From 94546b17b708874ef4536179a2c2bca29cd1cd24 Mon Sep 17 00:00:00 2001 From: Drew Yang <31813282+Yambottle@users.noreply.github.com> Date: Fri, 17 Dec 2021 13:46:06 -0600 Subject: [PATCH 001/176] Create README.md --- README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/README.md @@ -0,0 +1 @@ + From cfac635e7f07009fe10d800653c4c4bbd36858d6 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Fri, 17 Dec 2021 14:09:50 -0600 Subject: [PATCH 002/176] add gitignore --- .gitignore | 114 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 114 insertions(+) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..3a9d95d --- /dev/null +++ b/.gitignore @@ -0,0 +1,114 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +.idea/ + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy +scratchpaper.* + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +./.env +.env + +# virtualenv +.venv +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +# datajoint +dj_local_con*.json + +# emacs +**/*~ +**/#*# +**/.#* + +docker-compose.y*ml \ No newline at end of file From 1f324c1e588f07caa33e1b4a7aefe84ca35be536 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Fri, 17 Dec 2021 14:27:38 -0600 Subject: [PATCH 003/176] Initial structure --- .DS_Store | Bin 0 -> 6148 bytes LICENSE | 21 + README.md | 200 ++++ dj_example_local_conf.json | 26 + notebooks/1_Explore_Workflow.ipynb | 1517 ++++++++++++++++++++++++++++ notebooks/2_Explore_Export.ipynb | 277 +++++ requirements.txt | 7 + setup.py | 31 + temp_test.ipynb | 240 +++++ user_data/.DS_Store | Bin 0 -> 6148 bytes user_data/sessions.csv | 3 + workflow_behavior/.DS_Store | Bin 0 -> 6148 bytes workflow_behavior/__init__.py | 5 + workflow_behavior/ingest.py | 62 ++ workflow_behavior/paths.py | 11 + workflow_behavior/pipeline.py | 28 + 16 files changed, 2428 insertions(+) create mode 100644 .DS_Store create mode 100644 LICENSE create mode 100644 dj_example_local_conf.json create mode 100644 notebooks/1_Explore_Workflow.ipynb create mode 100644 notebooks/2_Explore_Export.ipynb create mode 100644 requirements.txt create mode 100644 setup.py create mode 100644 temp_test.ipynb create mode 100644 user_data/.DS_Store create mode 100644 user_data/sessions.csv create mode 100644 workflow_behavior/.DS_Store create mode 100644 workflow_behavior/__init__.py create mode 100644 workflow_behavior/ingest.py create mode 100644 workflow_behavior/paths.py create mode 100644 workflow_behavior/pipeline.py diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..31d6df26509b5acea7334c4f3dd42fd1222b9c74 GIT binary patch literal 6148 zcmeHKQBK1!40X04ZQ^I2llaLI*bT~5POt;OO3{#L$;6oW%~7}nT!0Jk%{}<%Iku)& z?GRrGkUcpsb?n4_k?NU<++tVGiKav}hB6K&7mQekR6%F<=b*H3m?# zS;ohLHW~xQfHAOVfWHqO%9tre!Sw0CkXiuX2<9Z1b1%U$UNKXQf>?n#2?a{1(-y-? zIP6~iGQ}t;;pDXWaC)-S4#lO@vA<95 literal 0 HcmV?d00001 diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..6bf141b --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 DataJoint + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md index 8b13789..be51242 100644 --- a/README.md +++ b/README.md @@ -1 +1,201 @@ +# Workflow for continuous behavior tracking +This directory provides an example workflow to save the continuous behavior data, using the following datajoint elements ++ [element-lab](https://github.com/datajoint/element-lab) ++ [element-animal](https://github.com/datajoint/element-animal) ++ [element-session](https://github.com/datajoint/element-session) ++ [element-behavior](https://github.com/datajoint/element-behavior) + +This repository provides demonstrations for: +Setting up a workflow using different elements (see [pipeline.py](workflow_behavior/pipeline.py)) + +## Workflow architecture +The lab and animal management workflow presented here uses components from two DataJoint elements (element-lab, element-animal and element-session) assembled together to a functional workflow. + +### element-lab + +![lab](images/lab_diagram.svg) + +### element-animal + +`subject` contains basic information of subjects. +![subject](images/subject_diagram2.svg) + +### element-session +`session` is designed to handle metadata related to data collection, including collection datetime, file paths, and notes. Most workflows will include element-session as a starting point for further data entry. +![session](images/session_diagram2.png) + +### This workflow +This workflow serves as an example of the upstream part of a typical data workflow, for examples using these elements with other data modalities refer to: + ++ [workflow-array-ephys](https://github.com/datajoint/workflow-array-ephys) ++ [workflow-calcium-imaging](https://github.com/datajoint/workflow-calcium-imaging) + + +## Installation instructions + +### Step 1 - Clone this repository + ++ Launch a new terminal and change directory to where you want to clone the repository + ``` + cd C:/Projects + ``` ++ Clone the repository + ``` + git clone https://github.com/datajoint/workflow-behavior + ``` ++ Change directory to `workflow-behavior` + ``` + cd workflow-behavior + ``` + +### Step 2 - Setup a virtual environment +It is highly recommended (though not strictly required) to create a virtual environment to run the pipeline. This can be done with either `virtualenv` or `conda` + ++ For `virtualenv`: + + + If not yet installed, run `pip install --user virtualenv` + + + To create a new virtual environment named `venv`: + ``` + virtualenv venv + ``` + + + To activated the virtual environment: + + On Windows: + ``` + .\venv\Scripts\activate + ``` + + + On Linux/macOS: + ``` + source venv/bin/activate + ``` ++ For `conda`: + + If not yet installed, run `pip install --user conda` + + + To create a new virtual environment named `venv`: + ``` + conda create --name venv python=3.8 + ``` + + + To activated the virtual environment: + + On Windows: + ``` + activate venv + ``` + + + On Linux/macOS: + ``` + source activate venv + ``` + +### Step 3 - Install this repository + +From the root of the cloned repository directory: + ``` + pip install -e . + ``` + +Note: the `-e` flag will install this repository in editable mode, +in case you'd like to to modify the code (e.g. the `pipeline.py` or `paths.py` scripts). +If no such modification required, using `pip install .` is sufficient. + + +### Step 4 - Jupyter Notebook ++ Register an IPython kernel with Jupyter + ``` + ipython kernel install --name=workflow-behavior + ``` + +### Step 5 - Configure the `dj_local_conf.json` + +At the root of the repository folder, +create a new file `dj_local_conf.json` with the following template: + +```json +{ + "database.host": "", + "database.user": "", + "database.password": "", + "loglevel": "INFO", + "safemode": true, + "display.limit": 7, + "display.width": 14, + "display.show_tuple_count": true, + "custom": { + "database.prefix": "", +} +``` + ++ Specify database's `hostname`, `username`, and `password` according to the database you plan to use (see [set-up instructions here](https://tutorials.datajoint.io/setting-up/get-database.html)). + ++ Specify a `database.prefix` to create the schemas. + + +### Installation complete + ++ At this point the setup of this workflow is complete. + + +## Interacting with the DataJoint pipeline and exploring data + ++ [Connect to database](https://tutorials.datajoint.io/setting-up/get-database.html) + ++ Import tables + ``` + from workflow_behavior.pipeline import * + ``` + This will create all tables defined in the elements in the database server. + ++ Preview the tables created by calling the classes, for example: + ``` + lab.Lab() + subject.Subject() + session.Session() + pose.DLCModel() + ``` + ++ If required to drop all schemas, the following is the dependency order. + ``` + from workflow_behavior.pipeline import * + + pose.schema.drop() + session.schema.drop() + subject.schema.drop() + lab.schema.drop() + ``` + ++ For a more in-depth exploration of the tables created, please refer to the example notebooks (TBD). + + +## Insert into Manual and Lookup tables with Graphical User Interface DataJoint Labbook + +DataJoint also provides a Graphical User Interface [DataJoint Labbook](https://github.com/datajoint/datajoint-labbook) to support manual data insertions into DataJoint workflows. + +![DataJoint Labbook preview](images/DataJoint_Labbook.png) + +Please refer to the [DataJoint Labbook page](https://github.com/datajoint/datajoint-labbook) for instructions to set it up. + +## Development mode installation + +This method allows you to modify the source code for `workflow-calcium-imaging`, `element-calcium-imaging`, `element-session`, `element-animal`, and `element-lab`. + ++ Launch a new terminal and change directory to where you want to clone the repositories + ``` + cd C:/Projects + ``` ++ Clone the repositories + ``` + git clone https://github.com/datajoint/element-lab + git clone https://github.com/datajoint/element-animal + git clone https://github.com/datajoint/element-session + git clone https://github.com/datajoint/workflow-behavior + ``` ++ Install each package with the `-e` option + ``` + pip install -e ./element-lab + pip install -e ./element-animal + pip install -e ./element-session + pip install -e ./workflow-behavior + ``` diff --git a/dj_example_local_conf.json b/dj_example_local_conf.json new file mode 100644 index 0000000..498909d --- /dev/null +++ b/dj_example_local_conf.json @@ -0,0 +1,26 @@ +{ + "database.host": "", + "database.password": "", + "database.user": "", + "database.port": 3306, + "database.reconnect": true, + "connection.init_function": null, + "connection.charset": "", + "loglevel": "INFO", + "safemode": true, + "fetch_format": "array", + "display.limit": 12, + "display.width": 14, + "display.show_tuple_count": true, + "database.use_tls": null, + "enable_python_native_blobs": true, + "database.ingest_filename_short": "", + "database.ingest_filename_full": "", + "custom": { + "database.prefix": "YourPrefix_", + "beh_root_dir": [ + "/Abolute/Path/Here/", + "/Abolute/Other/Path/" + ] + } +} \ No newline at end of file diff --git a/notebooks/1_Explore_Workflow.ipynb b/notebooks/1_Explore_Workflow.ipynb new file mode 100644 index 0000000..9b6b03e --- /dev/null +++ b/notebooks/1_Explore_Workflow.ipynb @@ -0,0 +1,1517 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d26010d6-acbc-4c90-8b62-a2448c50452d", + "metadata": {}, + "source": [ + "# DataJoint U24 - Workflow Session" + ] + }, + { + "cell_type": "markdown", + "id": "c5ffe5d2-5b2a-45c3-8d8f-8c20efa8c5eb", + "metadata": {}, + "source": [ + "This notebook will describe the steps to explore the lab and animal management tables created by the elements.\n", + "Prior to using this notebook, please refer to the README for the installation instructions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4351c4bb-9763-4d4d-8558-37662adc930e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting root@localhost:3306\n" + ] + }, + { + "data": { + "text/plain": [ + "DataJoint connection (connected) root@localhost:3306" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# change to the upper level folder to detect dj_local_conf.json\n", + "import os\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "import datajoint as dj\n", + "dj.conn()" + ] + }, + { + "cell_type": "markdown", + "id": "ee820754-bceb-476a-acf9-238fa8b201d9", + "metadata": {}, + "source": [ + "Importing the module `workflow_session.pipeline` is sufficient to create tables inside the elements. This workflow comes prepackaged with example data and ingestion functions to populate lab, subject, and session tables." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "868b79bc-f754-4d51-a327-94a209cde374", + "metadata": {}, + "outputs": [], + "source": [ + "from element_lab import lab\n", + "from element_animal import subject\n", + "from element_session import sessions" + ] + }, + { + "cell_type": "markdown", + "id": "2e19116d-bc32-4cea-9caf-f3e8eaa9b181", + "metadata": {}, + "source": [ + "## Workflow architecture" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1e7a0a8b-eaf1-41a1-bf08-1aff2f2812be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "

lab

\n", + " Abbreviated lab name\n", + "
\n", + "

lab_name

\n", + " full lab name\n", + "
\n", + "

institution

\n", + " \n", + "
\n", + "

address

\n", + " \n", + "
\n", + "

time_zone

\n", + " UTC offset suggested e.g., UTC+1\n", + "
LabAThe Example LabExample Uni221B Baker St,London NW1 6XE,UKUTC+0
LabBThe Other LabOther UniOxford OX1 2JD, United KingdomUTC+0
\n", + " \n", + "

Total: 2

\n", + " " + ], + "text/plain": [ + "*lab lab_name institution address time_zone \n", + "+------+ +------------+ +------------+ +------------+ +-----------+\n", + "LabA The Example La Example Uni 221B Baker St, UTC+0 \n", + "LabB The Other Lab Other Uni Oxford OX1 2JD UTC+0 \n", + " (Total: 2)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lab.Lab()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "63679df4-3064-402b-99ce-2f553dff877b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "`neuro_lab`.`#skull_reference`\n", + "\n", + "`neuro_lab`.`#skull_reference`\n", + "\n", + "\n", + "\n", + "lab.Project\n", + "\n", + "\n", + "lab.Project\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProjectUser\n", + "\n", + "\n", + "lab.ProjectUser\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->lab.ProjectUser\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project.Publication\n", + "\n", + "\n", + "lab.Project.Publication\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->lab.Project.Publication\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project.Keywords\n", + "\n", + "\n", + "lab.Project.Keywords\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->lab.Project.Keywords\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project.Sourcecode\n", + "\n", + "\n", + "lab.Project.Sourcecode\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->lab.Project.Sourcecode\n", + "\n", + "\n", + "\n", + "\n", + "lab.Equipment.EphysEquipment\n", + "\n", + "\n", + "lab.Equipment.EphysEquipment\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProtocolType\n", + "\n", + "\n", + "lab.ProtocolType\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Protocol\n", + "\n", + "\n", + "lab.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProtocolType->lab.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "lab.Equipment\n", + "\n", + "\n", + "lab.Equipment\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Equipment->lab.Equipment.EphysEquipment\n", + "\n", + "\n", + "\n", + "\n", + "lab.Equipment.CaImgEquipment\n", + "\n", + "\n", + "lab.Equipment.CaImgEquipment\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Equipment->lab.Equipment.CaImgEquipment\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab\n", + "\n", + "\n", + "lab.Lab\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Location\n", + "\n", + "\n", + "lab.Location\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab->lab.Location\n", + "\n", + "\n", + "\n", + "\n", + "lab.LabMembership\n", + "\n", + "\n", + "lab.LabMembership\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab->lab.LabMembership\n", + "\n", + "\n", + "\n", + "\n", + "lab.User\n", + "\n", + "\n", + "lab.User\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.User->lab.ProjectUser\n", + "\n", + "\n", + "\n", + "\n", + "lab.User->lab.LabMembership\n", + "\n", + "\n", + "\n", + "\n", + "lab.UserRole\n", + "\n", + "\n", + "lab.UserRole\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.UserRole->lab.LabMembership\n", + "\n", + "\n", + "\n", + "\n", + "lab.Source\n", + "\n", + "\n", + "lab.Source\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.Diagram(lab)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8cf0f64b-e523-4a94-9a43-fca4ed793f82", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Animal Subject\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "

subject

\n", + " \n", + "
\n", + "

sex

\n", + " \n", + "
\n", + "

subject_birth_date

\n", + " \n", + "
\n", + "

subject_description

\n", + " \n", + "
subject1M2020-12-30test animal
subject2F2020-11-30test animal
subject3F2020-12-30test animal
subject4M2021-02-12test animal
subject5F2020-01-03lmash_E105
subject6M2020-01-03hneih_E105
subject7U2020-08-30test animal
subject8F2020-09-30test animal
\n", + " \n", + "

Total: 8

\n", + " " + ], + "text/plain": [ + "*subject sex subject_birth_ subject_descri\n", + "+----------+ +-----+ +------------+ +------------+\n", + "subject1 M 2020-12-30 test animal \n", + "subject2 F 2020-11-30 test animal \n", + "subject3 F 2020-12-30 test animal \n", + "subject4 M 2021-02-12 test animal \n", + "subject5 F 2020-01-03 lmash_E105 \n", + "subject6 M 2020-01-03 hneih_E105 \n", + "subject7 U 2020-08-30 test animal \n", + "subject8 F 2020-09-30 test animal \n", + " (Total: 8)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "75576be2-2984-451f-a86b-f05f9ddec6b7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele\n", + "\n", + "\n", + "subject.Allele\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Zygosity\n", + "\n", + "\n", + "subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele.Source\n", + "\n", + "\n", + "subject.Allele.Source\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Allele.Source\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line.Allele\n", + "\n", + "\n", + "subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Line\n", + "\n", + "\n", + "subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.User\n", + "\n", + "\n", + "subject.Subject.User\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Strain\n", + "\n", + "\n", + "subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Strain->subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "subject.SubjectDeath\n", + "\n", + "\n", + "subject.SubjectDeath\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.User\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.SubjectDeath\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Source\n", + "\n", + "\n", + "subject.Subject.Source\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Source\n", + "\n", + "\n", + "\n", + "\n", + "subject.SubjectCullMethod\n", + "\n", + "\n", + "subject.SubjectCullMethod\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.SubjectCullMethod\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Lab\n", + "\n", + "\n", + "subject.Subject.Lab\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Lab\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line->subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line->subject.Line.Allele\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.Diagram(subject)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5243a782-93da-40fa-b243-03ddcb230c1d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "

subject

\n", + " \n", + "
\n", + "

session_datetime

\n", + " \n", + "
subject32021-04-30 12:22:15.032000
subject52020-04-15 11:16:38
subject62021-01-15 11:16:38
subject62021-06-02 14:04:22
\n", + " \n", + "

Total: 4

\n", + " " + ], + "text/plain": [ + "*subject *session_datet\n", + "+----------+ +------------+\n", + "subject3 2021-04-30 12:\n", + "subject5 2020-04-15 11:\n", + "subject6 2021-01-15 11:\n", + "subject6 2021-06-02 14:\n", + " (Total: 4)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "session.Session()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7e48d7c0-b7bd-4f0b-abcb-1aedc69d5310", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.ProjectSession\n", + "\n", + "\n", + "session.ProjectSession\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.ProjectSession\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionDirectory\n", + "\n", + "\n", + "session.SessionDirectory\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionDirectory\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionExperimenter\n", + "\n", + "\n", + "session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionNote\n", + "\n", + "\n", + "session.SessionNote\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionNote\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.Diagram(session)" + ] + }, + { + "cell_type": "markdown", + "id": "c510fe4d-09ed-472f-830f-4401bd6830d0", + "metadata": {}, + "source": [ + "(Workflow needs continued development to import geotyping tables)" + ] + }, + { + "cell_type": "markdown", + "id": "b60f5f4c-d366-4034-a40d-2d2095cb2a14", + "metadata": {}, + "source": [ + "## Explore each table" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9c0821e1-9125-4c41-bc9c-567f53d0a5e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Animal Subject\n", + "subject : varchar(32) \n", + "---\n", + "sex : enum('M','F','U') \n", + "subject_birth_date : date \n", + "subject_description=\"\" : varchar(1024) \n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "'# Animal Subject\\nsubject : varchar(32) \\n---\\nsex : enum(\\'M\\',\\'F\\',\\'U\\') \\nsubject_birth_date : date \\nsubject_description=\"\" : varchar(1024) \\n'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check table definition with describe()\n", + "subject.Subject.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "f6c110c0-0966-4283-a0ba-a7de2ce69e25", + "metadata": {}, + "source": [ + "## Insert data into Manual and Lookup tables" + ] + }, + { + "cell_type": "markdown", + "id": "54cf050e-882e-4672-be31-1ca3df52fa58", + "metadata": {}, + "source": [ + "Tables in this workflow are either manual tables or lookup tables. To insert into these tables, DataJoint provide method `.insert1()` and `insert()`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d5b43904-9711-4bce-8ae5-d0d797118dec", + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.insert1(\n", + " dict(subject='subject1', sex='M', subject_birth_date='2020-12-30', \n", + " subject_description='test animal'), skip_duplicates=True)\n", + "subject.Subject.insert1(\n", + " ('subject2', 'F', '2020-11-30', 'test animal'), skip_duplicates=True)" + ] + }, + { + "cell_type": "markdown", + "id": "49d43ca2-2cd3-4659-849f-5bcc09c1367e", + "metadata": {}, + "source": [ + "`skip_duplicates=True` will prevent an error if you already have data for the primary keys in a given entry." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9bf2c953-7b4c-4a70-99fd-124a4d28171b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Animal Subject\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "

subject

\n", + " \n", + "
\n", + "

sex

\n", + " \n", + "
\n", + "

subject_birth_date

\n", + " \n", + "
\n", + "

subject_description

\n", + " \n", + "
subject1M2020-12-30test animal
subject2F2020-11-30test animal
subject3F2020-12-30test animal
subject4M2021-02-12test animal
subject5F2020-01-03lmash_E105
subject6M2020-01-03hneih_E105
subject7U2020-08-30test animal
subject8F2020-09-30test animal
\n", + " \n", + "

Total: 8

\n", + " " + ], + "text/plain": [ + "*subject sex subject_birth_ subject_descri\n", + "+----------+ +-----+ +------------+ +------------+\n", + "subject1 M 2020-12-30 test animal \n", + "subject2 F 2020-11-30 test animal \n", + "subject3 F 2020-12-30 test animal \n", + "subject4 M 2021-02-12 test animal \n", + "subject5 F 2020-01-03 lmash_E105 \n", + "subject6 M 2020-01-03 hneih_E105 \n", + "subject7 U 2020-08-30 test animal \n", + "subject8 F 2020-09-30 test animal \n", + " (Total: 8)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7a10ddab-d0fd-45a0-8183-09c1b1933e0a", + "metadata": {}, + "outputs": [], + "source": [ + "# `insert()` takes a list of dicts or tuples\n", + "subject.Subject.insert(\n", + " [dict(subject='subject3', sex='F', subject_birth_date='2020-12-30', \n", + " subject_description='test animal'),\n", + " dict(subject='subject4', sex='M', subject_birth_date='2021-02-12', \n", + " subject_description='test animal')\n", + " ],\n", + " skip_duplicates=True)\n", + "subject.Subject.insert(\n", + " [\n", + " ('subject7', 'U', '2020-08-30', 'test animal'),\n", + " ('subject8', 'F', '2020-09-30', 'test animal')\n", + " ],\n", + " skip_duplicates=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "064ddaae-3410-47fc-be22-671d2afe7fb6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Animal Subject\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "

subject

\n", + " \n", + "
\n", + "

sex

\n", + " \n", + "
\n", + "

subject_birth_date

\n", + " \n", + "
\n", + "

subject_description

\n", + " \n", + "
subject1M2020-12-30test animal
subject2F2020-11-30test animal
subject3F2020-12-30test animal
subject4M2021-02-12test animal
subject5F2020-01-03lmash_E105
subject6M2020-01-03hneih_E105
subject7U2020-08-30test animal
subject8F2020-09-30test animal
\n", + " \n", + "

Total: 8

\n", + " " + ], + "text/plain": [ + "*subject sex subject_birth_ subject_descri\n", + "+----------+ +-----+ +------------+ +------------+\n", + "subject1 M 2020-12-30 test animal \n", + "subject2 F 2020-11-30 test animal \n", + "subject3 F 2020-12-30 test animal \n", + "subject4 M 2021-02-12 test animal \n", + "subject5 F 2020-01-03 lmash_E105 \n", + "subject6 M 2020-01-03 hneih_E105 \n", + "subject7 U 2020-08-30 test animal \n", + "subject8 F 2020-09-30 test animal \n", + " (Total: 8)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "markdown", + "id": "c47691a0-b016-4092-a5ad-fefff93c54dd", + "metadata": {}, + "source": [ + "For more documentation of insert, please refer to [DataJoint Docs](https://docs.datajoint.io/python/manipulation/1-Insert.html) and [DataJoint playground](https://playground.datajoint.io/)" + ] + }, + { + "cell_type": "markdown", + "id": "13f8a8ed-2656-46d8-82ba-cdf130c4873e", + "metadata": {}, + "source": [ + "## Insert into Manual and Lookup tables with Graphical User Interface" + ] + }, + { + "cell_type": "markdown", + "id": "4775dd80-8a54-47b7-a9ba-99995db9ff1a", + "metadata": {}, + "source": [ + "DataJoint also provides a Graphical User Interface [DataJoint Labbook](https://github.com/datajoint/datajoint-labbook) to support manual data insertions into DataJoint workflows. ![DataJoint Labbook preview](../images/DataJoint_Labbook.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv-nwb", + "language": "python", + "name": "venv-nwb" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/2_Explore_Export.ipynb b/notebooks/2_Explore_Export.ipynb new file mode 100644 index 0000000..fd4a50c --- /dev/null +++ b/notebooks/2_Explore_Export.ipynb @@ -0,0 +1,277 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3054518f-87bc-42ff-a3e7-84bf3d2a37f6", + "metadata": {}, + "source": [ + "# DataJoint U24 - Export Session" + ] + }, + { + "cell_type": "markdown", + "id": "79c15f36-039d-4304-96be-f56ba0d6b10a", + "metadata": {}, + "source": [ + "Same as before, import data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0e7fa407-d67b-403e-975c-bd0bd499d88c", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f11ca71-5e4f-460c-ad94-2037ef0f6448", + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj\n", + "dj.conn()\n", + "from element_lab import lab\n", + "from element_animal import subject\n", + "from element_session import session" + ] + }, + { + "cell_type": "markdown", + "id": "ab2a3f10-b96e-4f0d-9e54-0015bb9b8622", + "metadata": {}, + "source": [ + "Identify items for export with keys." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "76c040c8-15cc-4d61-ae5d-bc7646a6a0be", + "metadata": {}, + "outputs": [], + "source": [ + "session_key=(session.Session&'subject=\"subject5\"').fetch1('KEY')\n", + "mylab_key = (lab.Lab & 'lab=\"LabA\"').fetch1('KEY')\n", + "myproj_key= (lab.Project & 'project=\"ProjA\"').fetch1('KEY')\n", + "myprot_key= (lab.Protocol() & 'protocol=\"ProtA\"').fetch1('KEY')" + ] + }, + { + "cell_type": "markdown", + "id": "902e050c-3133-4fb7-850d-f0b954e1b634", + "metadata": {}, + "source": [ + "Get export function and related pynwb dependency, then export with keys from prev step." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "61fdbdce-a808-49ad-bb5d-f9b9894446fa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function session_to_nwb in module element_session.export.nwb:\n", + "\n", + "session_to_nwb(session_key)\n", + " Generate one NWBFile object representing all session-level information,\n", + " including session identifier, description, start time, etc.\n", + " \n", + " :param session_key: entry in session.Session table\n", + " :return: NWBFile object\n", + "\n" + ] + } + ], + "source": [ + "from element_session.export import session_to_nwb_dict, session_to_nwb\n", + "help(session_to_nwb)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e76fa25d-700b-4f9a-9bb4-62d2217288b6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/cb/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/pynwb/file.py:753: UserWarning: Date is missing timezone information. Updating to local timezone.\n", + " warn(\"Date is missing timezone information. Updating to local timezone.\")\n" + ] + } + ], + "source": [ + "mynwbfile=session_to_nwb(session_key)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "56ebdd79-f0dc-4925-aeb1-109887df361d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function elemlab_to_nwb_dict in module element_lab.export.nwb:\n", + "\n", + "elemlab_to_nwb_dict(lab_key=None, project_key=None, protocol_key=None)\n", + " Generate a dictionary object containing all relevant lab information used when\n", + " generating an NWB file at the session level. All parameters optional.\n", + " Use: mynwbfile = NWBfile(identifier=\"your identifier\",\n", + " session_description=\"your description\",\n", + " session_start_time=session_datetime,\n", + " elemlab_to_nwb_dict(lab_key=key1,project_key=key2,protocol_key=key3))\n", + " Note: The lab, project and protocol keys should specify one of their respective types.\n", + " \n", + " :param lab_key: Key specifying one entry in element_lab.lab.Lab\n", + " :param project_key: Key specifying one entry in element_lab.lab.Project\n", + " :param protocol_key: Key specifying one entry in element_lab.lab.PRotocol\n", + " :return: dictionary with NWB parameters\n", + "\n" + ] + } + ], + "source": [ + "from element_lab.export import elemlab_to_nwb_dict\n", + "help(elemlab_to_nwb_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "afc6555d-cd25-4d55-93e2-51c72ddba9ae", + "metadata": {}, + "outputs": [], + "source": [ + "from pynwb import NWBFile\n", + "lab_info = elemlab_to_nwb_dict(lab_key=mylab_key,project_key=myproj_key,protocol_key=myprot_key)\n", + "sess_info = session_to_nwb_dict(session_key)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "97e01cbf-b225-45d3-a030-0f7400e3526b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/cb/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/pynwb/file.py:753: UserWarning: Date is missing timezone information. Updating to local timezone.\n", + " warn(\"Date is missing timezone information. Updating to local timezone.\")\n" + ] + } + ], + "source": [ + "mynwbfile = NWBFile(**sess_info,**lab_info)" + ] + }, + { + "cell_type": "markdown", + "id": "1bccad09-d5e4-4200-bb73-08ddf98cfb80", + "metadata": {}, + "source": [ + "Learn more about using NWB formats [here](https://www.nwb.org/how-to-use/)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b914d8db-2584-403c-9f24-12a64103f1cb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/cb/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/hdmf/build/objectmapper.py:653: MissingRequiredBuildWarning: NWBFile 'root' is missing required value for attribute 'source_script_file_name'.\n", + " warnings.warn(msg, MissingRequiredBuildWarning)\n" + ] + } + ], + "source": [ + "from pynwb import NWBHDF5IO\n", + "with NWBHDF5IO('session_metadata.nwb', mode='w') as io:\n", + " io.write(mynwbfile)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c81b1c85-9623-4cc1-9438-79b0c5287756", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "root pynwb.file.NWBFile at 0x140190306285216\n", + "Fields:\n", + " experiment_description: Example project to populate element-lab\n", + " file_create_date: [datetime.datetime(2021, 12, 6, 17, 1, 11, 974467, tzinfo=tzlocal())]\n", + " identifier: subject5_20200415_111638\n", + " institution: Example Uni\n", + " keywords: ['Example' 'Study']\n", + " lab: The Example Lab\n", + " pharmacology: Subjects were administered 10ul sedative prior to surgery\n", + " protocol: ProtA\n", + " related_publications: ['arXiv:1807.11104' 'arXiv:1807.11104v1']\n", + " session_description: Successful data collection, no notes\n", + " session_start_time: 2020-04-15 11:16:38-05:00\n", + " source_script: https://github.com/datajoint/element-lab/\n", + " surgery: Craniotomy performed by session experimenter\n", + " timestamps_reference_time: 2020-04-15 11:16:38-05:00\n", + "\n" + ] + } + ], + "source": [ + "print(mynwbfile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e75c3795-96d6-4ed1-889e-3da58d9e8533", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv-nwb", + "language": "python", + "name": "venv-nwb" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..b4c240b --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +datajoint>=0.13.0 +element-lab +element-animal +element-session +element-behavior +ipykernel +pynwb \ No newline at end of file diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..ddbbcf2 --- /dev/null +++ b/setup.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python +from setuptools import setup, find_packages +from os import path +import sys + +here = path.abspath(path.dirname(__file__)) + +long_description = """" +# Workflow for monitoring behavior + +Build a workflow for continuous behavioral data using DataJoint Elements ++ [elements-session](https://github.com/datajoint/element-session) ++ [elements-behavior](https://github.com/datajoint/element-behavior) +""" + +with open(path.join(here, 'requirements.txt')) as f: + requirements = f.read().splitlines() + +setup( + name='workflow-behavior', + version='0.0.1', + description="DataJoint Elements for Continous Behavior", + long_description=long_description, + author='DataJoint NEURO', + author_email='info@vathes.com', + license='MIT', + url='https://github.com/datajoint/workflow-behavior', + keywords='neuroscience behavior deeplabcut datajoint', + packages=find_packages(exclude=['contrib', 'docs', 'tests*']), + install_requires=requirements, +) diff --git a/temp_test.ipynb b/temp_test.ipynb new file mode 100644 index 0000000..5a691f8 --- /dev/null +++ b/temp_test.ipynb @@ -0,0 +1,240 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "f13c2734-23db-435f-bc9e-68064cdcc82b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting root@localhost:3306\n" + ] + } + ], + "source": [ + "import datajoint as dj\n", + "dj.conn()\n", + "import csv" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cb32b7a1-37b4-45c1-8d03-02c8bbd09b04", + "metadata": {}, + "outputs": [], + "source": [ + "from workflow_session.pipeline import lab, subject, session\n", + "from workflow_session.ingest import *" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e94beab4-02f3-464d-989f-24651a8bef8a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "---- Insert 2 entry(s) into lab tables ----\n", + "\n", + "---- Insert 2 entry(s) into project table ----\n", + "\n", + "---- Insert entry(s) into publication/keyword tables ----\n", + "\n", + "---- Insert 2 entry(s) into protocol tables ----\n", + "\n", + "---- Insert 2 entry(s) into subject tables ----\n", + "\n", + "---- Insert 2 entry(s) into session.Session ----\n" + ] + } + ], + "source": [ + "ingest_lab(); ingest_subjects();ingest_sessions()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e306d9dc-ce9a-4485-b8da-eea7d852e5aa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "*lab lab_name institution address time_zone \n", + "+------+ +------------+ +------------+ +------------+ +-----------+\n", + "LabA The Example La Example Uni 221B Baker St, UTC+0 \n", + " (Total: 1)\n", + "\n", + "*project project_descri repositoryurl repositoryname pharmacology viruses slices stimulus surgery \n", + "+---------+ +------------+ +------------+ +------------+ +------------+ +---------+ +--------+ +------------+ +---------+\n", + "ProjA Example projec https://github element-lab videos generat \n", + " (Total: 1)\n", + "\n", + "*protocol protocol_type protocol_descr\n", + "+----------+ +------------+ +------------+\n", + "ProtA IRB expedited Protocol for m\n", + " (Total: 1)\n", + "\n" + ] + } + ], + "source": [ + "print(lab.Lab & 'lab=\"LabA\"')\n", + "print(lab.Project & 'project=\"ProjA\"')\n", + "print(lab.Protocol() & 'protocol=\"ProtA\"')\n", + "\n", + "lab_info = (lab.Lab & 'lab=\"LabA\"').fetch1()\n", + "proj_info = (lab.Project & 'project=\"ProjA\"').fetch1()\n", + "prot_info = (lab.Protocol() & 'protocol=\"ProtA\"').fetch1()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fd053496-eec1-4035-9369-fdf96e53fa95", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/cb/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/pynwb/file.py:753: UserWarning: Date is missing timezone information. Updating to local timezone.\n", + " warn(\"Date is missing timezone information. Updating to local timezone.\")\n" + ] + }, + { + "data": { + "text/plain": [ + "root pynwb.file.NWBFile at 0x140690194467040\n", + "Fields:\n", + " file_create_date: [datetime.datetime(2021, 12, 1, 14, 22, 4, 992693, tzinfo=tzlocal())]\n", + " identifier: subject5_20200415_111638\n", + " institution: Example Uni\n", + " session_description: Successful data collection, no notes\n", + " session_start_time: 2020-04-15 11:16:38-05:00\n", + " timestamps_reference_time: 2020-04-15 11:16:38-05:00" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pynwb\n", + "from element_session.export import *\n", + "session_key=(session.Session&'subject=\"subject5\"').fetch1('KEY')\n", + "mylab_key = (lab.Lab & 'lab=\"LabA\"').fetch1('KEY')\n", + "myproj_key= (lab.Project & 'project=\"ProjA\"').fetch1('KEY')\n", + "myprot_key= (lab.Protocol() & 'protocol=\"ProtA\"').fetch1('KEY')\n", + "session_to_nwb(session_key,lab_key=mylab_key,project_key=myproj_key)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "a3e1f490-7510-4f08-af66-c106f640928b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "root pynwb.file.NWBFile at 0x140337549119696\n", + "Fields:\n", + " file_create_date: [datetime.datetime(2021, 12, 1, 14, 21, 26, 638467, tzinfo=tzlocal())]\n", + " identifier: subject5_20200415_111638\n", + " institution: Example Uni\n", + " protocol: ProtA\n", + " session_description: Successful data collection, no notes\n", + " session_start_time: 2020-04-15 11:16:38-05:00\n", + " timestamps_reference_time: 2020-04-15 11:16:38-05:00" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mynwbfile" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "d982c732-8a8f-435d-ab16-aa37a0e99904", + "metadata": {}, + "outputs": [], + "source": [ + "session_key=(session.Session&'subject=\"subject5\"').fetch1('KEY')\n", + "session_identifier = {}\n", + "for k, v in session_key.items():\n", + " session_identifier[k] = v.strftime('%Y%m%d_%H%M%S') if isinstance(v, datetime) else v\n", + "\n", + "session_info = (session.Session & session_key).join(session.SessionNote, left=True).fetch1()\n", + "\n", + "def mytuple():\n", + " identifier='_'.join(session_identifier.values()),\n", + " session_description=session_info['session_note'] if session_info['session_note'] else '',\n", + " session_start_time=session_info['session_datetime']\n", + " return identifier,session_description,session_start_time\n", + "info=dict(identifier='_'.join(session_identifier.values()),\n", + " session_description='Note',\n", + " session_start_time=session_info['session_datetime'],\n", + " institution='')\n", + "info={k: v for k, v in info.items() if v} #drop empty\n", + "asstring = ','.join('='.join((str(key),val)) for (key,val) in info.items())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ef8b5fb3-8b19-403c-a954-150c1ec8cf41", + "metadata": {}, + "outputs": [], + "source": [ + "a=None;b=None;c=None\n", + "if [x for x in (a, b,c) if x is not None]: print('g')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91347a4f-9024-4a33-a1b1-42d903d9b132", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv-nwb", + "language": "python", + "name": "venv-nwb" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/user_data/.DS_Store b/user_data/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..3ae84be0c22145e2fff5833694964d892253c09a GIT binary patch literal 6148 zcmeHKOHRWu5FNKwDS}0pY><3~-XK)r1YN<3h9ZiTG!=y%3mO3#Xx`3TG#mnRS=S4SW^=r&_-PtEYDlvm1-9h6k zjIN%vc6dAgh_xH<&#G;2PrW1B(H(S$wb!&s>(UqMwjx^fub*V?ov36Ud!l;ggMuc; zfH7bU9E1U!*({S2L2HcxW55__7~t=Nhcaf0K`?$g(8MbMa0GJ{^!YBqIbJbS41(}L zoTLIJ)#(+(NjmIa<1)n{DCy+%^5L|y(;JG5)iJ)0;p8$wYmEV8AZ1`Ek5fMXFZbX7 z(Dk&@9G|rwdIM$QxFFc1pc9W`#PU&m28{x{=MFGa41%yg P>_;HdV2v?wPzJsMn15iU literal 0 HcmV?d00001 diff --git a/user_data/sessions.csv b/user_data/sessions.csv new file mode 100644 index 0000000..d3ad137 --- /dev/null +++ b/user_data/sessions.csv @@ -0,0 +1,3 @@ +subject,session_datetime,session_dir,session_note +subject5,2020-04-15 11:16:38,/subject5/session1,"Successful data collection, no notes" +subject6,2021-06-02 14:04:22,/subject6/session1,"Ambient temp abnormally low" \ No newline at end of file diff --git a/workflow_behavior/.DS_Store b/workflow_behavior/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..98c9ebd40b63a8e2e15e7490e5a65811048e407a GIT binary patch literal 6148 zcmeHKK}y3=5PiRPQR%`W2rjd9CG-Zd#1r%Y)udv@rYTex?&2*xg5cI0croAn8A?N1 zEP^631M}a{{F(eeFCmiw5Jr#FAus^YqY8EoIcyPG7i~$$EV3BlJr>9@L4g@=THbE> ziwwxKJH|Qgu*4HKpI?D{+A6Lr@PO6)b~rAoa;Zs#=Y8YKD$DYDmD6ALI$OPcTzId43)56*xy z;0*i#1FYF1y}qK4&VV!E46GTD??XZrObu(r`03yfTL5C0<|v#?FCj6BVQN?_a)lB! zl&GP>T8yCKv?u158rF&$j$qA4u$_Z-C?W37`zO&IAyxFz8E^)g3>@g?Kd*-YZK}fRYd%1#dQcLu@uu+O7ST*3hjwZh^b+%NDIY( N1R@PSI0L`Rz$cm9U-19{ literal 0 HcmV?d00001 diff --git a/workflow_behavior/__init__.py b/workflow_behavior/__init__.py new file mode 100644 index 0000000..ae798f2 --- /dev/null +++ b/workflow_behavior/__init__.py @@ -0,0 +1,5 @@ +__author__ = "DataJoint NEURO" +__date__ = "March 18, 2021" +__version__ = "0.0.1" + +__all__ = ['__author__', '__version__', '__date__'] diff --git a/workflow_behavior/ingest.py b/workflow_behavior/ingest.py new file mode 100644 index 0000000..277002b --- /dev/null +++ b/workflow_behavior/ingest.py @@ -0,0 +1,62 @@ +import pathlib +import csv +import re + +from workflow_behavior.pipeline import lab, subject, session, pose +from workflow_behavior.paths import get_root_data_dir +import element_data_loader.utils + +def ingest_sessions(session_csv_path='./user_data/sessions.csv'): + """ + Ingests DeepLabCut files from directories listed + in the sess_dir column of ./user_data/sessions.csv + """ + # ---------- Insert new "Session" and "ProbeInsertion" --------- + with open(session_csv_path, newline= '') as f: + input_sessions = list(csv.DictReader(f, delimiter=',')) + + # Folder structure: root / subject / session / probe / .ap.meta + session_list, sess_dir_list, = [], [] + + for sess in input_sessions: + sess_dir = element_data_loader.utils.find_full_path( + get_root_data_dir(), + sess['session_dir']) + session_datetimes, model_list = [], [] + + # search session dir and determine acquisition software + for file_pattern, acq_type in zip(['*.yaml', '*.other'], ['DeepLabCut', 'OtherUnspecified']): + beh_model_filepaths = [fp for fp in sess_dir.rglob(file_pattern)] + if len(beh_model_filepaths): + acq_software = acq_type + break + else: + raise FileNotFoundError(f'Recording files not found! Checked for files found in: {sess_dir}') + + if acq_software == 'DeepLabCut': + pass + # NEEDS WORK HERE + else: + raise NotImplementedError(f'Unknown acquisition software: {acq_software}') + + # new session/probe-insertion + session_key = {'subject': sess['subject'], 'session_datetime': min(session_datetimes)} + if session_key not in session.Session(): + session_list.append(session_key) + root_dir = element_data_loader.utils.find_root_directory( + get_root_data_dir(), sess_dir) + sess_dir_list.append({**session_key, 'session_dir': sess_dir.relative_to(root_dir).as_posix()}) + + print(f'\n---- Insert {len(session_list)} entry(s) into session.Session ----') + session.Session.insert(session_list, skip_duplicates=True) + session.SessionDirectory.insert(sess_dir_list, skip_duplicates=True) + + print(f'\n---- Insert {len(probe_list)} entry(s) into probe.Probe ----') + pose.DLCModel.insert(model_list, skip_duplicates=True) + + print('\n---- Successfully completed workflow_behavior/ingest.py ----') + + +if __name__ == '__main__': + ingest_subjects() + ingest_sessions() diff --git a/workflow_behavior/paths.py b/workflow_behavior/paths.py new file mode 100644 index 0000000..a784008 --- /dev/null +++ b/workflow_behavior/paths.py @@ -0,0 +1,11 @@ +import datajoint as dj +import pathlib + +def get_beh_root_dir(): + beh_root_dirs = dj.config.get('custom', {}).get('beh_root_dir', None) + return beh_root_dirs if beh_root_dirs else None + +def get_session_directory(session_key: dict) -> str: + from .pipeline import session + session_dir = (session.SessionDirectory & session_key).fetch1('session_dir') + return session_dir \ No newline at end of file diff --git a/workflow_behavior/pipeline.py b/workflow_behavior/pipeline.py new file mode 100644 index 0000000..8a5f29e --- /dev/null +++ b/workflow_behavior/pipeline.py @@ -0,0 +1,28 @@ +import datajoint as dj + +from element_lab import lab +from element_animal import subject, genotyping +from element_session import session + +from element_animal.subject import Subject +from element_lab.lab import Source, Lab, Protocol, User, Project +from element_session.session import Session + +if 'custom' not in dj.config: + dj.config['custom'] = {} + +db_prefix = dj.config['custom'].get('database.prefix', '') + + +# Activate "lab", "subject", "session" schema ------------- + +lab.activate(db_prefix + 'lab') + +subject.activate(db_prefix + 'subject', linking_module=__name__) + +Experimenter = lab.User +session.activate(db_prefix + 'session', linking_module=__name__) + +# Activate "behavior" schema ------------------------------------------------------ + +pose.activate(db_prefix + 'pose', linking_module=__name__) From aa01866417b8757deb6b86d77945809f65b35aa5 Mon Sep 17 00:00:00 2001 From: Chris Brozdowski Date: Fri, 17 Dec 2021 14:47:27 -0600 Subject: [PATCH 004/176] delete notes file --- temp_test.ipynb | 240 ------------------------------------------------ 1 file changed, 240 deletions(-) delete mode 100644 temp_test.ipynb diff --git a/temp_test.ipynb b/temp_test.ipynb deleted file mode 100644 index 5a691f8..0000000 --- a/temp_test.ipynb +++ /dev/null @@ -1,240 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "f13c2734-23db-435f-bc9e-68064cdcc82b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting root@localhost:3306\n" - ] - } - ], - "source": [ - "import datajoint as dj\n", - "dj.conn()\n", - "import csv" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "cb32b7a1-37b4-45c1-8d03-02c8bbd09b04", - "metadata": {}, - "outputs": [], - "source": [ - "from workflow_session.pipeline import lab, subject, session\n", - "from workflow_session.ingest import *" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e94beab4-02f3-464d-989f-24651a8bef8a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "---- Insert 2 entry(s) into lab tables ----\n", - "\n", - "---- Insert 2 entry(s) into project table ----\n", - "\n", - "---- Insert entry(s) into publication/keyword tables ----\n", - "\n", - "---- Insert 2 entry(s) into protocol tables ----\n", - "\n", - "---- Insert 2 entry(s) into subject tables ----\n", - "\n", - "---- Insert 2 entry(s) into session.Session ----\n" - ] - } - ], - "source": [ - "ingest_lab(); ingest_subjects();ingest_sessions()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e306d9dc-ce9a-4485-b8da-eea7d852e5aa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "*lab lab_name institution address time_zone \n", - "+------+ +------------+ +------------+ +------------+ +-----------+\n", - "LabA The Example La Example Uni 221B Baker St, UTC+0 \n", - " (Total: 1)\n", - "\n", - "*project project_descri repositoryurl repositoryname pharmacology viruses slices stimulus surgery \n", - "+---------+ +------------+ +------------+ +------------+ +------------+ +---------+ +--------+ +------------+ +---------+\n", - "ProjA Example projec https://github element-lab videos generat \n", - " (Total: 1)\n", - "\n", - "*protocol protocol_type protocol_descr\n", - "+----------+ +------------+ +------------+\n", - "ProtA IRB expedited Protocol for m\n", - " (Total: 1)\n", - "\n" - ] - } - ], - "source": [ - "print(lab.Lab & 'lab=\"LabA\"')\n", - "print(lab.Project & 'project=\"ProjA\"')\n", - "print(lab.Protocol() & 'protocol=\"ProtA\"')\n", - "\n", - "lab_info = (lab.Lab & 'lab=\"LabA\"').fetch1()\n", - "proj_info = (lab.Project & 'project=\"ProjA\"').fetch1()\n", - "prot_info = (lab.Protocol() & 'protocol=\"ProtA\"').fetch1()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "fd053496-eec1-4035-9369-fdf96e53fa95", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/cb/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/pynwb/file.py:753: UserWarning: Date is missing timezone information. Updating to local timezone.\n", - " warn(\"Date is missing timezone information. Updating to local timezone.\")\n" - ] - }, - { - "data": { - "text/plain": [ - "root pynwb.file.NWBFile at 0x140690194467040\n", - "Fields:\n", - " file_create_date: [datetime.datetime(2021, 12, 1, 14, 22, 4, 992693, tzinfo=tzlocal())]\n", - " identifier: subject5_20200415_111638\n", - " institution: Example Uni\n", - " session_description: Successful data collection, no notes\n", - " session_start_time: 2020-04-15 11:16:38-05:00\n", - " timestamps_reference_time: 2020-04-15 11:16:38-05:00" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pynwb\n", - "from element_session.export import *\n", - "session_key=(session.Session&'subject=\"subject5\"').fetch1('KEY')\n", - "mylab_key = (lab.Lab & 'lab=\"LabA\"').fetch1('KEY')\n", - "myproj_key= (lab.Project & 'project=\"ProjA\"').fetch1('KEY')\n", - "myprot_key= (lab.Protocol() & 'protocol=\"ProtA\"').fetch1('KEY')\n", - "session_to_nwb(session_key,lab_key=mylab_key,project_key=myproj_key)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "a3e1f490-7510-4f08-af66-c106f640928b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "root pynwb.file.NWBFile at 0x140337549119696\n", - "Fields:\n", - " file_create_date: [datetime.datetime(2021, 12, 1, 14, 21, 26, 638467, tzinfo=tzlocal())]\n", - " identifier: subject5_20200415_111638\n", - " institution: Example Uni\n", - " protocol: ProtA\n", - " session_description: Successful data collection, no notes\n", - " session_start_time: 2020-04-15 11:16:38-05:00\n", - " timestamps_reference_time: 2020-04-15 11:16:38-05:00" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mynwbfile" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "d982c732-8a8f-435d-ab16-aa37a0e99904", - "metadata": {}, - "outputs": [], - "source": [ - "session_key=(session.Session&'subject=\"subject5\"').fetch1('KEY')\n", - "session_identifier = {}\n", - "for k, v in session_key.items():\n", - " session_identifier[k] = v.strftime('%Y%m%d_%H%M%S') if isinstance(v, datetime) else v\n", - "\n", - "session_info = (session.Session & session_key).join(session.SessionNote, left=True).fetch1()\n", - "\n", - "def mytuple():\n", - " identifier='_'.join(session_identifier.values()),\n", - " session_description=session_info['session_note'] if session_info['session_note'] else '',\n", - " session_start_time=session_info['session_datetime']\n", - " return identifier,session_description,session_start_time\n", - "info=dict(identifier='_'.join(session_identifier.values()),\n", - " session_description='Note',\n", - " session_start_time=session_info['session_datetime'],\n", - " institution='')\n", - "info={k: v for k, v in info.items() if v} #drop empty\n", - "asstring = ','.join('='.join((str(key),val)) for (key,val) in info.items())" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "ef8b5fb3-8b19-403c-a954-150c1ec8cf41", - "metadata": {}, - "outputs": [], - "source": [ - "a=None;b=None;c=None\n", - "if [x for x in (a, b,c) if x is not None]: print('g')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "91347a4f-9024-4a33-a1b1-42d903d9b132", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv-nwb", - "language": "python", - "name": "venv-nwb" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 4ddf02f8ce660c17fec30379957bc5f8bedd6bf3 Mon Sep 17 00:00:00 2001 From: Chris Brozdowski Date: Fri, 17 Dec 2021 14:48:20 -0600 Subject: [PATCH 005/176] Delete .DS_Store --- .DS_Store | Bin 6148 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 .DS_Store diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 31d6df26509b5acea7334c4f3dd42fd1222b9c74..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKQBK1!40X04ZQ^I2llaLI*bT~5POt;OO3{#L$;6oW%~7}nT!0Jk%{}<%Iku)& z?GRrGkUcpsb?n4_k?NU<++tVGiKav}hB6K&7mQekR6%F<=b*H3m?# zS;ohLHW~xQfHAOVfWHqO%9tre!Sw0CkXiuX2<9Z1b1%U$UNKXQf>?n#2?a{1(-y-? zIP6~iGQ}t;;pDXWaC)-S4#lO@vA<95 From 5550ed8be2fecf31d3d04275f56057dc0be278b2 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Fri, 17 Dec 2021 14:54:15 -0600 Subject: [PATCH 006/176] remove ds_stores --- .gitignore | 4 +++- user_data/.DS_Store | Bin 6148 -> 0 bytes workflow_behavior/.DS_Store | Bin 6148 -> 0 bytes 3 files changed, 3 insertions(+), 1 deletion(-) delete mode 100644 user_data/.DS_Store delete mode 100644 workflow_behavior/.DS_Store diff --git a/.gitignore b/.gitignore index 3a9d95d..be6e35d 100644 --- a/.gitignore +++ b/.gitignore @@ -111,4 +111,6 @@ dj_local_con*.json **/#*# **/.#* -docker-compose.y*ml \ No newline at end of file +docker-compose.y*ml +.DS_Store +temp* \ No newline at end of file diff --git a/user_data/.DS_Store b/user_data/.DS_Store deleted file mode 100644 index 3ae84be0c22145e2fff5833694964d892253c09a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKOHRWu5FNKwDS}0pY><3~-XK)r1YN<3h9ZiTG!=y%3mO3#Xx`3TG#mnRS=S4SW^=r&_-PtEYDlvm1-9h6k zjIN%vc6dAgh_xH<&#G;2PrW1B(H(S$wb!&s>(UqMwjx^fub*V?ov36Ud!l;ggMuc; zfH7bU9E1U!*({S2L2HcxW55__7~t=Nhcaf0K`?$g(8MbMa0GJ{^!YBqIbJbS41(}L zoTLIJ)#(+(NjmIa<1)n{DCy+%^5L|y(;JG5)iJ)0;p8$wYmEV8AZ1`Ek5fMXFZbX7 z(Dk&@9G|rwdIM$QxFFc1pc9W`#PU&m28{x{=MFGa41%yg P>_;HdV2v?wPzJsMn15iU diff --git a/workflow_behavior/.DS_Store b/workflow_behavior/.DS_Store deleted file mode 100644 index 98c9ebd40b63a8e2e15e7490e5a65811048e407a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKK}y3=5PiRPQR%`W2rjd9CG-Zd#1r%Y)udv@rYTex?&2*xg5cI0croAn8A?N1 zEP^631M}a{{F(eeFCmiw5Jr#FAus^YqY8EoIcyPG7i~$$EV3BlJr>9@L4g@=THbE> ziwwxKJH|Qgu*4HKpI?D{+A6Lr@PO6)b~rAoa;Zs#=Y8YKD$DYDmD6ALI$OPcTzId43)56*xy z;0*i#1FYF1y}qK4&VV!E46GTD??XZrObu(r`03yfTL5C0<|v#?FCj6BVQN?_a)lB! zl&GP>T8yCKv?u158rF&$j$qA4u$_Z-C?W37`zO&IAyxFz8E^)g3>@g?Kd*-YZK}fRYd%1#dQcLu@uu+O7ST*3hjwZh^b+%NDIY( N1R@PSI0L`Rz$cm9U-19{ From eb3ba17bfed30abde0b0aba0361c63ec02828502 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Wed, 5 Jan 2022 18:41:33 -0600 Subject: [PATCH 007/176] repo standard items - changelog, contrib --- .gitignore | 1 + CHANGELOG.md | 11 + CONTRIBUTING.md | 3 + apt-requirements.txt | 1 + images/DataJoint_Labbook.png | Bin 0 -> 84080 bytes images/lab_diagram.svg | 179 ++ images/session_diagram.svg | 77 + images/subject_diagram.svg | 222 +++ notebooks/_All.ipynb | 2752 +++++++++++++++++++++++++++++ requirements_test.txt | 3 + setup.py | 17 +- tests/__init__.py | 120 ++ tests/test_export.py | 7 + tests/test_ingest.py | 33 + tests/test_pipeline_generation.py | 16 + workflow_behavior/ingest.py | 2 +- workflow_behavior/paths.py | 17 +- workflow_behavior/pipeline.py | 10 +- 18 files changed, 3457 insertions(+), 14 deletions(-) create mode 100644 CHANGELOG.md create mode 100644 CONTRIBUTING.md create mode 100644 apt-requirements.txt create mode 100644 images/DataJoint_Labbook.png create mode 100644 images/lab_diagram.svg create mode 100644 images/session_diagram.svg create mode 100644 images/subject_diagram.svg create mode 100644 notebooks/_All.ipynb create mode 100644 requirements_test.txt create mode 100644 tests/__init__.py create mode 100644 tests/test_export.py create mode 100644 tests/test_ingest.py create mode 100644 tests/test_pipeline_generation.py diff --git a/.gitignore b/.gitignore index be6e35d..680edde 100644 --- a/.gitignore +++ b/.gitignore @@ -113,4 +113,5 @@ dj_local_con*.json docker-compose.y*ml .DS_Store +*/temp* temp* \ No newline at end of file diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..ed66e14 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,11 @@ +# Changelog + +Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) convention. + +## [0.1.0b0] - [unreleased] +### Added ++ First beta release + +## [0.1.0c0] - 2021-11-15 +### Added ++ First draft begins diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..5836c18 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,3 @@ +# Contribution Guidelines + +This project follows the [DataJoint Contribution Guidelines](https://docs.datajoint.io/python/community/02-Contribute.html). Please reference the link for more full details. diff --git a/apt-requirements.txt b/apt-requirements.txt new file mode 100644 index 0000000..5ecd286 --- /dev/null +++ b/apt-requirements.txt @@ -0,0 +1 @@ +locales-all diff --git a/images/DataJoint_Labbook.png b/images/DataJoint_Labbook.png new file mode 100644 index 0000000000000000000000000000000000000000..2d8b83a1667c6ef112a13c8d070770053baea11d GIT binary patch literal 84080 zcma&N1yodR8#bzd(u2}5AR`@ufVAWwEfUh*-Q9w~&aX#6{$vJV9!I@&sW81p)X(TTAH* z`1RCYUQGDuY(Jwn@E=qgaaH>#PtbAx{CoN&DTVOKQ*k^A5uwj6DSN5UT=0|^A9oi9 z)MuTXcTbque+h?S)B2Ayy?GWVjPAdJOM8HX1?t8|X-Bnn{&QU->4_X1q(5?A!d+qJ ziCfJQUg?{cYuxr5Lqy!0BYf$h^T-^g;Ovb5y}qc% zMsSzeGx1Sq&Z__CJxD){X=fo&HhUQ!cQR)?V_h&fQd@DMs<5L2O2Cvj+TGrH8#%Eq zZ}T3~ozqU=pkdS1SKGiiv5NWci=GG6T+{@_i5gmeXdRmw|M_dNaIb{?IgzA&-_Mot z-G}xRzLQQ4`j(Zc{R5>w@JZ7MH@B|5fG-x2C86?6K2t zd@fcwi_y#ZSMA7tpj?x_y8G>u;5XGy8;JVlT)ZjL>zf2it#l%skAI z>4|57VNEZw{` zwjS5p>TXX{J!$*!S|X#`(IW)zv3p9ce-a)b6tLNIL>3nDq)5^=OYk$+Ch^KCfiUS(+0e{}V#*p_cC7U#A76mWp#a*UolYk9g&h}iM3hc{HLxyc+BR2jn< zQrY9U_8N#S`zd!k>VEJ{ch;CL@-~g^olaO0RCiANJj35R=fk4l`(4I1YD5yeK8gPd zy{V>6h7>``J3rE1+++v5TVfGL%eLV01~;Zvg1=t3ijgykszDGTKe=SMfKG(E=G9_| z^wlE9siW%TGYj6$S*x#*PHSUzD_`(yw&J%BOB=hOYwb#jbB%S6-y%ntN+oATgBtN& zMx3>aLR4aiWvcmZOy3oHwHYVJ#sl9e`P?sTdEL z*H1xo)=^kXST9)_HsfE`{cB`M>93@fbCuWoQ>aEEx~dvPTsdJgse*?3!eLeIL-E1hgX!i!*M9lGfz&adUngpgh@9 zQL0AL5@>_(0-d?QHi?#-R_X5g;-{3HPG6b8c1g3u7i}~b#HL$WL&I^kObtyfZ*;s2evGK+oZ|!o=gHbe`U#-$Z=D9q9q{-Qc>c9-gG$ycY*q`GjdB}Efaus)iVVR^OI)LROlE&hy77-teSP| zSGDZKvl`)0G&}Ka^r-*1X~eeWFN1)ey5b(Ayx95i%xPzDg@lXPr9=uQUD}sqS$z$7 zET-S4Ly$-v!v=@GmAUoSUlxO9Z+xJV7G{m#JcEyeD{ttxhk{2oIXvwKhG|9CNgb$K{Vy55AWB`+e_UI;1j0B=tjr4ziG77FL#ylG4>u;*pzh zDKO&Ytw_Lnykn6X6;IC1!9FVk&A)MCi>e@YRzX^+!+r+(1+?8VF*NlK&?C3}+B~N^ zmJ{=&9V1n%y>ZC0{v(HWZ>iZ7`%*wEVEF^~eHjR$GPM;;GBGa9yMQGe2}UCsZJqg)?V37O}x^5L2QVhO1=zt_k8VLeU@Um5za-5t1L@%2765f9{)9EcGp$07tfLK z)XmMc{A_4;qArRy<6@FPYmsc8QgVlAzPPC9ql|qb=0bH`am2#AQ3l>H_I>vk=l`k!zdK>74K^3<|+^sP4Mg2n#p z+tkDrV}cKQV!j#>@7xJ56vcQDsyuxq!A!wu%{)>B>x=mKO*r9jnIqr(Ne9Q{eLGZ` z!_d)g9rNd%u;SOp?y|LRX(^@^56BaWfMHjt8(6bXTVHVbNL%a_=x9=Rc96<%7!9fu zcvsEl=@n=fbBxyf>Z;FjjUw4eG_6=R`Hfc`4vrcnTe9cUL#$$Qs$xNC-bJ1efM1PV zIXiGHvugxZ!s{dj-eROcH$E#R2lT#mUxUfHoP~{G3ga%3-}->D~o|-P@T>3`)JXg zAFD8x^>i<8NzY5CgX&UYaAiwN+I>r4Q~c$>$w@|WOJQDt3{PvR>J}9;=vXt=QZpVW zB*h_5Gl}zYQ#8la%rZ{vMIwSSJyv4!8+%P`?s3?+lIG?I8GUOGqi?J_Ak!DdSf=k^ zS#^1(KYxSZo?_g%FIF}A%rO^%L3O*hollS#jsb`M>Y~;>!i>Tf$G^#xOY)H$zv%16 zzZ|<_?Qd-UH=)w|!u`_W9`KgiiuD7FFS4|^#vzB_YeZrx0e@saT6f}8db<{Iz3vbF zL86`n#G@i&^V|?BWrMH@^+3mpka4hUb+?QEDkG<&5VK0w8)4Gx)^3+a_T0 zlwMT}K7zjP^*`I*3WA8zv>jL|J6L&yI>?xBki|9$Dp>s;YuTYIRC~wH$dC74KTa*~ z2X?jvsQyj;qJkJc7FosEa>o!7(>jU69MUBMq%ys)it=i8vTtoKOqf74~@ocEQ%Mz45sp->LXILf|U1 z1*wRgZQPXZ2XdT@zi1hgP;)-1DJg`k>1S6^#kx0jBnp}Z?ni#0ygtCQra8Us^}7^1S;rLX4f> z#!0>qz$<8j%&xs?jgPe#$A7+DLFHJB8#5j8>9Lc_@6r6#f7~gO&=~_7*bX+GxMbm6 z?!3qv8dCaz)Jv82h}~p7*u=n_2c}Dg!)|3L+r7WjIRr`s!fe z6@tWnlVK6>>^8`wWy^ueZ+0&kyASxGOy9^GN}BI^SC`nKm{;~V1LN!5Yi99KDKMqC zcFarVq@Fh)K(lijbJpA0aNOvlQK-}~XN zhT}t-%<$;w<9_YZ$;pYI^6uK&ccWqAxT*2+@t>d1gq2;_*4A{IySuyjRFske0s>U4 zjOy#_>6oTwX1LrhEC>kV7mFF_=uCS;iDn)aKeNL7?E8S6X!Q(VE23t_IF9pk=T@=& zd$l=r8X~6V7rGtk$?}nJfPkHhgBf?kY-*=M)u>zHTurA_EYNJGVHax8*H$g;uK9y^f#Ia`Pyg;=;B42DieL`dke^RpnW z!8#QkZp zW8r4GO<&UvaY$cXRoVk}q^(<|EdnWPPDUtBA7Doe;JZyVm#yBsMYEP{7a=(TF3K(} z7OxeRoclXZq$$Ry|7x_zsLNeNFOx>ym?L-O*46RqSQ=T`x8`O|+oyQW;>(Ro>uF9! zGnY%B+Dp$XvGIg_pk!yQT*DTa?l+OP@3(GT?)@>}kWQn{vP18=Zh0+9nKAuUWLQ+` zMj=HcyGCjjSGCs-0|CTdEgzziuf!_PngqZsui9bz*1P z&?$EIdYXKxy9Ah_^h)g5Hzrcnx2?Drb#pXk)e7W~`TR;Vx7@kT$L8-(QjUOxw@Qdf z#PN7EnA9^VHc@bf=jdOxP5q$G1%CnY7F#0x9ZPK}NIK0NfkIoob%Xo!iyMMg&U zdTMWP|K`mbEUbvUJnA^QF$g+3x}&4x`PrG7xp^*WWp(vrPtS9ul$e;D3~Y<2>gx0E z5Q3_zDk?Dc-0Ip|PC-GC8$CU}O5V^yyehVeiptR;b4g*LuuqY4nWRZ!ZY~u=4@g;G zpQ1)YU45>r3ke3go*`KCesA_Ofw`9)(kvDm8{60FdCL(`QN{fknQILzuV`<=^Q!%Q z`H|ABLsj)4>1BiQGf_Mm(|{f5W=8mYCG4IVVqE#-WP8K>c$#TgJq_x6R@ihH>J57e zm|`%q6tsfEN11wyS?Q90Zpw7wNbx6-{wG@|aSoWkNIKtN1M%tbi$pV5qsXzl26E_w zUElBG_ixIcp!yhOZm(MtCI8GjdWEef#F9KOOzFiCh3*(p-qA335*ND-7mIkcvXn3o z8(F6t>z`TCB0d1Qj(+C48=gx#SX(O&pVbq+l2;457^yt@2lR3SC>7>W%{|a0b%i~s7l8_ zCqD6?4k_5MQNDZkjtj;}Pv22F%6Iv0r@dHN+m)M}TXo}LkO7s<{Y)+pEY+D(fxF># zvYn5`cQ)4Hk6zCJix7C=a)R+{xL@qKuCK@Igo;NH^QlofeJ(zjHBRqY7GCKIBf;jy zCMFK<7}Mz<&g5x>=o(_P^tz!ZC#xkk|G2+0$TsY|8+L-pHD!0L)K`Cq>pz$(NW;UN z6wgj@4xbMZ{Xx~gH=M&WMAz?o8Y#Akz4ZS~g zt+KtHWbdQS`}Yfx0W85Tpo09e!GW&s-is4*oWJLefqZpYNpB+_`Exj>r>6%?;O_0^ zNdj=r`m9`BDnIEy2t3}|+n+W=c$XlYmcPEco*Ko;eEjI&m6Vtmgv4baDT!8tU)oLG z+}xaL>;daqQF)o{;Naj_MMO+2`FZ@;FSPEYcebzRYF1YC2y{Yq`xLnH-;!x@8C#&b>g@|VJHQvj% z(Q>7{v7_QT;grIXA;&`QVRA-GGTxomgsp{SiN`@JJsxt-s3Zx=i{3Qt0 zmChP+qw^vD!XX@RIrZ<~1`UM}F!SH;P29ydKHThRmdwE*47M) zsp#p02kRZSqnb7$)1{icRw+qC3(B%l_I-c+o5kDrr@N>)Dry~&>$09dN?q-N(PnKAsJP9_EpuQy@iprE%XXqyCY zQ81$G5S3OoU%Riu9UZIrZ2E5f$_YUA#!E7k9G03(%CLWTZ$hyTFD)&dpU;(wAFvm9 z8EJ65yyv;Z67XoXSr(2IhFMr!!}dcT)}jGZLq8uko%aulAm9ma0{o-CfyY-|p`1g;pPF`>d|4bZ}GRw_kvHorVSn8K|j^Hii<@ zJs%SPRfAMsXFv_YJI%{l><;@b3!wBS;Si{~ySbL7t>gzMd-G`l8WtD1C@7CRy_5L8 z7BA&p7`oghMFU%acqElT0D=r^mXs1|PFDB~#I z2p$L??Fkp=As=f^2l4++2D@Yts5|fXbfu)wW3Vf`h7}b^2DvHkHVg98*R)U(^@-Ts z-Z^J)l&IG}T&$q0sgXWjD*uK{NJu2C@9z(vBqt@MxtuVMgXnPd^;gASdFIsBC4AJA zB9{S-;KntuoMD}$sc4J%XuX0KDHXUs!})XO-+q5LoiHGK(E>;Wq-T?v1u7tT@kan@t45T(Er>7BMZUlx^a8$tvpaN)UJX#6qaQM+))u%F*3Iw&l?)*>bASnai z{5Ht$?rvrQIzD4xf4>lBmC-Qv0i2zkonn!Q#VSQjR#w)`Y$u7`JVOHTu9O-eBma+T zH5No>;UOV7K?^P&%G$PvHHo~=Op?Fm<~BDrw3=LAvfF@y^nj>p3&e1v%=b>|Y|x;; zzCk>2zBgM`_bVbIA{#5K)BZ1i)%j0fQ5^aZkwNbt=h@QI()KcC6%;101a1QFX;GFS zc<+!|>b96+jExIA(FybukdoJWX2ityMdl^g@<8NLmRD1dk}@-`*AmZ>hBXn!s(FhY z6v1@NvD>{IJ3A*xse{C2B=k@tuatSNsI2Fko;x;aE**D5wu!b+j26^oHBixO&;qK% zsNy4HE2W(xCw@)sEQ&e}P3A29r}KnuY`i(j|G1Y4Bhza|M4fbf{CcTN4J0b$eg@Yk z>nYHh>gqQdbw@zua5ZIc)H{=V6q} zfChJ!8TmKy-5qt#=vd69dq2<}-@?_@)MUkep0@X4Sy)=mGHxC3mNhrH9P4qQM)-^J zpPxn3a(LZw^#P9MTlzf{$_^~MRJ%3pU*SH^k#V6YLqqIDpsK>=9v&XP%YS_x6CaO0@3|etg)6H; zLP8Ssv9Po>uC`rR5OdzMaQwC--*{N)8|Z!)9y*gH5A*wZg)l1+Ha@jT7N?B_lV*s# zBWJPV{o!oI7U$p|Jew)QvO<;+Z72qd$; z2_*{v+z7pXxPmaydsJU;igph`Y#%T{m}sC(+2$<^RfwhJKTCL-IRlw}f}&mVROrF$ zp{2!(pTA`htz)h?#8=ql%8l+o1{b%G&})$k=jPef^XJdncyMXOTtr}e&z~VbN4mMb zzP-Akb04HzLsmmxvVH3FV8Z-82_fwBjiEf6B1$gS!k>M`kOhOIP;7NMPDL5bA*}HMwK1tiOUTR0Vw}Qv z;q!!j6LW~#6-rb1MjJrH5v9&!?U?C;`)1MTn$cE?$;o~*NgzsAO;DRt>7Z=(YK%9SC4*72cyaOY@Z)jg zV`G&*fBvkfIQjGE&&kP5=1eg;=hvU(e*ID_&9*ZZK3+#j1QG4M`n|idVWDe^4+g7f z$(8O6(;&&o%Y)Nj&>WXNrSygR0;xPXoYw*CV{5(SN9(8EHsMR5HD)|oLNszWgyFuv zzQU?M{Gm5D*IeAZNlD3lxP8AGexVSb;Ve+{@-lET*JhMI>8w6Ev6G>A>PRDOsQY*h z6#t@P>+j^#piU|8c^{r`niyHh%c*AWS<*DXM5<(A5YV=cnGxeM{DnA1&fBwy3?YFZAETmh6ei@WDp^lYPJ=^u-&Hm=6xOWLW8F*n z%4&|YjRSSPq@-kFVNqRC|@I(6J1ORDS@lrNMRv##7eze34czF44i>4vyN;P{dVJ0CaW^He;I4euo zNsIw_Sgfs+t;qC8Nl8XV#^Tb_7T`R&*JJ^7Tv!|q{bLcETLsWyYlD(+-va+`e4cON z?#{Eb1G>7p3heXr^i)bpilJK;IA(l%DK=-U+JuCyrn{!XLVCMC2nIK$Kb#zv19+B^ zA2=`rEU1#Qa(sNe*jPsT7InDH1z8%Y^=L$Rkfg-Nmje1q8bG5=OK2fTRx>mdGetAy zb@uBqeTE|XehBRC8XQ;H7aZm5tH&jVMOs-?S_8eY%<%dlP zNtvOMekLX+dIBi$Bvp*iGaFVBf?*%1QWO+q_`h;W<1w(bRKx>UzWe7P-&vgAhw>}Gx}oBsj;xN# zlS)=QabbT549pJ*9t9P+5iNKbyaw+Q{CZ$uY`wa?&9n_ab9s~5@huRl1^9yHmFt)o z11}@N3;|Y6jZ}TT?8r8QrBYm60s!?}tFrDP=%ys-MQOBuHicVvvHL@b_Ap^NzivjPldAe-t=x%{ zeY0o7sGubAJbMG>_2+V{QK$0Oum7kEuwIt2gao45UqJQgzXta!%zh2pUfsm>zjYYu z1(F3u>q>{(u)A0EUg-i@XT4aZI4FL~VJO}XriQt*{l zeJ{pk553<(|7W#*vvJ8B?kR%vq1D}Pr@N9fYXH1=f=}cEOR;Vw?H<@+=-p)o>qt~d zo%R%R!@6|2*SxoBq==?LLb(6zvJ_>}1rdnwwMCyL!4)_}%TU z2dHkb_QHa~j1qy4+PC@nj(0rNX46{disnjy^z>iubts(=e>?5`v?S*9{p?+0@~;Kr z8@P`*nO%GY{uIz}A6^%nd3Wb%A0s=1fdRdtSw>XY z{Fc`|I~#c&Ik7KKw~POoOmd~M0yO)!5h>h3f>zv#gv*XNa{IsoFsc9k9 z2LQAqxo1tT`*k!g_sECFgWe_@)Wyu9$Efbx^>GZ7*vE4pwC6b09ozLbj}Upa)GzNW z8q;hsPX)b0rw?TL=y3hRqS`lgVLdxhUBOSWH#Sc*_#Jd)`vu?Ux-MM;b+qSREFHM+ zVGBQx8r$}#XJtKclMU;S?Q*!4{LYw_lKcOrTsItl&Z;dlz)jD@hvUN_y~*W+llYFZ zpJ2mYk;msQNP9a4@fgiF(&W4C&FusSTSw6(Z)S6Sh1JW6*3PxMvfj|UH9n^*hLik} zt7+a>Et+z2;b+XPBf<}rcvA3@fdLV>i}`d)rOW-xXn4e@qNe@hi(?P(pS-lE$Ho-L zm-i8(3F=7@0VaO@=;+quFCN8b3lR@N5(@UK8yiLsmK8uwcEX|Pg6Zk*r`Qe|^(PAM z+KAIu_7yRAXOIrH@4Mj*OPKum%k~XVW8QD`uie-nSH>d%Wp4&R#4{%&mjcX>L&RkPEQ^6C}uBm6gNI|PA#-dd{LF7>PK^$A|FXJkIXrd4kAs_Z_X`l zL5mK1bIH`lYkX7n>vx!ib~%*_!8jMn-iydT0Z8^tk#+)7QG6mAAtI1fr~KC}Ui(or z4Hkq$d%8rSDWabIl@%2bX6E>XUjs~JC!{A02Z}k$W$Lp)+95B0*S_W48-9yX!nbjlCb5#F*R!vjWP@#5rv7@wg=X z@fjFYH>AvXj1yW54+sL#P?Oks+f-HF`8p{WW4&g(mJW_Wly5&yJ16{M!St2)+>Crh zZh?TRRf1`SPwYDxn-xI(T%$&F@-RNOq3Md%`XBdP8*T>9cOrm*jfCFW?YGmY)pC2NKwCcF1E%s4S0}$#o-L;M$!E-HQ9foipWxS zoie}|D*SrHl+OdA+iBw1+S_qB|7A$&w6ACkKp+~erfgT^`YA)m!w=T*tsHB0NJj|UF&c6y9>{uBcqlQvCevrnx6NahUI|tU7sY zKx(D}KyPbzC%cT&E72im?yn+rtZ8=Y1eBT->FD||F>Jyy5E+@7#fZt=4`&8u|LTq; zfau_pyj7i4;r(HQN?p<&-0dvxbofne^JD(rB_($^aan75UI8e$`v9b=Q>$OJDM6U} z1m>_a4w=Ohm=Gc0FvUF#lxS_T8G)3Es2-7GySF=wFA$QT!akQ*0m>wvjyV}`Bc5|= z7V<$p>l=|-8~2)1*~H%vjeAc3P{#h(YUDwvM#HK7i?pYk1*;m=OOVqOyQS-+{ZG9f z#f}e*(LMF(UZ!}hNYCbm;`r=}vvY1rEiJHDXqvO);_xqSqk(XK(fcdb45#O zf;3&P_x<{x;31d8X`#ii<3SNFjc8$F(pJ-a`$M2Y_ZZdoU%ze z^eW1LW2rw6|8|N-_7@eWMjZj7-+tBwK;;5pIx5^js;bq68UVgA7Ct-Km6Ww8x>9Xl zEUjU5r?M2b)Xoz4|b#rVpLoD~&y7;LTcMjlKe1kPxh%Mw0k$YgmhNC*d&7?~L94X($kSXTKzw8T62vcKX*Ru3{JrutG&%Jm&9 zym#vA{_N1hqq(jgK6H3GvN~ zTwcl*gK{fX&tCtfs5!hn(cz;kq!9@Fo#TwU!I5)FFDiS%paZ~SxmpXTxXi;}pyy>i z7fvPPrPQk)%=|tu&vDG$C@4D};{7bA7gV5Q9M5}T7$Jrqd>9*hbsRMTaodX(o{0)v z>6%Ry`2DvS_*ZNg+0~9C5`JN+js-w6wCy%4J1G(S4k6-`d#O?VJvi ziD1}huVW`DLfQw#~W(d*Ce6lFU_zEYu?Z5awG)W34sYfVixsWYfqr+(JRz2H%DHX4T^ z{coK}4g((;7#UXj)K!NWN=rMKsY&X{J{h#N-OnrqJcy)iAHbhLdiKmVnumwS$jFHA zy}^mx9vmDQ8Ho%6%gTNM%TpsRDPLn~!6+L-g1IRDga2D+^eu;{eti@0+r-4nNK1=} zBq={X|2#vcyIGhHdOaO=s3?4@@s1^+rJ`PT3F2Zsvt$nHs0Ocxm7@UxVTdI+?AkEsIoFa`pd*#u?|Rc zQ z=!3wp{-;<(=3cYe9H^F3F4c@1akg{nsskeyko_#EQ>N{Pbar$&9WHwA{G{8`23pz9 zx3oDgua}pX@$m5A(%(q2XRr-KMMN@G?Ce;_RdTdESa7^hn!Dt;x3|Ye05sd<1<+?? z(roz5>?A&;BO@cDrA3B)Qc83}iv2p+!O6+#<3~&lTL3&e!r6cWzd9q^3p0Uysp1?U zhT5(^-^e25fJ#XX&d$~&55L}R>+SgvLk^ALV- zG|M}4o}mVaU-j@OK#aJ!xM%?&tu#LOa5BE6xGCG&f%*Bnoyns0)Vu4=bRQp|v3j6T zA1Wy-`pRS%kGu?Ld?2si{}8p8}MNy31e!cXxMBPtQd44rlS=7|qTu zWO{ynvK^F115kf6|K`SUa(VBK46JJtWM3g#Gd!cSCxew_YLglB|GsK~8u9jOYO;}u zZ*XD|u}Wh>!%eniMT_MK6ht=Y{n8wOw}OKDk5_vmTkO_@0|I(%$pCcVeC1Z^Q#yZ` zs?A6z4D0s#dNPDG%6-I|$mq1$3;=TBL9g|F=0J1md1&VP)yM z;$>rqPFGb{cGkS4KXJe4L=ATDZsRquvpZ?=yuB(D81C<%t+zM+_%SRp^5vr$qy{N* z=s3e{Xa!Kz?cH52bJU)v&)!)7>~ zNGt2E78^wY+R>~iE6l*Y{&GOC`NM|~d?tB9y=|9@{0ola0^ZsL$~5RwRg2E1dS6gc zC}BBUJbxR{f0e>{Y+x^zQo}w&RdYcDZm*zSgWwqSk91|+@2-x`r%Q!K$J{G;G&L6n z<9XAHLxO_=L=>yh{2d>k=iy>AEe-%|YR-m)hX+^+zuY}^?gP+b7dm$qER55;>(g_- z29POZK<@tD9y#n=w0k~?wW_jGmT*pWX&XM13Sg@pUdKdTqC4B$Lagpn@?S&#*j~dr zyzdO_%>L45|1_P6^7)r9g5KYCR9tA2uzf^yY`%mPRYg|#j5qrWJ0fW77Q=uNLlz^9 z9B?{9nwqIW1usdG^vSKvtUR`ZLCN30*ANmZO?ZUXbLhb5 zdE;PWLJP+BMU(1M(!~o84=;XuDU;gN^d4XW#k7ldgk3xDfOMa?0O}G4by5+N{Zbo| zA0489*!Yg6t?luvFaos0R}~5N9Mao8jJnB`>yGZf18nWdw8Wpz`Eyf0ClQoKfqWoo zlO0QX%kvOXLqbgAa_dszfEBPozXiuQTLhR2+hWL(5hIL|sGq(S6v#E@V}DY+Alz#l zLMCRQr@yNGElF;G4kJXISH4DZ78g-*L7_z2{N#)^{Z|=;B82CFMt-FrF*j4G4A3f< zyu?SFM17@gT^jvYqKijZN0=yQSZE->8?l|uT;KdWN<3jjpLoNDvUDV zIpUP^wZk9;x(si(5x2JMYY&XB%B~8*5rFF-ni%Vwm;CLN!VhTvv}3n%WM^Kwb)4}2pkYXhi@iHN~8F~E{^cp9+b zKpI7ed*-UWKC$n-gLGA2->5dHR*mx&?U|?GY_jM#wC2Y&fF~yV5yAJ)a8c(2NCE>1 zXx!;!rURYd8dz0PbbYQVaC-k>$4|%)A*4X-j2zIp=jivFa8jC21Y!#I(STt8!a;#g zzW`lF>c3C$CdH=tWWV}5dL~ei#5blt>-75v;|k2g1p0ps zz#o2n`TyrZ+t#qp6Y7HhB3nORL2TXuul(;Lfkr=Y>A?fqexUs4xsg7$eO?RC{t7C9 z1bFugA8-_5=$NCpyvCmo2_1m9Ly-Xaz6Boj56kuIpH5{U5Yql^rvLAD^u~Zh4C7@V zB&Z#D^goio;Q2faiRCYjB+@+K29S~psC+t+M&{Y4n=jSm+&RSm{uM{VIi?J#{x?%* z;kRNa{cRhDWXyxardVs5Z$ISr7UeJJ=*OnRE=dYF%0ti*Xz5DQ^5>viI7_o?&^Mua zz0v8pkY!J-c>fGzBy|F)hoK~FT=!tG^lub9kS;%QZxT< z!sGt^ZV8EI1atM$aGaV&e(AEiE^LzsS0Qd04!yX1Np0X;(NWCJ7Q=5t#Om{Pwq%)r z)Emzf-3-NyF*uDK76`~ir%w>X9RueIxNRC;|BMU1Xng-eCn#wK!pG)2KY?fw^`6Bv zc54H^f)j(|NxenwH%2vV9dO<7OTC(b^b*P+ajRV|ZNt0bZP+^M`q_?RydkFnELq=S zt8HRcER!IiC->{UzcRaj`X~1%+y>sSbcLzKK7o4zbw`?JQG<>L7USA6LF6C`5H+aa zD0l1X5}D5gSfHU$Sg!op-utN%Ya2W9lczvbx1)u45>E5H@}=q_$PJs;?m95k)N4hL z1`03&<@k?pRS>YGnMM-yDp||2UNmeatRMBg9c(JP)p22JBC}8?RAInsAqa1c4tqrloaO{viVF-x|VGGp*|KwRJ=mcX&6Soko zgvhU=DU9(XG?AG;D-wB~N1iB=Ee+Gy^+2I{TKmp>f!6%lqxFt;Cj_>4NFm+lC#vs> z&YiO(sKy$Y(IZb6&qI{_$v_IF(KJwJ+Bcfe3(ry|==&*udPM=EEw;_+%G?{g|K{&U{MVdrnw;Dv~${p7yHQn`dlp!#3esW)m0iLo@pmaBItruer_`Z#dJi@{Uxw}mvP_N4f5R=#T7P#RP*6|+ zyob48zv@!DxDk2+<0O=(( z6vIm1&6}#uatPxo;IXh*EHJ>v(M=mOYK{c!7Ph zS3sF+=KQ;6bvyx|!N1M6b;m^x@bmA67?eu^?6+UNYhi&8AXAF3U&UmeJU(2uB5($r zjyM331~^$q1g2}` zt%HXB5P$IU)4Z%KJ%AeNetCd5?)w$@{}$d2DCo-*apYw7PLBoY({pi`9AvXAEgSLU z*=^Sgl1@XLK42PT_Kju2ZSsEj-ie1(m3eIza`tkNw)g$K{ioB9hd_za7o*G%^d$i5 zzKNwJp8WN2GG}j?0!|&(TOWYRNDkoACpYG0HQ&FZXuN2{u26hDe7qM3^7u}0;h)aj z+=|VpUSq+8g-gbJG1#a_z~^yQe)KUa!x%(Y#cXST9kD!IIZt~$PkPDVPlIU16=)r` z=b6rJ^ZN;?4|w@?BJ=&~p_iA}3BGEjLI1%*qyJ|R`v(g8F?W70E9<5QVkzsnn*FXI z9O1YX*`{I5tSf!4IXfI;y~6&&^f#Og3pRGAc2+w}UwG715<5xI(a`@&@RdCtvkYi< z{bk$}8m5%XBck?1fdAbH5IodF(3j7)C$Iu5s;l=;2NeAt2*UtACX)uMb|1-_@?8an z|E<&hz=uij_`<|~(?~rq&H;h989bIqO%gJy`8u^19xKzCE5H0)QgM}E;H4R9#>xnm z7qYP9jZ~0>YeW&jNm&4l2K!d?$1{oJ52l3cMUMRVxzsJpphUUrpoQkgl1Ath9H1v)Y3lMzLp7vQ3jt6LGWOJGr=BRumy3C8ma3l_7Br0Yh5J%QNu9~2P|-B zK)`b*Z8}h5Qqs*)Cl)##9ggc9V>x>+)+(e`Dvd~J50T;6gW^^Mw$_8RDU#U3^QJ~2 zemWy~$?GR#rx0?Z?J2Rtk01RHBdNSE#F0)SML{Pl1ctaj1wjQ@C{nVTn)g3?UkS9W zCK{)8n990f3X4lidjJfsR>IR~BV?ZHRXby!zEV0EK>*Gg8ojB7S#e=$6pBG`7wMC7 zy}R$@a&)lwZg=oA7O}jx?p>?NoW-}@RdYRc>^7MrsuIY( z5@ust+4>Zcxr=Und1vH@c?7?`gB4jIOPDlkR~B*uU-YkW8Pv83hklzY!fXahc0 z&bjD8ig>Kq=v0x*6W+EnX@h~%v#ISthK+5fyazvGQt7fE?{=;(MjzHAkmq4qe(3x9i(m@YBAqXJ6Oi+_1^)Mi3r zF80vixGS>ao&(SbkEUYyLjkMs;f~zv{xA;W7V>`i>%cu)k(X~&bb}@%Y%H9&ndd-A z7U%9F`+yka){jNSkFTjQS4n5qaHC@rzJEN|i=NIZnXlwYdy{U}f`6S4wW_>hki!Q?;TW3% z%+u(4BjDpZjgFEZ5X}Ss8!K+ghF1!`bP5mE4%_3kkC!FLuRO+jdPH3?k+L5HMgTt@ zET&JJQ5OK9_2SIAMb4yEi}zdU>EYK2c&qs=4qqwgYz6apK(nivZ+rh>niAqN-WFuY zBX9siyAT^L%%I|RwvL}h0?Ay!MN@`2tyGzFG#!{sv)j`1!==_zxLk|Xlg|LiPRx^E z-Zt^U9M|=mu)gJdrPXrOH5$S*Tt+fH(tc}gu(5}4mVOp)Sq%Hi$7T|=0bt69n21C~ z%vnzH73&$PU0kr+)Dk|#86Jz)b=G5e5zk~SJtD&mF`&fV+jGE#ERH}E<*4Bp+Vqq(rI_{zX!7>Or zAKVvHmomu)`z~_=-;z1Ctf4C!9RencfY>OWk(&{z!GFp!#gg|aJ)Hmd>MG*?<=I&i zgOm98#-bu~V89GeP?hPZ*}x%4{>S9F^z_5EHKbj8TU#Mrpy3eO4Rkzhy8zDp5kM<- z?jEhqI$Tz{zifTf1Q@vH1+!JgP$f1twuy;}%toM4+p>-jxJZbNRRp-t)USYP6o9wd zXa|;eF_#2GQLu)N0+$T`t@qy=16EfzhZ*IR z51$8=lXG&y)`8YU=980IO38sDgWsgAwRtGl<@`CzGw04cOow|HKlJRQ2iQo|h}D0o z=|&`^7}dm0W*;orCSfv#mYBDMb%A%^^nI5)HIjOUCj!j<>zZECn6=GYrsdbM1H;+p zLC}XofhAFxD=?KXL+O@UR>s`=#O-0e-hRpDzPgM?`Yp#pYxZY3IUlT!KWzQ;i{z~y zEiElZ0d{spC=@Y2HS$&X9WWmBqc$?Ao~sIijf10F0W=X(vVL4IxtuzB$dHnf)+ZgC ze&`2g8h~!?-QDSES?dKozYvg$bvR4Xo$Wfl)mNZ!MKl@Ov9sNPFMWEH0VaT%5D@NJ z%qNrZ=3T-Mfe&x-L(0u#C51kK?Eu&`CoD4kD}tsH>l2Xb=g(IU-<->{9=>VdeMfTa z;QJkn{nTEkw*2*XUMmi*rFF+XfZO9``PW=Bi&l^5QM%KgZNRldZ;|o~N_aUQ&rThr zSN3Ay2^l(c5K#SNn^w&6>7}A4&5ys!q7-c zcZYO?l$1jV3`k1|f`mvn0@5iZEiK*MabNa6=iGb#=lk8C{ID6A_xJvuSkGGPk*j6) z-{}PqDyMWavqv|`>Ty8(+S0bEA}|UGdgy6d!^UFN`G^?gBwa_Xhf>I?Ql@ z3R@$yw2nthw`gO~a<|J0Edzv;V{_9bAh(4r=ECI(iaG9{XUhYK1l55kw8^Xcb#%jx zEBm(~ZR3XEfJIf)a#TN*bjm9VO3E0X&W2J^kAjQcn|qwt>h}7=daA(`9&%px3TW$^ z?{=hU%z3F?yGu(sG|!Wa{KaWye*jSin5|eeA_M;uN?#6odUyakv#abe-m=p~JsvLZ zQ0!2<-KXKNoSdA1ryfY*rFGj_^hodZ5^=`UaX8W&6jJl}mHZyh?)GdR*}w8#H$Yio zb8c9wbjb3(;J;(|5iF$YI{_VQQGab3;A>K$Ug$^vkdI$NfUz4;Sjq04h{ROp1G$rK zkt0zRX@@7;$io2gDu5gFS!%4sOc*h=L%}!_*pIyVEw1yy35@=$w`Gs@@PZ z6OXsfzKnEz?qcY-veHH27tj143cu;Ke3lV8uCW~rDtW7lBP+yA7JWDy>~?Zx^Gi!VyraW6N0~xi^}-d3R_$b{u>@r7it3rI9gBUiB2IN+k)tK7^VHeG1E@{Wnp!>I{Lmv z1$CU72PKqE;#GeBC)xy+Ap$vWqVxzBC;7dRG2mG7UXcGie<_#|1ODnc%YG6o8(TRT zVLQ7!*x4-zncM0yKS5oYoiV_O#E1%2D9#glVfRTk;g!)(iO#9SW zLAm_lR*5q4C8Vbr!uT7BE%cKV5sglJgZ%X>X+XOG2PB+R@W~DU31G${Ehs#m0MhQ6 z_iana6mX?Ok$y!`OEh^ z@WDCIC$@Be(7g-lW5B7G6H}U+oU8?!X%^(C$s#_y@?UdvLHZ!>24k1{nRyVR%>Od& zX46U=AfDQ|zGVJF%&Sb5iHGIg>s-TL43pO%%6RVI9vvx`hPA)uf(7eM+ZN9p9v*I) zEBgX}2Ll>k#IGf?!(kap9QP(gqDE7ox|qGU2x)UQH(%x+6s9e~Sws-8y3F|lrzXwE zTt-0Qd<}W*m*62=OLeSvsqqSM%&bj6bPPABU|l7_gRJ;NE5EjLe&|=OB{cD-Teq~- z*)&^cI-dYiPBdk9&sYp2_y?BV+8@<3&Lshc zl3uD^ck(H)42*H9x790OQ@;xCrKt=+7bBedf^Ugb4a;ByHM7yzZMR9jE^SPtwv(8S zS3}U**;xUFb{$AMWhN%?xBPrG5yFeDiITyA0qGcvH>i)z={4SQQ;Z%=SJ|$oX-gUz zWv+B}bPx~`*;-pK{E?wqL4bnO?r<$Lpt`2!?zloZMY>#r8;-$KiWYZ)OGWcUvc*dc}SZGG)a&W;m6M+o2ZKers8NwJ%X5xaTL%Z751kU(%xloX&A z^TrAQ^Z;6)aizJI%!A)p+s}x&w7y@YH;cTwy292N!cBpolPv7<=L}4B6!aD13(*|L z8Ul_*@icYUuE===;k9|c42x@LQNdmSPmqAQtu1qdX6F*%@^wu-C6mrpMci9@*c~>; z$PlLxpH2-gW?@8i2;@{x?oWNnukE4WH#aa~6|&|ZMI5=4YPzC&tZv(!Z-?!A%BEzd zzP3_w67V_OlPCk6s>PfxmrlUA+S)mSaUPS$-3jL?6lS(-n}6>!dBPXKW6=2PFTT$X zQzwkrww3;XsI5!Up(}BE&oa~mxo80N>{?iA56vY$5E2kraXajy3@9a4xqw`NFdv1-9Y*9y9@oI%Bd`po^s9836;zL` z3=Qo7BKrW)uiRY?*3>059Ug*NtUj38b*N-Cq-p`u@$1*FT8XgK#P1F;FyrdxMu>v`|{R6x%BA|D5eH2P<3CJ=A# z@o_#rmYV8r+TWQ1%Pf6S-Jbomo+uE;4^rMVY*MMvfNyJz9-K#5Nss-u5Dyx6qxg*0 ziqA#PW`08;FD1DwM>zl{?$=2BT`KFs}QBqWbbE<_aOhTf*%IImt@1^zhljgFO&6oXP3H^#^X??i z{Y8ZA7jkm49gfouqgP7l2kdE6_-PbA@0qN_-7=GEAE7rK9d9lLalpc0(N}Lj#FP+8 z*k7+Dn`ema%+FU;_YDsIsW;;u>4!S^Tm4GO0?Z=m_eF(f^pL+aJ&yQkH1u~{w!SFC zz8fSqufHn2f0KCH))23eRmKmmtmLAEHdQ6RApEo)2zzu&nfT*}KBhBmpB9@qbLp2~ zw?du&yu3lJLAlNBrgz6J`_Gw~@zex2{*>T;#C;Te?W}okV-vWyhckffCVP9Or2qSx zAm-G+dL~5mZ-Ubi7#?uc{n2A?V%T|`v$7X`wSXWNoeQgED`10bXFU0_m`-9!w&lgz ziu+zgRnI1KJ6$yq4*=POYiSx8@D8l=&GnEJBukl6qlE25n8fv*_ZhjrlA0PW3L$tW zaR*`LNxj;-x~9iQ>gqIuP|nMid^Gelr!xN6$jid#3&GnK!q~0!8P}L7mxvJh%(qKG z3ZESv03U4xHT*=0Q8TlqWEjRJ*&1SHWkp%!hK4Cn0H{!ndEWnRcH2<1M%QZqmC_Y` z<12V&Rn?7YKas&MyBIeM%acNMGsdMBou^D>M=QkM@OTk%ddFgH0Ji%@V+XvfaaKB! zlDNbXnlDm0CMo$~JfL=-UZ@8Nr~ZJ$7Jo?gGD&7zl8~YyR?u`ZUOWf-T?=%-Pmsify}5XW!eqL^ILTwk)WI z^5mxz&0Jr^*8<0UqJmQ&9TPWkWKLi~W4~kHQPVfkKu!=&?gE^(605IvcmJ$i`jCeV=6wc%pRDeB1mP_;^SxF{VOCUcQSJh6}mq$s;GCkomzK#2SQ! z{HejD4FzWqI;Ef1Go`;9t1u>cLVgH*`~WMG5%Yud6pQ!XD9PPP6189^oJ?q!I|qmy zI7dt;Bs_OCh};Epn0|ESqOODv1weY(3fsIVrEqgfwhS$_MhMyUoB*pgt!c9S5F+X- z>nl`YIwdfbOm;X3a{>1{{7ZYr9zknIACdIT-SOYh$hBD4&3<&%JQ#)G+jqaFD;(+R z4$w`LT19V?YLm7?^WBtQxI?_2%U(+Rp zpm=)eObmBxTdmrz3~pOr*+D-GPKyLd(mOMibxE1Whhn6A^1;T~mS#C&;HFu* ztS_YtB^MI8OI_PscHu}gJU!w!@V?X7x>gkPZi^Mr)GPWZ{5g{NH|m4^Mr=g!Y|bbWX~kk2q6s*y@6115oE6tJVAe${;Et0yC~wg{(u@AC3) zbhOj|^umgmKm+kU)1r=~6}J}ljxE-|w=SNh+)UG)bxDD2lKb)bjf0sWHz4OeN!rso-6c z89*6F*-H6p$C*Kt6;eMBH-}t&nR-L~#o@y*!HMW$0W!6=lE2MLsacnp+oOCwZXc9@ zj|xA$J!l|>JQyHLVPQ*>+6a=>l0dPh7)gY+Bj}ESyWR z!?pet>NuBfFxp6%&FJ0rZ_oSiYixX+Fc!v?!v|r!Hq->~n%*B>DDR%{dG&M%r@kGI z9-gbLt3rk0Ug8t=7UhBKMTuHLT?;>sF{bzyZ@8!xxq}@MZ#F$uNnHDVA~6&q)^--m)A956=?; zIsTrVFCYLiTFHFv*ET3$G0yBf!+9qOv`IB(KK{M*rJu(;LDkUCO;@=2qZlmYCNA(6 zb4d6e340QotWssbn9fJ{$>S2>sfUrYbc}E9m9dC}AmNkiZ^oro#4~7&Sn0+kW3ea~xO;iADdEqak&=e0_swYyuh1!`3j%p# zWuK`8cDPY}w?A_^s@s;K=C1{x$2uPD%(osJh>@7dUlntg-%iFy<5-j!FYa>#Bhqdr z=6N!jO=`m-Db5R7vGFkIi$hwj3$fQdEg3$GYYT16dpB|-!ShIdC&q681Kaw|n;<^j zDeX;?bqVqVFd0Mhrc%;@OgS66i}3=Fr8NYAaXDVr(lTpm8fi>vbGKdJk-1clSz6Pt zA%K6P!uS48eHH>ke7-Ta7d~lmJ{{lmG~w@9OaHXYA@wZ~?xnzX=bP_x1Fp#}yA7|} z*`y@yobp9PL{fg(&(`INoF|XGj>CrbGK@0Y9CAp8$^4^M#h_r z{L!U>=1y#`fLr3aRqmcbPG(4HR07Oyb8~1i#{{FU1&%BumnnT$5gi?8O2~+wXp^%v z5-z^aN%=vE`&YtwRAG*JuiCX9eW8j2>@|MWmjMbBLKcj=zv*oREj90184#r2TGV6>B_=D1OYm;R+R z$~q-8hwV5^;t_CasIa2LB3nOS6Zvt_0$?zJ&2>fJ+>b|0koOHLY$w=q?k=w@@R}|+ z!E8(7?a~DKU3HRE_vYps4Z>ObEJEZ-}fsJiZipONEr6#p?!vp2Kt zFhprXG3kfyklFb(>T!Xt7yqQq(Wl#0_HfiNt0>bY*A6 zQ4u&&FLCTj8mTnZN5hbPh*Z^)8W^Y^VGv5HzQoQ!RhNBE%&6wj^l@)3oqw;s#;KrY zKVX1tV0#9TUn?qH*G&HGKUB_qFF&Wy7bNAX!N>&o!|8iFzxNtIjqvo&8?B;6OzjTw z!&7#hN>sPX^1>?pkNd?XCJ(!4kYi;+M#N0rpNG8N9*hUf^t7pE74q%SVFznYFKVH#tBa(}a?Bsl5W9Kt?G(K7 zzPM0G!Fs+<-CJ+H4V|UPqye*CxWQ0?Eu6fex?;;9sxyjd3a+5g4b?F|lV{Y$fA1e5 zW-9Jxpx>hue{*v)^~**#>ic(lCnu+az7zphOCU&&)nccGtB;U8$@L8F@!(d?WAg9g z;D}A<*_a&+kR!E`v1b$s>0CH?HU!&PYhais8$!kAOU`)VQcaHO$MTcA3nmi~}$ z_~qFVPgrJpI(fiPx>SdjAY>=0Q%1Lgg%(ftuQ4$(@WX@S@ytroAi#zdk);k8vdx@p zG&7yM*^DH^U4Wzi`L4S|O*8&Y&uVw34!FT=Q@hC0#;3pzt$tF7cp%-FoJ=Pex!pX8 zY`i?3@ifNjdrd*62Hc2M@zoi{3E+~{e~Kz^vy`zki>A!+PQ5gwqS#$zIi^NF^u)X^ z>0*8imsEHk@_wP=XlpH{@^h9t3oF$jNytP0NzKb5Mr{m#$fQg^RI8|5+_olR&L(^w z?R2<7pe^1rY9>qJ${wPJaxiqnC40CL=;bv{i3_W_nR-EUI%{dq%UGDJQO;$?gss8~q%072+w!?Jj zugqhVGZP{FMrHtMw|@#ED|ZX7&l?Ys==}*T7>p+=|E0Z5`WjVG?|pNa;N?D9I(&3e zWl|b+f$&$rx~kWYiJT6$%{Y|pcC7>t4W*@_2^*IPMnyGijaOUnIQTJj>{goUp@*bSlFYTKZ=%4%bD(r#nkHnR>;76f$Lo9FcTQ59R~ zJvWpup)$8_9aw_c*qPD@o1dEo?(M6G0!)la%%0&aU;HYP0v@TVbQ;FY2-tV1y^PC< z`Vc#A!i!FIe)=b_0q^>zR0hjA(p4MEQ+cAXP5zwM3FH; z=3#H;SPw&r%&lQngyB_69zWNddUw-W%+E?U1C2C_*BuH;M^4mcSFg<5SGBQIDZ}&78AoiJ7I;T(H!$2#wHxKLhhur#zvH?61H| z$;i*?xvB_QqJzrWXsQaDKn`2hDYXG{1P}f<3;}bS8V)E?YE%M{hNnKZ2`3a6Quy*5 z&NBOXx8OwIg1tkVm34IHG#-+1Iub z(ECH2j&U<#7*Q!=T1Mo~wub08?b(ObNjP=mxob%hz2wg39L^~$k=QYk$M-NAidLbn ztl){D`d}7%iA)bqQ(554`&|6F3{r5KINbU9l)AD16tEnj0y%=(H7(WOk6Fifpv-jE zFL|h+;VBkg>W<##isPIj@1VKg|Je-F{F?bj`&{nbR4*Q>`m63D@ENQv0i_kLj+v4| zB1ZaD>-O>?MjU26Qa5uQZW{f|!T;73#o(Ex7gfXZiBVM7oS6xgGXsYMwVmpTNq zHVA)^h<*GF`EbbqIYX~o(cdnyJxpp_- z?d|EyWz=UrKN3?RQ7`!LDOv?+wf?nJ$%?xFNeLV>F6OpyOofwAG-?Vb}SS@nI(9Tv~B?s#ecK9}>V$Mn^tK>iiGY zbo!pCN{v4by&Eninu>f>n3rft(~IjO`!5}K=$=hWE>24Bva{nydJTz#CF=b*B~^kH zTv#_Gd$;ZztU)qA-wd+y8SuG*kB~9&8B~VmJ}=n;n(dIj{0tN|Be|8TF}=UplZU{c zr+Z3X&Yw1`OZ8*mK%#FfS$P_`wg30WdZA~?^=aOlILBNpFSu{)YS+dax-z~>(%vKp z|MGhwn!aN~bn64`RF*5|%~;sjWiUo=@F?cO>hRJN-?dXJ558R57_$AdD^T_**4o3F zkxFPcGwht2Z7D{(IYcH{xv4_MrQ1b`B+x3aS4 zxGxsczH^qg@G zQI?8X^JT_+RHFiM4-z4X&H)u6uZiQIt@_gIE$R`^+D2J8^Nu z$lnrVpbG%;h}iVWLcHs~1u(ANN}iI>Oy^L}NrN;>L*&5y0IYAMtRwJ`R0SJ;bKgtM zRmAIAE#&41PH-#Km^w64dkT@_vOAXBUXU2^&hgd8xa3WxH49p z!A?a)>ykdEFbPfa4$Vv3m6ql83q`?}%4c(7cFNe|#)$gYPfUZ@W_UeYgNUSCwBJTM)3z zv+6)bPgDc}4hsQunOj}G_8KC4cw{^*bG0i-?bovs70Sx}*Z!+dt>;T$C-kw{=MmA$ z$0BZjhofJ+9=Ryk;PHLgHO5P1Q;1(yz4^Q!UGKA#!fsS4XcpC~jB3CZ7=eqVdOJi8 z@9*u^{o&>O^5x3{hg@^-%VWmP4?17#nX~V=f#?DinM^pWX}~v1$8lt9xi#_Pc+3^w z`mckLggZ@G_Q#J&e@QmrqB?IssdPX!gc%NLFT^Oz$h^%llv@J5Pg1b(E6q3Mu_Xmy zo@BVBg(_LCfOaPD&P>|}iXqjt{CoxwZeidSRzlJ}yPM1By+qE$yzVL|j)Y1jbA**J zgziT10_OcvPl%S#>b&b_pqVtG0%AYBwp(=k$Mh$^t#!My-=eego_BC}p*rLRv(bNT zUJ6e>x$HopfH(=@ctP1%alQ%7h(%OvcJ5}b+a0h_;|$!=)cyR`f~I5)VOb# zH(ajf`kMX4NS6YYNYC~1v&TNpUkCc5J^gDN+Vnw~!fWoNN5f4kW1ST5ot^fLl0G(s zY+0sD({Lh2X>T+fPK5j0h)QZ$9D1o{ld^Fx*v-3C zid=rtg>o1nbr<`*h=%Gs+JEH9IpMir9)*Gb$Mlvh!n3tVZslS@lv)$K&{=rc28!hS`UuXB}sU2 zUP*JRs;R{5*_|1qTh^UVoj4rOPEAVpSzF(4Hju!F=VwBajho#wx&qn$0l$XxLzE2B ztLry;KV5xsvefrr)NV1fNqmO`+!v#*WE;r&;CMdfIGHG$O$787Lvkg3(|!l&TsZM& zJS@WTfVk(P(CkIt+fhHNv zE|KDK%>F`gp8T>5gZsvx$1C=>#eGqsTS_LRzFD~W9d@_GgGbsyLqSV_eeq~KqWvyI z&zqi&L0oW_9!(=mN%7?%rvN9a0a5x|?D2MXc2muOEB%rcMMJY!dQdaCk1kq7f;4KA z%?c_5Syxuc(&c$_oFWzcjg#CXju#p}ivh~ZTu0jxo&(;_1`Cc^9(<>0gRtF0&w3Jf z`eTdb?W1bhuIz1b=$S&U)y>M`WV#$c(R`xJ%BY^f%Sa$SdrSfzRI|8+&_Vz=0JCd(Wa<$pBrPO(5=v-rEdv ze&TmtO6D{R5hcOJEjR7JcoGDhZb?La&(nqWZoLyTGMX%ZrBVi556{fZ%+97;ogN;h zoCV{NX=*PLI%SYhsI`%Ox4*bvB-vOVrTgwDOLQ3kh)E*AF zEHUIl<6Ud>-%R(Vws*v88(Z4i9veN31uRUUEq#S>IbI+BA)GGKprH*%((!gQv3=&w zi<=;xpi)-u_%eGN+~_CD(U!ked@_Tt-!wiVkDJBvHWW-xcXvnt4^h}qz_W#j zp0j<}M-EbHLDzxcGbMCpr^8{XddfJ4M*yxyh`3Q~F*mo~XoF|g<+eqXmm&-IW7IWR zM-(y4ad%N>hIZ(cVbM?)rYax%wJWBtF&0{N{(0pwI+J9$!CFs=+Z8|I8jw%^A^yfw zn#xs$;Zlub&=a*}!CzJfZhpW$QiAnSlFTS`jDB~JC{RsVdPS8h=%VLhf4Mnl2L|Gu zBr~}SpqK%lyAX`&>FLvr*(*SuiT&Z(4O+X)Y%>|ac{4KurGn1kUN@#5cfK0(#?Exj zCr&eIb#;8Z9E)#+_S4mY$%?5waz;i0Q{@&cM+wYY{d2!>f1f5WZHT=+mi0~m3ZFM) zg~KM-;{jrCNTZQiab5;qk|Vk2gR3Bu1C6`%}rL{BVl4AUjxMI7w|5$`1E-hWGbeoiRi! zDH?B3W9jY3;e_zTg49+#BntOET$WquO|blRIggzdd$8-^XlV<${lE{J1xSVqP*6}N zE0-I;yWbQt-?@Qn>HcOya(0sHhq|(IOnT<>%e6NX#btp0`p6c!z$~cCdaRyS$g2Ad zGLhfa>Gy11XV1_uQ=ks^%6IFNB@3j+EN3IR3R6>4W47vls_jV?){JE6 z8B<}nAS3A3+{U=bfg`W=%ny%xvGO1eI+RAo= zQL(%ta&uRPvS1oT1kIFOe0();s$z=1f1g^~o~jV?oeS&$KA75Pf2WJ+?Eh4Se!0B7 z-D6d*2hNXK?jXu%ymj+lj%6fu-2LO_x)-yTQQ-O@JP;i!CQn()xz?(C+mDq6QxdFl z+DHhjSVPx+VsWH#iye3q@*ESU{1XEoQvc{auLdp421?uT>wni37& z*JjfrYbs7 zR&vJl_YkNYzW=C>GWd&XU_NmkCHlsl?f6n%%N885yFqn}EqlZ1iPJkwp#p}tY_{Ef zXw`&|R_C`HA1$=L9xNNFJB%5jhm~uElhy6@?!Kxx6UhIZiUeKVXjmI6vMhdaw^Z-r z9HUcjHxJ_rW{6IFLtN<;xd-)%pBa-Hv!+ z-gRM)5g+V$E7|575@V5gJ|!Txioi_)G2x**_h$51O78Z&AUM<<$+sdqhu`#)r=kT- zH}F9$qYFEXJQY|5!OjF=?l#%rOod!mKV)B^o+|NX2F8?-kzp$TR~FGzCF4-5(7FRSDiD>tmMjtP`oYV2qfxx{3nxV1(CQ2I*S@cPry_1Yv;uCTn+uC> z#WVURNCb3{$B}67PS+Hsej1U7e|hB~5b)#TwZmYN2*Wak%JU8Pa;6vRkD@K`B~YJ> z$Gd_Xg+<2yz**2)LU9fUt4In)N!b%%mu|Y%Ua@5o`5xLM@tur+%>&|Gw>#;(ubU}9}J%#ac#6w14(9lix z?AfKoL@@&|^VY1`8n68{61|(NtIr|DZ81cuKTV)Q^ZPTafi!V+2JCFiL7eg4-@jic z=!TQUiRhpbeB#>!ZfpPwd`im8!oh(pe*u&owYj;u76*D7+>Al6)_NjYATC1J72@P9 z>(-lZ^x+dT(^vb9RRmV7w1?r)(b3&cQc>AjT3hl}zdR{aheoM`hu$mqHr>v{F17hKm#80=lF7hr_#ngaB>xv*=TS9LdY;Pq#N$8VI$jX&Im*!Dh0w)vIx^ zAO1l?h4=}c&d-)frt)gK*tsq=nAyq`;vve8@?*feQ$NSPO(T{1X%~z7+Y{J6EW=|_ zkdfu)gmMZBY$6kXX%r-MLup6ox1KA%eEC2x%u=; zzBocenqn@?-CRW{%^@Kn#uH|I2l(6a>Z=z=0hF=u$CAhD^x$^?H>NRc3TH9u+aN&qsPJQeHbyI}{Y$`8;}94uown1|R!ty^rX|FE{csc^zgL-u~?a)h_TQ;5Tg# zQ~bnhVQFdD_?94YJ2aO8iJ2+}(EM-Sx=)>qxWwuKm0D9w%pkZm#;xFk7w+DpVPaNP zRY_G5vFQ_&lmEgU%oJ+|{8Yo|<04-B3zKDL!)TyH$C*=${95aGzOZlgTn8kFvOSQirj zTc4Pb345vhN*#li?f1uQjc(3U#)zFbBY564rKrR^~>FQ z`ui(OOWz6Odvw6DC;^dHf;htI;@g1AE4buu0YO2DKj+l0P$g|)pHmh+-{bYzrl#9M zjc;DJol(yqBwQh^805GCu^)}zJ_Ay@7$AMZhI3NwuC8{9wo+IPV4dL^^kFQxFy!ra zRRJeS(D-=O#~lLSou4HgQayEbdlY$1fY8AY6)WlfrLv9~QowIDLO)t)dCUUdl~_Jx zBrYndAXZbnldUn0YHqZ#|GBCEpl{g%<{;zv9CKWk+fK+Q9E1He{ue7m{ud1G=<=Q1 zC5Da9bWe^qUT2s(I7Dj<`Os1dE(7+WYviz=&3tl&T-EJ&!Ay=a! zm~j5vndMORW{lr!e?BRPnQ?w(q#UFuaNKwfpi9GJAd_bLajpqc7-&oyRNGKmKTaY# zcjw*K6gi!jByVHEa0;V;;C3N5KoW=HF?^=XR_Grj_4CU>2^%B=tNX;Zs>TDi8~!mB z4K@+%Os{|uH>GhKb~v67e<4R3cr~)rVkpHDeb*6Aec-bAtB<`yWnt5;M5D|?;?J^E z#227>OaNX6Vy>A+A1&_-{`f=|9kst=WBZLByE8q0#J5w~7Dt;e3R`F{d{{yu;Sr0N z7<_^jz4+_JGhg4eXLG&E2wwum1pgPUUG|rSQaJ-pe9eU3gkc5mUJx&{)VBRdCE$_+ zC?{K4qAtDHvVsY$hK465hz$6AZZ6&Zk8*~fii(P$Wa~DfjQESwDs0(L{O>QfzPdp~M6(19 z`Q~o{NERP&3{~wP$kHAj^1*$3w4iN`95jnVsx|h1P&R@F%9O!@rlmKyi4VMc_YPe7 z^K*a&c$V9NK#ihN_=G}u9RPF%1qDDV0rsG@Kp$8Ik3!=T6W_-?8!edtJtzaEg<*q- zJ!qS-B?1BhXjiPv&7V+;k~yRRbih%>ZGEVEXIegIjkU&cKAIYnM6*a+wII$PGzOrH z{}3Hr42GDp)X!KM8PVm#-yU}L12`XGT}WAM|Md;PpHIX98cqgm)314XsKMZkMNy9k zpm|%&B zp-=r)cGXb#_wYCcnnKay*O-ZD&=6%&ul)xp2KxGwgju2U{#Q=}g7b%gF^ zv%YRU+$S6xcHR0-ITp&r#RY|m@7jtHBv9hvQC}q{f%Pm?w$>IcVCT&eM!$4C+W-09 z-0=85kQV{)BAK@u2Bv#**qcH1Y~t9GoZw=)J#-f^myl=$HInGo{*u@mm-6ar!|ZpBK4;~g zlK_?Id?TTdRjh!Gr1}aV=LRnuC(3apafYzrz?`+d9z23()ps>DH6U5eTqbqLCVcv2 zYiCDBPR@EY4DPr_T};w*333=))3-AuKWifdmod)jE z@!tc&$#fj&o4%`LNxXddZd-;`m$#;}lEp3%Y;(bV27;0&kn`Dse6qA8@zFmhD`%^- zz1?kdMu!*>)~I`d#gVJqG#))ZMK}ow$)6fWK3?9=GCa`U{01Ao04uaJn%vgWG4s16TF@L`+!b8mPgR`?tKF5rNgfXyPBt+Rl2rO%OQw8=nT=O5cgUt*8Z0YIjq@$ve z&_+YaW!0;$t*`H$MT$5%IMAtef}#{Zs_7FyD%Pn0e+yEV8R!)P-Bp_EfCQ2S`}vuX zDp+B*H~Vd(%l(bt_#*f6B33qxI`Tcya{KNnu=-Y1Bsh6U829nx%6QcCUV0Eg*+BHN zCV#esP)KBHkxmd=26xkZ5H&FnCZT_@r84=5Q{2%J$CVnOY|s*sV12^lDhA9zc_j|zDo zn#WMQCnO{+XKn%(1}4Fm;GL-ht>a?zcr(ACV71+t>DKu7PrUYT#w&ZkIvwpx5ZrYu z?V@#C#-FRdT3k@g?+4csJQ_Vvu7XEpngR^sv??r5K;p_%N@wz9e*b=+N^Ba#U9-tR z$;pW$8US5ek5M6RSM`5D2-T_27G#(wdr1nOqS=(s^Ze|p@e(7W?{77pph2NfK+`7^ zbYlom)%^1QY2idZo!bWZb)@PAE+?B~X=!BDTZ3r=!K|&0Q&cG5g7h+<_kvx-|8
    SP9dj+#=;Ya8xG2k5b4(A-T6DC6j>Z43{K4UQD8}DR7-H} zWUS`3+>MiX1Z?dLnl}Z3!xR=4mZsHpQE6J~BTx_nXFRGZysX#w>xkqCG(2aoY(5EA zHlwEPjg4@&C`hN#H8bX(`vY=Z84@bseD;Ea0~Ed=WwGGB3CulnHa`HS29n`m3O6cm z2OiKyWEC;saXr3U4vl=%aYRo`d+?ahbiBaTD*QP6fG&zu96>YU`Zm-5l+nk4;|H6N z&?gJt7cbI9e5y-D@#vM*ml4mInB*DUU26J_PeThu{jZaAC1qs3ASbsn76x}uNq+h$ zn|?iE6riEr7#0@Bl)&83(9m~-4-qi$#`b!lCux@LS6ZQs$H@_95^3!#_33%oAuvhlf>%(HE<1C$eJ+G$^Q1o_)XKo1Iz$wHJ^A14c9GHwgX-#vsgX0ch0jPw@`u z9CXssO;mOf*PE{;rz`E-mS&);>cN*Mld~Uyo)sb8ugFQ-smal0v2xUqqr$}=75Ef5 zD^M9E$cd>^FbqMpN(}AULGk_tx8OT;xU=*9ifRWGWEN2PkC(m9lZqrPJF)=?1Ua7r z*&v7Y&`;2Mplh|LTB_vA>*_wc^hlq6apCR5Pc!qr4)3encQKol3I8Z5F_71$eH@l@q(QX?iM&Awh_J!xEp27#x0LjQ2;P^d9r!|G%#mjb&qIrH$X^v5kMk%R1QMhL^9eFyT}MXDtjE@BxM1`GAkg00>k-dV)9_ z|E`y<*e6J{>1b|#d_k0%30?u&D9QC_$bvM-W)blk-!$cNsx6I;f3km9?|_1hMJ7fF zGSG3JvJeu-0_ox4{%>Wm7WL3?KZ8)7tPd_BGVeVtSfan!OqQAmlR{8|GJ8t5=aI~1 zexbOwcCzH#0BWT0fJ)G$EXt{tf^f@g{|7D8m71{-hEc;z6sBMCRS`ld$|9enhf4eD z==AjcstKabWd&5$2@rBYntn0R3xVYog!8q|W>I(QFxLY@O9=X)QJss0zCKweGflSU zeWs_nk6D1iq4xQ6pg5cypr8}O!(}&lJAbNex7OC^Ri~g}`4Z^9x<05-Idub&={no2 ztLzcSMH`YXs2R@@(uU>TP!b^0SUWSqSs|k}&>F5P0^rC>WT8O*Ysc)xc!xbXgg0S}PHJp~1XgetnY>FDXVHa1MgBQMTCpU#m^Ah--lTaLPR{qM@a zE7kW-gc&YGvzi2$!}sL%l-% zY!o^|z&^%WB)ym+HTj`?V^r|rbiS^}uU_>pv;+g4D_yMNr`bA%Bw&PFUiFMOfABW4 z3}|DOrz;8Pnynli2M5?(K6`J6b8YuJsXyuJ27z)o%5kO^s>cjrIsz{s2&N=dsdS+t z(O#qZs@vQ1pzISXeDjmS!WZ~ag7tGiDS_n5k(e??UL6`19v%)XW2v&&#!E@#4#&)zoA8KsQxn)f?jcWda6l<07V2Sn&bT_ z>v8WVAWCg+^GEdps70`=q`Rg~R(cXaWx9dmL z3Xy2iSWgc+3HIj1HzAj0S>B6IyKhBkJe*aE5e z&FBAqG-wr8tN5Le8OW#rC}QXYuN&r67=rK5{AmSf2&$?K@*bd>tEeEhBajXIQWFwr zeUR;dN?Tg`{mk|GZuYfPcN8h#p;Z47h&KB^P+c4`vTTA_Zb89RA4gzhoxL?5b|}}Q z#QpVEN~^;qL7)R;z3FSJoit}>=WO~gCRBhX=d?#eWbl0v%uiUi$ z?|EsZ&?F1d8F>7keX#uhKg>7^(2`0Rs7%m;XAI0eLHYy^@+FwE7#ln0qF^2RkL&BF zw{S%5O$MGXZmdb3<*|@MVE5&pD|X2l9^W;Dts0k_Ox=Lg4EOZ>xF;4>sS5n6ODAw& zx*~Es?76bC44&jn=2h|GtOfX%yD$=d^T6O2|ATl92Sro>Pfox96NfJwCY@i7)mC6) zr#O$D_I$Hq!8!PyrL~xXfWZo2hcO-^{3~R>Q!0V5Q$fq>5i9zBJjOg&2?>BVuCQOO zfv=(X|NR;OfXuuN5?EG0(`YH7n#}X`$K-PP+lTAA|_?dLPC z6H|^02){Hsi&gUz4=x!hq&R`)rk6xrT|~>8!GOpM%s+!vh(_H3hb{vBKETFD!KUY? zNw2Jy8s{_8o;YhQA*l5&MUs+4Y&06 zL;L8gy-pb7WdS1bANU0w4NZ5kENG?K|NXn=yxqD1^aBLkU~y{vrY8R4!h#bR_&=%u z{=xVO?Um(E3UeOc7bgE?jzBkecSU98fG-am9UXy(_7s6aFBFt znS67w(Tzl14)x!cp0w`Q>jtUrr}@N%d8cnw7c?uwd0^}D!Hk}VCqB>0%IeS5R1~1*%*~1N@oDYWuYbF_JY`)b!u<*Tjqlhhe5kgj81ckFa1%Z#vgu<#-c+>$0 z5Q@wPdIv8re!*q%^&Fe&+1QkLNqK<2PFWQSg7-_YkW%n96pV~0 zLraf`zsQ{J&F=w&M}XbxYHN2FpMzonFlcIQ{;b}+4c_VAEv>IlmTN=;G|6GQvOwoI z9pHPIZGe%8H5jtIK_&@S;oJz4$>IWIi3}l6hp{*J^OP_PQQ(6HnpI|vkxW7N!kO>H z&-^2=t(ix4E<6NUT3Yxiz?cC{X@_>U9Dwh?`9{R%Pn~9ekwZ#hFKiUUJP*Zd4YgG3 zjyGf9Okbj*qu+~&!R?!!nVFP{0+pTNm(-zL@GBiua(lrBZBQLg7K^#AK52S>oyclh zs^(4!2ukn%-y`;mQI3|44E}C2Iwh-)q9JiltJ8W9vJ#+VPEKk+skqL0D8ImM;{^Ki z^K-8rpZ|@gfNQ%LnT%{&zP`7|K7-Ne`U(S(&RSgnxcYfZ%K!`#cNZ61Ha}z%B;sj) z&ij8jd+Vqw*KTiki!`{TLjhSdh_r-Ar=XH5DUFnLr{tnpAd*U>s34`JfOINdN_VFU z2*Nj)&w0*y>b&E9zx~G^d+f2duGsivcB%&UUTvk(2nZx65Db!m2Lbj?ff9H}&5F;}7SHQ3FS6{w-A$uKR@#s-` z5A4c-1aztqyg^C@1VK(tP6&e2qn6mxs9+8LXk8UoS0TM8oX>M^I6i9Tj&BEC?`*Sj znu9ZjoRm}}-`N6oCR9H|$pLwU{qXZ;^3C8Et!d$bGQ>tCF~tQq_&GOoj}Z|B?O;sj z(*!MEc+6-Il7IQhD-&NjLCfeqcK$oMrKUKFW8g)_VCLlXEP$^T19k#|96}-@)Uiq_ z44;qD(AIMn$8})3`TIqB*MR~x0%{1dw{E@=WAPUQnj}K4)DR$EG8;1byb^>p$1t=f zaYGwe@8+&znzab*vgg>7?#ezs$5hwS0Xu_F%>do69AVhR{UZH6;uNE;sXG0=DP+Ss zfj1)~tT4Qvw0yi>{|(k(N97g(1^{c^hu`Ys((%pxKSMexdP25eLySt{ zI{baFviNJjSBAl7Y1iM*i>lnZ6<_phf-sAtFenG&2eGueZ(_a1K4I$t7NFvVS+T(O%TqMQ=)>nB;*Jiic^fUcL39Qrro_l0~0qP#n5HfZ%BXxd_clR_2>~z zqBu*#FA7rP3hpoTWD3^{jN&Fgr#yd7Y~33J!ONQ1u48D3Vs*_s z@lv)mV3x4#y&@Szml_d6BWNo#v*x{mIdpb*b_$p4#*j5ci*3+-1%|gh=5o8>OuOIU z%R5c?91~4c;LL^98^T@VKgB>!cAGEdjY(QaMBvk>Pwk2p1Z+nNNtqQHoj1Q;*CAJ{ zEHA%&!?l3#5$w;1jyl6bX_%uHzCBZp`;9|H6)sR!T|HdL2Z=A5O!-8EH z0nv3>wydto#K{`l#rIni@%ND@Z6yl#W8m5^X`PrbP-YvsCM%wZ2OAo!3<1n*DLi;8 zTm*RT=WSZ8Ewt~A*iwq9jcm~gBp``)c6PqY5**7Q+<8zGOl}hYC0#*JmUn%Q; zuz4nLW1GUV)6IF$sRMAv+t-Zc0fpbt<>q4o%p+qKvzeZP;K4W$jZe<~hJ>f)-ileP zAkkerQ93C1=C-UvbgQ{%!+&{S+~ln*o4c!K6h~j1GsTqXe=FuH(qj zLfvNja?HBzlV+3dRMjmFIW$-VSu{pmR$ozqTYO=l|Qq$Adp5z0n8=E& zL=>f?7o|S+DnPwu#2R7$IH;*%&CT9MV$uchi9CX!|M6)3B*2m;3E)Wz;vlZsK!XtG zlQdg8&pO%;qzgM)@`_VT%*>(7=V^giIfDHyM@*Koa9cnO>s!sM@p24O{wHtkjZCAiP&^X_-qLgjtR7J@8cSY1xyuE7z1JC&&f;y130fR3L$=|jhH3ACyfhzX`eGmrI zJfy(F**lK>eRASx=k4XynWUwIN}PNb4!UoCvo>N-P8hA-Gp3h^#Y85#cJi9qmzvw# z@$PUXYU-lovCnHm3#Qu*v_i<*N}1f3=VIi{Y(BVYBp{%UbD+vVMSJr)fbK5d$doJo zbdV@lIDkt8mzq0r%SJt@4+*{3nZ~{I(7680;D{aq=*|d_Q^9k<&kGhj1dZTqIxCOk zDRVh>Y;4%!;o-SEkhO`p4={>}>8hz+EPvS3z;A5oNcA^tjo4IlbFlW{;&ec7*DNC9 zC?5nM0E4Ns`P{(o?t{LRL4p_0P)y^ysX04D>GSkM)SAv#kE#Q8nL5K-KsU zjN=6AJ+>c0(T}AQ3wll3-Q!FAO|}L88W4W zkT1GK9#j744c##qMu#6!Nuivnb}M#h-FcP!Q+h%UY#$wiCR@K`1co7_B~;ZreRAaS z>B-0$XYZ8(393R@cITV$i3WKH{%6;Y>{l;gp38B6ke!tZojRH2;^t$+IZY%@Q z+IRl)P%$dQ+TQ+)YOTvMf(*%pe^N(5jL+g-$cN)XW8RjHhmt?o8hfn;l*$ESVi>4+ zD(}tKL&ZKY&-0deumf;2yQIqc@(w1P}E8C>yLQ66o!p%&!dWHtVvHeu$9#GXO?4V@j zM@`hY*@DQGBoKQJ?@5EF-UuipU&#ys7x7HcSUTX;VGkZ?_^DS|=|it)AZiM|W;y^f zh$?0h(^-cC4E6y&&1^Y;gI6O!kIz&V?&*}~r70Y61j*fXG1?dAWnXpM3k^@NoV#Ma zU@i+6Oj3&CPbrhxUqfbT$2K2o32{CX1K}SHCwY3|Tyvtt`A+pk|BksQrI+M{>iWVt zE~u2OLre(RYLtK2#z#I`@yA#Y??|`PC&a_~^=6hTJ#=1UHT9EgDA7+AqR0b0S#WnQZ6zt50iC`0$GCuj-x4gT?PmA|ztd82xX2Pg9%tJ$a7( zII~3B)Pdb)dFCora`L`geEr#M>bKp%WG25Q!|6|hx?cdn(=pUt{55p`gPzCnldl(0 z{|%BxJv^sG!W!;=M^HdeaP`J*z*R3ecOUYG@B*QL?Y|*>GJMrVhv_cCC<~GcCS0UhSY3 zR^`yJOmV`6mD52e(+U>b-uox3-+S^#J9k%k5faX|G_x;~Gd);V=%(qM!R7lDeY)49 z6<09i$^F$+yui?zBhdz<@^h0hOlHK-Nr4AhBTY)@ywmv_2#->GJH5H$QVDii z$p)P1SNnBaS`D;qjHMn<+pv;LnHNKaGN) z7mejK|0d_t>F&PjdAGx+VVCy=o6R*@2Su1CMe*L=b-COMpMxOh^`>jTt>DzP)$gnGE+40|etxtk$>E;KG zJlm&45|`1Vxxc}9{lEQOdNPxW_S$4|-rcV^I>rg(4bOdnx04dr#8UK8U-wR>mzY)_ zb#58oVcu}Pbn?bTWxdlm_&G>P*@@j`;6-L3NTyYNPsaQ1iM{PpH&>G4vwl6}s1W|`%^z5RM{m#5A~HE1%OhA)m=`i#vE ze)fpH1ZeH(zX766Hx`5rhWR8KLQtw7D=t-k)?Y2(AKxx5o;qpF@G|prx!g5Dy@FM{ zzGyCd@C>ecxAuO0{Ej=h<^JzJ|*0@E(!d&aGjcc0p~O6ho-xqx3Bs_+r}&FEvn zPW3k8#~waQi~Ew!8npM$V6BYPI;F4`c&I4BiVP^98pc z;lbz1^Hag~;)#=qp67Ol3wf&D#EYr3U%wp>l%M&vkN$3HchpF_i_n#n`|AaqlI#C? zO5(RsA?ISHD#II`b9;MO>EPt*`hG*{;uXo3 zK!cMzBq8AfDVCm|o`4P&yswlcffo&%32iXt6N#L>^qZ-kUw*x1+|{zEwy zF0gm9iRy5rpE*0;9$?2tAFI1+d=zemJ#ygTpd%w|qS~IUMJe>$9-9MP7^vj8b8Jx3 zegKnuwHI)#QV_#rWie)uUAj~w{RyZ{i_scrgKXSDpFwHML`{4hkW@g!reR~EiVC3` zg#T8Qmp6~TudM9-{MjM`qP+nHfWO1=*xA)JmpVT`uhB_B&IaRt=Okg+hrj9P2+1{? z08tis{57w@9!47wYJy&_1dml-f?o=xBU(=6I5!q@6rw<<2I!p!AT=2)3kySq)=w?A0g`>W5ead} zpu%_DnUUcdk*A{D1HEnFF$mHzu8ZOa)F|Y!)!&qPa?eT#K)I3!W1`680Z<JRYTr2UQ5h5ERJAH~|9C6#dmwETJfpjALt)cBko{teR`|)t%FE5E0vB zm1kfQ`|G*)`N>5=<1JItLeb;P!P`LTU8fePLmxwWmouhBxddL{IxcLag9%t>$mY zEWHz`cALr$mC*dN3fOe_=*ZevAufPH!n;LF9+8@o(lOx%wqD?(&t`XJ|V}ApF)R?^=GNGOnDm;o8E1-R$c}X)tejsC!X9nQs7=Y z+nH^KwyIHG038V>SBM%)=T{onF(go4{87Nq3Q3(Sm_Qyaz68Cxj`{M1f7AvdEZ+j& z+Tpc6a;^U*cqxW4j-E?Y4FaICZKaUn<}RcuQdq%Pso|-KtgrX?^gMLtB(s)sb#e;4 z>JAE;?!jMA?_;*qtjB=p4E!AF46z=GFXf9NbCy0=X7Mv1SHmpQnuNvyCly5=l1gUHYH^vsMZ7C$_44l;`$NJ zGoiS-xA%=Qzv0cR50(4rG{ZqV44_{q=gPjQ&iAR{L%>a0R4y`!=4}LlZ^VP6g7#T6{j&x~Xn zB2v+4M*RQO65LDZ zGhQdvi`=^s3Zw+MM=5>EV&&HuR>aU(r*;9gDCyvtY#QcIn zCrRQgb~`NuyC&((IE!>r{&b|S8{n~lOK3zPI4Fn_-9)XLbveM`6_t4H;dUiHet}M@ zsitN^La=hY+}3#cD-hPf${g!$lk_Jt*^=Sg6V>~`?r2=?SPLN_uYtERI(E@~`C9)a z3@`FB@+K@SdvVjN%!<5#xSsl?3xm13>x!Q;#ZuXi*3&a>R4CrYnoYg!Jzn$$n$>0z zQ?*cw{6+%c++(M|gU(Ld65w;PRV~L-Q_yG1_jh$gPD(~v^7HW-HxpGzg6i-7EL`5E zW26#K@HUC}+xfyxK{$cNJJwr37o1NEl{iWQuut@l(n$z)a(s6B<=FyfrbIGp0ho;2 z?kE)y!I04N^kZG!GuLiV>fRnTpZ;95&=JIA%7syQ-zLX^(n}AG;WoMV)-S|s2igNT z8twFC_7SNblX12W-sDY#^WQgt-IwR8Hpzbr9^EC$wR+%ioBjZL#J9hcIXxI7c-;C1 zDFnACu1NVui@Ab$fqxu4%HyDpS1DP=-&8mfIzNevb-s9pQ@spB(B%H5S!&hKgntV( z(|zorbrj=MZWT$NAKTeg$>luAJthzd9KRcS(WEy5m~aKq$CLEE{oKmYCCA3#eGL_IA!dj38(cN-HZ_Dt^Ov>fG?sGE1m*9x|V!Jxhf$@d%lD=pM(kY&$>C3rn>+sylV=q!C)>OM6XD~>qnv+X(ZW{G z%G0iPo7?Ud#ZOx0)wggQ$X0!N6^{7%Co$k?h=}P{)HG3l_5d5<7&>zdYw-(z~-D&8zUJhGihv4q`UmB-2q-Cud~5^Y%YYxHleLfIp)wgQI=( zp<@boOekHV$X;)}PPj17;uDRYpfvGzp5_h8#;_$`omP(H^hzWlWSG7ZnERuHv)>o= zwQ7aBK&SK~hWjG?_5!fAE8r>KQhnJYMz7zhZiQ{GC7_u6E{w<#;^?-6`23^ zslov!5H9iDf2lBgo-J5|&aoe4C2H3`D<`sET0|>kY18jPb6ccWxiOJX6hOu-lgMZx z_8FjSGY2DX3HU97IDG#8yMU>xI9175s=t)^`Zy{vDdV}WAz!!PJf;fk&5Ias@YhX-4fRgS|oCRKlVFd z#3FL53V2S5?;uob6@$O`-Bn1Y<#+Wq*R&ygzJGfleaPxj)_VWzTN_yNmIfdE0P<0l zKXF~T=Lu3vF_tr{(O|7FjmeZ^=78;7^>c5!8Si}KJ7%T#zsOJn2OK*Nzf;4myqaP{ zf67M5x_x~#Ab90bs=0$?^8X}eq!wEU&LernPK}rOyk;}wsqL7XvvLkGUD>C`w7;b` zW=UsT$+V7R93<7CLX@R`^zeg1OWQY@2)VCE^dEnnP%*VFQ`LX*Kabj&7vdCTe_Q!f zj0}nH!DKaxO4dswbnOXX1pS*Sm&59@x)Ub%@=vm0Mdi=7(^SvxN_KX+iD#k<_{qAx zTJxmu6ejC4X?niMWn4HQhHsRSb3rrjqDzO^=q3nAL)C0pQQQx(`#uz6XS4mIP4)jG z81P?^L_rjHI{bnR{{eQwZQqYFsL0xEr8mIUGByULLcO24P#5QX9rXjFEt)9Yha}1SC4U7x)}Zy{=&FLqldvQ+WLh1U?~WcO!|6yR284VfJhMI z3fLFXR5^5YbK1}PXerz#hlYmkz7X#YTUb~i8|Ojn{~d=?dvW7B2y|f2Pk1=~_&qA# zhbJKFGJo`FpW>t@TLonIym#*`*i-12f}BO%y%3sNdwU+$maK)D89x6Lj6eEiULHp; z_rl@sJSYeXr2NAbuHfL{%z6Sp^;BO(+Zwp_j%F<|{Or=0`Jvu(t*U{0@ z)rI}W==`h~)V>|yTamW~Lh3P~E1(8|SPJ^hk3jb(F_4o7D^4{8H1O?1+ZG?r>kq!~ z@w)=1E%?=x%=m^12lQmkT|hyH5z_{ZpRZ+#5?g?vhA){?Qc%2rl*jiIU}3=yesFqL z?!jQJ!E!wHuq=dfz(OC^>bEEP-M+ZH~v~&iq_{E+V^D? zTAvUSs;J-W67xhDY=gW>SzaKTbyBNC@`vO_jo*=bw%Sr(rwUfEI>8=@L)Y{0o z8&a^cwgw)eePs}`;^7?ws4^d9Z>7BsZc$C!7zT;2pFYiiBk?9~9sYSgzvtB!JhyIL z{tZh&ejS``fhbdl@CrE#);Ku#Kya}24FjDP`PbEa$^O+2l>pxA$Lq zdzYg~><24ipo&kNlGWI>u_=O$q9aua0f{}!*PZ30;PK*}k-q)_O`VygWe|A)$W@mt zKcqg&iL2aUF5s~v^+7%)lBk2vFCCiSH3Ppd z3&+ySC0Y@$NO11)rX6M{YFb+47jo^-U)wI(M~skcQtMqvIt1x6xuof67h)85oahM! z?CVkN0&i(fi8n*sy47VtwnetW!|&#fve4ttr;<$$ArRKZyv)0Hc`n0u0b3 zf`A%GNlCpAwweY#^Vk4rPhsh`{j_UyeI?*VkFVT#O?j_$O>rCw_8YX#HX3};hp=iC z5>RxG!{}Nh8bG}|qQeg?KGCIWvv$OH*lthmPgVe-r2q1XP2{F3AkO- z9;kW^Yut|@-~~iHx~c0U{BLoY3rH!uAMp~3dOPXB@h=(w`0*y4hCv7;4cMROBAk}y z7%{@1Lt(>PGd4W#9qn;f_I;wO5_?nl<+vp1oB@?45oQaRKaTsALa{4j`^IWuiSQb3o0PceuR=Ik8{89rp?UR#U(WE z&C=|whPwKvx&Ex&>*kyIbY!ai%DeeiwVyxR?6VO+e3PHwx~1maM+-Ep#*&h~sCoO{ z7oO>%VU+33ie8Oo7*gWm)Z!Qr8%iQp40_0nEV=er00v1VZAO z)^O$lBj!p&4uRj%9_zY3Id<-QL@MMX>z;0~=GiWE#B-kj* zRV(*Vdfu*FN~E>uc*?{UWO~L zgsz18m$&yEDrLdm6{HuSxL@2|DF_J*Q(@hLY#SeOJtxzV1I!NeyuAk0Ac+yI2wF;9jQct-wm$*yKiX?uxTt>{0>%K~ zvlDw8RluN~CHM71+3{ksk&Y)+5s=T`d+u@zv)dP&d|oQcF^dOqBE*%$)CpA)G~VJO1*CtuSJNgEia=U?#wKF)0wE#UbO)saWRn?k6Stqf#UA;>D6+wg_$yDwHn_+* zP8}CI{uwxQbmH!IpjWh*x1bleew~uPkkbSaK~sTWBxrf`HiFpI2|VvUl2IAxz}1gElIDJ1-KUZZh8SI6o!2VWZFXONBIctNI%u1na~rG#vp1@ha6pv1hYC3^=G zSR(qSQtsFBapik^r^6i+|MTjla(nF{p%CEf#%%>n9HO+g)@MTr4RH=0a5wSef|0t=!KiAzIWHXV*j8K zPB)au>+J6S5MHy)*4NOzwUBe%#bkH_oOZbJ>&N=|9o3a9KKaUrLr9Bww+_(eWltG@ z%*e>ll%p%nj677wMIW#aJJIGsU47y4<<0l7hGJD=E10~cK&FRZ9sZ5d!Kzqhxes2c_ z#sF5L*G|yZUsC$v-hsKyzi#M_kB@!DMEX@9QzaLiP?BukIelj=#u?RNddUtR2mX$s3 ztiMh;J%91VY$j8Ez>_VyON7fYQxRJLi4=G`gG`%Q0DF|Z4VTb&O2-OR z=&gfF^Lvl?7CJXx(8{zS0|82yE3NKxk_iGNOJ=~g+`}_5U|g{aIqs)$@=U)3x-MqL zn~dqf!F~{_rmNG1L?$WsFc{rZRUI4|Io)V2{F;@4h$xB*AS_}bBqV&ZYjtZl1@0bV zx-%ea=cVl63RrrC{apQ>oT9w^PWwQWTr412pPrm*rk11R$GR6x``Uno(Wu}n0YNso z|F^(`&cqR<3F8ilq~#QtnNsEEG_Q^bTcF*P3uCpIfz7jrr;Ok8LcH?Uyka5h6Fu4F z`LRpKy458m>IO8=jlgYkC=U5d<8~GzU_r2+%Kc=~+S9`VuGi0VA+k!!CxKURB5-cG zfm~qkD<%hvLPSu|B@^1n->j-hi9*ELSSWdu!}+b}Ukqz@UTFp#Ehal0|Co=0tOSC4 zN$Hd-p%)?=(wWxLE6GjD;HIjDaq!PWeorL2ssH}HtbW8DgO8r^Vw76fzZ<`UEBxtG zas~!_bGwdjuAtDxY5}^L*~!$DDUHj#`%?UM+Y#yEJ`wNl@xA`C=YOZyxb-li#>U~L z$mN7=i)+Uf359Yw*7nocav(XMo1ZVGxWg`t>6|Jk5S_RY3I{wA5|jP?q<0vuwDo}! znEoYzX;khX4J&r0m!3d2( zqNmE$2rQ8kKU>U@s8BbuHJE2_FWJ>Hdvo%=HTilf{Y_8wZ`}5b>hS335TTq!d1|c< zG&C6sg5f-R(6jm+thMgJl~(@OV?)^Hy4T{>g=*P=MkqK~B3;5`i_T}QrMWry?5+r~ z)qq5sk&m@QtOjj5&9m>fZsaIx=%FZB8oXynnlvKou{WX`E{Z|S#_8_{Gb7y)i_-NC zAfcUfqG8yKj;sb#aDtf4&ifnxeR4nJY= zzP%}e=k^BmCQ#(LW13eO$R+)fu5)mN2UM3zV+|ovnV;B|ZUF@ZUoK8(b29*+rqAL| zX}s+>ybOgztsXh1frL7jZ|NAVH$`ZTSp8mz=Fh?(ii+A9V)gK-o6D2Hv zf%hj!>jk|g+5oq&DgfEoS3vcZ^ZnP`!=TeJhCC0f5`p)wg|uV~VtI^SghY=yx*x#_ z@}1BI`ixgH^tuqCG*PI(S}%iyjyJYvrs4=pX8N#m9icEy6xFw~e{qHoO!>pzgM;L? zPx7r?j;hRq@-CyAKJoM&G-(zTA4b&;M+=CWgHonGBP0JA4p-m!IPIulhdB;n>JXZ9 zP%(7Y7v9qYN+db}vYpc3zO{WjQnb_1hP+nKkbCi5bD{J(N#}*|jpC2;p}A~`2)4zw z;kSg?5Wr;uLuJ`C$%7g6%8mJOg?(rRqYzg<#|kA@rHFL-0P$n53Y zw}YGM_8$W&kgrN0rY!tZebxLY5Yy71#o5K{9&;n z85vvf=~}&-%v>S(B@IybqZ?`0*vWux@TFWbKSUU*S8mLO`wMq$@0Ul`Di2eL5 z1Z2L&w9Lg(Ji7o}?#SsA_Qz#Di9swj#Ne>fBHT&tq#HziYMqD3oZ(Zpd$e?965TLo zdiK&hP}vo1UOVrie46iO;OcZ#X=}mmbMpZOaKFxYxlCMC*PWED#z4XiGyDC-zpyp8 zdet)hk|-svjk6o~>y$chcLw<-t6W5ZC+&Ksg$K)F^(UEf?hj0wu=aho{IY&aB$sE& z^FE5MkDmIX1Mt$Y#*uu9=gxHB(>(LI#~N;PE5=coa(;7j@|B0fxHX88 zf<8LwM(k&0AVx2m!-K0dy*SB3p#r0>{++x<_0c=`l<3Pk+pPw~l2d&3_+9dIU*R3?Hd zH3fxFbo7Nsw`N2-iw&v;tOxQ;n!_47TBd26N5EZ!5~SW8`vA#xg{%y;IRyn)Qy;y! z)>W-ZY(UKnNi=5)41Bl8#nB&6o_)PP-%)Bc6R#H(kqO4V zw6gAExB+7PN{1T2&72^fWo1Sn{f5_GUSMD&@B|+rgVX1Dt7j!I(qPVxaYYnx`5^~eV!KOmN(ktKiKv(DHd$MRm{*QwL6jDxUTuxGb z?-^r0U>+@!ZO0?UG`1DO+w~nPM{0(`MyG`GF)@VmL;7rkp4Z$#YRU)+MJ>Y z8wV`!r0<}tOMhSpv^CHotzrL)tb1a-pCd58XvD`XYO{#8$kL>D1}p9TYurvX_vZWa zBDnI(YH2}9ch5)bEll@Sggfi_0F#EwAzs@s(r7x|BwKt%{6_!?Ux|zZw2olI4s9U0 zs{HVJ;Px)VUx{%S_MD*8;g$6FQ$>FaZ^D9^U`7!yB0*es;G)!tvHksYDWW>Sc4)Y3 z)jBj~?~j+PcAVn`%dn10H6mgHlT_8DiHK+c{A1SQ8;Bx=TNIf|SQ=t4r_+>*=jSTM z1pV4W{dF|64O<*2q`H5BErDhmlXK5K(mDVuCw7X=qUPd99u(X*J3@B z5v45+cFZfHC`PX5Ym=Yhj|>@T{)R214U4zr-LiV7e~NnTv0R?+Xd79AUYWZL#&yS= z8Eo9#Jso4}XySSMtYQc!VceA_`qbU6YNW<1B&2=PTt0>*Eq?W?yWN;X5;4XXaOx{F zrK7TQ3lGnSDrp@pb$XZ^YT-haU!?Y+0&6#e%z2X~htYFChkI{0 z(Aocr02f!+yJz$U98yx5eyW$=>2&u!xr5IfW~~V|tj^B^kTiT`gIbW8Cs>N?X4;{> zbtk%O7q%hiu6aeIvZD0#^mrS_HiBKV%pr0W$Bu^*K6~3noj3A4wM|s75*ZrO*Yl>J zX8=ztRGu^O7t=(Dxo?aCun|RG5|>sMc$g=t+2siKA9q4s1;c(KF=rUN!`}oUFd>l6 z0H+{Pu0)+YS&Crk)Ef7?@dOfjM|QkP_qN0zO)u zL4fAr)`gUEUW2MnQ+&i1F8qKju#YkefWyZL>F@*d(b>5QDf6(LaclxW;M9~`sV0y+ zUZd zc_oh}=vK#MC_d!%z9xl9SL0q^HilNZ+kuQ&lcwF0o2qf^Zfwk`TgTtntc_|9yy-Y2 z4Ko($MI)PmD$-ghFUV$wF`gOM_JfsmYkkWhfFR%t8sZlj9v+E63j=o->A&fdDiXlHESbL`OIZzWp#7-oA53=M+%0 zpNG33Nu)dv$r{7ZA`0BQ0Bk_b+oUN)UUK@kDjw&t#=L4KQs1tfu$J6LjL6%?$% ztn4iEV0@-WX0;k->k&uEsSTTv8B!cpHG}- zetrx$j=_5?QbJ}A7bqD{Zf+oIcLIS6G*m{QwVnfg)O6jk$l;^i#1+GtfUoyZHVAzKE}O3XMjo}Kj^goyXc_?*2!!kpMS zJskjDiILxLO5(`YTtQ1~i&?Q*OFJ7 zQ80Uc?zwG4d#U3cYbEh>m`qmJSBoCj>2*qgXk3;xx5giqlu-P@kY-omf#9@47up6F zFgO%MAm$#ktX%=H0%1PBnfAY)>|b74&s~%kg?Qzf5D?!(NCG-kOH*^u9#@;f%7_Z(2ZyK^|dFk*D8UYyRjgB8Qwe31OYMF2>*e8U9{ z1r#gpamuUK0yo0`IJ{}D7l0YyoFe3*1KMV~{Lw{KFE|c7hP5Z(2i4-^38jsnA z0LyID0i%Rg<17iu^A57QQ=pF@EHDX%te&dcT4*-2mBxUfS+UKLi!jwTcuYEsjf2|a zZ8*WIBxSL;w6t`F!vLWpf{bZVtYfk7*K)4>f5?6z_wX z*A<2_M1)_hy8kN_t-Nze)6392Kr=G3mfJb-4t<_G8^>JwmR{{6euen#}^T-4>Raen` zrQU9zkuhMLT0{^?(eayN`}Dof6ObNOKf=#C1;FJo9RAoKiMg3+Lc`?-O@9Bg-v=;6 zmGGtIHmJ;a=Xr*jXg+mhTZ}6b{hiTT)4@^%t5^zO;nfZLVojTnX8Z1Q49v*SO@%Lm<00o`ngxyDZ!q?n8EV`2b8UDGu z>vgy@3)2VkJnQ6RMjHx3grTc4&`*qFQLOk0^E)s*I<~^KP$G0qPw)3$H}8g3ba*)5 z_3Mo$P!KIUFu^CPxxjJ2hiQ^KEG#bOWZK{&*hL-OmzJG`LXm>3=Bz0Q#2E>(F59813BcczWVBbW_W;RLYyR z`X4Sr8YLMPtYz8bT;^ku{~_%(`U?0@*c15sIKbm*&qBs| zouG@FgyhrKliBdrz=O%k>Bx&6@CqPxM~iFrZo{{Ou-henCMp0m`^zJz`|fKK)p;g& zY4hDW;JRZ<-C(>&!Zp?p+5j(1cU8ieBy?gcQ6--&a!SrGP9tPGx4Ak?uQ5!&6?-IWLfFVwbU$tKpZbf(j zcOXhY)Ep00HzzlDoICd60`Kv4dVUx#`uJW}fh;O?aIaJmn7MZ!<-i{vOT%vpT@c6l zIE*1`6!|VN``~wFY z8(KPcHnt}AMSrc>0&9+~UvNj{DyUu+E{kQX7O}T8#U`4=dk7KqmgD3ZI$e@thXCSv zPC|ipVrmYDX~zX+0Kv>UW9+Z4A&w!}?FwvELw|fd1}OZo7Y^I`;cAd%eNk$$t*)w) z&Me_cNcFXDG5TpcTK^kde~!3@A3aImUWMCG9F`4yg9P`gE8nPU_ai3dmq4Y&K~|Oq zHm9JLjn4p%nw!Vom%YzR#FAB0QjV`26T|JFL}>vL(v}TZInpzcR)v3{yqqi8DJvpYhqg@L9~@RXD}xSTO_t1LKKv&R!ZBZ)$pKGdA& zbQ2+KeVfq5q8*VYis9ZMY5k2fBRLrvX0giQ?&Xy^pizE5;Q`kVEa@2gI_|zPL@?Dc zH{L$JmXZ$Y5)ui)EwrQR_m7}_85|y7{dq^^)XibHsu0Krr9U9Jo12@3gvNK>hU1_5 zg%6A0iPiKM9$?IG3|^Sb9T$7(i>)VLUME{wU+whUizx_%FNSZDOjY)8@Zyo~U*}Ur z92kUhAuLantyPILLB=1Wfm*AQn=7cWn!0Us6?(P{Yol_Alrd_~fx*EKEWSut-?i43 z7DoPxx4F6Hfb%i*O6K4b0a+X-#I=gnA>yC={9~g(_*&4&$h*fA1t#a82ix5oNFZyT zu0N#Mz1ohidQHLGi8Y*_Y?*ftwfamOm){f~`Bcx)=4sxc; zf(k4#$3150#zO3|(t+en1%$4d##I#e`Dbfrz%Aa}m|(8t7w?^U8KhI&T3+go4t_sAfgO?arEDx)!tnBcR6iJKb8oiOViUf__(&T9e(oTr~e@fR&&!U zKaWr3fzL)gp8NkqwII(=FIirc`YG6yJZO)r;GpmqRn6*`b4kdLPEE7{*yxw-^-lQm zdGfq5Q@8)a&m#1I)$7G)#}2t_Ju=2$L$j0uR4wE2uiX9kS8oWN0BRvhc>03W;i17T z#5lWaQ-Up$PpyUS?nkKY|Mq)#xpe%0?7ekVmFw0wjESg#BB7*9LAo0WVTm9iA)V48 zf`EXubcrkwkVaBUx{(%X5Tv^s>8^J!-Ouwr=Y7BNePeuoZ^s#Dk8#f4?8UvVdtP(? zYQDFNY(rGu4;MTgW-sR0W!^AvG?L+&{uI6iIlag!9C9Zoa<}__L^s2&nkp>tc7^!Z z_fdO4hL3^-Dj)yfKaxa|kMd2|yvU970omsA6R28lTTO94;hK|~ku#Swq1R0=N7Qo! zGkc?JZsO=A?5lepRvh0(k>>w^=L!e`8G2vNELwhI21i4fby(}Te|gx|{tXR6#0^2|>KAchi zv&r4JMfjh8E^Ppz$R3RByQnaMYJkrP{|WO5sYK`ek^vIKfe&3KddCS1Y829uY>|I^=^mjJ-W)ZVyjPV#Z4KQ!NvqX_=PmjLpl{4Lid z(*+qL1hda*Zu=`!g*{EfBX~%;=?ViN2x_x${rNOB*nGGPBA@TWZYcpWyex1ufs{y5 z^J(aFwe}0)L?DAVPsNP?Qmv;@U#ZlVZ z=ZaI6?C#6m#>cw!+?f+ELo5sjNXz<+s^93}{bIu4=ZA70q?=Gwz&%R_96hwQFvFtt zpWri#;j>wS3m7_h&jD(T(5|-<7jk9{Zk(-^9 zlarab4C(l?{NZn5=4|rJQJ2pH!d_exLR#7@P(@8uWmu)25mx=B!Sw@7@&6XMK2uhnhhf}5pa+T| zC{u@Fbbfz_ogs=F`X|GUpWo0llGlIHNE^VNppEqaAnSM#hPOxBf}E}!6vXrKu3FVD zAUL;U(Di$0^ZPozq0XfSLrxa(|0}qI4szJ`6)Oojxz_1bWr0~sKoUiubfS;v&%37n zNI_TzEh9su^pA4$@+C!j8k!dyu#r@j%}fo&Lm6(nION5QmL<2fnOqmDMr1>8o!PcpoB1&ji5 zEr3j3I8PP(Ix)wM-VUor60kX>vp0mQHW}yFb6p0^8+lRwqoExcc*2V<>Xb@pb zxOW7=fLIh{c=?Tl!oznH(J-AK;An0a;YA~w$4d98cHUo)%?OjBQq#8@^F&(J&lU zsqx)EyA2bidP*%EVKEzEMsqVW;tj3DJ@AgC{6<>&Ja*RU>jccjJNM2V5(fu|{6TOX zw!vnqR0V?ofBz*&Lad4-Kc$a&HJ;#6P_Q*Db91~`>e;j1_4P+jLC#TgU#=L!KY&vP z7?iM{Ox`lD8KHQgIQtkYH7*zPn_tVL1}Nd^x*+F9Gf2|K?*ocaYAIRQmeQr+3^%w9{}i#ZzaQ4=?6mT0?5luqr3Ej& zTY${K-&efe^%+#`=)6n7;+i~y^AaFH1a`?Hf-zVDR2Gkkh!_B!wtg!)g(iqXfkOcW zAB8g|r~!n7MUcq61o%NUys)aXlYm=5fXv{wpc4;2VZdh!v(O%xzi>#nRzPa)3(whn zC^_%bCuK-}0TvrT0f0d#!~w}H$}7@2`18s%gE$7+0Lnq|IDmu^Vq@6UyQN z)dM@Y)FAPJcWdyQJ^QID>?6p&2gO1j;iVuYB61!_xhMH*al`t~PArdQVhfjYQCbb+ zEa30Cv69W_PP7L>z~zud0D+k)Zxi=|D$bqq_|x~#|E1+Y)c`74-w4ZPaj?+KxPT#^ zMQ5Ptw@v|;3vDw%`j=+Q0P=|+tIQJxdaa4^=mwQKJ`quW6%upib5fE=V!lOSjrbs= zgp^I2^i3C7={oWjEHDwhy(cmf`!i59R3GU2D9Emy-Sfc*v6k_q?VGDd%oz%K+m;iJ4yO+63mnBfzjIiJ^Ijq z&CTf^>zTq6`3aGq$xo1fux5U zmK_0Q&!}0BZxs1<6j*6=LS{v^!;b0c&@1>JkL2`VLj(=d%#1--mSxwMlNl1@@ms*n z^Vl|k6S8&Y+9I1w;XF)9Pu%`TzA@oRLl-0jF@N%j`~WUCs=@JDxw8pDDWDsK%WC4x z9eZP~h_1;9upOSHB1B@TG$X=Xkvy7qECp*lfhff2(qbZ5y;2 z4Z;SX)@<0DQALQozwMSHbh3R*iQ}N#7+`mrEx=#^NzDxnB4H5M+W4wDeEZU)&*2He z00{($F&3E&U+%r)cHAjod*MP`qM7lz2LMikG_T0>-KS6eMq7*hCCm?ERQI)Mtkcp+ z0QW#2O9gPIhEX99li~a@nr4<8_oCPy&VWSH2b<*bmYaF)_x<{|7a&hzx#KcT)Y9n9 zRt`IZyBixD3*N@P_~+)lPNrKDoI;A%VF)kX_zhU2$#@+S4#K~9woWp<;Jup?9Ze$H zJnr-6;0nde<$O~^!w?{SF|Ni$N4HK*qEH-y$xG051Yhu!MMY3=hq-Rg0*b8y-V!P& zC;ti{(w(aH$*cf)SqV1;d8Z-Z+CT%-3vd2ry=Vd2F0%&B3R|5uplt?Ms+^A|@>X^k zMsQPGUftsQ;DjQH7R_tu%%|(Xq&H0yVEd=hNZW-HyClwAy=n<>QJjE7%@Y6(v$d-` z?vZesVKh#g)Ya92faoS1B9F70q-1i@-ObJJ{$JOyu?d7M7;MGA!`dTAGYw&O2A(Dy zxN>dOo&%jzH_*>sk;rFI@#e(P2-m#`G@h^Xg@t}Y;8A+Nu1;%{4zP4rEQX7}YH3>M zvFJ4pelm$*t^0UzPt|9}OFuC{e&c(k!|uxX*+W8d0SAucPs|R`9rPIlU93MrZ-^qQ zP0XWVV+(1Q;7y)#n&{Ms=7c`~pVtA}0zP@fxX*ik|NNH*nAC7P0}}3&RzmcL?<(D-=YfL6=*>xVyjjtAPrsvH)CczE|7?yo}2a&yk^>^9gNKvGq?UWWL9 zACz8TF)qlk3er;^`4+^vLbPtleWptmKW>fqL0d;Ow+xCqD)hY1MUVnD;4Dr}b%SP% zu=r0mans_`GOR$-LRmH?CsUr9D1lHqtW)e3%p2(LC2q4uhONe}-z$D>m!2of2Z$FgDS3jX%WJe@NV@o-2k0RW z-ikj+gREE!uoYiLegKh-U^knQ0riS+zk_22QvHo z|59|cOrk&nZQPT4t|OLQfV2wWC35CjbU5WBoPB!n%RRxw$KKD~aGC|h?^BIk2Q6cT zGBcF@Dun|lj{oDUAnKaJX%^>=u<%!YYN%*BwKTk4}K#WUKfby0R#~; zlHDq)ZW9wPt*)NHdIy-*;G4XUt<=`u?zLOf0{3=q84M^x14CEXx(Ue1KMYPxyqJMV z9CW7u_sifHtvmTRcW&OaRJ^$R8-6a_;$~j~%Y(zdNjibDS&l#lZ&g^sAOwvhB)#MZ zyWA}q_{H^V_i<5!vrZIvmtn)*({n;n5NOxbgz|5kq~m0wzb_yV>uf7>P8Jfi;+jV_E}i$$ zj~{$Q#gZ9%$%xeUTYe*F!y;V5ZRT!tPvK&KyY!6ZY2=oi>wi8Z(LjjrMUj09lSTf= z|8a#knPCP+d=BkLhBMqt{&(yTg+N)EXCV3_(SPV#8Fyug+2B393gEcqP+{(t zA^JXV4jBNW%fD3mW-(-wmj+|*{ie7xMcOe?(&PL1yPOK>s`OI>KCBHIIFW^h2+nUd zUQBH4BtL|T>lDCdI=OF&IXP=^y2A!NHGDK+H|;PA)a}K?P(QdTU?brrRmChM>|@al z`#oB1Nr3kJ81$`}lF226I`{aI-lYJ{Iw1UKHzb*ya`&HA)5{^ z!d+p{rPfF2$4cZhdBUZ&B5H|H2)#y zmWCU0Zgy6uA6$vRzOaHk1j`fSxuy(G@Ta ztv~XXFM^F1=;igRLqA?809(?>$_;g2Mp*b<3yV(O#Ws=o8=&+NnF8(9T|-#G4pk;c zJ0}~QNk3V_WlUg*R(dTS`UOyNh+N#;+A1*B@w>Qk;j27O0C$)lG5iB#z(O$v35R7E{>)2WRQ&1K>bw=GXVL`b` zKz0(!R9|s(a~I>g`=?A9sYbG*0Sybe76il_@Y$}MY35G&fPCl5byA+1-rg`#U3!3+ z7_L8@<$l{RFfjD!J=8lCZ{2{zQv#5`3EzkkA>g5u=Ah*bMtib)`Sh!+oY8Ge-Xv>o zKHxZdI}*`2 zLl>>Z)27>k)^)7(@2X! z8AwP5hFO$~^I*OK!1seggZHXFl%@OlV5%#q`6!^mvXJ-Yfl0p-uci$tU3JjIqa7)p zEV`fqXjpdHRaMGEg5f=?IYTxdnd&vNhXw8;m(kUi40W#7pw@#%N5>WN z+d=UtnL(opZDTNFMf{(;KPo^1;>f9wm~+lH`DYJ4e(}S?3ZkIqPxO0W|7-d?HCAUF z^+yml_&q1!(}!CDEi(MIy>_N4xlD-u^ia$4Om&(Se`beiMxtMWe4N2zP4&SeRH;;M zqxrate^097918AJv3=m@r|NvQjla8c(a^vejhcprlw|)9Nmvza>`R7zT)#l8qRU0U zY>}gd7X789!ZP6Q#~)*8IXS5q1sLy7Q~i-xz`2E&4BEN5C9fwc+A}(s^{ne% z^=a6HD2a%>{tHeY|DHw4wnmUyCEqk^0R#8vQ25?}PBf$S`~uCd@$Dh~Hjx21KK`#A z91h_I2qqw^UaLAOR_=_WdZoN8nCuR@3AtHdP)!t9SpxY7P|t#EZkzX^bn1q0Li?j) ziLSA{ys}mBX8UR@>9hD;$HA$hJ0Dy(Pl~s_(v<{*Vd?F>QRNj>4NNXQJr`3%bYzpk z8JalT41JMLJg@cTPwZ{FIWS+LK#DR@Tk8U=ZR9WQ?(CeD5BCF10!vT55AVHqt%Yk} zGUE^S{_~2$5(=u$!P=H3wxJ|eBIWCM6Fk7GogQ=wUGqwG&BUg;?w>q$D>{uXqYohr zTUNl_Q-yl*N)L<=?L@Uju#z+2Ei@a(Z2$@|GDL&AGCDF6Dn-laXl%eZAlEmUt6oxr z@rXeGk`E~)#D4ig9j^No^ilrR1LxXr;c8mL45lPPY2IDh1#Mk2S39Y^KTM`qC3s-D zLqv8q&hBVmhmvd^V!YZFWR$pAJ{Rs+!A3{GMSb-t*o2jS+U!%(^H0fbf(S{H;P%-G z?eo;!Slvk9#zh3WF~=57!BN%FfW2>5u3nk4hF{PFIY;Eo6HmNq>!cKhd*wSd{$){- zS_M~!TWAPA{T)=uN~Z&PEXG=g<0sUn#397s6PeaBtj7Gy7U^)1iUd>HWk zdzNsLdq&0ab0ji?Wr37~1N?Uc#@b0K#5q`VJ*#NZl&kfeQhW-00<C>)CTp6HWRQ{talk|G{|H&6pCL}P(Drc1Iq8~Re zdMcF^<(=7e6#F3P_x^18*UC3L>JTQ2Qi8*oS4+`npEx;n?}T^wUVnDzI5eF#-|Q;0 z$6s=r)wlX!=`?J6pfE<+_YFTkcCgPaX7g{A@dmz4SVqS;_E(<`6u5qK+6^M&))Khw zWPJ5c-jVS=*`ka?_uoc{K$ye+G?hCkiKKX1M0*bs)?9)RKBK`GTs_p_Xz3)U7)iFHA5NU`K`xoz=FcJ2VaZdUz{`r^!T#cboB`KF#|TA?GGx4 z`xc!8-2>I%E6CynCf{qTbwJ^}?)UKR`iA|*50qMu!E?J-^>pBDp=|;=28^oE?l#^( z59H6ec$^MW;2xr0p8x-Zq4B-{{7L$@1p6l`iu; zdbSjskGp4+tG_fAe>k^IyhoGq6XtW$N#S^ zA(~D6kLRN>hsuaNmBvxMfR;J`oiY;@6exV!IspTAb%Cxk#XRNwV4=x#Nb-IvneY@u zLKzG}#OMZ^^nZMF{!7!SfuBU$P8&yS)yhjsbSMZYI@_NPvLd3Qq%<}$X^&(VYY>%3 zQvhhe_s_>Oe3aRbL3jrNdqhk*{A^TIR2tu*i$Z5O-Zi~DgHrSccW^HV9T^`yNNtNe zIy{89;k>rYOe_#li6k_w?q|{2iI&~E>iyp@=g-HPlD4V*Wun&^;j$nbbr^i^6>p)5Z~2SFJJ^Fn9*@x+?5vq+LKG~4!X-9g3BWI4yw^|c zQXcQ-qikY6#87{pSrH63l&5=o)p@Mm=k28V5@YO*SXF@L7W$)A=d-pi+2nE1vLOwr zgB37M;EyWw!}`}(M&zhOv);5y%xU!3Qyj?R=86Em8u^J3V5QIy*0dv;iCc6J9WE5;{E z2ZbvzPQ`#w1C64>VG$l18`uP{V*-Om)bCTkcUijv^WPu?Q?vNsH`V7^=g}am)$3fN z>4nd`C_MQV`8sxKX=#+uZV4n9pmTy63zIV*#U>2^VvuKd1p=ED6^b(B*m#ofmXWB^ z;HT#YB;CgM{{5(rRACwg3uq!B$3#NHpJx*$K>&2p21e6E<{$oCDmEQ8g@s|qdpwEx zaxAGG@vf)nRu&endTLiXcung-T1lzeB%VirvX+*X0e8ZmGid5N$2$YM@a^q4F!I@7 zuW)WWx(bm3~BI5P*l}Z&asDReBE-50!V~Q^ewo2Sk{9sCI z1(HAGdjKW&%aHwXofRavw*>hpUHR~%Fpr3N ztte%Vry8#9Ze{Rv!qWMgZ9%8~w&%WJX@L^~-*ZSg<2o1@t8xA4Il|EnEQrH^6MUn} zagRyHkNyzmV*&*)^pXih&lb{rfPvsi>Go%ktVQJDc zOPs%er$GEDbj@w+K$x|J((&;Pja5|?L+}g!Cv`$qISrc^)nqvtK8G>L8%1{biOU}7q}`X3^s*P~2Ub_YhQ(0jA<>2wbA)DF|4>If8zt0Vw_{{yHYap?V@Azo67q*A^VUDRk=4jDjhDduJOSsF&&+o?Iw#>KOwFh@1rnhyEHeam$T` z&&fhG7f1D}B3j@1m;jd6>IV^lobDL(zIoyxK;Fey8!Ie9bW1%hO?64OHl##*#A`@nQV+;i9=LAfq zHBWB1!WRpn7WEHagvhd`7s;Bf+|M!u%a5_<&07$_>|JgVF9;kQL@|k-dloMkxDU~C z`sW$-=^?&@{42e(M$c{2oV10H7ONA)ZX5uQ2@9fC;*!~!nGlqIN6QT^q3@}36Mun| zEjvyF$p)RGei$+JTVDpXu0p0dNFSl9-ed(4J3!S}I+_VVT>64==bLG72EZdjLxYKe zk;uAvIhG#WY;7Q*#B?P1B_upTkyA~JUG)jza~-^&6_cCG(z6R?t=SN5mKKnZl>FbX z5wI7xh!{1Rlcx;GtbjcM7@S$V3SE1kNP7#Oix<_th$SK-a^uPP`A;%Z^0C(qYC5Kd zI>(eps$GRPXR1}u8!p_C7vkin6k&1|8WqVHxs^0sv!f2$==9*JaD;eZ#x! zVyrhCE^$4x64D6PE4-$LT48?(_lrRl&Xs0;$|sZd(LoYF7>-6+Xlbz&8{!R;+*Uy33$n&9(>voKIkN!%v;7xVVieAI?G;mr>&HA^_E=d?qFJc>LO6K2v`b z=gZHp;JtO-zM0->A`V$W{@9goH_mHDLgBN{0cZWAw=nQR_VYvM!$M8^hhQ181?rjl#u<|4|^#N2_(G6L(H+Kib`Kc5ysT3N^fiQi5S4^DZBiKmOeDTL(|n8k zf>?t|1Nk7hOVB3q0+d$r2-NPNL0E*VJKz(Ac5seKKVtX&af6ZlmLA6yR{A+N zA@*b*H{_8t;G(TW30{pmqUPz}X|e?_x4`=2K1jYW>`cgQ)PHUA0_#tm*^OXgx;jAQ zQh^5uT~n*W zppgmGBY+kNj}7z^YrfW4y7BHJz%Y^8FAa_`-DeVjW*%aoc*BEY3~0P0zqGjs;O=LB zke#oA0ixI#lqN}f6p8u)h5##D8IPv!0OXUWFcKKeYi*blC!p#M6lSh^s{4CnVzlr1 zyVd|Pd{2sQYJ3-_PT-zAtWd3``8d(Y`-A4e@;3=^D}xy?$>J4w;s6^1{|3m*BN^&X zW20W^I)u+YF^i)XfL2y}lSAcGLJ!bI`5 zI{$I@Mn7-qfbqT`_XeVXQ~`*hh4C1=CQu+Ve9)26;fFVVKL+U^&`$pSEu4)Y;iGrA zS1Rt_F(qk#*x}qJV)J?~F%6KR75f{5#|Ak!2bP&BFZ{?YIsJKlYxMDL);5&ExY;~S zb|vf_di2GzHXqU_?Cf&%%&&ejY%X`{-Knss7HtiZ^aBwLv9&GrEv(lGHDWYltW2il2|bu31AK)h*jHfx^Mr zy7D`;;7nq>p17VyNL~OY+8MuaS(-p(zFk9u(NEAh-z)&W%M%@0z791F+Bg+;{~zb5 zaZE2udz#JV|H=Hi&9PGY=x#3b@aY0!>U6ABy<>p?-y{cZqT;pI(o&w{CUE%MHG%Bm zt|~x{YHB4l&(~+BV&q{H~}!SAfxm)&P8V2hNawKn{Isu{Y%dJ ztAsO8obO^KIi>QgUedgr?5D0&hCiuD?|C4u(P9Q z>7*h34np7}z9ulgeDUI4HVdEi z?(QhU-(Bj;t)?UM0WOh@M7Z#;M(f@1rSZi?@FcOe`dAt4;aW;0;sKPN$9P&a3COqZ#%Yash8nAiI+ z5z0T=Ab_|s$Y6ca>s&!#CeI`ZyVz<-H|QyYSNr)f!^_#gKJn1-3@dT7E0`hs+*cZy z^**&ABpo`Bg^oywFcIJHk-G6D+uLcoA+Lr|Xn&Rg??YmGy5D};33MRGrqJra(qR=* zWo08saGZ)Cd+|<}^=N(414yueOJx=pS$rPn1xmjkSe{MOw{(ZgDfd%bFnUvJsg_G0 zqAN~ah3nH-fl`5~nauw{VG&dyG&$7Z>2qhv!aZ~e&vy?Q8wci3Vn`n=6rhA@kx z)pj=YAaqgqFL*|~RU+^cdv!1gvs@cuDIvASD3~fR%Xcipt?CqvX31JcJ3!KL3!o!L z64N1KB&cH1JV95^?nrO{L<*+O|((4`|+W&Q6N%oAnT^ZI(yn(k)G%|mN&ZLavS<=shi^kJz! zYRoOpGt|aM*))N$)D=v949D=M5Cmz`&Z5G&(KV!}1%Wji{Lt2pH~7`X&~qJwpIrnt zWRzijS1_?aTHRkYiUbTM300xL5VcU_1{TQt#gz1GctN*yuBgIhnJ~!n0<4I>bNe<> z1y|y2uMQfG0F(Y!_iZXjFu?~WOYDO`6PTI)c142LC+k}mv0t|cPI^LG5E2*^Wihcv z*m4P6HWd{*OcRolpWKp5zhD5&-9_R5`u!(RWn|9+P!q$v&H{UEC|R;Od>guve! zRqxK`#K|Z@X9rNIMMD?}$ZI%=rh;wgF8qPE6BXd=Sdug$!*k}07|YeE|E%?Y2v1Uz zg9(sGWMyR~NSx&q6yAWdGr86ujCsI1<+5#`&--tki6LyF`KvR*!u31{14xVDP7C0l z?rncQ>^G+aHZ~XtUT1r@kbDiMVf-IR;~$Kew%)l33NJYS3+%|;K$N%f2;WLf>Dyl| z=S`0ywZg)ySFX@7F}Xmx$KMfXY3bCc@aZ4Trq|i{SZt%19JveW{EZvQX**R#V}Mu{ zDHf{LUK1`P5T*CWoO1r`tH>l;d(Q`xkihLx>5O0<_9V=p8s|jt&U<4__Vo5?_qw`_ zuUweUtr(>IyvZLFzxPsW_4kU#!;0@tYy|~=EwY+++j2H-OXj5sF0ZjZ`g7?Y+i-r= zsE{|vq~+8Kf+2kWD1OB>FpY*7?}MpcCbCiw0|VL#38_vwd!_ zcHIsj`k)BAb`vh*^M?rr`ht}@dzle5zk-h(m0Pv5izik2Q1?ZDH_N_=Hb~+vnEeuq$QiNxT*d5&7%_%`7=Dl_jHv2I4_!OX^owo?g?%Egyk-oiVvka504f$ zo>Cb=kX;)RbkJRzCt|fD5JPJ9nkpL9IQLEa+4>jl*DCj5mFIf=*)g@@1#eu6?2Ccp zxojlz2SCHOQr@D}5P|Q04L%^Hb#rL?$Ck2q+)ab?7&Ub8!YLXe*;rI~-j1NCI-_ZqGMA1H7FyMFbr!a9XWEJ*J8TroIECAQjQ1~0(% zIYtJC9%KL^)lsLXlHFE2cS8_7o}zY;R7$Z4*HJlbHdB8(oy2`Q7t#zBVuHxe+p{u+ zdnRFw?=FD124qUVtyPybT~FYU^n-ngY6`r!?mpYN1rT8+Mxi*rNzYWhH|D75QA>?= z_I8khv)}dXXdHg0Y{(x_^DPTqzGD>UGN7Y~o1=RFHixnrY=Z%rO9=P`?QY2)szFAh@sG5h}^q+)BZAb*=J4EvIIxl2Kdrjnfd<=e~(d zlY7>b7ELmO2QwWH*QD|Xy2F10FXP#4+7=aCwHC!kAHqB`5ygjr5&zG>mG@wjT-%j1 z)qQ;RCK!>FsX!65*GdO5ouuYX>K2s{TJjsnEY;i>Zl>%JnIMSa%urV54nKqbx2%+(DefsibM0DHioL<3mEH?kN?y&jW@<5@|&Amf{S~W zqSG_c7N-wg>PjO-y~6rXOOJYAfEo!0t0JuuD3gL&fdI-FpT0u%|dfaXzdVKdS-)VlDwFKyCQ844vB5d`e7y{df-=H_IK<-l=L# z`NCsJT#6pPaQS*#p-?*kdnMOg`O6%})q@AB?pj5BHXmdae{{lEkzMFFy)zV6Jm9%4 zvM~EIAo0@!{y6W87^S{&u8qY@7mUosC7oc!C)_-&_rXo{OlY+SP;r=oKr_prUc3^f ztd*`&hTbgaVY0351F7GawXrvyw-2+4BVRMZ!8Q+h@P2Q?0~cM!?{(^-kpNBWonB0q z`*2lW{O8G3w|PsY0ifPUVYhO2GJbYRkC0JKAzeNN5Oj)x^F`D_2Rm{wX)|6{)?@8-Am!%8Xcy;x5H@dc9%w58m`1J1Y@9*n2CSH4Kaof9p7u^K%&Q#{XVAaQ9$fOmGw9?It27?-Ao+ zVC)C}*U!9&f#H($r}Y2(kNo?@{yi}0Chgz%<==AgZ?*jQviSF|{I^y3w^RDleEUKp z{%@oBZ=Xzz_wShS@0c*|0yAFlGLPZ6IWZLQrbh=Hs!g@bG z;Be1?7OmK9RQHt9GOYwh-0X#Eizkfvgzc@J^h`{BLBc2eQe&t&pZSEb*BBV@tPZ+} zt@>8B{XRZ$;&WQ~?W_>=&~9}E!cYLa0x=!uEcYYP>m-8uE2XTzQe_m;J7}|8RVx7L zYZs07D!G-#`BoepwO9@Tb91vG_fQnNytDPXV`MOeQ-8gFOV)x11LIK{<#9u59d^2? zvGDWMXT$=AGsRP{9DNpeeMHs$@zGw8sLgAi?>z~;6Uk*+*S3A<5Y~FW9E{=|gb)T2 zZdyw2O|k*0^kGe*-y?NvZlrRSkJesmKqB?A-!GSk}qLnVqnmG@~j?E zC`Au@Onw{a8>)`Y?gLojqEAHx$pX=Wcwhdc9oFG zBD02;S@^g0)C~L84(R~YdZ?3DV%)cxrI@L_*!bCnJlwWQY~N=dF>VE#P4pk{&Fi!L z!9^?HJr)OQO3m|q;YPp7^4ZuWy$y)N4GfL*XX*2A-)<3FSmki7HJS?|l?-Q6dSb}X z`qR;`oJKixQA=)iO z<~|`#5-EgeFgT>^d~V;CAdwQdye3x};O+)LtqA$rRIHgoF#I=}eT@)55PE<9n31&H zK~Z7ffd_+u@!1KDfxvffk7icjq-~-B68JOwhk1c#7l6LuFfc*^u0;Qp2EcIiUuA)= zz`&?dEq$#6ipD5zi_?{Y&fVpH3kc0L=*YKS9Xfa6@_SHWK;-pC5ad%VbU^~wz7HH$ zi2mg?{`naMAB{p8!H}g*W4)@j+3gGjO>4~Q1nh56Tpp@)wA*Y3kf{5!um|vj6m8|( zD@tG06cbdpMfmq8JW>S8!SM{y)sdqqROnq>m<9Gfz~1${e3_{d8^=XX+<0d$W>kBH zw@2NLqPqGz9nEib0q^{g&rA*s_jYhJ} z1lWlSz!f(0ZXowqn%38$MWYy{IYY1&=ga#VmH{b&FFtGE;vcW9?OenkOUea_QD13v z_Phf@x(irk%9k&n%4&93htXQKNu+~qb>;rl)mK`eKn@bjcxDnru2t~ltz9o);@n3W z!7`&x;hko%Fc+l(#6cxq$tTU}JaHPu7 zCr+fRZ$HtVT^q|Q@ldy{aT73ee*HhSGzMs1x20*B;f?Bbb`5oZO2YUw);od zE>lH5S@yS=78;jRI8`i+KFC(ev!CxBPhvIhCwn3BuelS!~!#Emea_!B>`inTuCF{j?BPO zoUK@^difi2Ql8%21P)0w(T5`EY+1WEyI;fEhL>dX_*zG202`(pbJYx9*m_GCeQS&z zB)q8QaHY1<*0&6zf{m|#GIB$scTOC3&fhYDokT|MVV*YhrerQ?!8RjpP z{V*3Ds;<810y$4aAl5SK)MYF0?ERUv^Gt>bD}As2tacWia=|y=qonnwL3^qcyQ42M zn0i05TBKu_Q@uzcpK`OjdF8OTfLYx(B%*`CE z%iXdGLQfV9x*4Kv&2WO8^qtfL6+$lQoeCb8`7`k%Y@&9DiL_lTQLTP?R$Ju`>9{44 zV<#Jh9|C;@2q;z$OV(Frm>jM;hhGnh*L2)ptB-efR$7bpDU@oyeKKvXej5tMJ8C|= z-%d88<*D}D)u8O7VAB`3v~13~3mR*oxqdMN1A~T6o8_-1bmHz6S4E3Fd6SDlt}Z&A znNvl~e>fGuOh+rNL&q#5Fgr7m06|4Bb;FX)OTd5&>7rNbh_v1 -fft_qjC983obxcG!cl_e=v zL#s6P?cRrr0TP9?SOjLwSwagsw|pGr9kB2#C$SV7N^tC*HirheaU3YC5aV(EXNylQ zB8HLq-3kegQ~phFVmIYw#Cns5heT^BlnUX|7pZSfo%JNzdD)7v2&N(kL>%&2srRC6 z7HKc$q#q2|&c&*8=Gg}9Ivp=2IeeTUvhWY>MqLOG>=mbqEX$Pc)m#mHv1dBzAYJeC z+N0N`*@3XO>AiGj9rMXpvxv;M&-+^uNv_Q${0W#psz!{OuUH?=E(&Nx@2ssSIoJkz z3mmL@A>-mVm7I{KL8FjJcFHN5$mrm_giImJMkpl0o#XhVK{N8EF)2Ia<_j zTDnj|^lDsdlU@|gRh-M=Vt)8dxx499Ju$%xU(V@$kVgB!^zghRb&J%B>c?EqoJVm4 zlkIA-X~F#Pvm6+-TYlsN3J=JDETYdjF1V-E*%Pxk1QcgrzkbvS|B*uajV2*)H zz04KI`L|?vsvL?;K}z*rTTcopcy_#1o+%n@Ik#A@6XyjM>E-MLj%oUwZ;6#d$xm-J zteSj@OWb%cW7rX6-w>ajrcst3mu}t`PvPiv|0$Brujm(3>AL$CGmrK)C*Vig-u^;$ z@^8J5+oWJVN;TZP14iv zP&0ZgCO5EYb2<<0ZUj0oM=n~I5Gpk=iGnpJVP?+c1o_(bIBy zLh0}%<+trvNqO8BrUrLz*$<76-76_3qf(uegH{4Ph?A53U($_6oleFhxUN%jj%j># zx6(+YwW-X+SLe4!Dt(?P2JOgG$nP{r>6LfH+|8KzWwn$dXg2h=C&Q%tRa|=AlUNHw zITB1Ue2cLX%sOmd-C6umrArNzFW+kF%&lFCXSicB#vAm^*h1~uaFSO<@M=KX^GV~@ooI^FAr?l3YNp*dVBWP#(~@P2ho?Ruf_d_m}~XL>|?As zpHyShHBypz2Qr1b1nKSMHW~Xf2z-XzSUcZL?}|v9jHv}LsHS6k^AobW9jG@4Jg@k{ zt^Y-}ay<5|KB(dgbtNk$J>%xD&(OoIySc0hEu{_371vFCc=(&83hn6ryi1dwVry zQ_~D{*ZoNd4dLQY&xZ=lxD_l$&ib(-rnMZh)C8nYEGUOC?8co-tY};UC%sod0nwYG z=rQe&rq6$OM95k3Q>P+qDxeS=Hp7~TeB1qOO-G^JHyHEz8LJkh2~}}XWtqy-t=x^5 zi)coxX2aE`8Vrn#UXBAhJtAd)X!2z#o2vq7NC`2~)Km`4nAuuP3bZH&$X6o2xG^Wt zxsbVzl6B(^>Fqy z{+HuYwu@t2zIpb+jze8kNM}@(w*qTMl$y*kVeFho$Vq&~M8{1kBXZMkE&A2UEJG;9 zA0^B2tV7qgwmw~=uQaT=uvD{0nEZr$MKy3dKT`#fP@y@$tN1cfCD20Lns51*Nsee@ zbuA`!J{?X`+tHKiAjde&uvlX&icFU) zsgUs#1Se%zeC+?>GND98D){hC_t4-_oo9}m6Oyh*A(vO(mFHQCZSOK3d(vXL)-}vM zO}@>=r?EY1bFyFP(iWSP90-T>hF(}p@!$!NW~_9DU>bZ-zHj2-Rd~o~EWTP(eZBMy zIxr7;f7O1U0PjmV>AbQgV{}0&Ha){NBjg1R2T=|jq^gF5%D5TT0qV97|2pJnb%bgyVJkq%#HPs1X~ zH&FArNl%SwHy-s%Y;AYn-W4BRu?1yUH8v~8BZtw-r{$b#a&}=Qr9P;LAx;QJwL;mL zi$pgSn-y}D82R0X(j~&^YHKs!@kUz1qA=EJ+*Cx0;n(`Qz=4G6*L%EgfdnX?md|zmLI+q^E@gtGTrCFvl!#wG&WLZl?SkM17;9 zw&QBiM;X4Zk?iO5R|Fh(d5vE3EPlU*lcPHAhDm-!i!*Q$!Z=y^tdNy|`= z!L++rm`r9sU-UgT=144y*~rL84|QfT(lhQBob@_pNJ!st&1DT)*h0+}NL?a zpyT54vk2jAgB4HNb3qD(HmQwBUg^5s)88ipNa z(KA9kty5|pWX6(5Qo5m}d&#e$4=~u)U_xd`w#{Q{>lJZPyH)Mee8)ZWOF<6Z+hOO| zyh)a*RYgFf(knhGGaLEyDs*{%anCbYp@GHij0g`;b!Wp|&?S{ZdxW`o=F;bTAr2bE z`RCzk5J1A--#N zMf;CJRy#$p@}r4T<$FiOY&;#Qp4Zf9?2)!nCzEiCx7`rD&!MvT{MN%r?WxABxNMTq z#8}$ri60xd_4UUWvko^Z9*}&c+6lxZ`t)f-xV7j!Gid z4^sRGs1wB=|FR}ZBQM&^CK=GEHG8(fiEWZC(sNP}8ZjH;&EYX8@z$(8i@y5p^Yy5x zjnwaLTnuz7$DCC$_T$31)DGy~(O;QJq3`t~*$UeguVk|>Ub!IS~$NBrc zuJ@n!d7t~ffA??sOPRAM8+V?owNYwe8pnBiUAhQ!WkBT%ApL3aunIkBsD$^;q+(h- z;*UNy)PjTh@0ieS){52z$)5vL{7S154>GgZIHYdyEQvzM;?{^*z7h@jEM68l8eHnH zfYmGva(Hu~iX8~tz*#*wpaS>)>mNZ#(thpz2DA>wNjMbr84@ny{d zd&Wk@MNmVWrJ3)&Kv#H;4-sKy=id(I zYTW)zqK_NTYJ|Q~el!4o*RoKTVh+=q6RNl+y#GZ`e zR?waeD4_)pI{s;Akw6Yw_db`P5PO%+;)$`a*BxKG7>Kn!d=|T53Hh{jIN>JPLKz%eoJ_gTgNsLs=U+X zM`v^)PWCH7eVoftZNU?ckOTuwzv(}uJx z^dGVbLaizvbP5_ki4i&cIQbvs^FCvx^+&3I_Vz2nS5&Jy`;LZDysB2(?^T~4w+u4d zRCWTNvW6NAk9E2ENj=pC^aT5piQMh!;{reZ&33oR2=w{g^v z-!~-%8Z|HjmQx$s?iarz*+VY=A4F+HH~ZY5(;4$`R^Y|%&|WF{W`)EYF0ba?w8ycB z%uH0PZJ%os0Mf>IC+HhLzI!->gLrRuI?5Xq7A@`Fz1j%HBJ2gi+p~x6geaiIzVGmy z3JX-gYSe&H)_}+MDg9Fb9&J~@425GENWj-E!pft3`ovG*Bps~x07p~JwN@cUlA(Jm zKLY0(J4R>$P60y`VayAz)aRfnLC|1n@UUI7{^)vHswK)&K0LY-$g+UVB1 z@^Bv(mMhh1-^2Q4Jf_ag)d>-1f#p0U9+)cxIRamg{XI){$VFd3v%D5}d~bDiwQ&Yp z32@o=ZaX?)*B)|%M~Z>1A?Is2UHRyCb1nwWU6OCL3Z)%1_%mV#J1@>Wi2K>4Ku1P< zI_tXu{^{H)Yw|vX_;D$f*QTdB=yA0>*>*U!x3GKo^78J+y1*~F(kE!&G?5Y3n2Or8 z)A5*EU*7Kz!E@2QU~pwpz@3=GAEK$HTQ%%Vbdx;s@XpF9SNqsuYAdC>Hk# zl4;zrJcCJ`JC4H8#?#hZFb!bu+H??+$`S3v0D{EAvyv~ctGIlm% zb?xlUyqL`X#yYAAp+4SCIr+2oG~LCN-Z8pT`Y~zsiKnwFZ9e|wxG}%`oOpC*EkzaJ zN!3SjXj93FT5zQ%R?nhJO1-jD>aaQDnhQ=ztFx(~NDRl!Zo=M&Rq&Wd7}q#mn67TN zh2y-x#syU9nC4P`zNZE0F%rzNs;ME&D0QP3jqqFpDEcAHYQ%O-(Ik`I8y)h-y&kD% zzXQrLi_<#EhRA)-S75XZ+fTll4SovXE0%gDZC$}UIIt#O{w3-0h&7miCp4oONO}2B z(FvFg6(={!?ZOYoU=AQOqr_~aBccUl&yKl=IvEGtjCf0(AnJ{kU#>c+bn{mg_YN~> zPYH`fxP1dqVAY`}TuTo|G&54!VMI)#R8@NFgn&nmt$OB8Rj+eYeU??90E8%h4|qtH z=S8QPRuDkRmAo+6cayO0H11~7KA}^1sU35yONX8A->A_N$|I3NS|dk#xkGzwY6KEL-0@>-^t_+T1#IP zK68gKBdHl$=c~TNW64>&4zP+)XB8nm7&(9@^nl8g0S5vG4&VucU)!6PE@FA;KR9Z_ zp{>dmo|L|P#3e;v11t&Wr0~ML!Qyj5-p9|^fR6(FL9@Io^5O6Y{nK%o-c7Z z=hgI&GM`94riM8P?uGNjr0W56~n5Dr}Oo42Ok57aPvj0Yt!M;g_ zQ&X>13)Z2aEZ19X*oB@weEv~xVZAue{x{dE$r~9qmw1=bUzHa`E`zkw*y2}ZT2N-g vf3Jy&DHDiOkkZF)DJvC}JSEu|cm3kG>x*)7*E<3|e2=+_jWOBq_t<{{c++3V literal 0 HcmV?d00001 diff --git a/images/lab_diagram.svg b/images/lab_diagram.svg new file mode 100644 index 0000000..f13349d --- /dev/null +++ b/images/lab_diagram.svg @@ -0,0 +1,179 @@ + + + + + + + + + +lab.Protocol + + +lab.Protocol + + + + + +lab.Project + + +lab.Project + + + + + +lab.ProjectUser + + +lab.ProjectUser + + + + + +lab.Project->lab.ProjectUser + + + + +lab.Project.Sourcecode + + +lab.Project.Sourcecode + + + + + +lab.Project->lab.Project.Sourcecode + + + + +lab.Project.Keywords + + +lab.Project.Keywords + + + + + +lab.Project->lab.Project.Keywords + + + + +lab.Project.Publication + + +lab.Project.Publication + + + + + +lab.Project->lab.Project.Publication + + + + +lab.Location + + +lab.Location + + + + + +lab.UserRole + + +lab.UserRole + + + + + +lab.LabMembership + + +lab.LabMembership + + + + + +lab.UserRole->lab.LabMembership + + + + +lab.Source + + +lab.Source + + + + + +lab.ProtocolType + + +lab.ProtocolType + + + + + +lab.ProtocolType->lab.Protocol + + + + +lab.Lab + + +lab.Lab + + + + + +lab.Lab->lab.Location + + + + +lab.Lab->lab.LabMembership + + + + +lab.User + + +lab.User + + + + + +lab.User->lab.ProjectUser + + + + +lab.User->lab.LabMembership + + + + diff --git a/images/session_diagram.svg b/images/session_diagram.svg new file mode 100644 index 0000000..a11a66e --- /dev/null +++ b/images/session_diagram.svg @@ -0,0 +1,77 @@ + + + + + + + + + +session.SessionNote + + +session.SessionNote + + + + + +session.SessionDirectory + + +session.SessionDirectory + + + + + +session.Session + + +session.Session + + + + + +session.Session->session.SessionNote + + + + +session.Session->session.SessionDirectory + + + + +session.ProjectSession + + +session.ProjectSession + + + + + +session.Session->session.ProjectSession + + + + +session.SessionExperimenter + + +session.SessionExperimenter + + + + + +session.Session->session.SessionExperimenter + + + + diff --git a/images/subject_diagram.svg b/images/subject_diagram.svg new file mode 100644 index 0000000..9864ba7 --- /dev/null +++ b/images/subject_diagram.svg @@ -0,0 +1,222 @@ + + + + + + + + + +subject.Subject.Source + + +subject.Subject.Source + + + + + +subject.SubjectDeath + + +subject.SubjectDeath + + + + + +subject.Allele.Source + + +subject.Allele.Source + + + + + +subject.Subject.Lab + + +subject.Subject.Lab + + + + + +subject.Line.Allele + + +subject.Line.Allele + + + + + +subject.Subject.User + + +subject.Subject.User + + + + + +subject.Zygosity + + +subject.Zygosity + + + + + +subject.Subject.Strain + + +subject.Subject.Strain + + + + + +subject.SubjectCullMethod + + +subject.SubjectCullMethod + + + + + +subject.Subject.Line + + +subject.Subject.Line + + + + + +subject.Subject.Protocol + + +subject.Subject.Protocol + + + + + +subject.Line + + +subject.Line + + + + + +subject.Line->subject.Line.Allele + + + + +subject.Line->subject.Subject.Line + + + + +subject.Allele + + +subject.Allele + + + + + +subject.Allele->subject.Allele.Source + + + + +subject.Allele->subject.Line.Allele + + + + +subject.Allele->subject.Zygosity + + + + +subject.Strain + + +subject.Strain + + + + + +subject.Strain->subject.Subject.Strain + + + + +subject.Subject + + +subject.Subject + + + + + +subject.Subject->subject.Subject.Source + + + + +subject.Subject->subject.SubjectDeath + + + + +subject.Subject->subject.Subject.Lab + + + + +subject.Subject->subject.Subject.User + + + + +subject.Subject->subject.Zygosity + + + + +subject.Subject->subject.Subject.Strain + + + + +subject.Subject->subject.SubjectCullMethod + + + + +subject.Subject->subject.Subject.Line + + + + +subject.Subject->subject.Subject.Protocol + + + + diff --git a/notebooks/_All.ipynb b/notebooks/_All.ipynb new file mode 100644 index 0000000..bb5aedb --- /dev/null +++ b/notebooks/_All.ipynb @@ -0,0 +1,2752 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4b38af7e-f9cb-4570-88e5-0ddb63ce4009", + "metadata": {}, + "source": [ + "# DJ-Imaging Merged" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "68f79307", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting root@localhost:3306\n" + ] + } + ], + "source": [ + "import sys, os\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "os.getcwd()\n", + "# Set up basics\n", + "import datajoint as dj; dj.conn()\n", + "import numpy as np\n", + "# Enable plotting and make plots pretty (seaborn)\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "sns.set(style='dark')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8168238e-20ce-4a93-9cff-d99d2e7e5092", + "metadata": {}, + "outputs": [], + "source": [ + "from workflow_behavior.pipeline import lab, subject, session#, DLCModel" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c8c5e29b-6ed9-44ae-94e7-6ced38c81de9", + "metadata": {}, + "outputs": [], + "source": [ + "import element_behavior" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d1e1d918-6301-4f6b-be3f-6a5fcea9ef45", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n" + ] + } + ], + "source": [ + "from element_behavior.dlc import DLCModel" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "51ee6b33-28b2-4bad-ad3b-3c5d38c55c37", + "metadata": {}, + "outputs": [ + { + "ename": "DataJointError", + "evalue": "Class DLCModel is not properly declared (schema decorator not applied?)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mDataJointError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 344\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 346\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/diagram.py\u001b[0m in \u001b[0;36m_repr_svg_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_repr_svg_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_svg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_repr_svg_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 328\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/diagram.py\u001b[0m in \u001b[0;36mmake_svg\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_svg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSVG\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 314\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mSVG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_svg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/diagram.py\u001b[0m in \u001b[0;36mmake_dot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 254\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/diagram.py\u001b[0m in \u001b[0;36m_make_graph\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 240\u001b[0m for n in graph})\n\u001b[1;32m 241\u001b[0m \u001b[0;31m# relabel nodes to class names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m mapping = {node: lookup_class_name(node, self.context) or node\n\u001b[0m\u001b[1;32m 243\u001b[0m for node in graph.nodes()}\n\u001b[1;32m 244\u001b[0m \u001b[0mnew_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/diagram.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 240\u001b[0m for n in graph})\n\u001b[1;32m 241\u001b[0m \u001b[0;31m# relabel nodes to class names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m mapping = {node: lookup_class_name(node, self.context) or node\n\u001b[0m\u001b[1;32m 243\u001b[0m for node in graph.nodes()}\n\u001b[1;32m 244\u001b[0m \u001b[0mnew_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m in \u001b[0;36mlookup_class_name\u001b[0;34m(name, context, depth)\u001b[0m\n\u001b[1;32m 719\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmember_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# skip IPython's implicit variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmember\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmember\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 721\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mmember\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_table_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# found it!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 722\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m'.'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'context_name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmember_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 723\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# look for part tables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/user_tables.py\u001b[0m in \u001b[0;36m__getattribute__\u001b[0;34m(cls, name)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;31m# trigger instantiation for supported class attrs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m return (cls().__getattribute__(name) if name in supported_class_attrs\n\u001b[0;32m---> 30\u001b[0;31m else super().__getattribute__(name))\n\u001b[0m\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__and__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/utils.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, owner)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__get__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mowner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/user_tables.py\u001b[0m in \u001b[0;36mfull_table_name\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;31m# for derived classes only\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatabase\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m raise DataJointError(\n\u001b[0m\u001b[1;32m 98\u001b[0m \u001b[0;34m'Class %s is not properly declared (schema decorator not applied?)'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m cls.__name__)\n", + "\u001b[0;31mDataJointError\u001b[0m: Class DLCModel is not properly declared (schema decorator not applied?)" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.Diagram(subject)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c0bb43af-01e1-4e7a-98b8-10701981db19", + "metadata": {}, + "outputs": [], + "source": [ + "schema=dj.schema()\n", + "schema.activate('neuro_dlc')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8e0faf9a-c6c4-4f81-bd9f-51701bccce40", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'neuro_dlc' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_2486/3655878047.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mneuro_dlc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'neuro_dlc' is not defined" + ] + } + ], + "source": [ + "neuro_dlc" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f59f55c5-8613-41b8-a8ec-5fed0b55f32f", + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute '__dict__'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_1842/4015721976.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdb_prefix\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'lab'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msubject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdb_prefix\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'subject'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdb_prefix\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'session'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdlc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdb_prefix\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'dlc'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Volumes/GoogleDrive/My Drive/Dev/element-animal/element_animal/subject.py\u001b[0m in \u001b[0;36mactivate\u001b[0;34m(schema_name, create_schema, create_tables, linking_module)\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m schema.activate(schema_name, create_schema=create_schema,\n\u001b[0;32m---> 28\u001b[0;31m create_tables=create_tables, add_objects=linking_module.__dict__)\n\u001b[0m\u001b[1;32m 29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute '__dict__'" + ] + } + ], + "source": [ + "lab.activate(db_prefix + 'lab')\n", + "subject.activate(db_prefix + 'subject')\n", + "session.activate(db_prefix + 'session')\n", + "dlc.activate(db_prefix + 'dlc')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2e30b543-4d31-4847-a647-84a1efdef06f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m\u001b[0m(973)\u001b[0;36m_find_and_load_unlocked\u001b[0;34m()\u001b[0m\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> up\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m\u001b[0m(991)\u001b[0;36m_find_and_load\u001b[0;34m()\u001b[0m\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> up\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m\u001b[0m(1014)\u001b[0;36m_gcd_import\u001b[0;34m()\u001b[0m\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> up\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/importlib/__init__.py\u001b[0m(127)\u001b[0;36mimport_module\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 125 \u001b[0;31m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 126 \u001b[0;31m \u001b[0mlevel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m--> 127 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 128 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 129 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> up\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Volumes/GoogleDrive/My Drive/Dev/element-animal/element_animal/subject.py\u001b[0m(24)\u001b[0;36mactivate\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 22 \u001b[0;31m \"\"\"\n", + "\u001b[0m\u001b[0;32m 23 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinking_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m---> 24 \u001b[0;31m \u001b[0mlinking_module\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinking_module\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 25 \u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mismodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinking_module\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"The argument 'dependency' must be a module's name or a module\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 26 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> inspect.ismodule(numpy)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "*** NameError: name 'numpy' is not defined\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> help('modules')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "*** No help for \"('modules')\"\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> quit\n" + ] + } + ], + "source": [ + "%debug" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5ca5792", + "metadata": {}, + "outputs": [], + "source": [ + "# Load base schema\n", + "db_prefix = dj.config['custom'].get('database.prefix', '')\n", + "lab.activate(db_prefix + 'lab')\n", + "\n", + "schema = dj.schema(dj.config['dj_imaging.database'])\n", + "schema.spawn_missing_classes()" + ] + }, + { + "cell_type": "markdown", + "id": "37faba43", + "metadata": {}, + "source": [ + "## Input new DLC model" + ] + }, + { + "cell_type": "markdown", + "id": "c8fb8a96", + "metadata": {}, + "source": [ + "This notebook shows the steps that have to be taken to insert a new deep lab cut model / processing method combination. At the moment it can only be run by administrators of the pipeline (with write permissions)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9fee9662", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9d6b5668", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up basics\n", + "import sys, os\n", + "sys.path.append('..')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "023a8c4c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Volumes/GoogleDrive/My Drive/Dev/dj-imaging'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d35e4006", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting root@localhost:3306\n" + ] + }, + { + "data": { + "text/plain": [ + "DataJoint connection (connected) root@localhost:3306" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import datajoint as dj; dj.conn()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b5bd51db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n", + "Deeplabcut package found\n" + ] + } + ], + "source": [ + "from imaging import *\n", + "# from helpers import *\n", + "import yaml" + ] + }, + { + "cell_type": "markdown", + "id": "63a0dfda", + "metadata": {}, + "source": [ + "### Current entries " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b959a437", + "metadata": {}, + "outputs": [], + "source": [ + "from imaging.dlc import *" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a4bc2fd7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "
    \n", + "

    dlc_model

    \n", + " lab-friendly model name\n", + "
    \n", + "

    dlc_task

    \n", + " \n", + "
    \n", + "

    dlc_date

    \n", + " \n", + "
    \n", + "

    dlc_iteration

    \n", + " iteration/version of this model\n", + "
    \n", + "

    dlc_snapshotindex

    \n", + " which snapshot index used for prediction (if -1 then use the latest snapshot)\n", + "
    \n", + "

    dlc_shuffle

    \n", + " which shuffle of the training dataset used for training the network (typically 1)\n", + "
    \n", + "

    dlc_trainingsetindex

    \n", + " which training set fraction used to generate the model (typically 0)\n", + "
    \n", + "

    dlc_scorer

    \n", + " scorer/network name for a particular shuffle, training fraction etc.\n", + "
    \n", + "

    dlc_cfg_template

    \n", + " dictionary of the config yaml needed to run the deeplabcut.analyze_videos()\n", + "
    \n", + "

    dlc_model_description

    \n", + " \n", + "
    \n", + " \n", + "

    Total: 0

    \n", + " " + ], + "text/plain": [ + "*dlc_model dlc_task dlc_date dlc_iteration dlc_snapshotin dlc_shuffle dlc_trainingse dlc_scorer dlc_cfg_te dlc_model_desc\n", + "+-----------+ +----------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +--------+ +------------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DLCModel()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8cf52706", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "
    \n", + "

    dlc_tracking_processing_method

    \n", + " e.g 2points_leftrightear\n", + "
    \n", + "

    method_description

    \n", + " \n", + "
    \n", + "

    dlc_tracking_processing_params

    \n", + " \n", + "
    \n", + "

    applicable_tracking_type

    \n", + " \n", + "
    \n", + "

    function_to_invoke

    \n", + " DLC processing method in the \"loaders.tracking_dlc\" module\n", + "
    \n", + "

    param_hash

    \n", + " \n", + "
    \n", + " \n", + "

    Total: 0

    \n", + " " + ], + "text/plain": [ + "*dlc_tracking_ method_descrip dlc_tracki applicable_tra function_to_in param_hash \n", + "+------------+ +------------+ +--------+ +------------+ +------------+ +------------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DLCTrackingProcessingMethod()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "46560b47", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "
    \n", + "

    dlc_model

    \n", + " lab-friendly model name\n", + "
    \n", + "

    dlc_tracking_processing_method

    \n", + " e.g 2points_leftrightear\n", + "
    \n", + "

    method_desc

    \n", + " description for this model-method combination\n", + "
    \n", + " \n", + "

    Total: 0

    \n", + " " + ], + "text/plain": [ + "*dlc_model *dlc_tracking_ method_desc \n", + "+-----------+ +------------+ +------------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DLCProcessingMethod()" + ] + }, + { + "cell_type": "markdown", + "id": "0f1d3deb", + "metadata": {}, + "source": [ + "### First insert new DLC model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8dd9f8a6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Volumes/GoogleDrive/My Drive/Dev/dj-imaging'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# os.chdir('openfield-Pranav-2018-10-30')\n", + "# os.listdir()\n", + "os.getcwd()\n", + "#os.chdir('/Volumes/GoogleDrive/My Drive/Dev/dj-imaging')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ad9ea562", + "metadata": {}, + "outputs": [], + "source": [ + "# ============================== SETUP for DEEPLABCUT ================================\n", + "# ---- DLC model ----\n", + "# load cfg from a config.yaml\n", + "wd = '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/openfield-Pranav-2018-10-30/config.yaml'\n", + "with open(wd, 'rb') as f:\n", + " # print(f)\n", + " cfg = yaml.safe_load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "953ca129", + "metadata": {}, + "outputs": [], + "source": [ + "new_model_name = 'my_model'" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4e8cc9b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Task': 'openfield',\n", + " 'scorer': 'Pranav',\n", + " 'date': 'Oct30',\n", + " 'multianimalproject': None,\n", + " 'identity': None,\n", + " 'project_path': '/Volumes/GoogleDrive/My Drive/Modules/DeepLabCut/examples/openfield-Pranav-2018-10-30',\n", + " 'video_sets': {'/Volumes/GoogleDrive/My Drive/Modules/DeepLabCut/examples/openfield-Pranav-2018-10-30/videos/m4s1.mp4': {'crop': '0, 640, 0, 480'}},\n", + " 'bodyparts': ['snout', 'leftear', 'rightear', 'tailbase'],\n", + " 'start': 0,\n", + " 'stop': 1,\n", + " 'numframes2pick': 20,\n", + " 'skeleton': [],\n", + " 'skeleton_color': 'black',\n", + " 'pcutoff': 0.4,\n", + " 'dotsize': 8,\n", + " 'alphavalue': 0.7,\n", + " 'colormap': 'jet',\n", + " 'TrainingFraction': [0.95],\n", + " 'iteration': 0,\n", + " 'default_net_type': 'resnet_50',\n", + " 'default_augmenter': 'imgaug',\n", + " 'snapshotindex': -1,\n", + " 'batch_size': 4,\n", + " 'cropping': False,\n", + " 'x1': 0,\n", + " 'x2': 640,\n", + " 'y1': 277,\n", + " 'y2': 624,\n", + " 'corner2move2': [50, 50],\n", + " 'move2corner': True}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cfg" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "df5b9306", + "metadata": {}, + "outputs": [], + "source": [ + "import deeplabcut" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d32dbfc8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- DLC Model specification to be inserted ---\n", + "\tdlc_model: my_model\n", + "\tdlc_model_description: my_model, Task: mouse_openfield, date: Dec15, iteration: 10, shuffle: 1, training_fraction: 0.95, latest snapshot\n", + "\tdlc_scorer: unknown\n", + "\tdlc_task: openfield\n", + "\tdlc_date: Oct30\n", + "\tdlc_iteration: 0\n", + "\tdlc_snapshotindex: -1\n", + "\tdlc_shuffle: 1\n", + "\tdlc_trainingsetindex: 0\n", + "\tdlc_project_path: /Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/openfield-Pranav-2018-10-30\n", + "\trepository_name: a\n", + "\t-- Template for config.yaml --\n", + "\t\tTask: openfield\n", + "\t\tdate: Oct30\n", + "\t\tTrainingFraction: [0.95]\n", + "\t\titeration: 0\n", + "\t\tsnapshotindex: -1\n", + "\t\tbatch_size: 4\n", + "\t\tcropping: False\n", + "\t\tx1: 0\n", + "\t\tx2: 640\n", + "\t\ty1: 277\n", + "\t\ty2: 624\n", + "Proceed with new DLC model insert? [yes, no]: yes\n" + ] + }, + { + "ename": "IntegrityError", + "evalue": "Cannot add or update a child row: a foreign key constraint fails (`group_shared_imaging`.`d_l_c_model__model_path`, CONSTRAINT `d_l_c_model__model_path_ibfk_2` FOREIGN KEY (`repository_name`) REFERENCES `#repository` (`repository_name`) ON UPDATE CASCADE)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIntegrityError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_37855/1316993212.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m DLCModel.insert_new_model(\n\u001b[0m\u001b[1;32m 2\u001b[0m **{'dlc_model': f'{new_model_name}',\n\u001b[1;32m 3\u001b[0m \u001b[0;34m'cfg'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m'project_path'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/openfield-Pranav-2018-10-30'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m'dlc_model_description'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'my_model, Task: mouse_openfield, date: Dec15, iteration: 10, shuffle: 1, training_fraction: 0.95, latest snapshot'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Volumes/GoogleDrive/My Drive/Dev/dj-imaging/imaging/dlc.py\u001b[0m in \u001b[0;36minsert_new_model\u001b[0;34m(cls, dlc_model, cfg, dlc_task, dlc_date, dlc_iteration, dlc_shuffle, dlc_trainingsetindex, dlc_snapshotindex, project_path, dlc_model_description, dlc_scorer)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransaction\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModelPath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;31m# -- Check and handle new TrackedBodyPart --\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m in \u001b[0;36minsert1\u001b[0;34m(self, row, **kwargs)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0mFor\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msee\u001b[0m \u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \"\"\"\n\u001b[0;32m--> 266\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_duplicates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_direct_insert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m in \u001b[0;36minsert\u001b[0;34m(self, rows, replace, skip_duplicates, ignore_extra_fields, allow_direct_insert)\u001b[0m\n\u001b[1;32m 328\u001b[0m duplicate=(' ON DUPLICATE KEY UPDATE `{pk}`=`{pk}`'.format(pk=self.primary_key[0])\n\u001b[1;32m 329\u001b[0m if skip_duplicates else ''))\n\u001b[0;32m--> 330\u001b[0;31m self.connection.query(query, args=list(\n\u001b[0m\u001b[1;32m 331\u001b[0m itertools.chain.from_iterable(\n\u001b[1;32m 332\u001b[0m (v for v in r['values'] if v is not None) for r in rows)))\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/connection.py\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, query, args, as_dict, suppress_warnings, reconnect)\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0mcursor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcursor_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuppress_warnings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 301\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLostConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreconnect\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/connection.py\u001b[0m in \u001b[0;36m_execute_query\u001b[0;34m(cursor, query, args, suppress_warnings)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mtranslate_query_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuppress_warnings\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreconnect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIntegrityError\u001b[0m: Cannot add or update a child row: a foreign key constraint fails (`group_shared_imaging`.`d_l_c_model__model_path`, CONSTRAINT `d_l_c_model__model_path_ibfk_2` FOREIGN KEY (`repository_name`) REFERENCES `#repository` (`repository_name`) ON UPDATE CASCADE)" + ] + } + ], + "source": [ + "DLCModel.insert_new_model(\n", + " **{'dlc_model': f'{new_model_name}',\n", + " 'cfg': cfg, \n", + " 'project_path': '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/openfield-Pranav-2018-10-30',\n", + " 'dlc_model_description': 'my_model, Task: mouse_openfield, date: Dec15, iteration: 10, shuffle: 1, training_fraction: 0.95, latest snapshot',\n", + " 'dlc_scorer': 'unknown'}) " + ] + }, + { + "cell_type": "markdown", + "id": "cfd0aa0e", + "metadata": {}, + "source": [ + "### ... then take care of processing method" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "28a5713a", + "metadata": {}, + "outputs": [], + "source": [ + "processing_method_name = \"left_right_ears\"" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "243b4256", + "metadata": {}, + "outputs": [ + { + "ename": "DataJointError", + "evalue": "The specified param-set already exists - name: nose_mouse_wj", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mDataJointError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;34m'applicable_tracking_type'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'OpenField'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m 'function_to_invoke': 'process_two_tracked_points'}\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mDLCTrackingProcessingMethod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert_new_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mdlc_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32mC:\\work\\python\\dj-moser-imaging\\imaging\\dlc.py\u001b[0m in \u001b[0;36minsert_new_method\u001b[1;34m(cls, dlc_tracking_processing_method, dlc_tracking_processing_params, applicable_tracking_type, function_to_invoke, method_description)\u001b[0m\n\u001b[0;32m 227\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 228\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# If not same name: human error, trying to add the same paramset with different name\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 229\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mdj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataJointError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'The specified param-set already exists - name: {}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 230\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 231\u001b[0m \u001b[1;31m# now validate the \"function_to_invoke\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mDataJointError\u001b[0m: The specified param-set already exists - name: nose_mouse_wj" + ] + } + ], + "source": [ + "# ---- DLC tracking processing method ----\n", + "dlc_method = {'dlc_tracking_processing_method': processing_method_name,\n", + " 'method_description': 'using 2LED processing method on nose and base of head (no space in attribute names)',\n", + " 'dlc_tracking_processing_params': {'left_point_name': 'nose',\n", + " 'right_point_name': 'mouse'},\n", + " 'applicable_tracking_type': 'OpenField',\n", + " 'function_to_invoke': 'process_two_tracked_points'}\n", + "DLCTrackingProcessingMethod.insert_new_method(**dlc_method)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "19273529", + "metadata": {}, + "outputs": [], + "source": [ + "# ---- Association of DLCModel and DLC processing method ----\n", + "DLCProcessingMethod.insert1({'dlc_model':f'{new_model_name}',\n", + " 'dlc_tracking_processing_method': processing_method_name})" + ] + }, + { + "cell_type": "markdown", + "id": "ae636cdb", + "metadata": {}, + "source": [ + "### Make sure tracked body parts are up to date" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0dc8cca2", + "metadata": {}, + "outputs": [], + "source": [ + "from imaging.tracking import TrackedBodyPart\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f5366340", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    body_part
    0bodycenter
    1bottom_left_corner
    2bottom_right_corner
    3chocolate_milk
    4cue_card_bottom_center
    5left_ear
    6leftear
    7lefthand
    8leftleg
    9miniscope
    10mouse
    11nose
    12nose_tip
    13right_ear
    14rightear
    15righthand
    16rightleg
    17tail_base
    18tailbase
    19top_left_corner
    20top_right_corner
    \n", + "
    " + ], + "text/plain": [ + " body_part\n", + "0 bodycenter\n", + "1 bottom_left_corner\n", + "2 bottom_right_corner\n", + "3 chocolate_milk\n", + "4 cue_card_bottom_center\n", + "5 left_ear\n", + "6 leftear\n", + "7 lefthand\n", + "8 leftleg\n", + "9 miniscope\n", + "10 mouse\n", + "11 nose\n", + "12 nose_tip\n", + "13 right_ear\n", + "14 rightear\n", + "15 righthand\n", + "16 rightleg\n", + "17 tail_base\n", + "18 tailbase\n", + "19 top_left_corner\n", + "20 top_right_corner" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(TrackedBodyPart.fetch(as_dict=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "16bf0c2a", + "metadata": {}, + "outputs": [ + { + "ename": "JSONDecodeError", + "evalue": "Expecting property name enclosed in double quotes: line 33 column 5 (char 983)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mJSONDecodeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_32742/164256071.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'..'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dj_local_conf.json'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/settings.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLOCALCONFIG\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msave_local\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/json/__init__.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[0mkwarg\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0motherwise\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mJSONDecoder\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mused\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 292\u001b[0m \"\"\"\n\u001b[0;32m--> 293\u001b[0;31m return loads(fp.read(),\n\u001b[0m\u001b[1;32m 294\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject_hook\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobject_hook\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0mparse_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_float\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_int\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_int\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 357\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 358\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 359\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m 335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 336\u001b[0m \"\"\"\n\u001b[0;32m--> 337\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 338\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 339\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m 351\u001b[0m \"\"\"\n\u001b[1;32m 352\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 354\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting property name enclosed in double quotes: line 33 column 5 (char 983)" + ] + } + ], + "source": [ + "import os\n", + "import datajoint as dj\n", + "\n", + "os.chdir('..')\n", + "dj.config.load(\"dj_local_conf.json\")\n", + "\n", + "import pathlib\n", + "import numpy as np\n", + "import datetime\n", + "import ipywidgets as widgets" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ecbc5300", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'dj_imaging.database'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_32729/2314122460.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdj_schema\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_virtual_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dj_schema'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dj_imaging.database'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/settings.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/settings.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'dj_imaging.database'" + ] + } + ], + "source": [ + "dj_schema = dj.create_virtual_module('dj_schema', dj.config['dj_imaging.database'])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "602862c1", + "metadata": {}, + "outputs": [], + "source": [ + "no_dlc_sess = (dj_schema.Recording - dj_schema.RecordingDLC\n", + " & (dj_schema.Recording.Data * dj_schema.Dataset & 'datasettype = \"DLC_tracking\"'))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "76f2c6c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    session_name

    \n", + " Meta session name (hash)\n", + "
    \n", + "

    recording_order

    \n", + " Order of session within meta sessions (zero index!)\n", + "
    \n", + "

    recording_name

    \n", + " Recording name: Hash of animal_id, datasource_id, timestamp and combined 'yes'/'no' label\n", + "
    \n", + "

    animal_id

    \n", + " \n", + "
    \n", + "

    datasource_id

    \n", + " \n", + "
    \n", + "

    animal_name

    \n", + " Animal name in mlims\n", + "
    \n", + "

    timestamp

    \n", + " Timestamp of session\n", + "
    \n", + "

    combined

    \n", + " \n", + "
    \n", + "

    timeseries_name

    \n", + " Timeseries name [e.g. MUnit_0]\n", + "
    \n", + "

    equipment_type

    \n", + " \n", + "
    \n", + "

    username

    \n", + " NTNU username\n", + "
    6c86cf0b2c3428a301eb2f70e95b30a9200270322020-08-19 15:23:06nofile2Pminiscope_Auser123
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*metasession_n *recording_order *recording_name animal_id datasource_id animal_name timestamp combined timeseries_nam experiment_typ username \n", + "+------------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +----------+ +------------+ +------------+ +----------+\n", + "6c86cf0b2c3428 0 1eb2f70e95b30a 0 0 27032 2020-08-19 15: no file 2Pminiscope_A user123 \n", + " (Total: 1)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "no_dlc_sess" + ] + }, + { + "cell_type": "markdown", + "id": "dc05d1bf", + "metadata": {}, + "source": [ + "### Recording selector UI" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3374d4b7", + "metadata": {}, + "outputs": [], + "source": [ + "sess_selector = widgets.Dropdown(options=no_dlc_sess.proj(\n", + " subject='animal_name', basename='timeseries_name').fetch(as_dict=True), disabled=False, description='Recordings:')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7c5fc28a", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "37c3dc258561420a840b4d01b76623b4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='Recordings:', options=({'session_name': '6c86cf0b2c3428a3', 'recording_order': 0, 'sessi…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sess_selector" + ] + }, + { + "cell_type": "markdown", + "id": "5a8e99f0", + "metadata": {}, + "source": [ + "### DLC model/method selector" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "59a86d38", + "metadata": {}, + "outputs": [], + "source": [ + "selected_sess = sess_selector.value" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "aa8161cc", + "metadata": {}, + "outputs": [], + "source": [ + "q_dlc_models = dj_schema.DLCModel & (dj_schema.Recording.Data * dj_schema.InferredRecordingDLC & selected_sess)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4d26eacf", + "metadata": {}, + "outputs": [], + "source": [ + "options = dj_schema.DLCProcessingMethod & q_dlc_models if q_dlc_models else dj_schema.DLCProcessingMethod()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a44c8a35", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "
    \n", + "

    dlc_model

    \n", + " lab-friendly model name (perhaps the same as dlc_scorer)\n", + "
    \n", + "

    dlc_tracking_processing_method

    \n", + " e.g 2points_leftrightear\n", + "
    \n", + "

    method_desc

    \n", + " description for this model-method combination\n", + "
    Resnet50_mouse_openfieldJun30shuffle1_latestSnapshotleft_right_ears
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*dlc_model *dlc_tracking_ method_desc \n", + "+------------+ +------------+ +------------+\n", + "Resnet50_mouse left_right_ear \n", + " (Total: 1)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "options" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fbb364ac", + "metadata": {}, + "outputs": [], + "source": [ + "dlc_selector = widgets.Dropdown(options=options.fetch('KEY'), disabled=False, description='DLC Models:')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4138001f", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "04b252f259874374827d670c13795774", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='DLC Models:', options=({'dlc_model': 'Resnet50_mouse_openfieldJun30shuffle1_latestSnapsh…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dlc_selector" + ] + }, + { + "cell_type": "markdown", + "id": "8a1b388b", + "metadata": {}, + "source": [ + "### Insertion" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cd774b32", + "metadata": {}, + "outputs": [], + "source": [ + "selected_dlc = dlc_selector.value" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f5b3fc9f", + "metadata": {}, + "outputs": [], + "source": [ + "dj_schema.RecordingDLC.insert1({**selected_sess, **selected_dlc}, ignore_extra_fields=True)" + ] + }, + { + "cell_type": "markdown", + "id": "4bfd4d02", + "metadata": {}, + "source": [ + "## Fill in Recording Type and Apparatus info" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "40d2eeb2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "210a524f", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "sys.path.append('..')\n", + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set(style='dark')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "757ca5ca", + "metadata": {}, + "outputs": [], + "source": [ + "from imaging import *\n", + "from helpers import *" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "5661fc37", + "metadata": {}, + "outputs": [], + "source": [ + "from ipywidgets import interact, interactive, fixed, interact_manual\n", + "import ipywidgets as widgets" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "b22ec094", + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import HTML, display\n", + "import tabulate" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "1505b09c", + "metadata": {}, + "outputs": [], + "source": [ + "def draw_tracking(session, sessiontype, apparatus):\n", + " try:\n", + " track = (TrackingRaw.OpenField & 'recording_name = \"{}\"'.format(session)).fetch1('x_pos','y_pos')\n", + " timeseries_name = (Recording & 'recording_name = \"{}\"'.format(session)).fetch1('timeseries_name')\n", + " sns.set(style='white',font_scale=1.2)\n", + " \n", + " figure = plt.figure(figsize=(7,7))\n", + " ax = figure.add_subplot(111)\n", + " ax.scatter(track[0],track[1], s=2, c='k', alpha=.1)\n", + " ax.set_title(timeseries_name,y=.97)\n", + " sns.despine(left=True,bottom=True)\n", + " ax.invert_yaxis()\n", + " ax.set_aspect('equal')\n", + " ax.get_xaxis().set_ticks([]);ax.get_yaxis().set_ticks([])\n", + " \n", + " except dj.DataJointError:\n", + " track = (TrackingRaw.Linear & 'recording_name = \"{}\"'.format(session)).fetch1('pos')\n", + " timeseries_name = (Recording & 'recording_name = \"{}\"'.format(session)).fetch1('timeseries_name')\n", + " sns.set(style='white',font_scale=1.2)\n", + " \n", + " figure = plt.figure(figsize=(7,3))\n", + " ax = figure.add_subplot(111)\n", + " ax.plot(track, lw=2, c='k', alpha=.1)\n", + " ax.set_title(timeseries_name,y=.97)\n", + " sns.despine(left=True,bottom=True)\n", + " ax.get_xaxis().set_ticks([]);ax.get_yaxis().set_ticks([]) " + ] + }, + { + "cell_type": "markdown", + "id": "b9116c0b", + "metadata": {}, + "source": [ + "### Execute to display widget and enter sessiontype and apparatus info" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "b485e5c0", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "02c86cc826a54cb495aed40c5bb3a0ac", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(Dropdown(description='session', options=('62a1f0a4383d854a',), value='62a1f0a4383d854a')…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a5f2fd5d235f4732a19992d91193f480", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(Button(description='Insert into DB', style=ButtonStyle()), Output()))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sessions = (TrackingRaw - Recording.Apparatus).fetch('recording_name')\n", + "sessiontypes = RecordingType().fetch('sessiontype')\n", + "apparatus = Apparatus().fetch('apparatus')\n", + "\n", + "if len(sessions) == 0:\n", + " raise IndexError('No session info missing!')\n", + " \n", + "im = interact(draw_tracking, session=sessions, sessiontype=sessiontypes, apparatus=apparatus)\n", + "button = widgets.Button(description='Insert into DB')\n", + "out = widgets.Output()\n", + "\n", + "def insert_dj(b):\n", + " recording_name = im.widget.children[0].value\n", + " session_type = im.widget.children[1].value\n", + " apparatus = im.widget.children[2].value\n", + " with out:\n", + " category = (Apparatus & 'apparatus =\"{}\"'.format(apparatus)).fetch1('category')\n", + " print('Inserting Recording: {} | Type: {} | Apparatus: {}'.format(recording_name,session_type,apparatus))\n", + " session_entry = (Recording.proj() & 'recording_name = \"{}\"'.format(recording_name)).fetch1()\n", + " session_entry['sessiontype'] = session_type\n", + " Recording.RecordingType.insert1(session_entry, skip_duplicates=True)\n", + " session_entry['apparatus'] = apparatus\n", + " session_entry['category'] = category\n", + " Recording.Apparatus.insert1(session_entry, skip_duplicates=True, ignore_extra_fields=True)\n", + "\n", + "button.on_click(insert_dj)\n", + "\n", + "im.widget.children[0].description = 'Recording'\n", + "im.widget.children[1].description = 'Type'\n", + "im.widget.children[2].description = 'Apparatus'\n", + "im.widget.children[3].description = 'Draw!'\n", + "widgets.HBox([button, out])" + ] + }, + { + "cell_type": "markdown", + "id": "db4095da", + "metadata": {}, + "source": [ + "## Work with offset corrected tracking data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2a86837d", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up basics\n", + "import datajoint as dj" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e8b4059d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "571607f8", + "metadata": {}, + "outputs": [], + "source": [ + "# Enable plotting and make plots pretty (seaborn)\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "sns.set(style='dark')\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "96af7d04", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting horsto@kavlidatajoint02.it.ntnu.no:3306\n" + ] + } + ], + "source": [ + "# Load base schema\n", + "schema = dj.schema(dj.config['dj_imaging.database'])\n", + "schema.spawn_missing_classes()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "26482530", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('..')" + ] + }, + { + "cell_type": "markdown", + "id": "568cc3c2", + "metadata": {}, + "source": [ + "### Get Tracking *raw* results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3aba003a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    session_name

    \n", + " Meta session name (hash)\n", + "
    \n", + "

    recording_order

    \n", + " Order of session within meta sessions (zero index!)\n", + "
    \n", + "

    recording_name

    \n", + " Recording name: Hash of animal_id, datasource_id, timestamp and combined 'yes'/'no' label\n", + "
    \n", + "

    animal_id

    \n", + " \n", + "
    \n", + "

    datasource_id

    \n", + " \n", + "
    \n", + "

    animal_name

    \n", + " Animal name in mlims\n", + "
    \n", + "

    timestamp

    \n", + " Timestamp of session\n", + "
    \n", + "

    combined

    \n", + " \n", + "
    \n", + "

    timeseries_name

    \n", + " Timeseries name [e.g. MUnit_0]\n", + "
    \n", + "

    equipment_type

    \n", + " \n", + "
    \n", + "

    username

    \n", + " NTNU username\n", + "
    f8557ddd091a94b2066f39b5352265e47574e9c63eece4fbe0949212020-12-18 13:30:49yestrial12Pminiscope_Ajorgensu
    f8557ddd091a94b21640777a7bd5111ab574e9c63eece4fbe0949212020-12-19 16:17:17yestrial22Pminiscope_Ajorgensu
    f8557ddd091a94b224d38c8f597146dde574e9c63eece4fbe0949212020-12-20 16:00:12yestrial32Pminiscope_Ajorgensu
    \n", + " \n", + "

    Total: 3

    \n", + " " + ], + "text/plain": [ + "*metasession_n *recording_order *recording_name animal_id datasource_id animal_name timestamp combined timeseries_nam experiment_typ username \n", + "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+ +------------+ +----------+\n", + "f8557ddd091a94 0 66f39b5352265e 574e9c63eece4f 0 94921 2020-12-18 13: yes trial1 2Pminiscope_A jorgensu \n", + "f8557ddd091a94 1 640777a7bd5111 574e9c63eece4f 0 94921 2020-12-19 16: yes trial2 2Pminiscope_A jorgensu \n", + "f8557ddd091a94 2 4d38c8f597146d 574e9c63eece4f 0 94921 2020-12-20 16: yes trial3 2Pminiscope_A jorgensu \n", + " (Total: 3)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Recording & 'username = \"jorgensu\"' & 'animal_name = \"94921\"'" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "adfc97d8", + "metadata": {}, + "outputs": [], + "source": [ + "# pick one session \n", + "key = (TrackingRaw & 'recording_name = \"66f39b5352265e47\"').fetch1('KEY')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "238e9775", + "metadata": {}, + "outputs": [], + "source": [ + "# Positions (offset corrected to begin with)\n", + "x_pos_raw, y_pos_raw = (TrackingRaw.OpenField & key).fetch1('x_pos','y_pos')\n", + "\n", + "# Raw signal from DLC part table \n", + "reward_x_raw, reward_y_raw, likelihood_reward = (TrackingRaw.DLCPart & key & 'body_part = \"chocolate_milk\"').fetch1('bodypart_x_pos','bodypart_y_pos','bodypart_likelihood')\n", + "filter_reward = likelihood_reward > .1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ba28e0b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Raw tracking results')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAALuCAYAAAC+de9yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAABYlAAAWJQFJUiTwAAEAAElEQVR4nOzdd1gT2dcH8G9oEqo0QaQqTUDBAooKNkBFQNfe6659bWvvva296yr2hgoqigoqNlBBVCzYKCoiVUB6Td4/eDM/YgIECE3P53l4NHfuzNwJAZIz95zL4nK5XBBCCCGEEEIIIYQQUkdJ1PYACCGEEEIIIYQQQggpCwWwCCGEEEIIIYQQQkidRgEsQgghhBBCCCGEEFKnUQCLEEIIIYQQQgghhNRpFMAihBBCCCGEEEIIIXUaBbAIIYQQQgghhBBCSJ1GASxCCCGEEEIIIYQQUqdRAIsQQgghhBBCCCGE1GkUwCKEEEIIIYQQQgghdRoFsAghhBBCCCGEEEJInUYBLEIIIYQQQgghhBBSp1EAixBCCCGEEEIIIYTUaRTAIoQQQgghhBBCCCF1GgWwCCGEECLU169fYWpqWuaXlZUVunfvjpkzZ+LVq1e1PeQKi4yMrO0hCLVgwQKYmppi48aNIvXftWsXTE1NMX369Goe2e+j5Os/KytLYHtdfe0QQgghvyqp2h4AIYQQQuo+S0tLyMjI8LVxuVykpqbiy5cv+Pr1K27evIktW7bAxcWllkYpuqSkJKxduxYxMTG4ePFibQ+H1CP02iGEEEJqBwWwCCGEEFKuHTt2QEdHR+i2mJgYzJo1C69evcLixYvRsWNHKCsr1/AIK+bBgwe4fv06LCwsansoYjF8+HC4uLhAQUGhtofyy/vVXjuEEEJIfUEphIQQQgipEl1dXWzbtg1SUlLIzs7GtWvXantIvx1VVVU0a9YMmpqatT0UQgghhJBqQQEsQgghhFSZrq4uDA0NAQBRUVG1PBpCCCGEEPKroQAWIYQQQsSCxWIBKK6N9bOYmBisXbsWbm5uaN26NSwtLdGpUydMnToVjx8/ZvplZmbC0tISpqamiImJETjOwIEDYWpqirlz5wpsCwgIgKmpKUaPHl3mOLt164aFCxcCAN68eQNTU1N069YNwP8Kd7u6uiIiIgKDBw9GixYt0KlTJ5w8ebLC1/Ozu3fvYuLEiejUqRMsLS3RrVs3LF++HImJiWWOmSc5ORk9evRgrjM3NxeA8CLuJa+lsLAQ//33H1xdXdGyZUu0a9cOU6ZMKbXwfm5uLjw8PODu7g5ra2vY2dlh/vz5iIuLYwrMe3l5iTTmbt26wdTUFF++fMGsWbNgbW0NGxsbzJs3j6/frVu3MH78eLRr1w4tWrSAo6Mj1qxZU+pzExMTgyVLlqBbt26wtLRE27ZtMXDgQBw6dAg5OTl8fcsrcr9x40aYmppiwYIF5V5Laa8dAMjPz8eRI0fQv39/WFtbo2XLlnB0dMSSJUuo6DshhBBSRVQDixBCCCFVFhUVhY8fPwIAWrRowbft4cOHmDp1KnJzc6GoqAg9PT3k5eUhJiYGt27dwu3bt7F582a4urpCQUEBbdu2xaNHj/Do0SPo6uoyx8nIyMCbN28AACEhIQJjuH//PgCga9euZY7V0tIS0tLS+PTpE+Tk5GBmZgYNDQ2+PhkZGRg/fjzS09NhZGSEqKgoNGvWrMLXU9LKlStx+vRpAICWlhaMjY0RHR2Ns2fP4s6dOzh//jy0tLRKHfePHz8wbtw4fPr0Cba2tti/fz9kZWXLvFYAKCwsxMSJE/Hw4UOoq6ujWbNmiIiIwO3bt/HgwQOcOHEC1tbWTP/MzExMmDABoaGhkJCQgLGxMfLy8nDp0iXcv38f+vr65Z5TmLlz5+LVq1cwMTFBfHw8tLW1ARQHPJctWwZPT08AgIaGBvPcnDhxAteuXcPBgwf5XleRkZEYOnQofvz4AWVlZZiYmCA7OxuvXr3Cy5cv4e/vj5MnT0JaWrpSYy1NWa8dLpeLadOm4d69e5CSkoK+vj4aNGiAT58+4fz587h69SqOHTsGKysrsY6JEEII+V3QDCxCCCGEVMnbt28xffp0cLlc6OjooFevXsy2/Px8LFy4ELm5uRgzZgyCgoJw6dIlXL9+HXfv3kWHDh3A5XKxd+9eZp8uXboAAIKCgvjO8+TJExQVFQEA4uLiBGZoPXjwAED5AaydO3di4sSJAABDQ0OcOXMGO3fu5OsTHx8PWVlZ3Lp1C97e3rh37x7at29fqesBAC8vL5w+fRpycnLYtWsX7t27B29vb9y5cwft2rVDYmIiM7NHmKysLEyYMAHv379H27ZtceDAAbDZ7DKvkyc6OhrPnj3Dtm3bEBgYCG9vb9y+fRtGRkbIz8/Hnj17+Ppv3boVoaGh0NPTg4+PD65cuYKbN2/i1KlT4HK5eP78uUjn/Vl4eDhOnTqFS5cu4cGDB/jzzz8BAEePHoWnpycaNWqEo0eP4uHDh/Dy8kJgYCCGDRuGlJQU/P3338jMzGSOtWPHDvz48QOjRo1CYGAgvLy8cOPGDXh5eUFFRQUvXryollpsZb127t27h3v37sHAwAC3b9+Gr68vvL298eDBAzg6OiInJwdbt24V+5gIIYSQ3wUFsAghhBBSrhkzZmDo0KF8X/3794e9vT369u2Ljx8/Qk9PDwcPHkSDBg2Y/V6/fo3s7Gxoampi3rx5kJGRYbapq6tj6tSpAIqDLBwOBwDQuXNnAMUBq5LpiLzUvNatWwPgn4UVHR2NmJgYNG3atNIzhH42btw4qKmpAQAaNmwIFotVqesBgAMHDgAonoXk7OzMtKuqqmLz5s2QkpLC48ePkZCQIDCOvLw8TJkyBS9evECrVq1w8OBByMnJVehaJk+eDBcXF+Zxo0aNMGHCBABAWFgY056SkoKzZ8+CxWJh165dMDIyYra1bdsW69evr9B5S3J2dkarVq0AANLS0lBQUEBeXh72798PAPj3339hZ2fH9JeTk8Py5cthZWWFuLg4XLx4kdn24cMHAEC/fv34ZlmZm5vj77//Ro8ePfhehzWBNyYHBwe+mXQKCgpYuHAhOnXqBGNj4xodEyGEEPIroRRCQgghhJTr9evXQtulpaXRo0cPdO7cGW5ubnwBHaA42BQaGorc3FxISkoK7M+bRcThcJCXlwc2mw1DQ0Po6enhy5cvePfuHZo3bw4AePToEeTk5DBixAg8e/YMISEh6NevHwDRZ19VRMm0uqpcz6dPn/Dp0ydISUmhb9++Avs0atQI3t7eaNSoERo2bMi3rbCwEDNmzMDjx49hbm6OQ4cOQV5evsLXwgsKlsQrul9yZtP9+/dRVFQEKysrmJmZCezTtWtXaGtr49u3bxUeg7Dn89mzZ0hLS4O6ujrat28vdD8XFxeEhYXh/v37TH0zPT09REdHY8WKFfjnn3/QunVrSEkVv60dPnw4hg8fXuHxVRUv3fXixYswMTGBo6MjVFRUAAA6Ojo4fPhwjY+JEEII+ZVQAIsQQggh5bp9+zZ0dHQAFKcFBgYGYt26dfjy5QuysrLQrVs3geBVSbKysnjz5g3Cw8Px5csXfPnyBR8+fEB0dDTTp+SMpc6dO+PEiRMICgpC8+bNkZSUhIiICHTq1Ak2NjYAgODgYKZ/dQSwfq6LVdnr+fLlCwCgSZMmpc6cMjExEdru6enJFGpPSUmp1HUAgKamptBrAMCkZQJgCo2bmpqWeiwzM7NKBbCEPZ8REREAgOzsbAwdOlTofj9+/AAAvueWVyz/xYsXGDlyJBQVFdG+fXs4ODigW7duUFdXr/D4qqp79+6wsrJCWFgYlixZgmXLljELAHTt2lWgNhwhhBBCKoZSCAkhhBBSITIyMujatStOnjwJDQ0NPHz4EBMnTmQCLT/jzZTq168flixZgoMHD+LmzZvgcDhwc3MTus/PdbB46YPt2rVDo0aNYGhoiK9fvyIuLg55eXkIDg5Gw4YNmfRCcSgtBa2i15OWlgYAFU77A4pXAzQzM4OBgQHi4+OxefPmCh8DgMjFzHljLau+VmVmgAHCn0/e7K/s7Gw8e/ZM6BcvqFZyppiVlRW8vb3h5uYGeXl5ZGRkwN/fH0uXLoWDgwPmzJmDjIyMSo2zsmRkZHD8+HHMmDED+vr64HA4CAsLw549ezBgwAC4uroiNDS0RsdECCGE/EpoBhYhhBBCKkVTUxObNm3CuHHjEBYWhvXr12PlypV8fT58+IBx48YhPz8fbdu2RZ8+fWBqaopmzZpBQUEB0dHRuHLlisCxbW1tIScnh9DQUOTn5zMBLFtbWwDFgazo6Gg8efIEampqyM3NhaOjo9C0PnGqzPXwgkE5OTkVPp+JiQmOHj2Kt2/fYuzYsTh79izc3NzQpk0bsVzPz3hjzcrKKrVPWdsqe74uXbowdcJE1axZM2zevBn5+fl48eIFHj16hHv37uHNmzfw8fFBTk6OQIH6kjXVSqrM90YYWVlZTJkyBVOmTEF0dDQePXqEwMBAPHjwAB8/fsSff/6JGzduCJ0RRwghhJCy0QwsQgghhFRahw4dMGjQIADA2bNn8ejRI77tJ06cQH5+Puzs7HD8+HEMGjQIVlZWUFBQAFC82p8wMjIyaN++PXJychAWFoaQkBDIycnB0tISQHEACyhOI7x//z4AoFu3btVyjVW9HgMDAwBAbGxsqbPUli1bhgkTJvClRQJAp06doKKigg4dOsDV1RVcLhdLly5Ffn6+eC/s//GKtvMKkgvz8eNHsZ2P99xERUWV2ufr16948eIFvn//DqA4NTMmJoZ5rmRkZGBra4sZM2bAy8sLa9euBQDcunWLCbbxApulPW9JSUlVvpbU1FSEhoYyqZ6GhoYYNmwY9uzZA39/f2hoaCA7Oxu3bt2q8rkIIYSQ3xEFsAghhBBSJXPnzmVqDq1cuZIvSBAbGwuguKaSsNlRFy5cYP5fshYT8L80witXruDz589o27YtU6i7Xbt2YLFYTABLSkoK9vb2Io9ZQqJyb4Eqcz1GRkZo3LgxCgoKcPXqVYF9UlNT4ePjg3v37kFRUbHUcy9cuBBKSkqIjIzEvn37KjX+8nTu3BlSUlJ49eqV0CDWkydPEBMTI7bztW3bFnJycvjy5QuTLvqzxYsXY/DgwdiwYQOA4mCTk5MTRo8eLXTVxg4dOjD/59UhU1ZWBsBfR4snIyODb0XL8pT22pkzZw6GDRvG9xrg0dTURNOmTQEIvs4JIYQQIhoKYBFCCCGkShQVFTFv3jwAxQGC//77j9nGm2Hj6+uLz58/M+0/fvzAunXr+AI6eXl5fMflrZzn7e0N4H+zrgBATU0NxsbGiImJwadPn9C6dWsoKSmJPGZeParExMQKzWaqzPWwWCxMmDABALBhwwY8fPiQ6ZOSkoI5c+YgOzsb7dq1Y1ZcFEZdXR2zZ88GAPz3339inQnFo6mpif79+4PL5WL69Ol8M6PevHnDfJ/FRUFBAWPGjAFQHAAqGcTKzc3FunXr8PjxY0hKSjIrEGpqasLW1hYcDgdz5szhC2JlZmZiy5YtAIBWrVoxAcFWrVoBAD5//oyjR48y/ZOTkzFz5kymULwoSnvt8Oqf7du3j+97DADXr19HaGgoJCQk0KlTJ5HPRQghhJD/oRpYhBBCCKmyPn364MKFCwgODsaBAwfg5uYGPT09jB07Fj4+PkhMTISLiwsMDQ3BYrHw6dMn5Ofnw8zMDPHx8UhLS0NiYiLfSnVaWlowMzPDu3fvAPAHsHiPebOEKpo+aGxsDBaLhaSkJPTo0QNaWlo4c+ZMuftV9nqGDRuG8PBwnD9/HuPHj4eOjg5TMysvLw9NmjRhZhiVZfDgwfD29mZWujtz5kylZ5OVZt68eXj9+jXevHmD3r17w9jYGEVFRYiIiICWlhbU1dWRnJwstnpjU6dORVRUFG7cuIGxY8eiSZMmaNiwIT5//swUbl+5ciWTPgoAa9euxYABAxAcHIzu3btDT08P0tLS+PLlC7Kzs9GwYUOsXr2a6W9ubg5nZ2f4+flh/fr1OHbsGJSVlREREQEpKSmMHz8ehw8fFmm8pb12+vTpgzt37uDmzZsYP34881wlJiYiMTERADB79mxmJhYhhBBCKoZmYBFCCCFELJYtWwZpaWnk5eUxxdx1dXVx+fJl/PHHH2jcuDE+ffqEuLg4mJmZYeHChTh//jw6duwIAAgICBA4Ji+NUFFREebm5nzbSga0eP1EZWhoiDVr1kBPTw9JSUmIiYlBcnJyuftV5XrWrFmDnTt3ws7ODunp6YiMjISmpibGjx8Pb29vaGtrl3t+CQkJrFq1ClJSUnjx4gVOnjxZoesWhYKCAk6dOoVp06ZBX18f0dHRSE1NxYABA3D+/Hmm3pesrKxYziclJYXt27dj27Zt6NixI7KysvD+/Xs0aNAATk5OOHXqFAYOHMi3j66uLi5evIghQ4ZAW1ubmYmnqamJMWPG4OrVqzA2NubbZ+vWrZg3bx5MTEyQnJyMhIQEODo6wsvLq0KrV5b22mGxWNiyZQsWL14Ma2trZGZm4t27d+ByuXBycsLRo0cxceJEsTxnhBBCyO+IxS1tORZCCCGEEEJ+0qFDB3z//h2nT5+uttUQCSGEEEJ+RjOwCCGEEEIIgOIVBrt164apU6cK3R4eHo7v379DSkoKpqamNTw6QgghhPzOKIBFCCGEEEIAAPr6+sjMzMStW7fg4eHBt2JeVFQU5s6dCwDo3bs3k0pICCGEEFITKIWQEEIIIYQwLl26hAULFoDL5UJFRQVNmjRBRkYGvnz5Ai6XC0tLS3h4eEBZWbm2h0oIIYSQ3wgFsAghhBBCCJ93797Bw8MDYWFhiI+Ph6ysLPT19dG7d28MHToUMjIytT1EQgghhPxmKIBFCCGEEEIIIYQQQuo0qoFFCCGEEEIIIYQQQuo0CmARQgghhBBCCCGEkDqNAliEEEIIIYQQQgghpE6jABYhhBBCCCGEEEIIqdMogEUIIYQQQgghhBBC6jSp2h5AXdS3b198/foVcnJy0NfXr+3hEEIIIYQQQgghhNR7nz9/RnZ2NnR0dHDp0qUK7UsBLCG+fv2KjIwMZGRkICEhobaHQwghhBBCCCGEEPLL+Pr1a4X3oQCWEHJycsjIyICioiKaN29e28MhhBBCCCGEEEIIqffevn2LjIwMyMnJVXhfCmAJoa+vj4SEBDRv3hwnTpyo7eEQQgghhBBCCCGE1HsjR45EcHBwpco1URF3QgghhBBCCCGEEFKnUQCLEEIIIYQQQgghhNRpFMAihBBCCCGEEEIIIXUaBbAIIYQQQgghhBBCSJ1GASxCCCGEEEIIIYQQUqdRAIsQQgghhBBCCCGE1GkUwCKEEEIIIYQQQgghdZpUbQ+AEEIIIYQQQgghtYPL5SI9PR2pqanIzc0Fl8ut7SGReoDFYkFWVhYqKipQUlICi8Wq9nNSAIsQQgghhBBCCPlNJSYmIiUlpbaHQeoZLpeLnJwc5OTkIDc3F5qamtV+TgpgEUIIIYQQQgghv6HMzEykpKSAxWJBU1MTSkpKkJSUrO1hkXqgqKgI6enpSEhIQEpKCuTl5aGgoFCt56QaWIQQQgghhBBCyG8oPT0dAKCmpgYVFRUKXhGRSUpKQkVFBWpqagD+91qqThTAIoQQQgghhBBCfkPZ2dkAAEVFxVoeCamveK8d3mupOlEAixBCCCGEEEII+Q0VFhYCAGRkZCp/kKJ8ICkIqO7i71xu8XmK8qv3PKRCeK8d3mupOlEAixBCCCGEEEII+Q3xVhyUkKhkaKAoH/h8GojzA2KvVF8Qi8stPn6cX/H5KIhVZ/BWH6yJ1SspgEUIIYQQQgghhJCKS3kKZH76//8/r54gFi94lfK8+HHmp+LzkjqBF8CqCRTAIoQQQgghhBBCSMWp2wGqrf73WNxBrJ+DV0Dx+dTtxHN8Uq9QAIsQQgghhBBCCCEVx2IBTdyrJ4hVWvCqiXvxeclvhwJYhBBCCCGEEEIIqZzqCGJR8KrW7N27F6ampti1a1dtD0UABbAIIYQQQgghhBBSeeIMYlHwipRCShwHKSoqwqlTp3DhwgVER0eDzWbD0tISo0aNQpcuXfj6xsXFCbSV1Lp1a5w5c4avLSEhAXv27EFgYCCSkpLQuHFjuLu746+//qracp+EEEIIIYQQQgipOl4QC/hf8In3r6jBJwpekTKIJYC1cOFCXL58GQoKCrCzs0NBQQGCg4MRGBiI6dOnY+rUqUzf8PBwAICpqSlMTEwEjmVoaMj3OD4+HoMHD0Z8fDzMzc1hYWGBZ8+eYefOnXj8+DE8PDwgLS0tjssghBBCCCGEEEJIZVUliEXBK1KOKgewfH19cfnyZRgaGuLkyZNQV1cHAHz8+BFDhw7F7t270bt3bxgYGAAA3r59CwD4888/4e7uXu7xV6xYgfj4eMyYMQNTpkwBAGRnZ2Pq1KkICgrCiRMnMG7cuKpeBiGEEEIIIYQQQqqqMkGsOhq8WrBgAby9vXHy5EkcPnwYQUFBUFBQwD///IP+/fsjMjIS+/fvx6NHj5CWloZGjRqhW7dumDx5MtTU1AAAd+/excSJE9GtWzfs27eP7/gzZ87E9evXMWLECCxdupRvW79+/fD27VsEBgZCVVUVQHH85eLFiwgPD0dGRgbYbDZMTU0xZMgQuLq6Mvt+/foV3bt3R48ePeDk5IR///0XaWlpMDY2xtmzZyEtLY0fP35g//79uHnzJpKTk2FoaIiJEydW8zNaNVWugXXlyhUAwJw5c5jgFQAYGxvDzc0NHA4HgYGBTDtvBpaFhUW5x46KisLdu3ehp6eHSZMmMe1ycnJYu3YtJCUlcfLkyapeAiGEEEIIIYQQQsSlIjWx6mjwqqQlS5bg+fPncHBwAJvNRvPmzfHw4UP0798fV65cgZqaGrp16wYZGRmcOHEC/fv3R0xMDACgffv2kJWVRUhICIqKiphjcrlcPHnyBAAQEhLCd77v378jPDwcVlZWTPBqxYoVmDVrFp49ewZzc3N07doVGhoaCAkJwT///IPjx48LjDs8PBzz58+HtrY2bGxs0KRJE0hLSyM1NRXDhw+Hh4cHuFwuunTpAi6Xi1mzZuHatWvV9TRWWZVnYO3cuROfPn1iZliVlJWVBQCQlJRk2t6+fQs5OTmBVEFhHj58CC6Xi65du0JCgj/Wpq2tDXNzc7x69QoREREwMjKq2oUQQgghhBBCCCFEPESZiVUPgldAcUDJx8cHjRs3BofDQVpaGsaMGYOCggLs3r0bTk5OAIqDUvv378f27dsxd+5cnD17FrKysmjXrh3u3buH169fw8rKCgDw/v17pKSkQFJSEh8+fMCPHz+grKwMAHjw4AETWAKAly9f4syZM9DX18fZs2eZoBYAHDlyBBs2bMDJkycxatQovnHHxMRgzJgxWLhwIQCAw+EAAHbt2oWPHz+iV69e2LRpE1Nb/ODBg9iyZUv1PZFVVOUZWDIyMjAxMREoph4QEIAbN25ATk4Ojo6OAIC0tDR8+/YNhoaGOHLkCNzd3WFlZYVOnTph6dKlSEhI4DtGREQEgOLZXMI0bdoUAPDhw4eqXgYhhBBCCCGEEELEqcyZWJx6EbwCACcnJzRu3BgAICEhgQsXLuDHjx8YMWIEE7wCABaLhcmTJ8PS0hLPnz/H8+fF19a5c2cAwKNHj5i+jx8/BgD06NEDXC4XT58+ZbY9ePAAAJgAVmZmJpydnTFr1iy+4BUADB48GADw7ds3oWMvGdSSkJBAfn4+vL29ISsri1WrVvHFciZMmMAE2OqiKgewSsrNzcX06dPh4uKCSZMmQU1NDQcPHmRSC3n1r968eYNt27ZBTU0N7dq1Q1FRETw9PdG/f39ERUUxx0tMTAQANGrUSOj5NDQ0AADJycnivAxCCCGEEEIIIaROSU9PZ+ox5eXl1fZwRFdaEOvVqnoRvAKKF6EriZf6165dO6H9O3XqBOB/qYHCAliPHj2CoqIihg4dyteXw+Hg4cOH0NbWhpmZGQCgQ4cO2LVrF3r16sXsn5eXh7dv3+LSpUtgsVgoKCgQGIeSkhKaNGnC1/bq1StkZ2fD2toaSkpKAvt07969tKeh1ollFUKeb9++4ebNm3xt79+/h42NDYD/1b8yMTHB3r17oaurC6C4KPvSpUtx9epVzJkzB15eXgCAnJwcAICsrKzQ8/Has7OzxXkZhBBCCCGEEEJIrSssLMTjx4/h4+ODgIAAFBYWAiiexDFz5szaHVxFCEsnLKkOB68AoGHDhnyP4+LiAACTJ08ucz9ePx0dHRgZGeH58+fIy8uDpKQkQkJCYGNjA2trazRo0IAJYL18+RJpaWl8wSqgeMLQxYsXcefOHURERCAhIQFcLhcsFgvcn+uK/T9hASreRCFNTU2h+/wc8KpLxBrA0tLSwuPHjyEhIYGgoCCsXbsWq1evRnZ2NiZMmIAxY8bA2dkZ8vLyfNPe5OTksGbNGoSEhODNmzd48eIFrK2tmdpZrHJexLw8TkIIIYQQQgghpL6LjIyEj48PfH19hWYcKSgo1MKoqojFApq4CQ9gNXGrs8ErQDAmwSvG3r17d8jJyZW6H28GFQA4ODjAw8MDz549g6ysLLKysmBrawsZGRlYWVkhNDQUmZmZTPpg165dmX0TEhIwYsQIfPnyBUpKSmjZsiV69uwJMzMztG/fHt26dRMaF/m5lriwa/lZyRrmdY1YA1hycnLMN69Xr15o3LgxhgwZggMHDmD06NFo0KABM+vqZ2w2G+3bt8fly5fx5s0bWFtbg81mAyiONArDay/rBUMIIYQQQgghhNR16enpuHHjBnx8fPDmzZtS+w0ZMgSjR4+uwZGJCZcLxPoI3xbrU6dnYP2sUaNG+PTpE8aNG4e2bduKtE+XLl3g4eGBoKAgJgBpa2sLoDgVMTg4GKGhoXjw4AETH+HZvn07vnz5gv79+2PlypWQlpZmtmVmZlZoUg9v5lVpNbOSkpJEPlZNE2sNrJ9ZW1tDT08PmZmZzBKSZeHVyuKlDvJqX5VW44r3xJZWI4sQQgghhBBCCKmrCgsL8fDhQ8yfPx9OTk7YsGFDqcGrhg0bYvv27Zg3bx5fAKNeELbaYElMYXfhqXB1DS9odf/+faHbFy5ciP79++P27dtMW5s2baCoqIigoCCEhoZCUVER5ubmAP5XS8vPzw+vXr1C+/bt0aBBA2bfsLAwAMCff/4p8L0PDAxk/i9KIMvS0hJKSkp48eKF0FjLvXv3yj1GbalSAIvL5WLTpk2YNWsWk4v7M15F+8LCQuzevRvTp0/H+/fvhfb9+vUrgOJUROB/qw/yViP8WWRkJIDimlqEEEIIIYQQQkh9EBUVhR07dsDFxQXTp0+Hv7+/0CLcPDY2Njh37hwcHBxqcJRiIix4pdoKaLGslNUJ634Qa/DgwWCz2fDw8IC/vz/fNi8vL3h7e+PDhw98K/pJSUmhQ4cOCA8Px9OnT9GmTRsmXc/KygqysrK4dOkSOBwOX/ogAGYFxDt37vC1v3jxAqtXr2Yei1LcX1paGsOGDUNBQQHmz5/PV1Pc09MTDx8+FPFZqHlVSiFksVi4ffs2Pn36hL59+zKV9XliYmIQHR0NOTk5GBoa4v379/Dz80PTpk0Fqvh///4dgYGBkJaWZqKP9vb2AICAgADMmTOHL3/z27dvePv2LZo0aQIjI6OqXAYhhBBCCCGEEFKt0tPTcfPmTfj4+OD169ci7SMpKYmpU6di1KhRQusZ1XmlBa946YI/F3bn/VvH0wm1tLSwfv16zJ07F9OmTYOJiQkMDAzw+fNnvH//HhISEti0aROTZcbTpUsX3Lx5k6l/xSMjIwNra2s8fvyY6VfSqFGj8PDhQ/z777+4ceMGtLW18fXrV7x58wbKysrQ0NBAUlISkpOTSy3bVNLkyZPx9OlTPHz4EE5OTmjTpg1iY2Px+vVrWFtb48WLF1V+jqpDlX8CBg0aBABYs2YN4uPjmfaEhATMnj0bhYWFGDZsGBo0aIDBgwcDAI4cOYLQ0FCmb1ZWFhYtWoTMzEwMGDAAGhoaAABdXV3Y29sz0Wme7OxsLFmyBEVFRRg7dmxVL4EQQgghhBBCCBG7oqIiBAYGMimC69evFwheqaqqwsbGRqB4tra2Njw8PDBmzJhfM3gF/C+IVQ9nYvXq1Qvnz5+Hq6srUlNTERAQgIyMDPTo0QPnz58XWEUQKC7kziuiXjKABfwvjdDc3FxghcDOnTvj4MGDaNOmDb58+YI7d+4gPT0dQ4cOxeXLl9GzZ08AgjO0SiMrKwsPDw/MmDEDCgoKCAgIQGZmJlasWIFhw4ZV+LmoKSxuaestiqigoABTp07FvXv3ICcnh9atW6OoqAhhYWHIzs5G586dsXv3biaVcMOGDThy5AgkJCTQunVrqKio4OnTp0hNTUXbtm1x6NAhpng7UDyLa+jQoUhKSoKJiQkMDQ3x7NkzJCUlwcHBAfv27YOUlFhr0WPkyJEIDg6Gra0tTpw4IdZjE0IIIYQQQgj5tUVFRcHHxwfXrl0TWmdISkoKnTt3hpubG6Kjo7Fnzx6+sjw9evTAokWLoKioWK3jfPv2LQCgefPm4j2wKMGrqvQndUpFXkdVibdUOfIjLS2Nffv24fTp0/Dy8kJISAgkJCRgYmKCfv36YdCgQXzR4gULFsDKygonT55EeHg4OBwO9PT08Oeff2L06NECBcl0dXVx/vx57Ny5E/fv38fnz5+hq6uLUaNGYfTo0WIPXhFCCCGEEEIIIRUlSopg8+bN4e7ujh49eoDD4WDFihV8NYfYbDbmz58PNzc3ZqZOvVOZYFQ9TickNUcs0R9JSUmMHDkSI0eOFKl/r169hE6nK03jxo2xfv36yg6PEEIIIYQQQggRu6KiIjx+/BhXrlzB3bt3hRZiV1VVRe/eveHq6sosVPbkyRMsWbIE379/Z/qZmZlh/fr10NfXr7Hxi11VZlJREIuUg6YvEUIIIYQQQgghFRAdHY0rV66UmSLo4OAAd3d32NnZMZlGBQUF2Lt3L44fP46S1XxGjhyJadOmCWQk1SviSAOkIBYpAwWwCCGEEEIIIYSQcmRkZDApgq9evRLax8zMjEkRVFFR4dsWExODRYsW4c2bN0ybqqoqVq5ciY4dO1br2KudOGtYURCLlIICWIQQQgghhBBCiBBFRUV48uQJrly5goCAgFJTBF1cXODm5sakCP7M19cX69atQ3Z2NtNmZ2eHVatWQU1NrdrGXyOqowA7BbGIEBTAIoQQQgghhBBCSoiOjoaPjw+uXr1aoRTBn2VlZWHjxo24evUq377Tpk3DiBEj+BY8q5eqc/VACmKRn1AAixBCCCGEEELIb6+qKYI/e/PmDRYuXIivX78ybTo6Oli/fj0sLCzEOvZak/yoeoJXPKUFsRpoABodxHMOUiUla7lVNwpgEUIIIYQQQgj5LYkrRbAkDoeDkydPYvfu3SgsLGTaXV1dMX/+fMjLy4v1GqqCxWKBy+WCw+FUbjaYalsg4wOQ+Un8wav/DZI/iKVgUHxeUifwAlisGpgRRwEsQgghhBBCCCG/lU+fPjGrCCYlJQlsFzVF8GfJyclYtmwZHj9+zLTJyclh0aJFcHFxEdv4xUVKSgoFBQXIz8+HrKxsxQ8gKQPoDwNSngLqdtWX1scLYjXQKA5eScpUz3lIheXn5wMofi1VNwpgEUIIIYQQQgj55WVkZMDPzw9XrlwpNUXQ1NQU7u7u6NmzZ7kpgj8LDAzE8uXLkZKSwrRZWFhg3bp10NXVrdLYq4ucnBx+/PiBjIyMygWwgOJgUk2k87FYlDZYB2VkZAAofi1VNwpgkRpz6tQpvHz5EjY2NnBxcamRFzghhBBCCCHk91VUVITg4GAmRZA3W6QkFRUVJkXQxMSkwufIz8/H7t27cfLkSb720aNHY8qUKSLP3qoNSkpK+PHjB75//w4pKSkoKSlBQkKiRtLBSP3FSztNT0/H9+/fARS/lqobBbBIjYiNjcWWLVsAAP7+/li3bh369euHzp07o1GjRlBVVUXDhg3r9C93QgghhBBCSP3w6dMnZhVBYSmCkpKSTIpghw4dKv055PPnz1i4cCHevXvHtKmpqWHNmjVo165dpcdfUxQUFKCqqoqUlBTEx8cjPj6+todE6iFVVVUoKChU+3kogEVqhJqaGuTl5ZGVlcW0eXl5wcvLi6+fkpIStLS0MHnyZHTu3Lmmh0kIIYQQQgippyqSItijRw+oqqpW+lxcLhc+Pj7YuHEjcnJymPaOHTti5cqVVTp2TWvUqBFkZWWRmpqK3NzcGl1VjtRfLBYLsrKyUFFRqZHZVwAFsEgNkZWVxeLFi7Fo0aIy+6WnpyM9PR1Lly6Fn59f5fOwCSGEEEIIIb+8mkgR/FlmZibWrVuHGzduMG1SUlKYOXMmhg4dWu/S71gsFpSVlaGsrFzbQyGkTBTAIjWmZ8+eePv2LU6cOMG0NWjQADo6OkhNTUVqaioT7c/MzERAQAB69epVW8MlhBBCCCGE1FE1lSL4s1evXmHhwoX49u0b06avr48NGzbA1NRULOcghAhHASxSo6ZNm4bnz5/j9evXAICGDRvi0KFDUFZWxvXr17F48WKm79KlSymARQghhBBCCAEgWoqgiYkJs4qgONP4OBwOjh07hr1796KoqIhp79OnD+bNmwc2my22cxFChKMAFqlR0tLS2LBhA4YMGYLMzEwkJCRg2bJlcHFxwZIlS/j6SknRy5MQQgghhJDfWVFREUJCQnDlyhXcuXNHaIpgw4YNmRTB6pgFlZSUhCVLliAkJIRpk5eXx9KlS+Hs7Cz28xFChKMIAalx2traWLVqFWbPng0AePDgAR48eCDQj2ZfEUIIIYQQ8nv6/PkzkyKYmJgosF1SUhL29vZwd3dHx44dq2018/v372PFihVIS0tj2lq0aIF169ahSZMm1XJOQohwFMAitaJLly4YPnw4Tp06xdeuqqqKc+fOQVFRETIyMrU0OkIIIYQQQkhNy8jIgL+/P65cuYKXL18K7VNdKYI/y8vLw44dO3D27FmmjcViYfz48ZgwYQJlixBSC+injtSa6dOn48WLF3jz5g3Tlp+fDzU1tVocFSGEEEIIIaSm1IUUwZ9FRUVh4cKF+PjxI9Omrq6OtWvXwsbGptrPTwgRjgJYpNbw6mG5ubkxbZmZmXj+/DlatWpViyMjhBBCCCGEVKe6kiJYEpfLxeXLl7Fp0ybk5uYy7Q4ODli+fDlUVFSqfQyEkNJRAIvUqiZNmmDVqlVYtmwZ0zZ+/HicOnUKzZs3r8WREUIIIYQQQsRJlBRBY2Nj9OnTp9pTBIWNbfXq1bh16xbTJi0tjdmzZ2PQoEFgsVg1NhZCiHAUwCK1TklJSaCt5B0PQgghhBBCSP1UVFSEp0+f4vLly2WmCPbq1Qvu7u41kiL4s7CwMCxatAhxcXFMW9OmTbF+/XoYGxvX+HgIIcJRAIvUOktLS4E2NptdCyMhhBBCCCGEiMOXL1+YFMGEhASB7ZKSkujUqRPc3d3RqVOnGkkR/FlRUREOHz6MgwcPgsPhMO39+/fH7Nmz6TMJIXUMBbBIrcvIyBBomz9/Pry9vSEhIVELIyKEEEIIIYRUVEZGBm7duoUrV64gLCxMaB8jIyP06dMHvXr1qtEUwZ8lJCRgyZIlCA0NZdoUFBSwfPlydO/evdbGRQgpHQWwSK3Jy8vDjRs3sHLlSoFtWVlZlGdOCCGEEEJIHcdLEeStIpiXlyfQp7ZTBH8WEBCAlStXIj09nWlr1aoV1qxZg8aNG9fiyAghZaEAFqlxiYmJ8PT0hJeXF9LS0gS229raYtasWRTAIoQQQgghpI6qDymCP8vNzcW2bdtw/vx5po3FYuGvv/7Cn3/+CSkp+nhMSF1GP6GkRnC5XLx69QqnTp3CnTt3UFRUJLTf+fPn0axZsxoeHSGEEEIIIaQ8mZmZzCqCdT1F8GcRERFYuHAhIiMjmTZNTU2sW7cOrVq1qsWREUJERQEsUq3y8/Ph7++PM2fOIDw8vMy+Fy5cQNOmTWtoZIQQQgghhJDycDgchISE1KsUwZK4XC4uXLiALVu28K2A2L17dyxdulToiuiEkLqJAlikWiQnJ+PChQu4cOECUlJSBLa3bt0az549Yx67u7tT8IoQQgghhJA6IiYmBleuXCk1RVBCQoJJEbS3t68TKYI/+/HjB1atWoWAgACmrUGDBpgzZw769etHJUsIqWcogEXE6s2bNzhz5gz8/PxQWFjIt01aWhq9evXCsGHDEBMTwwSwpKWlMXHixNoYLiGEEEIIIeT/ZWZmMqsIvnjxQmgfIyMjuLu7o1evXlBTU6vZAVZAaGgoFi9ejMTERKbNyMgI69evp5Il1SwlJQU/fvyAsrJynUojJfUfBbBIlRUUFCAgIACnTp3Cq1evBLZraGhg0KBB6NevH1RUVFBYWIgFCxYw2wcPHkyrfRBCCCGEEFILOBwOs4rg7du3haYIKisr86UI1uWZS4WFhTh06BD+++8/cLlcpn3QoEGYNWsWGjRoUIuj+3XFxsbC398ffn5+ePfuHdNubm4Oe3t7dO7cuc6/dkjdRwEsUmkpKSnw9vaGp6cnkpKSBLa3bNkSw4YNQ9euXfmmFF+7dg2fPn0CAMjJyWHs2LE1NWRCCKk2P378QEREBCIiIhAVFYWIiAikp6ejQ4cOGDFiBDQ0NGp7iIQQQggjJiYGPj4+8PHxqbcpgj/79u0bFi9ezFdgXklJCStWrECXLl1qb2C/qPj4ePj5+cHf3x9v3rwR2ic8PBzh4eE4cOAANDU10b9/f4wbNw4SEhI1PFryK6AAFqkUX19frFq1iq8QIgBISUnB2dkZQ4YMgaWlpcB+eXl52LdvH/N41KhRUFFRqfbxEkKIuOTk5CAyMpL54gWtkpOThfaPjIzEmTNn4O7ujtGjR0NXV7eGR0wIIYQU+5VSBH/m7++P1atXIzMzk2lr06YN1q5di0aNGtXiyH4t0dHRuHfvHgICAoRm35QlISEBe/fuha2tLVq2bFlNIyS/MgpgkQp78+YNVqxYwVfjSlVVFQMGDMCAAQOgrq5e6r6enp5MHrqKigpGjBhR7eMlhJDKKCgowOfPn/mCVJGRkYiNjeVLSRBFYWEhvLy84O3tDWdnZ4wbNw7GxsbVNHJCCCHkf0RJEVRSUkKvXr3Qp0+fepfmlZOTg82bN8Pb25tpk5CQwOTJkzFmzBhISkrW4ujqv48fP2LIkCEVfu9TGgUFBWhpaYnlWOT3QwEsUiEZGRlYsGABE7zS1dXFhAkT4OTkBBkZmXL39fDwYB7/9ddfkJOTq9bxEkJIRcTExODQoUMIDw/H58+fBRajKIuMjAyaNm0KIyMj5t/8/HwcO3aMuUPJ5XJx8+ZN3Lx5E/b29hg3bhysrKyq63IIIYT8xngpglevXkV8fLzAdgkJCXTs2JFJESzvvXxd9OHDByxcuBDR0dFMm7a2NtatW0czfKooJycHu3fvxpkzZ8R2zCFDhmDEiBE0I45UGgWwiMi4XC5Wr16N2NhYAIC8vDx2794tcjrMyZMn8ePHDwDFf1j69+9fbWMlhJDK2LhxI4KCgsrsIyEhAT09PRgZGaFZs2YwMjKCkZERdHR0mLu8P378wMuXL6GkpIRDhw7hxYsX8PDwwJMnT5jjPHjwAA8ePECbNm0wduxY2NnZ1as73uLEmw3w5csXaGtrQ15evpZHRAgh9VNWVhb8/f3LTBFs1qwZ3N3d4eLiUq9SBEvicrnw9PTE1q1bUVBQwLQ7OTlhyZIlUFRUrMXR/RqOHz8u1uDVpEmTMGHCBLEdj/yeKIBFRObp6Ylbt24xj5cuXSpy8Or79+84efIk83jy5Mn1ohAkIeT30qRJE6HtLBYLf/zxBwYOHAgDAwOBFYxycnIQHByMJ0+eICQkBO/evWOm2rPZbLRr1w6Ojo4YOHAgfH19ERAQwGwPDQ1FaGgozMzMMHbsWHTr1u23SneIiIjAoEGD+Nrmz58POzs7NG7cmP5WEEJIOTgcDi5duoSQkBDcu3cPubm5An14KYLu7u4wMzOr1zdMUlNTsWLFCjx48IBpk5WVxfz58+Hu7l6vr62u4HK5uHbtmtiOd/z4caH1kQmpKApgEZG8ffsWW7ZsYR4PHDgQzs7OIu9/6NAh5OTkACguDNmzZ0+xj5GQysrIyEBwcDAeP36Mx48fIzs7G+PGjcOQIUN+q0ACAebOnYtmzZrhzJkz+Pz5M9PO5XLh5eWF8PBwDBs2DN27d8eHDx+YgFVYWFip6YY5OTm4e/cu7t69C6D4znenTp0QERGBuLg4pt+7d+8wf/586OnpYezYsXBxcRE5eJOUlAR1dfV6+ab95+AVUDwTDiheGGT79u3o0KFDTQ+LEELqvJiYGFy9ehX//fef0O2/Qorgz4KDg7FkyRK+hVNMTEywYcMGGBgY1N7AfjHh4eH4+vUrgOJV4319fTFx4kS8f/8eANC4cWPMnz8frVq1ws2bNxEXFwcfHx++74u9vT0WLFiAxo0b18o1kF8Tiyuuamy/kJEjRyI4OBi2trY4ceJEbQ+n1mVmZmLYsGHMLzETExMcO3ZMYAZCaWJjY/HHH38wH+62b98OBweHahsvIeUpLCxEeHg4goKC8PjxY7x+/RocDkegX4sWLbBs2TI0a9asFkZZfTIzM5GTk1MvAh7BwcG4cuUKevToAXt7+xo7L4fDQVBQEE6fPo3Hjx9XeH8WiwVzc3Okpqbi27dvlRqDpqYmRo0ahb59+4LNZpfab8OGDfD09ISNjQ127NgBWVnZSp1PnDgcDuLi4hAZGYmoqChERkYiKSkJxsbGsLGxQatWraCoqIicnBx07NixzGP16dMHy5cvr6GRE0JI3ZaVlcWsIvj8+XOhfX6FFMGfFRQU4MCBAzhy5AhfMfFhw4Zh+vTpv0Rwri7ZunUrkz3j6uqKVatWYfXq1XyF8p89e8b8v6ioCLGxsejbt6/Q423evBl2dnZgs9m4fPkygoKCMGTIELRq1apar4PUTVWJt9AMLFImXt0rXvCKzWZj06ZNIgevAGD//v1M8MrKyqpGP4QSAhR/mI6KikJoaChCQkIQHBzMt8RyaV69eoWhQ4fizz//xNixY+t1KlNhYSGCgoJw9epV3L17F4WFhTAyMkLfvn3h4uKChg0b1vYQBezfvx8HDx4EUFwvKiAgABISEjVybgkJCXTq1ImZKXX27Fl4eXmVuU/Tpk1hY2ODdu3aoU2bNlBUVASXy8Xnz5/x8OFDBAYGIjQ0VOTC8AkJCfj333/x33//YejQoRg8eDCUlJT4+nA4HGZcISEh2LBhA1asWFGpa64MLpeLxMREZoVGXsAqKiqKmXVbUnBwME6dOgUWiwUzMzM0bdq03HNQkXtCyO+Ot4qgj48Pbt++LTRFsKSNGzeK9Pu1voiNjcWiRYuYBVGA4tXMV6xYQZ8rqgGHw8HNmzeZxz169AAA+Pn58fU7deoUWrRogbVr1yI6OrrM9zdz5swBANjY2CAkJAQAEB0dDU9PT3EPn/ziaAaWEDQD638uXLiAdevWMY/Xr1/P/BITxc/Lrh4+fJgi7WLy48cPHD9+HAAwceJEuvNUAofDQWRkJFNbKDQ0FGlpaaX2Z7FYaN68Odq3b4927drh2bNnOHz4MN8fYiMjI6xYsQLm5uZCj8HlcuvkbKbIyEhcuXIF165dQ0pKitA+UlJS6Nq1K/r16wcbG5saCxKV5cyZM/j333/52kre6asNM2bM4Ku3UVLr1q1x6NChco+Rk5ODkJAQBAYGIjAwUOjsLAUFBcjIyAh8v+Tk5DBgwACMGDECMjIyGDBgAN9U/ZJWrVoFV1dXEa5KNFwuF8nJycxsqsjISERERCA6OlqkYHBVHDp0CK1bt67WcxBCSF3ESxH08fEpdRVBYTPIWSwWOnTowCwy0qxZMxgYGNSJGboVdfPmTaxZswZZWVlMm62tLVavXg0NDQ2xnScpKQm7d+/G27dvoaWlhbFjx/62n1lCQ0Px119/AQAaNmyImzdvQlpaWux/i3V0dHDlyhWxHpPUDzQDi1SL9+/f832A7NevX4WCVwCwe/duJnjVqVOnCv0hqKsBgbogPT0dXbt2ZR7r6+vD3d29Qsfgcrk4cOAAM8MFAPT09GBlZYWmTZuiYcOGUFRUhKKiIiwtLctMYaptvIDV06dP8fTpUzx79oxZ8bI0GhoaaN++PTp27AgbGxuoqKgw22xsbNC9e3esXLkSb968AVBcaHrkyJFo0aIF8vPzkZubi5ycHOZfDoeDTp064Z9//im1EDhQPBOqsLCw2t9EpqenY+fOnaXOGpKSkmICdIWFhfD394e/vz+0tbXh7u6OPn36QFNTs1rHKAyXy8Xhw4exd+9evvbt27fX+FhKSklJ4UsldHV1RXR0NPP6ePnyJX78+AFlZeUyj8Nms+Hg4AAHBwdwuVx8+vQJgYGB8PT0ZGa6ZmZmokePHrC0tMTp06eZOlnZ2dnMikAlV1wSZtmyZejevXulfm5TUlL4Uv94X+np6RU6jrKyMpo1a4ZmzZqhadOmUFVVxevXr/H06VO8fftW5OOcOHGCAliEkN8GL0XQx8en1Bs3zZo1g5ubGzOD2tbWlm87l8tlbpTwsFgs6OrqMkGtpk2bolmzZtDT0xPbDPOcnBzMnDkTISEhGD58OGbNmlXpm2LZ2dnYuHEjfHx8mDZJSUlMnToVo0aNEuvNtg8fPmD69OlITEwEUPye7/Hjx1i2bFmFbwZxOBykpqZCSUmp3s7cLzn7ytHRsdLX0a5dO77Vl3+2f//+MvcvLCyElBSFKwg/moElBM3AKv7jOWzYMMTExAAonn1y/PjxCn3ofv78OcaPHw+g+I/m2bNnYWxsLNK+Fy9exJ49e+Du7o6ZM2dWePy/srS0NHTr1o2vzcPDA9bW1hU6ztu3bzF8+HCR+3t6eqJZs2Z1IqjI4XAQERHBF7Aq78N1w4YN0aZNG7Rt2xZt2rQR6VqKiopw5swZ7NmzB3l5eSKPr127drCwsEBYWBiePXsGYb9mZ8+ejREjRoh8zPT0dPz48QONGzcu8485l8vFjRs3sGXLFoEZPOrq6nBxcYGbmxs0NTXh5+cHb29vJghTEu/ubd++feHg4FAjb8K4XC527NjBzCzkGTBgABYtWlTt5y+Lh4cHdu/eDQAwNzdn/jaMGjWKef4WLFggtCC5KNLS0jBnzhy+Dytt27bFhg0bEBgYiKNHjyI6OrpCxwwICCgzoPbjxw++ABUvaJWamlqh8ygoKDCBqpJfqqqqQn/GvLy8sGbNmgqdo2HDhlixYgXVTySE/JI4HA5CQ0Ph4+ODW7dulbqKYM+ePeHu7g51dXV8/PgRRkZG0NTUxNmzZ7Fp06ZKnVtKSgoGBgZo2rQpjIyMmBnpFVnEhsvlYu/evTh8+LDAthUrVsDFxaVCgYi3b99i4cKF+PLlC9Omo6ODdevWiX0lu/v372PhwoVC094BYMKECZg4caJI73+Tk5MxfPhwJCUlASieOa2qqgp7e3sMGTJE5NXba5uTkxO+f/8OoPiGnZycHD58+IAXL15U6DhjxozBnTt3+L6PPPr6+hg+fDhOnjyJwYMHY+jQoXzbz58/j3///Rfm5ub477//6m0wkAhXlXgLBbCE+N0DWFwuF4sWLWKi72w2G6dOnarQyh5cLhfjxo1DWFgYAKBXr15Yu3atyPu7ubkhNjYWLBYL/v7+UFVVrdA1/Kq4XC5sbW1RVFTEtElISODp06cVPlZmZiYGDx7MtwpaeZSUlPg+oPLu3lX394fD4eDDhw94/PgxHj16xOTOi8LMzAwtW7aEjo4OCgsLkZ+fj4KCAuTn5yM/Px+ampro1KlTmQGtyMhIDBw4UFyXAwAwNjbGuXPnmMf5+fmIi4tDbGwsvn79im/fvjH/j42NZdK0lJSU0LFjR9jb26NDhw58NZFiYmKwbt06gbtdRkZGmDZtGjp06CD0DeTHjx9x6dIlXLt2TWggUFVVFa6urujbt2+1rfBTVFSE9evXC8wYa9KkCS5evFirKbKFhYVwc3NDQkICAGDlypVwc3MDAJw7d45ZMa9FixY4duxYpc9TUFCA1atX4+rVq0ybvr4+du7ciSZNmuDevXvw8PAQGnAsjY+PD5SUlBAdHc2k/fECVaWlH5aGzWYzP/e8lJRmzZpBQ0ND5MB2XFwc+vfvX279ltJ4e3tDX1+/UvsSUl9xuVxwOBwUFRUxXxISEpCTk6vtoZEqiomJwbVr1+Dj4yP0/RhvFUE3Nzc4ODhARkYGeXl5sLe35ytzoKqqynfTytPTE1FRUYiIiGC+YmNjhd5QE6Zfv35YsmRJuf0KCgpw+fJlvnIjpTl9+jTMzMzK7MPlcnHq1Cns3LmT7/p69eqFhQsXQkFBofzBi4jL5eLs2bPYvHkz87zIy8tjyJAhCAgIQFRUFNPX1dUVS5cuLTeIcvToUezcubPU7Wpqati7d6/IN/RrSmZmJt6/f4/3798jJCQE9+7dq/ZztmjRgq+mWVnOnTtX554zUjUUwBKz3z2A9fPd8TVr1sDFxaVCx7h//z4zc0pKSgpeXl7Q0dERef+ePXsy03iPHz8u9rstpQkLC8ObN2/g6OiIRo0a1cg5KyI4OBiTJk3ia3v06FGFiuqXlJOTg9OnT+P06dMVnnUhDJvNho6ODvT09NCkSRPIy8vzBYvK+39BQQG+fv2K7OzsKo+lMmRlZWFiYoKCgoIKpThVhpGREczMzPDt2zd8/foVSUlJIr+x5JGQkIC1tTU6deqE/Pz8MqdiS0lJoVGjRmjcuDEaN24MWVlZJCQkIDExEYmJicjIyIClpSX69OmD69evIzg4WOhxrK2t0adPHzg7O4slrbSoqAhPnz7FrFmzBIIakpKS8PT0hKGhYZXPUxU3b97EwoULARR/SPD19WUCaqmpqejRowfzRvvSpUvQ09MT+dh5eXl49uwZmjVrhkaNGoHL5cLDwwN79uxh+igrK2PLli1o3bo184b75/pg4iQrK4umTZsyX82aNYORkRG0tLSqPANz7ty5uH37NgDAwMAACgoKeP36dYWOsWXLFr4UakLqk/T0dMTExCA2Npa5ScG7UZGbm4vCwkIUFRWBw+Hw/V+YJk2awNraGtbW1rC1tYW8vDw+fvzI/L5v1KhRpd8fkOojSopg06ZNmVUE1dXV+balpqaie/fuZZ6Dt2jIoUOHoKCggKVLl0JJSQmfPn3iW3QjMjJSaG0taWlp3Lp1C4qKikKPz+Vy8eLFC2zfvl3kIARQdi3LlJQULF++nC/tkc1mY+HChWKt6ZiamoqbN2/iypUrePfuHdOura2N7du3w8jICBkZGZg3bx7fDUF7e3ts37691L+DXC4Xzs7OzMylshgYGMDBwQHW1tbM+86EhATExcUhPj4eCQkJzL8JCQngcDho2LAhlJWV+f4t+VVym4qKCmRlZYWONSkpiQlWvX//Hu/evWNKGNRVN2/ehIaGBrhcLjPBQVVVtU6XNyFlowCWmP3OAayPHz9i5MiRyM/PB1C55cs5HA6GDBmCiIgIAMCgQYOwYMGCCh3D3d2d+WW6efNmgZQ5ceNyuTh27Bhz10RWVhZjxozByJEjxf7LsbCwELm5ucwXl8stNy0MKA42de3alfneABX/sFxSUVER7t27hzNnziA0NFRoH0VFRVhbWyMlJaXUVcVIzWjQoAHYbHaZxejFwdDQEEePHkV6ejouX76My5cvM1PhS5KTk4O9vT309fXRpEkT6OrqQkdHR+CNdmnS09Nx6dIlnDhxosw3e66urtDU1GS+tLS0oKWlBQUFhWpPZ01ISMD+/ftx5coVJrj4559/YsqUKUyf3NxcjB8/ngl4tmvXDvv27UNBQQGeP3+O0NBQXLlyBQkJCWjZsiV69+4NU1NTaGlp4dGjRzhw4ADi4+MhIyODKVOmYPjw4ZCUlMSOHTsEZnMtXboUf/zxh8DiGpUlLS0NQ0NDJkDFm12lra1dLYX8g4KCMG3aNObx4cOHmTTzihoyZAjGjx+PvLw8aGtri2uIhFQJl8tFamoqkpOTkZiYiISEBCY4xQtUVfeiBz9TUlJiglkaGhrQ1NSEhoYG839NTU0oKyvXifIAv7KKpgg2b96c73tSWFiIu3fv4sKFCwgJCanwDa+mTZvCw8NDYCVbAMjIyEB0dDQiIiJw9OhR5v338uXL0adPH6HHO378eKVrU8rIyKBjx47YsGEDM6Pp8ePHWLJkCd8ssubNm2PFihX4+vUrzM3Nq1SXs6CgAIGBgfDx8cGDBw8EVstr0aIFtm3bxrzfzc/PR1ZWFjPDmmf37t1o166d0PRKYc/J7du3yw02VhdpaWm+wJakpGSZ9ajqsoYNG6Jp06ZITU3lK6fAZrOhqqoq8KWmpoZGjRrB2NgYTZo0qZb3NFlZWVi0aBEiIyMxb948KnFQQRTAErPfOYA1ceJEJj2radOmOHHiRIUDOL6+vsy0YzabjStXrkBNTa1Cxxg4cCAiIyMBAPPnz8fgwYMrtH9FFBQUYP369bh06ZLAtkaNGmHGjBno2bNnld/c5eTkYOPGjfD19RX4w8lms9G8eXNYWlrCwsICLVq0QEFBAa5fv47AwMBS7271798f+fn50NDQwMCBA0v9415QUICwsDA8efIEQUFBZc4usrCwwLBhwwSKNnK5XMTFxSEyMhJv3rzhK/5Oqo+WlhYGDx7MpKydPn0aHh4eNXJudXV16Ovr4/v37/jx44dIs/QcHR2xcePGUn9eIiIicPbsWfj6+lY6jQwo/pnR0tLiC241btyYL9BV2eBzeno6jhw5gjNnzvAFjJWUlHD+/HloaGggNjYW58+fx6VLlwTSLi0sLBATE1Phouc8bdq0KTWo7ObmhsLCQly/fr1CxzQwMICJiQlfsKpx48bYtGkTLl68CCcnJ6xYsQJsNpv5YFTR33kcDgc/fvzA9+/fkZyczHylpKQgLi6OmXnFY2Zmxnf3u7IWLVqEP/74o0L1WqpLXl4eQkND8eDBAzx+/BiysrKYMGECunTpQgGCei4rKwuJiYlISkpivhITE5GcnIyEhAQkJycjKSmpzGXk6zp9fX2YmJjAzMwMJiYmMDU1haqqap1YmVYc7t+/j507dyI/Px+7du2qkXTkr1+/MqsIlpUi6OrqigYNGiA8PBzx8fFITExEamoqpKSkKpQ2Xpafb8AIUzIFrn379ti7dy+4XC4T1OLVcCo5m7ayxowZg8mTJ2PPnj0CtS8dHR3RunVr7Nq1Czk5OVBVVcWZM2eErjqYm5uLxMREpKSkgMPhIDg4GBoaGmjevDlyc3Nx69YtXLhwocyfzW7duuHOnTuVug7eTZ+6Povpd8Vms+Hm5oZZs2aJbUZqUVERZs2ahYcPHwIoDhYePHgQVlZWYjn+74ACWGL2OwewRo4cyfyh1NbWxpEjRyq0RG1BQQH++OMPZml4Uf5YCjNixAiEh4cDAEaPHo0ZM2ZU+BiiyMjIwNy5c/nSpeTk5ARS2Fq0aIF//vkHLVu2rNR5YmJi8M8//zCz0qqDqqoq/v33X76VHoOCgnD27FmEhoaKPHvK1taWSQHU09ODjo4OdHR0mA+2RUVFyM3NxYULF8rM8ye/LzabjT59+qBr167M6nH379/HmTNnKlWvrTKkpKQwbNgwTJs2TejsRt5ruWSqDpfLhbe3Nzw8PJCRkcHX39TUFGPHjoWUlBS8vb0RFBRU4Tvg1WH37t14+fKlSAHlQ4cOwdraGhISEkhPT0eXLl3K3YdXp+3Zs2fo3LkztLS0EBYWhs2bN+P9+/ewsLBAQUEBkpKSkJKSwlefryaxWCw0bNiQ7+6rmpoaZGVloaurC3t7e76VRsUpISEBDx48wIMHDxASEiI0MNuqVSvMmjWrSunwycnJePLkCR4/foyPHz9CT08Pjo6OsLe35wvWcjgcJCUl4cuXL/jy5QsSExORk5PDt2pqyX8LCwvRqlUr9O/fX6QaI7xagrm5uUwaeF5eHgoKCiAhIQFJSUnmX97/paSk+NpKfhUVFSEiIgLh4eF4//49ioqKmBlC6urqzL/q6urVUgsvPz8fSUlJfIGokoEq3v9pBrIg3orFMjIykJGRQYMGDZj/8x7z2rS1tdGyZUtoa2tXezD369evePbsGcLDwxEeHi40Tblr167YsmWLSMfLycnB9evXcfnyZWRkZGD58uVlflAVNUXQzc0NvXv3hrq6OrZt21bjnzmaN2/OzLxt3rw5bGxskJiYWGbZEGlpadjb21c62FMVNjY2sLW1xfv37xEYGPjb/UxKS0tDX18fBgYG0NbWRmFhITIyMpCdnY2MjAxkZWUhMzMTWVlZ+PHjR7mrFf9uLCwssHXr1gp9ri3N5s2bcfr0ab42ZWVlHDt2TGhmzNu3bzF27FgYGhpi4cKFlf48+SuhAJaY/c4BrFevXmHChAnMimtGRkY4fPhwqTnwPytZ0FhJSQk+Pj4i71vSuHHjmJUuKloAXlTfvn3D9OnT+Yo09u7dG4sXL8b169exZ88egVXcevbsienTp0NLS0vk8wQGBmLhwoV8aQNsNhtsNhuysrJIT08XW0qBlJQUFixYgH79+sHPz6/CqZuE/GokJSXRtGlTxMbGMh+8a4qGhgZat27Ntxy1uN25cwdLly7lqxlSFisrK+zduxf//PMPHj9+XG3jqsu6d++Otm3bQkVFBRISEnxfjRs3hoGBQZmFeouKihAWFoZ79+7h0aNHFbox0aNHD0ybNg1NmjRh2nJychAaGorv37/D2NiYqfmWmZmJJ0+e4MGDBwgMDKzR2oCGhoaQkJBAXl4eX5AqLy+v1HpMNUFZWZkvsKWhoQFdXV2Ym5vD0NCQL1jN4XDw/ft3vuBUUlISIiIiRFq5llQPdXV1WFtbo2XLlrC2toapqalYVhf7/v07Ro8ezdxAFcW2bdvQuXPnUrdHRETgwoULuHbtGrKysph2dXV1nD59mi9lvmSK4O3bt0sNrgwcOBDu7u4wNzdnAnknT57E1q1bRR43IaTyTpw4AQsLi0rvf/78eaxfv555LCUlxczuMzMzw6lTp/iC9D8Hp11cXCq8EvOviAJYYvY7B7AA4MGDB5g1axbzJrV169bYs2dPudMus7Oz4e7uzgR9Zs6ciVGjRlVqDJMmTWJmRbVp0wb//fdfpY5TmtevX2PmzJl8AaqJEydiwoQJzC+dzMxMHD58GKdOneKbdtygQQOMGjUKo0ePLnMFIF4xZt70a6D4l9zixYvRu3dvPHnyBDdu3MCdO3dEuovUtm1bgdkrurq60NPTw5s3b/hqI+no6JQ7lVlHRweysrLVOiuMkN+Vubk5unXrBm1tbTRu3BhaWlrIz8/H5s2b8eDBA7GdR1paus7dZTUwMICamhpUVVXh7+8vsP3EiROQkJDA1q1bS02VJIT8HmRkZGBpaYmWLVvCxMQEioqKMDY2Fml105ycHPj7++PixYsVKiT+MwUFBfTv3x+jRo2CvLw8bt++jfPnzzM3UoVRVFTElClTYGdnB19fX1y9elWk4JmysjKMjIyYRTIOHjwolkV0CCGimz59OsaMGVPh/R49eoS///6b+YzcvXt3NGzYEBcvXgRQPEHh7t27kJaWBofDwYIFC3Dr1i2+Y9BCNMUogCVmv3sACyheer1k8XYnJyeBQoY/O3ToEPbu3QuguHbUpUuXICsrW6nzz5gxg/mQp6OjgytXrlTqOMI8efIEM2fOZGaZSUlJYfny5ejdu7fQ/l+/fsXOnTsFfgGpq6tj6tSpUFZWxp07dyAtLY3evXujVatWeP36NTZv3sz3hqpRo0ZYunQpHj58CD8/v1LfsFhYWKBXr15wcnISmOa6dOlSXLt2TWCfbdu2Yd++ffjw4UOp181isdCmTRuYmpqisLCQSQ0ipD4yNjaGg4MDDh8+DKB4xifNqKg/GjRowPwOJoSQmiArK1ul2ouEkF+HgoIC7t69W26NP15dtbNnz+LBgwfMpARzc3OMGjWKL9tlwoQJmDRpEr5//45ly5bh0aNHfMf6448/sHTpUvFfTD1UlXhL2cuekd+Wm5sb3r59i7NnzwIA/P39MXv27FKLhKelpfGtmDVp0qRKB68A8M32SkhIAJfLFVvNhE2bNjEfnJSUlLBlyxa0adOm1P46OjrYtGkTQkNDsWXLFqbocHJyMlauXMnX19vbG9ra2gJ34dq0aYMVK1Zg9uzZ+Pjxo8A59PT0YG5uDnt7e7x48QLp6elIT08XCGCV9gH9/fv35RbKV1VVxfPnz0WqQaSgoIDOnTszBbE1NDQgJyeHSZMmlbsvITXh48ePfD9LFLyqXyh4RQipaRS8IuTXVdEZ6ZmZmWjbti1u3boFVVVVcLlcxMbGIjIyEpGRkYiKisLNmzeF1vZs1KgRFixYIFDnOSkpCYMGDUJkZKTQOqn0O0g8KIBFhIqIiOCb6aOnp1dm0bsjR44w9QEMDAzg6upapfOXLNRaUFCAtLQ0sRTgLSwsxKdPnwAUz0g6duyYyKvQtG7dGrNmzcLEiRPL7CdsCrmUlBT++OMPvlREdXV1NGvWDJKSkvj8+TNu3LiBGzduMNsPHjwIIyMj9OjRAz169ICOjg709fWFph/t37+/3PF///693D48Y8aMQZMmTRAdHY3bt2/zFbknhBBCCCGEiEZKSgqurq64ffu2wCIxpOrmz5+PFi1aIDMzE5MnT67QIjuOjo5gs9lo2rSpyCt+uri44K+//hK4Geft7S3Q18LCgjnu9evXMXnyZOjo6Ig8PiKIAlhEwLdv3zBlyhTmF6yqqip27txZ6hTL+Ph4ZqYWAEydOlXoql8V8fNKQ4mJiWUGsLhcLlJTUxEXF4f4+HhIS0tDV1cXOjo6fMVB09LSmF9qysrKFVpCeePGjfD09KzglRR78uSJQJuCgoLQ9pIiIiIQERGBPXv2wNnZGStWrICCgoJIAavSmJqaAiietVWa3bt3V/r4pP6ysbFBSEhIbQ+DEEIIIeSXUVhYiEuXLtX2MH5Z5ZW5KU9OTo7IwSsAOHr0aJnbWSwWzM3NMWnSJEhISGDq1KlMe3k1pUn5KIBF+KSkpGDKlClITk4GAMjJyWH37t1ClwTlcrl49uwZ/vrrL772HTt24ODBg2Cz2ZCTk2NW3Cv5WFZWlq/95+0lC5IDxWmEJiYmCA8PR0REBOLi4phgVXx8PBISEoSuLsZisaCtrQ09PT3o6uryRcqVlZVFfl4CAgIqHbwqDW8mmKj8/PwQExODt2/fVum8ZQWuyO+NgleEEEIIqQlSUlLYtm0bJCUlMXXq1ArNmiGkrunQoQOsrKxgZWUFS0tLyMnJgcPhYNiwYUyfvn37lpnRRERDASzCyMrKwt9//40vX74A+N8fFjMzM75+eXl58PLywoULFxAdHS1wnPJWv6uMmTNnVmo/Xj5zbGysQCG9z58/448//oCenh709fWhq6sLfX196OnpoVGjRpCQkEB2djZu3bqFFStWVP0ixKCqwSsimoEDB+Lz58+UOkkIIYQQUg0KCwvx999/1/YwCKkSKSkpPH78WGim0o0bN5gFtho0aEC1hMWEAli/ic+fP+PcuXMoKiqChYUFFBQU0KBBA8jKykJWVhYNGjTAv//+ywRIWCwWNmzYABsbG4FjLVmyBLdv367pS6gWnz9/xufPn8W6rD2p3/z8/MDhcFBUVIT09HTMnz+fCeoSQggh9YW6ujpYLBaSkpJqeyiEEPJLGjZsmNDgFZfLxb59+5jHw4cPp9lXYkIBrN9Aamoqhg0bhpycHADA+fPny91nyZIl6Natm9BtwlbRA4oLiRsYGCAnJwfZ2dnIyclhvko+zs3N5dsmrH9MTEzlL5iQKnB2dq7tIRBCCCFCSUpKonHjxtDV1eX70tHRQZMmTUqtr5KdnY2PHz/i5MmTv8xNSEIIqW3Hjx/HkydP+Eq0zJ07FwMGDEB8fDzT1rNnz9oY3i+JAli/AS6XW6FlO52dneHu7l7q9nbt2gnMSHF0dIStrW2lx/iz48ePY/v27UK3WVhYYPr06aUGw4QFxH7+ys/PR5cuXXDnzh1kZ2eLbdyE3+TJk5mZTF++fEFUVBTfL3NCCCG/jkWLFsHCwgLy8vLYvHkzHj58KLZjt2jRAjIyMggLC+Nb0bcuUFZWxo8fP2rkXJaWlpg8eTJsbGzAYrFE3k9OTg5WVlZo0aIFsrOzER8fjytXruD27duIi4urxhGT6qagoAALCwvExsZWSxkPQmrD3Llz8eHDB1y+fFlgm76+Pj5//lwLoxLu5/rC586dw9ChQ9GuXTsEBQUBAIKCgmBkZFQbw/vlUADrN6CqqooDBw5gwoQJIvX38/PDgwcPYGlpCVtbW3Tr1g2GhobM9l69egnM4ho6dKhYx1zWCg1z585Fy5YtK3xMLpeLK1euYO/evcjMzMTVq1erMkQigpJTZwkhpCZs2bIFbdu2xdu3b+Hv74+LFy/W9pB+G1u3bsXo0aMxatQo7Ny5E0lJSXBxcUFRUVGljtekSRP8+eefcHFxYVYUzs7OxpMnT3D//n08fPgQ379/L/c4bDYbHh4eiI2NxaFDh/Du3btKjac0HA5HLMfZuXMnjI2NoaGhAQkJCURERMDDwwM3b95kClyHhYVh0qRJaNGiBdzd3VFUVMTctMvKykJWVhays7P5vnhtvJt4ZTE1NUVhYSEiIyPFck2/Kh0dHbRu3RopKSliDdRWRJs2bVBUVITY2FgEBwf/8kXQR44cCS0tLWhpaaFBgwaYNm2a2I4tJSWFnj17wtfXV2w/z6RqCgoKsHz5crRp0wbLli3j23bmzBlkZ2fD09MTBw8eFMv5Ro0ahRs3biAxMbHKxxo5ciQAwMnJiQlg3bp1C6NGjarysQnA4v7qv+0qYeTIkQgODoatrS1OnDhR28MRu8TERISFheHVq1cICwvD27dvy72baWBggG7dumHgwIHQ0NBA27ZtBfqMGjUKM2bMqNAdwdJcvnwZK1euFGjX1dXFpUuXKnyOpKQkrFq1CoGBgVUeGyF1xYQJE9C4cWPIyspCWloahYWFfLMQk5KScO7cudoeZr3Fe8P04sULvHz5ssIrh1YHCwuLCi31/DvZtGkTHB0dBdoLCgrQrl07kY6xc+dO2NraYtmyZfDz8yu1X9OmTaGiooLQ0NBKj/dn7dq1g6mpKfLz8/Hjxw+Eh4eLdIeZxWKBy+WCxWJBXl4e8vLyUFRUhIODA2xtbVFQUIDCwkLk5+fz/SusraCggPnKz88Hh8NBkyZN0KpVK2ZVJaB40ZenT58iMDAQgYGBAjN41NXVMXr0aPTr1w8yMjLw8PDAoUOHUFBQINJzoaKighkzZsDFxQVSUqXfa+VwOHj37h0ePHiAR48eIScnBxoaGlBTU4Oamhr8/Pzw7ds3AICWlhZOnDgBNTU15ibWmTNn6kTJAjc3NyxbtgySkpJCt3/+/BkeHh7w9fWtdDBQVCwWCyYmJmjXrh1sbW1RWFiIixcvUq1QFM9iMzU1BYvFwsuXL+vcTMDysNlsaGhoIDU1FRkZGbU9nArZsmULmjRpAmlpacjIyPB9vXjxAlOnTi1z/zZt2kBPTw+amprQ1NREdnY2/v33X74+rVu3hra2dqVucLPZbLRu3Rry8vIwMzNDp06doK2tzfzO/P79O06cOIHjx49X+Ni/q2HDhsHW1hbLly8XmOF67NgxtGjRAnFxcejdu7dYzte3b1+MHz8e/fr1E/lv1c8mTJiAvn374ty5czh27JjA9nv37kFRUbGqQ/0lVCXeQgEsIX71ANbP8vPz8e7dO4SFheHly5cICwtDcnKy0L5ycnJo2bIlHj9+LHT7rFmzmKhzVVy/fh2LFy8WaJ80aZLIM8mA4llXN27cwIYNG+rdH2tCSjNt2jSMGzeu3H5cLhezZs3C/fv3mTYZGRl069YNjo6OUFVVRWRkJD5+/IjIyEhERESILQ2mS5cu6NKlC44dOyZ0tdK6TEdHB3v27IGuri6SkpLw9u1bhIeH4/Hjx3j58mVtD6/S1NXVMW3aNHTq1IkJWuTn5yMrKwujR48W6RgjR46Es7Mz9PT08Pz5czx69AihoaHIzs6GrKwspKSkIC0tDQkJCSZ9PSIiolqvq2nTpti8eTMMDAxK7XPz5k0sXLiwzOMEBARAWVkZADBv3jzcunVLbGOUlZWFuro6GjVqhGbNmsHe3h6tWrWCvLx8mfulpqbi1atXePfuHWRkZKCiooKGDRuiYcOGUFZWRsOGDaGoqFhq4KMmcLlcBAcHY+vWrQI1Mhs2bIjhw4dj0KBBiImJwdatW/Hs2bMyj7dmzRo4OzuXGbgSVWRkJMaMGYOsrCwAxWmIBw8eZGZ53759G3PnzuXbx97eHhs2bACbzRY4XkpKCiIjIxEVFYWPHz8iIiICkZGRzPEr69y5czAwMGBmmZUmNjYWx48fh7e3d5WCJ2w2G3JycmCz2fj+/Xu5s7JI3cdisaCpqYnGjRtDS0sLmpqazGwlXpuCgoLADeD8/Hy8fPkST58+RXBwMF69elXtQdKadPr0aYHV1HkyMjKwbNky3Lt3T+zn1dDQwPz58wXqCXM4HPTs2bPUz1lENHZ2dmjWrBnOnj0r1kBy+/btoa2tDS8vL5H6y8jIID8/n3nMZrNL/X3aqFEjXL58ucwso98JBbDE7HcLYP2My+UiPj4eoaGhuHv3LoKCgipUQ2vjxo1wcnKq0hiEvakEAB8fHzRp0kSkY6SkpGDhwoUICQmp0lgIqWvMzMygoKAAGRkZvruRUlJSzP+lpaXh5eWF9PT0Gh+foaEh0tLSkJqaWuPnBgA9PT2oq6tDTk4OHA4HeXl5Zc6UMTAwQH5+PuLi4sBms9G7d2/Ex8fj7du39fZNZsOGDdG8eXM0b94cZmZmaN68ObS1tcudvcrhcPDy5UvcuHEDnp6epfbT1tZG165d0bVrV1hZWZUZQOFyuZg+fXq1zIB1dnbGsmXLmLvcZTl69Ch27twJAGjevDl27doFT09PNGrUCG5ubnzBg8uXL2PVqlUipeTIysrCyMgIxsbGMDExgYmJCYyMjH67u6xFRUW4evUq9uzZI/Bz8/Ob/J+pq6tj2bJlsLOzE3sw7uHDh5gxYwbzvbS1tYWKigoThOKRkpLCvHnz0L9//wrN8uZyuUhISEBERAQT0IqIiEBUVFSF7uJLSUlBTU2Nb4Vo3irRJR/LysoiIyMDV69eRWFhIVgsFtTU1KCiogJVVVVm9pmKigpUVFTAZrOZWXi8WXbp6enMV3Jycqk3JUntMDIywujRo9G6dWsoKSlBRkYGu3btwq1bt6CmpgZtbW2maH+TJk2gra0NLS2tcgOgosjJycHz588RHByMlJQUtGnTBl27doWSkhK4XC5CQ0Nx+/ZtREREMH9X2Ww2JkyYAAMDAyZwpqysjMjISJw/f16kxaOqw6hRozBz5swy+3A4HPz7778Vmq2upKSEixcvIiQkBI8fP0ZwcLDQ+q5SUlI4cuQILCws+NoTExMxe/ZshIeHi3xOcdHW1mZmpQLA/Pnz8f37d9y9e7fabzb9zho3boxdu3ahadOmtT2UOoMCWGL2uwewfpaTk4OgoCDs2rVLoHh7v379MGvWLEybNg1hYWEAAGlpaRw8eBBWVlaVPueDBw8wY8YMgfby7txyuVxER0dj5cqVePXqVaXPTwipX5ydnbFkyRIoKCiU2ufjx49YsWIF3r59y7RJSkqidevWiIqKEqmWDlAcsDAxMWECRObm5jAwMICkpCTi4+Px8uVLZGVlwcLCAkZGRpCUlET//v0rNBNNQkICI0aMQGZmpsh3AnV0dDBy5EjY29tDU1Ozyuncq1evhre3d7n9VFVV4eDggG7dusHW1hYyMjJ822NjY3Hy5EmhHxAUFBTQr18/dO/eHdnZ2QgJCcH169eRlpZW7gIk8+bNw+DBg8WSti7MkydPMHnyZKHb5syZA3V1dZiamkJHR6dWZ0DVNXl5ebh8+TKOHTtWanFwWVlZODg4oFevXrCzsxN4zYjbyZMnsXXr1lK3N27cGJs2bRL4oFkVRUVF+Pr1KyIiIvDx40dERUUhIiKiTqQik7pFWloanTt3hqurK+zs7MQSiKpLMjMzce3aNZw9e7ZShbelpKSgr68PFouF/Px8JsU5Ly+P+b+8vDzc3d3Rtm1beHp6wsbGBmPHji332GFhYSL1K8nDwwPW1tbMYy6Xi69fv+Lx48dMUCszMxNAcR2/06dPl3pDIzc3F+vWrRNIW1y8eDGUlZVx9+5d+Pr6Vmh8Jbm7u8PV1RUtW7Zkfs/27duX+Tzn6enJFBb/66+/mKBkixYt4OjoiCdPnjA1nEjlGBkZYc+ePdDQ0KjtodQpFMASMwpgCZeeno4uXboI3TZr1izs27eP+bChrKyMo0ePQl9fv1LnCg4OxqRJk/jaWrRoITSfuKCgAM+fP8e9e/dw7dq1WplxQv5HRUUF8vLy4HK5zFdhYSHS0tIqnVNeGhaLBVVVVairqzNfvPonGhoafO0yMjLgcDgIDQ3F1atXcevWLUqbqGaqqqro3r07HB0d0bp1a0hKSqKoqAh79+7FkSNH+PoqKioy3zMNDQ00atQIWlpayMnJwdOnTxEUFCS0sKqysjJWrlwJBwcHkcZUWFiIkydPYv/+/WXOCOGRlZWFqakpE6xq3rw5DA0NKxywOHjwIPbv3y90m7S0NKytrWFpaYlevXqhWbNmfEGZjh07ivxalZSUxJ9//olx48aJ9CGIw+Hg7t27UFBQgI2NDbhcLjgcDrhcLrp06cKcd9++fSgoKICfnx8CAgJKTZmSk5NDly5dMHv2bMTHx+PYsWO4deuWwEwmTU1NDBs2DP369SszjY4XEFywYAFf+7Zt29C5c+dyr68qygt6AMDy5cvh5uYGCQmJah1LfVRQUIDr16/jyJEj+Pz5M6SkpNChQwf06tULDg4OQlP0qguXy8Xq1atx6dIlgW3du3fHkiVLmPTR6pabm4vbt2+Dw+EgKioKkZGRiIyMrFMrAcrKykJJSQlKSkrIy8sTWieMzWZDTU1NYLYYm81GgwYN+IrLl/w3ISGhXhUbZ7FYsLa2Rrt27aCnpwcJCQlERUUhOjoa0dHRVZq10rp1a/Tu3RuOjo6/xYxNLpeLkJAQnDt3DgEBAeX2b9KkCdavXw9jY+Ny0654dQArMpazZ88K1MEqj6amJq5du1bm7/yYmBgMHTqUWeXcyckJGzZsEDq+9+/fw8vLS2CWmpSUFDZt2oQuXbqAy+Vi8ODBIr/WJkyYgJ49e5aaVj9x4kQmO2XHjh2wt7cHwF++RUFBAWvWrIGDgwPy8vLw559/Uv3NSurQoQN2795d28OocyiAJWYUwBKUm5uLefPmVWilFW1tbRw/fhyqqqpl9svLy8PTp0+Rn5/PFFf88uULxo8fz9dv2LBhmDNnDoDi9MDAwECmcGtF60/Iy8tXuWYFKX4Dm5ubK/Y3o1JSUgIBKGHBKRUVlUrPfMjJyWGCno8eParyqjMSEhKwtbWFs7MzunTpgoYNG/JtT01NxdWrV+Hl5VWrS/+yWCzo6elBW1sbXC6XuXvJ+5d3Hd27d0fLli2RnZ2NsLAwPH/+HBcvXiwzQMxisWBubo6OHTvC3t4ezZs3L/VNXnJyMmJjY5nv7c9vTl+/fg0PDw/cvXtXYF9zc3M4OjrCwsIClpaWlfog/PnzZ6xcuRIvXrxg2thstkCwijezqqo+f/6MP/74Q+g2DQ0NjBs3Dn379hX6Jn3OnDm4c+cOX5uOjg769u0LFxcX3Lt3D9u3b0deXh6z3cDAAF26dIGjoyPMzc2FnvfnD/Xt2rVDTEwM4uLiMGbMGCbI2LBhQ9y+fZt5811QUICQkBAEBAQgICAAKSkpIj8PxsbGGD16NJycnCo0yyA2NhajRo0Cl8vFmjVr0KFDB5H3razQ0FD89ddf5fabP38+Bg8eXO3jqa+KioqQkJAARUXFWv2QXlBQgHPnziEpKQkGBgZo2rQpDA0NoaSkVGtjKikrKwvp6enIzc1Fbm4u8vLymP+XbMvJyRFoL2sfCQkJJhjFq5tW8rGysjLfYyUlpRqp0VJQUICcnBxkZGTgzZs3CAoKwpMnT5CQkFDt5xaVoqIiOnbsCHl5eRQWFjKB/rZt26KoqAhbtmzBlStXKnzcyZMnw8XFReSSGL+iuLg4XLx4ES9evICGhgbMzc1hYWEBMzMzJv2/um4M5ObmYsqUKXx//0U1cuRIzJo1q9x+fn5+fDdeFi1ahAEDBgj0K2t2dskgVmFhIcLDw6Guro6nT5/i27dvuHz5MvPzwmazcfbsWejq6pY7tuXLl8PHxwdA8d/kTZs2gc1mQ1FREe7u7nyz0UeOHIm///4bs2bNooWwKsnAwEDkmfS/EwpgiRkFsPhVNPL/s8OHD6NVq1albt+wYUOZtVZKsrS0xPv37ys1k4fFYsHOzg79+vVDhw4dMGDAAL488N+NtLQ01NXVoaqqCkVFRXC5XBQVFSElJQXJycnVMpONzWaXOkuqZJuSklK1pQUJ8/37d9y4cQPXrl2r1PLqRkZG2LBhg0i57VwuF8+ePYOXlxdu3bol9llpwkhLS8PU1BTm5uZo3rw5LCwsKjWLSFghbAUFBdjZ2aFTp07o2LFjuQHrsvDuzh4+fFho7bq2bdti/PjxsLW1Fcvrgzcj7/v37zAxMYG+vn61poKVnJ4vjJqaGmbMmAFXV1e+dg8PD767d5MnT8aff/7J9xx8+fIFS5cuFZo63aZNG4wcORKdOnXi+0Dg5eWFNWvWiDT2CxcuCH19FxUV4dWrVwgICMCdO3cQGxsrdH9bW1uMGTMG7dq1q/T3rqioCEVFRdWebsYzfvx4PH/+vNx+M2bMELkIPiGkfCkpKXj9+jW+fPnCBOXy8vKYAB6vrb6tjLh//37Y2NjU6Psbwi82NhZubm6V3v/kyZOl3hT62bp163DhwgUAxe/DTp48CWNjYwDFs8G/fv2Kfv36lXkMSUlJbNq0CV27dhXY9vHjR4wePZrJfuncuTO2bNlSbuAvKCgI06ZNE2hv1aoVpk2bhgULFiApKYlpnzNnDn78+IH//vuv7AuuIXp6epg7dy46dOiAHz9+ID4+nvlKTU2Fvb09OBwO/v777xpfwGvAgAHM95zn77//rnCa6u+AAlhiRgEsfnl5eUx9kspavXo1unTpIjRVpGfPnkhMTKzKEEWmrKyMzMzMX2qFldK0adMGampq4HA4SElJQVJSEpKTk6s9bU5aWhp//PEHdHV1BQJVohRarm2RkZG4du0afH19K/S6lJCQQMeOHdG7d2907txZpDvYaWlpuHbtGry8vGp8pT5eapyFhQXMzc1hbm7OpEeUJicnB8uXL0d8fDzatGkDe3t7tGzZskqrhWVmZuL169dIT0/HiRMnhE5Rd3BwwLhx49CyZctKn6cuSE1NhZ+fH9LT05n0mmvXrgkUuz9w4ABsbGyQmpqKVatWCayQNGzYMPzzzz8CH4KKiopw4sQJ7N27V+iqPIaGhhg5ciTc3NwgKSkJd3d3fP36VaSxizIFnsvlYuvWrTh16hSA4p8JZ2dnjBo1qtRVoOqy1q1bC7S1bNkSXC4X79+/R4MGDTBlyhT069fvl6tZQ0h9cPXqVSxbtqzMPv7+/oiKimK+IiMjy62nKk46OjrYt2/fbz3bqq549OgRpk6dKnL/AwcOYNGiRcyMJB0dHVy+fFnkAGRubi5GjRrFTABQVFSEvLw80tPTK/SZSlJSEhs3bhRY0TAnJwerVq3CzZs3mbbly5ejT58+5R7Ty8sLGzZsEPpeQUJCQiArITQ0FCEhIXjw4AEuXbrEZLHIy8vD09MTkZGRePLkCR49eoSoqCiRr60iVFVVMXXqVLi7u4t8s7FXr141OquzV69euH79OvNYSUkJ165dK3fF4d9RrQewioqKcOrUKVy4cAHR0dFgs9mwtLTEqFGjhNZMio6Oxq5duxAaGoq0tDTo6elh0KBBGD58uNAPTwkJCdizZw8CAwORlJSExo0bw93dHX/99Ve13ImlAJagwMBA/P3332I5Vq9evTBq1CiYmJggMzOTqWPCYrGgoqJSoXQUUn14Kxv9nMKXnZ2N58+fl7l6SoMGDWBvbw8nJyd06tSpRmudiEtV6mUpKCjA2dkZrq6usLKyKvfNDpfLxYsXL+Dl5QV/f3+RajOVxsLCArKyssjJyUGjRo3w4cMHkWcaysnJMUXJeYGtJk2aVNvdYmEzungkJCTQo0cPjB07likw+ivKycmBt7c3jh49yqzc1qxZM8ybNw9Llizhuwta0tmzZ2FiYiJ0W1JSEp4/f467d+/Cz89P4I2om5sbFi9eDDs7O6ZuyL59+wTqDpbk5OSEjRs3lns9HA4HFy9eREZGBnr27Altbe1y96mrnJ2d+VbTa926NbZt2/Zb1KohpK4rKipC165dmWLZwuzduxft27cXaOdyuUhJSUFQUBD+/fffMo9RGWw2GwMHDsTIkSOhpqYm1mOTiuNwONi9ezeOHj0qUn9HR0ds2rQJQPHf0zVr1uDdu3dYvHixyPU2eaKjozFixIgK3zzW09MDl8tl6s/9HMQqKirC0KFDBbJjRFl5kefFixdYuHChSAGeo0eP8t1EjIuLw7t372BkZCSQtpiUlIQePXqINAZR9evXD//880+FP0/wZrkJC9TVhKlTpwqUxCHFaj2ANW/ePFy+fBkKCgpo06YNUx+joKAA06dP54t2v3v3DsOHD0dmZiZat24NNTU1PHnyBOnp6XBzc8PmzZv5jh0fH4/BgwcjPj4e5ubm0NXVxbNnz5CUlARbW1t4eHiI/c4nBbBKt3btWly8eBFA8ZTIpKQknD17lq+Pm5sbk1tN/sfKygppaWm1Wv8IEG99qU+fPsHf3x/+/v6lppjKysrC3t4ezs7O6NixI2RlZavjsqpVVepl6ejooHfv3ujduzd0dHTK7Z+eng5fX19cuHChSnexFi1axCwHn5qaivDwcL6v0gIjP1NUVGRSD7W0tJgl2tXU1KCqqgo5OblKBbhu376NuXPnCrRLS0ujT58+GDVqlEjP168iMTERf/zxh0hvctlsNnx8fERK14yLi8PZs2fL/FumpaUFX19ffP/+HU5OTkL7/Pvvv+jevXu55/uV/P333wgMDASbzcY///yDvn37UrF2QuqII0eOYNeuXaVuX7hwIQYOHFjp4yckJGD79u18s1tEISUlhaNHj4qcZkaqV2ZmJvr168d3M6IsP68wKA43b97EokWL+OrFslgsyMvLQ0lJCSYmJhg+fLhAzcVr165h0qRJfEGsf/75BwMHDsS3b9/4ZlpJSEiga9euWLhwYYVKOXC5XOTn52PixIl4+fIl3/hKjldPT0/oIhiliYmJYW7OVcb27dv5AnGlLeQlivv374sc1CtP+/btsWPHDuzfv19gMaLSzl3W6ti/s1oNYPn6+mLWrFkwNDTEyZMnoa6uDqA4L3fo0KHIysrC9evXYWBgAC6Xiz59+uD9+/fYtGkT84OXkpKCMWPG4P3799i5cydf1HbSpEkICAjAjBkzMGXKFABAdnY2pk6diqCgIMyfPx/jxo2ryiUIoABW6S5evIi1a9cC+N8d+aNHj2Lnzp1MH11dXXTq1AlnzpwR+/kHDRqE8+fPCy0a3rZtW9jb20NeXh5sNhtycnKIjo7G4cOHq1ywffHixWjUqBFycnJw5cqVCi0pq6WlBSMjIzx79qxKaZjlkZOT4wtElRacqq76UlFRUUwwq7TAC5vNhoODA5ydndGhQ4caKRQrblWpl9WqVSu4urqKtOIQl8vFq1evcPHiRfj5+fEV6K4IY2Nj7NixA1paWnztSUlJePv2LcLDw/HmzRuEh4cLpLKJokGDBkwwS1VVlS/ApaKiwvdYQUEBLBYLeXl5sLOzEzjWqFGjMHz48N92qWFhH8iUlJSwatUqtG/fHtevX0daWhr69OkjsEhAWaKiojB06NAy663x3mT9+++/Qn93P3ny5LdLk8vPz8eLFy9gZmZWZwp9E0KKlSxELYy40gRfvHgBb29vPHr0SCAIsn37dtjZ2eH169d49OgRPn36hH79+gmd9UVqXmRkZIWCmLwbFtUhLi4OaWlpzIIJ8vLyAjdEXr9+jVGjRgEoDoTev38f6enp+Ouvv/hWArW0tMT06dMxYcIEpq+vry/zGbwysrOz8fTpU8jLy0NbWxsaGho4dOgQDh48CIB/VlpFvHjxosKf05cuXYrVq1cLtJ8+fbrS5QhevXolllqVBw8ehImJCfr06YMfP34AKF4pWliB+8mTJ4u0EMzvqlYDWLwA0549e+Do6Mi3beXKlTh9+jSWLVuG4cOH4+HDh0wB3p8H+uzZMwwdOhQ2NjY4efIkgOI33S4uLtDV1cXNmzf5ftC/ffsGR0dHaGlpCazOVFUUwCpdeHg4RowYAaB4Zglv9ZVLly5h9erVtbY0soWFBQ4dOoQGDRogJSUFFy9exL59+6p83ObNm+P48eO4evUqNm3aVO31o0rTrFmzMoufq6ur16k0vYiICNy6dQt+fn749OmT0D5sNhudO3eGs7Mz7Ozs6mUwq7L1smRkZNC5c2e4urrCzs6u3BpSGRkZ8PX1xcWLF6u0ZPfs2bMxfPhwoQFMLpeLhIQEZoYWL6glzgKY0tLSUFVVhaSkJF9ao4SEBO7cufPbBwny8/MxYMAApi6VtbU11q9fD01NzSod9/Lly1i5cmWZfRo0aIBHjx4hLi4OvXv3FthekzVjCCGkPN+/f4eHhwfS0tL4as4AwK5du9CxY0exno/L5eLDhw8IDAzEu3fv0KVLF7i4uIj1HER8fl4FsCyqqqrw9/evE8X13717hzNnzsDBwYGZ9ZyUlIQpU6YgMjJS6D4sFguPHz+ulptMAQEBzGf0ypYE6Nq1KxPs6dOnD8aPH4+DBw/i6tWrQvsfPXoUY8aMEWjfsmWL0GL2ojp79mylgnAljRw5EoWFhcyNPi0tLaaO7fDhw/n6tm/fHtu2bauXn29qQq0GsPLz8/Hp0ycYGBgI1KPipRauXLkSQ4YMwfr163H06FEsWrRIaBS0Q4cOSE1NRUhICBQUFHD8+HGsXbsWo0ePxqJFiwT6DxgwAK9evcK1a9fEWiOFAlily8/PR6dOnZhc4nv37iEvLw+BgYFYu3ZtjeUY29raomPHjti+fTsTNFu+fDnu3LlT71alEcbMzAxbtmyBurp6vZ71wOVyERkZCX9/f9y8eRNfvnwR2k9OTg5dunSBs7Mz2rdvX2OrjIlLVeplqaqqwsXFBa6urqXWNOLhcrl48+YNLl68iJs3bzIrz1SUjo4O9uzZU+5yy1wuF7GxsQgPD0dERASSk5Px/ft3pKSkICUlBd+/f69SvS4AmDJlCv78888qHeNXEhkZiYMHD6J58+YYMWJElQrk8+Tk5GDChAlCC+SXdO3aNTRu3BgvX77ke/O4f/9+2NraVnkchBBSHZ48eYLJkycDKK5Vd+jQoVoeEakthYWFWLFiBXx9fUXqv2HDBjg7O1fzqKouPz8fHh4e8PDwEPisZW1tDQ8Pj1oaWflSU1Mxffp0JCcnM3UkS1sJsnfv3hg5ciSGDBnC1z59+nSMHDmySitF5+TkiCWwXTK9kvf6ycjIYGo6l2Rvb4/NmzfX689y1aXWa2AJw0v7k5SUhL+/P9TV1TF+/Hg8fPgQR48eFZo+Mnz4cDx9+hSenp6wsrLCsmXLcO7cOaxZs0boFFBegGzbtm1ivQtCAayyDR06FO/fvwdQXKxa3MUvy7NgwQIMHDgQLBYL8+bNw61bt2r0/GXh/YIqK1WHh8ViwdzcHPb29rhw4QIzNZ3FYsHHx6deFz8WhsvlIiIiAn5+fvDz8+ObDl2SvLw8unbtCkdHR9jZ2dW7X/pVqZdlZGQEV1dX9OrVq9w0uszMTFy/fh0XL17Ehw8fKj3eKVOmYPTo0ZV6nrlcLrKysphgFu8rNTWV7zEv4PVzYG/EiBGYNWtWnbjr+asrKCjA4cOHcejQoVJfkyVXL8rIyMDZs2dhampa4aK1hBBS01JSUvD161e0aNGC/qb8plJSUgSygcpy+/ZtqKioVOOIxC86Ohpr165lZkXLyMjg9OnTaNq0aS2PTDRcLhdt2rQps4+cnBx69+6N8+fPAwC2bt0qdFG4yhg3bhxevHghlmO1adMGBw8eBIvFQkBAAP755x+h/bp3747169eL5Ybkr6Qq8RaxPpO5ubmYN28eIiIiEBkZCW1tbWzatInJyeWl2JT2wYzXzvsgz+vfqFEjkfqTmmFmZsYEsMoLXp04cQJFRUV4+fIlXr16hVevXiE+Pr7S53Z1dcWgQYMQFxcHPz+/OhW86tmzJ6Kjo5nnRhg5OTnY2dmhU6dO6NSpE7M6TefOnZlpqS4uLr9c8AooDswZGxvD2NgYU6ZMwYcPH+Dn5wd/f38mZQoAsrKycPXqVVy9ehUKCgro2rUrnJyc0K5du3oRzGKz2ejZsyd69uxZ4XpZERER2L59O3bs2AE7Ozu4urqic+fOQtNDFRQUMHDgQAwYMADh4eHw8vLCjRs3KpzmunfvXuzduxfq6urYtWsXTE1NRd6XxWJBQUEBCgoK0NPTK7d/Tk4OE9CSkZGpdC0DUnHS0tKYNGkSHBwcsGzZMqF16tLT05n/KyoqUu0GQki9wavDSH5Pb968wciRI0Xu//Tp03q5KIehoSEOHjwIX19fBAcHo1+/fvUmeAWgzM9IPNnZ2QgLC8Pjx48hISEh1sCPra0tXwBrzJgxlSo0z2KxMGfOHCZY/vr161L73r59G8uXL8eqVauqNIOM/I9YA1jfvn0TWK3j/fv3sLGxAQDmg1Vpq5Dx2nmFrivan9QMMzMzXL58Weg2FRUVbNu2jW+pVaC4sLu1tTVycnLw5csXBAcHIzg4mMmJFtXVq1fx7t27KtUBqipVVVXY29sLPAc3btwQ2l9HRwcODg5wcHBAq1athAZhTE1N4eHhgY8fP/4WNRVYLBZMTU1hamqKadOm4d27d0wB+NjYWKZfZmYmfHx84OPjA0VFRXTt2hXOzs6wsbGpF8EsNTU1DB8+HMOHD69QvSwul4ugoCAEBQVBTk4Ojo6OcHNzQ6tWrQTecLFYLFhYWMDCwgKzZ8/GjRs34OXlhbdv31ZorMnJyRg6dCgAYPTo0Rg/frzYV05hs9nQ0dH5rVYXrGvMzc1x6tQpvHjxAvLy8hg7diyKiorAZrOZv9WEEEJIfXHu3Dls3LhRpL6TJk1iip/XVxISEnB1dYWrq2ttD6XClJSUIC0tLZCp4uXlhcjISCxevBj5+fn48OEDTp8+LbQWVlW0bduWKUwPFNepMjMzE7leGg+Xy4WBgQHzWFiJBlNTUyZgd/36dcjIyGDp0qX1MnBa14g1gKWlpcVES4OCgrB27VqsXr0a2dnZmDBhAhN1LG9qLy+9oaL9Sc0ouTSwoqIirK2tmbpTqampGDt2LMaOHYuJEydCWloa9+/fx5w5c8qtjyUhISHS97I2g1eysrJo3LhxqQE8oPh127p1a9jb28Pe3h76+voiHdvS0hKWlpbiGmq9wWKx0Lx5czRv3hx///033r59y8yuK1noOyMjA1euXMGVK1egpKSEbt26wdnZGW3btq0X03KbNWuG6dOnY9q0aRWql5Wdnc1ct7a2Nnr37o3evXsLnfEkLy+P/v37o3///ggPD4e3tzd8fX0rPCvr2LFjOHbsGOTl5bF161a0bduWUjJ+IQ0aNEC7du0AFK98+PbtW3Tq1AmNGzeu5ZERQgghosnPz8fEiRMRFhYmUv9Lly6JNFucVB9tbW14enri8+fPWLduHXNDd9euXdiyZQu+fPnCrMa8c+dOsNlsDB48WGzn//lzlqKiImxtbXH16lU8fPiwQscaM2YMNm3aBB0dHYSHhwtsb9WqFSwtLXHx4kUAxQvq6OjoYPz48ZW/AAIAEGsIUE5ODioqKlBWVkavXr2we/dusFgsHDhwAHl5eUwaTGmFh3nt8vLyACByfzk5OXFeBimHsbExEz3OzMzEunXrsHz5cub7wOVy4eHhgREjRuDjx494+vSpSMXdxRGIFPeMkZ/l5uYKjbKrqKjA1dUVGzduxJ07d3DgwAGMGDFC5OAVKcarCzZz5kz4+Pjg+PHjGDlyJLS0tPj6paen49KlS5gyZQqcnJywevVqPHnypMYWEagKCQkJ2NjYYOXKlbh16xbWrVuHjh07inRH5tu3b/jvv//Qt29fjB49GufPny91FqO5uTkWL14Mf39/LFu2DBYWFhUea1ZWFiZOnIg2bdpg/fr1FVppkdQPlpaWGDhwIAWvCCGE1BsJCQlo3769yMGrJ0+eUPCqjtDX14eDgwPWrl3LtAUEBCAgIAAjRozg+1y/e/dusa5wn5SUxPeYNyli9erVFT7W+/fvMXToUBw5ckRoSZ2AgAAsWLCAb6act7d3hc9DBFXrHDZra2vo6ekhMzMTMTExTC2r0mpW8V5UvNpWovYvrUYWqR6ysrJMvjVvWeE+ffrA09OTrzDfx48fMXz4cL6UMABo2rQpnJyc4ObmJvaUlZouKA8Uz1bx9/fHqlWr4OTkBEVFxRofw6+IxWLB0tISs2bNwrVr13Ds2DGMGDFC4Of9x48f8Pb2xuTJk+Hs7Iy1a9ciODgYRUVFtTRy0fHqZe3atQs3b97EP//8I3JdqFevXmH9+vVwcnLC3Llzcf/+faGLB8jJyaFv3744ceIEzpw5g4EDBzI3CSri/Pnz6NmzJ9q2bQs/Pz+RFioghBBCCBGn4OBg9OrVS6S+7du3x7Nnz+pF2YnfTZs2bZiFYwBg/fr1ePfuHRo2bMi0mZqaijUD4OebviEhIQAAZWVljBs3rsLHy87Oxu7du4VuS0hIwLt37zBjxgy+/qTqqhTA4nK52LRpE2bNmlXqzAcZGRkAxcuaGhsbAxCeAsblchEVFQVJSUk0a9YMAMrsDxQvNw6g3KXnifiV/JDNq7Wjra2NAwcO4J9//uH7vgcEBPDtKy8vjw4dOuDZs2fML476oEGDBnyPNTQ08ODBA7Ro0YLymasZi8VCixYtMHv2bPj6+uLo0aMYNmyYwIIQaWlpuHjxIiZNmoQePXpg/fr1CAkJqRfBLF69rNOnT+P8+fMYM2aMSMH5wsJC3L59GzNnzkSPHj2wadMmhIeHC71jZWpqioULF8LPzw/Lly9HixYtKjxODoeDBQsWoF27dli8eDE+fvxY4WMQQgghhFQEl8vFjh07MGnSJJH6e3h4YO/evdU8KlIVM2fOZBZfSE5OxujRo/nKh0yfPl2s5/s52yckJIR5vyzuelsAEB8fzxc8rQ+ZIvVBlT51s1gs3L59G76+vggMDBTYHhMTg+joaMjJycHQ0BD29vYAiqvx/+zZs2dISUlBmzZtmDQwXv+AgACBF9y3b9/w9u1bNGnSBEZGRlW5DFIJzZs3Z/5/8+ZNZjaGhIQE8yG8ZK2skl69eoWVK1cKzMyqixo3boxBgwZh586d2Lt3L99dgDlz5lRqJgupGgkJCbRs2RJz5szB9evX4eHhgSFDhjCrnfKkpKTg/PnzmDhxInr06IENGzYgNDS0XgSzePWyfH19ceDAAbi5uQldifBnaWlpOHv2LEaMGIGBAwfi6NGjSEhIEOjHZrPRp08fHDt2DOfOncOgQYMqlX57/fp1DB48GK1bt4anpycyMjIqfAxCCCGEkLJkZ2fD0dERx44dE6n/w4cPYW1tXb2DIlWmrKyMlStXMhMfSpo3b57AomBV9XMAKSEhgVkJXUFBAUpKSiIfa9CgQeX20dPT46vTSwEs8ajytBHeN2/NmjWIj49n2hMSEjB79mwUFhZi2LBhaNCgAWxtbWFsbIzAwEB4enoyfVNSUrBy5UoAwNixY5l2XV1d2NvbIyoqCjt27GDas7OzsWTJEhQVFfH1JzXHzs6OCea8evUK27Zt49uemZkJc3Pzeplvbm1tjenTp8PT0xNXr17FggULYGdnhy1btjBR+g4dOsDR0bGWR0okJCRgbW2NefPm4caNGzh06BAGDRoENTU1vn4pKSnw9PTEX3/9hV69emHjxo14/vx5nV8Aoir1sqKiorBz5064uLhg0qRJuHr1qtBi7sbGxliwYAFu3ryJFStWVPrNwoYNG9C5c2fMmDEDwcHBdf65JYQQQkjd9+XLF3Tq1Ampqaki9Q8NDaX6yPVIx44dceTIEaberYSEBFasWIEhQ4aI/VzCbmKXnFhTkcW0Hj58WGZqqqSkJAwMDPgCWFR+QzxY3CpWRisoKMDUqVNx7949yMnJoXXr1igqKkJYWBiys7PRuXNn7N69m4msvnz5EqNHj0Z2djasrKzQqFEjBAcH48ePHxg0aJBAEbWYmBgMHToUSUlJMDExgaGhIZ49e4akpCQ4ODhg3759Yl+BbOTIkQgODoatrS1OnDgh1mP/So4cOcKsFAEUF8Dr3bs3kpKS0KdPn1KL79dFFhYWGDZsGOzs7Phyr3lKLtErLS2NCxcuQFdXt4ZHSURVVFSEFy9ewN/fH7du3UJKSorQfurq6nBycoKTkxNatmxZb1JBv3//jhs3buDatWt49+6dyPux2Wx0794drq6uaNu2banXGxERAW9vb/j4+FSprtzEiRPh7u5OBcIJIYQQUmF+fn5YsGCBSH1XrVrFVzCb1C/p6em4c+cOLCwsmDJC4vbkyRNMnjxZoH3kyJGYNm0adu/eLbbP/k2bNsWFCxdQWFgIW1tbpj00NJRW9kbV4i1VDmABxR8WT58+DS8vL0RGRkJCQgImJibo168fBg0aJPAhKSIiAjt37sSTJ0+Qn58PfX19DBkyBAMHDoSkpKTA8ePi4rBz507cv38fGRkZ0NXVRZ8+fTB69GiBukTiQAEs0XC5XMydOxd37twBUFwj6ujRo7h06RLOnTtXy6MTzZQpUzBmzJgyg6DJycno168f80F+4sSJmDhxYk0NkVRRUVERnj17hlu3buHWrVul3sHT0NCAk5MTnJ2dYWlpWW+CWZGRkbh27Rp8fX0rtEqgpqYmXFxc4OrqCkNDQ6F9cnNzcfv2bXh5eeH58+eVHqOVlRWGDBmCLl26VMvvbEIIIYT8OoqKirBo0SL4+/uL1N/f319g9j0hPwsKCsK0adOEbrOyskKHDh2wb98+sZyrY8eO2LVrF9851dTU4OfnRwEs1IEA1q+GAliiy8rKwqhRoxAdHQ2guJB7WlpanV9lYfXq1ejVq5dIQYolS5bA19cXAKCjo4Pz58/Th/B6qrCwEM+ePYO/vz9u376NtLQ0of00NTWZmVmWlpb14g8Nh8NBaGgorl69ilu3bglNFyyNhYUFXP+PvfsOi+rq9gD8oyoKFooVLIhRg1ioIhYEZqQMDHajYhe7YtdY0CTGEo2oscRYUGNDQYoozNCLoAj2GmLHWBBUVOow9w+u53OcQQbmAALrfZ7vucyeffZew41wWGfvtXk8cLlcNG3aVGaf+/fvM6uy3r17V+E4hw8fDnd3d7lPWySEEEJI3fH27VsMGDBA7v4pKSkyF0AQ8qUrV65InDaopKQk89Cj8urevTuuXr0q0dasWTOEhYVh6dKlEAgEAIAxY8Zg/vz5Cs9XG1ACi2WUwCqfhw8fwsPDAx8+fKjuUMpkaWkJHx8f1K9fX67+KSkpEqutduzYAWtr68oKj1ShoqIipKamQiAQICoqSupo3U9atmwJBwcHcDgcGBsb14hkVm5uLmJjYxEaGoqkpCS561GpqqqiT58+4PF46NOnj8yimvn5+YiKikJAQABSU1MrHKOhoSGGDh0KR0dHmdt2CSGEEFK33L59G6NHj5ar75AhQ7B8+fJKjojUJiKRCB4eHkz5jfr166NPnz6IjIxUKJHl4eGBI0eOSN1vx8bGgsPhoKCgAEBJSZrK2h5Z01ACi2WUwCq/mJiYbz6j7OXlhbFjx8rdv7CwECNGjMDDhw8BABwOh6mDRWqXwsJCXLp0CQKBANHR0aWuMGrVqhUcHBzA5XLRpUuXGpHMqmi9rEaNGmHgwIHg8XilrkJ7+PAhsyqrtNVs8uBwOODz+bCysqKnqIQQQkgddPToUWzatEmuvqdOnYKhoWElR0Rqo1evXmHcuHHM4XO6urpYuHAhNm3ahMzMzAqNOWjQILx58wbR0dES7XPmzMG2bdsAAJ06dcKxY8cUC74WoQQWyyiBVTE7d+7E3r17qzsMmaZPn44pU6aU65r9+/fjjz/+AAA0aNAAp0+fhp6eXmWER74hhYWFSElJYVZmlVbEvHXr1kzNrE6dOtWIZFZF62W1adMGPB4Pzs7OaNWqldT7BQUFiImJgb+/P1JSUiocn66uLgYNGgRXV1fo6+tXeBxCCCGE1AyFhYWYMGECbt26JVf/5ORkmSvECZHX/fv3MWHCBOTk5AAAzM3NsXbtWqxYsaJC97Hdu3fH1KlTMWPGDIn2Jk2aMA94V65ciUGDBikce21BCSyWUQKrYkQiEby8vJCYmFjdoUgp74kPGRkZGDp0KPLz8wEACxYskHtJM6k9CgsLceHCBQiFQkRHR5eazNLX1weXywWHw8F33333zSezFKmXZWZmBh6PBwcHBzRs2FDq/cePHyMgIAAhISFyH3kti7m5Ofh8Puzs7KChoVHhcQghhBDybcrMzASXy5Wrr4aGxjf5Nwapmc6fP4/Zs2czWwcnTZqEadOm4a+//sKePXvKNZaWlhaio6Nhbm4u8/1WrVrh9OnTUFNTUzju2oISWCyjBFbFvXv3DqNHj0ZGRkZ1h8IIDw8v98qpuXPnIj4+HgDw3Xff4e+///7qSYWk9isoKEBycjIiIiIQHR1das03AwMDcLlccLlcGBkZffPJrIrWy6pXrx7s7OzA4/FgaWkptfWvsLAQ0dHRCAgIwMWLFyscX8OGDeHo6Ag+n19japARQggh5OsuXLiA6dOny9XXx8cH/fr1q+SISF2ze/duiWTVp//OTpw4Ue6yMQKBoNRkLK2+kkYJLJZRAksx//zzD0aMGFGlc6qrq6NXr16Ii4uTaK/IfuPY2FjMmzePee3r64tu3bqxEiepHT4lswQCAWJiYko9dbNt27bMyqwOHTp888mXitbL0tXVhbOzM3g8HoyMjKTef/LkCU6fPo3g4GBkZWVVOL4OHTqAz+fD2dkZ2traFR6HEEIIIdVDLBbj999/x5EjR+TqHxsbCy0trUqOitRFIpEIs2fPRnJyMoCSkjEHDhxAx44dYWpqWq6xdu/ejWnTpkm1t2zZEoGBgbT66guUwGIZJbAUV95/9IpwdnbGiBEjsHjxYrx48ULivY0bN8LBwUHusXJzczFkyBCmsN+gQYOwcuVKVuMltUt+fj6SkpIgEAgQGxtb6na89u3bg8PhMMmsb116ejpCQ0Nx7ty5ctXL6tSpE3g8HpycnKSSTIWFhYiLi0NAQACSk5MrfOKLiooK+vfvDzc3N/Tu3ZtWRxJCCCE1QF5eHuzt7eUuXVDeEiCElFd2djbGjBmD//77D0BJwunQoUM4cOAAjh49Kvc4ixcvxsaNG6XaV6xYgcGDB7MWb21BCSyWUQJLcYGBgfjpp58qdY5WrVphw4YNaNeuHSZNmoR79+5J9Slvocdt27bB19cXQEnhvYCAADRp0oSliEltl5eXh/Pnz0MgECA+Pr7UGzRDQ0OmAHz79u2rOMryKS4uxqVLlxAaGlquelnKysqwsbGBi4sL+vfvj3r16km8n5GRgcDAQAQGBuL169cVjk9XVxc8Hg98Ph9t27at8DiEEEIIqTxPnjwBn8+Xq+/48eMxZ86cSo6IkBLp6ekYP348s6PC2NgYr169KtcDXBsbG5k12qKiouhvSRkogcUySmAp7s2bN7Czs6vUOVJTU5nC8efPn5d6f8iQIVi+fLnc4/37778YOXIkRCIRAMDb21vuX7SEfCk3N1cimZWXlyezn5GREZPM+tYTMBWtl6WpqQkulwsej4fu3btLPE0tLCxEQkICAgICcP78+QqvygKAnj17ws3NDRwOBw0aNKjwOIQQQghhz9mzZ7FixQq5+oaEhKB169aVHBEhkuLj4+Hl5aXQfagsy5Ytw7Bhw1gdszagBBbLKIHFjhkzZjB7itlmaGiIkydPYu3atQgICJDZZ//+/ejRo4dc44nFYnh6eiI1NRUA0KNHD+zduxfKyspshUzqsNzcXCQkJEAoFCI+Pp453fJLHTt2ZGpmtWnTpoqjLJ+K1svS19dn6mXp6+tLvPfs2TMEBQUhMDAQr169qnBsGhoa4HK5cHd3R7du3Wj7ASGEEFINiouLMX/+fKkataW5ePEilQUg1ebo0aPYtGkTq2N27NgRx48fp3vRL1ACi2WUwGLH6dOn8fPPP1fK2N27d0fPnj2Z7X5fatWqFUJCQuT+YXHmzBmsWrUKQMnWp2PHjqFjx45shUsIIzc3F/Hx8Uwyq6CgQGa/Tp06gcvlwsHBAQYGBlUcZflUtF5Wz549wePx4ODgIFGgtaioCOfPn4e/vz8SEhIUehrWtm1b8Pl88Hg86OrqVngcQgghhMjv/fv3cp8c2KxZM4SFhVVyRIR8nVgsBofDUejAIVnoQDBplMBiGSWw2JGdnQ0OhyP3NiM2eXp6yjwJQpZ3795h0KBByM7OBlDy///PTyEkpLJ8/PgR8fHxEAgESEhIQGFhocx+nTt3ZpJZX65a+pZUtF6Wuro6+vfvDx6PB2tra4mnr8+fP2dqZZUnOfalTzW5Bg0aBBsbGzoNhhBCCKkkt27dwpgxY+Tqu3PnTvTq1auSIyKkbGKxGCNHjsQ///zD6rg8Hq/Sa0PXNJTAYhklsNgzffp0XLhwocrnDQoKknvVyrp163Dy5EkAJU+AAgICqH4OqXIfPnxAXFwcBAIBEhMTUVRUJLPf999/DwcHB3A4nG+6RkRF62Vpa2vDyckJPB4PnTp1YtpFIhESExMREBCA+Ph4hVZlaWtrw8XFBW5ubjXiREhCCCGkpvD19cW2bdvk6nv+/HnUr1+/kiMiRD6JiYmYPXs2K2MNHz4cfn5+AEoe1IaHh6Nx48asjF0bUAKLZZTAYs+JEyewYcOGKp2zW7dupW4t/NKNGzcwbtw45o/h3377Dfb29pUYHSFly8nJYZJZSUlJpSazjI2NweFwwOFw0LJlyyqOUn4VrZdlZGQEHo8HJycn6OnpMe0vX75kVmU9f/5codhMTEzA5/PB5XKhqamp0FiEEEJIXVVUVIRhw4bh0aNHcvVPS0ur5IgIkZ9YLMbEiRNx9epVVsYLCQnB4sWLcfv2bQDA/Pnz5V6VWBdQAotllMBSTGFhIY4cOYK4uDhcuXKlyue3t7fHb7/9VmY/kUgEDw8P5g9qGxsbbNu2jYrskW9KTk4OYmNjIRQKv5rMMjExAYfDgYODA1q0aFHFUcqvIvWylJSUYG1tDR6Ph/79+0NDQwNAyb/h5ORkBAQEIDY2VqHtyvXq1QOHw4GbmxvMzMzo5wAhhBAip9evX4PD4cjVd9KkSZg5c2YlR0RI+Vy6dAmenp4AAFVV1VLvt+WVmpqKwMBAph50mzZtcPr0abq//H+UwGIZJbAUExQUhDVr1lRrDN7e3uDz+V/t8/nqMHV1dZw6deqbri9ESE5ODqKjoyEUCpGcnAyRSCSzX7du3ZiaWc2aNaviKOVT0XpZDRo0gIODA3g8HkxNTZmTQl+9eoXg4GCcPn0az549Uyg2fX19pvB78+bNFRqLEEIIqc3Ks+3q3Llz9HuVfJM+L3vD4/Fw5swZhcY7evQo2rRpg4EDB+LDhw8AgD///BMWFhYKx1obKJJvUa6kmEgd1rRp0+oOAWvWrMH9+/dLfT8zMxN//PEH83rSpEmUvCLfPC0tLbi5uWH79u2IiIiAt7c3evfuDRUVFYl+165dw6ZNm+Do6IiJEyfi2LFjePXqVTVFLZuysjIsLS2xZs0aRERE4Ndff4WNjQ2TkCrNx48fERwcDE9PT7i6umLnzp149OgR9PT0MGnSJAQHB2PHjh2wt7eX+r7I6+nTp9ixYwecnZ0xe/ZsCIXCUk+LJIQQQuqqtWvXyp28Sk1NpeQV+SbduHGDSV4pKSmV+oC4PJKTk9GgQQO4uLgwbXTSJjtUy+5CSPn069cPfD4fQUFB1RrH9evXYWhoKPO933//ncmGt2nTBuPGjavK0AhRWOPGjcHn88Hn8/HmzRvExMRAIBDg4sWLElvprly5gitXrmDTpk3o0aMHuFwu7OzsJGpKVTcNDQ04OjrC0dGxXPWy/vvvP+zduxd79+6FiYkJeDweuFwurK2tYW1tjczMTISEhCAgIAAZGRnljkssFiMxMRGJiYlo3LgxnJ2d4e7ujo4dO1b0oxJCCCE1XkFBgdwnB+rq6kIgEFRyRIRU3OcrgExNTXHu3DmFx8zMzARQcpL4J3l5eQqPSyiBRSrJ7Nmzce3aNTx48KDK57a2toaOjk6pxdgvXrwokQFftmwZ1NXVqyo8QljXpEkTuLu7w93dHdnZ2YiOjoZAIEBKSgpzQIFYLMbly5dx+fJlbNy4EaampnBwcICDgwN0dHSq+RP8j46ODkaPHo3Ro0eXq17W9evXcf36dfz222/o378/eDweevfujQkTJmDcuHFISUmBv78/YmJiKlTX4O3btzh27BiOHTuGLl26wM3NDc7OztDS0qroRyWEEEJqnEePHmHQoEFy9d25c6fciS5CqktWVhbzdWpqKqtj/vfff0xbq1atWBm7rqMaWDJQDSx2FBYWQiAQ4PDhw7h3716VzBkREQFtbe1S3y8oKMCIESOYE1K4XC7Wr19fJbERUtWysrIQFRUFoVCIS5cuQdaPeyUlJZiZmYHD4cDe3v6r/36qS0XrZTVp0gQDBw4Ej8fD999/DyUlJWRlZSE4OBgBAQF4+vSpQnGpq6vDzs4O7u7uMDc3L3P7IyGEEFKTBQYG4qeffpKr74ULF6CmplbJERGiuDt37mDOnDnMqik2WFhY4M8//8SqVauYelorVqzA4MGDWZujJqMi7iyjBBa7xGIx1q9fj5MnT1bqPAcPHoSJiclX++zduxc7d+4EUFIM+vTp09/UVipCKsvr168RGRmJiIgIpKamlprMMjc3B5fLxYABA77JZFZubi5iY2MRGhqKpKQkuU8eNDQ0BI/Hg5OTE5o3b47i4mKkpqbC398fUVFRCp8207JlS7i6usLNzY2esBFCCKlVxGIxPD095V6dkpaWVskREcKuN2/eYPbs2bh58yYr4xkZGcHPzw+TJ09m/j3QisT/oQQWyyiBVTlMTU0rbWx5jibNyMjAkCFDmGLMixYtwg8//FBpMRHyrcrMzGSSWWlpaTKTWcrKyrCwsGCSWU2aNKn6QMtQnnpZnygpKcHCwgI8Hg92dnZo0KABsrKycObMGQQEBODx48cKxfRpfHd3dwwYMAD16tVTaDxCCCGkOr1//x79+vWTq6+HhwfmzZtXyRERUjkyMjLg6urKylja2tqIiIiAi4sLs43w9OnTaNu2LSvj13SUwGIZJbAqx+7du7Fnz55KGXv69OmYMmVKqe+LxWLMnTsXCQkJAIBOnTrh8OHDUFWlMnCkbnv16hWioqIgEAhw5cqVUpNZVlZW4HA4GDBgABo3blwNkX5deeplfaKhoQF7e3vweDyYmZlBWVkZaWlpCAgIQEREBAoLCxWKSVNTE87OzuDz+ejcufNXE+yEEELIt+batWsYP368XH3Pnj2LFi1aVG5AhFSiPXv2YPfu3ayMpaSkhOTkZFhbWzO7BZKSkujB5v+jBBbLKIFVOcpT9LG8QkJC0Lp161Lfj46OxoIFCwCU/EDx9fUtc7shIXXNy5cvERkZCYFAgKtXr8rso6KiIpHMatSoURVH+XUVrZfVrFkzuLi4wMXFBYaGhnjz5g1CQ0MREBDAymEUHTt2hLu7O5ycnL7J1WyEEELI53bt2oW//vpLrr6pqan0kIbUeDt37sTevXtZG+/w4cPw8PAAUHJIkVAoZG3smo4SWCyjBFblKCwshJWVFevjmpqafvWHzcePHzFkyBC8ePECADBkyBAsX76c9TgIqU1evHiBiIgICAQCXL9+XWYfVVVViWTWt3YiX0XrZRkbG4PH44HL5aJJkya4cuUKAgICIBAIFF6VpaqqCltbW7i7u8PKygoqKioKjUcIIYSwSSQSgcvlIjs7W67+VO+K1BaZmZng8/lyP/wsy5IlS7BhwwYAQNeuXXHo0CFWxq0NFMm30JFJpMpU1kkkLi4uX31/z549TPKqadOmmD17dqXEQUht0rx5c4wePRoHDx5EaGgo5s2bh65du0r0KSoqQmJiIlavXg17e3vMnTsXZ86cQU5OTjVFLUlDQwOOjo7Yvn07wsPDsWDBAnTu3LnM627evIkNGzaAy+Vi/vz5yMrKwsqVKyEQCLBo0SIYGhpWOKaioiJERERg1qxZcHFxwc6dO/HkyZMKj0cIIYSw5eXLl7CwsJAreeXj40PJK1Kr6OrqMiumACi8qvDzgvAtW7ZUaCzyP7QCSwZagcWed+/e4caNG7h+/Tpu3LiBxMRE1ueIjY0tdeVHeno6fvjhB4hEIgDA6tWr4ebmxnoMhNQVGRkZiIiIQERERKkntaiqqqJ3797gcrno168fNDU1qzjKr6tIvSwtLS04OjqCx+Oha9euuHbtGvz9/SEQCJiDIRRhZmYGPp8Pe3t7aGhoKDweIYQQUh6RkZFYtGiRXH2Tk5Ohrq5eyRERUvVycnLg4ODArLifPn06Tpw4gaysrHKP1b59e6YMxdixY+Hl5cVmqDUabSFkGSWwKq6oqAhhYWG4cOECrl+/rvCJXp/T09PDq1evpNpLe/ojFosxefJkXL58GQDQs2dP7N27l/boE8KSjIwMCIVCRERE4NatWzL7qKmpwcbGBlwuF3379kXDhg2rOMrSVbReVps2bcDj8eDs7AwtLS2cPXsW/v7+SE9PVzimhg0bYuDAgeDz+ejatSv9vCKEEFLpli5dCoFAIFdfWnVFaqvc3FwsXboU8fHxTJu3tzd69+6NH374oUJJrE+WLl2K4cOHsxFmrUAJLJZRAqviDh8+jC1btlTpnKX9Ig0ODsbq1asBlBSePnbsGIyMjKowMkLqjqdPnzI1s+7cuSOzj7q6Ovr06QMul4s+ffqgQYMGVRxl6SpaL8vMzAw8Hg/29vZ48OABsyorLy9P4ZgMDQ3B5/Ph4uICbW1thccjhBBCPpefnw9ra2u5+jo5OWHt2rWVHBEh1SM7OxteXl4SdV95PB5WrVoFVVVVvHr1CgMHDqzw+Fu3bkXfvn3ZCLVWoAQWyyiBVXG7d+/Gnj17mNcqKiro1KkTdHR0JLLZbNHX10dwcLBU+9u3bzF48GBmDz8t2ySk6jx58gRCoRACgQD37t2T2adevXro06cPOBwO+vbt+01tm3v9+jXCwsIQGhpaajLuS/Xq1cOAAQPg6uqK77//HuHh4fD398c///yjcDwqKiro168f+Hw+evfuDVVVVYXHJIQQUrelp6fLvSIkKCgIBgYGlRwRIdXjw4cP8PDwwMOHD5m2CRMmYNasWRIr4U1NTSs8h5+fHy2k+AwlsFhGCayKu3//PoYOHcq8DgkJQevWrfHw4UMMHjyY9fk0NDSQkJAgtc1m7dq18Pf3B1BSjNrf3/+bWu1BSF3x6NEjJplV2ha7+vXrM8msPn36fFPJrIrUy9LV1YWzszN4PB7y8vIQEBCA8PBwVk610dHRAY/HA5/PR7t27RQejxBCSN1z9OhRbNq0Sa6+qamptJ2d1GpRUVFYuHAhgJLC7YsWLcLIkSMl+ojFYpiZmVV4joSEBPpb9DOUwGIZJbDKJhKJEBERgby8PDg5OUkUcpw4cSKuXLnCvC6tdhVbvizifv36dYwfPx6f/tPevHkzBgwYUGnzE0Lk8+DBAwiFQgiFQvz7778y+9SvXx99+/YFl8uFjY0N6tevX8VRylbRelmdOnUCj8dDv379cOHCBQQEBMi9qqss3bt3h7u7OzgcDt0UEUIIKZNYLMaIESPkrtlI9a5IXZCWlobJkycDADp27IgTJ05I9VFkC6G2tjYiIiIUirG2USTfQvsQSLmJxWKsXbsWgYGBAIAzZ87g999/Z5JIenp6Ev0rM3kFAC9evGDmLioqwq+//sokr/r06QNbW9tKnZ8QIp/27dvD09MTnp6euH//PpPMun//PtMnLy+PadfQ0EC/fv3A5XLRu3dv1KtXr9piV1ZWhqWlJSwtLbF06VK562XdvXsXd+/exZYtW9C7d2+MHz8ezZo1Q2hoKM6ePavQqqyrV6/i6tWr2LhxIzgcDtzd3dG9e3d6Uk4IIUTKu3fv5L4nXrVqFdzd3Ss1HkK+FZ+vaH/69ClEIhFUVFQk+ihSEqJr164VvpZIowQWKRexWIwtW7YwySugZGnxxIkT8ccff6B58+Zo2bKl1HUqKipo06YNc5Qom169esXsKT558iTu3r0LoKQmzZIlS+iPOUK+QYaGhpg6dSqmTp2K9PR0pgD85/UHcnNzER4ejvDwcDRo0AD9+/cHl8tFr169qjWZpaGhAUdHRzg6OspdL6u4uBgJCQlISEiApqYmuFwufvvtNzx//hynT5/GzZs3KxxPbm4ugoODERwcjDZt2sDd3R0uLi5SDxMIIYTUXZ8ONipLXFwcNDU1KzcYQr4hTZs2hY6ODl6/fs3cUw0aNEiiT2k1XeXRqlUrRUMkn1Gu7gBIzbJnzx78/fffUu3//vsvxo0bJ3NJspGREc6fPw9/f384ODiwHtPTp08BlCSyduzYwbRPnjwZrVu3Zn0+Qgi7jIyMMG3aNPj7+8PPzw9TpkxBmzZtJPp8/PgR586dw7x58+Dg4ICVK1ciLi4OBQUF1RR1CR0dHYwePRpHjx6Fn58fxo0bh2bNmn31mvfv3yMgIACzZs3CgQMHYGNjg99++w3Dhg1TeCvg48ePsW3bNjg5OWHu3LmIiopCYWGhQmMSQgip2UQiEWJiYsrsl5aWRskrUucoKSnB3t6eef3LL7/A19dXok9ppS/kIW8NVSIfqoElA9XAku3Lgo8DBgxA//798fPPP0MkEgEANDU1YWhoiGvXrjH9VFRUcO7cOejq6mLq1KlISUlhPbbY2FisXbsWAoEAANC2bVucOHFCojYXIaTmEIvFSE9Ph0AgQHh4OJOo/pKmpiZsbW3h4OAAa2trqKmpVXGk0ipaL6tHjx7MDZRAIJA4ylkRTZs2BY/Hg6urK52AQwghddCLFy/g5ORU6vvff/+9zAfUhNQVz549w5gxY/DmzRumLSIiAtra2gAAV1dXZGRkVGhsDQ0NJCYmshFmrUFF3FlGCSxpQUFBWLNmDfO6V69e8PHxgbq6OpKTk7FgwYKv/pE2a9YsTJgwQaHTG8pj9+7dsLS0rJK5CCGVSywW4969exAIBBAKhV9NZg0YMAAcDgdWVlbfRDIrNzdX7npZn6ipqcHW1hadO3fGf//9h7CwMLx//56VeLp27Qo+nw8ulytx+AUhhJDa6/Lly5g0aZLM9w4cOIDu3btXcUSEfHtiY2Mxb948ACVJp6ioKNSrVw+FhYWwsrJSaOxTp07B0NCQjTBrBSriTiqVQCDATz/9xLzu3r07Nm/ezKxu6tWrF/bt24fZs2fj9evXMscICAiosnosjo6OlLwipBZRUlJCp06d0KlTJ8yaNQt37txhklnPnj1j+r1//x4hISEICQmBlpYWBgwYAC6XCwsLi2pLZlWkXlZhYSFTyF5bWxsDBw5EvXr1cP36dYVXZd24cQM3btzApk2b4ODgADc3N5iZmUFZmSoKEEJIbfV5iY3PXbhw4Zt42EPItyAyMpL52snJiam3euTIEYXHjo+PpwQWS2gFlgy0Aut/EhMTMW/ePBQVFQEAvvvuO/z1118yn9xnZGRg1qxZePToUVWHKWHp0qUYPnx4tcZACKl8YrEYt27dYpI9//33n8x+jRo1gp2dHbhcLszNzaGqWv3PbtLT0xEaGopz587JXRvByMgInTt3Rl5eHpKTk1lbldW6dWu4ubnBzc0NzZs3Z2VMQggh346kpCTMnDlToi0tLa2aoiHk25OTkwMul4v8/HwAwOHDh2FsbIyHDx9i8ODBCo+/YMECjB49WuFxagvaQsgySmCVSE1NxaxZs5h/yO3atcPevXuZvcCyvHnzBnPnzmWtdktpduzYIfWL+BMlJSXMnTsXHh4edAIhIXWEWCzGzZs3mWTW8+fPZfZr0qQJk8wyNTWt9mRWReplKSkpwdTUFPXr18fr16+/uprrS6qqqlBRUWF+rn85rrW1Ndzc3GBra0s1BAkhpBYpLi6GkpIS3RsTIoNAIMDSpUsBlCzYOHbsGPLy8uDh4YH79+8rPL6/vz/at2+v8Di1BSWwWEYJLODatWsYP34887ply5bYv3+/XE/nCwoKEB0djdWrV8v8I6mqDBs2DIsWLar2P1AJIVVLLBbjxo0bEAqFEAgEpa5watq0Kezt7cHhcGBqagoVFZUqjlRSReplNWjQAB06dEBhYSGePn0q96qsRo0aQU1NDR8/fpSZNGvUqBGcnZ3h7u6O7777rtyfhRBCCCGkpvi83rOrqytWr16N5cuXIywsTOGxW7VqhZCQEEoef4ZqYBFW3b17VyJ5BZQURZd3a4m6ujo6dOhQ5cmrbdu2Yd++fbh69SoA4OTJk3j27BnWr1+Phg0bVmkshJDqo6SkBBMTE5iYmMDLyws3btxgama9evWK6ZednY1Tp07h1KlT0NbWhr29PRwcHKotmVWRelkfP35kVrxqa2ujdevWyM/PR2Zm5lfnevfuHQBAS0sL+vr6UFFRkdj+/e7dOxw/fhzHjx9Hly5d4ObmBicnJzRq1IiFT0oIIYQQ8u34fNV5QUEB/Pz8WEleAcCAAQMoecUiWoElQ11egZWeni5VP6pFixY4e/ZsucYpLi7GsmXLIBQK2Qzvq9LS0pCfnw9vb28IBAKm/bvvvsO2bdvQrFmzKouFEPLtKS4uxrVr1yAQCBAREVFqkkdbWxsODg7gcDjo0aNHta/Mqki9LE1NTRQVFSE/Px/y/JpXUlKCkZER6tevj5cvX+LFixdSfdTU1GBnZwd3d3dYWFhQ4XdCCCGE1AqRkZFYtGgRAEBXVxdv3rxhakAras6cOVKLQ+o6RfItdPdJGA8fPpRZ/HzTpk3lHuvNmzdwcXHBuHHj2AhNLnfv3kW9evXw66+/YuLEiUz7vXv34OHhgXv37lVZLISQb4+ysjJ69OiBxYsXIywsDHv37sXw4cOho6Mj0S8rKwt+fn6YMmUKnJycsGHDBly+fFmuLX2VwcjICHPnzsXZs2exe/duuLq6QkND46vXvH//Hnl5eXIlr4CSbZf//PMPrl+/DhUVFVhZWcHS0lLidKrCwkKEh4dj+vTpcHV1xe7du5GRkaHQZyOEEEIIqW6fr8DKzMxkLXkFoFy1SknZaAWWDHVxBdbjx4/h7u4u8z1TU1P89ddfpS59zM/Px927d5kj3m/cuCFxtH1VmjFjBsaPHw9VVVUEBQXhl19+gUgkAlCyPWfDhg3o06dPtcRGCPk2iUQiXL58GUKhEJGRkcjKypLZT1dXFxwOB1wuFyYmJtW6Aqki9bLKS1VVFVZWVtDU1MSjR49KvQGzsLDAoEGDYGtri/r167MeByGEEEJIZbp48SKmTZsm0aapqSlRW1RPT0+iFIW8VFVVcfbsWejq6iocZ21BRdxZVhcTWB4eHrh58ybzWktLCx8+fGD+KPL29gafz4dYLMbjx4+ZRNWNGzdw7949VrPUivr++++xbt06GBgY4MKFC1i4cCE+fPgAoGQFxtKlSzF06NBqjpIQ8i0SiURIS0tDREQEIiIikJ2dLbNfs2bNwOFwwOFwYGJiUq21DeStl6WIDh06wMTEBEVFRYiLi2NqaH1OU1MTTk5O4PP56NKlC9V7IIQQQkiNkJqaiilTpki0rV+/njmZUElJSe5V7bJ4enpKJcjqMkpgsawuJrDGjRvHFALu2bMn9u3bBx8fHxw6dAhASULLxMQEN27ckPmHy5fU1NTQuXNnppBys2bNMGnSpEr9DF9atmwZhg4din///Rdz5szB8+fPmfc8PDwwd+5cquFCCClVUVER0tLSmJVZb968kdmvRYsWTDLL2Ni4WhM3FamXVR4aGhqwt7eHrq4u7t27h6SkJJk3dEZGRnB3d4eTkxOaNm3KehyEEEIIIWzZtGkTjh49yrweP348HBwcMGbMGFbG19bWxrlz5yRKM9RllMBiWV1MYKWnp8Pf3x9WVlawtbUFULJFZdiwYXJtBzQwMGCSVV27dsV3330n9Q+0T58++PjxY2WEXyobGxusWrUKAODl5YXbt28z79nb2+Pnn3+mLS+EkDIVFRUhNTUVAoEAUVFRePv2rcx+LVu2BIfDgYODQ7Ums4qLi3Hp0iWEhoYiIiICubm5rM9hbGyMvn37oqCgAOHh4TLrYamqqsLW1hZubm6wtrau9oL4hBBCCCGfy87Ohr29vUTbxYsXIRAIsGLFCgAlZSTKOuG5LL/88gucnZ0VGqO2oAQWy+piAqs0CQkJmDNnjkRbo0aNYGJiAmNjYyZh1bhx4zLHWr9+Pfz8/Cor1FI1atQIP/74I/r27Ytly5YhLi6Oea9r167w8fGBtrZ2lcdFCKmZCgsLcenSJSaZlZOTI7Nfq1at4ODgAC6XW61b6iq7XpampiZ4PB4MDQ1x9epVREREID8/X6qfnp4e3Nzc4ObmBgMDA1ZjIIQQQgipiBUrVuDs2bPMaz6fD29vb+zcuRN79+4FAFhbWyMpKUmheYyNjXHo0CEqsQBKYLGOEliSYmJicO3aNaYGioGBQbn/4b148QKDBg1CXl5eJUVZNkdHRyxatAh79+7FsWPHmPZWrVph+/btaN++fbXFRgipmQoLC5mndNHR0RLFPj/XunVrpgB8p06dqu3mpbLrZZmZmcHJyQmFhYUIDQ3FjRs3Su3n5uYGBweHMk9UJIQQQgipLMOHD0d6ejrz+scff8TQoUOxaNEiREZGAijJD7CRF/D19UW3bt0UHqemowQWyyiBxS6xWIyZM2ciOTm5ukOBnp4e1qxZg4cPH+K3335jardoampi8+bNsLCwqOYICSE1VWFhIZKTkyEUChETE1NqMktfXx9cLhdcLhcdO3astmRWZdbL0tbWhru7O7p3745Lly7hzJkzMgviN2jQAAMHDgSfz6/2YviEEEIIqXv27NmD3bt3M6+3bNmC/v37Y+jQobh//z4AYM2aNfD29lZ4roEDB2LdunUKj1PTUQKLZZTAYo9YLMbq1asREhJS3aFIGD58OExNTbF69WpmVZiqqipWrlwJV1fXao6OEFLTFRQUIDk5GREREYiOjmZOQv1SmzZtwOVyweFwYGRkVC0JnMqsl6WkpAQbGxu4u7ujuLgYISEhSEhIkFn4vX379nBzcwOPx4OOjg5rMRBCCCGElCYzMxNcLpd5/WkF1ucrs5ydnSW2GVaUiooKwsLC6vx9DiWwWEYJLPbcuHEDY8eOre4wZGrTpg1GjRqFv/76C69fv2baPT09MXXqVFoJQAhhxadklkAgQExMTKmHWbRr1w5cLhcODg4wMjKq4ihLVGa9rJYtW2Lw4MGwsbFBcnIyAgMD8fjxY6l+Kioq6Nu3L9zd3dG7d2+oqqqyFgMhhBBCyJdMTU2Zr62srLBr1y4cPXoUmzZtYn0u2kZICSzWUQKLPTdv3oSHh0d1h1EqZWVluLi44Nq1a3j06BHT7uLigpUrV0JdXb0aoyOE1Db5+flISkqCQCBAbGxsqaudDA0N4eDgAA6Hgw4dOlRxlCUqq16WiooK7OzsMHToUKiqqiIoKAhCoVDm90JHRwcuLi7g8/lUp5AQQgghrBOJRBJlZJSVlREREQEtLS1MnjwZV69eZXW+qKgoNGnShNUxaxpKYLGMEljsOnjwILZu3Vrl8yopKcncpiKLvr4+CgoKJOrAmJmZYfPmzWjUqFFlhUgIqcPy8vJw/vx5CAQCxMfHl5rM6tChAzgcDjgcTrUlcSqrXla7du0wdOhQ2NnZ4cKFCwgKCsKVK1dk9u3WrRvc3d3B4XDQsGFD1mIghBBCSN2Vl5eH3r17S7R5eXlh7NixePToEYYNG4aioiJW5mrVqhXOnDnDylg1GSWwWEYJLHZlZ2fD3t6+SuYyMTGBmZkZzM3N0b17dzRs2BBBQUH46aefykxmKSsrS22Xadu2LbZv3w59ff3KDJsQUsfl5uYiMTERQqEQ8fHxpZ7YamRkxJxm2LZt2yqOsvLqZdWrVw9cLhfDhg2DpqYmgoKCcObMGYnt3Z9oaGjAwcEB7u7u6NGjB233JoQQQkiFicVi9OrVC4WFhUxbu3bt4O/vDyUlJcyfPx8xMTGszGVvb4/ffvuNlbFqMkpgsYwSWOz5cklmZTI0NMSpU6dkvnfhwgUsXLiw1ELKX9O0aVNs2bKlzu9VJoRUjdzcXMTHxyMiIgLx8fHIz8+X2a9jx45MAfg2bdpUcZSVVy+rc+fOGDp0KDgcDi5fvoygoCDExcVBJBJJ9TUwMIC7uzt4PB709PRYmZ8QQgghdQuXy0VmZqZE2759+9CzZ08EBQVhzZo1rMwza9YsTJw4kZWxajJF8i3KlRQTIQDAynGj8srJySn1PSsrKxw4cADNmzcv97jZ2dnw9PSEUChUJDxCCJGLhoYGuFwuNm7ciMjISKxfvx729vZSNfn++ecf7NixA+7u7vjhhx9w4MABPHnypErjdHR0xPbt2xEeHo4FCxagc+fOCo97584d/PLLL3B2dsb58+cxffp0hIeHY968eTA0NJTo++TJE2zfvh2Ojo6YM2cOIiMjJZ6gEkIIIYSUpXHjxlJtp0+fBgBWD5P5/vvvWRurrqIVWDLQCiz2fH6iQ1W4ePHiV3/IvHr1CnPnzpVZkFhTUxMfPnz46lbDOXPmYNy4cbRlhRBS5T5+/Ij4+HgIBAIkJCSUmqjp0qULUzOrdevWVRxl5dTL6tGjB4YNGwZ7e3vcu3cPQUFBCAsLk7mqtmnTpnBxcYGbm1u1neZICCGEkJpj8uTJSEtLk2irV68eBAIBzp8/j2XLlrEyDxVwL0FbCFlGCSx2ZGVlwcHBoUrnPHfuXJmrrHJzc7F06VLEx8dLvWdqaoqXL1/i6dOnpV4/ZMgQLFmyhI52J4RUm/fv3zPJrMTExFKLixobGzOnGbZq1apKY6yMellNmjSBm5sbhgwZAl1dXURGRiIoKAipqaky+xsbG4PP52PgwIHQ0tJSeH5CCCGE1D6LFy9GRESEVPvy5cvRqFEjLFmyROE56tevj/Pnzys8Tm1AWwjJN0lFRaXKVyq9ePGizD4aGhr4/fffMXLkSKn30tLSYGhoCDc3t1Kv9/f3h5eXF96/f69QrIQQUlGamppwcnLCli1bEBkZiZ9//hl9+/aVSqzfvHkTW7duBY/Hg4eHBw4dOoT//vuvSmJUVlaGpaUl1qxZg4iICPz666/o3bu3Qr8X3rx5g0OHDoHP52PhwoVo2LAhdu3ahaCgIEyePBnNmjWT6H/z5k38+uuv4HK5WLFiBVJSUlir1UUIIYSQ2sHa2lpm+/379yXurTQ1NSs8h5WVVYWvJf9DCSxSaRo3bgwfHx9MmDAB48ePr5I55UlgASXJtcWLF2PhwoVS78XFxSEjIwPr16+Hrq6uzOvPnz+PiRMnyj0fIYRUFi0tLbi4uGDr1q2IjIzEmjVr0KdPH5nJLB8fH7i4uGDcuHH4+++/8fz58yqJ8VO9rD/++APh4eGYP38+OnXqpNCYSUlJWLBgAVxcXBAaGoqhQ4ciNDQUf/zxBzgcjsTnz8/Px9mzZzF16lTw+Xz89ddfVfbZCSGEEPJtGzhwIBo0aCDVrq6uDhUVFeZ19+7dKzxHdZR1qI0ogUUqVd++fTF79mzcvHmz1D5mZmaszVfeeiujRo3C77//LvWHXmpqKvbs2cP8ISRLeno6PDw8ZNbTIoSQ6qClpQVXV1ds27YNERER8Pb2ho2NjcTNFwBcv34dv//+O5ydnTF+/HgcPXqUtXpVZdHV1cWYMWNw7Ngx+Pn5Ydy4cVIrp8rj1atX2LNnD5ydnbFkyRIoKytj3bp1EAgEWLRokVQdrIyMDOzatQsuLi6YOXMmBAIBCgoKFP1YhBBCCKmhGjRoABcXF6l2kUgkcQ+lyP3CpUuXKnwt+R+qgSUD1cBiV2pqKjw9Pb9aHJ0tHh4emDdvXrmvu3HjBry8vJCVlSXRrquriz/++AMPHjzA2rVrZW4b1NDQwPr169G3b98Kx00IIZXp7du3iImJgVAoxIULFyASiWT269GjB7hcLuzt7aGnp1dl8YlEIqSmprJWL0tfXx9DhgwBn89H48aNcefOHQQFBeHs2bMyf443atQITk5OcHd3V3hlGCGEEEJqnn/++QcjRoyQaudwOMxp9M2aNVPogZ889ZrrAirizjJKYLHnw4cPGDFiBJ49e1Yl83E4HGzYsKFC12ZkZGDu3Lm4f/++RLu6ujp8fHxgaGiI1atXIzk5WepaJSUlLF68WOYPPUII+Za8efMGMTExEAgEuHjxosyaUEpKSujZsyc4HA7s7e1L3U5dGXJzcxEbG4szZ84gKSlJoYcfampq4HA4GDZsGLp164aCggLExMQgMDAQFy9elDl2p06d4O7uDkdHR5nHahNCCCGkdtq1axf++uuvUt83MDDAkydPKjz+8uXLMWTIkApfX1tQAotllMBiz9q1a+Hv719l8+nr6yM4OLjC1+fk5GDhwoVISUmRem/NmjXg8Xjw8/ODj48P8vPzpfqMGTMGc+fOldquQwgh36Ls7GxER0dDIBAgJSVFZkJHSUkJpqamTDJLR0enyuLLzMxEWFgYQkNDcffuXYXGMjIywtChQ+Hs7AxNTU08e/YMISEhCAkJkfmQRU1NDQMGDIC7uzssLS2hrExVFwghhJDabtWqVThz5kyljN23b19s3bq1UsauSSiBxTJKYLEjOTkZM2bMqJZ51dXVK3x9YWEhfvnlF4SEhEi9N336dEyePBmPHz/GypUrcePGDak+AwYMwC+//AINDY0Kx0AIIVUtKysLUVFREAqFuHTpUqnJLHNzczg4OMDe3h7a2tpVFl96ejpCQ0Nx7tw5hZbva2howNnZGUOHDkWnTp1QXFyMS5cuITAwEJGRkSgsLJS6pkWLFnBzc4OrqysVYSWEEEJqsSVLljBbBuvVqydz0UJF1atXD9HR0ahfvz5rY9ZElMBiGSWw2OHp6VktxepWr14NNzc3hcYQi8XYu3cvdu3aJfWeu7s7fvzxRwCAr68v/vzzT6l6MsbGxvDx8anSlQqEEMKW169fIzIyEkKhEGlpaV9NZnG5XNjZ2aFp06ZVEhub9bJMTEwwZMgQcLlc1K9fH+/evUNYWBiCg4Nx69YtmddYWFiAz+fDzs6uzt+AEkIIIbXN7t27sWfPHgAlf9N97TCyiti2bRv69OnD6pg1TbUnsEQiEY4dO4bTp0/j/v37EIlEMDAwgLOzMyZPnox69eoxfS9duoTRo0eXOparqys2bdok0fbgwQNs374dqampePPmDdq0aYPhw4dj9OjRlbKknxJY8nn27Bni4+Px8uVLZGZmIisri/m/LVq0wPXr11mfU11dvczTH6ytrbFjxw5W5jt37hyWL18u1W5jY4MNGzagQYMGuH37NlauXClVO6tVq1bYunUrOnTowEoshBBSHTIzMxEZGYmIiIhSk1nKysqwsLAAl8vFgAED0KRJkyqJja16WVpaWnBzc8PQoUPRtm1bAMC9e/eYwu9v376VukZTUxNOTk5wc3PD999/DyUlJYU+CyGEEEKqX0ZGBtzc3Fg9gExDQ4N54DZs2DAsW7aMtbFrompNYIlEIsyYMQMxMTFo0KABunfvDlVVVVy9ehXv3r1D9+7dcfDgQWY71aFDh7B27Vr07NkT+vr6UuOZmppi1KhRzOs7d+5g9OjReP/+PUxNTaGjo4MLFy7g3bt3MpNdbKAEVumKi4tx8eJFnDhxAnFxcVVysuDn5C2cl5qaytofE6mpqZgzZ47UU/4uXbpg69at0NXVRX5+Pnbs2IEjR45IfE80NTWxadMmWFpashILIYRUp1evXiEqKgoCgQBXrlwpNZllZWUFLpcLW1vbKiuEzla9LAsLCwwdOhS2trZQU1NDQUEBYmNjERwcjPPnz8v8zEZGRuDz+XBycqrSbZWEEEIIYd/s2bORmJgo1T5gwABER0eXe7xhw4bh5MmTAICWLVvizJkzdfrBV7UmsI4fPw5vb2906tQJf/31F3MsZFZWFmbMmIHLly/D09MTCxYsAAAsW7YMAQEBOHr0KMzMzL46tlgsBp/Px927d7Fx40bw+Xxm7PHjx+Pu3bvYtm0bBg4cqMhHkEIJLGnv3r3DmTNn4Ofnh8ePH1d3OGUKCgqCgYEBa+M9fPgQs2bNkir027x5c+zatQvt2rUDAKSkpMDb2xvPnz9n+qioqGDlypUKb2skhJBvycuXLxEZGQmBQICrV6/K7KOiooJevXrBwcEBAwYMQKNGjaokNjbqZeno6GDQoEEYNGgQWrZsCQB48eIFzpw5g6CgIDx9+lTqGlVVVfTv3x9ubm6wtraGqqqqQp+DEEIIIVUvLi4OXl5eUu39+/dHbGxsucfz8fHB8uXL8eHDBwCAn58fjIyMFA2zxqrWBNaIESNw5coVHDx4EL169ZJ4786dO+Dz+WjdujWioqIAAHw+H/fu3UNqaioaNGjw1bETEhIwadIkmR8sLS0NP/zwAywsLPD3338r8hGkUAKrpJD5lStXcPHiRVy8eBE3btyQ+dTZysoKPXr0gK6uLu7du8dklr8FFy5cgJqaGmvjZWVlYd68eVJbI9XU1PDnn3+iR48eAEpOMty0aZNUEfjffvsN9vb2rMVDCCHfihcvXiAiIgICgaDU7eOqqqro1asXOBwObG1toaWlVelxsVEvS0lJCX369MGwYcNgbW0NFRUViMVipKWlISgoCEKhUGaBV11dXbi6usLd3Z3VByqEEEIIqVwikQg8Hg8vXryQaNfR0cHr16/LPd7atWsRHR2NiIgIACUrvCZMmMBKrDWRIvkWhQtINW3aFIaGhujWrZvUe59WpXx6+llQUIB///0XhoaGZSavACA+Ph4A4ODgIPXep+2EqampeP/+vQKfgHzp/v37cHd3x9SpU7Fv3z5cv35dInnVsGFDjBw5EgEBAdi1axemTp2KIUOGVOgfMxt8fX1lticnJ7M6j7a2Nv7880+p/x4LCwsxceJE5geSlpYW1qxZg82bN0sUNT5//jyr8RBCyLeiefPmGD16NA4ePIjQ0FDMmzcPXbt2lehTVFSEhIQEeHt7w97eHnPnzkVoaGil/g5XUVGBpaUl1qxZg4iICPz666/o3bt3ucYQi8WIj4/HnDlzwOfzceDAAWRnZ8PMzAw//fQThEIhVqxYARMTE4nrMjMzceDAAfD5fEyePBnBwcEKFZwnhBBCSNVQUVHB4MGDpdor+vfuiRMn0LdvX+Y1/V1YcZV6CmFKSgrGjBkDAwMDRERE4ObNmxg8eDD69++PLl26QCAQICMjA7q6uhg4cCCmT58usb1g0qRJSEhIgK+vL6ytraXGHz16NC5dugQ/Pz90796dtbjr+gqszZs348iRIxJtSkpK6NKlC/h8PlxcXKQSkB8/fqzW0xQMDQ2liqiPHTtW5tJPRRUXF2Pbtm04dOiQ1HsLFiyQOKQgKysLO3fuREZGBhYuXEgF3QkhdUpGRgYiIiKYewBZVFVV0bt3b3C5XPTr1w+ampqVHpei9bJUVVVhZ2eHYcOGwdTUlKlj8e+//yI4OBihoaHIysqSuk5DQwMDBw6Eu7s7TExM6nT9C0IIIeRb9urVKzg5OaG4uJiV8fbv34+JEycCKKml/OXf23WJIvmWSivOIBaLsXXrVgAAl8sFAOZI6tjYWKSkpMDCwoI5rW7//v2IiorCsWPHmAKon1Zu6enpyZzjU3tmZmZlfYw66cttHRs3boS5uflXT5X6tEW0unyZvAJKDgyojASWsrIyvLy8oK+vj19//VXivc2bN+P58+eYN28elJWVoa2tjRUrVrAeAyGE1AStW7fGuHHjMG7cOGRkZEAoFEIoFOL27dtMn6KiIsTFxSEuLg5qamqwsbEBl8tF37590bBhw0qJS1dXF2PGjMGYMWMqVC+rqKgIAoEAAoEAhoaGGDp0KFxcXNChQwfMmzcPs2bNQmJiIgIDA5GQkMDc/Obm5iIwMBCBgYFo164d3NzcwOPxoKurWymfkxBCCCEVo6enhwEDBiAyMpKV8T6vpdyqVStWxqyLFN5CWJrff/8dKSkp0NXVxeTJkwGAuWG1tLREZGQk9uzZgwMHDkAgEMDa2hoPHz6Et7c3M8anpfb169eXOcen9o8fP1bWx6iTOnfuzHzdvXt3ODg4lHkk+qVLlyo5qv8pzwlPpqam4PP5OHjwIAoLC1mNY+jQodi+fbtU+5EjR7B06VKZNVEIIaSuat26NcaPH48jR44gODgYc+bMkfh9A5RsyY6JicGPP/4Ie3t7LFy4EAKBoFJ/zxsZGTHbGXfv3g1XV1fm5GR53L9/Hxs3bsTAgQOxZs0a3Lx5E2pqarC1tYWPjw/CwsIwZ84ctG3bVuK6hw8fYtu2bXB0dMS8efMQGxvL+u8pQgghhFTcsGHDWBsrIyOD+ZoSWBVXKQmsrVu3Ys+ePVBXV4ePjw+TcFi2bBnCwsKwa9cuiSSEtrY2NmzYgAYNGkAoFDJPQFVUVACgzCX2bC3rIyW6dOnCfH3v3j2IRKIyr/m8QO2sWbMwderUSonty7k++fKPoM89efIEW7duxeLFi1mPxcbGBsePH5c6Jj4iIgLTpk3D27dvWZ+TEEJqOn19fYwfPx5Hjx5FUFAQZs2ahe+++06iT0FBAaKiorB06VLY29tj8eLFEAgElVZHStF6WXl5eQgKCoKHhwfGjBmDwMBA5ObmQldXF+PHj0dAQABTE+vzBFlxcTFiY2Mxb948ODk5wcfHBw8ePKiMj0gIIYSQcjA3N4e+vj4rY1ECix2sJrCKioqwatUq7Ny5E/Xq1cMff/wBCwsL5n01NTW0b99eZn2L5s2b4/vvv4dYLGa2Gn66wcvLy5M536f2ytpiUFfp6elBR0cHQMkquFWrViEpKQlFRUWlXjN+/Hjs3r0bISEhmDhxIvr3719p8ZmYmEg9HT969GiZx5XL2mbIhu+++w4nTpyQOgr16tWrGD16tMQPK0IIIZIMDAwwceJEHD9+HKdPn8aMGTOkfp7m5+cjIiKCSWYtWbKkwqcKykNDQwOOjo74448/IBAIMH/+fHTq1Enu62/duoWffvoJAwcOxIYNG/Dvv/9CSUkJ3bt3h7e3N4RCIby9vdGzZ0+J67KysnDo0CEMGTIE48aNQ2BgIHPkNiGEEEKqlrKyMlxcXFgZ6/O/CVu3bs3KmHURawmsDx8+YNq0aThx4gQaNWqEffv2lTuJ8akGxKcb0mbNmgEovcbVq1evAJReI4tUnLGxMfP1uXPnMHPmTPB4PNy5c0dm/09Prj/9YyzPjX55de3aFba2thJtRUVFXz2mXF1dHT///HOlxdSsWTMcOHAANjY2Eu3Pnj2Du7s7k5QlhBBSurZt22Ly5Mnw8/ODv78/pk2bJnX4RV5eHoRCIRYvXgwHBwcsXboUUVFRpT7sUtSnelnHjh2Dn58fxo0bx9yflOX9+/c4ceIEhg0bhsmTJyM8PBwFBQVo0KAB+Hw+9u3bh9OnT2PChAlSdbCuX7+On376CRwOB97e3khLS0MlnrtDCCGEEBnYSmB9XgOLrVVddRErCay3b9/Cw8MD8fHxaNmyJY4cOSKx8uqTn3/+GTNnziz1+MmnT58CAFq0aAEA6NixIwAgPT1dqq9YLMb9+/ehoqJCJ7tVgmnTpqFNmzYSbS9fvkRgYKBc11fmyUrGxsZSx7Onp6dL/EHh7e0tkdlu0KABmjZtWmkxASUrAbds2YKhQ4dKtItEIowZMwYJCQmVOj8hhNQm7du3h6enJ06ePImTJ0/C09MT7du3l+iTm5sLgUCAhQsXwt7eHsuWLUN0dHSl1SBUpF5WWloali1bBi6Xi23btjFPYtu2bYvZs2fj7Nmz8PHxgZ2dHVNCAShJ2IWEhGDy5Mng8/nYv3+/3MXmCSGEEKIYfX19dO/eXeFxnj9/znzdsmVLhcerqxROYBUUFMDT0xM3b96EkZERjh8/LlXH4pPLly8jIiJC5ol19+7dw+3bt9GkSRNm9U/fvn0BQGbl/7S0NGRlZcHMzKxKjtyuS7Kzs3H58mWp4vmqqqpwdHSspqj+R1dXV2rV3dWrVyUSWIWFhdixYwdTm+rNmzeYOXOmzGPN2aSqqoply5Zh7ty5Uu/NmTNH7gQgIYSQ/+nQoQOmTZsGf39/+Pn5wdPTE+3atZPok5ubi/DwcCxYsAD29vZYvnw5YmNjUVBQwHo8itTLevfuHXx9feHq6orZs2cjNjYWIpEIqqqq6NevHzZt2oTw8HDMnz8fhoaGEtc+ffoUf/zxB5ycnDB79mxERERQ4XdCCCGkkjk7Oys8xqdV1Lq6uqhXr57C49VVCiewtm3bhitXrqBly5Y4fPgws3pKlhEjRgAAtmzZgn///Zdpz8rKwrJlyyASiTB58mSoq6sDKDmtsGPHjkhMTISfn59E/zVr1gAAJkyYoOhHIChJ+MTFxWHhwoUYOHAgfvvtN9y7d495v23btjh8+DB69Ogh95jKyuX7z6tXr14YNWpUmf2ys7Oltm9cuXJF4r+9V69eoU2bNti6dSvz39PTp0+xbt26csVUEUpKShg3bhw2btwo9d5PP/2E3bt30zYQQgipICMjIyaZdeLECUyZMkVqxfDHjx9x7tw5zJs3D/b29li5ciXi4uIqJZmlSL2sxMREzJs3D9bW1vjrr7+Y0gja2toYM2YMTp48iUOHDmHo0KES9T7FYjESExOxePFiDBw4EJs3b5a5Wp0QQgghiuNyuWXWW5YX1b9SjJJYgb+ks7OzYWtri7y8PBgbG0s9Kfzcpk2bUFxcDC8vL4SHh0NNTQ3m5ubQ0NDAhQsX8OHDBzg5OWHz5s0SS+evXbuGcePG4ePHj+jevTuaNWuGixcv4u3btxg+fHil1DXy8PDAxYsXYWlpicOHD7M+flXJy8tDbGwsunTpInVz/0lGRgb8/PwQGhoqc3WSuro6+Hw+vLy8ynWsOAD4+vpi27ZtcvWNj4+Huro6rKysyuz7999/o0mTJuDxeExbixYtMGnSJKxduxYA4OrqyiQ5IyMjsWjRIqbvrl275JqHDdeuXcP48eOl2l1dXbFixQqoqalVSRyEEFKbicVipKenQyAQIDw8nClJ8CVNTU3Y2tqCw+GgV69elfozOD09HaGhoTh37ly5tvz17dsXo0ePhoWFhcR2/NzcXERFRSEoKAiXLl2Sea2xsTHc3Nzg6OgILS0thT8DIYQQQkosWLAA0dHRCo/j7OyMX375hYWIai5F8i0KJbAEAgFmz54tV9+7d+8CKLnJPHHiBE6ePIn09HQoKyvDyMgIw4cPx9ChQ2XWTkpPT8e2bdtw4cIFFBQUoG3bthg5ciSGDRsmkexiS21JYC1ZsgRCoRDKysoYM2YMpk6dKpGESk1NhZeXl8wTjkxMTODq6oqBAwdW+CZYLBbDzMxMrr4DBgyQ+wfC2LFjMWPGDPTq1Uuiffny5UwCy8rKCrt27WLeW7FiBc6ePQugpK7K8ePHqyx59PTpU0yaNIl5sv6JhYUFNm/eTFtgCSGERWKxGPfu3YNAIIBQKPxqMmvAgAHgcrmwtLSstN8JIpEIqampCA0NhUAgKFd9Li8vL/D5fGY7/CdPnz5FSEgIgoOD8eLFC6nr1NXVYW9vDz6fD3Nz83KviCaEEEKIpC8XRVTUlClTMH36dBYiqrmqLYFVW9WWBNbgwYPx8OFD5nXr1q2xYsUKWFlZIT4+HosWLZLYTqGrqwsXFxe4urp+dTVdeYwfPx7Xrl1jZazPpaWlwc7ODm/evGHa5syZw6z4at++Pfz9/Zn3Xr16BXd3d+aEy3nz5sHDw4P1uErz9u1bzJ07V+p70bp1a+zfv59O0iSEkEogFotx584dJpn1+QlAn9PS0oKdnR24XC7Mzc0rLZmVm5uL2NhYnDlzBufPn5f7uh49esDLywsmJiYSD/pEIhEuXryIwMBAREdHo6ioSOraVq1awc3NDa6urlQ0lhBCCKmgf/75hymJpAhvb2/w+XwWIqq5FMm30CO5WuzLlVMZGRmYPn06Zs+ejfnz5zPJKx0dHfj4+ODs2bOYO3cua8krAMjMzGRtrM9lZWVJJX0+X1325RNpPT09jB07lnl99OjRSomrNI0bN8aePXukCgBmZGTA0dGRapcQQkglUFJSQpcuXTB37lyEhITg8OHDGDt2rFQiJycnB0FBQZg5cya4XC5++uknJCcny0wIKaKi9bKuXLmC8ePHw8zMDL6+vszKaRUVFVhbW2PDhg0QCoVYsmSJ1EE6z549w+7du8Hj8TBjxoxyrwIjhBBCCHDr1i1WxqEaWIqhBFYt1qRJE5ntiYmJEIlEAEqezO7fvx/9+vVjrTDd5z6teGJbSEiIVCF3AEzB9o8fP+L9+/dMe2FhIWJiYpjX+vr6lRLX16irq+Pnn3+Gp6enRLtYLMbw4cNLrWlCCCFEcUpKSjA2NoaXlxfOnDmDQ4cOwcPDA82bN5fo9/btWwQGBmLGjBngcrn45ZdfcPHiRdaTWbq6uhgzZgyOHTsGPz8/jBs3Dk2bNi3zum3btqFv375wdnbG7du3mfbGjRtjxIgROH78OI4ePYrhw4dLPMgSi8VITk7G0qVLweVysX79ety5c4fVz0QIIYTUVp//zlUEJbAUQwmsWuzzmhmzZs0Ch8OReN/Q0BAHDhyAgYFBpcVQWU95AwICoKOjI9H2/v17iaTW56uw/vzzT6YOm5qaGpYsWVIpcZVFSUkJ06ZNw+rVq6Xe8/T0RHh4eNUHRQghdYySkhK6du2KefPm4ezZs/D19cWYMWOkHoy8efMGAQEBmDZtGgYOHIhff/0VKSkpzEMgthgZGWHu3LkQCATYvXs3XF1dy7zm+fPnGD16NExNTbF582aJ37edO3fG0qVLIRAIsH79evTq1Uti62FOTg78/PwwatQojBw5EsePH5fYkk8IIYQQSWwlsGQtwiDyowRWLfZ5AktFRQUbNmyAj48PevbsiYEDB2Lv3r2VXnupMo4sB4AnT57g8ePHEm05OTkST9I/nfp09epVHDhwgGmfPXs2jIyMKiUuebm5uWH37t1S7cuWLYOvry+oNB0hhFQNJSUldOvWDfPnz2eSWaNGjZL6/ZidnY1Tp05h6tSpGDhwINatW4dLly6xmsxSUVGBpaUl1qxZg8TERPz666+wsLAo87ojR47A2toapqamSE1NZdrr1asHLpeLnTt34syZM5g2bRpatWolce29e/ewceNGcLlcLFmyBElJSawn6AghhJCarKioCPfu3WNlrMo4hK4uYX/PGPlmNGrUiPn67du3AIB+/fqhX79+VTJ/cXEx61suPnflyhWJ17ISWB8/fsSKFSuYhJC5uTlGjRpVaTGVh6WlJU6ePInhw4dLJKy2bduG//77D4sXL6YfcIQQUoWUlZXRrVs3JqF17do1CAQCRERESNR0zMrKwsmTJ3Hy5Eno6OjA3t4eXC4XPXr0YO3Ev0/1shwdHZGZmYmwsDDs2bNHYnu8LFOmTAEA2Nvbw9vbmznptmXLlvD09MTkyZORmpqKwMBAREZGMg+aioqKIBQKIRQK0bx5c6bwe3VsuSeEEEK+JQ8ePGBlZ9HnNZtJxdAKrFrs8xVY7969q/L5v1x91axZM4ktDGzLycmRWJL5/PlzbN68GRkZGQBKjkxfs2bNN3WceIcOHSAQCKT+QDh58iTmzJlTaTXECCGEfJ2ysjJ69OiBxYsXIywsDHv37sXw4cOltq+/fv0afn5+mDx5MhwdHbFx40ZcuXIFxcXFrMXyqV5WXFwc/Pz85DpFNzIyEv369YOpqSlCQ0OZeJSVlWFhYYG1a9dCKBRi2bJlMDY2lrj2xYsX+Ouvv+Dm5gZPT0+EhobS7yNCCCF11ucF3K2srKTeV1NTw8SJE8scp23btqzGVRd9O3/JE9Z9XsT90wqsr3n79i3S09NZu0n9MoHVsWNH2NrasjK2LE+ePJFYgXXq1CmcPn2aeb106dJv8ghxHR0dnDhxAjY2NhLtSUlJGD58OLKzs6spMkIIIUBJ0sfU1BRLly5lVkINGzYM2traEv0yMzNx/PhxTJw4EU5OTti0aROuXr3KajLLyMgI8+bNQ0pKCnbv3g1zc/Myr1m5ciXMzc0xZswYPHjwgGnX0tLCsGHDcPjwYZw4cQKjR4+WOgDm0qVLWLlyJbhcLtauXYsbN27QNndCCCF1yuf1r0xNTaUWHxQWFko9DJLly4dgpPxoC2Et9vkKrLISWE+ePMGoUaOYo7n19PTQtm1btG3bFm3atGG+btWqFdTU1OSa/8tllu3atcOQIUMQHR1dzk8in7t370oksF6/fs18zeFw4OTkVCnzskFDQwM+Pj74/fffcezYMaY9IyMD9vb2CAoKqtRi+4QQQuSjoqICc3NzmJubY/HixUhLS4NQKERkZKTEA4dXr17h6NGjOHr0KJo1awYOhwMOhwMTExNWViN/qpdlaWmJ3NxcREZGYtWqVV+95tatWxgyZAgAYOrUqRg5ciRzr9CxY0csWLAAc+bMQVxcHIKCgpCYmMgkqz58+AB/f3/4+/ujQ4cO4PP5cHZ2lkriEUIIIbXN5wszcnNzsWPHDvD5fIk+CxYsKHOc9PR01mOra5TE9BhNioeHBy5evAhLS0scPny4usOpsDt37jD1noyMjODn51dq35CQEHh7e5c5prKyMlq3bi0zufXlFsHs7GzY29szr4cOHYoff/wRpqamCnyqr/v7778xZswYibZGjRohKChIIqH3LTtx4gQ2bNgg1X7o0CF07dq1GiIihBBSlqKiIqSlpUEgECAqKqrUU/1atGjBJLOMjY1Z31qfmZmJgwcP4siRI3L119bWxvLly9GnTx+pB1QvX77EmTNnEBgYiKdPn0pdq6Kigv79+8PNzQ29e/eGqio9FyWEEFL7xMTEYP78+QBKtvWfO3cOp0+fxq+//lrusdLS0tgOr8ZRJN9CCSwZaksC69mzZ+DxeABKlisKBIJSb5SFQiGWLFmi0Hz169eHgYGBRHLry6RYWlpauRNYw4cPl0q+de/eHVevXpXqKxQKweFwJNpsbW3x+++/l2vO6hYXFwcvLy+pdh8fnyorwk8IIaRiioqKkJqayiSzSlsF3bJlSyaZ9f3337OezPrnn3+wbNky3L9/X67+Li4uGDFihFRiTSwW4/LlywgKCoJQKEReXp7Utbq6uuDxeODz+VTjgxBCSK1SWFgIJycnZGVlASg5dMvGxgbTpk1DSkpKucaKiIio86uXFcm3UA2sWkxXVxf169cHULKd7stT+z6npaXFfN2zZ08EBQVh69atWLBgAYYNGwYLCwuJAumy5OXl4Z9//kFERAT27dsnc0XXrFmzyv05ZCVyBgwYILNv06ZNpZ4Ad+7cudxzVrd+/frJfHru5eX11ZV0hBBCqp+qqiqsrKywcuVKCAQCZqvB579rAeC///7DoUOH4OHhAVdXV2zduhW3b99mrcZUx44dcerUKaSkpGDNmjVl9g8NDcXYsWNhZmaGAwcO4Pnz5wAAJSUlmJqaYs2aNRAKhVi1ahW6desmcW1mZiZ8fX0xaNAgTJo0CUFBQfj48SMrn4MQQgipTmpqanBxcWFeBwcHQ0lJCevWrSv3WLIWYRD50VrvWkxdXR1OTk5MIfPjx4+jZ8+eMvt+OmYbKNnXa2BgILPmUm5uLp48eYKHDx/i8ePHePz4MR49eoSHDx8iJyenzJjOnz9f7s9x7tw5qTZdXV18//33EidCACVbHJs1a4Znz54xbV26dCn3nN+CLl26IDQ0FHw+H0VFRUz7+vXr8fTpU3h5eX1TJyoSQgiRpqamBmtra1hbW+PHH3/ExYsXIRAIEB0djffv3zP9nj17hoMHD+LgwYPQ19dnVmZ16tRJ4ZVZKioqcHV1haurKz58+IB169bh7NmzX71m+/bt2L59O0xMTDB06FDY29ujQYMGaNiwIdzd3eHu7o4HDx4gKCgIZ86cYZ5KA8Dly5dx+fJlbNy4EVwuF+7u7ujWrVulnkRMCCGEVCY3NzdmtVBMTAyys7Ohra2Ndu3a4eHDh3KPc/To0VIXY5Cy0RZCGWrLFkKgZPvAiBEjAJQkd0JDQyUKnX/y6NEjDBo0CACgr6+P4ODgcs/15s0bPH78WCK59fDhw0opVjd//nykpKQgPj6eaWvevDnOnTuHcePG4fr160y7UCis0Sc+vH//HpMnT8a9e/ck2m1sbPD777/LXVSfEELIt6OwsBDJyckQCoWIiYmRSGZ9Tl9fH1wuF1wuFx07dmQ1CXT79m3MmDFDrpOKgZIthjweD+bm5lBRUWHaCwsLcf78eQQGBiI+Pl7mqYvt2rWDm5sbeDwedHV1WfsMhBBCSFUZO3Ysbty4AQBYuHAhAGDTpk3lHqeu18GiGlgsq00JLACYMmUKUlNTAQCTJk3CzJkzmffy8/Px8OFDREdHY8+ePQCAVq1a4cyZM6zNX5lF278UGxuLiRMn4t9//2XaasMPiMLCQqxatQrh4eES7a1atcKxY8ektqUQQgipOQoKCiSSWZ9OBP5SmzZtwOVyweFwYGRkxFoySyQS4ejRo9iyZYtc/VVVVTF69GjweDx06NBB4r3MzEyEhoYiKChI5hNpZWVl9OnTB+7u7rCxsaGHMIQQQmoMf39/rF27VuFxasPfp4qgBBbLalsCKzIyEosWLQIANGnSBOfOnUO9evXw+vVrDB06VOaT19atW6NRo0bQ0tJC48aNma8bNWpUarumpqbMm2lzc3OJp7HKysoyn85Wlt9++w1t27aFgYEB6tWrV2Xzsk0sFmPfvn3YuXOn1Htnz55FixYtqiEqQgghbPqUzBIIBIiJiSm1jlS7du3A5XLh4OAAIyMj1uZ/+fIlVq1ahYsXL8rVv2PHjhg0aBC4XK5EUVqxWIzr168jMDAQ4eHhyM3NlbpWW1sbLi4u4PP5MDQ0ZO0zEEIIIZUhJycHXC4X+fn5Co1z7tw5mbui6gpKYLGstiWwioqK4ObmxhRjXb16Ndzc3HDixAls2LCBtXmUlJSYZFbjxo2hpaUFLS0tCIVC1uZQhJKSElq0aIE2bdqgbdu2aNeuHfN1ixYtJLZDfMvCw8OxbNkyqfYTJ06gY8eO1RARIYSQypCfn4+kpCQIBALExsbKTAIBgKGhIRwcHMDlcllLBBUXFyMuLo45Nlweffv2haurK/r16wd1dXWmPTc3F0KhEMHBwaU+dTYxMQGfzweXy5Woy0kIIYR8S7y9vRESEqLwGHw+n6WIah5KYLGstiWwAMDX1xfbtm0DUHIq35EjR7BmzZoK1br6lqmpqaGwsLDc16mqqqJNmzbM/z5Pbmlra39zhWevXLmCiRMnSrXv3r0blpaW1RARIYSQypSXl4fz589DIBAgPj6+1GRWhw4dwOFwwOVy0a5dO1bmfv36NXx9fWWejluaoUOHgsfjwcTEROJ36OPHjxEcHIzg4GBkZmZKXVe/fn04ODiAz+fD1NT0m/v9SwghpG67c+cORo0apdAYXC4X69evZymimocSWCyrjQmsN2/ewNHREQUFBQCAAwcO4Mcff8R///3H9Bk2bBgWLlyInJwcvH37Fjdu3EBycjKSk5ORm5uLIUOGQElJCW/fvkVOTg7TLycnB+/evSv1Zroqde7cGXfu3GF1zIYNG6Jt27ZSya02bdpU61Pix48fw93dXaq9rmf0CSGktsvNzUViYiKEQiHi4+ORl5cns5+RkRFTM6tt27YKz1tcXIxLly7ht99+k6g1+TXa2toYMWIEnJ2d0bp1a6a9qKgISUlJCA4ORmxsrMRpu5/o6+uDz+fD1dUVzZo1Uzh+QgghhA1mZmZQJI3Su3dv/PHHHyxGVLNQAotltTGBBQBr1qxBUFAQAMDOzg5RUVES73t7e+PBgwe4efMmbt++LVV344cffmBqaclSWFjIJLM+/e9Tkmvjxo3sf6BvgI6Ojszklr6+vsT2icqSnZ0NNzc3qYK/EydOxMyZM+nJNSGE1HK5ubmIj49HREQE4uPjS63L8d133zHJLAMDA4XnzcrKQnBwMLO6Wx7dunXDoEGDYG9vL/EAKCsrC+fOnUNgYKDMxJiSkhJ69+4NNzc39O/fv0p+vxJCCCGl2bZtG3x9fSt8/ZcHq9U1lMBiWW1NYN27dw8jR46s8PWzZs2SuW1NHsOHD0d6enqF565plJSU0KpVKya51bZtW+Z/LVu2ZHWu/Px8TJ8+HVeuXJFo79u3LzZv3gxVVVVW5yOEEPJt+vjxIxISEiAQCJCQkMCsuv5Sp06dmALwiiazxGIxLl26hKNHjyI2Nlbu67hcLlxdXWFlZcX8nhKLxbh16xaCg4Nx7tw5vH//Xuq6xo0bM4Xfqe4jIYSQ6lBYWAgrK6sKX799+3bY2NiwGFHNQgksltW2BJZIJEJISAj279+Pp0+fVmiMxo0bIzg4GFpaWhW6furUqUhJSanQtfLasWMHdu3ahRs3bki09+/fH1paWhLbHT/9r7Sb+8rE5/Ph7e3N6pjFxcXYuHEj/Pz8JNpbtGgBf39/aGhosDofIYSQb9uHDx8QHx8PgUCAxMTEUutDdunSBRwOBxwOR2KLX0VkZ2cjNDQUe/bskZl8Ko2Hhwd4PJ5EQiovLw/R0dEIDAws9f7h+++/h5ubG5ycnCp8f0IIIYRUhJeXF+Li4ip0bWxsbJ3+vUUJLJbVtgRWcHAwVq9eXWY/DQ0NjBgxAsbGxtDX18eUKVOYG9DFixcrtHpry5Ytlf699PHxwcGDB3H58mWJ9n379qFnz54yr8nPz5fY7vju3TupRNenbZCf93n37l2FisUDJYXmExISoKamVqHrv8bPz09mQUChUAgdHR3W5yOEEPLte//+vUQyS1a9KQAwNjaGg4MDOBwOWrVqVeH5xGIxLl++jICAAJw9e1bu61q1aoWRI0fCyclJ4ndWRkYGQkJCEBQUhBcvXkhdp66uDjs7O7i7u8Pc3BzKysoVjp0QQgiRx+vXr8HhcCp0bWkn8tYVlMBiWW1LYAkEAixdurTMfosWLcIPP/wAANi0aROOHj0KADAwMMCpU6cUSrjs3LkTe/furfD18ujUqROeP3+Ot2/fSrSvW7cOAwcOZH2+vLw8qeTWlwXuv6wHVlRUhGHDhsHDw4P1eD5JSkqSuaf69OnTrBTxJYQQUnPl5OQgLi4OAoEASUlJpSazunbtCg6HAwcHB4W2vr99+xahoaE4deoUHj58KPd1VlZWGDRoEPr164f69esDKFlRnpKSgsDAQERHR8t8kNSyZUu4urrCzc1NoSQcIYQQUpYZM2YgOTm53NdRAosSWKyqbQkssViMsLAwLF++/Kv9nJ2d8csvv+DJkycYMmQIc1O7adMm2NnZKTS/i4sLnj9/XuEx5KGsrIw2bdpI3SDPnTsX48aNq9S5vzX//PMPRowYIdW+f/9+9OjRo+oDIoQQ8s3JyclBTEwMhEIhkpOTS01mmZiYMDWzmjdvXqG5xGIxrl69ioCAAJw5c6Zc17q7u8PV1RU9evRgDid5+/YtwsLCEBQUJPP0YSUlJVhaWsLd3R22traoV69eheImhBBCSnPp0iV4enqW6xo9PT2Eh4dXUkQ1gyL5FlpjXQcoKSnByckJaWlpGDVqVKn9bt68CaCkqNynm9gePXpgwIABCs0fHx9f6ckroKQOlKwb1JcvX1b63N+ajh07yvzBOHHiRISFhVVDRIQQQr41WlpacHV1xbZt2xAREQFvb2/Y2NhARUVFot/169exefNmODk5YcKECTh27Fi5f7cqKSmhR48e+OmnnxATE4PFixejQ4cOcl0bGBiISZMmwdzcHLt378aTJ0/QuHFjjBgxAkePHsWxY8cwYsQIiXoiYrEYFy5cwLJly8DhcLB+/Xrcvn1boWPPCSGEEKDkIcqePXvKnbwCSmoUk4qjFVgy1LYVWJ/Lz8+HtbV1qe9v27YNc+bMYV4fPHgQJiYmCs25efNmHDlyRKExFGFvb4/ffvut2uavTh8/foSbmxuysrIk2j09PTFt2rRqiooQQsi37O3bt4iJiYFAIMCFCxdQXFws1edTQorD4cDe3h56enrlnkcsFuP69evw9/eHQCBAfn6+3NcaGhrihx9+AJfLZRJX+fn5iI2NRVBQEJKTk2Umqzp27Ah3d3c4OTmhSZMm5Y6ZEEJI3ZWXl4ddu3bh1KlTyM3NrdAYJiYmOHjwIMuR1Sy0hZBltTmBBQBDhgzBgwcPyuzH5XJlFgQvr4sXL1ZZssTR0RHR0dESN8Fdu3bFoUOHqmT+b5FIJIKXlxcSExMl2vv27YstW7ZQsVtCCCGlevPmDaKjoyEUCnHx4sVSk1k9e/Zkklm6urrlnicnJwdnz56Fv78/0tPTy3XtgAEDwOfzYW1tzdTrfP78OVP4/dmzZ1LXqKqqwtbWFu7u7rCyspJadUYIIYR8iY26zs2aNavzO2IogcWy2pzAEovFGDhwIDIzM7/aT1VVFadPn1b4SO1Ppk6dWuox2GyaN28eLl68KJGsoX3GJXbs2IF9+/ZJtOnq6iIkJIRqgxBCCClTdnY2oqOjIRAIkJKSInOFk5KSEkxNTZlkVnlPwBWLxbh58yb8/f0RHh6OvLy8cl3/ww8/wNXVFZ06dYKSkhKKi4uRmpqK4OBgREREyFzl1axZM7i5ucHV1RUGBgblmo8QQkjdsXDhQkRFRSk0hpKSEpKTkyvlRPqaghJYLKvNCayMjAy4urqW2c/DwwPz5s1jbd5r165h/PjxUu1z586Furo6a1v8Ro0ahdatW0uMp6SkhAsXLkBVVZWVOWqysLAw/Pjjj1Lt0dHRaNy4cTVERAghpCbKyspCVFQUhEIhLl26VGoyy9zcHBwOB3Z2dtDW1i7XHO/fv0dYWBj8/f1x9+7dcl2roaGBKVOmwMXFhdnemJOTg/DwcAQFBTF1P79kZmYGPp8Pe3t7aGholGtOQgghtdu0adNw8eJFhce5cOECJbAogcWe2pzAOnPmDFatWgWgpH7E/fv3pfo0atQIwcHBaNSoEatzm5qaSrV9OkJ00aJFiIyMVHgOOzs7GBgYSO0rPnfuXIVPTqptSksmnjlzho4cJ4QQUm6vX79GZGQkhEIh0tLSSk1mWVhYgMvlYsCAAWjatGm55rh16xYCAgJw7ty5ctcd6dq1K3744QfY2toySan09HQEBwcjNDQU2dnZUtc0bNgQjo6O4PP5MDY2Zk4/JIQQUnd5eHiU+gBEXk2bNmXl796ajBJYLKvNCay1a9fC398fADB9+nQUFBRIbSubOHEiZs2axfrcNjY2UjednxJYxcXFMDc3V3iOpk2b4v379ygsLJRo9/X1Rbdu3RQev7YobSXeoUOH0LVr12qIiBBCSG2QmZnJJLMuX74sM5mlrKwMS0tLcDgcDBgwoFzF1D98+IDw8HAEBATg1q1bMDU1xaRJk3D27FmEhoaWeb2zszP4fD7MzMygrKyMwsJCxMfHIygoCAkJCTLjNTQ0BJ/Ph4uLS7lXkRFCCKk9Bg8ejIcPHyo0RqdOnXDs2DF2AqqhKIHFstqcwBo6dCiz6urPP/+EhYUFli1bJlEjauvWrejbty/rczs6Okodu52amgolJSUUFBSgV69erM/5yYYNG8DhcCpt/JooJycH/fv3l2pfv349uFxuNURECCGkNnn16hUiIyMhEAhw5coVmX1UVFRgaWkJLpcLW1vbcm1nz8vLg7q6OnMYSW5uLmJiYnDy5MlS5/vcxIkTwePx0K5dOybeM2fOIDAwEE+ePJEZa79+/cDn89G7d28qTUAIIXWIWCyGmZmZwuP07dsXW7duZSGimkuRfAsdP1aHvH37lkleqaioMCttPm0p/OTx48eVMv+XySsAGD9+PLZu3YqZM2eyPp+RkRHz9atXr1gfv6bT0tLChQsXpLYNLl26FLt3766mqAghhNQWenp6GDlyJPbv34+wsDAsXLhQajW0SCRCUlIS1qxZAwcHB8yePRtBQUF49+5dmePXr19f4iRdDQ0NODk5Yf/+/RAIBJg/f/5XE2L79+/H4MGD4eDggBMnTkBNTQ0TJkxAYGAg9u3bB1dXV4k6WCKRCNHR0fDy8oKzszO2bduGR48eVeA7QwghpKbZuHEjK+M0a9aMlXHqKkpg1SFXr15lvu7cuXOpxUlv3bpVVSHh+vXrOHjwIFJTU1kd197eHo6OjszrFy9esDp+baGmpoaQkBCp1Wl79uzB3LlzZW6lIIQQQsqrWbNmGDVqFHx9fXH27FksWLAAJiYmEn1EIhESExOZZNacOXMQEhKCnJyccs+nq6uLMWPGIDo6Gn5+fhg3blypfbOysrBhwwbY2dlh6tSpiI2NRdeuXbFmzRoIBAJ4e3uje/fuEtdkZmbC19cXgwYNwsSJExEUFISPHz+WO05CCCHfvsuXL+PEiROsjEUJLMXQ2uc65PLly8zXPXv2ZL7+8ulhfHw8CgoKoK6uXmWxVVSjRo2kntKqqqpizpw5Egk7SmCVTklJCRs2bECXLl2wbds2pj0+Ph4ODg4ICwur06dkEEIIYVeLFi0wevRojB49Gv/99x8iIiIgFApx48YNpk9RURESEhKQkJAAVVVVWFtbg8vlon///tDU1CzXfEZGRpg7dy5mzZqF1NRUnD59WqJ0wudSUlKQkpICABgyZAj4fD7c3NzA5/Px8OFDBAUF4cyZM3j9+jVzzZUrV3DlyhVs3LgRHA4H7u7u6N69OxV+J4SQWoLNv4UogaUYWoFVh3xeD6JHjx7M118Wonv//r3ETeS3rHfv3lJtI0eOhIGBgcSpg7K2LxJJ48ePh4+Pj0RbdnY2rKys8OHDh+oJihBCSK3WsmVLeHh44NChQwgJCcHcuXNhbGws0aeoqAjx8fFYuXIl7O3tMW/ePJw7d67cv5s+1dtat24dEhMTsXbtWqm5Pufv74+xY8fCzMwM+/btQ/369TF37lycO3cOPj4+sLW1hYqKCtM/NzcXwcHBmDhxIgYNGgRfX18qYUAIIbXApUuXWBurtF1QRD6UwKoj8vPzJY78/DyBJSuj3KhRo6oIi/Hzzz9X6LouXbpItU2ePBkAKIFVAf369YOfn59Ue9++fel7SAghpFK1bt0a48aNw+HDhxESEoI5c+ZI/Z4vLCxEbGwsli9fDjs7OyxYsADh4eHl3r73qV7W4cOHmXpZ9erVK7X/jh074OzsDFdXV5w7dw7m5ub4/fffERYWBi8vL7Rv316i/+PHj7Ft2zY4OTlh7ty5iI6OljohmRBCyLcvKChIYpeKov777z/WxqqLKIFVR9y+fRtFRUUAgDZt2kgcAy1rFdPnBdAVIRKJcOPGDZw6darUPt7e3hV+QpmTkwN9fX2Jtk/JNz09Pabt5cuXKC4urtAcdY2RkREiIiKk2h0dHau0PhohhJC6q3Xr1hg/fjyOHDmCoKAgzJ49G506dZLoU1hYiOjoaCxbtgx2dnZYuHAhBAIBcnNzyzXXp3pZSUlJ8PPzw9ixY0vtm5GRAW9vb/Tp0wdz585Feno6Ro8ejVOnTuHgwYMYPHgwGjRowPQvLi5GfHw8FixYACcnJ2zZsgX//vtv+b4ZhBBCqsWFCxcqvNCiNLq6uqyOV9dQAquOKG37IICvPnFUhEgkwvjx4zF27Fj8+uuvpfa7fft2hbPa2dnZcHd3l2j7dOOqoaHBJLOKioqQnZ1doTnqIm1tbZw/f16qfcyYMRAIBNUQESGEkLrKwMAAEyZMwLFjxxAUFIRZs2bhu+++k+hTUFCAqKgoLF26FHZ2dli8eHGFkllGRkbw8vJCSkoKdu/eDQcHh1L7xsfHY/r06bCwsMCWLVvQoEEDrFixAkKhED/99JPUcetZWVk4fPgwhg0bhrFjx8Lf379CBeoJIYRUjV27dpVrEYQ8NaS//P1FyocSWHVEaQXcASA5OVmq/+fFSSvq1atXEtsWSyNry5q8srOzpU4xioqKYr7+vEgeFXIvn/r16+PSpUvo0KGDRPvSpUuxe/fuaoqKEEJIXWZgYICJEyfi+PHjOH36NGbMmCG1ajw/Px8RERFYunQp7O3tsWTJEkRERJQrmfWpXtbGjRuZellfW53+999/Y9iwYTA1NUVgYCB69+6Nv/76C0FBQZg0aZLEqnAAuHHjBtauXQsul4uVK1fi0qVLdPIvIYR8Q16+fIlr164BAJSVlTFw4MCv9tfW1gafzy9z3BYtWrASX11FCaw6oLi4WGIF1pdHQctKIAUHBys8b/Pmzb96bDUbXr16JVFAFYDE9jeqg6UYZWVlnDx5EoMGDZJo37NnD2bPnk0324QQQqpN27ZtMXnyZPj5+cHf3x/Tpk2TeuiSl5cHoVCIxYsXw8HBAUuXLkVUVBTy8vLknudTvSw/Pz+mXtbX/Pbbb3BwcMDw4cNx9+5dTJkyBWfPnsX27dvh4OAAVdX/HQKen5+P0NBQeHp6gs/nY+/evfTAjRBCvgHR0dHM12ZmZpg5cyZ69epVan93d3fweLwyx9XS0mIlvrqKElh1QE5OjsQS9Y0bNyIrKwtASRG5+Ph4qWv8/f0VrhmlpKSEuXPnIiwsDBMnTlRorNJcv35d6pjqz29KaQUWO1auXImlS5dKtCUmJqJfv34QiUTVFBUhhBBSon379vD09MTJkydx8uRJeHp6ShVWz83NhUAgwMKFC2Fvb48ff/wRMTExyM/Pl3ueT/Wy0tLS4OfnhzFjxpTaNz09HYsXL0avXr2wfPlyaGlpYcOGDQgPD8eiRYukVnQ9ffoUO3fuhLOzM2bNmgWBQICCgoLyfSMIIYSw4vNFEfb29tDX18fOnTsxYsQImf01NTXRtWvXMsf9VJeaVAwlsOqARo0aoW/fvszr5ORkjBw5EmlpaQgICGBW0XTv3p2pGfXs2TOkpqayMr+enh66devGyliyfFkA/vOnr5TAYs/w4cPx559/SrR9+PABFhYW5a4xQgghhFSWDh06YNq0afD394efnx88PT3Rtm1biT65ubkICwvD/PnzYW9vjxUrViAuLq5cCSMjIyPMnz+fqZfVr1+/UvsKBAKMHz8eZmZmOH78OPr164cTJ04wWw81NTWZvmKxGOfPn8fSpUvB5XKxceNG3Lt3r/zfCEIIIRWSlZWFtLQ0ACWLMuzs7Jj3OnfuLPOazMxMPHjwoMyxDx8+zE6QdRQlsOoAJSUlbN68GePHj2faMjMzMXnyZOzbt49pc3Z2lnhayUbR83/++QfTp0+Hl5eXwmOVZuvWrRKvP3+i+fkeY9pCqDgLCwsEBQVJtdvY2FT4JElCCCGkshgZGWHatGkICAjAiRMnMHnyZLRp00aiz8ePH3H27Fl4eXnB3t4eq1atQlxcHAoLC+Wa41O9LB8fH6Ze1pcnJH/ur7/+gqurK3r37o179+5h9uzZEAgE+PXXX2FpaSnR9927dzh+/DhGjhyJ0aNH48SJE3j37l35vxGEEELkFhMTI7HI4/OTA7t06SLzmszMTBw8eLDMsXft2oX79++zE2gdpCSmIjZSPDw8cPHiRVhaWta6DGlCQgJWrlyJt2/fltlXIBCUesxnUFAQjhw5gtatW8PKygqWlpZo3749s51PLBbjjz/+gK+vb5XXSerSpQu6du0KDoeDpKQkHDhwAABgbm6OPXv2VGkstVVOTg769+8v1X748GEYGxtXQ0SEEEKIfMRiMf755x8IBAIIBAI8ffpUZj9NTU3Y2tqCw+GgV69eUFNTK9c8mZmZOHfuHLZs2VJmXxMTE3h6esLKygovXrxASEgIgoOD8fz5c6m+ampqsLOzg7u7OywsLKCsTM+jCSGETd7e3ggJCQFQ8jN36NChmDBhAnR1dfHkyROZxdr19fVL/X3yJRMTE/j6+kqVwqkrFMm3UAJLhtqcwAJKttItXboUV69e/Wq/6OhoNG7cWOZ7dnZ2ePPmjUSbrq4uLC0tYWFhgVatWmHq1KlshcyK1q1bMz+IiOKKioqknhQDwPr168HlcqshIkIIIaR8xGIx7t69C6FQCIFAgIyMDJn9NDU1MWDAAHC5XFhaWpY7mZWeno6AgAAcP368zL58Ph+jRo1Chw4dkJKSgsDAQERFRclcEdayZUu4urrC1dUVrVu3LldMhBBCZAsMDMRPP/0k0VavXj2MGDECd+/exYULFxSeIzQ0FC1btlR4nJqIElgsq+0JLAAoLCzEzp07v7rMUVNTExMmTMDIkSOhoaHBtGdnZ8Pe3r4qwmTdqVOnYGhoWN1h1BpisRjjxo3DjRs3JNonTZqEmTNnVlNUhBBCSPmJxWLcuXMHAoEAQqEQz549k9mvUaNGTDLL3Ny8XMkskUiE1NRUHDx4EElJSWX2nzNnDng8HtTV1XHu3DkEBwfj9u3bMvtaWFhg0KBBsLW1Rf369eWOiRBCiLSkpCTs3LkTN2/eZH1sDQ0NxMbGSpxKW5dQAotldSGB9UlcXBxWrVr11XoK7dq1w+7du9GsWTPk5eVh2rRpuHbtGvN+v379kJaWhvfv31dFyAqhbYSVY9OmTTh69KhEm5WVFXbt2lVNERFCCCEVJxaLcevWLQiFQgiFQvz3338y+zVu3Bh2dnbgcrkwMzMr1x8jubm5iImJwZYtW5CZmfnVvk2aNMGSJUvQv39/PH78GEFBQQgNDZV5/6apqQknJyfw+Xx06dKlzm5RIYQQRYnFYsTHx2PXrl24e/cua+NaWFhIHY5Vl1ACi2V1KYEFAP/99x9cXFy+2kdfXx+bNm3C9u3bkZiYCKCkOPzGjRthb28PkUiE27dvIyUlBRcuXMDFixerIvRya9u2LU6fPl3dYdRKQUFBWLNmjUSbqqoqLly4QDfPhBBCaiyxWIybN28y2wxLO9W4SZMmsLe3B4fDgampabmSWZmZmThz5gy2bdtWZl9LS0t4enrC2NgYsbGxCA4ORlJSksyaWMGmGAABAABJREFUo0ZGRnB3d4ezszOaNGkidzyEEEL+Jy8vD71792ZtPE9PT0ybNo218WoaSmCxrK4lsPLy8uDp6Sm1DawsCxYswOjRo2W+l5OTg19++QVCoVDqvfIUuGPbd999J1f9CVIxV69exYQJE6TaExMTJbahEkIIITVRcXExbty4wazMKu2EY21tbdjZ2THJLBUVFbnnSE9Ph5+fH06dOlVm3x9++AEjR46Empoazpw5g6CgIJl1vFRVVWFraws+n49evXqVKx5CCKnrevfujby8PIk2FxcXhIaGVmi8Xbt2wcrKio3QaiRF8i10bEkdV1RUhGXLlpU7eTVhwoRSk1cAoKWlhQ0bNmDGjBmoV68eAGD8+PEICgqCs7OzQjEr4lMspHJ0795d5g9yGxubUp9YE0IIITWFsrIyunXrhgULFuDs2bM4cOAARo0aBT09PYl+WVlZOHXqFKZOnYqBAwdi3bp1uHTpEkQiUZlzGBkZ4ccff0RKSgp2794NU1PTUvseO3YMfD4fzs7O0NLSwpEjR7Bnzx7weDyJe56ioiJERERg9uzZcHZ2xo4dO/DkyZOKfyMIIaSOSExMlEpeAahw8gqAXL8LiGy0AkuGurIC6/Xr15gzZ45EMdBevXph+fLlZSa1/P390b59+3LPGRYWhh9//FHme0KhEBwOp9xjllebNm2gp6cHXV1dNGvWDDo6OmjWrBl0dXWhp6cHPT09NGjQoNLj+FxxcTFevnyJJ0+e4MmTJ3j37h369OkDIyOjKo2DLbm5ubCxsZFqv3DhQrlPbiKEEEK+dcXFxbh27RoEAgEiIiJKrWmlo6MDe3t7cLlc9OjRA8rK8j1Lzs3NRXR0NNatW4cPHz58tW/Lli2xZMkSdOvWDVFRUQgMDCz1ns7MzAxubm5wcHCgldKEECLD1x4iVFS3bt1w4MCBOltmhbYQsqwuJLBiY2Mxb948me8tW7YMysrKWLt2banXV7TwXGpqKqZOnYri4mKp93x9fVFUVITJkyeXe1y2NWjQQCKh9Snh9XniS1dXt1yJruLiYrx48QJPnjzB48eP8eTJEzx69AhPnz7F06dPUVBQINFfV1cXZ8+erbGnUxQXF8PCwkKiJoePjw/69etXjVERQgghlau4uBhXrlyBQCBAZGQkXr9+LbOfrq4uHBwcwOVy0a1bN7mTWZmZmQgJCcH27dvL7Nu/f39MnToVqqqqCAkJwZkzZ5CdnS3Vr0GDBnB0dASfz0fXrl3r7B9VhBDypSVLlsgsi6OoHTt2wNramvVxawJKYLGsLiSw+Hy+QkvHFTk5ISUlBRs2bMD9+/el3uvcuTPu3LlT4biqWoMGDSSSWzo6OhCJRMjNzUVeXh5yc3ORm5uL169f48mTJygsLJR77ObNm+Ps2bM1/iZy6tSpSElJQdu2bbF//340bdq0ukMihBBCqoRIJMLly5chFAoRGRmJrKwsmf309PTA4XDA5XLRtWtXuZNZ6enp+PvvvxEcHFxm34kTJ2Lw4MG4e/cuAgMDkZiYKPOBoqGhIdzc3ODi4gIdHR254iCEkNpKLBZj/PjxuH79OqvjmpiYwNfXt8b/rVcRlMBiWV1IYJV3KaSysjJzk6OqqgqhUIjGjRsrFMPw4cORnp6u0Bi1kZGREaytrTFq1Cg0b968usNhRVFREZSUlKhoLCGEkDpLJBIhLS2NSWbJWgkFlDzA4nA44HA4cq+GEolESE1NxbZt23Dr1q0y+3t7e8PU1BSRkZEIDAzE48ePpfqoqKigb9++cHd3R+/evWvsinBCCGHDo0eP8OzZM8ycOZO1Mc+ePYsWLVqwNl5NQQksltX2BFZxcTHMzc3l7u/t7Y23b9/Cx8eHaVu/fj24XK5CcRQUFKBXr14KjSGPH3/8Eb/++ivzetGiRXj9+jWuXr2K1NRUAECLFi3w+vXrcq2QYtv48ePh7OwMQ0NDuZ+8EkIIIaTmKSoqQlpaGrPN8O3btzL7tWjRgklmGRsby5XMys3NRWRkJFavXi1zhdXnDA0NsXjxYqiqqiI4OBhCoRC5ublS/XR0dODi4gJ3d3e0a9dOrs9ICCG10cqVKxUq4P5J06ZNERYWVifrA9MphKRclJSU0KRJE7n76+npwcPDA7a2tkzbmjVr8PDhQ4XiUFdXl2rbtGkTFixYoNC4XxKJRBJzubq6okWLFrh69SrT1rlzZwwePBjdu3dnde7y8PX1xfDhw2FlZQUul4uZM2fKfCJKCCGEkJpNVVUVlpaWWLFiBYRCIXbu3Al3d3c0atRIot/z589x+PBhjB07Fq6urvDx8cHNmzfxtefPGhoa4PF4uHTpEgQCAaZNm1Zq3/v372PatGmYPHkyCgsLsXfvXnh7e6NHjx4S/V6/fo1Dhw5h8ODBmDBhAoKCgsosJk8IIbXRpEmTWDn0wtXVtU4mrxRFK7BkqO0rsADgypUrmDhxotz9586di969e2PBggV4+vQpgJKndocPH1boH/CXWxl//fVXODo6Yt26dTh58mSFx/3cgAEDEB0dzcpYVW3YsGFYtmxZdYdBCCGEkCpQWFjIJJ6ioqKQk5Mjs1+rVq2YmlmdO3eWa2VWeno69u3bh/Dw8DL7zpw5ExYWFoiJicGZM2dknqqooaEBBwcHuLu7o0ePHnWyjgshpG7KyspCWloaFi9eXOExgoKCYGBgwGJUNQdtIWRZXUhgAcCECRMkViHJw9zcHFevXmW22rm4uOCnn36q8E3Lrl278NdffzGvGzVqhJiYGACVc2Rpeejq6sLAwACqqqr477//mMRdeUyePBnDhg2DpqYmsrKykJWVhbt37yIkJKTMQoAqKirYsGED7OzsKvoRCCGEEFJDFRYW4uLFixAIBIiOjsb79+9l9tPX12eSWd99912Z92Sf6mVt2LABDx48+GpfFRUV/PLLL1BTU0NoaCji4uIgEomk+hkYGMDd3R08Hg96enryf0hCCKmhMjIy4OrqWqFrzc3NsWfPHpYjqjkogcWyupDAKi4uhpubG549e1au61RUVLBkyRKJmlLTp09H/fr10bRpU9jb25drRVZcXBy8vLwk2tLS0gAAp06dkpinss2ePRsGBgaoX78+nj9/jqioKCQnJ5drDB6PhzFjxqBjx47MDaRIJMKNGzeQkJCAhIQE3L17t9Tr1dTUYG5ujj59+qBfv35o3bq1Qp+JEEIIITVfYWEhkpOTIRQKERMTU2oyy8DAAFwuF1wuF0ZGRmUms3JzcyEQCLBmzZoyYzA2NsaUKVPw8OFDBAcHyzxNWklJCTY2NnB3d0ffvn1pewwhpFbIyclBTk4OWrZsCQBITEzE8uXLS10lW5aVK1di0KBBbIZYo1ACi2V1IYH16tUrDBw4UK6+bdq0YWox9erVCzt27MDq1asREhIi1bdhw4ZwdnbGoEGD0Llz5zLHfvr0Kdzc3CTakpOToa6ujvT0dAwfPlyuGMtr3rx5cHR0lPgecLlcCASCco1jYWGBcePGwdTUFPXr12fas7KykJSUhISEBCQlJeHdu3eljtGyZUv06dMHNjY2sLCwYGVPNSGEEEJqp4KCAolkVmm1qNq2bQsulwsOh4MOHTqUmczKzMzEiRMnsG/fvjJjGDx4MHr16oULFy4gLCxMZgxNmzaFi4sL3NzcYGRkJN+HI4SQb8SpU6ewf/9+vHz5EmKxGGKxGA0aNEBBQQGKiooUGjsmJkaq5mFdQgksltWFBJZIJMKiRYuY7XryUFJSwvHjx1G/fn3s2rULYWFhX+3//fffY9CgQXB0dETDhg1LjcPCwkKibfXq1XBzc0NxcTFsbW1LfcpYURYWFhg+fDguXrxY7jpbbdu2xfjx49G7d2+JJfLFxcW4c+cOs8rqawVWVVRU0LNnT9jY2KBPnz4wNDSkuhGEEEIIKbf8/HwkJydDIBAgJiZG5gmCANC+fXtwOBw4ODjIlUxKT0/Hzp075bpPnD17NjQ1NSEUCnHp0iWZfYyNjcHn8zFw4EBoaWmVOSYhhFQHsViM58+f4969e5g3b16lzfNpx1FdRQksltWFBNYn0dHRWL58OfLy8srsa2JigtatWyM8PPyrp998SUNDAwMHDsTgwYNlHgEtq9bVp3/Us2fPRmJiotxzsa1+/fqYNGkS+vXrJ7UUPycnB0lJSUhMTERiYiKysrJKHUdXV5dJWFlZWUFTU7MqwieEEEJIHZGXl4ekpCQIBALExcWVmswyNDQEh8MBh8OBoaHhV8cUiURISUnBTz/9hOfPn3+1r6amJmbOnImXL18iNDQUL1++lOpTr1492Nvbg8/nw8zMDMrKdCA6IeTb8P79e0yfPh03b94ss6+GhgZsbGywdOlSREZGYt26dXLPo6+vj+DgYEVCrfEogcWyupTA+kQsFuP8+fOYPXu2QuP06dMHWlpaiIiIYAq9f65jx44YPHgwnJ2dmSdwshJYiYmJ0NDQwL59+7Bjxw6FYiqv4cOHo3///ujZs6fEtkCxWIz09HQkJiYiISEBV65cQXFxscwxlJSU0K1bN/Tp0wd9+vSRq6gqIYQQQggb8vLycP78eQgEAsTHx5eazOrQoQNTAL5du3ZfHTM3NxdhYWH4+eefy5zfzMwM/fr1w40bNxAdHS1zu03r1q3h5uYGV1dXtGjRQq7PRQghleXQoUPw8fEp9X1bW1usWLECGhoaUFNTw4kTJ6CsrIzz58+Xa8FF3759sXXrVhYirrkogcWyupjA+uTly5dwdHQss5+VlRUmT54MMzMzbN68GUeOHAEAKCsr488//4SRkRFCQ0MREBAgs8hnvXr1wOVyMXjwYEyYMEHq/V9++QXOzs5ITU3FlClTFP9gcjAzM5M4EREAPn78iIsXLzJbA2U9TfykSZMmzCqrXr16oXHjxpUdMiGEEELIV+Xm5iIxMRFCoRDx8fGlrro3MjJiCsC3adPmq2NmZmbi8OHDct0n83g8tGzZEjExMfjnn3+k3ldSUkKvXr3A5/Nha2sLdXV1+T4YIYSwRCwWY/DgwXj06BGAkm3P3bt3R6dOnfDx40dcvnwZxcXFuHXrFp4/f17qIgZ5DBs2DMuWLWMr9BqJElgsq8sJLLFYjOHDh+Pff/+V+X6/fv0wefJkdO3alWkrLCzElClTcO3aNQCAjo4Ojh07Bl1dXYjFYly7dg3+/v4QCoXIz8+XKw5NTU3ExcUhLy8PvXv3VvyDfWHq1KkYMGAAPn78iIkTJwIouXE7ceIEHj16xCSs0tLSvlqk7/vvv2dWWXXp0gUqKiqsx0oIIYQQwobc3FzEx8cjIiIC8fHxpd6Xfffdd0wBeAMDg6+OmZ6ejs2bN+PChQtlzj98+HAUFhYiIiJC5uldjRo1gpOTEwYNGoTvvvtOvg9FCCEK2LlzJ/bu3cu8btCgAYRCITQ0NPDq1SssWbIEV65cYW2+WbNmMX9/1lWUwGJZXUxgFRcXIz4+Hnv37i113+/y5cthamqKwMBAaGtrY+zYscy2uBcvXuCHH37AmzdvAADm5ubYuXMnVFVVmetzcnJw9uxZ+Pv7Iz09vcyY4uLioKmpKXOLYXno6enB0dFR4v+XgYGBaNOmDXJyctC/f3+mvUWLFl+t8aCpqQlra2vY2NjAxsYGOjo6CsVGCCGEEFIdPn78iISEBAgEAiQkJKCgoEBmv86dO4PL5cLBwQH6+vqljicSiZCcnIwVK1bg7du3X527cePG4HK5ePToEVJSUmTWVu3cuTP4fD6cnJzq9GldhJDKs2TJEgiFQqn2tLQ0pKenY/bs2Xjx4gWrc/78889wcXFhdcyahhJYLKtLCSyRSISIiAjs27evzKSSvb09oqKimJuMNWvWwNXVlXk/OTkZM2fOZN4fPXo0vLy8pFYlicVi3Lx5EwEBAQgKCvpqQfguXbrg9u3bFfpsLVu2xJ49e9C6dWsAwIQJE3D16lUAgLe3N/Ly8pCQkFDmnmUjIyNmlVW3bt0kknKEEEIIITXdhw8fEB8fD4FAgMTERJl1TIGSlecODg7gcDjM/ZUsubm5CA4OxoYNG8qc28jICF26dEFqaiqePXsm9b6amhoGDBgAd3d3WFpaUuF3QggrxGIxzMzMZL63e/duLFy4EO/fvwdQUibH2dkZjx49wvXr1wEAc+bMwbZt28o9759//gkLC4uKB14LVHsCSyQS4dixYzh9+jTu378PkUgEAwMDODs7Y/LkyahXr55E/+vXr2PHjh24fv06Pn78CCMjI4wdO1YiGfK5Bw8eYPv27UhNTcWbN2/Qpk0bDB8+HKNHj66UX2J1JYH14MEDLFiwAA8fPpRoV1NTg52dHcLDw796vbGxsdT3588//8Sff/7JvO7UqRMWLVpU6iqqDx8+YMmSJTh//nzFPkQZli1bhiFDhkAkEsHR0RHZ2dllXqOhoQErKyv06dMHNjY2aN68eaXERgghhBDyrXn//j3i4uIgEAhw/vz5UkspGBsbM8msVq1alTpeZmYm/vrrL5w8ebLMubt164bGjRvjwoULMleEtWjRgin8/rUEGiGEyEPenT47duyAtbU1AODJkyeoX78+pk6dKvV3tJOTE86dO/fVsYKCgsrcml3bVWsCSyQSYcaMGYiJiUGDBg3QvXt3qKqq4urVq3j37h26d++OgwcPQkNDA0DJ6XJTp05FcXExLCwsoKGhgaSkJOTl5WHatGmYN2+exPh37tzB6NGj8f79e5iamkJHRwcXLlzAu3fv4Orqik2bNikSvkx1JYG1du1a+Pv7M681NDQwdOhQjBkzBnp6etizZw92794tdZ2SkhKzaurvv//G999/z7xXXFyMuXPnSq1q4nK58PLyKvWUmSlTpiA1NZWNjyWThoZGqSfwfM7MzAw7duygAqKEEEIIqfNycnKYZFZSUlKpyayuXbuCw+GAw+F89UTBf/75B2vXrmXqpn6NmZkZcnJycO/ePZnvW1hYwN3dHQMGDJA4NZoQQuR1/PhxbNy48at9XF1dsWbNGub127dvMWXKFKndSxYWFjA2Noavr+9Xx4uPj0fDhg0rHHNtoEi+ReHlSydPnkRMTAw6deqEsLAw+Pr6Yu/evQgPD0fPnj1x9epV7Ny5E0DJkb6LFi0CAOzfvx8HDx7E7t27ERISghYtWmD37t24ceMGM7ZYLMbixYvx/v17bNy4EceOHcMff/yB8PBwdOrUCSEhIWWuEiKl69Gjh8Trn3/+GfPmzYOenh4AYNSoUWjWrBmAkqSVg4MDjh07JnFK4alTpyTGUFZWxqZNmzB16lSJlXcCgQCDBg3Cn3/+KfP0G0XrXJVFVvLKxsYGS5YskSiip6OjQ8krQgghhBAAWlpacHFxwdatWxEZGYk1a9agT58+UuUUbty4gS1btsDZ2Rnjxo3DkSNHZNaN6dixI3x9fZGSkgIfH5+v3nOlpqYyyauePXtKne6ckpKC5cuXg8vlYt26dbh58+ZXy1IQQsiXRo4ciYCAgK/2uXbtGnPqYE5ODmbMmCGz9I6hoSFsbW3LnDMmJqYioZL/p3AC6/Tp0wCAH3/8UWKrlba2NlavXg0ACA0NBVCyXO7169dwdXVFr169mL5t2rTBwoULAUAiA5eYmIi7d+/C0tISfD5f5ti1eYVUZXN2dpY44e/333/Hhw8fmNeampo4ePAgvL29cerUKWzcuBGdOnXCsGHDmD5hYWFSp8jUq1cPU6dORUBAADgcDtOen5+PP//8E0OGDIFQKJS4yTA0NKyMj1gqS0tLbN++HSNGjEC/fv2Y9vv371dpHIQQQgghNYGWlhZcXV2xbds2REREwNvbGzY2NlK1Tq9fv47NmzfDyckJEyZMwLFjx/Dq1SuJPioqKujXrx+Sk5ORmJgotQPjS5cvX8bbt2+hoqKCTp06MYcIASVbHk+ePAkPDw+M+D/2zjusiex9+3foJfTQi4iACCoqCDbABtgQ1LWtsura9WtbFdva3bXsurbVtay9u/aKgA07CFJFFBGlSpMeAiTz/sGb+RESIEDo53NduZg5c+acM4jJ5J7nuZ/x43H27FmxLCMIBAIBAExNTXHq1CksXrxY5PEvX75g7ty5uH//PmbNmkX7M1d8HwKADh06iKyuWplTp04Rsb0e1FvA0tDQgJmZGbp27Sp0zNTUFACQnp4OoDxcDig3A6/MgAEDIC0tjcDAQLqN33/w4MFC/fnphCEhIbS5GqF2MBgMrFu3DioqKgCAlJQU7Ny5U6CPrq4uPD090b59e7rN1tYW5ubmAMqj6oKCgkSOr6+vj+3bt+PIkSMCpZBTU1OxYsUKzJkzB9nZ2QAaX8CquOaK15aQkFBleDyBQCAQCAQCAVBVVYWnpyf27dtHi1m9e/cW8qYNDw/HH3/8gSFDhmD69Om4ePGikJilqKgIb29vhIaGws/Pr0pPXKDcuiQ2NhYURUFZWZnOGuATFxeHnTt3wt3dHT4+Pnj27Bm4XK7kLpxAILRKOnfuXKWhO1Ae8blq1SrExsbSbWvWrEHHjh3pfTMzM5w7d67GuT5+/Fjl92dCzdRbwDp48CDu3bsHJSUloWN8h35+LvzHjx8BQEDM4MNkMqGjo4Ps7GxkZmYCAB2aJ6o/UC488Hg8fPr0qb6X0WbR0dHBqlWr6P3r168LiIiiYDAYAhF0/H/XqrCzs8PZs2exZs0aqKur0+3BwcGYPXs2srOzYWJiIqRiNxYqKir0DVBZWRkSExObZB0EAoFAIBAILQ01NTV4enpi//798Pf3x9q1a+Ho6CggZlEUhbdv32L79u0YMmQIZs6ciYsXL9L3/HxYLBY2btyI0NBQXLhwARYWFlXOW1hYSIthqqqqAtYVZWVlCAgIwMKFCzFs2DD8/fff5P6OQCAAKM8KqmgvU1BQAEdHR3h7e4s9xtKlSzF69Gh8/vyZblNTU8PLly/FOl+cohYE0TRYHVqKorBnzx4A5QbeAOgPmcpPS/jw2/kfZvzILXH7E+qGu7u7QKrfpk2bagy97tChA70tTtqdtLQ0xowZg+vXr+PHH3+kxapPnz5h1qxZKCgoEKuaTEXD+PpSsUR0ba+HQCAQCAQCgSCIhoYGRo0ahX/++Qd+fn5Ys2YNevbsKfCQkqIohISEYPv27XB3d8esWbNw6dIlZGVlCYxlaWmJixcvIjg4uMaiTXl5eeBwOADKH4pXJCMjA8eOHYOnpydmzJiBmzdvilXYh0AgtD4iIiLg4uKCvn37okePHujRowecnZ0FvheKQ2xsLBISEgSqpVa02akJUR5aBPFoMAHrr7/+QnBwMFgsFmbMmAHg/4y0q6oUwm8vKiqqU39C3WAwGFi1ahW0tLQAANnZ2Vi8eHG1wiA/hRCoOQKrIqqqqli2bBm2bNlC38zEx8dj5syZQuaconj37p3Yc9UEP1WQb8rHp6KSTiAQCAQCgUCoPZqamhgzZgwOHToEf39/rFq1SqSY9ebNG2zbtg1ubm6YPXs2Ll++TFtMAOUPQQcOHIjQ0FA8f/4c8+bNq3beitYilc3mQ0NDsWHDBri6umLTpk2IiIggXjQEQhuBoijs3LlTQHSqDUZGRvT27du3axTWqyMjI4O899SRBhGw9uzZg8OHD0NOTg67d++GpqYmgPIPIAaDUWOqGF9Q4JtCitufUHfU1dWxdu1aej8yMhLe3t54//69yP5mZmb0v8vXr19FVhasjqFDh2Lr1q10eHlCQgKio6PruPq6kZmZCR6Ph82bN+PVq1d0O4vFatR1EAgEAoFAILRmNDU1MXbsWBw6dAh+fn5YuXIl7OzshMSs4OBg/P7773Bzc8OcOXNw9epVgawARUVFzJgxA6Ghobh//75In9yKVOVrWlRUhOvXr2Pq1Kn44YcfcPLkSaEIMAKB0LpISUmhLY6q4++//xbZrqmpiX79+tH7L168qPNa2Gw28fGuIxIVsMrKyrBu3TocOHAA8vLy+Pvvv9GzZ0/6uKKiIiiKokN8K8MXQZSVlen+Fdtr6k+oH87OzvDx8aFvJr59+4aff/4ZAQEBQn0VFRXplD+KopCQkFDr+dzc3PD7778LGX42FkFBQdi4cSNu3LhBtw0fPrxa81ACgUAgEAgEQt3R0tLCuHHjcOTIEdy/fx8rVqxAjx49BMQsHo+HoKAgbNmyBa6urpg3bx6uX7+OnJwcuo+2tjZ27NiB0NBQnDlzRiA6ojZ8/vwZe/bsgbu7O5YsWYInT57UOp2IQCA0fzQ0NISiMkXx+fNnPHnyBJMmTRJoj4iIQEJCglhjiMO3b98kMk5bQ2LKQWFhIebMmYOLFy9CVVUVR48ehYuLi0AfHR0dABCqPsKnskcWv39VqWw1eWoRas+ECROwd+9eWhQsLi6Gj48PDh8+LBTmWNFYszZphBVxc3PD9u3bhUowNwa//fYbbt26Re+PGDECGzZsaJK1EAgEAoFAILQ1WCwWxo8fj3///Re+vr7w8fFBt27dBPrweDy8evUKmzZtgqurK/73v//hxo0byMvLo/tYW1vj5s2bCA4OxtatW+u0Fh6PhydPnmDJkiUYOnQodu/eTWwlCIRWhJKSEu7fvy/kk1cZBoMBFRUVLF26FKGhoVi+fDl9LCkpSWIV66vSRAjVIxEBKzc3F97e3nj69Cn09fVx9uxZgcgrPnzBQ1TVwIKCAqSnp0NTU5NO4eL3F2VyRlEU4uPjIS0tLWDATag/ffv2xcmTJwWeZB08eBCrVq0SiIarKGDVx4hu0KBB2LFjR53PlwQeHh5Yv349Ea8IBAKBQCAQmgBtbW1MmDABx44dg6+vL5YtW4auXbsK9OFyuXjx4gU2btyIwYMHY8GCBbh58yby8/MBlNuPuLu7035Z06dPr9NasrOzcerUKYwZMwZTpkzB9evXUVhYWO9rJBAITYuGhgaOHTtWbQZQxYqmADBx4kSsWLFC4mtZtGgRQkJCJD5ua6feAlZJSQlmzZqF6OhomJub48KFC7C0tBTZ18nJCQBEpqQ9fPgQXC5XIGqL3//BgwdC/UNDQ5GdnQ07O7saVVRC7TEzM8Pp06cFhEg/Pz/MmDGDrg5ZUTisbyWFAQMGYPfu3fUao654enoS8YpAIBAIBAKhmaCjo4Mff/wRJ06cwN27d/HLL7+gS5cuAn3Kysrw/PlzbNiwAYMGDcKiRYtw+/ZtWsxSVFTE/PnzERoaCl9fX/Tt27dOa4mMjKSjv9avX4/Q0FBivkwgtGDMzc3xxx9/0HY4ldHX1xdqGz9+vMRFrLKyMsycORO7du2S6LitHQZVz3fgP//8E0eOHIG+vj6uXr1KG7aLoqCgAG5ubsjLy8P+/ftpsSoxMRGTJ0/Gt2/fcP36dVhZWQEoD+UdOXIkPn78iM2bN2PcuHEAyp+KTJ06FbGxsfjnn38wcODA+lyCEN7e3ggKCoKDgwNOnz4t0bFbGqWlpfjzzz/x33//0W0sFgt//fUXVFRUMGrUKADlPmQPHz6ErKxsveYbNGiQgFlnQ6OoqIinT582mQ8XgUAgEAgEAkE8UlNTERAQAH9/f0RFRYnsIyMjg969e8PNzQ0uLi5CD7ojIiKwdOnSepm2GxkZwcvLCyNGjKAtTwgEQsuCoiiEhoZi5syZAu2PHz+GqqqqyHPmzZsnUPxLUsyYMQPR0dGYNm0a7O3tJT5+c6M+eku9BKzv37+jf//+KC4uho2NDczMzKrsyy8z+eDBAyxcuBBcLhc9e/aEsrIyXr16BTabjSVLlmDOnDkC50VERGDKlCkoKiqCra0tdHR0EBQUhNzcXIwbNw6bN2+u6/KrhAhYwly8eBF//PEHXfFRTk4OGzduxL59+5CSkgIAOHDgAHr16lWveaKjo+Ht7V3v9YrL6NGj8euvvzbafAQCgUAgEAiE+pOcnIyAgAAEBARUWclaVlYWffr0gZubG5ydnQUKP3G5XNy+fRsbN26s8xoYDAb69OkDT09PuLi41PtBLoFAaFzS0tIwbNgwet/HxwcTJkwQ2TcgIAA+Pj4Nuh5TU1NcvXq1QedoDjSZgOXn54cFCxaI1Tc2NpbeDg0Nxf79+xEeHg6KomBubo6pU6di6NChIs+Ni4vD3r178fr1a5SUlKBdu3aYMGECxo4d2yBpX0TAEk1QUBCWL19Oh2ZXZty4cVi5cmW95rh7926jCkpmZma4fPlyo81HIBAIBAKBQJAsycnJ8Pf3h7+/P2JiYkT2kZWVRb9+/eDm5gYnJycoKSnRx9hsNvbv349z587VeQ3q6uoYPnw4PD09YW5uXudxCARC45GcnExXoJeVlcWNGzegp6cn1C8lJQUTJkxAQUFBg6/pyZMnUFFRafB5mpImE7BaK0TAqpqvX79i0aJF+PLli9AxXV1d3L17V6AMcm1Zt24dbt++XZ8l1pqAgIBqU18JBAKBQCAQCC2DxMREBAQEwM/PT+ABekXk5OTg5OQEV1dXODk5QVFRkT6WmpqKtWvXIjQ0tM5rsLGxwciRIzFkyJBW/0WUQGjJUBQFb29vvHv3DgAwcuRIbNiwQajf/Pnz8fLly0ZZ06FDh0QWxGtN1EdvIcY/hFphYmKCU6dOoXfv3kLHvn37VuWNgjhQFNUgOcU1ERYW1uhzEggEAoFAIBAkj7GxMaZNm4bz58/j+vXrmD9/vkDlbKC8CNWDBw+wcuVKDBw4ED4+PvD39webzYa+vj7+/fdfhIaG4siRIwKRWuISHR2NrVu3wtXVFb/++iuCg4NpGw4CgdB8YDAYWLhwIb1/69Yt2h6HD4fDERCv6hOsIYpZs2ZhyJAh9P6HDx8kOn5rgwhYhFqjoqKCPXv24McffxQ69uOPP+LWrVtITEysdYWWT58+ITMzk57D1dVVIuutCVK+lEAgEAgEAqH1YWJigunTp+PixYu4evUq5s6dK5Tex+FwEBAQgBUrVmDQoEFYsWIFHjx4gOLiYtjZ2eHZs2cIDg6uUwWykpIS3L17F7Nnz8bIkSNx+PBhpKamSuryCASCBHBwcEDnzp0BlAdUxMXFCRznfz8FAG1tbYSEhGDs2LESmbtr166YM2cOunbtSrfVJyCkLUAELEKdkJGRwbJly7B27VqhY+vXr4enpydcXV2xbNkynDlzBlFRUSgtLa12zIrRV46Ojli2bJmA2WZDcf78+Qafg0AgEAgEAoHQdJiammLmzJm4dOkSrly5gjlz5qBDhw4CfYqLi+Hv74/ly5dj0KBBWLlyJR4+fIjS0lKMHz8eoaGhePbsGby8vGo9f0pKCg4ePIgRI0Zg3rx58PPzA4fDkdDVEQiE+qChoUFvVw7CqChg8auOpqenS2Tevn37AgAsLS3ptvfv30tk7NaKTFMvgNCyGTVqFLS0tLB48WKhY9nZ2Xj48CEePnwIAJCXl0fnzp3RrVs32NrawtbWVsAX4PXr1/S2o6MjtLW1MXfuXLqCZX2YOXMmjhw5UuVxiqIkHg5KIBAIBAKBQGh+tG/fHrNmzcKsWbPw6dMn2gD+8+fPdB82mw0/Pz/4+flBUVERLi4ucHNzQ+/evbFu3TqsW7cOiYmJWLlyZZXG8aLgW2a8evUKKioqGDp0KEaNGoWOHTs2xKUSCIQaKC0tRVpaGr1fVlYmcLyigKWpqYnS0lI8f/5cInP36tULgKCA9fnzZ5SUlEBOTk4ic7Q2iIBFqDfOzs7o0aOHgNklk8kUqtLA4XAQEhJCp+wxGAx06NAB3bp1g7W1tcAbAf8/87hx43Djxg18/PixXmuMiYmBkpISioqKRB4n4hWBQCAQCARC26NDhw7o0KED5syZg7i4OAQEBOD+/fsCBYvYbDZ8fX3h6+sLJSUl9O/fH25ubujVqxfOnj0LAHj58iX+97//1cpCIz8/H5cuXcKlS5fQsWNHeHl5YciQIVBTU5P4dRIIBNHs2rVL4LumlZWVwHEul0tvKygoICwsTEDkkpGRERK9xIWfuqisrAwFBQUUFxeDy+WCzWYTAasKSAohQSJU9KtydHTEo0ePcPHiRaxcuRJDhw6FgYGB0Dn8HOPLly9j06ZNAseePHlCV4NYvXp1vdf37NkzTJkyReQxFotVa78uAoFAIBAIBELrwtzcHHPmzMHVq1dx8eJFzJgxA8bGxgJ9ioqKcPfuXSxevBiDBg3CunXrEBgYCHt7e4SEhCA4OBiLFi2q9dyxsbHYvn07XF1dsWLFCrx8+VLgizOBQJA8r1+/xoULF+j9OXPmwNDQUKBPRRscBoOBvXv3Chyvy//3iuMB5VFexcXFAMoDQVRVVes8ZmuHRGARJIKzszO2b98OAAgPDweDwYCFhQUsLCwwbtw4AOW5wmFhYQgLC0N4eDhiY2OrrMjCTxtUVFSklen6cu3aNZHtmZmZ+Pr1K9q1ayeReQgEAoFAIBAILZeK97Fz587Fx48f6XTCpKQkul9hYSFu376N27dvg8lkYsCAAXB1dcWPP/6IKVOmID8/H7///jvu378v9txlZWV0SqOuri5GjhwJDw8PGBkZNcSlEghtmoSEBHq7e/fumDFjhlCfb9++0dvy8vKIjo4WOG5jYyPRdbRr145kB1UDEbAIEkFfXx+amprIzs5GcXExkpOThZ5Y6ejowM3NDW5ubgDKn2BFRUXh7du3OHz4sMgoKDabjeDgYImssWJuc2VevHhBBCwCgUAgEAgEggAMBgOWlpawtLTE/PnzERsbCz8/P/j7+yM5OZnuV1BQgFu3buHWrVtQUVHBgAED4Obmhk2bNmHr1q2Ij4/HsmXLBL6o1sS3b99w5MgRHDlyBD179oSnpycGDhwIBQWFBrhSAqHtoa+vT2/LyclBSko4Qa1i5dBbt24JHLt27RpmzZpV5/mLi4uhoKAgkLJsampa5/HaAkTAIkiMDh06IDs7GwDw4cMHIQGrMkpKSnBwcICDgwN69OiB2bNn08fc3NwQHh4uoHg3JC9evMDEiRMbZS4CgUAgEAgEQsuDwWDAysoKVlZWWLBgAWJiYuhoqZSUFLpffn4+bt68iZs3b0JVVZUWsy5evAhZWVk8evQIS5curdXcwcHBCA4OBpPJxJAhQzBy5EjY2NiQSA0CoR7IysrS25X9m4Fyy5u3b99Web6mpiYyMjLqPH92djYMDAzw9etXus3ExKTO47UFiIBFkBiWlpZ0tNTWrVshJycHJycnsc6tqHZ369YN27ZtA1AeNRUeHo63b9/i0qVLkl/0/+fNmzfgcDiQl5dvsDkIBAKBQCAQCK0DBoMBa2trWFtbY+HChXj37h0tZlWM2MjLy8ONGzdw48YNqKmpYeDAgXBzc0NQUBAA4N9//8Xhw4fFnregoACXL1/G5cuX0aFDB3h6emLYsGHQ1NSU+DUSCK2dN2/e0NudOnUSOh4SEoL4+HiR5y5cuBDr1q2r1/x8AatiBBbJCqoeYuJOkBjDhg2jnwJlZ2dj8eLF1SrWFZGR+T8tNSwsDFeuXEF4eDiUlJRgYGAAFRWVBlkzHw6HI/ZamzuhoaG4ceMG2Gx2Uy+FQCAQCAQCodXDYDBgY2ODxYsX4/bt2zh16hS8vb2hq6sr0C83NxfXrl3D3Llz4ebmhu3bt6NHjx4ICgrCw4cP4ezsXKt5P336hL/++gtDhgzBsmXLEBgYWOdqaARCW+T169f0tqOjo9Dx//77r8pz+/TpgydPntRrfn72UsUILCJgVQ+JwCJIjE6dOmHv3r3YsGEDsrKyQFEULly4gC5duggIVKKQlpYW2P/tt98acqkiefHiBXr16tXo80qSQYMG4fv37wDK87XnzJnTxCsiEAgEAoFAaDswGAx07twZnTt3xqJFixAVFUVHZqWnp9P9cnJycOXKFVy5cgWampoYNGgQJk2ahJ07dyIuLg6LFi0S6F8dZWVlePjwIR4+fAgWiwUPDw94enqSVCQCoRri4+PpqvcMBgM9e/YUOM5ms/Hw4UOR57JYLFy5cqXea+B/b6uIKB8uwv9BfjsEidK3b1/s2rWL3vf398eAAQOwcOFC3Lp1S6RRO79fU/PixYumXkKdoSgKTk5OIt8ECQQCgUAgEAiNj5SUFLp27YqlS5fi7t27OH78OCZOnAgWiyXQLzs7G//99x9mz54Nd3d3XLt2Db/99huCg4Nr/VA3MzMTx48fh5eXF6ZPn44bN26gqKhIkpdFILQKzp07R287OztDVVVV4DiXyxXwyKqIvb19tdFZ4sKPwNLR0aHbqis8RiACFqEBsLa2hp6eHr1fWFiIZ8+eYf369di/f79Q/2/fvuH06dMCbY6OjjA3N4eMjAw0NTXh4eGBLl26NOi64+Pj8fHjRwQHByM/P79B55IkFEXB0dERhYWFAu3jxo1rohURCAQCgUAgECoiJSUFW1tbLF++HL6+vjh69CjGjx8vUsy6dOkSZs6ciaFDhyIiIgJHjx7Fy5cv4e3tXas53759i40bN8LV1RUbN25EeHh4lQ+TCYS2RH5+Pm7fvk3vT548WagPk8mEm5ubyPN9fX0lso6SkhIAEPju3FhFzFoqRMAiSBwpKSns2bMHw4cPh7a2tsCxY8eOITAwkN6nKAq///670BgcDgdnzpzB69evERAQgI0bN2LEiBH08VGjRsHPz0/iax8/fjxmz56NGTNmSHzshoCiKNjZ2Qn5HQQEBBAzTwKBQCAQCIRmiJSUFLp3744VK1bg3r17OHLkCMaNGwctLS2BfpmZmbh48SKmT58ODw8PlJWV4dixY7h//75QulN1sNls3LhxA9OmTcOYMWNw4sQJZGZmSvqyCIQWw+PHj2nxyNzcHD169BDqU1ZWhrCwsDqNb2RkVGMfPT09eHh4AICAX564qcNtFSJgERoECwsLbN68Gb6+vrhx4wYcHBzoY+vXr0dpaSkAwM/PD0+fPhU6PywsDFu2bBFoU1NTo7cDAwNBURQCAgIaZP3fvn0Dj8drkLElBY/Hg52dnVD7w4cPiXhFIBAIBAKB0AKQlpaGnZ0dVq5cCV9fXxw+fBhjx44VupfLzMzE+fPn8fPPP2Py5MmwsLDAiRMncPz48VoVO0pISMDevXsxZMgQLF68GI8fP6bvywmEtkJF+5qKhcgqcvfuXQFz9dqQl5dXY5/z58/DwMAAgKCARVIIq4cIWIQGhcFgwNjYWEBoyc/PR2FhIQoLC7Fjxw66fezYsVi4cCG9f/v2bZw8eZLe7927N9TV1QEAWVlZ8PHxEcpVlhQ+Pj7N2kCPy+XC3t5eqP3x48f074hAIBAIBAKB0HKQlpaGvb09Vq1ahfv37+PQoUP44YcfoKGhIdAvIyMD586dw9SpU7Fy5Up4enrixIkTWLNmjdhz8Xg8BAYG4pdffsHQoUOxa9cuxMfHS/qSCIRmR15eHl69ekXvu7q6CvUpLS3F4cOH6zT+woULxRKwlJWV6e2KAhZJIaye5vsNndBqSEtLw6FDh+j9SZMmQV1dHS9evKBNx7W1tbFgwQJMmTKFDqUEgH379uHRo0cAABUVFWzbto1WyMPDw3HmzBn069dPoutVVFTE0KFDJTqmJCkrKxMZNv7kyZMGE/QIBAKBQCAQCI2HtLQ0evbsidWrV+P+/fs4ePAgRo8eLZCRAJR/2T1z5gymTp2KY8eOwdvbG4cPH8YPP/wg9lzZ2dk4ffo0fvjhB0yZMgXXrl1DQUGBpC+JQGgWPHr0iLZfsbGxgaGhoVCfmzdvIiUlpU7j//vvv2L1O3PmDL3N5XLrNFdbhAhYhAaHzWYLpOPxzcVDQ0PptpEjR4LJZILBYODXX3+l85ApisKaNWvw/v17AICtrS3k5OTo8/bu3Ytnz55JfL03b96U6JiSorS0VCAdk09gYGCtwscJBAKBQCAQCC0DGRkZODg44Ndff4Wfnx8OHDgALy8voQeXqampOH36NGbNmoUXL17gp59+wo4dO9C5c2ex54qMjMTmzZvh5uaGdevWISQkhBi/E1oVFdMHBw8eLHScw+HgyJEjdR6/qqqfpqamAvu3bt2itz9+/Ehvm5mZ1XnutgARsAgNTvv27dG1a1d6n19ytKKAVdE4T1ZWFn/88QethhcXF2Px4sXIyMjAb7/9Bg6H0+Br3rhxY51znhuKkpISODo6CrUHBgaCyWQ2wYoIBAKBQCAQCI2JrKwsevXqhXXr1sHf3x9///03Ro4cKfQgMyUlBadOnYKPjw++f/+OqVOnYvHixZCRkRFrnuLiYty+fRszZ86Ep6cnjh49SsylCS2enJwcvH79mt4XlT547dq1Bvlb55vG80lISKDFrri4OLrdwsJC4nO3JoiARWgUpk2bRm9fvnwZfn5+9H9UBoMhIHABgIaGBnbv3k3nBqenp8Pd3V2g3GlDs2bNmmZjallcXIxevXoJtT99+pSIVwQCgUAgEAhtEFlZWfTp0wcbNmxAQEAA9u3bBw8PD6F7w+TkZJw4cQK7d++Gnp4epkyZIlDduyaSkpKwf/9+DB06FAsWLEBAQECzuUcmEGrD8+fP6XS9Ll260CbqfNhsttgpgLWlckoiRVH48OEDAEEBy9zcvEHmby0QAYvQKDg5OaF9+/YAysMqV65cSYcjd+rUScDEjk+HDh2wfft2kVUh+vTp07ALBhAdHV1n8z5JwmazRV7vs2fPRP7eCC2L0tJSfP78GU+ePMGJEydw4sQJ5OfnN/WyCAQCgUAgtCBkZWXRt29fbNy4EQ8ePMCePXswYsQIITErKSkJJ0+exO3bt2FsbIxx48aJHfFBURSeP38OHx8fuLu7488//xRIfSIQmjuRkZH0du/evYWOX7p0CdnZ2Y22nvfv3yMpKQkJCQl0W4cOHRpt/paIeDGkBEI9kZKSwvTp0/Hrr78KHatYobAyffr0gY+PD7Zv3y7QPnPmTOzatQuTJk0SUKwlzbFjx9CrV69q19iQFBYWwsnJSaj92bNnUFJSaoIVEeoKj8dDWFgYPn36hK9fvyIhIQFfvnxBSkqKgEccUF6B88CBA9DR0Wmi1RIIBAKBQGipyMrKwsnJCU5OTigpKcGrV6/g7++PR48eCfjzJCYmIjExEQDQrl07WFhYICAgQKw5cnJycO7cOZw7dw7W1tYYOXIkhg4dSjxZCc2ad+/e0duVveGKiopw4sQJic/JYDBgb2+P4OBgkeuJjo6mvwvY29uTAIUaIAIWodEYOnQoZGRkEBISQn9g8sOYq2P8+PHw9/cX8My6ceMGbG1tsWnTJsyYMaNKs7z6QlEU1q5di4sXLzb6B3J+fj5cXFyE2ol41TJZv3497ty5I1bf+Ph4/Pzzz/jnn39gbGzcwCsjEAgEAoHQWpGTk4OzszOcnZ3B4XDw6tUr+Pn54fHjx2Cz2XS/L1++4MuXLwDK/Wvl5eXpIko18e7dO7x79w5//fUXBg4cCC8vL9jb20NKiiT7EJoPpaWliI2NpfdtbGwEjt+6dQu5ubkSnVNGRgZbtmzB48eP6TY7OzuEhIQAgNB3g7lz50p0/tYIEbAIjQaDwYCbmxvc3NxqdV56ejrCwsIE2q5fv45Ro0ahS5cuuHfvHr58+QIulwsOh4ObN2/i7t27Elt3WloafvvtN2zdulVkOmNDkJubiwEDBgi1E/GqZZKamlrt36Senh5MTEygq6uLu3fvgsvlIiUlBdOmTcOBAwdgaWnZiKslEAgEAoHQGpGXl4eLiwtcXFxQXFyMly9fws/PD4GBgQJi1ufPn+ltY2NjpKamoqysrMbxS0pK4OvrC19fXxgYGMDDwwMeHh5CPkMEQlMQFxdHe7cZGBhAQ0ODPkZRFC5evCjR+ZSUlPDXX3/BwsICmzdvptvnzp2LmTNngqIogQqfffv2Rffu3SW6htYIEbAIzZ5jx44JpVgB5WmEs2bNgre3t0AIqIODg0QFLADw8/ODk5MThg8fLtFxRZGdnS2ypCsRr1out27doj+gTE1NMWzYMLRr1w6mpqYwMjKCoqIi3Xfw4MHw8fEBh8NBdnY2ZsyYgb1796Jbt25NtHoCgUAgEAitDQUFBQwYMAADBgxAcXExnj9/Dj8/Pzx9+hTFxcV0P36KIQBoaWkhKytLrPFTUlJw6NAhHD58GA4ODvDy8kL//v0hLy8v8WshEMQhKiqK3q4cffX69WsBH6r6wmAwcPToUVhYWGDBggUoLCwEABgZGaF79+5QVVUVivaaP3++xOZvzRABi9DsqSp1r6SkBH///Tfu3r2LX3/9lf6CX/EJkiTZunUrunXrBkNDwwYZHwAyMzNFRqgR8ap5kpOTg/Pnz+Pdu3dITEwEm81G586dYW9vD3t7e9qE8ebNm/Q5s2fPhru7e5VjOjk54cCBA1i0aBEKCgpQUFCAuXPn4o8//kC/fv0a/JoIBAKBQCC0LRQUFDBo0CAMGjQIbDYbz58/h7+/v5CYJa54VRGKovD69Wu8fv0aTCYTw4YNg6enJzp16iTJSyAQaiQ6Opretra2Fjh2/vx5ic518+ZNGBoa4siRI3j58iXdvmzZMlAUJSRezZo1C1ZWVhJdQ2uFCFiEZs+UKVNw9epVfP/+XeRxvl/Q6NGjsXDhwgar4FZUVIQ1a9bg33//hYyM5P/rfPv2DUOHDhVqJ+JV8yQxMRHz589HUlKSQPujR4/w6NEjAACLxULv3r3psrlMJhP9+/evcezu3bvjyJEjmD9/PrKzs8HhcLBkyRJs3rwZQ4YMkfi1EAgEAoFAIACAoqIiBg8ejMGDB4PNZuPp06cICAjA06dPweFw6jV2QUEBLl26hEuXLsHS0hJeXl4YMmQI1NXVJbN4AqEKEhMT8ezZM3q/YgRWcnIynj59KrG5evToAUNDQwQFBeHgwYN0u5OTE1gsFjIzMwX6a2trY86cORKbv7VDnPUIzR4mk4n9+/eLPFYx9erq1asYPXo07t2712BriYiIwNGjRyU+bkpKChGvWhBRUVGYOnWqkHhVmczMTNy6dYveLygowKRJk3Du3LkqBVk+HTt2xLFjx6Cvrw8A4HK5WLNmDS5dulT/CyAQCAQCgUCoAUVFRbi5uWHHjh148OABtm3bhoEDB0JOTq7eY3/48AE7duyAm5sbVqxYgRcvXoDL5Upg1QSCIImJiZg5cyays7MBAOrq6gIClqTvrd++fYuMjAysXr1awOPq6dOnmDx5Mh4+fCjQPyMjAydPnpToGlozDKrib5UAAPD29kZQUBAcHBxw+vTppl4O4f+zdetW/PfffwJtd+7cwbZt2ySqmteElJQUjh07hq5du0pkvMTERHh6egq1E/GqeRIYGIiVK1fSIfVycnJYunQpunXrBgaDgdDQUISEhCA4OBg5OTlijTl37lx07NgRHTt2hI6OjkCxgPT0dMybNw/x8fF027x58zB9+vRGKypAIBAIBAKBwKewsBBPnz6Fn58fnj9/Thtj1xcdHR2MHDkSI0eOhJGRkUTGJLRt+OJVeno6gPL79j179sDR0RFAufVM3759m3KJNOfOnWszaYT10VuIgCUCImA1TyiKgp2dnUDbqVOnYGNjg0ePHmH79u3IyMholLUYGBjgwoULYDKZ9RonISEBo0ePFmon4lXzgaIovHv3DqmpqcjKysIff/xBFxVQVVXFnj17YGtrK3ReaWkpHj16hPPnzyM8PFzs+dTV1WFpaQkbGxtMmDAB2trayMnJwYIFCwRy93/66ScsWrSIiFgEAoFAIBCajIKCAgQGBsLPzw8vXrwQq1qhONjZ2cHT0xODBg0SyLggEMTl8+fPmDt3Li1eycvLY8+ePXBwcKD7HDx4EIcPH26qJQqwaNEiTJkypamX0SgQAUvCEAGr+cLlcjF8+HD6jahr1644fvw4GAwGCgoK8OuvvyIwMLBR1jJixAhs2rSpzufHxcVh3LhxQu1Pnz6FsrJyrcYqKSmBv78/jIyMRIophNrz+fNn3L9/HydOnEBJSYnQcQMDA/z9998wNTWtcSw2m41Tp07h6tWrtRJZu3fvTqesFhYW4pdffkFwcDB9fP78+Zg+fbrY4xEIBAKBQCA0FPn5+Xjy5An8/f3x8uVLiYhZysrKGDJkCDw9PWFjY0Me3BHEIjw8HIsWLUJeXh4A0eKVqOCIpsTZ2Rm7d+9u6mU0CvXRW4gHFqFFIS0tjSNHjtAm6hEREfD39wdQ7pWlo6NT6zENDAxqNT+f27dvw8/Pr9bzAUBsbKzExCsAOHbsGNauXYtp06YR0bUepKWl4eTJk5g4cSLGjBmDw4cPixSvOnbsiBMnToglXgHlHhKzZ8/G/fv3ERoaitDQUNy7dw8sFqva896+fUunISorK2Pfvn1wdnamj+/fv594YhEIBAKBQGgWqKioYMSIEdizZw8ePHiADRs2oF+/fvUqflRYWIgrV67gp59+wtixY3H69Gnay4hAEMXjx48xe/ZsWrxSVFQUEq8A4Pjx402xPAF69epFb7948QJhYWFNt5gWAhGwCC0OY2NjTJw4kd7fs2cPXRUlKCiIbu/Tpw8UFBRqHI9fIU4cKptLbtmyBampqWKfD5QbgFdcPwAwGIw6i1dA+Rs1n127duHEiRN1GqctkpOTg8uXL2PGjBkYNmwY9uzZg9jY2Cr79+7dG//++2+N4lNN6Orq4u7duxg7dqxAO4PBgLy8PL1f8YNMTk4O27dvR8+ePem27du3w9fXt15rIRAIBAKBQJAkKioqGDlyJPbu3YuAgACsX78effv2FXgYXFvi4+Oxa9cuDB48GEuXLkVgYKDEUhYJrYPLly9j6dKl9ANoTU1NHD58WEi84nK5+Pvvv5tiiQK8evWK3i4rK8PKlStpqxKCaOouhxMITciMGTNw69Yt5OTkIDU1FWfPnsW4cePw9etXAOVG6zt37kRWVha2b9/eYCbvBQUFGD58OIKCgsR6uhQeHo5p06YJtMnIyODRo0d1Fq84HA4+ffok0LZ3715QFCU0F6GcoqIiPHnyBPfu3cPLly9FVr2RlZUVMiX18PDAr7/+CllZWYmsQ0ZGBitXroSxsTF27doFiqJAUZRAmeqQkBD079+f3peXl8dff/2FOXPmIDo6GhRFYe3atVBWVoaTk5NE1kUgEAgEAoEgKVRVVeHp6QlPT0/k5ubi8ePH8PPzw+vXr+v8Zf3Ro0d49OgRWCwWRowYAU9PT7Rr107CK68fFEXh/PnzuHz5MqysrODo6AhdXV3Y29vXKyqNIJqzZ89i586d9L6RkRH2798PY2Njob4VHwZXxtjYGImJibWae/Xq1TA0NISuri6YTCaGDBlSq/P5ZGVlgcfjQUqKxBlVBfnNEFokKioqmDNnDr1/9OhRvHjxgt43MzODvLw8DAwMsHv3bvzxxx/1jpipjnnz5tXY582bN0KCkoKCQr3EKwD4+PGjSAFm3759+Pfff6s87+3bt/Dy8oK3tzdCQ0PrPH9Lgs1m48CBA3B1dcWaNWvw7Nkzgd8dg8FAr169sH79ety4cUPgXE9PT2zYsEFi4lXFOSdPnow///xTIPKKj6h/G346oZmZGYDyp0jLly9vM/+OBAKBQCAQWiZqamrw9PTE/v374e/vj7Vr18LR0bHOX9gzMzNx4sQJjBo1Cj///DNu3LiBoqIiCa+6bhw8eBB//vknEhIS4Ovri40bN2LevHnw9vam/XwJkuHNmzfYtWsXvW9tbY0TJ06IFK/27NlT5Ti2tra1Fq+OHz+OH374Ab1794aZmRl0dHSwcePGWo3Bx9TUlIibNUAELEKLZfTo0fQXeDabjZUrV9LHOnXqRG8zGAwMGjQIV69exc8//9wga3nz5g2ioqKqPP7q1SvMmjVLoE1ZWRn+/v5VilchISG4d+9ejR/CMTEx9LaTkxPs7e3p/QMHDuDgwYMC/flPuq5evYqvX78iOjoaM2bMQI8ePQTGakmIenpHURRKS0vBZrORm5sLPz8/jB49Gv/++y/YbLZA3y5dusDHxwd+fn44cOAAPD09oaenh1mzZsHS0hJr1qzB+vXrG9Q4dMCAAfj333+hpaUl0B4TE4Pdu3cjODhYICJMXV0dBw4coD3cSkpKsGjRIrx//77B1kggEAgEAoEgKTQ0NDBq1Cj8888/8PPzw5o1a9CzZ88632+FhYVh48aN6NevHzZs2ICwsDA0Vb0yNptd5YPk2NhYeHt748OHD428qtZJZmYmVq1aRX8f6NKlCw4fPgxNTU2hvg8ePMDJkyerHKs2lcMrzl85mGDYsGFin29hYUFvDx06tNbztzVIFUIRkCqELYfXr19j7ty5Qu0+Pj6YMGGCyHOysrLg6uraIOu5ceMG9PT0UFxcDBUVFQDlxuyLFi0S6Kempobbt29XKV5VDoEdPXo0fvnlFygpKQn13bRpE65fvw4AWLhwIcaPH48lS5YI+IG5ubmhuLgYHz9+RGpqKjQ1NUUaYPJ9mZpzhZfi4mLExsYiIiICkZGRCAgIEOojKyuLsrKyam9aTE1NMXz4cLi7u8PIyKghl1wrUlJSsGjRIqG0UKDchHLw4MFYvHgxNDQ0wOFwsHfvXpw/f57uo6GhgWPHjtUpjD4xMRG3bt2Cs7MzOnfuXK/rIBAIBAKBQKgL2dnZePDgAfz9/RESElIvEcrQ0BBjxozB8OHDoa2tLcFVVk9xcTH69OlD7w8ePBjS0tIICAigxQ5FRUVs27aNWEDUg7KyMsydOxchISEAyu+Dz58/L7KwV0xMDCZNmtQg6zAwMMCkSZMwcuRIvHv3Dv/++69A5XBRSEtLIygoCAwGA9++fUNeXp6AmNWaqY/eQgQsERABq2WxevVqIRPrsWPHYtWqVVWe8+LFC/zvf/9rsDXJyspi7969KCoqwtKlSwWOaWpq4saNG1WKV1evXsWWLVtEHlu4cCHGjRsnIGRNnDiRNh0/ePAgHBwcUFxcjF9++UXAGLA2dOzYEcbGxjA0NISJiQmMjIxgZGQEXV3dRs3JpigKSUlJtFgVFRWF2NhYkSmT4qKhoYGFCxfCw8Oj2eaX5+fnw8XFpcrjLBYLXl5euH79OjIzM4WO6+np4dixY9DT06vVvDNmzKDTELds2VKrp0cEAoFAIBAIkiYzMxMPHjxAQEAAQkND6yVmOTk5wcvLC/369ZO4JURloqKi8NNPP9H7hw8fhr29PV6/fo1ly5ahsLAQQHmmiI+PD8aPH9+g62mt7Nu3j64myGAwcODAATg6Ogr1+/btW7OLbhLXQ7k1QgQsCUMErJZFVlYWRo0ahYKCAoH2q1evwtTUVOQ5FEVhxowZePv2bSOs8P/Q1tbG1atXqxSv7t27hzVr1lQ7hqysLIYNG4aBAwciJycH69evp489efKEjvzicDhYunSpgDdYfZGVlYWhoSHc3d0xa9asBovUSkhIwO3bt3H37l2kpaVJZEwmkwlPT0/MmjWL/h01Z0pLS7F+/fo6VxiUk5PDxo0b4erqKrZQN3ToUHz79o3ev379OkxMTOo0P6FpKSsrw7179wAAI0aMaNZRlUlJSbh+/ToUFBQwadIkKCoqNvWSCAQCgdAMycjIwMOHD+Hv74+3b9/WS8zy9vbGyJEj0aFDBwmu8P8oLS3FhAkT8PnzZwDlDx/Pnz8PLS0txMXFYdGiRQKVzD08PLBy5cpm+xnIZrPx4cMHWFlZifRsbQoCAwOxePFien/27NmYPXu2UL+aHgw3BR4eHnX2yWoNEAFLwhABq+Vx5coV/PbbbwJtLBYLp06dqjIKRVRFwIZEX18fly5dqlK8evTokVC0Vm0xMjKClZUVLC0t4ejoiI4dO+LcuXPIycmBubk5OnbsCBMTE2RlZSE2NrZe861ZswZjxoyp13orkpOTg/v37+P27duIjo6W2Lh8DAwMsGfPnga7UWkIKIrCyZMnsXfv3ir7sFgseHh44ObNm8jKyhLZx8DAADY2NigsLERBQQEKCwuhrKyMiRMnws3NjRY3rl27hs2bN9PnycvLw9fXF2pqapK9MEKDwuFwsGrVKjx+/BhAeZTqDz/8UO05bDYbvr6+sLCwaLT00ffv3+P48eMICAigv4SYmppi+/btNYbQs9lsvHnzBkeOHKnSf1BTUxOGhoYwMDCAgYEBVFVVwWQywWQyoaSkJLCtrKwMZWXlBn8iTyAQ2g48Hg/R0dFQV1cXaSRNqB8ZGRl48OAB/Pz8EBYWVudxTE1NMXnyZLi5uYHJZEpugSi3Zpg8eTLy8/MBlEeA7d69GwwGA5mZmViyZInAPa+ZmRm2b9/e7O5VIyMjsXz5cqSnp0NHRwfz58/H8OHDmzSTIS8vDx4eHvTvtnfv3ti3b5/Qmr5//45BgwZJdO4LFy7g7t27OHXqVJ3HCAkJadYPFxsaImBJGCJgtTx4PJ6AeXlFunbtivHjx4sMGxUV8cRPzZI0jx8/hqqqKhISEvDhwwf0798fcnJyAMpTGhcvXoyysjKJzunp6Ylly5YJiWZlZWVYsmQJnj9/LvZYsrKyAibi8vLyOHv2LG2kX1u4XC5SUlLw/v17+Pr64unTpxK5fhkZGfpLaGWzdgDo27cvBg8eDGVlZTCZTPqLK//LrKKiotAHCkVRKC4uRmFhIYqKimgRiM1mo7CwkH6x2Wxoa2vDy8sL0tLS9b4WPvwqPSUlJQLtK1asgJeXF+Tl5ZGXl4edO3fi1q1btRrbyckJq1evhq6uLiiKgp2dncBxe3t77N+/n3yxr0BISAiOHDmCLl264Oeff25WT0vz8/Pxyy+/0F4QfCZNmgRVVVXo6OiAxWJBR0cH2traUFNTQ2FhIWbPnk0XcVi4cCGmTp3aIOujKArBwcE4ceJElSnOsrKy+OWXXzBu3Dj6/yKbzUZERARevnxZrxtGcRg0aBBGjRoFW1tbKCkptekbTELrJyYmBvv374elpSXmz58v0c+utkpubi5u3LiB//77D8nJyWAwGFi1alWNDxIIdefbt2+0mBUREVHncdzd3TFmzBjY2dlJ7L2/sm3J+vXr4enpCaD8s+3333/HnTt36OPKysq4cOECDA0NJTJ/faAoCleuXMGOHTuE7tEdHBywb9++Jrs/9PPzowt46ejo4Pz589DQ0BDoU/nBrCQIDg6GtLQ0eDweNmzYgNu3bwv1sbW1rdYM3tXVFdu3b5fouloaRMCSMETAapk4ODhUK4AcO3YM3bp1E2oPCgrCnDlzBNoMDQ2RnJws6SVi0qRJOHv2LACgQ4cOWLlyJd6+fYsDBw5IfC4+BgYG2LJlC33tFEVh5syZtNdRfdmwYQN0dXVhaGgIXV1doQ+ykpISfPnyBZ8/f0Z8fDw+f/6MT58+IT4+XiLzSxoGg0GLWjweD0VFRSgqKqpVmPr06dMxf/58ia4rMjISS5YsETDfnzt3LqZPny7wtOnly5dYsWKFUEptdSgpKWHBggXo06cPxo8fj+LiYoHjnp6eWLduHfkij/KnqRMmTKDFUSMjI6xfv15I+KsrbDYb79+/R0REBD58+AB5eXno6elBV1cX+vr69LacnBwoihL4t8/KysL06dPx9evXeq+DxWJhy5Yt0NXVhba2tsgCErWBy+Xi4cOHOHnyJN69eyd03M7ODtHR0QJ/e0wmE8OGDUN0dHSDRGVKggkTJsDBwQFqampQU1ODuro6VFVV26ynBaHlEBUVhdWrVyMpKYluW7JkCby9vZtwVS2b9+/f4+LFi7h3757QAycAGD58OGbPnt2sCse0RtLS0hAQEAB/f39ERkbWeZy5c+di5MiR0NXVrfeatm3bhkuXLgEov+e6dOkSXUWaoijcvHkT27ZtA4fDAQBMnjwZv/zyS73nrQ0UReH79+9IT09Heno6vn37hjdv3sDf37/KcyqKcY3N6dOnsWvXLgDAuHHjBKrRJycnw8PDQ+Jzjh07Fubm5khKSkJycjK+fv2KuLi4Gs/T1NSEt7c3KIrCly9f8PPPP7f5qEwiYEkYImC1THr06FHt8erU7spV/wiE+iArK4urV69K/OlZcnIyFi5cSPspAIC5uTlmzJiBwYMH02JGUVERbt26hejoaJFPhurCokWLMGXKFLH6crlcJCQkwNDQEAoKCjX2Z7PZ+Pr1K758+QIulwt7e/tGrRQkLmVlZZg+fbrQDTGDwcD48eOxYMECsaOxysrKkJ+fj5iYGLx48QKvXr2ql6iroaGB79+/1/l8cVBTU4OlpaVQFBd/X1tbW6SAffv2bZw8eRKJiYkCxxgMBgYPHoxp06ahffv2uHnzJrZu3VrrdTGZTDg5OUFZWRnJyclITk5GSkqKxCNaa7smvqDFF7cqilyV29XV1UVGfxIIDcWcOXMEqhUD5Z9dp0+fhoWFBflbFJOSkhIEBATg4sWLIsUSBoMh8ABMWloao0aNwvTp0yUijBCqJyUlhRaz6vogpH379pgzZw5cXFzozInawmazMXHiRPoBk52dHQ4dOiTwEOrJkydYsmQJAEBdXR2+vr5izcflcsFms8Fms1FcXIzi4mKB7Yr7lX/m5eXRglV6erpApkVlLCwssGXLFpw5c4aO9u/ZsycOHTpUp99JbWGz2QgLC0NISAhiYmLw8eNHupDRggULMG3aNFAUhYEDByI3N7dR1lQTCgoKmDt3LiZOnEgebFWCCFgShghYLY+PHz+KVb3Dz88PLBaL3qcoCu/evYOfnx/5tyZIHDc3N5SWliI1NZWuFLl58+Z6VffLz8/HsmXLhErzGhkZoW/fvlBXV0dmZibs7OwwYMAAyMvLIz4+Hvv27cOTJ0/qdT1//fUX+vfvj+zsbKxevZr+8uPj44MJEybQ/TZu3IgbN27AwsICZ8+ehYyMDHg8HlJTU5GQkIAvX77gy5cv9HZ6errQXJ07d4aLiwtGjRoFTU3Neq1bUhw8eBCHDx+uto+7uzssLCyQn5+PwsJC5OXl0T/T0tKQkZHRSKsVDZPJhJSUFKSkpJCTkyPx8TU0NMBisaCkpFRl+LyMjAyGDRuG7t27Iy0tDSEhIQgPD6/2xrkyw4cPx9ixY9G5c2f6CwCXy0VaWhoSEhLw9etXfP36Fbm5uQgICGhSMUtcZGRkaGFLXOFLTU2NpHwRagWPx0NGRka11biUlJRgZmYGU1NTmJiYoF27djA1NYWxsbFYDyXaAmlpabhy5QouXbpEe/BUxMzMDJMmTYKzszN+++032pOQj6ysLFavXt1k0SttkeTkZPj7+yMgIEBkJLA4jBgxAt7e3jX6NIoiIiKCFlkAYOnSpZg0aRJ9nMvlYsSIEXQxnW3btsHNzU1oHA6Hg8jISLx58wahoaGIiIgQGfEnSYYPH47Vq1dDUVERGRkZGDJkCH0d/AroDcmDBw+wZcuWKoUpvg9yTYWwGhJFRUVoaGhAU1MTGhoaMDExwYQJE5pFKmhzhAhYEoYIWC2PT58+YezYsTX2YzAYePHiBb5+/Yr79+/Dz89PIHyeQGgs9uzZAycnp1qfl5WVhadPn2Lnzp10CeaqUFVVxfDhwzFq1CiYm5sjLCwMe/bsqTYvvy6oqanh0aNH9H7FaEgFBQUsXboUly5dwsePH2s9tomJCa5evdqgRqFsNhuZmZnIzMxESUkJysrKwOVyQVEUuFwuuFwu7t+/j4cPHzbYGgji8ddff6FHjx4CAih/++vXr7USwSSNpqYmHVnXmLdWTCZTQPjS1NREhw4d0LFjR1hYWDQbAbgmeDweOBwOHUlQVFQk9JP/4vcR1V7xuIaGBjZv3oxOnTo19eU1CPn5+QgNDYWlpSX09fXpdg6Hg5SUFCQmJiI5ORmJiYlISkqi017q8/9ET08PJiYmMDU1Rbt27eiXnp5encRUfiSqkpIS2rVr16yrBBcWFuLcuXP4559/auxrZGSEuXPnwt3dHVJSUggJCcGBAwcEql+rqqoiICCARGY0AUlJSXRkFt//sbYsW7YMI0aMgKqqqtjn7N+/H0ePHgVQXi363LlzAl6yFR+UOTo64p9//kFxcTEiIyMREhKCkJAQRERENNhnHZPJhI6ODv3S1dWFra0tevfuLRCVWTmC89q1a2jXrp1Yc1AUVW2EZ3JyMi5duoQvX77A09MTDx48wN27d+t+URJCS0sLJiYmMDIygqGhIYyNjWFoaAgtLS1oamo2Kz/UlgARsCQMEbBaJrt37xbL3FdZWbnKL/6qqqpwcXGptRE2gVBXjh8/Dltb2xr75efnY+XKlXj58mWd5jEzM4O3tzdcXV0RHByMffv2ScyHbMSIEdi0aRMAID4+vkGMas3MzGBhYQF9fX3o6upCT08P5ubmUFVVRXh4OOzs7ARuHiiKQkFBAS1MZWRkICMjA1lZWcjIyKDbMzMzaxQCCW0LFRUVeHl5QVtbG2VlZXjx4gXevHkj9vlTpkyBjY0NNDU1kZeXh6tXr+LZs2cNuOLqkZeXh4mJCX2zbWRkBF1dXZSUlAiJQfxCFVWJRPztlhDRxkdaWhp37tyBjo5OUy9FYnz58gUHDx7E/fv3hY6xWCxkZWU1qogKlEcUGRsbo127dkICl7q6Ov2FtbS0FOHh4QgKCsKrV68QHR0tsFYNDQ16nHbt2sHY2BimpqYwMjJq9C+IwcHB2Lx5c70edJqbm2PevHlwcXEBUO5TuXLlStqn8tChQ+jZs6dE1kuoG4mJiQgICICfnx8dLV8bjIyMsHr1ajg4ONT4sK20tBTe3t748OEDAMDa2hrHjx+n0+9TU1MxfPhwur+Ojg6ys7Mb7T3X2NgYPXv2RM+ePWFnZyeQtVKRhw8fYtmyZfT+77//jiFDhgj1S0xMxI0bN/D8+XPk5eUhJycHHA4HNjY2mDhxIsLDwzF8+HB07twZsbGxOHr0KAICAhrs+urKnTt3BB4SEOoPEbAkDBGwWiZxcXEYN25crc9TUlLCwIED4ebmBkdHR8jKyoqsxkYgNCSzZ8+GiYkJ8vLykJeXh/z8fOTm5iIhIaFeJqSi8PDwwJgxY/Dp0yeJVGfp2bMnZsyYAXl5eRQXFwsVRSAQmgN///032rdvj7S0NCxcuJAWLjU0NODh4QEvLy+YmprS/SmKAofDQX5+PtLT0/Ho0SPcunWrydNACbWDwWDg3r17DSJgURRFC3taWloS9Y3icrlIT0+nI6cSExNx5coVkelqtUVdXV0ghXjEiBFgMBgCD+8MDQ3x008/0f6ECQkJSElJAY/Hq/f8dUVXV5dOaTQxMaG3DQwMJF4J7dmzZ1i4cKFYfXV0dGBjY4OOHTuCoiicO3dO6N9JSkqqyt9djx49oKGhAXV1dYGf/G1NTU1oa2vX+PeVlZWFiIgIhIeHIyIiAu/fv4eKigq6du2KLl26wNbWFlZWVpCXlxfvl9DGyMzMxLt37xAZGYnAwMA6RY17eHhg9uzZtEG7KD5+/IhJkybRotT48eOxYsUKxMfH488//6yyQq849O3bFzY2NigrKxN6lZaWgsvlorS0FDk5OYiMjBQq2lMZPT092NjY0K9OnTqByWQCEIy2X716Nf3wsrS0FI8ePcKVK1eE7C5aIuvWrYOXl1dTL6PVQQQsCUMErJZLxYoUopCWlgaXy4WCggKcnJwwZMgQ9OnTR+SHeUOUXiUQCAQCgdC4ODk5oW/fvujduzf09PSqFDt4PB7y8vLw/ft3gVdOTk6VbfxUHgMDA4wYMQLDhw8Xu7pUcXExXXggMTGRTvnjp/pJIuqiffv28PDwoFNejIyMwGQyMXv2bPrLpaGhIZYvX46QkBCB+94ZM2Zg3rx59H5paSmSk5MFUni/fv2KhIQEgQq5jY2UlBQMDAwEorYsLCzQpUuXOgtbFy9erLbMvaenJ4YNG4aOHTsKpZDl5+fj9OnTOHv2LF2xVlL89NNP6NKlCzp16oT8/HxarAoPDxereraMjAw6deokIGq1RTP54OBgnD17FoGBgSKPq6ur01GqdWX69Ono2bMnsrKykJqaSr9evHhR5zGZTCbYbDa4XK5Au4WFBX755Rc4OjqKPVZpaSmioqIQHByMN2/eiOVFyWAw0L59e1hbWyM8PJwuztK3b1/s27cPERER2LJli1iV+VoCXbp0wbFjx4jXZANABCwJQwSsls0PP/wgMjWKxWLh6NGjSEpKQrdu3QRC0Xk8Hi5cuIDCwkL89NNPiI2NxdSpUxtx1QQCgUAgEFoLrq6umDdvHlRVVWlRqqIXVVJSkkSi+VxcXEBRFIKDg6sUS8zMzDB27FgMGzaM9pg6dOiQUPUwOzs7hISE0PtSUlIIDAyEkpKSQL/c3Fzcv38fBQUFkJOTQ1lZGV6+fNnsoi1UVFTg5OQEFxcX9OnTB8rKymKfW1xcjBMnTuDw4cNQVVVFv3796Bc/AqUmsrOzcfz4cVy7dq1eQkhDw2Kx0KlTJ1hbW9MvLS0tADX7FYkLh8NBVlYW/crIyEBJSQns7e1haWnZaFUvi4uL0adPn0aZqzHQ0dHB3LlzMWLEiHqLLBwOBxEREQgJCUFwcDCio6PrbQ7PYDBgaGgIBoMhVIm4qTEwMEBKSkqVx3v37o1t27Y1a1++lgwRsCQMEbBaNjweD/v378fx48fRrl07fPnyhT62YsUKkdUKAwIC4OPjA6BcAAsICGiQCl0EAoFAIBAIdcXc3Bw///wzjIyMkJ2dTXv5ZWVlISUlBc+fP6/yXEVFRQwZMgRjx46Fnp4eBg4cWO1cDAYDFy5coA32o6KicOfOnTqbXjclMjIycHR0hIuLC1xcXKCtrd1oc/N4PHC5XBw6dAjHjh0TOm5iYgIzMzPEx8fj69ev9Zqrc+fO6NGjB2xtbdG5c2eEh4dj5cqVtU791NHRoSsEGxkZYdWqVbh58ybS0tKgrKyMkSNHYvDgwcjNzaVFKb7fZOW/y8zMzGpTX01MTODu7g5XV1eYm5vX6/qrIz8/H4sXLxYw0q+IvLw8NDQ0kJGRIRTh1NzQ1NRE//79ERERgezsbIEISFNTU1haWmLYsGHo3bs3ZGRkUFBQUGshprS0FPHx8YiKikJUVBSio6Px6dOnRvfXkzTTp09HXFxctZW5x40bh2XLlpECCw0IEbAkDBGwWhfr16+nfR0YDAYcHR2ho6ODTp06oV+/fjA0NKwyaotAIBAIBAKB0PqwsrLCTz/9BBcXlwY3iPf19cXq1asbdA6gPJpq2bJlkJaWxrVr1/Dy5csWJTiYmZlh8ODBcHd3R/v27ZGeno7w8HBERkYiMjISJSUl6NKlC+zt7dGjRw+xq6yWlJRgypQpVZq0u7q64rfffoO0tDR4PB4yMzPplL+0tDSkpqYiMzMT5ubmGDduHFgsFiiKwqdPn3D+/Hlcu3ZNkr8GIcaOHQtTU1PIyMhATU0NTk5OWLRoUY0FRphMJl0wgO9h5e/vj8WLF8PT07PGeTMyMvD69WvExMTg/fv3CAsLa1F/TxXx9PTE169fqxQw+fj4+GDChAmNtKq2CxGwJAwRsFoXubm5mDlzZpX52BWfMhEIBAKBQCAQCHzk5eUhJSUltp8Vg8HAzJkzoaioCCkpKUhJSeHkyZPIzMwUe87qTN9bIlJSUtDU1ASLxYKWlha0tLTAZrPx7NmzevmEdejQAXZ2djUKWg8ePMDy5curHIfFYqGgoAAcDgfKyspQUlICk8mEsrIy/arYxt+Wk5MDRVEoKytDbGwsLl++XKfrsLW1xbZt23Dx4kWcOnVK5L/9jz/+iMLCQsTFxWHMmDF09ee6EhwcLJR2WFJSgrCwMLx48QIvXrxoNV5W4qCoqIht27bBycmpqZfSJiACloQhAlbr4/v371i8eLHEq7kRCAQCgUAgEAiSpE+fPrCyskJpaSmuX78ukeqTTYmBgQE6dOiA9u3bo3379jA1NQWLxUJ0dDT27dsnlgE9QbLo6+tj7Nix6Nu3L0JDQ/HixQu8efNG4oUHWgJ6enrYs2cPLCwsmnopbYb66C0ksZPQJtDQ0MCJEyfw8eNHpKWlITExEa9evcKbN2/A4XCaenkEAoFAIBAIBAIA0BEwrYWUlBSkpKTg6dOnTb0Uwv8nNTUVe/fuxd69e5t6KU2KjY0Ndu3aBRaL1dRLIYgJEbAIbQYGgwFLS0tYWlqCy+XSBp7VGZ4SCAQCgUAgEAgEAqF1sWjRIowfPx4KCgpNvRRCLSACFqHNUVJSAh8fHwQGBjb1UggEAoFAIBAIBAKh0TExMUFBQYFAFcO2wJw5czBr1qymXgahjhABi9AmKCwsxI0bN8BgMHDt2rU2ZUpIIBAIBAKBQCAQCBX5+vUrxo0bh0uXLjX1UhoNb29vIl61cIiARWj1xMXFYfny5fjy5UtTL6XJkJOTg4uLC6ysrGBoaAh9fX2oqKggPDwc/v7+CAkJqVWFHQKBQCAQCAQCgdCyaUvilYODAxYsWNDUyyDUEyJgEVo18fHx+Omnn1BcXNzUS2lSSkpK4O/vjxcvXkBXVxdKSkqIi4sT6/dia2sLFxcXuLi4oH379tizZw9OnjxZq/mZTCYKCgrqunwCgUAgEAgEAoFAqDPbtm2DjAyRP1o65F+Q0KrZv39/mxevKlJYWIj4+Hix++vo6IDJZCIhIQEFBQVgsViwsrLCzJkzoampCVVVVXA4HKSkpCAwMBAfPnwQOQ4RrwgEAoFAIBAIBEJt+f3338FkMrFw4UKRx8eMGYOcnBwkJyfj/fv3Ivu4urpCXV29AVdJaCyIgEVotZSVleHRo0dNvYx6M3bsWHTt2hXKyspQUlKCkpISFBQUwOVyUVpaCi6Xi5KSEpSWlmLfvn2IjY2lz5WSkgKPx6vz3Onp6UhPT5fEZTQL5OXloaysDCaTCSaTSW8rKytDWVkZKioqUFJSAo/Hw9evX+l+FEWhpKSE/l3n5+cjJyeHfuXn50NBQUHgxePxkJaWVidjzGHDhqF9+/YoLCxEYmIiuFwuevbsCX19fRgZGYGiKLx79w6pqal4+/YtgoODG+C3RSAQCAQCgUAgNB5eXl6wsbFBfn4+Bg0aBB0dHfTp0wcURVV5TmpqKjQ0NGBnZ1elgJWUlNRQSyY0MkTAIrRabt++3dRLEEBeXh5mZmbo0KEDOnToAHNzc4SHh+Pff/+t9ry5c+eK/cSgY8eOcHV1pfdrEq+kpaVhY2MDXV1dZGdnIysrC5mZmc0+YsrIyAhmZmZQUVEREp+YTCY4HA6SkpKQm5uLAQMGwMbGBsrKypCVlW3qpYPNZiMhIQHx8fH069OnT0hOTqY/nIcMGYJ+/fpVO46FhQW9feXKFfz2228Cx6WlpeHs7AxjY2PExsbi9evXAMr90EpKSiR8VQRpaWmYmZnh48ePDToPg8GAk5MTunfvDgUFBZSVlaG0tBRlZWUoKytDSUkJysrKkJ2djfDwcCQnJzfoeuqDtrY2LCwsIC0tjeLiYhQXF6OgoACfP38WewwWi4V27drB1NQU7dq1Q7t27aCpqYmioiIkJCTg06dP+PTpEyIiIlBaWtqAV1OOoqIiFBQUoKioCFlZ2TbtvUggEAgEQm3w8PDA5MmTYWJiAhkZGTx79gyenp41nvfixYsa+8TExIDD4UBeXl4SSyU0IUTAIrRKioqKsGnTpqZeBgDgzz//hIWFBQwMDCAtLS32eUwmE/PmzatRvMrPz8fz58/x+PHjat/ApaSkYGdnh/79+8PZ2RmGhoZV9uVwOMjKyqJfmZmZyMzMpEWuoqIiyMvLC0UdVX5V7MPfLisrQ1RUFGJjYyErK4vc3FwkJSUJRI5VhsFgYMiQIZgzZw6MjY1r/N01ZxQVFdGpUyd06tRJoL24uBiJiYlQV1eHtrZ2rcYcPXo0EhIScO7cOXTo0AGenp4YOnQoNDU1RfYvLS1FdnY2MjIy6J/8f9/s7GwoKSmBxWJBW1sbUlJS8PPzQ2hoqMixtLW1kZGRUav1tlRMTU2hpaUFTU1NaGlpgcViQUtLCx06dICFhQXk5eVRUlKCH3/8sdpUXXV1dQwbNgx5eXnw9fVFWVmZyH4sFgu9evVCz5494ejoCB0dnTqvnaIopKamIioqCl+/fkVZWRm4XC7KysrA4/HoaE5+W8WfJSUltMBUXFwMDocDPT099OvXD927d4eUlBRKS0tRWlqKzMxMREREICIiAu/fv6/y2gAgIyMDGRkZMDMzQ5cuXWBra4uuXbvC1NQUUlJSAMrfiyIjI/HmzRskJydDX19fQLBiMplVjt+uXTtQFIXExMRqn9yKg7m5Obp3744ePXqgQ4cO0NLSgpqaGr1OceDxePj06RPCwsKQmJiI0NBQvHv3rl7rIhAIBAKhtXDr1i3cunWrwcaPiYlBt27dGmx8SfDx40fs2bMH3bp1w/Tp08FgMJp6Sc0OBlXfu7pWiLe3N4KCguDg4IDTp0839XIIteTTp08YO3Zso8xlbm6OuLg4kcdMTExw5syZar9glZaW4vfff8eNGzcE2o8ePYru3btXeV5ycjICAwPx+PFjhIaGgsvl1rjWsWPH1iqaS5JQFIXs7GyBqKN3797hw4cPNUZF9O/fH/PmzYO5uXkjrbblwuPxavWFujYcPHgQhw8fpvc1NTVx/fp1+u+7pKQEsbGxiIyMRGRkJEJDQ1ucsKWiooJevXqhR48e0NfXh6amJlgsFjQ1NSEnJyf2OFFRUZgyZYpI0cTd3R0+Pj7Q0NAAUP4eEBQUBF9fX8TExMDY2Bi9evWCg4MDTE1NW/SNC4fDQUxMDMLDwxEZGYnw8HBkZWXVeB6TyUTXrl3RtWtX2NrawsbGptr3UT4URSEuLg6PHj3CkydPEBMTU2VfHR0dujiFiYkJOBwOZGVlkZOTA11dXVq8bWzy8/MRFhaGkJAQhIWFITs7G3l5ecjPz6+3CNcWYTAYdPo9/6e6ujpsbGxgZmaGlJQUvH//HrGxsSRajkAgEJo5q1evhpOTE3R0dPDp0yeMGzeu1mPMmjULWlpa4PF4yM3NRV5eHnJzc5GTk4O8vDzk5eXB0tIS69atg7KycgNcRfX4+PggICAAALBo0SJMmTKl0dfQGNRHbyEClgiIgNUyKS0txZYtWxpUua+MpqamSI8jBQUFnDp1SmzRhcfj4e7duygqKsLw4cOF3jB5PB7evXuHJ0+e4MmTJ1WKZgCgr68PZ2dn2Nvb48SJE4iOjqaPycnJYciQIZg4cSI6duwo5lUKwuVyERcXh7i4OBQVFdERGaJ+FhcXIy8vDwkJCcjLy6v1XKdOnULnzp3rtE6CZOHxeDh27BiCg4MxduxYDB48uMZzsrOzERISghUrVjTCCmvH9u3bBdJtJU3lap1aWlpYs2YN+vfv32BzNncoikJKSgoiIyMRFhaGyMhIxMbG1pjqzGAw0KFDBzpCy9bWFsbGxmAwGCgtLcXbt2/x+PFjBAYGIiUlpcpxLC0t0b9/f7i4uMDKyqpFi4MVKSkpEfDkS01NxcuXL/Hs2TMUFRU19fIkCpPJhKysLL5//063MRgMDBgwAAMGDEDnzp2hoqICFRWVOqWM83g8pKamIi4uDh8+fMCzZ88QGRkpyUsgEAgEQj0xNTWFg4MDLCwscPPmTURFRUn8IU+/fv2wa9euWmXPSIIePXoI7FeVAdHSIQKWhCECVsvkzJkz+Ouvvxps/PHjx+OXX36Bo6NjjX23bt0Kd3f3es1XXFyMoKAgBAYGIjAwEJmZmVX2tba2hrOzMwYMGABzc3P6i1lRUREWLVqEkJAQoXO6deuGCRMmYMCAAdXe6LPZbERGRuLt27cIDw9HRESERL8U6enpwdramk6rs7KyQllZGbS1tVvNF8y2DEVR2Lx5MwICAjBz5ky4ubkhPz8fRUVFKCgoQEFBgdB2fn4+CgsLkZeXh6CgIIHxrKysYGxsDG1tbWhra0NXV5dO5dPV1cX379+hqKgIJpMJGRkZ8Hg8SEtLN/rfEofDwbJlyxAcHAw3NzcsXboUampqjbqGlgCbzUZ0dDQdoRUREYGcnJwaz1NXV4eFhQViYmKq9OyTlpYWSJs2MDCQ8OpbDt+/f8fHjx9RWFiI4uJiZGVl4du3b/j27RvS0tLw4cMHlJSUQFVVVSj9W1pamk4j5XA4Ai9J+OkpKirSKaGmpqYwNTVF+/btYWxsDAUFBQlcff0pKChAaGgovnz5gq9fv+L9+/cCD4cIBAKB0DSYmZlh9OjR+PPPPxtk/O3bt8PBwaHR7uH279+Po0eP0vu3bt2q1valpUIELAlDBKyWB5vNhoeHR50qvomLra0tNDU18e3bt2p9SyZOnIjly5fXaY7s7Gw8ffoUgYGBePnyJYqLi0X2k5WVhYODA1xcXODs7FytN05paSnu3r2LCxcuiPSZYrFYGDNmDMaMGQMWi4WMjAxarAoLCxMrQkIcFBUVYWZmhvbt28PMzAyWlpawsrKq0qeJQGjpUBQFLpcLGRliNykuFEUhKSmJFrPCw8MRFxcn9pNVZWVl9O3bF/3790ffvn2hoqLSwCtu2/B4PJSUlNBiVsUIXH47/1VRAJOTk0P79u3Rrl076OjotNiHFdnZ2XRE8rNnz/Dq1aumXhKhjSKOLyn/paenhy5duqBTp05QVFQEUH6vmJ6ejrS0NGRkZEBNTQ2mpqbQ1dUFh8NBeno67ZkYExODhw8foqioCFwuF7m5ucjIyEB+fj5YLBYWLlwIGRkZlJWVIS0tDRcvXhQrfZxAEEX37t1RWFiIDx8+iH2OiYkJFixYgPPnz0skgonBYMDa2hq9evWCo6MjbG1tG6ww1MWLF7F9+3Z639vbG0uWLAFFUS32s1IUzU7Aunr1KlatWoWzZ8/C3t5e4Fhqamq1KRQ9evTA+fPnBdq+ffuG/fv34/nz58jIyIC+vj5GjhyJmTNn1sqXRFyIgNXyOHHiBPbu3dvUy4CtrS0OHz5cqze1z58/06mBERERVX5RU1NTg5OTE1xcXNC7d28oKSnVam0URSEiIgLnz5/HgwcPhHyzZGRkoK2tjdTU1BrHYrFY6Nq1K9TV1QWe1ou6gVJUVISJiUmL/pJCIBCajsLCQkRHR9OiVkREBPLz8+njurq6cHFxwYABA9CjR49mUW2U0HbhcrlISUlBUlISXr9+jSdPnhB/rVaAtLQ0lJWVISMjA2lpacjKykJKSkrovobBYND3caLueSq3ycjIwNHREZ07d6aLhLBYLKioqOD58+eIiYmBqakpOnXqBBkZGeTn50NWVlaoYE5TePbVBg6Hg/v37+P8+fPVFu0hEJqSvn37AgCeP39ebT9FRUU4Ojpi6dKlEomO4ldh/vfff3Hp0qVq+3p4eGDdunWNntooaZqVgPX27Vv8/PPPKCoqEilgPXjwAPPmzUPHjh1haWkpdH779u0xf/58ej8tLQ3jx49HWloarK2tYWxsTJsDOzg44NixYxK/WSUCVsuiqKgII0aMECvtpCHR0tLCuXPnxKog9/79e9y7dw9PnjzB169fq+xnbGyM/v37o3///ujatavE3qwyMjJw5coVXLlypcanYgwGA2ZmZujevTtsbW3RrVs3GBgYEDGKQCA0CTweD1++fMGnT59gZGSEjh07kvcjQosgJycHUVFRSEhIQEFBATIzM/H161ckJibi27dvTb28NoeWlhbat29PR4bzt1ksFnlPaSAoikJYWBh8fX3x/ft3vHnzpsnv3wkEPgoKCvDw8MDIkSPh7e1dY/8ePXrgyJEj9Xq/OHfuXK3TH1uDuXt99BaJ5jXcv38fq1atqtYfh18VaMaMGRg5cmSNY27YsAFpaWlYtGgR5s2bB6BcsJg/fz5evHiB06dP4+eff5bMBRBaJBcvXmzwD7+JEyfC1tYWUlJSkJaWhpSUFLZv3460tDQAoPfFEa8qh4ZWhMFgwNbWFs7Ozujfvz9MTU0leRk02tramDNnDqZPn44HDx7g/PnztFGtnJwcXdKe/1JVVW2QdRAIBEJtkZKSor9oEggtCXV1dfTr1w/9+vUTq39paSntD5iXl4fk5GRa8AIAVVVVSElJ0X34r8LCQuTn56OgoABsNrshL0ki2NraQldXl45o4v/kb1d8MRgMsNlshIaGIjk5ucaxGQwGDAwMRApVJMW48WEwGOjevTu6d+8OLpeLgQMHSnwOBwcHrFmzBsbGxsjKykJ0dDSio6Px7t07cDgc6OvrIzk5udWaYxPqTnFxMf777z/8999/YLFYQv7DAwYMwIcPH+j3ntDQUDx79gxOTk61mic7Oxt///039PX18c8//9R6nVV5f7YVJCJgpaWl4a+//sKNGzegqKgo8h+cD987yMbGpsZx4+Pj8fjxY5iYmGDOnDl0u5KSEn777TcMHjwYZ86cIQJWG6awsFCg0ldD8ODBA7rkPZ9bt27R4hUALF68WKhqhChOnz6NXbt2CbQpKCigd+/ecHZ2hpOTU6N6QsnKymLIkCEYMmQIvn79isLCQpibm5MUHAKBQCAQmhhZWVloaGjQ9yDi3DtXhsvl0oIWv0gGX+jiF8zg+5LJy8ujqKgIubm5sLKygoqKCrhcLtLT02nDf775v5ycHHr27AkHBwf06NEDWlpakr58seDxeHj//j0CAwMRHh4OFRUVWqDi+6w1l2IABEEoioK8vLxASnhdYLFYMDMzQ4cOHeDg4ABnZ2c6IkZLSwvOzs5wdnYWeW50dDROnjyJBw8eSLyKHaFlI0rLyMzMxOXLl/HXX3/hv//+AwDs3bsXffr0ETtLhqKoGit5a2hooEuXLggMDBR5vK1rHxIRsHbv3o0bN26gc+fO+P3337Fly5YqBayYmBgoKSmJ9eT02bNnoCgKAwYMEMrtNjAwgLW1NSIjIxEXFwdzc3NJXAqhhREYGIi8vLwGG79v375C4tWHDx/w22+/0fuurq6YNGlSjWMdP34c+/bto/dtbGwwc+ZMODo6Ql5eXnKLriMmJiZNvQQCgUAgEAgSRFpaGqqqqq02klpKSgrW1tawtrZu6qUQaomMjAw2bNiA//3vfyKPr1ixAuPGjUNGRgauXr0KKysruLi4ICsrC58/f4asrCzMzMzq9bdtY2ODHTt24MuXLzh58iRu376NsrIyoX5MJhOFhYVE5GrjREZGYsmSJdi0aRNu374NNpuNT58+4c6dO3B0dERsbCwcHByqFc0re31X5smTJ3R0aEFBATIyMmBkZIQ7d+4gOzsbP/74Y5sX5SUiYJmZmWH79u0YOXJktSaCOTk5SElJgY2NDY4fP44bN27gy5cvUFFRwYABA/C///0Purq6dP+4uDgAgIWFRZXzRkZG4sOHD0TAaqNERUU16Pg7duwQ2M/Ly8PSpUvp0uHt27fH+vXrq819pigKhw8fxqFDh+i2bt26Yd++fVBWVm6YhRMIBAKBQCAQCM2YPn36QFlZGYWFhQLt586dg5WVFQBAR0dHIBOHxWKBxWJJdB3t2rXDunXrMHfuXJw9exb//fefQPptVSlb1WUdEVoW7u7uGDp0KG7duoUHDx5U2e/Vq1dwc3PDwIED8fDhQwDllkd8BgwYgJ07d4o8t7CwsEa/q4KCAlrAYjKZYDKZAAAvL69aXE3rRiIlK2bNmgUvL68aK2Dw/a+io6Oxa9cuaGlpwdHREVwuF5cuXcKYMWMQHx9P909PTwdQ/sYlCr7fEHnjaLv4+fk16Pj88sJAeZj62rVr6bxnRUVF/Pnnn9VWA6QoCvv37xcQr+zt7bF//34iXhEIBAKBQCAQ2jR//fWXUNuWLVuaJNpJW1sbixcvxr179zB//nzIyFQf69GWv4MeOHAAS5YsweDBgyUuKDYF9+/fx+LFi6sVryrCF68qExgYCA6HI/JYxe+DolBRUYG6urpY87dlJGriXhN8/ytLS0scOHAAxsbGAMpN2deuXYvbt29j2bJluHr1KgDQyndVYXL89upM4wmtl+zs7Bor6NWHvXv3CuwfPXoUT58+pfc3btxYbSosRVHYvXu3QGWFXr16YefOnQLCGIFAIBAIBAKB0Bbp2bMnxo4dS3sKAeXfGQ8cOCBQmb4hKC0tRVpaGpKTk5GUlISkpCSkpKTQ26LSCQnl7N69G3/99Re8vb2RnZ1do69TW4HL5SI2NhZdu3YVaI+Li8O5c+eqPXf69OnkO6IYNKqANXXqVLi5uUFZWVnAqFpJSQlbtmxBcHAwoqOjERYWhm7dutFmaDWVpuTxeA26bkLzg8PhYMiQIVUeNzIygq6uLkJCQuo8h729Pb398uVLHDx4kN739vau9o2aoijs2LEDFy9epNv69euHP/74o1n4XREIBAKBQCAQCM2B5cuXCwhYQPmD48DAQIF76dpCURRyc3OFhKnk5GQkJycjNTW1XpFeWlpaMDQ0hJGREYyMjGBoaAg9PT3Ex8fj6dOniImJwffv3+s8fnPmw4cPmDx5Mvbv39+gfsTNhR07dkBOTg6LFy+usW90dLSQgPXHH39Uq1mYmJhg/Pjx9V1mm6BRBSxpaWk66qoyioqK6NWrF27cuIHo6Gh069aNViCLi4tFnsNvry6Fi9D64PF4WL58eZVPRZycnLBmzRoMHTq0XvPwI/ySk5OxatUq+gPOzs4OCxYsqHZ9W7duxZUrV+i2/v37Y/v27aS6H4FAIBAIBAKBUAEZGRl4e3sLZC0AwMePH9G7d2/8888/6Natm8hzS0tLkZqaKiRO8bcr+2vVBjk5OVqYqihUGRgYwNDQEIqKisjOzkZUVBTevXuH+/fvIyoqqk0IOkC5v7U4hawq4+rqij59+mDjxo1CxyZPngxDQ0OEh4fD19dXEsuUCD4+PggODsa4ceNw6dKlavtW9GhOSkrC1KlTkZ2dXWV/V1dX+Pj4kCAHMWlUAasm+Pmz/NRBvvdVVfnFGRkZAv0IbYPt27fj2bNnIo8tW7YMo0aNwps3b+r1RMXb2xtAeaSXj48P/UHEYrGwbdu2KnPiuVwuNm3ahFu3btFtrq6u2LJlCxGvCAQCgUAgEAgEEUyePFlIwALK78V//vln/PnnnygtLRUSqtLS0up1z89isQSEqYrRVCwWSyATiM1mIyYmBi9evEB0dDSio6ORkpJS57nbClJSUrCxsYGdnR0GDhyIzp07AwAGDhyI3bt349q1a3TfM2fOwNnZGWvWrMHmzZsxefJkxMbGNtXSBVi1ahWio6PpfX19faSmpgr1e/36Nb29fv36asUrAPD398f27dslt9BWTqMKWH///Tc+fPiA+fPno2PHjkLHk5KSAAB6enoA/q/6IL8aYWU+ffoEoNxTi9A2OHfunFCIMR8HBwf8+OOPAIDnz5/Xax7+04QdO3bQxQekpaXxxx9/QEtLS+Q5ZWVlWL9+Pe7du0e3DRs2DBs2bKjRBJJAIBAIBAKBQGiLlJSUoKioCOPHj68yZXDZsmV1GltBQUFkBBX/Z1Vey2VlZfjw4QOioqIQHR2NqKgofPr0qUnM5cXl4MGDAhUbq6Jr166IiIhosHWwWCzo6+vDzs4O9vb26Natm8iMKRUVFaxduxaurq7YtGkT0tLSAJQboYeGhuLnn3/GL7/8gk2bNtFFtBoaBoNRpZgaEBBAbzOZTFy/fh2PHj3CypUrBfplZ2fj48ePsLCwgK6urljzlpaWkmAHMWnUb9WxsbHw8/ODmZmZkICVlZWF58+fQ1ZWFo6OjgDKU8EA4NGjR1i2bJlAlcOUlBTExMTA0NAQ5ubmjXcRhCahrKwMK1eurLLiAwAEBQUhMTERnz9/rjG0szo6duwIHR0dXL9+XeCJwLJly2BrayvynNLSUqxZs0bgjc3DwwPr1q2jvdwIBAKBQCAQCIS2BkVR+P79Ox05VdkwPT09vc7CEIPBgLa2tshUP0NDQ2hqatbop0xRFJKTk2mhKioqCu/fv6+ympw4yMjINLgJfPfu3VFQUICysjIsWLAADg4OCA4OxtixY5GQkCDQV1dXF6NGjcKIESNgYGAALpeLUaNG0QEkkmD37t1wcnKq8fddmV69euG///7D3r176UCFgoICoYJafLp37w4dHR3cv3+/3muuDEVRuHjxIhgMRrV/k3379oWsrCzc3NxgY2MDDw8PgePjx4/H4cOHsWXLFrFSIYl4JT6NKmCNHz8efn5+OH78OJycnGBnZwcAKCwsxOrVq1FQUICJEydCW1sbAGBsbAwnJyc8ffoUe/bswZIlSwCUVx389ddfweVyMW3atMa8BEIjU1paijt37mDTpk1i9f/hhx9QWlparzmdnJzw7t07bN26lW4bOnQoxo0bJ7J/SUkJVq5cicePH9NtY8aMwapVqwREVwKBQCAQCAQCoTXC4XBoQUqUYTrfIqYuKCoqCglT/H19ff1aewdlZ2fTKYB80So3N7de6zM3NwdFUSgoKEBBQUGNaWNMJhOqqqrIzc2tlU+XlpYWRowYAS8vL7Rr107ouLS0NK5evYorV67g9OnTMDU1xejRo9G3b1+Bh+rS0tK4efMmYmNjMXHiRPEvVgSKiorw9fWFiopKncdQVlbGqlWrMHjwYGzcuLHa1My3b9/WeR5xKCkpqbFP9+7d6W1DQ0O8fv2aDsLhM2vWLMybNw9v3rzBq1ev8L///U/ia22LNKqA1a9fP0ybNg3Hjx/H5MmT0aNHD2hoaODNmzf4/v077O3tsWLFCoFz1q9fj4kTJ+LgwYN4+PAh2rdvj9DQUGRkZMDZ2bne/+EIzZf8/HwsWbIEoaGhYp9TX/EKALp06QIfHx96rA4dOuDXX38V+TSBw+Fg+fLlAp5c48ePh4+PT62fPhAIBAKhcaAoCiUlJSguLgaHw6F/stlsFBcX06+Kx6prq3y+rq4uNmzYAENDw6a+VAKBQJAIFEUhKytLIIKqomE635u4LjAYDOjq6oqMoDI0NISGhkad76vZbDbev39PR1bV17dKWloaFhYWsLGxgby8PAoKCpCRkYGwsLBqRToZGRl0794dLBYLubm5+Pr1q9jRT1JSUujbty9GjRpFR/7UxJgxYzBmzJga++Xn54u1hupQVlaGn58fPDw8ICcnV6+xevbsif/++w83b95EaGgowsLC6vW31VDcuXMHY8aMoUVBWVlZ3LlzB8OHDxfod+DAAVy+fBnp6elVjsUP0iGIR6Mb86xcuRK2trY4c+YM3r17Bx6PBxMTE8yYMQNTpkwR+g9pbGxMhxQGBgbiy5cvMDY2xk8//YQpU6YQb6FWSkZGBubPn1+l/xmf9u3b4/PnzxKbV01NDefOnaM/2JSVlbFz5066ImZF2Gw2li5dilevXtFt3t7eWLx4MRGvCAQCoZZQFIWysjKRAlJ1opKoPjUJUBwOp0F9TJKSknDp0iVyU0ogEFoUxcXFtCglSqiqT0qdkpKSyAgqIyMj6Onp1Vv4AMotR+Lj4wXEqk+fPoHH49V5TBMTE3Tu3Bk2NjZgsVjIyclBWFgYHj16VG2UFYPBQMeOHeHg4AB9fX2kp6fjyZMnCA4OFntuIyMjOu2Pn6EkaSqaktcGJpOJgoICAOUF13777TccOXIE06ZNg5eXV70q6ikqKmL8+PEYP348ACA1NRUREREIDQ2t0gu5sYmMjMQ///wjEFWlrq4usm914lW7du3qVMmxLdMg6o8o07OKDB06FEOHDhV7PH19fYF0LkLrJjExEXPnzq3x6cjatWtx8OBBic6dm5srUDli8+bNMDExEerHZrOxaNEivHnzhm6bPn065s2bR8QrAoHQqigrKxNLSKpOTKougokfucThcOr1JaO5IYmn2gQCgSBJeDwesrKyREZQJScnV1n5XRwYDAb09fWrNExXU1OT6D0yRVFISUkR8K2KiYmpl8jGYrFosapz584wNjZGbGwsXr9+jUuXLuHLly/Vnm9gYABHR0c4OjrCyMgIQUFB8PPzw/v378Veg7y8PAYPHgxPT0/Y2dk1+PeKH374AZ8+fcLt27dhZ2eHkJAQsc47d+4c/P39cerUKTr9Mj09Hdu3b8fRo0cxZcoUjB49WmQQQG3R19eHvr4+3N3d8b///Q8uLi71HlMSHDt2DFZWVhg8eDAA1Jg2KoqpU6cSy5laQsKXCM2K9+/f43//+1+NbwAmJiYwNTVt0JDSadOmoX///kLtBQUFWLhwIcLCwui22bNnY/bs2Q22FgKBQKgIj8cTEH5qSm+rbQRTxWMNbULbVMjIyEBBQUHgJS8vX+U+f7viT4qi6IiukpISREZG4unTpwAALpfbxFdIIBDaImw2u8oIquTkZLH8faqCyWQKCVP8iCo9Pb0GNaL+/v07LVbxvatycnLqPB6TyYS1tTUtWNnY2EBdXR0REREICgrCgQMHEBUVVW20rqqqKhwcHGjRSl5eHv7+/jh79iwiIyNrtR5ra2t4enpiyJAh9fKSqi3KysrYtGkTNm3aBIqiaI/qmmCz2Zg2bRrGjx+P//77D6dPn6a/v2VmZmLnzp04fvw4vL29MXbsWJFVCOuCiooKHjx4AB8fH7HFtoZk/fr1MDU1hbm5eZ0ErK5duzbAqlo3RMAiNBuCg4OxZMkSFBUV1diXw+FgxowZDbYWBwcHzJs3T6g9Pz8f8+fPR1RUFN22YMECUkyAQCDQYkZ909vESaGThN9fc0RaWlqkkFSVgFTxmKKiYpV9RW3zfStKS0uRl5eH3Nxc5OXl0dv5+fn09vfv30Ueq06kak3RZAQCofnA4/GQkZEhVMmPL1TV5Us0HykpKTqKSpRQpaqqKsErqRo2m43Y2FiBVMDk5OQ6jycrKwsrKytaqOJHVwHAp0+f8PLlS1y5cgWhoaEoLi6udpzu3bujV69ecHBwQMeOHZGbm4uAgABs2LABb9++rVV6uoqKCoYNG4ZRo0bB0tKyztcnKSiKEkgNrI6MjAxYWFhASUkJU6ZMwbhx43Dt2jWcOHGCjuTLzs7Gnj17cPLkSXh7e2PcuHFQVlau9zo1NDRw4MABbNu2TaBifFPAt5Q5c+ZMnf7vrVu3rsbsNYIgRMAiNAuePHmC5cuXi/2k/9u3bw22Fl1dXfz+++8ClTqA8vTCefPmISYmhm775ZdfMHny5AZbC4FAqB8URaG0tLTe6W3ViUsVf7ZGGAyG2FFJol5VCU8VRSd5eXnIy8vX+ek9j8dDYWGhgMCUm5srsM0XoCqKVfn5+fWqjFUdZmZmDTIugUBo/RQWFoqs5McXrOrzEENVVVWkD5WhoSF0dXUb3V+4rKwMnz9/poWqqKgoxMXF1fkhAIPBgJmZmYBYZW5uTn++pKamIigoCAcPHkRwcHCNPlZWVlZwdHREr1690LVrVygoKCA3NxePHj3Cvn37EBQUVGtPxZ49e2L06NHo379/vbyiJE18fLxY4hUAoSwYRUVF/PjjjxgzZgyuX7+O48eP095POTk52LdvH06cOIFJkyZh4sSJ9Y4yk5WVxa+//gpzc3P8+eefAv8Genp6UFRUlKhPckWMjIwEDPgTExOxefNmAQN3FosFfX39GiPx6upB1pYhAhahyeHxePj111+bRZqKjIwM/vjjD2hqagq0Z2dnY+7cufj48SPdtmLFCtpckEAg1I7S0tJaG3RXTJmrKXWu4rGGNOtuSmqKSqpJSBIlJok6X0ZGptG8/TgcjpDAlJOTQ29XJ0Q1xb+zgoICVFVVoaqqCjU1NaiqqkJFRQVWVlbw8vJq9PUQCISWAZfLRXp6usgIquTkZHz//r3OY0tLS0NfX7/KVL/GTE+rTEXfKr5YFRMTU23UU00YGBjQYpWNjQ06deokkK6Wl5eHp0+f4vXr13j16hUSExOrHc/IyAiOjo5wcHBAz549aWPugoICBAQEwM/PD69evar19xYdHR14enpi5MiRzbZCbXh4uNh9s7KyRLbLy8tj/PjxGDVqFG7duoVjx44hNTUVQHkmy8GDB3H69Gn8+OOP+PHHH6Gmplbn9TIYDEycOBGmpqZYsWIFLb6lpaVh9uzZUFdXx/bt2+s8flX4+Phg4cKFAm0BAQFYunQptLS0kJWVhczMTNjb29coYFlYWEh8fa0dImARmgVN5RUiJSUl8ITHx8cHnTt3FuiTmZmJOXPmID4+HkD5m+WaNWswevToRl0rgdDQcLncalPYKotD/Mik6gSkqkSq1uoPJCsrW62fUsWIo/oIUPLy8s22YASXy0VBQUGdhKj6eLPUFSkpKSERii9EqaurQ0VFRegY/yWJqlkEAqF1UlBQIOBDVVGoSk1NrdeDW3V1dZERVIaGhtDR0Wk2VdpzcnKEfKvqI85paGjA2tpaQLCq/NC5pKQEwcHBeP36NV6/fo13795V+4BDTU1NwMeqorjEZrPh5+eH+/fv49mzZ7WOfJORkYGLiwu8vLzQq1cvoeyO5kZtBKyafIjl5OQwZswYjBw5Enfv3sXRo0fpqKXCwkIcOXIEZ86cwYQJEzB58mRoaGjUed29e/fGqVOnsGjRIlqgPHToEFxdXTFhwgRcuHChzmOLIjc3F66urvD39xdoT05OxuLFi7F27VoAgL+/P8zNzREXF1flWN26dZPo2toCzePdjdCmkZKSgpWVlYApOp+lS5di586dDTLv1KlTcebMGVrA8vDwwJgxYwT6pKenY/bs2XTVEQaDgfXr12PkyJENsiYCoTJ8s+6axKHKaW518VdqDlGQDQHfrLsuvkr87crRSVX1by2VZCiKQnFxschIp5qEqMLCwiaJhlJSUqqTEKWsrNxsxUACgdB84XK5+Pbtm0jD9KSkJLoyW12QkZERiKKq7EnFZDIleCWSobi4WMi3qmKaVW1RVFREp06dBMQqAwMDofdrHo+Hjx8/4tWrVwgKCkJoaGi1Kf1ycnLo0aMHHBwc0KtXL1haWgp8dnM4HLx48QL3799HYGBgnaLDzMzM4OnpieHDhwsJbM0ZUd/FqqKqCKzKyMrK0r8LX19fHD16lP5exWazcfz4cZw/fx5jx47FTz/9BC0trbosHaampjh16hR8fHwQHBwMoFxA6tSpEywtLfHhw4cqz7W1tUVaWprYFjVr167FxYsX8fz5cwHv5g8fPmD8+PG4evUq3r59Cy6XW614BQDm5uZizUn4P4iARWgWVPXF2c/Pr0Hm27VrF7Zt20bPa2lpiVWrVgl8KKampmL27Nn0h6+UlBQ2b96MoUOHNsiaCC0HiqJQUlIikfQ2UedX7NMUESmNgZSUVK2qvVXsW1FMqs7Qmx+p1FyeRDcFZWVlIsUmcYSophA0ZWRkhKKcKqbmVSVEqaioNGj1K1GUlpaioKAABQUFyM/PF/iZl5dHe3Lx2w0NDTFz5sx6pUsQCITGJT8/v9ooqvpEE2toaAhFUfHT/XR0dJp1tA6Xy0V8fLxAKuDHjx/r7FslLS0NCwsLgYqA7du3r/J3kJKSQqcEBgcHV1uNkMFgwNramo6ysrW1FfKdKi0txatXr+Dv749Hjx6hsLCw1tegqKgINzc3jBo1Cl26dGlxD0aysrIEBEcFBYVqxbvaVoKXkZHBiBEjMHToUAQEBODIkSN0dktxcTFOnz6NixcvYsyYMZg6dSq0tbVrfQ1qamr4+++/sXPnTly6dAkAEBMTU6MoFh4eji1btiAiIoI+rya8vb0xYcIEnDp1im57//49GAwGVq5cicmTJ9cYsWdsbIwRI0aINR/h/2i7d/WEZkN+fr5AVb+K1LYErThISUnh/PnztMrOZDLx559/QkFBge6TnJyM2bNnIyUlBUD5B+vvv/8OV1dXia+HIBkoikJZWVmN1dtqikoS19i7Nfoq8c26xY1KqouxN7+tscWGlgxFUSgqKhLLkLzysbrchEsCJpNZayFKTU0NCgoKjXLTz68YWVF4ys/PFxCdRIlQFdvrYv6upqaGmTNnNsAVEQiEulBWVkZHUYkyTM/Ly6vz2DIyMrQ4JUqokkQ1tsaAoiikpqYKpAHGxMTUqwCGiYkJOnfujM6dO8Pa2hodO3as1sw8NzcXwcHBCAoKwqtXr2qM7DIyMkLv3r3h4OAAe3t7kQ8OysrKEBoaCl9fXzx8+LDO/9Zdu3bFqFGj4OrqKuC91dKIiIgQ2GcymdUKWPxKg7VFWloa7u7ucHV1xYMHD3DkyBE6SqmkpATnz5/H5cuXMWrUKEydOhV6enq1Gl9WVhYrV66EmZkZduzYAR6PJ1a02IEDB3Dr1i24u7tj06ZNdJRYVZSUlODt27cCbfxCXxYWFti3bx+WLl1a7X3YypUroaioKMZVESpCBCxCk0JRFDZv3tyoc/J4PAQFBdH7v/32G4yMjOj9xMREzJo1ixa4ZGRksGPHDvTv379R19laKCsrq5WvUnViUk3G3q21bD0/kqgqX6XapMVVJzzJysq2uCeGLYnS0lKhKnmihChRr6bwDJOVlRXp+1SxTU1NDSoqKkLRUA0dOcDj8VBUVCRW5FNV7Y0dYcZgMIhZK4HQBOTl5YmMoEpOTkZqamq97h00NTVFRlAZGRlBW1u7RaaV5+bmCvlWVVeprya0tbXpaoA2Njawtrau0Uiew+EgPDycTguMiYmp9sGhhoYGevbsiV69eqFnz55VmqTzeDyEhYXBz88PAQEBdb4uDQ0NjBgxAp6enq2m4mzl9MGa/l9kZmaCoqg63zdKSUnB1dUVgwYNQmBgIA4fPoz3798DKL9funTpEq5evYqRI0di2rRptTa+HzduHExNTbF8+XLk5+fX2D8lJQWPHj3CwIEDceHCBfzzzz8C0VWiqBxo8fHjR5SWlkJWVhYODg44duxYtQW/jh49it69e4t3QQQaImARmpRr164hICCgyeafNWsWnJyc6P3Pnz9j9uzZ9FMFWVlZ7Ny5E/369WuqJTYIfF8lUSlrFX9WNOmuKYKpqvFau69SdWJSTWlxfEPv6vq0Jl+l1gCPx6OFEHGEKH6fvLy8ej2trisMBkMg0qkqHyhRQlTFqFRJU1ZWJhD5VFUaXnXpec0tClJKSgqamprQ0tICi8US2GaxWLCysoKJiUlTL5NAaHWUlpYiLS2tylQ/fmWyuiAnJ0dHT4kyTG/p0RPFxcX48OED7VsVFRVVL98qJpMJa2trOrrKxsZGrFQwHo+H2NhY2nj97du31VooyMvLw87OjvaxMjc3r/JeiaIoREVFwc/PD35+flWmvjGZTHC53CqrFzMYDPTp0wdeXl5wdnZudZHklQ3ca4pI43A4KCgoqHdVSykpKfTv3x8uLi549uwZjhw5QmfmlJWV4erVq7hx4waGDx+O6dOnw9jYWOyxHRwccObMGSxatAgJCQk19l+2bBmeP38ORUVFLF68GBRF4fTp07W6noSEBPphlYWFBeTl5av0ZAsNDcWNGzfg6elZqznaOkTAIjQZnz59wh9//NFk8/fp00cglSMuLg5z5syhn8bIy8tj9+7dcHR0bJT18FNaJJHeVtmDqXLf2lZRaSlIS0vX2ay7ophUU+qcvLx8s/amINQMh8MRaU7OF5uqE6KaQjhRUFAQEptERUPxI6D420wmU+ICKP+9qrIAJUp0qkqEagoxry4wGAxoaGgIiFFaWlrQ1NSkt/kvNTU1IjYTCA0ARVHIzc0VGUHFj6Kqz/syi8WqMtVPS0ur1fy/5nK5+Pz5s4Bv1YcPH+ocgSYrKwsrKyuB6CpjY2Oxf1/JyckCPlbVmd4zGAzY2NjA0dERDg4OsLW1rbYKLEVR+PDhA3x9feHv709bglSG//5dVFSEb9++ibw/NjAwgKenJ0aOHAldXV2xrq2lweFw8O7dO3rfwMBA6HdmbGyMtLQ0gd9RZmZmvQUsPgwGA05OTujXrx9evXqFw4cP06Ial8vFzZs3cevWLQwbNgzTp0+HqampWOMaGxvj5MmTWLVqFV68eFFj/8GDB+Px48eQlZXF/Pnzay1gxcbGCkRbW1lZVVvdcevWrejRo0ethLm2DhGwCE1CcXExVq5cWW2VkIZEX18fW7ZsoUWI2NhYzJkzh/7wVFRUxO7du2Fvby9k1l05Oknc9DZxBKjWCN9XSVxxSFzhqaLoxI9Uam1PwwjVw+VyaZGkKiFKVHRUfn5+k/x/k5KSEhn1JI4QVd2Nem2pKv1O3MinvLy8Fh9ZqaqqKlKMqixMqaurt+kiAARCY1FaWorU1FSRPlTJycn18vOTl5evMoLKwMCgxUdRiYKiKKSlpQmkAb57965eDw/Mzc3p6CobGxuYm5vX6r4rJycHQUFBCAoKwuvXr5GcnFxtfxMTEzg6OqJXr16wt7cXSyj59OkT/P394evri69fv4rso66uDltbW5SUlODr168iq9PJyspi0KBB8PLygr29fasRMavi/fv3Ap/rlpaWQgKWkZERCgsLBdIuMzIy0L59e4muhcFgoHfv3ujVqxfevHmDw4cPIyQkBED53/WdO3dw9+5duLq6YsaMGWJV8VNRUcHu3buxZ88enD17ttq+bDYbK1euxI4dOyAnJ4cRI0bg9u3bYq/fz89PwJi9cvqisbExLly4gLFjxyIlJQUlJSUIDw8nAlYtIHdlhCZh165d+PTpU5PNz2QysW3bNhQXF+Pt27dCby5sNhuLFy+uMoy4NVBXr6Sq0uIqVoar+JKRkSG+SoQqoSgKxcXFIg3JaxKimiqNTFFRUSDlTlwhSllZWSL/Fyqm31WXhlddel5rfF9jMplVilGVt4nYTSA0LhRF4fv37wLCVEWh6tu3b/V6X9LW1hYZQWVoaAgtLa1Wfx+Sl5dHR1XxRav6+FYZGBjQ1QBtbGzQqVOnWhuUFxcXC/hYvX//vtp/Y01NTbpSoIODA/T19cWaJzExEffv34efnx9tBl4ZJpMJFxcXKCsrIzk5GU+fPhUZeWZpaYlRo0ZhyJAhbapibGUzclHXbmRkhKSkJIG/q7oauYsDg8FAz5490bNnT4SGhuLIkSN4/fo1gPL3E35K6ODBgzFjxgxYWlpWO56MjAyWLl2KDh06YNOmTdX2ffToETZv3oz169fD3d29VgLWs2fPBPZTU1MF9n/77TcoKioKeJuKI8IR/g8iYBEanQcPHuC///6j93/55RcUFxfjwIEDjbaGjx8/4uPHj9X2aYoUF1lZ2Wr9lCqLRHUVoOTl5Vv9zRyhcSkrKxOZfieOENUU0TwyMjICpuNVCVH8inkV+9ZX/OBXvxMn8klUv5aSficJFBQUaD+p6tL3NDU1G9Szi0Ag1ExJSQmd4idKqKrPe5eioqLISn78n9VVsGttcDgcxMbGCghWiYmJdR5PQ0MD1tbWdCqgtbU1NDU1az0Ol8ulfaxevXqFsLCwai0rFBQUhHysxL03TUlJgZ+fH/z9/enKb5VRVFRE//79YWVlhW/fvsHX11ekqMdkMjF06FB4eXmhU6dO4l1sK6NyBUJR0W76+vpC7VX5iUmaHj164J9//kFERASOHDmC58+f08cCAgIQEBAAFxcXzJw5E9bW1tWO5eXlBWNj4xorAt+8eRPdu3fHsGHDoKamVm2Ka2X45vY8Hk/gfc/IyAg2NjZIT0+ni4UpKCgQAauWEAGL0KikpqZi48aN9P7gwYMxadIkMBgMuLi4VFupoangm3XXxVeJv11ReKrOV6m1hygTmjcURaGoqKjKqngVfaAqH6tPekd9YDKZYglRlbcVFRXrJOJSFEX7PdUl8qk1pN/VF1lZWbHS97S0tFp0SXICobVBURSys7NFRlAlJSUhIyOjzlFUDAYDOjo6QpX8+IKVhoZGm3zwxuVykZCQgKioKLx79w5RUVE1RjJVh6KiIqytrWnBysbGBgYGBnX+PExKSsLr16/p1MDqjL+lpKRgY2ODXr16wcHBAV27dq3VA6GMjAwEBATA19dXqPobH3l5eTg5OcHZ2RnFxcW4d+8e7t27J7KvnZ0dvLy8MHDgwFaZRiouFEUJeTSJ+uw1MjICk8kUaMvKymrQtVWma9eu2LdvH6Kjo3HkyBEEBgbSx548eYInT56gX79+mDlzJrp06VLlOHZ2drh69SpGjx5d7XwbN26Ep6cn3NzcBIIvaiI4OBgODg7IyckRaOd/560oGNrY2BCrglpCfluERqOsrAyrV6+mq8Ho6+tj7dq19IfmoUOHGnwNs2fPhrq6OiIiIoQ+0NavX4/OnTsLCE7y8vLkTYXQ4igtLa2yKl511fPy8vIEQpobC1lZWQFxqbIQJapiHt+gvLb/P/npd9nZ2UJCkzjbzSH9jsFgQFlZmf4dKCsrg8vloqSkhPbsq/yzodcsLS1NC1CVDc8rv5hMZpv8IkogtASKi4vpKCpRhunFxcV1HltRUZEWpioLVfr6+hL1+muJUBSFb9++CaQB1se3isFg0Cbr/Ff79u3rVYTm+/fvCA4OptMCqzJH52NqakpHWNnZ2dXa8Ds7OxsPHjyAn58fQkNDRX6WycjIoG/fvnB3d4empib8/Pywbds2FBUVCfVlsVjw8PCgo3AIQFJSEr5//07vGxgYiBQiDQ0NmywCqzI2NjbYvXs3YmNj8e+//+LBgwf0sWfPnuHZs2fo1asXZs2ahW7duokcw9TUFLt378bixYurnSs5ORnu7u61ErDmzJmDN2/eCKwLAIYNGwYAAgJs586dxR6XUA75Zk5oNI4cOUIr/FJSUti6dSv9Rpifn4+HDx822Nyqqqo4d+4cDAwM8OLFC+zatYs+pqmpiYMHD5LwTUKzgh/pUxchqilSzBgMBphMppAQxU+/q06Iqk3qFz/9LicnB4mJidVGPonabg7pdzIyMlBRUaGjxyqKUfx2FRUVutpfRTGqpKQEbDYb379/R2ZmJrKyshAXF1fnKlLVwWAwoKmpWW2UFH9fVVWVRJASCC0AiqKQmZkpEEVVUaiqj6cNg8GAnp5elYbp6urqRLyuQF5eHh1VxRes6hPR0q5dO4E0wI4dO9Y7tZLNZiMsLAyvX7/G69evERsbW21/LS0t2sPK0dGxTlX78vLy8OjRI9y/fx9BQUEiP9+kpaXh6OgINzc39OjRA4GBgTh27JhIDywpKSk4OzvDy8sLffr0IQ+mK1HZ/8ra2lqgIiEfIyMjKCsrC7Q1pAeWOHTs2BF//PEH4uLicOTIEQQEBNAi56tXr/Dq1SvY29tj5syZsLe3F3r/cXZ2xpAhQ+Dr61vlHJcuXcKiRYugo6OD9PR0sdd2+fJlHD58WKCN/723ooBla2sr9piEcsj/YEKjEBwcjH///Zfenzt3Lrp27UrvnzlzpkHn37p1KwwMDBAYGIhly5bRKTwsFguHDx8WuxQrgVBbOBxOjel3VQlRTRHpo6CgICQ2VRai1NXV6RQ9vhDFZDJrFDD4KYp8QSk5ORnv378XO/IpPz+/Wj+NxkJRUVGk6FSdGMXflpKSQkFBAbKyspCVlYXs7GxaiMrKysLHjx/p/YZKNVRXVxeKkqq4z4+iUldXr9eTegKB0DSw2WwkJyeLjKBKTk6uVxVWZWVlIWGKv6+np0cKJFQBh8PBhw8fBHyrqqqSJw7a2tp0NcDOnTujU6dOtY5uEgWXy0VMTAyCgoJoH6vqPosUFRVhZ2cHR0dHODo6okOHDnUSKQsLC/HkyRPcv38fL1++FDkng8GAvb093NzcMGDAAHz48AHXrl3Dli1bRPY3MTGBl5cXRowYARaLVes1tRUq+1916tQJAQEBAm0V72Uq0tQCFh9zc3Ns374d8fHxOHbsGO7du0ffQ7958wZv3rxB9+7dMWvWLDg4OAj8jS5duhTPnj2jM4Qqc/r0aSxZsgTu7u44ffq02Gvatm2bwD4/4q+0tFRAICQRWLWHCFiEBuf79+9Ys2YN/UbSs2dPTJ06lT6elZXVoALW3Llz0bt3bzx8+BArV66kP+T09PRw6NAhEkJMqBEul0tHQ1UUokSZk/OFKH6/+nxRqCsMBkOk7xN/ny8+VRaiVFVVq31aW1ZWhsLCQgFx6du3b7VKw2su6XdViU7ViVBMJhNMJlPoCxqbzUZ2djaysrJo8SkzMxMfPnwQEKeysrIa7O+ByWTWGCXFYrGgrq5OvmASCC0cHo+HzMxMkRFUycnJ9YrikZKSoqOoRAlVqqqqJIqqBng8Hj5//kz7VvFTAesKk8mkUwD5opW2trZE1kpRFBITE+mUwKCgoCq/yAPlfx+dO3dGr1694OjoiM6dO9f5M4XNZuPp06fw9/fH06dPUVJSIrJft27d4O7ujkGDBqG0tBQ3btyAt7e3UHU3oPwhnKurK7y8vNCtWzfytyoGYWFhAvuiqvkZGRkBgJAHVnMRsPiYmZlhy5YtmDVrFo4ePYo7d+7QEXxv377F3Llz0aVLF8ycORN9+/YFg8GAlpYW/ve//wkJThU5fPgwhgwZUisBqzL9+vUDl8vFli1b6L91AwMDIq7WASJgERoUiqKwYcMG+g1OXV0dW7ZsEXiqf/To0QZN65k+fTr8/PywevVq+k3MwMAAhw8fhoGBQYPNS2heUBSF4uLiGg3JRQlRhYWFTSK8KCoq1kmIUlZWFhkNxeFwhASljIwMxMfHiyVCNbf0u5oinyq38/2ixEl1KykpoUWnzMxMxMbG0mIUP2oqMzMT2dnZIn02JIGioqKQl1RFMYofNaWpqdmmKnARCG0BfhQVP9UvMTFRIIqqPtGoKioqIiOoDA0NoaurS0TuWkBRFNLT0wXSAKOjo+v8eSkjI4NOnToJiFXGxsYSTdHOzs4W8LESJQRVxMzMjPax6tGjh5CIURtKSkrw4sUL3L9/H4GBgVX+nmxsbODm5gZXV1doamri8ePHWLduHV6/fi3yfszGxgZeXl5wd3ev1/raGvn5+YiPjxdoU1NTE+pnaGgIQLg6YWFhIdhsdrMzwTcxMcHGjRsxc+ZMHD9+HLdu3aIDGCIjI7Fw4UJYW1tj5syZcHZ2xpgxY3DlypUqK9QfPHgQQ4YMqdeadHR0sGnTJty6dYtuGzlyZL3GbKsQAYvQoFy4cAFPnz6l9zdu3Cjw1CgpKQkXLlxosPk3bNiAe/fuYd26dfQHnrGxMQ4fPlynvHxC01NWVkYLT7URovLy8pok/UxaWrrKqnhVCVH8topfIiqm31UWlz5//iyWANUc0u8UFBRqJTpVFqsUFBTq/ES1rKxMIEpKlBjFb8/Pz5fwlZcjJycn0txclDDV3G4ICQSC5ODxeMjIyKgyiio7O7vOY0tLS0NfX58WpyoKVYaGhlBVVZXglbQt8vPzhXyr6hOFYm5uDmtra1qsMjc3l7iAyGazERoaSqcFVvUlnQ+LxaJ9rHr16lXvaK/S0lIEBQXh/v37ePz4cZURXhYWFnB3d4ebmxuMjIwQFxeHM2fO4M6dO8jNzRXqr6qqiuHDh8PLywsWFhb1WmNbpXL1QQMDA5H3P3wBq7IHFlAehdVcs1mMjIywdu1azJgxAydOnMC1a9doIevdu3dYsmQJLC0tMXPmTMybNw9LliypcqzNmzdDS0urzhGuhw4dEiiCMXLkSMyYMaNOY7V1iIBFaDDev38vYJY+adIkODk5AQC+fPmCa9eu4dSpUw26hsjISFy9epUWr0xNTXHo0CGJhV4T6gZfjKnKB0qUKMU/9v/YO++wts67/d8CscFmY5bZU8LYbGyDjQ0SXgwntpNmtWm60jdp0vZNmzZtk/dtm6a72X3bpmnsNImdBPAWmGHwYHuwNzZgg9l7SOj8/vDvnEroCLQZfj7XxWXp6Og5j2Tp6Jz73N/7Ozk5uSxztrKyUkmIWtg9z8LCAhwOR678bmHo+OjoKLq7u5UKUCu1/E4VB9RS5XfaMj8/j9HRUTm31MDAABNyLitMjY6O6uU95HK5SsUoOzs7OcHKysqKlDQQCA8Ik5OTrDlU9G1tcu7WrVsHT09PuU5+tFDl4uJCgqp1wNzcnEJu1a1btzQez83NTa4UMDg4GJaWljqc8X0kEgkaGhqY4PUbN24smWMVFRWFuLg4xMTEwNfXV+vfqfn5eVRVVUEkEqGgoIBVgALuH5cLBAIIhUL4+PhgYmKCqZqora1VWJ/D4SA2NhYZGRnYuXPnA9+5UlvY8q/YstnoihU28XslC1g0rq6uePnll/H1r38dH374Ib788kumjK+5uRn//d//DT8/v0XHqKqqUqvp0EJkxav09HT87Gc/I81vNIT8uhH0wtTUlFzeVHBwMJ577jncuXMHr732GioqKlift3HjRqSkpKC7uxt5eXlad9b64osvmNt+fn54//334eDgoNWYhP8gFovlHE5srqeFzij6sfn5eYPPl8vlyglMC4Uo+vZCIcrGxobJwWITl+hA8sVcUCuh/M7Y2Fjt7Cd6uY2Njcrld9pCURTGxsaUluzJZkoNDQ3pRZQyMjJasgMf/UcyYQiEB5P5+Xncu3eP1UHV09Mj15peXWgXFS1OLRSqdBHYTfgPUqkUnZ2dcrlVdXV1Go9nZ2cHHo/HuKtCQ0Nhb2+vwxn/B4qicPv2bZSWlqKsrAyVlZWL5lgZGxsjLCyMcVjxeDydXFySSqW4ceMGRCIRLly4oNRF6O7uDqFQCKFQyHQAv3HjBn7xi18gLy9P7kSfZsOGDUhLS0NaWhqJ/9AhC/OvQkJC0NXVpbCesgwsAOjv79fL3PSBs7MzXnrpJTz99NM4evQoTpw4wXze2tralnw+22dTE/z9/ZGVlQUzMzOYmZnB3t4e4eHh5MKDipB3iaAX3njjDUbBt7CwwG9+8xuYmprinXfeUSpeffTRR+DxeMyJYGJiIn7605/qZD6BgYF47733YGdnp5Px1hIURTEB5YuV37F1z1sOUYbD4TDCiqpCFF2OJxaLF+1w19fXp1SAWknld+pmP+mi/E5b6M+ZbNe9hR34ZEUpfXTg43A4sLW1VRCj2IQpW1tbcmWMQCBgYmKCcU0tFKru3r2r1b7K1taWNYeKdlGRLqD6Y2FuVW1trcbHNGZmZoxIRTus3Nzc9Pp7Ozg4yJQElpeXo6+vb9H1/fz8mLLAyMhI1lIwTaAoCnV1dcjNzUVubi7u3bvHup6LiwsEAgEEAgFCQ0PB4XAwODiIf/3rX8jJyWF1tnG5XCQlJSEjIwMxMTHk+6BjJBKJgsstJCQE7733nsK6dAkhm4ClTdOI5cLR0REvvvginnrqKRw7dgyfffaZQc9p/vCHPygse/TRR/Hf//3fBpvDaoYIWASdc+7cObmAuvj4eJSUlGBgYADnz59nfc7HH3+MkJAQuWVCoRDvv/8+65UAdeDxeHjnnXfWfObD7Oys0kyohaV5Cx1R2jrdNMHMzIw1B4pNiKKdP8bGxpBKpZiammJEN1kRqr+/n8mDkhWhJiYmluU1LoQWk9QRnWQdUCsxWHd6eppxRi0lTCnrMKQtNjY2CmIUW/menZ0dubpFIBDkkEgk6OvrkyvvkxWqlJU+qQKXy2WcUwuFKjc3NxI2bSDGx8fR0NDACFa1tbVa5VbRIev0n4+Pj97FlampKbkcq9bW1kXXd3R0ZDoFxsTE6DQ6g6IotLS0QCQSITc3Fz09PazrOTg4IDk5GUKhEJs2bYKRkREkEglKSkqQnZ2NkpISVje+n58fMjIysHfvXnLhWY80NzcrOIpCQkJYSzddXV0BrH4H1kLs7e3x/PPP48knn8S///1vfPLJJ8sWVbIwj4ygHHIkT9Ap/f39+NWvfiW3rKCgAAUFBYs+z9fXV2HZrVu3tBavwsLC8Pbbb68aq71UKsXExISC00lWiBoZGWENLZ+dnTX4fDkcjtIcKNnl5ubmMDIyYv44HA7TEY8WoWghqrOzU8EFpa8ub+ogW36niQPKUOV3umB2dlYh5HyhGEX/6euKlZWVlUrle/b29iQDg0AgLMr4+LjSHKq7d+9qVdJub2+vEJJO33ZyciKuEQMjm1tFO6s6Ozs1Hs/Ly4sJWOfxeAgMDDRI11eJRIL6+nrGYXXz5s1F3X6WlpaIiopCfHw8YmJi4O3trXMHWHt7O3JzcyESiZRmga1fvx7JyckQCASIiIhgPv9dXV3IycnByZMnWcVDS0tLpKamIiMjQ64ag6A/FuZfubm5sZ4vOTs7M8dZbI9rIwavFGxtbfHss8/iiSeewL///W/89a9/1fk2HnroIdjb22N2dhYzMzOYm5tDV1cXqqqqALC/twR2iIBF0ClVVVUaiQ0XL16EQCCQu6+L8sF3331XZzZpVaEoCjMzMwpOJ1WEqOUK6rawsFhUiOJyuQoCFM3CMrzR0VEmD4peri/njTqYmZkxeVZs3e2WEqGWs/xOF4jFYibYfClharHsDG0wMzNjuuwtJkzZ29uTDnwEAkFlxGIx46JiE6q06SpqYmLCiFJspX76COEmqIZUKsWtW7cYoUrb3ConJydGrOLz+QgJCTHYSSVFUejs7GQcVpWVlYs6QbhcLjZt2sTkWIWGhurFYdzV1cWIVspcX9bW1khKSoJQKER0dDTjFp+ZmcH58+eRnZ3NnKQvZPPmzUhPT4dAICC/+wbm2rVrcvdDQkLQ29ursB6dfwUo70K4VrCxscG3vvUtnDt3jjXMXhsCAgJw+PBhuWWnT59mvhvEbag6RMAi6JRt27Zh06ZNaGlpYU5G6ZPTxQIdr127xghYH374Id58802t57J//36txCuJRCLXJY6tNE9Zyd5y5CUZGxvLiVC0EGVlZQVjY2NwOBw5AYq+z+FwGMGNFqF6enrkhKmVUH4n2/1O3eynlVp+py3z8/MYGRlhuu8pc0nRHfj0AZfLXdIlRYtWlpaWq1oEJBAIywPd2GGhMEXf7+3t1ep3ysHBQaG8j77t6Oi4atyza53+/n65MsCbN29q7D63tLREWFgYI1bxeDyDd6geGBhgOgWWlZUtWYrl7+8vl2OlL/G0t7eXybSqr69nXcfCwgI7duxAamoq4uLi5JzQDQ0NyM7Oxrlz51gviNnb22P//v3IyMiAt7e3Xl4DYWnYOhB2d3crrCcbmm9iYgJzc3O50sO1JGABwNDQEKuQpy0lJSUKApZsow9bW1udb3OtQgQsgk6xsbHBhx9+yPrY3bt3UVJSwvoYfRVgcHAQb731lk7msn37dlAUhenpabnsJ7YcKLa8qOWqgaYFGNnyO1kBSlZ8khWkZLvkjY+P4969exgfH1+x5XfqiFGWlpYPTBmGVCrF6Ogoq0tqYTe+4eFhvTj2jI2N5fKjZIUp2WwpR0dHWFtbE1GKQCBojVgsRm9vr9JSP22coaampkpzqNzd3YnzYwUyMTHBdAOsra1FTU2NVifKYWFhcu4qDw8PgwuTk5OTqKqqQnl5OcrKypbseubs7IzY2FjExcUhOjoajo6OepvbwMAALly4gPPnzysIGzSmpqZITExESkoKtm/fLve9GRsbw7lz55CVlYXm5maF53I4HGzfvh0ZGRnYvn37mryouJro7e1VCP4PCQlhdR3RAe401tbWa1rAysnJ0UvlyOXLlzE8PCzntBoZGWFuEweW6hABi2AwvL295QQsIyMjUBTFhEGOj4/DxsZGZyfkP/7xj/HTn/5Uq2wLTeFyuYuW3y0UoejbwP1Q7PHxcQwMDKCjo2NFld/RApOqQeRrpfxOW+gOfLLle7Rrihaj6PvDw8N6+cxyOBzY2dkt6ZJycHDA+vXrieOAQCDoFIqiMDIywuqgol1U2vz+Ozo6sjqo3N3d4ejo+ED/Bq10xGIxmpub5coAOzo6NB7P39+fyazi8/nw8/NbFsFELBYzOVZlZWWoqalZ9Pfd2toaUVFRjGi1ceNGvX5uh4eHkZ+fj9zcXFRVVbF+/7hcLuLj45GamorExES5ygapVIrKykpkZ2cjPz+ftfrAw8MD6enpOHDgAJydnfX2WgjqwSZShoSEsGYWLxSwbGxs5ESrkZERiMXiNSNKlpaW6m3sv/71r/jxj3/M3CcOLM0gAhbBYGzcuFHufmhoKMRiMZqamkBRFGpqanTeKVAbIYDD4cDS0pJVfJIVnRaKUsbGxpibm8PExARu3bq14srv1M1+Wqvld7pgampKoXxvoUuKXq6vstL169criFELu/HZ29vD1taWdOAjEAh6ZW5uDnfv3lXo5EcLVdo4gs3NzVkdVPS/5ubmOnwlBH0hlUrR1dXFlAHSDitNcXNzkxOrQkJCls1RR1EU2tvbmRyrpXJhuVwuwsPDERsbi9jYWISEhOj9d3p8fBwFBQXIzc1FWVkZ6zGqkZERYmNjIRAIsGvXLoUcsHv37uHkyZPIyclh7UBoamqK5ORkpKenIzIyklwQW4EszL9ydXWFra0tiouLFdaVzcAC2DsRDg4OYsOGDbqd5DIgkUi02h8txfHjx3Ho0CH4+fkBkBewiANLdcjZDMFgLBSwvL29YWFhgaamJgBAU1PTslydUVaaB2DZyghlMTIyUggfV6cM70Eqv9MFMzMzrGKUbAA6/bew/bCusLa2ZhWlFpbv2dnZEXGRQCAYDIqiMDw8zAhSC4Wqe/fuaeyi4nA4cHJyYi31c3d3h729PXFRrUL6+/sZkaq2thbXrl3T+ILOunXrsGnTJoSGhjLlgMt90tff34+ysjJGtFqqnCogIABxcXGIiYlBRESEQcS2yclJFBcX4/z587h69SprN0MOh4PIyEhGtLK3t5d7XCwWo7i4GNnZ2bhy5Qrr9zw4OBjp6enYu3cv6ai2wmHLvwLAmsPGVkK4kIGBgTUhYLW2trJ217a3t1ea46wuf/rTn/D2228DuC/80RAHluoQAYtgMBYGNXp6esodeDQ1NS3LwSlFUXotMzQzM1MQnlTNfrKxsXngy+90gVgslsuTUuaSGhwc1JtoaWFhsaRLil5uiBbdBAKBwMbs7CzjomIr92M7uFcVCwsL1k5+Hh4ecHV1Jfu+Vc7ExAQaGhoYwerGjRtyJ2jqwOFwEBERAR6PxwhWrq6uy348NDExgerqapSWlqK8vBzt7e2Lru/i4oLY2FjEx8cjOjpaQRjSFzMzMygpKUFeXh6Ki4uVxlFs2rQJqamp2L17N2uIfUdHB7Kzs3HmzBnWE3hra2vs3bsXmZmZCAoK0vnrIOieqakpxjxAExISwurGMzY2VsheUyZgrQVu3LjBuvyXv/wlnn32WZ1s48qVK7h06RJsbW3l3F6yYfmExSECFsFgLNwBmpmZITAwkLnf0tKi14BKTbGyslLqfFrMAUU/JtuZhaA7JBIJhoeHl3RJDQ4OYmxsTC9zMDExUciTWuiSooUp0mqdQCCsBCiKwtDQEGsOVXd3N/r7+7VyUbm4uLA6qNzd3WFnZ7fsAgRBN4jFYrS0tMiVAWqTWxUSEgI+n4/Q0FDweDz4+PisCPe4WCxGbW0t0ymwpqZm0WgIa2trxMTEICYmBnFxcfD09DTYZ35ubg5Xr16FSCTCxYsXlYrNoaGhSElJgUAggKurq8LjU1NTyMvLQ1ZWltJA9+joaGRkZCApKYmU764y6urqFD7DISEhuHfvnsK6bDlsbO66pTporhauX7+usIzD4YDH4yEsLAw1NTU62c5vf/tbue/Nzp07iYClBkTAIhiMhTtAsViMgIAAcDgcUBSFzs5OeHp66nSbdPmdKg4oNhHKyspqRRxAPShIpVKMjIws6ZIaHBzEyMiI3jrwqeKScnBwIB34CATCimRmZoYp61uYQ9XT06NV+bOlpSWrg8rDwwMbNmwgF23WILK5VXTIujYncl5eXkwJII/HQ2Bg4Ipx31EUhba2NkawqqqqWtR1yOVysXnzZqYsMCQkxKDHjWKxGBUVFRCJRCgsLFTardPf3x9CoRACgYD1WJvOos3OzoZIJGJ9zU5OTkhPT0daWppCLhJh9cAm0oSEhKClpUVhOZuooiwDay3A5sDy8fGBjY0Ntm3bpjMBq7u7m7nN5XLxwgsv6GTcBwUiYBEMxsLMg5mZGVhYWMDDwwNdXV2gKAplZWVab+fQoUN45plnYGVlBQsLCyIwLDMURTFdFWWFqYWCFP2YPkLvORwO7O3t5TrwKevGt27dOhI4SiAQVjQURWFgYIA1h6q7u1urcg4OhwNXV1elgenr168nv6trnIGBAbncqsrKSo2jFhwcHLBp0yZGsAoJCVlx+Uj37t1jSgLLysqWPBkPCgpigtc3b95s8ND4+fl5VFdXIzc3FxcuXMDo6Cjrel5eXoxo5evry7rO0NAQzpw5g5ycHNZySGNjY+zYsQPp6enYunUruai7BljoqnN1dYWdnR2rg3Jh/hXALmCtBQdWX18fent7FZZv2rQJwP2MN32QmJiokBNNWBwiYBEMhqzaDAC3b98GcP9AoKurC8D97A1tSUhIYK3jJ+gOiqIwOTnJmiu1MAB9cHCQNTBUF9ja2iq4pOjyPbqcz8HBAevXrycHXQQCYVUxPT2Nnp4eVgdVT0+PVr+X1tbWCsIU7ajasGEDaQ7xADE5OYn6+nrGWXXt2jWNw4q5XC4iIyPl3FUr8XhsfHycybEqKytDZ2fnouu7uroiNjYWcXFxiIqKMliOlSxSqRQ1NTU4f/48Lly4oFRkc3Nzg1AohFAoZKocFjI/P4/S0lLk5OSgqKiI9RjNx8cH6enp2L9//7K8XoJ+kEqlCi4jOsD9ypUrCuuzCVhrtYRwYS4YTXh4OID/vE+6pqCgAK+99hqefvppnVcirVWIgEUwGLRgRdPW1gZAt6F1Dg4OiIuL09l4DxrT09NLuqToP12IjWxYW1sv6ZJydHSEra0tOckiEAirFqlUioGBAVYHVU9Pj1YlGUZGRoyLik2oWrdunQ5fCWG1IBaL0drayjirtM2tCgsLY8QqPp9v0LwndRCLxbh58ybTKZAtA0gWGxsbxMbGIiYmBrGxsfDw8Fi2JkP19fXIzc1Fbm4u+vr6WNdzdnaGQCCAQCAAj8dTOteenh6cPHkSJ0+eZB3LwsICKSkpyMzMxKZNm1bk/yVBO9rb2xXKTGlhpqSkRGF9tlLRtRrizpYBBgB8Ph/A/Sxnc3NzrUrwf/CDH+APf/iDwvKcnBycOnUKjzzyCH7wgx+Q794SEAGLYDAWHiR1dnZidnYWH330kc62sXfvXnC55GMty9zcHKsrii1nampqSi9zsLS0VJoltfA+yU8hEAhrhampKdZOfvTfwtJ6dbCxsWHNoXJ3d4eLiwsR+B9wKIpCV1eXXCmgNvkt/v7+jFDF4/Hg5+e3Yj9jFEWhtbWVybGqrq5eNMfKxMRELscqODh42Vzb9NxFIhFyc3MVqhdo7O3tkZycDKFQiPDwcKXRB7OzsygqKkJWVhYqKipYs0PDwsKQmZmJlJQUWFlZ6fT1EFYWbBlPizmLVHVgrQUBi+01WFtbw8fHB8D98vpNmzahvLxc422wiVc0UqkU//73v5GUlITIyEiNt/EgQM70CQZjoQNLKpXKtQ/VBampqTodb6UiFosxMjKyqBg1ODiIgYEBpYGe2mJmZqbgjFrokqKFKUPnQxAIBIIhmJ+fR39/v0InP/q2pqVYwP3sGVkXlaxQ5e7uTlxUBDkGBwflxKrS0lKNx3JxcVHIrVrpv+O9vb2MYFVeXr7od4/D4SA4OJjpFBgeHr7snfQ6OjogEomQl5en1BW3bt067N69G0KhEJGRkYuKbC0tLcjKysLZs2dZOzHb2tpi//79SE9Ph5+fn85eB2Flo0zAUuZIVCfEfX5+flXHddy9e1dhGZ/PlxOHeTyeVgKWKqzEsuuVBhGwCAZjoYAFAA0NDTrdhr4C9gzB/Pw8RkdHGeGJ/nd4eFhBmBodHdVLBz4ul8vqipK9T9+2srIiFlcCgbDmmZycZHVQ0WV/2mT8rV+/njWHinZREUcxgY2pqSnU19ejtrYW9fX1qKqqwvDwsEZjWVhYIDIyksms4vF4sLOz0/GMdc/4+DgqKysZ0erWrVuLru/u7s6UBUZHR6+I19jd3Y3c3FyIRCLWDnAAYGVlhaSkJAgEAsTGxi7qehsfH8f58+eRk5OD+vp6hcc5HA7i4+ORnp6OnTt3rlgHHUF/LOxAuGHDBtjZ2bGWz1laWrKKVWwOLIqiMDw8DEdHR53N1ZDQTsWF0AHuNEFBQXqdR2xsLAl0VwFyZEQwGGwhmcp+sDVlpQkqFEUxotRiLqmhoSEMDQ3pRZQyMjKSE6OUuaQcHR1hY2Oz4t5DAoFA0Cfz8/Po6+tTWuo3MjKi8dhcLheurq6sOVRubm4rrhsbYeUhFovR1tbGOKtu3ry5ZOj4YiwUq1xdXVfF7/7c3JxCjtVix0zr1q1jHFYxMTGsWT7LQV9fH5NpVVdXx7qOhYUFEhMTIRQKER8fDzMzM6XjURSF6upq5OTkIC8vjzWf1M3NDWlpaUhLS8OGDRt09loIq4uhoSGFklS6fJBNAKZL5xairMx0YGBg1QpYFy9exPj4uMLysLAwufv6CnKnefTRR/U6/lqBCFgEgzA+Ps5q525ubtbZNlJSUnQ21mJQFIWJiQm5QHM2YYpero8OfBwOh+nAt5QwZWtrqzQbgUAgEB4ExsfH5cQp2cD03t5erfbTdnZ2CjlUtFDl7Oy8qksqCIaFoih0d3czpYA1NTVa5VYFBwcjLCwMoaGh4PF48PHxWTWfR6lUitbWVpSWlqK8vBzV1dWLhiebmppiy5YtiI2NRWxsLIKCglbMsc/AwADy8/MhEokUHDA0pqam2L59OwQCARISEpYs2ezv78fp06eRnZ3NdPKWhcvlYteuXcjMzER0dPSKeS8Iy8di+VeVlZUKjylrsqXswkt/f/+qrYTJyclhXb5QwGLLBNMVnp6e2L59u97GX0sQAYtgEGSVfS6Xy5ws6FLA+sY3vqHV86enpxkBihaj6PK9hR345ubmdDRredatW8cqRtnZ2ckFoNvZ2ZHSEgKBQPj/SCQSxkXFVurHlgGjKlwulzWHihaqSOgxQVOGhoZQW1vLCFZXr17VeCwPDw+Eh4eDz+cjNDQUgYGBizp3ViJ3795FeXk5rl69ioqKikXLIjkcDkJCQuRyrFbS6x0ZGUF+fj5yc3NRWVnJ6hbjcrmIj4+HUCjEjh07ltyXSCQSXL58GVlZWbh06RJrbpG/vz8OHjyIPXv2YP369Tp7PYTVD5uAFRoaCgCs5XPKxBo6QmThZ1qbzrnLycjICGtmoLe3t0LWpD6F4K985StEaFYRcgZMMAiy+Vdbt25FeXk5ZmZmdFoyd/XqVfj7+8stm52dXdIlRf8t1qFGG6ysrOQEKbZufPRtkkdAIBAI7IyNjbE6qHp6enD37l2lIbSqYG9vz+qg8vDwgJOTEzmoJGjN1NQUGhoaGMGqvLxcY2HV2toa0dHRciHrq7EcdWxsDBUVFUxZIJuTSBYPDw+mJDA6OnrFCTTj4+MoLCxEbm4uysrKMD8/r7COkZERYmJiIBQKkZSUpFIzhtu3byMrKwunT59mFQmsrKywZ88epKenIzQ0dFWUhBIMD5v7j3ZgsUW6KBOwjIyMYGlpicnJSbnl/f392k9yGZibm2M9H114TqlPrKyssH//foNtb7VDBCyCQZAVsHx8fGBra4uTJ0/qdBt/+tOfUF1djfHxcUaU0lcHPnNz8yVdUvRjy93ZhkAgEFYDYrEYvb29Skv9tNmfm5iYsDqo6PsrvcMaYXUhFovR3t7O5FZdv359yZDxxYiNjQWfz2fcVau1S9Xc3Bxu3LjBBK/X19cveiHT1tYW0dHRjGilz/IdTZmamkJxcTFEIhEuX77MWo7M4XAQEREBgUCA3bt3w97efslxp6enceHCBeTk5KC6upp1nYiICGRkZGD37t1kH0ZYlLm5OYVgfzrAXRmL5cZZW1srCFgDAwPaTXKZcHZ2xsaNGxWajSn7ntra2mqVjclGRkYGcXOrARGwCAZBtiWwt7c3hEKhzgUs4H4In6aYmJgs6ZKiH7e0tNThrAkEAmHtQ1EU46JiC0zv7e3VykXl6OiotNTPwcGBuKgIeoGiKPT09DBlgDdv3tQqtyosLIwRq3g8Hjw9PVeto0YqlaKlpQVXr15FeXk5rl27xhoyTmNmZoaIiAimLDAgIGBFfm9nZmZw+fJliEQilJSUKH1NYWFhSE1NRXJyskqiI0VRqK+vR3Z2Ns6fP68gEACAg4MDDhw4gIyMDNKtjKAyDQ0NCuIq7b5SJiIry8AC7udg9fX1yS1brQIWAFY3p62tLeu6fn5+qKqq0tm2ORwOHnnkEZ2N9yBABCyCQZBVtaVSKZ577jmDbNfY2FjODSUrTNH3aXHK2tp61R4kEggEwkqAdlGx5VD19PRo5aIyMzNT6qByc3MjDgSCQaBzq+rr61FTU6NVbpWPjw82bdrEiFV+fn6rPkqgp6eHKQmsqKhY1KnA4XAQGhrKBK9v2rRpReVYySIWi3H16lWIRCIUFRUpjZ0ICQlBSkoKBALBogKALKOjozh79iyysrLQ2tqq8LiRkRG2b9+OgwcPYuvWrSQDlaA2i5UPKhOeFutYaW1trbBsNQtYbCK0MgeWLgQsa2tr5nhox44dK9JdupIhe0CC3qEoSk7A+uUvf8mo/WZmZotejdOUDz74AF5eXli/fv2KvHpHIBAIqxGKojAyMsLqoKJdVNpkGzo5OSkt9XNwcCAXGQgGZXp6Wi636urVqxqLsHZ2doiKipLLrVoLouvo6CgqKiqYssDu7u5F1/f09ERcXBxiY2MRFRWlUgbUciGRSFBRUQGRSISCggKl//d+fn4QCoUQCoXw9PRUaWypVIqKigpkZWWhoKCAtfTQ09MTmZmZ2Ldv36otGyWsDBbrQHjz5k2Fx5bK5V1rAhZbh1NlApavr69W23JycpLLP3z00Ue1Gu9BhAhYBL3T29srd6WKPrmxsLDAm2++iS+//BLnzp3TahsWFhZy2+jr68PmzZu1GpNAIBAeRObm5nD37l2lgelTU1Maj21hYcHayc/DwwOurq4kM5CwbEgkErncqqqqqiVDxRdj+/bt4PF4TG7VYlkzq4nZ2VncuHEDpaWlKC8vR0NDw6KitZ2dHVMSGB0drbIrabmYn59HdXU18vLykJ+fr7QT4saNGyEUCpGSkqJW2HNvby9ycnJw6tQp3LlzR+FxMzMzpKSkID09HREREUS0J2gNRVGsIhUtYBUXFys8tpRIwyZg9ff3g6KoVfmZZYsvULbP9vPz02pbJiYmjHkjICAAUVFRWo33IEIELILeKS8vZ13+pz/9CdPT08jLy9N6G9HR0cwO2NTUFJs2bdJ6TAKBQFiLUBSF4eFhRpBaKFTdu3dPYxcVh8OBs7OzQic/WrCys7NblQe3hLUFRVG4c+cOk1t148YNrXKrIiMjwePxmD9XV9c18zmXSqVoampCWVkZSktLcf36dczNzSld39zcnMmxio+Ph5+f34p3wlMUhZqaGpw/fx4XLlxQ6iRxc3NDSkoKUlNTERgYqPL/sVgsRlFREXJycnD16lXW/SuPx0N6ejqEQuGq7ChJWLl0d3djaGhIbpmLiwvjMGITsJYqaWP7jEokEoyNja247qCq4O/vr+AeVebAcnV11Wpbsu6rr3zlK2vmt8KQEAGLoHeU5QQ899xzEIvFOtlGRUUFc/uZZ57ReudCIBAIq5nZ2VlGkFrooOrp6VG6X1YFCwsLRphaKFS5urrC1NRUh6+EQNCeoaEh1NXVMX+XL1/WeCx/f39s2bIFoaGh4PF48PHxgbGxsQ5nu/x0d3czJYEVFRUYHR1Vui6HwwGfz0dsbCxiYmKwadOmVbEPoCgKDQ0NyM3NRW5uLnp7e1nXc3JygkAggEAgAJ/PV+tks7W1FTk5OThz5gxrFti6deuwd+9eZGZmIiAgQNOXQiAsymL5VwBYv99LCVhsDizgvgtrNQpYYWFhKCoqklum7BxVWzctXYpsZ2eH1NRUrcZ6UCECFkHv9PT0sC7XlXgF/Eck8/HxwVNPPaWzcQkEAmElQlEUBgcHWXOouru70d/fr/HYHA4HLi4urDlUHh4esLW1JVcMCSuW6elpNDY2MqWAJSUlrPkmquDk5ISYmBimDDAwMHDFhoxrw8jICMrLy1FeXo6ysjKlx200Xl5eTPB6VFTUqnIMtba2QiQSQSQSKc3rsre3R3JyMgQCATZv3qyWg2xychK5ubnIzs5W6uqLjY1FZmYmduzYsSY/T4SVxWL5V8rw8PBY9HFl3/nBwUG1SmpXCnw+X2FZSUkJAgMDFZbrKrvwoYceIt9/DSECFkHvdHZ2Kn3M19cX8fHx+Pe//61V8C/NT37yk1XfwYdAIBCA+6GitCjFJlRp0wDDysqKEaQWClWurq5kP0pYFUgkEnR0dDBiVXl5+ZLiizKMjIywc+dOpgwwJCRkVQkz6jAzM4Pr168zOVZNTU2LHoPZ29szDqvY2NhFu5OtRDo7OyESiZCXl4f29nbWdWxsbLB7926kpqYiIiJCrU5/FEXhxo0byMrKwoULF1gdri4uLkhPT8eBAwdIxzGCQWETsEJDQwFA6fd+qay6xRxYq5HQ0FBwOBy59+PixYv4+te/rrdtJiQk6G3stQ4RsAh659atW6zLz549yxwEcblc/Otf/9JqO2lpaYiMjNRqDAKBQDAUUqkUg4ODrA6qnp4erTr6cDgcuLq6Kg1MX79+PXFREVYVsrlVdXV1uHbtGmprazUeLyYmBmFhYYy7ai13eZufn0djYyPKy8uZHKvFXPAWFhaIiIhgXFb+/v6rbn/R09ODvLw8nD9/Hs3NzazrWFpaYteuXUhJSUFcXJzawv3Q0BBOnTqFnJwc1ou1XC4XO3fuREZGBmJjY9dcqSlh5TM+Ps4q2tIOLGWCv6YOrNXaidDKygq+vr5oa2tjltXW1qK/v5/1t8HKygqTk5NabZM0rdEcImAR9IpYLGbtsgJAbocQFham1XZsbW3xwgsvaDUGgUAg6Jrp6WmlDqqenp5Fw5CXwtramjWHyt3dHRs2bCAuKsKqZnh4mAlZr62txZUrVzQeKzQ0FOHh4eDz+eDxePD09Fx1gow6UBQll2NVXl6O8fFxpesbGRmBx+MhPj6eEfZW4/6jr68PeXl5yM3NVSpumpubIyEhAampqdi6davaJTwSiQRXrlxBTk4OiouLMT8/r7COr68vMjIysHfvXqVB0ASCIbh586aCy0o2wL2kpIT1eUvlPFlZWbEuX60CFgDs3r1bTsACgFOnTuHpp59WWJfP56OsrEyr7SnrcEpYGiJgEfRKd3c3a2tS4H4XBnoH6ePjo9V2XnjhBdja2mo1BoFAIKiLVCpFf3+/Qic/Wqha2PlHHYyMjBgXFZtQtW7dOh2+EgJh+ZienkZTUxNqa2tRU1ODwsJCSCQSjcZyc3NDdHQ0+Hw++Hw+fH19V6UYoy5DQ0OorKxkygKVXTyk8fHxQUxMDOLi4hAREbFqyyUHBwdx4cIF5Obm4tq1a6zrmJiYYPv27RAKhUhISNAow6a7uxs5OTk4efIka5mUhYUFhEIhMjMz1Q57JxD0xVL5VxcvXlR4XJUuqsr2F6u1hBAAvv71r+POnTs4ffo0s+ztt99mFbC0dV8BQH19PWJiYrQe50GECFgEvXL79m3mdkREBKqrq5n7ly9fxv79+wFA6xbLe/fu1er5BAKBoIypqSmlYel37tzRqiHFunXrWHOo3N3d4eLiolYOC4GwGpifn0d7ezvjrrpy5YrSDnBLYWpqyuRW8fl8BAcH6yxgd6UzPT2N69evo6ysDKWlpUrL5GgcHByYksDY2Fg4OzsbaKa6Z3R0FAUFBTh//jwqKytZc3yMjY0RHx8PgUCAnTt3Ks3sWYyZmRkUFhYiKysLlZWVrOuEh4cjIyMDKSkpsLS0VHsbBII+WUrAKi8vV3hclRB2Zd+nwcFBNWa3sjAxMcFrr70GAHIi1tTUlMJ3e7F8Z1WJiIjQeowHFXJkTNArHR0dzO2FbVxPnDjBCFjaHkiRkzwCgaAp8/PzjIuKFqe6urqY29rYvI2NjeHq6qq01G+1uh4IBFVYmFtVXV2Nuro6jcfbtm0b46wKDQ3Vup35amJ+fh4NDQ1MWeD169cXdalZWFggKiqKCV/38/Nb1a6g8fFxFBUVITc3F6WlpaylexwOB9HR0UhNTUVSUhLWr1+v0bYaGxuRnZ2Ns2fPMi3vZbG3t8e+ffuQkZGhdQUBgaAvJBIJaymtth0IgbXpwALu70OEQqGcgHXlyhUkJyfLrce2X1CXjRs3aj3Ggwo56yfoFVkH1sJSwpqaGlAUBQ6Ho1UQK4FAICzFxMSEQnmfrItK03Il4H4GH5uDinZRkeBewoPCyMgI46y6efMmrl69qvFYmzZtwubNmxl31YYNG1a1AKMuFEWhq6uLKQksLy9f9KTJyMgIYWFhiI2NRVxcHHg83qovnZyenkZxcTFEIhEuX77M6nblcDjYsmULBAIBkpOTNc6cGh8fx9mzZ5GTk4PGxkbW7Wzbtg0ZGRlISEhY9e8tYe3T0tLC2hGT7kCoDFW6ZCpzYK3mDCyalpYWufsffvghduzYofPvvKmpqU7He5AgAhZBr8gKWGxMTk7C0tISP/nJTww0IwKBsBaZn59HX1+f0sD0kZERjcfmcrmMc4qtq58mpSkEwmpnZmZGLrcqLy9PaUv2pfD09ERcXBxCQ0PB5/Ph7e39QAq/Q0NDTKfA8vLyJUsrfX19mZLAiIiINbEvmp2dxeXLlyESiVBSUoKZmRnW9cLCwiAQCJCSkqKxi5+iKFRWViI7Oxv5+fmsTTXc3d2Rnp6OtLS0VV12SXjwYCsfdHZ2ZkReZfEHqjiwLCwsYGRkxJgTOBwOKIrC9PQ0JicnlYa8rwaamprk7tfX1+NPf/oTXnrpJWaZvb29VhmngPL3n7A0RMAi6JWlaoTpHdyDeKBKIBDUY3x8XE6cknVU3b17l7WkRFXs7e3lxCnZ205OTmQfRXigmZ+fR0dHB+OuunjxosZX2q2srJCUlAQejwcej4fAwMAH9kr09PQ0qqurGdFq4ZX/hTg6OiIuLg4xMTGIjY1lbe++GhGLxbh69Sry8vJQWFiIqakp1vWCgoIgFAqRkpKikktEGffu3cOpU6eQk5OD7u5uhcdNTEywe/duZGZmIjIyUuucVgJhOVgY3QLIlw/W1NSwPs/NzW3JsTkcDqytrTE2NgbgfndP2u01MDCwagWse/fusXZm/PTTTxEREcGUEnp6emotYJFGPJpDBCyC3hgfH2e+3FwuF0lJScjLy2MeT0lJYcoBfve73+Gpp55alnkSCISVgUQiYVxUbKV+9IGSJpiYmDCiFFupHwnfJRDuQ1EUent7UVtbi7q6OpSXl7OWVKnKzp07ERYWBh6Ph5CQkAc6900ikaChoYFxWN24cWPR8mVLS0tERkYiPj4eMTEx8PHxWTNllBKJBJWVlRCJRCgoKMD4+Djrer6+vhAIBBAKhfDy8tJ4e2KxGCUlJcjOzsbly5dZ3YJBQUHIyMjAnj17yMklYdXD5sCSLR8sLi5mfZ6q4rCsgGVhYcEIWP39/Vp9V5cLiqLw+uuvMwL6xo0b4e3tzbxPr732GoKCguDp6Ql/f3/W91cd1sq+fDkgAhZBb9y6dYu5vXHjRggEAjkBS7ZTUGtrq0HnRiAQloexsTFWB1VPTw/u3r2rkJWnDg4ODgrlffRtR0dHchWdQGBhdHSUcVZdv34dpaWlGo8VGRmJzZs3MyHra8UhpCkUReHWrVtM8HplZeWiOVbGxsbYtGkTYmJimJLKtZS1JJVKce3aNeTm5uLChQtKG2R4enpCKBRCIBCo1BFtMTo7O5GdnY3Tp0+zOiasra2xd+9eZGRkIDg4WKttEQgrhd7eXvT19Sksl3VgXbx4kfW5qnZylS1ZNjc3Z26v1k6E+fn5cu/Jz372MwQGBuLRRx/FnTt3MDk5iZdeegkffvihVg5QAKvWobZSIAIWQW/I5l9t3LgRW7dulXuctrbOz8/jf//3fw05NQKBoCfEYjF6e3uVlvpp07nF1NRUaQ6Vu7u7ygddBMKDyszMDJqbm5mQ9dzcXI3H8vX1ZcLC+Xw+PDw8yBVl3D95k82xYjuJlMXPz48pC4yMjFxzblCKolBTU4Pc3Fzk5uYqLT11dXVFSkoKhEIhgoODtfosTU9PIzc3F9nZ2UpdElFRUcjIyMCuXbvkTr4JhLXAzZs3WZfLOrBkjQY06jinZN20ZmZmzO3V2IlwfHwcb7zxBnP/4MGDiIyMBAC88cYb+NrXvgaJRIKmpib88Y9/xJ07d7Ta3oNaNq8riIBF0BuyApaXlxcsLCxga2vLhCnfvn0bFEUhOjp6mWZIIBDUhaIojI6OsjqoaBeVpkHOwP2MFzYHlbu7OxwcHIiLikBQkYW5VQUFBUodL0uxfv16JreKz+fD19d3TTmDtGFqagpVVVUoLy9HWVnZko5yJycnxMXFITY2FjExMXB0dDTQTA0HRVFobGyESCRCXl4e7t69y7qeo6MjI1qFhYVpJVpRFIXa2lpkZ2dDJBKx5mg5OjoiLS0N6enp8PT01HhbBMJKhy3/SjbAXRnquBCVObBWYyfCf/zjH3LOsdnZWVRUVCAqKgo8Hg8/+MEPGIHrxIkTWm9Pm2oDAhGwCHpEdkdAq/QPPfQQ/vGPfzDLlQUIEgiE5UMsFuPu3busOVQ9PT2YnJzUeGxzc3NWBxX9L7kSTiCoD0VR6OvrQ21tLWpra3HlyhWtSvNTUlLA5/PB5/MRHBxM3I0ySCQS1NfXMw6rmzdvLppjZWVlhaioKEa08vLyWrNOtdbWVuTm5kIkEqGrq4t1HTs7OyQnJ0MgEGDLli1aX5QYHh7G2bNnkZWVhfb2doXHjY2NkZiYiPT0dGzduhVcLjn1Iax92JyHsuWDygQUVToQ0sg6sFa7gHXu3Dm5+2fOnMGZM2dw5MgR/OhHP8Lhw4dRVVWFCxcu6GR7o6OjOhnnQUUve/Evv/wSL7/8Mj7++GNERUUpPN7R0YG33noLVVVVGBkZwcaNG3H48GE89thjrD9kfX19eOedd3D58mX09/fD1dUVaWlp+MY3vkEseCsY2R1laWkpnn76aTz11FM4deoU7t27B6FQKGc5JRAIhoGiKAwPD8sJU7JCVV9fn1YuKmdnZ9ZSP3d3d9jb26/ZkzcCwVCMjY0xzira/aMpsbGxcrlVdnZ2Opzp6oeiKHR2dqK8vBxXr15FVVXVoiI+l8tFeHg40ykwNDR0TYsmt2/fhkgkgkgkYhWQgPtOjd27d0MoFCIqKkrr92N+fh5lZWXIzs5GUVERq4Do5eWF9PR0HDhwAA4ODlptj0BYTUxPT6OpqUlhuex5mbLSZnWynWRznFZzCeHw8LDSOX/xxRd49tlnYWNjg5///Oe4fv36qhTo1ho6/0W9du3aonlGjY2NeOyxxzAxMYGIiAiEhYWhrKwMv/zlL3Hjxg38/ve/l1u/t7cXR44cQW9vL0JDQ8Hj8VBdXY0333wTpaWl+OCDD4iNfYWSkJDA3L527RrGx8dhY2ODDz74AHV1dUhMTERRUZFOtjU1NbXmciMIBG2Ym5tjSvzYhCq6W4wmWFhYsLqoPD094erqSoRpAkGHzM7OoqmpCXV1dbh+/bpcMxR1CQwMlMut2rBhAxGUWejv72dKAsvKypY8IfP395fLsVrrjrWenh7k5eVBJBKxnigD9zsoJiUlISUlBfHx8To5Vr9z5w5OnjyJkydPore3V+Fxc3NzpKSkIDMzE+Hh4eSzTXggqa2tZXVYyQpYyrLh1BGwZDt1ruYQ98rKSrn7zz77LE6fPo3bt29DIpGgoKAAiYmJ+Otf/0rEqxWCTgUskUiEl19+mbXuHLh/Feull17CxMQEfvvb3yI9PR0AMDQ0hK9+9as4deoUUwtP8+qrr6K3txff+9738OyzzwK4L1Z897vfxZUrV3D06FE8/fTTunwZBB3h5OSE0NBQ1NfXY35+HleuXIFQKISbmxvc3NwAYMlabFXp6upCUFCQTsYiEFYDFEVhaGiI1UHV3d2N/v5+jV1UHA6HcVEtzKHy8PCAnZ0dOTEgEPTA/Pw8Ojs7UVtbi7q6Opw/f17jxgcODg7YtWsXQkNDwefz4e3tDWNjYx3PeG0wOTnJONlKS0uVOoloXFxcEBsbi7i4OERHRz8QDp979+4hLy8Pubm5SuMfzMzMkJiYCKFQiK1bt+qkJHxubg5FRUXIyspCeXk56+8an89HRkYGBAKBXC4PgfAgokyckg1wV9aBUFMHlux3fbU5sOLi4hASEoKRkRF85StfwWOPPQZTU1P8+c9/BgCcPn0aH3/8sVZl+QTdohMBq7e3F3/84x+Rk5MDCwsLODo6siqUly9fRlNTE2JiYhjxCrgvYrz66qt49NFHcfToUUbAam9vR1FRETZu3Ihvf/vbzPqWlpb41a9+heTkZBw7dowIWCuYxMRE1NfXA7i/s5QVJwEgIiJC6edFHZ588km8+uqr2LNnj1bjEAgriZmZGcZFxRaYPjMzo/HYlpaWrDlUHh4ecHV1JeXZBIKeoXOr6FLAkpKSJYWTxdi7dy/CwsLA4/EQGBhIvsOLIBaLUVdXx+RY1dTUYH5+Xun61tbWiI6ORkxMDOLi4rBx48YHQsQfGhrChQsXkJubi2vXrrGKR1wuF9u3b4dQKERCQoLO3PAtLS3Izs7G2bNnWfNi1q9fj/379yM9PR3+/v462SaBsBZgE7CcnJzkhPbi4mLW57q4uKi8HdkMLC6XCyMjI0ilUoyPj2N2dnbVuPFtbGzw8ccfg6IoZr8uFArxl7/8BRRFoaqqaplnSFiITgSsP//5z8jJyQGfz8evf/1r/PKXv2QVJEpKSgAAycnJCo9FRETAwcEBVVVVmJiYgLW1NS5dugSKopCUlKSQjeXm5obQ0FDU1NSgtbWV/HitUHbs2IH3338fAHDp0iWIxWI5G7mxsTFSU1Nx7NgxrbYjFovxhz/8gQhYhFUFRVEYGBiQc1HJClXaCLscDgcbNmxgzaFyd3eHra3tA3ECRiCsFMbHxxmxqqysTKuD4m3btiEyMhKhoaEICQmRO5EgKEJRFNrb2xmHVVVVldJqAeA/OVZ08HpwcPCazrGSZXR0FIWFhRCJREodT8bGxoiLi4NAIMDOnTt19vmbmJjA+fPnkZOTg7q6OoXHORwO4uLikJ6ejp07dxKRlkBYgFQqZRWwZN1XAJTGSKjj0pV1O05NTcHe3p45bh0YGFDLzbUSkD0mdnFxQUREBBGvVig6+TX29fXFG2+8gbS0tEW7idDWu8DAQNbHfXx8MDg4iLa2NoSHhzPrBwQEKN1uTU0NmpubiYC1QgkMDISLiwv6+vowMTGB69evIzo6Wm6dPXv2aC1gAfeDowmElcb09DTu3LnDWurX09OD2dlZjce2srJSEKbo+xs2bCD5gATCMjE7O4vm5mbU1dWhurpaq85FPB4P8fHx4PF4CA0NhZOTkw5nunbp7+9nHFZlZWVLXhAIDAxEbGwsYmNjsWXLljWfYyXLxMQEioqKkJubi9LSUtZQdA6Hg6ioKAiFQuzatQu2trY62TZFUbh27Rqys7ORl5fH+pvo6uqKAwcOID09Ha6urjrZLoGwFmlvb2ctO5fNv1KGsvNtZcgK1+Pj43BycmL2s4ODg6tOwFoIj8eTE7A2btyI2dlZpQH4BMOhEwHrm9/8pkrr3bt3DwCUHnzRy+kPP72+MmFi4fqElQeHw0FiYiJOnDgB4L4Lb6GAFRISAm9vb3R2dmq1LYFAoNXzCQRNkEqlGBwcZM2h6unp0Wr/ZGRkxLio2ISqdevWERcVgbDMSKVSJreqtrYWZ86c0bhJgouLC3bt2gU+nw8+nw8PDw/yHVeRiYkJVFdXM6LVUuWYGzZskMux0lUm52phenoaJSUlEIlEjEOejS1btkAgECA5OVmnWV8DAwM4ffo0srOzcfv2bYXHuVwukpKSkJmZiZiYmEUvkBMIhPvcvHmTdbmsgDU8PMy6Dp/PV2tbsg6siYkJODo6MvdXWw7WQiQSCT766CO5Za+99hr++Mc/6kzAki1ZJKiHQf3Q9AGdslBHejlt61Z3fcLKRFbAunjxIr7//e8rrPPCCy/ghRde0Go7C/O1CARdMT09zdrJj/6bm5vTeGwbGxtWB5W7uztcXFyIi4pAWGHcu3cPtbW1qKmpQUFBAbq6ujQax9jYGHv37kV4eDh4PB58fX3J910NxGIxU45ZWlqqtPMWjY2NDWJiYpgcqwdRHJydncWVK1dw/vx5lJSUKM1R5PF4EAqFSElJUSsTZykkEgmuXLmCrKwslJSUsP5/+fv7IyMjA3v37tWZy4tAeFC4fv0663LZEsKOjg7WdTw8PNTalqyANT4+LieSrXZzSVZWltz9pKQkhIeHw8vLS2kTC3WZmJgg5f8aYlABi66rXeqAgf5BU3d9wsokKioKFhYWmJ6eRldXFzo7O+Ht7S23zvbt27XaBp/PJ7ZygsZIpVL09/ez5lD19PRgaGhI47GNjY3h6urKiFOyQpW7u7tcG2ICgbCyGB8fR319PWpra3HlyhVcu3ZN47F27tyJiIgI8Hg8BAcHP1AlarqAoii0tbWhrKyMyRBbzOnG5XKxZcsWxMbGIiYmBiEhIQ9kF0axWIzS0lLk5eWhsLAQk5OTrOsFBgYiNTUVKSkpOi/9uX37NrKzs3H69GnWE1srKyukpqYiPT0dPB7vgRMWCQRdoUqAuzKXlrrfe1nxZaEDazULWLOzs3j99dfllv3yl78ERVEoLS3V2XaGhoaIgKUhBhWw6IM1ZVd86OV0W05V19dVxxOCfjAzM0NcXBwKCwsBAEVFRfjqV78qt46RkRFeeukl/Pa3v9VoG+Hh4dpOk7DGmZycZO3kR99my/xQlfXr1yt08qOFKhcXlwcm/JdAWM3Mzc0xuVWVlZXIz8/XeKzw8HAmt4rH4xEniYb09fWhrKyMybEaHBxcdP2goCCmLDA8PPyBFQklEgmqq6tx/vx5FBQUYGxsjHU9Hx8fCAQCCIVChQuL2jI9PY38/Hzk5OQoDULesmULMjIykJyc/MD+XxEIumJoaIjVEbww/0pZB0I3Nze1trdWSwjPnz8vd9/Pzw8WFhZoaWnRqTA3ODgILy8vnY33IGHQsypnZ2c0NDRgYGAAfn5+Co/TH3Y624rOvlL2YaHXJ+HdK5/ExERGwCopKVEQsABg9+7dGgtY6tpeCWuP+fl53Lt3j9VB1dPTo7TmXxW4XC5cXV0ZcUpWqHJzcyNXUAiEVYZUKsWtW7dQW1uLmzdvIicnR2MR293dHbt37wafzwePx8OGDRuIg0RDxsfHUVVVxeRYLZWN6ebmxgSvR0dHw87OzjATXYFIpVJcv34dIpEI+fn5Sp3DHh4eEAqFEAgE8Pf31+lnlaIoNDQ0ICcnB+fOnWMNk3ZwcMD+/fuRkZFBTt4IBB2iSv4VoLzMUN1zKTMzM3C5XEgkEkgkErmKgqUuNqxkFp4vpKWlAQCuXLmi0+1oU93xoGNQASsgIAAXL15Ea2srYmNj5R6jWxwbGxsz4hbdDYHuRriQtrY2AMq7GhJWDgkJCeBwOKAoCtevX8fIyIjCFWltOitpmkFCWF1MTEzI5VDJClV3797VykVlZ2enkENFC1XOzs4PZOkJgbBW6O/vZ3KrcnNzcefOHY3GMTMzw/79+xEWFgYejwdvb2+yb9ACsViMmzdvory8HKWlpairq1s0FmLdunWIiYlhRKsH/eIVRVGora1Fbm4ucnNzlboeNmzYgJSUFAiFQoSEhOhcYB0dHcW5c+eQlZWFlpYWhceNjIywbds2ZGZmYtu2bSTrjUDQA8qEKVU6EAL3qwnUxcbGhhF8ZDOrV7MD65FHHsHY2Bimp6exdetWJuamrq5Op9shApbmGFTASkhIwN///nfk5+fjsccek3usuroaQ0NDiImJYSyJCQkJAIDCwkL88Ic/lOtAcufOHTQ0NMDd3R3+/v6GexEEjbC3twefz0dNTQ0oisLly5exb98+hfXorCx1Yav5Jqw+JBIJ+vr6FALTaaFqdHRU47G5XC5rDhUtVNGlywQCYXUzPj6OhoYG1NbW4uLFi1oFrqakpDC5VYGBgTA1NdXhTB88KIpCa2sr47CqqqpSGhMBACYmJtiyZQvi4uIQExODoKCgB14wpCgKTU1NEIlEyMvLUyrGOjo6Ijk5GampqeDz+Trv4ieVSlFZWYmsrCwUFBSwdjH08PBAZmYm9u/fr9VFSgKBsDTKzoVkA9wXa3ymibBtbW3NKmCt5gwsc3NzPP/88wrLlYXfawoRsDTHoAJWTEwMAgICcPnyZRw/fhyHDx8GcP8/8LXXXgMAfO1rX2PW9/T0REJCAkpKSvCXv/wFL774IoD7X75XXnkF8/PzcusTVjY7duxgTiQuXryoIGDdvn1b49bjdXV1mJmZUdqxkrByGB8fV5pDdffuXczPz2s8tr29PauDysPDA05OTqQNN4GwxpDNrSorK0NRUZHGY0VGRmLbtm0IDQ1FSEgIKQ3WEb29vUzwenl5+aIH7RwOB8HBwYiJiUF8fDw2bdpEftf/P21tbcjNzYVIJMLt27dZ17G1tUVycjIjvOpD7Ovt7cXJkydx8uRJVvHMzMwMycnJSE9PR2RkJCmnJRAMwNzcHOrr6xWWOzk5yWVTKSvL5vP5Gm1X9nfS1NSUqbYZHh6GRCJZMxmw8/PzuHXrlk7H1Cba5EHHoJ8qIyMj/PrXv8ZTTz2Fn/3sZ/j888/h7OyM8vJyjI6O4vDhw9i1a5fcc37xi1/g0Ucfxfvvv4+CggL4+Piguroa/f39SExMxKOPPmrIl0DQgsTERLz99tsA7tcRi8VixkZ+/vx5/O///q9W49fX1yMiIkLreRK0QywWMy4qNqFqfHxc47FNTExYHVTu7u5wc3MjDR0IhDWMVCrF7du3UVtbi2vXriE7OxsURWk0lre3N3bv3o2wsDCEhobKHeATtGN8fByVlZUoLS1FWVmZUrGFxsPDg+kUGB0dTQLvZejq6oJIJIJIJGJiMxZibW2NpKQkpKamIjo6Wi8njGKxGBcvXkROTg6uXLnC+r0LCQlBRkYGUlNTifhLIBiYxsZGVhfkwvJBZS6iTZs2abRd2eqF6elp2NraYnh4mBGx1orz8s6dO1rFlLBBHFiaY3BZdNOmTThx4gTefPNNlJWVoaWlBV5eXvj+97+PQ4cOKazv6enJrF9cXIxbt27B09MTTz75JJ566qk1o+w+CPj5+cHNzQ137tzB1NQUqqurER4ejt/97nfIysrSevybN28SAcsAUBSFsbExBWGKvt/b27tohslSODo6Ki31c3BwIC4qAuEBob+/H3V1dbh58ybOnDmjcaaGtbU19u3bh/DwcPB4PHh4eBBXiA6Zm5vDjRs3mE6BdXV1iwqL69evl8uxUrd1+1rnzp07yM3NRV5eHhoaGljXsbCwwM6dOyEUChEfH6+3TKm2tjbk5OTgzJkzrG4BGxsb7N27F5mZmSSPlkBYRq5du8a6fKGApayk3tPTU6Ptyga3j4+Pw8HBgdlX9Pf3rxkBS9flg8DqDrpfbvSi/hw9enTRx/39/fHmm2+qPJ6rqytef/11badFWGY4HA4SEhLw2WefAQA+++wz/OEPf1Aa0q8uysILCeojFovR29urtNSPrbOQqpiZmbE6qGgXFWmlTSA8eExMTKC+vh61tbUoKChgLYVQlT179iAyMhI8Hg++vr4kMFrHSKVSuRyr6urqRXOsTE1NERERgZiYGMTFxSEwMJBciFhAf38/Lly4gPPnzys9wTQzM0NCQgIEAgG2b9+ut9LKqakp5ObmIisrS+lcoqOjkZmZiaSkJJiZmellHgQCQXVU7UB46dIl1vXc3Nw02q6sA2tiYgJOTk7Med1qzsFayFIdcTVBm4qUBx1iXyIYlJ07dzICljZZJWxUVVXpdLy1DEVRGBkZYXVQ0S4qTUtzgPsuKtkcKk9PT0aocnBwIO4HAuEBRiwWo7m5GbW1tbhy5QpKSko0His2Nhbbtm0Dj8dDcHAwEcD1xJ07d5hOgRUVFYtmd3A4HISGhjIuq/DwcCJysDA0NIT8/Hzk5uaiurqa9TeXy+Vi27ZtEAqFSExM1FuZPEVRqKmpwZdffom8vDzWPFJnZ2ekp6cjLS2NuOYIhBUERVEqBbgDUNr0QdPvtGy58Pj4uFw5PhGwFmexCz+ExSECFsGgREREwMrKCpOTk8wyExMTfP/738cbb7yh1diyYxLunyTevXtXqYtKm/fL3NyctZMf/S8J3SUQCMB9t05XVxdqa2tRWVmJnJwcjcfy9/dHcnIy+Hw+QkNDSVaSHhkbG0NFRQXKy8tx9epVdHd3L7q+h4cH4uPjERMTg6ioKI3asT8IjI2NobCwECKRCOXl5azl9kZGRoiLi4NAIEBSUpJe86SGhoZw+vRp5OTksJbIcLlc7NixA+np6YiPj3/gO0ASCCuR7u5upXlKFhYWmJ+fh7GxMWtGFo0uHFiTk5NyApamZf8rEX2UEEZHR+t8zAcFImARDIqJiQkSEhJw/vx5AICXlxd++9vfKrW+EpRDByQqc1H19fVp7KLicDhwcnJSGphub29PXFQEAkGB/v5+1NfX4/r16zh16pTGIaW2trY4cOAAwsLCwOPxsGHDBrLP0SOzs7O4efMmUxZYX1+/6O+HnZ0doqOjERcXh+joaOLIWYTJyUkUFRUhNzcXV69eZQ0C5nA4iIqKgkAgwK5du2BnZ6e3+czPz+Pq1avIyclBUVERa+dfHx8fZGRkYN++fbC3t9fbXAgEgvYsFqGSmJgIPp+Pf/7zn4teiND0wrNsBtbY2Bi8vb2Z+8SBtTgODg46H/NBgQhYBIPzve99DwCwYcMGPPPMM+ByuThy5IhOxm5tbYW/v79OxloJzM7OMi4qNqGKzeavKhYWFqw5VB4eHnB1dSUlHwQCYVEmJibQ0NCA2tpaiEQiNDc3azzWgQMHmNwqb29v4vTQM1KpFM3NzYxgde3aNczOzipd38zMDBEREYiNjUVcXBz8/f1JjtUiTE9Po6SkBLm5ubh06RLm5uZY1wsPD0dqaip2796t906YPT09yMnJwcmTJ3Hv3j2Fxy0sLCAQCJCZmYmwsDAiGBMIq4TFTAB0efDbb7+tl5K1hQ4sWVFmrTiwKIrSKvtXGcRFrjlEwCIYHBcXF/z6179m7ldWVups7IGBgVUlYFEUhaGhIVYHVXd3N/r7+7VyUbm4uCgNTLezsyMHqAQCQSXEYjFaWlpQW1uL4uJiXLlyReOxtm3bhu3bt4PH4yEwMBCmpqY6nKnhoAW8uro61NbW4u7duzh48CAeeuih5Z4aKz09PXI5ViMjI0rX5XA44PF4iI2NRUxMDMLDw1ft/5OhmJubw5UrVyASiVBcXKz0AhOPx4NAIEBKSgo2bNig1znNzs6isLAQWVlZqKioYF1n06ZNyMzMREpKit4ytggEgv5Q5Tzqww8/VPpYZGSkxttemIEl23VwrXTZ43A4CA8PR3V1tU7HJQKW5hABi7DsaFpiwoamNdz6ZGZmBnfu3EF3dzfzr6xQpc0VEUtLS1YHFe2iIt23CASCulAUxeRWlZWV4dSpUxqPFRwcDIFAAB6Ph5CQEFhbW+twpoZDLBajtbUVtbW1qK2tRV1dHTo6OhQuMPzpT3/CwYMHV8TFgZGRESbHqqysbMkcq40bNzIOq6ioKL1mL60VxGIxysrKkJubi8LCQqXZkgEBARAKhRAIBPDw8ND7vJqampCVlYVz586xdrqys7PD/v37kZ6eDl9fX73Ph0Ag6Ifx8XHcunVLqzE2b96s8XNlfyfWcgbWjh07dC5g6bNUfK1DBCzCsqPLGunGxkZs3LhRZ+OpAkVRGBgYkHNRyQpV2rw+DocDV1dXpYHp69evXxEnSgQCYfUyMDCA+vp6VFdXIysrS+PWzo6Ojjhw4ADCw8MRGhqq95IoXTE7O4u2tja0tLSgqakJzc3N6OjowLZt22BjY4Pa2lo0NTUpLQOTZfPmzcu2T56dncWNGzdQWlqKsrIyNDY2Lurgtbe3ZzoFxsTEwNXV1YCzXb3Mz8+jqqoKIpEIBQUFGB0dZV3P29sbAoEAQqEQPj4+ep/X+Pg4zp07h5ycHDQ0NCg8zuFwsHXrVmRkZCAxMZFc4CIQ1gCqZgi7ubkp7UDo6emp8fZlL0qNjY3J/e4PDg5CKpWuiXLzHTt24E9/+pNOxyQOLM0hAhbB4FAUJXeAr0uFvrm5GQKBQGfj0UxPT6Onp4fVQdXT07NodshSWFtbKwhTtKNqw4YN5CCTQCDojMnJSdTX16O2thZnzpxBe3u7xmMdPHgQUVFR4PF48PDwWPFiOn2xobm5mRGrWlpalL4Hp0+fXnQ8+vXSIpGlpSV+8pOf6HbSiyCVStHY2MiUBV6/fn1Rkc3c3ByRkZGIiYlhcqxW+v/ZSkEqleLGjRsQiUS4cOGCUue4u7s7hEIhhEKhQd5fiqKY7p4XLlxg/f93c3NDeno60tLS4OLiotf5EAgEw6KqgKVMvAKgVRMOWQFrcnIS5ubmsLa2xsTEBObn5zEyMrImGkFYWFjofEwiYGkOEbAIBuVXv/oVTp06hW9/+9v46le/CkC3rUmbmpo0ep5UKsXAwACrg6qnp0erOm4jIyPGRcUmVMl28CAQCARdQZe91dTUID8/X2kGjirs2LEDCQkJ4PP58PX1BZe7sg8fxGIxOjo6GJGK/nex3KelcHV1BY/HA5/PB5/Px8DAAH784x8zj//oRz/Sexl7d3c3ysrKUFZWhvLycoyNjSld18jICDweD3FxcYiJicGmTZvIBRE1oCgKdXV1yM3NRW5uLmvwOXA/11MgEEAgECA0NNQgomB/fz9OnTqF7Oxs1tJQExMT7Nq1C5mZmYiKiloTDggCgaDIYh0IVaW1tRUeHh4aCdyyAhbt3nZ0dGRCzwcHB9eEgHX37l2dj0lKCDVnZR+BEtYU/f39+OKLLwAAb731FqKiosDn8zUWndhYbKypqSnWTn70n1gs1ni769atY82hcnd3h4uLy4o/2SMQCKsbiqLQ3d2N2tpaXLlyBWfOnNF4rLCwMKSkpIDP5yMoKEgvVx51ydDQkJxI1dTUhM7OTkgkEq3GjYuLQ1hYGHg8Hng8nlx3paGhIbz00kvM/eTkZOzfv1+r7bExPDyMiooKRrRa7Co6cL9sjXZYRUZGkhwrNaEoCi0tLTh//jxyc3OVvt/29vZISUmBUCjEpk2bDCIQicViXLp0CdnZ2bh8+TKkUqnCOgEBATh48CBSU1Oxfv16vc+JQCAsHxKJBDU1NVqP88Ybb+Cdd97BhQsX1G7WIStgTUxMgKIoODo6orOzE8D9c7+AgACt57jc6FrAMjMzW/HHVisZclZNMBj0zgy4f5D4q1/9Cm+//Tb6+vp0to3BwUFUVlbKCVX0bW3C4o2NjeHq6qq01I+cJBAIBEMyODiIuro6VFVV4fPPP1fa8WwpNmzYgLS0NGzatAmhoaEr2tIukUjQ2dmJlpYWpgywublZJzmKISEhCA8PZ9xVnp6ejJNmcnISLS0tuHDhAhoaGtDU1IS2tjZGIHN0dMRPfvITnThvZmZmcP36dZSWlqK8vBxNTU2L5lg5ODgwGVaxsbGkRExD2tvbkZubC5FIpDQQef369di9ezeEQiEiIiJgbGxskLndunULWVlZOH36NOtxjLW1Nfbs2YP09HSEhISQslAC4QGhpaVFq0ZQskxMTODGjRuIjo5W63kmJiYwNzfHzMwMKIrC1NTUmgxy17WAtZKPtVYDRMAiGIyFB4VNTU1ISUnR6TYoisI3v/lNjZ5ra2vL6qByd3eHs7MzcVERCIRlYWpqCvX19bh586bSkiFVMDY2xkMPPcS4X11cXFbsye7Y2JhC+V9bW5tWTlkaR0dHxMXFMeWAgYGBTGnd0NAQGhsbkZ+fj8bGRjQ2NqK7u3tREekXv/iFxgej8/PzCjlWi71GCwsLREZGIjY2FrGxsfDz81ux/4crna6uLka0am1tZV3H2toaSUlJEAqFiI6ONlgJ5vT0NPLy8pCdna20RCgyMhIZGRnYtWsXuZJPIDyAqJp/pSqaNl6xtrZmhLSJiQk4OTkxj2kTwbKS6Onp0el45JxSO8i7RzAY2rZ51RYul8s4p9i6+q3W9u4EAmHtIBaL0dbWhpqaGohEIq3aNicnJ2P79u3g8/nw9vZekTk48/Pz6O7uZkSq5uZmNDc369SZu2PHDoSFhYHP5yMkJAQ2NjagKAo9PT1obGxEUVERGhsb0dTUpJaby83NDd/85jexbds2lZ9Dl3rSDqvy8vJFuz4aGRmBz+cjLi4OsbGx4PP5JMdKC+7evYvc3Fzk5eWhvr6edR0LCwvs2LEDQqEQ8fHxapfUaAqduZWdnY3z589jampKYR2602dGRoZWncMIBMLq59q1azod7/HHH8ff/vY3hIaGqvU8a2tr5rdzfHxcrtx+rTiwdH0O29PTg4mJCXLuqSFEwCIYjI8//ljv27C3t5cTp2RvOzk5GczyTyAQCEtBixl1dXUoKipCbm6uxmNt3rwZQqEQPB4PgYGBBjvpVoeJiQm0trbKiVWtra06K4EAgC1btmDTpk1MKaCzszNTetjQ0ICLFy+iubkZTU1NTMjsUhgZGcHHxwfBwcEIDAxEcHAwgoKCVG7AMTQ0hMrKSpSWlqKsrGzJUgRfX18mxyoiIoIc4GpJf38/8vPzcf78eaWOBVNTUyQkJEAgEGD79u0GdTSNjIzg7NmzyM7OZnWCGRkZISEhAZmZmdi6dSu5ck8gEADo3oE1PT2NqqoqtQUs2RiVterA0ocJo6GhQe2STcJ9yK8gwSBoG6a7FB9//DG8vLxgaWmp1+0QCASCpgwNDaG2thbl5eU4ceKExuVwHh4eSEtLQ3h4OEJCQlacwEG7m2g3Ff23VAC5unh6eiI2NpYJWffx8cHc3Byam5vR2NiIv/71r2hqakJra6vK77WZmRkCAwMRFBTE/Pn7+8Pc3Fyl59N5WU1NTWhqakJdXR1aWloWfY6joyOTYxUXFyd38E/QjOHhYeTn5yM3NxdVVVWsJaBcLhfx8fFITU1FYmIirKysDDY/qVSKsrIyZGdno7CwkPUYaePGjcjIyMD+/fs1Lu0hEAhrk76+PvT29up8XFn3lKos7ES41jKwpqammNdhbGyM+fl5nYxbV1dHBCwNIQIWwSDoM6PDzMwMISEhehufQCAQ1GVqagoNDQ24du0asrOzNRZvzMzM8NBDDyEmJgahoaEr7kR2enoara2tckJVS0sLa/mTNpiYmGDXrl2MsyooKAjT09NoampCY2Mj/v73v6OpqQm3b99eNK9KlnXr1iEoKAjBwcGMq8rLy0slpy5FUbh37x4jVNGuLlXyySwsLBAVFYW4uDjExMTA19eX5FjpgPHxcRQUFCA3NxdlZWWsXfqMjIwQGxsLgUCApKQklV10uuLu3bvIycnBqVOnWJ145ubmSE5ORmZmJjZv3kw+FwQCgZUbN27oZVwPDw+1nyMrYE1OTsqNoYsmK8tNV1cXc9vDw0NnbixlZeyEpSECFsEgTE5O6m3s2dlZDAwMrLgTOwKB8GAgkUjQ1taGmzdv4vTp01q1tabdIDweDx4eHivmBJaiKPT29srlVDU3N6Orq0tlwUgd4uPjmdyq0JWlwQEAAOI8SURBVNBQRqxqamrCBx98gMbGRrWu7G7YsIFxVIWEhCAoKEjlEHuxWIzOzk45oaqpqQljY2MqbdvY2BhhYWGMw4rH45EcKx0xOTmJ4uJinD9/HlevXmV1MnE4HERGRkIgEGDXrl2wt7c36Bzn5uZQVFSE7OxslJWVsX5feDweMjIyIBQKV5yjkkAgrDyUNXfQFnd3d7WfI1tCODY2Jnc+NjAwAIqiVsyxjCY0NDQwtz09PYmAtQIgAhbBIKxbtw42NjaLhtVqQ319PRITE/UyNoFAINDQ5XG1tbXIz89Hfn6+xmNFRUVBKBQiLCwMvr6+KybbZnZ2Fm1tbXIdAJubm/W2/w4MDER0dDT4fD6Cg4MhFouZUPV//etfauVVcTgc+Pj4MGIVnVulapfA8fFxOaGqubkZbW1tKpfBGxsbM9unSxF5PJ5By9PWOtPT07h06RLy8vJQXFyMubk51vU2bdqE1NRU7N69e1nKMltbW5GdnY0zZ85gdHRU4fF169Zh3759yMjIQEBAgMHnRyAQVi+6zr8C7jtANRH4FzqwrK2tYW5ujpmZGczOzmJiYkJO5FpNSKVSuQxnV1dXnY19584dDA0NGfyiylpgZRwtEx4ITp8+jR07duhl7IaGBiJgEQgEnTM0NIS6ujpcvnwZJ06c0Nht5O3tjbS0NGzevBlBQUErou09RVEYGBhgyv5osaqzs5O1/EoXrF+/Hjt27ACfz4e/vz+kUilTgnjs2DG0tLQoFSQWYmpqioCAADmxyt/fX6X3lqIo3LlzR06oUjeny9raGoGBgXKZWb6+visyQH+1Mzc3h6tXr0IkEuHixYuYnp5mXS80NBQpKSkQCAQ6PdFQlYmJCYhEImRnZ6Ourk7hcQ6Hg9jYWGRkZGDnzp3ks0IgENRmenoajY2NOh/X3d1dI6fUwgwsDocDR0dHpqR+YGBg1QpYJSUlaGtrA/Cf8v8TJ07obPz6+nps375dZ+M9KBABi2AwbGxsUFJSglOnTuG3v/2tTseWtXcSCASCJkxPT6OhoQGVlZX44osvNA4ftbKyksutUtX9o09ky+Bkxarh4WG9bjcpKQlhYWHw8PCAsbExbt++jaamJnzyySfo7OxUWRC0traWy6oKCgqCt7e3Sq61ubk5tLe3y+VVNTc3q+zqAgA3Nzc5sSowMBBubm6ruixipSMWi1FRUQGRSITCwkKl/1/+/v4QCoUQCATw9PQ08Czvi6HXr19HdnY28vLyWLtqbtiwAWlpaUhLS4Obm5vB50ggENYGFy5cwEsvvaSXsTUpHwTkSwjpyJiFApaPj4/2E1wGjh49ytx++OGHMTIyovFYUVFRqKyslFtGBCzNIAIWwaBYWVnhkUcewcMPP4znn38epaWlOhmX1BETCAR1kEgkaG9vx/Xr13Hy5Emt9iH79+9HYmIi+Hy+yrlK+mRoaEhOpGpqakJnZ6feu8GGh4cjIiICTk5OMDY2xuDgIBobG/HZZ5+hr69P5XGcnZ3lsqqCgoLg6uqq0vs6MjIi56pqampCR0eHyl2DuFwu/Pz8EBgYiODgYMbhtVqvHq825ufnUV1dDZFIhPz8fNbSOwDw8vJiRCtfX18Dz/I+g4ODOHXqFHJyclgzUbhcLpKSkpCRkYGYmBiVmgMQCASCMiYnJ/UmXgGaBbgD8g4sOhtyYQ7WakQsFsuF5T/22GP48ssvNR4vJCREQcBic+oSloYIWIRlgcvl4t1330VERIROxhsYGEB/fz9pP04gEBSgy8Vqa2tx7tw5FBcXazxWXFwck1vl7e0NIyMjHc5UPSQSCW7duqWQVWWIg0VXV1ds27YNVlZWMDExwfT0NJqbm/HFF1+oHG7O4XDg5eUlF64eGBgIOzu7JZ8rlUrR09ODxsZG5vU3NTXh3r17Kr8GuguhbF6Vt7c3CVg3MFKpFDdv3oRIJMKFCxcwODjIup6bmxsEAgFSU1MREBCwLEKxRCLBlStXkJ2djZKSElZh1M/PDxkZGdi7d69Kn2UCgUBQhVOnTul1fE0FLNkLPLRTVlbA0tTNvtx0dnYy+3hXV1c4OzvDwcFB4/HY3l9SQaQZRMAirBkaGhqIgEUgEDA8PIy6ujoUFxfj888/13gcf39/pKenIzw8HIGBgcuaVzM2NqaQVdXW1qZyXpS27Nq1C+bm5oyLpKOjA6dOncLs7KxKzzcxMWHcTIGBgQgJCUFAQIBKeVUzMzNMTpZsCaCyHCQ2PDw85ISqoKAgODs7L7tb7kGFoijU19cjNzcXubm5Sh16Tk5OjNOKx+Mt2/9XV1cXcnJycPLkSVaB2NLSEqmpqcjIyFjWeRIIhLXLsWPH9Dq+puXNsk1KaAFL9nxM2UWJlU5raytz29/fHwDg4uKi0VgbN25kzRbVRxfnBwEiYBGWlQsXLiA5OVknY5FOhATCgwcdZlpWVobPP/8cQ0NDGo2zfv16HDp0CNHR0QgJCVm2VvZSqRRdXV1yjqqWlhb09vYabA6hoaGwtLSEiYkJjI2NcefOHRQVFakc7G5tbc2IRHS4uqrOpsHBQUaoosUqdbKy6GB32XD3gIAA0gXQgFAUhc7OTkxOTsqJORRFobW1FSKRCCKRCD09PazPt7e3R3JyMoRCIcLDw5fN5TgzM4P8/HxkZ2ejqqqKdZ3NmzcjPT0dAoFgRTRmIBAIaxd1moxogqYOrHXr1jG3aQFL1qm0Wh1YdIYXACbDS9Nu0QcOHEB1dbXC8ujoaM0m94BDBCzCsvLMM8/obCxiwyQQ1jYSiQQdHR2orq7Gl19+iZaWFo3HoruAhYaGylndDcnk5KRC+V9raytrCLS+sLKygqOjI7hcLrhcLsbGxtTKA3N0dJQLVw8ODlYp3Hx+fh63b99GS0sLGhsbGdFKnSu1dnZ2cq6qwMBAeHl5aXyASdAcqVSKuro6FBQUoLCwELdv3wYAPPvss9i9ezdEIhHy8vLQ0dHB+vx169Zh9+7dEAqFiIyMXNa8qIaGBmRnZ+PcuXOswfH29vbYv38/0tPTV20wMYFAWF0YwqmjCwfW+Pg4AHkH1mrNwJLd/9PNeDS9oGJiYsJ6IYQEuGsGOcojLCtisVhnYxEBi0BYO9C5VTU1NThz5gwuX76s8VgJCQlITU0Fn8+Hh4eHwct76NdCizS0WKXMgaJPLCwswOVyYWJigrm5OUxMTDBdgxaDw+HA09NTIVzd3t5+yedOTU2htbVVLq+qpaVF5fJDDoeDjRs3KgSrOzo6klKtZUQsFqOqqgqFhYUoLCxkPUl599138e6777I+38rKCklJSRAIBIiNjV3W7LGxsTGcO3cOWVlZaG5uVnicw+Fg+/btyMjIwPbt20lOGoFAMCj6PsdxdHSEubm5Rs9dKgNrLQhYtEjX2Nio0Vgff/yxQoUAh8NBfHy85hN8gCECFmHZKC0t1ekJ3MDAAEZHR7F+/XqdjUkgEAzDyMgIamtrkZ+fj5ycHI3HCQ4ORnp6OrZs2QJfX1+Du3Gmp6eZvCb6r6WlBVNTUwadhzJUyY3icrnw9/dnHFW0u8nS0nLR51EUhYGBAcZRRQt2XV1dKl89Njc3l8vKCgoKgr+/PynPWiFMT0/jypUrKCwsRHFxMatDaTHMzc2RmJiI1NRUxMfHw8zMTE8zXRqpVIrKykpkZ2cjPz+f9YKah4cH0tPTceDAATg7Oy/DLAkEAgH497//rdfxNS0fBCB3bDA1NYX5+fk1J2DRIt2bb76p0VhsZZR8Pp80+tAQImARlg1tTlKVQa7GEwgrn5mZGTQ1NeHy5cv47LPPGMu5ujg4OODhhx9GbGwsgoKCDCpyUBSFvr4+OaGqublZLbGGw+GAw+GonC2lDywtLeXyqkJCQuDj47Okw0QikaCzs1Mur6qlpQXDw8Mqb9vR0VEhWN3Dw2NZy8cIioyOjuLixYsoLCxEaWmpys45GhMTEyQkJEAgECAhIWHZxci+vj6cPHkSJ0+eZL2IZmpqiuTkZKSnpyMyMnJZO40SCITlp7m5GTk5OdizZw/4fP6yzOHs2bN6HV8bAcvY2BhWVlaMm3tychK2trbgcrmQSCSYmJjA9PT0su/71UXWnd7f34+6ujqdjr9161adjvcgQQQswrLB5/MhEol0OuZq2zkSCGud+fl5tLe3o6qqCp9//jna29s1HuvQoUPYsWMHQkNDmTwCQzA7O4v29nYFsUod4W3dunVwcHDA3NwcZmdnMTAwAIqiDNqBxsHBgXFU0e4qd3f3JU/QJyYmGCcZ7a5qbW1VuQTcyMgI3t7eTKB6cHAwAgMDVSo/JCwPvb29uHjxIgoKClBVVaW2yMrlchEfHw+BQIAdO3YsW1MEGrFYjOLiYmRnZ+PKlSus3zvavbl37165khgCgfDg0tbWhkceeQQAUFJSgpMnTy7zjPSDpvlXNNbW1ozgMz4+zhzz0B1mBwYG4OnpqfU8DcnIyAhz+9SpU6itrdXp+CT/SnOIgEVYNvbs2YM//OEPOh1zZGRELjiQQCAYDoqicPfuXdy4cQM5OTkoLy/XeKydO3diz549CAsLg4uLi0HclXQJHC3W0EJVZ2enyifwHA4HXl5e8PT0xPz8PObm5jA4OIiOjg6MjY3p+RX8Bw8PD7lwdTozajEoikJvby/jqqL/VafU29LSEoGBgXJ5VX5+fhpnaxAMR3t7O5NnpU6QP42RkRFiYmIgFAqRlJQk15lquWhvb0dOTg5Onz7N6g60trbG3r17kZmZiaCgoGWYIYFAWMkcOnSIuS3blc6QGCKCwN3dXavnW1tbM2IVLWQ5OjquSgFLKpXi73//u9zv4K1bt9Da2qqzbdjb2yMkJERn4z1oEAGLsGzY29vDzc1Np21hv/vd7+Lvf//7ijhwJhDWOqOjo6itrYVIJMLp06c1HofP5yMzMxPh4eHw9vY2SMmOWCxGZ2cnU/qmSQmclZUVAgMD4e/vz4Sij46OIi8vD52dnfqbvAxcLhe+vr5M+R8tHi3leBGLxejo6FDIq1LHVebi4sIIVfR2VXF0EVYGUqkU9fX1TOfAW7duaTROZGQkBAIBdu/evSJcdVNTU8jLy0NWVhZu3rzJuk50dDQyMjKQlJRExFUCgcDKpUuX5O5nZmYuyzzOnDmj921oU0IIQO6Ygz6OkL1oxpYBtRKhKAo/+tGPkJ+fzyyzt7eHn58fKioqdLadrVu3kmMlLSACFmFZeeqpp/D666/rbLzW1lY899xzeP/990k5IYGgQ+jcqsLCQpw4cUKlMHA2XFxccOjQIcTExCAwMBCmpqY6nqkiw8PDciJVU1MTOjo6IJFIVB7Dw8MDgYGBCAgIgJ2dHebm5jAwMICzZ8/i2rVrepz9f7CwsGAyo+hOgH5+fkvmVY2Njck5qtR9/cbGxoxIRudVBQYGkoYZqxCxWIzq6mrGaaXpSUVYWBiEQiFSUlJWhOuZoijU1NQgOzsbIpGIdf/k5OSE9PR0pKWlaX2yRiAQ1j7PP/+83P1XXnllWebx8ccf630b2u4T2ToRyv42DA4OajW+obh48aKceBUZGYlXX30Vhw8f1ul2SPmgdhABi7CspKen61TAAoCamhr84Ac/wJ///GeDnBwTCGuN+fl5dHR0oLS0FCdOnEBXV5dG4xgZGeHw4cNISkpCSEiI3nNwJBIJbt26xZT/0WKVOh1wLCwsmA58/v7+8PT0hFgsRnt7O4qKilBQUKDHV/Af7O3t5bKqgoOD4eHhsegVO4qi0NPTwziqaHdVb2+vytu1trZmtkvnVfn4+JB96SpmenoaV69eZToHato0wcrKCl//+tchEAi0zkvRFUNDQzhz5gxycnJY8/WMjY2xY8cOpKenY+vWraRBAIFAUImFotGPfvSjZWsUdfv2bb2Ob2pqCgcHB63GYHNgyY65GhxYUqkU7733HnN///79+PnPf44zZ85ofNFWGTweT6fjPWgQAYuwrOjrpKi0tBSvvPIKXn/9dXLASiAsAp17dO3aNWRlZaGqqkrjsZKTk7Fv3z7w+XytD4aWYnx8XKH8r62tDXNzcyqP4erqioCAAEas8ff3x8zMDOrr61FWVobf/OY3enwF/8Hd3V0hXH0pV8vs7Cza2trk8qqam5vluuYshZubm4KrytXVlXRzXQOMjo6iuLgYhYWFuHr1qtqdA2V59NFHceTIEWzcuFGHM9Sc+fl5lJaWIicnB0VFRaxOQm9vb2RkZGD//v0roqyRQCCsHiiKUsjoPXLkyLLNRd/oovRf1oFFH4fIHseocyFxuSgsLERLSwsAwNzcHN/73vdgbGysFwfc/Py8zsd8kCACFmHNEBUVhYiICPzf//0fAODChQuwsbHBK6+8Qk7ICIT/z+joKGpqanDmzBmtuoBu3rwZGRkZ2LJlCzw8PPT2HZNKpeju7lYQq9RxFZmamsLf319OrAoMDMTk5CRqampw48YNvPbaa2qJP5pgbGwMHx8fuXD1wMDAJTueDQ0NyXUAbG5uRkdHh8rB8lwuF/7+/nJ5VQEBAaTT2hqjr6+PcQlq0jlQFgcHB/zlL39BaGioDmeoHT09PTh58iROnjzJBAPLYmFhgZSUFGRmZmLTpk3kd59AIGjEq6++Knf/nXfeWZ6JAGhqatL7NrQNcAfkHVh0wxrZDKzVIGBdvXqVuZ2QkAAHBweUl5crhLfzeDzU1dVpta2ZmRmtnv+gQwQswrKizQH2QiwsLPCtb30LY2Nj+PTTTwEAWVlZWLduHb73ve/pbDsEwmqBzq3Kz8/HZ599BrFYrNE47u7uOHToEOLi4uDr6wsuVz8/HZOTk3Llf83NzWhtbVXLuu3k5MQINLRYtXHjRsZZVVNTgzfeeEOn3WTYsLCwYErwaNHIz88PZmZmSp9Di3W0UEU7q9Sx3q9fv15OqAoKCoKXl9eSOVmE1UlHRweTZ6XtATUAfPvb38YzzzyzYsJlZ2dnUVRUhKysLKVdTcPCwpCRkQGBQAArKysDz5BAIKwlZmZmcOrUKbll8fHxyzQbMOcz+kQXAhZbBtZqE7BiYmLw5ZdfAgCKi4vR39+vcKHX399fJ43CiIClHUTAIiwrurxCKhaLweFw8MMf/hDj4+NM145//etfWL9+Pb761a/qbFsEwkqDzq26fPkyTpw4oXF3T1NTUxw5cgRJSUkICgrSSzMEiqJw584dRqShxaqenh6Vx6C779Hd72QD1sViMdra2lBTU4M333wTxcXFOn8NstjZ2SmEq3t6ei5avjw9PY3W1la5vCp1xTpPT09mu7Ro5eTkRJwnaxiKolBXV8eIVrrodunm5obf/OY34PP52k9QRzQ3NyMrKwtnz55lzeyytbXF/v37kZaWBn9//2WYIYFAWIssPFf44osvlmci/5+TJ0/qfRu6aGohe/GALcR9NQhYycnJCAwMRHNzM2ZnZ3H06FGFC4ipqanIzs7WeltEwNIOImARlhVduiDq6+sB3A+O/vnPf47x8XHmxPXNN9/EunXrcPDgQZ1tj0BYLiiKQl9fHyoqKvDFF18obRWvCnv27MHevXvB5/P10lVuenqayWqi/1paWtQq17O3t2fK/ug/b29vmJiYMGJYXV0d3n//fXz++ed6zYygc6Nkw9WXEo0GBgYYoY52V926dUvleZqamjKOMtpV5e/vT9wmDwhisRjXrl1DQUEBioqKcO/ePZ2M+8gjj+Cxxx7TydV3XTA+Po7z588jJyeH+T2XhcPhID4+Hunp6di5cydxFRIIBJ0yMDCA5uZm5r61tTV8fHyWcUaGQRcCFpsDy87ODhwOBxRFYWRkBGKxeEXvt42MjPD000/jxz/+MQCgoaFBQXgTCoWMS0sVIiMjWbNliYClHUTAIiwrHR0dWj3f1NSUCW2ma64BwMTEBG+88Qb+67/+i9lx/OpXv4K1tTUEAoFW2yQQDM3Y2Bhu3LiBU6dO4cKFCxqPExUVhYMHD2Lz5s1wcXHRqVOHoijcu3dPzlHV3NyM27dvqyzUGBkZwcfHh3FT0WKVrA19fHwcdXV1KCgowJkzZ9Dd3a2z18A2l4Xh6ovlRs3Pz+PWrVuMo4rO7BoaGlJ5u3T3QdpRRZdA6qtsk7AymZ6eRmlpKQoLC3Hx4kWNOwcuxN7eHo8//jgOHjyokzIIbaEoClVVVcjJycGFCxdYw+bd3NyQlpaGtLQ0bNiwYRlmSSAQHgQWnh8sLCU0NLra7y+FrjOw6HlzuVzY2dkxx0CDg4Mrfh9uaWnJ3DY1NcWtW7fkHnN3d1cr/iYzM5MIWHqAHBETlhVtQ4R5PB6uXbvG+piZmRn+9Kc/4Vvf+hYaGhpAURReeeUVWFtbY+vWrVptl0DQF7Ozs2hqasK5c+dw/Phxjd1EXl5eOHz4MGJjY+Ht7a3TTJvZ2Vm0t7cruKpkReSlWLdunVzpX1BQEHx9feU6k4rFYjQ3NyM/Px8FBQWoqKjQ2WuQxczMjBGMaPHI398f5ubmSp8zOTmJ1tZWuWD11tZWlbu9cTgceHl5yeVVLRTrCA8WY2NjTOfAK1euaNU5cCH+/v544oknkJqauiKugPf39+P06dPIzs5GV1eXwuNcLhe7du1CZmYmoqOjV0wmF4FAWJs0NjbK3Y+MjNSLK10dCgoKDLIdfWVgAfdzsGgBa2BgYMULWLLC0sIIjYcffhiAesKiMse0OnERBEWIgEVYVrS9uuHj4yMnYA0MDMidAFpbW+Ott97CM888g87OTkgkEvzwhz/Ee++9h/DwcK22TSBoi1QqRUdHBy5evIjPPvtMrbBuWSwtLZncqsDAQDkRSFtoS73sX2dnp8pXoDgcDjZu3KggVjk7O8s5wCiKQnd3N+rq6nD16lW9Xflcv369nKOKDjlXllcl6yyTzatSx/lFB7rLlgD6+fnpJV+MsLq4d+8ek2dVVVWltLU2XYahLrGxsXjyyScRFxe37NloEokEly9fxpdffonLly+z7kP8/f2RmZmJPXv2wNbW1vCTJBAIDyRf+cpX5O6/++67yzST/3Ds2DG9b8Pe3l4nxyJsGVjA/RwsuixzNeRgyQpLC4Wqhx9+GPPz82pFYChrruLq6qrZBAkAiIBFWEbu3r2L8+fPazVGaWmp3P2qqioIhUK5Zfb29nj33Xfx9NNPo7e3FzMzM3juuefwj3/8AwEBAVptn0BQFVoIKS0txeeff65Vx7ADBw5g3759CA0NlbNta4NYLEZnZ6eCq0qd8jcrKyuFrCplQs3o6Chqa2tx/fp1ZGVlqbUdVXF1dWUEIzpcfbHSyYXvAe2uGh0dVXmbTk5OjEBGdyH08PAgDhICQ2dnJwoLC1FQULDofmDdunWQSqWYm5tjSuVVgcvlIjU1FY8//jgCAwN1MWWtuH37NrKysnD69GkMDg4qPG5lZYU9e/YgPT0doaGhyy60EQiEB4uFTqevfOUrK8Kp2tbWpvdt6CoDUbYkXVb4cXBwYG5repHWkMgKWAtd/x4eHmodD1pZWaG6ulpheUxMDKKiojSfJIEIWITlQ5vgaZqFndYqKioUBCwA2LBhA9577z08/fTTGB4exsTEBL7zne/gn//8Jzw9PbWeB4GwkPHxcVy7dg1ffvmlVl3w4uLikJmZiYiICLkDAW0YHh5m8pnofzs6OiCRSFQew8PDQ85RFRAQADc3N9aTz7m5OTQ3N6Ompgbnzp1DbW2tTl4HDYfDgY+Pj4KzajH7//j4OCNU0e6q1tZWld8DOiNL1lUVEBAAe3t7Xb0swhqBoijU19czTqvFsh/pMN25uTn09/er5bqytrbGQw89hEcffRTOzs5az1sbpqenceHCBeTk5LAewANAREQE0tPTkZycTNyIBAJh2fjhD38od/8HP/jBMs3kPyhz4+oaXQlYshdTFzqwaNguYKw0lsqmUqd8cHJyUsGtxeVy8dprr5GLmlpCBCzCsmFnZ6fzMRfLyPHy8sI777yDb3zjG5icnMTQ0BC+853v4IMPPlj2g33C6mZubg6NjY04ffo0Pv/8c43H8fPzw6FDh7B161a4u7tr7USQSCS4ffu2gliljo3bwsIC/v7+cmLVYh3wKIpCV1cXampqUFJSgtzcXK1ew0Lojny0SBUcHLxoOR5FUbh7966co6q5uVlB/F4MKysrxlFG51X5+vrCzMxMVy+LsMaQSCSorq5GYWEhioqK0NfXx7qekZERQkJCANzPluvo6FD7xGXDhg147LHHkJGRsaydKSmKQl1dHXJycnD+/HnWMgsHBwccOHAAGRkZ2Lhx4zLMkkAgEP7D3//+d7n7P//5z1eEC7SlpcUg29FFB0Lg/rGikZERpFIpZmdnmY6DsrEuq82BJQt9AVnbYP1nn30WLi4uWo1BIAIWYRnx8/PT+ZhdXV3o6+tTunMIDg7GX/7yFzz77LOYm5vDnTt38Oyzz+If//jHsoc1ElYHdG7VhQsXcOLECY1L39avX48jR45g165d8PX11brLHO0okhWrWltb1So9cnV1ZUoAabFqqfK34eFh1NbWorKyEp9//rlOgyltbGwQHBzMiEZBQUHw9vZW+l6JxWK0t7fLCVVNTU1yVwOXgn4PZLepzFlGIMgyMzOD0tJSFBQUoLi4WGlTAzMzM2zZsgXGxsaYnp7GzZs31XI/0oSGhuLJJ5/Erl27lrVL5cjICM6dO4esrCy0trYqPG5kZITt27fj4MGD2Lp1K+moSSAQVgRSqVQh6yojI2N5JrOAL774wiDb0ZUDi8PhwMrKihF4JiYmYGdnJydgrbYMLFlooU+2K6Em7Ny5U6vnE+5DjiIIy4a9vT3WrVunVucyNszNzREWFsa4ryoqKrB//36l60dEROC3v/0tfvCDH2B+fh7t7e147rnn8P7778u1TyUQgPshy5cvX8Znn33GBFFqQmZmJvbt24eQkBCtymWkUim6u7vlHFUtLS3o7e1VeQxTU1P4+/vLlf8FBgYu2RWU7pB4/fp1nDx5Eu3t7Rq/joW4uLggMDCQyaoKCgqCq6urUuFoZGRELq+rsbFRLfcKl8uFr68vUwJIi1WyOQ4EwlKMjY2hpKSE6RyorPzAxsYGcXFxMDMzw+joKCoqKpSu6+fnh+npady7d49V2EpISMCTTz6JiIiIZRNWpVIpysvLkZWVhcLCQtZ5enp6Mvs92TISAoFAWAn8+Mc/lrv/t7/9bZlmoshqE7CA+79zCwUs2X3/ahCwlP0u08eGZ86c0Wp8cgFHN5B3kbBscDgc+Pn5yXUR1ISZmRm53JmlBCwASExMxP/+7//ipz/9KSiKQm1tLb7//e/jzTff1GkHN8LqYnx8HNXV1fj8889x+fJljcfZvn07HnroIWzevFkrZ9/k5CRaW1sZkYrOaVLH5eTo6MiIVPS/GzduXPJHVCqV4tatW6ipqUF+fj5KSko0fh2ycDgceHl5MSJVSEgIAgMDlZYUS6VS9PT0MO8B7a5SVpLFho2NDbM9Wqjy8fFZESGthNVHf38/CgoKUFRUhMrKSqWiqZOTExISEmBhYYH+/n5cunQJU1NTrOsGBQUhIiICg4ODuHLlioJr0MTEBPv378fjjz8OHx8fnb8mVent7UVOTg5OnTrFWoZrZmaGlJQUpKenL6vARiAQCItB5/TJEhkZuUyzWT50VUIIyOdg0ULWWikhpI/ltTk3AECOO3UEEbAIy4qvr6/WAhYAiEQi5nZlZaVKz0lNTcXY2Bh+85vfAADKy8vxk5/8BL/5zW+IQv4AMDc3h/r6emRnZ+PkyZMajxMUFITDhw9j69atcHZ21uiEjaIo3LlzR85R1dzcjO7ubpXH4HK5TKi4rFilatbc0NAQamtrceXKFRw/flzt18AGh8ORc1QFBwcjICBAqQNtZmYGbW1tcsHqzc3NSk/62XB3d2eEKroMcLHOgwSCKty6dYvpHLhYEwJvb28kJibCxsYGt2/fRl5entLMDF9fXwgEAvj5+aG4uBgnTpxQcDLRpcaHDh3SWRMHdZmbm0NRURFycnJQWlrKGizP4/GQnp4OoVC4pJOTQCAQlptHHnlE7r42x4G6Rh9dmdngcrk6dcfK7vvpizCyAtbQ0BDm5+dhbGyss23qGmXHzDY2NhgeHtZ6fCJg6QZylk5YVvSRg3X37l309PSoZIs9fPgwxsbGmBr4goIC/PKXv8QvfvELcsK7hpBKpejs7MT58+fx2WefaRzC6ODggMOHD2P37t3w9vbWqIvI9PQ0I9LQmVUtLS1q5TTZ29szZX/0n7e3t8o/jNPT02hsbGS6JKoTaK4MDoeDiIgIJlydzqtSNqehoSGFYPXOzk5IpVKVtmdiYoKAgAC5vKqAgAC5K4AEgqZQFIWGhgamc+Bi5bI8Hg87duyAra0tWlpacOrUKaUHuh4eHkhNTYVAIMDg4CA++ugjvP/++6zrPf744zhw4MCydehrbW1FTk4Ozpw5g5GREYXH161bh7179yIzMxMBAQGGnyCBQCBoQG9vL7q6upj7jo6OOnUiaculS5cMsh03NzedikmyTUToY1pTU1MmLkYqlWJkZGTZLsaoQlxcHD766COF5TY2Nrh48aLW4xMBSzcQAYuwrPj6+mr0PCsrK9YORzT//Oc/8corr6g01te//nWMjY3h2LFjAO5fhVm/fj1eeOEFImKtUvr7+1FYWIjjx49rldN06NAh7N+/H8HBwWr/6FAUhXv37sk5qpqbm3H79m1WBwMbRkZG8Pb2lhOqAgMD5a5oLQUdOl9bW4vTp0+jqqpKrdehjISEBDlnlbKg8/n5eXR1dcm5qtTthGhraytXAkgLdsQpSdAlEokE165dYzoHKsuVMzIyQmRkJJKSkuDg4IDr16/j+PHjSj/Trq6uSElJgVAohL+/P0QiEX7yk5+wBp6Hh4fjySefRGJi4rJcpZ6cnERubi6ysrKUOs1iYmJw8OBB7Nixg3TiJBAIq469e/fK3TdU3pSqfPLJJwbZjq5FO9kMUdkLxY6Ojkze8cDAwIoWsJRdfFq/fj3y8/M1GtPMzAyzs7MASAaWriDvImFZ0VTAWky8AoAvv/wSNTU1OHr06JKZVhwOBy+++CJGR0dx6tQpAMDRo0exfv16PP300xrNj2A4xsfHUVFRgePHj6O8vFzjcXbu3ImDBw9iy5Ytarein5ubQ3t7u4JYpU6DAhsbGwWhytfXV+0TxP7+ftTW1qKoqIj5PGtLcnIyQkNDGRFJNnNOlunpabkySPpfZaGYC+FwOPD09GS2Q7urHB0diZhM0AszMzMoKytjOgeOjo6yrmdmZob4+Hjs3LkTLi4uuHr1Ko4ePYq7d++yru/o6MiIVmFhYZiYmMAXX3yBF154QSEHhMPhYPfu3Xj88cexadMmnb/GpaAoCjdu3EBWVhYuXLjAmgHi4uKC9PR0HDhwQKehvwQCgWBIampq5O5v27ZtxZU9NzU1GWQ7ut6XszmwgPu/h/TF5P7+fgQFBel0u7pidnYW77zzDutjGzZsQFlZmdpjuru7yx0nEAFLN5B3kbCs6Krr3+bNm3H9+nW5ZS0tLYiLi8PLL7+MgwcPLno1m8Ph4Gc/+xkmJiZQWFgIAHj77bdhY2ODQ4cO6WSOBO2Zm5tDbW0tsrOzcfr0aY3H4fP5OHToELZu3ar2laCBgQG57nctLS3o7OxUufsdh8PBxo0bGZGKzqvSJD9renoa9fX1qKiowNGjR9UKd1fGnj17sGnTJiaviu07SlEUBgYG5BxV6rrLzM3N4e/vLxesvlg+FoGgK8bHx+U6Byr73lhbW2PHjh1ISkqCs7MziouL8Y9//EOu9EQWW1tbpKSkICUlBVu2bIGxsTHu3LmDP/7xj/jyyy8VtmNubo60tDQ89thj8PT01PnrXIqhoSGcOnUKOTk56OzsVHicy+Vi586dSE9PR1xc3IrOLSEQCARVeOqpp+Tu//nPf16eiShBLBYbbFu6FrBkhUBZB5Zsztbg4KBOt6lL/v3vf7NGari5uWFiYoK12+5SbNmyBT09PQDuH/8TAUs3kHeRsKxoI0LIcv36dabGeiGvv/46Xn/9dfz5z39GQkKCUpGAy+Xi17/+NZ5//nlUVFQAAH7zm99g3bp1EAqFOpknQXXo0rfTp0/j+PHjGoszLi4uOHLkCHbv3g0PDw+VRSKxWIzOzk4FsUqdcE0rKyuFrCo/Pz+NRJr5+Xm0t7fj+vXrOHbsmNKTaHXYu3cvYmJiEBwcrLQrn0Qiwa1btxihinZVqfM+ODo6Mq+fzqvy9PQkJ8QEg0GXFRcWFi7aOdDR0RFJSUnYtWsXHB0dkZ+fj3fffVdpKbK1tTV2794NoVCIqKgo5uC0vr4eR48eRV5enkKum729PR599FE8/PDDWnUp1QSJRIIrV64gJycHxcXFrO+Dr68vMjIysHfvXqVuSwKBQFhtnDt3Tu7+008/veKOQwzlvgJ0X0Iom0Eq68CSvVC8UjsR9vf34+9//zvrYwcOHEBeXp5G48qe527dupVUE+gIImARlg2KovDZZ5/pbLylyrVeeOEFbNmyBc8//zzCw8NZ1zEzM8Mf//hHfPvb30ZdXR0oisIrr7wCS0tLJCQk6GyuBEX6+/tx4cIFfPrppxqLM8bGxjh8+DAOHDgAf39/la90jIyMyAlVzc3NaG9vV+tqi7u7u5yjKjAwUGkulCr09fWhpqYGH3/8MW7cuKHRGLKkpqYiKSkJwcHBcHd3Zw2gn5iYYEogaVdVa2sr5ubmVNoGh8OBj48PI1bR78NKzjsgrF1u376NgoICFBYWKpSNyOLl5YWkpCQkJSXB3t4eeXl5+POf/6z0RMLS0hI7d+6EQCBAfHw8I/xKpVIUFxfj6NGjrFlzvr6+eOKJJ7Bnz54lS9t1TXd3N3JycpCTk8Oa1WVhYQGhUIiMjAyEhYWRg2wCgbDm+OlPfyp3/7/+67+WaSbKOXPmjMG2pWsBSxUH1koVsN5++22lF8q3b9+Ov/71r1qNb29vj5/97GdajUH4D0TAIiwbjY2N6Ojo0Hqc559/Hp988olKO8Vr167ha1/7GhITE/Ff//Vf8Pf3V1jHysoKb731Fp555hm0t7djfn4eL730Et59911s2bJF6/kS7gslV65cwYkTJ7QKFU9JScFDDz2EsLAwlVxN8/PzuHXrllxOVXNzs1o/qBYWFvD395cTq/z8/LTqfjc5OYm6ujr8+9//RnFxscbj0KSkpGDfvn3g8Xis4hFFUejt7WW6ANJ5Vd3d3Spvw8LCQk6kCgwMhL+/P8zNzbWeP4GgCRRFobGxEYWFhSgoKFi0gUNoaCgjWllbWyMvLw+///3vlQpdZmZmSExMhEAgwLZt2+Q+57Ozszh79iyOHTvG+psWHR2NJ554Alu3btWoc6mmzMzMoKCgANnZ2aisrGRdZ9OmTcjMzERKSorOSvoJBAJhpfH222/L3f/1r3+9TDNZHF1e2F8KNzc3nY4nm4Elm1UsK2Cp08DHUNTX1yutCIqNjdX6IrKRkRFef/11ODs7azUO4T8QAYuwbNjb28PY2Fjl7CA2IiMj8dWvfhWPPvoosrKy8Nvf/lal5xUXF6OkpAT79u3Dt7/9bYWduK2tLd599108/fTTuHPnDmZnZ/G9730Pf/vb31Zs+OBKRSwW48aNG/jiiy8gEok0Hmfz5s04fPgw4uPjVSq7GR8fZ8r+6LI3ddxEwP3QRlqkov/18PDQ6iRUIpGgoaEBJ06c0EkJ7e7du5GRkYHNmzezhs/TpZCNjY2MYNfU1KRWwLyzs7NcF8CgoCClLi4CwZDQnQOLiopQVFSkNFTdyMgIERER2LVrF3bu3AlTU1Pk5eXh17/+Na5du8aa3cblcrF9+3YIhUIkJCQoCDwjIyM4ceIEPvvsM4WSWiMjIwgEAjzxxBMICQnR3QtWgcbGRmRnZ+Ps2bNyZRw09vb22LdvH9LT0zVupEIgEAirBYlEgg8++EBuWWpq6jLNZmVgYWGh1YVXNpQ5sGQvpK40AYuiKPz+979Xmt+akZGBl19+WattPPfcc4iOjtZqDII8RMAiLBsuLi545JFH8PHHH2s8xvbt2wHcvzr+yCOPID09Hdu2bVPpuRRF4fTp0zh//jyOHDmCp59+GnZ2dszjzs7OeO+99/C1r30NQ0NDmJiYwHe/+1188MEH2Lhxo8ZzXstIpVK0t7fj5MmT+PTTTzUKPATul+MdPnwYKSkpcHFxWbScRSqVoqenR6HznbITWTZMTEzg7+8vJ1YFBgbKtQTWBIqi0Nrais8//xwnTpzQaizgP50SY2NjWfOqxsfH5YLVm5qa1CqFNDY2ho+Pj5xQFRgYCFtbW63nTiDoitnZWZSVlSE/Px8lJSUYGRlhXc/U1BTx8fFISkpCYmIiOBwOCgsL8eqrr6KiooL1gNXY2BhxcXEQCATYuXMna3eqrq4uHDt2DCdPnmRaY9NYWlrioYcewiOPPAJXV1edvF5VGBsbw7lz55Cdnc1a+sjhcLBt2zZkZGQgISGBdf9BIBAIa5EXX3xR7v6//vWvZZrJ4vT19RlsW/q4eCH7eyl78WQlO7Dy8vIUmoDRWFtbw8XFRavxd+/ejSeffFKrMQiKEAGLsKzExMRoJWAlJibK3bewsMDu3buRn5+v8hgSiQQff/wxsrOz8eSTT+Kxxx5jrrR7enri3XffxTPPPIOJiQkMDQ3hO9/5Dj744AOtd2prgf7+fpw7dw7Hjx9n7dyhCmZmZjhy5Aj2798PX1/fRV09k5OTaG1tlXNWtba2qhXw7ujoKOeoCggIwMaNG3XSGaSzsxNffvkljh07pvVY27Ztw6FDh7Bt2zaFkFGKotDT0yMnVDU3N6sl2llbW8u9D0FBQfD19TV4Ng+BoArj4+O4dOkSCgsLcfny5UU7ByYmJiIpKQlbt27F/Pw8ioqK8LOf/QylpaWsjl8Oh4OoqCgIhULs2rVLqWB748YNHD16FIWFhQril7OzM77yla8gMzPTYC3ZpVIpqqqqkJ2djfz8fFZ3qbu7O9LT05GWlkbKFwgEwgPH+Pg4Ll++LLcsLCxsmWazOHQDKUOg6/JBQD7EXdaB5ejoyNzu7+8HRVErImdxZmZm0S6Ue/fuxXPPPafx+F5eXvjFL36xIl7rWoMIWIRlpa6uTuPnenh4wNvbW2F5VFQUI2BFR0cjMjISx44dYy2lkGVychLvvfcePvvsM3zjG9/AwYMHYWJigsDAQLz55pv4zne+g9nZWdy9exfPPvss/v73v8s5ttY6k5OTKCkpwaeffoqbN29qPM6ePXtw8OBBbNq0SakLgKIo3LlzR85R1dzcrFZGE5fLZdxEsmKVLv7PZDskfvTRR0qtx6oSERGBxx9/HFu3blUQkObm5uRK/+jbS32eZXFzc1MIVtcmYJ5AMAT9/f0oKipiOgcqcxLSnQOTkpIQGRkJiUSCkpISvPLKK7h06ZLStuRbtmyBQCBAcnKy0kYDtAD20UcfseZjBQYG4sknn0RKSorBXE337t3DyZMnkZOTw7TnlsXExAS7d+9GZmYmIiMjSakvgUB4YHnooYfk7p89e3aZZrI0x48fN9i2dB3gDsgLWLIZWJaWlrCwsMD09DQkEgnGxsYM3oGXjWPHjqG3t1fp4wcOHNDq/+T3v/+9zss0CfchAhZhWdHmh+TrX/866wl4VFQUc7upqQnvvfceU6r48ccfY2pqatFxh4aG8MYbb+DYsWN49tlnIRQKsXnzZvz+97/Hiy++CIlEgo6ODjz33HP461//ypo7tNoRi8W4fv06Tpw4gQsXLmg8TlRUFA4dOoStW7cqfZ9mZmbQ2toqJ1a1tLSoJdDY2dkpZFV5e3vr5IRSLBajra0NhYWF+OSTT9SaFxv+/v74xje+gdjYWIUSxeHhYVy/fl0ur6qjo0PlnDgulws/Pz8EBgYiODiYEa0M5QghELSlq6sL+fn5KCwsRG1trVJxeOPGjUhKSsKuXbvA4/EgFotx5coVvPLKKygpKcHMzAzr83g8HoRCIVOerIzp6WmcPHkSH3/8Matwvm3bNjzxxBOIjo42iBAsFotRUlKC7OxsXL58mfV9CQoKQkZGBvbs2aN1+TOBQCCsdrq6uuRK1jw9PbFhw4ZlnNHi1NbWGmxb+hCwFmZgyTqtHB0dmQ7j/f39yy5g9ff345///KfSxwMCAlBeXq7x+L/5zW/g5+en8fMJi0MELMKyMDs7i66uLrUcNbIEBQXhwIEDrI/5+vrC3t4eQ0NDGBsbQ0tLC4KCgvCd73wHjz76KD766CN8+umnSk9waHp6evDTn/4U//rXv/Dcc89h69at+OUvf4mXX34ZFEWhvr4eL774It566y2YmZlp9DpWAnRO05dffonjx49r7Cby8vLCkSNHkJKSorTz3b1795iyP1qsunXrlsrbNDIygre3NyPM0J0AHR0ddXISOTk5iebmZpSXl+OLL77Qulbf0dERzz//PCIjI7FhwwZmjnRuV1lZmVwZoDrdENetW6cQrK4r0Y5AMBQURaGpqYnpHNjW1qZ03ZCQEMZp5evrC4lEgtLSUrz66qsoLCyUu+IrS2BgICNaLXXQPjg4iM8++wzHjx9XaHTA5XKxd+9ePP7446wdbPVBR0cHcnJycPr0aYWgeOD+Fe89e/YgIyPD4GHxBAKBsJJJT0+Xu//pp58u00yWZqlzEl3j7u6u8zHNzMzA5XIhkUggkUgwOzvLdOx1cnJiBKzBwUGD/YYq46233lo0fiQjIwO/+93vNBr74YcfhkAg0HRqBBUgAhZhWfj+97+Pq1evavV8ZWURdJ5Jbm4uAKC8vJzpHGhra4vnn38ejz/+OD788EOVsoqam5vx3HPPITIyEs8//zxefvllpv1uZWUlfvzjH+N3v/udTjKUDEF/fz9Onz6NTz/9VC3BRBYrKyscOXIEaWlp8PT0VBCP5ubm0N7eLlf+19LSgtHRUZW3YWNjIydUBQYGwtfXV2di4eDgIBobG1FTU4PTp09rnOFFY2xsjBdffBHR0dHw8fFhPg/T09Noa2vD1atXGaGqpaVFrdwuDw8PBAcHy+VVOTs7kxJAwqpkfn6e6RxYWFioNLuNw+HIdQ50dXWFRCJBVVUVPv74YxQUFCjtpunj4wOBQAChUMhaar6QtrY2HDt2DGfOnFEoVbSxscGhQ4dw5MgRuTBafTE1NYW8vDxkZ2crbd8dFRWFjIwM7Nq1izlBIBAIBMJ9Kisr5e6npKTAwsJimWazNA0NDQbdnj4ELOD+7+Xw8DCA+0Hu9O+T7IVtTc89dEVtbe2iXcC5XC42b96s8fjf//73NX4uQTVWxxk3Yc1RX1+v1fOXakcaHR3NCFiVlZV44okn5B63t7fH97//fTzxxBP461//ii+//HLJbVZVVeGpp55CUlIS9u/fz+z8Ll68iP/5n//Bq6++uuKyRiYnJ1FQUIDjx49rlTd24MABPPTQQwgNDVUQ6gYHBxkXES1WdXZ2qlz2xuFw4OnpqSBWLdV9UFUoikJ3dzcaGxvR2NiI/Px83L59W+txn3zySezYsQPBwcHMQdHg4CCamppw+fJlJq+qs7NTZYeZqakp0wGRdlUFBASsyTJVwoMF3TmwoKAAxcXFi3YOjIuLYzoH2tnZQSqV4vr16/jwww+Rn5/P6kQC7gu9QqEQAoEA/v7+S+4/KIpCZWUljh49ikuXLik87ubmhscffxxpaWlMYw99QVEUamtrkZ2djfPnz7MK3I6OjkhLS0N6ejo8PT31Oh8CgUBYzXzzm9+Uu//6668v00xUo7Cw0KDb01cjKisrKzkBiw5wXymdCCmKWtJZtX37djz22GMajW9iYkIuKhkAImARloWXX34ZP/7xjzV+/vT09KJXUmQFrurqakgkElaHlJOTE1555RU888wzePzxx5WeGMlSWFiocGJ0+vRp2NjY4Ic//OGyuWLEYjGqqqrw2Wef4eLFixqPExcXh8OHDyM2NlbuPRaLxejs7GSymeg/Vd4zGisrKzmBJiAgAP7+/jq7KiYWi9HR0YGGhgamFHCxkiRVSUlJwb59+8Dn82Fvb4/5+Xncvn0bzc3NKC4uZvK7BgcHVR7T3t6eeR9od5WXl9eqcfIRCEtBd38qKChYsnNgQkIC0znQ0tKSEXQ++OAD5ObmKr1iu2HDBqSkpEAoFCIkJESl/a9YLEZ+fj4++ugjNDY2KjzO5/PxxBNPYNeuXQodQHXN8PAwzpw5g+zsbLS3tys8bmxsjMTERKSnp2Pr1q1k/0AgEAhLkJ2dLXf/u9/97oq7wLwQbTqyqwuXy9Xbb4ls/qKyToTLKWCJRCLWhiwuLi7o6+sDABQVFWk8vrKmMQTdQo6ECMuCsrIPVamqqsL27duVPu7p6QlnZ2fcu3cPk5OTaGxsBJ/PV7r+hg0bcOHCBVRUVOBb3/rWkttnc9R88sknWL9+vcJVH31AURRaWlrw+eef4/PPP9d4HD8/Pxw+fBgCgUAuUHFkZAQ1NTVyQlV7e7vSLmBsuLu7MyKVPjrfTU1NMc4vuhSQ7QRQXXg8Hh5++GFERETAw8MD09PTaG1tRWNjI4qLi5kMr9nZWZXG43A42Lhxo1ywelBQkNyPOYGwVhgYGGBKAysqKhbtHLhz504kJSUhKioKJiYmTB6WSCRCXl6e0rJeBwcHRrQKCwtT+cRkYmICWVlZ+OSTTxQ6D3E4HOzYsQNPPvkkwsPD9XohYn5+HmVlZcjOzkZRURHre+Tl5YX09HQcOHBAaYdEAoFAIMhDURT+53/+R27Z17/+9WWajWpQFKV1N2t10KY8bilkKwZkmx7JHvMuVwnh9PQ0/vznPyssT09PR1dXFyNgEVY+RMAiGBypVIo333xTqzHKy8sXFbA4HA6io6Nx5swZAPfLCBcTsGiio6NRVlaGH/3oRxop8O+//z7Wr1+PI0eOqP3cxbh37x6ys7Nx/PhxtRxPstDzOnDgACMk0U6isrIypuStublZrR8Xc3Nz+Pv7y4lV/v7+Om0dOzQ0xORHNTQ0oLGxkQmD1AZ7e3scOnQICQkJCAgIwPDwMBobG9HS0oK33noLTU1N6O7uVvnAwtzcXE6wo9+LlZy7QCBoS1dXFwoKClBYWIiamhql3xdPT0+mcyCfz2eEp7a2NuTm5kIkEikt77W1tUVycjJSUlIQERGhljOqr68Pn3zyCb744guFoHdTU1OkpaXhscceg5eXl8pjakJPTw9OnTqFnJwc1gNlc3NzpKSkIDMzU+8iGoFAIKxFfv/73y96fyWiaUMrTdHnb93CToQ0siWE6lQr6JKPPvoI9+7dk1tmbW2NF198UafnbWKxmDRU0jNEwCIYnJGRETlVXhNKS0uXXCcqKooRsCoqKvDVr35VpbFNTEzwxz/+EcXFxXjhhRfUntsbb7wBc3Nzhe4nqjI5OYm8vDx8+umnaG5u1mgMAMjMzMRDDz2E4OBgGBkZYXx8HC0tLbh06RKTV6WOkwi471STFamCgoLg4eGhszIbiqJw584dNDY2Ms4qdbvzLUZaWhr27t2LwMBADAwMMKLdm2++iebmZqW5PGw4OjoqdAHU5XtBIKxUKIpCc3Mz0zmwtbVV6brBwcFM50A/Pz9GlOnq6oJIJIJIJFJa5mttbY2kpCSkpqYiOjpa7ZKHpqYmHD16FCKRSCGTz87ODocPH8ahQ4dgb2+v1rjqMDc3h6KiImRlZaG8vJxV3OPz+cjIyIBAINCp8E8gEAgPEmKxGJ988oncsl27di3TbFSnurraoNvTV4A7IO/Akr1gtNwh7n19ffjXv/6lsDwhIQEWFhYKwpY2TE9PEwFLzxABi2BwbG1ttR6jtbUVAwMDi5ZhyeZgXbt2TW1FPDExEWfOnMHLL7+MmzdvqjW/1157DRUVFfif//mfRctbxGIxSktLcfz4cVy+fFmtbciyfft2JrfK2NgYPT09aGpqwsWLF/F///d/aGlpUdrpiw0TExP4+/szYhWdWyVb264tEokEHR0djEhF/2krbtJER0fj4MGD8Pf3x+joKJqbm9HY2Ii//OUvaG1tVblO3cjICN7e3nJCVWBgoF5PegmElcb8/DyuX7+OwsJCFBUVKS3vozsHJiUlYefOnXBzc2Meu3PnDnJzc5Gbm8uaPQUAFhYW2LlzJ4RCIeLi4mBqaqrWPCmKwpUrV3D06FGUl5crPO7l5YUnnngCe/fu1WvQaktLC7KysnD27FnWkvn169dj3759yMjIWPZ24gQCgbAWePbZZ+XuLxSzVio5OTkG3Z4+BSzZ8wTZ377lDnF/8803MTMzo7B8x44d6O3t1WkJZ0NDA2JjY3U2HkGRZROwsrOz8aMf/Ujp49/+9rfx4osvMvdramrwzjvvoKamBlNTU/D398eTTz6JAwcOGGK6BB2iqyDFsrIy7Nu3T+njbm5ucHNzw507dzAzM4O6ujq1675dXV3xt7/9De+88w4++ugjtZ579uxZnD17Fu+++y7i4uJAURQaGxtx/PhxrX6sgoKCcOTIESQnJ8PIyAitra1oampCcXEx/va3v6G1tVVpWDIbjo6OcuV/+ggTn56eRktLi5xY1dLSorOwQzc3Nxw+fBh+fn6YnZ1luiG+9dZbSk+02bC0tJQLVg8MDISfnx/MzMx0Mk8CYTVBdw4sLCxEcXEx01loISYmJoiLi8OuXbuYzoE0/f39yMvLUxqcCgBmZmZISEiAQCDA9u3bNRKW5ubmcO7cORw7dozV0RUREYEnn3wS27dv11uY78TEBM6fP4+cnBzWrq8cDgdxcXFIT0/Hzp071RbnCAQCgcDOyMgIqqqq5JYFBQUt02zU4/r16wbdnoeHh97GVubAWrduHUxMTCAWizE9PY2pqSm9d/eluXnzJs6dO6ew3NjYGPHx8Vp1aWejoKCACFh6ZtkErIaGBgDAtm3bWJ0MISEhzO3Lly/jW9/6FqRSKaKjo2FhYYGrV6/ihz/8IVpbW+WELsKDw1ICFnC/jPDkyZMA7pcRahJcaGJighdeeAGRkZH4+c9/jtHRUbWev/CKkDo4ODjgyJEjSEtLg0QiYbrdXb58Gf+PvfsOi+Ls/gb+XXqXKoJYQAQRsYCIqCC9qAjYjWCNJbFE09RomomJpjwxmthjVMSugCXSOzbACiqKCnZEiiB1Yef9g3fnx7K7SNml6PlcF5fszOzMvQi7M2fOfc6ePXvw9OnTJt81kJOTg7GxsVCwStKZREVFRbh79y5u377NBqtyc3MldneDw+Fg4sSJMDU1BYfDQW5uLrKysrB79+5mZW/xp0PWL6xuaGjY4TvVECJNb968YTsHJicniw2Gq6qqsp0DR44cKXAiWlhYiJiYGERGRuLKlSsi//bl5OQwYsQIeHl5wdHRscUnsiUlJThx4gQOHTokdFeXw+HAzc0NgYGBTaqB2BIMw+Dq1asIDQ1FVFSUyCnZBgYG8PHxga+vLwwMDKQyDkIIeZ81TGiIjIxsp5E0j6RmHTSHNANY4mpgcTgc6OrqsrNBXr16hZ49e0ptHHw8Hk9sHTRra2uoq6vj6dOnEj1mXFwcVq5cSdcTUtRuAaxbt24BAH7++Wfo6+uL3a6yshJffPEFAGDPnj0YPnw4AODRo0cIDAzE9u3b4e7uLrWTU9JxnTlzBqtWrWr0wsfW1pYNYKWlpWH+/PktPp6DgwMOHz6Mr776ClevXm3xfhozefJk9kOYH6y6cOEC9u/f36wPOS0tLYEglbm5OXr37i3ROdkMw+D58+cCxdWzsrIkOo8cqOuW0r9/fygrKyMvLw9ZWVkIDQ1tckdEWVlZmJiYCE0BrN91kZD3WUFBAds58PLly2L/tnR0dNjOgba2tgLvJyUlJYiLi0NERAQuX74MHo8n9HwZGRkMHz4c7u7ucHFxETjRba6nT58iODgYYWFhQkE2ZWVl+Pv7Y/r06VKbKvHq1SucOXMGoaGhIgvPy8nJwdnZGf7+/hg2bBidyBJCiJQ8ePBAINvHzMys03R6lnT2T1O05rP3berXcWx43VI/gJWfn98mAaxz584hIyMDQN3ncs+ePdmO5aNHjwbQ/CL6lpaWjf6/vXr1CpmZmbCysmrhqMnbtFsA686dO9DV1W00eAXUzQsuKCjAhAkT2OAVAPTs2ROff/45Pv/8cwQFBWHjxo3SHjLpgEaNGoVNmzbB0dFR5Pr6dbCuXbuGqqqqVk0H09fXx9atWxEYGNho4eKmsLS0xOTJk6GpqclOA0xNTcXx48ebnK3Er8/UsFaVrq6uRDtY1dTUICcnhw1W8acC1r+7IgnKysoYOHAgdHR02KLz165da3J6tZqaGltYnZ9dZWxsTFN1CGngyZMnbOfAGzduiH3PMTIygouLi1DnQKBuekB8fDwiIyNx4cIFkYEvDoeDoUOHwsPDAy4uLgLTC1siIyMD+/fvR0xMjNCYdXV18cEHH2DChAkSrdfHV1NTg/PnzyMkJARJSUkig3Smpqbw8/PDmDFjJFLvkRBCSOMmTZok8Hjv3r3tM5AWOH/+fHsPQaLEZWABEAgqtkUnwoqKCoGu9wEBATh27Bj72MHBAQCanYH1008/vbVRV2xsLAWwpKhdAliPHz9GSUkJG/lsTFJSEgDA1dVVaJ2zszNkZWWRmJgo8TGSzmP58uVwcXHBl19+ia5duwqs69q1K3r27IlHjx6By+Xixo0bAkGtt2EYBpmZmTh69CjOnDkj0XFnZmY2686Luro6W5+JPw1QGvWZKioqkJ2dLVSvqrq6WqLHAQBtbW307NkTtbW17B20S5cuNem5hoaGbDZVv3790LdvXxgYGFDreUJEYBgG9+7dQ2xs7Fs7B5qbm8PZ2RkuLi4CnQOBuveHpKQkREZGIjk5Wez7wqBBg+Dl5QVXV9dW3wnn8XhISkrC/v37RWa/mpqaIiAgAN7e3lLp/PPo0SOEhobizJkzIovPqqiowNvbG76+vrC0tKT3IEIIaSMNA0A+Pj5SbdAhaYcPH27T41lbW0t1//UzsOpnxQGChdzbohNhQkICexxtbW3MmzcPBw4cYNd369YNABAdHd3kfY4bN459XmPaurPk+6ZdAlj8+lc6Ojr44YcfkJiYiBcvXsDQ0BDjx4/Hhx9+yF6U37t3D0BdOmhDampq6Nq1K54/f/7WjnTk3bF06VJs2bJFYFlsbCwuXryIxYsXY8qUKZCVlWXX2draslM80tPTGw1gPXv2DMePH8eRI0eaVQhdkjgcDnr06CEQrDIzM4O+vr7EL4xev34tkFGVlZWFhw8fSrQbR306OjpQUFBAXl4eeDweCgsLUVhY2Ohz5OTk0LdvX4F6VX379pVqCjQh74La2lpcv34dcXFxiIuLe2vnQCcnJzg5OQlNu6uursb58+cRHh6OpKQkse+NlpaWcHd3h4eHR5NO8N6msrISZ86cwYEDB0RO07Ozs8PMmTMxfPhwib83VlRUICYmBmFhYUKFgfmGDBkCX19fuLu7Q1lZWaLHJ4QQ8nZLliwRePzdd9+1z0BagMfjSayZUVNJu+RO/QBWYxlYbRHAqp/xraamBlVVVdTW1rLLWnLe4O/v36Su7u11Dfm+aJcAFr/+1cmTJ6GpqQlra2vo6+sjIyMDmzdvRlJSEvbu3QslJSX2F7x+1LY+PT09CmB1Mq3N5BF3EVZeXo5ff/0VZ86cwZo1a9C/f38AdQGsEydOAAAuX76MRYsWAah7Yz179iwOHz4s8uKoqdTV1SU2lc7Pzw8rVqyQeHCGYRi2flT94uovXryQ6HEa4nA4AsGwt6UMd+nShZ0CyK9X1atXL6lkVRDyLqqurhboHCguQFy/c6CDg4NQMwcul4tLly4hMjIScXFxQndS+fr27QtPT0+4u7ujR48eEnkNhYWFOHr0KI4ePYri4mKBdbKysvD29kZAQIDIG1utwTAMbt26hbCwMJw7d07ka9bW1oaPjw/8/PzQq1cviR6fEEJI0zXMXvrss886VQYsvxZTW5JmAXdAcAphwxpY9a/l22IK4eDBg6GoqIiqqio8evRIqPGVjIwMsrKymrVPS0tLREREvHU7acxaIf+nXTOwvL298dNPP7FFuJ88eYLFixfj6tWr2LRpE1atWsVGMMWlg/KXl5eXt8HIiSQ0tfi2OPxgFN9HH32Ec+fOIScnB0Dd71dgYCCmTZuGjz/+GEOHDmW3vXbtWqvTZ7t37w5VVVU8ePAANTU1Eq0DFRoaiqtXr2Lx4sVwdXVt0QdxbW0t25nvzp07uHPnDu7evdvs7omSIC6Ti8PhwMjISKhelZ6eXqc6+SCkI3jz5g3Onz+PmJgYpKSkiP08VFVVxahRo9jOgfXbXQN17x3p6emIiIhATEwMSkpKRO6nd+/e8PDwgKenJ4yNjSX2OnJychAcHIzTp08LnfypqalhwoQJmD59+ltrZzbX69evce7cOYSEhLBZ3/XJyMhg5MiR8Pf3x8iRIymgTggh7YxhGPzyyy8Cy2bMmNFOo2mZGzdutPkxpR3AaiwDS0dHh/2+LTKwFBUVMXToUKSkpAAA+y8fh8PBBx980OT9cTgcKCgoNKnDZWuvdUnj2iWAtXnzZjx+/Bg9e/YUKK5sZGSEDRs2wN/fH0eOHMFnn30GWVlZMAzz1otaUcVUScekoqKCcePGSaymFJfLxeHDh7Fv3z7s3r0bXC4XDMPg0KFDOHToUKv3r6WlhaKiIvZxS9qtbtiwAc+ePRMoJihObm4uvvzyS1haWmLp0qUYNmyY2G2rqqrYboX161VVVlY2e4zSoqioCFNTU4F6Vaampo12jySENK6goAAJCQmIi4vDpUuXxJ4saWtrs50Dhw0bJhR84fF4uH79OiIiIhAdHS02Y6t79+7w8PCAl5cXTE1NJRZoZhgGV69exf79+5GUlCQU9O7WrRtmzJgBPz8/oYBba/B4PKSmpiIkJARxcXEip3EYGRnB398f48aNE5sFTgghpO398MMPAo+bcn7d0YjK5FFWVpbq9DNpdebla9iFsP41fP3PUVH1JKXB3t5eZACLw+EgJSWlWSVT+Ns2DISJIm62EJGMdglg8S9oRbGwsEC3bt3w/Plz5OTkQFlZGSUlJWK7x/Ev1CV5Ykuki2EYiRa3O3HiBKqqqnD48GGpzCWvH7xqSF9fX6Courm5OQwNDbF3715s376dfbNbtWoVZs+ejZkzZ2L//v1NOm5mZiYWLVqE4cOHY+nSpejevbtAoOrOnTt4+PBhhwre6ujosD8Pfr2qnj17CtQkI4S0zNOnT9nOgdevX2+0cyC/CLuVlZVA50Dg/5pTREZGIjIyEi9fvhS5H319fXh4eMDd3V3ixclramoQGxuLoKAgkc0sLCwsMGvWLLi4uEBOTnKnKi9evMCpU6dw6tQpkSeYioqKcHNzg6+vL2xsbCgjlBBCOpiqqiqEhoYKLBs1alT7DKYVUlNThZbJy8tLNYAlifqUjZGXl4eSkhIqKyvBMAzKy8vZa/T6pX7aMoDFV79JFMMwWLZsWbP397a6vaRttEsA6210dXXx/PlzVFRUoGvXrigpKUF+fr7ItMe31cgiHc+rV68kGpkuKipCUFCQxPYniry8PExNTdG3b182WGVmZoYuXbqI3H7+/PkYMmQIvvrqK/ZNeu/evRg4cCBGjhzZpOg938WLF3Hx4kWJvA5J4XA46N27N1unih+0ojp0hEgOwzDIzs5GTEwM4uLiRE5v4zMzM2ODVqIypBiGwd27dxEREYHIyEix78Ha2tpwd3eHp6cnBg4cKBT8aq3y8nKEhYUhODhY5BgcHBwQGBgo0eARl8tFQkICQkNDceHCBZGBPwsLC/j6+sLb25saRBBCSAc2b948gcfHjh1rp5G0XMP6jnzSnkHRFlPg1dTU2Nfx5s0bNoClpaUFGRkZ8Hi8RpNTJKl3797o1q0bXrx4IZG6VAsXLpTAqEhrtXkA682bN9i4cSNev36N//3vfyLvrD558gRAXZS4b9++yM7Oxv3794UCWG/evMHLly+hra1NF86dCP//t6PS1dUVyKjq27cvevXq1ewsgKFDh+Lw4cP4+uuvceHCBQB1893V1NSkniIsScrKyuzPgh+s6tOnD3XdIkQKamtrcePGDbZzoLgpyxwOB0OGDIGzs7PIzoF8Dx48QGRkJCIiIpCbmytymy5dusDV1RWenp6wtraWSsZkfn4+Dh8+jGPHjgkVdpWXl8e4ceMwY8YMmJiYSOyY9+/fR1hYGM6ePSsyk1ZdXR1jxoyBv7+/xAvCE0IIkbyCggK2GRhQd47ap0+fdhxRy4irf/UuFP9WU1Njb96XlpaydStlZGSgra3Nrnv16pXUpzRyOBzY29sjJCREIvu7f/++RPZDWqfNA1iqqqqIiopCUVERUlNTBVL7ACAhIQFFRUUwMzND165d4eDggHPnziE6OhqjR48W2DY2Nha1tbVCy0nH9vjx4/YeAsvAwADW1tYCwaqG3bhaQ1tbG1u2bMG+ffuwZcsWAMJdOToSPT09oS6ARkZGEs/CIIT8n+rqaly+fBlxcXFISEgQm6IuJyfHdg50dHQU+171+PFjNmiVnZ0tchs1NTU4OzvDw8NDZG0sScnOzkZQUBDOnTsnVKerS5cumDJlCqZMmSJQ3LU1ysrKEBUVhZCQENy8eVPkNra2tvD394ezs7PU7/4SQgiRHE9PT4HHkqqn29bS09OFlvXo0UOq10hWVlZS23d99etgNezmq6enxwawCgoKpB7AAoARI0ZILIDVVHTdJF1tHsDicDiYMmUKduzYgR9++AH//vsvG5l99OgR1q1bB6CusxxQ90b1+++/IyQkBG5ubmyw6vHjx/j999/B4XAwe/bstn4ZpBVaUgS9tdTU1EQGjqqrq+Hg4AB3d3eJ1jopLS0VqlfVkcjIyMDY2FigsHrfvn2hpaXV3kMj5L1QVlaGlJQUxMbGIjk5WWznQBUVFYwaNQouLi4iOwfyPX/+HJGRkYiKihK4O12fsrIyRo8eDU9PT9jb2ws0UZEkhmFw+fJlBAUF4fz580LrjYyMEBAQAB8fH4lkcjIMgxs3biAkJARRUVEis1u7du0KX19fjB8/vk1OmAkhhEjW3bt3Beq+Dho0qNOetx4/flxomZKSklSP2dou7E1Vfxp+w27G9WdMtUUnQgAYNmwYO3WxrVApAulqlxpYH3/8MdLS0pCeng4vLy/Y2NgAqCuuVl1djblz52LMmDEA6gIPP/zwA5YtW4aFCxfC1tYWqqqquHjxIioqKrBixQr069evPV4GaaGrV69Kbd8yMjLo3bu3QEYVvzZTUlISli9fLrB9QUEBVq1ahVOnTmH16tXNvrBhGAavXr3C7du3cffuXTZY1R5BOnFUVVXZnwW/sLqJiQllHhDSxgoLC5GQkIDY2Ni3dg4cPXo02zlQXKApPz8fMTExCA8PFzsdQUFBAQ4ODvDw8MCoUaOkOvWXy+UiMjIS+/fvF1mva9CgQQgMDMTo0aMlMk2xsLAQZ86cQVhYGB4+fCi0Xk5ODqNHj4avry/s7e2pmQQhhHRi06ZNE3i8c+fOdhpJ69TU1Ii80SKtm0p8FhYWUt0/X2MZWO1RyF1dXR1WVla4fv16mxwPADQ0NNrsWO+jdglgKSkpYe/evdi7dy9Onz6NS5cuQUFBAYMHD0ZgYCA8PDwEtnd1dUVQUBD+/vtvtvOSubk5Zs+eDW9v7/Z4CaQV0tLSpLLfSZMm4bPPPhMbmBkyZAg4HA5bwFdBQYGda37+/HlMmjQJCxYsQEBAgMjpNDweD0+ePMGdO3dw584dNruqI3akWLBgATsF0NDQkDppEdJOnj59ytazunbtmtjOgd27d4eLiwucnZ1hZWUlNthSVFSEmJgYRERE4MqVKyL3JycnB3t7e3h6emL06NFS79JbWlqKEydO4NChQ0J3VDkcDlxcXBAYGIiBAwe2+li1tbU4f/48wsLCkJCQgNraWqFtjI2N4efnh7Fjx0p0SjghhJD2ER8fL/B4ypQpbVKQXBrENWSRZIMrUQwNDaW6f776AazS0lKBdfWbrrVVAAsABg4c2KYBLHFNvohktFsXQgUFBSxYsAALFixo0vbW1tb4559/pDwq0pkVFhY2mlWkrq6O/v37sy3bv/rqK9y6dQvHjh0DwzCoqqrCli1bcPbsWaxcuRLq6urIysrC7du3kZWVhbt373aawusXLlzA0KFDaaoMIW2M3zkwNjYWcXFxuHv3rtht+/btC2dnZ7i6uorsHMhXWlqK2NhYREZG4tKlSyLT4GVkZGBnZwcPDw84Ozu3yd2/58+f49ChQzh58qTQFEglJSWMHz8eM2bMQI8ePVp9rKdPnyIsLAynTp3Cy5cvhdYrKyvDw8MD/v7+sLKyoqA9IYS8Qz799FOBxytXrmynkbSeqIxpc3NzZGVlSfW4bXVNUD+A1bB8S/16l201hRCA1LvVN0QBLOlqtwAWeT81TCWVpNjYWMTHx8PJyUnsNkOHDmUDWHfu3MGqVavg4uKCRYsWsds8ePCgQ7VJVVdXFyis3q1bNyQlJeH48eNi2+3evHkTCxYswMiRI7F06VLqsEWIFPF4PIHOgeI6rXI4HAwePBjOzs5wdnZu9GSyrKwMCQkJiIiIwIULF0RON+RwOLC2toanpydcXFzaLNvo1q1bCAoKQlRUlFAwTVtbG9OmTcOkSZOgqanZquNUVVUhNjYWoaGhSE1NFbmNlZUVJkyYAHd3d6ioqLTqeIQQQjqevXv3Cjxes2ZNp75JkZCQILSsa9euUg9gtfYzuanq139qLAOroKCgTcbz119/tclx6qMphNJFASzSpp4/fy7V/X/66af4448/xHamrN+i/fDhw0hOThZ7sSmKioqK2GLL0sDhcHDy5EmhDl22traYPXs29u7di6NHj4ptu5uSkoLz58/Dy8sLH330EYyMjNpi2IS887hcLts5MD4+vtHOgXZ2dnBxccHo0aMbDTJVVFQgOTkZkZGRSEpKEvt3PXDgQHh6esLNzU3gZFCaeDwekpOTERQUJLJ7krGxMWbOnAkvL69W19fLyspCSEgIzp07J3TyCwBaWloYN24cxo8f3ynbpxNCCGkaHo+HzZs3CyybOHFiO41GMi5evCi0rC3q0rZV0K9+AKuxGlhtkYGVn5+PPXv2SP04DVEAS7oogEXalL6+PjQ0NIS6UkjS7du3YW1tjf/++w8hISHo0qULFBQUcOfOHaFof3OCVwAkFrySl5dnC8zzM6u4XC7WrVsnMAeeYRiMHz8e4eHhQh0ttLW18emnnyIwMBB79uzBiRMnRGZpMAyDc+fOISoqChMmTMD8+fMl1rKekPdJeXk5kpOTERcXh+TkZLEZpSoqKhg5ciRcXV0xYsQIgXT6hqqrq3HhwgVEREQgISFB7DTl/v37w93dHR4eHjAwMJDI62mKqqoqnDt3DkFBQSILpdva2iIgIAAjR45sVdvo0tJSnDt3DqGhoSK7tnI4HIwYMQJ+fn5wdHTstLVPCCGENN2aNWsEHu/YsaOdRiIZ4oI24pq6dEb16242vAnV1kXcpXm92RiaQihdFMAibUpdXR07duzAsmXLpBZ537lzZ7M6k8jIyMDExATa2tq4ffu2yDv+raGlpcUGqvjdAHv37g05OeE/v0OHDmHdunWIiYlhl1VUVGD06NFISUkR2UFMT08PK1euxOzZs/HPP/8gNDRU5AdhTU0Njh49itOnTyMwMBABAQGNXlgTQupq6yUmJrKdA7lcrsjttLS02M6BdnZ2jXYT4mdvRUZGIi4uTqhGBJ+pqSk8PT3h4eEhkTpSzVFcXIxjx47hyJEjQtllMjIy8PDwQGBgYKu6GjEMg7S0NISFhSE6OlpkxpmhoSF8fX0xfvx46Ovrt/hYhBBCOpeKigpEREQILLO1tW2n0UiGqELiysrKyM7Olupxzc3Npbr/+upnHzW8pqp/A72oqAg1NTUir4ck5cGDB1Lbd2MogCVdFMAibc7c3BwHDhzAvHnzmp0BJQ08Hk8iHxwcDgc9evQQqFdlbm4OXV3dJqftqqur45dffsHRo0exceNGgXUjR45EYmKi2KCTvr4+vvrqK8yaNQu7d+/G6dOnRRZ7rqiowM6dO3H06FHMmzcPkydPlnrrXkI6k6dPnyI+Ph5xcXG4evWq2M6BhoaGbOfAgQMHiu0cCNR1z7ty5QoiIiIQExOD169fi9yuV69e8PT0hLu7e7tMj3v8+DGCg4MRFhaGqqoqgXUqKiqYOHEipk2b1qossPz8fJw+fRqhoaEiPwPk5eXh4uICf39/DB06tFWZXYQQQjqnGTNmCDwODQ1tn4FIkKgAlqenp9Rfm42NjVT3X1/9DKyGN+jk5eWhqamJ4uJiMAyDoqIiqZZCsLe3h7a2dpt3jKcscemiABZpF3p6ejhy5AimTZuGx48ft/dwWszNzQ22trZsZpWoDKnm4nA4mDp1KgYOHCj04e3o6Ijjx48L1PJqqHv37vj2228xd+5c7Nq1C2fPnhV5AV5cXIzff/8dBw8exKJFizBmzJhGL8AJeVfxOwfyi7A3VkjV1NQULi4ucHFxQd++fRsNTvOLu0dERCA6OlpswVJDQ0N4eHjA09MTZmZm7VKc9saNG9i/fz/i4uKE3i+6du2K6dOnY8KECUJTmZuKy+UiOTkZoaGhSE5OFvme1LdvX0yYMAFeXl5095IQQt5jeXl5yMnJYR9ra2ujZ8+e7TcgCTl16pTQsraYDWFtbS31Y/DVP08QlWGuq6uL4uJiAHU3tKQZwFJTU8Py5cvxzTffSO0YoojqlkwkhwJYpN0oKysjJCQEQ4cObe+hNEpXVxempqa4fv26UH2a1NRUjBw5Uipt2y0sLJCYmAhHR0eB5ZMmTcKPP/6IMWPGNPr8Hj16YN26dZgzZw527NiBqKgokReNz58/x7fffov9+/dj6dKlcHBw6NTdXQhpCh6Ph5s3byI2NvatnQMHDRrEdg58WyMEhmGQmZmJqKgoREZGIi8vT+R2enp67PRAS0vLdvmbq62tRXx8PIKCgkS29TYzM8PMmTPh7u7e4ruJOTk5CA0NxZkzZ0TeAVVTU4O3tzd8fX1hYWFB7z2EEELg7e0t8DgkJKSdRiI5VVVVIsuUtEW2TltmdDeWgQXUXVfxZ760RR2ssWPH4uTJk7h27ZrUj8UnrgkPkQwKYJE2xeVykZOTgzt37iArK0vqLWOby8TEBGZmZgLF1evP13758iV+/fVXtkbV69ev8f333+PMmTP46quvYGxsLNHxqKmpIT09Hba2tgLTAdeuXYvU1FR8+eWXb836MjY2xoYNG/Dhhx9ix44dAvW16rt//z6WL1+OQYMGYdmyZRgyZIhEXwsh7Y3L5SI1NZXtHCguI4rfOdDZ2RmjR49+a9MDfgZXeHg4IiMj8fTpU5HbaWtrw83NDZ6enhg0aFC7TY2rqKjAqVOnEBwcLDJwN3LkSAQGBsLW1rZFAaWKigpERkYiLCxM7AmjjY0N/Pz84OLiIpHMVUIIIe+GjIwMgcf29vYtzv7tSEQ1KAEgkGkmLW3Z/KWxGlhA2xdy53A4WLVqFaZPny62JISktcXrep9RAItITUVFBe7du4fbt2+zwars7GyxRZAbM3/+fOzatUsKo6yzZ88e9OvXD0pKSo1u17VrV/z6669ITEzEhg0b8OLFCwBAeno6pk6dijlz5mDu3LkSbYfL4XBw+fJlTJkyRaAYYVhYGDIyMrBx48ZGpxTymZqa4tdff0VWVha2bduGxMREkdtdv34d8+bNg4ODA5YsWYK+fftK7LUQ0tbKy8uRkpLCdg4UVzBdWVkZo0aNgouLC0aOHNmklP6HDx8iIiICkZGRYk9ANTQ04OrqCk9PT9jY2LTrNN2CggIcOXIEx44dE6rBJScnB29vbwQGBsLU1LTZ++ZnnoWGhiI8PFxkx1ZdXV34+PjAz8+vzYvSE0II6Rxmzpwp8HjTpk3tMxAJE1X/auDAgYiLi5P6sduy1q2Kigr7fXl5OWprawXOfeoHsKTV0KshMzMzTJkyBUeOHGmT492+fbtNjvO+ogAWkYiioiJkZWXhzp07bHbVo0ePJBbplmbwCgAGDx7crO0dHR1ha2uLHTt24MCBA+DxeKipqcGuXbtw7tw5rFmzBnZ2dhIbn4yMDA4dOoSPPvoIV65cYZffv38fAQEBWLVqFcaPH9+kfZmbm2PTpk3IyMjAjh07kJKSInK7pKQkJCcnY+zYsVi0aBEMDQ0l8loIkbaioiK2c+DFixff2jnQyckJdnZ2TQo8P3nyBJGRkYiIiMC9e/dEbqOqqgpnZ2d4eHjAzs6u3Yt5PnjwAAcOHMDZs2eFfhbq6uqYPHkypk6d2qI6FEVFRfjvv/8QGhqK+/fvC62XkZGBg4MD/P39MWLECKl2GyKEENK5New6OGvWrHb/DJWUy5cvCy1zcnISOYW/M5OVlYWKigp7I6u8vFwgg67+uYa4THhp+Pjjj9ssgJWTk4OysjKB6ZREcuhMkjQLwzB49uwZsrKycPfuXTa7qjnF6vT19WFubo5+/fqhX79+uHjxIo4ePSrFUQvS0dHBX3/9henTp7PLnj9/3uz0WmVlZSxfvhxjx47Fjz/+iJs3bwKou8D96KOP4O3tjc8++wza2toSGbe8vDy2bNmCJUuW4OrVq+zyyspKfPfdd0hLS8Pq1aubPB1nwIAB2LJlC65fv45t27aJ/GBlGAZnzpxBeHg4Jk+ejHnz5kns9RAiSc+ePWM7B165cqXRzoH8elaDBg1qUkbUixcvEBkZiaioKGRmZorcRklJCY6OjvDy8oK9vb1EszBbgmEYpKWlISgoCMnJyULrDQ0NERAQgPHjxwvcLW0KHo+HS5cuITQ0FHFxcaipqRHapmfPnvDz88O4ceME7rYSQgghojAMg9WrVwssW7ZsWTuNRrIYhsH58+eFlr+rU+jV1dXZAFZJSYlAAKt+WYa2ysDij2ncuHE4c+aMRPdrZGQkVI6BYRjcvn27w9d57qwogEXEqqmpQU5ODjv9j59ZJWo+sygcDge9e/eGubk5zM3NYWFhATMzM6ipqSEuLg5HjhzBzp07pfwqhPXt2xfm5uaws7PDpUuXANQVY29qBpOo/f377784ceIEtmzZwk5ROnfuHJKSkrB8+XL4+flJpN6NsrIyNm3ahPnz5+Pu3bsC686cOYOMjAz88ssvzZoCNGjQIGzfvh1paWnYvn27QIYXX01NDQ4dOoTQ0FAEBgYiMDCQ7iqQdsUwDO7fv4/Y2FjEx8eLrS0B/F/nQGdn5yZ3+Xv16hViYmIQHh4uMu0fqAsqOzg4wMPDAw4ODh3iRJTL5SImJgZBQUEiU9gtLS0xc+ZMuLi4NHs647Nnz3Dq1CmcOnWKnT5dn5KSEtzc3ODv74/BgwdTQXZCCCFNtn37doHH69ate2c+R549eyZyubjzC0lqSpkRSVNTU2Ob2JSVlQmsq5+B1da1olavXi3xAJa9vT2OHTsmtPzw4cMUwJISCmARAHX1qrKzswWKq9+7d6/JXRQUFBTYwBD/q2/fvlBSUsKLFy9w8+ZNpKSkYMeOHQLZQ+3h4sWLcHJygqenJxvASktLa3EAC6ibJjN58mQ4Ozvjt99+Q2RkJIC67hs//vgjTp8+jTVr1rSotkxD6urq2Lp1K+bOnYtHjx4JrMvJyWGnFPr6+jbrg3/o0KHYtWsXUlNT8ffff7MZZfVVVFRg586dOHbsGObPn4+JEye+M6ndpOPj8XjIyMhAbGwsYmNjG+0cOHDgQDbTqqn1loqKihAbG4vIyEikpaWJzOKSk5ODvb09PDw8MHr06DZpf90UZWVlCAkJwcGDB4WCSxwOB6NHj0ZgYGCzA0vV1dWIj49HaGgoLl26JPJnYmlpCT8/P3h6enaYnwchhJDOg1+Go75x48a102gkT9w0wfDwcKkf28bGRurHaKj+uUDDxIe2LuJenzRuNPbs2VPk8tjYWFy/fh2DBg2S+DHfdxTAeg+9fv0aWVlZAsXVc3JymlyvSk1NDf369WMDVf369UPv3r0hJyeH0tJS3L59G6mpqdizZw8yMzPbdH5zU5WUlAhEy1NTU8EwTKvv9Ojq6mLDhg0YP348fv75Z7Yb2fXr1zF9+nQEBgZi/vz5rX4D1dbWZoNYDadvVldXY926dUhNTcVXX33VrEwpDoeDYcOGwdbWFufPn8e2bdtw69Ytoe2Kiorwyy+/4MCBA/j444/h5eXVbh3VyLuNy+UiLS2N7Rwo7mRHTk4Ow4YNYzsHNnXaWmlpKWJjYxEVFYVLly6htrZWaBsZGRnY2trCy8sLzs7OAh122lteXh4OHTqEkydPChWoV1BQwPjx4zFjxgz06tWrWfvNzs5GaGgozp49K1TwHagrTj927Fj4+flRowdCCCGt8vnnnws8/vfff9tpJNIhKtPK19cXYWFhUj92ewewGmZg1T8/KygokMj1V3tqrAHYpk2bsGfPnk79+joiCmC9Z/7880/s37+/ycGqrl27skEqfsDK0NAQHA4HXC4X2dnZuHLlCoKCgpCRkdGsQFhbCgsLw4oVKwS6+NWXl5eHpKQkODo6SuR4I0aMwLFjx7B7927s378fNTU1qK2txd69exEZGYlVq1Zh1KhRrTqGoaEhtm3bhnnz5qG4uFho/blz55CZmYlffvkFZmZmzdo3h8PByJEjMWLECCQmJmLr1q0iC1Y/e/YMa9euxd69e7F06VKMGjWK3qRJq5WXl+P8+fOIi4tDUlJSo50DR44cyXYObGqb7fLyciQmJiI8PBznz58XWcOJw+HA2toa7u7ucHNz63C13+7evYugoCCEh4cLBd20tLQwZcoUTJ48uVnjfvPmDSIiIhAaGiqy1heHw4GdnR38/Pzg5OTUpl2NCCGEvJtKS0uFOmO/a1kr0dHRQsv09fXb5NjNbVQlCfXPx0pKSgTWKSsrQ1VVFWVlZaipqUFxcTG0tLTaZFwVFRUS32djM4uuX7+OhIQEODk5Sfy47zMKYL1njh07JjLAxOFw0LNnT4Hi6mZmZuzFD794e2ZmJg4fPoybN2/izp07TZpiqKysDEtLSwwYMAADBgyAlZWVwPznvXv3YvPmzZJ7kSLcvn0bx48fR2lpKZYsWSJyetzy5csBAEFBQbC0tGz1MZWUlLBkyRJ4e3tj/fr1uHbtGoC6oM+yZcvg7u6Ozz//vEWdv/iMjY2xZcsWLFiwQOSb8qNHjxAYGIgvvvgCEydObHZwiT/1yMHBAbGxsdi+fbvIIGB2djY++eQTWFtbY9myZRg4cGCLXxN5P9XvHHjp0iWx7y2ampoYPXo0nJ2dm9w5EKhrdpCcnIzIyEgkJSWhqqpK5HZWVlbw9PSEu7t7q/42pYFhGFy4cAH79+8X2XShZ8+eCAwMxNixY6GkpISqqirk5+ejuroaBgYGIrMkGYbBtWvXEBoaiqioKFRWVgpt061bN4wfPx7jx4+nbqTtpKamBlwut0PUWSOEEEmaMmWKwGNJ1yhqb+Xl5SgsLBRaLmqGgzS0x7lM/QBWwwwsoG5M/OWvXr1qswBWVFSUxPfZMPja0ObNmzFq1CjqwixB9JN8z1hYWCA9PR1AXRe6sWPHwsLCAqampgKdqEpKSpCZmYmbN28iMzMTGRkZKCoqeuv+ZWRkYGpqygaqLC0tYWxs3Gix4PoBNS0trSYdp7n2798PDw8PqKurY9++faisrMQXX3yBlJQUoW0DAwMBADt37pRI8b0+ffpg9+7dOHXqFP744w92LnhUVBTOnz+PJUuWYNKkSc0uqMxnaWmJTZs2YcmSJeByuULruVwufvrpJ6SmpuLrr79uUY0aGRkZuLm5wcXFBVFRUdi+fTtyc3OFtrty5Qpmz56N0aNHY8mSJejTp0+LXhN5Pzx//hzx8fGIjY1ttHOggYEBW89q8ODBTf5bqa6uxsWLFxEREYH4+Hixd9769esHDw8PeHh4dMgATXV1Nc6dO4cDBw7g/v37IreRlZWFjo4O2xyjpKREIEjn4OCA3377ja1Z9+rVK5w5cwZhYWEi/5bl5OTg5OQEf39/DBs2rMXvT6R1nj59iuDgYISFhaGiogIDBw5k34s74u8qIYQ0x5MnT9hi30Dd7IJ37b1NXPdiUR2CpaE9ZkbUL18iqvmXrq4ucnJyANSdj7RVKYLTp09LfJ/1Zwns378fq1atEijan5OTg7CwMEycOFHix35fUQDrPePq6soGsFRUVDB16lRwuVxkZWWxgaqbN28KFQcXp1u3bmxm1YABA2BhYdGqO8TSmn5469YtZGRkYMCAAQDqsqO2bNmCmzdvYtasWSKfs2DBAgB185dbO7VQRkYGfn5+cHR0xB9//IGzZ88CqLsrsXHjRpw5cwZr166Fubl5i/Zva2uLjRs34vPPPwePxwNQV6NGQ0ODLXQdFRWF27dvY+PGjbCwsGjx6/D09ISrqyvCw8Oxc+dOkYW0ExISkJCQAB8fH3z00Ufo1q1bi45H3i38zoH8elaiuuTxmZqaskErc3PzJp+A1dTUIDU1FeHh4YiLixM7/bBPnz7w8PCAp6en2AKc7a2kpAQnTpzAoUOH3lrotLa2ttE09qSkJKxZswZjx45FWFgYEhMT2feK+vr06QM/Pz+MGTOmze6IEmE3btxAUFAQYmNjBT4Xb9y4gRs3buB///sfLC0t4erqCldX1yY3KiCEkI6kYQOlI0eOtNNIpEdU/StdXd0WFTBXVlaWyjQ4SaufgSUugMWXn5/fJmN68uQJew0sLZaWlvjzzz8xefJkgeWxsbEUwJIgCmC9Z1xcXPDLL78AqOu8N2vWLNy5c0dk5k5Dqqqq7FRAfnZVUwslN6b+yXn9CyoOhyPRgNbBgwfx008/CSzr378/1NTUxF7kAv83tXD9+vXw8vJq1Z0MbW1t/PDDD/Dx8cH69evx+PFjAHV3Z2bMmIEZM2Zg4cKFAtlwTeXk5IRvv/0W3377LYC6i19TU1MMGTKEvePw5MkTzJo1C5999hmmTJnS4tciJyeHcePGwdPTE//99x927tyJ58+fC213+vRpnDlzBgEBAZgzZw40NTVbdDzSedXvHBgXF8f+zjfE4XBgZWUFZ2dnuLi4NOuCvLa2FleuXEFUVBSio6NF1oQDgB49esDLywvu7u4S6QgqLU+fPsXBgwcRGhoqdKKqrKwMPz8/vH79Gv/991+j+5GTk4OqqipbhD06OlpkHQ4VFRV4enrCz88PAwYMoDp27aS2thaxsbE4cOCAyGnuDWVmZiIzMxObN29G37594erqCjc3t3ZpmU4IIc115coVgccuLi7NajzUWYi6udSlS5cWBbA0NTU7RQCr/mwPUddY9ac1tlUnQklmXy1ZsgTx8fHIyMgQWH7p0iUMHz4cAwYMEFjn5uYmsWMTCmC9d7p27QorKyvcvHkTPB5P7EmyrKws+vbtywaqBgwYgN69e0u901z9gNXQoUORmpoqsX2Hh4djxYoVAm+asrKysLGxQUJCAgBg5cqVyMvLw969e4Wev2bNGqxZswYrV65sVfAHAIYNG4ajR4/i33//xZ49e1BTUwMej4egoCBERkZi9erVLcr68vHxQWlpKX777TcAdScH2traWL9+PdavX4/y8nLU1NRg48aNSEtLwzfffNPkwteiyMvLw9fXF2PGjEFoaCh2794tdCeFYRgEBQXh6NGjmDdvHmbMmEF1XN5xXC4X6enpiIuLQ1xcXKOdA21tbeHs7AwnJ6dmBcT5718RERGIjo4WewwDAwM206o5mVztISMjA0FBQYiOjhYK3uvq6uKDDz7AhAkTIC8vD29vb3bdzJkzMWDAAGhoaKBLly7Q0NCAoqIiLly4gNDQULF3HAcPHgxfX194eHjQ32Q7Kisrw6lTpxAcHCww7YBv+PDhCAgIQL9+/ZCYmIjo6GhcvnxZoPnAvXv3cO/ePWzfvh3GxsZwc3ODq6sr+vbt26F/5wkh768PP/xQ4PHGjRvbaSTSw+PxcOHCBaHl4m60vU2XLl1E3jAWx8jIqEXHaa361xaiAlg6Ojrs922RgcXj8SQWwDIzM8OsWbMQGBgIOzs7gXVffvklEhMTsX37dpw8eRLR0dH46quvmt1MizSOAljvIR8fH6HAlaGhoUCRdXNz80bbgkpL/Ys2SQav+Dw9PXHp0iW2DgxQF0ziB7DS09Pxyy+/YOnSpfj333/x119/Ce1j48aN2LhxI5YsWYKZM2e2uCifoqIiFi1aBC8vL/z0009IS0sDUNcRcfny5XB2dsaXX37Z7C4lH3zwAUpKSrBz504AdVkXGhoaOHjwIFauXImsrCwAQExMDG7fvo0NGzawUytbSl5eHpMnT8b48eMREhKCf/75BwUFBQLbVFVVYevWrTh48CAWLlzIXoiTd0NFRQXbOTAxMfGtnQOdnZ0xatSoZgVQGYbB7du3ERERgaioKLx48ULkdrq6umzQqqNnFPF4PCQlJWH//v0i79KampoiICAA3t7e7N/L4cOH2a4+3bt3x5IlS9j3odu3b2Pv3r04d+5co5mlM2bMwGeffSaFV0SaKi8vD4cOHcLJkyeF/q/k5OTg7e2NgIAAgdogfn5+8PPzQ0lJCRISEhATE4MLFy4IZFE/fPgQu3btwq5du2BkZMROM7S0tOzQfwuEkPdHWFiYwOOFCxe+k7UWRdWYBCB0jtwUysrKzT5vtrGxafZxJKF+Jt3bMrBa8rNortTUVLHnjM312WefQVZWVmQSiIyMDBiGgYqKCgICAhAQECCRYxJBFMB6D02cOBEyMjLIz8+HhYUFBgwY0K4t4usHraRVA6u+f/75B4sWLWIf1y/UnpaWBh6PBxkZGcydOxdz5szB0aNHRd4V+uuvv/DXX39h9uzZWLhwYZO7oTXUu3dv7NixA2fOnMEff/zB3pWJi4vDxYsXsXjxYkydOrVZH+wLFy7E69ev2VoCJ0+ehIaGBvbu3Ys//vgDR48eBVDXEXHu3LlYvnw5pk+f3uqLG0VFRUybNg1+fn44duwY/v33X6G7TMXFxdi4cSOCgoKwdOlSuLu7Sz2zj0hHcXExEhIS2N/VxjoHOjo6wsXFBcOGDWtWcJxhGGRnZyMyMhIREREia64BddNzXV1d4eHhgSFDhnT436nKykqcPXsWQUFBImsO2tnZYebMmRg+fLjA32VNTQ0OHDjAPg4ICEB5eTnOnTuHkJAQ3L17V2hfHA4HdnZ2uHjxIrvs4MGDGDhwINzd3SX8ysjb3LlzBwcOHEBERARqa2sF1qmrq2Py5MmYOnVqo52jNDQ04OPjAx8fH5SVlSE5ORnR0dFITk4WKN7/5MkT7Nu3D/v27YOBgQFcXFzg5uYGKyurDv83Qgh5NzEMg++//15gGb/u7Lvmxo0bEtuXqalps6fbtVcAS0NDg/2+I9TAahgwbalRo0bB1tYWAPD3338Lrbezs6MbRW2AAljvIQ6HgwkTJrT3MEQSVVRY0nbu3CkQwOrTpw80NTVRXFyM4uJi3L9/n73jzeFwMHXqVEyZMgWnTp0S+sAFgL1792Lv3r2YNGkSli1b1qIufxwOBz4+PnBwcMCmTZtw6tQpAHVZLb/99hvOnDmDNWvWwNLSssn7++KLL1BaWsrWydm7dy80NDSwatUq2NjYYN26dSgrK0NNTQ1+++03pKam4rvvvkOXLl2aPf6GlJSUEBgYiIkTJ+Lo0aPYs2eP0B2YZ8+eYfXq1dizZw8++eQT2Nvb05t+J/DixQuBzoHi/ma7desm0DmwuZmKOTk5bKbVgwcPRG6jrq4OV1dXeHp6wsbGplO0KC4sLMTRo0dx7NgxoY6rsrKy8PLyQmBgoNh085iYGIFpZhcvXsT//vc/kXUMjYyM4OvrCx8fH3Tt2hVlZWVYtGgRMjMzwTAM1qxZgy5dumDYsGGSfZFECI/HQ3JyMg4cOMBm29ZnZGSEGTNmYPz48c2ezqmqqgpPT094enqioqICKSkpiImJQVJSEsrLy9ntnj9/juDgYAQHB0NXVxcuLi5wdXWFtbX1O5n5QAjpmP744w+Bxxs2bHhnz/9EFXBvKVNTU9y6datZz7G2tpbY8ZvjbRlY9QNY0q6BVVpaitjYWIns64svvgBQN6tEVHkGV1dXiRyHNK7jn+2Td179rKu2KkxYW1vLnrDLyMhg6NChbHHj1NRUoXauHA4Hvr6+GD9+PKKiorBq1SqhfR4/fhzHjx+Hl5cXPv/88xZltWlqauK7776Dj48PfvrpJzx8+BBA3R37mTNnYurUqfj444+bFCSTkZHBt99+i9LSUiQlJQEANm/eDA0NDUyYMAEWFhZYtWoV+2GYkJCA6dOnY8OGDRg4cGCzxy6KiooKZs+ejUmTJuHQoUPYvXu30IX2vXv3sGTJEtjY2OCTTz5p9XRGIlkMw+DBgwdsPavGOgf26dMHLi4ucHJyQr9+/Zp9Qvr06VM200pUJhFQ9zvl7OwMDw8PDB8+vNNMQ83NzcWBAwdw+vRpoUw1NTU1TJgwAdOnT290yjDDMPj1118FlvGnP/MpKCjAzc0Nvr6+sLGxEciyUVVVxZ9//ol58+YhNzcXNTU1WLFiBXbt2oX+/ftL4FWShviZdsHBwWzL8PqGDBmCwMBAODg4SCSIpKysDDc3N7i5uaGqqgqXLl1CdHQ04uPjBS4iXr16haNHj+Lo0aPQ0tKCs7MzXF1dMXTo0E7zN0UI6Xy4XK5AFjEAeHh4tNNopC85OVli+9LT0xPK2n2b9uoC/rYaWA0DWAzDSC2IGR4eLnaGQHOsW7eObTDU8NwLqJtaSMXa2wYFsEinIyMj0+pMrQcPHggEqRoGsD744AORz+NwOPDw8IC7uzuSkpLYDoX1hYeHIzw8HKNGjcKqVatgaGjY7PHZ2Njg8OHD2LdvH3bv3o3q6mowDIPDhw8jOjoaK1euhIuLy1vf7OXl5bFx40YsXbqUvVOwfv16qKmpwcPDA3v27MHmzZtx8OBBAHXZNfPmzcOSJUsQGBgosSkmampqmD9/PqZNm4YDBw5g165dQtukp6dj5syZcHFxweLFi2FsbCyRY5Pm4/F4yMzMZDsHiprixle/c2DPnj2bfay8vDxERUUhIiICmZmZIrdRUlKCg4MDvLy8MGLEiBZP121rDMPg2rVr2L9/PxITE4WmSHfr1g0zZsyAn59fo52XuFwuEhMT8dVXX4ntGNuvXz+2oUJjdcW0tbXx999/Y86cOcjPz0dFRQWWLl2KnTt3ok+fPi17oe2ksLAQe/fuxa1btzBv3jzY29u395BYjWXaycjIwNXVFYGBgVIN2CsqKsLR0RGOjo7gcrlITU1lg1n1p3YXFRXh5MmTOHnyJNTV1eHk5ARXV1cMHz4cCgoKUhsfIeT9s2TJEoHHDYNZ75KSkhKJZhe1pJN3e00Vf1sXQjU1NSgqKqKqqgpVVVV48+ZNq5pKNYY/q6W16genzpw5I7Q+MTERffr0wfDhwyVyPCIeh2mLokOdTGBgIC5fvoxhw4YhKCiovYfzztu1axe2bdvWpG19fHzw/fff4969e5g6dWqLj+nu7i5Q1+rhw4eYOHEigLo31bi4uCbdDWcYBhcuXBD6QK5v4MCB+Prrr1t8cfj48WP8/PPPAvVrAMDBwQErV65sUoDszZs3WLBgAe7cuQOgrkDwn3/+yV7wxcfH45tvvhH4kBk1ahTWrVvXog/MtykuLsb+/ftFdnvk8/Pzw8KFC5tdxJ60DJfLxZUrV9iglbiTLllZWYHOgY3V6RGnoKAA0dHRiIiIwLVr10RuIy8vj1GjRsHT0xMODg6dqkteTU0NYmNjERQUJDIoZ2FhgZkzZ8LV1bXRaY8PHjxAWFgYzpw5IxQEAereq8aMGQN/f3+Ym5s3a4zZ2dmYN28eW5tCXl4e8+fPx6xZszp8Bk5lZSXWrFmDuLg4dlm/fv3YQHx7evDgAQ4cOICzZ88KBRtVVFQwYcIETJs2rUU3NiSlpqYGV69eRXR0NGJjY8UW0FVVVYWDgwPc3Nxgb2/fqf4GCSEdz+vXr+Hs7Cyw7MqVK+00GulLSUnB0qVLJba/H3/8EWvXrm3Wc9rr58swDIYOHcreuGvYQAuou6Z7+vQpAODEiRNSuXF9//59TJ48WSL7Wr9+Pby9vVFQUABPT0+xyRQeHh748ccfO0VZi/bUmngLBbBEoABW26ofwJo7dy769u2Ln376SWTRv4EDB7JBj/pBp5YwNTWFgoICFBQUIC8vL9D1UEZGBiEhIWyqaFNcvnxZoLZWQ3369ME333wDKyurZo+VYRhERETgt99+Q2FhIbtcSUkJCxcuxAcffPDWi87CwkJ26hD/udu3b2enCz579gyrVq1CRkYG+5yuXbtiw4YNGDx4cLPH3BSFhYXYt29fo39nM2fOxJw5cyRSm4sI4ncOjI+PR2Jiosi/OaDud4XfOdDBwaFFd8mKi4sRGxuLiIgIpKWliWzYICsri+HDh8PT0xNOTk4tqifXnsrLyxEWFobg4GCBOlV8Dg4OCAwMhI2NjdjsyfLyckRFReHkyZMiO9zwffTRRwgMDGxVt9gbN25g0aJFqKysZJeZmJjg66+/xqBBg1q8X2nh8Xg4e/Ysvv32W6F1FhYWCA4ObodR1b0/p6amIigoCCkpKULru3XrhunTp8Pf37/D/U7zeDxcv34dMTExiImJQV5ensjtlJSUMGrUKLi5uWHUqFFQUVFp45ESQjo7JycntoMuUDdjoWvXru04Iunatm2byBkHLdG9e3cEBgZiw4YNzXpeewYIR48ezZ5XxsTEQEtLS2D9nDlz2BphO3bsYIujS9Iff/wh0Wv5zz77DL///vtbt9u3b1+LrvfeJxTAkjAKYLWt+gGsefPmYfHixXj58iW++eYbXL58WWj79PR09uLP29tb7Al3a3E4HJEF+t4mLS2t0W4qXbt2xXfffdeiThUlJSXYsmULTpw4IbDc1NQUa9eufWvtqhcvXmDOnDnsz0xNTQ3//PMPO52Sy+Xir7/+Evi9l5GRweLFizFr1iyppSLn5+fj33//xeHDh8Vus3TpUkybNo2yAFrp9evXAp0D63csq69Lly5wdHSEq6trszsH8pWWliI+Ph6RkZG4ePGiyNoNHA4Htra28PT0hIuLS6cMVObn5+Pw4cM4duyYUKq8vLw8xo4di4CAAJiYmIh8PsMwuHnzJkJCQhAZGfnWWoBubm745ZdfJDL2u3fv4vvvvxeqbTZp0iQsXbpUain9zXX58mX88ccfyMrKErlekj+TpuJyuYiIiMCBAwdE1mzr378/AgMD35pp11EwDIPMzExER0cjJiaGvTPekIKCAuzt7eHq6orRo0d3mN8RQkjH1fCms6mpKdsR+121cOFCgZvjreHk5AQTExPs2bOnyc/R19fHuXPnJHL8lhg3bhx7My8sLEwoKeDLL79ky7fws5skicvlwtvbW+DGf1swMTFBUFAQXa+8BQWwJIwCWG2HYRgsXryYnR63aNEiNvjD4/Fw8OBBbN68GTU1NexzLC0tsWnTJujo6CAyMlJkQXVJac2diytXrmDp0qViL0ZVVVXx7bffwsXFpdmBoevXr+PHH3/E/fv32WUcDgeTJk3CkiVLGr2gyMnJwbx589gpSTo6OtizZ4/AB0tiYiK++eYbgTtlI0aMwLp161pUnL6p8vLysGfPHhw7dkzsNmvWrMH48eM7/DSnjiQvL4/tHJient5o50AnJyc4OztjyJAhLbrorqioQGJiIsLDw5GSkiLwt8vH4XAwZMgQuLu7w83NDTo6Os0+TkeQnZ2NoKAgnDt3Tuh1amhosB1Mxb2+wsJCnD17FmFhYWI7LQJ1hVv5RU4BYP/+/RKtnVRTU4MjR45g69atAu9XOjo6+OKLL+Du7t5uHaKys7Px559/isxsqm/WrFn45JNP2mRMJSUlOHHiBA4dOiQ01ZbD4cDR0RGBgYEYMmRIp+2sxTAM7t69i+joaERHR7OZuw3JycnBzs4Obm5uGD16tFSmnBNCOr+G3fBSUlLe6Qv82tpaiWYULViwAM+ePRNZe0kcb29vrF+/XmJjaK5p06axN3cOHDgg1Cxm48aNOHLkCABg+fLlmDlzpkSPn5iYKLJWsaQpKytj+PDhmDRpEvT19dGrVy/q7NsErYm3dPxbguSdFhUVxQavOBwOnJyc2HUyMjIICAiAnZ2dQL2rzMxM+Pv7Y/To0VIv9P3HH3/go48+alH2ibW1NVJSUnD16lV8+eWXQnVGysrK8OWXX4LD4eCbb77BmDFjmhyUGTRoEA4ePIjg4GDs2LEDVVVVYBgGx44dQ2xsbKMXnb1798Zff/2FBQsWoKysDAUFBfjoo4/w77//svWMHB0dcfjwYaxatQo3btwAAJw/fx7Tpk3Dzz//DBsbm2b/PJpCX18fq1evxsyZM7Fr1y6RhRfXr1+P9evXY8OGDe16Yd3R1e8c2FjbZRMTE7ZzoIWFRYt+npWVlUhJSUFkZCQSExPFZnUNGDAAnp6ecHd377TTBvjTxfbv34/z588LrTcyMkJAQAB8fHygrKwMhmFQWlqKV69e4dWrV8jLy8OZM2dEZpeKk5+fz35vY2Mj8cLfcnJymDFjBlxcXLBhwwa2a2lBQQFWrVqFs2fPYtWqVTAwMJDocRuTn5+Pbdu2ISwsTOR004aMjIykPqbHjx8jODgYp06dEph2CdQVTB8/fjxmzJjRomYGHQ2Hw4G5uTnMzc3x8ccf4/79++w0w+zsbHa7mpoapKSkICUlhe3o6+rqChcXl04bmCaESFbDGq5jx459p4NXAARuMEuCmZkZrl692qznDB06VKJjaK63dSKsX0NVXC3G1ggLC5P4PhuSRuCNvB1lYIlAGVhto7S0FBMmTGDftKZOnYqVK1eK3PaLL75ATEyMVMbh6OgIS0tL7Nq1S2S2SK9evfD999+/dXre21y7dg3ffvstHj9+LHabzz//HP7+/s36YH/69Ck2bNgglKFgb2+P1atXi72wS09Px+LFi9nWsiYmJvjnn38EpnBxuVxs27ZNoNg6h8PBokWLMHfuXKnfYXj8+DG2bduG8PBwkevl5OSwZcsW2NnZSXUcnQGPx8OtW7fYIuziMiaA1ncOBOp+Ny5cuIDIyEjEx8ejvLxc5Hbm5uZs0Kp79+4tOlZHwOVyERkZiaCgIJHTxYC6OkwDBgxAQUEBG7B69eqVRNo3A3VB/Z07dwrdyZYkhmEQHR2NX3/9VSC7SFlZGR9//DGmTZsm1b/78vJy7N+/H/v37xcIEnE4nEYDWdu2bZPa+8D169cRFBSEuLg4oTHo6Ohg6tSpmDRp0nuTffTo0SPExMQgOjpaaOopHz/Lkh/MomYchLy/Gn5mpaamvvMZKkePHhVZr2rChAk4efJki/YpKysrshSDOEeOHBHouN7Wli9fjsTERADAb7/9BhcXF4H1YWFh+P777wEAXl5e+OmnnyR27MLCQnh6ejbr59Vcv/zyi0BnQtI8NIVQwiiA1TY2bNjAzn9XVlbG6dOnxU5P2717N7Zu3SrxMUyePBmrV6/G4cOH31o/ZebMmfjoo4+gqKjYqmPeuHEDP/30k9iLYKBu3vy0adOaXA9I3EWngoICFixYgMDAQJHZXYmJifjss8/YN/gBAwZg+/btQgV6U1JS8PXXXwu0Xh82bBjWr1/fJnfZc3Jy8OeffyIhIUHkej09Pfzxxx9C6cnvOi6Xi6tXr7JBq/qZOvXJyspi6NChcHZ2hrOzc4s6BwJ12RZpaWkIDw9HXFyc2KLvJiYm8PDwgKenJ3r16tWiY7WXyspKNgCVn5+P3Nxc7Ny5U6ijnDRwOBzo6OhAV1dX4EtHRwd6enro169fm3WvKy0txV9//SU0ndfCwgJr166FhYWFRI9XU1ODsLAwbNu2Tahehb29Pby8vPDdd9+JDWKdOnVKollY/E6SwcHBIovp9+nTBwEBAfD29oaCgoLEjtvZPH36lM3MaqzpgJWVFVxdXeHq6tqpA9mEkOY5cuSIQNfvTz75BLNmzWrHEbWNtWvX4r///hNYZm9vj7y8vEZLBkhSQkJCu9Yo/Prrr3H27FkAwLfffgtfX1+B9efPn2e7uA8dOhQ7d+6U2LGDg4PZYutqamoiM8CaSk5OTijBQVtbG+Hh4Z2ivmVHRQEsCaMAlvTV1tZi1KhRAlONlJWVMXToUIwYMQIjRowQqMkUHR2NL7/8EkBdRtSKFStw69YtxMXF4d69e60ay7Jly7B58+Ymbz9nzhz0798fycnJCAsLg7q6OkaMGIHx48fDzs6uyfWsbty4gd9//73Rk/7AwEAEBAQ0OeBQWlqKv//+G8eOHRO40DMxMcGaNWswZMgQoeecO3cOa9euZbcfNmwYNm/eLHRR9vLlS3z11VcCdcF0dHSwfv16DBs2rEnja63s7Gz8+uuvYotimpubY+PGje/EFB5xKioqcOHCBcTFxb21c+CIESPYzoEaGhotOh6Px8OVK1cQFRWF6OhotnZaQ0ZGRvDy8oKHhwdMTU1bdCxp4U/jy8/PFwhOFRQUCCx79epVq05ymiswMBDDhg1jA1Wampod7q709evX8cMPPwiccHM4HMyYMQOLFi1qdTc6hmGQlJSEP//8Ew8fPhRY17dvX6xYsQJ2dnZYuHAh0tLSRO5DRkYGFy5ckEhdvLKyMoSFheHgwYMiO0na29sjICAAw4cPp+nLDeTl5SEuLg7R0dG4evWq2GCjhYUFG8zqbAFuQkjT8Xg8oWls7dkVry15enoK3VRsagc7Sanf9Ko91K9x9dlnn2HGjBkC6+/du8eWiOndu3eLM9MaYhgGU6dOZae7u7q6SnwWzwcffIDPP/9covt831AAS8IogNU23tadw8jIiA1m9erVC5MmTWIzhbS1tbFhwwYMHToUP/74o8Te9FrL0tIS3333Hfr06dPk59y8eRNbtmwRe3EGAP7+/pg9e7ZQBw9xMjIy8OOPPwplefn5+eGTTz4RyuxqeIeMXwun4Z2Fmpoa7Ny5E//88w97ccLhcDB//nzMnz+/zS6+s7KysG7dOrHTV4YPH47vv/++xZlGHc3r16+RmJiIuLg4XLhwQWyNKQ0NDYwePRouLi6ws7NrUe024P+64kVERCAqKkqoUDVft27d2Eyrfv36tfmJUk1NDYqKipCfn49Xr16JDEjxv6SRQaWhocEGn5SUlJCeno6ysjKR23I4HNjb28PX1xdOTk6dqgkBl8vFvn37sGvXLoGfY7du3bBq1SoMGTIEFRUV7Fd5ebnIx5WVlQLLKyoq8OLFC6EabXp6eli8eDHGjh0LWVlZJCQkYMWKFWLHZ2ho2KzCtqLk5eXh0KFDOHnypFAQU05ODt7e3ggMDOxwwdmOqqCggA1mpaWliW0cYWpqChcXF7i5uaFPnz4UFCTkHbJ+/XqBrtmbNm2Co6NjO46obRQUFMDd3V1o+d69ezF79uw2G0d7Bwu3bt2K3bt3A6grQr9o0SKB9YWFhewUPDU1NXa6YWvdunULAQEBAOrqU/bt2xcZGRkS2TdfcHCwxDPR3zcUwJIwCmC1DX4NnfPnzyMlJUVsy26gLmAlIyMjcCEtIyODJUuWoFevXvjss8/aYshNIicnh2+++Qbjxo1r1vMyMjKwbds2XLhwQew27u7umDt3LszNzd+6v5qaGhw+fBjbtm0T6CympaWFTz/9FGPGjBG4WNi1axe2bdvGPh4/fjy+/fZbkRcUFy9exNq1awWm+tjY2OCnn35q06BRZmYmVq1aJfZ3x8vLC6tXr+6Ubd5fvnyJuLi4t3YO1NfXh5OTE1xcXFrcORCoC1rduXOHDVo9f/5c5Ha6urpwd3eHp6cnrKyspHLBWVFRIRSQEhWkKioqalKBb0mwtLREYGAgDAwM2Gl98vLySE9PR1hYGKKjo0UGFg0MDDB+/HiMHz++TYugS8OjR4+wfv16ibUFb0hFRQVz5szBBx98wNYB5HK5mDRpEls70NHRUegk19bWFjt27GjRMW/fvo2goCBERUUJ1crQ0NDAlClTMGXKFOjq6rZo/wQoLi5GQkICoqOjcenSJZG1JoG67Go3Nze4urrC3NycglmEdGJVVVWwt7cXWNbeAZW2EhcXJ/K65OLFizh9+nSbdQZs75/3vn378OeffwIAZsyYIfQz4fF4sLOzYz97z58/3+Ibr/XVL1Fjb2/f6HVVS5iYmODYsWP0GdVKFMCSMApgtY/Hjx+zway0tDShLk/iNLeoYVuZO3cujIyMYGhoiO7du0NfX79JwYWMjAzs3LkTycnJYrcZOXIk5syZ06Rizi9evMAvv/yC+Ph4geW2trb46quv2CkcDMPgf//7H4KDg9ltAgMDsXz5cpFv0vn5+VizZo1A5pi2tjZ+/PFHDB8+/K3jkqQbN25g8eLFYrNfPvjgAyxZskQiH4zS9PDhQ7ZzYGZmptjtTExM2HpWLe0cyJednY3IyEiEh4fjyZMnIrfR1NSEu7s73N3dMWTIkBZn2pWWliIvL09sQIqfLSXu/7E1lJWV2VpS/ACUhoYGUlNTxXb2GTp0KAIDAzFy5EiBqcH5+fk4c+YMQkJCRP7M5OTk4OLiAn9/f9ja2jZ5WnFnwDAMzpw5g//97394/fq1RPYpIyODiRMnYuHChUJ1EA8ePIjffvsNQN0d2g0bNrA1M/j8/f3x9ddfN/l4PB4PycnJCAoKQnp6utD6Hj16YMaMGWwnSSI5paWlSExMRHR0NM6fPy82M7J79+7sNMMBAwbQhQIhnczMmTMFsl7au6B4W9q0aRP2798vsExRUREXLlxAbW0tbG1tJXasadOmITY2Fi9fvhRYrq2tjejoaIkdpyVOnjyJH3/8EQDg4+PDFmyvz8vLix27JGpZVlVVwcPDQ2x5DUlYunQp5syZI7X9vy9aE2+hymOkw+jRowemTp2KqVOnorq6GteuXUNKSgoiIyORl5cn9nkdMXgFAHv27BF4LCMjg27duqF79+4wNDSEkZGRwPdaWlrgcDgYMGAANm/ejFu3bmHHjh1sS/v6+G3LBw0ahDlz5sDBwUHsCX63bt3wv//9D/Hx8diwYQP7QZGamoopU6Zg7ty5mDNnDhQUFLBixQqUlJTg9OnTAICgoCB06dIFc+fOFdqvnp4etm3bhl27dmHXrl1gGAaFhYVYvHgx5s6di4ULF7ZZccOBAwciKSkJ6enpmD9/vtD6gwcP4uDBg/j4448xe/bsDlN0kWEYZGZmskGrnJwcsdsOGDAALi4ucHZ2bnXdmNzcXERERCAyMlJsMVE1NTW4uLjAy8sLQ4cObdHPjMfj4c6dO0hMTERSUpLYKZ+toampKbLgecPv69dqKi4uxvHjx3H48GGhguEyMjLw8PBAYGCgQHo4l8tFSkoKQkJCkJKSIjIjztTUFP7+/vD29n5nO9JxOBz4+Phg1KhR+PPPPxETEwOGYaCsrCzyS0VFReB7JSUloW3MzMzQtWtXoWO9fv1aILNq/vz5In/uTT3hraysxJkzZxAcHCyyS6e1tTUCAwPh4ODwTgUdOxJ1dXWMHTsWY8eORVlZGVJSUhAdHY3k5GSBm1ZPnz5lu1Hq6+uz0wwHDRpE/zeEdHAFBQUCwSt5efn3JngF1N1UbYhfwLyxDtEtsWTJErbOVH0t6VZcVVWFf//9F6WlpfDy8mr1zQM1NTX2e3E3JvX09NjrklevXrU6gJWQkCDV4BWHw4G3t7fU9k+apmNcxRHSgIKCAoYNG4Zhw4ZhyZIlCA8Px759+97auYPD4UBVVRWysrISyw6QFB6Ph2fPnoksDAzUFd3mZ2vxv/z9/eHq6oqzZ8+KnLZz/fp1LF++HKamppg7dy7c3NzEBhqcnJxga2uL7du34+DBg2AYBlwuFzt27EB4eDi++uor2Nra4uuvv0ZpaSmbsfXXX3+htrYW8+bNE7pwkJWVxaJFi2BjY4OvvvoKBQUFYBgG//zzD65cuYKff/5Z5IWptNjY2CA9PR2XL1/GRx99JLR+69at2Lp1K9asWYMJEya0y139mpoaXLlyBbGxsYiPjxe6a8YnKysLGxsbNtOqtT/Hp0+fIioqChEREcjKyhK5jYqKCpycnODh4QF7e/sW1WmqqKjApUuXkJiYiOTkZLH1sxojJycn1I2PH4zS09ODtrY2+29zxvj48WMEBwfj1KlTQhmeKioqmDBhAqZPny4w1S83NxehoaE4c+YMCgoKhPapqqoKb29v+Pr6on///u9NpoiWlha+++47fPfdd1I7xq5du9gTUSMjI0ydOhXh4eFC272tq11BQQGOHj2KY8eOCXRSBeoClu7u7ggICIClpaXExk7eTlVVFR4eHvDw8GCbU8TExCAhIQHl5eXsdvz6ZIcOHYKOjg5cXFzg6uoKa2vrDnMzghDyfxpe4Dfsxvcu43K5uHbtmtDywYMHAwBbE0oS5OTk8OrVK5GlFGxsbJq9v0OHDrGdAA8dOgQNDQ1MnjyZbc7T3POb+uU7xAWV6ncyF9dJuzlOnTrV6n00pKGhgZKSEgB1P9du3bpJ/BikeeiTn3R48vLy8PHxwdixY5GcnIw9e/aIvLthZWWFhQsXwt7eXuhN9uHDh5g6darY2hsdQWVlJR48eNCi9rrZ2dn46quv8Pfff2PWrFnw8fGBoqKi0Haqqqr47LPPMGbMGKxfv54tnpybm4uFCxdi3LhxWLFiBX7++WcsXbqUnR64bds23LhxA99//73QFB+gbjri4cOHsXbtWly6dAkAcPXqVUybNg0//PADRo4c2ezX1FIcDgd2dnZIT09HUlISli9fLrTN+vXrsX79evz6669wdXWV+pgqKipw8eJFtnMg/4OwIX7nQCcnJzg6Ora4cyDfy5cv2aCVuAKWioqKcHR0hIeHB0aOHNmiaZbPnj1DUlISkpOTcfnyZbHTgmRlZdG9e3fo6ekJBajqf3Xp0kWigaAbN24gKCgIsbGxQid6enp6+OCDDzBhwgT2ZKuiogJRUVEICwsTO73Q2toavr6+cHNzo2lmUpCbmytwV/mTTz6BgoKCyC6Y4gJY9+/fx4EDB/Dff/8J/U6qqqpiwoQJmDZtWqevTfYuUFZWhouLC1xcXFBdXY2LFy8iJiYG8fHxAhc+BQUFOHbsGI4dOwZNTU04OTnB1dUVw4YN61SNEQh5V927d0/gXNvKykogSPGuu3PnjsjlVlZWAIDq6mqJHaumpoatD9mQsbFxs/fXMDBTUlKCf/75B//88w+MjY3h7u4ODw8PmJiYNGl/Tc3A4hN1k7A5Xr58ifPnz7dqH6KoqKiw5+1jx46V+P5J81ENLBGoBlbHd/XqVfz7778i60Tp6uqCw+GAy+Wiuroa1dXVHTpwJW0mJibo3bs3evXqBSMjI6ipqUFdXR3Kyso4d+4cW+iQT0NDAytWrICrqyuWL18uUARSRkYGFhYWsLa2ho2NDQYPHiwQZOHxeNizZw+2bdsmECiYNWsWPv7443a5wGAYBpGRkVi9erXYbXbu3CnU6rm1SkpKBDoHiqvppqGhAUdHR7ZzYGuDIYWFhYiKikJUVJTYVvZycnIYOXIkvLy84ODgIDC9rilqa2uRmZmJxMREJCYmsq2KRenSpQtGjRoFBwcH2Nvbt1lB/draWiQkJGD//v0iA95mZmaYOXMm3N3dIS8vz07nDAsLQ3h4uMiTLR0dHfj4+MDPzw89e/Zsi5fxXuLxeFi6dClbeNXa2hq7du0Ch8MRWVskLi5OoLNqTk4Ofv/9d6SkpAjt28DAANOnT4efn5/AyTVpOh6Ph7Vr1yI5ORkaGhpsVvGUKVPwySefSDSgy+VykZaWhpiYGMTFxYkMYAJ1F0pOTk5wc3ODnZ2dyBs4hBDpazh17cKFC+/V32NwcDB+//13oeWRkZGorKzETz/9hIsXL0rseCtXrhToIl7fgAEDoK2tDW1tbejo6LD/amlpsd9raGiwsysYhsHKlSvfWjvL1NQUHh4eGD9+fKOzA3JycjBhwgQAdVnUorKjtm/fzmZ9zZkzB0uXLm3S6xZl3LhxYme5tJStrS07A0ZBQQHR0dF07iAhVAOLvHeGDBmCIUOG4N69e9i3bx/Cw8PZ2igtmbL0LmtuVldJSQm+//57fP/990KZCTweD5mZmcjMzBR4s3FwcMDo0aPh4OCADz/8EEOGDMHq1avZ/4t9+/bh6tWr+Pnnn9s824HD4cDT0xPu7u4IDQ1lC0rWt2DBAgB1KdNN6fAoDr9zYFxcHNLT08XWZ+vatSvbOVAS02Bev36N2NhYREREIDU1VWTQSlZWFsOHD4eHhwecnJyaHUgqLy9HSkoKm2nVcCpWfX369GF/JwYMGNDiou8tUVFRgVOnTiE4OFhkgfURI0Zg5syZsLW1BYfDQXFxMf777z+EhoaKDMTJyMhg1KhR8Pf3x8iRI2nKUhs4cOCAQNegTz/9lM3Ia1izTE1NTShT8csvvxT6v1RQUMC6devg4uJC/4etlJ+fz07lfPPmDbv86NGjePjwYYs7QooiLy8Pe3t72NvbY/Xq1bhy5QpiYmIQGxsr8Fn/5s0bnDlzBmfOnIGysjIcHBzg5uaGkSNHdugMydLSUkyfPh3Pnj3DkCFD8PPPP0NVVRW5ubnIycmBs7Nzhx4/IfU17BA7adKk9yp4BdSV9mhISUkJY8aMQW1trcQ7J4vLEgcgNvO+PllZWWhpaUFLSws6Ojpsdnxj11LZ2dnIzs7GP//8g1mzZmHWrFki36eam4HVmuu3o0ePSjx4JSsrK1DP1MnJiYJXHQRlYIlAGVidz9OnT3HgwAGEhoaKbGUP1GWdyMnJic2EMTAwgJGRERQUFETeuSeS0b9/f3h7e0NNTY3NBlNRUYG6ujrU1NSgqqoKRUVFqdUS4vF42L9/PzZv3ix2m+Z0QsnJyWGDVo2dLBgbG7P1rCRRK+nNmzeIj49HREQELl68KDJYxuFwMHToUHh6esLFxaXZhcX5WUkhISEIDw9HRUWFyO3k5OQwbNgwODg4YNSoUW+tSSQN/DpHR48eFap/JycnB29vbwQGBsLU1BQ8Hg+XL19GSEgI4uLiRGZo9ujRA/7+/hg7dqzACRaRroyMDMyZM4f9fQ4MDMSKFSvY9UuXLhV4fzY3N8ehQ4cE9uHi4iIUYO0IHZneFQzDYPLkyWJvjLRF63Yej4ebN28iOjoaMTExePHihcjtFBUVMWrUKLi6umLUqFEd5uKjqKgIv/76q8iabg3NnDkTCxcupEAW6dAYhhGqu5SWlvZONV04deoUTp8+jV69emH48OGws7ODqqoqHjx4gClTprTLmGRkZEQ2N5GUwYMH47fffkNaWhoiIyORnJwsNA2ya9euWLZsGby9vQXObSsrKzFixAgAdedhly5dEjr3TUxMZEt92Nvb4++//272GC9fvoxFixY1+3lv4+npifT0dDaw9ueff8LBwUHix3lftSbeQgEsESiA1XlVVFTg1atXkJeXh4KCgsC//A/R4uJizJgxA8+fPxd6vrKyMkxMTGBsbIwzZ8609fDJ/ycnJ8cGs/iBLlVVVairqwssE7VOVlYW2traAmnRotTW1mLr1q34999/Ra5XVlbGqVOnhGo3MAyDW7dusUGrhw8fij2GpaUl2zmwd+/eLfpZ1FdRUYGkpCREREQgOTlZbK2pwYMHw9PTE66urtDV1W32cUpLS/Hff//h5MmTuHfvnshtdHR0MGrUKDg6OsLOzq7Z0xAl5cGDBzhw4ADOnj0r9PNQV1fH5MmTMXXqVOjp6eH58+c4deoUTp06JfLvX1FREe7u7vD19YW1tbXEgqg8Hg+5ubnIyMhAZmYmMjIy8OjRI2hra6NHjx4wMjIS+Ld79+5QUFCQyLE7k/rZKEDd38+ePXsEph4HBASwtfsAwNXVFb/++qvAfiZOnCjy79LExATbtm2jgKQE3L59GwEBASKzCX7++Wd4enq22Vj4gfaYmBjExMSIzLwE6rK5hg8fDldXVzg5ObW6xqA4RUVF2LZtG27fvo27d++iS5cuWLhwISZOnMiOd+bMmcjMzGzyPhcsWCCVCzRCJGXv3r0CNwZXrlyJqVOntuOIJOv169dwdnZu72EI4J/ficpcGjZsGBYuXIjCwkIUFBSgsLAQRUVFAo8LCwsFsmjrU1JSwuLFizFt2jSBLPqysjIkJCQgKChIqCGQlZUVvvjiCwwYMABA3XudnZ0de5Pw/PnzQnVWMzMzERgYCKBuamLDsiZv8/jxY7bDo6QtWLCAnd6ora2N8PBwyuCWIApgSRgFsN59d+7cwYcffijQ6Yi8u0xMTMQGwJSUlLBt2zaxmXndu3fHgQMHcPfuXbZzYF5enshtZWRkYGNjAxcXFzg5OUFfX7/VY6+qqsL58+cRHh6OpKQkseO0tLSEh4cH3N3dW9QhhWEYXLt2DSEhIYiKihKZyWhoaAhtbW0oKSnB2toaM2fObJfAFcMwSE9PR1BQEJKSkkSOMyAgAOPHj4ecnBzi4+MRFhaGixcvirzg7t+/P/z8/ODp6SmRGl35+fnIzMzEzZs3kZGRgVu3bolNnxeFw+Gga9eubECLH9TiB7naqo5YW2IYBqtWrUJUVBSAuiLrhw8fFsrmGzNmjEC2zcyZM4UaNcyfPx/p6ekij+Pg4IBNmza9N90ipenFixcYM2aM0HIbGxvs2rWrHUZU93t07949xMTEIDo6WuwNBllZWQwbNgxubm4YPXq0yOYkLfXrr78KZQV2794dq1atwrlz51rcke3YsWPo06ePJIZIiETV1tbC1tZWYFlbZGK2FYZhsGLFCqEpku3JwsICW7duhbe3t8jzwk2bNsHR0fGt+6murhYKatXU1GDEiBGNlvyora3F6dOn8ddffwlN7R87diyWLl2Krl27wtXVla1dGBkZKXRTNT8/n73hoampidjY2LeOmS8qKgorV65s8vbNYWlpiby8PDY4GBAQgE8//VQqx3pfUQ0sQpqpX79+CAkJQXR0NFsj6v79+2K7w5HOrSWdHfmePn3a6F03RUVFgc6B9YtJtxSXy8XFixcRGRmJ+Ph4scEPMzMztr5XU6c8NlRUVIQzZ84gNDS00WwyZWVl5Ofns9kx/MLK//vf/9CjR48WHbu5ampqEBMTg/379+P27dtC6y0tLREYGAgXFxfk5ORg27ZtCA4OFrs/ExMTzJs3D3Z2dtDU1GzRVIeysjLcunWLzazKyMjAy5cvm72f+hiGQV5eHvLy8kQGYjQ1NWFkZMQGuAwNDaGvr4+uXbtCQ0MDXC6XbWIh7vuamhpUVVWhpqYGXC4XysrK8PDwaLdpSvzAKd/XX38tFLxiGEboRFnUdNXG6rMlJSUhPDxcqM07aT5dXV2oqKgI3QhKT09HRUVFu/wucTgcmJmZwczMDB999BHu37/PZmbVzyatra3FhQsXcOHCBfz4448YOnQo2wWxtRl6DYNXQF1QvTXFiQFg8uTJ71RQgLw7vv32W4HHW7dubaeRSMeRI0c6VPAKAEaPHo3q6mqxNzWb2v1bQUEB3bp1a/aNT1lZWfj5+cHNzQ27d+/GwYMH2Uyrs2fPIiYmBnPmzBGogfbmzRuhAJaWlhY4HA4YhkFxcTG4XG6TGj6dOHEC69evb9aYmyM/P58NXmlra2Pu3LlSOxZpPsrAEoEysN5P/IsjfkDrwYMHKCkpQXl5OSorK1FRUYHy8vJWBUPIu8XKygqTJk1C37590b1791ZlxtTU1CA9PR0RERGIiYkRaB1fn7GxMTw8PODp6dniaYk8Hg9paWk4ceKE2BpQTaWuro6ff/6ZrXPQ0PPnz3HlyhUMHjy4xbWxysrKEBISgkOHDglN/eNwOHB0dMTMmTNhZmaGiIgI/P7772LrdYkjIyMDTU1NVFdXC6TUczgcGBgYoFu3btDS0kJxcTGeP38ukWKhlpaWePHiRatbR0uKlZUVtm/f3uaBh+zsbAQEBLB1NSZOnIg1a9YIbVdVVQV7e3uBZfzfvz59+iArKwtpaWk4cOBAo8fr0qULTpw4IdGsm/fVhQsX8P3334sM2u7ZsweDBw9u+0GJ8fjxYzaYJW76HofDwaBBg+Dq6gpXV9dmXdQVFRVh1apVbMcqaWjYcZOQ9lZRUSEULOnsgdakpCTk5uYiISFBbDZve9u0aRPU1dUxb948oXXjxo3DunXr2nQ8jx8/xqZNmxAXFyd2m/3797PTC+tzc3Njb079999/jb7v5uXl4eDBg216ff777793uOmj7wKaQihhFMAi4rx69QoeHh4i1+3evRtcLhdHjhxBfHx82w6MdBjOzs6YPXs22zRARkYGcnJykJWVhaysrMD3MjIyuHHjBmJiYhAfHy82c8TIyIgNWpmamrZ4+lN+fj5Onz6N0NBQsXVi3sbY2BgDBgxAeHg4W3OKw+Fg6dKlmDVrFjgcDioqKthpe/yuiHJycpg7d67QHTk+hmHYbCB+ttDTp0+xb98+JCQkiByLpqYmpkyZghcvXohsz0yab9iwYfjzzz/brHNURUUFAgMD2RsDffr0QVBQkFCdDL7Zs2fjxo0bzT5OZGQkZs6cyU4/dHd3F9t6nDRPRUUFgoKCsH37dqF1P/zwA8aOHdsOo2rcs2fPEBsbi+jo6EZ/nywtLTF48GAYGxvD2NgYJiYmIgNIly9fxueffy62noykSLO+WF5eHq5du4arV68iMzMT5eXlqK2tFfricDhQUlKCsrIylJWVxX6vrKwMDQ0NmJqaom/fvu/k1GdSlxl4//599vGJEydgbGzcjiNqnfpFxTuyc+fO4dKlS/juu++E1l2+fLndajVdvnwZv/32m8iuzn///bfQTSgAmDZtGu7evQtAdJCrsLAQUVFRiIyMbLTroqQoKiqyZTS8vLzw008/Sf2Y7yOaQkhIG3j69Cl8fHyElhsaGuLQoUPsyZmdnR0YhkFaWhpOnjyJmJiYVmW4kM6FX9xdkioqKnD27FmEh4cLBcH4QbKamhqR0+okxcrKCjY2NtDV1UV1dTWGDx/O1p9iGAabN29utLNjTU0Ndu7cyRbE1NXVFZrS1lzFxcXs/ohkXL58Gfb29li4cCGMjY3Z2lvSKnj966+/ssErRUVFbNiwAUpKSigrK8OTJ0/w+PFj9t/Hjx8LFY1tCktLS+jq6mLt2rVYsmQJgLraGfzunJ1RZWWl2CBfW1NWVsaCBQvg6+srNDVz27ZtHTKAxa+TFxAQgJcvXyIuLg7R0dG4cuWKQJ28zMxMoWwtbW1tNphlbGyM3r17Y/HixSLr60nLmzdvcODAAZibm7OZAbW1tXj69Cnk5OSgr68vUHi5IR6PhwcPHuDq1au4du0arl27JrKxhSQZGhpCR0cHMjIy7A0c/hf/saysLAwMDGBmZgZzc3OYmJgINLS4f/8+Hj9+jDdv3qCmpga2trbt0vWW1MnLyxMIXmloaHTq4BUAoQ57HcH27dtRXl7O1mDS1NRE165d8fTpU6FtR4wY0a6FxocNG4ZDhw4hJCQEW7duFbgxK6pbNgDo6emxASz+tL3Xr18jLi4O4eHh7I3QttCtWzf2Rpe2tja+/PLLNjkuaR4KYBHSBPn5+SKDV3PmzMGHH34oNOWGw+HA1tYWtra2KC0txZ07d3D//n1kZ2fj3r17uH//PhWQJ03WEaaY3bx5Ezdv3pTY/kR1zSGSZWxsjH79+uHZs2d48uRJs36PduzYIfBYQ0NDqJg8v8C8rq5uo1mBjx8/Rnp6OrS1tWFtbQ01NTUwDIOjR48iNDSU3a6qqgo//PADnjx5IlTrqjX409hGjBgBHx8fnD59GkBdNouNjU2nmpLFMAz+/PNPBAUFwdTUFJ9//rlQ8eT2kpGRIbSsM7Qc79q1K6ZOnYqpU6eisLAQcXFxiImJweXLl0W2p+cXOm5sapGcnJzEb1x5eXmhqKgIU6dOFdsdtv7xDQ0N2Vp5RkZG6NatGx4/fswGraSdLdbQs2fPmj31WlZWFr1794a5uTmeP38ulH2hra2Ns2fPtlnGKBHUMGBd//28s3JxcYGnpyciIiLaeygA6mrqmZubC9SINDc3B4fDwePHj4W27wgBXVlZWUyaNAmenp7YvXs3jh8/jh49emDIkCEit6/f7Ts3NxcbN27EiRMn2uXmf/0mMWvXroWmpmabj4G8HQWwCHmL169fi0zZb2pHIHV1dTaYxccwDB4/fozjx4+/tV4LIYS0xMOHD/Hw4UOoqKjgiy++gLm5OWRkZHDr1i0cP34ct27davK+SkpKRGajiKKhoQE9PT2UlZUJnAw2RUumB4oycOBAdl/1T5o/++wznD9/HgUFBSgoKMDvv/8u0Voh1dXVSEtLg7KystiT9ZbiB6/2798PALh37x4WLlwIDw8PfPrpp9DQ0MDDhw9x//593L9/Hw8ePEBeXh48PT0xe/ZsqXZezM7OxhdffCGwbMKECfj888+ldkxp0NbWxsSJEzFx4kS8fv0aqampePDgAR4+fIgHDx4gNze3SRka0rjwCg8PR3h4eJO2rampwaNHj/Do0aMm719JSQlWVlYYPHgwBg0ahK5du7LZvvWnwjMMw9YFfdvXq1evkJWVhQcPHojNvmhMbW0t+/ssSmFhITslyd/fH15eXrh06RLevHkjkOXF4XAEsrwACDzW0NDAyJEjJdI5uCl27dqFS5cuYdasWZ0iyCtKw4C1ra0tVFRUEB8fj/DwcFy5cgVDhgzB/PnzYWpq2k6jbD4ZGRn8+OOPkJGRwblz59p1LPWvM/gZSkBdAAuAyFIQHSGAxaeuro4VK1Zg+fLlYBhGbKOc+o0zNm/e3KbZrOJ4e3vDycmpvYdBxKAAFiGN4HK5WLx4sdDy9PT0Vl0McDgc9OzZE59++im0tLSwZcuW1gxTLGVl5WYXsyaEvFvKy8vx/ffft9nxSkpKJNbRVV9fH3369EGfPn1gYmKCqqoqbNiwodHnHDlyBB988AH72NLSEgUFBSgpKUFpaSlGjx6NkydPAgDOnDmDjIwMWFlZwcjICL6+vujatWuzxsgwDG7cuIGzZ88iIiKCbcDg4eGB2bNn49GjRzh8+DDu3bsHExMTLF68GMOGDWvmTwI4ePAgG7yqLzIyErGxsaitrRV54n/37l2oqKhg6tSpQuuqq6uRmZmJ58+fw8HBodl1irKysrB7927ExMQIrVuzZo1Ug2bS1qVLF7i5uQksq62txfPnz9lGLw8fPmQz+jobbW1tDB48mP0yNzdvUvevluByucjJyUFZWRlqa2vB4/FQW1uLlJQU3Lt3D/3790evXr1a9T4VEhKCkJCQVo3TysoKbm5ucHV1haGhYav2JU5KSgq2bdsGALh69Sp++OEHjBkzptX75XK5KCsrQ1lZGdt0iP9vSEgIkpOTYWFhAQcHB9TU1MDAwACysrIoLCzEX3/9BQBQU1PDb7/91qT3p5kzZwo81tHRgZubm0BmX1RUFKKjo+Hm5oYFCxY06aZvRyArK4vvv/8e9+7dE1nLydDQUCKNXBozZ84cgZ/XnTt32O/79esHQHTH3U2bNoHH47E1STsCDofT6FjqdybsCMErHR0dmjrYwVERdxGoiDvhS05OxrJlywSWrVu3DuPGjZPYMSorK7Fw4UKJTs+qb82aNVJtNUsIIe8KGRkZuLi4YNq0aRgyZIjIk+7a2lqUl5fjzp07OHbsGKKjo1t1TFtbW8yYMYOdXilKaWkpvLy82BsSDg4OUFFRafI0FxkZGWzduhXDhg3DvXv3EB8fj7S0NNy4cYMtVsuvE+bo6PjW/WVmZmLXrl1iW8v7+fnhm2++adLYWoPH46GoqAgvX77Ey5cv8fz5c9y/fx8vXrzAixcvkJeXx15Qq6qqwsDAAAYGBtDR0QHDMODxeFBTU4Ouri60tbWhq6sLXV1d6OjoQEtLS6COVGlpKYqLi/H69Ws2GFpcXNzhmwFYWVmxU4l1dHQwZMgQDBkyBPn5+cjMzERhYSEMDAwwceJEqdXOqa2tRWFhIfLy8lBaWoo3b97gl19+6RDT48WxtLSEq6sr3NzcYGRk1Kp9VVVVITg4GLt370ZlZaXQ+rVr12LChAlv3c+bN2/YmoBPnz5lawQ+efJEojXMvvnmG/To0QO9evWCjo6O0PtgREQEVq9e3ax9cjgceHh4YMGCBWLrZFVWVrLNYfhycnIwa9Ys9rGlpSV+/vnnVv+fNMXly5exaNGiJm/ftWtXkR1ZW+Kbb76Bn58fgLqfS/1Oz00JoO3duxcDBw6UyFikbfv27R2qnukff/yB0aNHt/cw3nnUhVDCKIBF+AoLC+Hj4yOQxRQWFoYePXpI9DhFRUWYP38+W8z4bdLT09m77Q8ePMC5c+dE3pkH6k4a6M+cEEI6Fx8fH9jZ2cHY2BgnT57EiRMn2HX+/v64e/cu7t27JzClTV5eHqampmzG2sGDBwXqzcnLywtdIDZkbm6Ofv36oV+/fnBzc2OL6peUlCApKalZGcPKysro378/9PX1oaenh27dukFPTw9du3aFvr4+tLW1BYJEDMOgqKgIz58/R15eHhiGgb6+PuTl5XH37l1kZ2fjxYsXyM/PZ4NW1CSlcYGBgVixYoXAssePH8Pf31+gxtfKlStFZuk1B8MwiImJwc2bN9kA4suXL5Gfn9+iKYQtMXLkSNjb24NhGDbbix+s5Gd+ARDIBMvKykJqaqrImmdA3d/EkiVLMHLkyBaNadOmTWLP0epzc3PDF198ITZIJa5TcUeirKwMb29vWFtbIzQ0FGlpaQLrORwOvLy8sGDBAvTq1Ytdvn37duzevVvs/0FDiYmJYgP+khIYGNikafN89bvXtVbPnj3ZunUtCU5GREQITM3raEpLS/Hff/8hLCxMILusvY0ZMwY//vhjew/jvUABLAmjABapLz09HUuXLkVlZSX69OmDo0ePSiUtt7KyEllZWTA2NoaGhgb27t2LrVu3CpycN+x42BCXy0VISMhbp9ioqqrip59+gp6eHtasWYOHDx9K9LUQQgghpP1NmzYNY8aMQbdu3aCtrQ0ZGRk8fvwYvr6+AtuNGDECM2bMQFFREYqLi1FQUIDr16+jpKQE06ZNazRDiGEYvHnzBrt372738+YPPvgACxYsaHbn1OLiYsTHxyM6OhqXLl0SGXCbMWMGevToAS0tLYGvLl26iO36yOPx4Obm1imCT+2hS5cu0NHRafIN3Pr+++8/nDp1Cnfv3sWHH37ITq2TlKVLlyIlJUWi+2wLH3/8MRQUFNip/HJycgJf8vLyQo9lZWXZfxUVFaGhoQF1dXVoaGhARUVFYtc9+fn52LJlCyIjIztcx0cTExP8+++/zZ5KT1qGAlgSRgEs0lBubi7i4+Ph6uraJmnLfFwut0U1KRiGQXJyMvbv3y+yU5KRkREWLVoEd3d31NbW4uTJk7h16xbbJSg/Px8Mw0ilk5Io48aNg4mJCZKTk3HlyhWpH48QQgghLaegoAAtLS1UVlaipKSkw2V6q6ur4++//0b//v0hIyODiooKvHjxAmVlZZCXl4e8vDwUFBTA4XBQUFCAV69eIT8/H69fv2br4MXFxSEpKemtx+JwOJCXlwePx6OMwHbSpUsXtgB4bW0tm2FXP/OuflYe/zGPx4Ouri7MzMyQn58PHo/HZqkGBga286vqGPiNDvgBrS5durDfi1res2dPkdlfsbGx+OGHH/D69et2eBWN09XVxb59+2BgYNDeQ3lvUABLwiiARd4lN2/exL59+xAXFyd0gqmrq4tJkyZh0qRJ0NbWZpdzuVy8fPkS8vLy8PLyksq4NDQ0MGnSJHz44YdQUlJil1dUVGD06NF0EkgIIYQQQkgnwuFw4OjoiOnTp8PW1hYVFRX45ZdfcOrUqfYemlhHjhxB375923sY7xUKYEkYBbDIuyg3NxdBQUE4ffq0UA0UfqBqxowZMDMzY5czDIOJEyciJycHAGBgYICysjKUl5cLBJgsLS2hqamJ3NxckW19G9O9e3dYWFhARUUF0dHRKC8vb/mLJIQQQggh5D2hrKwMTU1NoS8NDQ3IyMigtrYWXC4XNTU14HK5Ao8bfnG5XFRVVbHdhEtLSyVW16uj6t27N9uZmLSd1sRbpNNuhBDS4fTq1Qtr167Fxx9/jBMnTuDYsWNscV8ul4vTp0/j9OnTsLGxwfTp0+Ho6Ag5OTn8888/uHnzJuzs7KCoqMjur7q6GjU1NVBRURE4zoYNG3D06NEmj+vp06d4+vSpZF4kIYQQQggh7zglJSUMHToUAwYMgLm5Ofr27QsDAwOJ1KuqrKxEYmIisrOzAdR10rx//z5u376NoqKiVu+/I2lt8wrS9iiARch7RltbG/Pnz8fs2bMRHR2NgwcPCnRZSU9PR3p6OpSVlTFgwAAMGjQIVlZWqKysFAhgKSgoQEFBQWj/06dPR1paWosKchJCCCGEEEIaV1lZieTkZCQnJ4tcr6amhjFjxmDAgAEYMGAAevXqJTK4xePxcPXqVVy/fh0xMTG4ffu2tIfeoRgbG7f3EEgzUQCLkPeUvLw8vL294eXlhZs3byI4OBgxMTFsC+OKigqkpqYiNTWVfU6vXr0wcOBADBo0CAMHDoSJiQlkZGQE9turVy8cP34c9+7dw/Lly1vU/nfq1KmIiYkRaP/eVHJyctDU1GzRcxtyc3NDZWUlLly40GYtwAkhhBBCCGmNN2/e4OjRoyJnRejr60NWVhbPnj1rh5G1H0tLS2hra+Pp06d48OABTExMMGTIkPYeFmkmCmAR8p7jcDgYOHAgBg4ciLy8PBw9ehRnz57Fy5cvhbbNzc1Fbm4uTp8+DQBQVVWFlZUV+3xdXV1cu3YNV69exZUrV5Cfn9/s8cyaNQsnT55EaWmp2PH27dsXgwYNwuDBgzFw4EAYGhqKvKtUVVWFwsJCFBUVQVNTE926dcPdu3dx6dIlpKWlIT09HZWVlWLHEh0d3ezxE9JZrFy5EpqamoiNjUVqaiq1eSeEEELeA3l5ee09BKlauXIlRo0aJfb6gGEYvHjxArq6ui3q9k7aFxVxF4GKuJP3Hf+N/ebNm7h+/Tpu3LiBO3fuSCQLycTEBG5ubrC2toaVlRVu3bqF+fPni91+5cqV4HA4eP36NaysrDBgwACoqam1ehxAXe2vjIwMXL58GQkJCbhz545E9kuky8XFBba2tmz3Sg6HAxkZGfZf/vcA8OjRI6SmpiItLU2gC+fw4cPh7OyM7t27s9vX/6q/P1HrJP2coqIi5Obm4uHDh3j06BFyc3ORk5MjlVoTSkpKUFFRgYqKClRVVVFZWYnc3FyJH4cQQgghRNq0tbXx3XffYcSIEUIzQ0jHREXcCSESxeFwYGBgAAMDA3h4eACom2t/+/ZtXL9+nQ1sFRYWNmu/UVFR0NHREVhmZWUFDw8PREZGCiw3NDTEt99+C1tb29a9mEbIy8tjyJAhGDJkCBYuXIiKigpcvXoVoaGhTc6+srW1hZWVFQCgtrYWioqK6NmzJ3r27IkePXqgS5cu7LYMwyAnJwcJCQlITEzEtWvXpPGyhFhYWEBXVxdPnjxBTk4OOtN9C0NDQ7i6usLZ2RlWVlaQlZVt9j4+/PBDAHV1HhiGadE+pK1Lly7o3bs3Ro8eLbC8vLwcOTk5ePjwocC/jx49EugE2hyVlZWorKxs9t8vIYQQQkhHsXjxYowfPx56enrtPRTShiiARQhpEiUlJTbYA9QFY549e4YbN26wWVrFxcWwsLCAtbU1rK2t0bdvX5SXl+PNmzcwNDQUuV8FBQVs2LABH3/8McLCwnDhwgUMGTIEH3/8MVRVVdvyJUJZWRkjRozAiBEjAACvX7/GpUuXEBMTg+TkZFRUVAg9JzU1Fffv34ezszNcXV1hY2MjNh25trYWlZWVUFZWhr6+PgwMDFpUI6y5pFWQ09zcHHZ2dtDT04Oenh50dXXZf5WVlQHU1WC4desWbty4gZs3byIjIwNv3ryBrKws+yUnJwcZGRnIyclBVlYWmpqaGDlyJFxcXNC3b1+JdNQB0CnvyqmoqKB///7o37+/wHIul4unT58iJycHT58+RVlZGcrLy1FeXo6ysjL2ccPvRf0O1z9Wt27d0K1bN2hoaODNmzds5hg/6FdcXIy8vDy8fPkS1dXVUn3thBBCCCH1ubq6YsKECbCzs+uU53Wk9WgKoQg0hZAQ0lBVVRUuXbqE6OhoJCQkiK3Rpa6ujtGjR8PNzQ0WFha4ffs2m7GWkZHRaM0tPn6xfH6NL3V1dSQnJyMrKwsTJ05Ez5498ejRIzx48AAPHz7Ew4cP8eDBg1Zl5TSFjY0NJk+eDCcnJ5EdKEnHx+PxUFFRIRDcUlBQQLdu3aCurt7k/TAMg5KSErx8+RJ5eXnIz89HXl4eCgoK8OrVKxQUFKCwsBCvXr1ql0CXvr4+/vzzT5iZmQksLy8vx+PHj5Gbm4tHjx6xU0xbWg9EU1MT2tra0NHRgY6ODlRVVVFTU4Pa2lqBf8Ut4/F40NbWZjNeVVVVce3aNZw/fx4lJSVCx9PQ0ICXlxcUFRUhLy/PflVXV+Pu3btIT08X+95ECCGEdEZdunRBYGAgxo8fD11d3fYeDpGA1sRbKIAlAgWwCCGN4XK5SEtLQ2xsLOLi4lo1FUtRUREDBgxguztaWVlBS0urxePid1bhB7X4U86qqqpgbGwMKysr6OvrIyMjA1evXm00IwcADAwM4OPjA19fXxgYGLRoXOT9xTAMysrKhAJb/Mf872tra6Gtrc1+aWlpsV/1lwFgGzPw/+V/8R/37t0bs2fPbvZJ7uvXr9n6Y/wgFwDo6OhAV1dXIFClq6sLTU1NqRV/ZRgGGRkZCAsLQ2FhIfz8/ODg4NCsbMTCwkLcvHkT165dY7MfmxNMlJWVRY8ePdC7d28YGxsL/KumpgaGYUQG5mpra8HlcsHj8cDlclFbWwtlZWVoa2uDy+Xi1q1buHjxYpufXykqKsLY2BhGRkbQ1dUFl8tlA5gdlaamJpycnMAwDK5du9YhatXxA65ycnLgcDhQVFQU+NLW1kb37t1haGiI7t27w8DAAIqKiuzzb9++jaSkJGhoaMDDwwPa2toA6n5fFRUV2czriooK5Ofno7i4GLKyspCXl2ezdTMyMtgg9NOnT/Ho0SO8efOmzX4GlpaWcHZ2hqOjI2RlZVFYWMh+5efnIz8/H4WFhaisrER1dTVu3rwpkeP27t0bjo6O2L9/f5O2//nnn+Ho6Mi+z/LfJ+u/D/OXFRYWdrhGHl26dMHr16/bexiknbi7u8PX1xdWVlbNusFGOgcKYEkYBbAIIU1VW1uLa9euITY2FrGxsW/N5NDX1xfIrjIzM5N6BxQej4eamhrcv38fQUFBiIyMBI/HE7u9nJwcnJ2d4e/vj2HDhlGKNiHvAC6Xi6ysLFy/fh13795lszfLysqavS9dXV0YGxsLfPXu3Rt6enrNCrIxDIO8vDxkZmYiIyMDmZmZuHXrFsrLy5s9JnHk5OSgpaUFOTk5djqtNDNVO4s+ffrA0tISFhYWMDU1RZ8+faCpqdnew2qx4uJigRs3Dx48wIMHD0R2VG4uLS0tDBs2DMOHD8ewYcOafTPn4sWLWLNmTauacnTp0gVz587FlClTIC8vj1u3buHHH3/E3bt3BbbT1dXFBx98gIkTJzb7or+mpgavX7/G1q1bERISInIbHR0d9O3bF3fv3hV5805ZWRmqqqpQU1ODmpoaVFVVISsry2b8lpWV4c2bN3jz5g2UlZUxePBg2NjYwNraGv3792/0fKiqqgr5+fk4efIkDhw4QH/H76Bvv/0W48aN65C1SolkUQBLwiiARQhpCYZhkJmZiZiYGMTGxuLFixcwMzNjs6sGDhyIbt26tfmYUlJSEBQU9NZMA1NTU/j5+WHMmDGd+kKGENI0DMOgoKCADWbxMzZzcnJaNK1SRUWFDWbxM7a6d+8OLpcrVJNNVM228vJylJaW4vbt23Rx2kTq6urQ0tKCpqYmNDU1RX6vra3NLlNVVZVYXcHO4M2bN8jNzRXIOuJ/8bM2CwoKBDJ9FBUVYW1tDTs7OwwfPhympqatvpFTXl6Oo0ePYvPmza3aj4KCAvr27QsDAwPExMQINGX56quv4Ovr2+qbYlVVVVi+fDkuXboktO7KlSut2jcff9yt+V188OABwsLCcPr06Q6XPUaa79tvv4Wvr297D4O0kfcmgHX+/Hls374dWVlZ4HK5sLS0xPz58+Ho6CjR41AAixDS2VVXV+PcuXMICgrCgwcPxG6noqICLy8v+Pn5wdLS8r26sCGEiMfvgNmwC6a0a+21BVlZWaioqEBVVVXkv/wvVVVVqKqqslkl/C85OTmUlpaiqKgIpaWlbGDk5cuXAv82lukK1GXL9OrVi+1a27t3bxgZGeHVq1fstM/MzExUVVWhZ8+e6N27N3r16sUGCHv06ME2zCCtw+VyUVxcjJKSEhgZGQlMe5Q0hmFgY2Mjsf317NkTf/31F4yMjCS2T6AukGVvb88+TkxMhJqamkSPIQlcLhfx8fEIDQ3FxYsXBYJ6CgoK6NWrFxtYNzExgbGxMXr27Ak5OTk8f/4cOTk5yM3NRW5uLvue9+rVK3YfxsbGcHFxgbOzMywsLMSeJzEMgwsXLuDq1atsQF5FRQVGRkbsdFotLS2oqKiw7w+lpaV48+YNSkpKUFpaitevX+POnTtsBuG7pn///vjyyy8RFRWF4OBgdrmcnBwiIyPp5ul75L0IYJ08eRKrV6+GgoIChg8fDh6Ph0uXLoHL5WLdunWYOnWqxI5FASxCSGdVXFyM48eP4/Dhw43W5hoyZAh8fX3h7u5OF0CEkCarqanBs2fPhLK2Hj58KLU6RBwORyC41PB7UUEm/mNR2ysoKEg9WF9bW4vi4mK2JtKrV69QUlICAwMD9OrVCz169ICKiopUx0A6tsePH+Ozzz5Ddna2wPJ+/frhzp07TdrHwIEDsWnTJqle+JeVlUFFRaVT3OB6/vw50tPT0aVLF5iYmKBbt24tmo5WXl6OR48eQVVVFT169JDCSFuGy+Xi4cOHOH/+PI4fP45nz56195CapHv37vjwww/h4+MDGRkZMAyDq1evIiwsDLdu3cLUqVMxadKk9h4maUPvfAArLy8Pbm5uUFRUxMGDB9muQjdu3MCcOXPA5XIRFRUFfX19iRyPAliEkM7m8ePHCA4OxqlTp8R2OtTW1sa4cePg5+eH3r17t+0ACSHvNIZhUFhYiAcPHghkbuXl5UFJSUko4KSioiI26FR/GxUVFSgpKXWKi2dCmio2NhYJCQkwMTFBeHg4srKymvS8Ll264PPPP0d+fj4UFRXh7+8PJSUlKY+WdGT5+fmIjo5GUlISeDweevbsCRkZGcjKykJLSwt6enrQ1dWFnp4elJWVUVVVhby8POTl5eHFixcCX3l5eeByuS0ei4aGBiwsLNCnTx+YmJiwXxoaGhJ8xeRd0Jp4i5yUxiRRwcHBqK6uxsKFCwVaYg8cOBDz58/HH3/8gSNHjmDZsmXtOEpCCGl7N27cQFBQEGJjYyHqfgSHw8GoUaPg7++PkSNHSr1gPCHk/cThcNgujba2tu09HEI6rKtXr+Lzzz9v1nNGjhwJHx8fjB49WqrTG0nno6enh+nTp2P69OlNfo6pqanI5QzDoKioCM+fPxcIcKWnp+P27duN7m/nzp00BZC0iU4RwEpKSgIAuLm5Ca1zc3PDH3/8gcTERApgEULeC7W1tUhISEBQUBCuX78uchsjIyP4+fnBx8cHenp6bTxCQgghhIjS0gAUwzBvratGSGtwOBxoa2tDW1sblpaWIrdhGAa5ubnIzs5GcXEx9PX1MWrUKMqSJW2mwwewGIZBdnY2ZGRkYGJiIrS+d+/ekJGRQXZ2NhiGoT8eQsg7raqqCsuWLRPZUVBBQQHu7u7w9fWFjY0NvR8SQgghHUz//v2xYcMGrFq1qsnPSUlJQUpKCgBg8eLFmDdvnrSGR0ijOBwO20iCkPbQ4QNYr1+/RnV1NbS1taGgoCC0Xk5ODlpaWigoKEBZWVmH7I5BCCGS8vz5c6HglYWFBXx9feHt7Q11dfV2GhkhhBBCmsLDwwMeHh7Izs7GlClTmvXcv//+G76+vtDV1ZXS6AghpOPq8AGsiooKAGi0Sxa/eCEFsAgh7zojIyP4+Pjg5s2bsLOzg7+/v0BtQEIIIYR0DqampggJCcHMmTNRWlrapOeYm5tTrSFCyHurwwewZGRkmrxtJ2ioSAghrSInJ4fvv/++vYdBCCGEEAno1asXIiIi8O+//2LPnj2ora0V2sbFxQXDhg2DpaUlLCwsmnV9RAgh75IOH8BSUVEBUFf3RRx+y3j+toQQQgghhBDSGSgpKeGjjz7CxIkTce3aNVRWVuLu3btQUFDApEmTYGho2N5DJISQDqHDB7DU1NSgoqKCoqIi1NTUQE5OcMg1NTUoKiqCoqIiNDQ02mmUhBBCCCGEENJyXbt2hYeHR3sPgxBCOqwOn3/K4XBgamqK2tpa5OTkCK1/+PAheDwe1YAhhBBCCCGEEEIIeUd1+AAWADg4OAAAoqOjhdbxl40ePbpNx0QIIYQQQgghhBBC2kanCGBNmDABioqK2LVrFzIyMtjlN2/exO7du6GkpIQPPvigHUdICCGEEEIIIYQQQqSlw9fAAuraxq9cuRLr1q3DtGnTYGdnBwC4dOkSampqsHHjRujo6LTzKAkhhBBCCCGEEEKINHSKABYAzJgxA4aGhti9ezeuXLkCBQUFWFtb46OPPoK9vX17D48QQgghhBBCCCGESEmnCWABgLOzM5ydndt7GIQQQgghhBBCCCGkDXWKGliEEEIIIYQQQggh5P1FASxCCCGEEEIIIYQQ0qFRAIsQQgghhBBCCCGEdGgUwCKEEEIIIYQQQgghHRoFsAghhBBCCCGEEEJIh0YBLEIIIYQQQgghhBDSoVEAixBCCCGEEEIIIYR0aBTAIoQQQgghhBBCCCEdGgWwCCGEEEIIIYQQQkiHRgEsQgghhBBCCCGEENKhUQCLEEIIIYQQQgghhHRoFMAihBBCCCGEEEIIIR0aBbAIIYQQQgghhBBCSIdGASxCCCGEEEIIIYQQ0qFRAIsQQgghhBBCCCGEdGgUwCKEEEIIIYQQQgghHRoFsAghhBBCCCGEEEJIh0YBLEIIIYQQQgghhBDSoVEAixBCCCGEEEIIIYR0aBTAIoQQQgghhBBCCCEdmlx7D6Ajys3NBQDcvn0bgYGB7TwaQgghhBBCCCGEkM7v9u3bAP4v7tIcFMASoby8HABQWlqKy5cvt/NoCCGEEEIIIYQQQt4d/LhLc1AASwQjIyM8efIEKioq6NWrV3sPhxBCCCGEEEIIIaTTy83NRXl5OYyMjJr9XA7DMIwUxkQIIYQQQgghhBBCiERQEXdCCCGEEEIIIYQQ0qFRAIsQQgghhBBCCCGEdGgUwCKEEEIIIYQQQgghHRoFsAghhBBCCCGEEEJIh0YBLEIIIYQQQgghhBDSoVEAixBCCCGEEEIIIYR0aBTAIoQQQgghhBBCCCEdGgWwCCGEEEIIIYQQQkiHRgEsQgghhBBCCCGEENKhUQCLEEIIIYQQQgghhHRoFMAihBBCCCGEEEIIIR0aBbAIIYQQQgghhBBCSIdGASxCCCGEEEIIIYQQ0qFRAIsQQgghhBBCCCGEdGgUwCKEEEIIIYQQQgghHRoFsAghhBBCCCGEEEJIhybX3gMg0nH+/Hls374dWVlZ4HK5sLS0xPz58+Ho6NjeQyOd2MmTJ7F69WoEBwdj6NChQusfPnyILVu2ID09HcXFxejZsyemTJmCGTNmQEZGOF6el5eHv//+GykpKcjPz4eBgQHGjx+P+fPnQ0FBoS1eEumgamtrcejQIYSEhODBgweora1Fjx49MGbMGHz44YdQVFQU2P7mzZv4+++/cfPmTZSXl8PU1BQzZ86Ej4+PyP0393eVvF9qa2sRHByM48eP4+HDh1BWVsaAAQMwc+ZMODk5CW1P731EGoqLi+Hj44OXL18iKytLaD393hFJCg0NxcqVK8WuX7RoEVasWME+ps9dIklPnz7F33//jeTkZBQWFkJLSwtOTk5YtmwZ9PT0BLal9773G4dhGKa9B0Ekix9kUFBQwPDhw8Hj8XDp0iVwuVysW7cOU6dObe8hkk7o6tWrmDt3LsrLy0UGsO7cuYMZM2bgzZs3sLa2ho6ODi5duoSSkhL4+Pjgt99+E9j+xYsXmDp1Kl68eIH+/fujR48euHLlCvLz8zFs2DDs2bMH8vLybfkSSQdRW1uLjz/+GPHx8VBRUcGgQYMgJyeH69evo6SkBIMGDcK+ffugrKwMAEhJScHChQvB4/Fga2sLZWVlXLhwAZWVlUIn3EDzf1fJ++fLL79EWFgY1NTUYGNjAy6Xi9TUVHC5XCxbtgyLFy9mt6X3PiItK1aswH///QcAQgEs+r0jkvbzzz9j7969GDlyJLS1tYXWu7m5wcvLCwB97hLJunnzJubMmYPS0lKYmZmhZ8+eyMjIwIsXL9CzZ08cP34cXbp0AUDvfQQAQ94pL168YAYMGMDY2NgwWVlZ7PLr168z1tbWjJWVFfPixYt2HCHpjMLDw5khQ4YwZmZmjJmZGZOamiqwnsfjMT4+PoyZmRkTGhrKLi8oKGCXh4eHCzxn4cKF/6+9u4+puvz/OP48oBw7mnnDEM07CD+alJXm3R95s0pnG3Nz07yJiVZO13SzkVZaK6mWRokkWQqbS7GoLTPS0DJSYpnMe/MuBIwUEYVR3B7O4fP7g53P1yNg4e+QR3g9Nv7gut7Oc+bb93V4c13XxzQMw0xKSrLGKisrzZiYGNMwDDMlJaV135T4rc8++8w0DMOMioryqlfXrl0zn376adMwDDM+Pt40TdOsrq42x44da0ZGRpq//PKLFXvhwgVz3LhxpmEY5okTJ6zxW8lVaV927txpGoZhTp482SwpKbHGz507Z44YMcIcMmSImZ+fb5qmap+0nvT0dGvNNQzDa055J63hmWeeMQ3D+MefE7Tuii/V1taakyZNMg3DMD/99FNrvKamxly8eLFpGIYZFxdnmqZqnzTQfs02JjU1FafTSUxMDIZhWOPDhg3j+eefp7a2lrS0tNv4CuVOcvnyZZYtW8aSJUuor68nODi4ybjs7GzOnj3LqFGjmDp1qjXeo0cP3njjDQC2bNlijefl5fHTTz/Rv39/Fi5caI07HA7efvttAgMD2bp1a+u8KfF727dvB+DVV1+lV69e1vj1+bRz504AduzYwbVr14iKimLMmDFWbP/+/YmNjQW8c6+luSrtzzfffANAbGysV80bNGgQUVFR1NfXk52dDaj2SesoLi5m1apVPPLIIwQGBjaaV95Jazhz5gzBwcFe625TtO6KL+3atYuCggKioqKIjo62xu12O6+88grBwcHk5+cDqn3SQA2sNiYrKwto2OZ7I8/Y/v37/9PXJHeuhIQEduzYwQMPPEBaWhrh4eFNxt0s7zzbew8dOkRFRQUAP//8M6ZpMnHixEZn1fv06cPQoUO5ePEiubm5Pn5Hcifo3r074eHhDBs2rNHcwIEDAbhy5Qrwv9x7/PHHG8VOnDiRwMBAr5rX0lyV9icxMZH09PQm74ysrKwEsJoKqn3SGlasWIHT6WT16tVNzivvxNcKCwv566+/iIyM/MdYrbviS3v27AFg3rx5jeZ69+5NdnY2KSkpgGqfNFADqw0xTZPc3FwCAgKabDQMHDiQgIAAcnNzMXX1mfwL4eHhrF69mi+//JLBgwc3G+cp/Nfv+rteWFgY9fX1nD9/3it+0KBBzf69AOfOnbvl1y53ro8//pjvvvsOh8PRaO7EiRMAhIaGAvD7778DTedely5dCAkJobS0lKtXrwItz1Vpf4KCgjAMo9HFrpmZmWRkZOBwOKwPz6p94mvbtm0jKyuL2NhYBgwY0GSM8k587fTp0wD07NmTuLg4nnzySR588EEmT55MUlIStbW1VqzWXfGlU6dO0bFjR4YMGUJRUREbN25k5cqVxMfHc/z4ca9Y1T4BPYWwTSkvL8fpdNKjR48mn6jQoUMHunfvzrVr16isrKRLly634VXKnWTBggX/Ks6zG+bGp4R4eMY9H2Y88SEhIf8qXgQamvTr1q0DYNKkSQCUlJQAN8+9oqIirl69SnBwcItzVdq3mpoali1bRm5uLufPn6dPnz6sWbPGOlqo2ie+dOHCBd577z3Gjh3LnDlzmo1T3omvnTp1Cmh4EFS3bt0YPnw4vXr14uTJkyQmJpKVlcXmzZvp1KmT1l3xGafTSVFREaGhoWRkZLBixQqqq6ut+U2bNvHss8+ybNkyQLVPGmgHVhvi+Q/veTJXUzp16gT87xiEiC94cs+TXzfyjFdVVd1SvAjABx98QE5ODsHBwTz33HOAck9a16VLl9i9e7fXzoDrnwan/BNfcbvdLF++nICAAN555x1sNluzsco78TXPDqwpU6aQmZnJhg0b2Lp1K99++y1DhgzhyJEjJCQkAMo/8R3PUb/y8nKWL1/OE088QUZGBjk5Oaxdu5Zu3bqRkpJi3d+s3BNQA6tNufFs783oCKH4kuc+mJt94Aaor6+/pXiRdevWsXHjRoKCgkhISLAe8R0YGIjNZlPuSasIDQ3lwIEDHDx4kISEBOrq6oiLi2Pjxo2Aap/4TnJyMkeOHOHll1+mT58+N41V3omvJSYmsnPnTtasWeN1fL9v3768++672Gw20tLSqKur07orPuN0OoGGRtPo0aOJj48nLCyMrl278tRTT1n3ACYlJWGapmqfAGpgtSmeBef6c+o3qqmp8YoV8QXPrj9Pft3IM965c+cWxStPxeVy8frrr/PRRx9ht9tZv349I0eOtObvuusuTNNstu7dau554qV9czgcdO/enXvuuYcpU6awfv16bDYbn3zyCbW1tap94hNnzpzhww8/ZMKECUyfPv0f45V34mt2u52IiIgmryC5//77CQ0NpaqqioKCAq274jPX74yaNWtWo/kJEybQq1cviouLrdwD1b72Tg2sNqRLly44HA7KyspwuVyN5l0uF2VlZdjtdrp27XobXqG0VZ6z5c2dIb/xvoR/G9/cmXVpHyorK1m4cCFpaWl07dqVlJQUxo8f7xXjyRFPztzoVnOvufsVpH17+OGH6d+/PxUVFRQWFqr2iU+sXbuWuro66urqiI2N9fry7AzwfF9aWqq8k/+c596/6upqrbviM3fffTcdO3YEGnb7NcWzI7WsrEy1TwA1sNoUm81GREQEbrebgoKCRvP5+fnU19c3++QGkVvlebpHU4+hNU2TvLw8AgMDue+++/4xHrDum1Gutl/l5eVER0eTlZVF7969SU1N9dp55eHJpaaeXlRRUcGVK1fo0aOH9eG7pbkq7YtpmqxZs4alS5c2+YsgwNqh4HK5VPvEJzz3r2RnZ5Oenu715bnywfN9VVWV8k58qqKigtdee40lS5Y0W/f+/PNPoOFYtdZd8ZXr/92Li4ubjPE0n3r27KnaJ4AaWG3OY489BsAPP/zQaM4zduMOBpH/L0/e7d27t9Hc4cOHKS0tZcSIEdaTLz3xmZmZjc6dX7p0idOnT3PvvfcSERHRyq9c/JHT6WTBggX89ttvRERE8Pnnnzf74eJmNe/HH3/E7XZ71byW5qq0Lzabjb1797Jr1y6ys7MbzRcWFpKfn4/D4SAsLEy1T3xiy5YtnD17tskvzx0unu/79u2rvBOf6ty5M99//z27d+8mJyen0fy+ffsoKyvDMAxCQkK07opPjRs3DoCMjIxGc3l5eVy8eJGQkBD69eun2ieAGlhtzrRp07Db7WzatImTJ09a4ydOnCA5OZlOnToxe/bs2/gKpS0aNWoUgwYNIjs7my+++MIaLy0t5c033wRg3rx51rhnEcrLy2PdunXWeFVVFStXrsTtdnvFS/uSmJjI0aNH6d27N1u2bCE0NLTZ2MmTJ9OzZ0+2b9/Ovn37rPHCwkLef/99bDYbMTEx1nhLc1XanxkzZgDw1ltvcfnyZWu8uLiYF198EZfLxezZs7Hb7ap9clso78SXbDabVffi4uK8dsL88ccfrFq1CoBFixYBWnfFt2bOnInD4eDrr78mPT3dGi8vL2flypXU19czZ84cAgICVPsEAJupx9G1OampqaxatYqOHTsyevRoAH799VdcLherV69m6tSpt/kVyp0qOjqagwcPkpqayqOPPuo1d/z4cebOnUtVVRUPPfQQISEhHDx4kPLycmbMmEFcXJxXfGFhIbNmzaKkpATDMAgLC+Pw4cOUlJQwbtw4NmzYQIcOHf7Ltyd+oKysjAkTJlBTU0NkZCTh4eHNxsbHxwMNv4lbsmQJbrebkSNH0rlzZw4cOEB1dTVLly5l4cKFXn+upbkq7UtdXR0vvPAC+/btw+FwMHz4cNxuN8eOHaOqqorx48ezfv166yihap+0pqFDh+J2uzl79qzXuPJOfKmmpob58+dz6NAhHA4HI0aMABp+fnA6ncyfP5/ly5db8Vp3xZd27drFSy+9hMvlIjIykpCQEI4ePUpZWRljxowhOTnZuitLtU/UwGqjMjMzSU5O5tSpUwQFBTF48GAWLVrE2LFjb/dLkzvYzRpY0HDGPDEx0frAM2DAAGbOnMn06dOtYxDXKyoqIjExkf379/P333/Tr18/pk6dyty5c7Hb7f/FWxI/s2fPHhYvXvyvYq//ge7w4cMkJSVx7NgxTNMkIiKCmJgYpkyZ0uSfbWmuSvvidrvZtm0bX331FefPnycgIADDMJg2bRozZswgIMB7A7tqn7SW5hpYoLwT33I6nWzevJn09HQKCgoICgpi6NChREdHM2nSpEbxWnfFl06fPs2GDRvIycmhsrLSqk/z5s2zmlceqn3tmxpYIiIiIiIiIiLi13QHloiIiIiIiIiI+DU1sERERERERERExK+pgSUiIiIiIiIiIn5NDSwREREREREREfFramCJiIiIiIiIiIhfUwNLRERERERERET8mhpYIiIiIiIiIiLi19TAEhERERERERERv6YGloiIiIiIiIiI+DU1sERERERERERExK+pgSUiIiIiIiIiIn5NDSwREREREREREfFramCJiIiIiIiIiIhfUwNLRERERERERET8mhpYIiIiIiIiIiLi19TAEhERERERERERv6YGloiIiIiIiIiI+LX/A0QMYzYuXV2cAAAAAElFTkSuQmCC", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "image/png": { + "height": 375, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "figure = plt.figure(figsize=(10,6))\n", + "ax = figure.add_subplot(111)\n", + "\n", + "ax.plot(x_pos_raw, \n", + " y_pos_raw,\n", + " '-k', \n", + " alpha=.9)\n", + "\n", + "# Plot reward port tracking ('chocolate_milk' on top)\n", + "# ... if not present it will be filtered out \n", + "ax.scatter(reward_x_raw[filter_reward], \n", + " reward_y_raw[filter_reward], \n", + " color='orange', \n", + " s=200, \n", + " marker='x', \n", + " alpha=.5, \n", + " zorder=10, \n", + " label='reward')\n", + "ax.legend()\n", + "ax.set_title('Raw tracking results')" + ] + }, + { + "cell_type": "markdown", + "id": "d6f3f1c4", + "metadata": {}, + "source": [ + "### Do the same for Tracking results" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "454dd6d2", + "metadata": {}, + "outputs": [], + "source": [ + "# Positions (offset corrected to begin with)\n", + "x_pos, y_pos = (Tracking.OpenField & key).fetch1('x_pos','y_pos')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9e419294", + "metadata": {}, + "outputs": [], + "source": [ + "# Positions (offset corrected to begin with)\n", + "x_pos_raw, y_pos_raw = (Tracking.OpenField & key).fetch1('x_pos','y_pos')\n", + "# Raw signal \n", + "reward_x, reward_y = (Tracking.DLCPart & key & 'body_part = \"chocolate_milk\"').fetch1('bodypart_x_pos','bodypart_y_pos')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "82e95fd5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "figure = plt.figure(figsize=(10,6))\n", + "ax = figure.add_subplot(111)\n", + "\n", + "ax.plot(x_pos, y_pos, '-k', alpha=.9)\n", + "ax.scatter(reward_x[filter_reward], \n", + " reward_y[filter_reward], \n", + " color='orange', \n", + " s=200, \n", + " marker='x', \n", + " alpha=.5, \n", + " zorder=10, \n", + " label='reward')\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "98d3d732", + "metadata": {}, + "source": [ + "## Add timestamp to DLC pickle file" + ] + }, + { + "cell_type": "markdown", + "id": "4cbd3692", + "metadata": {}, + "source": [ + "Information about the timestamp of the tracking video (tracking start time) is not available from the set of DLC output currently. Users will need to manually add this info into the generated pickle file, under a variable 'Start TimeStamp'\n", + "Which can be done with the following example code (copyed from here: https://github.com/kavli-ntnu/dj-moser-imaging/pull/69)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8faf107c", + "metadata": {}, + "outputs": [], + "source": [ + "import scanreader # If you don't have it installed: pip install git+https://github.com/atlab/scanreader.git\n", + "from datetime import datetime\n", + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "da6a5ea8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting horsto@kavlidatajoint02.it.ntnu.no:3306\n", + "Suite2p not found\n" + ] + } + ], + "source": [ + "import sys \n", + "sys.path.append('..')\n", + "from imaging.utils import read_timestamp_rawtif" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "977b6865", + "metadata": {}, + "outputs": [], + "source": [ + "def add_timestamp_to_pickle(first_tif_file, matching_dlc_pkl_file):\n", + " timestamp_tif = read_timestamp_rawtif(first_tif_file)\n", + " video_datetime = datetime.strftime(timestamp_tif, \"%Y-%m-%d_%H:%M:%S.%f\")\n", + " \n", + " # Write to dlc pickle file\n", + " with open(matching_dlc_pkl_file, 'rb') as f:\n", + " pickle_dict = pickle.load(f)\n", + "\n", + " pickle_dict['Start TimeStamp'] = video_datetime\n", + " pickle.dump(pickle_dict, open(matching_dlc_pkl_file, 'wb'))" + ] + }, + { + "cell_type": "markdown", + "id": "fe318b6f", + "metadata": {}, + "source": [ + "### Configure filenames " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c7125bac", + "metadata": {}, + "outputs": [], + "source": [ + "tif_file = '/Volumes/moser/horsto/MEC data from Weijian/97046/20210313/96766_20210312_ML0000_AP-400_2Openfiled_00001.tif'\n", + "matching_dlc_pkl_file = '/Volumes/moser/horsto/MEC data from Weijian/97046/20210313/96766_20210312_ML0000_AP-400_2Openfiled_dlc/96766_20210312_ML0000_AP-400_2Openfiled_00001_trackingVideoDLC_resnet_50_OPENMINI2P_topcamera_20210305Mar5shuffle1_1030000_meta.pickle'" + ] + }, + { + "cell_type": "markdown", + "id": "92d8a812", + "metadata": {}, + "source": [ + "### Execute ... " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d683997c", + "metadata": {}, + "outputs": [], + "source": [ + "add_timestamp_to_pickle(tif_file, matching_dlc_pkl_file)" + ] + }, + { + "cell_type": "markdown", + "id": "5d78d095", + "metadata": {}, + "source": [ + "## Insert object locations for object sessions in the open field" + ] + }, + { + "cell_type": "markdown", + "id": "43262af8", + "metadata": {}, + "source": [ + "Extract sessions for which object locations have not been recorded yet. \n", + "\n", + "Create Napari (napari.org) viewer and let the user click on the center point of (round) objects and define a radius (second click). \n", + "Then extract centers and radius for each object and insert into database." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f11229b4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting horsto@kavlidatajoint02.it.ntnu.no:3306\n" + ] + } + ], + "source": [ + "import datajoint as dj \n", + "# Load base schema\n", + "schema = dj.schema(dj.config['dj_imaging.database'])\n", + "schema.spawn_missing_classes()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "38b94645", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "import seaborn as sns # Make plots pretty\n", + "sns.set(style='dark')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f919ac00", + "metadata": {}, + "outputs": [], + "source": [ + "# Create GUI (QT) context\n", + "%gui qt" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bdeaa83c", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('..')\n", + "from helpers_visual.enter_object_locations import extract_undefined_object_sessions, create_napari_object_viewer" + ] + }, + { + "cell_type": "markdown", + "id": "740f42da", + "metadata": {}, + "source": [ + "### User defined object location and radius" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d84b2c76", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of remaining sessions to define: 0\n" + ] + }, + { + "ename": "TypeError", + "evalue": "'NoneType' object is not iterable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_undefined_object_sessions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mobject_layer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj_sess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_napari_object_viewer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not iterable" + ] + } + ], + "source": [ + "session = extract_undefined_object_sessions()\n", + "object_layer, key, obj_sess = create_napari_object_viewer(session)" + ] + }, + { + "cell_type": "markdown", + "id": "2dbcba79", + "metadata": {}, + "source": [ + "### Confirm object position" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b3667595", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "image/png": { + "height": 415, + "width": 434 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Draw circles \n", + "figure = plt.figure(figsize=(7,7))\n", + "ax = figure.add_subplot(111)\n", + "ax.scatter(obj_sess['x_pos'], obj_sess['y_pos'],s=1, color='k', alpha=.5)\n", + "for no_, point in enumerate(object_layer.data):\n", + " if no_ == 0: \n", + " color='red'\n", + " else: # if second object\n", + " color='orange'\n", + " object_ = plt.Circle((point[1]/2,point[0]/2), 20, color=color, alpha=.5)\n", + " ax.add_artist(object_)\n", + "ax.invert_yaxis()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "abd7bd9b", + "metadata": {}, + "source": [ + "### Insert into database ... " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5761cc3d", + "metadata": {}, + "outputs": [], + "source": [ + "# First close the NAPARI window\n", + "#viewer.close()" + ] + }, + { + "cell_type": "markdown", + "id": "9c8d187c-6184-4b67-85a5-dc88d44a9077", + "metadata": {}, + "source": [ + "### Loop over objects and insert" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "ac8c1663", + "metadata": {}, + "outputs": [], + "source": [ + "from imaging.spatial_scores import ArenaObjectPos\n", + "for no_, point in enumerate(object_layer.data): \n", + " # Create dictionary and insert into session part table\n", + " object_dict = {\n", + " 'obj_name' : 'duplo_tower23',\n", + " 'obj_x_coord' : point[1]/2,\n", + " 'obj_y_coord' : point[0]/2\n", + " }\n", + " Recording.ArenaObject.insert1({**key,**object_dict},skip_duplicates=True,ignore_extra_fields=True)" + ] + }, + { + "cell_type": "markdown", + "id": "ee2ed9ef", + "metadata": {}, + "source": [ + "### Populate calibrated object positions and retrieve the session just entered " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "6a36a83a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 4.56it/s]\n" + ] + } + ], + "source": [ + "ArenaObjectPos.populate(display_progress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "28879de9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    object_hash

    \n", + " Object hash\n", + "
    \n", + "

    obj_name

    \n", + " \n", + "
    \n", + "

    session_name

    \n", + " Meta session name (hash)\n", + "
    \n", + "

    recording_order

    \n", + " Order of session within meta sessions (zero index!)\n", + "
    \n", + "

    recording_name

    \n", + " Recording name: Hash of animal_id, datasource_id, timestamp and combined 'yes'/'no' label\n", + "
    \n", + "

    apparatus

    \n", + " \n", + "
    \n", + "

    category

    \n", + " Arena / Apparatus category (openfield, linear track, etc.)\n", + "
    \n", + "

    obj_x_coord

    \n", + " x coordinate of object in pixels\n", + "
    \n", + "

    obj_y_coord

    \n", + " y coordinate of object in pixels\n", + "
    \n", + "

    dataset_name

    \n", + " 16 character hash\n", + "
    \n", + "

    trackingparams_id

    \n", + " Parameter set ID, starting with A\n", + "
    \n", + "

    obj_x_coord_calib

    \n", + " Object x coord [mm]\n", + "
    \n", + "

    obj_y_coord_calib

    \n", + " Object y coord [mm]\n", + "
    \n", + "

    obj_geometry

    \n", + " e.g. cube, cylinder\n", + "
    \n", + "

    obj_width

    \n", + " Object width in cm\n", + "
    \n", + "

    obj_length

    \n", + " Object length in cm\n", + "
    \n", + "

    obj_height

    \n", + " Object height in cm\n", + "
    \n", + "

    obj_desc

    \n", + " Object description\n", + "
    8c6e12b10ae58326duplo_tower23694a792ec2bedcae2a5daab937fa60cafSquare80Open Field132.21243.299c80e21167ee50e4A315.039579.728cube64.064.0230.0Colorful duplo tower, 23 cm high
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*object_hash *obj_name metasession_na recording_order recording_name apparatus category obj_x_coord obj_y_coord dataset_name trackingparams obj_x_coord_ca obj_y_coord_ca obj_geometry obj_width obj_length obj_height obj_desc \n", + "+------------+ +------------+ +------------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n", + "8c6e12b10ae583 duplo_tower23 694a792ec2bedc 2 a5daab937fa60c Square80 Open Field 132.21 243.29 9c80e21167ee50 A 315.039 579.728 cube 64.0 64.0 230.0 Colorful duplo\n", + " (Total: 1)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objects_example = ArenaObjectPos * ArenaObject & obj_sess\n", + "tracking_objects_example = (Tracking * Tracking.OpenField & objects_example).fetch1()\n", + "\n", + "objects_example" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "bedebca4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "image/png": { + "height": 415, + "width": 434 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Draw circles \n", + "figure = plt.figure(figsize=(7,7))\n", + "ax = figure.add_subplot(111)\n", + "ax.scatter(tracking_objects_example['x_pos'], tracking_objects_example['y_pos'],s=1, color='k', alpha=.5)\n", + "for no_, point in enumerate(objects_example):\n", + " if no_ == 0: \n", + " color='red'\n", + " else: # if second object\n", + " color='orange'\n", + " object_ = plt.Circle((point['obj_x_coord_calib'],point['obj_y_coord_calib']), radius = point['obj_width']/1.5, color=color, alpha=.5)\n", + " ax.add_artist(object_) \n", + "ax.invert_yaxis()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv-dlc", + "language": "python", + "name": "venv-dlc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "toc-autonumbering": false, + "toc-showmarkdowntxt": false, + "toc-showtags": false + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements_test.txt b/requirements_test.txt new file mode 100644 index 0000000..513fa42 --- /dev/null +++ b/requirements_test.txt @@ -0,0 +1,3 @@ +pytest +pytest-cov +djarchive-client @ git+https://github.com/datajoint/djarchive-client.git diff --git a/setup.py b/setup.py index ddbbcf2..dfa5d3a 100644 --- a/setup.py +++ b/setup.py @@ -1,24 +1,29 @@ #!/usr/bin/env python from setuptools import setup, find_packages from os import path -import sys + +pkg_name = 'workflow_behavior' here = path.abspath(path.dirname(__file__)) long_description = """" -# Workflow for monitoring behavior +# Workflow for monitoring continuous behavior -Build a workflow for continuous behavioral data using DataJoint Elements -+ [elements-session](https://github.com/datajoint/element-session) -+ [elements-behavior](https://github.com/datajoint/element-behavior) ++ [element-lab](https://github.com/datajoint/element-lab) ++ [element-animal](https://github.com/datajoint/element-animal) ++ [element-session](https://github.com/datajoint/element-session) ++ [element-behavior](https://github.com/datajoint/element-behavior) """ with open(path.join(here, 'requirements.txt')) as f: requirements = f.read().splitlines() +with open(path.join(here, pkg_name, 'version.py')) as f: + exec(f.read()) + setup( name='workflow-behavior', - version='0.0.1', + version='0.0.0b1', description="DataJoint Elements for Continous Behavior", long_description=long_description, author='DataJoint NEURO', diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..cde090e --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1,120 @@ +''' +fresh docker: + docker run --name wf-sess -p 3306:3306 -e \ + MYSQL_ROOT_PASSWORD=tutorial datajoint/mysql +dependencies: pip install pytest pytest-cov +run all tests: + pytest -sv --cov-report term-missing --cov=workflow-session \ + -p no:warnings tests/ +run one test, debug: + pytest [above options] --pdb tests/tests_name.py -k function_name +''' + +import os +import pytest +import pathlib +import datajoint as dj + +# ------------------- SOME CONSTANTS ------------------- + +_tear_down = True + +test_user_data_dir = pathlib.Path('./tests/user_data') +test_user_data_dir.mkdir(exist_ok=True) + +# ------------------ GENERAL FUCNTION ------------------ + + +def write_csv(content, path): + """ + General function for writing strings to lines in CSV + :param path: pathlib PosixPath + :param content: list of strings, each as row of CSV + """ + with open(path, 'w') as f: + for line in content: + f.write(line+'\n') + +# ------------------- FIXTURES ------------------- + + +@pytest.fixture(autouse=True) +def dj_config(): + """ If dj_local_config exists, load""" + if pathlib.Path('./dj_local_conf.json').exists(): + dj.config.load('./dj_local_conf.json') + dj.config['safemode'] = False + dj.config['custom'] = { + 'database.prefix': (os.environ.get('DATABASE_PREFIX') + or dj.config['custom']['database.prefix'])} + return + + +@pytest.fixture +def pipeline(): + """ Loads workflow_trial.pipeline lab, session, subject""" + from workflow_trial import pipeline + + yield {'event': pipeline.event, + 'trial': pipeline.trial, + 'subject': pipeline.subject, + 'session': pipeline.session, + 'lab': pipeline.lab,} + + if _tear_down: + pipeline.event.BehaviorEvent.delete() + pipeline.trial.Trial.delete() + pipeline.subject.Subject.delete() + pipeline.session.Session.delete() + pipeline.lab.Lab.delete() + + +# Subject data and ingestion +@pytest.fixture +def subjects_csv(): + """ Create a 'subjects.csv' file""" + subject_content = ["subject,sex,subject_birth_date,subject_description," + + "death_date,cull_method", + "subject5,F,2020-01-01 00:00:01,rich," + + "2020-10-02 00:00:01,natural causes", + "subject6,M,2020-01-01 00:00:01,manuel," + + "2020-10-03 00:00:01,natural causes"] + subject_csv_path = pathlib.Path('./tests/user_data/subject/subjects.csv') + write_csv(subject_content, subject_csv_path) + + yield subject_content, subject_csv_path + subject_csv_path.unlink() + + +@pytest.fixture +def ingest_subjects(pipeline, subjects_csv): + """From workflow_trial ingest.py, import ingest_subjects, run""" + from workflow_trial.ingest import ingest_subjects + _, subject_csv_path = subjects_csv + ingest_subjects(subject_csv_path=subject_csv_path) + return + + +# Session data and ingestion +@pytest.fixture +def sessions_csv(): + """ Create a 'sessions.csv' file""" + session_csv_path = pathlib.Path('./tests/user_data/session/sessions.csv') + session_content = ["subject,session_datetime,session_dir,session_note", + "subject5,2020-04-15 11:16:38,/subject5/session1," + + "'Successful data collection, no notes'", + "subject6,2021-06-02 14:04:22,/subject6/session1," + + "'Ambient temp abnormally low'"] + write_csv(session_content, session_csv_path) + + yield session_content, session_csv_path + session_csv_path.unlink() + + +@pytest.fixture +def ingest_sessions(ingest_subjects, sessions_csv): + """From workflow_trial ingest.py, import ingest_sessions, run""" + from workflow_trial.ingest import ingest_sessions + _, session_csv_path = sessions_csv + ingest_sessions(session_csv_path=session_csv_path) + return diff --git a/tests/test_export.py b/tests/test_export.py new file mode 100644 index 0000000..7712b7c --- /dev/null +++ b/tests/test_export.py @@ -0,0 +1,7 @@ +# from . import (dj_config, pipeline, lab_csv, ingest_lab, +# subjects_csv, ingest_subjects, +# sessions_csv, ingest_sessions) + + +def test_nwb_export(): + pass diff --git a/tests/test_ingest.py b/tests/test_ingest.py new file mode 100644 index 0000000..af396f1 --- /dev/null +++ b/tests/test_ingest.py @@ -0,0 +1,33 @@ +'''Tests ingestion into schema tables: Lab, Subject, Session + 1. Assert length of populating data from __innit__ + 2. Assert exact matches of inserted data fore key tables +''' + +from . import (dj_config, pipeline, + subjects_csv, ingest_subjects, + sessions_csv, ingest_sessions) + + +def test_ingest_subjects(pipeline, subjects_csv, ingest_subjects): + """Check length of subject.Subject""" + subject = pipeline['subject'] + assert len(subject.Subject()) == 2 + + subjects, _ = subjects_csv + for this_subject in subjects[1:]: + subject_values = this_subject.split(",") + assert (subject.Subject & {'subject': subject_values[0]} + ).fetch1('subject_description') == subject_values[3] + + +def test_ingest_sessions(pipeline, sessions_csv, ingest_sessions): + """Check length/contents of Session.SessionDirectory""" + session = pipeline['session'] + assert len(session.Session()) == 2 + + sessions, _ = sessions_csv + for sess in sessions[1:]: + sess = sess.split(",") + assert (session.SessionDirectory + & {'subject': sess[0]} + ).fetch1('session_dir') == sess[2] diff --git a/tests/test_pipeline_generation.py b/tests/test_pipeline_generation.py new file mode 100644 index 0000000..86267f7 --- /dev/null +++ b/tests/test_pipeline_generation.py @@ -0,0 +1,16 @@ +'''Test pipeline construction + 1. Assert lab link to within-schema children + 2. Assert lab link to subject + 3. Assert subject link to session +''' + +from . import pipeline + + +def test_generate_pipeline(pipeline): + session = pipeline['session'] + subject = pipeline['subject'] + + # test connection Subject->Session + subject_tbl, *_ = session.Session.parents(as_objects=True) + assert subject_tbl.full_table_name == subject.Subject.full_table_name diff --git a/workflow_behavior/ingest.py b/workflow_behavior/ingest.py index 277002b..a5cacc7 100644 --- a/workflow_behavior/ingest.py +++ b/workflow_behavior/ingest.py @@ -52,7 +52,7 @@ def ingest_sessions(session_csv_path='./user_data/sessions.csv'): session.SessionDirectory.insert(sess_dir_list, skip_duplicates=True) print(f'\n---- Insert {len(probe_list)} entry(s) into probe.Probe ----') - pose.DLCModel.insert(model_list, skip_duplicates=True) + dlc.DLCModel.insert(model_list, skip_duplicates=True) print('\n---- Successfully completed workflow_behavior/ingest.py ----') diff --git a/workflow_behavior/paths.py b/workflow_behavior/paths.py index a784008..14b3667 100644 --- a/workflow_behavior/paths.py +++ b/workflow_behavior/paths.py @@ -1,11 +1,18 @@ import datajoint as dj -import pathlib -def get_beh_root_dir(): - beh_root_dirs = dj.config.get('custom', {}).get('beh_root_dir', None) + +def get_beh_root_data_dir(): + beh_root_dirs = dj.config.get('custom', {}).get('beh_root_data_dir', None) return beh_root_dirs if beh_root_dirs else None + +def get_beh_root_output_dir(): + beh_output_dir = dj.config.get('custom', {}).get('beh_output_dir', None) + return beh_output_dir if beh_output_dir else None + + def get_session_directory(session_key: dict) -> str: from .pipeline import session - session_dir = (session.SessionDirectory & session_key).fetch1('session_dir') - return session_dir \ No newline at end of file + session_dir = (session.SessionDirectory & session_key + ).fetch1('session_dir') + return session_dir diff --git a/workflow_behavior/pipeline.py b/workflow_behavior/pipeline.py index 8a5f29e..c11a4e6 100644 --- a/workflow_behavior/pipeline.py +++ b/workflow_behavior/pipeline.py @@ -3,17 +3,20 @@ from element_lab import lab from element_animal import subject, genotyping from element_session import session +# from element_behavior import dlc, dlc_run, dlc_track from element_animal.subject import Subject from element_lab.lab import Source, Lab, Protocol, User, Project from element_session.session import Session +# from element_behavior.dlc import Recording, DLCProcessingMethod, DLCRecording, DLCModel, DLCModelMethod + +from .paths import get_beh_root_dir, get_session_directory if 'custom' not in dj.config: dj.config['custom'] = {} db_prefix = dj.config['custom'].get('database.prefix', '') - # Activate "lab", "subject", "session" schema ------------- lab.activate(db_prefix + 'lab') @@ -25,4 +28,7 @@ # Activate "behavior" schema ------------------------------------------------------ -pose.activate(db_prefix + 'pose', linking_module=__name__) +# dlc.activate(db_prefix + 'dlc', +# db_prefix + 'dlc_track', +# db_prefix + 'dlc_run', +# linking_module=__name__) \ No newline at end of file From 8350d396e9116efc0fabc6d15463799e9620b066 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Thu, 13 Jan 2022 17:37:04 -0600 Subject: [PATCH 008/176] Example ingestion in notebook 01 --- .gitignore | 3 +- CHANGELOG.md | 2 +- notebooks/1_Explore_Workflow.ipynb | 1044 ++++++++++- notebooks/_All.ipynb | 2752 ---------------------------- requirements.txt | 6 - setup.py | 6 +- user_data/recordings.csv | 4 + user_data/sessions.csv | 5 +- user_data/subjects.csv | 3 + workflow_behavior/__init__.py | 5 - workflow_behavior/ingest.py | 146 +- workflow_behavior/paths.py | 24 +- workflow_behavior/pipeline.py | 16 +- workflow_behavior/version.py | 2 + 14 files changed, 1158 insertions(+), 2860 deletions(-) delete mode 100644 notebooks/_All.ipynb create mode 100644 user_data/recordings.csv create mode 100644 user_data/subjects.csv create mode 100644 workflow_behavior/version.py diff --git a/.gitignore b/.gitignore index 680edde..5987d96 100644 --- a/.gitignore +++ b/.gitignore @@ -112,6 +112,7 @@ dj_local_con*.json **/.#* docker-compose.y*ml +*backup.y*ml .DS_Store */temp* -temp* \ No newline at end of file +temp* diff --git a/CHANGELOG.md b/CHANGELOG.md index ed66e14..fd2dab0 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,6 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and ### Added + First beta release -## [0.1.0c0] - 2021-11-15 +## [0.1.0c0] - 2021-12-15 ### Added + First draft begins diff --git a/notebooks/1_Explore_Workflow.ipynb b/notebooks/1_Explore_Workflow.ipynb index 9b6b03e..1b47585 100644 --- a/notebooks/1_Explore_Workflow.ipynb +++ b/notebooks/1_Explore_Workflow.ipynb @@ -5,78 +5,1063 @@ "id": "d26010d6-acbc-4c90-8b62-a2448c50452d", "metadata": {}, "source": [ - "# DataJoint U24 - Workflow Session" + "# DataJoint U24 - Workflow Behavior" ] }, { - "cell_type": "markdown", - "id": "c5ffe5d2-5b2a-45c3-8d8f-8c20efa8c5eb", + "cell_type": "code", + "execution_count": 1, + "id": "8b0d2410-e307-49ee-8adf-451bf7b24edc", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "# change to the upper level folder to detect dj_local_conf.json\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "import datajoint as dj\n", + "from pathlib import Path" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d25b109d-c8b2-46f6-8fde-cbd9135cdfc3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n", + "Connecting root@localhost:3306\n" + ] + } + ], + "source": [ + "from workflow_behavior.pipeline import lab, subject, session, dlc" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2125fdae-988b-47cd-9377-af7fd48c6093", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "---- Inserting 0 entry(s) into subject ----\n", + "\n", + "---- Inserting 0 entry(s) into session ----\n", + "\n", + "---- Inserting 0 entry(s) into session_directory ----\n", + "\n", + "---- Inserting 0 entry(s) into session_note ----\n", + "\n", + "---- Inserting 0 entry(s) into recording ----\n", + "\n", + "---- Inserting 0 entry(s) into config ----\n" + ] + } + ], "source": [ - "This notebook will describe the steps to explore the lab and animal management tables created by the elements.\n", - "Prior to using this notebook, please refer to the README for the installation instructions." + "from workflow_behavior.ingest import ingest_subjects, ingest_sessions, ingest_dlc_configs\n", + "ingest_subjects(); ingest_sessions(); ingest_dlc_configs()" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "4351c4bb-9763-4d4d-8558-37662adc930e", + "execution_count": 4, + "id": "3af29f80-63d4-4dd2-9f56-70579d27e9c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deleting 1 rows from `neuro_dlc`.`config`\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Commit deletes? [yes, No]: yes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deletes committed.\n", + "\n", + "---- Inserting 0 entry(s) into recording ----\n", + "\n", + "---- Inserting 1 entry(s) into config ----\n" + ] + } + ], + "source": [ + "import datetime\n", + "key={'subject': 'subject6', 'session_datetime': datetime.datetime(2021, 6, 3, 14, 4, 22)}\n", + "(dlc.Config&key).delete()\n", + "ingest_dlc_configs()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "77d22ee2-0a9d-4e28-88ac-c80b08d8540e", + "metadata": {}, + "outputs": [], + "source": [ + "dlc.Model.populate()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "967f0afd-6ec8-4fce-8bec-5af1d0291537", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    video_path

    \n", + " raw video path relative to session_dir\n", + "
    \n", + "

    config_path

    \n", + " config.yaml relative to session_dir\n", + "
    \n", + "

    shuffle

    \n", + " shuffle number to use (usually 1)\n", + "
    \n", + "

    train_index

    \n", + " train fract of those in yaml, 0-indexed\n", + "
    \n", + "

    snapshot_index

    \n", + " snapshot index, -1 for most recent\n", + "
    \n", + "

    task

    \n", + " task description\n", + "
    \n", + "

    scorer

    \n", + " scorer/network name in config\n", + "
    \n", + "

    multianimal

    \n", + " true for multi-animal\n", + "
    \n", + "

    train_fraction

    \n", + " training fraction specified by train_index\n", + "
    \n", + "

    iteration

    \n", + " iteration number\n", + "
    \n", + "

    pcutoff

    \n", + " threshold of likelihood\n", + "
    \n", + "

    model

    \n", + " DLC's updated GetScorerName()\n", + "
    \n", + "

    start_time

    \n", + " When the model started training\n", + "
    \n", + "

    run_duration

    \n", + " Seconds model run\n", + "
    \n", + "

    dlc_version

    \n", + " keeps the deeplabcut version\n", + "
    subject52020-04-15 11:16:38videos/reachingvideo1.aviconfig.yaml10-1ReachingMackenzie00.9510.4DLC_resnet50_ReachingAug30shuffle1_8002022-01-10 21:02:29282.4252.2.0.5
    subject62021-06-02 14:04:22videos/m3v1mp4.mp4config.yaml10-1openfieldPranav00.9500.4DLC_resnet50_openfieldOct30shuffle1_2002022-01-12 14:59:251569.052.2.0.5
    subject62021-06-03 14:04:22videos/videocompressed1.mp4config.yaml00-1demome10.9500.01DLC_dlcrnetms5_demoJul14shuffle0_200002022-01-12 18:16:523872.912.2.0.5
    \n", + " \n", + "

    Total: 3

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *video_path *config_path *shuffle *train_index *snapshot_inde task scorer multianimal train_fraction iteration pcutoff model start_time run_duration dlc_version \n", + "+----------+ +------------+ +------------+ +------------+ +---------+ +------------+ +------------+ +-----------+ +-----------+ +------------+ +------------+ +-----------+ +---------+ +------------+ +------------+ +------------+ +------------+\n", + "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Reaching Mackenzie 0 0.95 1 0.4 DLC_resnet50_R 2022-01-10 21: 282.425 2.2.0.5 \n", + "subject6 2021-06-02 14: videos/m3v1mp4 config.yaml 1 0 -1 openfield Pranav 0 0.95 0 0.4 DLC_resnet50_o 2022-01-12 14: 1569.05 2.2.0.5 \n", + "subject6 2021-06-03 14: videos/videoco config.yaml 0 0 -1 demo me 1 0.95 0 0.01 DLC_dlcrnetms5 2022-01-12 18: 3872.91 2.2.0.5 \n", + " (Total: 3)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dlc.Model()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4d60db46-efe9-4082-a6e6-b8f0eed17751", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m(665)\u001b[0;36mcheck_fields\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 663 \u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mfield\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 664 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mfield\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheading\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m--> 665 \u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'`{0:s}` is not in the table heading'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfield\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 666 \u001b[0;31m \u001b[0;32melif\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfield_list\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheading\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 667 \u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mDataJointError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Attempt to insert rows with different fields'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> up\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m(674)\u001b[0;36m__make_row_to_insert\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 672 \u001b[0;31m for name in self.heading if name in row.dtype.fields]\n", + "\u001b[0m\u001b[0;32m 673 \u001b[0;31m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# dict-based\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m--> 674 \u001b[0;31m \u001b[0mcheck_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 675 \u001b[0;31m attributes = [self.__make_placeholder(name, row[name], ignore_extra_fields)\n", + "\u001b[0m\u001b[0;32m 676 \u001b[0;31m for name in self.heading if name in row]\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> up\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m(320)\u001b[0;36m\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 318 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 319 \u001b[0;31m \u001b[0mfield_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# collects the field list from first row (passed by reference)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m--> 320 \u001b[0;31m \u001b[0mrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__make_row_to_insert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfield_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 321 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 322 \u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> up\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m(320)\u001b[0;36minsert\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 318 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 319 \u001b[0;31m \u001b[0mfield_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# collects the field list from first row (passed by reference)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m--> 320 \u001b[0;31m \u001b[0mrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__make_row_to_insert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfield_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 321 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 322 \u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> up\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m(266)\u001b[0;36minsert1\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 264 \u001b[0;31m \u001b[0mFor\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msee\u001b[0m \u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 265 \u001b[0;31m \"\"\"\n", + "\u001b[0m\u001b[0;32m--> 266 \u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 267 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 268 \u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_duplicates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_direct_insert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> up\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Volumes/GoogleDrive/My Drive/Dev/element-behavior/element_behavior/dlc.py\u001b[0m(241)\u001b[0;36mmake\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 239 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 240 \u001b[0;31m \u001b[0;31m# --------------- Insert to DataJoint dlc.Model table ---------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m--> 241 \u001b[0;31m self.insert1(dict(key,\n", + "\u001b[0m\u001b[0;32m 242 \u001b[0;31m \u001b[0mtask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Task'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 243 \u001b[0;31m \u001b[0mscorer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'scorer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> up\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/autopopulate.py\u001b[0m(153)\u001b[0;36mpopulate\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 151 \u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_allow_insert\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 152 \u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m--> 153 \u001b[0;31m \u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 154 \u001b[0;31m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSystemExit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 155 \u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> down\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Volumes/GoogleDrive/My Drive/Dev/element-behavior/element_behavior/dlc.py\u001b[0m(241)\u001b[0;36mmake\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 239 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 240 \u001b[0;31m \u001b[0;31m# --------------- Insert to DataJoint dlc.Model table ---------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m--> 241 \u001b[0;31m self.insert1(dict(key,\n", + "\u001b[0m\u001b[0;32m 242 \u001b[0;31m \u001b[0mtask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Task'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 243 \u001b[0;31m \u001b[0mscorer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'scorer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> cfg['Task']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'demo'\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> cfg['model']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'DLC_dlcrnetms5_demoJul14shuffle0_20000'\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> cfg['run_duration']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3872.9103260040283\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> cfg['start_time']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "datetime.datetime(2022, 1, 12, 18, 16, 52, 361727)\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> cfg['scorer']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'me'\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> quit\n" + ] + } + ], + "source": [ + "%debug" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3950c00d-a1a6-495f-a74e-8230c39458aa", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c70ce30f-1c8b-4c85-947d-545ccdd87427", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Connecting root@localhost:3306\n" + "Deleting 1 rows from `neuro_dlc`.`config`\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Commit deletes? [yes, No]: yes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deletes committed.\n" ] }, { "data": { "text/plain": [ - "DataJoint connection (connected) root@localhost:3306" + "1" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "059f736c-3515-4303-b4d2-14f8285e2489", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    video_path

    \n", + " raw video path relative to session_dir\n", + "
    \n", + "

    config_path

    \n", + " config.yaml relative to session_dir\n", + "
    \n", + "

    shuffle

    \n", + " shuffle number to use (usually 1)\n", + "
    \n", + "

    train_index

    \n", + " train fract of those in yaml, 0-indexed\n", + "
    \n", + "

    snapshot_index

    \n", + " snapshot index, -1 for most recent\n", + "
    \n", + "

    task

    \n", + " task description\n", + "
    \n", + "

    scorer

    \n", + " scorer/network name in config\n", + "
    \n", + "

    multianimal

    \n", + " true for multi-animal\n", + "
    \n", + "

    train_fraction

    \n", + " training fraction specified by train_index\n", + "
    \n", + "

    iteration

    \n", + " iteration number\n", + "
    \n", + "

    pcutoff

    \n", + " threshold of likelihood\n", + "
    \n", + "

    model

    \n", + " DLC's updated GetScorerName()\n", + "
    \n", + "

    start_time

    \n", + " When the model started training\n", + "
    \n", + "

    run_duration

    \n", + " Seconds model run\n", + "
    \n", + "

    dlc_version

    \n", + " keeps the deeplabcut version\n", + "
    subject52020-04-15 11:16:38videos/reachingvideo1.aviconfig.yaml10-1ReachingMackenzie00.9510.4DLC_resnet50_ReachingAug30shuffle1_8002022-01-10 21:02:29282.4252.2.0.5
    subject62021-06-02 14:04:22videos/m3v1mp4.mp4config.yaml10-1openfieldPranav00.9500.4DLC_resnet50_openfieldOct30shuffle1_2002022-01-12 14:59:251569.052.2.0.5
    \n", + " \n", + "

    Total: 2

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *video_path *config_path *shuffle *train_index *snapshot_inde task scorer multianimal train_fraction iteration pcutoff model start_time run_duration dlc_version \n", + "+----------+ +------------+ +------------+ +------------+ +---------+ +------------+ +------------+ +-----------+ +-----------+ +------------+ +------------+ +-----------+ +---------+ +------------+ +------------+ +------------+ +------------+\n", + "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Reaching Mackenzie 0 0.95 1 0.4 DLC_resnet50_R 2022-01-10 21: 282.425 2.2.0.5 \n", + "subject6 2021-06-02 14: videos/m3v1mp4 config.yaml 1 0 -1 openfield Pranav 0 0.95 0 0.4 DLC_resnet50_o 2022-01-12 14: 1569.05 2.2.0.5 \n", + " (Total: 2)" ] }, - "execution_count": 2, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# change to the upper level folder to detect dj_local_conf.json\n", - "import os\n", - "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "import datajoint as dj\n", - "dj.conn()" + "dlc.Model()" ] }, { - "cell_type": "markdown", - "id": "ee820754-bceb-476a-acf9-238fa8b201d9", + "cell_type": "code", + "execution_count": 9, + "id": "1486971c-9fb1-49a3-bccf-41ece67a3659", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " uses DeepLabCut h5 output for body part position\n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    video_path

    \n", + " raw video path relative to session_dir\n", + "
    \n", + "

    config_path

    \n", + " config.yaml relative to session_dir\n", + "
    \n", + "

    shuffle

    \n", + " shuffle number to use (usually 1)\n", + "
    \n", + "

    train_index

    \n", + " train fract of those in yaml, 0-indexed\n", + "
    \n", + "

    snapshot_index

    \n", + " snapshot index, -1 for most recent\n", + "
    \n", + "

    joint_name

    \n", + " Name of the joints\n", + "
    \n", + "

    frame_index

    \n", + " frame index in model\n", + "
    \n", + "

    x_pos

    \n", + " \n", + "
    \n", + "

    y_pos

    \n", + " \n", + "
    \n", + "

    likelihood

    \n", + " \n", + "
    subject52020-04-15 11:16:38videos/reachingvideo1.aviconfig.yaml10-1Finger1=BLOB==BLOB==BLOB==BLOB=
    subject52020-04-15 11:16:38videos/reachingvideo1.aviconfig.yaml10-1Hand=BLOB==BLOB==BLOB==BLOB=
    subject52020-04-15 11:16:38videos/reachingvideo1.aviconfig.yaml10-1Joystick1=BLOB==BLOB==BLOB==BLOB=
    subject52020-04-15 11:16:38videos/reachingvideo1.aviconfig.yaml10-1Joystick2=BLOB==BLOB==BLOB==BLOB=
    subject52020-04-15 11:16:38videos/reachingvideo1.aviconfig.yaml10-1Tongue=BLOB==BLOB==BLOB==BLOB=
    subject62021-06-02 14:04:22videos/m3v1mp4.mp4config.yaml10-1leftear=BLOB==BLOB==BLOB==BLOB=
    subject62021-06-02 14:04:22videos/m3v1mp4.mp4config.yaml10-1rightear=BLOB==BLOB==BLOB==BLOB=
    subject62021-06-02 14:04:22videos/m3v1mp4.mp4config.yaml10-1snout=BLOB==BLOB==BLOB==BLOB=
    subject62021-06-02 14:04:22videos/m3v1mp4.mp4config.yaml10-1tailbase=BLOB==BLOB==BLOB==BLOB=
    \n", + " \n", + "

    Total: 9

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *video_path *config_path *shuffle *train_index *snapshot_inde *joint_name frame_inde x_pos y_pos likelihood\n", + "+----------+ +------------+ +------------+ +------------+ +---------+ +------------+ +------------+ +------------+ +--------+ +--------+ +--------+ +--------+\n", + "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Finger1 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Hand =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Joystick1 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Joystick2 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Tongue =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject6 2021-06-02 14: videos/m3v1mp4 config.yaml 1 0 -1 leftear =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject6 2021-06-02 14: videos/m3v1mp4 config.yaml 1 0 -1 rightear =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject6 2021-06-02 14: videos/m3v1mp4 config.yaml 1 0 -1 snout =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject6 2021-06-02 14: videos/m3v1mp4 config.yaml 1 0 -1 tailbase =BLOB= =BLOB= =BLOB= =BLOB= \n", + " (Total: 9)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dlc.Model.Data()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a8e5027f-cee6-4076-9be8-c16dc6fd507a", "metadata": {}, + "outputs": [], "source": [ - "Importing the module `workflow_session.pipeline` is sufficient to create tables inside the elements. This workflow comes prepackaged with example data and ingestion functions to populate lab, subject, and session tables." + "key = (dlc.Config & \"subject='subject5'\").fetch('KEY')[0]" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "868b79bc-f754-4d51-a327-94a209cde374", + "execution_count": null, + "id": "57fa5f19-6fbf-465e-9bab-1f0110990ae4", "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "b7a304e3-a5cb-4ad3-93ce-6bc130e08e26", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "from element_lab import lab\n", - "from element_animal import subject\n", - "from element_session import sessions" + "# Actual guide - needs edits" ] }, { "cell_type": "markdown", - "id": "2e19116d-bc32-4cea-9caf-f3e8eaa9b181", + "id": "c5ffe5d2-5b2a-45c3-8d8f-8c20efa8c5eb", + "metadata": {}, + "source": [ + "This notebook will describe the steps to explore the lab and animal management tables created by the elements.\n", + "Prior to using this notebook, please refer to the README for the installation instructions." + ] + }, + { + "cell_type": "markdown", + "id": "ee820754-bceb-476a-acf9-238fa8b201d9", "metadata": {}, + "source": [ + "Importing the module `workflow_behavior.pipeline` is sufficient to create tables inside the elements. This workflow comes prepackaged with example data and ingestion functions to populate lab, subject, and session tables." + ] + }, + { + "cell_type": "markdown", + "id": "2e19116d-bc32-4cea-9caf-f3e8eaa9b181", + "metadata": { + "tags": [] + }, "source": [ "## Workflow architecture" ] }, + { + "cell_type": "code", + "execution_count": 3, + "id": "868b79bc-f754-4d51-a327-94a209cde374", + "metadata": {}, + "outputs": [], + "source": [ + "from element_lab import lab\n", + "from element_animal import subject\n", + "from element_session import sessions" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -1111,7 +2096,10 @@ { "cell_type": "markdown", "id": "b60f5f4c-d366-4034-a40d-2d2095cb2a14", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ "## Explore each table" ] @@ -1495,9 +2483,9 @@ ], "metadata": { "kernelspec": { - "display_name": "venv-nwb", + "display_name": "venv-dlc", "language": "python", - "name": "venv-nwb" + "name": "venv-dlc" }, "language_info": { "codemirror_mode": { @@ -1509,7 +2497,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.11" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/notebooks/_All.ipynb b/notebooks/_All.ipynb deleted file mode 100644 index bb5aedb..0000000 --- a/notebooks/_All.ipynb +++ /dev/null @@ -1,2752 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4b38af7e-f9cb-4570-88e5-0ddb63ce4009", - "metadata": {}, - "source": [ - "# DJ-Imaging Merged" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "68f79307", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting root@localhost:3306\n" - ] - } - ], - "source": [ - "import sys, os\n", - "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "os.getcwd()\n", - "# Set up basics\n", - "import datajoint as dj; dj.conn()\n", - "import numpy as np\n", - "# Enable plotting and make plots pretty (seaborn)\n", - "from matplotlib import pyplot as plt\n", - "import seaborn as sns\n", - "sns.set(style='dark')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8168238e-20ce-4a93-9cff-d99d2e7e5092", - "metadata": {}, - "outputs": [], - "source": [ - "from workflow_behavior.pipeline import lab, subject, session#, DLCModel" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c8c5e29b-6ed9-44ae-94e7-6ced38c81de9", - "metadata": {}, - "outputs": [], - "source": [ - "import element_behavior" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d1e1d918-6301-4f6b-be3f-6a5fcea9ef45", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n" - ] - } - ], - "source": [ - "from element_behavior.dlc import DLCModel" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "51ee6b33-28b2-4bad-ad3b-3c5d38c55c37", - "metadata": {}, - "outputs": [ - { - "ename": "DataJointError", - "evalue": "Class DLCModel is not properly declared (schema decorator not applied?)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mDataJointError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 344\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 346\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/diagram.py\u001b[0m in \u001b[0;36m_repr_svg_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_repr_svg_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_svg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_repr_svg_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 328\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/diagram.py\u001b[0m in \u001b[0;36mmake_svg\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_svg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSVG\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 314\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mSVG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_svg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/diagram.py\u001b[0m in \u001b[0;36mmake_dot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 254\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/diagram.py\u001b[0m in \u001b[0;36m_make_graph\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 240\u001b[0m for n in graph})\n\u001b[1;32m 241\u001b[0m \u001b[0;31m# relabel nodes to class names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m mapping = {node: lookup_class_name(node, self.context) or node\n\u001b[0m\u001b[1;32m 243\u001b[0m for node in graph.nodes()}\n\u001b[1;32m 244\u001b[0m \u001b[0mnew_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/diagram.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 240\u001b[0m for n in graph})\n\u001b[1;32m 241\u001b[0m \u001b[0;31m# relabel nodes to class names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m mapping = {node: lookup_class_name(node, self.context) or node\n\u001b[0m\u001b[1;32m 243\u001b[0m for node in graph.nodes()}\n\u001b[1;32m 244\u001b[0m \u001b[0mnew_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m in \u001b[0;36mlookup_class_name\u001b[0;34m(name, context, depth)\u001b[0m\n\u001b[1;32m 719\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmember_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# skip IPython's implicit variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmember\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmember\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 721\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mmember\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_table_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# found it!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 722\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m'.'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'context_name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmember_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 723\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# look for part tables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/user_tables.py\u001b[0m in \u001b[0;36m__getattribute__\u001b[0;34m(cls, name)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;31m# trigger instantiation for supported class attrs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m return (cls().__getattribute__(name) if name in supported_class_attrs\n\u001b[0;32m---> 30\u001b[0;31m else super().__getattribute__(name))\n\u001b[0m\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__and__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/utils.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, owner)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__get__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mowner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/user_tables.py\u001b[0m in \u001b[0;36mfull_table_name\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;31m# for derived classes only\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatabase\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m raise DataJointError(\n\u001b[0m\u001b[1;32m 98\u001b[0m \u001b[0;34m'Class %s is not properly declared (schema decorator not applied?)'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m cls.__name__)\n", - "\u001b[0;31mDataJointError\u001b[0m: Class DLCModel is not properly declared (schema decorator not applied?)" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(subject)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "c0bb43af-01e1-4e7a-98b8-10701981db19", - "metadata": {}, - "outputs": [], - "source": [ - "schema=dj.schema()\n", - "schema.activate('neuro_dlc')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "8e0faf9a-c6c4-4f81-bd9f-51701bccce40", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'neuro_dlc' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_2486/3655878047.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mneuro_dlc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'neuro_dlc' is not defined" - ] - } - ], - "source": [ - "neuro_dlc" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f59f55c5-8613-41b8-a8ec-5fed0b55f32f", - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'NoneType' object has no attribute '__dict__'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_1842/4015721976.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdb_prefix\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'lab'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msubject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdb_prefix\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'subject'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdb_prefix\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'session'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdlc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdb_prefix\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'dlc'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Volumes/GoogleDrive/My Drive/Dev/element-animal/element_animal/subject.py\u001b[0m in \u001b[0;36mactivate\u001b[0;34m(schema_name, create_schema, create_tables, linking_module)\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m schema.activate(schema_name, create_schema=create_schema,\n\u001b[0;32m---> 28\u001b[0;31m create_tables=create_tables, add_objects=linking_module.__dict__)\n\u001b[0m\u001b[1;32m 29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute '__dict__'" - ] - } - ], - "source": [ - "lab.activate(db_prefix + 'lab')\n", - "subject.activate(db_prefix + 'subject')\n", - "session.activate(db_prefix + 'session')\n", - "dlc.activate(db_prefix + 'dlc')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "2e30b543-4d31-4847-a647-84a1efdef06f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m\u001b[0m(973)\u001b[0;36m_find_and_load_unlocked\u001b[0;34m()\u001b[0m\n", - "\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> up\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m\u001b[0m(991)\u001b[0;36m_find_and_load\u001b[0;34m()\u001b[0m\n", - "\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> up\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m\u001b[0m(1014)\u001b[0;36m_gcd_import\u001b[0;34m()\u001b[0m\n", - "\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> up\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/importlib/__init__.py\u001b[0m(127)\u001b[0;36mimport_module\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 125 \u001b[0;31m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 126 \u001b[0;31m \u001b[0mlevel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m--> 127 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 128 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 129 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> up\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Volumes/GoogleDrive/My Drive/Dev/element-animal/element_animal/subject.py\u001b[0m(24)\u001b[0;36mactivate\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 22 \u001b[0;31m \"\"\"\n", - "\u001b[0m\u001b[0;32m 23 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinking_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m---> 24 \u001b[0;31m \u001b[0mlinking_module\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinking_module\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 25 \u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mismodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinking_module\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"The argument 'dependency' must be a module's name or a module\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 26 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> inspect.ismodule(numpy)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "*** NameError: name 'numpy' is not defined\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> help('modules')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "*** No help for \"('modules')\"\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> quit\n" - ] - } - ], - "source": [ - "%debug" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5ca5792", - "metadata": {}, - "outputs": [], - "source": [ - "# Load base schema\n", - "db_prefix = dj.config['custom'].get('database.prefix', '')\n", - "lab.activate(db_prefix + 'lab')\n", - "\n", - "schema = dj.schema(dj.config['dj_imaging.database'])\n", - "schema.spawn_missing_classes()" - ] - }, - { - "cell_type": "markdown", - "id": "37faba43", - "metadata": {}, - "source": [ - "## Input new DLC model" - ] - }, - { - "cell_type": "markdown", - "id": "c8fb8a96", - "metadata": {}, - "source": [ - "This notebook shows the steps that have to be taken to insert a new deep lab cut model / processing method combination. At the moment it can only be run by administrators of the pipeline (with write permissions)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9fee9662", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9d6b5668", - "metadata": {}, - "outputs": [], - "source": [ - "# Set up basics\n", - "import sys, os\n", - "sys.path.append('..')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "023a8c4c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Volumes/GoogleDrive/My Drive/Dev/dj-imaging'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "os.getcwd()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d35e4006", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting root@localhost:3306\n" - ] - }, - { - "data": { - "text/plain": [ - "DataJoint connection (connected) root@localhost:3306" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import datajoint as dj; dj.conn()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b5bd51db", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n", - "Deeplabcut package found\n" - ] - } - ], - "source": [ - "from imaging import *\n", - "# from helpers import *\n", - "import yaml" - ] - }, - { - "cell_type": "markdown", - "id": "63a0dfda", - "metadata": {}, - "source": [ - "### Current entries " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b959a437", - "metadata": {}, - "outputs": [], - "source": [ - "from imaging.dlc import *" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a4bc2fd7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "
    \n", - "

    dlc_model

    \n", - " lab-friendly model name\n", - "
    \n", - "

    dlc_task

    \n", - " \n", - "
    \n", - "

    dlc_date

    \n", - " \n", - "
    \n", - "

    dlc_iteration

    \n", - " iteration/version of this model\n", - "
    \n", - "

    dlc_snapshotindex

    \n", - " which snapshot index used for prediction (if -1 then use the latest snapshot)\n", - "
    \n", - "

    dlc_shuffle

    \n", - " which shuffle of the training dataset used for training the network (typically 1)\n", - "
    \n", - "

    dlc_trainingsetindex

    \n", - " which training set fraction used to generate the model (typically 0)\n", - "
    \n", - "

    dlc_scorer

    \n", - " scorer/network name for a particular shuffle, training fraction etc.\n", - "
    \n", - "

    dlc_cfg_template

    \n", - " dictionary of the config yaml needed to run the deeplabcut.analyze_videos()\n", - "
    \n", - "

    dlc_model_description

    \n", - " \n", - "
    \n", - " \n", - "

    Total: 0

    \n", - " " - ], - "text/plain": [ - "*dlc_model dlc_task dlc_date dlc_iteration dlc_snapshotin dlc_shuffle dlc_trainingse dlc_scorer dlc_cfg_te dlc_model_desc\n", - "+-----------+ +----------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +--------+ +------------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "DLCModel()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8cf52706", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "
    \n", - "

    dlc_tracking_processing_method

    \n", - " e.g 2points_leftrightear\n", - "
    \n", - "

    method_description

    \n", - " \n", - "
    \n", - "

    dlc_tracking_processing_params

    \n", - " \n", - "
    \n", - "

    applicable_tracking_type

    \n", - " \n", - "
    \n", - "

    function_to_invoke

    \n", - " DLC processing method in the \"loaders.tracking_dlc\" module\n", - "
    \n", - "

    param_hash

    \n", - " \n", - "
    \n", - " \n", - "

    Total: 0

    \n", - " " - ], - "text/plain": [ - "*dlc_tracking_ method_descrip dlc_tracki applicable_tra function_to_in param_hash \n", - "+------------+ +------------+ +--------+ +------------+ +------------+ +------------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "DLCTrackingProcessingMethod()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "46560b47", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "
    \n", - "

    dlc_model

    \n", - " lab-friendly model name\n", - "
    \n", - "

    dlc_tracking_processing_method

    \n", - " e.g 2points_leftrightear\n", - "
    \n", - "

    method_desc

    \n", - " description for this model-method combination\n", - "
    \n", - " \n", - "

    Total: 0

    \n", - " " - ], - "text/plain": [ - "*dlc_model *dlc_tracking_ method_desc \n", - "+-----------+ +------------+ +------------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "DLCProcessingMethod()" - ] - }, - { - "cell_type": "markdown", - "id": "0f1d3deb", - "metadata": {}, - "source": [ - "### First insert new DLC model" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "8dd9f8a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Volumes/GoogleDrive/My Drive/Dev/dj-imaging'" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# os.chdir('openfield-Pranav-2018-10-30')\n", - "# os.listdir()\n", - "os.getcwd()\n", - "#os.chdir('/Volumes/GoogleDrive/My Drive/Dev/dj-imaging')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ad9ea562", - "metadata": {}, - "outputs": [], - "source": [ - "# ============================== SETUP for DEEPLABCUT ================================\n", - "# ---- DLC model ----\n", - "# load cfg from a config.yaml\n", - "wd = '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/openfield-Pranav-2018-10-30/config.yaml'\n", - "with open(wd, 'rb') as f:\n", - " # print(f)\n", - " cfg = yaml.safe_load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "953ca129", - "metadata": {}, - "outputs": [], - "source": [ - "new_model_name = 'my_model'" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4e8cc9b0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Task': 'openfield',\n", - " 'scorer': 'Pranav',\n", - " 'date': 'Oct30',\n", - " 'multianimalproject': None,\n", - " 'identity': None,\n", - " 'project_path': '/Volumes/GoogleDrive/My Drive/Modules/DeepLabCut/examples/openfield-Pranav-2018-10-30',\n", - " 'video_sets': {'/Volumes/GoogleDrive/My Drive/Modules/DeepLabCut/examples/openfield-Pranav-2018-10-30/videos/m4s1.mp4': {'crop': '0, 640, 0, 480'}},\n", - " 'bodyparts': ['snout', 'leftear', 'rightear', 'tailbase'],\n", - " 'start': 0,\n", - " 'stop': 1,\n", - " 'numframes2pick': 20,\n", - " 'skeleton': [],\n", - " 'skeleton_color': 'black',\n", - " 'pcutoff': 0.4,\n", - " 'dotsize': 8,\n", - " 'alphavalue': 0.7,\n", - " 'colormap': 'jet',\n", - " 'TrainingFraction': [0.95],\n", - " 'iteration': 0,\n", - " 'default_net_type': 'resnet_50',\n", - " 'default_augmenter': 'imgaug',\n", - " 'snapshotindex': -1,\n", - " 'batch_size': 4,\n", - " 'cropping': False,\n", - " 'x1': 0,\n", - " 'x2': 640,\n", - " 'y1': 277,\n", - " 'y2': 624,\n", - " 'corner2move2': [50, 50],\n", - " 'move2corner': True}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cfg" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "df5b9306", - "metadata": {}, - "outputs": [], - "source": [ - "import deeplabcut" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "d32dbfc8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--- DLC Model specification to be inserted ---\n", - "\tdlc_model: my_model\n", - "\tdlc_model_description: my_model, Task: mouse_openfield, date: Dec15, iteration: 10, shuffle: 1, training_fraction: 0.95, latest snapshot\n", - "\tdlc_scorer: unknown\n", - "\tdlc_task: openfield\n", - "\tdlc_date: Oct30\n", - "\tdlc_iteration: 0\n", - "\tdlc_snapshotindex: -1\n", - "\tdlc_shuffle: 1\n", - "\tdlc_trainingsetindex: 0\n", - "\tdlc_project_path: /Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/openfield-Pranav-2018-10-30\n", - "\trepository_name: a\n", - "\t-- Template for config.yaml --\n", - "\t\tTask: openfield\n", - "\t\tdate: Oct30\n", - "\t\tTrainingFraction: [0.95]\n", - "\t\titeration: 0\n", - "\t\tsnapshotindex: -1\n", - "\t\tbatch_size: 4\n", - "\t\tcropping: False\n", - "\t\tx1: 0\n", - "\t\tx2: 640\n", - "\t\ty1: 277\n", - "\t\ty2: 624\n", - "Proceed with new DLC model insert? [yes, no]: yes\n" - ] - }, - { - "ename": "IntegrityError", - "evalue": "Cannot add or update a child row: a foreign key constraint fails (`group_shared_imaging`.`d_l_c_model__model_path`, CONSTRAINT `d_l_c_model__model_path_ibfk_2` FOREIGN KEY (`repository_name`) REFERENCES `#repository` (`repository_name`) ON UPDATE CASCADE)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIntegrityError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_37855/1316993212.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m DLCModel.insert_new_model(\n\u001b[0m\u001b[1;32m 2\u001b[0m **{'dlc_model': f'{new_model_name}',\n\u001b[1;32m 3\u001b[0m \u001b[0;34m'cfg'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m'project_path'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/openfield-Pranav-2018-10-30'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m'dlc_model_description'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'my_model, Task: mouse_openfield, date: Dec15, iteration: 10, shuffle: 1, training_fraction: 0.95, latest snapshot'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Volumes/GoogleDrive/My Drive/Dev/dj-imaging/imaging/dlc.py\u001b[0m in \u001b[0;36minsert_new_model\u001b[0;34m(cls, dlc_model, cfg, dlc_task, dlc_date, dlc_iteration, dlc_shuffle, dlc_trainingsetindex, dlc_snapshotindex, project_path, dlc_model_description, dlc_scorer)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransaction\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModelPath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;31m# -- Check and handle new TrackedBodyPart --\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m in \u001b[0;36minsert1\u001b[0;34m(self, row, **kwargs)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0mFor\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msee\u001b[0m \u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \"\"\"\n\u001b[0;32m--> 266\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_duplicates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_direct_insert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m in \u001b[0;36minsert\u001b[0;34m(self, rows, replace, skip_duplicates, ignore_extra_fields, allow_direct_insert)\u001b[0m\n\u001b[1;32m 328\u001b[0m duplicate=(' ON DUPLICATE KEY UPDATE `{pk}`=`{pk}`'.format(pk=self.primary_key[0])\n\u001b[1;32m 329\u001b[0m if skip_duplicates else ''))\n\u001b[0;32m--> 330\u001b[0;31m self.connection.query(query, args=list(\n\u001b[0m\u001b[1;32m 331\u001b[0m itertools.chain.from_iterable(\n\u001b[1;32m 332\u001b[0m (v for v in r['values'] if v is not None) for r in rows)))\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/connection.py\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, query, args, as_dict, suppress_warnings, reconnect)\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0mcursor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcursor_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuppress_warnings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 301\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLostConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreconnect\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/connection.py\u001b[0m in \u001b[0;36m_execute_query\u001b[0;34m(cursor, query, args, suppress_warnings)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mtranslate_query_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuppress_warnings\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreconnect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mIntegrityError\u001b[0m: Cannot add or update a child row: a foreign key constraint fails (`group_shared_imaging`.`d_l_c_model__model_path`, CONSTRAINT `d_l_c_model__model_path_ibfk_2` FOREIGN KEY (`repository_name`) REFERENCES `#repository` (`repository_name`) ON UPDATE CASCADE)" - ] - } - ], - "source": [ - "DLCModel.insert_new_model(\n", - " **{'dlc_model': f'{new_model_name}',\n", - " 'cfg': cfg, \n", - " 'project_path': '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/openfield-Pranav-2018-10-30',\n", - " 'dlc_model_description': 'my_model, Task: mouse_openfield, date: Dec15, iteration: 10, shuffle: 1, training_fraction: 0.95, latest snapshot',\n", - " 'dlc_scorer': 'unknown'}) " - ] - }, - { - "cell_type": "markdown", - "id": "cfd0aa0e", - "metadata": {}, - "source": [ - "### ... then take care of processing method" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "28a5713a", - "metadata": {}, - "outputs": [], - "source": [ - "processing_method_name = \"left_right_ears\"" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "243b4256", - "metadata": {}, - "outputs": [ - { - "ename": "DataJointError", - "evalue": "The specified param-set already exists - name: nose_mouse_wj", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mDataJointError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;34m'applicable_tracking_type'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'OpenField'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m 'function_to_invoke': 'process_two_tracked_points'}\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mDLCTrackingProcessingMethod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert_new_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mdlc_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mC:\\work\\python\\dj-moser-imaging\\imaging\\dlc.py\u001b[0m in \u001b[0;36minsert_new_method\u001b[1;34m(cls, dlc_tracking_processing_method, dlc_tracking_processing_params, applicable_tracking_type, function_to_invoke, method_description)\u001b[0m\n\u001b[0;32m 227\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 228\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# If not same name: human error, trying to add the same paramset with different name\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 229\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mdj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataJointError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'The specified param-set already exists - name: {}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 230\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 231\u001b[0m \u001b[1;31m# now validate the \"function_to_invoke\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mDataJointError\u001b[0m: The specified param-set already exists - name: nose_mouse_wj" - ] - } - ], - "source": [ - "# ---- DLC tracking processing method ----\n", - "dlc_method = {'dlc_tracking_processing_method': processing_method_name,\n", - " 'method_description': 'using 2LED processing method on nose and base of head (no space in attribute names)',\n", - " 'dlc_tracking_processing_params': {'left_point_name': 'nose',\n", - " 'right_point_name': 'mouse'},\n", - " 'applicable_tracking_type': 'OpenField',\n", - " 'function_to_invoke': 'process_two_tracked_points'}\n", - "DLCTrackingProcessingMethod.insert_new_method(**dlc_method)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "19273529", - "metadata": {}, - "outputs": [], - "source": [ - "# ---- Association of DLCModel and DLC processing method ----\n", - "DLCProcessingMethod.insert1({'dlc_model':f'{new_model_name}',\n", - " 'dlc_tracking_processing_method': processing_method_name})" - ] - }, - { - "cell_type": "markdown", - "id": "ae636cdb", - "metadata": {}, - "source": [ - "### Make sure tracked body parts are up to date" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "0dc8cca2", - "metadata": {}, - "outputs": [], - "source": [ - "from imaging.tracking import TrackedBodyPart\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "f5366340", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    body_part
    0bodycenter
    1bottom_left_corner
    2bottom_right_corner
    3chocolate_milk
    4cue_card_bottom_center
    5left_ear
    6leftear
    7lefthand
    8leftleg
    9miniscope
    10mouse
    11nose
    12nose_tip
    13right_ear
    14rightear
    15righthand
    16rightleg
    17tail_base
    18tailbase
    19top_left_corner
    20top_right_corner
    \n", - "
    " - ], - "text/plain": [ - " body_part\n", - "0 bodycenter\n", - "1 bottom_left_corner\n", - "2 bottom_right_corner\n", - "3 chocolate_milk\n", - "4 cue_card_bottom_center\n", - "5 left_ear\n", - "6 leftear\n", - "7 lefthand\n", - "8 leftleg\n", - "9 miniscope\n", - "10 mouse\n", - "11 nose\n", - "12 nose_tip\n", - "13 right_ear\n", - "14 rightear\n", - "15 righthand\n", - "16 rightleg\n", - "17 tail_base\n", - "18 tailbase\n", - "19 top_left_corner\n", - "20 top_right_corner" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.DataFrame(TrackedBodyPart.fetch(as_dict=True))" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "16bf0c2a", - "metadata": {}, - "outputs": [ - { - "ename": "JSONDecodeError", - "evalue": "Expecting property name enclosed in double quotes: line 33 column 5 (char 983)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mJSONDecodeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_32742/164256071.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'..'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dj_local_conf.json'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/settings.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLOCALCONFIG\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msave_local\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/json/__init__.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[0mkwarg\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0motherwise\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mJSONDecoder\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mused\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 292\u001b[0m \"\"\"\n\u001b[0;32m--> 293\u001b[0;31m return loads(fp.read(),\n\u001b[0m\u001b[1;32m 294\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject_hook\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobject_hook\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0mparse_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_float\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_int\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_int\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 357\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 358\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 359\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m 335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 336\u001b[0m \"\"\"\n\u001b[0;32m--> 337\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 338\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 339\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m 351\u001b[0m \"\"\"\n\u001b[1;32m 352\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 354\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting property name enclosed in double quotes: line 33 column 5 (char 983)" - ] - } - ], - "source": [ - "import os\n", - "import datajoint as dj\n", - "\n", - "os.chdir('..')\n", - "dj.config.load(\"dj_local_conf.json\")\n", - "\n", - "import pathlib\n", - "import numpy as np\n", - "import datetime\n", - "import ipywidgets as widgets" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ecbc5300", - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'dj_imaging.database'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_32729/2314122460.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdj_schema\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_virtual_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dj_schema'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dj_imaging.database'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/settings.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/settings.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'dj_imaging.database'" - ] - } - ], - "source": [ - "dj_schema = dj.create_virtual_module('dj_schema', dj.config['dj_imaging.database'])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "602862c1", - "metadata": {}, - "outputs": [], - "source": [ - "no_dlc_sess = (dj_schema.Recording - dj_schema.RecordingDLC\n", - " & (dj_schema.Recording.Data * dj_schema.Dataset & 'datasettype = \"DLC_tracking\"'))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "76f2c6c6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    session_name

    \n", - " Meta session name (hash)\n", - "
    \n", - "

    recording_order

    \n", - " Order of session within meta sessions (zero index!)\n", - "
    \n", - "

    recording_name

    \n", - " Recording name: Hash of animal_id, datasource_id, timestamp and combined 'yes'/'no' label\n", - "
    \n", - "

    animal_id

    \n", - " \n", - "
    \n", - "

    datasource_id

    \n", - " \n", - "
    \n", - "

    animal_name

    \n", - " Animal name in mlims\n", - "
    \n", - "

    timestamp

    \n", - " Timestamp of session\n", - "
    \n", - "

    combined

    \n", - " \n", - "
    \n", - "

    timeseries_name

    \n", - " Timeseries name [e.g. MUnit_0]\n", - "
    \n", - "

    equipment_type

    \n", - " \n", - "
    \n", - "

    username

    \n", - " NTNU username\n", - "
    6c86cf0b2c3428a301eb2f70e95b30a9200270322020-08-19 15:23:06nofile2Pminiscope_Auser123
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*metasession_n *recording_order *recording_name animal_id datasource_id animal_name timestamp combined timeseries_nam experiment_typ username \n", - "+------------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +----------+ +------------+ +------------+ +----------+\n", - "6c86cf0b2c3428 0 1eb2f70e95b30a 0 0 27032 2020-08-19 15: no file 2Pminiscope_A user123 \n", - " (Total: 1)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "no_dlc_sess" - ] - }, - { - "cell_type": "markdown", - "id": "dc05d1bf", - "metadata": {}, - "source": [ - "### Recording selector UI" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3374d4b7", - "metadata": {}, - "outputs": [], - "source": [ - "sess_selector = widgets.Dropdown(options=no_dlc_sess.proj(\n", - " subject='animal_name', basename='timeseries_name').fetch(as_dict=True), disabled=False, description='Recordings:')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7c5fc28a", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "37c3dc258561420a840b4d01b76623b4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Dropdown(description='Recordings:', options=({'session_name': '6c86cf0b2c3428a3', 'recording_order': 0, 'sessi…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sess_selector" - ] - }, - { - "cell_type": "markdown", - "id": "5a8e99f0", - "metadata": {}, - "source": [ - "### DLC model/method selector" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "59a86d38", - "metadata": {}, - "outputs": [], - "source": [ - "selected_sess = sess_selector.value" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "aa8161cc", - "metadata": {}, - "outputs": [], - "source": [ - "q_dlc_models = dj_schema.DLCModel & (dj_schema.Recording.Data * dj_schema.InferredRecordingDLC & selected_sess)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4d26eacf", - "metadata": {}, - "outputs": [], - "source": [ - "options = dj_schema.DLCProcessingMethod & q_dlc_models if q_dlc_models else dj_schema.DLCProcessingMethod()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a44c8a35", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "
    \n", - "

    dlc_model

    \n", - " lab-friendly model name (perhaps the same as dlc_scorer)\n", - "
    \n", - "

    dlc_tracking_processing_method

    \n", - " e.g 2points_leftrightear\n", - "
    \n", - "

    method_desc

    \n", - " description for this model-method combination\n", - "
    Resnet50_mouse_openfieldJun30shuffle1_latestSnapshotleft_right_ears
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*dlc_model *dlc_tracking_ method_desc \n", - "+------------+ +------------+ +------------+\n", - "Resnet50_mouse left_right_ear \n", - " (Total: 1)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "options" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "fbb364ac", - "metadata": {}, - "outputs": [], - "source": [ - "dlc_selector = widgets.Dropdown(options=options.fetch('KEY'), disabled=False, description='DLC Models:')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "4138001f", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "04b252f259874374827d670c13795774", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Dropdown(description='DLC Models:', options=({'dlc_model': 'Resnet50_mouse_openfieldJun30shuffle1_latestSnapsh…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dlc_selector" - ] - }, - { - "cell_type": "markdown", - "id": "8a1b388b", - "metadata": {}, - "source": [ - "### Insertion" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "cd774b32", - "metadata": {}, - "outputs": [], - "source": [ - "selected_dlc = dlc_selector.value" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f5b3fc9f", - "metadata": {}, - "outputs": [], - "source": [ - "dj_schema.RecordingDLC.insert1({**selected_sess, **selected_dlc}, ignore_extra_fields=True)" - ] - }, - { - "cell_type": "markdown", - "id": "4bfd4d02", - "metadata": {}, - "source": [ - "## Fill in Recording Type and Apparatus info" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "40d2eeb2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "210a524f", - "metadata": {}, - "outputs": [], - "source": [ - "import sys, os\n", - "sys.path.append('..')\n", - "import numpy\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.set(style='dark')" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "757ca5ca", - "metadata": {}, - "outputs": [], - "source": [ - "from imaging import *\n", - "from helpers import *" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "5661fc37", - "metadata": {}, - "outputs": [], - "source": [ - "from ipywidgets import interact, interactive, fixed, interact_manual\n", - "import ipywidgets as widgets" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "b22ec094", - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import HTML, display\n", - "import tabulate" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "1505b09c", - "metadata": {}, - "outputs": [], - "source": [ - "def draw_tracking(session, sessiontype, apparatus):\n", - " try:\n", - " track = (TrackingRaw.OpenField & 'recording_name = \"{}\"'.format(session)).fetch1('x_pos','y_pos')\n", - " timeseries_name = (Recording & 'recording_name = \"{}\"'.format(session)).fetch1('timeseries_name')\n", - " sns.set(style='white',font_scale=1.2)\n", - " \n", - " figure = plt.figure(figsize=(7,7))\n", - " ax = figure.add_subplot(111)\n", - " ax.scatter(track[0],track[1], s=2, c='k', alpha=.1)\n", - " ax.set_title(timeseries_name,y=.97)\n", - " sns.despine(left=True,bottom=True)\n", - " ax.invert_yaxis()\n", - " ax.set_aspect('equal')\n", - " ax.get_xaxis().set_ticks([]);ax.get_yaxis().set_ticks([])\n", - " \n", - " except dj.DataJointError:\n", - " track = (TrackingRaw.Linear & 'recording_name = \"{}\"'.format(session)).fetch1('pos')\n", - " timeseries_name = (Recording & 'recording_name = \"{}\"'.format(session)).fetch1('timeseries_name')\n", - " sns.set(style='white',font_scale=1.2)\n", - " \n", - " figure = plt.figure(figsize=(7,3))\n", - " ax = figure.add_subplot(111)\n", - " ax.plot(track, lw=2, c='k', alpha=.1)\n", - " ax.set_title(timeseries_name,y=.97)\n", - " sns.despine(left=True,bottom=True)\n", - " ax.get_xaxis().set_ticks([]);ax.get_yaxis().set_ticks([]) " - ] - }, - { - "cell_type": "markdown", - "id": "b9116c0b", - "metadata": {}, - "source": [ - "### Execute to display widget and enter sessiontype and apparatus info" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "b485e5c0", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "02c86cc826a54cb495aed40c5bb3a0ac", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(Dropdown(description='session', options=('62a1f0a4383d854a',), value='62a1f0a4383d854a')…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a5f2fd5d235f4732a19992d91193f480", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(Button(description='Insert into DB', style=ButtonStyle()), Output()))" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sessions = (TrackingRaw - Recording.Apparatus).fetch('recording_name')\n", - "sessiontypes = RecordingType().fetch('sessiontype')\n", - "apparatus = Apparatus().fetch('apparatus')\n", - "\n", - "if len(sessions) == 0:\n", - " raise IndexError('No session info missing!')\n", - " \n", - "im = interact(draw_tracking, session=sessions, sessiontype=sessiontypes, apparatus=apparatus)\n", - "button = widgets.Button(description='Insert into DB')\n", - "out = widgets.Output()\n", - "\n", - "def insert_dj(b):\n", - " recording_name = im.widget.children[0].value\n", - " session_type = im.widget.children[1].value\n", - " apparatus = im.widget.children[2].value\n", - " with out:\n", - " category = (Apparatus & 'apparatus =\"{}\"'.format(apparatus)).fetch1('category')\n", - " print('Inserting Recording: {} | Type: {} | Apparatus: {}'.format(recording_name,session_type,apparatus))\n", - " session_entry = (Recording.proj() & 'recording_name = \"{}\"'.format(recording_name)).fetch1()\n", - " session_entry['sessiontype'] = session_type\n", - " Recording.RecordingType.insert1(session_entry, skip_duplicates=True)\n", - " session_entry['apparatus'] = apparatus\n", - " session_entry['category'] = category\n", - " Recording.Apparatus.insert1(session_entry, skip_duplicates=True, ignore_extra_fields=True)\n", - "\n", - "button.on_click(insert_dj)\n", - "\n", - "im.widget.children[0].description = 'Recording'\n", - "im.widget.children[1].description = 'Type'\n", - "im.widget.children[2].description = 'Apparatus'\n", - "im.widget.children[3].description = 'Draw!'\n", - "widgets.HBox([button, out])" - ] - }, - { - "cell_type": "markdown", - "id": "db4095da", - "metadata": {}, - "source": [ - "## Work with offset corrected tracking data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2a86837d", - "metadata": {}, - "outputs": [], - "source": [ - "# Set up basics\n", - "import datajoint as dj" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e8b4059d", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "571607f8", - "metadata": {}, - "outputs": [], - "source": [ - "# Enable plotting and make plots pretty (seaborn)\n", - "from matplotlib import pyplot as plt\n", - "import seaborn as sns\n", - "sns.set(style='dark')\n", - "%config InlineBackend.figure_format = 'retina'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "96af7d04", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting horsto@kavlidatajoint02.it.ntnu.no:3306\n" - ] - } - ], - "source": [ - "# Load base schema\n", - "schema = dj.schema(dj.config['dj_imaging.database'])\n", - "schema.spawn_missing_classes()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "26482530", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "sys.path.append('..')" - ] - }, - { - "cell_type": "markdown", - "id": "568cc3c2", - "metadata": {}, - "source": [ - "### Get Tracking *raw* results" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "3aba003a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    session_name

    \n", - " Meta session name (hash)\n", - "
    \n", - "

    recording_order

    \n", - " Order of session within meta sessions (zero index!)\n", - "
    \n", - "

    recording_name

    \n", - " Recording name: Hash of animal_id, datasource_id, timestamp and combined 'yes'/'no' label\n", - "
    \n", - "

    animal_id

    \n", - " \n", - "
    \n", - "

    datasource_id

    \n", - " \n", - "
    \n", - "

    animal_name

    \n", - " Animal name in mlims\n", - "
    \n", - "

    timestamp

    \n", - " Timestamp of session\n", - "
    \n", - "

    combined

    \n", - " \n", - "
    \n", - "

    timeseries_name

    \n", - " Timeseries name [e.g. MUnit_0]\n", - "
    \n", - "

    equipment_type

    \n", - " \n", - "
    \n", - "

    username

    \n", - " NTNU username\n", - "
    f8557ddd091a94b2066f39b5352265e47574e9c63eece4fbe0949212020-12-18 13:30:49yestrial12Pminiscope_Ajorgensu
    f8557ddd091a94b21640777a7bd5111ab574e9c63eece4fbe0949212020-12-19 16:17:17yestrial22Pminiscope_Ajorgensu
    f8557ddd091a94b224d38c8f597146dde574e9c63eece4fbe0949212020-12-20 16:00:12yestrial32Pminiscope_Ajorgensu
    \n", - " \n", - "

    Total: 3

    \n", - " " - ], - "text/plain": [ - "*metasession_n *recording_order *recording_name animal_id datasource_id animal_name timestamp combined timeseries_nam experiment_typ username \n", - "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+ +------------+ +----------+\n", - "f8557ddd091a94 0 66f39b5352265e 574e9c63eece4f 0 94921 2020-12-18 13: yes trial1 2Pminiscope_A jorgensu \n", - "f8557ddd091a94 1 640777a7bd5111 574e9c63eece4f 0 94921 2020-12-19 16: yes trial2 2Pminiscope_A jorgensu \n", - "f8557ddd091a94 2 4d38c8f597146d 574e9c63eece4f 0 94921 2020-12-20 16: yes trial3 2Pminiscope_A jorgensu \n", - " (Total: 3)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Recording & 'username = \"jorgensu\"' & 'animal_name = \"94921\"'" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "adfc97d8", - "metadata": {}, - "outputs": [], - "source": [ - "# pick one session \n", - "key = (TrackingRaw & 'recording_name = \"66f39b5352265e47\"').fetch1('KEY')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "238e9775", - "metadata": {}, - "outputs": [], - "source": [ - "# Positions (offset corrected to begin with)\n", - "x_pos_raw, y_pos_raw = (TrackingRaw.OpenField & key).fetch1('x_pos','y_pos')\n", - "\n", - "# Raw signal from DLC part table \n", - "reward_x_raw, reward_y_raw, likelihood_reward = (TrackingRaw.DLCPart & key & 'body_part = \"chocolate_milk\"').fetch1('bodypart_x_pos','bodypart_y_pos','bodypart_likelihood')\n", - "filter_reward = likelihood_reward > .1" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "ba28e0b1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Raw tracking results')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "image/png": { - "height": 375, - "width": 600 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "figure = plt.figure(figsize=(10,6))\n", - "ax = figure.add_subplot(111)\n", - "\n", - "ax.plot(x_pos_raw, \n", - " y_pos_raw,\n", - " '-k', \n", - " alpha=.9)\n", - "\n", - "# Plot reward port tracking ('chocolate_milk' on top)\n", - "# ... if not present it will be filtered out \n", - "ax.scatter(reward_x_raw[filter_reward], \n", - " reward_y_raw[filter_reward], \n", - " color='orange', \n", - " s=200, \n", - " marker='x', \n", - " alpha=.5, \n", - " zorder=10, \n", - " label='reward')\n", - "ax.legend()\n", - "ax.set_title('Raw tracking results')" - ] - }, - { - "cell_type": "markdown", - "id": "d6f3f1c4", - "metadata": {}, - "source": [ - "### Do the same for Tracking results" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "454dd6d2", - "metadata": {}, - "outputs": [], - "source": [ - "# Positions (offset corrected to begin with)\n", - "x_pos, y_pos = (Tracking.OpenField & key).fetch1('x_pos','y_pos')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "9e419294", - "metadata": {}, - "outputs": [], - "source": [ - "# Positions (offset corrected to begin with)\n", - "x_pos_raw, y_pos_raw = (Tracking.OpenField & key).fetch1('x_pos','y_pos')\n", - "# Raw signal \n", - "reward_x, reward_y = (Tracking.DLCPart & key & 'body_part = \"chocolate_milk\"').fetch1('bodypart_x_pos','bodypart_y_pos')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "82e95fd5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "image/png": { - "height": 360, - "width": 600 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "figure = plt.figure(figsize=(10,6))\n", - "ax = figure.add_subplot(111)\n", - "\n", - "ax.plot(x_pos, y_pos, '-k', alpha=.9)\n", - "ax.scatter(reward_x[filter_reward], \n", - " reward_y[filter_reward], \n", - " color='orange', \n", - " s=200, \n", - " marker='x', \n", - " alpha=.5, \n", - " zorder=10, \n", - " label='reward')\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "98d3d732", - "metadata": {}, - "source": [ - "## Add timestamp to DLC pickle file" - ] - }, - { - "cell_type": "markdown", - "id": "4cbd3692", - "metadata": {}, - "source": [ - "Information about the timestamp of the tracking video (tracking start time) is not available from the set of DLC output currently. Users will need to manually add this info into the generated pickle file, under a variable 'Start TimeStamp'\n", - "Which can be done with the following example code (copyed from here: https://github.com/kavli-ntnu/dj-moser-imaging/pull/69)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "8faf107c", - "metadata": {}, - "outputs": [], - "source": [ - "import scanreader # If you don't have it installed: pip install git+https://github.com/atlab/scanreader.git\n", - "from datetime import datetime\n", - "import pickle" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "da6a5ea8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting horsto@kavlidatajoint02.it.ntnu.no:3306\n", - "Suite2p not found\n" - ] - } - ], - "source": [ - "import sys \n", - "sys.path.append('..')\n", - "from imaging.utils import read_timestamp_rawtif" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "977b6865", - "metadata": {}, - "outputs": [], - "source": [ - "def add_timestamp_to_pickle(first_tif_file, matching_dlc_pkl_file):\n", - " timestamp_tif = read_timestamp_rawtif(first_tif_file)\n", - " video_datetime = datetime.strftime(timestamp_tif, \"%Y-%m-%d_%H:%M:%S.%f\")\n", - " \n", - " # Write to dlc pickle file\n", - " with open(matching_dlc_pkl_file, 'rb') as f:\n", - " pickle_dict = pickle.load(f)\n", - "\n", - " pickle_dict['Start TimeStamp'] = video_datetime\n", - " pickle.dump(pickle_dict, open(matching_dlc_pkl_file, 'wb'))" - ] - }, - { - "cell_type": "markdown", - "id": "fe318b6f", - "metadata": {}, - "source": [ - "### Configure filenames " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "c7125bac", - "metadata": {}, - "outputs": [], - "source": [ - "tif_file = '/Volumes/moser/horsto/MEC data from Weijian/97046/20210313/96766_20210312_ML0000_AP-400_2Openfiled_00001.tif'\n", - "matching_dlc_pkl_file = '/Volumes/moser/horsto/MEC data from Weijian/97046/20210313/96766_20210312_ML0000_AP-400_2Openfiled_dlc/96766_20210312_ML0000_AP-400_2Openfiled_00001_trackingVideoDLC_resnet_50_OPENMINI2P_topcamera_20210305Mar5shuffle1_1030000_meta.pickle'" - ] - }, - { - "cell_type": "markdown", - "id": "92d8a812", - "metadata": {}, - "source": [ - "### Execute ... " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "d683997c", - "metadata": {}, - "outputs": [], - "source": [ - "add_timestamp_to_pickle(tif_file, matching_dlc_pkl_file)" - ] - }, - { - "cell_type": "markdown", - "id": "5d78d095", - "metadata": {}, - "source": [ - "## Insert object locations for object sessions in the open field" - ] - }, - { - "cell_type": "markdown", - "id": "43262af8", - "metadata": {}, - "source": [ - "Extract sessions for which object locations have not been recorded yet. \n", - "\n", - "Create Napari (napari.org) viewer and let the user click on the center point of (round) objects and define a radius (second click). \n", - "Then extract centers and radius for each object and insert into database." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f11229b4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting horsto@kavlidatajoint02.it.ntnu.no:3306\n" - ] - } - ], - "source": [ - "import datajoint as dj \n", - "# Load base schema\n", - "schema = dj.schema(dj.config['dj_imaging.database'])\n", - "schema.spawn_missing_classes()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "38b94645", - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "import seaborn as sns # Make plots pretty\n", - "sns.set(style='dark')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f919ac00", - "metadata": {}, - "outputs": [], - "source": [ - "# Create GUI (QT) context\n", - "%gui qt" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "bdeaa83c", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "sys.path.append('..')\n", - "from helpers_visual.enter_object_locations import extract_undefined_object_sessions, create_napari_object_viewer" - ] - }, - { - "cell_type": "markdown", - "id": "740f42da", - "metadata": {}, - "source": [ - "### User defined object location and radius" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "d84b2c76", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of remaining sessions to define: 0\n" - ] - }, - { - "ename": "TypeError", - "evalue": "'NoneType' object is not iterable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_undefined_object_sessions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mobject_layer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj_sess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_napari_object_viewer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not iterable" - ] - } - ], - "source": [ - "session = extract_undefined_object_sessions()\n", - "object_layer, key, obj_sess = create_napari_object_viewer(session)" - ] - }, - { - "cell_type": "markdown", - "id": "2dbcba79", - "metadata": {}, - "source": [ - "### Confirm object position" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "b3667595", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2UAAAM/CAYAAABRY3rCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydeXCb9Z3/37ovH7It2Y4SH3Fw4sQErMxCwhHSJhi627Cw0A2lG1o63ekCy3R7wNKdTpcy7W+XLXRaWhrobIG0tLTr0jQMYRuTOo0DSR0SLCeOEx/Eh2TLknVL1n39/vB8P3kkS7KchDhhv6+ZDNiW5UePHj3f7/tzvD+idDqdBofD4XA4HA6Hw+FwlgTxUh8Ah8PhcDgcDofD4fxfhosyDofD4XA4HA6Hw1lCuCjjcDgcDofD4XA4nCWEizIOh8PhcDgcDofDWUK4KONwOBwOh8PhcDicJYSLMg6Hw+FwOBwOh8NZQrgo43A4HA6Hw+FwOJwlhIsyDofD4XA4HA6Hw1lCuCjjcDgcDofD4XA4nCWEizIOh8PhcDgcDofDWUK4KONwOBwOh8PhcDicJYSLMg6Hw+FwOBwOh8NZQqRLfQBXA/fccw8mJyehVqvR0NCw1IfD4XA4HA6Hw+FwrjAmJiYQCoWwYsUK7N27d1G/y0VZEUxOTiIQCCAQCMButy/14XA4HA6Hw+FwOJwrlMnJyUX/DhdlRaBWqxEIBFBaWoq1a9cu9eFwOBwOh8PhcDicK4yzZ88iEAhArVYv+ne5KCuChoYG2O12rF27Fq+99tpSHw6Hw+FwOBwOh8O5wnjwwQfx/vvvX1C7Ezf64HA4HA6Hw+FwOJwlhIsyDofD4XA4HA6Hw1lCuCjjcDgcDofD4XA4nCWEizIOh8PhcDgcDofDWUK4KONwOBwOh8PhcDicJYSLMg6Hw+FwOBwOh8NZQrgo43A4HA6Hw+FwOJwlhIsyDofD4XA4HA6Hw1lCuCjjcDgcDofD4XA4nCWEizIOh8PhcDgcDofDWUK4KONwOBwOh8PhcDicJYSLMg6Hw+FwOBwOh8NZQrgo43A4HA6Hw+FwOJwlhIsyDofD4XA4HA6Hw1lCuCjjcDgcDofD4XA4nCWEizIOh8PhcDgcDofDWUK4KONwOBwOh8PhcDicJYSLMg6Hw+FwOBwOh8NZQrgo43A4HA6Hw+FwOJwlhIsyDofD4XA4HA6Hw1lCuCjjcDgcDofD4XA4nCWEizIOh8PhcDgcDofDWUK4KONwOBwOh8PhcDicJUS61AewWAKBAHbt2oV33nkHdrsd5eXl2LhxIx555BE0Nzcv9eFxOBwOh8PhcDgczqK4qjJlgUAAn/vc5/DKK6/A5XJh9erVSKVSePvtt3Hffffh6NGjS32IHA6Hw+FwOBwOh7MoripR9u1vfxvDw8PYuHEjDh06hD179uDdd9/FP/7jPyIajeJrX/saZmdnl/owORwOh8PhcDgcDqdorhpRNjo6is7OTigUCjz33HPQarUAAKlUiieeeAI33XQTvF4vXn/99SU+Ug6Hw+FwOBwOh8MpnqtGlL355ptIpVLYsmULqqur5/18x44dAIA//vGPl/vQOBwOh8PhcDgcDueCuWpE2alTpwAAGzZsyPnztrY2AMDZs2d5CSOHw+FwOBwOh8O5arhq3BcnJiYAAHV1dTl/XlNTA6lUikQiAYvFgrVr117Ow+N8TLFarbBYLJDL5YjFYqirq4PBYFjqw+JwOBwOh8PhfIy4akSZx+MBAOoly0YikaCkpARer5cey+EsFqEIGxgYwP79+2GxWOByuVBTU4Obb74ZjzzyyDxhxn6PBQ3Y/xsMhoyfcUHH4XA4HA6Hw8nmqhFlkUgEAKBUKvM+hv2MPZbDWQwHDhzAq6++imQyiUAgALvdDofDgWQyCWAuMOD1erFp06YMcWW1WvGrX/0KNpsNGo0GAOBwOKBQKHDzzTfj6NGj8Hq9GBsbg9frRXNzMx5++GGUlZVBLpfDarUCAIxG40cu2nIJRC4aORwOh8PhcJaWq0aUSSQSpFIppNPpvI9JpVIAAJFIdLkOi3OZEWayLqWYMZlM+O53vwuLxYJkMgmNRoNYLEaCjOHxeOZlYi0WC2w2GyYmJqBWqxGJRDA+Pg6xWIz3338f8XgcMzMz9FwulwsnTpyA0WjE7OwsAoEAVCoVNmzYgG3btlF2zePxIJ1OQyQSoaKiAgaDIaOE0mq1wmQywePxoKKiIud5yM7gMfFYW1uLnTt35vzeYsXa5RR1XEByOBwOh3Plwdfni+eqEWUqlQrxeBzRaDTvY2KxGIDC2TTO1YnVasWePXvw5z//GVKpFC6XC36/H2q1Gps3b85ZUrgY9u3bB4vFQtdQIBCAWDzfB6esrAxOpxNWq5X+Xl1dHWprawEAGo0GNpsNcrkcs7OzkMlkCAQC88RdIpHAiRMnIJfLkUgkIJFI4PF4cPLkSXg8HoTDYTqWdDoNmUyG8vJyrFy5EmVlZVi5ciXOnDmDsbExzM7OoqSkBOvWrcOjjz4Ko9FI50wouFpbW0k8AnNiEsC87zHBV4xYAzJFXXt7+0fWe5fvmAotBPl+xhcPDofD4XAuDYX2DJziuWpEmVarhd/vh8/ny/lzVnIGABUVFZfz0DgfMVarFc899xz279+PUCgEkUgEsViMZDIJqVSKwcFBEhMX+vynTp0iEQTMCaFsIcXo6elBLBZDe3s7rFYr3G43DAYDDAYD0uk0ampqAMwFCXw+HwKBAAYHB+c9TzqdRjQahUgkQiqVgsvlgtPpzPk3o9EoZmdn4XA4IJPJcOjQITrGdDoNv9+PUCiEZ599Fl/84hdJPI6OjuLcuXMIhUJobW0l8VhbW0uiKtf3hNk/9nUusSYUeqFQCLt374ZEIrngm3K2WBJmRvv6+jAwMACr1YpQKASTyQSTyYSenh4Eg8Gc4vHFF1/E+Pg4GhsbSbjzxYPD4XA4nIuHrdFOpxM2mw0jIyNwOBwwmUx8Xb0ArhpR1tTUBLPZjKmpqZw/t9lsSCaTEIlEqK+vv8xHx/kosVgsGBgYQDAYJCEmEokgkUggl8uh1WrzunIW+/zAXIlsPiHGcLlcOH78OKxWK/3X6XTSMUmlUmg0GlRVVWHFihX4m7/5G0xPT8Pr9cJms+V8TiYwiyGVSpF4FIlEEIlEVOIYCAQwPDyMZ555Bs3NzVCr1TCbzQiFQjCbzfB4PDkzWTt37pyXNRJm/wqJNaHQSyaTiEajcDgc9NjF3JSzxVJ7ezsOHDiA0dFR2Gw2KBQKnDt3DlKpFGazGV1dXXA4HLDZbFAqlQiFQujs7MSdd94Jg8FAgo09hvUC5hOcnP8bLDZLejFZVZ6R5XA4H1fYmj06OorZ2Vn4/X5YLBaYzWZoNJrL0if/ceOqEWXr16/HoUOH0NfXh89//vPzft7X1wcAtBnlfDywWq0YGRlBJBKBRCKBSCRCTU0NWlpaEAqFoNVq8eijj17UB7+urg4tLS04ffo0/H5/wcemUilEo1Ha2EejUSQSiYzHOBwOjI+Pw2Qyobu7G1/5yldwww03oKurC6FQKOOxTFwWK8rkcjnkcjkJsVAohEQigXQ6TSWTiUQCqVQKK1asgEajQSgUIsFitVrR3t5OQpRl+LJLE+VyOVpbW9Ha2kpCxm63w+l0QqPRoKGhAbW1tTAajTAajfQ7Bw4cgFqtzhByxWC1WtHZ2YnR0VESdX19fbDZbJTpS6fTkEgkEIvF0Gg0cDgc1N+nVCphs9lw7NgxOBwO6pfLRT7Bybl6WExZ6kJ9lblKYIWfgz179mRkW4G5HlQgs5/VZDKhr68PbW1tMBqN8wyANm3axDcpHwO40OZw5jh48CD27NmD6enpefugd955B7feemvBtZgzn6tGlN155534yU9+goMHD8LpdEKn02X8vKOjAwDwt3/7t0txeJxLDDOx6OrqwsDAAHw+H0pKSlBVVYUvfelLkMlkOHjwILZu3Uo9VBeKwWDAvffei5MnT6K/vx/xeDynoQwrm2TE43Eyl8lFKpWC1+vFCy+8gE984hMkpADQ86fT6Zy9a+zvsdJGRigUQjweh0ajQSQSySi5jMViEIvFSKfTUCgUAOYECBMsHo8Ho6OjVGKo0WiwZs0aMhEZGBhAd3c3lV3W1tZCr9cDAGWb2Pe2bduWscFk/62pqVn0hoWVGR4/fhwulwtNTU2ora1FW1sbHA4HQqEQbDYbysvL5x2XXq+nzOTg4CAcDgfUajUsFguMRiM2bdqE8fFxejzrBcyVHeRcGi7FpnWhPsF8wmohI5t8fZW5MrQ2m42yzz6fDzabDWvWrMHQ0BB6enoAAJs2bcIjjzwCu92Op59+Gg6HA3q9Hk899RRisRhsNhuGh4cRjUYxPj6OgYGBeeWyH1dH1GJeV6GvAVzSc7DY/lK2BgHnxfdi+lov93t4Oc8lh2MymfDiiy/SPTSbaDSKP/zhD1yULZKrRpQ1Nzfj9ttvx5/+9Cc89thjeOGFF6DT6ZBIJPDDH/4QPT090Gq1eOCBB5b6UDkXCdukv//++7BYLAiHw0gmk1AoFFCpVAgGg9i1axd8Ph8OHz4MALj//vsv6u91d3dDoVCgtrYWTqdzXkYLAIkdiUSCeDxedHYrEAhgdnaWniObaDSaM1vGMkPZwi8ej8Pr9eb8W0xQejwejI+PI51Oo7W1FUqlErFYDH6/H8lkEna7HZFIBG+//TZUKhVUKhX8fj9mZ2chFotRUlKCYDCIYDAIYK48OBQKIRgMUiZamG1jCLNuxWIymXDw4EFMTk4ilUohGAyS2GYijw3vFg7xZscgzIAAc2WUcrkcwNymmW2ku7q60NPTkzHSwG6359y4FPP//1c3PhcimBb7/IWeI1/5aa7vA8hbbsuypNm/xzK0ExMTEIvFiMfj9LedTifGx8cpmzs+Pg6LxYIzZ87A4XDA5XLRc9x5552ora2lgAYrYRaWyxYjJK/GnsdiXpdQ/GZ/zUaL5OoVvVTHIxRZo6OjUCgUeOihhyjL+eKLL84T3/musWLfw2LF22IFbaFzm+tcsmPnszRzcyHmUf8XEL72vr4+ut/lI1cvPacwV40oA4CnnnoKIyMjMJlM2Lp1K6655hpMT0/D7XZDJpPhhRdeQElJyVIfJuciMZlMePfddzE5OUnZqFQqhUQiAalUij/+8Y9kiBGPx/HSSy9h8+bNF3SDZCYi7733HkKhENRqdcGxC2KxmHrAihVlEokEDoeDMl/Z2S/WiyYSieaVAGR/vRDsmGKxGCwWCxwOB8xmM5YtWwan0wmFQoHJyUkkk0kqCWQllOx1l5aWQqPRwGAwQC6X09dswddoNNTPJTTQyEYYac62888mHA7Ta/X5fPjxj38MAGhra8v7O2wkAmPZsmUYGRlBNBrFnj17AID6EKPRKKamphCJRDA4OIh4PJ4z88bm0pWWlgJAxoYme3PzUW4ir1QuVDAthoWeI1/5ab7vC78nLLcVXlfCx7AMLTD3/tfV1dG1vmXLFrhcLuoPbWxsRF1dHeRyeUb2tq2tjTKy2WY0wnLZYoTk1djzWMzrEorf7K9Z4IcFxy72HBQS8qOjo+jt7YVMJgNwPtufS3znusaKfQ+B4sRbMd/LFrTZGeBC59JkMmFgYCDvc+USbYVYKPt5tbGQ8/BCAZOr/fXnI/u1G41GVFVVkcFeLti6yCmeq0qUVVdX44033sCLL76IAwcOYHh4GBqNBu3t7XjkkUfQ2tq61IfIuQSwWWDMuIWV5AFzG/bssQgOh+OCF+2uri4cOnQIHo8HYrEYer0eEokEZrM55+PZMQG5s165iMfj6OvryyhZFCKXy6HT6eD1eimjdqEwcQfMlU9GIhE4HA74/X6IRCISKUyMpdNppNNpxONxiMViqFQqXHvttbjrrrtw9OhRRKNRathl89I8Hg9+/vOfY2ZmBhaLZd4wbQAZkeZ4PA61Wo36+no0NTXNMxsxGo1Yv349PB4PlWP6fD4899xz2LBhA1avXj2vHyfXwO7x8XEyBGEZvlAoBL1eD4VCAa1WC5vNBofDgUgkAovFAplMRhnBSCSCiYkJyspWVVVBq9XS82RvbvJtfISGI8DcJmhsbAyJRAJbtmy56HLbpeRCBdNiWOg58pWf5vt+vscWej5hGS57ncKfbdq0CcD5sjaDwYCnnnoqo6eMPTe7xnNt1IoRkldjz2Mxr0sofnOJYQA5heylPJ66ujooFArIZDLEYjHqF66rq0NjY+M88W0wGNDe3k7vcy5Rn+89XIx4E37PZDJhZmYmZ79tvgxwoXOZ/fzZz5Ut2goFmPKZMwm/vpTzRC8H7H3K5SIofA/Zfb6trS2jeuNi535eqWRfv2VlZXjggQfwgx/8IKONQohGo8ELL7yAlStXXjXv/1JzVYkyYO5CePLJJ/Hkk08u9aFwPiIqKipQUVFB2RPh0PBcs+p8Ph/6+vqwcePGnH0A+bBarTh8+DBCoRBSqRTkcjk0Gg3q6+vhdrvnCSSZTAaFQkF9YGKxuGBPGaNQRk0qlaKmpgbxePyiBZlGo4FIJIJcLkc8HqfzFwqFEI1G6Rwmk0nIZDKIxWIq80smkxCLxairq8MDDzwAnU5HphkA4Pf7qQ8tFovB6XQiEonkHKYNICPSHIvFaM4ggIyetk2bNpF5Cdu4xGIxpNNphMNh9PX1wW63Uz8OE3TMflcoiFhJZ0VFBRobGwFkZrWsVit6enpgNpsxMDBAmUmDwYD6+nrYbDZYrVbEYjEkEgkolcqM58ne3OTa+LBStUOHDuHgwYOIRCIYGxuDy+WCRCLBnj178O1vfxvt7e0X9V4vFRcqmBZDMc+Rr0w21/eLKanNfkyurxd6PpaFy/f8drsdHR0d0Ol02Lp1Kz1PsULyaqLY15Xdg1pIDH8Ux2MwGPDQQw8BmCsjb2pqop8/8sgj88S31Wol0eFwOFBTU7Oo91A4z3JkZATpdDrDOClb0Gk0mgwH2fLyciSTSaxYsSJD0ObKAOc7lwAwMDBAv5stjoHiM7WFSn9DoRB27dqF8fFxAOdLQBdak4XHmV1pIZfLM0Se3W5Hd3c3qqqqUFtbi4GBAQqgXWgArK6ujnq2bTYbenp66HmY0ZVer6f7/Jtvvona2lo0NTVh2bJl6O/vh9frzQjO2e127N69m66xSzEu5nLj9/ths9lQUlKC2tpayOVynD59GlKpNK8oGx4exve//33o9Xps2bIFf/3Xf43JycmMwBUnk6tOlHE+/hiNRmzYsCEjYwbMleRNT09DIpFkPD6VSuHIkSO46667cvYB5LqBmUwm7N27lwZQy2QyKJVKiMVijI+PZ9jNMwr1cl0o6XQaVqs1o28lFws5NMrlcigUChIU8XicygETiQSVfjJYxo8JE7FYDJlMBr1eTwt6ttX95OQkotEoiVHWe5ZrLqAw0izMlCkUioznGhwchNvtRjAYRCqVwqpVqzA6Okrng93wc5mUsM1Mdokhy6oBmZs6tnHp7OwEcN704xOf+AQtnLt27cLIyAiAOUfHNWvWoLW1NWcfW66NT0dHB6xWK/r6+hAMBum9YFgsFnz/+9+H3W6njfnVxMUIJiELbTAWeo6l3qAsFpPJhCeeeAJmsxlyuRy9vb14/PHH6XUWKyQXet2LNdIwmUwUVKmoqLik0WxhdkH4mhYSv/nE8EKvNd/3hV+zwN2xY8cy+lOffPJJ6l21WOacZmOx2LzzkS9TnH3c2ef52LFjqKuro3LWrq4u/OIXvwAArFq1Ci0tLdiyZcs8Qed0OqlUvLy8nIKSJpMp53iTYs/lQplhoWgrlKXMDtIIBV4ymYTVap1XApqdOWJCa2xsDCdPnkQsFkMwGITf70cwGIRMJoNarYZeryfjJwD09czMDIC51oJIJIJ4PA6pVIpf//rXePzxxxfdb24wGMggyuPxIBgMZmQPNRoNWlpaAICcgVllxsjICK15rNqGjXQ5c+YMrQULVfbkuoaX0sn1wIEDeOqpp+DxeFBRUYGdO3diYGCAgtqFSCaTcDgc6O7uxokTJ5BMJskM6UKMwT7ucFHGueIwGAzYtm0bBgYG5okgof27MDrjdDphMpkoO5NMJtHX10elB1arFQcPHsTY2BgkEgnee+89uFwuJJNJNDc3QyKRQCaTwe12IxwOU7bmYhGLxVAoFAiHwzl/Xkxvmlwux4033ohjx47lFW9isRjRaDSnRT8jlUpRPxvr0YvFYjRqQCQSYXp6Grt378ZDDz1ECzezug8Gg7TgAKA+NJZtE5IdaRZGOoXP5XA4EA6HEYlEUFJSgurqaigUCoyMjECpVKK5uRkNDQ10rtgctIaGBmzbtg06na5gVD3XZvfOO++Ew+Gg6Cqba2YwGHDPPffgt7/9Lex2O2w2G40RyNdnIdx4+v1+Wrizs5OMRCKBiYkJ/PjHP8b+/fvxqU996qoTZ8WIrlwIN2HZPSzFPF+h3wcuXVblQgVftiW+kH379mFsbAyRSAThcBhHjx5FR0cHduzYUVBQFHrdRqMxI+qcbVqxfft2HD58mOz87733XrL31+v1NIA9EAhALBZDp9Nh8+bNGUPWizU7EL52oTlPrvf5QscZZD+2GOOO7du3w2QyFZx9mEgk8MlPfhK33HJLxs9Yr6lwAyyXy2lTKRQs2a9fuIF2OByYmppCc3MzHn/8cQDnN+8AqHw6FotR5o39s1qtJJCE9z+1Wo1YLIaNGzcWfX0KWUgMF5upzRWkqampgclkgtvthslkojWclYACc5v8V199FclkEoFAAD6fj8rX2ZrISuxVKhVKSkrg9XoRjUapBJ+tRWw9EvZkJxIJWK1W/OhHP8Lq1atzZmUKXd9GozGjhBM4nz1saGjApk2baP/BHsMCjgqFAkqlEkqlEg6Hg0rj2fqnUCgKCt1c1zYLBizk5PpRYDKZ8L3vfQ9msxnpdBqRSAQ9PT0oLS3Nu6/JRiwWI5FIIBAI0O8wp+er2dDoo4CLMs4VidFoxI033ojJyUm6qYvFYsr2ZAuZsbExjI2NobGxERaLhfqd3nrrLRgMBrz22mt45513EAqFqPQQmIsOt7S04J577qENRHl5OcLhMDkZXow4KykpQV1dHc6dO4dIJLLo35dIJLj77rvxr//6r3jiiSdw6NAh+hnrHZNIJGRMketvsPPGnBxZSSjLeLESzHg8jpGREbjdbgDAk08+SYs+W2h7enpw8uRJEnRutxsdHR3Uzylc5Apt3uVyOQ22Zudcq9Xi5ptvxm9+8xtUVlZCo9Hg4YcfRmtra8Ymj81By44UFntDL5TtYYsxgAzHvHx9FgcOHMB//Md/YGZmBiKRCDKZDNFolGbJCTOWYrEYSqUSyWQSTqcTXq8Xo6OjGBoampfRvdoyQQsh3Ggsdsg4K0lmZhnZv8/eG+FGvKysDH6/f55oKcbRbrHOh1arFXv27EFHRwdCoRDKy8vxzW9+M+O6HRoaontWKpWC2+3G3r17MTg4iJaWFrS2tmJoaAj79++HWCzG9ddfj3vvvZdKboPBIAKBALxeLyKRCFwuFzo7OxGJRFBeXo7Pfe5zqKiowOjoKD744API5XJMTk7SKAyWoevp6YHNZqPMeyAQoNLlWCyG9957D2vWrMHWrVvniZuysjLaTL744osk7nQ6Hfbt24dwOIyqqipcd911AHIPkwfyuxKy5xTOg8uXHcjOWJlMJsosCY07HA4HysrK6Bi6u7vR398Pi8WC2dlZCsydPn0aPT09KCkpoeyHz+fDuXPnMkqnDxw4QBvv9vZ2EqTf+ta3YLfbUVNTg8997nN0bPF4HENDQwiHwzh79iySySRcLhcsFgs9TzKZxOjoKHw+H+RyObZs2ZKRAcsOjl3IHMhiyc4qFkOu+7wwq/SFL3whIwt74MAB/Pu//zucTifS6TTUajWJlmQymdESwErsa2pqUFpaiunpaQBzxlBsZqVEIoFUKoVYLKZgGCMYDNLmP/tzX+hznr1GsNcEzC8XFToDs/cnl0FUVVUVOXwWuqfkysayjGQhJ9diEQZ5CplvAXOfq2effRY2my2j/SGdTmPLli14/fXXMTU1lfdvKRQK6HQ61NbWwmAwwGw2w+fzQa/Xo6qqCiaT6YIMjRbzGq42uCjjXJEYDAaIRKIMZx8WhRscHJxXw5xMJpFIJPDII48gFoth79698Pl8+Mtf/oLly5djYGCA7OAlEgmUSiVKS0uxbNky3HPPPfPs13/0ox9RGeSyZcswMTGRt24aAJmRCAWcVCrFtddeiy9/+cv4f//v/1FZ3EIwc5NUKpVhWvH4449jfHwcVquVSg5ZT8K1114LkUiE3t5eBAIByGQyqv1WqVQIBALQ6XRwu91wu92IRqOQSqWIRCKIRqMZM9e8Xi/GxsYysoystv+2226jMgwWofR6vTlFCzDfSl4YPddoNNi2bRtl0VhPWW1tLbxeLyorKxGLxTIW/UtV7pBPMLLFmIkAlnX1eDzzFkq73Y6vfOUr8waOq1QqVFdXY/ny5eTUl0qlSHiyTBrbFOcq6ym0Ybgcgu1SL3rCjQYzXmGlp06nk0pJhX9b2Djf398Pm80GpVKJ5cuX0++zKDYTI2KxGKdPn4ZOp8O5c+cAAOXl5dixYwdOnDhBG+cdO3YgFouhq6sLFosFCoUCn/rUp1BRUbEo50OTyYRdu3bh+PHjVGrtdrvx4x//GM3NzbBYLEilUtTHyfpRWVZ6enoaPT09SKfTCAaDiEajEIlEsFqt9DlzOp1QKpVIpVIIhUJ0X5ydnYXf74fH48H//M//oLm5GQ6Hg0Z2hEIh+qx7PB7Mzs5SrymAjOCUSCSikqtdu3ZRwOCDDz6ARCLB6dOnsWLFCmi1WjQ3N+Pdd9+FzWaj+1QwGEQ6ncbs7CxSqRQkEgkqKyshkUgyskoWy5zbIRM+7PwKP282mw2bNm2CTqcjw4VAIECZqEceeSSjbI71XbENMDvXsViM+oD1ej0CgQAGBwcpQ8VKi9PpNEKhEKampnDddddh1apVsNlskMlk8Hq99Nln/VIsU8/Wg+7uboyPj1PG6+jRo6iqqkJDQwP1OKVSKYTDYezdu0wGz2UAACAASURBVJeOjb0H7H2fmprCyMgIfvnLX6K+vp76cLZu3ZoRHMtVFnmhpb7ZJYTC8yicYSkcR5J9T8h+fuFnvaGhAc3NzXT8JpMJP/zhDzEzM0PPJZVKUVpaitnZWcqSsT5yhUKBlpYWfOYzn8HRo0cBzAkdlUqFqqoqaj0oKSnB6tWr8fvf/x7Hjh2jQJhUKoXZbIbZbM64l+YrQxVSTPYw+3cK9fEtdM8Wvhe5+nZbW1thMBgwNDR0QQY4rFqou7sbfr+f7nv19fWora1FSUkJtm/fDmCuN3DFihXYt28fzp07l1GhU1JSgptuugk1NTW455578Oqrr+YtYfzJT36Ssb7b7faMjPL09DRCoRACgQBGRkZyZt6zs5zCbDhbIy+0T+9KhIsyzhWJyWTC7373u4yMWCKRwKZNmzA8PJzxWKlUCo1GQ9kaFslhkVp2s2e9aCUlJbjhhhtgNBqxZcsW1NTU0OK2ceNGKoOMRCJIp9MIBAKoqKiA3W7PeaxKpZIyJMD5zQ6LLLe2tuJb3/oWHn744YxMFovyCW94K1euxG233Yb9+/fD4/EgnU7D6/XCYrFg48aN+M53voPXXnuNBmpLpVKsXbsW3/jGN+B0OjExMUGCbefOnfiHf/iHedE8q9VKfSS9vb348MMPKZPHjmVsbAyvvvoqxsbGMDo6iv7+fsRiMeh0Ovj9fnocc20E5juGCaOlAOZlORoaGqDT6ebddPV6Pc6dOwev10tN1sJF8KO+8QqzfKw5e2hoiBq82Ry0l19+eZ4gY5Hduro63HTTTRQNZEOFQ6EQYrEYZXxZmQt7X1i0P9+GYTHDay8U9jcGBgYwOTmJFStWoLW1ddGLnvCYsntPhMYrbEB8e3s7Tp8+jcOHD9NmXi6X49SpU/D5fAAArVaLpqYmKt1bsWIFRdjlcjlmZ2chEong9Xrh8/kQi8Xgcrnwwx/+EMlkErFYDAqFAkNDQ9BqtRgdHaUNxfDwMDZs2JCzPC3f69u1axd6enroHpNOp5FIJGCxWGC1WskgR61WQ6VSUX8MK1dLpVIkDFgGGwCVcrH7BTMhCofDiEajGbMb0+k0HA4HAoEAlEolRCIRVq5cSdlC1geyfv16uN1ueL1exGIxGnXBSipdLhfi8TimpqbwxhtvQK/Xk+AKBAKYnJyEVqvFuXPnyOgHwLzeW5b1sNvtaGpqgl6vp6wSKwtmGfJcpc8Mds1YLBaYzWa4XC5EIhFs2rQJn/70p2mTPDIygs7OTni9XjQ3N2PLli1Qq9Xwer2UfT969CisViu8Xi9EIhH0ej1EIhE8Hg/12y5fvhwPPfQQzXMcGBigviDWL8Xug+z4AECn09E9MB6PY3JyElVVVTAajRgZGcnI/LD+XlblwN77cDhMmXW/309C/OTJk9i/fz/uvPNOCmDly+gCmBdIAeZb6Qs3yLt376bPChOhrBS+q6sLMpmM7ns+ny9jjAhz0s0uUc1lBmQymfDyyy9nfFbkcjkqKyuxbt06+gyyvm6Xy0X9ZHV1dZQBGx0dhVwux7Jly3DLLbdAJBJhaGgIHo8HExMTaGtrw/HjxwHM3YvZ2jI8PJzhpLiQYVEuill7cpWF5vr/bHK5WOZ7D++9996cmb/sjL+wXxSYW+tZ24Zwzurw8DAFgvfu3QulUgm3252RxVSpVNTioFAo8NOf/pTuCYVKGD0eD2UUgfNmSCaTCf/5n/+J3t5eeL1eJBIJHDlyBAcOHMDOnTvxne98BzMzM6iursYLL7yQsUdgglrYz6dWqylbfrVnzbgo41yR9PX15cxMqdVqVFVVkQBSKpVQq9W49tprEYvF8Ktf/QoWiwXpdBpSqRQqlQp1dXXw+XwIh8NQKBT45Cc/iS996Ut5F7e+vj643W6Kttntdlp0hbDFvaGhAQ6HA4lEgtwJw+EwSktLEQqF0NHRgebmZjQ1NeHMmTMZz1FXVwer1YpUKoWmpiZ8/etfp0je0NAQ1Go1Wlpa6HEmk4nOi16vx6pVq/CNb3wDRqMRb7/9Nt2kRCIR/vCHP+D222+fV4YivMFt3boVFsucU+Irr7yC0dFRiqyfOHECp06dog2jSCSC3++nDQRjamoKBoMhY5EDzos0oV28MEuSz72PNVl7vV4Eg8ELKtG4FLB+O1b6tHz5chKVrPckm6qqKqxZs4ZMTJqamvDggw8iFothZGQEv/jFL+B0OpFKpVBZWUlZjx/84AcA5gIGer1+niNbIcF2KZy9hJhMJrz33nsYHh5GMpnEzMwMQqEQRWqLIdfnKjvSzBr6R0ZGYLFY0N/fj6GhIXi9XjLeAeY2shUVFdiwYQM2bNiAdDqNw4cPw+Fw4M0330R5eTlSqRRWrlwJiUQCn88HhUKB06dPIxqNIhaL0WdGJBIhGo3SgHhmjMMGrp86dQrLly+HwWAgIcFeDzt25vg2PT2NM2fO0IZSpVKRi6tcLkc0GqWofyqVglQqRUVFBerq6hAIBGC32zE7OwupVIp0Ok09oRKJBFqtloJCwlJjVnbIshlVVVUUOJqdnYVEIoFKpYJEIsGOHTvwxz/+ESMjI2hubsbWrVtRU1OD0dFRBINBVFZW4qabbsLU1BT6+/upbJk15rNzxkZrsHNUUVEBtVpNwR+VSoVwOAy5XE79uadOnUI0GsXw8DDKy8vpuWKxGGpraynSz75vNBrpc9/Y2EiBmJ07d0Iul8PhcOQ0WXI6nejt7YXZbEY8HqfX2draSp8JNtqDiciamhpUVlZCrVbD7XYjHo+jqamJ7qP5TBXsdjsGBwdht9uRTCYxMDBAPVM6nY7OB8t+x2Ixch4UIpPJ6D1l/59dIs/O+czMDNxuN4aHh3H48GE8+uijqKmpQUdHBw4ePAiHwwGtVouzZ88iHo/T9c9EE5tfxj5jIyMjVOo+MTFBLolqtRoSiQSJRALl5eUIhUIUcFIqlZQ5ZPdCthHOttNnwUPh2AC73Y6vfvWr+PDDDzPOQX19Pe677z6Mjo5idnYWDQ0N2LBhA5xOJ6RSKex2O13nMpmMhFw8HkcsFsOJEyfgdrthNpshEonQ0tKSUS4OgCpB2OfjN7/5DTweD7Zu3TqvPLGYrOOlIJeA6uzszBh5IOwXPHbsWMY5zu4lzCXofvazn6GnpweRSCTDZdnr9ebsS2dBbJZFZrBKpfLycuj1eng8ngxzlYXo7++H1WrNWAfsdjsee+yxnJ8Ndl9lP2P7kp/85Cf0GCaoWWAnFovhyJEjOHPmDBobG6/6rBkXZZwrkra2NigUiozMklKppOGtJ0+ehNPphEgkImc/YE4IuN1ulJeXI51O49prr8XKlStp4ckuB8tVxtDW1oaysjLapLCyRDbkuaamhv791V/9FTo6OpBMJlFVVYWWlpaMPimz2YyzZ88ikUjM21SwuV9ssbn55pup5l6j0eD666+n8hWDwYBjx45hYGAAvb291Ld0/fXXU7/XG2+8AYfDQcfLNo+FrGdZZE8ul8Pj8SASiWT0mGXfwFnkXwjb+Oarwc81dLlQSRzr6xodHaVzsxQwa+R4PI4zZ87AarUiHA5TIGDZsmU4efIkPV6r1eIb3/gGEokEjRMQNuSzgedCN1GRSITTp0/T5rSkpAQ33ngj7rrrrgwTE+EmUSjY5HI5du/ejRMnTsxz9so3HiJfaSJ7/FtvvYWRkRES3yKRCFNTU+jp6ckoRSlkxpDrc7Vx48aM95u9NpfLhUQiQWWi8XicsjdsxEMikYBMJsPQ0BDGx8dhNpsRiUSoH6e0tJQyaMuWLcP09DQSiQQ++OADup7ZkHSFQkEGPHq9Hna7PcMswO/3Q6lU4siRI+ju7qYNImvaN5vNVNYqkUggl8szRLZOp8M111yDUCiEU6dOIZFIQKVSQalUorq6GnfccQemp6dpA1RfX0+b5/HxccrKm81mDA8PUza6qakJ8Xic+qFkMhl0Oh2dTybYUqkUysrKEIvFoNFooNVq6TNYVlaG5uZmnDt3DvX19diwYQMUCgWAuYoDFlxi2RsmHFhZmVqtRl1dHdatW0fZ3w0bNgAAlbrt3r0barUaoVCIMkd+vx9vv/02PB4P9Hr9vL4og2HOGChXediOHTvgcrmohw2YM4rYt28fuft5vV5IpVLaRAoDKiwQ1NzcTCKL3WdVKhX1+QgdW4XldzqdDgaDAZ2dnfD5fAgGg3C73XjppZfgcrng9/vpvWDi2mKx4MyZM7SGMMrKyrBq1SoyPSgpKaEyTnYNMlHPAkBMJPf29mLXrl0oLS3F0aNHMTU1hVQqBZvNhsHBQcrIarVaEk0GgwGBQACBQAAulwsOhwMKhQIejwd+vx+RSISCAswEiplesUAkM4gKh8Noamqi60qj0WDFihXo6+vLyCybTCYSxA6HI8PGnpFIJFBaWkrXJitl7u3tRX9/PwBg/fr12LBhA4aGhtDb24vS0lKsW7eOPsPRaBQzMzPweDyQyWSUTWWf93Q6DZPJhJKSEsqSs3lvvb29lHnMHu7+UW7o8811E5biZQcrF8rqseu1r68PoVAIb7/9Nsxmc0YgivXdLWQqlo9wOExjYxbTY3/u3Dn4fL6MvtIzZ85gcnIy5+NTqRRl1RinTp3K+FrY73f48GG8+OKL5OjMxNxSBXIvBVyUca5IjEYjvvjFL+KnP/0p4vE4FAoFPv/5z2fMC2EigtWTs808E0UsWphrhguD3eCEJSkGgwGf+tSn8Nprr1FDMTBXDlFaWoq7774b119/PW1AZmdnEYlEoNfryRFQLpejr68P//u//4uJiQlqqBeSTCbh9Xohl8vJ5Yu5EbHyvubmZjreuro6KjtgPXRjY2N4/vnn8e6779JGlMEc1Yph3759cDgcGRtYtkgLyWUkwjbOhWrwgeLd8ViWgi3sBw4cIFeyy00wGMTk5CQ1/mu1Wlo4jUYjjh8/DrfbDYVCgZtvvhnbtm0DABJkwkWUZQMGBwcRDAah1+sxOTlJAgQAlYcIRZQwUprtOskMA7KdvYTDu4Hz4yGAOZMGdgzCgd4HDhxAf38/9WIx4cJEjLDfSKvVUl9W9oYGOD/PR9g3xrK87Bqw2+0UqQdAGwfhHECWpfV6vfjzn/8MtVoNsVgMj8dDIkQulyMcDsNisSASiSAYDMLn85GRACvNUalUlIVMp9N0TbHh8azH1OfzYXR0lOy5RSIRysrKKEjBxIZEIkFZWRn1PrINbklJCR588MEMF7rh4WE6R+waKfR5YELX7/dj3759lAXNVeIJnL+PBQIBSCQSNDU10fXLsufs7zU1Nc0zy+nr68N9991HjqNjY2MIBAJQq9WoqKigDERtbS127NhBJib5jp9twuLxOHQ6HTo6OmjDtH79etoQsw2YsGQ4GybYmJX87t27ybKevQ/M6EEikWQYIwDIGwjK15+abxPc1tZG51upVCIYDGJqagqJRAJyuRyNjY3Q6/UkRrLvlSKRCPX19fiXf/kXcoTUaDQIBoOQSCQIhUKoqanBDTfcgMrKSrz99tsYGhqift9wOExifWZmZt69OZlMIhKJQC6XY9WqVdBoNJR5jsViZObCDDFY9lMikUCn01EZWTKZREVFBYlDsVgMq9VKwRFhkO3w4cNkWMLK0vbu3QuTyUQOu3V1dfOqXtLpNKamptDZ2UmZ0fHxcbz33nuYnp6GTCZDbW0txsbG8P7772N2dhbr16/HPffcQ+src/GNRCK0Vgl70Nn9IxwOU0aSmeR0dXXBZDJBpVJBo9HA6/VScOKjLIPLDlYJ+xRra2uxceNGcgNmZJuOZB8TK+8UjpIRwoLJzC0x1zxUdr+VSCQ5nadjsRg+/PDDBUf35Prb2f3DY2NjeR2ihcEARnbPMXD+fvHKK69kXFusrPujMMG5XHBRxrlieeKJJ1BfX4+DBw9i69atuP/++2G1WvGzn/0Mf/rTn2jR8/l8OHLkCLZt25bhVFVohguDlaRMT0/D5/Ph3Xffpdlb11xzDVwuF8RiMSorK6ms4uzZs9QTxCzopVIp5HJ5xmaalQqdO3duXu8RY3JyEsuWLUM8Hqffy9fTYjAY8MUvfhGTk5OYmZkhow7WfyYUfSKRCGvXrsXWrVuLPt+sz0EkEkGr1QIA9b4U+h1hGZKQQvX1+WCb0eHhYbjdbszMzNCcmMstyiwWCxwOBwlhqVSK1atXkzGM3W5HdXU1EokEiRR2jPmawlk2gF2ffr8fHR0dNEh29erVNMyWldNkbxKzXSfZBlzo7HXs2DEaDwHMlYGYTCaMjIxQ6R8AEkR79+6F3W7HzMwMEokEiQ2NRkNldWfOnEEikYDH44FIJMLJkyfR0NAwb0Mj7CVsampCT08Pjh49Cr/fj+XLl6OhoQE333wz3nzzTUxMTCAcDkOr1aK+vh6RSIQMGMLhMPUhsk0py4yXlpZCJpOhpqaG+jK9Xi/1VgUCAcowqdVqlJSUYN26dZRRZwLJYDBQ5rmyshLXX389hoeHSUSxTT+Lzgv/q1KpcNNNN+HWW29Fd3c3zpw5g1gsRlkq4fWfK5tY6HoW/m62ADIajdi2bduChgJApmMcm8GV3a8iHIa8c+dOVFZW4re//S31dNxxxx0QiUTo7u6GRCKByWRCa2srBQWyX4vRaMQTTzxBQRWFQpExr4r9V9iLlH1M2eVdFosFHo8H/f39lCGSSqUoLy/HypUrqWRTOPz5YgaQ5/pdo9GIp556ikwQOjo6YDabKbsEANXV1VRWyvqMWcBFLpdDq9XOmzHGPjfsb7DXPDIyAo/HA6/XS/2DzBU4H6yPSqvVQq/X4/Tp0xRsY71BGo2GKklSqRSam5txzTXXYHBwkPrG2BwuYK7MUaVSUaaJbdqj0SiVOur1enR0dMBms2FycpIyVv39/ZiYmMiZoYlEIpiamkIkEsHZs2cxNjZGfdRMZO/fvx9TU1PkbFtfX0+Dodvb22E2m6mVIRAIkLmLUqlEY2Mj/V2DwYCRkRGa8xaNRuH3+6FQKMgwpKKiIsM05qMYt5F9L88e3J0tyBjZ12r2/YRZzueivLycrjNhCSkwd71cd911WLlyJc6cOQOHwwGxWJyzT6xY+3sGM2m57bbb8Je//AXDw8Po7++ne1L2Y9k1lR3MCAaD2LNnDx577LF5v7d161ZyRAXmMtG33XbbVZslA7go41zh3H///RnDH1mpgfAGEQ6HcerUKZhMJnz6059e1AeS9Y95vV4EAgF897vfhUKhQElJCdauXYs77rgDra2tMJlM6O/vh9lshsViIWMRvV6P0tJSapwXwjbhNTU1eOWVV6hMSwgTdbW1tTSTJttyWXgDbm9vh06nQ3d3NwKBAE6dOkUZASFarRZ///d/X/S52L59O7q7u6msgJWwlJeXU2kI2wwII7RKpRLXXHPNRUWmsucwjY6OknUum1/T1dV1WYdlAvMHYDc3N+PRRx+lbK3JZKIMgt/vx5/+9Cfquyq04cv+fmtrK7q6uuByuch4JrsfS9ijUUwUNfvYAeCtt97CwMAApqenKXvExJbH44Hb7aaG/6qqKrjdbrKwZyYWLBIJgNz3SktLUVFRMa+XUK/X49ixYxgZGSHRbrfbMTIygqNHj1IJV2lpKVasWEERX1ZqduzYMczMzNDGln12FAoFZb20Wi22b99OGTuHwwGz2QylUonZ2VkSYzt27MiwdBcKF1YS2djYiO3bt+PAgQMQi8U4deoU9aG2tbVBLpdTiVhFRQVWrlxJpcXCHiYmDBZ634sl1+8uFPBgry/bTj3bICa7X8Viscyb0cQEYG9vL/WIZrutMpEhLFVjphlyuRx79uyh8m29Xo+RkRHqoQmFQhlD4YHMUmd23Exos8xYY2MjVq5ciYceeihn1utSn3PgvFEBAKpCYMKAlVkz50i5XI4jR47gyJEjdB0lEgnqgxH2BeV6f1lwxmq10sgNFixjJWHZ932pVIrjx49T8ILNlWKz6ACgsrIStbW1GdUdLMgkdMZja21JSQk0Gg2qq6vhcrnofWSlbGq1GpOTk+SkFw6HqU8uHo+T0MomHA7DZrPB5/NRtQnryVy+fDnNeIvH4xCJRJicnMTPf/5zAMDrr78Og8FA2WzgvHGWXC7H+vXrcffdd8NsNmNkZARerxfpdBoymQwAMsqVDQYDWlpaaKZmV1dXTsOqQq7CxQq2XPfrxToK5+rXXbduHfbv358xDoDBsoPhcHieS2JZWRkMBgP1jCsUCsRiMeoTvRiqqqpw7bXXoqOjAydPnqRxHmz9EIlE1JKSSqXQ1dWV8zqJx+N4/vnnodfr5w0Cv//+++H1evHLX/4SSqUyo1rlaoWLMs5VgTBamt3TxG7EhX43V28NMFeSotFoIJFIaPMnFovh9XpRXV2Nm266CRs3bqSN829+8xv4fD7YbDYYDOdNKZjLUXYts8FgwGOPPYY1a9bgpZdewuDgYM6B2KwsJtty2Wq14rnnnqNm/ccff5w2BqxEzev1orS0lG54KpUKt95666JuTkajEc8++yy6u7tx4sQJjIyMUM9eZWUlNm/eTP0pJ06coBk8zA2v0GKSq7E52/JcOL+KuSqxJu1oNIre3t7Lni1jopot1rlEYSKRoPLDPXv2wGq14utf/3rBPr5cWRPWY7Rnzx5UVVVlNH0LNwYOhwM1NTUA5s+Ey3Xsa9asQXd3N2ZmZvDBBx/A7/dTSZ9CoaDIKNs4MfHFzD2EsDl3DJFIhPLyctx4441khgCcz87MzMyQ4QEjmUzC7/fTc4vFYiSTSSxbtgyhUIiufdYHyvquzGYzPvzwQwSDQSQSCVxzzTUU9TaZTNi5c2fG3KCBgQGMjY1lCCfh+c8+T7k2ScyBT6fTUcY5ezPGEI7UWEr3r1wbto0bN+YUXwZDbhe6fEI/n5EPE1XRaJRmD8pkMiqZZed006ZN8Hg8GBoaImt64dBdVvILnM/gCo0k9Ho9mpubUVdXlyEmisk8Xqpzm12F8fTTT88bn8GymVarFaFQCMuWLZsnNosJYrFrStjzxJ4nFouRuyjLIkmlUqjVaoTDYYTDYUQiEXKGVCqV1Nuo1WpJMGeL2GyXu2AwiNnZWTQ3N6OlpQUDAwPo7++H3+8nIcXm3LGxCBKJhKouAFAvU3a5JQtIsqHP7Herqqpw4403ZvwO62ljTE1NwW63ZwQ5mRAsKSlBOBzGG2+8QfbxTAiw8uKKigpIJBJcd911uOuuuzIylMLsMrCwqzDr0xP2JRYiV0BlMdcuK4EUOkree++9OH36NE6cOIHZ2VnEYjESaGyeaC5KS0up91Kn0yEUCqG0tJSCH+w9vRBmZmbwve99j9ZxYbZULBajoqIC3/rWt7B582Y8//zzBfvdQqEQvvOd7+QcBP5P//RPuOuuu5b83nup4KKMc0WTPThWo9HghhtuwPHjxxGJRGhu0fXXX5/zhpivt0ZYkvLlL38ZzzzzDC0MbHHQarUZjeirV69GfX09zp07RyV7xZpStLe3o7W1FS+//PK8OmjmQmcwGKDRaGj4ZF1dHQ4ePIiuri4EAgGYzWZs2LCBonXZooEtKux1LfbmJLSr/d73vke218yp7tFHH8XGjRtx4MABfPOb3yTzg9/97ne45ZZbcg7ozdfYzL5mBgfC+VVsTlAkEqE+B9ZDeLkptGAajUbKcLKeor6+PuzevTtvD1yuTTNbZEdGRhCJRKDVahGPx6l3Dci/MSjUmG4wGNDc3Ize3l56brbAsgg6iy6zBZH1r+RaiJngZ6Ju+fLl2LlzJ/UHMdhr6uzsxIkTJ+j7LFLPIuhSqZQs1w8ePAiVSgWVSoWSkhIAINt85hzH+igBUD8X28gLjUSsVisOHDgAj8dDZZWFzn+hTVJ7e/u8c1roOS7nhqBYg5V84ou9nnyltoUyssB58S0MprjdbnJiFM7fY/+OHTuG3t7ejB4aNg9Jr9dTcIqJl+zyroVMgj4q8s1GYpULt912G/X+sR7YbMMQYS9oscfOzpuwJxoAlQq6XC6UlJSgubkZSqUSGo2GqgxYGaLQeIoJsmLEQ/Y1s2PHDnJ6HRwcJNEGnB+LoFAoUF1djWXLliGRSJCRidVqhdPppD5A4SDiaDRK5ahlZWW4/fbbUVNTgzfeeIPMdKRS6bwsUK5NfDqdht/vp1EX2aWeqVQK1dXVuPvuu9Hc3DxvnSx0nedyFY5EIpiYmKAM3OXofWYGVNFoFDabDV1dXfB4PLj11ltxyy23wOfz4Z133iFHTrFYDKlUSkE14XlsbW2lHlN2Plkg7WIEGTAXsMwnBuVyOT772c9i8+bN+NWvfoUPPvhgwefz+/15jcsu9733o4SLMs4VC1sIhYNjm5ubcdddd+Guu+4CMPdhLLRIM7t3lolhi4nwsSUlJairq6PInUqlwvr16/Hoo49mPE7YJK/RaCibZDQaMTU1taAphcFgwPbt23H48GGyngdAg1GZi5kQp9NJx8WsvLOfU/i3illsF8JoNOKTn/wkWYonk0n4fL6MAaMss5JIJDA5OYnu7u6cZXf5GpvZ162trTkb8uVyOfbt20cLRDQaJXvdK+XmazAY8PDDD+O73/0urFYrbSCY+1q+6zF708w2P2xwrt/vR1lZGVpaWrBjxw4A+TcGoVAInZ2dGb0IueaDsc3Z9PR0RsSamWqwbEWhaKVEIsGKFStwxx13UF9HoQVSLpejp6eHTAKampowODhI/Y/JZBIikYgELZtFNjY2hp6eHrS1tWH16tXkesjcDZubm7Fjxw6YTKZ5Zir5zvFCoiWbQnPfin2OS8lCwQ4mDBcrvtjPii29yiW+WWkkMNeozzJler1+nsFLrp4a1hOiUChw2223ZdjkX0h518VSyLJcOBsJAJVdshEHLpcLVqsV0WgUFRUVCAQCZC4zNjaGkZERMkVaDNnnfsuWLXjnnXcQjUZRVlaGz3zmM2hsbKSRDcIyxHw91sX8OZfCUQAAIABJREFUzexrhr0fBw8exOuvvw63241QKEQz9qqrq/HP//zPdCzA+dlpv/vd73Dw4EEqSWYIhVM0GsWhQ4cQi8Uo88gCR9nz8NgQ9uyWALZeCbN1wJxwLC0thdFozOj/XehcF3IVttlsFJTzer3z7sUfBcIKnZmZGfT29qKnpwdyuRxqtRp6vR6JRAKrVq2iwDUbwcBMnBirVq2i1zc+Po6f/vSn8Hq9Oc1ALiUVFRW4/vrr6V6a7biYj1z9aB83uCjjXLGwDywrD1yxYsU8owO2gOZDLpdT9guYi8AKs1lMZITDYVRXV2PFihUZgzqFsEWKuYD94he/AAA0NjZSCQCL2ue7KcdiMaxatQpOp5PmVTHb6P3791NkkbmlsZkswNymmA3I/iixWq0U5WS26DMzM/jBD36A6upqDAwMZCyEzAa8kOAAcjc2F3LGPH36NIDzWZ3Ozk643e6Mvq6lhkXK9+3bh6GhIZSUlOTsKWIUKhdj11V/fz+8Xi9cLheA/NFbFgFn9vssg5pvPth9992Hn/3sZzhz5gzC4TA17LM+jkI9BGKxGBqNBrW1tQiHwzSKoRBGoxFf+9rX8N577+HWW29Fa2srnnvuObzzzjvw+XzzypmEzM7O4r333oPT6SQTkJaWlozodj4HwEIW0gvZSwP5s2mLeY5LSaHsarYwvBTiq1iEzyfc/LPh9ENDQ3jrrbcyMkvZ8+rY62DW9fv27YNEIqF7w0dx3IVYyLLcYDDQHDAWyJiamoLb7YbL5UI0GsX09DQGBwepB2z16tUIh8P4wx/+gFgshn379uHZZ5+9qHtYLBaDXq/H1NQUYrEYjh49is2bN2dk1hgXc+7ylUbv3LkTra2t6OvrQzAYRHd3NxwOB1paWtDY2DivX46NMPB4PPM+98K1hM0TA85b8UulUjQ0NGBqagqhUIgqUsrKypBIJDA7O5vT/IT1u1ZXV6OpqQk6nQ7r16+fV8682HMgvH6zB3AL78Uf5TXLKnSYWy5zd1apVDQgnc04ZcZErOWAwcpdDYY5J9yXXnoJo6OjF5UdKxY2XB2Yu4euWrWKZqIW4tSpU1dUYPajgIsyzhWLXC7Hhx9+CIvFQvMrGhoaMgRZ9mYFQMbmoKenB9FoFGq1GnK5HPX19Rmlg6wEQ6FQoLa2Fp/97Gfx6U9/Ou8xGQwGcuVj4kKr1cJgMGTYvua7cdTV1UGtVtOCA5x3PfT7/UilUuS8OD4+jpdeeomiiqFQCENDQ/NKqgqR3QeRazZVrgj8Bx98gNnZWbIa9vl8OHnyJEpLS6FUKkmwicVi3HLLLdRzU0x/Sr6m/GxYZJT983g8VIb69NNPXzE3ZmGPH+tdtNvt8yLUAMj9Lntuj3DhdzgcNKRTGHnN3hh0dnbi2LFjmJqaIodKnU5XcD5Ya2srDh48iJdeegljY2MAQGYl2b2aAGgo6+rVqxEMBuFyudDT04OTJ0+ipKQEd999NxobGzOuL/baDIa5GUCBQIAc+x5//HEybikGJgBZ+VQxImMhYbKQK1+24Mm2yC7mOS6G7M9kMcGObJfWy/3ZEP5No9FIZYrCzBILWOUTyayUTdhLebkyZPmGs+eyLG9ra6NrndmyM7dB1gclHJbNeqpYIOLcuXN46623LkqU1dXVQavVoqSkBJFIhO49l/N9F973WIAo3/rX2dkJr9dbMBADICOAypxTr7vuOjz00EOYnZ3Fm2++iWQySe6wR44coXWK/WP27uXl5Vi7di2eeOKJSxrEE17rbD1j92J27X7UGbNcgbx4PE6ZMuEAcVZ90tXVlVEmKJVK0draCqvVin/7t3/D8PDwR3KsuRCO+tm5cye2bNmCw4cP4/XXX6dgdS7S6fRlqUxYSrgo41yRWK1W7NmzJ2M2RjQaxcGDBykimGvzxDaMbJYSEw7l5eV0kyoUOS+21p4528VisXkOcG+99RbeeeedvLX7gUCAygmkUilkMhm0Wi2cTifi8Tg0Gg3a29vx/PPPZ9jDxmIx/PKXv8SaNWsKCjOTyYS+vj7EYjF0d3cjHA7D7/dDp9MhEAhQvxrrH3M4HGhsbKRZQMePH8fZs2cRi8WonI0tljKZDMuXL0dbWxsqKirQ2NiYkVUstj+l2Jsq23SwPqdQKITh4eElscgvBtZfyHo42OIo7JURNv0PDAxkBBMMBgMaGxsRiURgNpsBAKOjo2SkITynd955J0ZHRzE4OEizdx588MGCWRy2mB89ehQTExMUHFAqleTGyBZEiUSClStX4tlnn6XS1P7+foyOjtLvnj59GmvXriV7bFZqKJfLyeGNXcPsuqiuriaDj4X4whe+gLa2tkVvzAtdYwtdf8J7Qi6L7Et13WUb3uRzSVyMGceVAjtm4cxIZsufL5PLXjsrDxf2EbOBz7kCSxeLMLiXPZy9kGW51WpFa2srli1bBpPJhA8++AAulwuxWAyxWIzuWX6/PyPgkUgkLjribzDMuTOy+6HX60VPT89ld6hlx8IEQk9PD7q6uubd1yorKzN6VxdCKpWioqICN9xwQ0ZlxObNm8mEp6Ojg9Z3ljmrqKhAMBiETCbDunXrLktVBbsXs+vEZrPh0KFD6OvrK7p/70L/LsuKsmBgoc9IV1dXhthRq9U4ceIEXnjhBRrYfTlggb7s17Fx40bcfvvt+OpXv4rR0dGcwkzooPtxhYsyzhWJxWLB4OAgZYlYNslut+O//uu/8NBDD83brLjdbrz77rs0O8bpdKKiogKVlZVobm6e59YFFBc5z8ZgmDPYWL16NQ1xZZkAh8OB3t7evI2/JpMJdrsd0WiUxFh7eztOnDiB0dFR2O12aoxmN3khDocDzzzzDHQ6Xc6bvclkwtNPP43JyUmKTLLhppOTk3Qeh4eH0dfXRxFei8WCWCyGqakpmM1mEmQikYhMT5gDV7ZAyD43l2qjNDQ0BKVSSYIhmUxCJpMVdNpcSliQgGUHmI11MBikHpRQKDTPYS7b0YsNRA0GgxgbG8Pg4CDef/991NfXZ2wyDAYD1qxZQ0YwrOeumOv57/7u7/CXv/wFfr8fGo0G27Ztg81mw4cffgiRSIQVK1agtbUV27dvp7/HNl6vvPIKWVWHw2F8+OGHUCqVGfb1EokEPp8PUqmUNrpsQz46OkqzxQohkUhw4403XvZSVeE9wel0Zlhks1LpfBn6Yu4hucyLAJBLnDBbxDKdxQQ7riSE51C4QcyV9RNmclkWnZ334eFhRKNRDA4OIh6PU6BjsaYV2QgF8UKGHLky+9lVGg8++CA2bNiAsbExSCQSnD17lnp+2BwzZrTA+qMuNuJvNBpxzz334Le//S28Xi+VvC/FNcGCpMFgcF6QdHx8nGaj5VrTspFIJCgtLaW+7uz3d3h4GHv37sXk5CQNutbpdKivr4dEIsHk5CQqKiponuTlgF3vnZ2dOHToEM6ePXvZzD+KuQ+YTCb8/ve/zxDFsVgMr7zyyjyX3cuByWTKGZQwGo340Y9+hJdffhnHjx+Hz+dDIBCASCSCSqVa1NzVqxUuyjhXJHK5PGOQpFKpRF1dHaampujG/uSTT2bMbxoYGKASQOagmE6nEQwG4XA4CpY7LfamaTDMuTEeOXKEmmdbW1uhUCggk8nInEK4SLJySqfTSVmydevWobq6Gl6vF9FoFOl0Gg6HA4cPH845cDqZTMLtduO///u/sXr16gyzBavVil//+tcYHx+Hz+fLqNVn9ebMYTIej8Pv95PbntPpxP79+8kKVy6XUxN1RUUFbQAuVySWLfAymQwlJSVQqVSQSqUoLS1FW1vbFdNTJiQ7O1BMpiyXoxeDiU+n04lwOAyLxYJwOIwHHniAIqLpdBoajYacxkZGRmA0GjN6OnLR3t6O73//+9TvpdPpsHv3bqxfv556f3L1khgMcwYezzzzDBlzrFq1al6mLB6PQy6XUymRXC7H6dOnyU5b9//Z+/LguKoz+9Pb61Xq1tKS3FbLsoxkYdnGbQpMgMHEHsWZsMTjpEwWUgNMKmypmSoqmWQmIRQkMEwYJlRmYpjMEEwqw+IBB2IziREyCARY2FELy5Ily1q79aRepN737feHfvfy+un1or0Nfaqowq1+r9967/2+73znVFZidnaWBiFCSCQSGdW2lht8yiD5jy+RLRRY5KOGyffi44oXEe8pYsIrl8tppYYr1FGoAVgmZDpmtVqNWCyG8fFxjI2NAUDG607Eb4hyn91uRyKRwOjoKBQKBRwOx4LpaUJ9Y9nM2YXOg/8MsCxLe+lqamrwwAMPoKOjA6+99hpmZmYQDAZp5l8sFqOiomJZMv5cT7lc1PmVBr/C3N3djffeew8OhwMikYiOB7kojKQXT6/Xp7UaEHuYd955B36/H6lUCuXl5di+fTv2798Pg8FAK635sl6WE6Ri1tPTQ9cAqyX+kQskAcsFqeiuNlKpFNra2qDVavG9731v3nWprq7GlVdeCZ/Ph/7+figUCqhUKhiNRpw/fx5PP/10moL2pw3FoKyIgkQ0GqXml8Dci6pUKmGz2dL8iciENDIygkAgQGW1VSoVSkpK4Ha7qULSoUOHlrUXiWEYampKVLWI4afb7YZcLk+r6pBAg0h+V1ZWQqfTUQoj6aEiinpCGaxEIgGHw4H/+7//w5tvvomjR4/i/vvvh0ajQXt7O7q6uuD3+6mMsFKpBAA0NDRAr9dDIpFgZmYGgUAAer2eKlwRxSVuUCaXy6FQKFaNBsIFmeA3bNgAq9WK2tpa1NXVZa3SrTWEqgNCPWVC/89V9AI+Cd4YhsGxY8cQCAQQCoVw/vx5HD58mErm6/V6bNu2jT5bAwMDePrpp/O6Tq2trbS/7Xe/+9080/Js21VWVqKnpwe1tbUoLS2d11PmcrnQ3d2N7u5uTE9P409/+hOUSiUN1q677jo0NDTg9ddfR39/v+DvEO+llUY2YY9MlfRMnl3AJ9U07v3le/ERewedTof6+noA6YbJayH7vtJgWRbt7e04ffo0hoaGAIAmEVpaWgSvO9cDjCQ6SIVkdnYW0Wg073E9U99YNBpdMFuCYRgkEgna/wtg3j4PHjyImZkZfPTRR2l0LLlcjubm5mW5t5mog2vxDPGP5ezZs7Db7bTSIZPJIBaL0xRfhZIyZD4EQINMAHj22WfR3t4Oj8eDVCoFrVaLTZs2pc1Na+0VSGilAFZd/CMbamtr51FHlUrlmgRlwNw65sMPP5xX2TWbzWnCKVu2bKEsnddff52OA9XV1VCr1QWboF0KikFZEQUJhmFgt9vpoDE+Pk754tyFGvF3AkArOyqVClqtFrFYDB6PB7FYDIlEAna7fVnpHdFoFFqtFqOjo0ilUjh+/Dh+8IMf4I477qBmqlyJfDKRr1u3jg6Q09PTsFqttHLg9/sRj8ezNkWTSlcsFoPFYsFTTz2F2tpaTExMwOv1QiaT0cXdunXr6MKZ37vCXUSTZuFgMEgrMKlUCgaDYVVpINyKRWtrKyYnJwHMLV4PHDhQ8ANwrooGPwNPwFdX5P7/5OQkOjo6EIvF4Pf7YbFYqLGnSqXC3r17YbfbqejH8PAwxsbGaF9Hruedq4BHTMtzgTT5Z/obMCd7PDQ0hJmZGbhcLoTDYZSUlKCmpoZSiZ988smMv1FRUYGNGzfmPJalIpfEvVBPZDYvI37flJAXX1NTE+2VIterUPvDlgpC12xvb0dnZycNToG5MXRgYAAVFRXzglluzwx3zPJ6vXjmmWfQ19cHn8+XV49ptr4x7m/lez5cGf/W1lZUV1fPq6gSmntFRQVeeuklTE1NQSQSQa/XY/fu3Uu8qp+ATx0kZt5ExXI1gwFyLA6HA1NTU5SJAcwlWZRKJVV7bWhooFR9UkmUy+VQKpVQKBQYGBhAMBjEsWPHYLVaYbVaKROG2OPwq6SFUFEmpt9c8Q8icgMIJ+ZW+phPnz6dZhEgEolw5ZVX4uTJk2mfrYbyIkEymUyrFrMsi8OHD+PMmTOIxWK4/PLLceONN2Lfvn20Zw6YYxQcOXKEJkUeeuihgl8XLATFoKyIggQxmySIx+OUtki8sQyGObPlcDgMr9dLKYCEjieTySiNUalUoqqqalmbRLkKWFy6IjDHi+eq4hHpXELFUavVmJmZSVNwVKlU1PMlFovN81kRAqEhsixLe1IUCgVMJhPuuOOOjHRNLrim0d3d3Th9+jQmJyehUCjoomg1wK9YtLS0QCKRwO/3o6KiYs2yeqsBoYU/wS233AKWZXHhwgUkEgnEYjGUl5ejrq4ujaZDqIPT09NwuVwYGRnJSZ0h7xl/kbocMJlM2LFjB8LhMILBICQSCaqrq1FeXg6GYfD0009nvKdEfGQ1FliLkbjn3y8hmfdcXnxCypCfNrAsi6effho9PT2w2Wzzkk2pVAplZWXzglluwMq/1izLUo8jYlSd6xiIzxhJPCzGyJlAKImRqaJqMBhoxezMmTOIx+P49re/vexjajYVy9XuM2MYBsPDw1QIC5h7n+VyOVpaWqBQKKDX66lAVF9fH+3Fm52dhcPhoIHauXPnMDMzg3A4TH0gNRoNLr/8cvz4xz8u2MU4oTKSgEzo+Qaw7AJCQmrKZrMZbW1tae/dunXr0NTUhA8//JBaoRC2TiY6+WJBrCP4uOuuu9JaO44cOYLR0VHqrabT6dLmLuLNFgwGYbVaaaJvtSjuq4ViUFZEwSJTUELoD319fXTA9vl8dOAm2Z5UKgWNRkN9MPhm0EsFoSoEg0G43W7o9Xq6oFOr1fD5fJiZmcGxY8eQSCTQ398Pn88HlUqFRCIBv99PVbpIFUEmk9FesFQqRRtciWCE0LXw+XzQarVQqVTUCFvIZy3XuQCgnm0ajQZNTU0ZA7uVAH8xSyqKhCL0aVddygSSeb1w4QJ9Nr70pS/hqquuSlsACtG9urq6qHoj6UMTMiAmYh/LSQ01GAw4cOAAGIbB4OAgTawoFAocP34cLMtmVGRLpVIYGxvD448/jl/84hcr3ii/VCVDfuDA71HK5MX3aQd5Hkl1jMiWkzFapVLhuuuuS/NeGxoaQjgcxtjYGE6dOpUWnHHpTZWVlVCr1Whubs64KCPPOFFEJSqQS3nO+dRFhmHQ1dUFo9Eo2MtJ3gOv14tIJILx8fFl7/viU6e5vVVGozGrGfpyg2VZeDweOrcRGv2VV16JBx54YF5CgnvvyP1SKpWYnp5GKBSiEulisRilpaW4+uqrC8qrMhP44wp3fhMSe8rn/ggFXUI0aa7HXm9vL3w+H92HRCJBQ0MD6uvrUV9fj5GREZoErqqqgt/vh9/vR1lZGVwuV05BpmxgGAb33nsvJiYm8Pvf/56+99dccw3uvvtueu6HDh1Cf38//H4/NBoNtmzZkrb2IFVnorz5L//yLzQom5iY+FR5lxWDsiIKEgaDAVVVVbQRnAupVAqDwYDnn38eFosFkUiEqlsBSBvAr7jiCnz9619fsT6k6upqlJSUYHp6GoFAADabDdFoFNXV1YhGo/D5fDh79iwqKysRi8WgVCqhVqtRW1tL6ZUk+LzssssQDofTTHxTqRS2bt2KVCqFM2fOCNILSJ+ZVquFVCqlcvcLhdlsxtjYGMRiMerq6laVtgjMbxQnymv59Dl9mmEwGLBx40a88847SKVSiMfj0Ol08xaAXLoX18NseHiYqtdxvWt6enrQ19cHlmWxadMmAJ/0Qy3kWmdaKJDF4YULF2gvaCwWA8uyUCqV0Ol0aGhowMjISEap7M7OThw9ehTf/e53F3n1ch8vsLy0p2xVk88iyH2Px+MIh8NpY1hpaSl27tyZ1qtHEgp2ux3T09OUitva2oonn3wSH3/8McRiMXbs2EHpTULXll8hIz5jSxFd4FMXTSbTPAsDoX0TJgURhVqJ6hX3Geb2VgHz1UJX+llUKpWUqVJWVoadO3fmFUjxg8tz587hxRdfxOzsLBiGwcGDB3HgwIFL5l3KlKzh9w5ns3/g+j5ynzUSdAnRpIFPPPbsdjvC4TD1R62vr0dNTQ22bt2KJ554Av/1X/+Frq4uaj1z0003Ydu2bQCAxx9/nPa/LgZisRgdHR3UwJr4YWo0GrAsC5vNhieeeALnz5+Hz+eDXC5HeXm54NqDXEuWZVFTU4PJyUnEYjFMTk5+qrzLikFZEQWJaDSKyy67DIFAAH6/Py1QqaiogMFgwOnTpxEKhSjVqaGhAQzDULPompqaFfUJAeYCmd7eXjgcDvh8Pvh8PpSUlFB5cJKB0mq1kMvl0Ol0OHjwIC2/O51OJBIJ1NfX4+DBg9i0aRPtRyNwuVyorq6mik4EpGkamKN0ulwulJaWpmXF8gVRhpyengYA1NfXF4QU+UL6nD6tYFmW0nvi8Tg0Gg3Kysoyfp9LnSF0RiJhT6qtpNrQ19cHiUSCiYkJaugsJPPO/X8+lUwoO8sVtXC73QAAvV4/LzAkoiATExM4cuSIoGR2JiGQxSCbqMdyohB6WwoBBoMBKpWKjl38pJJUKk2zeOBWe8fGxigVd3p6Gs8++yw++OADRCIRWq3i+4aZzWa6iBwcHKQBHnnmlqqCx6cuWq3WrP2IBEQUiiizrrStB/f56+rqyniMK1FBM5lMuOGGGzAwMAC5XI4vfvGL2LNnT977J9+zWCzYu3cvtm7dShWWC706lg1CvajcuY5UiLn2Dw6HA8FgEDKZDPX19ZBIJHSMJEFXJpo08dizWCyYnJyEWCxGeXk5tFot3G43Dh8+jJtvvhk33XQTZmZmcO7cOXi9XnR2duLUqVPwer1LCshIknxwcDBNtCyRSKC7uxvPP/883nvvPUxNTcHv90MqlUKr1ea8zxaLhdqxJJNJnD9/XlCp+lJFMSgroiDBMAw8Hg/UajUqKyths9mo71FLSwsaGhpQXV2NQCBAm1YPHjxIMykAVkWlz+VyYXZ2FoFAABKJBDabDUNDQwiFQvD7/Ugmk4jFYhCLxQCAcDgMm80GlUoFt9tNDZlJhcNkMuH06dMwm81pdgDxeBxXXHEFHA4HJBIJ9Ho9Wlpa0NHRAZfLhWAwCJFIhFgsBpvNhiNHjtDrkQ9IozjhcpNs3WqBuzjYtWsXzGZzkbr4/2E2m+F2u6FQKKBWq7Fly5a8M858OmNNTQ3l+JP3JB6PQyqV0gUA+U2u1DaQ3v8AfLKgEMrOckUtGhsb52WAySKQex5XXXUVvvOd78wLwLds2bI8FxK5RT2KWF5Eo1Ho9XrMzMzQZyybmAC32sv1cgPmqqYkWRWPx9My52azGceOHUN3dzeCwSDUajWUSiVKSkqwfv36JVXIuGMTv/+Qby6daZyKRqOoqamB2+2GTCYDy7KrFmBk6plcqQQFl2q2mGBP6LjuvPPOJR9XIUCod5hv/0ASaFzfR4ZhoNPpYDAYMhqbC9Gkq6urwTAMHA4H3G43xGIxYrEYent7EY/H8cEHH2D9+vUYGhqi65VQKJTWBsIFEdvJB8QOJRwOz/vb7OwsnnvuOfq3iooKNDQ0YP/+/TlbL4xGIyQSCU30EA/WTwuKQVkRBQmibOhwOLB+/Xrs378fH3zwAWQyGVpaWrBnzx5UV1fjmWeeQSwWQ11dHS3vj4yMQC6Xr3i2mmVZdHZ2wuVyUf48gDSZ33g8Do/Hg3PnzlEBj/7+fojFYkQiESQSCSoNDMwN0o888ggeffRRTE9PY9OmTairq8sol016LMbHxzExMYFYLIaLFy9ibGwMr7/+Ou655x7cdtttOc+FP3GvZkYyU7WlSF38pILpdrupNHC+vZHcBS5fcbOtrQ2hUAgulwtSqRTxeJx6qWXyTssUsHFFQvgLBVINA3InSVpbW/HrX/86LTC7/vrrceDAgUVevflYjKhHEYsHSa4Rj8HKykqcP3+eLvj8fj/a29vxrW99K207/rP74YcfpvW2EC+rN954A+3t7bQnh1TjotEoNb1fSoVMKEDg217kIz9vNBqh1+sxMDCAyclJtLe3r5q1RyY6ba4ExVKqaEuZez9riZNMCTR+pay5uRkHDhxIe9aELAD4QR8RmiGVS5/PB4/Hg0gkArFYjNnZWcTjcdqvnEn1Wa1W5/SY44J4omZKwnB75NVqdd7CLQaDAXfeeSdsNhtdF+3YsSPv4yp0FIOyIgoSZDJPJBKwWq24/fbbceDAgbQByGKxYMOGDRgfH0cgEEBPTw9GRkbQ3d1NfU6IHP1KwGKxYHR0lC4EwuEwVbIrLS1FOBxGLBZDNBqFx+OhmR1CuSRCHkQhkkw+JpMJv/zlL/OSziWD2KFDh2i1ixzPzMwMHnnkEZSXl6O1tTXjeZDJd638kfiTMKm2FKmLn1QwxWIxSkpK0szC84XQAolINgNz97+uri5NkQ4Q9k7jB2xCSnb8fhZCZ8xHor+1tRVHjx5FR0cHKioqFixYk8+1WKqoRxH5g1shisViqKqqgsFggN1uRzweRygUwrPPPksDeD7Iszs2Npa2uJPL5ejv76cJAr/fT4VEZDIZrfYvRryGG4yYzWacPn0aDocDzc3NsFgstJdzIVUmg8GAzZs3o729HT6fD729vTll/JcTQmNAtgTFatF8hZCtssd/b1dTwGQlkS2Bli2plU/wazDMCc0Qyvr4+DgikQhSqRSSyWRaQJYJpOK1kKAslUpl7BXmQiQSobGxcUHzGtcr81KntfJRDMqKKEgIeYC1tLSkiRsIUUl6enpo8ON2u3NKgi8GXCEDvV4PsVhMB594PA6GYbB9+3aUl5fjrbfegtvtprL8KpUKgUCAZn3FYjG0Wm2aciOQmeZAVL64f4tGowiHw4I9G6FQCJ2dnRmDsrWcfAn497G2thY9PT1F6iLmrg3JUHq9XnR0dKClpWXJk5DBYKDvi1KpRENDw7xJP5t3GteTib9dvv0smZDNA20hyLRgK/Z7rR6MRiMaGhpob2M4HMbGjRshEomoAMHo6Ch++tOforKyUvDU2T3CAAAgAElEQVS+m81mPP/887SvmIg4EQousQIB5hIIjY2NuO222xYV0BMJ/7GxMUq7/PjjjxGLxRAMBvGVr3wFwOKqOWVlZVCr1Wn9NWuJbAmKtaxWGQwGKkS0Y8cOOvfx5ylgfmBMjjVTkEaorsDqtDcsFEJj03KMhaRyPDg4iNnZWbpOEIvFkEgkNEDLVNXiB2NisRgVFRVURToWiy3Y40wqlSKRSECpVOIb3/hGzu/zx/PlmicKDcWgrIiCBNcDLBwO08GUv7jKNKkQR3hi3rgcwQY5BtLnUFNTg1tuuQU9PT20IVYqlaK2thb33XcfAODixYsYGhpCMplEY2Mjtm7disHBwTS1IYPBkLOHK1vwZDQaUV9fTxvJA4EA7VfTarW4/vrrM+63EKgi3PtIqHVF6uIcyLMxMDCAkZER9Pb24oknnphnmrpQCBng5srCZgrYcvH/14IuWAjJhiIyU7PKy8upD2UikcD09DSOHTsm+Ez39PRgfHycVsylUikaGhrQ3NxMAzORSASJRIKmpiY88sgji343uMcpl8vh9/spzWp6ehq//vWvqS/ZQp9rk8lE32XSH7TWyJSgWEi1ajnAl3Yn1XWHw0Er7/x5CkDaZ1xatdA7z5Vej8Vi2Lx5Mx544IFLdmHPFbYpKyuj5yEUdJLkHjeBAQAlJSXUC1QsFkMul6cljTOBCKmNjIxkZbIQBU6h/ZEgLplMYnZ2Nuc5Dg4OLruvWyGiGJQVUZAwGD7xAOvv78/Iw+dPKsTTiUiCE/PGpQYbZJHX29uL6elpKBQKAHOKcg0NDejt7UUymURZWRm+9rWvobq6Gr/73e8glc69YgqFApFIBDfffDM2btwIh8OBRCIBtVpNsz7ZkC14Io3V11xzDf33+++/j7GxMezbty9rlWyljIMXCnIf33jjDfT29sLlcqGpqekzTV0kMJlMePPNN2GxWOByuTA8PIzDhw8viZorZIC7EORbbVorumAhJBuKmAOXmsW1atDr9XA4HAiHwwiHw3j11VexefPmeT2wxFqEQKVS4dZbb8XWrVsxMzODCxcuIJlMQqFQ4HOf+xyi0Wiab9FiAwk+/SqRSKC3txe//OUvcfXVV+OGG25AX18fKioq8r4OBw4cwKFDh+B2u3H06NEVpdcvBULv7UolOvj75Uu7k2MQChK5nwEQDNxIsu/QoUPo7OyE3+9HKpWiyoQ/+tGPstL7CxF8by9iOwAAvb29tA3hmmuuwcaNG+m6iPs8E7EMUukCQMXFcgVlyWQSExMTgt6pXBgMBno/+CDHEo1GcfLkyXnvPcuyeOSRR/DRRx9BJBKhpKQEGo0GwKd7PC8GZUUULEwmE3bv3o2+vr4F8fANhk8kwbkGmksBWeSRiphOp6OUyZGREXi9XkSjUVxzzTXYs2cP/f7s7CxUKhUkEglqamrQ19eHjo4ORCIRaLXavA2ac1UchILTbFhp4+CFgEsH5cryq9XqzzR1kYAkKBwOB4aHh5FMJhGJRJY0MfENcFfyOq8FXbAo6LH2MJvNaT0f/HF5x44duHjxIhXxcDqdeOyxxxCNRrF37166j5dffjltMRmJRFBTU4O2tjY4HA5qTlxRUYHR0VG8/PLLWSlu2Z5Fk8mEbdu2YWhoCOvXr0coFKIy/CKRCOFwGENDQ3A6nRgYGKC9cu+++25ePlwsy2JsbIyq4a22yu1CwH9vlzvRQcZ9voIrX9qdBIVCyR0+xZpLq2YYht57i8WCvr6+NOn0aDSKixcv4qGHHspInS1EsCyLw4cPo7u7Gy6Xi1aihoaGAABWqxV+vx/Dw8N47733UFZWhs2bN8NqtVLqrEgkon373ISckFKiEPKl4OZDaxSLxTSg5OLo0aM4efIkgsEgVa8m4/qneTwvBmVFFDQWy8Nfzgw9t6LU1NRE5b3JQqO6uppWqbiBjVqthk6ng0wmQ11dHfR6PTo6OtDf349wOEwlYPNVHFrOigN3gt2wYQMqKyvXLCAjE6fP56OyvVVVVQW9YFltmEwmfP/738e//du/wW63Q6VSLXpiyoe6eKkgW99YUdBj7WA2m/Hwww/D4XBAr9fjoYceomMj9760t7fj9OnTNDM/MzODn/3sZzh06BAAwOfzzfMgCoVCOHToEKqrqxEKhVBZWYmSkhLU1NRAIpFkpbgtJJBQqVS4++670draig8++AC9vb2YnJxENBrF7OwsFUsglbylVq8LHctJaeQnBblsDSFpd0A4ucP/jPtskTnObDZTcQs+UqkUWJbFSy+9VHBBGf+6coNYotxMesLKysqgUCjg8/kQCARoD1g8HofD4YDD4YBYLKafE1phJoEPsVic9h0SWC20b2xqairndzQaDWpra+ed+9tvv03fMZFIBIPBgK997WsF2Qu4nCgGZUUUNLiZy4Uq9CxHhj6filK231EoFKioqEBzczMqKiqoB4lCociLtih0PpkEPxaCQqkkkIlzaGgIPp8P0WgUarV6TcyrLwVMTU3BZrMhkUjAZrMt6P7zM9OXurplLjpVUdBj7dDT0wOHw4GZmRn6b65JNLkve/fuxf/+7//CbDZToQG/3w+/3591/3/+85+xdetWmuwiCZy2trZ57Ihs4xx/4Ws2m9Hb20tplSzL4vbbb8eePXvw9NNP49ixY1QoIZlM0oVrKpXKq3pN+srGxsZQX19PKdvkb4X8vC6U0phNLZFbHRNScCW/t5hj5G6nVqupZQ2BRqOBSCSiNMZ4PI733nsPZrO5YOacTDYxhEUSCASgVqvBMAz1nmRZFi6XK6PiYTKZhEgkglQqhUajgUQigcvlmifiIRKJsG7dOtTU1MDr9WJmZgaJRAIMw8Dr9QoGt5mQj1ojUcrmwmKxQKPRQC6XQyKRoKKiAn/3d393ydFMF4NiUFZEwYNUnIg090Kw1MbkxVaULBYL5aynUikEAgHo9Xr4/X7U1NSguro6L9qi0Pkslde/1hL4XJDgkCyC5HJ5sUqWAceOHcPExATC4TASiQQ6OjryWkTwBWqCwSAcDgc0Gs0lTQUp9o0VLnbs2EF97/R6fUYfIYPBgIcffhgPPvggenp68s7EJxIJeDyeeabQQr5NmSqmQmOpy+WaJ4ZAjvOGG25AZ2cn7W8j4gpTU1PQaDRoaGjI+S6RvrKenh5Eo1E8+OCDGB8fh0QiwTXXXIOf/OQnBf0M50tp5CczuUFzpurYcp+3wTAnkvT+++9jdnYWyWQScrkcGzZsgEqlwtmzZ9OqndzEwVqAu1YRsokZGRnBwMAAgDlLiFQqBZ1OB61Wi8nJSUxNTdHqmVAwxDAMGIahlWWbzQaVSpVWiRaLxdi/fz/uuususCyLF198EW63m+6P0AjzBcMwiMfjWYOz8vLyedfdaDSipaUFwBwF8s477/xMBGRAMSgrosBBghsykC9k4bXUAGYpQhgMw1BKXiwWg0QiwcjICPx+P+RyOS677DJUV1fnfSwES12IFpoqHcm+8lUtCyVjWShgWRYXLlygVBKxWIzKysq8tuMK1CQSCdjtdqRSKVRUVKz5/c8XQsmVQqn2FjEfJpMJDz30UF4+QiaTCX//93+Pe++9l8re5wOHw0El0wnyobgR8MdSs9mM7u5uxONxqFQqbNu2jR43y7I4fvw49ZhkGAYGgwFlZWWIx+N504AJdfjDDz9Eb29v2vm+9dZb2Lp1K7773e/mfQ3WGpneQVJxtNlsSKVSGBsbg0KhgNvtRjgcRmNjo2B1bLlhMplw3XXXIRKJ0JaBiooKDA8Po7S0FB6PBxKJBFqtdk0NiIUqY3ybmNdffx0+nw/xeBwVFRVQq9Worq6GWq3GzMwMYrEYRCIRysrK4PV608Q6xGIxrr32WtTW1sLj8WBychIqlQpqtRoKhQJ+vx8Mw+Av/uIv8I//+I/0fkQiEYRCIcTjcVq9JlL22RIoIpEIWq0WX/7yl9HZ2Ynh4eGM392yZYvgO/tZpZ8Xg7IiChokuAkGg5iengbDMHlvu5QAZqlCGFzT1FQqBZlMhqmpKYTDYUilUlgslkVl9pe6EC3E6gL/9wudxrMWMJvNiEQitFp8xRVXYM+ePTm34wvURKNRKnkcCATw+uuvF3wGMlMi4bM8cV8KyNdHiGVZvPvuuwsypgXmaE8syy46gcMfS2dnZ9Hd3Q2v1wutVoudO3fSqs+JEyfgdrsRjUYhlUqhUCigUChov06+NGAiOEEqZVyEw2G88sor0Gq1y26avlLIRGkkgk2BQAAMw8ButyMSiVB6ulqtpveN9P6t1Plec8012Lx5M8rKymi1Dphj4BB63J133rmmiUD+vByNRuf1x5H1BACsX78e9fX12Lx5Mw3Wkskk9Q/btWsX/vjHP6b1gnk8HojFYlgsFsRiMUilUtTV1dH9kMovuYdPPvkkent7qWk0eT/lcjmkUmmaKIhMJkNpaSmCwSBEIhGMRiPuuusu3H777fjbv/3bjEEZ6RUTwmeVfl4MyoooCAhx+zs6OuD1eqn8vFarzTjxcRX8CCVvKQHMYmiL3HMgpqnAHNVGLpfTBl2RSASFQrGozD63srQYFGJ1gb/oLrQq2Up58+QLs9mM1157DXa7HRUVFWhsbMR9992X1/PIF6hxuVywWq1UsautrQ1tbW0FHZjlsoP4LE7cnyZYLBacPn16Qb0qAKg89mLBDyhOnjwJp9OJUCiEVCoFt9tNx6aRkRF4PB7U1tbC6XSipKSELur1ej3tuckFo9EIv98vKLAgEokwOTmJf//3f8eJEyewb9++SyI44/c6O51OBAIBKBQK6HQ66HQ6hMNhmlTlCmOtJGuDP6/s2bOHCnPx1wprJXLFXS8IKU4Cc+P/6OgoEokEmpuboVKpUFZWho0bNwIA+vv76bvDlaoXi8WUhptKpTA5OQmn00nZOtXV1fjCF74g+IwdPXoUp0+fRiAQSKuIiUQiAEBFRQWmpqZon1pdXR2tqslkMmzatIkmDUkfnBBSqRQ+/PDDNAuLzzqKQVkRaw6hRfkvfvELjI2NIZVKgWEYKJVKeDwewYmPZVk8/fTTGBgYQCAQQF1dHRoaGtDa2oqWlha0tLQsqPqyGNqi2WzG4cOHEYlEoNfrsXnzZqhUKiQSCXi9XkxNTUEkEkEmk0Gj0dB+i8WCmGT29fXlnMz4QUWhVBcySSIXQvWOYK3pnkT+eKGKnZkqvWazGW+99RaltsRiMXR2dhZ0UFaIiYQilg9GozGjOEEmiMVi1NbWLjmBww/q4/E4EokEwuEw/vSnP0Gn02F6ehpWqxVKpRLbt2+Hy+WiqpI33HADjh8/jkgkgra2tpzqiwaDAbW1tejq6qKfaTQaJBIJxONxRCIROBwOzM7Oor+/H2azGd/73vcKZjzkg5sM5feLNTY2UioeqZ5x6enZ+tEWMj/xjafJ8ZA+LKvVCofDQROZazX38Y+TP6/w52WuF5nP50NJSQkNxHp7e/Huu+9CIpHA4/Gk/Q5hQTAMQ+mxUqmUGqETk2i9Xo+mpqZ518FsNuP1119HOBymVHmRSESFeIjZM6mciUQiBINBRCIRSKVS6HQ67N69m97LycnJrNdlYmIiL6ujzwqKQVkRaw7+4NzZ2QmWZeHz+WhmRq1Wo6amRrBSRvqRyMsfDAbhcrnQ0dGByspKGpTlg8XQFsnC+cyZMwiHw1AoFGhvb0c8Hkc0GkUwGEQymaR+Okqlkk4SSxUfIf/OtJ9s1K+1BPe4gDnp69XwzFooSG+E2+1GMBikGezVun5msxksy9LnKl/FzmyV3l27dlG6mFarxfXXX7/Sp7EkFFIioYjlh8FgwLe//W1873vfy0ph1Gq18Hq9VB3unnvuWfZngfTKxONxjI6OoqOjAyUlJYhEIohEIpQGTBCNRiGRSKj3Gn8sFgowZDJZWhVDIpGgpKSEmviSeSMajaKzs3NFq2VLYQFwx/BEIkEDSiE1xUwy9/xki9B8BQgHUkTA6NixYxgdHUVpaSmMRiOCwSCGh4eRSCQQCAQQiUTgdDrx4osvor29HQDm2dosBzJJ2AsFYUIG2bt27QIwN+YfPXoUb7/9NoaHh+H1eukzWV5eDo/HA4fDAQA0UOJCo9HAYDCgvLwcFosFyWQSsVgM4XCYim5Eo1E4HI55SW6z2Yyf/exnmJiYQDwep/2TtbW1mJiYQDQaRSwWS+tXSyaTsNvtkEgkYBgG27dvp1Uyi8VC13BckM/EYnFaxXutWSmFgGJQVsSag58J37BhA1555RUqNSyRSNDY2JhT3UoikUAsFkOhUOD8+fMIhUK4ePEigPyqL6R3YGRkhE4u+dAWuQtnYM5HJxKJIBgM0oGNONKXlJRALBZjenoap06dWtSksJDKQSH2kJHjIFL44XAYOp0OBoOhoDyzuL0RsVgMExMTCIVC6OnpwR133LHiNEvy+263GyUlJWhsbMxbsTPTM2IwGPDP//zPOHr0KMbGxrBv376Cq5IJTcyFkEgoYuVw2223oa+vD88++6zg3w8fPgxgjlZVWlpK/YqWA+R5c7vdkEgk9PNQKIRoNIq6ujpq9kxUYt1uN+3vzDQWZ0qI7du3D2+++Sa8Xi+kUik2b96MVCoFuVyOixcvwul0ApgLEP1+/7xAcKnnSWh73OoWqWjlS+fjz5V6vZ4qG2ZSU+S/w0LJlq6urnniK4QVwr2G5Np2dnZSOwVgLiAhgiJk/UAUA30+H+RyOXQ6HSQSCcbGxtDX17coFWKhACyThL1QENbS0gK1Wg2VSgUAcDqdNBh77733qO1JKpWCVCqFRCKBXq9HY2MjgE9MnhOJBLVoAACdTod7770X1113Hb3HLMvi2LFjVG0yFArRahq3J5NlWTz66KM4c+YMpdcSmju5nplAjlWj0aC5uTlNjOmyyy6D3W5P+z55d2pra3HttdfCZDKtOSulUFAMyopYcwhx+8ViMTUNVKlUqK+vz7hg53q/6PV6hMNhWCwWao4bi8VyVl+4vQNkUMinasNdOGs0GhiNRmg0GnR3d1M+diqVglarxeWXX45rr70WXV1dVHrZbDYvODOUq68sF1e9EMCVwp+enkYoFIJEIikYzyyWZfHggw/io48+gkKhQE1NDUQiEfr7+2l2caWNYs1mM8bGxiAWi2E0GvM2GgeyV5cMBkPBKrwVJ+bPLh555BG0tLTghRdewMjICMLhMEwmE5566in6DCx3AoFvXq9WqymzQS6Xg2EY7N69G9FolFb1BwYGqPAUqQIJvWeZEmItLS249tprce7cOZSXl2Pv3r3Q6XTo7e1Ff39/Gi1MqVSirKxsSednsVjg9Xpx/PhxuN1ueDwe6m1FqlvBYBCHDx+GRCLJ+d4Rqj53X6RdYKHBDT9Q489XwHzzb5vNhtdeew1jY2Np1wsApegBSBO5EIvF8Pv9VIWxrKwMdrsdp0+fxtmzZyESiVBfX48bbrgBVqtVUDFUiKpJrpWQhD0/COOeFxEw83g8mJqagsVigU6ng9vtht1uRzgchkgkQmlpKTZt2oTPf/7z2LhxIz0mMvczDIMf/vCHsNlsAOaCMu73gLl5qr29nSYclEolrXjNzs7S7508eRJnz56d1++YSCRooiAbpFIpampqsHv37rT7+0//9E946KGHMDAwQCX7U6kUysvLcf311+Puu+8WDMgLJYG82igGZUUUBLjNwn/605+o/GoqlcLU1BTOnj1LJWCFMnAHDhxAR0cHpFIpbRonpfcvf/nLWel9fEPdmpqaef43mWCxWGhTc01NDc3gPvvss3j11VdpH1xzczO+//3vo7q6mk7oarU6jWe/0AWoUF+Z0KK2UKhffDpHS0sLVCoVPv74Y7oYKISgsa2tDQ8//DBGR0cBzC2OamtrUVlZiZmZGWoqu5I8eG6VDsCizLQvxepSoVZ2i1gd3HbbbbjttttW7fe4z5ter8fll18OiUQCv98PjUaDkpISVFdX0zHU6XRSehyh02d6z7LJxc/MzNAA4siRI1i3bh36+vrSKhIymQzl5eWLev653oQOhwNDQ0MIBAIIhUJQKpUIBALYtGkTrW5x6YfkGIWoeAzDUKp+LBbD5ZdfnvdcmQ/4ySRgbp4j19Dr9eJXv/oV9eTi0ugIRCIRJBIJZagolUpEIhFK20ulUgiHw4jFYmniWyMjI+jo6EAikYBer8f9999PKZEABKmawCfUSu693rFjB/07t3LY09OD2tpaHD9+HP39/fB4PIjH42BZFnq9HkajEVVVVXC5XNBoNNiyZQvuu+++eWM/91oPDg7iN7/5DSKRCK28cWE2m9HV1UU9U4lSokwmw4ULF6jIhtPpFBSgyRcMw2DTpk3zrH5MJhOeeeYZKljy2muv0fMjlT+g2DtMUAzKiigokBeXWzFJJpMYHh7Gxo0bBRdpLMviqaeeQnd3d9o2EomESr7ywTfU5ZtZ5jvJ8AcSk8kEm81GfT9KSkqg0+lwzz330IGVO8EfO3YMw8PDCAaDC1qAcul/DocDJ0+eRGNjo6Boxq5du9Z8Ycvv1QNAq2RarRY6na4gqIsvv/wyHn/8cTqhAqB8/vvuuw+HDh3ChQsX4Ha7F00/zQck2JfL5SgrK1uQmfalzMsvTsxFrDSyMQlaW1vR0dGB999/H16vl/b+3nTTTTQ46evrg0qlyvl8ClWrSbJlfHwcHo8HyWQSfr+f0t9JQCaVSlFRUUEX8vlW5bnz2tjYGKanpxGPxxEMBmlfqlqtxqZNm9KqW6T6Q7yruMlCLhUvkUhQawCGYaDT6Za9x5Yf5HKv4YkTJ2C1WjE7OwuNRoOamhpYrda07eVyObZu3QqVSoXLLrsMZ86cQSAQoD3nyWSSGiJLJBJIpVKIxWKEQiEkk0mEw2EkEgk899xzKC0thVwux1VXXYXp6WlcuHABSqUSOp0uTQRM6F5zjcyBuWTfyMgI2trakEgkqL1CIpGg/Vj79u1DeXk5XC5XmkR9Nhw4cAA2mw0DAwO0DYAPriF6JBKhCowOh4OuO9atW7dgc2gu4vE4AoEAjh49CofDgeuvv55Wtsk9JQEiUW0k+gEkqVwoCeS1RDEoK6Kg4HK5aJWMCyL0ITQJ/sd//AfefvvtNAUvsVgMqVSKeDw+r5mVb6irUCgWbWZJ+qCISarNZsOPfvQjXLx4EdFoFCqVChs3bkRpaWnaNmSAIrSZoaEhjI2NAUAa5z8THcRoNEKtVlOq5gsvvIC6ujro9fpFmV2vFIQUFkk2j/jPkQlyKZ5Dy4G2tjY8/vjjcDqdafx5qVSKm2++GSaTCfv378fhw4fhdDrTJrTlBsMwSCQSqK2tRUNDw6KEai5F+l9xYi5iJZEPk6C6uhozMzO0Us1NvuSijvPBDzAsFgscDgei0Sil3cXjcUgkEjrmiEQiKlDl8XjQ1dUFh8ORl8oud14jC+zKykrEYjGa/Lr55ptpBYi7PxJEOJ1OtLe3C1Lx9Ho9dDodrrzySsjl8rx7XJcC7jVUqVTUQNnr9aKhoQGhUAgzMzP0+wzD4Ktf/Sqqq6vx3HPPYXZ2FoFAgFbQSEAEzF3r6upqaio9MTEBj8cDhUKBRCKBP//5z2AYBsFgME3wxWg0zhMBE+qZI//u6urCyMgIuru7IRaLoVarsWXLFjAMA4lEgkAggPr6+kWJuhCm0KFDh+B2u3H06NG0IN5kMqGxsRGzs7N0jRQOh+F0OtHf3w+v10sVF7mKjQsFUWX81a9+hUgkgj/84Q/4+c9/TgMzIog2MjICv98PpVKJwcFB2sZBrtdnfcwvBmVFFBTKysqgUChoQAXMZb4Ix5tvNGk2m/GHP/xhnqQyGYB1Ot28PiW+oa5Op8vYnCwELpWjr68PJ06cQDgcRm9vL8RiMTWJBkCPQSg4ikaj0Gq1uHjxIrxeLx599FHqbabVajPy9ck5VFdXQywWw+fzUTqGSqVaVHC5EuBXx9RqNfX0IdeFNBIvRfhkOY7z6NGjeOGFF2j2mgjG1NXV4eDBg7j77rsBzD13sViM9pgsxMx8IcfT1tZGeyIXUkG8FOl//MpecWIuYqUg9H7wmQQGg4H2KJPeX/57tBBLEgKWZTE0NISLFy8iFovRKk15eTm0Wi019dXr9fibv/kbsCyLsbGxjMqOmc6NzGtVVVWor6+nVfZc/V7cFgIuZZBPxVtM79hygRscSSQS2O12rFu3Li0o83g8+NWvfoVYLAaPx0PXBsSSBkDaZ9u3b8c999wDo9EIm82WRjG0Wq30XnEFX4C5YBeYC7hyXQti+0D+q62txY033oh9+/YBWLpMP3m27HY7HA4HvedkbP3Od74DAPj4448RDAaRSqWomuiRI0cAACMjI4jH47TnbKE2FQzDYHBwkAZ1s7OzOHHiBA3KSJ9/NBqFUqkEwzBIpVJrOvcXIopBWREFBYZhKH2AIBaL4ezZsxgfH6ceZEQq93/+539oYy+BQqGAXC6nwhv8gIhbhdDr9XlL4/J5+hMTE2BZlk6CfX19qKurAzCX0ZNIJNixY0fGbKLRaKTVMHIOPp8P5eXlsNvtEIlE9HPShM2l/g0PD4NlWcTjcUilUtTX1y8ouFxp8GXZd+7cCa/XS1XMCI1Co9FALBZjbGxs1f1KWJbFI488gvb2dhpIy+VyaLVa/PCHP5zX3xKNRlFTU0OpPSshTEKu2+TkJHQ63YIqiJca/e9Sr+wVcWkh3/fDZDLRwEutVsPpdNLem8UkPshzfvr0aUopVCgUMBqNuPrqq3HgwAGcO3cOMzMzaGlpgdlshs1mo4k5/jEI7Z9vFL9YyfdcVLy1fD937NiB2tpamrRtbGwEy7LzvjcxMZH2b4lEQnupxGIx7Z/S6XT4xje+QeXoiXAL8EnQFYlE0NDQkCb4QsQ6+GMXCepIEpmbwC0pKaG9bhUVFWm0z6VeU5fLBZfLhVAoBLvdjtHRUbS1teG5556DTCZDXV0ddu/eDdVhntsAACAASURBVKfTiYsXL1IBMr/fj56eHkQiEfh8PkilUmzatAmNjY34/e9/n/fvSyQSKrnPBbd1xGg0Qq/XY926dZDJZNi8eTN6e3vp+udSSCCuBopBWREFhb6+vnku8slkEmNjY1QZS6VSUalcs9mc1uwrEokgl8tRU1ODiooKAJ/I6lqtVqhUKnzwwQdwu93Q6XQ4cOBAXgtePjUkHo/D5XKlUQL8fj8cDgeamppgNBpxxRVXZKUjGAwG7N69m/L3CRiGQWNjo6BKFpf6Nzs7i3g8jlgsBoZhIBaLC6Ivi4C/ACorK4NEIgHLsnA4HHC5XNTyQKvVIhKJrHrG7OTJk+js7EQwGAQw11xfW1uLBx98UFDpzWg0oqGhIa+eksWCUFN9Ph9mZmbQ3t6e9zW51Oh/l2Jlr4hLC/xKbD70Q+73Tp06hfb2dloV449rDMPkrJaQ55xlWUSjUYjFYlRVVeGv/uqvcPDgwbRggKjQEdGp5uZmOg4IVeYW462ZC9moeGsJk8mEhx56iFazotEoHnjggby2lclk2LBhA+655x4MDg7mtAQxmUzzgtHq6mr67LAsmzZ2nTx5Eq+88gq1CLj//vtpiwLpxSMWOXa7ne5nOa5rWVkZNBoNpXa++uqrsNvt8Pv9kEgkWLduHcrLy6nPH0E0Gk0LaqPRKGw2G2688cYF/b6QX1pJSQkA0P2bzWbaR04UHUn1Uq1WF3wCcbVQDMqKWHOQChQA2nQrhHg8jtLS0jSpXL/fD5FIlCZ/6/f7MTs7i2AwiPHxcZw5cwY2mw1isRhisRhKpRJisRg7d+7MWeng90SRqlhlZSUSiQS8Xi8NyogqUyQSwTe/+c28gr09e/ags7OTqj7V1NTg/vvvR319/Tw/GdKEDcz12MlkMkgkEnoMpGF8LfuygPRFkJCSVjAYhNPpRDKZpIO5z+dDIpHAxMTEqi3MWZZFR0cHotEoRCIRxGIx9Hp9xoAMWJ2gx2AwYPPmzWhvb4fP50Nvb29eFUTudSeZ30LHpVbZK+LSQiYzYi79kE/H475HlZWVcDgcGBgYgFarBcMwOHjwIO0jrq2tnSePnokVAYCKeajVauzcuZMGZPzvct+JxsZGDAwMYGBgAKWlpfQYyLEuxlvzUgYxogaAxx57TLAHnQvCMCkrK8Nf//Vfo7W1NW9rBaFglFtB5fZvkz5jQqXs7OyEz+ejvXixWAxKpZL2gb/00ksLor9mg8lkwpYtWxAIBOD3+6l5NjC3biKWPaRiKBREETgcDvznf/7ngn6fuwYjcLvdOHToEM6dOwdgzkrC7XbTwNHpdEImk6GqqgqhUAg2m+1T/dzmi2JQVsSagmVZPP300zh16hQAYNu2bdiyZQu6u7vncZoZhoFWq0Vrayuqq6vR19eH5uZmeL1eeL1e+j2SlSIDBV82Nx6Po7KyEnK5POMiMJM6I5cawjAMHnvsMQwNDdEBifSJ5UtrMxgM+MlPfoL29nbMzMxg9+7dgkEVX8mJUCLef/99vPDCC5idncX4+PiCqiorAe4iKBgMQqPRpNE0yGKmqqoKL7zwAiKRCFU39Pl86OnpwdjY2KoEFRaLhQa9MpkMBoMB//AP/5Bxwl7NoKesrAwymYz2ReQykL1UaIBC/WOXUmWviEsLQpVY4BPvK74/F9/012QyYWJiAtPT07BarXA6ndRvSSKRUOrX5OQkVWrkB3YAaJWL9KxKJBLs3LlT8HnnvxM2m40ew/j4OEZHRzEwMIC7776bKvotxFvz04SBgYGMf5NKpTAYDGhqakIymUR9fT327NmTc5/Z1Gv5lHxu/7bNZsM777wDANDr9bj++uvx7rvvQqVSQa/X4ytf+QqOHz8OlmXhdrvhdrsxMjKCEydOLFnB0mAw4L777sMTTzyBc+fOzRPrKCkpQUtLC4LBIM6ePUstGTJByGogG7hecQQk2XrmzBm4XC66JuLqBcTjcYyPj8Nms6G/vx+PPvromieV1xrFoKyINYXFYqHNzIlEAgqFAldffTV8Ph+Gh4fpy0u42IQCYjKZ6MTV3NyM559/Hj6fj+43FosJZm8IGhoaMvZ65avOyLIs1q9fj/HxcVptUalUGYU9MsFgMOBb3/pWzu/w6STAXBD45ptvUo78SioC5gOz2YzTp09jeHiYGlq++OKL0Ol0qKyshNVqpb1bRG44HA5TNTKi3tTU1LTigzPDMPB4PFCr1dBoNDkDstUMegwGA81oBoNBdHd3Y8+ePTnpUYVMA8x0DQuFGlXEpwfcXh5+JdZms1EvKi41HJhv+mu1WqFWq5FMJpFIJDAzM4OPPvqIzi21tbXQarVpVGODwUADO7VajUAgQPuQo9EoFfjIZgrNfScsFgukUikVSZidncXbb78NrVZLF/jl5eWorq4uKPr6auCmm25CZ2cnIpEIANA5eOPGjbjllltw4MABAOlCGtmCrlzjvJAFDrcvjFArd+zYgerqarz77rt025aWFtovSJ6H6enpvNU1c8FkMuH73/8+HnzwQbhcLhooSSQS3Hrrrdi7dy+cTidCoRD1eCPXbaUglUoRCoXonA8IB3zhcBhDQ0Po6OgoBmVrfQBFfLbBMAz1TvH7/fD5fJicnKSKRyQjSYIyLsjEZTQa0d3djY8++iit0TSVSkEsFs/L4iQSCUqhe+ONNwCADgRCVMVM6owWiwUajQalpaVUFWrnzp2rIhNMwDAMFfqQSCTUgHItwLIsjhw5gp6eHtqjRUAyg1x4vV6o1WqUlJTA6XQCmMu4eb1e9PT0rPjgzBXt2LRpU5ptAR+rHfQQOwVg7jnOFWxfCjTASyFwLOLSB39h3draSvtabDZbmrLpzTffDLPZTHtE+UqDO3bsQG9vL62qqVQqyGQy+P1+aj6cSqXgcrkQj8fR29uLjo6ONPsPQtlKJpOU7UFEOPIBGeO5iEQi6OnpQTKZRCwWQyKRgFKpRFtbW96eZp8GECGmV199FTKZDOvXr8e2bdvm9XKT/88VdOUao3JV9rnUyq6uLgQCAQSDQSpkQZQ+TSYTTpw4ga6uLlit1jRZ+KXAZDLha1/7GqampuBwOCAWi7F9+3a6JjGbzfB4PDR4jcViglWupYKoitbW1qK/vz/tb5kS5eFwGFJpMSQpXoEi1gxE+lsikUCj0SCRSEAsFiMQCECn08Fut1MPF41Ggw0bNuCKK64QdLe/9dZbMT4+DqfTmeZBolKpEIlE0rIzyWQS4+Pj+OlPfwqRSASRSITa2lpUVVUBgCBVUYgSSEQfgLlAb/fu3VmrGSuBaDQKvV4Pp9OJmpqaRfmcLBfa29vx0UcfzQvIsiESiWD79u104pJKpaisrMSOHTtW8EjnQNSgAoFAzmB2tYMerl+MSCTKenwk87uWUtX54FIIHIu49MFfWBOJd+IJ6Xa7EQqF0NTUhNLS0pymv6RvSC6X45prrqFGvZOTk1AoFGBZllLBYrEYKioq6BxEKmWEDWA0GvHFL34x73mCZVn09PRAr9djamoKMzMzSKVStPdaJpNBoVBAoVDkLZ3/acNtt902TyU3E3IFXfmMUflW9rPty2AwYN++fRgZGcHw8PCyysLv2bMHg4OD1Ez6vvvuS2PW1NTUwOFwQKFQzKtiLQfq6upwxRVX4JZbbsG7776L7u7uvLZjGAYbN25c1mO5FFEMyopYM5ABcmRkBBMTE4hEIlAqlTAajSgtLYVaraYVs4aGBtx6660Zudd79+7FW2+9hQ8//JCW5MViMcrKyiCXy+HxeBCNRiGTyWivjs/no6X8mZkZlJSUoKKiIm8j6ULohyEUPJlMBrFYvKaT8czMzIK56AaDATfeeCMmJiZoA/yePXuoKtRKno/NZsP09HRek9Jq32uWZRGLxSAWi6FSqTL2nxRaL1k2alAhvC9FfPrBXwwDcz1kFy5cgM/nQywWo4k3bm8jAfffpNqRTCZRVVWFq666CkajkVLQBgYGYLfbqbehXq/H3r17ASAtsMvVM8wH337F4/Fg+/btGB8fh9vthlwup8lEvV4PANSmg/xmtnfxswCh888VdC3nGJVrXwbDJ354drt92SxhDAYD7r33XsHfJYlkl8uF3t7eZQ/IysvL8eMf/5gGl3/84x/z3lapVH7mqYtAMSgrYg1BBsienh7aDxaLxTAwMAC1Wk0rZ8Ac1W3Hjh1pNATuoGOz2XD+/Pm0Kk0ikYBKpcJdd91F+fsMw+D48ePUK8vpdMLv99Os40KNpNe6H2Y1fLPyxe7du/Hb3/52XpNxNhw6dAgsy1JxFJ1Oh9HRUbz88ssrGmSwLItDhw6hu7sbqVQKFRUVOTPMq32vCf1WqVRm7D8pJEpgPgHiWr8vRXx6kUv1lXgjKhQKVFVVUYPdbCBzVDAYRCKRAMMwadswDAOWZem8Q6hZ3OecZVlMTU1heno6L3ohv6dZLpejtrYWzc3N2LRpE11Mr1+/Hrt27RI0IC60ZM1qI1v/aq6gaznGqHxFoUwmE06dOoXp6ellrZZlOgeDwUDFthZqDp0PgsEgZmdn6W8Tteh8sNL9bZcKikFZEWsGMkAS5UUA1OFdIpFALBbTjKBaraYCH2azGYcPH6amjrfffjuOHz8Oh8Mxj6+sUqnmUUVaWlpoE3hfXx86OjpoP9ZCDDcLIRPJ9c3KZTC60qiursa1116LP/7xj1RVMRsuu+wy6gVDMoYKhQISiYQGGWazeUWusdlsxtDQELxeL2QyGRKJREHR6QwGA9RqNVwuF4LBYEalrEKiBBZSgFjEZwtCi3Cj0ZgWpHHVdEniLRfIIpbMN0ePHsW5c+dw4cIFqsq7bt06KlTldDrnVTvyfS/49iukB7esrAx6vR4zMzNwOByIxWJYv349GhoaMhoQZ/vNpcxbmbZdy7lQ6Leznf9KJ4YWEhCvVLUsG6LRKEpLS3POz4vdN7EEYFkWHo8n721lMllxzkAxKCtijWGz2TA7O5v2GZFIl8lkkMvlSCQSuHDhAn7729/CYDDg8OHD6OrqQigUgtvtBsMwOHv27LyGVYlEgmuvvTZrtt5kMmHPnj0LnlAKJRNJAlshk9PVPB5ibWCz2WA0GiESiTAzM0MHaCFcfvnlNIAkdAu+Jxt3EbXcPVOkH6OkpAS7d+/Omb1ezkUHoSd9/PHHmJ2dnWdiGo1GUVJSQnvtnnrqKZSXl6d9p9B6yQopQCziswX+ItxsNtM+MjI+33TTTTCZTLBYLPB6vfjv//5viEQi3HzzzVkDNKKYaLVaMTw8jJ6eHoTDYcjlcjQ1NWHTpk0YGRlBJBKBTCabt71QtY0PvgG0Wq1GY2Mj1Go1Nm/eTKX4iaE0qZAtVPhnIfMWf8zLtG22z5crgMu2L6HfXsuxaKHJqZWqlmWC0WiESqXK6Ae7FCSTSSiVSnR1dWFoaCirZQEfTU1NxTkDxaCsiDUEy7I4fPgw/H4/Vd1RKBRQKpVU6UqhUNB+o1OnTuHZZ5+Fz+ejkq/9/f0IBoO04sFVqVIqlUgkEjkrR4vJnBVSVcBgMMBisSAQCKzZ8XD7H9RqNT73uc9h3759eOSRRzAyMiK4zenTp2lWkHsPSKO90+lEe3u7oJ/QUoNOg8GA+vp66HQ6NDc3Z/WvWe4A3Gw2U+okUZ1sb2/Hz3/+cxp0GY1GqjhKJLufe+45tLS0FAQ9SWiRVOwZK2ItwLIsnE5nmpEvAMHx2WAwoK+vDw899BCsVitSqRSOHz+OZ555JmNgRhb4RMKcCHfodDoq5FFaWopoNIpt27YJClFxq21CFEaz2Yze3l64XC40NTXRnmaSpOJ6kfErZELI9C4KBa/5BjuZ5rxMXnALCeC4v8s/nmzbZDqmtRyLFhoQrna1zGAwoK6ubkXoiwDw5ptvYnBwEBMTE5iamsp7u6ampuKcgWJQVsQawmKxIBKJQCwWo7y8HCUlJbjlllsQDocxODgIjUaDs2fP0jJ7IpGA3W7HunXroNPp4PP5wDAMvF4vQqHQPNngYDCIN954A9FoFPfee++yvvCFVhXIJxu7GkgkEvD7/RgfH4fZbMbnP/95WCwWQQEQElzzQSZVoppG9sv1E1oKrZGr+mkwGHDgwIGs+1iOAJzrm3T48GF0d3dTyhMAeDwedHZ20qDMYDDgzjvvxOjoKJU29vv9MJvNsNlseO211zA2NkZpjasZhGdbJBV7xopYTfArTHv37qVBERk7uOPzyy+/jH/913/F1NQUnVesViseffRRvPLKK4K/QRb4J0+exIkTJ2CxWGj/cXV1Nbq6uiCXy2E0GjOq35Jqm5BCIsuytFICzPXhkEpJV1cXpqen866Q8Y+b/z3uvMVnIuQKdjLNeUKfLySAyxV8Zdsml8LhWiWqFhoQrna1rKWlZUXoi8Cc4MyFCxdgt9vzDvzEYjFKSkpW5HguNRSDsiLWDKQfKhQKwWq1orKyEu+//z5qampQVVWFuro6VFVV4fDhw0gkEhCJRLjqqqvwl3/5lwDmvK88Hg/kcjnOnz8/b5BJJpOw2+34+OOPceLEibwns3xQaFWBfLKxKwmTyYRt27YhHo8jGo0iFAqhr68PZrM5oyJjPB7HiRMn0NLSIpil5l7jbLTGhVaJSFba7XajsbExpzjKUgNw7mIjkUjA7XYjlUrRXjaRSASJRIItW7akbUcCtGeeeQZWqxVOpxNHjhyBw+HAzMwMEokEWlpaCp6eU0QRKwXus7hhwwb6mdDCuK2tDY8++ihcLte8ueLDDz/EY489ltVjcnBwEHa7HT6fD1KpFDabDadOnYLNZgMA1NfX56y2AfPHEIvFAofDgXg8jsrKyjQBEqPRCLVaDZVKBZVKRW1bFgvumMplIpDjyBbsZJrzMn2ebwDHvQ5C40quwKsQ5mGhgDKbwAcfq10tKy0tRW1tLYaGhpZ1vxKJBCUlJVAoFPD7/YhGo5DL5fB6vVn90Orq6nDzzTcv67FcqigGZUWsKVpaWsAwDJRKJYaHhxEMBuF2uyGTyaiXhlarpb1jFy5cwDe/+U384Ac/oL0Bzz33HKV6SSQSAJ+oYIlEIkxNTeGdd96Bw+FYVppXoVUFuNlYAMseiOaCWq1GZWUlAoEAamtr4XA4snqWJRIJ9PT04NChQ3j44YcFjzMfWuNCzlMoK51PQENog5lUELOBu9jQ6/XQ6XS4+uqrqfqnx+NBc3Mz6uvr523b2tqKwcFB/OY3v4Hf78fHH3+MaDSKeDyOsrIy1NfXrzp1sdCqxEV8dpGr8kMWxizL4pVXXqHy9iKRaF5g9tJLL8Fms+GOO+6YF1wRejbLsgiHwxCJRHC73dBoNNBqtVi/fj02b95MqXtCfcyk95cPhmEwPT2NaDSKWCyWti2x7fB4PLRH98033xQ8xnwhxETIN9jJNOfxP19oAAdkHldyBV6FMA8vR6JqJatlpIeZ/A7DMBnfg6WAVMb27duHwcFBOBwOyOVyWK3WNEE3ApFIhLKyMnz9618vyuH/fxSDsiLWBHzaCfFamZ6eRiqVgsPhgN1uR2lpKRiGgVQqRSqVgs1mg8Viwa5duyi9o7S0lJoOl5SUwGAwYGhoCIFAALFYDMFgEL29vQCWHqgUguJiJnApjNPT0+jq6lr2QDQTSE9bMplEXV0ddu3ahUAggIsXLyISidAsmUgkglKpBDBXKQsGg7hw4UJeWUH+YmIx50mOUy6Xo6ysLKssNt8riGRBhYRUhJriyb/5iw0izEGqf2S/QsENCSL9fj/1LQPmREoqKiqwf//+VX8OCyU7XcRnG3yhm0yVHzLXeDweSKVSaracSqXS1OECgQDOnDmDYDCI/fv3py2IZ2dn4fF4EA6H6WI2mUzC5/NBIpEglUqhu7sb3d3dUKvVGVV8ifgIdwwhtiYkGUlUhknPdX9/P/x+PxKJBM6fPw+VSoVgMJgxkZUvVjrYyTeAy+d4CiHwygShnsbFJKpWqlpGRLjee+89BINBbN68GV/4whegVquXPSgDgHPnzuGOO+5I80o7efIkzGbzPNl7sh4omkZ/gmJQVsSagE87IY3NXq8XR44cgcfjQSQSgVwux2WXXYbe3l4kk0nE4/G0filCgQTmsjQlJSWUt59MJmlQlkgkMDo6CqVSuehAZa3FFXKBTGonTpyggQq/f2E5kS3wIN45IyMj6Onpgd1up4Hz/v37MTAwgPfee2/e/VzMeVqtVgQCgbwmMEIHIlUmfnaO2/vV1tZGvYIILTMQCEClUqX1tAHpTe2tra1pwdbtt98+b7GRr3Ki2WyG1WpFPB5HIpFAIpGARCKBQqHAl770pTXLLhbyIqmITz8ySeALVX4IXdnv98NkMkGr1eKrX/0qAOCHP/whXC4XJBIJSktLEQ6HMTQ0hJdeeokGTmQffr8f8XgcYrGYBmWEmeFwOGCz2SAWi5FMJjE2Noa+vr609zsbPU+v12N4eBhut5tWSCwWC6Xox+Nx+P1+pFIpRKNRdHd3L5vRcCG9x4V2PLmQqadxseewEtUyi8WCgYEB2ts9Ozv7/9i78ui2yjt7tctabMm2bCMvsZ04MTGBKB2IO4GEJjVpm0DbzJCWUyjptAMk5bRTSIfOdIYeCtMN5gxdcKGlxS1Lk5SmSUPaZjHFIaE2SSwnsY2XWLYlW5Ys2dq3p23+8Pk+nqQnW97iBHT/SY5tSe89vfd9v+X+7oXVakU4HIZAIJiWVjgXeL1eOByOhO9y8+bNOHHiBE6fPo1wOIx4PA4ejweJRIJVq1Zlu2QsZJOyLJYEyUE8e7BZqVRCLBbD6/UiFApBpVKhoqICDocDGo0mYQaIiyPvcDhoJyEcDlPpc5VKNa9E5VqYpdFqtdi6dSs9z8Wil3EFRcn0HK1Wi71791Ka6cjICNauXQsAuHDhAhQKBeLxOKdi2Uwg52kwGNDT04PJyUk0NzfPuIEROlAwGEw5H7aPEREWIUIkhYWFCIfDKCkpSaFJ1dXVJdwXHR0dKfcJ6eymu3bpjtnhcIBhGKpOSqr0Mpls0auLV3NXOIsPJ9heXgaDgVLeyTPGVfxobm7GwMAAxGIxdDpdguhTYWEhOjo6IJPJ8M4778BsNsPpdMLhcMBgMODYsWMoKiqCzWajzx7pVpPEjC2DLxaLEQwG4XQ6YTAYEhRjGxoa0tLz0nVIbDYbgsEgAoFAwnXw+XxX4nJnMQOSi8uFhYXz7i6Se8HpdMJms82b3VNeXg6VSgWBQEDnvXt7e+mestCQSqUpNH+tVovvf//7aG5uRmdnJ1pbW+FyuZCbm4svfvGL2f2FhWxSlsWSIB1VgS0/7PP5YDQa4fP54HK5wOPx4HK5UjorXBx5uVyO4uJitLa2IhQK0Q4JUbybizrhtTJLM938wkKBS1oZAE1W2PScZJrfD3/4Q3R3dyMYDKK6ujqtYtlM0Gq1WLVqFY4dOwa32z1j9dhsNlMp+ng8joKCggT5ZtIVk0qlKC0thUQiQVlZGaLRKDZu3IgbbriBkyZFxDaAqfti7dq1dK6P6z6ZjZlsb28vpXgoFAoAU/OShB61WLjau8JZfPjAvicBwGg0UgozWc+T1xvSJfN4PFAqlVi1alXC73U6HX2ObrvtthS6cltbG2QyGV0viNppKBRCOByGUChEfn5+gqdYb29vQmGHrAVmsxl1dXVU2Cj5OEiHxGQy4dChQ9i4cSPi8XhKQkbOM9tdWFosFG0xGTqdDl1dXdQGYb5jCFqtFjt37sTw8DAMBgNVqk5Wq14orF27Nq1w13333YejR4/CYDCAx+OhtrYWubm5i3Ic1yqySVkWSwYuqgJJKA4ePIjOzk54PB5MTExAKpUiFoth2bJladXy2Ime2+3GG2+8QWXP77vvPgCYszrh1WbUmwm45hcWClwD9kNDQ7BYLODz+bDZbJwJkl6vp8PyUql0QYKLUCgEhmHgcDg4JfbZn93d3Q2HwwGxWIxoNJpAKyKvValUqK6uhk6nwxtvvIFQKISxsTGaPCYPyJPAjl1gIKIkXPdJcnLvdrvx7LPPQigUQqVS0Up9PB6H0WhEMBiEUChEVVUVPvGJT0CtVi+qXDJwbXSFs/hwgX1PEhVW0r2eSUFVIBBQ2nI6kP1Ip9MlUKMJW2P16tW46aabAACHDx/G6OgohEIhSktLE2hryRRotmIsET4gzxJ7jaivr0dPTw8MBgO6u7sBIGUGB5jqRPzHf/xHyvPI/txrZY+6lsBmDgBYUNoiG+nGEOZqA2M2m3Hq1CkqBLbYWLlyJQCgra2N0/+OPAcMw0Amk121xe2lQjYpy+KqBKkuEnEDv98PoVAIu91Oq6LpDGyPHDmCX/3qV3Qmbd26dXTTDoVCGBgYAJC519W12DVY7KA6HW2UKIgFg0EcOnQoIekiCzJRLVu5cuW0EtSZQK1WQ61WIxwO0/+nw+DgICYnJxGLxcDn81FVVUWDGHaCSbpQJpOJ01soXZd3psF0Lh+boaEhPPnkkxgdHaX3KOH4Ey+kWCwGoVAIhUKBmpqaWUktzxXXSlc4iw8+2MkG+zkFQCnE6e5PnU5Hkx2VSpXRWkOo0ZcuXYLP5wPDMJBKpVAqlbj55pvp8/eHP/yBUonZtDX2s89WjD1y5Aja29shEong9/tRUlKSoBSp0+lw/PhxmM1mhMNhSlsmc2rAVHL50EMPUbsM9jV65ZVXUkymr4W96mpHMrU9mbK+ELTFZCSPIczWBoa915B9xul0LpphNBvd3d34+c9/jqGhIVRWVmLjxo3o6upCYWEh1Go1+vv7YTKZEI/Hcfny5UU/nmsN2aQsi6sKZHM5e/YsHA4HeDweeDwepW6VlZWBYZiETUgikVCJ4P379+Ppp5+mlI/CwkJq7Enmifx+P4xGI5qbmwGAUy1Lr9ejo6MDa9euBcMw11zX4EqYSXPRRj0eD6xWK0ZHR9Hd3Y3GxkZs2rSJJkvEj6eoqIgqnM0HOp0Ot912G90A0r2fQPf/dAAAIABJREFUXq/H3/72N+oLplQqMTg4iP379ycoIiYn6AtlTMqV2Lvdbjz33HMYHh7mpJKEw2HYbDaIxWIUFRVBo9EseHKUbm5sOiW0LLK4Ukh+btjPKYAZ70+tdsocfrYMCavVip6eHroHrVixgq4BZrMZVquVzuRMZ6vBXiOPHz8OkUgEhmHgdDoRDAapZYher0dhYSH1aiIFRXIsfr8fMpkMe/bswYMPPpjyOYSmOTIyQgWJAODAgQMoKChAfn7+onfXP0hIJ/gklUoBpFLWF6NoxVX4zCQG4XpmKisrYTKZ4Pf7E5L8xcDk5CSl4nZ2duJPf/oTvZ+rqqpw8eJF2gG+ePEiDh48iIcffnjRjudaQzYpy+KqgslkQldXF3p6eqjpMJkP02q1qKurQ3l5OfR6Pc6ePYuBgQEqsV5cXIw333wzgfIRi8VQU1MDYMrHKy8vDzabDfF4HF1dXXC5XIjFYrhw4QKqqqqwfft29Pb24sCBA3C73RCJRGhoaFhw3vhiQ6u9cmbS7M1DLBajqakJExMTCAaD6O7uRldXF+RyOZYvXw6j0cjpxzOfz96xYwdNoLm6U3q9HocOHcLIyAji8ThUKhXKysogEAjoJscwTEoHaiETk+TO5cGDB/Gb3/yGJqnTIRwOIxQKpczDzBczdYCvNSW0LD54MJlMCYIeyc9pJvcn278xE5Ens9mMQ4cOYXJykiZDGo2GPh+EUsZl9sz1XmT92LVrF4CphItYwPh8vpQuyK5du2A2m+FwONDb24ucnJyEwmPy++v1ejQ3N1M7mby8PGg0Gly+fJnaaeTk5GDDhg14/PHHP/TPdDINMVkYZjrBJ5VKlZayvhjgKnyWlJRALBZz0gPJ+bD3GoZhsHv3bojFYhw6dAjhcBiRSGRWiZlQKEQsFsvoNVqtFmNjY/D7/VRpMRaLIRAIJCRkwNTeRqi6WUwhm5RlcVWAXZkiqosEPp8Pfr8fKpWK0jZaW1sxOjpKfWPIYrpu3TocO3YsYfE4fPgwrFYrNm7cSOWFzWYzQqEQlRkmA9Z6vR4ulwsOh4O2+l9//XVs3rwZd9555zVVbSTByOjoaMaS8XNBum6L2WzG6OgoXC4XJicnEQgEkJ+fT1UhZ5oDyfSzify8zWZLSDz1ej0aGxthNBqpnHV+fj6WL1+OL33pS9Dr9YuqUMk+RrvdDgCQyWQApvzyxsfHMxq25vF4EIvFczKung7ZubEsrnYQY+VkQY/ZYDZUXFKoGBoaQigUAp/Pp0Fl8jHNVFziKno89thjVBRJq9VyCgeRAJzMK6Xbd8j7ky4On89HeXk5tm7dCrvdjs7OTtjtdioW8re//Q0bNmyg89UfNiQnXMkUWGJlwiX4tHLlSk5GzZVaL5MLn8mWK+zj4LrftVot1q1bh1OnTsHn84HP54NhmIwTs2g0SmXsgfeVR5NRVlaGf/mXf8GBAwdgs9kS5O+JpREbAoEAq1evnutl+UAim5RlseQgm8v58+fh8XiQn5+f8jcjIyNYsWIFGIahBsAKhYI+5OPj41Sxau3atXjvvffA5/MRDocxOjqKnp4eVFdXo6SkBOPj4+DxeAgEAtQ4MRqNwm63IxgMIhwOJ3CvGYbB6OjogvPGFxtEcTIYDC6Y50kyuAIPnU6H4uJi6PV6HDlyBKdOnUIwGITdbodUKsWKFStQXV0970TIbDbj2LFjMBgMdAaRbRjb2NiIt99+G6FQCDk5OVixYgWKi4uxceNGqoI2XaVzIWYJiXHnhQsXYLfbodFoIJVKaRcYAPVrCYVCKUaeYrEYubm5uPXWWxdcbS07N5bF1Q5irEwC57kUcghrIF03HUiU2rdYLPB6vSgvL4fH4wGPx6PCRcCU5UVeXt6Mx8RV9CBeatP5qwGgr9NoNBgfH+c8XrL2kS5OUVER1qxZg82bN9NjZa8n0WgUExMTs75+1xLSdcGAVIVdlUoFAJRCSqxMkgWfrhZxL9I1a2trm7GYxqXySeYrBQIBLl++PKtOGbmPhEIh1Go1CgsLkZ+fj4sXL8Lr9UIsFmPFihXYu3cv6urqcNNNN+Gdd96hcZRQKEzZ26RSKW699Vbs2LFjztfkg4hsUpbFksNkMuH48ePo6elBPB6HWCyGSCRKCFwjkQg6OzshFotRXFxMNy8ywzQyMoKLFy9icnKSJl8DAwMYGxtDLBaDz+dDWVkZDd4HBgbo/wlisRhCoRAEAgGEQiEikQh4PB6kUiny8vIWZS5rMUEUvYaGhuBwOODz+Ra8G8IOPPx+f4KnilarhcPhwN///nd6bYVCIdavXz8v3xWAe7CdnViYTCZYrVYEAgHEYjFEo1Fcf/31EIvF0Ov1GBsbw7333jutaMZCdJL0ej3efvttGI1GxGIxuN1uqFQqlJeXw+Fw0A6aUqlEZWUljEYjAoEAeDweCgsLccMNN2Dr1q3YvHnzvK8XlzBJdm4si6sZ5eXlqK6unldHe7puOlf3hFDV5XI5fD4fDeSbm5upcpzL5cLy5cunLS5xFT241pRkfzVgSjmXdAeTJdGT176ysjJoNJqELo7VakV+fj4cDgf16qysrMSmTZvm8jVc9ZipC0aEOdgJV2VlZcLfsK1MuLpiVwuSlY/tdjvMZnPCvUH2RHYhT6vVYvfu3QgEAhgeHua0WpgJhYWF2LBhA3bt2oXi4mIcOHAAZ86cgdvtRlFRESwWC/R6PY4ePQqn00lfF4vFaFLG5/OxbNkyPPTQQ/Pe1z6IyCZlWSw5hoaG0N/fTx9ahmEgFovB4/ESqis+nw9msxk6nS5lhslqtcLpdILH4yE/Px833XQTpZ643W5oNBrk5ubS17322mspXGYej4e8vDwEg0FK/ePz+RAIBAiHwzh48CD9/GtlISH+N8D0A+lzBVtQhCuAUKvVUCgUcDqdiMViGB8fXxDjUxLc2Gw2lJSUpCR65eXlKC4uRk5ODkKhEBQKBRQKBRwOR8ZJ1kJ1khiGQTweRzwep7L3u3btwr59+3D06FF4vV54PB7U1NTggQceQEtLC/h8PtavXz9nDzc2puv4ZefGsrhasVA2JOmKK8n0P6lUipqaGmzZsgWFhYV0dtlkMsFms2FgYIAGl2VlZTMWl9IVPdIZSLPfh0sSnXgqstkBXGsfMJXU8fl8aDQa5Ofno66uDl/4whc+MN5mXPL003XB2MIc7IQLSJwpm87K5GoBua9IEtrc3ExtbzIpJHq9XsRiMQgEgozVGPl8PvLy8rBr1y7s2LGDvufOnTsxMTFBhT1OnToFt9uNoaGhhNeTWAoACgoK8J3vfCdFQTSLKWSTsiyWHFyDnjKZDLm5uVQ6FXhfBhlI3cRsNhsMBgNisRjsdjteffVV+Hw+eDweCAQCdHR0oLm5Gbt27cL69evx3HPPpXzmsmXLkJOTA6PRSDnUhI9PKCc9PT04fvw459D11QSyabndblgsFrhcLlgsFlit1kWRxucKIIgc/urVq+F0OqkJ+C9/+Uu0trbi0UcfnfM1JD5jGo0G1dXVKUGJVqvFnj17EAgE0N/fD6FQiN7eXhQVFWUs2LIQnSRCGWltbQWPx8Pq1atplXFgYAButxvRaBSRSAT9/f3o6+tDQUEBfD4fxsbGZv15XMjOjmVxtSO5k7uQNiTpiitc/oSku0A+y2q1IhwOw+v1wmazQaFQIDc3FxqNJqNuf/I+lQmVkvwdWxKdiDtwyd4nH4der0dTUxOMRiPEYjE2bNiARx555APzzCffG5l0waYT5rgWxY20Wi0d40imx6YrJJrNZrz44otob28Hn8+HUqlEKBTi7Jjx+XwUFxejtrYWFRUVYBgGW7duTUmkktk4AoGAxlxktEQsFuOWW24BMMVsuv/++7MJ2TTIJmVZLDluvfVW/OlPf4LdbqeeTIWFhdiyZQva2trQ3d1NfaW4gvji4mJ86lOfwl//+ld4PB6MjIxgcnISwWCQ/k0gEKAL0oMPPojrr78ep0+fpoIiK1aswL//+7/j1KlTGB0dpQOtQqEQAoEAIpEIgUAABoMBZrMZfr8/QdL9ajLtNJvNeOaZZ9DR0YGJiQlEIhGEw2GYzWY8/fTT+OY3v7mgCSVXAMGuAO/cuROdnZ3w+/0IBoMIBoM4ffo0eDwefvjDH85pTuvEiRNwOp0Ih8NpO5c6nQ733HMPmpqaYDAYMDg4iJycHPq9zfS56QRMZgOtVou9e/fSGQ/yuT/72c+g1+tppTIej8PpdOL48eOQSqVUenkhEqjs7FgWVzO4ErCFLCSwOwsOh4M+i8k0MC66GlHs7evrA8Mw4PP5qKiomFZxcaZzTUelnO64yWunYwcQtLS0wGw20/1PrVZfE4lGpki+NzLtggFXTpjjSiB5XSfU/Ouuuy5lnoyIXrW3t1Obh4KCAjrmwR4VAaau46c//Wl8+ctfnvGa6XS6hDnJj33sY/jFL34Bm81GffVuueUWNDU1Qa1WY3h4mNIts0hFNinLYsnR0NCAH/3oR3jxxRfR3d1NJVt5PB7Ky8sxODiIUCgEm82W0ulh8+snJycRjUbT+nCEQiG0t7fjySefRFVVFa677jpq4rlt2zYMDg7i3LlzlF4nl8uxcuVK5OfnQywWIxgMwmAwIBgMoq+vD/v27aPUQJvNBovFgry8PEpPW4pOmtlsxo9//GMcPnyYbso8Ho+qLQUCATz11FP4r//6r3kfX3LSkq6rlJubi5UrVyYMrIfDYXR0dMxJEZLIZHd3d0MsFuONN95AXV1d2sSMGLL6fD5cvnwZ7e3tM577Qlbqk6uvZrMZR44cSSgaAFNzkxaLBUVFRSkzcrNB8veSnR3LIh3ILA6AJaNlpxPEWOhCQmtrK12v6+vrsXv37hmfi/LycqhUKuTm5sLhcEAmk1EGwFwwl2Szq6sLBoMB0WgUSqWSdvrTdeqIqIJAIIBUKqVdo2sNXKIdYrEYdrs9waIm0y7YBw3JiowHDx5MuL/JPUpEr8i8HZnbJ7P5YrEYg4ODVMlaKpXiH/7hHzJKyNjHwV5HVq5cSbvBOp0ObW1ts7Kl+DAjm5RlcVWAzA08++yzVP3q4sWLCAQCCIVCYBgGVqsVLS0tCRsi2eSIj41IJKK+LlyqWLFYDB6PB/39/eDxeJDL5RgfH8cLL7yQYObJ4/FQVlaGhx56iM4XWK1WNDU1wWw2w+l0wul0Uu60xWKB2+3G4OAgpFIpRkZG8JnPfGZBZoIyBVH6e/311xMCfkL/jEQicLlcuHDhAh577DHceuutVG55Lp/FlbSkC2zq6urQ29tL5yKAqe7l66+/DofDMauB3/LyckgkEojFYurhlW6R12q12L59OwYGBjAxMYHh4WHs378fDocDe/fuTfuZi0n5I7YLySCce4FAgDvuuGNO985030t2E8yCDdJRZ1Nr9+zZc8WLSVwV/4WYJQMSVRWHhoaokMPQ0BAV2ZjuvbVaLfUXczqdtODGFgpJ53mVybnOJM9/7NgxdHV14b333oNIJMLq1aun7fSbzWacPXsW8XgcMpkM//iP/4gtW7bMfKGuMrDXMTK2QAqfJSUl0Gg02LJly5LI019NIOt6W1sb5/2t1Wqh1+vR19cHv99P55qJKjPDMCgrK8OyZctox6ympgZ79uyZ9fUk3TIy3/alL32J/o4oQctkskWZbf8gIZuUZbGkYG9qZP6mo6MDHo8HExMTcLvdiEQiiMfjCIfDEAoTb9lkoQkin+zxeGhSJpfLKX+aKClqNBrYbDaYzWbaYWODx+NBpVKlLPpE6j1Z5Ukul6O/vx/RaBSTk5NwuVwYGxtDX18fdu/efUU2DL1ej5MnT6Z0YEQiEQQCAZVcDwQC6O7upibZH//4x6kyV6YB2WySFjJHMTExgSNHjsDj8SAej4NhGDQ3N+P06dM4ffp0xsamJNGy2WyIRqM0QeOiG5rNZvzlL3+BxWJBKBRCNBqF3+9Hf3//tMe8GJV6ttGrQCCgg9bEhywnJwdCoRBVVVVYuXLlgooaZJFFMsg6NjY2hmg0StXSnnjiiSt6zyRX/KfzYJoNkgN7jUaDvLw8hMNhaDSajJ9pYvHBtbYQlgbpYgGY9rjTda3ZPp1E6OrEiROUdk3YIwKBYFprloMHD+LMmTMIBoMQiUSoqKi45p7/ZKsT4u1I/Op8Ph9kMtk1Z1GzmCgvL0dlZSUsFgsAoLKyMuH+5vF4EIlEEIlEKCwsBDAlgmI2m7FmzZqExH0uHfPsvrNwyCZlWSwZuKr6u3fvhl6vx29/+1vo9XpqcMjn8yGRSKiqEkHyvIBarYbD4aCGwQBQVVWFFStWQK1WQ61WQ6VSQa1WY3BwEC+88AJNyNi0R6FQiDvuuCNlYSGVKTZdAgAV1XjppZfw3nvvwePxwOVyzZmiN5dr2drayulDE4vFUjjj8XgckUgEDocDv//97yGRSPDaa6/hM5/5DMrLy5Gfnz/t4jzbiu+JEycQDoexceNGOBwO9PX1UTqj3+9Ha2trynVKDlTYIgB6vR4ikQhWqxVSqRQHDx4E8P5gNwmKmpubcfLkSbjdbsRiMdpJncniIJmSMV8kK70pFAosW7aMJpakelhaWjovD7fs/FgWmcLhcCQIGvn9fnR3d+PAgQPYuXMngJk7P7NFujlNdsV/PsEd+/3ZgeKyZcug0+ng8XjgdDoTRKMyARcF+cCBAzh9+jRGR0cBABKJBAUFBQBAVRunu3ZWqzUhEWULeBDfwpGREVq4USqVMyaTQ0ND1LA3FothcnJyVue51OCyOtFoNACmCp9c9icfdpB7fseOHaivrwcAWlxta2uDWCymHar8/HwsX74cExMT8Pl8qKioSBG3mQvYxfFoNJqytxJREpJUZ5O29MgmZVksGcim2d/fT805t23bBqvViv7+ftpu5/P5EIlEKCoqglqt5nwv9qCpTqeDXC6HWCxGPB6Hx+PBpUuXoFKpEA6H6dxXcXExFfrg8/nIzc1FJBJBKBRCQUHBjEE7F3e9sLAQjY2N6O7uplLnra2t0Gq1iyoCwjbUTpacz0T2NhQKYXR0FL/4xS/A4/Egk8mg0+lw//33Izc3l5Oek4mKGHmNwWDAwMAAli9fji9+8YvYt28fTp06RRWawuEwBgcH0dbWliBznKw0Rj6TCK4Eg0EMDw+jr68PdrsdkUgESqUSExMT+PrXv46hoaEErvz1118PpVIJhUKBEydOTDtoD6RSMub63ZlMJpw/fx69vb2QSCRYuXIlNBoNzp8/j7GxMUQiEZSWllI66XxFDbLzY1lMB7PZjPb2dgBTwS7DMBAKhfB6vThz5gwt7iQXOeb7mTPNac6lqMAu3rC7bA0NDQnvlZ+fD6VSicnJyXkFhoQm/vbbb2N8fByxWAwSiQQKhQIqlQpyuZwyKZLPk7y2p6eHBsUkASMUfJ/Ph+XLl0MikUCtVlNLj6KiohkFRm6++Wb88Y9/RDQahVAoxM033zzr87tS4Cq6pbM6Aa4uMa2rBemeKfbPiUpxOByGVqtFNBqFVCpFeXl5CgV0riDxQGNjI8xmMw4ePJiwt2aLhZkjm5RlsWQgPONgMAiLxYLW1lbodDq0tLTA6XTSWSgej4eqqip89KMf5aTXJbfOR0ZGUFFRQefDgCk+OqHpkKSDYRiawJCgPRaLIScnB2q1GiKRaNYqQTqdDk888QQ1VQwGg7DZbGhqaoJAIFiwACcZ5FpKpVK6cc0FpKPGMAxOnz4Ng8GA0tJSuojabDZUVlZix44dGauIicViSj2xWCzQarV45JFH4HK50NnZiUgkgmg0ikOHDqGzsxMajQYFBQU0kfP7/XR+7+TJk8jPz0cwGIRWq6XfK1tEZGJiAq+88gqam5tRVlZG6YEVFRW4++670d7ejv7+fvh8vmm7mAtJyRgaGkJHRwd8Ph8EAgE0Gg2USiWlLPJ4PPh8vjlTcrjEPbLIIh30ej0uXboEv98PuVyO1atXIxgMwuPxIBAI0FlZsoaSWdD5JPuZPE+zLSokB59EEAqYWsPSmTJzVfNncx49PT0YHx9HPB6HXC5HdXU1amtraaeiubkZfX19tNhIAuUDBw7g1KlT9Bj9fj9qa2shkUiwfPnylAIUYUCwpd2nQzgcRm5uLlwuFzQaDRQKxZzOcbHB1REj58wO3tliJtk1LRXpnin2zzUaDVQqFQoKCugzQuwDgIW7rmazGf39/RgfH4fNZksoIGSLhZkjm5RlsWTQaqc8Lnp6emCz2WCz2WAymeD3+xPodmKxGEVFRQmmhWywW+cej4cmX7W1tQBAZWCJoiMw5ZeRrNBot9tRWFiInJwcMAyDw4cPo7e3d9YzYVrtlAw8wzAJwcLIyEjCJr2QYF/LycnJWSVlfD4fCoUCgUAg4bqHw2GMjIxgdHQUHR0ddDbNYrGgoKAgYTOYjq7DMAxKSkrgdDpportt2zY8+OCD+MlPfkLfw+FwoLe3FwMDA1CpVPD5fMjJyaHdzuHhYVqVXrduHW6//XYIhUI8/vjjnOc1NjZG6Y0KhQIf+chHqOAIuxCQ3MVkV3AXqrpHVEUJbVSv1+PChQvIycmBRqOBTCZLmQPIFAupFJnFhwsCgQB5eXm46667kJ+fnzIrS5IB4pE1n3tsJg+lTIsKbMVIAAnBp0QiSfAhTH6vhoYGNDU1IRQKZdQp54Lb7YbVakUsFoNYLMbNN9+c0OEmiVQoFEpYY06cOIGzZ89SmxKxWEx9FomoSXIniLxnJowEs9mMlpYWhEIhqrx4tSJZoIvMiSUn0tl1bHqw/TrZz1Tys8a+vw4ePIiBgYGEQvhCXGeHwwGHw4FgMEj/n8XssWRJWTAYhE6n45QuJ/jUpz6F//u//0v5+dGjR/Hyyy+jp6cHfD4fNTU1uOeee/CZz3xmMQ85i0WAVqtFOBxGOByGxWKh/GeJREJNDQUCAXJzc9MmGuzWeXd3N959910oFApUV1dDqVSCYZgEPyj2v2wQdcJYLEbNe5MrPrM5r2S52sVYCNnQ6XR4+eWXU4Q+ZkIsFkNpaSmKi4vx97//nVI6gfepj4RmKBKJUFBQgIKCggQhlWS6DvB+Vb28vBwajQYDAwNwOp30/HU6HW655RbEYjEwDAO5XI54PI7x8XFKORSLxZBKpbS6HYvFqO0BobWkS8rIuYVCIfB4PJw9exZisRirVq3C0NAQxsfH0dPTg8bGRiiVSrp5JVOgFoIuo9VqU+45cp8RtavZ3BPpZmeA7JB1FjNDp9NhzZo16O/vR01NDVX65JqVTXePsX+fqUBPOpGLmRI+dqGELf29Zs2aBHn0mZ5XhmHmJc2t1+vx/PPPw+FwgM/no7KyEnfeeSe2bduWcJ7EUNfpdMLn86Gjo4Mq9AJT82fFxcW46667plVZnY2vmV6vp4WvnJwc1NTULIktSybgEuhKl0hnwQ1yb4RCIUgkEjQ0NEzbmSLPENn/nE4nbDYbjh07lpER+kxQq9W0uKtQKBJGTbKFw8yxZElZX18fYrEYlEolampqOP+muro65Wc//vGP0djYSH/P4/HQ0dGBjo4OnDt3Dk899dSiHncWCwvSRSEBPcMwqKurQ1FRESwWCw3MfT7ftHQThmFohYYYFAcCARQVFYHH4yEnJwfBYBA8Hg88Hg+xWIyzIBAOh+H1ehEMBsHn8+dV8WFvLmazGT09PbDb7bQjuNCLUldXF86ePZsi6pEJCNWGK1llIxqNUmNvAFRuurm5OaFrxp7xu/fee7Fq1Sq8++678Hq99PzXr1+P3bt3U8qP1+vFc889B5/PB6/XC4FAQFUaAdDv8brrrsOXvvQlev0+8YlP4E9/+lPaY47FYlRxcXJyEsXFxVSpanh4GMPDw1RAhgRPw8PD8Pv9aGlpQU1Nzaw6WMmzEm63G3/5y1/SFhXMZjPn65OFTYD3B7jZG1wy5SfL188iE8jlcjoDRZBuVhZAimx9ulkWrkSN/fP169cn/JyttAekFhWSKYpms5n+rc1mw5YtW6htyUxr6nykuYnfU1dXFwKBAHJyclBcXMyZ+CQb6q5du5ayQUjRa8WKFTOqrGZacCHdOSLbv3LlyjnJmi822PcBu2iZnRObPdjzd8uWLUvZX5LjD7YSaWVlJbUYeOutt9DR0TFvb1WtVou8vDwEg0Hk5eUlfJfZwmHmWLKkrLe3FwDwsY99DE8//XRGr2lpaUFjYyPkcjleeOEFOsR69uxZ7NmzB7///e+xfv163HnnnYt23FksLMrLy1FdXQ2ZTJbgSl9YWAi/349AIEBVwZKHR5Pfp7KyEiaTCePj4wiFQnC73dSH46Mf/SgikQguXLhAfc+i0SiCwSDtAvF4PAiFQirBH4/HUyo+cwXpCBJK41znGabDsWPHOLtkPB4PUqmUyipzIRqNwm63z/gZYrEYN954Y0JFzmw2o6urC8D7gVsytbG9vR0jIyMIh8O4fPlygoQ96RA9++yz9DuTSCQQiUTwer20up2Xl4e1a9fiG9/4RsLm8cADD+DPf/5z2nNjd0ftdjtaWlrw6U9/Gj09PVQqH5gKUknw5Pf7YTQacenSJcTjcdTX10/ra0ag1+vR1NQEp9MJl8uFkpISuN1uei24EIlEsG/fPnR1dXF26pJNQevr6xMEcurq6rKUnyxmBb1ej6GhITgcDmg0mhmDpOTKe7rOGZkTkkgk1N/rjTfeQF9fH/h8PlQqFbZv347c3Fy43W4cOHAAVqsVkUgEGo0GHo8HfX19CZ06u92eMh9DFPkqKyuvmOm1yWSC1WoFwzB0LnnTpk2cn83VqSB2Ks3NzbDZbBnJ8qejp7FhNpvxq1/9Cu+++y6i0SgqKiqol9lSggg+dHd3o6ysDBUVFejt7U1gVLAT9Cxmh9mIZyQrkW7ZsgXj4+N466230N3dTeORudB5CRiGgUajgdPphEajSUgSs0IfmWPJk7J0XTIu/PznPwcAfO1rX0umJGF+AAAgAElEQVRQFbr55pvx7W9/G4899hgaGxuzSdk1BK7N3mAwwO12QyAQIBKJIBwOw+l0oqenZ1qT4N27d6O4uBj79u3D6OgoFZCYnJxEZWUlRkZGEAqFEAqFUFhYiMrKSlx//fXo7+/HxYsXqRFxMBikgTyh9MwXXB3BhUZlZWWCrD+BRCLBww8/jLq6Ovzrv/7rrDppAoEAtbW1dIaivLw85flK/g4BpCRpJpMJwWAQ4XCYeocJhcKE5KOnpwderxexWIz68RAhFh6Ph6KiopSEDJjqbqVLyJIRj8cxMTFBrRGi0SilaK5cuZJ6Eh07dgxms5med2trK5qbm7Fy5cq0iY/ZbEZTUxPOnTsHr9eLnJwc+Hw+qniVDjKZDOPj47h06RIVRSHBbkdHR4opaH19PadATjbAySITkK4K8TTKtGOU3EVLDrLI2n3+/HmIxWKMjo7CaDTCbrcjFotBKBRCpVKhs7MTZWVlGBwcxPj4OCKRCAoLC8Hn8+HxeDA0NAS9Xg+5XE4tI5RKZQJFkXSXZ5uQzUeaWywW01kwHo+HW265BZs3b874epH/t7a20ud5OkxHT2P/zTe+8Q20tbVRa421a9deFQnZd7/7XRw7dgwMw4DP56OgoAByuRwikQi1tbXZbsk8MZN4BrsrmZwUkfujo6MDYrGYxj7z+U7EYjEtaA4ODlKqbibHmsX7WFL6IpB5UjY8PAy9Xg8+n48dO3ak/H779u347ne/C4PBgJ6eHirykMXVD/bmZbVaYbFYEAgEqLKVz+ejHmXTBQ9WqxXvvPMO3G53wgwZn8/H6OgorFYr/H4/hEIhZDIZ8vPz8clPfhJbtmzBSy+9hIGBAQSDQZjNZqrGyO4KzQfJHcHFqBTt2LEDJ0+exNmzZxN+Ho/HIRQKcfLkyVlTG/Py8nDPPfdg7dq1dNica8NPDkCSk7RDhw5BIBBQhbdXX30VN9xwA02miDKhRCIBMJVI+nw+euwKhSKtuXVZWdmsFCflcjl0Oh0cDge6urrgcrmoAiI5l61bt+Kdd97B5cuXaWX81KlT0Ov1nDLXpKJPurBSqRRyuRzLly9HdXU1SktLsX///pROJqFokgTLaDQmzMisXbuWKpQB73cGANBOR9b3JYvZwGQywWaz0WRovjOz7CCLGLn7fD6q7sguEpH19/Lly/D7/XTek2EYhMNhuFwuAEB/fz+kUimGh4chEomwevVqbNy4Efn5+QCmrEfEYjHt0GV6/POp2JNOgN1uR0lJCe68885ZX7fZJIVc9LRkeujBgwfxzjvv0Gvs9XohEomWfC0gxuRkTSaeaSTB7O/vx9DQEGVnXKluZyZgX2OAe24yHU13KY4xmQ6czoSdbWFDzmv79u1U3Xg2hurpjsnlcsHlciEYDOLAgQOoq6ubltacRSqumaTswoULAKZ42Lm5uSm/FwqFuOGGG9DW1oZz585lk7JrFAzDIC8vD4ODgxCJRFAoFCgsLERpaem0HHnSpSBqTjwejyZkAoEApaWl1J9DJpNBo9FQBS6dTkcliWUyGS5evEiNqKuqqmYti88FIkaSiYrWfD6jsbERP/vZz3D48GEaEOXn58NgMMxohMzn82lyEo/HIRAIoFarUVZWRoU5ZnMs7HPcs2cP+vv7MTAwgFgsBo/Hg/feew86nS4h+SAm3CKRCB6PB5FIBCKRCOXl5aivr6c+ZuS99Xo9fvOb32SclPF4PKxevRparRabN29Ge3s7zpw5g0gkgvb2dmzevJke+549ewCAzmkIBAI6a0aGo61WK1Vz02g00Gg0+MhHPgKJREJpWuXl5bBarWhubsbIyEjC8RDREvaaVl9fnzAjU1xcnGAKSs6dPbMynZJdFlmwQbqxJBGa6z3C1QnatWsX/H4/Lly4kKBwKxAIUFBQgBUrViAUCiEvL4+KbcTjcdx4442QyWT072tqauDxeGA2m8EwDBiGwblz56iiYV5eHqUHazQa1NfXZxTYz6diLxaL4XK5IBKJwOfz53TdZjPTVl5eDr/fD6vVinA4jLNnz2L//v3UlmT37t0YGhpKSHoJ7X4pYTab0dzcnOKXSYqhDMPA5XLhueeeox6k9fX1s1Y5XgwQH7mhoSFoNBpaFJbL5bR4YTabceTIEYyOjqKmpgZ79+4FsPBG69MdYybeZGx7CPac96VLl+DxeBKsWTIRB8tkX4nH4+DxeAiHwzAajTTmyIp8ZI4lScqsViscDgfkcjlisRieffZZdHV1IRaLoaqqCp/97GdRV1eX8Bqj0QgA0y5ipaWlADDt/EYWVzdIKz0nJwfhcBg8Ho8OphYXF6d9nclkQigUQiwWg0KhoAEHkXsnVU0i7d7e3o7h4WEYjUbs27cPCoUCy5Ytww033ACpVIqBgQFIpVK0t7fDbDbPeyGZjYrWfKDVavG9730Pd999N1599VUqqGOz2WgVOh3Ky8tpImI0GnH+/HkoFAro9Xpa8ZordDod/vmf/xk/+clP4PP5qNBKOBxGQ0MDTfgsFgusVivsdjv9/nNycnDrrbdCr9cnLOwA8L3vfQ+tra0JgUl5eTnuuOMOHDt2jHavSKCSk5ND1xCtVot169ahtbUVDocDb731Fqqrq/Hwww/TY37iiSeg1+sxOTlJC0kWiwVtbW20g0Xk7tetW4c777yTU3TAZDLRThwbfD4fGo0Ga9euTfAiSg52k699uuAyq3KVxUxYKCo1lwCN2WymRQgyCyoUClFXV4cHHngAubm5tIBC5krZr2e/H7vgkWywPD4+Dh6PB5fLhYGBAQwNDc3b4H2mcyVy85FIBHl5eYtCQWejq6sLp06dgtvtxuDgILq6umjBTK/Xo7i4GFu3bqUFOGBqPamoqFjU45oJpBMrFL4fXgoEAlRVVUGlUsFiscDr9dL5YYFAgI6Ojhnp4YsNMpv35z//GT6fD3l5eaioqIDL5cLExAROnDgBhUIBoVCIvr4+RKNRDAwMwOVyYXx8HH6/HyqVCg899BAaGhoW7Tgz9SZj20MAoCyyQCCASCQCgUCAZcuWAZgqPMrl8mmFZNhCIVxFEJ1Oh/r6epw5cwY+nw+hUIjOQmdFPjLHkiRlZJ4sHA7jU5/6VAKl6vTp03jllVfwwAMP4JFHHqE/n5ycBIBpRReIglrWH+HaBBkM9nq91JuMyOLPRPUg9MBAIEApHN3d3XTxIbNhOp0OZrMZZrMZg4OD6OzsRDQahdVqhcFgwLlz5+j9KJVKUVRUhNLSUrz55ptUhW8uCwqZtyBBxWIvTGQ2ir2QkueOC2KxGF//+tfxuc99DgDQ1tYGu92O4eFhWCyWBTneHTt2oLOzE6dPn6ZBGdvqgGEYKJVKWCwW8Pl8apXgcrlw+PBhAFPzV0S2++zZs2hvb0+ZoVMqlXjooYdw22234cknn8To6ChV3gSmZjo2bNgAnU4HtVoNHo9HRWVee+01qFQq2jED3u9IyeVy2oG32Wzw+XwIBoP0XCQSSdpqPeH1j46OJnQPysrK8LWvfQ11dXWzrrRyJWtZlassZsJCUKn1ej0aGxvR19cHsViMNWvWAAAuXbqEcDgMmUyGm266CWKxGBs3bkyQfScVdy7lwuRiRHFxcQIdC0BCp0wkEsHpdMLhcMBisUzrl0g+e7ZFC3KuFy9epJ0HqVQ6J7EmIrBCpPLTPZ96vR7PPPNMQiGN3c0IBoP47W9/ixdeeAHbtm3DH/7wB7qu9Pf3z/q4FhJEdKu7uxterxfxeBwSiQTV1dXYuXMn3njjDSrH7nK54PV6MTk5id/97ndUyGW+SoCzBemQ/fnPf8b4+DiAqQQ3FAphbGwMTqcT8XgcIpGIimYRxsfJkycpDZ/P58Nms6GwsHDRjp9NwZXL5bDb7TCbzQmiMNXV1dDpdBgZGaF0RYvFQs3hAVB2htVqpR3odPc02Vf6+vrg9Xrx7rvvQqlUYvny5VizZg3dL/fu3Qu1Wo2jR49iYmICfX19qK+vz4p8zAJLkpSRijPDMLjrrrvwla98BVVVVbDZbNi/fz9++ctf4oUXXkBBQQHuv/9+AO8vSNMZIpJ5FLbPUhbXDggP3WKxICcnB9dffz2dT5jpYSb0wNHRUQDAxMQE8vLyEIvFUFFRkVDVJF0GolZFEI/H4Xa7qWw+AOpb9tprr6GiogLV1dVzqsYSyhDxZVkM9cVkJFMmy8rK8IMf/CClwrt8+XJ89atfpQkZsDhqSVqtFo8//jjefPNNtLS0QCAQoLq6OsHwkghYuFwuepzRaJTS/gQCAYqKivDyyy+jubmZc0aroKAAJpMJubm5KC4uptX4eDyOQCCA9vZ2PP300/jmN79J6ZMWiwWRSASTk5PYv38/ent7KWWQrVpVX18PhmEo/QgACgoKqNpcukDQZDLhgQceoMPU+fn5uP3227F9+3a6eS/U7CLX95alNGbBRl1dHerq6uY0y0Oo4u3t7XC5XMjJyaH7MklaNBoNbr/99hT/o9kmRezCAztBY3fa2IbXyX6J8ylakE7gvn370NHRAbfbTYPvkZERnDlzZlaBd6YCK3q9Ht/+9rdx+fLlad9vcnKSMg+EQiEYhkEsFsNf//pXnDhxYlG7NdNBq9Vix44dePfdd2G32ylF+6abbkJDQwMtQInFYrS0tODMmTOw2WyYnJzE6OgoFAoFgPkpAc4WJpMJQ0NDtONIGBrEkoUkvLFYDHK5nAqGJVvrxGIxOBwOdHR0LGhSlmyzQiwXjEYjmpubaUeKdJV1Oh1llhCF3pKSEsocEQqF4PP5GBkZgd1up4Je6bq/ZF8h18nn8yESieDs2bM4evQoTp8+jVtvvRUA0NnZibGxMUQiEbS2tqKkpAS7du3K2h5kiCVJylauXIl77rkHWq0WDzzwAP15aWkpHnnkESiVSjzzzDP46U9/irvvvhsymQwCgQAAt+kvAfkdF00oi2sD4XAYgUCAqibu2bMn44eZdMD6+/vh9/sRj8dRVFTEOcCq1Wpx9913J3TG2MjJyUFhYSEKCwvh8XgoTWYuhqPAlVFfTEYyZfLee++FSqXC0aNH6ZD99u3b8eCDD6a8drHUksj7bt68OeW9tdop09Wenh4MDg5yvj4ajeL8+fPUFJoNkUiEkpKSBF+x2tpaGAwGaoFAEu/33nsPTU1NeOyxx/DQQw/hBz/4ASYmJhCPx+Hz+dDa2oqhoSFUVlYmCG+QbutMg+AEybSPFStWIC8vD5WVlfjyl7+84BsU1/eWpTRmQZB8L8wlcNTr9TCbzVSJkO01SooktbW1nIa08+nkcnWG2c9jsl8i13tnWmwi1ha9vb0YHBxMCMzJeb744otYtWpVxskPEfmQSCRQq9VpBVZaWlpgMBimnfPh8XiIRCJoaWmhMzwEkUgEp0+fXrKkDJja73Jzc6kacE5ODqqqqgCkJtoMw8BgMKC/v5+KHpGEeKHXqXTFKdLdMxqNEAgEkMlkWLVqFaVXikQiymwglFEiCJaMnJwc2p2a6XO5fs7lU0msJkiXmKiShkIhKJVKyhLz+/1YtmwZRkZGEp6zuro6VFdX0+5sJBKBy+WCw+Ggs/cOhwN9fX2w2+2cFPp7770XYrEYRqMRbrebJqQulwtnzpzBxYsXAUxRIUnxIhKJ4MiRI5iYmMCjjz6a3XcywJIkZRs3bsTGjRvT/v7+++9HY2MjPB4Pzp49i02bNiEnJwfA9F0wEuiSjlkW1xZ0Oh0d8CZCEwzDZCT1TaqQw8PDtKLJ5/MRCASwatUqzsXgc5/7HI4fP45jx44lJPsFBQXYsGEDPvvZz0Kv19PFsKSkZM5do/Lycmg0Gvh8vnmrHGUKrgDoc5/7XEJHbDpwBUELBfZ76/V62s0Ti8WYnJyctrBC6BfJkEgkKCoqSgh2duzYgZ6eHjgcjgRFzsnJSbS3t+PNN9/Evffei8LCQrS0tMBoNFJTTTL3Wltbm8KhT96w0oH9HchkMgCYkxz3bJD8vWUpjVkQzPde0Ov1OHToEMbHx6kI0NatW6mZPHsmLB2NdzE68CR4ZVtxlJeXpwS4JLhMFj1iz8eJxWK89NJL6Ovrg8PhoHuJXC6n8zjAVFfw3/7t3/DKK69klNwSJoBarU5QUU1GQUEBZWskF6EFAgHkcjnt4DAMQ/fKaDRKvTZXr1496+u4EGB3dNjnEYvFONkh7CIS8a3r7+/H+Pg4Dh06RMcOFurY0hWntNopS536+npMTk4iPz8fYrEYzz33HHJzc6FSqXDbbbfh85//PBiGwcmTJzkbBES4hKhKkvuK3cFtaGignd5khUQgVRSDPLNk9GFsbIyK6EgkEmg0GlRWVgIA/QzitwkgoZhIjmVoaAjnz59P8PAcHx/Hb3/7WwDAmjVrKOWYPfe5c+dOGI1GnDhxgo4UxWIx+P1+an0AIOHeDYVCuHDhApqamq5o9/NaxZKpL04HsViMFStW4OLFi/SGIJWA6cQKnE4nAFDZ3CyuLRDFOzLczaa2zQQyXEw2KR6PB4FAgNzc3GnnEB9++GH09/djeHgY0WgUQqEQgUAAg4ODsFqtCQso+Xe2MsxLhdkofS0VCFXHarVCrVbjuuuuozOBXJ5r04EkOuzNn8jTE3oP2SgikQhMJhOampoojYvMG5KNy2azwWg0YmJiAhMTEynBQTKlhF3Z5PKHIXTHTOi4C4mscWcWBPO5F/R6PZ5++mkYDAaEw2FUV1fjnnvuwbZt2+jfLKb64UxIfm+AW/XNarXi+PHjCIVC1LD95ZdfRmtrK903iEw4MKUamJubi7q6OgwMDFDRMWAq5vjZz36GX/3qVwt2Hlu2bMGZM2dw+vRpeL3ehC4Yj8cDn8+HVCqFVCql9HqhUAilUgk+n4+6ujoapC8U0snEk/8nU0lLSkpQWloKhUJBPUdPnz7NKRjFLiIxDIOmpiYYDAZ0d3cvaCA/U0EiuZjV1taGvLw8jI+Po6amBjt27KB7hFarxdDQUMKeAkwlKCaTCf/5n/+JyclJDA8P49KlS7BYLJBIJPD7/WhqaqJJNFshkcQVycdInlky+hCLxahlkFqtxoYNG7Bz586Ev2fPYyazUUhyxjBMgp2C0+mEx+MBn8+neymxbxCJRDRR++QnP4mOjg6alAGgLBQydyeTyRAMBhGLxcDn8xGPx+ftg/ZhwZIlZaTaxFboYYPc6CKRCABQXV0NAHRmiAtk7mSp1YeymB+qqqpQWFiYILYwE8rLyyGRSGjgS8yHb7nllmkrbTqdDv/93/+Nl156iaox+nw+XLx4EUajEVu3bsXevXs5aWDJCdt0QcZ8TEuvJK7U7BH5nOPHj2NoaAherxdOp5Py9IkABwmMMgGpHrNl5wktxWQyYXx8HAzDIBKJUGNxh8OBlpaWhO+PbFwHDhzAe++9B7PZDKfTSWfMmpubqUcY2ShLSkpQXV2NhoaGlOpncqB4pWe70lEaszNmHz7MNSnS6/V46qmn0NfXh1AohPz8/Fl3MdJ5Ky0k2IF1W1tbSoALgBq8k2TnjTfewFtvvUUFwhQKBfh8PpRKJYApb8CKigrs2rUL+/btwyuvvJLwmemo1snIVOSDzN42NTWhqakphZro9XrB5/NRW1uLmpoa9Pb2QigUIhQK0XVoIQsvyRRsAHSGDwBlFhDRFT6fD5PJhLy8PESjUZrk9vb24pVXXpmWPq3T6XD8+HGYzeYFpzFOV5DgWg+JBQJR+STFPq1Wi0cffRQA0NPTg/Hx8YTZ9Gg0CovFghdffBHLly+ne5haraYqojabLUUhkRxP8jGyZ8PLysrAMAyam5upNcLOnTvTMjjS3V/k56OjoxgZGaFKyPF4HFKpFDweDzabjQqDCAQCes/GYjHaCSQgSsnk/MkoAfEhVKlUV4whdK1jSZKyhoYGmEwmPPHEE5xUKoZhMDAwAGBKhAAAVXfq6+tLWBAIIpEIOjs7AQA33XTTYh5+FosEvV6PJ554gi5Ys5Fh12qnPHIAUM+oTL1ryPDxt771rYQqqMvlQmtrK90U2JU2UvEiqkUlJSWQyWSoqKjgNDlmKyNdqW7FbBPBKzV7xP4cUiUEphbzaDSK1atX0874H//4R7rhcdF52ODxeJDJZAl8fjYtxeFwwOl0oqWlBd3d3bTSaDQaYTQaE85Zq9WipqYmoes2OTmJZ555BidPnkQwGIRYLEZeXh6AqSBFJpOho6ODs9LJPp6lSILYn5udMftwYq5JkdlsRmNjI7q6uhAIBCCRSFBaWppW2Cbde1zpe44rCCfWKaSYFo1GcfbsWTidTupjSTr2drsdhYWFVKhAp9PBbrfj0KFD8Hq99HNuvPHGFP/EZBDap8FgQDweT5h75epCud1udHZ2cjIFIpEIPB4PbDYbdu3ahYcffpizY08wUwFmJrPkdBRs8n+TyQSPx0MZCYFAAKOjo1ShkKzLExMTMyr5kn3c7/fj4sWL6O7uxpNPPokLFy7M6n5L995cxSm9Xp/iO0Z+JxKJaILBngPX6XT40Y9+hAMHDuDXv/41JiYmUj5PIBBAIpGgpqYmwefsxIkTVPmUFHbZ15vrGA8ePEhnnHfv3p0w1zzXa6LT6XDDDTdQ+4p4PE67wkqlEkqlknZhw+EwotEoLcpwjRGRvZkk4sSeCACNx7L7zMxYkqSspqYGRqMRhw8fxs6dO1PmR/bv3w+/34/S0lLceOONAKZEQG644QZ0dnbij3/8I+XfErzxxhvw+/1YsWJFisdZFtcGOjo6YLPZ6AI3GwUjYjI6V5UfrVaL++67j7blCU+ffW+yaQQ2mw3hcBhutxvhcBh2u53SNI4cOYJvfetbdNCaCG4QZaSGhoYrsjjNlqp0pWaPkv1Uqqur6WcSKWFCx3A6nbTYkpubS6XrXS4XXfxJMCCTybB3715OmW32eezYsQPNzc2YmJhAYWEh9axLPmfiu0I2w/z8fPT398Pr9dJOv0KhQE5ODp03TObyi8Xiqy4BSv6eZ5IRz+Lax3ySIpPJRJMWHo8HiUSCj33sY7Pqki3FXGO6riBh3UgkEpSUlODMmTN0Hqaqqgp79+7FyMgI2traYLPZ6GwzCeDXrl2LS5cuIR6Po7a2FkqlEvv370/wbyLnSNbcpqYm6PV6Oqfq8XgAcHehbDYb+vv7MTk5mXaGPhwOY2xsDIODg9i2bVtKEkaSRACc7A4uEaBkejW5R9JRsImxstFoRDgchkKhQHl5OWw2G/UgA6ZGT/Lz87F8+fKM9iGdToeqqiqcPHkSDMPA6XTi+eefR29vL77//e/POzFLLk6dPn0a3d3diEajGB4ehk6nw5YtW9Dc3IzR0VGIxeK0YmE7d+7E22+/DZfLlSCLX1RUhJtvvhk7duxIiUe4aIXpjhFAAp3eYrGgvr4+5TvnwkwiIsBUAkW8YYGp+8rn80Gr1UIgEOC6667D2NgY8vPzqfcYsapJB4lEAplMhpycHLhcLggEAkxOTl4RxekPApYkKfvyl7+MN998E+fPn8dTTz2Fb37zm5BKpYjH4zh06BB+9KMfAQAeffRRqroIALt378ZXv/pV/O///i+WLVuG2267DQBw7tw5/M///A8A4MEHH8yqL16jWLt2LTQaDQBQQ91MsFBV2IaGBnz729/GT3/6U4yNjVFZ3GQ+dltbG0ZGRhAMBqFQKChNw263IxAIwO124/nnn6edPhKQ2Gw2LFu27IooL5LjZUviz3RNFnv2iD2Dxf6cf/qnf6ID3oFAgCblWq0W3//+9zllsI8cOYLBwUEUFRVh+/bt8Pv9WLt2bUaBIknAyTGRuVWuc66vr08ItGpqajA8PIxIJELfp66uLmHjZW+6V6PIRnKQxSUjnokqWBbXDuZzH5LZSIPBAK/Xi9zcXFitVjpbk+l7LMVcY3KASxI1IurR0tKCYDBIFeiIbLvZbIbNZkvwciPXMBKJYOPGjVi/fj2KiorQ3NyM/v5+BINBDA0NUXly8kzV1dXB6XTC6XQiHA5TUYTkGSLSeSLmyqQbT7oYySyBYDCI48ePo6qqijJCkvfCurq6FHaHQCBIEZFI7oIB798jXLN65P96vR42m416et5xxx20m+JwOKBQKLB69Wrs3LkTubm5Gasonz9/PiHwJx3NhVRk1Ov1uHTpEkZHRymdPRKJYGJigv7O4/FAqVSmFQvTarVUvXdychJCoRANDQ3UBzOTpGs6EDXp6ZIg8nczKe4CSLk3CJ2fz+eDx+NBJBJR306pVEotDSKRCKqqquD1emEymWC1WmkSyoZQKMSmTZugVqths9lgMBjgcrngdDrx0ksvLap/2wcFS5KUfeQjH8G3vvUt/PCHP8Qrr7yCgwcPoqqqCuPj47TK/PWvfz1hgBgAPv7xj+MLX/gCXn31VXzlK19BZWUlhEIh9fP4/Oc/j7vuuuuKn08WCwOdTofvfOc7NInI9OFNphUeO3aMU445E1RWVqKqqgoulwvhcBgCgQBms5kOxz7//PPo6elBMBiEVCqFQqHARz/6UajVarz22mtUKpoYmSZXGq9kQJIsiT/TwPRc500yPZbpKrYMw2Dfvn0p8xZcG1iyLP1CU1q4jpdssKQTNzExwUlTJe/JPqarTWSDfc5cMuJAqkAC189mQ5HKYmkx3zVILpejsLAQQqEQCoUihQ490/e/mGvLXNDV1QWDwYCRkRGIxWJKtSLMnHTHy76GW7dupe/FVmwlIMlNXV0dVCoVVCoVnE4n/X/yDBHpQsnlclgsFlRWVsJut0OpVCISiWB4eJhKmANTNMaLFy/imWeewa233ordu3enJN/EnwoAp7BEpkJEXOsw+f7XrFlD14UtW7Zgy5YtlC6uVqtn7YVnMpmgUCgobY5goTotpOPZ3NwMi8UCqVSK3Nxc2hnatGkTzGYzpeyJxeJpxcIaGhpQWFg467glk+MkUvgymQwajQa1tbXQarUJdFmuBIyrCAMg5d5QqVRQq9XwevP9n/AAACAASURBVL1QKBSIxWKIRqO0UCmVSpGXl0dNve+991709vbixRdfTBg9IKitraVKrPF4HKdOnaId4s7OTjQ2NuKJJ55Y8uf/asaSCX3s2rULN954I5qamnD+/Hn09vYiLy8PDQ0NuP/++3HzzTdzvu7xxx+HTqfDq6++it7eXkSjUdTV1WHnzp0ZS31ncfWCBNwzgUvhjoguENrJXDpmbGEIh8MBr9eLQ4cOYWRkBK+//jr1rCG0OYFAgNHRUUgkEtx+++04dOgQGIahAjbkOLm444uNTKrjXJLRi01ZBJBidUDMMAmNxm63T1uJX8jjTBdwcF07dpct0/e+moJRAnIuXDLimWzoyQH51UbRzOJ9zHcNIrOpIpEIubm5KCoqSgja9Xo9Ghsb4XQ6UVtbi927d8+4ziwlTCYTDAYDzp8/Dz6fj/z8fCiVStTV1WHz5s3079J12ZLPg3Te2CbWwPvJDTspIUkZez6KqwvFZgaQ7+zIkSP49a9/DZvNRmmN4XAYw8PDEAqFtKPPThzZBSwiwc7u/k3XBeP6nqaTeE9W+ZsrysvLUVdXh2AwiK6uLspI2bx587wTHrJWEUVEPp+P6upqrF+/HnK5PCGpIhQ8mUw24/lkGrfM5jiPHTsGg8GA0dFRyOVybNiwAZs2bUoRkuJar9MVYZLvDXJeoVAI0WgUVqsVly9fpgWFvLw8bNq0iVIX9Xo9ZDJZgrAJARkh+M1vfgNgimXy8Y9/HB0dHQiHw3A4HLh06dKi+M99kLCkkvjr1q3DunXrZv26O++8E3feeeciHFEW1wK4ui51dXW0kkZoJ3Ohimm1U8IQBQUFePPNNzE6Oopz587hrbfeQjgcRiQSoVL769evRywWw8jICDUe5vP51KtkqeeJZqqOX4lgOh1lkYufzw5umpub0dXVtWQB/nw6C1cq0V0IZNIRSKcKRjBT8n81BeQfNizEM04EMcrKyhIElADg6NGj+N3vfodz584hFArBbDanDPRfbUl7eXk5otEo7QrU1tbi9ttvz4hdwfUsk59NZypPaM2ENllcXJz2PbmOQa/X4/Dhw/B6vdTyhS2s4Ha74XA40j7P5N90MunJlGUACXNp7KSOJDRSqRRAaoFtrmB//r333otNmzZRujqQ3vtuNiBrFeloFhUVYc2aNdixY0fCezMMg4qKCoTDYVRUVCzayAEX7bC5uRmnTp0CwzCw2Wzw+/0IBoOYmJiA2WzOKAGbroDAdV+S77epqQlGo5HeZ36/H++88w7y8vKoGub4+HhCR5ggEong0qVLkEgkEIlEGBoaQnV1NXJzc2GxWBCPx+FwONDe3r4g3+UHFVelT1kWH27MRg2KzZOXy+XQaDQJlcC5QKudGuAdHBzE+Pg4xsfHqVwskUresmUL7rvvPhw8eBADAwN08QyFQhAIBFCr1RgZGVnSeaKZujSLPe80E2WR63hJVX4pZ7Dm01m42gLQTDCbjgDXvTST1PS1dj0+SJjvM54sUkS8mvR6PZqammA2m2EwGODz+RCPx2G321MCtqttrtJqtVLhErlcjoqKijnT3dnIJLkibIDZFpxaWlroTB8A6v0kEAggEAgQCoXQ3t5ObWRmyzCYSfbe5/NR+iP5flUq1YJRsrnWCZLoLWQHKpmumU6huby8HBqNBj6fb9Gk3Ln2x4MHD6K5uRlOpxM5OTkoKyuDUqkEwzD4f/a+PbjN8sz+6PZJsixbsq3YKJbjGOxcRCAKlHib0NCk3nS3YZbmDyhdOqTLbltSZtptoWWn02Qo093+dtvZ0ikJnYFiWEpZtk3pECiOccDBpXYMlkhixxcsK5YtS5YtyZZ1v/3+8Lwvnz5/ullyrKQ6M52CkfRd38vzPOc5x2KxYMuWLVkHYOkSCOn+5vf7MTAwQD3wbDYbpqenEY/Hae9dKhXkeDwOmUyGqqoqNDY2or6+HpFIBEKhkKor9/f3QyqVltaCFCgFZSUUFXJVg2Lz5Ddt2oQDBw6gpqamIL1GR44coVQRt9sNkUiEqqoqPPjggzSzZrPZMDw8TJuzE4kEpFIpdDod6uvrYTKZrqoMPt91pLoPa93rlomymO6c/H4/5fNfTeQbRBTbBnS1yHZBJ39PFbBdL/fjWkW2YzxVNZNQ/cbHx3HjjTciHA6vMJEOh8O0csMW5sr1HNYahHr3yiuvYHBwkAaaqUQcCo18xoJYLKbCCgKBAAqFAgzDQKFQwO12IxKJYGBgAK+++mqSb1U6sJ95KsEP9j8TX62Wlpa0Ac1qQIQ1iKfXWs0T600nT3XPgWW1aYvFAq/Xi3A4DLlcjg0bNkCpVNLq5MjICK+i42rZGNxxbzAY8MQTT6CrqwuvvfYaxsfH6fnw2TNIpVJqgyAUClFXV4cvf/nLVHzGarVi8+bNVAQtFothcnISZrO5tBakQCkoK6GokI0aFLDcpErUDdk8+UKWxQ0GAx577DG0t7fDbrdDJBLhq1/9KpW6J585c+YMzGYzhEIhRCIRNm7ciF27dsFoNF51GXwu0lHH1nqBWo03m1a7rBhJPOA6OzszCpQUEvkGEcWyAb3aSLUpyIZCW6I2rh2yGePpEhEMw2BycpImpywWC95//32Mj4/D4/FAqVRSLydi+s4VRVjvjTD7Gvv7+zE6OopgMAgAlNVwNZDP3LB582bU1tZidnYWAoEADMOgvLwcarUaAoGA0heJvH+qZBKbTs7uTWpra0spe0/+ORu2Qy5gn0tvby/sdjs9/lrOm9kEMISxka3HZzbgq4yxE5D19fVobGyE3W5HOBxGS0sLjh49SpVA3W43fD5fQemifIJOVqsVBw4cwM0334wTJ06gv79/hVk0sDw3tLS0QKfTweFwQCKR4Bvf+EbS/sjhcEClUuGGG27A5OQkotEowuEwYrHYX83amCtKQVkJRYVMalB8qnhrueAbDIa0viKkoub3+zE6OgqGYXDHHXdArVZjYGDgqsvgs5Fu0l3rfqd8vNmIkEo+vYGrRb5BVTFsQIsJ6e5Hidq4Nsi1pzFdIsJms9GeWZfLheeeew433HADotEoGIYBwzCora3FlStXIBAIsH379iS62WoNqwsNdsUvFApBIpFApVIl9cetNdhjYXFxER0dHVRYgh2g8AU9BoMB+/fvx/DwMKanp2mfUXV1NW644QaMj49jfn4+rUEze7xxlRhJIMfXNsD+53yqMexrczgcNPEmlUrp/6vV6qIwGS5Eco07Dsk4Gx0dhdPphF6vT0pAGo1GHD58GK2trQA+6aGrra2ltNdCJvq44570c3/00UdYWlrCpk2b0NraiitXrlA7B2A5kVFWVobq6mrodLqUdgdc9WfS/lFeXo7PfOYz6/6MixWloKyEogOpgpHFkjRHA/wbiN27d6/pAM+0qSElf3Ke5Ly5qnZXG3yTLntyX8tNMDn2aoLS9aw2FSKoWqtA91pFqvtRojYWHqsJdNONt/fff5/6BsZiMYyOjqKiogIymYxWErxeL3bs2LFCVbBYgm6bzUb9luRyOQQCATZu3Ij9+/dnTfXL5VjcuYP7N4fDgaeffhpOpxOVlZX4+7//ezgcDkxOTmJqago1NTW0CnHo0CG6nrS2tqK6uhpGoxGXL1+mgeWnP/1pjIyMIBQK0R5oNu2bHH9ubo6ON0JF3LRpU1JvUqqeuFzpkECyOIjZbKbvgUajgd1ux9DQECKRCLZt2waVSoWWlhaaaF1vkHWAvffIBXxVsdHRUczOzmJpaQmhUAi9vb1obW1NSkCGw+EVVlBrlejjjnuXy4WzZ8/iypUrSCQSGBkZQU9PD7ULEIvFqK2txRe+8AVUVFTgo48+QjgcxunTp3HkyBEASJLqJ4mQixcvwu/3QyAQoLKyErfeeiuVzS9hJUpBWQlFA74qGJDcHM2lWWSSTr9a4Nt4komUYRgqK14oykc2kzN30gVSy5oXGquhLhKsR7WJr7qw2u+WkB3+Wqmea4nVBLrpxtulS5eSPhuLxbBhwwZ4vV5Ks2IYBvv27cOuXbuSVAWLIehmrylKpRLbt2+HSCRCU1PTmgRk2fj7mUwmOJ1OSkn7/e9/j3g8jvn5eYRCIUxMTCAej+P9999Hd3c3vv/978NoNMJsNiMWi6G6uhq7du2CVCrFoUOH0NPTQyXNY7EYLBYLTp06RZ8Fu0dboVDQQKwQVEQ+OiSfOMj4+DilAvp8PgSDQfrukGD+alvGZINchFlS9Yv5/X787Gc/g9lsThIC8/l8ALLzsVyLRB933BuNRgQCgSQRD7/fT+1/pFIp7r//fjzyyCPo6+vDpUuXaHJgfn4ecrmcjq0HHngAOp0OPp8PgUCAer7W1tbi6NGjRfWMiw2loKyEogHfIg4kBxKEZlEs0unpQM6nUNnibOiI3ONzKSlXo3qXD3WR4GpWm/LJ6BdLNeBaRCZqYynQzQ58no1AbmOcb7zZbDbqh0WgUqnwxS9+ES+88AImJiYQi8UgkUgwOTkJt9tNE2dkw73eQXdXVxfeeustLC4uUj/TQghB8SGb9ctqtWLnzp3QaDQIBoOIRqMQCASIRCKQSCQIBoOIxWKIRqOIRCKYnJxER0cHlpaWMDAwAIlEgu3bt2Pfvn3UnHdychKxWAwymQyBQAButxvnzp1DdXU1mpub6fELKYQFpKZD8omD3HjjjUmVMgCorq6GVCrFkSNHiqI6xkUuSYVU/WIA4PV6cfnyZczNzUEgEKC6upp6/bF95NI9k7WaD7nj/o477kBHR0cSsyUcDkOhUEAul8PhcMBms0Gn01Hp+0AggNHRUcRiMSr0QxhMLS0t+Mtf/kJ/i9CdS0iNUlBWQtEg1YaC/Td21Slb6fT12uCxDSAJdz+fbHEmOiI380nUxgCklc4tNPKhLq7Hs8ono18M1YBrGamCgVKgmx347lW6MZ7L+LJarVTIIxqNQi6X4+tf/zr0ej02bNgAlUoFYFmEQiQSrbAoIZvOwcFBVFdXr9k9SAWj0Yhf/vKXmJ6eBrBstMyWWi80slm/yH0/fvw4uru7MTk5Sb/v9Xrh9XoxNjaG+fl5xONxMAyDxsZG9Pb2IhAIIBQKIRwOo7+/Hy6XC3a7HVKpFHq9HolEAuPj40miH/Pz80nVsUIKYbHnPjYdMpU4CLunjHy/mJMuuSQ4+JSGyTjs6urCO++8Q9UzAeDgwYPUvgBIz6C5WvOhVqvFsWPH0NDQgGeffZYmZEgVltAsScBFKIsTExM0MCcVNXKvDh06hD/96U+w2WwQCARXvUf8WkQpKCuhaJAqaGhra4PJZEJ9fX0SRYK92KSTel6PDR45LptLn2/DMDfzDCCJIsHeDBHPk97eXgDL/QgPP/zwValApZK1z7QhXK9nlQ+NrkTBKzxSBbql6tlK5NJjm+v4IpU3IuJhMBigUqnw6quvwu/3o7a2FiqVCn/7t3+LmZkZlJWVJVVM/H4/xsbGYLFYAACjo6N0DroaOH36NGZmZigdKxgMYmpqqmC/z0d5ztbfj1RI+PqwFhcX8eKLL2J2dhZ6vR5btmzBH//4R4RCIUoLJ/1jPp8PKpWKikacOnUKJpMJi4uLCAQC8Pl8eVfHUvmGcuc+dlKQ/blUxyz2MazVauneY+fOnWkTHHzrAPn85cuXkxKTsVgMarW6KBJ/fO/wgQMH8PLLLydVyUlQxg64SHXzxIkTtO+0qakpqa+0trYWOp2OKmu6XK6rbnNzraEUlJVQ1GAr+JhMppw9ydarksFW+9Jqtdi9e3fOBqWk0tXb28srSQx8QkfkqmkRzxOn04lYLAaTyUSrZmu9sSWLGVvWHkCSBDPfhnC9nlU+FcT16H+73sG3wSlUwH69BXb5ZPOzGV9+vx9CoRByuRyBQAAvvPACpTM1NDSgqamJNu2ze4tIgEbkvAHAYrFQlsPVegYCgYD+s0KhwM6dOwvyu6neR76kV7pEWCpxDb1eT+9RR0cHtYWRSqXYuHEjXC5XkopkW1sbVQrmrhn5VMcy+Yamm/uu9fHFVQ9kW7NkW6E2Go3UxgBYfh+lUmlO57FWib9U77BOp8OGDRuSTOAVCgV27dqVFHAByxXBcDgMj8eDRCKBRCKRRE80Go2YmJigyo0ulws2m60o6arFglJQVkLRgG+S4AY3DMOgrKwMCoUiq8VmvSoZDMPAbrfD7/djYWGBN9OWDuReENNImUwGYKUBM1tMhO3XtnPnTpjNZtrr4XK58Nvf/hZdXV0AUHDzTy64svYmkynjhjAfcZB8kU8F8Wr2v/01gC/Q7evry6l6lkoFL11gd60EbNzzzDYpkOtc2NXVhZ6eHiwuLkIgEFCvIpFIhA0bNqxINLGz42ROOnXqFDUEbmxsBMMwV60a3traiv/93//FwsICpFIpvv71r+e1GUxn/FvoBBJ7TiE9aNFoFFKpFAsLC/B4PAiHwygvL4dSqaSVGPK9bPqUsgH7Ovl8Q9da+Xg9wdcukO75c++FzWZDV1cXPB4P5HI5YrEYKioqsGfPnpzew0wVu0JdH3mHtVot/u3f/g2PP/443G43Kioq8M///M84fPgw75o9MTEBn8+HRCKBiYkJGI3GpM/FYjH6z3zm8iUkoxSUlVA04Jsk2MGN0+lEQ0NDTr+5XpUMm81GTVXr6uoy9lVxvVyIfDHJVqlUKt6NFHvx5vqp1dbWgmEYnD17Fm63G5cvX4bZbIZSqUQ8HofFYklqzF9Lud2dO3dSSlMsFsPi4iKVzwU+8UhZb7PtbHCtbN6vZXAD3VyqZ6n+nm4jfa30saWr0GRCrnPh/Pw8wuEw4vE4zfQrlUooFAps3bo1Y+W/trYWDz/8cJLv0tWqhttsNvT29kIsFkMqlaK2thb19fV5/V4qIYfVJJAy+ZKxYTAY8M1vfhPPP/883G43Lly4QDe3lZWVGdeFXJGKlsfnG3o9Q6fTQaFQ0GCUy1jJ9PyNRiOVg6+oqMBnP/tZfOYzn8k5EZquYpfv9aW6hra2NtTU1MBkMqGsrAx+vx8Oh2PFccPhMFQqFaampmiljA2tVgulUgmXywWhUIht27aVqmQZUArKSiga8E0SVqsVdXV18Pl8UCgUmJ+fh8fjgUKhyHpB5y5Qa72pJhsCj8cDiUQCjUaTtueNBCROpzNJoUqhUKClpSXrqhbfQhwOh2lzuEQiQXl5ORiGQTAYhMfjgdlsXtGLVogAjZvdIxNxe3s77HY7jh8/DplMBp1OB4Zh4PF44PF4IBQKsWHDhqtKccjlfbhWNu/XG3KpnqXa+KfbhFwrfWz5BjW5bNb1ej1UKhUikQjkcjn27NmDvXv3Qq1Wp5yL+MYH13eJL7jONkDJBjabDSdPnsSbb75J5xSxOL+tDtlgk6of12yZ713huy7yW9z5nsiIp+pbCofDqKiowPDwMKWUqtVqSl1Mdb+yeX+5PWPpaHlA8Qt0FAJkXSay9V6vlyaGAf7nnw5isRg7duxYMRaywVolMjIlacj6+8QTT1D66te+9jU0NjbSz+t0Otx6662Yn59HIBDALbfcssI8nlAXhUJhUtWsBH6UgrISigapJommpiaarSKLkt1uX1XD6NXYVFutVjidTgQCAQDA1NQUTp06hc2bN9PFmlTARkZGYLFYYLfbEY1GEQ6H4fP5UFZWlneDttVqhc/ng1KpBABs3LgRW7duxZYtWzAyMpLkI0OqWOwALV/5fm52jwSIH374Ib03H3/8MZRKJaRSKeRyORKJBOLxOHp7e9eMWsk9z1zeh5La4vohm+pZur+n24SsZR9bIXG16Ng2mw3nzp2DRCJBZWUlGhoasHfv3iTFOD5kGh/cZwAgSRCpsrKS+latNiljtVoxPDwMj8dDBQp0Ol3G3+MLohwOB7q7uzE8PEzFCgBgbm6OVpKsViscDkdSz2xbWxuvaTIA3vkeADo6OpKqj9x+LmJ+HQ6HIRaLIZFIkqiLfNeT6f3lfkav12ek5a33GFhrcFsHpFIp6uvrM5ptc2EwGLBjxw74fD4wDIORkZFVeaqu5ZjPdA2k7cDhcEAkEuH//b//B5VKhY0bN+I73/kODAYDDh8+DLvdDo/HQ99xArfbTdf6RCKBUCi0JmtmsSXP8kEpKCuhqEAGFFnowuEwrd7Mzc3B7/djfHw8K0ogH8imYWxsDE6ncwX/uRBYXFzEwMAAlYmdnJzEuXPnUFNTA6VSiUAggKWlJchkMsjlctr4W1NTg0gkQpUa8w1KuNQTdrWNa/rJVU4Dkjn0uZ4HHx/f5XLh0qVLdJIGlvnmHo8HKpUKFRUVkMlkCAaDVHp3rSfYXIOsktpi8SBVkJUu+Eq1CcmlEreeyJaCmO8mxWg0UhU/YDkI6ejogM1mSxmc2mw2zM3NZVTFZT8Dco/Hx8fh9XoxMTGB8vJyAFg1TUun00GlUtF5tbq6Gp///OdXVLMApAwO6+rqUFZWhuHhYTgcDkSjUVRXV6O6uhperxddXV1U2Zab3AI+2cxyTZMB0IQcme8rKytht9vR19cHp9PJS7fVaDSUuh8Oh+F0OqkCcao5KJu5jfsZvV7/Vzm/8fULkqqoWq1GU1NTziwSrXZZydDpdGJ2dhYWi2VV+431asEAlvsZFQoFJBIJIpEI5ufnMTs7i/HxcQSDQTz11FO0f9zj8SSt2zabDSMjI1QkqKqqClu3bi34O2U0GqmoWKqK87WEUlBWQlGBT0qe7RBPeN3pKIHpQHjiwWAQdru94BUZm82GF198ETMzM4jH4wCWA494PE6DylAohGg0ilAoRLO4jY2NaG1thVarLVh/F5nMieoi+zr5etHYAZpCoUji0Oc60ZHgxe/3w+v14vXXX8fFixep+hoXCwsLAIBoNEp7zj772c+umacQ9zyB7DYh67lAlrAS6YKs1Wx+sqnEAeubmc10bflW+Aj92uVyIRqNoqysDAzDwO12w263827uuVWdAwcO5CTEROTzQ6FQ2uNkA61Wi3vvvRczMzPw+XwwGAzYv39/WiVBUiFiB1EymQwOhwNerxdisRgKhQJarRahUGiF8AXbp4vbQ8utlBFqOpnvTSYTDciATypmRPiovLycVunq6upQW1uLvr4+BIPBrO4t+R55f9NJuWdrZnw9IV2/IAA0NDRg3759q6rcGgwG9Pb2wm63w2w249e//jUYhkFbW1tO57dez8NgMODxxx/H888/j8nJSVitViQSCUSjUYyOjtIgk/T9sxlMhDHEMAw2bdqEm2++mVcsJB/YbDa0t7ejr68PgUAAgUCgKJJn+aAUlJVQVCBZKvbiSAwHC5Fh0Wq1aG1thcViySt7lQpWqxXT09M0ICMQCoUrKmVqtRq7du3KegOzWvT29sJisaC3t5fXJ4gvQJubm0NXV9eqqgSEjx+NRuF0OhGJRCiNMRWnPJFI0MwksOwp9LOf/QxOpxObN29es/uzmiBrNRv+Eq49pHo31oPWmKvpcz4VPjb9WiwWo6KiAlu2bKHKrtx52GazoaOjA2azmdqV1NTUZD2WyD02mUx4+umnEQ6HqQnyakCol0tLSwiHw/D7/TQxlUpJUK/XQ6FQQKVSQSKRoKGhgbIHZDIZKisrcd9990Gv1yclroCVJslsoSW+njLuc6ytraUBGamYXbx4EV6vF+FwGJFIBDKZDOPj4wgEAhgaGsLY2Bi9tl/96le45557VgQN3KQcuTfZSLlf7/NbOiVN0i9Gev8mJyfR2dm5qsot2W/09/dTGuBPfvIT1NTUZBXkkf5Ii8WCxsbGq+rzR9DW1ga9Xo+zZ8/iF7/4BU0QkIp2OBxGZWUlZmdnUVlZSRlMRKQtEAjA6/XC6XSu+j6mgtVqhcfjgdvtRiKRwNTU1DXvg1YKykooKrAzp1zTZdIjRYK11dLr2NmrQlfLdDodNm7ciLGxMRqYicVifO5zn6NZItJTlq5hvlDgNpWT7GwqkIDDZrNhcHBwhQF0OhAJ4I6ODoyNjcHlciX5sgiFQohEoqyafROJBF2QNBoNampqcMstt+Duu+8uuABIKcgqIRX43o1MQU+hM9urMX3OlYLGrZ5IpVJIJBL4fD4EAgHEYjHe5BEfsyFX2hu5x0NDQ5DL5fD7/RCJRKs2eiY9ZcQf6sKFC/D5fGhsbKTUSm5ARa6JiA+Ra3U4HCvEitgqt+R4fM86XRWX++8PPPAAOjo60NfXB7PZjLm5OcTjcTAMg82bN2Nubg4+nw9DQ0MIh8NwuVxIJBKUfdDT04PHH3+ctwIzODgIu92OwcHBrHrGrndko6RJ6KM+ny9v+rJWq8XCwgIikQgSiQQWFhZgMpmyWseMRiPefPNNuFwuDA0NZVy/c0Eu8xR5R2tra/HMM88gEong1ltvpWNkYWEBAoEACwsLdK9AgjWr1QqBQACbzYbq6uqCVrKIUBixHKivr19VW0sxoRSUlVB00Ov10Ov1vFQ+do8Ul14HZKcMtZbVMq1Wi+985zsIBAK4fPkyhEIh9uzZg2PHjtHfvxYkYbXalQbQfBkuo9GI7u5uiMViXLp0CefPn8fCwgLC4TASiQQEAgHi8TgqKyuhVCohl8tht9vh8/moKlMqkOqZ1+vF+Pg4PvjgA3R1deHnP//5NXEPS7g+kYnWWOgqWq6Vr1yrv3znfOTIETidTpjNZkQiETgcDoyNjdHNKsMwsNlsGBsboxWyurq6Fd5luWDnzp2orq6m/VurNXpmGIb2bxGauMPhgEajSRJPApBULSEJPwC00qfVankrUOzrK9S6cfDgQZjNZly4cIEmw+LxONxuN5RKJZxOJxKJBPWEAj7pyQ0Gg3j++efpuknA7aG+4YYbSj1jKSpj3PFSqP7hcDiMuro62O12CIVCVFZWZv1uE/EYYLmK+v77769KwZGL1c5TpGrGvldshWx2rz/DMFhYWIBEIkE0GoVWq12Td06pVFKKdUNDwzX/TpeCshKKBuxSvUajwa5du6BWq3nV07j0sE3dGgAAIABJREFUOqPRSDOC2UwypFpmtVoxPj6Orq6uglWtDAYDfvGLX/D2cl1tGAwGGoA2NjbmFMywDaCBlepgRqMRP/jBD/Dxxx8jFAoBQJJXCZGiZhgGlZWVaGlpwcLCAsbGxpKqZUKhEABWUD4JyGeJt9rp06cLFpRdT6pNJVwdpAt61kKdM9PmkO8dzqX6y3fOu3fvxmOPPYYTJ05gaGgIV65cwf/93//hzJkz0Gg0cDqd8Pl8EAgEKCsrQ0NDA5qamlYdkAHLFaiGhgYEAgE0NDSgtrY2q+9xxTtMJhM0Gg3m5+dpkJVIJKBQKFbMxex/Xu9gRavVYsuWLTh//jy8Xi/i8TiCwSBcLhfcbjfi8Ti8Xi+i0Sj9jlgshlgsRjweh9/vXzFHc3uoR0dHcfjw4YL7UhYzsq2Mpapg5rs+MAyDRCKB6upqSCQSfO1rX8uautjd3Z30t0uXLq3qHLjIZ57i670lCtnc3kWJRAKZTIZNmzbhrrvuymt+4IPRaITT6YRSqcSGDRsKWklcL5SCshKKBoRqNzU1hUgkgrfffhvl5eVobW3Fo48+SicDNr0O+GQxzTWbvGXLFnR1dcHr9eLixYsF7S0rJkpca2trVj5nXHCppFx1MJPJRDPMbBCp5vLycsRiMUgkEohEIpSVlWFkZCQp+BIIBBCJRBmrZgTxeBwzMzOw2WwAktXTjEYjVTZTq9W8lVY2ss0WlgK3ErhINb6zEVdYTV9Kqs3hajLe3HNJdc4GgwH33HMP7HY7FhcX4Xa7qb9hKBRCIBAAwzDQaDR5VcgIjEYjxsfH4Xa7MT4+vmI+5ruHfOIdTqeTVpVisRhisRhUKhXvho39m+st4ENYB6FQCHK5HCKRCKFQCB6PBwKBgAaYXEilUojFYkxNTeHdd9+lczSwPD9u2bIFFosFDocDFosFNputINWWYsZqKmNreS7EI3Tr1q1obGzM6ntnz55d0VdJ+rjyRSFVhAmrhtB8yf6M7dVaXV2NDRs2FOTcCcgxSCUx16RzsaIUlJVQVPD7/VhaWkI8HkcgEMDi4iJ6e3tXLNDcjQoAGqQpFArMzc1RT5BUGyK1Wg2FQgG/349wOIyxsbFV+YgUK7gbtlwnLG6vg9PppKIrWq0W9fX1EAgEK75HsucikYhme2OxGNxuN1QqFe0rEwgEqKqqQjAYzDooE4lEmJ+fx8mTJwGAmor7fD4MDAxgbm4OAFBZWUn9lVLJGWeTLSxGr6oSihd8AVSqdyhVkMH+G5Gm37lzJ68Saa4Z71TnkmqTajAYsHPnTjpGCU1oenoaSqUSFRUV2Lp1a8Ez4MCydHxfX1+SZD272kFsUvjEO4ivF6n2q1SqFfMf371Ya7VXPpBg7MMPP4TZbEYgEIBOp0NFRQXGx8fhcrlWJL4IiFotAEopra6uTmKOKBQKaDSaNemhLgbwmXavpjLG97v5zv3c4CRb1WibzYa33nprxbqoVqtzOn4qFKoKCPB7kpJkrUwmg0wmozYSg4ODBVtDyTGkUinUavV1USUDSkFZCUUEg8GAuro6zMzMIBQKQSAQIJFIQCKR8H6eO7E+8MAD6Orqwrlz5/D6669jcHAQBoMBp0+fpl5YbFNSQu0bHh6mwiETExN5GZcWE/KlUpHFjsg7E3oCwzB444030Nvbi9raWiwuLiYtHgKBgFIeSbY6kUjA4XCgqqoKN998MwDgrrvuQk1NDUwmE06dOpWSvkggkUhQUVEBl8sFi8UCYHkDVlZWBo/Hg9nZWZpNDofDScEenyl2NtnCtaCjlXB9gzsv8b1DAFZs+Lh/MxgMePrpp+F0OqHRaHD8+PEkkYls32E2Ur3PqTapWq0Whw8fps37MpkML774IhYXFyEWi9Ha2ppWES6XCiHDMKioqIBcLsdNN92EkZER9PT0IBaLoaGhAdPT0ytM7olXF1e8gyRqQqEQwuEwlEpl1vdircG+Jw6HA0888QSsViuCwSAkEgkUCgUaGxtx5MgRDA4O4g9/+AOMRiOliHPBMAy9D1qtFrFYDC6Xi17bpk2b0NDQAKfTCY/HA5/Pd83OY5kCMBJoFKIyVoj1s6OjA06nEzKZLGW1lg9ExIwNsVhc0GpTodg8fPeJ0GZVKhVkMhlEIlHBxxmxjKivr0dTU9N1sWcDSkFZCUUErVaLb3zjG/jJT34Cl8tFKxuf+tSnsh5wH3zwAYaGhhCJRBAIBGAymWA2m5OapR977DGaKXz44YfR0dGBd999F5cvX6YBYCFlW9cLfBu2bDdJfNnGcDhMvcwuXryI0dFRzM7OAgDkcjkAULoQCcRIYA18YnpaU1ODu+++m0rdHzx4EB6PB0ajEYlEAnV1dbBYLEl0HaFQSGmQWq2WGqmyN2Ber5e3UiaVSlOaYmdarAtJ8yjhrxN871CqQI39t56eHjidTszPzwMAuru7EQ6Hs65yAdlTFflgs9nwwgsv4LXXXsPi4iKkUmmSGEY0GsUf//hHHDp0iJdmSOYKUq3ZsmULr+Is8Rr63e9+h1AohOrqatTU1MBoNFLp948++ggVFRVQq9VJRs2bNm1KKd5B+k2I1Qa5x+TcsjG6LjS48yrDMHA6ndQLrba2Flu3bqWJQYPBAL1ej8cff5y3n4hhGOzatQsqlQrbt29Hd3c37HY7zp07B6VSSa9t3759Se/OtTiPZROAkWe/msoYF/nM/VxV0o0bN+YUOLjdbkQiEcjlcgSDQTAMg8bGRtx99905XcPVAAmONBoN731SKpVQKBQpLTVWA1KdC4VCkEqlaGtru+b3awSloKyEokJbWxutntTX16OioiJtAMHljpPMKMMwiEQikEgkCAQCVMVqaGgIJ06cwBNPPEEn6oMHD8JkMlEJ6IsXL+K5557DQw89dE33GPFRPLOlY/BlG3fv3k0lm0dHR+FwOGglKh6PU7oiGyQgI4jH45iensaLL74IANiyZQv27t0LjUaDr33ta9izZw/C4TAsFguee+45WCwWGuCR3xMKhdiyZQtqa2sxODiImpoa6PV6HDhwgLenLJMpdjrKUiFpHiX8dSLVO8S34eMa+X700UcAQG0hBgYGsq5y5UpVZMNoNOL48eMYGBhYMYbZiEQi+M1vfkOl2I1GI1VsJcmQqakpuN1uvPnmm1AqlUnejADwox/9CB0dHVS1LRAIoLOzkwaAhOpMkj3V1dXQaDR0k5dOvIMt/sQwDE6ePEmZEQ0NDVSRcS3pfOn6m3bt2kVNpSsrK3H//ffjwIEDKyikjz76KB555BEsLS0l/XY8HsdNN92EQ4cOob29HaOjo3C73VAoFLjllltw//3302vjVlmLGXxrbC4BWKHmbKIEnev7Qc51NaqkNpsNAwMDiMViqKyspP3gqzWwTnWMQlIXucFRX19fUgKntbWVJk4KWZ0jiZlrXQafjVJQVkLRgWQIM4GvmtPU1ARgufn50KFDVAFxaGgICwsLWFxcRH9/P06dOoVHHnkEwPIifuTIEbhcLnz44YewWCz4zW9+g76+PnzrW99K8n7p7OzEU089BZ/PRxfLYlvguGpkBOnoGNlk1IkE9scff4z5+fkkFbB4PA6hUEj/x+dFJhaLIZPJIBQK4XA4EIvF4HA40NPTQ/s+tmzZgra2NuzevRt33nknzp49i/fffx8ffvgh5ubm4Ha70d3djQsXLkCtViMajUIikWDHjh1oamrC8PAw4vE4vvjFLya9Q/mYYheTaEsJ1ya471CqjSP3byRBtXPnTtTW1lKBGz4p/mw2samCONLXVFNTg9raWjz11FP46KOP0gZkBLOzs3jjjTeg1WrR3t6ODz74AJFIBNu2bYNKpYJcLqe9Uh6PB36/H06nE11dXXC73fjzn/+ctKkiEvYMw0CpVEIkEgFYpirPzc1BLBZj7969aG5uBsMwtALGvSYiQECu69KlS+jt7cX09DSA5eCPnH8hwZ1/0/U37d+/H3q9foUXGvf3jEYjtm7dig8//HDFMxkcHMTCwgImJyfhdrtpYnJiYiLpvrDfC/a/FxtSJRNyCcDynbPz7cfmnmsuPZdnz55Fb28vlpaWUFlZiba2NkpvLgQK2SedKjjiXn+hkx5sEbJsfVSvFZSCshKKEtlkcjJxx8nf9Ho9JiYm8Pzzz1M6y8svvwyVSoX9+/dDq132ornttttgMpkQiUQQDodx8eJFfO9738Pjjz+O++67D52dnfj+97+P2dlZJBIJWK1WNDU10eBuvcC3CTCbzYjFYrSXgtCH+Og6mTLqDMNQZczh4WGqbMZGWVkZqqqqIBQKqUHk/Pw8rZzJ5XLcdttt2LlzJ/7whz9Q81N2YJdIJNDR0UGDYHIO+/fvx/Hjx9HT04NgMIhoNEr7x4h6o8PhSOpte+edd/Dtb38bO3fuTKKvrMYUu4QS1gJ8G0f2e0pELr761a/S/85VOQNy28TygVhbmM1maljs9/t5ezzZdGRgmVY8OzuLn//852hsbEQ4HKbVadLD293dDZvNhkgkQoWBLBYLzp8/Tz/PRSAQgFQqxbZt23Dw4EFYLBZ0dXVRKmIikcDc3Fxar0oAOHXqFHp7ewEsJ2aI8IdQKIRSqeRVlc0H3GfBNWsmaxRJFgKZk5BGoxEXL16EzWZbMe9Go1H85S9/oWbfYrEYoVAIQqFwxWevFdGidMmEtQjAcjmHbEDWYz5xqWy++9Zbb2Fubo5SAgsl7kFQyF5K0jtGWCh89kVrUZklCZdMPqrXIkpBWQlFgXTZxVSLRzruOF8V7Z133oHH40E0GoXD4cAzzzyDkZER2qi+b98+nDlzBn6/H6FQCNFoFE6nE8eOHcPk5CSMRiPm5uboYhcIBPDKK69gz54969ZkyrcJMJvNlP4glUqhVCqpx1djY+MKug53kiY9V6Tvore3FxaLBZOTk1hYWEAoFIJYLIZQKEQ8HodUKsWOHTvwN3/zN2huboZWq8WpU6fQ39+PyclJiEQi6HQ6PP744wiHwzh37hxmZmbofRQIBPR/S0tLKxQwtVotjh49CovFgpGREUpnksvlqKiooL1k7ADP6/XimWeewS233EI3h6RatmnTJphMJsTjcbS3twO4Ngy9S/jrQKrNc2dnJ55//nlIJBKqcqbValP2p1mtVhgMBkxNTSUFcVyYTCZcuXIFXq8XAFJKr1dVVUGn08HpdCIej2NxcRFCoZBK5KtUKmi1Wtx2222QSqW0L6q2thbz8/MYHh6GVCqFSCTCwMAApZUDoNUwMifE43GEQiGUl5fjwIEDAEBpzRqNBqOjo7BYLLDb7VRY6OzZs7DZbDCbzZBKpWhubobJZILdbqeV+JaWFgSDQWg0GjQ0NNAkE1tVdrXPjFTi2c9Cr9fzBsaEVplJjY6o95Hf5kM0GkU8HqdzMrnHzc3NSfNaMYsWsdf/dMmEq8VaWG0/Wb6BLxEbi0QiEIlEqK+vL/jadLX6pNl7sTfeeANAYT1b2T6q+Y7fYkIpKCth3ZEpu5hqsKXLxvBV0b7xjW/gySefxPT0NCKRCGw2G9577z2qiGQwGPDjH/8Yr7zyCk6fPo2FhQUkEgksLS3h5MmT0Ol0K7LH09PTST1qVxvc69Tr9TRrGo1GwTAMGIZBMBiE2+2m/Qtnz57F3Nwc9u3bl9SoS3qunE4n7HY7JBIJPB4PhEIhXSjKyspQX1+Pbdu2weVyQSQSoampCffeey+9B7W1tTh06BAWFxfpptBgMMBoNNKAKhwOQ6VSoa6uDktLS2AYJuXkajAY8L3vfQ9PPvkk3G431Go1vvnNb6K8vBxvvPEGOjo6koIyYFl0xGQyoby8HH6/H3V1dXA6nRgaGsL8/DyCwSDNQl4vWbb1wrXSZ3ktwGq1wmw2Y3x8HPPz83j11VchFovx6quv0iCEfI6vIsYwTJLIAHnvU73jZWVllMoHLAdGMpkMZWVlNDiSy+XYtWsX7rrrLtTX1+OZZ57B6OgoAoEAxGIxlEoltm7dymtMrNUuCyqRRM+JEycAfOJRKJPJcMstt6C2thbnz5+nIgexWIz6lX3hC1+gv0EoyB6Ph7IaEokEFUIZGhqCSCSC0WhELBaDUCiESqVacX7AcvKPrSpLqpO5vMNcrzQ2G4FUwdhjo6+vL+vgyGq1YnJyMol1wAeGYSAQCCAWixEOhyGTyVawAIpVtIgvkFnvPt7VVnryDXzdbjftG5RIJGsi817IKhaRpff7/byqnjabDSdPnqTV6kxqrbmgWN/nfFEKykpYd/AFFtkOtlSZs1RVNFIhI70ZXOoMWUR37dqFY8eO0QkyFAphcnJyBX0nHA7DbDZf1SxNuqwiOxMVCoWg0WjQ0tKC0dFRqlT4+uuv4y9/+QsikQjeeOMN7NixgzbqbtmyBQMDAxgfH4ff7wfDMFSKV6/Xw+v10mDqS1/6Usrm8VTPJRwOo6GhAYuLi2AYBnfddRfuvvtuqtKW7nmzRWDY/RdarRaXLl2C1WqllMRNmzYBAJaWlhAOh+HxeBAMBum7RqoB8/PzsNvtafvrSkgPo9GIEydOwOFwoLa2Fvfee29GgZ4SVoK8dxaLBWNjY1hYWMDc3Bw+/vhj6tnIFjFKRRUi7zgZwz6fb0Wyg/2O+/1+lJeX07lQrVbj1ltvRWtrKy5duoQLFy6AYRg0NDTg4MGDMBqNsNvt8Pl8EIvFuOmmm3DfffetEKhgg8wHb7zxBjweDzWdVqlUuOeee3D48GEAwMmTJ3H+/HlMT0+v8EDkUpCB5Yo4GdsikQhLS0uIRCLwer0oLy+HUqlES0sL9uzZk5Q0ImBTtNlzUC4m3OzqGFcNktvPBeS2mWQYBhMTE/D5fLx9ukKhEI2NjdixYwetRopEIhrwsqumfGqz6zHXcY/JF8js3r17XeeO1d6XfAMFtVoNtVqNSCSCDRs2YPPmzTl9P1sUquKYSXmRzGdE+dhisRRkr5QPRbTYUQrKSlh38AUW3OxiriAbFTZ3HwD279+PkZERvPfeewiHw1TZiIv77rsPg4ODeO655+jfUin82Gw2LC4u5nyOq0E2WUWtdqXSFmkWHxsbw/nz5+H1ehGLxWC322lWddOmTVCr1bSBlhxDo9HQ++RwOJJ43Lkarup0OpSVldFeEo/Hg9ra2qwzd3z9FwaDAT/84Q8ptauhoQGtra1gGAanT5+mwSkABINBzMzMQCAQ0Mzz3NwczSpfK30XxQKbzYYTJ07gvffeo31AIyMj2L59O5qamkr3L0uwJbRJQOb3+yEWi+F2u6nfn1gsRllZGf7hH/4hbRKEO4bT9ZCSBIZYLIZUKsX999+PI0eO0IpdVVVVkjnrqVOn4HK5KGXu85//PL7yla9kvD6j0UgrXEqlEs3NzTh69GjSeH744YfR2tqKrq4uWK1WSolkg9vvypbdDwQCCIVC9H41NDRAr9fzBmTs+8atXrFtM/i+l6k6xv0Od5Of7Xxns9kQCoUQiUSS6N4SiQQ1NTXYsmULvvvd76K2thYvvfQSZDIZpqamqHcTqZryGWSvx1zHd8xiq3is5r6wn28+VSiDwYA777wTw8PDvO9+IbDWyots6HQ6NDY2wmq1IhwOZ22enem41/MaXQrKSlh3pGvgzRd83H2y8APpOc5VVVUQi8UraHFcBAIBDA4OJqk0rhWyzSryZcIGBwepAAihJtXV1aG5uRnAyoCYYZgVWSir1Zo3j3t2dhYulwuxWAx9fX2UnpTP825ra4Ner18RiJJAmmz8urq64HQ6YbVaqXJjTU0N/Vwx910UI4xGIyYnJxEKhRCPxxEOh7G4uIiRkRFqyF66f5lBAqALFy4gHA4jEolAqVRCKpVCIBAgEokgHo8jGo1i48aNKC8vT/lb3KCFbwyz33HSPxuPx6n0PMBvzmqz2agqo0AggFqtzpjNJ5uoixcvwm63QygUQqfTUe8sdg8pmbe0Wm3aJn72/MZWViWm8tFoFFVVVbjrrruyUr5jBwZ8thnc77PvYarqGPf6uZvIbMcFESZJJBIQiUQoLy/H5s2b8aUvfYkKVQHgfebp5rP1mOtSrV/rTVfMdI6ZAudUgW+u0GqXzdrXSsCi0MqLhGZ944038iatyfXY7XZ4PB5q8J4Prvc1uhSUlVAU4C5SqbI5uWR5cpGE5kNNTQ0UCgUWFhYALFNF2JsWgkgkQuWH1wpsQ9ZUWcV094YtXXvTTTdh3759UCqV2LdvHy8FMdX94etfyaUPw2q1UhpWPB6nMveFAPu5chcfrXbZs6yqqgpbt26lPXZlZWUIhUK0UlZsWdtiBJFP9/v9GBoawtLSEuRyOWKxGFQqFZRKJaLRKOx2O3p7e9fUA+paBxmzi4uLsNvtdG7ZunUr6urqcOjQIYTDYbjdbvT09ODChQtYWFhIeV/Zc0CqjSH3HSeKpvF4HIFAAD09PXSDxc2C//KXv6Q9W1VVVdi+fXtKIQIuvY+M8w0bNqCxsREjIyMYGBjg3RySJv6pqamMwT2b1vj666/TfjSXy4X6+vqs3j12IJuNbUYukt/5bCINBgNaW1vR09MDv98PhUIBqVSK6upqSsHn3gcuUs1naznXpVqLUh2zUHS6QiDX+1LoIGEtBSwKea4Mw9BWALvdnlLJOBwOQ6lUwuVy8fad5YrrWQ4fKAVlJRQhUmVzcs3y5Lvo7N+/HwMDAzCZTBCLxbj99tsxNjaG4eFhSikimJmZQV9fHxiGoYtloTajfEqS3Ow3m/7EVj4j4N4L9j3NJUOZijqUbdZNp9OhoqKCGk2LRCJMTk7i+eefT+nTk8t9InRVt9uNixcvwu12w+/3o729HSKRCAqFAhqNBs3NzbRHsK6ujm5Cc6UWFUt292qhs7MTTz75JOx2OzVnLysrw0033YRPfepT2LVrF9xuNzo6OuB2u+F0OtHR0ZGTT89fC9jjmhjF+nw+aLXaFRUem82G7u5uhEIhBINBWu3lJqyymR+57/h7772XpHq4sLBAK05+vx8ajQYmkwmjo6N47rnn4Ha7IRKJsH37dhw9ehRarRZGozGp15OP3tfS0gKFQkFZCukCHyK1HQqFsg7utVottm7divfffx9er5cGddmCHdyRnjVu4omcayaaWrZqgtmc06OPPooDBw7A7XZjbm6OKkZye2FTfT/VeXL/G4BVCZ1wke49zGV+XS/keo6FDm7XMlgu5G+Hw2HU1dXRinKq9o5MfWe5Qqu9fuXwgVJQVkIRIlU2J9csDxm8XF+fbEEWRPbk/NJLL2F6eho+nw+hUAgAqJLgv/7rv8LpdCIWi0Eul+POO+/EsWPH8l7gOjo6YDababNsOBxekQU3Go3o7+/H6OgoxGIx/H5/kiIk30KzWioD2bzkoiLG/u7BgwcxOjqKxcVFKJVKvPXWW3TSPn78eM6BGbtf5eLFizRYIP8fi8UQCoWoweWBAwfQ0tKCc+fOUeXIXGSXbTYburq6kr5/vfHa+WA0GvHf//3fmJqaoop38XgcSqUSmzdvxkMPPUTfKyJNbrfb8e6778JkMq1IFPy1gz2fEdGL6upqXrNZ0pMRjUYhk8kglUp5m+qzHY/sd5wI+hDfv0AggEQigQ0bNtAseF9fHyYnJ+H1eqkXoEajgcFgoIH64uIitFotHnzwQQwNDWFkZATz8/O48cYbsXfvXkQiEWqCbTQaeT0T2efX2tqK4eFhzM3N8QahfNDr9QCWFSSJwEmuSJV4ItQrNq2RrxpZaDVBPgYAUYzMZoPL/T63/ziftYB9zVyhmVTvYTFVxdjguzfZfraQgSb5PW4/fCFQyHPV6XRoampK+y7abDacOnUKNpsNKpWKt+9sNbhe5fCBUlBWQhEhE0Uv1ywPaUQltL3VZFO4k7NarUY8Hqcmq2q1GrfddhvefvvtpExRKBTCn/70J+zZsydjE3y68+dKW/NdNwkSSKAjFAoxNja2gvLDvZZ8qQyrzbodOHCA+gz5/X5MT09TU1iTyZT1xt1ms+Hs2bPo7u6Gx+PB9PQ03TRKJBJK9amqqqJy+yTz/cEHH1AVyba2NgwODuJXv/oV9u7dm7Y30Gaz4ac//Sk6Ozvh8/kgk8kwPz8PvV5/3SwKfCCCHlNTU1RmnHg/VVVV4dOf/vSKBEBHRwfeffddXL58GRKJBEDJeiBdBSWVkpjNZkN/fz8mJiYgkUhQXl6OQ4cOZaTUZTse2ZL4xH9sZmYGmzdvRkNDAwDQhFA0GqUiOUajEY888gg+/PBDTE5OAlhWM33yySchkUgorZUcAwDMZjMAUCVYrmciG1qtlkrep6NHseFwOGhPXiwWw0svvYQ777xz1fM+STyNjY0BAGQyGRUISqVmuZZqgvkkGtMFXvmsBXxMjmuNAp5LUJpvj2C2yNbLLlcU6lyzeReNRiO12NFoNLDZbHkn5mw2G+bm5tImda5llIKyEooC2VD0cs3yrEVDKMMwVLlQKBSiubkZFouFt3QfjUbxzDPP4Oabb17VRMTuA6urq8Pu3bt5aWBnz55Fb28v/H4/EokExGIx3QSnQ75UhkzPIxW9T6v9xLdocXERTz/9NEQiETQaDXbu3JnVsYkM+0cffYSlpSW6WSW9TaFQCIFAAPPz85ifn8e2bdtw8OBB6PV6tLe344MPPkAkEsG2bdvw61//Gp2dnQgGg3jttdfw05/+lDcw6+zsxLPPPosPP/yQbmLD4TDGxsbw2muvQavVXreVIKPRiNHRUfj9fshkMqjVashkMtjtdthsNrzwwgtoaWlJsik4ePAgTCYTJBIJwuEwQqHQdZXRzBWpKigkI84XsBKfn66uLrhcLojFYuj1elRUVKz43GolokdHR2n1C1h+p0dHR6FUKvEv//IvdG4bGxuDVCpFNBpFLBbD5OQkrFZrEo07Ho/D7XbT/ltChySy/iqVCjKZDB6PByqVKq0PU7b0KDbMZjNlMMTjcVy6dAmnTp3CI488ktW94ILQKIPBIMLADC3AAAAgAElEQVThMAKBANRqNRQKBXQ6Ha3Ss4VB1jIoySfRmG495AqdzM3NJQmw8J1HqiA0HA4XPUWRi1z2CldDaGItj1Fo9cV8kt6rOSabFp0uqXOtohSUlVAU4JvY+aghuWR51oKbPTU1lWTUKRKJMDs7m/LzFosFP/jBD/DjH/845w07m4vd1NTEG5B1dnbihRdegNPppNSmTZs24Y477sh4vELQJFI9j0yZR/b3+LzH0oFUbchGiJhRb9++HVVVVXj33XfhdDrp5iwQCMDn88Hj8UAkEsHhcFDT26GhIbjdbhpkzc3N4dSpUyuCss7OTnzve9+D0+lM2oQmEgksLi5iaGgI7e3tAHDd+aYQMAwDuVwOhmEwNzeXZDgcjUbR3d2d9Py0Wi2OHDkCv98Pj8dTEDnkaxl8Gy2dTpc2I261Lvv8eL1eRKNRKBQKqFSqFQI/+VDPvF7vCmPieDwOs9lMEw0dHR1wOBwrlGjZY4ENMj/KZDJqQB+LxSCRSFBdXQ273Z6xV4zMfyqVKqt3x2azUaN7gmg0ij/+8Y/Ys2fPqhImhEZpsVjgcDhoTx+wXJU7deoUTCYT9UUD1jYoyWeznm49ZK8Fvb296OrqSlmhyaYyVqwUxVRId2+4QczVEINaq2MUWn0x07toMBiwY8cOjI2Nobm5Oe+kJVf1tKam5pp6z7JBKSgroSiwFpPQWnCz6+vrIRKJIJFIwDAMDAYDpeSkgs1my4mWR76TyQPEaDTiJz/5Ca3UicViVFVV4YEHHkhr5MrFWtAk2HK5fr+fV5SALHR83mPpYDQaqeofsEwp3bVrF44ePQqbzYa+vr4khcxEIgGv1wuTyUQ3nwzDQCKRIJFI0E0WgUgkWnHMnp4eLCws8G5CQ6EQNaH+2c9+hoWFBTQ3N2P37t0YGhrKSIm8FkBU4CwWCy5cuJAUkAHL92BmZmZFdr22thZ1dXXweDyw2+1wOBzX3SKaLfjmuEwbG4ZhIJPJUF1djUQigZaWFhw5cgTAJ6IM+WbVVSoVBALBindbLBZjcHAQP/7xj2EymZK8/TJBqVTi9ttvRzweh0wmo9L3Go0G5eXlEIvFcLlctNrGt/H/1a9+hd7eXgBAdXV1xmNarctGyTfccAOmp6cRi8UgEAjgcrnQ3t6+qkw+EW1qbGwEANjtdgDL9Mvu7m709vZSmX8iZMAwzJpVifJRnsvEbNBql/u2fT5f0rvkcDjQ3d2N6upqHDhw4LqojHGR6t6kCmLW+nrXqq+skBW4bPdsJJFUCDn86115ESgFZSUUEW644QYwDIN9+/YVdKIrZNBRUVGBG2+8kU6Wb7/9NlQqFaamplJmjaPRKOrr63M6Dpu6uGnTJl7qjslkgs/no9lrsViM5uZmtLS0FITSkg/SyeXmk62z2WwYGBigNgVSqRSf+9zn8K1vfQta7bJpdnNzM1wuF9xuN+LxOIRCIYRCYVKWn5hXczejDMNg69atK467d+9e/O53v6PVNy78fj8uXbpE5cUvXbqEV199FQKBAL/73e/w8MMP4/Dhw9fkhgX4xG/m9OnTeO+991b890Qigb6+Ppw8eRIPP/xwUq+K0+nElStXYLPZVr05vlaRSRDA4XCkVCYjiRmRSIT6+nps2bIFhw4dokbBherhKSsrg1wuh9/vT/r7wsICjh07lvKdT4ebbroJ//Ef/5EkliGXyzE5OYmJiQlMT09DIBBgcHAQi4uLK75/9uxZdHV10XPq7+/P6HlHNmx33HEH/H4/xsfH4XK5AHwiBrTaHiyFQoGDBw9S/726ujrU1NQAAO2t3LNnD/bt25ezIm0uIH08q1WeY1ew2IqZxBaFYRgoFAqUlZVBoVDAYrHgqaeegtPphEQiQUdHBx588MGkz1yLlTGCTOIeqdbHq3W9hU6YFjL5nU1wSuZ/Moby3V/k+/5fCygFZSWsO0jfBMmKzs/PF2ygFTro0Ol0iMfjCIVCiMViuHz5MsrLy7Fx40ZIJBJYrdYVFB+lUrmiByQTspGR3blzJ+rq6hCNRhEOh7Ft2zbo9fqcJtq1okmk6wdZ7TMxGo1ob2+nnnBSqRR1dXX4zGc+k9R3ePToUQQCAfT398Pv90MoFEIikVCBFi7EYjGEQiE11TabzSsqPm1tbTh27Bi+//3vJ/XfEMRiMSpWwkYikYDH40F7ezv8fn9RqzSm6zWw2Wz4n//5H3R2dvJefzQaxczMDIaHh1f0qkil0r/KvrJMggCZquGk2jwyMkLNmjs7O6HX6wtaqdi3bx/OnDmDqakpBAIBCAQCmrTItjLGhkQiwZe//OWka62trcWrr76KixcvYmpqigqGxGIxXtn6iYmJpDljaWkpo58hd5PocDhw4sQJjI2NwePx5OSZx1W93bRpE5qbm7F//34avNhsNuzYsQNOpxONjY249957U85thbTPyFd5jggkvfzyy1hYWEBlZSVNRCkUCvh8PgSDQZhMJrz55puYnZ2l70F/fz8EAgEUCgU8Hg98Pl9e15Lq/EjScy37hfjGJ4Ck53Q1qIqpsBYJ00JX+djJN/a/E2TrZZYLbDYbXC4XPB7Pdae8CJSCshKKAKRvgih8WSyWgg20tZhUiWR0IpFALBaD1+uFXC7HXXfdhf7+foyMjCR9vqKiIqfjZkNdBJYXrOPHj8NkMqG+vp4eJ5f7tlZUjHRyuatR0TQajXjttdcwNDSEYDCIyspKVFVV8fahGQwG3H777TAajUgkEohGo/R5EQiFQiQSCRrYLS4uYmFhAX6/HwMDA7xZ9fvuuw8vvvgiTCZTzvfD7XbDbDYX7QJCVCXPnDkDn8+HxsZG/NM//ROlwRqNRvT09NDKAx/8fj+cTmfSwsvtKysrK6MiAgAK/t4VEzJtqjJVwxmGweTkJObm5pBIJMAwDDQaDfR6fUF7eAwGA6UolpWV4e2338Y777zDG3xnAsMwuP/++3Hfffcl/V2r1aK5uTlpDJKgjI9F0NjYCIVCgYWFBdozOjIysiJZkkrinRzznnvuwSuvvILZ2VlYLJaM1TK2cAfJ8LNVb8l304kNcJ9NIft4gPx8n8i5vPfee7BYLIhGowgGgxCJRFShNhgMYnR0FAsLC3TuJAiFQtQ8PBwOIxgMoqurCy0tLQUZx0TAaWhoCIlEAq2trXj00UfXZH7gjk+j0UgrU1eTqpgKa7F3KbS3ZqZ3ezViPZmORyjDAGil9npCKSgrYd2h0+nQ2NhIB1pjY2PKgbaaSUWv10Ov1xck62a1WqFQKGhlBVhuivd4PDh//jzt7SLVMrlcjrvvvjun47L7sW688ca0E1mu/Vh8IBsZ0o9VSK+VVOqLuRg0v/TSS7h48SKsViuCwSBkMhm2b9+Oe+65J+mZst8NvV4PkUhENxTkWQkEAmp4LJFI0NDQgIaGBnR3dycF2d3d3RgbG8O+ffuS7u+3v/1tPPLII7SfjQ98/TlEIjyTqlk+6OzsRE9PD+1hI1YJFy9ehFKpRGtra5JJ9uDgIObm5rBv3z6cOHECb775Jv2t0dFR/PCHP4TRaMSjjz4KABkXVDIOSMBFkgUjIyOYmZlBNBrF8PAw/H4/rYqzFeuuB4GUXEyDM/13m82W5IdIhD7ImC/k5oo9j3g8nqR3IR2I2BGZ926//faUSoekL/Hdd9+l5s7btm3jZREcOHAARqMR58+fRyAQQE1NDXw+H4xGI71uABmDHYPBQDdxmYRF2HON3W6HTCbDxo0bqeotsNzHNzc3l1JsgG9uW42fYypkm7Dj+57RaMTY2BhVqJRIJJDL5ZTyDSxvcu12O+LxOJ0zif2FQCCATCZL6rkNh8M4d+4c9Z1rbW3NeZ0lY8ZiseDkyZOwWq0IhUIQi8Xo7e3NmXaaLbiKk+TekOTw1aYqclHovrJCJweAzIknnU4HjUYDn89XEKEn0vMolUqhVqvTqrdeqygFZSWsO7TaZYn01tZWAKkpC7lOKtzPF0KufHFxEXNzc5DL5ZQOR6oxc3NzqK6uxvbt2+FwOFBWVoY77rgDhw8fzukYa1Hyz4S1mLDTLWbZLHRsCpHb7YZEIkFTUxOtvrCfJ/f8iW/YwsICtS8QCoWQyWSoqqpCTU0Nlc0fGxtDLBaDSCSiG4433ngDiUQCZ86cSVLObGtrw49+9CM8++yzmJ+fh9PppNQeoVCIiooKbN++HVqtFufOnaOGvGTRP378OKqqqvDYY48VVPzj2LFjeOGFFxCNRvHSSy/hH//xH/HnP/856dra29upGh4Aquj385//nLcqEolEMDg4SBfabConbrcbnZ2dGBgYgMfjQTQaRSgUQiQSgVAohFqtRiQSobQnv98Pv9+P9vZ2iESiVW/sigG5mAZnK2EvEAioBL3T6aT3bS03imfPns3qczKZDH/3d3+HaDSKiYkJaDQafPe730075h999FEYDIaUxu3cz7Ll5hUKRZL0PJfGyRfsaLWfqCe63W74fL6UQRHZYBKapEqlQlNTE3bu3LniPDIZX3M3pnzVs9UE1dn0GnPBbg+IRCIoKytDU1MTbr75ZjQ0NGDfvn20p4zQPgOBAEwmE6LRKCoqKlBXV4f5+XkkEgnccsstKCsrg9PppEHa6OgoQqEQLBYLBgcHM77XbE/Szs5ODA4O4vz580n9hZFIBAKBIOt7kytIf1J3dzcmJyd5K6PFgEL1la0FHTLbal4wGCyI0BP3eNejBU0pKCuhKKDVLje9m0ymlAM310ml0JOQzWbD6dOnsbS0BJFIhKqqKvj9foTDYQiFQjAMg+bmZtxzzz3QarWrzvyTkr/H44FEIimI4WImXA3vlVxANriDg4OYmppCfX09GhoaaGaMqLmRCh+7/wNYro7W1NRAIpFAIBBApVJh7969uOmmm6DX62E0GmlGnAQMpHldJpPB7XYjFovRd5J9/xsbG7Fjxw4MDg4iHo8jEolALBajvr4elZWV+OpXv0orVa+++ir+/Oc/U3GDRCKBmZkZPPbYY/iv//qvnCmnpB9kbm4Oer0e4XAYZ86cwe9//3tanQsGg3juueeSvheLxRCLxVYoTWbChg0boNPp0N7eTsVV2GAYJmljGI1G6blwq4XxeBzBYBBarZaaEvt8PuorNz09jWAwiOHhYZw5c2ZF4F2MWI1pcKYECPlNrVaL+vp6WK1WxONxqmK61mOTSLungkAgAMMwqK+vx0MPPZS0oee7Vi698Ctf+QpV8Uv37pPPk6rg2NgYOjo6aNDER+Pkg8FgwODgIMxmc0rFNrYhbUtLC/3/hYUF/Pu//ztmZmaQSCSgVqvR0tKCAwcOoKamJquxy62eAZkrfKlAfNPYIhuZ0NXVhZ6eHszOzkIikUCj0SRV/0g/ELGg0Wq1+M///E86z9TU1GBgYACJRAIqlQp33303fSYkqPL5fLDb7fB4PDCbzSmTLFxfNzL2x8bGVii6CgQC+vzXAqTqSNYBqVSK+vr6lH6g64H1UEvMBZmqeUTow2KxgGGYvIWeSCC9GvP0awWloKyEooDRaMQTTzxBnd+PHz++YjLOdVIp9CREaBXxeByVlZXYvHkzBAIBrFYrlas+evRo3ouITqdDWVkZAoEArly5gq6urjWvHBSb1KzRaER/fz/GxsYALGetDx8+zKs819nZCbPZnJTlZBgGoVCI0hVramrw0EMPwWAwwGazIRwOQ6vVYmBgAGNjY6isrATDMGhoaEA8HofL5UIoFIJIJILP50uiHJKNUTweh0KhoAG61+tFTU0NjEYjrdTde++9CIfD9B0hcDqdeOqpp9DS0pJ2Y8be1ALA0aNHYTKZkqhFXGGZQuG2227Dd77zHWi1Wnz00Ue8kuncoAxASnEIgUAAjUYDg8GAe++9FwBWbOwmJycxOjoKs9kMv9+PJ554omgX3mz8mviQbqPF/c3W1lZcvnwZLpcLEolkhUfZWuCzn/0s3nrrrZT/PZFI0GQHSRjlymzIpdJHPnf69OmkXpJsaZxkI8dVbAOS3z92j5hWq8WpU6fw5ptv0mq4VCpFdXU1zdDn8l6yr7eQdEYu2BWocDiMxcVF/Pa3v6X0YZVKha1bt9KALN3zIcIXNpsNIyMjUKlUaGxspNdOPltbW8sbaE1NTa2onrGDIEIPlUqlaG5uxsLCQpJYklgspsyIQoOdyCPHVKvVKf1A1wtXWy1xtUhVzdPpdIjFYojH4wgEAnkLPa2HYfXVRikoK6Eo0N3dDavVCq/XCwC8vl65TiqFnoSIeEUgEEAkEsGDDz5Iqy5A4ZSiHA4HxsbGKF0klfBEIZFq47IeEx5p5r1y5QoWFxchEAgwPz9Pgyn2hsZkMtEJuq6uDrt370Z9fT2ef/55jI2NIR6PQyKRQK1W0+oau0kfWKZhVVZWoqGhAU1NTTAYDHA6nVRS+9lnn8WlS5dw7NgxuhkhlCir1YpIJILZ2Vm6Sa2urk7qR3jggQcwPz+Pl19+OSlg8Xg8aTdm5FxJht/v96O/vz/pM6tRx8sEuVyO1tZWPPjgg/Se3fr/2fvy4DbrO/1Ht63DkmzLMoqPXHacOCZWKE1SSEOTdaFLuqVpN9l2YQuls5Qs02O30E7bKT+6S1tKWWZnt4F2C4QupSEzDaExFIfYxOTASUhkx3ccn7IVndZ9WOfvD8/3yyvp1WnZScHPTAYSW++l9/2+n+P5PM+mTTh9+nTc70UikbSzdcB8IkbMfPl8PjweD6ampgCANbB76aWXcO3aNUQiEYyMjCz6fZ8v2Lqz2aogput2MBM2n8+HFStWoLS0FJFIBCUlJdi7d++iX4+dO3dm/B2Px5ORzlrIKj+ZJSH3EikaZZvcEcXCmZkZeL1etLe349q1azAajTSJIJTA8vJyBINBTExMwOVyxc2j1tfXL5javRA6I7kOPp8viYrJXC9IouVyuTA7O4toNAqpVIpt27ZRy4pz586l9ZJMRCoKWmJHk617ZjQa6VqdSA8lVMf29nb83//9H6Uw8vl8hEKhgs/gpmNh3Gi06ULHMItBe870nMtkMohEIipUtJDEkjBcSCJ9vRk9i4HlpGwZ1x0GgwFTU1MIh8Pg8/mQy+Vobm5m/d1cFpVCKw2RxGVmZgYOhwOtra0oLy/H3XffveBtE+h0Ojz99NOYmJigtLqFKhZli4VKLRcCJNidmpqis2B8Pp/OQSUGNM3NzRgbG4NYLIZKpUJzczMOHjxIOy0CgQClpaVoaGhIopiJxWIA84mNSqWCWq1GS0sL+vv7MTU1RT2S/H4/3n33XWg0GnzjG98AMD87VVRUhNraWior7XQ6WecRNBoNvv3tb6OrqwtXr14F8KFAQjoFNSL4cv78eQQCgZyph7lCoVBAKpVSg9/W1lYqKPCpT30KlZWVtFMBIKlzxiZwQmiMAoGA2ga88847EAgE1FsO+LAb8sc//jHl9m8UsAW/hVBBBOI71kajEQaDAS6XCxKJBBs2bMjZWiNfJH7XbAiFQpSSxrbOFrLKTxLZubk5WK1WtLa2YmZmBhs3bkQ4HE4S5En1eZJYkJk2wswQiURxM2Imk4nOn0ajUXA4HKxcuRL/+I//WJDAOF86Y6pryiwSkCTL6/VCo9HQbr5KpYo7/mxnlwkFjXgNHjhwIElkiZxXqu4ZWatJAYNtdtRqteLNN9+E1+tFNBoFj8fLKlnMFWRdJSqSoVAIe/bsuWGp0uTcU8nO54pCx0XpnnNyzKWlpQsW5vg4KC8Cy0nZMm4AkAeX0M727du34AVyMYQrAFCvpcHBQZooFKqjRMx1iTqWSCSCVCqlL6/FxvX0ZAHm1QNfeukl+P1+6PV6cDgcyGQyVFdXY9OmTfQFnhjQMNHf34++vj5YrVaIxWLU1dXhi1/8Inbu3Em/I3KOwLzYBZfLhdPphMlkospmiZRAv9+P48eP0wpdb28vfD4fKisr8ZWvfAUrV66klCFyXEwlS0LvYEIul6edXyAeXxwOZ9GH3rdv34777rsvjoLkcDjofW6xWFBTUwOHw4G5ubmkhInH46G6uhpmsxnBYJAqWQKgvn4AqPDHoUOHMDQ0hP/3//4fvbf1ej2kUimkUintStwogRLb7BizO5sL5Umn02FiYoL6PDGDTnJ/t7W14eTJk+jt7UUgEIBCoSiIelm2x7dhw4aMSZnNZkNfXx+lD4tEIuzevTtuTrJQVX7SnSZqjF6vFx0dHXj33XfB4/GSBHlSfZ4IfvB4vLhEjClMAYCadt98881YtWoVxGJxxsQv1/PJh86YOFMDzIsSMSX8NRoNLRCtXr0a9957L6anp5PsQ4LBIORyOcxmM+RyecriH1mHBAIBAoEARkZGcOjQobTCE4ndM/L9p5s/FAqF8Pv9tPtP1pBC3/NCoRAulwtcLhfRaBQlJSVLVvjMB4WMZRZL0CvVnBexbygrK4NCoVjQvsi6yeVyUVFR8ZFUXgSWk7Jl3ABITAayoc+kAgmemLLFQOHa3MwXVCHNcEml0+FwIBqNorS0FCtWrEhKKBI/U+hOYCEleHOBTqfDv//7v2NmZgbAvLdbcXEx6urqcMcdd8QFvYkBDaHzWCwWqjhIlDE/+9nP0tmIxHPs6uqCz+dDKBSCXC6nHUKtVovKykpMTk7S5CwWi8FiseDkyZNQKBSwWCwIBALwer1444038Oijj9KAh+3F19bWlkT1U6vV9LwMBgOOHDmCgYEBVFVVYdOmTdBoNLj11lthMBgwNjYGYL47YbPZCnrtt2/fjkOHDgFAHAXp4MGD9D73+XwYHx+nFWxixh0KhRCLxSCRSFBcXIxPfepTEAqF8Pl8OHnyZMp9hsNhXLp0CT/72c+ooIdQKIRIJEJTUxMUCgXuv//+G+Klm2l2LJeELLHaCyDJJkGj0eDOO+9Ed3c3iouLAQArVqxYkiCEeXxMaw82BAIB/OIXv4BAIIDdbkc4HMaf//xnNDc3Y9u2bUlm2QuFVqtFTU0NtcYIh8P0+AwGAyvlPfHzTB8qrVbLmqyQJInQGXft2kVFMBYDudAZmTM1ZE2YmJhIkvBvbm7OKDQlFAqpD5zT6UzZKdNo5tVuyf4dDgfMZjN6e3sz0osTv/9U9wMR0QqHw+ByubQgt2PHjoJTF0mnXiqVoqqqKqUC6I2CQtOACx0XpZrzIv/ucDioQXy+YxGJ6yaZbfwoYjkpW8Z1BwmU29vbYbPZ8pZNTZwXSidbvJBjZZrhFqJ6zaRDOZ1OrF+/ngalqRaexeoEAoWT4M0FnZ2dMJvNmJuboxXMuro6NDY2pg16SSWOz+djaGgIExMTtNIajUYxMjKS5L2m0Who1c1ut6OqqipuiH/Xrl2orKzEs88+i5mZGXg8HkSjUQSDQerVQ6q6brcbg4ODOHDgAPbv349gMIgLFy7g1KlTtDuk0+kwPj4ed9wKhQJ79+6FTqdDe3s7Xn75ZQwODgIArQSq1WqoVCqqBAkA69evx8TEBH3JLRQPPvggfvrTn9K/swVNDocD4+Pj8Hg8tKIsFApRX18Pq9UKm82Gubk5uN1uSpH6j//4j4z7jkajGBoawvPPP4+6ujpKT8t07y81EgOZbGfHUm2L+OwUFxfD7Xajvb096VlLXGcaGhqW5Howj6+2thajo6Npf58pzADMX5vz589DKBQWPKDWaDTYv38/AMBsNtPCWyQSAYfDYTWhZtuGUCiks8BsgSRRYSz0uyPdMWVLZ2R6WCoUCqoUC3w4o5VqvUxM9HIx9tVqtZSSSLwPM/m+5QKmN5lYLKadyYUUaFPtx2g0wuPxoK6u7oZSWkyFQtOAC82GSZXokX83GAwIBAJJ89a57uOj7k9GsJyULeOGgMlkwp/+9CfajWBTX0wFnU6H73//+xgcHEQ0GkVZWRn+5m/+JifZ4lygVqupZP1CvDeYhp79/f20Wp7YGWL+fjr57UKc4/WSxi8vLwefz6dGpeXl5VAoFGnNUUl3aXh4GKOjowgGg3GVfaFQCI/Hg9deey0uuCFVN71ej2AwiPr6emzZsgUXLlyAWCxGf38/Tpw4QWdKioqK4HA4EIlEUFRUhOrqani9XgSDQXg8HrhcLgwMDOCZZ56By+XC5OQkDTDkcjmOHTuGvr6+uGOPxWJ4+eWXodfrYTab4/x5otEozGYzIpEIrFYrXC4X/H4/uFwuhoaGkgLhXCGTyaDRaLBv3z489NBD1GTaZrPFUbRIINbW1gaTyZQk7ODz+eB0Oum/O51OTE5O4siRI7h8+XLG4yDzHBMTEzCbzVAoFNRW4kaiE7EFMvl2gJjbIgITqZ41tVoNmUwGo9FI/ckWG6TIQToIHA4HJ06coDOa2cJkMhU0mWGufU888QT0ej0uXLiAI0eOwOl0YuPGjUnzdkwlwr6+PrzxxhsYHR1FLBZDbW0thEIhndMkFHpmUW/Xrl1LJvyQic5I/utyuegcmEAgoOa8mfz92Ip4RLgKABViyPYYLRYL7HY7LBYL2traFpzYMLvkJKFPxRDJB8x7Id8u9/VCoWnAhVZgTJXokTlOhUIBgUCAmpqavBPBxHXpRinYLQaWk7Jl3BDo7u6GxWKh1KxMVBQCnU6HL33pS7QrAczPOnR2dqKmpqag9BmCxMHnfLw3iKFnR0cHnS0Jh8MYHh6Gx+NJ4mbnK7+dK/LxwSkEiOcQoa8QI+d0wTmhIF69ejXp95RKJf7u7/4OgUCAtYJnsVjgcDgQCoWg0+lw7tw5OoRO5p4I5HI5KioqEIlEsGLFClRWVmJubg52u51aJNjtdrjdbrjdbkrp43K56O/vpxRJJhwOB95//31EIpGUKnbRaJSqLpJOmdPpzEsCn8fjQalUora2FmvXrqXb0ul0+M1vfoPOzk5Ws2xCpTt79ixGR0fh9/vB4XDA4/Go5DUw390TCASwWq04e/ZsxgCex+NBLpcjEAggFovRhLq0tPSGGeBmJgLZBjLpKMWJhtEulwutra2sYi86nQ5/+OM5Ha0AACAASURBVMMfcObMGXpfLbYSJaEbkXnWlpYWaLVaPProowCAH/7wh3j55Zez2lYhBUlSJRSvvfYaAoEAZDIZampq4q4fk30wNTUFk8kEi8VCn6PZ2VmoVCp6vwmFwjg1TaLCeD0C9sQgVygU0vOPRCIQiUSIxWJQqVRZFx5TeegxFXePHDmS1uKAgOn7ZjQa6dqZL6tCp9Ph4MGDcDgci9IlJ+/aiYkJrFy5Env27MnbQ/R6gcQxBoMhifmRz7aAwgmHAPPv78bGRtZ7hxQy8y1ysK1Lfy3fWz5YTsqWcUOgubkZKpUKAKiKXjb4n//5n7gAmsBoNOLll1/GsWPH8IMf/AAtLS0FO9ZCzJXp9Xr09PRAr9fTQAGYr553dXXhm9/8Jp5//vk4EYRCUahuRASDQaxduxaRSITSBM1mM0ZGRtKeXygUSkpSOBwOvvrVr+L+++/HK6+8AiC5gkeMlP1+P3w+H8LhcEp5eafTidraWnziE5/A1q1bIRQK0drairVr14LP58NoNMLv97P6dbEZLgMfmjmnE+9QKpVUdIN5vrmCy+VCJBIhEolgZmYGVqsVZWVlAOZpo+fPn4fH46HG1okFEY1m3m+tv7+fytWHQqG4504oFKK4uBgWiyXlOTOPR6FQwOPxIBwOU9l8ImhxI1BT2BKBTHNFbIUTpngEswuzbt06DA8PY25uDpFIBGKxmM5ymkwm/OhHP8L4+DiVgbfb7ZSmtljnm5iUJN7PjzzyCGZmZvDee++lLZbw+Xxa/Fis7r3VakVvby/cbjdkMhkVkAAQR50ithZOpzNpnSX0vXXr1uHIkSMYGhqC1+tdUEW/EEjsZjDPn8/nY2pqCrFYDE6nk4ppZEKqbgZR3J2ensbo6Cj1FEuXYJHja2trowlZvmq9BoMBzzzzDHp6esDlcrFt27aCd8lJ8Y4IoWzdurWgislLhUKNLCymcAjzXiSUQ+a6lc9+mMJKbOvSRw3LSdkybghotVo8/vjjVMEn20rZxYsXU/7MYrHA6/XipZdeoma+hUAh5sqqq6upTDgbTCYT/vd//xf19fXYsWNHQSlU6ZDOB2cxwaTSjI6OwmQyYWJiAuPj4xgeHqbeOkxotVrU1dXBZDLF0f8A0ICFLXHVaDT49Kc/jYsXL9JOVyZlQ5lMhocffhj9/f14/vnn4fV6UV1dja1bt+LEiROYnZ3N+9xJx4iZ5ESjUUxNTYHH4+W93aKiImrcGQwGIRQKEYvFIBQKoVAoIJFI0NvbSwVI+Hw+ZDJZUkHEYDBgenoaxcXFtAuYeN9yOByYTKasunjESJSoNDL/HcANQU0hfjgkoMjmOUj0GDt48CB4PB4qKyvR2NgIo9GIkZERBAIBdHd3IxAIgMvlYm5uDv39/ZBIJGhqasK1a9cwNDREgw8+nw+lUgmlUrko55pO4p8JjUaDn//85+jo6EBvby/6+/thtVqhVCpx5coVWsm+9dZbCyqewLb2jYyMUOn0cDhMPbeYnTRiLQCAGteSe4zD4UCpVKKoqAi9vb04d+4cHA4H5HI5GhoalsQPLh0S13ZyLiMjIwiFQlSciHg3kvUtVac21VpIrhNJWAglP9P9TjroJCGTSCRJgjXZoKOjA93d3XA6nRAIBPB4PDdEl/xGRKFGC5ZKOIR8j7Ozs/B4PHnPHxLqYjr7mI8SlpOyZdwwUKvV2LBhA9Rqddaf8fv9KX8mEAjA4XAgEAhyWniY/PNUNIeFzntoNBp89rOfxcWLF1N2Pzo6OnDy5ElKKVuKztj1ksVnVl+JNxPpQpKOYuI5k8F/r9eLrq4umtRwOBx4vV5K82DrcOzatYtWUInkvNPppJ2LxK7Z2rVr0d/fj+9///swm80AgPHxcdx+++1Qq9WIRqOwWq05mznzeDzcc889cDgcePPNN+OSmoVUBIuLi2n3gCRQkUgETU1NqKmpgVqtxvHjx3H58mUqt69UKvH5z3+eGkaTIO+5557DBx98gPHx8SQpfCLKku45ZAOhLTIRDoeTkuvrgXz9cNjmxQglltBz9Xo9bDYbIpEIeDwehEIhpSe63W7Mzs5S2wFgPiCpra3F7bffvmjJai4S/+Q5TYROp0N3dzeqqqriJPEXArLN5ubmJBn44eFhcDgcSvHV6/VwOBzw+Xx0xomslxcuXMCxY8dgNptpAYJ4dw0MDCAUCsHn84HD4dCZxuvdqWWCuTb6/X64XC5IpVIIhcI4H7CWlhaqgsfWAWEr4pFtJ3qKZWNonfhZNsEaJpjbM5lM6O7uxtDQUFyivBiFRq1WS+0Q/ppV+wr1bl5K4RC32425uTkEAgFYLJacE8CPG3URWE7KlnGDIN+WulQqTZIaB+aDaJlMBi6XmzRvkGr/JBEjvjvkWFavXo2WlhYYDAYA84t8e3s7urq64Ha7EQgEcpr30Ol0OHToEE6cOMGakBETXo/HAw6HQ+WeH3jggSVZkNLxwxcTpPra0dFBO1exWAxWqzUljZHMvDz55JM4f/48TUB0Oh3C4TD16WELRr73ve9RyhihJE5NTeHatWtU8h6YT5wikQief/55mM1mmkx4PB68++67qK2txYYNG9Db20s7ZkKhEBwOB8FgMC5REwgEUKvVMBgMiEajlFbI5XILapYcCoUwPT0d19Ei8252ux0dHR2Ynp6Ok/y32+145ZVXcOrUKaxduxarV6/GwMAAzp8/D4vFwtoFEwgECIVCOSejqX7f5XIVfHYqnbw4W/ElXz8cZjeCrCNisZjSejQaDUZGRmCxWMDj8dDU1AStVku7cqRDTWitPB4P9fX1+Na3vrVozyKb2mA+4gdarbYgwS75TlwuF37961/DYrHQ7hUwz35obGyE1+uFQCCAWCxGcXExBAIBioqKkmacqqur0draimg0SmdDvV4vPB4PZmZmqHiPVCpFRUVFTiyNpQSzMwXMP9+bNm3C2NgY7VJ0d3fn1QEhiRDTUwzIztCaUEW9Xi/tELMJf5C5sbm5OYjFYpw7dw5msxlCoZD+EQgEuHbtGnQ6XcG+A3I//TXOkSUicX3JdyasUGIfiTOyidvS6/Xg8XjgcrlUyCXXBJCpNrpmzZqPPHURWE7KlnEDIHGeAcjuhWIwGNDQ0JBkclpVVYX9+/fjxIkTWSnVMRNCUuEeHR2lFD4AOHDgACYmJhAKhVBVVQWXywWr1UpnE7KFTqfDY489FlchTASHw0n6U1VVBZ1Oh87OTvj9fszMzGBmZgZmsxkWiwV8Ph9f/vKX4+TNc0U6fvhSQqVSQSqVAphPiObm5vDGG2/AYDCwBgdarRZcLjfOrNhgMNCgLdW9xFaVffzxx2EymeK+Ux6Ph6GhIRgMhqTEidwrzASIz+ejvr6eVoStViv4fD5EIhFuvvlmtLW10e/e6/Xi1VdfpYlfoZCKRnjq1CkA7EkR8UBzuVwYHByk83rpkkUulwuJRAK32530M5FIhKKiIrjd7rj9kaIDG/x+f8FktoFkmwySYJFuGKFtMYsvC/HDYd5TiUa5er0eMpkMUqkUSqUSu3btwt133027tna7HadPn8bZs2cRDAZRUVGB733vewWdh2WCGSgT0YilLsQAyQUxo9FI/zgcDgQCAdpVBD7sOpK1v7KyEiqVCmVlZRgaGoqbcQJA5/II3ZzMkAIfzltqtVrcfvvti0YRLQQ0mnmT3pmZGTgcDvT09EAmk0GlUlFlurGxsbxFmpj3bqICpE6nSxnEM6mibMIfBoMBBw4cQFdXF4LBIIqKimC1WgHMK7jyeDwUFxcjEongypUreQlnJYIoGzO7f0tl77KYIMe/0JmwhXYksymiJ6p75uM76XK5KFXZaDRmVAj9KGA5KVvGdQNz4WQGR5la6iQ5GRoaQm9vb9zPxGIxdu/ejbNnz1JqSipvDIPBQDnt09PTsNls0Gg01Hl+enoaGo0GIpEIBoMBRqMRgUCAypWHw2Hw+XxUV1dnHbiRama6zoJUKqUdFLL94eFhPPvssxgcHEyZBL7wwgsYHx/HU089dd255vmCBFKlpaXUnNjr9WJsbIz1ezQYDHjqqadw5syZuO2Ql38u9IxDhw5Br9cnfTehUAj9/f1UlCIxoSAKicT0lHxnxLeMdGJcLhd+8pOfJBUKiNH1UiDTfogSYraFhmAwiHXr1gEAtRgIBoPU+PsrX/kKTp8+jTNnziAWi6G4uBgikQh6vT6J8sjhcFBaWlrQWUZyT5OZp6GhIYRCIQgEAqq+Se4xsViM7u7ugvnhJAY+iVQfpsIl+b2dO3ey2hMUGkQ19oMPPkAoFMLmzZuvi9ogW0HMYrFAKpVSiqFcLkddXR2AD68b0+Q8UUyFdCdJUSQSiaCkpCROHZVsVyQSUYGpzs5OBINBHD9+/IbyyWOCULqJxcbq1auhUCgwNzeH9957r2DWCcx7VSKRpE1uMgl/6HQ6XL58GXa7nTJAmCCiRyRxHh8fx+HDh/Oe6yP3FPFSE4lEAPJ/n2WicS41bpT3dKZjIEWEzs5OlJeX5zSWAnxoKO50OuHz+T4WIh/AclK2jOsEMqty/vx52O12anyZbp6BJFHPPfccNRpO7C74fD6sWrUKMzMzNCBma5sbDAb86le/Qnt7O/x+P2KxGORyOQQCAdatWwefz4eqqio6Y8Dj8SjdBQCdPygtLcVdd92V9aLY3NyMyspKuFyuuG4GqdiWlZVBLBZTaXiPx4Pu7u60giZMnDx5Es899xyrMEYmXK95ssRjkEgkiMViCAQC9DoIBIKk75G8fI8dO8a6La/Xm3Xl/7XXXsOJEydYk5ZYLIa5uTnweDxIpVKqPkjmWQgIZbGiogJ79+5NCupeeukl1lmqv2ZEIhH09PRAoVBgzZo1uOmmmyAWi1FTU0OTin379sXNBwHAk08+iffffz9uWzfddBN9Pgpt8UDmBi0WC03KBAIBVCoVQqEQKisrIZFIIBAIIJFIUF9fX7BucSppfQCsxub33XffgveZCcSsN90amS3yDVoTGRIqlYomSSKRCH/7t38Ln8+H5ubmpK4jwE7bSry+ZB5FoVCAy+VidnYWXC4XcrkcN998M6LRKGZnZ3H58mWqxCqTyWCxWPDoo4/ecIkZUY4lQjlTU1MA5ostJCErhEgTk+JmtVrR3t6eMQBnCn8kPsNMmn4iK4DP56O2tpZe/7GxMdhsNvT392P//v05fwckYSDFL6VSmfeaQuKEkZER1NXV4Xvf+17aa0oKzUQgSKlUFrz7XKj39EKe22wM1omPaFdXFwCkFOxKBZ1Oh/HxcTpnbLValztly1jGYkGn0+G9997D9PQ0DVLFYjEEAgGd82HjpJ8/f56+iFLh2Wefxb59+wCkbpvr9XqMjIzQ5IjP5yMcDsPn8+H999+nymjRaBSBQAArV67E7t27cfHiRfD5fLjdbkgkEjQ0NGDnzp1Zn7dWq8Uvf/lLtLa2YmJiAj6fDzU1Ndi8eTOuXbuGM2fOoKenB8FgECKRiHVeLh2i0Sg++OCDvF/I12uejECj0WDdunV49913EQ6HEQ6HIRAIUF5envQ9HjlyBL///e9ZLREAYGZmBr/+9a9RXl6e8cXe0dGRlj4oEokgkUiwbds2bNy4ET09PRgaGooTv5BIJCgpKWE1sgXmE/LS0lLY7fa8pO0zoZAUSHLvp/o7E2Qe7fLlywgEAli/fj2uXLkCPp9PaUiJM0cPPPAAzGYzRkdHAcxf389+9rP41Kc+tSj3HlGiVKlUcDqdlPKWSGe8dOlSQY2DU0nrF1KWOtfjIR2mhVKLyPbyOQ82xUdiCtva2kpNtZnby2U+CviQgkcSPofDgWAwiEgkArfbjcHBQUilUojFYszNzcHj8SAajcLhcGB0dLQgNLrFQE1NDS5duoRoNAo+nw+FQgGZTAaJRAIAcWIdQP7BN7mWBoMB/f39AD5MAti2mTirBMx/BxqNBp/85CfR2dlJBW4CgQAtCHzyk5/E9u3b8fbbb8PpdNLkzG63Y3Z2Fj/+8Y9zSsyYSQu5Xjt27Mjre+zo6EB7ezvcbjempqawefNmVqEbUjB+++23MT4+Tm1BysvL0dDQgIaGhoJ1vgsxE7bQ5zYbg3W9Xo+JiQk6kjIxMZF1XKLT6XD06FEYjUZalK2qqlrulC1jGYsFYrYbjUbpXIrFYqHmpFu3bqVVFSbVxmQyZdy20WjE4OAgAOBLX/oS60IoFApRVFQELpcLLpcLPp+PysrKuBkxoVCIQCAAu90OiURCq7iRSATbt2/HqlWr8grcUg3Fv/nmmzhy5AiteOYbuAcCgZyrZzfKPJnBYMDw8HDc4svlcpN+75133sGzzz5LxTjYQAK7zs7OjOezc+dOnDhxgnV7ZWVl+NznPoempibs3LmT3pM/+9nPMDY2Fve7xcXFKS0StFotfvCDH+D555/H9PQ0XC5XXmqDHA4HcrmcUu+IUXUhZ9KICAmPxwOfz4dEIqHqiKmS4GAwiIGBAfT19YHD4eDtt99GX18fHnrooaRBcK1Wix07dsDr9cJms4HD4eDkyZPgcrkFvfeICEE0GkVNTQ22bNmC5ubmpOMJBoNUrKCQxsGpaD6J/55uZqdQSOejlu8+86FSJXbImIqPRBxgId5XBMzg3O12w2q10ufF5/MhEAhAKBRCLBZTb0QejweBQEAN5K8HNSwVyPd39epVhEIhcLlcFBcXY+/evVTxEkDcfVSI5J8t2Uq1TWYix/ydhx56CLfffjvGx8dx+fJljIyMUG/K7du34+zZszAajVQYibAR+vv78cwzz+ArX/lK1u9acrxkNGJqagrvvPNOXgn2+Pg4/H4/wuEwQqEQnYUD4gscR44cQXt7O73HiM2K3+/HxMQETpw4gdbWVjz99NMFS8yA/A2gc31uyblarVb6uUzrZHV1NVauXBk3n5tNXELivYGBAQSDQZSUlKC2thaNjY0feTl8YDkpW8Z1gMFgwKVLlygFsLy8HGvXroXD4WCtqjCpNkVFRSmDQiaOHz8OABgYGIjrlBB6QXt7O1wuF2QyGXg8HtauXYtt27bBZrPB6/XGGbwSA9+5uTnMzMwgEAjA4XBgZGQk54FZ5kJuMBgozSEWi8XRHhaCbdu2LfoivVggQbRSqaTzE0RJ8MCBA3jiiSeg0Wjw4osvpk3IgHlaTCAQwNTUVEb/HNJZffXVVzE7O4vZ2Vk6W7R+/XraEWUGH4QWRzplTqcTPB4vbTWvpaUFjY2N6OjowPHjx9HR0ZEznZFQO0lBo6SkBHNzc2ll6Xk8Hi0siMVi+Hw+Klecav+hUAjFxcUoLi5GY2MjampqYLfb8dZbb6V8Bkk3jVA+L1y4AL1eD6lUisrKSjqro9FosHXrVpw/fx6zs7P02Uplf5ALmFX8RKpPKmr0YlF3U3ns5DKzs1CwBVTAfCKayRA7E7K9bpnUbZnfS6G+B2YyceHCBfT09EAgENAZTkID9Pv9lD4ulUrB5XKpgMaNFAQy12iSvPj9fvT398fNXzHvnVTreq7ds3QiIGzPa+J+g8Egdu7ciba2NvT19SEWi0Emk0Eul+Pdd9/FxMQE3G43YrFY3Jrq8/nQ19eHgwcPss76pfNlY6pCpjrOdCDvZy6XCx6PB6VSicbGRrz55psYHx9HT08PeDwenTl3u910No6oDgYCAZpgXr16Fa2trQWjRC8k2c5lvUvsjmWiLRJoNBo8/PDD2Lp1KwBknVQTewuPx4Pi4mI0NTXhnnvuuW7snaXGclK2jCVHR0cHurq6EAgEIJfLcdddd2H37t04cuQI5YEzqypk5mHDhg0QCoU4c+YMpQdkwszMDFpbW6FWq6HT6XDs2DF88MEHsNvtVBGvrq4Ot9xyC/bu3QsgudLIDCa8Xi+mpqYwNjZGJfKzrcC98847eOmllyh9xul0wmazIRwOQyqV0sHzdFSxTJDL5bjtttty/tyNME+WeByNjY3o7+/H6OgoXC4XLl26hBdeeAEPPvggK4VVIBCguLiYqnn5/X6qppbNC3nfvn3Yt28fpaKQwX+n05mkKAbMJx7MhCYcDsNsNuP1119HKBTC5z73OUxPT1PvJiJKQBJyDoeDoqKinD2+ANCkiJgPk+ICGaZPBBEjKS0txd///d9jx44d6Ovrw7lz59DT04Pp6emkZJLD4UAqlaKhoQF33HEH7rzzTiqyk01hBJhPVGdnZxGLxVBeXg6fz0dfsFqtFjU1NRgfH6fV5enpaVy4cKGgtJxMVJ9M0s75Ip3HTq4zOws5hnwCqmyRDZUqlZgHmydaIahZiccHAK2trSguLkZ5eTnKysowPT0d96wQU3WyBqvV6gWJvCwGyNqoUqkwOztL3yNnzpxBMBhMqYCXuK6nC+izSdayeVck/o5QKMRzzz1Hiy5kRthsNmN6ehqBQABisZga08diMar86nQ60dvbS7tC5H2bKTFZ6DtNp9PRriQRKHnllVcwOTkJs9mMaDQKsViMuro6RKNRlJWVoaSkhM6nk4IgUxW4UPPECy2i5vKcEcsOh8OBuro67Nq1C+Xl5Vk/n7n8LjAf7zmdThQVFUEikWDv3r2LpkB7I2I5KVvGkkKn0+H111+nLxWVSoXNmzdDq9XSFyHwYVWFGdgoFArcf//9CIfDtBOWCbFYDP39/Xjuuedw4cIFDAwMxIl1yGQy3HbbbSkrjcwKIUnsjh49SlvriRSXxJeawWBAe3s7ent7cfr0aSqjLxQKMTc3R4NbIiSxevXqJJpEKpSWlsLtdlPFP6lUis985jN5V+Ku9zwZkOzF8uSTT9JKo8ViwYsvvog//OEPSRLsxPdpdnYWLpcLoVAIfD6fXt9cXsjkGEhll01RDACamppQVFSU1LHzer1oa2ujMxQkIQmFQli1ahVVS/N4PLjtttswNDSE6enpnK4TqcSSSm00GoVarYbT6UxJYyQzenV1dTQpKi0thclkookTEZ/h8XgoLy9HfX09Ghsb4wLnVHL7TAiFQuohSKhhLpcLV65cwaFDh6jJ7P79+zE7O4uhoSH4fD54PB68+uqrGBsby2uuiy1Y2bJlS8ptLOZsF9OUmU05LN3MTqHAvB61tbU5B1TZIBNbgHkMRMQjlSdaIZXuyLauXLmCiYkJFBUVoaKiAgqFArFYDF6vN664cdNNN+Gmm26CVCql820L3TfbeWTyzUv3GdJpJ0bNdrsdLpcLRqMxpQBHYvCdqtOV7bPAts3EY0/8HUIlnJmZoZ5xEomEWs/w+XzcdNNNWL16NUZHR2GxWOg6Hg6HEYlE6LxfKgpw4vkvJMEn7/nJyUmEw2FEo1G43W5cvHiRUltJUc1qtaKqqgqlpaWoqanBzMwMLBYLKioqMDc3Rz0pq6ur8fnPfz7rY0iHQhRRs2H5kFlbQkGUSCRZr8lEzI2Ydmcj8kE8WeVyObxeL9asWcM6n/1RxnJStowlA+EKk4VZIpHQABFgXyQSAxuDwYBbbrkFp0+fhs/nS9oHh8NBWVkZbDYbrX6aTCb60mIGrIQGlu0QMDk+jUZD/X1Wr14dN1BNhtdFIhF2796Nv/zlLzhx4gSlQBKuOfMPmQnSaDS4//770dPTgxdffDFtB2X9+vV4+umn4zovQPYUASZulHkyAnKN33zzTej1elqpJNLJbPRAhUKBpqYmvP7663SGiCQsZrM57+NgGraSZJpg165dOHHiBE6dOpXUOWLK3nM4HHo/jo+PQyQSQS6XY+PGjWhsbMTPf/5zmEwmPPbYYxgYGMh4XGKxGKtXr4ZKpUI4HMbk5CRCoRAVsJiZmWH9HI/Hi3veyAt3ZmaGyoSLRCJqcK1QKLBlyxbs2bMnrmBBjL1TQSqVYv369TCbzVSohsxscjgcTE9PQyKR0GDpnnvuwdGjRzE6Ogqv1wun00ltMkjylu09TdQWs/VqIlVgQhsuZJcqXeCUGMQWsjuUjr55PQouiceQqiNZyASZbOvixYsYGRmh3QuFQkHnxsrLyyl1lnSs5+bmUFZWRiX3gdzndTJ1oth+lu4zOp0OBw4cgMPhQENDAw1uDxw4gLm5OfouTXWvJ75XU92X2VIdyQgA2Vbi8TGNmgk9tqOjA16vFxwOByKRCDU1NXQ+lXSPmpub8Z3vfAc6nQ49PT04dOgQ7HY7fWcC88UeNgpwqsSEnHcu3yVzpikajVLKK6GAE0VgmUyGqqoqWnyqra3F5s2bKUWcfI/vv/8+OBwOdu/eXbB3a2LxMt97NRMIBTQfi5BEu6NMnyVibg6HA06nE2vWrImLrz4uWE7KlrFkILNh0WgUpaWlWL16Nfbv3w8gWRqagG32wmKxoLS0lHaIAFDfMJlMhk2bNsHn8+HatWs0MXO73ayCEU6nE3/5y19yWixJVy8xiNLr593nL126BIFAAIvFApvNBo/HQz2uioqKwOPxUFZWBqlUCrPZjFAoBKVSiW9961toaWmBVqvF1NQUlZItKSmB1+tFMBiEUqnEXXfdlaSWtpDF/nrPk6WqEJOuVybKB5fLxX333QeBQAAOhxM31xSNRhc0sK/RzHutkCScOTCu0Wjw85//HB0dHWhtbcXAwAAcDkdSp4p5/GQOxO/3IxAIYN26dejo6IBSqcRjjz2G//7v/8bly5cRCoXo/U06WER0Q61W4+tf/zqkUilGRkYgEokwODgIgUCADRs2YNWqVRgcHKQzM3Nzc+Dz+bj55pvxb//2b3H3q9frhUwmo90vuVwOjUaD8fFxTE9P48SJE7jtttvirt369evpvcmGNWvWYNOmTRgaGoLD4YBYLMb69etxxx134K233oLT6cTU1BQuXLiA1tZWeL1e1NbWori4mNIohUIhzGYzent7odPpFuV+ZKsCFzIAIPcOsQLIFJgXijKZK31zIfvKtN1cqKGFXIf0ej36+/tx/vx5WsTxeDwoKyujndtVq1bR9xGh99psNszOzqKrqwtKpRK33nprXJKREYMVGgAAIABJREFUTdcr3XlkK/zCTIgOHDhAKYoWi4WySYjPHlNFNBukKgJkQ3VsaWmJkzlvamqC2WzGpUuXEAqFqJ8nKfDs3r0bwWAQly5dovTQm2++GbfffjveeOMNKrQlFovR1NREn4Py8nJ0dXXF+cpJpdK4Amqu1NlsE33mDHtJSQnWrVuHWCwGqVSKsrIy2sUj1ic6nS6uoEkSenJMi0W9I+eRbyEjm+eXzMRWVVUtuHuc6ViYvonr169Pa4/0UcZyUraMJUN1dbLDu1qtTruokIVXp9NhZGQEQ0NDGBoaokmWVCpFbW0tTCYTZmZmqNTxP/3TP2HVqlV0gB6YX2x1Ol1cByoajeKtt95KSnIyLVhsQVR1dTXtNASDQQgEAlRUVODatWsIh8OoqKjAli1bqKoiWcSnp6fR3Nwc1zF86KGH0NDQgLKyMuzatWtRF6brOU+W7qVZWlqKkpIS+Hy+lJQ5Pp+P9evX49Of/jSEQiGOHz8Og8GAUCiEoqIiFBUVQaFQLMjfJBgMUkU4AGhra6MvC3J/NjY24rHHHoPNZov7LIfDSamM6HA4cPDgQRqsyOVyqFQq3H777XC5XPB4PJTmaLfbaadwcnISBw4cwPr166FSqVBTU0NNmckzRe7dU6dO4c0330RDQ0PSPZ5Y8Fi3bh2USiVGRkZw9epVOJ1O8Pl8dHd3x72Mf/zjH2Pfvn0pjWpDoRBuv/129PT0QCaTQSqV4qGHHkJJSQn6+vowNDREKZ6BQABFRUXUaBqYV2a9dOkSNX9tb28HkL4LrNPp8Mc//hF9fX10OJ/L5aKtrQ3Nzc10aJ/pG0SSUnKPFHqGiFCvSaefJPOLVQRJVDUk205H31zIvjIFg7kGxIVch6qrq+HxeOIUbH0+H7q6uugssUwmg9frpb/jcDhgs9loUWd6ehoWiwXj4+PgcDhJFKxU55fuPFL9LF33iiRf0WgUwWAQ4+PjOHfuHAYHBykVkHlM6WiFBGzvr0xUR5/Ph6NHj2JoaAgWi4UWQjkcTpzwiMlkwvT0NKLRKHQ6HdRqNRwOB/XC+/znP09n++RyOSKRCJqbm7Fx40ZanK2ursamTZvgcrmoJ5ZWq4VSqYwTbcpUzMj2WWNep8QZ9k9/+tPYuHEjgsEgnf8kfnYlJSWsieFSJRLk/K5cuQKLxZJ1ASvb5zfVTGw20Gq12Lp1K6UvpkroyLpFLCuEQiEUCsXHMiEDlpOyZSwhMi36QOpFs7+/nyp2lZSU0KADmJ/HMpvNdLbHbDajq6sLe/bsiataAfPeVq+88gpt9wOAy+WKW8zypdEQ+iE5ptWrV6OlpQV9fX2w2WzUpyRTwscM5pbCl6PQ9KlckO6lqdFoUFZWBqfTSTn8sVgMpaWlUKlUCAQCEAgEuPXWW+lxP/nkk+ju7kZVVRVMJhM6OzvB4/HylkQGPgyYfD4fpqamYLPZMDY2FhegEf+jRBAqy+zsLOu2vV4v7ZoFAgFqZAzMm2WSThdz20QQg8h5s80JkYCMKJ1ZrdYk/79U37tOp8PJkycBACqViho+E2i1Wvz617/GT3/60yRLAADYsGEDpqenk+YCSFHG6/XCaDTC6/UiEAhAoVAkUeuUSiUsFgvtlun1elb1NXK83/3udzE6OkoDag6Hg2vXruHq1atU9MXj8UCpVGL79u3Ys2cPDfbq6uoWhbab6t5O1ZFYyPPH5vu1mAWWbILdXJPPQq5DGo0Gn/nMZ3D58uUkmjsRkCD/T+4ZNnEls9kMv98PHo+XRMFKdX7pziPVz1L9OwlQKyoqEA6HsWLFCnR1dWFwcBB+vx9CoRASiQTBYJC1q0XeI9m+x1JRHcnaZzAY4PP5IJFI6LxocXEx1Go1YrEYbr75ZsoOCQaD9L8ymQwVFRVoamqizxmzQLt79+6kYyXKfYRaPDw8TGfpsn0nJxaerFZrkhJvohgOMP/+FgqFkMlk0Ol0uHbtGu69915UV1cnzX8WqsudDZjUUa1WS+nabrcbZrMZhw4dgkajybiWZfv8jo2NYXR0FGvWrMk5FtFo5tUXyfGyIZGyuGHDBqod8HFMyIDlpGwZS4x0i37izA4Bc66MDAg7nU54PB7I5XIIhcK4img4HIbb7WatEj/yyCNQqVT40Y9+RDtmgUAgLmgm+xsZGcmp+gSwUxsTF8h8q3vZVkH/mpCuqhwMBlFTUwObzQa73Y5wOIzi4mJs3rwZ+/fvZ52jY3rAnTt3DpcuXcLk5OSCPY8aGxths9lgtVoxOTmJmZkZ1NfX47777qPnUVZWlvS5aDSKqqoq6ovEBg6Hg+LiYuqb5/F4aDcpFaLRKDQaTdo5IZ1Oh1OnTsFoNILP51NKV2JAY7VaqbAMuV8ff/xxSrtje8G3tLTgwoULeO655+KCWQ6Hg+7ubta5AGbXm4jfELppbW1t3FyEVqtFf38/ent7M6qddnd3w2g0xh0HkeSfmJigtE9gvovX09OD0dFRlJSUQKVSFcwoOhGp5tsSA3AgfwoSAXONZFM1LDTyUeDLJkEsZIC7Z88e9PX14dSpU9QTk3SuJRIJVCoV7HZ7UnebiWg0CqfTCZFIlOQbme780p1Hqp8l/jsJWN1uN0pLS1FUVARgvoMXiUQgEomgUCjQ0NCA6urqpPcGeS4W0pEl9+oLL7yAS5cuwe/3o7S0FJs2bYLFYoHf70dVVRUaGhpQV1cHoVCI//qv/6KiQuQaNjU10eeMHAuT0pp47DqdjlrZkOItWctTnUs6Q2uipnvs2DF0dXVh69atcR1zsm+xWAxgvqsqFosRCARoUk/iietVwCTCGUzq6K5du6BWq+HxeCj19plnnsEvf/nLBXelhUIhjEYjfD4fjEZjTmwTpmp1f38/jEZj0ruHjbJIVH7/WuOZQmA5KVvGdYVGk3pmh4DwmqVSKZxOJ3w+H1VOXLNmDVasWIHh4WEa9AoEAvD5/LhFhFSY7HY73n777bjOw9zcXJzEPgmmAoEAjEYjurq6cgraFhpYZMvtz7UKmgqLqUDHtq9ElS62uRsgnu46MDBAfWwcDgcMBgPuvvvutPvKJuHP5nhJB+LChQtUPTMcDuO9996j1FKSSJw9ezZpG0TJkEhw8/l8hEKhOBVQoVCI2dlZOBwO2h1LB4FAAK1WG6camoienh4YDAYEAgHw+Xxcu3YN/f39+M1vfoN77rkHAHDgwAFcuXIFQqEwzrA9lcE5E7Ozs0ndBdKVEwqFWLNmDavkObPTMDs7i+npafz2t79FXV0dVq9eTe8/ksARtdNAIECfY+Y5Nzc3o7KyMkmRk4AI7PD5fHA4HBiNRjqz98lPfrJgRtG5gHkdsmULpILBYKBdv1SqhoVGpq5WrjYDi1Fg0mg0+MlPfoL29nb8+c9/hs1mg0QiwYYNG9DU1ITGxkb84he/wOnTpzNui/hqCYVCnDt3jtpbLIaNgl6vh8vlwvPPP4+BgQH4/X4UFxdDKBRSmnJdXR0UCgV27NgR55/IfG80NzdTRslCu6bDw8N0XrasrAzbtm3DtWvX6DuD2Mk89dRT9PkiliGEgn/33XenfdcwO1rt7e2wWCyUMsqMAZhreaKBM1PpDwD9WWdnJy5duoRIJAKFQoGJiQm0t7ejpqYGjY2NcfsGQL1Kyf8zr99SdsaY0Ov1mJiYoNRRr9cLi8WCQCAAu92OUChEi07t7e20YMiGbLrSwWAQlZWV9Pyz7ZSlssAg58Bc/8ns3sedssjEclK2jOsO5sxOYkeDyWsOhUKQy+Ww2WwQCATUs0WpVKKkpIQqvZWWliIajeLpp5/GF77wBUilUlqZJzLIzBmlWCwWZ9qs0WgoF9put8Pr9WZdmcsHmeSESaWw0FVQgqUS+mB7IQNgnbsB4l8cLpcLhw8fxsDAAGZmZtDe3p4xUc4m4U91nOTak2vT3d0Nk8lEaU+BQADT09M4fPgwTYzEYjH4fH7cvSWXy6l0fVVVFcrKynDzzTfDbrfj8uXLuHr1KoLBIIxGY5xpKhPE7JmJUCiEurq6lOei0+nQ2toKv9+PaDSK4uJiSKVS9Pf34/Llyzh27BiKiopgs9moSXR3d3dOXeE777wTf/zjH1l/plAooFKpUr5ktVotnf9zuVyUXgjE08DInwMHDmBkZAQOh4POmGk0GhoQP/vss/jRj36Enp4e1uOJxWIIhUJwu910/kUkEiESiSwqxc/r9cLn88WtIYnP+0JmqRKpV4vV9SP7Slyn2PaTa5Gn0EWhxOMkXSbiKbVnzx6qCrhp0yacPXs2oy9kNBqF2WzGs88+C5VKRZVOSREBSC1WlS10Oh2eeeYZTE1Nwel0UnN3ANTiw+v1oqKiAjt27IhTRQXYA+1E1kY+7yydTge9fl65mKxju3btAoCkdxQx/SUg1gPMQDwT5XNkZAS/+93vYDabodfrsW7dOhgMBjgcDmod8s4779D/Go1GuN1uuj4YjUbU19fTpNHtdmNgYAAul4sWwIhRvUAgQHV1Nf7lX/6FUqzJOZNrWsikO1+QwotKpYJKpYLX66ViSCaTKS5h8vv9cQXDVMiUXJLZOsJ2IAWJTNeC+R0nWmAkzleqVCqsXLnyY09ZZGI5KVvGdUcqmg8QT8shD3hZWRkkEgnq6+tx5coVdHZ2wmaz0ZdGKBTC5cuXEYlE0N3djbVr18JqtdJKulAoBJ/PpwuZWCzGypUr446J0KdIoJAYKOXjwcGGdEpsiV2jxaqCLpXQB9tQcnl5eUavGfJ3k8mE/v5+KpmeqRoIpE/42cDWkaysrKT0N4JYLIaJiQkcPXoUNpsNDz/8MBobGyGTyWiCr9FosGHDBlqhr6ysRFNTEw3innjiibiZLLaEjMvlQigUss7FpOv8dXZ2wmq1xs3LKJVKmEwmWCyWuH0RlUe3252VqAZBS0sLBAJBEq3L5XLFFTnYoNHMz1/6fD5cvnwZNpsNgUCABk9MaLVa3HPPPTh06FDcjBmZvyOB8Xe/+11861vfSvo8E8RSQSAQoKioCJs2bVpUil/iupbqec+XEsUMgGpraxet65dL4pRrkaeQRSG25zedwiYRdmBSiwndldmt5vP5sNvtmJubg8PhAIfDgdfrhVgshk6ni3tX5JNUGgwGPPPMM1RlkQ3EFsTj8eDs2bNJqqhAcqDN/PtCkl+hUIiioiKIRKK4ubpERovT6YRQKITf7weHwwGPx4NCoaDnlA3lc2RkBHa7nXaAxsfHYbfbadffYDCgrKwsrjDJ5XLjrpvNZov7WSwWi0vMnU4nDAYDvF4vBAIBpqen8cADD9DrtNDvs5BILLx87WtfAzDfvRwaGsLExAT9XZJ08ni8BT9HTJEPrVabNTMn8TtmdpOBD4sXBEVFRaisrIRarc7rWD9qWE7KlnFDg9AWVCoVFc4gsyXAPLXAYDDA7/dTOhgRTgDmJe8nJiZQWloKmUwGiURCFZ/sdjs1HSaVP4JMgRLTg4NQJIgJdr5BFfk72+ezqYLmi4UEhbmABKlEov7o0aPYu3dvTglhOByG3+9HKBRCW1tbxmpgrhRGnU6HCxcuUF88lUqFlpYWiMViPPfcczSxLy4uBjCv2HbhwgVMTk5S2ixR8/va175G+fgSiSRujgEAvvzlL+P9999PS1UkhqmJEIlEaQ2ny8vLwePx6N9DoRAmJyfjvPEI+Hw+SktLIRKJ0Nvbm5M/WH19PR18Zx7z1atXsXbt2rSBAUm2jEYjHA4H5ubmYLfb0draivLyckprIf8tLS1FIBCAw+GAxWKh3mxer5dWtiUSSVxSJhKJkq4vj8cDj8dDcXExTCZT0uD/YiJdpyCfQD6Rtng9hT0Ici3yFLIoxDZXlU5h0+l0JhUoioqKIJPJqMCHWCyG1+ulSoPV1dWYm5ujYioAkuahcllLifrczMxMXJed0G15PB7tWgQCAfj9foyOjuLgwYM5iRflm/xqtVo0NTXR7kyqZ8ZgMEAgEEAmk6G4uBjhcBhFRUVQq9XUSzMbSqtSqYRSqaRWMStXroRIJKJrKbnuzMIkSbYJ5XHHjh20m5b4sz179uDMmTNUITIcDqOqqmrB16nQINfLarXGFV7q6uqwZcsWGAwGHD58GAaDIc7SpKmpacH+XsxieG1tLaanp7O+Jsx4gqzfbLOzjY2NrEyCjzuWk7JlXHekovkwlXkUCgVaWlqgVqvxzjvvYGxsDJFIBDKZDGVlZdQwl8vlQiAQxG0/EAhg8+bN2Lx5M5RKJWZnZ/H73/+efv4LX/hCykQo0yIRiURgtVpx7NgxvPfee3FzOdkgl4AkXRX0rwGEFjo0NISxsTEMDAygtbUV999/f0aKiMFgwPDwME3IiAJhJrpdLhRGIqs+ODiIcDhM5wwtFgvuvfderFu3Dq+//jqmp6dhs9ngdDoRiUQwMjJCjUZjsRittK9atQoajSZJMIOpoHXffffh5ZdfzthZSoRarU5SRWRi586dOH36NN599134/X7EYjH4fD7ccsstuHz5MmZnZxEKhSAQCFBVVYVbbrmFmnw6HA6MjY3FSf+nwn333Ycf/vCHSfSvUCgEm82WMTDQarWoq6vD1atXEYvF4Ha70dnZSavhRqMRcrkcTqeTmv9WV1dTNTgSqHV1dWHdunWsCRiXy407Pi6XS2d0FjMYYFvXUj3vudLKlpK2COSeODU2NqKxsTHrY8r197M9zkRGAXNOkiRDzHuGy+VCpVLhrrvuwqpVq6BUKmG32/HGG2/AarWipqYGd9xxB5qbm+OCTVKYIF6aZA4nW+rm2NgYNSdm2kTU1tZCrVZj69atMBqNOHr0KE0mcvVfzDf51Wg02LVrFywWCxwOB+szQzz/iC8hSQx6enrA4/Fw5MgRAMjqumi1Wmzfvp2yUJhUSWaQn1iYJL+Tzc8MBgOqq6thMpnQ0NCAkpKSBV+nQiLx+WYrvGg0Guzduxc2mw1DQ0NQKBTYu3cvpWHmQtNP/F1mMTwfZg7ZXmISxkzsGhsbUzKkPs5YTsqWcd2RStiCKPMEg0HccsstCAaDVKb10qVL4HK5WLFiBWpqanDlypW4YX4mioqK6KAxML8YXblyhS76O3fuzPmYiQcHmTXy+/0IBoOYmJjI6UW5VF2qdFhKoQ/mLFEoFKKDvmTGIxX0ej2mpqbg9Xpp8pNIm0uFdD5jwIdJ0tGjRzE4OEipggKBAJOTk9Rv5+6770ZJSQleeukl2Gw2qshGZpQIIpEIna0gw+djY2OUnvGrX/0KXV1dEAgEqK+vR1VVVU5JmVAoxF133ZVWiEOjmRc52LhxI1599VXMzs4iFotBLpfjZz/7Gfr7+6nC2I4dO6BWq+O6v0ajEefOnaMJaar7YePGjaioqKD0MCZUKlXG+4gEfIODgxgYGEAoFILFYoHdbqd0ZbPZTOlia9asQU1NDerq6mC329HW1kbnPpVKJTQaTZySaiLtkxjVlpWVUSn8xQoG2OiLpEjAFLbJ1+B2KWiLBNmuU4nnkkkshhTeiIXIQm0JUjEK2GS5dTodDRAJeDweNmzYgAcffDBufSCmyBKJBAKBAAaDIS6BJPskPla5UjctFgvWrl2LHTt2AAClGnu9XohEIjQ2NqKlpQUbN27EgQMH4HA4oFKpcrp32a5NtsUArVaLrq4uTExMsAbQpABBOpLEpmNsbAyTk5NUSZapZMjWaSPH8vDDD7MqKbKdU2KhMtXPgPnvvKOjgypJlpSUoKamJolGeb3fyYnPN5vtCTlWtmuVCenWHPJMEhNwIvqUKzOHxGpDQ0P0XVBZWUmppaT7lkqV+OOK5aRsGdcVTEoD8fMig6tE3INQN8jCKRKJwOVyqZrd3NwcuFwupWUl+kUxFxtSZctnIUvcJvHgICIiALBy5cqcg7zr3fFaSrqGRpPs5ZbN9aqurkYoFKLfs1AozNpbiklhJD5jvb29lPr4zjvv0DklMpfA4/HA4XAQCoXiFDiJTDARjJFKpeDxeEmdIr/fj87OTmqrQDyOANAZFy6XC7lcnlFpkQ319fUZf0ej0eCRRx6BQqHAa6+9RvdTUlKC73znO6y/r9Vq0dbWRhMyIDmJZcJgMFApf+Y1KCoqwhe/+MWszkWr1aKqqgoDAwP038LhMDweDyoqKqiwglwup8kfUb4jwTK5F/bs2YPh4eGUCXssFkNJSQn++Z//mT6rS/nssRlK5/L8MdXmlrqan806le25MAshAwMD9LkrxNqTOEdFig1er5fScoH55zDRlH7FihXYv39/UuDLtHJ4+eWXqdDO/v37aXJGkhxm1yyVLxZ59yQWJElwfO7cObz22mtJ11GtVqOysjLvQDbx2hSiGMek0dbV1VHVQuY9Sv6NKAcm0sjZjiVToS7XYyRy8oSGKRKJUFFRwWoaT/6u1+vj/r5USOxUpesg5xM/pHpOSTH83LlzsNvtUCgUaG1tRWNjY877EQqFmJqagtFohMlkQllZGbRaLa5cuYJAIIC+vj767lyoZc1HCctJ2TKuG5gLMQD09vZidnYWx48fx3e/+904c0mmMs/9998Pi8VCzWIJHYlQ25hBrkgkglAoxLFjx+iCT4QBFrrok0VKq9XGGTr+tS0sS03XyKfqptFo8MADD2B6ehp2ux1KpRLf/OY3s/7svffei8OHD0On02F4eJh6Va1cuZLOMgkEAmg0GurZVVFRQQU+CGUHmOftEwNagUCALVu2YHR0FNeuXaP7jEQiOHXqFKt3mUAgoAIearUapaWlGBkZYRX6YAOPx0vqAKXDzp07MTw8nLLKnXit7rzzTpqQGY1GnDx5Et3d3azGzcC8UE5JSQl8Ph/C4TBEIhFuu+02NDY2ZnV8Go0GO3bsoH5SBEqlEl/96lep/1F3dzdNFsViMQwGQxztzWQy4fjx46ziIwSxWAzj4+P4wx/+gCeeeGJRn1WdToeJiYk4yheApGAo2+ePTcTiRlCGYyKbcyHnQTzoCF2PWXgrBJj7MRqN1OdLp9PBbDbTpJjQocvKyvD444+z3uMazbyfFSmwBAIBuN3upLmuxATu0KFD6OrqopR2tuSDrSuT6jqy3VP5fve5FAPSKYkyaXabN2+mJs+J96jJZEpJI1/swqBOp6PCIAAgk8lQXV0dZ2bNxFKyR9j2zRTZaGlpKfi+U91fev28TL3H40E4HIbP58uZJksQDAapoFosFsOlS5fQ19dHpfIFAgGN38bHx/OyrPkoYjkpW8Z1A3Mh9ng8mJycxNzcHPx+P61okmSHqcyj1Wrx6KOP0gVepVKhpKQEOp0uSXktEong6tWr1CCUKGYVctG/3p2uheJ60DXyuWaNjY34xCc+gf7+fqhUKpSXl+e0Pz6fD6vVSqvMw8PDUCgUUCgUqK+vh0QigVqtxltvvQWn0wkOh4O6ujoA8S8uhUIBuVyOQCCAFStWYPv27XHiMwSzs7MIBAJYv349Nm3aRF/+n/70p+kMwP79+2G1WnHu3Lmke1csFiMcDrOqsTEH0wsNcj+0tbXh5MmTGBgYoC/MxHk8QuMdGhrC5OQk3G43OBwOAoEADh8+jLKyMpSWlmYsVuzcuRNtbW04ffo0FfZYu3ZtnAeTWq2mCVni7I5QKMTzzz+P3t5ezM3N0WQ6sRNCcPHiRfznf/4nnnrqqUW538mMDZvqX2IwlO3zlxi4ZkP7zffY86FDkc+wnQubzYTD4YBQKMTq1atpBz3f74LtmMl+CDVYoVAAAI4ePYpgMAin04l169ZBq9WipqYGO3bsSNt5r66uxsqVKzE6OkqLfw6Hg1UxVqfT0WSQ+AA++OCDrMnHli1bWDs1bDTXdEqSuSKXYgCboAyZyRsbG6OCEIR+PDo6Cp/PF3eP6vX6lEq4i1kYJNfN7XaDy+VCqVRi8+bNaecwr6fYR6LIRrb+YLkg1ZpTXV0NsVhMBZKEQiHEYnFe30d1dTXUajWuXLlC4zqbzYZgMEgFp3g8HiKRCCKRCKUEf9yxnJQt47qBuRCbzWaqPiUQCGjAncoNntltEQqF6Ovrg8PhwNDQEO2UxWIxWu0pKSlBaWkpampqrtvw7o2Mv4bEUq/Xw+fzwW63w+Px4ODBgzhx4gTOnTuHlpYWPPTQQ2k/T9TTCIhcM1NoRK/Xo6+vD6Ojo6ipqaFcfqFQSF9gRMrd4XCAy+Xit7/9LU36E+Hz+XDt2rW4rgwbdfbrX/86XnzxRQSDQXC5XNTU1OCmm25CT09P3IwU8KF3Ti7XjVT4JRJJVgEG6Zh1d3dTyXCDwYCOjg7U1dXRYyc03sOHD9PvJxaL4eLFi+jr60M4HEZ5eTl27tyZVgBHo9HgX//1XwHMG4UT0QOdTkfV3oD55356ehoCgQCXLl3C5OQkfD4fzp07h6GhIQQCAUoL3bBhAwYHB+F0OlmvV67zn7mAdBZEIhGUSmUcRYotGMqGLrUUHe18OgSZqGepbCYAsKqSFuqYyUyfUqmESqXC5s2b0dnZSWcX169fj4aGBtTV1cXtX6fT4YUXXoDFYsGePXuwb98+AB9S1tVqNQ4fPkwTO2aFnySHxMzX5/PB4/HgT3/6E+x2O+67776sEyE2mmuqeyofsAXmicltKkEZAFSghKmIWFVVhTfeeIMK8DCvTSIljyR26ZL5QoB53SorK3HbbbfhG9/4Rtp9XE+xj6XaN9s732QyYXJyks5JBwIB6PX6ON+2XLa/d+9eDA8P04IjKZJxuVysW7cOdrsdbrcbYrG4QGf114/lpGwZ1w3MlwJ5aC0WCxobG7Fz586M1Sry/8QvrKysDBUVFZidnaXzaARisRgNDQ2LrlL214hCmWAvNqqrqyESiSAQCOD1etHZ2UlpdufPn4fL5cKjjz6a8vM7duzAm2++icnJSfD5fGzevJmVkrd69WqIxeK4mbXEoFImk9HuUOIMYyLInAnZVuLLkMy88Pl88Hg8NDY24q677qL+OcTuQSwWU6npbMyzCcjTWUZlAAAgAElEQVQcHFuglA6ke+Hz+ai33KuvvoqamhpKAdZoNDCZTLhy5QrcbjdisRii0Sj8fj/8fj+A+Y7O0NBQxgRIq9XiH/7hH6gfGZH9JrSpWCwGiUSCmpoaqFQqWrl3u92UUsbj8VBSUoJdu3Zh8+bNlIoaiUSSklui5LgYYCYEK1eujFPeZHvW0iVDSxG4EuQz38aU7CafIf9ldsaYHb5Cnkfi9okkPfM+J98F8dATCAQYHR2F0WjE+++/j+PHj9NZ10ceeYR6P509exZ//vOfsW3bNqxatQparRa33nor+vr6MDQ0FCf6kZjAEBl2t9sNu92OM2fOUBNerVab1s6D7XtIdU8tBJlmzJjHoVKpYDab447PYrGgsrISW7ZswZ133gm9Xo/Kykp4vV7I5XJ0d3dTlkui7xVz1m8x5sgIhEIhNeJOR21OvC5LzR5Zyuc8cX8kAT9w4ABGR0fh8XgQjUbh8/kwODiIgwcPZm2TwkRJSQlWrVqFkZER8Pl8ykARCAS47bbb4Pf7qeDacpdsHouWlH3729/G22+/jQsXLsTJjTJx+vRp/O53v8Ply5f/P3tvHtXWfaaPP9quNhDCoIBlwBgbsI2JwW1qN0nj1C71tHHGqc80SXfntNPG6ZyZpnHantPJZPLtSc/J0qZL9sk0TpNmcVLXHZOpMSETO3YCSYxIMDaLERiBLCFAQmhff39wPm+urq6ExGbSH885PY2R7tVdPsu7PO/zIhaLYfXq1di7dy+++c1vJvTY4WNqagqPP/44jh8/Drvdjry8PGzduhX79+8nqtEyPjmw2+149913MTQ0hJycHBQUFOBb3/oWTfyZIkZ8xTiNRkNZjXA4jEAgQGIMLBKz0CplnzRcTu78TNcllk3Yt28fFe8zox+YNrpffvllfOMb30h5/fX19XjwwQdx4sQJyt6I0YaEG2JbW1uCgXTixAl0dnZiZGRkRoeMYWxsDCaTKem3TSYT7rnnHpw/fx7hcJjqs+rq6tDV1QWFQoG8vDwUFRXh2muvRWNjI6amptDZ2TljOwCGUCiEvLw8jI6OIi8vL6ssG79x88DAAOx2O6k2WiwW2O12fO9738Po6ChisRjRUoCPm2GzjGQmDhBr2s6oX8yoZe/a4/HQc2JZzPfffx9dXV1QqVTIycnB7t278d3vfhcAqJZOpVKht7cXZrMZ8Xgcer1+0cd6urmWrvB+IQUQhJhNfZtQspvjuJSZMT5lc76ePf+a+bTWaDSKYDAIn88Hh8OBkydPoru7m2ofmcMulUpRWFgIh8OBDRs2JPT/i8VieOutt3D69Gno9Xps3rwZ69ato89HRkYoQCJUzNu1axdcLhdljMfGxvBf//VfUKlUWLlyJTZt2pTyGYi9B7vdvqBqdakcQSaSxFdkFb5TvhAQqwXn16NeddVVNJ8NBgMaGxsxMTEBm80GpVJJvzff85FlHF0uF9GiM63FEzqsC+kkLfY8F/u9lpYWfPjhh0TzlMvlJODEmBbZviPWUDwUCsHr9SInJwd+vx95eXkoKyuj4PtSDwgvJhbEKXvllVdw7NixtN957bXX8POf/xzAxzzW3t5e/PKXv8Tbb7+NJ598kuoCGKampvD1r38dvb29UKvVqKqqwqVLl/D666/jjTfewJNPPomrr756IW5pGQsAk8mE++67DxaLBZFIBEVFRdi0aRMZjdlGq1gEZv369TAYDKiurkY8Hkdvby9F45Y6bXGxs1aLxZ3P5r7SGa9FRUUJzcH5UCqVGWVjZorICQ1GoYEkl8vh9XqTWi+kgkqlwujoKG6//XaSGW5ubsaPfvQj/PrXv8a5c+cQDAYhkUigVCqxefNmUoT0+XyQy+UwGAwZO4BCsI1RIpEk0a0yAZPDHhoaQjgcpoxVaWkp7rnnngQ5fCa7z3EcfD4f1Go1Nm3alKRolwpszvODLawfGT9Txs9iNjY2guM45OXlYdu2bQly5owqynEcmpubcebMGZjNZuh0OrzxxhuoqqpakAitmDACkCzyMVNNzWLXtsymvo1JdjMwRcyFyoylu2a+JD1rqWAwGGCz2dDT0wOn05k0j2KxGMbGxtDb2wun0ylah8hqpZiDFolEqAfeiRMnqHH82bNnsWLFChQXF1OrFavVSrU0ExMTkEgkiEaj6OjoSDn2hO8BAA4ePIjOzk74/f4FUasTG4Ostu3IkSNwOp3o7+8HkPqdCutRz58/D4VCAZ/Ph+LiYspsW61WYsfk5+cv2N7MxmkgEKCa4Wx/azECl4s9z8WyyydPnoTb7UYsFoNer8fatWupFclsyz5CoRBUKhWcTidisRgUCgXUajVUKhV6enqwY8eOBXU+P4mYd6fs5Zdfxn333Zf2Oz09Pbj33nshl8vx8MMP40tf+hIAoLe3Fz/4wQ/w9ttv45lnnsHtt9+ecNw999yD3t5ebN26Fb/73e+g1+sRiUTwyCOP4JlnnsGdd96JlpYW5OTkzPdtLWMB0NHRQZFwuVxOnG9hz5CZjGwmNMAiMUqlEnv37p2RMrTUcDmyVgvNXxdKUmdyX+k2KObACyGXy1FTU7MgGzvfQOI4DocPHwbHcSgoKEA4HMbExASpSalUKsrSskbSSqUSf/vb34j6E4lE8OabbyIcDqOrq4uCEAqFArW1tVizZg1aWlpgt9vh8Xggl8thtVrJEczNzU2pGiaGUChElKLi4uKsC8eNRiOqq6vR0dEBmUyGVatWUT1LX19f0ve3bNmCXbt2AZg2uLKlC7M5z7IPHMdRTRmrp+NnMRmFddWqVUmUMP76UVRUBI7jMDw8jKGhIVLLXAgVxlTzKtVcS1d4v5Sk74WS/D6fjyTOTSZTyma3C1GzKlzXWWaRSdKzLB1T7UxHNWaOmVBsR4hIJELrD6PB+f1+HDp0iM6dm5uLr3zlKzAajdixYwdOnDhBQhOsrUcoFJpRrIe/5o2NjcHlclEbmOHh4XlXqxOucywT3tzcjMHBQfT390MulxMFOtU7NRo/rkdVKBTUJ5K1BGlpaSF6aG1t7YKWFLA6thUrVoDjOGzfvl2UIZEOi+EwLfY8F/4eALjdboTDYdpjfvCDHyAUCpHa8WzeEWtlEw6HEY1G6fyBQAAOh2NRBVQ+KZg3p8zlcuGBBx6gzu3p8PTTTyMSieDb3/42OWTAdO+dhx56CN/4xjfw3//937jtttsotW02m9HU1ASlUomHH36YlJTkcjnuvvtudHV14d1338WLL76I73//+/N1W8tYQNTV1cFgMACYlhm/5ZZbMlowhZvx/v37E3orFRQUJBiews1jqTppl0PxaSG586kkqWe6r3QbFMdxkEqlUCqVJLmbl5eH2trajLMxswEbQ6+//joGBwdJvvuLX/wiABAvftOmTWhubsb777+Pnp4eGodCYy8Wi+Gdd94heXyFQoENGzbgrrvuwtjYGC5evAi/3w+5XA6FQoHJyUmqiaqoqEhbjyJEaWkp1cml6p2UDlarFT09PVSzxfj/VqsVubm5Cd/V6/X48Y9/PO/1LqnOx+rl2MbPDHOx8Ww0GlFZWUm9DOPxOOx2e9o+bHO5drF5JVTUE7tfq9WKtra2rJQZFwNijaFHRkYQDAbR2NiIYDBIinGpmt0u1LWwQI/Y8yoqKoLZbE5otyAGNoaEkEqlAJDUi5AhHo8n7DeBQIB67hmNRtxxxx0AgA8//BCTk5MAgA0bNqQs6xC7R61WC47jkJubC5/Ph8LCwgVT5QM+rqFlFFCr1Uo1r5kEdhjVHEBCY3CWMeY3mb7hhhvm/T6AROri8PAwSkpKRFVyZ8JiOEyLPc9ZBrSjowMlJSWw2+2wWCyIx+OQSCRYuXIlQqEQCa1l2hNU7Heqq6vx3nvvAQDVHC9EC4y/F8yLU9be3o79+/fD5XIhNzcXP/rRj/CLX/xC9Lt+vx9NTU0AgH/6p39K+vzTn/401q5di/7+fpw6dYpoEX/9618Ri8Wwfft2XHHFFUnH3XzzzXj33Xfxt7/9bdkp+4Sgvr4e9957LxkpmUz6VJsx663EBBqYspOwf9hSraECLp/i00JEsQFxSepM7ivdBhUKhWAwGGCxWKDRaLB69WrccsstWTkpswVfkjocDkOlUmHTpk1J47aoqAg1NTVoaWkhGtvw8HAS5TIcDkOhUGDFihVYu3Yt/v3f/x0A8Mgjj+DChQsk7a7RaKgmUqVSURYpU7Dn2dLSgpMnT+Lo0aNZFW2ze2CGFIt4v/DCC5DJZCRrLJFIsHHjxoT2FQsNYb0cX7VObH6zzHprayskEgkikQgFcxZ6LRBT1BP+XjpnYyGuJxsjkMm82+12OBwOcBxHEueMKsiyYwstpsSuxeVyAUgM9Aifl9FopD6WswFrJJ/OEWGOm1QqhU6nw7XXXkuf1dfX47777qP5J5PJYDAYZgyOCCmimzdvhtlsRjQaxdDQEAYHB+ed+iWUuWfvde3atQl9PjOtD+X3owSQ0GR6toZ+pmDPz2q1knpsQUFB1sFOsQwi+/t8YqHmuRjYWmQ2m/HXv/6VMlfMYWIBg/kIEqvVaqpNY6ioqJhTC4y/Z8yLU8aaGX7hC1/APffcg0gkktIpY3K0OTk5qKqqEv3O5s2b0d/fjw8++ICcso8++gjANDVGDHV1dQCA8+fPw+PxLFMYPyEQ1vjMZCikyibxa1GAaQGR559/noyvjRs34o477kAoFLps/UdmwuWIii9U1pD1tgFAktSZ0lTSXVNpaSn0ej20Wi38fj8KCgpQVVVFDvdCNvFmzkkkEoHb7SZqj9C4FqPfHThwQFQ2n+M4XHXVVbjjjjtQX1+PZ599Fna7HX6/H7FYjDI6ubm5WL9+PUn4z+bePvjgA1p/2f1kch6m+qbX6ymzzahq8XgcBoMBk5OTiMfjCIfDizqnhPVyAwMD6OzspECAmGLrgQMHYDKZ0NfXh+7ubtG+SXPFTEp2YtcGfOxspLr++UK2wSmTyYQjR47AbDbD4/FQcIRPU1ysZtaz6dclVN7MBsLsmUQioaxVOByGXC7H6tWrceWVVyIYDGLXrl2oqalJyHYajUZ861vfws6dO4nO3dLSkjY4IgzS6fV6KBQKuFwuxONx/Pa3v53Xmkg2Jvgy9xUVFfReOY7L+v3ys79i0voLOU74IiX9/f3kOM8m2CnMIM5XQPdysXbYWtTf3w+HwwG3241oNEpBBdazj08Fnm2QeM2aNVAqlQm033g8vqjBu08S5sUpq6qqwssvv0yLA1+9SIihoSEA081PUxXKr1q1CgBo8+L/d6qBUVRUBLlcjkgkAovFgg0bNmR/I8u4rMjEUGAGIqNiCccDS7ePjo6is7MTk5OTpB4UCATw4x//+LL1H8kE/DoC/r8XAguVNeRv7kNDQ1Rjkm4TZg6V0+lET08PvF4vtFotqqurkZ+fD6PRSIZBZWUlTCYT4vE4HA4H3n//ffT19aG9vR3t7e0IhULYtm0bDhw4MK/Pj40Vt9sNn8+HkZERmM3mlIYzP/JZVlaG7u7upO/k5ubipptuorWzrq4ORUVFcLlc1G9PIpGgsLAQ119//axpdhaLBcFgkJ7hbKgjgUCA1OuYQV5ZWQm5XA6TyYRwOIzOzk50dHQsWvE2v14uLy8PbW1t6O/vh0KhSFof+AbQDTfcQOMUANVFzRfSKdkB4mvPfDcHzvb6Uo0rk8mEhx56CP39/fB6vZDL5bR3b9u2bUFpiqmuPdt+Xbt27aKm8GLQarXUyiEdpFIpNBoNamtrUVZWBp/PB6VSid27d0On05FK4gMPPEC0Pf66ajQa6fpnevb8IJ3b7cahQ4eohxQAXLp0CY2NjTMGNfl1gGJOlbC1gVDmXuy77PoyBT/YUFVVtSgqyHyGwIULFzA2Nobu7m7Y7fZZr6HzGdC9nKwdthaNjIygv78foVCIVGk3b95MZSTzESTesWMHXnnlFXz00UckkBMMBpdUQHwpYV6csk2bNmX8XRaxys/PT/kdVi/GooX8/2afCSGTyZCTk0PFsMv45GGuix47/uzZsxgdHYXH40EkEkE8HofX60V7ezvOnj27ZGo0hJiNKMZcsFA1bPwonM/nQzgcTilDzBoSHzt2DBaLBeFwmJyGYDCIlpYWcBwHjUZDGRmFQoFoNIpQKASr1Yrnn3+eFnqXy0Uy1lu2bME3v/nNWd+HmIEzNTWFaDRKSlKZOjdf//rX8fbbbycYfTKZDOvWrUswqurr63HnnXfid7/7HRlgBQUF2Lx585zqnkpLS2EwGFBeXp51to0ZkS6Xi6LOTJJ+YmICJ06coGxCIBDAq6++ihtvvHFR5harlwNAss1TU1PIzc2lDCow/S6feOIJfPjhh1AoFLj99tvR0NCAhoYGHDx4EMFgUDTrOZfrEnPAampqUFNTIxqgEKOJLtQzzEb6/uDBgzCbzVSekJeXl0A/m49sAb857UzZGCbeUFJSQrVKM6GhoQG//e1v8eKLL8JiscBsNlPQA5h+L5cuXSKHgw+JRAKZTAa5XA6dTof8/HxotVoA0wHk/Px8nDx5El6vF8B0AHlkZIScvKamJtTV1SWI1fCfPcdxCVk1IXp7e3HkyBFqlM7AWk7wn6PQyAeQFCBbv349NXJP19qgrq4uwQGbrROxmMEGIYxGIyKRCPx+P7xeLyYnJ9OqXqbDfJcXXI4acgbmcI2OjuKDDz5APB6HVCqlGkghFVhY55otKisrYbVa4fF4UFhYiPXr1y+5gPhSwaI3j2Z1FazoXwxM3IO/aGZyHPtsofp4LGP+wTd8Z8qCAeJS02yRYMd7PB5SBZNIJLR5RaNRjI+PLyp3O1PMVhRjLlioGjZhb5vi4uKE8/Mjt4cPH0ZLSwvGxsYQjUahUqmQl5eH3NxcTE1NkROkVqvhcrkoQj85OUn9huLxODlyEokEsVgMHo8Hf/nLX8gAzhap6GcymYzqwLLhxTc0NOCJJ57AE088gQsXLkCpVKK8vBz/8A//QEXWbMMLhULQ6/UIhULQ6XS45pprcPPNN9PmOJeAgkqlQnFxcVbUEfY+x8fHMTw8jIKCAsosnTx5MikIJpfLF83AMBqN5FiFw2F4PB4A00qW4+PjVLNjMpnw5ptvUjH7hQsXcMstt0Cj0ZBAxXxSGIVRZgBJIhkMQkVDAAteb5NpFJwZjm63GxqNBlVVVbjtttsoKzQfDtkTTzyB1tZW6j8nbE4u/D6/CXFDQ0PG19DQ0ICamho8/PDDGB0dJftCq9Viw4YNCa0dGCQSCdRqNeLxOOLxODweD/x+P8bHxyGXy+k6cnNzoVQqMTU1BZ/PB7/fj9zcXAwPD+Ott97Cq6++Cq/XC47jsG3bNlx33XUkPpGqBtJkMuHXv/41yeCzLAOjNKvVaqrvFNaCAR+zLWw2G7q7u2Gz2Yg1whx+sdYGrNen8Lpm60QsZrBBDExQjO0vM6lepoLYnJ6Lo3K5asgZjEYjysvLaU+VSqW46qqrktaduWb0+I69SqVK2M+WkYxFd8pYU2hhlIcPsc9kMhlisVja41ghYab9g5ZxeSGc7A0NDSm/x3fc0slKb9u2DR9++CEcDgdJFDN1uyuuuALbt29f+BubBWYrijEXLFQNG/+8QsoM/51Ho1FYrVZyvGQyGfWa2rJlC9rb29HZ2UnGGj9TFggE6DhgOhAjl8uhVqsBTDvgZrMZjz/++Kwkz1PRz1hWRqlUYt++fVkZziwzw5pIDw0N4dSpU3jttdcS6jf4UeX169cnOGSz3RzTBTNmAquP6+jogE6ng8PhQDwep/o3fs2NXq/HVVddteAGBn9NYJLbsVgM+fn5UCqVkMlk6O7uxgsvvICGhga0t7dT6wJgWnDgscceIwrcpk2b5n2+8YM/wgbk7PmLrYGLUZclvL5UcLvd6O/vp9qpPXv2pFynZwOLxYLBwUE4HA6EQiGo1WoaT2KqmGxeMpXHbBUIDx48iGPHjhHtMD8/H6WlpTh27JhozSfLkCkUClpvIpEIKTXGYjGqVcrNzQXHcQgGg9Dr9SgoKIDBYIDVasXExAT8fj84jkN3dzfcbjdkMhneeecdjI6OYnJyEqtXr07Iqh08eJDo2BKJBFqtFlKpFGq1GrFYDFdeeSV0Oh1MJhMOHjwIl8uFycnJpCBYcXExVCoVgsEg4vE4nE4nBgYG0NbWJhoIMBqNouN1tk6E8LiFDDaIob6+Hj/84Q/x7LPPQqFQwGQyoaamZlbzS6xGbraMloXaf7PBmjVrUFBQALvdDo7j4HA4ksRn5prRE77/ZYcsPRbdKWNGEz8LJgRbaPlZMbZYZ3vcMpYuhJO9o6NDtOGqcPFLt5DV19djzZo1GBoaQiQSoc2stLQUd95556JvCJmAiWJotVpUVVVBq9Vi27ZtC1oIzTdqF6L+h18bx39PfNW0VatWkfMZDodRUlKCXbt2kZLijh07kmhNrGfVSy+9hPfee4+ixgCopxeD2+1Gb28vTCZT1s+Rn+1jWaH52ERZpJ9lRCORCEKhELxeLzQaDc0BsajyXDbHuURlrVYrGhsbcfHiRXg8HigUCly6dIkMVBYMY72IWPR+oSDmyLB702q1KCgoIAEPAHj88cfR19cnasAHg0HYbDZ4vV5cffXV8zrf+HOMUe4MBoNoc+je3l44HA7U1NQsmET4bMDqwyORCDUEn0+UlpaivLycFE1Z8MVms5EqJt9Rncs4bm5uxvPPP0/tKRQKBfLy8jA0NISJiYmk/odSqRS5ublYt24dZDIZbDYbJicnEYlEEAwGycFnEuK33HILenp64HA4qM7MZDJBrVZDoVBQpkyv1yMYDGJ4eBhOpxMTExOIx+OYmJiA3W7Hq6++CrlcTi014vE4FAoFFAoFSkpK4PF4oNfrUVVVBbfbjWeffRZmsxmxWAwbNmxIqgVraGhAW1sbFAoFIpEIVCoVPvzwQ5jN5pSBALHnPJv1j82BxQw2iEGn06G4uBgXL16EzWabc0Z8LmuxWH+9y4X6+npceeWVeP/996kkQHgvc83oMTZDqlYgy0jEojtlrCYsVcEtAJK5XbFiRcJxbrc75XHRaJT6kKSrV1vG0gF/smu1WirO5zcdFVv8tm7dmnJiG43T/VF8Ph96e3shkUhQWVlJ6nZLDUJO/5YtW2bdqHE2v7lQdWup6hv4WSCDwYC9e/dSrYXwnoUblslkooX9rrvuwq9+9Su89957VMsBgAwrJkYw26w5nxYnrDdK9awyoRay8Tw6Ogqv14vCwkJEIhGqUWFzQEwyei6b41w2RiYS4vf7SUaeqVDyIZFIYLfb0dLSgtbW1gULLAgVCkOhkChVUKPRUDZ2fHw8LctiamoKv/zlL+F2u3H33XfP+RqF8xoAUd34/ZoY5Zo5h62trQuuSpcNGNVLJpMhFApRa4b5AJsve/fuJUfeaDRSs2fmVB88eJD6Y80UlEuHpqamhLUCAAoKCuhvfKq7TCbDxo0bUV5eDoPBgOrqagDAwMAATp06hcHBQUQiEUilUlxxxRX4t3/7NzQ0NCStATU1NcQY4NeUNTc3w+v1YmJiAlKpFMFgEJFIBGazmTL+EokEUqmUekdFIhFYrVYaLwMDAxgYGEB/fz9cLhfy8/Oh1+upcTuj1jGlzJycHEgkEpSUlEAmk9GeGgqFkgJzqRywbJyIpdR+hl8aASDrXo1i55vNWryUngkw/T5vvvlmUlWdnJxMEjxiY4EFSLNFJq1AlvExFt0pY/SfdE38RkZGAEwrlvGPGxoaos+EYHQoiUSScNwyli74k721tRXt7e2icrnZLn6sL8xCyqPPF/hOp8FgIFpLNn2k5vKb7N/z/TusmJ6JfTAj1Ov1QiqVguM4VFdXJ7VESAWTyYQ777yTNrNHHnkE3/nOd3D69GnR7zOaj0ajmfW9MdqR0+mERqOByWRKaQxmutky4yAWi1H9W25uLlwuFz766CNSNhSTjJ5Lpm4uGyMTCdHpdGnV6Zjh2NfXh0AggMHBwXkbx/y6KzHRAKGxyFet++Mf/0gZVVaTI4ZIJILf//73GBsbw6233jqnIA5/jjFDkL3bxsbGBCdj27Zt1FYmW2ppJsi2DlFIDS0sLITH4wHHcXjnnXfwuc99bl7eZ6r5UlRURDV+0WgULpeLHGtGacw0u8+/F36QF5imIFdWVsJut0Or1ZKTFY1GsW7dOnz2s5+F3W6HyWTC0NAQampqsHv3boyMjGBwcBAKhQJ6vR4HDhwgSqdwHPL/zR9PRUVFMJlMaGlpwZkzZ0htNRqNUkaV1aQFg8EE5dRwOAyn0wm73U5S43q9nmpc7XY70Rk5jsPmzZthMBhQW1uLcDiMPXv20LhMt6fyr104hrIJQC2l9jN8Bdm5rE2zXYuX4jPR6XSorKxEf39/2sbgTNk62+e2FO95KWPRnbKqqioq2jebzeSk8cGM6c2bN9Pfamtr8dZbb6GjowPf/va3k47p6OgAMK3yMp/RvGUsLFixMZMIXr16dYJcbrrFL93GwKd8LWXwI27RaJREB4CFW7zmq8A4ndSy2+1GX18fPB4Puru78dxzz2Hz5s0YHR2F0+mEWq1Ge3t7xlnBl156CRcuXKBi+5deegm5ubkpNxCVSoXc3FyUlZVlXXfC7o1v/ANIq4qZzcbT399PdCOmFsnUryKRCDQaTUrJ6NnSXeayMRqN07Wa3d3dMJvNAD5WwGOZBaZMV1hYCIlEQvWR80EVEtYhBoPBGUUD2N9eeOEFBAKBhOsVZkv4iEajePXVV9HT04N777131o4Zf44B05k4rVZLqqL8Oc76ATEHZT7r2rKNzAszfF6vF263mzI28yVlnW488td8t9uNxx57jJyQYDAIs9mcNgvLX5dYIEKr1cLhcEAul5NTHo/Hcfr0afh8PsjlclRWVkImkyEcDsPv96OxsRGjo6OIxWKUPaipqcH4+DjVk6nV6ln1RGXzmOM4XNaIyFUAACAASURBVLx4EXK5HHa7nWprCwsLodfrYbPZEgIh8Xgcfr8f0WgUgUAABoMBFRUVxBApKirCAw88gLa2NlK6PnPmDGpra8FxHNRqNS5evJgVnVCMLpyuQTvD5Ray4EOoINvd3Q2v10u25mwCXemc1lRYSs+EQVgrLdYa5HJR5///iEV3ypRKJa677jo0NTXh1VdfxU9/+tOEzz/44AMMDAxAr9fjuuuuo7/v2rULv//97/Hmm29ibGwMhYWFCccdOnQIAPCP//iPC38Ty5hXzDRphQ4Wi9SlMzaWGk0gFfgGCDMiZopgzudvzibjwoylxsZG2Gw2mo86nQ6bN2+GXq/HsWPHMDExAa/Xi9HRUQwODqKpqYnqGtRqNdra2tDX14fKysoZm4kycQlg2jCxWNL3IszJycGVV16JioqKWT1HoWJYWVkZhoaGUm5KwjHMl7lm3+c4Dg888ADefvttOo6JG8jlcsjlcur/NVd6jRCZKJumQ319PY4fPw6r1YpwOIyysjKMjIyQgcjkv2UyGRoaGqjX3HxLRxsMBspwzCQawI7z+/3Q6/XQaDQIBAIIBAIps2UAqN5nttLZQDILgBnyYlmK+ahVTIVsjSlhhi8QCEAqlSInJwerV6+e9Xzig19Dy6eq88EM3ra2NuTl5aGnpweRSARnz56FzWbD4OAgUWSNxsR605aWFlovgsEg3G43OI6jAAiD3+8nQaj8/HxyrhiNa2JighrdymQyOBwODAwMYGhoiMRIGAVxts/h0KFD6O3thc/nI8eX395DKpVCJpMhHo/TGhGPx4k6rFQqsWfPHuor1dbWRlRjJkYSDAZx5swZVFRUQKFQQKPRiFIWU0Gs9juTMbWQ4zpb8GuEh4aGSBmYUa3n0oImU1tjqdTXCZGOqs8gVmOdzfmXyjj4JGDRnTIAuP3229Hc3IznnnsOVVVV+MpXvgJguh8H4/Pv27ePREGA6QzYF77wBbzxxhv4l3/5Fzz66KNUj/HII4+gtbUVer0eX/va1y7HLS1jjkjXv4fJJg8ODqK8vBz79++f0dj4JKXM+RG3oqKiRVm8xBzdmcBX+RoeHsbk5CRcLhfi8TgGBwchk8lw+vRpqNVqeL1eBINBxGIxylCEw2Ey8qRSKex2O0ZHRzEwMIALFy5gzZo1KRUNKysr0dzcTI6Z1+vFZz/7WdpI+JDL5Thw4ADKy8vnTa54+/btaG5upn+XlpYmRUeZEe50OnH48GFqgM2uNxqNivLy8/Pz6Vq3bNmCnp4eHD16FMePH89a4XGhwCLxAIhm5fF4qI43Ho/D5/Ohvb0dV111FdUIzQd1mC+SwRQqMzFq+P2sWF2Qy+XCa6+9BrPZnNYxY8Izc4HROC3F393djf7+fqjVarzzzjvYt29f0vXPNgM6E7KNUgszfN3d3YjH4ygsLEww/mcLYSZupmBMaWkp9Ho9dDodXC4X5HI5QqEQ7HY7Sb17vV4oFApSiBwcHKRSBqVSiXA4DKlUSiIdDPF4HNFoFHq9HoWFhXC5XBgbG6NASV5eHgBQbZfT6cTLL7+MqakpcpRkMhl6e3tnFUAxmUykCsp+JycnBwUFBdQSg2W35HI5cnNzKdMrkUjAcRzWrFmDyspK+m2W9XA6nejo6EAkEqHsssvlwsaNG5PGwUxZHuEYqquro0xvqjG10EJS2YLvGPT19aGpqQlOpxMOhwMOh4MEbGZjJ2Riayz1IDFTsE3VGiQTxy0dFmp9+3vEZXHKNm7ciAMHDuDBBx/Ez372M/z+979Hbm4uent7EYvFcP311+P73/9+0nH33nsv+vr6YDKZsGPHDqxbtw6XLl3CxMQEFAoFHn300VlRCZZx+SBcrBidUajY19raSs1hGXUlnbExl8jO5cRCL158as/zzz9PmaoDBw7MSGs6ePAgPvjgA1Lg8/l8kEgkZACFQiGEw2H6f0C8PUUsFiNHKhaLIRwO48KFCyRJLbbYl5WVQa1W02+yHl5f/epX8ac//YmcNalUirvvvhu33HLLnJ6TWHSPOcwcxyU0+WZqmRzHUTbJ5XJRlg0AUaTEFN6+973voa6ujoRtTp06hTNnztCYnY/C6LlI4jMUFRXhi1/8Ij2f/fv3J33H7Xbjueeew8aNG2fdI44PfiBAr9ejoaEho3MK+1nt3buXjrvmmmtw9OhReL1eXLhwAa2trUnHFxYWzkk6m107a/obCAQgkUgQDAZhtVqTmB5z7T+XCplGqfm/zzdeLRYLfD4fysrKEoz/2YJvwAqp6qmunwUDWI1Ubm4u7QVs/49EIpDL5dBoNIhEIvRvYNr5mpycpKwXA/vvoqIixGIxXLp0CVNTU9BoNKioqMCuXbvgcrlw5swZnD59GhaLhY5RKBTIycmBSqWCw+GY1XxyOp0JPf7i8Tjy8vJw0003oaamBs8++yxUKhXVx+bk5CAajWLFihXwer1YuXIl9Hp9wt7G3rfBYIBEIiFJfYVCgTVr1pAyIwCSxOdTPPmZR36tZqq1cC71tYsNdi8soMYX4WGZspkaeYtBLPAhnM9LPUicSfAmFAohGAyiv78fwNK7h78XXBanDAC++93voqqqCn/4wx/Q2dmJ0dFRVFRUYM+ePdi3bx/1M+PjiiuuwGuvvYYnnngCzc3N6O3thVarRUNDA/bv34+amprLcCfLmAv4i5XP50tS2ko16fmZiVSfzyWys9hYKKNM+Btss3Q4HDhz5gx8Ph8GBgawZcsWUkgUgklJj4yMIBAIQK1WQ6vVYu3atRgbG0Nubi4mJiYQDAapX5jf70cwGEwyhBiY0hmfnsMyb2KLPTPIfD4fZDIZfD4fOI7Drbfeivb2dgwMDEAmk+Hzn/889u7dOy/PSqywndE2meMllUoRi8XQ3d1NdM1wOIycnBwUFxejvLwcwDT9sq+vL0GyHwA0Gk1Cn6/S0lKiA3o8Hlit1llJ+gshVDrNlh4pFjxhWTI+IpEIJiYm0NHRAbVaPaeN22Qy4aGHHkJ/fz9isRg+9alPZVwfyNYVsX5WfHGZ5uZmnDlzJuEdyGQykkCf7fWzIMa5c+cQDAapvtFgMCTQpZhi31wpVOkwU6BHzIguLS1FY2MjXC4XFAoFDAbDotAWxSAMBlitVjidTvT09KC7uxtTU1OQSCSQy+UwGo3kkEUiEeTm5uLs2bO0BnEcl5C9DwQCuHDhAuRyOWWgpFIpDAYDZQV/8Ytf4M0330xYx8LhMK1BNpstKejH5i275lAoBLfbjeHhYdTV1aG+vh5DQ0NJ45ll7U6ePJmgRsx6pIXDYahUKhiNRrjdbqhUqqS9zWg0YteuXTCbzbBYLJiYmEB+fj7KysrIIRPWaDJBke7uboTDYeqbyMaisH6K3ZvY+1vqDojQyQQ+ppeL1crNtDeLnU9Ygzebcb+YYPZSOnVeNtYZ9TPTQPdi2DZ/T1gQp6ykpAQ9PT0zfu9zn/scPve5z2V1bp1Oh5/+9KdJtWjL+GQiE6GL+vp6UigrLy9PiJSnUwSaKSW/VLBYkUX+Zjk6OgqPx4NoNEqOGf962CZ1+vRpPP3009SKorCwELW1tbj55puh0+lI5IPVc7DI76lTp3Dy5EkEAgE6L+sfGIvFqLaBHc9okOfPn6c+QgzNzc148cUX4fV6IZPJoFarsWbNGqqLePDBB3HixAkUFhbOmV7F7l+ssN1sNqOvry/B8dLpdAgEAhgbG4PP50MgEEBOTg6qqqpw00030VhtamqC3++H3W4nRw6YdmIOHTqEs2fPwmAwoKioCC6Xiwr5WY0MML1xnj17FuPj49i+fXtWWShhjVNLS0tWKlpidSVCB5MhHo9DpVJBqVTOyvhgRt+RI0dgNpspS5bp+bIx/oeHh0ncgUGtVqOoqGhOxpPJZILVaiVD/4orrsCePXuQn5+PlpaWhCDUxMQEbDYblEolgMU3YsWMaAAp++XNBpnSFoUGHMuUBoNBGAwGuq7i4mJqp8FqyJhqKcdx6Orqgs/nw/DwMNRqNfx+P1QqFdasWYOamhocP34cbrcbkUiEhExkMhk5Qg6Hg4Ih+fn5kMlkSVlutm5VVVUlOFdPPfUUnn76acq6FRYWoqCgAP39/YhGo5DL5dQknu/oMVXapqYm2Gw2qFQqVFZWoqioCHa7HQMDAwiHw3C73TCbzYhEIujv70cgECDpfb6TUVRURNk1o9GI6upqWCwWjI2NJdVo5ufnU30wv2+icCyyUgKWXd62bRv2798vWn8ELB1hB7HeYEJ6nljDbCC5T6qYo8YvB+A/X36gORO67uVCJuq8oVAIxcXFNP8yCZAt1azpUsZly5QtYxlAZkIXjColjLbMFJHLZnO4nNGcxYossucxPj5OTphEIqHMAJ/OYjabaYFmTgKLRt90000kAc0HcxKYUXzu3Dm43W7qs6PX62E0GmG326FQKGA0GiGTyfDee++RUXzp0iX88Y9/JNqY1WrFk08+CavVSpLVTHiDvc9MZfUzfcepCtv7+/vh8XgSHK/t27dTw1ibzYa8vDzo9fqkWrBdu3ZRsKGvrw8ul4uc2VAohM7OTqJ+BgIB2vDGx8dx6tQpnD9/njKJAHD8+HHcf//9WTtmfKVTdq8zjTUxJ6eurg6VlZUYGxtL+G5OTg42bNiA1atXY9++fVmPY7aJd3Z2kqBCfn4+yX1ncq3Z1CzV1dUlUWyVSiVJ4s82S9ba2orR0VGEQiFEo1FMTEygp6cHe/fuTQpCsXeqVqvnTLWezTomtk4yJcBVq1ahoqJizjTUTGiLYsEQRpkOhUIoLy+HSqWi+p9QKIQbbriB+r6x9eDhhx/GqVOn4PP5SO2S1Yh95zvfwY4dOzA2NoZ3332X5hlzynQ6HaLRaELPuDVr1sBgMMButyMSiSQ4UmztZL/9yiuv4P7776d6Ra/Xi6mpKYyNjdHaAQAvvvhi0jPKzc2FRqOhwBaTuW9oaMDhw4fR39+PUCiE4eHhhHpIk8mEWCxG6q3MeT1//jy1HlIqlWhvb6e2M/y5zM/WsnWsuLhYdM+0WCwYHByktWxwcFC0/mgpCTtk065EOA9SBSzEenDy1x32fPktHdauXTsjXXchMF/tCzJRaZzNeZeRiGWnbBmXHcKolVgKXYyCk4lqYyYNcy93NGexIov858E3LGKxGN58800MDQ2REcP6i7HILjBNX5jJAWLP0mw2IycnBwaDgRpSbtq0CfX19Thz5gxGR0fhcrkwNTWVINQRi8UwOjpKm19TU1OSbDxfAChTWK1WPPzww+jo6IBWq8W//uu/ijqWgHhhu9lshl6vJyoX3/FK1xqA/+z5wYeuri4cO3YMbrcbPp8PbrcbLpeLjD7mKASDQYyPj2N8fJx6GLEmzbNRB8x2rKVzcn7+85/j/vvvh9lshkQiwWc/+1ns2bMHOp1uVsaY1WpFU1MTZcc4jkuQ+57pXvnHM9riTEZQfX09qqurqaUKAKxbtw433HBDVtcuvAaHw0EBj1gsBrfbjY6ODmzbtk00CAWA5sLhw4dhtVqzdgpnI38vrCNj44Ffj9fQ0DCv9NlU485iSext2NHRkdC0XKVSoby8nBQTmVHI3xtef/11nDp1CjabDfF4HGq1Gnq9HkVFRairq6NM+ne+8x24XC5cunQJAIiWrdFooNfrEYvFEmovCwoK4PF44PP5EjIEOp0Ot912G/3+c889lyQgEw6HodPpiN7NRI/40Gq1qKqqQjAYJGEavuy/1WrF4OAgUQ758Pv9+OijjxAMBiGVSqFUKpGXl4exsTGqZZyYmIDFYkEsFsPq1auxc+dOFBYW0jxl6/pM61hpaSnKy8thsVioKXUq0ZClIPABZO4YpHImM3XU+EGHnTt3AphuNv6///u/RPlzu91Z16zNFoxxwJxtr9cLo9GI2traJEZJJvNzNiUhSzFrutSx7JQt47JDrK9MJg1uZ4rIZdowly2yfX19CbSVxcJiRhaZQEYsFoNcLkcsFoNMJoPT6UR/fz/Wrl0LpVKJtWvXknEHTEeSN27ciL1792aUZXI4HCgrK0NtbS0uXLiAnJwc1NTUYPv27ZQZstlsSZQglUqFmpoauN1u3HvvvbBYLHA4HFQLotVqwXFc1mIVLS0tOHbsGCYnJyGRSPDzn/8chYWFooa+WI0Au7bS0tKk7MtMNTv887LvFRUVoaenB4ODg8jLy4NGo6EaPLVaDaVSSeqGLAoulUoBTEfn8/PzUVJSgueffx5nz56FVqvFjTfeOKPjInQOZ1LfTJfhqK+vx+9+97t5Gbd8Z95ms2HVqlVJhmk2x6eK9Isd96UvfYkcAa1WKypgMpt7WL16NTQaDSYnJzE1NQW3203ZF2awMsGEsbExtLS0oLe3F/39/bNqvJ1NVFrMgdu6dauoY5tpHZ9YRJ6/tosp7PKPEdaslJSUwGw2Q6fTkQNw3XXXobGxMaXz6nQ64fP5qDm7UqnEli1baG4wB+fkyZNQq9XYsGEDqqqq0NXVBYvFgpUrV6K4uJjqmjmOQ0dHBykf8iX1OY5DQ0NDQnBHrH42Go3C6XTiy1/+Mpqbm+F0OhPOI5fLUVRUBJlMBoPBQGIc7FoZg2HFihXIz89PkuBn1EpWKyeRSGjd4NftxmIxGAwGaiUhFjiaiU5qNBqxd+9e2Gw2uFwuaLVa2O32tDVZlxvZOAZitMZMHDXh34xGI5qbm/H+++9jbGwMHMchLy8vqXH8fD8foS3FGAcTExPweDyIx+N4/fXX0d7eniDulakNkm1JyFLLmn4SsOyULeOyQtgYlqX6fT5fRkZ3OoM4UyOltHS6h1MgEEigrSxGJEuMl76Q4DgOSqUS69evx6VLlygTptVqUVZWliA5ni5imup++DQ3AJiYmEBOTk6Ccl5RURFF8Lq7u6kWS6VS4cYbb8Stt96K+++/HyaTCZFIhOrPFAoFOZLZ9toaHx8n6lA8HsfIyAipHpaXl2P37t0JDo2wRoApF/p8PoyOjmbzyEXBV0M0GAxYsWIFjEYjotEorrvuOkxOTuKZZ54h53nFihVYtWoVRkdHoVKpsGrVKvzqV7+iwnypVIqWlhb85je/ycgxA8RrJYRgcyNVf7P5Grd8Z764uDjBMF2o4/lrz/XXX4+cnBzs2rUrwcjOhg7Iz/Sw7ENhYSEGBgbw0Ucfwe/3JwUT2POzWq3o6uoi+pjL5cpaaCQb4zMdLStbx5Y9p1SULv75GLWOgX9MTU0NiouLqb60q6sL1dXVGBwcJEofqwEcHh5Gf38/uru7k1pHsAAHWyc0Gk3Ces7PHjDHPxgMkjrm7t27qV62ubkZXV1dMJvN5Jwylcc1a9bg1ltvTbifffv24Wc/+1mSIzsxMYEzZ87ghz/8IV577TUMDw/D6/UiHo9DqVRCqVRSywcmxvH6668nUArz8vKgVqshk8koW6bVapGbmwulUknZQdbYOh6PQ6PRQKVSQaPRQC6XZ5X5FGtFw8ZqIBCgPm+PP/44rd/MYGdjaikY4ZkyZtIdn4mjxg92dXR00LhhfRI5jkuqmWf/Px8Oi9CWYtRov9+PQCBAolsej4eUVWdiIwkxm8zXYtk2fy9YdsqWcVnBNw5ycnIwPDxMztFcZewzXUCMRiMJiTidzllLhmeDy0GZ5MuEFxcX43vf+x4ZD0ajMSsHTOzcfJpbRUUF/u///g8jIyOIxWLYsGEDOjo6KFvJjFaTyZRQqH/dddfh9OnT6OzsTHCimDGi1WpRXFyctfDA9u3bcfDgQdoQgWm6ybFjxwAAx44dw6OPPppQFyesETAYDLDZbGhra4PD4ZjTOxOOTWHvrUcffRQej4f6DMViMdjtdgSDQUxOTsJsNidQoGKxGIaGhnDixImMKI2ZBixYPyi+YMtCQPg8snHIhMdrtVpcccUVMx4jzAJ+9atfpWyRyWTCxMQEent7E5QS+e9I6LDxMz0OhwNDQ0MYGhqCVqvF+vXr0zbTZoaesNXC2NgYTCZTRnMzm6h0uvqZ2TjG6ShdTCFxbGwMarWasoA1NTUJx6xcuZIUBgcHB3HkyBFs2bIF5eXllOFhPbKYDL3ZbIbZbEZXVxd27NiBoaEhANOS9aFQiGoy6+vr8a1vfQvAdDZtcnKSDNVIJJIgYKDT6bB161a0tbXBbDbj/Pnz1IxZIpFAo9Hg+uuvxz//8z8nzTXWiuPPf/4zent7E9abS5cu4dy5c/jJT36CQ4cO0XPJz8/HunXrEuTqWV2lzWajJtCs1yEfTJQjFApBq9VSL8hwOAyZTAa9Xg+9Xg+n04nu7m5IJBJah9l7SjVWxFrR2O12HDlyBBaLBQqFAh6PB8PDwxgfH4dGo0FlZeWSUxjMlDGTDfiOhjDby+/lKZfLKYi2fft2WK1WqpnnOG5ebAD2+2ICLqtWraIaZaY4qtVqUVlZKfp+MlGanIuDu4yZseyULeOygh+J5zgOJSUlsFqtGav7pEOmRgorhuZv/gu9oVyOAliTyYTOzk64XC5UVlaS8TEf4N+PwWBAf38/hoeH4XK5kJubi+Hh4SRnRuictba24ujRo0mNfVkxPavRESpwZoL6+npce+21+Mtf/iL6+eDgIF566SUyfoUG+86dOzE6Okr3MFc1z5nGJhM1AaadUpvNNuM5pVJpUv+rVBAa5WL9efiy7uFwGAUFBQsyTpkhkGlTaDHY7XaqQbPb7aQumc6REssCmkwmPP744/joo4+oSbBOp8O6deswMjJCgYHq6mq0t7fD4XBQFiEUCiEvLw+jo6OQy+XUlJZfwyNGGeVLjLPaHjYfDh06hOHhYZSUlKCgoABlZWVplTezodLORMtK55AJjbdUATDWByoSiSAUCkEul2N0dBSdnZ1EFfT5fJiamoLJZKLIfiQSgcvlgsPhSKp/Ypn2I0eOED2MNaFXKpWIx+MIhUIIhUKUOXr++ecxOTkJvV6Po0ePYmJiAvF4HAqFAjU1NQiFQkkCU6WlpVAqlSSgwWpr8/PzcfXVVyMUCom2lbjllltwyy234KmnnsKDDz5IAQ12TzabDX6/H3K5HIWFhVi3bh1lyIzGj1UAmQCMwWAgmXqv10tZNnZO1n6EMQrYvsl6QCoUCqIznjt3DkqlEmazGV6vN2H8zjRunE4njh8/jnPnziEQCKCiogLV1dUUSGXUyaWmMDjfey1/7Nvt9gR1UJvNhnPnziEUCqGiogJFRUXQ6XSoqKjAjh07qBa4rq4OoVBI9Lqyyc6LBQ9Xr15NaxQTYmLjuKSkBFdffbWoSnEmgeJsHdxlOfzsseyULWPJIDc3F1qtFgUFBSmbMGaLmYwUoeTyYm0oi10Ay9S1mHGfLf0v3XkZZYOvKqdQKBCLxaDX61FQUACDwZDSmTEajVQ71t7eThFppVKJaDQKtVpNVJzZZMnYdQrr14R44403KGre0NCQ8H6YEczuYT7eWbqxuX37dhw+fBgDAwMJ9SepwHEcrr32WuzYsSPj3xYKTgg3Y4vFgmAwSFTW2Urcp8NcMsb83nGPPfYYHA4HVCoVSkpKSJSB3/eQtTbgq/vxwQystrY2MtoBUCuGNWvWYHx8HMFgEO+99x4uXbpEDnNVVRUAkMBHJBIhh0Sr1dL4EdLB2N+EEuOFhYVwOBzo6OiA3+8nQQqZTIbjx4/jzjvvFBVUSVfXxZdLF6NMZxoFT/XOxJw8xkBgNTUqlQo2mw02mw29vb3YtGkT2traqM5KqVRSDZlWq6UAjBjNigUS4vE49Rxj9ZfAtLIsc0zMZjMee+wxqFQqOJ1OovzGYjHodDo0NDRQWw273U5O8tVXX42zZ8+SwAfHcYjH4zh16hTa29vTGq/nzp2DVquldUer1WJiYgLPP/88hoaGEIlEUFRUhPXr1+Pmm2+mcwizvvyGzhzH4amnnsK7775Lwh7MGVIqleQEjIyMIBKJEF1Oo9HQOGblASMjI1QmUFBQkHANAJJa0eTn5ycIr+j1euzevRu9vb0YHx+ngAUbY+xdXW5wHIdoNDovAVd+3Wg0GoXf78fAwACpgwLTwi5sP9y3bx8FhQAkODTCPYbZO9msh2LBQwBobW0lpU2DwUB7VrrzZeK8zrVudSmMh6WOZadsGZcV/NoaYHoTZ1FRILO6l5mQyrFjmYAPPvgA4XAYW7ZsWTTJ2myoRvMB9pxZT5q59h0CxCWsmeHQ3NxMEt+bN2+G3W5PS9/iR6W9Xi/VRLAmsBqNBjk5OVllyfjv3WQyob+/P6EeQwin04m+vj4A07Qgsfez0NQNftbkhz/8If7zP/8zqW+bEDU1Nfj2t7+ddY82ZtwK+/OYTCZydpRKJTZu3Ehqk/N9z7ONYvPHHvsf62cWDodJjppfw8FaG/BbHbC1x+v1ktofy7AwsFoMjuOonxOjlgKAz+fDkSNHaOwyhU7Wa85ms8FutyfJjrO1TkxinM0HlUpFin1+v5+cjCeffJJoYvzeSTNJdQNIS8cUi4IL189U70wsyFBfX4+uri4yiI1GI5qamuB0OuFwOHDo0CF0dnaSbH1tbS3Wr1+P6upq5Ofnpw2Q6XQ6rFq1ihxo1udLqVQiFotBo9HA7/cjHo+TcmkwGKT3Fo/H4XK58P7778NsNqOzsxNut5scNo7jsHbtWhgMBqJh+3w+BINBOJ1OrFixArW1tSmN12AwSPW6SqUSEokEFosFHo+HBBPi8TgqKytnrFli76CoqAj/8R//QfRaiUQCl8uFDz/8kIRCvF4vAoEAHA4HAoEAlEolFAoFyf2r1WqoVCpwHIepqSk4nU6cPn0aoVAoaUzwW9EA08Y+c5pzc3MxNjYGv98PqVRKWeeFbISeLfiU/flQEn3zzTdx7NgxKq9gDh/r51dcXIyCggIolcokxVjhOsv2GLbeA5mvh2LBUBY8ZHs931HjZ5pTIZNA8VzrVpedspmx7JQt47JCOMnZIibkSAMfG4vZODHpojWLkQkQB9tTdwAAIABJREFUu57FFvcAUj/nuUC46LJmzgASxDzMZvOMPaOMxmnZc2ZIuN1uMkClUinC4TDy8vIyvjbhezcajeT4pwJ/YxV7PwtRm/DKK6/gzTffxJYtW1BSUoKWlhZ0dnaS2hy/XYAYlEolvva1r5EBLoSQGid2vcLIvFBcgOM4XHXVVVSHMl/IpsmzEMJaVK1WS0bpbbfdliDUwKLErB4JmJ4DJSUl6OjogMFggFarJWdqw4YNaG9vJ+NdKpWSGM2GDRugUqkQCoXgcDggl8vhcrmoUaxarUZFRQX0ej39nQV/rrrqqqT7YBLjLINdXl5OY2/fvn0AAJfLhfHxcVy4cIEcNK/Xi87OzgRBpHR1XRcvXiTpfSZYw88iMieAL0nPF//gr5/ZZvn5qovsvTN1x6GhIfh8Psp0VVRUZESlY8/u05/+NNxuNzweD4qLi6HX6zEyMgK/3w+dTofCwkLE43FcunQJU1NT4DgO4+PjlI2PxWL485//DLfbjcnJSRIIYe99amoKRqORat2AaSYAq+WSy+VJtc9sXBsMBnzmM5+Bx+OhGqOpqSly8HU6HamLsuPE9gaxPUzYskGYDVUoFDhy5AiCwSB8Ph9KS0sRi8XgcrkwNDSE6upqbNmyBX19fZiamoLf74fZbE4aE8LrYKwHv98Pn8+HJ598EufPnyenJz8/H06nM2kMXi4KG79OMhslUQb++jk8PIxHH30UTqeT3mFOTg5WrVqF6upq7Nu3j9RUxe411bzp6uqCzWYjunU6YSW+zH2q4Ao7P/v/TJk/mQSKs6kpW2w20N8Llp2yZaTFQnOChZMcEG/CyDcWxaJQqZAuWlNamtgMcSEyAXxcznS+kK42H/SSVIsuGzMAEiJ2mfSMuvvuu/H444/j3Llz1FuFRbl7e3thMBgyirgJ37vRaKS6NDEoFAp86lOfStk02Gq14tChQzh16hTcbjfWr18/58jfU089RY1mGxsbodFoqK9bMBiE1Wqd0SljdZhiYOppra2tCIfDqKysxB133JE0b/hjg0mzM8Pc5XJBoVAgEAigp6cnY4n6mZBtk2f+cWIR4i9/+cs4d+4crr322gRaotBIYv9mDlswGEQ0GoXX6yW6z/bt2xEMBtHb25tAo+3r64PVakVJSQn0ej2++c1vYnh4GP/zP/9DdLFoNAqlUondu3ejsbGRBCeCwSBlqBkdjN3v/v37sW3bNgCJjjNTKmVZy/vvv58cZVZLxBdESjUf+Q43AJpTQiU4t9uNvr4+eL1eOq/Y+rl169aMa3X56x27t/r6erS1tWF4eJgMXGBa1XDlypUZjy3hszMajTh8+DAuXLgAl8uFQCCAwsJC3HTTTSQl/9Zbb+HUqVNEsWYOKhNC4FOFY7EYpqamiLIqRDQaxcjICBobGwGAgnuHDx/G4OAgDAYDbrzxRgDA0aNHYbVaoVQqqfH8rl27sHPnzpRZTn7wcKaMgzCAtGnTJrzxxhvkhDAnlPU7dLvdKC4uxs6dO0kchY2J4eHhpNYwfNpeOBxGSUkJlEolNbxnrIb8/HxySNOJWcxEs800Wy6WTeQfz6cuMuEcsTrAVOd/+OGH0draimg0ivHxcfj9fvo8FAphcnISsVgMX/nKV2hdTXVuMadHmD1j9e1i98mXubfZbFAqlXQd/NrwubBw2DhirRjE2EWZBiYXmw3094Jlp2wZKcGMuu7ubuj1elGDbj5+g21iZrMZ27ZtS+JIFxYWYmxsDEePHkV7ezsUCgUAZJSpSCVowHEcKSGtX78+bfH8fOFypPPFNqq5Ooapms4KjQu+aiG/2Ws61NfX47777oPJZMILL7yAkydP0mexWAxdXV0Znae0tBTj4+OkgFVWVoaCggL09/cnfE8qlaKgoACbNm3CXXfdJToG2OZ84sQJclKYMM1sYTKZ8P/+3/9L+JvP58PFixeRl5cHuVyeYACkQjAYRE9Pj2gjbIvFgu7ublgsFoTDYbhcLjz00EO4++67RR0z9v66urqoV5RCoYDL5cLo6ChsNtus+meJQVgLkQlteCa6LBOLqKmpoXMJjVUhZZPV2wQCARLlqK6uBgDk5ORAo9Fg1apVsFqt8Hg8AICLFy+SGqZCocD4+DjVNUmlUkgkEuh0Osp0BYNBVFRUJDToZQ4TM3xSNatm1/v666/T+Vl9pUQiSRBESmUE8YNeQqeUrwTX2NgIr9cLv9+PvLw8ir6LOXqZZPnF1ju73Y5nn30WZrMZU1NTJN4jlUqRm5uLmpqatOdM9XwYTp8+DY/HQwwIs9mM9vZ27Ny5Ez6fD2+99RYJb0SjUchkMjqWCXkw6jT7jrCvGB+BQABNTU04c+YMiouLyXl3uVwwGAyor6/HihUroNFoqAck3xlL96z4wcNsMg5WqxW9vb2QyWS0Vq1cuRI9PT1E3Q4Gg2hsbERtbS1qa2uxbds2qlezWCxwOp0JrWH4IlGrVq3C1q1bUVdXh8OHD2NychLhcBhXXnklKcQyqqCYmAWQvAeJ/S2Vo8XuUbgWCOtiASQFXpgAUCbrF8tI2Ww26gEnBGvj88wzz+Azn/lMRu1I+L8rfLcAEijV7BoGBwep5QATgMnPzxcdD3MNpM81QJDufpcxM5adsmWkhMlkwttvvw2LxQKZTIZAIIAHHnhgXieZUHa3qKgooSiX3/Dz+PHjCZHnTJwaZqiYTCacPn0aP/nJT8BxHJxOJ6ampkhVbnx8fF7oaOlwOcQ9hIvrXB3DVE1nGYTG9pYtW+B2uxEMBtHc3JzRM2aft7S0JNWAxWKxjCgof/rTn3DixAmEw2EMDw/j1KlTot8rKCjA/fffnzZLw8YoizJLJBJotdpZq4NarVbs3bs35eeTk5PIyckRbUQrRDgcxrlz50Q/Y+M8FAohEolgamqKKEqp3oMwo8rqoAYHB2Gz2eB0OmGzZdc/i49Uma5M5JlT0WWZdDmfdpdJsIbRhFgUndGB4vE4ZU/0ej22b99OhjZTVDSbzXj66aeh1+sRDAbJAM7Pz4fBYMDY2Bjq6+vx05/+NMlA4gcvzGZzxpl/luVVKBQoKipCbm5u0rNLR7llQS+j0ZhUP8So3NFolPoKsmuebbRbqG7pdrvx7LPPore3Fx6Ph/p9rVixAgCwfv166HS6jM8vhkgkQs+J0RM7OzvhcDjQ29ub1NqBzWeJREKS4exY1iya1RSKgdX6sax+LBbDpUuXIJVKEQqFcPLkSchkMly4cAEymQw1NTVJDhl7VqnmQ7bvgNUU5efnQy6XY9WqVdDpdEksgUAggPb2diiVSmzbtg1/+9vf8OGHH8Lv98PtdqO3t5ecqNbWVlgsFnLY6+rqKJPLMpUAqL3J6tWrKesjpCfz57HP50NTUxOuuOKKjJy3VM6BsF6UT90VBl7Y5+kyvMwxZeOA9XhjKpPC8RAMBtHR0ZF1YFf4boFpOiMwPQ6Yc+xwOJCXl4eqqipUVlaSAIxw35oPNs58BgiWkT2WnbJlpAUzTiQSCXp6ehIoDfONcDhMBcvColyj0ZgUec5mQTh06BDeeustqhNhiy07H8soLKRTttjpfLHFdTaLKt84nsmpE56fKXaxDFWmz5htqnq9HuPj4/T3kpKSGa+5ubkZzzzzTEIfr1SQSqUpsxR8MMNNLpejoKAA69evT3sd6aKVhw8fntGhY1mZTLBx48aU1+D3+8mpjcViGTlVfMNHKNHO75+VKQ2Ifz1imS6xZyRmXKQau/z+YHw6X6bQarXYu3cvXYvJZIJCoYBMJiPHjDlXfX19+Otf/4ru7m6iDzJRiLy8PNx4443w+XwJkvxiYPVbZ86coeudiQqk0WigVquRl5eHm2++WVR9MdXvdHd3Ix6Po7u7G06nkxyEAwcO0PGpqNyzjXazHneTk5MYGBjA4OAg+vv7MTU1hby8PKxevRqf//znZxQB4oPV1DidTlEhkJqaGqjVahLSYEG+kZERTExMiJ6TZTn52TA214HpNYKNBSFYgCYSiWBychKjo6OIRqOQy+XQ6XSQyWTkFDK1x5aWFlRVVSXR7lLNh2wzH8Ia0erqagwMDKC9vV303qempgBMOwOsZjEUCqGvr48orA6HA06nE36/H729vTh8+HBCz0l2ncyhYOUGbL0Q0pNZKwSbbbrvIwuMpHLegPTOgbBeNB11N9044687AGi+6XQ6fOMb38Do6CjsdjvOnj2L4eFhBINBopCz8otsIZxffBuBLwLCcRyuu+66hLEjxHywcYTP1u124ze/+Q0KCgqwc+fOJHGSdFiWxM8ey07ZMlKivr4eVVVVcLlciEajVCg+nzAajSgvL6cmlzKZLGVRLr/GIh2PXIgHH3wQLS0tCdEt/n+zJpuLEfVZzHS+mBGbrWMoZkinc+qE52eGWbYGM7v2q6++GiaTCePj41Cr1WnpTcxg+8Mf/pBxFiuTd2E0GpGXlwe32w2FQoGdO3eiuroaJpMJdrudKHTs/1l2KZUCWarM1mzAcRyuueaapL9brVa0t7fD6/US5Y1F/fv7+9MqOqYSFuA7Zy0tLWhtbc2qxiydMMxM301XyxQKhVBc/HHz30zePcsmMJqQ8Fpqa2vh9XohkUhw4sQJ1NTUYOvWrSgtLcX777+PgYEBot/F43EolUpwHAe1Wg2Hw0FZADHhBOBjtVGO4xAOh2fM/IdCIZSVlSEcDqOsrCzjHoPMYWWsAK/Xi4mJCcRiMdhsNmzZsoWoXnxBjrmuUUzchPUe02g0UKlUiEaj0Ov1WLt2LdFoMzXcGJ3+7bffhtPpRE5ODjZu3JhAq9fpdLjyyivR3d2N/Px8XHPNNQiFQujo6Mjq+iUSCfLz86k/GRsjQsTjcVy8eBESiSQpCFRSUoLCwkJqUC2RSODz+XDy5EmYTCZighw6dAgulwulpaXYuXNnwpo628yH0TjdMqCmpgYmkwkjIyNQKpXQaDSUEQWmHfA9e/agvr4+KUvp9/tx4sQJbN++nertIpEI3G63aBCTv/az2lQxejL7XlNTU0LfRzGVwGyyh2K2gVBkZqZxxl93mDOu1+uxevVqcByH7373u0TnPHHiBHw+HzQazbyWPwhtBH4dqliWlY/5yGTxn63b7cYjjzwCs9kMiUSC06dP4wc/+EGCOEmqMbksiT87LDtly0gJo9GIu+66C48//jhcLhfWr18/r3VXjFojk8lgNBqxe/dunDx5kqJrYguKMDI306R/6KGH8OqrryadR6lUEn2mtrYWd9xxx6IsGIsZOZqPzJyYIZ2JQhM/25KtwSy89muvvRZNTU0YHR1NKkBnYAZbR0cHLl26lFGWDADVIaUb10wJcWRkBFKpFKdOncLp06chkUgSxDny8vIwOTlJdVgqlQpAsmroxo0b8frrr8/YNy0TKJVKnDhxIiHLwjdemaPIcVxC1mx4eDjlOVNFWxmF0Ov1oq+vD4FAIOMas0yUFvlzI5taptLSUpICNxgMGWVb0l2L0WjEzp07YbFY0N/fj87OzoRaPKYSCkxnNOPxONEXCwsLaYxPTU3BarUSZY5vxGab+WfqsGvXrs2KJcAcVpfLRdLwrD4mGAxSxlMoyDFXsDHEKL/xeBy5ublYvXo13TtfGGEmKhnHcejo6EB3dzdGR0fh9/sxNTWFQCCQQMctLS1FWVkZrFYrqSTu3bsXRUVFuHjxIvUyY2DZKyGkUilycnKQk5MDu92eVrlVbB7L5XLs3bsXNpsN7733Hvx+P1F2ZTIZOe1tbW2kXnj+/HlYLJaE+ZRt5oMv7gNMZ79YoLOyshIrV66Ex+PB2NgYotEoqqurKcNTWlqKjz76iLL0nv+PvW+PbvI+7//oLlmWLV9lBDa2iS/B3ExGoRkJK8ylWUmT0R2SrKFNTrc1l+1sp6On2052Sfrrmq23dV3IetoN0qRt4qQ0KSThEhOcmGBCsABjY2wsy5YsbMuybtb99vvD53n66rUkywYSkvn5JwHsV9Kr7/v9PpfPZXoaR44cwcjICNxud4pQkt/vT9tgo+9SyE1Nxyc2Go3Yvn17iu9jOj+6bGeNeN1kyw3o2rnCmqVSKdxuN58jDoeDC8gHH3yQEQQ3OozGVFuCuSao1wuNQ/dq3759zKeVSCQ4c+YMGhsbc1qTHwWH/pMQi0XZYmQNEl64EYUEPbQLlaud66E3mUx47rnn0v7u0qVL8dnPfhbr16+fV2dYmCRkgl5l+90Pu3MkPojm+x4yTdtyfd9ihcv5QMvodSorK3H58mX2oxIS0CmOHz+Oo0ePsppbLnwsYIa/NRcXoLKyEnq9Hvn5+ZienobP52M1No1GA7fbDYlEwom6QqGAQqGAXq9PgfHQ/S4rK0NJSQkcDkdOxtDAzL1LpwAnk8lSPIbsdjsGBgZw/vx5TExMQCKRoLq6GnV1dTh58iT8fj8AZFRspM+bqdtK/0Yc0FzgkLkoLaZblzcC6purWXxzczOOHj3K3BIhF49UQvfv3888M61Wi8bGRjbvNplMaGtrg8ViAZDerJ3WnFBeOpMq3UK8loTS7MTr0mg0eOmll+Dz+SCTybBkyZIbkjwplUpMTk6mGDSvXLkSd955J4qLi+e0VxBKfwvtGYgrBYAVKIeGhnD8+HHU1dWhsrKSJwtut5snXBs2bMBbb72VAuFTqVTQ6XSswCiMWCzGU/5cBHeEIZFIcOedd6KpqQlvvvkmfD4ff/5du3bBZDIhLy+PZfaj0SgXkHa7Hd3d3dx4mu/kw2q1pvje6fV6GI1Gbj7QHiGGIk9OTgKY2c/8fj9P1q9cucLqjMlkEmq1mq0jsp3XRuOMqjI9a+n4xLkUEAtFlixkTdOaI5+3aDTKZ5ZUKp03BP96Rbp7kOkcv95N33Xr1qGgoIDPVYLvO53OjLL9FIv8s4XFYlG2GHPGjYLczaU8NNfml+2hJ/hMpolJeXk5QxFyDUro3G43PB4PKioqUFZWljOE68PsHGXanBeinnQtyXEuh3Mu16BEa2JiAhaLhTHtxC9pbW3F2NgYS5IrFIqcpmUFBQVzcgGEUw1Kwj0eD2KxGHQ6HZYuXZoyKROuCwAM4wFm+GR79+6Fx+PJ+fOr1Wqo1eqUooyU9/Lz8xEKhWA2m7F3715YLBYEAgH4fD4kEgmGMG3btg0+nw99fX0wGAxzJlSZvnP6t/lwzMTiL+mUFjPBFXNZJ2IoYqY1TXtCLmbx9J07HA6YzWaeLNG1hVDqdA0a4kRmM2sXK88CwPPPP49z584hLy8Pu3fvRnV1NQYGBlj5rq6ubs7mldjLSFgIW61WnD9/Ht3d3dBoNHjvvfcAzAgi5OfnX5fkyW63s//b5OQkFAoF6uvruSAxmUxpp6uZpL9jsRj7gq1YsQKNjY0IBAI4dOgQpqamYDab8dxzz/EUsaWlBatXr+bvTalUMreMQiqVoqqqCo8++ihaW1vR19cHr9eb0tAh4+lcQyKRQCqVorKyEvfffz/+53/+Bx0dHfD5fNBoNFi7di1aWloYUjg1NQWZTAav14tgMAi5XI5oNAqr1ZrSeJpr/xVPmIW+d42NjSl8SVq7QijywYMHWYmyuLgYXq+XZe6pYJVKpVAoFAzps9ls8Hq9WX0QSZArWzFzo3ILoRT+XByydGuOPm95eTnKysrQ19eXEYL/UfCm0u2XwLUrK6eLNWvWwOVy8XlXUVGB/v7+OX/vek3t/q/FYlG2GB9ZCB9a4uLMR0I920NvtVpZvlwul6cIfOh0OuzatWveBdl3v/tdDA4OIhQKQa1Ww+PxYHBwMGcI14fVOco2DVvIe8gFXpRt07Xb7ZiammJuyUKK0ebmZlYAu3z5Mvbv34/W1lYMDAwgHA7D7Xbzd5xMJmE0GvnAyhQNDQ34+7//+5QpWabPI/aMEvJA1q9fD2BGothoNKYkQELye0VFBXp7e7MWZMRhIDPdoqIi9hcibphGo8Htt9+OlpYWXL58mX2nhoaGMDo6ikQiAbVajYKCAlRUVKCqqgpGoxElJSWcBHZ2ds56r8LI9p3Tv4k5ZsJnYC4oovg+X8uzkevvWq25mcUL3xtNxAhiSJYac02MxWILdF3xJF84CYpEIjh8+DA8Hg8kEgkGBwexZs0a+Hw+BAIBKBSKrJ1peo0XXniBk0u1Wo26urqU4rOiogIWiwV+vx8XL16EyWTie0IG1wtNNOn1e3p64HA4cOutt8JgMOChhx7KKI8uTIqpsRIOh+FyuQCAeVkVFRWora3Frl27YLVama9G16WivKmpaVYjqKWlJUXcSa1W40/+5E9w3333ob6+Hnv37kVXVxfLuycSiaxTbLI+SCaT/L0AM8/vLbfcgjfffBMdHR2YmJhgLudrr72GhoYGNDU1MSdHq9Xiz/7szwAAhw8fxsWLFxEMBjEyMpICG85lCk17/c6dO1FSUsLCDJmaDiTgcfbsWUilUqhUKuZWU+OJgj6DRCJhEZXW1lbodDp0dXUhEolg06ZNKcIx1yrAs9DIdbIsvHe05khuvry8HNXV1dxYCwQCGBwcnAXB/6h4U+n2vOvd9DWZTHjyySfZvoIanT09PTk3z29U0f1JjsWibDE+0qAHlja2QCAAh8OB/Pz8nKYqmR56pVLJ/B46bOgAXbVqFaqrq3N+j9RhN5vNcLvd0Ol0yM/PR3FxMdxud84y4R9W5yjb5nw930MuBxJBZahzO1dSmSmMRiMaGhpw9OhROJ1OVlITEtApotEovF7vLDl9iUSC0tJSLF++HC0tLdi5c+e8oJ201k6fPg2dToepqSk4nU688cYbKCgoYC+qbBCdAwcO4ODBgykJHyn9yWQy1NbWQiqVptwnkvaXy+UoLi7Gvffey1NeSp6JkE0eS4WFhaiurobD4cDJkycxMjICh8OBcDiMUCiEkZGRFBEKoedXrrBcSuzIIBzI3LEVyz6nu8/XYniaiyKYGEqbziw+3Rog5UVh4TBXAkbv6fjx42hvb8fBgwfnbNwIYbHJZBJ+vx99fX3Iz8+HQqFARUVF2ombMOjZp4JGr9fP4uTt2LGDCxoi8JNJ9pEjRzA+Pg4AKZDbXOGSR44cQU9PDy5dugSJRMIFGQl6CBNJobGw0My6rKwMKpUK9fX1LP2droHQ2NgIh8PB/CiPx8NTpk2bNjGXKi8vD+3t7bBarfzcSaVS9qNrbm7Gli1b0NPTwx6YZAGTjfdJHDGDwYBoNMowv6mpKUxNTTEMEJiZGlksFjz99NN4+OGHUybHdXV1AGZUY2k6Z7PZcipixHs9TSGpyM8WQrEZsi2RSqWorq6epVSpVCp5n5VIJPD7/RgZGUE0GmXj9BMnTqQIxyxEgOd6xFy0CNozJycn+d7RmksnN0+NNeK+Cc+vj4o3lWm/vJ5NX7IZIKG3RCKBUCiEq1evwufzzTmFXIyFxWJRthgfedDG1t/fj6mpKT4cNBrNgje5SCSCwsLCFCPR/Px8LF26FHq9nidzuUx5WltbMTQ0hGg0iqKiItTW1uLhhx9GJBJJ4QulmwCI48PoHM01OaD3YLfbU7r+841cDiRK2rPBuHKNoqIiyGQy5jtIpdK0P5dMJhGNRvnfqTBvaGjApz/96YzF45EjR2A2m5mPkWnt0f0NBAIYGBjgSVWm3xF+5zt37sS7776L06dPIx6Po6SkBGvXrkUikUBZWRmqqqrQ19fHflj0GWQyGeRyOVauXJkCuxUWimVlZRgZGYFCoUBlZSVuueUWDA8Pw263Y3R0FAUFBYjFYtBoNKz4R69DUwUqOGprazPCy4TrRTwRIn5KNigiGTenExK5lmcjnSKY+D3PVfhlg1Fmet/Z4syZM+jt7eXEUPg7zc3NKcpqd955J3p7ezE0NIREIgGZTAa1Wo3CwkJUVVVx0Z8tCLa1bNmyjNDqgoIC6PV6WK1WhvgSb4aKBwAMXTty5Ai2b9+e9bMKod02mw1SqRTxeBwFBQUZza3FhsQqlSqF+zRXc4AMjy9cuMD7DClMAkjxSAsEAimFhlQqZbEbu92OoaEhVlosLi7GsmXLYLPZcPXq1VlcTmqgFBYWYs2aNVi5ciXMZjNzz+rq6uB0OnHlyhXI5XIkEglIJBLEYjH4/X5MTk5mtHbIy8tDNBrFsmXLmGMmXK9zTZgB5LxGjcYZiG4gEEBvby+mp6e5uCSZfGCGZ7ZlyxacPn2aRUBIxZHschKJBAKBANrb25lXKeQzfpjJ+1y0BiG/VSj2k2nNZds3Mr3WhwFpFO+XuTanco1169ZBq9VCoVDweev1etHR0QG5XI76+vqc+a2LkXssFmWL8ZGHUDxgenoaoVAoK7xIHOk2QKVSienpaYahEElVoVAgHA7jwIEDAH7XDU63IZtMJuzdu5cPrHQSzMTTyDYByPQes73/hYTdbmdxgaKioowiBvSz1wq7yAU2RmpWRUVFqK6unrdilfDeUBLb2dkJiUQCjUaDRCIBj8cDj8fDKndarRYlJSXMqfrc5z6HqqqqtL5GwnthNpv5fmRLIozGGZ7cq6++yibkua5Xo9GIH/7whzh+/DgmJyexZcuWFClnYGYNAWBPp2AwyH45mZJjEiMpLCzkBoROp+POP8lxq9VqKJVK1NTUYGJigjvExP0gSIoYZprNY6ylpQWHDh3C5cuXedKRTWXxRsB458OxyNaQyEXkRPhv2Z5dglBPT09Do9HMWh9GoxE7d+5koY/m5maUlpbipz/9Kfr6+gD8TpAomzcRhRi2tXPnzrTPW2VlJQwGA/r6+hCLxSCVSlFcXIxbb72VvztgRnFuZGQETqcTZrMZd955J2w2G9atWweDwcDJn9FoxPe//32cP38eUqkUjY2NMBgMPD0WJ6pKpZKFUGiCXlZWNov7lO1zChPrkpISDA8PI5FIsLcYTaooSD2QJl/kLSVUKp2enoZCocDq1avxxS9+Ea2trSgoKIDFYuGzRKPRQKlUstrm6dOncerUKZSVleEzn/nl5vTIAAAgAElEQVQM1q5dy/eDmkdqtZqbIVqtlmXaxcWWTqeDRqNBQUEBqqqqUiaJ9Mylm9SKLUhy4VJRNDc3495770UoFMLo6Cimp6cxNTWVwsdVKpW4//77UVhYiIGBAea3Tk1NsSk1wasjkUjKtC6TsM+Nikyeb+mmY8uXL08rw58uMjWM0hVsHwWkUfhs5SJXn0sYDAb88R//MV555RU4HA7EYjEoFAq4XC4olUqEQqEPbfr5fykWi7LF+MhD2OEhjkUmeJE40m2AwAwUhCBfiUQCCoWCu8cOh4MPbOoGE5RLCJfZv38/urq64PV6oVQqGTomTHSEm7W4k05S6AR7MpvN/LkIziMm5M/H90l8H5566im8/fbbiEQi0Gq1+OxnPzsrKUt3OAELg13caDhmuu92z549KckgwSNHRkY4YVi3bh02btyI3t5erFy5EsPDw+jq6soo9y2Eu1RUVGDjxo1ZJwOU/I6PjyMcDuPWW2+FXq/Pab3S+6Z1Kn4flZWVePDBB/GjH/0IIyMjPDVevnw5Nm/ezF3odNekrjclZk1NTXA6nbBYLFCr1fD5fBgaGkI4HMbbb7/NYiG7du3idZCpKBUWPeLnxe/3o7Ozk20APvWpT+Huu+/mZAfALB7WfNfNT37yExw7dgwrV66EXC7HxMQEgJnEYceOHVz8CxXBMk1yM+0ZwvufTeQklyYMMJPMOhwOSCQSKJVK7NixI6XIFT77DoeDlR2feOIJbhKoVCqsWrUqp2bGXLAt4eeg79xisUAqlaK8vBxf/vKX2ZAaAAtg2O122O12tLe3Ix6P8+SOBBz0ej16enowPT0NuVyOYDCIDRs2oKamhkUtjh8/zkX72NgY20bQa9O+l0sIv1fazyORCOLxOOLxOBwOB44ePYqRkRHmQJESqVwuh1wux7Jly5hLZ7FY4HQ6EQqFEI/HYTab8eabb0Imk6G+vh4bNmzASy+9hEAgwKp8xM0kuX/6/bvuuov32HA4jFgshnA4jPz8fMhkMpSWlsJkMrHvHa2F1tZWWK1W6HQ6vh9iDh7BydJx8ug7W4hKJ/F1JyYmMD09PUuJMpFIoKenB3v27EmBSk9NTbFfHBWgdrsdb775JgYGBpBMJrFq1aqMYjrXO7IpEmaajl2PYlFcsGXbd27EWZmOGzcX2iPXa5rNZuZMKhQKnuQqFApUV1cvQhdvQCwWZYtxUwRtbMLJUy6bSaYO+djYGJLJJEpLS1kW/Ctf+QrLEVM3mKBn4XAYo6Oj7L1EfDHioRUWFnI3O1OIoVyUcMXjcbjdbly6dIknFwBmqT0lEglYLBZ0dnaioaEh42QnXZhMJpw5cyZFzrinpycFepTtcFro5joX5MxkMqXIU8/nkMgGJaMgaW0h74SKYJ/Ph7feegtutzutVxSFeAKSqSCjRHpgYIBhjrkUccLfTwdFOnfuHH77299Cp9Nh/fr1aGlpwZUrVzip02q1uO222/Doo49mfQ2DwQCdToeenh54vV688847PH3wer145plnEIvFEAgEkEgkEIlE4Ha78dZbb+Gxxx5jQZbi4uJZ6054j4QHf15eHtxuN3w+HyKRCE+EhHyMTNOqXNfBT37yE3znO99BNBrFqVOnUrylZDIZ3nrrLTzxxBOzfi/T1Eu8ro4fP47Dhw+ju7sb09PT0Ol0+KM/+iP85V/+JV/LZDLxRIuS6bngjHa7HYFAAJFIhKcI9PdCMQ6VSsW/T/9tbm7G6OhozoqlufjA0c9RMVhYWAi9Xg+FQpFiSE3rsrS0lPerYDDInJJQKIRgMMi8tWAwmCKkZLPZ4HQ6UVhYiLKyMgwMDCAej7PgkkQigVqtRjKZREFBwbwn6OK1SObO0WgU4+PjLEhBE2zyGaOpoFwuh81mQ09PD5qamlBdXY3Lly9zQTIwMICrV6/CaDSiqamJYcS05mKx2CyuWTweh9frxblz51BeXo5YLMZ8smQyiVgshry8PExNTWFsbIwbdiQadOHCBfh8PhQVFeFTn/oU3w/h+l23bh0n21qtFgMDAzh06BCjPZqamq7JYoY+h1jgRMiX27hxI06fPs3nZzweRyAQYP+ysbExhoKqVKp5eepda2QqhoR/P5/p2EIj00T9Rk3PxE0KIQw4F0pFtmsSeiIvLw+VlZXYvn07kskkLBYLampqMD4+nvXawn3zw/B1+yTEYlG2GDdVzJdXkinxIs5PPB6HVqvlrq0QNgKkKn/5/X4myWu1Wuj1enzqU59CPB7Hli1bsHXr1jkhDtRJn5ycZCn0/Px87piTTLCQRAuA4QATExOwWq04evQoZDLZLEWrbCGXy/lwJEjaG2+8AbPZzAaUZrMZg4ODWLFiBU8zlEolJ4TXu4PX1taGwcFBKJXKeYt8zAVzy2QQSsnywMAAfD4ffD4fkskkli5dCq/Xi3379s06JGhtZII3Etyqu7ubJ5HE88m1IEsHRerq6sJ7772HeDwOqVSKcDiMrq4ulhxWqVQwGAz40pe+NOdrkJoaTX8tFgsikQgnUhUVMybCcrkcTqcTsVgMPp8Pvb29uHjxIq5evZpyL4UhXNv0vFBzQ+jrU1dXh9tvvx1HjhzBunXr0iruzXeN0dSbQpg0xuNxDA8P42c/+xnUajU3HKiATzf1EjdPfvrTn+LKlSt8zVAohOeeew6/+c1v8C//8i8oLi7G008/zcnvP//zP6O5uTknGKZCoYBSqUxpxlDCQ89+UVFRVuGLuRRLc/Veo5+z2+08pSosLGQOIfG8hNdavXo1+7CNjIyw9YNwUrZixQoMDg6y7x5NjgKBAEZGRnjyolAooNFoIJfLUVFRAa/XywbrcyV3whCvxf379yM/Px8ej4d5L3K5HMlkEpFIBJFIhIuoRCKBcDiMRCKB/v5+KJVKPProo1AqlWhtbYXL5UIkEmEVRuLCZrPXIBN5o9HI9hpCKX3yU1MqldDr9ZiYmEBbWxtzuRwOB3w+HwuuUCEp9B4EZpouLS0taG9vx8jICI4cOcIKm8DMHrYQWDDtG9PT03wOCZ8xtVoNh8OR4p3W2NjI5sok5R8MBnliSEFF5IcRmZ5H8d/faChlOiRAtgbOtU7QxJ9PCN0EFiaTL+RNj4yMQKvVorq6Gk1NTThw4AA6Oztx+PBhyGQyzq3E1yb1RoK0076ZLT4Ke4GbLRaLssW4aWIhD2QmKNSDDz6II0eOIBgMwm63w+FwpJ200P8TR0Io3JEL2Tzd+6EJQU9PD8sBFxQUIBwOY9myZaitrZ3V9WxoaMDly5dhsVhw+fJlOJ1OJBIJnDhxAs3Nzdi9e/ecr1tQUACv1wuVSoXbbrsNfX19LE1NkyShRLHRaITBYLhhHTwi8vt8Puh0OjQ0NMzr2ungYkIYXDrlMUrUiKN49epVhrq99957uHLlCkOLNmzYgL/4i7/A008/jZGREZSWluKpp56aNe2gqQZxvGQyGcrLy2dNyITrl94P8DveYToo0pkzZ1gkJJFI4P3330+5B3q9Ho8//nhOhxlNSvLy8mbBSyorZ5QH8/Ly2K/MZrMhmUwiGAzCYrHA5XJlLZ6EDRMxD44+q1KpxDPPPMMH8eOPP37N/LGWlhb2FksX0WgUH3zwAeRyOXQ6XYr8trjJI+acvPbaaykFmTC8Xi+++c1vQq/XY2pqihN6MhtPtz5ff/11fl0AWL16NaxWK5v40j2Kx+MoLi6GUqnkho+44x2Px7MatFKz4NVXX0Vvby97rwEz3x8l9sQ1IksP8huqrKxk2W9aX2IfNyEMdXx8nLveQk5Zc3Mzenp6sG/fPgQCAdhsNhZTCofDCIfDkEgkzHNcvnw5qqurMT4+DofDAYfDMQs+nqk5Itznhf8eCATQ39/PUzKaXNG0meTrZTIZVCoVGzcfOnQITU1N+OpXv4r+/n689957DGP0+/3o7e1l5d50oVarUV1djd/7vd/DqlWrAMyopYbD4ZSpUyQS4eJrdHQUWq0WsViMPcrIH83r9eLEiRM4e/YsCgsLoVQqodPpAACdnZ0AAIvFwugK4HcKm+Q/lo27TL6OQvsOi8WC3t5ePnNIaRGYEfnIz89Hb28vnn32WSiVSrS0tODRRx9luXij0Yienh60tbXh5MmTKa8ZjUa5mLvRke28+LD9ssTrM5sgSDYo9Xzg8Ol+ZyHiRMJrmkwmHDx4EKOjo/D7/bDb7bz+/H4/JBIJPB5PWlG2c+fOweFwwOl08p/TnWNir7h08NP/S4XaYlG2GDdFXItDfbrpGnUtX3vttZx8UhYKn8x2PSoMT58+DYfDgbq6upREXpjY0mc1mUz41a9+hQ8++ACBQAAejwcvvfTSnLwSgqwlk0no9XqUlpbC4/EgEAhgcnISLpcLpaWlsySKM0E+rke4XK4UJbSioqJ5X0NY5IrXRya4KCUoly9fxvj4OCcYJKVPCdZ7773H5rkAMDIygkceeQSPPPIIVq5cCZfLhcnJSbZCoARJq9WisbFxVkEmhIb6/X50d3cDmIFW7ty5cxYUqbu7O6WrnC4cDgfeeOMN3HHHHSnPg1C6HgDj/69cuQKVSoWlS5di586dAGYnJkqlEgcOHGCxEYlEgtHRUZSVlS0Izip8/vbt24exsTGWKbfZbNecEH3ta18DADbdJiU7YTef7mMsFkNlZSXOnTs3C/KXbg319vZmfe1oNJrS7Xc6nSmwMIIIk9jEhQsXIJFIeJqUl5cHvV4PmUyGAwcOoKenB5cvX4bb7cbQ0BC0Wi3a29vR1NQ0az3TBFIcQgiixWKB1WrlSYzD4eApDH3OvLw8DA8PY3h4GD6fD3q9HnV1dbj33ns5MQfS+7gJiyPaHynEXXGyoFixYgX0ej22bt2Kixcvoq+vD2q1GmVlZQB+182naStNBW02G8LhMPs+5trxb25uxpNPPskFx+XLl9HX18ewSTI9TiaTUCqVSCaT7DNGhuAbN27E17/+dSSTSXzwwQcMA6diKp00vkqlglqtRjwex9TUFN566y08++yzCIVCcLlcaX3OaKI8PT0NtVrNKpv0s8lkEqOjo8jPz8fQ0BDkcjlUKhWKi4t5/yTYqNBLS/w9iYVs7HY7vve976GjowOBQACFhYUoLCyETqfDyMgIF7BUHNLErqKiAk6nE16vF06nE9/61rdQWlqaAk82mUzo6uqC3W5nc2lS63M6nXN6Il6vEDfFxOuFYMcfRWQqnNI1FkmoYz5N0kwIo4WKKtG9dLlcPAUPhUJYv349qqurMTg4iGg0ing8DrVanVbkat26dfzMl5WV8RRZ+BrCRng6PhyAFH4t8fE/ybFYlC3GTRHzUU/LNRbikzJf+ORcUV5eniILLEzkxa8l5Nr84z/+Iy5duoRoNIorV65g7969ePLJJzPitn/zm9/A4/Hw1IU4FnTIJpPJlGmJGOpJ/81l087VSuDy5ctcyKxevXrBm6lQrn50dJShNJ///OfTwkUDgQAuXLiAU6dOzUqMMnW8KUKhEH784x+juLgYyWQS+fn5nGCXlZVl5PoJ1y/xrOhwIRih+FBub2+f5aWWLjo6OtDW1oZt27bNUomsra1lLklfXx/GxsYYQnbx4kUcOHCAJdcfffRRTkwMBgOUSiUOHjyIYDAIk8mE6upq3HHHHbP82+g7yEXgYtmyZczdicViWLZs2TU/UySsMjk5yX9HUC/x9xmLxTA4OIgTJ07A4XCkvLd0e0xJScm83ks8Hscrr7yC2tpanDlzBs8//zw3Hoh3JJPJ+PmjZ4uSm3PnzjEna3JyEpFIBMPDwwgGg3jggQe4EKH1LDZoFfPRpFIpixjRa3Z3dyMajSIajcLj8SAcDiMYDCIQCECn02HFihV47LHHZk3JW1pa5vRxy/T9kBchJWpFRUUYHh5OUVSke07rn5pSYvg4+acJffRojWdqHgnXGKnmarVaJJNJFBUVobKyEh6PByMjI/wdLVmyBGVlZfzempub8bd/+7f413/9V5w7d44LsXQFWUFBAYqLi+H3+xGJRDA+Po4rV67A5/OxBH6mkEgkLJ5AMHNqHhH/jBRXib+m0WjYW1Or1SIej+POO+/Etm3b+H7QfRA3iDZt2oShoSGcOHECk5OTbExPhRgpFNMzJfRyo8Ye/YzP58O5c+cAgGGUVqsV/f39zKNTq9WszGe1zvjGkW/kjZqAiBsuc62XjyLS7YPiogmYbWlA/800/cx2DzMVg3OpQdO9JOP6SCTCDd6dO3eivr4e77zzDiKRSEaRq+bmZjz++OPo6OjA5s2bU87/dLzaZcuWzeLDEeWiq6uLIeBz8Ws/7rFYlC3GTRHizUmpVObsG5XtmumKkA8jxAfjfGSBm5ubcf/99+MHP/gBpqam+NAXwvOEic7+/fvZwFOhUEAikWBgYADAjBCCXq9HcXFxVqgnXXcublmuhGWrdcY3KD8/n+X5F7KRClWgiJ8SCoXQ2dk5q2tP0D1SYFuoXG88Hsfk5CQUCgWi0SiMRmPKhJMONHpdYDZPSTjlIBihcOp3+vRpno6QL1SmiEajcDqds8jXJF1PXJKCggLYbDbE43G4XC5cvHgR7e3tcDgcGBgYSPGIMxqNWL9+Pd555x34fD4WCBgcHEyBmZpMJk6+6LOVlJRkfC4LCgrQ1NSEgYEBVFZWwmazwW63L/gQPXbsGL73ve/h4sWLKX9P0w6VSoVkMskqeMThIol0IXQq3R5TXV3NSWSuYbfb8a1vfYshPBRUXEulUuTl5WHFihXcKab7FAgEIJfLodVq4XA4EI/HEQqF0N/fjxdffJEnH0ajMa1YAJkzW61WJJNJlJeXQ6/Xo6qqCqOjoxgdHQUw05kmlbSJiQmEw2H2WPzGN74Bg8Ewa39N1zjI9PmFBfqRI0dYMVetVjMHKS8vjzmNFJkKKTF8XNw1nw9fKhKJ8EQ7Pz8fmzdvxo4dO7B3714W0KFiCADvrRSVlZXwer0IBALw+Xwp/maUND7yyCO4ePEiLBYLysrK4PP5MDg4mCJAo1KpmM9GhRjxzwwGA2655RaUlZVBqVTitdde48IsGo0yJJF8wfR6PU++yQ/ugw8+QEVFBUwmU8p+TPvEwMAAQqEQ+vr6MD4+Dp/Ph3g8zgUjFe7xeJy/O4LWkjCJQqHgqbRCocCSJUsQiUSwZ88eTE1NMcSWoMV6vR5+vx+BQICfy97eXoTDYRQUFPBzAFxbw1Uc4obLQvl1H3akg1z29PQAmG2wLoY35mpmnw7CLS7ahbmJ8F6SsiYwY25PiqUPPvggVq1alVXEgyZh9HtNTU2zXkPIq62trU1LGVGpVFAoFMzHvxkK7BsZi0XZYtwUIdychBLyufhGZbtmS0sLbxwf5oMsVnyaryzw1q1b0dHRgfb2diSTSQQCgVmwJJqSEORIJpMhGo1yYpZIJFBYWIiVK1fyppkJ6gnkdkjmCncUmtjmYnqbKYTiJBqNBjqdDslkEn6/f5YfDiXIOp0ONTU1GBwcZENXACkKanMFSWcXFRWlQBWzHWjiw1XIuQFmYITCg1Sr1WL16tWQSqWYmpriA0ocMpksBd4mXAMVFRUwGo1wuVzsu0QCCv39/bDb7QwjPHbsWAo8t7m5mYUcpqenuZh75ZVXUFpaip6eHvzyl79kRbvS0lIkEgmoVCrIZDJUVVXNei4rKyvR1NQEjUaDsbExnD59GmazeUFWDyaTCd/61rcwNDSU9t8lEglKSkpgMBjgdrvhdruhUqmgVCoRjUYxNjaGtrY2/g5oP2hvb4dcLseBAwfgcDhQU1ODkZGRlAIrW0SjUUxOTmb8ebVajR07dmDbtm18bw4cOIDh4WGWlF+3bh1D3GjaNTExgbGxMZ5sNjQ0sPgMMPN89vT0oKenB3K5HPn5+VCr1ZDJZIhEIjyRF9p60BRLaDNCE7J0+6u4cSCecI2Pj6cIgQBgmXuyHKG/i8fjWSHjwhDDx8ViMgBy5vjSeybhikgkAoPBgC1btqCnp4eLLCr6fvCDH7AY0fr161kYamBgIGXiJZfLsWrVKjz55JNobm5GfX09ny3AjAplV1cXIpEIiouL8elPfxqXLl1CMBhkflkkEsGKFSvwhS98AfX19VAqlbh48SIqKip4KkHQwaamJjYe1+l03GQKh8Os5OtwOFBQUJDSIKF9gr4XEv8h+KZcLofBYMD09DRDVQlenkwmoVKp2HiceH/T09NoaGjApk2bsG/fPlitVkQiEajVaixZsgTFxcUsjmWz2RhiSWG321M84qghYLPZUkREFjo9E+6N8Xj8ulIRbnSIz2ThOZIN3rhQ+ft0RfuvfvUr/n5JyGr58uVcyBM3c3h4GCUlJSnvg+w8xK9Nr9Pf38/fMf09QWMBZOWRGo2/s3pxu90pk+1PaiwWZYtx0wRtTkROna/kuDgI9pRt47gRkas8dbYwGo24++674XQ64XA4UFRUxGIlPp8PHo+HpyS1tbVwOp0YHBxkOI1EIkFBQQFuvfVWPPbYY3N+7lyLrVww6mIT21z9ctKFUqlkcRKFQoHGxkZ+bZfLhTNnzrCoBDBjEqvVarF582Y4nU6cPXuWO+LZCjKVSoWCggL4fD7k5+dDq9WyZ9Odd96ZAiMRHmhi/gtNG6noIcEFWodiVT1gBuJqNBrR0dGR8R6QfLewcUFS99///vfR39/P8tTUVbRarTy9SSaTeP311xEKhXhy3NzcDJ/Ph8LCQrhcLu6Qm81mPPnkkwgGg5x80esRPKu4uBiNjY3YtWvXrOmHkEs5OjqKwcFBtnqYT3HW3t6OiYmJjN9bSUkJGhoa4PF4eKIciURQXl6OcDgMn8+Hrq4uOBwO/o5IOczv9zO09pZbboFMJps1jUsXJG2fbbKmUCgwOTmJgYEBnlifP3+eYYTLly+HxWLh5LuyspILHpvNhlAoBKvVmtJMOnfuHMxmM+x2O2QyGaRSKXOMaC2lk/oWiz8ASJmQCfdXIH3jAAD7KI6NjbGoyPLly6FWq+FyuaDRaHg9CAu3XOT8hZFpetbW1pbzRGV8fJxhzPSsWa1WbN26FZcvX8b58+fhdru5cCGRG5lMhrKyMjz00EOYmJhAMBjE+Pg4TwoSiQRDaMVny4MPPoh///d/x4EDB/D2229DoVDg4sWLbDpdW1vLhtN6vR6rVq2CwWDAU089hffff58nv2q1GnK5HHV1dYhGo7zOSOyFiiSawCkUilmQL3oG6d6NjIzgwoULbL2wfPlynpJptVpoNBo0NDQAAHOvy8vLsWHDBi4MhCqCVGAqFAqUl5fz1IT2iQMHDsBsNvOUD5jZf8LhMCYmJrgZQo2QcDiMtrY2TE1Nob+/n+kG85meUcNFuO4+ah7ZQkNcpGWCNwrl77VaLSYnJxmVkK24FRbtZrMZVquV0ScHDhxAfX09FAoFli5dikAgwEq9lFNkglmmex1gxsh9enoabW1tCxJSMxgMqKiomBea4eMci0XZYtx0QVOWsrKynCXH08WNFLFIF+nMoOcDWxQHqZoJJ0FWqxWxWIwTZOoINjU14eDBgzCZTPD7/Twhe+yxx3KaUuVKCM4EgRSGcLq1YsWKBcMIgVReYFVVFSeeSqUSzz//PC5duoRIJIJgMMhJ79TUFK5cuQK73c4F2VyRSCSg1WpRV1eH5cuXY3p6Gg6HAzKZDIcOHWI+REtLS0oX2uVyMf8lHA7z5IGKRDEUS3yQ0s/QGk0XMpkMJ0+ehNPphMFgQCwWQ1NTE8bHx/Hiiy+ir6+PDyziheTl5c3iwgQCAXR0dKC6uhqBQADnzp3D8PAwnE4npFIpQ5tGRkZQVFTESaRGo4HBYMDq1atx+vRpOJ1Onpxlgsts376dTdrHxsZSpkBiEYd017Db7fD5fCwcAIDhpFKpFCUlJXj44YdhNpthNpvZh0oul8Pn8/H3TsIgZWVlOHfuHCwWC+x2OwtjEEl9586dfJ251slc01a/34+TJ0+io6MD4XCYE+1oNAqdTsf3NRaLsV/X0NAQRkZGEIlE4HK5eFI7Pj4Oi8XCBRHBdMXKfNmkvoXTr2effRZ9fX3w+/28FmnSk0mOn9ZOXl4ec4VI3l0mk/GUiVTWaGqfi5y/MI4dO8b8E2rkkM/hxMREykQlU9jtdoZzB4NBFBUVsQiB0WjEzp074fV6uZhSKBSsDkkFE+2XDocDbrcbFy9eZBEMem5WrlzJZ0sgEGA/yA0bNsBsNqO7u5shzPn5+aioqMCOHTtw6NAhLhry8vLQ3t7OxQs1g6qqqtDc3IyRkRGW5Kep1d69e1kQpbi4GFVVVSm8PSE8mc6G1tZWhm3SBJv2hpqaGqxduxY7d+7kqSolzcIGE12XFDVp/T3++OO47777Ur6DyclJHD16NKUoA35XmFksFgQCAWi1Wp7ydnd3s4UJADQ2Nqasm1wmaARvI6uGTwLMbS54I1kmUOOC9tZssEa6ZltbG37605/yFCwajSKRSODcuXOQSCR4//33+d/IPmPr1q3YtWvXrPeRKV+gvZgaTWq1mvdYMbQ5U1yL1+nHMRaLssW4qYI6kOSBIoR+LQTSQDyj+XpkzTfSEVfr6+vnDVsUhnhDNplM7HcWDofhdru5I9jc3IzOzk5UVVVBrVZj+/btc/qqZXutbL+XDgIpDOF0ay7Vy7lCzAsUepGR6lwsFsPExAQXf9PT0xgbG8t4zWXLlkGj0cBisXCSTz5DGzZsSKsMJ+bdpOO/CLleBG0LBAKzDD3FqnJWqxVnzpzhzrowiouLUVJSAq/Xi+PHj7N6JHlfkZCD0KgWACc34vD5fGzsqlAo4HK5MD09zb9HvkNKpRIrV65EXl4eqqqqsGXLFhgMBlZwi8fj6OrqSllj1JQAZhoKwvtEMsriIjadohYVD+3t7QiFQqzcV1tbywn/ww8/jKamJrzwwgsoKSnB2NgYixTQd0IcM7/fj4mJCVy9epWFC4QJXGFhIZYuXYoHHngAv8Tj2EwAACAASURBVPzlLxEKhRjqKhQSSScski5oIkAiLpRAkyrh2rVrYTabkZ+fj4mJibQTOiEcube3l6Xk/+AP/gDLli1Da2sr3G43dDpdzo2ftrY2HDlyBF6vF4WFhdBoNNBoNDh27FiKMEKmxgH9f0lJCVQqFXbs2IGf//znPGUdGBhIgc8B2afpx48fx9DQEDcJfvCDHyAQCODFF1/EU089hTvuuAOdnZ2sAhcOh5lLmumzChUkNRoNli5dmiJCQPeVOFYajQalpaVQqVSoqqpKQRW0tLRw4X7hwgUkk0kYjUa2BBD6ONntdrz33nvYtWtXCsSYDLofeughfm2bzQa/3w+dTsdFPjVFAHDThfYzKpD+3//7f9yAUavVKC4uTrEzEN9fEvkh3qVMJmPzeLo/9fX1bEovnKqK+UpCARpqkhmNRgQCAZ7OEP+0o6ODC3Tgdz5uK1asYKSDcD0JJdapISE8N3LlMV/Pc+dmimzwRqNxRtVS2Ngju5VMYiH0/8XFxaipqWHxF1L6lEqlLBpEDSi5XI7q6uoUZMRc1BBCWZDlg9frTREXyyUfI8uPkZGRBXmdfhxjsShbjJsqaMrS29vLhp5CBcXr6aN1vUKoDig2hL3WDUS8IVN3amxsDKFQCGNjYzCZTNxJTiQSKCsrQ11d3bzvk5CEK/xzrkHdTOHBnavqZbZIZ+xcWVkJvV7PE4dcX4OSrz179sDr9eK///u/EY1Gccstt6Qkt2JlOKFYTCb+C4C0k7K5oBpG44whq9lsxqlTp+BwOKBWq9HU1IS7776b/etIlpgSfZlMxgUa+S7NFbFYDFarFUVFRZy0iAsNtVqNz33uc9i2bVsKJ8donBEH6ezshM/nQ3d3N08uSPGuv78fEokEdXV1eOyxx/D5z38ezc3Ns4pYt9vNvBggVVHrwIEDaG1t5QI1EokgkUiwqe3SpUtZNIA4lePj4xgfH0c8HmceIcED4/E4Ll26hO7ubiQSiZQJYjAYxJUrV7B06VKsXr0at912G6vLRaPRlElrLgUZBSXaMpkMJSUl2LhxI5qbm9Hf3w+z2Qyfzwe5XA6v15vxGvR69Hk8Hg/WrVvHXkE0yZlLRIeK5d/+9reYnJxkVVaDwcDTLLEwghCO+/LLL2NgYAA7duzA3Xffzcnd/v370dHRwc9ed3c3vF7vnA0ekmc/evQoc6iE0KTp6Wn8+Mc/Rn5+Pjc3aNJosViyTssqKytRVlaGgoICRCIRLF++HAaDIeXftVototEoAoEA1Go11q9fP6uwFcIT6+vrcccddyAWi3Fzwmq1Mj+xu7ub1RcB4LHHHmNosc1mYyEEu90OrVbLE3XiEsrlcuauER+UlGvXr1+PpqYm7N27l30vyc8tm8DCU089haNHjyIcDkMul6O4uBg6nQ6lpaUwm83M91u7du2sKYqQQkCTQFLCBGYEbUpKSmCz2VjltLm5Gc888wysVivzU6mJoVKpYDAY8IUvfCFFLVLIvaXGDRUBhYWFbGuRDfEinKAtRG354xjic1rcBBF6oIrFQoRNFmCmkN2wYQPi8TgaGhpQX1+PQ4cOobe3lwszQok88sgjaZ+PTNQQpVIJu93OPL/h4WHU1NTg7rvvzqmJRFPv3t5ehEIh1NbWpohVfVJjsShbjJsqKisrU8j6brcboVCIR97zGV2TAqBYVvp6hRCuSHC2pUuXMkl6obBF4fWFiY2YK0DdRuIN2Gw2LFu2bMHFYK4dybl+V6vVptgALLQwFb8fsdrirl27cObMmVnTpUwhl8tRVlaGxsZGvqdNTU1pk0cxtyXXnxErY+ZKNDcajdizZ0/aaxQVFaGhoQFarRanTp1COBxOga55vV54PJ6c7gEww7vr6OiASqWCRqNJmQCREEBXVxcKCgqYe0SS+kVFRdBqtfB4PJiYmMA777wDpVKJffv24dKlS1xkuN1ufPe738U3vvENfg6ERez+/fuZ++Z2u3HkyBGsW7cOJ0+exE9+8pNZfJSpqSkUFRWxuIg40aiqqoLD4ZhVmMrlcshkMni9XkQiEfanEl47FApheHgYGzduREFBAfLz81OMfxcSxMEoLy/Hpz/9aXzpS19CJBKByWRiVbOrV69mvQZN+igKCwtTEk0yOBaLKghDOMGnpFYmkyE/Px8GgwE6nY6fLbEwgt1uxwMPPMDFRldXF/7zP/8T+fn50Gg0nNxTRKNR2Gw2ANmn6VarlaFq6eTmAaRwqcrKyrgoSyfeIvysVqsVDQ0N6Ovrw+TkJJxOJ0ML6T01NDTgyJEjrHS4fv16fP7zn5/1HoVCTRs2bMDGjRtn7UlLlixBMplkY/ErV66gvb0dW7ZsYVVEYdK6adMmfm80FQLA8DEAGB0dxb59+1K83cbHxxEOhyGVSpGfn4/bbrsto3VFa2sr3nvvPW4oxGIxuFwu3HLLLbjnnnvQ1dWFgYEB1NXVcZEkDmGiT02g0dFRhEIhaDQaftZoIj84OMgS6gR1o2coPz8ft9xyC+rr61PglRS0N4jPUvL3bG5uZjqDUIlUeA4KLR0+CrXlDzPSndPiJojwzBKuZYIj0+RZr9fz9L6mpgZGoxHV1dUIhUJQqVSMltiyZUvK+ZsLNYT2W5oGBwIBDA0N5YweMplMsNvtPBmmdfJJj8WibDFuqjAaZ9R2AKQofNHGO5+NdqHGiblEJp+NhYqSZLq+uEAST2kmJydx8OBB9Pb2MtxpocIa6ZSe0hUV6fD94iQmnejAtb4f8cZPEKRsEwwy8l2yZAk0Gg22b9+ekszMBcW81p+Zz2cXX0O8Br72ta9h8+bNmJycZHjTO++8g1deeWVOrzNxJBIJhn6qVComcUejUXi9XnR1daGvrw/AzD20Wmc8vbZs2YIVK1bAbDYjEong5ZdfxuHDhyGVSjE9Pc0Fz/T0NC5dupTir5fuHrndbng8Hpw4cQIvv/wyRkdH0xYZyWQSZWVl/HyJ15vBYJjlDSWTyVBeXo6lS5diaGgIU1NTnJSQMIhEIsH09DQGBgbw3HPPMXSPlPCuJUgJtbe3F8eOHWM+YiAQSFtA0r2m9UwJO/3Z4/Fw0btixQr2JRTDSIVB98nlciEvLw+lpaWQy+XYtGkTdu/enZaLRPFf//VfXJBRkMpluojH4zzJExd3QlhrZWUliouLs3p5abVanuSSwl8oFGIzYqvVip6enhTPK+LLJZNJeDweBINBRhYIPevoM4RCIZ42iyPd2UEFz5kzZ+D1euFwOGA0GrFmzRoEAgEEg0HY7Xa8/PLL6OnpYV4dfQ+0/qPRKEMBhUI8FIlEAk6nk6G3r7/+OiYnJ/l+kIeiWERFaBAt/o7oOd+6dSuamprmVCQWTjvFPnJer5cLWo1GA5vNhtLSUsRiMRQXF6O4uBibN29Gb28vbDYbFArFnKp5wnONRILo3o2OjqaIRgGYdfYCuVs6fNwj3bm4cePGjA1DAEzjKCsr42kZfZcTExPo6emBVquFwWBgbnZJSQmWLFmSNp/JJbeqrKxETU0NhoaGEAwGuUDLBVZqt9vR1taG0dFRqNVqrFy5MmffxI97LBZli3HThPAw/+Y3vzkLAz3fjVY4Wbrekcln43oUZMLrz2WWarfbcfToUSiVSgSDQfj9frS3ty9IaVLstSXsQgqTn3TFoniTvtYpofj9pNv4hWbC6YLUwb761a+yV861JtofZojXACUdwO+eFQCzknsSxMglJBIJCxEUFBSwCSxxsmQyGeRyOSYnJ/Hmm2/C6XRCLpczpDAcDrOYRUFBAQoKCiCTyVioo7+/Py3crLm5mb2yTp8+jcHBQf4dcZJM8Cu9Xp/yfFGiAcwUC1KpNOVzKRQKNDQ04Itf/CJaW1sxPj6OwsJCaLVanD17lo22E4kEpqenMT4+DplMhuLi4oyFx0Kir68PVVVViEQirBDn9/tnTYlUKhX0ej2mp6chk8mQSCQgl8vZkLewsJBFZ2iSOD09jc7OThw/fhxbt25NK8xA1hTZzM/TxS9+8Yt5fU6dTpdiSK3VatHQ0ICuri50d3cDAFavXs1qsWSOnK4ompycRGtrK2QyGfMy1Wo1RkdHuaDKy8vjKZjJZMK7774Lm80GqVQKnU7HYil2ux0lJSX8vJw4cQLBYJC79/T3whAXJcePH8fhw4cxNDQEl8uFWCzGCrt5eXkoKipi+fDx8XGUlJSgpqYGZWVlKdYAdrudX1eoYigWkJFIJIjH4/B6vTh79iyAmeegoqKCoYMWiwUXL17E7t270dLSgra2Npw4cQJOp3NWk0YqlbLCYq6KxGIkgHiSVVhYyF5409PTaGpqQnV1Ne69914uOj0eD/R6fc6wM6PxdyJBYk4vifUoFAp0d3djYmICQCpVIJcG2sc9rqXZrNVqU0RdLBYLBgcH4fP5WNWZOMXkTelwOFLgzHa7HS6XC0uWLJlFKxAHPRsymYynXbnASk0mE7q7u+Hz+aDT6bB27VpEIpFr8rz8uMRiUbYYN0WkS/bFyjwL5TqRgqGws3qtQcnO9YQrCmM+aojk49Hb2wuLxYJXX30VTqczrSpXthAmIpOTk2hra5tVFGYqFufikSwk5rrmli1b8Itf/AJ2uz3t7xMkiGTE56sG91EFFVxiL5fJyUmGdFByZDabZyW1uRZkwIziG02H3G43G9xSUkgQR7/fD5vNxnAkcQJJpsEk2uB0OiGTydjIPN1hajTOiCa8/vrrGBsbg8fjSYFRFhUVQa1W87OQqVM6MjLCxaKwC0uiCuPj49DpdJiamoJWq8WZM2dShGBkMhkKCwthMBiwdOlS+P1+1NfX48yZMznfx2yRSCRYeODNN99Ee3s7vF4vYrEYZDIZG7wTrCcej6OgoABOpxMWiwXhcBglJSXQ6/WcoFIBGgqFEI1G8dprr7H8P0FNAaRYU9x5551s85ALnyMTtDBTRKNR/OhHP4LZbIbT6UQoFEJHRwe8Xi/DEUdGRnKCG4fDYYbYkUDOkiVLuCBTKBSw2Ww8UaFkj8RZyEcvGo2m+LBZrVZMTU3x+o3FYjh16lTG9QkAzz77LBtkCydb0WgUXV1dWL58OUNNieNpMBhw++2347XXXkMikcD+/ft5T+3v7+d7IJfLkZeXB7VaDbfbzYILhYWFLMhBMDCdTocVK1ZAp9Ph5MmTbE9w9uxZPPTQQ+js7GQJfnGQ+qNQGZcK0lzPBjEEmZJ08hRVqVS49957U/hzer0e1dXV84KdpZvSATOTHeKwkfXH6tWrr0nh+OMY8z1rrVYrF9JarRaRSCSF69vW1sZqoUqlkukjZAIPgIWZRkZG4PF4MD09jaKiItxxxx0Zv1sqrKanpxGNRqFSqeDxeOYlwEJKtefPn4fZbL5pdQWuZywWZYtxU4Qw2RdKDGebzsz3uvTn6wEtFCY7O3fuvC5YZzHsJ9eNt7m5Gffeey/DEdxuN/r6+picPZ97RoevyWRKwfErlUr2MMpULH7YXcrm5mbs2bMH3/72t1mVUBjJZBIul4ulptO955stxGudJI/b2tp4wqLVauFwOODz+RCNRrPCwOYKg8GAoqIiTE5OsjqcRqNhxaxYLIZgMMjJIR3spCwokUhQVFSE+vp65Ofnw+l0wufzQaVSQaVSQS6Xo6+vDy+88ELKGiRIW2trK7q6umZZF+j1evzVX/0V1q1bN6uxQMI6DocDNpsNVquVVTTz8/NZPSwUCqG/vx8///nPUVBQAKlUigsXLsxS5qytrcUXvvAFNDU14Z133oHf78fq1auxZs0aPPfcc/MuToRBBtcVFRXYv38/Xn311VnXKy8vR01NDUpKShCPx1kcJJFIoLu7G8FgEGNjY/jSl77EvBBaz263G/F4HGazGcPDwwgGgxgcHERDQwPq6uo4ATcajSnWDnPtByaTib9jcWQyYff5fDhw4AAUCgVPgYSCD0I1t2yhVCqh0WigUCiwZcsW1NXV8TN79epVViMl82T6nTVr1vD/r1+/HuvXr0cymURxcTEn7ePj4yzAQu9tYmICra2tszz3gJnz4vz585iYmEhpdtBnkkgk0Ov1KCkpQV5eHhsz79q1C4cOHYLZbIbL5YJOp8PAwADC4fAstdNoNMrWDFKpFKWlpVCr1XyvJRIJiouL2eLEbrfj5MmTvI7cbjd+9rOfIS8vD8lkEvn5+YjFYlygLl++HP/wD//ASfi1KBRm2uPFvnTXGuIpnXCiTgWZXq9n+wAAac3OP6nJ+3zO2kyqlMJC22QyYWhoCOfPn2e/PiDV0mVwcBBTU1PMBSXhnblyKvLgSyaTs3ixmaK5uRmbNm2CxWKBWq2GTCb70KyNPupYLMoW46YI6obTxkEdUCpMFlpYXW9emVBp0eFwYPny5ddF5SkbhyyXaG5uxrp167gjLeyqA7NlcbNdV1x0Njc3p5VHvtGHXrp7Qp+BYBT5+fnYvXs3fvazn8Hv93OiBIA75tPT0/ja1772sTio00EWp6am0NnZiampKTYNFhraUhK8kDCbzdBoNMjPz0dJSQnKysowNDSEiYkJLpTi8Tgn2R6PB36/nzv8VVVV2L17N1atWoVjx47hzJkzmJqa4kKYuq3CCSXxf95//3309/enfX7y8vLSqsvRmnj++ecxNTU16/ckEglKS0vh9/uZN3fp0iU25iUlQ2H84R/+If7mb/4Gp0+fThEGuu+++xAOh/HCCy/M+75WVVVBKpVi2bJlbML6xhtvpC3wCMpHhPZEIoG8vDyG20ajUTgcDrS3t+PP//zP+dk7fvw4RkdHWcmR5M7D4TAOHz4Mg8HAyRgJcNCEJtseSk2A4uJi3j8oPvvZz0KlUuHs2bOIxWIoLCxkAYhkMolIJMJTo3A4zLC8XJQrCTq6du1aJBIJVFdXz+LKiScoPT09GBgYYA7MXXfdhZqaGjQ1NaXsWTS9OXfuHJYuXYrJyUnm501NTeHXv/41nE4ny8NTKJVKLnyFoVAoUFxcjN///d9nbh7tScDvPLOCwSCrjQLgJgrdD1JSJEVMMoOmBHbVqlXYtGkTampqUqZB4mKKXkev16O+vh733HMPF7BCdc5IJILCwkJMTEzknCDPFel86QDws+RwOFKarPMNIaQRAEMnPR4P+vr68Oyzz/LrCdUFP2lTlVy82tLFXKqUdC3iQVKjWaxATPSIeDyOWCyG0tJSVFdXZ8ypqLA6f/48LBYLJBIJHA5HTo0Ao9GIRx99NKMC8ic5Fouyxbgpwmg0oqWlBa+++iqCwWDKBn8thdX15JVRQmg2m/mwv16bxLVO9GgT27RpE/9ZuJEJ1erEh5V4s6f3Mjo6Cr1ez/BPYaGQi+njtUY64ZGenh7u/ns8HigUCoRCIfbqkkqlLJcejUZRUFCAzZs3f2y4BuK1rlQqceTIEUxOTiIWi7GfWmVlJXw+HyKRCIaGhuB0OlPgf7kGccJisRjuuecebNu2DV6vlzlYgUAALpeLoWFUnKlUKjQ1NeGJJ57gwmlychKnT5+GRCKBTCZDXV0dm57Tc/LSSy/hRz/6EUZHR5m3li4KCwtnCRkAM2vil7/8ZdqCjD7P1atXuSCgIN6YONRqNdauXZv23ldWVuL+++/HG2+8kfH1MoXRaMQTTzwBAGhvb8evf/3rjHzGSCQCm82GYDCIvLw81NTUAECK4AmJupSUlHDhQFYRbreboXnnz59HPB5nIQyhpxRNdObaswh2REk+qf5VVVXhT//0T2EymXhfaWlpwfPPP4+TJ0/C5XLxNIm4fsKpUC5RVVWFlpYWno6lgxQK/+7cuXPw+/3o7u5maCYZLmfaOzweD9auXQuLxQKbzYZAIACLxYKDBw+ivr4eu3fvTrnvRqMRIyMjvJ6kUimUSiXq6+tx1113ccEnVsUtKyvDunXrmM+aTCZRW1vLaqBSqRSNjY1QqVTo7u5GLBZDKBSCz+dDaWkpysvLcf/996coQ9rtdjz//POzlDuJt1ZfX8+Kp+lCqVSyofp8oWSZQthQpfch9HETN1kXug/TejcajTh37hxfU+gJKTQ7Bz45U5VrQQuRTQQZxqd79umZd7vdqKur43VPTTSj0Yju7m5IJBKoVCpW8sxmx2E0zpi1B4NBbh55PB7Y7fackEXiaenHoal6PWKxKFuMmyJoOjM+Pg6Px5NS8FwPvtL14JVRkeBwOFBRUXHdlBaBa5voCYsq4QGeSRaXPksmaCh5+fh8PjidTk4wiNvxYXWqxIe9y+XC2NgY+vr6YLfbUzrxFKTutGvXLoRCIWzevJkVu27GyGR7QH9nMplgs9kQj8chl8vR2NiYYrZstVrh9Xqxb98+nDp1asGdb5rOULFNVgFKpRKHDh3Cq6++yoUhTSA/85nP8PsgZbrBwUFEIhHu0Atl1t99911885vfzInz5nQ62YNPeH8qKytnTW+EQZOaXGByMpkMt99+O3+GdPsMFVd/93d/N69729nZia985Su49dZbEQwGMTQ0lPXn4/E4pqamEAqFuACmz0LhcrkYHnj33Xfj2LFjkMlkMBqN2L17Ny5evMjqlW63G93d3ZBKpVixYgXy8vIQiUQQjUbT8m+EColUDBLU8TOf+QzC4TA/S2IbCYPBgNraWjzzzDOcIEcikXlPb+PxOHvEkWJntohEIigoKEAwGEwpwrVaLZqamliEQ6vVwuVyobu7Gy6Xi1VyrVYrfvWrX7H/ndPpxGuvvZaSaFZWVsJgMKC0tJRNdoEZLt/Zs2fhcDjw9ttv48KFC2zkrtfrsWrVKmzbtg2bNm1Ca2sruru7oVarsWrVKjz22GPo6elBSUkJVq1ahYMHD6K/v5+njcAMpHX16tWzkleyE0g3cY1EIrjnnnuyJrw3wsuLGqpCCCPJtAuVFBfK501njSIUA6HGD4mBiBtBn4S4ETQMCuIoE6yblE+JrnDs2DF0d3ezIAhB3IUWB5mue+zYMfT398PtdiMWi8Hj8WS18MgUH5em6vWIxaJsMW6KmKvgof8uROyDukA2mw1KpRLhcBhf/epX5/2Qk7hHWVnZvJQWc4EdLLTwzNZBE29k6Yq+TPK6DQ0NaGtrg8/nw+DgIDZv3pyxe32jQnzYX758GUDqdCcdnyoWi2HNmjWzvIdutpjL9gAAjh8/jkgkwup79957b8rnop8rLS3F448/zt9jusjLy8Pq1auxdOlS9Pf3s+iCRCKBRqNBU1NTynWFXcpIJIITJ05geHiYi7LCwkL+eZPJhN7eXrhcLkgkErjd7pTPYrfb8b//+785FWQ0iQAwSwGUJM4zBcncZwpSZczLy0NJSQm+/OUvz7me77vvPvzHf/wHq5HlGk6nEx0dHcz1yRYEEQ2Hw3C73fwZxMWl2+1Ga2srTp48CZVKhUQigfr6ekQiEWzbtg0nT57E22+/zTYDZWVlWLZsGa5evcrTlYGBAZSWlqZMeIjsD8woJK5evZqTXIItmUwmnlQI7xlN14VG2AuF08bjcZw9exZ//dd/jRdffDHrd1NZWQmXywWfz8em4DKZLIU3EwqFYLFYMDIygsHBQZZn3759O3p6enDw4EFW/EwkEhgYGMDx48cZKg0AGzZsQCAQgNfrxcDAADweDyKRCBfbZA9B8MN4PA6fzwelUomenh4MDw8zb6yhoQEtLS3MFX3hhRcYhu31ellpc/v27WltDiorK1FXV4eBgYFZz5JKpZpTXbaysvKGeHmlgzDSGS4sniYnJ+etoJfpjBJCWQ8cOACHw8Hqgh8GvP7DjGtp2or9WsWNLpPJBIvFAqlUivLycjQ0NDD0l/Ykl8sFhUIBnU4HrVabFbYofN2xsTEW1qE9cL5ojv9rsViULcZNEeJNR1zwLHR8b7fbcfDgQZhMJni9XsTjcVy+fBlXrlzBd77znbTXMJlM7OMihqYI/VLSdZvFRdV83vdCukG5dtAyFX3poCcA2CSYDvmioiKeuNH1PowQHvbAjGfN1atXuSCjQpk6x3K5HOvXr/9YmEzO9d3Z7XZcvnyZISNr1qzB1q1bM14vXddbLpcjPz8farUaW7ZswZ49e7hIam1txeHDh+FyudDY2MiwT3EQDMVms7EqHykrCoPgo8DMlGX//v34p3/6JxQWFiIYDDKvaa4gyf2JiQnk5+fzd3/gwAH88Ic/nPP3iVeo0WhQVVUFmUzG60UqlUKhUMDtdqOmpiblM2d7Vq9FIn++NgzJZDJr4uJyueD1eqHRaLBkyRJotVp+phsbG3Hy5El+nqempqDRaOB0OrlwsFqtaG9vBzAj0GC32xleRM/a/fffz9OhdCqs4iB58rlCyKfKFoODg3jmmWfw7W9/O+PPvPvuuzhz5gxzH+naFRUVsNlscDgcsFgsiEajLIGfl5fHQhQmkwm1tbXw+/2sWjg9PY0jR47wcyZcD+S5ROuL9krigwHg4tBqteLpp5+GQqHgyYBWq0VRURG/f1JC7OvrYzn9RCLBP59pH9+zZw9GR0fR2dmZsu8tX74c69aty3pfrwfqJF1kOkeE9IHOzk60tbXNiVYRn6OZChI6L8VcULvdjtLS0uvyuW6WuJbvLZPVjdCygqZk1dXVKCoqQldXF4aHh1mZt76+HlqtFvX19SnCObm8LqmmxmIxlJSUoLi4eOE34v9ALBZli3FTxFybTjp1xnTKbOI4fvw4Tp06lTIyj0ajOHHiBNra2lL4A8DMQf3kk09yt/Vzn/scgsEgLBYLG1VSZ1oYQr6ZSqViiNmNhB0A8+ugpSv60kFPDAYDk3T7+vqg1+szctIWSj5eyOcjCCN14SUSCYxGI1atWsXThdWrV2Pt2rUYHx+/6THoc313JGUcCoVYoj5dmEwmfP3rX5/FMwFmprt33XUXQw2FU9Rdu3axoqJQYVN8z0wmE/bu3YsLFy4gkUhAKpXO6nrTeuno6EAgEMDk5CReeeUVvkamZJymSCTAIZFIIJfL4fP54PV6UVJSwrDZN998gOWH8gAAIABJREFUk4Vs5gq1Wo2NGzfigQcemAX1/Ld/+zf8f/a+Pbip80770V2WZFm+CJmDLYyJjYNSsMhSPN0UWihNMiHTDt1J2t10Q3Y7syXpNNttMpkmu8l0L/3aTTqd3W5DpjsttOy0KZ2lpLjdAAsNYIIJxDIYG1+wMbJ9LFmSJVn3+/eH531zdHQky7Jsi66efxpqXY7ec857fpfn9zw+nw9TU1NpMzW57lWTyYTLly/n9d3FQDblQwISwFdWVqZ5QO3atQunT59GLBZDPB6HXq+H0WikapTk/A0ODuLatWu4desWEokE1Go1KioqIJVKodfr6bXCsiztTmbbX1iWpTLaZF8kPmuk89PS0kJNln/zm9/ktQb/+7//i+eeey5j9pUIavzwhz9MU+2USCTUB629vR29vb2Qy+UIBAKIRCK0G/nee+8hHo9jbGwMwWAQlZWVVCgjHA7j2rVr+MlPfoJt27bRpIlI1JPgUqFQ0Fmd/v5+Oq+o0+mgVqsxOTlJxT+qqqpQU1OTIVxDVPGIUTlJxiORSE66K8Mw+MpXvoLR0VFqY/GZz3wGf/VXf7VqxSjuc8Tj8eDIkSMAQK8j0q3hzvgJ7c/ZCiO5YoNc/porJUq1nOA+YwuZ5eauH9eWIRKJoLe3F+FwmHbJiLVPf38/gsEgFAoF9u3bR+c1gY/YSvl+r8lkwsmTJzE1NYWWlpZ7omC6mignZWWUDHJ1iriVOKvVirt37+Lw4cPYsGEDjEZjVp+wsbExOufARTwex82bN+m/ycZ3+fJl2Gw22Gw2pFIpHDlyBJWVldTIUqfT0eCEu1mSqueHH35IAz2DwVB09UchkAHoQr1aslFP9u/fT5O1zs5OQTVHvnx7sR+A3AcKoZ7abDaq/PbFL34R+/fvT5uPO3nyJD2m5ubmklXgyifYUCgUkMlkVJZeqJv25ptvYmxsTPA71q9fj7/4i78QfBDy15arVkfWzGKx4PXXX6d+MwBQUVEBhUKRIXX/wgsvwGg04tKlS7h+/Xrad2Xrjmi1WnzhC1/AmTNn4Ha7IZVKaWcpHo+nBQTcJC8XJBIJzGYzvvnNb6b9boZh6PVKBCj6+/vpa3Ldq//+7/+O7du35/X9S4VCoUBDQwNYlhVUiwQ+MtRub2+n3QKSFH3jG99Af38/9bsaHh4GALqHqVQqsCwLq9VK7Qvuu+8+qgBJFOyA/Cr0ExMTVKAkmUzi4YcfRmVlJT788EO4XC4A83vhl770JQDIOykLhULUFJtrXGy1WhEOhzP8CT/96U/jK1/5SsZx3rlzB0NDQ2leaZcuXaJKiNxOFwAqdHP79m2MjIxgdnYWdrudrl8ymUQ4HIbX60Vraysee+wx2nlobGxEd3c3bty4gUQiAaVSic2bN1P/Lu5xkfkuj8cDqVSK2dlZ6ufkdrtz0vzInGxXV9ei5maXIhixEIji5K1btyCTyQCACvXkSpy4BT6usjGQ7oOZD/Wf29l1uVzo7e2FVqst6edALhTrfHFp5P39/dS3jBRRuDOM/EKtxWKhdN7FHgv5O7c7V0ZulJOyMkoahDrodrupTDTLslRpzul04vbt2/jggw9gNBrxxBNPQKvV0moqy7JZZ0xIskYkugmvmvjXxONxWmVVKpWoq6uDTqejD0B+QkKqxbFYDJFIJIP7XuxqndAAdCEQUvy7cuUKnE4nTdYIjYEr9sHvXhbii5YPuA/kV199FQ888ADGx8fx8MMPpwUj5HiIl00gECgps2ihruJCwca+fftw8+ZNpFIpQbW0iYkJQWqdVCrFZz7zGXzta1/LeV1wKUD8LhEwT28bHh6mcztSqZRK6I+OjiIUCqXJXZPuWyAQwI0bN9Kkv/kCHBKJBPfddx+2bt0KqVSK/v5+TE9Po6qqColEAkajEVqtllaH161bh6mpqZxrvGbNGjzxxBN4+umnBdeViJUQmpnT6Uxbi71791LqMn9uqqmpCePj4zm/fyEQY+5sQiRKpRIPPfQQKioqEIvFqEolH/fddx8+9alPYc2aNfjnf/5nBINBiEQiGI3GtAD0ypUrsFgs8Pv9VOkvEolgYGCAJikajQZbtmyBx+PB8PAwxsfHYbFYFrxG+Sbn7e3taff+lStXcPjwYfT09MDlcuHIkSNoaWnJe60CgQBOnDiB8+fPw+PxwOPxpKmBcrtkCoUCu3btSuskmM1mGAwGnDt3Du+88w69tlUqFcLhMGpqauD3+1FRUZHxjPD7/ejr60MkEkE0GoVCoaBGtkSQY2ZmBlevXkVzczNisRiqqqowOztLPZyIbcQTTzyRtk9x143MdwHztNS7d+/ShGahfYvMpi0Gy8nc4BaRSNJMrqNsiRMw3zXjJt2FKBvzkw6Xy0XtNiQSSdF/60qh2OeLTycliRK/qJ3N5mCxx8L1lCTP5HvxPKwkyklZGSUFbuAKAIcOHcLFixdpMEUUxLiGhE6nk24WQ0ND2LBhA5VL93g80Gg01AyXi8uXL+Pll19GRUUFLly4QH2g6urqqMknmUWRSqV00JV0LPgy8QcOHAAwL0TR3NycwX0vNkh3jiQhhW522TomarUaarWaJmJCnTAutZB00vjm38XG9u3baXeMC343lRx/KShwFVrx1Gq1aGlpwejoKKqqqtDb25smE9/Y2Ii2tjawLAu73Q4AqK2txd/93d/hySefzPv4hLpEExMTNCglSVU8Hk+baWJZFnfu3EFnZye++tWvYu/evXjqqaewa9cuXLhwARcuXEB9fT2MRiNu3ryJ3t5eRKNRyGQymEwmPPjggzCbzVT63+v1Apjv/nLvIQB45JFH8MEHH2Qcu1wuh1gsRktLC5566qmcgb/JZKIJD1/chKiFEcEhss5kT/r617+OF154IS9lRyGQIJxh5iXWJycnaWKhVquxbds2KuLys5/9jHaZ+CAJws2bN3Hjxg06D6VUKmmSSfYC/nklSScR7wCArVu34vHHH8fx48dpJ/rs2bMAkLX7LmRyTuh6JIDjBukkiRoYGMh7vcLhMK5fvw65XE7NgjUaDd2XuRRP/rwWn+pYW1sLnU5Hqc3Eb6miogJ+vx8ajQZer5ead2s0GppAK5VK6HQ6bNy4ESMjI9QDD5jf95xOJ4LBIG7fvp02DyiRSGAwGHLOLXLN4f1+P7X0WK59azmZGwzD4MCBAwgGgxgZGcHMzAx++ctfwu12U9ESPiWWdM244wFEHbOQ5we3sBKLxeD3+6HT6Sjd9F7Dcpwvch64yrh89oXT6Ux79pPvXcyxLKeF0B8zyklZGSUDi8VCW+YkeOnt7YXNZkMoFEqTASZGo0RZjFRPiUIWGcSWSqVYs2YNDUK4AZXNZsPRo0epWAQxTkwkEjCbzXC5XJiZmYFMJkNVVRWMRmPODYphmGX30+AmrWQmgXjBLMVzRqhjsn79euzZs4e+hu8ZJZTMASiaLw0fCyU23Crg2bNnc0qnrzQKrXgStTRAeF0Z5iN/utnZ2byHsPnIRlNrbm5GKBSissZ+vx/RaBRisZjOKHk8Hty5cwcOh4Oq+jEMgx07duDFF1+k38GyLM6dOwen0wmTyZQ2p9DV1YVAIIC5uTmoVCro9foML72BgQGoVKo04Yzdu3ejpqYGkUgEer0eQ0ND6OnpyTr3qNVqsWXLFty+fRv33XdfWsAs5IvHrSjX19fj7//+7/GLX/yCBvaLSdDq6+tx4MAB7NmzB3a7HZ2dnbhx4wYikQgMBgOeffZZGAwGHDt2DLFYjIqpSKVS2r0Xi8W0gm21WtOOIRwOIxQK0b2A/G5+McVgMMDhcFAaKpl/ZVkW4+PjmJmZQV9fHxwOB7q7uzOq6EI0M/L5/PuTW6jS6/WLlmAPBoMIh8M0WH/kkUcwNjaGiYkJWK1W2O12OidH9j8u80GpVNL1Wr9+PbZv304LDmTPImuo0+mQSqWwadMmWnjTaDRobGzEI488ApPJhKNHj+L06dO007Zt2zYAwMDAAFwuF/V0UygU0Ol0aGxsTAtCiRIwmXHmmsMT/zDS2VkO5ENHXQrMZjM+//nP0w67w+HA2NgYhoaGqCqiXC6HyWTC2rVr4XK56HwZAEo5X0pBj1gliMViVFdXo7m5GQcOHMjYC0q5W8M9zuU6X0LFYu4zVq1WU1sT8rrFHMtyWgj9MaOclJVREmBZFkeOHMGVK1cQCoXgdrsxMjICn88HqVQKuVyOVCqFRCIBsViMqqoqfOELX8C+fftw9OhRdHd3QyQSoaqqCnq9nnqd6fV6GAwGvPPOO7QyzVXrI/MB5L/r6+vR1taG5uZmWsUE5jcwfpdIaINarq4YWSNu0GMymYruOcOvzDEMQ7sHQqpZ3N9rMBjSfGkAFLVjlk9iwzDzQ+UkUVWr1SVBlyi04kmCqGPHjsHlcmFycjKDklmsa47/OdwAbnx8HD/84Q8RDAYRj8czKHXE54n4THHVS7mft3v37rR7hlzT4+PjNKDl0xxJEuDxeFBZWQlgXja/o6MD/+///T8AEKRFceceiQDPJz7xCcjlcjAMA6PRmCZuQvz5SOJ34sQJSpsjcuMKhQIPPvgggPk5iVwWBHwEg0Fcu3YNDzzwAAwGA7Zt24bq6mp0d3fD4/Hg6NGjUKvVlDJkNBrh8XhoEqzT6TA7O4tEIoFgMAixWJxGu5PL5VTqnNv1EypgEGn76upqqkZIBvz7+vpgs9kwMzMDm82G8fFxeu9z15Nf/c4mXW4wGGhya7PZcoqYEHol/9oiEvTBYBBf/vKXEY1Gcfr0afz3f/83vF4v4vE4uru7YTKZcOzYMVy8eBEejwdVVVVobW1Nq/jz9yx+4m0ymXD27Fl4PB7odLo0A2dy3pxOJ3bt2kWLcGfPnsVPf/pTKm4ikUiogi33Oub7Qc3NzeHEiROYnZ2lyr5+vz+DQlosrERSYjabcfr0aSqk4vP5MDg4SAuuNpsNVVVV8Hq9UCgUGB0dBQDqkyjEglgMuIWsRCKBnTt3wmAwLOs8XTEhdJyFCHwUAu49vH79etTV1RW8RoVaCP1fRzkpK6MkQGZj3G43UqkUrFYrjEYjRCIRmpubcf/992N0dBR37tyBRCLBQw89RL3GyIMV+Ch5IkPvJFi4evUqpqenKRXG7/dnDHiLRCJs2bIFBw4coA+tfOZxVgr8oIdQvIrpOcOvpC6mw8MwDPWlAYrfMcs3sSlmB7FYWGqF2uVy0dmxlaRkkuM8fPgwlcPPBpVKBZVKhRdffBF2ux0GgwEvvfQS7HY7xsbGUFNTQztMJNgg15fL5aJBLAlk+bMmXq8XH/vYxyCXy7Fz5840k18+LYqbKIyNjaGnpwcikQgWiwWJRAKpVAq1tbU4fvx4mlIbMN9xunv3LqVKV1ZWor6+niYMJGlabEfD5XKhu7ubKv719fXB6/XSebCpqSmsW7cOyWQSRqMRbW1tsFqt6OvrQzQahU6nQ11dHcbGxiCTySiFOxQKQaFQwGAw0IISIDz/IUQp4hZbuPMmhFJGTNu5cyVTU1NQKpVoa2vDE088kUFn5kuXc9X3hPy0JBIJGhvnjcH5SRkwH1yPjIzAZrPBarXi5Zdfxr59+/Duu+/C5XLB5/PhxIkTmJqagsvlgtvtpkbyO3fuRGtrq+B9J0TlApB2HfELC1wPM/L/jYyMQCqdD6dSqRSlzjkcDrr2RE01FArR59OxY8eoOXxlZSU0Gg2kUilsNhu6u7sLFm8SwkolJXwaI6GeRiIRSrWfmZmhiqypVArBYBBSqRQjIyNF+X7udWyxWDA9PQ2TybSsSshLBUmYueMYwMoeZ65n7GKuH5Zlcfz4cbAsS+fwS2mtSxnlpKyMkkBjYyN0Oh2qq6vh9/uhUqkwPT1NO2Nf/OIX05Ivvrw3ML958Sl2AGC32xGLxaDRaBCLxdDc3Ayz2Yzr16+jt7eXGuhqNBrs379/xapSiwVfwQrAohUPF6qUCv19MR0e8kDkdsyKJbaRb2ITjUZRVVWFmZkZVFVVFaWDWAzkk8QLrT8JaMlsC1cCfSUwMTGBoaGhnOtYWVmJF154AcPDwxgdHaVU4m9961twu91U+ECr1WLNmjV07rChoQE+nw8+nw8ikQharRZGozFj1kSpVGLdunU5KTDZrg8y1+Tz+eiMnFwux9TUFPU3Aua7e4FAgHanQqEQKioqUFlZidbWVnR0dMBisVAjXGDeN08oiRBCKpVCIBCA3W7HyMhIWgIil8shl8uh0+loEvjEE09QahfLsojFYqipqcGDDz4Ih8OByclJBAIBSCQSrF27Fu3t7ZRuZLfbaZVayCxeSAyH/N1sNsNsNmd0kBobG2G32+n5CofDsFqtaQUxs9mMrq4umsgQVUgyd0VmToWuH5/Pl9NcPJFIwOPx4MSJE3A4HPjkJz8Jj8dDu6o2mw1nzpxBdXU1lEolNUt/4IEHFtwDuPdaY2NjVsGXbCAFBaLqGY/H4fP5MDIyQk215XI5rFYrnY/2er1QKpWIx+OoqanBxo0b8cADD+Dy5ctULKeYAfly27NwYTab8e1vfzvt2uDS20mnrKqqCpOTk5DJZEgmk1QgqxjPCpIEDw4OQq/Xg2GYZVdCLhR82qDQPNdKgWEYyOVy7Nq1K+08LOb64RbT9Ho9WJYtS+HniXJSVkZJgF9dC4VCEIlEkMlklJqXLajNVcEhNB4yk3HffffhwQcfpNXOQ4cO4erVq0gkEvjKV76yaDWrlQR/Zurtt99GU1MTDh48mHdClqvSlcsjhjxc8z1ObseMaya6VOST2Mjlcjqf4XA4MDIysiozBIulCmVbf6FkPJdcdrHR2NiITZs2USlyIWzatAmtra149913aTctHo+neaclEgm4XC4qCkMk3xOJBEKhEKqrqynduKWlBT09PXT2RqfT5UWBEaJgkn3FbrdT+qVMJkNLSwvUajVNOtrb22nSQBJBlmUxOzuLCxcuIBqN0rmYxsZG9Pf3Y2hoaEFFSAK5XI6GhgbE43GEQiEqdy+TybB+/Xr8yZ/8SdrnMwxDO4tutxs+nw8Mw+Dxxx/HnTt38J//+Z+U8kmSu7q6OtjtduoXRaiaQPoA/8aNG+l1plarMTIygs7OzrQu5mOPPUY7SHK5nAZaXEolmT0j1gI/+tGP4HA4cPXqVbS1tdG1rKqqws2bNwWVQpVKJRQKBZ3FIgwGQkcnwh4EqVQKH374ITW85iIcDsNut0Or1VJa5vHjx+FwONLm5/gg996HH35I388wTJrgSy6YzWY0NzdTHzOxWEw7Qm+99Rbq6uoQjUahVquRTCaRSCQQCASgUCjo/PS+fftw4cIFeo8VuyO+EvYsXPDvRUL1JF1C8r9zc3PUcqW5uTmrX+JiQZLgmZkZzM7Ooqenh1JfV3OmjC9mxu+OkVnuurq6FT1OMovZ3d0NYL6zzxeVKtWk9o8J5aSsjJIBGRJ+++23MTMzQ2e8+CpsfOSq4BD60tjYGCQSCSQSSVor/eDBg9i3b1/JD/4SMMy8d1RPTw9mZmYwMTGRd+dkoUpXrr/39/dnnSvLdpxCptQrscZc/x/SkWFZdkVnCPJJgIU6YkLrz6fjnD17Nu/zUAwwDEOtCM6dO4dbt27R6j/B3bt38corr1DZfNLx4CueJpNJ+P1+hEIhKvculUohlUpRW1sLr9dLxRvUajVaW1sFJZuB/JNeg8GA+vp6hMNh3HfffXSWi2sqzRfqkcvl6O3txe9//3sMDg7C7XZjcHAQ0WiUdtItFktWuw0hyGQyrF27ls7fkRlZIp2+f//+jISBZVk6A0veV1dXB2A+mfH5fDTxqq+vx9zcHA4fPoyxsTGqcnnr1i288847aGxsRDAYhN/vx7p162A0GpFIJDA1NYUPPvgAbrebKhhyr73+/n4cPnyYnjdiHq5UKhEOhym9saurCw6HAy6XC+FwGBKJhHbVJicnaYLNhVgspkk46RyNjY0hmUxiw4YN+LM/+zP8+te/xgcffJA2hyaVSmnyIzTfmEwmEQqFKH2QSM0Hg0FBz7CJiQl8+OGH6O7upoq7xMB5MaI8Y2Nj1D6CdMP6+/vx5ptv4tlnn0VbWxscDgdd623bttHu5kIdcSIxD2RXxcyFpVKol4pcBTWTyZSh/rtUiiVJgoH5fcjhcKTdvwTLPWfHT8K4HTEAVJae2x0rJm01X5DZYVJMHR8fT6M9C4kGZYPZbEZHRwfGx8fR1NRU7pItAuWkrIySAhk2J5tWNlNoLshmRygq3ASOyDIT/zCZTJYmK77Sc2HFwJ07dzA9PY1wOAyn0ykY7AhhoUpXtr8XSnshUuqEJrYcg+tCaGxshF6vx9TUFObm5tJmYlbqXOdas3w6YlyDcm6Xkszl8D9zucEwDPbv34/3339fkK7ncrng9/shlUqhUCgoVTgSiWTMbsbj8YzOh0QiQTwex5o1ayjlNVe1eDHzDSTYJbS5lpaWtMCM31kj/zYYDOjr68PQ0BCA+bXnd3xz0e34SCaTGBgYgEQiQSQSoaIQiUQCsVgsw+6ACEMQyXuxWEyPQS6XIxAIIB6PQyqVwmw20yLI2NgYNeJ2u92Ix+OYnJzE4OAgPR9ExVEmkyGRSKTR+7gdGovFgu9+97uU3kiG9puamrBp0yYMDQ3R7tr69evR1dUFnU6HmpoarFu3DoODgzTJIWqS5FpQKpXYuHEj/vqv/xpNTU00ESa059raWgSDQbzyyiv4n//5H/zXf/0XQqEQqqqqsHXrVgSDQZqocju4RGRj48aN0Ov1sNlsYFkW4XAYIyMjePvttzOKGo2NjTSZAkApiPlKqVssFjgcDiiVSnqu5+bm6OeQ7iJRSiVJGf/ZVl9fD5fLhVgsRq+1M2fO4Be/+AXu3r0Lr9dLWSMvvPDCopkdpfq8I8cl5JdY6PHOzc3B6/UimUxCJBIhHA7Tju/58+dRV1cHk8lUtCRQCELiXOT3EX+6YDC4at0xLhobG9HU1ESFaJqamugzqBDT6IMHD94TSpelhnJSVkZJgV/NAz5SUct2Y9vtdgwODtI5CuLXRD6DyDJ7PB54vd5lkWtfKZBALRqN0oowvxuRDflUSk0mE0wmU1qwUChtgajZhcPhZRlcXwhKpRKJRAK1tbVFpVDmg1xrtlBHjJyf/v5+vPXWW7DZbJDJZGhoaIBGo6EzTStNH5mYmEhTLuWCdCa4qK+vx4YNGzA8PIxIJEKTL6lUSv9bLBZTQ2qGYaDT6VBbW7tgtZisIZnNypXwL0X5cs+ePXQ2xWg0piUvZrMZH//4x3Hu3Dnq+cWHSqWCVCqlFgIOh4Nab5DEVaVSwePx4L333qP7EgBquiqTyagAhFQqxfnz5zE9PQ2fz0eVGN9//31s3boVHo8HPp+PztGRxCWZTFIfLj7EYjE9D9XV1ejo6AAwPw92+fJleL1ehMNhSKVS1NTU4Itf/CIYZl5QyWQypVHQ6urqYDAY8Mwzz9C5KJFIhLm5OSgUCiiVSqokmUqlsHbtWty9exef/OQnaadSSCjowIEDePTRR9Hb24uGhgZcuHAB4+PjuP/++9Hb20uTMpFIhJqaGjz//PM0qCR0TpZlqZgUv0jDMAyefvpp/OM//iMtLjzwwANUSj0XuKqKUqkUBoMhrSikUqnQ1taWofzI/wyLxQKPx4Oenh7Mzc2hq6sLMpmM3jtczM7O4pVXXqEWFAvhXpCCz+WPles9XD86kuz6/X7827/9GxVRAYC+vj5897vfxa1bt+DxeCCTybBx40asXbsWfr8fQPELXULiXHwqOilqrEZ3jAtSeKutrUVtbS0VUiokUeael4XitzLSUU7Kyig5kAdXvhWa3t5eeL1eBAIBeL1enD9/HtFoNO19L7300rKIT6w0JiYmKHVMLBZDo9GgpqYm7/fnO5cnpDi22Ic6wzCUwuB2u+FwOJbVVJqAdEaSyST0ej3t2KwkhTLXLF6uJIGcH9KhGB8fpyIZHo8nbVZmpUGOmxjuLgQSpKpUKtot0el0WL9+PQKBAKqrq2nCLJFIqADP5OTkggIL+Sb8i6Xd8MHt3NfX12fMu7z66qt46KGH0N3djQ8//BBWq5W+VyKRYP/+/fjMZz6DU6dO4Q9/+ANVBVSr1TAajVAoFPB4PHA4HBgYGEBFRQUsFgv6+/upQuL69euhUqlgt9sxMDAAq9UKpVKZ1tVxOp3UK44kziqVCgqFgr4um3Im2UfUajWVyid7QTAYRCwWox2umpoaMAyT1l3Yu3cvOjs7ce3aNUSjUTz44IPUg66/v58mo7FYDEqlEk1NTQgGgwgGg9TYmV+YENqrd+zYAbPZjCtXrtA5O3Jvc3/L2rVrodFo0s6hkPQ9P+B/8sknUVNTg1OnTqGpqSlvaXYu7bC+vh6f/exn0draShMFcgzcDijfFuLQoUM4d+4cJicn085TNusAAJiZmcH58+cXTMruBSl4vtAFoRjnej1XTMJqtVKFWmKfMzc3l7Z+gUAA165dowl8IpHArVu3EA6HsXnzZkG1wUITWW5Swt3riYgOv+hcCsky10aDW3xabFFLSOG1ubm5JK+7UkQ5KSujZJEvba69vR16vR7APL2mrq4OPT09aSawdXV1dJC/mBLyKw25XE6FClKpFO6///6i8LUXWutCaS8kqCUb9Ep0KbkPESIqsdyJeLYHuNAsXrYkl/sZRBWUG1QQ8RvS/VvpogLDzItmjI6Ooq+vL6c8PgHLslCr1RCJRFAoFNi6dSuVUCdJEoCMeRIisED+xl9XfsIvpFRXjGCUe66yzbs89dRTeOqpp8CyLL7zne/gzJkziMViWLt2LXbu3Im9e/fCZDIhmUyiu7sbMpkMDz74IPbs2QO324133nkH8XicCl7Mzs6ir68PHo+HKk5OT0/jJz/5CTWIJn5uwPysmkQiwfXr12lXLplMIhaLoa6ujs428ecAAVAxDaPRiIqKCqjVarAsm0axqq2tpSq6DayUAAAgAElEQVSPwWAQvb29aXtFb28vpUXK5XJK+SNr43K5cOPGDTrjptVqsXbt2jSfM36yS4SCSFfY6XRScRuSkBPJea6ao0gkQiwWw9mzZwEg7TwxzEfS99kq+Hv37l00JZAcj06nQ1NTU5pVA39vPnPmDN566y3E43Fs2bKFUrxu3LiBqampvO4pArlcTucLc2ElVRcLBfcY9Xo95ubmIJFIBGdnyX1N/PQIPZSICBHw5z2JCTsXxDScr+q6lL2D/16hghB/LysF5MvgWOh4cym8lspvLWWUk7IyVhSLqT7lW6Exm8147bXXqIQxMYoE5ikCZ8+ehcPhoNXP1VZfWgqi0Sj0ej2sVis11c6Gxa41Mc7NRY1bbPUwW+WbSH0v13kgNExS1QeKqwLJRbYHeK5giJvkcqu+XCrL2rVrAXwklhGLxRAMBiGRSFbNf81sNuP555/Ha6+9Rrs+4XA4azBJqHMkaK2trRWkXAnRZEjHKFtwwzAMampqoFarBfeHQoNR/jWe77wLwzB4+eWXUV1dnTHgTmaAuEINAPDGG29Qz6/NmzdTBT4y16HX6/Hwww/jJz/5Sdr8WjKZhEwmo9RGjUYDiUQCg8GAUChEaZFEzOPGjRvw+XxpNDhCJVUoFKitrYXf76c0TC7FSq1Ww+VyIRKJwG6306IWeR0pdgGgCofcdZmdnYVIJKIJ4MMPP4yWlpY0Fb5syS5X3Ka7u5uKX8jlckrV5Aba8Xgc0WgUExMTVOiHf98BWJXOkcViwT/90z/h7t27SCaTmJqaQm1tLUwmE/XnzAfEvuVTn/oUdu/eveDr7wXVPO4x+nw+qswKZM7jnjp1is5NAvOKo0IUT2JeDAAajQYdHR24efMmJicn6Wv0ej22bt2awd5Yyt5Bjo/cE0LiIqWIXDHAYoqy5FwS5VWuwXwZC6OclJWxYlhs9SkXBYwPQgsgIJWd4eFh/PznP4fD4YDNZkNHRwcee+yxovye1QARLFGr1XQuR+iBsRyUlUIUBYF0iXyy4XMTkGIGRUI0zOVWgSQKn6QySM5HPsEQv+pLhAK0Wi0tNEilUly6dAk3btyARCKhlJjV8l+LRqNYt24d5HI5qqurMTg4mBHwE4jFYiiVSuj1erS0tOR8OPPXC/jIADkYDOLIkSOQSCQ0QTtz5gwikQgUCgVVVOVeg9zggJ+QZ7tWc13j+ZxPhsk+4M4PbH73u9+hr68PPp8PlZWV2LVrF7RaLZVK5853AfNJFAkypVIpnUmRy+WUqrt//35akCLdSLlcjqNHj6Krqwsej4eeE2Ki3dzcnDHLx6VYWSwWsCxLFR3ff/99HDhwIC1BJqqV/N9M5qQUCgXUajU2b96M3bt3p71GKNnl/m8gEMDIyAjC4TAGBwcpZdHj8QjS++bm5iCXy+F2uyGTyTKKF9kC7kLpalwhmVzeYr29vVR4BZinH7777ru4c+cOampqYLPZIBaLBe/ruro61NXV0UR/w4YNec8gFUo/X0kwDIOqqirqQUfM3VtbW2mXFEAaLa6hoQF6vR6bNm3CiRMn6PkE5hNXco9UVFTgoYcewrZt2zA3N0eFh7Zt20YVT/lrUkgiK0Tbu5eSEbvdDpvNlmF7Umghloxa5ENHL+MjlJOyMlYMhVafFivHzt1EnE5n2t/cbndRPFBWA4TzTarjDQ0NWe0CFrvW+QQWuT7TYrHgzTffhMfjQVtbW4Z3GnejdjqdOHnyZEYSUwwIHSMwH9AWk8LIvcbkcjmdv+F2sPKhKZLj5fpxkQc5oVuxLEv9vrgc/dV42BNRAzIo/8ADD+CRRx7Bz372M7Asi3g8TgU8CM22oaEB3/jGN+icUba1568XMH/vA+k0VACUQudwOLB+/XpEo1HBhEooIQeyd0oW6m7mE9wuluorkUigVqtRXV0NuVwOn8+HiooKNDU1gWHm57suX76c1o2sr6+HVCqlM186nQ6bNm1CNBoVDDINBgOlS6ZSKVy6dAk9PT0QiUSorq7G9u3bM9QAuZ+xYcMGqnLIsiw1g+VSAIUKQ+Ra0el0aG1txRNPPJFBG+QHwHK5PMNIV6fT0fNNkrJsnaV169ZR+f5AIJBhXJtN5bTQIlau5J+L9vZ2VFdXU8GVZDKJ8fFxAPOJpFgspvRP4r8mk8nwN3/zN3j66aeXtGct9ppcKbAsi3PnzuGnP/0pVTklmJycxLVr1wDM7wNEudDhcKC+vh47duxAQ0MDfvzjH+Py5ctp7yWql/F4HBUVFWhubsbQ0BA8Hg9qa2vR0dGR098z172eLUkhewf3+JZ7frpYYFkWR44cwcDAAGKxGKXHA4V1lfkd6Xz9/sooJ2VlrCAKqT4tNrkQ4nOT2RO9Xo+enh46yJ2v6XKpgKyF3++nst7ZNv3FrnU+r8/2GpZl8f3vfx8ffPAB4vE47t69i9bWVnz5y19Oez8JDCwWC2w2G3w+H0ZGRgRnXQpBLvUuLh2LO59S6PfwZY7r6+tp549b6eYHQ0LXJ/fYhCwguAECoXytVlGBK2pAAu3W1lbodDp873vfg9PppEbtpHjw9NNPY+/evTSYIb9JCPz14s9zEeoclzJHzjN3ryD+dGvWrMlIyAEIJu5Cg/n8+4AcH8uySy7uMAyDpqYm6HQ6tLW1gWEYHD9+HMPDw4jFYjRZICbVRLADmO+yVFZWIhgMQqfTQaFQ4Pz58+jq6so5VF9dXQ23203piCKRCAMDAwBAhVaEjpMYcA8PD8Pj8VA6Ya5uN1GtJFYHu3btovc+f9aLGwBzzyORCu/o6KCiDjabDclkEj6fj85ZAvNdWYZh8Oijj+Kdd96h1gFCv4fPwLBYLOjr66PFkcUUbhgmP09Gs9mMf/iHf8C//Mu/wGq10uTSZrNBIpEAmO/wqNVqSkf9/Oc/j29961t5HYcQSll1kWVZvPHGGzhx4oSgzQYwb/8ilUpppyWRSECv10Ov10MqleLYsWPo6+sTfC+h+yYSCbjdboyPj0MsFmPNmjV5eXtmKzTk20m/VxIyYP56j0QitLNO5kKXMo9I3js8PLygQm4ZH6GclJWxYiiERrHY5IK/ibAsi46ODuoN87Of/SyNyngvbRKL2fQLWWshOfx8PtNisWB4eBihUAjJZBKzs7O4cOFC2sA7F9FoFFVVVbhz5w5SqRQ6OzthMpkAfCTqQP473ySEr95FDFnJe5ZqvsxV0+rt7U2bGTCZTGhubs5LQIZ/fUaj0WXpviwXuNegWq3G8PAwLBYLfD4fYrEYpZMlEglUV1djy5YtaGpqKrgTwf3dfIqcEGWOO8tw5coV6PX6nEk6vzPDn10DkJF8FYMaTLreRPxi586diEajGBwcpLL5DocDs7OzmJycFDTh9vl8kEqlEIlEiEQiuHPnDg1EuTOb/f39eP311zE9PQ21Wo3Kykp4vV7qVxaNRjE6Opqzi2w2m/H5z38eb7/9NlWL5IpsCImsDA8Pw2q1IhqNUspYPjOW/PND7mOuSAfLsjh58iRu3LgBuVyOtrY2tLW1YdeuXTAYDLDb7bQQR46H/7sIA4Mkl2SOrxC7CeLJuFA3fu/evbDZbDh06BA1EFcqlVTkRSaTUWVOklwXilJXXZyYmKAiMdmQTCapufLQ0BAikQgSiQQCgQBOnz4Nq9VKE1ouJBJJ2j0zNTWV5r9VqDjWQknKQs/QUkVjYyOam5sBZM6FFjqPSGbUIpEIJiYmcOLECXofl5Ed5aSsjBXFYoPLxSYXXCqJz+dLU+G6lzbJbFjMpr/QWnMTDe6gfa5NM9tnymQyiMViAKCJVDb5e0L5k0qliEQiYFkWZ8+exfT0NE2qANDkub6+nooW7Nq1S/D4+NX1urq6jG4T6fLkU/XjVpiB9FmGqqoqeL1eOjPAn8ER+sxsEslcMYlSBnc9uDTUs2fP4u7du4jH45RyBcwHUxqNBiaTackVVwL+Ogn9W0hURsiUNVtnBvhoML8QAZd8QeYQBwYGIJfL0dnZiX379lGhDZFIBL1eTzsnKpUKSqWSdobIPKlMJoNCoaDUQjIrRhINADh37hwtIMzOzlI1TKlUioaGBirlLqSCyAVXSTWRSKCyshJAZrDGnZMMBAKQyWSor69HTU1NXgHeQnu+wWCg9xxXOIX7uoMHD+YswnDPITHxJclRbW3tos4lsLji4Z49e6iITTKZpCbXmzZtQnV1NRiGKUo3vBjX6XKCBO25IBKJUF9fj02bNqGnp4fe0+FwmMrfb968GQAwODhITdPJMwiYv5+np6dpd38pxdhcbBHyjFAoFPfEns6HUGxRSHGXgGHmFXIHBwfpXnfkyJEyjXEBlJOyMkoei9nguFQSlmUxPj5OxRNMJlMalREQrqCWInL5iC32cywWC+7cuYPu7m5EIhEolUqEw2E64JsPRZSvTldVVUWV0FpaWuD1evHee++ht7cXBw4coLNRJDEhAV0oFALLsrhw4UJapRkAndNyOp2Ym5uDTCbDe++9h9deey3j9y+Ffin0+/j0RL7E78aNGzPoo0JUF6Gkt1DPrNWCUHJCkhYy8+Xz+SCRSDIUAokAB1B4xXUxYJh0URlup4X/umydGW6nViioLYaaXWNjIxQKBRXuiUQimJychNFoRDAYhFarRXV1NTUSVqlUaG1txebNm3H58mWqLBiNRjE7OwsA1IDbaDTi9u3buHv3LrxeL03ICEiyRkysn3vuOWi12qwqiNw149L0stFtybqRoJnMQOZTvMh2frIlyLk6y7mKMPyOLwBYrVZMTk7CYrHQDvZinjv5ilKR71yzZg30en1GV79YKHXVRYZh8PWvfx2vvPIKWJbN6AaLxWLIZDIolUoMDQ3RjjfXdJl7DZ47dw5vvvkm7SCTLnAsFoPb7cbmzZuX/NzPxRa5evUqxsbGaLxxryQfuWKLpdJfzWYzTp8+DZZl6T5XasWBUkM5KSvjjw6ESjI3N4dAIEDFE7jV1UJpbKuFYlQ9iRjHwMBA2kyJUqmERqNBVVXVgtQdoeAoGo3CaDQiFouBYebnZMbHx3Hr1i2IxWI4HA4888wzdJ4kkUjA4/FQKXWv14toNEqNhclDV61Ww2azIRQKYXZ2llKluIapQh2cbA+QXIGTkPgGWWsyM8aV+G1ubs5JH+WuE1+k4l6RSCbIdu1xA5Tx8XGcO3cu7X1arZZWq5dScV0sFvtd2V6fLagtxm9hmPk5LQCIRCJobm5Om5Oz2Wy4ceMGHA4HPB4PJBIJKioq8Oijj0IqlaK/vx8DAwOIx+Np9K+pqSnY7XbqeZXLfDiRSMDr9UKr1WLHjh0LSv4DmTQ9fkeaO9fZ0tICtVpNO0DkdxeyXtkEfHKdg1xJCf8c2u12vP766wgEArh16xYqKioK2mPzEaUiySKZXyMotgDVSt5zhYLQOX/84x/DarUiHo9DLBZDpVLRLvDs7CwCgUBaxxtIP/dnzpzBb37zGyq0QuiLyWQSSqUSGzZsQFtbG1wu15Kf+0IFg+7ubkxNTSEUCiGVSt1TyUe2/b1YPo9kHtXj8UCv15dccaDUUE7KyigZFGsomdAikskk5HI5dDpdWsWeX0Fdbs+sYoD8plw+YrnWj6gr9fT0YHZ2lsphi0QiqpJXX1+/ILVDaAMng8EbN25Ec3MzraaLxWK43W6Mjo7i8OHD0Gq1cDgc0Ov1iMVi0Gg0iMfj0Gg00Ol0aTLb3M++efNmmrqf1WpNk0jmd3BywW634/Tp04hEIvTBzP8crvgGn56YbcaNv/bcddLr9VAoFBlzTfcK+Opyc3NzNIAEgKtXr+Ktt97KkFLWaDQZgfBK3V+F0KSFumm5glqn00nVXRf7u1iWRTQaFZSWJ/TLyclJmtQS/61oNIq9e/eit7eXKswBoIbRNpst54wOFzKZDG1tbfQc5dttVqvVEIvF8Pl8VEiD77VH5jpJwNzT07OkQDiXQmO2gHGh7hU55yzLore3FzKZjAb1ROhgMci3cMadtbHZbHmJpiwGQkyGUgY5n1VVVYjFYtiwYQP9N58mzu/cAh/5v5HZy4qKCmofUVlZiebmZphMJrS0tMBqtRadzkniCY1GA2Be/ZOvjlvKgitC99aVK1fgdDqLQn81GAyor6/PeD6UIYxyUlZGSSCfqozQxsaliHGDG8JlJkPzRDKbP9eznJ5ZK4mF1o+oK6VSKSgUCjqTUlFRgZqaGppQLUSLFArQLRZLml8U+QyHw4HR0VFqdMtNTMxmMzo7O6lc9oEDB6hwA5AeJJvNZohEIvzqV7+icv3Hjh1DS0vLopU5jxw5gmvXrtFgV0iNj2XZrPz6fNee/6C71yiLXDDM/HD2lStXqGeU0WiEXq/HyMgILl26lKYMCMxT47Zs2XLP/VY+hIJalmVx6NAhdHd3A8CC8tp8kPcTg2nyXrI/kY6ZSqVCMBjEwMAAIpEIvF4v3ee0Wi3i8TgVitBoNFQCPhfEYjGlTe7evRsvv/wygI+6NHxLAqHuTSAQwNTUFBwOB44fPw4AOHPmDPXaUygUaG1tRV1dHaLRaFECO36CvBjmQK7uFXcWyOv1YvPmzXQ/Wuxx5ksXJM+n8fHxvERTFoNSF/fgg2VZdHZ2IhAIQCQSYcuWLXjkkUfQ3t5OlQAX2jfPnz+fVowQiUSQSCR0HvNzn/scNdkmdOtiqPAS8KmwfEpvqZ8TQkvu7e1FQ0MDpTATO4qlFhNJ0rqQj18Z8ygnZWWUBBZ6yAptbABw6NAhDA4OIhAIpClWyeVyzM7OIhwOw2azYW5uTlBhjStWIPS9pYKFNraF1o9Ufz/2sY9BLpdj69at0Ol0ix4s58+VdHZ2Umoe8YsC5hOpF198kb6OdNC432MymdLoQ9/73vfoa/kPrt27d2NoaAgXL16kErvbtm1b1EODJKaxWCyjGp4tSc+WpOaiO05MTGDHjh0lTx3KFyzL4tixY+jv70c4HIZcLkcwGITL5cL169czEjJgXl593759S/7elVi/xX4PoWsSquH4+HhGtz2XOfWxY8dw8eJFeDweqgJrt9vpvUJEH4giKQCMjo5Su4XGxkbo9XpK0RKLxaisrITf76fy8MB8AqbVaqFSqejcWXV1NdauXYv29nYcPHgQgHC32WKxpN27XJETh8NB50evX78OsVgMu90Oj8dDvyOb0uVSusT8BJn/uUJrvtC+SP5eDG8pbnC7kFkuEU0hioA+nw9qtXrJa1Tq4h5csCyLU6dOwePxUGGgQqTk6+rqaBJG6PhyuRypVAobNmxAS0sL/bylqvAKYaGOeq5zUgodNKIEa7PZqBomYbS0tbUJzo0uBvmwfMr4COWkrIySAL8Dw/eXEdrYnE4nLl68CJZlIRaLEQwGoVKpYLFYcPr0aQQCAYRCIVRVVWFycjKrwlp/f/+Cxp+rjYU2tlxVWrLpRiIRWgVeiiwtd65Eo9EgFotBr9dnfK/ZbBaULSfg0oeOHDmCS5cuUe75rl27MqgqHR0d6O3thcPhwMzMDG7fvo3du3fnfGhwH3qNjdllf4UUBQHhoCaX1xh/9qhUA6LFYGJiAna7nd4jwPy8mE6ng0wmy3h9Q0MDfvCDHyzpGsu3urzUoKaQKnZjYyOamppoQK3X69MS+b179woKZpDvunr1KtxuN01m3W43Tp8+jWvXriEUCkGr1WJwcBAMw+ATn/gEpQbr9Xo4nU7qv5VIJJBIJBAKhTA9PU3nWRQKBWpra6naYUVFBW7cuIFQKASdTocnn3wSu3fvBsMwgnNkAHDkyBF0d3cjFAohFAqliZyo1WokEgnE43FMTEygoqICkUgE69atg16vz7gfl6M4wQ+EAWGT24WeK/x9cyneUtzglpjlAsJzb1xqZXd3N4LBIGUaLJW6X8riHgT8DuX9999fcIdy9+7d6OrqwgcffACZTAaj0QiFQgGNRpNBIySFhXxVePNFrr1+IcXG1e6gCVHt9Xo93d+IIX2hsNvtsNlsZfpinignZWWUBPgdGL4Bp9DGNjIyArfbjVgsBolEAq1WS19D/EwqKioQi8XQ0NCQYTabz/feK8hVreNWg7ndrELBDXSIRHy2gCKfxIQcn9PpRDKZxMDAQIahNJkhW7duHaxWKx2kJkppQsIfANJkig8cOJB1jbgJIqG4CCW3JHErxGvsXkVjYyO0Wi3tzCiVSmzfvh379u1DKBRCV1cXVV3U6XR46623luxFk0/FvxhBTSGdBYZhcPDgQXR0dGB2dhYul4tSpQGgt7dX8DPJd4XDYVRXV6OyshLt7e2orq6mxq0ikQjBYBBjY2OYmJjAzZs30dDQALlcjkAggJMnT1KqFvGGi8fjaR5xEokETU1NUCqV8Pv9CAaDVBAkFovR7jggvK8SdVYiRDE5OUnnCOVyOfx+P10LiUSC2dlZQTVS7notxz3B/dxsIiUL7e8LdTkWA+61FAwGcezYMbhcrqzUeG7ncXR0FBs3bix4b16M4FEpgGvW3dDQsOQO5auvvopz587h/PnzkEgkgsUBgmwzVIWsVz5FoWzXWKl0NYWo9r29vWm2IoUeGym4DgwMIBqNora2tqS7t6WAclJWRskgGo0iEolgdHQUoVAozedKaGOrrq6mAY1arcb27dsprbG/vx9utxtWqxWJRAIWiyXrXE++xp+riXx42ULBD1cNrVhCE+RccP2gamtrCw4oGhsbIZFIIJVKaYLd1dUFk8mU1mEgSobr16+H3W6nv49s9ADSZnU6OjowNjaGnp4e2tF56aWXaIf0d7/7HQBkzI0JPUD55tT89fxj6YoJgWHmZeZv3rwJr9cLpVJJDVi/+c1vUqU6tVqNr371q0UxB82n4l+MoKbQzgL5Hq5/HREk4Koocj+T37XZtWsXnXXp7u6miVQqlcKdO3fg9/uRSqXAsiwdlLfZbPD5fLTqTIxzuXLi8Xic+voFAgH4fD44HA6EQiEqD879HfyOU3d3N5xOJ8RiMaRSKfR6PTo7OyGRSDAzM4P+/n6EQiHI5XKoVCo6j8oNqlealpXrPPL3d4vFIuhvRjrChc5+ku6lRqOBzWaDy+WCx+OBQqEAICwoJZfLqfWHzWYriKmRzbKiVGGxWHDixAlMTExAJpNBr9cvqUNJQJ4d2ZRBCbjX/EI2ELmwmKKQ0PNhIXbLSt0/Qs88g8GAsbExKtqzlHkyUgwiitilykYqFZSTsjJKBuQB5fP5aLfC4XCkedJwNyiz2Yxt27ahu7s7zSySYeaFCc6fP49YLIaxsTFUV1dnlSIvZcpHLtPhfN7LTSSK6YfDMAza29vx/vvvQywWL2njZhgGzzzzDOx2O7xeL4B5I9DXXnsNu3btQnV1dRq9YsOGDWAYJi0QJhX+7u5uajq9adMmKBQKyGQymvBzk7eLFy8iGo2io6MDL7zwQlpilmsuYP369YKGxH/MqK+vRyqVQiKRgM/nw6VLl+j19O1vf3vZ6WlCn1uM+3YpnZJc80hCtF1u18bj8eDq1aswmUyU5kaS3Z07d1IRHKI+R/yVxGIxNR0Wi8WoqqqCSqWC2+1GMBikyZnf78f+/fupWMLRo0fR3d0NkUiE8+fPUxEbclzcjhNXSU6v10MsFsNms2FycpJ2fohQ0EMPPYTPfe5zWQsYK0XLynUeudTvYDCIX/7yl3RP7ejowP79+3HmzJm05FporjUXCHWRCHdUVlZS4Y7q6mpBQSlgXqSCCCGRmcFCZhxLoeOSD7idk3A4jObm5iWZOZPPFCqO5NoPyDWfjb6bz/ovdd3zKQCu5P2zHN9BRgZCoRAmJychk8nuWTbSSqGclJVRMmBZFjKZjD7wuV2QbBWvPXv20BkjMnAPAJ2dnXC5XPD5fKiurqaKY0I0BX7ljKsAuFqwWCw4f/48rFYrgHkBitbW1rRgiitLLiQywKXZrV+/PmvlsNDjI55nqVRqyYns3r17UVdXhxMnTmBwcBAjIyMIh8Po7+9HR0dHmnEo8T0ivzUbqqurM7ygiDDH9evXcffuXSSTSXR1dWHbtm1oaWnJ+iAmVXAyO7ccZq+ljMnJSSSTSZqY3blzJ6OTXWws9LnFop4Vevy55pGyfSYpDgwMDNCK8Wc/+1naBXc4HJicnKRS+Vz1OQA4duwYJicnMT4+TkU+Pve5z6G2thZvvfUW5ubmIJVKodFo0N/fj1gshvb2duzZswcTExMYGxvDjRs38Prrr+PFF1/MacIOzAtQzM3NYXh4mHboSOKXSCRgtVqz0qVXOkngrjmfxhwIBDAzMwOfz4dQKASfz4eKigqMj49Tuuno6Cjm5uYwMzOz6OMmazswMACJRIJIJIKWlhZKowOQNqtKCkgXL16E2+1GdXU19Hp9XlL/fJRyUZEPbudEqVTSAupSsBQqpBCVMd9Z1mIwUBYqAJJ/r/SzhtBqJyYmEA6HYbFYCqaW8lk1NputpAsHq41yUlZGSYAYMHo8HlRUVFD6zUKbndlsRnd3N2w2G2w2G5WpJjMaKpUK69atw759+3LSFLh0pNUevLVYLHjllVdw9+5dxONx6PV6GkB87GMfA8MwabK1AARFBoolacsH1/PM6/WioqKCbuBLWS8iDPK9730Pt2/fRiQSwezsLCYmJmhy1d3djZ6eHtTX14NhmDS5671791KpaUKvE+pa9Pf3Uw8okUiEubk5nDp1in6ukGw2EUopxjD+vQJuYNvQ0ACpVAqxWAyRSAS/308fsqsp8byatFHS+cpHbY9ALpdjbm4OEokEsViMynhzZzTJuvLp1izLwuVywe12U2PoYDCImzdv4o033kBzczMOHz4MmUyG2tpa/OIXv4DX64Ver8dzzz0HhmFgtVrh8XgwNjaGI0eOZFSsuYkuEb0ZHx8HgAwj6mQyicnJyYyAbbWTBH6nQaVSobu7mypQisViyOVyVFZWoqmpidJNydqKRKKcVEJ+J4sE6ERIhMwwc5MD/qwqACqJn0wmUVlZiY6Ojj1wnpgAACAASURBVIIsBIpVnFgJ5BJbKgQkbuCK7iyGCslfO/5cILfwxP3O5WKgALmFQZb7HJPvmJubg9VqxczMDNxuN86ePVvwbyT0d0IrLfXCwWqjnJSVURIgM1MKhYJWnXN1LggYJt3zhfj0qFQqiEQiyvHv7+/H2NgYnfUQetiVQoUKmKe0jI2N0YF6iUSCNWvW0GDqxIkTsNvtmJqaAjBPeyJKblyRgeWi2U1MTMDj8SASiUAqlaKyspIqwy3V94VhGBw4cAAulwu9vb1IJpMIBAJgmHnZfq5qFl9QIRqN4uDBg4K0MW4FvbOzE/F4nCYYCoUC4XA464O42EIp9wL4ga3JZEJrayuuX78OkUiEcDiMwcFBALnvk1KQfBZCMY5LSG0v12eR18tkMqjVajQ0NFBvQLPZnFZNBuYVECUSCS0UkD1Sp9PB7/fTa1gmk2FiYgJ79+6lNhOXL1+mn0U6zgcOHIDD4cDY2BiSySSl8/KPmdwvJJEglEWi7kj80Yj8vtvtTmMgrHaSwA+sp6am4HQ6qRWG0WhEY2Mjdu7cSQ2uDQYDjh07Bo/HQxkWP/jBD/Cnf/qnCIVCqK2txZ49ewBkGs0fP34c4+PjUKvV2Lx5MyQSScaMHX9NAKQlE+3t7bRjVOiMYyndX7kg5AG5WBCz8pGRETgcDiiVSuh0uoKokPy1EyqQcAtPfCp7MRko5Hj4908uSmOx9lj+7DaZw04mk0suupYiG6lUUU7KyigJ8KtDRLI5H3A9X9RqNe7cuYPe3l643W6kUilcu3YNbrcbYrE4J998tSu8BHV1dTRhEIlEqK2tRUtLCyQSCWw2G0KhUJrZaCKRoF1FvsjActDs5HI5vF4vNBoN5HI5Hn30UQSDwaL5vpjNZnzpS1+ifilGo5F2DLjnR0hQYaHghNBnpFIpnTP5+Mc/Dr1eT6kV/Acxdyblj9Vnhf9g5xcoTCYTtFot1Go1peABWLCjUAqd5+U6rsUWccjr/X4/WlpaaCeF/I1rGp1IJOj1T/5Orv/29na0trbC4XBAKpVCp9PRc0Cuf7lcjtOnT8PtdiMSieDatWvYtWsXnnnmGdpN48uF80E6gVNTU6irq0M8HodEIqEeZalUClqtFkNDQ+jp6ckwzl2tc83dJ3w+H2ZnZ6k6pVwuR2NjI5599tkM2hwR5nA6nQgGgxgeHsYf/vAHyOVyqNVqWCwW7NmzJ+2cnz9/ns6x6vV6/OVf/iVaW1sFA2T+mhAFTwAFWQiUasEjG/j3XaG0RZZl8cYbb9AZyaqqKupRulQqJJ9uNzk5iUAgkNYNXok4gX+tZNtripmscb9Dr9djzZo1CAaDiEaj1PutEJAEmvyuQoVV/q+gnJSVURIotLpKNp69e/eCZVmcPXsWP//5z2G32xGPxyESiRCJRDA3N4e2tracfPPVrvASEN+Vq1evUvnxbdu24c6dO3C5XHC5XFCr1VSinE+VyeUNVgyQ2T+lUom2tjbU19ejr6+vaB1GMifW1tZGH4DktwipRC30W4W8ykKhEPx+Pz796U9j//79AJDWqSAqbaSy98cK8sDkixDwZ+gYhkFlZSUUCgUkEglkMlmaOIEQFpu0rFSQWayO+GKDM+6akk4KkNl5IbNkZ86cSaP78K9/ruE0f3jebDbjz//8z3H06FFMTU3h1q1b+P73v481a9YgFotBrVbnRcMlyoVEjILQJglqa2sRCAQwPDwMv99PqZxEZGQ19lHuOo2MjGBgYIAyCUhiSa5Z7vxtIBCASCTK+I3hcBixWAzvv/8+Fe0glPC6urq0766pqclb+TBb4ppPQluqBY9cKNZ9Z7FY0NXVhZmZGUgkEtTW1i5JUp8PQrcbGxvD6OgoHYvgFhtWOk7IttcsNlnLtccKSePfvHkT77zzDqxWK44ePZq3QAdXoOz48eN0rKSpqYkqZHKPt4yPUE7KyigZLLa6KkSxcjgcmJubQzKZhEgkglQqRU1NDdra2mj1Z6FjAD5SYVqNDYNh5n1XLBYLZmdnMTw8jK6uLlitVgSDQchkMrS2tkKtVtNAOh+RgWKAcPgdDged2csmAV7o5+fi6/N/20K/VejhRKr/FRUVtNvI571z1dJI1yKXHcG9CIvFgiNHjoBl2Qzp7v7+/rQZOhLE1tTUoKKiAjqdDpWVlTnP92KSlpUMMotV6SadpHxmyoTmEoH5QgCXVs1ViDUYDLTCzP1O7h6Vy8pj9+7dOH/+PJ0RuXXrFoaHhxGNRqHX68GybNp3EIowX7I9kUhArVZjfHw8zaOMdJvVajW1RWBZFoODg3jvvfdgMpkWrWJYLHA7hhKJBGKxmFKyiCy3kM1FW1sbYrEYJiYm0hKzRCIBu92Od999Fzt27KD7EgAMDQ2lzbGuBEqFar8YFOu+u3TpEhwOB2KxGMRiMbRabdESMgKGyRyL4CY8K124zZYILiZZA4QLQLlox/39/RgZGcHc3BwmJibQ3NyMr33tazmPlU+DZFmW7m86nQ4MwxR9zv2PCeWkrIx7AkIboRDFqqmpCVarFQ6HA/F4HJWVlbj//vuxbt06OrCai2JXKhVIskleuXIFly5dQl9fHzXDrq+vp0HBSj8ciCqTz+cDME8PMhgMGfMShZpxFpuvn+3hJBTMch9KROSAUDkUCkXGg8RisaCzsxOpVAqPP/74igVkxQARa+nu7kYwGKTG6+QBz5+hE6qi8gN4vuId6WDn0zFZySCzWJXufGbK+EqoZE3Je4mMd1VVFaXXEdjtdpw+fRqRSERwz+KeE7VanTHTyTAMdu3ahf7+fvh8PkgkEiQSCUSjUUSjUbjdbhw6dAjd3d2IxWJUXIkkUgBoIunz+TI6okS51GAwoKWlBUNDQ3Smd2xsDE6nE8DqJgzRaBRr164Fy7IQi8WIxWIIBoM4cuQItm/fnjF/63a7EQgE6P7ApeomEgk6W0N+V2NjI/bv378osZdioFSo9ovFUufJzpw5g9/85jf0WlSr1UVPyAj4YxFOpxMWi2XV6HdCBcjFJGv8WUv+zCq/4AMATqcToVAI0WgUyWQSf/jDH7B///6cv5mvhqnT6aDT6ehz4Mtf/vKqddHvBZSTsjJWFIVUmbIlSvyNhwzMb9q0Ce+//z4mJyfh9/sxMjICm82GZDIJpVIJIHugUGoVSOLdlkgkEAqFoFKpoNfrV21uo7GxEQqFAkqlkhpCTkxMYMeOHQty3PP9/GIGG0KSxyzLplGQ+FYJ5Hdw1dLIA5rQlSwWC772ta/Rh9eJEyfw0ksv4cknn1zS8a4UyHXucrkAzNsHPPzww9TMmPvbyWB2rgSL33UAkEaHXOgaWOkgsxj3zkJ7RS7DceCjxLeqqooqMRIaIjAv9HHt2jXEYjGEQqEMARoSkBH6qVDBaffu3bSTo1arcfv2bQDzwWwqlcL4+DhNPioqKmhyRq5rcoyVlZWUAkgQi8XgdDoxOzuL6elpeDyetL+TLvNqJgzkutLpdFT1cnh4GNPT0wgGg6ivr6fnhGEYnDhxAgMDA0gkElizZg1N5ogvnFgshsfjwcmTJxEIBKDX6yljIR+xl3yQ6xnJ/VspUO3zBcuyOHTo0JI7iqdOnaKJPwBK771y5UqafUSxaIz8+0to1nO1134xyRrZe7L9Dv7zu6qqis62E+bRQsJOfDXMnTt34uc//zm1mih7lOXGsiVlzz//PN59911cvXoVWq024+/f//738eMf/zjnZ/z+97/Hxo0b0/6/mZkZ/Md//Afee+89zM7Oora2Fp/85Cfx3HPPYe3atUX9DWUUF4UOpWYLfoQ2HpZlwbIskskk4vE4/H4//H4/NURVKpU5xRpKrQIZjUZRX18Pm82GYDCIWCyGwcFB2O32VevgEXl6QnnjVveXmtQWm6/P/Twyp8OlRjKM8OAx/31cXvzQ0BCi0Wgaxclut+ONN95Aa2trWsDBHXImiV1XVxceeughSmFbDcjlcjidTohEIqRSKaxduxYtLS10vYXWLNdsAve8q1QqAEAwGMwqK83HasxpLAX5+BRNTEzQuZSNGzfi8ccfp0qowEeJr8/noyqB3ISI2HpIJBJMTk4KKsGRtSdzXQ6HI02UgGEYqkjqdDpx8uRJjI6Owmg0oqamBk1NTbDZbGmdMu5v4QpmaLXaNJ8yANS3jiT3XMhkMmzdunVVzyXDMNi3bx9u3ryJaDQKn89HVXmJcTw5J+fOncPVq1fhcrnoLDIASoWXSCTQaDRIJBLo6elJs1whCWsx5mlzPSP5f8t3fm01wbIsjh07hosXL8Lj8cBmsxVsGF1TU5P2b6lUih/96EeoqqqC1+tFVVUV5HJ5mrLmUsC9v3KxJkoRQlR//r5OklpSmOR6mxLfRDI/rNPpFvzNZK24apjE5sHhcCCZTBbsefZ/BcuSlP3qV7/Cu+++m/M1Q0NDAIDW1lZoNBrB11RUVKT9e3p6Gk8++STsdjsqKyvR2toKq9WKX//61zh9+jSOHj2KTZs2FedHlFF0FKIgBOROlLgbD8uyaTMaarUaiUQCiUQCUqkUEomEdsqyodSCw8bGRuj1ekSjUcRiMcRiMSoXv1p0ObPZDKfTicOHD8Pj8aSJDJRaUgukU0H51MhcvkDc95GOAjDvL1RZWZnh2+R0OnH+/HmYzWaajJ09exY9PT3w+XxQqVRwOp2IRqP47W9/i3/9139dtcQsGo2irq4O09PTgt4xQmtGEqz29vaMRI1PpQOQpmY5NjZGTb+51CV+crfa91s+yNenSC6Xw2q1YnZ2FjKZDAyTbpRLqvBcLzBuwYj4Oc3NzUEmk2WdHSPqoJFIJEOUAMiUuCfnmzALiAIgw2TOlJFj7OnpQU9PD53NInNaqVQKyWQyo4tGVA43bNhQxJUvDFqtFg0NDZiengYwn2SpVCrodDq6TizL4sSJE3C5XPS3hMNhqoJLLARcLhe1BpBIJPRvRBBnqftdrqJWqbE48gHpkH3wwQeYmZlBMplELBYr+POMRiNUKhX8fj816XY4HJiZmUEikcDt27eRTCbR39+P4eFhHDx4cMlrRPa2YDAIhUKBffv2QavVlkR8sFhw91giksUvVpJCk8/ng81mQzgchkKhQHNz84K+ckLsJYvFglgsRmOYMnKj6EnZ22+/jW9/+9sLvo4kZT/60Y9gNBrz+uznn38edrsdjz32GL7zne9AqVQiFArh1VdfxW9/+1v87d/+LTo7OyGRSJb0G8pYHixWQYiAYRYeqCeb/+DgIAKBAIxGI/R6PQBQ8Q+ZTEa7ZkRZLx/p4tVGbW0tDAYDrfJqNBq0t7ev2vGQyufQ0BBSqRQd8hfqXgKLmy9bCv1xIWpstuuPT2/kH29jY2NaR0GpVAoGm9FoFMFgEEePHsWFCxfg8Xhw9+5dOpQOgHYZEokETp06tWpJmVwuRyQSoTL3+/bty7lmXN+e3t7eDOrLjh07Ms77qVOn8N5772FwcBB9fX04e/YsFal59tlnYTAYVmV+c6mD+vnOPbIsS/29AoEAWJZNS8pIFR4ApQdxpe2zdSv5gT/DZBcl4L9OaB9daA36+/tx+/ZtxONxyGQyiMViSKVSyOVy6HQ6eDwezM3NUe+yuro6GI3GkqEpNTY2QqfT0ZkyYL4L+YlPfCIt4fH5fGnJZTwep52xZDKJVCqFaDSKeDyOiooK1NTUQPv/2fv24KbOM/1Hd1myLMm2bCMs34gNRCEg0hbaBEhhWbYtJCndybZp05Kddidkuttb0ky37S9NdzrdJN1m252QpNu0dNNtCcsQsklaCDUJBAoEapkYG9uKZWHJsmTJ1v1++/3h+b4cHR9dLV/I+pnJBNu6nHO+73zne9/3eZ+npgbV1dUVM5XPl9RaigmvQjh48CBeeuklRCIR8Hg8ai2Sj/7W3d2NqakpbNu2bVbiUa1WU+aIUCiEVqsFn8+HUqmEzWaj1dBAIACLxVKRwJXcN0Tp1Gg03hBql4WQK1lJvE1NJhMsFguqqqqgUCjw6U9/umAimCup7XQ6IZPJUFVVlXfslzGDigVlXq8XTzzxBI4ePVrwtT6fDw6HA1VVVUUvLOfOncOVK1eg0WhoQAbMVNN+/OMfo6+vD2azGSdOnMAnP/nJOZ3LMuYHuapQhR42xTTUE963y+WCSqXCmjVrcO+99wIAZzaIvHYxM19cwgjMf5Nj7u/vRzwexy233IKqqio88MAD814ly7dxNRqNGB4ehs/no+psXFWWcgIsJuUrHA4X/VAt5rtyzb9CVD1CAVu9ejVOnz4NgUCAM2fOcB7H4cOHkUwmkUwmIZVKaYKISfkCZmg3bW1tBc+r0DkzqZGlBK+nT5+mPZadnZ2zKObMzyYbeUKfy0XhYSczNmzYgFdeeYXS84CZPqRgMIgDBw5Ar9cXNHSvNOba81gMbZHA4/HQvstctgqkykWEN7q7u6kJOldWO9caZTAYcOHCBdo7xnVMpZpdA+8HoHa7HVVVVZBKpaiurqZBWDAYhEQigUQiAZ/Ph06nw86dO2E2myEQCGZJ9S8GtFotbr75Zrz88sv0PvT5fDhy5Ai2bNlCK/zt7e0YGhrKyubL5XIIhUJMT0/T36XTaaRSKchkMojFYphMJnR2dlbEVD4fU2OpsTgK4fnnn8dzzz2XxSiIxWKU+cEEWW9effVV/PnPf0YymcQbb7yBH/3oR1nPOoPBgC1btsBisUCj0VCKYjweh9/vx+HDhzE8PAyxWIy2traKBa6ERkzWqmIo2TcKuFgOOp2OXkOynyL9xoXAZi/19vZCoVDA5/Nxjv0yslGRoKynpwf79++H1+uFQqHA17/+dfzLv/xLztcPDw8DmKFn8Hi8or7j5ZdfBgDs3r17FgVNKBTiM5/5DH7yk5/gD3/4w3JQtoTBVYUiDxu2/DMBu5KWr8oFzPQyMPtjmBsbo9EIk8kEo9GIgYEB8Pl8XL16FZ2dnfMq38z07WD6ELGFEZj/TqVS8Hq9uHbtGng8HhobG/HII48sSEBWaOMqFotpBm3r1q2c16wcug0RNiGVmWI9wooRXSDXnw2ujCH7M7RaLTo7O9HT04P+/n7qpcYGeWgDMwqP69atg91ux3vvvYdMJkMtGj70oQ9Rf7RiwDV/mH1u69aty0mjY7//xRdfxKlTpxAIBKBQKCCTyaDT6Ti9ZeLxOLq6unDvvfdS+lwu9UU2SD+k1+ulrAbSz2MymeD1epFIJKjq40Jk/udCASuWtkheOzQ0BLFYDIVCAa1Wi56eHoyOjqK9vT3rfXV1dZBIJEin0/B6vZwZ/kpU78m5m0ymWb1nucCslI6NjUEul1MltatXryIWiyGTyUCtVqO1tRWtra0YGBiAw+GARCLhpFsuBux2+6zEyMTERNaxkWw+Ccr4fD6AmedGLBbLEpgQiUSQy+UYGRlBNBqlQUElkG+slxqLIxeMRiOefvrpWRTvWCxGqa0E5L7q6+vDwMAArVg6nc5ZNH2SIMv1/Nfr9WUlqgqBizHA7u+8UcHcfxFBE/JcEQgE0Gq1BRUXuUDGlVBJM5kMXC7XB9r3sxKoSFBGaBN/9Vd/he9///tIJpN5gzJmP1mxePfddwEAGzdu5Pw7oXNdunSp6M9cxtICkZ9lK4ixMzlso1uCdevWweVyoa2tjW6y2Qt3f38/zGYzbDYb+Hw+otEoeDweRkZG5mUDwTTnJT02TU1NkEgklALGFEYgx0RMoROJBEQiEeLxOGpqahYky1SoWkX6UIiS1o4dOzg/pxy6DdnIk/Et9nzzfRd5OBD58aamJs4AvNDxkp+JWh0Zw1wIhUK444478Pbbb2N8fBypVAp6vR533313SVlWruOXSCTU/yWVSlH1N+a9Q+aex+OhcuWBQACXLl2iwWMsFsPY2BicTidNEhBvGYfDQU22ZTIZ9u3bV5K6mU43Y9RNfN8aGxtx5coVeL1eeL1eTE1NQSQSYeXKlQu2uZkLBawUuwbS8K5QKMDj8WAymXDx4kXE43FIpVKsWrUKu3btgtPphMvlQiwWg0AggFwun5XhL4ZuSb7P4/EAAGfARapy0WiUs/eMC06nE2KxGLfccgtkMhlVYgyHw4hEIohEIuDz+RCLxfj4xz8Os9lMVRgJ1Wwp0OzuuOMOHDlyhB6bSCTCLbfcksVMIPcSAZHAJxtJiURCFei6urpQVVUFsVhMq8Bnz56FXq+vyDxmj/lieGPNBb29vVlBLBMajSbrHMh9RZI3UqmUJiG5aPqLEbSSwOXw4cOYmpqCzWZbMgmHSoBQqYmgiUwmQzQahdfrxapVq8rad5B9BGkpAcBJ415GNioSlHV1deHQoUP0QttstryvJ0FZR0cH/vCHP+DNN9+E0+mESqXCRz/6Uezdu5camQIzdAHymbkW+JUrVwIA5bdzKT4uY+kiXwabmckxmUwYHBykm0pidFuMoh75DpfLlRXw+Hy+ecnWMzOADocDyWQS8XgcoVAIq1atohQwpjAC0yBao9HgM5/5DF577TXEYjF0dHQsyAanULWKZM4KefNw0W2K6fsiG/lSxiMftYeMOwkyQ6EQ5wOV3cdD+n2Yr5mcnEQ0GkUqlUJtbS3i8Tin8hww05Ny/vx5DA8PI5FIQCKRQKPRlByQEfEa5vGvWrUKKpUKGo0GoVAIYrEYHo8HDoeDHjfxoCJ/r66uRlVVFSKRCP38TCZDM9Lk/tNoNNQcmki1e73eLGPjcseEi6Z0/vz5nD1tlcZcKGClBHTM15IxCQaDyGQyiMVi6OnpgclkQlNTE/h8PtxuNxWP2Lp1KwBQme9ifJGYYh9WqxXHjh2DVqudVWUgyRTix5VvU3ny5En8y7/8C/x+P2QyGZRKJaLRKORyOaqrqyEQCGj1SaFQoL29nQYocrmcCrssBdTX12P37t0wmUwQi8W44447srL/YrEYUqmUzvlUKoVMJoNkMglg5l6Wy+Vob2+HXq/Hjh07IBaL8cwzz0AoFCKZTMJiseC3v/3tnBMMbKbCzp07F80bq1ycOHFilvgLwfHjx3H77bdT2qFYLKYVWMJYSSaTnD1li42pqSka2OdTcb4RwVyzAGBwcLBkxgoTZB8Ri8WoOFCxzLj/y6hIUHbLLbeU9HoSlD377LN0ESc4ceIEfvWrX+G5556jcvher5dmsHIt8kqlkv7b4/EsB2U3GJj0ALaJKgGpcpGHk1wuh8lkon0pGo2GemFwBXjsTRWhYFXa34SABAMke11fX0/pWh0dHVkUMGBm7gPA+Pg4lZPduXMn9Hr9gmZJmdUqpVKJ3t7erL6QUnpT2Pzycvu+ikGuLCmbepIvACfv5zpOo9FIm8kJDZGMLRcEAgHee+89eL1eZDIZVFdXY9u2bXnPid1nyKyQabVamkAg88dut2dVwphGoUQxMpVKQaFQQK1Wo62tDVNTU3QNBmbWzg0bNtBEB7k3zp07h8OHDyMWi9H+wVLBHhPys8lkwqVLlxCNRuHz+RZUTbTUbHo53lDMeez3+/H000/TwJYgEAhALpejpqaGWnikUin09/fDaDTSqmUxvkgk4BocHITZbMbAwAAOHjw4697kMsRlGk4zz/m5557D2NgYkskkeDwenE4nRCIRYrEY7rzzTjQ2NiKVSoHP56OhoYEGgcz+TEL3XcxAwmg04vHHH6fPiEcffXSWbcXJkychEAiwdu1a+P1+eL1e6mVGIJPJ8IlPfAL33nsvPZf6+nocO3YMg4ODtO9urtUTdoKSmTAhf1/KQdm3vvUtvP322zn/7nQ68U//9E+or6+nAT7wfr9ZOVS5hQCX3PtSPM5yodW+LwQkEoloAKpUKsuqlJF9BFEcFggEy0IfRWDBzaMzmQxMJhOAmUzDD37wA9x5550Qi8W4dOkSnnzySZhMJnzlK1/BK6+8AoVCkUURYlbQmGD2mUWj0fk9iWVUHGRBOHjwILxeLw4ePAgA9OHJrHI1NTVhzZo1mJqawuDgIBwOB5RKJeV6E0NPLiECpvpYJTaB+So/RASjubkZGo2GLuK5AsBdu3Zl9e0QafWFpq2QalUkEsHo6Cg8Hg/MZjMVHzAajejr66PUq2J9Ryol6VzqNWFXwAoF4PmOUyQSUdrS1NQUNckEZhT0ampq6EZOoVBgamoKiUQCfD4fLS0t2L59Oyc1iU0zlMvlqKurowmHpqYmbNq0CRs2bMg6fjKHua4JUYwEsnvOtm7disceewzT09Oora3F1772Nfo5zHsjHo/j0qVLeO+998p+MOc6tm3btuGtt96iG+UNGzYsSYrWXLyhmMFffX09Dh06hGPHjiEYDNLX1NTUYOfOnTh+/Dji8Tg0Gg3q6+vR09NTsi+SwWDAG2+8Abvdjmg0SucVVzU4n+E0qc6Gw2GqOpjJZBCNRmkV6a233sKtt96K2267jZqxnzx5kqqJEvrrUggkTp8+TdUVAcxKAJD1zOv1YuXKlaiqqoJSqYRKpYLJZEIoFIJIJMK6deuyAjLg/efT448/TvvuTCbTnOYwO3nITpgs5erMSy+9hMOHDxd8XTQazWJUCQQCiEQiRKPRJetjxZbGX4rHOBcwk63ADHOHx+OVnZDT6WbsfNRqNTKZDEQiEVpaWpaFPgpgwYOyWCyGL37xixgfH8fXvva1rAVm69atuPXWW3HXXXdhfHwcL774Ih566CHacAvMVjEjYJbKl0ukNybi8ThisRj6+vqQyWRgs9nwve99DwaDYdaCWFdXh7GxMbphbWxspEECAKxZswabN2+e5YlUqvpYPhQy+jx58iRisRgkEgn27t1bspwswF2xmW+Q4PXixYuYnJzExMQE/H4/9TO6cOECrFYrPB4P4vF4Ub0pwOzNBpf8fKFqWrnqeaVUR3LR1AwGA9atWwc+n49AIJAl6gHMBGx33nknFAoFent7aaY9mUxCKBQiHo9n9W6RwPvw4cMwmUy0aktEH6qqqhAKhdDc3IyOjo68LBFofQAAIABJREFUtEeuitT+/fvpmLF9q+rr67MCMK57QywWw+fzAUDZD+Zc42UwGPDYY4/RY5hvefxyA75KJRKIJ5hSqcQLL7xAe8iqq6sRiUSwYsUKBAIBfO5zn8OWLVuokEyxoirA+8bu4XCYGknnojEy+0eY58W0Fkkmk5DJZDSYAUArd4FAAKFQCJs2bUIikaCfc/DgQdobB4D2Ey5WIGG322m1TygU0qow8+8XLlygG1FCswqHw5DJZKiuroZQKIRarcYXv/hFzuvPFLTx+Xx46aWXMDQ0VLZHFhdboJD65lKA0WjED3/4w5y0xXwgfqJOpxOjo6PzcHRzBzNxHIvFloSyaCXBXOtkMhmEwpnwgCTkyl1DpVIplEolWlpaFqwF40bGggdlUqkU3/jGN3L+XaVS4b777sPTTz+NN998Ew899BAVQgCQM8pm/r6QQfAyliZ0Oh1SqRTC4TDC4TCCwSD+7d/+DU8++eSsBXF4eDirGkZ49wDoA5bZ68Os8BDKWaVpJkajkSo/TU9P0+xrKXLJzM11PjXA+UY8Hqe+PmRMyDGEQiHa8A6gYG8KAbtila/vL9c5V2qTXMpxkh6t/v5+aqIci8WykkXAzAbUbrdTSiGT+pRMJtHf348XXniBJg9aW1vR29uLoaEh+P1+iMViKJVKKBQK+P1+ajqcSCTK8kDKF4iSIMFut+PixYtwu92zriuAskRXmOAaL/J/nU6HBx54AMD8zvW5yOBX2htq3759GBgYwJUrV6jv1ZUrVzAxMQGxWIw///nP2LJlS9kUXoPBgHvuuQcHDx6E2WzGu+++i6eeemqWaitbPInQGNnWIiqVKisoA2YSoBKJBFKpFM3NzTQ5waRaVldXIxgMIpFIYHR0FN3d3bTfdyFhtVoRDodRXV2N6upq3HfffVnXgaxnEokEarUa27Zty6IEE3pme3t7zpYIwiwgtGC/349oNDonehub9r3UAzK73Y4nnniC9lsxIZVKc7KXiD8fCeSCwSCOHDmC22+/fcn1kxF5d6ZA11KnkpYCdk8Z2T/5fD74/f6y7G1CoRDS6TRaWlqwadOmD4yNwHxiwYOyYrB27VoAoNlCuVxONyderzdr4hAwF4Pa2tqFOdBlVBRarRZbt27FpUuXEAqFEI1G0dPTg6NHj+KrX/1qlleITCajJofMbCLTsPbatWuwWCzo7++HwWDA73//e5hMJlRVVVUke8ve2HR3d6Ovr49KKhM1RbFYXJZc8mKahep0Ouh0Oly7dg3xeBxKpZJe46amJqxevbpgfxYXyGYj1ya8GAXEhbgm7N4yALh8+TLsdjutaPH5fFRVVQGYkTVfsWIFhoeHqYcTG9FoFKdOnUIqlaIbkRUrVlBqh1KpxObNm9HR0YE333yTNkjPl+omW+Kdi/JLhEQ0Gk1Z15qrOsr1cJ/PcZ1LID+XHsdcn/etb30LBw4cwMDAADweD4RCIfX88nq9sFpnzLjL/S5CYxwbG8P09DTi8TgOHDiAxx9/PKtaykVjlMlkCIVCSCQSVNhlcnIyq4WAJGvi8TiMRmNWb+7Ro0cxNTWF0dFRhMNhuu4NDAzAaDTi4YcfXtBNmVgsxtjYGKLRKJRKJfR6/ay/E4p5R0cHtm/fjv7+frz88ssIBAJIJpOoq6uDSqXKOSfJtRSLxZicnKTVZY/Hw6kAXArm6qu3ELDb7fjJT36SU/k6FouBx+NxejXK5XJKkSM0WYfDsaA9psWASwF3qVNJSwVzrXO73QiHwxgZGUFTUxNsNlvJayib3dTc3MwpnrWMbCxaUBaNRgtWtEQiEYCZh0BbWxtMJhPGx8exZs2aWa8dHx8HMBOQkcbRZSxd5Mr+7dixA2+88Qb+/Oc/IxqNIhAI4Fe/+hVWr14NvV6ftXFjU+a0Wm2WYW0ikYBUKoXZbMaZM2eoCl5NTU1FaAfsRezQoUNwOBw0OyQQCJBOpxEKhfDMM8+gvr6+pAdNpTeEpUCr1WLHjh1UKprI4rKrSLn8ugoh1yacuVnMdVwLdU3IZn54eBihUAhTU1PUriAej0Mul4PP50MqlaKlpYWuV7ko1gCoNDCheDU1NUGtViOVSmHbtm3Q6/U4efIkqqurIZfL6WaRXJ9CWfNisurkNczqWGtr66wkR7ljywR7vpw+fRqXLl2iCpDk4T6f4zrXgK8U6isX2GNiMBiwbds2XLlyBX6/n86jqqoqShOdS3WE0BhtNht8Ph8CgQCGh4c5+8uYNMapqSkEg0GkUimk02naI/noo4/if/7nfzA6OkpFP/h8Pux2O+rq6mjwRuYLUSeNRqNIJpNIJBKIRqPo7u5GR0cHPvzhDy/Yema326liqdvtxtWrV+kabDQaaQ+zSqWi1ejDhw8jEokgmUxCJBKhsbER+/btK0gd3bZtG/r7++F0OlFTU4Oenh709PTMKZhi9rsBS1PkgzBEiFIlG5lMBjweD1VVVTQhxefzKSsgGo0iGAzS9/N4PDQ3Ny/kKRQEV097Z2fnYh9WRcFcc3Q6HU3SkKIIV9IuH5jsJq/Xi2eeeSanHc0y3seCB2VHjhzB448/DpVKlVOh59q1awBA1ReBmUZ1k8mE3t5eTl+k3t5eAMD69evn4aiXUUnky/5ptVp885vfhNVqxcjICNLpNNxuN5588kk8+eSTBTduTH5/JpNBQ0MDUqkU7e/JZDLw+Xy4cOFCRVSeyIbNbreju7ubUniAGcoan8+nqn/Hjh0rORic64ZwLmAqtbEDJ3LOhIJYqsIaV/8cySoDuT3rmN8/3yDHMj09Tek1IpEIyWQSCoUCdXV14PP5EIlEWLNmDbZu3Yrf/OY3uHz5MlURZSOdTkMoFFLqF9v7i1QQg8EgOjs7sx7+zGytRCLBvn37ZqnIFcqq56uOsZMcZMNONrXlbgjJe5599lm8/fbb8Hg8UKvVs6rV8zWui5ncyDcmsViM0oRbW1uRTCbR1NRE76tc41wMDAYDPv7xj2N0dJQa9nKBmc0mohaEzicSiRAKhbBhwwbs2bMH3/nOd3D+/Hl6L2i12qzeULfbTZMOMpkMdXV1sNlsmJqaQiqVgs/nw+9+9ztcvXp1QTdmRNo+GAzixIkTqK2thVarxcGDB3H58mUkEgls3LiRVv4uX75MFVY1Gg2+8Y1vzLr+XGI9J0+epJYnoVAIAwMDUKlU2LBhQ1n3DnmmjIyMUNn4cioz80l/JD158XgcIpGI9s5mMhnw+Xzw+Xy63pF5NDExAaFQCJVKhaGhIbhcLvD5fExNTSGTyeDWW29dcurZbFbM1NQUxsbGFl1ZtFLgWqeYlfSenh5qOVSKKTfRCTCZTNRP84NG+6w0Fjwo0+v1iMfjmJycxLlz53D77bdn/T0UClH1nr/5m7+hv9+1axeOHj2KV155BV/96lezVBiTySSOHj0KALjrrrsW4CyWMRcUohMZDAb87d/+LX72s59RBTC73U57I4jkNwD6QGRmeJiGtZs3b4bH48G7775LKxiJRAKDg4MVVXkilaV33nmHZpsFAgH1vonFYjAajXj22WfLbgBfaBTazM61v4sZ3DEfCHq9nn5uOBzGiRMnFoyLzpakDwQCCIfDiMfjUKvVEAgEEAgEqK2txb333otIJIL6+nps374dWq0Wer0ep06dQnd3Ny5cuDCLtioUCrFy5UqsXbsWDz30UJbCJpD/4a/X62E2m9HT00Orcswgv5jxYL6GqzrGBKF2aTSaOVF1iJrf4OAgvF4v0uk0FArFgkpKlxrwVWojm29MpFIpIpEIqqqqUFdXh4aGBjr2+ca52OMnFZt4PI5169ZxBnbkHj98+DAsFgsNyIhnFNNiIZ1O0zaCpqYmrFq1CrFYDM8//zyluMrlcnR2dtK1d3R0FL/85S/h8XjA4/EQDAbx7rvvzroW8wFSudPpdHRNttlsOHToEGVQuN1uiEQijI6O4ujRozCbzTCZTDSQraurQ319PadVBXMDS8bZbrfD5/PB4/HQQFCn0+W0HsgHUiULBAJQKBRYvXp1Ue/Pd6zFisYUA3Jfu1wu1NbWoq6uDl1dXfjwhz+MoaEh8Hg8dHV1YWJiImuN5DpWv9+Pw4cPw+v1oqura8nRAtmsmO7u7iWhLFopcK1TmzZtyqqkt7a2or6+vqRzJV5l6XQaqVSKJnKW2vguJSx4ULZ27VrcfvvtOHfuHL7zne/g5z//OVVDcjqd+Pa3v43x8XF0dXXh7rvvpu/bunUr9Ho9+vv78c1vfhNPPPEEqqurEY1G8f3vfx+jo6Nob2/Hrl27FvqUllEiiqET7d27F1evXsXZs2cRi8WQSCRgNptx4MCBLPEBLmNNLsPal19+GRMTE7R6QYQUKgmDwYCPfOQj8Pv9iMfjaGhowOc//3mYTCacP38eY2NjiEQiN5S/Sb7NbKX6gNgPBEJTJRXGixcvwuVyzXtGkis4jMfjCAQCNONXX18PmUyG5uZmXL58GdFoFG1tbdi+fTuA9x/e27dvxw9/+EOcOnWKVmiFQiFaWlrwla98hW5QcmUouR7+er2eVjDI8TA3BMWMB/s1ubKebPXQcoRGmNfUbDYjFApBpVJBJBJVzJJiPlDJPh6uMbHb7ejp6aFUQJFIBB6Ph40bN9J59MYbb+Qc50LHTuZOKBSCQqGASqXKK7LhdDpx+fJl2Gw2JJNJqNVq/MM//APa2tqyNu8ajYb2v4XDYbz++uu0Ckb8h774xS+iq6uLWk9otVqYzWZaTYlEIohEIjCZTGX12RYL5hg2NDSgqqoKXq8XXq8XTqcTPp8PDoeDUuY8Hg8GBwcp1Y7A5XLNeuYwk0bA+6I1ZM26cuUKgPeFT9555x34/X60tLRg9+7dqKmpKSkoIlTnXB6t+YIwdoKLqY7JVicu5doyBWEcDgdWrlxZVvWTmZhrampa0nZG5LzcbnfJVL6ljlzPjrk+45mep1qtFnfeeeey2EcBLEpP2RNPPIEvfelLGBkZwWc/+1k0NzejuroaJpMJyWQSLS0teP7557MoF3w+H0899RS+8IUv4E9/+hO2bt2KtrY2mmVRKpV45plnIBAIFuOUllECiqETabVa/L//9/9w6tQpvPzyyxgfH0cqlYLX60U0GqXKdlzGmuwmea1WiwcffBBjY2NwOp1UPcxisZScvSx0Xg8//DAMBgOmpqawbds2GAwGvP7667h06RLi8fgHyqOjUrQwrkDBYDDgxIkTNCCbb8oDyfoSXzBgJghKpVJIJBJIp9OIxWJIJpNobm6GWCyGyWSC1+uFw+GYFWiT+Xv77bejr68P4XAYDQ0N2LNnzyz1t1zz1263o7+/HwCoxPju3bsBzFDf2PLCucaDXfUpZsyYPRStra1lz1vm57S0tFA6JnszuJQU5iqp8MlF0z18+DAuXrxIxQ38fj/MZjNOnz4NtVoNg8GAffv2AeAe51wgPVLEhJdUrcg9les9Tz31FK5du4ZIJAKBQICqqipUV1dn+bFptVps3LgR/f39tL+SaZxOArXz58/j2rVrGBoaQnV1NfR6Pe6//350dHTg2LFjNMkhEAjw2muvQa/Xz8t4syvC99xzD4AZOw+LxUIDUCbIz6RPlPRAsZ857N5m9n1lsVjw7//+73A6nYjH4/D7/fjLX/4Ci8WCq1evorOzs6gAhlhwmEwmes+QgIj83el00jHv6OiYFTAyj5UEiePj44hGo1QEq5hAityfRDW3r68PDoeDCjjMVVmvUlTp+QSb+l0qlW8pI1c7gVgspvdorqRAPhDmEjDjMbxhw4YPxPWaTyxKUKbRaHDkyBH813/9F44fP47R0VG43W7qxfP3f//3WTL4BKtWrcKxY8fwzDPP4PTp0xgeHoZSqcRdd92Ff/zHf0RLS8sinM0HAwu9KSqGTkQWisbGRvz617+m5oMAaNayWGNNYmr6s5/9jD6Qu7u74fF4Krq4arVa3H///bN+J5fLEQwGIZfLlxclFnIFCmwz7fnKSOZS1jIYDDh9+jTNnPN4PNTX1+Ozn/0sPB4PLBZLwfNizwU28mUiyXVhKuQ1NTXN6kNjfyc72OGq+uSbg3a7vWLZYPb5sc138x3jXDCX9axStE0C8v1kHC9fvkyr9gKBAEKhkFaPDh06RDfKjz76aNHnYLfbaY9UPB7Hbbfdhj179uSkpjLfMzIygmAwSKm5sVhslvcgqe4FAgEEAoGsgAyYoYTbbDbYbDbKRiBS6LFYDL29vfB6vQiFQtSIupQKYKnIVRE2GAwwGo0wm81UJZENiUSCVatWQS6Xo7q6etYzhySN2GND7qtNmzahtrYWjz32GMbGxgDMBESBQABisRgjIyMAUDQtOx6P49q1a/jud7+LdDpNq15NTU3IZDJwuVz0mnOJYZFjJQFVKBSCw+GAx+OBw+EoOAbM+5MEdkR4RK1WF/RQLAaLqTTMBa71g2mp09XVVTKVb6mDvU6RKqhEIoHb7UZzc3NZveMfZG+3+cC8BGXNzc0YGhrK+xqZTIYHH3wQDz74YEmf3djYiB/+8IdzObxlsLCUZXdJZrCmpoaaMLONNAsZa5IFVq/XY//+/XjuuedgsVgwNTVFF5/5bNiNx+NoaWlBIpH4QDnaz9e8YWaDK9kDkQtsZS1m1nfdunX4wx/+QDdUzEzfunXr4HK50NbWltUbVsqxMgOvXH8nWWRiCKzX6/GpT32qpHMrtupT6WwwM+Bmer4xP6/S3nNzmZeVoG2y6WRkk2OxWKjhPanK1NTUYNWqVeDz+ZReRzbKpcjiW61WSvMmBuSFxs1qtdIqUHV1NaqqqqBUKiEUCmdVK8iGlKg5coFdeYpGo+jv78fQ0BCCwSAVf+DxeFAqldRmoZIJQeZncSV6SOBks9nw5JNPzqLLMVUCq6uroVKpOJ855LNyYefOnbh06RJ+8YtfUIsUiUSCuro6aDSaLFp2rjWuu7sbZ8+ehdPpRDKZRF9fH71+6XQaDocDQqEQPB4ParWajnmugBGY2T8ZjUZ0d3fD5XIVFA9hMwg0Gg0kEklW32AlEpqVYl1UAlzrB4Ask/HFNESfL5DzJlXQRCKBSCRCrSHcbjeqqqpKXp/ZVkZLsQq6lLAkfcqWsbCo9KaokuCiUeXK9DudTk7qFrvRecOGDYhGowiFQhCLxVkbIUIbq+TDgWySVq1a9YFytK/UvGEHAqFQCH19fQCAzZs3z7swCltco6Ghgf5tx44dMBqN6O/vpxuh7373u/Sh/NnPfpaTRlRqgJpPbVKn00EulyMWi8HhcMyqYhR7bsVkoNm0r0pkg8n7cwVK5PyIOM9c74+5zMu50DbZvTapVAoKhYJmnIm/l1AopMp0NTU1ePDBBxGPx3HhwgVajSnWAoFAp9NBo9GgtbUVKpWqoIQ7MLMu+Xw+CIVCJBIJNDY2IpPJ5PVgSqVSeS0f2AiHw0gkErSnkggfabVabN68GUDueVEMjEYjent7sWHDBjQ2Ns76LCYFk/megYEBrFy5Ejabjarl8vl8qNVq1NTUIBwOZ0n+F8PsYGP9+vWoq6uD0+kEj8dDbW0t7rvvPiQSCRqQAaB9XkwhDrFYjDNnzmB6epoGu5lMhkrKAzNKruS6plIpfOxjHysYMJLfkzmaD1wMgo6OjnlLlDGv8WLSmbnWD2CmUiqVSqFSqW6ovvBiQc6bVME1Gg2mp6dplbeqqgoSiaTk9XmpVUGXOpaDsmUs6ZuGfWxEfpkp4sH14CAPd/YCG4/HsX//fqrKODQ0lLURqnT1p1KCCUsR8yH0IZPJ4PV66YbBYrHMe5KAiybIDI4efvhh2t82ODiIyclJpNNpjI+PY+PGjQCAAwcOoKenh25YywkEcgURZANrsVgwOTkJi8VStHJosRloZs/IfKwFC5n4mcu8LPe9zCwz6XPm8XiQSCRQKBQAgIaGBuj1ely/fh3Xr19HIpFAe3s7ampqsGnTpiyamdVqhdPpnCViVOiaSaVSNDU1obGxseAxkyZ8QqX0eDxobW1FY2PjrHXKYDBg8+bNSKVSsFgsWWbSXCBWEcTzDJgJ6EiAkUgkONfnUubFyZMn8dhjj1GLhfvuu6/gZ5EeuuHhYfh8PqqQKxAIaP+L3+/H2NgYamtr53QPGAwG3HLLLdRSIxgMorGxEXq9nlYNCB3Q5XJlCXEEAgE4nU7w+fyszyRVRj6fj3g8Tq9vKpXCn/70J2zZsqXg9SvUv8X2MeRiEMwH2H1ri8Xc4VoDnE4nUqkUFTRZqiJFcwE7Obl582aIxWK89tpr1MuvmGQPF/R6PfR6/QemB28+sRyULWNJUQfYYNOf2Is1eaiPjIzQhwyzRM61wObLyBGfqEptHislmLAUQfjiJFNd7nViPwxCoRClFREFuPkGkybIHnutVotdu3bBbDZjdHQUmUwGiUQC09PTePnll+H1ejEwMACPx0P7kSodCBgMBly4cAFW64x/X3d3d9EPuGJ6yOZLNpsg3zlWusl/rutZORsIQu8j4g6kmiEWi9HQ0IC2tjZK9XI6nThw4AC8Xi9aWlqyroXb7aYVM+aGHci/FpVzDUl1raqqCtFoFIlEgpotp9PpWb0fZJN2/PhxuN1uZDIZGnASCXniUSUSiSASiRAOh2n/UTqdpkbSSqWSVoQIVTCVSuX0UyNgbtx/9rOf0Xs1EAjg1VdfxUc+8pGcfZB2ux0//elPYTQaEYlEqIeWSqWiptZKpRIHDx5ENBqF2+2eM3XXYDCgp6cHwWAQAHD27Fno9fpZzzRmgGaz2RAIBLKOEZix01izZg02btyI1atXw+v14vjx43jvvfcQCoVw5coVnDp1itLtcoGpFMm+5vl8DOc7IGP3rRUz7+cD7PUDmEkAeL1eJBKJD2xgkWvd1Ov1Za+ldrsdzz77LCwWC6X5LyM/loOyZQBYXJPiYmG32/NKETNFGshiWmiDxj7vXBLW5S5K7ICjHL+apQqmebTL5SrLS4mr/wNAlsLYQl2rYoKjmpoaqNVqBAIBqmR2/PhxqqTH5/Oxfv36ko65mCBCq9Vi9erV6O7uRiAQQF9fX8V89riqyVy0r7kgX29Zvk3iXL6vnA0EMzgttIFgeiwdO3YMVqsVIpEIt956KxXx0Gg0nH03bPlv8t2XLl3C+Pg4qqur0dHRAYlEUpTYSjkVPmYFlsfjIZFIwOl04vr167Db7ZSixVZ13LRpE+0v2rhxI9RqNWUtEFqcRCLBxz72Mfzyl79EIBCgQhTJZBKhUAjXrl2jdhFerxejo6NQqVTUb5QrKUA2eFeuXEEkEsHk5CT9G1F/3Lx5c05xk+7ubvzlL39BIBBAJpOhSpMSiQSNjY1IJpM4cuQI3G43TZ719/dTkahyoNfraVDl8/nw7rvv4re//W0WtZL0qzGFOGKxGLVWEQqFkEgkuOmmm7B9+/Ys9VKlUon/+I//gNvtpkEalx8Ye9xziS+w6cv5fAwrCeb3kr61xZScZ64fFy9ehNlsxsDAAK0czZdq6GKDnBNX7285YIuGfBBpn5XGclC2jCWNfJk7thQxydSyHyClbNC4smRzoTMWosbdyJgL9YiLJsqW4CavY9JV5xP5giNSiUin0+js7ITL5aK0JD6fD5lMhnQ6jYaGBrS3t5f13WRjm+t81Wo1xGIxEokE/H7/LAW8crFQ9OV8vWVLQaGrmPnMDMQIrcdmsyEUCiGRSKCjo4PaHuQKsrmqWsCMYbTZbEYkEgEw09Oxd+/eoqqWzHWmFJAKLOklIebeDocDZ86cgVgsxnPPPUcVAzs7O3H33Xejs7Nz1jGxRSYA4M0338TY2BhisRgVqAiHw4hGo/jd736Hmpoa2t/rdrvh9/vhcDigUCiy5ofRaMTPf/5znD9/HqFQCACyetsEAgFkMhlGR0cBgHMOT01NIZlM0veRYyFzjtAAE4kE+Hw+otEoxsbG5pREY1YDM5kMhoaGaBDG7P8i/yZCHIcPH4bZbEY8Hgefz4dGo8FNN92UZSb/hS98ATt27MCJEyfg9XqRSqVgtVqLStYQ8YXx8XGEQiH6HnZ/50Ilxdhr0EIIPJVybBKJBGKxGNFolPaOLvZxzQfY+63GxkZcuHABsVgMa9asmff+7mUsB2XLWOIoJXNXqY0cO0s2VzpjPmrcjYy5bOaL3QDPpyooVwU0VwDPJbH92muv0erBTTfdlKXEWO7x5DtfknRIpVIIhULo6ekpmBUvBgtJX8417pVQ6JqrOEAx/avPPvssBgcH4XQ6EYvFEIlEaKBcXV1NKWtcGedCfXsSiQRSqRSZTAYrV66kFbZSkE8whgvMatn09DSA9wUlLl26hLfffpueq0AggNlsxtDQUM55x147ZTIZ2traAABVVVXo7+9HMpmkPVbRaBSZTAbJZBISiQR+vx9Op5MeC+mte+SRRzA8PEwrbkwIhUIoFAp4vV7853/+J9RqNbZs2TJrA7lt2zYcO3YMZrM5SzAjmUwiHo/T3jeRSISqqio6RnNdqzOZDDKZDNLpNL1/c62V5Pq9+eabWefX1dWFHTt2ZJnJE4VOQq32+XxIJBJFJWtI8BWNRuFwONDd3U2/fzGwkGtQLuRaP7RaLfbt24dwOIzh4WF4vd6SxJZuJJD1eXh4GMFgEB6PB36/H3w+Hy6Xq+RKF5fX3jLyYzkoW8aSRi6/GYJiN+7lbtgqVUVYymIq5aLYBynXtS90PbjMnCsZyJYa8HGdK5NrT45vLhuKfIGq3W7Ha6+9hkgkQrPuLperYtdkoejLuQKfuQqMVCqAJ/1kWq12Vv+q0WjEqVOnqJKiVCqFTCZDdXU1mpubsxrhufr0mJ/HVQlgG0YzNzDFrF9WqxVmsxmDg4PQaDRFU6wMBgP6+/up0XEikYBAIIDP54Pf70csFqOBWiAQwNjYWMF5x/S60+v1NOvu8Xhgs9mQSCTA4/FoMETok9FoFH6/H2vWrKHz4MSJE7BarZwBmUA/BwsTAAAgAElEQVQggEKhgEajgdPpzPJFI9UM5rX78pe/jBdeeAEOhwNSqZQqYXq9XtoLqFarsXbtWjQ0NORcm5hjkW9sDAYDWltb4Xa7kUqloFQqsX79+oL0sF27duGtt96C3+9HTU0N7r//fuj1emomzzwuohB74cIFiMViDA0NFazuMYPxyclJ9PX1weVyoba2lqoRy+XyBU0eLtQaxIVC64fBYMA999yDQ4cOUSGqYn3mbiSQ9dnlcsHlciEYDCKZTEIgEJTdDy+Xy6FSqSCXyyt8tB9MLAdly1jSINSmXGIS811xyRV4lBrkLYVM4HygEAc9n3lxruuRy8y5koFsOdRL5qaB6aXGFo8pF4XEMMjmWCgUQi6Xz0kEZbEkp9l040KBSrGYq7ojc85JJBJ86EMfmvV5o6OjcDgciMVi4PP5aGlpwS233ILdu3ejpqaGjsXFixepch15f29vb8G+PYPBwOmHVez6JRaLMTY2hsnJSXg8nqLFYMiYEIl8t9sNiUSC6upqRCIRxGIxWlnKZDKw2Wx5+/7YFKiNGzdiaGgIZrMZbW1tCAQCCAaDWUEWqZaFQiFMTk7i5ptvpgqQzc3NtIrFhEgkQm1tLT7ykY8AmJHfB2aUHwOBALq7u2cp7O7cuROf+MQn6Djv3r0b8Xgco6OjlKal0+lor1yhJCA72OYam7q6OohEIvD5fCgUCpjNZly9ehWpVArbtm3jrDqSPrazZ8/ijjvuoD/n8l7bsWMHXC4XNeguZv6TYJz4Uk1OTsLn81EqpMPhqEh/Z7FYajL4XAF2f38/fS4RW4MPQisCAVkLTp06hYmJCWqwTmwASq10VVrE6f8CloOyZSxpsMUkgOwm8GIqUHPdsLE33OUGeYuZCZwvFLoW+a59ruvBVKycLynmuVQuCY3twoULACrnpZYvUCXHl8lkIJPJsHr1auzdu7es71xss3jyXexK6FwERsoZT+YmkFSZenp6qGpgU1NTVv/qG2+8QauUPB4Pq1evxqOPPsoZPLH7Xzds2EDPcz6ovsDM9SPZ6HQ6XXIldWpqCqlUCmq1GuvWrUNHRwdeeeUVSl8kG3SRSITXXnsNdrs9K2hhS6kPDw9DrVajrq6OUreZypQikYiqDgKgQi/hcBh/+ctfkEgk8NBDD6GmpgZ6vR6XL1+m/WB8Ph9tbW348pe/DL1ej6NHj6KlpQXRaBQ8Hg+xWAx9fX0wmUyIRqOQSCT0GuW6x/bu3UsrTj09PfRaEkVNMk/yBdskOcWcV1NTU+DxeFSwY2RkBCMjI0gmk+jv70dPTw+nSfvOnTtniYyQNZPde0oCBnJPFyv0QirA3d3d6Ovrg9vtBo/Hg0gkQlNT04KpBS/2mlTM+kGuF7FHIb2kH7T+Mq1Wi87OTtx0001IJpOoqanB9u3bce+995Z8nqSfUqPRfGAYQvON5aBsGUsazIcg08eFuXAXanCvNHVwrkEewWJmBisF9viwKR3lXHv2e9gBWTnXjf2euVQurVYrLBbLvHip5ao8Op1ODAwMIBgMQiAQUJ+iclCp+Vsu5qMSWup4clU8JBIJRCIR7W9jK/mp1WoIBAIkk0kIhULcfPPNWQEJM8jk6n/lqoKxj4nIR2s0GrpRL/YeEovFUKlUaGhogEgkKrqSSo7d5XJRb7WNGzeis7MTV69eBTBT7UkkEnA4HLDZbPj973+PV199FXfccQcefvhhAMgKSAFQs/OxsTEaoAIzfmVSqRRKpRKf/OQnoVKpMDY2hkOHDsHv9yOdTmNychLnzp0DADz00ENYt24dpqenMTY2BmCGErV//3783d/9HV5//XVYLBZEIhE0NzdDIpFgenoaDoeD3iNqtZrTEoUJrXam93dsbAyDg4MQCoUYHBxEIpFAU9P7xsnMsWAH22KxeJaCp8fjQTqdphRdk8lEK4WZTIaq0xXbB5grgClnPWNeC5fLRT0Yyfku1CZ6sdekYq+fVvu+PcrIyAgcDgcuXLgArVa7ZIRJKgGdbsazD5jpdd22bVvJ52W323H06FHY7XaoVKoPlEfrfGI5KFvGkgZzQ5LPvyRfg3ulqYOVCPIWOzNYKTB9hrgoHeVc+2KojaVct3wUynKuuU6nQ1tbG6xWK+LxODQaTcU2L7mOtbe3l/bfCAQCJBKJG7a/cb4qoaWMJ3sTGI/HOXu6mJ+nUqmgUqng8/nQ2NhIVTZzBZns9xc6PlKlIT1rVquVrmeF7iHCKBAIBOjs7MxJi+N6Hzn2sbEx6jM2NDQEvV6Pjo4OyGQyNDU1QavV4qWXXoLb7UY4HEYwGMTZs2exY8cOAEBfXx88Hg+am5tRXV0NlUpFZf+ZAa7T6aR0dEKHunjxIoaGhnDlyhUqoR+LxajSIAAolUrU19cjmUxi9erVaGtrozL8DocDwPuKleT3oVCIGuEWQ+X0+/1499134fF4kMlkEAgEIJPJqP8lV6WNGWyz51V/fz9VcyTX8cqVK0gkEgBAqaEejwdms7moPqX5CGCYlbZSrlelsNhrElD8+qHVvt+PR/rLuJLFNzK02pm2kQMHDmB0dBQHDhzAQw89VBJ90Wg04syZM9Q+g1TWl5Efy0HZMiqOSlaAuHpQyMONKbBQ6CFVSepgrqChlPNe7MxgJUA2grFYDIlEAkqlklM9r5xrX4jaWMp1q0S/EbvKtnfvXjgcDni93oo2MHMdKzCjwFZbWwtgpkrwwAMPlD1fKp2kKAVMAYhKmNJWUsCHvJ+rf9VoNOL48eMAgNraWmzatIluMCodZKZSKaRSKertQ1T28lWLCf1yZGQEq1atQmdnZ1Hfzzx2uVwOgUAAAAiFQrMCEABURCISiSCTySAYDOLMmTNIJBKwWq2IRCKIRqNYuXIlEokEVq5cOStAJXQ79njo9XpUVVXh2rVrmJ6epkqCx48fR3V1NV1neDwelc83Go2wWCzg8/loaGigwQRbnr/Ysejv70cgEKBBUyAQgEAgQGtrK/XQY69N7J+Z8yocDtPPUCqVCIVCtE+HjHUikYBcLi+6T4lr7s41ybeYa8JS+P5SwQximabfLpcLp06d4rSMuNFAqLXT09MYHh4GADz++ONFn9P09DQ8Hg+i0Sg8Hg9VVF1GfiwHZcuoKOajAsR86HFRgBYjy8Z+EJd63oXkt5c62HQtYvhZV1c3r2OQa0OS72E+1/4xrnElFDeSKa1UUM01L0glo66uDh/96Eexe/duuqllCo6UktmuZJKiEJhS8KQ/VC6Xc/bRlPq5lRTwyWWGbrfbceDAAfT09CAWi6Gurg4bN26ctf6Ew2FIJBJOQaJiYDAYsHnzZgwODiIUCqGlpSWn+h/7vMViMRwOB8LhcEkCDcz5RpILRBSDVLUGBgaoCuDevXshFotx7Ngxam78xhtvgM/nIxKJUDEQqVSK5ubmogNUMh5GoxEymQxGoxF2ux2hUAijo6NoamrKCmpCoRAOHz4MhUJBq2RsO4py5rhQKMwSIOHz+WhsbIRAIMjrocdcg8i88vv9ePrppxEOh8Hj8bBq1So0NDTQnjpgpvePmGe3t7fP8g3Ld62Yc7dS1i2L+exZ7O8vBexk8dGjRzEyMgKr1Yrf/e53aGlpQUdHxw1bNSPPd7fbjWQyiVQqBafTWdK8qq2thVqtRjKZhFqtpknFZeTHclC2jIpivitAXAv3fGTZSs3Al3reXBXApUpl5JKAZtK1lEolVTIjKnTzNQbssQYKm3vPZX7kqlwNDw9jbGys5E1wIbCPlS1AUVtbi8bGRgClCY4sVv8iM4Bg0o9bW1tRX18/p2OptIBPrs8zGo1477336OZaKBRCrVZnfU4lzK+1Wi32799P14RcPSq55mRTUxMNqIrtN+S6n8i/nU4nHn/8cTgcDiSTSej1erS0tKCxsREdHR24evUqAoEApSgKhUKIxWKIRCKo1Wp0dHSUVDEkFT8AVEwlFovB5/Ohvr4eDQ0NkEgkMJlMiEQiVAJfKpVSdbi5zu329nY0NjZiYmICAKBQKFBfX4/q6mpOFgBJipC+MIlEgn379mHTpk349a9/TSt+CoUCK1euRFtbG+rr62G326loSSwWw+TkJABAJpMhGo0W9MEivyfXaynQ/+aCxeyvLve7meuH3W6HxWKBzWaDz+ejZus3qmy+1WpFNBqFQCBAKpWCUCjMUpgtBgaDAbfddtuyR1mJWA7KllFRLNbDgf2Qmkv2neshW2hBKee8yaJeiSxnITCrFfF4fNb/8/WqsIMeJuVJqVQikUggFovBaDRWLKDMVwVhPgzZ185oNHI+YMvNwrLH1e/344knnqBZ/PlQKWMfayqVQjweRzAYxOjoKM2iG41GvPPOO7BarRCJRDkFRxarfzFXNZWpaDgXlHLPFbPxYiqFyeVyuN1unDx5EocOHYLT6QSPx4NYLMatt946az2ohPk1kK2uR9ayYs+b2ZhfKEnARcllHgMAHD58GFarFV6vFzweD5cuXcLg4CCtGhHjZ4J0Og25XI6bb74Z99xzT1lVUHJura2t8Pl8iMViAGZohESFcWJiAjweD8lkEhqNhiq7VWLTZzAY8LGPfQy9vb0IBoNoampCQ0MD5HL5LNo8ua/6+vqoeXN1dTWAGUZHc3Mz9Xji8XgYHx+Hx+PBxo0b0dDQQKm8hK5KJPOJB2G+DT2XUA3x2LvRTI0Xs7+6Ut9N6IyEhqpUKm9o2XydToc1a9bQOVpdXY2GhoaSP0cul0MqlSIQCMDpdN5Q12CxsByULSMnyskgVbpqVewxVGJxNRqNOHjwIOx2O1wuFwKBAIRCIVwuFx555JG8D/25nPd8BbJs2hizsuXz+ej/mcpiJEADkCVvzQwYc4mvzGUzykaxVRA2/aq7uxsulwttbW0Vl6n3+/349a9/DbPZjHg8TjOHlVYpY855YMYgNxKJIB6PY2RkBK+++iquXLmCP/3pT7BarVQNMJfgyGL0L3KJX7DnWCXHJt/nFbM2MPsjST/X888/j4mJCWpITBQN9+zZM+v9Op2Obtzlcvmc5gNThZFrHjOpfszfFVuty3U9mPPO6XTi8uXL1JMtHo9THzGpVJpFwSMQCoWQSqW49957Z0m5FwtybidOnKAVh3Q6jfHxcap6unLlSuoTZzAYYLFYEIvF0N/fX7DKWAyInUEqlaL+aGwlTuD9+2pychLT09OIx+NIJBLwer1UBEir1UIgEKCxsRGhUIhWiffv34/p6Wm88MILsNlsEAgEWL16NVpaWmgvYb4NPfOeZqsS32gVicXsr67Ud7OZL729vXT8KvlcXCiQqn1dXR3OnTuHSCQCoLTrY7Va4XK5cP36ddjtdhw8eLAsBsH/NSwHZcvgRK4Ht9FonKWcxUaluOFc2cBiKT25Kibsz2dufg8ePIjLly9nle2DwSCGh4fx1FNPFRWYzXVBJ8cy1/4yLtrYyMgIwuEwJicnwePx6P9DoRA9f4FAkNVbwvZbIsfEJb5Cqgt2u33O419soMo8FpPJhN/85jd0U1MJOhP5DgB44oknMDIyAq/XC7Vajc7OzrIrArnAnvN6vR6hUIj26sRiMZw6dYpuAAHQDPuOHTsKBq7zXb1me1XNp9ccUNw9V8zGi1n95fP5MJvNmJqaQjqdprQ8YlQ835teZqU+3zxmK84yq3VAbupULvojU9Le4XBgdHQUwEwFMZPJUA+wdDqN2tpaGogAoFXErq4u1NTUzOn8tdoZw+jr16/D7/cjmUzSv5nNZkxOTqKxsRFDQ0Po7e3F9PQ00uk0Dh06hM7OTgCgSYBi1VmZdGGyHorFYiqlz3WPk/vKarVCJpOBz+dT1UmxWIwLFy4gEomgpqYGN910E+RyOaWXks9ramrCc889h2QyiZaWFuzdu7eoDT2zj9Hv90MkEiEYDAK48USjFpN6WcnvZq5FjY2NdPxuRDopMHM+27Ztw+joKDUUL4Wmr9PpsmxGYrHYDTc3FwPLQdkyOMH14CY9BjabDSKRCF//+texZcsW+kDr7+/H2bNncccdd8zKlJZTdcuXDWQHaMyHVCAQQHd3NwDkzYyzN7+xWAzxeBxSqRTt7e2IRCK4fv06AoEAzGbzvGZ6mLSlStApmNeO0MZWrVqVs1ImkUiyKl7AjJkrl98S83iBmQcQ2Uh2d3fjwoULc5ZULqXySI7F7XaX9V3FwGq1IhaLIZ1OQ6VSoaOjo2SJ4GK/h3nf6fV6Kr8/OTmJUCiUtUkFssUCuALiSlevcyGfeXIlA7JS15JiNl7M17hcLgSDQXqdq6qqsGbNGnz605/OKTNPNvPhcBihUGhOmw+Px4NQKIREIoFQKASPx8P5fbkq2KTP8a233kJvby/27duXJZDErOoBgNvtzqqIk76maDSKZDKJdDpNEwCZTAZisRh1dXXwer30eIgx8uDgIP73f/93zvPMZrMhk8lkiW4QBINBGoAwEYlEcPXqVWpWzRXQsBNxbKr67t276TUMhULo6uri9FciNHeZTAapVAqFQgGFQoHOzk7s27cP8XicVhVVKhUVtWHP25qaGrS2tuL69etU8XLXrl10Hc6V6GJWRkUiEV3Hb8QAYKHWp4X87sU8p7mCzbDxer2w2WwAUFK/rFarxe7du2Gz2ZBIJCpqHfNBxnJQtgxOcG1kTpw4AZvNRvsrfvKTn+DixYsAgPHxcapO9t///d+477778KlPfWqWGEMpHii5aHK5TKSJr8bY2BhisRgUCgWA9zOH7Iwoe/PL7MkgnkVPPfUUzGYz3fiW0rg710CUeeylgj1+JIjN1VPGrngBmJXVzQWtVks3pcPDw4jFYrBYLOjv758TXa2YKgjzGhP1OmK+S/5eiQcioSkCKLrPsNzvYY4bkfbevHkzXn/9dbz55pvw+/1Z7yHVi1dffRUmk4nz2CpVvc4H5tzNFczPFaUkLbgU8biOhbyOzFWTyQSz2YxEIgGhUIg1a9bge9/7Xt7xrlTG3W63Y2hoiFZGeTwelaFnHjfX9zGpf2+99RYGBgYgFosRDoezREBIwiwajWJwcBDhcJj20bW2tmZZPIyMjNBkRDqdBo/HQzQaxejoKMLhMH1dIpGgSZEXX3wRNpsNP/7xj8se9w0bNqC+vh5er5cGhMWASMxXVVXNopGykwbA+8bvgUAAUqkUU1NTWLlyJSYmJjAxMUG925gbUaPRiAMHDmB4eBiJRAJisRgSiQQNDQ20cm632yGXy6FSqagqJNc9yKW2SuYi8Vrr7u7m9N8klVHS+zZf1eiFwEKsTwv93czPXUwhk1LAxbCx2+2IRqOw2+2oqqoqeg9kt9tx5swZTE1N0STFMgpjOShbBie4Mj0bNmygm4V0Oo1gMIj+/n5MTExgamqKvjcSieA3v/kNhoeHsXHjRuj1ejgcDphMJkSj0aI37CTQ6u3tRXNzM81M5jKRJgpIxHyVNIDrdLpZfRp79+6dJQXNdTyPPPIIDh48CK/XC5/Pl5V9zrdJK7fiVanNHfPa5aOaMsHMpgMoqxpBKFder5dWFwklstKGpFzXeP/+/VlVO67NTDmYz8wn+4HN9T3kAU82gplMBhqNhlYx4vE4nE4nleh/4IEHKqqEWQy4AspKf3exSQuuubFp06aiXqfT6aiRcTqdxsc//nGqeJkLZNyYfV7lnh8RkKmqqgKPx4PFYpklkZ5vnuzatQu9vb0Qi8W0x8npdMLtdqOzsxMajQahUAherxfhcBgjIyOQyWRZQTQwE3x0d3ejr68PPp8PwWCQ0hWJyTQXUqkUrl69OqdqocFgwJe+9CU8//zzsNvtVPCjEIgAyNjYGCQSSZa4AHPukCqhx+NBPB6nBs4ejwc9PT3Uh00gEODcuXOoq6vDihUrcPr0aYyMjGBychKBQAASiQRKpRI6nQ7r1q0rOVHDRQVnU5dzzXWmME2pSpdLDTdK0FIOFlPIpFSQe8RkMtEqL5NhU4p4CekBJRX1SlrHfJAh+MEPfvCDxT6IpY6XX34Z4+PjWLlyJfbu3bvYh7NgUCgUaG5uphWnTCaDt99+GxMTE/SBHI/HswIyAtL/Ul9fj46ODkQiEfj9fgSDQSQSCfrgHhgYgNlsRnt7O/0eu92OgYEBuFwuKlARDAaxc+dOrF27Frfddhs8Hg+EQiGampqwZcsWKBQKmEwmyuNXq9X467/+a3zuc5+DVqvFmTNncOTIEYyOjsLj8UCv12PXrl2IRqPo7++HyWSCx+PBli1bshaNFStWYP369UilUnA4HOjr64PVaoXNZsOHPvQhesxsDAwM4OLFi7h+/TqEQiF0Oh2am5s5X8s0pu3q6kJ7ezt0Oh26urrg8Xhoz1Ax4Lp2k5OTWdc3F5jjzR77QlAoFGhvb0d9fT39mc/nIxaLYXR0FNevX4fFYoHT6SzqWNjnw3UNuK7x2rVr4fF4YDQai7r2pYDrmuQ7vmLP77e//S0uXrxI7wPSU8P+vEwmg5GRESSTSaxevRo/+tGPsGfPHqhUKgCAz+eD1+tFMBjE5cuXMTY2BqvVWtL1LgfkGiiVStx6663Q6XSz7qNKgfR7se99Noq9/3LNIbVajWvXrmFiYgLXrl3Du+++C51OhxUrVuQ8NkKbJmp85Vx3cn6kpzWVStEm+zVr1mR9Xq57VKFQoKmpCZOTk2hoaIBMJsPAwAB8Ph/8fj/uvPNOpFIpmmBraWmBTqfDrl27sHbtWnr/d3V1Yd26dVAqldBoNKitrYVSqYRUKoVUKkUmk6HiHgKBIItWq1KpcPfdd+e9XoWg0WgorTIcDlPRDS5IpVKIxWLIZDKEQiEEg0E4HA5cvXoVa9euxYoVK3Dt2jV0d3dTamNDQwPq6upQW1sLv9+PSCSCSCRCKZvkv0AggOHhYbz++usYHBzE5OQkkskklf7fvHkz7rrrrqygaGBgAEajEW63GzKZLO8aRMZxbGwsay6qVCqMj49DIpHQe4r5jDx27BhVb7zlllvQ0dExr/f5fIFrDVyI9arcNbtUlLIXWGzw+XxcuXIFJpMJXq8XK1aswJ49e7Bnzx5IpVL4fD64XK6izoN8lt1uh0AgwNq1a/GJT3zihpyjpWIuMcNypWwZRcNqtSKVSkEgEAAANc/lglQqpVLChIJFKhihUChvtYtZPvd6vbDb7QiHw4jH4zTbzWUibTAYsG7dOly+fBnJZBKTk5M0cz06OoqpqalZVJhLly5hYGCA/p6L6ggADQ0NSKVSiEajiMfjGB4ezspes1/vdruzemqY5tDke0gf3r/+678iGAxCKpVCr9fj05/+NPR6fcnZtVyeUMzzmk+Qag7pnSCZ31AoRFXK+vr68hqj5jqffH2EQHZVcaEaxyuRAS2FrkoEGYg8MbkfyL1FqLahUAhTU1NIpVLzrvxVbEWqUii2YlnsHMj1ung8jmg0iunpaUSjUQQCgYI9paVU8cj9Ybfb4fF4oFaraWWRKVxz4sQJ2mPG9Xm5KgwGg4GukefPn0dvby9Vkw2Hw1nVGbvdnu+Sw263w+v1QqPRUOVJu91OE1xqtRoqlQp//OMf8c4771DzaLvdPieKr1Y7owDn8Xjwxz/+Eel0miYD2VU6Pp8Pg8GAa9eu0aAqHo9jcHAQP/3pT7Fp0ya88soruH79OhKJBK5fvw6JRIJt27Zhz549AIC+vj7qQ5dIJBCJRJBIJJBIJKjfGPleoVAIvV6Pe+65h1Ngh1nFKnYNYivJEiq4RCKBwWBAd3c3pqamsG3bNsTjcTgcDthsNgQCATgcDgwPD1dEcXahUQnKfr5KG/t+Iz2EqVQK69evR3t7+7zaCFRSmXW+odVqsXr1anR3dyMQCFC/NbKeFOp15EKuivoyuLEclC2jaOh0OnR2dmJsbIw2orMbsQm3PRQKZWU2c23Y2epEzAVaKBRibGwMmUxmlkEvF1/bYrHg0qVLsFgsSKfTGB0dxR//+Eds2rSJKg/yeDysWrUKWq0WJ06cgNfrpT1VJCPJpDoSaeRAIACLxUIzpDweDyaTiW5o2L0KRLmQPLAJLYX994GBAVitM0aNqVQKFosF77zzDvbt28epkJZvM8ol7lEpT6hSwBwbIgJCKghWqxXHjh2jcyEf8gm9kAAoF4Wr0mqWhY6P/Fzq55cSQOZ7rcFgwCOPPIIDBw5gYGCACiHM9yZgMeSsC/WAsHvE2ONeDF1Up9Ohra0NIyMjSKVSSCaTVNwh36Yv31iyPRDHxsYoLVCtVmPjxo3YuHEj1Go1tFot1Go17Y3kGsdCSQFyncRiMd566y0IBAJoNBps2LCB/o1YATBVHJmfwe4TrK+vR2NjI+x2O5xOJ1wuF9xuN/bt2we1Wo3JyUm4XK6yzdS5/BS9Xi8NyPh8Pvh8/qzkWjgcxrlz57J+R3oth4aGqAgOoV0SGuaRI0dgt9uhUqmwbt06ADNJj2g0irNnz8Lj8YDH46Gqqgp8Pp/aT6xfvx7//M//zLmGMe0VJBIJp1AIF5hz0e12o7u7G+Pj45BKpfjFL36BoaEhxONxHD16FJ///Oept5nL5YLdbsf09HTFFGdzoRj15VIx1yRaPosHtpKpSCSCy+WC1+tFNBrFuXPn0NDQgC1bttCA9oNMpSwGarUacrk8q2cUyKZoF9MeQCTxyb22TF8sDstB2TJmgWvhJQvV/fffj46ODrz44otwOBxZ7xMKhfj2t7+NtrY2vPTSS1RRinkjsjfsXJshon5lMploplIikaC3t3dWpposyP39/XjnnXdmiSCQalkymUQymYREIoHP58OBAwcQj8fh8/lw8803Q6VSYd++fdBqtXj99ddx4cIFOBwOJJNJ8Pl8BINBGoBKJBJkMpn/z963B0d13mc/e79Lu0irlQ6SAGEE9iLDyvZAC7ZaKKWxSexQF6epncBMO+NLOk06Tp2kk7jpF3fsxm2cTn0hdmsc5wKkUfCAL7IijGwuEhithCzQBYS0Ky272l3tTXu/fV2nxLYAACAASURBVH9o3pezZ8+uVtJKQKxnxmMQq90957znPb/L83se9Pf349VXX0VZWRk1yOUqF5aXl9OqJnuWIRQKQalU0uCCJLCJRAIejwdnz57FihUrMjpt+byLgOwZg2J6Qs0V7OtttVoxPDyMixcvFqRkmUvohbwXee9c84jczmux+fzF6MjNZlZtpteaTCY89NBDiEQilLq20EHaYnUlCWYKmGa63rn+nftepEuzdu1afPDBBzTA4BYUCrXtYBsNk33F7/cjEAggkUggGo3i5MmT6OjogFQqhVgshlgsxuTkJFQqFU3O2CgkISaJyFNPPYWxsbGsYHqm92Dvx8lkEn6/nx6HxWKB1+ul3bcnn3wSDQ0NGBoawpo1a2YdtBOfSDK/S1Rh/X4/hEIhRCIRZDIZlEol3QdyQSgUQqvVUo8/n8+Xcw7u9OnTdHbmtttuQyqVglQqBcMwtGtGaPMOh4POJM/UMR0bG4NOp5tVx5C9b3V0dCASicDtdlPp+0QigUgkgjfeeAN33HEHpqamKPPE4XBQG4Nig/hM/frXv0Y4HMby5cvx0ksvFSUxY5jM+edCWRS5RLtIAZN7v8ViMSrPHo/HEYlEAEzHCETwhfxeMZ8XxVRmXWiQ5N5gMECr1WLdunUZ15hhrot6DQ0Nwel05mS+kMKW3W6nqtZzLdZ8nrCUlC0hA62trZROV1VVhWeffRYGgyFjoyIPK6IORjxqGhoasHLlyoIDtVzBEFEQI3NocrkcLpeLd8DUarXi/PnzMJvNWZUdYPrhXFpailQqhWAwiHA4jLNnz0IkEkGtVqOhoQF/8id/kjUkTXw1yFxHMpmkD3SBQAC5XI6xsTFcuXIFWq0W8XgclZWVUCqVCAQCUKlUGcfOFRUhnTLyfpcvX4bP5wMwnaCRwXbSaTt27BhaWloQCARw8eJFGAwGfOMb36Dfl686ezOZiJpMJnz44Yew2WyIx+MFeZawH9ZsoZfZBP/sweV8D5C5gJ0kEdU08vNc4Ct4zNT54X4mH2WWTeHt6OgAAKr6tpCYTVI5XxSSYM+UZMyms0eOTafT4cCBA7wFBe77sSnW5DtbrVYMDg6it7cXExMTAIDy8nIoFArI5XJKXQam59Li8TgNIlOpFEQiEYLBIKXCkbUz0z5byPma6T3IPUhMqY8dO4ZoNAqv10tnsFKpFAYHB9HX10cVB9kKjoWABP2ffvoppqamoFAoEAwGsXr1ahgMBvh8PoTDYVRWViKdTtMkKxeIEBWR1SdFLzJHx4XX68WlS5cAABKJBCtWrMD999+PDz74AMPDw7hw4QLcbje+/vWv57RFYJ9TlUqFqakpTE5Ooq2tbdb0OIZhqJLsxMQEVb5Mp9OIx+NwOp3o7e2lyXI6nUY6nea1T5gPSLfp9ddfx/nz5+l5vHz5Mg4ePFiU/YXdrXU6nTMW6/gKIdw1TO5Lcj7Ky8sRj8dRWlpKbUXi8ThSqRR0Oh2NW2baH+bSRVvswtVcQRhCHR0diMViqK+v5y0+kPUdiURgt9vR0dHBu75JYctgMOCjjz5CLBablaT+5xVLSdkSKMxmM37yk59gdHSUdoW6u7txxx13ZGxUZKCaiDlUVVWhrq4OdXV1eSlBhYJhGOrVolAoqDkm6UQ1NzfD6XTijjvuwIULF3D27FneB7RIJILBYMC3v/1t2O127N+/H2NjY7T7pVQqodVqsxIyhmGgUqkgFosRiUQoZSadTkMikWD58uUwGo2UEhGJRLB8+XKsW7eOVjWTySQdcuej05FZN/LwPXr0KE6cOIFwOAyxWExV9IaGhtDW1ob29nbKgw8Gg3jrrbewdu1aKm9NHiZOpxMrVqzIG7DcCDAMg7179yIUCtH5FEIVZdOV2DRWthqZ0+mcU+ev0AfIfI4LKKy6ajab8cMf/hBOpxN6vR7PPvvsnIMavoCbfMZiSA9zg5PFeMjmC5gKpRDOJUAiBQWLxYKpqSl4vV6agHNnR7kS7K+++ir6+/vh8XiofHpDQwMttpCZMgDo6upCb28v/H4/PB4P3YMTiQTcbjd+8YtfQK1W02KZyWTKS9MtJAEtZK9mm1ITWvSaNWtQWlqKwcFBSmXv7e2lx6PX62fVEWhra0N/fz/19lKpVFi9ejX0ej3q6+uxYcMG9PT0wOv1YnR0NMurjw98e2C++RaBQEATssrKSqxatQoCgQChUIh2qF577TW0t7fn9ShkmMy5nNnM0bJhMpnQ19eH4eFhWCwWKjZFFFcdDgd9LgHTzzuPx1M0GxDSufzss88wODiYJbQyNDQ0788AZk+B5iuE8K1hdiF08+bNkEqlOHz4MKUxEqGXu+++G42NjQDy7w9zZV0sZuFqPiAjIGQmvaKignbaueMBpGCQb96VYGhoiHbLFArFTd0pvBmwlJQtAcD1SiVRlhKLxVCr1di4cSMMBkPGRlVVVYWSkhIoFAps2LABu3fv5r1x53PjcbsQJDh3u914//33qSJZOp3OeECr1WqUlZVR1bF9+/ZRz5ePP/4YLpcL4XCYmikTyiIbsVgMer2ePojS6TR0Oh3WrVuHhoYG7Nq1K8Mwmfj/kHm7sbExRKNRRCIRDA4OUil4UkG32Wzo6+vL2NyHhobQ2dlJA6B4PE5n1kjFjzwUiXzzm2++CaPRCCB/cHizgKwjQhtxOBxobm6mwVhtbS2laRFKTi6hl0Ix2wfITOCrlJIgYXBwMG83rru7G06nk6qVdnd3zzkp4wYmZrMZfX19lK4jEAjoz4v9ALxREs+5AqZCKISFepbxgWGmTVDNZjNisRicTiel8ZE5UWIOzH4/sj+Mj48DmJ7VqK2txfbt2/HAAw8AQMb137ZtG8xmMzweD9555x2YzWZaFAKAaDSKaDQKsVhM104+mu58GAts5KJFS6VSvP322+jo6IBAIEBXVxfi8TgkEsmsZhnNZjN+/etfw2q1QiAQoKGhAV/72tcQi8XQ1taGDz/8EHK5HLFYDNeuXcPU1FReJca5QC6X47bbbsPjjz9OrSQAYNmyZbQLRbr7LpcLk5OTeb3rdDodPW+z8Vljg80aAYDJycms42b/XaFQAJjfXCe7uHHgwAF0dnbC4XDwnu/S0tI5fQYXhaxT9v3L93ruGuZLhN59990My5z6+nqsW7cOHo8HBw8eREdHB5544omc+8N85mfZzwr2328mEGohudYejwdtbW0AkLXPk4IB2f9yiX5YrVZEo1GEw2EIBAIkk8mbMja5mbCUlC0BwPWbRygUory8HBUVFfjWt75F577Ig9jv9+Pll1+mN+N99923YBQp9kZGEqsf//jHCAQCOSueZGg3Foth586dtJPEMAyefPJJAKDzNrmqnTU1NZBKpTQYIvMJ3/3ud7P41US4hGw0fX19tIM2MTEBu92e5ctGZJ7Zm7vRaIRIJIJYLKYzJUTiHAClixIpa6FQCIlEQoPxfMHhzQLCRfd6vRgZGcHRo0czglbCuSd/VqvVGBsbo12uufLR2ZS++Qhf5EpGSDcuGo3m7cZt3LiRJp1EcGGu4AYmAKg/HPEtS6fTRe8MAjdG3APIXXEuhEI4X4XIWCwGgUCAaDSKYDBI7zm2AEaucyASiSAUCsEwTF4vK3ZgaTQa8corr8BqtSIUCqGsrAxWqzVDrCPfXI3ZbEZ5efm850pnokXbbDY4nU5cvXoVk5OTUCgUqKmpmdUsY3t7O65evYqpqSmo1Wrcdddd2LFjB37xi1+go6MDPp8PcrkcMpmM0rqFQmFREjNyPh966CHs2rUr69qYTCZ8+umndB9OJBIIhULUgzEXFYthGCiVSigUCiiVyjnfH2zWyNWrVzE2Npbz2UeKXXMRyuAWP0lXbnJyMqsrKRAIoNVq8dWvfnVOx8QFua9zefzx3b+FFFa4idrk5CQVIFMoFGhoaEBdXR3eeOMNTExMwGq1YvPmzXjggQdyzkjNlYZ4s3uVkfs8EolApVJRuwen00nHQrjaAFzRj46Ojiwv0pqaGuj1epSUlCAWi30u5PDni6WkbAkApm+euro6ANNCFnv37s2aJSNVO6JeJBKJMDY2tmDfiWxkw8PDkMlkuOeee6g/Ti54vV787ne/QyqVQnt7OyYnJ/HII48AmH7A/vCHPyxoM29qakJfXx88Hg8UCgX27NnDG0xxN372RkWqcmwj5Wg0SpXx1Go1kskkpFIpSkpKYDQaMTQ0hPLycshkMni9XsjlctTV1SGdTsPv98PtdsPv90MqlaKsrAwACg4ObzTY1MQrV67AZrMhEAhQGWq9Xo+VK1cCuN4pA6bXQWVl5Q2nZOZKRtjdOK/Xm7MbZzKZ8OyzzxZFvYyPEtvX1wdgei6JqIstxGD5jZyR4N5vNpttxi5xsZJI0n0UCARUwAfIfQ5MJhNWr16NRCKB2tpauocU8tnsvYpQe/1+PxXr4O7N7LkalUqV0cFnq9HNlkI1Ey2aFDyIlyQwXXAoZG2TeaXf/OY3VKCJzNS1trbirbfewsTEBJ1BJbPBRDSAb4aYQCwWz0hxFAqFuO222/Dd736XFu+4aGpqwtGjRxGJRDIMrMlcXa61FIvFUFtbi3g8jtra2nntXeReVyqVGBwczPlekUhkRgEUglyJGBFT0uv1EIlEVIxKIBBg2bJl2LJlC7RaLbZu3ZrznM0VpNDBVfTju383bdo0q3vYZrNhcHCQKnJKJBJqrO7xeBAOhzExMZFXKGU+NMQbVcgqFOT7RSIROppCio1kH+GbN2WLfgQCAfpsI0JkhMrb3d1N75+b7dhvNiwlZUugMBqNMBqNYBgGsVgMZrM5ayMpZrV/JhC1vq6uLkgkEoRCIRgMBioNzIVIJKK0wWQyiVAohJdeegn19fWzFlXYtm0bBgYGqNphoQaA5P35fNm8Xi96e3sRiUSgUCigUqlQVVWFAwcOYNeuXTAajSgrK6ND8mVlZRkJcktLC06cOIHe3l4A0wEMwzC3xBAxME1XJIaqJMkkRrW33347TCYTmpqaaHeWBAxlZWXzOrZiqF/NFPzz0TkI3Y0rxFGszjKbuma1WmEymTA2Nobq6mp8/PHH1NKh2GviRs1IcJMKdvU5X5e4WCqZ7M6H0WjEtm3b8p4Dh8MBi8UCv98Pp9M5Z0o3Xzems7Mz51wNkVPnU6ObbaWeBGZENZZLUyJB13vvvUcTl87OTvT19eV9f3Ltjh8/jpGREfrzdDqNc+fO4YMPPsDo6CilcwuFQoTDYUSjUUoHzNctW7NmDa5evUq7R3xIpVJYsWJF3uTCYDDgy1/+Mo4cOQK3201VeNPpdF7pf2Kxsnr1ajprPR8wDINVq1ZR9kau4/H5fGhpacmak2Yjl58l10bFZDLh5z//Oc6fP49EIgGdToe9e/cuCDOGPOv7+/uh1+spLb+QOdFC35+wMMTi6bCXCH6o1WoEAgGkUil0dHTk9S2b6z18s4t9kPtcLpcjHA7TuU6+0RTu71VWVsJqtcJiscDtdiMSidBOuc1mw8DAAAKBAI3ZbrZjv9mwlJQtISu4Aa6rA3KDUIZhilbtnwk1NTWQyWRUxtbv92NychJKpZLOknEVEZVKJaLRKFWqisVic5rdYZhp5aD5iJWw6Y2Eoy8QCKiqWjKZhM/no93GvXv3ZoldsD97586dOH36dMaxtbe3o6qqCkajseg0tWJ60pCZxfHx8QwD2HA4DKlUilAoBIvFgubm5owNnWGYeR9bMX1wcgX/XDrH0aNHaQBcV1e3YHQVdjeZfB6xZyBdu4XAXIOTuYKP/sOuPnO7xIV4kc0G3M4H3wA8F93d3fD5fAgGg/D5fGhvby+YSjgT3SnXXA3AP18630q9z+fD6Ogo3G53VifDYrFk0NxGRkbwrW99C1/60pdQUVEBo9GIkpKSDDEfs9mMc+fO4eLFixmfIxAIYDAYcOXKFcTjcYjFYiiVSqRSKWrmPJMZrUAgoKa3+SAUClFRUZHz34nQRTQaxZ133omJiQlcunQJk5OTCIfDcDgc6Ovry9ob5+pTNhMmJyfzJmWkcHnixAl0d3fTYh55/rBNwnP5WXLprg6HAwMDA/D7/YjH4/M2BM8Fv9+Prq4u6rl2+PDhjG7vfGm4JOmQSCSU+q9SqSgjJhKJIJFIwGq14uDBg3n9t+aCG1XImi38fj+1oHA6neju7s6b4JPjkkql9JnDhtVqpTO4AoEAgUBgMQ7jlsZSUnYLYz4mh7nmEZRKJSKRCLxeL1avXo0vfvGLKC8vX7Bqfz4wzHXFPofDgWvXrlElLJ1Oh9LSUuh0OireIRKJoFAoUFZWBqfTCYlEgpqamjl38+YTeLLPL3t+JRQKZdAoiGeK1+ulCRDf7xFoNBrIZDIIBALYbDYaZK1Zs6aogfJ8lAL51qXZbKbUEJJUEjuFcDiM0dFR+P1+pFIpKvwRDAYhEAiKcmykCzyX5C5f8M8Gm85BzMqDwSCUSuWCUTbId2N/ntvtxvj4OEKhECKRyIKIfSw2+JIKtgBFPuGPXF5k5LWF7KGE3q1UKnPSA7lgswpKS0thsVhgsVioGly+tThTEsUX5PEVD8ianGu3gQRVIyMjCIfDcLlciEajlCVx6tQpHDp0KIu54PF48Pbbb1NGwMqVK+H3+1FdXY3a2loqiEMoygTLly/Hl770JZw5cwZSqRQKhQLr169HT09PRkKWS9qeXbCaCQzD4Ctf+Qrvv7El+mOxGO666y7cfffdcDqdVBkzEAigvb09SyJ/IZRwCf1OrVbnpG0SuuGlS5doglZZWUmNykOhECQSCRoaGjKS9nwJD3nOEo+vYoBNnSRqnYcPH8a1a9foM7GrqwuhUIh2Quci8sQGm2Iuk8moh6PBYMB9990HYHr0gfxnt9uLvmcvdiFrNiD3OVtUi3QUuTZEXDAMgz179sDtdlNmEYkVSGFdoVDQrvcSfTE/lpKyWxTzGRzN5/MBAP39/QiFQrDb7bTbM5/vOZ/qEFG6YtNGlEol9Z6Jx+NgGAZerxepVIrSRQiVa6G7eXzIdW3InAgZaJZKpTh27Bg1Sz1x4gR+85vfQKVSYd26dVkG0YSGtGzZMtpxc7vdCIfDCAQCdPCcvJZ7zvmuRa7rc/ToUQwNDVFhjmPHjlFKZnt7O8rKymjQx0Zraytee+01BINBqNVq1NXVQSwW48yZM1TQw2AwoL6+HsFgEOPj40gkElAoFJBKpYhEInC5XPD7/ZT2yT62uRQf2NdiLmthNp028lpy/1RWVhaVrsK9Xnyfl0wm4XQ6EQgEEAwGi+ZdNN97eT7gXgNCbeXrRhTaFZrNHspOgvjogXy/x54hlEgk6OrqwtDQECKRCBX/IVYG3PNayJrjBnnc4gEAOi80125DTU0N3XeJeExvby+cTidef/11+Hw+6q/IBeluJRIJOu/ocrlosYA9o0VAOlBGoxH9/f1YtmwZ4vF4VkImk8moZxfx8CKduELx0EMPIRaLZanG2Ww2tLS0UNEcQkVsamqC2+2mc0hKpZI3yFwIqhoJmpVKJdRqNZ3fYyORSKCkpARutxuxWIzOlRLBDsJKcDqd2L59e1axlQ8mk2lehuBcsA3CnU4n9Q51u91ZVFStVguNRlO0c8immBO/Vfb939TUhIGBgZwzVPPBjdw7C0FNTQ01Io/H4/D7/bS4XWhRcfPmzVnFJoaZVq4ldj4ymWzJQHoGLCVltyjmQ0fhUytjBxyhUAhXrlyZt7jCfBWHzGYznn/+eZqM6PV6LF++HNeuXaNURoFAAJFIBLlcDoPBgC1btvAmC4uJfNeGG0gZjUa0tLSgs7MT/f391FzW6/VmKZixH/ZkADcQCNBqczQazVBj5A758/la8V2f1tZWmiyS7/Lb3/6WGm/bbDYIBAJ8+OGH+Md//Ef6oG5tbcX3vve9DJrMmTNnMs4N8Xn75je/SSmdpPK+du1aDAwM0Mquy+WiBrCFmE3P9loUikKoJ3yS62y6VjHWY677ift5bKlyqVSKgYGBeXsX3Wj1MO41yNeNKDQonu3aIPcusbSY6f2B66wCm81GlQqJxYXdbs95v86F7pRLkTOXKmWh0Gg0kEgkkMlkAKb3mcuXL9NkLReEQiEUCgUkEgmlKCsUCmi1WohEIly4cCErEJ+amsLp06dpN4gIGxHFWWBaiKqkpASJRIIWHMgeUSjEYjFaWlpw5swZGAwGqsTLpgP7fD7cfvvt0Gq1GbNU5J5Sq9XQ6/W8cuDz6czzwe/348KFC/D5fNBoNIjFYlnPZpVKhZ07d+LTTz+leyr5OemcSSQS2sko5Hs5HA4EAgE6YzQfcA3C0+k0IpFIhoAOMP2MWLt2Lfbs2UPtCYq117CvC7eIsWbNmhnnROeCG713FgKGmZ4NJbY8iUQCTqcT1dXVM+5x+QqfRMxHIpHA4XBALpcvGUjPgKWk7BbFfKpx+X6XYRhK05lvtWi+AXF3dzempqao901lZSXuvfdefPTRR3A6ndQ/hsxnEQ+oG32zz+baMMx1yWO3253hR8b32kcffRRtbW1wu92477770NjYiPb2dohEIqqeyXfO+a4F32sB4M0334TL5cr4bKfTiUOHDlFZcGBabOBHP/oRHn/8cTgcDvzP//wP7HZ73nNDDL3JQ4/MPLApWCTB6OvrQ0tLC3w+H1WpnC2KVbXORz0phuR6Icin/sj9bjabDZcvX6ZiE/OljNxI9TAuFTif6Ap5bSFdobmujbkkTOR3uN6GAP89SN6bJKDkPWY6P7kUOecjkABMF8SILP3Y2FhGQiYUCiGVSiGXyymjQS6Xo6mpCQ0NDaiqqsLp06fh9XppgmOz2XDu3Lms/SIajaK9vR2JRIL6ZYrFYgiFQshkMiiVSpSVlUEoFGJoaGjOsviJRAJXr16lyq/Xrl3D//t//w82mw29vb3weDyorq7Gpk2b6EyNzWZDc3MzRkZGUFFRgcbGRgwMDKCtrS2j6/nqq69m0bjmA5vNhp/97GcYGxtDKpVCKBRCaWlpltKiSCTC9u3bsX379ow1wJ0pKzQhI0nUxYsXEY/HqS3DXO97q3XadodQIcPhMIRCIUQiEcrLy6kljEqlgkAgwMcff5zFFpkrzGYzXnnlFXi9Xqxbtw4mkynnXGax97WbXXmRYNeuXWhtbaXrTCQSYWpqasbvmu/4yL/ZbDZEIhHYbLZ5r6M/dCwlZbco5hIY5PpdAFl0xmJU+OcbEG/cuBFVVVUApuXjH3/8cRiNRrjdbjgcDjgcDurdJRQKIRaLqUz8jcRsrw15vdFoRFtbG6xWK7RaLe/vORwO/Pa3v4XT6cSJEyfw7LPPZlT3AP5ALNe14P7MarVCIpHQqiUbhCpGhFcikQh6enrwzDPPIJVK5U0ogekKc2NjI5588smcnUP23w0GA7q6unDlyhX4/X40NzfPusI2n/ukUCzWQ3c29xMJwvx+P/r6+qjk+GJ8djHBR7UmlDyu6MpsK9JzWRuk8gsUHtyyP4/QmHPdr1KplFd0ie94yHfhzrexCwLzXfvc7rzBYMDPf/5zRCIROhdaUlKCu+++G6FQCJcvX0Y6ncbmzZvxgx/8gH7mvffem9HNZRgmZ2fL6/VCKBRSyiRJ+rRaLcrKyiCVSjE4OJg1jzZbEGGSeDyOS5cu4bnnnkMsFsPVq1chlUpRX1+fIXJAzjWZs62trUUwGMy4710uF32N3W6flV9bLpjN5ozjTSaTEIlE0Gg0VDhBIBDgwQcfzNhXCcifZ5sgspMoQuGc631PCil6vR533XUX/H4/kskkHA4HSkpKMoyorVYrrFYrL1tkLjCbzfj+97+P/v5+qppJ/MgWQ3zjZldeJDCZTPj+97+PF154ARMTE1T45J133oHNZuPdT2dSJSbH7na7MTY2BoZhbupzcDNgKSm7hTGfqg77d/nklYtR6Z9vQJzL1+mJJ57A5s2b0dPTgzNnzsBmsyGZTGLNmjVYv379vL93MTDba8PueBBKX3NzM1W7Iu/V3d1Nu2rk7/v27cv4LL5znuta8P3MaDQiGo1icHAQPp+PykqLRCLU1taitLSUzpvFYjFMTk7S5DgfVq1ahZ/+9Kcz0v/YVTbycBaJRBgZGbkpK2wL/dDN1QnJdx7GxsYgFouRSqUgFovn7Se4GMktH7gJb3d3N6/oCpkDIsqT5HcLKYgUeiw2mw2vvvoqNSLfvHnznCr53M/ko2WOjo5CKBTS5Id7PCQB7e3thd1uh1wu5z3mYlT+uZSvTz75BE6nkxpqE0U78jOZTEb34c7Ozoz1QqiByWQyr48YKe6wlVpDoRBNEvhmqnKJfxSCaDSKnp4eRKNRmvSQLmEu8HnVuVwuat5OpPvni8nJSdotFAgEUKlUWL9+PWpra2G323H58mXcf//9+Pa3v12UzyMg4jZE9XLXrl1zWktcAZovfvGLlCZPZPm9Xi9sNhsUCgXC4XDRzp/NZsNzzz2HCxcu0OcTWyhlITpjXDAMgx07dtA45mZ7frFhNBpx55134uTJk/D5fIjH4xgaGqL/xi0KFaJKbDKZ0N3djfLycmi12qKpkf6hYikpuwVR7KHRhQwq57vp8Sk9kvcsLy+Hy+WiiklTU1O3PF+ZKBSNjY3hypUrGYIADMMU5BOX65zz/ZyvU/Xoo4+iqakJUqkUp06dwq9+9StMTU1BrVZj7dq1AKar9xMTEwiHw0ilUhAKhVkD+Vz82Z/9Wc6EjK/DIZVKqaQ7oarOlsK4GHz+hUxY5kqN3LhxIzQaDYLBIDQazbz8BHMpic7nfdjJRb6fsYVMkskkqquradJF9ipCTXI4HNSoeSGSY6vVipGREfr5xSoScO9BcrxEMQ+Y9iQka5+dgJKZKq1WS4+5WM8H7tpjGAaDg4Nwu920k0465GfPnkUymaT3f0dHBxwOR0YHz2qd9qI6f/48QqEQb2JFwPYgS6VSNBkTCAQzmkLPFkQFNhKJZCSDfr8/nQoYfwAAIABJREFUax545cqV0Gq1WLduHbZt2wYAtHNKXsP2sytGl6elpQXAtG8lKcysXLlyxus733VAkonx8XE6r8wNzGf6bKlUioMHD+LUqVOQSqXYuHEjysvLYTKZKHXd7/fj5ZdfpmtdLBZTKuN8z98LL7yAjo6OjGeSwWBYVPEvYpFAZmBv5viEFCKUSiX1BYxEIhgfH0dHR0dG0sWdyeNTJbbZbDh27BhGR0dprLBQtgp/KFhKym4xLESQeStVcoDMDR+YHgYn4iQVFRU3ZTelUJAglNBfJiYm0NvbS2XNDQYDHn74YbhcLjQ1NS3I5sYOEk0mE7Zs2YL29nYEAgGMj4/D6XRi9erVuO+++2CxWCi9Ra/X48SJE9SIMxqNIhwOQywWY/Pmzdi7dy/v5+Wi/xFvqMnJSarUOFsKY7GphbkCnWJWXHPZVQCz+/6k2hyLxeByubK6FoV+l/movOaiSOcSmuH72Y4dO2j32Gw2Z9CrAeDf/u3f0NXVhXQ6jaqqKqxbtw579uwp+h5QU1ODlStXwmKxIB6PQ6/XFz3xIwk+CcSvXr2KQCAAr9eL1tZWANOCOsSXrrq6Gnq9nqqeAXMzieYDWXtDQ0O4fPkyOjs7IRQKqfAG6UwlEgl4vV7qKSaXyxGNRjEyMkIDbZK4kFm0QCCQVbwhScz4+DjC4XDGvxUyOzaXLhkRiUomk/S7CYVCqFQqGI3GjFnF1tZWiEQiMAyD3bt30w4tEWrp6+uD0WjM8LObr1DWK6+8gq6uLkQiEWg0GuzcuROPPPLIjL976NAhvPXWW9BoNLjrrrvmvA5isRii0Sj1fTObzTMmemTP6Ovrw6VLl+B0OpFIJCCRSFBfX0/PKdkzOzs7UVlZiWAwCLFYTOfI5+uLdujQITQ3N2eti5KSkkWND26VmTLgevyxYsUKTE1N0RnyVCoFp9NJv/tMtEUCQoENh8OIRCKwWCw4cuQI7aAtIRtLSdkthoW4wYtVySm0MjdffzV20KHT6aiyFzA9H3Ar85VJUGY2m9HW1kbpSR0dHZTyMTw8TCWaFwMGgwGxWAzj4+MZhsg7duzIGh4nhtPV1dWIxWLweDzQ6XR5529ydWpramqg1+uRSqUwNTWFQCAAv9+PtWvX0gB+JhSzC7wYXbd8dhWz+f7t7e1wOp1UYvy1116jD8/ZfO+57jfc4zAajQULzbB/ZjabMTExgWg0SuWZ2fTqX/ziF7hw4QKl2Pr9/pw+cvMFCcbtdju8Xu+81ejyfQ4R/wkGg0ilUlTanNA3x8bGoFAoshJQLhV9Ps+Hmpppw91AIEDnd4kKX3l5ObxeL8LhMKWFpdNpVFZWYu3atbSbHwwGqa+b0+nE2NgY9fkiEAgE0Gg0aGpqwvLly/HLX/5yTt+X3V0rFCUlJdDr9fD7/YhEIpDL5bjzzjuxbdu2DBXfXGqf3PvDaDQWTSjLbDbDYrEgEokgHo8jHA6jo6MDq1atokUrvmdoa2srfvCDH9BO5LVr1wrucHEhlUqpWuj58+cRDodRVlaWdx8hxuCXLl1CIBCgnc10Og21Wp31O2ya5MWLFxEOh+m84lxhs9nw7//+77zrIV+HdiFwq8yUAdlFoStXrlBrCLvdTmeVZ6ItAplzhNXV1bTzf/HixTlb3HwesJSU3WJYiBu8GIleoQHrfANb7nc1mUxYuXIlrl27BrVanTVfVcj3Xuw5mZnA7roQs9JgMIju7m5K/yFdwsXY2Mg5Hx8fh1wux7p169DU1JThgUSqXnMxFs9F/2OYacPPkydPUmNRt9uNlpaWLMPW2b73XLBQFc98nTG2XcVsvn9ZWVlGNyMej8/pe892vyHH4nK5soLVQoRm2D9jB/O5PN9cLleWpHYxLADyHV8kEkE4HEYwGFywqjdZt8ePH6fqqpWVlaiursbp06cRCAQQjUbpbClBMZ8P5P4j1MRkMgmVSoU77rgDTU1NsFgsOHToEFVqVSqV+Mu//Evcc889Gcp/LpcLhw8fRmdnJxWmYIMoIAaDQaxatQrV1dWwWq2UTpgr0RKJRACud8iIQFE0Gi24ayYUCrF27Vr09/dDJpNh8+bNePrpp7OuaS6jcu75JvvffPcbm82Gjo4ORKNROrNHlC/3799PaclKpRL79u3Djh076O8SujnB8PAwjh49Oid5fjIj6PV6kUwmcerUKZSVldFjZFOOzWYzTp06hTNnzsDr9SKRSEAkElEfOa1Wi507d9LXs88RYeqQ66ZSqebsi2Y2m/Hd7343pxJwPgo2OY5CiomFYiHp7QsBdlEolUrBYrFAIBBQi6R8tEU2i4nEB8B08UMkElHa8lwtbj4PWErKbjEsxA1ejAd5oQHrfCrv5GZnf9ft27dj/fr1WWIgheBm9w9hm12qVCqIxWIqDR+PxxdtYyMV80gkgkgkArfbDZvNRq9jKBRCS0tLhlLZbJGL/mcymXDbbbfRIA0AfD7frI67WNTCuSYohVB98nXG5vL9169fD4ZhYLfbUVZWhttuu63g7839/oWosXKVAFUqVQa1JVewmk98hpg0O51OVFZWZsiTEzQ1NeHDDz/E6OgoEokEysvLFyxZIoGy3W5HLBbLmPNaKNhsNohEIshkMmreTirXcrmc91iL6ZEllUop1VAmk+H222+nnl6dnZ0YGBjAuXPnAACNjY2U1sfG0NAQLl26lLNDQTzI9Ho9DAYDqqqqkEqlEAwG4XK56H3PhkQiwdatW7F8+XKcO3cO165dQywWg0ajwbJlyzK8EvOBdOxkMhk0Gg0aGxt5K/65jMpzUf+LMUs2MjICmUyGtWvX0sRsYmICw8PDiEQilDbucDjonBaArPOVTqdx9uxZSoGfDWpqaiASiSAQCCgVemJiAhaLha59s9mM//iP/0BPTw+8Xi/tcpWXl6O+vh5SqRQVFRX48pe/TNkV3OeuzWZDPB5HIpGg3VidTgeHwzHr+ODAgQMYHBzkfd0999yTk0ZPZlMvXryIqakp6HQ63HvvvfOW5b8ZC78zgc3YIYUxYvxsMBh4n4Ps65pMJhGNRjE2NkaLg4lEgiboSqXypu4Y3kgsJWW3IIo5v0Ler5jSyfkCv7kkgHyBKztInC0/OVc1/2ar3HCpjB9++CGtVBNfssXY2EjFfGRkhHbtAFCp26GhIYTDYTidzqImtuQ67dmzB16vFxcuXIBAIEAoFMLQ0NCiP+Rmc58UmvAXqzPGBZEdDwaDWLFiBbZv347y8nK6XmaaL5tNwYJPCXDNmjUZn5krWM23lzEMk7FX8CX9JpMJzz33HNrb2yk9Zq6edjPBarUiGAxSURsy57VQ3Wp2h1qr1dICTTgchkqlQkVFRc6giN29nivMZjNee+01uFwuJBIJlJSU4C/+4i/o+9bU1MBoNEKhUEAmk2Hv3r1ZnRPi4UV8Jbkg6o1isRihUAiHDx/G8PAw9TvLNZMVj8exYcMG/M3f/A3+6Z/+CRaLBYlEAqFQCBUVFZBIJAWr9yWTSUoR5euy5qIukuPko/7Pl6JPkn8A2LBhA3bv3g2bzYYjR46gt7cXoVCIWgb4fD50d3fT67JlyxZ8/PHHGe85OTmJgwcP0mclO4nJV3BhGAb79u3D0NAQVQIl5ysWi8Fms+Hf/u3fcO7cuYzzLRAIUFpainvuuQerVq2izAY+ldS2tjZ8/PHHGV3oyclJjI+Po6urC88880xeE2miijoyMgK5XE67e1zLhdraWjz88MM53+PAgQM4d+4cPB4PTULnK+Zzsxd+c4GsD1LYITO9ra2tePTRR3mfUexnmV6vp4lZPB5HKpWCRCIBAJrYL4EfS0nZEgDMP9ErNGCdSwLIF7jOVQWOy4eeaVD1RoNhpue0ent7qT/O1772tYyB6bkIOMwW7K4dCfgYhqEG35cuXYJCoaBzQcWg77AfZg8++CCi0SgVAWhpacHAwAAVOJgpQSpWpbLQ+4RcM6/XCyB3ws9XpChG0UUqlcLn80EgEMDn82UEY3NJFvMFJuRYyYNWq9XSeav5CosU0qUjXTiz2ZwRPBQ7WWKL8KRSKYTDYdjt9gUr5rA71Ha7HRaLBSqVCvX19fT/AoGAimgUk15rNpvx4x//GENDQ4hGo5QGm06nsywazGYzJicnaXfqs88+w8cffwyHw0E76dzkSigUQqvVQq1W0wRsfHwcAoEAgUAAIpGIqrXlwhtvvIGSkhIa+JEkLpFIwGAwYHx8fEYaYygUgl6vRyQSwcTEBEZGRrI6SvkKiexzThgDGzduzKB2z4WiHwwGIZPJoNPp6B7HDpAvX76MS5cu0SSpurqa/v7u3bvx3nvvoaenJ+N9ySwPMD13dv78eVgsFlRWVqK8vBz33XdfxhwdwY4dOzAwMID9+/dTD7lQKASpVIq2tjZ0d3dnJcAqlQqVlZVoa2tDIBDAwYMH8dhjj2F0dBRdXV0YHR1FbW0tVCoVWlpacOnSJQSDQYTD4Qz/uMuXL2fNw5JzRPYEdjentLQU9fX1aGpqwujoKNxuN+LxOKRSKdRqdU5qs9lspqI6ZM0oFIp5i/ncSiIfBHzzwCKRiM70Wq1WbNq0Kes4uPdJVVUVvF4vXC4XqqqqMDk5Cb/fj3A4jKtXr86pc/t5wFJStoSiodBgcrZBZy4+/1zA5UPzVfNvViSTSQSDQQgEAmzatGlRq3B8ybTVakVJSQlSqRS9RkNDQzh27BiVwi7UiJybOHEfZgzDQCgUApge1CaiI8QyINfn3IhKJbfSrVKpstbsXLzHZoNYLEYVzcgsAFB4kFBoR5scq9VqRSgUwooVK3D77bfj6tWrOHr0aIadQyHJ8XwKMMROgh08FJtRsGPHDkilUlgsFgALO7jP7VAD095o5eXlkEqlaG5uzvBM2717d1HmyUjXYHh4GOFwGFKpFAKBAAqFghoZE4rq2rVr0dXVldG9IWprwHXVRK6HGMMw+NGPfoSPP/6YHsPy5cspLS4YDM4ofT81NYX/+q//QllZGX1vgUCAiooKbNiwAR6PB++9915Ok2pgek/t7e3FmjVrYLfbqagSu9CTr5DItmyw2+3o7OxEd3c3FaYBZh+I882pERAp+cOHD8PlcsHj8VDxG/a5vf/++2nHUSgUQiwW02vT3d2Nvr4+nD9/nha6FAoFurq6cOTIETz++OMZM2rAdKLX0dGBTz/9FMlkElVVVYjFYnC73VmJr0wmw5YtW+D3+6k6r9PpxIsvvojly5fj4sWLSCQSSCQSWL16NQYHB+HxeHi9LlOpVMY8rNlszigOcgWfpFIp7rvvPlq0ZBgG7777Lg4ePEhZHtzrYbPZ0NbWhuHhYWrpQJRE5yvmcyuJfBCwVVedTieqqqoKOgY2swcAvY/9fj/EYjFqa2vpvjk1NUX3tCVkYikpW0IWbiYOdD4+/1zA98C70cc4E0wmExoaGhAMBiGVSmm1b7GrcNxkmqhmAdPBjUajQUtLC6WwhUIhHDhwgIoUcKuc5M/soWDyOu510ul0qKyshMvlQiwWg0QioR5Jw8PDWZ/DR6ngO0cLsdb5Kt0zJYrFMGtngyhXBoPBjGpvoUFCoR1tq9WKwcFB2Gw2pFIpJJNJOBwOxGIxGpCbzeaseTO+DmehMsv5jpkEyAtBYWRT1XIdQ7GRq0Pd2dmZ4ZnW39+P7u7ugosg+WA2m+mMT1lZGdRqNZRKJZXFdjqdmJiYQCAQwNmzZxEIBODz+ahqHjDdCSspKYFGo4FOp8Po6Cj9rhKJBA8++CB27NgBo9GIzZs3088+evQofD4fFTIhkMlkkMvl8Pv9GUmA3++ns1WpVAp6vR7btm3Dnj17AABerxcnTpzI2TFLpVKYnJyETqeDVquFy+XKkP4G8u8R5D5paWlBZ2cnZTPIZLI5reFC5jgZZtqjk8waKpXKrOv30Ucf0fOm0WhQU1OD5cuXo66ujnbygOsiKeFwGOFwGF6vF88//3zGjBqBWq2m97jdboff70dZWRlNpuPxOAQCAaVEymQymowTKtu1a9eogIvH40FbWxsmJycRj8chkUhQWloKn89H59LWr1+PDRs20OISkK3YajKZaPFi5cqVWd0+cg8R03LuvmA2m9HV1UU7tgCQSCTgdrsxODiIH/3oR6isrMQXv/jFOVGCiznjuRjgdugHBwexe/fugvcVsl9ZrVZarPP5fJiamoJIJIJIJIJarYZOp1ukI7q1sJSULSEDNxsHOh+ffy4oxvzcYoNhGGzfvp0qMVosFuzfvx9bt269oVU49rkkogxsM1vC6yfBGLvKSSqQwWCQPrDZleVNmzZlXCdgevaAcNP1ej0qKioAgPf3yf+5wjDsc7RQa52dHMhksqz3XIxk2uFwYGRkBBMTE1SeHMi8ZlKplJ6rXPMkM30vv9+Pzz77jHZJgOteVGq1GslkEh0dHRgZGYHdbqey5VxTdDb1UK/X55RZzgfSyVooCiN3ZmKh5iJyKdOxhSRI0k2SJYfDgRMnTsx7rtNms+Hw4cO4dOkSZDIZGhoasGfPHpjNZnrfksQsFAohFApBpVJBJpMhmUwikUhALBajpKQEGzZswJ49e1BSUgK/34/9+/fDbrdj/fr1VGyBXTyRSqVUGp0oOhKIRCIqMGC326kXIjGUZxiGmjoTiwCbzQa1Wg2xWJxzvoysWZ1OR6v6FosFfr8fnZ2dGQWjfIl4RUUFvR6EIVCo2Aj73HP3o08++QTvvvsuqqursWXLFnpcXV1dkEql0Gg0aGhooMkC6XKOjY0hmUyipKQEd999N5588smMoHrfvn1wOByYnJykz1TiD8adUQOm9+6BgQFKKR0bG8PPfvYz1NTU4K677sLmzZvR3d0Nh8MBYFpspKmpCdFolM4CV1RUQCQSweFw0A6Y1+uln0+SML1eD6fTiQ0bNtB1wn4O9PX1Acikez/xxBN5E+eZ9gViTM42RY9EIjh37hztwLe1teHv/u7vClb+LfaM52KB26EPBoMFMxbIHtnX10d9Cklxzmaz0fnRYhir/6FiKSlbQgZuNAeaG5CwK/sqlQoul2vectfFmNlZbJBqH/lvYGAAPT09eOqpp/IOQS80yLkkJqoAMuZdBgYGaKACXK9ykuoumengqyyzr5PNZqPVcxLwE+opCZzI50il0qyH4djYWJYxutVqxfDwMK5cuYJQKDSntc5XRZ8pCFhISovZbEZ7ezuOHz+O/v5+auzb2NhIO5Xke8wnISXH3dHRQQUAgOkgV6/X0yD57rvvphLTwDSlhczvEFN0h8OBH//4xxgeHkYqlUJjY+OcvcYWksJIKLpqtZpS1RZC3IY7U5fLQ5L4hY2NjSEYDGJqaorOdc51Hb/xxhv46KOPkEwmIZFIsGrVKtrRIonTK6+8Qrsfcrkcd9xxBzZs2ICenh7aIa2vr8euXbsyAlHyHux7he94pVIpzp8/n/H9YrEYrl27htLSUuj1euh0OmqHsGLFCnzrW9/K2getVitGR0dnFPwga5d06MRiMQ4fPgyNRkMLPuPj44hEIlnFBJvNhhdffBFDQ0NYvnx5RjeFXDf26/OB++x966238Prrr9OO4bFjx6DVaqFUKuH3+6nxNek4bd++nXY5E4kEli1bhtWrV1O1TDZ27NiB8vJy6in50Ucf4dChQ4jH4wgGgxndN0Lvm5iYoOcqHo9jcHAQ4XAY9fX12L17N77yla9Q9cLx8XEMDw/je9/7Hj777DO43W6Ul5fj5MmTdC8n15VQKwkb4plnnuFNrAhy2afkO7/59gXSaTt16hRcLhc937FYjB5vIpHAyMgIDh06BJvNNqfreSvMkxFwO/SFPqPIs21oaAhApuk7Kdal02mEQqF5GYP/IWMpKVtCBhaaA52PCpKrc8GWZiVqXje6g7fYIOdh//79GBgYgNfrhUgkwtjYGPbt23dDvhNRWAOmN3F2B4btUVJbW4umpiYYDIaMxA1AztkzvhkzQn2QSCRUIpysAYPBAKvVCr/fj9dffx39/f2QSCQIhUIYHx+nD2SDwQAA9LWk6j40NAS/30+Pi2+Ncn/Ot17Je7tcLohEIgwPD2N8fBwMw2Dbtm0LOkdmNpvxwx/+EMPDw9RXCJj2uuN6u80nYGAft9VqzZgDqaiowNe//nWsWbOG7h3Xrl0DgKwZJKvVijfffJPSZAKBAHQ6HWQy2Zz3nYWiMLJp1PF4HKWlpQuS+HGvCzGL5l4nQpENBAJUSEMulyOZTM6aLtfW1oZ33nkH4+PjsFqtGYE3Ca5I0NvZ2QmRSASxWEypa01NTTAajVi1ahU8Hg8GBgbg8XjQ3NyMvr6+DL+nmbrGNpsNly9fpgI5BMlkEuFwGEqlEhUVFdi5cydKS0vhdrvR1NTEG9yNjIzgypUrM56D8vJyeDwe+Hw+xGIxeL1eTExMYHJykhaMtFotNVFmi7scP34cH3zwAQKBAAYGBrB+/Xo88MADGSbeM1mG5LJ7OX36dAaF0+120w6hQCCASCRCIpHA4OAgTp06hd///vdIJpOYmJiARqPBmjVreBMyAiIeYrPZ8LOf/Ywm1FNTU3j77bexbNkylJSU4Ny5czh58iQikQiEQiFVzCQUNHbHqqmpCX19fQgEAujt7YXNZsNjjz1Gj5P8NzAwgFgsBqFQSMU5DAZDhoJnLsylqJpvX2AYBk8//TR0Oh1++9vf8lowANNrcGpqqmBxn1txnsxsNtOu/FyeUSROUSqVGBsbQyKRQDQapfPgwPXzuDRTxo+lpGwJGVhIet9MdLFcgSI7CLkVq07FAHlwb926FT09PRCJRNDr9di4cSPv6xa6c2az2fCv//qvOHHiBBKJBO666y585zvfof9GBoUjkQicTieVeudSEnMlP9x1wp5fk8lk2LVrVwb1jmEYOBwOPPfcc7TjolarKXVveHgYQ0NDUCqVdCg/mUxCJpPRWZhjx46hvLycVzmN7ztx1yuXnul2u2mQe+DAAXR1dQHAgs2RkQCenZAB09XK4eFhHD58mFK7+IKUQtcO6TD29/dDKBRCo9HQYe7Vq1dn0Xu4+4lOp8Ply5dhtVqpMalIJEJJSQnq6uoKCsxyYaEojGwaNQnUFQpF0WfXuIHcxo0bKS2XHdgR/zBivCwSiVBaWoqmpqZZJdevvvoq3nvvPTidziyRBQAIBAIZzATSLayqqqJdq5MnT+L//u//UFlZSY2GicBDW1sbpf3x+T2R+RXSmSFiEuwZMDKXRLq+CoUCPT09eROO1tZWPP/889S+Ix+USmXGfAtZi0RYilARyUwk+zp89NFH8Pl8AKYLTAcPHsSWLVt4BUD4uqrsfQWYplE2NjZi27ZtsFgs1AOOgHg9AcjoAAaDQXR0dECtVgMA6urq8NBDD83YiTCbzXjhhRdw9uxZ+r6pVAoDAwN4/vnnUV1dTQUfSGeUYRgYDAaUlJSgqakp437X6XSQSCS8dgYktiCdR4/HA5FIhNWrV+Oxxx5b0JmrmfYFhmHQ2NiII0eO5HwPpVKJ6urqghOsW21Uwmw245//+Z/hcDhgMBjw3HPPzfoZRZ4hq1atwp133on+/n6Ew2HqUUhmBcXipdQjF5bOzBKysFD0vpmq8/kqS7di1alY4CYETz31FKXjAcCbb76JjRs3wmAwLNo84PHjx9HS0kIfvCdPnsQTTzyBFStW0AF/Ul1mU9UeeOCBjO/E9/341smmTZvobE11dTWdcWEfZ3t7O0ZHR2lwGQ6HsXz5csTjcTgcDsTjcRw+fBirVq3C1NQU9Ho9HTqOxWJUmSxXZ4L7c+6aBK7TM/V6PWw2G2KxGJLJJNxuN/r6+pBKpeZMlZwJGzduhEqloiIopKotk8kwNTWFU6dO0eSYG6Q0NzcDuN65zLd2iPrgxMQErXRLpVKIRCI658cGdz9hGAYulwvBYJB+T61Wi/r6enz729+eN6VlISiMfAJBx44dK/rsGjuQI4IJfF3k5uZm6g9YWloKtVqNVatWQafTFUzvtlqt6O/vh9vt5k3IiIkvOX/sz43FYigtLUUoFKL032AwCIZhcPXqVXg8HtoJ8Xq96O/vz3kdgsEg7Yy53W7qBQdMF2BEIhEN6uLxONxuNy5evIhXXnmFJh7c921ubs6aS8uFsbExANOd3EQiQTtxpNvLMAyv+TkRsGHD7XbjwIEDeOaZZ7IEQPjWIilwfPbZZ/B6vZDJZLSbf++996K1tZUeu0wmQyqVymsTQK4b+c75YDab8c1vfhOXL1/O+jci+kE6GuRaajQaPPzww9QqgTs3xzAMVCoVpqamoFKpsq6Lw+GgCnxktqisrGzOdOXZYKZ9wWQyoaqqiibIXCQSCWzatInXIP0PAe3t7RgZGaHWBO3t7VlzhaSLxre2uHZDtbW1SKVSuHLlSoZdBVEB7erqKng+7/OEpaRsCYuGmRKrfJWlW63qVExwE4KSkhLs27eP0tVI9f7hhx9eNA771atXsyqhVqsV165dg16vh8lkws6dOylVjU9qOhf41glb+Y5ITo+NjcHpdFK/k/Ly8gyaRDqdRmlpKZRKJcRiMcLhMGKxGOLxOJ1fYwfXRJmMrzORy0+M2/kj9MxkMolly5bBYrFAJBJBoVDQhMxuty+IubHJZMJ3vvMdvPDCC7h27RrS6TQ0Gg2USiXS6TTcbjc++eQT+r3ZQQrpKIRCIQD5104sFqP0U4FAALVajWXLltHuxkzrzmazZQyACwQCrFy5sigJGbAwBRySxJKgZKFm19hUNm7HFpj2JBwaGkJ3dzfcbjeAaT8lALBYLFk2BNz3Ze+dUqmUSpED0x2isrIylJWVwePxQCwWw+fz0bVKkriJiQmIRCL4fD4kk0kwDAOfz4fKyko6gxUOh6moi0gkosqxXLA9GAOBAMrKyqBSqRCPx6HVavGlL30J7e3tuHjxIqXtpdNp+P1+9PT0wOv1Zh2vzWbD1atXZ0xeCKxWK376058CuG4MCacNAAAgAElEQVRfQdTy2OAWF44dO0al/4FpxUkibkQKSTt37qRrhG8tSqVS2O12+Hw+mthOTk7iueeeg1gszqAvqlQqhMNhxONxWsiIRCKUurp582ZeI+9cYAtzcEHOsUqlontDKpWCWCyGVqvNufZtNhtUKhVKSkpQW1ub8Ywg3ndEMESj0aCurg51dXWLUmSdidrMMAz+4R/+AX/7t3/La8cQjUbxy1/+Ert37y7o82420bRcIGMINpsNQqGQjgiUl5fT15BYg+xN3/zmN/HII49kvA87TiF2Q2vXrqVm4ZOTk1S9Mx6PU3XGm/Gc3EgsJWVLWDQUkljl69ItVAfvZkeuILO7u5sqoUUiEVy9enXRuok6nS7Lewi4zhcXiURYs2YNdDodVY3k84jhA986Yc9o6PV6GvzZ7Xa0tbUBuC49bDabaUW5o6MDf//3f4+amhqavO7bty9DFIArQEDm07iD5IUMmLO7HM3NzQiHw5BIJPjTP/1TfPbZZ7hy5QoqKytpF63YBQaj0Yg777wToVAIEokEd911FxobG3Hy5EmcOXMGdrsdv/rVr2A0GrNEdABk0bP4UFNTg5qaGoyPj0MikcBoNNLgrdB1R7yvAKC0tBSPPPJI0Ya+ud2mfAqThYJdFHA6ndixY0fRZ9fYQRxbUTQUCuHw4cNwu91wOp2wWCzw+XwQCoWQy+VQKBR01o3MULFnmMj7Dg8P04DdZDIhFotlCDoIhUIYDAZKR5VIJBked8R3CJgO2sPhMJWSf/TRR1FSUoK2tjacOnUKyWSSUqyJR9FMyrlkvXq9XiQSCahUKpSVlUGj0VCqqEwmg1QqpcnJ4OAggOl1z56XJAlrISCeWcC0aqDL5Zqx+9na2or3338fkUiEzletXr2aqs6S9UDWIrejRkD8BO12O50NTKfTvGbbJFgm5+av/uqv8Ed/9Ec4efIktm7dyiukkg+EXUE61lywFVUJJBIJnSMDMp8zhOLp9XohFoszrDhIQnbp0iV4PB4a9D/44IOL1i0phNpcXl4OmUyW0yPPYrHg+PHjWf5ofLgVhD4IhfmTTz6hXffly5fDaDRi27Zt9HXd3d2wWq2YmJiAQCDASy+9hPr6+ow9mxunMAyDvr4+aiSu0WgwNjZG11WuQs3nHUtJ2RJyYiHmk+aTWHGFJW62DW6hkCsh2LhxI0pLS+HxeJBIJODxePDYY4/lFcwoFrRaLeRyeUalGJgOHogfDgne2XNWhapnctcJ34bPVvFzOp1oaGjAn//5n+PKlSvwer1Ip9Po7e3F73//e/z1X/81zp07h5UrV2YEcHyflWuNFrJ2yWsIvVImk2HdunXYsmULQqEQNSRlz6cUs4JKEpCysjJotVps374dDzzwAFwuF86dO4dwOIzJyUkcOXIEW7duRTQahU6nw65du3iT0Vwgyn8SiQRf+MIXZhUQmkwmbN26FadOnUI6ncbWrVszAoBigHyHYlWquQEWoRUWc3aNK7kvk8mgVqsxNDQEm81GE5F4PA6pVIr6+nrceeed1JzXYrEgnU6jq6sLg4OD6O3txdNPPw2r1Yq+vj50d3dDLpcDmBbGqampgVQqpQEoEfaQy+W0U8UOrGOxGGpra6nVQ0lJCRU9iMfjaGpqwvnz55FIJCAQCKDVanH77bdDo9FQVdTOzs6MNcL1mCLfye/3IxwOY//+/VT2XqvVoqysDC6XC/F4nFbcx8fHM7rwNTU1vEE16WLlA/EtyyfmYLPZ8Oabb8LlciGVSkGlUuGRRx7Brl27cq4Hsgdyu3rE2kAkElF1Oj6Q4JWIJjAMQ5Ue2UbPhaw/Ng3tpZdewn//93/j5MmTlCbJBnt/F4vFWLZsGaVGckWPDh8+TOdMKyoqqD+jzWbDK6+8gkuXLsHn80EsFlNlzzVr1izqczxfh5t8z5mKBx999FFBSRl7XpJ0YG82kO73+Pg4kskkysvL8YUvfIHOHhNs3LiRFtLS6TTi8XiWbQIfm4A9h1tdXQ2NRgOLxVJwoebziKWkbAm8WIzW+2wSBlLR6ejoAICcg+OfJ5hMJnz1q1/FoUOHEIvFEAqF0N3dnVUhL/Y1JPQgboBDAqe6uroMk+9iqGdyK87k+Ag10uv1wm63o7GxEWq1ms6oxGIxHDlyBOfPn0c4HMbp06cxPDyMp59+GgC/0Mh8QbyCLl68SA14ySwXUWZsa2tbkAoqEWIgiTF5aDY1NeHEiROw2+2IRqM4efIkfve731HjXY/Hg6effrpgLxqn0wm73Q6JRIJjx47BaDQWPBTOMNNqZ9u3bwewcAWWYlaq+brVRBG0WBRGrr/dH//xH+Odd97B1NQUvF4vNBoN9Ho94vE4KisrUVdXh0cffRQOhwPt7e0Ih8Po6uqiqmZ2ux0mkwnr16+nFepgMEgTjk2bNqGurg4dHR00GWCbfkejUTrfRDy76urqaJBpsVhgNpsRiUQwODiI999/n5o4l5SU4M4776T+WKRzTJIvsnczzHWPKalUiv3791OBoHQ6TTtESqUSK1euREVFBVWelcvl9P8jIyOUxsww06INH3zwQcb5lUgkMyZlKpUK69aty9vxtVqtkEgkdF6zqqoKu3btyhnws9chV4mRdObdbndO6f6ysjKsWLGCeo9JpVI0NjbOqbPMFkAh88lqtTqn4iAwXWgjAfmlS5fw/PPP4yc/+Qm939ndFo/HA51Oh5UrV9Lvd/z4cfT09MDn80EgEKCkpAQNDQ20W7+YyEdtJibSM1koEDXZPwTU1NRQk29gmkrON99nMpmwd+9eWiSprq7mFRnjYxMAmFEwZwnXsZSULYEXC916z0Wp4XsdCWRHRkbovM/IyMhNSQdYCORLrrZt20Y9wsjgP1H5WohrSB7Ahw4dyqK8EPPNWCyG7u5uWikmgUkx1DPZ3inEP4ls8iqVCgMDA5DJZBm/Q2TxSUDW0dGB48ePU5XIhehWRaNRGowSiXdyLtieblz6z3ySRLZsu0wmy0iMTSYTnn32Wfzyl7/EmTNnYLVaaeAbi8UwNDRU8DWpqalBMpmkZsFkhma2SfZC37vFrFSTKnB7ezudtSj27BqXXnX69GlIJBKk02nI5XKsWbMGDz30EBiGod1wYDrQPnnyJK5evYpAIEATrFAohJGREdTX10Ov12N0dBTpdJqaI5vNZgwNDWV0Z8h6EAgEiEQiaG9vp2qvpNgSi8Xg9/vxL//yLxmdlFgsRhMTlUqFPXv20D393XffxSeffIKJiQlYrVbaRSHHzTAM3n33XVy4cIF6EQKgQjBqtRo7d+7EsmXL4HQ6KZWvpKSEFmTY3bJvfOMbOHv2LCYnJwFMi1+Ul5fzdoOEQiEkEglMJhMef/zxGX0fycwZMN1d3LdvH5WW51sP+ZQYgen9iMxyku8jl8vpcW/fvh16vR6vvfYaFUqw2+2z9utsbW3Fs88+S+dNE4kE3n77bYyPj+ek6wHTs4bpdJombidPnsRTTz2F7du3Y9WqVejp6cGHH35IFVg1Gg29vmazGb/73e8wOTlJO7yrV6+ma2Oxn9/cbg778z0eT5YVAx+qqqoK+izyzCPFkJshXuF7xlRUVECj0WBqagpisRgDAwNZa8tms8Hn86GhoSFjzbPBxybgMnz4BHOWkImlpGwJvFhotUOiOnX+/HlKzeDSf7hqPnq9nkqcE6rL5wH5kiv2Q8bn81HFMa46YLGMt5ubm3HkyBFeqWlCrRkbG8uSgC7GeuLb9B944AG6yZMOlEKhgEKhoAEjSRbFYjFSqRSkUilcLheV7WeLhRQDfr8ffr8fdXV1qKyszBq656OjFqOryZZtX7FiRRY1hASOPT09dDYFmJZ6ZvuKFQKNRkNNX5VK5U1zL7KDDgLig0ZEDWayZMj1vs3NzbRTPzAwgCeeeKLo4kMkqRkfH4dcLodWq0VjY2POwlVnZyf6+vrQ399PkxlS9VYqlVi1ahVqamrorNjk5CQmJibwn//5nwiHw1k+XmwT8HA4jP7+fpoAkfPg8Xjwv//7v5Qqy0UymcTY2Bjef/99ShX2eDzweDyIRCL0z3wg3539d1LUWL9+PQwGAzo6OmC325FKpaiHWDgcRjAYhNlsptfjG9/4Bl588UUqhCESiXg/b/ny5di4cSP27dtXULc333wpV/iH0DVzKTECoP6IBDqdDl/5yldw/vx52O12mM1mCIVCej8nk8lZiyTs378fP/3pTzN8zjweDy5evDhjIsKeJSQ4e/Yszp8/j5KSEgSDQSQSCQiFQmrjQPaaAwcOYHh4OCPRViqVi6K2yAduN4cdc+h0Omg0mrxdQwDUdmAm3GjF6EJ8Ncka1Gg0AK5faz6VULvdjqmpKaqwzMVMQioEi1GQu5WxlJQtgRe5HjzFAmmbS6VSxONx3mo7n5rPQlOebkbMtLnHYjFIJBL6wB0cHITf78+g/BXDeNtsNuNXv/pVXtPHiooK6PV6XkoXEeKY67Xjbvp+v58GPZs2bcroQGk0GgwMDCAUCkEsFqOqqgrhcBhyuRwbN25EU1MTmpubacBeqDLkTDCbzXj55ZcxNjYGiUSC3bt383aAuQ8mvsSb/L/Q+49QF4m/El8QQGZ4iIpeMpmEVqvFF77whYKP3Wq1IhQKIR6PQyAQIBAIFPR7hYKvmlvIz7hBh9FohNPpxOjoKJ0XqayspF1VINMCgBwb3/m2Wq28nfpNmzYVdR8i/mOBQACRSAQ1NTXYvn17zrVZU1ND6VZECVOr1UKtVtNhfYZhsGvXLhw/fpyKWvT19VGqYS4Q0R5iFtzd3U0Npl0uV875J2BaPOOdd95BY2MjHn30Ueh0Ouh0OsTjcfpnLkwmE+644w54vV74fD76/qSg0traikcffZSuX2LQzjAM7c6xqVFSqRTLli2D2+2GRCLhLSQRjzy2OEchmGnulC8A5lNidDgclNZHvk80GsW1a9cgFAoRDAYRDAZp54x0DmdTCGltbcWLL76Y0Y0Dpq8R2QNygShn8iGZTGY8C4RCIaWmAsDhw4cxMDBAk05CXdRqtTesiJOvwEn2xt///vdZs9JsfPrppwXPRefqyi00CvHVZBdu161bR1/L9+woJMHkdvqbm5tpgqfVavP6Ci7hOpaSsiXkxEJWNBiGwd69ewGAypHPtBF8nhIxNmZKkGtqaqDVaqHRaOD1ehGPx+mcD5c6yJ1pmA3a29sxMTGRNxi7//774XA4Mjjj3I4n+7hmex7Ipu/1evHyyy9nzNawz5NUKsXbb78Ns9lMFc0I9WdiYgJ9fX3/n70rj26rvLNXu6zFlrxb3u14IY5JFBpCE2ggbqAcoGk9U2Bm4ExTONOWtidtaafQhbJMlzNlWgZmUmihhAIdSFsITSgkxkkMTmLjxnLseLflXbYsyZK1754/fL4fT5stOw5Ni+85nDa2LL339L3v/Zb7uxdVVVUYHR2F1WqFyWTCoUOHUFFRcVHrjPmcmc1miMVivPHGG7juuuuWfb/otS4Wi1fUOVuKuhh9Db/85S/j0KFDOHHiBCYnJxEMBvH8888jMzMzqYcmK6ikpKRQZ+diqDnR3a3o8072Z9FBR01NDSQSCUQiEfx+P2w2G7xeLwmuAB9YAHCNvxk1liuYU1hYiJKSEvIwWutOPRMxYup1jPoKIGFXgV23vXv3wmg0wul0Ii8vD1/5yldiKHjMCoKBiWcwH71EYFRGn88Hp9OJqamppKXmvV4vnn32WSrEXHfddTRTlqhQcd9998HlctEMEhMVMBqNEeIbgUAAfr8f8/PzpPwYPavJfKeEQiEZXbtcrogiglKpRGZmJnVC18pvLtEcGSuSWa1WNDY24tSpUzQrBnzQoTx16hRyc3OhUqkgEolQXl4OiUQCk8kElUqF/fv3J32cL7/8ckxCxufzl6Qscl+31PrggsfjQa1Ww2g04sUXX0RzczPsdjt4PB7EYjFSU1NxxRVXXJQ5/MViueQiKysLFRUV0Ov1camuwOI9kcx+t1RX7lKAu48ulYAB8W1d2H4QL8ZItkjPnau0WCwYGxvD3Nwc7WWPPPLIRzKGWwnWk7J1JMSlUu7jvu93vvOdhJ/x16w0XU5Y7ntgCa7JZMLw8DCCwSAFeVzqYLyZhmSvqcFgwKlTp5asIMrlcuj1esjl8ojqPpOzZwP8o6Ojq+7YsU1/bGwMDocDNpstoiPH/tPpdHA4HFAqlXC73UQptFqtmJ6exsDAACorK5GdnU2iBV1dXVhYWMA111yDb33rW6tab0ylilGlRCLRqmwAElV0E60F5vdktVpRWVm5rKqVxWKB2WyG2+2m4OPgwYNJBQ6s82IymSJkyleCRH5cNTU1cTuGyfxsqSKOzWaDWCwmmk50p4z7fm63GwcPHoRAIIhIiOvr68nHq66ubsnvY6XX4qWXXkJXVxcmJiYQDAYhFouRk5OTsCodXQl/4IEHyFA+UWKtUqkoIZHJZNi/fz/0ej2OHTuGubm5hME3twjDTciYCAcrdsTD7OwsiQ9dc801uOaaa+j4WJcb+OD702q12LdvH77//e8TrS4QCEAqlUYIrEgkEni9XlKBjO6U5+bmoq6uDps2baLnR05ODk6cOIGWlhZMTU0hLS0NVVVVOHHiBMbGxmCz2SJm3S4GifZcrVaLw4cPo6enB1arFU6nM+a6h0IhzM3NQSaT4Z/+6Z+oUGQ0Gpc0740Hg8FA9wgDE0dJ1AFj4PF4pMS5HPh8PjIzMyGTydDU1ITm5maST09NTUVJSQlKSkoSzo5/mEjE2GDFS5lMhurqavT19cVNzEQiUVL73Ycxl5+omBUtshGdgF0qdWbuHuxwODA2NoZwOIxAIACbzXZZzNVd7lhPytYRF5dSuS/6fRPx+D/sStPliGS/B61Wi29/+9s4cOAABgYGYLPZIih5bKbh1KlTNEeykg1yYmICvb29CX/P5/NRUVGBrq4ulJeXR1T32UbNFPusVisFgyvt2DHxBgBU6eWKOLBklAU+fr8feXl5EAgERLkLBoOwWCzo7e1FSkoKampqYDAYYDQaydtstTNmZrMZKSkpkMlkZKCabEclujMdz0A73lpgilasi7OcqAULPpRKJXw+H4l2JCvYwa6xSCSC0WiEVCpdUhJ+KZoh148LWAyY4lWyk/lZvKCDHQ+j1Mjl8ojEgBvUcI2/ucfEkkC2F7GEV6fT0ftyu7WJrtlS1MiZmRnMzs6SaXNOTg5uuummhB5O3IDPYrHA5XLhM5/5TMKAV6vVYseOHTh58iT8fj8+9rGP4bbbboPRaMSpU6eS7oYAH8x5qdVqpKWlYWxsjOa2FhYWqCvDZPELCgoi1q1Go6FrGZ0c79mzB++++25EMMzj8SKu73vvvYfe3l6aiWP0uOiqP7ve+/bto/diwkhisZiOJREuJmDl7rksIQOAt99+GyMjI7R3RSe53H9PTk4iEAjglltuofdcaULT2NgYMbPG4/GQnZ0dd04sGmKxGEKhEAqFImFywhJyiUSC8vJySkSZsbhIJEJFRQUJQ/w1n9/R+2f0teTSv7OysqBSqfDuu+/S/c7j8Yg22tTUBIPBsOQ5rbUk/lJJWHQxK57IBhD5jFlJjJfsa7n3ILO14PF4SElJQXV19WUze3w5Yz0pW0dcXKoqz0re91JXmv4WsJJroNVq8ZnPfAavvPIKZmdnY2Sit2zZgjfeeIOqt8l2NxoaGvCb3/wm4UNcJBLhK1/5Ck6fPk1KaNz3Zhs1o2ex5CzZjl10cMR8jZhqmt/vh06ng9FoRENDA3UcPB4PxGIxRCIRNmzYgNzcXAwODsLhcCAQCESYtKampkIoFBJdK1kYDAacOHECIyMjEAqFeOuttyg5yszMXHXlPV6CwTXQBj5IFo4dOwaTyUTCEMt9JptbSk1NRWlpaYTRbzJiMGxNGgwGeL1eGAwGZGRkRKzNRJ2waJoh8+MqLi6mQCmeOle8ACMZM28g1puIWzDgvpYb0Dc0NETM/kTT0Q4dOoTu7m709PTA4/HAYrFE+N8lQ8lkn8XWpEQiIXlquVwOtVqdMMk1m82Qy+VQKBTo7u5Gf38/zp8/jx/+8IcJqYF1dXUwmUyw2Wz0fbGOzUqQkpKCrKwsXHPNNZDL5SgoKIDT6cQNN9yAqqoq/Pa3v8XIyAjR7JifGVu3jOI7ODgIYFEZkSUjHR0d6Ovri6DbLSwsRCT0hw4dgtPppMRmcnIy4jzjXW/283gUVzajplKpVhWwJoJGo4mYI+vt7UV/f3/C18ejhba2tq7oM7kwGAw4duwYzayxpILP55PoDRdMZZHP50OtVpMMfmpqKiYnJym5U6lU2LJlC4aHh2E0Gkl1c2hoCAUFBTh79iyCwSCEQiHUajX27t1LieVfE0s9S6Pp3/X19cjJycFrr72Gc+fOgc/nw+fz0T7y+9///qJZFStBvHnZ6HUcr0iVzD6+1rEY9x4UCAQoKCjADTfcgPr6+o9c/LYarCdl64iLS6UctJL3ZcEjn8+/bM0XLzWSvV4sCNRoNDT7Ei1g4ff7kZubC7PZDI/HgwsXLixbeW1oaMDXv/71JRW6SkpK8IlPfAIXLlzA7Ows0tLSYuhz7AGh1WoTqpDFq0onCo6kUil8Ph9mZ2cxNTWF1tZWVFRUQCAQwGq1QiQSoaCgAC6XCwqFAnK5HFu3bkVGRgaGhoZgs9mQl5cHk8mEEydOAFhMzPx+P9LS0pJ6eBgMBjz66KNobGykBzabwRCLxZDL5RdF1Yl+qCaaO9Pr9RgfH4dcLk84r8M95tdeew0DAwPkobZr1y4Ai4qCyYjBRFOzcnNzI9Q9ASTshMWjGUbPb7FzX+paJPpZPCQjgBL9fvGMtLnnbLFYYDKZ4HK54Pf7IwyMuecfL4Bi82vse3O73fB6vUQTCwaDMJlMaGlpoUQEQMx7M0VaoVBI3l3Rhq5caLVatLS0YHR0lPZT5i24XNeEQSQSoaqqCnfccQeJLkVfp2gj8Wip+C1btkCv18PhcMDtdiMtLY26LAUFBbBarRHzTswQm3Vo2bpighfRfklLBZCJKK7Rps9rVRDkFqTeeeedFf0tn8+PMIZeKXQ6HUZGRkiQJyUlBWKxGLOzszGUxNTUVFK3zM7ORllZGZqbmzE8PIzy8nIUFBRQ18jhcKClpQU8Ho++p1AohJmZGbzyyisAQHNkAoFgxdTmZLHSTuZSz1L2fXOVazWaRWsF7ucdO3YMr7/+OoxGI0KhEE6dOgWtVou777475vMYI2EtJPHjFROi1/FKpeZXEoutNB6cmFhU1+7p6aHC0zqSw3pSto64iFep/zDflwWPg4ODEZ48HzVoNMvP1UUnLkzAwmaz0cMAAMnl2+12mM1m/PrXv6agOBGam5uXVde78cYbIRaLMTU1BYfDgampqSXlcKNVyLjJRbT0N9vch4eHYbFYcOjQIVgsFthsNng8Hvj9flIRy8rKgkajQWVlJeRyOTIyMqDT6WAwGJCSkoKysjK43W54PB6UlpYiKysLZ8+epdkHpnCmVCqXnckCFoOes2fPUlWfdZxkMhmys7Oxb9++uJ0jJiuuVqtXROnhBnjsPWdmZjA5OQmXy0VKaez37Ppx7zOmIsjoRZ2dnXC5XEhPT4fP58PU1NSyFgHcNVlQUAC/3x+h7slNQqI7YYlohpcKyQqgxDtH7uuiKcBjY2MIBAJQKBTw+/1QKpUR99pSAZTVaqVursPhoMSOqQxKJBJ4PB6Mj4/jwIEDGBwchNvtRmVlJT71qU9FKNJu3boVQ0NDABY7TgUFBSu6PlqtFvfffz8eeOCBpNa8UCjEpz71qYggdLlkOd733d3djcbGRng8HshkMmg0GtTU1MDv90OtVkMgEFDnaGFhARaLha6tTCaDQqGARCKJ8EJjiBdAcgP46GOZmIg1AV/LoiT7jJVQRJVKJe655x588YtfXPXnMusBptiYkpICp9MZk5CJxWKalbz77rtpPs9gMNAeLZPJ0NbWBp/Ph3A4jFAoBB6PBz6fH/ezFxYW4PP5YDAY8N3vfhfHjx/HV7/61TWbJ9PpdDhw4ABsNhuqq6vJjHw5JJonS1Zd8KabbsKZM2cwMDBACeqxY8doxpSLaLXgi0lOowtLiZKwleylK92HV6KezMSgWJG0s7MTJ06cIJbAOhJjPSlbR0IkW4m+FO/LgkfWoTGZTB9J+mIyc3Xxqmi1tbVUTW9sbMSZM2eIguHz+eB2uzE9PY3nn38+gnYVjWuvvRa/+93vYtS7GFJTU7F582Z0d3fDYDDQ4Hp3d3fCB3D0w4AlXu3t7RCJRHC73WSSOzg4SGa3LMjweDyUPAmFQvh8PpICv/XWW0l5js3L2O12DA4OQqvVYmZmhmYetm3bhjNnziAUChGVUSAQRMypLAehMHILFQgEUKlU2LhxI9Eq2cyXyWTC+Pg45ufnyagzPT0d9957L+64446kPg/4wEBbLpcTzc3pdMJqtaK9vR0TExM4fPgwiZywRDcnJwdms5lmJlwuF0QiEaxWK+RyOUKhUFyLgHgzYdw1qdFo0NfXB5PJhOrq6pgkJFEn7FInYxMTE+RHl8i7bSXgUoC9Xi98Ph9ycnLA5/NRVFSUcN6NG0CJxWK89tprRHFNT0+HQCCAzWYj0RRgsdPQ29sLr9cLu92OcDgMm80GHo+HkpISSnJrampgs9lw8uRJKBQK6HS6hPdzosp9SUkJrr76arS0tCyryKdUKklIaKUBIPf1arUacrkcbrcbCwsLmJiYgM1mo2KKTCaD3W4nARFGx66srERJSQlUKhWqq6tRX18f97O4VFRGm+baH3DnmFnAq1AoKHhe68KBWCyGVCpN6rUZGRl44YUXLjqBsdlslEQtLCzAarXGTQz5fD4CgUAE7Tz6/IFFk+vW1lZ6FiylwsvATKfffvttNDc3Izc3F7W1tbjnnntWfX7M3uL06dPw+XwYGxtDVS1fsoIAACAASURBVFXVkgH/UvNkbK+It09FQ6NZVAidnp5GX18fwuEwJicn4xaxWPEqugu70rW0VGFpteuSu6cv58233CxePGg0i2JQbW1t8Hg8GB0dxQsvvEBJ3ToSYz0pW8dF4VIpNF5qCeq/FSRDo4lHydFqtdDpdDhy5AhOnTpF9CSJRELzVIFAgIxIE313e/bswec+9zm8/PLLMQFbamoqbr75Zmi1Wjz33HPweDwIhULweDwYGRlZ8ryigzQmXe71ejE4OIhnnnmGEnIWlIrFYtjtdohEImRnZ6OkpISoRlNTUxAIBNDpdES9m5iYQFpaGkZGRsDj8XD8+HF6n9zcXJSWluKaa65BS0sL3G43zZoFg8GIOZVE0Gq12LZtG9577z0KVPh8PpxOJ3p7e2GxWBAIBCASiWCz2RAMBmG32+FwOEhgw2g04uGHHwaAhIkZo21ZrVaMjIzg9OnTMJlMyMrKws6dO7F9+3ZYLBYYjUb4fD5YLBaEw2GqaMvlckxOTqK4uJiqtnv37kVaWhp0Oh29V1VVFZn7sqDdaDRSUMHmiIBIpcKuri709/cjGAxS12OlVJq1AldanvmRyeXyiE7dxYBRgKenpyngrK6uxvbt2yNEaxLNu7W2tsLlckEikSA3Nxfbt2/HxMQErSEej4e8vDzMzs6SuiULflkn88Ybb0RlZSXNvnV1dcFut8Pn80XIxkcjWnhALBajtbUVdrsdubm5uOKKKzA7OwufzxfhE8bA4/EQDofR19eHX/7ylzS7uFwgGw/ME2p0dJSMnVlnlc/nIy0tjRJfYDHB+PnPf46dO3eSN9lyMypms5nomjMzM5TwxpslstlsdM9zg+e1Wrt+vz+mgJMI+fn5yMnJuejP5M4KLiwsJOzUeb1e2Gw2lJSURBQtos///vvvx/79+yMMx1cyf+t0OjE0NIShoSGcPHkSL7300qoCdLYveTweBAIBWCwWvP322wlFcdjfJFKzXensoFarxZ133oknn3wyonAQD9HzrCspLF+KwhJ735Wc82qpvKmpqVAqlTAYDAiHw5ibm1uSXr2ORawnZetYNS6VQiPwgZ8SCwITVe1Xe9x/jYBxJeDS3ZKhVcQLAicmJjAwMACXy0VUoFAoBKFQCB6PB6VSmVSgunPnTjQ1NdGDpbKyElu2bEFRURH50jCTWUZpiWcOmwgazQeedf39/ZiensbExAR8Ph898Jg57IYNG1BRUUEBYUNDAyknzs/PQyaTQafT0bVjRrqsA7SwsECzONzk9fjx4zhy5AgAEKUqGWRlZaG8vBxzc3Ow2+1ERfN4PDCbzWTsLRKJkJmZiZSUFEilUszMzFCS63Q6ceDAAVRWViInJ4cSMAAYHx/HqVOnMDs7S1RNj8eDcDhMog21tbXIysqixNBut8Pr9dJ37na7yVuHff8ulwv79u2DXC6PUD5kQgQsaD9w4ADa29sRDAahUqlIEEGpVKK4uBihUAgWiwULCwsU+C2nSraWiCeo0dXVhZmZGUilUlRUVKCurg6ZmZlrcr8XFhYiKysLKSkp8Hq9ABbXQLSKaHRAG30/s25VS0sLfD4fzedIpVI4nU64XK6YIgib29HpdKirq4tQbXS5XFCpVEndz8yW4sUXX6QZOYlEArlcjtraWgwNDUUIaTDw+XzweDyMjIygr68P77//PgQCAYqKipZVn4wG29+5wirA4no3GAwxEv0LCwuYm5uDTqeDXC5f0vaBazEwMzNDFDu1Wp1wloiJ1jAJ75Wor3KLJkycJTpRHR0dxezsbFLvp1AoLpoVwijSQqEwRtUxHnw+H3krJoJWq8Vtt92Gp556ir4bsVgcs07Ys8XtdifsvNpsNvzP//wPnnvuuRWe2Qefyb7XcDiMiYmJJb+zRPTE1SYcNTU1yMzMpAJnouu2WgpjtLfnWhaWVnrOq6XyFhYWoqamBlNTU/D7/UhPT4+Z/1xHLNaTsnWsGpdaHZEb2BgMBrz55psxNJTVUAEuVSK5Vog+xmRpFdG/Y7NjLLFhXj8ikQgAUFFRkZSRp0ajQVpaGhwOB0QiEerq6lBQUBChpBgOh2kmKycnB6WlpSs6Z61WC7PZjLa2NszPz8fMPYRCIZK0r6+vh1arJTVClsCoVCrI5fKINXLrrbcCAEZGRmA2m+kaRCsUvvzyy+Dz+VhYWIBAIEBbWxuuu+46APEFSNjPTSYTpqenqarPvLCqqqogl8vpO2RdJlZYOH36NP74xz8SfctgMODee++FSqWieblwOAyPx0OBDUt6WYDFqo82mw0ajQYZGRmYnZ3FhQsXKGFgf8eCqFAoRJ28//iP/0Bubi6CwSBcLhcJSrAE9vTp06TOx+fzIRQKiXbJxBmuv/56XLhwASaTCVarFT6fL4L6eCmRSJGMux64Yg5rAY1GQ2p9wWCQOoxs3ile4ShaOl+r1aKrqwudnZ1ESWL/MVEji8UCHo8XUYVnczqDg4PQ6XTQarWQy+UIh8MQiURQqVQJZ+aYSMH4+DhGR0cRCATIr4p1t6VSKanzsaSTJdvsvhAIBLDb7dSNFYvFZMbNiiHxxHri/Zy7Z+Xk5ODYsWMwGAxE5YyGQCCgjtNSwk/sucRM7PPz81FdXU1WCPFmidxuN8bHxzE3N4eenh4cOXIkqXVjMBjw+OOPo7m5me7lQCCA4uJi7NmzB3v27KF1mozptlQqRUZGxqoC72eeeQZHjx5FbW0tNm7cCJPJhOzsbOrML0XJDofDkMvlVFBJhMzMTGRkZGBubo66rlxqu1AoRGZmJsLhcMQeFA9TU1MrPkdgsftUVFREhThGv2T3fbz1lqhwudqEw+/3IysrC1NTU/D5fDh69Ghc2jCXwmiz2XDw4EEAWLZbxI2tiouL17ywtJJzXi2VV6PRYPv27VTUq62tXZMO8N871pOyjwAuJcXwUig0RiO68sm4+atJAi91IrkWiD5Gv9+/LO8biP2eu7u7STZeLBZjy5YtuPfee6nCnGyw6vf7IZFIiEb4xBNPoKKigugjgUAAYrEY2dnZUCqVKzI35aK7u5tofvHAkgd2/Nz1x/yngEVvHkaty8rKwq233opDhw5hfn4eAoEgRqFwYmICIpEIfD4ffD4fHo8HjY2NOHfuHIqKipCRkRGTwDc0NODYsWOwWCwQCAREs0pJScG1116Lu+++G36/H2KxOCah1mq1uOWWW7B582Y8+eSTMBgMcLvd1LVgiK5wM0WzhYUFhMNhCAQCpKenR8zSicVivPjii2hqaqIgRSKR4Morr0R+fj7Gxsag1+thtVpht9sxOzuLqqoqhEIhWK1WmEwmjI2NwWAwYHp6mmhsLNiy2Wwwm8004zM2NoZvfOMbyMjIwOnTp+HxeC5aaSwZsCRDr9fH9Tjj+pGt9XGIxWLMzc3Remxvb0d7ezsVULgWACwg+8tf/kLrViwW09wiS6iys7MRCoVgNptJ2Y0ZM0fDbrdT4ssogFarNaFADds/9Xo9JicnyUuMJdqM0sxmNrngdqp5PB510NjnCIVCZGVlAQAOHz4MgUAQ0TVbib+RTCajc4+GVCqFRqOBXC6nxCX6HNnex2iarIOhUqmokBPvc1nA2djYiBdffBFerxdnzpxBY2NjXFW9hoYGNDc349prr4Xf78fp06dJEZKhp6eHRI/6+/sxODiYVFImFApXJAjC8NBDD1HXqb29HXl5eVAoFJBKpdi2bRu2bt2KtrY2NDQ0wOFwxD0W7rpKdM9s2bIFxcXFkEgkCIfDxJBg6yQYDEbsYUvhs5/97IrPE1jc98vKygAAQ0NDsNvt4PF4aGpqgsvlQlNTE0QiEWpqamIKmtwibyLhl2SPgbEw/H4/fD5fBEOD+5ns911dXeDxeHC73XjkkUcSfhbX8oJrFbKWhaXlxMPi/c1KP1+n0+FXv/oVpqamsLCwgKmpqcsy3rrcsJ6U/Z3jUlMMPwwVNZakWK1WBAIBSKXSVUvkf1iJ5MVgNccYr7s2Pj5Oxp6MNseduUoWdrsdvb291L1yOByYmJhAZmYmsrKyEAgEIrpBq32ACIVCqshzZ2m4MJlMCYfRWRDY3d0Ni8WCwcFBktdm1LDs7OyYLhmjWVitVgwNDcHlcmF2dhazs7PQ6/XQaDTYsGED0WNeffVVPPTQQ+SDlp6eTrTNrKwsbN26Namk9I477oDf78djjz0WN5jmJmEikQhXXHEFbrzxRgCL8yICgQAjIyMxs3Q5OTnQarX405/+BLvdjtLSUtx2222UqL/wwguw2+3w+/0IBALo6ekBsDgDEAqFEA6HYbVaqXMSDAaRmpqKXbt24cKFC3A6nbDb7TQTNzk5idtvvx1+vx96vf6ilcaWAzfJYOt9tbLQK/1cZkzucrng8XggFAphMpmoW8A8uLieXD6fjwoXEomExGm4lGKTyUTBSyJIJBLk5+eT0mNjYyMsFguysrJIqCXedefKfWdlZUUYFxcVFWHXrl04efIk+vr64PV6ac0xOiqTNff7/dQNYn5WUqkUOTk5GBgYwPT0NAKBADweDwVfyRbBGhoa8Nhjj8X1TOPz+UR9YoULJvfOBGyin3EsWbXZbMuqqbKA8+zZs1hYWKCk02KxxLz21VdfxcMPPwyPx4PXX38dN9xwQwzVknvd/X4/5ubmYjr/icDureWC14aGBjz33HPU/T9//nzE72dnZyGVSlFYWIi6ujrccsst2L17NwQCAd577z2aiWRghc7lCio5OTn45Cc/SQURp9OJhYWFmK4uFykpKaitrUVPTw+cTif4fD7q6+tXrSzJ3ffb2tpw5MgRGI1GtLe3491336UusNfrpVljbuwTLyZKpugZfQyMcs/mbbnMEe7zUCwW0x4RCARw/vz5hEqE0bTFurq6NS8sJSMethbo6OjA/Pw8vF4veDwexsfHL+lz4e8F60nZ3zn+FjpDy4FLMRkeHobf70/aVycaH1YiuVqsRAmKi+jvuaOjA8DiQzQQCCA1NRVOp5MCmmTP32Aw4Le//W2ELP7CwgKUSiWuueaaixr2j54HMhqNSE9PR0pKCtRqNcxmM80rMZjNZjz33HP47ne/m7DizqqA8/PzmJiYQDgcJhPUeOpPbE3k5eXh6aefxvz8PP0uGAzCaDRCIpGQb9RTTz0Fp9MJYHH+bGpqiqiFNpsN7e3tScvd19XV4eWXX0ZXVxf9TCgUIiUlBSUlJdi8eTMUCgVKSkpifKEmJibw6quvYmxsLGKIXKNZtAUoLCyk3wGgB3F5eTn5bIVCIbhcLnR2diItLQ35+fmYn5+H3+/HzMwMNm3aRNVfs9mMHTt2wOFwYGBggBJ+di+uhdJYMtDpdOjq6oLNZkN+fn6MyMal+EyDwYBf/vKX6OjooCBboVAgJycHubm5RJfdsmULBWChUAgFBQXUyZNIJNixYwf+8Ic/RHSE7XZ7xJqLh4yMDOzYsYNUMwHg//7v/2iOktGC4l13pi7IEki1Wg2hUIiCggLU1NRg586d0Ov1GB0dpQTc5/PRPc+oyTweDx6PhwonIpEIAoEAZ86cgcvlgtfrhUgkwuTkJAVfyRaYDhw4kNDEms/n495778Vtt91GlgTM/whYtOSIfsZptVpSKU22sFVTU4OUlBQEAgGkpKSgpqYm4vcGgwFPPfUU0QAtFgveeecdSrjiJSYmkymm271U4s3n8yEQCJY83oaGBuzfv3/ZNSMUCiNYARMTEzRfKhQK6bj5fD7y8vJQXV29rBfmSy+9hLa2Nuj1eur0MhsQtubjHYdKpYJUKkUgEIBSqcS2bdvivj/3ebDUM4r97OjRo3A4HPB6vfB6vQgGg1TEmpubg1qtjvBIXEmhYDlotVoSmjKbzWhsbMTw8DCp+w4PD6Ovrw+BQIBmfEOhEOx2O5qammKESaK7/8XFxRFG92uFDysm3LJlC2QyGY1LFBUVXbRIyUcB60nZ3zkuZWfoYrtwydIqWdB86NAh9PX1wWazrXgYO/r9LrdkDLi46xn9PTOD1pycHKqkMzW6lczlMWVDLtjcWCJK0GrOVaPRYHR0FAqFAhqNhuZreDxeRCU6GAzirbfeglgsxt133x1BFWPn4vf7kZqaCq/XS90g9reJhu1ZIhNPTYxVz10uF44ePUoP+ejXLCwswOl0oqurCyaTCd3d3ZRcx6MxMmzcuBEzMzPwer3Iz88nmla0eEK8bmiieztaaQ/4QDGRzbu8/vrrFOAzFbPp6WkKvB0OB3g8HsrLy2E0Gslby+v1Ij09nYKwN954A0ajEZWVlTAYDDCZTPB4PDh27FiMAMbFgtkLMIpUPJGNSwGm6MiEI4qLi5GTk4Ndu3aRvxb3u2XJqU6ni1gDBw4cwPnz5yPmbZKRFr/qqqvw0EMPAVi8J48fPw6j0Qi73U6znEygJpG6oF6vJ3XN2tpaXH/99bjpppswMTEBi8VC821KpRIikShiHjUtLQ08Hg8ulwtSqRRqtRp8Ph9ms5lmQFlHjXnXAUsXwVjnsbOzE+3t7QnPPRgMor+/H7fddhv5RLGZJUYbjncfrMRXCVjsFF955ZUYHBxERUUFUlNTI37PlEq5cDgcRO9lhS927qxQw8Dj8SASiZYMTFUqVYS/YTw0Nzcvm5Dx+XykpqZGsAKYd5RUKo34+4WFBWzYsAH79u1b8lnMrEt6enpIGRdYFCbZvXs32traSMGSJZc8Hg8SiQTZ2dlQKBTweDxQKBQRQlDxFFMBLPuMYjO9oVAIwWAwgl0hEAhwww03QCgU0h7IzO3X2oOOy9BgnVymuGsymci8mzsTzISX2HmxuVObzYb5+Xnq/l8KJs9Kz3+14y9stpzNH7JC4TqWxnpS9neORA/FtZgzu5iKy0oTEI1Gg4qKCqq6/D3iYq5n9PfMhVwuR319Pfx+P1X0VqK8pNFooNfrIyrCJpNp2aHwpdYY91zdbjcGBwcp0FapVOTdVFFRAYfDgQsXLkRIg3d3d+PIkSM4ffo0XC4XdXQ0Gg0p5KWlpdHcG7CYeDBlx3jnzOYEohEKhRAIBKjynmiAXSAQUKWdBcEHDx7EzMwMxsfHUVRUhKuuuioiSO/o6IDb7UZBQQHUajXuvPPOhBS8eLOGyXZ9NRpNjHdYb28vzp07B2AxiCopKUF3dzdd51AohLm5OWzfvh1erxd6vT7Ci47P50MsFsPpdOL9999Hb28vBgcH4fP5aD5Jr9cnbeyaDFggFggEIsRTPiwwH7pt27bB7/ejvb0dBoOBqEitra0wm83w+XwkHc5mQltbW2Gz2ZKmsnHBui0ajQZGoxHnz5+Hw+GgYJRrLBtPXXBsbIwEH1JSUiAWiymZNRqNGBkZgcfjgUAgQEZGBsLhMKmmsfmcf/iHf8Dk5CS2bNlCKqGNjY3o6uqC2+2GQCBAaWkpampqIo4hXhGMdR5bWlowPT29rD/aO++8Q1QupVJJFhrMMB5ARBd8pb5KwAc0ZjZDGr2PjoyMxBUgYmuirq4O27dvx9tvvw2bzUaeiowqzLqLSyVlbA5qKVx77bV49tlnl3yNQCCAQqGIOHeNZtE7inXvGIWRx+NhdnZ22T2ksLCQvAwZhEIhrrjiCtxzzz1Qq9U4f/48wuEwduzYgfHxcUxNTaGiogJbt27F2bNnIZfLkZaWFlNoYvPiEomEEjZGCY7H7DAYDBgYGMD4+DiCwWBM8qtSqXDzzTfTOuWa2991111rzpbhPn/FYnGENyVL0hYWFjA/P0+emmx96XQ6/OxnP4Ner0c4HMYVV1wR0/1fayRbsLiYQjHz6OTxeBAIBKisrLwsi+GXG9aTso8Aoh+KazVndjEVp5UmIEwZrra2FiaTKUasIVlcznL4F1vB437Pra2t9ECQy+UUGLKKHvczlromGo0G999/P2w2G/r7++Hz+UgFbSk0NDTg+eefTzhwzT3XUChEVXq1Wo1du3ahv78fLpcLJSUlqKqqwv/+7/9ifHwcwGLAIZFI0NjYiImJCSwsLFCgyY6ZzZQwGXEmXTwzM5NQhUyj0WD//v144IEHIobVmfw881vhQiwWQ6FQQCgUktx5IBBAfn4+JBIJZmZmcO7cOfKYGhkZQWtrKxQKBebn55GWlob5+XkUFBSQMh/7DriKfkD89ZGo6zsxEWkUHC+Bu/HGG9HT00OBenV1NYLBIIaHhynpUqvVyMnJQWtrK6krMnADNKPRCKPRSLL9wWAQQ0ND8Hg8a5o4icVi+j4DgcAlv4fZvcFdUyUlJdi6dWtEcYMFfqOjo5DL5RgfH6eqOQvSzGYzMjIyIBAIVnwcJ0+exNe+9jV8//vfx4EDB9DT0wO/30+03A0bNqC6uhoVFRURx84EA7KysmC1WsHj8SCVSrF58+aIAJf55gkEAjKE1+v1sNlsUKlUuPbaa2OSBY1GQ5YS7N/J0pgnJiYwOjpKnQ42R5oIVqsV7777Lv07IyMDKpUKlZWV1C1nHRYAST1bove95ajt7PpFQyQS4R//8R9xzz33QKPRkN+gTqdDR0cHiU9MT0/D5XJBKBQmTEIHBweXNeZmHfKlBDX4fD727t0btzOZmpqK6upqUm9l9MPlOtsajQabN29GS0sL/H4/+Hw+srOz8aUvfSmCysed72VJysGDB+F2u+Hz+SJm/FgcwDpvarUaJSUlABDD7GDiPRqNhvz5WOdWJBLBYrFQ8pudnY3u7m76HJfLFbEetm/fvuZ7B3cvjjaM5yZpEomEVI+ZEXZPTw/cbjcJN12qhGylRtAXS3VkXqhs9nody2M9KfsIYq04xcs9xJbCShKQtRp+vZzl8Fc7S5bofex2O2ZmZiICQyB+R225a6LVavHYY4/hwIED6OzshNfrXZICqtPp8Nhjj1HCNDIygsHBQfJFY5/BrSy+9tprsFgs9IAdGRkhtbiCggJ88pOfxIkTJ+B0OlFZWYkrr7wShw8fJr8aPp8fkSiymRI+n08CMcBiQreUMfSePXuQmZmJJ598MkLmmpuMAIuBWHl5OVENp6amSNWsqKgI27dvR0FBAf7zP/8Tfr+fkrmpqSmYTCakpKSQYEN5eXlEZTTROl3J/RZNX+S+niV7KpWKRCfY9SkuLqYAmak/tra2kmhBNNj8BjtHHo+HtLQ08t5aaxgMBlITzc3NvaQzClxBEYlEgltvvRW33nor3TeMrhQKhTAyMkJBl0wmQ2ZmJmQyGdLS0tDU1ASLxQKXy4VQKASVSgWPx7OiYwmFQmhpacH9999Pim8swWKd4fHxcZw7dw4tLS2or6+PSFb27t2L5uZmdHZ2QiwWQ6/X480334RWq4XVaqVhfKFQiM2bN0OlUkEikUAmkyE9PT2h7+BqqeCFhYUoKSmhxCIlJYVUQeMhHA7j5MmTCAQCkMvlqKioQG5uLtLT06HT6UiApq+vD9XV1cv6OsWzKeju7kZmZiZqampiCiI6nS5GTIOhuLiYEjIuuD6Ig4ODcDgcZB+SCFarNann8cc//nG8/vrrS75GoVBE/Jsr+FJcXIx9+/bhzJkzVBRrbW2FXq9PKNZkMBig1+uRkpICgUCAgoIC7N+/n5J17lpgCemWLVvg9/ths9mI6sn80LhFg4qKigjFVHa8jNnR3d0Nh8OBvr4+aDQaMvvm8XjQaDRQqVTIysrCuXPnYDabYbfb8bvf/Q5FRUVE318rn69kkShJ4+7FbG24XC4IBALk5+cnZVOzGsRTrE2GKbPaQrFGo6G5U5FIdNnEWpc71pOyjyAuBad6NX+XbIAZ7dmx2uHXy1X0ZK2SRe77hEIh6sKIRKIIqmF0R417TRKJgGi1Wtx33334r//6L1JaO3DgAO67776YaltTUxMMBgMFzCMjI7BarUhLS4ugGXL5+KySqdfr0dnZCYvFQoHrE088gYcffjjCSNxoNOLs2bOkgFdTUwOxWIzW1lZ0dHSgvb0dW7duxfXXXw+z2Yyuri6Ew2EadF8KOTk5qKurw/nz5yM8eLjIyMjAhg0bUFxcjN7eXvh8PvKKysrKgkgkwltvvQWr1RrTAWCD9hKJBOXl5SgrK4uojC61Tld7vwGx64ypzIXDYfLaYT5LwGIgHAwGSSAg0eyT3++HWq1GOBxGQUEBPvaxj9E8BZNLX676n+zxt7S0wGazkRT7xexd0aa/3GCQUUu7u7sjRCW+853v0JrVaDRE1zx//jxdN7FYjJycHAgEAvJ7s9lsNOdTWlqK+fn5hGtrKUxPT+Paa6+lOa6KigpotVoIhUK88cYb1B3PyMiI2DMrKiqgVqtJWZQ79yiTycgnLTs7GyqVCv39/eS5V15eTtdrrfZLjWbRPJrd0xqNBi+++CKOHj0aV8RJIBDA4/HAZDJFeDYBQEtLC5xOJ2w2G13TvXv3oqKiIu6zhXUnmH/S6OgoDh8+THL8GRkZ2LZtG9FjJycn8cILLxDtTyKR0AxpRkYGHnzwwYTXhc30MbVN9l8icGltS+Gaa67BG2+8kfC9GLWWm1xFP/fr6+tRX1+PY8eOobW1FZOTkxgeHsbo6CjR/LjndeLECepapaWl4c4774xLtdTpdPje976HsbExBINB5OTkkBm93+9HZmYmTp8+jTNnzkAkEqGoqChhodVsNgNYnN2bm5uD3W5HIBCg/y8Wi+Hz+UhlMzs7m+Ycg8EgAoEAZDIZtFotgsFg0hLwlwKJ9m4mnJOamorPfvazq57TXgqJFGtX4lEmFotjihXLfSbrjrrd7mXHHdaxiPWk7COIi+lwXQxYEAR84JGVzGevVRJ5ucrhr1WyyH2frKwsiMViCIVC2Gy2hP4z0V5fS4mAGAwGDA8Pw2g0kt/Kz372M3z729+O2GwzMzPB5/Pp3+FwGPPz82SI3NbWFqGwZTabMTExQd5P0V0vp9OJtrY2PP744/QzjUaDH/3oR2hqakJGRgY2bdqEr3/96xgeHqbk4a233sL3vvc9aDQaSpy8Xi9efvllXH311XEfE/C9HQAAIABJREFUEKyCbjAYlhRgsNls+Mtf/oL3338fMpkMMpkMtbW10Gq1GBgYwLFjxzA8PAyn0xlDzRKJRFCr1bj99tuxbdu2mHvwYtYp65SazWa4XC7YbDaMjo5Cp9MhMzMzYp0xIRVgMTDQ6/XIyMig8w6HwxgfH48wrI6HcDiM4uJiZGVlkTgJu5bcWQ7uLB2jI7MEB1jeN49RMhnNdbW0SLYPHTlyBDqdDi6XC5mZmdi6dStRDycnJ5GVlQWTyQSBQIBAIEDCHey8+vr6MDU1BaVSidLSUlRUVMDr9aKkpAT19fXo6OigYDcQCFACXFtbi/z8fBw+fDhmRonH40Emk9G9Eg1mTbBr1y4AQH9/P8bHx+FwOKjgEAgEkJGRQfcQW0OFhYXo7u6m+R0298iURNPS0lBdXQ2z2QyTyQSlUknm0tx5nLVMzNj3r9PpaK1FQyQSISMjA+np6URb5x4DM/Nm5z8+Po6mpqa4Zr7AB90Jm81GRQg2JwksUh+bmpqgVCpx5swZjIyMUKdcKBQiJycHN9xwA/Ly8rBr164lA01ud4p5py0FuVyO9957jyiQibB7927s2LEDzc3NcX8fDAZx+vRpSCSSZTvtN910E0wmE1wuF62LaJVeo9GI119/nUSXxGIxdU+jn+uvvPIKOjs76VzZ+gIWv8sLFy7QHCL7m1tvvTXhiIXD4UBaWholxVNTUwiHw7Db7eDz+Zifn4dYLMbU1BQyMzNpnk4ul6O8vBxyuRwDAwNwuVwkAc++m6UEmD4MMKaFXC6nLu2lAHcd5ubmrmhmjb1mpYVjq9VKvoZOpzNhF3wdkVhPyj6iuJiK+2rAHewGFh+kyQoArFUS+ddKRpfDpUo6NRoNvF4vGf3GU6vkXpOlREB0Oh2efPJJmusCFjfd4eFhHDhwAJ/5zGcoWNq9ezcaGhpw+vRp+Hw+Cv4DgQBmZmbw/PPPo6WlBdnZ2UT/YkavTGZdJBLRw1wsFtOcAReMEgIAO3bsoONmCIVC+M1vfoO7774b77zzDoDF5MNms6GjoyMmmDIYDGT0a7fb4fF4YgycGXw+H+bm5iJmyVgFX6fTwWazQSwWIy0tjc5HKpVCqVRCoVCguroaO3fujNu1W+06jab5AoDX68XMzAxR2rjrQ6vV4uTJk0Qh8/v9SElJgUQioSCUnaNAIEiYmAUCAVx11VWoq6uLoUmyDqjFYkFHRwdEIhEFgIwuxzy8amtrUVdXFzObxJ1NkcvlNHeSbNU1WmqbyXp3dnZSB9Dr9UIikSA1NRUDAwPweDwwGo0oKChAfn4+FAoFJScmkwkTExOw2Wz0/WVlZaG+vp4SzJycHAp2ZTIZHA4HbDYbzeCxdcOl1aanp+Nf/uVfsHHjRoyMjNA92d7eTqqhBoMBf/jDH7Bjxw7U1NTQ9VUoFCQ0I5PJsGnTJmzatImKFtx1xZJKk8lEnTWm3MgogDMzMzQX6fP51oxdED3HxdZsc3Mz+vv74fV66Z5jyn1bt27Fpz/9aaSnp8ckZOx6V1dXAwDGxsbg8/nQ09ODgwcPJrRlEIlENNvF4/FiutlM4t9kMkXco1KpFNdeey3279+/7HWInunr6elZ1hhar9fjwQcfhM1mIx8vLhWQy3b4xS9+gTvvvJMEZaIxMzODmZmZZTvt0esiepYLWLyuY2NjCIfDRDfUarUwGAx4/PHH0dTUBL/fj/z8/IjCWDSYCBIXnZ2dZOHC1kV0gVGtViMjIwMejwepqakwGo3koweAEkX2fTO6cWpqasxzTafTobu7O6JrVFZWFjE6ACwtyb9W8Pv9KCoqQiAQuGSS8fHMqFc6s7aawrFarYZarUYgEKD/v47lsZ6UreNDAXewGwBGR0dXrC64Fpvjh52MJouVyjfHQ7x5sf7+fno4J+qWcSvWXBEQRgdkg9pDQ0MRf8fj8RAMBjE4OIiDBw/i+PHj+PznPw+tVouf/OQnOHHiBLq6ujA8PIyRkREaxJ6dncX8/DzRgFJTU1FYWIiysjLw+Xxs2LABZWVlOH78OCwWC2pra1FfX5/wvJ955pmYhIzBbrfTfAx74PH5fBQUFMS8dmJiAj6fjwQymNR1MBiMSUi4XSaJRAK32w2xWBzTeayqqgKAuPS4hoYGmldi140btDJhltbW1hjlseiOM3vf7u5ukvRmMwws+Pb7/dizZw+ampqoKvvxj38chw8fJjrWxo0bIZPJ0NPTQ0PabM6Px+PB4XDEBJZMVSzagJXNtfH5fIyMjMDv91Oiy5I+1kUUCARkXMvMyFmgxJ2NMplMsNlscLlcMBqNywZO0ZTNmpoazMzMYHp6OmIOLhwOw+FwQKlUQigU0pzN7OwsamtrqWvIBGm4a4nR3HJycuhYo1Xe2PfNjkMmk5FSp0Qiwd69eyMCfYPBgAcffBBnz56NoPOFw2H09PSgrKwMNTU1EWI5paWlMBgMKCoqIqonK4INDAxQEYz9x7zB5ufnaW7SYDBAJpOhoKAA27dvx5YtW9DQ0BAjK76SQsFS86tszQ4MDNBcm1QqhUKhgEgkQlVVFb75zW/GTcCjixB79+5FU1MTrV2fzxf3GaPValFbW0sUUqFQCJFIFNG1ZPe3SCSi+18kEmHPnj341re+lVRCxj22/Px8XLhwYdmkDFgs+DzxxBPQ6/UIBoP485//jHA4jJycHPzgBz/Anj176Lr+27/9G5lZR4PP5ydd5GNrgu0ZZrMZR44cwYULF2gWzOfzITU1FZWVlbjvvvug0Wjw5ptv4sSJE/Rcn5ubW/azouH1evHMM8/g6quvpk57dIFRq9Xi6NGjRItmZulpaWkksV9dXU0qw9H7Jfe5BiwmrMxTjN1fBw8ehEAgiJDk5865XYq4gc2Dulyui6Zjx8NazePHKxwvJ5qm1WpJPIqrkrqOpbGelK3jQ0H0YHdJSUnMBrTcTZ7o95ezouJyWKka0nKITjqZYpzVaqWgd6lrxJJDjUZDQWQoFKKZGC4UCgWZDTO5dLfbjV27dkGtVmP37t246667YDAY8N///d/485//DLvdTqp9LJBg/8sk6cvKylBaWopHHnmE6G5LcdkbGhoSns/27duhVquRlpZGD99wOIxDhw4hMzMzIhESi8WQyWQIhUIIh8Pg8/lIS0sDn8+Hy+WCx+NBMBik+RupVAoAFOR/61vfwnXXXYe9e/dSognEVlxZMNPW1obh4WGkpKREnAvXhyzah81oNOLAgQMYHByk4Wnm1TU3NwedTgePxwO/348dO3aQSiFTnXz33XcpUO/v70dqaioZ2obDYWg0Gtxzzz348Y9/TBQkqVSKqqoqOJ1O9Pf3x52Famtrw0svvUSGqCxxZB0HZisglUqpI8XtlLFumdlsJnN4mUyGjo4OqtDy+XxMTU3B7XZjbm6OqujRfm5cRFd4WSKTl5cXEUAKhUJK0ORyObxeL9xuNzweD3p7ewEsBmnhcBgVFRUoLS2lThtTgYxXTeaqvDF1OrFYjO7ublRWVsJut6Ompiam89LY2Ijm5ua49gtSqZT2Cq7CW0NDA8m5A0hYBDMYDGhqasLg4CAsFgspAYZCIczOziIzMzPC/y2RrPhKEhNuQsztWMzNzWFkZISSFab8eNNNNy1rwM693haLBX/84x+RmpqK1NRU5OXlISsrK24SqdFoUFdXB5PJRB6MrJvp9/shk8lIqGbjxo3g8/mYnZ1FVVVVQtP6pY4tKysLoVCIxFmiu0jxfsaEKrjFIKfTiSeffBIAcOjQIRiNRhgMhoSiMQqFYtV0U7FYTJL2TGlRrVYjOzsb+/bto+eU1Wolk/GLwfz8PPr6+shnb/v27TFsgZqaGpp/4/P5KCoqws6dO7Fr164lKYjxipVcT7Hc3FzqCLPONgDqbPf19UUUHP+WwNbh4OAgVCrVqinf0dfwyJEj+PWvfw2PxwO1Wo2vfe1rMZRbo9GI4eFhok0+88wzeOihh/7mYrQPG+tJ2d8Y/lYTEI0mcrA7Hg1lKc4yoz8ySWpW9V2JSMbleO0utfgIUxXT6/VE8YiH6OsIICKoEIvFyM7OhsFgQCgUglwux9atW+n9e3p64PV60dPTg+7ubqowfvnLXwaw+JBnNCquwSqwSGmxWq1ISUnB7OxsxN9Hq8ixhwr3Ibxnzx6cPXs27nmNjo7C6XRCIpFQ18tut6O1tRUulwv79u2DTqej9x8cHITT6aQOhlAoRHl5OSWTs7OzJCjAEjefz4dgMAi3240//elP6O/vx7333ouzZ89S4FlRUUEVdp1Oh1deeQUDAwMU/Pl8PjQ1NaGrq4u499yExO1247nnnkNHRweGh4fhcrnA5/MxNDQEPp+PxsZGyOVyzM/P0/fc0tICj8cDHo+H3NxcTE5OxgTqSqUygqJps9mQk5ODnTt3wufzwW63o7i4GBs2bEBzc3OMCiXD0NAQfvKTn6C5uRlf/OIXSbJ6ZmaGkq/MzEwUFRXh9ttvjzAXNhgMsFqt6O/vJzodG0TfsmULHa/D4cDs7Cz5p9ntdgwODsLj8cTcN9xEO7rirtVqkZeXB4PBQPQ1pVJJiUFOTg5JxC8sLNCx+nw+6qps2rQJfX19mJubg9lsxoULF1BXV7ckDZkd36OPPoq2tjbweDwUFxfj5ptvjrnnW1tb4yZkfD4fN998c8Q+F530sRkgqVSKtLQ0ov+y6jajb5rNZni9XiwsLNAaEAgEUCqVEcEbSzijZcWX26cSJcTAB/OrzLZCIpEgLS0NlZWVCTtjib7b9vZ2DA4OAlgM7llHl80QxUsitVotmYH7/X5oNBpceeWVKCsrQ3NzM/r6+pCVlUWB+EqfG2KxmLzjWMDPrDOi5whFIhF5zXERjy48NDSEhx9+GCaTifadRLjyyitX9CyJFopiBRJ2LMxM/ujRowAWE2gunT1ZCIVCbNy4EQ6Hg5LilJSUuD57ACI64lxKcG5uLm6//fakRyC4r+N2sFnhj9sRBhb3x/HxcSo4AkhIh10toq1L1vr5z9gKXBr7art+7Bq++uqr+NGPfkTr1Waz4cEHHwSAiMSso6MDJpOJOuDvv/9+3BGKdURizZKy0dFRPPfcczhz5gyMRiPEYjEqKirw6U9/GnfccQeEwtiPam5uxrPPPovOzk4aFq+vr8ddd92V0MvF4XDgwIEDOH78OIxGI9LS0rB9+3Z8+ctfjvBo+XvE5SzpngyWog4ul5zodDq89957mJmZwcDAAKqqqmhjXUo9kPsAj+46XA7X7lKIj0Qnn3v27MHBgwdhs9lw8OBBAIgJepYLoFi3SqlUIhwOE33q9ttvh9FoJHEMJg3vcDjQ0dFBAhMulwtKpRJpaWnQ6/URXQpGEwQWu2Yejwdutxujo6OUmAwODlLVktHbGG3s6quvRmlpKUZGRmKuxejoKH7/+98jPT2dxC+Y8EhnZyceeOABqFQqpKSkICUlBaOjoxGBSGZmJvH9RSIR+vv7KahyOp0x3cNQKITx8XH89Kc/hc/ng8PhAJ/Px/j4OLZu3Yrdu3fjwIEDOHfuHLxeL0QiEfLz80nSnOs9JBQKIZfLoVAo0NPTg7a2NrhcLgSDQQiFQupuMTUyi8VCD0qHw4GzZ89SwAuAzHZVKhX8fj+ysrIiRFkWFhbQ1taGRx99lNQvs7KykJGRgf7+foyNjSWkX7Fu6rvvvov8/HxYrVaaHWFdSrlcTh5F3MCAdSvVajXm5uZIOY2JKLBkw26344UXXkAoFCIRk3A4jK6urgjvueh9Mp7NBI/Hw/z8PPx+P3WeCgoKSC0yIyMDQ0NDsNvtZPwKfEBXZMpufr+fOpB1dXXLzgI2NjaiqamJugsOhwNPP/00dW2BxW4p15OLux62bt2acJ6JS0NuaGggw9ZPfOITNKvH1FbtdjutnVAoBJFIRIkRd3aJge1TbAY0UXGHXf94CTE75pqaGlitVhw7dgxTU1NYWFig7vhy3Yh43y3b0xgWFhYwPj4Ok8mE1NRUALHPE41GQwIher2eAuOqqiqUlZVR0L8SQSruMTLFRTbbxLrGBoMhIimTSCSQy+Ww2+1UvAiHwwnvM9axX86APCsrC/fcc0/SxwxEPgOEQiEpHzIEAgH09/fDaDTiwoULKCgowODg4IrN0DUaDb761a8ShdZms6GwsBBbt26FzWbDoUOHUFNTA51OFzP3tVbGz/G+U25RA1iMJQ4fPkyegInosBeDSy0+xtb5Stgy0YhWqX3yySdj1qfP58Ozzz6L6667jt57y5YtSE1NhdVqJfrvOpbHmiRlTU1N2L9/PzweD8RiMUn+dnR0oKOjA++88w6eeeaZiI38D3/4A773ve8BWFyYMpkMAwMD+PGPf4z33nsPTz/9dEwi53A48M///M8YGBhASkoKKisrMT09jTfffBPvvPMOnn76aezYsWMtTumyxOUq6b5acJOH5TYnq9UKs9lMClvHjh3D7t27l1QP5NK/2IxIsv4cHybWYp6MIV7izh4ovb29tDFGV/zicfiZx05LSwsuXLhAHHzm/bVnzx56uDGKU2NjI9rb24nWEi0wwb4HFlADoNkNHo9HQgUikQglJSXUKWEdFJPJhEAggPn5eQwPD1OSlp+fT4IFXAQCAXR3d0MqlSIvLw/T09MkT83U12ZmZpCeno6qqqqYYpDH4yGxDkaz44JJZLNz4/P5NGPFukpMct5sNkOn02F4eJiOMy0tDbt370ZFRQWOHDmCYDAIpVIJh8NBc2NOpxN2u50C+dTUVJSVlaGqqgpHjx6lAJv7oFxYWKBuXjAYRFdXF4BFmlZhYSHm5+cxNDSEvr6+CAVGg8GAmZkZmqnh8/mYmJiAXq+P27mJhtPpxOjoKM3SyeVyZGRkoK+vD5OTkyTF3tLSEuFJxOSax8fH4XK5iD7FZJQLCwvxy1/+EpOTk/B4PBHec0ajEU899RQAYHJyEiKRKGKfZFVubpB55swZeL1eWoNFRUW45ZZbyG6ho6MDxcXFOHPmDFwuF804MbpkTU0NXnvtNRKJ8Pv9MXTFeLBYLDS/BywG2V1dXfjBD36AT33qUwCA3/72t7BYLBF/JxQKkZeXh02bNsV9X+5eyr6v4eFhlJeXo7Kyko6J3ecmkwlGoxHz8/MAFtX6SktL8YUvfIEoqFxwizs+nw8NDQ1xuwaJEmJuUUwul1NwaLVaoVAokJOTkxQ9LPoZyMQl2HlwEQgESCwkXrCr1Wpx/PhxjIyMwGw2w2az4Ve/+hUeeOABoh+vZj/W6XTU8S4oKMDk5CT27NmDmpoa8Pl8vPvuu2SLsW3bNrjdbpw/f56KLIxKyZCSkkIdeXaPx6M8MojFYtx+++0rptpxE+/Ozs6YwJvtCW63GwKBAH19fdSZXwk2bdpEz7pHHnkkouDS0dFBe2B5eTksFgt1kbjUxkvx7I5O1Ni/WUFTIpEsWYxY7WdeCvEx7n7A2CzsnlxJ4tfQ0ICnn34ao6OjcLvdZNIdD+FwOCKuysnJwc0334xXXnkFfr8fSqXysom5LmdcdFI2NzeHb37zm/B4PPjsZz+L73//+2RaePbsWdx///04c+YMfv7zn+OBBx4AsDjL8MMf/hBCoRCPP/44br75ZgCLw8hf/OIX8d577+HZZ5/Fl770pYjP+sEPfoCBgQFs374dTz75JFQqFYLBIH7xi1/g2WefxTe+8Q00NjbGmCb+veBylXRfDeIlD0ttTszLg8fjgcfjwev1xnDPo1WWuPQvRiNRKBRUKf5rY63nyYD4iXthYSEkEgn4fD75+cSrHMe7/oxewUQwgMXNV6/X4/e//32M95lWq8WhQ4dw+vRpeDweuFwu+P3+GKNoq9UKo9EIAFAqlZBIJJibm4NcLsfmzZuxceNGqFQqGAwGCmiYYtzMzAxEIhENfbP5NLlcHne+gsldM0oRC2i4QQ0L6nJyckj2ORAIYGJiAkajEVdeeSXUajWkUmlMcqJUKlFdXU30zOnpaXR2dtLvmT+VUCjEkSNHMDs7S6bLTEpdLBYTlYpds7GxMchkMvD5fAgEAlq/mzdvJn84jUaDV155heSH2fkpFAoyhQ4Gg5SwFhQUwOl0Ynx8nKhrXASDQRI9EAqFcLlclGDz+XxIpVLqsMQLCkOhEJqbm3H11Vdj165dEQIPTHJ7dnYWMzMz5IeUl5eHrq4uTE5OkuolE/5g9LOamhr09fVhdnY2bqegvb0dP/zhD8m8urq6GllZWXA4HDhy5Agp1NXW1sLtdqOtrY2qtxKJBC6XC93d3TRLyRLE+fl5EjdhCpOMzlZaWhqhwJnMfrxr1y4cP34cQ0NDtI68Xi96e3sxODgIHo8Xs4b5fD6USiVSU1PjVrrj7SPj4+OYm5sjo20Gllx5vV7qWLOgWi6Xx03IGPx+PwQCAdHH2Jwnd8+IlzRt3749wg+RzewAIDoo66AmQqLuW2FhIf793/8d9957bwyVT6FQYOvWrQnNeDUaDT7/+c9jcHAQVquVRIi6u7vx9a9/PeGxLAUmsMLuZUbvM5lMuOuuu6DVavHaa6+hp6cHGzduRH19PZ577jn09vbS9y4QCCIoxR6PB1KplAo+LDli9iFMGZV9j1xVwpWAPQOeeOIJsidhEAgESEtLg1QqRX5+Pvx+P7xeL1wuV4xIylIoLi4mkRCGwcFBvP766+jr66PuOvMly8/PJybFuXPn8NZbbwEAPve5z+HRRx9d8TmuFOz5tlwx4mLAfdZy/71arDS24oKr9AkAP/3pT8kSYqlCAJ/PR1lZGe2B7Bja29upmOnxeNa9ypKA4OGHH374Yt7gxRdfRFNTEyorK/HrX/+aht+BxSSiqKgIf/7zn9Hf3499+/ZBKBTiJz/5Cfr6+nDXXXfhC1/4Ar2e+Q2xTetf//VfqVum1+vx6KOPQiwW46WXXqIAiM/nY+fOnTh37hyGhoagVCpx1VVXXcwpxeD111/H1NQU8vPzl1SBu9RgfjiFhYURbeK/RfT09KC1tZWoEoWFhbjiiitQUFAApVIZ8VrmRD81NUVKPtu2bUNdXR2USiWUSiUKCgqgUCig1+shFAqRm5uLnTt3YnZ2lt6/trYWJ0+exOzsLHp7e1FVVYW8vLy/0hWIfw3iqQKuBHw+P+IasHUiFArx/vvvE+Vq27ZtZArLwK4j9/qz9wuFQiSJzDA2NoYdO3ZEXEOlUomioiJYLBZIJJKIYygoKEBeXh6qq6uRnZ1NancajQaBQAAulwuBQAB8Ph/9/f1obW3F6dOnodfrsXv3blx//fUoKytDXV0dHTurKofDYaImRs9iCAQCUlFzuVzU0YoOXLKzs4mGFw6HsbCwQDQ1n8+H/Px8VFdX01yTQqFAcXExqqqqsHnzZnz1q1/FJz7xCUpinE4nxGIxUlNTUVBQgKGhIRLKYB02v99PXQ2RSASv14v8/HyoVCqo1WpSL8zIyEBpaSnuuOMOfOELX0BlZSWARUpiW1sbiW/k5OTgiiuuwO23304dyYWFBfB4PAgEAtjtdhiNxoSzYTKZDOnp6cjNzYVarUZ6ejr8fj/NEZaVlUEul5M6JSuScB/WwWAQs//P3pdHt1WfaT/aN8uWF3lR7HiL4xAncRwgCSQk4GAoQygp7dDlQEunnLYwS3s6dChn2jJ8HaYbhQ4tgU6nJdCyNEDY0oIxTuuAS0ISy4mXeImXyLYsWbb23Vq+P3zelytZsuVswDTvOT4EW9K9usvvvsuzTE1h3bp1KCkpgcFgQGVlJQoKCgCAfaEikQii0ShOnjzJ6ntKpZI/T6FQ8CSKJkputxvhcDhlckDiHABQX18Pp9MJk8nE0J1AIACn04m+vj6Gi+Xl5WH58uUJkNDh4WH09fXxPtK0paSkBBs3bmQIrsVigcvlglgsRnV1Na688sp5a1dylJSUoLCwEENDQwl8LhK/IUVIYWRlZeHyyy9HRUUF30/C7SSvI+FwGCdOnGDj8qqqKqxbtw7AXNJF8FkSMqGGV1VVFQoLC+FwOLgQFIZYLMbx48fh8/lQWFiINWvW4JVXXsHhw4cxPDyMyspKPjbC9Yd4i/T7wsJCvh/lcjmqq6v5mZbq+FGCd/jwYUxNTaGpqQmXXHIJryvV1dVQq9UM16V9vfTSS/GjH/2I75V050MqlaK9vR2zs7OIxWLIzs7Ghg0bFj2XqaK3txdGoxFutxsKhQJqtZqbLFarlbl+69at4wI4OzubocnxeJyFRoTXQSwWg0qlglQq5XsEmCtqaZJG12pWVhZuuukm1NfXL3n/Dx06hN/85jdwuVwsfENqmGq1Gvn5+ewTSLYCSqUy5aQSQML7Gxsb8fjjj/P5MBqN+OlPf4rXXnsN4+PjvC5SRCKRBKXVmZkZhEIh9gt0uVy45pprlvwdlxomkwm9vb0wm83n7DktDOH1TffRmVx7FEvJrYTR0tKC++67DwcPHsSRI0cQCATQ3d0Nt9vNa326WLNmDe677z4+t7QP/f39sNvtfC1t2LDhjK7Lj1ucTc1w1pOy999/HwBw3XXXpeSBbd++HRKJBIFAAENDQ6isrERzczMA4DOf+cy811922WWorq7G0NAQ3n33XezYsQMA2L1++/btKCwsnPe+W2+9Fe+99x7eeOMNfPWrXz3br/WRjaXi288kkmWMz4c4xlKmfjStIa+SlStX4pZbbkkJsUnuCAlx4s3NzQgGg3A6nZBIJCm9qi5knI/JZ7qJFxUG09PT8Hq92L9/f1pz1XSfZzab0dvby3/z+XzYvXs3HnjggYymbsK/19TUoKysDKdPn0ZBQQFyc3MZtkdQPeJPeb1e7N69m1UdCVpJEwu32429e/fixIkT3D0mLhV5N+Xl5WFsbIyTtuTEl8Q2vF4vFzAk9ECv7+3tRV5eHhvqkghENBpFSUkJrFYry87X1dWhtbWV949UAynZok731NQU7HY7iouLUVNTg7Vr1zLsi7ruMzMzmJ3dVzN8AAAgAElEQVSdxW233ca/J5n8cDiMnJwcjIyMQKPRoLKyks28t2zZwkqN1KmkoiZdRKNRFBQUoLKyEjt37oTVamUPIrlcjvr6egwPD/PEhOSyAfD0TCQSwWKx4KGHHsKKFSsSpnpCSCzBUsfHxzEzM5Pw0KdkMy8vDyaTibks6SZ0wAdTPqfTiba2NjidTub9qVQqnr5R8q1SqbBq1SpUVlYy5JngsqTMlpOTA5fLxcIj1BQUmtqStHqmkGiLxYKZmRnEYjHk5OSgoKCAk85U4g4ymQwNDQ244oorUt5PROYnkYL8/HwuZr1eL9ra2tDY2AhgruPf0dEBl8sFqVQKtVqNcDiM7OxsaLXaeebxABKeBcIgqGuy2mSqe1+4JtC0XKlUoqysbFGZ7nTTN2GQp9fDDz+McDgMnU6HO++8M6PzUVFRgerqavT19UEmk52V4AKdC51OB71ezzYOJpMJXV1dXOxotVps2bIF99xzDxoaGrBr1y48+eSTXITQVJ+udZVKhRUrViA/Px/j4+NwuVzM06yoqGCRIqVSifXr13PetJQwGo34yU9+gunpaUYWFBYWQq/XQywWw+128z2rUCiQl5eHZcuWYXR0lKdlMpkMBQUFUKlUiMViyM/PR319/TxPUrPZjN27dyd4oaW69sncO1X86U9/uiDTsmSKxFJtIRaLc01LOZO8wmw244knnsDp06dZtGrt2rUoKSlBJBJBMBiEVCrlAotCLpejsbERP/jBD1LSIfLz82Gz2RCPx7nJdzEWjrMuyv75n/8ZN954I1avXp3y78KOTzQaZQ+RrKystB2s+vp6DA0N4ejRo7y4EBxow4YNKd9D49aTJ0/C6/X+n4Uwnu9IZUArfEifq4UoOXEHMM+PiUKIdbdYLPB6vWlhBOlw4cDcNUJEfr1ez9fMhxXnC0+eqnAnuXmVSsUTg0wXf/q8O++8E9/5zncShDB6e3tx4MABLrKESdhCn5384BAWIZTEUAFJ60ayqqNwG+FwmBOaYDDI3Xi73Y5gMIjJycmEh3tyYh+Pxxk+RN1fSjQoMbBYLBgfH08oDChxeu211/DSSy8lENLlcjmeeOIJWK1W5n0Bc118qVQKqVSKYDDIn7d9+/aE42g2m1mIQKlUYv/+/SgoKJgnm6/T6ZCVlYXZ2VlkZ2fz+WloaMADDzwAo9GIwcFBNDc3p+UDUPh8PvT29iISiaC5uZnhrgqFAlqtFt3d3fB6vaitrYVYLObpDkE0qSij95lMJpw+fRpf/OIXeTJAP+ST5XQ6+Ti63W5IJBIolUqeFJ48eZInXekKMgCspEb8STrWcrmcIbIEt1Kr1aiqqsK//uu/JjRuhI0coTIbCYUIE6isrCxW6rRYLBnxTcxmMw4ePMjG0wUFBbj99tsxMjKCl156aV5hRl5TJHqSSaxZswY1NTXweDw8bSB+4sjICAKBAIvIkBlwTk4OF9xCtU+TycRTNIPBwFNHWkdTJX/p7n36PSmf0lSzoKBgSWtFuiTza1/7Gqsnbt26FU1NTRkdLzIdJlGXVHYtS41gMAiPx4MbbriBmw6jo6M8zff7/Th06BCfl+HhYbayoPUhJyeHBXk2b96M22+/HeFwGKOjo3jsscfgdrvhdrtht9tx9dVX45vf/OaC0vALhdlsxsMPP4zh4WFGKwi9FckTjPZTLpezzx4VkADYT23FihVs3pxqf2hNomtA6P2XaZSXly/p9Wca9Jw+E1uITOJcNmepoZ5K3GihoMY3QVZDoRDKysrwuc99Dp2dnSgtLcXbb7+Nl156iZ+TWVlZuPHGG1P69hFMenBwEBMTEwiFQqioqLgIXcwgzrooq6+vX3Ac2drayvyJiooKvP322wCA0tLStOPQZcuWAUCCISz9O90FW1RUxJLXY2NjuOSSS87o+/ythzDpIOw/LZznWhyDHtKZqKXV1dVx0iPkNCxlfxoaGnD//fczZvrDXiDOl0R/qs81GAzYuXMnuru7WYhiqaTliooKbNy4ER0dHcyHcTgcePPNN9HR0bGkwj25c06KfELfpe7ubk5gSdXR7/ejr68Pzc3N7KMEzCVWw8PDLCc/MTGBoqIihr4lJ/OkOEgJcDwe54KF1Ohqa2sRCoUwPj6eMGGhz6IpnFwuZwESIqSTctfAwAB3ggmOR5wRmuSRXHxdXR0XBD09PXjllVcwPDzMnKZQKITOzk6GO5LozR133AFg7kEqxPXTcTYY5mT4Sco6OYT8FWCOw3L8+HEMDQ0lyJW73W6Mj48zzFClUkEmkyEUCjHsjmBNlHwGg0H09/fj0UcfRUdHB09Furu70dPTwxLmNJmkY5yTk4OysjLYbDa43W6EQqEFCzIAPLUUBu2DzWZLSHbXrVvHEzw6TsnHTBipOE20LbPZjOLi4oySSqPRCKfTCblcDrlczue9sbER4XAYf/3rXzEyMsI8mm3btuFzn/scwuFw2u58sqx2OBzG3Xffjd27d6O3txcTExPYu3cvHA4HBgYGWFX0kksuwfDwMMLhMGpqaqDT6RiWazKZ0NHRAa/Xy42cwcFBVgfVaDQJ92sma9hCvLB0ryORlkyTzKampoyLMdpWS0sLwuEw8vPzcc0116REYWQaY2NzZu3Dw8OIxWKYnJzEJz7xCb6WybScJpkOh4PfQ/BD8srLyspCTU0Nrr766oS1DpgzozeZTJidncWpU6ewd+9ebNmyZd4EMdNobW1FV1cXX8PEkQwEAvB6vSgvL8f27dtx5MgRhj7bbDaexlIDiwo1p9OJ/fv3p+XzAeC8UKPRoKysDF1dXfxcEYvFWLVqFQKBAEZHR+fd+xqNBt/97nfP6LueSRBfcqm2EJl+dlNTE+clZ/qZZ6POXVZWlmCPQs9HUr9tbW3FyZMnec2Ty+Wora1lVddUEQ6HWZ02Go1iZGQEVqv1Y027uRBxXn3KvF4v/vu//xsA0NjYCK1Wy1LYubm5ad+n0+kAgBMs4b/pb8khkUiQlZUFp9OZ8L6LsbRIHtUDH0zKzpewiLAQ9Pv92LNnDyQSSYKCIvGatFotysvLz3h/KJH4sON82Rss9LnZ2dmoqalBf38/ZDLZkkm3ZWVluPTSSxlrHolEEA6HMT09DZfLBZvNtiQfEnpd8v5SYtHQ0IAdO3awqiMlDT6fD3/5y1/Q2dmJO+64A0VFRdi/f3/CFCgej8NutyM7O5v5T8LIyspCNBpFIBBgf6ZoNMoQbJJ/DgQCnISQGAFNbHJzc3HrrbciEAjAZDJhfHwcBoMBGo0GHR0d6O3t5YYGwUc1Gg1GR0dZjp14IlKpFPv27QMwl+wMDg7C5XIxV4P2LxwOo7e3l3H+AwMD2LFjB+69994Fk2Oz2czcO2HQ56YKn88HqVTK0yaaGAaDQU4gSUmPDLfD4TCqqqowOzsLm83GHD8S4RkbG8OhQ4cwMjLC06KcnBzIZDLYbDbmi1HhcOrUqQQYV7pYiIQOgAVQZDIZli9fjl27dmV87S+mKKhSqTJSZqNJsNPphEaj4WdWS0sLbrvtNtx1113YuXMnRkdH0dvbi61bt6Kuri7t/UzFC8F9xWIxJ7gGgwHbt2/H8ePH+d4kfiRxLE+cOAGPx8Pqof39/SwOI5VKeXJDTQe9Xg+lUjnPhDaT+z2Txhu97qGHHkJnZycn+2q1OmE6nu7zl9LgotdPT09jeHgYXV1dEIlEOHz4MCorKzP+XslRVlbGjSC/3w+n04mpqSls3boVd955J0NrT58+DZVKhf7+fhQVFQEArwNKpRKFhYVYsWIFqqqq5hVkZWVlKCws5MQ5Go3C7XafMRy/paUFTz/9NEOR4/E4T5uVSiUaGhpQXFyMxsZGNDY2csOJxEyWLVuGSCTC0Eyy5yB9gXSIFplMBplMhtzcXHz+85+Hz+fDyMgIJBIJrrnmGnz/+9+H1WrFgw8+yEIoUqkUpaWluO+++y74c/x8Ca1RY4DUhc9USORsYJAGw5xP37FjxxAKhSCRSBCJRFh9kXi49PyQSCQoKipa8ByUlZUx0kXYVPwo5F8f5ThvRVk4HMY3vvENmM1mqFQqVjOiTohQECQ56CEhJKNn8j76WybSzRcjdaSCFZ5vw2XhYpcsXU+TgY6ODshkMqxevZoTqo9zx+V82Rss9LllZWXQ6/UYGhrC1NQUXnnlFeb5ZBJ0bcjlckxMTPB0aGJiAtnZ2QgGg2htbeXXZtLZXuw40NSCuEiDg4MwGo0J8v6XX345enp65nEP4vE4LrvsMhw8eJCLI4poNAqpVIqsrCzEYjFkZWVBqVQylJBEQWj6IpVKodVqoVKpMDQ0xCIVy5cvR1FREZ588kk22fb5fCwUoVQqoVQqoVarmasklLcOBoPQaDQMKyPp/HA4zDAiqVQKiUQCq9WKZ555hpOhcDiM1157DZOTkwnFbKpoaWmB2WyeB40rKChgDklySCQSZGdnIycnB3q9Hmq1GmazOWEKRoUIoR5isRjcbjfWr1+PqakpjI2NsWy20+lEd3c3Jicn+XirVCpUVlZCJBIx94C672+99RZPA1PxTZL3VagWRx1fKsTy8vKgUChQUFCAVatWLSkxWIzTlKky29jYGKsiSqVSZGdn8/OR+FhWqxV+v5/XOKFqIX2GEF0wPDyMU6dOweFwQCKRsHH02NgYnE4ngsEgX2tCGsHk5CQnS6TsNzo6CqfTiby8PD5mkUiEDaVdLheWLVuGqqqqJSdWmfDCAODAgQP405/+xJLwdJxGR0fTrpFLbXAlQ/QJ8hwKhRgFQJYNtI4JIawLfbbBYEB9fT2b2ZNZvc1mQ01NDTZt2oTKykrs2bMH09PTMJlM2L9/P1wuFyuBFhYW4qabbsLll1+eEtpvMBhw7bXXsnATTdrPBI5vNBrxgx/8YB4sm4STSNmWpo+kECiRSBi6WF5ejo0bN+LFF19kj0m5XM4CSanOGxm3h8Nh5qn95Cc/QVtbGwoKChKgzo8++iiMRiPsdjvy8vI+tGf/uZpoJce5ygXOpmg0m83o6upilUUA6OnpwTPPPMPPeWEQv3ix/dTr9dzY02q1Hzpl5OMQ56UoC4fD+Jd/+Re8++67AIAHHngAVVVVAMCd6IW6mqn+RrKvC71P6Ht0Mc48UvGyzvf2hFC2lpYWNu8sLS1FS0sLxGIxSzIvxkH4OEQyOf9cdd0WWpgNhg8MUwcGBtDR0ZFSqGOhoA78s88+y4k8TXMUCgW6urowNjaWYPBcW1vL/ISF+IILmdIKoa4jIyOQyWTMI3v11VcxPDw87z10raxcuRKdnZ0JfxOJRNBqtSgsLERNTQ0KCgpYkQ4Aq5nR1J3gPEJlRo/Hg1dffRU6nQ6nT59ms2UqUHNzc5krITSlJogSrVeUgBKUTMjpUKvV0Ol0CAQCLIJCxUckEmGLAGGynsyHam9vx+uvvz6vsJHJZPja176Gp59+GqOjo/OOX15eHlauXImxsTGGM8tkMi54CG4oPGd+vx/9/f0YGxvDtm3bsHHjRrz11lsYGxtDJBKBx+NBKBSCSqVCTU0N9Ho9tFotTp06xQIqsVgMgUAAExMT/D0XC4LcUCFGHmsymQyFhYXYvn07T++WGgvdU8RhI8n9hRIqt9uNEydOMFrE4/FArVazMbjb7cZjjz0Gm82GnJwcfOELX2AIrl6v522TGi2pRJJdhUwmw6lTpxhp4PF4oFKpGHpLQYUvNRuys7MRi8VgsVjYTFqv1yMvL4+3Z7PZUFxcjE2bNs2b3CwWZrMZ09PT0Gg0i6Icurq64PV6+f9pTViI55WMtEiGNifvCx07m82G8vJyrFy5EkNDQ7Db7YjH45icnGSVztnZ2QSxl6qqqgWhlGazGcPDw1AoFFAqlaw2Kdx/g8HAVhNerxfLli1j3g0JDlmt1gQ7CWHBabVa8eqrr7KVQHFxMb70pS8tuVA2m8145plnGFKZKqLRKMbGxniKT+Iier0eGzZsgEKhwB133MHHNRgMsoovefqlO280dQ8Gg3jqqadQW1ub0opgMX7yhYpzNdFKjnM1gTtTjrrZbMa3vvUttLe3A/hAXOXgwYOMihCGXC5Hbm4urFbrgoInRqMRMzMzkEqliEajyM3N5anwxUgf57woc7vd+Md//EdWZfzOd76Dm2++mf+uUqkAJE7BkoMWCOFUjLDKS33fxfjoRzL0REi0b2lpYehRVVXVgov8xVh8YW5oaMArr7zCsuTHjx9Ha2srbr/99ow+n6Ar5BlHUDOCThF/hwye+/r60NraOk+gQ7i/mZjSCl8v5FBFo1GGUgqDGjNvv/02w4LoNdRZ3rRpE2Pi9+zZw9yxnJwcTpyIS1VVVTWvuIvFYizT7HK5uIiTSCRYvnw5T8eOHz+ekPQke6TFYjGGnwmnO/n5+di6dSsqKyvR39/PAigajYaTxerq6oRknaYnQuVAi8WSEj1A8M0bb7wRjz32WMLfZDIZysrK4Pf7uWAmPlYgEGAJeYPBwP8eHx+H3W5nbt3IyAhuuukmmEwmzMzM8ISNik673Y5rr70W3d3dGB8fRzweh1arRTQaRSQSWXCtTw6CoNLaT+eatmc2m1nKPxN1veQ1Kd09Rf5yJEK0EISxp6eHjx0wB+8Ph8Pwer149tln2dzc4XDA4XDgf/7nf7i5QY0oq9XKcG6LxYJ4PM7nNhwOw263w2AwwGazQSaT8XERBvEZSbxl69atGBsb4262UqlkHtGll17Kk97i4uIzKsiEU6nFlBZJKIbuD5FIhPz8/AV5XslCUIcPH2ZfsGRopPD+IEVNKrL6+vq46CAPxNnZWUxNTUEkEjE3lNYqKkiExZDRaERHRwd8Ph+0Wi2uvfZabNu2LeE7m81mzMzMwOl08rSf1iiyAKJrFEDCFKW1tRVvvPEGBgYG4PF4oNPpsHbt2iWrLdKxMBqNfP1QIyN5am6329HX1welUgm/34/y8nLs2LEDBQUFCfeDTCbjqerXv/71BQ24CRI5OTmJUCiEqakpPPjggygoKPjIwtvOF7oFmOPM19XVLWkKmLxGLQXCK3yt0WhM8NUEEgcjdI2qVCqoVCoUFhayh+VC0+tDhw5xs5KQEEuhN/ytxjktyiYnJ3HnnXfi1KlTEIlE+O53v8uyuhTECUvnawGADQSF8pk6nQ5utzvt+6LRKHecF+KrXYyPVqSDnhgMBvbtGBkZYZPMpqam/xM3dTI5/1wu8At1FoVck2AwCIfDgT/84Q9Ys2ZN2oehkLvy2GOPcRKqVqsZwlZeXo7rrrsOVqs1weB5MYEOILUp7ULHgsjHY2NjaG1txcGDBzn5lEgkPAkIBoOwWCwJfkHk+fWJT3wCX/nKV/g6czqd7OkVj8fZfFkikTDsSCaTob29nScxcrkc+fn5zL2KxWKQy+UIhUKYmJjAunXrEsRHFgriXpEnll6vh0QiwcTEBBQKBW655RYmTff09LBABP2uubkZMpkMFosFfX198Hg8XOjMzs6m5FzRJBFAgik2CWHU1NQwhAmYM15uaGhAe3s7JiYmGEpE+7F37172iqLkzmAw4O677wYAnrgRTNPpdOLQoUOs5gbMrd0EgSTODAmJLBYKhQKXXXYZG28TwZzur5qamoz4qAutSckRDodRXFzM8LNknqZwcmkymRKaB1R80tRVr9cjKysLkUgEgUAA4+PjbPydl5cHp9OJI0eOwOl0cofa7XYn7E8kEmHDcZPJxPBPYWRnZ2PNmjWYnp6GXq9HZ2cn/H4/ZDIZampqGNJHRtRLEfNIDmEiW15eviDKgSBtGo0GXq+XxU5CodCC/Fcqmpubm7kgS15HkidkyVM/YSNQqAKbbIugUCjgdDoT4NPCJtLIyAgmJydZQnzt2rW48cYbE/bX4XBwwTc7O4uSkhKUlpZCJpMl2C/QNSrkeB88eJDFf7RaLaqrqxcU01jovPT09PA0WiwWIy8vD/F4nP3t6PcqlQo6nQ7FxcUJ10VywUuQ7kgkgj//+c+YmZlBY2MjPvvZz6Y8Z1//+tdxzz33IBQKIRKJYGRkBA8//DB+/OMffySf8eeDU5bK+P1M3ifk3isUCla+TAW7TQXfJcoQMFecr1y5EiKRCH6/H7m5uVi3bh0KCwtRV1cHo9HI211oek2cYoLg0/1yMRaOc1aUDQ0N4R/+4R84GfvhD3+Im266ad7rCMZIqkqpghaK5cuXJ7zPZDLx35LDYrHwREX4vovx0Y7FOFAKhYKhakK57497kIywEJZ0oaKxsRFvvvkmy8X39vbi/vvvx9e+9jV+KAg5hbSA04/T6YROp8Oll17K0CSXywWr1crFAyU3yQIdVGhv3ryZH+yZQhiFIUyCyIBXKpVCJBLN4yARtIsmBkqlEhs2bEi4zkjimQyIjx07xhDEmpoayGQytLS0cKEjl8uxevVq3HzzzXj33XdZFMHr9cJkMgGYE06IRCKL8qEoSIVw7dq12LBhA06dOsWwOHqwPv744xgdHUVFRQUaGxthtVoTIG96vZ5NgQHwBJOKUmGUlJSgrq4O7e3tXLBJpVLs2rULN998M59/ku2mbqxarYZSqYTNZkNfXx/C4TDq6uqgUqmQnZ3NBeuyZcuYO0TS/L/+9a9x7NgxxGIxOBwODA4OoqioCBqNBsXFxRgfH2ceX0VFBSoqKjA9PY3Ozk7+TsIgWLtIJIJUKkVJSQluuOEG7N+/H/39/Th9+jTDGJMtB9LFUjriQp4mFZl0D5GEts/ng8fjgdPpZCVDOt9KpZJ5e+vWrcO2bdvQ09ODt99+G319fQyVdTqdGBoaglgsxvT0NE+kk6H60WiUFSvtdjtPJ4XHq7q6GldccQX6+vowNDTEAg96vZ4nWanUW4X+eJkmzUv1owTmpM7JhwtAxuvB9ddfzwWZcFupJmTJAhrCojtZBVbIKZPL5dizZw8/k8jImF576NAhXn/S3fe9vb0JU2C3240vf/nLaZNomtJOT08zDDk3NxdVVVXsSZhJCKcjbreb94PsLNasWQOJRIKOjg4WR8rNzcWGDRtw9913z7OOEH5uR0cHPB4PT6WfeeYZxGIx/OUvfwGAlIUZqc3OzMww4oJ4qB/FoizVxPxsFZTPdPpG7xscHITNZmP14WPHjkEikaC7uxulpaUJsFtqLqVS2C4tLUV2djbEYjE+97nP4aabboLRaITD4UB/fz/zq2mit9h3JgQBcbHLyspQX1//kZ2CfpTinBRlY2Nj+NKXvsQL4i9/+Uts2bIl5WtXrlwJlUoFp9OJ4eFhLtKEQUmAUGp/7dq1rLj2xS9+cd57CFZUU1PDF9rF+OjHYtyqyy+/HH6/n71y/i9AFwmbThCY8zX9S/fAMBgM+MQnPoHjx4/z1OLEiRP41a9+hRUrVmBqaop9SpYvX46enh6YzWbk5eWxapxer0djYyPeeustWK1WuFwu5k0J1ROFAh19fX2YmJjA0NAQRkdHE7xelgJhpBgbG+MinWTm6eGeHARfpCQ3+bOpYCNfJ5ouETzy1KlT6O/v52RFJBKxEbler0cgEIDZbGbPLQAs6pFJEKxbLpdDKpVyN10IizMajXjnnXc4cdm8eTOmpqaYKE+TV9o+dSd1Oh00Gg3L2VNRWVY2Zz7tcDgSpmjRaDRBhEEoLNHV1cXco9nZWQwNDUGtVsNgMLBcOnXLJyYmOKGmZMBqtfL5icVicLlcbHgs9NAD5iZft912G+rq6vD9738fnZ2d884tfRcSLQHmJkF33HEHdu/ezQXH2rVrWTxgsVhKIWEwGFBbW4vOzk5uPBiNRvT09PCxIp4Q+cjFYjGEQiEolUps3boVW7duTeBcNjU1oaSkBA8++CBfs6SASZMkgpO63W6+tsViMXw+H4aHh9mcHPgAyiuRSKDRaGAwzJm3k5JoT08PCzdQcZKKK3UmarFL4bkIjzswp0IaDAYTiquF3p9uW5SELoUXlwmPKRQKQa/XJxTeExMT3FzJyspKidoZHx+f9zk9PT249dZbeX9T7YvZbEZPTw+AuXts27ZtGXN0hOcPAAYGBuB0OhGJRKBUKrFy5Upcf/31aG9v57WULBvq6+t5PU51XTz00EN4+eWXUzZM/X4/Dhw4kFCUmc1mhnkKp+oSiQSVlZUf6We88BicCwXlM+WW0/sIDWIymbihSZzMwcFBht0KJ8cLKWwLvwOhSDo6OuYZxGcCj5TJZFAqlaioqEBtbS127dr1kSy2P2px1kVZKBTC3XffDZvNBq1Wi9/85jcL+pYpFAps27YNzc3NeOGFF3Dvvfcm/P3o0aMYGRmBTqfDtm3b+PfXX389fvGLX+DAgQOYnp7mBzDF3r17AQCf/OQnz/YrXYyPQAgXPK1Wi+XLl2P79u3/J25qYZJQXl5+XqZ/iz0wGhsb8fTTT7NwRSwWw9DQEKsAxuNx9PT0ID8/H16vlycSt956K+RyOdavX4+ioiIYjUao1WrI5XLo9XpMT08nJE/ChOL3v/89fD4fLBYLHA4Henp68PDDD6OiooKLIZqgZ9IxJIUvkr2XSCTzpkHA3LSosLCQ+QuhUAjd3d1oaGiA2WzG3r170dXVxZ1eoXfZ7OwsJ1FU0ABzxdvQ0BCeffZZlJWVYXBwkBNvggqSamI6yfnkfSwtLWWRC5vNxtwx8sAirhHBTh0OB9avX4+cnBw4HA6EQiE+d2q1GuFwmPehsrISwWCQC+5YLAaz2cwiCsTZBeZQD8kJsNFoRFdXF6xWK4A5VS3itZGyokwmQyQSYVU1i8WC9vZ2njAcOXJkntCGSCRiw+Ti4mKGoANzMPbOzk7IZDKYTKaUxTYJjiiVygTY19jYGLRaLbKysiCXyxMmo+lC2MTItJAwm83o7++H2+1OENOgiTIw98yLx+Msdb5p0yZEIpEElbnkqKioQHV1NU6cOMGQWrFYjKysLJjNZsTjcW4QUJFGP9PT08jJyeHkmhJetVqNtWvXsnpiQ6Q0eKsAACAASURBVEMDmpubAQAmk2lBm4yldvSTG0ILQRaTuXs0YfT7/cwRzNSwV7je0FQvucheKi8uOYTwabJ6GBgYgNfrhcfj4eL3kksuwcjICH7+858nmH/feOONaG9vZ1jw7Owsnn32WRiNRp6U6XS6eXy15ONjNBpZeXWx70P379TUFBwOB1wuF8OztVotdu3ahR07duDo0aO81gcCAYyMjODZZ5+dh4KgaZ7RaMSrr76a9hlGnECj0cjv3bdvH9555x1MT08jEolAJpNBKpWiqKgIW7du/dg8488Fx8xqtabl/C4UwmYQTV23bdsGtVoNp9MJl8uVALtNNnfPVGH7TCCbQvhvIBDA9PQ0dDpdxs3Wv/U466LsiSeewMDAAEQiEX72s58tWJBRfP3rX0dLSwueeuoprFy5Ep/61KcAzHVvvv3tbwMA7rjjDu4eA3MTsGuvvRZvv/02/umf/gm//OUvUVBQgEgkgkceeQSHDh2CTqfD5z//+bP9ShfjAkY6bpVwPE9QnXA4fE5v6vNl3rxYXAjoYiYPjJKSEpZ3l0gkrLREcDDyxRKLxczpO3r0KHbt2sUdWo1Gg2XLli2aPCUnFCaTCSdOnGCPKzomGo0GGo0mI8+nlpYW9jFSKBQ8vSGvK2Cu2Ln00ktx7bXX4sknn0QoFILX68Urr7yC4uJiGI1GvPvuu7BarVyQyeVyxGIx9tGhKQLxxqg4mJ2dxczMDKanpzlBViqVKC0tBQAUFhbCbrdjZGRk0fMlEomwatUq9pHS6/Xw+XxQKpXQ6/UoKyvDkSNHWAQkNzeX1azWrl3L/CmRSISioiL2YbPb7dDr9Vi1ahXy8vLw2muvIRwOIxKJwO/3Y//+/aisrMSKFSvQ3d3NJp8HDhzAbbfdxl3t1tZW7rKTsMD4+DheffVVuFwuDA4OzvODI25Jd3c3LBYLZmZmEjhVSqUSOTk5XMg6HI4EOB5NKf/85z+zYmGqIMVGvV6fMHXWaDQIhULweDzo6OhYcFKWqomxmBEv8ZRMJhM8Hg9EIhE8Hg8MBkMCHLe+vh5Wq5W70ZmYE8vlcvaiI084iUQCv9/P14hEImGxDpFIBLfbjWAwiFgsxtBG8niLRqNQq9W44oorcOutt/L2r7/+egwPD/OUl+CXZ5OcZTpBSOa2kEorMDclGxoagk6nY0gxkD7xFa7lwHzFwjNRpVsokqdXNpsNXq+X19BQKIS+vj4cO3YMkUgEe/fuxRe+8AXccsstuOqqq7B161Z0dHSwtYXVasXMzAw3mrKysuD3++cp49KzcSkmxpQkWywWuFwuNmMXi8XQ6XS4+uqrWfDojjvugN/vx/Hjx3nq7/F40NfXx0gGIQTUYDAsWFAoFAq0t7ejs7MTy5cvh0KhgNlsxtTUFILBIMRiMXN0HQ4H/vd//xc+nw/r16+/4M/lpUa6eyLTvMJsNmPPnj3o7e3F7Ows8vPz09oHJBdQcrkc/f39fOxJRGfHjh0pYbep0DLJ/58qzgSySXwyalTF43GMj49nxBe/GGdZlIXDYfzud78DMPeAfeKJJ/DEE0+kff33vvc9rF69GqtXr8Y999yDn/zkJ/jOd76DX/ziF9BqtRgYGEAsFsPVV1+Nr371q/Pef//997NPUWNjI1asWIHJyUnY7XbIZDL88pe/RFZW1tl8pYtxgSNdgUILHpGtHQ5HgvT32cb5Mm/OZLsXArq4WBJlNBpx6tQpLmBI+Ssej0MmkyX4T8ViMSiVSkQiEQwMDOD555/HoUOHkJ+fz2ReAAy3A1InCvQgaGhowK9+9SuGT5LnFTA3jaqoqFh0ekhFp9frRVFREUMJCdZFBZLBYMDatWtRW1sLjUbDk6aBgQE8+uijEIlEPJ0jXlJ2djbq6+uxYsUKOBwOjI2NYWJiAvF4HDMzMwgGg5zs0vSMipFoNIrGxkbYbDb2fDKbzYsqCZLqHcmXNzQ04OjRo3xcenp68Kc//YkTmVWrVkEul+Pf/u3f2FhVLBYjJycHRUVF3MCiqRspYdF+i8ViOJ1OvPTSS1Cr1cwFI5n9V199FXV1dWhpaWEYHk0cKYF7+OGHMTY2hlgsBp1OB6VSifz8fLYBoA740NAQpqen4XA4uAGQm5uL9evXQ6VS8T1IiabX64VIJEJWVhZsNhvsdvs8XhQdb/ou0WgU4+PjCd3Y2tpatLa2wuPxoKura0Hlr0y73kLhDiLWj4+PQywWc0MgHA4nwHH9fj9PGTJNNMPhMAoKCtjqgc4LTTmpyIrH42xeHYvFuCAgGK+wSI7H46ipqUmAX42NjaG2thajo6NwOBxpRYeETZXFItNjSa+jKROptFZXV7P3Wjwex7p16wCkLwaT1/K6ujr+XJvNxsqqYrEYmzdvzhjGmknQcTlw4AD+8Ic/sB0Iyb0Dc2uC3+/Hr3/9axw/fhzbt2+HSqXiCTetJ2S+Ho/H2SQ++Zpdir0Avb65uRk2m43FfKgxolAocN111+Gb3/xmAp/ugQcewIEDB/Dmm2/yta7T6dj6gRqoBFteKEh1z+v1YnZ2FtXV1dDpdCgsLITD4YBSqWT12kAgAK/Xix//+Me4/PLLcemll16w5/KZRLqCJdO8wmg0wmw2IxgMQqlUQqFQzDuXyY0LANzIJAj0mZi5n2lk8v1IC4CGKmq1Grm5uRecO/9xjbMqyvr7+xluEggE0NHRseDrhdCUr3zlK1i5ciV++9vf8li9qqoKN998M+644455Mr7AXOf5xRdfxOOPP46WlhYMDAxAo9GgqakJd911F+rq6s7m61yMCxwLFSjJkxXqMp+rm/p8yttmst3zCV0EFudyUJFLyW4wGIROp4NOp4Ner8eVV16J6elphtF5vV5OCqempmCxWKDT6RjCJpfLodVqAWTGw9m1axeOHDnC3WVhkj0+Po7BwcG0CWxyYkIKUocOHeLP0Wq1yM7O5r/19PQgNzcXk5OTLNff29ubwJ8gmNny5cvR1NSE3NxcOBwONDQ0YGBggDlz5CdExZiwYJDL5ZicnMTIyAgLopSWlrJgR6pQqVRsyH3q1ClEo1H09fXx1FKtVqO5uZmneQqFAqFQCE899RQOHz6MYDDIohE0UXn99dfR09PDEt8mkwmBQADRaJQTeIK6kfIlXYs0odq/fz/DJIE5rlZeXh4MBgM6OzuZ/C2Tyfh4BINBVFZWskx+KBRCXl4exsfHGcapVqvxmc98Bl/5ylcSurput5thigqFgvkRNAEEwNui4pKMSUUiEcbHx9HT08MKn4uFsOObySRImJAITe4LCgrg9/shlUrhcrn4+wgVRdOZJaeLsrIyZGdnJzwHibMWi8Wg0Wggk8mg1+uh0+mg1Wrh9/vR0dHBwgm0H8Bc03Tjxo0wGAz44x//iOPHj+O9996DSCTCihUrUFFRkdHkvqenBxaLJS2UcClFA/FiRCIRJ+TUWPF6vQgEAsjKysKGDRsWFGhJ9imTy+Xw+/2w2+2YnJzEwYMHuRB566230NHRgXvuueecrfdWqxVHjhzhiSaQaHtB16vL5cKRI0cQCARgsVgwPT3NE3iCDNJPNBrFzMxMgnIrXX89PT3wer245pprFpy6JgucqNVq5jgSBzMVJYCeHY2NjQkiPy0tLQCQYCeQm5vLxYgwqGCjdUGj0WD58uXs80Z+k319fXj//fcTIOOhUAj9/f1YtmzZPAXNDwPZslAkT5wWyisIdUDva21txcTEBJRKJVavXp2goknfdXp6ep4oh9/vh16vh0KhQE1NzZJUG5ca6RoeC+VNNHEFPuBdkrAXgCWLBf2txVkVZWvXrkV/f/8Zv/+qq67CVVddtaT3ZGdn4957753HRbsYH79IV6AIF1/hYrMUD4/F4nzI237UtrsQlyM3NxcymWwe9EShUCA/Px9SqRS33357Ag8gFAol+PgEg0Hk5eVhdnYWKpVqHgRpoWhoaMCDDz6I119/HW1tbRgZGcHs7CybEzc3N8NsNqf1GhL6HhkMBuzfv5+FPkhcQ6FQ8L/7+vpY2Y8SACKZk2ofccicTidaWlo40SAp+NraWgSDQbhcrgSTYprc0LSR+GlSqRSzs7MM+0sXBBMlny9K7uLxOCQSCQYHBwGApySk+EZCDSQ8AsyJe0xPT+PkyZPMaaKOKk2pyJCalCRJll1YXHo8HjQ3N2Pjxo1YuXIl/46EWMhnCJgrRGtqatDd3Q2/34+xsTEsW7YMkUgExcXFyMrKwuTkJPvVFBYWYufOnfOuzz/+8Y8A5iCnfr8fbrc7QVmR+GrC/aSET6lUQiwWY2BgAMCcEbfP5+NmAZ3DVNfRYhC3VAkSJUXl5eXMaTObzcz/Oxf3OXkBEnyYJsBqtRo5OTmoqalJMDMeHBzEyZMn+boDgPz8fOj1emzduhU7d+7Evn37cODAAZjNZvaLm5mZwV133bWoMuVijaxMPMmSoVhU2NN3i0Qi8Hq9CbBhANi0aVNa9Uc61jMzMxgcHGRuqt/v5+ksBTVozkUTjpLsV155Bb29vdzYEhq8U3FGP8LrmlRGpVIp8vLyIBaL4XA4GG1A038Kglr39vYiHo/DZrNhy5Yti54vmpLROReJRMjOzsbq1asXTOaT70+hbQBd4wDw93//93j++edhtVohkUhQVlbGTXKz2QyHwwGTycQWDy+88ALa29thsVh4TRMG8dBSKWheaGTLUoIaEsDcGubxeOB2u3H48GG43W7s3bsXg4ODkMlkfCzJ4J0Uww8fPsxTeLqPhM1H4ANRjoVMzM9VJN/zdXV1Ga1rxLukIpSus4/6OfwoxDk3j74YFyPTSJW4pBvXn8tuECUGF2JRSxVnYhR5tpHcZWxoaMCmTZvQ1tbGD2ryjurr6wMwJ+awfv16dHZ2Mkxx+fLlWLVqFZsB08RgfHwcMzMzLMySCSmfFm4qxmZmZpCfn49oNAqHw4Hh4eF5vmZEWHc4HFwsEHGcvH+o0xwIBCCVSmG32+H1ellQQhjEt3G5XKioqMD4+Djcbje6uro4IfT5fLDZbNDr9VzgUMFHnDu1Wg2ZTAan08lwO1JgEwpAJAdNeYLBIEMXhXw4miL09fVxwUkS6TQtoiAYInHchNsQWgZUV1dDpVLB4/HAZrPxMUg+LiaTCSUlJdz1bG1t5Y5tdnY27r//frS1tcFkMmFsbIwLfLlczhxDMtC2Wq1cuFZWVqZUjXM4HJidnU2A4AFzUx6aJKTaT5lMhry8PBZqOXnyZAJnpbq6ms11Kenp7OxkVVEqJFOpiiWvR8IJEK0f1LQQWjosNqleLIxGIyYmJiCVSiGXy5GTkwOlUslTsq9+9auoqKhIgE299957fOzomgsGg6ipqeGpZF9fHywWCzfACDqcm5u76CRvoUIz2QcslSdZcmJdUlKCjo4OWCyWhAJUJpPBYDBws6GtrY0TvFQJncEwp1j59ttvY3x8HBMTE8jKyprny0dBvKrFQghVTX5OGI1G7NmzJ4EfRbDvgoICNpWnRgRdv7m5ucjOzmY4M0FOZ2dnsX79ephMJgwNDfF1RMUd2YucOnWKkQVTU1Noa2tL+1yk8zUxMYH+/n5eq7VaLWpra3H33Xcv6bpM1+S77bbboNfr8Ze//IUbFqQmCgA/+9nPGAGQSSiVSlx99dUJ5/jDQrZQJE+5hLYvNM3s6OjA2NgYpqamWI33kUcegV6v5+ej1+uFSqViuDMwd48eOXIEx44dYy88msInG3XTd79QOUvyPb9U30LhZD2TKdvFuFiUXYwPMVLxFFJ5aCxG8l5KfJgdtzM1ijzX26Xv/MMf/hD/9V//hbfffpu5BiQWcOLECQSDQbz66qsJKk5VVVUJ0s3UAfR4PLDb7ZiammIYYybny2g0YnR0FDKZDJdeeikXfCaTCb29vTCbzRgeHsYtt9zCiQkllbTN0dFRLlLowU9GpjSBSuUbRMmSRCKBSqVifo7X6+XpCzBXZJw6dYo9iEQiEbRaLSsWEudOLBbzdqnrTwbTU1NTKb8/dcm9Xi8b5tI2iewvfC9N55K/C4k+OJ1OluKXSCSQSqUoLi5GYWEhRkZGuCu7c+dOPPnkkwiHw3C5XCkT2Gg0yvLcDQ0NLMdNCTklJwSNzM3NhVarRU1NDYA5juHU1BRz3ug4xWKxlFOW/v5+tgQQhkajgVQqnTfVFYlEUKlUzF0gSXhSa6TEdv369ZDL5fjxj3/MymQKhQJDQ0OQSCRsOUD7IUw4hOtRcoIkhBr5fD5MTEzAZrNh3759aSXEMwkSZrDb7dywyMrKgsFgwMzMDKqrq1FRUcFFlBCmJoTNAXNJ/+joKIA5Pyyr1crXPUFeL7/88ozWouRCE0js7At9wFJ10ZMT61AohOnpaTZ1p2JerVYzj2xwcBAdHR0sWEPqncnXT3t7OwYHBxPgmuXl5dBqtfOEdiwWCx555BEUFBSk/d5UdNH1QmsfQe+E0zGtVouqqioWBiKeq9frZfuInJwcGAwGyOVySCQS9PT0JAgSBQIB+P1+fPrTn8bevXvhdDr5/v/GN76Bv/71rwkTODp/o6OjuOeee1BRUYEtW7YkFI/UfHvhhRfgcDiY+1lbW7skf7PFwmAwpBSMMRgMuP/++5eMppLL5aisrEw5DQUuLLIFmLu//t//+384cuQIpFIpT59tNhtcLhcjJ4QIB2qECbnO9DzQarVMtRkcHITb7WZRDJ/Ph+rqap7CpzLqvpCFjPCel8vlfO9nAsUeGxvD8PAw8xAznbL9rcfFouxifKjR09ODZ555BjKZDD09PWhqakrroXEubuIPs+P2YfPYhNul/27evBn9/f1MUCe4Xzwex9jYGPucGAwGFBUVoby8HM3NzayO9fvf/559vWiiQaReuVy+IH5cqAoGzE3mbr31VlitVvznf/4npqamuBPd29vL4hTRaBSzs7OwWq04ePAgT6rowUeTP/oBPvBqEgZNiHJyclBaWoobbrgBe/fuRTweR35+Pq666io4nU6cOHECIyMjCTCoQCAAnU7HHAiz2cydfuFUx+Px4IYbbsDLL7+csjtPkx6CIVIUFxfzxDH59cLXiUQiJlWT2mAsFkNBQQEKCwuxceNGbN68GQcPHmRBgSuvvBJ1dXW44YYbEA6HceLECTY0Tv782dlZVuRLNflJ9rwpKirC6OgoF2Q2m40nlCTeQROe5GvU5/NBq9VCLBbj9OnTXIQR3EtYiFJhnJ+fj0gkgpmZGeaeUWJOBU0wGMTTTz+Nrq4uBAIBaLVaLv7o+Hd3d/P1KPTsSdUpTr6WSW3M4/EwH/Bs7m06FsBcgiqXy6FWq1kogawnCMJKsEryyCJrAgqJRILf/OY36OzsZJn58vJy1NbW4oorrmAIcCZByb6w0UPG2IFAAKWlpWl9wJKPpcFggFqthtfrZQNjg8GA9evXY+fOnTCZTMzhHB0dhUqlSstTO3bsWAI/1+fzQaFQYOPGjYhEIgneX7Ozszh9+jSef/55dHZ2Yv369fOgrXv27MHRo0fh9XqhVCrhcrkwOTmJw4cPQywW83RMqVSipqaGPZgIdkbXChVYZWVl2LFjBxwOB37729/yhBoAC7L4fD5s2bIFtbW1ePTRRzE8PIxf/epXCecSAE/q5XI5q6kCYKPnuro6Lh5ffPFFzMzMJKyJX/7yl895Q9BgMGDz5s0YHR3F1NQURkdH0dbWNs9zLZPw+Xw4efLkvM8/1wqamUZrayva2tpYE8Hn82Fqaorh3CTSAsytS4QkUKvVUKvV0Ov1LFEvl8uxfft2NDY2AvjAYJ4EzYTFvxAi+mHysGibmTazqbHldrt5fRgcHIRcLv/QzuHHKS4WZRfjQwuj0Ygf/ehHnPwDwPbt2xNuXKvVyg/Oc3ETf5gdt48Kj00ulydAstauXQudTsfFFfF0hA8Tl8uFvr4+5v3I5XLU1tayATAwx19ZtWoVK0EJE5RUizgln1RU5OfnAwBDAmnyZbfb4ff7EYvFkJOTg7y8PPh8PvT392NoaAg5OTkMlaLupVDRjGTDZ2dnWVBASCzPycnB/fffj3A4zD6IZOhqtVoxMTExD3pDZp9CNTNgLgEuLi5GIBBgFbYTJ05wUZI8kZqdnYXJZJp3zsbGxtLCr4RBPDgSkyDol06nw/bt25nXQbCocDiMBx54ACtWrIBKpeIpgN1uRzQaZUgqFT0FBQWw2WwMI03ukAqn3SMjI3juued4mhGNRhOmWwqFAtdeey3uuuuuBRN2ALDb7QxhFMI/hTxBUh8kGXipVJpg3A3MFc/d3d2Ynp5mU9V4PI6ysjKGEE1NTeG5555jrqRCoeBzsGnTpkUlocvKyqDX6/maJS7bmQYJYMhkMgBzSaDJZEJubi5UKhXUajVef/11WCwWTvTC4TB/v/z8fFitVvaACgaDeOWVV1gkJC8vD2vWrMG99957VoUjWZZ4PB6Ew2FeMxbyARNCt4E5yNehQ4fYS07IQ9u2bRuOHz/OMEAAXEBSsk/b0ev18+4vuq8++clP4sUXX2SPPWBuSvfaa6+htbUVhYWF+Lu/+zvodDoAwMjICEZGRhAMBlmEJxwOY2BggDlgubm5LAkv9BMjrpDQK09o59Df3w+73Z6wn1KpFDk5OdDr9Whra4Pb7cbw8HACn0wY8XgcBQUFmJiYSFB1tdvtaG9vh9vtxsGDBzE+Ps5rOjBXMBgMBmRnZy96fs8kGhoauMlmsVjQ19fHjdWlRCQSwe9+9zusW7cuwXT6TCfPZxvU8KFzRuqT9KwUNvzo/3ft2oX6+voEqGM6aXqCA6Z6zUeFS7cUdVqhGBJZdUQiEezfvx91dXVLEjz6W4yLRdnF4LjQ6kZCBTfyZxFCL0idkcjK58Kj7MPsuJ3PbS907pK3mw6SRRh5eg+dl87OThw+fBgnTpxg09FYLIbJyUlotVqePOh0Otxyyy1oaGjA4cOHF13EKRH3+/2wWCyc2O/cuROrVq1irxOxWMyFXyAQgFwux/T0NKsOhkIhTupuvvlmHD9+PMHbKj8/H5/+9KdRV1eHp556ig2NKXQ6HYqKivDOO++gu7ubO/9TU1OIxWI8QaSEXyKRoKSkBC6Xa14XGwCysrIglUrZ0DoQCHDyJBaLmTOQygyZIpOCjIIKV4LuFRQUwG634/XXX8eLL74ImUyGkydPcqHicrlw7NgxAHMJIcHFfD4fBgYGEAwGWbxDoVBwcmuz2dImBYcOHcL+/fvTwjSBOUXMbdu2peRtkfR3XV0dHA4H2tra5r1fJpOhrq4OZWVlGB8fRzAY5HNP38Xj8fDEin5HqpV0XGnKWlVVBZ1Ox4U8cRBTTXqToYLJSdKOHTtw6tQpWK1W6PX6s1JVpalDX18fpqamWOBEIpHA5XJBJBJxB3pkZAQymYwnazKZDJdccgny8/MRj8e5wUB/J6idUOntTIKsTEhmXaFQoLCwMEGaWxipoNsGgwH33HNPghiA8L07duzAwYMH0dHRwVw5Mp1PPv5NTU3Yv38/3G43JBIJZDIZHA4H3G431Go1rrrqKvzxj3/k+y4QCCAQCMDtdsPpdGJsbAxerxeRSARisZibRKtXr0Z9fT2am5u5UUBm3DfffPM8ef3kwkHYDAPmCuy8vDz2m5NKpTAYDKioqIDNZmOoIa13qUIsFrOKbHIEg0H09PTw8RJGbm4uLrvssvPWDBROyyYmJvDWW2+ltAIh0Z6FIhqNYs+ePbjqqqtgNBrhcDhY6fFC88C3b9+Offv2sTASWRYA4GtF2IAiMZ4bb7wxo89fqNg8G3TNucznMvVWFe5vVlYWLBYLIpEIP1Mv8sgWj4tF2cUAMJ/QThKm5/MGWr9+PfLy8hAMBqFWq3HllVcm/P1cwv2SF6gPa2E4H9tuaWnBk08+yUlrKsVCISZcLpenhWSlgrUUFRXBZrPxlIl4VJQsUgJgtVphNpvR0NCQ0VTQYDCgvLwcBw4cgMVigd/vh0ajgc1mw5e//GVs3rwZdrsdBw8exNGjR5nfcfTo0QR1M1JRJMW3vLw8vPfeewkKidu3b2delBDqRH9vbW3FG2+8wWINIpGIZc4lEglLjlOn3OPxYGJiYt53isVisNvtDI0jry0ynhbCQ891hEIhhEIhvPfee5yY5ubmcuKcKiKRCAKBAPLz81FfX4/BwUGIRCK2CCDj3omJibTmnwcOHEBzc/OCBRkwp97405/+FG+88QY2bdrE4huPP/44Dh06BGBO6ZL2WSaTMYeP5J8/+9nPsn+aEDpH5tmTk5MJU01hcipU+PN4PDh9+jTWrl2L8vJyjI+Po7S0FMuXL19w0ptuTTIYDCymQrL4ZxMNDQ146623GFYMgJVBFQoFDAYDRkZG4PP5WCAFmJse5ufnY+fOnZiZmYHRaER3dzcUCgWysrJQX1+Pu+++e0nwNbPZjH379qG3t5ftA/r7+xEKhaDT6fjeprUkldmtULky+bilWw8NBgPuvvtu7N69GwMDA3A6nWhra2OrAb/fzxPccDiMFStWwGw2Izs7G4FAAJOTk4jH43j//feRk5MzT7mTggo0ilgsBo/Hg8LCQtTV1WHLli1s7kx8IZ1Ox0UCHaPkBJiKRUJ6AHPFRllZGZYtW4aSkhKsW7cOdXV16OzsxB/+8AdWSFwoFmrokNdgqqJHpVKlnFIvFmazmREEtI6mex0wx9lbiEe2WEFG4XK58NBDD6GjowMOh4M9aMlm49vf/jaampqW9F3OJBoaGvC9730PTz75JMOE6RypVCpcccUVGBoawunTp9mKoqCg4Jxs+0zRNedywrYUb1Wa8qvVasjlcixfvpwh08K14mKkj4tF2cUAkGjkSRj+TBT0ziaKioqwatUqhqmRpDZt81zB/T5scY/zOZUzGo34wQ9+wHwSh8OBuro63tZChPVMO47CSdvo6Ch+//vf49SpUyyM4fF44Pf74XQ68frrr3ORt9hUsKWlBQ8+aVag3gAAIABJREFU+CBmZmb4d1T4PPnkk0xGHx8fx+HDhxmqlhyUjJ88eRJWqxUNDQ1YvXo1d6MrKyu5CCspKUn4DJlMhunpaRw8eJAnSVSoaTQa9jwCPjAtjsfjsFqtadXEPB4Pcz6o2KSuOKndnY+iLPmYAODvmol59cjICPPiAHBSK5PJoNPpoNFoMD09DbPZnHB9vfzyywnnMF0Eg0EMDg5icHAQb731Ftrb27Fq1Sr09fXBZrMx9K+goAASiQSlpaVcGBcVFaGoqAgdHR1c+Pf09EAqleLIkSMYGhpCKBRKmfDRlIl+aL2hCSZ5yWUy6U23JoXDYTbAJln8swmCxQFzMLzu7m4Wg7HZbKipqYFarcbU1FSCWqdUKoXb7YbRaGRvKOrkb9u2LS1/LJ3SoNlsxn333Zeg0pqXl4e8vDxotVosW7aMBV62b98OACnVc4G5+yITLzRhNDQ0YNeuXXj++efhdDpZ0Eav18NiseDw4cNsf0BiM9XV1ejo6GCuFpk3Z1oMUJDqaTgcxi233ILa2lo2VA4EAmhtbeVzlaqAFyI9yATc6XRyA6CgoCBh0vbrX/86QX01XaQSLALmmg4EeU21No2Pj+Odd95JgAQuFkajET/72c/Q0dGBUCiEp59+Gvfee2/CZ1DR1tbWhoGBAZw6dSrjz18o1Go1C96QjyHdV5OTk/j3f//3BcVazmU0NTWhrq4Ora2teO655zA1NQWRSIRPfepTyM7OZisKsViMVatWMWfsbONM0TVLaWgvlqOQ4rHT6URNTU3Ga5tWq+W10uPx4Morr7w4JcsgLhZlFwPABx0ZIpw6nU5YLJbzOm4mXkAoFILH40FfX19CN17IVznb7XwYAhsXohhsa2tLkP82mUwszAAggbCuUqng8/nOyMyWzsemTZtw1VVXYd++fXjzzTfZUJqmSxMTExl1wQHg3XffnSd+EY/H4fF4MDQ0hN27d0Or1eLQoUNsfJwqotEo+8F861vfwqc//WlUVlbCbrcjKyuLIW9msxkvv/zyvIeKUqlEOByG3W5nFUUqDCiZI5NpgjGmS5zI70sulyMej7OaJfBBsXe+CzIKEn0gif50kZOTg7KyMlYlJGVL8lrLzs5GdXU1TCYT3n//fWRnZ+Pmm29GXV0ddu/ejaGhobScuXTh8/nQ3t6OmZkZTE1NMUeDilaZTIbS0lKUl5cDAE/EqMg9ceIEhoeHEY1GWWSCkvDkEIqXZGVl8ZSXplk0DczPz+drI13xlS5JKisrQ1VVFQBwQX62QffwI488wl5yAOD1ejE4OMgiM6TgmZubi2XLliErKwuDg4PQ6XS47rrrsHPnzgUTOqF6o8lkgkajwapVq3DLLbegra0Nhw8fToB/2u12Nq42mUw4efIk5HI5ZmZmsHnz5gT13GAwyBxDvV7P06OlrINUgA8PD0OhUGDnzp3cqLHZbFz0KRQKPgZdXV2sUJiVlYXCwkK2nUgVyZA6uVyO0tLSBPPv2267Dbm5uXj++ecxNTWFrq4u9gAj30bgg+eLkHdH1xXZFZjNZuTn58NoNGJsbAyDg4MJRuHCSOYsJQepVtIUj2CYqeLAgQMZF2UkeNLR0cFwymAwiAcffBB5eXmoq6uD0WhEa2srDh06BKfTybzGswlCPUxOTrJFAvFzheF0OtHZ2XnBVIwNBgNuv/12rFmzhqefRUVFuP/++1msJj8/n0VfzibOFtmTaUN7sRyFfPhMJhPkcjk0Gs2CDRXiiVMjU6VSwWQyYXZ2Fs899xxWrlx5wc7XxzUuFmUXA0AiYV+oQHY+x8302X6/n/1TUt30Qq+LMylsLrTARiqzWeD8FIMFBQVQKBQMv4nFYujr64PRaERBQQFCoRDC4TCr81VXV5/1MTAYDKisrEQoFOKHsEKhgEajwbJly+ZNU9LF1q1bsXfv3oSHrVKphFarRSQSYS4TcYRkMtmCE59YLIaBgQH8/Oc/h1KpRE5ODlavXs0Ql4ceeghHjhxJeI9SqcSyZcsgl8sZ/icWi7lYJJ8hgh6S1xeJkRCxm4oegjYVFhayV5FwkkHS+4tBlM42CHYphLalCpKfrqyshN/vx+joKMO0ALC6X0tLC0QiERfGw8PDqKqqQl9fH9xuN6RSKQoLCwGAPe0WC6/XyxMger3T6YROp4NUKoVer0dDQwNmZmbQ19fHogUTExPsD0RTr4WKQUpayQ+O1pnKyko2bU1e7xbqUAuTJGHy1NTUhN27dzPcj47fmU7JadJCPDKKeDzORSgwd+9t3rwZN910EwwGA/bt24ehoSFGPiykrmg2m7F3714cOXIEZrOZlTLNZjM355KvHzrW5NtHCfvo6Cg2b96cINjS19cHu93OgjhlZWVLniJSIbdnzx42Tm9qauKCiSZyGo2GCzNSvovFYigsLERZWRlycnLQ398Pt9s9bx2h+5saKitWrMDMzAx0Oh1mZmbwwgsvwO12c4HY1dUFi8WCqakpXmNpP2j9IygXWYWQabNarYbBYIBGo2HlPZPJxBzU5IKKJtep7inyQqT9X6h5pVareYKTCYJjbGwMoVAowfMQmPMT/OEPf4gtW7ZgdHQUQ0NDcLlcmJ2d5Un0UkKpVDJPj8R3SOkQmLt/U00HS0pKGBZ6IYO8uoA5VUR6FmRnZ6O+vv6sp2Tnopmb6YRtoYY1FeVk/VBVVZWWM0oh5J5pNBocO3aM1T8lEskFLaI/rnGxKPs/FmcDl6NkI1NzwHMBzaOEWyKRQK/Xz7vphbBKm83GggBL/V4XQtyDBAuoqE02mz0fxWBjYyM6OjrQ2dkJq9WKcDiM/v5+tLa24vbbb+fufTQaRX19PSorK88pV1AsFkOr1aKkpAQGgwFerxevv/56RgV0U1MT/uM//gNPPPEEvF4viouLsXPnThw9ehSDg4M8McnPz0d2djYkEgl8Ph9L6KeLYDCI2dlZhMNhOJ1OhMNhjI2NobOzcx78Ua1W45JLLkkQMKH3CpMLmnKRzxAVYsmeYZSU+Hy+hMSPjKABcOf3fAUlceSZtlAIC63y8nKGPyVH8ueQeTh5q0UiESxfvhzXXXcdWlpaMDIykpB8p0rW6DgnRzwex4YNG6DX6zEwMIDR0VEmjPv9fp540GQs2SKAIJhUMJMi5eTkJHw+HyKRCK644grccMMNnCAsVnylimQersPhYC87KmqoADwTji6tfbFYDCUlJfB4PFzAUiEqkUhQWFiIm266iYUFzGYzRkdH4XA44PP5UjaDaK1qbW1lzo5IJOJppd1uh9VqRSwWg1arRTgcTmg8AHPFdyQSgUajQTwe5yKanh/T09MYGRnhNZ6Ed85kHQyHw8wlE06uhCq9VLRZrVasXbsWSqUSHo8HMpkMEokE2dnZqKmp+f/svXlwlPd9P/5a7X0f2pXE6hZIAgkBwsZWAnyxoYQcduMwDXacuLEnbRPTzLRx4/E0nUma8bT1xH+0SVo7aZqYBGo7ZIrxlQRjQbDBkcHWCiSBDnTuaqXVSrurvbSn9veHfu83z65W0kqIw7HeMwxY1u4++zzP5/O8j9eBaDTKnnJ0T5LZO/neEWeNPBHFYjH+8Ic/4N/+7d/SmpeDg4Ow2+3ccAkEAmhubub9r7a2Fq+99hpDlhOJBMNJY7EYm23H43EolUqYzWaEQiGEQiG+vhKJhKGjwntdLBZj3bp1zC+kJgWFSCTiyaFcLsfGjRuh0Whgs9kWVcal69TX1zdnr5qZmUFfXx/bSpC5uUwmw/T0NBwOR87X9d5778UPfvADWK1WHDlyBD//+c/n+MoJ+b8ajQYGgwHbtm3D1772tVue4MtkMkxNTUGtVkOj0eDAgQPX/WxdKrJnvjwslwnbQg1rKspjsRgUCgXnhgsdh5B7VlNTg97eXlbG1Wg0t6SI/qjFalH2JxQrBZfLZTGvxGfZ7XbujpE5auaip25jNBrF2NgYQ/OWW3DeqKDzQR1U8q/JZja7kmG1ziqY/fznP8err77K+PvTp09DKpXioYceYv+l/v5+hMPhFXmQNTY2oqGhAR6Ph1Xg+vv72acFyP4wyXyAPPjgg9i5c2faz0pKSvDyyy/D6/WipKQE69evR3V1NaxWK86dO4d///d/n1e4goKKJZfLBb/fD4/HM8czhyYnr7zyCvLy8rjjSUmvMCj5p243eaVldnDj8TgmJycxOTmZZoJMkDnhVOhGRTaj7PkilUphfHwcP/7xjzkJzwWCmI0b19PTg9HRUWg0GlaYIysD6uSLRCKeNs439TQYDNi+fTs6Ojpw4cIFeL1eRKNRfg1dH3ovKvgkEgkTyjUaDTweDzweD0NO6XWxWAy9vb0wGo3weDycvGYWC5n36nzG0h0dHRgfH4fX600ToHG5XBgbG0MkElkWRzfTA04mk+H3v/89RkZG0nyRKGGioGkO7c2ZfoHEM3U6nejt7WVhEovFwmqhRqMRer0ekUgEer0ewGwDjURvtFotIpEILBYLlEolQ86Aa3utzWZjaWwqMMrKypa1D2ZLHoV7us1mg8fjgc/ng0qlwp49e7Bnzx5ukCWTSb539Ho92x6QwipdM0pCyQie7m9a18888ww2bNiAgoICbNy4kXmjZPwbi8U4mbbZbJiYmOAGAan2jY2Nwev14sqVK0gkEtBoNNDr9SgrK4PFYkFtbS2Gh4d5Ck1iL/F4HIlEgq0vCgoKsH//fhgMBvzmN79h7zOpVAq5XI6ysjL85V/+Jerr63Hs2DG0tLTg/PnzqKio4AIXmD/pt1qt8zbAyIOxqqoKNTU1rPgaj8fxf//3f/PuIZ/4xCfYUPnzn/88nnzySf5/u3fvRnd3N7xeLx8bBRXM27Ztwz/8wz/c8mKMgrikU1NTMJlM180lBZaG7LnePGyhhnUmJHshxVan04kTJ05wk6G8vJyfrwaDAVKpFH/zN39z21y32zlWi7I/obiZ3KmV+KxcFr3Vek1m1+fzzdv5vdVB58Pr9QKYTSznM5td6XC5XGhubsb4+DgXE+S9dOXKFXzqU5/izjkd6/Uek9VqRVVVFd566y32iQKuQfTkcnnWJDfbAySzYM5MKqn7aLPZ8N5777GnWmZhJpxESaVSnlj96le/4o63MMjDiaCfUqmUhQHmC4VCAQDzKrkJIxOGlA1mR/L4typisRgXD8RJyQWCJDRMFUYgEEA8HodOp4NMJkvzcUulUpDL5aisrIROp2Mz58xwOBx49tlnEQqFFiy+iYNDhYlWq+VCuKqqCoWFhWhvb2e/LplMBpFIBLFYjFgshnfeeQcnTpxANBqF0WjEzp07WZ0u817du3fvnOkC3d8ul2sOb08qlaKwsBCRSIT3hf7+flYLzGX9ZSZMAHDp0qW05gLBZYX3tvB1MpmMj1utVqO2thZnzpzB5cuXWbWSiubi4mIUFxfD7XbDYrEAAKtxFhYWorq6Gps2bUJlZSW6u7sRCoUQCAQYNkd7M3BNcbGsrIwT7IKCAhYDWWosxC/ONKFXq9W87wo9oE6ePJkGdwTAAiFA+vrMdv/HYjF0dXWhq6sLEokEWq0WRqMR4XAYSqUSFosF8Xicz91LL72EiYkJ9kqkaRfJpxNXVSaT4bOf/Sy2bdvGQisEgTty5Ag6OzsRj8exa9cuTE9PsxgS3VPAtakl+dBt2LABTz75JAoLC3HixAkW0wFmn01WqzUnBMfmzZtx+vTpOT9PpVKs6mq329mAXqVSQa/Xp3msSaVSlJWV4W//9m8X5LNZrVY8/vjjqKmpwW9+8xvY7XaEw2HI5XLI5XJ84QtfuG4rh5UO8ijs6+vD+Pg4jh8/vuhEab4QNn1yRfasRB62UMNa6CuYCyeV9kcShaJ7wWKxsHrmaiwcq0XZn1DcTO7USnxWti5NtlF8ZpJ+O8qqZna1b4alAMWZM2fgdDrnJBLJZBI2m409QyghWYnzZ7PZcPr0aYY1CZOZRCKRVWlpsQcIYdi7urqwfft27Nq1K+2+OHToEPr7+xEMBmE2m2E0GtHf388+YjSNoqkJdf0vXbqU1vUGZmGLJSUlCAQC8Hq9c7hhwiDYEFkJDA0NLVnJjYKOE5hNVu699158+OGHczrDS43FCqmFRDgy4Um58kLmez/qalM3XfjZ8XgcdrudJz/Z5L2TyeSiao4EUaTjJJ4fmYYPDAxg165dcDqd8Hg8GB4eZll4Uubs6+vjIpIUZzNFGuhebWtrm3PvlpaWIhAIzCnQxWIx6urqcPDgQS4YSECJ1AJz3R8yE6ZNmzahtbWVJ4U6nQ7r16+fs6bpdaQiSXDgtrY2Fp8RWkaIxWKUlpbiiSee4IKqubmZJy8FBQVoaGhIUxYk+OPg4CCAa9M8IaSzpqYGCoUC8Xgcjz322HV3yrPxi0lcgIoqoZ8lnQen04n6+nqsWbMGJpMJVqsVZ86cgdvthsvlYrhgrpFIJDA1NcU+igaDAX6/HxaLBclkEsPDw2hra2MhFrVajcLCQni9XuamUlFDU1WZTIZjx47h4sWLkEql3Izq7e1lo3f6zu+//z5+/etfY2hoCHl5ecxdlcvlqKqq4oKMkuVQKMQQQxJxyYXv+IMf/ABf+tKX0NfXNwc66fP5mLNKjSWtVovCwkKeDK5duxZPP/10ztfdap0V09izZw9OnDjBapeEOrmdCjIg3VOwp6cHra2teO655/D9739/ScearWGZixDXjcr5svkKzhe0V7rdbhQVFWH9+vVsxzEwMMBiTMtFOX3cYrUo+xOK6+VOLYUjtlI8LXqd3W6Hy+XKinW/WZyw64lbeYz5+fkMCctMLGZmZphoSw+Q6z02KpAIRpVJTg8Ggzh69ChMJhN0Oh0/KCYmJgDMFiaBQCBNoY6kt0+dOoWZmRmcPn0amzZtwt///d/zlIw8fIxGI6qqqvCZz3wGb7/9Nrq6uhAOhzE1NcUwOaVSCY1Gw8a5yWQSarUa8XgcKpUKjY2N2LZtG15//XU+P9lgizQ9I3iW0Dh0sci8HiaTCVKpFJOTk0gmk9BoNGhsbMSnPvUpPP300zzJXEqYTCZ89rOfxejoKM6ePZs2dSMOHClB5pJ4ZjsHFGKxOCd4Y0FBAWpra9Hd3Q2pVMrvSROZcDjMRs3LCYVCwddCqDQnFovZUHpoaAiXL1+G2WxmA+ZkMgmtVot169ahu7ubp6IEg7RYLHyvZiY7W7Zs4cKZkh+CX0ulUla/UyqVqKurw3e+8x3mVzU2NuLEiRN4//33MTIygr6+vpzgjNngk0ajEWq1GjMzM5BKpfjsZz+7oPdUpqouGcITQoFI/Dqdjid49FktLS3cZKmrq0s7ViqGSJ3UbDajqamJ4XlCY/rF1B9zjfmaOqWlpeylNzMzg+PHj2P79u3smUb8LzoHer0eBoMBn/zkJ1FdXQ2PxzPvPU+cRLFYnNZAoCkt3ddU5DqdThgMBoyNjfEEjuCGpNAnEomwYcMGlJSUYGhoiKeMb7zxBn77298yDPHy5cuorq5OU6jNZs3gdrshFou56UZ+bcRrdLvdKCsrYxj4UhPjP/uzP0MqlcLIyAj7Bmo0Gm7e0LRPLpezzQ1NuB599NFlFeIEY6dJ40p4/92oaGxsxPHjxxGPxxEMBnHx4kXmdOcay5143ai8YynHk7lXms1mnDt3ju0kZDIZNBrNbYtyut1itSj7E4tc+WCZi5h4BtFoFFVVVTlhk1eCpyXsyIyPj6dNDIQL+EZzwlYibvYx0nUsKipCcXExdyfj8fgc2JdYLF42rCIziABMIgDElaJiJRaL4eLFi3j66adRV1fHcB63242rV68iEAjA6XTiueeew8GDBxlic/78eU56ZmZm0NbWhm9961t4+OGHMTExAZ/PB41Gg5qaGn7d6OgowuEwenp6GL4oEomQn5+P3bt3s3Q6CXiQSmQymcSFCxdw9epVPu5s4huJRIKLB0o4c+2o0++RyMBDDz2EK1eu4OLFi4hEItBoNHj//fdhMpmwceNGnD9/flEoIyWDeXl5MBqNWLNmDaamppi3aLfbkUqlUFpaiqamJpSVlcHlcqG9vZ2hfNmmfFRALvTdclVVI/U5SuAyiy/6HCqIlhLkKQaAlT/Ja8xkMuHKlSsMWx0dHQUANsEmRU6j0QiXy4Xp6Wme6q1ZsyYtWc2W7BQWFs7ZNy0WCwwGA2QyGcrLy/Hnf/7nczr6VqsV+/btY/l2gjMuZDkyH3yyvb0dSqUS0WgUSqUSg4ODcLlc8+47QtifUICImjM//elP0d3djdraWtTX16e9lnhNpHhpMpnw9a9/nf8/8alisRg3fQoLC+c1pr/eWGgq4HA4WHlwcHAQv/jFL5BKpWCz2eZM0QcGBqBQKNDR0QGz2TxHxl0sFsNgMCAvLw8KhQKlpaXIz8+H0+lEJBLhzz137hwCgQALeIyPj0MmkzF8WtiUoYlYMplkqO2Xv/zlNGhpd3d3WoEYDAYxPDyMVCoFiUSCsbExLkzoujY3N+O1117j91UqlWhpaUFraysaGhrShKbmE6HIZvZN/37++efxzjvvwOPxQKvVMuJizZo1yM/PZ56hSqVCWVkZdu3alXWdLDWcTifeeOMNBINB5jWuBF/rRoTVasWuXbtw8eJFRKNR+P1+HD9+HBs3bsz5eXs9E68bkXcIVRQXO55MuPThw4dZLZfUTPPy8m5blNPtFqtF2ccsso3JgWt+VrTx3ayOBnVkOjs7uROnVCrZW2o1sofwOtKDkbr2pNhFhRkpIa0UHp+6ohs2bIBMJoNWq0VXVxc6Ojo4oZiensbIyAgrJkYiEYyMjCAQCLCct9frxejoKGpqajAyMpLVQ8jr9eLo0aNQq9Usg/zAAw/ww27v3r3o7e3lqRgpNiaTSdTU1MDr9bLwBkn4B4NBNrte7EFPvmOU1BFMaKl+YzqdDhcvXsTExARSqRRUKhXi8TguX74Mg8GA6elp7qLT+xKsjGBZJGIh7LhLJBI2DlcoFKioqEB+fj77iHV0dGB4eBg+n49fQxLUwiJ0Jb3ThHLWlLBS8UXKfcLvmC0IVpdNSIW4CiQtbjAYsHnzZrz88stccNL0Yt26dWwqrdFosH79euj1ev7uUqkUSqWS7RSEVg6ZyU62/yboktvtRn5+PmpqarKusWzF0UJJirBTHQ6Hcfz4cbhcLvh8Pk7Sw+Ew+vv7cejQoTTIXrbPFvKrKFl+//33oVKpYDKZ0vwh6fP7+vp4fYRCIfz4xz/GXXfdxWsvm2F2rpD05cR8UwG73Z4GT45EIjh+/PgcSKlWq+WpTiQSgUgkyiohL5PJUFtbC5/Px9xYoWATAHzve9+bs36o6KNElkym6T3J51Cj0eD//b//h8bGRi5gJiYm4Ha7mXtGrzGZTAwzpXNM55OSf+KcaTQa5OXl8cTd7XbPKzQlNAsXFoZ0rYuKilBfX4/BwUF4PB6W68/Ly0NxcTEMBgMeeeSReQuwlZiKUuOPFBdv53xg9+7dOHPmDHuCOhyORdelMG4nNFCmimIuvoK0x7z55pvMFab1SM/OpfoTflxjtSj7mEW2sTRwzdSSEu6btQFSh4ikUwkOYjQaVxfwAmG329Hf34/u7m7EYjEudEhymQozjUaDsrIyHDx4cEWmZMIN22Aw4L777kN3dzebnwoTnGQyCbPZDIvFwlAGgvmQmtiVK1dgt9vTVPSEIRKJEA6H2fw6Ly8PAwMDnDyTXLVMJoPVakUikcDExAT8fj+OHDmCyclJVu+Lx+OcPGUqBy4UEomEYXuUmADXVA5zmTANDQ1heHiYzVBVKhXLu09OTnJhQN8ZmE3KSOlxZmaGJzIk665UKqHT6eBwOOByubhwm5mZwdGjR9OKMTLHJT8xuVzO5ss32syakkpqHADgwjZzOkmQSzpvwqKU1Lzoft+2bRt3/8+cOZPGQ8vLy0NjYyNkMhna29sRDodZQY+KWRL/UKvVyMvLWxa8xmq18mS6t7d3jhl65u9mFkcA0pQRgdk1JoT6Dg8PY3p6mhXeCKJJSpLRaDSn484sKmnvJeEImUyWlqwXFRVhcHCQ749QKIQzZ87wPkLPirVr16KqqirN4034XVZCEXihGBwcxPj4OP93tvWYTCYRDAZRUVGB4uJiSKVSNkufmJhImx6bTCY0NDSwomJRURHvNcDs3ktCFrS2qOCjxslDDz2EqakpnD9/nif3oVAIfr+f+anCc2Wz2SCXy7F582bY7XbI5XLU1tZix44dLKpCSpok/tHT0wO/389eisA1KDsAVFRUZJ1UZjb0SDTJYDAAAPuS1dfXo6KiApcvX2YfslgshlgsBqPRyEX4jXhOk4BGRUUFDAbDbSfwkRlWqxUHDx7Es88+i56eHlaYXKqc/e3wHYUcsfLy8iVNKMmGQyqVcqOE9n2n07mqvphDrBZlH7OYb0yeq/TpSgd1iKxWK375y19iZGSEu31OpxNAdg+hj3v4/X709vaySIVOp4NCoYDZbGbvIOrwriSEiIrBvr4+vmbUGcucaqjValRVVUGj0cBisWB8fBwSiYQNQoVcDOrWU+FEIfTNikajGB8fx69+9Su0tLRg3759KCoqYgiVXq+HWCxmPgeZ2pLqGQCWp85VMp7EFEwmE0/iCCdP4hJk5ptNSVAYBF8SwkyFnlNUeNBEiwqZdevWQalUQqFQIBaLsZkwyVG73W4m+8fjcV47wskjTaANBgP+7u/+DjU1NfjXf/1XFkrxeDw3tDij7yaXy7mQzcZPIx84SgTkcjmkUikXYmQWHQwGcfnyZQSDQZw8eRKnT59OM7k1GAzYuXMn3n77bS7CE4kEBgYG2BA5Ho9z4afX65clgkP33cDAAFKpFN544w3U19cvuN6EvK3MgoX8tnw+H/toRaNRTE5OoqSkBBqNhouoSCSC4uJiWCyWnA3bM0OlUsHtdkOj0bDhNU1svvjFL2JoaIj3YQCc8OfaTV9JReD5ztd//dd/Mfc/D16eAAAgAElEQVRooUgkErDb7bjvvvtgNpuhUqlw+fJlxGIxdHR0IBKJcLHu8/nQ2NjIoiBCrvPevXuxfv16uN1uBAIB5Ofnz3l+NjY24rvf/S5OnTqF3//+9wxd9fv98Pv9+OEPf4iamhrmvdG5LCoqwl/91V8xFzdz0kj774cffsj3u1Qq5QlcWVkZtm7dCqPROO++b7PZ0N7ePsf4mgx/qQCk15PdACXZIpEIfr//pnC8FAoFioqKUFhYeMM/63qjsbERjz32GJ555hkEg8FFeXA3o2GxnFgulNLpdLIIkVarRW1tLQYGBhAKheBwONDc3Lwq9JFDrBZlH7PIxP8KJVhJbvhmb4B0TADw61//GrFYDOFwGEePHmWT2ttp07rVYbPZ8MILL2BqagqJRAJ6vR4VFRWoqKhAbW0tWltb4Xa706ShVyqIRxIOh+FwODAzM8MKdCQ2kkgkWBnv9OnTUKlUSKVSCIfDiEajPC0S8qeKi4vR1NSE//3f/02TU5bJZFAqlYjH4wyFJH5Zf38/6uvrIZfLkUqloNVqOWGlCYywuNFqtTAYDJicnOSO9WKRSqXYcPvSpUsYGBiATCaDQqGA1+vlRI4mk/Td5XL5HANqCppo0gSJgooxlUrF3C/qgt9xxx2or6+HzWZjZUOtVssQUYPBgEgkwpOlbNw0muzFYjEm5AOzE5Cqqir09/fD5/PlzB1batB1Ia4OgDTuIwm00DUkVbuysjJ0d3enFV3JZBJutxvPP/88lEol+vr6AFwTZigoKMDbb78Nl8vF8EkqgMn3iUIqlaKoqGhZIjilpaXsixaJRFhYIpf3yYQoHj16FJ2dnVxskpGxVCqF0WiExWJBKBSCz+eDWCzGpk2bYLVa4fP50gyLc+nMk9Jfc3MzfD4fFAoFn9+hoSFIpVKEw2GYTCYuyuLxeFqjLJdu+kqqw2Ur8C5fvgy/35/zPRuNRvEf//EfAGYhjXfccQeblff39yMSiSAYDPIe2tDQAABpnxuLxVi6/Z133oFYLIbFYklT1KTzDMzy3RwOR9p0fmRkBD/60Y9wxx134MMPP8Tg4CCkUik2btwInU6XpryXOUEhnhVNy81mMzcvAKC6unpe5T6hfQBNNDQaDQoLC7mBl+nFV1JSwlBMuVzO8OeTJ0/mDM9bathsttveBidbkEE5iRwtNB26mRZGuQbdt3v37s1JnVMYVOyHw2FotVqUlJRgYGCAG612u/22+I63e6wWZR/DoEWRKXn61ltvwefz4a233lq2ahLFcngEu3fvhtPpZL+LyclJThiA7BLqH7cpGikf9vT0wOfzQa/Xo66uDg888ACsViva2trYtPdGPMyEPBKr1YpkMgmPx8NqegQ3JKNfr9eLWCzGal1UhFABQ8mUTCbD5s2b8cEHH+D999/nz5NIJDAYDCgsLORON5mUTk1NsZz1zMwMhoeHYTKZ+LV5eXlQqVSIRqOsQmgymVgIIldZ+97eXoRCIYyMjCASiUClUkEikbDAASVHcrkcEokESqUSO3bsQFlZGU6dOoXe3l7+LII7kUokyWVLpVKEQiHI5XJYrVakUikWy7Db7fjwww8Ri8XQ2NgIi8UCqVTKiSMZ+AaDQYa1yWSytO9H0D+Xy4X//M//xNTUFJ83UmZraGhAT08Pzp8/f0NI9TQdpKJcp9NhamoqLaEmNT9gdtK6c+dO5Ofn4+LFi3OK6Hg8juHhYZ6sEVRGJBLB6XSy8AIVsTTl6ezsZG5dXl4eSkpK0NDQsGyVuEcffRThcBi9vb1Lkn4WwgfHxsYwODiI0dFRzMzMQK1WQ61Wo7q6mhN+AGhuboZcLufv1Nvbi7GxMTZDXmi9CzvzyWQSTqcTgUAgbYpKvxeLxeDz+dIaJKlUCr/5zW+g1Wrx6U9/OqdiK5MrA8yFa+Ya2Qo8Mr2enJxcsil7MpnE+fPnsXPnTpSUlGB6ehoOh4Mn4D6fD2NjY6ivr89qXF1TU8OWIwAYQikUzSJhl8wpeiKRwDvvvIOTJ08CmF2fWq0WGzduXNDEvLOzEw6Hg3mFGo2GxX7ovlmo8KViR8gN02q1aZOxTIirxWJBTU0NIpEIPB4PAoEAm6/fiCQ7m+/c7cwnE8ZSfMtupoXRQpGNX3g9TXCiEwwNDXFzUCqV3va8wNslVouyj2lkdmnb2trQ19cHn88Ho9EIAMvqgmVKECeTSezatQu7d+9etKCiBzjJRzscDgDXjJiFC/p2Hf3f6CA+A/k/yWQyHDhwAPX19XMMHG/ERk+G3yqViqE8nZ2daG9vR0tLC1wuF4BrSn2UKGzcuBHDw8M8uSDDWWA2Qbl69SoOHz6MoaEhFrsgo1UAqKmpwf79+3Hu3Dm8+OKL8Hg8DDtTqVQYHR1luXqdTodAIMDm0MSFCAaDLDe9lATO6/UiGAxyIUUJERU+dB1UKhWkUikqKirw9a9/HY2Njdi8eTOefvppjI6OMrdELBZjcnKS+XAlJSXsVUSTLhIjSCaTmJiYYLlrmoxR0l5eXo5kMgmlUsmecVqtFjMzM3C5XIjFYgwXBWYTa4fDgeeff54LvKqqKlRXV6OrqwsOh4M5cysR2XiCJMQSiUQYLkVQRjqfUqkUxcXF2LZtG44ePcr3A30HilgsBrlcjry8PFYMSyQSvD4kEgmKiorQ0NAAkUgEl8uFiYkJFp+RSCSw2+3YvHkzIwWWuo80NjbigQcewKFDh+B2u+F2uxdUVRQWKPX19ZDJZPB6vQylI27RV7/61TkwtpaWFhiNRigUCvaKAgCj0bjoehfu+aQaWVRUhHg8jurqahw8eBAAuKBQqVSYmpqC0+nk9RIOh/HCCy/A7XbjkUceyambvhBcc6n8vUwxBKvViq9+9av4yU9+wkJRSw2PxwOJRIKpqSk+36QYS8VK5gQJmFtUkwfd4OAg2tvbEYvFoFAosgoYUZOEgiwrqAlDa1eYKAPABx98gNHRUZ6UV1RUwGw2IxKJwGg0LjjtzSx2luJXBoAbYmQvkkwmb0iSTb5zcrl80e90u4VQ/CcX37JczJlvZGQ2aqLRKCtg22y2JTW9Gxsb0dTUhMHBQYbay+VyiMViVFZW4uDBgx+Z63grY7Uo+5iGsEuTTCY5gSV+TK7kcWHQAm9vb+fiIRaLobOzE93d3eyns9jDuaCgABaLhRPRbEart2r0f/LkSZw9exZ1dXXsUE9dsJsxtZPJZIjH49BqtYjH46ipqYFOp5tj4Hj33Xez79BKBiVGQqhrY2MjJyQkOAGAE2WLxYIdO3agsLAQLS0t6OnpgdvthtfrZUjd1NQULly4wOqGwDUOllwu53uA+B8dHR1Qq9XYsGED/vjHPzLfKJVKobq6GslkEpcvX54jJJELzCmbsmI8HodEImHTV4KN6nQ66HQ66PV6VmsrKCjg5DAYDLInmV6vx8MPPwwAeOGFF5jH5Xa7GapInBZhcknTNLlczg9N8oAym82cuNF6qa2txZkzZ5BIJODxeLImqj6fD93d3exbZDabMTw8zDyTlYrM800muuTpQyIJgUCA4ZxqtRpr1qzB7t278eqrr7KlgVwuZ9gnFQkEFTUajTAajfB6vXC5XAxVjMVicLlc0Ov1qKqqQiKRQH9/PxKJBAvPTE9P4+jRozh9+jSampqWlKhSyGQyeDweRCIRDA8Po7e3l4UzAKQVVSRLD4D/LZxqKpVK3Hnnndi7d++8n6fVaqFWqxfcI4H0IjCzM08NlYmJCezatYv3scLCQjaGVigUyM/Ph9fr5WOMxWLo7e1FLBbLyeCWYiX3bJfLxQqEAwMDUCqVyM/Ph8fjWfL9GwqF0NXVhUgkgqqqKjz00ENZi7DMY81sIrrdbjidTnR3d7PMPvFaKajhQNxIYSQSCfzud7+D2+1mfplwzQeDQfZUlEqlKC0txTe+8Q3YbDZuki1m8ptZ7Cw2Hbbb7XC73ejt7U2zqyFF04XsGJYbmffpR00corGxES+99BI/k3p7e+dAmpdiznwjgvaFiYmJtEaNXC5HeXk51Gp1mkpsrhZJjz/+eJo0vlwuZ/XnjwIv8HaI1aLsYxqZ3DKCUTgcDpSUlKSpaeUa9NAlyBpxSAKBAAYHB/khPN/DmTYqUoXLnLAtlGDcjLH4yZMn8Y//+I/w+Xw89dBqtewHQxvYcvDY2SKzq04TSKlUCr1ej5KSkjTrAOH5uBEFmTA6OzvZyuArX/kKX5P29vY04YpIJIKenh787Gc/w4YNG5grRMk4wW4AzBH5AMDTH2F3WqVSYXp6Gl6vF8ePH2dTY3qfuro6nD9/PqvvGEVm4UXFVm1tLT7xiU/gF7/4RRqHiX5Hr9ezL5tIJEJhYSH70Zw7dw4jIyOYnJzEH//4RwwODuK///u/WaK6tLQU27ZtQ2lpKfr7+9HS0sKTIWqIkBpgeXk5+vr6IJFIYDKZcODAAWzcuJGLLyHcyOl0zum41tfX47nnnuNCN1tMTk5CJpPhjTfewLZt21jl8UZGPB7Hvn37MDQ0BJvNhkQiwbDPqakpnnZOTk7i7bffhtfrhdfr5SkZJbSpVIof+FbrrJnugQMH8Lvf/Y6NxikpikajuHLlCsbGxjgpBsCyzYlEgqcZXV1dOHToEE8j5yt2KAgZcPz4cQSDQQQCAcTjcRw9ehSJRALhcBipVIonm1S4ExohHA6jvLwc9957LwtHrFmzBvX19XNgfpRU033Z1NTERTkVn5lS9JnNr0woIU1hhPwgEndob2+H2+2GwWBAfX09++upVCpUV1cvec8V7tlqtXrJ4iROpxPPP/88urq6EAqFoNPp2GeQJnvE5cyFL5qXl4fS0lIEAgFMTU1BIpEwLDtXJTyr9ZoHHQBcuHBhju8ZhVgsZnn6gYGBNLVQikgkgkuXLsFkMiEUCmHt2rWQy+UM0UwkEqyM+a1vfQt79+5lruliIZzs0bpZLIhDnKkqmkgk8OGHH+L73/8+vve9761YUXE9nKbbNbLdi7eSTybcF4SIC2HuMjExgebm5mWZWdPeEwgEuPEwOTmJEydO3PC85E8hVouyj3EIHzzkN0IP+OvF/ANgGA9BuughPl9BJVSWIjjY7t27AWSHK95sX4+zZ8/C7/ezx43b7UYkEkFnZyerLIXDYU7qrgdWmblxArPSz8QfITI3bXLZEvMbFfRAoe6pzWbD5z73OYbRUGJPk67p6Wm4XC6GlFFQx5f81YT+VgBYEEKpVKKtrY07bdS9TSaTmJqaglKpZEGPRCKBnp6eNInsbEHEeBLW+MQnPoGmpiZuAgQCAfzyl7/k3ycOEnG3aAoojKtXr8LhcCAej6Ozs5OnzzSJFovFfK9++9vfhs1mg9frRWtrK9rb2xlGduDAAeh0Ovj9fjgcDmzZsiVtipGZeFOiajAYeE0TpG54eJgLX2FyQMXJ9PQ0fD4fzpw5w0p6dO1IwISCjHKvJyKRCN577z0As1PERCIBo9EIqVTKojEk+U7FLIl0SKVSpFIpGAwGrF27FhUVFTxB0Gq1LPGt1WphNpvhdru5KCJII0mX63Q6iEQiBAIB5OXlscKmwWBANBqFw+FANBrF4OAgOjs7OVkR7o+klEj8NZoeBAIB9Pf3M++QvqtOp4NEIoHRaERFRQWAa55Q27dvR39/P3p7e1FcXIx33nlnTpc609CV7olM2HJVVRXvjZmJ39133817w/vvvz9vc6y3tzetqSESibB27VrMzMxg37592L9//5L3GOGUvaWlJWdxEgp63cjICACwPQQ1HWQyGQoKClhtc6EGw86dO1FbW4v33nsPIyMj3Cwym81LgkAKi4i2tja0tbUt+P2ffvppjI2N4cSJE7DZbGmcPQqRSMTXuKqqCnv37sUbb7yB1tZWFsM5cOAAT1Lfffdd/OQnP8HU1BT0ej2+8Y1v4MEHH8z6+Xv37mV4ai5CHSSBn63BRXDwtra2FbNb+VOgJNhsNm4SiMViVtkUxq3kkwkVlNeuXYv7778/zcuOLDmExdpyGvQkhS8SieBwOHia/FG9rjcrVouy1QCwMq7w2Ujd1METFgqZUzrC0E9MTDCfJh6PM4QSAE6cOIH+/n7uSGYmGDcjduzYgTfffJO7P0ajERqNBslkkiWCM3HZy+2ACRMqlUoFAGn8kaqqqrSC7GZCIQYHB7mg8Pv9aGlpgdU66xUlfHgTtIyUD6kIoMSVEiGdTsfFC4VcLodWq4VGo0EikeANXaVSobe3l3+XCj+a8kxPT+PDDz9cMCGjAqumpgbRaBQWi4XhbzRtmJiYQF5eXtr7kHR8MpmE0WiEyWRCWVkZ+zk5HA7ukk9OTkKv10MkEnGy/9hjj6WtAfr37t27s66TbJG5Tm02G9599104HA4uEg8ePMgeQpWVlejr62OhC1LFJHl6nU7HMvR0Tk0mE5RKJdasWYNAIMBEe1JcI6jhciKVSuHy5cts3CsSidDf348NGzYwpJF+j46HinaS/bZYLLjnnnuwffv2NOPbEydO4MqVK4hEItBqtezxRsqdBoMBRqORrQgMBgMXnZWVlfj85z/P00gSaCCVT0pkaY1ZLBaMjY3h8uXL/HmFhYWMElAoFGwYTJOysrKyNJU+4Brk2WazMd8xFArxRIxUGSUSCVpaWuD3+1FYWMgJ+YkTJ9DZ2YmrV6+ygA0ZQS+W+JWWlqZBIEtLS9PQCiqVChaLBQqFguHoFosFlZWVy953CSkRCoWW3IUnDyRSUzWbzQiHw3wvisVifPGLXwQAlqGPxWJzGjRr1qxhs+aBgQG+zxQKBXPKconMfXfv3r2ora2F0+nMCpOWyWRwuVzo6enh+zhbUbZp0yY89dRTacV/W1sbq0zqdDpUVlbCZrPhX/7lX9DS0sLNEpfLhaeeegrDw8N48skn57w38VhpL13s3A8ODqKrq2vOeie0hsViwZYtW3I6X4vF7ahGuJSgqTkpmhoMBuZq3k58MqGC8tjYWJoQSWYzeKmWOkLRELKJ8Pv9kEqlOd9zH/dYLcpWA8DKKRlmJo3zvRf9PLPLq9VqUVdXB7FYjKqqKjbLzCZgcaPVFzPff+/evfB4PPjlL3/JXCk6DofDAaPRiF27dsHpdDLELJcHvHAjowexsCtOpHPin2RCqm7kwyzzHJw8eRLPPPMMm9vG43G43W6cOXOGRT6A2YJs3bp1qKqqQmVlJZLJJLxeL8LhMEtPk/qiED4GzBZNpNRECmMOh4MhaJlKZjRJociFN5ZMJtlY+dKlS+jo6MCFCxfY06y7uzvtPUnhLxqNMozx4YcfZhjXsWPH2McHmH3wSSQSlJaWQqlU4rHHHpuXI3S9DRGCeMbjcbhcLjz33HPw+XxIpVLw+/1sGaBWqyEWi+HxeDAzM4NgMIji4mKUlZVhZGQEExMTbGhN4iMSiQR+vx9utxsPPfQQHn30UfzsZz/DW2+9tagv23yRKXAQi8UwPDwMo9GYNkkFrk1LNRoNTCYTpqamMDU1hVdffRUul4v5X729vXjnnXcYhiaTyVBbWwu3281Kj/v27UMqlcJbb72F8fFxViglXp/JZEJjYyNzqohTQY2Wvr4+fs3k5CQbk9NesGnTJoavGgwGHDhwgKcuVqt1XgQCJXIkwEB+UW63G8PDw7DZbAxdE4vF8Pv96OjowMmTJ9HZ2YnOzk4As/d0QUEBF1jZhDEWCyE3taysDHfffTekUmna1Ph6Y6lTAkp2W1tbIZPJoNfrmTc6OjoKADwNBWYL7IceegipVAomkwkDAwN44YUXGC7o9Xrh8XgAXNsrCM4snAwvFpn7biwWw4EDBzA+Pg6Hw8GCPrSPDA8P4+c//zlDpRUKBaRSaVojq6SkBE899RQnyTabDc8++yyGhoZYNKm6uhrBYBCPPfZYGs+LIh6P46c//Sm2bNmStucsZwJy+fLltOm4WCxGQUEB1q5dy3DsWCy2LI+8zMicBH+UlPqEfPqxsTGGxj7wwANzmqS3mk8mVFAuKiriPcrpdKY1v8vLy2E2m5dUkGU2KZxOJ7xeb5oB+kfput6KWC3KVuOWwQbooSZMdhoaGvDAAw8wV6KtrY03CaGABYAbeszznZOKigqsX78evb29TAxXKBRchBHcMhNiJpweCgsw4vMJi04qwghOtn///jmwNWFcj9lj5nEJ3z/bOSAIJyUaJMQhhGoBQH5+Pr7whS9Ar9fzFMBiseD++++HTCbDP//zP2NoaAipVIoTJAqlUomamhqWrR8bG8PIyAjD2YSRl5eXVS2QJPezBU0vxsfHeWInEokwOTkJiUTCkxRhEGeNYH9lZWVpfEfyqiJBkPz8fJSVlSEej6OoqAg6nS6na7LUIMWrlpYWTlgvX76M0dFRLjDJ56q8vJzFAsivzOVyYWRkhA2oaepIUFk6l8FgEC+99BKamprwyU9+Et3d3ejq6lqR75BKpdiPiK4Z+b4RTLShoQHBYBATExMIBoMIh8MYHBxksYmJiQnIZDLmme3YsQPV1dUoKSmZo2DY09MDu92O0dFRLhDHxsYwMDAA4FqRTGIPQs7t2NgY9Ho9nE4nNwgSiQTGx8dx9epV9gcsKipa1EQ6M5EjVTuyOCDD7PHxcV5bQs7d2NgYnE4nFxJUfAtjoYKfJlbkt3f06FHU19enNYPIVsNgMKRJWl9PEp4NUTGfTD7JyzudTubjlZaWMoeToItisRhyuRxnzpzBiy++CJFIhLq6Ohw8eBCNjY1wuVw4f/48KzRSgUtFfywWg0gkQl9f34I+c7Rn+v1+LoapwCGxl/z8fExPT7M1CEUikYDX62XlS/I6nJychFKpxM6dO/HXf/3Xcwqynp4eBAIB6HQ6lJeXY/PmzTh8+HDWgkz4WWfPnuWibLkTkB07duC1116Dx+NBXl4eCgoKUFhYiEAgALvdDpvNBrPZjKKiouuy0cnVjPx2DGExQ8+ogoKCeW02bvVEMFNBOXNCvlz15szv5XQ6mXu+nKnbxzVWi7LVuGWbRKakMG0EC3ElCLI3Hx9ipWK+c0KQn0gkgrGxMXR3d8+r1kawzGPHjmFwcDCtA07fidS1hIUpTY1IBICgaAtNHYnTsGXLlkUTClKFy6YEJyxAs52Duro6Vh+USCRoaGjAo48+CqfTCY1Gg1AohLy8PBQVFaG3txdOpxNXrlxBPB6HXq/H1q1b8ZWvfAUvvvgiv2+276PVajE4OMjS8fMVWDMzM3MEOQDM8ekShsFggFQqZSgicA0uJ5ThFwZx1ugzVSpVmgLcmTNnODEkIQSCx93I7qCQnwbMTgF+9KMfpYkdzMzMQCKR4I477uBEkiIajaKjowMajYYFVEhwhSZhVHC73W60trbyRCubSmVmSKVShvYRvDPba2ZmZtIESRQKBeRyOcuEJxIJtjYgnzOaVNN5KCwsxNTUFKRSKdra2uDxeLJyGPLz85GXl5c22UwkErh48WJasTEf5/bMmTMYGxvj19PUsaurC4lEAlqtFqWlpYt6hlEi5/P5+LtFo1G89NJL/HvUcKACQq/Xo6mpCbt27cLJkyd57yR4EPF8MuWss6EKSktLMTIygitXriCRSODSpUvQ6/U82abilWCmRUVFyM/PXzIXLFsIBQHma645nU4899xzaG1tRTKZhMFggMVigclkwpo1axAKhSCRSCCXy6FWq6FUKtHb28scSuL3PvXUU3j88cfR1NSE5uZmhmKSt6BGo0FPT09W6Xrh9aLp6fDwMDo7OyEWi5Gfn4+HH34Y9fX1OHz4MM6dO4dwOAyZTDaHx5mXl4eysjJs3rwZTU1NDDf2+XxYv349qxMLv/uVK1cQDAah1+tRVFQEpVKJM2fOsF1MthCJRDAajdixYwf/TLiXL2UCsnfvXvzgBz/AiRMnIBaLMTU1hb6+PhbvisViGBoagtls5tfQs5A+d7EpbbYJzY3wSVzpyLT/GRsbQ0lJyRwj8cy41f5k2SbolE9dj3pz5vfyer24cOEC3G431q9fv6Sp28c5Vouy1bhlm0Qmt0xY2Lz55ptob2/H+Pg4dDod1q9fjwMHDqwYEXWxmO+cWK1W9uIgjkOmNLTQPJQkcX0+H1QqFYqLi+Hz+bgAI3WttWvXzpmU5Trupy4jbarZyNuZnTCpVMrG3GQaS8WNsAAVngMyhGxoaIDH48E999yDRx99lBPiHTt2sDpkaWkpotEoRkdHeQISj8dx4sQJ7N69G3fccQdOnTqVtXBKpVLo6+tjr57lhBAiJwyCVZJsuvDnSqUSCoUCPp8vrTBTKpUQi8WIRqPMdXzllVdw6tQpFBQUcEJNiWN5eTnq6+tvmoKYsHigBGd8fDzNm2x8fBxHjhxJU7gk/zKaGFZWVkKtViMQCLARNRUd9DdxwciTbbHrQ8cgk8mYF5YLvFQoMBCNRnH27FkWIRGLxdBoNBgaGuIik7rsZBBNyaOQwyAURQkGgxCLxWmTOb/fP69CGJ1jm82Gzs5OBINBNiTX6/VQKpWYnp5GKBTiydV86zZzLRYXFzOclvYK4gASbFOv16O4uBj79u3jCa2wUOzs7MSLL77I3/ull15i3lJtbS0bjavVaqxfvx719fU4ffo03nvvPV4HwWAQwWCQhTSEQWbmkUgE5eXlAFamEZbZ+BEWkzabDT09PZiammKIKAmxvPfee7BYLPD7/YhGo6zGSiqmIpEojZdM/GOafvr9frzxxhvMKZXL5RgZGUF1dTUnqZloBppo+v1+RgvQZL2zsxMnT57kSQlNbIW8VFJRJTTFkSNHIBaLYbVa5win0HcPBALstadUKrmAJghkLBaDRCJBYWEhCgoKAMxCIL/whS+kQRev5xm/d+9ehqIdOXIEANDb28u2EiKRCMFgEAMDA3juuefYrgGY2+jLjJWY0NyKyJxyKxQKFBcXL1rM3K7qkkLoqLD5vZTIzOcOHz6Mnp4exONxqFSqOcJYq5E9Vouy1VgW92ClQ1hI0BSnvz7dCw0AACAASURBVL8fHo8HJpOJ+QDXS0TNNRY6J42NjWljeZJ2BsDy2GQEu2bNGoTDYcRiMajVahgMBuTn56dN/7IpuwG5+57lMunMhIpKpVJIpVIYDIY5SnDCApQmcCUlJXjjjTdw9uxZpFIpbNy4EXv27EmbKggnNiS1np+fD5fLxYl4JBKB3W7H/v378e677+LcuXNzJifDw8OQSCQ5Je/zxXxCFDMzM7h69SqA2Y4yyfMXFRWhpKQEk5OT8Pv9SKVSkEqlLJahUCjQ3NzM75NMJuHxeOD1epkfotfrUVdXxzyCW7GOrFYrnnjiCUQiEbS2tvLEjGT8gdkEm6ZNUqkUiUSCIXjFxcWorq6GSCTCe++9xxBIOnf0h2S1qTBbyH7A4/Gwsmau1zSzWCf+Ifm4ORwOOBwOfOc738FXv/pVjI2NsRm1wWBg2Kjwfqau9tjYGCt/KhQKzMzMQKFQoK+vD3/4wx/mVQgjOFlfXx8SiQSqqqqwbds2bN26FR6PB6+99hpCoRDi8Th6enpw6tQp1NfXp3mVkdx0Zld6y5YtOHz4cJqowszMDMRiMQoLC/GlL30pbb3R+9F/x2IxlJWVoaOjA263m5sOer0e58+fx+joKKLRKKLRKE6fPg2FQsGG6EsJMikWntfr4fYKk8FMbySrdVZxUqlUQqvVYtOmTfB6veypRLxNobeeXC6H2WyGXC5HZWXlHGsXOr4TJ06wINP09DSCwSAUCgUA4NixY1nRDCTKUVhYiGQyCbFYzH6ahw8fZksHYLYZQXsLmc6r1WoYjcY5k4lskyGv14t4PA65XA69Xs8WCX6/n++JyspKiESinE2gr1dYQvhMpKJ2bGyMzawnJiaYw0pB9hPzFfA3y19zpUNo/wPMoi8WK2ZuF3XJbNyvlYKO0p5Ewly0v6jV6o/E9PN2iNWibDUArIz64lJjvk2KJM+npqYQi8VYaICUGJcDw1hOzHdO6OFESd7rr7+O48ePQ6vVMjk/Eomwmhf5Jen1epY7z8V6IJfvlcvUUPg7mRO5bEpwNFUQQjNefPFF2O129inauHFjVrEVISQpGo1Cq9WioKAAbrcbMpmM/cwA4P7778fo6Cj6+/vTCjNSy1MoFCxtLZFIYDAYmAM2n6eWTCZjafz5CoDJyUmW65XJZGn30MzMDLRaLWZmZlBQUIC77roLU1NTuHTpUlbYHfmxkaDJ9fAqVioaGxvxwx/+EMeOHWMVulAoxIbKVJQpFAqeFlGBNT09DbFYjO7uboYvms1mlJWVQaFQsJEsQQp1Oh2LjCwUdE0zFS0XisypGhWXQtih0+nE//zP/6CkpAT5+fkAZguHuro6bN++PWsCSjBBoR0DnZe+vj7MzMxwc4G6vh0dHTh+/DiGhoYQCARgMBhQWVmJr33ta3zPf/DBBxgYGOAJ+o9//GM2eScOU2VlJcrKytLW65YtW9DZ2YmOjg6GZwKz97JOp0NZWRlqamrmnTTQMVosFsRiMQQCAf48EtIhfhqpUZLAyVIiLy8PW7duZXPlxeCHi4WQR0S2EU6nk7lS9fX1jEqoqKjAfffdx9w+KtqGh4d5DUqlUpjNZnzmM59BU1NT1iQwczKj1+vhcDhY0AWYhc6SOh2hGYTwyT179kAmk7Gn59GjR9MUYUl4iJQOlUolpFIpq+gCC0+ubDYbzpw5A5FIxHDVhoYGtLS0MGRYqVRiw4YNi/roCb/zSghLCJ+J9fX1sNvt6O3txYkTJ+ByudjKglAXer0e4+Pj6OnpyfrMyzwPH4WCDLjWTMgFrkhB3n9U3N8qFcLMJm5bWxvGxsZYrMzpdC7rHsn0ka2oqGBe8vr162/76eftEqtF2Wrcssg24QHA0vgknKBSqbgTd7soNAmLR+I8kIw7ycFarVbceeedOHfuHACgrKwMOp0uDep4PbHY1FBYWBF37P777+eOfbaCkHhwQrgOKSxSYh+NRtn8dr6imrqfNGkxmUxM0KdjGhwcRF5eHrRabZo5KQlplJSUYGJiAh6PB2vWrIHVaoXD4cD4+DhCoRCCwWBa4iUSiVjKfXh4OCvXjIKul0qlQllZGVQqFQuPVFZWMsT05MmTbO5MEv+ZBd+aNWvw9a9/PafkaLGJAiVkZrM5TUhkqWG1WvHNb34T+/fvZ/sCYJa/cvHiRfh8Plaf1Ol02LRpE/PGLl68CL/fj1gsBrlcjpKSEjz66KMAgJdffhkDAwOIRqPMpcp1+kU+Y/OZWQuDEm2aUgh/nhlutxt+vx9Go5Gl5FtaWtDR0YHvf//7fA4bGxvR0NDAkxWCvQHX7h0qHpubm7khMTw8zFMAaq6sXbuWobt0vu+77z6cP3+evyMlpwRvBcCqpQcOHGAxo8OHD6O5uRlTU1NsJi6Xy3kakjntoSA4Jq0jOg90Pch43Gq1YmRkhIsfOo9LKZANBgOamprwzW9+My1hWyofWbgG6LUOhwOBQADDw8MQi8VsM9HY2MhwQ+GaIe4scM0uQSqVorCwEGazGSMjI3jnnXcAzE7/hT5zwimlXq+HQqGAxWJhg3KFQoGKigqo1eq05lUoFOJJrHCdv/nmm2htbeUpNE3EqGmhVCpRWVnJ3nR07jKRGAB4EvbCCy+kTWNpQqpWq1nMJRKJMAeOnpvznfcbxRmnAo2acxaLha/j9PQ07w89PT1wOBy815KPHgAWVbkV0vDLCeEzVSjEtVgRQ+gfKlJIIfVWRGYhvGXLFvT397PlR0tLy5KvRbbmzOOPP47a2lpMTExg165dt/21vV1itShbDY4bLTGfGdk4S7SwtVot1q1bB7/fD51OB61Wi9dff527mwaD4ZYrNJWWlvKEgfzVCgoKUFFRkTaBGh0dXZJEfq6xEHk7G+a9uroaZrM56wNEuKmSBDh19KgDTx5BlLgsJoYSCoU4uSDVNFJiHBsbY2XA6upqNv6VSCTYunUr9u7di1QqhXPnzmFmZgY+n4+np0LxCJfLxckJwQ1HRkYWlWuXSCQ8UZucnGSxBb1ej23btuHChQu4fPkyX1fy5CktLYVer8fbb7+NcDgMhUKBhx56CJ/73OcW/LxcJgo2mw3/9E//hMHBQUilUrS2tuLb3/72dd3j2aa9mcU6HQ8wC+siyfC8vDwYDAbce++9fM+0tLSwciEVG7lMXMgnbGpqKudjF4lELOgwMTGx4OdQESQs4C5duoTnnnuOCzOr1Yo9e/bg6tWruHz5MnPmADAPSaFQsDE83acej4d5kVqtFjU1NXjyySfnrCOaank8HjaJpoaDz+djmOfAwAAuXrzIwirNzc1sUwCABT3uvvvueeHZTqcTR48exbvvvstrQFiMKRQK1NbWYvPmzaitrcXZs2fh8XiWDSGSy+Vwu9149tln0+wdlsJVygabKioqgt1u54lXfn5+Gn8YQNqeJuTO1tfXw2KxYHJyEkajEWq1Gk6nkydrCoWCvd4OHToEsVgMtVrNCpeUIPv9fiiVSqRSKVgsFoYC0mRnYmICzc3NPDkTFjU0GaVQqVS444470NrayoX+I488goqKijnPVSFP8dChQxgbG2OURTgcZjN4KkzLysrYDJ34Y8ePH2f7mPmmlDeaM57JJTp27BjvLYlEguGhMpmMnym0/7S0tAAAGhoa0t7vdoz5nqmLrSni+brdbsjlchiNRjQ1Nd2y75mNmkHrhlRwl1q4Z8sFqFgfGxvjCfftxKO7XWO1KFsNALcG75y5mQvl70kWmhKlcDicplCYn59/yzHKVquVJwhEGM8GY7hRfL35HrbZJHoNBsO8D+RM9SsivldXV7Mvmkwmw69+9SuMj4+jvr4ee/bs4c8Vfj4l/D09Peju7uZrFIvF0NHRwSqNwCw3o6KiAvn5+bDZbMwVIVJ5Z2cnLl26lKa+SMTyWCwGr9cLtVqN/Px81NXVYfPmzXj11VcXhWYRlygWiyESiSAajUKv10Or1cJisbDxMCVlZrMZn/70p7F161ZOxGUyGXp7e1FdXY39+/cveq1y6Va3tbVxgi0Wi9Hb23tDIC6UDGabQuzbtw9ut5s5RxaLBf39/Thy5Ah3P5uamjAwMIBXX30Vvb29i0IXAaC4uBglJSW4ePFizscpEolw5513MndjPjVNCrpeQtVJn8+Xdg6tViukUinkcjlD+cifqqSkBFqtNk1sR61Ws1+bRCJJk1nP9vk0RZbJZDhw4AC2b9+Oc+fO4dVXX8XExAR78h0+fJingTRVoPfQaDQ8Icks9p1OJ5qbm3HixAmGJQsnYMDs/X3XXXcxzNBut6O1tRUKhYK5fXR+cglqYHR2dmJmZgaDg4Pc3FmIe0t7AQA+jkxfr6985SuQyWQYHx9P42Rli8zXr1mzhov8qakpnnTKZDLe74Q+c8Tf2rNnD8bHx5n3Qv599NmZwk20FwHp+6zNZsPp06eRl5fHhtb5+fl8jsViMdauXYuKiop50RFOpxOHDh1K4+AQsiBzGksFqEKhwN13342LFy+yefnk5OS8FgwLXaOVCmHzp7CwEE1NTfB4PAzZo+J4ZmaG1/Pg4CDcbjebntvtdrz11lvzQsBvdtM4M+i7kBn5Qs9UYH51xqqqqlsOcRdeL+K8VlRULNtLLFsuIlyvwsbIreTTfRRitShbDQA3XxY/E3+cqcBEsKVwOMywDSEf6nZRaCKz2YUeFvSzxWAmy4lM8nYmZ2IxzHs29Suh+IjwOxGHQPgz4tYBYNjj2bNncenSpTnJejAYRDweR35+PstCk4fQW2+9hUAgAJVKhVQqhbGxMVy9epWFE0hunrg/lEz7fD5IJBIm3BNvaqEgoQpKimOxGILBIFQqFdxuN7q6ulBQUICtW7ciGo1i/fr1zB2i+Pa3v72kBCGXbvWWLVtQWFiI6elpyCQSbCwrQ7lMBtjtQCoF5OUBUimg0QAqFZBhtLzUyDZFEyZwNCEQ7gmkYgfMJonf/e53cyrKpqenYbVa50jyLxTJZBJvv/02n7fMIDVHYHZKtXPnTpw9exZutxupVAoajQalpbNG7G+++Sa8Xi9aW1tZ0ptMpsViMUwmE+655x40NTWxrxlwzdiZvLrmK8gAMNwzHo+jvLwc27ZtQ2FhIcLhMKxWK5v/OhwORCKRORYMMpkMlZWVPC3KZjr7/PPP47e//S0mJyd5ElNYWIjx8XFW5duyZQueeOIJNDY2pvFJCwsLF504Zgu9Xs/wPQAYGRnB66+/ngbFy7avPP/88zwJaWpqwv79++esAavVigMHDmBychJdXV0IhULo6uriJoDwfWkCr1KpoFarYTKZIJfLEQgEIJfL4ff7sWHDBhgMBjz66KNp6pQnT55ktAIdd39/P0KhEBfgVKBlrs1sRY3Q1BmYVWg1mUy46667cPLkSW6skBLtfGG32xGNRhkuTO9VXl6eNo11uVwYGBjA0NAQc9OmpqZYJbWvrw/Nzc23DAKYjVsMABs3bsSZM2fg9/vR19fHJtjt7e0AZgsbarT19/fD6XQiHA7PEUuar2l8swq1TPhhQ0PDgiJjy1VnvFkh5KLS9Pl6hNPmK/xpvQsbI8Ct49N9FGK1KFsNAHMfeDey4MncYOvr67OqkWU+SIlYLTSDvdGRy6afLSnJfI+VnkLOR94WkolzeQjQ73u9XpSUlCz4+/N9T1KipI3X5XLNO9WQSCSoqalJ8+Sx2WwIhUIsKU6eSOvWrUMgEGAVOhKmiEaj0Gg0/HPymDp9+jTDYxYKYdFGpPxQKISRkRFYLBaIxWJUV1fjS1/6Esxmc9Zrv9g1z3busnarZ2aAyUlgZASNTid+cuedGJRKoc/LQ1VeHrRHjsz+zuzBzhZmcvnsH50OMJkAqxUoLp79swIm1fTd5psQUNCUk/g0C8Xk5CTefvvtnPhkFFR0z8zMQKfTMV9KLBZDp9MhPz+fi/OmpiZ85jOfQVdXF/+eTCZDOBzG4cOH0d7ejlAohEAgAK/XyxL/Wq0WKpWKzXZjsVjavUmNFPrdhabzlHgGAgH09fXB7/dzsysYDKK6uhrr169HV1cX/vjHPzJ8ksRs5HI58vPzWQUwM+x2O7q6uuDxeFjdT6lU4sEHH8SaNWtw4cIFVFRUsLx6Juf0/vvvh9PpnGPWvliQ8i0FKXUuFHa7nSchANjTKtsasFqtePzxx3H06FH8/ve/xwcffIDR0dFFzbdlMhlPH5PJJGpqanDPPfek7V/C6U0mXGu+IP/BzGMU7ldk6kxcWJFIBIlEgqtXr/L+IpVKUVtbu+B3KC0thUqlYhEcggxTUQKk+7WRci4VohKJhKf97e3tWY2vV+L5I3wOAnNFoeYrmIQJf35+PgYHB9Hf34/+/n7o9XpUVVWlTf2mp6fR29uLl19+Oc0Lbz7+eebnZh7bSoXNZmPuZkFBQdYptvA8EXdxKeqMNyuE1ysQCMDn82F6eho1NTUrKpxGzzziNHd3d98QGsefWqwWZatx08Nut6O/v5/hiPX19VkVmDI7nVS43azR90oVU5nfdyW6RMJiij4DQFo3z2KxLCrRm+33gVnS+VLl+An2uH79evh8Pk7WSXqfpJy//OUvp72v1+tl0Y5EIgGj0Yjdu3fDZrPB4/Ggu7uboWaJRAIymQxWq5Whdl1dXejs7OSpWq5BsvDxeJwVBInLQYXuSt5nnNglEkBPD9DVNftnYgIIBIBAAGWBAMqmp4F4HAiFAJlsthADZqdlySQQi80WajLZ7MRMq50txrTa2QJtwwZg/frZf1/HNG0x2BM1cnIJIv0vBkHMFsFgEFbrrDVBIBCAVqtFSUkJnE4nRkdHUVhYiK1bt7KYgFgsZvU78twi0RmakAKziTRBHoPBIHw+HyKRSBrfIxvvdb610dnZyQWCWCyGw+HAvn370l5/4MABAMCpU6cwMTGB6elpvPLKK/D7/dDr9YjFYvD5fFCr1XMMoEtLS6FQKNguQiaToa6ujvlXDz74YNrxZHJOKysrUV5ezoXu9UQ2zuZC6muZnKpsqIGuri5W9vR6vTyNE76GhFxCoRAcDgfMZnMaZzfXhpLwvdxuNyKRCAvfLASzIrhhT08PfD4f5HI5KyISrN5kMrHwyH333bfouaSprVQqhUajwebNmxkeTtzBDz74gKGahBwgyLVEIlmw2bEcFExmESYs7oF0/7H53j/z/isrK0Nvby+rNMZiMVitVmzbtg3bt2/HoUOH4HQ64fP54HK5AFwTAlkMHke/Sw3CbEbkyy3WMp+TQsGWzN8Tnie1Wo2amhqmANwuQiaUj3R1dXFTi453ucXSQrmS0D7oRlkY/SnFalG2GgDmPvBu5HhZJpOx5PDY2Ni8/Bb6W8h3omO9GYt6pSCdmd/3ek0U51NyIjXIeDzOkMXMhEJ4jumak4F0U1MTXC4XG18vRB6nyHxYEh9Mq9WiubkZkUgEBQUFeOSRR5BIJGA2m1FYWJh2TCRCkEgk4PF4MDAwwA89j8fDCQdNyLRaLYqLi5FIJLBjxw44nU42G15KqFQqbNiwAR0dHczrSSaTuPvuu+eYua5IuN3A++8Dly4B4+OzxdjExGyxRUVVRQWgVKYXY9kimQSiUSAcBvx+wOGY/VulAj78EDCbZ4uyO+8E7rhj9ufLiIUmglarFX/xF3+BZ555hs89qVPK5XLmMJEC53J952ZmZuD1elFWVga5XI5QKITR0VEugqanp3H27FkA4ERZIpGgoKAA1dXVAJCmVkfHKhKJYDAYGPqXl5eHQCDAk6RMo9dMqI8w0XI6nSxWQROvLVu2zFvYUlef4FqDg4NQq9WsGjo8PMy+eMIpgMVigclkQiKRwIYNG/DEE0/Me30y1yYA5OfnQyKRXDcf9/z58zh58mQa1DOb+lpTUxMALCqhb7fbMTIygkQiwQ2S7u7utMlPJpqjpKSEFfCSySTq6upyXrNCFV+VSoWuri6Ew2E4HA4A4IZS5p5vt9vh8/kQCASYi7hhwwa+Z9atWwer1cpG1MK9LlvYbDYuBs1mM6qqqnDw4MG0c3Xq1Cl+9lEQDLy0tJTVRLNBXum8LUXoYz4kixA6Scq2dF9ne//Mn+/atQvDw8NwOp3cPHO5XPD7/di7dy8KCwsZLkywP6ES4ELwOPo72/P6em0bchHpyORlE3dxPqTFrQy/388m9SSuU1RUdF3iI7kW5jfSwuhPJVaLstUAcOMVmoQRi8WYhF1UVMQds/lgF0K+k9Cs+UYv7pU6J9m+73KC5NIDgQCLMVAxZbVa4XK5MDw8zNyszIIs88GU+f2sVisOHTqEDz74gB+a8xWiwgIvU9r55MmTXBySkIHFYsHQ0BBaW1vhdDrTkrGBgQFOxkKhEI4dOwaXywWZTMY8FuLPmM1m6HQ6XL16Fe3t7XjllVdYan2pYTQaUVdXh7a2Nv5ZJBJBIpFYuXsrlQL6+4H33gOuXAGcztk/Mtls4bRxI6BWL32iJRbPFloq1ez7ALPTs6mp2ULv0qXZz+vqAk6fBrZsAbZvB/5/L6+VCvIHGx8fh0QigUqlYlPuNWvWIBAIcNGcl5e3rOsEzCahpGhHFhmxWIxFAjr+P/a+PLjJ+07/0fXqtCQfskA+ABPbgDks0hbStCENIeluyW7L7pJsS7rp7M60pJ12d9OdnWm3zTbtr5NOJ8m0kyW9UqBJmpTNUlIgqXFNgASwIVgGI+MDX7ItW5ZkHdZ9/v5Qv5+8ryzJBwba1M8Mk2Bk6dV7fL+f4/k8z9WrkMlkcLvdSKVS0Ov1+Pu//3sSYbFYLBgcHMQvf/lLek+RSCSgAgIZyuSRI0fAcRyGh4cFzwsLMHp7exGNRjE0NEQUK9b5YQJEGzZsoIB8tsR27969sFgsuHTpEux2OziOg1qthtPpFAS/LpcLTqcTer0e5eXlJOTBkGuuJ/vZtFqt8Pl86OjomFdXORuBQAD79++n+V5+4M6Olz9/yFBIsbWiooLOrVgsht/vx9GjRwGAviejGU9OTsLj8WB6epo6SAcPHiQPxUIqb3x/NLlcDrPZTEJSJpMJer0eSqWSupF8VFVVQa/XQ6PRwOfzgeM4yGQy3HPPPVi5ciVKSkrQ0tJCv1do/bRYLDh69CjGxsagUChmiMiwc8XotnzIZDKysdBoNCgtLc1ZSMouLMyX/QBAwGTJ7pSx98tHS82+/9RqNcrKyuj5jcViOHToEGKxGMxmM1ECnU7nDCXA7Oco3/0NCPfrfPfcbN0zporp9Xrh8/lmiHRkz2Xx45SbwbRYDFgsFuzfvx9erxfJZBJarRYVFRXYsGHDDYmP5GMUcBx3y+LKDwqWkrIlALg1Ck0MHMeRcEdNTU1eShC/zc4Wj1gshpaWFgHf/GaBz4m+EVRVVaGmpgZAppOwkE4Zk0tnRsvLli3D2rVrBZvE1atX4XK5qOPJTCCzq3jA+0ET/5qzoXPWFWAy9tnIleAxdbG2tjZMTExgaGiIaIGRSATHjx8nIQ32+SwYY3NcrFNlt9vxzjvvQKvVkniBSCSCSCQi6Xqfz0cy4wulYjkcDrz11luC4JR93qJgYgJobs4kSDZbZnasvBzYtCmTiC02xGKguDjz5447AI8n00EbGAD6+4GLF4G77gK2bVvUz+c4DlKpFOl0WiBewSh5NpsNAEhZrlBiJvpjcsqopez3+IWMcDgMjuNopjCdThM1kt8FU6vVggDx+PHjOZ895v3E7q+Ojg7YbDasW7dO0DFhnRqRSIR4PA6v14uJiQnB+pBKpTA9PY22tja88MILgvm0QmhtbcU777wDj8eD4uJiga0EC3IKUajydQPYH35wvm3bNpw5cwa/+MUvyFB6vmCGyLkC90LBV6FCl8FgQH19PdxuNwmLnDt3Dm63G1arFcuXL8fZs2dht9uRSqXQ09ND1yKdTmN8fBz79++HVqslBc1ctDEWpLOORklJCWpqagTzy8eOHUM0GkVzczOMRqPgPlq3bh3OnTtHwkKjo6PkLbd+/fpZZ7PZtTp79ix6e3sBZApE27ZtE1xTdq7WrFkDv99P82tSqRRbtmzB5s2b0dLSQt8ju9g3nw5RNvWUf42y/eLYOcw3c8cH/+dtbW0IBoNkpC2RSBCPx9Hb2yuYITObzbBarRgYGMiZGOd7fyC30nGue262c8PmBgcGBpBKpbB27VrBvHUuCxn+TPyfwuxYNhj1dmBgAIFAAHq9HuvWrZshqrIQ8ONHPqOArwfAmANLKIylpGwJhEIV3cVCriol/wHmL44cx8Fms2FychJTU1PQarXQaDS3nMbIONELTQRNJhN27NhBtMDsjX4u6OjogN1uRyAQgEgkgs/nE/j5sMRramqK5q88Hg+OHz8ukOTNVq7Mvub85JEvx8xHIVon2wCLi4vhcDgoQGf0D7Y5nj9/HhzHwWw244knnoDP58P169eRSCQgk8moCs6Cc5FIRBVj1pEB5i7rnQvMg4iPoqIirFq1asZr5zWTEA4DJ04AFy4Ag4MZ2mJVVSZR+mOicdMhEmVEQEpKMhTHkRGgtRUYHwcsFuATnwC2bi1MkZwDzGYztm7dinfffReBQIAMtiUSCYqKihAIBObdHWOdCJ1OB7FYjO7u7hmvicVikEgkkEgk1NVgn6NQKKDT6chgmMHj8RCVkgl9iMVimnPj+5YxZTuDwTAjMFQoFEgmk6ioqIBarabny+l00vtNTk6iu7s75xqVi0bMPIKY4Ed1dTW2bdtG3Y1soYFsqlGhZzJXAPq5z30OV69excWLFxGNRqkjzSCVSqHValFZWYlYLIb+/n4qXiiVSuzZswdSqVQQeOWioGcjX/GPUan5XoNerxeJRILWrmg0KvBz419LINPl9Pv95LHY39+PoaEhtLa2CpKzbBokO3aWXLN7y+l0QqVSCc5lc3Mzfv7zn2NqagrpdBpVVVW0Xk1MTBQUEOFfK6vVip6eHoTDYUgkEnAch+Li4rznas+ePfjDH/4Ah8OBuro6bNq0n4RdhQAAIABJREFUCSaTqWAiPFf6fa77I1/ni39s8wU/OQJAIhPsz8TEBBULF7pf5ophct1zrHiYq3tmsVhw5MgR9Pf3w+v1ori4GHq9XpBo5ZqlXrFiRcG5xtsNi8VC1NHi4mLU1NRg9+7d0C6COBTw/rlva2ujGXq3242xsTF6nuYb9/wlYikpW8ItRXaVcnR0NO/GEYvFoFarkUgkEI/H4Xa7UVpaSovfrWiFL9ZcGVM983g8Mzb6uaCxsREqlYpoVmq1GrW1tfQeFosFfX19NH/F6F4soJHL5XNSV5xLt7RQtZsloFeuXIFCoUA0GoVMJkMqlcKyZctQUVGB8+fPo62tDadOncKTTz4Js9mMr371q9i/fz98Ph8GBwfh8/koaGZzSiwoTCQSiEQi1FWZC5g6GjsmVlVOpVLgOA7pdBoqlQoPPvggdRdZ1Y/N8PGH2/Neu/5+4Le/zdAGh4aAZcuAj3zk1iVjuaBSAfX1QGVl5vjGxjIzbd3dwGc+k+msLRAmkwlf//rXUV1djbNnz8Lv95OhukajwbVr1wRJ2WwJGku6U6kUXC4XIpFIXppdMplEKpVCOp2GWCymTlwikUB1dTUAkBR+Op2GxWKh2THWHQoEApBKpQIBEhYkL1++HA6HA+Pj4+jv78cnP/lJBINBpFIpVFdXY8uWLSgvL8fRo0dx4cIFem/2HYPBIPx+v4AFkI9GzEQxQqEQwuEwLBYLqRUCmFVoIN8zma9LDmSSVGbwy4dMJsO6detQUVGBTZs24fLly/D5fLR2bd26lfwZCwXu+ZArcM4O2JnEPbuWy5Yto+4KA7vm/ONevXo1rTlerxeTk5PUuWdFtXzgCxIAoMIPny7/7rvvwuv1EsUykUjgQx/6EJ13ALPOZvv9fvT29iKRSFBHt7a2Nid9jH+u2Lzuyy+/jJaWloJdCL4Vwmz7Za49Lhf19EaQi0YJvG8izadD2u12dHR0UPdpIftlNrLvuULds87OTthsNkSjUSiVSlRUVGDnzp0CcZpcs9R/ysbIbA/zer3QaDSoq6vD7t27YbFYFt2blj9DP9uM5hJmYikpW8ItRfZi1tjYSMFC9sbB5IIZDW5iYgKPPfbYDCWvW3m8C0kE8wlzzAdGoxG1tbXwer3gOA5btmwRbOLnzp3D5OSkQD6e8fIBUGVstireXLqlVqsVk5OTqKmpyTnHwHxnWCdDrVajvLycqKo+nw9utxuJRAJHjhyBy+XCsWPHkEwmSVKcIZlM0lxNNubagZFIJNDr9aisrMRf//Vfo7u7G01NTRS81dXVobKyEtu2bcN9990HAIJZRhbgyeVyAHk2lkQi0x07cyaT7KRSwObNCxbYuClQq4GNGzM0ymvXMv+124FPfQq4gXkCkynjNRWLxeicaTQaTE1N5VTpmw2sQ5ptipwL6XRakAyxe8JqteKJJ57AxMQEYrEYlEoltFottFotwuEwUd5YEscSO7lcTsbh165dw+DgIKLRKEQiEUZGRnDXXXcJKuIOhwNOp5P8olgXV6vVwmAw4NixYwIlv3wB8N69e1FXV4cjR45gbGwM165dg1KppFmyQl0ydg1yeWnxZ2J0Oh3RSh0OBwlrMMjlcqL8hUIhMjD3+/1UXa+rqyMhCva5iwE+Vby1tZXmpHbu3EliIhaLBUajkbqJJSUlRC8DMklaVVUVHnzwQcRiMbS2tmJoaAgTExOYnJwkyXgA1JlkiRMAQecjmUwiEomgu7sbbrcbJ06cwM6dOyGXy+nzmGpddXW1IKmyWq0IhUI5qXdspod1JouLi7F27VrBOS0EvpJvvi5EtgpgLsW7QnTF+exN2Qbhc+3E8e+fbGokf+1l9+yNimNlo1D3zOv1Eh2VrRFvvfUWQqEQMUjMZrOArvennJABQkn/6upqfPrTn4ZWq70p3rRshp6pyHIcd0sL6X/uWErKlnBLkWsxLGS+PD4+TtQkiUSCUChE80u3CtkGzfNFLpXD+b4Pox5IJBKoVCps3ryZ3uM3v/kNXn/9der+SCQSlJeXY+XKlQgGg4sqydvc3IwnnniCpMlz+fCwOTEm3S2Xy0nR0OFw4NSpUyQdfuXKFbz55ptkFL6QIL4QRCIRamtr8dBDD6GhoYGCZeYxVF9fj927dwvODducmYUBm5kqLi7OvbEEg8Brr2VUD/v6Mh2p6uobNne+aSgtBT784Yws/7lzGXGQiQngwQcXTGdkz3VTUxPa2towODgIu91OczDzQTaVbq5g82WpVAo2m02QcITDYcRiMWzcuBE1NTUYHh6mjinfSoElVFNTU3A6nfRMpdNpeDwejI6OYu/evRSANzc30+8yKp1EIkFZWRnNnWXPpeUKgE0mE+rq6mAwGDA4OEi0X47j0NLSgv7+fnAcl1eOm70Hv3POvLTi8ThWrVpFx/M///M/iEQiJJDBoFKpYDabMTU1RYa3TBFSJpNBp9PhC1/4wg0JAhQCSyRZwlRbWwutVitY7++55x4MDQ1h5cqVMBqN+PGPfyxQ1bx69SrKy8uxY8cObN26FfX19Whvbyc1P6ZomatAxq5LMpmE1+vF2NgY2SoolUpcvXoVarUaSqUSiUQCEokEdrsdJ0+ehNvtpjWEUe+8Xi8OHDgAANR937dvH65du0YzPXV1dQKT6NkwWxcCwAwVwGzFu7nSFWeDxWLBs88+i56eHuqu5jJXn41tkj13xpg0Op0OgUAAoVAIhw8fXnTqW6Hu2fT0NBUuBgcHYbPZkEwmyUOQfyyL7UG62Cgk6c8XcFksATW2//f39yMcDqOoqGhJCn8eWErKlnDLkb0Y5uvOnDx5khZD5imUPSdyM5HPoHm+yDU4PZfP5lfhmB8Z6zyx+QO73Y6DBw8KuklMFGP9+vUIhUJobGxctEDqt7/9LTweD8mU//a3v8WOHTsErzGZTNi+fTu6urowNDSEcDiMc+fO4e6774bZbMaTTz6JI0eO4MqVK7BarQiHwwtW5ZsNMpkM9957L3bv3o2XX34ZVqsVV65cQTAYpHmk7M2CXS9mYVBINAAuF/Dyy0BHR2Zea+PGjLT9nzpkMmDdukwy1tGRkdf3eIC///uMMuQCYDJlvOMGBgbQ3t6O6elpSoxEIhFkMhmSyeSCpfGzIRaLoVAoBMWI4uJiTE1N5UzsOI5DTU0NfD4fpqenqYPGcRyCwSCSySR55iWTyRm0PpaYeTwe6mBNTEwgEomQsTT7bkwdNRQKCeY4C1GEWTCj1WoRi8VQVFQEq9WKzs7OgkWQbNjtdjz77LOwWCxUVBgbG8Py5cvhdrvh9/vJF5B/biorKxEMBmmtYd12Zk68evXqRZs/yXfchRgFJpMJu3btQkdHBxobG2E0GnH16lWcOXMG09PT1GnX6XTUQWJrpV6vRyQSIVZGLnlzdl38fj+ee+45hEIhxONxKJVKhMNhJBIJOBwOxONxcBxHIiPXr19HOBym94rFYohGo+jq6qIOj9FoxMmTJ3H58mX4/X7IZDKsWLFiXgkZIFTyNZlM4DiOZuM4jpuhVjzXWbP50BVZd+zVV1/FhQsXEA6HIRKJEAwGceDAgRnJ03w6cdmJkcvlIlGnG5Fsnwv4zybHcThw4ADcbregUBgOh+n7s3nQQglnvlnkXD/P3vPn8jtzQaGiML87vVgCaiaTCVu3bqXiSigUmtVsfgnvYykpW8JtR77F5sqVK7QgisVibN68+aZVaXNhsebJ5jqrxZDLCsBgMGDDhg1wOp0wGAz0OraI85OaZDKJzs5OOBwOrFu3blEHbIuyEo7svzOYTCbE43EKdHt7e2nDZtfw7bffJn8wADckmZ4LTJZ9586dZLbNOmTxeBxSqRRjY2M5K7fZ1BT2XwFGR4Ff/Qq4fDkjprF5M/BHiuOfBUQiYPnyjC+a1QpEIhkT60cfvSFfs61bt+LChQuCjVgqldJ9mkgkCprdzhVMlZF10hnlTCaTCUQ7GBVJq9XCbrejq6uLqL4cx0Gj0dDvsESMLxzC3kOlUkEmk+H06dNob2+n2aN0Og2FQgGlUolAIIBIJEIWAMuXLxcI8rBzlG+mMzuYOXfuHHUb+cWYQjh8+DBaW1up2MFU7mw2G4LBIJm0A5lnRKlUYs2aNdi0aRN9TiAQgFarRTwex+joKNLpNFwu16LTyPgYGZndZ5GJQjmdTuzZswff/va38eyzz+LEiRN0nsbHx6FQKCg51+v18Hg8lKQZDAYYDIYZBTJ2XY4fP07niRmVazQajI6OQiKRkOWDVquFx+NBLBajmTsgk1jI5XKIxWIyJbdYLGhqaiJvKK1WS6p38wFT8mXzbiyJNBgMsNvtdG74KoAABHONN0pXZHNXbC6OgT3XuSTn57r/8dfeixcvwmq1knBVX1/fTbfCyfVsRqNRmmsbHh7G5OQk+acVOpf5aJu5fu5wOLBv3z44HA6ao+T7hBZ6r7nMgKvVauj1+hmddpZYBoPBRaUxms1mtLa2oru7m9R32TO71C0rjKWkbAm3FfkWm+bmZly8eBFAJnDQaDRkRHqrsJjebWyxt9vtOeX/gfeTLFaBZ/Q5n8+HYDCIBx54gHxwXnvtNbS2tmLXrl3QaDQzpMZjsRj8fj/6+/sXZVCa4f7778fbb78Nn8+HZcuW4ZFHHsn5ulgshsrKSlIji8fjMzZsqVRKlDOO41BUVAS/33/DxrZAphKu1+vxta99DUajEYcPH8bExASkUinUajVisRjkcjlqa2tzXlf+5pzzHk2ngYMHM5RFsTjjA8YTIfizgl6fmSnr7ATi8Yy32j/9UyZZmyfY9W5oaIDL5YLb7aa5jEgkQiIai5GUARAEhayTJZfLKYFiM17JZJK8pZhYBwDqQjMBiXziMXK5HCaTCXfccQckEgmZoVZXV2NkZIT80Zhyn0gkQiwWg9frhc1mo7mb2Z5BJgdutVrR1dWFaDSKYDAIlUo1Jy8hi8WCl156SdA5TyQSRNHMTjalUikljQ0NDbBarRgaGqJELJ1Oo6ioCPF4nJQYbxYYNS8ejyMej884V3yfOKfTCYvFgk996lP43Oc+h+vXr2N0dBThcBgymQxOpxNqtRqRSIRmysRiMcLhML785S/POpfMcRw4jqPkqa6uDn6/H4cOHUJfXx/S6TQMBgNSqRT5ybW3t+O+++6DyWTCzp07cfXqVSSTSYyOjuLs2bPo6ekhy436+nps375d8Jlzmc/iz969+uqr6OzsRDQaRSAQwObNm6nDL5fLiVWSa3+da5KUfUzsGni9XqhUKhgMBjKLr62thcFggMvlQnNzM9kKsORirmMH7HiOHTsGmUxG9gvd3d14+eWXb1lgbzabydB6amoKk5OTiMfjZO6ey1aGf1y5iroAcOjQIVy8eJG6+S0tLThy5Ag6OzupABgKhQT7dr73yr627N/m001bzDgnG36/n1RUFzMO+SBjKSlbwm1FrsXG4XDg6aefJs68RqPBnXfeSSIMtwrz7XDNhkIDz9nD2Wq1GqtXr4bNZiMancViQXFxMdra2ohCVV9fD5lMRkpkrPJdWloKnU6H6urqReGLs825tbUVa9euRTwezztfwpS/mALe4OAg9Ho9DAYDqVy1trYilUpBo9FALpdj3bp12LlzJ44dOwaLxUJV54WCiXjE43E0NTUJJPm3bNmCRCKBsrIyCqLYcc9lcx2/fBmmjg6Ez55FKBoFt2kTiv5cEzIGlSqTWHZ0ZKT8pVLg85+fF5Ux+x7+whe+gNOnT6Orqws+n4+6WXy1xPliNp+zdDqNeDyO8vJy1NbWQiQSwe12IxqNUmcj+/f58vjMKy87cSktLcWOHTtQXV2Nnp4e8rTatm0bBgcHqdpcVFSE8vJy6HQ6RKNR+P1+dHZ2wul0zokaxGaS2traKNhnwj01NTWzBtDPPPMMxsfHBT9nnQZ27vjfjam59vT0oKenB0NDQyS2I5FIUF1djcrKSmi1WtTU1NzUQX273Q6ZTEaGs9kJIKv4R6NRTExMULeCUaJfeeUVnD9/Hm63G3q9HnfddRc8Hg9OnTpFfmzBYBAvvPACnnvuubzn0mQyYeXKldDr9VizZg22b99Or43FYnjttdfg8XhIJIYpgDqdTgo6mZ2Aw+FAJBLBG2+8QdcgW16dffcXXngBra2tAICtW7cKPO5y2Sj4/X4SFGFCJlVVVeSb1dzcnNPUmxWcZtsLLBYLnnnmGfT19UGpVGLz5s3YvHkzqUWq1WrU19ejuLiYCm2tra04evQo+vr6EAgE6BkvJMefa81ls4VyuZzmuMbGxgBkZuYaGxtx9uxZDA0N4cEHH5xBoV9MMArx4OAgze8xxgWQv/Ody1D5hRdeID9CZu/zxhtv4OrVqzQbmU6noVQqBQlSrsQpe1+yWCwCBVF2bYDCiqD8RH+xwLreLpeLbB8WInD2l4ilpGwJtxW5FpumpiYyg2VzIP/+7/9+Wyos7DP5crgLQT5pav77swV2xYoV2L59O8rKytDX14empibadKPRKOx2O82uNDU1wefzkeGxWq3GQw89hHvuuWfGRnnixAlSjprvsTPKCpPXr6uryzlfkh2Y79ixg6T5GVgAq9FoYDAYcPfdd2P37t1wOBwUfA4MDNxwYuZyubBv3z4YjUak02lUVlbmVYwslDDz79Gq4mLccf48whYLbCMjGFKroensxMaNG/NSOf9sIJdnEjOLJWM0rVQCn/3snAVL+Mpwq1evxs6dOwFkggW+lxRT51wIWKeNBexSqRRVVVVwuVy0ZgAgv6poNIrS0lKkUikkEgm4XK6cXbpUKkVS/NlJGzOq/v3vf49Vq1ahtLSUfMTMZjNcLhcsFgvNlj388MNoaGhAc3MzPTPMx2q2SrHdbsf3vvc9tLW10XEw37Nf/epXqK+vzxuAskAoO9llSqhyuRxFRUU0f8V+7vf7MTQ0BACYnJxEMpmEUqmETqfDpk2bsGvXrpuuLme329HS0oKxsTFwHAeDwQCO4wSsAkbv7O7uhsvlEiRBTEiDzWzFYjGsWrUKmzdvxuXLl+FwOABkEtS+vj488cQTePnll3OuA83NzYjFYpDJZLSOMvA7mdeuXSMVWWaezuidVVVV4DiOupTA+yqwmzZtmlFgZF51zIz48uXLaGpqIvoho7Mz9T9GkWT3SDgcxltvvQWNRkOsA5VKNWdT72xYLBZ861vfQldXF50Lj8eD69evw2g0klIt/9zEYjHqYLIZPI7j4HQ6yRqCPy/Fvlf2mst8wtieK5VKiWlx/fp12O12/OIXv8D4+DgSiQROnDiBZ5555qYkZvzOIEuWQqEQYrEYjh07hoaGhoLJPZ8G39HRge7ubuochcNhjI2NYWJigu4R9n1VKhV27NghoDznKhDzry0A8lP1eDw4ceIEFAoFqqqqUF5ePqsC4o36sfLB7k9GC89FR15CbiwlZUu4LeDPQ2WrGzY2NqK0tBShUAgKhQKf//znb+ksWfZx3qi6Uq4ZsVzy/9liIIz/b7fbyTuFUSiATLDGOhCsEmc2m/G1r31NUNl1Op1ob2+H7I9eWfOdL+NvTAAEKoS5Krj85JIdK6vSsWqcWq1GXV0dli1bht27dwMADhw4gK6uLpoLmq0rwgL7fK+JRCIYHh7G6Ogo7rzzzoIebey4+/r6KLCYsSEODaGurQ26jg5M+XwYVqvh9fmAP5p5/9knZUAmMdu4MZOYKZVARQVw771z+lW+MpzNZkNfX1/exHqhs4OMKrVu3ToYDAasXbsWGzduBMdx+NGPfoTr16+Thcbw8DDKy8vhdrvh8XjgdDoFM4zZx846H9lgJuMSiQRTU1MoKysjZUaj0UhJZiQSoeo5n/rEzKVnk/a22+34zGc+QwwBPpLJJCYmJvCTn/wkbyBYVVWFhoYGjIyMkMk882SLxWLUmS4rK4Pf76c5P0bDDAaDmJiYgFKpJGn57MD7ZoHNfPp8PpKdZ+bBfJEdjuMwNTVF7AH++TSbzVi9ejXcbjdEIhF6enqwa9cugbcZA5tx/cY3viH4OTN27ujogFKpnBF4swSwublZoM4ZiUTQ19eHl156idbXbdu24dy5c3S/cRyH1atX55S/ZyIvIpEIqVQKIyMjOHXqFJxOJxoaGjAwMIBLly7R9/3P//xP1NTUoK2tDUDmefJ6vUgmk9DpdNDr9fMy9QaEe/K+ffvQ09NDBYxEIoFAIIBr167BZrMhEokI5htZ585ms8Hn8yEej0MikSCRSKC/vx8/+MEPUFFRQbT3mpqanF08ANi3bx/a29uRTCZhNBoFNhYejwd2u13grzc1NZVTcGoxwN+Xa2traT9JpVICOn4+sH9j+38wGIReryeaIptjZV16VjwRi8Ww2+05Zx75f+cnakDGz5CZcvPPT2NjIx555JG8tNjFmp/nHxub5WaMlaWEbG5YSsqWcMuRnaSwTYQtOkajEdXV1XA4HOA4DlevXsXHP/7x2/JQL8Zixd4jewC70AKbnRCwGQKXywWxWAyRSISioiIYjUYUFRXllb3nV6wikQgGBwdx6NAhgfDAbOA4DslkEhUVFYIACQBeeOEFkqjeu3dvXh86psD02muv0bA9Xya3ra0N0WiUKqJ+v39ORsNzQTKZhNPpLOjRxqhRbAaFUaP418F05QowMACMj0O2aRPU3d1Ii0TQaDTQ6XRzOpY/C6hUwNq1QFdXJjEzmYC6ull/jSnDuVwuuFwuvPHGG6iqqkJFRQVsNtuizSOxe0SlUsHpdKK1tRX19fUoLS0lqXsmgc+CQ9Y5Y/NtuSh8EolkBq2SJTasEy2VSmkOjXW+2OuSySQCgQBOnz5NyQz7wxKM5ubmvEWR559/PmdCxsA8+/KtQSZTxsh78+bNGBwchEQiwYkTJ8jLKxaLYWpqigJ7iUSCuro63HvvvWhsbMTp06cxMjKCSCQCg8EgMKe/FWCqhclkEidOnCBz5qKiIgwNDaGlpQXDw8MU9Ot0uhn3FKOe+nw+9Pb24vTp0znNx9PpNLq7u2f8nOM4mk2LRCLwer2C82232/Gzn/0M/f39gs+Ox+Ow2+04ceIENm/ejD179kAmkwleU1RUlFfcw2TKKNaOjIzAZrNBJBLBbrejtLQUDQ0NkMvl4DhOMJtbVVVFiqZARmlWrVajpqYGDzzwgIB2Odt15Bcfk8kkHA4HmbGLxWKo1Wqk02myUmCdYUa3DAaDmJ6epjkp/lylx+OBz+fD5OQklEolzUjm6uJZLBb09fXB5/NRx3TVqlX0/mzGMtuQnllVHD9+nLrA999/P+LxOHp6elBfX4+vf/3r8yru5jK8tlqt2L9/P2Qy2ZzpvKzg4PF4UF1djTVr1qCsrAw9PT1wOp3o7++Hz+eDWCymvd3r9c7Yg3KBP6vOxhm6u7upCMpm4PhMH/6cILvf2dwtE8BZKM2QX6RlxSp2zbKTzCXkxlJStoRbDpaksNmFwcFBaDQaACDPMiZRHQwGMTQ0dNsGRBdjCJYF/GwwOl9ykI+bzn7W0tICjUaDdDoNjUaDj33sY3j00UcLUotMJhMee+wxhEIhdHV1YXh4GK+++ireeecdfOlLX5q1usjoPNFoFHK5HLt27aKF9fjx4yRh3d/fj/r6+pyeNy6XC2fOnMHw8DDi8ThUKhU+8pGPkH+O3W5Hb28vJX6XL19eNNl0hsrKyoKzY4waNTQ0BI/HM5N7390NvP125r/r16NIp8PGjRvJlPcD0SXjo6Qk0yWzWoHXXwe+8hVgFjl0Vu1PpVIIBALo7+9HaWkp/uEf/gG///3vMTAwgOnp6QXPkgHv0x8HBgYwNjaG8vJyKBQKXLhwAePj40SbYrYQ8XgcCoUCOp0OUqmUaEPZAiHsM1gwyST8xWIxioqKoNVqyVzZYDAIzIHNZjNqa2sxPT1NaofZgTwTCggGg2hpaUFJSQkAoaADC3DzQalUorKysuAaxIo4QEZx7+LFizPON0sUYrEYRkdHUVlZiebmZgwMDCAej6OiouKWG72azWbU1dWRCTdLAFgSPDk5iZ6eHgQCAcTjcZSWlkKv1wuOcWRkBLFYjK7NtWvXSAkzWz1VpVLhU5/61IzjsNvtUKvVJBaT/RmHDx9Ge3t7XgpsIBDA2bNnYTQacfDgQUoQZDIZVq9ePUPcI/scWK1WlJaWChgV/HuECWdwHAeHwwGDwYCpqSkqdOl0Omg0mhlzhYWQTa03GAwwGo1wu91wOp2QyWQoKyuDUqnE8PAwUqkUCZyweS+v14vp6WmyYCgqKiLFzlAoRKwOiUQCk8mUs4sHgERUlEolioqK8MlPfhL33XcfdfAOHz6Mjo4OjI6OwuVy0SxoX18fLl26RN8pnU6jubmZ/j4+Po5Lly7h1VdfnbMlTS6FRCY+otfrBfTCfOBTMWUyGQwGAxVE+Z1JJpDk8XjQ1NSUew+aw7Gq1WqsWbMGpaWlGB0dxfj4OD0DHo8HP/3pT3Hw4EFEo1EoFApas6RSKTFX5vK98h3HU089he7ubqxZswZ33303CebcTNXWDxqWkrIl3HLwfaAYXS0cDlMFkOM4CqSYYertGhBdbLGPfJhN2pZVusrLy1FdXU1zWHM5HrPZjE9/+tOYmJjA1NQUvF4vXC4Xnn76aZSVlRXcpPhdvhUrVsyoTPMrctkdArvdjuPHj2P//v2wWq0kE65QKCCXy4n+yAbcmX/SjSrzSSQSwWyQTqfDP//zPwMoTEdlQRH7N7rnolHg+PFMQrZiBfDHrlhRUdEHLxnjo7o6Yyzd1wc0NQH/8A8FX84SWzbXw3Ec5HI5Vq1ahbq6OoRCIfK8mi9YtyoWi5GYRyqVwuTkJPR6PZmPMxER1rmKRqMoKSnBP/7jP0Kv16OzsxOvv/76jKRMLBaTcTRT1kun0zTYHwwGEQwGYTAYsGXLFpw7d446X3v27MHjjz+Offv2kSodE9YBMsnWwMAApqam4Ha70d/fTz5oJpNOOZ6gAAAgAElEQVQJX/3qV9HQ0JCzo8OgVqvxkY98JCf1jQ/+OuL3+zExMVGQBjw6Ooo//OEPiMfjBTv5NxsmkwmPP/44mS6zYgcL8EdGRjA2NkYKnmVlZXjsscdmsAL0ej30ej3df8wgmyXYWq0WtbW1+Lu/+zs8/PDDgmNg87fhcBilpaWora0VfAaTtedfp1yCNYODg3j66acxNTWFVCoFhUIBg8GAL33pS7N2PbKtOPjzdKxgyTpKQ0ND0Ol0KC8vp+/n9XoRDofnpHTHF29yOp207tXU1GDHjh04duwYjh49SkWMmpoarFy5Em63G8FgENXV1WTPMjQ0RPYDOp0Od999NxoaGvDUU09hYGAAQIYCKZPJsHLlyhmBP/841Go1ysvLsWbNGsF+AoC68Ozcu91uAJiTSf309DSef/55vPjii7O+NpeIxokTJ/Dee+8hHo9j8+bNs3b+7Xa7gJJfU1MjmKvify+2B9vtdvT09ACY6dOX7zOyzcLZPDrHcXjppZfQ2toKkUiE/fv3o6+vL2fBk3XpKioqFsxoOHz4MJqamhCLxTA0NASpVIrVq1dDoVCgtrZ2qUs2RywlZUu45chW+7ly5YpguLu5uRkSiQRVVVWoq6vDQw899GfNR2adv3zqR3OZW+MnsnK5HNu2bZvXOTGbzWhsbMTY2Bgpkfl8Phw5cqTgjBmjLuaiNZhMJuosMHoUX/L+5ZdfxsmTJ9Hd3U2BTCqVgtFoxGOPPQYgI1TS3d2N0dFRgVrXQiCTyaBQKKDRaFBaWoo1a9ZAJpMJ1LkK0VHzJuCnTgG9vUAqleke/aVAJMrQFt97L/OnsRGorS34KyaTiRJvkUiEj370o4IOADvv8wVLKpRKJSVkrHs2NTVFCoMcx6G4uBjxeByBQAChUAjj4+Pw+/1YtWoVNBoNFAqFwFya0Rfj8Th1tNetWwe1Wk1qhMFgkAyBk8kktFotnE4nBb/s+fR6vejs7MTIyAhaW1uxdetWOJ1OeL1eShyB96X8HQ4HnnjiCXpdLojFYtTV1eGJJ54oGNhkV8wnJiYQCAQK0nwTiQS6urrQ2NhIQgC3OiFjYHN42UkJkJERt9vtCIVCSKVSUCqVMBqNgt/nswL4fk9jY2Mk1KBSqfBXf/VXMxIy4P11mnVHmZCL3W7HyZMn8fOf/xyjo6MCkRSNRkOdCJFIRHSwaDRKyWN5eTn+7d/+bU4zT+y85yrQ8YtdfJNto9EIiURC6oT8md98yBZvUigUqKioECTkVquVustTU1MYGxvDxo0baT/mXx+WVAWDQZoTNplMePrppwWfOzU1BYfDQTReIDNvdfHiRYyNjUGj0aCmpgZbtmxBY2OjgHZnsVjw3HPPkSgN62CzxHsuKEQP5iN732OzbJFIRFBULASLxSL4HTaPOFfwZ7BzPY/5ZtX5nVVWpOnt7cXIyEheBkoqlaI9eqFKzZcuXaKELh6P49y5c1i2bBmpSS9hblhKypZwW8BEIVQqFYqKisBxHIxGIzo6OjAwMEAbm0gkKjiHcbOxGEIf/IQq17D/XObWTKaMVPZcZlNywWQyYe/evTAajTh06BAFh93d3XjyySdzqmllUxezq5tM4czn80GpVAqoPiMjI7h06ZIgIQMym93HPvYxGI1G2lA8Hg8F2guFQqFAcXExdWJSqRQeeOCBGRSl2eioMyikExPAO+9kZsk2bpyzEuEHBgpFpjvY25vpFn75y8AfBWNygfm/BYNBqNVqnDt3Dh//+MexZ88eRKNRXLlyZUGHwYKGiooKbNq0CT09PXC5XDRfxO4dpoBYUlJCCUkkEsGvf/1r/N///R8SiYRgrowZS4vFYhLCUSgU2Lx5M+677z5YLBa0tLSgtbWVAnuZTAa5XC5QM2MBvdPpxOTkJHV2jEYj5HI5Be8ABIFROp2G2+3G8ePH8ypSSiQSTE9PU7c5O1jP9jccHh6meTu+qls+SKVSbN26FWVlZTeVDTAX5KJw2+12mm9jginZYjwMLEGZnp4mRsHZs2dhs9mQTCYhkUhQWVk54/1ZIqhWq5FKpRCJRNDT0wOLxYLm5ma89dZb6O/vpwSX3S9sBo51qvhqi1KpFOvXr8c3v/nNOQfjc9lvmMomM9m+5557MD4+DpVKNUMKPR/YnsOEePR6PWpqaighYx0bJn4jEokwMjKC5cuX52RXsIQjO5lk9EaGWCyGq1evIhwOo6mpCeXl5RgYGMDAwADC4TAAwGAwoLGxkYzC2Xk4ffo0hoeHiXK3ECr0rl27Zn1N9r63YsUK/OEPf8Dk5CSKiopmdFBzgdEWnU4nNBoN6urqZv0d4P3CgNfrJYaK1WoVzLWx17HnvVCH22w201zpbIhGo5icnERLS8u8FBjZ85PdrQwEAsTMKS0tXfIomyOWkrIl3DawWatUKgW/348333wTBoMBPp8PiUQCbrebqlW364FeDKEPtmG1tbUhFAph3759ePzxx6kK63K5oFarZ5WsZca0/Ar9fI7FZDLhK1/5Cu6++24cOXIE3d3d6OvrQygUwqVLl9De3o6vf/3rgmptIeoix3HQ6/VobGyEXq8XbDocx9GMSvYxMFUmNvxcXFwMj8dDm/JCoNPpsH37drS1tZFKZL5zMGc6ajoNHDsGXL8OGI3AB5mqWAgVFZnktLc3k6AW8AvkOA4ulwvJZJKk1lnw3NPTU5CiNxtSqRT0ej2+8Y1vwOFw4Hvf+x7ZRPARCoUoGQqHw0in0/D7/ZQMsZkxINNdZXM4TCWMBeT33XcfBZ81NTW4fPkyJBIJ0bv4QRKjVMnlcgCZSrHX68Xly5dJXfTq1auw2Wzki5ZIJGYkaEAmCSsqKoJEIkE4HIZUKqUgtr29XRCs5/I3XLFiBUKhELq7uwU0XolEkrNS7na7513Fv1Xgfz+FQoGioqIZQg98nDx5EufPn8f09DSmpqZwxx13QKVSoaSkBB6PB1KpFGfOnCFFxewkqL6+XjBX2tHRgYmJiRkBJ+uuspkm1lHi34tMpGQ+55VvK5FP1MXv96Ovr48UCdevX08iIYxp0t7eXjCwZp2gyspKgXgTf+0PBoMkHpJIJKBUKgt2iHIl1OvXr0dHRwf9PZVKYXx8nMQ/NBoNvF4vZDIZ0uk0KioqsHXrVsRisRn0QSZgwRKx+Sq4rly5El/84hdnfR1/3zMYDHjjjTcwMDCAQCCAmpqavGItDLloi7P9DgMrGjI6qcfjwcDAABVjk8kkUeb5z3u+Dje/e3zlyhXY7fa8iWwwGER3dzd1HudCfz18+DDefvttSKXSGQU3xsaRSqWw2WxLc2VzxFJStoTbBr64wsjICFW9S0tLMTY2hmAwiHA4jLq6uts2U7YYQh92ux2HDh2C1WpFOBzG4OAgAODxxx+naqBarRaoEeY7FkavuhEjRkYT+sEPfoCenh5SGjt16hTMZjMeffTRWb87v5rIEjL+phOLxVBaWorx8XHaPBm18OzZs0S5iMfjUCqVC/atAjIV6Q0bNuD++++nytyaNWvyboL5BFVm4MqVjNDF1BTwkY8s+Pj+7CEWZ2iMVitw+jSweTOg1+d8aSwWQ2VlJSYnJxGNRjE6OoqjR48iEAiQwuaN4K677qLr94lPfAL9/f0zZO7FYjHkcjkqKyvhdDpJzpuBGSgz+tmWLVvw0Y9+FF6vl2YiGHWIP2P4+OOPC0Qj+F5LAwMDsNlskMvl0Ov1CIVCEIvFkEgk0Gq1eOyxx7Bv3z4EAgH4/X5SnO3t7RWcE5FIhHXr1mHPnj0oLi7GoUOH0NnZiWQyiZGRESo4sIApl78hABw5cmRG0icSiaBWq0mVkp0rlUq1aMqYi4nseRmNRkM0KLVanfMZHhwcpBlXdl7VajUpF46Pj+P48eMwGo34yle+MqPo1tDQgA0bNtCazJKS9evXIxKJEMOABcYs0OQ4DjabTXAsYrEY5eXl8/rOfFuJbMl/dk6OHTuGYDCISCRCCpTsmWhra5u1iFhIvIn9O79QyGiYy5Ytm1O3h49HHnkE7777LsnIM5n8UCiEzs5OOr+sw8escYD3vbfUajXa29tx+fLlG7pPNRoNLBbLjGc416gA22eTySSSySRRpL1e75zEPRZKW+SPdjA6KPvca9euIZlMQi6Xo6SkBHV1dTQ/VqjAaDab8Z3vfAcWiwWDg4M4f/482traZhRB0+k0HA4H2Snkiy/YLOLRo0fx9ttv0zORnewxK45wOAybzYazZ8/+SRZ+/tSwlJQt4bbCbDajtbUVkUiEhoeZHDpzgs/mgc8mirGYWAyhj5GRETgcDkQiEVqoHA4Hjhw5AofDQZ0opkZ4K8AqaKOjo+js7CSp4xdffFGgwJTvu+fqovGvS1VVFdatW4eBgQGEQiFKugYHB/HjH/8YQKZLkUgkMDo6mtMfaq7Q6XTkKcTUvZhB9A3dKxcvAjYbsGoVIP0LXyp1ukwiNjaW8TD7xCdyvox5ZTGj2UgkgitXrkAsFtPmvVCw7gQzoWWWGcxclgl2KJVKJBIJ1NXVobGxEcPDw7h27dqM2RM2B+l2u9He3o7JyUkSiGDBPz/AZUEcv7PCvJZYZ0OlUqG2thbxeBxarZZks0dGRlBUVETHJpFIYDAYkEwm0dXVRcdUUlKCD3/4w2Qu/Oqrr1IClU6nZwRMufwNR0ZGIJFIEI/HIZVKKTFjn1lXVweLxUKeYOx5BYCf/vSnaG5uxo4dO+bUVbhZyDUvk0wmodfrIZFIUF1dPSNAt9vtGBsbI1oqx3Goq6vD1atXKUlj+8y+ffsgk8nw0EMPzTh/ZrMZLS0tOHPmDHVLdu/ejT179qCpqYkSablcjrq6Ojz44IN49tlnZ3Ruli9fjkceeWRe35vZSrDZrOx1dWRkhOjZGo1mhjrkXIqI2Wu33W7PaegMZJLEysrKnIW3ucBsNuP555/H0aNHceXKFdhsNvj9fnpemTF1TU0N7r33XkG3hyUnLS0tOHHiBAnnLBR6vZ46TnxRE6ZWyleBZJBIJOT/lkqlkEwmC0q7s3k/r9c7L9oiHyzBZs8yx3E4cOAA2R+wudns+bFcx8Lf+9jrdu3aheeffx6vvfbajCJZKpXCxMRE3ve1WCw4cOAAKSYX6lqzQkgikYDf78fBgwdRX19/U/zkPkj4C480lnC7wOfxAyDn+e3bt4PjOHz3u98lZSun00neWgBueMZrvuBTOvh/nys4jkMikQDHcRCLxSgpKUE4HMa7774Lv9+PmpqaOXXhZhMMmS/MZjP+67/+C9/61reIatHf348nn3yS5gbyfffszZ/juBnXZfPmzXj33XdpQxOLxXnNexcKhUKByspKjIyMQKPRIJFICJLEBd8rDkeGtujzAevWLdrx/lnDZMooMV66BGzblumgzXhJJpHnOA5+vx9erxcikWhOs02zQSaT4dKlS0SPjEajKC4uRjgcpooyM1/l+wHV1NRApVKhvb2dzNYB0LxZZ2cnuru7SbyhoqICpaWlOUVu+J0VJv+tVquxevVqgefizp07odVqBcWAZcuWYc2aNbDZbAiFQhgYGIDf76f5NkalZOfKYrFgZGQE4XAYHMehqqoKDzzwAMnp8893duGByaMnk0moVCqS89+4cSN27dqFw4cPo7u7G3q9Ho8//jgA4Bvf+AZeeeUVJBIJtLW1YWxsDE899dSs18Vut6OlpQVut5vEMW4U2d6O7LwxVTeDwTBjvWTro0KhgFgshk6nw8WLF+F0OuF0Oklqn9GqfvjDH1JQzj9/drsd7733Hrq6ukhpr6ysDFu2bEFZWRn27duH3t5ekoZ/7733aH1kqKysxKOPPjpDjGQ2VFVV0f2aa13dsWMHDAYDVqxYMYMyztDQ0EAdp1zrHX/tVqvVAoEOvqGzSqUCkKEDl5aWLrhLxRJddp+cOXMGfr8f4+PjdH+uWbMmp3enxWKBxWLBxMTEDe0bHMdBq9UiGo1SAYX5pWV3xBsaGuB0OjEyMoJ4PE7UZ75fYT7w78Fly5bNmbaYC7kYHdlm6oUSslx7n8Viwb59+0hFMhfi8TjefffdGSb1FosFP/zhD2ksYTZ2i1QqhUwmo/Pncrnwk5/8ZMb7LkGIpaRsCbcc2UaVfFUytvm99957eOWVVxCLxeD3+3Hy5EnEYjHBpgHcuPP8fI93IYlgLBZDdXU1wuEw5HI5FAoFGUayhe1f/uVfZn3PQkqIC4XZbMYjjzyC73//+1SBnZiYwLFjx2gjzf7uAGaYarIZMT69qri4GCqVCm63m6qii5mQAZkNpL+/HyqVChKJhAxJswNodkxzvm6XLmXmqIxGQCJZ1GP+s4VOl1GgHBvLJGf19TlfZjKZsG3bNlitVjgcDvj9/nkppOUDm23p6+tDRUUFye2zDT8UCkEqlZK4AAuWOY7Dhg0bsGfPHrz++uvUBQBA1Ea+ZDqjLp46dYqSLL7IDRPtYe9jMBjw0EMPgeM4HDt2DNFoFBaLRbBO8JOnvr4+vPLKKxgaGhIERul0Gk6nE52dnQJzV7lcDrlcjnXr1mF8fJyCyOwKP/+ztm7diosXL5JE/8MPP4wPf/jD9Dq+vDqQMYH/7W9/Sx3rVCqFV155BZ/5zGcKdgUYjencuXNIJBI4ceIE/t//+383nJhlJw4ASHm2uLhYIC3OjqW3txfpdBpFRUXweDxwu904f/48IpFITlGIcDiMX//619i/f79Ara6pqQler5dobuz+YqIfXq+XTMkvXboEt9uN6elpAJnOyvLly2mG2OFwYO/evfMSZOInidlrGOsWsaCfn/Rlr9WF6Ns7duxAR0cHZDIZ2tvbBfTN7PPOErYb3W9MJhMeffRRwfwb+z65Egy73Y6jR4/CZrPdEJMCyNzPBoMB+j/Srvlqhezvvb29RHfu6+sjxVQgc11VKhXq6uoK3tvsfqmoqEBNTc2i0fX4yqRzYX2w+4b5t/3ud7/DnXfeiVAohIsXL8LlchWcyRsaGsLLL79Ma9hvfvMb7Nu3Dx6Ph4pha9euRV9fn6A7zX/PeDyOsrIysjuKx+MYHR3NKdCzhPexlJQt4ZaDv9EYDIYZSmZM+lin05Ha1ujoKAYGBgSbxq0yOL1RsQ9W/QQAm81G83NMWpktVoUwmxLijeC+++7DkSNHcPHiRVKn6+3tpW5m9sA1v6rIAkO+RDObd6uqqkJlZeWM4HMu0Ol0CAQCs5pIJ5NJ8jYrKyvDypUrBcHwgu6VeBy4fBkYHwc2bJjXcX+gIRJlumXj45mkNU9Sxu5V5hfndrsXZWZJrVbD6/WSfcauXbtIRIDZPDCDdqfTiaGhIVIGXbZsGR5++GF4vV78+te/FgTorAqu1WpRUlICqVRKnbNgMCjoErCguampCW1tbSS6U1ZWBpfLhampKVK0a2pqElT/WeXb7/fnFMFhdEr2c7PZjA0bNiAYDEIkEuHSpUvQaDSYmpqC0+nEwYMH8d5778Hr9SKVSmHZsmW4++67sWvXLrS1tZF8fyAQwMWLF4nSyz8W4H0T+GwqUyQSwY9+9CN8//vfn7HW8GlMrOOXTCYxPj6Ojo6ORQlGGxoaoFKpcPnyZdhsNvh8PlRWVqKmpobmp/hJZWtrK0KhEMnTR6NRTE1NIRaLzSmo51MmfT4f1q1bRz5pLS0tVEBknUvmYxeJRJBKpcBxHJYtW4aHHnoIb7/9Nok11NXV0Vz0bMIJLOjesmUL/ZyfJPX19cHpdOZkS+Raq/mJN/t/h8NBND6DwSAQi8hl6LzYowL8e6/QfXLy5ElcuHBB8JxI/lggm21fyEYikcBvfvMbPPzwwzkl/VtbWxEIBDA5OYmuri7yPNTpdJBIJOA4DrW1tQV9Am/mHg3MYxYa78/FDQ4O0no0ODhIPoxM9IfjOEgkEuoiK5VKrF69GoFAABMTExgZGYHVasV3vvMdKqwplUpUVFTgiSeeQG9vL/77v/+b1ijWiQYySdnU1BTZ5ohzMCuWMBNLSdkSbjmyqW/8jgvbbPnKT/F4nNrgfK71rZJvvlGxDxbIHTp0CIODg2SACmQqayaTCY2NjQXfg224o6OjKC4uLshrny9MJhO++c1v4tvf/jYGBgYglUqhVqupWs/ftAHMSFBdLheGhoaQSCSg0WhQX19P12XdunU4derUvI5HrVaTrHo2cpm18vGxj30sZ3diXveK1QrY7QDHARrNvI79Aw+jEbhwAbh2LUPt/KORNh9MQY4pefE37BuBz+ejOSFmn8GG8YGMkIVCoYBer6ckigXVzIC+qqqKkkU+GB2npKQEVquVum6smp7tz/fggw+SrxhTmmSFiVgsRqIPTqdzRmfdarXOSICYKIhUKkVVVRV1DlhXYWBgAENDQ/S64eFhnD17lsy0AaC3txfnz5/HL3/5SzidTvqOiUQCw8PDsxaTWCeSH/BevHgRL7zwAvbu3QsAghmX9957D5FIhNQs2ZqWLTk/X7DkyGq1Eq1aJpNhw4YN5F/Fl0tvaGhAd3c3xsbGKDkCMnRXdt+xSr5UKqXAUavV4rOf/Sx9Jl9UhEmMl5eXo6WlRVBArKyshM1mQzQaxfT0NFm3aLVa/Ou//is0Gg3efvttABmWxJkzZ2CxWAqyLLK7XPw9kS/80N3dLejy5Jsn49MS+R0v5l/Hp2Y+9NBDM8QismmEtxp2ux2nT58mWwtGHdTr9UilUmTMPR9EIhG8/vrrkEgkM7qX9fX1aGpqgsvlomeK3XN/+7d/i+Li4oJ0QUConLl69erbKp7DuuHZDIVAIIDy8nKUlJQQvfr+++/HiRMn0NnZSabTfDbOd7/7XcH7hMNhYjatXLkStbW1dD/xLSNYMsbWBqYqy3EcFVSWOmYzsZSULeGWIztYBt6fWQLe31zYHEEqlUIoFIJMJkNzczP27NlD1A72frfyeBf6eW63mxIyk8mE5cuXY9OmTdi5c+esCRarfLEh5dbW1lk3ifnAbDbjqaeewoEDB2iwuaWlhf69uLgYJpMJHMdhenoaYrGYVM1aWloEQ789PT1kPsm6gfMJyPleP4zeyXEcdDodtFotbDabYMOTSCTQ6/VoaGiAVqsVvNd8qouES5cySdnShjETHAcUF2eonRYLcO+9OV6SUZDz+XzUxWH0wBuxPWDJHQC8+eabmJqaQiKRgE6nQzgchk6nQ3V1Nfk2KZVKBAIBMqAHMvdWcXExJicnZ7w/mzdhXZ9oNAqbzYYvf/nLM+4hRgFjHYdjx44JaG2su59tXWG32zE9PU1D8EDm/i0pKSG5a353jfkM2Ww2eL1eSKVS6gblQjweh8PhEPxMLBZjzZo1eYtJZrOZVHCnp6dhsVioszQ9PY3Lly8LOuRMDS4ejxONrqysDE6nExUVFXmPjd8JAt5P8PgFOZYcWa1WXLp0iUSfFAoFAoEAGhsbZ8ilm0wmWi9YNZ4VdnQ6Hfr7+ynpvuOOO3DnnXcimUySqXwuURHm2QVkkmjg/QLi6dOnqUPIOipAZoZGo9EIzqdCoaAkmn3nXAJE2bOK7L6Sy+XYuXMnJicnaTYunycVf59yuVyUTPJnw1QqFZldM2rsYu4jiwXW5WPXTS6Xo6GhAStWrMBHP/pRvPTSS+ju7ibvP5lMNqckKBQK4fLlyzO62EDm+c+2NDAajcQGmQ2zKWfeajBBIX7yynEcampq8Dd/8zfUvXU4HPjZz34Gr9cLn8+HtWvXwmg0Uqcve08FMv6mzzzzDP7pn/4JRqMRDocDk5OT5GnHZnM5joPH40EqlYJUKoVUKsWvfvUrum4bN26cU/zzl4SlpGwJtwUsWM43r8VXXurr60MymURPTw8mJiagUqngcDgwNDSElStXzouzf6PHu1CwAeCioiKUl5fj7rvvJuGSkZERSmIKfT7b6L1e76IIfWSDLYyMljQ0NET0JyaioFQqiR9eWloKq9WKzs5O+Hw+8l5iwixlZWUIBALkFzVXRKNR6hoAoKCMbSDZtC9W2auurr5xOqvTuSTwMRtMpsw5am/PCH5kDXwzBTl+oUUul6Ompobk3RcC1gXRaDQk2w2AkhWRSIRQKIRly5Zh+/btgvXjpz/9qUCOHgD5IDJ4vV74/X5BEOPxeMi8NRt830CNRoPR0VEyL1epVDOsK9had/36dSiVSipYrF+/HgaDAZFIBCtXriTlxcypzqikOp1OognOd75mzZo1+OIXv5i3Q2OxWFBfXw+j0YijR4/OoHYODQ3h+PHjCAaDCAQCNJuzefNmShrOnDmD1tZWhMPhnAWjbD81AETvW7ZsGQwGA+rr69HT0wObzYbe3l4SCEqn04hGowgGg6QMyWcuFBcXw2AwwO12U6HI7/ejrKwMcrkcEomEvOGcTifuv/9+wfXMFhXJTniyC4hutxtjY2OChIzfKTSZTNi7d6/AN4wJdzB6/gsvvCDYv/hdrunpaQwODmJ0dBQcx+Hq1auorKyEz+cTJIy5rid/X2XJZHanDABKS0shl8vnrQx4K8AEQQYHBykp02q1uPfee7F7926YTCbU1dVh37596OrqQjqdxsaNGzE4OEiy8fnAfBTffPNNDAwMUOxQXFxM1GB2/0ul0nkJtcRiMeh0OkxOTpJVwe3Etm3bcOzYMVy/fp06wx/60IfQ0NCA7du303VvampCIBCggpLD4YDD4UBzczOMRiMeeeQRKoIxxGIxtLe3U6e8vLwcoVAIXq8X6XQaZWVl2LVrF0pLS/G73/0OfX19iMViGBsbw8DAAGKxGJLJJCwWC86fP78oc6gfFCwlZUu4bcimjADvVxJZ9TAUCsHpdMLlciEej2NychK/+tWvIJPJEAwGMTExMWPo+2Yd6410yrIpkAtRkmT2AUNDQzfkU1YIsVgM0WgUTqcT8Xgc4XAY09PTtPiywAMALly4QAEuowqVlJTAZrOhs7MTgUAAGo0GRqMRLpdL4BU1F7DqeDKZpOpjLgneoqIiKBQKAb+WW3sAACAASURBVG1ywRgaAtxuoLR0SeAjH/T6zNzd5CTg8QA8NUDg/RlKu91O19xoNGLVqlWwWq0LTsrS6TSmp6dp3pTNDLGAO5VKob+/H8899xw++9nPIp1Oo7OzkyiFLMA3Go144IEH0Nvbi8uXLwuq47m8dsbHx3MWTfjPNPtO3d3dRI8Ti8VULbfb7XjxxRdx8uRJOJ1OSugMBgM2bNiAXbt25fVNMpvN+I//+A8cOHAAPT09ZAqdjVzzNsw3jQVYQKYL4fF44PV60dbWhpGREaL45VK5c7vdeOutt4gGyub5+Mcbi8UEpsvZHSGXyzVD1Y91FbxeL/r7+9HR0YHp6Wn4/X6iP4nFYkrO2HvEYjFBosRsRRj1kv3X5XKRnxhbM6anp3H+/HlBUpa9NufqQLG/sznCYDAoOE9SqRRr166lwJL/O9kCDWyGjyn8lZaWkuQ+KyRYrVbqaLH7Z/Xq1Tk7ZLmQj41ys+bEFhMjIyMYGRmh4gPzHaytrRV0kJn3Fvu7w+HAvn37MDIyQt3uYDBICYBKpcLy5csRDAZJyIPFDmazGTU1NZiYmBAkGjt37pzzcXMcR+JdjGp9O2E2m/HDH/4Qp0+fRllZGRoaGnKuMY2NjVi+fDmAzPqn1+sFXf6qqips2rQJFy5cEBQiotEorl+/DgA0m8ae2ZUrV1KMMz4+DgAYHh6mWIKtUdFoFA6HY9HmUD8IWErKlnBb0NzcjP379yOVSiESieTkyDNDTjaszTbnYDBI3kTzFZBYCG5UfRHITYFkRp9M9amQKpHdbseBAwfw7rvvQqFQYOXKlYvwzWaCUTBY1YxVwACQrD0LlEKhEMbGxrBhwwaqdldXV+Ps2bMYGhpCJBJBPB5HdXX1vNT3xGIxEokE/QFAn5ed2LHjiUQiAtrkgjE+DgQCwB+DuSXkgEiUmbULBDLnKyspY9S+vr4+6myuWbMGWq1W0OWRSqXz7vpEIhFcv34dX/jCF9DV1YV33nmH7gm2NgwMDODFF18kKh0zgGWdqUgkggsXLlCAXwgymQwDAwMCJTL+92TPNMdxOHz4MK5fvw6v14tQKESJT09PDyn18VX6lEolSkpKSN66EJj62qFDh+B0OjE5OYlUKkUzG0qlEnfddReAjOpkKBSCUqlEcXExIpEInZMrV65gYGAA4XCYPIQYvbQQBYz5uXEchzvuuANGo1FwLsxmM6xWKwYGBkhAILs7xmZT+QbQExMTkMlkcDqdCIfDJNIhEolgNBrJ6iCdTsPj8SAUCs3wXbJYLCR8kUgkoFar4Xa7AWSCPuZlx5K1K1euCNaJ+dDTq6qq6HMYLZtdS6bayd4zn3AHQzKZhMfjwcmTJzE4OIjHHnsMQKaDyGYjy8rKIBaLZ+2Q5UIudkeh4/lTAcdxNFMOZPzFcj0j2d/PZDLhO9/5jkDZkYlcABkKvsfjwcGDB+H3+xEMBunfTSYTtmzZgq6uLgQCAajVanz+85+fV6Lwp9YpA963I5jtNU8++SROnz4Ni8WCvr4+mudnYyIajQZKpZLEiNh8n1QqxfT0NCVkbI4sEonA4XDAbDZjz549aGhowJEjR3DlyhXE43EqoikUChiNxlln6v+SsJSULeGWw2Kx4Omnn8bIyAhEIhHMZnPOCqDdbqcNmQ1py+VylJWVQSQS0bzKza5I3aj6IkP2JjKXOTFG5fjf//1fWtCkUil0Ot1NsQPgm5dqNBpEo1GUlJQgGo1CLBbDYDBAoVDA6/VCLBZDo9Fg+/btNCgOZCrrTqcTHo8HxcXFUKvVFGixIIbjOEQikZyzZmzhz/4Zo0bwIRaLEYvFIBaLF4fSOTGRSTbKyxf+Hn8J0GiA6enM+WpomPHPsVgMsViM5OZZx4mPhcpc+/1+AJn5KdaRKioqgkqlosSdJS1VVVXQ6XTkbca6WP39/Xk7dqzay0RCfD4fKZHlmi1jATibE0ulUqRmxuTY2f2ZTqchlUqh1WphMBjQ2NhYMGjKnsOqra3Fhz/8YbS3tyMUCkGtVsNgMOCTn/wkdu3a9f/Z+/LoNssz+6t9sSTLi7zIuxM7ThwnUdoUB4akJKShEJamHWBa2ibTmbYJHSgFWuYMB34ZpmVa1pkWSplSUuBQSHvMlgCOcYgTJ8QkWM7ixEu8ypYly9Zq7dvvD/V9+CTLthwnIRDfcziArO379H3v+yz3uRfAp50wAGhtbYXBYEBHRwcOHTpEHYTE+4gZ4wLTi+nYbDa0trZOKiAlztg1NDTE2ZeUlJRMWidY8Nze3o5XX30VDocD6enpyM3NhUKhQDgcjkucQ6EQ9Ho96urq4pQkgVgnjCVdTNmWiTVUVFQQ5ZXH45G8PXfOL9XOkdlspnPLKLPRaBQejwenTp3Czp07ie7KFSPhJvRs5qytrQ1WqxXDw8MYHx+Hx+OBUqmEyWSCQCBAbW0t0cym6qKmivNRWLwYMBqNaGtrg0ajgcfjgUqlwrp164i2OBO4e2yy+8poNKK1tRVutxtisZgKeUDst1UqlZBIJKitraX7KVVcap2y2YDZ3+zZs4dmXru7u/HHP/4Rq1evRnl5ObxeL4aGhmitXbt2Lf7617/C4/FAoVAgJycHHR0d8Hg8GBkZwc6dO6l4w/5hM+tisRhlZWX0PvNdsk8xn5TN46Kjra2NOMxMACBZQsZkatmQqFqtxpVXXomcnBycOnUKNpsNgUAAu3fvvqCGhHNVX5wKM82JsdmDxsZGDA8PUxAbCoVgtVovCH2RUc+8Xi/a29spsBYIBBCJRFixYgWuueYavPXWWxCJROTFwj3327ZtQ21tLSVlYrEYv/71r+H1eiEQCLBixQqEw2F88skns6omJkvI2LyISqWa+28TDn+alM2rLk4PpTJ2rhISLQaxWIyhoSHqxrBknwk78Hi8pIE/E0aYzmyaz+ejv78fgUAAUqkUMpkMK1euxK233oo33ngDLS0tJGRQW1uL7u5uug6Li4uxd+/epAkZExTIy8uDVqtFNBrFyMgIdeK411ZisvTKK6/gxIkT8Pl8dF1y1Q+ZoTEzPt60aROWL18+pT8Tdx6JO4fldruh0Whw1113AUBSVThuB6mpqQnj4+MYGRkh2wAuDZmrXMkSs+lEeQKBAMbGxuI6EFyKIhNLATDJvmSq78l8HJlqHVMD7O/vx/Hjx8ljLBQKYWhoCE888QT27t2LHTt2UCegqKgIVqsVXq+X6Kysg7V8+XLk5uZS4hcIBOj7zyZZYWyFsbExKkgpFAqi2k1MTKCnpwdSqRRisZiU+DweT9y6zmbO9Ho93nzzTVKus9vt8Pl85MW2fv163HDDDVP+FrPB+SosXkgkWhJUVVWhvLw85YQsFWi1nyqaMuEUNvvKRigKCgriZq5m8/1ZxzkvL++S6JSlCqPRiO7ubioqWa1WHDp0CC0tLTh48CB+/vOfY+3atXHecjabjQpjQCyxGxoagtfrTVrImq3X2uWK+aRsHhcdXA6zRCLBlVdeOek5TBiDgQUMbCEYGxujdrrf77+gm8xs6C2zBaP9sKCAG/gZDAbyWuIGkUKhEF/96lcvyPGyY/3DH/6AU6dOkYAH684VFBRgYGAAKpWKlLsSVTATO4JGoxHLli0jOuTPfvYzUi9LVIqbDRh9Kzs7m4RT5nROxsZi3R+xGBDOL43TgtEXOUbMXAQCARQWFtJ9qlarce2116K3t5cSHRYEcaFUKpGWlgav1ztltyYQCNAsQ01NDdRqNbZs2YLc3FwcOHAASqUSgUAANTU1SE9Ph9FoxMjICPLz87FmzRqUlpZiaGiIgiam6CgSiVBcXIybb74ZFRUVeOedd9Db24tIJIKBgQG6Vpk8ORNOyMrKIkogqyIzas74+DgpJjLhmtzc3EnCN8kSMaYAybzQgJh6XElJCSoqKqaloBmNRjzxxBNobW0lqWpGL1IoFCgrK6PzyEzj+Xw+sRLYeUn8DVhyxP0cJlqRlpaGwcFBmv9kszrTrZtMsEWj0ZAYBqua//GPf6RCDp/PJ6p6IBDAJ598gq1bt+If/uEfUFVVRfQq1qEUCARIS0vDt771LaIF9vb24tChQwiFQmhtbUV1dTXa2tqSzjQng16vh9FoRDAYRGZmJsrLyyGTydDf3w8gVjSy2+0YHBycdC6SdU2ys7Nx6623kuG4RqMBAPIOY+fnfKzzF6qweD6h1+tx8uRJ2Gw2FBYWpjw/N1totTG1TpfLhcHBQfptGG0/GAzOWradFZHtdjtEIhE0Gs0leY6TgWtBEYlEIJPJSPAoEAjAaDRiaGgIW7duJS82k8kEl8sFHo8HsVgMsVgMuVyOkpISWCwW6kgnXvdzFUy7HDAfeczjooPLYR4cHEw6syEWi0kxaGRkBKFQCDabDWfOnIFWq0VhYSGCwSBUKhXKy8sv+AJ4oRaTxLkUg8EAs9lMlf7S0lL09PSQqhubtfj6179+3r8LFxqNhmZNQqEQ1Go1ioqKUFZWhtbWVlgsFmg0GuzevRsCgWDaKjNL2hQKBTQaDQKBALRaLTIzMzE6OjplZX4mTzIgRmErKSk5P9XU+Xmy1CGVAqFQTOjD5Zp0zoqKilBdXQ2ZTEaFl927d0MulyMjI4N8vBJhtVoxPj4+7e8eDAZhMBiwatWquMCN+RsqlUqo1WqUl5eTvLrP54PdbkdRURG+973vwefzoaOjgwL9aDQKtVqNhQsXYu3atcjNzcXevXtJ0nlgYAD/9V//RUGHyWSi61OtVpMaKaMrssAsMzMTPT09lMyIRCL09/djYGAAx44dQ2VlJTZt2gS9Xj8pEWO+WNw5LLfbnVJQvW/fPrS2thLVMy0tDQsXLkR2djauu+46rFu3Dvv27aMONhDrmkkkEohEIqjVaqSnp6O/v39S15JJ4re0tKCrq4tEK5iJNkuu2H0+XeeJO3O2fv166qbt2bMHp0+fpqJQMlgsFrzxxhsQi8XIzMyEVCql9UogEKCioiKu41FeXo79+/fD7/ejtbWVZmGm8v3ignW1mNJmZWUltm/fTusfS1TZTA1T9mRdE6PRSB0GrVYbR23csmVLnJExS/qZ4MdcqYYs4U/0A71UwBRAGxsbYfp7kUej0VyQhIx9nsfjIb8to9GI7Oxsou2np6entK9xodfryUcwJyfnooiPnS8wf7UzZ85QB9jhcNC6kJ6eTjNf3I6rRqNBRUUFqcZWV1fj448/hlqtht/vR1paWsp+qnMVUvsiYT4pm8dnAp1ORyaryTxcGhoaSFo6PT0dNpuNaEHMk+pS3WRmC/bdk3nlbN68Gbm5ufjTn/5E56C8vPyCUSNYoHTy5ElIJBJEo1FotVqsWrUKt9xyC3Jzcym44AaQwNRVZlal9Xg8ZLSr1+uhUCimFBeQy+XQaDQYHR2dVk6fz+fPWtVxSsxTF1MHjxdLxNhcWUJSllhsYGbDTFZ9qnmuVJQZmUS6XC6PC9wSzXNbWlpw/PhxUgxjdg0qlQpbtmzBzp07MTIygszMTCxevBhWqxUCgQB1dXWora3FlVdeie7ubjIJ7unpwcjICCn6sblIn8+HgoIC5ObmwmazwWg0QiAQICsri0SJgHhqpt/vp9lL9p0SE7FEE2EgddW8vr4+6hqxQPG2226bFOgyvyAAREe+5ZZbUFZWBp1Oh4MHD+Khhx6Ku8dkMhmOHz+O3t5euFwuun9FIhFyc3OhVCqnTXCSKTKWlJQgOzt70pxYKh6HjFK5cOFC6qpJJBIsXLgwzo6A0eHD4TB1SUQi0YxdGaPRiGeffRatra2UaHLnYD744AMMDQ1RAqZSqYjRwToIjY2NOHnyJICY4S7rQDDPOW7Xk7FEzgfV8FKfJePuN6zQcTGSGnZNsX8z2j4Qm1m12+10zc90/lnCbjAYIBKJUFpa+rmZkWKdalZACAQCdA/b7Xao1WrcdddddDyJHVe2PrEOv0gkglwuJxucVPxUL/Vr9GJjPimbx0WHXq9HW1sb5HI5wuFwnHs88Gk1ZmhoCDKZDAsXLsTAwADC4TBKSkqwceNGrFu37qLfuHOt5kz3elatYj5ObrcbcrkcgUAAZWVlkMlkRI1QKpXnrTM4lYnp6OgoGXaLxWLccssttDBzg+1ED55kSBQBYEa7icIPDGKxGCtXrkRGRgZOnjxJBuIMCoWCZIslEgmEQmFSM9BZw2yOJWWFhef+HpcTGIXRbAYqKib9mXVJWlpa4Pf7ySfsfBQU7HY7mpub8fWvfz2ONsvMfUdGRsjclzsrptFoyJ6BmU/z+XzU1NSgtbUVXV1d6OnpoS4aC7CZKbFIJIJKpYJarcby5cthNpupe1VSUoIPP/yQqICRSISCk2g0imAwCLVajYmJCSo0sK7aVIlYMmGR6cC6DsPDw5BIJCQFvmrVqkn3R0ZGBon4ALHuxC9+8Ys4ufjbbrsNvb29+L//+z/yD8zPz4dAIEB3dzf5BxYXF6O0tHRaaX/23bjUT6bImLh+MDGMQ4cOYWxsLC55TIZoNAqlUomJiQkyGS8vL4+brxsaGqKZOiA2E8OKP9OtHfv27cPx48dpDs1ms5FAhE6nw9NPP42mpiYIhUL09vaiu7ubft+cnBxkZGSQ2idXqdHn82FwcBDd3d1x5yuxuDA2NnbONMZLeZaMa4ljt9sBxM5XTU3NBU1qtFotRCIRzUmzdYrtUSKRiDzhZupKcxP2cDiMioqKS7JLxgTDxsfHqaDAneHz+XwoKyuDQCCAw+Egeu6WLVvifgt2ntra2uJEipiS9MTEBHJzc0nEazrxrWTFGeDSukY/C8wnZfO4qNDr9dixYwdGRkYQDodJXpm5xwPxqoR+vx8LFiwAEPO5GB0dRWdnZ5zB6sXAXKs5M73e6XRSVR6ILX5sQ2AmuXw+H2lpaVizZs15SQqTfScWEFgsFkp8EoeWuZSkVAd3mS/T0NAQ+Hw+LcTJFPhUKhVuvPFGtLa2AkCcaAIQo6+lp6dDKBSiuLgYfr+f/IPmVGXz+WL+W58j1azPFCJRjMI4Q8BcVFQEjUYDHo933iwsotEozGYznnvuOZhMJmRmZkKr1aKurg7vvfceJVJSqRRpaWm45pprUFNTg87OTrzzzjvo7u6Gw+GgaykjI4Oue4PBgMHBQfB4PCiVSlRWVlKCJpfLifbm8XiwZs0aDA0NobCwkGaDmKCH3W6HzWYjawggRrljiRBL0oqLi6dNZlJFYteBBfZVVVX47ne/O+l9dTodvva1r+H48eMQiUT48Y9/nNQku6ioCHK5HKFQiPy4lEol0apramriqIepfDeJRILKyso4RcZEEZD77rsP69evxyuvvIIDBw5Me+xM1dJut5Mn3f79+6njB4Cq+EyxlQmfTOdvqNfr8cYbb8DpdMYpx3KDTSY20tLSQgJUNpsNkUgEXq8XfD4fTqcTPB6P1CWZQbTD4UB9fT06OzuxaNEiEm5hxYXBwcE50RgvxVkybnLOqMAFBQXQaDQkfX8hg3Kj0RjnaceSa7ZHTUxM0O/EjUuSQa/Xo6uri9QW1Wr1Jdcl0+v1ePjhh6lT+8Ybb+DBBx/E0NAQ2tvbcebMGeqSp6WlwWazYXx8HDKZDE1NTXR+uCwmZrbO1BUTCwns/mBKjolIZiYvl8svmP/q5wnzSdk8Lira2tpgsVgwPj4OPp9PG1ti0J+oSsiMNJlJ6cWupsy14jjd641GI3bv3g23202eTl/96lepetve3k4UrEgkQovfbJAsAUv2na644oo4E9OzZ8/CYrHQbEoiUp214ybabrcbDodjSkn0r3zlK6iurkZTUxNtnlyEQiF4PB7U1NRg2bJlZB7LzC7P+boIhYBIBPh7V2QeM4DPjyWxM0jba7XauOrp+UIkEsGpU6fQ3t6OrKwsLFmyBO3t7XEd2OzsbFx77bXYtm0bDAYDWltb0dPTQ+qvCoWCgoYNGzZALpdjYGCAfKi0Wm0cVXpsbAyNjY0YGBiA1+tFW1sbVCoV2traYLfb4ff7yYCd+ftxjzkcDmPBggWYmJggelBubu55oWEbDAa0t7ejq6sLQqEQWq2W1NSYeXRi4sPOy3SfnZGRQTYYTCTj1ltvRV5eHnWPEqmHXCTriLAkeLoAnK0tVqsVzc3NM86XskIfWyf7+vrw2muvob29HSUlJVCpVJDJZABAyrIOh4O+UyL0ej0ee+wxDAwMIBKJQKFQICMjA0uWLEma4DCPR9YJDQQCcLvdcDqdyM/Px6JFi3DVVVchPz8fzz//PK2F3d3d6O3txd69eyEQCLBs2TJoNBra5wCgpKQkjg3A2CYzWSoAMRXM6urqC57spIJkyfmFFPWYCowBwrzQgHiavclkQn9/P5599lmsXbuWjJeZeiz7t81mg1gshkwmg1KpPOeC6YUCE/zR6/V0/5w9exb//u//jgULFmBkZATBYBCBQAC9vb20N/N4PAwMDODkyZOQy+Wora2ltSJZHJNIV3/55ZcxPDwMi8WCurq6SWtP4mxaKrT1ywXzSdk8LipWrFgBjUZDwfbChQuTbnBcVUJWefH5fPB4PPB4PFMmCRcKc604MuGSRKomC1hMJhO8Xi+kUiny8vJogzIajWhubqYBcolEgqGhoZQ/dzqKwFTHxBbPd955B52dnQiHw/jNb36D7Ozsc64CchPt4eHhaVUX9+3bRzMawWAwqeAHG6bXaDRwuVznp8o2n5TNDnx+7HzNkJQZjUY0NTVNOxt4rmBzH8y2YXR0NO7v2dnZ2LZtG13TeXl5GB4exsTEBAoKCiCTySAQCNDY2EhmxoFAAKFQaFJxBIgF6i6XC6FQKG5ua/HixYhEIjRjBoBoiVyTbJlMhquuugpmsxlHjhyBy+XCu+++i6NHj5KK5GzvMXaP9/f3o729HU6nE2lpaSgrK4PT6Zy2YDFTUYUZ8LLvzwy6Q6EQampqkqrGJr6eOys7m44I66jweDx8+ctfxscffzzlc5kEvlAopLkyv9+P0dFRtLe3o76+niiTeXl5ZF4LIE7en6GhoQFPPfUURkZGyC+rsrISW7duhUqlSprEMtuHkZERokgyBTun04mqqipUV1dj9+7dmJiYoEKk2+1GIBAglUyXy4WysjL4/X7YbDbweDy6zi0WC0pKSvD8889jYmIC+fn5ePjhh6f05OIW4i6FDg4LxrnJ+WxNsecKRo3t7++Pm/9iiUV9fT3279+PkydPwufz4fjx48jOzoZGoyEvPUZv1Gg0qKmpQV5eHkpLS7F+/fqLcgypgkn/J+6fJpMJ4+PjJIwjEong9XrjZkfZLK1SqaQCASuuJttvuXR1i8VCsVqyIjo3HpJIJFTEOi9eo59zzCdl87io0Ol0uPPOO9Hc3IwlS5agtLR00gaXTC0KAP785z+jrq4OAwMDeOaZZ+aUJMwW3ErQbCvaXOESiURClAiuFG1PTw9EIhEUCgU2bdpE1dCdO3eSIlVWVhby8vJICSmVz+VSBBLnN6Y7JoPBgL6+Png8HkQiEfT29mL37t1zOt8s0QZiKntTzYn4fD6cOHECUqkUUqmUAhwGoVAIPp8Pq9WK3bt3k9oak5Q+Z7CkLOHz5jEFUkzK6urq0N7eTtVQNs8x05zQ1B8bE/zhUiGj0egkJU+RSITVq1fDbDZTBTcUCqG3t5cCZZVKBYPBgIyMDOTk5MDn85EsNPPUA2IzE06nE7t27cLp06fh9XrB4/Hg9/uhUCgQiUTgcrlofgyIXadCoZBUU8ViMaqrq7F582ZSazMYDLBarRgcHIREIoHH48GOHTtSXl+49zhbJ9gsm1arJTXEc5lNYnL3zc3NVARjFGqmUDnVephYDLJYLMjLy0u5I2I0GvH444+jqakJ4XAYpaWl1J1IBj6fT35wbL4sLS0NOTk5CAaDcLvdsFqtcLvdGB8fJwEUoVCI4eFh7Nmzh5JEvV6PRx55hGbQMjMzUVlZifvvv3/a9Y8rFnHmzBkMDw8jGAzSdWC322melnuNicVi8nYKBoPw+/2w2+3Izc2F3++na21gYAAejwfvvfceBgcH6X5qa2tL+r0upXkyruUDl+Z2MeiKyb7HVHRhrVaLjRs3oq2tjejWzHDdbreDx+NhdHQUPB6P5r6nouBeCmAquAMDA3C5XPR4NBol5hEAGptIRCQSgVKppDgNiBURGINpqs9kFhdMQTrR45EbDzH12Zlm0y8XzCdl87ioYNVPl8uFgYEBXH311ZMSsmSzV3q9HgcPHoTNZkM4HIZAIJhyM7pQSJWqxwWXusOqnGwh5G6a4XAYYrEYFRUVUKlUZFR67Ngx2jxycnLwrW99a9pj5s6Ncd+/pKQk6eYx1TGxhVUoFNKMRldX15x8c1gSqNfrwefz0dTUNO2MEaOs8jmdK+Y/5PV64ff7iaolFAoRiUSg1+vPfWPk8+cTstkgGo2dr78bESdDQ0MD/vznP2NiYoJmcphh/LmC66PFTYAYdRAAqbi1tLSgubkZGo2G5sUcDgcF5azDBsRoXqwKzDq1dXV1AGIdiu7uboyPj8cpKkokEvD5fAwODmJ0dDSOkpueno5QKETXuFwux0033UTXZ3t7O9LS0nD69Gmiup0+fXpW1zD3HhcKhRCJRFS8YYnTbCXW2Rry1ltv4a233iLPItapv+666+K6C4mvnU7MI9WEbNeuXXjvvfcoGbRardNSnGQyGbKzs+F0OpGenk7CI7W1tRCLxXjqqafgdDop6WZiLDKZDD09PXjttddw5MgR1NbW4sCBAxgeHiZhk+zs7BkTMnYuuBSuQ4cO4cMPP0QwGITX6yVbE7VajS996UsIh8Ok5AkAXV1dGBgYQDQaRX5+PlavXo3x8XEMDg6ir68PwWAQAwMDcDqdxCCQSCRxRTpu8jM2NjalkMq5gP227FinE3SZbnb5s1JNTnUuXKvVYsuWLfB4POju7kY0GkVaWlrSTtlMFNzPGmw2c+XKlfjwww/R0tJCnf5UxZbuGQAAIABJREFUUFJSgh/+8IdkLbFnzx60trZidHQULpdr2rVKqVRCo9Fg8+bNSamLLB5SqVQXzAf284j5pGweFxUzVe+S/R0Adu7cSUGPUCiEQqFIuWP0WSGRupOeng6Xy0VqW2yTZLLIKpWKTCcNBgPJ8opEIjidTgqwqqurp1QzStz8uPTE2WweWq0W9957L+x2O/r6+iASiZCWljbnait77W9/+9uURB+4NCMgFnzL5XIa0mcGt6FQCE6nMykVKWUIhbEEY4bZlXn8HYzqOYXRtl6vx29+8xuMjY0RvSwajdKs1bmCBedSqRQymYw6pMzEF4hVgsfHx8nAdHx8HB6Ph7oU3MSOx+MhIyODxCrq6+uJgtPR0UFUH5b4i0Qi+mz2HizgZ2CqhN3d3fQZKpUKS5cuBRAfwDc2NuLll19GMBjExMREytcwk7NmAaPJZEJJSQn4fD62bt1KScRsJNYbGhpIPGVoaCgueOPz+cjIyMDy5csnfQ+uGmuqYh5THdMrr7yCv/3tb3EU9ZlmTpYtW4bFixdT9b64uDhOtt5sNuP1118nZdlIJAKhUIisrCwAwOjoKEwmEzo6OjAwMBCX3K9atYok7Gf6/twil06no64oS1Knszk4ePAgnn76aeqWrV27FkBs71Or1RgbGwOfz6dkLDFBZp3N48ePw263o6ysDMXFxTOKsEwHlojZbDa0trbi5MmTCAQCSEtLQ3FxMcrLy3HHHXfQcXDNz6eaXU60ALhYmE3nUKfTYceOHZOS0MSZss9DEsHWmoqKCohEIuj1eqIWctcsdm0xRdrKyko8/vjjccUIJmLj8/novxPBTMAZbTLRqyzZ2MS5FLy/qJhPyuZxUTHTbFaip5VYLIZer4fRaEQkEkF2djZycnJwzz33fKYc+VTk8bkVofT0dExMTGB8fBz9/f3o7OzE5s2b4XK5yCOE689iNpvhcDggk8nA4/GQnZ09o5hFss1vLhUonU6HRx55hKTsz5dJ9759+9DV1TXr1/F4PAgEgrgAmCmquVwuuFwutLa2xtklzMrGQCj8lJI3j5kxTVJmNBrx5JNPore3l6r6zOJhKqrMbMDn81FUVIQf/OAHWLduHXbu3IkXX3yR/s5V7GRJF1PQA2LJfUlJCc25cYUQNm7cCIvFAq/Xi9bWVlitVnrPpUuXoqCgAMuXLycLC5ZkMrl7qVSKrKwsouiyjkxFRUVSFdOxsTHs3bsXo6OjyMjIQEZGxozHn0hNrqqqAoC46jPDdHMg7L0MBgOcTicefvhhou1xwQyaKysrqfuRGIQz38LZiHkkwmAw4JNPPiEvxFSQl5eH//iP/4jrCnZ0dKCvrw9AbB1bt24djEYjDh48SDRBtVqNyspKWCwWjIyMUMHA7XaDz+eDx+NBrVbDYrHg9ddfT0q30+v1aGpqQnZ2Nqqrq3Hq1CmcPHkSSqUSmzZtoufqdLop1yF2Pg8fPoxgMAiPxwOJRAKj0YjR0VH4/X5EIhGkpaVBrVbDZrPB6XRScYoli4xN0t/fj3A4TII3jA4/W7AkjyWUoVCIimFMKEcul0Ov19P8dzLvyktFAXKque6p8EVLFBi9lq0DMpkMf/nLX+BwOMDn87FgwQJ8+ctfhkKhQFlZWVLbIbY+BYPBlNeqRMxlFORywHxSNo+LisQbEojNa3CrJVxPq7q6OuIwq1QqVFRUYPv27Z95QpYKDYK7GYXDYarUA0BHRwfefPNNkuGVSqW0eQOxhCo9PR2jo6P0Pn6/nxLVZEi26cx1Y9HpdHGzI0D873UuYJ2T6aBSqSASiTA+Pk6PsUF+RoUDYklZNBqlYMBgMMSZkM/KxkAkiiUZKVI7LnuEw7HOYpKkrLGxEXq9noQwFAoFbrnlFhw6dAh9fX1zVtvKysrCP/zDP5A1htlshkwmoyRILpdDpVKRml1VVRXq6+tpto3H40Gj0dBrmCwz8KkXT2NjIyVkAKgoxGa+9uzZg7Nnz5K6XlFREVauXAmxWIyOjg588MEHiEajEIvFyM7ORnFxcdJAUKfTYeXKleju7kZFRUVKaxurRtvtdhQUFCArK4vmOGYT+HLvka6urqQJmVQqxfr164l+yiidbrc7LghnQ/sVFRXnPC9UVFQEl8s1SaVTrVZDIBDErQcM3G4REzZobW0Fn8+HxWIh6uEdd9yB/Px8CkTT09MxPj6OtrY2WocVCgVRpgUCAeRyOc6cOUPdg46ODqKAHjx4EI8//jgsFgvC4XDcrKNQKERTUxN+8YtfUCLO9XRKNsNrt9tpfol1UJkSYGFhIQmkWK1WvP322zAajejo6CAKmc1mw+joKDEQPB4P9Hr9lPvFdGC0+46ODjo+uVwOpVJJrIni4mLa37hKelzPvZlmly8WpprrvpyQ7HdYtWoVXnzxRYTDYeTl5eH222+fdv3R6XS4+uqr0dHRAbVanfQc6nQ61NTUzGo9m8enmE/K5vGZYGxsDN3d3ejs7CRaBwua2QZpsVhgNBqJA61Wq+MoKZ8VUqVBsOCura0NhYWFOHDgAOx2Ow2e9/f3w+FwYPHixaS8xt5HLBaTNK3f78eVV16Jw4cPw+/3J5W3vpCbDkvs5urVxrB27Vq8+uqrGB4eBhDrZOTk5MQJNSgUiklqfUyBkpvQ+f1+Eo6QSCRQq9WTTMhTHnTPygLkcsDtBtTqWR/XZQe3G8jJiZ03DvR6PV5++WXqTAmFQlRXV+Mf//Ef4fP54Ha7YTabZ0zMp0J2djZuueUW/OAHPwAA7Nq1C319fSRwIRaLUVNTg6VLl0KpVNJsVUdHB06fPg0gZqvA/M28Xi/Nm3E7rEw4g4HP56Oqqoq6Ek1NTejv74fX64XVakUkEkFOTg7WrFmDffv2xV2bIpFoysF4ANQF4SaHU8FoNOLIkSMwmUwIBoMYHBwEEDN/TkZVY/RFrroZe5wJcXR1dSVNlmUyGRYtWgSZTEbJFzsOj8czKQif67yQVqvF97//ffznf/4nHA4HgFh1fsOGDRgZGZnkV5aeno4rr7yS/r+oqAgSiQSRSARWqxV+vx+PPfYYJWbf/e53sXTpUrS1tcHtduOpp56irnsoFCJRFrYOsbUDiK0/TCyktbUVH330URy9k9GogVhRbWRkBM899xzRMGtqakhJOHH9LCoqglgsJvoYKzAxCxSuQIper8df/vIX2Gw22O12vPbaa5iYmMD7778/STxnfHwc77///qz2TC7t3u12Q61WQyQSoaamBitXrkRGRkbcTBkAEnCa6hr4rLtOiXNM58PA/vOIxN9hw4YNyM7OpiJ4stgi8fWbN2+e8fnTrWfnK474ouK8JWX9/f144YUXcPjwYZjNZhItuOmmm3DbbbfRYDTDE088geeff37a93z33XfJOJhhdHQUv/vd77B//35YrVZkZWXh6quvxp133on8/PzzdTjzuEBglIi9e/fC4XBAoVDQ78aCItYZMhqNOH36NNGdvF4vXnrpJRrITtZevxhIlY6RaLS4efNm1NbWoru7myqQUymSMXlltoF7PB5KVJNRGC/0ppMoWMI+81zOv06nw7333otf/epXmJiYgFqtxurVq3HgwAHqTCSKJgCxOSGpVErBGhATA1EoFMjKysKiRYuwffv2uEBnVrSZvDxAqQQ4ssDzmAYTE0B5OcBZdxlt8ezZsxTgq1Qq3HzzzXRNymSyOfmVBYNB2Gw2mM1m1NXVkQBQWloamUgrlUoMDw+T2t62bduQnp5OAiPRaJQCbIlEApPJNKmjoFAoKNni8XjIysqC1+tFQ0MDnnnmGfI7Y8lld3c3TCYT9uzZQ8moQqFAMBjE2NgYjhw5gn379tEMDpvX6e7uJgnpVCShWZIlkUhIoZStC8n8whLpi2KxeJIqa0ZGBnp7e+Nex8QzGOWUJV8s0Eo2I3U+1uPbbrsNmZmZqK+vR2ZmJpYvXw6dTod9+/bh448/jks8mM/cnj17AMSCxoqKChw+fBhAbAbmzJkzeOyxx7B27VoYjUZMTEzg1KlTMBqNSRVAp7o2w+EwXC4XTp48OWNBgdkhuN1uWi+7u7uhVquJ7sr9nbVaLdauXYv29na4XC4oFAqo1WoolcpJAilspisSiSAYDOLYsWP4+OOPaWYzEWfOnJn2uyaCdWFtNhuKi4tRVVVFHY+pft/PuhM2Ey4VCuWlCG4RPBWvz5men6wINJNuwKV4zXxWOC9JWVNTE+6++254vV6IxWKUlZXB4XCgra0NbW1t+OCDD/CHP/whbtPr7OwEAFRWVkKhUCR9XzZIzTAyMoLbbrsNZrMZSqUSlZWVGBwcxF//+lfs3bsXL7/8MhYtWnQ+DmkeFwgGgwGHDx8mr62JiYlJHG/WYXrvvffigp6RkRGMjIygvb0d77zzDhYsWIAf/ehHSWX1LySmo2NMpX4IxBazG264AXq9Hn19fdBoNFN6tDD1Q7fbDY1GgxUrVtDmnpeXB7FYHEeDuZCbTqJgCVOdms1nMLNTuVxOi7VCoYDb7YbX68XZs2cBxIIZ5hOVCBZwCAQCCvij0SiCwSAqKiomUS9mTZvJzwcUCiChQzKPJAgGY/+oVHGdsrq6OnzyySeUgInFYixfvpxohqxTwO1GzAasU2qxWNDU1ERm9KyjcOONN6K7uxt9fX3o7+9HNBqFwWCASCTCmTNnEI1GaTaRiSUIBALk5eUhEAjQdcoMbdlAv1Qqpc9tbm6mmTMmde/3++H3++Hz+UhEJD09Hbm5uRgbG4PH40EwGMQbb7yB6upq5Obm4vHHH8eRI0fA4/FIMTCV+4rRlAsLC2mekh3DVK/lylizLiBXlbW2thZ8Ph/Nzc0IBoMQiUQkSpKXl4fy8vIpBSouxLq7YcMGbNiwIe6xdevWobm5Gfv374fH4wGPx4PX68Vjjz2GQCBAKnlpaWmIRqMIhUKIRCKw2WzQ6/XU2TrXDi3DdK/n8XhYsWIFUSDD4TB1mgoKChAOh8krLfG3WrduHTo7O4kaduuttyb1RCsqKkJVVRWGh4dJBY/dS3K5HFKplOTPZTIZbrjhhpSPjduFBWLd11tvvXVWAieXIthewIQ75vEpZhs7pKILMN0M60x/v9wx56TMarXiZz/7GbxeL77xjW/gwQcfpCTro48+wr333ovDhw/jySefxAMPPECvY0nZM888g+Li4pQ+6+6774bZbMYNN9yAX/3qV5BKpfB6vXjooYfw9ttv46c//Sl2795NEsfzuPQgFouJtgbEgmq32z2JbsdMS6faAEOhEDo7O/GLX/wCK1asQF5eHvh8PpYsWTJJgvVCINkmNJX6IVe0ZLY0Q5/PRxskSzCcTmec+AZr/1+oaiVLLoeGhiCTyVBVVZXSRs2g1+uxY8cODA0NURVYIpHAbrdTkGSxWCAWi6e9d2UyGYqLixEMBuNmfYBY92QqCmnK5yIvL5aUud3zJtIzYWIidq7y8ug8JcrfSyQSLFmyBPfeey/9BosWLcIHH3wwyXsuVYhEIqhUKqSlpaGjo4O6HYwmmJ6eTmIw3KB8//79RI/l8/lQKBRQKBRkYK/RaHD06FG8++67cDgckMvlSE9PJ5+vvr4+BAIBmEwmXHPNNTh+/Dh9H7lcjq6uLqLbssQvGAySBDvzBerp6cGzzz6LNWvWUPDL5/ORlZWVkocXd/1ghQmWYE61lnDV0Hw+H1auXJlUlVWn06Gurg79/f1YtWpVnDfYVAIVFxNarRYPPfQQCgoK8OabbxJ9z2azQSAQQCaTIS0tDSKRCFarlWarwuFwSkqvc4VMJsOPfvQjLFmyBE8//TRMJhPUajXWr1+PsrIytLa2wmAwQK1WT/lbVVZWwmQywel04sUXX8TWrVuTnvdt27Zh0aJF+O1vf0v7KbOB+PGPfwyPx0OiR7fddlvKx8C6HFKpFGq1moSnvihggiSpWENcLpht7MAdy1ixYsUkUS1u88XtdscpWHIfn87r7HLGnJOyv/3tb5iYmEBlZSV++ctfxgVVq1evxo4dO/CTn/wEr732Gu655x5IJBI4HA6YTCbIZLKUs+RDhw7h+PHj0Gg0lJABsYXw0UcfxcmTJ9Hb24v6+npcf/31cz2seVwgsNY3Fy6Xi7jJwKfdtIkUaGSMvsFQX1+PU6dO4aGHHopTCbsYnbRknTGuaElDQwOqq6tTohmygfX+/n7w+XyaiygqKsKvf/1rHDt2jAIN1v6/UNVKVtli3YBkw/bTgXUzrFYrQqEQAoEA1Go1xGIxSVMXFBRQ1yxxloyBddGWLVuGjz76CH6/H3w+nwxJ50zZlEqB7GxAIgE8nljSMY/k4CZlmCx/LxaLsWDBAjzyyCNxkt0NDQ0YGRk5Z6EPoVAIpVKJoaEh2Gw2UspjsvDj4+PkzcSV4WdGqazIMzExAb/fD6FQiMHBQZjNZpjNZpr/YYmOSCQCj8ejDlh6ejpKS0vx8MMPo6mpCYODgzh9+vQk6XiBQACVSkXeVEyMxuFw4PTp0yguLia/NrFYjNzcXKxYsYLmvaa6jw0GA3p7e9HT0wO1Wg2fzwe73Q61Wg2j0TitoTNDRkYG1q1bN8lLipnqXsqBqlarxQ9+8AMMDg6iqamJqO3MQD43NxfhcHjSPOD5hEajodlgIHY+q6qqyCftwIEDsFgsmJiYQFpaGlauXAkAZBWgVCrR3t4e19VnBT0Wx7ACAlOQTOwaarVarFu3Dg0NDRgdHSXPs7vuumvaJGym/ZCt9Wq1GqWlpZ/5/Pb5BNufGV14Tp6Wn3MkXgfTxQ7JfOe4YxksbmMFaSb+Mzo6ip6eHhgMhrgkOLFIdDn/Dskw56Ts448/BgB87WtfS1rlXrt2LQQCAbxeL3p6erBkyRKSwy4vL0+5YvrGG28AiMm7soSMQSgU4pvf/CYef/xxvPvuu/NJ2SUMp9M5KXj2+Xzo7e0lad329nYyTU4FXBqUz+fDqVOnKLhJZlp5vjxGuGaaOp0uaVvfYDDE8a+rq6tTogqwgXU+nw+73Y7e3l48++yzKC4uRl9fH7xeL2QyGSQSyQVv/2u1WtTW1qK/vx+jo6Po7+9PaSFlizlTDvP7/dQpy83NRXFxMdEz169fj4mJCTz++OOkUJkIj8eD8fFxOJ1OonABsSCY+bvNGdy5svmkbGpMTMTEUPLzk8rfZ2dn4+c//3lcULdv3z4cPXp0Tl2LaDSKkZERSCQSms0Ri8UoKCiAw+FAR0cH5HI50cZUKhVycnLg8XjwySefkJlzJBKhRKunpycuSWTzQEx0wePxIBKJQCqVQiwW07oRCATQ0dGBwcFBSsp4PB7Ndi1cuJDUDAOBAMLhMMLhMMbHx2E2m7F48WJIpVKiiCX6OyW7v9ra2ohi6PF4oNVq4XK54PP50NjYmNQPixUtmMJaYhD2eRu812q12L59O1pbW+Mq7UVFRfjlL3+J999/n4Qnzjduv/123HHHHWhqaoJQKERZWRmAmNpoV1cXOjo6YLfbaRZxbGwMExMTNFvIJOWbmpriZqJZwmC32+k5wWAQZrMZzz33HKqrq+l57PozGAzIyspCaWkpAOCb3/zmjAnZ5+l3Pt9gCSdjnxw5cuSSNn6+UJjNdaDX6/Hkk09ieHgYWm3Mu5QxBhK9ZLkKnGz+cmxsDFarFXw+H2vXroVWq6Vi2lwVeL+omHNS9m//9m+44YYbsGTJkqR/Zx4bwKcGkNx5slRx4sQJAKCqUyKYkfDRo0dTfs95XHx88MEHk8xjg8EgXRsmkwlnz56lIOZcoFAoMDY2hqNHj+LgwYPw+/3weDzUsWKLkUajQW5uLkKh0IyqjsmqRczDBQBqa2uxbdu2pDQAbhLGHuP6IiWDVqvFli1bYLFYKNg9ceIEjh49ikAgAJlMhrKyMlJsTPb9zmeHUKfTEd3KYDDgzTffJMrTVOeLu/DfeeedGBoaglwux8jICLKysrB06dK45LilpQXl5eVTehSxgFYkEiEUCiEajVIHgs2SzlWun+bK5sU+pofLBRQWAnl5SeXvv//978dV941GY1xnY7bg8/nUWWLqegKBgGapVq1aheHhYVgsFvD5fAwPD5M64HXXXYeKigpcc801eP7552EymWiuLRQKTVpnMjIysGDBAgQCAQwMDMQpgq5ZsyZOSIYlWuw50WiUxCN0Oh2uv/56PP/887SeRSIR+P1+NDc3U/JWVlaWNNBJvIYbGhrw9NNPUzePdYoZNdpiscBiscBms8FgMKC7uxsCgYA6gQ6HA1arFS+//HKcYtrncfA+NzcXGzduxF/+8hcEg0HIZDJ84xvfoLVRIpEkFfHIyMg4J4N5Pp+PTZs24YknngCASV2u9vZ2YgMw70kgJk61a9cuPPjgg6ioqKBZr0AggPr6eqKrcgt6BQUF+PjjjzE+Pk5dv3379tEsIAukWZJRWFiI0tJSbN68edpjYF3Wjo4OaDQaVFdXT/qd9Xo9+vv7YbPZoNFoPhfXQqrgFhdtNhssFkvcb3C5INX7nRXbmpubEQgEqGt77733Ji0scx+Ty+Xo7Oyke/DEiRPo7+9HUVEROjs7IRaLoVQqUVNT84Xqxp4PzDkpW758OZYvXz7l3xsbGxGNRiESiaiiw5Ky8vJyvPvuu/jwww9hNptJhW3z5s2QSCT0HpFIhIQhpqqGFxQUAAB5fXDNM+dxaYAZZCZCJpNRUtTe3o6FCxcSVW62iZlIJIJQKMRLL71Ecx4SiQQ5OTkAgJ6eHvILO3nyJKxWK3g8Hvbu3Yt77rkHgUCAHsvIyKAFI7GyZDAY0N/fT+Ib/f39MBgMuOKKK+IWOC7/urCwMK4aPtNipNPpcP/992Pnzp3o6+tDd3c3vF4vqcGxcwog7n03bNiQUtV9NmAbWkdHB3p7e3H69Gns3LlzSvncxIVfpVJh69atlKzp9XqMjIzEfTc2U6ZQKJJSVxn1NRQKgcfj0bXhcDhw5MiRpPYKs0ZBAZCeDnR0AAsWAOc4+/SFhtcbE/lIT4cxFMLbb79NYgNM/j4xQGQdY7lcTslbqpDL5SguLobH40FGRgZJzTNJeKFQiNLSUkgkEhK+sFgscLlccLvdiEajtG/ceuut2LlzJ3g8HnWRfD4fXVNyuRzLli1DOBwmSXXW/cvLy8P69evjvptSqZykLMy6HBUVFcjOzkZFRQUdcyAQQDAYhMPhoO+XrHiTbJ9rbm6Oo/YyM9+MjAzk5uaitLQUbrcbJpMJVqsVo6Oj4PF4cDgcFOD7fD4cOnSIjoPNgHyelOnYGuLz+fCVr3wFHo8Ha9asgcPhwOuvvz4lvVosFqOyshItLS0zfgafz8fChQuxevVq2O12mlVOBq6IRGNjI5qamjA6OkrF6GAwiEAggO3bt2Pnzp2w2+1wOBx4//330dDQgK1bt2LDhg20r4jFYgiFQuzbtw/RaBRyuRxjY2NEW/V4PHG+kalCLBZjcHAQo6OjsFqtaGxsjCsMJop8fBFFGFiMwUSrWlpaYLFYLquuYarCHgaDIU4FORgMwmg0oq2tLaniKvf6ffnll2numzEF3nrrLRL3UiqVNG95uZz3VHFBfcomJibwP//zPwBiykJKpRLAp0nZ73//+0ldk/r6evzpT3/Cc889R3L4drudArCpHMTT09Ppv20223xSdgmCDREnIisri6p2GzZsgEajwfXXX4+XXnoJbW1tSd+Lz+fHBebssaysLIyNjaGnp4dmSKRSKSorK4n2ajKZEIlE0NvbC6/Xi2g0CqPRiOeeew5jY2MYGxsDj8dDdnY2rr76aixatIgkgtlxFBUVobS0lDYwpgDJwB16ZQlSW1sbef2w95lpQcrNzcXXvvY1tLa2Ynx8HCMjIwBiFfmhoSG89tpryMzMjHvftrY2Sog8Hs95qwbqdDrs3bsXRqMRwWCQvHSmmk1ItvBzZ2JYcMHlqYtEIuTm5kIoFCalMbLOBBeBQADHjh2DUqmk3/icK7xlZbHErLMTcDjm/cqSwWQCcnOBpUtR9847ZMgMfCp/n0xNtLy8HOPj42hvb4+zNZgJoVAIEokESqUSdrsdUqmUBsVDoRC6urpw6NAhfPOb38TQ0BAKCwvx3nvv4ciRIxCJRNDr9ejq6qKEhXWz+Hw+lEol0tPTIZVKkZ6ejtWrV6Ovrw+nT58mmfpoNIpIJAKTyQSz2RzXYfJ4PFAqlXGzY0zow2q1QqvVkkk1+5vf74fb7UYkEqGujk6ng06nm1bRdcmSJVCpVBTsqFQqVFdXIysrC8XFxST5brFYKClQq9VxNHCmSmiz2ZJSuy9VSXMu2EyK2WxGNBpFXl4ehoaG4Pf7MTQ0hJGRkTiKrFQqxYIFC3D//fejs7MTH3/88YxFAbaOMBPqSCQCs9k8pSE2o4MyI/Bdu3ZhYGAACoUCy5cvp/Oam5uL+vp6vP/++2hra0M0GoXZbEZ2djYV6X7/+9/j1KlTNCtZU1OD6upq7N+/H06nE93d3XA6nTNKjyeCSekDsesg0ZuPdcmYYMgXTeQD+DSBrq+vp4QsFRn4LxJSFfYoKipCdXU1rXNSqRQCgWDKRJbdA3v27MHJkycRDodJnVYoFEIkEgGILz7Nd8km44IlZYFAAHfffTeMRiNkMhl++tOfAogtBt3d3QBilZj/9//+H7761a9CLBbj6NGj+M1vfoPu7m7867/+K9566y0olco4ygu3g8YFd84sGW1hHp89ioqKkJ2dDbPZTI8JhUJoNBoEAoG4AdK8vDz89Kc/xX//93+T5xELdhQKBW666SYsWLAAH330ETo6OhAMBpGdnU1+O1KplKroQqEQixYtgkKhQHV1NSYmJrBr1y5IpVL4fD4IhUKoVCoEg0EKxIBYh7ajowMmk4mq8qOjo+jq6kJRURG2bduG2tpaAJhUcUwcerVYLJOMVmeqQnLfJy0tDVdccQXOnj0Lu92OjIwMeDwe2Gw28Pl8BINBshZg8vkej+e8VQPLi+BjAAAgAElEQVRZYLhp0ybqNE43xzXVwi8Wi2Eymei7MaUm1lmbmJjAkiVLYLfbcfjw4ZQ7pUwWXCqVzq3Cy+cDK1fGOmUjI/NJWSIikdh5Wb4c7xiNeP7550ltUSqVxsnfc8Guh+rqavzwhz+c1UcGAgEcP34cQqGQZr1YxwmIVXD7+vpIeddisWDlypWUnHR3d5OkvUKhoMA0FAphwYIFUKlUWLt2LYlfvPjii3C73XEdPaYS++abb1J3WCwW4+zZs3C73STDzvYqi8WCF198EStWrIBIJILP54PP56N7wel0IhgMQiAQYNOmTXR/JN6fiWvAP/3TP6Grqws5OTm49tprEQgEcOTIEQwODqKhoQEbNmxATU0NPb+2thbHjx/Hq6++CpfLBZFIhMWLFyMjIwOtra1xokRXXHFF3OdeSr5T3CJXY2Mjenp6EAqFIJfLYbfbkZaWBolEgoyMDAwMDFCXis/nQ6vV4tprr0V1dTWqq6tx9OhRfPDBB9N+nsfjoThFLBZjZGQEPB4Px44dQ2VlJbZs2ZI0oGTX+bp16+Lmjbm/78aNG9HQ0EB2HhMTE2hra6OkvL+/H3a7HTweD2q1GsXFxQgEAkhPT0dfXx94PB52796NLVu2AAAl3alImVdVVVGxS6PRYGxsjNgW3C7ZF03kgwv2G7AiJqP/Xk5IRRRMq9Xivvvug06nQ39/PwAQRXymRDYYDILH40Gj0UCtVqOgoIBGNqYqPs0jhguSlAUCAdx1111obm4GAOzYsQPl5eUAYjz4733vexgeHsbdd98dt5CsWbMGy5Ytw0033YTh4WG8/PLL2L59Oxl+AlMbO3Kl089VbnkeFxZarRb3338/HnjgAZr9KCgoQFFREXWouB2e4eFhFBYWUlcmFAohOzsbN954I6mErVixAq+//joNmEokEqjVajidTlL5LCoqwuHDhyEQCJCWlkZJVjQaxcKFC7F06VJs3LgRBw4cgMPhiOuUqdVqCAQC8Pl8eDwedHV1wWw2o6urC9u2bUvqAcOl7rHvxP69adOmlAVMuO/D/IRuu+02ktY/cuQILBYLTCYT0tPTEQ6HqRp7vqqBer0eTU1NJD8eiUSQlZUFv9+P06dPo729fdq5uGRBpkgkgkgkilNM5HqXMInx2VBXXS4XGfzOGStXAvv2Af39MZre3yt88wBgtQIyGTq8Xjz0xz+SbYVIJEJ5eXmc/H0i5qoOymg0YrE4Tu2QiWVwu8XV1dUoLS2FwWAg5sTChQvJQoLNNx4+fBh+v58CU9bR6+vrg8vlivsct9uNQ4cOweVyYf369ejr64PJZCLqJncP8vv9RJFiEv1sJjIQCKCqqooKCXq9nkyaEwMVtgZ0dXXB7/cjLy8PNTU1VGBpaWmB2+2OS664xRAgFmwzaf/Kykrce++9yM3NJTGMxAJRYiI4VXfoYoCJKTEBE5fLhe7ubrhcLlJvZsWoDRs24NChQ+js7ITD4aAZvuHhYXzyySdYu3YtrrjiCjz66KPIy8vDq6++Oq3nGIs1mOIsj8fD+Pg4XWNTUbeB6a91rVaLm2++GX19fQgGg8jPz6eZeMbAYN0JVhgcHx+HWCwmGyC73Y5Tp06ho6MDZrMZ4XA4ros71eeyQqLNZkNnZycaGxtpDf+id8m4YKwcrjLydL/n5QqtVovvfve7AEBz9KwINV1BllmUZGRk4Oc///kkv7358zw1zntS5nQ6ceedd5Iq4wMPPICbb76Z/i6VSnHPPfdM+Xq1Wo1vf/vbeOqpp/Dhhx9i+/btkMvl9PepAlru44nqjPO4dLBhwwZkZ2eTelV6ejq6urrQ2NhIxp8lJSXUYTIajQiHwzQUr1ar0dvbi1deeYWGnbntcEbDcTqdeOaZZyCVSuF0OmG32zExMQG5XE5Va2bk+Z3vfAc6nQ7V1dWora2NmynTarVoaGiggIDNgTQ3NyM3NxerVq2aJK6ROKOh0+mwe/du+P1+HDhwAEAswEvmlcKtUCceGzcwYhUnlngNDw/TcXV2dmLRokUUrLHO1NGjRzE2NpZygPWHP/wB//u//0tKSVxxBeb39MgjjyA7Oxu5ublJK8PsmPR6PWw2G5qbmzE8PEz+TtxKLaOkRaPRKRUYpwKb43E6nSlReaaFWg1UVQHd3YDZHBO0mEcMRiPcKhX+2NYG698TMj6fT5tvKmI5cwGfz4dKpYLH40EoFIJUKsX111+P22+/HQ0NDZDL5XSv2Gw21NfXw+l0QiQSTfLXa2lpmVS0uOKKK+iefP755zE0NER7SyQSQU9PD4ndeL1e+h7JwEQ9IpFI3FzGmTNnaO2z2WwYHx9Hd3c3lErlpHlItgYwqpnBYACfzydKcjKaMEsIjEYj6uvrYbFYoFQqUVRURAbrRqOROkeJ9ytLBPV6PZxOJ06cOIGrrrrqos/dcGXiTSYTJBIJZDIZzZamp6dj48aNqKiooADx+PHjRJFntMNgMIje3l7qhmi1Wtx9990wmUw4cOBAykUyNsvLuqjnusYYjUYMDAygsrISwWAQW7dupftGq9Vi8+bNZH7OipNyuRzLly9Hb28vzQn29/fDarXC4XBAJBJRt206sGujpaWFOqUejwcnT55EV1cXotEoXRNfdLDr6HKkMF5IsPEGticnduHnMT3Oa1I2MjKCf/mXf8HZs2fB4/Hw4IMP4o477pj1+yxevBjApyIGzBAyGAzCbrfTJsQFN4jLzMw8xyOYx8WATqcjP52xsTFKHlg3iPm97N69G1qtFg6HA3l5eZBIJPD7/XGqQSyISmyHt7S0IC8vD263G5mZmUTv02g0GBwcRCAQICXGuro6qpIlW5RZwtHY2IiWlhaYzWYMDw/jhRdewNGjR1FdXT1JXIM7o8GVxWcBA6NIcjeCZFK103G/uTQMNi8zOjoKg8GAvXv3UiIKAKOjo+jt7UV+fj6uvvpqbNu2bdoNqKGhAY899tgkz7DEma6BgQG88MILcLlcOHr0KMRiMdatW4f77rsPAOi8nTx5khKmaDSKtLQ0WCwWqtTm5+eTdwnrPMwGPB4PAoEAwWAQLpdr7nSUL30JOHYM6OqKzZjNd98Bnw9wOnHK50P9yAglGunp6XjggQcmeSlxwb221Wr1rJNuhqKiIhQXF0MgEMBqtaKiogJ33303dYi59wqbHQsEAnC5XMjKyoq75llCwzV3Bz6loOXm5uK5555Df38/nE4nJViMah0KhaZMyIDYvpWZmRlH1wZAptYajSZOlIPNXHPXBFbR9/v9GBkZwcTEBHW4GCU52RrBzjfr1hUUFKC8vJwSMu46kxiAswTHbDbD4/HA5XJBrVZf9KCVKxMPxObJ2eyuxWJBaWlpnKz8nj17cOLECTgcDqK6s9njSCSCpqamuHX+0UcfRV1dHV566SWYzeY4WwPu+iOVSskOIRwOY/HixSgvLz9nijSXql1SUjJp/p0JUTgcDgiFQmi1WuTl5aGsrAwVFRXo6elBXl4eid6wDmxzczMKCwspyZyu+MZN5l0uF3p7ezE2NgaBQIDR0dFzOq7PG1IVvJhHDGzmkJk+T7ceiEQiiMVimiObR+o4b0lZT08P/vmf/xkmkwkikQiPPvoobrzxximf7/P5ZuxosR+Uz+ejtLQU3d3dGB4eRlVV1aTnMlf7zMxMKOb9hS5pcIMCILYpMAqKTqeD2WzG7t27yRT1jjvugEqlokSNPZctoonJlNFoxNjYGDQaDQAQvU8ikWDp0qVobGyE0+lEJBKB3W4n5cSZqCg6nQ5PPvkkPvjgA+qYnT17FllZWXHiGsDkGQ22+LN5FqYSyN0IkknVJqo5JvtuTPnryJEj6O/vR09PDxwOxyRRFbfbDaPRiI6OjhkDrPr6+pRmM0OhED2XUYEaGhpQXl6O7u5uGI1GDA8Pw+VyUQDBFDJ9Ph8dKzeJEolEKCgoSDk44PF40Gq1UCgUZKY7ZzpKZWUsGevqApzOmCLj5Q6TCVaRCHsGBuD4+zyXWCzGDTfcMK0/EhB/befm5qaclMnlckgkEuo+y2QyKBQKhMNhSKVSuFwuom1x1wGj0Yjx8XEolUqEQiFkZGRMKtbNRGHasGEDqqur0djYiPr6evT19cHn80EikSArK4v2nKlQVlaGmpoavPXWW3GPs/tErVZDrVaTKqJMJotbE7jUvf7+fvJXEwgENDel1+txww03xB03K3Yxc9e8vDxcccUVJPbT0tIyrSS2VqtFcXExxGIxFY+CweBFD1pZElRQUACNRkOUOlasTZZ0sDlDHo8HiUQSV6w5dOgQ0TvZtfKTn/wEixYtwiOPPEJqlUqlEkqlEl6vF+np6SgsLMS1114Lj8eDwsLCSVSs2WK6ZIBZR1itVgQCAWg0Glx11VW49dZbAYAScjZbz+aJA4EAGhoa8NFHH0EgECASiaCwsBD33Xdf0mIJd+b36NGj+OSTT6gTOJcu4OcJqQpezGN2ypw6nQ41NTVob29HTk7O/HmdJc5LUmYwGPD973+f2sC/+93vcNVVVyV97t/+9jfs2LEDarUaBw8eTPqcM2fOAACpLwJATU0Nuru70dbWNkmWGACp9E0nzz+PSwN6vR6HDh3C8PAwJBIJbZBs89i5cyeZR3/pS1+iTfCVV16hwH6qGz1xHoIl8BaLBTKZDHv27CGZV1YRTdV4WKvV4jvf+Q7Onj2LkZERBAIBZGVlIS0tjcQ1gMkbbeLibzab0dbWhhUrVkxLe0w1COImjawz1draiqGhobhOF5/PB5/Ph8Viwd69eyEWiyd1F9g5FAgEEIlEKVF7EhVUnU4n3n77bbjdbiq+MPU5JrawbNkyaDQaSk6rq6vR3t4OtVqNqqqqWdHcZDIZpFIpsrKyyHyVUczOeUMQCACdLpaU9fcDy5Zd3t2yYBC+3l58NDaGpokJhMNhCAQC5Ofn4/bbb0/6Eq65Oqv2A58qp87UDRUIBJBIJBSAMnEZiURCxRSxWDzJmoGtAe3t7RAIBFi4cCGWL1+elJI1E4WJzVSsX78e+/btQ1NTE3mOmUymKeeXhUIh0tLSyGMwESdOnMDQ0BDS0tIQDAapAOn3+2E2m2E2m7Fz504YjUbY7Xbw+XyIxWLk5OSQ4l6iAW7i2seo4Hl5eXHqq6l0CNauXYv9+/djZGQECoUCW7duvejUxYaGBvj9fkgkEmzevBm5ubnTdvh0Oh2+8pWvoKmpiZJ2uVxO3mVTrQtcSj3XP1EsFp93RUq21k+ldmkwGEg1mCWWa9eupb/pdDoMDw/D7/fj+PHjcR554XAYTqeT/t9ut+Phhx+OU3bkQqvVwmw24/jx4xCJRJBKpcjMzERVVdVl0zWa65zr5YJzUeZkhVguE2keM2POSZnf78f27duJt/7CCy9MmxhVV1cjEAhgdHQUhw4dmpS8ud1u7Nq1CwBw3XXX0eMbN25EXV0d3nrrLfzkJz+JU2EMhUKoq6sDANx0001zPaR5XEAYjUa883cZbb/fD4FAAJ/Ph6ysLAQCARgMBqLdSaVSSCQSogD29vbi2LFjcLlc0Ov1aG1txX333Zd0HoLRIWtrazE+Pg6j0YjTp08DiK+mMl8jRjGaqWqm0+nw8MMPo6mpCXq9HlarFW63m8Q1Znq92WwmmiNL4qaiPc52EUtMzpgindlsRlpaGhQKBex2O8bGxvDmm2/io48+oqSV0SVZMGi321FcXAyr1UoD50xOOxVqIfMKkkqlWLJkCTIzM9HW1oZgMIiCggLceOONpMLEbAMEAgHNVBw6dAgNDQ0pHbfP54Pb7YZGo0FWVtaMg8gpY/Vq4OhRwGgELBbg7153lyV6ejDg9eKg3Y7evwsvKRQKfPvb304a8BmNRjz++OMkS79mzRps3rwZRqMRu3btItPj6cA8wGw2G4LBICQSCfLz83HHHXdg165diEQi8Hq9kyr7bK04c+YM+Hw+FAoFCQMlgpugpKWl0YxjMqpwRUUFzeHI5XLk5+eTXQv3nuDz+Vi8eDGysrIwNPT/2fvy6DavOu1H2ytrsSxZVuTIS2wn3mKnjcOUuG1oaIwboAmUlK9lDpQW5hugZWaYw3RmmMMEvsJhoKUw00I6zbA0DUyadhg3pQWapE7q0jR20liOHTteYkveZO37vn5/qPfmlSx5ix27jZ5zciiJreXVq3vv7/d7likUFRVRkxyCWCwGi8UCp9NJXSWdTidGRkboxM5gMCAQCEAkEqGwsBDV1dXYtm0bdDodent7EQgE4PP5oNVqU6Zj6VRwhmFSmhxzFQUEZJ0jzaPrrTEi67jFYsGGDRvo3jDfhO873/kOTp06hZ6eHlgsFthsNohEIsjlcqrZy7QuEI3uSoIYJej1elRUVGSkkJeVlUEul6OwsBCBQACVlZUprsSxWAxOp5Peo9XV1fD7/QgEArTRwb4XHQ5HRq0ZaZgcO3YMAwMDiEajqK6uxq5du1J0lzcC1prT6FoA+5oAi3PmJNpXp9MJHo83LxMph1Rcc1H27LPPYnh4GBwOBz/5yU/mnVTV19fj9ttvx5kzZ/Av//IvePrpp6nzkMlkwj/90z9henoaNTU1KQYhd9xxB+2mf/Ob38Tjjz9O6Ur79++HTqdDZWUldu/efa1vKYcVxOTkJKanpykVh1AIJyYmKIXN5XJBJBJBIpFgz549MJlMaGtro26DAKitfSwWw1NPPZUybSIufhKJBBqNBj6fD1NTU/D7/eByubQrzePx4PV6cf78eezfvx81NTUAMK/jGNFl/OEPf4DFYkEwGJxFI2Ij3SKfbKp+v39e2uNCkL6psLt/+/btSyl8/vznP8PtdsPj8dBrQK472aS7u7sBAJs2bUJJSQkEAgHi8Tj4fD7OnTs3L/VMIBAgPz8fKpUKGo0Ge/bswVtvvUWpV2RRZ4vO0w9gtbW1VEdKwDAMCgsL4fV6U8Kl4/E4vF4vpqamEI1GwefzZ2VKLQkSCbB7N2A2A/39QGEhwF/RaMe1CacTM5cv49TMDP4oECD+nv39tm3bsgbqEtqd0WgEl8vF4OAgwuEwHA4H7fDPBS6XS7U8Xq+XHjgjkQj6+/vpBFckEqVMugl1mdBkw+EwZDJZ1olvOv2XaBwzmVqwHUKJPtVqtdI1gOiYKioqcPvttyMcDkMsFqOpqQmxWAyvvPIKpU0TRCIR6i5LtE8ulwtut5t+N0nUA5DM+JyYmIDdbkdpaSkkEgl1JkyfjqUH36dTp+cz7rgehUo2ZJvmzTfhI5/nrl27Ut53bW0tFArFqrlIAle/E8QxN9O0QaPRUKv7UCgElUqFkZERjI2NwWKxQCqVYmpqCsFgEC6XC1//+tdhNBpx6dIlAMDZs2cxOjpKHy8/P5+er9ivg0xhzWYzZTNUVlbekAVZuo77Rnr/mUAaaiMjI6iurkZLSwt8Ph+NnJhvSsYwDJVOEFv8G2Xyuhy4phNGOBzGb37zGwDJjvizzz6LZ599NuvP79+/H5s3b8bjjz+OBx98EKOjo/jc5z6H0tJSSKVSjIyMIBqNory8HAcPHkzRmXC5XPz4xz/GF77wBbzxxhu44447qHWs2+1GQUEBDhw4AB6Pdy1vKYcVRllZGUpKSugmAiSth/l8Pj04EYOOjRs3wmg04vHHH8fY2FhKXh35vd/97nfYvHkztb+XSCQpWioSphqJRGiuDcksIrowr9cLn88Hs9kMhUKBeDwOvV6Pzs5OugBl6ioTLn8mehIbJOzU6XSisLCQbqpGoxGlpaVZaY8LwXybCrtAIxSCI0eO0MJXLpdDIpGguLgYOp0OFy9ehNPphFAopFocUiy1tLRAKBTixIkTsz4L4KoGVCqVoq6uDnv37oVGo6Fd67y8PMjl8lmLeqYD2PHjx6kWhI3q6mrodDpqGAJcFebb7XZEo1EEAgHYbLZZtLYloakJ0GqTgck6HVBdvfTHej8iHofr3Xfx9swMTiUSsAELsr/Hez9HaHdyuRwMw6CjoyOloM7+tHH4fD6sW7eO5n/l5eUhGo3i9OnT0Ov1NFS6trY2hb5HQuGrqqogkUgymjKkNzJIEG+2KUw6JBIJampqMDk5SXVLDocDiUQCNpsN77zzDurr69HS0kILgTvvvBNPPfUUJiYm4H2PAkrMJdiFWjgcBp/PR2VlJY2P8Hq91IFRr9fT70ZNTQ20Wi2N36irq0tpKLH1Y8TFOJPJ0FpCJoofsLAJH0F6sd3d3U2LUvLvaxXECIu8dpKVyTa7mpychFAoxNTUFHbv3o0HHngABoMBo6OjmJycRCQSgUwmwxe/+MWUwtpgMFB5QDAYRH5+PqqqqmgxuJavy0pgvunrjYhDhw7h1VdfRSQSwcTEBIqKihCLxVBaWkrNguYC0ULabDao1Wq0tLTc8Nd0MbimomxoaIh23AOBAO2wZwP5WZVKhd/97nc4fPgwXn/9deh0OlitVlRVVWH37t348pe/nGKDT7Bx40YcO3YMBw4cQEdHB4aHh1FQUIBPfepT+Nu//VuUl5dfy9vJ4TpAo9Fgx44d6OzshN1uRyKRAI/Ho2GdDQ0NNNNOKBTi0qVL0Ol0GYsAgn//93/HJz/5yVkHD1KcVVRUUFt4Ho8HsVhMw6DJYYidgRQMBmE2m2E0GmkwdXFxMaqqqtDa2gqDwQCdTkdfeyKRgMPhoAJ0to7GYDCgvb2djv4ZhkFRUREmJydRUFAAmUx2TWLjxWwq5KDS0NCAjo4OTExMwO/300Drl156CW63G0CysLrzzjvh9/tTbMabmpqwYcMG/O///i9sNhtisRi4XC4KCwshEAjg8XgQDocxNjYGnU6X0hlmO8ClI92emx26SxCJRDA9PU1t89kgWVmEZulyueB0Oq99k+VwgD17krqyzk5ArQbS3NI+0JiawoTZjIFoFF3vTWtkMtm89vdNTU244447MDg4CLlcjkceeYTqo4RCYYpuhsPhZJycEZfCm266idJsBwcH6XcSSBZ+hHI4OTmJ/v5+9PT0QCQSYcuWLZQmm24ElN7IWIjOihRuRM+mVqsRiURoU6mqqgqXL1+msRRutxvl5eX0OplMJpp/qFKpMDMzA5fLNet5GIbBhg0b6CGZUNdIxiLDMPTQXVhYSB0kyRoTDofpc6bTM4HMJkNrBZk+GwBLmmawi+3h4WEa0rx169Z53WdXAk1NTWhubqb0xbm+P+zXbrFYUFBQALVajc2bN+OFF15ANBrF6Ogo3nzzTerCqdVqYbVaweVyIRaLZ02ySUSC0+lEJBJBXl4eqqurcc8996zqBHE1kXNgTIVWq8XLL7+MQCCARCIBn8+HCxcuUC1va2vrvPeJ2+3G1NQUdT+9Ee+ra8E1FWVbtmzB0NDQkn5XLBbja1/7Gr72ta8t6vfUajW+973vLek5c1gbUCgUUKlUCAaDiEaj4HK5MJlM+O1vf4va2lrcdtttVMxMrKfnAjlkALMPHqSQaG5uxsjICAYHBzE2NkY71QQSiQQ7duzAtm3bMDQ0BL1eTyl1kUiEFnjPPPMM9Ho9XC4XfQybzYZXXnkFQ0ND8Pl86OvrQzgcpnQip9OJaDQKqVSKyspKnDt3jmbNMAxzTWLjpWwqTU1NCIfDmJiYgMVigUqlwiuvvILx8XFEIhFIJBLcfPPNdENnF4zEBKSoqIgWRxwOB5FIBHw+H9FoFMFgECMjIzh48CBEIhEUCgUqKytTHOAIstlzq9Vq1NfX07xDIDkZNRqNs1whE4kEdbAj+jI+nw+LxXLt1vjJFwN85COAzQZcvpy0y78RaIxuN2w9PXjX5cIJhkGMw0FeXh4+97nPzWl/D1wNqmXrEtra2uB0OunEy+v1ZtUncjgcyOVyrFu3DlVVVdR97re//S0CgQDi8TjEYjGlRj755JMQCAQYGRlBIBBAIBCA2WyG3W6HVquFVqulB89sLqfzNUcIfZFMuS9evIiCggL4fD5oNBpEIhFqKBGJRGA0GvH73/8eNpsNjY2NeOaZZ2A2m6mGjBjvsKdkIpEIGo0Gd911F8xmMzVAIsUEAOoWSRoc7LzCdMOSdJMh8n7Xqn4m02cDYMnTDLI+Ep2LzWZDMBhclXDk9O/EfM9PXrvNZsPU1BSApHlHQUEBzGYzYrEYRkZGACTdcgndmzhIfvzjH6fPQSiLTqcTLpcL9fX1kMvleOihh26ITLJsYE9Uc0ga5rGjIcRiMaRSaYq0YC4QzbDNZkM0GkVBQcGCcwBzSOIGOFnksNZAiiSRSISxsTGaX+V2u2GxWDA1NQUul4tAIEC7fh6PBxwOJ4XmSJBusgEgZZElhxNSABCqIskdEolEuPvuu2neEduKmkx5CHWE0CFJQUmKEkLPczqdsFgsCIfD8Hq9NHuIUJJ0Oh09yBUXF1/zgrVUW192MUd0bjabDRwOBxKJZJa2YHJykrpG9vf3p+gCiY0ywzDg8/ngcDiIRqPUfYnP50OlUs0qyMjjZjpwTU5OorKykubnAEkKM9t6n0wMEokEwuEwpqenaaFNXsuybQh33gmMjQEuV7Iwa2z8YLsxhsNwnz2LP1ss6ACgf8+Ns76+nmpeFoLz58/j2WefpQYzpGBJj2tIB6G63nXXXWhsbKT3d2trK6anp1FZWUldC3U6HYxGI+LxOJ3cAsnczOeffx6RSAQMw6C5uRkPP/wwtVrPFK0BXC0EMul9yKTDbDbDZDIhkUhAJBKBYRh88pOfBJfLxbvvvgufz4dwOAydTgeXy4UTJ05gZmaGajPZjQxyP+fl5aGsrAy33XYbDh06ROllmzdvhlgsphORf/7nf571fSd5hWSqne7+St5XWVnZmg5yXaqWLBvI+sgwDDVWWU0spgHHLsh9Ph8uXryIwsJCKJVKFBUVQa/XIxwOo7e3F0CyoCcNQK/Xi4mJCWi1Wly6dAnHjh3D1NQU4vE4Nm/ejI9+9KMZ1+MbFf39/TAajVn1pB90EKaBWCymGlYej4ddu9BJXCYAACAASURBVHYhPz9/wdN1rVaL3t5eOJ1OMAwDhmFu+OnjYpErynK47iAdw+PHj+M3v/kNDQsmlr5GoxGJRAICgQDRaJR2/4jOQiQS0QgEhmHwwx/+MGWzMxgMGRdZdldMpVKhq6sLgUAAH/7wh2lBRl4fcTEkJhnEIpl0+0moKoFMJqPifxJYK5FIUF5eToueYDAIHo8HuVwOpVK5bHSJuTZ6ti05m6LCLuZ6enpw4sQJOrVUq9U00DS9w1pQUECpCaQgJZoaok8YHx+Hy+UC573JSklJCWprazMedrMdkMnh7NZbb6WLPNsQgcPhzLLsZ08+ORwO1Gr18m0IAgFw//2AwwGcPQuMjwMVFcvz2GsN8TgC3d24aDLhbDCIU++ZTahUqpTvSSawDXcOHjyI48ePzyqM57PDJ0VVIBCAXq/H8PBwSnQCMeiRSqXUKMPn8yEWi0EoFKKoqAjxeBxcLhcOhwOBQAAMw0Cv10Or1VLn13Q6zkJE/01NTejs7KSGRaSrbLPZUFxcjI9//OOwWCwYGRlBKBRCJBKhNG1yf5JJBtHNESdKsVgMpVKJV199FVarlZogud3ulKiITNmFczVn3m9mBulUZgDXRPHWaDS47777YLPZFkQdXEsgRjXE2IM0FImZAqFtj4+PU70Z0U8fOXIEp06dgtvthtvtRiwWQ2FhIeRyea4gY+FG15WlG5Ft2rQJOp0OfD4fNpsNXC4X5eXl2Llz57zXhUiRyLpYWVl5Q13L5UCuKMthVaDRaLB792709fXBbDbTwowYteTn5yMSidCpUzwep5S7O++8E1/84hcxMDCAHTt2zKJSzbXIsguuTMVK+mtM/3u1Wk2pkERgT6Y1EomE2n6T3yfFHFsXUlNTg8LCwhXn8RML5s7OTgCgkwL2tQCAp556ir4HckAcHh7G+fPncfr0aWpMkpeXR12YQqEQpV7x+XyIRCJs27aNCs6JaQg5zA8NDaG7uzvlUJieRUQ+x66uLpSVldGDmNvtxksvvQStVguLxUI1PHPRWsVi8fI7iSkUwP/5P4DHA1y4AOTlAe918D8wSCSAoSEYjEZ0er14hWEQA1AgleLBBx+ck7bINtpwu90YHh7OOKmci7JIsuyIU+Pbb7+NeDxO3QcbGhogkUho3l5+fj6lEScSCTqxCgQCsFgsmJiYgM/nA8MwqHiviE53+iRYyOGMTMuIayqhUkajUQwODqK5uRmVlZXUiATALMt80lwieiFiWFJWVkbpdWR6RqbAHo+H6smyIVtzhryvkZERWCwWaLXaNXlQykZlXg7L8sVSB1cL6e+1rKwMVVVV0Ol08Pv9CAaDVO9DQDIDVSoV9Ho9/R75fD7MzMxQQ5n8/Hxs3LjxhjT0mAtsV9VliVJ5n4G97qlUKqjVaggEAkxOTkKn02FycpKew+YzznI4HLOy83JYHHJFWQ6rArL5PPDAAxAIBPjjH/8Ir9eLSCSCQCCAsrIyqidjb0ChUAhmsxkVFRW4//77Mz72QnRWS9VxsamQNpsNdrudFpSEsnT33XenvEe1Wo3W1lZqrqHVajOGny43iFMbcXfMlBcyOTlJXfJ4PB74fD7sdjt+/vOf0w4/h8NBQUEBpFIpJBIJhoaGaKFMCrKNGzdSp7n0w5TVakV7ezuGh4dTDoXpWUTsPB5SvBGq1dDQEM6cOUMPupFIhE5UMmHTpk102res2LQJ+PSngWgU6OkBuNwPTn5ZIgGMjMA8MYETFgte4vPhAea1vycg+WDd3d3gcrmzQsXnglQqRSAQoJs40Qj6/X5KZSwuLqb3Lpkc2Gw2Sg2Ox+PQaDRoaWmh2YYMw9AmCbkv+/v7AcxeGxaqz2xqasLWrVtht9vpoZc0jEiBKBKJAFw1KyEHZw6HA4Zh4Pf7EYlE4PF4wOPxUFJSQmlnRJtGTEFIjEdxcTEMBsOicgxJRAC5ZumB02sJ2fRkyzXluxbt7vVAtonmF77wBaxfvx5PPfVURtovYZh85StfgVQqxZkzZ6iuljAz+Hw+ampq8Mgjj7xvpoQ5rCzYrAb2utfa2or+/n4cOXIEJpMJ0WgUDMNApVLNO0VUKBTgvEfr53A4UCgU1+W9fJCQK8pyuO5I33y2bNmC06dPU4pFNBqF2+2GUqmkhQHpHvP5fMhksjm7WUvVWS30tZOFDEgeWIldLPsgx36PxHyEmIeQrv9K0yTKysqo8ySQdKFMv25lZWVoaGiAw+HA1NQUBAIBdZiLRqN0YSW5gc8//zyNARAIBCgoKMBNN91E6UZssAvYzs5OhEIhGI1GtLe3w+Fw0J8jHUogs6Cf/H66wQfbICEdfD5/5Tqe27cDgQAQjwMXLyb/7v1emL1XkAWtVpy02XAEgOG9AmIh9vdA8l4SCoXUwp3QWklBQujIAGZNOcPh8KyuKilmtmzZQgt+EgxMAuZJtheJYyDmB2ztVPp9mW1tSDfVmMvF9OGHH4ZSqcSLL75ItTpWqxW/+tWvoFAoaL4YkGwkCQQC8Pl8MAwDsViMkpISmidFpoORSATBYBBCoRD5+fmQyWTUFKW8vBxisRjHjh1DOBxekEkDew3yeDyQy+U0cHotUrQyUZlvJGpZtveq0WjQ2Ng4Z5RENBqF3+/Hd77zHbz00ks4deoU7HY7CgsLccstt2Dbtm1rshBfCyAul8Sx+YN8jxGkn8HSoybYtvZ5eXlQq9ULlluwi7IcFo9cUZbDdUf65rN+/XrqpEaoQAzDwO12QyqVQiQS0VwxsVicUVORjvlE+0tBOveaGIWUl5dTZ0EgSb+zWq0YGxvD6Ogo5HI58vLyaCFCuv4rTZMgh8fm5mYAmWmapIBtaGigxiYDAwPw+/1IJBIQCoWorq7GP/7jPyIcDtP8MtLx/8QnPoHbb799zg0/3SChq6sLp06dojq0vLw8+P1+lJaWzjqUETenS5cuzcos4/F41AI/HcPDw9ceHj0Xdu4ESBHR15cs0srL35/mH7EYMDAAxGI4H4vhV8Egxt77J4VCMa/9PZty9dBDD8FgMODixYuU2gdcDRH1+XwpBTlBJpojcflsaWnB3XffDa1WiyNHjkCv1wMALVpkMhlCoRB8Ph8ikQiOHz8Og8GQMdMqExWO3Whpa2vD4OAg3nnnHTzyyCNQq9VZf76hoYFSdUiwNVkb6uvraZFJphV79uyh79PhcMBms8HhcCAajSIWiyEej0MikdApMIl1qK+vR11dHd5991309vbC5/PRDvRcdKJ0WpJGowGPx1uT1t+ZqMzkfd0oluVzTWp7enrmpP2S6BK2fq6zsxMulwsTExPYs2fPB77QWCrSYyNIxMYH+Xqln8HC4XCKARDDMDSug8fjYfv27di3b9+814Sc3dj/m8PikCvKcrjuSN98CAeeFGUcDgcej4dSdzQaDWKxGMbGxsAwDEwm07yL5kqI29MPOcTpLRaLUbtq8pwAaA4YkDw8lZaWQqVSpYS7rjQWQtlJNzZxu914/vnn6TX+h3/4B6rBc7vdlComlUppNtR8lJimpib09/fj/PnzGB8fp3boFosFXC4XfD4fIyMjqK+vp9TO9vZ2vPXWW7h48SKlYKZDpVJBJBLRzYXAZrPhBz/4AZ5++umVuc4cDrBrFyASAQwD9PYmC7OamiSl8f2CUChZVEqlGCkowA8GBzHMcrdsbW1dkI6MrQMi0RCZQLSL84HL5UImk6GyspLeWz09PXC5XFSnWVRUhJqamlmTaIfDgbGxMRw6dIgWIdnyrth/5/F4MDAwALPZDIFAQDPVQqEQ6urqqF60s7MTPp8PV65cgdVqpRRfPp8PPp+P0tJSNDU1wWazwWKxQCgUpky1iNbTYrHA4/EgFothZmYGtbW1NGKD5DIS6mZRURF0Oh0NqHY4HPPm8KWvswsNXl4NkLV1amoKCoUCBoOBrpErxXpYa5jrvW7duhXl5eUYGBjI+LuxWAxvvPEGPvKRj9AmGIl/GRgYwKFDh+bVA92oYBuAdXZ2or29/QPrwpiNspje7AiHwygoKIBOpwOHw8HFixdRWVkJYO4G986dO3Hs2DG6xu7cuXPl3swHFLmiLIdVQUNDA9avX4/CwkI4HI4URzZiYS2TyVBSUoLi4mKsX78eJ06cgNPpXBDFYCVoL+kdNYvFgunpaZjNZrS1taG5uZk+ZyQSSTlchUIhiEQi1NTUUD0V+7WuhQMHm25YWVkJDoeDiooKqNVqGAwG9PT0oKioCFarFRwOB0KhEA6HA0ajcd7rSzY+hmEwMjJCnStjsRhisRgikQhisRhGR0cRj8dx+PBhjI6Owmq1ZtUmxWIxeDweCASCjI5+PT09aG9vxwMPPLB8F4kNDge47TagsBB46aVkYabVAvX1wHsh5msaViswPAyUlmKyqAj/3NWFwWCQNkiKiorwuc99bs6HSDeROHv2LGw226yfEwqFYBgmawg8cdQkrokCgQB5eXkpHX6xWIxwOEy1j6WlpZTWCIAeqogTYygUosV8trwr9t9FIhG4XC46uert7UU4HAaXy4XBYIBer6duiDweDxMTE/T9kOZRY2MjqqqqUF1dTXMAMxmKWCwWWmASerZer4fD4aAauU2bNqG2tpZOH8kEjdA1id00e/pHHp84xs5ViC2HgcZygZgtEIozW/e21rVgy4ls77WpqQlPPPEEHnvsMVy4cCGFuk0mtewivampCSdOnIDBYEAwGKQuvDfKdVwsyJ7s8/k+sFTZ+SiLbJSVlVGGj9frxcDAAILB4IKKVYFAAB6Ph3A4TPWsOSwcuaIsh+sKtkMbWRxUKhVKS0upToTL5cJms0GpVGLnzp3YtWsXgGTuEPmd+WgsSwlVng/sTqbVasXRo0dpwaDX69Hc3EzDPklXPx6Pw+/3IxaLwWAwYGJiAjweD+Xl5VCpVACSQdcSieS6TtDmQjrHntiIj42NIRQKYevWrdSIgeSaLeT6EmrNmTNnYLPZZmnCotEorFYrwuEw1SWFQqE5JyuhUAjT09MZ/y0QCOCJJ57AW2+9hYaGBuzcuXNlRO51dcD//b/Aiy8maYBabdIuX6NZm3TGaBQYHU3a+zc0wFFRgf/X2YmLU1M0FkGhUODRRx+d93qxA5XHxsZgMplm/Qyxep+ZmZlFQSXgcDiQSqWUEkxsmNnRDM8//zw10igoKAAAmM1mAJljLE6ePDkrtyvTmkDcHP1+P7XZj8Vi1HiErEc+nw/BYBB8Ph9yuTzl9RP6IWnGXLx4ER6PZ1bUAwC43W4MDAxQDS2ZttvtdgQCAQDJKWVjYyO++c1vAkiak5DDDpfLhUQiAY/HowY6ZA0hDSufzweVSkW1Z5kKsrVmk69UKiGXy2kAvFarXTNF4/XCXIVyU1MTvvvd7+LrX/86Jicn6fopFAqhVCpRV1eXkrn30EMPwe/3Y2RkBE6nc80avKwVrMSZYS1hPsoiGxqNBnv27MGlS5cQCoXg9XphMpnmNfvo6emB2+2mRkbPPfccGhoacvfcIpArynK4riALw+joKD30i8Vi7N69G0qlEjqdDiaTCaFQCDqdDufPn8euXbsWTWNhUxKWE+nmFWwTDeI8ePDgQXR3d1PnyFAoRI0zzGYzOBwO3G43pQM4HA6EQiHo9Xp0dnauenGWvjk5HA709fXB6XSipKQEdXV1mJiYwIULFxCJRBYd5vqpT30Kg4ODsNvtKf8Wj8cRj8dhs9moU91cOgoyDZgLdrsdf/zjH/HHP/4Rv/nNb/CjH/1oTjrekqHRAA8/DLz+OvDOO8mAabMZ2LgRWAkXyKUgkUi+prGxpL1/czPw8Y/jv8+fx9mLF+nURiaT4Stf+UpWd1M2NBoNamtrce7cOTgcjoxFF9EnBN+bwmUDmToRt0/2waijowN6vZ66ygkEAhiNRnR1ddFQ5fSpSiY9WPoaQpwZg8EgIpEICgoKUFBQQEPgCZ2afGdJHMPWrVvR1dWV8p7Gx8fp9I00KzZu3Ih7772XPr9Wq8Vzzz0Hp9NJi0/yfojVOYfDoVlSZPo3ODgIl8sFLpdLX09vby90Oh3i8TiEQiFEIhGmp6fpY09MTNDiFUBKV3ytGGiQCQ7Rs0YiEZSUlEAikdCp51opGlcaC83Je+yxx/D000/DYDAgLy8PlZWV2L17N1paWlJ+vqmpCffccw+OHj0Kh8Nxw5hYLBXsMwbDMMuqR19NLJSymA6ZTIbS0lJ4vV7w+fwFmX1s3boVUqmUsp+Itf77/RpeT+SKshyuC8jCoNfrMT4+Dj6fj4KCApSXl6O4uBgtLS1oaWmBVqvFsWPH0NvbC6/Xm0LJyEbtmKu7mClEejkwl4nGPffcg9OnT1O9CI/HA4/Ho5lDHA6HuhuxXQfNZjOMRiP0ev2qctrZmxPJCBsbG0MikUB1dTWqq6tx4cIFKgS2WCyLWnhbWlpw5swZvP7661knJ/Plm5DpwnxBxGwYjUb88Ic/XLnOXV4ecM89SV3Zq68mi59LlwC5HKiqSv77asHpTE7HgCS9sr4e2LcP2qkpHHnhBXg8HpoHthD7e3KYJlliJG8rk+MWKSbi8TilHpICkIAU5EBSR1VRUZEy4VEqlVSzJRaLUVtbSzWJYrE4a6bYfH9HpkpAsnhct24dKioq4PP50N3dDYfDQZ+XTL6LiorwiU98Alu2bMGBAwfgdDrp/cqe/rrdbly+fBn/9V//hf7+fjQ0NOCll17CwMAAAoEAdYxkGAZCoZA6z/L5fEgkEvzpT3+CxWKhejdC3SQuln6/H36/H3K5nDorssPVyXV2Op2z9HWrORVgHxJPnjyJvr4+GI1GCIVClJaWYvv27Vi3bh3a29tXvWi8nlhoodza2oqGhoasOZvpDsEVFRWQSCSIxWJz5tzlcLUAW2tT5KViMZTFdLjdbkxNTQEACgoKsHv3btogz4ampiZ861vfwo9//GO43W6IRKIP3MRxpZErynJYcZCFob+/n7qH8fl83HLLLdi7d2/KpqLRaMAwDMbGxsDhcOByuebcSObqLq50N5g9NWN31ZqamrB//3489dRTmHqPEkZc1YRCITgcDjZs2ED1MKRTTIwKzGYz+vr6VlUDQJ738ccfR19fHywWC/Ly8mA2m6HRaOa12p/vsffu3YuRkRGMjIwsKWCSuC6SA+hCCzOSE7ei13Xz5mQR9vbbwJkzgE4HvPtucjpVWpqcnF0PWmM8DlgswNQUEIkAlZXJyV1LC3DzzQCXi6NPPgmj0UinNmVlZfPa37NDyV0uF6Xnks5oerZgIpGgzQmFQoH8/HyMj4+n/Awp5kQiERQKBT7zmc+kUCeLi4uxYcMG+Hw+1NTU4Pbbb8fQ0NAseuJiQDK8JBIJqqurUyjEQHKqZbfbab7exMQEJBIJ6urqoNFo0NnZCY1GQ0N9M92D4XAYvb29GBoaApB0MiOdZ6VSiWAwiHA4TF0kbTYbAoEAHA4HOjs7IZVKUzIByTWMRCL090pLS8HhcHDlyhW4XC5IJBI0NjYiGo1CKpVCLpfP0tdt3779uhtosKdibN2f0+kEkHT6rKqqoi622fLkPqhYTKE8V4MyXR4gFovptT558mTO8GMerJUp8nJgMZRFNgwGAzX7isViqKiogEKhWND00G63Y3p6GoFAAF1dXejv73/fXr/VQK4oy2HFwTYDcLvdiEQiNKSY/DsAWuD09/fTzl5xcfGcFLW5FlCidyE5WCuxsWcrCltbW2EymfDiiy/STnphYSHC4TBKS0tprhdbC6PVatHe3k47x6utAZicnKR8cqKxIW6Lzc3NqK2thUKhWNJr1Gg0yM/Ph0qlohz0xYA9keHxeAu23l1KAbgk5OUBH/sY8Bd/AZw+ndSZGQzA4CDA4wFqNVBUlHRvXE4kEoDHkyzGTKak4ciGDcli8NZbk3/ea3KcPHkSJ0+epN8vkUiEz372s/PqyCYnJzE4OIjJyUl63RmGgUgkgsvlmpNyyuFwoNPpMrzsq+YiH/3oR6mOFEgWRwcOHMDMzAw4HA4CgQC6u7shkUhoY2Ox9196jmCmxyH/zZ48kC4zKS6I2UcikQCPx4NSqUQ4HKZTZIFAgGAwSDOmSFaZTCbD7bffDq1WS6d9n/70p/HOO+9geHiY6jXVajXy8vJgMpmg0+moaQN5PoZh0NzcjJdffpnq68h3VaPRQCgUYs+ePdBqtbSAZRgGXV1dKVluK236QQr5np4eeDweSKVSlJaW0siNTJraG8V1kWCxFP1MYMsDPB4PzGYzSkpKaOwIABw/fhy7d+++Ia7pUvBB0pYt9b2cOnUK586dg8/now2fo0ePQiKRoKqqKuv00GAw4PDhw3QfcLlcePvttzNKBtaS0dBaQq4oy2HFQRaG6upqeL1eBAIBCIVClJSUpOgGWltb0dbWhj//+c9wOBxQKBRQqVQpgczpX+LVXkCzFYUGgwFDQ0MIBoOUNsLn87FhwwaaaZZ+ACT/32KxUE3Lam6gZWVlUKlUUKlUlG7mdrtx9OhRlJaWoqqqal46QzaEw2EUFxdTalumokwkElFNXvpBnz0dY4cUL+R5u7q6UFRUdH02A7kc+MxnktOpd98Fzp8HpqeTRVN3d7JAUiqT07P8fEAoXNzjJxKA358sxNzupKMin58s+G66KTmx2749+d/v0eWAZKHzxBNPUIt1gUCAmpqaeWmLQLIAIy6BiUQCIpEISqUSkUgka3QBABqwnA3RaBTBYBB33HFHyufS09MDo9EIq9UKHo+Hvr4+lJSUoLq6GkVFRYv6DMkaYrVa6fd2w4YNC3ocq9WK/v5+jIyMpLwnIHkP5uXl4a677oJUKsWJEyfg8XjA4/HoRJm8R6FQiKKiIrS2tkIoFEKv16OiogL79u1DbW0tfvSjH4HH40EkEkGj0aCsrAwXL17E9PQ0nTARxz0+P7mFs697PB6H3W6HQCDAhg0bIJPJUrQyJ0+enBUL8J//+Z/0dTz88MNL/l6QaRhwNbR7cnIS58+fR3t7O1wuF/h8PlQqFaqqqmbRqQwGAy0YbyTXRYJrfc9kP7TZbOjv7wePx4PP50NZWRn8fn9GHWYOqfggaMvYZ6WlFPq9vb10T04kErDb7eju7obwvf0p2/SQXCuyN4fDYUil0oyvj0x00yNDbnTkirIcVhxs0w2RSASdTgeVSoUdO3agu7sb4+Pj8Pv9OHbsGAYHB6lQPT8/H83NzXSzzjSRytRdZB+82C6CK0FDyFYUEttrv99PC9B169ahuLh4ziKL5HkR+sla2EDz8/PBMAyCwSAcDge8Xi/0ej2dnC3ldZGCb3R0lE4FyELO5/PR3NyMvXv3IpFI4PLly3j55Zfh8XjohIBdgEUiEfB4vAU/969//Wv09fXh1ltvvX7XVSZL5pp95CNJG/rLl5P/azYnXRANhmRhxeEkp2dCYfIPw1zNPUskkkHP4XAyXywUShZkAsHVom7rVmD9+qQj5ObNGQOttVot9u/fj9HRUXrtVCoVvvGNb8xLWyTfK1IMcDgcRCIRmEymeaeV6W6b6SABzP39/SmdVULP4/P54HA4EIlESwpgT5+OSSQSbNiwYc7HSadek4bSrbfeii1btkAkEkGv19MpXyQSwZUrV+hkLFM8QDweh0qlgkwmw8MPP5yydk1OTqK6uhqDg4PweDw4efIk8vPzUV9fj/r6ely+fJlO3uPxOMxmM06cOEFp0UDS7bKxsREAaEOIrJVdXV2zmkhWq5UabRiNRrrmLgRarRY9PT3YunUr1Go1pbUCwJYtW2h0yMDAAOx2O2KxGORyOW6//Xbcd999s7RQHxQtz2qB7IfHjx8HkLym5eXlaGlpgdlspvtJNh1mDkm8n7Vlmb5HC6EsspGfnw8+n08139FolGrkY7FY1vWyrKwMeXl5dD9PJBLo6OjA5z//+Vla3rGxMZw7d46Gnz/22GPvi+u70sgVZTlcF5ADh1gsBsMw9EtbXFxMO3iBQABOp5NSobZu3ZrSbc1GU2R3FwlNRq/XUyON+Q5e14qGhoYUOiJw1fbaZrOBYRjU1tamZCqxu8GZHi8YDMJms1G799WYmBEThEAgAD6fTxdZMv0bHh6G2+0GsHgqgkZzNeB0YmKC/j2Xy0VJSQn27t2bMoW788478fLLL2NqagoGgwFGozFlerYYWmIikcDFixfB5/Ovv12vQAA0NCT/RKNJvdnERHJ6Nj0NuFzJEGp24ZVIJP9wOMkCTSRKTuAYJklPLCpKuj+WlCQnY+vXZ9WsGQwG/PSnP8XAwAB1O5RKpXjwwQcXFBJ94cIFjI+Pw2Qyzet8CSQL7IVQS4mBRSKRwODgIP7whz/QBsVzzz0HhUKBaDQKpVKJzZs3L5q2aDAYcPz4cYyNjdHssJaWlqwT0/SJGmkWRaNRhMNhTE9P42tf+xruv/9+Kojn8/nQarUYHx9HIBBAJBLJaGRDmhnDw8MpFEIgeaipqqqCzWbDpUuXEAwG4Xa7sW7dOuzcuZMG0pP8tFgshsuXLyMWi4HP50MkEuHDH/4w7r33Xrz22muzdESZmkjs6WUkEsHIyAgNjk+/HmwKp8lkwmOPPQaLxQKVSoXPfvaz0Ov1dGo3MjICuVwOo9FIs9ckEgk2b948qyADPlhantWERqPB7t27YbFYoFQqaag7AFqQSSQSWK3WWZ9zDlfxfr0fl+N179mzB2+99RYmJiYQiURSJvM333zznI8nl8tT9uZ0HTfR8nq9XgSDQYRCIVy8eBGnTp2ik/sbGbmiLIfrBnamkdFoxPDwMBobGzE6OgoOh0MPMyQUdd++fYumKbLtlVUqFR588EFUV1evCFUtvSPFNgh47rnnKMWLUEiKiooAZO++sUf6ly9fhsvloq5sAK77xIxcc7/fD4FAAJfLBbvdjmg0SjOZXnvtNRQVFc2iRC3kNZL4AJ/PRxfxeDyOqakpHDx4EENDQ9i3bx+1Lf/Xf/1XAEBbumW6AgAAIABJREFUWxt+9rOf0WnEUhCJRDA+Pr66uj0+H6iuTv4BkoWXw5EszAgd0edLTsfi8WRBJhAkJ2LkT2EhkIEekg1tbW24cOECLagYhlmQ2+Lk5CQuXLiAs2fPLrgAJqHeC0FJSQlkMhkikQhGR0dx6NAhvPDCC7h8+TLsdju4XC5UKhV1LFwMSKNmcHAQZrMZee+5YGb73NMnagDolI64P1ZXV2d0vZuZmYFKpYLVaqWZY5kwOjqK//iP/4BWq8Wjjz6a0mD6whe+gFAoRHMBuVwuhEIhDbUmpimkuUXMQrhcLvLy8qBUKjE1NQUejzdrKpJNt0QaJCQfTKfTYc+ePZDJZJTyyDaPqKqqAsMwsFgsdBpotVpTDICIVkwikcBoNKKgoCBrbhqw+lT0DxKyfc6EsdLZ2Yn29vZVdfld63i/3o/L8bpJWHlHRwcGBwfR19cHu92OgoICmEymjMW8VqvFoUOHoNPpUtgsiUQCIyMj0Gq1NErI5/NRp9hYLAa3242Ojo4lyyE+SMgVZTlcN5DpiF6vh8PhwMTEBN555x3Y7XZ4PB7ahSWZOwaDgRY6SxVBKxSKRY/uF4pMHSkA+MlPfoKenh6EQiEwDIOCggLqUDiXBo108vv6+jA9PU31J4WFhatCOSGGJT09Pbj33nsRDoeh0+lw+vRpTE9PIx6PIxQKoaOjA319fTCbzbSbnqkTnglXrlyZpReLxWKYnp7GW2+9RXPrgOTB8eGHH8bf/M3fwOPx4Je//CWCweCS3hufz0c8Hl+0nf+KgsNJFlmFhSvy8FqtFkeOHKE0UKFQiM2bN8/rtggki7fh4eFFTSSFQiG1m58PsVgMDQ0N1OVwbGwMPp8PXq+X3h82mw0CgYC6gS70QPnzn/8cbW1ttJkgFArh8Xjw85//nE6g2HoG8h0dHh6GQqFATU0NqqqqIBQKIRAIsGPHDvzVX/3VrOc1mUxgGAY7d+6k3d9siEajMJlMOHXqFJqamvDAAw+kPM7ExAQ1BZFIJFi3bh0aGhrw5ptvUic9tVqNUCgEv99Pi18SLL1161ZKn063Qk/XLWk0yXiP48eP480338Tly5fB5XJx6dIlVFdXQygUIhQKzcqWbGpqgkqlAgCoVCrs3LkTarU6JSaEXE/2hC3b57UcRhfvJ6y00UEmfRphrPh8vvfdBOh6gy27eL+A3FOLsb7P9hhlZWX4+7//exgMBvzqV7/CiRMn4Pf7MTExMeueMRgMOHToEN599126PrANkM6ePYs333wTAoEATqcTXC4XXq8XoVAI8XicMhBy92KuKMvhOoNQkoxGI4xGI52+xONxxGIxcLlcRCIR+Hw+OByOlN9N32QybWpNTU208KuoqKA6ipXY+BiGQSwWg0qloh2ptrY26lrE4/FQXl6OL3/5yynBnuldLPaEjNCTCO0pFAohFAqhvr5+VfKEyATMYrGgtbUVRUVFqK2tpdQolUqFwcFBDA0Nwev1gsvlwmw2w2azzWsYcODAAepSxwaPx4NQKEQgEIBer4fL5QKPx4Ner6eL9oMPPoixsTG88cYbC6LRsZGXl0cPqUajEW63e8XukbWEo0ePptjfl5eX4/vf//6cAmuDwYC2tja8/vrrc5p4ZMJCJ5lcLpe+npaWFhw7dgwDAwOz7o1wOAyj0QilUom8vDwYjcY5N3Fi63z06FFK1QSS3ym3243Dhw9DKBTitddew49//GN6Hch3zG63w+v1QqVSoaKign7PSSHIpvT19/fjyJEjcLlcKCgooJO1uZBIJODxeHD8+HEUFhbS5z906BAGBgbg9/uRl5eH/Px8AMkw1+9+97v4xS9+gcuXLyMcDqfkvfF4PKxbt466GFqtVvT09EAgEMxrhU4ob+TnSTE8OjqKjRs3QigUYuPGjXRSRrIlGxsbqaaM3UBLf+yF4kYx91hN/dz7dQK0WliprNPlxnLcU5keAwCmp6cxMzODaDSK3t5eKlsgIE7NwWAQPB4PJSUl1JjIarXC7XZDoVBAJpPRZhMxmCL+AXK5PHcvIleU5XCdwe6Gut1u/OhHP4LT6aQ2+YlEggbJDg0NZeW8z2X8QcTzmZzGlmtBJQVLKBSCUCikepzjx49T1yLSoWYXZJm6wV1dXRgbG8Pg4CACgcCsIsPv90OhUKC1tfW6a8rIVM/v99MAWgAoKipCRUUFOBwOOjs7adQBl8uFxWKhlulzvd7e3t5Zf7d9+3bk5+fD7/fD6XTC7/fTqQ7biVOj0eC+++5Df38/7fguBBwOBxqNhj62w+HAD37wA8hkMtx8880phWQmHc1a3ZDnw1Ls7w0GA773ve/h+PHjiy58FwoulwupVIrCwkI6sdJoNPjJT35CnQbZwczhcBherxe1tbULMug4duxYSkHGBtFHjo+Po6OjI+VaeDwe2hDx+/2z9GfpeVAulwuTk5OIx+PweDz08JEtHJ3EAwCATqfD0aNH0dnZCaVSCafTiXA4DLFYDKlUiurqavpeTSYT9Ho9nR4zDAPue0YwXC4X4XCYvr7XXnsN4+PjCIfDEIlE834fNRoNHnroIQCA0+mkBSax1SdURvZ3QaPRXLNr2o1ojb2aeqUbbSJ5LXg/6cqIecbg4CBEIhHMZjM+//nPL+r7mY39Mz09nbIWk1BpAmLcJZVKEQgEMDMzA4/HQ/+drKONjY0oLy9HbW0turu70dfXh3A4jJqamqy05hsNuaIsh+sOspkbDAZ85jOfwenTp+lkjBRoTqcz45icYL7F0mq1oru7G729vZTitpwLKnl+YhpARu9cLpeamHA4HHC53FnPm94NZhgGExMTlG7E4/HA4/EQj8fB4XBgsVjw6quvwuFwpFA6V3pTZU8ChUIhnE4nRkdH4XQ6IRQKUVZWho997GMAQLU+hCLm8/nmDP0GkmLiixcv0oWeZMk99NBD6OnpocVqJBKBXC6n+h4CmUyGm266CTMzMwsuGgQCAWZmZugGQ6YxPB6P0s+2bdsGjUaToqMhmp4PfehDuPnmm1c1P26xWKj9PdtJj+TmdXR0rFhBxufzoVAoIJfLUVdXB7VaDQBQq9UQiUTg8XiQSqUoKCiA0+mE2+1GNBqF3+/H+Pj4nE0WckDJVhSxweVyoVQqU36XfAcFAgGEQiFdr4gpBjsPym63w+Vy0eLP7/fTmAC73U7/XiAQQC6XIy8vD3w+HzabjRZuIyMjGBoaQnFxMbhcLjZv3gy5XI49e/akXP+enh64XC5KbQZABfikqXXmzBlIJBK6ljIMQ7+v86GpqQlqtZo2zchEXKvVrsiU4EZ1XFztaRW5xu9Hu/frCbauOp0GvNbAMAxGR0cxPj6OSCSCwcFB9PT0pLAA5kO2+7K6uhrj4+OIRqNYv349tm7dmvJ7bGkKcY5Nh1wux0c/+lFqWLZr166U+IzcPZhErijLYVXA3ow3b96M2tpadHR0QKvVwmq1IhQKoa+vD+fPn6eFB/vQmGnxIBk57e3t6OrqorlG69atW7bwaPIcOp0OHo8nhboIADfddBMmJiao6yKHw8m6kJPH6u7uhs1moyHNRMgvEAgQiUQQDodhtVrx9ttvw2KxUMtpku+20IPMYiY/6ZPA2267DS+88AJcLheNGSDOelu2bAGXy4XD4UAwGKSFWUdHx5yUqa9+9auYnp7GkSNHEIvFwOFw6JSAuIcRExC/348///nPEIvF2LVrF8rKymh3bjF2+NkKjFgsBqPRiMOHD9MctuLiYhgMBtjtduq8d+HCBeTn50Oj0WDv3r0pZjRrEcRtkRQo2ezvtVotvv3tb2NmZgb5+fnYv38/7Hb7gjVhSwHJJYtGo7DZbNRhVKvVwul0AkgW3vX19TCZTOjt7aV6qvHxcTz//PNZ3TPdbjdGRkYQj8fp9ygTeDweamtrqYU8cLXrW1xcDIFAgNtuuw1tbW30e9fc3Ix9+/bRPCiTyUTt/okDIp/PR2FhIW655RZKxSwrK4NYLEYsFoPVakVjYyP4fD4mJycxMzNDaduNjY308AJcNQbq7+9P0XHxeDyEw2EEAgFwOBzE43GMj4/jwIEDqKqqQjAYRGVlJWQyGf7iL/5iwZ8LaRp1dXVlNAtZTryfJhHLidWeVt2oxfBiQXTVhw4dmuVkutJgT5BNJtMsmnCmnydOpwCoBjydBTAXst2Xjz76KKqqqqDX67F79+6Mj9fU1ITOzk709PTMij/h8/nYuHFjioP0jUJVXixyRVkOqwL2ZrxhwwZUV1dDrVZTE5BwOAy73Y7Dhw/DZDLhjjvuwIEDB6ir4ne/+92UxQNIHl76+vowPDwMs9mMaDQKPp8PoVC4qOydbCAubqdOnYLVaoVIJEJ1dTV27NgBILnI7Nu3DwMDAzRHiAQvsx+DFEbkoGe32+FwOKh1eCwWox16AFQDFAqFYDabEQwGqeOaxWKh3aZMWW2kAMvkoKZSqVBbWwuFQgGNRkN/jhSBRqMR09PTyMvLw/nz51FQUACJRJJCz1y/fj0mJibg8/kQDocp9XR8fBzHjh2bV1t29913Y3x8HBcuXKBcc/IeiMj6pZdewpkzZxAOh/HLX/4S586dQ2VlJcrLy6FWqyEWi+d0ulsootEoncQ4nU6Mj4+jvr6eThzIRM9ut8Nut+PSpUt45pln8I1vfANf/epXr/n5VwJst8VEIpHV/r6jowMjIyPw+/2wWq144okncMsttyzK2GMpCIVCKcUfMfhwOp2QSqWoqanBHXfcgTNnzmB4eJgWV+FwGJcuXUqxWgauNjqOHTsGr9eLcDhM3Raj0SgtXghI3ho5bAHJAtViscBqtUIgEOCNN96gYe4AoNfrYTAY0NDQAJvNBrlcTr87BQUFyM/PRyQSgcfjgclkoq6IHo8HQqEQY2Nj4HK5UKvV2L17N37/+9/DYDDQe29iYgKlpaW0MGIXLURX1tHRgStXruDcuXO06UPebygUwsDAAJRKJeRyOXg8Hk6cOIHh4eFFBUNfj2nOak+MVgurTdm8UYvhpYA4KF8Psy32vk2kFwAwODgIm80GqVSKb33rW7PWb4PBgLfeemvWhIrD4aSwANjPke3ey1Ys+f1+xONxaLXajM0wjUaD2traWQUZwzBoaGhYkKFUDrmiLIdVQvpmzDAMDZcmwv94PA63250SVEwWnZ6eHnzpS1+iX/I//OEP1AGQOP4Qp7Xy8vJlSYvXarU4d+4cpqenaVfb6/ViZmYG3d3dePTRR2EwGOjrFwgEtMggh0ViBxuLxWAwGGCxWBAMBmctZOS1E/dF0g0PhUJUD+XxeBAMBtHe3k4ft7i4GK2trbMKsHQHNZfLhcHBQbS3t0MgEEAikUClUsHlcqG4uBhisRgWiwUzMzP0Z9VqNcrKysDj8RCNRlFXV0ff+8zMDBKJBCQSCaX6OZ3OFHOObPdBeXk5jUMIBAIwmUx0Y9BoNDh9+jQ1gonFYujt7cXIyAi1J18IRW0p8Hg8GBoagkqlyhp87HK58G//9m+Qy+W4//77V+R1LBUnT57Er3/9a3g8HsTjceTl5WW1vzebzbTYjsfjGB0dxcDAwIq/RlLs+v1+aLVajI6OAkhu5OXl5bjnnnvQ1NSEmZkZjI+PY2BggDYvSJg5gVarxTPPPIPR0VG4XC7EYjGIRCIASKEVk+sBJKdNbrcbRqMRWq0W/f396Ovrw+joKDweD3g8HpxOJ+RyeYrTYGdnJ83fCQQC1NTnU5/6FAoKCvA///M/uHLlCiwWCzXrKC0tpXTCSCQCmUyGwsJClJWV4dKlSzR3LBgM4rXXXkNDQ0PKOkmypTQaDWw2G/r6+uD1esEwDIqLi6HT6eh3gUyX/X4/ent7aRbkYppTmbrmy11MrPbEaDVArMNDoRCqqqpWZUp1oxbDSwGJ8iH5bst5rdjfJ+DqVJwwAiwWC8LhMAwGA1wuFxwOB5599lmEw2FK+TMYDHjyySdx6tSplH2Kw+HgQx/6EFpaWlKejz0hZbs0ApjVyCX/Rujg5PyQbU9XKBSz3JRVKtW8hlI5XEWuKMthVUBoAWQkT5zVQqEQZDIZpTUplUpIJBIMDw9Td7+CgoIUTjPJviBdpW3btsHr9cLtdkOtVuORRx5ZlilZe3s7da8jCAaDmJiYgN1uR1FREQKBACwWC6LRKBiGQX5+PkwmE06ePIm+vj6qTyotLaUHPbfbPcvanUw2amtrUVdXB5PJhKmpKXA4HOTl5SESidAcKIvFQvVoQLJgJXoXYmGd7qAmEAgwPT0Nj8eDWCwGr9cLp9MJDocDl8sFPp+PQCAAs9lMD80kQ27Tpk2Ix+MoLy+HUqmkEzIgyRvfsGEDNSIgmU3ZDFsIF31wcBBjY2PQ6XQ4dOhQCkVk9+7dOHHiBA2x5HA4CAQCiEajsxw6lxvks0nfaNiIRqM4derUnEUZod6WlpbCaDTCZrPNsmJfThgMBjz99NOwWCx00lpWVpaxW0m+P2xkM8dYbiQSCVps22w2OBwOqiUjVB2yVvT19aWEUUskEigUCvrd/O1vf4srV65QmmZhYSHq6+sBJKMX0u8VPp8PlUqFuro6ekA1Go1wOBzgcDgQCASQyWSoq6tLycsDgPb2dgwODsLtdlPXWDLpqq2tpY2TeDyORCKBqqoqGjBPdFpVVVX0879y5QqGh4cp9TcUCmFychLbt2+flS1FGjpEr0by07Zu3Uopm6FQCHw+Hy6Xa8FZcZnA7pqvFOXtRqIxsa3DSQG9GlOqG7EYXgvIVoQVFxejoaGBTi+JjnvDhg0Aks0/YnplNBqpMVBtbS10Oh3efvttuFwuqkMHgIKCAnzkIx9J+WyzmXcRvbbFYqGZgqRBq1KpoFarqTO00WjMKsloamrCpk2bcOHCBQDJwrC4uJiyEHKYH7miLIfrhvQFKd1uXSKR0I6+UCiETCZDUVERbDYbPWzJZDLs2LGDdo80mqu5K0KhEAqFAnv37kVTU9OybjharRZ9fX1UYM/lcpFIJBAKhRCNRuF2u3H06FFaUJIcrMnJSXR0dMBoNFKdjEKhQFVVFVpbW9Hf34/Dhw/PcjMCkgukRqPBjh07oNVqaacOAJRKJV08iR6L6GDYGUXssFfSFWMYhh5kifsRe1JGskS8Xm9KARoIBHDx4kWIRCJs2rQJFRUVaGxsRHV1NSwWCzgcDvLz8/Hggw8iHA7D4XBgaGho3pDSpqYmHDt2jD4HOZCSn21tbcW3v/1t/OIXv4DT6aRTgEQiQQ/oKwXyGc8FhmGwa9eurP/+4osv4sknn4TX66UBwBwOB0eOHMEXv/jFZdGlpX+3nnrqKQwNDdHPTygU4q677oLBYJhlFjMyMkIF/6sNQn8NhUKUVmswGNDR0YHp6WkAyY2eHCQYhqFdYrvdTumWXC4XRUVF+Mu//EswDIPvf//7SCQSlEpMKIV79uxBS0sLvW79/f20mFIqlVCr1di3bx+amproNSNFrEQiAY/Ho5P5UChEtV+EFgkkp3E33XQTvf8bGhpmrU07duyAQqGATqejzRq20yhZ44aHh8Hlcqn1NIfDgd/vx/T0NBobG3HbbbdhcnIS09PTNL+ssrKSrg/k9S/lflsuyttqU/dWE8Q6nKzDCzVgWQmwDbduhEiQpYJ890iD81ru+2xFGAA0NDSkTC/ZU6z+/n4899xz8Hq9tFk6OTmJ9vZ2ev4AQHXs8XgckUgEnZ2dKfsLe0LKnsaJxWIAyaaU3++H2WymDdrR0VGqGScMIPYeko677roLOp0Obrebmjnl6LELR64oy+G6gOixSH5Yc3NzSsemp6cHtbW1lO5GaE0cDgc2m41qhhiGgU6nwzPPPINIJIIvfelLsxYz0l1fiUWALEr5+flYt24dxsbGaKFgs9lowK1YLEYwGMTw8DCUSiVUKhWqq6shkUhojhDZECcmJrI+H5/Ph0wmS+lqmkwmvPbaa+Dz+bSojUajKC4uxr333pvioJbN1IMcMokeja0pIwWbVqtN0boRBAIBDA0NQa1W48yZMzAYDHQzUKlUkMlk2L59O7q6utDd3b2gQ1x+fj6EQiE4HE5Gl6v7778fUqkUR48ehdPphEQigc1moyYcJEqBGKuwD+DLCS6Xi7q6OgBJbZlCocBf//VfZ52SabVaPP7443RyyMb09DQOHjwIk8m0KK0PQSb9gUQiwcTEBN5++206feVwOJDJZOjo6EBbWxvi8Tiqq6shk8kQDocxNjZGp6zXAvYU61rh9/tx9uxZaLVatLW14dSpU5iZmaEFMofDQWFhIV5++WWcOXMGDoeDFmQ8Hg9KpRIf/vCHaXNGrVbDarXSyRUp2m6++eaUcPnW1lb893//d0p+Trb7SKlU0nuOx+Nh06ZN9PoTg5pEIoFAIEALSiBzeDOZhrW3t2fMgyMUKo/HA5vNRp83EolQo5Te3l6Ew2FEIhHE43GIRCKIxWLs3r0bhYWFdNK21Lyl5aC83egGE2VlZaiqqgKQbJSstg34jf55LARLve/Tmw/pTY1M55ZszWTSzCETc71eTynWZE0TiUSQy+WQyWTU2Km7uxvf/va38Xd/93f0zEHOEmTfYDd7JRJJyqRMIBDAbDbD6XQiFArRyJvOzs5ZjonkXurr64NYLEY4HM7ljy0BuaIsh+sCspgYDAb09/dDLBZTq1mj0Yiuri4a0srlcnH58mVYrVY4HA7a8RUIBNiwYQPcbjcuXLiAWCwGvV6PAwcOrDgVg4RSE02Z3++H2+2mVvBsEIMRtj383r17U3KOCE6ePJnRPpaA6LjIQU6r1eKnP/0ptFotwuEwtd2PRCIpWpSFFKXsTmn6tSOmBPn5+QgEAhnplWfPnsXly5fhcDgQiUSosQBZgBe6mZEpjVQqpTTKTC5X7OBxsok9//zzuHDhAj2sk+nBStHv4vE4XC4XDh48uCDqYUdHx5wUS/Jea2pq8MADD8z7eJkKMXbHk1ghsz+vRCIBs9lM9Zak0cEwDKLR6LIVryR7ay6q52LQ29uLX/3qV1RLwdYORiIRDA8PQ6/Xw+12U6t/qVSKuro63HPPPdi1axe9f+rq6mA2m2EymRCNRsHlciEWi2cdKtra2vDuu+/C4XDQkHG9Xg/gaqj08ePHKS20rq4O27dvpxRsq9WKV199FR6PhxaogUAAWq12likJG+TQBiBjR57QfHt6emAymSi9mRSisViM0o+JHlWlUqG5uRktLS2020+aYMTpcjFrJbt4XCpudIOJtUYbvNE/j4UgXWqxkOuTqdhN3w+zFWFzrREajYY2UwnTxefz0cKsuroaGzduhMVioetEe3s7hoaG8I1vfAMVFRUoKyujjSjSvM2mKTMYDDh27Bg1LyPSAYvFMuteIfeS2WymUgfiXpu7pxaOXFGWw3WD3++H1+uFz+fDn/70J3z961+Hz+ejX3KxWIympiYMDg7CbDanHMJIt3/nzp14/fXXqc6HhKTu379/Rb/4Gk0ylFqpVOLYsWPUkU+pVNLONQGPx0NFRQWd7lVUVGTM4XjxxRfxyiuvZH3O/Px83HfffSmajmeeeQbnz5+H1+ulUyGxWExpdk6nM+PGmk5vy8Rrl0gkUKvVOH36NKanpxEIBCAQCKiDJcn0IiCUDjI9VKvVKZ3f9AMIgIw0GbJZTU5OUi2g0WjMmO/GfrzJyUk6WSOFAHFQXCyILmchpiE2m23BNsNKpXJOTU88Hsf09DR+9rOfUcoKGwsRgpOJKQkuzuREyS6U/j97Xx7dVnmn/WjfN1uyHHnP6uAE4rQ0aYCGSZoJHUJL05YuUyB0esrSTjlnSqenZ2bK8BXmpNDTaafTQDvtNBQOHdzWhYEMmOAUh4Q6SYns2HK8xEtsWZasfd+l7w+f9+Xq6kqWHds4Qc8/BMuW7r26931/y/N7HkL7XGrq55UKrhCfLZJoZLNZvPXWW2hubqadWCb8fj/1E1Or1WhqasIXvvCFnGQMeO/ZFYvFaGtro0UeUs0lIHN/brcbqVSKCs889dRTaGlpQX19PYD35i4ILZiZ3NhsNrzxxhs59EUAJV3rYkUMMs+2YcMGqv4pFApzkj9CQSIG6ffee2+OcT27COZ0OhfVGSGFkcV03MoCE6trhq78fcwPYg9DWCnFJPHJek3Ui5nJLpkPLTUJKwR2cjY+Po7/+7//g9/vh9/vx0c/+lH09vbC5XJRurzVasUPf/hDfOhDH8oRl+Hq2jNB4pajR4/SOXUAnHNlpJsPvGdkn81m8c477+TNtpVRGOWkrIwVQWtrK6qrq2lAGYlE8PLLL0OtVtOB0urqalRUVMDlcuUFeIRypNVqoVQq6c9TqRQnNWw5YDKZcNddd8HtdmNwcBBSqZQO5JL5HR6Ph+uvvx6f+cxnwOPxoNPpChojHjt2rGiXoqqqCmq1mv6/2WzG8PBwThdIoVDgr/7qr2A2m2mQPjw8nLPoM6t2ZNEkSo2E106EVLxeL8LhMHg8HpRKJRKJBEQiUUEvMBJEKxQK3HvvvXmJCrMbV4gmQyqRIyMjmJ2dpQkul0AIexNRKBRL0pnJZrMFkwqpVIpMJkO/q0wmg5mZmZJmc/bu3Yv29nacPXs25+dVVVU0gSLv98QTT2BoaAhNTU30mjFVNbkGwZVKJc6ePUtnCq4GkHkuNmWysrKSFhYIvF4vLl68SAsD7L9Jp9OQSCRobW3Ft771rYKJsslkgkAgoCawAoEAKpWKBqFERt/j8YDP50MsFiMejyMcDiMWi1EBDWCuuFRdXY0dO3bkdZtMJhMOHToEq9WKcDiMeDwOsViM6667bt4kvlAXhfnsAEBTUxMqKyuhVqsRDodhsVjA4/FQU1ODPXv2QC6XUxEZ5rzQl7/8ZXR0dNCEbDHy3lfaWVltnaKVxmqbp2PT2T5oZtKFipXsDhBRHmT6KXIVPpn7rEKhQENDQ06yu5QJOXmvM2fOoL+/H6Ojo6iuroZarcYDDzwueMgcAAAgAElEQVSARx99FFarlQopuVwu9PT0LPi5J+MQbW1tiEajCAaDqK6u5oxdwuEwvF4vVYsmFh3lDmzpKCdlZawITCYTHnjgAXz/+99HIBCAXC6HSCSC0+mERqOB0WjEvn37YDQaUVVVhZGRkZxgm0hODw0NwefzUVl2oVAIn8+36OH1xZzHwYMHqaSxQqHA17/+dYjFYjrg73A4YDabUV1dnVe1Z6K2tpZK/7PB5/MBIKcaRShcYrEYfD4fCoUCra2t2LZtG+LxOAYHB+H3+9HW1kY9iQCgra0N586dQywWg1arBQAa3BJeO1FwJJV34rlEOlelKLgxk2U25gvmCP9cpVJBJpMhGAyWNP9ClO+WEwqFAjKZDDMzMzTxvnDhAh599FHccMMNaGpqyhHOYFdBv/rVr8JqtVLbAIFAgLVr1yIUCqG/vx/AXKI3NjaG//zP/0RlZSX1hPP5fNRni2sQ/B//8R+vqoQMQJ5XGABKJ5RIJDlJGTDXEVMoFJz3oFAoRG1tLb74xS8WTXrMZjNVPQTes5xwOBzo7OzESy+9BKvVilQqhYaGBmzevBl9fX3weDxIpVLYsGED7ZSRJLkQ/a+1tZUGRV6vFzqdDvfcc09J6xNX0MZ8duRyOaVyV1VVob6+HkKhEE6nE+vXr8fevXspNYmrEEJM2Ql9fKGdkaXorKymTtFKYrXOb5FjWI3HtpRgJ8TFipVMUayRkRFMTk4iEAhQKjoRJ+Py9iTeq3v37uUcWVhqsOcUxWIx9u3bB71ej//4j/9Ad3c3FZkix7/Q59ZiseDtt9+G2+2GXC7PESIimJqagtPppEwNqVSKdevWYe3ateUO7AJQTsrKWDG0tLTgwx/+MEZGRlBTUwODwUCpQEQ2ft++fVCr1VAoFHRYniQgAoEAw8PDsFqtEAgE9OdKpXJFKzFsM0m9Xo8dO3Zg3759OHPmDF588cV5K8k2m40KEvj9fsjlclrJJ3LWEomEqhIajUYMDQ3RZEmj0dAFr7a2Fu+88w5VSJqdnaWbiNVqhdVqRSwWg06no2IjZPNhCn789re/RTAYRDweR0VFBbZs2YK33367JFpaMBhEb28vbr/9ds7XuXzpmFRGtiqUz+dDX19fQU8Um82Gjo6OZRHzYCMej0Ov10OhUCCZTILP5+Py5csYHx/H6dOnUVVVhe3btwOY+76lUin279+PlpYWJBIJmEwm3HLLLfjf//1fRKNRpNNpnD17FjfccAMqKyvh8XioUEkoFEI8HofL5YJQKKTUPAA5iZ7P58Pjjz++Il5iSw1SuWWCz+cjFArR55qZtBG6JdN/UCAQQC6Xo6qqCjfffPO8Xaiuri4qCAPMddjfeustDA4OIplMUmn7iooKNDU14eGHH4bD4UBXVxeEQmHRxJsLpMNMEqgruU/ZXmXAXFCYTqfpPUaCaeZzRRRjiVKbWCzGXXfddUWdqg96p2uhYCYCq3l+azUf21KAKyFmFzsAUG8/UnQl9jGkiCkUCmGz2SCTyXD06FH4fD5YrVbU1taivr4+pztWiCGz1CBME3LMZB67tbUVTzzxBL72ta/BYrEgnU5DLpejvr5+QcdlNptx+PBhTE1NIZvNYvPmzZx+h0Rgi6zTBoMBn/rUp4oWpsvIRzkpK2PFQKgRZA5q586dmJ2dzaHT9PT0UJ8iErzx+XwIBALqz0OSIrVajbVr1654JaZYtVgsFiOdTlN5+kLHZTabMTExAaPRiA0bNqChoQFOpxP9/f1wu91Ip9MYHByEzWZDb28vPvvZzyIcDlPK1f79+7FhwwaIxWK0t7djYGAAbreb0rMikQja29upZLdcLodKpcLevXsLDhebTCYcOXIEPp8Pzc3N2LhxI86dOwefz5cXRLORzWbR19dX8HU2TYZw9JlVWfJ6IBDAz372s4KeKGSDtVgsuHDhAsLhcEnf22IRDofhcrmgVquh0+moyAsRmJidnYXFYoHL5YLH4wEAqkBlMBhw/fXX4+6778af//xnqrSZyWRgsVhQX18PjUZD72tCk2QG8X6/H9lsFk6nE3w+nyqUrpSX2HJDKBRCq9XShIvdRSOdNTLMLhKJUFFRgeuuuw779++fd9MnCqfMdQSY8xi0Wq00mFCpVFi3bl3OXGQikaD+PMVUXbloaaSzt1ifMOZ7stVXSQBmNptzKvbPPfccLXoBwNDQEPV3dDqdcLvdePDBB3MUJxeKhXa6Vhtlb6XAZdS7Wue3rqXZMq77jSvp5Cp2hMNhOq9LfD6Buf3NaDSCz+ejurqadvTNZjNisRhcLhdkMllBQa/lBrtQzEyqa2trMTo6imw2izVr1mD37t0Leu+enh6EQiH6GSKRKK8IZrPZ8Nxzz2FiYoLO61ZUVFzxnPEHEeWkrIwVAzthIQ82WUhIByedTqOuro4aSBOjZMJljkQikMlk2Lp1K+68884Vq0gRFJv9OH78OOLxOCQSCfbt21cwgGOaXRNz2uPHj8Pj8cDv91MKgM/nw+joKFwuV86mSQLRY8eO4ezZs7Db7dRGgIAEskKhEHK5PMeMtxDt6rHHHqPnZbFYKO2BDaK0xwygz5w5g8985jMwGo349Kc/nSdaweTAc1VlyTF1dHRAo9HQbh67y3DixAm8+OKL9PrNh/kUAaVSaZ66JBPE7oAkWa2trbhw4QJ4PB5isRhUKhWAOaNpYnBN7BFmZmbgcrkgFotRXV2dY3+QSCQwMTGB6upqpNNpThorMNfVmZycpIp/iURi3iT5agHx0RIIBKitrcWlS5fyfofYGxDjdKVSmWMzUUzUxmQy4cSJExgZGYFOp4NOp4Pb7c7z4NNoNNi8eXPOXFqp3YNCtDS5XE5l6Re6PnG9J0mkSEJOhu4TiQR27NiB5557Dm+88QZCoRCGhoagUqkQDoeRTCYhFArhdrvR09NTVAVyqbFaKXsrAfb9k0gkVm2X8VrpgBa637iSTvY5A8gpGgKgBRlgjq1y1113Qa1WQywW4+jRo5DJZIjH45DJZHS29f24duT8SFGWFDJJIbyyshIKhWJemjcXtm3bRhk9fD6f+h0yceLECXR2dtIColwux8WLFwFg0YJCH1SUk7IyVgTFEhamR8/LL79Mq74ymYzOj/H5fNTW1kIsFtPK+kMPPbTgBWapUGz2w+l0oqGhoSBliW12TXzLjEYj5HI5xsfHcxT0iNJjS0sLWlpa6MJPxAmI/xKhfbFn8aqqqnDvvfeWZFLMPC8y2MsF8j0wu1TxeBzd3d0AgLfeegs/+clP8hIzgLsqa7PZqG2C0+mE3++HyWSiHHmCb33rW2hrayuYwDDPu7a2FvF4nNIDucymRSIRGhsbMTMzk6cuyUY8HofVasXo6CgVi9DpdNQrSiQSUY8oQkckHlKnTp1CJpNBdXU13G43rSCmUilO43A2iLXCtQa5XE4N2detW4ctW7bgyJEjnM8On8/H2rVrsWXLlrzgA0DOPcSk577wwguYmJiASCSiM1e//OUv4XA4kM1mIRaLodfrUVVVBZvNRtXViJoYKRgVs3RgJ28ulwsKhQJSqRQKhaKo2Wqp70meS7FYTE1emZ3kiYkJRCIRSvUkktSECh2Px+kM3VIFj/N1wa51WlwxFEoEVuv5M7tKzP9fzZjPC4xZ8CukfMgW6gHek4ofGRlBR0cHvF4vVCoV9eBkwufzQavVvq+ec4UojGStID5mW7ZsWfB7t7a24ktf+hKee+45hEIhAPnPMYlZiGx+PB5HKpXC4OBg2Tx6gSgnZWWsCJgJi8FgQE9PDw1+yGJKxCYAoL6+nnZ5UqkU1q1bh0wmQ0UAVCpVTgC1GlAqBYTLr4QgEonAaDTmzL9kMhn8+te/xtatW6FWqzE+Pg6tVouuri709fUhGo1Cq9VCp9NRHnwikYBEIsHmzZvxzW9+kzM5mg96vZ5TRIOo58nlcqoeyIbf78cf//hHzs/lqlAS00m73Q6pVEqpD2SDefPNN/Hf//3f84paaDQaPPzww7jjjjvonNz58+cRi8XocafTaeohl06nMTQ0BIFAUNT8mKhRAnO0t3Q6DaFQCJfLBWDOi0qn06Gurg6JRAKzs7NUej2TyWBiYgICgQB6vR4ASu7yXSsQCASQSCSIxWI594tarYbP54PL5UIwGMT111+P6667DmNjY4jFYjnJWUVFBe655x60tLTkBR9A7j0kkUgAAKdOnYLf70cqlYJMJkN9fT0+//nPY+PGjTh69ChsNhtdU/r6+jA1NYU33ngDhw4dAjD3PRXrogLc85KkuBAKhQqarXKB6UNXaC1JJBKorq5GOByGRqOha6lOp4NIJKJqqJs3b6YKsUKhEJ2dnQgEAjh//jw6OztRUVEBAItO0Erpgl1LtLiF4mrrPl1tXc1SvMCY99tCEmLyu6RgSD6D+X6kkLpavl8uCqPL5YJIJIJIJCqomFgKWlpaIJPJEAqF8kYKbDYbvF4vZUIx1WtFIhEkEskH6rm/UpSTsjKWFaR67fF4oFAoYDAYqE/O2NgY7RLV1dWhsbGRBqs33HADamtrcf78eaxfvx7Dw8Po7e2lyQqhcy3GK2e5MN8mzAy42F0v4L3EVSaTQalU5phK+/1+dHd3QywW49SpU1T8gagp6fV63HfffdiyZQssFgvGx8fR2NhIfYoWM9exZ88e/P73v8f58+cpXU4oFEKpVKKqqgrZbBaRSKRgh6lY54l5zkS1ipyLVCqllNXz58/jtddem9fnSafT4e///u9x//3353yGyWTCo48+iv7+fsTjcepHJhAIcvjuXO9POo88Hg8ymQwf/ehHkUql8O677yIWi9FELpFIgM/nQ6fT4bbbbkN/fz/C4TDtnDHVBOfrxl2rIPRMpVJJzU41Gg3Wrl2LkZERBINBOBwOnD59mhZj1qxZA6FQCI/HA6PRiF27dmHPnj2UvtfT04Oenh709vYilUohEAjQhE+n09GCR29vLwDAYDDQeQoSUJHO2sTEBCYnJzE2NoapqSlYrVbIZDKMj48jkUigsrIyp9rLfp7Y/nmkqykUCsHj8fLMoAkK+dAxFeC4fP2I2hpzLQ2Hw9Dr9YhGo/jIRz6C+++/n/79iRMnqDCO3W7HM888AwB0tvfBBx9c8BpaShfsaktMlhqruTPGBvv7NJvNq+57m084pZAX2GIx3/27mr5fNoUxEAigs7MTk5OTlFK42OSIWQhiJ3eks6rT6cDj8aBSqRAKhaBSqaDRaHDgwIFVc42uBpSTsjKWDTabDU8//TROnjyJZDKJ66+/Hs3NzQCA6elpjI6OYmJigiZWBw8eRGVlJfR6PUQiEX7xi1/A4/Hg7NmzqKiogN/vp8EOqaLz+fxV0xovlviQqt7Y2FiO8SyzS8as8tXW1uL06dNUOAKYC2yj0Sg12SXUqkwmgzVr1iCVSlHVJXIszM9eaAXU4XAgFApBoVBQWgIAaLVarF+/HoFAAB6Pp+BMlt/vL2hVYDababfDYDBAoVBg48aNAIDZ2VkMDw9TCfpiMBgM+O53v4vPf/7znK+7XC44HA5qZEmuI9vclw2RSETPi8w19vX1Yfv27Whubobb7UYikaBds1gshpmZGXR1dVFZYD6fX5D++UFELBaDRqOBSqWiqpSf+cxn8Itf/ILOPvn9/hx1xbvuugu7d+/OSU4sFgvOnz9P7w9yn/N4PFRUVGDXrl244447aMFDr9ejp6eHzlQSkIDKZDKhq6sL2WwWly5dgtfrRSwWo5VfqVSaU+0t9DyR4gehLjY3N9Pf4eoSsd+H6UMHvDcrxgYJFJmeY2Rd1Ol0WLt2LT1/Ap1OB6VSiUAggFAoRM3n1Wo1FY5Z6BpaahdsNQWuZRQGW/iCSQNeDYXPUoVTlup+Y+7nVyKMs1JgUhh9Ph+eeeYZWK1WuFwuCAQCzM7OLvq9SSGIy0qD0LyJMFsgEIBUKoVAIEBFRcWKKCRfSygnZWUsG6ampjA4OEgNDFOpFG666SasXbsW4XCYdkfsdjvMZjMsFgvsdjsmJyfR19eHsbExmngR9R+CTCYDp9OJCxcurAqfpmKJD5FvJwaUkUgE4XA4TyWJXZWzWCz4yU9+gsuXL1OaIOnOGI1GNDc3I51OI5FIwGaz4X/+53/Q19eHu+++m6obKhQKVFZWYmxsjFJDSw3Aurq6YLVaacdOKBRCJBJBLpfj4x//OF5++WXq0cLj8fISEK/Xy/lZZrMZTz31FIaHh5FMJrFt2zbceOONcDqd6OnpwbvvvluSIfT69evx5JNPFtwwjx8/ju9973t0dogJqVRKOfBckEqluOmmm3D27Fn4/X6k02n4/X4MDg5SOhihi/D5fMRiMbjdbsTjcSqvXEY+stksVRqtr6/HLbfcgoqKCvzwhz/E4OAg/T6IrPKGDRtokYHMnZ48eZImb+z39vl8CIVCOR1oYvvABTLrarfbUVVVhWg0So3ERSIRdDodDAYDPvzhD8PhcOR0dtkdIrbv0R133AGTyUQTSgA5NhDsSj/bh65YVdtkMuV4jhH1OLaIEvP3ZTIZ0uk0fRbInGVjY+Oi/cY+yF2wQmAyIrg6nasVzO/T5XKhs7PzfZ8FLNYZW07hlKuNyklAzJr7+voQj8cRjUbpWnYlJs7k3jCbzZyvbdq0CWfPnkUkEoFEIoFEIgGPx4PP5yuZul3GHMpJWRnLhrq6OkgkkhwPpomJCezcuRNisZgq0ZFAxG63Y3h4mCoQkiSMzP6wKWbZbBZ+vx8Wi2VRM1OlohTqXyEqD7tDZjKZ4Pf7C1bPmdi3bx9aWlpw4sQJdHV1UQEUtmHxr371K5w7dw6RSATT09PQ6XTwer0YGRmhhtHJZLKkz2QiEonkdMDI9ZfL5YhEIhCJRAiFQpSOxsbExATa2tpykqbjx4/jySefxOTkJKLRKEQiES5evIizZ8+WnFyTYFmv12NiYoJK0VdUVNAgOBAI4KmnnoLdbs+7b8jAc2VlJTo6Ojipi7FYDEajEUajkSbEPB6P3pekOqjRaDAyMkI3v3g8jtraWmq4nclkrhnp+sWAKUHP5/NhNBrpDAIwl6Dffvvt6OjowMjICFKpFJVUvv766+nvtLe3o7u7mybIfD6f03g9k8kgEAjkPH9sdTXmv9mGr5/+9KepDYTT6aT0yTfffBN/+MMfoNFoIBaLaSdPoVBQbzDmexkMBszOztJgpJT5F5I8lhpkspMih8NBO4Lsv00kEpRexERjY+OiqIvMYyhGyfygBWKFGBFXS1DP7PhaLBYAKzsLOB+dd6WEU65WgZq6ujrK3CB7EjDHIqisrLzi75EUzpljIzabDUNDQ4jH4xCLxdi4cSN27NiBM2fOwOfzFaRul8GNclJWxrLBZDJh586d6O3tpao9p0+fxmuvvUb9Pu68807s2bMHDocD6XQaMpkMPB4PqVQqZ6anUGBL5J6XC6VWzApRecxmM06fPg273Y7Gxkbceuut2LZtG2cFtdBn7dmzBzqdDkDhoXxCs4tEIgiFQqiursbU1BTcbjd4PB7WrFkDo9FYUKaf67wvXrzIGfSqVCrU1tZiYmIiZ+6NCy+++CI2btyI+++/H2azGd///vcxMTGR06Ganp6e93iAuWRKo9FQe4SxsTEcPnwYYrEY0WiUctgNBgPGx8cxPT2dR53g8/nQaDR44IEHkEgkYLFYMDU1ldcxSyaTGBgYwPr165FKpZBKpeD3+xEIBBCNRrFmzRp8/etfh1qtxrlz5/DrX/8aHo+Hem2pVCokk0maIJPuhEKhoGakRD7/WgYpyohEIjQ3N6OmpgZvvfUW9fY5f/48TCYTGhsbodFoEAgEIBQKsX//fqjVanR2dlJ/QrvdTml6CoUCLS0tkEqlGB0dpTRf0s0Vi8V5nStgzoeI/W+m4euePXuwZ88eSg0knW2SDI6NjVH5a6PRiHA4jPb2dnpcwWAQSqWSznoROeiFzL8sJHhhBtGk4+d0OvMEkOrq6tDc3EyTBQB0vtLhcCxZwHS1dhiWClNTUxgbG8OFCxeQTqc5GRFXA7jEmJhd3itBsULJfHTelejMMinIZF24WoQqTCYTdu/eDYvFQn0vyZxXc3PzFV2zQokqEWkjsQBhmUQiEYyNjeWp5JZRHOWkrIxlAaEb9ff3I5vN0sRqYGCABsBWqxVSqRRGoxGvvvoq7HY7otEoIpEIIpEIXVDm85jq7u6GRqOhohZLiWIVM/bmwhbvsNlseOWVV2CxWJBKpZBIJPDVr361IJWK67OA3I2K628bGxvpbJlcLseWLVuwZcsWjIyMwOl0IpFIwOl0QiaTUbW6+a5TZ2cnBgYGOI18a2pqMDQ0RI+vGLLZLNrb2/GRj3wEzzzzDDXGJijVb0ssFkMikdCuVjKZRDQapZX/bDaLWCyGWCwGn8+HYDDIyWUnnZShoSEcPHgQ1113HQKBAA26mefJ5/MRDodhMBggkUgwPDxMffT4fD6VR66rq0Nvby/Onz+PVCpF56MITZV5/6bTaWzatAk+nw/j4+MlnfvVDHJPqlQqeL1eTE9P025YJpPBX/7yF7z77rvQaDSora2lXoQAqBor8cUhMwvZbBbRaBR6vR733nsvHn/8cfh8PnqvZjIZHD9+PCeoI5TSSCSS8++Ghgbs3bs3z/B1//79GBsbg9vthkgkgsFggNVqpfcZoeZIpVI4nU7Mzs7C6/VCoVCgoqICGo0mRwWtUNFmqSr981X2TSYTHnzwQVRWVqKtrQ0zMzMA5goiR48eXTIVW5KUkGT2aktGrhREgpywO0wm01UV1DPBTPivNNFmUjqZ1HoAdG6NnYRx0XmXez6RXcjZu3fvVUe927NnD4aGhtDb20uv+WJMo9kotIYRRhQR6BIIBEgkEpwS/VfTdXy/UE7KylhykIXt3LlzGBwcpIEsVzfiwoUL+MEPfgCv14tQKAQ+n59DY5uvk8Dj8XD+/HmMjY1heHj4iqg4XCi0EBWqwjMTp6mpKarelslkEIvFilItuQwgS6FR7N27F2azGSMjI9iwYQP27t2LqakpCAQC8Hg8iMViKBSKnCCR6xoxN86TJ08iGAzmXf90Oo3u7m6cPXu2ZFre6Ogo7rnnHvB4PGrEWQpIQs7j8ei5yGQyyGQyOl9HrhNXp4xNvySIRqN44YUXcNNNN+Ghhx6Cx+PBwMAAve7EpHh6epp2U5qbm1FfXw9gjj6p1Wpp9c9kMuGhhx7KkViPRqN5c5DAXLU3mUxyXttrEalUCtFoFAKBAMFgkHq4iUQi8Pl8uN1upFIpjI+PUz+tcDhMvfkMBgPkcjkuXbpE/5Yk4sQ3TiqVgs/n0+tJumrMoK5Qp4w8r4XWDKlUirq6OuzduxdisRivvvoqfD4fpSAbDAaEw2EMDQ3B5XJRARgyFE/8zZZ7/oot0uByufJEdkwmE+666y643W50dnbC4/EgHo/D5/MtWfJUyEPtgwKmSp3JZMKtt96K/fv3X9XBaDFqfin3M3OvTKfTiMfjdC8CQPcEdhK2UDrvUoB5rg0NDdDr9Vfdd0cKMFNTUwgEArBarXkiR4t933379uVRpE0mEw4dOoRIJAKfzweDwUBnANkS/VfbtXw/UE7KylhykGrp4OBgTgWbC16vl84kzdcV40IqlUI6nYbH48Hg4OCSPvhk0+GSpmYu3uzNhRyDWCxGLBajA/WxWAyDg4MFFQmZ6kmkulRIYYr9d4888kjO5uVwOKhfmUwmo39X6D3YGyf5O6IuyMSlS5cgEolKvo7RaLRkFUIej4fPf/7zaGpqwk9/+lNKC8tkMtSDSaVSob6+HgaDATfeeCOUSiXnTNmzzz6Lixcv0qSAHEc2m8XMzAwOHz6MgwcPQiaTQS6XQyAQQKFQQKVSQSgUYnJyktJCR0ZGUFNTQxUiBQJBTvWPLbHOFKFh3tM8Hg8jIyOrVpGKBNFE7OJKQUy2yQwWeV5FIhG93mxxDalUCr/fD41GA4lEApFIBK/Xi1QqBalUSm0ZKisrIRQK0dTUhOnpaUqR5vF4UCgUeUEdkD9TVkgptaOjA06nkz7Ter0eO3bsQEtLS56IAxEpcrvd9DyYySPBclb5mYP43d3d6Ozs5LQLIQGb0WhEW1sbIpEIrFYrJiYmAOCKg99i0tkfBLBV6q72hAzgLkyW2j1jilyRrrdEIkFDQ0PB4ghX0E/eaz67mSu5f69m2iIby7HWFKNIG41GVFdX5xRBiXotER+6Wq/lSqOclJWx5CDt7EwmM6+kOekg8fl8iEQi6nFRahdGKBTSof9wOLxkldn5Nh3mgiOXyxEMBqFQKHIWn0QiAY1GQ4MzMhtTLHFkG0CWyqNnL8LM4Ki+vp6TosUEM8k0GAzQarX46Ec/ipmZGfT29uYF6EsRsDMhEomwa9cufPvb36Zqe2fOnKHGz8T0WSAQwGq1Uk+qy5cv5wwcM69TS0sLzGYzvF4vdDodTp8+jRdeeAHJZBKJRAKnTp3CqVOnIBQKIZfL0dDQgC1btmD//v04efIknXsiXlMksYzFYvD7/ZzqmSRBm5qawrlz5/DLX/6Sql4CyAnaCcj9C3D7pa0kiEy6WCwGj8eDzWab9xnmAjkngUAAqVSKzZs3Y+fOneju7sbU1BSdZ1Kr1WhqaqLfrcFgQGVlJQYHBynldnx8HIFAAHw+H2vWrEFVVRXi8TgVCjIYDLj11lvx9ttvU8qoTCbjnNEq9G8CLqGGQnRD5rP8kY98BPF4HIlEAuvWraOd6XA4jBMnTlCV0SutVhcDme8Ih8O4fPkyIpEIOjo68hIDk8mEG2+8EefOncO7776LeDyOn/3sZ7juuuuuWJSimHT2tQzm2nOtKVJydXnPnDlTlMFBxhdIgYo8SwaDARs3bkRFRUUOo4TcJ4WC/vnUjbleW0iidi3QFufDlSauxVg7ZN0hAk7t7e0YGRmBz+eDVqsteZa9jHJSVsYygLSzSaeMCfX6cnkAACAASURBVIlEQoM10oUAQAM/pVKJAwcOYHBwEGazed5KazKZpIFkfX39klVm55slO378OOLxOO0iCQQCSCSSnMWnrq4ORqMRUqkUkUiEzqEUSxxL9f6ZD+zgqNgGYzab8ec//xkAaIWQ2R185ZVXcPjw4WWpeovFYnzyk5/Ed77znbzA8R/+4R9w5MgRDAwMIBqN5sjvJxIJ+Hw+2O12ztk7pncUEx0dHVTogIAYD1ssFmrH8JWvfAWbNm3C66+/jkAggFQqhcrKShpoE0pooc6lyWSis2anTp2ivlASiQQKhQKxWAyRSIR2VORyOYRCIf2s9wvxeBzZbBahUChHdXIhHWyDwQC9Xg+9Xo90Og2tVouHHnoIiUQCY2Nj8Pl8lCJKZs6YRQNg7ruUy+WYnZ3FpUuXEI/HIZFIsHXrVojFYvT19SESiWB0dBRyuRzr16+H2WymtMfXX38dn/jEJxacBDFnokwmE3bs2MHZ7WAHggcPHsTOnTsBzH3/x48fpx30F154gXb+vvjFLy7L7CsBkwLNFhthC3+IxWJaFEsmk+Dz+VdEMyrGLLiWwZUUXA2+VgsBey3lotqTRMzr9WJoaAgTExOw2+2QSCSora2l/o5mszlnTyLvWyzRmy8hmG8W+8tf/jJ9jeu+vBZoi8WwFHOBxWITokZMZPcvX75Mxza2b9/+geuYXwnKSVkZy4LW1lbcfffdePzxx3NUeT772c/iC1/4Arq6unD06NGcLgKRvd+8eTM+9alP4bHHHoPFYinacSMPO+lcLFVlthBlgymjTbpZJMBuaGjIWXzIrNHMzAxGR0fB4/HQ1NRUdIFiVyUdDgc1g9RqtTh06FDBQNNsNueY5JZSsTWbzXjsscdoV2L37t1obW1Ff38/3G43xGIx7r//fgDAk08+SekJAoGAWh0sBiKRCJ/61KfykjEmWltbceeddyIWi2F6eprK1MvlctqhJN9NKXMPra2t2L17N9rb2zk7fZlMBn6/H36/H4899hjuvPNOxONxSo00GAyQyWSwWq2QyWSorKzkPG7mZ951113weDywWq0A5u6rW2+9FYODg/jTn/5EkzWhUEi7U/OBKW6yHHC73eDz+TmeYaVCrVajqqoKUqkUJpMpp+Jss9k4Z7y4igbk3m1vb8c777xDhROI7x5JOkgnq6WlBceOHaPql/F4HD09PQtOypgzUX6/n1NeHsgPBBOJBG6//Xb6utFoxNTUFP785z/TxMjr9VKRjeVSJSTrB1GPtFqtCIfDMJvNeUUPotJGqttqtXrRhaD5OhnXUueIjatVPn2hYH+PTKo9USDt6+ujRSuJRAIA1OsPeE+8B8i/TvMF/Qt5jf2dMH1Q52O+XIsd3qW4R5kUaa7Xdu7ciYmJCVitVsTjcYTDYUgkEqTT6Wvuei4nyklZGcuGPXv24NSpUzh9+jQA4KabbsLDDz8MAHQolAkSCJ48eRJisZjOiJQS+CcSiSVTs+Oq+ALIoTcQ3jk7uGQvPkajERs3boTH44FYLEZ9ff28CxSpHtpsNhw9ehRnzpyB1+uFSqWC0+nEfffdB7VaTTdHm82Gzs5OPPvss/B6vTAajXjiiSfmpV/YbDa89NJLsNvt8Hg8EAqFOHHiBF577TX63bzxxht44okncP/990Or1eLpp59GPB6HwWCgc2vFOjsSiSSPilpTU4P9+/eXJMrS2tqK7u5u2O128Hg8hMNhVFVVwWAwYOfOnTnnWMrcwyOPPIJ0Oo3f//73RT/X7/fjt7/9LUQiEZLJJBwOBxKJBKXXOZ1OnD59Gm63GwcPHix4nwCgyRyZdXI4HPD5fMhms+Dz+TQhSyQSeQqQXAnRQpMx4k9z6dIl+kwVQzabLUkVs7q6Gh6PhxYZeDwelEolrFYrkskkAoFATleIvamT+T+uYJ08AyMjI3jllVfoPNnWrVuxZ8+enLkuouhWWVkJh8MBkUiENWvWYNu2bQu6TkAu7Vej0aCnp4dTNWy+jjY5frFYTC0ACG2WdHeXc75s//796Ovrg8fjQSgU4jRw3bNnD86fP4/u7m7weLwFWWawMZ9P47UskX8tBvTsBIzre2RS7Yk4j9PppHO/dXV1aGxsxKZNmzA0NITBwUFOSjABF03ySl5jPp8A8pI0Nm2SdOOvRaodV2dzseDyKgPm9mqLxQKFQoHJyUkIhUJks1moVKqlOo0PBMpJWRnLCoPBgObmZmi1Wtpxef755/Hyyy/ndSuIEEBfXx9sNhsVAyAdGfYcDjtZGx0dxfPPP39FG3+hIIJJrWhoaMD27dvpnAipiheiRQBARUUFdDoddu7cWfKxTU1NIR6PUxqZz+fD8PAwDh8+jA0bNmDt2rVobW3Fq6++ir6+PkxMTFC1u66urqLHZbPZ8MMf/hA9PT2IRqP02tvtdkor5fF41JC2tbUVjY2N2L59O70GN954I373u9/B7XYXPIf169fjxhtvRH9/P/V3IobTpQamJOiJx+MQCoWw2WyorKzMoZgUm3sYHh6G0+mkRsUHDhzAyZMnMTs7W/RzE4kETTiIp5lSqUR9fT1CoRAuX76M4eFhXLx4EfX19bSLQ4bauWTYnU4n3G43AoEARCIRFbJQKBQIh8N0LimbzeYku0KhsGRaI6EHk+fL7XYjHA5fMS2S2AQolUrcd999+PjHP47/+q//whtvvEHnQkOhEO2msjvCzDkTUsQo9qzabDYAc2uIQCCA0WhES0tLHpWKfM/JZJLep3feeeei5rfq6upgMBjgdrvhdDoL0v+KBYlMtLa24tFHH0VXVxedgVupwD0YDCIejyMWi2FycjJvvox0Mp1OJ3w+H1Qq1aJpRoWS1Gu9i8Sksl8rAT3XHsj1PTKTUaJCSp79rVu30i751NQUzp8/D6fTierqakoJBvK9z7go5wQLeY39fAKgZtgKhSJnDWppaaGU5XXr1l2TVDt2Z3OxEvXFnmdm0e3kyZM4d+4cEonEB9Ia40pQTsrKWDaQ4U9ipptIJDA1NQWLxQKHw5Hzu0qlEk1NTXC5XPB4PIhEIlAqldDpdNT3KZVK0cSMq5IvkUhyqtBsOl+px8y16LAlp4eGhhAOh2nAVmiGgHCtdTodGhsbFxQokrmwaDSK4eFhKqceCoUwOjoKAOjp6cHly5fh9XppkppKpfDmm2+ira0NVVVV2LVrV15Q2dnZiTfffBOhUAhSqRQ7duxAMpnEyMgIDd5FIhGMRiPtOHBdg2IJGTDXcdq1axeeeOKJvM1+vsDUZrPh6aefxokTJ+BwOMDn86FQKNDY2MhZ7eOaeyA8d7vdju7ubojFYhw+fDjHdoGA0AgLiZgQjzOr1QqRSIRQKIRsNov+/n5qHK3VapFMJlFdXZ0jAMOk70xOTiIej0Or1cJgMMDpdCIQCECpVKKhoQGXL1+mPlIAcqiE84HMV7KtB7isAUoF6YAZDAZoNBrcdtttOHjwIEwmE26//Xa8++67cDgc4PF4VLBDJBLh+uuvp/c7+S57enoQDAZp9ZSLZgqAJm8TExPg8XjQ6/VYv349Z8DETghIh7uQymkpiMViCIfDsFqtOXNW7A5CKe9PVCBXksZH5KiJ4IrVauVMMEl1mzyTYrF4USbBhZLUpZqRXa0g+4XT6cyjr18tYN+XXHsg+3sk3WmSjB48eJCqzwLI68oy/5YkZMvdQWU/n+T+dLlc6OzspOcnl8sxMjKCcDi8IjYOhdaB5VaXZIuILSZRKqXj1t3djVOnTsHhcFAV4w+aNcaVoJyUlbFsKLQhu93uvMDXaDTi7rvvRltbG/V5IiaExOOMGMdyobKyEh/60Ifo5zBnpQwGAx599NGSEqJCx8ysAo2MjFB1OGD5qr/MQCcQCOR5JJEuSzKZhFwup92kWCxGN0dSnd+9e3fOMU5MTOQIrdxwww3YvXs3nV/LZDLYuHEjDhw4kHPdSKfilVdewalTp4oeP1HEJDMrpXYXCKampuh1TqVSEIvFMBqN4PP58Pl8OHr0KFwuFxKJBLxeLyYnJ+HxeLB//35asSY8d5/Ph3A4jFOnTsHv9+cFT3w+HzqdDgKBAC6XqyhllnhdkW5tJpOhin+xWAwCgQADAwOIx+NYs2YNGhoaaNDS0dEBYM60V6vVwmg0YmZmBsFgkD4Ts7OzOZ9f6twe8XPLZDJLGhwSyq3D4YDT6cTLL78Mh8OBgwcPwuPxwGg0IhaLQSgUQiqVgsfjYc2aNbjjjjvod0ySLLvdDj6fT6leYrEYx44do5VrQvkkIgFElbW6uhpr164tKKxC7iumOe1igj1SSCIFEJlMtmAZ8EJgB4nLmaSRgg4A2pUl6xWzY7YU1455HuziFFfHYjFJ32rF1Z50ct3TXOfE/h65ktFCRYpiLIZSvc+W4llhjgWwu2bEL1Gj0eSsnVyfW+rPuH5eTClyIeqS5Jot5Hosxb06X8dtamoKExMT8Hq9SKfTtJB6NRYr3i+Uk7Iylg2FgvDGxkb09PTQBEskEqGpqQmNjY349Kc/jZmZGfj9fgQCASSTyaJdAh6PB51Oh8997nP467/+a/o5xGeIdHJKGfovRT3MYrEUlMvmAgnyfD4fJiYm8gbu5wNzo9Pr9ejp6UFtbS2VLT9+/DhkMhkCgQA8Hg/GxsaotxcA6snFPEabzQav15tjfkwEPgpRHtk+ZqRTxwbxoJmZmeHszpTaXQDmaIukA0jk0uvq6pBIJHDx4kXw+XyYzWak02m43W6qcNnZ2Yknn3wS+/bty+sEtLa24tSpU/D5fEin05BKpdTMWCqVQiQSQaPR0O4sVxEgmUzC5/NBKBRCpVJBJBJBp9NR2uCFCxfoHJ3NZoNEIqFBy/79+2mlkhzPuXPnkEwmEY1G5+08FgOhPC7EoLsU+Hw+xONx6hXm8/nA5/MxPj4Oq9UKl8sFiUSSkzRVV1fT4EssFmNkZIQmnURFcd++fTh+/Dj6+vpgt9shlUqh1WoBgCbyVVVVaGxszJsfZIPcV1zBHvlvKQEM6a5mMhmIxeIcOef5ZMAXguWeteJKtsi69dZbb6GnpweHDh3Ked4XQzUs5TyYwfC1Nl+20ELTagPXd75jx46CiTQz6V5IgM/FYijV+2ypRWSY39nIyAjefvtthMNhiEQiiMViei6FkqFSflbouAs9YwtRlywkWjLfteC6VwmlnLxOYgOdTldwvU0kEojH4zQGYB4rmSOcmpqC1+uFUqmEVqstd8oWgHJSVsaygisIb25uhkgkotUTqVRKxQjq6uowNDSEnp4eSjUoBr1ej8rKSuzcuTNnw9i2bRsMBgMNvGtra4u+TykBA7M6yOTGF9sMSJBH5jpeeuklmEymBc+7sI0byfGRoEosFqO9vR2xWAwTExM5ncj169fnHCMJVGtqaiAWi3HXXXfR4ymUNDE3BkJjY1LsgDlFxs997nPYunUrnn76aTidTmg0Guh0ugWdK/OciTeYSqXCbbfdhgMHDuDo0aOUPkiUL8ncHTCXRHR0dNBgmitoeuaZZ2hHlnigSSQSGI1G7NixA16vF729vRgfH4fP5+NMMLPZLIRCIQ3iGxsbMT09nSNskslkEAwG8zquTHXNZDJZMAFcDJZaUj8QCCAYDNLrG41GYbPZYLfbEY1GkUql6Gv19fXQ6/UA5rxqnE4nLl26hEwmQztPMpkMyWSSvgdJwLRaLRobGwHMzZEpFIp5kzE2uGhWC/EwMplM2LhxI3p6epBOpxGLxWCz2WA0GpfUWHYlZq2YzzLp0r711lsYGBgAn8/H6OgoZDIZgLk1+WMf+xiCwSCtbpdyfgs5D67g8mpNZgiuNmVJ9vEWY4YUS4iuNBldSPdsOURkyDm4XC5akFOpVDmMEq7PBVDSz0qlgZLrvRB1yULHUMq1YH6vx44dQ2dnJ/r6+pBIJCASiRCNRqmS9c0334xHHnkk732YCrVsuqfJNGdOv2nTJvT19WFgYACTk5N47rnnFjXD9kFEOSkrY8Wh0WigVCrh9/upgAcRXSAPtdlsxm9+8xt4PJ6CnbKamhrU1dVBqVTCbDZTah0wx2n/+te/jn/6p3/CxMQEfvzjH+e8zkaxhZ8kPeygbL6EjJzPzp07MTg4iLGxMfT19eGpp56iJsmlolBAQxZEo9GIBx98EDt37sRrr72GV199FclkElKpFJ/4xCdy3ou9yO/du3fez2fPkzE7QMT0e9euXXj44YfhcDig0WgQi8Wg0Wg4r1Epwcz4+DilQWSzWTQ1NeVcM6L25ff7qZgFUTQMhUJ0poi9EXm9XhgMBkxNTSEYDEIul0On02Ht2rVYu3YtnZciVcTx8XH87ne/o0IqTJCkkBhMK5XKHNVEHo+XpwLIDJY7OjoQjUYLJmTFPMKEQmFRSu9SIZvN5pwPUcEkxu1SqRQymQw+nw9msxl+vx8ikQg+nw/BYJDSMSUSCUwmExU1AZBzT5EEDFg4NYegEM2q1ADGZrNheHgYHo8HHo8H8XgcnZ2dOfTKpTCWXWnaG+nS9vT0gM/nw+PxwOVyIZVKQSgUYmpqCj09PfB4PMhmsyUfz0LOg72GlCr4slpxtXX+Ch1vseRqPmGHKznfUrpnxX4+X0GglD3GZDKhsbERWq0Wzc3N2LNnz7zHU+rPSqGBMq9lqeqSwHuiJcUsAIrNpz3//PPo6+vD6OgogsEgnQWORqOIx+NUsZXN7LHZbOjp6YFGo6HrvM1my4tlbDYbLl68iIsXLyKdTsNut2P79u20s1hGYZSTsjJWFCToUavVtMqeSqVgtVrpAkAWgaeffrrgPA3prrndboRCIVRWVuZRldra2jA9PQ1gjr74la98Ba+//jrn+3FV2Mmsi9PppBuZwWBYcFDW2tqKN954g7b0h4eHF5yYcQU0zONau3YtvvzlL+P222+nMtzBYBAymQxGozHnvRZa5WTTOk+cOIGLFy/SwWG1Wo2GhgYqZmGz2aBQKKBSqTgNvUsJZmw2G7q7u+nfEtEXcj2ZHUJCueju7kZPTw+t+P3oRz/Ctm3b0NLSQm0DiI9OOp1GIBCgZtQ1NTU5kuDknFtbW3H77bfjpptuwuHDh3H27Fl6TAqFAtlsFolEArFYDMPDwwByhTlEIhH6+/vxgx/8AAcOHKB/Sz7DbrfnWQYwQWbEgLmEiJg5i8Vi6HQ6BIPBkmTulwpSqZQeC6H6CIVCSmmZnJykibpIJIJQOLfFkOOuqKigRQ0igFEoEFks2MEeOzAqlKiRzjxJ9BOJBPx+P0ZHR2nxaOPGjUtiLPt+0N5MJhMOHToEp9NJ5zAzmQxSqRQ8Hg/8fj/i8ThkMhmcTmfJ3buWlha0tLTMuyYyz9nlcuGVV17B6OjoVavOthLdzsWCKyEpdLzFkquVLB4sNGEpdmyl7jHHjx+HQCCAyWSixbj5jqfUnxU7n2JdrELXhvlaMQsAdsGDbe3T0dGBsbEx+Hw+iMViSsEXiUTwer3wer10fWfCbDbjRz/6ESYmJihFPpvN5tltkPtsenqaKgknk8l5WU9lzKGclJWxoiAzVmQGx+PxUGECQmMiv8dUW2SCBKSXL19GOBymIg1sqhLb5LCvrw/f+9738P/+3/+jPyPdEK/XizVr1sBkMiGbzaK9vZ2KDRB6WTgchlwuX3BQxgyGhoeHEQwGMTo6iqNHj87b0mdurmz1qMHBQQSDQfh8vhw1JYvFQq9dKpWCxWLBvn378o6plHNgb24ajQbPP/88XZRTqRRisRhmZmZgMs0pXnZ2dmJ6ehpisZjOgTFRSjBDKn1k3ovdJWMeP1G2I/fA6Ogoenp6EI/H8frrr6O6uho6nQ7T09PUyFwmk0GtViMWi0GlUiGVSsHhcOD48eMAQKmiCoUCmzZtAgDccsstsFqtmJ6epmbGfD6fJktcHd1sNotLly5hdnYWZrMZfD4fwFyyFolEEAwGiw5BEzVFHo+Huro6ZDIZOBwOqvTIVjFdajA7dQKBAHV1daiqqoLL5UJtbS20Wi3i8TisVisymUyO+iTxZ3v11VcRiURgMpnwwAMP5HjsAVeWgM2HQoFRIYqjQqGgJtWk0+z1eqngSKm0vlKPrdC5LxctrrW1Fd/+9rdx5MgRDAwMwO/308Q5EAgglUohHo9T+41in89eG0opMJFzNpvNsNvtCAaDGBkZ4VRDXU3g+j5Wq8hHoYSklOPlUhddyeLBQhKWYsdWyh4zNTU1rxR+oc8t5WfFfn6lYL8vu+BB1CUjkQiOHj0KgUBARZRIMbempgYGgwGbNm2CTqeDyWRCf38/Ojo6EI/H0dzcDJPJhGPHjsHj8eDpp5+m1xMALcYNDg7mdNSY9xVhU9TU1GD37t1Lfh2uRZSTsjJWFGRjiEQidM7E7/eDx+NhaGiIUs7q6uqg1+vz6FtSqRQmkwmhUAiBQIC+9s4771BFoL6+PiiVSjQ2NubNPT333HO45ZZbsG/fPirT/fbbb9OhVI1GA4VCQcUMgLn5FhJoLnbzJcHQU089heHhYcRiMfh8vqLVVVKZmp6eRkVFBW699VZotdqc60HodMxgUa/XUzodmXtiYiEBH3PjGhwcxODgYF5nh6g+Op1O9Pb2oru7G36/HxqNBps2bcr7jFKDGR6PB5FIBLVazUkVJefBVKYk8vJEQY/H49HEkVQFFQoFtm7diu3btyObzcLtdlOVR7lcjq6uLvT19cFqtSIQCOCll15CNpuFRqOhxxSNRhGNRul7Fkqu+Hw+JBIJotEoYrEYEokE7Rolk0mq3lgIGo0Ger0etbW1qK+vRzgcptLzhP67XNi+fTvWrl2Lt956C/F4HLW1tfjOd75DRWaYxs1yuZxSEE0mE32tvb0der0eIpEIDzzwQF5xYCVQLIBhd84aGhrwsY99DAAovZVQYqurqxfkM7hQMKnSV6IgOR9aW1vx2GOP0WKUz+dDW1sb7bim02kMDQ3hxz/+Md544w3cc889eYk0kLs2LLTblUgkoNFoMD4+jlQqhV//+tfQ6/WL8pZbbizXXNVyoVhHrNjxFjvP1XJubBQ6tvn2GMLYmZyc5JyNuhpBrgVTXZK5NzN9M5kz8cDcPWI0GtHa2oq9e/diampORfFf/uVfMDk5ybm/pVIpBAIBXLx4Ea+88kpOt4wUPxUKBUwmE+6+++5V+WyvRpSTsjJWFGRjINLggUCAJhBsQ+FNmzbBbDbD5/MBmAvSVSoVpdwwk5N0Oo2XXnoJer0eXq8X2WwW69evx7Zt29DT00N/L5FIoL29Hfv27aMLz+zsLGKxGA2c1Wo1hEJhjvIbCTSvZPNtbW3Ffffdh8OHD8Pv99Ogn4Dt1fSjH/0Ib7/9NhXtOHv2LIRCIQwGA+rr6yGRSFBZWYmampqcYLGlpQWVlZXUVqC/vx/Hjh2ji+JC5iDIUG8gEIDb7c5LyIgsfCgUwtjYGO0EAnPCDVwiH6UEMyaTiQb6er0eW7ZsodeIBJNDQ0NwOp0YGRmhoh9EQZHQ7EhALRAIsH37dmzfvj1PWYoEI+TzJicnMTU1RRUk0+k0vT+VSiWA9zpIfD4fcrk8R/GSiUwmg7q6Ouh0OkqLI8mUWCwGn8+HUCiEz+dDKpUCn8+nZumkA6dQKNDa2ordu3fj5z//OcbHx3OMrZcDIpEIFRUV+NKXvoRDhw4V9fsrpNhJ6L92ux0qlWrZu3oLQTGK45YtW2A2m+F0OinFT6lULsjvcKFgq5uSQApYfhGQM2fO4Ny5c9QKIp1O06KD1+vFhQsXsHnzZjoTQv6u2MD/fKirq4NYLAaPx4Pf78fAwACOHDmCxx57bNUlAMs5V3UlKFRcK5aQFDve1UzHXCiK7THMmSqiulhdXX3NyLYzz51dNANAaY3F/OIcDgd+9rOfYXx8vGjhL5VKwev14uTJk/jVr36Fv/u7v8PU1BQSiQRSqRQUCkXerF4ZxVFOysp4XzAzM4P+/n7a7SIqdkxp3Onp6Tx5cmLYS37OTBLIbAShkXk8HnzjG9+A2+2mcyPAezMkXPKtGo0G9fX1MBgMC1Z+KwVqtRq1tbVwOBxIpVL4zW9+Q+XSmdXxlpYWTE9P56goEq82q9UKv98PtVoNjUaTZ0qdSCRQVVVFP8NsNiMcDsNisaClpWVBG6/NZoNIJEI6neZU9VOr1YjH45QzzlzAk8lkyRx5NhKJBAwGA9xuN4RCIfr7+/PmwkgQGIvFEIvFIJPJoFAosGHDBsjlcsRiMVitVsTjcVRVVWHv3r24/fbbOY+F6UHndDqp5xePx6O/Ryhs9fX1sNls9Dvh8j1jXoOpqSnU1tbib/7mbzA8PIxTp04hmUyisrISW7duRUVFBX7729/C4XDQQkMsFkM2m6ViE+RaWSwWKmgyHwitVyqV0tnKUpFKpTAwMIBAIECtBQqh2HdJpP7T6TS6urqwZ8+eVRfocXXOnE4n9Y4jNGBSYFiMz9Z83WlmQKxUKpFMJnO83JbT26uurg4tLS0A5sR1AoEALRLE43EkEgna/WaKJSUSCVRXV9Mgb6FBrUqlovMmgUAA58+fp4HdarpH3i+a4nxmwoWKawvp4DE/Y7XSMReLQusSedZmZ2eRTCZRU1OT54F4talqssE8d2bRDMgVUSqkeNnT04NAIFAyE8Pv9+NPf/oTJBIJWltb4ff76X584MCBq/Iavl8oJ2VlrChsNhu++93voqurC8lkkirWkSSIcJj7+vrg9Xqh1+sRDAZp9ysajYLP56OhoQHRaDQnkAXmAgXCY66oqEBtbW2enPn4+DjMZjNaW1tx8OBBVFZWQigUoqmpibb/ASx5Qga8VyHOZDIIBAI4e/Ys/H4/mpqacqrjLS0tqKmpwaVLlzhnlUKhEFKpFCoqKrBz504AoJ5QLpcLfD4fqVSKJg1jY2NQKBRoaWnh3HiPHz+OJ554ApOTk5DL5di1axd0Oh16e3tht9sBgDMRUCqVUCqVmJ2dzTtO6CNRwwAAIABJREFUYv69UNhsNoyMjFATymAwiGeffTZvLkylUqGmpgbJZBIajQZarRYHDhygVCsgtwpINiGuyikxNyZ8+/r6eshkMrjdbng8HgCgFdX169fjk5/8JE6fPo2LFy9Ss+1CmJ2dxauvvorTp0+jqamJzk5u27YNX/3qV2E2m6koBqHMEpojMEc3OXfuHEZGRmC1WudNyEQiESQSCWQyGZqbm+H1emmCXiqy2Sz8fj/a2tqKqpYWQ2trKzZs2EDVvYg642rcoJlBjMPhgFQqRUVFBZxOJ1W47O/vx6OPPgqBQECFdZjdVmaww+56z9edrquro4bkxPA+nU5DLpejvb19WVUK2ZX1/v5+vPzyyxgYGKBWCKlUCqOjo+js7KTrolgshkQiwbp16woaexcCKYwplUqEw2FaUPv9738Pr9fLKcW90uCa512pIH0+oYr5ulqldPC4PmM10jGXGuRZy2QyEIlEOV6EwNWnqjkfuOjbBIUS8W3btsFkMsHn8+UUhoshHo/DbrfDarXSYs26deugVquX8GyufZSTsjJWFGazGe+++y590LPZLKWdEcGGzs5OjI6OQiwWo7m5mc5fkWA0kUhQfjQXBAIBGhoacO+990KtVkMkEuW8Ho/HcfToUQDI6U6RYIooF1kslmUxdb3hhhvwzjvv0Bb/6OgodDodtFptjjKdWCyGxWKB0+nMC/rJdbPZbAiFQnj++edx/vx5XL58mVLwiMcXCSpHR0dhMplyFO8A4N/+7d9w5MgRen3j8TiOHTuW83kSiYQmDEzweDzcfPPNsFgsGBoayulc6vX6kgM1Ni1xcHAQNpsN8XgcfD4fLpcL6XQ6by5s7969RamlbBoHe6M1m804evQobDYbfD4fpFIpampqsGPHDmzbtg2vvvoq2traqFfZ8PAweDweRkZG4Ha785JRolpFEmKCdDoNl8sFl8sFoVBIVRlPnDhBv08+nw+VSgWZTEYTTwK/308lzItBJBJh7969UCqV8Pl8GB8fx+Tk5KK8y4jp+WJpTCaTCQ899BCOHDmCkZER+Hy+PKWu1QamItvWrVuRyWTw7rvvIh6Po6enB0KhEBKJBEBhvyRiis3sepfanSYWC3a7HZlMBna7na6PkUgEHR0dJVlxLBTsynpFRQV6e3vxxz/+kd53AoGAqjICc2tnPB6nhvEdHR0lUzxJMLh161YIBALY7XaEw2G43W50dHRAp9O9rx0zrsCcdFGBpRWnWYhSIsFSdLW4PmPHjh2r9tlcKphMczY1pOinUqlyiofXEo1zPhTqqra2tuLee+/F97///RwBNolEQmehmZDJZKitrYXdbseOHTtgMBgQDoc5hb7KKI5yUlbGioMtPJFMJjEwMIATJ07QwM3v90MqlcJqtdKZHgIyb0M6NOzOQTabpXLlYrEYmzZtwszMDP09sVhMgyyy+BKVIo/HA7vdTmeSlnpBNpvNeOWVVyhtkyj4abVaHDp0KCe5mJqawoYNGxAIBHICfLKBZLNZ8Hg8DAwMYGJigsq1s69HOp1GKBTC+Pg4Xn/9dXz3u9+lCcmRI0dw4sSJebsvpAPJRl1dHf72b/8Wx48fh0ajQW9vLzKZDCorK/HNb36T89qRBAx4TxqeTUskdgnkHOVyOTZt2pSjFlVKcE+CTULTIPRE8vlHjx7FX/7yF8RiMSiVSmovQAJfi8WCeDxON6FYLIahoSEq9sFOdoRCITQaDdRqNZWHZ4PYQFy6dAn//u//DrVajVAoBB6PB7fbTZNp9vVn3gMCgQA1NTWYmZnJqWSaTCZ84xvfoGbBXq8Xo6OjRa9RMRBa02Jgs9mQSCSwe/duxGIxeL3evLnR1Qaz2Uy79Bs3bkR9fT2dMSSy/8lkEqFQqKBfEnNdAVCwO80EUaUlaqNisRixWIwWOUghIBqNYmxsbEHPwELATkYeeeQRtLW1wW6301lNsVicI3udSqXwzjvvQCAQoLq6Go8++ui8iRkzGOzp6cEPfvADAHNrms/nwx/+8AckEgk8+OCD78u9wuX7ZLFYlrx7slilxIVQFAvhWqMrFgM78W1tbUV3dzcA5CmqLkal8mpGoa5qJBKBQqGg8YdAIOBMyORyOb72ta/h9ddfRzgcxrPPPov6+vqVOvxrDuWkrIwVBaG9sDE7O4tf/epXSKfT8Hg8SCaTSKVSNLAlRrnAXFI2PDyMLVu2IBAIUONpAtJ9+ulPf4qbb74Zd9xxBzWEJRXwtWvXYtu2bZQuSIbrSVVIq9Uu+Ub185//HD/96U9zKk/AXCB/4MCBvECGbA56vR6hUAgKhQKhUCgn0BeLxbjuuutw8uTJon5XABCNRtHe3o7bbrsNRqMRR44cQVdXVx69kwtEdIIJtVqNz372s9Q3bPfu3QgEArBarTkVc6aqHDMBSyQSUCgUUCgUebRErVZLZwk1Gg2+9KUv4cYbb7yiAEShUCAWi2FychIvvfQSvF4vnZmRSqXYuHEj7rzzzpxAV6fTQafTIRqN0mJANpulATQpFhAxEZlMBo1Ggw0bNiCZTOZICLNB6B4OhwM8Ho9STtkgsvvs7+OOO+6AWq2mSb5er8c3v/lNet33799f0JcPmLt3iMk3uzNHXiedyIWCGWwqFAo0NjZSufnVqnJGuuSErqtQKLB792643W7w+XxcvnwZsVgMmUwGPp+PCpewTeWZ68p8fmwEbB/CjRs3Ynh4mArIEPuPvr4+XLp0CZ2dnVTtcikTF2YyEolEYDAY8M///M+wWCzo6uqCQCDA8ePHsW/fPigUCgSDQdrRJvdoT0/PguTx29vb89auQCCAiYmJ9y2BZwfmABbdPSkWwC9WKRFYvMjI+0nLfD9AVJYnJiaoz6hYLMbg4CBmZmaoAAbBfNf+WqM3coHMkldWViIej9NCIVvQqrGxEf/6r/8Kq9VKGU2RSARutxtarRYGg2FVF+FWI8pJWRkrikQiQRXs2AgEAnRjJyp6JPiVyWRQKpXweDzIZrNwOBxUIc7n8+UkKmReKxQKoauri9IXhUIhKisrsWXLFrS0tMBoNBZUKVpqkY8XX3wRTz75ZF4CJBQKsXHjRk7etck0528GzM3BMRMXYK5bsmvXLly+fDnPQFggEFBlPyIaAcxVv/4/e18eFWd9r//MvjLMAANkwhYiS0IWJnHBJhpPYqrWeNW0jV1cu6mpt+21eupPr9p6bHut9ni0muZqe8WjrREtmho1iEQx0YCJDIRAWMJAGHiZYWaYGWbff39wvl/fGWbYISThOccjYZjlfed9v9/P8nyehxgsHz58OKlqYDzikzIOhwMOh4OXXnoJAOh3qtVqqd3A+++/H6OSSKrt5DhIB49IrMfL1R85cgSDg4MoKioaZ+w5XRDKSkdHB/R6Pdrb2wGMBVwbN26ESCTCnXfeOS6Y1Gq1+OY3v4ljx47BarVCIBDQYJx4v2RmZkIul4PD4dDkef369RgaGpowKSOIRqP0+4m3gCAJjFQqjdkcBQIBpFIp7rvvPuzcuTNhAHH48GF8+eWXSd+XmGYHAgH09/ePu4Y2btw4Y7XBeJl5rVaL0dFR+P1+1NbWTurPdzZAulVisRhKpZKuAVlZWSgpKcGePXuob6HRaMSePXvo7IRMJosxlU+kSDnR8SYKBEnwTIyW+/v7weFw4PV6EQwG4fF40NHRMaeURrZlidFoRGNjI8xmM8rKysDj8dDR0QGr1YqysjJUVFTgyy+/pNcvEWsqLy+f8vvpdDqqxMuGUChEQUHBWevexH8fAKjM+HSKdZMF8DNVSpwpEn2eyy67bE7fY7GBzAsbjUZwuVy0t7djeHiYioKZzWasWbMG9913H33OROd+Mnrjud5FY18jpaWluPzyy2Gz2cAwDL788ktaQOHxeEhPT0dZWRkyMjKQmpoKp9MJj8dDPThXrFgxpXvlXD9nc4mlpGwJC4q+vj46P8MO8gmFQK/Xg8fjAfg60He73VAoFDQBIzNSBoMBPB4vKfWOJGfNzc1wuVy009bf3w+bzUZnxsimlEzaey5w6NChcVQ2kUiEdevWYePGjUkXLhIU79mzZ5zoR0pKClasWIH+/v6YLg778ezsbOq3BIx1EaVSKf7xj38k7I4kQ7yIB5/Ph8fjQX9/P5555hnIZDIIBAKaUDU1NY1TSQwGgxAKhTQBI2a8RO2STckCgM7OThrwzgW0Wi0++ugj6m1mMpmwZcsW7NixY8Jq9M6dO1FYWIhAIIC3336bJqRisRgbNmzA9773PVrxb2hogM/ng8lkwje+8Q20t7dP2sFkn1OpVEqtDBQKBXg8HiKRCFQqFdatW4eDBw/C6/UiJSWFKuYlCiAYhsHzzz8/odBKJBLB8PAw0tPTkZ6eDqlUCofDAR6Ph1WrVuGRRx6Z8X0QH2ympaXRmSS20fliQvxnJtehRqNBUVERioqKYLPZEAqFEAwG6Zyhx+NBfn5+jKn8TALqZM/RaDQoLCwEAEoj6unpoZ/hgw8+QGtra0xSOFOQZKSmpgaNjY0YGBiA2+2GVCrF6dOnwTAMTCYT9u3bhx07dlBquVQqxUUXXYRf/vKXdDZ4KmtpfX39OOYAMHavni3qIkH89zFZ92QmJsZzQUOcCtgJ/oUyLxWPcDiMYDCI3t5eSkMGxgqVBw8enHLhb6JE+nzoosUX1L75zW/SecrPPvsML730EjweD8LhMDo6OlBXV4dt27ahtLQUw8PDVIQsGo1SxsBEePPNN/Hqq68iJSUFGzduPCfP2VxiKSlbwoKBKPw5HI6Y3/N4PKxcuZIumi6XC1wul3Z4QqEQGIbB4ODguOQgkTIhGwKBgPo/yWQyZGdng8fjLbjnzNatW3Ho0CHa6SgsLMTPf/5zWg2e6H0DgcA4JUMul4vCwkJs2bIFtbW1WLNmDZWWDgaDVO2uuLgYaWlp1KxbKBSiqakJ3d3dMz4WQnsjr+nz+eB2u8Hj8eB2u6kIRiKVxOzs7JgELJlIR2NjI9xuNzwez5zNIWk0GuzYsYN63505cwZNTU0TyrQTIRC73U6tCLhcLhQKBYqLi3H//ffH0DTJ8LjZbIZGo4FarabeU5PN7ZGkLDs7G0qlEhs3boROp0NXVxeGhoZgNptp8pusu0pgMBgmpKXyeDwolUpwuVwYjUYolUpcd911WLFixZzMKiXqNDQ0NNBO9GKcX5koQGbLxg8MDFAzb+Br35+5PKZE4iHkPgHGrsumpibU1dWho6MDXV1dMBgM0xYnSpZMZGZm0u4s6ZgRum80GkVrays8Hg+8Xi8tUFx77bW0Sz7VwNTj8Yyj7HK5XDgcDlRXV8+6Qz6XSLY/THS8U5lPms99J/7zEbo4odouxvtwrqHVailLwmQy0WuOJGWRSITOGU/le5honZiKSMhMu0IL1U0SCoUIh8PUkoO8H3nPf/3rX/B4PADG7t+TJ0+iuLgYHo8Hfr+f3s88Hg9yuXzCvfvNN9/Eb3/7W7hcLlqM37Jly6K5588GlpKyJSwY3nnnHWrGCnxNgRMIBBgYGKDUnEgkQk1Mp+LHFA/iMUUG5kdGRiAWiyGVSrFx40Z4vV4a/M5mU5rOInnLLbcAGDPUXbVqFe64446Ei7VOp8P+/ftx9OhReDwe8Pl8rFu3DlarNSbIjkQikEqlMRTMvr4+PPHEE9RsOxKJoLu7GwUFBXA6ndRguaamJsa0erpIS0vDz372M9TX1yMcDlPKQiAQgFAopP+fjkpiPNg0qrmcQ1IoFMjKyqKb8+nTp5NuGjqdDk8//TR6enrg8/kgEAgQDAaRlpaGwsJCPPjggzH0Pq1Wi7a2Nuj1ehiNRjAMQxNTMhc2kQoioaR5vV64XC5azXU6nTHdNqlUipGREXR3d084o3TJJZegpqZmXDJPLA82btyI/fv3w2w2QyAQYMOGDQl93GaCRBLx5wIm6laxqc7sBGk+AqX44C4QCMTQzMh7ffbZZ9S/0Gw2Q6/Xo6amBjk5OUlnO9nfSXwyAXwt3+90OqFUKuncLltwiQS1ZPbX4/GgsbGRUmnjA9Nk50kqldLzSRCJRNDS0oKOjg6cPHkSjz322KII0mbSDVuoTthEiO98bNu2jSrjLobzOt/QaDS49957KUPiwIED6O3txenTp2kBmCQE03nNyWZDE8UXkxUskl1jM33edEHUZ4mqKtsqgBxfRkYGvadJ4pWbm0uLqITKrFQq4XK5xsUaDMPg0KFDVHjM5XLRmNDpdF4QhYKJsJSULWHBkJKSEvNvYs4bCoXgcrmoVDgRUJhqUsam7XE4HDrnQ5QNSSudy+Xi1KlTKCgomDXVZyY0hVtuuYUmZ4le769//SveeOONcep7X3zxRcLnHD9+HNXV1VQAw2KxQCKR0KQMGBOTcDgcWLZsGfr6+uD3+2mVa6ZQKpUoLy/HDTfcECPgQebHCOVwNgpxGo0G27dvx2OPPYahoSHY7Xb88Y9/nHUQkZubi/T0dGqGTTbqeDAMg8rKSuj1etjtdqSkpCA1NRU5OTlUKTN+3iqe+tXR0UGvvUQdXS6XC5FIRK91LpdLOxFerxcOhwMcDmcc/ZH489XU1IBhmITXnkajwWOPPYa8vDx89tlnGBwcpNXISy65BL/4xS/AMAzWrl0Ls9k8zoB8Nkh0b5B5rbnsfC40JpszIX8DzD5ImkqHhd0BIPLTRqMRBw8eRE9PD/h8PlVDzMrKSvidJEqeyO/UajWUSiUd3o9EIpDL5YhGo7jooovA5/MRjUYRiUQonZz4P7I/u1AoTLpWbtmyBVVVVTAYDOOEhPx+PxoaGlBXV4fbbrtt2udwLjGbbth8d8ImAsMw44RoFrMlxXyB/R2UlZVBp9Ohrq4OTU1NCAQCqKiogFAoxCuvvDJlW4dk7zNREj5RAj/RNTbT500X5H3MZjPy8/Opgi67AHXDDTegp6cHXq8XCoWC+qSazWa6fwWDQQwPD8Nut+PZZ59FRkYGpTU/88wzqKuro0Je5L6XSqUJi9UXGpaSsiUsGL73ve+hoaEBDMOAw+FArVbDbDYjGAyCy+XSDZ+IGNjt9nHKionATtxIQkcCaK/Xi4GBAchkMni9XthsNqjV6pj5j5kgXqVsNsP2Op0O//jHP7B///6EcujJ4PP58MILL6C4uBjr16+nSZDT6UQwGKSdQqFQCK1WC5fLNW0T4XhwuVx4vV4IhcKYjS5ZNX42+PDDD3HixAmEQiGYzWZUVlbi4YcfntVrajQalJaW4vDhwwgEArRzGG+QTL7f0dFRSKVSFBcX46677qLG1BMdG6F+qdVqKgoRn5SJxWJkZGSAz+eDx+PR7pfb7aYKf+T/8YhGo7DZbGhvb4dMJoNOp4uhCZKfTSYTTCYTUlNTwTAMxGIxUlJSsHr1ampIHC9QMRdIFEAQ9cvFTF+cCRLRDNlehzMNkqbSYWF3AIRCIZqbm9HY2IgTJ05gdHQUkUgEPB4Pzc3NWL16NfR6PXp6euDxeOjrJkom2L/TaDTw+XwwGAyQSqXg8XjIzc2Fy+VCR0cHXC4XrYx7PB7YbLZxn32yztkvf/lLPPfccxgeHh63/o2OjuLVV1/FmjVr5qxokAwzUUkEFkc3LB6EdUGuw/m4z89VkH1Lq9VSaxShUIgXX3yRUjwfeughbN++fVavnwgTJfATXWMzfR6Q+LpOdq2z38fj8eD//u//IJfLkZ6eDqfTCR6PR2fhiWWPQqGATqfD4OAgjcVIYkYUiIkiq8FgQHd3N5xOJ7XvIWypb3/720mL1hcSlpKyJSwYtFot/vu//xt79+6F3W6HzWajXS65XI6CggI6TxCNRpGVlUWVfMjNTlQFJxIw4HA4sNlsUKlUEIvFyMnJoXQ6YG48WZKplE03ANPpdLjvvvtw5syZGVE1nU4nWlpawDAMrFYrVq5cCaVSCbPZTA23V65cie9973sQCoVoaWmBTqdLGOxP5z1ffvll/PSnP03YLZqrTb+5uZkmkGSoeC6wbNkyBAIBRCIROJ1O1NXVweFwYPfu3XSjfv3113HixAlqx3DjjTdOukmTAF2v16O/v592fuPl7AFg+fLlSE1NpZ56IpGIdsgIpz+RJwwBkWUn8ugAqCAKCcL6+vrQ3t4e4/nm8XhQX1+PSCQCkUiE4uLiWRco4pEsgCBm1FNV/DwXEF+cmcjrcLoFi6ncS+y/ycrKgtlshtfrpdetWq2maohGo5GuV6SoEj/3ZzAYxs2vMQwDmUxGE0+RSEQH+smaRSiUnZ2d0Ol042jK7OthdHQUTz31FPx+P9RqNQCgqKgIADAyMkLX9kgkgmAwiP7+frz33nvzmpTNRiUROLvdMDbYyVhfXx+9DouKiub8Pj/Xwf7OXnnlFRiNRhiNRvB4POzdu3dcoW6u3nOiudWJlDhn8rzJKMqJrvWysjKEw2FUV1fDbrfTUQkyErJ69WooFAqsXLkSeXl5yM3NxbFjx2IKKmz2EhkXIZ+1qKiIqv0Gg0HKblq9evWcnedzGUtJ2RIWFIFAgHpZEbl7LpcLLpeLXbt24eTJk2hubobT6YRYLEZFRQVkMhmljx05cgSnTp1K+vpyuRxSqZSKXpDEpLi4eE659PFUtZmqyu3btw99fX2z+iyhUAgmkwl1dXVIT0/Hhg0b6DwTj8eDy+VCVlYWrapXVVVh3759034fklzY7XbU1NSgr68Pv//976fUJZtuQEq6qUSlUyQSzdm8k8fjgVgspjROq9VKVRNXrVqFAwcOwGg0IhAIgMPhQKVSTYnyaTAYoNfrceLECUQiEXC5XDr8zAaHw0FWVhZKS0vR39+PtrY2OptHug4pKSlUMTSZmE0oFILFYsHp06dpd5kcHwlmnU4nnbEk59LtdkMikUClUs3bsH9ZWRnKyspoZf79999HU1MThoeH4XQ6pzxUv5gRTw2byOtwIVTZ2IFbvF9gY2Mjle/Pzs6miQ8JTNkFBWIPQT5f/CydUCjEH/7wh3HvHw6H0d/fj8rKSmokTY6T/RrEsD0QCGDZsmU0eMvIyACXy0U0GoVCoYBer4fb7UYgEMCxY8fwwgsvIBQKYcuWLTNK0GbaCYs/t4ulG0ZAjmt0dBRVVVXo7++nM0HA/HhunmvQ6XSor69HRkZGQmGn8vJyuj5GIhEYjcZ5W6OmMrea6BqbyfMmoyiT32k0mhhRq9bWVlitVoRCISrgxefzoVKpaIGbzLibTCY0NjZS4RSBQICMjAxYLBaEQiHI5XK6f2o0GjzwwAPYsGEDvvjiCzQ1NVGPTZVKNdtTe15gKSlbwoJiZGQEDocDXq+XbsaEqkUCBbFYjHA4jOXLl6OwsJBu7I2NjThy5EjSLplAIEBqairy8/MhlUphNpvh8Xio9PpcUzc0Gg2uueYampCxN76pJiHE82o6kEgkMa1/spEQcQgul0uHZ8lMksFgwGWXXUY/y7Fjx9DT0zOt92X7afn9fpw5cwb19fWUK54s6Ez2WCLhAfJvnU4Hk8kEkUgELpeL7373u3NGbSgvL0d2djat1JFz99VXX+Grr76KsU8g19Rk3ksMw6C7uxv9/f00kVq5ciWGhobgcrkSVhF37tyJd999F52dnbBarVTuXCKRIC0tDQBoRy9RF5UkWENDQ1i+fDlsNhskEgn4fD76+/vpRpiamooVK1aAw+HA7XZTC4K59uIj54H9XZPg2WazwWazwefz0Z/PZSQypNVoNEm9DhsbGxdEinwiAYLCwkL62SwWCxiGiZlZ0ev1+Oqrr6ioDvGSS/SaN954I06ePEnnMcnc5MDAAABQzztynOS/xsZGKmDD4/Fgt9tjBJ9Id3nVqlXg8/lob29HJBJBZ2cn9Rb85z//idtvvz2hMuNMhRIWg0riVME+RgA0mSb+W8FgEBKJBKtWrUJpaem83OfnEnQ6HR588EHKXti/fz+2bNmCFStWxJwXuVxO59z5/LMTGs/0Gpvovp+MokyKRqRYYrPZYvZA4j8ok8mo4nNTUxOdD25ubobZbKZ/H41GUVpaCp1OR+1dcnJyYj7rrbfeiq1bt9I1dC5nms91LCVlS1gwMAyDrq4uABhH6fJ6vTh58iQNkvPy8nDZZZfFzGkJhUIMDQ0lfG1ilByJRKBUKpGXl0eV+zIzM1FRUTFvQVAi09epVsUvvvhi1NXVTYtO6PV6kZaWhm3btsFisaCnpweDg4NUsdJsNlOqgVQqhUgkilEvzM3NxZYtW+D3+zE0NERFJqbzGYhVQX9/Pw0SkgWdyap18bM4tbW19N9SqTSm0rZq1aopf7bJoNVqcccdd+Dvf/87BgcH6UwjofgBYzRZsViM4uJi6r0UD3LcxHicyIRLpVLk5eXhqquuQk5ODl566SXaGSDnLhgMIhAI4KabbkJLSwvt7EokEmg0Gtxxxx147733cOzYsZjPxQa53v1+P+0ek89N/j4lJQUbNmzA7t27Y9Qx56van+w6UKlUUKlUCAaD9OdzEeQ77+rqQkNDA8xmM03KiNH0dClGCwGyThFqW11dXYx8fm5uLl0nfD4fpcElu0a2bt2KV155hSZlRKwmIyMDSqUS6enpCY+TJIfA2LyYQCCA1Wqlsvo+nw9KpZKez7/+9a+wWCzw+Xw06BscHMSePXug1+vxwAMPAECMsp7f748p5pHHE12X7ARnsXfC2EkYWSfLyspgNBrR09OD0dFRusZIpVJs2rQJu3btWlTHcjZQX1+P/v5+Sps+fvw4Tp48CalUCoVCgXXr1qGnpwdDQ0Pg8XhISUlBcXHxeZEkJOuixf+OFEsCgQDC4TCNBbhcLvLz81FSUkI76FlZWVRNNzs7G+Xl5Thw4AC4XC59bl9fH+RyOYLBIKRSKe2mxd9bFRUVF3zRIB5LSdkSFgxEgU2lUiEcDsfMD3C5XDAMg4KCAqoSFS+cQegu8eIfUqkUHA6HUiPNZjNGRkZgtVohEAjmvQoTX6WailcJwc5u5kPGAAAgAElEQVSdO/Hxxx/j2LFj03pPl8uF1NRUXH755aiqqoLL5YLD4UAkEoHH46F0NZfLhe7ublRXV8dUvu+9915UVFSgt7cXNpsNer0eR44cmbIyI5/Px/Lly+nxTRR0Jnos/hw1NzfH/Hu+g/aysjKa1BDzW4lEAoFAAAC0s3r11VdDoVDQrkJ8IkZEPAhtTSAQ0GCJXL+BQAAWiwV6vR7hcBgSiQSZmZl0g3r88cdRX18PnU6HkZERFBUV4YorroBcLofT6URnZ2fM/A45/wqFAgKBgNIVSfeBVHpVKhVWrlxJZ+UWAok8boCxRHjDhg3o7u5GUVHRORnwsIstZFB9qpiIYrRQ/kMajYauwfFrk0ajwZ133gmPx4Ouri7Y7XY0NDTQ6zeRiAy7QEY6ZRaLBVu3boVQKER5eXlCChabykjuIfY8JLvD2tXVhS+//JKqvRG4XC40NDTg0KFDYBgGer0e3d3dcLvdNKCcTCghUfGMbTtwtjCVJIx8f2VlZXS2mTBCotEoKioqlhIygNL4+Xw+pcaGw2G43W6Mjo7CaDSiq6uLUuXFYjEKCwuxe/fu8+bcJeqixf+OFEu8Xi9aW1upNZFMJsO3v/1tqvDMTuqISEpWVhbuuece9Pb2UrNoh8MBqVQKqVQKgUCA+vp6NDU1JZ1rOxf3g/nCUlK2hAVDvLJPW1sbhoaG6M0vk8lQXFyMUCiUcEMnQ6J9fX3UgFogEGDlypUwGAwIhUIQi8UwmUyIRCIIhUIoLCycty7ZVI5zsqq4RqPBnj17cMstt0Cv10/5PUKhEE6cOIHa2lpq6kq8VtiVLgAYGhrCsWPHUFhYSBOF+FkSIndNgvuJwOVysWbNGqSlpcUo6cXPEbGPMVFAyj5H5eXldEHPzs7GsmXLkJqaikAgQAUxElXaZgKGYejcGOmSkRmYrKwsrF+/HiaTCWazGS+++CKys7NRWFgY080jiRjplBDRDHINE/ohMJaQbN68mQptlJSUxGz6Wq0WgUCADj8TNc/y8nKsW7eOFi8IZRUY64hlZWUhPz8fo6Oj6OrqolQR8t0mk+6fL0zmcSOTyaBUKmkAfq6BXUhQq9UoLi6Gz+ejRZ/JOuSJgqOFmDVjY6K1SavV4qabbsK+fftgt9up4imPxxuXNHV2dtI1mMDtdmNkZARVVVUoKiqC2WymhSA22OfBYrHgyJEj2Lx5M8rKysatEaR49N5776GhoQEOh4PSfDkcDiwWC+0UER9GuVxOu36NjY20O8wWMCFzjq2trZRKuxhsGuKvh2RJGAAazBJVO2JNAmCp8wDQGSmj0QilUkn3E1K8ZYNYO0gkEtx8880XXJKg0YxZ0LjdbjgcDlrgTUtLw6ZNmxKej7a2NhiNRtpxv/baa/Hmm29SZkdubi4kEgnEYjF4PF5MIchisaC1tZXa9yyGe2+xYCkpW8KCgU2h2bdvX8wczbJly6BWq9HV1QW32w29Xg+GYcZtLllZWTF872AwSIftyUAzn89Hb28vxGIxNBrNgi+wUxkKj6+Ob926dVpJGaEI2Gy2mCQqkdJfOBzG0NBQQoVIEmi6XC4UFxdDJBLRjg0bAoEAF198Mfx+P4qLi2MSY5PJFEM9THS+4wPSROeITf1qa2ujUvFSqRQ6nQ46nW7WgSuZBfrggw8wOjpKhTWEQiEikQg1vHa73VQ+3O12QyqVxnTzSCJGurok4CPVf51OF0MPI8ElkDhgSqTm2draCoPBQA3XSaUXGEvKVCoVysrKUFRUBKFQiLa2NqSnp2PNmjXzSlFMhniPG3Yn6XzwKYtPaLRaLRXTiJ8bm6pNxnS66nOBydYmYoBOuoEMw8Dr9dLONVHDTdTZj0ajGBkZAY/Hg06ng9VqnVDBTqfT4dlnnwXDMPjkk0/w85//HAUFBeM+L1nDdTodWlpacODAAXg8HqSmpqKsrAyBQIDeN6mpqVAqldixYwdqa2upkTu7sGIwGGAymdDQ0ACj0QgAZ8WmIVGHNP56mCgJYz+PXeA533HVVVehp6cH2dnZ2L9/f8LrS6fT4emnn0ZXVxecTidSUlKQn58PuVyOjo4O+P3+cT6oYrEYa9euxdatWxfycBYFSEHNZDJR0/hgMIiioqKEjID461Sn09GxiXA4DJlMhhtvvBHFxcV0TyTzrF1dXdTGJRAIoKio6IIWoYnHUlK2hAUFodD4fD66MJJB0pKSEjQ1NaGrqwt+vx99fX00sAWAZ555Bh988AGcTmfMaw4PDyMtLQ0lJSXYsWMHvvjiC4jFYtopOBvB30QDu4mq45s3b8Y777wDq9U65ffw+/3j5sCSyeo7nU46hE+6MCSJYCcDKpUKpaWl4wyrBQIBrr/+epSXl+OBBx6AwWCgyoTx1MOpBpaJEjWNZkwF6pVXXqFWCBKJhIq2TOf148EwDKqqqnDkyBGa6PD5fKxYsQJr1qwBENu1I+ckOzs7YTcvvvIOIKmgw2QD3CRYJmqeAwMDGBkZoca9xBqAKCnKZDI4nU50dHQgEAjg1ltvnbGvzlwgkUltPIX1XPcpS0a9Ix2hmdhkTNZVnw9q40TXIrtwVldXR5Vh1Wo1LVacOnUq6ToVCoVgtVoRjUap3USyrk19fT1Onz5NO2zPPPMMNm7cOG4ejP2ZMzIy0N/fj87OTshkMnrts9UhiVAQKWoEg0G43W54vV40NzdDoVBAJBJRUSShUIiSkpJ53ycmoiWyZ/umk4RdaLjqqqvQ3d0NYOx8XnLJJTh27Ng4OvCePXvQ3t4Op9MJLpdLGREjIyO0k0/Uank8HjIzM5GTk3Ne0RaTYaJigNlsRk5ODrKysqBQKFBYWJhwrWavdfGjKMTLjO0tmJWVRdeUqqoqjIyMwOVygcPh4MyZMzCZTOf9eZ8qlpKyJSw4cnNzUVpaijNnzsBut1OFvWg0iuzsbBgMBoyMjGB4eJhK0w4PD+Po0aPjEjJgrBNktVrBMAy++OIL+P3+BaduTQeJquPbt2/HI488gueeew4DAwNJZdDZIItaPNgeIQR+vx/Dw8MYHByEwWDAW2+9BZlMhtLSUuzcuZMazxI63tq1a9He3k47mQUFBSgvL0dzczPcbjc1pw4Gg+OSldkE3GRDPXXqFFwuF9LS0pCVlRUj5z0dhUv2DFh1dTUOHz6MoaEhalKekZGB22+/HVu3bk3YtYsXxkgm5EAwG0EHjeZrNU+32w2z2UwNpIlCHVFiDIfDEAgEM7ZimEuwiwznu0ktSQ7ik2+dToeMjAxs376d3kvE9mMiwYzJZs0WktrI/kxEjIcUt8h3Wl1djerq6gmfT4Izp9OJ06dPJ702+Xw+7fKHw2G4XC6cOHECQPLCS25uLtRqNXp6eujcm1arjZkFI+bdpAtG5kV7e3upmumqVatod9zn86Gzs3Pc3OhsE+HpzIaxizeJrofz8V6aCUhCxsYtt9yCw4cP03/rdDpqUAyMiR2tXbsWeXl5cLvdVMp97dq1VDX1bDALzgaSrSnsWWDSUZ7onBC6Y2VlJfx+P7q6uiAUCqFQKOB2u6kXKBvd3d1oamqiUvt8Ph+BQACDg4OorKxMSHW+ELGUlC1hwUHoXCUlJTh48CAGBgZgt9vR1dWFNWvWoLGxkfo2eTweNDQ0oL+/f8IuUiQSQX9/PzIyMhAKhcbRpxYTkgXut9xyC9LS0vDwww/T2YCJkCxxk0qlCQ16BwcHAYx1FskArt1uR0VFxThp/7vvvhttbW1obW2FTCbDDTfcQBNc8tllMhnuuuuuCVXnpgudTof29nY4HA4IBAJavYx//akErOy/CYfD6O3txfDwMLhcLhQKBbKzs3HppZdS35rJ5n8m+j378dmouJHnV1dX48SJEwiFQtSnTSAQUM8zqVSKzMxMpKSknFUPIoZhUFNTA71eT2mLiUxqCX3Rbrejr6/vnPcpY9/DMpkMDQ0NtGiwfft26PV69PT0wGg00sRhosQs0WMTURvnWxwkUcdGo9Ggvb190plTAiJzT2a72J+VYRjYbDY6B8uehyXm1omg0WhQUVFBqduJqLDkWhOJRFAqlRCJRHQWLRAIQCKRQKlU4uKLL4bP56OG5olUYZPZd0yG6c6GxRsFn8v3xnxCrVbTAiBBIto/KUpyOBxkZGTg6quvxrZt2wCAClScr4WjiZBMCTl+FngqxexAIAC/30+tddavXw+9Xo9oNAqLxULn9shsX1dXF0wmE7hcLtLS0iCVSuF0OqmC8LlIaZ8PLCVlS1hQsDsXRUVFAMbodDabDWazGfv378fg4CBsNhv1GiOKQJN1j/h8PpxOJ/Ly8ha1WeZEgbtCocDq1aths9li1MamCj6fj5ycHIyMjIzbvNgg6kpDQ0Oorq5Gd3c38vPzoVarqeFsooVZq9Xi8ccfR3NzM/07ckxzsaCOjIzQhToSiWDjxo0x70GQbHMhG65Go0FzczNNFvh8PhWVEQqFuPzyy2miOdcbwVyci8bGRthsNnrN+3w+CIVCyGQycDgcpKen4+qrr4bH40koijPfIJLpRBaeTfNMRneRyWTw+XxTSlQWO9j3sMViQV1dHb0WA4EATRzYAf90jzVZ8WYyT8BkycN0Hku0RtXW1uLf//73uM9JOvNE1p7P59Oq+65du/Daa6/h6NGjCAQC2Lx5My655BJ0dnbixIkT9HlcLhcqlQoymQzZ2dlgGCZppZ4995aoc04o2QCoIA9RgyPHRqS9h4aGYl5nKvYd8QWg+I4YuSZmMhu2hFjodDo0NzcjJycHCoUCTz/9NO68884Jn6PValFcXIzR0VGqSnz8+HHaFbuQz3WiNYVQfe12e9IZskQQCoUwGo2Urr1p0ybk5OTAYrHA7XbjwIEDyMjIQGVlJRobG+mcukQiwWWXXYbrrrsuxsJiscZrC42lpGwJCwYSTLCHr9VqNQoKCqBWq6lqIBFgiEajkMvl1Hw3WYWWzNkIBAKkpKQkVH5bbEi2OeTm5qKsrAwSiQRfffXVlDpmBAKBAAqFAldffTU6OztRX1+f9JxFo1H4fD74fD4cPHgQBw8eBJfLRWpqKjZv3ozHHnss6flLlrDNBdLS0pCSkgKXywUej4fe3t4Yk1uC+M1FKBTir3/9KxoaGugsWmpqKjgcDtRqNQYGBqioR0FBAW644QZcf/3183IMs4XBYMDw8PC4eUHiYyYWi5GSkoIvvvgCPB4vqcrdVDEdGiibitXa2gqj0QixWIzly5eP8xVkg93hGBwcRHNzM+rq6nDbbbfN6DMvBpB7mGEYtLW1Afg60MnNzUVDQwP6+vpmPEOXrHgzkefWdA3cJ3qMfXyvvfYaHn300YQzqzfddBNKS0sRCoVoR8hqtWLLli1oa2vDv//9b1pgevvtt/HJJ58gJSUFTqcTwWCQ0nFtNhsyMjIglUpjOo/xSZDJZIJQKMSGDRtolzv+GAj9iniXpaWlQSgUYsuWLfQ5wHi/JvIdsr/LyTqWbOouMKZCSdSEyXzl0mzY9KHT6fC73/0ORqORXltlZWUJ//aqq67Cp59+CmDsnO7evZsKfZBu6FInZvyaAgB1dXXo6emhRb+prlVEGXl4eJjutcFgEHw+H16vF3a7Hc3NzbDb7bDZbAiFQhAKhVCr1diwYQO2b9+eUHH1QsecJGUdHR146aWX8OWXX8JutyMjIwObN2/Gj3/8Y6xYsSLhc44cOYK//e1vOHHiBCKRCPLz87Fz507ceuutlMoQD6fTiT179uCjjz6CyWRCamoqLrvsMtx7772067KExQuyucWr2m3btg0ZGRkYHR3Fiy++CKlUikAggNLSUuTl5eHkyZN0oJQNDoeDlJQUCAQCCAQCqNVqhEIh8Hi8mGrPQvkAzQXYi+bdd9+NW265ZRwVUSqVgsfjjZuvi0QiyMzMhFQqBcMwkEgkU6YakefbbDZ88MEHyM/Px//7f/9vTo5pOtBqtVi9ejXcbjdCoRACgUDCzTR+czEYDOjr66NKkoQGtWzZMqxevRrAWFKTmpqKFStWLMpZQwKSmMd7MwGg/w6FQlSSH5id+MkzzzyDtrY2ZGZm4v777x93bpJRsYiUuFKpjLFbSAatVou6ujq0tLTAaDTijTfeiBkGP1eRKHlKVEyZyTqUqHiTrIM2UfIw08fId3/48OGEa4lQKMR1111Hv0OdTkeN7TUaDQ4cODDuGrZarXC5XAiFQnRNJxYmo6OjlLY+MDAAs9kcQ3XV6XR45JFHYDKZkJWVRdUd44+BdNlef/112O12DAwMICcnZ9IiV7JEOBnVkP2+RDjC4/EgPz+f7mtLSdjUwb5HmpubYTabYTabEQqF8Nlnn2FgYCDh/RU/a6bVanHXXXfhf/7nf2i3jFgGnCuxwHyBvaa8//77aGpqwsjICFJTU6cleCMUCuFwOMDhcGA2m9HU1IRIJIJgMEgfI2wnYjkQCoXgcrliZjgv1O8hGWadlB09ehQ//elPEQwGIZfLUVRUBIZh8NZbb+G9997Dc889h6uuuirmOW+//TYeeeQRAGMbjFQqRVdXF/7whz/g8OHD2Lt3b4zsOTCWkP3gBz9AV1cXJBIJiouLMTQ0hPfffx8ff/wx9u7di2984xuzPZwlzCNIMGG1WuniypZQr6mpQWpqKtxuN9LS0pCbm4tAIIDe3l6qPscGn8+nHTafz0eD7nA4TGcS4iuZ54J7PHuhuuGGG7Bv3z76mFwux759+/Dyyy9j//79Mc8Lh8M4ffo0ent74fF44PP5Eop+EPB4PCocEf86Bw4cwB133LHg50mj0WDXrl3Q6/XUM2Wi+RL25ysoKKCJDJE7jkajKCgogMPhwLJly5CVlXXWFDmnCo1GgwceeACFhYV47bXXwDBMTNcsEAggJycHy5cvpzOAM+nEMAyD5557jgbOJLB56qmnYuZoJqJiTeee0mg0yMvLg0AggM/ng8PhQHNz8zmflAGJDeTZFgCE6tnX14eCggLce++9M74GkyUOE4nMzPQxg8FAKcCJoFAoqIeY2+1Ga2srldLevXs3CgoKqEw2G2zPPQKfz0fnvlwuF2w2G0ZGRmIUHOvr69HX10fVFOvr66HVahMeA0mYGIaBz+cDwzBIT0+ftLPI7hCSWbhkdPP42UIg1gB7Ma8ziw3x34dWq6WFRXL9dHR0YOXKlQmf//7778ecc4VCgZycHHR3dyM1NZXKvi+0cM5iRm9vL0wmE/x+PxVAmSoYhqEzozKZDGazGVarFVKpFFwul6oz8vn8GIGq0dFRmM3mpc5lEswqKRsZGcGvfvUrBINB/OAHP8BDDz0EkUiEYDCI5557Di+//DJ+/etf49ChQ0hNTQUAdHZ24vHHHwefz8czzzyD6667DgDQ1dWFu+++G4cPH8bf/vY33HPPPTHv9eijj6KrqwuXXXYZnn/+eSiVSoRCITz77LP429/+hv/6r/9CXV0d5HL5bA5pCfMIjWZMsYcITgiFQmg0Gupzpdfr4XA4kJ6eDoZh0NfXB7/fD5fLlfD1yNwCmd3g8XgIBoMQi8Wora2l4hBtbW1oaWlBKBRCS0sLNm/efM4syH/+858hk8lQU1ODdevW4Xe/+x00Gg1uvvlmHD16lM4dkQSEzImRn5MlZCqVCldffTW6u7vR0tIy7u+MRiOefPJJ/PSnP13woFmhUKCoqIh60UyF467RxIrH9Pb2IhqNYt26dTh58iRaWloAABdddBGysrLm+xBmDY1Gg/vuuw+bNm3Cn//8Zxw/fjymMzowMIA777xzxqphxK+tpqaGdmIDgQCGh4epjxNR1iJy6JNRsabynhkZGcjKyoJYLKbzi+cj4pMEm81GFQF7enpQUlJCrT5mgkQV5olmVWf6mFAoRH9/PzV5ZYPP50Mul6Onp4eKuBiNRni9XjidTlRWVmLHjh3IysqK6Wxwudxx1FxgrFtmMBioX6DFYkEkEsGRI0eg1Wqxbds2eg+QObSMjIwJj4FtURA/8zhVWiIJ4InCY3y3JZ4ONttOTKJuTvzvEs2xxSvFnktgGAZ///vf8cknn8BisQAYs0wwm83jEnoiLBGP++67D5s3b0ZxcTF27NgR08lxOByw2WwL6gm42MEwTMzeH98Imey5ZJ44EAggPT0dUqkUPp8PwWCQWg7k5OTQgjp5H5FIBJFItDRDlgSzSso+/PBD2O12lJaW4tFHHwWXywUwNtvy61//Gp9++im6u7vxwQcf4Pvf/z4A4KWXXkIoFMLtt99OEzIAKC4uxtNPP40f/vCH+Pvf/4677roLIpEIwJi6Tk1NDUQiEZ555hkolcqxD8/n48EHH0RbWxuOHj2Kf/7zn/jZz342m0NawjwjEAiAx+NhZGSEzjQdO3YMdrsdDMNAJpOBYRhKcUkk+U4QiUTQ2toak4AEg8GYiujo6Ciam5vpQn/y5EmoVKpzakF+4okn8MQTT8T8bvv27fjTn/6EmpoajI6O4pNPPqHqSSkpKVSlLx4SiQQqlQo/+clP4HA4YLVakZ2dTSkiBD6fD7W1tejq6sIdd9xB6UgLAaFQCJFIBI1GA5FIlLRTFg8SIG3dupUKfoyMjOAvf/kLhoeHwePxYDAYzqnvXqvV4k9/+hOqq6vxyiuvwGaz0WA4EAjESIFPFUQxkZiosoUaRCIRncM5fvw4gsEgNmzYgBtuuGFWVCx2kEtoyVu2bDkvumSJEB+s63Q6BINB2g06ePAgVCrVnHdTJqIDzeSxQCAAmUw2LonicrlYvXo1RCIRnQ12u9206h4Oh2G323HgwAGkp6djeHgYoVAIHA6HCoEkEm4KBoMYGhqC3+9HIBBAKBSC2WzGa6+9Rg2pia8lodImOwb2d5AoYZmsQzidub34950pEr0+gHHzcqTjQ7pzbLEdtVp9TjBCCBiGwRNPPIGPP/44huo6Hc9OYOxa/eyzz3DkyBG8/fbbVF0zFAohNTUVKpUqxluLva9ciLRGg8EAHo8HsViMSCQCHo8XQyuc7Llms5nOivX29mLFihXw+/0YGRkBl8sFj8eDVquFWq2GXq+nXnFSqRQ7duy4YM7zdDGrpEylUuFb3/oWKioqaEJGwOFwcNFFF6G7uxtDQ0MAxuYhampqAADf+c53xr3exRdfjJUrV6KnpwdHjhyhEqb79+9HJBLBli1bkJmZOe55u3btwtGjR/Hhhx8uJWWLHMQPQywWw+fzYXh4GA6HA3a7HX6/n1ZUPR4PXSiEQiG4XG7CRMPj8UAsFkMikSAlJQXp6ekx6os1NTUxnRZCjTkfqjRlZWXQ6XSIRCL4xje+Aa/XS42WHQ4HgDGKIvG4EgqFSEtLQ3l5OXJyctDZ2UmH5levXg2r1QqTyUSTM4/Hg46ODvz2t7/F73//e1x88cX4/ve/P6+bPZHP7e/vx8DAANLT01FdXT0tIQt2kPTCCy9QOWwul3tOVuhI16ykpAR79+5FMBhEXl7ejCmLRGzH7XYjJSUFwWCQDmgbjUZUVVUhEAhQc3GRSDTr75wd5Obn5+Pyyy8/bxMygvhgvaioCE6nkwYxe/fuRXl5+ayojPMN4inZ399PBXQAIDMzEz/4wQ+wZs0amuyYTCYMDw+jq6sLUqkUQqEQfr+f+gG63W4IBAKoVCq6zgSDwXEdfeJhJBKJEA6HEQqFqKcl8Rzj8Xi46KKLJu2iT5aIToWWONXu2kwQnwwkU39k/665uXncHBtRwXM4HOjp6UFfXx/a2tom9Zs622AYBlVVVfj8889npDYcD7J3WSwWKl5FPFCFQiH11rLb7aisrAQwZmx8NvwAzzaI519WVhZsNhuUSuWUlWKFQiHMZjNcLhf8fj/cbjcsFgs4HA5cLhei0SjEYjH6+vooe4l4mxJj6iUkxqySsm9961v41re+lfCxcDiMU6dOAQDy8vIAgHqcyOVyFBcXJ3ze+vXr0dPTQyVMAVBDyQ0bNiR8DqHAEMPZJQrj4gThdBNzZ6lUilOnTiEQCEAkEiEUClFxh0gkAj6fT5MtouxjtVpjBs4JbS89PR2lpaXYtWsXFAoF3YTKy8uRkpJCPTP4fD42btx4zi667E08PtBdu3YtWltbcebMGVqNZncRw+Ew3G43enp6YLPZIJPJaIdEKpXiiiuuwIcffhjj+8JWafzkk09w7NgxXH/99XjggQfm/BwyDIPKykp8/vnnsFqtiEQisFgsGBkZmRHdS6fT4ZNPPqGqiyqVCtdee+05+92XlZVh9erV6Ovro7+bboWXXDNmsxl5eXm4/PLLqXWA0+mkqlkajQYbN26ESCSakxk8tjnpYrarmC9oNGOKcJWVlejt7UVfXx9GR0fh8/lQUlKCoqKiRRk4E1rwwMAAGIahyZNUKkVxcXFMYm0wGJCZmQm73Q6VSoUtW7aAYRhIpVKakPJ4PIhEIhiNRnR2dtLiWzAYpJ00sViMiy++GKFQCEePHoXD4aA09rS0NPB4POTk5ECtVqO7uxsWi2XGRYNkSdtM5vama0eQqCuW7PXZvysvL6czfqRTJpPJYDQaqfekzWaDXq+nFGRyHy+mQgihUB85ciTpiMJsQaxVBgcH8fOf/xxXXHEFQqEQTp06BYFAAAD45je/CaPRiO7ubtpV3bVr16K7F+cLCoUCPB5vylZCJI6z2+10Do3EZmR8icPhwOPxYHBwkKpCy2QySKVSlJaWXnDr/3QwL5L4RNGrr68POTk5VHq6v78fAJCTk5OUlrZ8+XIAoFUh9s/JvsisrCzw+XyEQiEYDAasWrVqzo5lCXMHdkCoVqthtVpht9sRCoUglUqhVCrh8XhoZYskZaTbk52djauuugr/+te/aGLG4XAQiUTgdrvB4/GgUChiKF1arRa33347/vd//xderxdZWVlYv379vB/rXNMhSMJSW1sLLpeLwsJCXH311TGBLtmsS0tLIZVKMTo6iqGhIZqckco0MNblLikpwZdffgmXywWj0Yje3hqVIZcAACAASURBVF7k5+ejv78/obAKALhcLhw6dGhO6YzE86q7uxtGo5FeE5FIBBwOB1arFQcPHoyRsk70GuzzrdPp8OSTT6KrqwuRSARKpZJKaJ+rSCQewfZrmkqFlwR9Ho8HIpEIO3bswI4dO7Bnzx50dXVBKBSitLQUO3funLMKO7sYcy7YVcwXiMl6VVUV3nnnHTgcDng8HrzzzjtQq9UoLCxclFV6jUaDFStWgM/nIxAIgMvlQqlUjtuP4xMKcq8lmn2qra1FJBJBZ2cnotEosrKyIBKJMDIyAplMBpVKhZ07dyI1NRXvvvsu7Yjx+XxkZWXRSn1DQwMAoKKiYs47jtOZ25uJ5UCirthll12W8PXjf0fmpePPK5n1cbvdlEJKEhCPx4ObbrppzpgOxEOM7Vc53ecfPnwYBoNhWirBM4Xb7cbBgweRnZ1Nr2XCvpHJZHA6nejv76eCFYu5gz0XIBREUoSbyNIk/nlkn2aDMC6AsbhMJpPRGI8oHhcVFZ0ztNqzhTlNyl5//XW89tprMBgMCIfD2LBhA5566ilIJBIAoOZxKpUq6WuQeTEit8z+mTwWDx6PB7lcTitES1icYAeEo6OjCIVCMcOfKpUK0WgUNpsNfr+fUlgyMjLg9XqRnZ1Nb3ZCzyPKPxKJJIaaxg7Sd+7cCZPJhL6+PqjVavo4MPOhbLZRaXzwSiqAc6G0BgC1tbV4+OGHY4bl+/r60NnZiRUrVlDBlKysLLp5j46OoqqqCi6XC3a7HRwOBxKJBLm5ubj00kuh0WjQ0NBAvYK8Xi94PB6uvfZaNDU10fObCCMjI2hpaZkTny9yrhoaGiiVKSUlhXb4SELZ29uLqqoq7Nq1C8DXJtFarZaKUtjtdgiFQqxfvx719fVoaWlBIBCAWCxGYWEhdu/efU5vBuz7JxwOY2RkZNpUKiK2QyrotbW1uPXWW/G73/0u5pzO5XliF2Py8/OnbE56PkKjGVMXtVqt6OjogMlkwuDgIAwGA6X3TyUwWmjccMMNOHz4MBiGQWpqKn7xi19MOWFJNG9F5Ozr6upgNptRUFCA4uJifPTRRxgeHkZHRwfq6+uRm5uL1NRUWK1WhEIheL1eWK1WcDgcBAIBGI1GhMNhdHR0LNisaKJkbSaWA8m6YsmSwfjzmOi8skV4hEIhKisrqdppd3c39u3bh4aGhlnPnBFrAoZhoFAo8Oijj2L79u3Tfh3CikkmSDUfcLlc2LhxIxQKBQoLC2lC2dzcTJOIvr6+c2r2eCZgmz87HA6Ul5dP6XgJ60GpVMJoNCb8m2g0SmMP8vrn8wzxXGJOk7Ljx4/HUGtMJhM+//xzSl8krU6xWJz0NYi4B3t+aCrPI49NV9ZzCQsHjUYDrVaL5uZmOuitUCjA5/NRXFwMmUxGK3ykk1pUVIT09HSYzWZIpVK4XC7w+XxIpVLI5XKsXbuWJmR33nkngDFp3Hjz0XvvvZfKUtfV1aGuro7SaSarUNfW1mLPnj0wmUxIS0uDXC5HKBSiMrAymQylpaU0+Tp06BB9faPRiIqKihkv7rW1tdi9ezc8Hk/M74kh5pkzZ+im29nZSd+rra0NJpMJYrEY6enpyMjIQFlZGbZt20Y3bbPZDC6XC7lcjvz8fBQWFmLr1q0YGhrC888/n1AhDRijJtfW1sb4E80UBoMBHR0dGBwcBI/Hw/Llyym92e12Y2hoCD6fDy6Xi9Ia2dLbpOve1dWFkZERRCIRfPzxx5SuGY1GIZFIcPPNN5/zG0J8QtXV1RWjijhVSgjDMBgZGaEzOqRCPx8BCMMwsFgsM/qc5ysIJbCmpgaffvop2tvbweVyMTAwgMbGRpjN5kU3C0QEZybrjEw0w5Xo79gqnsCYCrPBYEBXVxelPgaDQfj9fkSjUarIKhaLKXUdGOuCTFUQaD4wE8uBiWbaZoNE3wHDMLDb7RgeHobRaKQzZzPpzDIMg+effx5tbW0IhUIYGRnB3r17qWfcVKHValFRUYFPP/0UZrM56X4z1/B6vbjxxhtRUFAQc97Ly8tp/EgeO58RCASQnZ1N46SpFMsYhkF1dTUYhhnnnxqPUCgEiUQCt9uN1NTUC7oYNx3MaVL24IMP4qmnnoLJZEJ1dTVefvll/Pa3v4Xb7cZPfvITago9UVUk0WPJ/JTYIDf0RGp9Szi7YBgGBw4coBLKMpkMGRkZyMvLw65du/Dhhx/SDhrwtewxgdlshs/nQ3p6OmQyGW655RZs3bo1ZlN//fXX0draCqPRSBN1EnQS+hcJ4P1+P339ZFWx2tpa/Od//ieVYmbTaoVCIQQCAbhcLgYHB1FSUoKtW7eipqYGFosF4XAYwWAQvb29+M1vfoPh4WFs3LgRmzZtmlLAxTAM/vCHP4xLyMi5kcvlUKvVMZttR0cH3G43rFYrVSrLzc3FbbfdFkP/M5lMMBqNiEajyMjIwI033kgff/DBB6FQKFBdXQ2v1wu3201pc0QxjUjm33TTTZNSGQk9ERjfhREKhXRxJ4lUamoqeDwebrvtNpw8eRJffvklbDYbRkdHqf0Bkd62Wq0QCoVwuVxUrY0NiUSCtWvXntO0RTaIeimZKSktLZ1W1ZthGNTV1aGnp4d2mecr+GB3jNVqNS0ILIYk42xDo9HgmmuugdlshkQiwejoKAQCAf1eKysrwePxFpXwAOnCzCXiE4iKigrasSCKuSQhA8YoUg6HA6mpqVAoFBCLxQgEAsjLyzurQd9ECdZkj83nd0sos2yfPGL8TlStp9OZra2txfPPP4+TJ0/StZbQJKfbWdJoxvwYN2zYgDfffBPNzc0Llph5PJ6YMQdSKKmoqAAw92yBxQZSLFOr1dPyuiSU0+Hh4UnvNz6fT+dBzWbzWS2anEuY06SMzIPl5eXhV7/6FeRyOZ5++mns2bMHu3btojTGZHLdAOgXze6KEQPB6T5vCYsHRIabPTNEAvCUlBSYTCa0trbCZrPRQVFgTOQlJSUFIpGIyiTLZDKUl5fTJIIsno2NjXTDIbQ1dtBJKpZENcjn84HH42F0dDTpgjHREDJJAng8HkwmE959911Eo1FK3yUzbm+88Qb6+/sRiUTw6aef4q233sKqVasm7dAZDIakdFwOh4OrrroKmzZtitlsLRYLRkdH4XQ6qeKgUqlEUVFRzPuwq2R5eXnjHr/77rtx991304TKZrPhvffew/HjxxEIBOhM0+nTp6HT6ZIKf+h0OuzZswfd3d0QCATjZj9IQEVmnEQiEXp7eyEQCPDFF19gx44dMBqN8Pl8iEQiUKvVdB6RdMII1TVeYlsgEGD9+vX49a9/fd5ssLm5uZDJZOByuejv74fX60Vvb++UgzudTofW1lY4nU6kpKSgpKRk3s4NuyOdnZ29oLYK5wLYwTqZsyJ0PBLIABeWn5JWq0V5eTlGRkbAMEwMxZ2AiDekp6dDqVRStsPZ7mxMdA/Od/I1EdhdSZKcEQn96XRma2tr8dhjj2FoaChmBozD4SScMZzqZyM2JtXV1fjwww/R1tY2JzNmyfzw0tPTE/ojns3vaCHBnnGUyWTTKpbZbDbYbLaEhWIulwuhUEil71UqFaUdOxwO1NfXT0tF+ULFvAh9ENxxxx147rnn4Ha70d7eTmfCJppXIcODaWlp9HdKpRKjo6NJnxcOh2knY6J5tSWcHbBluC0WC6RSKbxeL1JTU5GVlYXs7GxYLBaYzWYEg0FEo1Eq7uFwOOg8GXGPz8rKSkgJZM/cnD59miYPBGQDIBsTkV0XCATUbDr+NTdv3ozXXnstaUGABAihUAjt7e1YuXIlBAIBxGIxRCIR0tPTqdgEADoHQTadiQKu3NxcrFixggZn8e87OjqK66+/ftxm29/fD7FYDJfLBZVKlVDtKDc3F4WFhZNWydgblUqlgs/nw8mTJ+H3++H3+xEMBql9RSLKTGVlJZqamjA6OgqxWIyOjo6Y6iz7cxAFMavVSoewBwYGkJKSgpSUFKhUKrqBHDp0CPX19dTfbnR0FDweL6ZTplAo8N///d/nPG0xEXw+HxwOBxiGAY/Hg9VqnZJdgc1mo/cEEVSYDzAMg88++wx2u53K6y9hPNj3FxFvIAnadCrY5wtIx8Lr9VLmBNtHj8/nIzMzE0qlkha+LmTxmOmAnZzV1NTQhAz4ujMrk8kSdt4ZhsHevXthMpnGJUxZWVm45557ZnX+NZox24/77rsPv/nNb/D666/P+LWAsYLcj3/8Yxw7dgxms5lS27Ozs/HYY4+dl3vCVGEwGKDX69HT04OVK1ciIyNjyiyL3t7eGMEwAg6Hg+LiYtx8881obW2F3W6HyWSicQ97/CDZ9XWh+cQlw6ySMqfTiTNnziAvLy+h74BAIMDy5cvR29sLq9WKwsJCAIgRLIjH4OAggK9l9AGgsLAQ/f399LF4kOoih8OJed4SFgfYg/4ZGRm0K5KWloZrrrkGW7duxeHDh+H1ehEOh8Hn85GWlkZpc6TSsmLFCmRkZMQM57JBkq6qqiqcOnUKFosFPp+PCkSQTSnRxmQ0GhMmSNu3b8dPfvITvPjiixMeYzQahcPhQG1tLfLz8yGXy6lYRXp6OtxuNyKRCAQCAQQCAQKBALq7u6lUfyKQuTAej5fQaJUsePHzGUSFy2azTWhQW1ZWhrKysilXybRaLdatW4eBgQEMDw/TmY5klGGDwUD953g8HuWXk3NOuoRsag8R7fD7/SgsLER5eTn0ej1UKhUKCgroZ2WbRL/77rtob2+H1+uNScquvPLK827zJRRcYlXgdDoRjUbR3NwMv98/4ZwIwzDo7OyEUChESkoK1q5dO+fnh2yuFouFGpNKJJJx8ulLGI9ECVp8kHIhBC8ajQY//OEP0dzcDK/Xi2g0CpFIhJSUFJqoknngYDAItVqN+vp6nDx5Emlpaec99Wy2YFNnpVIp7cwODg7C5/Ohr68PDQ0NKCkpgUqlgsvlQnV1NTo6OhAKhagxsEQiwbJly/CjH/1oRiIf8aitrcWRI0fA4/EgEAhm1S0LBoN49NFHJxTkulDBFvgwGo2T0goJW+a9995DY2PjONVFIrS3bNkyrFixAps2bUJtbS30ej29vux2Ozo7O9HR0YGWlhZs3ryZ7lPsov1itG5YaMwqKbv55pthMBjw5JNP4rvf/e64x8PhMOWFZ2Zmori4GBKJBHa7HXq9niZpbJDZE7Zs+dq1a/Hpp5+iubkZt99++7jnNDc3AxgThSBmiktYPGAPOhPj6P7+/phK/YEDB+iQLek+kf+AsQKA0+nE8uXLJ6yKajQaFBUVUYUgm82Gzz//HIFAICZYJRsTMdEFgGPHjuHo0aPjVILuvPNOdHZ24uOPP570WE0mEywWC+RyOfx+P06ePIlvf/vbcDgcGB4eRmZmJvr6+tDS0gK73Y6HHnoI+/fvR0VFRczMF6lMDg0NJaRgiMVi3HzzzeOOna3CRTYkNsgCyxZCmeoCqNFoUFFRgZaWFrjdbuo5uGnTpoSvIRQK4XA4IJfLIRQKcfnll8PhcNBggCTB7M+t0YyXe57o85D/KisrYTQa6axDRkYGSktL8frrr0+YmJ5rIPeS1WqlxS1idzBRcQH4WgIZGKOazzWdMJ4Wo1arcemll86Z19mFhGRiDezzO1sFvcUKhmEQCARw00034b333qOzsSkpKUhPT8eZM2cQCAQwPDyMiy66CP39/TGU3NWrV2P37t0XdGA3GRJRZ91uN4xGI4aHh2EwGFBXV4dIJAKz2UyZIqRbKZfLkZ2dTfet2YAwKtieanOhxvjYY4/hiSeeOO/uj9mCjC44HA4IBAIwDDPuXiGxw+joKA4cOIDOzk6cPn06ocG3XC7H6tWrkZmZibq6OmRnZ2P79u1UUMpgMOD111+nTDe32w2VSoW6ujqEQiG43W4cOXIEer0eYrEYZrMZDz744AV7/84qKbv88sthMBhQVVWF73znO+Mq5u+88w6cTieUSiXWr18PoVCIK6+8EjU1NXjrrbfwm9/8Jubvjx8/jt7eXiiVSlx55ZX099dccw3+8pe/4NChQ7BYLMjIyIh5XlVVFQDgP/7jP2ZzOEuYJ2g0Y6pxzc3NkEqlVB3PbrdTn5nBwUHaDSIbAVmYCT2QDI0mokKxK2LAWCLP5XJhsVjAMAz0ej10Oh29hrZs2YKsrCwAY1Swr776Ch999BEikQg++ugj/P73v6eLgkajwR//+EeUlJSgtrYWHo8HAwMDSY83HA7TBWh4eBg6nQ7PPfccrQrdfffd1PXe6/XinXfewcGDB7F//348/PDD0Gq1qK6uRnt7OwKBADV3VigU8Pl8kMvluP/++yesTup0OrrJqdVqSvesra2lQihE6VSn0025+k6EOeRyOaRSKXbt2oWdO3cmfB57bm3lypW48sor0dbWRs9RsgodCUjJHKLZbKazdTqdbtx7kWF2g8GAvr4+Sll5++23abfwiiuuOC98Z8i99Nlnn9ENUi6XU5rqRHQ3UiElfjJzfS7izcy3bduGjIyMper0HIGc366uLvj9/lkp6C1WxCeel156KaWZazQaaptBFN1SU1MRDoeh1+vh9XrhdDrh8/lQWVk5jo5+IXQZp4P4zixbDKSnpwdOpxMejydGaIV0ycLhMCQSyayUhYGvxYDefPNNWhxN5pE5Xbz66qt44okn5uS1zifk5uZCrVajp6eHxmDs4g67c9Xd3U2VepN1Lv1+P5xOJ6xWK52/ZxiGemieOXMmZgaNjHC88cYbMJlM9BoLBAKIRqMIBAIJ798LBbNKyn70ox/h3XffxYkTJ/D444/joYceop2qDz74AE8++SQA4P7776cB2D333IPa2lq8+uqrlIMKjEnhPvjggwDGOhNEFAQY64BdffXV+Pjjj3HffffhhRdeQEZGBkKhEJ599lk0NDRAqVTi+9///mwOZwnzBGIeq9frYTQa4XQ6YbFYoFQq4Xa70dvbS0U3SGIWXykLBAKwWq1UNY4NkoDY7XaqzEXorC6XC06nEydOnIDNZkNLSwv8fj/eeust3HvvvXC73bDb7RgZGaGJkslkQnNzc0ylRqPR4OGHH8add96Jhx566P+z9+bBUd/3+fiz92ov7UpaHYuEDiwJENeSwSi2QTWEYE9w7ZApdhLTkMk0jWk6mTZh0rSdpk5ifxM7TusmxnWdxDi2m4BdTGuSIGRhg8GAZWt1sLoWrcSu9NEe2vu+f3/o937ns5eQhATC3mcmE7OSdj/7Od7v1/G8nmfWpIyNZDIJi8WCY8eOUTENQjljIxQK4YMPPsDf/d3fYePGjejs7KTURg6Hg+3bt+Ob3/zmnFUbjxw5gg8//BDhcBhyuRxDQ0MQCARIJBKUelhZWQmpVEq7ZtervhP1TNIlq6+vx5YtW2adiWPPrZH3PXz4MJXWzbfwsjcGk8lEF+7MDYSA/JtU9axWK5xOJ6LRKEKh0C3znckXCM6mSHk96PV6jI+PU3uI2tpafOtb34JCoch7bzAMg56eHhQXF89LAnk+yJT+/jh2cW4l2EJFRDBpIQp6yxmZib1Wq6XroFAoRHNzM/VdlEql2Lx5M95+++00NgGh4xGWQKaQynJStFwuyBQD6ezspN1Hu92OeDxOz3k4HEYsFsPU1BR6e3sBLFytkFiiLIWVUS7K/8cB7AI0YUvM9/wLhUJwuVzEYjGMj4/TYicphJKZM+LbRuIpdtxC/h2LxTAxMYF4PI7Gxka6B5DnOBaLUaox+WyNRoOBgQGqUs1+X+K/+UkSOGLjhpKy+vp6PP300zh06BCOHj2Kt956iwoT2Gw2ADMqbg8//DD9m7Vr1+I73/kOnnrqKfzDP/wDfv7zn0Mul1MxhD/7sz/D17/+9azP+v73vw+DwQCdTocdO3bgjjvuwNTUFJxOJwQCAX7xi19AJpPdyNcpYIlANtrR0VFadUkkEohGo7hy5QrMZjOSySSkUimVQzeZTGmLKukq2e12XLlyhSZMOp0OP/rRjzAyMoJoNAqJREI7a2wJd5fLBavVShf/a9eu4Z133kFzczOt4pAZsIqKipzqTMDM5nXnnXfiwoULaRsJkdbPpBpyOBxEIhEqFlJdXQ0+n59TGSoej8NgMGB0dDTtZwKBYM7zPzqdDq+99hqGh4fp8QWDQRiNRmq4WlRUBIVCgaamJtTV1UGn082p+m42mxGJRJBMJiGTya6ruJU5L6bRaKDT6TA+Pg673Q6325230sqeQ5RKpdROg6g+5kp0iFwvUQeLx+PUgHoppd/zgVSBh4aGAAAbNmxAa2srotEoDXoAZClSXg/T09NIJpPgcDgQCAS48847s7qm7GQQAE1wPR4PVq1atWhqdZlJ51L4LhUwg1xCRQaDga6Lt3uikcvTrqSkBDweD06nk3ozSqVSqt778ssvIxgMIplM0rmzhoYGNDQ0QCgU0q7bJ1nRcj7ITM6AGZGGrq4ulJSUYGxsjKrxulwuvPzyyyguLsY999yTV4F3NhDmhVAozEmNuxFUVVUt6vstB7AL0Ha7HR6PB6lUCq2trXM6/wzD4Ac/+AHOnz+PSCQCgUCASCSCEydOwO/34/3336cqznK5PE3/gc/n0zlygUAADodDraoEAgGqq6uxdetW7N69GwAoK0YqlUIikaCrqwvJZBI1NTW47777YDKZssS5gD/R8T9JAkds3LD64n333YdVq1bhxRdfxMWLFzEyMgK5XI6dO3fiL//yL6nvAxtf+9rX0NTUhF//+tfo7++HzWZDQ0MDHnzwQRw4cIAGYGyUl5fjjTfewPPPP4+Ojg5qnLpr1y489thjaGlpudGvUsASga2K6Pf7aYUmEonAaDSCy+VS6fY777wTjY2NeOGFF+B0OtPeJ5VKIRAI4MSJE1i3bh0qKirwzDPPUJEDDodDqy7shInH4yEajaa9RoxISfdJKBRCr9djbGwMdXV1lNqYC3v37sWVK1dw4cIFeoy5kjGhUIja2lokEgmMjo4CmFEXLSsro/YRHo8nS+yD/V48Hg8rV65EW1vbdc9zR0cHfvjDH8JisVCBkcbGRgDA2NgY/H4/nbXzeDzo6+ujFgMqlYrOE/T39+ekCZLOVygUQiwWw549e667CeSajSEIBoN45513aBDABjlOMo8ml8sBIK2zl1nxHhsbw9TUFBXCAECpn6tXr77pQRhJEk0mE2KxGLq7u3H8+HHU1NRgenqaKsb29PTkPN/50NbWhtOnT8NqtaKiogIPPPBA2s/ZFLDKykpUVVVRRawVK1bQjfNGz0fm55BrUQh2lw7sOcqnn34agUAAV65cgcvlglAopIJGtxvyyXQDM8Gd3W6nptLxeJzakRDbDx6PB7lcjs2bN+Ohhx6iokekWi+TyagoyCdN0XIhyHyOSWFdp9Ph6aefxsDAADweD8LhMMLhMN59911otVrs379/Xp9DLFG8Xi+lsi0W2D5kHweQArTBYKA2MMFgEKlUKq8CciY6OzvR3t5OWRJcLhfRaBTBYBAffvghLX7L5XJwOBwUFRUhFAqBz+dT9loikYBSqcSGDRtQXFwMhmEglUqpMBcpyrELdDqdDolEAgzDoLy8HDKZDPv27cOzzz6bpWzN5XLxqU996rZcxxYDiyKJ39jYiKeeempef7Nt2zZs27ZtXn+jUCjw3e9+N2sWrYDlDXYFvaenh1Z6wuEwotEokskk7R7t2bOHJkm5EI/HMTIygn/5l3/B2rVrMTg4mMZ5TyaTWQ+5QCAAn599q6tUKkSjUbp4V1RU4NVXX4VOp8PU1FTeyrNGo8Ff//VfQ6/XZyWObFRVVWHXrl14/fXX6WtEpbGyshLr1q3DwMAA+vr6smgBKpUKVVVV0Gq1eOSRR/J2ydhUhpdeegmTk5NU7RCYmcdct25dWnVtenoafr8fo6OjKC0txQMPPIDW1lbavbFYLGk0QXY3ZNeuXfQzdDodWlpa5rV4arVaKhZiNpvx3nvvYWBgAF/60peo0Amhu7rdbkxMTKC6uhpSqRTNzc2Ynp7G0NAQJicnYbfb02gXvb29Wb5G5FzOJaldCpDgMZlMIhwOI5VKQSgUoqioCBKJBOFwGE6nEydOnMiZnOaCVqvFE088gZ6eHmzatClN1IVsgMTzLxgMwmAwwGKxAADUavWCE7LMrhg76AUK3YebiWg0CoVCQQMqg8EAj8cDh8NxW85OZtIW2TLdpDv4u9/9DjabDaFQKItJoFKp0NTURBVXyd+SYqDFYkFxcXFBPv8GodVqcejQIRw+fBjd3d3UL9Lv9+PcuXPzFg8ihb7BwcFFTcgAYHh4eFHf71aCYRgcPnwYvb29CIVCVHCFiKEFg8G8nqZsnDp1Ko22TvalSCRCi+N8Ph+xWAwSiQTBYBBSqRRFRUVobm4Gl8uF3+/HvffeS2fJ2TFIJkWYnRh3dnZicnISJpMJzz//PLhcbk6KKdGg+KRiSX3KCiiAgCzUZ8+eRVNTEywWCywWCzweDxKJBOLxOB321Gg0VISCgFC1gJluU39/P8bHxylfOZVK0cUkExUVFYhEIlRyH5jZyIkIAQEJDAwGQ1rAnwkyn5PPVBqYSb6mpqbwy1/+MitJjEajGBgYwNTUVNbP+Hw+1qxZgy9/+cvX3eDY1eVEIkF534TrHY/Hce7cOaxbtw7f/e53YTabYTAYcOzYMRiNRgiFQohEorQghohqBAIBOpPB7oa0tLSAx+NlKSjOFRrNjA/RCy+8AKvVCpfLBZ/Ph6NHj4JhGJq8WywWMAyDcDgMhmFQVFQEr9eLSCQCk8lEzWXfeustWhXn8XhZyXdRURH27dt3S5ScSAJ65swZmrzzeDzU1dWhqakJQqEQXV1dmJycxMDAwLyGm9niJjqdjm6GUqmUqqgBSPNyKioqQmlp6by+w2wbbuYMWaH7cPNAglkyZxuJROB0Oufddb3VYN9f+e4l0rVxuVxU/EggEFBvZOYCAwAAIABJREFUw4aGBmzduhVWqzWroPboo4+mWZ+UlpYWPPNuEFqtFo8//jiOHz+OX//613C73VSgYSH7QXFxMa5evbrox2k0GtHR0bEocv23GjqdDlevXqXxApnlIl3izHgpHzLVmMl7kdENtl3KypUrEQgEYLfbUVdXh+3bt+PkyZMoKipKS6DJ83n58uW8RTqr1Ypr167B4XAgGAxiamoKQPrcH4fDgUgkooXGTyoKSVkB88ZCVaxIsG2xWOByudICaKLGODY2hrKyMiiVSvpwAzNm4hqNBgaDgVaHAoEAbbErFAqoVCpcvXo1a2hYJpNhw4YN6O7upvM4xcXF2LhxY9rxk0DA4XDA5/PRbhE5djJYS0yac9Fs2chnOA2k20WwwePxsH379jnRQNgmkCUlJYjH41AqlXA6nbQj19/fjyNHjuC73/0utm7dCq/Xi0AgALFYDKlUmkZB1Gq1VDGJBEaZ3ZCWlhZafZ5NQXE2aDQaPPTQQ7SzRRJyMtzLpruSYxGJRNQoOplMwul0IhaL4eLFizhz5gx27NiBhoYGjIyMYHR0FPF4HDweD01NTdi7d++8j3ExoNFo8J3vfAcrV65Ee3s7nE4n6uvrsXv3bsq7n56eTjPLnmtQQ+bVxsfHIRaL0xJlYMYyQalUoq2tjc4FWCwWDA0N4dVXX53T/FFm0p85k7N169bCDNktAkk4Wlpa0NnZie7ublrgyCeGs9yQSX/dtWvXrEJGFRUVdIZFLBZj37592LhxIy3KHD16NGdAWF5eDrVa/Yk0414qaDQa1NfXo7q6mnZVrjdjnAsvvPACnnjiiSU5xkQigfb29ts+KWMYBp2dnTlZOWS2GJjpDDIMk/e5ZxiGFrbzIZlMorq6Oq2QSdZ3UvjMV5AlIweZFGHS5RsaGsoqZHM4HHC5XGqxsGrVKhw8eHDZr11LiUJSVsCckOlbQcx95zNcXlNTA6lUSml0hNZFEI/HqWllpkFhdXU11q9fj/HxcTo/xuFwaHdkzZo12LVrF06dOoVz587RCgyPx4NWq8XevXvxhS98AS+//DJGRkYgEAjQ29sLnU6XVpXx+XyIRCIIh8O0W6bX66lypEAggNvthlgsRmlpKa34LBYikQhOnjyJ+++//7rVIiJx7vP54HK5UFZWRgfeCaUzFArB7Xajvb0d1dXVeOmll+DxeBCLxVBdXZ1m+p5PqCGXoh6hQx4+fBjbt2+fN21Fq9Xi+9//Ps6ePQuTyUTfP1Mwgph+er1ePPfccwgGg7TSTaiqPT092LFjBx599FFUVVXh5ZdfhsPhgFwux7e+9a1busBrNBrs27ePygBXVlam8e4PHDgAYKb7KxKJ5mTkSTqepDigVCrR2NiI2tpaSKVSAKAzd8RDiN0tmGuHk52Qq9VqiEQiKsBANtzCDNmtA1uU4dixY1R8yG633xaKjJkFHzaVPBcmJiYoK0IoFKKqqgqf+9zn6M8zO2355tSW8zm5XUAShenpaSgUCqxdu3beXoRHjx7Fk08+uWQqiYlEAna7fdZE5XYAoaPH43GIxWI6M09o+olEgj73s63rZrMZJSUlWSqKbMTjcVitVgB/Yjex3y9fN5s9chCLxehz9sILL+Dw4cNwuVw5r7NQKERZWRnuueeewvP5/6OQlBVwXbBV5KxWK6LRKH3A5kNX0GhmzIc/+OAD2Gy2LHGMZDJJk77Mig7DMPD5fNQfhXQCfD4feDwehEIhduzYgR07duCpp57C//7v/9KB1M985jN0syceGKOjo1mUMVIJ4vF4EAgElBJAlCODwSAEAgEEAgGUSiXUajXMZjP1JFssBIPBLEn+TLAlzqemphCPx+H3+6FQKCAWi2nCWlVVBY/Hg1OnTsFkMiEcDiMYDEKlUuWsbGYG2bkSNaLC2N/fj2AwiI8++gg6nW7e6ltarTZrHoq9EbDf6/Lly9TzrLq6GlNTU/B6veBwODCbzfjJT36CAwcOYP/+/Vi3bl3avNWtRmaSyaYaNjc3o6KiglosdHR0XNcmgCTihKorEAjQ1taGxsZGej0zz+fu3btpQpavW5Cp2JiphHe9TkYBtwbsxJ8Uj0gCvpwVGedLf62urgbwJ+Em0hUGsp8xs9mM6enpvHNqBdwYSKJADLvb2trmvda++OKLi+ZJlgvJZBKXL1/G8ePH8c1vfnPJPudmQSAQQK1WY+vWrTh//jyuXbuWllyJRKK8zxBRNi0tLYVIJJrVgsBqtWJsbCzrdbb6a+Z7t7e346OPPoJerwefz8exY8fQ09ODZ599Nm8CKBQKodVq8fnPf57OkxdQSMoKmAWZVfnJyUkkk0lwuVwUFRUhkUjMm66g1Wpx5513wu/3g2GYNH5/KpVCJBKhPjQCgYAGnh6Ph8q/FhcX49Of/jQkEgm6u7sRjUapOp/VaoXNZoNSqUQkEsHatWvTukFarRYnTpwAh8NBKBRKo4yROQ1gZoE7cOAAKioqqA+eQCDAypUrqRmzVqvF1q1b8b3vfS+NqsjhcCgF02Aw5D0XIpGIqh+RhauoqAgajSavJD+5LkTi3G63g8/nIxwOg8/nY/Pmzdi8eTNUKhU0Gg16enrw7rvvQqfTwe/3U+pmQ0PDnCubmQlSTU0NRCIRHTAOhUJzVn+ay/vnAtvzDABqa2vh9/vhcDhgMBhw9epVBINBPP744zTZWy5gJzuEcjo0NESNw4lapFgsBpC70MH2j7Hb7VCr1WhsbEQ4HEZdXV3Wppb59/m6oOz3Z3cVAFDvukIFc/kj1/zUQmY+bwbYz8N86K8KhQKrVq1CT08PUqkU3n77bWzbti2ros++j9lFhQJtcfHB4/Goeu98wDAM7cgsJXw+H1555RXcfffdy2pPmA/IbPLQ0BCUSiUeeeQR1NTU4Oc//zkCgQB4PB5WrFiRdz9nr+1siXs22N2zWCyG1157DUqlMmeyREYc9Ho9du3ahY6ODuj1evT19VEP1jNnzlA1x3yf19jYiH/+53++ba/LUqGQlBWQE8QPIxKJIJFIUEENLpcLkUhEPWHmCyL00NTURKmGpGOWSqVgs9nA4/GgUqnSZspisRhVHCKKgcBMAGu32xEMBqHT6XD69GmMjo7C7/dDJpNBLpdnbcZyuRwikYhWnthUrMwggSxiYrEYEokEMpkM27dvpzx1IhX8k5/8BIFAAAKBAA0NDdi4cSNNGDJBZtz+/M//HJWVlbS6BMzQB65Xdczl4SWVSlFRUYGdO3emUXoqKipoEEMS4Gg0igcffHDBi6FGo8GBAwcwOjoKt9tNu3RzUX9aKMi16ezsxG9/+1tqEk4UDKPRKEZGRpadyAHbV0apVOKuu+6ixr8ulyutUhyLxXDlyhWMjIyk3X/El4qYBldWVkKtVqO5uRkqlWrOCdNsyS+bSsblcmkllVgqkAHx5XRuC0iHRqOZU0f0ViKXjcJcpctramqgUCjA4/EQCoWobyn7nsxUcdy5cycVdCrcu4sHdqIgEongcrnmRRPU6XQ5BVeIcEUuwa7roaSkBHw+H06nM21d9Xg812WeLGdoNBrs3buXxmNEvOTKlSu4dOkSeDwetQjKBfJM9PT04Nq1a3RtJ/GUVCqlBWqCiYkJ/PSnP8WpU6fw7W9/O22+jE057unpof8m8vwA6J6cCbVaTefH/umf/um2vSZLiUJSVkAWGIbBkSNH8OGHHyIajWLt2rVp1KhwOEyNHhdaiZ2amoLP56NDqgQk2C4rKwOfz6cPdiqVopQ8sVhMPzMWiyEWi8FisWBsbAwMw1BJeA6Hk5U4kuCytLSUzoWxkRm4ms1mBAIBWK1WWK1W8Pl8DAwMwGq10irSww8/jKamJvT09NA5rZqaGuj1epw7dy4tWbn33nvxxS9+MS2Qns8gcqbBKlHbIwpJmYucRqPBnj17cO7cOVoNE4vFNyw/rNVqcd9992F4eDiLhrpU0Gg0iMfj8Hg8sNvtCIfD2LBhA6WxLkR0ZClBBpy7uroQDAZRUlICu91ODdJzVRHdbjf+7//+DyMjI2hubsbw8DDGx8dhsVggFouxYsUKrF69Gg6HA93d3XTO70bBFlcxmUyUqgsgry9cAcsPuWh85PXlgBuxUdBoNGhra4Ner4fP58spWpBJiZytYLFQwaoC/pQoHD58GAaDAS+++CLOnj2LgwcPznk9yhW0ExGvfBAKhdSfKxOf+tSn8Bd/8Rc4fvw4Ojs7aSG3pKRkVubJ7YBoNAoej4fJyUkEAgG0tLTggQceQCAQgM1my6sWzTAMDAYDzGYzxsbG0s65Wq2m82lsQ3ZgJt5yOBz44IMPcPjwYTz++OOUTcQW+qqurqaiZ5lU1FzerZ/97GfxhS98ofDMzYJCUlZAFsjMUCwWg1AohFKpxIEDB6h/GFsaeyGVWJ1Oh66uLly9ejUrMCUzW9XV1Tkfai6XC7VaTRcXMmckEonwzjvvwOFwUMEEQqlgb/yZweeFCxdm9fYhv6/X66mkfigUwiuvvIKuri4cOHCA0uVyJUT/9m//huPHj0OhUMzqNzYX5Bpc12g0OH78OFXFywWFQoHm5maaBC/WJqVUKiESiag071y/w40EQps2bUJxcTHtMolEIrS2tuZNSm8VGIbBr371K3R3d1Pard/vp/OIxIQ7EAjQzYxUiRmGgd1uxzvvvINQKETnGysrK9HQ0IDGxkaYTKZF9Qdj09+AmUqpSqXCypUrsz6L/D/7GmbOoxWC3VuHXDQ+Qre+1dfjRm0UduzYgeHhYfT29kIgEGQVYq5H0yXIZ3xewNwRjUYRDofhcDgQCoXg9Xrx9NNP49ChQ9ddhzUaDRVxmg+IcXgmBAIBxGIxOjs7odFoaMFQKpXiW9/61rLZFxYKIpLm8/ngcDjQ2dmJ/fv3o66uDmazGaOjo+js7Ex7xnU6Hb7xjW/QeWU2OBwOGhoaMDAwgEgkApFIhM9//vOYmpqi/yMWRaOjozTh02g02LVrF+3a6XQ61NbW5mTJkHiNJHpisRjr16//2Jl6LzYKSVkBWcg1W0UWNYZh0NLSgpaWlgVt8kRSfnJykhpHE0gkEtTX16OmpoZ6bhEQeh+Xy4VQKKSbOTnOgYEBeL1eKslKOmFSqTTL8+bRRx/FsWPHMDg4CIZh4Ha70dramvO7kEWov78fZrMZiUQCyWQSk5OTmJ6epnNM+c7Drl27Fk2SN5fBKsMwGB8fh9vtpt5imcdSU1ODzZs30wTqq1/96qJsUiqViio+lpWVXXeuIDOpXEigqNVq8aUvfQlHjx5FJBKBRCJZdhQlIoxz+vRpulmJRCI0NDSgpaUFu3btouplpAASiUQQj8cRi8UgEAgwOTlJTXKFQiHq6+vx2c9+Fjt37gQw070iPk2LRVEj9Dej0YhAIIC6ujq0tbWho6MDwEwQLRQKs4JZADnn0dg/ZytpLpfr9HEGWStGRkYQiUQwPj6OS5cu0XXuZlyHfAI+N2KjoNFosH37drz//vsIBAJ47rnnUFZWlraezWVGtWB8fuOoqalBXV0dRkdHEYvF4Pf7MTo6isOHD+Ohhx66bpdSqVTO6vWZC7m6aMXFxXj00UcxPT1NlWJXr16NBx54gN7rt7sCIzDTJSQz3P39/WAYBs3NzWhvb4fX60V3dzdNnhiGwd/8zd/kTMiAmU4Ymf8CZkYqPvvZz+Jzn/scdDodnnzySfT09CAWi2FycpL6gZLzyZbGDwQCOa8L2QuCwSAtBq9bt27pTtDHBIWkrIAs5Ns4M6uLCwnsCR1QLBZnSaSShMfn88FkMqX9nBgkqlQqbN++nR4TSbCIElQqlQKPx0M4HEZZWVne79fY2Eh9NQKBwKzzUNFoFFKpFEKhkPKwSSCda65hqZBZZRYKhbh06RI1Cs4XoN9oIJQP5DySTfB690OuQFGv18+7Sr1jxw4wDJN2Hy6nDddsNmN8fJxuRlKpFBs3bkyjrZLOKrkmVqsVR44cwbVr1zAyMpK20cXjcUgkEjQ1NaXNOQIzyQ9Rw8pFXSE/W+g5qqioSLt3cgWzAOhrRIiF0IsyLSVIt4+drBWStMUHWSvIHOLk5CRGR0fR29sLLpeL4uJiCIXCBdlZzAWzdaLmkjTNhomJCYTDYbjdbvB4vAXNCxWMz28c7PnwEydOYGJiAvF4HAaDAUeOHMHp06fTCrpsuFwupFIpcLncG6a/19XV4f7770dHRwf1tgyFQtDr9XR84XbuhpK5ZLKnkOIdAelouVwuGseQdXo2cLlc8Pl8FBUVYe3atVQJmcycT09PY2xsDOFwGDqdjsY5mRTGtWvX4u23305j6xCF1Hg8TsdOBAIBGIa57buWS41CUlZATuTaOBejukgeaJlMlvWzSCSCwcFBKsIBgErUk67MypUr06otJDGQSCSU1xwIBBAMBuH1egEAx44dw759+9KOVavVYv369QgEAhAKhbMaLxK1QZFIhFQqhbKyMrhcLroY5jJ1nCvmGzizu5Q6nQ7j4+PgcrkoLy/P2+0DFt9PiviS8Hg8Ol8wFwVFdqDodrupYfR8jm2pkszFAqkgWywWxGIxNDY25py1YF8Ts9kMt9uNgYEBBAKBtN8TiUSoqKhIowYGAgF6/ux2e1ZyS7p1586dQywWQ2tr65wsC8h7B4NB2nndunVr2t/lCmbJa5mdMiDdUiIQCEAikdBkrUAfWxqQZ0Sn06GzsxOXLl2C2+2G1+uFWCzG1atXEY/HF2xncT0sZSdq06ZNUKvViEQiSCaTadL4c8VyX0NuF2g0GmpDcuTIETAMA5vNBqPRSItHmRYfDMNgeHgYPp9vUeaRe3t78eSTT+LZZ59Fe3s72tvb0dPTAw6HA4lEQufG81GvlzPY8/1EPZnH41EGhUqlgkqlQiwWo/8NzOxBq1atwsDAQN73ttvtVMBs48aN0Ov11INWrVajrKwM165dQywWw/T0NF588UUIhULK/jl8+DAYhsHly5chEomy/M9isRj9t0AgWNBz+kkE71//9V//9VYfxHLHm2++icnJSaxYsQJ79+691Ydzy8DlcmE0GsHn81FZWYlt27bNW4FRLpejvr4eIyMjGBkZyRoOTaVSdOCUx+NBIpFAoVBgxYoVKCkpgVgshsvlQn19Pf1smUyG0dFRakgN/Glg2OVyUYXG1atX07+Ry+WIx+NgGAapVIqqNBIvnMxj5vP5+OCDD+jv1tfXU7VFmUyW9t5zBQmc33jjDZw7dw79/f2oqalBVVUV/fnAwAC4XC58Ph9effVV9Pf3IxQKoaKiAh0dHRgaGkIqlcK6devwwAMPLEgRcyEYGBjAO++8g9HRUSgUCmzYsCHnuWODnDfSwZTL5ZBKpSgpKaFKmXOFXC5HdXX1Tfu+84FcLsfq1atRV1eHu+++G1/84hfR1NQ069/Y7Xa88cYbsNlsWd4zGzduxLe//W36HuQ5tNlscDgciMVikEgkaffvwMAAFQzxer2wWq2orKzEhg0brnscOp2Oet5kPuPkGpKfaTSatNd27tyJLVu20J9XVVXBaDTSDXvlypWoqalBeXk5+vv7ce3aNfD5/LzPXgELh1wuR1NTE+LxOIxGI+0skaDO7/cjEonA4/GgsbHxuvfoXEDWLKFQCJvNdkN7RT5UVVWBw+Ggq6sLsVgMJpMJzc3NdN2cK3KtIew1l7ye67UC0lFVVYWNGzeisrISJpMJ09PTiMfjqKioQENDQ9qzPTAwgP/5n//J6YW1UJjNZiSTSdx77714/fXX6book8lQVVWF8vJycDgcnDp1CjqdDkajMS2GWK4YGBjAhx9+iImJCTqjRQRMwuEwtm3bBrvdTmOAL3zhC5DL5ZDL5fjMZz6D1157La+Spd/vRyqVQlFREZxOJ/7whz9gZGQEDMNApVKhsbER165dQyAQQCwWo5T75uZmuN1unDp1CkajEUajET6fL837TCQSUaYTYYvcfffd9Pg+7riRnKHQKStgzlis6qJGo8GWLVtw4sSJvL+TSqXA5/Npx8xiscDj8dAHur29Hbt376bdhp07d8JsNsNkMiGVSkEmk8HpdCIYDMLpdGJ8fDyrWqvVaim1KpFIzKrep1Ao0NjYiNHRUaxcuRKrV69GMpmEzWbD+Pj4giiMOp0OPT09VDHS7/dTM2tCZ4tEInQWyWg00o5DT08PFThRqVSzdsmWAl6vFwaDAX6/HxaLZc7Kh+R6kU7fpUuX0NnZuSAa43LGfDuT0WgUZWVluHr1Kn2Nx+Nh7dq1aV4uZE5Hq9XCYDDQDTmTulpTUwOlUkkVTL1eL9rb22c16STdTzL4vWvXrrxzlpmvZ77G/m+2GiCZZQKWZi6ugGxotVpqqRCNRtHY2AiHw0G7ZYulWppJWVxKs/GpqSlEo1H4fD7Y7fZFkTzPRbkEUBAEmSPIeens7ITRaIRQKEyznCEQCoVp69xi4Ve/+hXefPNNhMNhJBIJ8Pl8CIVCrF27FsFgEO3t7VTFFrh1c4SEHeNyua5racKe7/f7/RgcHKQibCMjIzh58iSAGdEtAGlUdo1Gg69//ev493//97zHEo/HEQqFMDY2BpfLRQt8iUQCe/bswfDwcJqfnM/nw/PPP4+vfe1r8Hq9cLvdOd9XIpHQArtAIMAdd9yxJDTpjyMKSVkB88Ji0eCmpqbA4/Hy/jyVStH/eb1equIjFothMpkQCoXQ09NDOesajQaxWAx8Ph/FxcXYtGkT3n33XYTDYarYmLk5ZCoJdXR0ZFEtCGpqaqBWqxEIBKBWq9HW1gaHwwGLxQKLxYJLly7NeW6HLMqdnZ2UwsHlcqlxdmdnJ06cOIHR0VF6jqqqqmCxWChnniyc1dXVaGhouKk8bYZhcPLkSQQCAYTDYRQXF89bRQsAlfIlXPRP8rA9oXaq1Wo4nU4IBALU1tbikUceof4z7KCRdJLlcjmUSmVWUq7RaHDw4EFMTU1R24KRkRH86Ec/wl133ZUzOdPpdOjv74fb7UZjY+OCrmku5Foz8pmYFrD40Gg0aG1txfj4OGw2G5LJJO677z709vbC7XbPaR50LsikLEaj0SVRWmMYBiaTCfF4PG29v1Fcb16SvPZJXaPmAnLOSkpKUFRUBKFQCJ1OB6vViitXruDy5cs4c+YMPB7Pknw+e5QgkUjAZDLh9ddfR2NjI1wuFwKBAJRK5S2bIyTsmPfee48mZZs3b0ZDQwNcLhfq6urSkhc2Dfns2bO0O5xKpTAyMgKbzUZ9V3NR2Q8dOgQANDHj8Xhps/rxeJyOfESjUZpI1dfXQ6vVYu3atXjnnXfSvsPAwAAmJibyiohwOBxwOBwUFxcjFAphxYoVuOeeewqzZHNEISkr4KaDYRhcvHgxrd2diWQySRcJMjtAJPq5XC7tzkxMTODzn/88VCoVlceXy+Xo6uqCz+cDl8ulJqK5NtNMJaHrbbrhcJgO0JJAhyz2+f6WdDe8Xi8uXbqEvr4+eL1ehMNhiMVi1NXVYXp6GqlUCgaDASaTCWazGdFolAqilJSUoLi4GDabDSKRCO+///51OxpLAYZh0N7eDrfbjWQyCZlMBqVSOa8NjiQXbPEHIlxy+fLl24bvv5jQaGYMuYEZrzKhUAi5XI7z589T64VoNEoDRJlMhlgshhUrVuRNyrVaLR555BEcPnwYDocDNpsN7e3tuHDhArq7u+kcEbtIcD3RmMVCrtm1T9o1v5kg3TJSRLJarTh48OCidrJulniGTqeD3W5HaWkppFIpdu/evSgKe/mOny1q4PV6P7Fr1FyQaTnT2dmJt99+GxwOBxMTE9Tf9GYgmUwiHA4jHA7D7/dDLpdTn9ObuWeyQdgxFosF0WgUkUgEZ8+exe9//3vEYjEUFRXlnPHU6/Xo7e1Nm9MKh8OYnp4GMEPFDYfDcLlcWXPahw4dosnZD3/4Q/zXf/0XneVLJpPwer20AA7MFMQ/+ugjdHR0wGAwZH0Hn8+HJ598Mu935PP51LqAdOKWmxjXckYhKSvgpuPMmTPo7e3NmifLRCwWQzwep4tFJBKBxWJBSUkJkskknE4nnE4nxsbGcOedd0KtVgMA+vr6YLVaaSu+oqIib5UmU0koH5WHzKWNj49DKBTiyJEjOHDgANavXz+rZxupjHV2dmJiYgLJZJJSM6VSKVpaWqDRaKBQKNDb24twOEyrVcDMArlx40ZoNBp4PB5wOBxMTU0hFAohlUotakfjemAnUx6PB2vWrKEedvNZcElF2m63o7KyElu3bsWmTZvS/O9ud5rQQvzYtFotKioqYDabMT09jbfeegvd3d0QCAQIBoNoa2uDVCqFWq2mNBy73T6rzHBLSwtWrVoFj8eDYDCIZDKJRCIBvV6P9vZ2et77+/thsVjmJBqzGGAHwFKpFNPT0x8L2erlCna3jNhnzKWTNZf7mP07Sy2eQSxVTCYTYrEYmpqaYLVacfToUUilUiSTSVy9ehWrVq3C3XffPa9gMB89n7Ap3G43nnvuOaogupT0zNsRJOHQarVQq9VUIZcE55lqyzcTgUCAinpxOJxbogJI7l2fzwc+nw8+nw+JREL93oj6NDF7ZotAkWKZUChELBZLU+YlCohkdixXLNLR0YHz58+DYZgsxUu2OiJh7Vy7dg0//vGP86pSXy92i0QiSCQSSCQScDqd0Ov1i2YN9HFHISkr4KZjbGwMkUhkTr+baS6dSqVQVVUFkUiEoaEh+Hw+RKNR9PX14eDBgygtLUVXVxdNVEQiEdra2mZVJcxFYQTSVZqIAqNQKEQ4HAbDMGAYBrt27UJPTw82bdqUpTBlNpvR1dWFEydOZCk0ksV0w4YN2LNnD5555hmEQiG62BHp/ZKSEpjNZrz22mtUlTIcDmNiYgIKheKmzuMQepvL5UJ1dTW2bt1K5/rmg8yK9O7duz9WvkE34sdGqH4Mw+D06dP0ehsMBoTDYdTV1WH16tXUqyYcDsNsNqO3tzdL4ZFhGBxxwNIQAAAgAElEQVQ/fhw2mw08Hi+tEhoOh3H58mX09PQgEonQzbe8vBzr169f8oCFTcv5uM4VLjeQGdrZikhszMVgOdfvLKU5LCmOEX8ru92OUCiEq1evwuFwwOfzAQDOnz+PP/zhD1i3bh1aW1spHet691Yuqi1hUzAMQzu7AHDkyBHweLy0c0PW/U+aJ19HRwd++MMfgmEYJBIJ1NXVwefz0euxXEA6qhcuXMDnPve5m/rZRC1ZJBJhzZo12LBhA+rr63H+/HmcP38ewWAQRUVFaGxszJoPJoVjl8uFcDicllSFQiEYjUasXr06517T0dGB733ve3A6nZR9RFBSUgKlUomqqioIBAK43W5MT08jFArB6XTC4XDM+3vGYrE0cRFCMy5gbiicqQJuOurr61FUVEQTp0wp1dlQXFyM/fv3o6SkBP/4j/9IFYTYRtNisRhCoRA8Hg/r16/Hjh07Zn1PQiMYHR0FABw/fhzvv/8+BAIBWlpa6IZ74MABBINBjIyMwG6343e/+x3tztntdjqPRnxF3G43DAZDXrf7srIybN68GVqtFs3Nzbh06RJdwGQyGSorK+F2u/HGG28glUpBJBKhvLwcLpcLiUQCAoEAzc3NN2XTJ1U+UrFTq9ULSsiA/BXp5eAbtJAOVyZIgmkwGODz+dDT04PGxkbs3LlzXskZud9MJhOlpqjVarS2tmJsbAwcDof65XV3d1ORGPL+Z86cQWdnJzweD6LRKK2QElVTu90OtVoNkUiEpqamBRt6LxQajYbSGD8Oyfhyx3yFmtj3MVHlJK+Tv7/ZxRRSHBOLxbTDMDw8DL/fn6UyZ7fbcfHiRVy+fBnl5eXYsWMHHnvssQUXkcg8b2VlJUQiESKRSNo8LIAsWjbx5Ps439MMw+DZZ5+l+ycAjIyM3MIjmh3JZBKvvfYa7HY7Hn/88UW/NpmJuVAopMqFZP9cvXo1vva1r0Gj0aClpQUrVqyA3+/HunXrskYtyHP7y1/+El1dXXRmi8Q9oVAIfX194HK52LNnT9b3OX/+PNxud1YhnM/n45577sFf/dVf0QICERhzu93o7++/4e4ml8tFaWkp6uvrb+h9PkkoJGUF3HTs2LEDJ06cQHd3N6LR6JySMiKrqlAo8Pbbb2P79u1Yu3YtQqEQBAIB6uvrMTw8DLvdjuLiYpSWlqK4uBi7d+++7vEIhUJYLBZaGSLzaCTRI4GGVqvFQw89hBdeeAGjo6Ow2WyQyWQoLy+nvweA+oqQqlbmd+NyuWhqasJdd91FOxJ79uzBxYsXMTU1Bblcji9/+cs4ffo0jEYjTV5JEEK6aZl+VkuJ+XiizQW51PryGZbfLF+ZuXQG5gISxBE1UKvVitHRUVy9ehX33HPPnN+3oqKCJuaJRAJFRUWQSqU0aQoGg+jr64PH46EKWmSo/uTJkzhz5gwVduDz+VQ0RigUQiKRoLa2dslV8q6HgonvzcV8hJpqamoglUrpHO1bb72FEydOgMfj0WRDKBQikUhArVYv+fUja8Fdd92FoaEhTE5OQq/X55X8BmbmbghdbWhoaMFJI/GH1Gg0NNDu6OiARCKh35skqJmefB/3QsORI0fQ29t7qw9jXojH4/jDH/4Au92Ow4cPL8r1IbO5ROnUYrGguLgYHo+HdqEy90+ieOtyuVBZWZmVkJH3HBsbwwcffIBAIJBmzUC8UoPBIAYHB6k3Kxv33HMP3njjjax5Pj6fD6fTiT/+8Y+QSCTwer1QKBTYs2cPJiYmwOfz8fbbb1+XqsgGl8tNm0/jcDhYuXJlQeRjHigkZQXcdGg0Gjz44IMwmUxwOBy0GpPPSJLL5UImkyGZTGJychJWqzXNfJLMZl27dg2jo6PQaDRYtWoVzGYz5VHPFghHo1GIRCJ4PJ60GTaRSIRYLJYWaGg0GjqTQY63traWzsUYDAYwDEO9PTIDBoVCgf3792Pnzp1pQbBWq8UTTzxBqZAMw8Dv96dVqlKpVFp3kcjhLzUyu2R1dXVZEu2LEdBnBoyLlSTlQ+axL1bVnySYpEI6NTWFZDIJo9FIP2cu70u6SITWyhbG0Wq1ePzxx3H8+HH893//N7V9OHbsGCYnJylFmD2/WFRUBA6HA41Gg2984xtQKBS3nF61HJLx+YBUvB0OB9ra2j7WwQZ7Ds1qtaKvr49W2x0OBw0qieCQVqulhaml6D6QLtSHH36IqampOf2dQCCgLIP5ChKRz33++ecxPj6Ourq6tE4bmf8k96jVakUikaDzv0TA6ONcaHjhhRfShCNuN9xIog6kd8XYs7lk77fZbOBwOBAKheDz+Vn08Hx7DqGenzp1Cna7HV6vF8lkEvF4HGKxmI4yuFwuGrPEYjFcunQpa3Zr165dePTRR/Haa6+lUR/j8TguXLiA8+fPg8/nQ6FQYM2aNfR5ttvt1NNwLtdXJBLh3nvvxUcffUQ7yBwOBw0NDctqDV/uKCRlBSw65hJQrVu3DmVlZQgGg/D7/eDxeLQtz+fzweFwqNAHl8ulZoShUAjRaDSNKuHxeOigNwlcE4kExsbGEAqFaAChUqngdDpRUlKSRtGqqakBj8ejVR4OhwOBQIDi4mI8+OCDad8hGo1CqVTCbDaDw+GgqKgIGzZsQDQaxbFjxzA4OIhAIJAmGkKOSSqV4p577skrjKHVaqHVaumC7Pf7KeUslUpBIBAgkUjQIGPNmjU3JSgkyYFYLE6TXyc0TeKltthJ01JSo3IlfIvdtWlsbKQm48FgEGKxGF6vN6eYTK5nhhyP3W6nFVG2J55GM+P3d/bsWdpNGxoagsfjSUvgeDwepFIpysvL0dTUhC9/+cvLKpm42cn4QkAq1m+99Rbef/99xONxnD59Gk888cSyOpeLDTKHBoBW5MPhMEZHR3Hq1CnIZDJKgz158iS91xabBnv8+HH88Y9/hMViydkNAED3DVLZl0gkkMvlKCoqQl1dHQ4ePDjv48nsfLAZAuz7lu3xp1Qq8eijjy6LosdS4ujRo3jqqadm7VQud8jl8gX79GValBBLG2CG3h+LxWinjNidZD4XuYTGdDodnnzyScokSqVS1PRdKpWCz+eDy+UiHo+jpqYGNpuNrvlnz57Fnj17stakr3zlKwgGgxgaGoLX68W1a9fS5v1isRhcLheGhoZosheNRueVbLe2tuLee++FwWCAw+FAKpWCWCyeVYiqgGwUkrICFhXXC6hI8Dk9PQ25XI5gMEgrPSKRCCUlJZDL5XTYFPiTwSGhYOXiOZMhb2KWGw6H4fV6EYlEYDAY8B//8R/w+XyIRCIoKSnB/fffj71794JhGLhcLqxfvx7j4+Pw+/2Ix+Pg8XgQCoW4cuUKtm3blraI3nHHHZRWWFJSQod1u7q6qLwsSdiKi4shl8uhUqnQ0NAwp8CAJEEA6HdWKBTUJy0Wi6G5uRl///d/f1MofdPT05BKpWhsbERlZSVNHAlNk3TvFpumkytJupEOCvtvcyV8W7duXRT1OPYzUF5eji1btlCTW4FAkOWHl++ZYYthdHZ20ios2xOvpqYGq1evhs1mg8vlooUNQgnm8XhQqVQQi8UIhUIoKiqiQjbLFctN9IVcn/7+fgwMDNAOvdVqXRTT4uWMTEGW3t5eGAwGRCIRTExMoLGxEbW1tTQonZiYQCQSwfj4OC5durQoydnRo0fx/PPP5zWqBWbmiFUqFZ3jIckR8fKrr6+/ofs+kUggEAjkVaNjK8rW1tZCoVAsqeDJQsBe/wDk/O+5XiedToef/vSns9ra3A4IhUJ45ZVX8vqTEuTad9jrFJnNbWxspEUJNtU1Hz08U2js+PHjGBsbS9tXgZk4gAhyyGQyGI1GCAQCNDQ0gMfjwe/3I5lMwu/351yTNBoN9u7dSz8nHxQKRZoS5FwgEolQUVGBtrY2nD9/HlarFVwuFwKBAK2trdi5c+d1z2UBf0IhKStgUTFbQMUOPsnP2L4biUQCCoUCKpWK/g4B6RoJBAK6yLHB5XLB5XLphhwIBMDhcJBMJuF2u9NoiVNTU7h48SIsFgsGBgbgdDqhUCggFoshkUgoNTEQCGB8fDztO2g0GmzevBl6vR5+vx+rVq2CSqWi8q9shTuysFVXV6O+vp4aXbNB6FDj4+Oor6/Hjh07aDKi0Wjg8/mQSqVQVlZGBU4A3BQxhkwVQbZQxeXLlxGJRKh3nEgkWnSaTia1DcCCOyiZic+uXbtydsXmM3OTD2yVyqamJjz00EOw2Wy4fPkyrbjnkjzO9cywj8dut8Nms2V1yx577DGUlpbiwoULCIVCKCkpwfDwMBX3KCsrg0KhQCgUui08wRY7Gb9RkOvjdrshFotpx1ouly+KafFyB7nPtFotjh07Bo/HQ+dk2tra0NjYSOlbgUAAFosFNpsNFoslb3KWa93LdV0ZhsHLL7+cNyETCoWora1FbW0tYrEYgsEgpqamoFAooFQq6Uwvue+B3AlIvvtLq9Vi/fr1dD85e/YsWlpastbx5TwbyZ51CgQCkEqlAJDzv3Ndp1zn5ezZs3k7lrcTXC4X3n77bWi1Wuzfvz/n7+QrmmVe84XO5jIMA6fTSf1OJycn07qPQqEQK1euRFlZGZ3zVCqVEIlE2LJlCzo6OjA+Pk47bfnWJBIbOZ1OcLlcSCQSKl0PzFANPR4P1Go1vF5vzg4okc1n/01lZSUaGxvx4Ycfoquri1qvlJSUZPnBLUcWxHJDISkrYFEx2+bEDj4lEglEIlGaZwaXywUwUxXKJfxBONVyuTwtmRMKhSgvL0cqlYLD4UA0Gs3qpmW+XygUgtVqBcMwdDNXKBRIJBKIRqPg8/lQq9VQq9UYGRmBwWCASqWCRqPB8PAwkskkJBIJ1Go1NBoN5HI5ZDIZ/Wwej4fi4mLw+XyEw2HweLysRJJhGPzgBz/AO++8g2g0CqlUiu7ubuzfv58OlXd3d8NsNkOpVGLdunU3tSpvNpthNBoxOjqKVatWoaysLK1j2NDQAGCmUjZfr7K5gp2UXL58eV4dlNk6Y9FodNE8lTIr0JcuXaLm3wDoNSMG5ZnPxVwCukzz387OTvq6RqPBvn37qLk0SaJjsRgVq1EqlRCLxTfVQmGhWMxkfDHAvj7BYBDd3d0IBAKfOJlncp85HA46Y8VOpioqKmjwPz4+npacDQ0N4fTp09izZw+Gh4dx9OhRmM1makDPNjNnQ6fT5a3YFxUV4Stf+Qr27NlDqb0//vGP4fF4qBVKOByG3W6HVCqFUCjMeR/NRsPWaDTYuXMnXQv7+vrw9NNP49ChQ2lrcb7ZyFsNdpfXYrGkzSEHg0FIJBIAM4b14XCYzqV6PB5IpVJwuVzqjUgSNqvVig8//PC2pi2y4fV60d/fn/fn+Ypmi3HN2fPaZDSitLQUk5OTCAaD4PP5aGxsRENDA53Tam5uRmlpKcrKytDS0oLh4WFcvXoVoVAIa9euBYCcBuder5eqQRNqpUQioQldIpGAw+GAw+GgolCZyLzmpODhcDiojD55Xp1OJ86fP5+2Riw3FsRyxCdrVylgyTHbQpVpGEsqMna7nc5NWa1WWCwWRCIROktGfJUAUElvmUxGB1I1Gg1KS0sRiUTgcDiyjBG5XG6awqNEIsGWLVswMDBAlYLIz8hxSKVSbN26FbFYDP/5n/9JZ9HWrl1L58+EQiGam5tp8F1WVobi4mLU1taiqakJer0eExMT9P2EQiEuX75MBSDOnTuH9957j3ruJJNJ6PX6NP+b+++/HydPnkzzULtZixhRpSRS0Gzu/a0IQubSQckcvJ6tM7YYXbHMyp9Go8HQ0BDcbjd4PB7l7ROaSi5Pu7mcS7bogs1mQ39/P8xmM06fPk07sOT9t27dSrvDRJlUIpHcVlSjG0nGl+JYyPW5ePEilYoOhUIfe/piJggNKt99TDpq7OSMJDTEt3FqaiqtQOXxeKDX67Ouq06nw4kTJxAKhSCTyeg6CcwU8FpbW6msOABcvHgRgUCAVv/7+vrQ1NRE/4aYGZP7SKfT4cyZM3jzzTcxOTlJ943M49BqtTh9+jRMJhPcbjeMRmOW/QT7+99KkK4YACq8Qrq8AKBSqVBXVwcgvVNGkuirV69iYmIirVBKqPOk60l+7+OSlMViMVy+fBk6nS7nszxb0Wwh15xQ/4eGhrBmzRqqqBgKhWC321FZWUl93kpLS7F//35ayAaAU6dO0T2utbUVzc3N6O3tpbHT008/DYFAAKVSSfcGnU6Hl156CXa7ne5JpBCdqwA+Hyl8h8MBPp+fZRBOhNnYz9Ny7igvFxSSsgIWHfkWKvZ8AjAT9F+7dg3BYBDBYBDAzPxYNBpNk8pnb+CpVAqhUAiVlZW0U1VeXk6FPTIXE+JWTzYZHo+HsrIy9PT0wOfz0YSNdLai0SilgFVVVeHChQuYmppCLBZDOByGXC6HVCpFJBJBMpnE8PAwWlpa6GvRaBQqlQobNmyA3W6Hx+OhvmnHjx+H3W6HyWSCx+OBzWaj/G4OhwOxWAy1Wp3mfzMxMQEej0f/3d7evmB/sPmCYRiEw2H4/X5UV1dndfpudhByvQ7Krl270NHRAaPRCK/XC4FAAKfTCbvdTv3myL1ntVoXJaFkV/6CwSAMBgMmJycRjUYhFovB4/EobYokiWxPO/Z3u95xENGF/v5+mEwmGI1Gamjb1taG7u5u2O121NXV4Stf+Qq6urpQV1eH+vp6dHZ2wu12U+ojOfblVNXPh3wb+c2kNJLrIxQKcfr0aYTDYRQXF38i6ItssLtK5P7LpFJnJmcnTpxAX18f9UrKXEc4HA7UajUtWpHre+TIEeh0Ovh8PlRXVyMWi2F8fBzAjF8l6QoQlJaW0oIZAOoTSYpiBoMBUqmUquV2dnbi0qVLtLpfUlKSk4at0cx4BtrtdhiNRiSTSUQikWVX5dfpdDh8+DAMBgOd59m7d29aIZR0u4D0mTKSRJ8+fTqtqJlMJun+TObpwuEwQqHQnL1FbwfY7XacPXuWzkuz15XFKkASyu5LL70Eg8GAZDKJzs5O2pEMh8Ow2WwYGRmBRCJBMpkEl8vFlStX6Pz7iRMnMDg4CLfbTROviooKOhtvs9lQVFRE/Vs7Ozvxt3/7txgYGIDRaITP50MikaAxT21tLRiGuSGT71QqBbfbnSWYkkwm4fF44PV68corr1DF2uXYUV5OKCRlBdx06PV6WCwW+Hw+uN1uSrMipsmkqpdKpaisNxuE03znnXfSxcrpdGJsbCyL6kKUithza1NTU7Q9T5KiaDSKQCCAlpYWcDgc1NXVoa2tDXq9HkKhkKpAer1ecDgcOl9it9sRjUbR3NyMzs5O+Hw+9Pf3Y/PmzZBKpRCLxXC5XDh//jwVEYlEIvD7/fS7CYVCOij7mc98BjqdjtLcNm3aBLvdTrtVZC5Jq9ViYmIC1dXVUCgUtPvmcrmgUqlmnTnLF8xm0vB+85vfYHBwEKlUCoODg+jq6sr5N7lmQ5YqYGYnL7///e/R398Pq9UKu90OoVAIo9GI7u5uahlANrtLly5Bo9FAr9dnmbveiE8XO2EgYgfEQ2bFihVoaGjIKywy389iFzVOnDiBgYEBBAIB9Pb2YnR0lHaJjUYjBgYGIJPJqCgM22+KBKOBQOC24PXnCormO5uQ636cyz1KOg/kudJoNFi9ejV4PB4aGxuXvWjKYoIt7kMKVISWmGtelp3I6vV6WnDLhEwmwwMPPEALKoFAAHK5HOPj45RFYbFY8NBDD4FhGEqT+s1vfoNQKERl6tetW4d169bBYDBAJBKhqKiIzuSaTCYAM6p4RHjgd7/7HXw+H+LxOGQyGVasWIEtW7bk/O5arRaHDh2ixroikWjBqn1LAXJturu74fV6IRaLMT4+PitNO/O/tVotJBIJfvnLX9KuOofDQXFxMZqbm6FWq2lnLRaLYXp6ekHHyufzIRAIco4Z3CokEgmYTCZ0dHRQZkqmWuJCOmKkAKbRaPDKK6/gzJkztMBKkMtvNBqNUhVEch3LysrA4/HofDyZeydUeaKWyH7GXC4X/t//+3/QarVpdHYSb913331QqVS4ePEibDYbjbnmi3wsjNHRURw6dIiOmxDF2q1bt4JhmJw0y086CklZATcVJDg1GAyYmJigPmWk6qPRaBAOh2nCkmksLRAIwOfzEQqFYDQa8Ytf/ALDw8PgcrmYnp7OSsokEklWIBCLxXKqC7lcLtTW1lKne61Wi4MHD8LtdmNwcJAKh0xMTMDv9yMYDMJkMqVtzmShNJlMsFgscDgcsNlsSCQSiMVikEgklH5JPl+lUuGOO+5ALBaDTqfLShIqKirQ3t5OE7JQKIT29nYEAgHE43GsWrUKPp8PHo8Hfr8fKpUK27ZtS/PUISC+O0NDQ1AqlTh48CCtDrKDXIlEAp1OR+WlXS4Xjhw5glOnTuG+++7D3r17YbVa8eSTT6K3txexWIzOxFVVVeHkyZMoKSnBpz/96SUJ+kliYjQa4ff7EY1GoVar6b1E+PmEmhoIBNDT05Nl7gogjS4632NlJwxerxcnT55EfX19TvnjTOnjhYAEBxqNBj/72c/Q19cHr9dL1beAGS4/URpVKpUAgC1btmB8fBwulwt2u50m+sDtwevPDIpm8/fJlXxldlSvXLmCc+fOpRkh55pneuaZZzA4OIhIJIKysjI0NjYiGo3C5/PBbDZTwZVPAsxmM9xuN/x+PzgcDoLBIKUl2u32rDkrYObc6/X6nB0yAjK3e/78eZhMJgQCASoBTtYft9uN119/Pc0A1+fz4dSpU2htbYXVasWRI0coNX3lypWYnJzE5OQkgBl1xomJCQQCAfpcEipeLBZDdXU1ysvL0d3dDYZhchZqyHdjK+Xl6hTeCuh0OjAMQynLQqEQwWAQXq93zgmFRqPB9773PTQ0NOC3v/0tHA4HVCoVNm7ciL1799LzAQA/+9nP8NFHHy3Inywejy8bXzOhUAiZTAa1Wo1gMIiXXnoJ165dQzgchkwmw/j4OPR6/bz3hcyupVKpxMjICJxO55zfg4gJqdVq2vFVq9Wora2FyWQCn89HMplET09PzuI1AfFLk0qlsNlsAGYK3iUlJairq8O2bdvo/qXX6/HWW29haGhozsdJkO/5djgctHtNFGsrKirw6quvUsG0e++9F3v37r3lz9FyQCEpK+CmgizqFoslK4mqqqqCSCSirXdgZtEkCw5RWCTeZmSBmQ1kmJkEoAS5NoV4PI733nsPiUQCvb29KCsrQ0VFBZRKJZXaJzx6ssgkEgno9XoMDw/TpC0YDOK3v/0t3XxIVYt8n5aWFtxxxx0YGRlBOBym0s0jIyOUK75v3760YfPdu3dToQiLxQK/308XO4PBAA6HQ6u+kUgEPT09OQNGMkfBMAxNbp966qmsIJf4oLDP19TUFCwWCwYHB3H69GkAQH9/P12ME4kE3n33XUxPTyORSNBz1NbWtiiLLbtrcfbsWQwMDFAZeHKsbW1tAGaCOKJURTpCmV3HyspKiESiNLroQjtYwAyVksw57t27N0sIgC19vJD5wMzKq1gsBofDQSKRgEwmg0QiodQVj8eDVCqFYDAIhmGQSqWwfv166PV6OJ1OiEQiqNXqOasbLjcZ43zzhZnJVzQaxfT0dBrF9JlnnsGVK1cQCATonEZmUicUCvHEE0/go48+oiqW5P/D4TDcbjdcLhc6OztvalDOPj52kHwzro1QKITH40FRUREVV5qYmIDL5cLIyEiaAAZb8W9oaAiRSCTNqoGAJBBvvvkmpTcCuWdacnUUnE4nZSgMDAwgFoth8+bN2Lx5M0QiESQSCV0DMi0lHnvsMbS2ttL36uzspPdIvkJNNBoFj8fDxMQERkdHFxy0LyaIWITb7aaCPoRq+eMf/xgAsgyFZ8PDDz+Mhx9+eNZnvq6ujnZdFgL2HPfNAvve43A4KCsrw759+6gqLbugB8wkM2SGcD5jA+yuJZktJsId80lGk8kkvF4vPvjgA3R0dIDL5aK6upqyiwKBAGUVkdn5fJ1HsiezP7+oqCiNokn2lrq6OoyNjc27Y5bvu5G4jUjnb9q0CWazGXq9HhcvXkQ0GsXg4CCMRmNOsZ9PGgpJWQE3FVarFX19fbBarWkPcSqVwqc//WlcuXIlLRlgV4AIl38+IPzpuYDP5yMSicDj8QAAenp6sHbtWtqJAGZU7FasWIHJyUlEIhFEIhGcPXsW0WiUUhzJogmASs/KZDLE43FKe9mzZw8qKipogPXKK6/AbrcjEAjQ6jLpYgHZHZnnnnuOJiOkUyYWi+mx+3y+ND8rAFRchCg9RaNR6HQ6OuDMDnJra2vx1ltvZZ1vIrrS29tLE1X2ubZarfTfyWQyjQ45H+h0Opw8eRIA6Ll6/vnn8d5771E/OjLXR6gYBHK5HOFwGFKpNK3CS7qO7KCWCILkUkWcDzJ9inJVDYkkMTE+n08CSLo2ZN6AdGwEAgFKSkqwatUqfPWrX0U0GsWlS5dgMBhgMBgQjUZx7do1HD9+HOFwGCaTid7LVVVVWLduHaxWa5YoCsMwAP7UHVhuMsa5KI1sQZBgMIjDhw9Tqll5eTlqa2vh8/kwODiI6elpaluRSCSykjqbzYa+vj56Hcnv2u12OqOaTCZht9tx5swZxGIxbNq0aUlEPzLFa9j0W7VaDQBpVFRg8ZM0hmHQ09OD4uJiBAIBrFr1/7H35cFt1mf+H9m6ZcmSLflQfCe2k9ghcTiSbrI4TUhDizmabWmXpoUWZimlx7TTnWULAwVm6Sy03ba7hKMHBFgW0tYhJRxpcIhzOiRYjhMnPuVb1n3fku3fH57nyStZdpwQYH/bPDMMiQ9Fet/v+/0+x+dYjHXr1uGdd95Bb28vAoEABgYGsH37dlx//fXo7e1l8QiyZVCr1bDZbLwf5+bmMh9GqNp2MZGXlweHw4FoNMrPs0wmQ0NDAwtdlJaWwsLHbyoAACAASURBVGQyweFwwOv1MuRrzZo1KXsjmWQTDDlTo0Zo6E4CGukWF590kK+lXC6HVquFz+dDIBBANBpFIBDACy+8wGq+FxPzTdgqKyuhUqkuSRZfJpNxA1OoovxxhUajQV5eHqqqqqBSqWCxWCCRSPDtb3+b9zrh86VQKHhKJaQNmM1m9h8T7o/pE/rR0VEWm6F9IhaLpeQ1Cw2/359yjeeCjF4oxxGK5AAzk+Mbb7yR76/JZMKDDz4Is9mMaDR6WUVcyPT6mmuuwfe//31u2hBiiXxo+/r6/r9AbXzccaUouxIfewg3rI6ODobzCSM7OxtKpZLhJhQfdcOWSCQZO7SZ4uqrr8bk5CTL4ROJ3+l08u8XFhZCr9cjFovB4/FAJpMhHo9jbGyMD/P05CIvLw96vR6hUIi77Pv27UvpXgYCASZUE+wEAB599NGUiRn9mcRK0jll7e3t6OzsnOVJRbBFkt+n8Hq9OHLkCG666aaUJHd0dBSLFy/G6dOnMyZLCy12JyYm8F//9V/47ne/e8HNlrrrR44cwa5duxAIBJCdnY2DBw/iG9/4Brq7u/k6KxQKFBUVIRqNQiaTQa/XIxAIYO/evSz9bDAYEI/HU0xcMyUaVKhdir8M/d5CVKWkUilGRkYYwtLX17egf9NisWD79u1oa2tDNBqFWCyGQqFATU0Nrr76arYkoMNOyEPw+XzweDyznitgJpn7z//8T5w6dYrFZKiYoYKOzD//N8oYp99L4T2gyYnVakV2djZWrlyJW265BUNDQzhy5EgKx7SyshLAjIiO2WyGw+FIgYMCM4kkmbMqFApoNBrk5uZCpVLh1VdfZX+fRx555CMXZuncTioUaX8Rwm9pekRIAJPJxJzd9AL6UqedVKyazWb4fD4sXrwYVVVV2LRpE+rr6/HUU09hYGAAiUQCvb29sFqtiEajPCkvKChARUUF8vPzcezYMfT392NychK5ubm8T11KQbZy5UosXbqUeU75+fkZ7Tlova5YsYKvS6bpMO1/lJwDmAU1FvI6hbzMT1NFTuhdRYbx1KwCZj7D5RaIamhogEQiuajfkclk+OpXvwqj0YhTp07B7/ejv78fdrv9Y4UzxuNxGI1GfPWrX2XxGQCoq6sDkLqPCJt2XV1d3IggO4RTp04hmUwiFAqx+uHWrVtnNbUMBgMKCwuRnZ0Nj8dzSQXZQoPyG0JNXCgkEgnWrVuHrVu38tc6OjowMTGRglK6XEFK2mKxmDm4RqMR3/zmNzE2Nga73Q6ZTIZFixbB6XTCYrH8rzhfPq24UpRdiY81qBggX5v6+vo5oYMOh+OyGlLK5XJcddVV6OrquuBGo9Vq8dBDDwEAyz03NDTg+PHjKCkpgdPphEKhgEKhQHZ2NiQSCXJzc1FQUMAmpUSUBc5DJUg6v7q6mrurxIcjiAx5o0kkEoYNkoBDS0tLRlNL6gRn+hpxxgKBACcUo6OjGBoaQiQSmXX9Dx48yBuhcDOsrq5mHkn6hi+TySCTyXgyN1+89NJLiEajc0ITqBh788030dbWxlMMYGZdjIyMwOVyQavVQiqVIpFIQKfT4bOf/SwLtFDCSlPKi/HkulQSd/r06EKqUqSUSMX57t27YbFYOHF+/fXXsX//fmzcuBFf+cpX+PdGR0dhs9l4apxIJOByudDU1ASNRpMifvHYY4/xNHEhB3QwGMSRI0dQVVWFkpISyGQyDA4Osiw2TVs/TRljWh9utxsikYgFN2jCQl9raGjge9DX14f29nZuQJjNZhw8eBB+vz8FBkfPHz0zoVAIZWVlKC8vRywWY6l0uu4ikQgKhQJNTU24+uqr0dfXh9OnT8PlcgEAT80vBgY6VxFWVFSEuro6LogNBgNkMhkWL14856QMwJxcu0zTzoUUasIpME23qKEUj8dx6623Yvfu3QgGg/D7/fB4PBCLxSgtLYXBYEBZWRnq6uoQj8cxMjKCwcFBeL1e+P1+yGSylISVkre5+CkUeXl5uPXWWyGVSlOUYdORAZngrPN53wmvgRBqTJ9VCPeiSZxUKmWF1YU0WD4uqCk1/BKJBPNrFy1ahFgshgMHDqCjoyOjIMulhNFoRE5ODq/7C4VMJsPPfvYz3tfoOvT19eH3v/89+vv7P7bCTCqV8v0CzguNEewUmD1ZfuaZZ9DW1oZEIgGJRMJ8bWoU0lR2aGiIucr0zNEUTSaTIRKJXJJwxkKDhMyys7Mhk8nmNFkXhkajSaFHAEBJSQnC4fDHNrWMRCLo6elJoVRs3rwZer0era2tEIvFsNlsaGlp+dThwJ92XCnKrsTHGtRRdDgcfLgXFhbC7/enJI0ikQgWi+WCh/HFREFBAex2+5yvSROH4uJi/OQnP+FNm/5vsVjQ29sLqVSKVatWQavVoqmpidURSWLYaDTixRdfZANH6qoDM12pgoICVvyia0GS7cFgkOWgFy9eDL/fD5vNBr/fD6/Xi71792LTpk0XtUGFQiGMj4/D4XCgubkZhYWFKC0tRUVFBXvACRPTycnJWdMPo9GIpqYmfPDBBwiHwynqSllZWSgtLcXKlSuxa9euCyb/09PTMJlMKQkiccMAoKenB93d3ejs7EQoFJp1MEgkEjQ2NqbwxZYuXYq7774bXV1d2Lt3L/R6PUZGRhCPx/nwzMvLu+C1utQkKZPQhBAONVdQ4T09Pc1F+ujoKA4dOoSHHnoI0WgU7733HgYGBnDXXXfBZrOxoIDwuhD8VDgFbG5uxrvvvnvR0BPyiZLL5bj33nuxc+dO9iGSSqXIy8tjA91PmlNGTZ2DBw/yxDo3NxcKhQKRSIQbBiSR/p3vfAdr1qxhGJtwmuv1ejE+Ps4THGBmLdtsNpw5c4Y5OUuXLsXtt9+OlpYWvPLKK5iYmOBmERVltbW10Ov1kEqlOHDgAIAZON7IyAhGRkZS5MfnKhIoIZyrCANmuvnCgpgKi7k4ZQAYhicsoDOt1/R/e65CTSqVMm/R5/PxmgTAa5j8JIGZ510ikcBoNCKRSLBCI020hEpxU1NTUCgU/GeJRAK5XA65XD5vg87tduP5559HfX09fD4fK6kajcaUaX+6YTw9LxfyviPuGEEYf/GLX8Dn86G6upqbS0Iu6UKgvfMViR/lmSL4IsEzxWIxKisrccsttyCZTOLAgQM4d+5cymQr0797sXvhhg0bsGPHjjm/n5WVhdzcXBQXF+Oee+5JaTRR1NXVoba2lif7lzvkcjmuueYanp7Sfe/t7eXzsa+vj43Dyeuxu7ub731eXh6kUimkUinb24hEIkilUlRUVKCkpARHjx5lo20AGBkZwfDw8EeSmr9QSCQSqNVqxGIxiMViqNXqBRVltbW1KYX5c889h+3bt8+COF7uGBgYwEsvvZSyJ1Ij+fjx43j99df/16ExPo24UpRdiU8s4vE4bDYbJicnUVxcjEgkwp22qakpjIyMzBLk+ChBMumZigaJRII77rgD69atm0XUF05uOjs7IRKJsHz5cu4y1tXVzZLnvvbaa+F2uzE6OspJCXm5lJaWpsjqvvjii8y/KikpgcFg4OlJaWkp1Go1Ojs7uVi6GIU3UkIjGGR3dzdDV4jY/s477+DNN99EMpmESCTCokWLMk4/NBoNampq4PP5WD0SmDnovvSlL6GyshKHDx+G1Wq94PtyOp3w+/145ZVX8O6772JwcBDBYBByuRwKhQKJRCJjp1Qmk+F73/seHyKPPvooX/tDhw7h0UcfZZuC9N+32+148cUX8ZOf/CTje/ooSdKlmGAajUZUVFRALBbD5/NBo9HwNO/pp5/m9RKNRvHnP/8ZTqcTH374IYaGhmbBX0KhEAYHB1O+NjQ0dMlcgHg8zpOl73znOwDOF7+XKgn9UYISRKfTie7ubkxMTLBYRDweZ4GWeDzOgibRaJRNfS0WC/R6PfvnLF++HBUVFVAqlXA6nZwAxmIx7NmzB5OTk+x7qNfrMTo6iqKiIigUCu5EEyzM4/HgqaeeQl1dHerq6nD//fdjbGwMYrEYJpMJfX19iEajbLZLjZvW1lacOHGCGxxUGM1XhKVzo+a6B8KvZ5rYZlqvLS0tOHToEF+LTIUaef95vV44nU7k5OQw99Xn8/EkcWpqCmKxmOXOE4kEe0GSYAEwI38v3I/Tr+3U1BTvSxcKr9eL7u5uiMXiWUqqKpWKrSDKy8tnPaMX8r6TSqX8fbvdjo6ODoRCIfT396Oqqgpbt27l9Un3z+Vy4bnnnsNtt92WcRqV7mmYLiZCP3Mhi4b0s4d4RolEAsFgkK9HfX09CgsL0dHRwQIWXq8347+bDsdcyPv57ne/i7a2NvT09GR8r2KxGDKZDCtWrMDf//3fz+JuWa1WFmFJn46SfygJghDqZPHixdiyZQvGx8cZEZBMJiEWi9l7y2AwQCKRYPHixdiwYUNKU5OgnuQzNzw8zNL8kUiEueKhUAi5ubn8mmKxGFqtFqWlpVi2bBkjRAoLC5lXmUgkUFpaCqlUirGxsY/Ny00kEjFSJzs7G4lEAtnZ2VAoFJDL5XPK0wMza/2f/umf+Ho899xzeOyxxy77e5wryKQbSF1bdF+o2f23bCp9pSi7Eh9rNDQ0YO3atRgaGoJKpcLIyAgSiQTy8/Oh0+lw8OBBPow/SlKZKaLRKHPKhJGVlYU77rgDTzzxxKzfoUT9yJEjOHPmDMMKjUYjHxqUoFosFrz11lt8mFVUVOBzn/scd9Z27twJm82GQCAAm83Gr0HGq9nZ2QgGg5BKpSkS5YsXL4bZbEYsFrto6XThRIwUmkhKf9u2bbjpppsAACdPnoTdbodGo8Gtt96a8dAtLS1l3P3g4CBPsa677jqsW7cOFosFy5YtS+HAzRXBYBDPP/88xsfH4XQ6+T7HYjFkZ2dj0aJFUCgUcDqdCAaDfLh+73vfS+mwCpWifvrTn/K/m+nwm5qawuuvvz6LY0IhTJJcLhc6Ojqg0WhSJNLn6h5nEpqYLywWC/bt25cCt3Q6nXA4HDCZTMjLy2P1LOIH9PX1YWJiIiMfIZlM4tSpUyn4+2uvvRbNzc2XDJeJxWLYvXs3nn/++ZTi95PuWAqLZWDGjoFgbTKZDDqdjj2ovF5viu3B8PAwdu7ciZGREUQiEeh0OhQVFaGpqQnxeBwejydlj6G9B5hZoyqVCj09PWhvb2fek1arhUwm40ZHJBJBIpFAX18f8vPzodFo8M1vfpMn6zS1cDqd6OnpYR6K0LJCmHgspAi7mHuQqYBOX682mw3/8z//g6GhIWRlZaGgoIBheMIisaOjA2azmbmlGo0GRqMRdrudE9jp6Wmo1WpMT09DoVBArVbzxJ+mepRkKxQKaLVaFj5QqVQMISWI6ELOAFoPeXl5SCQSSCaTGBsbQ35+PlwuF2KxGEPmGxoaZk3NMz2/6U0aMmv3eDw83Usmk2hubmZvRLIH8Pl8OHXqFI4ePYo333wTX/rSl3DXXXcBOJ98ZvI0pGnMfFxAinQqgJDLFAgE2ONzenoaYrEYHR0d2LJlC78PUoYV/rstLS04efIkLBYLe29mej/p0E8qrjZs2MAS8ukxOTkJr9eLjo4O/O53v+PzSPjZlUol84KFNjgSiYQhedRwmZqawsDAAHbt2oX169dj27Zt2LFjB1vDiMViLsIz2TPQfad8ZGxsDPF4HJFIhD0d6X2VlZVh6dKlAIDu7m709vYiHA5jcHAQZ8+eRSQSSYHaTk9PQyaTweFwoKurCyqVClKpFNnZ2R+ZkkHm0sT3IkoETZSpaKytrYVUKsXZs2fnbHDW19dDo9Hw1+abdH4ckUgk8P3vfx+VlZXQ6XRsywPMNBXtdjtDsv9W40pRdiU+1jAajbjvvvu4q0geGCqValZHdD6vjfkiKysr4yY0PT3NGHBhyOVyLF++HMDszuPo6CjMZjN6enr4oKFNV9hNpc4iKYzJ5XJUV1ejpqYGa9aswfHjx5GdnY2JiQlYLBY8+OCD+OEPf4i6ujpWYKRJYWdnJxKJBIqKirhzqFarIZfLsWLFioviAND1Xrt2Lfr6+hiGoVQqGRJgNBo5mSksLER9ff2cryUkvxNW3mg0cjJQUFCA5cuXw2w2IxAIpPgICWNychK9vb1IJpOYnJxkXl5hYSFWr17N3czW1lYcOXIEkUgENTU1qKioyPh6e/bsWdBh5/f755w0UpLkcrm40zk1NQWXy8XF6HzQpIVOjywWC4tIWCwW+P1+hMNhSCQSdHZ2wuVywev1cgdXp9PhmmuugVKphMViySgFLpVK4fV6+bNZLBacOXMGJSUlrO54KXHkyJGM/EL6HOlJLHU9L6csvLAwUCqVUCqVrKC2cuVKFBcXo7i4GO+99x5z7UZHRxGNRlnZlRIiSvR37NjB9gDzwaTMZjNzSORyOa/R0dFRNq2nAqOgoAAqlYqVLoEZ2JKwS06G83K5nCFxarWap2dA5snW5S6E03lrb7zxBlwuF8v82+12NDc3o7a2FgC4Y11SUoI9e/YgHA6zWhoZ34fDYYaOFhcXo6ysDAaDATU1NTh48CDOnj3LMt4ajQYikQgVFRW4/vrrsXPnTi4gzp07d1EmwtT0EovFzKMi/izt98FgkOFkfr8f2dnZPLVMn/ySPPfhw4fR3d0Nt9sNl8uFvr4+DA0NpXBtpqam0N/fzyqz1EQRCsOEw2E8/fTTeOONN1BTU4OcnBxu9KSLiZDqK4CUKVomYQ4hFcBqtSI/Pz+Fb1hWVsaKhslkkptxmzdvxuc+9zkAM+uqubkZLpcLDocDe/fuxeDgIPtyFRUVZXw/wgkkcB62mpuby4UVBcmf015vsViwe/duADNn75IlS6DValFeXg5gRpSnrq6O3wc9n8LPQjE1NYXx8XG8++672LBhA3JyctggHJhpVrlcLnR1dc15blKxTU1iohjcfvvtMJlMiEQi8Pv9qKurg16vx8mTJ2G1WrmgpMKRgsRVxGIxRkdHudDT6/UoLS2F2WzG0NDQgtd3euh0OhQUFGB4eJg9/Kho1Wg0KXDsZcuWobe3N+MeR3nHpz2FikajOHfuHICZfYZoC4R48Xg8WL16NU9r/9biSlF2JT72EE6W2traMDAwAK/XC6PRCI1Gw1CAhaoHUeh0Otxyyy0YGBjAiRMnMk4IpqenZ00acnJyoNPpMnI8SktLU/gR9L6Ir0C/Q543tCFqtVoUFRVBKpXi+PHjzGmZnp6G0+mEx+PBww8/jMcee4w7l9Sd9Pl8yM3NxdKlS6HX67F3717IZDKUlZVh9erVCyaRZ7rer7zyCh/89N6cTicMBgO8Xi8rFF7otYDzXDshH6O8vBxf/OIXceLECVitVubmpAf5qOTm5sJgMKCkpARr165Fbm4u8vLyOFEqLCxEPB5naEtvby+cTidPGSlxFUrvzxfkQzXXZyMOARkCZ2VlYXx8HG+++Sbef/99jI6Owu12IxwOs30AXYtMQg3pX0uXMc/Ly4PT6eRGgkgkwsTEBJxOJ2KxGCQSCaqqqnDzzTejoaEBVVVVePrpp1MKUIlEgsnJSfT19bFPVnNzM/70pz99ZA5DNBrNKC6TaYqwc+dO5lyuWLGCC+t0ueiFRCYlSyqWHQ4Hw3USiQSLRVByQkkRyWtbLBbk5OSgpKQE2dnZCAQC8Hg8CAaD80J7gJmpHCWiwEyXmrwHKfEm6wu73Y6RkRHs37+ffYAICkVBks9KpRJarRYSiWSWdP5HgYbOxwOaCy4GzBSPxBOjwvXQoUP44IMPeCoxODiII0eO8N8JJipcuzSZuP/++1NEZ+rr61kog7g6Qi7c7bffjvfeew9Hjx696EmCUqmESCSCy+WC1Wrl/Z2aKvR8AOCuu9lshsvlwvvvv4/6+nrU19fD4/HA4/FwY4ymYbm5uVxgjY6OcsJNQROgCwWZV9OUjvYOj8fDwjRjY2NYtWoVCgsL0dbWhqysLJ7yHj16FI2Njdi4cSOMRiPcbjcXBsCMAq/L5WLY7datW2GxWFKacQBS4IpSqRSDg4MYGRlhmH00GoVcLkdNTU0K9DKTRQDtp1arFeFwGF6vFzk5OfD7/TytqqmpQTgchtPpRDKZxPT0NDweDyYnJ9k77tprr4Xf78eHH34IsVic8sxIpVIsWrSICzK73T5LUMrn83GjhM4WEoEaHR3Fq6++irq6ujmnZaSg2dLSAofDwXxOt9uNP/7xjwiHw3jkkUdw1VVXweFwzJK0p8JMqOJKVgTDw8MoKirCkiVL8NWvfhUejwe//OUv4XA4uFFzMXkOnXVisRi5ubl8vxKJBMxmM+9VdrudPU+FIRKJIJPJoFaroVarU75355134vHHH//EPeMoCH0gDLvdjj/84Q84evQovvjFL16Ux97/hbhSlF2JTyyE0AGPx4P8/Hx8+9vfRldXF7q7uzE8PHzRRZlWq8U999wDl8vF3ZcLhc/nYz5OJrGGpqYmtLa2MvFVLBajrKyMf8ZqtbJIBck9U+dbKI3b1NSEvr4+hr84nU48++yz+Na3voW77roLFosFLS0tOH36NLxeL4sEEPRwenoahw8fxt69e1FRUYH77rvvomFM6Z1ZgoQ5HA6G3cwFkTSZTGhtbUV+fj7q6+u5KCKeHpkPb9y4ERs3bsTOnTsxPj6eUfkvmUzC6XTi85//PG699dYU/yuCytAkavPmzdizZw86Oztx6NAhTE9PQ6VSsSBKZWUlPvzwwwVdA+JEZQryXqLEWSaTcQfy8OHD/HUScHjzzTd5CrV27Vrcd999/BnmEm8QJjRFRUUoLCyEXC7H8PAwq3d6vV643W5uIMTjcej1ehiNRmzduhVtbW04cuRIiqlpIpFAJBLBW2+9xZLXl4NUnkwmM4rL0Long3Py7fF4PMjOzmY+Jcm0Cws1uv5Cf7h0GBQVrkJ5/1dffZUTy0gkwibEMpkMRqMxpdtO0EC6RiqVCuvXr2fFP4fDwZP5+RIQ6s6LRCIkEgmewFCHnJL1SCTC/Nj5xIRoktTU1ASlUgm9Xs+cVGD+RstcBddcxRbtQVT8NDc3Y2hoiCFOY2NjEIlEkMvlmJqawuLFizlJtlgs6Onp4UQ4/fmVyWTMryOolk6nQ2NjI5qamjIqwaZbTVgsFvz85z9HR0cH3G43fD7fJYk7jI2NZfw6FZf0mtnZ2RCJRFAqlZiYmGAritHRUbS0tPA6kMlkXOyQSTgA5jFS8XKp4fV6ea9vb2+Hx+NBTk4OFAoFlEolWlpasHbtWp4WOJ1OjI6OYnJyEl1dXWhvb8fq1athMplY3GHx4sUAzhtqk7+lUIFXCBUcHx/HwMAA9u3bB4fDwRNFrVaLqqoqaLVaNDY2MlpEKpUyZ5Kma6FQiItcam4EAgG43W7mBUqlUrjdblb5pWKJYMLT09Nwu93485//zM8NFQ1krqxWq6FSqfDlL38ZOTk5eOutt5j3KYxTp06xVycwAz+WSCTM2Z1PCZU+JwBWxN2/fz9efPFFWK1WTE1NwefzIRQKIT8/HwqFAtPT0ylrS7hWALDnIU1OKyoqEAwGsWvXLkgkEuTk5LC36cVEIpHgNU9T+ng8zj5qQlN7v9+Pqakp5mjKZDLk5OTAaDRyY0EoonHvvfcCAHbt2oXx8XF+Rj7NIGuN/v5+HDt2DE8++eTfVGF2pSi7Ep9oEHSAEonKykrU1tbi8ccfv6jNSiwWIxAIwGw2o7GxEXfeeSceeOCBBf1uLBbDf//3f+OGG27ISATXaDQoKyuD1+tlAQCPxwOLxcKEVJ1OB4PBwEmnUNmJijyNRoMf/vCHePjhh1k5zmq14rXXXsOKFSs4gSfVLEpAabOnZBKYUS4qLCyEVquF0+lEY2PjgmCN1IUXvjeCJhFkMlNitG/fPjzyyCM8pVi0aBEnAg6HA2KxmLkGlHC5XK6M6okUU1NTeP/993H99dfDZrMxX0Vo0grMqAju3bsXDoeDu6sKhQIDAwOYnp5GS0vLBdcKKUR++9vfzpj4mkwm/PKXv8TQ0BAXpkqlEsuXL8fg4CDGx8dThE1UKhUrWgIz+Hd6v2azmb2jTCYT7HY7urq6GAZIxQNNmLxeLxQKBU8QLBYLfvvb3+L06dNcDPj9fhw/fhylpaW48cYbWXY9/drSoX8xzQxhVFRU4JprrsEbb7yBZDKJZDKJgYGBFFgkJWkqlQqBQABjY2NcsJAAjNPp5CkSEc+pUGtrawNwHvKULuVOk4ezZ89yg0Aul89KEIhTkpeXB5VKhW3btvF05tChQ/j5z3/O6nOrV6/G3XffDQD49a9/zWp/ZDR+Id8gtVrN3BaJRMKcV+F1Jo7RfKIUxO9xOp0Ih8OYnJzE4cOHmYOaqdEihEcLhReIS0kTKOLhjI2NIRaLobu7m59pggv7fD7erwKBAKMRDAYDfD4fdDodw8/i8ficZr6JRALV1dX82jU1NTzNnSvSJ/Z79uzByZMnEY1GP5bOvEKhgFQq5b2B1qxEIkmZxtE6p6BiDABPX71e7yzY3KUGwej7+/vZNJeKKUriz5w5A5VKBZ/PxwUNFZmtra0wmUw8hVUoFAgGg+zJmJWVxZYFXV1dKQksTUaI10yFAyXuGo0GjY2NsNlsOHz4MP70pz8hNzd3lqJlKBRiiDVNy0QiEfx+P0N1SSqe4P7ATMGr1Wrh9Xq5MAsEAvD5fDzZJHEYmvCEw2EMDQ1hx44dWL9+PdRqNQwGwyw/s/Q9jwzec3JykJ2dje7ubnR3d6c0eoQh5PipVCq0trby9I1ej3jpy5YtQ2FhIfr6+jA2NoZwOIzs7GzI5fJZtAuRSAStVguFQoHnn3+ezwmDwcB/FkZRURFWr14Ns9mMvr6+efdyem/UvKOJHRVr0WgUyWSS9zr6fjKZZLg1eYHZbDa0trZCr9fjm9/8Jn7zm9/8ryjKgPP30u124/Dhw1eKsitxJT6OoG4hQVpsNhvefPNNnD17FmazecGvjPTjzgAAIABJREFUQ0p78XgcAwMDbN66kGSLYmRkBH19ffj617/O2G+hQtPKlSvhcrlgt9uRSCTw7rvvMtaZDlSVSpUC0cqk6EWwk9///vdMDrbb7Thx4gSkUikaGxvZ1JSgRcBM4Wi327l7ODk5iRdeeIEhVC+99BJuvPFGGI1G9gVSKBRzFmvpB1AoFIJcLofBYJiFMbdYLHj22WfZqJmEJ8LhMNxuN3fmli1bxt1Zk8mEU6dOcXI+V4TDYfzsZz9DeXk5J4bEY5BKpdi7dy9OnToFh8PBxSIlcVQAzJXQFRYW4t5772V+G0Ef0vk0+/fvx3PPPYehoSFOmvV6PZYvX46bb74Z7e3tzA2ghFsulyM/P587xBUVFSyYQFCekZERtLS0MKeBOrZUPNBkgxLqzZs3871yu9183eLxOF544QUWHdm8eTM2bdqEvXv3cidUGAspyBYtWoRIJMLTX4lEgvz8fKxYsWKWsbTFYsHOnTtnTX6VSiVPOChpFL4H8p0Seu0NDAxw0hIMBuF2u9Hd3Y2SkhIUFxcjHA7DYDCw/H4ikYDX6+UpY6YgGGJXVxf77VRUVODqq69Gd3c3DAYDbr75Zi4IiJhPieiFgiB6VVVVaGxsxJkzZ3DmzJlZ647gfDSRmWtdejweDAwMYGJiApFIBNFolEU18vPzUzyD0uHRcrmcOUarVq3Ciy++iJMnTyIej6OqqopV12KxGKtKer1e9kgiDhhdW2owqFQqhMNhjI+PQ6vVcmI3V8hkMqxatQrRaBSxWAxyuZyNYOl9Z4LwNjc34y9/+QvMZvOcfNPLEQTtKi8vh1wu5yJBJpMxjG6uUCqVWLRoEXsvikQi/h2JRMLmtzShyHSftVotFzPBYJAnq8SvomkdPbt0z+j16NpQ4UXPF3FcqUFI78Pj8SAvL4/3Z4IIWq1WVookWFhZWRn6+vp4WiyXyyEWi5GTk4MlS5bweUCNJWo8hUIhKJVKtLa28nq0WCwwGAx8Dgivh/A9kwiGRCJhoQ56/8JrQz+j1+tRVFSEiYkJFvDxeDzo7e2FWCxm+GUsFkMgEJjzHCChEo/Hg5aWFv68Y2NjeOihh2bBhgnG2N7eDpvNNmudUMMpGo1i6dKl+Na3voVf/epXvKfl5eXB4/GkrG0S4Pjwww95GpyVlcUQZ2FoNBrcdtttaGpqwpEjR7B9+/Y5vT/pGRVaTxD0c2pqimHGFDTZc7vdCAaDbO0xNDSE7du3o7+/n9d4NBqdV2AnKyuL9/VPMgj++rcUV4qyK/GJBBnb7t+/H4lEAhqNBsuWLeMD52IePFJb8/l88Pl8OHv27CW9p7a2Ntx9990pXk/AebEM8gmjbrTVasWxY8dSOp/xeBwrVqzgDbq8vBx2ux3JZBImk4k9kEKhEILBIBtuDg4Oor+/H2+//TZqamqwdOlSVFZWciL5xhtvIBgMcoJIXSMqVMLhMF5++WVOpKampiCVSrFnzx489dRTswqzdChjc3MzH77CIFEKobgEEeapy07Tgf7+fjz22GN44okn2Ax2IR1lIuar1WpWuWpsbGQIW/p6oMN+vsR36dKl+NGPfsTqU6FQCHv27AEwMxUzm80sMEJkeYrJyUkuUCmh/OCDD3D27FnmCfl8PlRUVGD9+vXMByEITFFREUKhEFQqFU+DhImERqNh8RfqaJeXl/O97OjoYCEFkUiEoaEheL1evrfkr/TWW2/xeyZ4ynzPDR3gpABGQg10H30+H06cOMF8EIpkMonTp0+jtbWVp4Aulwsul2tOU2pKxhQKBfR6PRdXUqmUE9xoNMowzb6+PqjVauTk5MBgMOAf/uEfsGfPHsRiMS4kSFo9UwwPD7NYxX333YfS0lJUVVUxf5LW//79+9kIm+71hULIPTMYDDCZTBgfH591ralRQXy3YDCYMWmhgpW4aQSxm56exv79+zE4OMid/HR4tFwuh9VqxfHjx3H06FHYbDZ+NicmJlBZWclKjtQYoL1GLBbDYDDw3kAFWywWYxVK6upXVlZywkuFI3XblUolysvLsWLFCrS3t88SDsrENyTILXk3pgc1kej60NQxPUisJdO+QnA5kUiEvLw8FlmqrKzkvTAWi8Fms7HCIzDTkNBoNFx0LF68mDlx6TBvnU6H8fFxPqcI6kYKnFNTUygtLcXDDz/MRrhk7eJyuRCJRHg6QQmmSqWC0WhEMBiEzWbD9PQ0dDodcnNzYbPZUuxBqCEnTE6FkD0SwcnKykJ+fj4mJydRUlLC+1tRUREaGxvhcrk48SZTb/r+qlWrWP2XGgEOhwNGo5Gn40LVUbIxII4wvc/s7Gye0tA9JrsX+hrBGWmfoKisrMSaNWtQUlKC//iP/2CLmOnpaeba0v41H+SV1G3Tv9bR0YHt27fj0UcfBZDq69fW1oZDhw7B4/FArVYzfJegvtTcGBoaQn5+PrRaLex2O+/P5JlIkUgkuGhWKpUsU5/uA0aczA8++ADj4+MIhUKsKptpvRcVFSE/P589K3NzcxEKhS6oWEpy/5FIBCdPnuSGDcWFeLbUtCRe4CddJB07dixFZfj/elwpyq7EJxL79+/HgQMHeMMk/6GVK1ciFotxJ/CTjEQigccffxxut5snH/Tgd3V14ciRIynKW8lkEiMjI/z7Xq8Xr776KnM2CMpAEAK5XI6qqiokEgnGqUciEYYJhUIh2O12dHd348CBAygtLUVJSQluuOEG5OXlpSQpKpVqFrxIKOcNzGyuIyMj2LNnT0YsvRDKSJ3UUCjExs5Cbk8ymeROrLBYpAkb8R/m6updKAiyFolEUF5ejurqai4ACC4j9D8j2eRMoVAo4PP58NJLL8HpdDLhWqfTweFwYGpqCufOnUvpKAtDJBIhJycHoVCIuRNarRb5+fnw+/2soBcKhVBdXc1FPHkEGQwGhoTSvaIEtaqqig//9Emq3+/HCy+8wAqDwkIrGo0iNzcXMpkMpaWlePvtt/l+Z2dno7KyEitWrMDZs2fR398/Z6FEExJSyhOLxdyhpmuRDgWlyejExAQnmGfOnJlVvNHPUtcbAL83Sp6CwSDGxsa4KBQS4+12O1/3eDyOu+66C/F4nFXvFAoF+w9Fo1GIRCKo1WqeiHu9XoaRrlmzJqPEeWtr6yVx7ZLJJBwOB95+++2MCQ9dR0rcqqqqkEwm2eOIbCKIfyWTyRCNRhmiBswki2azGePj4wiHw3j00UeZv5afnw+pVIqysjKMj4/DbDbD5/Mx9C47OxuxWAwjIyOora3Fpk2boNfr0dvbi7/85S8YGBiAVCqFTqdDeXk5wuEwzp49i+npad5rqeCJRqMpDQepVIquri7o9XoUFhamiFGQiEsmY2ryJXv55ZdnGc4L10tRURG2bt2K0tJSHDx4EO3t7Qw7pWtP65maMTQRoPWTnZ3NaoF5eXnMd3vvvfdQVFSUIv2en5+PkpISuN1unvjl5+cDmJkipaMLMnHh0hVohdeIhDiAGXh++mR+7969OHDgAJxOJ3OvhHsZcajEYjHcbjeUSiUXAnQNhE0pmpgIOaZ0zni9XuzZswdNTU0M7QVmOLC1tbXcUKL7Rt+vq6tDcXExRkdH8eGHH0Kv13MhQmqCJKozNTUFnU6H4uJifr8EWRaq6BkMBmzYsAFerxcffvghIpEIcnNzGdLd1NQEk8nEeyWpTVqtVrzyyiusYkpCK4lE4oIFxFzFCV2bdKn/uro6boJNTU2hoqICX/jCF3Dq1CmGwlLjRaVS4eTJk3C5XMxdq6qqgkQiwcmTJ1mynhQgdTodbrzxRuzatWvWHkRnDvHWaCI4OTk5y7ONfj43Nxd1dXUoKytjy5h4PI733nsPHo8nIxc0vYCiHORiIplMcpOHECsfZ66W3oyLx+N/U2bSV4qyK/GxByVH6RtqKBTC4sWLsW3bNvz0pz/9SLKxlxoDAwP413/9V1x33XVYvXo1ysvLsWvXLphMpgU53FP3G8CsjTccDqOrqwsymYwT0vRCCjhfoASDQQwMDODo0aMAwN11SqKLi4sRCATYuypTN21qagq9vb3weDxz+t0IhTpUKhUrUFFxOT4+jmQyicLCQk5a2tvbIZFIcNVVV8FgMKCtrW2WefGlRCwWYxPQ0dFR7or+/d//Pf7617+mFH3k2yY8sChR8Pv96Ovrg0QiYV4bJTakZgbMwLCEXXORSASdTofJyUk4HA7uGBsMBqxcuRJerxehUAhlZWWzTGZpOqBSqVKUB91uN7+uEN4qVHtUKpV4+umn4XA4YDAYsH79egSDQUQiEUilUpSXl6OyspI91tavX8+TMoVCge985zv4yle+gldeeQVPPvkkm7ALg5I5WhdisRhyuRzFxcVc1GRKcmQyGReyVLhnmqoC58UUqAMuk8lYcU6r1aK2tpYnymKxmNeeVCpFQUEBQ676+/shkUjw2c9+FuvWrcOKFSu4CCQYoVarBQAcPnwYnZ2dkEql3EwRJsI2mw179+6FWCzmZ4gKzbkmLqTeKLx28yUfNBkn4Y5NmzaxN2FfXx8XDdSoCQQCbPYqVNCLRqMIh8Po7OzE73//e3g8HgwODsLr9aKyshLxeBxKpRLBYJD3F7rO1PyxWq0wGo1oaGhAaWkp+07F43HmvdrtdkQiEZw7d465Vnl5edwYSW84CGG/Qmn2uYypiQc0F9eTPOb0ej0effRR5ojU19dj+/btrOJZWFgIv9+P8fFxLmqlUilKSkpw7bXXIplMwmw2w+/3IxaLQaVS8cTNYrHweiURE+Jybtu2LaW4nM+DL10NM5MCLV2j9MgE49yyZQs/P2NjYzAYDPD7/YhGo1AqlSgtLUU8Hse5c+cwPT0NjUaD6urqlAItEolwUUaJu1gs5v0imUyy+TFB+O66664UGf3JyUmsXLmSP1NfXx+am5t5rUxMTPAUUCqV8l5LjVTii9LUTCaTwW63w+/3Izc3F1VVVVi2bBlMJhNLyt9zzz0AzitPpqvokugNwXlpTSxdupT5i7W1tRCLxdi1a9e8sOb5QqVSQSaTwe12w2q1oq+vj88bg8EArVaLeDyO6upqfPnLX8a6desAnC++xWIxTp06hcHBQcRiMWg0GpSXl+Pqq6/G5s2bceTIEezYsYMhxPF4nKfa6XsnKVSS8jJNBMkyg857YRNCJBLB7Xajr68PW7ZswbXXXstnkVQqRXd3N4CZM5I81S5n4RQOh5Gfn8/KpENDQx+JZzlfCBt/1Ij7tGX8P8m4UpRdiY89iJ+TnviIRCKMjo7innvuwU9/+lP84Ac/uOTJy0eJWCzGsByz2XxJimBzhVCxiaYgmcb/QqgaKaAJCcdEjCbFLII2pkdOTg7i8Tja2tpQXV09S8YdAPOaSJmLOtVarRbV1dXQarVMcFar1WzAC4DFPUi84XKE1+tFa2srJ25UnGYqGEglcXp6GsXFxQyvSSaTWLx4MU/IwuEwKisrOZEnRUD6L5FIQCwWo7y8HNPT07PEJ4SGqULFQKG3kdAWgK4rSd/TlEyYxJGBtNVqhdVqxcTEBK/3goIC3HTTTRgaGpolIAOcTwAPHz6M5cuXo6KiAiaTCT09PVCr1RmLMvIVCgaDvKai0Sh8Ph+MRiPkcjlPXmitUVJA00GJRDLv8zA5OQm1Wg2FQgGJRMKG2CqVCtdeey2USiV0Oh0SiQQKCgrwd3/3dxgbG2O5+qGhIZjNZpjNZiQSCZw5cwZmsxl6vZ7lkkdHR5FMJrFx40YAQE9PDxYtWgStVouSkhL88pe/RDAYZAGC7u5u+Hw+KBSKFEgtTWLSP4+QN0n/XYg7QZPAq6++GuvWrWPoIflmAUB1dTWuuuoqtLa2wmKxwOfzMWeJIGAkQOJyubBv3z74fD7mMBEkraysjF9bWFAJIWpCY3uhWbBwDTocDigUCshkMjbTFoqJCBOfdEii0Dw4E9ybpPWFQftHRUUFmpqaIJVKZ9kBNDQ04NFHH01J2ukZsdvtLCNeX1+PW265BWvWrJklhDLXhFr4njOp713uyGSxQgWdEDre0dGB48ePY2xsjBUsT5w4wc9aTU0NNmzYgFWrVvH+I/SItFgs8Hg86Onp4WIrOzsbnZ2dLLphtVrx1FNPIZFIwOFw8ET88OHDyMvLg0QiYc4ycP78oak3cS9pb6Bzi4pCEtmKRCK8r5AIECkV2+12tLS0sP1AZ2cnFxdWqxVNTU249957YbPZWLyG4I4WiyXFWLi1tRWrV69Gf38/BgcHGcqYHtRgoVyDnu1EIsEWD1Qo+Xw+WCwWLFmyBDk5OXwNCC1B64qaKHQ25+TkYPny5WwfQA2R2tpaPPTQQ6yU6Ha78cYbb6TkPBKJBPfffz9b35w5c4Yn1oS2mZyc5MYi2U/Q1G5gYABvv/021q1bx2uYfGCpIfXEE0+gvb39oosymt5maqzQGvjyl7+MZDKJl19+eZbwyuUKasRMTU1BLpczd/5vJa4UZVfiomM+b5xMUVpaCq1WC51OB5fLxYeIQqFAdnY2RkdHodfrodPpEAqFPnEyKTCz8fb09HxkvPR8RtbC/wPnlb7o4KbOHcE/aKJAvAJK9gjSlYljJZfLcebMGcRiMe7CkaKS0WhkjD7xTmhiQkpzjY2N0Ol0KckaySyPjo7C7/dj+/btrDB5uYKUxubDxlNkZWWhoKAAd9xxB7RaLcxmM/Ly8pCbmwuTycTTJ5pe7du3j3kSarUaoVAIOp0OW7ZsQXV1dUaZ9gutbVLhFMIWrVYrBgYG4Pf7Ybfb+bUyFXI5OTnIyclh3kVjY2NKB59+nmJ0dJQlql955RUcP36ci2qdTofs7OxZptEqlYrXD11j4hXpdDosXryYJ4FCzpVYLIZKpcLSpUthMBjQ3t6Ojo6OOe+FUqlEZWUlXC4X8w6AmUO+sbGRRXUWLVqE5cuX87RIpVJhy5YteO2111gCPBwOo6Ojg6GV09PTGBsbQ3NzMwYHB1FdXY3u7m6WOD98+DCi0SiysrJY9tnpdCISiUCpVKbwceYrLmnPoX2JJg/zhdvtxiuvvIIDBw6grq4ON9xwA+RyOXJzcxnWCJyH4xC8kZI/IU8oKyuL+SH0O2Q50djYiHg8jvz8/AUVVJnk6IHMUy56rtPXu3CtCs2D55q+b9myBS0tLfB4PMjKykJFRQVuuOEGrFy58oJ+demTKXrvQn6X8DPSzwvfO73nTA2UTyqE14z+LpyS058LCwuZl0eWImRWT4V0unF0eiELnD+HnU4nWlpakJOTA2CGB+l0Onk9ESyaeIwOhwNZWVk8TSF4KHH0NBoNampqUFBQwPxnKhTcbjf/Dom+AOebFF6vF8PDw6yi2tvbC4VCwVMj4dnY2dkJv9+PsbExnDx5kpVDSanUYDDgyJEjeO+997ihQCqiJDYjkUhgtVoRi8XYo46aM/R56dknPzq5XA6Xy8W0AoLmEayURHP0ej1yc3P5vWi1WqxevXpONcfNmzfjmWee4aKMoMLCWLduHSv8+v1+5OTksM0LiXWcPn0asVgMSqUSN9xwA5YsWQKLxYJ33nmHVTQ7Ojr43xeuLZPJBIvFckGY51xxoSIrNzcXmzZtwsjICNra2niPJE725cijsrOzUVtbC4lEgurqamzduvWSX+v/x7hSlF2Ji4q5uoHzBXVvaRJFHd/y8nLm3OzcuZMLNkr0cnNzU1T4Pu6YbyPJy8u7oFysWCyGTqdDMplEIBDgjg8pUKUXmyS3f+211+LrX/86G38eO3YMo6OjKCwsxOLFizE0NITOzk4W/aDEMVOiSSRg8lvq6urin+vr6+Pf1+v1/B5FIhGMRiOuu+465kekJ2vEn3rjjTfQ3t4+5+adlZXF4gIEMbqcQeIVIpEIx48fRzQaZXghHajU/dXr9XxwkcKgz+djsvZ85qIXG0ajkQ2PPR4P2x8Ipe3TOWVCSJXwgE2HRgLnp3d1dXWc+BkMBshkMtTU1PBUQSj9TRApYceUOrJarRaLFi3CBx98kPJ9Wh+kRGk0GiGVStHT05OxSKEiRKVSwW63M0Rx2bJluOuuu1BYWAiVSgW5XI6RkRHs3r0bXq8Xcrkc1dXVqK6uxo033oi+vj6EQiHuGpMAAr3n4eFhBAIBtLS0cDJJXXtgZuput9uZayUSiRCLxbiBMdeznf494uxRx/pCe8/k5CSGh4cxPDyM999/H+Xl5SgoKAAwk5STlxsJZ8TjcchkMlYIpElITk4OFi1aBLfbDa1WiyVLlqRMSzMVWXMVVLSO5vqaxWLhNZnp54BU/qPQa48+V/rvbN68GU8++SR7Km7duvWSi6L0Amahn/FyF2GX0nxMV9/NFMLJmfAeXAhWmel16H6S0bNKpUJ+fj5MJhPOnj0LuVyOyspKAMDg4CALLNGkjM7ivLw8lJWVQa1W4/rrr8emTZsAnC90aTq3b98+dHd3c5GTKQiFAYCh5ekFGTDTlNi3bx+qqqr4OZBIJJBKpbyHDw0NweFwwOVyQaVS8Vqk633VVVfh3XffhcVi4UYITe5JuZi+TkIuhNqh5jBN0AkKT36LkUiELSPInPtCBf98511eXh7UajV+9atfIRQK8ftaunQpbr75ZgDAn/70Jy4w6f+NjY3YsWMHI0IIqv/WW2/x/Qdm9oPBwUE2m77YIDjsfN/v7e3Fpk2b8OMf/xgtLS04ePAg89vWrl0Lt9uNX/3qV7xXXOy0Ljs7G5/5zGfwwAMPfGrNlU87rhRlV+KCITyc5usGzhcNDQ3453/+5xSYwtq1aznpEMrZEwm2trYWSqUSfX19H+vnmy/EYjEWLVqUIjox38/SpIz4O1qtlhNWl8s162AKBAI4e/Ys4vE4brrpJlgsFoyMjLAnzNe+9jUAwC9+8Qv09vZCIpGgqqoKa9aswV//+le0t7fPkiYX/lmomEidebFYnKJQpVQqsX79etx9990ZO7tCmW6aaMyljJefn4+6ujr4fD709/fPeUjJ5XKGbi00CPalVCohkUg44QVmCpDi4uIUTzBKiugQDgaDrJIXDofR3NyMwsLCC67fTCba1IkmDks8Hse2bduwd+9evmZyuTxF2n7btm0pMKZ4PI4tW7YAQEqSbDKZcPr0aXg8Huh0OgDgJKWurm4WlLSrqwsvv/xyxgM1U1GRnZ2N0tJSVFZWMh+HggoEin379mF8fBwVFRUIBAKzDJqpuCMFPyrK//Ef/xENDQ0sKkPKdfQ5SJqdzMhbW1vR3d3N3fp06frJyUlu2mQKktwXfkbiXdGkID1EIhErxglfhzhWwNyT70xBBtd5eXkwGo3M2UuXUSdYDim90Wfu7e1FVlYWFAoFPv/5z6dwluYrsi4mLtRUE+71tFb9fj/27NnDk7u5io3Nmzdfdj+hS/mMlyMutfmYaRo5189ejvuZ6d8Fzj/3iUQCd955J+rq6mAymZjvSpB4j8eDiooK1NfXpyT3wnOAOGcNDQ3YuHEjTCYT/vCHP8xZlAFgIQyNRjPnpIyaPzRZlcvlWLNmDXtXVlRU4Prrr0d/fz+AmSlNWVkZIwKWLl3KzxBxtSORCHJyclBcXIySkhJYrVb2YtNqtQz5HBkZYRVOg8GAcDjMzZLe3l6GPlosFigUClitVjidTmg0Gn7/mYp24fcpyH5gw4YNrL5LsG9SjSVEh9vtZgGhqakpnDx5ksVF6LyNx+Nobm7G/v37kZOTw7L2K1asgN1uv2Qu2Vz7JAC2hgmFQiysVFNTA5PJxN6n1AQlaDGpzgobhfOFWCzGsmXL8I1vfGNBHqz/V+NKUXYl5g2LxYJnnnkGQ0ND3AFN7wYutKM4F6wGmJHELSwshM1mg1QqxaJFi5CXl4e8vLxPrSgjsQa1Ws2QhPmCcNe0SRYUFGDJkiWcdCkUCoyPj8/aNCcnJ1FaWorXX38dr776KmP1h4aG8Nxzz+Hhhx/Gk08+mcINMxqNyM3NTTGYTg+ZTIaioiIoFAqMjo5yAUPwNKEZqcfjYWWq9OkNFQl2u50hBdPT0+jv72c4CyXBOTk5zHnJysrCqVOnZhUGWVlZKCsrw+TkJAYHBxec9GZlZUGr1bIPDE2kyD/I6/WitLR0Fh9L2MG22+0YHR1FJBKB1+vF2rVr512zJpMJDz74IMxmM6amprj40Gg0UKvVKQWg0WhkUr9CoUBfXx8rcgHgwwzAnJMw4utRE4DEWDJBSUtLS9HV1YXf/e53GB4eXhD0EwCvz7q6OixfvhyBQACBQIDFA4AZKCZxPxwOB5YsWYKlS5fC5XLNMoGma7hv3z7k5+enSNKTsIfRaGQlTILnAoDNZsO+ffsAnPduIj4YSU2TmMjFFPAEhQTOi5FkkrQXmqwC54swupZzwYTnC6/Xi4qKCvbhI29BspKQSqWorq7GZz7zGQQCARw5cgTnzp3j4tDr9eL5559HXV3dZS9IaHpHnlTCplomHpnT6URbW1uKt97fQuf6UpuPl6OITD9PL/bvwEyBPD4+jlgsBpPJhLq6Om76Ce8xNeKEfNeuri4uQjMVpzfddBOkUimGhobmbFYqlUpcddVV2LJlC4qKitDW1gabzQaVSgW3242BgQGIRCL09PTA5/OxRcGZM2dgMBiwevVq6HQ61NXV4f7778fhw4exfv16Li6BmXOQvE5JsZDMqUUiEW6//fZZHqTpSpqDg4PweDyoqqqCVqvFvn37eBJP/E6annV0dCA/P589LQcHB5Gdnc0NNwAs5kNnvFKpRElJCfR6PU8MtVotF1F0VtF6m5qagkaj4XOVJoDCvY+QOMFgECqVivOLvr4+hkxfTEilUuTn50MsFiMWi2VspFKzTdiUmcuX9cc//jH279+P1157DT6fDxKJhBvVer2eKSvCPZVg/LW1tX/TBRlwpSi7EvMEmcgeOnSIDV3Xrl3LCnKrVq0CgIvqKM51aDU0NGDNmjXo6+tDXl4epFIprFYrqwp9GkFTGUogLxSU0FJkagAfAAAgAElEQVQxeccdd6Curg779u1j7tHIyAiOHz/OB4darcY999yDQ4cO4aGHHkoR7xCJRPjggw9gMplw0003zbpumzZtQm9vL44ePYrx8XHmrdCksbCwEPfccw/OnDnDalkKhQIFBQVsAkxY+tOnT6O/v5+7pbm5uXjggQdQV1eHlpYWlthevXo1HyQ2m40nHATb0+l0MBgMuP766/Hss89mNOtVqVSora1FVlYWRkZGFpxsV1ZWYtGiRZBIJCw5rFarkZubi1gsljIpIoETKs7IJPSFF17gAy+TUEp6dHR0wGKxcHFF5qparRZVVVW47bbbmLsGnO9a7927F5FIBGfPnmW1vHQJ8eHhYWRlZaXg/8n4laaswkNbmFzQRO2RRx6Zk98nl8shlUpndSojkQj27t2L8fFxLF++HG63G2NjY6wC5vf72VBYeOjefvvt/P6F4gPCAri1tRV6vR42m407pgRnosM4EokgHA7DaDSio6MDVqsV0WiUFcfi8TgkEgkUCgVPs6hAo2sllNa/UMGUaSImhC3RJJn8+Ohr9HP094WuU5FIhGuvvRZXX301PB4PDh8+DJPJxEbTRUVFqKmpQWNjI5qbm2d1t6enpzE4OAiTybTgBJ/EL4DU+5GesNO+Sp5UxP0BMvPIqDAnmOwnBSX/tGOhUERhzNecnOt7mQqs9MK4ubk5pSkq5Nlt3rw55e90/hI6IN1Tbq5i82K/XldXh8bGRhw7dgyhUIh9xSgikQjq6uqQl5eHgwcPMsR827ZtsNls+NGPfoSBgQGmK8jlcuTl5XEzxO/3s6ckALZuIW8zt9vN+/sDDzyA3/zmNywoRo1Buo+0V9LetHHjRthsNjz33HM4d+4cgJmGUG5uLp/LwPnGDjX9pqamYLVaIRKJcOzYMUgkEshkMrhcLhiNRiQSCeaykoUANWXNZjMAYMmSJVi+fDlWrVqFuro6fp5ovVksllnWI3Ptb0RT0Ov1UCqVqK6uhkqlQldX17zPaVZWFiQSCWpqatDU1DRrgjgyMpJiG0N5kF6vT2nKzDUZNhqNqK6uhkKh4PtrNBrx4IMPoqGhAc888wz2798Ph8OB7OxsqNVqVFZWoqioiNWG/5bjSlF2JTIGHQ4nTpyAx+Ph7vE777yDoaEhZGVlwWw2Y+3atSmbNnleXQoWWKVSIRwOs6Q2KZZ9WiGRSKDVahEOh6FWqy9ojkwmoKtXr8bXvvY17vgIp4NUyAwNDcFisWDVqlXIy8vDE088MatIEIlEs+BlwjAaZ0yum5qa4Pf7sXPnTni9XuTn57Mhc0NDA3JycjA0NMQ8nrKyMnz44Yc8FZDL5QgEAnA4HKzURx5aX/jCF3D69GnmIqxevZo7rvF4nBN1k8nE0vrAzATE5XKldN3I5LW4uBhKpZI7hguZ8Gg0GmzYsAG33XYbJyEqlSplSkNfa2lpwenTpwHM+PPcd9996Orqwm9/+1t0dnbyeyI+BUEHgVSRD5PJhLGxMeY50T0m+CIAnDx5kvkXwPnkWCKRoKysjNXuSBHv+PHjkEqlKCoqYrNfn8+H6elpVFdXs5ErAJ44zdXIaG1tncUfkEgkqKiowOc+9zmUlpZi9+7dOHHiRMohPzU1Ba/Xiw8++ABmsxnV1dUoKSlBNBpFT08Pyyq/8cYbuPfee9nvKL0g/Otf/4pYLIauri5OHtva2hCPx6FSqaBSqdgTSyi9TuT7UCiU8nmrq6sRCATg9XrZliGZTEIsFkOtVuOqq67C1NQUampqIBaLceDAAfZRm08cKCcnh6fBBEsiaBXta1TQE3yTfKMqKiowMTHBSa5er0dlZSUCgQD6+vpmiavQ9bn55pv5+ScBI7vdzp13mkiQ6IJQ5ZJ4fW1tbRcUyaA198wzz3ACu3btWmzduhUWiyVFDGTbtm2Ix+Nsdi5UbQTOJ4bksSaRSFiogBRY/69JU89VLM2VcM5XXM3VnJzre5m+nl4Etba2sqS91WpFfn5+yvc7OjoyTj7nKirn+7pQuCj952l6REX86OgowuEwPxdSqTSlKIvFYnjzzTdZ2XdycpJf/8CBA+jt7eWfnZycZJGnrKwsPjf8fj80Gg1Pb8rLy9HZ2Yne3l6eOC1fvhy33347qqurEQwGEQqFoNFocPbsWUQiEXR1daGhoQFPPvkkhoeHIRaLsXv3bsRiMXR0dHAzJBKJwOfzseop7fn0Z+KFp5t6T01NYWRkBK+++ioMBgPGxsaQk5PDn5f8GL1eLyQSCSYmJqDT6dDe3o7W1laetFGxs3v37nlFOlQqFcM1s7OzUVdXhw0bNrBQmlQqxR//+Mc5f1+r1aKhoQE33nhjir+ecIJIZ6XNZuOiSiKRcHEqjPk4qUuXLmVNACFV5b777sPatWu5iUtNhL9F/limuFKUXYmMQYdDNBqFTqdDLBaD3W7Hnj17eBrjcrlQW1vLm7xKpZqVBCz0ISPTSpqM2Wy2BU+oPq4gEn9OTg4MBgMKCgrg9XpZWS89kskkQqEQFAoFCgsL+etCQjbJpo+MjCAUCuEvf/kLduzYMYsPRFj3a665ZpYhaaZrWldXlyItTQkhFQEE+QuHwwgEAjAYDAyNKywsxNtvv82cHMKWE7yQ+DUqlQo6nW4WpPW+++5jCXOr1YqsrCxEIhEEAoGUIlYmk6G2tpYPevJCevvtt+fFwdOhsH79ehQWFrIKoTBhpcLX6XTitdde40R/aGgIzc3NePbZZ2eZRjscDvz6179GSUnJLDn8hoYG9hGjKY/wPVJhY7VasX//fiQSCZSUlODgwYOcHK9YsYKTc5vNhn//939ndTWaNkciEe6+kg9Vps+XKfR6PU97gJlJbX19PR5//HG+//X19fi3f/s3LrZIJIXur8/nY2VE6gYTyfzs2bPYs2cP/uVf/mVWAvriiy+yYhowkxwSKZ9Mo9VqNYCZrjclcMJ7Oj09jcLCwll8GJPJhJ07d+LYsWNcNCmVSjQ0NOD222/notBsNiMYDEIul8Pr9WbsDsvlcqxdu5ZNwGm6R/5flEjRvSV4bkNDA/+f1M4I0itMYB566KEUZcrc3Fx897vfnaWW19XVhdOnT2NkZAROpxPBYBDRaBQTExMIh8NQKBRYtGgRF4rE+1wIbG50dJSvPQCcOnUKAwMD8Hg8sNvt0Gg0KdPWqqoqVv5LL7KKi4vR19fHzTGNRgOj0Yg1a9awYMRCp3LCWOikaCG/kz71WMgZk+m1LsQbS0845/v5+eCOFzOFSi+aaDIEzKzZQCAAlUrFsOmSkhL88Y9/hNVqhdfrxWc/+1msWbNm3inGQnlv9PObN29mLvi+fftQWFiI0tJSTE5OIhKJ8GRbuEdOT08zfw0A/3nnzp0ZOUYikYj5quPj47wPOZ1OPkPi8ThisRhzS8mg3O12w+VysUQ7NU+poXvmzBkMDQ3x1Kuzs3PWfi70A5PL5cjKysLk5CRycnJQWVmJUCiE4eFhnmKR3QjZithsNobPkwgXAFa3BcDXiPYtUmIlfzm3231B/hXtl62trQxBb2xsxL59+9De3o7R0dFZfqkUpNz6gx/8YF4+Y0NDA+rq6hgFQ40n8oRcSAiLL3pN4Rq8UnzNHVeKsiuRMYSHg8vl4mJLODEIBoPQ6XTYuHFjijRvb28vHA7HRcFv/H4/QwkoPqo8/UeNRCLBiR4VTReS1SbZ3UyfnQ5hgvvF43E2gBVGTk4OrrvuOkxNTTEcYc+ePbMEUoAZ6KjZbOYNt6urC1arlQsDgiSQPH5PTw9Dt7Zu3Yo777wTo6OjOHPmDHtJTUxMICsrC4FAAOPj45BKpVCr1TAaZwxHBwcHcejQIeZnrV27ljHtRB4vKSlBMBhkPo5IJGKjVCHn6+GHH0Z9fT1+/vOfZ5yYZWVlQSaTYfny5YjH4ymflwxHhTLYpaWlKZysiooKdHV1ZZy4Tk5OoqenBxMTE6iqquKiNRwOY2BgABMTEzwlpkNaGMQ12LFjB8LhMItXUHLscDg4qRIWMZFIBAaDAatWrUJHRwcUCgWvNaH0+IWw9Rs3bsThw4dx7Nj/a+/ew6Oqzv2BfyfJzCSTmSTkQkIIIQlkJxIQQlWKCFiUQgGhYivqQan1WAVrEdRfbXu84KmnFbSnnrYIooKF1hYpiiIVEAQFxYAmXAK5kDuZZHKdTO4zk9m/P3jWciaZ3CAwAb+f5/F5ZG7Zs2btmfXutdb7fgGNRoOxY8fi8ccf7xIQ/N///R8yMzNlivmGhgYcOXIEZWVlcDgc8iq3qDXm/jm0t7d3CQxE6QaRMU2v12PChAmyRpuYKYuPj0dzc7McZAUHB3ukfDcajXKfXedz5b333pMDHlEzTSQWyMzMxKZNm2SGRrFBXxRod6fRaJCfnw+Hw+HxOWo0GgQHB+Paa6/FsGHDZF8NDg5GUlKSDP6A84MI9zTkYmAOAE1NTbLN/Pz84HA4kJmZieTkZI/Z15kzZyItLQ1vv/02Tp06JWskNTQ0yMLa4eHhmDFjhty35z4zIXhb7lZTUyNTdre2tqKmpkbuIRWJS4Dzs4Die8k986i4cLN3716cPHkShYWFsFgssgCuGPTGx8dj69atKCkpQVtbG0wmk5yJBtDlQs2FzBT19hz3PZ6qqmLHjh349a9/3eOFq+5eq7/7xnp6fE/LHTvPQonZeTFj7v6czkETcL4uX05OjjyXDAYD4uPjkZaWhuzsbDkT4+fnh5deegnh4eFy5sXb+/F2u8g4KBIXub83b0shJ02ahGnTpsnvVnEeilkcsXQ6NjYWLpdLvrb4bnBPemUwGGSQ1NTUJGerxMUjcd6K13ev6yVKqYhjVFUVWq0W7e3t0Gq1OHfunNzT5F6HUCyHFt8voaGhCAgIkMum9Xo9QkJCcO2118Jut8sl6+K8BYDhw4ejpaUFVqtVLntsbGyUKflF2n6R9MtgMMhxk/hdFDUz29vbYbVaERISIoNNb8aPH4+HHnoIt912m9fka91lXgwPD8eUKVO8BmTeiL3L3V2A6QsGXxeGQRl55f7j8M4778gkFoKoSu++vEosl2lvb5eBQXcncucNt3/6058G5X4FcdVfFJDsbflia2srzpw5gw8++ADANzNWYlAsHtfR0SEHh97+Zm5urrxit27dOhQXF6OjowNhYWEoLi5GdnY20tLSUFhYiK+++go6nQ7V1dUICQmRP57ANwkbxBVNse/M398f77//PmbPnu1x9byjowNRUVEwm83yxxM4P3NXVFSEs2fPyvorbW1tqK+vR1FRkUxp3tLSAoPBgKqqKgQFBcnMc/7+/jAajTJgcR/w/vznP0dKSgqef/551NTUyL1EYqYtIiJCBoTZ2dk4ffo0XC4XMjIyEB8fj9raWrmU44knnuhyhW779u3YvXu31w3MIkgKDAxEQkKCXCYkUqIbDAaZtVL8iLsPEvR6Perq6uRymrCwMDnrJq4sugcxqqrKpTvV1dWYN28eAMhECr2lHncngtrOs6MffvihvF8ETiIzlni9hQsXyj6Zm5sr64bFxsYiLy8PbW1tSE5OliUr3In+AsCjZk90dLRsd/F9sG/fPpSWlkKn0yElJQXz5s3D559/LmcMvV15LSsrk4ljxPmi1+vlvpI9e/bg2LFjsr5QW1ubR0IVd62trSgsLJQDI71eLwPE//zP/8TUqVM9kqYcOnQII0eORFlZGSwWC7Kzs1FUVIQhQ4bImSKxXMzpdOLcuXMy0HS5XGhpacE//vEPnDlzBt/5zncAeCZxEcXd7XY7SkpKZGIg8b4zMzMRERGB2tpaVFZWorCwEL/4xS8wc+ZMGYy6z7aK/UbBwcGIi4uTS5BbW1s9CoK3trZi8+bNHst63Zf9ilTj4sq+SAIEQGYaLSws9FjCJWYKRP9zX2bnnkCnv/uVenpO5z2ex44dw8svv4zHH3/c696qnl6rv/vGenq8+L0UbQGc/87PyspCXFycvK25uVkWJ3bPnlpTUwOLxeIxkBX9cunSpdi9e7csOC0ulOzZs0cWKxf9qLa2Fhs3bux3kpjegkpv94k9zVlZWXIPmMhoKOpW3XLLLdDpdHLVgcjMum3bNlRUVCAuLg4mkwmNjY3yt8XlcsFgMMggyT27oGgXADKxlKiPJYrWuxd61mq1qKurQ0xMjFyKN2LECDQ2NiIsLAw2mw2jRo1CcnIyUlJSAEB+H4oSJGJZ/siRI3Hdddfhq6++gtVqRWhoKPR6vcdFD7GXTfzeBQcHIy0tzeNYxH5h8R03b948ZGZmyqA9Ojoa77//vixzIOj1ejzwwAOyv7l/vuLziYqKgsVikVshtFotFi1ahGnTpslZ/927d3cp4t4dBlaXH4My6pY4IW02Gw4cOCCDicjISKSmpmLlypVdlnt897vfRXFxsfxx9zaw3Lt3L1566SXU1dXJq/R9STnvC6K4cn80Nzfj008/RXFxsaz9IeqsxMbGymxw7kkw3GcnxHIIvV4vl5SJ9e46nQ719fWorKxEWlqanDESX/oikBBLE/38/GQw4p6C3uVyoampCQcPHsRjjz3mkapdZNEDgJycHHklz+FwoKmpCUFBQfIKvqjLJpI5hISEyAGimJ0qLy+Xe4hE0Un3gMFutyMyMhJPPfUUgPP9aOfOnfjggw/k7KwoKZCXlyezT4kltGKGpLKyEhMnTuyyDGnhwoU4deoUsrKyYLfb5SBYLMMNDQ3FrFmzMGPGDDn4qa6uRlpamqy5Jq4giwCgubkZiYmJiIiIQE5ODhoaGhAVFYWHH35YXlxwD4KSkpLQ2toq0y2fOXMGQUFBmD59On75y196tH13S8s6Exc23AN/sZ9OXJGOioqSg5WIiAiEhobCaDRi3rx5mDt3rnyeGDyKY+9pnX9PS6PEQH/Dhg0ymUxdXR2cTieOHz8Os9mM2bNnY8yYMV7fi5jtFO2Vn58v6/UEBwejvr5eDsrF7Jd7oOCNuJou+ufw4cNxxx13ICEhAcD5wdQbb7yBffv2oa6uDtu2bYOiKGhqakJ5eTlaW1vh5+cnM7mJwEecR97k5+fLoEacK1lZWbK/2u12ORvgnt1R1IPKzs5GW1sbNBoNfvOb3yA3NxfHjx/H6dOn5XeF+34jg8GA4cOHy/cbFxcnZyj9/PxQXl6OmpoaNDY2wt/fH8XFxTLJilii7XA4EB4eDpvNJi+IiEGyewkNwW63w2w2Y+PGjYiJiUFDQ4PXZcj93d/U030TJkxASEgIamtr5eeal5eHgwcP9nsW60KW8vX2eLFSYd++ffJ7QdQFFH2lrKxMlojIzs7Gtm3bUF1djQMHDuDZZ59FdHR0l1lH91n19vZ2WTxdLHUVWU5FYNTXbJF9eW89ne9Lly6VCX3cA83O3x2RkZEeS4DdL4aINhGFpDt/D7knFBKP7bwH2Nv/uxcfd99/nJWVhS+//BLl5eUyU3BKSopcCuv+XSQ+U9F3RCF398y5Yp+fyEYrbhMBl6iP1jlBi3sbpaWlebTvz3/+c6xZswYbNmyQF2H+8Ic/dFtX0/3z+d73vod169bB6XTi+uuvl7NjmZmZWLVqlQyOH3nkkR6/7/tbp48GBoMy6pWoPXPo0CGMGTNGzgB4O1HF/gnx5dN5YJmZmYn/+q//6lOK+ctNzIqIWayLIeqPuc8w1tbWorm5GU6nUw7qxFINg8HgsXlY/BcYGCiXlgUFBUFRFBiNRjQ2NqKoqAjJycmora2FzWaTP8pCdHS03BwukhyIpYSiLlxOTg7MZrPHFTH3/VliKV94eDhqamqg1+tlvZjm5mYkJCTIHyoAXX6IsrKycODAAZw5c0YOQktLS7FmzRpotVo0NDRAr9fLDdDXXnstli5dioSEBFn3LSAgADabDRUVFbItTSaTLKYt9ko5HA6ZgMOdmFHqvG9JXAFNSEiQP8gipb0IjESqY28/+OJHrPOeI29/X2RkbGtrQ05OjlwiI86jzm0vlmaK57svI4mNjUV2drbcKC4GAsXFxcjNzYXNZpNLYyoqKmRgK/q2RqPBe++9h5tuugllZWVyJstgMGD06NFISkrqdamK+zGbzWbs379fznL+6U9/krPCYu+Y+zmQk5OD+Ph4KIqCffv2YciQIR77/eLj45Geng6dTieLRouZ3kOHDqG8vFym7/c2u+5e89CdKN7c0dGBHTt2YP/+/XC5XCgvL0d9fb1MXa/RaHDy5Eno9Xo0NTXJNrNYLGhsbPTIzChmlzt/XwQEBMiC5ZGRkfJChSjYK9rDaDTKq9pNTU2Ijo6W+80AyAsS//rXv6CqKtra2uSMn/t+I5EJzuVyQavVYsiQIfJikLiYIpYhBwcHIyEhQSZZEXtBQ0ND4XA4kJCQAH9/f1RWVsqLMZ3fn06ng06nQ21tLWpqamThX6PRiHHjxnmcBxeyv6m7+9LT0/H000/jhRdekOdHUFAQIiMjZV/obharu7/TlwGnuHAxYcIEWdaiM/cZObH0s7GxEX5+fmhqakJERIRcfSDavKioSBZHBs4H7kOHDvWYdUxJSYHZbEZ7ezvCwsLk7Lv7SoJx48bBbDbDaDQiLS3tghKy9NQWvS2F7KmQOfDNcrjuXq+nz6Dzd2p3z/P2/95K8ERHR6OwsBA5OTmoqqqSFzxyc3Plslv31+rcd9xfE+j6eyBu69wW/SmEDgBPPvkknnzyyW7bpbvnL1q0yCPoFbdnZWWhoqJCJlJZt24dbDabx3JzMQPvLUkQA7PLg0EZ9UlfC4P2djUxKyurS8KFwSAmJkYGS94SO/SXGIi6L6kSS+DE1WcxUPbz8wNwftZJbPB3uVwwmUxQFAUGg0Fe3UpKSsKRI0dw9uxZHDp0SC4VEQkjDAYD9Ho9KisrkZ+fL/fiNDU1eQxURSHmgoIC7Nu3D4qieAQIIhDIzs6WyyqmTp0Kp9MpU/nW1dUhPDwcwPkEATqdDmlpaV2y9e3YsUMGV9HR0Th37hyam5vl2nqxt660tBTl5eU4deoUKioq0NjYKLMYOhwOmYBk9OjRMnNkc3OzXD4YEhKCtLQ0r5+Ht0FAd6nmxTKkoqIivP7660hKSupSH6fzFdX777+/y990v99isSAnJwdlZWXQarUIDg7GvHnzPF5HLOUV6Z7dC6i+9dZbsnh4QECA3CNoMBgwatQoAEBFRYWcQRJp3jUajQzG3PtjZWUltm3b5nG8/v7+qKmpQW1tLYYNG+bRJ9wDr+nTpyM6Ohr79+/HkSNHcPz4cY+9DO6zKt3NJJWWlqKqqgp6vR7+/v7w8/NDSEiIrD03YsQIzJkzRwbbqqri6NGjqKmpQUdHBwIDA+Hv7y9njXQ6nZw1EDNqnf+2RqNBe3u73FcpZqk6tw0AefHB/ZwVM17udYPCwsIwatQoREREyOQaUVFR0Gq18gq/2HslzlWxr81kMmH48OGorq6Wtdu87TEV3w+icHxsbCx+8pOfIDo6Grm5uXJGJSUlBbt370Z9fT3a2trQ1tYmZ8qCg4MxYcIEpKSkYMiQIbIPR0dHe8wOR0VFQa/Xy5nyqqoqnD17FlVVVXJ2z73UgviOFEvDoqOjPUpECL0N6r3p7r6ZM2ciMjISa9euhdVqRWpqKmbMmAHA+2C4t7/Tm84zDM8++6zXiy/uM3IAPAqHh4WFISYmBqmpqQCAgoICxMTEyHp2AOQ+U9FvhJqaGlkvcOTIkTLbq/tFJbGvz1czG4N1mZu34xIrejIyMuSSdLPZjJycHK/Zo739dvQWUPa3rw80b38rLi5Onqd2u10u+RRbNMR3aedSGEDf6/TRxWNQRgOupy+fCRMmyDXgAPpdmHUgabVaAJDLBDvXBxEbh/tTtBaAHLgAkMv6xG1ig7Hg/v6Dg4Mxe/ZsNDQ0oLCwUF7VnzRpEioqKlBaWoqDBw8iPz9fphlubW1FcHCw3LclBppiMCaWNXZuY7vdDo1Gg+rqarz55pvQ6XRITEz0SOUtEhRkZ2dj9+7dOH78uCxYLa706vV6uWdKq9VCp9Nh2LBhCAkJwfjx4+F0OuXAOyEhAXFxcbDZbLKumnj/YmBcUVGBiooKj88oLy8P8fHxcrZBJCkQyU/EwNZoNOLTTz+F3W7v06Zk9+BTpMUXz/nggw/wySefoL29XS4HBc4P/kXKcff6Qe6DIpvNhuzsbJSWlsq/9dVXX6GkpAROp1Put/jiiy9gsVhk1iwxm9nU1CQ3vOfm5iIjIwNFRUVoa2vz2AAvjqe1tRX19fUem+fF7E1AQIC8rbfzTGRjPHPmDOrr6xEREQF/f38sWLAAX375Jfbt2weHw4EPP/wQsbGxOHr0qJyVc+/PfSX26rm/D5HevqOjA2VlZQgODpbLcuvq6mRgIPq6eK77jKnoc+7nmQjiRRAhznMxs6zVamX/FYVv77zzTlgsFlkktqioSG76F7Nx4uqy+4yqTqeT5QEAeMygpqWloaKiAk6nEyNHjsSSJUvw9ttvy6QrIhubuCik0+kwduxYjB49Wi7Bcu/bS5cu9bhqbzab5VJwEbyGhoZi0aJFXjMVepsddl9aBZwPSj799FP5eVut1i4X1rRaLUJDQ/u8V+VipaenY9WqVV4H0AMtKyury2xWb8vIdDodNm/eLANwMQMtVhWItr7lllswduxYj5l2sTdTfLeI7HqAZ5mM7i4qUe/S09MRHx+PkpISuFwuuT/4ap4dEhctz549ixEjRsBkMsn9d2KmTOxrvtpLYQxmDMroshJXusVg3JcZFl0uF3Q6nVzS5+1YwsLCoNfrYbVa5UDOfXO+O41GI7/ck5OTkZqaiqysLNTV1UGv1yM8PFwmchAJCsTzAgMDMWzYMJntrKqqCk1NTbBYLCgsLER4eDiqq6thtVrlJneNRgOdTofQ0FBoNBo4HA4EBATIwCwkJASJiYmora1FcXGxx/I/ADK9sBjM5+Tk4PTp04iPj5dXeUWdmJMnT8qCmBEREdBqtWhqahBr994AACAASURBVJKZosSyL7Fsy9/fH4cPH5aFNMUskMhYKAbMBoNBBo/eZiYdDgfq6urQ1taG0aNHy7ToIhAsKChAWFgYDAYD6urqUFJSgqysLCQnJ2PixIlyVgDwfhXZW7HWgwcP4vDhw3KJqVjiITaeZ2VlQafT4dNPP0V1dTUKCwvl8pXS0lJkZ2fLYDwmJgZarRbFxcWyz4i9ZaWlpXKWU8wGuSeBUVVV1usR94uARNSpEcFHZWWlnBnT6/Xy8e6BT29E6QGn04mqqipZj7CoqAhBQUFy6V5lZSVsNpvX5BreziWNRiPTQYvj0ev1MJlMMgFKUFAQxo8fjy+//BItLS1yKVx8fDwaGhrk/kRRbkKkxBab/cWeQrFvUgRY4uJEdxdXoqKiEBgYKItjG41GAN4zjbnPaLrvh/Q2KHZPeuL+Wk888QTS09NRW1sr6wgqioKDBw+itLQU1dXVcpklcD7b2uOPP97j/r7ullr1tqzW/TV6Wt0gAoAtW7bgs88+Q11dnUefiY6ORmpqKmbNmuV1luxSuVwzD+JCIvDNbFZvx/Tll18CgExEMWnSJMyaNavL8jf35X/ur+EebHt7jvvfov6LjY3FsmXLAJwvvxMdHY3p06fj66+/7nM2zivNiBEjkJaWhoiICI+SNIBnUiixr9nbRSC69BiU0WWTmZmJp556alAk9RD7QUR6+ISEBJkBSwyIg4KCEB4eDkVRYLfbce7cOTidThQVFXl9TaPRKOvHpKWlYebMmXITsNgULGYIGxsbPTKcGY1Gj6tUYimBWDolrmiJ+igBAQEIDQ3FqFGjcOutt+Ljjz9GQUGBXEYmBuQGgwH19fVy9kDMJgjuA1Wn04nCwkKUlZXJvW1idk8kWxDL4kTA4B5IuQfaIsOcqA0jZkBELRoAHrOHbW1t3S4XFRkSy8vLkZ2dLfcsihk74PwgWKPRoL6+HtXV1SgqKsKhQ4cQGhqKcePGITg42OsVUPd9IC0tLdi0aZNHbTbRV8T+NlVVYbPZcOLECdTW1sq9Tvv375d736xWq6xBU1dXhyFDhnQJCMRnL4qyCmJGx70dAciU6YmJiYiMjERtbS0aGhrgcDhk3R4R7AwbNgyNjY0yiBIBmngPIkGGu4CAAKSnp8vMey0tLR4ZOyMiImAymeBwOBATE4PY2Fj5N8SxiiKy4eHhiImJ8fib3/ve9zBlyhSYzWYUFRXB6XQiIiICH3/8MfLz85GcnIyFCxciNDRU/nvevHnYu3evTBgEACNHjkRCQgIsFgvKy8sRGxuLKVOmoLq6Glu2bJH9XpRwEEsixQWOzrXSbr31VpmZrK8zq33R03K9e++91+O2zumnxX4i98yWfeX+dzsP9i/keN3vF+fNhg0bUFpaCpfLhbi4ODz88MOXNRi73NLT0/Hss8/2KcAVOicXEQEZ0Ld+1NuSObp4nWdbAcgg5WqcHfJ28cVbX+5p7xtdegzK6LLZuXNnt3U0LjWNRoPw8HB5lWjcuHGoqamR+wTEoEIUmiwvL5eJLBYuXCivIm3YsAGFhYVdXl/swxAJLsTVzc41Z/qSLcpgMCA5OVkGbg0NDYiJiYHBYJDZ09zrlZWVlcFkMsnBvJh1Eok6VFWVBSV7Wy7qHggA52fTTCYTwsPD0dLSIgMDMcsmEpeINhg6dCjq6urkDE/n1P9BQUFyltBkMiEmJkYmLvFWm0XsfRPZG0UdpfDwcBk4ieyQYnZFLAN0Op2w2+3Iz89HWFiYDOC6y8wm0oLbbDa5JC4gIABRUVGyxADwzbJXg8Egl9OJmTSTyeS1uLjIMgpABi8i8BWfifvyU/d6gCKj5q233orly5cDAPbv348NGzagvLxc1t3R6/WIjY3FihUrYLFYcPDgQdhsNpmCPyIiAjNmzEBDQ4Pc/yT6+AMPPOCxb66pqQmvvfaaDGQfeeQRWCyWLnvKTp48iebmZlitVvm+pk+f3u0eH/dBgNlsRkVFhTwnRXDSeYbAW3Y39yyVd955J7Zu3YrAwEA0NzfD398fI0eOxNKlSz2KP+fn52PLli0eWRGHDRsms1D6mvusyWAcFIlBXVpaGnbu3AlVVT2WO1/NOieq6E1vs480OPQ003w1fmYXckGALi8GZXRZXe49ZGJJ1rBhw7B06VKPfRXeUr66F4n19uV84403Yv/+/TLICQgIQGRkJIYPH47W1lY5U9LW1gaz2YzFixd7ZOvqboOw+7In9yx7nZdJAd6X4YlN5GK5YHV1tRysi2VuYgmYSC4iZmVE7S0AcgmiezCQlpaGWbNmQaPRYMiQIYiNjfVINSySB7gfr5gROX78OGw2G2pqahAXF4f4+HikpKRAVVWEh4fL1wKAL774wmOGSgQo/v7+cu+PeypynU6HkJAQtLW1obW1FXFxcQgPD4fVapWzjVqtFsnJyR4zZd1lZhNBMQC5fEMUfhV1vIBvZjv1er3chxgVFYWOjg5YrVaYTCZZOsBgMMjPQQSXQ4cOlQGjmMkSs48i4BNLFEUAYbfbZTa/2NhYOasr9pnFxcVhwYIFckkcAFnUXaSadr/K313/dv9/RVF6nB0Q+6j6+nqddTdw9TZD0F2wJkyfPh179uxBRUUFTCYTli9fLhMTieebzWaUl5fj8OHDUFUViYmJmD59erfH5yuDfVDU3wDl22qwf47UFT8z8jUGZXTZzJs3Dx999JFc+nI5iJmIiIgIJCcn93lJSHf3zZgxA19//TWysrIQEBCA+fPny2VZR44cQXFxMSorK2UtsQtZl+5eUsDbZmNv/3YPLETRXvfsfefOnYPdbsfWrVvR0NAg02ObzWYZrAHnZ7KSkpJgMplgt9sRHR2NZcuWdRmE9bbEofPg39v+G/fXyszMRFhYmCw+LpZcApAzgxqNBpGRkfDz85M1YVJSUjyKfYokBaId3I+lL5nZ3FPSewuEAciZG1ETaOzYsXK/n8gYGRUVhbi4OIwcOVLuExL76MQMnMj42draiubmZuj1elkH0Ol0IiAgQBb/7ujowLlz55CZmYnY2FhERkbKoFWj0cBkMmHy5Mk9pp3u7j13pz+D7wsdzPT1eZ0fZ7FYsGfPHrS3tyM7OxuLFy/GCy+80Gtpgt/97nfYt2+fx54uIiKiwYBBGV026enp+POf/4ydO3di48aNsqjxQBDJBLzVLWpvb5eBSGfu9Wf6WuH+iSee8DrDlp6e3mWpVX/XpbvvcRL/7u+g1dsyMCEpKQnr1q1Dc3MzjEYjYmNj0d7eLmdcgoODkZaWhnvvvdcjiPIWXHVXr6e74+rJvn378PHHH8ukIe5JVYKCgjB69GgkJCTIAqDejq3ze+2pvk1/j9f9trKyMuTl5SErKwuqquLs2bMyCUhgYGCXtOXu+4RKS0vR3Nws9wSKukVBQUGIj4+Xy1LFezx69Cj+/ve/y2BPmDFjBg4dOoSMjAyZJONq2wPhzr1Om06nw8aNG1FQUOBRkHfSpEm9nsPe9nQRERENBgzK6LISKX+3b9/ude9Nf4ilbQAQGBgIo9EIi8XSZXmkyG4oAjYxwDt+/Dg++ugj2O12xMTEdFt/prOeBu4Xuyek8wbxCx1od3eMdrsdtbW1KC8vh1arxZgxY3DDDTfAbDajqakJRqMR1dXVMJvNmDt3LsxmMz788EOPoCI4OBipqamy0ObFKisrQ3Z2Nmw2m9cZ1GHDhuGuu+7qNhnDhczSdBfI9YXNZsOZM2fQ0tICl8uFtrY2mfUwNjYW1157LebNmyf7knufyMvLw549e2TKYVGTLj4+3iNDmyCy6In02O6v+cwzz3SpnXa1Eefqvn37cPLkSVn3qaWlBY2NjTI76tUckBIR0bcDgzK6rMxmM55//vmLDsiA80vNkpKSEBUVhaCgILzzzjteAzKTyYTExETU1NQgMzMT27dvx2effQaz2Yz29naZhKK7+jP9dTHr0i/HBnGxl0mkL7/77rsBQA58KysrceTIEcTGxmL79u3IyspCY2MjHA6HrE9ktVrljM7FGjFiBEJCQrzeFxgYiPnz5w9oMobOafD7W4/m3LlzsoYdAI9kHS0tLcjLy8PevXsRHR3tsU8qNjYWI0aMQEVFRZeMnJ0ztAmxsV3TY7vfdzUGYoL4nE6ePImCggKZTVKv18PlcmHIkCFISkrCT37yk6u6HYiI6NuBQRldVmVlZTh16tSAvJZItjF37ly8//77XVKqi5mLOXPmoLW1Ffv27UNHRwfMZjOqqqrkVfeAgAAEBwf3WH/mcrqUg+309HR897vfxZEjR6DT6ZCamuoxy1JdXS2z8u3cuRP79u1DQ0MDAgICZH2ygd4PGBsbi/nz56OwsFBm8dNqtQgICMDkyZOxcOHCAf17F7pEVIiLiwMAWZhZp9NBo9HAYDDAYDDAarV2u5+wc9At/n5PAfjVHnx1R3xOVqtV7oHUarUIDg5GVFQUwsLC+p0ynoiIaLBiUEaX1YgRIzBs2LBua331hUhu4HK5UFJSgvfeew+Kosg6XMD55BAmkwljx47FsGHDcPLkSZSUlMjB3NChQ1FfX4/AwEBZa+fbMLgTe+K8LXtLT0+XSUYA4Msvv5RJJsLCwvC9731P1gFzX0o3EG655Rbk5eWhuLgYUVFRHoWfBzogudglona7XZYnEAG9SMMuXqun1+0cZH0bA66+cP+cvGX5vFrTVhMR0bcTgzK6rGJjY/HrX/8av/71r3Hy5Ml+p8cPCgqSCQ86OjpkIV8AuP7665Gbm4ugoCAMHz4ckZGRSEpKwoQJE1BdXQ0AHpXs6+vrL9nAfzDrbubFYrGgvb0ddrsdx44dk1kDQ0NDMWbMGDzwwAMAep/ZudBj6m6Z3kAbiCWiBoMBRqMROp0OQ4YMgVarxYgRI3DLLbcgMjLSJwGD2WzG/v37ZS2xK/0iw+VYyktERDRYMCijyy49PR1vvPEG9u3bh1OnTuGrr75Cbm5ul2VxI0aMwNChQ1FRUYGqqiqZlc9gMMi6Tg6HA1arFRaLBaGhoRg1ahRGjhzZZXDcOYX7lT5gHWiZmZn4zW9+g8LCQrS2tsrshwAwZMgQLFu2zGsdqYF0OZfpXczfEktAxayeew00XwX4Yq/mwYMHoaoq9uzZgxdeeOGK7+ff1qWbRET07cOgjHzCPTX13r178fvf/x7V1dVoamqCXq9HcnIyVq1aBQBYtWoVKisr4XK5oKoq2traYDKZEBQUhPb2dhiNRkRHRyMmJqbbwTEHdz3LysqCxWJBS0tLl715ISEhV/zgfiB1ntUDLs3sYX9kZmYiIyMDTU1NUFUVZrN5wBLXEBHRleViMgyT7zAoI58SKa/j4uIQHR2NBQsWICEhQQ52X331VeTl5clZNFVVERERgbFjx8JkMqGlpQV6vV7WhOKX0IWZMGECoqOj0djYiObmZo/7+lKP7NtmMO4LCwoKgp+fHzQaDUJCQjBhwgRkZmZi586dAOCRpn+wch9IAL4PdomIrjSZmZnYtGkT2tvbkZSU1O8Mw+Q7DMrIp0SGtaamJowcOVIGZGVlZaipqUFOTo5HkenAwEDMmTMH8+bNg9lsBuCZrIJfPBcmPT0dL7zwAp5//nlkZWXJmm7h4eH4wQ9+4OOjo96kp6djxowZOH78OLRaLR5++GEAwP/7f/8PBQUFAICDBw9izZo1gzYwcy9V0LlcAAcVRES9M5vN2LRpE44dOyZ/x/ubYZh8h0EZ+VTnTHg6nQ6vvvoqiouLERwcjPr6emi1WqiqiuDgYEycOBGJiYnYvn07mpub5eANYEB2sWpqapCdnS2/yAWz2TxoB/J0nrdEKRs3bpS1+IDziVwG85JG91IFBoMBANDS0iLv4/lNRNSzsrIytLe3w+FwQKfTQa/X9zvDMPkOgzLyqc4Z1jIzM/HZZ5+hqqoKRqMRMTExiImJQUhICFJTU1FfX4+tW7eipqYGgYGB8Pf3R3FxMbKzs3k1/SKJQNddS0vLRZUvoMun85LKuLg4BAQEyCWN4eHhg6YWnzedU+AD38yUcVBBRNS7ESNGICkpCQDk1g6Oi64cDMrI59wHk/v370d9fT1aW1vhcDgQFhaG1NRUREVFobi4GCdPnpRFo/V6PYYMGYL6+vpui/VS34WEhMDPz88jC6a/vz+OHz8Os9nMtr3CiH1l2dnZ0Gq1SExMxKlTpxAdHT0oP8sLKaxNRETfYCmRKxuDMhpUhgwZAqPRCJvNBofDgYqKCkRHRyM+Ph55eXmwWq0yHX5HRweMRiPi4uJ4NX0A3HXXXThy5AjMZjNcLhcCAwOh1+vh7+/PgPcKNGLECKSlpQEATpw4gc8//xxffvklDh8+jGeeeWZQfp6DMYEKEdGVhNmmr1wMymhQSU9Px5gxY1BfX4+mpiY0NTXh9OnTOHfuHBoaGqCqqiw47XQ6YbfbkZqaijvvvJNfQhcpPT0df/zjH5GVlQWDwYDPP/9cZm9iwHvlEVdM169fj8zMTLS0tECj0eDo0aPIzMzk+UJERDSIMCijQSU2NhbLli1DXV0dsrOz4XA40NDQgIaGBjidTmg0Gmg0GhmY6XQ6JCcnc4A5QNLT02UiiKlTp3IJxBUuNjYWP/zhD/HJJ5/AZrNBVVUEBgb6+rCIiIioEz9fHwBRZ+np6fiv//ovTJ06FbGxsejo6EBrayucTif8/Pxk4eiIiAjcdNNNgzab3JUuNjYWkyZNYkB2hUtPT8fTTz+N73znOxg/fjxmzJjBc4aIiGiQ4UwZDUrp6elYtWoV/vCHP6CiogIAZFr81NRUhIWFYfr06ZgxY0aPQQOr2hMBM2fORFpaGs8FIiKiQWrAgrKcnBy89tpryMjIgNVqRWRkJG666SY88MADSExM7PL4l19+Ga+99lqPr7lr1y6MGjXK47aqqir8+c9/xoEDB1BXV4eIiAhMnToVjzzyCIYNGzZQb4cGgdjYWPzHf/wHjh8/jpKSEuh0OkyZMgW33XabR8Ho7rCqPdE3uPmbiIho8BqQoOyLL77Agw8+CIfDAaPRiOTkZJjNZrzzzjv44IMP8Morr+Dmm2/2eE5ubi4AQFEUGI1Gr68bFBTk8e+KigosWrQIFosFJpMJiqKgtLQU77zzDvbs2YPNmzcjJSVlIN4SDRLp6elYvXo1Dh48iMjIyF5nxgT3qvYOhwMAC9ASERER0eB00UFZXV0dHnvsMTgcDtxzzz146qmnoNfr4XA48Morr2DDhg14/PHHsX//foSGhsrniaDsL3/5C+Lj4/v0t5YvXw6LxYK5c+fif/7nfxAYGIjW1lY888wzeP/99/HYY49h586d8Pf3v9i3RYOIe/KJvhJV7e12O6vaExEREdGgdtGJPv7973/DarUiNTUVTz/9NPR6PQBAq9Xi8ccfR3JyMpqamrBr1y75nIaGBlRWViIoKKjPA+XDhw/j+PHjiIqKkgEZcH427Xe/+x0SExNRWFiI3bt3X+xboquAqGr/ne98B9dddx2r2hMRERHRoHXRQdmQIUMwZ84c3HPPPfDz83w5jUaD0aNHA4BM1gAAeXl5AICkpCRoNJo+/Z13330XADBv3rwuKZ0DAgJwxx13AIBH8EffXqJG0/33349f/vKXzDZHRERERIPWRS9fnDNnDubMmeP1vo6ODpw5cwYAPJYouu8n66sTJ04AACZOnOj1/gkTJgAAjh492ufXpKsbExsQERER0ZXgkqXEN5vNeOmll1BcXIy4uDjMnTtX3ieCsqSkJOzatQuffPIJLBYLwsLCMHnyZCxcuFAugwQAl8uFc+fOAUC3yx2HDx8OALBarbDZbAgJCblUb42IiIiIiGjADHhQtmXLFmzevBllZWXo6OjAxIkT8eKLL3pkUhRB2auvvoqWlhaP5+/evRtvvvkm1q1bJ9PhW61WdHR0ADi/XNIb9yQi9fX1DMqIiIiIiOiKcNF7yjo7duwYiouLZRBlsVhw+PBheb+qqsjPzwcABAcHY/Xq1cjIyEBWVhY2bNiA5ORklJaW4sEHH0RjYyMAoL29XT7ffQbNnfs+s7a2toF+W0RERERERJfEgAdlTz75JE6cOIG9e/di6dKlsFgseO655/D6668DOB9g3Xfffbjtttvw9ttvY8GCBQgNDUVQUBCmTZuGLVu2IDo6GuXl5di8efP5g3RLIKKqqte/63K55P/3NXkIERERERGRrw14UDZ8+HDo9XrEx8fjsccew4oVKwAAa9euhc1mQ2BgIFasWIGXXnrJ6/6wsLAw3HPPPQCATz75BABgMBjk/Xa73evfdb+9c3ZGIiIiIiKiwWrAg7LOlixZAp1Oh+bmZpw+fbpPz7nmmmsAnE8WApxf5qjVagGc31/mjfvt4eHhF3PIREREREREl81FB2WNjY04deoUbDab1/u1Wq3MjFhbWytv78u+LxGI+fn5ISEhAQBQXl7u9bHi9vDwcBiNxj4fPxERERERkS9ddFB2++2344477sDu3bu93t/R0YGamhoAwNChQ7Ft2zaMGzcOM2fO7PY1RW0zkX0RAMaNGwcAyMrK8voccfv48eP7/yaIiIiIiIh85KKDssmTJwMAtm7d6jUJx7vvvovGxkaEhYVh/PjxSEtLg91uR1VVlUdWRqG5uRlbt24FAMyePVvePmvWLADAjh07PLIxAoDT6cT27dsBAPPnz7/Yt0RERERERHTZXHRQ9tOf/hQ6nQ4nTpzAs88+61F3bNeuXfjtb38LAFi5ciV0Oh2uueYaTJkyBQDwq1/9ymPmy2KxYNmyZSgvL4eiKFiwYIG8b9q0aUhLS4PFYsHKlSvR1NQE4PwyyF/96lcoKipCYmKiDN6IiIiIiIiuBBddPDoxMRFr1qzBk08+iX/+85/44IMPkJiYiOrqalRVVQEAHnroISxatEg+58UXX8SSJUtQUFCAu+66C3FxcTAajcjPz4fT6UR8fDzWr18PnU4nn+Pn54c1a9Zg8eLF+PjjjzFt2jQkJCSgrKwMNpsNoaGh+Mtf/gJ/f/+LfUtERERERESXzYBkX5w9eza2b9+OBQsWwGg0Ii8vD06nE7fccgveeustrFy50uPxUVFR2LZtG1asWIHU1FRUV1ejuLgYSUlJePTRR7Fjxw7ExsZ2+TujRo3Ce++9h0WLFsFkMiEvLw86nQ7z58/Htm3bPPagERERERERXQkueqZMSE5OxurVq/v8eIPBgIcffhgPP/xwv/5OdHQ0nn/++f4eHhERERER0aB0yeuUERERERERUfcYlBEREREREfkQgzIiIiIiIiIfYlBGRERERETkQwzKiIiIiIiIfIhBGRERERERkQ8xKCMiIiIiIvIhBmVEREREREQ+xKCMiIiIiIjIhxiUERERERER+RCDMiIiIiIiIh9iUEZERERERORDDMqIiIiIiIh8iEEZERERERGRDzEoIyIiIiIi8iEGZURERERERD7EoIyIiIiIiMiHAnx9AFeCkpISAMCZM2dw7733+vhoiIiIiIhosDlz5gyAb2KH/mBQ1gctLS0AgMbGRmRkZPj4aIiIiIiIaLASsUN/MCjrg7i4OJw7dw4GgwEjR4709eEQEREREdEgU1JSgpaWFsTFxfX7uRpVVdVLcExERERERETUB0z0QURERERE5EMMyoiIiIiIiHyIQRkREREREZEPMSgjIiIiIiLyIQZlREREREREPsSgjIiIiIiIyIcYlBEREREREfkQgzIiIiIiIiIfYlBGRERERETkQwzKiIiIiIiIfIhBGRERERERkQ8xKCMiIiIiIvIhBmVEREREREQ+xKCMiIiIiIjIhxiUERERERER+RCDMiIiIiIiIh8K8PUBUN80NjZi7dq12LNnDywWC0JDQzFp0iQsXboUycnJvj68K15bWxvS09Phcrm6fcycOXPwv//7v11u//DDD7F582bk5OTAz88PycnJuPvuu/HDH/7wUh7yVWH58uX46KOPcPToUYSEhHh9zKFDh/D666/jxIkTcLlcGDlyJBYuXIjFixfD39/f63N4vvRNb+3/8ssv47XXXuvxNXbt2oVRo0Z53FZVVYU///nPOHDgAOrq6hAREYGpU6fikUcewbBhwwb0PVxJiouL8cYbb+Dzzz+HxWKBTqdDcnIy5s+fj0WLFiEgoOtPMvv/wOhv27PvD5ycnBy89tpryMjIgNVqRWRkJG666SY88MADSExM9Poc9vuB09/2Z9/3HY2qqqqvD4J61tjYiHvuuQd5eXkICgpCUlISKioqUFdXB71ej3Xr1uHGG2/09WFe0U6cOIEf//jHMJlM3X5533jjjXj00Uc9bnvllVewdu1aAEBSUhI0Gg0KCgoAAD/+8Y/x29/+9tIe+BXsn//8J5555hkA6DYo2LZtG37zm98AAEaMGAGDwYD8/Hy4XC5MnToV69at6zKY4vnSN31p/5/97Gc4ePAgFEWB0Wj0+jovv/wyYmNj5b8rKiqwaNEiWCwWmEwmxMfHo7S0FI2NjQgNDcXmzZuRkpJyad7UIHbw4EEsX74cra2t0Ol0SEhIQENDAywWC4Dz3y/r16+HTqeTz2H/HxgX0vbs+wPjiy++wIMPPgiHwwGj0Yj4+HiYzWZYrVYEBgbilVdewc033+zxHPb7gXMh7c++70MqDXrLly9XFUVR7733XrW+vl5VVVV1OBzq6tWrVUVR1BtuuEFtbGz08VFe2bZu3aoqiqI+8cQTfX7OgQMHVEVR1PT0dDUjI0PenpGRoV533XWqoijq+++/fykO94r39ttvq6mpqaqiKKqiKGpDQ0OXx+Tk5KhjxoxRx4wZo+7atUvenpubq958882qoijqq6++2uV5PF9615f2V1VVnTZtmqooilpSUtLn1/7xj3+sKoqirlixQm1tbVVVVVVbWlrUJ554QlUURZ09e7bqdDoH5H1cKWpra9WJEyeqiqKov/zlLz363+eff65OnjxZVRRF/d3vfidv5qLCxwAADelJREFUZ/8fGBfS9qrKvj8Qamtr1RtuuEFVFEV97rnn1La2NlVVVdVut6tr1qxRFUVRJ06cqFqtVvkc9vuBcyHtr6rs+77EoGyQKygoUFNTU9Vx48apFouly/1LlixRFUVR169f74Oju3r893//d7/bcdGiRaqiKOrGjRu73Pfuu+/KLyL6Rn19vfrUU0/JYKCnoGDlypWqoijqb3/72y73HT16VFUURb3uuuvkD42q8nzpTX/a32q1qoqiqOPHj1ddLlefXv/QoUOqoijqlClT5A+z4HA41FmzZqmKoqgffvjhgLyfK8X69etVRVHUefPmeR2Y7NmzR7a16M/s/wPjQtqefX9gbNmyRVUURZ0/f77a0dHhcZ/L5VLnzp2rKoqi/v3vf5e3s98PnAtpf/Z932Kij0Fux44dcLlcmD59OoYOHdrl/jvvvBMA8O9///tyH9pVJS8vDwD6vO68pKQEmZmZ8PPzw8KFC7vcP2/ePAQHB6OwsBA5OTkDeqxXqq+//hqzZs3C9u3bYTKZ8PTTT3f72NbWVuzevRsA8KMf/ajL/ddddx1GjRoFm82GQ4cOydt5vnSvP+0PfHNOiGW5ffHuu+8CON//AwMDPe4LCAjAHXfcAeD8foRvk4yMDADA97//fa/7YaZPnw5/f3+0traioKCA/X8A9bftAfb9gTJkyBDMmTMH99xzD/z8PIebGo0Go0ePBnB+6RvA7/2B1t/2B9j3fY1B2SB34sQJAMDEiRO93j9hwgQAwJkzZ9DU1HTZjutq09+g7Pjx4wCA0aNHe92LExAQgLFjxwIAjh07NkBHeWUrLi6G1WrFrbfeip07d3ZZx+7u9OnTcg28oiheHzN+/HgAnu3L86V7/Wl/AMjNzQWAbtvfm762/9GjR/v8mleDRx99FL///e/x/e9/3+v97e3tMslQR0cH+/8A6m/bA+z7A0Ukx1q0aFGX+zo6OnDmzBkAQHx8PAB+7w+0/rY/wL7va8y+OMiVlJQAOL/Z1Zvo6GgEBATA6XSirKwM11xzzeU8vKuCxWJBfX09goOD4XK58Mc//hHZ2dlwuVxITEzE7bffjrS0NI/nlJaWAuj+cwGA4cOHA/jmM/y2UxQF//jHP5Ceng4AOHfuXLePFe0bFxfX7dU6b+3L86V7/Wl/4Jsf56SkJOzatQuffPIJLBYLwsLCMHnyZCxcuBB6vV4+3uVyydfsrv3FZ2a1WmGz2brNuHm1GT9+vBxMerNv3z6oqgqtVouEhAR8/PHHANj/B0J/2x5g37/UzGYzXnrpJRQXFyMuLg5z584FwO/9y6W79gfY932NQdkgV19fDwAICwvzer+/vz+MRiOsVqt8LPWP+BJyOByYM2cOHA6HvO/QoUPYsmULfvazn2HlypXy9rq6OgDnlwd0R3xm/FzOEzOHfXGh7cvzpXv9aX/gm/Pi1VdfRUtLi8d9u3fvxptvvol169bJtMhWq1XONHT3uYWGhsr/r6+v548zgKamJrzyyisAgBkzZsBkMrH/Xybe2h5g379UtmzZgs2bN6OsrAwdHR2YOHEiXnzxRQQFBQHg9/6l1lv7A+z7vsbli4NcW1sbAHRZp+tO3CceS/0jli7a7Xb84Ac/wPvvv4+TJ09i//79eOihh6DRaLB+/Xq89dZb8jl9+VzE1aT29vZLePRXpwttX54vA0NVVeTn5wMAgoODsXr1amRkZCArKwsbNmxAcnIySktL8eCDD6KxsRGA5+fgfiXVnfvnwvY//52zfPlymM1mBAUF4bHHHgPA/n85dNf27PuXzrFjx1BcXCwH8RaLBYcPH5b3s99fWr21P/u+7zEoG+TExmS1h3JyYj18XzdlkidFUXD33Xfj8ccfx5o1a5CSkgKdTofhw4dj5cqVcobsT3/6k7xy1JfPRdzHz6X/+tO+/X0ez5fetbe347777sNtt92Gt99+GwsWLEBoaCiCgoIwbdo0bNmyBdHR0SgvL8fmzZsBwGMjeXft716c/dve/na7Hb/4xS9kwoJVq1YhKSkJAPv/pdZT27PvXzpPPvkkTpw4gb1792Lp0qWwWCx47rnn8PrrrwNgv7/Uemt/9n3fY1A2yIlp5Z5mW+x2O4CerxJR96ZNm4bnnnsOP/vZz7zev2TJEhgMBjQ2NsqNqv35XLq7ekTdu9B+z/NlYAQGBmLFihV46aWXvO4TCAsLwz333AMA+OSTTwAABoNB3i/auDP327/N7W+z2fDAAw/ItnvqqaewYMECeT/7/6XTW9uz7186w4cPh16vR3x8PB577DGsWLECALB27VrYbDb2+0ust/Zn3/c9BmWDnFgj3dDQ4PX+jo4OOY3c0zpsunA6nU6mjjWbzQB6/1yA82utASA8PPwSH+HV50Lbl+fL5SM2y4tzIjg4GFqtFsA3n01n7rd/W8+LiooK3H333cjIyIBGo8HTTz+N+++/3+Mx7P+XRl/avi/Y9wfGkiVLoNPp0NzcjNOnT7PfX2ad278v2PcvLQZlg5xYUlFeXu71/srKSnR0dECj0XikNaX+cTqdcDqd3d4vpuXFl09vnwvwTXY7fi79J9pXfPF7I9revX15vgysvqz/F+eEn5+fzF7XXfuL28PDw2E0GgfmIK8gBQUFuOuuu3D27FlotVqsWbMGixcv7vI49v+B19e2F9j3L15jYyNOnToFm83m9X6tVisz89XW1rLfD7D+tr/Avu87DMoGuXHjxgEAsrKyvN4vbk9OTvaYRqa+mzlzJsaOHYt//etfXu+32+2yqKjIOCQ+l7y8PDQ3N3d5jtPpxKlTpwCgx3TM5J2iKAgKCoLVakVhYaHXx2RmZgLwbF+eLwNj27ZtGDduHGbOnNntY0SNG3FOAH1v/2/jOVFWVoYlS5agsrISBoMB69evx2233eb1sez/A6s/bc++P3Buv/123HHHHbIgdGcdHR2oqakBAAwdOpT9foD1t/3Z932PQdkgN2vWLADA/v375cnjbuvWrQCA+fPnX9bjupokJydDVVXs2LHD60bVf/7zn2hpacHw4cNx7bXXAji/Nnvs2LFwOp2ymr27nTt3oqWlBaNHj+5S44x6p9frMW3aNADAO++80+X+Y8eOoaioCGFhYfJxAM+XgZKWlga73Y6qqiqP7FxCc3OzbMvZs2fL20X779ixo8v+DqfTie3btwP49rV/e3s7li1bhurqaphMJmzatAlTpkzp9vHs/wOnv23Pvj9wJk+eDOB8v/P22/ruu++isbERYWFhGD9+PPv9AOtv+7PvDwIqDXrLli1TFUVRFy1apFZXV6uqqqoOh0NdvXq1qiiKesMNN6iNjY0+Psor17Fjx9SUlBRVURT1+eefV1tbW1VVVVWXy6Vu375dHTt2rKooirpz506P5+3du1dVFEWdMGGC+umnn8rbjx49ql533XWqoijqjh07Lut7uZKUlZWpiqKoiqKoDQ0NXe7Pzs5WU1NT1WuuuUbdvn27vD03N1e9+eabVUVR1LVr13Z5Hs+Xvumt/e+//35VURR16tSpamZmpry9srJSve+++1RFUdR58+ap7e3t8r6Ojg719ttvVxVFUZctWybbubW1VX3iiSdURVHUWbNmqU6n89K/wUHkj3/8o6ooipqSkqIeOHCgT89h/x8YF9L27PsDo7CwUP5+Pv3002pzc7O878MPP1THjx+vKoqi/uMf/5C3s98PnAtpf/Z939Koag85RGlQqKqqwuLFi1FSUgK9Xo/Ro0ejoqICdXV10Gq12LhxI66//npfH+YVbdOmTXjxxRfhcrlgMBiQmJiIqqoqVFdXAwCWL1+OZcuWdXne888/j7/97W8AgISEBAQEBODs2bMAgLvuugurVq26fG/iCnPu3DnccsstAICjR496LSj5xhtvYPXq1QDOz06aTCbk5eXB5XLh5ptvxtq1a2U6ZIHnS9/01v7V1dVYsmQJCgoKoNFoEBcXB6PRiPz8fDidTsTHx+Ott95CbGysx/MKCgqwePFi1NXVITg4GAkJCSgrK4PNZkNoaCjefvttj6UvVzu73Y4bb7wRjY2NCAoKkhvlu/P0009jzJgxANj/L9aFtj37/sD56KOP8OSTT8Jut8vf1urqalRVVQEAHnroIVl2RmC/Hzj9bX/2fd9iUHaFsNlsePXVV7F3715UVlYiODgY119/PZYuXcrlcQPk66+/xqZNm/DVV1/BarUiNDQUEydOxJIlS3r8Mv/ggw/wt7/9Dbm5uejo6MDo0aNx5513YtGiRazJ0YO+BGUA8Nlnn+HNN9/EyZMn0dbWhpEjR2LBggX4yU9+Ap1O5/U5PF9615f2b2lpwV//+ld89NFHKCoqgkajwYgRIzBr1iz89Kc/7XZ/hsViwV/+8hccPHgQtbW1CA0NxY033ohHH330W7XRHgBOnjyJH/3oR31+/F//+ldMmjRJ/pv9/8JdTNuz7w+c/Px8bNiwAV988QXq6+thMpmQnp6O++67D9/97ne9Pof9fuD0t/3Z932HQRkREREREZEPMdEHERERERGRDzEoIyIiIiIi8iEGZURERERERD7EoIyIiIiIiMiHGJQRERERERH5EIMyIiIiIiIiH2JQRkRERERE5EMMyoiIiIiIiHyIQRkREREREZEPMSgjIiIiIiLyIQZlREREREREPsSgjIiIiIiIyIcYlBEREREREfkQgzIiIiIiIiIfYlBGRERERETkQwzKiIiIiIiIfIhBGRERERERkQ8xKCMiIiIiIvKh/w9dMeS8dBZVvAAAAABJRU5ErkJggg==", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "image/png": { - "height": 415, - "width": 434 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# Draw circles \n", - "figure = plt.figure(figsize=(7,7))\n", - "ax = figure.add_subplot(111)\n", - "ax.scatter(obj_sess['x_pos'], obj_sess['y_pos'],s=1, color='k', alpha=.5)\n", - "for no_, point in enumerate(object_layer.data):\n", - " if no_ == 0: \n", - " color='red'\n", - " else: # if second object\n", - " color='orange'\n", - " object_ = plt.Circle((point[1]/2,point[0]/2), 20, color=color, alpha=.5)\n", - " ax.add_artist(object_)\n", - "ax.invert_yaxis()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "abd7bd9b", - "metadata": {}, - "source": [ - "### Insert into database ... " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "5761cc3d", - "metadata": {}, - "outputs": [], - "source": [ - "# First close the NAPARI window\n", - "#viewer.close()" - ] - }, - { - "cell_type": "markdown", - "id": "9c8d187c-6184-4b67-85a5-dc88d44a9077", - "metadata": {}, - "source": [ - "### Loop over objects and insert" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "ac8c1663", - "metadata": {}, - "outputs": [], - "source": [ - "from imaging.spatial_scores import ArenaObjectPos\n", - "for no_, point in enumerate(object_layer.data): \n", - " # Create dictionary and insert into session part table\n", - " object_dict = {\n", - " 'obj_name' : 'duplo_tower23',\n", - " 'obj_x_coord' : point[1]/2,\n", - " 'obj_y_coord' : point[0]/2\n", - " }\n", - " Recording.ArenaObject.insert1({**key,**object_dict},skip_duplicates=True,ignore_extra_fields=True)" - ] - }, - { - "cell_type": "markdown", - "id": "ee2ed9ef", - "metadata": {}, - "source": [ - "### Populate calibrated object positions and retrieve the session just entered " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "6a36a83a", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 4.56it/s]\n" - ] - } - ], - "source": [ - "ArenaObjectPos.populate(display_progress=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "28879de9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    object_hash

    \n", - " Object hash\n", - "
    \n", - "

    obj_name

    \n", - " \n", - "
    \n", - "

    session_name

    \n", - " Meta session name (hash)\n", - "
    \n", - "

    recording_order

    \n", - " Order of session within meta sessions (zero index!)\n", - "
    \n", - "

    recording_name

    \n", - " Recording name: Hash of animal_id, datasource_id, timestamp and combined 'yes'/'no' label\n", - "
    \n", - "

    apparatus

    \n", - " \n", - "
    \n", - "

    category

    \n", - " Arena / Apparatus category (openfield, linear track, etc.)\n", - "
    \n", - "

    obj_x_coord

    \n", - " x coordinate of object in pixels\n", - "
    \n", - "

    obj_y_coord

    \n", - " y coordinate of object in pixels\n", - "
    \n", - "

    dataset_name

    \n", - " 16 character hash\n", - "
    \n", - "

    trackingparams_id

    \n", - " Parameter set ID, starting with A\n", - "
    \n", - "

    obj_x_coord_calib

    \n", - " Object x coord [mm]\n", - "
    \n", - "

    obj_y_coord_calib

    \n", - " Object y coord [mm]\n", - "
    \n", - "

    obj_geometry

    \n", - " e.g. cube, cylinder\n", - "
    \n", - "

    obj_width

    \n", - " Object width in cm\n", - "
    \n", - "

    obj_length

    \n", - " Object length in cm\n", - "
    \n", - "

    obj_height

    \n", - " Object height in cm\n", - "
    \n", - "

    obj_desc

    \n", - " Object description\n", - "
    8c6e12b10ae58326duplo_tower23694a792ec2bedcae2a5daab937fa60cafSquare80Open Field132.21243.299c80e21167ee50e4A315.039579.728cube64.064.0230.0Colorful duplo tower, 23 cm high
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*object_hash *obj_name metasession_na recording_order recording_name apparatus category obj_x_coord obj_y_coord dataset_name trackingparams obj_x_coord_ca obj_y_coord_ca obj_geometry obj_width obj_length obj_height obj_desc \n", - "+------------+ +------------+ +------------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n", - "8c6e12b10ae583 duplo_tower23 694a792ec2bedc 2 a5daab937fa60c Square80 Open Field 132.21 243.29 9c80e21167ee50 A 315.039 579.728 cube 64.0 64.0 230.0 Colorful duplo\n", - " (Total: 1)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "objects_example = ArenaObjectPos * ArenaObject & obj_sess\n", - "tracking_objects_example = (Tracking * Tracking.OpenField & objects_example).fetch1()\n", - "\n", - "objects_example" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "bedebca4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "image/png": { - "height": 415, - "width": 434 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# Draw circles \n", - "figure = plt.figure(figsize=(7,7))\n", - "ax = figure.add_subplot(111)\n", - "ax.scatter(tracking_objects_example['x_pos'], tracking_objects_example['y_pos'],s=1, color='k', alpha=.5)\n", - "for no_, point in enumerate(objects_example):\n", - " if no_ == 0: \n", - " color='red'\n", - " else: # if second object\n", - " color='orange'\n", - " object_ = plt.Circle((point['obj_x_coord_calib'],point['obj_y_coord_calib']), radius = point['obj_width']/1.5, color=color, alpha=.5)\n", - " ax.add_artist(object_) \n", - "ax.invert_yaxis()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv-dlc", - "language": "python", - "name": "venv-dlc" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "toc-autonumbering": false, - "toc-showmarkdowntxt": false, - "toc-showtags": false - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/requirements.txt b/requirements.txt index b4c240b..fc7a8e6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1 @@ datajoint>=0.13.0 -element-lab -element-animal -element-session -element-behavior -ipykernel -pynwb \ No newline at end of file diff --git a/setup.py b/setup.py index dfa5d3a..c369bb9 100644 --- a/setup.py +++ b/setup.py @@ -23,11 +23,11 @@ setup( name='workflow-behavior', - version='0.0.0b1', + version=__version__, description="DataJoint Elements for Continous Behavior", long_description=long_description, - author='DataJoint NEURO', - author_email='info@vathes.com', + author='DataJoint', + author_email='info@DataJoint.com', license='MIT', url='https://github.com/datajoint/workflow-behavior', keywords='neuroscience behavior deeplabcut datajoint', diff --git a/user_data/recordings.csv b/user_data/recordings.csv new file mode 100644 index 0000000..9779961 --- /dev/null +++ b/user_data/recordings.csv @@ -0,0 +1,4 @@ +subject,session_datetime,video_path,camera_id,frame_rate,config_path,shuffle,train_index,snapshot_index,config_notes +subject5,2020-04-15 11:16:38,videos/reachingvideo1.avi,1,30,config.yaml,1,0,-1,Reaching example provided by DeepLabCut repository +subject6,2021-06-02 14:04:22,videos/m3v1mp4.mp4,1,00,config.yaml,1,0,-1,Openfield example provided by DeepLabCut repository +subject6,2021-06-03 14:04:22,videos/videocompressed1.mp4,1,00,config.yaml,0,0,-1,Multianimal - not fully trained diff --git a/user_data/sessions.csv b/user_data/sessions.csv index d3ad137..4eb7371 100644 --- a/user_data/sessions.csv +++ b/user_data/sessions.csv @@ -1,3 +1,4 @@ subject,session_datetime,session_dir,session_note -subject5,2020-04-15 11:16:38,/subject5/session1,"Successful data collection, no notes" -subject6,2021-06-02 14:04:22,/subject6/session1,"Ambient temp abnormally low" \ No newline at end of file +subject5,2020-04-15 11:16:38,"Reaching-Mackenzie-2018-08-30/","Successful data collection, no notes" +subject6,2021-06-02 14:04:22,"openfield-Pranav-2018-10-30/","Ambient temp abnormally low" +subject6,2021-06-03 14:04:22,"demo-me-2021-07-14/","multi-animal" diff --git a/user_data/subjects.csv b/user_data/subjects.csv new file mode 100644 index 0000000..862269b --- /dev/null +++ b/user_data/subjects.csv @@ -0,0 +1,3 @@ +subject,sex,subject_birth_date,subject_description,death_date,cull_method +subject5,F,2020-01-01 00:00:01,rich,2020-10-02 00:00:01,natural causes +subject6,M,2020-01-01 00:00:01,manuel,2020-10-03 00:00:01,natural causes \ No newline at end of file diff --git a/workflow_behavior/__init__.py b/workflow_behavior/__init__.py index ae798f2..e69de29 100644 --- a/workflow_behavior/__init__.py +++ b/workflow_behavior/__init__.py @@ -1,5 +0,0 @@ -__author__ = "DataJoint NEURO" -__date__ = "March 18, 2021" -__version__ = "0.0.1" - -__all__ = ['__author__', '__version__', '__date__'] diff --git a/workflow_behavior/ingest.py b/workflow_behavior/ingest.py index a5cacc7..3406338 100644 --- a/workflow_behavior/ingest.py +++ b/workflow_behavior/ingest.py @@ -1,62 +1,120 @@ -import pathlib +# from pathlib import Path import csv -import re -from workflow_behavior.pipeline import lab, subject, session, pose -from workflow_behavior.paths import get_root_data_dir -import element_data_loader.utils +from workflow_behavior.pipeline import subject, session, dlc +# from workflow_behavior.paths import get_root_data_dir +# from element_data_loader.utils import find_full_path -def ingest_sessions(session_csv_path='./user_data/sessions.csv'): + +def ingest_general(csvs, tables, + skip_duplicates=True): """ - Ingests DeepLabCut files from directories listed - in the sess_dir column of ./user_data/sessions.csv + Inserts data from a series of csvs into their corresponding table: + e.g., ingest_general(['./lab_data.csv', './proj_data.csv'], + [lab.Lab(),lab.Project()] + ingest_general(csvs, tables, skip_duplicates=True) + :param csvs: list of relative paths to CSV files + :param tables: list of datajoint tables with () """ - # ---------- Insert new "Session" and "ProbeInsertion" --------- - with open(session_csv_path, newline= '') as f: - input_sessions = list(csv.DictReader(f, delimiter=',')) + for insert, table in zip(csvs, tables): + with open(insert, newline='') as f: + data = list(csv.DictReader(f, delimiter=',')) + prev_len = len(table) + table.insert(data, skip_duplicates=skip_duplicates, + ignore_extra_fields=True) + insert_len = len(table) - prev_len # report length change + print(f'\n---- Inserting {insert_len} entry(s) ' + + f'into {table.table_name} ----') - # Folder structure: root / subject / session / probe / .ap.meta - session_list, sess_dir_list, = [], [] - for sess in input_sessions: - sess_dir = element_data_loader.utils.find_full_path( - get_root_data_dir(), - sess['session_dir']) - session_datetimes, model_list = [], [] +def ingest_subjects(subject_csv_path='./user_data/subjects.csv', + skip_duplicates=True): + """ + Inserts data from a subject csv into corresponding subject schema tables + By default, uses data from workflow_session/user_data/ + :param subject_csv_path: relative path of subject csv + :param skip_duplicates=True: datajoint insert function param + """ + csvs = [subject_csv_path] + tables = [subject.Subject()] + ingest_general(csvs, tables, skip_duplicates=skip_duplicates) - # search session dir and determine acquisition software - for file_pattern, acq_type in zip(['*.yaml', '*.other'], ['DeepLabCut', 'OtherUnspecified']): - beh_model_filepaths = [fp for fp in sess_dir.rglob(file_pattern)] - if len(beh_model_filepaths): - acq_software = acq_type - break - else: - raise FileNotFoundError(f'Recording files not found! Checked for files found in: {sess_dir}') - if acq_software == 'DeepLabCut': - pass - # NEEDS WORK HERE - else: - raise NotImplementedError(f'Unknown acquisition software: {acq_software}') +def ingest_sessions(session_csv_path='./user_data/sessions.csv', + skip_duplicates=True): + """ + Ingests to session schema from ./user_data/sessions.csv + """ + # ingest to session schema + csvs = [session_csv_path, session_csv_path, session_csv_path] + tables = [session.Session(), session.SessionDirectory(), + session.SessionNote()] - # new session/probe-insertion - session_key = {'subject': sess['subject'], 'session_datetime': min(session_datetimes)} - if session_key not in session.Session(): - session_list.append(session_key) - root_dir = element_data_loader.utils.find_root_directory( - get_root_data_dir(), sess_dir) - sess_dir_list.append({**session_key, 'session_dir': sess_dir.relative_to(root_dir).as_posix()}) + ingest_general(csvs, tables, skip_duplicates=skip_duplicates) - print(f'\n---- Insert {len(session_list)} entry(s) into session.Session ----') - session.Session.insert(session_list, skip_duplicates=True) - session.SessionDirectory.insert(sess_dir_list, skip_duplicates=True) - print(f'\n---- Insert {len(probe_list)} entry(s) into probe.Probe ----') - dlc.DLCModel.insert(model_list, skip_duplicates=True) +def ingest_dlc_configs(recording_csv_path='./user_data/recordings.csv', + skip_duplicates=True): + """ + Ingests to DLC schema from ./user_data/recordings.csv + """ + csvs = [recording_csv_path,recording_csv_path] + tables = [dlc.Recording(),dlc.Config()] - print('\n---- Successfully completed workflow_behavior/ingest.py ----') + ingest_general(csvs, tables, skip_duplicates=skip_duplicates) if __name__ == '__main__': ingest_subjects() ingest_sessions() + ingest_dlc_configs() + +''' +# Folder structure: root / subject / session / [fill in here] +# session_list, sess_dir_list = [], [] + +for sess in input_sessions: + sess_dir = element_data_loader.utils.find_full_path( + get_root_data_dir(), + sess['session_dir']) + session_datetimes, dlcmodel_list = [], [] + + # search session dir and determine acquisition software + for file_pattern, acq_type in zip(['*.yaml', '*.other'], + ['DeepLabCut', 'OtherUnspecified']): + beh_model_filepaths = [fp for fp in sess_dir.rglob(file_pattern)] + if len(beh_model_filepaths): + acq_software = acq_type + break + else: + raise FileNotFoundError('Recording files not found! Checked for ' + + f'files found in: {sess_dir}') + + if acq_software == 'DeepLabCut': + pass + # NEEDS WORK HERE + else: + raise NotImplementedError('Unknown acquisition software: ' + + f'{acq_software}') + + # new session/probe-insertion + session_key = {'subject': sess['subject'], + 'session_datetime': min(session_datetimes)} + if session_key not in session.Session(): + session_list.append(session_key) + root_dir = element_data_loader.utils.find_root_directory( + get_root_data_dir(), + sess_dir) + sess_dir_list.append({**session_key, + 'session_dir': sess_dir.\ + relative_to(root_dir).as_posix()}) + +print(f'\n---- Insert {len(session_list)} entry(s) ' + + 'into session.Session ----') +session.Session.insert(session_list, skip_duplicates=True) +session.SessionDirectory.insert(sess_dir_list, skip_duplicates=True) + +print(f'\n---- Insert {len(dlcmodel_list)} entry(s) ' + + 'into dlc.DLCModel ----') +dlc.DLCModel.insert(dlcmodel_list, skip_duplicates=True) +''' diff --git a/workflow_behavior/paths.py b/workflow_behavior/paths.py index 14b3667..88fbe90 100644 --- a/workflow_behavior/paths.py +++ b/workflow_behavior/paths.py @@ -1,18 +1,24 @@ import datajoint as dj +from .pipeline import session +from pathlib import Path -def get_beh_root_data_dir(): - beh_root_dirs = dj.config.get('custom', {}).get('beh_root_data_dir', None) +def get_beh_root_dir(): + beh_root_dirs = dj.config.get('custom', {}).get('root_data_dir', None) return beh_root_dirs if beh_root_dirs else None -def get_beh_root_output_dir(): - beh_output_dir = dj.config.get('custom', {}).get('beh_output_dir', None) - return beh_output_dir if beh_output_dir else None - - -def get_session_directory(session_key: dict) -> str: - from .pipeline import session +def get_session_dir(session_key: dict) -> str: session_dir = (session.SessionDirectory & session_key ).fetch1('session_dir') return session_dir + + +def get_beh_output_dir(session_key: dict) -> str: + """ Returns session_dir relative to custom 'beh_output_dir' root """ + beh_output_dir = dj.config.get('custom', {} + ).get('beh_output_dir', None) + if beh_output_dir is not None: + return Path(beh_output_dir, get_session_dir(session_key)) + else: + return None diff --git a/workflow_behavior/pipeline.py b/workflow_behavior/pipeline.py index c11a4e6..682f337 100644 --- a/workflow_behavior/pipeline.py +++ b/workflow_behavior/pipeline.py @@ -1,16 +1,16 @@ import datajoint as dj from element_lab import lab -from element_animal import subject, genotyping +from element_animal import subject from element_session import session -# from element_behavior import dlc, dlc_run, dlc_track +from element_behavior import dlc from element_animal.subject import Subject from element_lab.lab import Source, Lab, Protocol, User, Project from element_session.session import Session -# from element_behavior.dlc import Recording, DLCProcessingMethod, DLCRecording, DLCModel, DLCModelMethod +from element_behavior.dlc import Recording, Config, Model -from .paths import get_beh_root_dir, get_session_directory +from .paths import get_beh_root_dir, get_session_dir, get_beh_output_dir if 'custom' not in dj.config: dj.config['custom'] = {} @@ -26,9 +26,7 @@ Experimenter = lab.User session.activate(db_prefix + 'session', linking_module=__name__) -# Activate "behavior" schema ------------------------------------------------------ +# Activate "behavior" schema ----------------------------------- -# dlc.activate(db_prefix + 'dlc', -# db_prefix + 'dlc_track', -# db_prefix + 'dlc_run', -# linking_module=__name__) \ No newline at end of file +# db_prefix + 'treadmill', +dlc.activate(db_prefix + 'dlc', linking_module=__name__) diff --git a/workflow_behavior/version.py b/workflow_behavior/version.py new file mode 100644 index 0000000..bd697a6 --- /dev/null +++ b/workflow_behavior/version.py @@ -0,0 +1,2 @@ +"""Package metadata.""" +__version__ = '0.0.0b1' From a580a4c1b201123c3fe4d911d8ef087172a0d933 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Thu, 13 Jan 2022 17:41:19 -0600 Subject: [PATCH 009/176] minor: remove debug notebook cell --- notebooks/1_Explore_Workflow.ipynb | 468 ----------------------------- 1 file changed, 468 deletions(-) diff --git a/notebooks/1_Explore_Workflow.ipynb b/notebooks/1_Explore_Workflow.ipynb index 1b47585..7ff1867 100644 --- a/notebooks/1_Explore_Workflow.ipynb +++ b/notebooks/1_Explore_Workflow.ipynb @@ -308,464 +308,6 @@ "dlc.Model()" ] }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4d60db46-efe9-4082-a6e6-b8f0eed17751", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m(665)\u001b[0;36mcheck_fields\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 663 \u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mfield\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 664 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mfield\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheading\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m--> 665 \u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'`{0:s}` is not in the table heading'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfield\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 666 \u001b[0;31m \u001b[0;32melif\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfield_list\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheading\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 667 \u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mDataJointError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Attempt to insert rows with different fields'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> up\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m(674)\u001b[0;36m__make_row_to_insert\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 672 \u001b[0;31m for name in self.heading if name in row.dtype.fields]\n", - "\u001b[0m\u001b[0;32m 673 \u001b[0;31m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# dict-based\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m--> 674 \u001b[0;31m \u001b[0mcheck_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 675 \u001b[0;31m attributes = [self.__make_placeholder(name, row[name], ignore_extra_fields)\n", - "\u001b[0m\u001b[0;32m 676 \u001b[0;31m for name in self.heading if name in row]\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> up\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m(320)\u001b[0;36m\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 318 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 319 \u001b[0;31m \u001b[0mfield_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# collects the field list from first row (passed by reference)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m--> 320 \u001b[0;31m \u001b[0mrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__make_row_to_insert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfield_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 321 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 322 \u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> up\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m(320)\u001b[0;36minsert\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 318 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 319 \u001b[0;31m \u001b[0mfield_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# collects the field list from first row (passed by reference)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m--> 320 \u001b[0;31m \u001b[0mrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__make_row_to_insert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfield_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 321 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 322 \u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> up\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m(266)\u001b[0;36minsert1\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 264 \u001b[0;31m \u001b[0mFor\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msee\u001b[0m \u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 265 \u001b[0;31m \"\"\"\n", - "\u001b[0m\u001b[0;32m--> 266 \u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 267 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 268 \u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_duplicates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_direct_insert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> up\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Volumes/GoogleDrive/My Drive/Dev/element-behavior/element_behavior/dlc.py\u001b[0m(241)\u001b[0;36mmake\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 239 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 240 \u001b[0;31m \u001b[0;31m# --------------- Insert to DataJoint dlc.Model table ---------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m--> 241 \u001b[0;31m self.insert1(dict(key,\n", - "\u001b[0m\u001b[0;32m 242 \u001b[0;31m \u001b[0mtask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Task'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 243 \u001b[0;31m \u001b[0mscorer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'scorer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> up\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/autopopulate.py\u001b[0m(153)\u001b[0;36mpopulate\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 151 \u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_allow_insert\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 152 \u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m--> 153 \u001b[0;31m \u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 154 \u001b[0;31m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSystemExit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 155 \u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> down\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Volumes/GoogleDrive/My Drive/Dev/element-behavior/element_behavior/dlc.py\u001b[0m(241)\u001b[0;36mmake\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 239 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 240 \u001b[0;31m \u001b[0;31m# --------------- Insert to DataJoint dlc.Model table ---------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m--> 241 \u001b[0;31m self.insert1(dict(key,\n", - "\u001b[0m\u001b[0;32m 242 \u001b[0;31m \u001b[0mtask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Task'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 243 \u001b[0;31m \u001b[0mscorer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'scorer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> cfg['Task']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'demo'\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> cfg['model']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'DLC_dlcrnetms5_demoJul14shuffle0_20000'\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> cfg['run_duration']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3872.9103260040283\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> cfg['start_time']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "datetime.datetime(2022, 1, 12, 18, 16, 52, 361727)\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> cfg['scorer']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'me'\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> quit\n" - ] - } - ], - "source": [ - "%debug" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3950c00d-a1a6-495f-a74e-8230c39458aa", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "c70ce30f-1c8b-4c85-947d-545ccdd87427", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Deleting 1 rows from `neuro_dlc`.`config`\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Commit deletes? [yes, No]: yes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Deletes committed.\n" - ] - }, - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "059f736c-3515-4303-b4d2-14f8285e2489", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    video_path

    \n", - " raw video path relative to session_dir\n", - "
    \n", - "

    config_path

    \n", - " config.yaml relative to session_dir\n", - "
    \n", - "

    shuffle

    \n", - " shuffle number to use (usually 1)\n", - "
    \n", - "

    train_index

    \n", - " train fract of those in yaml, 0-indexed\n", - "
    \n", - "

    snapshot_index

    \n", - " snapshot index, -1 for most recent\n", - "
    \n", - "

    task

    \n", - " task description\n", - "
    \n", - "

    scorer

    \n", - " scorer/network name in config\n", - "
    \n", - "

    multianimal

    \n", - " true for multi-animal\n", - "
    \n", - "

    train_fraction

    \n", - " training fraction specified by train_index\n", - "
    \n", - "

    iteration

    \n", - " iteration number\n", - "
    \n", - "

    pcutoff

    \n", - " threshold of likelihood\n", - "
    \n", - "

    model

    \n", - " DLC's updated GetScorerName()\n", - "
    \n", - "

    start_time

    \n", - " When the model started training\n", - "
    \n", - "

    run_duration

    \n", - " Seconds model run\n", - "
    \n", - "

    dlc_version

    \n", - " keeps the deeplabcut version\n", - "
    subject52020-04-15 11:16:38videos/reachingvideo1.aviconfig.yaml10-1ReachingMackenzie00.9510.4DLC_resnet50_ReachingAug30shuffle1_8002022-01-10 21:02:29282.4252.2.0.5
    subject62021-06-02 14:04:22videos/m3v1mp4.mp4config.yaml10-1openfieldPranav00.9500.4DLC_resnet50_openfieldOct30shuffle1_2002022-01-12 14:59:251569.052.2.0.5
    \n", - " \n", - "

    Total: 2

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet *video_path *config_path *shuffle *train_index *snapshot_inde task scorer multianimal train_fraction iteration pcutoff model start_time run_duration dlc_version \n", - "+----------+ +------------+ +------------+ +------------+ +---------+ +------------+ +------------+ +-----------+ +-----------+ +------------+ +------------+ +-----------+ +---------+ +------------+ +------------+ +------------+ +------------+\n", - "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Reaching Mackenzie 0 0.95 1 0.4 DLC_resnet50_R 2022-01-10 21: 282.425 2.2.0.5 \n", - "subject6 2021-06-02 14: videos/m3v1mp4 config.yaml 1 0 -1 openfield Pranav 0 0.95 0 0.4 DLC_resnet50_o 2022-01-12 14: 1569.05 2.2.0.5 \n", - " (Total: 2)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dlc.Model()" - ] - }, { "cell_type": "code", "execution_count": 9, @@ -994,16 +536,6 @@ "dlc.Model.Data()" ] }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a8e5027f-cee6-4076-9be8-c16dc6fd507a", - "metadata": {}, - "outputs": [], - "source": [ - "key = (dlc.Config & \"subject='subject5'\").fetch('KEY')[0]" - ] - }, { "cell_type": "code", "execution_count": null, From 87a1f4994868ab3a3234c32e6f6c9a9f1e600d66 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Fri, 21 Jan 2022 14:13:50 -0600 Subject: [PATCH 010/176] new dev branch --- workflow_behavior/pipeline.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/workflow_behavior/pipeline.py b/workflow_behavior/pipeline.py index 682f337..5e2f058 100644 --- a/workflow_behavior/pipeline.py +++ b/workflow_behavior/pipeline.py @@ -28,5 +28,5 @@ # Activate "behavior" schema ----------------------------------- -# db_prefix + 'treadmill', dlc.activate(db_prefix + 'dlc', linking_module=__name__) +# treadmill.activate(db_prefix + 'treadmill', linking_module=__name__) From 3494447ad249a21a90fdfbf5b1ba0fda970222a3 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Wed, 26 Jan 2022 20:47:31 -0600 Subject: [PATCH 011/176] Refactor readme, remove images, add docker. See details Docker: add dev and test environments/dockerfiles changelog/version.py: revise version number remove images: lab, session, subject diagrams README: refactor with links to images. Direct to central install.md for instructions 1_Explore_Workflow: update to show new functionality requirements: pinn versions tests: Minor edits to supress linter errors user_data: move config parameters to new csv ingest: update for config parameters paths.py: update for cross-element consistency pipeline.py: Minor edits to supress linter errors --- CHANGELOG.md | 5 +- Dockerfile.dev | 32 +++ Dockerfile.test | 39 ++++ README.md | 183 ++------------- docker-compose-dev.yaml | 32 +++ docker-compose-test.yaml | 44 ++++ images/lab_diagram.svg | 179 -------------- images/session_diagram.svg | 77 ------ images/subject_diagram.svg | 222 ------------------ notebooks/1_Explore_Workflow.ipynb | 360 ++++++++++++++++++----------- notebooks/2_Explore_Export.ipynb | 277 ---------------------- requirements.txt | 5 + tests/__init__.py | 2 +- tests/test_export.py | 7 - tests/test_ingest.py | 3 + tests/test_pipeline_generation.py | 2 + user_data/config_params.csv | 3 + user_data/recordings.csv | 8 +- workflow_behavior/ingest.py | 68 ++---- workflow_behavior/paths.py | 11 +- workflow_behavior/pipeline.py | 9 +- workflow_behavior/version.py | 2 +- 22 files changed, 433 insertions(+), 1137 deletions(-) create mode 100644 Dockerfile.dev create mode 100644 Dockerfile.test create mode 100644 docker-compose-dev.yaml create mode 100644 docker-compose-test.yaml delete mode 100644 images/lab_diagram.svg delete mode 100644 images/session_diagram.svg delete mode 100644 images/subject_diagram.svg delete mode 100644 notebooks/2_Explore_Export.ipynb delete mode 100644 tests/test_export.py create mode 100644 user_data/config_params.csv diff --git a/CHANGELOG.md b/CHANGELOG.md index fd2dab0..45a271c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,10 +2,11 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) convention. -## [0.1.0b0] - [unreleased] +## [0.0.0b1] - [unreleased] ### Added + First beta release -## [0.1.0c0] - 2021-12-15 +## [0.0.0a1] - 2021-12-15 ### Added + First draft begins ++ Added Docker files diff --git a/Dockerfile.dev b/Dockerfile.dev new file mode 100644 index 0000000..1ee34f0 --- /dev/null +++ b/Dockerfile.dev @@ -0,0 +1,32 @@ +FROM datajoint/djlab:py3.8-debian + +USER root +RUN apt-get update -y +RUN apt-get install git -y + +USER anaconda + +RUN mkdir /main/element-lab \ + /main/element-animal \ + /main/element-session \ + /main/element-behavior \ + /main/workflow-behavior + +# Copy user's local fork of elements and workflow +COPY --chown=anaconda:anaconda ./element-lab /main/element-lab +COPY --chown=anaconda:anaconda ./element-animal /main/element-animal +COPY --chown=anaconda:anaconda ./element-session /main/element-session +COPY --chown=anaconda:anaconda ./element-behavior /main/element-behavior +COPY --chown=anaconda:anaconda ./workflow-behavior /main/workflow-behavior + +# Install packages +RUN pip install -e /main/element-lab +RUN pip install -e /main/element-animal +RUN pip install -e /main/element-session +RUN pip install -e /main/element-behavior +RUN pip install -e /main/workflow-behavior +RUN pip install -r /main/workflow-behavior/requirements_test.txt + +WORKDIR /main/workflow-behavior + +ENTRYPOINT ["tail", "-f", "/dev/null"] diff --git a/Dockerfile.test b/Dockerfile.test new file mode 100644 index 0000000..6372bd9 --- /dev/null +++ b/Dockerfile.test @@ -0,0 +1,39 @@ +FROM datajoint/djlab:py3.8-debian + +USER root +RUN apt-get update -y +RUN apt-get install git -y + +USER anaconda +WORKDIR /main/workflow-behavior + +# Option 1 - Install DataJoint's remote fork of the workflow and elements +# RUN git clone https://github.com/datajoint/workflow-behavior.git /main/workflow-behavior + +# Option 2 - Install user's remote fork of element and workflow +# or an unreleased version of the element +# RUN pip install git+https://github.com//element-lab.git +# RUN pip install git+https://github.com//element-animal.git +# RUN pip install git+https://github.com//element-session.git +# RUN pip install git+https://github.com//element-behavior.git +# RUN git clone https://github.com//workflow-behavior.git /main/workflow-behavior + +# Option 3 - Install user's local fork of element and workflow +RUN mkdir /main/element-lab +COPY --chown=anaconda:anaconda ./element-lab /main/element-lab +RUN pip install -e /main/element-lab +RUN mkdir /main/element-animal +COPY --chown=anaconda:anaconda ./element-animal /main/element-animal +RUN pip install -e /main/element-animal +RUN mkdir /main/element-session +COPY --chown=anaconda:anaconda ./element-session /main/element-session +RUN pip install -e /main/element-session +RUN mkdir /main/element-behavior +COPY --chown=anaconda:anaconda ./element-behavior /main/element-behavior +RUN pip install -e /main/element-behavior +COPY --chown=anaconda:anaconda ./workflow-behavior /main/workflow-behavior +# RUN rm -f /main/workflow-behavior/dj_local_conf.json + +# Install the workflow +RUN pip install /main/workflow-behavior +RUN pip install -r /main/workflow-behavior/requirements_test.txt diff --git a/README.md b/README.md index be51242..2ed600e 100644 --- a/README.md +++ b/README.md @@ -14,188 +14,37 @@ The lab and animal management workflow presented here uses components from two D ### element-lab -![lab](images/lab_diagram.svg) +![element-lab]( +https://github.com/datajoint/element-lab/raw/main/images/element_lab_diagram.svg) ### element-animal -`subject` contains basic information of subjects. -![subject](images/subject_diagram2.svg) +![element-animal]( +https://github.com/datajoint/element-animal/blob/main/images/subject_diagram.svg) ### element-session `session` is designed to handle metadata related to data collection, including collection datetime, file paths, and notes. Most workflows will include element-session as a starting point for further data entry. ![session](images/session_diagram2.png) + + ### This workflow This workflow serves as an example of the upstream part of a typical data workflow, for examples using these elements with other data modalities refer to: + [workflow-array-ephys](https://github.com/datajoint/workflow-array-ephys) + [workflow-calcium-imaging](https://github.com/datajoint/workflow-calcium-imaging) - ## Installation instructions -### Step 1 - Clone this repository - -+ Launch a new terminal and change directory to where you want to clone the repository - ``` - cd C:/Projects - ``` -+ Clone the repository - ``` - git clone https://github.com/datajoint/workflow-behavior - ``` -+ Change directory to `workflow-behavior` - ``` - cd workflow-behavior - ``` - -### Step 2 - Setup a virtual environment -It is highly recommended (though not strictly required) to create a virtual environment to run the pipeline. This can be done with either `virtualenv` or `conda` - -+ For `virtualenv`: - - + If not yet installed, run `pip install --user virtualenv` - - + To create a new virtual environment named `venv`: - ``` - virtualenv venv - ``` - - + To activated the virtual environment: - + On Windows: - ``` - .\venv\Scripts\activate - ``` - - + On Linux/macOS: - ``` - source venv/bin/activate - ``` -+ For `conda`: - + If not yet installed, run `pip install --user conda` - - + To create a new virtual environment named `venv`: - ``` - conda create --name venv python=3.8 - ``` - - + To activated the virtual environment: - + On Windows: - ``` - activate venv - ``` - - + On Linux/macOS: - ``` - source activate venv - ``` - -### Step 3 - Install this repository - -From the root of the cloned repository directory: - ``` - pip install -e . - ``` - -Note: the `-e` flag will install this repository in editable mode, -in case you'd like to to modify the code (e.g. the `pipeline.py` or `paths.py` scripts). -If no such modification required, using `pip install .` is sufficient. - - -### Step 4 - Jupyter Notebook -+ Register an IPython kernel with Jupyter - ``` - ipython kernel install --name=workflow-behavior - ``` - -### Step 5 - Configure the `dj_local_conf.json` - -At the root of the repository folder, -create a new file `dj_local_conf.json` with the following template: - -```json -{ - "database.host": "", - "database.user": "", - "database.password": "", - "loglevel": "INFO", - "safemode": true, - "display.limit": 7, - "display.width": 14, - "display.show_tuple_count": true, - "custom": { - "database.prefix": "", -} -``` - -+ Specify database's `hostname`, `username`, and `password` according to the database you plan to use (see [set-up instructions here](https://tutorials.datajoint.io/setting-up/get-database.html)). - -+ Specify a `database.prefix` to create the schemas. - - -### Installation complete - -+ At this point the setup of this workflow is complete. - - -## Interacting with the DataJoint pipeline and exploring data - -+ [Connect to database](https://tutorials.datajoint.io/setting-up/get-database.html) - -+ Import tables - ``` - from workflow_behavior.pipeline import * - ``` - This will create all tables defined in the elements in the database server. - -+ Preview the tables created by calling the classes, for example: - ``` - lab.Lab() - subject.Subject() - session.Session() - pose.DLCModel() - ``` - -+ If required to drop all schemas, the following is the dependency order. - ``` - from workflow_behavior.pipeline import * - - pose.schema.drop() - session.schema.drop() - subject.schema.drop() - lab.schema.drop() - ``` - -+ For a more in-depth exploration of the tables created, please refer to the example notebooks (TBD). - - -## Insert into Manual and Lookup tables with Graphical User Interface DataJoint Labbook - -DataJoint also provides a Graphical User Interface [DataJoint Labbook](https://github.com/datajoint/datajoint-labbook) to support manual data insertions into DataJoint workflows. - -![DataJoint Labbook preview](images/DataJoint_Labbook.png) - -Please refer to the [DataJoint Labbook page](https://github.com/datajoint/datajoint-labbook) for instructions to set it up. - -## Development mode installation ++ The installation instructions can be found at [datajoint-elements/install.md]( + https://github.com/datajoint/datajoint-elements/blob/main/install.md). -This method allows you to modify the source code for `workflow-calcium-imaging`, `element-calcium-imaging`, `element-session`, `element-animal`, and `element-lab`. +## Interacting with the DataJoint workflow -+ Launch a new terminal and change directory to where you want to clone the repositories - ``` - cd C:/Projects - ``` -+ Clone the repositories - ``` - git clone https://github.com/datajoint/element-lab - git clone https://github.com/datajoint/element-animal - git clone https://github.com/datajoint/element-session - git clone https://github.com/datajoint/workflow-behavior - ``` -+ Install each package with the `-e` option - ``` - pip install -e ./element-lab - pip install -e ./element-animal - pip install -e ./element-session - pip install -e ./workflow-behavior - ``` ++ Please refer to the following workflow-specific +[Jupyter notebooks](/notebooks) for an in-depth explanation of how to run the +workflow ([01-Explore_Workflow.ipynb](notebooks/01-Explore_Workflow.ipynb)). diff --git a/docker-compose-dev.yaml b/docker-compose-dev.yaml new file mode 100644 index 0000000..91939c2 --- /dev/null +++ b/docker-compose-dev.yaml @@ -0,0 +1,32 @@ +# docker-compose -f docker-compose-dev.yaml up -d --build +# docker-compose -f docker-compose-dev.yaml down + +version: "2.4" +x-net: &net + networks: + - main +services: + db: + <<: *net + image: datajoint/mysql:5.7 + environment: + - MYSQL_ROOT_PASSWORD=simple + workflow: + <<: *net + build: + context: ../ + dockerfile: ./workflow-behavior/Dockerfile.dev + env_file: .env + image: workflow_session_dev:0.0.0b2 + volumes: + - ./apt_requirements.txt:/tmp/apt_requirements.txt + - ../element-lab:/main/element-lab + - ../element-animal:/main/element-animal + - ../element-session:/main/element-session + - ../element-behavior:/main/element-behavior + - .:/main/workflow-behavior + depends_on: + db: + condition: service_healthy +networks: + main: diff --git a/docker-compose-test.yaml b/docker-compose-test.yaml new file mode 100644 index 0000000..1b86e35 --- /dev/null +++ b/docker-compose-test.yaml @@ -0,0 +1,44 @@ +# docker-compose -f docker-compose-test.yaml up --build +# docker-compose -f docker-compose-test.yaml down + +version: "2.4" +x-net: &net + networks: + - main +services: + db: + <<: *net + image: datajoint/mysql:5.7 + environment: + - MYSQL_ROOT_PASSWORD=simple + workflow: + <<: *net + build: + context: ../ + dockerfile: ./workflow-behavior/Dockerfile.test + env_file: .env + image: workflow_behavior:0.0.0b2 + environment: + - DJ_HOST=db + - DJ_USER=root + - DJ_PASS=simple + - DATABASE_PREFIX=test_ + command: + - bash + - -c + - | + echo "------ INTEGRATION TESTS ------" + pytest -sv --cov-report term-missing --cov=workflow-behavior -p no:warnings + tail -f /dev/null + volumes: + - ./apt_requirements.txt:/tmp/apt_requirements.txt + - ../element-lab:/main/element-lab + - ../element-animal:/main/element-animal + - ../element-session:/main/element-session + - ../element-behavior:/main/element-behavior + - .:/main/workflow-behavior + depends_on: + db: + condition: service_healthy +networks: + main: diff --git a/images/lab_diagram.svg b/images/lab_diagram.svg deleted file mode 100644 index f13349d..0000000 --- a/images/lab_diagram.svg +++ /dev/null @@ -1,179 +0,0 @@ - - - - - - - - - -lab.Protocol - - -lab.Protocol - - - - - -lab.Project - - -lab.Project - - - - - -lab.ProjectUser - - -lab.ProjectUser - - - - - -lab.Project->lab.ProjectUser - - - - -lab.Project.Sourcecode - - -lab.Project.Sourcecode - - - - - -lab.Project->lab.Project.Sourcecode - - - - -lab.Project.Keywords - - -lab.Project.Keywords - - - - - -lab.Project->lab.Project.Keywords - - - - -lab.Project.Publication - - -lab.Project.Publication - - - - - -lab.Project->lab.Project.Publication - - - - -lab.Location - - -lab.Location - - - - - -lab.UserRole - - -lab.UserRole - - - - - -lab.LabMembership - - -lab.LabMembership - - - - - -lab.UserRole->lab.LabMembership - - - - -lab.Source - - -lab.Source - - - - - -lab.ProtocolType - - -lab.ProtocolType - - - - - -lab.ProtocolType->lab.Protocol - - - - -lab.Lab - - -lab.Lab - - - - - -lab.Lab->lab.Location - - - - -lab.Lab->lab.LabMembership - - - - -lab.User - - -lab.User - - - - - -lab.User->lab.ProjectUser - - - - -lab.User->lab.LabMembership - - - - diff --git a/images/session_diagram.svg b/images/session_diagram.svg deleted file mode 100644 index a11a66e..0000000 --- a/images/session_diagram.svg +++ /dev/null @@ -1,77 +0,0 @@ - - - - - - - - - -session.SessionNote - - -session.SessionNote - - - - - -session.SessionDirectory - - -session.SessionDirectory - - - - - -session.Session - - -session.Session - - - - - -session.Session->session.SessionNote - - - - -session.Session->session.SessionDirectory - - - - -session.ProjectSession - - -session.ProjectSession - - - - - -session.Session->session.ProjectSession - - - - -session.SessionExperimenter - - -session.SessionExperimenter - - - - - -session.Session->session.SessionExperimenter - - - - diff --git a/images/subject_diagram.svg b/images/subject_diagram.svg deleted file mode 100644 index 9864ba7..0000000 --- a/images/subject_diagram.svg +++ /dev/null @@ -1,222 +0,0 @@ - - - - - - - - - -subject.Subject.Source - - -subject.Subject.Source - - - - - -subject.SubjectDeath - - -subject.SubjectDeath - - - - - -subject.Allele.Source - - -subject.Allele.Source - - - - - -subject.Subject.Lab - - -subject.Subject.Lab - - - - - -subject.Line.Allele - - -subject.Line.Allele - - - - - -subject.Subject.User - - -subject.Subject.User - - - - - -subject.Zygosity - - -subject.Zygosity - - - - - -subject.Subject.Strain - - -subject.Subject.Strain - - - - - -subject.SubjectCullMethod - - -subject.SubjectCullMethod - - - - - -subject.Subject.Line - - -subject.Subject.Line - - - - - -subject.Subject.Protocol - - -subject.Subject.Protocol - - - - - -subject.Line - - -subject.Line - - - - - -subject.Line->subject.Line.Allele - - - - -subject.Line->subject.Subject.Line - - - - -subject.Allele - - -subject.Allele - - - - - -subject.Allele->subject.Allele.Source - - - - -subject.Allele->subject.Line.Allele - - - - -subject.Allele->subject.Zygosity - - - - -subject.Strain - - -subject.Strain - - - - - -subject.Strain->subject.Subject.Strain - - - - -subject.Subject - - -subject.Subject - - - - - -subject.Subject->subject.Subject.Source - - - - -subject.Subject->subject.SubjectDeath - - - - -subject.Subject->subject.Subject.Lab - - - - -subject.Subject->subject.Subject.User - - - - -subject.Subject->subject.Zygosity - - - - -subject.Subject->subject.Subject.Strain - - - - -subject.Subject->subject.SubjectCullMethod - - - - -subject.Subject->subject.Subject.Line - - - - -subject.Subject->subject.Subject.Protocol - - - - diff --git a/notebooks/1_Explore_Workflow.ipynb b/notebooks/1_Explore_Workflow.ipynb index 7ff1867..cb55ee5 100644 --- a/notebooks/1_Explore_Workflow.ipynb +++ b/notebooks/1_Explore_Workflow.ipynb @@ -17,11 +17,10 @@ }, "outputs": [], "source": [ - "import os\n", + "import os; from pathlib import Path\n", "# change to the upper level folder to detect dj_local_conf.json\n", "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "import datajoint as dj\n", - "from pathlib import Path" + "import datajoint as dj; dj.config.load('dj_local_conf.json')" ] }, { @@ -34,25 +33,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n", - "Connecting root@localhost:3306\n" - ] - } - ], - "source": [ - "from workflow_behavior.pipeline import lab, subject, session, dlc" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2125fdae-988b-47cd-9377-af7fd48c6093", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Connecting root@localhost:3306\n", "\n", "---- Inserting 0 entry(s) into subject ----\n", "\n", @@ -62,20 +43,21 @@ "\n", "---- Inserting 0 entry(s) into session_note ----\n", "\n", - "---- Inserting 0 entry(s) into recording ----\n", + "---- Inserting 3 entry(s) into recording ----\n", "\n", - "---- Inserting 0 entry(s) into config ----\n" + "---- Inserting 3 entry(s) into config ----\n" ] } ], "source": [ + "from workflow_behavior.pipeline import lab, subject, session, dlc\n", "from workflow_behavior.ingest import ingest_subjects, ingest_sessions, ingest_dlc_configs\n", - "ingest_subjects(); ingest_sessions(); ingest_dlc_configs()" + "ingest_subjects(); ingest_sessions(); ingest_dlc_configs(skip_duplicates=True)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "3af29f80-63d4-4dd2-9f56-70579d27e9c9", "metadata": {}, "outputs": [ @@ -83,7 +65,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Deleting 1 rows from `neuro_dlc`.`config`\n" + "Deleting 1 rows from `wf_dlc`.`config`\n", + "Deleting 1 rows from `wf_dlc`.`recording`\n", + "Deleting 1 rows from `wf_session`.`session_directory`\n", + "Deleting 1 rows from `wf_session`.`session_note`\n", + "Deleting 1 rows from `wf_session`.`session`\n" ] }, { @@ -97,34 +83,189 @@ "name": "stdout", "output_type": "stream", "text": [ - "Deletes committed.\n", - "\n", - "---- Inserting 0 entry(s) into recording ----\n", - "\n", - "---- Inserting 1 entry(s) into config ----\n" + "Deletes committed.\n" ] + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "import datetime\n", - "key={'subject': 'subject6', 'session_datetime': datetime.datetime(2021, 6, 3, 14, 4, 22)}\n", - "(dlc.Config&key).delete()\n", - "ingest_dlc_configs()" + "multianimal=(session.Session & 'session_datetime > \"2021-06-03\"').fetch1('KEY')\n", + "(session.Session & multianimal).delete()" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "77d22ee2-0a9d-4e28-88ac-c80b08d8540e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populated Model and Data tables from: reachingvideo1DLC_resnet50_ReachingAug30shuffle1_800\n", + "\n", + "Populated Model and Data tables from: m3v1mp4DLC_resnet50_openfieldOct30shuffle1_200\n", + "\n" + ] + } + ], "source": [ "dlc.Model.populate()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, + "id": "4ef08929-4d27-4b2c-bb84-e30f4b4a595d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    scorerDLC_resnet50_ReachingAug30shuffle1_800
    body_partsFinger1
    coordsxylikelihood
    0208.178589631.4389040.286353
    1208.230087631.8498540.293349
    2208.575089631.3552860.270262
    3208.384003631.0619510.279657
    4207.791412631.4551390.292992
    ............
    251367.111267456.9003300.178846
    252367.781586456.2545170.158984
    253366.738342462.9418950.182887
    254366.690765463.3888850.176299
    255182.641144645.3834230.157941
    \n", + "

    256 rows × 3 columns

    \n", + "
    " + ], + "text/plain": [ + "scorer DLC_resnet50_ReachingAug30shuffle1_800 \n", + "body_parts Finger1 \n", + "coords x y likelihood\n", + "0 208.178589 631.438904 0.286353\n", + "1 208.230087 631.849854 0.293349\n", + "2 208.575089 631.355286 0.270262\n", + "3 208.384003 631.061951 0.279657\n", + "4 207.791412 631.455139 0.292992\n", + ".. ... ... ...\n", + "251 367.111267 456.900330 0.178846\n", + "252 367.781586 456.254517 0.158984\n", + "253 366.738342 462.941895 0.182887\n", + "254 366.690765 463.388885 0.176299\n", + "255 182.641144 645.383423 0.157941\n", + "\n", + "[256 rows x 3 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dlc.Model.Get2DTrajectory(dlc.Model & \"subject='subject5'\",joint_name=['Finger1'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "id": "967f0afd-6ec8-4fce-8bec-5af1d0291537", "metadata": {}, "outputs": [ @@ -194,30 +335,21 @@ "

    video_path

    \n", " raw video path relative to session_dir\n", "
    \n", + "

    paramset_idx

    \n", + " \n", + "
    \n", "

    config_path

    \n", " config.yaml relative to session_dir\n", "
    \n", - "

    shuffle

    \n", - " shuffle number to use (usually 1)\n", - "
    \n", - "

    train_index

    \n", - " train fract of those in yaml, 0-indexed\n", - "
    \n", - "

    snapshot_index

    \n", - " snapshot index, -1 for most recent\n", - "
    \n", "

    task

    \n", " task description\n", "
    \n", "

    scorer

    \n", - " scorer/network name in config\n", + " scorer/network name in config, human labeler\n", "
    \n", "

    multianimal

    \n", " true for multi-animal\n", "
    \n", - "

    train_fraction

    \n", - " training fraction specified by train_index\n", - "
    \n", "

    iteration

    \n", " iteration number\n", "
    \n", @@ -225,7 +357,7 @@ " threshold of likelihood\n", "
    \n", "

    model

    \n", - " DLC's updated GetScorerName()\n", + " DLC's GetScorerName()\n", "
    \n", "

    start_time

    \n", " When the model started training\n", @@ -233,73 +365,55 @@ "

    run_duration

    \n", " Seconds model run\n", "
    \n", + "

    fps

    \n", + " Source video framerate, frames per second\n", + "
    \n", "

    dlc_version

    \n", " keeps the deeplabcut version\n", "
    \n", " subject5\n", "2020-04-15 11:16:38\n", "videos/reachingvideo1.avi\n", - "config.yaml\n", - "1\n", "0\n", - "-1\n", + "config.yaml\n", "Reaching\n", "Mackenzie\n", "0\n", - "0.95\n", "1\n", "0.4\n", "DLC_resnet50_ReachingAug30shuffle1_800\n", "2022-01-10 21:02:29\n", "282.425\n", + "30.0\n", "2.2.0.5subject6\n", "2021-06-02 14:04:22\n", "videos/m3v1mp4.mp4\n", - "config.yaml\n", - "1\n", "0\n", - "-1\n", + "config.yaml\n", "openfield\n", "Pranav\n", "0\n", - "0.95\n", "0\n", "0.4\n", "DLC_resnet50_openfieldOct30shuffle1_200\n", "2022-01-12 14:59:25\n", "1569.05\n", - "2.2.0.5subject6\n", - "2021-06-03 14:04:22\n", - "videos/videocompressed1.mp4\n", - "config.yaml\n", - "0\n", - "0\n", - "-1\n", - "demo\n", - "me\n", - "1\n", - "0.95\n", - "0\n", - "0.01\n", - "DLC_dlcrnetms5_demoJul14shuffle0_20000\n", - "2022-01-12 18:16:52\n", - "3872.91\n", + "30.0003\n", "2.2.0.5 \n", " \n", " \n", - "

    Total: 3

    \n", + "

    Total: 2

    \n", " " ], "text/plain": [ - "*subject *session_datet *video_path *config_path *shuffle *train_index *snapshot_inde task scorer multianimal train_fraction iteration pcutoff model start_time run_duration dlc_version \n", - "+----------+ +------------+ +------------+ +------------+ +---------+ +------------+ +------------+ +-----------+ +-----------+ +------------+ +------------+ +-----------+ +---------+ +------------+ +------------+ +------------+ +------------+\n", - "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Reaching Mackenzie 0 0.95 1 0.4 DLC_resnet50_R 2022-01-10 21: 282.425 2.2.0.5 \n", - "subject6 2021-06-02 14: videos/m3v1mp4 config.yaml 1 0 -1 openfield Pranav 0 0.95 0 0.4 DLC_resnet50_o 2022-01-12 14: 1569.05 2.2.0.5 \n", - "subject6 2021-06-03 14: videos/videoco config.yaml 0 0 -1 demo me 1 0.95 0 0.01 DLC_dlcrnetms5 2022-01-12 18: 3872.91 2.2.0.5 \n", - " (Total: 3)" + "*subject *session_datet *video_path *paramset_idx *config_path task scorer multianimal iteration pcutoff model start_time run_duration fps dlc_version \n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +-----------+ +-----------+ +------------+ +-----------+ +---------+ +------------+ +------------+ +------------+ +---------+ +------------+\n", + "subject5 2020-04-15 11: videos/reachin 0 config.yaml Reaching Mackenzie 0 1 0.4 DLC_resnet50_R 2022-01-10 21: 282.425 30.0 2.2.0.5 \n", + "subject6 2021-06-02 14: videos/m3v1mp4 0 config.yaml openfield Pranav 0 0 0.4 DLC_resnet50_o 2022-01-12 14: 1569.05 30.0003 2.2.0.5 \n", + " (Total: 2)" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -310,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "1486971c-9fb1-49a3-bccf-41ece67a3659", "metadata": {}, "outputs": [ @@ -380,18 +494,12 @@ "

    video_path

    \n", " raw video path relative to session_dir\n", "
    \n", + "

    paramset_idx

    \n", + " \n", + "
    \n", "

    config_path

    \n", " config.yaml relative to session_dir\n", "
    \n", - "

    shuffle

    \n", - " shuffle number to use (usually 1)\n", - "
    \n", - "

    train_index

    \n", - " train fract of those in yaml, 0-indexed\n", - "
    \n", - "

    snapshot_index

    \n", - " snapshot index, -1 for most recent\n", - "
    \n", "

    joint_name

    \n", " Name of the joints\n", "
    \n", @@ -410,10 +518,8 @@ " subject5\n", "2020-04-15 11:16:38\n", "videos/reachingvideo1.avi\n", - "config.yaml\n", - "1\n", "0\n", - "-1\n", + "config.yaml\n", "Finger1\n", "=BLOB=\n", "=BLOB=\n", @@ -421,10 +527,8 @@ "=BLOB=subject5\n", "2020-04-15 11:16:38\n", "videos/reachingvideo1.avi\n", - "config.yaml\n", - "1\n", "0\n", - "-1\n", + "config.yaml\n", "Hand\n", "=BLOB=\n", "=BLOB=\n", @@ -432,10 +536,8 @@ "=BLOB=subject5\n", "2020-04-15 11:16:38\n", "videos/reachingvideo1.avi\n", - "config.yaml\n", - "1\n", "0\n", - "-1\n", + "config.yaml\n", "Joystick1\n", "=BLOB=\n", "=BLOB=\n", @@ -443,10 +545,8 @@ "=BLOB=subject5\n", "2020-04-15 11:16:38\n", "videos/reachingvideo1.avi\n", - "config.yaml\n", - "1\n", "0\n", - "-1\n", + "config.yaml\n", "Joystick2\n", "=BLOB=\n", "=BLOB=\n", @@ -454,10 +554,8 @@ "=BLOB=subject5\n", "2020-04-15 11:16:38\n", "videos/reachingvideo1.avi\n", - "config.yaml\n", - "1\n", "0\n", - "-1\n", + "config.yaml\n", "Tongue\n", "=BLOB=\n", "=BLOB=\n", @@ -465,10 +563,8 @@ "=BLOB=subject6\n", "2021-06-02 14:04:22\n", "videos/m3v1mp4.mp4\n", - "config.yaml\n", - "1\n", "0\n", - "-1\n", + "config.yaml\n", "leftear\n", "=BLOB=\n", "=BLOB=\n", @@ -476,10 +572,8 @@ "=BLOB=subject6\n", "2021-06-02 14:04:22\n", "videos/m3v1mp4.mp4\n", - "config.yaml\n", - "1\n", "0\n", - "-1\n", + "config.yaml\n", "rightear\n", "=BLOB=\n", "=BLOB=\n", @@ -487,10 +581,8 @@ "=BLOB=subject6\n", "2021-06-02 14:04:22\n", "videos/m3v1mp4.mp4\n", - "config.yaml\n", - "1\n", "0\n", - "-1\n", + "config.yaml\n", "snout\n", "=BLOB=\n", "=BLOB=\n", @@ -498,10 +590,8 @@ "=BLOB=subject6\n", "2021-06-02 14:04:22\n", "videos/m3v1mp4.mp4\n", - "config.yaml\n", - "1\n", "0\n", - "-1\n", + "config.yaml\n", "tailbase\n", "=BLOB=\n", "=BLOB=\n", @@ -513,21 +603,21 @@ " " ], "text/plain": [ - "*subject *session_datet *video_path *config_path *shuffle *train_index *snapshot_inde *joint_name frame_inde x_pos y_pos likelihood\n", - "+----------+ +------------+ +------------+ +------------+ +---------+ +------------+ +------------+ +------------+ +--------+ +--------+ +--------+ +--------+\n", - "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Finger1 =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Hand =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Joystick1 =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Joystick2 =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject5 2020-04-15 11: videos/reachin config.yaml 1 0 -1 Tongue =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject6 2021-06-02 14: videos/m3v1mp4 config.yaml 1 0 -1 leftear =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject6 2021-06-02 14: videos/m3v1mp4 config.yaml 1 0 -1 rightear =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject6 2021-06-02 14: videos/m3v1mp4 config.yaml 1 0 -1 snout =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject6 2021-06-02 14: videos/m3v1mp4 config.yaml 1 0 -1 tailbase =BLOB= =BLOB= =BLOB= =BLOB= \n", + "*subject *session_datet *video_path *paramset_idx *config_path *joint_name frame_inde x_pos y_pos likelihood\n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +--------+ +--------+ +--------+ +--------+\n", + "subject5 2020-04-15 11: videos/reachin 0 config.yaml Finger1 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject5 2020-04-15 11: videos/reachin 0 config.yaml Hand =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject5 2020-04-15 11: videos/reachin 0 config.yaml Joystick1 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject5 2020-04-15 11: videos/reachin 0 config.yaml Joystick2 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject5 2020-04-15 11: videos/reachin 0 config.yaml Tongue =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject6 2021-06-02 14: videos/m3v1mp4 0 config.yaml leftear =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject6 2021-06-02 14: videos/m3v1mp4 0 config.yaml rightear =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject6 2021-06-02 14: videos/m3v1mp4 0 config.yaml snout =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject6 2021-06-02 14: videos/m3v1mp4 0 config.yaml tailbase =BLOB= =BLOB= =BLOB= =BLOB= \n", " (Total: 9)" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -2009,7 +2099,7 @@ "id": "4775dd80-8a54-47b7-a9ba-99995db9ff1a", "metadata": {}, "source": [ - "DataJoint also provides a Graphical User Interface [DataJoint Labbook](https://github.com/datajoint/datajoint-labbook) to support manual data insertions into DataJoint workflows. ![DataJoint Labbook preview](../images/DataJoint_Labbook.png)" + "DataJoint also provides a Graphical User Interface [DataJoint Labbook](https://github.com/datajoint/datajoint-labbook) to support manual data insertions into DataJoint workflows. ![DataJoint Labbook preview](https://github.com/datajoint/datajoint-labbook/blob/master/docs/sphinx/_static/images/walkthroughDemoOptimized.gif)" ] } ], diff --git a/notebooks/2_Explore_Export.ipynb b/notebooks/2_Explore_Export.ipynb deleted file mode 100644 index fd4a50c..0000000 --- a/notebooks/2_Explore_Export.ipynb +++ /dev/null @@ -1,277 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "3054518f-87bc-42ff-a3e7-84bf3d2a37f6", - "metadata": {}, - "source": [ - "# DataJoint U24 - Export Session" - ] - }, - { - "cell_type": "markdown", - "id": "79c15f36-039d-4304-96be-f56ba0d6b10a", - "metadata": {}, - "source": [ - "Same as before, import data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0e7fa407-d67b-403e-975c-bd0bd499d88c", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f11ca71-5e4f-460c-ad94-2037ef0f6448", - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj\n", - "dj.conn()\n", - "from element_lab import lab\n", - "from element_animal import subject\n", - "from element_session import session" - ] - }, - { - "cell_type": "markdown", - "id": "ab2a3f10-b96e-4f0d-9e54-0015bb9b8622", - "metadata": {}, - "source": [ - "Identify items for export with keys." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "76c040c8-15cc-4d61-ae5d-bc7646a6a0be", - "metadata": {}, - "outputs": [], - "source": [ - "session_key=(session.Session&'subject=\"subject5\"').fetch1('KEY')\n", - "mylab_key = (lab.Lab & 'lab=\"LabA\"').fetch1('KEY')\n", - "myproj_key= (lab.Project & 'project=\"ProjA\"').fetch1('KEY')\n", - "myprot_key= (lab.Protocol() & 'protocol=\"ProtA\"').fetch1('KEY')" - ] - }, - { - "cell_type": "markdown", - "id": "902e050c-3133-4fb7-850d-f0b954e1b634", - "metadata": {}, - "source": [ - "Get export function and related pynwb dependency, then export with keys from prev step." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "61fdbdce-a808-49ad-bb5d-f9b9894446fa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function session_to_nwb in module element_session.export.nwb:\n", - "\n", - "session_to_nwb(session_key)\n", - " Generate one NWBFile object representing all session-level information,\n", - " including session identifier, description, start time, etc.\n", - " \n", - " :param session_key: entry in session.Session table\n", - " :return: NWBFile object\n", - "\n" - ] - } - ], - "source": [ - "from element_session.export import session_to_nwb_dict, session_to_nwb\n", - "help(session_to_nwb)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e76fa25d-700b-4f9a-9bb4-62d2217288b6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/cb/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/pynwb/file.py:753: UserWarning: Date is missing timezone information. Updating to local timezone.\n", - " warn(\"Date is missing timezone information. Updating to local timezone.\")\n" - ] - } - ], - "source": [ - "mynwbfile=session_to_nwb(session_key)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "56ebdd79-f0dc-4925-aeb1-109887df361d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function elemlab_to_nwb_dict in module element_lab.export.nwb:\n", - "\n", - "elemlab_to_nwb_dict(lab_key=None, project_key=None, protocol_key=None)\n", - " Generate a dictionary object containing all relevant lab information used when\n", - " generating an NWB file at the session level. All parameters optional.\n", - " Use: mynwbfile = NWBfile(identifier=\"your identifier\",\n", - " session_description=\"your description\",\n", - " session_start_time=session_datetime,\n", - " elemlab_to_nwb_dict(lab_key=key1,project_key=key2,protocol_key=key3))\n", - " Note: The lab, project and protocol keys should specify one of their respective types.\n", - " \n", - " :param lab_key: Key specifying one entry in element_lab.lab.Lab\n", - " :param project_key: Key specifying one entry in element_lab.lab.Project\n", - " :param protocol_key: Key specifying one entry in element_lab.lab.PRotocol\n", - " :return: dictionary with NWB parameters\n", - "\n" - ] - } - ], - "source": [ - "from element_lab.export import elemlab_to_nwb_dict\n", - "help(elemlab_to_nwb_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "afc6555d-cd25-4d55-93e2-51c72ddba9ae", - "metadata": {}, - "outputs": [], - "source": [ - "from pynwb import NWBFile\n", - "lab_info = elemlab_to_nwb_dict(lab_key=mylab_key,project_key=myproj_key,protocol_key=myprot_key)\n", - "sess_info = session_to_nwb_dict(session_key)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "97e01cbf-b225-45d3-a030-0f7400e3526b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/cb/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/pynwb/file.py:753: UserWarning: Date is missing timezone information. Updating to local timezone.\n", - " warn(\"Date is missing timezone information. Updating to local timezone.\")\n" - ] - } - ], - "source": [ - "mynwbfile = NWBFile(**sess_info,**lab_info)" - ] - }, - { - "cell_type": "markdown", - "id": "1bccad09-d5e4-4200-bb73-08ddf98cfb80", - "metadata": {}, - "source": [ - "Learn more about using NWB formats [here](https://www.nwb.org/how-to-use/)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b914d8db-2584-403c-9f24-12a64103f1cb", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/cb/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/hdmf/build/objectmapper.py:653: MissingRequiredBuildWarning: NWBFile 'root' is missing required value for attribute 'source_script_file_name'.\n", - " warnings.warn(msg, MissingRequiredBuildWarning)\n" - ] - } - ], - "source": [ - "from pynwb import NWBHDF5IO\n", - "with NWBHDF5IO('session_metadata.nwb', mode='w') as io:\n", - " io.write(mynwbfile)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c81b1c85-9623-4cc1-9438-79b0c5287756", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "root pynwb.file.NWBFile at 0x140190306285216\n", - "Fields:\n", - " experiment_description: Example project to populate element-lab\n", - " file_create_date: [datetime.datetime(2021, 12, 6, 17, 1, 11, 974467, tzinfo=tzlocal())]\n", - " identifier: subject5_20200415_111638\n", - " institution: Example Uni\n", - " keywords: ['Example' 'Study']\n", - " lab: The Example Lab\n", - " pharmacology: Subjects were administered 10ul sedative prior to surgery\n", - " protocol: ProtA\n", - " related_publications: ['arXiv:1807.11104' 'arXiv:1807.11104v1']\n", - " session_description: Successful data collection, no notes\n", - " session_start_time: 2020-04-15 11:16:38-05:00\n", - " source_script: https://github.com/datajoint/element-lab/\n", - " surgery: Craniotomy performed by session experimenter\n", - " timestamps_reference_time: 2020-04-15 11:16:38-05:00\n", - "\n" - ] - } - ], - "source": [ - "print(mynwbfile)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e75c3795-96d6-4ed1-889e-3da58d9e8533", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv-nwb", - "language": "python", - "name": "venv-nwb" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/requirements.txt b/requirements.txt index fc7a8e6..d9aa5d0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1 +1,6 @@ datajoint>=0.13.0 +element-lab==0.1.0b0 +element-animal==0.1.0b0 +element-session==0.1.0b0 +element-interface @ git+https://github.com/datajoint/element-interface.git +ipykernel==6.0.1 diff --git a/tests/__init__.py b/tests/__init__.py index cde090e..b549577 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -59,7 +59,7 @@ def pipeline(): 'trial': pipeline.trial, 'subject': pipeline.subject, 'session': pipeline.session, - 'lab': pipeline.lab,} + 'lab': pipeline.lab} if _tear_down: pipeline.event.BehaviorEvent.delete() diff --git a/tests/test_export.py b/tests/test_export.py deleted file mode 100644 index 7712b7c..0000000 --- a/tests/test_export.py +++ /dev/null @@ -1,7 +0,0 @@ -# from . import (dj_config, pipeline, lab_csv, ingest_lab, -# subjects_csv, ingest_subjects, -# sessions_csv, ingest_sessions) - - -def test_nwb_export(): - pass diff --git a/tests/test_ingest.py b/tests/test_ingest.py index af396f1..6997a57 100644 --- a/tests/test_ingest.py +++ b/tests/test_ingest.py @@ -3,6 +3,9 @@ 2. Assert exact matches of inserted data fore key tables ''' +__all__ = ['dj_config', 'pipeline', 'subjects_csv', 'ingest_subjects', 'sessions_csv', + 'ingest_sessions', ] + from . import (dj_config, pipeline, subjects_csv, ingest_subjects, sessions_csv, ingest_sessions) diff --git a/tests/test_pipeline_generation.py b/tests/test_pipeline_generation.py index 86267f7..05ec2d0 100644 --- a/tests/test_pipeline_generation.py +++ b/tests/test_pipeline_generation.py @@ -4,6 +4,8 @@ 3. Assert subject link to session ''' +__all__ = ['pipeline'] + from . import pipeline diff --git a/user_data/config_params.csv b/user_data/config_params.csv new file mode 100644 index 0000000..b5085d7 --- /dev/null +++ b/user_data/config_params.csv @@ -0,0 +1,3 @@ +paramset_idx,shuffle,train_fraction,snapshot_index,filter_type,track_method,scorer_legacy +0,1,.95,-1,,,False +1,0,.95,-1,median,ellipse,False diff --git a/user_data/recordings.csv b/user_data/recordings.csv index 9779961..e336c21 100644 --- a/user_data/recordings.csv +++ b/user_data/recordings.csv @@ -1,4 +1,4 @@ -subject,session_datetime,video_path,camera_id,frame_rate,config_path,shuffle,train_index,snapshot_index,config_notes -subject5,2020-04-15 11:16:38,videos/reachingvideo1.avi,1,30,config.yaml,1,0,-1,Reaching example provided by DeepLabCut repository -subject6,2021-06-02 14:04:22,videos/m3v1mp4.mp4,1,00,config.yaml,1,0,-1,Openfield example provided by DeepLabCut repository -subject6,2021-06-03 14:04:22,videos/videocompressed1.mp4,1,00,config.yaml,0,0,-1,Multianimal - not fully trained +subject,session_datetime,video_path,camera_id,config_path,config_notes,paramset_idx +subject5,2020-04-15 11:16:38,videos/reachingvideo1.avi,1,config.yaml,Reaching example provided by DeepLabCut repository,0 +subject6,2021-06-02 14:04:22,videos/m3v1mp4.mp4,1,config.yaml,Openfield example provided by DeepLabCut repository,0 +subject6,2021-06-03 14:04:22,videos/videocompressed1.mp4,1,config.yaml,Multianimal - not fully trained,1 diff --git a/workflow_behavior/ingest.py b/workflow_behavior/ingest.py index 3406338..feeb03e 100644 --- a/workflow_behavior/ingest.py +++ b/workflow_behavior/ingest.py @@ -2,8 +2,8 @@ import csv from workflow_behavior.pipeline import subject, session, dlc -# from workflow_behavior.paths import get_root_data_dir -# from element_data_loader.utils import find_full_path +# from workflow_behavior.paths import get_beh_root_data_dir +# from element-interface.utils import find_full_path def ingest_general(csvs, tables, @@ -54,13 +54,21 @@ def ingest_sessions(session_csv_path='./user_data/sessions.csv', def ingest_dlc_configs(recording_csv_path='./user_data/recordings.csv', + config_params_csv_path='./user_data/config_params.csv', skip_duplicates=True): """ Ingests to DLC schema from ./user_data/recordings.csv """ - csvs = [recording_csv_path,recording_csv_path] - tables = [dlc.Recording(),dlc.Config()] - + # First, ConfigParamSet + with open(config_params_csv_path, newline='') as f: + config_params = list(csv.DictReader(f, delimiter=',')) + for paramset in config_params: + dlc.ConfigParamSet.insert_new_params(**paramset, + skip_duplicates=skip_duplicates) + + # Next, recordings and config files + csvs = [recording_csv_path, recording_csv_path] + tables = [dlc.Recording(), dlc.Config()] ingest_general(csvs, tables, skip_duplicates=skip_duplicates) @@ -68,53 +76,3 @@ def ingest_dlc_configs(recording_csv_path='./user_data/recordings.csv', ingest_subjects() ingest_sessions() ingest_dlc_configs() - -''' -# Folder structure: root / subject / session / [fill in here] -# session_list, sess_dir_list = [], [] - -for sess in input_sessions: - sess_dir = element_data_loader.utils.find_full_path( - get_root_data_dir(), - sess['session_dir']) - session_datetimes, dlcmodel_list = [], [] - - # search session dir and determine acquisition software - for file_pattern, acq_type in zip(['*.yaml', '*.other'], - ['DeepLabCut', 'OtherUnspecified']): - beh_model_filepaths = [fp for fp in sess_dir.rglob(file_pattern)] - if len(beh_model_filepaths): - acq_software = acq_type - break - else: - raise FileNotFoundError('Recording files not found! Checked for ' - + f'files found in: {sess_dir}') - - if acq_software == 'DeepLabCut': - pass - # NEEDS WORK HERE - else: - raise NotImplementedError('Unknown acquisition software: ' - + f'{acq_software}') - - # new session/probe-insertion - session_key = {'subject': sess['subject'], - 'session_datetime': min(session_datetimes)} - if session_key not in session.Session(): - session_list.append(session_key) - root_dir = element_data_loader.utils.find_root_directory( - get_root_data_dir(), - sess_dir) - sess_dir_list.append({**session_key, - 'session_dir': sess_dir.\ - relative_to(root_dir).as_posix()}) - -print(f'\n---- Insert {len(session_list)} entry(s) ' - + 'into session.Session ----') -session.Session.insert(session_list, skip_duplicates=True) -session.SessionDirectory.insert(sess_dir_list, skip_duplicates=True) - -print(f'\n---- Insert {len(dlcmodel_list)} entry(s) ' - + 'into dlc.DLCModel ----') -dlc.DLCModel.insert(dlcmodel_list, skip_duplicates=True) -''' diff --git a/workflow_behavior/paths.py b/workflow_behavior/paths.py index 88fbe90..4b4c4c9 100644 --- a/workflow_behavior/paths.py +++ b/workflow_behavior/paths.py @@ -1,21 +1,20 @@ import datajoint as dj -from .pipeline import session -from pathlib import Path -def get_beh_root_dir(): - beh_root_dirs = dj.config.get('custom', {}).get('root_data_dir', None) +def get_beh_root_data_dir(): + beh_root_dirs = dj.config.get('custom', {}).get('beh_root_data_dir', None) return beh_root_dirs if beh_root_dirs else None def get_session_dir(session_key: dict) -> str: - session_dir = (session.SessionDirectory & session_key - ).fetch1('session_dir') + from .pipeline import session + session_dir = (session.SessionDirectory & session_key).fetch1('session_dir') return session_dir def get_beh_output_dir(session_key: dict) -> str: """ Returns session_dir relative to custom 'beh_output_dir' root """ + from pathlib import Path beh_output_dir = dj.config.get('custom', {} ).get('beh_output_dir', None) if beh_output_dir is not None: diff --git a/workflow_behavior/pipeline.py b/workflow_behavior/pipeline.py index 5e2f058..7c3e7e7 100644 --- a/workflow_behavior/pipeline.py +++ b/workflow_behavior/pipeline.py @@ -1,16 +1,17 @@ import datajoint as dj - -from element_lab import lab from element_animal import subject +from element_lab import lab from element_session import session from element_behavior import dlc from element_animal.subject import Subject from element_lab.lab import Source, Lab, Protocol, User, Project from element_session.session import Session -from element_behavior.dlc import Recording, Config, Model -from .paths import get_beh_root_dir, get_session_dir, get_beh_output_dir +from .paths import get_beh_root_data_dir, get_session_dir, get_beh_output_dir + +__all__ = ['get_beh_root_data_dir', 'get_session_dir', 'get_beh_output_dir', + 'Subject', 'Source', 'Lab', 'Protocol', 'User', 'Project', 'Session'] if 'custom' not in dj.config: dj.config['custom'] = {} diff --git a/workflow_behavior/version.py b/workflow_behavior/version.py index bd697a6..c5b7d5c 100644 --- a/workflow_behavior/version.py +++ b/workflow_behavior/version.py @@ -1,2 +1,2 @@ """Package metadata.""" -__version__ = '0.0.0b1' +__version__ = '0.0.0a1' From 9b368cb1ec7a6fb37ed9ee5b0a8fed0a78139ae5 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Thu, 27 Jan 2022 07:51:40 -0600 Subject: [PATCH 012/176] Cleanup: README, versioning, add ToDo notes --- CHANGELOG.md | 6 ++++-- LICENSE | 2 +- docker-compose-dev.yaml | 2 +- docker-compose-test.yaml | 2 +- images/DataJoint_Labbook.png | Bin 84080 -> 0 bytes requirements.txt | 1 + setup.py | 4 ++-- tests/__init__.py | 4 ++++ tests/test_ingest.py | 9 +++++++++ tests/test_pipeline_generation.py | 5 +++++ workflow_behavior/pipeline.py | 1 - 11 files changed, 28 insertions(+), 8 deletions(-) delete mode 100644 images/DataJoint_Labbook.png diff --git a/CHANGELOG.md b/CHANGELOG.md index 45a271c..130e05a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,11 +2,13 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) convention. -## [0.0.0b1] - [unreleased] +## 0.0.0b1 - unreleased ### Added + First beta release -## [0.0.0a1] - 2021-12-15 +## 0.0.0a1 - 2021-12-15 ### Added + First draft begins + Added Docker files ++ Dragt integration tests ++ Add example data featuring DLC examples diff --git a/LICENSE b/LICENSE index 6bf141b..2f92789 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2021 DataJoint +Copyright (c) 2022 DataJoint Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/docker-compose-dev.yaml b/docker-compose-dev.yaml index 91939c2..ff22c72 100644 --- a/docker-compose-dev.yaml +++ b/docker-compose-dev.yaml @@ -17,7 +17,7 @@ services: context: ../ dockerfile: ./workflow-behavior/Dockerfile.dev env_file: .env - image: workflow_session_dev:0.0.0b2 + image: workflow_session_dev:0.0.0a1 volumes: - ./apt_requirements.txt:/tmp/apt_requirements.txt - ../element-lab:/main/element-lab diff --git a/docker-compose-test.yaml b/docker-compose-test.yaml index 1b86e35..fbec52f 100644 --- a/docker-compose-test.yaml +++ b/docker-compose-test.yaml @@ -17,7 +17,7 @@ services: context: ../ dockerfile: ./workflow-behavior/Dockerfile.test env_file: .env - image: workflow_behavior:0.0.0b2 + image: workflow_behavior:0.0.0a1 environment: - DJ_HOST=db - DJ_USER=root diff --git a/images/DataJoint_Labbook.png b/images/DataJoint_Labbook.png deleted file mode 100644 index 2d8b83a1667c6ef112a13c8d070770053baea11d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 84080 zcma&N1yodR8#bzd(u2}5AR`@ufVAWwEfUh*-Q9w~&aX#6{$vJV9!I@&sW81p)X(TTAH* z`1RCYUQGDuY(Jwn@E=qgaaH>#PtbAx{CoN&DTVOKQ*k^A5uwj6DSN5UT=0|^A9oi9 z)MuTXcTbque+h?S)B2Ayy?GWVjPAdJOM8HX1?t8|X-Bnn{&QU->4_X1q(5?A!d+qJ ziCfJQUg?{cYuxr5Lqy!0BYf$h^T-^g;Ovb5y}qc% zMsSzeGx1Sq&Z__CJxD){X=fo&HhUQ!cQR)?V_h&fQd@DMs<5L2O2Cvj+TGrH8#%Eq zZ}T3~ozqU=pkdS1SKGiiv5NWci=GG6T+{@_i5gmeXdRmw|M_dNaIb{?IgzA&-_Mot z-G}xRzLQQ4`j(Zc{R5>w@JZ7MH@B|5fG-x2C86?6K2t zd@fcwi_y#ZSMA7tpj?x_y8G>u;5XGy8;JVlT)ZjL>zf2it#l%skAI z>4|57VNEZw{` zwjS5p>TXX{J!$*!S|X#`(IW)zv3p9ce-a)b6tLNIL>3nDq)5^=OYk$+Ch^KCfiUS(+0e{}V#*p_cC7U#A76mWp#a*UolYk9g&h}iM3hc{HLxyc+BR2jn< zQrY9U_8N#S`zd!k>VEJ{ch;CL@-~g^olaO0RCiANJj35R=fk4l`(4I1YD5yeK8gPd zy{V>6h7>``J3rE1+++v5TVfGL%eLV01~;Zvg1=t3ijgykszDGTKe=SMfKG(E=G9_| z^wlE9siW%TGYj6$S*x#*PHSUzD_`(yw&J%BOB=hOYwb#jbB%S6-y%ntN+oATgBtN& zMx3>aLR4aiWvcmZOy3oHwHYVJ#sl9e`P?sTdEL z*H1xo)=^kXST9)_HsfE`{cB`M>93@fbCuWoQ>aEEx~dvPTsdJgse*?3!eLeIL-E1hgX!i!*M9lGfz&adUngpgh@9 zQL0AL5@>_(0-d?QHi?#-R_X5g;-{3HPG6b8c1g3u7i}~b#HL$WL&I^kObtyfZ*;s2evGK+oZ|!o=gHbe`U#-$Z=D9q9q{-Qc>c9-gG$ycY*q`GjdB}Efaus)iVVR^OI)LROlE&hy77-teSP| zSGDZKvl`)0G&}Ka^r-*1X~eeWFN1)ey5b(Ayx95i%xPzDg@lXPr9=uQUD}sqS$z$7 zET-S4Ly$-v!v=@GmAUoSUlxO9Z+xJV7G{m#JcEyeD{ttxhk{2oIXvwKhG|9CNgb$K{Vy55AWB`+e_UI;1j0B=tjr4ziG77FL#ylG4>u;*pzh zDKO&Ytw_Lnykn6X6;IC1!9FVk&A)MCi>e@YRzX^+!+r+(1+?8VF*NlK&?C3}+B~N^ zmJ{=&9V1n%y>ZC0{v(HWZ>iZ7`%*wEVEF^~eHjR$GPM;;GBGa9yMQGe2}UCsZJqg)?V37O}x^5L2QVhO1=zt_k8VLeU@Um5za-5t1L@%2765f9{)9EcGp$07tfLK z)XmMc{A_4;qArRy<6@FPYmsc8QgVlAzPPC9ql|qb=0bH`am2#AQ3l>H_I>vk=l`k!zdK>74K^3<|+^sP4Mg2n#p z+tkDrV}cKQV!j#>@7xJ56vcQDsyuxq!A!wu%{)>B>x=mKO*r9jnIqr(Ne9Q{eLGZ` z!_d)g9rNd%u;SOp?y|LRX(^@^56BaWfMHjt8(6bXTVHVbNL%a_=x9=Rc96<%7!9fu zcvsEl=@n=fbBxyf>Z;FjjUw4eG_6=R`Hfc`4vrcnTe9cUL#$$Qs$xNC-bJ1efM1PV zIXiGHvugxZ!s{dj-eROcH$E#R2lT#mUxUfHoP~{G3ga%3-}->D~o|-P@T>3`)JXg zAFD8x^>i<8NzY5CgX&UYaAiwN+I>r4Q~c$>$w@|WOJQDt3{PvR>J}9;=vXt=QZpVW zB*h_5Gl}zYQ#8la%rZ{vMIwSSJyv4!8+%P`?s3?+lIG?I8GUOGqi?J_Ak!DdSf=k^ zS#^1(KYxSZo?_g%FIF}A%rO^%L3O*hollS#jsb`M>Y~;>!i>Tf$G^#xOY)H$zv%16 zzZ|<_?Qd-UH=)w|!u`_W9`KgiiuD7FFS4|^#vzB_YeZrx0e@saT6f}8db<{Iz3vbF zL86`n#G@i&^V|?BWrMH@^+3mpka4hUb+?QEDkG<&5VK0w8)4Gx)^3+a_T0 zlwMT}K7zjP^*`I*3WA8zv>jL|J6L&yI>?xBki|9$Dp>s;YuTYIRC~wH$dC74KTa*~ z2X?jvsQyj;qJkJc7FosEa>o!7(>jU69MUBMq%ys)it=i8vTtoKOqf74~@ocEQ%Mz45sp->LXILf|U1 z1*wRgZQPXZ2XdT@zi1hgP;)-1DJg`k>1S6^#kx0jBnp}Z?ni#0ygtCQra8Us^}7^1S;rLX4f> z#!0>qz$<8j%&xs?jgPe#$A7+DLFHJB8#5j8>9Lc_@6r6#f7~gO&=~_7*bX+GxMbm6 z?!3qv8dCaz)Jv82h}~p7*u=n_2c}Dg!)|3L+r7WjIRr`s!fe z6@tWnlVK6>>^8`wWy^ueZ+0&kyASxGOy9^GN}BI^SC`nKm{;~V1LN!5Yi99KDKMqC zcFarVq@Fh)K(lijbJpA0aNOvlQK-}~XN zhT}t-%<$;w<9_YZ$;pYI^6uK&ccWqAxT*2+@t>d1gq2;_*4A{IySuyjRFske0s>U4 zjOy#_>6oTwX1LrhEC>kV7mFF_=uCS;iDn)aKeNL7?E8S6X!Q(VE23t_IF9pk=T@=& zd$l=r8X~6V7rGtk$?}nJfPkHhgBf?kY-*=M)u>zHTurA_EYNJGVHax8*H$g;uK9y^f#Ia`Pyg;=;B42DieL`dke^RpnW z!8#QkZp zW8r4GO<&UvaY$cXRoVk}q^(<|EdnWPPDUtBA7Doe;JZyVm#yBsMYEP{7a=(TF3K(} z7OxeRoclXZq$$Ry|7x_zsLNeNFOx>ym?L-O*46RqSQ=T`x8`O|+oyQW;>(Ro>uF9! zGnY%B+Dp$XvGIg_pk!yQT*DTa?l+OP@3(GT?)@>}kWQn{vP18=Zh0+9nKAuUWLQ+` zMj=HcyGCjjSGCs-0|CTdEgzziuf!_PngqZsui9bz*1P z&?$EIdYXKxy9Ah_^h)g5Hzrcnx2?Drb#pXk)e7W~`TR;Vx7@kT$L8-(QjUOxw@Qdf z#PN7EnA9^VHc@bf=jdOxP5q$G1%CnY7F#0x9ZPK}NIK0NfkIoob%Xo!iyMMg&U zdTMWP|K`mbEUbvUJnA^QF$g+3x}&4x`PrG7xp^*WWp(vrPtS9ul$e;D3~Y<2>gx0E z5Q3_zDk?Dc-0Ip|PC-GC8$CU}O5V^yyehVeiptR;b4g*LuuqY4nWRZ!ZY~u=4@g;G zpQ1)YU45>r3ke3go*`KCesA_Ofw`9)(kvDm8{60FdCL(`QN{fknQILzuV`<=^Q!%Q z`H|ABLsj)4>1BiQGf_Mm(|{f5W=8mYCG4IVVqE#-WP8K>c$#TgJq_x6R@ihH>J57e zm|`%q6tsfEN11wyS?Q90Zpw7wNbx6-{wG@|aSoWkNIKtN1M%tbi$pV5qsXzl26E_w zUElBG_ixIcp!yhOZm(MtCI8GjdWEef#F9KOOzFiCh3*(p-qA335*ND-7mIkcvXn3o z8(F6t>z`TCB0d1Qj(+C48=gx#SX(O&pVbq+l2;457^yt@2lR3SC>7>W%{|a0b%i~s7l8_ zCqD6?4k_5MQNDZkjtj;}Pv22F%6Iv0r@dHN+m)M}TXo}LkO7s<{Y)+pEY+D(fxF># zvYn5`cQ)4Hk6zCJix7C=a)R+{xL@qKuCK@Igo;NH^QlofeJ(zjHBRqY7GCKIBf;jy zCMFK<7}Mz<&g5x>=o(_P^tz!ZC#xkk|G2+0$TsY|8+L-pHD!0L)K`Cq>pz$(NW;UN z6wgj@4xbMZ{Xx~gH=M&WMAz?o8Y#Akz4ZS~g zt+KtHWbdQS`}Yfx0W85Tpo09e!GW&s-is4*oWJLefqZpYNpB+_`Exj>r>6%?;O_0^ zNdj=r`m9`BDnIEy2t3}|+n+W=c$XlYmcPEco*Ko;eEjI&m6Vtmgv4baDT!8tU)oLG z+}xaL>;daqQF)o{;Naj_MMO+2`FZ@;FSPEYcebzRYF1YC2y{Yq`xLnH-;!x@8C#&b>g@|VJHQvj% z(Q>7{v7_QT;grIXA;&`QVRA-GGTxomgsp{SiN`@JJsxt-s3Zx=i{3Qt0 zmChP+qw^vD!XX@RIrZ<~1`UM}F!SH;P29ydKHThRmdwE*47M) zsp#p02kRZSqnb7$)1{icRw+qC3(B%l_I-c+o5kDrr@N>)Dry~&>$09dN?q-N(PnKAsJP9_EpuQy@iprE%XXqyCY zQ81$G5S3OoU%Riu9UZIrZ2E5f$_YUA#!E7k9G03(%CLWTZ$hyTFD)&dpU;(wAFvm9 z8EJ65yyv;Z67XoXSr(2IhFMr!!}dcT)}jGZLq8uko%aulAm9ma0{o-CfyY-|p`1g;pPF`>d|4bZ}GRw_kvHorVSn8K|j^Hii<@ zJs%SPRfAMsXFv_YJI%{l><;@b3!wBS;Si{~ySbL7t>gzMd-G`l8WtD1C@7CRy_5L8 z7BA&p7`oghMFU%acqElT0D=r^mXs1|PFDB~#I z2p$L??Fkp=As=f^2l4++2D@Yts5|fXbfu)wW3Vf`h7}b^2DvHkHVg98*R)U(^@-Ts z-Z^J)l&IG}T&$q0sgXWjD*uK{NJu2C@9z(vBqt@MxtuVMgXnPd^;gASdFIsBC4AJA zB9{S-;KntuoMD}$sc4J%XuX0KDHXUs!})XO-+q5LoiHGK(E>;Wq-T?v1u7tT@kan@t45T(Er>7BMZUlx^a8$tvpaN)UJX#6qaQM+))u%F*3Iw&l?)*>bASnai z{5Ht$?rvrQIzD4xf4>lBmC-Qv0i2zkonn!Q#VSQjR#w)`Y$u7`JVOHTu9O-eBma+T zH5No>;UOV7K?^P&%G$PvHHo~=Op?Fm<~BDrw3=LAvfF@y^nj>p3&e1v%=b>|Y|x;; zzCk>2zBgM`_bVbIA{#5K)BZ1i)%j0fQ5^aZkwNbt=h@QI()KcC6%;101a1QFX;GFS zc<+!|>b96+jExIA(FybukdoJWX2ityMdl^g@<8NLmRD1dk}@-`*AmZ>hBXn!s(FhY z6v1@NvD>{IJ3A*xse{C2B=k@tuatSNsI2Fko;x;aE**D5wu!b+j26^oHBixO&;qK% zsNy4HE2W(xCw@)sEQ&e}P3A29r}KnuY`i(j|G1Y4Bhza|M4fbf{CcTN4J0b$eg@Yk z>nYHh>gqQdbw@zua5ZIc)H{=V6q} zfChJ!8TmKy-5qt#=vd69dq2<}-@?_@)MUkep0@X4Sy)=mGHxC3mNhrH9P4qQM)-^J zpPxn3a(LZw^#P9MTlzf{$_^~MRJ%3pU*SH^k#V6YLqqIDpsK>=9v&XP%YS_x6CaO0@3|etg)6H; zLP8Ssv9Po>uC`rR5OdzMaQwC--*{N)8|Z!)9y*gH5A*wZg)l1+Ha@jT7N?B_lV*s# zBWJPV{o!oI7U$p|Jew)QvO<;+Z72qd$; z2_*{v+z7pXxPmaydsJU;igph`Y#%T{m}sC(+2$<^RfwhJKTCL-IRlw}f}&mVROrF$ zp{2!(pTA`htz)h?#8=ql%8l+o1{b%G&})$k=jPef^XJdncyMXOTtr}e&z~VbN4mMb zzP-Akb04HzLsmmxvVH3FV8Z-82_fwBjiEf6B1$gS!k>M`kOhOIP;7NMPDL5bA*}HMwK1tiOUTR0Vw}Qv z;q!!j6LW~#6-rb1MjJrH5v9&!?U?C;`)1MTn$cE?$;o~*NgzsAO;DRt>7Z=(YK%9SC4*72cyaOY@Z)jg zV`G&*fBvkfIQjGE&&kP5=1eg;=hvU(e*ID_&9*ZZK3+#j1QG4M`n|idVWDe^4+g7f z$(8O6(;&&o%Y)Nj&>WXNrSygR0;xPXoYw*CV{5(SN9(8EHsMR5HD)|oLNszWgyFuv zzQU?M{Gm5D*IeAZNlD3lxP8AGexVSb;Ve+{@-lET*JhMI>8w6Ev6G>A>PRDOsQY*h z6#t@P>+j^#piU|8c^{r`niyHh%c*AWS<*DXM5<(A5YV=cnGxeM{DnA1&fBwy3?YFZAETmh6ei@WDp^lYPJ=^u-&Hm=6xOWLW8F*n z%4&|YjRSSPq@-kFVNqRC|@I(6J1ORDS@lrNMRv##7eze34czF44i>4vyN;P{dVJ0CaW^He;I4euo zNsIw_Sgfs+t;qC8Nl8XV#^Tb_7T`R&*JJ^7Tv!|q{bLcETLsWyYlD(+-va+`e4cON z?#{Eb1G>7p3heXr^i)bpilJK;IA(l%DK=-U+JuCyrn{!XLVCMC2nIK$Kb#zv19+B^ zA2=`rEU1#Qa(sNe*jPsT7InDH1z8%Y^=L$Rkfg-Nmje1q8bG5=OK2fTRx>mdGetAy zb@uBqeTE|XehBRC8XQ;H7aZm5tH&jVMOs-?S_8eY%<%dlP zNtvOMekLX+dIBi$Bvp*iGaFVBf?*%1QWO+q_`h;W<1w(bRKx>UzWe7P-&vgAhw>}Gx}oBsj;xN# zlS)=QabbT549pJ*9t9P+5iNKbyaw+Q{CZ$uY`wa?&9n_ab9s~5@huRl1^9yHmFt)o z11}@N3;|Y6jZ}TT?8r8QrBYm60s!?}tFrDP=%ys-MQOBuHicVvvHL@b_Ap^NzivjPldAe-t=x%{ zeY0o7sGubAJbMG>_2+V{QK$0Oum7kEuwIt2gao45UqJQgzXta!%zh2pUfsm>zjYYu z1(F3u>q>{(u)A0EUg-i@XT4aZI4FL~VJO}XriQt*{l zeJ{pk553<(|7W#*vvJ8B?kR%vq1D}Pr@N9fYXH1=f=}cEOR;Vw?H<@+=-p)o>qt~d zo%R%R!@6|2*SxoBq==?LLb(6zvJ_>}1rdnwwMCyL!4)_}%TU z2dHkb_QHa~j1qy4+PC@nj(0rNX46{disnjy^z>iubts(=e>?5`v?S*9{p?+0@~;Kr z8@P`*nO%GY{uIz}A6^%nd3Wb%A0s=1fdRdtSw>XY z{Fc`|I~#c&Ik7KKw~POoOmd~M0yO)!5h>h3f>zv#gv*XNa{IsoFsc9k9 z2LQAqxo1tT`*k!g_sECFgWe_@)Wyu9$Efbx^>GZ7*vE4pwC6b09ozLbj}Upa)GzNW z8q;hsPX)b0rw?TL=y3hRqS`lgVLdxhUBOSWH#Sc*_#Jd)`vu?Ux-MM;b+qSREFHM+ zVGBQx8r$}#XJtKclMU;S?Q*!4{LYw_lKcOrTsItl&Z;dlz)jD@hvUN_y~*W+llYFZ zpJ2mYk;msQNP9a4@fgiF(&W4C&FusSTSw6(Z)S6Sh1JW6*3PxMvfj|UH9n^*hLik} zt7+a>Et+z2;b+XPBf<}rcvA3@fdLV>i}`d)rOW-xXn4e@qNe@hi(?P(pS-lE$Ho-L zm-i8(3F=7@0VaO@=;+quFCN8b3lR@N5(@UK8yiLsmK8uwcEX|Pg6Zk*r`Qe|^(PAM z+KAIu_7yRAXOIrH@4Mj*OPKum%k~XVW8QD`uie-nSH>d%Wp4&R#4{%&mjcX>L&RkPEQ^6C}uBm6gNI|PA#-dd{LF7>PK^$A|FXJkIXrd4kAs_Z_X`l zL5mK1bIH`lYkX7n>vx!ib~%*_!8jMn-iydT0Z8^tk#+)7QG6mAAtI1fr~KC}Ui(or z4Hkq$d%8rSDWabIl@%2bX6E>XUjs~JC!{A02Z}k$W$Lp)+95B0*S_W48-9yX!nbjlCb5#F*R!vjWP@#5rv7@wg=X z@fjFYH>AvXj1yW54+sL#P?Oks+f-HF`8p{WW4&g(mJW_Wly5&yJ16{M!St2)+>Crh zZh?TRRf1`SPwYDxn-xI(T%$&F@-RNOq3Md%`XBdP8*T>9cOrm*jfCFW?YGmY)pC2NKwCcF1E%s4S0}$#o-L;M$!E-HQ9foipWxS zoie}|D*SrHl+OdA+iBw1+S_qB|7A$&w6ACkKp+~erfgT^`YA)m!w=T*tsHB0NJj|UF&c6y9>{uBcqlQvCevrnx6NahUI|tU7sY zKx(D}KyPbzC%cT&E72im?yn+rtZ8=Y1eBT->FD||F>Jyy5E+@7#fZt=4`&8u|LTq; zfau_pyj7i4;r(HQN?p<&-0dvxbofne^JD(rB_($^aan75UI8e$`v9b=Q>$OJDM6U} z1m>_a4w=Ohm=Gc0FvUF#lxS_T8G)3Es2-7GySF=wFA$QT!akQ*0m>wvjyV}`Bc5|= z7V<$p>l=|-8~2)1*~H%vjeAc3P{#h(YUDwvM#HK7i?pYk1*;m=OOVqOyQS-+{ZG9f z#f}e*(LMF(UZ!}hNYCbm;`r=}vvY1rEiJHDXqvO);_xqSqk(XK(fcdb45#O zf;3&P_x<{x;31d8X`#ii<3SNFjc8$F(pJ-a`$M2Y_ZZdoU%ze z^eW1LW2rw6|8|N-_7@eWMjZj7-+tBwK;;5pIx5^js;bq68UVgA7Ct-Km6Ww8x>9Xl zEUjU5r?M2b)Xoz4|b#rVpLoD~&y7;LTcMjlKe1kPxh%Mw0k$YgmhNC*d&7?~L94X($kSXTKzw8T62vcKX*Ru3{JrutG&%Jm&9 zym#vA{_N1hqq(jgK6H3GvN~ zTwcl*gK{fX&tCtfs5!hn(cz;kq!9@Fo#TwU!I5)FFDiS%paZ~SxmpXTxXi;}pyy>i z7fvPPrPQk)%=|tu&vDG$C@4D};{7bA7gV5Q9M5}T7$Jrqd>9*hbsRMTaodX(o{0)v z>6%Ry`2DvS_*ZNg+0~9C5`JN+js-w6wCy%4J1G(S4k6-`d#O?VJvi ziD1}huVW`DLfQw#~W(d*Ce6lFU_zEYu?Z5awG)W34sYfVixsWYfqr+(JRz2H%DHX4T^ z{coK}4g((;7#UXj)K!NWN=rMKsY&X{J{h#N-OnrqJcy)iAHbhLdiKmVnumwS$jFHA zy}^mx9vmDQ8Ho%6%gTNM%TpsRDPLn~!6+L-g1IRDga2D+^eu;{eti@0+r-4nNK1=} zBq={X|2#vcyIGhHdOaO=s3?4@@s1^+rJ`PT3F2Zsvt$nHs0Ocxm7@UxVTdI+?AkEsIoFa`pd*#u?|Rc zQ z=!3wp{-;<(=3cYe9H^F3F4c@1akg{nsskeyko_#EQ>N{Pbar$&9WHwA{G{8`23pz9 zx3oDgua}pX@$m5A(%(q2XRr-KMMN@G?Ce;_RdTdESa7^hn!Dt;x3|Ye05sd<1<+?? z(roz5>?A&;BO@cDrA3B)Qc83}iv2p+!O6+#<3~&lTL3&e!r6cWzd9q^3p0Uysp1?U zhT5(^-^e25fJ#XX&d$~&55L}R>+SgvLk^ALV- zG|M}4o}mVaU-j@OK#aJ!xM%?&tu#LOa5BE6xGCG&f%*Bnoyns0)Vu4=bRQp|v3j6T zA1Wy-`pRS%kGu?Ld?2si{}8p8}MNy31e!cXxMBPtQd44rlS=7|qTu zWO{ynvK^F115kf6|K`SUa(VBK46JJtWM3g#Gd!cSCxew_YLglB|GsK~8u9jOYO;}u zZ*XD|u}Wh>!%eniMT_MK6ht=Y{n8wOw}OKDk5_vmTkO_@0|I(%$pCcVeC1Z^Q#yZ` zs?A6z4D0s#dNPDG%6-I|$mq1$3;=TBL9g|F=0J1md1&VP)yM z;$>rqPFGb{cGkS4KXJe4L=ATDZsRquvpZ?=yuB(D81C<%t+zM+_%SRp^5vr$qy{N* z=s3e{Xa!Kz?cH52bJU)v&)!)7>~ zNGt2E78^wY+R>~iE6l*Y{&GOC`NM|~d?tB9y=|9@{0ola0^ZsL$~5RwRg2E1dS6gc zC}BBUJbxR{f0e>{Y+x^zQo}w&RdYcDZm*zSgWwqSk91|+@2-x`r%Q!K$J{G;G&L6n z<9XAHLxO_=L=>yh{2d>k=iy>AEe-%|YR-m)hX+^+zuY}^?gP+b7dm$qER55;>(g_- z29POZK<@tD9y#n=w0k~?wW_jGmT*pWX&XM13Sg@pUdKdTqC4B$Lagpn@?S&#*j~dr zyzdO_%>L45|1_P6^7)r9g5KYCR9tA2uzf^yY`%mPRYg|#j5qrWJ0fW77Q=uNLlz^9 z9B?{9nwqIW1usdG^vSKvtUR`ZLCN30*ANmZO?ZUXbLhb5 zdE;PWLJP+BMU(1M(!~o84=;XuDU;gN^d4XW#k7ldgk3xDfOMa?0O}G4by5+N{Zbo| zA0489*!Yg6t?luvFaos0R}~5N9Mao8jJnB`>yGZf18nWdw8Wpz`Eyf0ClQoKfqWoo zlO0QX%kvOXLqbgAa_dszfEBPozXiuQTLhR2+hWL(5hIL|sGq(S6v#E@V}DY+Alz#l zLMCRQr@yNGElF;G4kJXISH4DZ78g-*L7_z2{N#)^{Z|=;B82CFMt-FrF*j4G4A3f< zyu?SFM17@gT^jvYqKijZN0=yQSZE->8?l|uT;KdWN<3jjpLoNDvUDV zIpUP^wZk9;x(si(5x2JMYY&XB%B~8*5rFF-ni%Vwm;CLN!VhTvv}3n%WM^Kwb)4}2pkYXhi@iHN~8F~E{^cp9+b zKpI7ed*-UWKC$n-gLGA2->5dHR*mx&?U|?GY_jM#wC2Y&fF~yV5yAJ)a8c(2NCE>1 zXx!;!rURYd8dz0PbbYQVaC-k>$4|%)A*4X-j2zIp=jivFa8jC21Y!#I(STt8!a;#g zzW`lF>c3C$CdH=tWWV}5dL~ei#5blt>-75v;|k2g1p0ps zz#o2n`TyrZ+t#qp6Y7HhB3nORL2TXuul(;Lfkr=Y>A?fqexUs4xsg7$eO?RC{t7C9 z1bFugA8-_5=$NCpyvCmo2_1m9Ly-Xaz6Boj56kuIpH5{U5Yql^rvLAD^u~Zh4C7@V zB&Z#D^goio;Q2faiRCYjB+@+K29S~psC+t+M&{Y4n=jSm+&RSm{uM{VIi?J#{x?%* z;kRNa{cRhDWXyxardVs5Z$ISr7UeJJ=*OnRE=dYF%0ti*Xz5DQ^5>viI7_o?&^Mua zz0v8pkY!J-c>fGzBy|F)hoK~FT=!tG^lub9kS;%QZxT< z!sGt^ZV8EI1atM$aGaV&e(AEiE^LzsS0Qd04!yX1Np0X;(NWCJ7Q=5t#Om{Pwq%)r z)Emzf-3-NyF*uDK76`~ir%w>X9RueIxNRC;|BMU1Xng-eCn#wK!pG)2KY?fw^`6Bv zc54H^f)j(|NxenwH%2vV9dO<7OTC(b^b*P+ajRV|ZNt0bZP+^M`q_?RydkFnELq=S zt8HRcER!IiC->{UzcRaj`X~1%+y>sSbcLzKK7o4zbw`?JQG<>L7USA6LF6C`5H+aa zD0l1X5}D5gSfHU$Sg!op-utN%Ya2W9lczvbx1)u45>E5H@}=q_$PJs;?m95k)N4hL z1`03&<@k?pRS>YGnMM-yDp||2UNmeatRMBg9c(JP)p22JBC}8?RAInsAqa1c4tqrloaO{viVF-x|VGGp*|KwRJ=mcX&6Soko zgvhU=DU9(XG?AG;D-wB~N1iB=Ee+Gy^+2I{TKmp>f!6%lqxFt;Cj_>4NFm+lC#vs> z&YiO(sKy$Y(IZb6&qI{_$v_IF(KJwJ+Bcfe3(ry|==&*udPM=EEw;_+%G?{g|K{&U{MVdrnw;Dv~${p7yHQn`dlp!#3esW)m0iLo@pmaBItruer_`Z#dJi@{Uxw}mvP_N4f5R=#T7P#RP*6|+ zyob48zv@!DxDk2+<0O=(( z6vIm1&6}#uatPxo;IXh*EHJ>v(M=mOYK{c!7Ph zS3sF+=KQ;6bvyx|!N1M6b;m^x@bmA67?eu^?6+UNYhi&8AXAF3U&UmeJU(2uB5($r zjyM331~^$q1g2}` zt%HXB5P$IU)4Z%KJ%AeNetCd5?)w$@{}$d2DCo-*apYw7PLBoY({pi`9AvXAEgSLU z*=^Sgl1@XLK42PT_Kju2ZSsEj-ie1(m3eIza`tkNw)g$K{ioB9hd_za7o*G%^d$i5 zzKNwJp8WN2GG}j?0!|&(TOWYRNDkoACpYG0HQ&FZXuN2{u26hDe7qM3^7u}0;h)aj z+=|VpUSq+8g-gbJG1#a_z~^yQe)KUa!x%(Y#cXST9kD!IIZt~$PkPDVPlIU16=)r` z=b6rJ^ZN;?4|w@?BJ=&~p_iA}3BGEjLI1%*qyJ|R`v(g8F?W70E9<5QVkzsnn*FXI z9O1YX*`{I5tSf!4IXfI;y~6&&^f#Og3pRGAc2+w}UwG715<5xI(a`@&@RdCtvkYi< z{bk$}8m5%XBck?1fdAbH5IodF(3j7)C$Iu5s;l=;2NeAt2*UtACX)uMb|1-_@?8an z|E<&hz=uij_`<|~(?~rq&H;h989bIqO%gJy`8u^19xKzCE5H0)QgM}E;H4R9#>xnm z7qYP9jZ~0>YeW&jNm&4l2K!d?$1{oJ52l3cMUMRVxzsJpphUUrpoQkgl1Ath9H1v)Y3lMzLp7vQ3jt6LGWOJGr=BRumy3C8ma3l_7Br0Yh5J%QNu9~2P|-B zK)`b*Z8}h5Qqs*)Cl)##9ggc9V>x>+)+(e`Dvd~J50T;6gW^^Mw$_8RDU#U3^QJ~2 zemWy~$?GR#rx0?Z?J2Rtk01RHBdNSE#F0)SML{Pl1ctaj1wjQ@C{nVTn)g3?UkS9W zCK{)8n990f3X4lidjJfsR>IR~BV?ZHRXby!zEV0EK>*Gg8ojB7S#e=$6pBG`7wMC7 zy}R$@a&)lwZg=oA7O}jx?p>?NoW-}@RdYRc>^7MrsuIY( z5@ust+4>Zcxr=Und1vH@c?7?`gB4jIOPDlkR~B*uU-YkW8Pv83hklzY!fXahc0 z&bjD8ig>Kq=v0x*6W+EnX@h~%v#ISthK+5fyazvGQt7fE?{=;(MjzHAkmq4qe(3x9i(m@YBAqXJ6Oi+_1^)Mi3r zF80vixGS>ao&(SbkEUYyLjkMs;f~zv{xA;W7V>`i>%cu)k(X~&bb}@%Y%H9&ndd-A z7U%9F`+yka){jNSkFTjQS4n5qaHC@rzJEN|i=NIZnXlwYdy{U}f`6S4wW_>hki!Q?;TW3% z%+u(4BjDpZjgFEZ5X}Ss8!K+ghF1!`bP5mE4%_3kkC!FLuRO+jdPH3?k+L5HMgTt@ zET&JJQ5OK9_2SIAMb4yEi}zdU>EYK2c&qs=4qqwgYz6apK(nivZ+rh>niAqN-WFuY zBX9siyAT^L%%I|RwvL}h0?Ay!MN@`2tyGzFG#!{sv)j`1!==_zxLk|Xlg|LiPRx^E z-Zt^U9M|=mu)gJdrPXrOH5$S*Tt+fH(tc}gu(5}4mVOp)Sq%Hi$7T|=0bt69n21C~ z%vnzH73&$PU0kr+)Dk|#86Jz)b=G5e5zk~SJtD&mF`&fV+jGE#ERH}E<*4Bp+Vqq(rI_{zX!7>Or zAKVvHmomu)`z~_=-;z1Ctf4C!9RencfY>OWk(&{z!GFp!#gg|aJ)Hmd>MG*?<=I&i zgOm98#-bu~V89GeP?hPZ*}x%4{>S9F^z_5EHKbj8TU#Mrpy3eO4Rkzhy8zDp5kM<- z?jEhqI$Tz{zifTf1Q@vH1+!JgP$f1twuy;}%toM4+p>-jxJZbNRRp-t)USYP6o9wd zXa|;eF_#2GQLu)N0+$T`t@qy=16EfzhZ*IR z51$8=lXG&y)`8YU=980IO38sDgWsgAwRtGl<@`CzGw04cOow|HKlJRQ2iQo|h}D0o z=|&`^7}dm0W*;orCSfv#mYBDMb%A%^^nI5)HIjOUCj!j<>zZECn6=GYrsdbM1H;+p zLC}XofhAFxD=?KXL+O@UR>s`=#O-0e-hRpDzPgM?`Yp#pYxZY3IUlT!KWzQ;i{z~y zEiElZ0d{spC=@Y2HS$&X9WWmBqc$?Ao~sIijf10F0W=X(vVL4IxtuzB$dHnf)+ZgC ze&`2g8h~!?-QDSES?dKozYvg$bvR4Xo$Wfl)mNZ!MKl@Ov9sNPFMWEH0VaT%5D@NJ z%qNrZ=3T-Mfe&x-L(0u#C51kK?Eu&`CoD4kD}tsH>l2Xb=g(IU-<->{9=>VdeMfTa z;QJkn{nTEkw*2*XUMmi*rFF+XfZO9``PW=Bi&l^5QM%KgZNRldZ;|o~N_aUQ&rThr zSN3Ay2^l(c5K#SNn^w&6>7}A4&5ys!q7-c zcZYO?l$1jV3`k1|f`mvn0@5iZEiK*MabNa6=iGb#=lk8C{ID6A_xJvuSkGGPk*j6) z-{}PqDyMWavqv|`>Ty8(+S0bEA}|UGdgy6d!^UFN`G^?gBwa_Xhf>I?Ql@ z3R@$yw2nthw`gO~a<|J0Edzv;V{_9bAh(4r=ECI(iaG9{XUhYK1l55kw8^Xcb#%jx zEBm(~ZR3XEfJIf)a#TN*bjm9VO3E0X&W2J^kAjQcn|qwt>h}7=daA(`9&%px3TW$^ z?{=hU%z3F?yGu(sG|!Wa{KaWye*jSin5|eeA_M;uN?#6odUyakv#abe-m=p~JsvLZ zQ0!2<-KXKNoSdA1ryfY*rFGj_^hodZ5^=`UaX8W&6jJl}mHZyh?)GdR*}w8#H$Yio zb8c9wbjb3(;J;(|5iF$YI{_VQQGab3;A>K$Ug$^vkdI$NfUz4;Sjq04h{ROp1G$rK zkt0zRX@@7;$io2gDu5gFS!%4sOc*h=L%}!_*pIyVEw1yy35@=$w`Gs@@PZ z6OXsfzKnEz?qcY-veHH27tj143cu;Ke3lV8uCW~rDtW7lBP+yA7JWDy>~?Zx^Gi!VyraW6N0~xi^}-d3R_$b{u>@r7it3rI9gBUiB2IN+k)tK7^VHeG1E@{Wnp!>I{Lmv z1$CU72PKqE;#GeBC)xy+Ap$vWqVxzBC;7dRG2mG7UXcGie<_#|1ODnc%YG6o8(TRT zVLQ7!*x4-zncM0yKS5oYoiV_O#E1%2D9#glVfRTk;g!)(iO#9SW zLAm_lR*5q4C8Vbr!uT7BE%cKV5sglJgZ%X>X+XOG2PB+R@W~DU31G${Ehs#m0MhQ6 z_iana6mX?Ok$y!`OEh^ z@WDCIC$@Be(7g-lW5B7G6H}U+oU8?!X%^(C$s#_y@?UdvLHZ!>24k1{nRyVR%>Od& zX46U=AfDQ|zGVJF%&Sb5iHGIg>s-TL43pO%%6RVI9vvx`hPA)uf(7eM+ZN9p9v*I) zEBgX}2Ll>k#IGf?!(kap9QP(gqDE7ox|qGU2x)UQH(%x+6s9e~Sws-8y3F|lrzXwE zTt-0Qd<}W*m*62=OLeSvsqqSM%&bj6bPPABU|l7_gRJ;NE5EjLe&|=OB{cD-Teq~- z*)&^cI-dYiPBdk9&sYp2_y?BV+8@<3&Lshc zl3uD^ck(H)42*H9x790OQ@;xCrKt=+7bBedf^Ugb4a;ByHM7yzZMR9jE^SPtwv(8S zS3}U**;xUFb{$AMWhN%?xBPrG5yFeDiITyA0qGcvH>i)z={4SQQ;Z%=SJ|$oX-gUz zWv+B}bPx~`*;-pK{E?wqL4bnO?r<$Lpt`2!?zloZMY>#r8;-$KiWYZ)OGWcUvc*dc}SZGG)a&W;m6M+o2ZKers8NwJ%X5xaTL%Z751kU(%xloX&A z^TrAQ^Z;6)aizJI%!A)p+s}x&w7y@YH;cTwy292N!cBpolPv7<=L}4B6!aD13(*|L z8Ul_*@icYUuE===;k9|c42x@LQNdmSPmqAQtu1qdX6F*%@^wu-C6mrpMci9@*c~>; z$PlLxpH2-gW?@8i2;@{x?oWNnukE4WH#aa~6|&|ZMI5=4YPzC&tZv(!Z-?!A%BEzd zzP3_w67V_OlPCk6s>PfxmrlUA+S)mSaUPS$-3jL?6lS(-n}6>!dBPXKW6=2PFTT$X zQzwkrww3;XsI5!Up(}BE&oa~mxo80N>{?iA56vY$5E2kraXajy3@9a4xqw`NFdv1-9Y*9y9@oI%Bd`po^s9836;zL` z3=Qo7BKrW)uiRY?*3>059Ug*NtUj38b*N-Cq-p`u@$1*FT8XgK#P1F;FyrdxMu>v`|{R6x%BA|D5eH2P<3CJ=A# z@o_#rmYV8r+TWQ1%Pf6S-Jbomo+uE;4^rMVY*MMvfNyJz9-K#5Nss-u5Dyx6qxg*0 ziqA#PW`08;FD1DwM>zl{?$=2BT`KFs}QBqWbbE<_aOhTf*%IImt@1^zhljgFO&6oXP3H^#^X??i z{Y8ZA7jkm49gfouqgP7l2kdE6_-PbA@0qN_-7=GEAE7rK9d9lLalpc0(N}Lj#FP+8 z*k7+Dn`ema%+FU;_YDsIsW;;u>4!S^Tm4GO0?Z=m_eF(f^pL+aJ&yQkH1u~{w!SFC zz8fSqufHn2f0KCH))23eRmKmmtmLAEHdQ6RApEo)2zzu&nfT*}KBhBmpB9@qbLp2~ zw?du&yu3lJLAlNBrgz6J`_Gw~@zex2{*>T;#C;Te?W}okV-vWyhckffCVP9Or2qSx zAm-G+dL~5mZ-Ubi7#?uc{n2A?V%T|`v$7X`wSXWNoeQgED`10bXFU0_m`-9!w&lgz ziu+zgRnI1KJ6$yq4*=POYiSx8@D8l=&GnEJBukl6qlE25n8fv*_ZhjrlA0PW3L$tW zaR*`LNxj;-x~9iQ>gqIuP|nMid^Gelr!xN6$jid#3&GnK!q~0!8P}L7mxvJh%(qKG z3ZESv03U4xHT*=0Q8TlqWEjRJ*&1SHWkp%!hK4Cn0H{!ndEWnRcH2<1M%QZqmC_Y` z<12V&Rn?7YKas&MyBIeM%acNMGsdMBou^D>M=QkM@OTk%ddFgH0Ji%@V+XvfaaKB! zlDNbXnlDm0CMo$~JfL=-UZ@8Nr~ZJ$7Jo?gGD&7zl8~YyR?u`ZUOWf-T?=%-Pmsify}5XW!eqL^ILTwk)WI z^5mxz&0Jr^*8<0UqJmQ&9TPWkWKLi~W4~kHQPVfkKu!=&?gE^(605IvcmJ$i`jCeV=6wc%pRDeB1mP_;^SxF{VOCUcQSJh6}mq$s;GCkomzK#2SQ! z{HejD4FzWqI;Ef1Go`;9t1u>cLVgH*`~WMG5%Yud6pQ!XD9PPP6189^oJ?q!I|qmy zI7dt;Bs_OCh};Epn0|ESqOODv1weY(3fsIVrEqgfwhS$_MhMyUoB*pgt!c9S5F+X- z>nl`YIwdfbOm;X3a{>1{{7ZYr9zknIACdIT-SOYh$hBD4&3<&%JQ#)G+jqaFD;(+R z4$w`LT19V?YLm7?^WBtQxI?_2%U(+Rp zpm=)eObmBxTdmrz3~pOr*+D-GPKyLd(mOMibxE1Whhn6A^1;T~mS#C&;HFu* ztS_YtB^MI8OI_PscHu}gJU!w!@V?X7x>gkPZi^Mr)GPWZ{5g{NH|m4^Mr=g!Y|bbWX~kk2q6s*y@6115oE6tJVAe${;Et0yC~wg{(u@AC3) zbhOj|^umgmKm+kU)1r=~6}J}ljxE-|w=SNh+)UG)bxDD2lKb)bjf0sWHz4OeN!rso-6c z89*6F*-H6p$C*Kt6;eMBH-}t&nR-L~#o@y*!HMW$0W!6=lE2MLsacnp+oOCwZXc9@ zj|xA$J!l|>JQyHLVPQ*>+6a=>l0dPh7)gY+Bj}ESyWR z!?pet>NuBfFxp6%&FJ0rZ_oSiYixX+Fc!v?!v|r!Hq->~n%*B>DDR%{dG&M%r@kGI z9-gbLt3rk0Ug8t=7UhBKMTuHLT?;>sF{bzyZ@8!xxq}@MZ#F$uNnHDVA~6&q)^--m)A956=?; zIsTrVFCYLiTFHFv*ET3$G0yBf!+9qOv`IB(KK{M*rJu(;LDkUCO;@=2qZlmYCNA(6 zb4d6e340QotWssbn9fJ{$>S2>sfUrYbc}E9m9dC}AmNkiZ^oro#4~7&Sn0+kW3ea~xO;iADdEqak&=e0_swYyuh1!`3j%p# zWuK`8cDPY}w?A_^s@s;K=C1{x$2uPD%(osJh>@7dUlntg-%iFy<5-j!FYa>#Bhqdr z=6N!jO=`m-Db5R7vGFkIi$hwj3$fQdEg3$GYYT16dpB|-!ShIdC&q681Kaw|n;<^j zDeX;?bqVqVFd0Mhrc%;@OgS66i}3=Fr8NYAaXDVr(lTpm8fi>vbGKdJk-1clSz6Pt zA%K6P!uS48eHH>ke7-Ta7d~lmJ{{lmG~w@9OaHXYA@wZ~?xnzX=bP_x1Fp#}yA7|} z*`y@yobp9PL{fg(&(`INoF|XGj>CrbGK@0Y9CAp8$^4^M#h_r z{L!U>=1y#`fLr3aRqmcbPG(4HR07Oyb8~1i#{{FU1&%BumnnT$5gi?8O2~+wXp^%v z5-z^aN%=vE`&YtwRAG*JuiCX9eW8j2>@|MWmjMbBLKcj=zv*oREj90184#r2TGV6>B_=D1OYm;R+R z$~q-8hwV5^;t_CasIa2LB3nOS6Zvt_0$?zJ&2>fJ+>b|0koOHLY$w=q?k=w@@R}|+ z!E8(7?a~DKU3HRE_vYps4Z>ObEJEZ-}fsJiZipONEr6#p?!vp2Kt zFhprXG3kfyklFb(>T!Xt7yqQq(Wl#0_HfiNt0>bY*A6 zQ4u&&FLCTj8mTnZN5hbPh*Z^)8W^Y^VGv5HzQoQ!RhNBE%&6wj^l@)3oqw;s#;KrY zKVX1tV0#9TUn?qH*G&HGKUB_qFF&Wy7bNAX!N>&o!|8iFzxNtIjqvo&8?B;6OzjTw z!&7#hN>sPX^1>?pkNd?XCJ(!4kYi;+M#N0rpNG8N9*hUf^t7pE74q%SVFznYFKVH#tBa(}a?Bsl5W9Kt?G(K7 zzPM0G!Fs+<-CJ+H4V|UPqye*CxWQ0?Eu6fex?;;9sxyjd3a+5g4b?F|lV{Y$fA1e5 zW-9Jxpx>hue{*v)^~**#>ic(lCnu+az7zphOCU&&)nccGtB;U8$@L8F@!(d?WAg9g z;D}A<*_a&+kR!E`v1b$s>0CH?HU!&PYhais8$!kAOU`)VQcaHO$MTcA3nmi~}$ z_~qFVPgrJpI(fiPx>SdjAY>=0Q%1Lgg%(ftuQ4$(@WX@S@ytroAi#zdk);k8vdx@p zG&7yM*^DH^U4Wzi`L4S|O*8&Y&uVw34!FT=Q@hC0#;3pzt$tF7cp%-FoJ=Pex!pX8 zY`i?3@ifNjdrd*62Hc2M@zoi{3E+~{e~Kz^vy`zki>A!+PQ5gwqS#$zIi^NF^u)X^ z>0*8imsEHk@_wP=XlpH{@^h9t3oF$jNytP0NzKb5Mr{m#$fQg^RI8|5+_olR&L(^w z?R2<7pe^1rY9>qJ${wPJaxiqnC40CL=;bv{i3_W_nR-EUI%{dq%UGDJQO;$?gss8~q%072+w!?Jj zugqhVGZP{FMrHtMw|@#ED|ZX7&l?Ys==}*T7>p+=|E0Z5`WjVG?|pNa;N?D9I(&3e zWl|b+f$&$rx~kWYiJT6$%{Y|pcC7>t4W*@_2^*IPMnyGijaOUnIQTJj>{goUp@*bSlFYTKZ=%4%bD(r#nkHnR>;76f$Lo9FcTQ59R~ zJvWpup)$8_9aw_c*qPD@o1dEo?(M6G0!)la%%0&aU;HYP0v@TVbQ;FY2-tV1y^PC< z`Vc#A!i!FIe)=b_0q^>zR0hjA(p4MEQ+cAXP5zwM3FH; z=3#H;SPw&r%&lQngyB_69zWNddUw-W%+E?U1C2C_*BuH;M^4mcSFg<5SGBQIDZ}&78AoiJ7I;T(H!$2#wHxKLhhur#zvH?61H| z$;i*?xvB_QqJzrWXsQaDKn`2hDYXG{1P}f<3;}bS8V)E?YE%M{hNnKZ2`3a6Quy*5 z&NBOXx8OwIg1tkVm34IHG#-+1Iub z(ECH2j&U<#7*Q!=T1Mo~wub08?b(ObNjP=mxob%hz2wg39L^~$k=QYk$M-NAidLbn ztl){D`d}7%iA)bqQ(554`&|6F3{r5KINbU9l)AD16tEnj0y%=(H7(WOk6Fifpv-jE zFL|h+;VBkg>W<##isPIj@1VKg|Je-F{F?bj`&{nbR4*Q>`m63D@ENQv0i_kLj+v4| zB1ZaD>-O>?MjU26Qa5uQZW{f|!T;73#o(Ex7gfXZiBVM7oS6xgGXsYMwVmpTNq zHVA)^h<*GF`EbbqIYX~o(cdnyJxpp_- z?d|EyWz=UrKN3?RQ7`!LDOv?+wf?nJ$%?xFNeLV>F6OpyOofwAG-?Vb}SS@nI(9Tv~B?s#ecK9}>V$Mn^tK>iiGY zbo!pCN{v4by&Eninu>f>n3rft(~IjO`!5}K=$=hWE>24Bva{nydJTz#CF=b*B~^kH zTv#_Gd$;ZztU)qA-wd+y8SuG*kB~9&8B~VmJ}=n;n(dIj{0tN|Be|8TF}=UplZU{c zr+Z3X&Yw1`OZ8*mK%#FfS$P_`wg30WdZA~?^=aOlILBNpFSu{)YS+dax-z~>(%vKp z|MGhwn!aN~bn64`RF*5|%~;sjWiUo=@F?cO>hRJN-?dXJ558R57_$AdD^T_**4o3F zkxFPcGwht2Z7D{(IYcH{xv4_MrQ1b`B+x3aS4 zxGxsczH^qg@G zQI?8X^JT_+RHFiM4-z4X&H)u6uZiQIt@_gIE$R`^+D2J8^Nu z$lnrVpbG%;h}iVWLcHs~1u(ANN}iI>Oy^L}NrN;>L*&5y0IYAMtRwJ`R0SJ;bKgtM zRmAIAE#&41PH-#Km^w64dkT@_vOAXBUXU2^&hgd8xa3WxH49p z!A?a)>ykdEFbPfa4$Vv3m6ql83q`?}%4c(7cFNe|#)$gYPfUZ@W_UeYgNUSCwBJTM)3z zv+6)bPgDc}4hsQunOj}G_8KC4cw{^*bG0i-?bovs70Sx}*Z!+dt>;T$C-kw{=MmA$ z$0BZjhofJ+9=Ryk;PHLgHO5P1Q;1(yz4^Q!UGKA#!fsS4XcpC~jB3CZ7=eqVdOJi8 z@9*u^{o&>O^5x3{hg@^-%VWmP4?17#nX~V=f#?DinM^pWX}~v1$8lt9xi#_Pc+3^w z`mckLggZ@G_Q#J&e@QmrqB?IssdPX!gc%NLFT^Oz$h^%llv@J5Pg1b(E6q3Mu_Xmy zo@BVBg(_LCfOaPD&P>|}iXqjt{CoxwZeidSRzlJ}yPM1By+qE$yzVL|j)Y1jbA**J zgziT10_OcvPl%S#>b&b_pqVtG0%AYBwp(=k$Mh$^t#!My-=eego_BC}p*rLRv(bNT zUJ6e>x$HopfH(=@ctP1%alQ%7h(%OvcJ5}b+a0h_;|$!=)cyR`f~I5)VOb# zH(ajf`kMX4NS6YYNYC~1v&TNpUkCc5J^gDN+Vnw~!fWoNN5f4kW1ST5ot^fLl0G(s zY+0sD({Lh2X>T+fPK5j0h)QZ$9D1o{ld^Fx*v-3C zid=rtg>o1nbr<`*h=%Gs+JEH9IpMir9)*Gb$Mlvh!n3tVZslS@lv)$K&{=rc28!hS`UuXB}sU2 zUP*JRs;R{5*_|1qTh^UVoj4rOPEAVpSzF(4Hju!F=VwBajho#wx&qn$0l$XxLzE2B ztLry;KV5xsvefrr)NV1fNqmO`+!v#*WE;r&;CMdfIGHG$O$787Lvkg3(|!l&TsZM& zJS@WTfVk(P(CkIt+fhHNv zE|KDK%>F`gp8T>5gZsvx$1C=>#eGqsTS_LRzFD~W9d@_GgGbsyLqSV_eeq~KqWvyI z&zqi&L0oW_9!(=mN%7?%rvN9a0a5x|?D2MXc2muOEB%rcMMJY!dQdaCk1kq7f;4KA z%?c_5Syxuc(&c$_oFWzcjg#CXju#p}ivh~ZTu0jxo&(;_1`Cc^9(<>0gRtF0&w3Jf z`eTdb?W1bhuIz1b=$S&U)y>M`WV#$c(R`xJ%BY^f%Sa$SdrSfzRI|8+&_Vz=0JCd(Wa<$pBrPO(5=v-rEdv ze&TmtO6D{R5hcOJEjR7JcoGDhZb?La&(nqWZoLyTGMX%ZrBVi556{fZ%+97;ogN;h zoCV{NX=*PLI%SYhsI`%Ox4*bvB-vOVrTgwDOLQ3kh)E*AF zEHUIl<6Ud>-%R(Vws*v88(Z4i9veN31uRUUEq#S>IbI+BA)GGKprH*%((!gQv3=&w zi<=;xpi)-u_%eGN+~_CD(U!ked@_Tt-!wiVkDJBvHWW-xcXvnt4^h}qz_W#j zp0j<}M-EbHLDzxcGbMCpr^8{XddfJ4M*yxyh`3Q~F*mo~XoF|g<+eqXmm&-IW7IWR zM-(y4ad%N>hIZ(cVbM?)rYax%wJWBtF&0{N{(0pwI+J9$!CFs=+Z8|I8jw%^A^yfw zn#xs$;Zlub&=a*}!CzJfZhpW$QiAnSlFTS`jDB~JC{RsVdPS8h=%VLhf4Mnl2L|Gu zBr~}SpqK%lyAX`&>FLvr*(*SuiT&Z(4O+X)Y%>|ac{4KurGn1kUN@#5cfK0(#?Exj zCr&eIb#;8Z9E)#+_S4mY$%?5waz;i0Q{@&cM+wYY{d2!>f1f5WZHT=+mi0~m3ZFM) zg~KM-;{jrCNTZQiab5;qk|Vk2gR3Bu1C6`%}rL{BVl4AUjxMI7w|5$`1E-hWGbeoiRi! zDH?B3W9jY3;e_zTg49+#BntOET$WquO|blRIggzdd$8-^XlV<${lE{J1xSVqP*6}N zE0-I;yWbQt-?@Qn>HcOya(0sHhq|(IOnT<>%e6NX#btp0`p6c!z$~cCdaRyS$g2Ad zGLhfa>Gy11XV1_uQ=ks^%6IFNB@3j+EN3IR3R6>4W47vls_jV?){JE6 z8B<}nAS3A3+{U=bfg`W=%ny%xvGO1eI+RAo= zQL(%ta&uRPvS1oT1kIFOe0();s$z=1f1g^~o~jV?oeS&$KA75Pf2WJ+?Eh4Se!0B7 z-D6d*2hNXK?jXu%ymj+lj%6fu-2LO_x)-yTQQ-O@JP;i!CQn()xz?(C+mDq6QxdFl z+DHhjSVPx+VsWH#iye3q@*ESU{1XEoQvc{auLdp421?uT>wni37& z*JjfrYbs7 zR&vJl_YkNYzW=C>GWd&XU_NmkCHlsl?f6n%%N885yFqn}EqlZ1iPJkwp#p}tY_{Ef zXw`&|R_C`HA1$=L9xNNFJB%5jhm~uElhy6@?!Kxx6UhIZiUeKVXjmI6vMhdaw^Z-r z9HUcjHxJ_rW{6IFLtN<;xd-)%pBa-Hv!+ z-gRM)5g+V$E7|575@V5gJ|!Txioi_)G2x**_h$51O78Z&AUM<<$+sdqhu`#)r=kT- zH}F9$qYFEXJQY|5!OjF=?l#%rOod!mKV)B^o+|NX2F8?-kzp$TR~FGzCF4-5(7FRSDiD>tmMjtP`oYV2qfxx{3nxV1(CQ2I*S@cPry_1Yv;uCTn+uC> z#WVURNCb3{$B}67PS+Hsej1U7e|hB~5b)#TwZmYN2*Wak%JU8Pa;6vRkD@K`B~YJ> z$Gd_Xg+<2yz**2)LU9fUt4In)N!b%%mu|Y%Ua@5o`5xLM@tur+%>&|Gw>#;(ubU}9}J%#ac#6w14(9lix z?AfKoL@@&|^VY1`8n68{61|(NtIr|DZ81cuKTV)Q^ZPTafi!V+2JCFiL7eg4-@jic z=!TQUiRhpbeB#>!ZfpPwd`im8!oh(pe*u&owYj;u76*D7+>Al6)_NjYATC1J72@P9 z>(-lZ^x+dT(^vb9RRmV7w1?r)(b3&cQc>AjT3hl}zdR{aheoM`hu$mqHr>v{F17hKm#80=lF7hr_#ngaB>xv*=TS9LdY;Pq#N$8VI$jX&Im*!Dh0w)vIx^ zAO1l?h4=}c&d-)frt)gK*tsq=nAyq`;vve8@?*feQ$NSPO(T{1X%~z7+Y{J6EW=|_ zkdfu)gmMZBY$6kXX%r-MLup6ox1KA%eEC2x%u=; zzBocenqn@?-CRW{%^@Kn#uH|I2l(6a>Z=z=0hF=u$CAhD^x$^?H>NRc3TH9u+aN&qsPJQeHbyI}{Y$`8;}94uown1|R!ty^rX|FE{csc^zgL-u~?a)h_TQ;5Tg# zQ~bnhVQFdD_?94YJ2aO8iJ2+}(EM-Sx=)>qxWwuKm0D9w%pkZm#;xFk7w+DpVPaNP zRY_G5vFQ_&lmEgU%oJ+|{8Yo|<04-B3zKDL!)TyH$C*=${95aGzOZlgTn8kFvOSQirj zTc4Pb345vhN*#li?f1uQjc(3U#)zFbBY564rKrR^~>FQ z`ui(OOWz6Odvw6DC;^dHf;htI;@g1AE4buu0YO2DKj+l0P$g|)pHmh+-{bYzrl#9M zjc;DJol(yqBwQh^805GCu^)}zJ_Ay@7$AMZhI3NwuC8{9wo+IPV4dL^^kFQxFy!ra zRRJeS(D-=O#~lLSou4HgQayEbdlY$1fY8AY6)WlfrLv9~QowIDLO)t)dCUUdl~_Jx zBrYndAXZbnldUn0YHqZ#|GBCEpl{g%<{;zv9CKWk+fK+Q9E1He{ue7m{ud1G=<=Q1 zC5Da9bWe^qUT2s(I7Dj<`Os1dE(7+WYviz=&3tl&T-EJ&!Ay=a! zm~j5vndMORW{lr!e?BRPnQ?w(q#UFuaNKwfpi9GJAd_bLajpqc7-&oyRNGKmKTaY# zcjw*K6gi!jByVHEa0;V;;C3N5KoW=HF?^=XR_Grj_4CU>2^%B=tNX;Zs>TDi8~!mB z4K@+%Os{|uH>GhKb~v67e<4R3cr~)rVkpHDeb*6Aec-bAtB<`yWnt5;M5D|?;?J^E z#227>OaNX6Vy>A+A1&_-{`f=|9kst=WBZLByE8q0#J5w~7Dt;e3R`F{d{{yu;Sr0N z7<_^jz4+_JGhg4eXLG&E2wwum1pgPUUG|rSQaJ-pe9eU3gkc5mUJx&{)VBRdCE$_+ zC?{K4qAtDHvVsY$hK465hz$6AZZ6&Zk8*~fii(P$Wa~DfjQESwDs0(L{O>QfzPdp~M6(19 z`Q~o{NERP&3{~wP$kHAj^1*$3w4iN`95jnVsx|h1P&R@F%9O!@rlmKyi4VMc_YPe7 z^K*a&c$V9NK#ihN_=G}u9RPF%1qDDV0rsG@Kp$8Ik3!=T6W_-?8!edtJtzaEg<*q- zJ!qS-B?1BhXjiPv&7V+;k~yRRbih%>ZGEVEXIegIjkU&cKAIYnM6*a+wII$PGzOrH z{}3Hr42GDp)X!KM8PVm#-yU}L12`XGT}WAM|Md;PpHIX98cqgm)314XsKMZkMNy9k zpm|%&B zp-=r)cGXb#_wYCcnnKay*O-ZD&=6%&ul)xp2KxGwgju2U{#Q=}g7b%gF^ zv%YRU+$S6xcHR0-ITp&r#RY|m@7jtHBv9hvQC}q{f%Pm?w$>IcVCT&eM!$4C+W-09 z-0=85kQV{)BAK@u2Bv#**qcH1Y~t9GoZw=)J#-f^myl=$HInGo{*u@mm-6ar!|ZpBK4;~g zlK_?Id?TTdRjh!Gr1}aV=LRnuC(3apafYzrz?`+d9z23()ps>DH6U5eTqbqLCVcv2 zYiCDBPR@EY4DPr_T};w*333=))3-AuKWifdmod)jE z@!tc&$#fj&o4%`LNxXddZd-;`m$#;}lEp3%Y;(bV27;0&kn`Dse6qA8@zFmhD`%^- zz1?kdMu!*>)~I`d#gVJqG#))ZMK}ow$)6fWK3?9=GCa`U{01Ao04uaJn%vgWG4s16TF@L`+!b8mPgR`?tKF5rNgfXyPBt+Rl2rO%OQw8=nT=O5cgUt*8Z0YIjq@$ve z&_+YaW!0;$t*`H$MT$5%IMAtef}#{Zs_7FyD%Pn0e+yEV8R!)P-Bp_EfCQ2S`}vuX zDp+B*H~Vd(%l(bt_#*f6B33qxI`Tcya{KNnu=-Y1Bsh6U829nx%6QcCUV0Eg*+BHN zCV#esP)KBHkxmd=26xkZ5H&FnCZT_@r84=5Q{2%J$CVnOY|s*sV12^lDhA9zc_j|zDo zn#WMQCnO{+XKn%(1}4Fm;GL-ht>a?zcr(ACV71+t>DKu7PrUYT#w&ZkIvwpx5ZrYu z?V@#C#-FRdT3k@g?+4csJQ_Vvu7XEpngR^sv??r5K;p_%N@wz9e*b=+N^Ba#U9-tR z$;pW$8US5ek5M6RSM`5D2-T_27G#(wdr1nOqS=(s^Ze|p@e(7W?{77pph2NfK+`7^ zbYlom)%^1QY2idZo!bWZb)@PAE+?B~X=!BDTZ3r=!K|&0Q&cG5g7h+<_kvx-|8
      SP9dj+#=;Ya8xG2k5b4(A-T6DC6j>Z43{K4UQD8}DR7-H} zWUS`3+>MiX1Z?dLnl}Z3!xR=4mZsHpQE6J~BTx_nXFRGZysX#w>xkqCG(2aoY(5EA zHlwEPjg4@&C`hN#H8bX(`vY=Z84@bseD;Ea0~Ed=WwGGB3CulnHa`HS29n`m3O6cm z2OiKyWEC;saXr3U4vl=%aYRo`d+?ahbiBaTD*QP6fG&zu96>YU`Zm-5l+nk4;|H6N z&?gJt7cbI9e5y-D@#vM*ml4mInB*DUU26J_PeThu{jZaAC1qs3ASbsn76x}uNq+h$ zn|?iE6riEr7#0@Bl)&83(9m~-4-qi$#`b!lCux@LS6ZQs$H@_95^3!#_33%oAuvhlf>%(HE<1C$eJ+G$^Q1o_)XKo1Iz$wHJ^A14c9GHwgX-#vsgX0ch0jPw@`u z9CXssO;mOf*PE{;rz`E-mS&);>cN*Mld~Uyo)sb8ugFQ-smal0v2xUqqr$}=75Ef5 zD^M9E$cd>^FbqMpN(}AULGk_tx8OT;xU=*9ifRWGWEN2PkC(m9lZqrPJF)=?1Ua7r z*&v7Y&`;2Mplh|LTB_vA>*_wc^hlq6apCR5Pc!qr4)3encQKol3I8Z5F_71$eH@l@q(QX?iM&Awh_J!xEp27#x0LjQ2;P^d9r!|G%#mjb&qIrH$X^v5kMk%R1QMhL^9eFyT}MXDtjE@BxM1`GAkg00>k-dV)9_ z|E`y<*e6J{>1b|#d_k0%30?u&D9QC_$bvM-W)blk-!$cNsx6I;f3km9?|_1hMJ7fF zGSG3JvJeu-0_ox4{%>Wm7WL3?KZ8)7tPd_BGVeVtSfan!OqQAmlR{8|GJ8t5=aI~1 zexbOwcCzH#0BWT0fJ)G$EXt{tf^f@g{|7D8m71{-hEc;z6sBMCRS`ld$|9enhf4eD z==AjcstKabWd&5$2@rBYntn0R3xVYog!8q|W>I(QFxLY@O9=X)QJss0zCKweGflSU zeWs_nk6D1iq4xQ6pg5cypr8}O!(}&lJAbNex7OC^Ri~g}`4Z^9x<05-Idub&={no2 ztLzcSMH`YXs2R@@(uU>TP!b^0SUWSqSs|k}&>F5P0^rC>WT8O*Ysc)xc!xbXgg0S}PHJp~1XgetnY>FDXVHa1MgBQMTCpU#m^Ah--lTaLPR{qM@a zE7kW-gc&YGvzi2$!}sL%l-% zY!o^|z&^%WB)ym+HTj`?V^r|rbiS^}uU_>pv;+g4D_yMNr`bA%Bw&PFUiFMOfABW4 z3}|DOrz;8Pnynli2M5?(K6`J6b8YuJsXyuJ27z)o%5kO^s>cjrIsz{s2&N=dsdS+t z(O#qZs@vQ1pzISXeDjmS!WZ~ag7tGiDS_n5k(e??UL6`19v%)XW2v&&#!E@#4#&)zoA8KsQxn)f?jcWda6l<07V2Sn&bT_ z>v8WVAWCg+^GEdps70`=q`Rg~R(cXaWx9dmL z3Xy2iSWgc+3HIj1HzAj0S>B6IyKhBkJe*aE5e z&FBAqG-wr8tN5Le8OW#rC}QXYuN&r67=rK5{AmSf2&$?K@*bd>tEeEhBajXIQWFwr zeUR;dN?Tg`{mk|GZuYfPcN8h#p;Z47h&KB^P+c4`vTTA_Zb89RA4gzhoxL?5b|}}Q z#QpVEN~^;qL7)R;z3FSJoit}>=WO~gCRBhX=d?#eWbl0v%uiUi$ z?|EsZ&?F1d8F>7keX#uhKg>7^(2`0Rs7%m;XAI0eLHYy^@+FwE7#ln0qF^2RkL&BF zw{S%5O$MGXZmdb3<*|@MVE5&pD|X2l9^W;Dts0k_Ox=Lg4EOZ>xF;4>sS5n6ODAw& zx*~Es?76bC44&jn=2h|GtOfX%yD$=d^T6O2|ATl92Sro>Pfox96NfJwCY@i7)mC6) zr#O$D_I$Hq!8!PyrL~xXfWZo2hcO-^{3~R>Q!0V5Q$fq>5i9zBJjOg&2?>BVuCQOO zfv=(X|NR;OfXuuN5?EG0(`YH7n#}X`$K-PP+lTAA|_?dLPC z6H|^02){Hsi&gUz4=x!hq&R`)rk6xrT|~>8!GOpM%s+!vh(_H3hb{vBKETFD!KUY? zNw2Jy8s{_8o;YhQA*l5&MUs+4Y&06 zL;L8gy-pb7WdS1bANU0w4NZ5kENG?K|NXn=yxqD1^aBLkU~y{vrY8R4!h#bR_&=%u z{=xVO?Um(E3UeOc7bgE?jzBkecSU98fG-am9UXy(_7s6aFBFt znS67w(Tzl14)x!cp0w`Q>jtUrr}@N%d8cnw7c?uwd0^}D!Hk}VCqB>0%IeS5R1~1*%*~1N@oDYWuYbF_JY`)b!u<*Tjqlhhe5kgj81ckFa1%Z#vgu<#-c+>$0 z5Q@wPdIv8re!*q%^&Fe&+1QkLNqK<2PFWQSg7-_YkW%n96pV~0 zLraf`zsQ{J&F=w&M}XbxYHN2FpMzonFlcIQ{;b}+4c_VAEv>IlmTN=;G|6GQvOwoI z9pHPIZGe%8H5jtIK_&@S;oJz4$>IWIi3}l6hp{*J^OP_PQQ(6HnpI|vkxW7N!kO>H z&-^2=t(ix4E<6NUT3Yxiz?cC{X@_>U9Dwh?`9{R%Pn~9ekwZ#hFKiUUJP*Zd4YgG3 zjyGf9Okbj*qu+~&!R?!!nVFP{0+pTNm(-zL@GBiua(lrBZBQLg7K^#AK52S>oyclh zs^(4!2ukn%-y`;mQI3|44E}C2Iwh-)q9JiltJ8W9vJ#+VPEKk+skqL0D8ImM;{^Ki z^K-8rpZ|@gfNQ%LnT%{&zP`7|K7-Ne`U(S(&RSgnxcYfZ%K!`#cNZ61Ha}z%B;sj) z&ij8jd+Vqw*KTiki!`{TLjhSdh_r-Ar=XH5DUFnLr{tnpAd*U>s34`JfOINdN_VFU z2*Nj)&w0*y>b&E9zx~G^d+f2duGsivcB%&UUTvk(2nZx65Db!m2Lbj?ff9H}&5F;}7SHQ3FS6{w-A$uKR@#s-` z5A4c-1aztqyg^C@1VK(tP6&e2qn6mxs9+8LXk8UoS0TM8oX>M^I6i9Tj&BEC?`*Sj znu9ZjoRm}}-`N6oCR9H|$pLwU{qXZ;^3C8Et!d$bGQ>tCF~tQq_&GOoj}Z|B?O;sj z(*!MEc+6-Il7IQhD-&NjLCfeqcK$oMrKUKFW8g)_VCLlXEP$^T19k#|96}-@)Uiq_ z44;qD(AIMn$8})3`TIqB*MR~x0%{1dw{E@=WAPUQnj}K4)DR$EG8;1byb^>p$1t=f zaYGwe@8+&znzab*vgg>7?#ezs$5hwS0Xu_F%>do69AVhR{UZH6;uNE;sXG0=DP+Ss zfj1)~tT4Qvw0yi>{|(k(N97g(1^{c^hu`Ys((%pxKSMexdP25eLySt{ zI{baFviNJjSBAl7Y1iM*i>lnZ6<_phf-sAtFenG&2eGueZ(_a1K4I$t7NFvVS+T(O%TqMQ=)>nB;*Jiic^fUcL39Qrro_l0~0qP#n5HfZ%BXxd_clR_2>~z zqBu*#FA7rP3hpoTWD3^{jN&Fgr#yd7Y~33J!ONQ1u48D3Vs*_s z@lv)mV3x4#y&@Szml_d6BWNo#v*x{mIdpb*b_$p4#*j5ci*3+-1%|gh=5o8>OuOIU z%R5c?91~4c;LL^98^T@VKgB>!cAGEdjY(QaMBvk>Pwk2p1Z+nNNtqQHoj1Q;*CAJ{ zEHA%&!?l3#5$w;1jyl6bX_%uHzCBZp`;9|H6)sR!T|HdL2Z=A5O!-8EH z0nv3>wydto#K{`l#rIni@%ND@Z6yl#W8m5^X`PrbP-YvsCM%wZ2OAo!3<1n*DLi;8 zTm*RT=WSZ8Ewt~A*iwq9jcm~gBp``)c6PqY5**7Q+<8zGOl}hYC0#*JmUn%Q; zuz4nLW1GUV)6IF$sRMAv+t-Zc0fpbt<>q4o%p+qKvzeZP;K4W$jZe<~hJ>f)-ileP zAkkerQ93C1=C-UvbgQ{%!+&{S+~ln*o4c!K6h~j1GsTqXe=FuH(qj zLfvNja?HBzlV+3dRMjmFIW$-VSu{pmR$ozqTYO=l|Qq$Adp5z0n8=E& zL=>f?7o|S+DnPwu#2R7$IH;*%&CT9MV$uchi9CX!|M6)3B*2m;3E)Wz;vlZsK!XtG zlQdg8&pO%;qzgM)@`_VT%*>(7=V^giIfDHyM@*Koa9cnO>s!sM@p24O{wHtkjZCAiP&^X_-qLgjtR7J@8cSY1xyuE7z1JC&&f;y130fR3L$=|jhH3ACyfhzX`eGmrI zJfy(F**lK>eRASx=k4XynWUwIN}PNb4!UoCvo>N-P8hA-Gp3h^#Y85#cJi9qmzvw# z@$PUXYU-lovCnHm3#Qu*v_i<*N}1f3=VIi{Y(BVYBp{%UbD+vVMSJr)fbK5d$doJo zbdV@lIDkt8mzq0r%SJt@4+*{3nZ~{I(7680;D{aq=*|d_Q^9k<&kGhj1dZTqIxCOk zDRVh>Y;4%!;o-SEkhO`p4={>}>8hz+EPvS3z;A5oNcA^tjo4IlbFlW{;&ec7*DNC9 zC?5nM0E4Ns`P{(o?t{LRL4p_0P)y^ysX04D>GSkM)SAv#kE#Q8nL5K-KsU zjN=6AJ+>c0(T}AQ3wll3-Q!FAO|}L88W4W zkT1GK9#j744c##qMu#6!Nuivnb}M#h-FcP!Q+h%UY#$wiCR@K`1co7_B~;ZreRAaS z>B-0$XYZ8(393R@cITV$i3WKH{%6;Y>{l;gp38B6ke!tZojRH2;^t$+IZY%@Q z+IRl)P%$dQ+TQ+)YOTvMf(*%pe^N(5jL+g-$cN)XW8RjHhmt?o8hfn;l*$ESVi>4+ zD(}tKL&ZKY&-0deumf;2yQIqc@(w1P}E8C>yLQ66o!p%&!dWHtVvHeu$9#GXO?4V@j zM@`hY*@DQGBoKQJ?@5EF-UuipU&#ys7x7HcSUTX;VGkZ?_^DS|=|it)AZiM|W;y^f zh$?0h(^-cC4E6y&&1^Y;gI6O!kIz&V?&*}~r70Y61j*fXG1?dAWnXpM3k^@NoV#Ma zU@i+6Oj3&CPbrhxUqfbT$2K2o32{CX1K}SHCwY3|Tyvtt`A+pk|BksQrI+M{>iWVt zE~u2OLre(RYLtK2#z#I`@yA#Y??|`PC&a_~^=6hTJ#=1UHT9EgDA7+AqR0b0S#WnQZ6zt50iC`0$GCuj-x4gT?PmA|ztd82xX2Pg9%tJ$a7( zII~3B)Pdb)dFCora`L`geEr#M>bKp%WG25Q!|6|hx?cdn(=pUt{55p`gPzCnldl(0 z{|%BxJv^sG!W!;=M^HdeaP`J*z*R3ecOUYG@B*QL?Y|*>GJMrVhv_cCC<~GcCS0UhSY3 zR^`yJOmV`6mD52e(+U>b-uox3-+S^#J9k%k5faX|G_x;~Gd);V=%(qM!R7lDeY)49 z6<09i$^F$+yui?zBhdz<@^h0hOlHK-Nr4AhBTY)@ywmv_2#->GJH5H$QVDii z$p)P1SNnBaS`D;qjHMn<+pv;LnHNKaGN) z7mejK|0d_t>F&PjdAGx+VVCy=o6R*@2Su1CMe*L=b-COMpMxOh^`>jTt>DzP)$gnGE+40|etxtk$>E;KG zJlm&45|`1Vxxc}9{lEQOdNPxW_S$4|-rcV^I>rg(4bOdnx04dr#8UK8U-wR>mzY)_ zb#58oVcu}Pbn?bTWxdlm_&G>P*@@j`;6-L3NTyYNPsaQ1iM{PpH&>G4vwl6}s1W|`%^z5RM{m#5A~HE1%OhA)m=`i#vE ze)fpH1ZeH(zX766Hx`5rhWR8KLQtw7D=t-k)?Y2(AKxx5o;qpF@G|prx!g5Dy@FM{ zzGyCd@C>ecxAuO0{Ej=h<^JzJ|*0@E(!d&aGjcc0p~O6ho-xqx3Bs_+r}&FEvn zPW3k8#~waQi~Ew!8npM$V6BYPI;F4`c&I4BiVP^98pc z;lbz1^Hag~;)#=qp67Ol3wf&D#EYr3U%wp>l%M&vkN$3HchpF_i_n#n`|AaqlI#C? zO5(RsA?ISHD#II`b9;MO>EPt*`hG*{;uXo3 zK!cMzBq8AfDVCm|o`4P&yswlcffo&%32iXt6N#L>^qZ-kUw*x1+|{zEwy zF0gm9iRy5rpE*0;9$?2tAFI1+d=zemJ#ygTpd%w|qS~IUMJe>$9-9MP7^vj8b8Jx3 zegKnuwHI)#QV_#rWie)uUAj~w{RyZ{i_scrgKXSDpFwHML`{4hkW@g!reR~EiVC3` zg#T8Qmp6~TudM9-{MjM`qP+nHfWO1=*xA)JmpVT`uhB_B&IaRt=Okg+hrj9P2+1{? z08tis{57w@9!47wYJy&_1dml-f?o=xBU(=6I5!q@6rw<<2I!p!AT=2)3kySq)=w?A0g`>W5ead} zpu%_DnUUcdk*A{D1HEnFF$mHzu8ZOa)F|Y!)!&qPa?eT#K)I3!W1`680Z<JRYTr2UQ5h5ERJAH~|9C6#dmwETJfpjALt)cBko{teR`|)t%FE5E0vB zm1kfQ`|G*)`N>5=<1JItLeb;P!P`LTU8fePLmxwWmouhBxddL{IxcLag9%t>$mY zEWHz`cALr$mC*dN3fOe_=*ZevAufPH!n;LF9+8@o(lOx%wqD?(&t`XJ|V}ApF)R?^=GNGOnDm;o8E1-R$c}X)tejsC!X9nQs7=Y z+nH^KwyIHG038V>SBM%)=T{onF(go4{87Nq3Q3(Sm_Qyaz68Cxj`{M1f7AvdEZ+j& z+Tpc6a;^U*cqxW4j-E?Y4FaICZKaUn<}RcuQdq%Pso|-KtgrX?^gMLtB(s)sb#e;4 z>JAE;?!jMA?_;*qtjB=p4E!AF46z=GFXf9NbCy0=X7Mv1SHmpQnuNvyCly5=l1gUHYH^vsMZ7C$_44l;`$NJ zGoiS-xA%=Qzv0cR50(4rG{ZqV44_{q=gPjQ&iAR{L%>a0R4y`!=4}LlZ^VP6g7#T6{j&x~Xn zB2v+4M*RQO65LDZ zGhQdvi`=^s3Zw+MM=5>EV&&HuR>aU(r*;9gDCyvtY#QcIn zCrRQgb~`NuyC&((IE!>r{&b|S8{n~lOK3zPI4Fn_-9)XLbveM`6_t4H;dUiHet}M@ zsitN^La=hY+}3#cD-hPf${g!$lk_Jt*^=Sg6V>~`?r2=?SPLN_uYtERI(E@~`C9)a z3@`FB@+K@SdvVjN%!<5#xSsl?3xm13>x!Q;#ZuXi*3&a>R4CrYnoYg!Jzn$$n$>0z zQ?*cw{6+%c++(M|gU(Ld65w;PRV~L-Q_yG1_jh$gPD(~v^7HW-HxpGzg6i-7EL`5E zW26#K@HUC}+xfyxK{$cNJJwr37o1NEl{iWQuut@l(n$z)a(s6B<=FyfrbIGp0ho;2 z?kE)y!I04N^kZG!GuLiV>fRnTpZ;95&=JIA%7syQ-zLX^(n}AG;WoMV)-S|s2igNT z8twFC_7SNblX12W-sDY#^WQgt-IwR8Hpzbr9^EC$wR+%ioBjZL#J9hcIXxI7c-;C1 zDFnACu1NVui@Ab$fqxu4%HyDpS1DP=-&8mfIzNevb-s9pQ@spB(B%H5S!&hKgntV( z(|zorbrj=MZWT$NAKTeg$>luAJthzd9KRcS(WEy5m~aKq$CLEE{oKmYCCA3#eGL_IA!dj38(cN-HZ_Dt^Ov>fG?sGE1m*9x|V!Jxhf$@d%lD=pM(kY&$>C3rn>+sylV=q!C)>OM6XD~>qnv+X(ZW{G z%G0iPo7?Ud#ZOx0)wggQ$X0!N6^{7%Co$k?h=}P{)HG3l_5d5<7&>zdYw-(z~-D&8zUJhGihv4q`UmB-2q-Cud~5^Y%YYxHleLfIp)wgQI=( zp<@boOekHV$X;)}PPj17;uDRYpfvGzp5_h8#;_$`omP(H^hzWlWSG7ZnERuHv)>o= zwQ7aBK&SK~hWjG?_5!fAE8r>KQhnJYMz7zhZiQ{GC7_u6E{w<#;^?-6`23^ zslov!5H9iDf2lBgo-J5|&aoe4C2H3`D<`sET0|>kY18jPb6ccWxiOJX6hOu-lgMZx z_8FjSGY2DX3HU97IDG#8yMU>xI9175s=t)^`Zy{vDdV}WAz!!PJf;fk&5Ias@YhX-4fRgS|oCRKlVFd z#3FL53V2S5?;uob6@$O`-Bn1Y<#+Wq*R&ygzJGfleaPxj)_VWzTN_yNmIfdE0P<0l zKXF~T=Lu3vF_tr{(O|7FjmeZ^=78;7^>c5!8Si}KJ7%T#zsOJn2OK*Nzf;4myqaP{ zf67M5x_x~#Ab90bs=0$?^8X}eq!wEU&LernPK}rOyk;}wsqL7XvvLkGUD>C`w7;b` zW=UsT$+V7R93<7CLX@R`^zeg1OWQY@2)VCE^dEnnP%*VFQ`LX*Kabj&7vdCTe_Q!f zj0}nH!DKaxO4dswbnOXX1pS*Sm&59@x)Ub%@=vm0Mdi=7(^SvxN_KX+iD#k<_{qAx zTJxmu6ejC4X?niMWn4HQhHsRSb3rrjqDzO^=q3nAL)C0pQQQx(`#uz6XS4mIP4)jG z81P?^L_rjHI{bnR{{eQwZQqYFsL0xEr8mIUGByULLcO24P#5QX9rXjFEt)9Yha}1SC4U7x)}Zy{=&FLqldvQ+WLh1U?~WcO!|6yR284VfJhMI z3fLFXR5^5YbK1}PXerz#hlYmkz7X#YTUb~i8|Ojn{~d=?dvW7B2y|f2Pk1=~_&qA# zhbJKFGJo`FpW>t@TLonIym#*`*i-12f}BO%y%3sNdwU+$maK)D89x6Lj6eEiULHp; z_rl@sJSYeXr2NAbuHfL{%z6Sp^;BO(+Zwp_j%F<|{Or=0`Jvu(t*U{0@ z)rI}W==`h~)V>|yTamW~Lh3P~E1(8|SPJ^hk3jb(F_4o7D^4{8H1O?1+ZG?r>kq!~ z@w)=1E%?=x%=m^12lQmkT|hyH5z_{ZpRZ+#5?g?vhA){?Qc%2rl*jiIU}3=yesFqL z?!jQJ!E!wHuq=dfz(OC^>bEEP-M+ZH~v~&iq_{E+V^D? zTAvUSs;J-W67xhDY=gW>SzaKTbyBNC@`vO_jo*=bw%Sr(rwUfEI>8=@L)Y{0o z8&a^cwgw)eePs}`;^7?ws4^d9Z>7BsZc$C!7zT;2pFYiiBk?9~9sYSgzvtB!JhyIL z{tZh&ejS``fhbdl@CrE#);Ku#Kya}24FjDP`PbEa$^O+2l>pxA$Lq zdzYg~><24ipo&kNlGWI>u_=O$q9aua0f{}!*PZ30;PK*}k-q)_O`VygWe|A)$W@mt zKcqg&iL2aUF5s~v^+7%)lBk2vFCCiSH3Ppd z3&+ySC0Y@$NO11)rX6M{YFb+47jo^-U)wI(M~skcQtMqvIt1x6xuof67h)85oahM! z?CVkN0&i(fi8n*sy47VtwnetW!|&#fve4ttr;<$$ArRKZyv)0Hc`n0u0b3 zf`A%GNlCpAwweY#^Vk4rPhsh`{j_UyeI?*VkFVT#O?j_$O>rCw_8YX#HX3};hp=iC z5>RxG!{}Nh8bG}|qQeg?KGCIWvv$OH*lthmPgVe-r2q1XP2{F3AkO- z9;kW^Yut|@-~~iHx~c0U{BLoY3rH!uAMp~3dOPXB@h=(w`0*y4hCv7;4cMROBAk}y z7%{@1Lt(>PGd4W#9qn;f_I;wO5_?nl<+vp1oB@?45oQaRKaTsALa{4j`^IWuiSQb3o0PceuR=Ik8{89rp?UR#U(WE z&C=|whPwKvx&Ex&>*kyIbY!ai%DeeiwVyxR?6VO+e3PHwx~1maM+-Ep#*&h~sCoO{ z7oO>%VU+33ie8Oo7*gWm)Z!Qr8%iQp40_0nEV=er00v1VZAO z)^O$lBj!p&4uRj%9_zY3Id<-QL@MMX>z;0~=GiWE#B-kj* zRV(*Vdfu*FN~E>uc*?{UWO~L zgsz18m$&yEDrLdm6{HuSxL@2|DF_J*Q(@hLY#SeOJtxzV1I!NeyuAk0Ac+yI2wF;9jQct-wm$*yKiX?uxTt>{0>%K~ zvlDw8RluN~CHM71+3{ksk&Y)+5s=T`d+u@zv)dP&d|oQcF^dOqBE*%$)CpA)G~VJO1*CtuSJNgEia=U?#wKF)0wE#UbO)saWRn?k6Stqf#UA;>D6+wg_$yDwHn_+* zP8}CI{uwxQbmH!IpjWh*x1bleew~uPkkbSaK~sTWBxrf`HiFpI2|VvUl2IAxz}1gElIDJ1-KUZZh8SI6o!2VWZFXONBIctNI%u1na~rG#vp1@ha6pv1hYC3^=G zSR(qSQtsFBapik^r^6i+|MTjla(nF{p%CEf#%%>n9HO+g)@MTr4RH=0a5wSef|0t=!KiAzIWHXV*j8K zPB)au>+J6S5MHy)*4NOzwUBe%#bkH_oOZbJ>&N=|9o3a9KKaUrLr9Bww+_(eWltG@ z%*e>ll%p%nj677wMIW#aJJIGsU47y4<<0l7hGJD=E10~cK&FRZ9sZ5d!Kzqhxes2c_ z#sF5L*G|yZUsC$v-hsKyzi#M_kB@!DMEX@9QzaLiP?BukIelj=#u?RNddUtR2mX$s3 ztiMh;J%91VY$j8Ez>_VyON7fYQxRJLi4=G`gG`%Q0DF|Z4VTb&O2-OR z=&gfF^Lvl?7CJXx(8{zS0|82yE3NKxk_iGNOJ=~g+`}_5U|g{aIqs)$@=U)3x-MqL zn~dqf!F~{_rmNG1L?$WsFc{rZRUI4|Io)V2{F;@4h$xB*AS_}bBqV&ZYjtZl1@0bV zx-%ea=cVl63RrrC{apQ>oT9w^PWwQWTr412pPrm*rk11R$GR6x``Uno(Wu}n0YNso z|F^(`&cqR<3F8ilq~#QtnNsEEG_Q^bTcF*P3uCpIfz7jrr;Ok8LcH?Uyka5h6Fu4F z`LRpKy458m>IO8=jlgYkC=U5d<8~GzU_r2+%Kc=~+S9`VuGi0VA+k!!CxKURB5-cG zfm~qkD<%hvLPSu|B@^1n->j-hi9*ELSSWdu!}+b}Ukqz@UTFp#Ehal0|Co=0tOSC4 zN$Hd-p%)?=(wWxLE6GjD;HIjDaq!PWeorL2ssH}HtbW8DgO8r^Vw76fzZ<`UEBxtG zas~!_bGwdjuAtDxY5}^L*~!$DDUHj#`%?UM+Y#yEJ`wNl@xA`C=YOZyxb-li#>U~L z$mN7=i)+Uf359Yw*7nocav(XMo1ZVGxWg`t>6|Jk5S_RY3I{wA5|jP?q<0vuwDo}! znEoYzX;khX4J&r0m!3d2( zqNmE$2rQ8kKU>U@s8BbuHJE2_FWJ>Hdvo%=HTilf{Y_8wZ`}5b>hS335TTq!d1|c< zG&C6sg5f-R(6jm+thMgJl~(@OV?)^Hy4T{>g=*P=MkqK~B3;5`i_T}QrMWry?5+r~ z)qq5sk&m@QtOjj5&9m>fZsaIx=%FZB8oXynnlvKou{WX`E{Z|S#_8_{Gb7y)i_-NC zAfcUfqG8yKj;sb#aDtf4&ifnxeR4nJY= zzP%}e=k^BmCQ#(LW13eO$R+)fu5)mN2UM3zV+|ovnV;B|ZUF@ZUoK8(b29*+rqAL| zX}s+>ybOgztsXh1frL7jZ|NAVH$`ZTSp8mz=Fh?(ii+A9V)gK-o6D2Hv zf%hj!>jk|g+5oq&DgfEoS3vcZ^ZnP`!=TeJhCC0f5`p)wg|uV~VtI^SghY=yx*x#_ z@}1BI`ixgH^tuqCG*PI(S}%iyjyJYvrs4=pX8N#m9icEy6xFw~e{qHoO!>pzgM;L? zPx7r?j;hRq@-CyAKJoM&G-(zTA4b&;M+=CWgHonGBP0JA4p-m!IPIulhdB;n>JXZ9 zP%(7Y7v9qYN+db}vYpc3zO{WjQnb_1hP+nKkbCi5bD{J(N#}*|jpC2;p}A~`2)4zw z;kSg?5Wr;uLuJ`C$%7g6%8mJOg?(rRqYzg<#|kA@rHFL-0P$n53Y zw}YGM_8$W&kgrN0rY!tZebxLY5Yy71#o5K{9&;n z85vvf=~}&-%v>S(B@IybqZ?`0*vWux@TFWbKSUU*S8mLO`wMq$@0Ul`Di2eL5 z1Z2L&w9Lg(Ji7o}?#SsA_Qz#Di9swj#Ne>fBHT&tq#HziYMqD3oZ(Zpd$e?965TLo zdiK&hP}vo1UOVrie46iO;OcZ#X=}mmbMpZOaKFxYxlCMC*PWED#z4XiGyDC-zpyp8 zdet)hk|-svjk6o~>y$chcLw<-t6W5ZC+&Ksg$K)F^(UEf?hj0wu=aho{IY&aB$sE& z^FE5MkDmIX1Mt$Y#*uu9=gxHB(>(LI#~N;PE5=coa(;7j@|B0fxHX88 zf<8LwM(k&0AVx2m!-K0dy*SB3p#r0>{++x<_0c=`l<3Pk+pPw~l2d&3_+9dIU*R3?Hd zH3fxFbo7Nsw`N2-iw&v;tOxQ;n!_47TBd26N5EZ!5~SW8`vA#xg{%y;IRyn)Qy;y! z)>W-ZY(UKnNi=5)41Bl8#nB&6o_)PP-%)Bc6R#H(kqO4V zw6gAExB+7PN{1T2&72^fWo1Sn{f5_GUSMD&@B|+rgVX1Dt7j!I(qPVxaYYnx`5^~eV!KOmN(ktKiKv(DHd$MRm{*QwL6jDxUTuxGb z?-^r0U>+@!ZO0?UG`1DO+w~nPM{0(`MyG`GF)@VmL;7rkp4Z$#YRU)+MJ>Y z8wV`!r0<}tOMhSpv^CHotzrL)tb1a-pCd58XvD`XYO{#8$kL>D1}p9TYurvX_vZWa zBDnI(YH2}9ch5)bEll@Sggfi_0F#EwAzs@s(r7x|BwKt%{6_!?Ux|zZw2olI4s9U0 zs{HVJ;Px)VUx{%S_MD*8;g$6FQ$>FaZ^D9^U`7!yB0*es;G)!tvHksYDWW>Sc4)Y3 z)jBj~?~j+PcAVn`%dn10H6mgHlT_8DiHK+c{A1SQ8;Bx=TNIf|SQ=t4r_+>*=jSTM z1pV4W{dF|64O<*2q`H5BErDhmlXK5K(mDVuCw7X=qUPd99u(X*J3@B z5v45+cFZfHC`PX5Ym=Yhj|>@T{)R214U4zr-LiV7e~NnTv0R?+Xd79AUYWZL#&yS= z8Eo9#Jso4}XySSMtYQc!VceA_`qbU6YNW<1B&2=PTt0>*Eq?W?yWN;X5;4XXaOx{F zrK7TQ3lGnSDrp@pb$XZ^YT-haU!?Y+0&6#e%z2X~htYFChkI{0 z(Aocr02f!+yJz$U98yx5eyW$=>2&u!xr5IfW~~V|tj^B^kTiT`gIbW8Cs>N?X4;{> zbtk%O7q%hiu6aeIvZD0#^mrS_HiBKV%pr0W$Bu^*K6~3noj3A4wM|s75*ZrO*Yl>J zX8=ztRGu^O7t=(Dxo?aCun|RG5|>sMc$g=t+2siKA9q4s1;c(KF=rUN!`}oUFd>l6 z0H+{Pu0)+YS&Crk)Ef7?@dOfjM|QkP_qN0zO)u zL4fAr)`gUEUW2MnQ+&i1F8qKju#YkefWyZL>F@*d(b>5QDf6(LaclxW;M9~`sV0y+ zUZd zc_oh}=vK#MC_d!%z9xl9SL0q^HilNZ+kuQ&lcwF0o2qf^Zfwk`TgTtntc_|9yy-Y2 z4Ko($MI)PmD$-ghFUV$wF`gOM_JfsmYkkWhfFR%t8sZlj9v+E63j=o->A&fdDiXlHESbL`OIZzWp#7-oA53=M+%0 zpNG33Nu)dv$r{7ZA`0BQ0Bk_b+oUN)UUK@kDjw&t#=L4KQs1tfu$J6LjL6%?$% ztn4iEV0@-WX0;k->k&uEsSTTv8B!cpHG}- zetrx$j=_5?QbJ}A7bqD{Zf+oIcLIS6G*m{QwVnfg)O6jk$l;^i#1+GtfUoyZHVAzKE}O3XMjo}Kj^goyXc_?*2!!kpMS zJskjDiILxLO5(`YTtQ1~i&?Q*OFJ7 zQ80Uc?zwG4d#U3cYbEh>m`qmJSBoCj>2*qgXk3;xx5giqlu-P@kY-omf#9@47up6F zFgO%MAm$#ktX%=H0%1PBnfAY)>|b74&s~%kg?Qzf5D?!(NCG-kOH*^u9#@;f%7_Z(2ZyK^|dFk*D8UYyRjgB8Qwe31OYMF2>*e8U9{ z1r#gpamuUK0yo0`IJ{}D7l0YyoFe3*1KMV~{Lw{KFE|c7hP5Z(2i4-^38jsnA z0LyID0i%Rg<17iu^A57QQ=pF@EHDX%te&dcT4*-2mBxUfS+UKLi!jwTcuYEsjf2|a zZ8*WIBxSL;w6t`F!vLWpf{bZVtYfk7*K)4>f5?6z_wX z*A<2_M1)_hy8kN_t-Nze)6392Kr=G3mfJb-4t<_G8^>JwmR{{6euen#}^T-4>Raen` zrQU9zkuhMLT0{^?(eayN`}Dof6ObNOKf=#C1;FJo9RAoKiMg3+Lc`?-O@9Bg-v=;6 zmGGtIHmJ;a=Xr*jXg+mhTZ}6b{hiTT)4@^%t5^zO;nfZLVojTnX8Z1Q49v*SO@%Lm<00o`ngxyDZ!q?n8EV`2b8UDGu z>vgy@3)2VkJnQ6RMjHx3grTc4&`*qFQLOk0^E)s*I<~^KP$G0qPw)3$H}8g3ba*)5 z_3Mo$P!KIUFu^CPxxjJ2hiQ^KEG#bOWZK{&*hL-OmzJG`LXm>3=Bz0Q#2E>(F59813BcczWVBbW_W;RLYyR z`X4Sr8YLMPtYz8bT;^ku{~_%(`U?0@*c15sIKbm*&qBs| zouG@FgyhrKliBdrz=O%k>Bx&6@CqPxM~iFrZo{{Ou-henCMp0m`^zJz`|fKK)p;g& zY4hDW;JRZ<-C(>&!Zp?p+5j(1cU8ieBy?gcQ6--&a!SrGP9tPGx4Ak?uQ5!&6?-IWLfFVwbU$tKpZbf(j zcOXhY)Ep00HzzlDoICd60`Kv4dVUx#`uJW}fh;O?aIaJmn7MZ!<-i{vOT%vpT@c6l zIE*1`6!|VN``~wFY z8(KPcHnt}AMSrc>0&9+~UvNj{DyUu+E{kQX7O}T8#U`4=dk7KqmgD3ZI$e@thXCSv zPC|ipVrmYDX~zX+0Kv>UW9+Z4A&w!}?FwvELw|fd1}OZo7Y^I`;cAd%eNk$$t*)w) z&Me_cNcFXDG5TpcTK^kde~!3@A3aImUWMCG9F`4yg9P`gE8nPU_ai3dmq4Y&K~|Oq zHm9JLjn4p%nw!Vom%YzR#FAB0QjV`26T|JFL}>vL(v}TZInpzcR)v3{yqqi8DJvpYhqg@L9~@RXD}xSTO_t1LKKv&R!ZBZ)$pKGdA& zbQ2+KeVfq5q8*VYis9ZMY5k2fBRLrvX0giQ?&Xy^pizE5;Q`kVEa@2gI_|zPL@?Dc zH{L$JmXZ$Y5)ui)EwrQR_m7}_85|y7{dq^^)XibHsu0Krr9U9Jo12@3gvNK>hU1_5 zg%6A0iPiKM9$?IG3|^Sb9T$7(i>)VLUME{wU+whUizx_%FNSZDOjY)8@Zyo~U*}Ur z92kUhAuLantyPILLB=1Wfm*AQn=7cWn!0Us6?(P{Yol_Alrd_~fx*EKEWSut-?i43 z7DoPxx4F6Hfb%i*O6K4b0a+X-#I=gnA>yC={9~g(_*&4&$h*fA1t#a82ix5oNFZyT zu0N#Mz1ohidQHLGi8Y*_Y?*ftwfamOm){f~`Bcx)=4sxc; zf(k4#$3150#zO3|(t+en1%$4d##I#e`Dbfrz%Aa}m|(8t7w?^U8KhI&T3+go4t_sAfgO?arEDx)!tnBcR6iJKb8oiOViUf__(&T9e(oTr~e@fR&&!U zKaWr3fzL)gp8NkqwII(=FIirc`YG6yJZO)r;GpmqRn6*`b4kdLPEE7{*yxw-^-lQm zdGfq5Q@8)a&m#1I)$7G)#}2t_Ju=2$L$j0uR4wE2uiX9kS8oWN0BRvhc>03W;i17T z#5lWaQ-Up$PpyUS?nkKY|Mq)#xpe%0?7ekVmFw0wjESg#BB7*9LAo0WVTm9iA)V48 zf`EXubcrkwkVaBUx{(%X5Tv^s>8^J!-Ouwr=Y7BNePeuoZ^s#Dk8#f4?8UvVdtP(? zYQDFNY(rGu4;MTgW-sR0W!^AvG?L+&{uI6iIlag!9C9Zoa<}__L^s2&nkp>tc7^!Z z_fdO4hL3^-Dj)yfKaxa|kMd2|yvU970omsA6R28lTTO94;hK|~ku#Swq1R0=N7Qo! zGkc?JZsO=A?5lepRvh0(k>>w^=L!e`8G2vNELwhI21i4fby(}Te|gx|{tXR6#0^2|>KAchi zv&r4JMfjh8E^Ppz$R3RByQnaMYJkrP{|WO5sYK`ek^vIKfe&3KddCS1Y829uY>|I^=^mjJ-W)ZVyjPV#Z4KQ!NvqX_=PmjLpl{4Lid z(*+qL1hda*Zu=`!g*{EfBX~%;=?ViN2x_x${rNOB*nGGPBA@TWZYcpWyex1ufs{y5 z^J(aFwe}0)L?DAVPsNP?Qmv;@U#ZlVZ z=ZaI6?C#6m#>cw!+?f+ELo5sjNXz<+s^93}{bIu4=ZA70q?=Gwz&%R_96hwQFvFtt zpWri#;j>wS3m7_h&jD(T(5|-<7jk9{Zk(-^9 zlarab4C(l?{NZn5=4|rJQJ2pH!d_exLR#7@P(@8uWmu)25mx=B!Sw@7@&6XMK2uhnhhf}5pa+T| zC{u@Fbbfz_ogs=F`X|GUpWo0llGlIHNE^VNppEqaAnSM#hPOxBf}E}!6vXrKu3FVD zAUL;U(Di$0^ZPozq0XfSLrxa(|0}qI4szJ`6)Oojxz_1bWr0~sKoUiubfS;v&%37n zNI_TzEh9su^pA4$@+C!j8k!dyu#r@j%}fo&Lm6(nION5QmL<2fnOqmDMr1>8o!PcpoB1&ji5 zEr3j3I8PP(Ix)wM-VUor60kX>vp0mQHW}yFb6p0^8+lRwqoExcc*2V<>Xb@pb zxOW7=fLIh{c=?Tl!oznH(J-AK;An0a;YA~w$4d98cHUo)%?OjBQq#8@^F&(J&lU zsqx)EyA2bidP*%EVKEzEMsqVW;tj3DJ@AgC{6<>&Ja*RU>jccjJNM2V5(fu|{6TOX zw!vnqR0V?ofBz*&Lad4-Kc$a&HJ;#6P_Q*Db91~`>e;j1_4P+jLC#TgU#=L!KY&vP z7?iM{Ox`lD8KHQgIQtkYH7*zPn_tVL1}Nd^x*+F9Gf2|K?*ocaYAIRQmeQr+3^%w9{}i#ZzaQ4=?6mT0?5luqr3Ej& zTY${K-&efe^%+#`=)6n7;+i~y^AaFH1a`?Hf-zVDR2Gkkh!_B!wtg!)g(iqXfkOcW zAB8g|r~!n7MUcq61o%NUys)aXlYm=5fXv{wpc4;2VZdh!v(O%xzi>#nRzPa)3(whn zC^_%bCuK-}0TvrT0f0d#!~w}H$}7@2`18s%gE$7+0Lnq|IDmu^Vq@6UyQN z)dM@Y)FAPJcWdyQJ^QID>?6p&2gO1j;iVuYB61!_xhMH*al`t~PArdQVhfjYQCbb+ zEa30Cv69W_PP7L>z~zud0D+k)Zxi=|D$bqq_|x~#|E1+Y)c`74-w4ZPaj?+KxPT#^ zMQ5Ptw@v|;3vDw%`j=+Q0P=|+tIQJxdaa4^=mwQKJ`quW6%upib5fE=V!lOSjrbs= zgp^I2^i3C7={oWjEHDwhy(cmf`!i59R3GU2D9Emy-Sfc*v6k_q?VGDd%oz%K+m;iJ4yO+63mnBfzjIiJ^Ijq z&CTf^>zTq6`3aGq$xo1fux5U zmK_0Q&!}0BZxs1<6j*6=LS{v^!;b0c&@1>JkL2`VLj(=d%#1--mSxwMlNl1@@ms*n z^Vl|k6S8&Y+9I1w;XF)9Pu%`TzA@oRLl-0jF@N%j`~WUCs=@JDxw8pDDWDsK%WC4x z9eZP~h_1;9upOSHB1B@TG$X=Xkvy7qECp*lfhff2(qbZ5y;2 z4Z;SX)@<0DQALQozwMSHbh3R*iQ}N#7+`mrEx=#^NzDxnB4H5M+W4wDeEZU)&*2He z00{($F&3E&U+%r)cHAjod*MP`qM7lz2LMikG_T0>-KS6eMq7*hCCm?ERQI)Mtkcp+ z0QW#2O9gPIhEX99li~a@nr4<8_oCPy&VWSH2b<*bmYaF)_x<{|7a&hzx#KcT)Y9n9 zRt`IZyBixD3*N@P_~+)lPNrKDoI;A%VF)kX_zhU2$#@+S4#K~9woWp<;Jup?9Ze$H zJnr-6;0nde<$O~^!w?{SF|Ni$N4HK*qEH-y$xG051Yhu!MMY3=hq-Rg0*b8y-V!P& zC;ti{(w(aH$*cf)SqV1;d8Z-Z+CT%-3vd2ry=Vd2F0%&B3R|5uplt?Ms+^A|@>X^k zMsQPGUftsQ;DjQH7R_tu%%|(Xq&H0yVEd=hNZW-HyClwAy=n<>QJjE7%@Y6(v$d-` z?vZesVKh#g)Ya92faoS1B9F70q-1i@-ObJJ{$JOyu?d7M7;MGA!`dTAGYw&O2A(Dy zxN>dOo&%jzH_*>sk;rFI@#e(P2-m#`G@h^Xg@t}Y;8A+Nu1;%{4zP4rEQX7}YH3>M zvFJ4pelm$*t^0UzPt|9}OFuC{e&c(k!|uxX*+W8d0SAucPs|R`9rPIlU93MrZ-^qQ zP0XWVV+(1Q;7y)#n&{Ms=7c`~pVtA}0zP@fxX*ik|NNH*nAC7P0}}3&RzmcL?<(D-=YfL6=*>xVyjjtAPrsvH)CczE|7?yo}2a&yk^>^9gNKvGq?UWWL9 zACz8TF)qlk3er;^`4+^vLbPtleWptmKW>fqL0d;Ow+xCqD)hY1MUVnD;4Dr}b%SP% zu=r0mans_`GOR$-LRmH?CsUr9D1lHqtW)e3%p2(LC2q4uhONe}-z$D>m!2of2Z$FgDS3jX%WJe@NV@o-2k0RW z-ikj+gREE!uoYiLegKh-U^knQ0riS+zk_22QvHo z|59|cOrk&nZQPT4t|OLQfV2wWC35CjbU5WBoPB!n%RRxw$KKD~aGC|h?^BIk2Q6cT zGBcF@Dun|lj{oDUAnKaJX%^>=u<%!YYN%*BwKTk4}K#WUKfby0R#~; zlHDq)ZW9wPt*)NHdIy-*;G4XUt<=`u?zLOf0{3=q84M^x14CEXx(Ue1KMYPxyqJMV z9CW7u_sifHtvmTRcW&OaRJ^$R8-6a_;$~j~%Y(zdNjibDS&l#lZ&g^sAOwvhB)#MZ zyWA}q_{H^V_i<5!vrZIvmtn)*({n;n5NOxbgz|5kq~m0wzb_yV>uf7>P8Jfi;+jV_E}i$$ zj~{$Q#gZ9%$%xeUTYe*F!y;V5ZRT!tPvK&KyY!6ZY2=oi>wi8Z(LjjrMUj09lSTf= z|8a#knPCP+d=BkLhBMqt{&(yTg+N)EXCV3_(SPV#8Fyug+2B393gEcqP+{(t zA^JXV4jBNW%fD3mW-(-wmj+|*{ie7xMcOe?(&PL1yPOK>s`OI>KCBHIIFW^h2+nUd zUQBH4BtL|T>lDCdI=OF&IXP=^y2A!NHGDK+H|;PA)a}K?P(QdTU?brrRmChM>|@al z`#oB1Nr3kJ81$`}lF226I`{aI-lYJ{Iw1UKHzb*ya`&HA)5{^ z!d+p{rPfF2$4cZhdBUZ&B5H|H2)#y zmWCU0Zgy6uA6$vRzOaHk1j`fSxuy(G@Ta ztv~XXFM^F1=;igRLqA?809(?>$_;g2Mp*b<3yV(O#Ws=o8=&+NnF8(9T|-#G4pk;c zJ0}~QNk3V_WlUg*R(dTS`UOyNh+N#;+A1*B@w>Qk;j27O0C$)lG5iB#z(O$v35R7E{>)2WRQ&1K>bw=GXVL`b` zKz0(!R9|s(a~I>g`=?A9sYbG*0Sybe76il_@Y$}MY35G&fPCl5byA+1-rg`#U3!3+ z7_L8@<$l{RFfjD!J=8lCZ{2{zQv#5`3EzkkA>g5u=Ah*bMtib)`Sh!+oY8Ge-Xv>o zKHxZdI}*`2 zLl>>Z)27>k)^)7(@2X! z8AwP5hFO$~^I*OK!1seggZHXFl%@OlV5%#q`6!^mvXJ-Yfl0p-uci$tU3JjIqa7)p zEV`fqXjpdHRaMGEg5f=?IYTxdnd&vNhXw8;m(kUi40W#7pw@#%N5>WN z+d=UtnL(opZDTNFMf{(;KPo^1;>f9wm~+lH`DYJ4e(}S?3ZkIqPxO0W|7-d?HCAUF z^+yml_&q1!(}!CDEi(MIy>_N4xlD-u^ia$4Om&(Se`beiMxtMWe4N2zP4&SeRH;;M zqxrate^097918AJv3=m@r|NvQjla8c(a^vejhcprlw|)9Nmvza>`R7zT)#l8qRU0U zY>}gd7X789!ZP6Q#~)*8IXS5q1sLy7Q~i-xz`2E&4BEN5C9fwc+A}(s^{ne% z^=a6HD2a%>{tHeY|DHw4wnmUyCEqk^0R#8vQ25?}PBf$S`~uCd@$Dh~Hjx21KK`#A z91h_I2qqw^UaLAOR_=_WdZoN8nCuR@3AtHdP)!t9SpxY7P|t#EZkzX^bn1q0Li?j) ziLSA{ys}mBX8UR@>9hD;$HA$hJ0Dy(Pl~s_(v<{*Vd?F>QRNj>4NNXQJr`3%bYzpk z8JalT41JMLJg@cTPwZ{FIWS+LK#DR@Tk8U=ZR9WQ?(CeD5BCF10!vT55AVHqt%Yk} zGUE^S{_~2$5(=u$!P=H3wxJ|eBIWCM6Fk7GogQ=wUGqwG&BUg;?w>q$D>{uXqYohr zTUNl_Q-yl*N)L<=?L@Uju#z+2Ei@a(Z2$@|GDL&AGCDF6Dn-laXl%eZAlEmUt6oxr z@rXeGk`E~)#D4ig9j^No^ilrR1LxXr;c8mL45lPPY2IDh1#Mk2S39Y^KTM`qC3s-D zLqv8q&hBVmhmvd^V!YZFWR$pAJ{Rs+!A3{GMSb-t*o2jS+U!%(^H0fbf(S{H;P%-G z?eo;!Slvk9#zh3WF~=57!BN%FfW2>5u3nk4hF{PFIY;Eo6HmNq>!cKhd*wSd{$){- zS_M~!TWAPA{T)=uN~Z&PEXG=g<0sUn#397s6PeaBtj7Gy7U^)1iUd>HWk zdzNsLdq&0ab0ji?Wr37~1N?Uc#@b0K#5q`VJ*#NZl&kfeQhW-00<C>)CTp6HWRQ{talk|G{|H&6pCL}P(Drc1Iq8~Re zdMcF^<(=7e6#F3P_x^18*UC3L>JTQ2Qi8*oS4+`npEx;n?}T^wUVnDzI5eF#-|Q;0 z$6s=r)wlX!=`?J6pfE<+_YFTkcCgPaX7g{A@dmz4SVqS;_E(<`6u5qK+6^M&))Khw zWPJ5c-jVS=*`ka?_uoc{K$ye+G?hCkiKKX1M0*bs)?9)RKBK`GTs_p_Xz3)U7)iFHA5NU`K`xoz=FcJ2VaZdUz{`r^!T#cboB`KF#|TA?GGx4 z`xc!8-2>I%E6CynCf{qTbwJ^}?)UKR`iA|*50qMu!E?J-^>pBDp=|;=28^oE?l#^( z59H6ec$^MW;2xr0p8x-Zq4B-{{7L$@1p6l`iu; zdbSjskGp4+tG_fAe>k^IyhoGq6XtW$N#S^ zA(~D6kLRN>hsuaNmBvxMfR;J`oiY;@6exV!IspTAb%Cxk#XRNwV4=x#Nb-IvneY@u zLKzG}#OMZ^^nZMF{!7!SfuBU$P8&yS)yhjsbSMZYI@_NPvLd3Qq%<}$X^&(VYY>%3 zQvhhe_s_>Oe3aRbL3jrNdqhk*{A^TIR2tu*i$Z5O-Zi~DgHrSccW^HV9T^`yNNtNe zIy{89;k>rYOe_#li6k_w?q|{2iI&~E>iyp@=g-HPlD4V*Wun&^;j$nbbr^i^6>p)5Z~2SFJJ^Fn9*@x+?5vq+LKG~4!X-9g3BWI4yw^|c zQXcQ-qikY6#87{pSrH63l&5=o)p@Mm=k28V5@YO*SXF@L7W$)A=d-pi+2nE1vLOwr zgB37M;EyWw!}`}(M&zhOv);5y%xU!3Qyj?R=86Em8u^J3V5QIy*0dv;iCc6J9WE5;{E z2ZbvzPQ`#w1C64>VG$l18`uP{V*-Om)bCTkcUijv^WPu?Q?vNsH`V7^=g}am)$3fN z>4nd`C_MQV`8sxKX=#+uZV4n9pmTy63zIV*#U>2^VvuKd1p=ED6^b(B*m#ofmXWB^ z;HT#YB;CgM{{5(rRACwg3uq!B$3#NHpJx*$K>&2p21e6E<{$oCDmEQ8g@s|qdpwEx zaxAGG@vf)nRu&endTLiXcung-T1lzeB%VirvX+*X0e8ZmGid5N$2$YM@a^q4F!I@7 zuW)WWx(bm3~BI5P*l}Z&asDReBE-50!V~Q^ewo2Sk{9sCI z1(HAGdjKW&%aHwXofRavw*>hpUHR~%Fpr3N ztte%Vry8#9Ze{Rv!qWMgZ9%8~w&%WJX@L^~-*ZSg<2o1@t8xA4Il|EnEQrH^6MUn} zagRyHkNyzmV*&*)^pXih&lb{rfPvsi>Go%ktVQJDc zOPs%er$GEDbj@w+K$x|J((&;Pja5|?L+}g!Cv`$qISrc^)nqvtK8G>L8%1{biOU}7q}`X3^s*P~2Ub_YhQ(0jA<>2wbA)DF|4>If8zt0Vw_{{yHYap?V@Azo67q*A^VUDRk=4jDjhDduJOSsF&&+o?Iw#>KOwFh@1rnhyEHeam$T` z&&fhG7f1D}B3j@1m;jd6>IV^lobDL(zIoyxK;Fey8!Ie9bW1%hO?64OHl##*#A`@nQV+;i9=LAfq zHBWB1!WRpn7WEHagvhd`7s;Bf+|M!u%a5_<&07$_>|JgVF9;kQL@|k-dloMkxDU~C z`sW$-=^?&@{42e(M$c{2oV10H7ONA)ZX5uQ2@9fC;*!~!nGlqIN6QT^q3@}36Mun| zEjvyF$p)RGei$+JTVDpXu0p0dNFSl9-ed(4J3!S}I+_VVT>64==bLG72EZdjLxYKe zk;uAvIhG#WY;7Q*#B?P1B_upTkyA~JUG)jza~-^&6_cCG(z6R?t=SN5mKKnZl>FbX z5wI7xh!{1Rlcx;GtbjcM7@S$V3SE1kNP7#Oix<_th$SK-a^uPP`A;%Z^0C(qYC5Kd zI>(eps$GRPXR1}u8!p_C7vkin6k&1|8WqVHxs^0sv!f2$==9*JaD;eZ#x! zVyrhCE^$4x64D6PE4-$LT48?(_lrRl&Xs0;$|sZd(LoYF7>-6+Xlbz&8{!R;+*Uy33$n&9(>voKIkN!%v;7xVVieAI?G;mr>&HA^_E=d?qFJc>LO6K2v`b z=gZHp;JtO-zM0->A`V$W{@9goH_mHDLgBN{0cZWAw=nQR_VYvM!$M8^hhQ181?rjl#u<|4|^#N2_(G6L(H+Kib`Kc5ysT3N^fiQi5S4^DZBiKmOeDTL(|n8k zf>?t|1Nk7hOVB3q0+d$r2-NPNL0E*VJKz(Ac5seKKVtX&af6ZlmLA6yR{A+N zA@*b*H{_8t;G(TW30{pmqUPz}X|e?_x4`=2K1jYW>`cgQ)PHUA0_#tm*^OXgx;jAQ zQh^5uT~n*W zppgmGBY+kNj}7z^YrfW4y7BHJz%Y^8FAa_`-DeVjW*%aoc*BEY3~0P0zqGjs;O=LB zke#oA0ixI#lqN}f6p8u)h5##D8IPv!0OXUWFcKKeYi*blC!p#M6lSh^s{4CnVzlr1 zyVd|Pd{2sQYJ3-_PT-zAtWd3``8d(Y`-A4e@;3=^D}xy?$>J4w;s6^1{|3m*BN^&X zW20W^I)u+YF^i)XfL2y}lSAcGLJ!bI`5 zI{$I@Mn7-qfbqT`_XeVXQ~`*hh4C1=CQu+Ve9)26;fFVVKL+U^&`$pSEu4)Y;iGrA zS1Rt_F(qk#*x}qJV)J?~F%6KR75f{5#|Ak!2bP&BFZ{?YIsJKlYxMDL);5&ExY;~S zb|vf_di2GzHXqU_?Cf&%%&&ejY%X`{-Knss7HtiZ^aBwLv9&GrEv(lGHDWYltW2il2|bu31AK)h*jHfx^Mr zy7D`;;7nq>p17VyNL~OY+8MuaS(-p(zFk9u(NEAh-z)&W%M%@0z791F+Bg+;{~zb5 zaZE2udz#JV|H=Hi&9PGY=x#3b@aY0!>U6ABy<>p?-y{cZqT;pI(o&w{CUE%MHG%Bm zt|~x{YHB4l&(~+BV&q{H~}!SAfxm)&P8V2hNawKn{Isu{Y%dJ ztAsO8obO^KIi>QgUedgr?5D0&hCiuD?|C4u(P9Q z>7*h34np7}z9ulgeDUI4HVdEi z?(QhU-(Bj;t)?UM0WOh@M7Z#;M(f@1rSZi?@FcOe`dAt4;aW;0;sKPN$9P&a3COqZ#%Yash8nAiI+ z5z0T=Ab_|s$Y6ca>s&!#CeI`ZyVz<-H|QyYSNr)f!^_#gKJn1-3@dT7E0`hs+*cZy z^**&ABpo`Bg^oywFcIJHk-G6D+uLcoA+Lr|Xn&Rg??YmGy5D};33MRGrqJra(qR=* zWo08saGZ)Cd+|<}^=N(414yueOJx=pS$rPn1xmjkSe{MOw{(ZgDfd%bFnUvJsg_G0 zqAN~ah3nH-fl`5~nauw{VG&dyG&$7Z>2qhv!aZ~e&vy?Q8wci3Vn`n=6rhA@kx z)pj=YAaqgqFL*|~RU+^cdv!1gvs@cuDIvASD3~fR%Xcipt?CqvX31JcJ3!KL3!o!L z64N1KB&cH1JV95^?nrO{L<*+O|((4`|+W&Q6N%oAnT^ZI(yn(k)G%|mN&ZLavS<=shi^kJz! zYRoOpGt|aM*))N$)D=v949D=M5Cmz`&Z5G&(KV!}1%Wji{Lt2pH~7`X&~qJwpIrnt zWRzijS1_?aTHRkYiUbTM300xL5VcU_1{TQt#gz1GctN*yuBgIhnJ~!n0<4I>bNe<> z1y|y2uMQfG0F(Y!_iZXjFu?~WOYDO`6PTI)c142LC+k}mv0t|cPI^LG5E2*^Wihcv z*m4P6HWd{*OcRolpWKp5zhD5&-9_R5`u!(RWn|9+P!q$v&H{UEC|R;Od>guve! zRqxK`#K|Z@X9rNIMMD?}$ZI%=rh;wgF8qPE6BXd=Sdug$!*k}07|YeE|E%?Y2v1Uz zg9(sGWMyR~NSx&q6yAWdGr86ujCsI1<+5#`&--tki6LyF`KvR*!u31{14xVDP7C0l z?rncQ>^G+aHZ~XtUT1r@kbDiMVf-IR;~$Kew%)l33NJYS3+%|;K$N%f2;WLf>Dyl| z=S`0ywZg)ySFX@7F}Xmx$KMfXY3bCc@aZ4Trq|i{SZt%19JveW{EZvQX**R#V}Mu{ zDHf{LUK1`P5T*CWoO1r`tH>l;d(Q`xkihLx>5O0<_9V=p8s|jt&U<4__Vo5?_qw`_ zuUweUtr(>IyvZLFzxPsW_4kU#!;0@tYy|~=EwY+++j2H-OXj5sF0ZjZ`g7?Y+i-r= zsE{|vq~+8Kf+2kWD1OB>FpY*7?}MpcCbCiw0|VL#38_vwd!_ zcHIsj`k)BAb`vh*^M?rr`ht}@dzle5zk-h(m0Pv5izik2Q1?ZDH_N_=Hb~+vnEeuq$QiNxT*d5&7%_%`7=Dl_jHv2I4_!OX^owo?g?%Egyk-oiVvka504f$ zo>Cb=kX;)RbkJRzCt|fD5JPJ9nkpL9IQLEa+4>jl*DCj5mFIf=*)g@@1#eu6?2Ccp zxojlz2SCHOQr@D}5P|Q04L%^Hb#rL?$Ck2q+)ab?7&Ub8!YLXe*;rI~-j1NCI-_ZqGMA1H7FyMFbr!a9XWEJ*J8TroIECAQjQ1~0(% zIYtJC9%KL^)lsLXlHFE2cS8_7o}zY;R7$Z4*HJlbHdB8(oy2`Q7t#zBVuHxe+p{u+ zdnRFw?=FD124qUVtyPybT~FYU^n-ngY6`r!?mpYN1rT8+Mxi*rNzYWhH|D75QA>?= z_I8khv)}dXXdHg0Y{(x_^DPTqzGD>UGN7Y~o1=RFHixnrY=Z%rO9=P`?QY2)szFAh@sG5h}^q+)BZAb*=J4EvIIxl2Kdrjnfd<=e~(d zlY7>b7ELmO2QwWH*QD|Xy2F10FXP#4+7=aCwHC!kAHqB`5ygjr5&zG>mG@wjT-%j1 z)qQ;RCK!>FsX!65*GdO5ouuYX>K2s{TJjsnEY;i>Zl>%JnIMSa%urV54nKqbx2%+(DefsibM0DHioL<3mEH?kN?y&jW@<5@|&Amf{S~W zqSG_c7N-wg>PjO-y~6rXOOJYAfEo!0t0JuuD3gL&fdI-FpT0u%|dfaXzdVKdS-)VlDwFKyCQ844vB5d`e7y{df-=H_IK<-l=L# z`NCsJT#6pPaQS*#p-?*kdnMOg`O6%})q@AB?pj5BHXmdae{{lEkzMFFy)zV6Jm9%4 zvM~EIAo0@!{y6W87^S{&u8qY@7mUosC7oc!C)_-&_rXo{OlY+SP;r=oKr_prUc3^f ztd*`&hTbgaVY0351F7GawXrvyw-2+4BVRMZ!8Q+h@P2Q?0~cM!?{(^-kpNBWonB0q z`*2lW{O8G3w|PsY0ifPUVYhO2GJbYRkC0JKAzeNN5Oj)x^F`D_2Rm{wX)|6{)?@8-Am!%8Xcy;x5H@dc9%w58m`1J1Y@9*n2CSH4Kaof9p7u^K%&Q#{XVAaQ9$fOmGw9?It27?-Ao+ zVC)C}*U!9&f#H($r}Y2(kNo?@{yi}0Chgz%<==AgZ?*jQviSF|{I^y3w^RDleEUKp z{%@oBZ=Xzz_wShS@0c*|0yAFlGLPZ6IWZLQrbh=Hs!g@bG z;Be1?7OmK9RQHt9GOYwh-0X#Eizkfvgzc@J^h`{BLBc2eQe&t&pZSEb*BBV@tPZ+} zt@>8B{XRZ$;&WQ~?W_>=&~9}E!cYLa0x=!uEcYYP>m-8uE2XTzQe_m;J7}|8RVx7L zYZs07D!G-#`BoepwO9@Tb91vG_fQnNytDPXV`MOeQ-8gFOV)x11LIK{<#9u59d^2? zvGDWMXT$=AGsRP{9DNpeeMHs$@zGw8sLgAi?>z~;6Uk*+*S3A<5Y~FW9E{=|gb)T2 zZdyw2O|k*0^kGe*-y?NvZlrRSkJesmKqB?A-!GSk}qLnVqnmG@~j?E zC`Au@Onw{a8>)`Y?gLojqEAHx$pX=Wcwhdc9oFG zBD02;S@^g0)C~L84(R~YdZ?3DV%)cxrI@L_*!bCnJlwWQY~N=dF>VE#P4pk{&Fi!L z!9^?HJr)OQO3m|q;YPp7^4ZuWy$y)N4GfL*XX*2A-)<3FSmki7HJS?|l?-Q6dSb}X z`qR;`oJKixQA=)iO z<~|`#5-EgeFgT>^d~V;CAdwQdye3x};O+)LtqA$rRIHgoF#I=}eT@)55PE<9n31&H zK~Z7ffd_+u@!1KDfxvffk7icjq-~-B68JOwhk1c#7l6LuFfc*^u0;Qp2EcIiUuA)= zz`&?dEq$#6ipD5zi_?{Y&fVpH3kc0L=*YKS9Xfa6@_SHWK;-pC5ad%VbU^~wz7HH$ zi2mg?{`naMAB{p8!H}g*W4)@j+3gGjO>4~Q1nh56Tpp@)wA*Y3kf{5!um|vj6m8|( zD@tG06cbdpMfmq8JW>S8!SM{y)sdqqROnq>m<9Gfz~1${e3_{d8^=XX+<0d$W>kBH zw@2NLqPqGz9nEib0q^{g&rA*s_jYhJ} z1lWlSz!f(0ZXowqn%38$MWYy{IYY1&=ga#VmH{b&FFtGE;vcW9?OenkOUea_QD13v z_Phf@x(irk%9k&n%4&93htXQKNu+~qb>;rl)mK`eKn@bjcxDnru2t~ltz9o);@n3W z!7`&x;hko%Fc+l(#6cxq$tTU}JaHPu7 zCr+fRZ$HtVT^q|Q@ldy{aT73ee*HhSGzMs1x20*B;f?Bbb`5oZO2YUw);od zE>lH5S@yS=78;jRI8`i+KFC(ev!CxBPhvIhCwn3BuelS!~!#Emea_!B>`inTuCF{j?BPO zoUK@^difi2Ql8%21P)0w(T5`EY+1WEyI;fEhL>dX_*zG202`(pbJYx9*m_GCeQS&z zB)q8QaHY1<*0&6zf{m|#GIB$scTOC3&fhYDokT|MVV*YhrerQ?!8RjpP z{V*3Ds;<810y$4aAl5SK)MYF0?ERUv^Gt>bD}As2tacWia=|y=qonnwL3^qcyQ42M zn0i05TBKu_Q@uzcpK`OjdF8OTfLYx(B%*`CE z%iXdGLQfV9x*4Kv&2WO8^qtfL6+$lQoeCb8`7`k%Y@&9DiL_lTQLTP?R$Ju`>9{44 zV<#Jh9|C;@2q;z$OV(Frm>jM;hhGnh*L2)ptB-efR$7bpDU@oyeKKvXej5tMJ8C|= z-%d88<*D}D)u8O7VAB`3v~13~3mR*oxqdMN1A~T6o8_-1bmHz6S4E3Fd6SDlt}Z&A znNvl~e>fGuOh+rNL&q#5Fgr7m06|4Bb;FX)OTd5&>7rNbh_v1 -fft_qjC983obxcG!cl_e=v zL#s6P?cRrr0TP9?SOjLwSwagsw|pGr9kB2#C$SV7N^tC*HirheaU3YC5aV(EXNylQ zB8HLq-3kegQ~phFVmIYw#Cns5heT^BlnUX|7pZSfo%JNzdD)7v2&N(kL>%&2srRC6 z7HKc$q#q2|&c&*8=Gg}9Ivp=2IeeTUvhWY>MqLOG>=mbqEX$Pc)m#mHv1dBzAYJeC z+N0N`*@3XO>AiGj9rMXpvxv;M&-+^uNv_Q${0W#psz!{OuUH?=E(&Nx@2ssSIoJkz z3mmL@A>-mVm7I{KL8FjJcFHN5$mrm_giImJMkpl0o#XhVK{N8EF)2Ia<_j zTDnj|^lDsdlU@|gRh-M=Vt)8dxx499Ju$%xU(V@$kVgB!^zghRb&J%B>c?EqoJVm4 zlkIA-X~F#Pvm6+-TYlsN3J=JDETYdjF1V-E*%Pxk1QcgrzkbvS|B*uajV2*)H zz04KI`L|?vsvL?;K}z*rTTcopcy_#1o+%n@Ik#A@6XyjM>E-MLj%oUwZ;6#d$xm-J zteSj@OWb%cW7rX6-w>ajrcst3mu}t`PvPiv|0$Brujm(3>AL$CGmrK)C*Vig-u^;$ z@^8J5+oWJVN;TZP14iv zP&0ZgCO5EYb2<<0ZUj0oM=n~I5Gpk=iGnpJVP?+c1o_(bIBy zLh0}%<+trvNqO8BrUrLz*$<76-76_3qf(uegH{4Ph?A53U($_6oleFhxUN%jj%j># zx6(+YwW-X+SLe4!Dt(?P2JOgG$nP{r>6LfH+|8KzWwn$dXg2h=C&Q%tRa|=AlUNHw zITB1Ue2cLX%sOmd-C6umrArNzFW+kF%&lFCXSicB#vAm^*h1~uaFSO<@M=KX^GV~@ooI^FAr?l3YNp*dVBWP#(~@P2ho?Ruf_d_m}~XL>|?As zpHyShHBypz2Qr1b1nKSMHW~Xf2z-XzSUcZL?}|v9jHv}LsHS6k^AobW9jG@4Jg@k{ zt^Y-}ay<5|KB(dgbtNk$J>%xD&(OoIySc0hEu{_371vFCc=(&83hn6ryi1dwVry zQ_~D{*ZoNd4dLQY&xZ=lxD_l$&ib(-rnMZh)C8nYEGUOC?8co-tY};UC%sod0nwYG z=rQe&rq6$OM95k3Q>P+qDxeS=Hp7~TeB1qOO-G^JHyHEz8LJkh2~}}XWtqy-t=x^5 zi)coxX2aE`8Vrn#UXBAhJtAd)X!2z#o2vq7NC`2~)Km`4nAuuP3bZH&$X6o2xG^Wt zxsbVzl6B(^>Fqy z{+HuYwu@t2zIpb+jze8kNM}@(w*qTMl$y*kVeFho$Vq&~M8{1kBXZMkE&A2UEJG;9 zA0^B2tV7qgwmw~=uQaT=uvD{0nEZr$MKy3dKT`#fP@y@$tN1cfCD20Lns51*Nsee@ zbuA`!J{?X`+tHKiAjde&uvlX&icFU) zsgUs#1Se%zeC+?>GND98D){hC_t4-_oo9}m6Oyh*A(vO(mFHQCZSOK3d(vXL)-}vM zO}@>=r?EY1bFyFP(iWSP90-T>hF(}p@!$!NW~_9DU>bZ-zHj2-Rd~o~EWTP(eZBMy zIxr7;f7O1U0PjmV>AbQgV{}0&Ha){NBjg1R2T=|jq^gF5%D5TT0qV97|2pJnb%bgyVJkq%#HPs1X~ zH&FArNl%SwHy-s%Y;AYn-W4BRu?1yUH8v~8BZtw-r{$b#a&}=Qr9P;LAx;QJwL;mL zi$pgSn-y}D82R0X(j~&^YHKs!@kUz1qA=EJ+*Cx0;n(`Qz=4G6*L%EgfdnX?md|zmLI+q^E@gtGTrCFvl!#wG&WLZl?SkM17;9 zw&QBiM;X4Zk?iO5R|Fh(d5vE3EPlU*lcPHAhDm-!i!*Q$!Z=y^tdNy|`= z!L++rm`r9sU-UgT=144y*~rL84|QfT(lhQBob@_pNJ!st&1DT)*h0+}NL?a zpyT54vk2jAgB4HNb3qD(HmQwBUg^5s)88ipNa z(KA9kty5|pWX6(5Qo5m}d&#e$4=~u)U_xd`w#{Q{>lJZPyH)Mee8)ZWOF<6Z+hOO| zyh)a*RYgFf(knhGGaLEyDs*{%anCbYp@GHij0g`;b!Wp|&?S{ZdxW`o=F;bTAr2bE z`RCzk5J1A--#N zMf;CJRy#$p@}r4T<$FiOY&;#Qp4Zf9?2)!nCzEiCx7`rD&!MvT{MN%r?WxABxNMTq z#8}$ri60xd_4UUWvko^Z9*}&c+6lxZ`t)f-xV7j!Gid z4^sRGs1wB=|FR}ZBQM&^CK=GEHG8(fiEWZC(sNP}8ZjH;&EYX8@z$(8i@y5p^Yy5x zjnwaLTnuz7$DCC$_T$31)DGy~(O;QJq3`t~*$UeguVk|>Ub!IS~$NBrc zuJ@n!d7t~ffA??sOPRAM8+V?owNYwe8pnBiUAhQ!WkBT%ApL3aunIkBsD$^;q+(h- z;*UNy)PjTh@0ieS){52z$)5vL{7S154>GgZIHYdyEQvzM;?{^*z7h@jEM68l8eHnH zfYmGva(Hu~iX8~tz*#*wpaS>)>mNZ#(thpz2DA>wNjMbr84@ny{d zd&Wk@MNmVWrJ3)&Kv#H;4-sKy=id(I zYTW)zqK_NTYJ|Q~el!4o*RoKTVh+=q6RNl+y#GZ`e zR?waeD4_)pI{s;Akw6Yw_db`P5PO%+;)$`a*BxKG7>Kn!d=|T53Hh{jIN>JPLKz%eoJ_gTgNsLs=U+X zM`v^)PWCH7eVoftZNU?ckOTuwzv(}uJx z^dGVbLaizvbP5_ki4i&cIQbvs^FCvx^+&3I_Vz2nS5&Jy`;LZDysB2(?^T~4w+u4d zRCWTNvW6NAk9E2ENj=pC^aT5piQMh!;{reZ&33oR2=w{g^v z-!~-%8Z|HjmQx$s?iarz*+VY=A4F+HH~ZY5(;4$`R^Y|%&|WF{W`)EYF0ba?w8ycB z%uH0PZJ%os0Mf>IC+HhLzI!->gLrRuI?5Xq7A@`Fz1j%HBJ2gi+p~x6geaiIzVGmy z3JX-gYSe&H)_}+MDg9Fb9&J~@425GENWj-E!pft3`ovG*Bps~x07p~JwN@cUlA(Jm zKLY0(J4R>$P60y`VayAz)aRfnLC|1n@UUI7{^)vHswK)&K0LY-$g+UVB1 z@^Bv(mMhh1-^2Q4Jf_ag)d>-1f#p0U9+)cxIRamg{XI){$VFd3v%D5}d~bDiwQ&Yp z32@o=ZaX?)*B)|%M~Z>1A?Is2UHRyCb1nwWU6OCL3Z)%1_%mV#J1@>Wi2K>4Ku1P< zI_tXu{^{H)Yw|vX_;D$f*QTdB=yA0>*>*U!x3GKo^78J+y1*~F(kE!&G?5Y3n2Or8 z)A5*EU*7Kz!E@2QU~pwpz@3=GAEK$HTQ%%Vbdx;s@XpF9SNqsuYAdC>Hk# zl4;zrJcCJ`JC4H8#?#hZFb!bu+H??+$`S3v0D{EAvyv~ctGIlm% zb?xlUyqL`X#yYAAp+4SCIr+2oG~LCN-Z8pT`Y~zsiKnwFZ9e|wxG}%`oOpC*EkzaJ zN!3SjXj93FT5zQ%R?nhJO1-jD>aaQDnhQ=ztFx(~NDRl!Zo=M&Rq&Wd7}q#mn67TN zh2y-x#syU9nC4P`zNZE0F%rzNs;ME&D0QP3jqqFpDEcAHYQ%O-(Ik`I8y)h-y&kD% zzXQrLi_<#EhRA)-S75XZ+fTll4SovXE0%gDZC$}UIIt#O{w3-0h&7miCp4oONO}2B z(FvFg6(={!?ZOYoU=AQOqr_~aBccUl&yKl=IvEGtjCf0(AnJ{kU#>c+bn{mg_YN~> zPYH`fxP1dqVAY`}TuTo|G&54!VMI)#R8@NFgn&nmt$OB8Rj+eYeU??90E8%h4|qtH z=S8QPRuDkRmAo+6cayO0H11~7KA}^1sU35yONX8A->A_N$|I3NS|dk#xkGzwY6KEL-0@>-^t_+T1#IP zK68gKBdHl$=c~TNW64>&4zP+)XB8nm7&(9@^nl8g0S5vG4&VucU)!6PE@FA;KR9Z_ zp{>dmo|L|P#3e;v11t&Wr0~ML!Qyj5-p9|^fR6(FL9@Io^5O6Y{nK%o-c7Z z=hgI&GM`94riM8P?uGNjr0W56~n5Dr}Oo42Ok57aPvj0Yt!M;g_ zQ&X>13)Z2aEZ19X*oB@weEv~xVZAue{x{dE$r~9qmw1=bUzHa`E`zkw*y2}ZT2N-g vf3Jy&DHDiOkkZF)DJvC}JSEu|cm3kG>x*)7*E<3|e2=+_jWOBq_t<{{c++3V diff --git a/requirements.txt b/requirements.txt index d9aa5d0..cc5d4e0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,5 +2,6 @@ datajoint>=0.13.0 element-lab==0.1.0b0 element-animal==0.1.0b0 element-session==0.1.0b0 +element-behavior==0.0.0a1 element-interface @ git+https://github.com/datajoint/element-interface.git ipykernel==6.0.1 diff --git a/setup.py b/setup.py index c369bb9..7b1047f 100644 --- a/setup.py +++ b/setup.py @@ -7,7 +7,7 @@ here = path.abspath(path.dirname(__file__)) long_description = """" -# Workflow for monitoring continuous behavior +# Workflow for monitoring DeepLabCut post estimaton + [element-lab](https://github.com/datajoint/element-lab) + [element-animal](https://github.com/datajoint/element-animal) @@ -24,7 +24,7 @@ setup( name='workflow-behavior', version=__version__, - description="DataJoint Elements for Continous Behavior", + description="DataJoint Elements for DeepLabCut pose estimation", long_description=long_description, author='DataJoint', author_email='info@DataJoint.com', diff --git a/tests/__init__.py b/tests/__init__.py index b549577..c1a7786 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -118,3 +118,7 @@ def ingest_sessions(ingest_subjects, sessions_csv): _, session_csv_path = sessions_csv ingest_sessions(session_csv_path=session_csv_path) return + +''' TO DO +- Add csv and ingestion fixtures for config params and recordings +''' diff --git a/tests/test_ingest.py b/tests/test_ingest.py index 6997a57..77a4932 100644 --- a/tests/test_ingest.py +++ b/tests/test_ingest.py @@ -34,3 +34,12 @@ def test_ingest_sessions(pipeline, sessions_csv, ingest_sessions): assert (session.SessionDirectory & {'subject': sess[0]} ).fetch1('session_dir') == sess[2] + + +''' TO DO +- add ingestion of recordings and config params +- test launch of analyze videos +- Encode analysis outcome specifcs from Model.Data + e.g. assert mean(Model.Data & "joint_name = 'Finger1'").fetch('x_pos')) == Value +- post example data to djarchive? +''' diff --git a/tests/test_pipeline_generation.py b/tests/test_pipeline_generation.py index 05ec2d0..6b6e86f 100644 --- a/tests/test_pipeline_generation.py +++ b/tests/test_pipeline_generation.py @@ -16,3 +16,8 @@ def test_generate_pipeline(pipeline): # test connection Subject->Session subject_tbl, *_ = session.Session.parents(as_objects=True) assert subject_tbl.full_table_name == subject.Subject.full_table_name + + +''' TO DO +- Add relative table assertions for DLC schema +''' diff --git a/workflow_behavior/pipeline.py b/workflow_behavior/pipeline.py index 7c3e7e7..44acb9c 100644 --- a/workflow_behavior/pipeline.py +++ b/workflow_behavior/pipeline.py @@ -30,4 +30,3 @@ # Activate "behavior" schema ----------------------------------- dlc.activate(db_prefix + 'dlc', linking_module=__name__) -# treadmill.activate(db_prefix + 'treadmill', linking_module=__name__) From 8e7ca78d861c500ae73416479fc7f8b36e0abd86 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Wed, 2 Feb 2022 09:20:42 -0600 Subject: [PATCH 013/176] README+CHANGELOG clarity --- CHANGELOG.md | 4 ++-- README.md | 12 +++++------- 2 files changed, 7 insertions(+), 9 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 130e05a..fb5ed72 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,7 +8,7 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and ## 0.0.0a1 - 2021-12-15 ### Added -+ First draft begins ++ First draft begins, reflecting precursor pipelines + Added Docker files -+ Dragt integration tests ++ Draft integration tests + Add example data featuring DLC examples diff --git a/README.md b/README.md index 2ed600e..f5eec6f 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ This directory provides an example workflow to save the continuous behavior data + [element-lab](https://github.com/datajoint/element-lab) + [element-animal](https://github.com/datajoint/element-animal) + [element-session](https://github.com/datajoint/element-session) -+ [element-behavior](https://github.com/datajoint/element-behavior) ++ [element-deeplabcut](https://github.com/datajoint/element-deeplabcut) This repository provides demonstrations for: Setting up a workflow using different elements (see [pipeline.py](workflow_behavior/pipeline.py)) @@ -24,13 +24,11 @@ https://github.com/datajoint/element-animal/blob/main/images/subject_diagram.svg ### element-session `session` is designed to handle metadata related to data collection, including collection datetime, file paths, and notes. Most workflows will include element-session as a starting point for further data entry. -![session](images/session_diagram2.png) +![session](https://github.com/datajoint/element-session/blob/main/images/session_diagram.svg) - +### Assembled with element-deeplabcut +![element-deeplabcut]( +https://github.com/datajoint/element-deeplabcut/blob/main/images/diagram_dlc.svg) ### This workflow This workflow serves as an example of the upstream part of a typical data workflow, for examples using these elements with other data modalities refer to: From 3a3ba77080d6aee2d3a530e2b5f481584ce19d74 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Mon, 21 Feb 2022 11:00:48 -0600 Subject: [PATCH 014/176] draft ingest edits --- user_data/config_params.csv | 6 +++--- user_data/recordings.csv | 8 ++++---- workflow_behavior/ingest.py | 4 +++- workflow_behavior/paths.py | 19 +++++++++---------- workflow_behavior/pipeline.py | 19 ++++++++++++++++--- 5 files changed, 35 insertions(+), 21 deletions(-) diff --git a/user_data/config_params.csv b/user_data/config_params.csv index b5085d7..d3ef1fc 100644 --- a/user_data/config_params.csv +++ b/user_data/config_params.csv @@ -1,3 +1,3 @@ -paramset_idx,shuffle,train_fraction,snapshot_index,filter_type,track_method,scorer_legacy -0,1,.95,-1,,,False -1,0,.95,-1,median,ellipse,False +paramset_idx,shuffle,train_fraction,filter_type,track_method,scorer_legacy +0,1,.95,,,False +1,0,.95,median,ellipse,False diff --git a/user_data/recordings.csv b/user_data/recordings.csv index e336c21..b59def5 100644 --- a/user_data/recordings.csv +++ b/user_data/recordings.csv @@ -1,4 +1,4 @@ -subject,session_datetime,video_path,camera_id,config_path,config_notes,paramset_idx -subject5,2020-04-15 11:16:38,videos/reachingvideo1.avi,1,config.yaml,Reaching example provided by DeepLabCut repository,0 -subject6,2021-06-02 14:04:22,videos/m3v1mp4.mp4,1,config.yaml,Openfield example provided by DeepLabCut repository,0 -subject6,2021-06-03 14:04:22,videos/videocompressed1.mp4,1,config.yaml,Multianimal - not fully trained,1 +subject,session_datetime,recording_start_time,file_path,camera_id,config_path,config_notes,paramset_idx +subject5,2020-04-15 11:16:38,2020-04-15 11:17:00,videos/reachingvideo1.avi,1,config.yaml,Reaching example provided by DeepLabCut repository,0 +subject6,2021-06-02 14:04:22,2021-06-02 14:07:00,videos/m3v1mp4.mp4,1,config.yaml,Openfield example provided by DeepLabCut repository,0 +subject6,2021-06-03 14:04:22,2021-06-03 14:07:00,videos/videocompressed1.mp4,1,config.yaml,Multianimal - not fully trained,1 diff --git a/workflow_behavior/ingest.py b/workflow_behavior/ingest.py index feeb03e..4606dbc 100644 --- a/workflow_behavior/ingest.py +++ b/workflow_behavior/ingest.py @@ -1,5 +1,6 @@ # from pathlib import Path import csv +from distutils.util import strtobool from workflow_behavior.pipeline import subject, session, dlc # from workflow_behavior.paths import get_beh_root_data_dir @@ -63,12 +64,13 @@ def ingest_dlc_configs(recording_csv_path='./user_data/recordings.csv', with open(config_params_csv_path, newline='') as f: config_params = list(csv.DictReader(f, delimiter=',')) for paramset in config_params: + paramset['scorer_legacy'] = bool(strtobool(paramset['scorer_legacy'])) dlc.ConfigParamSet.insert_new_params(**paramset, skip_duplicates=skip_duplicates) # Next, recordings and config files csvs = [recording_csv_path, recording_csv_path] - tables = [dlc.Recording(), dlc.Config()] + tables = [dlc.VideoRecording(), dlc.VideoRecording.File()] ingest_general(csvs, tables, skip_duplicates=skip_duplicates) diff --git a/workflow_behavior/paths.py b/workflow_behavior/paths.py index 4b4c4c9..46466c6 100644 --- a/workflow_behavior/paths.py +++ b/workflow_behavior/paths.py @@ -1,23 +1,22 @@ import datajoint as dj -def get_beh_root_data_dir(): - beh_root_dirs = dj.config.get('custom', {}).get('beh_root_data_dir', None) - return beh_root_dirs if beh_root_dirs else None +def get_dlc_root_data_dir(): + dlc_root_dirs = dj.config.get('custom', {}).get('dlc_root_data_dir', None) + return dlc_root_dirs if dlc_root_dirs else None -def get_session_dir(session_key: dict) -> str: +def get_session_directory(session_key: dict) -> str: from .pipeline import session session_dir = (session.SessionDirectory & session_key).fetch1('session_dir') return session_dir -def get_beh_output_dir(session_key: dict) -> str: - """ Returns session_dir relative to custom 'beh_output_dir' root """ +def get_dlc_processed_data_dir(session_key: dict) -> str: + """ Returns session_dir relative to custom 'dlc_output_dir' root """ from pathlib import Path - beh_output_dir = dj.config.get('custom', {} - ).get('beh_output_dir', None) - if beh_output_dir is not None: - return Path(beh_output_dir, get_session_dir(session_key)) + dlc_output_dir = dj.config.get('custom', {}).get('dlc_output_dir', None) + if dlc_output_dir: + return Path(dlc_output_dir, get_session_directory(session_key)) else: return None diff --git a/workflow_behavior/pipeline.py b/workflow_behavior/pipeline.py index 44acb9c..bcab534 100644 --- a/workflow_behavior/pipeline.py +++ b/workflow_behavior/pipeline.py @@ -8,10 +8,12 @@ from element_lab.lab import Source, Lab, Protocol, User, Project from element_session.session import Session -from .paths import get_beh_root_data_dir, get_session_dir, get_beh_output_dir +from .paths import get_dlc_root_data_dir, get_session_directory +from .paths import get_dlc_processed_data_dir -__all__ = ['get_beh_root_data_dir', 'get_session_dir', 'get_beh_output_dir', - 'Subject', 'Source', 'Lab', 'Protocol', 'User', 'Project', 'Session'] +__all__ = ['get_dlc_root_data_dir', 'get_session_directory', + 'get_dlc_processed_data_dir', 'Subject', 'Source', 'Lab', 'Protocol', 'User', + 'Project', 'Session'] if 'custom' not in dj.config: dj.config['custom'] = {} @@ -27,6 +29,17 @@ Experimenter = lab.User session.activate(db_prefix + 'session', linking_module=__name__) +# Activate equipment table ------------------------------------ + + +@lab.schema +class Device(dj.Lookup): + definition = """ + camera_id : int + """ + contents = zip([1, 2]) + # Activate "behavior" schema ----------------------------------- + dlc.activate(db_prefix + 'dlc', linking_module=__name__) From 598b0e2974a92d0d6de9c6543be9f8fc0c883a1d Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Tue, 22 Feb 2022 17:19:49 -0600 Subject: [PATCH 015/176] new notebook for modeling use --- CONTRIBUTING.md | 3 +- README.md | 2 +- docker-compose-dev.yaml | 6 +- docker-compose-test.yaml | 10 +- notebooks/1_Explore_Workflow.ipynb | 2808 ++++++++--------- requirements.txt | 3 +- setup.py | 10 +- user_data/recordings.csv | 3 +- user_data/sessions.csv | 4 +- .../__init__.py | 0 .../ingest.py | 5 +- .../paths.py | 0 .../pipeline.py | 2 +- .../version.py | 0 14 files changed, 1425 insertions(+), 1431 deletions(-) rename {workflow_behavior => workflow_deeplabcut}/__init__.py (100%) rename {workflow_behavior => workflow_deeplabcut}/ingest.py (94%) rename {workflow_behavior => workflow_deeplabcut}/paths.py (100%) rename {workflow_behavior => workflow_deeplabcut}/pipeline.py (97%) rename {workflow_behavior => workflow_deeplabcut}/version.py (100%) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 5836c18..7383395 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,3 +1,4 @@ # Contribution Guidelines -This project follows the [DataJoint Contribution Guidelines](https://docs.datajoint.io/python/community/02-Contribute.html). Please reference the link for more full details. +This project follows the [DataJoint Contribution Guidelines] +(https://docs.datajoint.io/python/community/02-Contribute.html). Please reference the link for more full details. diff --git a/README.md b/README.md index f5eec6f..0bf9780 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ This directory provides an example workflow to save the continuous behavior data + [element-deeplabcut](https://github.com/datajoint/element-deeplabcut) This repository provides demonstrations for: -Setting up a workflow using different elements (see [pipeline.py](workflow_behavior/pipeline.py)) +Setting up a workflow using different elements (see [pipeline.py](workflow_deeplabcut/pipeline.py)) ## Workflow architecture The lab and animal management workflow presented here uses components from two DataJoint elements (element-lab, element-animal and element-session) assembled together to a functional workflow. diff --git a/docker-compose-dev.yaml b/docker-compose-dev.yaml index ff22c72..a599179 100644 --- a/docker-compose-dev.yaml +++ b/docker-compose-dev.yaml @@ -15,7 +15,7 @@ services: <<: *net build: context: ../ - dockerfile: ./workflow-behavior/Dockerfile.dev + dockerfile: ./workflow-deeplabcut/Dockerfile.dev env_file: .env image: workflow_session_dev:0.0.0a1 volumes: @@ -23,8 +23,8 @@ services: - ../element-lab:/main/element-lab - ../element-animal:/main/element-animal - ../element-session:/main/element-session - - ../element-behavior:/main/element-behavior - - .:/main/workflow-behavior + - ../element-deeplabcut:/main/element-deeplabcut + - .:/main/workflow-deeplabcut depends_on: db: condition: service_healthy diff --git a/docker-compose-test.yaml b/docker-compose-test.yaml index fbec52f..dfd7d7b 100644 --- a/docker-compose-test.yaml +++ b/docker-compose-test.yaml @@ -15,9 +15,9 @@ services: <<: *net build: context: ../ - dockerfile: ./workflow-behavior/Dockerfile.test + dockerfile: ./workflow-deeplabcut/Dockerfile.test env_file: .env - image: workflow_behavior:0.0.0a1 + image: workflow_deeplabcut:0.0.0a1 environment: - DJ_HOST=db - DJ_USER=root @@ -28,15 +28,15 @@ services: - -c - | echo "------ INTEGRATION TESTS ------" - pytest -sv --cov-report term-missing --cov=workflow-behavior -p no:warnings + pytest -sv --cov-report term-missing --cov=workflow-deeplabcut -p no:warnings tail -f /dev/null volumes: - ./apt_requirements.txt:/tmp/apt_requirements.txt - ../element-lab:/main/element-lab - ../element-animal:/main/element-animal - ../element-session:/main/element-session - - ../element-behavior:/main/element-behavior - - .:/main/workflow-behavior + - ../element-deeplabcut:/main/element-deeplabcut + - .:/main/workflow-deeplabcut depends_on: db: condition: service_healthy diff --git a/notebooks/1_Explore_Workflow.ipynb b/notebooks/1_Explore_Workflow.ipynb index cb55ee5..0da995b 100644 --- a/notebooks/1_Explore_Workflow.ipynb +++ b/notebooks/1_Explore_Workflow.ipynb @@ -8,9 +8,31 @@ "# DataJoint U24 - Workflow Behavior" ] }, + { + "cell_type": "markdown", + "id": "b811f8c8-5851-445e-bfba-b61dfd68388d", + "metadata": { + "tags": [] + }, + "source": [ + "First, please install both `element-deeplabcut` and `workflow-deeplabcut` locally. We \n", + "recommend launching a new conda environment and using `pip install -e ./`. For more\n", + "information, see our [install instructions](https://github.com/kabilar/datajoint-elements/blob/main/install.md). " + ] + }, + { + "cell_type": "markdown", + "id": "a9152fd1-faed-4492-8a1b-fc0b04bb54f0", + "metadata": { + "tags": [] + }, + "source": [ + "Next, let's change directory to the main workflow directory." + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "8b0d2410-e307-49ee-8adf-451bf7b24edc", "metadata": { "tags": [] @@ -20,76 +42,103 @@ "import os; from pathlib import Path\n", "# change to the upper level folder to detect dj_local_conf.json\n", "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "import datajoint as dj; dj.config.load('dj_local_conf.json')" + "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", + " + \"workflow directory\")" ] }, { - "cell_type": "code", - "execution_count": 2, - "id": "d25b109d-c8b2-46f6-8fde-cbd9135cdfc3", + "cell_type": "markdown", + "id": "bf1e4ef2-522d-4a45-a06c-2ba685dfb88c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting root@localhost:3306\n", - "\n", - "---- Inserting 0 entry(s) into subject ----\n", - "\n", - "---- Inserting 0 entry(s) into session ----\n", - "\n", - "---- Inserting 0 entry(s) into session_directory ----\n", - "\n", - "---- Inserting 0 entry(s) into session_note ----\n", - "\n", - "---- Inserting 3 entry(s) into recording ----\n", - "\n", - "---- Inserting 3 entry(s) into config ----\n" - ] - } - ], "source": [ - "from workflow_behavior.pipeline import lab, subject, session, dlc\n", - "from workflow_behavior.ingest import ingest_subjects, ingest_sessions, ingest_dlc_configs\n", - "ingest_subjects(); ingest_sessions(); ingest_dlc_configs(skip_duplicates=True)" + "Second, download the example data we'll be using from the DeepLabCut repository. We will\n", + "use the [example openfield data](https://github.com/DeepLabCut/DeepLabCut/tree/master/examples/openfield-Pranav-2018-10-30) \n", + "from the DeepLabCut github repository. If you have already cloned this repository, you \n", + "may have this data on your machine already. [This link](https://downgit.github.io/#/home?url=https://github.com/DeepLabCut/DeepLabCut/tree/master/examples/openfield-Pranav-2018-10-30) via [DownGit](https://downgit.github.io/) will start the single-directory download \n", + "automatically. After downloading, please add the path to this directory to the `custom`\n", + "field of your datajoint config file as `dlc_root_data_dir`. " ] }, { "cell_type": "code", "execution_count": 3, - "id": "3af29f80-63d4-4dd2-9f56-70579d27e9c9", + "id": "0ad68223-3600-4da9-a3e7-141962fefaf6", + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj; dj.config.load('dj_local_conf.json')\n", + "from element_interface.utils import find_full_path\n", + "data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'],\n", + " 'openfield-Pranav-2018-10-30')\n", + "assert data_dir.exists(), \"Please check the that you have the folder openfield-Pranav\"" + ] + }, + { + "cell_type": "markdown", + "id": "43dcb79d-72f8-468a-be2a-59866505b888", "metadata": {}, + "source": [ + "Later, we'll use the first few seconds of this video as a 'separate session' to model\n", + "the pose estimation feature of this pipeline. `ffmpeg` is a dependency of DeepLabCut\n", + "that can splice the training video for a demonstration purposes. The command below saves\n", + "the first 2 seconds of the training video as a copy." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "10218fae-c1ab-43cb-8c6a-37dcd38f8ff6", + "metadata": { + "tags": [] + }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Deleting 1 rows from `wf_dlc`.`config`\n", - "Deleting 1 rows from `wf_dlc`.`recording`\n", - "Deleting 1 rows from `wf_session`.`session_directory`\n", - "Deleting 1 rows from `wf_session`.`session_note`\n", - "Deleting 1 rows from `wf_session`.`session`\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Commit deletes? [yes, No]: yes\n" - ] - }, - { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Deletes committed.\n" + "ffmpeg version 5.0 Copyright (c) 2000-2022 the FFmpeg developers\n", + " built with Apple clang version 13.0.0 (clang-1300.0.29.3)\n", + " configuration: --prefix=/usr/local/Cellar/ffmpeg/5.0 --enable-shared --enable-pthreads --enable-version3 --cc=clang --host-cflags= --host-ldflags= --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libbluray --enable-libdav1d --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librist --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvmaf --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-videotoolbox\n", + " libavutil 57. 17.100 / 57. 17.100\n", + " libavcodec 59. 18.100 / 59. 18.100\n", + " libavformat 59. 16.100 / 59. 16.100\n", + " libavdevice 59. 4.100 / 59. 4.100\n", + " libavfilter 8. 24.100 / 8. 24.100\n", + " libswscale 6. 4.100 / 6. 4.100\n", + " libswresample 4. 3.100 / 4. 3.100\n", + " libpostproc 56. 3.100 / 56. 3.100\n", + "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4':\n", + " Metadata:\n", + " major_brand : isom\n", + " minor_version : 512\n", + " compatible_brands: isomiso2avc1mp41\n", + " encoder : Lavf56.40.101\n", + " Duration: 00:01:17.67, start: 0.000000, bitrate: 228 kb/s\n", + " Stream #0:0[0x1](und): Video: h264 (High 4:4:4 Predictive) (avc1 / 0x31637661), yuv444p(progressive), 640x480, 225 kb/s, 30 fps, 30 tbr, 1000k tbn (default)\n", + " Metadata:\n", + " handler_name : VideoHandler\n", + " vendor_id : [0][0][0][0]\n", + "Output #0, mp4, to '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4':\n", + " Metadata:\n", + " major_brand : isom\n", + " minor_version : 512\n", + " compatible_brands: isomiso2avc1mp41\n", + " encoder : Lavf59.16.100\n", + " Stream #0:0(und): Video: h264 (High 4:4:4 Predictive) (avc1 / 0x31637661), yuv444p(progressive), 640x480, q=2-31, 225 kb/s, 30 fps, 30 tbr, 1000k tbn (default)\n", + " Metadata:\n", + " handler_name : VideoHandler\n", + " vendor_id : [0][0][0][0]\n", + "Stream mapping:\n", + " Stream #0:0 -> #0:0 (copy)\n", + "Press [q] to stop, [?] for help\n", + "frame= 2330 fps=0.0 q=-1.0 Lsize= 2164kB time=00:01:15.56 bitrate= 234.6kbits/s speed=4.83e+03x \n", + "video:2137kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.297295%\n" ] }, { "data": { "text/plain": [ - "1" + "0" ] }, "execution_count": 3, @@ -98,160 +147,150 @@ } ], "source": [ - "multianimal=(session.Session & 'session_datetime > \"2021-06-03\"').fetch1('KEY')\n", - "(session.Session & multianimal).delete()" + "vid_path = str(data_dir).replace(\" \", \"\\ \") + '/videos/m3v1mp4'\n", + "cmd = f'ffmpeg -y -ss 2 -i {vid_path}.mp4 -vcodec copy -acodec copy {vid_path}-copy.mp4'\n", + "os.system(cmd)" + ] + }, + { + "cell_type": "markdown", + "id": "9ac69bc0-4e62-4094-b506-39dcc9f93515", + "metadata": {}, + "source": [ + "Now, we can activate the `dlc` schema and import some data from files stored in this\n", + "directory under `user_data/.csv`. This includes parameters like shuffle and \n", + "training fraction that DeepLabCut uses." ] }, { "cell_type": "code", "execution_count": 4, - "id": "77d22ee2-0a9d-4e28-88ac-c80b08d8540e", + "id": "d25b109d-c8b2-46f6-8fde-cbd9135cdfc3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Populated Model and Data tables from: reachingvideo1DLC_resnet50_ReachingAug30shuffle1_800\n", + "Connecting cbroz@tutorial-db.datajoint.io:3306\n", "\n", - "Populated Model and Data tables from: m3v1mp4DLC_resnet50_openfieldOct30shuffle1_200\n", - "\n" + "---- Inserting 0 entry(s) into subject ----\n", + "\n", + "---- Inserting 0 entry(s) into session ----\n", + "\n", + "---- Inserting 0 entry(s) into session_directory ----\n", + "\n", + "---- Inserting 0 entry(s) into session_note ----\n", + "\n", + "---- Inserting 0 entry(s) into video_recording ----\n", + "\n", + "---- Inserting 0 entry(s) into video_recording__file ----\n" ] } ], "source": [ - "dlc.Model.populate()" + "from workflow_deeplabcut.pipeline import lab, subject, session, dlc\n", + "from workflow_deeplabcut.ingest import ingest_subjects, ingest_sessions, ingest_dlc_configs\n", + "ingest_subjects(); ingest_sessions(); ingest_dlc_configs()" + ] + }, + { + "cell_type": "markdown", + "id": "2f5fd85c-29eb-4b65-bad1-11fcd096f103", + "metadata": {}, + "source": [ + "For model training, we'll work with the following session and parameters." ] }, { "cell_type": "code", "execution_count": 5, - "id": "4ef08929-4d27-4b2c-bb84-e30f4b4a595d", + "id": "53017c86-1512-4a18-8f19-556f6ad94644", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
      \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
      scorerDLC_resnet50_ReachingAug30shuffle1_800
      body_partsFinger1
      coordsxylikelihood
      0208.178589631.4389040.286353
      1208.230087631.8498540.293349
      2208.575089631.3552860.270262
      3208.384003631.0619510.279657
      4207.791412631.4551390.292992
      ............
      251367.111267456.9003300.178846
      252367.781586456.2545170.158984
      253366.738342462.9418950.182887
      254366.690765463.3888850.176299
      255182.641144645.3834230.157941
      \n", - "

      256 rows × 3 columns

      \n", - "
      " + " /* Show the tooltip text when you mouse over the tooltip container */\n", + " .djtooltip:hover .djtooltiptext {\n", + " visibility: visible;\n", + " }\n", + " \n", + " \n", + " \n", + "
      \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "
      \n", + "

      subject

      \n", + " \n", + "
      \n", + "

      session_datetime

      \n", + " \n", + "
      \n", + "

      camera_id

      \n", + " \n", + "
      \n", + "

      recording_start_time

      \n", + " \n", + "
      subject62021-06-02 14:04:2212021-06-02 14:07:00
      \n", + " \n", + "

      Total: 1

      \n", + " " ], "text/plain": [ - "scorer DLC_resnet50_ReachingAug30shuffle1_800 \n", - "body_parts Finger1 \n", - "coords x y likelihood\n", - "0 208.178589 631.438904 0.286353\n", - "1 208.230087 631.849854 0.293349\n", - "2 208.575089 631.355286 0.270262\n", - "3 208.384003 631.061951 0.279657\n", - "4 207.791412 631.455139 0.292992\n", - ".. ... ... ...\n", - "251 367.111267 456.900330 0.178846\n", - "252 367.781586 456.254517 0.158984\n", - "253 366.738342 462.941895 0.182887\n", - "254 366.690765 463.388885 0.176299\n", - "255 182.641144 645.383423 0.157941\n", - "\n", - "[256 rows x 3 columns]" + "*subject *session_datet *camera_id *recording_sta\n", + "+----------+ +------------+ +-----------+ +------------+\n", + "subject6 2021-06-02 14: 1 2021-06-02 14:\n", + " (Total: 1)" ] }, "execution_count": 5, @@ -260,13 +299,15 @@ } ], "source": [ - "dlc.Model.Get2DTrajectory(dlc.Model & \"subject='subject5'\",joint_name=['Finger1'])" + "train_key={'subject': 'subject6', 'session_datetime': '2021-06-02 14:04:22',\n", + " 'camera_id': 1, 'recording_start_time': '2021-06-02 14:07:00'}\n", + "dlc.VideoRecording & train_key" ] }, { "cell_type": "code", "execution_count": 6, - "id": "967f0afd-6ec8-4fce-8bec-5af1d0291537", + "id": "a90b0534-a028-443c-b219-80dd79016748", "metadata": {}, "outputs": [ { @@ -322,95 +363,52 @@ " }\n", " \n", " \n", - " \n", + " Parameters to specify a DLC model training instance\n", "
      \n", " \n", " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", "
      \n", - "

      subject

      \n", - " \n", - "
      \n", - "

      session_datetime

      \n", - " \n", - "
      \n", - "

      video_path

      \n", - " raw video path relative to session_dir\n", - "
      \n", "

      paramset_idx

      \n", " \n", "
      \n", - "

      config_path

      \n", - " config.yaml relative to session_dir\n", - "
      \n", - "

      task

      \n", - " task description\n", + "

      shuffle

      \n", + " shuffle number to use (usually 1)\n", "
      \n", - "

      scorer

      \n", - " scorer/network name in config, human labeler\n", + "

      train_fraction

      \n", + " training fraction\n", "
      \n", - "

      multianimal

      \n", - " true for multi-animal\n", + "

      model_prefix

      \n", + " DLC model prefix, often empty\n", "
      \n", - "

      iteration

      \n", - " iteration number\n", + "

      filter_type

      \n", + " filter type, blank if none (e.g., median, arima)\n", "
      \n", - "

      pcutoff

      \n", - " threshold of likelihood\n", + "

      track_method

      \n", + " tracking method, blank if none (e.g,. box, ellipse)\n", "
      \n", - "

      model

      \n", - " DLC's GetScorerName()\n", + "

      scorer_legacy

      \n", + " legacy naming for DLC < v2.1.0\n", "
      \n", - "

      start_time

      \n", - " When the model started training\n", - "
      \n", - "

      run_duration

      \n", - " Seconds model run\n", - "
      \n", - "

      fps

      \n", - " Source video framerate, frames per second\n", - "
      \n", - "

      dlc_version

      \n", - " keeps the deeplabcut version\n", + "

      param_set_hash

      \n", + " hash identifying this parameterset\n", "
      subject52020-04-15 11:16:38videos/reachingvideo1.avi0config.yamlReachingMackenzie0
      010.4DLC_resnet50_ReachingAug30shuffle1_8002022-01-10 21:02:29282.42530.02.2.0.5
      subject62021-06-02 14:04:22videos/m3v1mp4.mp40config.yamlopenfieldPranav0.95000.4DLC_resnet50_openfieldOct30shuffle1_2002022-01-12 14:59:251569.0530.00032.2.0.5
      c70007c1-32b1-ae9b-cb83-7d11cbf78f37
      \n", " \n", - "

      Total: 2

      \n", + "

      Total: 1

    \n", " " ], "text/plain": [ - "*subject *session_datet *video_path *paramset_idx *config_path task scorer multianimal iteration pcutoff model start_time run_duration fps dlc_version \n", - "+----------+ +------------+ +------------+ +------------+ +------------+ +-----------+ +-----------+ +------------+ +-----------+ +---------+ +------------+ +------------+ +------------+ +---------+ +------------+\n", - "subject5 2020-04-15 11: videos/reachin 0 config.yaml Reaching Mackenzie 0 1 0.4 DLC_resnet50_R 2022-01-10 21: 282.425 30.0 2.2.0.5 \n", - "subject6 2021-06-02 14: videos/m3v1mp4 0 config.yaml openfield Pranav 0 0 0.4 DLC_resnet50_o 2022-01-12 14: 1569.05 30.0003 2.2.0.5 \n", - " (Total: 2)" + "*paramset_idx shuffle train_fraction model_prefix filter_type track_method scorer_legacy param_set_hash\n", + "+------------+ +---------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", + "0 1 0.95 0 c70007c1-32b1-\n", + " (Total: 1)" ] }, "execution_count": 6, @@ -419,13 +417,23 @@ } ], "source": [ - "dlc.Model()" + "train_key['paramset_idx']=0\n", + "dlc.ConfigParamSet & train_key" + ] + }, + { + "cell_type": "markdown", + "id": "9d113e2d-54d5-4fbb-ad91-faf4a66c0879", + "metadata": {}, + "source": [ + "Now, we'll insert this combination into the `TrainingTask` table, and ask DeepLabCut to\n", + "train the model for us, for one quick iteration." ] }, { "cell_type": "code", "execution_count": 7, - "id": "1486971c-9fb1-49a3-bccf-41ece67a3659", + "id": "8bad2548-ce93-475f-93d2-a9028fa7b848", "metadata": {}, "outputs": [ { @@ -481,7 +489,7 @@ " }\n", " \n", " \n", - " uses DeepLabCut h5 output for body part position\n", + " Info required to specify 1 model\n", "
    \n", " \n", " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + " \n", "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", "
    \n", @@ -491,130 +499,34 @@ "

    session_datetime

    \n", " \n", "
    \n", - "

    video_path

    \n", - " raw video path relative to session_dir\n", - "
    \n", - "

    paramset_idx

    \n", + "

    camera_id

    \n", " \n", "
    \n", - "

    config_path

    \n", - " config.yaml relative to session_dir\n", - "
    \n", - "

    joint_name

    \n", - " Name of the joints\n", - "
    \n", - "

    frame_index

    \n", - " frame index in model\n", - "
    \n", - "

    x_pos

    \n", + "

    recording_start_time

    \n", " \n", "
    \n", - "

    y_pos

    \n", + "

    paramset_idx

    \n", " \n", "
    \n", - "

    likelihood

    \n", + "

    training_id

    \n", " \n", "
    subject52020-04-15 11:16:38videos/reachingvideo1.avi0config.yamlFinger1=BLOB==BLOB==BLOB==BLOB=
    subject52020-04-15 11:16:38videos/reachingvideo1.avi0config.yamlHand=BLOB==BLOB==BLOB==BLOB=
    subject52020-04-15 11:16:38videos/reachingvideo1.avi0config.yamlJoystick1=BLOB==BLOB==BLOB==BLOB=
    subject52020-04-15 11:16:38videos/reachingvideo1.avi0config.yamlJoystick2=BLOB==BLOB==BLOB==BLOB=
    subject52020-04-15 11:16:38videos/reachingvideo1.avi0config.yamlTongue=BLOB==BLOB==BLOB==BLOB=
    subject6
    subject62021-06-02 14:04:22videos/m3v1mp4.mp412021-06-02 14:07:000config.yamlleftear=BLOB==BLOB==BLOB==BLOB=
    subject62021-06-02 14:04:22videos/m3v1mp4.mp40config.yamlrightear=BLOB==BLOB==BLOB==BLOB=
    subject62021-06-02 14:04:22videos/m3v1mp4.mp40config.yamlsnout=BLOB==BLOB==BLOB==BLOB=
    subject62021-06-02 14:04:22videos/m3v1mp4.mp40config.yamltailbase=BLOB==BLOB==BLOB==BLOB=
    1
    \n", " \n", - "

    Total: 9

    \n", + "

    Total: 1

    \n", " " ], "text/plain": [ - "*subject *session_datet *video_path *paramset_idx *config_path *joint_name frame_inde x_pos y_pos likelihood\n", - "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +--------+ +--------+ +--------+ +--------+\n", - "subject5 2020-04-15 11: videos/reachin 0 config.yaml Finger1 =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject5 2020-04-15 11: videos/reachin 0 config.yaml Hand =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject5 2020-04-15 11: videos/reachin 0 config.yaml Joystick1 =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject5 2020-04-15 11: videos/reachin 0 config.yaml Joystick2 =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject5 2020-04-15 11: videos/reachin 0 config.yaml Tongue =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject6 2021-06-02 14: videos/m3v1mp4 0 config.yaml leftear =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject6 2021-06-02 14: videos/m3v1mp4 0 config.yaml rightear =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject6 2021-06-02 14: videos/m3v1mp4 0 config.yaml snout =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject6 2021-06-02 14: videos/m3v1mp4 0 config.yaml tailbase =BLOB= =BLOB= =BLOB= =BLOB= \n", - " (Total: 9)" + "*subject *session_datet *camera_id *recording_sta *paramset_idx *training_id \n", + "+----------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n", + "subject6 2021-06-02 14: 1 2021-06-02 14: 0 1 \n", + " (Total: 1)" ] }, "execution_count": 7, @@ -623,71 +535,333 @@ } ], "source": [ - "dlc.Model.Data()" + "train_key['training_id']=1\n", + "dlc.TrainingTask.insert1(train_key, skip_duplicates=True)\n", + "dlc.TrainingTask()" ] }, { "cell_type": "code", - "execution_count": null, - "id": "57fa5f19-6fbf-465e-9bab-1f0110990ae4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "b7a304e3-a5cb-4ad3-93ce-6bc130e08e26", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "# Actual guide - needs edits" - ] - }, - { - "cell_type": "markdown", - "id": "c5ffe5d2-5b2a-45c3-8d8f-8c20efa8c5eb", - "metadata": {}, - "source": [ - "This notebook will describe the steps to explore the lab and animal management tables created by the elements.\n", - "Prior to using this notebook, please refer to the README for the installation instructions." - ] - }, - { - "cell_type": "markdown", - "id": "ee820754-bceb-476a-acf9-238fa8b201d9", - "metadata": {}, - "source": [ - "Importing the module `workflow_behavior.pipeline` is sufficient to create tables inside the elements. This workflow comes prepackaged with example data and ingestion functions to populate lab, subject, and session tables." - ] - }, - { - "cell_type": "markdown", - "id": "2e19116d-bc32-4cea-9caf-f3e8eaa9b181", + "execution_count": 8, + "id": "93fe3dac-b5b4-4ae5-ae2f-38a59ca1841a", "metadata": { "tags": [] }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Config:\n", + "{'all_joints': [[0], [1], [2], [3]],\n", + " 'all_joints_names': ['snout', 'leftear', 'rightear', 'tailbase'],\n", + " 'alpha_r': 0.02,\n", + " 'apply_prob': 0.5,\n", + " 'batch_size': 1,\n", + " 'clahe': True,\n", + " 'claheratio': 0.1,\n", + " 'crop_pad': 0,\n", + " 'crop_sampling': 'hybrid',\n", + " 'crop_size': [400, 400],\n", + " 'cropratio': 0.4,\n", + " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/openfield_Pranav95shuffle1.mat',\n", + " 'dataset_type': 'imgaug',\n", + " 'decay_steps': 30000,\n", + " 'deterministic': False,\n", + " 'display_iters': 1000,\n", + " 'edge': False,\n", + " 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]},\n", + " 'fg_fraction': 0.25,\n", + " 'global_scale': 0.8,\n", + " 'histeq': True,\n", + " 'histeqratio': 0.1,\n", + " 'init_weights': '/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt',\n", + " 'intermediate_supervision': False,\n", + " 'intermediate_supervision_layer': 12,\n", + " 'location_refinement': True,\n", + " 'locref_huber_loss': True,\n", + " 'locref_loss_weight': 0.05,\n", + " 'locref_stdev': 7.2801,\n", + " 'log_dir': 'log',\n", + " 'lr_init': 0.0005,\n", + " 'max_input_size': 1500,\n", + " 'max_shift': 0.4,\n", + " 'mean_pixel': [123.68, 116.779, 103.939],\n", + " 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/Documentation_data-openfield_95shuffle1.pickle',\n", + " 'min_input_size': 64,\n", + " 'mirror': False,\n", + " 'multi_stage': False,\n", + " 'multi_step': [[0.005, 10000],\n", + " [0.02, 430000],\n", + " [0.002, 730000],\n", + " [0.001, 1030000]],\n", + " 'net_type': 'resnet_50',\n", + " 'num_joints': 4,\n", + " 'optimizer': 'sgd',\n", + " 'pairwise_huber_loss': False,\n", + " 'pairwise_predict': False,\n", + " 'partaffinityfield_predict': False,\n", + " 'pos_dist_thresh': 17,\n", + " 'pre_resize': [],\n", + " 'project_path': '/Volumes/GoogleDrive/My '\n", + " 'Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30',\n", + " 'regularize': False,\n", + " 'rotation': 25,\n", + " 'rotratio': 0.4,\n", + " 'save_iters': 50000,\n", + " 'scale_jitter_lo': 0.5,\n", + " 'scale_jitter_up': 1.25,\n", + " 'scoremap_dir': 'test',\n", + " 'sharpen': False,\n", + " 'sharpenratio': 0.3,\n", + " 'shuffle': True,\n", + " 'snapshot_prefix': '/Volumes/GoogleDrive/My '\n", + " 'Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1/train/snapshot',\n", + " 'stride': 8.0,\n", + " 'weigh_negatives': False,\n", + " 'weigh_only_present_joints': False,\n", + " 'weigh_part_predictions': False,\n", + " 'weight_decay': 0.0001}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selecting single-animal trainer\n", + "Batch Size is 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n", + " warnings.warn('`layer.apply` is deprecated and '\n", + "2022-02-22 17:11:26.399703: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading ImageNet-pretrained resnet_50\n", + "Max_iters overwritten as 1\n", + "Training parameter:\n", + "{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1/train/snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'sgd', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 1, 'dataset_type': 'imgaug', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': False, 'pairwise_predict': False, 'all_joints': [[0], [1], [2], [3]], 'all_joints_names': ['snout', 'leftear', 'rightear', 'tailbase'], 'alpha_r': 0.02, 'apply_prob': 0.5, 'clahe': True, 'claheratio': 0.1, 'crop_sampling': 'hybrid', 'crop_size': [400, 400], 'cropratio': 0.4, 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/openfield_Pranav95shuffle1.mat', 'decay_steps': 30000, 'display_iters': 1000, 'edge': False, 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]}, 'histeq': True, 'histeqratio': 0.1, 'init_weights': '/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt', 'lr_init': 0.0005, 'max_input_size': 1500, 'max_shift': 0.4, 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/Documentation_data-openfield_95shuffle1.pickle', 'min_input_size': 64, 'multi_stage': False, 'multi_step': [[0.005, 10000], [0.02, 430000], [0.002, 730000], [0.001, 1030000]], 'net_type': 'resnet_50', 'num_joints': 4, 'pos_dist_thresh': 17, 'pre_resize': [], 'project_path': '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 50000, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25, 'sharpen': False, 'sharpenratio': 0.3, 'covering': True, 'elastic_transform': True, 'motion_blur': True, 'motion_blur_params': {'k': 7, 'angle': (-90, 90)}}\n", + "Starting training....\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-02-22 17:11:36.969368: W tensorflow/core/kernels/queue_base.cc:277] _0_fifo_queue: Skipping cancelled enqueue attempt with queue not closed\n", + "Exception in thread Thread-8:\n", + "Traceback (most recent call last):\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1380, in _do_call\n", + " return fn(*args)\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1363, in _run_fn\n", + " return self._call_tf_sessionrun(options, feed_dict, fetch_list,\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1456, in _call_tf_sessionrun\n", + " return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\n", + "tensorflow.python.framework.errors_impl.CancelledError: Enqueue operation was cancelled\n", + "\t [[{{node fifo_queue_enqueue}}]]\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/threading.py\", line 932, in _bootstrap_inner\n", + " self.run()\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/threading.py\", line 870, in run\n", + " self._target(*self._args, **self._kwargs)\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 83, in load_and_enqueue\n", + " sess.run(enqueue_op, feed_dict=food)\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 970, in run\n", + " result = self._run(None, fetches, feed_dict, options_ptr,\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1193, in _run\n", + " results = self._do_run(handle, final_targets, final_fetches,\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1373, in _do_run\n", + " return self._do_call(_run_fn, feeds, fetches, targets, options,\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1399, in _do_call\n", + " raise type(e)(node_def, op, message) # pylint: disable=no-value-for-parameter\n", + "tensorflow.python.framework.errors_impl.CancelledError: Enqueue operation was cancelled\n", + "\t [[node fifo_queue_enqueue\n", + " (defined at /Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py:69)\n", + "]]\n", + "\n", + "Errors may have originated from an input operation.\n", + "Input Source operations connected to node fifo_queue_enqueue:\n", + "In[0] fifo_queue (defined at /Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py:68)\t\n", + "In[1] Placeholder (defined at /Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py:61)\t\n", + "In[2] Placeholder_1:\t\n", + "In[3] Placeholder_2:\t\n", + "In[4] Placeholder_3:\t\n", + "In[5] Placeholder_4:\n", + "\n", + "Operation defined at: (most recent call last)\n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/runpy.py\", line 194, in _run_module_as_main\n", + ">>> return _run_code(code, main_globals, None,\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/runpy.py\", line 87, in _run_code\n", + ">>> exec(code, run_globals)\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel_launcher.py\", line 16, in \n", + ">>> app.launch_new_instance()\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/traitlets/config/application.py\", line 846, in launch_instance\n", + ">>> app.start()\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelapp.py\", line 677, in start\n", + ">>> self.io_loop.start()\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tornado/platform/asyncio.py\", line 199, in start\n", + ">>> self.asyncio_loop.run_forever()\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/asyncio/base_events.py\", line 570, in run_forever\n", + ">>> self._run_once()\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/asyncio/base_events.py\", line 1859, in _run_once\n", + ">>> handle._run()\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/asyncio/events.py\", line 81, in _run\n", + ">>> self._context.run(self._callback, *self._args)\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 457, in dispatch_queue\n", + ">>> await self.process_one()\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 446, in process_one\n", + ">>> await dispatch(*args)\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 353, in dispatch_shell\n", + ">>> await result\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 648, in execute_request\n", + ">>> reply_content = await reply_content\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/ipkernel.py\", line 353, in do_execute\n", + ">>> res = shell.run_cell(code, store_history=store_history, silent=silent)\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n", + ">>> return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_cell\n", + ">>> result = self._run_cell(\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2947, in _run_cell\n", + ">>> return runner(coro)\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n", + ">>> coro.send(None)\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3172, in run_cell_async\n", + ">>> has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3364, in run_ast_nodes\n", + ">>> if (await self.run_code(code, result, async_=asy)):\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3444, in run_code\n", + ">>> exec(code_obj, self.user_global_ns, self.user_ns)\n", + ">>> \n", + ">>> File \"/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_7154/2075415569.py\", line 1, in \n", + ">>> dlc.ModelTraining.train_model(training_id=1,maxiters=1)\n", + ">>> \n", + ">>> File \"/Volumes/GoogleDrive/My Drive/Dev/element-deeplabcut/element_deeplabcut/dlc.py\", line 243, in train_model\n", + ">>> train_network(model.yml_path,\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 178, in train_network\n", + ">>> train(\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 169, in train\n", + ">>> batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n", + ">>> \n", + ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 69, in setup_preloading\n", + ">>> enqueue_op = q.enqueue(placeholders_list)\n", + ">>> \n", + "\n", + "Original stack trace for 'fifo_queue_enqueue':\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/runpy.py\", line 194, in _run_module_as_main\n", + " return _run_code(code, main_globals, None,\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/runpy.py\", line 87, in _run_code\n", + " exec(code, run_globals)\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel_launcher.py\", line 16, in \n", + " app.launch_new_instance()\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/traitlets/config/application.py\", line 846, in launch_instance\n", + " app.start()\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelapp.py\", line 677, in start\n", + " self.io_loop.start()\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tornado/platform/asyncio.py\", line 199, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/asyncio/base_events.py\", line 570, in run_forever\n", + " self._run_once()\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/asyncio/base_events.py\", line 1859, in _run_once\n", + " handle._run()\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/asyncio/events.py\", line 81, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 457, in dispatch_queue\n", + " await self.process_one()\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 446, in process_one\n", + " await dispatch(*args)\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 353, in dispatch_shell\n", + " await result\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 648, in execute_request\n", + " reply_content = await reply_content\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/ipkernel.py\", line 353, in do_execute\n", + " res = shell.run_cell(code, store_history=store_history, silent=silent)\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n", + " return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_cell\n", + " result = self._run_cell(\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2947, in _run_cell\n", + " return runner(coro)\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3172, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3364, in run_ast_nodes\n", + " if (await self.run_code(code, result, async_=asy)):\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3444, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_7154/2075415569.py\", line 1, in \n", + " dlc.ModelTraining.train_model(training_id=1,maxiters=1)\n", + " File \"/Volumes/GoogleDrive/My Drive/Dev/element-deeplabcut/element_deeplabcut/dlc.py\", line 243, in train_model\n", + " train_network(model.yml_path,\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 178, in train_network\n", + " train(\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 169, in train\n", + " batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 69, in setup_preloading\n", + " enqueue_op = q.enqueue(placeholders_list)\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/ops/data_flow_ops.py\", line 350, in enqueue\n", + " return gen_data_flow_ops.queue_enqueue_v2(\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/ops/gen_data_flow_ops.py\", line 4063, in queue_enqueue_v2\n", + " _, _, _op, _outputs = _op_def_library._apply_op_helper(\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py\", line 744, in _apply_op_helper\n", + " op = g._create_op_internal(op_type_name, inputs, dtypes=None,\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 3697, in _create_op_internal\n", + " ret = Operation(\n", + " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 2101, in __init__\n", + " self._traceback = tf_stack.extract_stack_for_node(self._c_op)\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The network is now trained and ready to evaluate. Use the function 'evaluate_network' to evaluate the network.\n" + ] + } + ], "source": [ - "## Workflow architecture" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "868b79bc-f754-4d51-a327-94a209cde374", - "metadata": {}, - "outputs": [], - "source": [ - "from element_lab import lab\n", - "from element_animal import subject\n", - "from element_session import sessions" + "dlc.ModelTraining.train_model(training_id=1,maxiters=1)" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "1e7a0a8b-eaf1-41a1-bf08-1aff2f2812be", + "execution_count": 9, + "id": "2d83a13d-1518-45b5-9516-caee7c9bc70e", "metadata": {}, "outputs": [ { @@ -747,349 +921,96 @@ "
    \n", " \n", " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "
    \n", - "

    lab

    \n", - " Abbreviated lab name\n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", "
    \n", - "

    lab_name

    \n", - " full lab name\n", + "

    camera_id

    \n", + " \n", + "
    \n", + "

    recording_start_time

    \n", + " \n", "
    \n", - "

    institution

    \n", + "

    paramset_idx

    \n", " \n", "
    \n", - "

    address

    \n", + "

    training_id

    \n", " \n", "
    \n", - "

    time_zone

    \n", - " UTC offset suggested e.g., UTC+1\n", + "

    snapshot_index_exact

    \n", + " latest exact snapshot index (i.e., never -1)\n", + "
    \n", + "

    config_template

    \n", + " stored full config file\n", "
    LabAThe Example LabExample Uni221B Baker St,London NW1 6XE,UKUTC+0
    LabBThe Other LabOther UniOxford OX1 2JD, United KingdomUTC+0
    subject62021-06-02 14:04:2212021-06-02 14:07:00011=BLOB=
    \n", " \n", - "

    Total: 2

    \n", + "

    Total: 1

    \n", " " ], "text/plain": [ - "*lab lab_name institution address time_zone \n", - "+------+ +------------+ +------------+ +------------+ +-----------+\n", - "LabA The Example La Example Uni 221B Baker St, UTC+0 \n", - "LabB The Other Lab Other Uni Oxford OX1 2JD UTC+0 \n", - " (Total: 2)" + "*subject *session_datet *camera_id *recording_sta *paramset_idx *training_id snapshot_index config_tem\n", + "+----------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +------------+ +--------+\n", + "subject6 2021-06-02 14: 1 2021-06-02 14: 0 1 1 =BLOB= \n", + " (Total: 1)" ] }, - "execution_count": 5, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "lab.Lab()" + "dlc.ModelTraining()" + ] + }, + { + "cell_type": "markdown", + "id": "bb4d8a96-bb1b-4138-9af6-4c9db319c89d", + "metadata": {}, + "source": [ + "Next, we can optionally describe each of these body parts in our `BodyPart` lookup \n", + "table by providing a list of descriptions in the order given by the PoseEstimation \n", + "method. If you skip this step, the same items will be inserted during the following\n", + "step." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "63679df4-3064-402b-99ce-2f553dff877b", + "execution_count": 10, + "id": "341f00e8-39b9-4833-8a32-b8b7ac907036", "metadata": {}, "outputs": [ { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "`neuro_lab`.`#skull_reference`\n", - "\n", - "`neuro_lab`.`#skull_reference`\n", - "\n", - "\n", - "\n", - "lab.Project\n", - "\n", - "\n", - "lab.Project\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.ProjectUser\n", - "\n", - "\n", - "lab.ProjectUser\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Project->lab.ProjectUser\n", - "\n", - "\n", - "\n", - "\n", - "lab.Project.Publication\n", - "\n", - "\n", - "lab.Project.Publication\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Project->lab.Project.Publication\n", - "\n", - "\n", - "\n", - "\n", - "lab.Project.Keywords\n", - "\n", - "\n", - "lab.Project.Keywords\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Project->lab.Project.Keywords\n", - "\n", - "\n", - "\n", - "\n", - "lab.Project.Sourcecode\n", - "\n", - "\n", - "lab.Project.Sourcecode\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Project->lab.Project.Sourcecode\n", - "\n", - "\n", - "\n", - "\n", - "lab.Equipment.EphysEquipment\n", - "\n", - "\n", - "lab.Equipment.EphysEquipment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.ProtocolType\n", - "\n", - "\n", - "lab.ProtocolType\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Protocol\n", - "\n", - "\n", - "lab.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.ProtocolType->lab.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "lab.Equipment\n", - "\n", - "\n", - "lab.Equipment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Equipment->lab.Equipment.EphysEquipment\n", - "\n", - "\n", - "\n", - "\n", - "lab.Equipment.CaImgEquipment\n", - "\n", - "\n", - "lab.Equipment.CaImgEquipment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Equipment->lab.Equipment.CaImgEquipment\n", - "\n", - "\n", - "\n", - "\n", - "lab.Lab\n", - "\n", - "\n", - "lab.Lab\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Location\n", - "\n", - "\n", - "lab.Location\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Lab->lab.Location\n", - "\n", - "\n", - "\n", - "\n", - "lab.LabMembership\n", - "\n", - "\n", - "lab.LabMembership\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Lab->lab.LabMembership\n", - "\n", - "\n", - "\n", - "\n", - "lab.User\n", - "\n", - "\n", - "lab.User\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.User->lab.ProjectUser\n", - "\n", - "\n", - "\n", - "\n", - "lab.User->lab.LabMembership\n", - "\n", - "\n", - "\n", - "\n", - "lab.UserRole\n", - "\n", - "\n", - "lab.UserRole\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.UserRole->lab.LabMembership\n", - "\n", - "\n", - "\n", - "\n", - "lab.Source\n", - "\n", - "\n", - "lab.Source\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['leftear', 'rightear', 'snout', 'tailbase'], dtype='object', name='bodyparts')\n" + ] } ], "source": [ - "dj.Diagram(lab)" + "from element_deeplabcut.readers.dlc_reader import PoseEstimation\n", + "print(PoseEstimation(data_dir).body_parts)\n", + "description_list = ['Left Ear', 'Right Ear', 'Snout tip', 'Base of the Tail']\n", + "dlc.BodyPart.insert_all_from_model(train_key,skip_duplicates=True,\n", + " description_list=description_list)" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "8cf0f64b-e523-4a94-9a43-fca4ed793f82", + "execution_count": 11, + "id": "256abce9-1ee6-4868-8c11-38ea998ef216", "metadata": {}, "outputs": [ { @@ -1145,369 +1066,141 @@ " }\n", " \n", " \n", - " Animal Subject\n", + " \n", "
    \n", " \n", " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + " \n", + "\n", + "\n", + "\n", + "\n", "
    \n", - "

    subject

    \n", + "

    body_part

    \n", " \n", "
    \n", - "

    sex

    \n", - " \n", - "
    \n", - "

    subject_birth_date

    \n", - " \n", - "
    \n", - "

    subject_description

    \n", + "

    body_part_description

    \n", " \n", "
    subject1M2020-12-30test animal
    subject2F2020-11-30test animal
    subject3F2020-12-30test animal
    subject4M2021-02-12test animal
    subject5F2020-01-03lmash_E105
    subject6M2020-01-03hneih_E105
    subject7U2020-08-30test animal
    subject8F2020-09-30test animal
    leftearLeft Ear
    rightearRight Ear
    snoutSnout tip
    tailbaseBase of the Tail
    \n", " \n", - "

    Total: 8

    \n", + "

    Total: 4

    \n", " " ], "text/plain": [ - "*subject sex subject_birth_ subject_descri\n", - "+----------+ +-----+ +------------+ +------------+\n", - "subject1 M 2020-12-30 test animal \n", - "subject2 F 2020-11-30 test animal \n", - "subject3 F 2020-12-30 test animal \n", - "subject4 M 2021-02-12 test animal \n", - "subject5 F 2020-01-03 lmash_E105 \n", - "subject6 M 2020-01-03 hneih_E105 \n", - "subject7 U 2020-08-30 test animal \n", - "subject8 F 2020-09-30 test animal \n", - " (Total: 8)" + "*body_part body_part_desc\n", + "+-----------+ +------------+\n", + "leftear Left Ear \n", + "rightear Right Ear \n", + "snout Snout tip \n", + "tailbase Base of the Ta\n", + " (Total: 4)" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "subject.Subject()" + "dlc.BodyPart()" + ] + }, + { + "cell_type": "markdown", + "id": "27d0929a-9a0d-465b-a552-9a4a2938cbc6", + "metadata": {}, + "source": [ + "Now, we'll insert the model into the central `Model` table." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "75576be2-2984-451f-a86b-f05f9ddec6b7", + "execution_count": 12, + "id": "fe416a66-6c9f-4d47-9435-239f4b7fc984", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "dlc.Model.insert_new_model(train_key,config_paramset_idx=0,model_name=\"FirstModel\",\n", + " model_description=\"First inserted model\", training_id=1)" + ] + }, + { + "cell_type": "markdown", + "id": "73cc8877-0a53-483d-b5ee-7adef6c4d45a", "metadata": {}, + "source": [ + "The `ModelEval` table runs DeepLabCut's evaluation function and stores the result." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "5109aae8-f896-47fa-830b-cec07cf29ee2", + "metadata": { + "tags": [] + }, "outputs": [ { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele\n", - "\n", - "\n", - "subject.Allele\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Zygosity\n", - "\n", - "\n", - "subject.Zygosity\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele->subject.Zygosity\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele.Source\n", - "\n", - "\n", - "subject.Allele.Source\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele->subject.Allele.Source\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line.Allele\n", - "\n", - "\n", - "subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele->subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Line\n", - "\n", - "\n", - "subject.Subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.User\n", - "\n", - "\n", - "subject.Subject.User\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Strain\n", - "\n", - "\n", - "subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Strain\n", - "\n", - "\n", - "subject.Subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Strain->subject.Subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "subject.SubjectDeath\n", - "\n", - "\n", - "subject.SubjectDeath\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Protocol\n", - "\n", - "\n", - "subject.Subject.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject\n", - "\n", - "\n", - "subject.Subject\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Zygosity\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.User\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.SubjectDeath\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Source\n", - "\n", - "\n", - "\n", - "\n", - "subject.SubjectCullMethod\n", - "\n", - "\n", - "subject.SubjectCullMethod\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.SubjectCullMethod\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Lab\n", - "\n", - "\n", - "subject.Subject.Lab\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Lab\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line\n", - "\n", - "\n", - "subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line->subject.Subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line->subject.Line.Allele\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "Config:\n", + "{'all_joints': [[0], [1], [2], [3]],\n", + " 'all_joints_names': ['snout', 'leftear', 'rightear', 'tailbase'],\n", + " 'batch_size': 1,\n", + " 'crop_pad': 0,\n", + " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/openfield_Pranav95shuffle1.mat',\n", + " 'dataset_type': 'imgaug',\n", + " 'deterministic': False,\n", + " 'fg_fraction': 0.25,\n", + " 'global_scale': 0.8,\n", + " 'init_weights': '/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt',\n", + " 'intermediate_supervision': False,\n", + " 'intermediate_supervision_layer': 12,\n", + " 'location_refinement': True,\n", + " 'locref_huber_loss': True,\n", + " 'locref_loss_weight': 1.0,\n", + " 'locref_stdev': 7.2801,\n", + " 'log_dir': 'log',\n", + " 'mean_pixel': [123.68, 116.779, 103.939],\n", + " 'mirror': False,\n", + " 'net_type': 'resnet_50',\n", + " 'num_joints': 4,\n", + " 'optimizer': 'sgd',\n", + " 'pairwise_huber_loss': True,\n", + " 'pairwise_predict': False,\n", + " 'partaffinityfield_predict': False,\n", + " 'regularize': False,\n", + " 'scoremap_dir': 'test',\n", + " 'shuffle': True,\n", + " 'snapshot_prefix': '/Volumes/GoogleDrive/My '\n", + " 'Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1/test/snapshot',\n", + " 'stride': 8.0,\n", + " 'weigh_negatives': False,\n", + " 'weigh_only_present_joints': False,\n", + " 'weigh_part_predictions': False,\n", + " 'weight_decay': 0.0001}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running DLC_resnet50_openfieldOct30shuffle1_1008 with # of training iterations: 1008\n", + "This net has already been evaluated!\n" + ] } ], "source": [ - "dj.Diagram(subject)" + "dlc.ModelEval.populate()" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "5243a782-93da-40fa-b243-03ddcb230c1d", + "execution_count": 14, + "id": "836fa695-2f2c-474f-8ae4-8a54094807a4", "metadata": {}, "outputs": [ { @@ -1567,242 +1260,164 @@ "
    \n", " \n", " \n", - " \n", - "\n", - "\n", - "\n", - "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "
    \n", - "

    subject

    \n", - " \n", + "

    model_name

    \n", + " user-friendly model name\n", "
    \n", - "

    session_datetime

    \n", - " \n", + "

    train_iterations

    \n", + " Training iterations\n", + "
    \n", + "

    train_error

    \n", + " Train error (px)\n", + "
    \n", + "

    test_error

    \n", + " Test error (px)\n", + "
    \n", + "

    p_cutoff

    \n", + " p-cutoff used\n", + "
    \n", + "

    train_error_p

    \n", + " Train error with p-cutoff\n", + "
    \n", + "

    test_error_p

    \n", + " Test error with p-cutoff\n", "
    subject32021-04-30 12:22:15.032000
    subject52020-04-15 11:16:38
    subject62021-01-15 11:16:38
    subject62021-06-02 14:04:22
    FirstModel100811.356.040.49.35.1
    \n", " \n", - "

    Total: 4

    \n", + "

    Total: 1

    \n", " " ], "text/plain": [ - "*subject *session_datet\n", - "+----------+ +------------+\n", - "subject3 2021-04-30 12:\n", - "subject5 2020-04-15 11:\n", - "subject6 2021-01-15 11:\n", - "subject6 2021-06-02 14:\n", - " (Total: 4)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "session.Session()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7e48d7c0-b7bd-4f0b-abcb-1aedc69d5310", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session\n", - "\n", - "\n", - "session.Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.ProjectSession\n", - "\n", - "\n", - "session.ProjectSession\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.ProjectSession\n", - "\n", - "\n", - "\n", - "\n", - "session.SessionDirectory\n", - "\n", - "\n", - "session.SessionDirectory\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.SessionDirectory\n", - "\n", - "\n", - "\n", - "\n", - "session.SessionExperimenter\n", - "\n", - "\n", - "session.SessionExperimenter\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.SessionExperimenter\n", - "\n", - "\n", - "\n", - "\n", - "session.SessionNote\n", - "\n", - "\n", - "session.SessionNote\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.SessionNote\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" + "*model_name train_iteratio train_error test_error p_cutoff train_error_p test_error_p \n", + "+------------+ +------------+ +------------+ +------------+ +----------+ +------------+ +------------+\n", + "FirstModel 1008 11.35 6.04 0.4 9.3 5.1 \n", + " (Total: 1)" ] }, - "execution_count": 10, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dj.Diagram(session)" + "dlc.ModelEval()" ] }, { "cell_type": "markdown", - "id": "c510fe4d-09ed-472f-830f-4401bd6830d0", + "id": "760865e8-a012-4fee-bdcc-5349151ff3d2", "metadata": {}, "source": [ - "(Workflow needs continued development to import geotyping tables)" + "Finally, we can use this model to conduct pose estimation for separate videos. In this\n", + "case, we'll use the `m3v1mp4-copy.mp4` clip we generated earlier. First, this line is\n", + "inserted into the `PoseEstimationTask` table before conducting the actual estimation \n", + "via the the `PoseEstimation.populate()` method. " ] }, { - "cell_type": "markdown", - "id": "b60f5f4c-d366-4034-a40d-2d2095cb2a14", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, + "cell_type": "code", + "execution_count": 7, + "id": "ebca0624-a6cb-4261-8032-6bdd355b0be8", + "metadata": {}, + "outputs": [ + { + "ename": "DuplicateError", + "evalue": "(\"Duplicate entry 'subject6-2021-06-03 14:04:22-1-2021-06-04 14:07:00-FirstModel' for key 'PRIMARY'\", 'To ignore duplicate entries in insert, set skip_duplicates=True')", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mDuplicateError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_7216/1998148785.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mestim_key\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdlc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModel\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m'model_name=\"FirstModel\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'KEY'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mestim_key\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'task_mode'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'trigger'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdlc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPoseEstimationTask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestim_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mdlc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPoseEstimationTask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m in \u001b[0;36minsert1\u001b[0;34m(self, row, **kwargs)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0mFor\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msee\u001b[0m \u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \"\"\"\n\u001b[0;32m--> 266\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_duplicates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_direct_insert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m in \u001b[0;36minsert\u001b[0;34m(self, rows, replace, skip_duplicates, ignore_extra_fields, allow_direct_insert)\u001b[0m\n\u001b[1;32m 335\u001b[0m 'To ignore extra fields in insert, set ignore_extra_fields=True')\n\u001b[1;32m 336\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mDuplicateError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m raise err.suggest(\n\u001b[0m\u001b[1;32m 338\u001b[0m 'To ignore duplicate entries in insert, set skip_duplicates=True')\n\u001b[1;32m 339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDuplicateError\u001b[0m: (\"Duplicate entry 'subject6-2021-06-03 14:04:22-1-2021-06-04 14:07:00-FirstModel' for key 'PRIMARY'\", 'To ignore duplicate entries in insert, set skip_duplicates=True')" + ] + } + ], "source": [ - "## Explore each table" + "estim_key = ((dlc.VideoRecording & 'session_datetime>\"2021-06-03\"').fetch1('KEY'))\n", + "estim_key.update((dlc.Model & 'model_name=\"FirstModel\"').fetch1('KEY'))\n", + "estim_key['task_mode']='trigger'\n", + "dlc.PoseEstimationTask.insert1(estim_key)\n", + "dlc.PoseEstimationTask()" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "9c0821e1-9125-4c41-bc9c-567f53d0a5e5", + "execution_count": 16, + "id": "9937220f-b08b-4836-a1a2-a5933096d6ac", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "# Animal Subject\n", - "subject : varchar(32) \n", - "---\n", - "sex : enum('M','F','U') \n", - "subject_birth_date : date \n", - "subject_description=\"\" : varchar(1024) \n", - "\n" + "Config:\n", + "{'all_joints': [[0], [1], [2], [3]],\n", + " 'all_joints_names': ['snout', 'leftear', 'rightear', 'tailbase'],\n", + " 'batch_size': 1,\n", + " 'crop_pad': 0,\n", + " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/openfield_Pranav95shuffle1.mat',\n", + " 'dataset_type': 'imgaug',\n", + " 'deterministic': False,\n", + " 'fg_fraction': 0.25,\n", + " 'global_scale': 0.8,\n", + " 'init_weights': '/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt',\n", + " 'intermediate_supervision': False,\n", + " 'intermediate_supervision_layer': 12,\n", + " 'location_refinement': True,\n", + " 'locref_huber_loss': True,\n", + " 'locref_loss_weight': 1.0,\n", + " 'locref_stdev': 7.2801,\n", + " 'log_dir': 'log',\n", + " 'mean_pixel': [123.68, 116.779, 103.939],\n", + " 'mirror': False,\n", + " 'net_type': 'resnet_50',\n", + " 'num_joints': 4,\n", + " 'optimizer': 'sgd',\n", + " 'pairwise_huber_loss': True,\n", + " 'pairwise_predict': False,\n", + " 'partaffinityfield_predict': False,\n", + " 'regularize': False,\n", + " 'scoremap_dir': 'test',\n", + " 'shuffle': True,\n", + " 'snapshot_prefix': '/Volumes/GoogleDrive/My '\n", + " 'Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1/test/snapshot',\n", + " 'stride': 8.0,\n", + " 'weigh_negatives': False,\n", + " 'weigh_only_present_joints': False,\n", + " 'weigh_part_predictions': False,\n", + " 'weight_decay': 0.0001}\n", + "/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n", + " warnings.warn('`layer.apply` is deprecated and '\n" ] }, { - "data": { - "text/plain": [ - "'# Animal Subject\\nsubject : varchar(32) \\n---\\nsex : enum(\\'M\\',\\'F\\',\\'U\\') \\nsubject_birth_date : date \\nsubject_description=\"\" : varchar(1024) \\n'" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Using snapshot-1008 for model /Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1\n", + "Starting to analyze % /Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4\n", + "The videos are analyzed. Now your research can truly start! \n", + " You can create labeled videos with 'create_labeled_video'\n", + "If the tracking is not satisfactory for some videos, consider expanding the training set. You can use the function 'extract_outlier_frames' to extract a few representative outlier frames.\n" + ] } ], "source": [ - "# check table definition with describe()\n", - "subject.Subject.describe()" - ] - }, - { - "cell_type": "markdown", - "id": "f6c110c0-0966-4283-a0ba-a7de2ce69e25", - "metadata": {}, - "source": [ - "## Insert data into Manual and Lookup tables" - ] - }, - { - "cell_type": "markdown", - "id": "54cf050e-882e-4672-be31-1ca3df52fa58", - "metadata": {}, - "source": [ - "Tables in this workflow are either manual tables or lookup tables. To insert into these tables, DataJoint provide method `.insert1()` and `insert()`." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "d5b43904-9711-4bce-8ae5-d0d797118dec", - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject.insert1(\n", - " dict(subject='subject1', sex='M', subject_birth_date='2020-12-30', \n", - " subject_description='test animal'), skip_duplicates=True)\n", - "subject.Subject.insert1(\n", - " ('subject2', 'F', '2020-11-30', 'test animal'), skip_duplicates=True)" - ] - }, - { - "cell_type": "markdown", - "id": "49d43ca2-2cd3-4659-849f-5bcc09c1367e", - "metadata": {}, - "source": [ - "`skip_duplicates=True` will prevent an error if you already have data for the primary keys in a given entry." + "dlc.PoseEstimation.populate()" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "9bf2c953-7b4c-4a70-99fd-124a4d28171b", + "execution_count": 5, + "id": "f6c60d2d-f34b-4feb-83cd-88e094b6a9cb", "metadata": {}, "outputs": [ { @@ -1858,78 +1473,575 @@ " }\n", " \n", " \n", - " Animal Subject\n", + " \n", "
    \n", " \n", " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", "
    \n", "

    subject

    \n", " \n", "
    \n", - "

    sex

    \n", + "

    session_datetime

    \n", " \n", "
    \n", - "

    subject_birth_date

    \n", + "

    camera_id

    \n", " \n", "
    \n", - "

    subject_description

    \n", + "

    recording_start_time

    \n", " \n", + "
    \n", + "

    model_name

    \n", + " user-friendly model name\n", + "
    \n", + "

    post_estimation_time

    \n", + " time of generation of this set of DLC results\n", "
    subject1M2020-12-30test animal
    subject2F2020-11-30test animal
    subject3F2020-12-30test animal
    subject4M2021-02-12test animal
    subject5F2020-01-03lmash_E105
    subject6M2020-01-03hneih_E105
    subject7U2020-08-30test animal
    subject8F2020-09-30test animal
    subject62021-06-03 14:04:2212021-06-04 14:07:00FirstModel2022-01-26 11:22:34
    \n", " \n", - "

    Total: 8

    \n", + "

    Total: 1

    \n", " " ], "text/plain": [ - "*subject sex subject_birth_ subject_descri\n", - "+----------+ +-----+ +------------+ +------------+\n", - "subject1 M 2020-12-30 test animal \n", - "subject2 F 2020-11-30 test animal \n", - "subject3 F 2020-12-30 test animal \n", - "subject4 M 2021-02-12 test animal \n", - "subject5 F 2020-01-03 lmash_E105 \n", - "subject6 M 2020-01-03 hneih_E105 \n", - "subject7 U 2020-08-30 test animal \n", - "subject8 F 2020-09-30 test animal \n", - " (Total: 8)" + "*subject *session_datet *camera_id *recording_sta *model_name post_estimatio\n", + "+----------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n", + "subject6 2021-06-03 14: 1 2021-06-04 14: FirstModel 2022-01-26 11:\n", + " (Total: 1)" ] }, - "execution_count": 16, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], + "source": [ + "dlc.PoseEstimation()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3f12ee64-e1e1-425c-99bd-060176ce6779", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    scorerFirstModel
    bodypartsleftearrightearsnouttailbase
    coordsxyzlikelihoodxyzlikelihoodxyzlikelihoodxyzlikelihood
    078.326073102.6610490.00.31450674.472694109.3662570.00.23949671.408234114.3236160.00.31547878.313431110.9945530.00.273981
    168.676651102.0676730.00.29485674.428070109.6935730.00.23672271.155952114.5973740.00.29793478.757637110.8692700.00.284565
    268.009209101.7143940.00.38691472.86501397.3321150.00.21742171.49594197.1097340.00.31698478.588943111.0119250.00.267579
    367.158943101.4643780.00.45962567.72011695.4340670.00.22882268.80291794.2680130.00.32908478.494675111.5275040.00.254363
    466.202774100.6793900.00.45719067.56582694.5998840.00.21919868.94625993.6288830.00.30956978.469231112.2232820.00.229301
    ...................................................
    2325356.629333376.8256840.00.092403346.879303381.0070500.00.096084343.775360384.1889340.00.125122425.655670422.1686100.00.212187
    2326350.492767387.1819760.00.118965410.790131397.7976070.00.110812352.447784385.3256530.00.144934418.131439406.1693120.00.195705
    2327351.700409387.6010740.00.150138411.026489397.9878230.00.121694353.609650385.4432980.00.193439420.859375404.9639890.00.217717
    2328354.063324388.1681820.00.137858427.673187413.4660950.00.121844354.449799385.9833370.00.178257422.556610404.8189700.00.243224
    2329432.365479442.0434570.00.118110427.488220413.4584350.00.128129355.735626386.1260680.00.160270436.392853422.4031680.00.222099
    \n", + "

    2330 rows × 16 columns

    \n", + "
    " + ], + "text/plain": [ + "scorer FirstModel \\\n", + "bodyparts leftear rightear \n", + "coords x y z likelihood x y \n", + "0 78.326073 102.661049 0.0 0.314506 74.472694 109.366257 \n", + "1 68.676651 102.067673 0.0 0.294856 74.428070 109.693573 \n", + "2 68.009209 101.714394 0.0 0.386914 72.865013 97.332115 \n", + "3 67.158943 101.464378 0.0 0.459625 67.720116 95.434067 \n", + "4 66.202774 100.679390 0.0 0.457190 67.565826 94.599884 \n", + "... ... ... ... ... ... ... \n", + "2325 356.629333 376.825684 0.0 0.092403 346.879303 381.007050 \n", + "2326 350.492767 387.181976 0.0 0.118965 410.790131 397.797607 \n", + "2327 351.700409 387.601074 0.0 0.150138 411.026489 397.987823 \n", + "2328 354.063324 388.168182 0.0 0.137858 427.673187 413.466095 \n", + "2329 432.365479 442.043457 0.0 0.118110 427.488220 413.458435 \n", + "\n", + "scorer \\\n", + "bodyparts snout tailbase \n", + "coords z likelihood x y z likelihood x \n", + "0 0.0 0.239496 71.408234 114.323616 0.0 0.315478 78.313431 \n", + "1 0.0 0.236722 71.155952 114.597374 0.0 0.297934 78.757637 \n", + "2 0.0 0.217421 71.495941 97.109734 0.0 0.316984 78.588943 \n", + "3 0.0 0.228822 68.802917 94.268013 0.0 0.329084 78.494675 \n", + "4 0.0 0.219198 68.946259 93.628883 0.0 0.309569 78.469231 \n", + "... ... ... ... ... ... ... ... \n", + "2325 0.0 0.096084 343.775360 384.188934 0.0 0.125122 425.655670 \n", + "2326 0.0 0.110812 352.447784 385.325653 0.0 0.144934 418.131439 \n", + "2327 0.0 0.121694 353.609650 385.443298 0.0 0.193439 420.859375 \n", + "2328 0.0 0.121844 354.449799 385.983337 0.0 0.178257 422.556610 \n", + "2329 0.0 0.128129 355.735626 386.126068 0.0 0.160270 436.392853 \n", + "\n", + "scorer \n", + "bodyparts \n", + "coords y z likelihood \n", + "0 110.994553 0.0 0.273981 \n", + "1 110.869270 0.0 0.284565 \n", + "2 111.011925 0.0 0.267579 \n", + "3 111.527504 0.0 0.254363 \n", + "4 112.223282 0.0 0.229301 \n", + "... ... ... ... \n", + "2325 422.168610 0.0 0.212187 \n", + "2326 406.169312 0.0 0.195705 \n", + "2327 404.963989 0.0 0.217717 \n", + "2328 404.818970 0.0 0.243224 \n", + "2329 422.403168 0.0 0.222099 \n", + "\n", + "[2330 rows x 16 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dlc.PoseEstimation.GetTrajectory(estim_key)" + ] + }, + { + "cell_type": "markdown", + "id": "b7a304e3-a5cb-4ad3-93ce-6bc130e08e26", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "# From scratch didactic guide - needs work" + ] + }, + { + "cell_type": "markdown", + "id": "c5ffe5d2-5b2a-45c3-8d8f-8c20efa8c5eb", + "metadata": {}, + "source": [ + "This notebook will describe the steps to explore the lab and animal management tables created by the elements.\n", + "Prior to using this notebook, please refer to the README for the installation instructions." + ] + }, + { + "cell_type": "markdown", + "id": "ee820754-bceb-476a-acf9-238fa8b201d9", + "metadata": {}, + "source": [ + "Importing the module `workflow_behavior.pipeline` is sufficient to create tables inside the elements. This workflow comes prepackaged with example data and ingestion functions to populate lab, subject, and session tables." + ] + }, + { + "cell_type": "markdown", + "id": "2e19116d-bc32-4cea-9caf-f3e8eaa9b181", + "metadata": { + "tags": [] + }, + "source": [ + "## Workflow architecture" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "868b79bc-f754-4d51-a327-94a209cde374", + "metadata": {}, + "outputs": [], + "source": [ + "from element_lab import lab\n", + "from element_animal import subject\n", + "from element_session import sessions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e7a0a8b-eaf1-41a1-bf08-1aff2f2812be", + "metadata": {}, + "outputs": [], + "source": [ + "lab.Lab()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63679df4-3064-402b-99ce-2f553dff877b", + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(lab)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8cf0f64b-e523-4a94-9a43-fca4ed793f82", + "metadata": {}, + "outputs": [], "source": [ "subject.Subject()" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, + "id": "75576be2-2984-451f-a86b-f05f9ddec6b7", + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(subject)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5243a782-93da-40fa-b243-03ddcb230c1d", + "metadata": {}, + "outputs": [], + "source": [ + "session.Session()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e48d7c0-b7bd-4f0b-abcb-1aedc69d5310", + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(session)" + ] + }, + { + "cell_type": "markdown", + "id": "c510fe4d-09ed-472f-830f-4401bd6830d0", + "metadata": {}, + "source": [ + "(Workflow needs continued development to import geotyping tables)" + ] + }, + { + "cell_type": "markdown", + "id": "b60f5f4c-d366-4034-a40d-2d2095cb2a14", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Explore each table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c0821e1-9125-4c41-bc9c-567f53d0a5e5", + "metadata": {}, + "outputs": [], + "source": [ + "# check table definition with describe()\n", + "subject.Subject.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "f6c110c0-0966-4283-a0ba-a7de2ce69e25", + "metadata": {}, + "source": [ + "## Insert data into Manual and Lookup tables" + ] + }, + { + "cell_type": "markdown", + "id": "54cf050e-882e-4672-be31-1ca3df52fa58", + "metadata": {}, + "source": [ + "Tables in this workflow are either manual tables or lookup tables. To insert into these tables, DataJoint provide method `.insert1()` and `insert()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5b43904-9711-4bce-8ae5-d0d797118dec", + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.insert1(\n", + " dict(subject='subject1', sex='M', subject_birth_date='2020-12-30', \n", + " subject_description='test animal'), skip_duplicates=True)\n", + "subject.Subject.insert1(\n", + " ('subject2', 'F', '2020-11-30', 'test animal'), skip_duplicates=True)" + ] + }, + { + "cell_type": "markdown", + "id": "49d43ca2-2cd3-4659-849f-5bcc09c1367e", + "metadata": {}, + "source": [ + "`skip_duplicates=True` will prevent an error if you already have data for the primary keys in a given entry." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9bf2c953-7b4c-4a70-99fd-124a4d28171b", + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "7a10ddab-d0fd-45a0-8183-09c1b1933e0a", "metadata": {}, "outputs": [], @@ -1952,128 +2064,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "064ddaae-3410-47fc-be22-671d2afe7fb6", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " Animal Subject\n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    sex

    \n", - " \n", - "
    \n", - "

    subject_birth_date

    \n", - " \n", - "
    \n", - "

    subject_description

    \n", - " \n", - "
    subject1M2020-12-30test animal
    subject2F2020-11-30test animal
    subject3F2020-12-30test animal
    subject4M2021-02-12test animal
    subject5F2020-01-03lmash_E105
    subject6M2020-01-03hneih_E105
    subject7U2020-08-30test animal
    subject8F2020-09-30test animal
    \n", - " \n", - "

    Total: 8

    \n", - " " - ], - "text/plain": [ - "*subject sex subject_birth_ subject_descri\n", - "+----------+ +-----+ +------------+ +------------+\n", - "subject1 M 2020-12-30 test animal \n", - "subject2 F 2020-11-30 test animal \n", - "subject3 F 2020-12-30 test animal \n", - "subject4 M 2021-02-12 test animal \n", - "subject5 F 2020-01-03 lmash_E105 \n", - "subject6 M 2020-01-03 hneih_E105 \n", - "subject7 U 2020-08-30 test animal \n", - "subject8 F 2020-09-30 test animal \n", - " (Total: 8)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "subject.Subject()" ] diff --git a/requirements.txt b/requirements.txt index cc5d4e0..5194ea3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,6 +2,7 @@ datajoint>=0.13.0 element-lab==0.1.0b0 element-animal==0.1.0b0 element-session==0.1.0b0 -element-behavior==0.0.0a1 +element-deeplabcut==0.0.0a1 element-interface @ git+https://github.com/datajoint/element-interface.git ipykernel==6.0.1 +pygit2 diff --git a/setup.py b/setup.py index 7b1047f..ddb82fb 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ from os import path -pkg_name = 'workflow_behavior' +pkg_name = 'workflow_deeplabcut' here = path.abspath(path.dirname(__file__)) long_description = """" @@ -12,7 +12,7 @@ + [element-lab](https://github.com/datajoint/element-lab) + [element-animal](https://github.com/datajoint/element-animal) + [element-session](https://github.com/datajoint/element-session) -+ [element-behavior](https://github.com/datajoint/element-behavior) ++ [element-deeplabcut](https://github.com/datajoint/element-deeplabcut) """ with open(path.join(here, 'requirements.txt')) as f: @@ -22,15 +22,15 @@ exec(f.read()) setup( - name='workflow-behavior', + name='workflow-deeplabcut', version=__version__, description="DataJoint Elements for DeepLabCut pose estimation", long_description=long_description, author='DataJoint', author_email='info@DataJoint.com', license='MIT', - url='https://github.com/datajoint/workflow-behavior', - keywords='neuroscience behavior deeplabcut datajoint', + url='https://github.com/datajoint/workflow-deeplabcut', + keywords='neuroscience deeplabcut deeplabcut datajoint', packages=find_packages(exclude=['contrib', 'docs', 'tests*']), install_requires=requirements, ) diff --git a/user_data/recordings.csv b/user_data/recordings.csv index b59def5..a552194 100644 --- a/user_data/recordings.csv +++ b/user_data/recordings.csv @@ -1,4 +1,5 @@ subject,session_datetime,recording_start_time,file_path,camera_id,config_path,config_notes,paramset_idx subject5,2020-04-15 11:16:38,2020-04-15 11:17:00,videos/reachingvideo1.avi,1,config.yaml,Reaching example provided by DeepLabCut repository,0 subject6,2021-06-02 14:04:22,2021-06-02 14:07:00,videos/m3v1mp4.mp4,1,config.yaml,Openfield example provided by DeepLabCut repository,0 -subject6,2021-06-03 14:04:22,2021-06-03 14:07:00,videos/videocompressed1.mp4,1,config.yaml,Multianimal - not fully trained,1 +subject6,2021-06-03 14:04:22,2021-06-04 14:07:00,videos/m3v1mp4-copy.mp4,1,config.yaml,Openfield example provided by DeepLabCut repository,0 + diff --git a/user_data/sessions.csv b/user_data/sessions.csv index 4eb7371..9048b5b 100644 --- a/user_data/sessions.csv +++ b/user_data/sessions.csv @@ -1,4 +1,4 @@ subject,session_datetime,session_dir,session_note subject5,2020-04-15 11:16:38,"Reaching-Mackenzie-2018-08-30/","Successful data collection, no notes" -subject6,2021-06-02 14:04:22,"openfield-Pranav-2018-10-30/","Ambient temp abnormally low" -subject6,2021-06-03 14:04:22,"demo-me-2021-07-14/","multi-animal" +subject6,2021-06-02 14:04:22,"openfield-Pranav-2018-10-30/","Model Training Session" +subject6,2021-06-03 14:04:22,"openfield-Pranav-2018-10-30/","Test Session" diff --git a/workflow_behavior/__init__.py b/workflow_deeplabcut/__init__.py similarity index 100% rename from workflow_behavior/__init__.py rename to workflow_deeplabcut/__init__.py diff --git a/workflow_behavior/ingest.py b/workflow_deeplabcut/ingest.py similarity index 94% rename from workflow_behavior/ingest.py rename to workflow_deeplabcut/ingest.py index 4606dbc..18f8e23 100644 --- a/workflow_behavior/ingest.py +++ b/workflow_deeplabcut/ingest.py @@ -2,10 +2,7 @@ import csv from distutils.util import strtobool -from workflow_behavior.pipeline import subject, session, dlc -# from workflow_behavior.paths import get_beh_root_data_dir -# from element-interface.utils import find_full_path - +from workflow_deeplabcut.pipeline import subject, session, dlc def ingest_general(csvs, tables, skip_duplicates=True): diff --git a/workflow_behavior/paths.py b/workflow_deeplabcut/paths.py similarity index 100% rename from workflow_behavior/paths.py rename to workflow_deeplabcut/paths.py diff --git a/workflow_behavior/pipeline.py b/workflow_deeplabcut/pipeline.py similarity index 97% rename from workflow_behavior/pipeline.py rename to workflow_deeplabcut/pipeline.py index bcab534..62b9e96 100644 --- a/workflow_behavior/pipeline.py +++ b/workflow_deeplabcut/pipeline.py @@ -2,7 +2,7 @@ from element_animal import subject from element_lab import lab from element_session import session -from element_behavior import dlc +from element_deeplabcut import dlc from element_animal.subject import Subject from element_lab.lab import Source, Lab, Protocol, User, Project diff --git a/workflow_behavior/version.py b/workflow_deeplabcut/version.py similarity index 100% rename from workflow_behavior/version.py rename to workflow_deeplabcut/version.py From 4daf26206cc5e73cc17c078784e0a77d0ee7fb45 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Mon, 28 Feb 2022 16:43:19 -0600 Subject: [PATCH 016/176] recordings.csv edit for new dev --- user_data/recordings.csv | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/user_data/recordings.csv b/user_data/recordings.csv index a552194..d83292b 100644 --- a/user_data/recordings.csv +++ b/user_data/recordings.csv @@ -1,5 +1,5 @@ -subject,session_datetime,recording_start_time,file_path,camera_id,config_path,config_notes,paramset_idx -subject5,2020-04-15 11:16:38,2020-04-15 11:17:00,videos/reachingvideo1.avi,1,config.yaml,Reaching example provided by DeepLabCut repository,0 -subject6,2021-06-02 14:04:22,2021-06-02 14:07:00,videos/m3v1mp4.mp4,1,config.yaml,Openfield example provided by DeepLabCut repository,0 -subject6,2021-06-03 14:04:22,2021-06-04 14:07:00,videos/m3v1mp4-copy.mp4,1,config.yaml,Openfield example provided by DeepLabCut repository,0 +recording_id,subject,session_datetime,recording_start_time,file_path,camera_id,config_path,config_notes,paramset_idx +0,subject5,2020-04-15 11:16:38,2020-04-15 11:17:00,videos/reachingvideo1.avi,1,config.yaml,Reaching example provided by DeepLabCut repository,0 +1,subject6,2021-06-02 14:04:22,2021-06-02 14:07:00,videos/m3v1mp4.mp4,1,config.yaml,Openfield example provided by DeepLabCut repository,0 +2,subject6,2021-06-03 14:04:22,2021-06-04 14:07:00,videos/m3v1mp4-copy.mp4,1,config.yaml,Openfield example provided by DeepLabCut repository,0 From 365a090fd3cc63f8bcd071ab82b97aaf4ee9ac7a Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Thu, 3 Mar 2022 19:16:31 -0600 Subject: [PATCH 017/176] See Details - README - add install instructions - Notebook/1_Explore - major revision demonstrating new helper functions - user_data - revise per table redesign - ingest.py revise for paramset redesign - paths.py - remove get_session_directory - remove arg from get_dlc_processed_dir - pipeline.py minor change per paths change above --- README.md | 4 +- notebooks/1_Explore_Workflow.ipynb | 1691 ++++++++++------------------ user_data/config_params.csv | 7 +- user_data/recordings.csv | 9 +- user_data/sessions.csv | 6 +- user_data/subjects.csv | 2 +- workflow_deeplabcut/ingest.py | 58 +- workflow_deeplabcut/paths.py | 23 +- workflow_deeplabcut/pipeline.py | 7 +- 9 files changed, 688 insertions(+), 1119 deletions(-) diff --git a/README.md b/README.md index 0bf9780..922c11d 100644 --- a/README.md +++ b/README.md @@ -38,8 +38,8 @@ This workflow serves as an example of the upstream part of a typical data workfl ## Installation instructions -+ The installation instructions can be found at [datajoint-elements/install.md]( - https://github.com/datajoint/datajoint-elements/blob/main/install.md). ++ The installation instructions can be found at the +[datajoint-elements repository](https://github.com/datajoint/datajoint-elements/blob/main/gh-pages/docs/install.md). ## Interacting with the DataJoint workflow diff --git a/notebooks/1_Explore_Workflow.ipynb b/notebooks/1_Explore_Workflow.ipynb index 0da995b..93e3321 100644 --- a/notebooks/1_Explore_Workflow.ipynb +++ b/notebooks/1_Explore_Workflow.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "8b0d2410-e307-49ee-8adf-451bf7b24edc", "metadata": { "tags": [] @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "0ad68223-3600-4da9-a3e7-141962fefaf6", "metadata": {}, "outputs": [], @@ -73,12 +73,43 @@ "assert data_dir.exists(), \"Please check the that you have the folder openfield-Pranav\"" ] }, + { + "cell_type": "markdown", + "id": "343dbd62-8f60-4802-9106-bd5aea2bfe9e", + "metadata": {}, + "source": [ + "As part of the DeepLabCut demo setup process, you would run the following additional\n", + "commands, as outlined in their \n", + "[demo notebook](https://github.com/DeepLabCut/DeepLabCut/blob/master/examples/JUPYTER/Demo_labeledexample_Openfield.ipynb).\n", + "These steps establish the project path within the demo config file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "640f83f8-23c3-4058-894f-5fce872e8e18", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded, now creating training data...\n", + "The training dataset is successfully created. Use the function 'train_network' to start training. Happy training!\n" + ] + } + ], + "source": [ + "from deeplabcut.create_project.demo_data import load_demo_data as dlc_load_demo\n", + "dlc_load_demo(data_dir / 'config.yaml')" + ] + }, { "cell_type": "markdown", "id": "43dcb79d-72f8-468a-be2a-59866505b888", "metadata": {}, "source": [ - "Later, we'll use the first few seconds of this video as a 'separate session' to model\n", + "Later, we'll use the first few seconds of the training video as a 'separate session' to model\n", "the pose estimation feature of this pipeline. `ffmpeg` is a dependency of DeepLabCut\n", "that can splice the training video for a demonstration purposes. The command below saves\n", "the first 2 seconds of the training video as a copy." @@ -86,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "10218fae-c1ab-43cb-8c6a-37dcd38f8ff6", "metadata": { "tags": [] @@ -96,59 +127,24 @@ "name": "stderr", "output_type": "stream", "text": [ - "ffmpeg version 5.0 Copyright (c) 2000-2022 the FFmpeg developers\n", - " built with Apple clang version 13.0.0 (clang-1300.0.29.3)\n", - " configuration: --prefix=/usr/local/Cellar/ffmpeg/5.0 --enable-shared --enable-pthreads --enable-version3 --cc=clang --host-cflags= --host-ldflags= --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libbluray --enable-libdav1d --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librist --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvmaf --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-videotoolbox\n", - " libavutil 57. 17.100 / 57. 17.100\n", - " libavcodec 59. 18.100 / 59. 18.100\n", - " libavformat 59. 16.100 / 59. 16.100\n", - " libavdevice 59. 4.100 / 59. 4.100\n", - " libavfilter 8. 24.100 / 8. 24.100\n", - " libswscale 6. 4.100 / 6. 4.100\n", - " libswresample 4. 3.100 / 4. 3.100\n", - " libpostproc 56. 3.100 / 56. 3.100\n", - "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4':\n", - " Metadata:\n", - " major_brand : isom\n", - " minor_version : 512\n", - " compatible_brands: isomiso2avc1mp41\n", - " encoder : Lavf56.40.101\n", - " Duration: 00:01:17.67, start: 0.000000, bitrate: 228 kb/s\n", - " Stream #0:0[0x1](und): Video: h264 (High 4:4:4 Predictive) (avc1 / 0x31637661), yuv444p(progressive), 640x480, 225 kb/s, 30 fps, 30 tbr, 1000k tbn (default)\n", - " Metadata:\n", - " handler_name : VideoHandler\n", - " vendor_id : [0][0][0][0]\n", - "Output #0, mp4, to '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4':\n", - " Metadata:\n", - " major_brand : isom\n", - " minor_version : 512\n", - " compatible_brands: isomiso2avc1mp41\n", - " encoder : Lavf59.16.100\n", - " Stream #0:0(und): Video: h264 (High 4:4:4 Predictive) (avc1 / 0x31637661), yuv444p(progressive), 640x480, q=2-31, 225 kb/s, 30 fps, 30 tbr, 1000k tbn (default)\n", - " Metadata:\n", - " handler_name : VideoHandler\n", - " vendor_id : [0][0][0][0]\n", - "Stream mapping:\n", - " Stream #0:0 -> #0:0 (copy)\n", - "Press [q] to stop, [?] for help\n", - "frame= 2330 fps=0.0 q=-1.0 Lsize= 2164kB time=00:01:15.56 bitrate= 234.6kbits/s speed=4.83e+03x \n", - "video:2137kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.297295%\n" + "File '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4' already exists. Exiting.\n" ] }, { "data": { "text/plain": [ - "0" + "256" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vid_path = str(data_dir).replace(\" \", \"\\ \") + '/videos/m3v1mp4'\n", - "cmd = f'ffmpeg -y -ss 2 -i {vid_path}.mp4 -vcodec copy -acodec copy {vid_path}-copy.mp4'\n", + "cmd = (f'ffmpeg -n -hide_banner -loglevel error -ss 2 -i {vid_path}.mp4 -vcodec copy '\n", + " + f'-acodec copy {vid_path}-copy.mp4')\n", "os.system(cmd)" ] }, @@ -158,54 +154,55 @@ "metadata": {}, "source": [ "Now, we can activate the `dlc` schema and import some data from files stored in this\n", - "directory under `user_data/.csv`. This includes parameters like shuffle and \n", - "training fraction that DeepLabCut uses." + "directory under `user_data/.csv`. Subject and session data imports like these are \n", + "common across DataJoint workflows. They include fields like `subject_birth_date` and \n", + "`session_datetime`\n", + "\n", + "The recordings file specifies all videos across sessions, including both model training\n", + "videos and videos for later analysis. The config parameter csv is used in the \n", + "`ModelTrainingParamSet` table, which features a longblob field for any parameters \n", + "required in model training. Both shuffle and trainingsetindex are required in this \n", + "field, but many others can be added to later be passed to DLC's `train_model` function.\n", + "In this case, `maxiters` to only run a handful of training iterations for our example \n", + "model." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "id": "d25b109d-c8b2-46f6-8fde-cbd9135cdfc3", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting cbroz@tutorial-db.datajoint.io:3306\n", - "\n", - "---- Inserting 0 entry(s) into subject ----\n", - "\n", - "---- Inserting 0 entry(s) into session ----\n", - "\n", - "---- Inserting 0 entry(s) into session_directory ----\n", - "\n", - "---- Inserting 0 entry(s) into session_note ----\n", - "\n", - "---- Inserting 0 entry(s) into video_recording ----\n", - "\n", - "---- Inserting 0 entry(s) into video_recording__file ----\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'workflow_deeplabcut'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_12766/1191413193.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mworkflow_deeplabcut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipeline\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlab\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdlc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mworkflow_deeplabcut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mingest\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mingest_subjects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mingest_sessions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mingest_dlc_items\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mingest_subjects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mingest_sessions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mingest_dlc_items\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'workflow_deeplabcut'" ] } ], "source": [ "from workflow_deeplabcut.pipeline import lab, subject, session, dlc\n", - "from workflow_deeplabcut.ingest import ingest_subjects, ingest_sessions, ingest_dlc_configs\n", - "ingest_subjects(); ingest_sessions(); ingest_dlc_configs()" + "from workflow_deeplabcut.ingest import ingest_subjects, ingest_sessions, ingest_dlc_items\n", + "ingest_subjects(); ingest_sessions(); ingest_dlc_items()" ] }, { "cell_type": "markdown", - "id": "2f5fd85c-29eb-4b65-bad1-11fcd096f103", + "id": "d2738f21-591a-406b-8cd0-9a43ff6273d6", "metadata": {}, "source": [ - "For model training, we'll work with the following session and parameters." + "Let's look at the tables this populated." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "53017c86-1512-4a18-8f19-556f6ad94644", + "execution_count": 6, + "id": "95434ff7-d60c-40df-9ebc-c0ca3f99fa32", "metadata": {}, "outputs": [ { @@ -261,53 +258,91 @@ " }\n", " \n", " \n", - " \n", + " Animal Subject\n", "
    \n", " \n", " \n", - " \n", - "\n", - "\n", - "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "
    \n", "

    subject

    \n", " \n", "
    \n", - "

    session_datetime

    \n", + "

    sex

    \n", " \n", "
    \n", - "

    camera_id

    \n", + "

    subject_birth_date

    \n", " \n", "
    \n", - "

    recording_start_time

    \n", + "

    subject_description

    \n", " \n", "
    subject62021-06-02 14:04:2212021-06-02 14:07:00
    subject1M2020-12-30test animal
    subject2F2020-11-30test animal
    subject3F2020-12-30test animal
    subject4M2021-02-12test animal
    subject5F2020-01-01rich
    subject6M2020-01-01manuel
    subject7U2020-08-30test animal
    subject8F2020-09-30test animal
    subjectXF2020-01-01manuel
    subjectYM2020-01-01manuel
    subjectZM2020-01-01manuel
    \n", " \n", - "

    Total: 1

    \n", + "

    Total: 11

    \n", " " ], "text/plain": [ - "*subject *session_datet *camera_id *recording_sta\n", - "+----------+ +------------+ +-----------+ +------------+\n", - "subject6 2021-06-02 14: 1 2021-06-02 14:\n", - " (Total: 1)" + "*subject sex subject_birth_ subject_descri\n", + "+----------+ +-----+ +------------+ +------------+\n", + "subject1 M 2020-12-30 test animal \n", + "subject2 F 2020-11-30 test animal \n", + "subject3 F 2020-12-30 test animal \n", + "subject4 M 2021-02-12 test animal \n", + "subject5 F 2020-01-01 rich \n", + "subject6 M 2020-01-01 manuel \n", + "subject7 U 2020-08-30 test animal \n", + "subject8 F 2020-09-30 test animal \n", + "subjectX F 2020-01-01 manuel \n", + "subjectY M 2020-01-01 manuel \n", + "subjectZ M 2020-01-01 manuel \n", + " (Total: 11)" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "train_key={'subject': 'subject6', 'session_datetime': '2021-06-02 14:04:22',\n", - " 'camera_id': 1, 'recording_start_time': '2021-06-02 14:07:00'}\n", - "dlc.VideoRecording & train_key" + "subject.Subject()" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "a90b0534-a028-443c-b219-80dd79016748", + "execution_count": 7, + "id": "057f467c-a012-4425-8bec-960b1ae153bd", "metadata": {}, "outputs": [ { @@ -363,79 +398,80 @@ " }\n", " \n", " \n", - " Parameters to specify a DLC model training instance\n", + " \n", "
    \n", " \n", " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "
    \n", - "

    paramset_idx

    \n", + "

    subject

    \n", " \n", "
    \n", - "

    shuffle

    \n", - " shuffle number to use (usually 1)\n", - "
    \n", - "

    train_fraction

    \n", - " training fraction\n", - "
    \n", - "

    model_prefix

    \n", - " DLC model prefix, often empty\n", - "
    \n", - "

    filter_type

    \n", - " filter type, blank if none (e.g., median, arima)\n", - "
    \n", - "

    track_method

    \n", - " tracking method, blank if none (e.g,. box, ellipse)\n", + "

    session_datetime

    \n", + " \n", "
    \n", - "

    scorer_legacy

    \n", - " legacy naming for DLC < v2.1.0\n", + "

    session_dir

    \n", + " Path to the data directory for a session\n", "
    \n", - "

    param_set_hash

    \n", - " hash identifying this parameterset\n", + "

    session_note

    \n", + " \n", "
    010.950c70007c1-32b1-ae9b-cb83-7d11cbf78f37
    subject52020-04-15 11:16:38Reaching-Mackenzie-2018-08-30/Successful data collection, no notes
    subject62021-06-02 14:04:22openfield-Pranav-2018-10-30/Model Training Session
    subject62021-06-03 14:04:22openfield-Pranav-2018-10-30/Test Session
    \n", " \n", - "

    Total: 1

    \n", + "

    Total: 3

    \n", " " ], "text/plain": [ - "*paramset_idx shuffle train_fraction model_prefix filter_type track_method scorer_legacy param_set_hash\n", - "+------------+ +---------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", - "0 1 0.95 0 c70007c1-32b1-\n", - " (Total: 1)" + "*subject *session_datet session_dir session_note \n", + "+----------+ +------------+ +------------+ +------------+\n", + "subject5 2020-04-15 11: Reaching-Macke Successful dat\n", + "subject6 2021-06-02 14: openfield-Pran Model Training\n", + "subject6 2021-06-03 14: openfield-Pran Test Session \n", + " (Total: 3)" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "train_key['paramset_idx']=0\n", - "dlc.ConfigParamSet & train_key" + "session.Session * session.SessionDirectory * session.SessionNote" ] }, { "cell_type": "markdown", - "id": "9d113e2d-54d5-4fbb-ad91-faf4a66c0879", + "id": "63b3e2a4-a860-4ccb-a2b0-62fafb7d412c", "metadata": {}, "source": [ - "Now, we'll insert this combination into the `TrainingTask` table, and ask DeepLabCut to\n", - "train the model for us, for one quick iteration." + "Note that the video recording filepaths are specified relative to the root directory\n", + "defined within the workflow. This allows multiple users to operate on the same \n", + "filestructures across different machines. Because the root directory is passed as a \n", + "list, there can be multiple root directories on a given machine." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "8bad2548-ce93-475f-93d2-a9028fa7b848", + "execution_count": 8, + "id": "e043aeae-0039-43db-ac18-4fb198aa5b76", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Root: /Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/\n", + "Root: /Users/cb/Documents/U24_SampleData/\n" + ] + }, { "data": { "text/html": [ @@ -489,7 +525,7 @@ " }\n", " \n", " \n", - " Info required to specify 1 model\n", + " \n", "
    \n", " \n", " \n", - " \n", + " \n", + "\n", + "\n", + "\n", + "\n", "\n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "
    \n", @@ -502,366 +538,55 @@ "

    camera_id

    \n", " \n", "
    \n", - "

    recording_start_time

    \n", - " \n", - "
    \n", - "

    paramset_idx

    \n", + "

    recording_id

    \n", " \n", "
    \n", - "

    training_id

    \n", - " \n", + "

    file_path

    \n", + " filepath of video, relative to root data directory\n", "
    subject6
    subject52020-04-15 11:16:3813Reaching-Mackenzie-2018-08-30/videos/reachingvideo1.avi
    subject62021-06-02 14:04:2212021-06-02 14:07:0001
    1openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4
    subject62021-06-03 14:04:2212openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4
    \n", " \n", - "

    Total: 1

    \n", + "

    Total: 3

    \n", " " ], "text/plain": [ - "*subject *session_datet *camera_id *recording_sta *paramset_idx *training_id \n", - "+----------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n", - "subject6 2021-06-02 14: 1 2021-06-02 14: 0 1 \n", - " (Total: 1)" + "*subject *session_datet *camera_id *recording_id *file_path \n", + "+----------+ +------------+ +-----------+ +------------+ +------------+\n", + "subject5 2020-04-15 11: 1 3 Reaching-Macke\n", + "subject6 2021-06-02 14: 1 1 openfield-Pran\n", + "subject6 2021-06-03 14: 1 2 openfield-Pran\n", + " (Total: 3)" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "train_key['training_id']=1\n", - "dlc.TrainingTask.insert1(train_key, skip_duplicates=True)\n", - "dlc.TrainingTask()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "93fe3dac-b5b4-4ae5-ae2f-38a59ca1841a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Config:\n", - "{'all_joints': [[0], [1], [2], [3]],\n", - " 'all_joints_names': ['snout', 'leftear', 'rightear', 'tailbase'],\n", - " 'alpha_r': 0.02,\n", - " 'apply_prob': 0.5,\n", - " 'batch_size': 1,\n", - " 'clahe': True,\n", - " 'claheratio': 0.1,\n", - " 'crop_pad': 0,\n", - " 'crop_sampling': 'hybrid',\n", - " 'crop_size': [400, 400],\n", - " 'cropratio': 0.4,\n", - " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/openfield_Pranav95shuffle1.mat',\n", - " 'dataset_type': 'imgaug',\n", - " 'decay_steps': 30000,\n", - " 'deterministic': False,\n", - " 'display_iters': 1000,\n", - " 'edge': False,\n", - " 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]},\n", - " 'fg_fraction': 0.25,\n", - " 'global_scale': 0.8,\n", - " 'histeq': True,\n", - " 'histeqratio': 0.1,\n", - " 'init_weights': '/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt',\n", - " 'intermediate_supervision': False,\n", - " 'intermediate_supervision_layer': 12,\n", - " 'location_refinement': True,\n", - " 'locref_huber_loss': True,\n", - " 'locref_loss_weight': 0.05,\n", - " 'locref_stdev': 7.2801,\n", - " 'log_dir': 'log',\n", - " 'lr_init': 0.0005,\n", - " 'max_input_size': 1500,\n", - " 'max_shift': 0.4,\n", - " 'mean_pixel': [123.68, 116.779, 103.939],\n", - " 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/Documentation_data-openfield_95shuffle1.pickle',\n", - " 'min_input_size': 64,\n", - " 'mirror': False,\n", - " 'multi_stage': False,\n", - " 'multi_step': [[0.005, 10000],\n", - " [0.02, 430000],\n", - " [0.002, 730000],\n", - " [0.001, 1030000]],\n", - " 'net_type': 'resnet_50',\n", - " 'num_joints': 4,\n", - " 'optimizer': 'sgd',\n", - " 'pairwise_huber_loss': False,\n", - " 'pairwise_predict': False,\n", - " 'partaffinityfield_predict': False,\n", - " 'pos_dist_thresh': 17,\n", - " 'pre_resize': [],\n", - " 'project_path': '/Volumes/GoogleDrive/My '\n", - " 'Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30',\n", - " 'regularize': False,\n", - " 'rotation': 25,\n", - " 'rotratio': 0.4,\n", - " 'save_iters': 50000,\n", - " 'scale_jitter_lo': 0.5,\n", - " 'scale_jitter_up': 1.25,\n", - " 'scoremap_dir': 'test',\n", - " 'sharpen': False,\n", - " 'sharpenratio': 0.3,\n", - " 'shuffle': True,\n", - " 'snapshot_prefix': '/Volumes/GoogleDrive/My '\n", - " 'Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1/train/snapshot',\n", - " 'stride': 8.0,\n", - " 'weigh_negatives': False,\n", - " 'weigh_only_present_joints': False,\n", - " 'weigh_part_predictions': False,\n", - " 'weight_decay': 0.0001}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selecting single-animal trainer\n", - "Batch Size is 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n", - " warnings.warn('`layer.apply` is deprecated and '\n", - "2022-02-22 17:11:26.399703: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading ImageNet-pretrained resnet_50\n", - "Max_iters overwritten as 1\n", - "Training parameter:\n", - "{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1/train/snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'sgd', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 1, 'dataset_type': 'imgaug', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': False, 'pairwise_predict': False, 'all_joints': [[0], [1], [2], [3]], 'all_joints_names': ['snout', 'leftear', 'rightear', 'tailbase'], 'alpha_r': 0.02, 'apply_prob': 0.5, 'clahe': True, 'claheratio': 0.1, 'crop_sampling': 'hybrid', 'crop_size': [400, 400], 'cropratio': 0.4, 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/openfield_Pranav95shuffle1.mat', 'decay_steps': 30000, 'display_iters': 1000, 'edge': False, 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]}, 'histeq': True, 'histeqratio': 0.1, 'init_weights': '/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt', 'lr_init': 0.0005, 'max_input_size': 1500, 'max_shift': 0.4, 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/Documentation_data-openfield_95shuffle1.pickle', 'min_input_size': 64, 'multi_stage': False, 'multi_step': [[0.005, 10000], [0.02, 430000], [0.002, 730000], [0.001, 1030000]], 'net_type': 'resnet_50', 'num_joints': 4, 'pos_dist_thresh': 17, 'pre_resize': [], 'project_path': '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 50000, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25, 'sharpen': False, 'sharpenratio': 0.3, 'covering': True, 'elastic_transform': True, 'motion_blur': True, 'motion_blur_params': {'k': 7, 'angle': (-90, 90)}}\n", - "Starting training....\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-02-22 17:11:36.969368: W tensorflow/core/kernels/queue_base.cc:277] _0_fifo_queue: Skipping cancelled enqueue attempt with queue not closed\n", - "Exception in thread Thread-8:\n", - "Traceback (most recent call last):\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1380, in _do_call\n", - " return fn(*args)\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1363, in _run_fn\n", - " return self._call_tf_sessionrun(options, feed_dict, fetch_list,\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1456, in _call_tf_sessionrun\n", - " return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\n", - "tensorflow.python.framework.errors_impl.CancelledError: Enqueue operation was cancelled\n", - "\t [[{{node fifo_queue_enqueue}}]]\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/threading.py\", line 932, in _bootstrap_inner\n", - " self.run()\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/threading.py\", line 870, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 83, in load_and_enqueue\n", - " sess.run(enqueue_op, feed_dict=food)\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 970, in run\n", - " result = self._run(None, fetches, feed_dict, options_ptr,\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1193, in _run\n", - " results = self._do_run(handle, final_targets, final_fetches,\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1373, in _do_run\n", - " return self._do_call(_run_fn, feeds, fetches, targets, options,\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1399, in _do_call\n", - " raise type(e)(node_def, op, message) # pylint: disable=no-value-for-parameter\n", - "tensorflow.python.framework.errors_impl.CancelledError: Enqueue operation was cancelled\n", - "\t [[node fifo_queue_enqueue\n", - " (defined at /Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py:69)\n", - "]]\n", - "\n", - "Errors may have originated from an input operation.\n", - "Input Source operations connected to node fifo_queue_enqueue:\n", - "In[0] fifo_queue (defined at /Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py:68)\t\n", - "In[1] Placeholder (defined at /Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py:61)\t\n", - "In[2] Placeholder_1:\t\n", - "In[3] Placeholder_2:\t\n", - "In[4] Placeholder_3:\t\n", - "In[5] Placeholder_4:\n", - "\n", - "Operation defined at: (most recent call last)\n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/runpy.py\", line 194, in _run_module_as_main\n", - ">>> return _run_code(code, main_globals, None,\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/runpy.py\", line 87, in _run_code\n", - ">>> exec(code, run_globals)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel_launcher.py\", line 16, in \n", - ">>> app.launch_new_instance()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/traitlets/config/application.py\", line 846, in launch_instance\n", - ">>> app.start()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelapp.py\", line 677, in start\n", - ">>> self.io_loop.start()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tornado/platform/asyncio.py\", line 199, in start\n", - ">>> self.asyncio_loop.run_forever()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/asyncio/base_events.py\", line 570, in run_forever\n", - ">>> self._run_once()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/asyncio/base_events.py\", line 1859, in _run_once\n", - ">>> handle._run()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/asyncio/events.py\", line 81, in _run\n", - ">>> self._context.run(self._callback, *self._args)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 457, in dispatch_queue\n", - ">>> await self.process_one()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 446, in process_one\n", - ">>> await dispatch(*args)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 353, in dispatch_shell\n", - ">>> await result\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 648, in execute_request\n", - ">>> reply_content = await reply_content\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/ipkernel.py\", line 353, in do_execute\n", - ">>> res = shell.run_cell(code, store_history=store_history, silent=silent)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n", - ">>> return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_cell\n", - ">>> result = self._run_cell(\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2947, in _run_cell\n", - ">>> return runner(coro)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n", - ">>> coro.send(None)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3172, in run_cell_async\n", - ">>> has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3364, in run_ast_nodes\n", - ">>> if (await self.run_code(code, result, async_=asy)):\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3444, in run_code\n", - ">>> exec(code_obj, self.user_global_ns, self.user_ns)\n", - ">>> \n", - ">>> File \"/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_7154/2075415569.py\", line 1, in \n", - ">>> dlc.ModelTraining.train_model(training_id=1,maxiters=1)\n", - ">>> \n", - ">>> File \"/Volumes/GoogleDrive/My Drive/Dev/element-deeplabcut/element_deeplabcut/dlc.py\", line 243, in train_model\n", - ">>> train_network(model.yml_path,\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 178, in train_network\n", - ">>> train(\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 169, in train\n", - ">>> batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 69, in setup_preloading\n", - ">>> enqueue_op = q.enqueue(placeholders_list)\n", - ">>> \n", - "\n", - "Original stack trace for 'fifo_queue_enqueue':\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/runpy.py\", line 194, in _run_module_as_main\n", - " return _run_code(code, main_globals, None,\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/runpy.py\", line 87, in _run_code\n", - " exec(code, run_globals)\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel_launcher.py\", line 16, in \n", - " app.launch_new_instance()\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/traitlets/config/application.py\", line 846, in launch_instance\n", - " app.start()\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelapp.py\", line 677, in start\n", - " self.io_loop.start()\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tornado/platform/asyncio.py\", line 199, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/asyncio/base_events.py\", line 570, in run_forever\n", - " self._run_once()\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/asyncio/base_events.py\", line 1859, in _run_once\n", - " handle._run()\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/asyncio/events.py\", line 81, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 457, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 446, in process_one\n", - " await dispatch(*args)\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 353, in dispatch_shell\n", - " await result\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 648, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/ipkernel.py\", line 353, in do_execute\n", - " res = shell.run_cell(code, store_history=store_history, silent=silent)\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n", - " return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_cell\n", - " result = self._run_cell(\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2947, in _run_cell\n", - " return runner(coro)\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3172, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3364, in run_ast_nodes\n", - " if (await self.run_code(code, result, async_=asy)):\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3444, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_7154/2075415569.py\", line 1, in \n", - " dlc.ModelTraining.train_model(training_id=1,maxiters=1)\n", - " File \"/Volumes/GoogleDrive/My Drive/Dev/element-deeplabcut/element_deeplabcut/dlc.py\", line 243, in train_model\n", - " train_network(model.yml_path,\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 178, in train_network\n", - " train(\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 169, in train\n", - " batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 69, in setup_preloading\n", - " enqueue_op = q.enqueue(placeholders_list)\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/ops/data_flow_ops.py\", line 350, in enqueue\n", - " return gen_data_flow_ops.queue_enqueue_v2(\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/ops/gen_data_flow_ops.py\", line 4063, in queue_enqueue_v2\n", - " _, _, _op, _outputs = _op_def_library._apply_op_helper(\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py\", line 744, in _apply_op_helper\n", - " op = g._create_op_internal(op_type_name, inputs, dtypes=None,\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 3697, in _create_op_internal\n", - " ret = Operation(\n", - " File \"/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 2101, in __init__\n", - " self._traceback = tf_stack.extract_stack_for_node(self._c_op)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The network is now trained and ready to evaluate. Use the function 'evaluate_network' to evaluate the network.\n" - ] - } - ], - "source": [ - "dlc.ModelTraining.train_model(training_id=1,maxiters=1)" + "from workflow_deeplabcut.paths import get_dlc_root_data_dir\n", + "for d in get_dlc_root_data_dir():\n", + " print(f'Root: {d}')\n", + "dlc.VideoRecording.File()" ] }, { "cell_type": "code", "execution_count": 9, - "id": "2d83a13d-1518-45b5-9516-caee7c9bc70e", + "id": "bf24a52f-9e3e-411c-9001-523ae498988b", "metadata": {}, "outputs": [ { @@ -917,52 +642,44 @@ " }\n", " \n", " \n", - " \n", + " Parameters to specify a DLC model training instance\n", "
    \n", " \n", " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n", "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    camera_id

    \n", - " \n", - "
    \n", - "

    recording_start_time

    \n", - " \n", - "
    \n", "

    paramset_idx

    \n", " \n", "
    \n", - "

    training_id

    \n", + "

    paramset_desc

    \n", " \n", "
    \n", - "

    snapshot_index_exact

    \n", - " latest exact snapshot index (i.e., never -1)\n", + "

    param_set_hash

    \n", + " hash identifying this parameterset\n", "
    \n", - "

    config_template

    \n", - " stored full config file\n", + "

    params

    \n", + " dictionary of all applicable parameters\n", "
    subject62021-06-02 14:04:2212021-06-02 14:07:00011
    1OpenFieldacf342ee-75e0-6782-b5ef-f0d7d359aa17=BLOB=
    2Reaching8ea3dc9b-e9eb-2709-97ee-9abe32068830=BLOB=
    3ExtraExampleee0be706-5703-acbb-0b8b-6c8e56d8ac68=BLOB=
    \n", " \n", - "

    Total: 1

    \n", + "

    Total: 3

    \n", " " ], "text/plain": [ - "*subject *session_datet *camera_id *recording_sta *paramset_idx *training_id snapshot_index config_tem\n", - "+----------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +------------+ +--------+\n", - "subject6 2021-06-02 14: 1 2021-06-02 14: 0 1 1 =BLOB= \n", - " (Total: 1)" + "*paramset_idx paramset_desc param_set_hash params \n", + "+------------+ +------------+ +------------+ +--------+\n", + "1 OpenField acf342ee-75e0- =BLOB= \n", + "2 Reaching 8ea3dc9b-e9eb- =BLOB= \n", + "3 ExtraExample ee0be706-5703- =BLOB= \n", + " (Total: 3)" ] }, "execution_count": 9, @@ -971,46 +688,108 @@ } ], "source": [ - "dlc.ModelTraining()" + "dlc.ModelTrainingParamSet()" ] }, { "cell_type": "markdown", - "id": "bb4d8a96-bb1b-4138-9af6-4c9db319c89d", + "id": "cd5622ce-d1ab-48d7-ae53-ca133e96aa97", "metadata": {}, "source": [ - "Next, we can optionally describe each of these body parts in our `BodyPart` lookup \n", - "table by providing a list of descriptions in the order given by the PoseEstimation \n", - "method. If you skip this step, the same items will be inserted during the following\n", - "step." + "We can take a closer look at the parameters specified with the `fetch` command. Using\n", + "the `ingest_dlc_items`, this naturally captures the full `config.yaml`" ] }, { "cell_type": "code", "execution_count": 10, - "id": "341f00e8-39b9-4833-8a32-b8b7ac907036", + "id": "f17e266c-86b6-463f-9f4c-b4d24ca7ecc4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Index(['leftear', 'rightear', 'snout', 'tailbase'], dtype='object', name='bodyparts')\n" + "[{'param_set_hash': UUID('acf342ee-75e0-6782-b5ef-f0d7d359aa17'),\n", + " 'params': {'Task': 'openfield',\n", + " 'TrainingFraction': [0.95],\n", + " 'alphavalue': 0.7,\n", + " 'batch_size': 4,\n", + " 'bodyparts': ['snout', 'leftear', 'rightear', 'tailbase'],\n", + " 'colormap': 'jet',\n", + " 'corner2move2': [50, 50],\n", + " 'cropping': False,\n", + " 'date': 'Oct30',\n", + " 'default_augmenter': 'imgaug',\n", + " 'default_net_type': 'resnet_50',\n", + " 'dotsize': 8,\n", + " 'filter_type': '',\n", + " 'identity': None,\n", + " 'iteration': 0,\n", + " 'maxiters': '5',\n", + " 'move2corner': True,\n", + " 'multianimalproject': None,\n", + " 'numframes2pick': 20,\n", + " 'pcutoff': 0.4,\n", + " 'scorer': 'Pranav',\n", + " 'scorer_legacy': 'False',\n", + " 'shuffle': '1',\n", + " 'skeleton': [],\n", + " 'skeleton_color': 'black',\n", + " 'snapshotindex': -1,\n", + " 'start': 0,\n", + " 'stop': 1,\n", + " 'track_method': '',\n", + " 'trainingsetindex': '0',\n", + " 'x1': 0,\n", + " 'x2': 640,\n", + " 'y1': 277,\n", + " 'y2': 624},\n", + " 'paramset_desc': 'OpenField',\n", + " 'paramset_idx': 1}]\n" ] } ], "source": [ - "from element_deeplabcut.readers.dlc_reader import PoseEstimation\n", - "print(PoseEstimation(data_dir).body_parts)\n", - "description_list = ['Left Ear', 'Right Ear', 'Snout tip', 'Base of the Tail']\n", - "dlc.BodyPart.insert_all_from_model(train_key,skip_duplicates=True,\n", - " description_list=description_list)" + "import pprint\n", + "pprint.pprint((dlc.ModelTrainingParamSet & 'paramset_idx=1'\n", + " ).fetch(as_dict=True))" + ] + }, + { + "cell_type": "markdown", + "id": "2f5fd85c-29eb-4b65-bad1-11fcd096f103", + "metadata": {}, + "source": [ + "For model training, we'll work with the following session and parameters. First, we \n", + "insert a training task into the cue." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "53017c86-1512-4a18-8f19-556f6ad94644", + "metadata": {}, + "outputs": [], + "source": [ + "key=(dlc.VideoRecording&'recording_id=1').fetch1('KEY')\n", + "key.update({'paramset_idx':1,'training_id':1,\n", + " 'project_path':'openfield-Pranav-2018-10-30/'})\n", + "dlc.TrainingTask.insert1(key, skip_duplicates=True)" + ] + }, + { + "cell_type": "markdown", + "id": "4011030a-3174-47b2-a673-3ce24f5874fb", + "metadata": {}, + "source": [ + "In the next step, all new entries in this table will undergo training." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "256abce9-1ee6-4868-8c11-38ea998ef216", + "execution_count": 16, + "id": "67cbe9c3-b043-49ac-9572-71c0a161e2dc", "metadata": {}, "outputs": [ { @@ -1066,141 +845,79 @@ " }\n", " \n", " \n", - " \n", + " Specification for a DLC model training instance\n", "
    \n", " \n", " \n", - " \n", - "\n", - "\n", - "\n", - "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "
    \n", - "

    body_part

    \n", + "

    subject

    \n", " \n", "
    \n", - "

    body_part_description

    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    camera_id

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    paramset_idx

    \n", + " \n", + "
    \n", + "

    training_id

    \n", + " \n", + "
    \n", + "

    model_prefix

    \n", " \n", + "
    \n", + "

    project_path

    \n", + " DLC's project_path in config relative to root\n", "
    leftearLeft Ear
    rightearRight Ear
    snoutSnout tip
    tailbaseBase of the Tail
    subject62021-06-02 14:04:221111openfield-Pranav-2018-10-30/
    \n", " \n", - "

    Total: 4

    \n", + "

    Total: 1

    \n", " " ], "text/plain": [ - "*body_part body_part_desc\n", - "+-----------+ +------------+\n", - "leftear Left Ear \n", - "rightear Right Ear \n", - "snout Snout tip \n", - "tailbase Base of the Ta\n", - " (Total: 4)" + "*subject *session_datet *camera_id *recording_id *paramset_idx *training_id model_prefix project_path \n", + "+----------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", + "subject6 2021-06-02 14: 1 1 1 1 openfield-Pran\n", + " (Total: 1)" ] }, - "execution_count": 11, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.BodyPart()" - ] - }, - { - "cell_type": "markdown", - "id": "27d0929a-9a0d-465b-a552-9a4a2938cbc6", - "metadata": {}, - "source": [ - "Now, we'll insert the model into the central `Model` table." + "dlc.TrainingTask()" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "fe416a66-6c9f-4d47-9435-239f4b7fc984", + "execution_count": 17, + "id": "3cf0b309-7cee-456e-84b7-a0004e28e16e", "metadata": { "tags": [] }, "outputs": [], "source": [ - "dlc.Model.insert_new_model(train_key,config_paramset_idx=0,model_name=\"FirstModel\",\n", - " model_description=\"First inserted model\", training_id=1)" - ] - }, - { - "cell_type": "markdown", - "id": "73cc8877-0a53-483d-b5ee-7adef6c4d45a", - "metadata": {}, - "source": [ - "The `ModelEval` table runs DeepLabCut's evaluation function and stores the result." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "5109aae8-f896-47fa-830b-cec07cf29ee2", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Config:\n", - "{'all_joints': [[0], [1], [2], [3]],\n", - " 'all_joints_names': ['snout', 'leftear', 'rightear', 'tailbase'],\n", - " 'batch_size': 1,\n", - " 'crop_pad': 0,\n", - " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/openfield_Pranav95shuffle1.mat',\n", - " 'dataset_type': 'imgaug',\n", - " 'deterministic': False,\n", - " 'fg_fraction': 0.25,\n", - " 'global_scale': 0.8,\n", - " 'init_weights': '/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt',\n", - " 'intermediate_supervision': False,\n", - " 'intermediate_supervision_layer': 12,\n", - " 'location_refinement': True,\n", - " 'locref_huber_loss': True,\n", - " 'locref_loss_weight': 1.0,\n", - " 'locref_stdev': 7.2801,\n", - " 'log_dir': 'log',\n", - " 'mean_pixel': [123.68, 116.779, 103.939],\n", - " 'mirror': False,\n", - " 'net_type': 'resnet_50',\n", - " 'num_joints': 4,\n", - " 'optimizer': 'sgd',\n", - " 'pairwise_huber_loss': True,\n", - " 'pairwise_predict': False,\n", - " 'partaffinityfield_predict': False,\n", - " 'regularize': False,\n", - " 'scoremap_dir': 'test',\n", - " 'shuffle': True,\n", - " 'snapshot_prefix': '/Volumes/GoogleDrive/My '\n", - " 'Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1/test/snapshot',\n", - " 'stride': 8.0,\n", - " 'weigh_negatives': False,\n", - " 'weigh_only_present_joints': False,\n", - " 'weigh_part_predictions': False,\n", - " 'weight_decay': 0.0001}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running DLC_resnet50_openfieldOct30shuffle1_1008 with # of training iterations: 1008\n", - "This net has already been evaluated!\n" - ] - } - ], - "source": [ - "dlc.ModelEval.populate()" + "dlc.ModelTraining.populate()" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "836fa695-2f2c-474f-8ae4-8a54094807a4", + "execution_count": 18, + "id": "869268e6-0bc9-4e4e-8455-d4d3c02b46a8", "metadata": {}, "outputs": [ { @@ -1260,164 +977,133 @@ "
    \n", " \n", " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "
    \n", - "

    model_name

    \n", - " user-friendly model name\n", + "

    subject

    \n", + " \n", "
    \n", - "

    train_iterations

    \n", - " Training iterations\n", + "

    session_datetime

    \n", + " \n", "
    \n", - "

    train_error

    \n", - " Train error (px)\n", + "

    camera_id

    \n", + " \n", "
    \n", - "

    test_error

    \n", - " Test error (px)\n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    paramset_idx

    \n", + " \n", "
    \n", - "

    p_cutoff

    \n", - " p-cutoff used\n", + "

    training_id

    \n", + " \n", "
    \n", - "

    train_error_p

    \n", - " Train error with p-cutoff\n", + "

    latest_snapshot

    \n", + " latest exact snapshot index (i.e., never -1)\n", "
    \n", - "

    test_error_p

    \n", - " Test error with p-cutoff\n", + "

    config_template

    \n", + " stored full config file\n", "
    FirstModel100811.356.040.49.35.1
    subject62021-06-02 14:04:2211115=BLOB=
    \n", " \n", "

    Total: 1

    \n", " " ], "text/plain": [ - "*model_name train_iteratio train_error test_error p_cutoff train_error_p test_error_p \n", - "+------------+ +------------+ +------------+ +------------+ +----------+ +------------+ +------------+\n", - "FirstModel 1008 11.35 6.04 0.4 9.3 5.1 \n", + "*subject *session_datet *camera_id *recording_id *paramset_idx *training_id latest_snapsho config_tem\n", + "+----------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +------------+ +--------+\n", + "subject6 2021-06-02 14: 1 1 1 1 5 =BLOB= \n", " (Total: 1)" ] }, - "execution_count": 14, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.ModelEval()" + "dlc.ModelTraining()" ] }, { "cell_type": "markdown", - "id": "760865e8-a012-4fee-bdcc-5349151ff3d2", + "id": "05ae9e9a-48c2-4ce6-926c-4b03354687f2", "metadata": {}, "source": [ - "Finally, we can use this model to conduct pose estimation for separate videos. In this\n", - "case, we'll use the `m3v1mp4-copy.mp4` clip we generated earlier. First, this line is\n", - "inserted into the `PoseEstimationTask` table before conducting the actual estimation \n", - "via the the `PoseEstimation.populate()` method. " + "To resume training from a previous instance, one would need to \n", + "[edit the relevant config file](https://github.com/DeepLabCut/DeepLabCut/issues/70) and\n", + "adjust the `maxiters` paramset to a higher threshold (e.g., 10 for 5 more itterations).\n", + "Emperical work from the Mathis team suggests 200k iterations for any true use-case." ] }, { - "cell_type": "code", - "execution_count": 7, - "id": "ebca0624-a6cb-4261-8032-6bdd355b0be8", + "cell_type": "markdown", + "id": "9380568a-fb2e-4c11-9488-9020111a0bd2", "metadata": {}, - "outputs": [ - { - "ename": "DuplicateError", - "evalue": "(\"Duplicate entry 'subject6-2021-06-03 14:04:22-1-2021-06-04 14:07:00-FirstModel' for key 'PRIMARY'\", 'To ignore duplicate entries in insert, set skip_duplicates=True')", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mDuplicateError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_7216/1998148785.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mestim_key\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdlc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModel\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m'model_name=\"FirstModel\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'KEY'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mestim_key\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'task_mode'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'trigger'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdlc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPoseEstimationTask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestim_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mdlc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPoseEstimationTask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m in \u001b[0;36minsert1\u001b[0;34m(self, row, **kwargs)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0mFor\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msee\u001b[0m \u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \"\"\"\n\u001b[0;32m--> 266\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_duplicates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extra_fields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_direct_insert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/datajoint/table.py\u001b[0m in \u001b[0;36minsert\u001b[0;34m(self, rows, replace, skip_duplicates, ignore_extra_fields, allow_direct_insert)\u001b[0m\n\u001b[1;32m 335\u001b[0m 'To ignore extra fields in insert, set ignore_extra_fields=True')\n\u001b[1;32m 336\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mDuplicateError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m raise err.suggest(\n\u001b[0m\u001b[1;32m 338\u001b[0m 'To ignore duplicate entries in insert, set skip_duplicates=True')\n\u001b[1;32m 339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDuplicateError\u001b[0m: (\"Duplicate entry 'subject6-2021-06-03 14:04:22-1-2021-06-04 14:07:00-FirstModel' for key 'PRIMARY'\", 'To ignore duplicate entries in insert, set skip_duplicates=True')" - ] - } - ], "source": [ - "estim_key = ((dlc.VideoRecording & 'session_datetime>\"2021-06-03\"').fetch1('KEY'))\n", - "estim_key.update((dlc.Model & 'model_name=\"FirstModel\"').fetch1('KEY'))\n", - "estim_key['task_mode']='trigger'\n", - "dlc.PoseEstimationTask.insert1(estim_key)\n", - "dlc.PoseEstimationTask()" + "Next, we can optionally ingest all body parts from a given config with one command, including\n", + "a list of body part descriptions." ] }, { "cell_type": "code", - "execution_count": 16, - "id": "9937220f-b08b-4836-a1a2-a5933096d6ac", + "execution_count": 26, + "id": "a20db1a5-e279-4553-baf2-24e5772f6d6b", "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "Config:\n", - "{'all_joints': [[0], [1], [2], [3]],\n", - " 'all_joints_names': ['snout', 'leftear', 'rightear', 'tailbase'],\n", - " 'batch_size': 1,\n", - " 'crop_pad': 0,\n", - " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/openfield_Pranav95shuffle1.mat',\n", - " 'dataset_type': 'imgaug',\n", - " 'deterministic': False,\n", - " 'fg_fraction': 0.25,\n", - " 'global_scale': 0.8,\n", - " 'init_weights': '/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt',\n", - " 'intermediate_supervision': False,\n", - " 'intermediate_supervision_layer': 12,\n", - " 'location_refinement': True,\n", - " 'locref_huber_loss': True,\n", - " 'locref_loss_weight': 1.0,\n", - " 'locref_stdev': 7.2801,\n", - " 'log_dir': 'log',\n", - " 'mean_pixel': [123.68, 116.779, 103.939],\n", - " 'mirror': False,\n", - " 'net_type': 'resnet_50',\n", - " 'num_joints': 4,\n", - " 'optimizer': 'sgd',\n", - " 'pairwise_huber_loss': True,\n", - " 'pairwise_predict': False,\n", - " 'partaffinityfield_predict': False,\n", - " 'regularize': False,\n", - " 'scoremap_dir': 'test',\n", - " 'shuffle': True,\n", - " 'snapshot_prefix': '/Volumes/GoogleDrive/My '\n", - " 'Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1/test/snapshot',\n", - " 'stride': 8.0,\n", - " 'weigh_negatives': False,\n", - " 'weigh_only_present_joints': False,\n", - " 'weigh_part_predictions': False,\n", - " 'weight_decay': 0.0001}\n", - "/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n", - " warnings.warn('`layer.apply` is deprecated and '\n" + "Existing body parts: []\n", + "New body parts: ['leftear' 'rightear' 'snout' 'tailbase']\n" ] }, { - "name": "stdout", + "name": "stdin", "output_type": "stream", "text": [ - "Using snapshot-1008 for model /Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1\n", - "Starting to analyze % /Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4\n", - "The videos are analyzed. Now your research can truly start! \n", - " You can create labeled videos with 'create_labeled_video'\n", - "If the tracking is not satisfactory for some videos, consider expanding the training set. You can use the function 'extract_outlier_frames' to extract a few representative outlier frames.\n" + "Insert 4 new body part(s)? [yes, no]: yes\n" ] } ], "source": [ - "dlc.PoseEstimation.populate()" + "dlc_config_path = 'openfield-Pranav-2018-10-30/config.yaml'\n", + "bp_desc=['Left Ear', 'Right Ear', 'Snout Position', 'Base of Tail']\n", + "dlc.BodyPart.insert_from_config(dlc_config_path,bp_desc)" + ] + }, + { + "cell_type": "markdown", + "id": "341a9d4f-b681-46ca-aef1-36e9ace3bb57", + "metadata": {}, + "source": [ + "Alternatively, the above step will be included when inserting a model into the model \n", + "table." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "f6c60d2d-f34b-4feb-83cd-88e094b6a9cb", + "execution_count": null, + "id": "1f4db829-51fa-4e10-aa37-1e314a1d1d0f", + "metadata": {}, + "outputs": [], + "source": [ + "dlc.Model.insert_new_model(model_name='OpenField-1010',dlc_config=dlc_config_path,\n", + " shuffle=1,trainingsetindex=0,\n", + " model_description='Open field model trained 1010 iterations',\n", + " body_part_descriptions = bp_desc,paramset_idx=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "6e1e905a-0a0e-4e13-9884-3b9089eae54f", "metadata": {}, "outputs": [ { @@ -1477,385 +1163,263 @@ "
    \n", " \n", " \n", - " \n", - "\n", + " \n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "
    \n", - "

    subject

    \n", - " \n", + "

    model_name

    \n", + " user-friendly model name\n", "
    \n", - "

    session_datetime

    \n", - " \n", + "

    task

    \n", + " task in the config yaml\n", "
    \n", - "

    camera_id

    \n", - " \n", + "

    date

    \n", + " date in the config yaml\n", + "
    \n", + "

    iteration

    \n", + " iteration/version of this model\n", + "
    \n", + "

    snapshotindex

    \n", + " which snapshot for prediction (if -1, latest)\n", + "
    \n", + "

    shuffle

    \n", + " which shuffle of the training dataset\n", + "
    \n", + "

    trainingsetindex

    \n", + " which training set fraction to generate model\n", + "
    \n", + "

    scorer

    \n", + " scorer/network name - DLC's GetScorerName()\n", "
    \n", - "

    recording_start_time

    \n", + "

    config_template

    \n", + " dictionary of the config for analyze_videos()\n", + "
    \n", + "

    project_path

    \n", + " DLC's project_path in config relative to root\n", + "
    \n", + "

    dlc_version

    \n", + " keeps the deeplabcut version\n", + "
    \n", + "

    model_prefix

    \n", " \n", "
    \n", - "

    model_name

    \n", - " user-friendly model name\n", + "

    model_description

    \n", + " \n", "
    \n", - "

    post_estimation_time

    \n", - " time of generation of this set of DLC results\n", + "

    paramset_idx

    \n", + " \n", "
    subject62021-06-03 14:04:22
    OpenField-1010openfieldOct300-112021-06-04 14:07:00FirstModel2022-01-26 11:22:34
    0DLCresnet50openfieldOct30shuffle1=BLOB=openfield-Pranav-2018-10-302.2.0.6Open field model trained 1010 iterations1
    \n", " \n", "

    Total: 1

    \n", " " ], "text/plain": [ - "*subject *session_datet *camera_id *recording_sta *model_name post_estimatio\n", - "+----------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n", - "subject6 2021-06-03 14: 1 2021-06-04 14: FirstModel 2022-01-26 11:\n", + "*model_name task date iteration snapshotindex shuffle trainingsetind scorer config_tem project_path dlc_version model_prefix model_descript paramset_idx \n", + "+------------+ +-----------+ +-------+ +-----------+ +------------+ +---------+ +------------+ +------------+ +--------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", + "OpenField-1010 openfield Oct30 0 -1 1 0 DLCresnet50ope =BLOB= openfield-Pran 2.2.0.6 Open field mod 1 \n", " (Total: 1)" ] }, - "execution_count": 5, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.PoseEstimation()" + "dlc.Model()" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "3f12ee64-e1e1-425c-99bd-060176ce6779", + "execution_count": 27, + "id": "d4cdb0c4-147b-4e50-9874-32cb46b7f663", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    scorerFirstModel
    bodypartsleftearrightearsnouttailbase
    coordsxyzlikelihoodxyzlikelihoodxyzlikelihoodxyzlikelihood
    078.326073102.6610490.00.31450674.472694109.3662570.00.23949671.408234114.3236160.00.31547878.313431110.9945530.00.273981
    168.676651102.0676730.00.29485674.428070109.6935730.00.23672271.155952114.5973740.00.29793478.757637110.8692700.00.284565
    268.009209101.7143940.00.38691472.86501397.3321150.00.21742171.49594197.1097340.00.31698478.588943111.0119250.00.267579
    367.158943101.4643780.00.45962567.72011695.4340670.00.22882268.80291794.2680130.00.32908478.494675111.5275040.00.254363
    466.202774100.6793900.00.45719067.56582694.5998840.00.21919868.94625993.6288830.00.30956978.469231112.2232820.00.229301
    ...................................................
    2325356.629333376.8256840.00.092403346.879303381.0070500.00.096084343.775360384.1889340.00.125122425.655670422.1686100.00.212187
    2326350.492767387.1819760.00.118965410.790131397.7976070.00.110812352.447784385.3256530.00.144934418.131439406.1693120.00.195705
    2327351.700409387.6010740.00.150138411.026489397.9878230.00.121694353.609650385.4432980.00.193439420.859375404.9639890.00.217717
    2328354.063324388.1681820.00.137858427.673187413.4660950.00.121844354.449799385.9833370.00.178257422.556610404.8189700.00.243224
    2329432.365479442.0434570.00.118110427.488220413.4584350.00.128129355.735626386.1260680.00.160270436.392853422.4031680.00.222099
    \n", - "

    2330 rows × 16 columns

    \n", - "
    " + " /* Show the tooltip text when you mouse over the tooltip container */\n", + " .djtooltip:hover .djtooltiptext {\n", + " visibility: visible;\n", + " }\n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    body_part

    \n", + " \n", + "
    \n", + "

    body_part_description

    \n", + " \n", + "
    leftearLeft Ear
    rightearRight Ear
    snoutSnout Position
    tailbaseBase of Tail
    \n", + " \n", + "

    Total: 4

    \n", + " " ], "text/plain": [ - "scorer FirstModel \\\n", - "bodyparts leftear rightear \n", - "coords x y z likelihood x y \n", - "0 78.326073 102.661049 0.0 0.314506 74.472694 109.366257 \n", - "1 68.676651 102.067673 0.0 0.294856 74.428070 109.693573 \n", - "2 68.009209 101.714394 0.0 0.386914 72.865013 97.332115 \n", - "3 67.158943 101.464378 0.0 0.459625 67.720116 95.434067 \n", - "4 66.202774 100.679390 0.0 0.457190 67.565826 94.599884 \n", - "... ... ... ... ... ... ... \n", - "2325 356.629333 376.825684 0.0 0.092403 346.879303 381.007050 \n", - "2326 350.492767 387.181976 0.0 0.118965 410.790131 397.797607 \n", - "2327 351.700409 387.601074 0.0 0.150138 411.026489 397.987823 \n", - "2328 354.063324 388.168182 0.0 0.137858 427.673187 413.466095 \n", - "2329 432.365479 442.043457 0.0 0.118110 427.488220 413.458435 \n", - "\n", - "scorer \\\n", - "bodyparts snout tailbase \n", - "coords z likelihood x y z likelihood x \n", - "0 0.0 0.239496 71.408234 114.323616 0.0 0.315478 78.313431 \n", - "1 0.0 0.236722 71.155952 114.597374 0.0 0.297934 78.757637 \n", - "2 0.0 0.217421 71.495941 97.109734 0.0 0.316984 78.588943 \n", - "3 0.0 0.228822 68.802917 94.268013 0.0 0.329084 78.494675 \n", - "4 0.0 0.219198 68.946259 93.628883 0.0 0.309569 78.469231 \n", - "... ... ... ... ... ... ... ... \n", - "2325 0.0 0.096084 343.775360 384.188934 0.0 0.125122 425.655670 \n", - "2326 0.0 0.110812 352.447784 385.325653 0.0 0.144934 418.131439 \n", - "2327 0.0 0.121694 353.609650 385.443298 0.0 0.193439 420.859375 \n", - "2328 0.0 0.121844 354.449799 385.983337 0.0 0.178257 422.556610 \n", - "2329 0.0 0.128129 355.735626 386.126068 0.0 0.160270 436.392853 \n", - "\n", - "scorer \n", - "bodyparts \n", - "coords y z likelihood \n", - "0 110.994553 0.0 0.273981 \n", - "1 110.869270 0.0 0.284565 \n", - "2 111.011925 0.0 0.267579 \n", - "3 111.527504 0.0 0.254363 \n", - "4 112.223282 0.0 0.229301 \n", - "... ... ... ... \n", - "2325 422.168610 0.0 0.212187 \n", - "2326 406.169312 0.0 0.195705 \n", - "2327 404.963989 0.0 0.217717 \n", - "2328 404.818970 0.0 0.243224 \n", - "2329 422.403168 0.0 0.222099 \n", - "\n", - "[2330 rows x 16 columns]" + "*body_part body_part_desc\n", + "+-----------+ +------------+\n", + "leftear Left Ear \n", + "rightear Right Ear \n", + "snout Snout Position\n", + "tailbase Base of Tail \n", + " (Total: 4)" ] }, - "execution_count": 8, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.PoseEstimation.GetTrajectory(estim_key)" + "dlc.BodyPart()" + ] + }, + { + "cell_type": "markdown", + "id": "c561a772-910d-405e-b181-986dee983563", + "metadata": {}, + "source": [ + "Next, all inserted models can be evaluated with a similar `populate` method, which will\n", + "insert the relevant output from DLC's `evaluate_network` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a43ec42d-9f1d-4499-8f3f-e8e387ae9212", + "metadata": {}, + "outputs": [], + "source": [ + "dlc.ModelEvaluation.populate()\n", + "dlc.ModelEvaluation()" + ] + }, + { + "cell_type": "markdown", + "id": "fc4d764d-1458-49f7-86cb-3d3958b302c4", + "metadata": {}, + "source": [ + "To put this model to use, we'll conduct pose estimation on the video we made earlier.\n", + "Here, we can also specify parameters accepted by the `analyze_videos` function as a \n", + "dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f7b8a96a-72ab-489e-9325-46db98dc6bec", + "metadata": {}, + "outputs": [], + "source": [ + "key=(dlc.VideoRecording&'recording_id=2').fetch1('KEY');\n", + "key.update({'model_name': 'OpenField-1010', 'task_mode': 'trigger'})\n", + "dlc.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True},\n", + " skip_duplicates=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ad622b3-25d3-4da1-9506-bddb3a4f7f24", + "metadata": {}, + "outputs": [], + "source": [ + "dlc.PoseEstimation.populate()" + ] + }, + { + "cell_type": "markdown", + "id": "9d113e2d-54d5-4fbb-ad91-faf4a66c0879", + "metadata": {}, + "source": [ + "By default, DataJoint will store the results of pose estimation in a subdirectory\n", + "> processed_dir / videos / device_<#>_recording_<#>_model_\n", + "\n", + "Pulling processed_dir from `get_dlc_processed_dir`, and device/recording information \n", + "from the `VideoRecording` table. The model name is taken from the primary key of the\n", + "`Model` table, with spaced replaced by hyphens.\n", + " \n", + "We can get this estimation directly as a pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "257fbd67-ff7d-459f-a942-4f1774f31709", + "metadata": {}, + "outputs": [], + "source": [ + "dlc.PoseEstimation.get_trajectory(key)" ] }, { "cell_type": "markdown", "id": "b7a304e3-a5cb-4ad3-93ce-6bc130e08e26", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -1867,8 +1431,8 @@ "id": "c5ffe5d2-5b2a-45c3-8d8f-8c20efa8c5eb", "metadata": {}, "source": [ - "This notebook will describe the steps to explore the lab and animal management tables created by the elements.\n", - "Prior to using this notebook, please refer to the README for the installation instructions." + "This notebook will describe the steps to explore the lab and animal management tables \n", + "created by the elements. Prior to using this notebook, please refer to the README for the installation instructions." ] }, { @@ -1876,7 +1440,9 @@ "id": "ee820754-bceb-476a-acf9-238fa8b201d9", "metadata": {}, "source": [ - "Importing the module `workflow_behavior.pipeline` is sufficient to create tables inside the elements. This workflow comes prepackaged with example data and ingestion functions to populate lab, subject, and session tables." + "Importing the module `workflow_deeplabcut.pipeline` is sufficient to create tables \n", + "inside the elements. This workflow comes prepackaged with example data and ingestion \n", + "functions to populate lab, subject, and session tables." ] }, { @@ -1961,19 +1527,10 @@ "dj.Diagram(session)" ] }, - { - "cell_type": "markdown", - "id": "c510fe4d-09ed-472f-830f-4401bd6830d0", - "metadata": {}, - "source": [ - "(Workflow needs continued development to import geotyping tables)" - ] - }, { "cell_type": "markdown", "id": "b60f5f4c-d366-4034-a40d-2d2095cb2a14", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -2087,14 +1644,6 @@ "source": [ "## Insert into Manual and Lookup tables with Graphical User Interface" ] - }, - { - "cell_type": "markdown", - "id": "4775dd80-8a54-47b7-a9ba-99995db9ff1a", - "metadata": {}, - "source": [ - "DataJoint also provides a Graphical User Interface [DataJoint Labbook](https://github.com/datajoint/datajoint-labbook) to support manual data insertions into DataJoint workflows. ![DataJoint Labbook preview](https://github.com/datajoint/datajoint-labbook/blob/master/docs/sphinx/_static/images/walkthroughDemoOptimized.gif)" - ] } ], "metadata": { diff --git a/user_data/config_params.csv b/user_data/config_params.csv index d3ef1fc..dd2ed76 100644 --- a/user_data/config_params.csv +++ b/user_data/config_params.csv @@ -1,3 +1,4 @@ -paramset_idx,shuffle,train_fraction,filter_type,track_method,scorer_legacy -0,1,.95,,,False -1,0,.95,median,ellipse,False +paramset_idx,paramset_desc,config_path,shuffle,trainingsetindex,filter_type,track_method,scorer_legacy,maxiters +1,OpenField,openfield-Pranav-2018-10-30/config.yaml,1,0,,,False,5 +2,Reaching,Reaching-Mackenzie-2018-08-30/config.yaml,1,0,,,False,5 +3,ExtraExample,Example/config.yaml,0,0,median,ellipse,False,1 diff --git a/user_data/recordings.csv b/user_data/recordings.csv index d83292b..6727ada 100644 --- a/user_data/recordings.csv +++ b/user_data/recordings.csv @@ -1,5 +1,4 @@ -recording_id,subject,session_datetime,recording_start_time,file_path,camera_id,config_path,config_notes,paramset_idx -0,subject5,2020-04-15 11:16:38,2020-04-15 11:17:00,videos/reachingvideo1.avi,1,config.yaml,Reaching example provided by DeepLabCut repository,0 -1,subject6,2021-06-02 14:04:22,2021-06-02 14:07:00,videos/m3v1mp4.mp4,1,config.yaml,Openfield example provided by DeepLabCut repository,0 -2,subject6,2021-06-03 14:04:22,2021-06-04 14:07:00,videos/m3v1mp4-copy.mp4,1,config.yaml,Openfield example provided by DeepLabCut repository,0 - +recording_id,subject,session_datetime,recording_start_time,file_path,camera_id,paramset_idx +1,subject6,2021-06-02 14:04:22,2021-06-02 14:07:00,openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4,1,0 +2,subject6,2021-06-03 14:04:22,2021-06-04 14:07:00,openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4,1,0 +3,subject5,2020-04-15 11:16:38,2020-04-15 11:17:00,Reaching-Mackenzie-2018-08-30/videos/reachingvideo1.avi,1,1 diff --git a/user_data/sessions.csv b/user_data/sessions.csv index 9048b5b..2246678 100644 --- a/user_data/sessions.csv +++ b/user_data/sessions.csv @@ -1,4 +1,4 @@ subject,session_datetime,session_dir,session_note -subject5,2020-04-15 11:16:38,"Reaching-Mackenzie-2018-08-30/","Successful data collection, no notes" -subject6,2021-06-02 14:04:22,"openfield-Pranav-2018-10-30/","Model Training Session" -subject6,2021-06-03 14:04:22,"openfield-Pranav-2018-10-30/","Test Session" +subject5,2020-04-15 11:16:38,example-dir/subject5/,Successful data collection. No notes +subject6,2021-06-02 14:04:22,example-dir/subject6/,Model Training Session +subject6,2021-06-03 14:04:22,example-dir/subject6/,Test Session diff --git a/user_data/subjects.csv b/user_data/subjects.csv index 862269b..bf63d78 100644 --- a/user_data/subjects.csv +++ b/user_data/subjects.csv @@ -1,3 +1,3 @@ subject,sex,subject_birth_date,subject_description,death_date,cull_method subject5,F,2020-01-01 00:00:01,rich,2020-10-02 00:00:01,natural causes -subject6,M,2020-01-01 00:00:01,manuel,2020-10-03 00:00:01,natural causes \ No newline at end of file +subject6,M,2020-01-01 00:00:01,manuel,2020-10-03 00:00:01,natural causes diff --git a/workflow_deeplabcut/ingest.py b/workflow_deeplabcut/ingest.py index 18f8e23..be371e9 100644 --- a/workflow_deeplabcut/ingest.py +++ b/workflow_deeplabcut/ingest.py @@ -1,15 +1,18 @@ # from pathlib import Path import csv -from distutils.util import strtobool +import ruamel.yaml as yaml +from element_interface.utils import find_full_path + +from .pipeline import subject, session, dlc +from .paths import get_dlc_root_data_dir -from workflow_deeplabcut.pipeline import subject, session, dlc def ingest_general(csvs, tables, skip_duplicates=True): """ Inserts data from a series of csvs into their corresponding table: - e.g., ingest_general(['./lab_data.csv', './proj_data.csv'], - [lab.Lab(),lab.Project()] + e.g., ingest_general(['./lab.csv', './subject.csv'], + [lab.Lab(),subject.Subject()] ingest_general(csvs, tables, skip_duplicates=True) :param csvs: list of relative paths to CSV files :param tables: list of datajoint tables with () @@ -28,8 +31,8 @@ def ingest_general(csvs, tables, def ingest_subjects(subject_csv_path='./user_data/subjects.csv', skip_duplicates=True): """ - Inserts data from a subject csv into corresponding subject schema tables - By default, uses data from workflow_session/user_data/ + Inserts data from ./user_data/subject.csv into corresponding subject schema tables + :param subject_csv_path: relative path of subject csv :param skip_duplicates=True: datajoint insert function param """ @@ -43,7 +46,6 @@ def ingest_sessions(session_csv_path='./user_data/sessions.csv', """ Ingests to session schema from ./user_data/sessions.csv """ - # ingest to session schema csvs = [session_csv_path, session_csv_path, session_csv_path] tables = [session.Session(), session.SessionDirectory(), session.SessionNote()] @@ -51,19 +53,39 @@ def ingest_sessions(session_csv_path='./user_data/sessions.csv', ingest_general(csvs, tables, skip_duplicates=skip_duplicates) -def ingest_dlc_configs(recording_csv_path='./user_data/recordings.csv', - config_params_csv_path='./user_data/config_params.csv', - skip_duplicates=True): +def ingest_dlc_items(config_params_csv_path='./user_data/config_params.csv', + recording_csv_path='./user_data/recordings.csv', + skip_duplicates=True): """ - Ingests to DLC schema from ./user_data/recordings.csv + Ingests to DLC schema from ./user_data/{config_params,recordings}.csv + + First, loads config.yaml info to dlc.ModelTrainingParamSet. Requires paramset_idx, + paramset_desc and relative config_path. Other columns overwrite config variables + Next, loads recording info into dlc.VideoRecording and dlc.VideoRecording.File + :param config_params_csv_path: csv path for model training config and parameters + :param recording_csv_path: csv path for list of recordings """ - # First, ConfigParamSet + + previous_length = len(dlc.ModelTrainingParamSet.fetch()) with open(config_params_csv_path, newline='') as f: - config_params = list(csv.DictReader(f, delimiter=',')) - for paramset in config_params: - paramset['scorer_legacy'] = bool(strtobool(paramset['scorer_legacy'])) - dlc.ConfigParamSet.insert_new_params(**paramset, - skip_duplicates=skip_duplicates) + config_csv = list(csv.DictReader(f, delimiter=',')) + for line in config_csv: + paramset_idx = line.pop('paramset_idx') + paramset_desc = line.pop('paramset_desc') + config_path = find_full_path(get_dlc_root_data_dir(), + line.pop('config_path')) + assert config_path.exists(), f'Could not find config_path: {config_path}' + with open(config_path, 'rb') as y: + params = yaml.safe_load(y) + params.update({**line}) + + dlc.ModelTrainingParamSet.insert_new_params(paramset_idx=paramset_idx, + paramset_desc=paramset_desc, + params=params, + skip_duplicates=skip_duplicates) + insert_length = len(dlc.ModelTrainingParamSet.fetch()) - previous_length + print(f'\n---- Inserting {insert_length} entry(s) into #model_training_param_set ' + + '----') # Next, recordings and config files csvs = [recording_csv_path, recording_csv_path] @@ -74,4 +96,4 @@ def ingest_dlc_configs(recording_csv_path='./user_data/recordings.csv', if __name__ == '__main__': ingest_subjects() ingest_sessions() - ingest_dlc_configs() + ingest_dlc_items() diff --git a/workflow_deeplabcut/paths.py b/workflow_deeplabcut/paths.py index 46466c6..c556756 100644 --- a/workflow_deeplabcut/paths.py +++ b/workflow_deeplabcut/paths.py @@ -2,21 +2,20 @@ def get_dlc_root_data_dir(): - dlc_root_dirs = dj.config.get('custom', {}).get('dlc_root_data_dir', None) - return dlc_root_dirs if dlc_root_dirs else None - - -def get_session_directory(session_key: dict) -> str: - from .pipeline import session - session_dir = (session.SessionDirectory & session_key).fetch1('session_dir') - return session_dir + dlc_root_dirs = dj.config.get('custom', {}).get('dlc_root_data_dir') + if not dlc_root_dirs: + return None + elif not isinstance(dlc_root_dirs, list): + return list(dlc_root_dirs) + else: + return dlc_root_dirs -def get_dlc_processed_data_dir(session_key: dict) -> str: +def get_dlc_processed_data_dir() -> str: """ Returns session_dir relative to custom 'dlc_output_dir' root """ from pathlib import Path - dlc_output_dir = dj.config.get('custom', {}).get('dlc_output_dir', None) + dlc_output_dir = dj.config.get('custom', {}).get('dlc_output_dir') if dlc_output_dir: - return Path(dlc_output_dir, get_session_directory(session_key)) + return Path(dlc_output_dir) else: - return None + return get_dlc_root_data_dir()[0] diff --git a/workflow_deeplabcut/pipeline.py b/workflow_deeplabcut/pipeline.py index 62b9e96..e414140 100644 --- a/workflow_deeplabcut/pipeline.py +++ b/workflow_deeplabcut/pipeline.py @@ -8,11 +8,10 @@ from element_lab.lab import Source, Lab, Protocol, User, Project from element_session.session import Session -from .paths import get_dlc_root_data_dir, get_session_directory -from .paths import get_dlc_processed_data_dir +from .paths import get_dlc_root_data_dir, get_dlc_processed_data_dir -__all__ = ['get_dlc_root_data_dir', 'get_session_directory', - 'get_dlc_processed_data_dir', 'Subject', 'Source', 'Lab', 'Protocol', 'User', +__all__ = ['get_dlc_root_data_dir', 'get_dlc_processed_data_dir', + 'Subject', 'Source', 'Lab', 'Protocol', 'User', 'Project', 'Session'] if 'custom' not in dj.config: From 4c9cae12a90801a0e41c1df7f226b3e87170239f Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Tue, 8 Mar 2022 09:11:44 -0600 Subject: [PATCH 018/176] Draft notebooks. WIP Integration Tests --- notebooks/00-DataDownload_Optional.ipynb | 189 ++ notebooks/01-Configure.ipynb | 233 ++ notebooks/02-WorkflowStructure_Optional.ipynb | 1906 +++++++++++++++++ ...xplore_Workflow.ipynb => 03-Process.ipynb} | 1155 ++++------ notebooks/04-Automate_Optional.ipynb | 466 ++++ notebooks/05-Explore.ipynb | 461 ++++ notebooks/06-Drop_Optional.ipynb | 95 + tests/__init__.py | 126 +- tests/test_ingest.py | 8 +- 9 files changed, 3819 insertions(+), 820 deletions(-) create mode 100644 notebooks/00-DataDownload_Optional.ipynb create mode 100644 notebooks/01-Configure.ipynb create mode 100644 notebooks/02-WorkflowStructure_Optional.ipynb rename notebooks/{1_Explore_Workflow.ipynb => 03-Process.ipynb} (50%) create mode 100644 notebooks/04-Automate_Optional.ipynb create mode 100644 notebooks/05-Explore.ipynb create mode 100644 notebooks/06-Drop_Optional.ipynb diff --git a/notebooks/00-DataDownload_Optional.ipynb b/notebooks/00-DataDownload_Optional.ipynb new file mode 100644 index 0000000..ce26222 --- /dev/null +++ b/notebooks/00-DataDownload_Optional.ipynb @@ -0,0 +1,189 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# DataJoint U24 - Workflow DeepLabCut" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download example data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We've structured this tool around the example data available from the DLC. If you've already cloned the [main DLC repository](https://github.com/DeepLabCut/DeepLabCut), you already have this folder under `examples/openfield-Pranav-2018-10-30`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[This link](https://downgit.github.io/#/home?url=https://github.com/DeepLabCut/DeepLabCut/tree/master/examples/openfield-Pranav-2018-10-30) via [DownGit](https://downgit.github.io/) will start the single-directory download \n", + "automatically as a zip. Unpack this zip and place it in a directory we'll refer to as your root." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Directory structure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After downloading, the directory will be organized as follows within your chosen root\n", + "directory.\n", + "\n", + "```\n", + " /your-root/openfield-Pranav-2018-10-30/\n", + " - config.yaml\n", + " - labeled-data\n", + " - m4s1\n", + " - CollectedData_Pranav.csv\n", + " - CollectedData_Pranav.h5\n", + " - img0000.png\n", + " - img0001.png\n", + " - img0002.png\n", + " - img{...}.png\n", + " - img0114.png\n", + " - img0115.png\n", + " - videos\n", + " - m3v1mp4.mp4\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For those unfamiliar with DLC...\n", + "- `config.yaml` contains all the key parameters of the project, including\n", + " - file locations (currently empty)\n", + " - body parts\n", + " - cropping information\n", + "- `labeled-data` includes the frames coordinates for each body part in the training video\n", + "- `videos` includes the full training video for this example\n", + "\n", + "As part of the DeepLabCut demo setup process, you would run the following additional\n", + "command, as outlined in their \n", + "[demo notebook](https://github.com/DeepLabCut/DeepLabCut/blob/master/examples/JUPYTER/Demo_labeledexample_Openfield.ipynb).\n", + "These establishes the project path within the demo config file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from deeplabcut.create_project.demo_data import load_demo_data as dlc_load_demo\n", + "dlc_load_demo('/openfield-Pranav-2018-10-30/config.yaml')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For your own data, we recommend using the DLC gui to intitialize your project and label the data. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make new video" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Later, we'll use the first few seconds of the training video as a 'separate session' to model\n", + "the pose estimation feature of this pipeline. `ffmpeg` is a dependency of DeepLabCut\n", + "that can splice the training video for a demonstration purposes. The command below saves\n", + "the first 2 seconds of the training video as a copy.\n", + "\n", + "- `-n` do not overwrite\n", + "- `-hide_banner -loglevel error` less verbose output\n", + "- `-ss 0 -t 2` start at second 0, add 2 seconds\n", + "- `-i {vid_path}` input this video - be sure to change the root\n", + "- `-{v/a}codec copy` copy the video and audio codecs of the input\n", + "- `{vid_path}-copy.mp4` output file" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sh: your-root: No such file or directory\n" + ] + }, + { + "data": { + "text/plain": [ + "256" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vid_path = '/openfield-Pranav-2018-10-30/videos/m3v1mp4'\n", + "cmd = (f'ffmpeg -n -hide_banner -loglevel error -ss 0 -t 2 -i {vid_path}.mp4 '\n", + " + f'-vcodec copy -acodec copy {vid_path}-copy.mp4')\n", + "import os; os.system(cmd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next notebook, [01-Configure](./01-Configure.ipynb), we'll set up the DataJoint config file with a pointer to your root data directory." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "toc-autonumbering": false, + "toc-showcode": false, + "toc-showmarkdowntxt": false + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/01-Configure.ipynb b/notebooks/01-Configure.ipynb new file mode 100644 index 0000000..dc978b7 --- /dev/null +++ b/notebooks/01-Configure.ipynb @@ -0,0 +1,233 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# DataJoint U24 - Workflow DeepLabCut" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Configure DataJoint" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "- To run `workflow-deeplabcut`, we need to set up the DataJoint configuration file, called `dj_local_conf.json`, unique to each machine.\n", + "\n", + "- The config only needs to be set up once. If you have gone through the configuration before, directly go to [02-Workflow-Structure](./02-WorkflowStructure_Optional.ipynb).\n", + "\n", + "- By convention, we set the config up in the root directory of `workflow-deeplabcut` package. After you set up DataJoint once, you may be interested in [setting a global config](https://docs.datajoint.org/python/setup/01-Install-and-Connect.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import datajoint as dj\n", + "from pathlib import Path\n", + "# change to the upper level folder to detect dj_local_conf.json\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", + " + \"workflow directory\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure database host address and credentials" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's set up the host, user and password in the `dj.config` following [instructions here](https://tutorials.datajoint.io/setting-up/get-database.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "dj.config['database.host'] = '{YOUR_HOST}'\n", + "dj.config['database.user'] = '{YOUR_USERNAME}'\n", + "dj.config['database.password'] = getpass.getpass() # enter the password securely" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should be able to connect to the database at this stage." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.conn()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure the `custom` field in `dj.config` for element-deeplabcut" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Prefix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Giving a prefix to your schema could help manage privelages on a server. \n", + "- If we set prefix `neuro_`, every schema created with the current workflow will start with `neuro_`, e.g. `neuro_lab`, `neuro_subject`, `neuro_imaging` etc.\n", + "- Teams who work on the same schemas should use the same prefix, set as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config['custom'] = {'database.prefix': 'neuro_'}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Root directory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `custom` field also keeps track of your root directory with `dlc_root_data_dir`. It can even accept roots. element-deeplabcut will always figure out which root to use based on the files it expects there. \n", + "\n", + "- Please set one root to the parent directory of DLC's `openfield-Pranav-2018-10-30` example.\n", + "- In other cases, this should be the parent of your DLC project path." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config['custom'] = {'dlc_root_data_dir' : ['your-root1', 'your-root2']}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check that find the path connects." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from element_interface.utils import find_full_path\n", + "data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'],\n", + " 'openfield-Pranav-2018-10-30')\n", + "assert data_dir.exists(), \"Please check the that you have the folder openfield-Pranav\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save the config as a json file\n", + "\n", + "With the proper configurations, we could save this as a file, either as a local json file, or a global file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config.save_local()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The local config is saved as `dj_local_conf.json` in the root directory of this `workflow-deeplabcut`. Next time you import DataJoint while in this directory, the same settings will be loaded.\n", + "\n", + "If saved globally, there will be a hidden configuration file saved in your computer's root directory that will be loaded when no local version is present." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# dj.config.save_global()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the [next notebook](./02-WorkflowStructure_Optional.ipynb) notebook, we'll explore the workflow structure." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/02-WorkflowStructure_Optional.ipynb b/notebooks/02-WorkflowStructure_Optional.ipynb new file mode 100644 index 0000000..94d720d --- /dev/null +++ b/notebooks/02-WorkflowStructure_Optional.ipynb @@ -0,0 +1,1906 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# DataJoint U24 - Workflow DeepLabCut" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook gives a brief overview and introduces some useful DataJoint tools to facilitate the exploration.\n", + "\n", + "+ DataJoint needs to be configured before running this notebook, if you haven't done so, refer to the [01-Configure](./01-Configure.ipynb) notebook.\n", + "+ If you are familar with DataJoint and the workflow structure, proceed to the next notebook [03-Process](./03-Process.ipynb) directly to run the workflow.\n", + "+ For a more thorough introduction of DataJoint functionings, please visit our [general tutorial site](http://codebook.datajoint.io/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To load the local configuration, we will change the directory to the package root." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os; from pathlib import Path\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", + " + \"workflow directory\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Schemas and tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By importing from `workflow_deeplabcut`, we'll run the activation functions that declare the tables in these schemas. If these tables are already declared, we'll gain access." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting cbroz@tutorial-db.datajoint.io:3306\n" + ] + } + ], + "source": [ + "import datajoint as dj\n", + "from workflow_deeplabcut.pipeline import lab, subject, session, dlc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each module contains a schema object that enables interaction with the schema in the database. For more information abotu managing the upstream tables, see our [session workflow](https://github.com/datajoint/workflow-session). In this case, lab is required because the pipeline adds a `device` table to the lab schema to keep track of camera IDs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0, + "title": "The schemas and tables will not be re-created when importing modules if they have existed." + }, + "source": [ + "`dj.list_schemas()` lists all schemas a user has access to in the current database" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "title": "`dj.list_schemas()`: list all schemas a user could access." + }, + "outputs": [], + "source": [ + "dj.list_schemas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`.schema.list_tables()` will provide names for each table in the format used under the hood." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "title": "Each module imported above corresponds to one schema inside the database. For example, `ephys` corresponds to `neuro_ephys` schema in the database." + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['video_recording',\n", + " 'video_recording__file',\n", + " '#model_training_param_set',\n", + " '#body_part',\n", + " 'model',\n", + " 'model__body_part',\n", + " 'training_task',\n", + " '__model_evaluation',\n", + " 'pose_estimation_task',\n", + " '__model_training',\n", + " '__pose_estimation',\n", + " '__pose_estimation__body_part_position']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dlc.schema.list_tables()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`dj.Diagram()` plots tables and dependencies in a schema" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "title": "`dj.Diagram()`: plot tables and dependencies" + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.Model.BodyPart\n", + "\n", + "\n", + "dlc.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelTraining\n", + "\n", + "\n", + "dlc.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "dlc.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.TrainingTask\n", + "\n", + "\n", + "dlc.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.TrainingTask->dlc.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "dlc.PoseEstimation\n", + "\n", + "\n", + "dlc.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.PoseEstimation->dlc.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording\n", + "\n", + "\n", + "dlc.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording->dlc.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording.File\n", + "\n", + "\n", + "dlc.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording->dlc.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "dlc.PoseEstimationTask\n", + "\n", + "\n", + "dlc.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording->dlc.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelEvaluation\n", + "\n", + "\n", + "dlc.ModelEvaluation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.Model\n", + "\n", + "\n", + "dlc.Model\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.Model->dlc.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "dlc.Model->dlc.ModelEvaluation\n", + "\n", + "\n", + "\n", + "\n", + "dlc.Model->dlc.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelTrainingParamSet\n", + "\n", + "\n", + "dlc.ModelTrainingParamSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelTrainingParamSet->dlc.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelTrainingParamSet->dlc.Model\n", + "\n", + "\n", + "\n", + "\n", + "dlc.BodyPart\n", + "\n", + "\n", + "dlc.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.BodyPart->dlc.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "dlc.BodyPart->dlc.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "dlc.PoseEstimationTask->dlc.PoseEstimation\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.Diagram(dlc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Table tiers \n", + "- **Manual table**: green box, manually inserted table, expect new entries daily, e.g. Subject, ProbeInsertion. \n", + "- **Lookup table**: gray box, pre inserted table, commonly used for general facts or parameters. e.g. Strain, ClusteringMethod, ClusteringParamSet. \n", + "- **Imported table**: blue oval, auto-processing table, the processing depends on the importing of external files. e.g. process of Clustering requires output files from kilosort2. \n", + "- **Computed table**: red circle, auto-processing table, the processing does not depend on files external to the database, commonly used for \n", + "- **Part table**: plain text, as an appendix to the master table, all the part entries of a given master entry represent a intact set of the master entry. e.g. Unit of a CuratedClustering.\n", + "\n", + "### Dependencies\n", + "\n", + "- **One-to-one primary**: thick solid line, share the exact same primary key, meaning the child table inherits all the primary key fields from the parent table as its own primary key. \n", + "- **One-to-many primary**: thin solid line, inherit the primary key from the parent table, but have additional field(s) as part of the primary key as well\n", + "- **secondary dependency**: dashed line, the child table inherits the primary key fields from parent table as its own secondary attribute." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "title": "`dj.Diagram()`: plot the diagram of the tables and dependencies. It could be used to plot tables in a schema or selected tables." + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line.Allele\n", + "\n", + "\n", + "subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelTraining\n", + "\n", + "\n", + "dlc.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Strain\n", + "\n", + "\n", + "subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "dlc.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Line\n", + "\n", + "\n", + "subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelEvaluation\n", + "\n", + "\n", + "dlc.ModelEvaluation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.Model\n", + "\n", + "\n", + "dlc.Model\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.Model->dlc.ModelEvaluation\n", + "\n", + "\n", + "\n", + "\n", + "dlc.PoseEstimationTask\n", + "\n", + "\n", + "dlc.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.Model->dlc.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "dlc.Model.BodyPart\n", + "\n", + "\n", + "dlc.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.Model->dlc.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelTrainingParamSet\n", + "\n", + "\n", + "dlc.ModelTrainingParamSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelTrainingParamSet->dlc.Model\n", + "\n", + "\n", + "\n", + "\n", + "dlc.TrainingTask\n", + "\n", + "\n", + "dlc.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelTrainingParamSet->dlc.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "dlc.BodyPart\n", + "\n", + "\n", + "dlc.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.BodyPart->dlc.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "dlc.BodyPart->dlc.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.User\n", + "\n", + "\n", + "subject.Subject.User\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.PoseEstimation\n", + "\n", + "\n", + "dlc.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.PoseEstimationTask->dlc.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionDirectory\n", + "\n", + "\n", + "session.SessionDirectory\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.ProjectSession\n", + "\n", + "\n", + "session.ProjectSession\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionExperimenter\n", + "\n", + "\n", + "session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.TrainingTask->dlc.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Source\n", + "\n", + "\n", + "subject.Subject.Source\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele.Source\n", + "\n", + "\n", + "subject.Allele.Source\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.SubjectCullMethod\n", + "\n", + "\n", + "subject.SubjectCullMethod\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Lab\n", + "\n", + "\n", + "subject.Subject.Lab\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording.File\n", + "\n", + "\n", + "dlc.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Zygosity\n", + "\n", + "\n", + "subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.PoseEstimation->dlc.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "subject.SubjectDeath\n", + "\n", + "\n", + "subject.SubjectDeath\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line->subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line->subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele\n", + "\n", + "\n", + "subject.Allele\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Allele.Source\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionNote\n", + "\n", + "\n", + "session.SessionNote\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording\n", + "\n", + "\n", + "dlc.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording->dlc.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording->dlc.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording->dlc.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Strain->subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionDirectory\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.ProjectSession\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionNote\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->dlc.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.User\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Source\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.SubjectCullMethod\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Lab\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.SubjectDeath\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->session.Session\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# plot diagram of tables in multiple schemas\n", + "dj.Diagram(subject) + dj.Diagram(session) + dj.Diagram(dlc)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['#device',\n", + " '#user_role',\n", + " '#skull_reference',\n", + " '#lab',\n", + " 'equipment',\n", + " 'equipment__ca_img_equipment',\n", + " 'equipment__ephys_equipment',\n", + " '#user',\n", + " '#protocol_type',\n", + " '#source',\n", + " '#project',\n", + " '#location',\n", + " '#lab_membership',\n", + " '#protocol',\n", + " '#project__keywords',\n", + " '#project__publication',\n", + " '#project__sourcecode',\n", + " 'project_keywords',\n", + " 'project_publication',\n", + " 'project_source_code',\n", + " 'project_user']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lab.schema.list_tables()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.PoseEstimationTask\n", + "\n", + "\n", + "dlc.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording\n", + "\n", + "\n", + "dlc.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->dlc.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->session.Session\n", + "\n", + "\n", + "\n", + "\n", + "dlc.TrainingTask\n", + "\n", + "\n", + "dlc.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording->dlc.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording->dlc.TrainingTask\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# plot diagram of selected tables and schemas\n", + "(dj.Diagram(subject.Subject) + dj.Diagram(session.Session) \n", + " + dj.Diagram(dlc.VideoRecording) + dj.Diagram(dlc.TrainingTask)\n", + " + dj.Diagram(dlc.PoseEstimationTask)) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "title": "Each datajoint table class inside the module corresponds to a table inside the schema. For example, the class `ephys.EphysRecording` correponds to the table `_ephys_recording` in the schema `neuro_ephys` in the database." + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    camera_id

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    file_path

    \n", + " filepath of video, relative to root data directory\n", + "
    subject52020-04-15 11:16:3813Reaching-Mackenzie-2018-08-30/videos/reachingvideo1.avi
    subject62021-06-02 14:04:2211openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4
    subject62021-06-03 14:04:2212openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4
    \n", + " \n", + "

    Total: 3

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *camera_id *recording_id *file_path \n", + "+----------+ +------------+ +-----------+ +------------+ +------------+\n", + "subject5 2020-04-15 11: 1 3 Reaching-Macke\n", + "subject6 2021-06-02 14: 1 1 openfield-Pran\n", + "subject6 2021-06-03 14: 1 2 openfield-Pran\n", + " (Total: 3)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# preview columns and contents in a table\n", + "dlc.VideoRecording.File()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0, + "title": "`heading`:" + }, + "source": [ + "`describe()` shows table definition with foreign key references" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Specification for a DLC model training instance\n", + "-> dlc.VideoRecording\n", + "-> dlc.ModelTrainingParamSet\n", + "training_id : int \n", + "---\n", + "model_prefix=\"\" : varchar(32) \n", + "project_path=\"\" : varchar(255) # DLC's project_path in config relative to root\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "'# Specification for a DLC model training instance\\n-> dlc.VideoRecording\\n-> dlc.ModelTrainingParamSet\\ntraining_id : int \\n---\\nmodel_prefix=\"\" : varchar(32) \\nproject_path=\"\" : varchar(255) # DLC\\'s project_path in config relative to root\\n'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dlc.TrainingTask.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`heading` shows attribute definitions regardless of foreign key references" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "title": "`heading`: show table attributes regardless of foreign key references." + }, + "outputs": [ + { + "data": { + "text/plain": [ + "# \n", + "model_name : varchar(64) # user-friendly model name\n", + "---\n", + "task : varchar(32) # task in the config yaml\n", + "date : varchar(16) # date in the config yaml\n", + "iteration : int # iteration/version of this model\n", + "snapshotindex : int # which snapshot for prediction (if -1, latest)\n", + "shuffle : int # which shuffle of the training dataset\n", + "trainingsetindex : int # which training set fraction to generate model\n", + "scorer : varchar(64) # scorer/network name - DLC's GetScorerName()\n", + "config_template : longblob # dictionary of the config for analyze_videos()\n", + "project_path : varchar(255) # DLC's project_path in config relative to root\n", + "dlc_version : varchar(8) # keeps the deeplabcut version\n", + "model_prefix=\"\" : varchar(32) # \n", + "model_description=\"\" : varchar(1000) # \n", + "paramset_idx=null : smallint # " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dlc.Model.heading" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "title": "ephys" + }, + "source": [ + "## Other Elements installed with the workflow\n", + "\n", + "[`lab`](https://github.com/datajoint/element-lab): lab management related information, such as Lab, User, Project, Protocol, Source." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(lab)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[`subject`](https://github.com/datajoint/element-animal): general animal information, User, Genetic background, Death etc." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele\n", + "\n", + "\n", + "subject.Allele\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Zygosity\n", + "\n", + "\n", + "subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line.Allele\n", + "\n", + "\n", + "subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele.Source\n", + "\n", + "\n", + "subject.Allele.Source\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Allele.Source\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Strain\n", + "\n", + "\n", + "subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line->subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Line\n", + "\n", + "\n", + "subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line->subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Source\n", + "\n", + "\n", + "subject.Subject.Source\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.User\n", + "\n", + "\n", + "subject.Subject.User\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.SubjectDeath\n", + "\n", + "\n", + "subject.SubjectDeath\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Strain->subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "subject.SubjectCullMethod\n", + "\n", + "\n", + "subject.SubjectCullMethod\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Lab\n", + "\n", + "\n", + "subject.Subject.Lab\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Source\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.User\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.SubjectDeath\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.SubjectCullMethod\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Lab\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Line\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.Diagram(subject)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "title": "[subject](https://github.com/datajoint/element-animal): contains the basic information of subject, including Strain, Line, Subject, Zygosity, and SubjectDeath information." + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Animal Subject\n", + "subject : varchar(32) \n", + "---\n", + "sex : enum('M','F','U') \n", + "subject_birth_date : date \n", + "subject_description=\"\" : varchar(1024) \n", + "\n" + ] + } + ], + "source": [ + "subject.Subject.describe();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[`session`](https://github.com/datajoint/element-session): General information of experimental sessions." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionDirectory\n", + "\n", + "\n", + "session.SessionDirectory\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.ProjectSession\n", + "\n", + "\n", + "session.ProjectSession\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionDirectory\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.ProjectSession\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.Diagram(session)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "title": "[session](https://github.com/datajoint/element-session): experimental session information" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-> subject.Subject\n", + "session_datetime : datetime(3) \n", + "\n" + ] + } + ], + "source": [ + "session.Session.describe();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary and next step\n", + "\n", + "- This notebook introduced the overall structures of the schemas and tables in the workflow and relevant tools to explore the schema structure and table definitions.\n", + "\n", + "- The [next notebook](./03-Process.ipynb) will introduce the detailed steps to run through `workflow-deeplabcut`." + ] + } + ], + "metadata": { + "jupytext": { + "encoding": "# -*- coding: utf-8 -*-" + }, + "kernelspec": { + "display_name": "venv-dlc", + "language": "python", + "name": "venv-dlc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/1_Explore_Workflow.ipynb b/notebooks/03-Process.ipynb similarity index 50% rename from notebooks/1_Explore_Workflow.ipynb rename to notebooks/03-Process.ipynb index 93e3321..57f7585 100644 --- a/notebooks/1_Explore_Workflow.ipynb +++ b/notebooks/03-Process.ipynb @@ -2,41 +2,36 @@ "cells": [ { "cell_type": "markdown", - "id": "d26010d6-acbc-4c90-8b62-a2448c50452d", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "# DataJoint U24 - Workflow Behavior" + "# DataJoint U24 - Workflow DeepLabCut" ] }, { "cell_type": "markdown", - "id": "b811f8c8-5851-445e-bfba-b61dfd68388d", - "metadata": { - "tags": [] - }, + "metadata": {}, "source": [ - "First, please install both `element-deeplabcut` and `workflow-deeplabcut` locally. We \n", - "recommend launching a new conda environment and using `pip install -e ./    `. For more\n", - "information, see our [install instructions](https://github.com/kabilar/datajoint-elements/blob/main/install.md). " + "## Interactively run the workflow\n", + "\n", + "The workflow requires a DeepLabCut project with labeled data.\n", + "- If you haven't configured the data, refer to [00-DataDownload](./00-DataDownload_Optional.ipynb) and [01-Configure](./01-Configure.ipynb).\n", + "- To overview the schema structures, refer to [02-WorkflowStructure](02-WorkflowStructure_Optional.ipynb).\n", + "- If you'd likea more automatic approach, refer to [03-Automate](03-Automate_optional.ipynb)." ] }, { "cell_type": "markdown", - "id": "a9152fd1-faed-4492-8a1b-fc0b04bb54f0", - "metadata": { - "tags": [] - }, + "metadata": {}, "source": [ - "Next, let's change directory to the main workflow directory." + "Let's change the directory to the package root directory to load the local config, `dj_local_conf.json`." ] }, { "cell_type": "code", "execution_count": 1, - "id": "8b0d2410-e307-49ee-8adf-451bf7b24edc", - "metadata": { - "tags": [] - }, + "metadata": {}, "outputs": [], "source": [ "import os; from pathlib import Path\n", @@ -48,161 +43,123 @@ }, { "cell_type": "markdown", - "id": "bf1e4ef2-522d-4a45-a06c-2ba685dfb88c", "metadata": {}, "source": [ - "Second, download the example data we'll be using from the DeepLabCut repository. We will\n", - "use the [example openfield data](https://github.com/DeepLabCut/DeepLabCut/tree/master/examples/openfield-Pranav-2018-10-30) \n", - "from the DeepLabCut github repository. If you have already cloned this repository, you \n", - "may have this data on your machine already. [This link](https://downgit.github.io/#/home?url=https://github.com/DeepLabCut/DeepLabCut/tree/master/examples/openfield-Pranav-2018-10-30) via [DownGit](https://downgit.github.io/) will start the single-directory download \n", - "automatically. After downloading, please add the path to this directory to the `custom`\n", - "field of your datajoint config file as `dlc_root_data_dir`. " + "`Pipeline.py` activates the DataJoint `elements` and declares other required tables." ] }, { "cell_type": "code", "execution_count": 2, - "id": "0ad68223-3600-4da9-a3e7-141962fefaf6", - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj; dj.config.load('dj_local_conf.json')\n", - "from element_interface.utils import find_full_path\n", - "data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'],\n", - " 'openfield-Pranav-2018-10-30')\n", - "assert data_dir.exists(), \"Please check the that you have the folder openfield-Pranav\"" - ] - }, - { - "cell_type": "markdown", - "id": "343dbd62-8f60-4802-9106-bd5aea2bfe9e", - "metadata": {}, - "source": [ - "As part of the DeepLabCut demo setup process, you would run the following additional\n", - "commands, as outlined in their \n", - "[demo notebook](https://github.com/DeepLabCut/DeepLabCut/blob/master/examples/JUPYTER/Demo_labeledexample_Openfield.ipynb).\n", - "These steps establish the project path within the demo config file." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "640f83f8-23c3-4058-894f-5fce872e8e18", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Loaded, now creating training data...\n", - "The training dataset is successfully created. Use the function 'train_network' to start training. Happy training!\n" + "Connecting cbroz@tutorial-db.datajoint.io:3306\n" ] } ], "source": [ - "from deeplabcut.create_project.demo_data import load_demo_data as dlc_load_demo\n", - "dlc_load_demo(data_dir / 'config.yaml')" + "import datajoint as dj\n", + "from workflow_deeplabcut.pipeline import lab, subject, session, dlc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inserting entries into upstream tables" ] }, { "cell_type": "markdown", - "id": "43dcb79d-72f8-468a-be2a-59866505b888", "metadata": {}, "source": [ - "Later, we'll use the first few seconds of the training video as a 'separate session' to model\n", - "the pose estimation feature of this pipeline. `ffmpeg` is a dependency of DeepLabCut\n", - "that can splice the training video for a demonstration purposes. The command below saves\n", - "the first 2 seconds of the training video as a copy." + "In general, you can manually insert entries into each table by directly providing values for each column as a dictionary. Be sure to follow the type specified in the table definition." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "10218fae-c1ab-43cb-8c6a-37dcd38f8ff6", - "metadata": { - "tags": [] - }, + "execution_count": 5, + "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "File '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4' already exists. Exiting.\n" - ] - }, { "data": { "text/plain": [ - "256" + "subject : varchar(8) # \n", + "---\n", + "sex : enum('M','F','U') # \n", + "subject_birth_date : date # \n", + "subject_description=\"\" : varchar(1024) # " ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "vid_path = str(data_dir).replace(\" \", \"\\ \") + '/videos/m3v1mp4'\n", - "cmd = (f'ffmpeg -n -hide_banner -loglevel error -ss 2 -i {vid_path}.mp4 -vcodec copy '\n", - " + f'-acodec copy {vid_path}-copy.mp4')\n", - "os.system(cmd)" + "subject.Subject.heading" ] }, { - "cell_type": "markdown", - "id": "9ac69bc0-4e62-4094-b506-39dcc9f93515", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "Now, we can activate the `dlc` schema and import some data from files stored in this\n", - "directory under `user_data/.csv`. Subject and session data imports like these are \n", - "common across DataJoint workflows. They include fields like `subject_birth_date` and \n", - "`session_datetime`\n", - "\n", - "The recordings file specifies all videos across sessions, including both model training\n", - "videos and videos for later analysis. The config parameter csv is used in the \n", - "`ModelTrainingParamSet` table, which features a longblob field for any parameters \n", - "required in model training. Both shuffle and trainingsetindex are required in this \n", - "field, but many others can be added to later be passed to DLC's `train_model` function.\n", - "In this case, `maxiters` to only run a handful of training iterations for our example \n", - "model." + "subject.Subject.insert1(dict(subject='subject6', \n", + " sex='M', \n", + " subject_birth_date='2020-01-01', \n", + " subject_description='manuel'))" ] }, { "cell_type": "code", - "execution_count": 1, - "id": "d25b109d-c8b2-46f6-8fde-cbd9135cdfc3", + "execution_count": 7, "metadata": {}, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'workflow_deeplabcut'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_12766/1191413193.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mworkflow_deeplabcut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipeline\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlab\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdlc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mworkflow_deeplabcut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mingest\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mingest_subjects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mingest_sessions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mingest_dlc_items\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mingest_subjects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mingest_sessions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mingest_dlc_items\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'workflow_deeplabcut'" + "name": "stdout", + "output_type": "stream", + "text": [ + "-> Subject\n", + "session_datetime : datetime \n", + "\n" ] } ], "source": [ - "from workflow_deeplabcut.pipeline import lab, subject, session, dlc\n", - "from workflow_deeplabcut.ingest import ingest_subjects, ingest_sessions, ingest_dlc_items\n", - "ingest_subjects(); ingest_sessions(); ingest_dlc_items()" + "session.Session.describe();" ] }, { - "cell_type": "markdown", - "id": "d2738f21-591a-406b-8cd0-9a43ff6273d6", + "cell_type": "code", + "execution_count": 8, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# \n", + "subject : varchar(8) # \n", + "session_datetime : datetime # " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "Let's look at the tables this populated." + "session.Session.heading" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "95434ff7-d60c-40df-9ebc-c0ca3f99fa32", + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -258,317 +215,96 @@ " }\n", " \n", " \n", - " Animal Subject\n", + " \n", "
    \n", " \n", " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + " \n", + "\n", "
    \n", "

    subject

    \n", " \n", "
    \n", - "

    sex

    \n", - " \n", - "
    \n", - "

    subject_birth_date

    \n", - " \n", - "
    \n", - "

    subject_description

    \n", + "

    session_datetime

    \n", " \n", "
    subject1M2020-12-30test animal
    subject2F2020-11-30test animal
    subject3F2020-12-30test animal
    subject4M2021-02-12test animal
    subject5F2020-01-01rich
    subject6M2020-01-01manuel
    subject7U2020-08-30test animal
    subject8F2020-09-30test animal
    subjectXF2020-01-01manuel
    subjectYM2020-01-01manuel
    subjectZM2020-01-01manuel
    subject32021-04-30 12:22:15.032000
    \n", " \n", - "

    Total: 11

    \n", + "

    Total: 1

    \n", " " ], "text/plain": [ - "*subject sex subject_birth_ subject_descri\n", - "+----------+ +-----+ +------------+ +------------+\n", - "subject1 M 2020-12-30 test animal \n", - "subject2 F 2020-11-30 test animal \n", - "subject3 F 2020-12-30 test animal \n", - "subject4 M 2021-02-12 test animal \n", - "subject5 F 2020-01-01 rich \n", - "subject6 M 2020-01-01 manuel \n", - "subject7 U 2020-08-30 test animal \n", - "subject8 F 2020-09-30 test animal \n", - "subjectX F 2020-01-01 manuel \n", - "subjectY M 2020-01-01 manuel \n", - "subjectZ M 2020-01-01 manuel \n", - " (Total: 11)" + "*subject *session_datet\n", + "+----------+ +------------+\n", + "subject3 2021-04-30 12:\n", + " (Total: 1)" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "subject.Subject()" + "session_keys = [dict(subject='subject6', session_datetime='2021-06-02 14:04:22'),\n", + " dict(subject='subject6', session_datetime='2021-06-03 14:04:22')]\n", + "session.Session.insert(session_keys)\n", + "session.Session()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inserting recordings" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "057f467c-a012-4425-8bec-960b1ae153bd", + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    session_dir

    \n", - " Path to the data directory for a session\n", - "
    \n", - "

    session_note

    \n", - " \n", - "
    subject52020-04-15 11:16:38Reaching-Mackenzie-2018-08-30/Successful data collection, no notes
    subject62021-06-02 14:04:22openfield-Pranav-2018-10-30/Model Training Session
    subject62021-06-03 14:04:22openfield-Pranav-2018-10-30/Test Session
    \n", - " \n", - "

    Total: 3

    \n", - " " - ], "text/plain": [ - "*subject *session_datet session_dir session_note \n", - "+----------+ +------------+ +------------+ +------------+\n", - "subject5 2020-04-15 11: Reaching-Macke Successful dat\n", - "subject6 2021-06-02 14: openfield-Pran Model Training\n", - "subject6 2021-06-03 14: openfield-Pran Test Session \n", - " (Total: 3)" + "# \n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "camera_id : int # \n", + "recording_id : int # \n", + "---\n", + "recording_start_time : datetime # " ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "session.Session * session.SessionDirectory * session.SessionNote" + "dlc.VideoRecording.heading" ] }, { "cell_type": "markdown", - "id": "63b3e2a4-a860-4ccb-a2b0-62fafb7d412c", "metadata": {}, "source": [ - "Note that the video recording filepaths are specified relative to the root directory\n", - "defined within the workflow. This allows multiple users to operate on the same \n", - "filestructures across different machines. Because the root directory is passed as a \n", - "list, there can be multiple root directories on a given machine." + "The `VideoRecording` table retains unique recordings file specifies all videos across sessions, including both model training\n", + "videos and videos for later analysis." ] }, { "cell_type": "code", "execution_count": 8, - "id": "e043aeae-0039-43db-ac18-4fb198aa5b76", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Root: /Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/\n", - "Root: /Users/cb/Documents/U24_SampleData/\n" - ] - }, { "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    camera_id

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", - " \n", - "
    \n", - "

    file_path

    \n", - " filepath of video, relative to root data directory\n", - "
    subject52020-04-15 11:16:3813Reaching-Mackenzie-2018-08-30/videos/reachingvideo1.avi
    subject62021-06-02 14:04:2211openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4
    subject62021-06-03 14:04:2212openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4
    \n", - " \n", - "

    Total: 3

    \n", - " " - ], "text/plain": [ - "*subject *session_datet *camera_id *recording_id *file_path \n", - "+----------+ +------------+ +-----------+ +------------+ +------------+\n", - "subject5 2020-04-15 11: 1 3 Reaching-Macke\n", - "subject6 2021-06-02 14: 1 1 openfield-Pran\n", - "subject6 2021-06-03 14: 1 2 openfield-Pran\n", - " (Total: 3)" + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "camera_id : int # \n", + "recording_id : int # \n", + "file_path : varchar(255) # filepath of video, relative to root data directory" ] }, "execution_count": 8, @@ -577,219 +313,180 @@ } ], "source": [ - "from workflow_deeplabcut.paths import get_dlc_root_data_dir\n", - "for d in get_dlc_root_data_dir():\n", - " print(f'Root: {d}')\n", - "dlc.VideoRecording.File()" + "dlc.VideoRecording.File.heading" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The related part table allows for multiple files for a given recording session." ] }, { "cell_type": "code", "execution_count": 9, - "id": "bf24a52f-9e3e-411c-9001-523ae498988b", + "metadata": {}, + "outputs": [], + "source": [ + "recordings = [{'recording_id': '1',\n", + " 'subject': 'subject6',\n", + " 'session_datetime': '2021-06-02 14:04:22',\n", + " 'recording_start_time': '2021-06-02 14:07:00',\n", + " 'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4',\n", + " 'camera_id': '1',\n", + " 'paramset_idx': '0'},\n", + " {'recording_id': '2',\n", + " 'subject': 'subject6',\n", + " 'session_datetime': '2021-06-03 14:04:22',\n", + " 'recording_start_time': '2021-06-04 14:07:00',\n", + " 'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4',\n", + " 'camera_id': '1',\n", + " 'paramset_idx': '0'}\n", + "dlc.VideoRecording.File.insert(recordings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training a DLC Network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we'll add a `ModelTrainingParamSet`. This is a lookup table that we can reference when training a model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " Parameters to specify a DLC model training instance\n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    paramset_idx

    \n", - " \n", - "
    \n", - "

    paramset_desc

    \n", - " \n", - "
    \n", - "

    param_set_hash

    \n", - " hash identifying this parameterset\n", - "
    \n", - "

    params

    \n", - " dictionary of all applicable parameters\n", - "
    1OpenFieldacf342ee-75e0-6782-b5ef-f0d7d359aa17=BLOB=
    2Reaching8ea3dc9b-e9eb-2709-97ee-9abe32068830=BLOB=
    3ExtraExampleee0be706-5703-acbb-0b8b-6c8e56d8ac68=BLOB=
    \n", - " \n", - "

    Total: 3

    \n", - " " - ], "text/plain": [ - "*paramset_idx paramset_desc param_set_hash params \n", - "+------------+ +------------+ +------------+ +--------+\n", - "1 OpenField acf342ee-75e0- =BLOB= \n", - "2 Reaching 8ea3dc9b-e9eb- =BLOB= \n", - "3 ExtraExample ee0be706-5703- =BLOB= \n", - " (Total: 3)" + "# Parameters to specify a DLC model training instance\n", + "paramset_idx : smallint # \n", + "---\n", + "paramset_desc : varchar(128) # \n", + "param_set_hash : uuid # hash identifying this parameterset\n", + "params : longblob # dictionary of all applicable parameters" ] }, - "execution_count": 9, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.ModelTrainingParamSet()" + "dlc.ModelTrainingParamSet.heading" ] }, { "cell_type": "markdown", - "id": "cd5622ce-d1ab-48d7-ae53-ca133e96aa97", "metadata": {}, "source": [ - "We can take a closer look at the parameters specified with the `fetch` command. Using\n", - "the `ingest_dlc_items`, this naturally captures the full `config.yaml`" + "The `params` longblob should be a dictionary that includes all items to be included in model training via the `train_network` function. At minimum, this is the contents of the project's config file, as well as `suffle` and `trainingsetindex`, which are not included in the config. " ] }, { "cell_type": "code", - "execution_count": 10, - "id": "f17e266c-86b6-463f-9f4c-b4d24ca7ecc4", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'param_set_hash': UUID('acf342ee-75e0-6782-b5ef-f0d7d359aa17'),\n", - " 'params': {'Task': 'openfield',\n", - " 'TrainingFraction': [0.95],\n", - " 'alphavalue': 0.7,\n", - " 'batch_size': 4,\n", - " 'bodyparts': ['snout', 'leftear', 'rightear', 'tailbase'],\n", - " 'colormap': 'jet',\n", - " 'corner2move2': [50, 50],\n", - " 'cropping': False,\n", - " 'date': 'Oct30',\n", - " 'default_augmenter': 'imgaug',\n", - " 'default_net_type': 'resnet_50',\n", - " 'dotsize': 8,\n", - " 'filter_type': '',\n", - " 'identity': None,\n", - " 'iteration': 0,\n", - " 'maxiters': '5',\n", - " 'move2corner': True,\n", - " 'multianimalproject': None,\n", - " 'numframes2pick': 20,\n", - " 'pcutoff': 0.4,\n", - " 'scorer': 'Pranav',\n", - " 'scorer_legacy': 'False',\n", - " 'shuffle': '1',\n", - " 'skeleton': [],\n", - " 'skeleton_color': 'black',\n", - " 'snapshotindex': -1,\n", - " 'start': 0,\n", - " 'stop': 1,\n", - " 'track_method': '',\n", - " 'trainingsetindex': '0',\n", - " 'x1': 0,\n", - " 'x2': 640,\n", - " 'y1': 277,\n", - " 'y2': 624},\n", - " 'paramset_desc': 'OpenField',\n", - " 'paramset_idx': 1}]\n" - ] - } - ], + "outputs": [], "source": [ - "import pprint\n", - "pprint.pprint((dlc.ModelTrainingParamSet & 'paramset_idx=1'\n", - " ).fetch(as_dict=True))" + "from deeplabcut import train_network\n", + "help(train_network) # for more information on optional parameters" ] }, { "cell_type": "markdown", - "id": "2f5fd85c-29eb-4b65-bad1-11fcd096f103", "metadata": {}, "source": [ - "For model training, we'll work with the following session and parameters. First, we \n", - "insert a training task into the cue." + "Below, we give the parameters and index and description and load the config contents. We can then overwrite any defaults, including `maxiters`, to restrict our training iterations to 5." ] }, { "cell_type": "code", - "execution_count": 15, - "id": "53017c86-1512-4a18-8f19-556f6ad94644", + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "DataJointError", + "evalue": "The specified paramset_idx 1 already exists, please pick a different one.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mDataJointError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_7336/3428268629.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m 'maxiters': '5'}\n\u001b[1;32m 15\u001b[0m \u001b[0mconfig_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m dlc.ModelTrainingParamSet.insert_new_params(paramset_idx=paramset_idx,\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0mparamset_desc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparamset_desc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m params=config_params)\n", + "\u001b[0;32m/Volumes/GoogleDrive/My Drive/Dev/element-deeplabcut/element_deeplabcut/dlc.py\u001b[0m in \u001b[0;36minsert_new_params\u001b[0;34m(cls, paramset_desc, params, paramset_idx, skip_duplicates)\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mskip_duplicates\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'paramset_idx'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mparamset_idx\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m raise dj.DataJointError(\n\u001b[0m\u001b[1;32m 187\u001b[0m \u001b[0;34mf'The specified paramset_idx {paramset_idx} already exists,'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 188\u001b[0m f' please pick a different one.')\n", + "\u001b[0;31mDataJointError\u001b[0m: The specified paramset_idx 1 already exists, please pick a different one." + ] + } + ], "source": [ - "key=(dlc.VideoRecording&'recording_id=1').fetch1('KEY')\n", - "key.update({'paramset_idx':1,'training_id':1,\n", - " 'project_path':'openfield-Pranav-2018-10-30/'})\n", - "dlc.TrainingTask.insert1(key, skip_duplicates=True)" + "import yaml\n", + "from element_interface.utils import find_full_path\n", + "from workflow_deeplabcut.paths import get_dlc_root_data_dir\n", + "\n", + "paramset_idx = 1; paramset_desc='OpenField'\n", + "config_path = find_full_path(get_dlc_root_data_dir(), \n", + " 'openfield-Pranav-2018-10-30/config.yaml')\n", + "with open(config_path, 'rb') as y:\n", + " config_params = yaml.safe_load(y)\n", + "training_params = {'shuffle': '1',\n", + " 'trainingsetindex': '0',\n", + " 'maxiters': '5',\n", + " 'scorer_legacy': 'False',\n", + " 'maxiters': '5'}\n", + "config_params.update(training_params)\n", + "dlc.ModelTrainingParamSet.insert_new_params(paramset_idx=paramset_idx,\n", + " paramset_desc=paramset_desc,\n", + " params=config_params)" ] }, { "cell_type": "markdown", - "id": "4011030a-3174-47b2-a673-3ce24f5874fb", "metadata": {}, "source": [ - "In the next step, all new entries in this table will undergo training." + "Then we add training to the the `TrainingTask` table. The `ModelTraining` table can automatically train and populate all tasks outlined in `TrainingTask`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Specification for a DLC model training instance\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "camera_id : int # \n", + "recording_id : int # \n", + "paramset_idx : smallint # \n", + "training_id : int # \n", + "---\n", + "model_prefix=\"\" : varchar(32) # \n", + "project_path=\"\" : varchar(255) # DLC's project_path in config relative to root" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dlc.TrainingTask.heading" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "67cbe9c3-b043-49ac-9572-71c0a161e2dc", + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -893,19 +590,31 @@ " (Total: 1)" ] }, - "execution_count": 16, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "key=(dlc.VideoRecording&'recording_id=1').fetch1('KEY')\n", + "key.update({'paramset_idx':1,'training_id':1,\n", + " 'project_path':'openfield-Pranav-2018-10-30/'})\n", + "dlc.TrainingTask.insert1(key, skip_duplicates=True)\n", "dlc.TrainingTask()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dlc.TrainingTask.populate()" + ] + }, { "cell_type": "code", "execution_count": 17, - "id": "3cf0b309-7cee-456e-84b7-a0004e28e16e", "metadata": { "tags": [] }, @@ -917,7 +626,6 @@ { "cell_type": "code", "execution_count": 18, - "id": "869268e6-0bc9-4e4e-8455-d4d3c02b46a8", "metadata": {}, "outputs": [ { @@ -1032,78 +740,131 @@ }, { "cell_type": "markdown", - "id": "05ae9e9a-48c2-4ce6-926c-4b03354687f2", "metadata": {}, "source": [ - "To resume training from a previous instance, one would need to \n", + "To training from a previous instance, one would need to \n", "[edit the relevant config file](https://github.com/DeepLabCut/DeepLabCut/issues/70) and\n", - "adjust the `maxiters` paramset to a higher threshold (e.g., 10 for 5 more itterations).\n", + "adjust the `maxiters` paramset (if present) to a higher threshold (e.g., 10 for 5 more itterations).\n", "Emperical work from the Mathis team suggests 200k iterations for any true use-case." ] }, { "cell_type": "markdown", - "id": "9380568a-fb2e-4c11-9488-9020111a0bd2", "metadata": {}, "source": [ - "Next, we can optionally ingest all body parts from a given config with one command, including\n", - "a list of body part descriptions." + "## Tracking Joints/Body Parts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The DLC schema uses a lookup table for managing Body Parts tracked across models." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "body_part : varchar(32) # \n", + "---\n", + "body_part_description=\"\" : varchar(1000) # " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dlc.BodyPart.heading" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This table is equipped with a helper function to insert all body parts from a given config, and can accept a list of descriptions in the same order. To see the order, you can do a dry run of the function and check the confirmation message." ] }, { "cell_type": "code", - "execution_count": 26, - "id": "a20db1a5-e279-4553-baf2-24e5772f6d6b", + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Existing body parts: []\n", - "New body parts: ['leftear' 'rightear' 'snout' 'tailbase']\n" + "Existing body parts: ['leftear' 'rightear' 'snout' 'tailbase']\n", + "New body parts: []\n" ] }, { "name": "stdin", "output_type": "stream", "text": [ - "Insert 4 new body part(s)? [yes, no]: yes\n" + "Insert 0 new body part(s)? [yes, no]: no\n" ] } ], "source": [ - "dlc_config_path = 'openfield-Pranav-2018-10-30/config.yaml'\n", "bp_desc=['Left Ear', 'Right Ear', 'Snout Position', 'Base of Tail']\n", - "dlc.BodyPart.insert_from_config(dlc_config_path,bp_desc)" + "dlc.BodyPart.insert_from_config(config_path,bp_desc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, include this description list when declaring a model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Declaring a Model" ] }, { "cell_type": "markdown", - "id": "341a9d4f-b681-46ca-aef1-36e9ace3bb57", "metadata": {}, "source": [ - "Alternatively, the above step will be included when inserting a model into the model \n", - "table." + "If training appears successful, the result can be inserted into the `Model` table for automatic evaluation." ] }, { "cell_type": "code", - "execution_count": null, - "id": "1f4db829-51fa-4e10-aa37-1e314a1d1d0f", + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'bp_desc' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_7336/572428548.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrainingsetindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mmodel_description\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Open field model trained 5 iterations'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m body_part_descriptions = bp_desc,paramset_idx=1)\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'bp_desc' is not defined" + ] + } + ], "source": [ - "dlc.Model.insert_new_model(model_name='OpenField-1010',dlc_config=dlc_config_path,\n", + "dlc.Model.insert_new_model(model_name='OpenField-5',dlc_config=config_path,\n", " shuffle=1,trainingsetindex=0,\n", - " model_description='Open field model trained 1010 iterations',\n", + " model_description='Open field model trained 5 iterations',\n", " body_part_descriptions = bp_desc,paramset_idx=1)" ] }, { "cell_type": "code", "execution_count": 24, - "id": "6e1e905a-0a0e-4e13-9884-3b9089eae54f", "metadata": {}, "outputs": [ { @@ -1243,7 +1004,6 @@ { "cell_type": "code", "execution_count": 27, - "id": "d4cdb0c4-147b-4e50-9874-32cb46b7f663", "metadata": {}, "outputs": [ { @@ -1340,309 +1100,120 @@ }, { "cell_type": "markdown", - "id": "c561a772-910d-405e-b181-986dee983563", - "metadata": {}, - "source": [ - "Next, all inserted models can be evaluated with a similar `populate` method, which will\n", - "insert the relevant output from DLC's `evaluate_network` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a43ec42d-9f1d-4499-8f3f-e8e387ae9212", "metadata": {}, - "outputs": [], "source": [ - "dlc.ModelEvaluation.populate()\n", - "dlc.ModelEvaluation()" + "## Model Evaluation" ] }, { "cell_type": "markdown", - "id": "fc4d764d-1458-49f7-86cb-3d3958b302c4", - "metadata": {}, - "source": [ - "To put this model to use, we'll conduct pose estimation on the video we made earlier.\n", - "Here, we can also specify parameters accepted by the `analyze_videos` function as a \n", - "dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7b8a96a-72ab-489e-9325-46db98dc6bec", "metadata": {}, - "outputs": [], "source": [ - "key=(dlc.VideoRecording&'recording_id=2').fetch1('KEY');\n", - "key.update({'model_name': 'OpenField-1010', 'task_mode': 'trigger'})\n", - "dlc.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True},\n", - " skip_duplicates=True)" + "Next, all inserted models can be evaluated with a similar `populate` method, which will\n", + "insert the relevant output from DLC's `evaluate_network` function." ] }, { "cell_type": "code", - "execution_count": null, - "id": "1ad622b3-25d3-4da1-9506-bddb3a4f7f24", - "metadata": {}, - "outputs": [], - "source": [ - "dlc.PoseEstimation.populate()" - ] - }, - { - "cell_type": "markdown", - "id": "9d113e2d-54d5-4fbb-ad91-faf4a66c0879", + "execution_count": 16, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "model_name : varchar(64) # user-friendly model name\n", + "---\n", + "train_iterations : int # Training iterations\n", + "train_error : float # Train error (px)\n", + "test_error : float # Test error (px)\n", + "p_cutoff : float # p-cutoff used\n", + "train_error_p : float # Train error with p-cutoff\n", + "test_error_p=null : float # Test error with p-cutoff" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "By default, DataJoint will store the results of pose estimation in a subdirectory\n", - "> processed_dir / videos / device_<#>_recording_<#>_model_\n", - "\n", - "Pulling processed_dir from `get_dlc_processed_dir`, and device/recording information \n", - "from the `VideoRecording` table. The model name is taken from the primary key of the\n", - "`Model` table, with spaced replaced by hyphens.\n", - " \n", - "We can get this estimation directly as a pandas dataframe." + "dlc.ModelEvaluation.heading" ] }, { "cell_type": "code", "execution_count": null, - "id": "257fbd67-ff7d-459f-a942-4f1774f31709", "metadata": {}, "outputs": [], "source": [ - "dlc.PoseEstimation.get_trajectory(key)" - ] - }, - { - "cell_type": "markdown", - "id": "b7a304e3-a5cb-4ad3-93ce-6bc130e08e26", - "metadata": { - "tags": [] - }, - "source": [ - "# From scratch didactic guide - needs work" - ] - }, - { - "cell_type": "markdown", - "id": "c5ffe5d2-5b2a-45c3-8d8f-8c20efa8c5eb", - "metadata": {}, - "source": [ - "This notebook will describe the steps to explore the lab and animal management tables \n", - "created by the elements. Prior to using this notebook, please refer to the README for the installation instructions." + "dlc.ModelEvaluation.populate()\n", + "dlc.ModelEvaluation()" ] }, { "cell_type": "markdown", - "id": "ee820754-bceb-476a-acf9-238fa8b201d9", "metadata": {}, "source": [ - "Importing the module `workflow_deeplabcut.pipeline` is sufficient to create tables \n", - "inside the elements. This workflow comes prepackaged with example data and ingestion \n", - "functions to populate lab, subject, and session tables." + "## Pose Estimation" ] }, { "cell_type": "markdown", - "id": "2e19116d-bc32-4cea-9caf-f3e8eaa9b181", - "metadata": { - "tags": [] - }, - "source": [ - "## Workflow architecture" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "868b79bc-f754-4d51-a327-94a209cde374", - "metadata": {}, - "outputs": [], - "source": [ - "from element_lab import lab\n", - "from element_animal import subject\n", - "from element_session import sessions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e7a0a8b-eaf1-41a1-bf08-1aff2f2812be", - "metadata": {}, - "outputs": [], - "source": [ - "lab.Lab()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "63679df4-3064-402b-99ce-2f553dff877b", - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(lab)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8cf0f64b-e523-4a94-9a43-fca4ed793f82", - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "75576be2-2984-451f-a86b-f05f9ddec6b7", - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(subject)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5243a782-93da-40fa-b243-03ddcb230c1d", - "metadata": {}, - "outputs": [], - "source": [ - "session.Session()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e48d7c0-b7bd-4f0b-abcb-1aedc69d5310", "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(session)" - ] - }, - { - "cell_type": "markdown", - "id": "b60f5f4c-d366-4034-a40d-2d2095cb2a14", - "metadata": { - "tags": [] - }, "source": [ - "## Explore each table" + "To put this model to use, we'll conduct pose estimation on the video generated in the [DataDownload notebook](./00_DataDownload_Optional.ipynb). Here, we can also specify parameters accepted by the `analyze_videos` function as a dictionary." ] }, { "cell_type": "code", "execution_count": null, - "id": "9c0821e1-9125-4c41-bc9c-567f53d0a5e5", "metadata": {}, "outputs": [], "source": [ - "# check table definition with describe()\n", - "subject.Subject.describe()" - ] - }, - { - "cell_type": "markdown", - "id": "f6c110c0-0966-4283-a0ba-a7de2ce69e25", - "metadata": {}, - "source": [ - "## Insert data into Manual and Lookup tables" - ] - }, - { - "cell_type": "markdown", - "id": "54cf050e-882e-4672-be31-1ca3df52fa58", - "metadata": {}, - "source": [ - "Tables in this workflow are either manual tables or lookup tables. To insert into these tables, DataJoint provide method `.insert1()` and `insert()`." + "key=(dlc.VideoRecording&'recording_id=2').fetch1('KEY');\n", + "key.update({'model_name': 'OpenField-5', 'task_mode': 'trigger'})\n", + "dlc.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True},\n", + " skip_duplicates=True)" ] }, { "cell_type": "code", "execution_count": null, - "id": "d5b43904-9711-4bce-8ae5-d0d797118dec", "metadata": {}, "outputs": [], "source": [ - "subject.Subject.insert1(\n", - " dict(subject='subject1', sex='M', subject_birth_date='2020-12-30', \n", - " subject_description='test animal'), skip_duplicates=True)\n", - "subject.Subject.insert1(\n", - " ('subject2', 'F', '2020-11-30', 'test animal'), skip_duplicates=True)" + "dlc.PoseEstimation.populate()" ] }, { "cell_type": "markdown", - "id": "49d43ca2-2cd3-4659-849f-5bcc09c1367e", - "metadata": {}, - "source": [ - "`skip_duplicates=True` will prevent an error if you already have data for the primary keys in a given entry." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9bf2c953-7b4c-4a70-99fd-124a4d28171b", - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a10ddab-d0fd-45a0-8183-09c1b1933e0a", "metadata": {}, - "outputs": [], "source": [ - "# `insert()` takes a list of dicts or tuples\n", - "subject.Subject.insert(\n", - " [dict(subject='subject3', sex='F', subject_birth_date='2020-12-30', \n", - " subject_description='test animal'),\n", - " dict(subject='subject4', sex='M', subject_birth_date='2021-02-12', \n", - " subject_description='test animal')\n", - " ],\n", - " skip_duplicates=True)\n", - "subject.Subject.insert(\n", - " [\n", - " ('subject7', 'U', '2020-08-30', 'test animal'),\n", - " ('subject8', 'F', '2020-09-30', 'test animal')\n", - " ],\n", - " skip_duplicates=True)" + "By default, DataJoint will store the results of pose estimation in a subdirectory\n", + "> processed_dir / videos / device_<#>_recording_<#>_model_\n", + "\n", + "Pulling processed_dir from `get_dlc_processed_dir`, and device/recording information \n", + "from the `VideoRecording` table. The model name is taken from the primary key of the\n", + "`Model` table, with spaced replaced by hyphens.\n", + " \n", + "We can get this estimation directly as a pandas dataframe." ] }, { "cell_type": "code", "execution_count": null, - "id": "064ddaae-3410-47fc-be22-671d2afe7fb6", "metadata": {}, "outputs": [], "source": [ - "subject.Subject()" - ] - }, - { - "cell_type": "markdown", - "id": "c47691a0-b016-4092-a5ad-fefff93c54dd", - "metadata": {}, - "source": [ - "For more documentation of insert, please refer to [DataJoint Docs](https://docs.datajoint.io/python/manipulation/1-Insert.html) and [DataJoint playground](https://playground.datajoint.io/)" + "dlc.PoseEstimation.get_trajectory(key)" ] }, { "cell_type": "markdown", - "id": "13f8a8ed-2656-46d8-82ba-cdf130c4873e", "metadata": {}, "source": [ - "## Insert into Manual and Lookup tables with Graphical User Interface" + "\n", + "." ] } ], @@ -1666,5 +1237,5 @@ } }, "nbformat": 4, - "nbformat_minor": 5 + "nbformat_minor": 4 } diff --git a/notebooks/04-Automate_Optional.ipynb b/notebooks/04-Automate_Optional.ipynb new file mode 100644 index 0000000..b69bd0e --- /dev/null +++ b/notebooks/04-Automate_Optional.ipynb @@ -0,0 +1,466 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# DataJoint U24 - Workflow DeepLabCut" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Workflow Automation\n", + "\n", + "In the previous notebook [03-Process](./03-Process.ipynb), we ran through the workflow in detailed steps. For daily running routines, the current notebook provides a more succinct and automatic approach to run through the pipeline using some utility functions in the workflow.\n", + "\n", + "The commands here run a workflow using [example data](https://downgit.github.io/#/home?url=https://github.com/DeepLabCut/DeepLabCut/tree/master/examples/openfield-Pranav-2018-10-30) from the [00-DownloadData](./00-DataDownload_Optional.ipynb) notebook, but note where placeholders could be changed for a different dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os; from pathlib import Path\n", + "# change to the upper level folder to detect dj_local_conf.json\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", + " + \"workflow directory\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ingestion of subjects, sessions, videos and training parameters\n", + "\n", + "Refer to the `user_data` folder in the workflow.\n", + "\n", + "1. Fill subject and session information in files `subjects.csv` and `sessions.csv`\n", + "2. Fill in recording and parameter information in `recordings.csv` and `config_params.csv`\n", + " + Add both training and estimation videos to the recording list\n", + " + Additional columns in `config_params.csv` will be treated as model training parameters\n", + "3. Run automatic scripts prepared in `workflow_deeplabcut.ingest` for ingestion: \n", + " + `ingest_subjects` for `subject.Subject`\n", + " + `ingest_sessions` - for session tables `Session`, `SessionDirectory`, and `SessionNote`\n", + " + `ingest_dlc_items` - for DLC tables `VideoRecording` and `ModelTrainingParamSet`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "---- Inserting 0 entry(s) into subject ----\n", + "\n", + "---- Inserting 0 entry(s) into session ----\n", + "\n", + "---- Inserting 0 entry(s) into session_directory ----\n", + "\n", + "---- Inserting 0 entry(s) into session_note ----\n", + "\n", + "---- Inserting 0 entry(s) into #model_training_param_set ----\n", + "\n", + "---- Inserting 0 entry(s) into video_recording ----\n", + "\n", + "---- Inserting 0 entry(s) into video_recording__file ----\n" + ] + } + ], + "source": [ + "from workflow_deeplabcut.pipeline import lab, subject, session, dlc\n", + "from workflow_deeplabcut.ingest import ingest_subjects, ingest_sessions, ingest_dlc_items\n", + "ingest_subjects(); ingest_sessions(); ingest_dlc_items()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting project variables\n", + "\n", + "1. Set your root directory in your DataJoint config file, under `custom` as `dlc_root_data_dir`" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'CommentedSeq' object has no attribute 'keys'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_8305/2210132270.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mconfig_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata_dir\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'config.yaml'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdeeplabcut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_project\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdemo_data\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_demo_data\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdlc_load_demo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdlc_load_demo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m in \u001b[0;36mload_demo_data\u001b[0;34m(config, createtrainingset)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mtransform_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcreatetrainingset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Loaded, now creating training data...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m in \u001b[0;36mtransform_data\u001b[0;34m(config)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This is not an offical demo dataset.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"video_sets\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 63\u001b[0m cfg[\"video_sets\"][str(video_file)] = cfg[\"video_sets\"].pop(\n\u001b[1;32m 64\u001b[0m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'CommentedSeq' object has no attribute 'keys'" + ] + } + ], + "source": [ + "import datajoint as dj; dj.config.load('dj_local_conf.json')\n", + "from element_interface.utils import find_full_path\n", + "data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config\n", + " 'openfield-Pranav-2018-10-30') # DLC project dir\n", + "config_path = (data_dir / 'config.yaml')\n", + "from deeplabcut.create_project.demo_data import load_demo_data as dlc_load_demo\n", + "dlc_load_demo(config_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m(62)\u001b[0;36mtransform_data\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 60 \u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This is not an offical demo dataset.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 61 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m---> 62 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"video_sets\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 63 \u001b[0;31m cfg[\"video_sets\"][str(video_file)] = cfg[\"video_sets\"].pop(\n", + "\u001b[0m\u001b[0;32m 64 \u001b[0;31m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> up\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m(39)\u001b[0;36mload_demo_data\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 37 \u001b[0;31m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 38 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m---> 39 \u001b[0;31m \u001b[0mtransform_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 40 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mcreatetrainingset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 41 \u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Loaded, now creating training data...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_8305/2210132270.py\u001b[0m(7)\u001b[0;36m\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 3 \u001b[0;31mdata_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config\n", + "\u001b[0m\u001b[0;32m 4 \u001b[0;31m 'openfield-Pranav-2018-10-30') # DLC project dir\n", + "\u001b[0m\u001b[0;32m 5 \u001b[0;31m\u001b[0mconfig_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata_dir\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'config.yaml'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 6 \u001b[0;31m\u001b[0;32mfrom\u001b[0m \u001b[0mdeeplabcut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_project\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdemo_data\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_demo_data\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdlc_load_demo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m----> 7 \u001b[0;31m\u001b[0mdlc_load_demo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> config_path\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PosixPath('/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/config.yaml')\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> config_path.exists()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> down\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m(39)\u001b[0;36mload_demo_data\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 37 \u001b[0;31m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 38 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m---> 39 \u001b[0;31m \u001b[0mtransform_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 40 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mcreatetrainingset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 41 \u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Loaded, now creating training data...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> createtrainingset\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> down\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m(62)\u001b[0;36mtransform_data\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 60 \u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This is not an offical demo dataset.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 61 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m---> 62 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"video_sets\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 63 \u001b[0;31m cfg[\"video_sets\"][str(video_file)] = cfg[\"video_sets\"].pop(\n", + "\u001b[0m\u001b[0;32m 64 \u001b[0;31m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> cfg\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ordereddict([('Task', 'openfield'), ('TrainingFraction', [0.95]), ('alphavalue', 0.7), ('batch_size', 4), ('bodyparts', ['snout', 'leftear', 'rightear', 'tailbase']), ('colormap', 'jet'), ('corner2move2', [50, 50]), ('cropping', False), ('date', 'Oct30'), ('default_augmenter', 'imgaug'), ('default_net_type', 'resnet_50'), ('dotsize', 8), ('filter_type', ''), ('identity', None), ('iteration', 0), ('maxiters', '5'), ('modelprefix', ''), ('move2corner', True), ('multianimalproject', None), ('numframes2pick', 20), ('pcutoff', 0.4), ('project_path', '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30'), ('scorer', 'Pranav'), ('scorer_legacy', 'False'), ('shuffle', '1'), ('skeleton', []), ('skeleton_color', 'black'), ('snapshotindex', -1), ('start', 0), ('stop', 1), ('track_method', ''), ('train_float', 0.95), ('trainingsetindex', '0'), ('video_sets', ['/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4']), ('x1', 0), ('x2', 640), ('y1', 277), ('y2', 624)])\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> cfg[\"video_sets\"].keys()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "*** AttributeError: 'CommentedSeq' object has no attribute 'keys'\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ipdb> cfg[\"video_sets\"]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4']\n" + ] + } + ], + "source": [ + "%debug" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. For this demo, we generate a copy to show pose estimation. This is recording_id 2 in `recordings.csv`" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "File '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4' already exists. Exiting.\n" + ] + }, + { + "data": { + "text/plain": [ + "256" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vid_path = str(data_dir).replace(\" \", \"\\ \") + '/videos/m3v1mp4'\n", + "cmd = (f'ffmpeg -n -hide_banner -loglevel error -ss 0 -t 2 -i {vid_path}.mp4 -vcodec copy '\n", + " + f'-acodec copy {vid_path}-copy.mp4') # New video copy, first 2 seconds\n", + "os.system(cmd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Pair training video with training parameters, and launch training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "key=(dlc.VideoRecording&'recording_id=1').fetch1('KEY') # replace w/relevant IDs \n", + "key.update({'paramset_idx':1,'training_id':1,\n", + " 'project_path':'openfield-Pranav-2018-10-30/'})\n", + "dlc.TrainingTask.insert1(key, skip_duplicates=True)\n", + "dlc.TrainingTask.populate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Add this model to the `Model` table and evaluate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dlc.Model.insert_new_model(model_name='OpenField-5',dlc_config=dlc_config_path,\n", + " shuffle=1,trainingsetindex=0,\n", + " model_description='Open field model trained 5 iterations',\n", + " body_part_descriptions = bp_desc,paramset_idx=1)\n", + "dlc.ModelEvaluation.populate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Add a pose estimation task, and launch pose estimation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "key=(dlc.VideoRecording&'recording_id=2').fetch1('KEY') # change relevant ID\n", + "key.update({'model_name': 'OpenField-1010', 'task_mode': 'trigger'})\n", + "analyze_params={'save_as_csv':True} # add any others from deeplabcut.analyze_videos\n", + "dlc.PoseEstimationTask.insert_estimation_task(key,params=analyze_params,\n", + " skip_duplicates=True)\n", + "dlc.PoseEstimation.populate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "6. Retrieve estimated position data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dlc.PoseEstimation.get_trajectory(key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary and next step\n", + "\n", + "+ This notebook runs through the workflow in an automatic manner.\n", + "\n", + "+ In the next notebook [05-explore](05-explore.ipynb), we will introduce how to query, fetch and visualize the contents we ingested into the tables." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv-dlc", + "language": "python", + "name": "venv-dlc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/05-Explore.ipynb b/notebooks/05-Explore.ipynb new file mode 100644 index 0000000..150bc80 --- /dev/null +++ b/notebooks/05-Explore.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# DataJoint U24 - Workflow DeepLabCut" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os; from pathlib import Path\n", + "# change to the upper level folder to detect dj_local_conf.json\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", + " + \"workflow directory\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from workflow_deeplabcut.pipeline import lab, subject, session, dlc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Workflow architecture\n", + "\n", + "This workflow is assembled from 4 DataJoint elements:\n", + "+ [element-lab](https://github.com/datajoint/element-lab)\n", + "+ [element-animal](https://github.com/datajoint/element-animal)\n", + "+ [element-session](https://github.com/datajoint/element-session)\n", + "+ [element-calcium-imaging](https://github.com/datajoint/element-deeplabcut)\n", + "\n", + "For the architecture and detailed descriptions for each of those elements, please visit the respective links. \n", + "\n", + "Below is the diagram describing the core components of the fully assembled pipeline.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(dlc) + (dj.Diagram(session.Session) + 1) - 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Browsing the data with DataJoint `query` and `fetch` \n", + "\n", + "+ DataJoint provides functions to query data and fetch. For a detailed tutorials, visit our [general tutorial site](https://playground.datajoint.io/).\n", + "+ Running through the pipeline, we have ingested data of subject6 into the database.\n", + "+ Here are some highlights of the important tables.\n", + "\n", + "### `subject.Subject` and `session.Session` tables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject & session.Session" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "+ Fetch the primary key for the session of interest which will be used later on in this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "session_key = (session.Session & 'subject = \"subject3\"' & 'session_datetime = \"2021-04-30 12:22:15.032\"').fetch1('KEY')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `scan.Scan` and `scan.ScanInfo` tables\n", + "\n", + "+ These tables stores the scan metadata within a particular session." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan.Scan & session_key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan.ScanInfo & session_key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan.ScanInfo.Field & session_key" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `imaging.ProcessingParamSet`, `imaging.ProcessingTask`, `imaging.Processing`, and `imaging.Curation` tables\n", + "\n", + "+ The parameters used for Suite2p or CaImAn are stored in `imaging.ProcessingParamSet` under a `paramset_idx`.\n", + "\n", + "+ The processing details for Suite2p and CaImAn are stored in `imaging.ProcessingTask` and `imaging.Processing` for the utilized `paramset_idx`.\n", + "\n", + "+ After the motion correction and segmentation, the results may go through a curation process. \n", + " \n", + " + If it did not go through curation, a copy of the `imaging.ProcessingTask` entry is inserted into `imaging.Curation` with the `curation_output_dir` identical to the `processing_output_dir`.\n", + "\n", + " + If it did go through a curation, a new entry will be inserted into `imaging.Curation`, with a `curation_output_dir` specified.\n", + "\n", + " + `imaging.Curation` supports multiple curations of an entry in `imaging.ProcessingTask`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.ProcessingParamSet()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.ProcessingTask * imaging.Processing & session_key" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example workflow, `curation_output_dir` is the same as the `processing_output_dir`, as these results were not manually curated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.Curation & session_key" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `imaging.MotionCorrection` table\n", + "\n", + "+ After processing and curation, results are passed to the `imaging.MotionCorrection` and `imaging.Segmentation` tables.\n", + "\n", + "+ For the example data, the raw data is corrected with rigid and non-rigid motion correction which is stored in `imaging.MotionCorrection.RigidMotionCorrection` and `imaging.MotionCorrection.NonRigidMotionCorrection`, respectively. \n", + "\n", + "+ Lets first query the information for one curation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "curation_key = (imaging.Curation & session_key & 'curation_id=0').fetch1('KEY')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "curation_key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.MotionCorrection.RigidMotionCorrection & curation_key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.MotionCorrection.NonRigidMotionCorrection & curation_key" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "+ For non-rigid motion correction, the details for the individual blocks are stored in `imaging.MotionCorrection.Block`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.MotionCorrection.Block & curation_key & 'block_id=0'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "+ Summary images are stored in `imaging.MotionCorrection.Summary`\n", + "\n", + " + Reference image - image used as an alignment template\n", + "\n", + " + Average image - mean of registered frames\n", + "\n", + " + Correlation image - correlation map (computed during region of interest \\[ROI\\] detection)\n", + "\n", + " + Maximum projection image - max of registered frames" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.MotionCorrection.Summary & curation_key & 'field_idx=0'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "+ Lets fetch the `average_image` and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "average_image = (imaging.MotionCorrection.Summary & curation_key & 'field_idx=0').fetch1('average_image')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(average_image);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `imaging.Segmentation` table\n", + "\n", + "+ Lets fetch and plot a mask stored in the `imaging.Segmentation.Mask` table for one `curation_id`.\n", + "\n", + "+ Each mask can be associated with a field by the attribute `mask_center_z`. For example, masks with `mask_center_z=0` are in the field identified with `field_idx=0` in `scan.ScanInfo.Field`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mask_xpix, mask_ypix = (imaging.Segmentation.Mask * imaging.MaskClassification.MaskType & curation_key & 'mask_center_z=0' & 'mask_npix > 130').fetch('mask_xpix','mask_ypix')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mask_image = np.zeros(np.shape(average_image), dtype=bool)\n", + "for xpix, ypix in zip(mask_xpix, mask_ypix):\n", + " mask_image[ypix, xpix] = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(average_image);\n", + "plt.contour(mask_image, colors='white', linewidths=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `imaging.MaskClassification` table\n", + "\n", + "+ This table provides the `mask_type` and `confidence` for the mask classification." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.MaskClassification.MaskType & curation_key & 'mask=0'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `imaging.Fluorescence` and `imaging.Activity` tables\n", + "\n", + "+ Lets fetch and plot the flourescence and activity traces for one mask." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "query_cells = (imaging.Segmentation.Mask * imaging.MaskClassification.MaskType & curation_key & 'mask_center_z=0' & 'mask_npix > 130').proj()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fluorescence_traces = (imaging.Fluorescence.Trace & query_cells).fetch('fluorescence', order_by='mask')\n", + "\n", + "activity_traces = (imaging.Activity.Trace & query_cells).fetch('activity_trace', order_by='mask')\n", + "\n", + "sampling_rate = (scan.ScanInfo & curation_key).fetch1('fps') # [Hz]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(16, 4))\n", + "ax2 = ax.twinx()\n", + "\n", + "for f, a in zip(fluorescence_traces, activity_traces):\n", + " ax.plot(np.r_[:f.size] * 1/sampling_rate, f, 'k', label='fluorescence trace') \n", + " ax2.plot(np.r_[:a.size] * 1/sampling_rate, a, 'r', alpha=0.5, label='deconvolved trace')\n", + " \n", + " break\n", + "\n", + "ax.tick_params(labelsize=14)\n", + "ax2.tick_params(labelsize=14)\n", + "\n", + "ax.legend(loc='upper left', prop={'size': 14})\n", + "ax2.legend(loc='upper right', prop={'size': 14})\n", + "\n", + "ax.set_xlabel('Time (s)')\n", + "ax.set_ylabel('Activity (a.u.)')\n", + "ax2.set_ylabel('Activity (a.u.)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary and Next Step\n", + "\n", + "+ This notebook highlights the major tables in the workflow and visualize some of the ingested results. \n", + "\n", + "+ The next notebook [06-drop](06-drop-optional.ipynb) shows how to drop schemas and tables if needed." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv-dlc", + "language": "python", + "name": "venv-dlc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "metadata": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/06-Drop_Optional.ipynb b/notebooks/06-Drop_Optional.ipynb new file mode 100644 index 0000000..27de194 --- /dev/null +++ b/notebooks/06-Drop_Optional.ipynb @@ -0,0 +1,95 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# DataJoint U24 - Workflow DeepLabCut" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os; from pathlib import Path\n", + "# change to the upper level folder to detect dj_local_conf.json\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", + " + \"workflow directory\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Drop schemas\n", + "\n", + "+ Schemas are not typically dropped in a production workflow with real data in it. \n", + "+ At the developmental phase, it might be required for the table redesign.\n", + "+ When dropping all schemas is needed, the following is the dependency order." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Change into the parent directory to find the `dj_local_conf.json` file. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from workflow_deeplabcut.pipeline import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# dlc.schema.drop()\n", + "# session.schema.drop()\n", + "# subject.schema.drop()\n", + "# lab.schema.drop()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv-dlc", + "language": "python", + "name": "venv-dlc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tests/__init__.py b/tests/__init__.py index c1a7786..f0769a6 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -1,4 +1,4 @@ -''' +''' deeplabcut fresh docker: docker run --name wf-sess -p 3306:3306 -e \ MYSQL_ROOT_PASSWORD=tutorial datajoint/mysql @@ -11,6 +11,7 @@ ''' import os +import sys import pytest import pathlib import datajoint as dj @@ -18,6 +19,7 @@ # ------------------- SOME CONSTANTS ------------------- _tear_down = True +verbose = False test_user_data_dir = pathlib.Path('./tests/user_data') test_user_data_dir.mkdir(exist_ok=True) @@ -35,6 +37,17 @@ def write_csv(content, path): for line in content: f.write(line+'\n') + +class QuietStdOut: + """If verbose set to false, used to quiet tear_down table.delete prints""" + def __enter__(self): + self._original_stdout = sys.stdout + sys.stdout = open(os.devnull, 'w') + + def __exit__(self, exc_type, exc_val, exc_tb): + sys.stdout.close() + sys.stdout = self._original_stdout + # ------------------- FIXTURES ------------------- @@ -44,6 +57,12 @@ def dj_config(): if pathlib.Path('./dj_local_conf.json').exists(): dj.config.load('./dj_local_conf.json') dj.config['safemode'] = False + dj.config['database.host'] = (os.environ.get('DJ_HOST') + or dj.config['database.host']) + dj.config['database.password'] = (os.environ.get('DJ_PASS') + or dj.config['database.password']) + dj.config['database.user'] = (os.environ.get('DJ_USER') + or dj.config['database.user']) dj.config['custom'] = { 'database.prefix': (os.environ.get('DATABASE_PREFIX') or dj.config['custom']['database.prefix'])} @@ -52,24 +71,29 @@ def dj_config(): @pytest.fixture def pipeline(): - """ Loads workflow_trial.pipeline lab, session, subject""" - from workflow_trial import pipeline + """ Loads workflow_deeplabcut.pipeline lab, session, subject, dlc""" + from workflow_deeplabcut import pipeline - yield {'event': pipeline.event, - 'trial': pipeline.trial, + yield {'dlc': pipeline.dlc, 'subject': pipeline.subject, 'session': pipeline.session, 'lab': pipeline.lab} if _tear_down: - pipeline.event.BehaviorEvent.delete() - pipeline.trial.Trial.delete() - pipeline.subject.Subject.delete() - pipeline.session.Session.delete() - pipeline.lab.Lab.delete() - - -# Subject data and ingestion + if verbose: + pipeline.dlc.VideoRecording.delete() + pipeline.subject.Subject.delete() + pipeline.session.Session.delete() + pipeline.lab.Lab.delete() + else: + with QuietStdOut(): + pipeline.dlc.VideoRecording.delete() + pipeline.subject.Subject.delete() + pipeline.session.Session.delete() + pipeline.lab.Lab.delete() + + +# Subject data and ingestion @pytest.fixture def subjects_csv(): """ Create a 'subjects.csv' file""" @@ -79,7 +103,7 @@ def subjects_csv(): + "2020-10-02 00:00:01,natural causes", "subject6,M,2020-01-01 00:00:01,manuel," + "2020-10-03 00:00:01,natural causes"] - subject_csv_path = pathlib.Path('./tests/user_data/subject/subjects.csv') + subject_csv_path = pathlib.Path('./tests/user_data/subjects.csv') write_csv(subject_content, subject_csv_path) yield subject_content, subject_csv_path @@ -88,8 +112,8 @@ def subjects_csv(): @pytest.fixture def ingest_subjects(pipeline, subjects_csv): - """From workflow_trial ingest.py, import ingest_subjects, run""" - from workflow_trial.ingest import ingest_subjects + """From workflow_deeplabcut ingest.py, import ingest_subjects, run""" + from workflow_deeplabcut.ingest import ingest_subjects _, subject_csv_path = subjects_csv ingest_subjects(subject_csv_path=subject_csv_path) return @@ -99,12 +123,15 @@ def ingest_subjects(pipeline, subjects_csv): @pytest.fixture def sessions_csv(): """ Create a 'sessions.csv' file""" - session_csv_path = pathlib.Path('./tests/user_data/session/sessions.csv') + session_csv_path = pathlib.Path('./tests/user_data/sessions.csv') session_content = ["subject,session_datetime,session_dir,session_note", - "subject5,2020-04-15 11:16:38,/subject5/session1," - + "'Successful data collection, no notes'", - "subject6,2021-06-02 14:04:22,/subject6/session1," - + "'Ambient temp abnormally low'"] + "subject,session_datetime,session_dir,session_note", + "subject5,2020-04-15 11:16:38,example-dir/subject5/," + + "Successful data collection. No notes", + "subject6,2021-06-02 14:04:22,example-dir/subject6/," + + "Model Training Session" + "subject6,2021-06-03 14:04:22,example-dir/subject6/,Test Session" + ] write_csv(session_content, session_csv_path) yield session_content, session_csv_path @@ -113,12 +140,59 @@ def sessions_csv(): @pytest.fixture def ingest_sessions(ingest_subjects, sessions_csv): - """From workflow_trial ingest.py, import ingest_sessions, run""" - from workflow_trial.ingest import ingest_sessions + """From workflow_deeplabcut ingest.py, import ingest_sessions, run""" + from workflow_deeplabcut.ingest import ingest_sessions _, session_csv_path = sessions_csv ingest_sessions(session_csv_path=session_csv_path) return -''' TO DO -- Add csv and ingestion fixtures for config params and recordings -''' + +@pytest.fixture +def recordings_csv(): + """Create a 'recordings.csv file""" + recording_csv_path = pathlib.Path('./tests/user_data/recordings.csv') + recording_content = ["recording_id,subject,session_datetime,recording_start_time," + + "file_path,camera_id,paramset_idx", + "1,subject6,2021-06-02 14:04:22,2021-06-02 14:07:00," + + "openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4,1,0", + "2,subject6,2021-06-03 14:04:22,2021-06-04 14:07:00," + + "openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4,1,0", + "3,subject5,2020-04-15 11:16:38,2020-04-15 11:17:00," + + "Reaching-Mackenzie-2018-08-30/videos/reachingvideo1.avi,1,1" + ] + write_csv(recording_content, recording_csv_path) + + yield recording_content, recording_csv_path + recording_csv_path.unlink() + + +@pytest.fixture +def config_params_csv(): + """Create a 'config_params.csv file""" + config_params_csv_path = pathlib.Path('./tests/user_data/config_params.csv') + config_params_content = ["paramset_idx,paramset_desc,config_path,shuffle," + + "trainingsetindex,filter_type,track_method," + + "scorer_legacy,maxiters", + "1,OpenField,openfield-Pranav-2018-10-30/config.yaml,1,0,," + + ",False,5", + "2,Reaching,Reaching-Mackenzie-2018-08-30/config.yaml,1,0," + + ",,False,5", + "3,ExtraExample,Example/config.yaml,0,0,median,ellipse," + + "False,1" + ] + write_csv(config_params_content, config_params_csv_path) + + yield config_params_content, config_params_csv_path + config_params_csv_path.unlink() + + +@pytest.fixture +def ingest_dlc_items(ingest_subjects, ingest_sessions, + recordings_csv, config_params_csv): + """From workflow_deeplabcut ingest.py, import ingest_dlc_items, run""" + from workflow_deeplabcut.ingest import ingest_dlc_items + _, recording_csv_path = recordings_csv + _, config_params_csv_path = config_params_csv + ingest_dlc_items(config_params_csv_path=config_params_csv_path, + recording_csv_path=recording_csv_path) + return diff --git a/tests/test_ingest.py b/tests/test_ingest.py index 77a4932..3e2d860 100644 --- a/tests/test_ingest.py +++ b/tests/test_ingest.py @@ -24,7 +24,7 @@ def test_ingest_subjects(pipeline, subjects_csv, ingest_subjects): def test_ingest_sessions(pipeline, sessions_csv, ingest_sessions): - """Check length/contents of Session.SessionDirectory""" + """Check length/contents of session schema""" session = pipeline['session'] assert len(session.Session()) == 2 @@ -36,9 +36,13 @@ def test_ingest_sessions(pipeline, sessions_csv, ingest_sessions): ).fetch1('session_dir') == sess[2] +def test_ingest_dlc_items(pipeline, recordings_csv, config_params_csv, + ingest_dlc_items): + """Check length/contents of VideoRecordings/ConfigParams""" + pass + ''' TO DO - add ingestion of recordings and config params -- test launch of analyze videos - Encode analysis outcome specifcs from Model.Data e.g. assert mean(Model.Data & "joint_name = 'Finger1'").fetch('x_pos')) == Value - post example data to djarchive? From bbf2d5df30c29f854ebc632c0604e9d787a07e1b Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Wed, 9 Mar 2022 15:57:06 -0600 Subject: [PATCH 019/176] update NBs 0 - 3 --- notebooks/00-DataDownload_Optional.ipynb | 33 +- notebooks/01-Configure.ipynb | 23 +- notebooks/02-WorkflowStructure_Optional.ipynb | 1833 +++++++++-------- notebooks/03-Process.ipynb | 1031 +++++++-- 4 files changed, 1857 insertions(+), 1063 deletions(-) diff --git a/notebooks/00-DataDownload_Optional.ipynb b/notebooks/00-DataDownload_Optional.ipynb index ce26222..a3830ca 100644 --- a/notebooks/00-DataDownload_Optional.ipynb +++ b/notebooks/00-DataDownload_Optional.ipynb @@ -78,17 +78,27 @@ "As part of the DeepLabCut demo setup process, you would run the following additional\n", "command, as outlined in their \n", "[demo notebook](https://github.com/DeepLabCut/DeepLabCut/blob/master/examples/JUPYTER/Demo_labeledexample_Openfield.ipynb).\n", - "These establishes the project path within the demo config file." + "These establishes the project path within the demo config file as well as the `training-datasets` directory, which DLC will use for model training" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded, now creating training data...\n", + "The training dataset is successfully created. Use the function 'train_network' to start training. Happy training!\n" + ] + } + ], "source": [ + "your_root='/fill/in/your/root/with\\ escaped\\ spaces'\n", "from deeplabcut.create_project.demo_data import load_demo_data as dlc_load_demo\n", - "dlc_load_demo('/openfield-Pranav-2018-10-30/config.yaml')" + "dlc_load_demo(your_root+'/openfield-Pranav-2018-10-30/config.yaml')" ] }, { @@ -129,17 +139,10 @@ "tags": [] }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "sh: your-root: No such file or directory\n" - ] - }, { "data": { "text/plain": [ - "256" + "0" ] }, "execution_count": 3, @@ -148,7 +151,7 @@ } ], "source": [ - "vid_path = '/openfield-Pranav-2018-10-30/videos/m3v1mp4'\n", + "vid_path = your_root + '/openfield-Pranav-2018-10-30/videos/m3v1mp4'\n", "cmd = (f'ffmpeg -n -hide_banner -loglevel error -ss 0 -t 2 -i {vid_path}.mp4 '\n", " + f'-vcodec copy -acodec copy {vid_path}-copy.mp4')\n", "import os; os.system(cmd)" @@ -164,9 +167,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "venv-dlc", "language": "python", - "name": "python3" + "name": "venv-dlc" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/01-Configure.ipynb b/notebooks/01-Configure.ipynb index dc978b7..1dcf6a4 100644 --- a/notebooks/01-Configure.ipynb +++ b/notebooks/01-Configure.ipynb @@ -33,13 +33,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", - "import datajoint as dj\n", - "from pathlib import Path\n", "# change to the upper level folder to detect dj_local_conf.json\n", "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", @@ -62,10 +60,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + " ····\n" + ] + } + ], "source": [ + "import datajoint as dj\n", "import getpass\n", "dj.config['database.host'] = '{YOUR_HOST}'\n", "dj.config['database.user'] = '{YOUR_USERNAME}'\n", @@ -155,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -211,9 +218,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "venv-dlc", "language": "python", - "name": "python3" + "name": "venv-dlc" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/02-WorkflowStructure_Optional.ipynb b/notebooks/02-WorkflowStructure_Optional.ipynb index 94d720d..76a8329 100644 --- a/notebooks/02-WorkflowStructure_Optional.ipynb +++ b/notebooks/02-WorkflowStructure_Optional.ipynb @@ -36,11 +36,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "import os; from pathlib import Path\n", + "import os\n", "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", " + \"workflow directory\")" @@ -62,17 +62,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting cbroz@tutorial-db.datajoint.io:3306\n" - ] - } - ], + "outputs": [], "source": [ "import datajoint as dj\n", "from workflow_deeplabcut.pipeline import lab, subject, session, dlc" @@ -97,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "title": "`dj.list_schemas()`: list all schemas a user could access." }, @@ -115,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": { "title": "Each module imported above corresponds to one schema inside the database. For example, `ephys` corresponds to `neuro_ephys` schema in the database." }, @@ -123,10 +115,13 @@ { "data": { "text/plain": [ - "['video_recording',\n", - " 'video_recording__file',\n", + "['#body_part',\n", " '#model_training_param_set',\n", - " '#body_part',\n", + " 'video_recording',\n", + " 'video_recording__file',\n", + " 'training_video',\n", + " 'training_video__file',\n", + " 'training_video__video_recording',\n", " 'model',\n", " 'model__body_part',\n", " 'training_task',\n", @@ -137,7 +132,7 @@ " '__pose_estimation__body_part_position']" ] }, - "execution_count": 8, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -155,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": { "title": "`dj.Diagram()`: plot tables and dependencies" }, @@ -163,163 +158,138 @@ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", - "\n", - "\n", + "\n", + "\n", "\n", - "dlc.Model.BodyPart\n", - "dlc.VideoRecording.File\n", + "\n", - "\n", - "dlc.Model.BodyPart\n", + "\n", + "dlc.VideoRecording.File\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.ModelTraining\n", - "dlc.ModelEvaluation\n", + "\n", - "\n", - "dlc.ModelTraining\n", + "\n", + "dlc.ModelEvaluation\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.PoseEstimation.BodyPartPosition\n", - "dlc.BodyPart\n", + "\n", - "\n", - "dlc.PoseEstimation.BodyPartPosition\n", + "\n", + "dlc.BodyPart\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.TrainingTask\n", - "\n", + "\n", + "dlc.Model.BodyPart\n", + "\n", - "\n", - "dlc.TrainingTask\n", + "\n", + "dlc.Model.BodyPart\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.TrainingTask->dlc.ModelTraining\n", - "\n", + "dlc.BodyPart->dlc.Model.BodyPart\n", + "\n", "\n", - "\n", - "\n", - "dlc.PoseEstimation\n", - "\n", + "\n", + "dlc.PoseEstimation.BodyPartPosition\n", + "\n", - "\n", - "dlc.PoseEstimation\n", + "\n", + "dlc.PoseEstimation.BodyPartPosition\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.PoseEstimation->dlc.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording\n", - "\n", - "\n", - "dlc.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording->dlc.TrainingTask\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording.File\n", - "\n", - "\n", - "dlc.VideoRecording.File\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording->dlc.VideoRecording.File\n", - "\n", + "dlc.BodyPart->dlc.PoseEstimation.BodyPartPosition\n", + "\n", "\n", "\n", - "\n", + "\n", "dlc.PoseEstimationTask\n", - "\n", - "\n", - "dlc.PoseEstimationTask\n", + "\n", + "dlc.PoseEstimationTask\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.VideoRecording->dlc.PoseEstimationTask\n", - "\n", + "\n", + "\n", + "dlc.PoseEstimation\n", + "\n", + "\n", + "dlc.PoseEstimation\n", + "\n", "\n", - "\n", - "\n", - "dlc.ModelEvaluation\n", - "\n", + "\n", + "\n", + "dlc.PoseEstimationTask->dlc.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelTrainingParamSet\n", + "\n", - "\n", - "dlc.ModelEvaluation\n", + "\n", + "dlc.ModelTrainingParamSet\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "dlc.Model\n", - "\n", - "\n", - "dlc.Model\n", + "\n", + "dlc.Model\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.Model->dlc.Model.BodyPart\n", - "\n", + "\n", + "\n", + "dlc.ModelTrainingParamSet->dlc.Model\n", + "\n", + "\n", + "\n", + "\n", + "dlc.TrainingTask\n", + "\n", + "\n", + "dlc.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelTrainingParamSet->dlc.TrainingTask\n", + "\n", "\n", "\n", - "\n", + "\n", "dlc.Model->dlc.ModelEvaluation\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "dlc.Model->dlc.PoseEstimationTask\n", - "\n", + "\n", "\n", - "\n", - "\n", - "dlc.ModelTrainingParamSet\n", - "\n", + "\n", + "dlc.Model->dlc.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "dlc.TrainingVideo.File\n", + "\n", + "\n", + "dlc.TrainingVideo.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording\n", + "\n", - "\n", - "dlc.ModelTrainingParamSet\n", + "\n", + "dlc.VideoRecording\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.ModelTrainingParamSet->dlc.TrainingTask\n", - "\n", + "dlc.VideoRecording->dlc.VideoRecording.File\n", + "\n", "\n", - "\n", + "\n", "\n", - "dlc.ModelTrainingParamSet->dlc.Model\n", - "\n", + "dlc.VideoRecording->dlc.PoseEstimationTask\n", + "\n", "\n", - "\n", + "\n", "\n", - "dlc.BodyPart\n", - "dlc.TrainingVideo.VideoRecording\n", + "\n", - "\n", - "dlc.BodyPart\n", + "\n", + "dlc.TrainingVideo.VideoRecording\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.BodyPart->dlc.Model.BodyPart\n", - "\n", + "dlc.VideoRecording->dlc.TrainingVideo.VideoRecording\n", + "\n", "\n", - "\n", + "\n", + "\n", + "dlc.TrainingVideo\n", + "\n", + "\n", + "dlc.TrainingVideo\n", + "\n", + "\n", + "\n", + "\n", "\n", - "dlc.BodyPart->dlc.PoseEstimation.BodyPartPosition\n", - "\n", + "dlc.TrainingVideo->dlc.TrainingVideo.File\n", + "\n", "\n", - "\n", + "\n", "\n", - "dlc.PoseEstimationTask->dlc.PoseEstimation\n", - "\n", + "dlc.TrainingVideo->dlc.TrainingVideo.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "dlc.TrainingVideo->dlc.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelTraining\n", + "\n", + "\n", + "dlc.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.PoseEstimation->dlc.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "dlc.TrainingTask->dlc.ModelTraining\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -444,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": { "title": "`dj.Diagram()`: plot the diagram of the tables and dependencies. It could be used to plot tables in a schema or selected tables." }, @@ -452,609 +493,655 @@ { "data": { "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "subject.Line.Allele\n", - "session.SessionDirectory\n", + "\n", - "\n", - "subject.Line.Allele\n", + "\n", + "session.SessionDirectory\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.ModelTraining\n", - "dlc.PoseEstimationTask\n", + "\n", - "\n", - "dlc.ModelTraining\n", + "\n", + "dlc.PoseEstimationTask\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Subject.Strain\n", - "\n", + "\n", + "dlc.PoseEstimation\n", + "\n", - "\n", - "subject.Subject.Strain\n", + "\n", + "dlc.PoseEstimation\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "dlc.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", + "\n", + "\n", + "dlc.PoseEstimationTask->dlc.PoseEstimation\n", + "\n", "\n", "\n", - "\n", + "\n", "subject.Subject.Line\n", - "\n", - "\n", - "subject.Subject.Line\n", + "\n", + "subject.Subject.Line\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.ModelEvaluation\n", - "\n", - "\n", - "dlc.ModelEvaluation\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.Model\n", - "\n", - "\n", - "dlc.Model\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.Model->dlc.ModelEvaluation\n", - "\n", - "\n", - "\n", - "\n", - "dlc.PoseEstimationTask\n", - "\n", + "\n", + "subject.Subject\n", + "\n", - "\n", - "dlc.PoseEstimationTask\n", + "\n", + "subject.Subject\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.Model->dlc.PoseEstimationTask\n", - "\n", + "subject.Subject->subject.Subject.Line\n", + "\n", "\n", - "\n", - "\n", - "dlc.Model.BodyPart\n", - "\n", + "\n", + "session.Session\n", + "\n", - "\n", - "dlc.Model.BodyPart\n", + "\n", + "session.Session\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.Model->dlc.Model.BodyPart\n", - "\n", + "subject.Subject->session.Session\n", + "\n", "\n", - "\n", - "\n", - "dlc.ModelTrainingParamSet\n", - "\n", + "\n", + "subject.SubjectDeath\n", + "\n", - "\n", - "dlc.ModelTrainingParamSet\n", + "\n", + "subject.SubjectDeath\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.ModelTrainingParamSet->dlc.Model\n", - "\n", + "subject.Subject->subject.SubjectDeath\n", + "\n", "\n", - "\n", - "\n", - "dlc.TrainingTask\n", - "\n", + "\n", + "subject.Zygosity\n", + "\n", - "\n", - "dlc.TrainingTask\n", + "\n", + "subject.Zygosity\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.ModelTrainingParamSet->dlc.TrainingTask\n", - "\n", + "subject.Subject->subject.Zygosity\n", + "\n", "\n", - "\n", - "\n", - "dlc.BodyPart\n", - "\n", + "\n", + "subject.Subject.Strain\n", + "\n", - "\n", - "dlc.BodyPart\n", + "\n", + "subject.Subject.Strain\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.BodyPart->dlc.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "dlc.BodyPart->dlc.Model.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.User\n", - "\n", - "\n", - "subject.Subject.User\n", - "\n", - "\n", + "subject.Subject->subject.Subject.Strain\n", + "\n", "\n", - "\n", - "\n", - "dlc.PoseEstimation\n", - "\n", + "\n", + "subject.Subject.Source\n", + "\n", - "\n", - "dlc.PoseEstimation\n", + "\n", + "subject.Subject.Source\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.PoseEstimationTask->dlc.PoseEstimation\n", - "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Source\n", + "\n", "\n", - "\n", - "\n", - "session.SessionDirectory\n", - "\n", + "\n", + "subject.Subject.Lab\n", + "\n", - "\n", - "session.SessionDirectory\n", + "\n", + "subject.Subject.Lab\n", "\n", "\n", "\n", - "\n", - "\n", - "session.ProjectSession\n", - "\n", - "\n", - "session.ProjectSession\n", - "\n", - "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Lab\n", + "\n", "\n", - "\n", - "\n", - "session.SessionExperimenter\n", - "\n", + "\n", + "subject.Subject.User\n", + "\n", - "\n", - "session.SessionExperimenter\n", + "\n", + "subject.Subject.User\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.TrainingTask->dlc.ModelTraining\n", - "\n", + "subject.Subject->subject.Subject.User\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", + "\n", + "subject.SubjectCullMethod\n", + "\n", - "\n", - "subject.Subject.Source\n", + "\n", + "subject.SubjectCullMethod\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Allele.Source\n", - "\n", - "\n", - "subject.Allele.Source\n", - "\n", - "\n", + "\n", + "\n", + "subject.Subject->subject.SubjectCullMethod\n", + "\n", "\n", - "\n", - "\n", - "subject.SubjectCullMethod\n", - "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", - "\n", - "subject.SubjectCullMethod\n", + "\n", + "subject.Subject.Protocol\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Subject.Lab\n", - "\n", + "\n", + "subject.Subject->subject.Subject.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "dlc.ModelTraining\n", + "\n", - "\n", - "subject.Subject.Lab\n", + "\n", + "dlc.ModelTraining\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.VideoRecording.File\n", - "\n", + "\n", + "dlc.Model.BodyPart\n", + "\n", - "\n", - "dlc.VideoRecording.File\n", + "\n", + "dlc.Model.BodyPart\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Zygosity\n", - "\n", + "\n", + "subject.Allele\n", + "\n", - "\n", - "subject.Zygosity\n", + "\n", + "subject.Allele\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Subject.Protocol\n", - "\n", + "\n", + "subject.Line.Allele\n", + "\n", - "\n", - "subject.Subject.Protocol\n", + "\n", + "subject.Line.Allele\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.PoseEstimation->dlc.PoseEstimation.BodyPartPosition\n", - "\n", + "\n", + "\n", + "subject.Allele->subject.Line.Allele\n", + "\n", "\n", - "\n", - "\n", - "subject.SubjectDeath\n", - "\n", + "\n", + "subject.Allele.Source\n", + "\n", - "\n", - "subject.SubjectDeath\n", + "\n", + "subject.Allele.Source\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Line\n", - "\n", + "\n", + "subject.Allele->subject.Allele.Source\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "dlc.TrainingTask\n", + "\n", - "\n", - "subject.Line\n", + "\n", + "dlc.TrainingTask\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Line->subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line->subject.Subject.Line\n", - "\n", + "\n", + "\n", + "dlc.TrainingTask->dlc.ModelTraining\n", + "\n", "\n", - "\n", - "\n", - "subject.Allele\n", - "\n", + "\n", + "dlc.ModelEvaluation\n", + "\n", - "\n", - "subject.Allele\n", + "\n", + "dlc.ModelEvaluation\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Allele->subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele->subject.Allele.Source\n", - "\n", + "\n", + "\n", + "session.ProjectSession\n", + "\n", + "\n", + "session.ProjectSession\n", + "\n", "\n", - "\n", - "\n", - "subject.Allele->subject.Zygosity\n", - "\n", "\n", - "\n", - "\n", - "session.SessionNote\n", - "\n", + "\n", + "dlc.TrainingVideo.File\n", + "\n", - "\n", - "session.SessionNote\n", + "\n", + "dlc.TrainingVideo.File\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "dlc.VideoRecording\n", - "\n", - "\n", - "dlc.VideoRecording\n", + "\n", + "dlc.VideoRecording\n", "\n", "\n", "\n", "\n", "\n", "dlc.VideoRecording->dlc.PoseEstimationTask\n", - "\n", + "\n", "\n", - "\n", + "\n", + "\n", + "dlc.TrainingVideo.VideoRecording\n", + "\n", + "\n", + "dlc.TrainingVideo.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", "\n", - "dlc.VideoRecording->dlc.TrainingTask\n", - "\n", + "dlc.VideoRecording->dlc.TrainingVideo.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "dlc.VideoRecording.File\n", + "\n", + "\n", + "dlc.VideoRecording.File\n", + "\n", + "\n", "\n", "\n", "\n", "dlc.VideoRecording->dlc.VideoRecording.File\n", - "\n", + "\n", "\n", - "\n", - "\n", - "subject.Strain\n", - "\n", + "\n", + "dlc.TrainingVideo\n", + "\n", - "\n", - "subject.Strain\n", + "\n", + "dlc.TrainingVideo\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "subject.Strain->subject.Subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "session.Session\n", - "\n", - "\n", - "session.Session\n", - "\n", - "\n", + "dlc.TrainingVideo->dlc.TrainingTask\n", + "\n", "\n", - "\n", + "\n", "\n", - "session.Session->session.SessionDirectory\n", - "\n", + "dlc.TrainingVideo->dlc.TrainingVideo.File\n", + "\n", "\n", - "\n", + "\n", "\n", - "session.Session->session.ProjectSession\n", - "\n", + "dlc.TrainingVideo->dlc.TrainingVideo.VideoRecording\n", + "\n", "\n", - "\n", + "\n", "\n", - "session.Session->session.SessionExperimenter\n", - "\n", + "session.Session->session.SessionDirectory\n", + "\n", "\n", - "\n", + "\n", "\n", - "session.Session->session.SessionNote\n", - "\n", + "session.Session->session.ProjectSession\n", + "\n", "\n", "\n", "\n", "session.Session->dlc.VideoRecording\n", - "\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject\n", - "\n", + "\n", + "session.SessionNote\n", + "\n", - "\n", - "subject.Subject\n", + "\n", + "session.SessionNote\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "subject.Subject->subject.Subject.Strain\n", - "\n", + "session.Session->session.SessionNote\n", + "\n", "\n", - "\n", + "\n", + "\n", + "session.SessionExperimenter\n", + "\n", + "\n", + "session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "\n", "\n", - "subject.Subject->subject.Subject.Line\n", - "\n", + "session.Session->session.SessionExperimenter\n", + "\n", "\n", - "\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "\n", + "\n", "\n", - "subject.Subject->subject.Subject.User\n", - "\n", + "subject.Line->subject.Subject.Line\n", + "\n", "\n", - "\n", + "\n", "\n", - "subject.Subject->subject.Subject.Source\n", - "\n", + "subject.Line->subject.Line.Allele\n", + "\n", "\n", - "\n", + "\n", + "\n", + "dlc.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "dlc.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dlc.BodyPart\n", + "\n", + "\n", + "dlc.BodyPart\n", + "\n", + "\n", + "\n", + "\n", "\n", - "subject.Subject->subject.SubjectCullMethod\n", - "\n", + "dlc.BodyPart->dlc.Model.BodyPart\n", + "\n", "\n", - "\n", + "\n", "\n", - "subject.Subject->subject.Subject.Lab\n", - "\n", + "dlc.BodyPart->dlc.PoseEstimation.BodyPartPosition\n", + "\n", "\n", - "\n", + "\n", + "\n", + "dlc.ModelTrainingParamSet\n", + "\n", + "\n", + "dlc.ModelTrainingParamSet\n", + "\n", + "\n", + "\n", + "\n", "\n", - "subject.Subject->subject.Zygosity\n", - "\n", + "dlc.ModelTrainingParamSet->dlc.TrainingTask\n", + "\n", "\n", - "\n", + "\n", + "\n", + "dlc.Model\n", + "\n", + "\n", + "dlc.Model\n", + "\n", + "\n", + "\n", + "\n", "\n", - "subject.Subject->subject.Subject.Protocol\n", - "\n", + "dlc.ModelTrainingParamSet->dlc.Model\n", + "\n", "\n", - "\n", + "\n", "\n", - "subject.Subject->subject.SubjectDeath\n", - "\n", + "dlc.Model->dlc.PoseEstimationTask\n", + "\n", "\n", - "\n", + "\n", "\n", - "subject.Subject->session.Session\n", - "\n", + "dlc.Model->dlc.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "dlc.Model->dlc.ModelEvaluation\n", + "\n", + "\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Strain->subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "dlc.PoseEstimation->dlc.PoseEstimation.BodyPartPosition\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1072,27 +1159,23 @@ { "data": { "text/plain": [ - "['#device',\n", + "['#user',\n", " '#user_role',\n", - " '#skull_reference',\n", " '#lab',\n", + " '#project',\n", + " '#protocol_type',\n", + " '#source',\n", + " '#device',\n", " 'equipment',\n", " 'equipment__ca_img_equipment',\n", " 'equipment__ephys_equipment',\n", - " '#user',\n", - " '#protocol_type',\n", - " '#source',\n", - " '#project',\n", - " '#location',\n", " '#lab_membership',\n", - " '#protocol',\n", + " '#location',\n", " '#project__keywords',\n", " '#project__publication',\n", " '#project__sourcecode',\n", - " 'project_keywords',\n", - " 'project_publication',\n", - " 'project_source_code',\n", - " 'project_user']" + " 'project_user',\n", + " '#protocol']" ] }, "execution_count": 7, @@ -1106,112 +1189,92 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", - "\n", - "\n", + "\n", + "\n", "\n", + "dlc.VideoRecording\n", + "\n", + "\n", + "dlc.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "\n", "dlc.PoseEstimationTask\n", - "\n", "\n", "dlc.PoseEstimationTask\n", "\n", "\n", "\n", - "\n", - "\n", - "session.Session\n", - "\n", - "\n", - "session.Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording\n", - "\n", - "\n", - "dlc.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", + "\n", "\n", - "session.Session->dlc.VideoRecording\n", - "\n", + "dlc.VideoRecording->dlc.PoseEstimationTask\n", + "\n", "\n", "\n", - "\n", + "\n", "subject.Subject\n", - "\n", - "\n", - "subject.Subject\n", + "\n", + "subject.Subject\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Subject->session.Session\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingTask\n", - "\n", + "\n", + "session.Session\n", + "\n", - "\n", - "dlc.TrainingTask\n", + "\n", + "session.Session\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.VideoRecording->dlc.PoseEstimationTask\n", - "\n", + "\n", + "\n", + "subject.Subject->session.Session\n", + "\n", "\n", - "\n", - "\n", - "dlc.VideoRecording->dlc.TrainingTask\n", - "\n", + "\n", + "\n", + "session.Session->dlc.VideoRecording\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1219,13 +1282,12 @@ "source": [ "# plot diagram of selected tables and schemas\n", "(dj.Diagram(subject.Subject) + dj.Diagram(session.Session) \n", - " + dj.Diagram(dlc.VideoRecording) + dj.Diagram(dlc.TrainingTask)\n", - " + dj.Diagram(dlc.PoseEstimationTask)) " + " + dj.Diagram(dlc.VideoRecording) + dj.Diagram(dlc.PoseEstimationTask)) " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "title": "Each datajoint table class inside the module corresponds to a table inside the schema. For example, the class `ephys.EphysRecording` correponds to the table `_ephys_recording` in the schema `neuro_ephys` in the database." }, @@ -1302,34 +1364,20 @@ "

    file_path

    \n", " filepath of video, relative to root data directory\n", " \n", - " subject5\n", - "2020-04-15 11:16:38\n", - "1\n", - "3\n", - "Reaching-Mackenzie-2018-08-30/videos/reachingvideo1.avisubject6\n", - "2021-06-02 14:04:22\n", - "1\n", - "1\n", - "openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4subject6\n", - "2021-06-03 14:04:22\n", - "1\n", - "2\n", - "openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4 \n", + " \n", " \n", " \n", - "

    Total: 3

    \n", + "

    Total: 0

    \n", " " ], "text/plain": [ - "*subject *session_datet *camera_id *recording_id *file_path \n", - "+----------+ +------------+ +-----------+ +------------+ +------------+\n", - "subject5 2020-04-15 11: 1 3 Reaching-Macke\n", - "subject6 2021-06-02 14: 1 1 openfield-Pran\n", - "subject6 2021-06-03 14: 1 2 openfield-Pran\n", - " (Total: 3)" + "*subject *session_datet *camera_id *recording_id *file_path \n", + "+---------+ +------------+ +-----------+ +------------+ +-----------+\n", + "\n", + " (Total: 0)" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1351,7 +1399,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1359,7 +1407,7 @@ "output_type": "stream", "text": [ "# Specification for a DLC model training instance\n", - "-> dlc.VideoRecording\n", + "-> dlc.TrainingVideo\n", "-> dlc.ModelTrainingParamSet\n", "training_id : int \n", "---\n", @@ -1371,10 +1419,10 @@ { "data": { "text/plain": [ - "'# Specification for a DLC model training instance\\n-> dlc.VideoRecording\\n-> dlc.ModelTrainingParamSet\\ntraining_id : int \\n---\\nmodel_prefix=\"\" : varchar(32) \\nproject_path=\"\" : varchar(255) # DLC\\'s project_path in config relative to root\\n'" + "'# Specification for a DLC model training instance\\n-> dlc.TrainingVideo\\n-> dlc.ModelTrainingParamSet\\ntraining_id : int \\n---\\nmodel_prefix=\"\" : varchar(32) \\nproject_path=\"\" : varchar(255) # DLC\\'s project_path in config relative to root\\n'" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1392,7 +1440,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "title": "`heading`: show table attributes regardless of foreign key references." }, @@ -1412,13 +1460,12 @@ "scorer : varchar(64) # scorer/network name - DLC's GetScorerName()\n", "config_template : longblob # dictionary of the config for analyze_videos()\n", "project_path : varchar(255) # DLC's project_path in config relative to root\n", - "dlc_version : varchar(8) # keeps the deeplabcut version\n", "model_prefix=\"\" : varchar(32) # \n", "model_description=\"\" : varchar(1000) # \n", "paramset_idx=null : smallint # " ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1456,285 +1503,286 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Line\n", + "\n", - "\n", - "subject.Allele\n", + "\n", + "subject.Subject.Line\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Zygosity\n", - "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", - "\n", - "subject.Zygosity\n", + "\n", + "subject.Subject.Protocol\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Allele->subject.Zygosity\n", - "\n", + "\n", + "\n", + "subject.Allele\n", + "\n", + "\n", + "subject.Allele\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "subject.Line.Allele\n", - "\n", - "\n", - "subject.Line.Allele\n", + "\n", + "subject.Line.Allele\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "subject.Allele->subject.Line.Allele\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "subject.Allele.Source\n", - "\n", - "\n", - "subject.Allele.Source\n", + "\n", + "subject.Allele.Source\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "subject.Allele->subject.Allele.Source\n", - "\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject.Strain\n", - "\n", + "\n", + "subject.Zygosity\n", + "\n", - "\n", - "subject.Subject.Strain\n", + "\n", + "subject.Zygosity\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Line\n", - "\n", + "\n", + "subject.Allele->subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Lab\n", + "\n", - "\n", - "subject.Line\n", + "\n", + "subject.Subject.Lab\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Line->subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Line\n", - "\n", + "\n", + "subject.Subject\n", + "\n", - "\n", - "subject.Subject.Line\n", + "\n", + "subject.Subject\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Line->subject.Subject.Line\n", - "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Line\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Protocol\n", + "\n", "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Lab\n", + "\n", "\n", "\n", - "\n", + "\n", "subject.Subject.User\n", - "\n", - "\n", - "subject.Subject.User\n", + "\n", + "subject.Subject.User\n", "\n", "\n", "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.User\n", + "\n", + "\n", "\n", - "\n", + "\n", "subject.SubjectDeath\n", - "\n", - "\n", - "subject.SubjectDeath\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Strain\n", - "\n", - "\n", - "subject.Strain\n", + "\n", + "subject.SubjectDeath\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Strain->subject.Subject.Strain\n", - "\n", + "\n", + "\n", + "subject.Subject->subject.SubjectDeath\n", + "\n", "\n", "\n", - "\n", + "\n", "subject.SubjectCullMethod\n", - "\n", - "\n", - "subject.SubjectCullMethod\n", + "\n", + "subject.SubjectCullMethod\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Subject.Protocol\n", - "\n", - "\n", - "subject.Subject.Protocol\n", - "\n", + "\n", + "\n", + "subject.Subject->subject.SubjectCullMethod\n", + "\n", "\n", + "\n", + "\n", + "subject.Subject->subject.Zygosity\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject.Lab\n", - "\n", + "\n", + "subject.Subject.Source\n", + "\n", - "\n", - "subject.Subject.Lab\n", + "\n", + "subject.Subject.Source\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Subject\n", - "\n", + "\n", + "subject.Subject->subject.Subject.Source\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Strain\n", + "\n", - "\n", - "subject.Subject\n", + "\n", + "subject.Subject.Strain\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "subject.Subject->subject.Subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Zygosity\n", - "\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Source\n", - "\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "subject.Strain\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.User\n", - "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.SubjectDeath\n", - "\n", + "\n", + "\n", + "subject.Strain->subject.Subject.Strain\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.SubjectCullMethod\n", - "\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "subject.Line\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Protocol\n", - "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Lab\n", - "\n", + "\n", + "\n", + "subject.Line->subject.Subject.Line\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Line\n", - "\n", + "\n", + "\n", + "subject.Line->subject.Line.Allele\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1745,7 +1793,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": { "title": "[subject](https://github.com/datajoint/element-animal): contains the basic information of subject, including Strain, Line, Subject, Zygosity, and SubjectDeath information." }, @@ -1777,67 +1825,100 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "session.SessionDirectory\n", "\n", - "\n", - "session.SessionDirectory\n", + "\n", + "session.SessionDirectory\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "session.ProjectSession\n", "\n", - "\n", - "session.ProjectSession\n", + "\n", + "session.ProjectSession\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "session.Session\n", "\n", - "\n", - "session.Session\n", + "\n", + "session.Session\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "session.Session->session.SessionDirectory\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "session.Session->session.ProjectSession\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionNote\n", + "\n", + "\n", + "session.SessionNote\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionNote\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionExperimenter\n", + "\n", + "\n", + "session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionExperimenter\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1848,7 +1929,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": { "title": "[session](https://github.com/datajoint/element-session): experimental session information" }, diff --git a/notebooks/03-Process.ipynb b/notebooks/03-Process.ipynb index 57f7585..6244e3a 100644 --- a/notebooks/03-Process.ipynb +++ b/notebooks/03-Process.ipynb @@ -34,7 +34,7 @@ "metadata": {}, "outputs": [], "source": [ - "import os; from pathlib import Path\n", + "import os\n", "# change to the upper level folder to detect dj_local_conf.json\n", "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", @@ -50,17 +50,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting cbroz@tutorial-db.datajoint.io:3306\n" - ] - } - ], + "outputs": [], "source": [ "import datajoint as dj\n", "from workflow_deeplabcut.pipeline import lab, subject, session, dlc" @@ -70,7 +62,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Inserting entries into upstream tables" + "#### Inserting entries into upstream tables" ] }, { @@ -82,20 +74,20 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "subject : varchar(8) # \n", + "subject : varchar(32) # \n", "---\n", "sex : enum('M','F','U') # \n", "subject_birth_date : date # \n", "subject_description=\"\" : varchar(1024) # " ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -106,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -118,15 +110,114 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Animal Subject\n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    sex

    \n", + " \n", + "
    \n", + "

    subject_birth_date

    \n", + " \n", + "
    \n", + "

    subject_description

    \n", + " \n", + "
    subject6M2020-01-01manuel
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*subject sex subject_birth_ subject_descri\n", + "+----------+ +-----+ +------------+ +------------+\n", + "subject6 M 2020-01-01 manuel \n", + " (Total: 1)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-> Subject\n", - "session_datetime : datetime \n", + "-> subject.Subject\n", + "session_datetime : datetime(3) \n", "\n" ] } @@ -137,18 +228,18 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "# \n", - "subject : varchar(8) # \n", - "session_datetime : datetime # " + "subject : varchar(32) # \n", + "session_datetime : datetime(3) # " ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -159,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -225,21 +316,23 @@ "

    session_datetime

    \n", " \n", " \n", - " subject3\n", - "2021-04-30 12:22:15.032000 \n", + " subject6\n", + "2021-06-02 14:04:22subject6\n", + "2021-06-03 14:04:22 \n", " \n", " \n", - "

    Total: 1

    \n", + "

    Total: 2

    \n", " " ], "text/plain": [ "*subject *session_datet\n", "+----------+ +------------+\n", - "subject3 2021-04-30 12:\n", - " (Total: 1)" + "subject6 2021-06-02 14:\n", + "subject6 2021-06-03 14:\n", + " (Total: 2)" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -260,22 +353,22 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "# \n", - "subject : varchar(8) # \n", - "session_datetime : datetime # \n", + "subject : varchar(32) # \n", + "session_datetime : datetime(3) # \n", "camera_id : int # \n", "recording_id : int # \n", "---\n", "recording_start_time : datetime # " ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -294,20 +387,46 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "recordings = [{'recording_id': '1',\n", + " 'subject': 'subject6',\n", + " 'session_datetime': '2021-06-02 14:04:22',\n", + " 'recording_start_time': '2021-06-02 14:07:00',\n", + " 'camera_id': '1'},\n", + " {'recording_id': '2',\n", + " 'subject': 'subject6',\n", + " 'session_datetime': '2021-06-03 14:04:22',\n", + " 'recording_start_time': '2021-06-04 14:07:00',\n", + " 'camera_id': '1'}]\n", + "dlc.VideoRecording.insert(recordings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The related part table allows for multiple files for a given recording session." + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "subject : varchar(8) # \n", - "session_datetime : datetime # \n", + "subject : varchar(32) # \n", + "session_datetime : datetime(3) # \n", "camera_id : int # \n", "recording_id : int # \n", "file_path : varchar(255) # filepath of video, relative to root data directory" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -316,34 +435,153 @@ "dlc.VideoRecording.File.heading" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "recordings[0].update({'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4'})\n", + "recordings[1].update({'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4'})\n", + "dlc.VideoRecording.File.insert(recordings, ignore_extra_fields=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    camera_id

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    file_path

    \n", + " filepath of video, relative to root data directory\n", + "
    subject62021-06-02 14:04:2211openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4
    subject62021-06-03 14:04:2212openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4
    \n", + " \n", + "

    Total: 2

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *camera_id *recording_id *file_path \n", + "+----------+ +------------+ +-----------+ +------------+ +------------+\n", + "subject6 2021-06-02 14: 1 1 openfield-Pran\n", + "subject6 2021-06-03 14: 1 2 openfield-Pran\n", + " (Total: 2)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dlc.VideoRecording.File()" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The related part table allows for multiple files for a given recording session." + "The `TrainingVideo` table handles all files generated in the video labeling process, including the `h5`, `csv`, and `png` files under the `labeled-data` directory. While these aren't required for launching DLC training, it may be helpful to retain records. DLC will instead refer to the `mat` file located under the `training-datasets` directory." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "recordings = [{'recording_id': '1',\n", - " 'subject': 'subject6',\n", - " 'session_datetime': '2021-06-02 14:04:22',\n", - " 'recording_start_time': '2021-06-02 14:07:00',\n", - " 'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4',\n", - " 'camera_id': '1',\n", - " 'paramset_idx': '0'},\n", - " {'recording_id': '2',\n", - " 'subject': 'subject6',\n", - " 'session_datetime': '2021-06-03 14:04:22',\n", - " 'recording_start_time': '2021-06-04 14:07:00',\n", - " 'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4',\n", - " 'camera_id': '1',\n", - " 'paramset_idx': '0'}\n", - "dlc.VideoRecording.File.insert(recordings)" + "dlc.TrainingVideo.insert1({'video_set_id': 1})\n", + "csv_path = 'openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.csv'\n", + "dlc.TrainingVideo.File.insert1({'video_set_id': 1,\n", + " 'file_path': csv_path})" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "video_key = (dlc.VideoRecording&'recording_id=2').fetch1('KEY')\n", + "video_key.update({'video_set_id': 1})\n", + "dlc.TrainingVideo.VideoRecording.insert1(video_key)" ] }, { @@ -362,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -376,7 +614,7 @@ "params : longblob # dictionary of all applicable parameters" ] }, - "execution_count": 5, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -411,22 +649,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "ename": "DataJointError", - "evalue": "The specified paramset_idx 1 already exists, please pick a different one.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mDataJointError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_7336/3428268629.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m 'maxiters': '5'}\n\u001b[1;32m 15\u001b[0m \u001b[0mconfig_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m dlc.ModelTrainingParamSet.insert_new_params(paramset_idx=paramset_idx,\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0mparamset_desc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparamset_desc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m params=config_params)\n", - "\u001b[0;32m/Volumes/GoogleDrive/My Drive/Dev/element-deeplabcut/element_deeplabcut/dlc.py\u001b[0m in \u001b[0;36minsert_new_params\u001b[0;34m(cls, paramset_desc, params, paramset_idx, skip_duplicates)\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mskip_duplicates\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'paramset_idx'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mparamset_idx\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m raise dj.DataJointError(\n\u001b[0m\u001b[1;32m 187\u001b[0m \u001b[0;34mf'The specified paramset_idx {paramset_idx} already exists,'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 188\u001b[0m f' please pick a different one.')\n", - "\u001b[0;31mDataJointError\u001b[0m: The specified paramset_idx 1 already exists, please pick a different one." - ] - } - ], + "outputs": [], "source": [ "import yaml\n", "from element_interface.utils import find_full_path\n", @@ -457,17 +682,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "# Specification for a DLC model training instance\n", - "subject : varchar(8) # \n", - "session_datetime : datetime # \n", - "camera_id : int # \n", - "recording_id : int # \n", + "video_set_id : int # \n", "paramset_idx : smallint # \n", "training_id : int # \n", "---\n", @@ -475,7 +697,7 @@ "project_path=\"\" : varchar(255) # DLC's project_path in config relative to root" ] }, - "execution_count": 7, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -486,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -546,16 +768,7 @@ "
    \n", " \n", " \n", - " \n", - "\n", - "\n", - "\n", + " \n", "\n", "\n", "\n", @@ -584,37 +794,45 @@ " " ], "text/plain": [ - "*subject *session_datet *camera_id *recording_id *paramset_idx *training_id model_prefix project_path \n", - "+----------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", - "subject6 2021-06-02 14: 1 1 1 1 openfield-Pran\n", + "*video_set_id *paramset_idx *training_id model_prefix project_path \n", + "+------------+ +------------+ +------------+ +------------+ +------------+\n", + "1 1 1 openfield-Pran\n", " (Total: 1)" ] }, - "execution_count": 8, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "key=(dlc.VideoRecording&'recording_id=1').fetch1('KEY')\n", - "key.update({'paramset_idx':1,'training_id':1,\n", - " 'project_path':'openfield-Pranav-2018-10-30/'})\n", + "key={'video_set_id': 1, 'paramset_idx':1,'training_id':1,\n", + " 'project_path':'openfield-Pranav-2018-10-30/'}\n", "dlc.TrainingTask.insert1(key, skip_duplicates=True)\n", "dlc.TrainingTask()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: numpy==1.20 in /Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages (1.20.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ - "dlc.TrainingTask.populate()" + "pip install numpy==1.20" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "tags": [] }, @@ -625,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -685,16 +903,7 @@ "
    \n", "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    camera_id

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", + "

    video_set_id

    \n", " \n", "
    \n", "

    paramset_idx

    \n", @@ -570,10 +783,7 @@ "

    project_path

    \n", " DLC's project_path in config relative to root\n", "
    subject62021-06-02 14:04:2211
    111
    \n", " \n", - " \n", - "\n", - "\n", - "\n", + " \n", "\n", "\n", "\n", @@ -723,13 +929,13 @@ " " ], "text/plain": [ - "*subject *session_datet *camera_id *recording_id *paramset_idx *training_id latest_snapsho config_tem\n", - "+----------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +------------+ +--------+\n", - "subject6 2021-06-02 14: 1 1 1 1 5 =BLOB= \n", + "*video_set_id *paramset_idx *training_id latest_snapsho config_tem\n", + "+------------+ +------------+ +------------+ +------------+ +--------+\n", + "1 1 1 5 =BLOB= \n", " (Total: 1)" ] }, - "execution_count": 18, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -764,18 +970,19 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ + "# \n", "body_part : varchar(32) # \n", "---\n", "body_part_description=\"\" : varchar(1000) # " ] }, - "execution_count": 9, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -788,27 +995,63 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This table is equipped with a helper function to insert all body parts from a given config, and can accept a list of descriptions in the same order. To see the order, you can do a dry run of the function and check the confirmation message." + "This table is equipped with two helper functions. First, we can identify all the new body parts from a given config file." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Existing body parts: ['leftear' 'rightear' 'snout' 'tailbase']\n", - "New body parts: []\n" + "Existing body parts: []\n", + "New body parts: ['leftear' 'rightear' 'snout' 'tailbase']\n" + ] + }, + { + "data": { + "text/plain": [ + "array(['leftear', 'rightear', 'snout', 'tailbase'], dtype='\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrainingsetindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mmodel_description\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Open field model trained 5 iterations'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m body_part_descriptions = bp_desc,paramset_idx=1)\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'bp_desc' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "--- DLC Model specification to be inserted ---\n", + "\tmodel_name: OpenField-5\n", + "\tmodel_description: Open field model trained 5 iterations\n", + "\tscorer: DLCresnet50openfieldOct30shuffle1\n", + "\ttask: openfield\n", + "\tdate: Oct30\n", + "\titeration: 0\n", + "\tsnapshotindex: -1\n", + "\tshuffle: 1\n", + "\ttrainingsetindex: 0\n", + "\tproject_path: openfield-Pranav-2018-10-30\n", + "\tparamset_idx: 1\n", + "\t-- Template for config.yaml --\n", + "\t\tTask: openfield\n", + "\t\tTrainingFraction: [0.95]\n", + "\t\tbatch_size: 4\n", + "\t\tcropping: False\n", + "\t\tdate: Oct30\n", + "\t\titeration: 0\n", + "\t\tproject_path: /Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30\n", + "\t\tsnapshotindex: -1\n", + "\t\tx1: 0\n", + "\t\tx2: 640\n", + "\t\ty1: 277\n", + "\t\ty2: 624\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Proceed with new DLC model insert? [yes, no]: yes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Existing body parts: ['leftear' 'rightear' 'snout' 'tailbase']\n", + "New body parts: []\n" ] } ], @@ -859,12 +1137,12 @@ "dlc.Model.insert_new_model(model_name='OpenField-5',dlc_config=config_path,\n", " shuffle=1,trainingsetindex=0,\n", " model_description='Open field model trained 5 iterations',\n", - " body_part_descriptions = bp_desc,paramset_idx=1)" + " paramset_idx=1)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -954,9 +1232,6 @@ "

    project_path

    \n", " DLC's project_path in config relative to root\n", "     \n", - " \n", + " \n", "\n", "\n", "\n", @@ -976,9 +1251,8 @@ "\n", "\n", "\n", - "\n", "\n", - "\n", + "\n", "\n", "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    camera_id

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", + "

    video_set_id

    \n", " \n", "
    \n", "

    paramset_idx

    \n", @@ -709,10 +918,7 @@ "

    config_template

    \n", " stored full config file\n", "
    subject62021-06-02 14:04:2211
    1115
    \n", - "

    dlc_version

    \n", - " keeps the deeplabcut version\n", - "
    \n", "

    model_prefix

    \n", " \n", "
    \n", @@ -966,7 +1241,7 @@ "

    paramset_idx

    \n", " \n", "
    OpenField-1010
    OpenField-5openfieldOct300DLCresnet50openfieldOct30shuffle1=BLOB=openfield-Pranav-2018-10-302.2.0.6Open field model trained 1010 iterationsOpen field model trained 5 iterations1
    \n", " \n", @@ -986,13 +1260,13 @@ " " ], "text/plain": [ - "*model_name task date iteration snapshotindex shuffle trainingsetind scorer config_tem project_path dlc_version model_prefix model_descript paramset_idx \n", - "+------------+ +-----------+ +-------+ +-----------+ +------------+ +---------+ +------------+ +------------+ +--------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", - "OpenField-1010 openfield Oct30 0 -1 1 0 DLCresnet50ope =BLOB= openfield-Pran 2.2.0.6 Open field mod 1 \n", + "*model_name task date iteration snapshotindex shuffle trainingsetind scorer config_tem project_path model_prefix model_descript paramset_idx \n", + "+------------+ +-----------+ +-------+ +-----------+ +------------+ +---------+ +------------+ +------------+ +--------+ +------------+ +------------+ +------------+ +------------+\n", + "OpenField-5 openfield Oct30 0 -1 1 0 DLCresnet50ope =BLOB= openfield-Pran Open field mod 1 \n", " (Total: 1)" ] }, - "execution_count": 24, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1003,7 +1277,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1089,7 +1363,7 @@ " (Total: 4)" ] }, - "execution_count": 27, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1115,7 +1389,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1124,14 +1398,14 @@ "model_name : varchar(64) # user-friendly model name\n", "---\n", "train_iterations : int # Training iterations\n", - "train_error : float # Train error (px)\n", - "test_error : float # Test error (px)\n", - "p_cutoff : float # p-cutoff used\n", - "train_error_p : float # Train error with p-cutoff\n", + "train_error=null : float # Train error (px)\n", + "test_error=null : float # Test error (px)\n", + "p_cutoff=null : float # p-cutoff used\n", + "train_error_p=null : float # Train error with p-cutoff\n", "test_error_p=null : float # Test error with p-cutoff" ] }, - "execution_count": 16, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1146,7 +1420,117 @@ "metadata": {}, "outputs": [], "source": [ - "dlc.ModelEvaluation.populate()\n", + "dlc.ModelEvaluation.populate()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    model_name

    \n", + " user-friendly model name\n", + "
    \n", + "

    train_iterations

    \n", + " Training iterations\n", + "
    \n", + "

    train_error

    \n", + " Train error (px)\n", + "
    \n", + "

    test_error

    \n", + " Test error (px)\n", + "
    \n", + "

    p_cutoff

    \n", + " p-cutoff used\n", + "
    \n", + "

    train_error_p

    \n", + " Train error with p-cutoff\n", + "
    \n", + "

    test_error_p

    \n", + " Test error with p-cutoff\n", + "
    OpenField-55148.49156.750.482.5576.76
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*model_name train_iteratio train_error test_error p_cutoff train_error_p test_error_p \n", + "+------------+ +------------+ +------------+ +------------+ +----------+ +------------+ +------------+\n", + "OpenField-5 5 148.49 156.75 0.4 82.55 76.76 \n", + " (Total: 1)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "dlc.ModelEvaluation()" ] }, @@ -1166,7 +1550,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -1201,9 +1585,328 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    scorerOpenField-5
    bodypartsleftearrightearsnouttailbase
    coordsxyzlikelihoodxyzlikelihoodxyzlikelihoodxyzlikelihood
    0-2.4220834.3448210.00.550124103.509773154.8433690.00.49445326.76992627.6440770.00.34510112.27134725.3874950.00.420643
    1-3.5973484.7843530.00.570660129.002899158.9589390.00.497367113.209633111.1482240.00.39640111.66239125.4034960.00.409297
    2-1.8883464.0475950.00.52188726.2521845.5799910.00.431996111.761734114.3339690.00.43143812.38860125.3766400.00.381368
    3-2.6635054.9796670.00.55342326.8005876.1330340.00.429278634.74499528.0706960.00.35368511.83953624.7477650.00.389143
    4-3.1019334.9465460.00.552119117.008659145.3593750.00.427354125.948250110.6968310.00.40327211.64713024.0265390.00.382323
    ...................................................
    58-2.1798614.9173210.00.54336043.7868734.2421620.00.44074970.17988611.2572650.00.38580330.41210622.0749440.00.387526
    59-3.1255555.4284800.00.52246143.4959454.9912090.00.433459180.951401125.3253560.00.38751530.75188422.1980090.00.371095
    60-2.4750675.3631920.00.55059743.6919524.5685880.00.41862628.47232829.5186940.00.37250231.05481922.1894820.00.383042
    61-2.8770435.1240610.00.55832243.8440064.6317580.00.43881585.56198912.0519970.00.37468330.82567022.1802860.00.397028
    62-3.1326885.0888510.00.53553826.9758094.3689770.00.42704985.59262812.0825240.00.38396830.76311121.9663640.00.377716
    \n", + "

    63 rows × 16 columns

    \n", + "
    " + ], + "text/plain": [ + "scorer OpenField-5 \\\n", + "bodyparts leftear rightear \n", + "coords x y z likelihood x y z \n", + "0 -2.422083 4.344821 0.0 0.550124 103.509773 154.843369 0.0 \n", + "1 -3.597348 4.784353 0.0 0.570660 129.002899 158.958939 0.0 \n", + "2 -1.888346 4.047595 0.0 0.521887 26.252184 5.579991 0.0 \n", + "3 -2.663505 4.979667 0.0 0.553423 26.800587 6.133034 0.0 \n", + "4 -3.101933 4.946546 0.0 0.552119 117.008659 145.359375 0.0 \n", + ".. ... ... ... ... ... ... ... \n", + "58 -2.179861 4.917321 0.0 0.543360 43.786873 4.242162 0.0 \n", + "59 -3.125555 5.428480 0.0 0.522461 43.495945 4.991209 0.0 \n", + "60 -2.475067 5.363192 0.0 0.550597 43.691952 4.568588 0.0 \n", + "61 -2.877043 5.124061 0.0 0.558322 43.844006 4.631758 0.0 \n", + "62 -3.132688 5.088851 0.0 0.535538 26.975809 4.368977 0.0 \n", + "\n", + "scorer \\\n", + "bodyparts snout tailbase \n", + "coords likelihood x y z likelihood x \n", + "0 0.494453 26.769926 27.644077 0.0 0.345101 12.271347 \n", + "1 0.497367 113.209633 111.148224 0.0 0.396401 11.662391 \n", + "2 0.431996 111.761734 114.333969 0.0 0.431438 12.388601 \n", + "3 0.429278 634.744995 28.070696 0.0 0.353685 11.839536 \n", + "4 0.427354 125.948250 110.696831 0.0 0.403272 11.647130 \n", + ".. ... ... ... ... ... ... \n", + "58 0.440749 70.179886 11.257265 0.0 0.385803 30.412106 \n", + "59 0.433459 180.951401 125.325356 0.0 0.387515 30.751884 \n", + "60 0.418626 28.472328 29.518694 0.0 0.372502 31.054819 \n", + "61 0.438815 85.561989 12.051997 0.0 0.374683 30.825670 \n", + "62 0.427049 85.592628 12.082524 0.0 0.383968 30.763111 \n", + "\n", + "scorer \n", + "bodyparts \n", + "coords y z likelihood \n", + "0 25.387495 0.0 0.420643 \n", + "1 25.403496 0.0 0.409297 \n", + "2 25.376640 0.0 0.381368 \n", + "3 24.747765 0.0 0.389143 \n", + "4 24.026539 0.0 0.382323 \n", + ".. ... ... ... \n", + "58 22.074944 0.0 0.387526 \n", + "59 22.198009 0.0 0.371095 \n", + "60 22.189482 0.0 0.383042 \n", + "61 22.180286 0.0 0.397028 \n", + "62 21.966364 0.0 0.377716 \n", + "\n", + "[63 rows x 16 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dlc.PoseEstimation.get_trajectory(key)" ] From 21f74946b52edc6cbd453e5d72e38b158714b04b Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Fri, 11 Mar 2022 12:12:35 -0600 Subject: [PATCH 020/176] Split dlc schema --- workflow_deeplabcut/pipeline.py | 25 ++++++++++++++++++++++--- 1 file changed, 22 insertions(+), 3 deletions(-) diff --git a/workflow_deeplabcut/pipeline.py b/workflow_deeplabcut/pipeline.py index e414140..62e90f4 100644 --- a/workflow_deeplabcut/pipeline.py +++ b/workflow_deeplabcut/pipeline.py @@ -2,7 +2,7 @@ from element_animal import subject from element_lab import lab from element_session import session -from element_deeplabcut import dlc +from element_deeplabcut import train, model, pose from element_animal.subject import Subject from element_lab.lab import Source, Lab, Protocol, User, Project @@ -38,7 +38,26 @@ class Device(dj.Lookup): """ contents = zip([1, 2]) -# Activate "behavior" schema ----------------------------------- + +@session.schema +class VideoRecording(dj.Manual): + definition = """ + -> Session + -> Device + recording_id: int + --- + recording_start_time: datetime + """ + + class File(dj.Part): + definition = """ + -> master + file_path: varchar(255) # filepath of video, relative to root data directory + """ + +# Activate DeepLabCut schema ----------------------------------- -dlc.activate(db_prefix + 'dlc', linking_module=__name__) +train.activate(db_prefix + 'train', linking_module=__name__) +model.activate(db_prefix + 'model', linking_module=__name__) +pose.activate(db_prefix + 'pose', linking_module=__name__) From 4bbdfc062b2e258dc35d14f3ecf5dde520963466 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Mon, 21 Mar 2022 15:51:18 -0500 Subject: [PATCH 021/176] See details. Docker, Notebooks, JupyText, ingest.py Update docker Update NBs for 2 schema split Add jupytext py files Update ingest to reflect VideoRecording in pipeline Add ingest feature for train.VideoSet --- CHANGELOG.md | 2 +- Dockerfile.dev | 32 - Dockerfile.test | 39 - README.md | 48 +- docker-compose-dev.yaml | 32 - docker-compose-test.yaml | 44 - docker/Dockerfile.dev | 31 + docker/Dockerfile.test | 42 + .../apt-requirements.txt | 0 docker/docker-compose-dev.yaml | 37 + docker/docker-compose-test.yaml | 51 + images/attached_model_only.svg | 149 ++ images/attached_train_model.svg | 238 +++ notebooks/00-DataDownload_Optional.ipynb | 5 +- notebooks/01-Configure.ipynb | 5 +- notebooks/02-WorkflowStructure_Optional.ipynb | 1539 ++++------------- notebooks/03-Process.ipynb | 477 +++-- notebooks/04-Automate_Optional.ipynb | 627 ++++--- ..._Optional.ipynb => 05-Drop_Optional.ipynb} | 31 +- .../py_scripts/00-DataDownload_Optional.py | 98 ++ notebooks/py_scripts/01-Configure.py | 107 ++ .../02-WorkflowStructure_Optional.py | 148 ++ notebooks/py_scripts/03-Process.py | 280 +++ notebooks/py_scripts/04-Automate_Optional.py | 129 ++ notebooks/py_scripts/05-Drop_Optional.py | 44 + ...{05-Explore.ipynb => temp05-Explore.ipynb} | 7 +- tests/test_ingest.py | 1 + user_data/recordings.csv | 2 +- user_data/sessions.csv | 2 +- user_data/train_videoset.csv | 2 + workflow_deeplabcut/ingest.py | 22 +- workflow_deeplabcut/pipeline.py | 9 +- 32 files changed, 2329 insertions(+), 1951 deletions(-) delete mode 100644 Dockerfile.dev delete mode 100644 Dockerfile.test delete mode 100644 docker-compose-dev.yaml delete mode 100644 docker-compose-test.yaml create mode 100644 docker/Dockerfile.dev create mode 100644 docker/Dockerfile.test rename apt-requirements.txt => docker/apt-requirements.txt (100%) create mode 100644 docker/docker-compose-dev.yaml create mode 100644 docker/docker-compose-test.yaml create mode 100644 images/attached_model_only.svg create mode 100644 images/attached_train_model.svg rename notebooks/{06-Drop_Optional.ipynb => 05-Drop_Optional.ipynb} (88%) create mode 100644 notebooks/py_scripts/00-DataDownload_Optional.py create mode 100644 notebooks/py_scripts/01-Configure.py create mode 100644 notebooks/py_scripts/02-WorkflowStructure_Optional.py create mode 100644 notebooks/py_scripts/03-Process.py create mode 100644 notebooks/py_scripts/04-Automate_Optional.py create mode 100644 notebooks/py_scripts/05-Drop_Optional.py rename notebooks/{05-Explore.ipynb => temp05-Explore.ipynb} (98%) create mode 100644 user_data/train_videoset.csv diff --git a/CHANGELOG.md b/CHANGELOG.md index fb5ed72..d5b8aee 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,4 +11,4 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and + First draft begins, reflecting precursor pipelines + Added Docker files + Draft integration tests -+ Add example data featuring DLC examples ++ Add example data download instructions diff --git a/Dockerfile.dev b/Dockerfile.dev deleted file mode 100644 index 1ee34f0..0000000 --- a/Dockerfile.dev +++ /dev/null @@ -1,32 +0,0 @@ -FROM datajoint/djlab:py3.8-debian - -USER root -RUN apt-get update -y -RUN apt-get install git -y - -USER anaconda - -RUN mkdir /main/element-lab \ - /main/element-animal \ - /main/element-session \ - /main/element-behavior \ - /main/workflow-behavior - -# Copy user's local fork of elements and workflow -COPY --chown=anaconda:anaconda ./element-lab /main/element-lab -COPY --chown=anaconda:anaconda ./element-animal /main/element-animal -COPY --chown=anaconda:anaconda ./element-session /main/element-session -COPY --chown=anaconda:anaconda ./element-behavior /main/element-behavior -COPY --chown=anaconda:anaconda ./workflow-behavior /main/workflow-behavior - -# Install packages -RUN pip install -e /main/element-lab -RUN pip install -e /main/element-animal -RUN pip install -e /main/element-session -RUN pip install -e /main/element-behavior -RUN pip install -e /main/workflow-behavior -RUN pip install -r /main/workflow-behavior/requirements_test.txt - -WORKDIR /main/workflow-behavior - -ENTRYPOINT ["tail", "-f", "/dev/null"] diff --git a/Dockerfile.test b/Dockerfile.test deleted file mode 100644 index 6372bd9..0000000 --- a/Dockerfile.test +++ /dev/null @@ -1,39 +0,0 @@ -FROM datajoint/djlab:py3.8-debian - -USER root -RUN apt-get update -y -RUN apt-get install git -y - -USER anaconda -WORKDIR /main/workflow-behavior - -# Option 1 - Install DataJoint's remote fork of the workflow and elements -# RUN git clone https://github.com/datajoint/workflow-behavior.git /main/workflow-behavior - -# Option 2 - Install user's remote fork of element and workflow -# or an unreleased version of the element -# RUN pip install git+https://github.com//element-lab.git -# RUN pip install git+https://github.com//element-animal.git -# RUN pip install git+https://github.com//element-session.git -# RUN pip install git+https://github.com//element-behavior.git -# RUN git clone https://github.com//workflow-behavior.git /main/workflow-behavior - -# Option 3 - Install user's local fork of element and workflow -RUN mkdir /main/element-lab -COPY --chown=anaconda:anaconda ./element-lab /main/element-lab -RUN pip install -e /main/element-lab -RUN mkdir /main/element-animal -COPY --chown=anaconda:anaconda ./element-animal /main/element-animal -RUN pip install -e /main/element-animal -RUN mkdir /main/element-session -COPY --chown=anaconda:anaconda ./element-session /main/element-session -RUN pip install -e /main/element-session -RUN mkdir /main/element-behavior -COPY --chown=anaconda:anaconda ./element-behavior /main/element-behavior -RUN pip install -e /main/element-behavior -COPY --chown=anaconda:anaconda ./workflow-behavior /main/workflow-behavior -# RUN rm -f /main/workflow-behavior/dj_local_conf.json - -# Install the workflow -RUN pip install /main/workflow-behavior -RUN pip install -r /main/workflow-behavior/requirements_test.txt diff --git a/README.md b/README.md index 922c11d..370937b 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,27 @@ -# Workflow for continuous behavior tracking +# DataJoint Workflow - DeepLabCut -This directory provides an example workflow to save the continuous behavior data, using the following datajoint elements +Workflow for pose estimation using +[DeepLabCut](http://www.mackenziemathislab.org/deeplabcut). This workflow assumes you have +already declared your project and labeled training data. Then, DataJoint can be used to +launch model training and run pose estimation inferences. + +A complete DeeLabCut workflow can be built using DataJoint Elements. + [element-lab](https://github.com/datajoint/element-lab) + [element-animal](https://github.com/datajoint/element-animal) + [element-session](https://github.com/datajoint/element-session) + [element-deeplabcut](https://github.com/datajoint/element-deeplabcut) This repository provides demonstrations for: -Setting up a workflow using different elements (see [pipeline.py](workflow_deeplabcut/pipeline.py)) +1. Setting up a workflow using different elements (see [pipeline.py](workflow_deeplabcut/pipeline.py)) +2. Ingestion of model training parameters, and launching training. +3. Ingestion of model information, and launching evaluation. +4. Using an ingested model to run pose estimation. ## Workflow architecture -The lab and animal management workflow presented here uses components from two DataJoint elements (element-lab, element-animal and element-session) assembled together to a functional workflow. + +The deeplabcut workflow presented here uses components from 4 DataJoint elements +(`element-lab`, `element-animal`, `element-session`, and `element-deeplabcut`) +assembled together to a functional workflow. ### element-lab @@ -23,18 +34,21 @@ https://github.com/datajoint/element-lab/raw/main/images/element_lab_diagram.svg https://github.com/datajoint/element-animal/blob/main/images/subject_diagram.svg) ### element-session -`session` is designed to handle metadata related to data collection, including collection datetime, file paths, and notes. Most workflows will include element-session as a starting point for further data entry. + ![session](https://github.com/datajoint/element-session/blob/main/images/session_diagram.svg) ### Assembled with element-deeplabcut -![element-deeplabcut]( -https://github.com/datajoint/element-deeplabcut/blob/main/images/diagram_dlc.svg) -### This workflow -This workflow serves as an example of the upstream part of a typical data workflow, for examples using these elements with other data modalities refer to: +The DeepLabCut Element is split into `train` and `model` schemas. To manage both model +training and pose estimation within DataJoint, one would activate both schemas, as +shown below. -+ [workflow-array-ephys](https://github.com/datajoint/workflow-array-ephys) -+ [workflow-calcium-imaging](https://github.com/datajoint/workflow-calcium-imaging) +![assembled-both](./images/attached_train_model.svg) + +If training is managed outside DataJoint, one could only activate the `model` schema to +still manage various models and execute pose estimation. + +![assembled-model](./images/attached_model_only.svg) ## Installation instructions @@ -43,6 +57,12 @@ This workflow serves as an example of the upstream part of a typical data workfl ## Interacting with the DataJoint workflow -+ Please refer to the following workflow-specific -[Jupyter notebooks](/notebooks) for an in-depth explanation of how to run the -workflow ([01-Explore_Workflow.ipynb](notebooks/01-Explore_Workflow.ipynb)). +Please refer to the following workflow-specific +[Jupyter notebooks](/notebooks) for an in-depth explanation of how to ... ++ download example data ([00-DataDownload.ipynb](notebooks/00-DataDownload_Optional.ipynb)) ++ configure DataJoint settings ([01-Configure.ipynb](notebooks/01-Configure.ipynb)) ++ run the workflow ([01-WorkflowStructure.ipynb](notebooks/01-WorkflowStructure_Optional.ipynb)) ++ ingest data and launch tasks ([03-Process.ipynb](notebooks/03-Process.ipynb)) ++ automate tasks ([04-Automate.ipynb](notebooks/04-Automate_Optional.ipynb)) + +?? keep? [05-Explore.ipynb](notebooks/05-Explore.ipynb) diff --git a/docker-compose-dev.yaml b/docker-compose-dev.yaml deleted file mode 100644 index a599179..0000000 --- a/docker-compose-dev.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# docker-compose -f docker-compose-dev.yaml up -d --build -# docker-compose -f docker-compose-dev.yaml down - -version: "2.4" -x-net: &net - networks: - - main -services: - db: - <<: *net - image: datajoint/mysql:5.7 - environment: - - MYSQL_ROOT_PASSWORD=simple - workflow: - <<: *net - build: - context: ../ - dockerfile: ./workflow-deeplabcut/Dockerfile.dev - env_file: .env - image: workflow_session_dev:0.0.0a1 - volumes: - - ./apt_requirements.txt:/tmp/apt_requirements.txt - - ../element-lab:/main/element-lab - - ../element-animal:/main/element-animal - - ../element-session:/main/element-session - - ../element-deeplabcut:/main/element-deeplabcut - - .:/main/workflow-deeplabcut - depends_on: - db: - condition: service_healthy -networks: - main: diff --git a/docker-compose-test.yaml b/docker-compose-test.yaml deleted file mode 100644 index dfd7d7b..0000000 --- a/docker-compose-test.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# docker-compose -f docker-compose-test.yaml up --build -# docker-compose -f docker-compose-test.yaml down - -version: "2.4" -x-net: &net - networks: - - main -services: - db: - <<: *net - image: datajoint/mysql:5.7 - environment: - - MYSQL_ROOT_PASSWORD=simple - workflow: - <<: *net - build: - context: ../ - dockerfile: ./workflow-deeplabcut/Dockerfile.test - env_file: .env - image: workflow_deeplabcut:0.0.0a1 - environment: - - DJ_HOST=db - - DJ_USER=root - - DJ_PASS=simple - - DATABASE_PREFIX=test_ - command: - - bash - - -c - - | - echo "------ INTEGRATION TESTS ------" - pytest -sv --cov-report term-missing --cov=workflow-deeplabcut -p no:warnings - tail -f /dev/null - volumes: - - ./apt_requirements.txt:/tmp/apt_requirements.txt - - ../element-lab:/main/element-lab - - ../element-animal:/main/element-animal - - ../element-session:/main/element-session - - ../element-deeplabcut:/main/element-deeplabcut - - .:/main/workflow-deeplabcut - depends_on: - db: - condition: service_healthy -networks: - main: diff --git a/docker/Dockerfile.dev b/docker/Dockerfile.dev new file mode 100644 index 0000000..b9fb531 --- /dev/null +++ b/docker/Dockerfile.dev @@ -0,0 +1,31 @@ +FROM datajoint/djbase:py3.9-debian-fcd8909 + +USER anaconda:anaconda + +COPY ./workflow-deeplabcut/docker/apt_requirements.txt /tmp/ +RUN /entrypoint.sh echo "Installed dependencies." + +RUN mkdir /main/element-lab \ + /main/element-animal \ + /main/element-session \ + /main/element-deeplabcut \ + /main/workflow-deeplabcut + +# Copy user's local fork of elements and workflow +COPY --chown=anaconda:anaconda ./element-lab /main/element-lab +COPY --chown=anaconda:anaconda ./element-animal /main/element-animal +COPY --chown=anaconda:anaconda ./element-session /main/element-session +COPY --chown=anaconda:anaconda ./element-deeplabcut /main/element-deeplabcut +COPY --chown=anaconda:anaconda ./workflow-deeplabcut /main/workflow-deeplabcut + +# Install packages +RUN pip install -e /main/element-lab +RUN pip install -e /main/element-animal +RUN pip install -e /main/element-session +RUN pip install -e /main/element-deeplabcut +RUN pip install -e /main/workflow-deeplabcut +RUN pip install -r /main/workflow-deeplabcut/requirements_test.txt + +WORKDIR /main/workflow-deeplabcut + +ENTRYPOINT ["tail", "-f", "/dev/null"] diff --git a/docker/Dockerfile.test b/docker/Dockerfile.test new file mode 100644 index 0000000..ef69b5b --- /dev/null +++ b/docker/Dockerfile.test @@ -0,0 +1,42 @@ +FROM datajoint/djbase:py3.9-debian-fcd8909 + +USER anaconda:anaconda + +COPY ./workflow-deeplabcut/docker/apt_requirements.txt /tmp/ +RUN /entrypoint.sh echo "Installed dependencies." + +WORKDIR /main/workflow-deeplabcut + +# Option 1 - Install DataJoint's remote fork of the workflow and elements +# RUN git clone https://github.com/datajoint/workflow-deeplabcut.git /main/ + +# Option 2 - Install user's remote fork of element and workflow +# or an unreleased version of the element +# RUN pip install git+https://github.com/element-lab.git +# RUN pip install git+https://github.com//element-animal.git +# RUN pip install git+https://github.com//element-session.git +# RUN pip install git+https://github.com//element-deeplabcut.git +# RUN git clone https://github.com//workflow-deeplabcut.git /main/workflow-deeplabcut + +# Option 3 - Install user's local fork of element and workflow +RUN mkdir /main/element-lab \ + /main/element-animal \ + /main/element-session \ + /main/element-deeplabcut \ + /main/workflow-deeplabcut + +COPY --chown=anaconda:anaconda ./element-lab /main/element-lab +COPY --chown=anaconda:anaconda ./element-animal /main/element-animal +COPY --chown=anaconda:anaconda ./element-session /main/element-session +COPY --chown=anaconda:anaconda ./element-deeplabcut /main/element-deeplabcut +COPY --chown=anaconda:anaconda ./workflow-deeplabcut /main/workflow-deeplabcut + +RUN pip install -e /main/element-lab +RUN pip install -e /main/element-animal +RUN pip install -e /main/element-session +RUN pip install -e /main/element-deeplabcut +RUN rm -f /main/workflow-deeplabcut/dj_local_conf.json + +# Install the workflow +RUN pip install /main/workflow-deeplabcut +RUN pip install -r /main/workflow-deeplabcut/requirements_test.txt diff --git a/apt-requirements.txt b/docker/apt-requirements.txt similarity index 100% rename from apt-requirements.txt rename to docker/apt-requirements.txt diff --git a/docker/docker-compose-dev.yaml b/docker/docker-compose-dev.yaml new file mode 100644 index 0000000..bf4d705 --- /dev/null +++ b/docker/docker-compose-dev.yaml @@ -0,0 +1,37 @@ +# docker-compose -f ./docker/docker-compose-dev.yaml up -d --build +# docker-compose -f ./docker/docker-compose-dev.yaml down + +version: "2.4" +x-net: &net + networks: + - main +services: + db: + <<: *net + image: datajoint/mysql:5.7 + container_name: workflow-deeplabcut-dev-db + environment: + - MYSQL_ROOT_PASSWORD=simple + workflow: + <<: *net + build: + context: ../../ + dockerfile: ./workflow-deeplabcut/docker/Dockerfile.dev + env_file: .env + image: workflow-deeplabcut-dev:0.1.0a4 + container_name: workflow-deeplabcut-dev + environment: + - TEST_DATA_DIR=/main/test_data/workflow_dlc_data1/,/main/test_data/workflow_dlc_data2/ + volumes: + - ${TEST_DATA_DIR}:/main/test_data + - ./apt_requirements.txt:/tmp/apt_requirements.txt + - ../../element-lab:/main/element-lab + - ../../element-animal:/main/element-animal + - ../../element-session:/main/element-session + - ../../element-deeplabcut:/main/element-deeplabcut + - ..:/main/workflow-deeplabcut + depends_on: + db: + condition: service_healthy +networks: + main: diff --git a/docker/docker-compose-test.yaml b/docker/docker-compose-test.yaml new file mode 100644 index 0000000..b906c23 --- /dev/null +++ b/docker/docker-compose-test.yaml @@ -0,0 +1,51 @@ +# export COMPOSE_DOCKER_CLI_BUILD=0 # some machines need for smooth --build +# .env file: TEST_DATA_DIR= +# docker-compose -f ./docker/docker-compose-test.yaml up --build +# docker exec -it workflow-deeplabcut_workflow_1 /bin/bash +# docker-compose -f ./docker/docker-compose-test.yaml down + +version: "2.4" +x-net: &net + networks: + - main +services: + db: + <<: *net + image: datajoint/mysql:5.7 + container_name: workflow-deeplabcut-test-db + environment: + - MYSQL_ROOT_PASSWORD=simple + workflow: + <<: *net + build: + context: ../../ + dockerfile: ./workflow-deeplabcut/docker/Dockerfile.test + env_file: .env + image: workflow-deeplabcut-test:0.1.0a4 + container_name: workflow-deeplabcut-test + environment: + - DJ_HOST=db + - DJ_USER=root + - DJ_PASS=simple + - TEST_DATA_DIR=/main/test_data/workflow_dlc_data1/,/main/test_data/workflow_dlc_data2/ + - DATABASE_PREFIX=test_ + command: + - bash + - -c + - | + echo "------ INTEGRATION TESTS ------" + pytest -sv --cov-report term-missing --cov=workflow_deeplabcut -p no:warnings tests/ + tail -f /dev/null + volumes: + - ${TEST_DATA_DIR}:/main/test_data + - ./apt_requirements.txt:/tmp/apt_requirements.txt + - ../../element-lab:/main/element-lab + - ../../element-animal:/main/element-animal + - ../../element-session:/main/element-session + - ../../element-deeplabcut:/main/element-deeplabcut + - ..:/main/workflow-deeplabcut + depends_on: + db: + condition: service_healthy +networks: + main: diff --git a/images/attached_model_only.svg b/images/attached_model_only.svg new file mode 100644 index 0000000..639079b --- /dev/null +++ b/images/attached_model_only.svg @@ -0,0 +1,149 @@ + + + + + + + + + +session.VideoRecording + +session.VideoRecording + + + +model.EstimationTask + + +model.EstimationTask + + + + + +session.VideoRecording->model.EstimationTask + + + + +model.Estimation + + +model.Estimation + + + + + +model.EstimationTask->model.Estimation + + + + +model.Estimation.BodyPartPosition + + +model.Estimation.BodyPartPosition + + + + + +model.Estimation->model.Estimation.BodyPartPosition + + + + +model.Model + + +model.Model + + + + + +model.Model->model.EstimationTask + + + + +model.Model.BodyPart + + +model.Model.BodyPart + + + + + +model.Model->model.Model.BodyPart + + + + +model.ModelEvaluation + + +model.ModelEvaluation + + + + + +model.Model->model.ModelEvaluation + + + + +model.BodyPart + + +model.BodyPart + + + + + +model.BodyPart->model.Model.BodyPart + + + + +model.BodyPart->model.Estimation.BodyPartPosition + + + + +session.Session + + +session.Session + + + + + +session.Session->session.VideoRecording + + + + +subject.Subject + + +subject.Subject + + + + + +subject.Subject->session.Session + + + + diff --git a/images/attached_train_model.svg b/images/attached_train_model.svg new file mode 100644 index 0000000..601c17c --- /dev/null +++ b/images/attached_train_model.svg @@ -0,0 +1,238 @@ + + + + + + + + + +session.VideoRecording + +session.VideoRecording + + + +model.EstimationTask + + +model.EstimationTask + + + + + +session.VideoRecording->model.EstimationTask + + + + +train.VideoSet.VideoRecording + + +train.VideoSet.VideoRecording + + + + + +session.VideoRecording->train.VideoSet.VideoRecording + + + + +model.Estimation + + +model.Estimation + + + + + +model.EstimationTask->model.Estimation + + + + +model.Estimation.BodyPartPosition + + +model.Estimation.BodyPartPosition + + + + + +model.Estimation->model.Estimation.BodyPartPosition + + + + +subject.Subject + + +subject.Subject + + + + + +session.Session + + +session.Session + + + + + +subject.Subject->session.Session + + + + +session.Session->session.VideoRecording + + + + +model.BodyPart + + +model.BodyPart + + + + + +model.BodyPart->model.Estimation.BodyPartPosition + + + + +model.Model.BodyPart + + +model.Model.BodyPart + + + + + +model.BodyPart->model.Model.BodyPart + + + + +train.VideoSet.File + + +train.VideoSet.File + + + + + +model.Model + + +model.Model + + + + + +model.Model->model.EstimationTask + + + + +model.Model->model.Model.BodyPart + + + + +model.ModelEvaluation + + +model.ModelEvaluation + + + + + +model.Model->model.ModelEvaluation + + + + +train.ModelTraining + + +train.ModelTraining + + + + + +train.TrainingParamSet + + +train.TrainingParamSet + + + + + +train.TrainingParamSet->model.Model + + + + +train.TrainingTask + + +train.TrainingTask + + + + + +train.TrainingParamSet->train.TrainingTask + + + + +train.TrainingTask->train.ModelTraining + + + + +train.VideoSet + + +train.VideoSet + + + + + +train.VideoSet->train.VideoSet.VideoRecording + + + + +train.VideoSet->train.VideoSet.File + + + + +train.VideoSet->train.TrainingTask + + + + diff --git a/notebooks/00-DataDownload_Optional.ipynb b/notebooks/00-DataDownload_Optional.ipynb index a3830ca..ef96a59 100644 --- a/notebooks/00-DataDownload_Optional.ipynb +++ b/notebooks/00-DataDownload_Optional.ipynb @@ -127,7 +127,7 @@ "- `-n` do not overwrite\n", "- `-hide_banner -loglevel error` less verbose output\n", "- `-ss 0 -t 2` start at second 0, add 2 seconds\n", - "- `-i {vid_path}` input this video - be sure to change the root\n", + "- `-i {vid_path}` input this video\n", "- `-{v/a}codec copy` copy the video and audio codecs of the input\n", "- `{vid_path}-copy.mp4` output file" ] @@ -166,6 +166,9 @@ } ], "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, "kernelspec": { "display_name": "venv-dlc", "language": "python", diff --git a/notebooks/01-Configure.ipynb b/notebooks/01-Configure.ipynb index 1dcf6a4..7bbd4e9 100644 --- a/notebooks/01-Configure.ipynb +++ b/notebooks/01-Configure.ipynb @@ -157,7 +157,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's check that find the path connects." + "Let's check that find the path connects with a tool from [element-interface](https://github.com/datajoint/element-interface)." ] }, { @@ -217,6 +217,9 @@ } ], "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, "kernelspec": { "display_name": "venv-dlc", "language": "python", diff --git a/notebooks/02-WorkflowStructure_Optional.ipynb b/notebooks/02-WorkflowStructure_Optional.ipynb index 76a8329..95acfba 100644 --- a/notebooks/02-WorkflowStructure_Optional.ipynb +++ b/notebooks/02-WorkflowStructure_Optional.ipynb @@ -67,14 +67,14 @@ "outputs": [], "source": [ "import datajoint as dj\n", - "from workflow_deeplabcut.pipeline import lab, subject, session, dlc" + "from workflow_deeplabcut.pipeline import lab, subject, session, train, model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Each module contains a schema object that enables interaction with the schema in the database. For more information abotu managing the upstream tables, see our [session workflow](https://github.com/datajoint/workflow-session). In this case, lab is required because the pipeline adds a `device` table to the lab schema to keep track of camera IDs." + "Each module contains a schema object that enables interaction with the schema in the database. For more information abotu managing the upstream tables, see our [session workflow](https://github.com/datajoint/workflow-session). In this case, lab is required because the pipeline adds a `Device` table to the lab schema to keep track of camera IDs. The pipeline also adds a `VideoRecording` table to the session schema." ] }, { @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "title": "Each module imported above corresponds to one schema inside the database. For example, `ephys` corresponds to `neuro_ephys` schema in the database." }, @@ -115,42 +115,35 @@ { "data": { "text/plain": [ - "['#body_part',\n", - " '#model_training_param_set',\n", - " 'video_recording',\n", - " 'video_recording__file',\n", - " 'training_video',\n", - " 'training_video__file',\n", - " 'training_video__video_recording',\n", - " 'model',\n", - " 'model__body_part',\n", + "['video_set',\n", + " 'video_set__file',\n", + " 'video_set__video_recording',\n", + " '#training_param_set',\n", " 'training_task',\n", - " '__model_evaluation',\n", - " 'pose_estimation_task',\n", - " '__model_training',\n", - " '__pose_estimation',\n", - " '__pose_estimation__body_part_position']" + " '__model_training']" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.schema.list_tables()" + "train.schema.list_tables()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "`dj.Diagram()` plots tables and dependencies in a schema" + "`dj.Diagram()` plots tables and dependencies in a schema. To see additional upstream or downstream connections, add `- N` or `+ N` where N is the number of additional links.\n", + "\n", + "While the `model` schema is required for pose estimation, the `train` schema is optional, and can be used to manage model training within DataJoint" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": { "title": "`dj.Diagram()`: plot tables and dependencies" }, @@ -158,861 +151,152 @@ { "data": { "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "dlc.VideoRecording.File\n", - "\n", - "\n", - "dlc.VideoRecording.File\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.ModelEvaluation\n", - "train.VideoSet\n", + "\n", - "\n", - "dlc.ModelEvaluation\n", + "\n", + "train.VideoSet\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.BodyPart\n", - "\n", - "\n", - "dlc.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.Model.BodyPart\n", - "\n", - "\n", - "dlc.Model.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.BodyPart->dlc.Model.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "dlc.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "dlc.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.BodyPart->dlc.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "dlc.PoseEstimationTask\n", - "\n", - "\n", - "dlc.PoseEstimationTask\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.PoseEstimation\n", - "\n", - "\n", - "dlc.PoseEstimation\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.PoseEstimationTask->dlc.PoseEstimation\n", - "\n", - "\n", - "\n", - "\n", - "dlc.ModelTrainingParamSet\n", - "\n", - "\n", - "dlc.ModelTrainingParamSet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.Model\n", - "\n", - "\n", - "dlc.Model\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.ModelTrainingParamSet->dlc.Model\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingTask\n", - "train.TrainingTask\n", + "\n", - "\n", - "dlc.TrainingTask\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.ModelTrainingParamSet->dlc.TrainingTask\n", - "\n", - "\n", - "\n", - "\n", - "dlc.Model->dlc.ModelEvaluation\n", - "\n", - "\n", - "\n", - "\n", - "dlc.Model->dlc.PoseEstimationTask\n", - "\n", - "\n", - "\n", - "\n", - "dlc.Model->dlc.Model.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingVideo.File\n", - "\n", - "\n", - "dlc.TrainingVideo.File\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording\n", - "\n", - "\n", - "dlc.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording->dlc.VideoRecording.File\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording->dlc.PoseEstimationTask\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingVideo.VideoRecording\n", - "\n", - "\n", - "dlc.TrainingVideo.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording->dlc.TrainingVideo.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingVideo\n", - "\n", - "\n", - "dlc.TrainingVideo\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingVideo->dlc.TrainingVideo.File\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingVideo->dlc.TrainingVideo.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingVideo->dlc.TrainingTask\n", - "\n", - "\n", - "\n", - "\n", - "dlc.ModelTraining\n", - "\n", - "\n", - "dlc.ModelTraining\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.PoseEstimation->dlc.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingTask->dlc.ModelTraining\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(dlc)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Table tiers \n", - "- **Manual table**: green box, manually inserted table, expect new entries daily, e.g. Subject, ProbeInsertion. \n", - "- **Lookup table**: gray box, pre inserted table, commonly used for general facts or parameters. e.g. Strain, ClusteringMethod, ClusteringParamSet. \n", - "- **Imported table**: blue oval, auto-processing table, the processing depends on the importing of external files. e.g. process of Clustering requires output files from kilosort2. \n", - "- **Computed table**: red circle, auto-processing table, the processing does not depend on files external to the database, commonly used for \n", - "- **Part table**: plain text, as an appendix to the master table, all the part entries of a given master entry represent a intact set of the master entry. e.g. Unit of a CuratedClustering.\n", - "\n", - "### Dependencies\n", - "\n", - "- **One-to-one primary**: thick solid line, share the exact same primary key, meaning the child table inherits all the primary key fields from the parent table as its own primary key. \n", - "- **One-to-many primary**: thin solid line, inherit the primary key from the parent table, but have additional field(s) as part of the primary key as well\n", - "- **secondary dependency**: dashed line, the child table inherits the primary key fields from parent table as its own secondary attribute." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "title": "`dj.Diagram()`: plot the diagram of the tables and dependencies. It could be used to plot tables in a schema or selected tables." - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "session.SessionDirectory\n", - "\n", - "\n", - "session.SessionDirectory\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.PoseEstimationTask\n", - "\n", - "\n", - "dlc.PoseEstimationTask\n", + "\n", + "train.TrainingTask\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.PoseEstimation\n", - "\n", - "\n", - "dlc.PoseEstimation\n", - "\n", - "\n", - "\n", - "\n", + "\n", "\n", - "dlc.PoseEstimationTask->dlc.PoseEstimation\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Line\n", - "\n", - "\n", - "subject.Subject.Line\n", - "\n", + "train.VideoSet->train.TrainingTask\n", + "\n", "\n", - "\n", - "\n", - "\n", - "subject.Subject\n", - "\n", + "\n", + "train.VideoSet.File\n", + "\n", - "\n", - "subject.Subject\n", + "\n", + "train.VideoSet.File\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "subject.Subject->subject.Subject.Line\n", - "\n", + "train.VideoSet->train.VideoSet.File\n", + "\n", "\n", - "\n", - "\n", - "session.Session\n", - "\n", + "\n", + "train.VideoSet.VideoRecording\n", + "\n", - "\n", - "session.Session\n", + "\n", + "train.VideoSet.VideoRecording\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "subject.Subject->session.Session\n", - "\n", - "\n", - "\n", - "\n", - "subject.SubjectDeath\n", - "\n", - "\n", - "subject.SubjectDeath\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.SubjectDeath\n", - "\n", - "\n", - "\n", - "\n", - "subject.Zygosity\n", - "\n", - "\n", - "subject.Zygosity\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Zygosity\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Strain\n", - "\n", - "\n", - "subject.Subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Strain\n", - "\n", + "train.VideoSet->train.VideoSet.VideoRecording\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Source\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Lab\n", - "\n", - "\n", - "subject.Subject.Lab\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Lab\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.User\n", - "\n", - "\n", - "subject.Subject.User\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.User\n", - "\n", - "\n", - "\n", - "\n", - "subject.SubjectCullMethod\n", - "\n", - "\n", - "subject.SubjectCullMethod\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.SubjectCullMethod\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Protocol\n", - "\n", - "\n", - "subject.Subject.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "dlc.ModelTraining\n", - "\n", + "\n", + "train.ModelTraining\n", + "\n", - "\n", - "dlc.ModelTraining\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.Model.BodyPart\n", - "\n", - "\n", - "dlc.Model.BodyPart\n", + "\n", + "train.ModelTraining\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Allele\n", - "\n", - "\n", - "subject.Allele\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line.Allele\n", - "\n", - "\n", - "subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele->subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele.Source\n", - "\n", - "\n", - "subject.Allele.Source\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele->subject.Allele.Source\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele->subject.Zygosity\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingTask\n", - "\n", - "\n", - "dlc.TrainingTask\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingTask->dlc.ModelTraining\n", - "\n", - "\n", - "\n", - "\n", - "dlc.ModelEvaluation\n", - "\n", - "\n", - "dlc.ModelEvaluation\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.ProjectSession\n", - "\n", - "\n", - "session.ProjectSession\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingVideo.File\n", - "\n", - "\n", - "dlc.TrainingVideo.File\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording\n", - "\n", - "\n", - "dlc.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording->dlc.PoseEstimationTask\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingVideo.VideoRecording\n", - "\n", - "\n", - "dlc.TrainingVideo.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording->dlc.TrainingVideo.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording.File\n", - "\n", - "\n", - "dlc.VideoRecording.File\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.VideoRecording->dlc.VideoRecording.File\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingVideo\n", - "\n", - "\n", - "dlc.TrainingVideo\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingVideo->dlc.TrainingTask\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingVideo->dlc.TrainingVideo.File\n", - "\n", - "\n", - "\n", - "\n", - "dlc.TrainingVideo->dlc.TrainingVideo.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.SessionDirectory\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.ProjectSession\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->dlc.VideoRecording\n", - "\n", + "\n", + "\n", + "train.TrainingTask->train.ModelTraining\n", + "\n", "\n", - "\n", - "\n", - "session.SessionNote\n", - "\n", + "\n", + "train.TrainingParamSet\n", + "\n", - "\n", - "session.SessionNote\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.SessionNote\n", - "\n", - "\n", - "\n", - "\n", - "session.SessionExperimenter\n", - "\n", - "\n", - "session.SessionExperimenter\n", + "\n", + "train.TrainingParamSet\n", "\n", "\n", "\n", - "\n", - "\n", - "session.Session->session.SessionExperimenter\n", - "\n", + "\n", + "\n", + "train.TrainingParamSet->train.TrainingTask\n", + "\n", "\n", - "\n", - "\n", - "subject.Line\n", - "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.Diagram(train) #- 1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "model.Model.BodyPart\n", + "\n", - "\n", - "subject.Line\n", + "\n", + "model.Model.BodyPart\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Line->subject.Subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line->subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "dlc.PoseEstimation.BodyPartPosition\n", - "\n", + "\n", + "model.Estimation.BodyPartPosition\n", + "\n", - "\n", - "dlc.PoseEstimation.BodyPartPosition\n", + "\n", + "model.Estimation.BodyPartPosition\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.BodyPart\n", - "\n", + "\n", + "model.EstimationTask\n", + "\n", - "\n", - "dlc.BodyPart\n", + "\n", + "model.EstimationTask\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.BodyPart->dlc.Model.BodyPart\n", - "\n", + "\n", + "\n", + "model.Estimation\n", + "\n", + "\n", + "model.Estimation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.EstimationTask->model.Estimation\n", + "\n", "\n", - "\n", - "\n", - "dlc.BodyPart->dlc.PoseEstimation.BodyPartPosition\n", - "\n", + "\n", + "\n", + "model.Estimation->model.Estimation.BodyPartPosition\n", + "\n", "\n", - "\n", - "\n", - "dlc.ModelTrainingParamSet\n", - "\n", + "\n", + "model.BodyPart\n", + "\n", - "\n", - "dlc.ModelTrainingParamSet\n", + "\n", + "model.BodyPart\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.ModelTrainingParamSet->dlc.TrainingTask\n", - "\n", + "\n", + "\n", + "model.BodyPart->model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "model.BodyPart->model.Estimation.BodyPartPosition\n", + "\n", "\n", - "\n", - "\n", - "dlc.Model\n", - "\n", + "\n", + "model.Model\n", + "\n", - "\n", - "dlc.Model\n", + "\n", + "model.Model\n", "\n", "\n", "\n", - "\n", - "\n", - "dlc.ModelTrainingParamSet->dlc.Model\n", - "\n", - "\n", - "\n", - "\n", - "dlc.Model->dlc.PoseEstimationTask\n", - "\n", - "\n", - "\n", - "\n", - "dlc.Model->dlc.Model.BodyPart\n", - "\n", + "\n", + "\n", + "model.Model->model.Model.BodyPart\n", + "\n", "\n", - "\n", - "\n", - "dlc.Model->dlc.ModelEvaluation\n", - "\n", + "\n", + "\n", + "model.Model->model.EstimationTask\n", + "\n", "\n", - "\n", - "\n", - "subject.Strain\n", - "\n", + "\n", + "model.ModelEvaluation\n", + "\n", - "\n", - "subject.Strain\n", + "\n", + "model.ModelEvaluation\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Strain->subject.Subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "dlc.PoseEstimation->dlc.PoseEstimation.BodyPartPosition\n", - "\n", + "\n", + "\n", + "model.Model->model.ModelEvaluation\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], + "source": [ + "dj.Diagram(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Table tiers \n", + "- **Manual table**: green box, manually inserted table, expect new entries daily, e.g. Subject, ProbeInsertion. \n", + "- **Lookup table**: gray box, pre inserted table, commonly used for general facts or parameters. e.g. Strain, ClusteringMethod, ClusteringParamSet. \n", + "- **Imported table**: blue oval, auto-processing table, the processing depends on the importing of external files. e.g. process of Clustering requires output files from kilosort2. \n", + "- **Computed table**: red circle, auto-processing table, the processing does not depend on files external to the database, commonly used for \n", + "- **Part table**: plain text, as an appendix to the master table, all the part entries of a given master entry represent a intact set of the master entry. e.g. Unit of a CuratedClustering.\n", + "\n", + "### Dependencies\n", + "\n", + "- **One-to-one primary**: thick solid line, share the exact same primary key, meaning the child table inherits all the primary key fields from the parent table as its own primary key. \n", + "- **One-to-many primary**: thin solid line, inherit the primary key from the parent table, but have additional field(s) as part of the primary key as well\n", + "- **secondary dependency**: dashed line, the child table inherits the primary key fields from parent table as its own secondary attribute." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "title": "`dj.Diagram()`: plot the diagram of the tables and dependencies. It could be used to plot tables in a schema or selected tables." + }, + "outputs": [], "source": [ "# plot diagram of tables in multiple schemas\n", - "dj.Diagram(subject) + dj.Diagram(session) + dj.Diagram(dlc)" + "dj.Diagram(subject.Subject) + dj.Diagram(session.Session) + dj.Diagram(model)" ] }, { @@ -1189,105 +508,107 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", - "\n", - "\n", + "\n", + "\n", "\n", - "dlc.VideoRecording\n", - "subject.Subject\n", + "\n", - "\n", - "dlc.VideoRecording\n", + "\n", + "subject.Subject\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.PoseEstimationTask\n", - "session.Session\n", + "\n", - "\n", - "dlc.PoseEstimationTask\n", + "\n", + "session.Session\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "dlc.VideoRecording->dlc.PoseEstimationTask\n", - "\n", + "subject.Subject->session.Session\n", + "\n", "\n", - "\n", + "\n", "\n", - "subject.Subject\n", - "model.EstimationTask\n", + "\n", - "\n", - "subject.Subject\n", + "\n", + "model.EstimationTask\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "session.Session\n", - "VideoRecording\n", + "\n", - "\n", - "session.Session\n", + "\n", + "VideoRecording\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "subject.Subject->session.Session\n", - "\n", + "VideoRecording->model.EstimationTask\n", + "\n", "\n", - "\n", + "\n", "\n", - "session.Session->dlc.VideoRecording\n", - "\n", + "session.Session->VideoRecording\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "from workflow_deeplabcut.pipeline import VideoRecording\n", "# plot diagram of selected tables and schemas\n", "(dj.Diagram(subject.Subject) + dj.Diagram(session.Session) \n", - " + dj.Diagram(dlc.VideoRecording) + dj.Diagram(dlc.PoseEstimationTask)) " + " + dj.Diagram(VideoRecording) \n", + " + dj.Diagram(model.EstimationTask)) " ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "metadata": { "title": "Each datajoint table class inside the module corresponds to a table inside the schema. For example, the class `ephys.EphysRecording` correponds to the table `_ephys_recording` in the schema `neuro_ephys` in the database." }, @@ -1377,14 +698,14 @@ " (Total: 0)" ] }, - "execution_count": 9, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# preview columns and contents in a table\n", - "dlc.VideoRecording.File()" + "VideoRecording.File()" ] }, { @@ -1399,7 +720,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1407,8 +728,8 @@ "output_type": "stream", "text": [ "# Specification for a DLC model training instance\n", - "-> dlc.TrainingVideo\n", - "-> dlc.ModelTrainingParamSet\n", + "-> train.VideoSet\n", + "-> train.TrainingParamSet\n", "training_id : int \n", "---\n", "model_prefix=\"\" : varchar(32) \n", @@ -1419,16 +740,16 @@ { "data": { "text/plain": [ - "'# Specification for a DLC model training instance\\n-> dlc.TrainingVideo\\n-> dlc.ModelTrainingParamSet\\ntraining_id : int \\n---\\nmodel_prefix=\"\" : varchar(32) \\nproject_path=\"\" : varchar(255) # DLC\\'s project_path in config relative to root\\n'" + "'# Specification for a DLC model training instance\\n-> train.VideoSet\\n-> train.TrainingParamSet\\ntraining_id : int \\n---\\nmodel_prefix=\"\" : varchar(32) \\nproject_path=\"\" : varchar(255) # DLC\\'s project_path in config relative to root\\n'" ] }, - "execution_count": 11, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.TrainingTask.describe()" + "train.TrainingTask.describe()" ] }, { @@ -1440,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "metadata": { "title": "`heading`: show table attributes regardless of foreign key references." }, @@ -1465,13 +786,13 @@ "paramset_idx=null : smallint # " ] }, - "execution_count": 12, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.Model.heading" + "model.Model.heading" ] }, { @@ -1503,290 +824,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Line\n", - "\n", - "\n", - "subject.Subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Protocol\n", - "\n", - "\n", - "subject.Subject.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele\n", - "\n", - "\n", - "subject.Allele\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line.Allele\n", - "\n", - "\n", - "subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele->subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele.Source\n", - "\n", - "\n", - "subject.Allele.Source\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele->subject.Allele.Source\n", - "\n", - "\n", - "\n", - "\n", - "subject.Zygosity\n", - "\n", - "\n", - "subject.Zygosity\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele->subject.Zygosity\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Lab\n", - "\n", - "\n", - "subject.Subject.Lab\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject\n", - "\n", - "\n", - "subject.Subject\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Lab\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.User\n", - "\n", - "\n", - "subject.Subject.User\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.User\n", - "\n", - "\n", - "\n", - "\n", - "subject.SubjectDeath\n", - "\n", - "\n", - "subject.SubjectDeath\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.SubjectDeath\n", - "\n", - "\n", - "\n", - "\n", - "subject.SubjectCullMethod\n", - "\n", - "\n", - "subject.SubjectCullMethod\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.SubjectCullMethod\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Zygosity\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Source\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Strain\n", - "\n", - "\n", - "subject.Subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "subject.Strain\n", - "\n", - "\n", - "subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Strain->subject.Subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line\n", - "\n", - "\n", - "subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line->subject.Subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line->subject.Line.Allele\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dj.Diagram(subject)" ] @@ -1962,7 +1002,8 @@ ], "metadata": { "jupytext": { - "encoding": "# -*- coding: utf-8 -*-" + "encoding": "# -*- coding: utf-8 -*-", + "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "venv-dlc", diff --git a/notebooks/03-Process.ipynb b/notebooks/03-Process.ipynb index 6244e3a..a21b7f8 100644 --- a/notebooks/03-Process.ipynb +++ b/notebooks/03-Process.ipynb @@ -50,12 +50,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting cbroz@tutorial-db.datajoint.io:3306\n" + ] + } + ], "source": [ "import datajoint as dj\n", - "from workflow_deeplabcut.pipeline import lab, subject, session, dlc" + "from workflow_deeplabcut.pipeline import lab, subject, session, train, model" ] }, { @@ -80,7 +88,7 @@ { "data": { "text/plain": [ - "subject : varchar(32) # \n", + "subject : varchar(8) # \n", "---\n", "sex : enum('M','F','U') # \n", "subject_birth_date : date # \n", @@ -104,13 +112,13 @@ "source": [ "subject.Subject.insert1(dict(subject='subject6', \n", " sex='M', \n", - " subject_birth_date='2020-01-01', \n", - " subject_description='manuel'))" + " subject_birth_date='2020-01-03', \n", + " subject_description='hneih_E105'))" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -184,8 +192,8 @@ "
    \n", " subject6\n", "M\n", - "2020-01-01\n", - "manuel \n", + "2020-01-03\n", + "hneih_E105 \n", " \n", " \n", "

    Total: 1

    \n", @@ -194,11 +202,11 @@ "text/plain": [ "*subject sex subject_birth_ subject_descri\n", "+----------+ +-----+ +------------+ +------------+\n", - "subject6 M 2020-01-01 manuel \n", + "subject6 M 2020-01-03 hneih_E105 \n", " (Total: 1)" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -252,6 +260,17 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, + "outputs": [], + "source": [ + "session_keys = [dict(subject='subject6', session_datetime='2021-06-02 14:04:22'),\n", + " dict(subject='subject6', session_datetime='2021-06-03 14:43:10')]\n", + "session.Session.insert(session_keys)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { @@ -318,7 +337,7 @@ " \n", " subject6\n", "2021-06-02 14:04:22subject6\n", - "2021-06-03 14:04:22 \n", + "2021-06-03 14:43:10 \n", " \n", " \n", "

    Total: 2

    \n", @@ -332,16 +351,13 @@ " (Total: 2)" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "session_keys = [dict(subject='subject6', session_datetime='2021-06-02 14:04:22'),\n", - " dict(subject='subject6', session_datetime='2021-06-03 14:04:22')]\n", - "session.Session.insert(session_keys)\n", - "session.Session()" + "session.Session() & \"session_datetime > '2021-06-01 12:00:00'\"" ] }, { @@ -353,28 +369,29 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "# \n", - "subject : varchar(32) # \n", - "session_datetime : datetime(3) # \n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", "camera_id : int # \n", "recording_id : int # \n", "---\n", "recording_start_time : datetime # " ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.VideoRecording.heading" + "from workflow_deeplabcut.pipeline import VideoRecording\n", + "VideoRecording.heading" ] }, { @@ -387,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -398,10 +415,10 @@ " 'camera_id': '1'},\n", " {'recording_id': '2',\n", " 'subject': 'subject6',\n", - " 'session_datetime': '2021-06-03 14:04:22',\n", - " 'recording_start_time': '2021-06-04 14:07:00',\n", + " 'session_datetime': '2021-06-03 14:43:10',\n", + " 'recording_start_time': '2021-06-03 14:50:00',\n", " 'camera_id': '1'}]\n", - "dlc.VideoRecording.insert(recordings)" + "VideoRecording.insert(recordings)" ] }, { @@ -413,42 +430,42 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "subject : varchar(32) # \n", - "session_datetime : datetime(3) # \n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", "camera_id : int # \n", "recording_id : int # \n", "file_path : varchar(255) # filepath of video, relative to root data directory" ] }, - "execution_count": 11, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.VideoRecording.File.heading" + "VideoRecording.File.heading" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "recordings[0].update({'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4'})\n", "recordings[1].update({'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4'})\n", - "dlc.VideoRecording.File.insert(recordings, ignore_extra_fields=True)" + "VideoRecording.File.insert(recordings, ignore_extra_fields=True)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -528,7 +545,7 @@ "1\n", "1\n", "openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4subject6\n", - "2021-06-03 14:04:22\n", + "2021-06-03 14:43:10\n", "1\n", "2\n", "openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4 \n", @@ -545,13 +562,13 @@ " (Total: 2)" ] }, - "execution_count": 13, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.VideoRecording.File()" + "VideoRecording.File()" ] }, { @@ -563,25 +580,128 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ - "dlc.TrainingVideo.insert1({'video_set_id': 1})\n", + "train.VideoSet.insert1({'video_set_id': 1})\n", "csv_path = 'openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.csv'\n", - "dlc.TrainingVideo.File.insert1({'video_set_id': 1,\n", - " 'file_path': csv_path})" + "train.VideoSet.File.insert1({'video_set_id': 1,\n", + " 'file_path': csv_path})" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "video_key = (dlc.VideoRecording&'recording_id=2').fetch1('KEY')\n", - "video_key.update({'video_set_id': 1})\n", - "dlc.TrainingVideo.VideoRecording.insert1(video_key)" + "rec_key = (VideoRecording & 'recording_id=1').fetch1('KEY')\n", + "train.VideoSet.VideoRecording.insert1({**rec_key,\n", + " 'video_set_id': 1, 'recording_id': 1})" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    video_set_id

    \n", + " \n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    camera_id

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    1subject62021-06-02 14:04:2211
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*video_set_id *subject *session_datet *camera_id *recording_id \n", + "+------------+ +----------+ +------------+ +-----------+ +------------+\n", + "1 subject6 2021-06-02 14: 1 1 \n", + " (Total: 1)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.VideoSet.VideoRecording()" ] }, { @@ -600,13 +720,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "# Parameters to specify a DLC model training instance\n", "paramset_idx : smallint # \n", "---\n", "paramset_desc : varchar(128) # \n", @@ -614,13 +733,13 @@ "params : longblob # dictionary of all applicable parameters" ] }, - "execution_count": 16, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.ModelTrainingParamSet.heading" + "train.TrainingParamSet.heading" ] }, { @@ -649,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -668,9 +787,9 @@ " 'scorer_legacy': 'False',\n", " 'maxiters': '5'}\n", "config_params.update(training_params)\n", - "dlc.ModelTrainingParamSet.insert_new_params(paramset_idx=paramset_idx,\n", - " paramset_desc=paramset_desc,\n", - " params=config_params)" + "train.TrainingParamSet.insert_new_params(paramset_idx=paramset_idx,\n", + " paramset_desc=paramset_desc,\n", + " params=config_params)" ] }, { @@ -682,13 +801,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "# Specification for a DLC model training instance\n", "video_set_id : int # \n", "paramset_idx : smallint # \n", "training_id : int # \n", @@ -697,18 +815,18 @@ "project_path=\"\" : varchar(255) # DLC's project_path in config relative to root" ] }, - "execution_count": 19, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.TrainingTask.heading" + "train.TrainingTask.heading" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -800,7 +918,7 @@ " (Total: 1)" ] }, - "execution_count": 20, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -808,26 +926,8 @@ "source": [ "key={'video_set_id': 1, 'paramset_idx':1,'training_id':1,\n", " 'project_path':'openfield-Pranav-2018-10-30/'}\n", - "dlc.TrainingTask.insert1(key, skip_duplicates=True)\n", - "dlc.TrainingTask()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: numpy==1.20 in /Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages (1.20.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "pip install numpy==1.20" + "train.TrainingTask.insert1(key, skip_duplicates=True)\n", + "train.TrainingTask()" ] }, { @@ -838,12 +938,12 @@ }, "outputs": [], "source": [ - "dlc.ModelTraining.populate()" + "train.ModelTraining.populate()" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -935,20 +1035,20 @@ " (Total: 1)" ] }, - "execution_count": 23, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.ModelTraining()" + "train.ModelTraining()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To training from a previous instance, one would need to \n", + "To start training from a previous instance, one would need to \n", "[edit the relevant config file](https://github.com/DeepLabCut/DeepLabCut/issues/70) and\n", "adjust the `maxiters` paramset (if present) to a higher threshold (e.g., 10 for 5 more itterations).\n", "Emperical work from the Mathis team suggests 200k iterations for any true use-case." @@ -965,7 +1065,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The DLC schema uses a lookup table for managing Body Parts tracked across models." + "The `model` schema uses a lookup table for managing Body Parts tracked across models." ] }, { @@ -988,7 +1088,7 @@ } ], "source": [ - "dlc.BodyPart.heading" + "model.BodyPart.heading" ] }, { @@ -1000,7 +1100,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1017,13 +1117,13 @@ "array(['leftear', 'rightear', 'snout', 'tailbase'], dtype='\n", - " .Relation{\n", - " border-collapse:collapse;\n", - " }\n", - " .Relation th{\n", - " background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n", - " font-weight: normal; font-family: monospace; font-size: 100%;\n", - " }\n", - " .Relation td{\n", - " padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n", - " }\n", - " .Relation tr:nth-child(odd){\n", - " background: #ffffff;\n", - " }\n", - " .Relation tr:nth-child(even){\n", - " background: #f3f1ff;\n", - " }\n", - " /* Tooltip container */\n", - " .djtooltip {\n", - " }\n", - " /* Tooltip text */\n", - " .djtooltip .djtooltiptext {\n", - " visibility: hidden;\n", - " width: 120px;\n", - " background-color: black;\n", - " color: #fff;\n", - " text-align: center;\n", - " padding: 5px 0;\n", - " border-radius: 6px;\n", - " /* Position the tooltip text - see examples below! */\n", - " position: absolute;\n", - " z-index: 1;\n", - " }\n", - " #primary {\n", - " font-weight: bold;\n", - " color: black;\n", - " }\n", - " #nonprimary {\n", - " font-weight: normal;\n", - " color: white;\n", - " }\n", - "\n", - " /* Show the tooltip text when you mouse over the tooltip container */\n", - " .djtooltip:hover .djtooltiptext {\n", - " visibility: visible;\n", - " }\n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    body_part

    \n", - " \n", - "
    \n", - "

    body_part_description

    \n", - " \n", - "
    leftearLeft Ear
    rightearRight Ear
    snoutSnout Position
    tailbaseBase of Tail
    \n", - " \n", - "

    Total: 4

    \n", - " " - ], - "text/plain": [ - "*body_part body_part_desc\n", - "+-----------+ +------------+\n", - "leftear Left Ear \n", - "rightear Right Ear \n", - "snout Snout Position\n", - "tailbase Base of Tail \n", - " (Total: 4)" - ] - }, - "execution_count": 29, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.BodyPart()" + "model.Model()" ] }, { @@ -1389,7 +1345,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1405,13 +1361,13 @@ "test_error_p=null : float # Test error with p-cutoff" ] }, - "execution_count": 30, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.ModelEvaluation.heading" + "model.ModelEvaluation.heading" ] }, { @@ -1420,12 +1376,12 @@ "metadata": {}, "outputs": [], "source": [ - "dlc.ModelEvaluation.populate()" + "model.ModelEvaluation.populate()" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1525,13 +1481,13 @@ " (Total: 1)" ] }, - "execution_count": 32, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.ModelEvaluation()" + "model.ModelEvaluation()" ] }, { @@ -1550,14 +1506,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ - "key=(dlc.VideoRecording&'recording_id=2').fetch1('KEY');\n", + "key=(VideoRecording&'recording_id=2').fetch1('KEY');\n", "key.update({'model_name': 'OpenField-5', 'task_mode': 'trigger'})\n", - "dlc.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True},\n", - " skip_duplicates=True)" + "model.EstimationTask.insert_estimation_task(key,params={'save_as_csv':True},\n", + " skip_duplicates=True)" ] }, { @@ -1566,7 +1522,7 @@ "metadata": {}, "outputs": [], "source": [ - "dlc.PoseEstimation.populate()" + "model.Estimation.populate()" ] }, { @@ -1585,7 +1541,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -1902,13 +1858,13 @@ "[63 rows x 16 columns]" ] }, - "execution_count": 5, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dlc.PoseEstimation.get_trajectory(key)" + "model.Estimation.get_trajectory(key)" ] }, { @@ -1921,6 +1877,9 @@ } ], "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, "kernelspec": { "display_name": "venv-dlc", "language": "python", diff --git a/notebooks/04-Automate_Optional.ipynb b/notebooks/04-Automate_Optional.ipynb index b69bd0e..9f5ea29 100644 --- a/notebooks/04-Automate_Optional.ipynb +++ b/notebooks/04-Automate_Optional.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "tags": [] }, @@ -36,7 +36,27 @@ "# change to the upper level folder to detect dj_local_conf.json\n", "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", - " + \"workflow directory\")" + " + \"workflow directory\")\n", + "from workflow_deeplabcut.pipeline import lab, subject, session, train, model, \\\n", + " VideoRecording" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you previously completed the [03-Process notebook](./03-Process.ipynb), you may want to delete the contents ingested there, to avoid duplication errors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# session.Session.delete()\n", + "# train.TrainingParamSet.delete()\n", + "# train.VideoSet.delete()" ] }, { @@ -54,12 +74,12 @@ "3. Run automatic scripts prepared in `workflow_deeplabcut.ingest` for ingestion: \n", " + `ingest_subjects` for `subject.Subject`\n", " + `ingest_sessions` - for session tables `Session`, `SessionDirectory`, and `SessionNote`\n", - " + `ingest_dlc_items` - for DLC tables `VideoRecording` and `ModelTrainingParamSet`" + " + `ingest_dlc_items` - for `VideoRecording` and `TrainingParamSet`" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -69,22 +89,23 @@ "\n", "---- Inserting 0 entry(s) into subject ----\n", "\n", - "---- Inserting 0 entry(s) into session ----\n", + "---- Inserting 3 entry(s) into session ----\n", + "\n", + "---- Inserting 3 entry(s) into session_directory ----\n", "\n", - "---- Inserting 0 entry(s) into session_directory ----\n", + "---- Inserting 3 entry(s) into session_note ----\n", "\n", - "---- Inserting 0 entry(s) into session_note ----\n", + "---- Inserting 3 entry(s) into #model_training_param_set ----\n", "\n", - "---- Inserting 0 entry(s) into #model_training_param_set ----\n", + "---- Inserting 3 entry(s) into video_recording ----\n", "\n", - "---- Inserting 0 entry(s) into video_recording ----\n", + "---- Inserting 3 entry(s) into video_recording__file ----\n", "\n", - "---- Inserting 0 entry(s) into video_recording__file ----\n" + "---- Inserting 1 entry(s) into video_set ----\n" ] } ], "source": [ - "from workflow_deeplabcut.pipeline import lab, subject, session, dlc\n", "from workflow_deeplabcut.ingest import ingest_subjects, ingest_sessions, ingest_dlc_items\n", "ingest_subjects(); ingest_sessions(); ingest_dlc_items()" ] @@ -95,235 +116,34 @@ "source": [ "## Setting project variables\n", "\n", - "1. Set your root directory in your DataJoint config file, under `custom` as `dlc_root_data_dir`" + "1. Set your root directory in your DataJoint config file, under `custom` as `dlc_root_data_dir`. For the purposes of this demo, we'll ask DeepLabCut to structure the demo config file with `load_demo_data`" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'CommentedSeq' object has no attribute 'keys'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_8305/2210132270.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mconfig_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata_dir\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'config.yaml'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdeeplabcut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_project\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdemo_data\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_demo_data\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdlc_load_demo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdlc_load_demo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m in \u001b[0;36mload_demo_data\u001b[0;34m(config, createtrainingset)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mtransform_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcreatetrainingset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Loaded, now creating training data...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m in \u001b[0;36mtransform_data\u001b[0;34m(config)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This is not an offical demo dataset.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"video_sets\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 63\u001b[0m cfg[\"video_sets\"][str(video_file)] = cfg[\"video_sets\"].pop(\n\u001b[1;32m 64\u001b[0m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'CommentedSeq' object has no attribute 'keys'" - ] - } - ], + "outputs": [], "source": [ "import datajoint as dj; dj.config.load('dj_local_conf.json')\n", "from element_interface.utils import find_full_path\n", "data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config\n", " 'openfield-Pranav-2018-10-30') # DLC project dir\n", "config_path = (data_dir / 'config.yaml')\n", - "from deeplabcut.create_project.demo_data import load_demo_data as dlc_load_demo\n", - "dlc_load_demo(config_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m(62)\u001b[0;36mtransform_data\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 60 \u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This is not an offical demo dataset.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 61 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m---> 62 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"video_sets\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 63 \u001b[0;31m cfg[\"video_sets\"][str(video_file)] = cfg[\"video_sets\"].pop(\n", - "\u001b[0m\u001b[0;32m 64 \u001b[0;31m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> up\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m(39)\u001b[0;36mload_demo_data\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 37 \u001b[0;31m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 38 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m---> 39 \u001b[0;31m \u001b[0mtransform_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 40 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mcreatetrainingset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 41 \u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Loaded, now creating training data...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_8305/2210132270.py\u001b[0m(7)\u001b[0;36m\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 3 \u001b[0;31mdata_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config\n", - "\u001b[0m\u001b[0;32m 4 \u001b[0;31m 'openfield-Pranav-2018-10-30') # DLC project dir\n", - "\u001b[0m\u001b[0;32m 5 \u001b[0;31m\u001b[0mconfig_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata_dir\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'config.yaml'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 6 \u001b[0;31m\u001b[0;32mfrom\u001b[0m \u001b[0mdeeplabcut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_project\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdemo_data\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_demo_data\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdlc_load_demo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m----> 7 \u001b[0;31m\u001b[0mdlc_load_demo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> config_path\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PosixPath('/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/config.yaml')\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> config_path.exists()\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> down\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m(39)\u001b[0;36mload_demo_data\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 37 \u001b[0;31m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 38 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m---> 39 \u001b[0;31m \u001b[0mtransform_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 40 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mcreatetrainingset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 41 \u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Loaded, now creating training data...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> createtrainingset\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> down\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m(62)\u001b[0;36mtransform_data\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 60 \u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This is not an offical demo dataset.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 61 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m---> 62 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"video_sets\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 63 \u001b[0;31m cfg[\"video_sets\"][str(video_file)] = cfg[\"video_sets\"].pop(\n", - "\u001b[0m\u001b[0;32m 64 \u001b[0;31m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> cfg\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ordereddict([('Task', 'openfield'), ('TrainingFraction', [0.95]), ('alphavalue', 0.7), ('batch_size', 4), ('bodyparts', ['snout', 'leftear', 'rightear', 'tailbase']), ('colormap', 'jet'), ('corner2move2', [50, 50]), ('cropping', False), ('date', 'Oct30'), ('default_augmenter', 'imgaug'), ('default_net_type', 'resnet_50'), ('dotsize', 8), ('filter_type', ''), ('identity', None), ('iteration', 0), ('maxiters', '5'), ('modelprefix', ''), ('move2corner', True), ('multianimalproject', None), ('numframes2pick', 20), ('pcutoff', 0.4), ('project_path', '/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30'), ('scorer', 'Pranav'), ('scorer_legacy', 'False'), ('shuffle', '1'), ('skeleton', []), ('skeleton_color', 'black'), ('snapshotindex', -1), ('start', 0), ('stop', 1), ('track_method', ''), ('train_float', 0.95), ('trainingsetindex', '0'), ('video_sets', ['/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4']), ('x1', 0), ('x2', 640), ('y1', 277), ('y2', 624)])\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> cfg[\"video_sets\"].keys()\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "*** AttributeError: 'CommentedSeq' object has no attribute 'keys'\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "ipdb> cfg[\"video_sets\"]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4']\n" - ] - } - ], - "source": [ - "%debug" + "from deeplabcut.create_project.demo_data import load_demo_data\n", + "load_demo_data(config_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "2. For this demo, we generate a copy to show pose estimation. This is recording_id 2 in `recordings.csv`" + "2. For this demo, we generate a copy to show pose estimation. This is `recording_id` 2 in `recordings.csv`. If you already did this in the [00-DataDownload notebook](./00-DataDownload_Optional.ipynb), skip this step." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": { "tags": [] }, @@ -341,7 +161,7 @@ "256" ] }, - "execution_count": 9, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -366,18 +186,20 @@ "metadata": {}, "outputs": [], "source": [ - "key=(dlc.VideoRecording&'recording_id=1').fetch1('KEY') # replace w/relevant IDs \n", - "key.update({'paramset_idx':1,'training_id':1,\n", - " 'project_path':'openfield-Pranav-2018-10-30/'})\n", - "dlc.TrainingTask.insert1(key, skip_duplicates=True)\n", - "dlc.TrainingTask.populate()" + "key={'paramset_idx':1,'training_id':1,'video_set_id':1, \n", + " 'project_path':'openfield-Pranav-2018-10-30/'}\n", + "train.TrainingTask.insert1(key, skip_duplicates=True)\n", + "train.ModelTraining.populate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "4. Add this model to the `Model` table and evaluate." + "4. Add this model to the `Model` table and evaluate.\n", + " - Include a user-friendly `model_name`\n", + " - Include the relative path for the project's `config.yaml`\n", + " - Add `shuffle` and `trainingsetindex`" ] }, { @@ -386,18 +208,20 @@ "metadata": {}, "outputs": [], "source": [ - "dlc.Model.insert_new_model(model_name='OpenField-5',dlc_config=dlc_config_path,\n", - " shuffle=1,trainingsetindex=0,\n", - " model_description='Open field model trained 5 iterations',\n", - " body_part_descriptions = bp_desc,paramset_idx=1)\n", - "dlc.ModelEvaluation.populate()" + "model.Model.insert_new_model(model_name='OpenField-5',dlc_config=config_path,\n", + " shuffle=1,trainingsetindex=0, paramset_idx=1,\n", + " model_description='Open field model trained 5 iterations')\n", + "model.ModelEvaluation.populate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "5. Add a pose estimation task, and launch pose estimation." + "5. Add a pose estimation task, and launch pose estimation.\n", + " - Get all primary key information for a given recording\n", + " - Add the model and `task_mode` (i.e., load vs. trigger) to be applied\n", + " - Add any additional analysis parameters for `deeplabcut.analyze_videos`" ] }, { @@ -406,12 +230,11 @@ "metadata": {}, "outputs": [], "source": [ - "key=(dlc.VideoRecording&'recording_id=2').fetch1('KEY') # change relevant ID\n", - "key.update({'model_name': 'OpenField-1010', 'task_mode': 'trigger'})\n", + "key=(VideoRecording & 'recording_id=2').fetch1('KEY')\n", + "key.update({'model_name': 'OpenField-5', 'task_mode': 'trigger'})\n", "analyze_params={'save_as_csv':True} # add any others from deeplabcut.analyze_videos\n", - "dlc.PoseEstimationTask.insert_estimation_task(key,params=analyze_params,\n", - " skip_duplicates=True)\n", - "dlc.PoseEstimation.populate()" + "model.EstimationTask.insert_estimation_task(key,params=analyze_params)\n", + "model.Estimation.populate()" ] }, { @@ -423,11 +246,330 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    scorerOpenField-5
    bodypartsleftearrightearsnouttailbase
    coordsxyzlikelihoodxyzlikelihoodxyzlikelihoodxyzlikelihood
    0-0.051000479.6207280.00.2733382.7253636.9041590.00.0899344.700935-7.5217900.00.2695982.38509416.4985430.00.227193
    10.028559479.5801700.00.2701502.3214107.1489790.00.0834235.155587-8.2365470.00.2464041.57686916.5681690.00.219187
    20.011300479.5625000.00.267837643.8314214.7395150.00.0821024.967218-6.4458490.00.2459353.41466416.4375740.00.199375
    30.280110479.5580440.00.273800643.8950204.5520260.00.0825535.191478-7.3123840.00.2409732.43559116.5924680.00.219349
    40.269247479.5125730.00.267830643.9069824.6196610.00.0831534.552286-7.5772260.00.2329770.99674216.6169490.00.223073
    ...................................................
    58-0.043869479.5482480.00.264180644.0790415.2654050.00.0840586.878569-8.1917700.00.2473270.69133016.9791790.00.240952
    590.213278479.6058650.00.288912643.8714605.3038520.00.0865547.673346-9.1639530.00.2070052.23173217.3976440.00.211547
    60-0.011851479.3172300.00.267723644.0430915.2021060.00.0837786.811278-9.1307250.00.2321062.03793517.6341740.00.229108
    61-0.020756479.2878420.00.269066643.9953615.3278440.00.0855176.878219-9.7602600.00.2329770.56998017.5098530.00.241432
    62-0.054609479.2832640.00.266023644.2104495.4729770.00.0852727.383186-9.0418800.00.2238432.44974917.4412380.00.214529
    \n", + "

    63 rows × 16 columns

    \n", + "
    " + ], + "text/plain": [ + "scorer OpenField-5 \\\n", + "bodyparts leftear rightear \n", + "coords x y z likelihood x y z \n", + "0 -0.051000 479.620728 0.0 0.273338 2.725363 6.904159 0.0 \n", + "1 0.028559 479.580170 0.0 0.270150 2.321410 7.148979 0.0 \n", + "2 0.011300 479.562500 0.0 0.267837 643.831421 4.739515 0.0 \n", + "3 0.280110 479.558044 0.0 0.273800 643.895020 4.552026 0.0 \n", + "4 0.269247 479.512573 0.0 0.267830 643.906982 4.619661 0.0 \n", + ".. ... ... ... ... ... ... ... \n", + "58 -0.043869 479.548248 0.0 0.264180 644.079041 5.265405 0.0 \n", + "59 0.213278 479.605865 0.0 0.288912 643.871460 5.303852 0.0 \n", + "60 -0.011851 479.317230 0.0 0.267723 644.043091 5.202106 0.0 \n", + "61 -0.020756 479.287842 0.0 0.269066 643.995361 5.327844 0.0 \n", + "62 -0.054609 479.283264 0.0 0.266023 644.210449 5.472977 0.0 \n", + "\n", + "scorer \\\n", + "bodyparts snout tailbase \n", + "coords likelihood x y z likelihood x y \n", + "0 0.089934 4.700935 -7.521790 0.0 0.269598 2.385094 16.498543 \n", + "1 0.083423 5.155587 -8.236547 0.0 0.246404 1.576869 16.568169 \n", + "2 0.082102 4.967218 -6.445849 0.0 0.245935 3.414664 16.437574 \n", + "3 0.082553 5.191478 -7.312384 0.0 0.240973 2.435591 16.592468 \n", + "4 0.083153 4.552286 -7.577226 0.0 0.232977 0.996742 16.616949 \n", + ".. ... ... ... ... ... ... ... \n", + "58 0.084058 6.878569 -8.191770 0.0 0.247327 0.691330 16.979179 \n", + "59 0.086554 7.673346 -9.163953 0.0 0.207005 2.231732 17.397644 \n", + "60 0.083778 6.811278 -9.130725 0.0 0.232106 2.037935 17.634174 \n", + "61 0.085517 6.878219 -9.760260 0.0 0.232977 0.569980 17.509853 \n", + "62 0.085272 7.383186 -9.041880 0.0 0.223843 2.449749 17.441238 \n", + "\n", + "scorer \n", + "bodyparts \n", + "coords z likelihood \n", + "0 0.0 0.227193 \n", + "1 0.0 0.219187 \n", + "2 0.0 0.199375 \n", + "3 0.0 0.219349 \n", + "4 0.0 0.223073 \n", + ".. ... ... \n", + "58 0.0 0.240952 \n", + "59 0.0 0.211547 \n", + "60 0.0 0.229108 \n", + "61 0.0 0.241432 \n", + "62 0.0 0.214529 \n", + "\n", + "[63 rows x 16 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "dlc.PoseEstimation.get_trajectory(key)" + "model.Estimation.get_trajectory(key)" ] }, { @@ -438,11 +580,14 @@ "\n", "+ This notebook runs through the workflow in an automatic manner.\n", "\n", - "+ In the next notebook [05-explore](05-explore.ipynb), we will introduce how to query, fetch and visualize the contents we ingested into the tables." + "+ The next notebook [06-Drop](06-Drop_Optional.ipynb) shows how to drop schemas and tables if needed." ] } ], "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, "kernelspec": { "display_name": "venv-dlc", "language": "python", diff --git a/notebooks/06-Drop_Optional.ipynb b/notebooks/05-Drop_Optional.ipynb similarity index 88% rename from notebooks/06-Drop_Optional.ipynb rename to notebooks/05-Drop_Optional.ipynb index 27de194..f55b2f0 100644 --- a/notebooks/06-Drop_Optional.ipynb +++ b/notebooks/05-Drop_Optional.ipynb @@ -9,9 +9,16 @@ "# DataJoint U24 - Workflow DeepLabCut" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Change into the parent directory to find the `dj_local_conf.json` file. " + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [] }, @@ -32,14 +39,7 @@ "\n", "+ Schemas are not typically dropped in a production workflow with real data in it. \n", "+ At the developmental phase, it might be required for the table redesign.\n", - "+ When dropping all schemas is needed, the following is the dependency order." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Change into the parent directory to find the `dj_local_conf.json` file. " + "+ When dropping all schemas is needed, drop items starting with the most downstream." ] }, { @@ -57,21 +57,18 @@ "metadata": {}, "outputs": [], "source": [ - "# dlc.schema.drop()\n", + "# model.schema.drop()\n", + "# train.schema.drop()\n", "# session.schema.drop()\n", "# subject.schema.drop()\n", "# lab.schema.drop()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, "kernelspec": { "display_name": "venv-dlc", "language": "python", diff --git a/notebooks/py_scripts/00-DataDownload_Optional.py b/notebooks/py_scripts/00-DataDownload_Optional.py new file mode 100644 index 0000000..487cecd --- /dev/null +++ b/notebooks/py_scripts/00-DataDownload_Optional.py @@ -0,0 +1,98 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.13.7 +# kernelspec: +# display_name: venv-dlc +# language: python +# name: venv-dlc +# --- + +# %% [markdown] tags=[] +# # DataJoint U24 - Workflow DeepLabCut + +# %% [markdown] +# ## Download example data + +# %% [markdown] +# We've structured this tool around the example data available from the DLC. If you've already cloned the [main DLC repository](https://github.com/DeepLabCut/DeepLabCut), you already have this folder under `examples/openfield-Pranav-2018-10-30`. + +# %% [markdown] +# [This link](https://downgit.github.io/#/home?url=https://github.com/DeepLabCut/DeepLabCut/tree/master/examples/openfield-Pranav-2018-10-30) via [DownGit](https://downgit.github.io/) will start the single-directory download +# automatically as a zip. Unpack this zip and place it in a directory we'll refer to as your root. + +# %% [markdown] +# ## Directory structure + +# %% [markdown] +# After downloading, the directory will be organized as follows within your chosen root +# directory. +# +# ``` +# /your-root/openfield-Pranav-2018-10-30/ +# - config.yaml +# - labeled-data +# - m4s1 +# - CollectedData_Pranav.csv +# - CollectedData_Pranav.h5 +# - img0000.png +# - img0001.png +# - img0002.png +# - img{...}.png +# - img0114.png +# - img0115.png +# - videos +# - m3v1mp4.mp4 +# ``` + +# %% [markdown] +# For those unfamiliar with DLC... +# - `config.yaml` contains all the key parameters of the project, including +# - file locations (currently empty) +# - body parts +# - cropping information +# - `labeled-data` includes the frames coordinates for each body part in the training video +# - `videos` includes the full training video for this example +# +# As part of the DeepLabCut demo setup process, you would run the following additional +# command, as outlined in their +# [demo notebook](https://github.com/DeepLabCut/DeepLabCut/blob/master/examples/JUPYTER/Demo_labeledexample_Openfield.ipynb). +# These establishes the project path within the demo config file as well as the `training-datasets` directory, which DLC will use for model training + +# %% +your_root='/fill/in/your/root/with\ escaped\ spaces' +from deeplabcut.create_project.demo_data import load_demo_data as dlc_load_demo +dlc_load_demo(your_root+'/openfield-Pranav-2018-10-30/config.yaml') + +# %% [markdown] +# For your own data, we recommend using the DLC gui to intitialize your project and label the data. + +# %% [markdown] +# ## Make new video + +# %% [markdown] +# Later, we'll use the first few seconds of the training video as a 'separate session' to model +# the pose estimation feature of this pipeline. `ffmpeg` is a dependency of DeepLabCut +# that can splice the training video for a demonstration purposes. The command below saves +# the first 2 seconds of the training video as a copy. +# +# - `-n` do not overwrite +# - `-hide_banner -loglevel error` less verbose output +# - `-ss 0 -t 2` start at second 0, add 2 seconds +# - `-i {vid_path}` input this video +# - `-{v/a}codec copy` copy the video and audio codecs of the input +# - `{vid_path}-copy.mp4` output file + +# %% tags=[] +vid_path = your_root + '/openfield-Pranav-2018-10-30/videos/m3v1mp4' +cmd = (f'ffmpeg -n -hide_banner -loglevel error -ss 0 -t 2 -i {vid_path}.mp4 ' + + f'-vcodec copy -acodec copy {vid_path}-copy.mp4') +import os; os.system(cmd) + +# %% [markdown] +# In the next notebook, [01-Configure](./01-Configure.ipynb), we'll set up the DataJoint config file with a pointer to your root data directory. diff --git a/notebooks/py_scripts/01-Configure.py b/notebooks/py_scripts/01-Configure.py new file mode 100644 index 0000000..44712dd --- /dev/null +++ b/notebooks/py_scripts/01-Configure.py @@ -0,0 +1,107 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.13.7 +# kernelspec: +# display_name: venv-dlc +# language: python +# name: venv-dlc +# --- + +# %% [markdown] tags=[] +# # DataJoint U24 - Workflow DeepLabCut + +# %% [markdown] tags=[] +# ## Configure DataJoint + +# %% [markdown] tags=[] +# - To run `workflow-deeplabcut`, we need to set up the DataJoint configuration file, called `dj_local_conf.json`, unique to each machine. +# +# - The config only needs to be set up once. If you have gone through the configuration before, directly go to [02-Workflow-Structure](./02-WorkflowStructure_Optional.ipynb). +# +# - By convention, we set the config up in the root directory of `workflow-deeplabcut` package. After you set up DataJoint once, you may be interested in [setting a global config](https://docs.datajoint.org/python/setup/01-Install-and-Connect.html). + +# %% +import os +# change to the upper level folder to detect dj_local_conf.json +if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') +assert os.path.basename(os.getcwd())=='workflow-deeplabcut', ("Please move to the " + + "workflow directory") + +# %% [markdown] +# ### Configure database host address and credentials + +# %% [markdown] +# Now let's set up the host, user and password in the `dj.config` following [instructions here](https://tutorials.datajoint.io/setting-up/get-database.html). + +# %% +import datajoint as dj +import getpass +dj.config['database.host'] = '{YOUR_HOST}' +dj.config['database.user'] = '{YOUR_USERNAME}' +dj.config['database.password'] = getpass.getpass() # enter the password securely + +# %% [markdown] +# You should be able to connect to the database at this stage. + +# %% +dj.conn() + +# %% [markdown] +# ### Configure the `custom` field in `dj.config` for element-deeplabcut + +# %% [markdown] +# #### Prefix + +# %% [markdown] +# Giving a prefix to your schema could help manage privelages on a server. +# - If we set prefix `neuro_`, every schema created with the current workflow will start with `neuro_`, e.g. `neuro_lab`, `neuro_subject`, `neuro_imaging` etc. +# - Teams who work on the same schemas should use the same prefix, set as follows: + +# %% +dj.config['custom'] = {'database.prefix': 'neuro_'} + +# %% [markdown] +# #### Root directory + +# %% [markdown] +# The `custom` field also keeps track of your root directory with `dlc_root_data_dir`. It can even accept roots. element-deeplabcut will always figure out which root to use based on the files it expects there. +# +# - Please set one root to the parent directory of DLC's `openfield-Pranav-2018-10-30` example. +# - In other cases, this should be the parent of your DLC project path. + +# %% +dj.config['custom'] = {'dlc_root_data_dir' : ['your-root1', 'your-root2']} + +# %% [markdown] +# Let's check that find the path connects with a tool from [element-interface](https://github.com/datajoint/element-interface). + +# %% +from element_interface.utils import find_full_path +data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], + 'openfield-Pranav-2018-10-30') +assert data_dir.exists(), "Please check the that you have the folder openfield-Pranav" + +# %% [markdown] +# ## Save the config as a json file +# +# With the proper configurations, we could save this as a file, either as a local json file, or a global file. + +# %% +dj.config.save_local() + +# %% [markdown] +# The local config is saved as `dj_local_conf.json` in the root directory of this `workflow-deeplabcut`. Next time you import DataJoint while in this directory, the same settings will be loaded. +# +# If saved globally, there will be a hidden configuration file saved in your computer's root directory that will be loaded when no local version is present. + +# %% +# dj.config.save_global() + +# %% [markdown] +# In the [next notebook](./02-WorkflowStructure_Optional.ipynb) notebook, we'll explore the workflow structure. diff --git a/notebooks/py_scripts/02-WorkflowStructure_Optional.py b/notebooks/py_scripts/02-WorkflowStructure_Optional.py new file mode 100644 index 0000000..3f4406d --- /dev/null +++ b/notebooks/py_scripts/02-WorkflowStructure_Optional.py @@ -0,0 +1,148 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.13.7 +# kernelspec: +# display_name: venv-dlc +# language: python +# name: venv-dlc +# --- + +# %% [markdown] tags=[] +# # DataJoint U24 - Workflow DeepLabCut + +# %% [markdown] +# ## Introduction + +# %% [markdown] +# This notebook gives a brief overview and introduces some useful DataJoint tools to facilitate the exploration. +# +# + DataJoint needs to be configured before running this notebook, if you haven't done so, refer to the [01-Configure](./01-Configure.ipynb) notebook. +# + If you are familar with DataJoint and the workflow structure, proceed to the next notebook [03-Process](./03-Process.ipynb) directly to run the workflow. +# + For a more thorough introduction of DataJoint functionings, please visit our [general tutorial site](http://codebook.datajoint.io/) + +# %% [markdown] +# To load the local configuration, we will change the directory to the package root. + +# %% +import os +if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') +assert os.path.basename(os.getcwd())=='workflow-deeplabcut', ("Please move to the " + + "workflow directory") + +# %% [markdown] +# ## Schemas and tables + +# %% [markdown] +# By importing from `workflow_deeplabcut`, we'll run the activation functions that declare the tables in these schemas. If these tables are already declared, we'll gain access. + +# %% +import datajoint as dj +from workflow_deeplabcut.pipeline import lab, subject, session, train, model + +# %% [markdown] +# Each module contains a schema object that enables interaction with the schema in the database. For more information abotu managing the upstream tables, see our [session workflow](https://github.com/datajoint/workflow-session). In this case, lab is required because the pipeline adds a `Device` table to the lab schema to keep track of camera IDs. The pipeline also adds a `VideoRecording` table to the session schema. + +# %% The schemas and tables will not be re-created when importing modules if they have existed. [markdown] +# `dj.list_schemas()` lists all schemas a user has access to in the current database +# %% `dj.list_schemas()`: list all schemas a user could access. +dj.list_schemas() + +# %% [markdown] +# `.schema.list_tables()` will provide names for each table in the format used under the hood. + +# %% Each module imported above corresponds to one schema inside the database. For example, `ephys` corresponds to `neuro_ephys` schema in the database. +train.schema.list_tables() + +# %% [markdown] +# `dj.Diagram()` plots tables and dependencies in a schema. To see additional upstream or downstream connections, add `- N` or `+ N` where N is the number of additional links. +# +# While the `model` schema is required for pose estimation, the `train` schema is optional, and can be used to manage model training within DataJoint + +# %% `dj.Diagram()`: plot tables and dependencies +dj.Diagram(train) #- 1 + +# %% +dj.Diagram(model) + +# %% [markdown] +# ### Table tiers +# - **Manual table**: green box, manually inserted table, expect new entries daily, e.g. Subject, ProbeInsertion. +# - **Lookup table**: gray box, pre inserted table, commonly used for general facts or parameters. e.g. Strain, ClusteringMethod, ClusteringParamSet. +# - **Imported table**: blue oval, auto-processing table, the processing depends on the importing of external files. e.g. process of Clustering requires output files from kilosort2. +# - **Computed table**: red circle, auto-processing table, the processing does not depend on files external to the database, commonly used for +# - **Part table**: plain text, as an appendix to the master table, all the part entries of a given master entry represent a intact set of the master entry. e.g. Unit of a CuratedClustering. +# +# ### Dependencies +# +# - **One-to-one primary**: thick solid line, share the exact same primary key, meaning the child table inherits all the primary key fields from the parent table as its own primary key. +# - **One-to-many primary**: thin solid line, inherit the primary key from the parent table, but have additional field(s) as part of the primary key as well +# - **secondary dependency**: dashed line, the child table inherits the primary key fields from parent table as its own secondary attribute. + +# %% `dj.Diagram()`: plot the diagram of the tables and dependencies. It could be used to plot tables in a schema or selected tables. +# plot diagram of tables in multiple schemas +dj.Diagram(subject.Subject) + dj.Diagram(session.Session) + dj.Diagram(model) + +# %% +lab.schema.list_tables() + +# %% +from workflow_deeplabcut.pipeline import VideoRecording +# plot diagram of selected tables and schemas +(dj.Diagram(subject.Subject) + dj.Diagram(session.Session) + + dj.Diagram(VideoRecording) + + dj.Diagram(model.EstimationTask)) + +# %% Each datajoint table class inside the module corresponds to a table inside the schema. For example, the class `ephys.EphysRecording` correponds to the table `_ephys_recording` in the schema `neuro_ephys` in the database. +# preview columns and contents in a table +VideoRecording.File() + +# %% `heading`: [markdown] +# `describe()` shows table definition with foreign key references +# %% +train.TrainingTask.describe() + +# %% [markdown] +# `heading` shows attribute definitions regardless of foreign key references + +# %% `heading`: show table attributes regardless of foreign key references. +model.Model.heading + +# %% ephys [markdown] +# ## Other Elements installed with the workflow +# +# [`lab`](https://github.com/datajoint/element-lab): lab management related information, such as Lab, User, Project, Protocol, Source. + +# %% +dj.Diagram(lab) + +# %% [markdown] +# [`subject`](https://github.com/datajoint/element-animal): general animal information, User, Genetic background, Death etc. + +# %% +dj.Diagram(subject) + +# %% [subject](https://github.com/datajoint/element-animal): contains the basic information of subject, including Strain, Line, Subject, Zygosity, and SubjectDeath information. +subject.Subject.describe(); + +# %% [markdown] +# [`session`](https://github.com/datajoint/element-session): General information of experimental sessions. + +# %% +dj.Diagram(session) + +# %% [session](https://github.com/datajoint/element-session): experimental session information +session.Session.describe(); + +# %% [markdown] +# ## Summary and next step +# +# - This notebook introduced the overall structures of the schemas and tables in the workflow and relevant tools to explore the schema structure and table definitions. +# +# - The [next notebook](./03-Process.ipynb) will introduce the detailed steps to run through `workflow-deeplabcut`. diff --git a/notebooks/py_scripts/03-Process.py b/notebooks/py_scripts/03-Process.py new file mode 100644 index 0000000..a8f9e97 --- /dev/null +++ b/notebooks/py_scripts/03-Process.py @@ -0,0 +1,280 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.13.7 +# kernelspec: +# display_name: venv-dlc +# language: python +# name: venv-dlc +# --- + +# %% [markdown] tags=[] +# # DataJoint U24 - Workflow DeepLabCut + +# %% [markdown] +# ## Interactively run the workflow +# +# The workflow requires a DeepLabCut project with labeled data. +# - If you haven't configured the data, refer to [00-DataDownload](./00-DataDownload_Optional.ipynb) and [01-Configure](./01-Configure.ipynb). +# - To overview the schema structures, refer to [02-WorkflowStructure](02-WorkflowStructure_Optional.ipynb). +# - If you'd likea more automatic approach, refer to [03-Automate](03-Automate_optional.ipynb). + +# %% [markdown] +# Let's change the directory to the package root directory to load the local config, `dj_local_conf.json`. + +# %% +import os +# change to the upper level folder to detect dj_local_conf.json +if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') +assert os.path.basename(os.getcwd())=='workflow-deeplabcut', ("Please move to the " + + "workflow directory") + +# %% [markdown] +# `Pipeline.py` activates the DataJoint `elements` and declares other required tables. + +# %% +import datajoint as dj +from workflow_deeplabcut.pipeline import lab, subject, session, train, model + +# %% [markdown] +# #### Inserting entries into upstream tables + +# %% [markdown] +# In general, you can manually insert entries into each table by directly providing values for each column as a dictionary. Be sure to follow the type specified in the table definition. + +# %% +subject.Subject.heading + +# %% +subject.Subject.insert1(dict(subject='subject6', + sex='M', + subject_birth_date='2020-01-03', + subject_description='hneih_E105')) + +# %% +subject.Subject() + +# %% +session.Session.describe(); + +# %% +session.Session.heading + +# %% +session_keys = [dict(subject='subject6', session_datetime='2021-06-02 14:04:22'), + dict(subject='subject6', session_datetime='2021-06-03 14:43:10')] +session.Session.insert(session_keys) + +# %% +session.Session() & "session_datetime > '2021-06-01 12:00:00'" + +# %% [markdown] +# ## Inserting recordings + +# %% +from workflow_deeplabcut.pipeline import VideoRecording +VideoRecording.heading + +# %% [markdown] +# The `VideoRecording` table retains unique recordings file specifies all videos across sessions, including both model training +# videos and videos for later analysis. + +# %% +recordings = [{'recording_id': '1', + 'subject': 'subject6', + 'session_datetime': '2021-06-02 14:04:22', + 'recording_start_time': '2021-06-02 14:07:00', + 'camera_id': '1'}, + {'recording_id': '2', + 'subject': 'subject6', + 'session_datetime': '2021-06-03 14:43:10', + 'recording_start_time': '2021-06-03 14:50:00', + 'camera_id': '1'}] +VideoRecording.insert(recordings) + +# %% [markdown] +# The related part table allows for multiple files for a given recording session. + +# %% +VideoRecording.File.heading + +# %% +recordings[0].update({'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4'}) +recordings[1].update({'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4'}) +VideoRecording.File.insert(recordings, ignore_extra_fields=True) + +# %% +VideoRecording.File() + +# %% [markdown] +# The `TrainingVideo` table handles all files generated in the video labeling process, including the `h5`, `csv`, and `png` files under the `labeled-data` directory. While these aren't required for launching DLC training, it may be helpful to retain records. DLC will instead refer to the `mat` file located under the `training-datasets` directory. + +# %% +train.VideoSet.insert1({'video_set_id': 1}) +csv_path = 'openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.csv' +train.VideoSet.File.insert1({'video_set_id': 1, + 'file_path': csv_path}) + +# %% +rec_key = (VideoRecording & 'recording_id=1').fetch1('KEY') +train.VideoSet.VideoRecording.insert1({**rec_key, + 'video_set_id': 1, 'recording_id': 1}) + +# %% +train.VideoSet.VideoRecording() + +# %% [markdown] +# ## Training a DLC Network + +# %% [markdown] +# First, we'll add a `ModelTrainingParamSet`. This is a lookup table that we can reference when training a model. + +# %% +train.TrainingParamSet.heading + +# %% [markdown] +# The `params` longblob should be a dictionary that includes all items to be included in model training via the `train_network` function. At minimum, this is the contents of the project's config file, as well as `suffle` and `trainingsetindex`, which are not included in the config. + +# %% +from deeplabcut import train_network +help(train_network) # for more information on optional parameters + +# %% [markdown] +# Below, we give the parameters and index and description and load the config contents. We can then overwrite any defaults, including `maxiters`, to restrict our training iterations to 5. + +# %% +import yaml +from element_interface.utils import find_full_path +from workflow_deeplabcut.paths import get_dlc_root_data_dir + +paramset_idx = 1; paramset_desc='OpenField' +config_path = find_full_path(get_dlc_root_data_dir(), + 'openfield-Pranav-2018-10-30/config.yaml') +with open(config_path, 'rb') as y: + config_params = yaml.safe_load(y) +training_params = {'shuffle': '1', + 'trainingsetindex': '0', + 'maxiters': '5', + 'scorer_legacy': 'False', + 'maxiters': '5'} +config_params.update(training_params) +train.TrainingParamSet.insert_new_params(paramset_idx=paramset_idx, + paramset_desc=paramset_desc, + params=config_params) + +# %% [markdown] +# Then we add training to the the `TrainingTask` table. The `ModelTraining` table can automatically train and populate all tasks outlined in `TrainingTask`. + +# %% +train.TrainingTask.heading + +# %% +key={'video_set_id': 1, 'paramset_idx':1,'training_id':1, + 'project_path':'openfield-Pranav-2018-10-30/'} +train.TrainingTask.insert1(key, skip_duplicates=True) +train.TrainingTask() + +# %% tags=[] +train.ModelTraining.populate() + +# %% +train.ModelTraining() + +# %% [markdown] +# To start training from a previous instance, one would need to +# [edit the relevant config file](https://github.com/DeepLabCut/DeepLabCut/issues/70) and +# adjust the `maxiters` paramset (if present) to a higher threshold (e.g., 10 for 5 more itterations). +# Emperical work from the Mathis team suggests 200k iterations for any true use-case. + +# %% [markdown] +# ## Tracking Joints/Body Parts + +# %% [markdown] +# The `model` schema uses a lookup table for managing Body Parts tracked across models. + +# %% +model.BodyPart.heading + +# %% [markdown] +# This table is equipped with two helper functions. First, we can identify all the new body parts from a given config file. + +# %% +model.BodyPart.extract_new_body_parts(config_path) + +# %% [markdown] +# Now, we can make a list of descriptions in the same order, and insert them into the table + +# %% +bp_desc=['Left Ear', 'Right Ear', 'Snout Position', 'Base of Tail'] +model.BodyPart.insert_from_config(config_path,bp_desc) + +# %% [markdown] +# If we skip this step, body parts (without descriptions) will be added when we insert a model. We can [update](https://docs.datajoint.org/python/v0.13/manipulation/3-Cautious-Update.html) empty descriptions at any time. + +# %% [markdown] +# ## Declaring a Model + +# %% [markdown] +# If training appears successful, the result can be inserted into the `Model` table for automatic evaluation. + +# %% +model.Model.insert_new_model(model_name='OpenField-5',dlc_config=config_path, + shuffle=1,trainingsetindex=0, + model_description='Open field model trained 5 iterations', + paramset_idx=1) + +# %% +model.Model() + +# %% [markdown] +# ## Model Evaluation + +# %% [markdown] +# Next, all inserted models can be evaluated with a similar `populate` method, which will +# insert the relevant output from DLC's `evaluate_network` function. + +# %% +model.ModelEvaluation.heading + +# %% +model.ModelEvaluation.populate() + +# %% +model.ModelEvaluation() + +# %% [markdown] +# ## Pose Estimation + +# %% [markdown] +# To put this model to use, we'll conduct pose estimation on the video generated in the [DataDownload notebook](./00_DataDownload_Optional.ipynb). Here, we can also specify parameters accepted by the `analyze_videos` function as a dictionary. + +# %% +key=(VideoRecording&'recording_id=2').fetch1('KEY'); +key.update({'model_name': 'OpenField-5', 'task_mode': 'trigger'}) +model.EstimationTask.insert_estimation_task(key,params={'save_as_csv':True}, + skip_duplicates=True) + +# %% +model.Estimation.populate() + +# %% [markdown] +# By default, DataJoint will store the results of pose estimation in a subdirectory +# > processed_dir / videos / device_<#>_recording_<#>_model_ +# +# Pulling processed_dir from `get_dlc_processed_dir`, and device/recording information +# from the `VideoRecording` table. The model name is taken from the primary key of the +# `Model` table, with spaced replaced by hyphens. +# +# We can get this estimation directly as a pandas dataframe. + +# %% +model.Estimation.get_trajectory(key) + +# %% [markdown] +# +# . diff --git a/notebooks/py_scripts/04-Automate_Optional.py b/notebooks/py_scripts/04-Automate_Optional.py new file mode 100644 index 0000000..ada1d81 --- /dev/null +++ b/notebooks/py_scripts/04-Automate_Optional.py @@ -0,0 +1,129 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.13.7 +# kernelspec: +# display_name: venv-dlc +# language: python +# name: venv-dlc +# --- + +# %% [markdown] tags=[] +# # DataJoint U24 - Workflow DeepLabCut + +# %% [markdown] pycharm={"name": "#%% md\n"} +# ## Workflow Automation +# +# In the previous notebook [03-Process](./03-Process.ipynb), we ran through the workflow in detailed steps. For daily running routines, the current notebook provides a more succinct and automatic approach to run through the pipeline using some utility functions in the workflow. +# +# The commands here run a workflow using [example data](https://downgit.github.io/#/home?url=https://github.com/DeepLabCut/DeepLabCut/tree/master/examples/openfield-Pranav-2018-10-30) from the [00-DownloadData](./00-DataDownload_Optional.ipynb) notebook, but note where placeholders could be changed for a different dataset. + +# %% tags=[] +import os; from pathlib import Path +# change to the upper level folder to detect dj_local_conf.json +if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') +assert os.path.basename(os.getcwd())=='workflow-deeplabcut', ("Please move to the " + + "workflow directory") +from workflow_deeplabcut.pipeline import lab, subject, session, train, model, \ + VideoRecording + +# %% [markdown] +# If you previously completed the [03-Process notebook](./03-Process.ipynb), you may want to delete the contents ingested there, to avoid duplication errors. + +# %% +# session.Session.delete() +# train.TrainingParamSet.delete() +# train.VideoSet.delete() + +# %% [markdown] +# ## Ingestion of subjects, sessions, videos and training parameters +# +# Refer to the `user_data` folder in the workflow. +# +# 1. Fill subject and session information in files `subjects.csv` and `sessions.csv` +# 2. Fill in recording and parameter information in `recordings.csv` and `config_params.csv` +# + Add both training and estimation videos to the recording list +# + Additional columns in `config_params.csv` will be treated as model training parameters +# 3. Run automatic scripts prepared in `workflow_deeplabcut.ingest` for ingestion: +# + `ingest_subjects` for `subject.Subject` +# + `ingest_sessions` - for session tables `Session`, `SessionDirectory`, and `SessionNote` +# + `ingest_dlc_items` - for `VideoRecording` and `TrainingParamSet` + +# %% +from workflow_deeplabcut.ingest import ingest_subjects, ingest_sessions, ingest_dlc_items +ingest_subjects(); ingest_sessions(); ingest_dlc_items() + +# %% [markdown] +# ## Setting project variables +# +# 1. Set your root directory in your DataJoint config file, under `custom` as `dlc_root_data_dir`. For the purposes of this demo, we'll ask DeepLabCut to structure the demo config file with `load_demo_data` + +# %% +import datajoint as dj; dj.config.load('dj_local_conf.json') +from element_interface.utils import find_full_path +data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config + 'openfield-Pranav-2018-10-30') # DLC project dir +config_path = (data_dir / 'config.yaml') +from deeplabcut.create_project.demo_data import load_demo_data +load_demo_data(config_path) + +# %% [markdown] +# 2. For this demo, we generate a copy to show pose estimation. This is `recording_id` 2 in `recordings.csv`. If you already did this in the [00-DataDownload notebook](./00-DataDownload_Optional.ipynb), skip this step. + +# %% tags=[] +vid_path = str(data_dir).replace(" ", "\ ") + '/videos/m3v1mp4' +cmd = (f'ffmpeg -n -hide_banner -loglevel error -ss 0 -t 2 -i {vid_path}.mp4 -vcodec copy ' + + f'-acodec copy {vid_path}-copy.mp4') # New video copy, first 2 seconds +os.system(cmd) + +# %% [markdown] +# 3. Pair training video with training parameters, and launch training. + +# %% +key={'paramset_idx':1,'training_id':1,'video_set_id':1, + 'project_path':'openfield-Pranav-2018-10-30/'} +train.TrainingTask.insert1(key, skip_duplicates=True) +train.ModelTraining.populate() + +# %% [markdown] +# 4. Add this model to the `Model` table and evaluate. +# - Include a user-friendly `model_name` +# - Include the relative path for the project's `config.yaml` +# - Add `shuffle` and `trainingsetindex` + +# %% +model.Model.insert_new_model(model_name='OpenField-5',dlc_config=config_path, + shuffle=1,trainingsetindex=0, paramset_idx=1, + model_description='Open field model trained 5 iterations') +model.ModelEvaluation.populate() + +# %% [markdown] +# 5. Add a pose estimation task, and launch pose estimation. +# - Get all primary key information for a given recording +# - Add the model and `task_mode` (i.e., load vs. trigger) to be applied +# - Add any additional analysis parameters for `deeplabcut.analyze_videos` + +# %% +key=(VideoRecording & 'recording_id=2').fetch1('KEY') +key.update({'model_name': 'OpenField-5', 'task_mode': 'trigger'}) +analyze_params={'save_as_csv':True} # add any others from deeplabcut.analyze_videos +model.EstimationTask.insert_estimation_task(key,params=analyze_params) +model.Estimation.populate() + +# %% [markdown] +# 6. Retrieve estimated position data. + +# %% +model.Estimation.get_trajectory(key) + +# %% [markdown] +# ## Summary and next step +# +# + This notebook runs through the workflow in an automatic manner. +# +# + The next notebook [06-Drop](06-Drop_Optional.ipynb) shows how to drop schemas and tables if needed. diff --git a/notebooks/py_scripts/05-Drop_Optional.py b/notebooks/py_scripts/05-Drop_Optional.py new file mode 100644 index 0000000..0dd09e8 --- /dev/null +++ b/notebooks/py_scripts/05-Drop_Optional.py @@ -0,0 +1,44 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.13.7 +# kernelspec: +# display_name: venv-dlc +# language: python +# name: venv-dlc +# --- + +# %% [markdown] tags=[] +# # DataJoint U24 - Workflow DeepLabCut + +# %% [markdown] +# Change into the parent directory to find the `dj_local_conf.json` file. + +# %% tags=[] +import os; from pathlib import Path +# change to the upper level folder to detect dj_local_conf.json +if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') +assert os.path.basename(os.getcwd())=='workflow-deeplabcut', ("Please move to the " + + "workflow directory") + +# %% [markdown] +# ## Drop schemas +# +# + Schemas are not typically dropped in a production workflow with real data in it. +# + At the developmental phase, it might be required for the table redesign. +# + When dropping all schemas is needed, drop items starting with the most downstream. + +# %% +from workflow_deeplabcut.pipeline import * + +# %% +# model.schema.drop() +# train.schema.drop() +# session.schema.drop() +# subject.schema.drop() +# lab.schema.drop() diff --git a/notebooks/05-Explore.ipynb b/notebooks/temp05-Explore.ipynb similarity index 98% rename from notebooks/05-Explore.ipynb rename to notebooks/temp05-Explore.ipynb index 150bc80..189f946 100644 --- a/notebooks/05-Explore.ipynb +++ b/notebooks/temp05-Explore.ipynb @@ -426,13 +426,14 @@ "source": [ "## Summary and Next Step\n", "\n", - "+ This notebook highlights the major tables in the workflow and visualize some of the ingested results. \n", - "\n", - "+ The next notebook [06-drop](06-drop-optional.ipynb) shows how to drop schemas and tables if needed." + "+ This notebook highlights the major tables in the workflow and visualize some of the ingested results. \n" ] } ], "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, "kernelspec": { "display_name": "venv-dlc", "language": "python", diff --git a/tests/test_ingest.py b/tests/test_ingest.py index 3e2d860..6185d1b 100644 --- a/tests/test_ingest.py +++ b/tests/test_ingest.py @@ -41,6 +41,7 @@ def test_ingest_dlc_items(pipeline, recordings_csv, config_params_csv, """Check length/contents of VideoRecordings/ConfigParams""" pass + ''' TO DO - add ingestion of recordings and config params - Encode analysis outcome specifcs from Model.Data diff --git a/user_data/recordings.csv b/user_data/recordings.csv index 6727ada..d8799cb 100644 --- a/user_data/recordings.csv +++ b/user_data/recordings.csv @@ -1,4 +1,4 @@ recording_id,subject,session_datetime,recording_start_time,file_path,camera_id,paramset_idx 1,subject6,2021-06-02 14:04:22,2021-06-02 14:07:00,openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4,1,0 -2,subject6,2021-06-03 14:04:22,2021-06-04 14:07:00,openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4,1,0 +2,subject6,2021-06-03 14:43:10,2021-06-03 14:50:00,openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4,1,0 3,subject5,2020-04-15 11:16:38,2020-04-15 11:17:00,Reaching-Mackenzie-2018-08-30/videos/reachingvideo1.avi,1,1 diff --git a/user_data/sessions.csv b/user_data/sessions.csv index 2246678..322869b 100644 --- a/user_data/sessions.csv +++ b/user_data/sessions.csv @@ -1,4 +1,4 @@ subject,session_datetime,session_dir,session_note subject5,2020-04-15 11:16:38,example-dir/subject5/,Successful data collection. No notes subject6,2021-06-02 14:04:22,example-dir/subject6/,Model Training Session -subject6,2021-06-03 14:04:22,example-dir/subject6/,Test Session +subject6,2021-06-03 14:43:10,example-dir/subject6/,Test Session diff --git a/user_data/train_videoset.csv b/user_data/train_videoset.csv new file mode 100644 index 0000000..bb7b67a --- /dev/null +++ b/user_data/train_videoset.csv @@ -0,0 +1,2 @@ +video_set_id,recording_id +1,1 diff --git a/workflow_deeplabcut/ingest.py b/workflow_deeplabcut/ingest.py index be371e9..c3304c3 100644 --- a/workflow_deeplabcut/ingest.py +++ b/workflow_deeplabcut/ingest.py @@ -3,7 +3,7 @@ import ruamel.yaml as yaml from element_interface.utils import find_full_path -from .pipeline import subject, session, dlc +from .pipeline import subject, session, VideoRecording, train from .paths import get_dlc_root_data_dir @@ -55,18 +55,19 @@ def ingest_sessions(session_csv_path='./user_data/sessions.csv', def ingest_dlc_items(config_params_csv_path='./user_data/config_params.csv', recording_csv_path='./user_data/recordings.csv', + train_video_csv_path='./user_data/train_videosets.csv', skip_duplicates=True): """ Ingests to DLC schema from ./user_data/{config_params,recordings}.csv - First, loads config.yaml info to dlc.ModelTrainingParamSet. Requires paramset_idx, + First, loads config.yaml info to train.TrainingParamSet. Requires paramset_idx, paramset_desc and relative config_path. Other columns overwrite config variables - Next, loads recording info into dlc.VideoRecording and dlc.VideoRecording.File + Next, loads recording info into VideoRecording and VideoRecording.File :param config_params_csv_path: csv path for model training config and parameters :param recording_csv_path: csv path for list of recordings """ - previous_length = len(dlc.ModelTrainingParamSet.fetch()) + previous_length = len(train.TrainingParamSet.fetch()) with open(config_params_csv_path, newline='') as f: config_csv = list(csv.DictReader(f, delimiter=',')) for line in config_csv: @@ -79,17 +80,16 @@ def ingest_dlc_items(config_params_csv_path='./user_data/config_params.csv', params = yaml.safe_load(y) params.update({**line}) - dlc.ModelTrainingParamSet.insert_new_params(paramset_idx=paramset_idx, - paramset_desc=paramset_desc, - params=params, - skip_duplicates=skip_duplicates) - insert_length = len(dlc.ModelTrainingParamSet.fetch()) - previous_length + train.TrainingParamSet.insert_new_params(paramset_idx=paramset_idx, + paramset_desc=paramset_desc, + params=params) + insert_length = len(train.TrainingParamSet.fetch()) - previous_length print(f'\n---- Inserting {insert_length} entry(s) into #model_training_param_set ' + '----') # Next, recordings and config files - csvs = [recording_csv_path, recording_csv_path] - tables = [dlc.VideoRecording(), dlc.VideoRecording.File()] + csvs = [recording_csv_path, recording_csv_path, train_video_csv_path] + tables = [VideoRecording(), VideoRecording.File(), train.VideoSet.VideoRecording()] ingest_general(csvs, tables, skip_duplicates=skip_duplicates) diff --git a/workflow_deeplabcut/pipeline.py b/workflow_deeplabcut/pipeline.py index 62e90f4..23d2e1f 100644 --- a/workflow_deeplabcut/pipeline.py +++ b/workflow_deeplabcut/pipeline.py @@ -1,12 +1,12 @@ import datajoint as dj -from element_animal import subject from element_lab import lab -from element_session import session -from element_deeplabcut import train, model, pose +from element_animal import subject +from element_session import session_with_datetime as session +from element_deeplabcut import train, model from element_animal.subject import Subject +from element_session.session_with_datetime import Session from element_lab.lab import Source, Lab, Protocol, User, Project -from element_session.session import Session from .paths import get_dlc_root_data_dir, get_dlc_processed_data_dir @@ -60,4 +60,3 @@ class File(dj.Part): train.activate(db_prefix + 'train', linking_module=__name__) model.activate(db_prefix + 'model', linking_module=__name__) -pose.activate(db_prefix + 'pose', linking_module=__name__) From 4338899b0e9284e0afc975f0426ad344dc93d0d6 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Mon, 21 Mar 2022 17:07:24 -0500 Subject: [PATCH 022/176] revise version info --- CHANGELOG.md | 3 ++- docker/docker-compose-dev.yaml | 2 +- docker/docker-compose-test.yaml | 2 +- workflow_deeplabcut/version.py | 7 +++++-- 4 files changed, 9 insertions(+), 5 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index d5b8aee..f570b45 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,9 +6,10 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and ### Added + First beta release -## 0.0.0a1 - 2021-12-15 +## 0.0.0a0 - 2021-12-15 ### Added + First draft begins, reflecting precursor pipelines + Added Docker files + Draft integration tests + Add example data download instructions ++ Added Notebooks to demonstrate use diff --git a/docker/docker-compose-dev.yaml b/docker/docker-compose-dev.yaml index bf4d705..4cd0bae 100644 --- a/docker/docker-compose-dev.yaml +++ b/docker/docker-compose-dev.yaml @@ -18,7 +18,7 @@ services: context: ../../ dockerfile: ./workflow-deeplabcut/docker/Dockerfile.dev env_file: .env - image: workflow-deeplabcut-dev:0.1.0a4 + image: workflow-deeplabcut-dev:0.0.0a0 container_name: workflow-deeplabcut-dev environment: - TEST_DATA_DIR=/main/test_data/workflow_dlc_data1/,/main/test_data/workflow_dlc_data2/ diff --git a/docker/docker-compose-test.yaml b/docker/docker-compose-test.yaml index b906c23..9f77e37 100644 --- a/docker/docker-compose-test.yaml +++ b/docker/docker-compose-test.yaml @@ -21,7 +21,7 @@ services: context: ../../ dockerfile: ./workflow-deeplabcut/docker/Dockerfile.test env_file: .env - image: workflow-deeplabcut-test:0.1.0a4 + image: workflow-deeplabcut-test:0.0.0a0 container_name: workflow-deeplabcut-test environment: - DJ_HOST=db diff --git a/workflow_deeplabcut/version.py b/workflow_deeplabcut/version.py index c5b7d5c..683759e 100644 --- a/workflow_deeplabcut/version.py +++ b/workflow_deeplabcut/version.py @@ -1,2 +1,5 @@ -"""Package metadata.""" -__version__ = '0.0.0a1' +""" +Package metadata +Update the Docker image tag in `docker-compose.yaml` to match +""" +__version__ = '0.0.0a0' From d0d94b60aaabb62cf8ddbc2f8921db8cc851f2c8 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Tue, 22 Mar 2022 12:34:59 -0500 Subject: [PATCH 023/176] Update notebooks for separate video tables across schemas --- README.md | 7 +- dj_example_local_conf.json | 4 +- notebooks/00-DataDownload_Optional.ipynb | 19 +- notebooks/02-WorkflowStructure_Optional.ipynb | 542 +++++------ notebooks/03-Process.ipynb | 907 +++++++++--------- notebooks/04-Automate_Optional.ipynb | 382 ++++---- .../02-WorkflowStructure_Optional.py | 8 +- notebooks/py_scripts/03-Process.py | 60 +- notebooks/temp05-Explore.ipynb | 462 --------- .../{recordings.csv => model_videos.csv} | 3 +- user_data/train_videoset.csv | 2 - user_data/train_videosets.csv | 9 + workflow_deeplabcut/ingest.py | 13 +- workflow_deeplabcut/pipeline.py | 16 - 14 files changed, 931 insertions(+), 1503 deletions(-) delete mode 100644 notebooks/temp05-Explore.ipynb rename user_data/{recordings.csv => model_videos.csv} (63%) delete mode 100644 user_data/train_videoset.csv create mode 100644 user_data/train_videosets.csv diff --git a/README.md b/README.md index 370937b..f5534d0 100644 --- a/README.md +++ b/README.md @@ -43,12 +43,12 @@ The DeepLabCut Element is split into `train` and `model` schemas. To manage both training and pose estimation within DataJoint, one would activate both schemas, as shown below. -![assembled-both](./images/attached_train_model.svg) +![assembled-both](https://github.com/datajoint/element-deeplabcut/blob/main/images/diagram_dlc.svg) If training is managed outside DataJoint, one could only activate the `model` schema to still manage various models and execute pose estimation. -![assembled-model](./images/attached_model_only.svg) +![assembled-model](https://github.com/datajoint/element-deeplabcut/blob/main/images/diagram_dlc_model.svg) ## Installation instructions @@ -64,5 +64,4 @@ Please refer to the following workflow-specific + run the workflow ([01-WorkflowStructure.ipynb](notebooks/01-WorkflowStructure_Optional.ipynb)) + ingest data and launch tasks ([03-Process.ipynb](notebooks/03-Process.ipynb)) + automate tasks ([04-Automate.ipynb](notebooks/04-Automate_Optional.ipynb)) - -?? keep? [05-Explore.ipynb](notebooks/05-Explore.ipynb) ++ drop tables ([05-Drop](notebooks/05-Drop_Optional.ipynb)) diff --git a/dj_example_local_conf.json b/dj_example_local_conf.json index 498909d..19a7612 100644 --- a/dj_example_local_conf.json +++ b/dj_example_local_conf.json @@ -18,9 +18,9 @@ "database.ingest_filename_full": "", "custom": { "database.prefix": "YourPrefix_", - "beh_root_dir": [ + "dlc_root_data_dir": [ "/Abolute/Path/Here/", "/Abolute/Other/Path/" ] } -} \ No newline at end of file +} diff --git a/notebooks/00-DataDownload_Optional.ipynb b/notebooks/00-DataDownload_Optional.ipynb index ef96a59..3e28d86 100644 --- a/notebooks/00-DataDownload_Optional.ipynb +++ b/notebooks/00-DataDownload_Optional.ipynb @@ -87,18 +87,23 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded, now creating training data...\n", - "The training dataset is successfully created. Use the function 'train_network' to start training. Happy training!\n" + "ename": "AttributeError", + "evalue": "'CommentedSeq' object has no attribute 'keys'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_1395/750526841.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0myour_root\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdeeplabcut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_project\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdemo_data\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_demo_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mload_demo_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myour_root\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/openfield-Pranav-2018-10-30/config.yaml'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m in \u001b[0;36mload_demo_data\u001b[0;34m(config, createtrainingset)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mtransform_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcreatetrainingset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Loaded, now creating training data...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m in \u001b[0;36mtransform_data\u001b[0;34m(config)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This is not an offical demo dataset.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"video_sets\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 63\u001b[0m cfg[\"video_sets\"][str(video_file)] = cfg[\"video_sets\"].pop(\n\u001b[1;32m 64\u001b[0m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'CommentedSeq' object has no attribute 'keys'" ] } ], "source": [ "your_root='/fill/in/your/root/with\\ escaped\\ spaces'\n", - "from deeplabcut.create_project.demo_data import load_demo_data as dlc_load_demo\n", - "dlc_load_demo(your_root+'/openfield-Pranav-2018-10-30/config.yaml')" + "from deeplabcut.create_project.demo_data import load_demo_data\n", + "load_demo_data(your_root+'/openfield-Pranav-2018-10-30/config.yaml')" ] }, { diff --git a/notebooks/02-WorkflowStructure_Optional.ipynb b/notebooks/02-WorkflowStructure_Optional.ipynb index 95acfba..b82e6c2 100644 --- a/notebooks/02-WorkflowStructure_Optional.ipynb +++ b/notebooks/02-WorkflowStructure_Optional.ipynb @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": { "title": "Each module imported above corresponds to one schema inside the database. For example, `ephys` corresponds to `neuro_ephys` schema in the database." }, @@ -115,15 +115,15 @@ { "data": { "text/plain": [ - "['video_set',\n", + "['#training_param_set',\n", + " 'video_set',\n", " 'video_set__file',\n", " 'video_set__video_recording',\n", - " '#training_param_set',\n", " 'training_task',\n", " '__model_training']" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": { "title": "`dj.Diagram()`: plot tables and dependencies" }, @@ -151,23 +151,13 @@ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "train.VideoSet\n", - "\n", - "\n", - "train.VideoSet\n", - "\n", - "\n", - "\n", + "\n", "\n", - "\n", + "\n", "train.TrainingTask\n", - "\n", "\n", "\n", - "\n", + "\n", + "\n", + "train.ModelTraining\n", + "\n", + "\n", + "train.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", "\n", - "train.VideoSet->train.TrainingTask\n", - "\n", + "train.TrainingTask->train.ModelTraining\n", + "\n", "\n", "\n", - "\n", + "\n", "train.VideoSet.File\n", - "\n", "\n", @@ -195,49 +198,30 @@ "\n", "\n", "\n", - "\n", - "\n", - "train.VideoSet->train.VideoSet.File\n", - "\n", - "\n", - "\n", - "\n", - "train.VideoSet.VideoRecording\n", - "\n", + "\n", + "train.VideoSet\n", + "\n", - "\n", - "train.VideoSet.VideoRecording\n", + "\n", + "train.VideoSet\n", "\n", "\n", "\n", - "\n", - "\n", - "train.VideoSet->train.VideoSet.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "train.ModelTraining\n", - "\n", - "\n", - "train.ModelTraining\n", - "\n", - "\n", + "\n", + "\n", + "train.VideoSet->train.TrainingTask\n", + "\n", "\n", - "\n", - "\n", - "train.TrainingTask->train.ModelTraining\n", - "\n", + "\n", + "\n", + "train.VideoSet->train.VideoSet.File\n", + "\n", "\n", "\n", - "\n", + "\n", "train.TrainingParamSet\n", - "\n", "\n", "\n", - "\n", + "\n", "train.TrainingParamSet->train.TrainingTask\n", "\n", "\n", @@ -258,10 +242,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -272,30 +256,31 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", - "\n", - "\n", + "\n", + "\n", "\n", - "model.Model.BodyPart\n", - "model.BodyPart\n", + "\n", - "\n", - "model.Model.BodyPart\n", + "\n", + "model.BodyPart\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "model.Estimation.BodyPartPosition\n", - "model.PoseEstimation.BodyPartPosition\n", + "\n", - "\n", - "model.Estimation.BodyPartPosition\n", + "\n", + "model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.BodyPart->model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "model.Model.BodyPart\n", + "\n", + "\n", + "model.Model.BodyPart\n", "\n", "\n", "\n", - "\n", + "\n", + "\n", + "model.BodyPart->model.Model.BodyPart\n", + "\n", + "\n", + "\n", "\n", - "model.EstimationTask\n", - "model.ModelEvaluation\n", + "\n", - "\n", - "model.EstimationTask\n", + "\n", + "model.ModelEvaluation\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "model.Estimation\n", - "model.VideoRecording\n", + "\n", - "\n", - "model.Estimation\n", + "\n", + "model.VideoRecording\n", "\n", "\n", "\n", - "\n", - "\n", - "model.EstimationTask->model.Estimation\n", - "\n", - "\n", - "\n", - "\n", - "model.Estimation->model.Estimation.BodyPartPosition\n", - "\n", - "\n", - "\n", + "\n", "\n", - "model.BodyPart\n", - "model.VideoRecording.File\n", + "\n", - "\n", - "model.BodyPart\n", + "\n", + "model.VideoRecording.File\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "model.BodyPart->model.Model.BodyPart\n", - "\n", + "model.VideoRecording->model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimationTask\n", + "\n", + "\n", + "model.PoseEstimationTask\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "model.BodyPart->model.Estimation.BodyPartPosition\n", - "\n", + "model.VideoRecording->model.PoseEstimationTask\n", + "\n", "\n", - "\n", + "\n", "\n", + "model.PoseEstimation\n", + "\n", + "\n", + "model.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation->model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", "model.Model\n", - "\n", - "\n", - "model.Model\n", + "\n", + "model.Model\n", "\n", "\n", "\n", - "\n", - "\n", - "model.Model->model.Model.BodyPart\n", - "\n", - "\n", - "\n", + "\n", "\n", - "model.Model->model.EstimationTask\n", - "\n", + "model.Model->model.ModelEvaluation\n", + "\n", "\n", - "\n", - "\n", - "model.ModelEvaluation\n", - "\n", - "\n", - "model.ModelEvaluation\n", - "\n", + "\n", + "\n", + "model.Model->model.PoseEstimationTask\n", + "\n", "\n", + "\n", + "\n", + "model.Model->model.Model.BodyPart\n", + "\n", "\n", - "\n", - "\n", - "model.Model->model.ModelEvaluation\n", - "\n", + "\n", + "\n", + "model.PoseEstimationTask->model.PoseEstimation\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -508,107 +527,105 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", - "\n", - "\n", + "\n", + "\n", "\n", - "subject.Subject\n", - "session.Session\n", + "\n", - "\n", - "subject.Subject\n", + "\n", + "session.Session\n", "\n", "\n", "\n", - "\n", - "\n", - "session.Session\n", - "\n", + "\n", + "model.VideoRecording\n", + "\n", - "\n", - "session.Session\n", + "\n", + "model.VideoRecording\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "subject.Subject->session.Session\n", - "\n", + "session.Session->model.VideoRecording\n", + "\n", "\n", - "\n", + "\n", "\n", - "model.EstimationTask\n", - "model.PoseEstimationTask\n", + "\n", - "\n", - "model.EstimationTask\n", + "\n", + "model.PoseEstimationTask\n", "\n", "\n", "\n", - "\n", - "\n", - "VideoRecording\n", - "\n", + "\n", + "model.VideoRecording->model.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject\n", + "\n", - "\n", - "VideoRecording\n", + "\n", + "subject.Subject\n", "\n", "\n", "\n", - "\n", - "\n", - "VideoRecording->model.EstimationTask\n", - "\n", - "\n", - "\n", + "\n", "\n", - "session.Session->VideoRecording\n", - "\n", + "subject.Subject->session.Session\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from workflow_deeplabcut.pipeline import VideoRecording\n", "# plot diagram of selected tables and schemas\n", "(dj.Diagram(subject.Subject) + dj.Diagram(session.Session) \n", - " + dj.Diagram(VideoRecording) \n", - " + dj.Diagram(model.EstimationTask)) " + " + dj.Diagram(model.VideoRecording) + dj.Diagram(model.PoseEstimationTask)) " ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "metadata": { "title": "Each datajoint table class inside the module corresponds to a table inside the schema. For example, the class `ephys.EphysRecording` correponds to the table `_ephys_recording` in the schema `neuro_ephys` in the database." }, @@ -698,14 +715,14 @@ " (Total: 0)" ] }, - "execution_count": 17, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# preview columns and contents in a table\n", - "VideoRecording.File()" + "model.VideoRecording.File()" ] }, { @@ -720,7 +737,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -743,7 +760,7 @@ "'# Specification for a DLC model training instance\\n-> train.VideoSet\\n-> train.TrainingParamSet\\ntraining_id : int \\n---\\nmodel_prefix=\"\" : varchar(32) \\nproject_path=\"\" : varchar(255) # DLC\\'s project_path in config relative to root\\n'" ] }, - "execution_count": 18, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -761,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": { "title": "`heading`: show table attributes regardless of foreign key references." }, @@ -786,7 +803,7 @@ "paramset_idx=null : smallint # " ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -833,7 +850,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "metadata": { "title": "[subject](https://github.com/datajoint/element-animal): contains the basic information of subject, including Strain, Line, Subject, Zygosity, and SubjectDeath information." }, @@ -843,7 +860,7 @@ "output_type": "stream", "text": [ "# Animal Subject\n", - "subject : varchar(32) \n", + "subject : varchar(8) \n", "---\n", "sex : enum('M','F','U') \n", "subject_birth_date : date \n", @@ -865,111 +882,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "session.SessionDirectory\n", - "\n", - "\n", - "session.SessionDirectory\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.ProjectSession\n", - "\n", - "\n", - "session.ProjectSession\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session\n", - "\n", - "\n", - "session.Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.SessionDirectory\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.ProjectSession\n", - "\n", - "\n", - "\n", - "\n", - "session.SessionNote\n", - "\n", - "\n", - "session.SessionNote\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.SessionNote\n", - "\n", - "\n", - "\n", - "\n", - "session.SessionExperimenter\n", - "\n", - "\n", - "session.SessionExperimenter\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.SessionExperimenter\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dj.Diagram(session)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "metadata": { "title": "[session](https://github.com/datajoint/element-session): experimental session information" }, @@ -979,13 +901,23 @@ "output_type": "stream", "text": [ "-> subject.Subject\n", - "session_datetime : datetime(3) \n", + "session_datetime : datetime \n", "\n" ] + }, + { + "data": { + "text/plain": [ + "'-> subject.Subject\\nsession_datetime : datetime \\n'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "session.Session.describe();" + "session.Session.describe()" ] }, { diff --git a/notebooks/03-Process.ipynb b/notebooks/03-Process.ipynb index a21b7f8..c2b634d 100644 --- a/notebooks/03-Process.ipynb +++ b/notebooks/03-Process.ipynb @@ -50,17 +50,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting cbroz@tutorial-db.datajoint.io:3306\n" - ] - } - ], + "outputs": [], "source": [ "import datajoint as dj\n", "from workflow_deeplabcut.pipeline import lab, subject, session, train, model" @@ -106,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -118,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -190,10 +182,10 @@ "

    subject_description

    \n", " \n", " \n", - " subject6\n", - "M\n", - "2020-01-03\n", - "hneih_E105 \n", + " subject5\n", + "F\n", + "2020-01-01\n", + "rich \n", " \n", " \n", "

    Total: 1

    \n", @@ -202,17 +194,17 @@ "text/plain": [ "*subject sex subject_birth_ subject_descri\n", "+----------+ +-----+ +------------+ +------------+\n", - "subject6 M 2020-01-03 hneih_E105 \n", + "subject5 F 2020-01-01 rich \n", " (Total: 1)" ] }, - "execution_count": 6, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "subject.Subject()" + "subject.Subject & \"subject='subject5'\"" ] }, { @@ -258,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -269,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -351,7 +343,7 @@ " (Total: 2)" ] }, - "execution_count": 11, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -367,243 +359,34 @@ "## Inserting recordings" ] }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# \n", - "subject : varchar(8) # \n", - "session_datetime : datetime # \n", - "camera_id : int # \n", - "recording_id : int # \n", - "---\n", - "recording_start_time : datetime # " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from workflow_deeplabcut.pipeline import VideoRecording\n", - "VideoRecording.heading" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `VideoRecording` table retains unique recordings file specifies all videos across sessions, including both model training\n", - "videos and videos for later analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "recordings = [{'recording_id': '1',\n", - " 'subject': 'subject6',\n", - " 'session_datetime': '2021-06-02 14:04:22',\n", - " 'recording_start_time': '2021-06-02 14:07:00',\n", - " 'camera_id': '1'},\n", - " {'recording_id': '2',\n", - " 'subject': 'subject6',\n", - " 'session_datetime': '2021-06-03 14:43:10',\n", - " 'recording_start_time': '2021-06-03 14:50:00',\n", - " 'camera_id': '1'}]\n", - "VideoRecording.insert(recordings)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The related part table allows for multiple files for a given recording session." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "subject : varchar(8) # \n", - "session_datetime : datetime # \n", - "camera_id : int # \n", - "recording_id : int # \n", - "file_path : varchar(255) # filepath of video, relative to root data directory" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "VideoRecording.File.heading" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "recordings[0].update({'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4'})\n", - "recordings[1].update({'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4'})\n", - "VideoRecording.File.insert(recordings, ignore_extra_fields=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    camera_id

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", - " \n", - "
    \n", - "

    file_path

    \n", - " filepath of video, relative to root data directory\n", - "
    subject62021-06-02 14:04:2211openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4
    subject62021-06-03 14:43:1012openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4
    \n", - " \n", - "

    Total: 2

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet *camera_id *recording_id *file_path \n", - "+----------+ +------------+ +-----------+ +------------+ +------------+\n", - "subject6 2021-06-02 14: 1 1 openfield-Pran\n", - "subject6 2021-06-03 14: 1 2 openfield-Pran\n", - " (Total: 2)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "VideoRecording.File()" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The `TrainingVideo` table handles all files generated in the video labeling process, including the `h5`, `csv`, and `png` files under the `labeled-data` directory. While these aren't required for launching DLC training, it may be helpful to retain records. DLC will instead refer to the `mat` file located under the `training-datasets` directory." + "The `VideoSet` table handles all files generated in the video labeling process, including the `h5`, `csv`, and `png` files under the `labeled-data` directory. While these aren't required for launching DLC training, it may be helpful to retain records. DLC will instead refer to the `mat` file located under the `training-datasets` directory." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "train.VideoSet.insert1({'video_set_id': 1})\n", - "csv_path = 'openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.csv'\n", - "train.VideoSet.File.insert1({'video_set_id': 1,\n", - " 'file_path': csv_path})" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "rec_key = (VideoRecording & 'recording_id=1').fetch1('KEY')\n", - "train.VideoSet.VideoRecording.insert1({**rec_key,\n", - " 'video_set_id': 1, 'recording_id': 1})" + "labeled_dir = 'openfield-Pranav-2018-10-30/labeled-data/m4s1/'\n", + "training_files = ['CollectedData_Pranav.h5',\n", + " 'CollectedData_Pranav.csv',\n", + " 'img0000.png']\n", + "for file in training_files:\n", + " train.VideoSet.File.insert1({'video_set_id': 1,\n", + " 'file_path': (labeled_dir + file)})\n", + "train.VideoSet.File.insert1({'video_set_id':1, 'file_path': \n", + " 'openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4'})" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -659,49 +442,43 @@ " }\n", " \n", " \n", - " \n", + " Paths of training files (e.g., labeled pngs, CSV or video)\n", "
    \n", " \n", " \n", " \n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "
    \n", "

    video_set_id

    \n", " \n", "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    camera_id

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", + "

    file_path

    \n", " \n", "
    1subject62021-06-02 14:04:2211
    openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.csv
    1openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.h5
    1openfield-Pranav-2018-10-30/labeled-data/m4s1/img0000.png
    1openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4
    \n", " \n", - "

    Total: 1

    \n", + "

    Total: 4

    \n", " " ], "text/plain": [ - "*video_set_id *subject *session_datet *camera_id *recording_id \n", - "+------------+ +----------+ +------------+ +-----------+ +------------+\n", - "1 subject6 2021-06-02 14: 1 1 \n", - " (Total: 1)" + "*video_set_id *file_path \n", + "+------------+ +------------+\n", + "1 openfield-Pran\n", + "1 openfield-Pran\n", + "1 openfield-Pran\n", + "1 openfield-Pran\n", + " (Total: 4)" ] }, - "execution_count": 36, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "train.VideoSet.VideoRecording()" + "train.VideoSet.File()" ] }, { @@ -720,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -733,7 +510,7 @@ "params : longblob # dictionary of all applicable parameters" ] }, - "execution_count": 37, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -768,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -785,7 +562,8 @@ " 'trainingsetindex': '0',\n", " 'maxiters': '5',\n", " 'scorer_legacy': 'False',\n", - " 'maxiters': '5'}\n", + " 'maxiters': '5', \n", + " 'multianimalproject':'False'}\n", "config_params.update(training_params)\n", "train.TrainingParamSet.insert_new_params(paramset_idx=paramset_idx,\n", " paramset_desc=paramset_desc,\n", @@ -801,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -815,7 +593,7 @@ "project_path=\"\" : varchar(255) # DLC's project_path in config relative to root" ] }, - "execution_count": 39, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -826,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -918,7 +696,7 @@ " (Total: 1)" ] }, - "execution_count": 40, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -932,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "tags": [] }, @@ -943,7 +721,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1035,7 +813,7 @@ " (Total: 1)" ] }, - "execution_count": 42, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1100,24 +878,24 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Existing body parts: []\n", - "New body parts: ['leftear' 'rightear' 'snout' 'tailbase']\n" + "Existing body parts: ['leftear' 'rightear' 'snout' 'tailbase']\n", + "New body parts: []\n" ] }, { "data": { "text/plain": [ - "array(['leftear', 'rightear', 'snout', 'tailbase'], dtype=' \n", " OpenField-5\n", "5\n", - "148.49\n", - "156.75\n", + "245.06\n", + "247.52\n", "0.4\n", - "82.55\n", - "76.76 \n", + "239.24\n", + "238.07 \n", " \n", " \n", "

    Total: 1

    \n", @@ -1477,11 +1306,11 @@ "text/plain": [ "*model_name train_iteratio train_error test_error p_cutoff train_error_p test_error_p \n", "+------------+ +------------+ +------------+ +------------+ +----------+ +------------+ +------------+\n", - "OpenField-5 5 148.49 156.75 0.4 82.55 76.76 \n", + "OpenField-5 5 245.06 247.52 0.4 239.24 238.07 \n", " (Total: 1)" ] }, - "execution_count": 49, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1501,19 +1330,169 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To put this model to use, we'll conduct pose estimation on the video generated in the [DataDownload notebook](./00_DataDownload_Optional.ipynb). Here, we can also specify parameters accepted by the `analyze_videos` function as a dictionary." + "To put this model to use, we'll conduct pose estimation on the video generated in the [DataDownload notebook](./00_DataDownload_Optional.ipynb). First, we need to update the `VideoRecording` table with the recording from a session." ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "key=(VideoRecording&'recording_id=2').fetch1('KEY');\n", + "key = {'subject': 'subject6',\n", + " 'session_datetime': '2021-06-02 14:04:22',\n", + " 'recording_id': '1', 'camera_id': 1,\n", + " 'recording_start_time': '2021-06-02 14:07:00'}\n", + "model.VideoRecording.insert1(key)\n", + " # do not include an initial `/` in relative file paths \n", + "key.update({'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4'})\n", + "model.VideoRecording.File.insert1(key, ignore_extra_fields=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    camera_id

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    file_path

    \n", + " filepath of video, relative to root data directory\n", + "
    subject62021-06-02 14:04:2211openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *camera_id *recording_id *file_path \n", + "+----------+ +------------+ +-----------+ +------------+ +------------+\n", + "subject6 2021-06-02 14: 1 1 openfield-Pran\n", + " (Total: 1)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.VideoRecording.File()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to specify if the `PoseEstimation` table should load results from an existing file or trigger the estimation command. Here, we can also specify parameters accepted by the `analyze_videos` function as a dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'subject': 'subject6',\n", + " 'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n", + " 'camera_id': 1,\n", + " 'recording_id': 1,\n", + " 'model_name': 'OpenField-5',\n", + " 'task_mode': 'trigger'}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "key = (model.VideoRecording & {'recording_id': '1'}).fetch1('KEY')\n", "key.update({'model_name': 'OpenField-5', 'task_mode': 'trigger'})\n", - "model.EstimationTask.insert_estimation_task(key,params={'save_as_csv':True},\n", - " skip_duplicates=True)" + "key" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "model.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True})" ] }, { @@ -1522,7 +1501,7 @@ "metadata": {}, "outputs": [], "source": [ - "model.Estimation.populate()" + "model.PoseEstimation.populate()" ] }, { @@ -1541,7 +1520,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1597,98 +1576,98 @@ " \n", " \n", " 0\n", - " -2.422083\n", - " 4.344821\n", + " 0.790677\n", + " 7.965729\n", " 0.0\n", - " 0.550124\n", - " 103.509773\n", - " 154.843369\n", + " 0.397091\n", + " 115.835762\n", + " 164.004028\n", " 0.0\n", - " 0.494453\n", - " 26.769926\n", - " 27.644077\n", + " 0.518405\n", + " 58.818291\n", + " 4.837649\n", " 0.0\n", - " 0.345101\n", - " 12.271347\n", - " 25.387495\n", + " 0.514612\n", + " 4.134376\n", + " 463.009460\n", " 0.0\n", - " 0.420643\n", + " 0.717231\n", " \n", " \n", " 1\n", - " -3.597348\n", - " 4.784353\n", + " 2.807120\n", + " 10.973466\n", " 0.0\n", - " 0.570660\n", - " 129.002899\n", - " 158.958939\n", + " 0.435590\n", + " 10.124892\n", + " 470.653931\n", " 0.0\n", - " 0.497367\n", - " 113.209633\n", - " 111.148224\n", + " 0.514644\n", + " 15.192053\n", + " 472.954376\n", " 0.0\n", - " 0.396401\n", - " 11.662391\n", - " 25.403496\n", + " 0.509128\n", + " 4.339864\n", + " 462.988220\n", " 0.0\n", - " 0.409297\n", + " 0.711722\n", " \n", " \n", " 2\n", - " -1.888346\n", - " 4.047595\n", + " 9.415764\n", + " 16.290619\n", " 0.0\n", - " 0.521887\n", - " 26.252184\n", - " 5.579991\n", + " 0.400282\n", + " 10.313096\n", + " 470.749420\n", " 0.0\n", - " 0.431996\n", - " 111.761734\n", - " 114.333969\n", + " 0.513927\n", + " 15.203813\n", + " 473.046204\n", " 0.0\n", - " 0.431438\n", - " 12.388601\n", - " 25.376640\n", + " 0.509683\n", + " 4.241215\n", + " 463.060944\n", " 0.0\n", - " 0.381368\n", + " 0.709923\n", " \n", " \n", " 3\n", - " -2.663505\n", - " 4.979667\n", + " 8.467562\n", + " 15.072682\n", " 0.0\n", - " 0.553423\n", - " 26.800587\n", - " 6.133034\n", + " 0.407272\n", + " 10.299086\n", + " 470.716309\n", " 0.0\n", - " 0.429278\n", - " 634.744995\n", - " 28.070696\n", + " 0.515085\n", + " 14.914599\n", + " 472.946564\n", " 0.0\n", - " 0.353685\n", - " 11.839536\n", - " 24.747765\n", + " 0.507931\n", + " 4.296385\n", + " 463.385590\n", " 0.0\n", - " 0.389143\n", + " 0.704007\n", " \n", " \n", " 4\n", - " -3.101933\n", - " 4.946546\n", + " 1.952696\n", + " 10.845516\n", " 0.0\n", - " 0.552119\n", - " 117.008659\n", - " 145.359375\n", + " 0.388948\n", + " 10.309416\n", + " 470.719910\n", " 0.0\n", - " 0.427354\n", - " 125.948250\n", - " 110.696831\n", + " 0.511848\n", + " 14.834159\n", + " 472.920166\n", " 0.0\n", - " 0.403272\n", - " 11.647130\n", - " 24.026539\n", + " 0.504538\n", + " 4.267960\n", + " 463.363556\n", " 0.0\n", - " 0.382323\n", + " 0.702786\n", " \n", " \n", " ...\n", @@ -1711,98 +1690,98 @@ " \n", " \n", " 58\n", - " -2.179861\n", - " 4.917321\n", + " 5.497818\n", + " 12.181496\n", " 0.0\n", - " 0.543360\n", - " 43.786873\n", - " 4.242162\n", + " 0.503961\n", + " 10.725180\n", + " 470.430847\n", " 0.0\n", - " 0.440749\n", - " 70.179886\n", - " 11.257265\n", + " 0.505526\n", + " 15.931270\n", + " 474.692963\n", " 0.0\n", - " 0.385803\n", - " 30.412106\n", - " 22.074944\n", + " 0.507564\n", + " 9.060750\n", + " 481.278442\n", " 0.0\n", - " 0.387526\n", + " 0.704268\n", " \n", " \n", " 59\n", - " -3.125555\n", - " 5.428480\n", + " 4.192788\n", + " 10.005349\n", " 0.0\n", - " 0.522461\n", - " 43.495945\n", - " 4.991209\n", + " 0.455334\n", + " 10.476208\n", + " 470.846588\n", " 0.0\n", - " 0.433459\n", - " 180.951401\n", - " 125.325356\n", + " 0.499014\n", + " 3.508626\n", + " 26.821339\n", " 0.0\n", - " 0.387515\n", - " 30.751884\n", - " 22.198009\n", + " 0.537064\n", + " 3.786860\n", + " 462.760376\n", " 0.0\n", - " 0.371095\n", + " 0.689251\n", " \n", " \n", " 60\n", - " -2.475067\n", - " 5.363192\n", + " 2.216149\n", + " 10.115728\n", " 0.0\n", - " 0.550597\n", - " 43.691952\n", - " 4.568588\n", + " 0.420141\n", + " 10.644203\n", + " 471.036102\n", " 0.0\n", - " 0.418626\n", - " 28.472328\n", - " 29.518694\n", + " 0.487316\n", + " 3.166887\n", + " 26.835373\n", " 0.0\n", - " 0.372502\n", - " 31.054819\n", - " 22.189482\n", + " 0.548109\n", + " 8.188313\n", + " 481.524902\n", " 0.0\n", - " 0.383042\n", + " 0.707340\n", " \n", " \n", " 61\n", - " -2.877043\n", - " 5.124061\n", + " 5.196610\n", + " 10.838953\n", " 0.0\n", - " 0.558322\n", - " 43.844006\n", - " 4.631758\n", + " 0.484508\n", + " 178.007233\n", + " 72.935913\n", " 0.0\n", - " 0.438815\n", - " 85.561989\n", - " 12.051997\n", + " 0.576688\n", + " 4.478888\n", + " 26.513628\n", " 0.0\n", - " 0.374683\n", - " 30.825670\n", - " 22.180286\n", + " 0.531905\n", + " 4.350879\n", + " 462.553345\n", " 0.0\n", - " 0.397028\n", + " 0.703052\n", " \n", " \n", " 62\n", - " -3.132688\n", - " 5.088851\n", + " 2.678554\n", + " 10.277241\n", " 0.0\n", - " 0.535538\n", - " 26.975809\n", - " 4.368977\n", + " 0.426758\n", + " 10.260103\n", + " 471.321564\n", " 0.0\n", - " 0.427049\n", - " 85.592628\n", - " 12.082524\n", + " 0.502590\n", + " 15.026831\n", + " 472.492065\n", " 0.0\n", - " 0.383968\n", - " 30.763111\n", - " 21.966364\n", + " 0.528700\n", + " 8.123420\n", + " 481.642578\n", " 0.0\n", - " 0.377716\n", + " 0.707681\n", " \n", " \n", "\n", @@ -1810,61 +1789,61 @@ "" ], "text/plain": [ - "scorer OpenField-5 \\\n", - "bodyparts leftear rightear \n", - "coords x y z likelihood x y z \n", - "0 -2.422083 4.344821 0.0 0.550124 103.509773 154.843369 0.0 \n", - "1 -3.597348 4.784353 0.0 0.570660 129.002899 158.958939 0.0 \n", - "2 -1.888346 4.047595 0.0 0.521887 26.252184 5.579991 0.0 \n", - "3 -2.663505 4.979667 0.0 0.553423 26.800587 6.133034 0.0 \n", - "4 -3.101933 4.946546 0.0 0.552119 117.008659 145.359375 0.0 \n", - ".. ... ... ... ... ... ... ... \n", - "58 -2.179861 4.917321 0.0 0.543360 43.786873 4.242162 0.0 \n", - "59 -3.125555 5.428480 0.0 0.522461 43.495945 4.991209 0.0 \n", - "60 -2.475067 5.363192 0.0 0.550597 43.691952 4.568588 0.0 \n", - "61 -2.877043 5.124061 0.0 0.558322 43.844006 4.631758 0.0 \n", - "62 -3.132688 5.088851 0.0 0.535538 26.975809 4.368977 0.0 \n", + "scorer OpenField-5 \\\n", + "bodyparts leftear rightear \n", + "coords x y z likelihood x y z \n", + "0 0.790677 7.965729 0.0 0.397091 115.835762 164.004028 0.0 \n", + "1 2.807120 10.973466 0.0 0.435590 10.124892 470.653931 0.0 \n", + "2 9.415764 16.290619 0.0 0.400282 10.313096 470.749420 0.0 \n", + "3 8.467562 15.072682 0.0 0.407272 10.299086 470.716309 0.0 \n", + "4 1.952696 10.845516 0.0 0.388948 10.309416 470.719910 0.0 \n", + ".. ... ... ... ... ... ... ... \n", + "58 5.497818 12.181496 0.0 0.503961 10.725180 470.430847 0.0 \n", + "59 4.192788 10.005349 0.0 0.455334 10.476208 470.846588 0.0 \n", + "60 2.216149 10.115728 0.0 0.420141 10.644203 471.036102 0.0 \n", + "61 5.196610 10.838953 0.0 0.484508 178.007233 72.935913 0.0 \n", + "62 2.678554 10.277241 0.0 0.426758 10.260103 471.321564 0.0 \n", "\n", - "scorer \\\n", - "bodyparts snout tailbase \n", - "coords likelihood x y z likelihood x \n", - "0 0.494453 26.769926 27.644077 0.0 0.345101 12.271347 \n", - "1 0.497367 113.209633 111.148224 0.0 0.396401 11.662391 \n", - "2 0.431996 111.761734 114.333969 0.0 0.431438 12.388601 \n", - "3 0.429278 634.744995 28.070696 0.0 0.353685 11.839536 \n", - "4 0.427354 125.948250 110.696831 0.0 0.403272 11.647130 \n", - ".. ... ... ... ... ... ... \n", - "58 0.440749 70.179886 11.257265 0.0 0.385803 30.412106 \n", - "59 0.433459 180.951401 125.325356 0.0 0.387515 30.751884 \n", - "60 0.418626 28.472328 29.518694 0.0 0.372502 31.054819 \n", - "61 0.438815 85.561989 12.051997 0.0 0.374683 30.825670 \n", - "62 0.427049 85.592628 12.082524 0.0 0.383968 30.763111 \n", + "scorer \\\n", + "bodyparts snout tailbase \n", + "coords likelihood x y z likelihood x \n", + "0 0.518405 58.818291 4.837649 0.0 0.514612 4.134376 \n", + "1 0.514644 15.192053 472.954376 0.0 0.509128 4.339864 \n", + "2 0.513927 15.203813 473.046204 0.0 0.509683 4.241215 \n", + "3 0.515085 14.914599 472.946564 0.0 0.507931 4.296385 \n", + "4 0.511848 14.834159 472.920166 0.0 0.504538 4.267960 \n", + ".. ... ... ... ... ... ... \n", + "58 0.505526 15.931270 474.692963 0.0 0.507564 9.060750 \n", + "59 0.499014 3.508626 26.821339 0.0 0.537064 3.786860 \n", + "60 0.487316 3.166887 26.835373 0.0 0.548109 8.188313 \n", + "61 0.576688 4.478888 26.513628 0.0 0.531905 4.350879 \n", + "62 0.502590 15.026831 472.492065 0.0 0.528700 8.123420 \n", "\n", - "scorer \n", - "bodyparts \n", - "coords y z likelihood \n", - "0 25.387495 0.0 0.420643 \n", - "1 25.403496 0.0 0.409297 \n", - "2 25.376640 0.0 0.381368 \n", - "3 24.747765 0.0 0.389143 \n", - "4 24.026539 0.0 0.382323 \n", - ".. ... ... ... \n", - "58 22.074944 0.0 0.387526 \n", - "59 22.198009 0.0 0.371095 \n", - "60 22.189482 0.0 0.383042 \n", - "61 22.180286 0.0 0.397028 \n", - "62 21.966364 0.0 0.377716 \n", + "scorer \n", + "bodyparts \n", + "coords y z likelihood \n", + "0 463.009460 0.0 0.717231 \n", + "1 462.988220 0.0 0.711722 \n", + "2 463.060944 0.0 0.709923 \n", + "3 463.385590 0.0 0.704007 \n", + "4 463.363556 0.0 0.702786 \n", + ".. ... ... ... \n", + "58 481.278442 0.0 0.704268 \n", + "59 462.760376 0.0 0.689251 \n", + "60 481.524902 0.0 0.707340 \n", + "61 462.553345 0.0 0.703052 \n", + "62 481.642578 0.0 0.707681 \n", "\n", "[63 rows x 16 columns]" ] }, - "execution_count": 54, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.Estimation.get_trajectory(key)" + "model.PoseEstimation.get_trajectory(key)" ] }, { diff --git a/notebooks/04-Automate_Optional.ipynb b/notebooks/04-Automate_Optional.ipynb index 9f5ea29..82a6ed3 100644 --- a/notebooks/04-Automate_Optional.ipynb +++ b/notebooks/04-Automate_Optional.ipynb @@ -37,8 +37,7 @@ "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", " + \"workflow directory\")\n", - "from workflow_deeplabcut.pipeline import lab, subject, session, train, model, \\\n", - " VideoRecording" + "from workflow_deeplabcut.pipeline import lab, subject, session, train, model" ] }, { @@ -79,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -89,19 +88,21 @@ "\n", "---- Inserting 0 entry(s) into subject ----\n", "\n", - "---- Inserting 3 entry(s) into session ----\n", + "---- Inserting 2 entry(s) into session ----\n", "\n", - "---- Inserting 3 entry(s) into session_directory ----\n", + "---- Inserting 2 entry(s) into session_directory ----\n", "\n", - "---- Inserting 3 entry(s) into session_note ----\n", + "---- Inserting 2 entry(s) into session_note ----\n", "\n", "---- Inserting 3 entry(s) into #model_training_param_set ----\n", "\n", - "---- Inserting 3 entry(s) into video_recording ----\n", + "---- Inserting 2 entry(s) into video_set ----\n", + "\n", + "---- Inserting 8 entry(s) into video_set__file ----\n", "\n", - "---- Inserting 3 entry(s) into video_recording__file ----\n", + "---- Inserting 2 entry(s) into video_recording ----\n", "\n", - "---- Inserting 1 entry(s) into video_set ----\n" + "---- Inserting 2 entry(s) into video_recording__file ----\n" ] } ], @@ -116,12 +117,12 @@ "source": [ "## Setting project variables\n", "\n", - "1. Set your root directory in your DataJoint config file, under `custom` as `dlc_root_data_dir`. For the purposes of this demo, we'll ask DeepLabCut to structure the demo config file with `load_demo_data`" + "1. Set your root directory in your DataJoint config file, under `custom` as `dlc_root_data_dir`. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +130,22 @@ "from element_interface.utils import find_full_path\n", "data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config\n", " 'openfield-Pranav-2018-10-30') # DLC project dir\n", - "config_path = (data_dir / 'config.yaml')\n", + "config_path = (data_dir / 'config.yaml')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. For the purposes of this demo, we'll ask DeepLabCut to structure the demo config file with `load_demo_data`. If you already did this in the [00-DataDownload notebook](./00-DataDownload_Optional.ipynb), skip this step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "from deeplabcut.create_project.demo_data import load_demo_data\n", "load_demo_data(config_path)" ] @@ -138,7 +154,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "2. For this demo, we generate a copy to show pose estimation. This is `recording_id` 2 in `recordings.csv`. If you already did this in the [00-DataDownload notebook](./00-DataDownload_Optional.ipynb), skip this step." + "3. For this demo, we generate a copy to show pose estimation. This is `recording_id` 2 in `recordings.csv`. If you already did this in the [00-DataDownload notebook](./00-DataDownload_Optional.ipynb), skip this step." ] }, { @@ -177,7 +193,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "3. Pair training video with training parameters, and launch training." + "4. Pair training files with training parameters, and launch training." ] }, { @@ -196,10 +212,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "4. Add this model to the `Model` table and evaluate.\n", + "5. Add this model to the `Model` table and evaluate.\n", " - Include a user-friendly `model_name`\n", " - Include the relative path for the project's `config.yaml`\n", - " - Add `shuffle` and `trainingsetindex`" + " - Add `shuffle` and `trainingsetindex`\n", + " - `insert_new_model` will prompt before inserting, but this can be skipped with `prompt=False`" ] }, { @@ -209,7 +226,8 @@ "outputs": [], "source": [ "model.Model.insert_new_model(model_name='OpenField-5',dlc_config=config_path,\n", - " shuffle=1,trainingsetindex=0, paramset_idx=1,\n", + " shuffle=1,trainingsetindex=0, paramset_idx=1, \n", + " prompt=True, # True is the default behavior\n", " model_description='Open field model trained 5 iterations')\n", "model.ModelEvaluation.populate()" ] @@ -218,7 +236,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "5. Add a pose estimation task, and launch pose estimation.\n", + "6. Add a pose estimation task, and launch pose estimation.\n", " - Get all primary key information for a given recording\n", " - Add the model and `task_mode` (i.e., load vs. trigger) to be applied\n", " - Add any additional analysis parameters for `deeplabcut.analyze_videos`" @@ -230,23 +248,23 @@ "metadata": {}, "outputs": [], "source": [ - "key=(VideoRecording & 'recording_id=2').fetch1('KEY')\n", + "key=(model.VideoRecording & 'recording_id=2').fetch1('KEY')\n", "key.update({'model_name': 'OpenField-5', 'task_mode': 'trigger'})\n", "analyze_params={'save_as_csv':True} # add any others from deeplabcut.analyze_videos\n", - "model.EstimationTask.insert_estimation_task(key,params=analyze_params)\n", - "model.Estimation.populate()" + "model.PoseEstimationTask.insert_estimation_task(key,params=analyze_params)\n", + "model.PoseEstimation.populate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "6. Retrieve estimated position data." + "7. Retrieve estimated position data." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -302,98 +320,98 @@ " \n", " \n", " 0\n", - " -0.051000\n", - " 479.620728\n", + " 5.966216\n", + " -4.787393\n", " 0.0\n", - " 0.273338\n", - " 2.725363\n", - " 6.904159\n", + " 0.032592\n", + " -1.522350\n", + " 8.632778\n", " 0.0\n", - " 0.089934\n", - " 4.700935\n", - " -7.521790\n", + " 0.053609\n", + " 2.076265\n", + " 16.415096\n", " 0.0\n", - " 0.269598\n", - " 2.385094\n", - " 16.498543\n", + " 0.139537\n", + " 3.148022\n", + " -6.657187\n", " 0.0\n", - " 0.227193\n", + " 0.054340\n", " \n", " \n", " 1\n", - " 0.028559\n", - " 479.580170\n", + " 4.879360\n", + " -3.865869\n", " 0.0\n", - " 0.270150\n", - " 2.321410\n", - " 7.148979\n", + " 0.040094\n", + " -1.018066\n", + " 9.007607\n", " 0.0\n", - " 0.083423\n", - " 5.155587\n", - " -8.236547\n", + " 0.069977\n", + " 1.774640\n", + " 17.406301\n", " 0.0\n", - " 0.246404\n", - " 1.576869\n", - " 16.568169\n", + " 0.176452\n", + " 2.704738\n", + " -8.274201\n", " 0.0\n", - " 0.219187\n", + " 0.067940\n", " \n", " \n", " 2\n", - " 0.011300\n", - " 479.562500\n", + " 7.582597\n", + " 141.982101\n", " 0.0\n", - " 0.267837\n", - " 643.831421\n", - " 4.739515\n", + " 0.023750\n", + " -1.379965\n", + " 6.661551\n", " 0.0\n", - " 0.082102\n", - " 4.967218\n", - " -6.445849\n", + " 0.060571\n", + " 2.861758\n", + " 13.935984\n", " 0.0\n", - " 0.245935\n", - " 3.414664\n", - " 16.437574\n", + " 0.148534\n", + " 2.233558\n", + " -6.290349\n", " 0.0\n", - " 0.199375\n", + " 0.044671\n", " \n", " \n", " 3\n", - " 0.280110\n", - " 479.558044\n", + " 6.314935\n", + " -4.641110\n", " 0.0\n", - " 0.273800\n", - " 643.895020\n", - " 4.552026\n", + " 0.026313\n", + " -1.371757\n", + " 7.696576\n", " 0.0\n", - " 0.082553\n", - " 5.191478\n", - " -7.312384\n", + " 0.059226\n", + " 2.387363\n", + " 16.957266\n", " 0.0\n", - " 0.240973\n", - " 2.435591\n", - " 16.592468\n", + " 0.144777\n", + " 2.781236\n", + " -7.757162\n", " 0.0\n", - " 0.219349\n", + " 0.058866\n", " \n", " \n", " 4\n", - " 0.269247\n", - " 479.512573\n", + " 5.441626\n", + " -5.161995\n", " 0.0\n", - " 0.267830\n", - " 643.906982\n", - " 4.619661\n", + " 0.030603\n", + " -1.186847\n", + " 7.544275\n", " 0.0\n", - " 0.083153\n", - " 4.552286\n", - " -7.577226\n", + " 0.063246\n", + " 2.337324\n", + " 18.026840\n", " 0.0\n", - " 0.232977\n", - " 0.996742\n", - " 16.616949\n", + " 0.148035\n", + " 2.534595\n", + " -9.052482\n", " 0.0\n", - " 0.223073\n", + " 0.055323\n", " \n", " \n", " ...\n", @@ -416,98 +434,98 @@ " \n", " \n", " 58\n", - " -0.043869\n", - " 479.548248\n", + " 2.117847\n", + " -3.214342\n", " 0.0\n", - " 0.264180\n", - " 644.079041\n", - " 5.265405\n", + " 0.033657\n", + " -1.280500\n", + " 9.483593\n", " 0.0\n", - " 0.084058\n", - " 6.878569\n", - " -8.191770\n", + " 0.046280\n", + " 2.093328\n", + " 17.112661\n", " 0.0\n", - " 0.247327\n", - " 0.691330\n", - " 16.979179\n", + " 0.158959\n", + " 2.268270\n", + " -8.589610\n", " 0.0\n", - " 0.240952\n", + " 0.051529\n", " \n", " \n", " 59\n", - " 0.213278\n", - " 479.605865\n", + " 3.116104\n", + " -4.693888\n", " 0.0\n", - " 0.288912\n", - " 643.871460\n", - " 5.303852\n", + " 0.024087\n", + " 0.219144\n", + " 8.388535\n", " 0.0\n", - " 0.086554\n", - " 7.673346\n", - " -9.163953\n", + " 0.053968\n", + " 3.630704\n", + " 16.375135\n", " 0.0\n", - " 0.207005\n", - " 2.231732\n", - " 17.397644\n", + " 0.108622\n", + " 1.831684\n", + " -6.993159\n", " 0.0\n", - " 0.211547\n", + " 0.040950\n", " \n", " \n", " 60\n", - " -0.011851\n", - " 479.317230\n", + " 3.547711\n", + " -4.983343\n", " 0.0\n", - " 0.267723\n", - " 644.043091\n", - " 5.202106\n", + " 0.023138\n", + " -0.428721\n", + " 7.312115\n", " 0.0\n", - " 0.083778\n", - " 6.811278\n", - " -9.130725\n", + " 0.042809\n", + " 3.867933\n", + " 16.821577\n", " 0.0\n", - " 0.232106\n", - " 2.037935\n", - " 17.634174\n", + " 0.097812\n", + " 1.444253\n", + " -7.488105\n", " 0.0\n", - " 0.229108\n", + " 0.031486\n", " \n", " \n", " 61\n", - " -0.020756\n", - " 479.287842\n", + " 2.383031\n", + " -3.214643\n", " 0.0\n", - " 0.269066\n", - " 643.995361\n", - " 5.327844\n", + " 0.030314\n", + " -1.092042\n", + " 8.263630\n", " 0.0\n", - " 0.085517\n", - " 6.878219\n", - " -9.760260\n", + " 0.054284\n", + " 2.453674\n", + " 17.717480\n", " 0.0\n", - " 0.232977\n", - " 0.569980\n", - " 17.509853\n", + " 0.136344\n", + " 1.779472\n", + " -8.309467\n", " 0.0\n", - " 0.241432\n", + " 0.046111\n", " \n", " \n", " 62\n", - " -0.054609\n", - " 479.283264\n", + " 3.069550\n", + " -5.201364\n", " 0.0\n", - " 0.266023\n", - " 644.210449\n", - " 5.472977\n", + " 0.022396\n", + " -0.602118\n", + " 6.999699\n", " 0.0\n", - " 0.085272\n", - " 7.383186\n", - " -9.041880\n", + " 0.049786\n", + " 4.244837\n", + " 15.407806\n", " 0.0\n", - " 0.223843\n", - " 2.449749\n", - " 17.441238\n", + " 0.109382\n", + " 2.311671\n", + " -6.654770\n", " 0.0\n", - " 0.214529\n", + " 0.037103\n", " \n", " \n", "\n", @@ -515,61 +533,61 @@ "" ], "text/plain": [ - "scorer OpenField-5 \\\n", - "bodyparts leftear rightear \n", - "coords x y z likelihood x y z \n", - "0 -0.051000 479.620728 0.0 0.273338 2.725363 6.904159 0.0 \n", - "1 0.028559 479.580170 0.0 0.270150 2.321410 7.148979 0.0 \n", - "2 0.011300 479.562500 0.0 0.267837 643.831421 4.739515 0.0 \n", - "3 0.280110 479.558044 0.0 0.273800 643.895020 4.552026 0.0 \n", - "4 0.269247 479.512573 0.0 0.267830 643.906982 4.619661 0.0 \n", - ".. ... ... ... ... ... ... ... \n", - "58 -0.043869 479.548248 0.0 0.264180 644.079041 5.265405 0.0 \n", - "59 0.213278 479.605865 0.0 0.288912 643.871460 5.303852 0.0 \n", - "60 -0.011851 479.317230 0.0 0.267723 644.043091 5.202106 0.0 \n", - "61 -0.020756 479.287842 0.0 0.269066 643.995361 5.327844 0.0 \n", - "62 -0.054609 479.283264 0.0 0.266023 644.210449 5.472977 0.0 \n", + "scorer OpenField-5 \\\n", + "bodyparts leftear rightear \n", + "coords x y z likelihood x y z \n", + "0 5.966216 -4.787393 0.0 0.032592 -1.522350 8.632778 0.0 \n", + "1 4.879360 -3.865869 0.0 0.040094 -1.018066 9.007607 0.0 \n", + "2 7.582597 141.982101 0.0 0.023750 -1.379965 6.661551 0.0 \n", + "3 6.314935 -4.641110 0.0 0.026313 -1.371757 7.696576 0.0 \n", + "4 5.441626 -5.161995 0.0 0.030603 -1.186847 7.544275 0.0 \n", + ".. ... ... ... ... ... ... ... \n", + "58 2.117847 -3.214342 0.0 0.033657 -1.280500 9.483593 0.0 \n", + "59 3.116104 -4.693888 0.0 0.024087 0.219144 8.388535 0.0 \n", + "60 3.547711 -4.983343 0.0 0.023138 -0.428721 7.312115 0.0 \n", + "61 2.383031 -3.214643 0.0 0.030314 -1.092042 8.263630 0.0 \n", + "62 3.069550 -5.201364 0.0 0.022396 -0.602118 6.999699 0.0 \n", "\n", "scorer \\\n", - "bodyparts snout tailbase \n", - "coords likelihood x y z likelihood x y \n", - "0 0.089934 4.700935 -7.521790 0.0 0.269598 2.385094 16.498543 \n", - "1 0.083423 5.155587 -8.236547 0.0 0.246404 1.576869 16.568169 \n", - "2 0.082102 4.967218 -6.445849 0.0 0.245935 3.414664 16.437574 \n", - "3 0.082553 5.191478 -7.312384 0.0 0.240973 2.435591 16.592468 \n", - "4 0.083153 4.552286 -7.577226 0.0 0.232977 0.996742 16.616949 \n", - ".. ... ... ... ... ... ... ... \n", - "58 0.084058 6.878569 -8.191770 0.0 0.247327 0.691330 16.979179 \n", - "59 0.086554 7.673346 -9.163953 0.0 0.207005 2.231732 17.397644 \n", - "60 0.083778 6.811278 -9.130725 0.0 0.232106 2.037935 17.634174 \n", - "61 0.085517 6.878219 -9.760260 0.0 0.232977 0.569980 17.509853 \n", - "62 0.085272 7.383186 -9.041880 0.0 0.223843 2.449749 17.441238 \n", + "bodyparts snout tailbase \n", + "coords likelihood x y z likelihood x y \n", + "0 0.053609 2.076265 16.415096 0.0 0.139537 3.148022 -6.657187 \n", + "1 0.069977 1.774640 17.406301 0.0 0.176452 2.704738 -8.274201 \n", + "2 0.060571 2.861758 13.935984 0.0 0.148534 2.233558 -6.290349 \n", + "3 0.059226 2.387363 16.957266 0.0 0.144777 2.781236 -7.757162 \n", + "4 0.063246 2.337324 18.026840 0.0 0.148035 2.534595 -9.052482 \n", + ".. ... ... ... ... ... ... ... \n", + "58 0.046280 2.093328 17.112661 0.0 0.158959 2.268270 -8.589610 \n", + "59 0.053968 3.630704 16.375135 0.0 0.108622 1.831684 -6.993159 \n", + "60 0.042809 3.867933 16.821577 0.0 0.097812 1.444253 -7.488105 \n", + "61 0.054284 2.453674 17.717480 0.0 0.136344 1.779472 -8.309467 \n", + "62 0.049786 4.244837 15.407806 0.0 0.109382 2.311671 -6.654770 \n", "\n", "scorer \n", "bodyparts \n", "coords z likelihood \n", - "0 0.0 0.227193 \n", - "1 0.0 0.219187 \n", - "2 0.0 0.199375 \n", - "3 0.0 0.219349 \n", - "4 0.0 0.223073 \n", + "0 0.0 0.054340 \n", + "1 0.0 0.067940 \n", + "2 0.0 0.044671 \n", + "3 0.0 0.058866 \n", + "4 0.0 0.055323 \n", ".. ... ... \n", - "58 0.0 0.240952 \n", - "59 0.0 0.211547 \n", - "60 0.0 0.229108 \n", - "61 0.0 0.241432 \n", - "62 0.0 0.214529 \n", + "58 0.0 0.051529 \n", + "59 0.0 0.040950 \n", + "60 0.0 0.031486 \n", + "61 0.0 0.046111 \n", + "62 0.0 0.037103 \n", "\n", "[63 rows x 16 columns]" ] }, - "execution_count": 4, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.Estimation.get_trajectory(key)" + "model.PoseEstimation.get_trajectory(key)" ] }, { diff --git a/notebooks/py_scripts/02-WorkflowStructure_Optional.py b/notebooks/py_scripts/02-WorkflowStructure_Optional.py index 3f4406d..4c9eeee 100644 --- a/notebooks/py_scripts/02-WorkflowStructure_Optional.py +++ b/notebooks/py_scripts/02-WorkflowStructure_Optional.py @@ -93,15 +93,13 @@ lab.schema.list_tables() # %% -from workflow_deeplabcut.pipeline import VideoRecording # plot diagram of selected tables and schemas (dj.Diagram(subject.Subject) + dj.Diagram(session.Session) - + dj.Diagram(VideoRecording) - + dj.Diagram(model.EstimationTask)) + + dj.Diagram(model.VideoRecording) + dj.Diagram(model.PoseEstimationTask)) # %% Each datajoint table class inside the module corresponds to a table inside the schema. For example, the class `ephys.EphysRecording` correponds to the table `_ephys_recording` in the schema `neuro_ephys` in the database. # preview columns and contents in a table -VideoRecording.File() +model.VideoRecording.File() # %% `heading`: [markdown] # `describe()` shows table definition with foreign key references @@ -138,7 +136,7 @@ dj.Diagram(session) # %% [session](https://github.com/datajoint/element-session): experimental session information -session.Session.describe(); +session.Session.describe() # %% [markdown] # ## Summary and next step diff --git a/notebooks/py_scripts/03-Process.py b/notebooks/py_scripts/03-Process.py index a8f9e97..24ef793 100644 --- a/notebooks/py_scripts/03-Process.py +++ b/notebooks/py_scripts/03-Process.py @@ -76,57 +76,23 @@ # %% [markdown] # ## Inserting recordings -# %% -from workflow_deeplabcut.pipeline import VideoRecording -VideoRecording.heading - -# %% [markdown] -# The `VideoRecording` table retains unique recordings file specifies all videos across sessions, including both model training -# videos and videos for later analysis. - -# %% -recordings = [{'recording_id': '1', - 'subject': 'subject6', - 'session_datetime': '2021-06-02 14:04:22', - 'recording_start_time': '2021-06-02 14:07:00', - 'camera_id': '1'}, - {'recording_id': '2', - 'subject': 'subject6', - 'session_datetime': '2021-06-03 14:43:10', - 'recording_start_time': '2021-06-03 14:50:00', - 'camera_id': '1'}] -VideoRecording.insert(recordings) - -# %% [markdown] -# The related part table allows for multiple files for a given recording session. - -# %% -VideoRecording.File.heading - -# %% -recordings[0].update({'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4'}) -recordings[1].update({'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4'}) -VideoRecording.File.insert(recordings, ignore_extra_fields=True) - -# %% -VideoRecording.File() - # %% [markdown] -# The `TrainingVideo` table handles all files generated in the video labeling process, including the `h5`, `csv`, and `png` files under the `labeled-data` directory. While these aren't required for launching DLC training, it may be helpful to retain records. DLC will instead refer to the `mat` file located under the `training-datasets` directory. +# The `VideoSet` table handles all files generated in the video labeling process, including the `h5`, `csv`, and `png` files under the `labeled-data` directory. While these aren't required for launching DLC training, it may be helpful to retain records. DLC will instead refer to the `mat` file located under the `training-datasets` directory. # %% train.VideoSet.insert1({'video_set_id': 1}) -csv_path = 'openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.csv' -train.VideoSet.File.insert1({'video_set_id': 1, - 'file_path': csv_path}) - -# %% -rec_key = (VideoRecording & 'recording_id=1').fetch1('KEY') -train.VideoSet.VideoRecording.insert1({**rec_key, - 'video_set_id': 1, 'recording_id': 1}) - -# %% -train.VideoSet.VideoRecording() +labeled_dir = 'openfield-Pranav-2018-10-30/labeled-data/m4s1/' +training_files = ['CollectedData_Pranav.h5', + 'CollectedData_Pranav.csv', + 'img0000.png'] +for file in training_files: + train.VideoSet.File.insert1({'video_set_id': 1, + 'file_path': (labeled_dir + file)}) +train.VideoSet.File.insert1({'video_set_id':1, 'file_path': + 'openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4'}) + +# %% +train.VideoSet.File() # %% [markdown] # ## Training a DLC Network diff --git a/notebooks/temp05-Explore.ipynb b/notebooks/temp05-Explore.ipynb deleted file mode 100644 index 189f946..0000000 --- a/notebooks/temp05-Explore.ipynb +++ /dev/null @@ -1,462 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# DataJoint U24 - Workflow DeepLabCut" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import os; from pathlib import Path\n", - "# change to the upper level folder to detect dj_local_conf.json\n", - "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", - " + \"workflow directory\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "from workflow_deeplabcut.pipeline import lab, subject, session, dlc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Workflow architecture\n", - "\n", - "This workflow is assembled from 4 DataJoint elements:\n", - "+ [element-lab](https://github.com/datajoint/element-lab)\n", - "+ [element-animal](https://github.com/datajoint/element-animal)\n", - "+ [element-session](https://github.com/datajoint/element-session)\n", - "+ [element-calcium-imaging](https://github.com/datajoint/element-deeplabcut)\n", - "\n", - "For the architecture and detailed descriptions for each of those elements, please visit the respective links. \n", - "\n", - "Below is the diagram describing the core components of the fully assembled pipeline.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(dlc) + (dj.Diagram(session.Session) + 1) - 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Browsing the data with DataJoint `query` and `fetch` \n", - "\n", - "+ DataJoint provides functions to query data and fetch. For a detailed tutorials, visit our [general tutorial site](https://playground.datajoint.io/).\n", - "+ Running through the pipeline, we have ingested data of subject6 into the database.\n", - "+ Here are some highlights of the important tables.\n", - "\n", - "### `subject.Subject` and `session.Session` tables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject & session.Session" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "+ Fetch the primary key for the session of interest which will be used later on in this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "session_key = (session.Session & 'subject = \"subject3\"' & 'session_datetime = \"2021-04-30 12:22:15.032\"').fetch1('KEY')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `scan.Scan` and `scan.ScanInfo` tables\n", - "\n", - "+ These tables stores the scan metadata within a particular session." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scan.Scan & session_key" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scan.ScanInfo & session_key" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scan.ScanInfo.Field & session_key" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `imaging.ProcessingParamSet`, `imaging.ProcessingTask`, `imaging.Processing`, and `imaging.Curation` tables\n", - "\n", - "+ The parameters used for Suite2p or CaImAn are stored in `imaging.ProcessingParamSet` under a `paramset_idx`.\n", - "\n", - "+ The processing details for Suite2p and CaImAn are stored in `imaging.ProcessingTask` and `imaging.Processing` for the utilized `paramset_idx`.\n", - "\n", - "+ After the motion correction and segmentation, the results may go through a curation process. \n", - " \n", - " + If it did not go through curation, a copy of the `imaging.ProcessingTask` entry is inserted into `imaging.Curation` with the `curation_output_dir` identical to the `processing_output_dir`.\n", - "\n", - " + If it did go through a curation, a new entry will be inserted into `imaging.Curation`, with a `curation_output_dir` specified.\n", - "\n", - " + `imaging.Curation` supports multiple curations of an entry in `imaging.ProcessingTask`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imaging.ProcessingParamSet()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imaging.ProcessingTask * imaging.Processing & session_key" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example workflow, `curation_output_dir` is the same as the `processing_output_dir`, as these results were not manually curated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imaging.Curation & session_key" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `imaging.MotionCorrection` table\n", - "\n", - "+ After processing and curation, results are passed to the `imaging.MotionCorrection` and `imaging.Segmentation` tables.\n", - "\n", - "+ For the example data, the raw data is corrected with rigid and non-rigid motion correction which is stored in `imaging.MotionCorrection.RigidMotionCorrection` and `imaging.MotionCorrection.NonRigidMotionCorrection`, respectively. \n", - "\n", - "+ Lets first query the information for one curation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "curation_key = (imaging.Curation & session_key & 'curation_id=0').fetch1('KEY')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "curation_key" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imaging.MotionCorrection.RigidMotionCorrection & curation_key" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imaging.MotionCorrection.NonRigidMotionCorrection & curation_key" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "+ For non-rigid motion correction, the details for the individual blocks are stored in `imaging.MotionCorrection.Block`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imaging.MotionCorrection.Block & curation_key & 'block_id=0'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "+ Summary images are stored in `imaging.MotionCorrection.Summary`\n", - "\n", - " + Reference image - image used as an alignment template\n", - "\n", - " + Average image - mean of registered frames\n", - "\n", - " + Correlation image - correlation map (computed during region of interest \\[ROI\\] detection)\n", - "\n", - " + Maximum projection image - max of registered frames" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imaging.MotionCorrection.Summary & curation_key & 'field_idx=0'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "+ Lets fetch the `average_image` and plot it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "average_image = (imaging.MotionCorrection.Summary & curation_key & 'field_idx=0').fetch1('average_image')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.imshow(average_image);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `imaging.Segmentation` table\n", - "\n", - "+ Lets fetch and plot a mask stored in the `imaging.Segmentation.Mask` table for one `curation_id`.\n", - "\n", - "+ Each mask can be associated with a field by the attribute `mask_center_z`. For example, masks with `mask_center_z=0` are in the field identified with `field_idx=0` in `scan.ScanInfo.Field`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mask_xpix, mask_ypix = (imaging.Segmentation.Mask * imaging.MaskClassification.MaskType & curation_key & 'mask_center_z=0' & 'mask_npix > 130').fetch('mask_xpix','mask_ypix')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mask_image = np.zeros(np.shape(average_image), dtype=bool)\n", - "for xpix, ypix in zip(mask_xpix, mask_ypix):\n", - " mask_image[ypix, xpix] = True" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.imshow(average_image);\n", - "plt.contour(mask_image, colors='white', linewidths=0.5);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `imaging.MaskClassification` table\n", - "\n", - "+ This table provides the `mask_type` and `confidence` for the mask classification." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imaging.MaskClassification.MaskType & curation_key & 'mask=0'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `imaging.Fluorescence` and `imaging.Activity` tables\n", - "\n", - "+ Lets fetch and plot the flourescence and activity traces for one mask." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "query_cells = (imaging.Segmentation.Mask * imaging.MaskClassification.MaskType & curation_key & 'mask_center_z=0' & 'mask_npix > 130').proj()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fluorescence_traces = (imaging.Fluorescence.Trace & query_cells).fetch('fluorescence', order_by='mask')\n", - "\n", - "activity_traces = (imaging.Activity.Trace & query_cells).fetch('activity_trace', order_by='mask')\n", - "\n", - "sampling_rate = (scan.ScanInfo & curation_key).fetch1('fps') # [Hz]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(16, 4))\n", - "ax2 = ax.twinx()\n", - "\n", - "for f, a in zip(fluorescence_traces, activity_traces):\n", - " ax.plot(np.r_[:f.size] * 1/sampling_rate, f, 'k', label='fluorescence trace') \n", - " ax2.plot(np.r_[:a.size] * 1/sampling_rate, a, 'r', alpha=0.5, label='deconvolved trace')\n", - " \n", - " break\n", - "\n", - "ax.tick_params(labelsize=14)\n", - "ax2.tick_params(labelsize=14)\n", - "\n", - "ax.legend(loc='upper left', prop={'size': 14})\n", - "ax2.legend(loc='upper right', prop={'size': 14})\n", - "\n", - "ax.set_xlabel('Time (s)')\n", - "ax.set_ylabel('Activity (a.u.)')\n", - "ax2.set_ylabel('Activity (a.u.)');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary and Next Step\n", - "\n", - "+ This notebook highlights the major tables in the workflow and visualize some of the ingested results. \n" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:percent" - }, - "kernelspec": { - "display_name": "venv-dlc", - "language": "python", - "name": "venv-dlc" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "metadata": { - "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/user_data/recordings.csv b/user_data/model_videos.csv similarity index 63% rename from user_data/recordings.csv rename to user_data/model_videos.csv index d8799cb..13f7146 100644 --- a/user_data/recordings.csv +++ b/user_data/model_videos.csv @@ -1,4 +1,3 @@ recording_id,subject,session_datetime,recording_start_time,file_path,camera_id,paramset_idx -1,subject6,2021-06-02 14:04:22,2021-06-02 14:07:00,openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4,1,0 2,subject6,2021-06-03 14:43:10,2021-06-03 14:50:00,openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4,1,0 -3,subject5,2020-04-15 11:16:38,2020-04-15 11:17:00,Reaching-Mackenzie-2018-08-30/videos/reachingvideo1.avi,1,1 +3,subject5,2020-04-15 11:16:38,2020-04-15 11:17:00,Reaching-Mackenzie-2018-08-30/videos/reachingvideo1-copy.avi,1,1 diff --git a/user_data/train_videoset.csv b/user_data/train_videoset.csv deleted file mode 100644 index bb7b67a..0000000 --- a/user_data/train_videoset.csv +++ /dev/null @@ -1,2 +0,0 @@ -video_set_id,recording_id -1,1 diff --git a/user_data/train_videosets.csv b/user_data/train_videosets.csv new file mode 100644 index 0000000..ad02fc0 --- /dev/null +++ b/user_data/train_videosets.csv @@ -0,0 +1,9 @@ +video_set_id,file_path +1,openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.h5 +1,openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.csv +1,openfield-Pranav-2018-10-30/labeled-data/m4s1/img0000.png +1,openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4 +2,Reaching-Mackenzie-2018-08-30/labeled-data/reachingvideo1/CollectedData_Mackenzie.csv +2,Reaching-Mackenzie-2018-08-30/labeled-data/reachingvideo1/CollectedData_Mackenzie.h5 +2,Reaching-Mackenzie-2018-08-30/labeled-data/reachingvideo1/img005.png +2,Reaching-Mackenzie-2018-08-30/videos/reachingvideo1.avi diff --git a/workflow_deeplabcut/ingest.py b/workflow_deeplabcut/ingest.py index c3304c3..0631181 100644 --- a/workflow_deeplabcut/ingest.py +++ b/workflow_deeplabcut/ingest.py @@ -3,7 +3,7 @@ import ruamel.yaml as yaml from element_interface.utils import find_full_path -from .pipeline import subject, session, VideoRecording, train +from .pipeline import subject, session, train, model from .paths import get_dlc_root_data_dir @@ -54,8 +54,8 @@ def ingest_sessions(session_csv_path='./user_data/sessions.csv', def ingest_dlc_items(config_params_csv_path='./user_data/config_params.csv', - recording_csv_path='./user_data/recordings.csv', train_video_csv_path='./user_data/train_videosets.csv', + model_video_csv_path='./user_data/model_videos.csv', skip_duplicates=True): """ Ingests to DLC schema from ./user_data/{config_params,recordings}.csv @@ -64,7 +64,8 @@ def ingest_dlc_items(config_params_csv_path='./user_data/config_params.csv', paramset_desc and relative config_path. Other columns overwrite config variables Next, loads recording info into VideoRecording and VideoRecording.File :param config_params_csv_path: csv path for model training config and parameters - :param recording_csv_path: csv path for list of recordings + :param train_video_csv_path: csv path for list of training videosets + :param recording_csv_path: csv path for list of modeling videos for pose estimation """ previous_length = len(train.TrainingParamSet.fetch()) @@ -88,8 +89,10 @@ def ingest_dlc_items(config_params_csv_path='./user_data/config_params.csv', + '----') # Next, recordings and config files - csvs = [recording_csv_path, recording_csv_path, train_video_csv_path] - tables = [VideoRecording(), VideoRecording.File(), train.VideoSet.VideoRecording()] + csvs = [train_video_csv_path, train_video_csv_path, + model_video_csv_path, model_video_csv_path] + tables = [train.VideoSet(), train.VideoSet.File(), + model.VideoRecording(), model.VideoRecording.File()] ingest_general(csvs, tables, skip_duplicates=skip_duplicates) diff --git a/workflow_deeplabcut/pipeline.py b/workflow_deeplabcut/pipeline.py index 23d2e1f..e18b250 100644 --- a/workflow_deeplabcut/pipeline.py +++ b/workflow_deeplabcut/pipeline.py @@ -39,22 +39,6 @@ class Device(dj.Lookup): contents = zip([1, 2]) -@session.schema -class VideoRecording(dj.Manual): - definition = """ - -> Session - -> Device - recording_id: int - --- - recording_start_time: datetime - """ - - class File(dj.Part): - definition = """ - -> master - file_path: varchar(255) # filepath of video, relative to root data directory - """ - # Activate DeepLabCut schema ----------------------------------- From ff7cf589bdabacf5137cb14a7264e5a00a002600 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Tue, 22 Mar 2022 14:53:44 -0500 Subject: [PATCH 024/176] add `Session = session.Session` to pipeline.py, from code review --- workflow_deeplabcut/pipeline.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/workflow_deeplabcut/pipeline.py b/workflow_deeplabcut/pipeline.py index e18b250..19598e5 100644 --- a/workflow_deeplabcut/pipeline.py +++ b/workflow_deeplabcut/pipeline.py @@ -5,7 +5,6 @@ from element_deeplabcut import train, model from element_animal.subject import Subject -from element_session.session_with_datetime import Session from element_lab.lab import Source, Lab, Protocol, User, Project from .paths import get_dlc_root_data_dir, get_dlc_processed_data_dir @@ -26,6 +25,7 @@ subject.activate(db_prefix + 'subject', linking_module=__name__) Experimenter = lab.User +Session = session.Session session.activate(db_prefix + 'session', linking_module=__name__) # Activate equipment table ------------------------------------ From 20038267292149114b6787d34d79e79f2b582aad Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Tue, 22 Mar 2022 16:15:08 -0500 Subject: [PATCH 025/176] minor README updates --- README.md | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index f5534d0..2ce1f8e 100644 --- a/README.md +++ b/README.md @@ -25,8 +25,7 @@ assembled together to a functional workflow. ### element-lab -![element-lab]( -https://github.com/datajoint/element-lab/raw/main/images/element_lab_diagram.svg) +![element-lab](https://github.com/datajoint/element-lab/blob/main/images/lab_diagram.svg) ### element-animal @@ -52,16 +51,16 @@ still manage various models and execute pose estimation. ## Installation instructions -+ The installation instructions can be found at the -[datajoint-elements repository](https://github.com/datajoint/datajoint-elements/blob/main/gh-pages/docs/install.md). +The installation instructions can be found at the +[DataJoint Elements repository](https://github.com/datajoint/datajoint-elements/blob/main/gh-pages/docs/usage/install.md). ## Interacting with the DataJoint workflow Please refer to the following workflow-specific [Jupyter notebooks](/notebooks) for an in-depth explanation of how to ... -+ download example data ([00-DataDownload.ipynb](notebooks/00-DataDownload_Optional.ipynb)) -+ configure DataJoint settings ([01-Configure.ipynb](notebooks/01-Configure.ipynb)) -+ run the workflow ([01-WorkflowStructure.ipynb](notebooks/01-WorkflowStructure_Optional.ipynb)) -+ ingest data and launch tasks ([03-Process.ipynb](notebooks/03-Process.ipynb)) -+ automate tasks ([04-Automate.ipynb](notebooks/04-Automate_Optional.ipynb)) -+ drop tables ([05-Drop](notebooks/05-Drop_Optional.ipynb)) ++ Download example data ([00-DataDownload.ipynb](notebooks/00-DataDownload_Optional.ipynb)) ++ Configure DataJoint settings ([01-Configure.ipynb](notebooks/01-Configure.ipynb)) ++ Run the workflow ([01-WorkflowStructure.ipynb](notebooks/01-WorkflowStructure_Optional.ipynb)) ++ Ingest data and launch tasks ([03-Process.ipynb](notebooks/03-Process.ipynb)) ++ Automate tasks ([04-Automate.ipynb](notebooks/04-Automate_Optional.ipynb)) ++ Drop tables ([05-Drop](notebooks/05-Drop_Optional.ipynb)) From 20e0f0691a978b7200c75195c7786e932204787d Mon Sep 17 00:00:00 2001 From: Chris Brozdowski Date: Tue, 22 Mar 2022 16:53:57 -0500 Subject: [PATCH 026/176] Update README.md from code review suggestion Co-authored-by: Kabilar Gunalan --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2ce1f8e..279594b 100644 --- a/README.md +++ b/README.md @@ -60,7 +60,7 @@ Please refer to the following workflow-specific [Jupyter notebooks](/notebooks) for an in-depth explanation of how to ... + Download example data ([00-DataDownload.ipynb](notebooks/00-DataDownload_Optional.ipynb)) + Configure DataJoint settings ([01-Configure.ipynb](notebooks/01-Configure.ipynb)) -+ Run the workflow ([01-WorkflowStructure.ipynb](notebooks/01-WorkflowStructure_Optional.ipynb)) ++ Visualize the workflow ([01-WorkflowStructure.ipynb](notebooks/01-WorkflowStructure_Optional.ipynb)) + Ingest data and launch tasks ([03-Process.ipynb](notebooks/03-Process.ipynb)) + Automate tasks ([04-Automate.ipynb](notebooks/04-Automate_Optional.ipynb)) + Drop tables ([05-Drop](notebooks/05-Drop_Optional.ipynb)) From 3c4fe47707b1ecde71edc5df37cd340d5191b61b Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Wed, 23 Mar 2022 15:29:31 -0500 Subject: [PATCH 027/176] Add issue templates --- .github/ISSUE_TEMPLATE/bug_report.md | 39 ++++++++++++++++ .github/ISSUE_TEMPLATE/config.yml | 5 ++ .github/ISSUE_TEMPLATE/feature_request.md | 57 +++++++++++++++++++++++ 3 files changed, 101 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md create mode 100644 .github/ISSUE_TEMPLATE/config.yml create mode 100644 .github/ISSUE_TEMPLATE/feature_request.md diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..31fe9fc --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,39 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: 'bug' +assignees: '' + +--- + +## Bug Report + +### Description + +A clear and concise description of what is the overall operation that is intended to be +performed that resulted in an error. + +### Reproducibility +Include: +- OS (WIN | MACOS | Linux) +- DataJoint Element Version +- MySQL Version +- MySQL Deployment Strategy (local-native | local-docker | remote) +- Minimum number of steps to reliably reproduce the issue +- Complete error stack as a result of evaluating the above steps + +### Expected Behavior +A clear and concise description of what you expected to happen. + +### Screenshots +If applicable, add screenshots to help explain your problem. + +### Additional Research and Context +Add any additional research or context that was conducted in creating this report. + +For example: +- Related GitHub issues and PR's either within this repository or in other relevant + repositories. +- Specific links to specific lines or a focus within source code. +- Relevant summary of Maintainers development meetings, milestones, projects, etc. diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000..d31fbac --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,5 @@ +blank_issues_enabled: false +contact_links: + - name: DataJoint Contribution Guideline + url: https://docs.datajoint.org/python/community/02-Contribute.html + about: Please make sure to review the DataJoint Contribution Guidelines \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..1f2b784 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,57 @@ +--- +name: Feature request +about: Suggest an idea for a new feature +title: '' +labels: 'enhancement' +assignees: '' + +--- + +## Feature Request + +### Problem + +A clear and concise description how this idea has manifested and the context. Elaborate +on the need for this feature and/or what could be improved. Ex. I'm always frustrated +when [...] + +### Requirements + +A clear and concise description of the requirements to satisfy the new feature. Detail +what you expect from a successful implementation of the feature. Ex. When using this +feature, it should [...] + +### Justification + +Provide the key benefits in making this a supported feature. Ex. Adding support for this +feature would ensure [...] + +### Alternative Considerations + +Do you currently have a work-around for this? Provide any alternative solutions or +features you've considered. + +### Related Errors +Add any errors as a direct result of not exposing this feature. + +Please include steps to reproduce provided errors as follows: +- OS (WIN | MACOS | Linux) +- DataJoint Element Version +- MySQL Version +- MySQL Deployment Strategy (local-native | local-docker | remote) +- Minimum number of steps to reliably reproduce the issue +- Complete error stack as a result of evaluating the above steps + +### Screenshots +If applicable, add screenshots to help explain your feature. + +### Additional Research and Context +Add any additional research or context that was conducted in creating this feature request. + +For example: +- Related GitHub issues and PR's either within this repository or in other relevant + repositories. +- Specific links to specific lines or a focus within source code. +- Relevant summary of Maintainers development meetings, milestones, projects, etc. +- Any additional supplemental web references or links that would further justify this + feature request. From 4317fd77a5b81195ea52b38a2cf17f5f15685894 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Fri, 15 Apr 2022 18:10:48 -0500 Subject: [PATCH 028/176] Add citation section --- README.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/README.md b/README.md index 279594b..097d232 100644 --- a/README.md +++ b/README.md @@ -64,3 +64,17 @@ Please refer to the following workflow-specific + Ingest data and launch tasks ([03-Process.ipynb](notebooks/03-Process.ipynb)) + Automate tasks ([04-Automate.ipynb](notebooks/04-Automate_Optional.ipynb)) + Drop tables ([05-Drop](notebooks/05-Drop_Optional.ipynb)) + +## Citation + ++ If your work uses DataJoint and DataJoint Elements, please cite the respective Research Resource Identifiers (RRIDs) and manuscripts. + ++ DataJoint for Python or MATLAB + + Yatsenko D, Reimer J, Ecker AS, Walker EY, Sinz F, Berens P, Hoenselaar A, Cotton RJ, Siapas AS, Tolias AS. DataJoint: managing big scientific data using MATLAB or Python. bioRxiv. 2015 Jan 1:031658. doi: https://doi.org/10.1101/031658 + + + DataJoint ([RRID:SCR_014543](https://scicrunch.org/resolver/SCR_014543)) - DataJoint for (version < enter version number >) + ++ DataJoint Elements + + Yatsenko D, Nguyen T, Shen S, Gunalan K, Turner CA, Guzman R, Sasaki M, Sitonic D, Reimer J, Walker EY, Tolias AS. DataJoint Elements: Data Workflows for Neurophysiology. bioRxiv. 2021 Jan 1. doi: https://doi.org/10.1101/2021.03.30.437358 + + + DataJoint Elements ([RRID:SCR_021894](https://scicrunch.org/resolver/SCR_021894)) - Element DeepLabCut (version < enter version number >) \ No newline at end of file From 886d3f4a6dc8de49bdd711cd997b9f40b06c6273 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Fri, 15 Apr 2022 18:11:29 -0500 Subject: [PATCH 029/176] Add links to elements.datajoint.org --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 097d232..1fec3a1 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,10 @@ This repository provides demonstrations for: 3. Ingestion of model information, and launching evaluation. 4. Using an ingested model to run pose estimation. +See the [DataJoint Elements documentation](https://elements.datajoint.org) for +descriptions of the other `elements` and `workflows` developed as part of this National +Institutes of Health (NIH)-funded initiative. + ## Workflow architecture The deeplabcut workflow presented here uses components from 4 DataJoint elements @@ -52,7 +56,7 @@ still manage various models and execute pose estimation. ## Installation instructions The installation instructions can be found at the -[DataJoint Elements repository](https://github.com/datajoint/datajoint-elements/blob/main/gh-pages/docs/usage/install.md). +[DataJoint Elements documentation](https://elements.datajoint.org/usage/install/). ## Interacting with the DataJoint workflow From 0e19c7fe1fa1ec88f5ca9d8fe2b5bc792aeb806c Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Fri, 15 Apr 2022 18:12:21 -0500 Subject: [PATCH 030/176] Remove upstream element images to shorten readme --- README.md | 15 --------------- 1 file changed, 15 deletions(-) diff --git a/README.md b/README.md index 1fec3a1..a3f3aef 100644 --- a/README.md +++ b/README.md @@ -27,21 +27,6 @@ The deeplabcut workflow presented here uses components from 4 DataJoint elements (`element-lab`, `element-animal`, `element-session`, and `element-deeplabcut`) assembled together to a functional workflow. -### element-lab - -![element-lab](https://github.com/datajoint/element-lab/blob/main/images/lab_diagram.svg) - -### element-animal - -![element-animal]( -https://github.com/datajoint/element-animal/blob/main/images/subject_diagram.svg) - -### element-session - -![session](https://github.com/datajoint/element-session/blob/main/images/session_diagram.svg) - -### Assembled with element-deeplabcut - The DeepLabCut Element is split into `train` and `model` schemas. To manage both model training and pose estimation within DataJoint, one would activate both schemas, as shown below. From 2417a4be377cd384910282cb0e48a538e49e4fc1 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Fri, 15 Apr 2022 18:12:41 -0500 Subject: [PATCH 031/176] Add links --- README.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a3f3aef..d57fe52 100644 --- a/README.md +++ b/README.md @@ -23,8 +23,11 @@ Institutes of Health (NIH)-funded initiative. ## Workflow architecture -The deeplabcut workflow presented here uses components from 4 DataJoint elements -(`element-lab`, `element-animal`, `element-session`, and `element-deeplabcut`) +The deeplabcut workflow presented here uses components from four DataJoint Elements +([element-lab](https://github.com/datajoint/element-lab), +[element-animal](https://github.com/datajoint/element-animal), +[element-session](https://github.com/datajoint/element-session), +[element-deeplabcut](https://github.com/datajoint/element-deeplabcut)) assembled together to a functional workflow. The DeepLabCut Element is split into `train` and `model` schemas. To manage both model From 11991b1ea807a6fa10a562b2893448c9a9b236f1 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Fri, 15 Apr 2022 18:15:06 -0500 Subject: [PATCH 032/176] Fix format --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d57fe52..8f7c37d 100644 --- a/README.md +++ b/README.md @@ -64,7 +64,7 @@ Please refer to the following workflow-specific + DataJoint for Python or MATLAB + Yatsenko D, Reimer J, Ecker AS, Walker EY, Sinz F, Berens P, Hoenselaar A, Cotton RJ, Siapas AS, Tolias AS. DataJoint: managing big scientific data using MATLAB or Python. bioRxiv. 2015 Jan 1:031658. doi: https://doi.org/10.1101/031658 - + DataJoint ([RRID:SCR_014543](https://scicrunch.org/resolver/SCR_014543)) - DataJoint for (version < enter version number >) + + DataJoint ([RRID:SCR_014543](https://scicrunch.org/resolver/SCR_014543)) - DataJoint for < Python or MATLAB > (version < enter version number >) + DataJoint Elements + Yatsenko D, Nguyen T, Shen S, Gunalan K, Turner CA, Guzman R, Sasaki M, Sitonic D, Reimer J, Walker EY, Tolias AS. DataJoint Elements: Data Workflows for Neurophysiology. bioRxiv. 2021 Jan 1. doi: https://doi.org/10.1101/2021.03.30.437358 From e60d017a719b4c1f04772df8fddd3be784c74ef7 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Fri, 15 Apr 2022 22:04:31 -0500 Subject: [PATCH 033/176] Update text --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index 8f7c37d..27209f8 100644 --- a/README.md +++ b/README.md @@ -17,9 +17,7 @@ This repository provides demonstrations for: 3. Ingestion of model information, and launching evaluation. 4. Using an ingested model to run pose estimation. -See the [DataJoint Elements documentation](https://elements.datajoint.org) for -descriptions of the other `elements` and `workflows` developed as part of this National -Institutes of Health (NIH)-funded initiative. +For more information on the DataJoint Elements project, please visit https://elements.datajoint.org. This work is supported by the National Institutes of Health. ## Workflow architecture From 85849698a213efca4eea3e2cacdee7b557269139 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Sun, 17 Apr 2022 09:59:46 -0500 Subject: [PATCH 034/176] Add link to element.datajoint.org --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 27209f8..0bcb042 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,9 @@ This repository provides demonstrations for: 3. Ingestion of model information, and launching evaluation. 4. Using an ingested model to run pose estimation. -For more information on the DataJoint Elements project, please visit https://elements.datajoint.org. This work is supported by the National Institutes of Health. ++ See the [Element DeepLabCut documentation](https://elements.datajoint.org/description/deeplabcut/) for the background information and development timeline. + ++ For more information on the DataJoint Elements project, please visit https://elements.datajoint.org. This work is supported by the National Institutes of Health. ## Workflow architecture From 0253e7e545306dd44d1ee04ec009211bb9aaffc0 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Sun, 17 Apr 2022 10:30:30 -0500 Subject: [PATCH 035/176] Fix format --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 0bcb042..f5b4774 100644 --- a/README.md +++ b/README.md @@ -17,9 +17,9 @@ This repository provides demonstrations for: 3. Ingestion of model information, and launching evaluation. 4. Using an ingested model to run pose estimation. -+ See the [Element DeepLabCut documentation](https://elements.datajoint.org/description/deeplabcut/) for the background information and development timeline. +See the [Element DeepLabCut documentation](https://elements.datajoint.org/description/deeplabcut/) for the background information and development timeline. -+ For more information on the DataJoint Elements project, please visit https://elements.datajoint.org. This work is supported by the National Institutes of Health. +For more information on the DataJoint Elements project, please visit https://elements.datajoint.org. This work is supported by the National Institutes of Health. ## Workflow architecture From 2259f3a35914435a6f48376cce967560b14d520e Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Mon, 18 Apr 2022 11:14:38 -0500 Subject: [PATCH 036/176] updates corresponding to element-deeplabcut PR #15 --- notebooks/00-DataDownload_Optional.ipynb | 18 +- notebooks/02-WorkflowStructure_Optional.ipynb | 14 +- notebooks/03-Process.ipynb | 193 +++++++++++++++--- notebooks/04-Automate_Optional.ipynb | 28 ++- .../py_scripts/00-DataDownload_Optional.py | 4 +- notebooks/py_scripts/03-Process.py | 51 ++++- notebooks/py_scripts/04-Automate_Optional.py | 38 ++-- user_data/model_videos.csv | 6 +- workflow_deeplabcut/ingest.py | 6 + 9 files changed, 269 insertions(+), 89 deletions(-) diff --git a/notebooks/00-DataDownload_Optional.ipynb b/notebooks/00-DataDownload_Optional.ipynb index 3e28d86..0245df1 100644 --- a/notebooks/00-DataDownload_Optional.ipynb +++ b/notebooks/00-DataDownload_Optional.ipynb @@ -83,23 +83,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'CommentedSeq' object has no attribute 'keys'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_1395/750526841.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0myour_root\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/Volumes/GoogleDrive/My Drive/Dev/DeepLabCut/examples/JUPYTER/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdeeplabcut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_project\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdemo_data\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_demo_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mload_demo_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myour_root\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/openfield-Pranav-2018-10-30/config.yaml'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m in \u001b[0;36mload_demo_data\u001b[0;34m(config, createtrainingset)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mtransform_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcreatetrainingset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Loaded, now creating training data...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/deeplabcut/create_project/demo_data.py\u001b[0m in \u001b[0;36mtransform_data\u001b[0;34m(config)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This is not an offical demo dataset.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"video_sets\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 63\u001b[0m cfg[\"video_sets\"][str(video_file)] = cfg[\"video_sets\"].pop(\n\u001b[1;32m 64\u001b[0m \u001b[0;34m\"WILL BE AUTOMATICALLY UPDATED BY DEMO CODE\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'CommentedSeq' object has no attribute 'keys'" - ] - } - ], + "outputs": [], "source": [ "your_root='/fill/in/your/root/with\\ escaped\\ spaces'\n", "from deeplabcut.create_project.demo_data import load_demo_data\n", diff --git a/notebooks/02-WorkflowStructure_Optional.ipynb b/notebooks/02-WorkflowStructure_Optional.ipynb index b82e6c2..e1ba283 100644 --- a/notebooks/02-WorkflowStructure_Optional.ipynb +++ b/notebooks/02-WorkflowStructure_Optional.ipynb @@ -625,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "title": "Each datajoint table class inside the module corresponds to a table inside the schema. For example, the class `ephys.EphysRecording` correponds to the table `_ephys_recording` in the schema `neuro_ephys` in the database." }, @@ -693,13 +693,13 @@ "

    session_datetime

    \n", " \n", "
    \n", - "

    camera_id

    \n", + "

    recording_id

    \n", " \n", "
    \n", - "

    recording_id

    \n", + "

    file_id

    \n", " \n", "
    \n", - "

    file_path

    \n", + "

    file_path

    \n", " filepath of video, relative to root data directory\n", "
    \n", " \n", @@ -709,13 +709,13 @@ " " ], "text/plain": [ - "*subject *session_datet *camera_id *recording_id *file_path \n", - "+---------+ +------------+ +-----------+ +------------+ +-----------+\n", + "*subject *session_datet *recording_id *file_id file_path \n", + "+---------+ +------------+ +------------+ +---------+ +-----------+\n", "\n", " (Total: 0)" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } diff --git a/notebooks/03-Process.ipynb b/notebooks/03-Process.ipynb index c2b634d..eaf986e 100644 --- a/notebooks/03-Process.ipynb +++ b/notebooks/03-Process.ipynb @@ -50,9 +50,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting cbroz@dss-db.datajoint.io:3306\n" + ] + } + ], "source": [ "import datajoint as dj\n", "from workflow_deeplabcut.pipeline import lab, subject, session, train, model" @@ -103,14 +111,14 @@ "outputs": [], "source": [ "subject.Subject.insert1(dict(subject='subject6', \n", - " sex='M', \n", - " subject_birth_date='2020-01-03', \n", + " sex='F', \n", + " subject_birth_date='2020-01-01', \n", " subject_description='hneih_E105'))" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -182,10 +190,10 @@ "

    subject_description

    \n", " \n", " \n", - " subject5\n", - "F\n", - "2020-01-01\n", - "rich \n", + " subject6\n", + "M\n", + "2020-01-03\n", + "hneih_E105 \n", " \n", " \n", "

    Total: 1

    \n", @@ -194,17 +202,17 @@ "text/plain": [ "*subject sex subject_birth_ subject_descri\n", "+----------+ +-----+ +------------+ +------------+\n", - "subject5 F 2020-01-01 rich \n", + "subject6 M 2020-01-03 hneih_E105 \n", " (Total: 1)" ] }, - "execution_count": 3, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "subject.Subject & \"subject='subject5'\"" + "subject.Subject & \"subject='subject6'\"" ] }, { @@ -250,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -261,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -343,13 +351,13 @@ " (Total: 2)" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "session.Session() & \"session_datetime > '2021-06-01 12:00:00'\"" + "session.Session() & \"session_datetime > '2021-06-01 12:00:00'\" & \"subject='subject6'\"" ] }, { @@ -1335,23 +1343,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "key = {'subject': 'subject6',\n", " 'session_datetime': '2021-06-02 14:04:22',\n", - " 'recording_id': '1', 'camera_id': 1,\n", - " 'recording_start_time': '2021-06-02 14:07:00'}\n", + " 'recording_id': '1', 'camera_id': 1}\n", "model.VideoRecording.insert1(key)\n", " # do not include an initial `/` in relative file paths \n", - "key.update({'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4'})\n", + "key.update({'file_id': 1, \n", + " 'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4'})\n", "model.VideoRecording.File.insert1(key, ignore_extra_fields=True)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1417,13 +1425,13 @@ "

    session_datetime

    \n", " \n", "
    \n", - "

    camera_id

    \n", + "

    recording_id

    \n", " \n", "
    \n", - "

    recording_id

    \n", + "

    file_id

    \n", " \n", "
    \n", - "

    file_path

    \n", + "

    file_path

    \n", " filepath of video, relative to root data directory\n", "
    \n", " subject6\n", @@ -1437,13 +1445,13 @@ " " ], "text/plain": [ - "*subject *session_datet *camera_id *recording_id *file_path \n", - "+----------+ +------------+ +-----------+ +------------+ +------------+\n", - "subject6 2021-06-02 14: 1 1 openfield-Pran\n", + "*subject *session_datet *recording_id *file_id file_path \n", + "+----------+ +------------+ +------------+ +---------+ +------------+\n", + "subject6 2021-06-02 14: 1 1 openfield-Pran\n", " (Total: 1)" ] }, - "execution_count": 3, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1456,7 +1464,134 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we need to specify if the `PoseEstimation` table should load results from an existing file or trigger the estimation command. Here, we can also specify parameters accepted by the `analyze_videos` function as a dictionary." + "To automatically get recording information about this file, we can use the `make` function of the `RecordingInfo` table." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    px_height

    \n", + " height in pixels\n", + "
    \n", + "

    px_width

    \n", + " width in pixels\n", + "
    \n", + "

    nframes

    \n", + " number of frames\n", + "
    \n", + "

    fps

    \n", + " (Hz) frames per second\n", + "
    \n", + "

    recording_datetime

    \n", + " Datetime for the start of the recording\n", + "
    \n", + "

    recording_duration

    \n", + " video duration in seconds\n", + "
    subject62021-06-02 14:04:2214806406330None2.1
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id px_height px_width nframes fps recording_date recording_dura\n", + "+----------+ +------------+ +------------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n", + "subject6 2021-06-02 14: 1 480 640 63 30 None 2.1 \n", + " (Total: 1)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.RecordingInfo.populate()\n", + "model.RecordingInfo()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Next, we need to specify if the `PoseEstimation` table should load results from an existing file or trigger the estimation command. Here, we can also specify parameters accepted by the `analyze_videos` function as a dictionary." ] }, { diff --git a/notebooks/04-Automate_Optional.ipynb b/notebooks/04-Automate_Optional.ipynb index 82a6ed3..5423007 100644 --- a/notebooks/04-Automate_Optional.ipynb +++ b/notebooks/04-Automate_Optional.ipynb @@ -26,11 +26,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting cbroz@dss-db.datajoint.io:3306\n" + ] + } + ], "source": [ "import os; from pathlib import Path\n", "# change to the upper level folder to detect dj_local_conf.json\n", @@ -73,7 +81,11 @@ "3. Run automatic scripts prepared in `workflow_deeplabcut.ingest` for ingestion: \n", " + `ingest_subjects` for `subject.Subject`\n", " + `ingest_sessions` - for session tables `Session`, `SessionDirectory`, and `SessionNote`\n", - " + `ingest_dlc_items` - for `VideoRecording` and `TrainingParamSet`" + " + `ingest_dlc_items` - for ...\n", + " - `train.ModelTrainingParamSet`\n", + " - `train.VideoSet` and the corresponding `File` part table\n", + " - `model.VideoRecording` and the corresponding `File` part table\n", + " - `model.RecordingInfo`" ] }, { @@ -88,11 +100,11 @@ "\n", "---- Inserting 0 entry(s) into subject ----\n", "\n", - "---- Inserting 2 entry(s) into session ----\n", + "---- Inserting 0 entry(s) into session ----\n", "\n", - "---- Inserting 2 entry(s) into session_directory ----\n", + "---- Inserting 0 entry(s) into session_directory ----\n", "\n", - "---- Inserting 2 entry(s) into session_note ----\n", + "---- Inserting 0 entry(s) into session_note ----\n", "\n", "---- Inserting 3 entry(s) into #model_training_param_set ----\n", "\n", @@ -102,7 +114,9 @@ "\n", "---- Inserting 2 entry(s) into video_recording ----\n", "\n", - "---- Inserting 2 entry(s) into video_recording__file ----\n" + "---- Inserting 2 entry(s) into video_recording__file ----\n", + "\n", + "---- Inserting 2 entry(s) into _recording_info ----\n" ] } ], diff --git a/notebooks/py_scripts/00-DataDownload_Optional.py b/notebooks/py_scripts/00-DataDownload_Optional.py index 487cecd..86435a3 100644 --- a/notebooks/py_scripts/00-DataDownload_Optional.py +++ b/notebooks/py_scripts/00-DataDownload_Optional.py @@ -66,8 +66,8 @@ # %% your_root='/fill/in/your/root/with\ escaped\ spaces' -from deeplabcut.create_project.demo_data import load_demo_data as dlc_load_demo -dlc_load_demo(your_root+'/openfield-Pranav-2018-10-30/config.yaml') +from deeplabcut.create_project.demo_data import load_demo_data +load_demo_data(your_root+'/openfield-Pranav-2018-10-30/config.yaml') # %% [markdown] # For your own data, we recommend using the DLC gui to intitialize your project and label the data. diff --git a/notebooks/py_scripts/03-Process.py b/notebooks/py_scripts/03-Process.py index 24ef793..2698d68 100644 --- a/notebooks/py_scripts/03-Process.py +++ b/notebooks/py_scripts/03-Process.py @@ -52,12 +52,12 @@ # %% subject.Subject.insert1(dict(subject='subject6', - sex='M', - subject_birth_date='2020-01-03', + sex='F', + subject_birth_date='2020-01-01', subject_description='hneih_E105')) # %% -subject.Subject() +subject.Subject & "subject='subject6'" # %% session.Session.describe(); @@ -71,7 +71,7 @@ session.Session.insert(session_keys) # %% -session.Session() & "session_datetime > '2021-06-01 12:00:00'" +session.Session() & "session_datetime > '2021-06-01 12:00:00'" & "subject='subject6'" # %% [markdown] # ## Inserting recordings @@ -127,7 +127,8 @@ 'trainingsetindex': '0', 'maxiters': '5', 'scorer_legacy': 'False', - 'maxiters': '5'} + 'maxiters': '5', + 'multianimalproject':'False'} config_params.update(training_params) train.TrainingParamSet.insert_new_params(paramset_idx=paramset_idx, paramset_desc=paramset_desc, @@ -207,6 +208,9 @@ # %% model.ModelEvaluation.heading +# %% [markdown] +# If your project was initialized in a version of DeepLabCut other than the one you're currently using, model evaluation may report key errors. Specifically, your `config.yaml` may not specify `multianimalproject: false`. + # %% model.ModelEvaluation.populate() @@ -217,16 +221,41 @@ # ## Pose Estimation # %% [markdown] -# To put this model to use, we'll conduct pose estimation on the video generated in the [DataDownload notebook](./00_DataDownload_Optional.ipynb). Here, we can also specify parameters accepted by the `analyze_videos` function as a dictionary. +# To put this model to use, we'll conduct pose estimation on the video generated in the [DataDownload notebook](./00_DataDownload_Optional.ipynb). First, we need to update the `VideoRecording` table with the recording from a session. + +# %% +key = {'subject': 'subject6', + 'session_datetime': '2021-06-02 14:04:22', + 'recording_id': '1', 'camera_id': 1} +model.VideoRecording.insert1(key) + # do not include an initial `/` in relative file paths +key.update({'file_id': 1, + 'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4'}) +model.VideoRecording.File.insert1(key, ignore_extra_fields=True) + +# %% +model.VideoRecording.File() + +# %% [markdown] +# To automatically get recording information about this file, we can use the `make` function of the `RecordingInfo` table. # %% -key=(VideoRecording&'recording_id=2').fetch1('KEY'); +model.RecordingInfo.populate() +model.RecordingInfo() + +# %% [markdown] +# Next, we need to specify if the `PoseEstimation` table should load results from an existing file or trigger the estimation command. Here, we can also specify parameters accepted by the `analyze_videos` function as a dictionary. + +# %% +key = (model.VideoRecording & {'recording_id': '1'}).fetch1('KEY') key.update({'model_name': 'OpenField-5', 'task_mode': 'trigger'}) -model.EstimationTask.insert_estimation_task(key,params={'save_as_csv':True}, - skip_duplicates=True) +key + +# %% +model.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True}) # %% -model.Estimation.populate() +model.PoseEstimation.populate() # %% [markdown] # By default, DataJoint will store the results of pose estimation in a subdirectory @@ -239,7 +268,7 @@ # We can get this estimation directly as a pandas dataframe. # %% -model.Estimation.get_trajectory(key) +model.PoseEstimation.get_trajectory(key) # %% [markdown] # diff --git a/notebooks/py_scripts/04-Automate_Optional.py b/notebooks/py_scripts/04-Automate_Optional.py index ada1d81..eb80543 100644 --- a/notebooks/py_scripts/04-Automate_Optional.py +++ b/notebooks/py_scripts/04-Automate_Optional.py @@ -29,8 +29,7 @@ if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') assert os.path.basename(os.getcwd())=='workflow-deeplabcut', ("Please move to the " + "workflow directory") -from workflow_deeplabcut.pipeline import lab, subject, session, train, model, \ - VideoRecording +from workflow_deeplabcut.pipeline import lab, subject, session, train, model # %% [markdown] # If you previously completed the [03-Process notebook](./03-Process.ipynb), you may want to delete the contents ingested there, to avoid duplication errors. @@ -52,7 +51,11 @@ # 3. Run automatic scripts prepared in `workflow_deeplabcut.ingest` for ingestion: # + `ingest_subjects` for `subject.Subject` # + `ingest_sessions` - for session tables `Session`, `SessionDirectory`, and `SessionNote` -# + `ingest_dlc_items` - for `VideoRecording` and `TrainingParamSet` +# + `ingest_dlc_items` - for ... +# - `train.ModelTrainingParamSet` +# - `train.VideoSet` and the corresponding `File` part table +# - `model.VideoRecording` and the corresponding `File` part table +# - `model.RecordingInfo` # %% from workflow_deeplabcut.ingest import ingest_subjects, ingest_sessions, ingest_dlc_items @@ -61,7 +64,7 @@ # %% [markdown] # ## Setting project variables # -# 1. Set your root directory in your DataJoint config file, under `custom` as `dlc_root_data_dir`. For the purposes of this demo, we'll ask DeepLabCut to structure the demo config file with `load_demo_data` +# 1. Set your root directory in your DataJoint config file, under `custom` as `dlc_root_data_dir`. # %% import datajoint as dj; dj.config.load('dj_local_conf.json') @@ -69,11 +72,16 @@ data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config 'openfield-Pranav-2018-10-30') # DLC project dir config_path = (data_dir / 'config.yaml') + +# %% [markdown] +# 2. For the purposes of this demo, we'll ask DeepLabCut to structure the demo config file with `load_demo_data`. If you already did this in the [00-DataDownload notebook](./00-DataDownload_Optional.ipynb), skip this step. + +# %% from deeplabcut.create_project.demo_data import load_demo_data load_demo_data(config_path) # %% [markdown] -# 2. For this demo, we generate a copy to show pose estimation. This is `recording_id` 2 in `recordings.csv`. If you already did this in the [00-DataDownload notebook](./00-DataDownload_Optional.ipynb), skip this step. +# 3. For this demo, we generate a copy to show pose estimation. This is `recording_id` 2 in `recordings.csv`. If you already did this in the [00-DataDownload notebook](./00-DataDownload_Optional.ipynb), skip this step. # %% tags=[] vid_path = str(data_dir).replace(" ", "\ ") + '/videos/m3v1mp4' @@ -82,7 +90,7 @@ os.system(cmd) # %% [markdown] -# 3. Pair training video with training parameters, and launch training. +# 4. Pair training files with training parameters, and launch training. # %% key={'paramset_idx':1,'training_id':1,'video_set_id':1, @@ -91,35 +99,37 @@ train.ModelTraining.populate() # %% [markdown] -# 4. Add this model to the `Model` table and evaluate. +# 5. Add this model to the `Model` table and evaluate. # - Include a user-friendly `model_name` # - Include the relative path for the project's `config.yaml` # - Add `shuffle` and `trainingsetindex` +# - `insert_new_model` will prompt before inserting, but this can be skipped with `prompt=False` # %% model.Model.insert_new_model(model_name='OpenField-5',dlc_config=config_path, - shuffle=1,trainingsetindex=0, paramset_idx=1, + shuffle=1,trainingsetindex=0, paramset_idx=1, + prompt=True, # True is the default behavior model_description='Open field model trained 5 iterations') model.ModelEvaluation.populate() # %% [markdown] -# 5. Add a pose estimation task, and launch pose estimation. +# 6. Add a pose estimation task, and launch pose estimation. # - Get all primary key information for a given recording # - Add the model and `task_mode` (i.e., load vs. trigger) to be applied # - Add any additional analysis parameters for `deeplabcut.analyze_videos` # %% -key=(VideoRecording & 'recording_id=2').fetch1('KEY') +key=(model.VideoRecording & 'recording_id=2').fetch1('KEY') key.update({'model_name': 'OpenField-5', 'task_mode': 'trigger'}) analyze_params={'save_as_csv':True} # add any others from deeplabcut.analyze_videos -model.EstimationTask.insert_estimation_task(key,params=analyze_params) -model.Estimation.populate() +model.PoseEstimationTask.insert_estimation_task(key,params=analyze_params) +model.PoseEstimation.populate() # %% [markdown] -# 6. Retrieve estimated position data. +# 7. Retrieve estimated position data. # %% -model.Estimation.get_trajectory(key) +model.PoseEstimation.get_trajectory(key) # %% [markdown] # ## Summary and next step diff --git a/user_data/model_videos.csv b/user_data/model_videos.csv index 13f7146..711d88a 100644 --- a/user_data/model_videos.csv +++ b/user_data/model_videos.csv @@ -1,3 +1,3 @@ -recording_id,subject,session_datetime,recording_start_time,file_path,camera_id,paramset_idx -2,subject6,2021-06-03 14:43:10,2021-06-03 14:50:00,openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4,1,0 -3,subject5,2020-04-15 11:16:38,2020-04-15 11:17:00,Reaching-Mackenzie-2018-08-30/videos/reachingvideo1-copy.avi,1,1 +recording_id,subject,session_datetime,file_id,file_path,camera_id,paramset_idx +2,subject6,2021-06-03 14:43:10,1,openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4,1,0 +3,subject5,2020-04-15 11:16:38,1,Reaching-Mackenzie-2018-08-30/videos/reachingvideo1-copy.avi,1,1 diff --git a/workflow_deeplabcut/ingest.py b/workflow_deeplabcut/ingest.py index 0631181..35a2856 100644 --- a/workflow_deeplabcut/ingest.py +++ b/workflow_deeplabcut/ingest.py @@ -95,6 +95,12 @@ def ingest_dlc_items(config_params_csv_path='./user_data/config_params.csv', model.VideoRecording(), model.VideoRecording.File()] ingest_general(csvs, tables, skip_duplicates=skip_duplicates) + # Populate RecordingInfo + previous_length = len(model.RecordingInfo.fetch()) + model.RecordingInfo.populate() + insert_length = len(model.RecordingInfo.fetch()) - previous_length + print(f'\n---- Inserting {insert_length} entry(s) into _recording_info ----') + if __name__ == '__main__': ingest_subjects() From fdd12ac243ec652c5011e1d2f257612de28f8635 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Mon, 18 Apr 2022 15:13:05 -0500 Subject: [PATCH 037/176] Update format --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f5b4774..db73c79 100644 --- a/README.md +++ b/README.md @@ -64,9 +64,9 @@ Please refer to the following workflow-specific + DataJoint for Python or MATLAB + Yatsenko D, Reimer J, Ecker AS, Walker EY, Sinz F, Berens P, Hoenselaar A, Cotton RJ, Siapas AS, Tolias AS. DataJoint: managing big scientific data using MATLAB or Python. bioRxiv. 2015 Jan 1:031658. doi: https://doi.org/10.1101/031658 - + DataJoint ([RRID:SCR_014543](https://scicrunch.org/resolver/SCR_014543)) - DataJoint for < Python or MATLAB > (version < enter version number >) + + DataJoint ([RRID:SCR_014543](https://scicrunch.org/resolver/SCR_014543)) - DataJoint for `` (version ``) - -+ DataJoint Elements - + Yatsenko D, Nguyen T, Shen S, Gunalan K, Turner CA, Guzman R, Sasaki M, Sitonic D, Reimer J, Walker EY, Tolias AS. DataJoint Elements: Data Workflows for Neurophysiology. bioRxiv. 2021 Jan 1. doi: https://doi.org/10.1101/2021.03.30.437358 - - + DataJoint Elements ([RRID:SCR_021894](https://scicrunch.org/resolver/SCR_021894)) - Element DeepLabCut (version ``) \ No newline at end of file +This repository demonstrates the functionality of the +DataJoint Element for markerless pose estimation with +[DeepLabCut](https://www.deeplabcut.org/). DataJoint Elements collectively standardize +and automate data collection and analysis for neuroscience experiments. Each Element is +a modular pipeline for data storage and processing with corresponding database +tables that can be combined with other Elements to assemble a fully functional pipeline. + +Installation and usage instructions, including those featured in this repository, +can be found at the +[Element documentation](datajoint.com/docs/elements/element-deeplabcut). + +![element-deeplabcut diagram](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/diagram_dlc.svg) From b141b1e0d7144d72c3d43232abcb4f7e5f9faacb Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Thu, 20 Oct 2022 15:17:15 -0500 Subject: [PATCH 065/176] Add class docstring for Device --- workflow_deeplabcut/pipeline.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/workflow_deeplabcut/pipeline.py b/workflow_deeplabcut/pipeline.py index b56e918..73dab71 100644 --- a/workflow_deeplabcut/pipeline.py +++ b/workflow_deeplabcut/pipeline.py @@ -41,11 +41,19 @@ @lab.schema class Device(dj.Lookup): + """Table for managing lab equiment. + + In Element DeepLabCut, this table is referenced by `model.VideoRecording`. + The primary key is also used to generate inferred output directories when + running pose estimation inference. Refer to the `definition` attribute + for the table design. + """ + definition = """ device : varchar(32) --- - modality : varchar(64) - description=null : varchar(256) + modality : varchar(64) + description=null : varchar(256) """ contents = [ ["Camera1", "Pose Estimation", "Panasonic HC-V380K"], From 65849581b46fa7f21702fb5e025aae8286c2c51a Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Fri, 21 Oct 2022 18:15:02 -0500 Subject: [PATCH 066/176] Update changelog. Add attribs to Device --- CHANGELOG.md | 4 +++- workflow_deeplabcut/pipeline.py | 5 +++++ 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index d85b560..12565be 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,7 +4,9 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and ## 0.1.1 - Unreleased - Added - Integration tests -- Changes - Dataset for didactic notebooks +- Added - Docstrings for mkdocs deployment +- Changed - Dataset for didactic notebooks +- Changed - Revert Equipment -> Device ## 0.1.0 - 2022-05-10 - Added - Process.py script diff --git a/workflow_deeplabcut/pipeline.py b/workflow_deeplabcut/pipeline.py index 73dab71..89cb2cd 100644 --- a/workflow_deeplabcut/pipeline.py +++ b/workflow_deeplabcut/pipeline.py @@ -47,6 +47,11 @@ class Device(dj.Lookup): The primary key is also used to generate inferred output directories when running pose estimation inference. Refer to the `definition` attribute for the table design. + + Attributes: + device ( varchar(32) ): Device short name. + modality ( varchar(64) ): Modality for which this device is used. + description ( varchar(256) ): Optional. Description of device. """ definition = """ From 3eec2720dfeb206e129b52e8c11f5c0f19fcf230 Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Thu, 27 Oct 2022 13:57:54 -0500 Subject: [PATCH 067/176] Update CHANGELOG.md Co-authored-by: Kabilar Gunalan --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 12565be..438421f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,7 +2,7 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) convention. -## 0.1.1 - Unreleased +## [0.1.1] - 2022-10-27 - Added - Integration tests - Added - Docstrings for mkdocs deployment - Changed - Dataset for didactic notebooks From 2913c8642bc70c641dadd1d2a3e0d7b3e444ce70 Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Thu, 27 Oct 2022 13:58:02 -0500 Subject: [PATCH 068/176] Update README.md Co-authored-by: Kabilar Gunalan --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index c65a1e0..de78e3e 100644 --- a/README.md +++ b/README.md @@ -7,8 +7,7 @@ and automate data collection and analysis for neuroscience experiments. Each El a modular pipeline for data storage and processing with corresponding database tables that can be combined with other Elements to assemble a fully functional pipeline. -Installation and usage instructions, including those featured in this repository, -can be found at the +Installation and usage instructions can be found at the [Element documentation](datajoint.com/docs/elements/element-deeplabcut). ![element-deeplabcut diagram](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/diagram_dlc.svg) From a836e0abf2c1cab9a832afc1a7a1586cb8f2579e Mon Sep 17 00:00:00 2001 From: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> Date: Thu, 27 Oct 2022 14:01:09 -0500 Subject: [PATCH 069/176] Update CHANGELOG.md --- CHANGELOG.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 438421f..1f830eb 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -19,3 +19,5 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and - Added - Draft integration tests - Added - Add example data download instructions - Added - Added Notebooks to demonstrate use + +[0.1.1]: https://github.com/datajoint/workflow-deeplabcut/releases/tag/0.1.1 From d1dee80467ff71ac9740c0436a5973ad6ecbce57 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Thu, 27 Oct 2022 14:24:08 -0500 Subject: [PATCH 070/176] Add remaining suggestion from #14 --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index de78e3e..90d7aa3 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,11 @@ # DataJoint Workflow - DeepLabCut -This repository demonstrates the functionality of the -DataJoint Element for markerless pose estimation with -[DeepLabCut](https://www.deeplabcut.org/). DataJoint Elements collectively standardize -and automate data collection and analysis for neuroscience experiments. Each Element is -a modular pipeline for data storage and processing with corresponding database -tables that can be combined with other Elements to assemble a fully functional pipeline. +The DataJoint Workflow for DeepLabCut combines multiple DataJoint Elements for +markerless pose estimation with[DeepLabCut](https://www.deeplabcut.org/). DataJoint +Elements collectively standardize and automate data collection and analysis for +neuroscience experiments. Each Element is a modular pipeline for data storage and +processing with corresponding database tables that can be combined with other Elements +to assemble a fully functional pipeline. Installation and usage instructions can be found at the [Element documentation](datajoint.com/docs/elements/element-deeplabcut). From 872d9a0bda2ca0f337a3d151ce28b2cfa798df47 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Thu, 27 Oct 2022 14:24:35 -0500 Subject: [PATCH 071/176] Fix typo --- workflow_deeplabcut/pipeline.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/workflow_deeplabcut/pipeline.py b/workflow_deeplabcut/pipeline.py index 89cb2cd..1ac87c6 100644 --- a/workflow_deeplabcut/pipeline.py +++ b/workflow_deeplabcut/pipeline.py @@ -41,7 +41,7 @@ @lab.schema class Device(dj.Lookup): - """Table for managing lab equiment. + """Table for managing lab equipment. In Element DeepLabCut, this table is referenced by `model.VideoRecording`. The primary key is also used to generate inferred output directories when From 5a3a1e5361115b757a4c886be1328ad99cb4e094 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Thu, 27 Oct 2022 15:07:21 -0500 Subject: [PATCH 072/176] Fixed typo in README --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 90d7aa3..8ee02f0 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,9 @@ # DataJoint Workflow - DeepLabCut The DataJoint Workflow for DeepLabCut combines multiple DataJoint Elements for -markerless pose estimation with[DeepLabCut](https://www.deeplabcut.org/). DataJoint +markerless pose estimation with [DeepLabCut](https://www.deeplabcut.org/). DataJoint Elements collectively standardize and automate data collection and analysis for -neuroscience experiments. Each Element is a modular pipeline for data storage and +neuroscience experiments. Each Element is a modular pipeline for data storage and processing with corresponding database tables that can be combined with other Elements to assemble a fully functional pipeline. From 32511f92cb21773e5862d185b5891818ddd96188 Mon Sep 17 00:00:00 2001 From: Tolga Dincer Date: Tue, 29 Nov 2022 09:02:35 -0600 Subject: [PATCH 073/176] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8ee02f0..4923fce 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,6 @@ processing with corresponding database tables that can be combined with other El to assemble a fully functional pipeline. Installation and usage instructions can be found at the -[Element documentation](datajoint.com/docs/elements/element-deeplabcut). +[Element documentation](https://datajoint.com/docs/elements/element-deeplabcut). ![element-deeplabcut diagram](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/diagram_dlc.svg) From 43635d5b06b3827bf26172e7e206c4f41232b8ef Mon Sep 17 00:00:00 2001 From: CBroz1 Date: Mon, 5 Dec 2022 11:29:51 -0600 Subject: [PATCH 074/176] =?UTF-8?q?=F0=9F=90=9B=20Debug=20mkdocs=20noteboo?= =?UTF-8?q?ks=20rendering?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- notebooks/03-Process.ipynb | 336 ++--------------------------- notebooks/py_scripts/03-Process.py | 13 +- 2 files changed, 23 insertions(+), 326 deletions(-) diff --git a/notebooks/03-Process.ipynb b/notebooks/03-Process.ipynb index a5d9569..a7bb68f 100644 --- a/notebooks/03-Process.ipynb +++ b/notebooks/03-Process.ipynb @@ -701,326 +701,25 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Config:\n", - "{'all_joints': [[0], [1], [2]],\n", - " 'all_joints_names': ['head', 'bodycenter', 'tailbase'],\n", - " 'alpha_r': 0.02,\n", - " 'apply_prob': 0.5,\n", - " 'batch_size': 1,\n", - " 'clahe': True,\n", - " 'claheratio': 0.1,\n", - " 'crop_pad': 0,\n", - " 'cropratio': 0.4,\n", - " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_from_top_trackingFeb23/from_top_tracking_DJ95shuffle1.mat',\n", - " 'dataset_type': 'imgaug',\n", - " 'decay_steps': 30000,\n", - " 'deterministic': False,\n", - " 'display_iters': 1000,\n", - " 'edge': False,\n", - " 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]},\n", - " 'fg_fraction': 0.25,\n", - " 'global_scale': 0.8,\n", - " 'histeq': True,\n", - " 'histeqratio': 0.1,\n", - " 'init_weights': '/Volumes/GoogleDrive/My '\n", - " 'Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/models/pretrained/mobilenet_v2_1.0_224.ckpt',\n", - " 'intermediate_supervision': False,\n", - " 'intermediate_supervision_layer': 12,\n", - " 'location_refinement': True,\n", - " 'locref_huber_loss': True,\n", - " 'locref_loss_weight': 0.05,\n", - " 'locref_stdev': 7.2801,\n", - " 'log_dir': 'log',\n", - " 'lr_init': 0.0005,\n", - " 'max_input_size': 1500,\n", - " 'mean_pixel': [123.68, 116.779, 103.939],\n", - " 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_from_top_trackingFeb23/Documentation_data-from_top_tracking_95shuffle1.pickle',\n", - " 'min_input_size': 64,\n", - " 'mirror': False,\n", - " 'multi_stage': False,\n", - " 'multi_step': [[0.005, 10000],\n", - " [0.02, 430000],\n", - " [0.002, 730000],\n", - " [0.001, 1030000]],\n", - " 'net_type': 'mobilenet_v2_1.0',\n", - " 'num_joints': 3,\n", - " 'optimizer': 'sgd',\n", - " 'pairwise_huber_loss': False,\n", - " 'pairwise_predict': False,\n", - " 'partaffinityfield_predict': False,\n", - " 'pos_dist_thresh': 17,\n", - " 'project_path': '/tmp/test_data/from_top_tracking',\n", - " 'regularize': False,\n", - " 'rotation': 25,\n", - " 'rotratio': 0.4,\n", - " 'save_iters': 50000,\n", - " 'scale_jitter_lo': 0.5,\n", - " 'scale_jitter_up': 1.25,\n", - " 'scoremap_dir': 'test',\n", - " 'sharpen': False,\n", - " 'sharpenratio': 0.3,\n", - " 'shuffle': True,\n", - " 'snapshot_prefix': '/tmp/test_data/from_top_tracking/dlc-models/iteration-0/from_top_trackingFeb23-trainset95shuffle1/train/snapshot',\n", - " 'stride': 8.0,\n", - " 'weigh_negatives': False,\n", - " 'weigh_only_present_joints': False,\n", - " 'weigh_part_predictions': False,\n", - " 'weight_decay': 0.0001}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selecting single-animal trainer\n", - "Batch Size is 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n", - " warnings.warn('`layer.apply` is deprecated and '\n", - "2022-07-18 13:46:02.685255: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading ImageNet-pretrained mobilenet_v2_1.0\n", - "Max_iters overwritten as 5\n", - "Training parameter:\n", - "{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': '/tmp/test_data/from_top_tracking/dlc-models/iteration-0/from_top_trackingFeb23-trainset95shuffle1/train/snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'sgd', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 1, 'dataset_type': 'imgaug', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': False, 'pairwise_predict': False, 'all_joints': [[0], [1], [2]], 'all_joints_names': ['head', 'bodycenter', 'tailbase'], 'alpha_r': 0.02, 'apply_prob': 0.5, 'clahe': True, 'claheratio': 0.1, 'cropratio': 0.4, 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_from_top_trackingFeb23/from_top_tracking_DJ95shuffle1.mat', 'decay_steps': 30000, 'display_iters': 1000, 'edge': False, 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]}, 'histeq': True, 'histeqratio': 0.1, 'init_weights': '/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/models/pretrained/mobilenet_v2_1.0_224.ckpt', 'lr_init': 0.0005, 'max_input_size': 1500, 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_from_top_trackingFeb23/Documentation_data-from_top_tracking_95shuffle1.pickle', 'min_input_size': 64, 'multi_stage': False, 'multi_step': [[0.005, 10000], [0.02, 430000], [0.002, 730000], [0.001, 1030000]], 'net_type': 'mobilenet_v2_1.0', 'num_joints': 3, 'pos_dist_thresh': 17, 'project_path': '/tmp/test_data/from_top_tracking', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 50000, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25, 'sharpen': False, 'sharpenratio': 0.3, 'covering': True, 'elastic_transform': True, 'motion_blur': True, 'motion_blur_params': {'k': 7, 'angle': (-90, 90)}}\n", - "Starting training....\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-07-18 13:46:12.505604: W tensorflow/core/kernels/queue_base.cc:277] _0_fifo_queue: Skipping cancelled enqueue attempt with queue not closed\n", - "Exception in thread Thread-6:\n", - "Traceback (most recent call last):\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1380, in _do_call\n", - " return fn(*args)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1363, in _run_fn\n", - " return self._call_tf_sessionrun(options, feed_dict, fetch_list,\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1456, in _call_tf_sessionrun\n", - " return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\n", - "tensorflow.python.framework.errors_impl.CancelledError: Enqueue operation was cancelled\n", - "\t [[{{node fifo_queue_enqueue}}]]\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/threading.py\", line 932, in _bootstrap_inner\n", - " self.run()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/threading.py\", line 870, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 83, in load_and_enqueue\n", - " sess.run(enqueue_op, feed_dict=food)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 970, in run\n", - " result = self._run(None, fetches, feed_dict, options_ptr,\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1193, in _run\n", - " results = self._do_run(handle, final_targets, final_fetches,\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1373, in _do_run\n", - " return self._do_call(_run_fn, feeds, fetches, targets, options,\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1399, in _do_call\n", - " raise type(e)(node_def, op, message) # pylint: disable=no-value-for-parameter\n", - "tensorflow.python.framework.errors_impl.CancelledError: Enqueue operation was cancelled\n", - "\t [[node fifo_queue_enqueue\n", - " (defined at /Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/core/train.py:69)\n", - "]]\n", - "\n", - "Errors may have originated from an input operation.\n", - "Input Source operations connected to node fifo_queue_enqueue:\n", - "In[0] fifo_queue (defined at /Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/core/train.py:68)\t\n", - "In[1] Placeholder (defined at /Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/core/train.py:61)\t\n", - "In[2] Placeholder_1:\t\n", - "In[3] Placeholder_2:\t\n", - "In[4] Placeholder_3:\t\n", - "In[5] Placeholder_4:\n", - "\n", - "Operation defined at: (most recent call last)\n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/runpy.py\", line 194, in _run_module_as_main\n", - ">>> return _run_code(code, main_globals, None,\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/runpy.py\", line 87, in _run_code\n", - ">>> exec(code, run_globals)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel_launcher.py\", line 16, in \n", - ">>> app.launch_new_instance()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/traitlets/config/application.py\", line 846, in launch_instance\n", - ">>> app.start()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelapp.py\", line 668, in start\n", - ">>> self.io_loop.start()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tornado/platform/asyncio.py\", line 199, in start\n", - ">>> self.asyncio_loop.run_forever()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/asyncio/base_events.py\", line 570, in run_forever\n", - ">>> self._run_once()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/asyncio/base_events.py\", line 1859, in _run_once\n", - ">>> handle._run()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/asyncio/events.py\", line 81, in _run\n", - ">>> self._context.run(self._callback, *self._args)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 456, in dispatch_queue\n", - ">>> await self.process_one()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 445, in process_one\n", - ">>> await dispatch(*args)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 352, in dispatch_shell\n", - ">>> await result\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 647, in execute_request\n", - ">>> reply_content = await reply_content\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/ipkernel.py\", line 335, in do_execute\n", - ">>> res = shell.run_cell(code, store_history=store_history, silent=silent)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/zmqshell.py\", line 532, in run_cell\n", - ">>> return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_cell\n", - ">>> result = self._run_cell(\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2947, in _run_cell\n", - ">>> return runner(coro)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n", - ">>> coro.send(None)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3172, in run_cell_async\n", - ">>> has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3364, in run_ast_nodes\n", - ">>> if (await self.run_code(code, result, async_=asy)):\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3444, in run_code\n", - ">>> exec(code_obj, self.user_global_ns, self.user_ns)\n", - ">>> \n", - ">>> File \"/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_49051/2802763993.py\", line 1, in \n", - ">>> train.ModelTraining.populate()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/datajoint/autopopulate.py\", line 229, in populate\n", - ">>> error = self._populate1(key, jobs, **populate_kwargs)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/datajoint/autopopulate.py\", line 281, in _populate1\n", - ">>> make(dict(key), **(make_kwargs or {}))\n", - ">>> \n", - ">>> File \"/Users/cb/Documents/dev/element-deeplabcut/element_deeplabcut/train.py\", line 250, in make\n", - ">>> train_network(dlc_cfg_filepath, **train_network_kwargs)\n", - ">>> \n", - ">>> File \"/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/training.py\", line 207, in train_network\n", - ">>> train(\n", - ">>> \n", - ">>> File \"/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 168, in train\n", - ">>> batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n", - ">>> \n", - ">>> File \"/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 69, in setup_preloading\n", - ">>> enqueue_op = q.enqueue(placeholders_list)\n", - ">>> \n", - "\n", - "Original stack trace for 'fifo_queue_enqueue':\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/runpy.py\", line 194, in _run_module_as_main\n", - " return _run_code(code, main_globals, None,\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/runpy.py\", line 87, in _run_code\n", - " exec(code, run_globals)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel_launcher.py\", line 16, in \n", - " app.launch_new_instance()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/traitlets/config/application.py\", line 846, in launch_instance\n", - " app.start()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelapp.py\", line 668, in start\n", - " self.io_loop.start()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tornado/platform/asyncio.py\", line 199, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/asyncio/base_events.py\", line 570, in run_forever\n", - " self._run_once()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/asyncio/base_events.py\", line 1859, in _run_once\n", - " handle._run()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/asyncio/events.py\", line 81, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 456, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 445, in process_one\n", - " await dispatch(*args)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 352, in dispatch_shell\n", - " await result\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 647, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/ipkernel.py\", line 335, in do_execute\n", - " res = shell.run_cell(code, store_history=store_history, silent=silent)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/zmqshell.py\", line 532, in run_cell\n", - " return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_cell\n", - " result = self._run_cell(\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2947, in _run_cell\n", - " return runner(coro)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3172, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3364, in run_ast_nodes\n", - " if (await self.run_code(code, result, async_=asy)):\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3444, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_49051/2802763993.py\", line 1, in \n", - " train.ModelTraining.populate()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/datajoint/autopopulate.py\", line 229, in populate\n", - " error = self._populate1(key, jobs, **populate_kwargs)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/datajoint/autopopulate.py\", line 281, in _populate1\n", - " make(dict(key), **(make_kwargs or {}))\n", - " File \"/Users/cb/Documents/dev/element-deeplabcut/element_deeplabcut/train.py\", line 250, in make\n", - " train_network(dlc_cfg_filepath, **train_network_kwargs)\n", - " File \"/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/training.py\", line 207, in train_network\n", - " train(\n", - " File \"/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 168, in train\n", - " batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n", - " File \"/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 69, in setup_preloading\n", - " enqueue_op = q.enqueue(placeholders_list)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/ops/data_flow_ops.py\", line 350, in enqueue\n", - " return gen_data_flow_ops.queue_enqueue_v2(\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/ops/gen_data_flow_ops.py\", line 4063, in queue_enqueue_v2\n", - " _, _, _op, _outputs = _op_def_library._apply_op_helper(\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py\", line 744, in _apply_op_helper\n", - " op = g._create_op_internal(op_type_name, inputs, dtypes=None,\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 3697, in _create_op_internal\n", - " ret = Operation(\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 2101, in __init__\n", - " self._traceback = tf_stack.extract_stack_for_node(self._c_op)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The network is now trained and ready to evaluate. Use the function 'evaluate_network' to evaluate the network.\n" - ] - } - ], + "outputs": [], "source": [ "train.ModelTraining.populate()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Output cleared for brevity)\n", + "```\n", + "The network is now trained and ready to evaluate. Use the function 'evaluate_network' to evaluate the network.\n", + "```" + ] + }, { "cell_type": "code", "execution_count": 24, @@ -2444,11 +2143,6 @@ "source": [ "In the [next notebook](./04-Automate_Optional.ipynb), we'll look at additional tools in the workflow for automating these steps." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { @@ -2456,7 +2150,7 @@ "formats": "ipynb,py:percent" }, "kernelspec": { - "display_name": "Python 3.8.11 ('ele')", + "display_name": "Python 3.9.13 ('ele')", "language": "python", "name": "python3" }, @@ -2470,11 +2164,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.11" + "version": "3.9.13" }, "vscode": { "interpreter": { - "hash": "61456c693db5d9aa6731701ec9a9b08ab88a172bee0780139a3679beb166da16" + "hash": "d00c4ad21a7027bf1726d6ae3a9a6ef39c8838928eca5a3d5f51f3eb68720410" } } }, diff --git a/notebooks/py_scripts/03-Process.py b/notebooks/py_scripts/03-Process.py index 2f5a363..6eeb4ce 100644 --- a/notebooks/py_scripts/03-Process.py +++ b/notebooks/py_scripts/03-Process.py @@ -6,9 +6,9 @@ # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.13.7 +# jupytext_version: 1.14.1 # kernelspec: -# display_name: Python 3.8.11 ('ele') +# display_name: Python 3.9.13 ('ele') # language: python # name: python3 # --- @@ -154,6 +154,12 @@ # %% tags=[] train.ModelTraining.populate() +# %% [markdown] +# (Output cleared for brevity) +# ``` +# The network is now trained and ready to evaluate. Use the function 'evaluate_network' to evaluate the network. +# ``` + # %% train.ModelTraining() @@ -269,6 +275,3 @@ # %% [markdown] # In the [next notebook](./04-Automate_Optional.ipynb), we'll look at additional tools in the workflow for automating these steps. - -# %% [markdown] -# From 3a6a635f1f21b579b32ece2b345c1002bdbb4ffc Mon Sep 17 00:00:00 2001 From: CBroz1 Date: Mon, 5 Dec 2022 11:35:41 -0600 Subject: [PATCH 075/176] =?UTF-8?q?=F0=9F=90=9B=20Debug=20mkdocs=20noteboo?= =?UTF-8?q?ks=20rendering=202?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- notebooks/04-Automate_Optional.ipynb | 493 +------------------ notebooks/py_scripts/04-Automate_Optional.py | 2 +- 2 files changed, 3 insertions(+), 492 deletions(-) diff --git a/notebooks/04-Automate_Optional.ipynb b/notebooks/04-Automate_Optional.ipynb index c0c9399..c2c879d 100644 --- a/notebooks/04-Automate_Optional.ipynb +++ b/notebooks/04-Automate_Optional.ipynb @@ -184,498 +184,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "---- Populating __model_training ----\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ModelTraining: 0%| | 0/1 [00:00>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/runpy.py\", line 194, in _run_module_as_main\n", - ">>> return _run_code(code, main_globals, None,\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/runpy.py\", line 87, in _run_code\n", - ">>> exec(code, run_globals)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel_launcher.py\", line 16, in \n", - ">>> app.launch_new_instance()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/traitlets/config/application.py\", line 846, in launch_instance\n", - ">>> app.start()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelapp.py\", line 668, in start\n", - ">>> self.io_loop.start()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tornado/platform/asyncio.py\", line 199, in start\n", - ">>> self.asyncio_loop.run_forever()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/asyncio/base_events.py\", line 570, in run_forever\n", - ">>> self._run_once()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/asyncio/base_events.py\", line 1859, in _run_once\n", - ">>> handle._run()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/asyncio/events.py\", line 81, in _run\n", - ">>> self._context.run(self._callback, *self._args)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 456, in dispatch_queue\n", - ">>> await self.process_one()\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 445, in process_one\n", - ">>> await dispatch(*args)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 352, in dispatch_shell\n", - ">>> await result\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 647, in execute_request\n", - ">>> reply_content = await reply_content\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/ipkernel.py\", line 335, in do_execute\n", - ">>> res = shell.run_cell(code, store_history=store_history, silent=silent)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/zmqshell.py\", line 532, in run_cell\n", - ">>> return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_cell\n", - ">>> result = self._run_cell(\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2947, in _run_cell\n", - ">>> return runner(coro)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n", - ">>> coro.send(None)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3172, in run_cell_async\n", - ">>> has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3364, in run_ast_nodes\n", - ">>> if (await self.run_code(code, result, async_=asy)):\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3444, in run_code\n", - ">>> exec(code_obj, self.user_global_ns, self.user_ns)\n", - ">>> \n", - ">>> File \"/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_35367/1370140438.py\", line 4, in \n", - ">>> process.run(verbose=True, display_progress=True)\n", - ">>> \n", - ">>> File \"/Users/cb/Documents/dev/workflow-deeplabcut/workflow_deeplabcut/process.py\", line 43, in run\n", - ">>> table.populate(**populate_settings)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/datajoint/autopopulate.py\", line 229, in populate\n", - ">>> error = self._populate1(key, jobs, **populate_kwargs)\n", - ">>> \n", - ">>> File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/datajoint/autopopulate.py\", line 281, in _populate1\n", - ">>> make(dict(key), **(make_kwargs or {}))\n", - ">>> \n", - ">>> File \"/Users/cb/Documents/dev/element-deeplabcut/element_deeplabcut/train.py\", line 250, in make\n", - ">>> train_network(dlc_cfg_filepath, **train_network_kwargs)\n", - ">>> \n", - ">>> File \"/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/training.py\", line 207, in train_network\n", - ">>> train(\n", - ">>> \n", - ">>> File \"/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 168, in train\n", - ">>> batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n", - ">>> \n", - ">>> File \"/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 69, in setup_preloading\n", - ">>> enqueue_op = q.enqueue(placeholders_list)\n", - ">>> \n", - "\n", - "Original stack trace for 'fifo_queue_enqueue':\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/runpy.py\", line 194, in _run_module_as_main\n", - " return _run_code(code, main_globals, None,\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/runpy.py\", line 87, in _run_code\n", - " exec(code, run_globals)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel_launcher.py\", line 16, in \n", - " app.launch_new_instance()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/traitlets/config/application.py\", line 846, in launch_instance\n", - " app.start()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelapp.py\", line 668, in start\n", - " self.io_loop.start()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tornado/platform/asyncio.py\", line 199, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/asyncio/base_events.py\", line 570, in run_forever\n", - " self._run_once()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/asyncio/base_events.py\", line 1859, in _run_once\n", - " handle._run()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/asyncio/events.py\", line 81, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 456, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 445, in process_one\n", - " await dispatch(*args)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 352, in dispatch_shell\n", - " await result\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/kernelbase.py\", line 647, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/ipkernel.py\", line 335, in do_execute\n", - " res = shell.run_cell(code, store_history=store_history, silent=silent)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/ipykernel/zmqshell.py\", line 532, in run_cell\n", - " return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_cell\n", - " result = self._run_cell(\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2947, in _run_cell\n", - " return runner(coro)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3172, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3364, in run_ast_nodes\n", - " if (await self.run_code(code, result, async_=asy)):\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3444, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_35367/1370140438.py\", line 4, in \n", - " process.run(verbose=True, display_progress=True)\n", - " File \"/Users/cb/Documents/dev/workflow-deeplabcut/workflow_deeplabcut/process.py\", line 43, in run\n", - " table.populate(**populate_settings)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/datajoint/autopopulate.py\", line 229, in populate\n", - " error = self._populate1(key, jobs, **populate_kwargs)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/datajoint/autopopulate.py\", line 281, in _populate1\n", - " make(dict(key), **(make_kwargs or {}))\n", - " File \"/Users/cb/Documents/dev/element-deeplabcut/element_deeplabcut/train.py\", line 250, in make\n", - " train_network(dlc_cfg_filepath, **train_network_kwargs)\n", - " File \"/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/training.py\", line 207, in train_network\n", - " train(\n", - " File \"/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 168, in train\n", - " batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n", - " File \"/Volumes/GoogleDrive/My Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 69, in setup_preloading\n", - " enqueue_op = q.enqueue(placeholders_list)\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/ops/data_flow_ops.py\", line 350, in enqueue\n", - " return gen_data_flow_ops.queue_enqueue_v2(\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/ops/gen_data_flow_ops.py\", line 4063, in queue_enqueue_v2\n", - " _, _, _op, _outputs = _op_def_library._apply_op_helper(\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py\", line 744, in _apply_op_helper\n", - " op = g._create_op_internal(op_type_name, inputs, dtypes=None,\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 3697, in _create_op_internal\n", - " ret = Operation(\n", - " File \"/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 2101, in __init__\n", - " self._traceback = tf_stack.extract_stack_for_node(self._c_op)\n", - "\n", - "ModelTraining: 100%|██████████| 1/1 [00:29<00:00, 29.53s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The network is now trained and ready to evaluate. Use the function 'evaluate_network' to evaluate the network.\n", - "\n", - "---- Populating _recording_info ----\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "RecordingInfo: 100%|██████████| 1/1 [00:00<00:00, 4.74it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "---- Populating __model_evaluation ----\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ModelEvaluation: 0it [00:00, ?it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "---- Populating __pose_estimation ----\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PoseEstimation: 0it [00:00, ?it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", - " \n", - "
    \n", - "

    px_height

    \n", - " height in pixels\n", - "
    \n", - "

    px_width

    \n", - " width in pixels\n", - "
    \n", - "

    nframes

    \n", - " number of frames\n", - "
    \n", - "

    fps

    \n", - " (Hz) frames per second\n", - "
    \n", - "

    recording_datetime

    \n", - " Datetime for the start of the recording\n", - "
    \n", - "

    recording_duration

    \n", - " video duration (s) from nframes / fps\n", - "
    subject62021-06-02 14:04:22150050012360None2.05
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet *recording_id px_height px_width nframes fps recording_date recording_dura\n", - "+----------+ +------------+ +------------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n", - "subject6 2021-06-02 14: 1 500 500 123 60 None 2.05 \n", - " (Total: 1)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "key={'paramset_idx':0,'training_id':0,'video_set_id':0, \n", " 'project_path':'from_top_tracking/'}\n", diff --git a/notebooks/py_scripts/04-Automate_Optional.py b/notebooks/py_scripts/04-Automate_Optional.py index 39ee04b..07f7817 100644 --- a/notebooks/py_scripts/04-Automate_Optional.py +++ b/notebooks/py_scripts/04-Automate_Optional.py @@ -6,7 +6,7 @@ # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.13.7 +# jupytext_version: 1.14.1 # kernelspec: # display_name: Python 3.8.11 ('ele') # language: python From 415145c1b00183623a8e1ac1766f0e05b9702f3f Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Fri, 10 Mar 2023 15:13:19 -0600 Subject: [PATCH 076/176] Update requirements --- CHANGELOG.md | 32 ++++++++++++++++++++------------ requirements.txt | 12 ++++++------ workflow_deeplabcut/version.py | 2 +- 3 files changed, 27 insertions(+), 19 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 1f830eb..0daa4ac 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,22 +2,30 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) convention. +## [0.2.0] - 2023-03-10 + +- Update - Requirements for released packages + ## [0.1.1] - 2022-10-27 -- Added - Integration tests -- Added - Docstrings for mkdocs deployment -- Changed - Dataset for didactic notebooks -- Changed - Revert Equipment -> Device + +- Add - Integration tests +- Add - Docstrings for mkdocs deployment +- Update - Dataset for didactic notebooks +- Update - Revert Equipment -> Device ## 0.1.0 - 2022-05-10 -- Added - Process.py script -- Added - Adopted black formatting into code base -- Changed - Device -> Equipment + +- Add - Process.py script +- Add - Adopted black formatting into code base +- Update - Device -> Equipment ## 0.0.0a0 - 2021-12-15 -- Added - First draft begins, reflecting precursor pipelines -- Added - Added Docker files -- Added - Draft integration tests -- Added - Add example data download instructions -- Added - Added Notebooks to demonstrate use +- Add - First draft begins, reflecting precursor pipelines +- Add - Added Docker files +- Add - Draft integration tests +- Add - Add example data download instructions +- Add - Added Notebooks to demonstrate use + +[0.2.0]: https://github.com/datajoint/workflow-deeplabcut/releases/tag/0.2.0 [0.1.1]: https://github.com/datajoint/workflow-deeplabcut/releases/tag/0.1.1 diff --git a/requirements.txt b/requirements.txt index 3436837..5ad5bc8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,8 +1,8 @@ datajoint>=0.13.0 -element-lab>=0.1.0b0 -element-animal>=0.1.0b0 -element-session>=0.1.0b0 -element-deeplabcut>=0.0.0a0 -element-interface @ git+https://github.com/datajoint/element-interface.git +element-lab>=0.2.0 +element-animal>=0.1.5 +element-session>=0.1.2 +element-deeplabcut>=0.2.2 +element-interface>=0.5.0 ipykernel>=6.0.1 -pygit2 +pygit2 \ No newline at end of file diff --git a/workflow_deeplabcut/version.py b/workflow_deeplabcut/version.py index 4c135c9..0822c47 100644 --- a/workflow_deeplabcut/version.py +++ b/workflow_deeplabcut/version.py @@ -2,4 +2,4 @@ Package metadata Update the Docker image tag in `docker-compose.yaml` to match """ -__version__ = "0.1.1" +__version__ = "0.2.0" From f9d56ebb19c6e1b93911a7416aa23bdeaa80e3ee Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Fri, 10 Mar 2023 15:15:52 -0600 Subject: [PATCH 077/176] Update changelog --- CHANGELOG.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 0daa4ac..d762cf4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -22,10 +22,10 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and ## 0.0.0a0 - 2021-12-15 - Add - First draft begins, reflecting precursor pipelines -- Add - Added Docker files +- Add - Docker files - Add - Draft integration tests -- Add - Add example data download instructions -- Add - Added Notebooks to demonstrate use +- Add - Example data download instructions +- Add - Notebooks to demonstrate use [0.2.0]: https://github.com/datajoint/workflow-deeplabcut/releases/tag/0.2.0 [0.1.1]: https://github.com/datajoint/workflow-deeplabcut/releases/tag/0.1.1 From 836a4e2b9fba7d05565eeecbe338347961590bb8 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Tue, 14 Mar 2023 13:55:45 -0500 Subject: [PATCH 078/176] Update command --- docker/docker-compose-test.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docker/docker-compose-test.yaml b/docker/docker-compose-test.yaml index f724d86..5fcd41e 100644 --- a/docker/docker-compose-test.yaml +++ b/docker/docker-compose-test.yaml @@ -6,7 +6,7 @@ # TEST_CMD="pytest" # pytest --dj-{verbose,teardown} False # options # # to do nothing, set as "True" # export COMPOSE_DOCKER_CLI_BUILD=0 # some machines need for smooth --build -# docker compose --env-file ./docker/.env -f ./docker/docker-compose-test.yaml up --build --force-recreate --detached +# docker compose --env-file ./docker/.env -f ./docker/docker-compose-test.yaml up --build --force-recreate --detach # docker exec -it workflow-deeplabcut /bin/bash # docker compose -f ./docker/docker-compose-test.yaml down --volumes From e1d82b247f6421bb9703dba62288b2cbba5043d1 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Tue, 14 Mar 2023 13:56:11 -0500 Subject: [PATCH 079/176] Remove element-interface installation from source --- docker/Dockerfile.test | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docker/Dockerfile.test b/docker/Dockerfile.test index ad4b722..1fa14d7 100644 --- a/docker/Dockerfile.test +++ b/docker/Dockerfile.test @@ -8,8 +8,7 @@ RUN /entrypoint.sh echo "Installed dependencies." WORKDIR /main/workflow-deeplabcut -# Always get interface/djarchive -RUN pip install --no-deps "element-interface@git+https://github.com/datajoint/element-interface" +# Always get djarchive RUN pip install --no-deps "djarchive-client@git+https://github.com/datajoint/djarchive-client" # Always move local - conditional install in setup.sh From a6b2c9fc9bca604f02adda7ae3a40fdfa3926a44 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Sun, 23 Apr 2023 22:28:21 -0500 Subject: [PATCH 080/176] Add GitHub Actions workflows --- .../u24_workflow_before_release.yaml | 18 +++++++++++++++++ .../workflows/u24_workflow_release_call.yaml | 20 +++++++++++++++++++ .../u24_workflow_tag_to_release.yaml | 15 ++++++++++++++ CHANGELOG.md | 3 ++- 4 files changed, 55 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/u24_workflow_before_release.yaml create mode 100644 .github/workflows/u24_workflow_release_call.yaml create mode 100644 .github/workflows/u24_workflow_tag_to_release.yaml diff --git a/.github/workflows/u24_workflow_before_release.yaml b/.github/workflows/u24_workflow_before_release.yaml new file mode 100644 index 0000000..28a5ff5 --- /dev/null +++ b/.github/workflows/u24_workflow_before_release.yaml @@ -0,0 +1,18 @@ +name: u24_workflow_before_release_0.0.1 +on: + pull_request: + push: + branches: + - '**' + tags-ignore: + - '**' + workflow_dispatch: +jobs: + call_context_check: + uses: dj-sciops/djsciops-cicd/.github/workflows/context_check.yaml@main + call_u24_workflow_build_debian: + uses: dj-sciops/djsciops-cicd/.github/workflows/u24_workflow_build.yaml@main + with: + jhub_ver: 1.4.2 + py_ver: 3.9 + dist: debian diff --git a/.github/workflows/u24_workflow_release_call.yaml b/.github/workflows/u24_workflow_release_call.yaml new file mode 100644 index 0000000..8196673 --- /dev/null +++ b/.github/workflows/u24_workflow_release_call.yaml @@ -0,0 +1,20 @@ +name: u24_workflow_release_call_0.0.1 +on: + workflow_run: + workflows: ["u24_workflow_tag_to_release_0.0.1"] + types: + - completed +jobs: + call_context_check: + uses: dj-sciops/djsciops-cicd/.github/workflows/context_check.yaml@main + call_u24_workflow_release_debian: + if: >- + github.event.workflow_run.conclusion == 'success' && github.repository_owner == 'datajoint' + uses: dj-sciops/djsciops-cicd/.github/workflows/u24_workflow_release.yaml@main + with: + jhub_ver: 1.4.2 + py_ver: 3.9 + dist: debian + secrets: + REGISTRY_USERNAME: ${{secrets.DOCKER_USERNAME}} + REGISTRY_PASSWORD: ${{secrets.DOCKER_PASSWORD}} diff --git a/.github/workflows/u24_workflow_tag_to_release.yaml b/.github/workflows/u24_workflow_tag_to_release.yaml new file mode 100644 index 0000000..3a6ce58 --- /dev/null +++ b/.github/workflows/u24_workflow_tag_to_release.yaml @@ -0,0 +1,15 @@ +name: u24_workflow_tag_to_release_0.0.1 +on: + push: + tags: + - '*.*.*' + - 'test*.*.*' +jobs: + call_context_check: + uses: dj-sciops/djsciops-cicd/.github/workflows/context_check.yaml@main + call_u24_workflow_build_debian: + uses: dj-sciops/djsciops-cicd/.github/workflows/u24_workflow_build.yaml@main + with: + jhub_ver: 1.4.2 + py_ver: 3.9 + dist: debian diff --git a/CHANGELOG.md b/CHANGELOG.md index d762cf4..8f51ac2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,9 +2,10 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) convention. -## [0.2.0] - 2023-03-10 +## [0.2.0] - 2023-04-23 - Update - Requirements for released packages +- Add - GitHub Actions workflows ## [0.1.1] - 2022-10-27 From 11f6773d4ba8a3e90ec9c913d45bc14aefa497bd Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Sun, 23 Apr 2023 22:29:36 -0500 Subject: [PATCH 081/176] Update changelog --- CHANGELOG.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 8f51ac2..8a52d87 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,7 +7,7 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and - Update - Requirements for released packages - Add - GitHub Actions workflows -## [0.1.1] - 2022-10-27 +## 0.1.1 - 2022-10-27 - Add - Integration tests - Add - Docstrings for mkdocs deployment @@ -29,4 +29,3 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and - Add - Notebooks to demonstrate use [0.2.0]: https://github.com/datajoint/workflow-deeplabcut/releases/tag/0.2.0 -[0.1.1]: https://github.com/datajoint/workflow-deeplabcut/releases/tag/0.1.1 From b5661841e836605be99adce91dcc57ae8138026f Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Wed, 2 Aug 2023 17:33:36 -0500 Subject: [PATCH 082/176] paths,pipeline moved; train and dlc_reader modif --- element_deeplabcut/paths.py | 24 ++++++++ element_deeplabcut/pipeline.py | 73 ++++++++++++++++++++++++ element_deeplabcut/readers/dlc_reader.py | 8 +-- element_deeplabcut/train.py | 22 ++++++- 4 files changed, 120 insertions(+), 7 deletions(-) create mode 100644 element_deeplabcut/paths.py create mode 100644 element_deeplabcut/pipeline.py diff --git a/element_deeplabcut/paths.py b/element_deeplabcut/paths.py new file mode 100644 index 0000000..b42b264 --- /dev/null +++ b/element_deeplabcut/paths.py @@ -0,0 +1,24 @@ +import datajoint as dj +from collections import abc + + +def get_dlc_root_data_dir() -> list: + """Returns a list of root directories for Element DeepLabCut""" + dlc_root_dirs = dj.config.get("custom", {}).get("dlc_root_data_dir") + if not dlc_root_dirs: + return None + elif not isinstance(dlc_root_dirs, abc.Sequence): + return list(dlc_root_dirs) + else: + return dlc_root_dirs + + +def get_dlc_processed_data_dir() -> str: + """Returns an output directory relative to custom 'dlc_output_dir' root""" + from pathlib import Path + + dlc_output_dir = dj.config.get("custom", {}).get("dlc_output_dir") + if dlc_output_dir: + return Path(dlc_output_dir) + else: + return None diff --git a/element_deeplabcut/pipeline.py b/element_deeplabcut/pipeline.py new file mode 100644 index 0000000..148cb70 --- /dev/null +++ b/element_deeplabcut/pipeline.py @@ -0,0 +1,73 @@ +import datajoint as dj +from element_lab import lab +from element_animal import subject +from element_session import session_with_datetime as session +from element_deeplabcut import train, model + +from element_animal.subject import Subject +from element_lab.lab import Source, Lab, Protocol, User, Project + +from paths import get_dlc_root_data_dir, get_dlc_processed_data_dir + +__all__ = [ + "get_dlc_root_data_dir", + "get_dlc_processed_data_dir", + "Subject", + "Source", + "Lab", + "Protocol", + "User", + "Project", + "Session", +] + +if "custom" not in dj.config: + dj.config["custom"] = {} + +db_prefix = dj.config["custom"].get("database.prefix", "") + +# Activate "lab", "subject", "session" schema ------------- + +lab.activate(db_prefix + "lab") + +subject.activate(db_prefix + "subject", linking_module=__name__) + +Experimenter = lab.User +Session = session.Session +session.activate(db_prefix + "session", linking_module=__name__) + +# Activate equipment table ------------------------------------ + + +@lab.schema +class Device(dj.Lookup): + """Table for managing lab equipment. + + In Element DeepLabCut, this table is referenced by `model.VideoRecording`. + The primary key is also used to generate inferred output directories when + running pose estimation inference. Refer to the `definition` attribute + for the table design. + + Attributes: + device ( varchar(32) ): Device short name. + modality ( varchar(64) ): Modality for which this device is used. + description ( varchar(256) ): Optional. Description of device. + """ + + definition = """ + device : varchar(32) + --- + modality : varchar(64) + description=null : varchar(256) + """ + contents = [ + ["Camera1", "Pose Estimation", "Panasonic HC-V380K"], + ["Camera2", "Pose Estimation", "Panasonic HC-V770K"], + ] + + +# Activate DeepLabCut schema ----------------------------------- + + +train.activate(db_prefix + "train", linking_module=__name__) +model.activate(db_prefix + "model", linking_module=__name__) diff --git a/element_deeplabcut/readers/dlc_reader.py b/element_deeplabcut/readers/dlc_reader.py index a7f6a32..321003c 100644 --- a/element_deeplabcut/readers/dlc_reader.py +++ b/element_deeplabcut/readers/dlc_reader.py @@ -191,8 +191,8 @@ def reformat_rawdata(self): return body_parts_position -def read_yaml(fullpath: str, filename: str = "*") -> tuple: - """Return contents of yml in fullpath. If available, defer to DJ-saved version +def read_yaml(fullpath: str, filename: str = "dj_dlc_config") -> tuple: + """Return contents of yaml in fullpath. If available, defer to DJ-saved version Args: fullpath (str): String or pathlib path. Directory with yaml files @@ -204,7 +204,7 @@ def read_yaml(fullpath: str, filename: str = "*") -> tuple: from deeplabcut.utils.auxiliaryfunctions import read_config # Take the DJ-saved if there. If not, return list of available - yml_paths = list(Path(fullpath).glob("dj_dlc_config.yaml")) or sorted( + yml_paths = list(Path(fullpath).glob(f"{filename}.y*ml")) or sorted( list(Path(fullpath).glob(f"{filename}.y*ml")) ) @@ -225,7 +225,7 @@ def save_yaml( Args: output_dir (str): where to save yaml file - config_dict (str): dict of config params or element-deeplabcut model.Model dict + config_dict (dict): dict of config params or element-deeplabcut model.Model dict filename (str, optional): default 'dj_dlc_config' or preserve original 'config' Set to 'config' to overwrite original file. If extension is included, removed and replaced with "yaml". diff --git a/element_deeplabcut/train.py b/element_deeplabcut/train.py index ff85146..9a25950 100644 --- a/element_deeplabcut/train.py +++ b/element_deeplabcut/train.py @@ -195,7 +195,7 @@ def insert_new_params( if existing_paramset_idx == int(paramset_idx): # If existing_idx same: return # job done else: - cls.insert1(param_dict) # if duplicate, will raise duplicate error + cls.insert1(param_dict, skip_duplicates=True) # if duplicate, will raise duplicate error @schema @@ -248,6 +248,8 @@ def make(self, key): from deeplabcut.utils.auxiliaryfunctions import ( GetModelFolder as get_model_folder ) # isort:skip + + from deeplabcut.utils.auxiliaryfunctions import edit_config """Launch training for each train.TrainingTask training_id via `.populate()`.""" project_path, model_prefix = (TrainingTask & key).fetch1( @@ -257,7 +259,7 @@ def make(self, key): project_path = find_full_path(get_dlc_root_data_dir(), project_path) # ---- Build and save DLC configuration (yaml) file ---- - _, dlc_config = dlc_reader.read_yaml(project_path) # load existing + _, dlc_config = dlc_reader.read_yaml(project_path, "config") # load existing dlc_config.update((TrainingParamSet & key).fetch1("params")) dlc_config.update( { @@ -273,7 +275,21 @@ def make(self, key): } ) # Write dlc config file to base project folder - dlc_cfg_filepath = dlc_reader.save_yaml(project_path, dlc_config) + dlc_cfg_filepath = dlc_reader.save_yaml(project_path, dlc_config, "dj_dlc_config",mkdir=False) + + + # ---- Update the project path in the DLC pose configuration (yaml) files ---- + pose_cfg_path = list(project_path.rglob('train'))[0] + #pose_cfg_path_rel = + edits ={ + "project_path": project_path.as_posix() + } + pose_cfg = edit_config(pose_cfg_path / 'pose_cfg.yaml', edits, "dj_pose_cfg") + + # Write pose cfg file to base project folder + pose_cfg_filepath = dlc_reader.save_yaml(pose_cfg_path, pose_cfg, "pose_cfg",mkdir=False) + +################# # ---- Trigger DLC model training job ---- train_network_input_args = list(inspect.signature(train_network).parameters) From 4afc06385d47af7d23c76d9761685007c17d0ad3 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 3 Aug 2023 11:48:00 -0500 Subject: [PATCH 083/176] [WIP] Update project path in pose config --- element_deeplabcut/train.py | 59 ++++++++++++++++++------------------- 1 file changed, 28 insertions(+), 31 deletions(-) diff --git a/element_deeplabcut/train.py b/element_deeplabcut/train.py index 9a25950..ff916b0 100644 --- a/element_deeplabcut/train.py +++ b/element_deeplabcut/train.py @@ -195,7 +195,9 @@ def insert_new_params( if existing_paramset_idx == int(paramset_idx): # If existing_idx same: return # job done else: - cls.insert1(param_dict, skip_duplicates=True) # if duplicate, will raise duplicate error + cls.insert1( + param_dict, skip_duplicates=True + ) # if duplicate, will raise duplicate error @schema @@ -241,14 +243,17 @@ class ModelTraining(dj.Computed): # https://github.com/DeepLabCut/DeepLabCut/issues/70 def make(self, key): - from deeplabcut import train_network # isort:skip + from deeplabcut import train_network # isort:skip + try: - from deeplabcut.utils.auxiliaryfunctions import get_model_folder # isort:skip + from deeplabcut.utils.auxiliaryfunctions import ( + get_model_folder, + ) # isort:skip except ImportError: from deeplabcut.utils.auxiliaryfunctions import ( - GetModelFolder as get_model_folder - ) # isort:skip - + GetModelFolder as get_model_folder, + ) # isort:skip + from deeplabcut.utils.auxiliaryfunctions import edit_config """Launch training for each train.TrainingTask training_id via `.populate()`.""" @@ -275,27 +280,30 @@ def make(self, key): } ) # Write dlc config file to base project folder - dlc_cfg_filepath = dlc_reader.save_yaml(project_path, dlc_config, "dj_dlc_config",mkdir=False) - + dlc_cfg_filepath = dlc_reader.save_yaml(project_path, dlc_config) # ---- Update the project path in the DLC pose configuration (yaml) files ---- - pose_cfg_path = list(project_path.rglob('train'))[0] - #pose_cfg_path_rel = - edits ={ - "project_path": project_path.as_posix() - } - pose_cfg = edit_config(pose_cfg_path / 'pose_cfg.yaml', edits, "dj_pose_cfg") + model_folder = get_model_folder( + trainFraction=dlc_config["train_fraction"], + shuffle=dlc_config["shuffle"], + cfg=dlc_config, + modelprefix=dlc_config["modelprefix"], + ) + model_train_folder = project_path / model_folder / "train" - # Write pose cfg file to base project folder - pose_cfg_filepath = dlc_reader.save_yaml(pose_cfg_path, pose_cfg, "pose_cfg",mkdir=False) + pose_cfg = edit_config( + model_train_folder / "pose_cfg.yaml", + {"project_path": project_path.as_posix()}, + ) -################# + ################# # ---- Trigger DLC model training job ---- train_network_input_args = list(inspect.signature(train_network).parameters) train_network_kwargs = { - k: int(v) if k in ("shuffle", "trainingsetindex", "maxiters") else v - for k, v in dlc_config.items() if k in train_network_input_args + k: int(v) if k in ("shuffle", "trainingsetindex", "maxiters") else v + for k, v in dlc_config.items() + if k in train_network_input_args } for k in ["shuffle", "trainingsetindex", "maxiters"]: train_network_kwargs[k] = int(train_network_kwargs[k]) @@ -305,18 +313,7 @@ def make(self, key): except KeyboardInterrupt: # Instructions indicate to train until interrupt print("DLC training stopped via Keyboard Interrupt") - snapshots = list( - ( - project_path - / get_model_folder( - trainFraction=dlc_config["train_fraction"], - shuffle=dlc_config["shuffle"], - cfg=dlc_config, - modelprefix=dlc_config["modelprefix"], - ) - / "train" - ).glob("*index*") - ) + snapshots = list(model_train_folder.glob("*index*")) max_modified_time = 0 # DLC goes by snapshot magnitude when judging 'latest' for evaluation # Here, we mean most recently generated From 0135a381a36d60dde0c7e45a51ce725cbb5beefd Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 3 Aug 2023 11:58:24 -0500 Subject: [PATCH 084/176] Move workflow pipeline files --- {workflow_deeplabcut => notebooks}/ingest.py | 0 {workflow_deeplabcut => notebooks}/load_demo_data.py | 0 {workflow_deeplabcut => notebooks}/paths.py | 0 {workflow_deeplabcut => notebooks}/pipeline.py | 0 {workflow_deeplabcut => notebooks}/process.py | 0 5 files changed, 0 insertions(+), 0 deletions(-) rename {workflow_deeplabcut => notebooks}/ingest.py (100%) rename {workflow_deeplabcut => notebooks}/load_demo_data.py (100%) rename {workflow_deeplabcut => notebooks}/paths.py (100%) rename {workflow_deeplabcut => notebooks}/pipeline.py (100%) rename {workflow_deeplabcut => notebooks}/process.py (100%) diff --git a/workflow_deeplabcut/ingest.py b/notebooks/ingest.py similarity index 100% rename from workflow_deeplabcut/ingest.py rename to notebooks/ingest.py diff --git a/workflow_deeplabcut/load_demo_data.py b/notebooks/load_demo_data.py similarity index 100% rename from workflow_deeplabcut/load_demo_data.py rename to notebooks/load_demo_data.py diff --git a/workflow_deeplabcut/paths.py b/notebooks/paths.py similarity index 100% rename from workflow_deeplabcut/paths.py rename to notebooks/paths.py diff --git a/workflow_deeplabcut/pipeline.py b/notebooks/pipeline.py similarity index 100% rename from workflow_deeplabcut/pipeline.py rename to notebooks/pipeline.py diff --git a/workflow_deeplabcut/process.py b/notebooks/process.py similarity index 100% rename from workflow_deeplabcut/process.py rename to notebooks/process.py From 9c123b5b562ded14e5c8a71c6e3ad18b373f742b Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 3 Aug 2023 11:59:24 -0500 Subject: [PATCH 085/176] Remove jupytext scripts and workflow version --- .../py_scripts/00-DataDownload_Optional.py | 111 ---- notebooks/py_scripts/01-Configure.py | 99 --- .../02-WorkflowStructure_Optional.py | 122 ---- notebooks/py_scripts/03-Process.py | 277 --------- notebooks/py_scripts/04-Automate_Optional.py | 143 ----- .../py_scripts/05-Visualization_Optional.py | 136 ----- notebooks/py_scripts/06-Drop_Optional.py | 44 -- notebooks/py_scripts/09-AlternateDataset.py | 578 ------------------ workflow_deeplabcut/__init__.py | 0 workflow_deeplabcut/version.py | 5 - 10 files changed, 1515 deletions(-) delete mode 100644 notebooks/py_scripts/00-DataDownload_Optional.py delete mode 100644 notebooks/py_scripts/01-Configure.py delete mode 100644 notebooks/py_scripts/02-WorkflowStructure_Optional.py delete mode 100644 notebooks/py_scripts/03-Process.py delete mode 100644 notebooks/py_scripts/04-Automate_Optional.py delete mode 100644 notebooks/py_scripts/05-Visualization_Optional.py delete mode 100644 notebooks/py_scripts/06-Drop_Optional.py delete mode 100644 notebooks/py_scripts/09-AlternateDataset.py delete mode 100644 workflow_deeplabcut/__init__.py delete mode 100644 workflow_deeplabcut/version.py diff --git a/notebooks/py_scripts/00-DataDownload_Optional.py b/notebooks/py_scripts/00-DataDownload_Optional.py deleted file mode 100644 index f0a4de9..0000000 --- a/notebooks/py_scripts/00-DataDownload_Optional.py +++ /dev/null @@ -1,111 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.13.7 -# kernelspec: -# display_name: Python 3.8.11 ('ele') -# language: python -# name: python3 -# --- - -# %% [markdown] tags=[] -# # DataJoint U24 - Workflow DeepLabCut - -# %% [markdown] -# ## Download example data - -# %% [markdown] -# These notebooks are built around data provided by DataJoint, including a well-trained model. For similar content using data from DeepLabCut, see [09-AlternateDataset](./09-AlternateDataset.ipynb). -# -# DataJoint provides various datasets via `djarchive`. To pip install... - -# %% vscode={"languageId": "shellscript"} -pip install git+https://github.com/datajoint/djarchive-client.git - -# %% -import os; import djarchive_client -client = djarchive_client.client() - -# %% [markdown] -# We can browse available datasets: - -# %% -list(client.datasets()) - -# %% [markdown] -# Datasets have different versions available: - -# %% -list(client.revisions()) - -# %% [markdown] -# We can make a directory for downloading: - -# %% -os.makedirs('/tmp/test_data', exist_ok=True) - -# %% [markdown] -# Then run download for a given set and the revision: - -# %% -client.download('workflow-dlc-data', - target_directory='/tmp/test_data/', - revision='v1') - -# %% [markdown] -# ## Directory organization -# -# After downloading, the directory will be organized as follows: - -# %% [markdown] -# ``` -# /tmp/test_data/from_top_tracking/ -# - config.yml -# - dlc-models/iteration-0/from_top_trackingFeb23-trainset95shuffle1/ -# - test/pose_cfg.yaml -# - train/ -# - checkpoint -# - checkpoint_orig -# ─ learning_stats.csv -# ─ log.txt -# ─ pose_cfg.yaml -# ─ snapshot-10300.data-00000-of-00001 -# ─ snapshot-10300.index -# ─ snapshot-10300.meta # same for 103000 -# - labeled-data/ -# - train1/ -# - CollectedData_DJ.csv -# - CollectedData_DJ.h5 -# - img00674.png # and others -# - train2/ # similar to above -# - videos/ -# - test.mp4 -# - train1.mp4 -# ``` - -# %% [markdown] -# We will use this dataset as an example across this series of notebooks. If you use another dataset, change the path accordingly. -# -# - `config.yaml` contains key parameters of the project -# - `labeled-data` includes pixel coordinates for each body part -# - `videos` includes the full training and inference videos -# -# This workflow contains additional functions for setting up this demo data, including adding absolute paths to config files and shortening the inference video to speed up pose estimation. - -# %% -from workflow_deeplabcut.load_demo_data import setup_bare_project, shorten_video - -setup_bare_project(project="/tmp/test_data/from_top_tracking", - net_type = "mobilenet_v2_1.0") # sets paths -shorten_video("/tmp/test_data/from_top_tracking/videos/test.mp4", - output_path=None,first_n_sec=2) # makes test-2s.mp4 - -# %% [markdown] -# For your own data, we recommend using the DLC gui to intitialize your project and label the data. -# -# In the next notebook, [01-Configure](./01-Configure.ipynb), we'll set up the DataJoint config file with a pointer to your root data directory. diff --git a/notebooks/py_scripts/01-Configure.py b/notebooks/py_scripts/01-Configure.py deleted file mode 100644 index 73cfbe5..0000000 --- a/notebooks/py_scripts/01-Configure.py +++ /dev/null @@ -1,99 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.13.7 -# kernelspec: -# display_name: Python 3.8.11 ('ele') -# language: python -# name: python3 -# --- - -# %% [markdown] tags=[] -# # DataJoint U24 - Workflow DeepLabCut - -# %% [markdown] tags=[] -# ## Configure DataJoint - -# %% [markdown] tags=[] -# - To run `workflow-deeplabcut`, we need to set up the DataJoint config file, called `dj_local_conf.json`, unique to each machine. -# -# - The config only needs to be set up once. If you already have one, skip to [02-Workflow-Structure](./02-WorkflowStructure_Optional.ipynb). -# -# - By convention, we set a local config in the workflow directory. You may be interested in [setting a global config](https://docs.datajoint.org/python/setup/01-Install-and-Connect.html). - -# %% -import os -# change to the upper level folder to detect dj_local_conf.json -if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') -assert os.path.basename(os.getcwd())=='workflow-deeplabcut', ("Please move to the " - + "workflow directory") - -# %% [markdown] -# ### Configure database host address and credentials - -# %% [markdown] -# Now we can set up credentials following [instructions here](https://tutorials.datajoint.io/setting-up/get-database.html). - -# %% -import datajoint as dj -import getpass -dj.config['database.host'] = '{YOUR_HOST}' -dj.config['database.user'] = '{YOUR_USERNAME}' -dj.config['database.password'] = getpass.getpass() # enter the password securely - -# %% [markdown] -# You should be able to connect to the database at this stage. - -# %% -dj.conn() - -# %% [markdown] -# ### Configure the `custom` field - -# %% [markdown] -# #### Prefix - -# %% [markdown] -# A schema prefix can help manage privelages on a server. Teams who work on the same schemas should use the same prefix -# -# Setting the prefix to `neuro_` means that every schema we then create will start with `neuro_` (e.g. `neuro_lab`, `neuro_subject`, `neuro_model` etc.) - -# %% -dj.config['custom'] = {'database.prefix': 'neuro_'} - -# %% [markdown] -# #### Root directory - -# %% [markdown] -# `dlc_root_data_dir` sets the root path(s) for the Element. Given multiple, the Element will always figure out which root to use based on the files it expects there. This should be the directory above your DeepLabCut project path. - -# %% -dj.config['custom'] = {'dlc_root_data_dir' : ['/tmp/test_data/', '/tmp/example/']} - -# Check the connection with `find_full_path` -from element_interface.utils import find_full_path -data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], - 'from_top_tracking') -assert data_dir.exists(), "Please check the that you have the from_top_tracking folder" - -# %% [markdown] -# ## Save the config as a json -# -# Once set, the config can either be saved locally or globally. -# -# - The local config would be saved as `dj_local_conf.json` in the workflow directory. This is usefull for managing multiple (demo) pipelines. -# - A global config would be saved as `datajoint_config.json` in the home directory. -# -# When imported, DataJoint will first check for a local config. If none, it will check for a global config. - -# %% -dj.config.save_local() -# dj.config.save_global() - -# %% [markdown] -# In the [next notebook](./02-WorkflowStructure_Optional.ipynb) notebook, we'll explore the workflow structure. diff --git a/notebooks/py_scripts/02-WorkflowStructure_Optional.py b/notebooks/py_scripts/02-WorkflowStructure_Optional.py deleted file mode 100644 index dbe928a..0000000 --- a/notebooks/py_scripts/02-WorkflowStructure_Optional.py +++ /dev/null @@ -1,122 +0,0 @@ -# -*- coding: utf-8 -*- -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.13.7 -# kernelspec: -# display_name: Python 3.8.11 ('ele') -# language: python -# name: python3 -# --- - -# %% [markdown] tags=[] -# # DataJoint U24 - Workflow DeepLabCut - -# %% [markdown] -# ## Introduction - -# %% [markdown] -# This notebook introduces some useful DataJoint for exploring pipelines featuring the DeepLabCut Element. -# -# + DataJoint needs to be configured before running this notebook (see [01-Configure](./01-Configure.ipynb)). -# + Those familar with the structure of DataJoint workflows can skip to [03-Process](./03-Process.ipynb). -# + The playground tutorial on [CodeBook](http://codebook.datajoint.io/) provides a more thorough introduction. - -# %% [markdown] -# To load the local config, we move to the package root. - -# %% -import os -if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') -assert os.path.basename(os.getcwd())=='workflow-deeplabcut', ("Please move to the " - + "workflow directory") - -# %% [markdown] -# ## Schemas, Diagrams and Tables - -# %% [markdown] -# Schemas are conceptually related sets of tables. By importing schemas from `workflow_deeplabcut.pipeline`, we'll declare the tables on the server with the prefix in the config (if we have permission to do so). If these tables are already declared, we'll gain access. -# -# - `dj.list_schemas()` lists all schemas a user has access to in the current database -# - `.schema.list_tables()` will provide names for each table in the format used under the hood. - -# %% -import datajoint as dj -from workflow_deeplabcut.pipeline import lab, subject, session, train, model - -dj.list_schemas() - -# %% -train.schema.list_tables() - -# %% [markdown] -# `dj.Diagram()` plots tables and dependencies in a schema. To see additional upstream or downstream connections, add `- N` or `+ N`. -# -# - `train`: Optional schema to manage model training within DataJoint -# - `model`: Schema to manage pose estimation - -# %% `dj.Diagram()`: plot tables and dependencies -dj.Diagram(train) - -# %% -dj.Diagram(model) - 1 - -# %% [markdown] -# ### Table Types -# -# - **Manual table**: green box, manually inserted table, expect new entries daily, e.g. Subject, ProbeInsertion. -# - **Lookup table**: gray box, pre inserted table, commonly used for general facts or parameters. e.g. Strain, ClusteringMethod, ClusteringParamSet. -# - **Imported table**: blue oval, auto-processing table, the processing depends on the importing of external files. e.g. process of Clustering requires output files from kilosort2. -# - **Computed table**: red circle, auto-processing table, the processing does not depend on files external to the database, commonly used for -# - **Part table**: plain text, as an appendix to the master table, all the part entries of a given master entry represent a intact set of the master entry. e.g. Unit of a CuratedClustering. -# -# ### Table Links -# -# - **One-to-one primary**: thick solid line, share the exact same primary key, meaning the child table inherits all the primary key fields from the parent table as its own primary key. -# - **One-to-many primary**: thin solid line, inherit the primary key from the parent table, but have additional field(s) as part of the primary key as well -# - **Secondary dependency**: dashed line, the child table inherits the primary key fields from parent table as its own secondary attribute. - -# %% [markdown] -# ## Common Table Functions - -# %% [markdown] -# -# - `()` show table contents -# - `heading` shows attribute definitions -# - `describe()` show table defintiion with foreign key references - -# %% Each datajoint table class inside the module corresponds to a table inside the schema. For example, the class `ephys.EphysRecording` correponds to the table `_ephys_recording` in the schema `neuro_ephys` in the database. -model.VideoRecording.File() - -# %% `heading`: show table attributes regardless of foreign key references. -model.Model.heading - -# %% -train.TrainingTask.describe() - -# %% ephys [markdown] -# ## Other Elements installed with the workflow -# -# - [`lab`](https://github.com/datajoint/element-lab): lab management related information, such as Lab, User, Project, Protocol, Source. -# - [`subject`](https://github.com/datajoint/element-animal): general animal information, User, Genetic background, Death etc. -# - [`session`](https://github.com/datajoint/element-session): General information of experimental sessions. -# -# For more information about these Elements, see [workflow session](https://github.com/datajoint/workflow-session). - -# %% -dj.Diagram(lab) + dj.Diagram(subject) + dj.Diagram(session) - -# %% [session](https://github.com/datajoint/element-session): experimental session information -session.Session.describe() - -# %% [markdown] -# ## Summary and next step -# -# - This notebook introduced the overall structures of the schemas and tables in the workflow and relevant tools to explore the schema structure and table definitions. -# -# - The [next notebook](./03-Process.ipynb) will introduce the detailed steps to run through `workflow-deeplabcut`. diff --git a/notebooks/py_scripts/03-Process.py b/notebooks/py_scripts/03-Process.py deleted file mode 100644 index 6eeb4ce..0000000 --- a/notebooks/py_scripts/03-Process.py +++ /dev/null @@ -1,277 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.14.1 -# kernelspec: -# display_name: Python 3.9.13 ('ele') -# language: python -# name: python3 -# --- - -# %% [markdown] tags=[] -# # DataJoint U24 - Workflow DeepLabCut - -# %% [markdown] tags=[] -# ## Interactively run the workflow - -# %% [markdown] -# -# The workflow requires a DeepLabCut project with labeled data. -# - If you don't have data, refer to [00-DataDownload](./00-DataDownload_Optional.ipynb) and [01-Configure](./01-Configure.ipynb). -# - For an overview of the schema, refer to [02-WorkflowStructure](02-WorkflowStructure_Optional.ipynb). -# - For a more automated approach, refer to [03-Automate](03-Automate_Optional.ipynb). - -# %% [markdown] -# Let's change the directory to load the local config, `dj_local_conf.json`. - -# %% -import os -# change to the upper level folder to detect dj_local_conf.json -if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') -assert os.path.basename(os.getcwd())=='workflow-deeplabcut', ("Please move to the " - + "workflow directory") - -# %% [markdown] -# `Pipeline.py` activates the DataJoint `elements` and declares other required tables. - -# %% -import datajoint as dj -from workflow_deeplabcut.pipeline import lab, subject, session, train, model - -# Directing our pipeline to the appropriate config location -from element_interface.utils import find_full_path -from workflow_deeplabcut.paths import get_dlc_root_data_dir -config_path = find_full_path(get_dlc_root_data_dir(), - 'from_top_tracking/config.yaml') - -# %% [markdown] tags=[] -# ## Manually Inserting Entries - -# %% [markdown] -# ### Upstream tables - -# %% [markdown] -# We can insert entries into `dj.Manual` tables (green in diagrams) by providing values as a dictionary or a list of dictionaries. - -# %% -session.Session.heading - -# %% -subject.Subject.insert1(dict(subject='subject6', - sex='F', - subject_birth_date='2020-01-01', - subject_description='hneih_E105')) -session_keys = [dict(subject='subject6', session_datetime='2021-06-02 14:04:22'), - dict(subject='subject6', session_datetime='2021-06-03 14:43:10')] -session.Session.insert(session_keys) - -# %% [markdown] -# We can look at the contents of this table and restrict by a value. - -# %% -session.Session() & "session_datetime > '2021-06-01 12:00:00'" & "subject='subject6'" - -# %% [markdown] tags=[] -# #### DeepLabcut Tables - -# %% [markdown] -# The `VideoSet` table in the `train` schema retains records of files generated in the video labeling process (e.g., `h5`, `csv`, `png`). DeepLabCut will refer to the `mat` file located under the `training-datasets` directory. -# -# We recommend storing all paths as relative to the root in your config. - -# %% -train.VideoSet.insert1({'video_set_id': 0}) -project_folder = 'from_top_tracking/' -training_files = ['labeled-data/train1/CollectedData_DJ.h5', - 'labeled-data/train1/CollectedData_DJ.csv', - 'labeled-data/train1/img00674.png', - 'videos/train1.mp4'] -for idx, filename in enumerate(training_files): - train.VideoSet.File.insert1({'video_set_id': 0, - 'file_id': idx, - 'file_path': (project_folder + filename)}) - -# %% -train.VideoSet.File() - -# %% [markdown] tags=[] -# ### Training a Network - -# %% [markdown] -# First, we'll add a `ModelTrainingParamSet`. This is a lookup table that we can reference when training a model. - -# %% -train.TrainingParamSet.heading - -# %% [markdown] -# The `params` longblob should be a dictionary that captures all items for DeepLabCut's `train_network` function. At minimum, this is the contents of the project's config file, as well as `suffle` and `trainingsetindex`, which are not included in the config. - -# %% -from deeplabcut import train_network -help(train_network) # for more information on optional parameters - -# %% [markdown] -# Here, we give these items, load the config contents, and overwrite some defaults, including `maxiters`, to restrict our training iterations to 5. - -# %% -import yaml - -paramset_idx = 0; paramset_desc='from_top_tracking' - -with open(config_path, 'rb') as y: - config_params = yaml.safe_load(y) -training_params = {'shuffle': '1', - 'trainingsetindex': '0', - 'maxiters': '5', - 'scorer_legacy': 'False', - 'maxiters': '5', - 'multianimalproject':'False'} -config_params.update(training_params) -train.TrainingParamSet.insert_new_params(paramset_idx=paramset_idx, - paramset_desc=paramset_desc, - params=config_params) - -# %% [markdown] -# Now, we add a `TrainingTask`. As a computed table, `ModelTraining` will reference this to start training when calling `populate()` - -# %% -train.TrainingTask.heading - -# %% -key={'video_set_id': 0, - 'paramset_idx':0, - 'training_id': 1, - 'project_path':'from_top_tracking/' - } -train.TrainingTask.insert1(key, skip_duplicates=True) -train.TrainingTask() - -# %% tags=[] -train.ModelTraining.populate() - -# %% [markdown] -# (Output cleared for brevity) -# ``` -# The network is now trained and ready to evaluate. Use the function 'evaluate_network' to evaluate the network. -# ``` - -# %% -train.ModelTraining() - -# %% [markdown] -# To resume training from a checkpoint, we would need to -# [edit the relevant config file](https://github.com/DeepLabCut/DeepLabCut/issues/70) (see also `update_pose_cfg` in `workflow_deeplabcut.load_demo_data`). -# Emperical work suggests 200k iterations for any true use-case. -# -# For better quality predictions in this demo, we'll revert the checkpoint file and use a pretrained model. - -# %% -from workflow_deeplabcut.load_demo_data import revert_checkpoint_file -revert_checkpoint_file() - -# %% [markdown] jp-MarkdownHeadingCollapsed=true tags=[] -# ### Tracking Joints/Body Parts - -# %% [markdown] -# The `model` schema uses a lookup table for managing Body Parts tracked across models. - -# %% -model.BodyPart.heading - -# %% [markdown] -# Helper functions allow us to first, identify all the new body parts from a given config, and, second, insert them with user-friendly descriptions. - -# %% -model.BodyPart.extract_new_body_parts(config_path) - -# %% -bp_desc=['Body Center', 'Head', 'Base of Tail'] -model.BodyPart.insert_from_config(config_path,bp_desc) - -# %% [markdown] jp-MarkdownHeadingCollapsed=true tags=[] -# ### Declaring/Evaluating a Model - -# %% [markdown] -# We can insert into `Model` table for automatic evaluation - -# %% -model.Model.insert_new_model(model_name='FromTop-latest',dlc_config=config_path, - shuffle=1,trainingsetindex=0, - model_description='FromTop - latest snapshot', - paramset_idx=0, - params={"snapshotindex":-1}) - -# %% -model.Model() - -# %% [markdown] -# `ModelEvaluation` will reference the `Model` using the `populate` method and insert the output from DeepLabCut's `evaluate_network` function - -# %% -model.ModelEvaluation.heading - -# %% -model.ModelEvaluation.populate() - -# %% -model.ModelEvaluation() - -# %% [markdown] -# ### Pose Estimation - -# %% [markdown] -# To use our model, we'll first need to insert a session recoring into `VideoRecording` - -# %% -model.VideoRecording() - -# %% -key = {'subject': 'subject6', - 'session_datetime': '2021-06-02 14:04:22', - 'recording_id': '1', 'device': 'Camera1'} -model.VideoRecording.insert1(key) - -_ = key.pop('device') # get rid of secondary key from master table -key.update({'file_id': 1, - 'file_path': 'from_top_tracking/videos/test-2s.mp4'}) -model.VideoRecording.File.insert1(key) - -# %% -model.VideoRecording.File() - -# %% [markdown] -# `RecordingInfo` automatically populates with file information - -# %% -model.RecordingInfo.populate() -model.RecordingInfo() - -# %% [markdown] -# Next, we specify if the `PoseEstimation` table should load results from an existing file or trigger the estimation command. Here, we can also specify parameters for DeepLabCut's `analyze_videos` as a dictionary. - -# %% -key = (model.VideoRecording & {'recording_id': '1'}).fetch1('KEY') -key.update({'model_name': 'FromTop-latest', 'task_mode': 'trigger'}) -key - -# %% -model.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True}) -model.PoseEstimation.populate() - -# %% [markdown] -# By default, DataJoint will store results in a subdirectory -# > / videos / device__recording_<#>_model_ -# where `processed_dir` is optionally specified in the datajoint config. If unspecified, this will be the project directory. The device and model names are specified elsewhere in the schema. -# -# We can get this estimation directly as a pandas dataframe. - -# %% -model.PoseEstimation.get_trajectory(key) - -# %% [markdown] -# In the [next notebook](./04-Automate_Optional.ipynb), we'll look at additional tools in the workflow for automating these steps. diff --git a/notebooks/py_scripts/04-Automate_Optional.py b/notebooks/py_scripts/04-Automate_Optional.py deleted file mode 100644 index 07f7817..0000000 --- a/notebooks/py_scripts/04-Automate_Optional.py +++ /dev/null @@ -1,143 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.14.1 -# kernelspec: -# display_name: Python 3.8.11 ('ele') -# language: python -# name: python3 -# --- - -# %% [markdown] tags=[] -# # DataJoint U24 - Workflow DeepLabCut - -# %% [markdown] pycharm={"name": "#%% md\n"} -# ## Workflow Automation -# -# In the previous notebook [03-Process](./03-Process.ipynb), we ran through the workflow in detailed steps, manually adding each. The current notebook provides a more automated approach. -# -# The commands here run a workflow using example data from the [00-DownloadData](./00-DataDownload_Optional.ipynb) notebook, but note where placeholders could be changed for a different dataset. - -# %% tags=[] -import os; from pathlib import Path -# change to the upper level folder to detect dj_local_conf.json -if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') -assert os.path.basename(os.getcwd())=='workflow-deeplabcut', ("Please move to the " - + "workflow directory") -from workflow_deeplabcut.pipeline import lab, subject, session, train, model -from workflow_deeplabcut import process - -# %% [markdown] -# We'll be using the `process.py` script to automatically loop through all `make` functions, as a shortcut for calling each individually. -# -# If you previously completed the [03-Process notebook](./03-Process.ipynb), you may want to delete the contents ingested there, to avoid duplication errors. - -# %% -safemode=True # Set to false to turn off confirmation prompts -(session.Session & 'subject="subject6"').delete(safemode=safemode) -train.TrainingParamSet.delete(safemode=safemode) -train.VideoSet.delete(safemode=safemode) - -# %% [markdown] -# ## Ingestion of subjects, sessions, videos and training parameters -# -# Refer to the `user_data` folder in the workflow. -# -# 1. Fill subject and session information in files `subjects.csv` and `sessions.csv` -# 2. Fill in recording and parameter information in `recordings.csv` and `config_params.csv` -# + Add both training and estimation videos to the recording list -# + Additional columns in `config_params.csv` will be treated as model training parameters -# 3. Run automatic scripts prepared in `workflow_deeplabcut.ingest` for ingestion: -# + `ingest_subjects` for `subject.Subject` -# + `ingest_sessions` - for session tables `Session`, `SessionDirectory`, and `SessionNote` -# + `ingest_dlc_items` - for ... -# - `train.ModelTrainingParamSet` -# - `train.VideoSet` and the corresponding `File` part table -# - `model.VideoRecording` and the corresponding `File` part table - -# %% -from workflow_deeplabcut.ingest import ingest_subjects, ingest_sessions, ingest_dlc_items -ingest_subjects() -ingest_sessions() -ingest_dlc_items() - -# %% [markdown] -# ## Setting project variables -# -# 1. Set your root directory in your DataJoint config file, under `custom` as `dlc_root_data_dir`. - -# %% -import datajoint as dj; dj.config.load('dj_local_conf.json') -from element_interface.utils import find_full_path -data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config - 'from_top_tracking') # DLC project dir -config_path = (data_dir / 'config.yaml') - -# %% [markdown] -# 2. Next, we pair training files with training parameters, and launch training via `process`. -# - Some tables may try to populate without upstream keys. -# - Others, like `RecordingInfo` already have keys loaded. -# - Note: DLC's model processes (e.g., Training, Evaluation) log a lot of information to the console, to quiet this, pass `verbose=False` to `process` - -# %% -key={'paramset_idx':0,'training_id':0,'video_set_id':0, - 'project_path':'from_top_tracking/'} -train.TrainingTask.insert1(key, skip_duplicates=True) -process.run(verbose=True, display_progress=True) -model.RecordingInfo() - -# %% [markdown] -# For the purposes of this demo, we'll want to use an older model, so the folling function will reload the original checkpoint file. - -# %% -from workflow_deeplabcut.load_demo_data import revert_checkpoint_file -revert_checkpoint_file() - -# %% [markdown] -# 3. Now to add such a model upstream key -# - Include a user-friendly `model_name` -# - Include the relative path for the project's `config.yaml` -# - Add `shuffle` and `trainingsetindex` -# - `insert_new_model` will prompt before inserting, but this can be skipped with `prompt=False` - -# %% -model.Model.insert_new_model(model_name='FromTop-latest', - dlc_config=config_path, - shuffle=1, - trainingsetindex=0, - paramset_idx=1, - prompt=True, # True is the default behavior - model_description='FromTop - latest snapshot', - params={"snapshotindex":-1}) -process.run() - -# %% [markdown] -# 4. Add a pose estimation task, and launch via `process`. -# - Get all primary key information for a given recording -# - Add the model and `task_mode` (i.e., load vs. trigger) to be applied -# - Add any additional analysis parameters for `deeplabcut.analyze_videos` - -# %% -key=(model.VideoRecording & 'recording_id=1').fetch1('KEY') -key.update({'model_name': 'FromTop-latest', 'task_mode': 'trigger'}) -analyze_params={'save_as_csv':True} # add any others from deeplabcut.analyze_videos -model.PoseEstimationTask.insert_estimation_task(key,params=analyze_params) -process.run() - -# %% [markdown] -# 5. Retrieve estimated position data. - -# %% -model.PoseEstimation.get_trajectory(key) - -# %% [markdown] -# ## Summary and next step -# -# + This notebook runs through the workflow in an automatic manner. -# -# + The next notebook [05-Visualization](./05-Visualization_Optional.ipynb) demonstrates how to plot this data and label videos on disk. diff --git a/notebooks/py_scripts/05-Visualization_Optional.py b/notebooks/py_scripts/05-Visualization_Optional.py deleted file mode 100644 index 9884afb..0000000 --- a/notebooks/py_scripts/05-Visualization_Optional.py +++ /dev/null @@ -1,136 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.13.7 -# kernelspec: -# display_name: Python 3.8.11 ('ele') -# language: python -# name: python3 -# --- - -# %% [markdown] tags=[] -# # DataJoint U24 - Workflow DeepLabCut - -# %% [markdown] tags=[] -# ## Setup - -# %% [markdown] -# The notebook requires DeepLabCut pose estimation already processed via DataJoint. -# -# - If you don't have data, refer to [00-DataDownload](./00-DataDownload_Optional.ipynb) and [01-Configure](./01-Configure.ipynb). -# - For an overview of the schema, refer to [02-WorkflowStructure](02-WorkflowStructure_Optional.ipynb). -# - For step-by-step or autmated ingestion, refer to [03-Process](./03-Process.ipynb) or [03-Automate](03-Automate_Optional.ipynb). - -# %% [markdown] -# Let's change the directory to load the local config, `dj_local_conf.json` and import the relevant schema. - -# %% -import os # change to the upper level folder to detect dj_local_conf.json -if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') -assert os.path.basename(os.getcwd())=='workflow-deeplabcut', ("Please move to the " - + "workflow directory") - -import datajoint as dj # Import relevant schema -from workflow_deeplabcut.pipeline import model - -# Directing our pipeline to the appropriate config location -from element_interface.utils import find_full_path -from workflow_deeplabcut.paths import get_dlc_root_data_dir -config_path = find_full_path(get_dlc_root_data_dir(), - 'from_top_tracking/config.yaml') - -# Grabbing the relevant key -import pandas as pd -key = (model.PoseEstimation & "recording_id=1").fetch('KEY') - -# %% [markdown] tags=[] -# ## Fetching data - -# %% [markdown] -# In the previous notebook, we saw how to fetch data as a pandas dataframe. - -# %% -df=model.PoseEstimation.get_trajectory(key) -df_xy = df.iloc[:,df.columns.get_level_values(2).isin(["x","y"])]['FromTop-latest'] -df_xy.mean() - -# %% [markdown] -# ## Plotting - -# %% [markdown] -# We plot these coordinates over time. - -# %% -df_xy.plot().legend(loc='right') - -# %% [markdown] -# Next, we'll make a copy of the data for the next plot. - -# %% -df_flat = df_xy.copy() -df_flat.columns = df_flat.columns.map('_'.join) - -# %% [markdown] -# Here, we can overlay the traces of each point over time. - -# %% -import matplotlib.pyplot as plt -fig,ax=plt.subplots() -df_flat.plot(x='bodycenter_x',y='bodycenter_y',ax=ax) -df_flat.plot(x='head_x',y='head_y', ax=ax) -df_flat.plot(x='tailbase_x',y='tailbase_y', ax=ax) - -# %% [markdown] -# Our visual check shows that these trajectories are more-or-less aligned. - -# %% [markdown] -# ## Video Labeling - -# %% [markdown] -# This Element adds to the DeepLabCut tree structure by sorting results files into output directories. Let's see where they're stored using `infer_output_dir`. - -# %% -destfolder = model.PoseEstimationTask.infer_output_dir(key) -destfolder - -# %% [markdown] -# When labeling videos, we need to provide this as an additional argument. -# -# Note that DataJoint handles paths as `pathlib` objects, while DeepLabCut requires strings. - -# %% -from deeplabcut.utils.make_labeled_video import create_labeled_video - -video_path = find_full_path( # Fetch the full video path - get_dlc_root_data_dir(), ((model.VideoRecording.File & key).fetch1("file_path")) -) - -config_paths = sorted( # Of configs in the project path, defer to the datajoint-saved - list( - find_full_path( - get_dlc_root_data_dir(), ((model.Model & key).fetch1("project_path")) - ).glob("*.y*ml") - ) -) - -create_labeled_video( # Pass strings to label the video - config=str(config_paths[-1]), - videos=str(video_path), - destfolder=str(destfolder), -) - - -# %% [markdown] -# The video should now be labeled at this path - -# %% -from IPython.display import FileLink -FileLink(path=video_path) - -# %% [markdown] -# In the next notebook, [06-Drop](./06-Drop_Optional.ipynb), we'll demonstrate dropping schemas in this Element. diff --git a/notebooks/py_scripts/06-Drop_Optional.py b/notebooks/py_scripts/06-Drop_Optional.py deleted file mode 100644 index 0dd09e8..0000000 --- a/notebooks/py_scripts/06-Drop_Optional.py +++ /dev/null @@ -1,44 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.13.7 -# kernelspec: -# display_name: venv-dlc -# language: python -# name: venv-dlc -# --- - -# %% [markdown] tags=[] -# # DataJoint U24 - Workflow DeepLabCut - -# %% [markdown] -# Change into the parent directory to find the `dj_local_conf.json` file. - -# %% tags=[] -import os; from pathlib import Path -# change to the upper level folder to detect dj_local_conf.json -if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') -assert os.path.basename(os.getcwd())=='workflow-deeplabcut', ("Please move to the " - + "workflow directory") - -# %% [markdown] -# ## Drop schemas -# -# + Schemas are not typically dropped in a production workflow with real data in it. -# + At the developmental phase, it might be required for the table redesign. -# + When dropping all schemas is needed, drop items starting with the most downstream. - -# %% -from workflow_deeplabcut.pipeline import * - -# %% -# model.schema.drop() -# train.schema.drop() -# session.schema.drop() -# subject.schema.drop() -# lab.schema.drop() diff --git a/notebooks/py_scripts/09-AlternateDataset.py b/notebooks/py_scripts/09-AlternateDataset.py deleted file mode 100644 index 698650b..0000000 --- a/notebooks/py_scripts/09-AlternateDataset.py +++ /dev/null @@ -1,578 +0,0 @@ -# -*- coding: utf-8 -*- -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.13.7 -# kernelspec: -# display_name: Python 3.8.11 ('ele') -# language: python -# name: python3 -# --- - -# %% [markdown] tags=[] -# # Workflow DeepLabCut - Alternate Data - -# %% [markdown] -# ## Introduction - -# %% [markdown] -# -# This notebook provides a general introduction to DataJoint use via Element DeepLabcut. It follows the same structure as other notebooks in this directory, but uses data from the DeepLabCut team. -# -# We recommend the other notebooks as they provide access to a pretrained model and allow for a more in-depth exploration of the features of the Element. - -# %% [markdown] -# ## Example data - -# %% [markdown] -# -# ### Download -# -# If you've already cloned the [main DLC repository](https://github.com/DeepLabCut/DeepLabCut), you already have this folder under `examples/openfield-Pranav-2018-10-30`. [This link](https://downgit.github.io/#/home?url=https://github.com/DeepLabCut/DeepLabCut/tree/master/examples/openfield-Pranav-2018-10-30) via [DownGit](https://downgit.github.io/) will start the single-directory download automatically as a zip. Unpack this zip and place it in a directory we'll refer to as your root. - -# %% [markdown] -# ### Structure - -# %% [markdown] -# The directory will be organized as follows within your chosen root -# directory. -# -# ``` -# /your-root/openfield-Pranav-2018-10-30/ -# - config.yaml -# - labeled-data -# - m4s1 -# - CollectedData_Pranav.csv -# - CollectedData_Pranav.h5 -# - img0000.png -# - img0001.png -# - img0002.png -# - img{...}.png -# - img0114.png -# - img0115.png -# - videos -# - m3v1mp4.mp4 -# ``` - -# %% [markdown] -# For those unfamiliar with DLC... -# - `config.yaml` contains all the key parameters of the project, including -# - file locations (currently empty) -# - body parts -# - cropping information -# - `labeled-data` includes the frames coordinates for each body part in the training video -# - `videos` includes the full training video for this example -# -# Part of the demo setup involves an additional -# command (as [shown here](https://github.com/DeepLabCut/DeepLabCut/blob/master/examples/JUPYTER/Demo_labeledexample_Openfield.ipynb)) to revise the project path within config file as well as generate the `training-datasets` directory. - -# %% -your_root='/fill/in/your/root/with\ escaped\ spaces' -from deeplabcut.create_project.demo_data import load_demo_data -load_demo_data(your_root+'/openfield-Pranav-2018-10-30/config.yaml') - -# %% [markdown] -# ### New video - -# %% [markdown] -# Later, we'll use the first few seconds of the training video as a 'separate session' to demonstrate pose estimation within the Element. `ffmpeg` is a dependency of DeepLabCut -# that can splice the training video for a demonstration purposes. The command below saves -# the first 2 seconds of the training video as a copy. -# -# - `-n` do not overwrite -# - `-hide_banner -loglevel error` less verbose output -# - `-ss 0 -t 2` start at second 0, add 2 seconds -# - `-i {vid_path}` input this video -# - `-{v/a}codec copy` copy the video and audio codecs of the input -# - `{vid_path}-copy.mp4` output file - -# %% tags=[] -vid_path = your_root + '/openfield-Pranav-2018-10-30/videos/m3v1mp4' -cmd = (f'ffmpeg -n -hide_banner -loglevel error -ss 0 -t 2 -i {vid_path}.mp4 ' - + f'-vcodec copy -acodec copy {vid_path}-copy.mp4') -import os; os.system(cmd) - -# %% [markdown] tags=[] -# ## Configuring DataJoint - -# %% [markdown] -# ### DataJoint Local Config - -# %% [markdown] tags=[] -# - To run `workflow-deeplabcut`, we need to set up the DataJoint configuration file, called `dj_local_conf.json`, unique to each machine. -# -# - The config only needs to be set up once, skip to the next section. -# -# - By convention, we set a local config in the workflow directory. You may be interested in [setting a global config](https://docs.datajoint.org/python/setup/01-Install-and-Connect.html). - -# %% -import os -# change to the upper level folder to detect dj_local_conf.json -if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') -assert os.path.basename(os.getcwd())=='workflow-deeplabcut', ("Please move to the " - + "workflow directory") - -# %% [markdown] -# ### Configure database credentials - -# %% [markdown] -# Now let's set up the host, user and password in the `dj.config` following [instructions here](https://tutorials.datajoint.io/setting-up/get-database.html). - -# %% -import datajoint as dj -import getpass -dj.config['database.host'] = '{YOUR_HOST}' -dj.config['database.user'] = '{YOUR_USERNAME}' -dj.config['database.password'] = getpass.getpass() # enter the password securely - -# %% [markdown] -# You should be able to connect to the database at this stage. - -# %% -dj.conn() - -# %% [markdown] -# ### Workflow-specific items - -# %% [markdown] -# **Prefix:** Giving a prefix to your schema could help manage privelages on a server. -# - If we set prefix `neuro_`, every schema created with the current workflow will start with `neuro_`, e.g. `neuro_lab`, `neuro_subject`, `neuro_imaging` etc. -# - Teams who work on the same schemas should use the same prefix, set as follows: - -# %% -dj.config['custom'] = {'database.prefix': 'neuro_'} - -# %% [markdown] -# **Root dir:** `custom` keeps track of your root directory for this project. With multiple roots the Element will figure out which to use based on the files it expects. -# -# - Please set one root to the parent directory of DLC's `openfield-Pranav-2018-10-30` example. -# - In other cases, this should be the parent of your DLC project path. -# -# We can then check that the path connects with a tool from [element-interface](https://github.com/datajoint/element-interface). - -# %% -dj.config['custom'] = {'dlc_root_data_dir' : ['your-root1', 'your-root2']} - -from element_interface.utils import find_full_path -data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], - 'openfield-Pranav-2018-10-30') -assert data_dir.exists(), "Please check the that you have the folder openfield-Pranav" - -# %% [markdown] -# ### Saving the config - -# %% [markdown] -# -# With the proper configurations, we could save this as a file, either as a local json file, or a global file. DataJoint will default to a local file, then check for a global if none is found. - -# %% -dj.config.save_local() # saved as dj_local_conf.json in the root workflow dir -# dj.config.save_global() # saved as .datajoint_config.json in your home dir - -# %% [markdown] tags=[] -# ## Workflow Structure - -# %% [markdown] -# ### Schemas, Diagrams and Tables - -# %% [markdown] -# Schemas are conceptually related sets of tables. By importing schemas from `workflow_deeplabcut.pipeline`, we'll declare the tables on the server with the prefix we set. If these tables are already declared, we'll gain access. For more information about lab, animal and session Elements, see [session workflow](https://github.com/datajoint/workflow-session). -# -# - `dj.list_schemas()` lists all schemas a user has access to in the current database -# - `.schema.list_tables()` will provide names for each table in the format used under the hood. - -# %% -import datajoint as dj -from workflow_deeplabcut.pipeline import lab, subject, session, train, model - -dj.list_schemas() - -train.schema.list_tables() - -# %% [markdown] -# `dj.Diagram()` plots tables and dependencies in a schema. To see additional upstream or downstream connections, add `- N` or `+ N`. -# -# - `train`: Optional schema to manage model training within DataJoint -# - `model`: Schema to manage pose estimation - -# %% [markdown] -# #### Table Types -# -# - **Manual table**: green box, manually inserted table, expect new entries daily, e.g. Subject, ProbeInsertion. -# - **Lookup table**: gray box, pre inserted table, commonly used for general facts or parameters. e.g. Strain, ClusteringMethod, ClusteringParamSet. -# - **Imported table**: blue oval, auto-processing table, the processing depends on the importing of external files. e.g. process of Clustering requires output files from kilosort2. -# - **Computed table**: red circle, auto-processing table, the processing does not depend on files external to the database, commonly used for -# - **Part table**: plain text, as an appendix to the master table, all the part entries of a given master entry represent a intact set of the master entry. e.g. Unit of a CuratedClustering. -# -# #### Table Links -# -# - **One-to-one primary**: thick solid line, share the exact same primary key, meaning the child table inherits all the primary key fields from the parent table as its own primary key. -# - **One-to-many primary**: thin solid line, inherit the primary key from the parent table, but have additional field(s) as part of the primary key as well -# - **Secondary dependency**: dashed line, the child table inherits the primary key fields from parent table as its own secondary attribute. - -# %% `dj.Diagram()`: plot tables and dependencies -dj.Diagram(train) #- 1 - -# %% -dj.Diagram(model) - -# %% [markdown] -# ### Common Table Functions - -# %% [markdown] -# -# - `
    ()` show table contents -# - `heading` shows attribute definitions -# - `describe()` show table defintiion with foreign key references - -# %% Each datajoint table class inside the module corresponds to a table inside the schema. For example, the class `ephys.EphysRecording` correponds to the table `_ephys_recording` in the schema `neuro_ephys` in the database. -model.VideoRecording.File() - -# %% `heading`: show table attributes regardless of foreign key references. -model.Model.heading - -# %% -train.TrainingTask.describe() - -# %% [markdown] tags=[] -# ## Running the Workflow - -# %% [markdown] -# `Pipeline.py` activates the DataJoint `elements` and declares other required tables. - -# %% -import datajoint as dj -from workflow_deeplabcut.pipeline import lab, subject, session, train, model - -# Directing our pipeline to the appropriate config location -from element_interface.utils import find_full_path -from workflow_deeplabcut.paths import get_dlc_root_data_dir -config_path = find_full_path(get_dlc_root_data_dir(), - 'openfield-Pranav-2018-10-30/config.yaml') - -# %% [markdown] tags=[] -# ### Manually Inserting Entries - -# %% [markdown] -# #### Upstream tables - -# %% [markdown] -# We can insert entries into `dj.Manual` tables (green in diagrams) by directly providing values as a dictionary. - -# %% -session.Session.heading - -# %% -subject.Subject.insert1(dict(subject='subject6', - sex='F', - subject_birth_date='2020-01-01', - subject_description='hneih_E105')) -session_keys = [dict(subject='subject6', session_datetime='2021-06-02 14:04:22'), - dict(subject='subject6', session_datetime='2021-06-03 14:43:10')] -session.Session.insert(session_keys) - -# %% [markdown] -# We can look at the contents of this table and restrict by a value. - -# %% -session.Session() & "session_datetime > '2021-06-01 12:00:00'" & "subject='subject6'" - -# %% [markdown] tags=[] -# #### DeepLabcut Tables - -# %% [markdown] -# The `VideoSet` table in the `train` schema retains records of files generated in the video labeling process (e.g., `h5`, `csv`, `png`). DeepLabCut will refer to the `mat` file located under the `training-datasets` directory. - -# %% -train.VideoSet.insert1({'video_set_id': 1}) -labeled_dir = 'openfield-Pranav-2018-10-30/labeled-data/m4s1/' -training_files = ['CollectedData_Pranav.h5', - 'CollectedData_Pranav.csv', - 'img0000.png'] -for idx, filename in training_files: - train.VideoSet.File.insert1({'video_set_id': 1, - 'file_id': idx, - 'file_path': (labeled_dir + file)}) -train.VideoSet.File.insert1({'video_set_id':1, 'file_id': 4, 'file_path': - 'openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4'}) - -# %% -train.VideoSet.File() - -# %% [markdown] tags=[] -# ### Training a Network - -# %% [markdown] -# First, we'll add a `ModelTrainingParamSet`. This is a lookup table that we can reference when training a model. - -# %% -train.TrainingParamSet.heading - -# %% [markdown] -# The `params` longblob should be a dictionary that captures all items for DeepLabCut's `train_network` function. At minimum, this is the contents of the project's config file, as well as `suffle` and `trainingsetindex`, which are not included in the config. - -# %% -from deeplabcut import train_network -help(train_network) # for more information on optional parameters - -# %% [markdown] -# Here, we give these items, load the config contents, and overwrite some defaults, including `maxiters`, to restrict our training iterations to 5. - -# %% -import yaml - -paramset_idx = 1; paramset_desc='OpenField' - -with open(config_path, 'rb') as y: - config_params = yaml.safe_load(y) -training_params = {'shuffle': '1', - 'trainingsetindex': '0', - 'maxiters': '5', - 'scorer_legacy': 'False', - 'maxiters': '5', - 'multianimalproject':'False'} -config_params.update(training_params) -train.TrainingParamSet.insert_new_params(paramset_idx=paramset_idx, - paramset_desc=paramset_desc, - params=config_params) - -# %% [markdown] -# Now, we add a `TrainingTask`. As a computed table, `ModelTraining` will reference this to start training when calling `populate()` - -# %% -train.TrainingTask.heading - -# %% -key={'video_set_id': 1, 'paramset_idx':1,'training_id':1, - 'project_path':'openfield-Pranav-2018-10-30/'} -train.TrainingTask.insert1(key, skip_duplicates=True) -train.TrainingTask() - -# %% tags=[] -train.ModelTraining.populate() - -# %% -train.ModelTraining() - -# %% [markdown] -# To resume training from a checkpoint, we would need to -# [edit the relevant config file](https://github.com/DeepLabCut/DeepLabCut/issues/70). -# Emperical work from the Mathis team suggests 200k iterations for any true use-case. - -# %% [markdown] jp-MarkdownHeadingCollapsed=true tags=[] -# ### Tracking Joints/Body Parts - -# %% [markdown] -# The `model` schema uses a lookup table for managing Body Parts tracked across models. - -# %% -model.BodyPart.heading - -# %% [markdown] -# Helper functions allow us to first, identify all the new body parts from a given config, and, second, insert them with user-friendly descriptions. - -# %% -model.BodyPart.extract_new_body_parts(config_path) - -# %% -bp_desc=['Left Ear', 'Right Ear', 'Snout Position', 'Base of Tail'] -model.BodyPart.insert_from_config(config_path,bp_desc) - -# %% [markdown] jp-MarkdownHeadingCollapsed=true tags=[] -# ### Declaring/Evaluating a Model - -# %% [markdown] -# We can insert into `Model` table for automatic evaluation - -# %% -model.Model.insert_new_model(model_name='OpenField-5',dlc_config=config_path, - shuffle=1,trainingsetindex=0, - model_description='Open field model trained 5 iterations', - paramset_idx=1) - -# %% -model.Model() - -# %% [markdown] -# `ModelEvaluation` will reference the `Model` using the `populate` method and insert the output from DeepLabCut's `evaluate_network` function - -# %% -model.ModelEvaluation.heading - -# %% -model.ModelEvaluation.populate() - -# %% -model.ModelEvaluation() - -# %% [markdown] -# ### Pose Estimation - -# %% [markdown] -# To use our model, we'll first need to insert a session recoring into `VideoRecording` - -# %% -key = {'subject': 'subject6', - 'session_datetime': '2021-06-02 14:04:22', - 'recording_id': '1', 'device': 'Camera1'} -model.VideoRecording.insert1(key) - -_ = key.pop('device') # get rid of secondary key from master table -key.update({'file_id': 1, - 'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4'}) -model.VideoRecording.File.insert1(key) - -# %% -model.VideoRecording.File() - -# %% [markdown] -# `RecordingInfo` automatically populates with file information - -# %% -model.RecordingInfo.populate() -model.RecordingInfo() - -# %% [markdown] -# Next, we specify if the `PoseEstimation` table should load results from an existing file or trigger the estimation command. Here, we can also specify parameters for DeepLabCut's `analyze_videos` as a dictionary. - -# %% -key = (model.VideoRecording & {'recording_id': '1'}).fetch1('KEY') -key.update({'model_name': 'OpenField-5', 'task_mode': 'trigger'}) -key - -# %% -model.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True}) -model.PoseEstimation.populate() - -# %% [markdown] -# By default, DataJoint will store results in a subdirectory -# > / videos / device__recording_<#>_model_ -# where `processed_dir` is optionally specified in the datajoint config. If unspecified, this will be the project directory. The device and model names are specified elsewhere in the schema. -# -# We can get this estimation directly as a pandas dataframe. - -# %% -model.PoseEstimation.get_trajectory(key) - -# %% [markdown] -# -# . - -# %% [markdown] pycharm={"name": "#%% md\n"} -# ## Workflow Automation - -# %% [markdown] -# Below is a more automatic approach to run through the pipeline using some utility functions in the workflow using the `process.py` script to automatically trigger all computed tables. -# -# Because we just inserted all the data, we'll delete using the command below to start over. - -# %% -from workflow_deeplabcut.process import run -safemode=None # Set to false to turn off confirmation prompts -(session.Session & 'subject="subject6"').delete(safemode=safemode) -train.TrainingParamSet.delete(safemode=safemode) -train.VideoSet.delete(safemode=safemode) - -# %% [markdown] -# #### Automated Ingestion -# -# Refer to the `user_data` folder in the workflow for CSVs to fill in various tables. -# -# 1. Upstream tables: -# - `subject.Subject` via `subjects.csv` -# - `session.Session` via `sessions.csv` -# 2. `train` schema: -# - `train.TrainingParamSet` via `config_params.csv` -# - `train.VideoSet` via `train_videosets.csv` -# 3. `model` schema: -# - `model.VideoRecording` via `model_videos.csv` -# - `model.Model` via `model_model.csv` -# -# Run automatic ingestion via functions in `workflow_deeplabcut.ingest` - -# %% -from workflow_deeplabcut.ingest import ingest_subjects, ingest_sessions, ingest_dlc_items -ingest_subjects(); ingest_sessions(); ingest_dlc_items() - -# %% [markdown] -# #### Setting project variables -# -# Set your root directory in your DataJoint config file, under `custom` as `dlc_root_data_dir`. - -# %% -import datajoint as dj; dj.config.load('dj_local_conf.json') -from element_interface.utils import find_full_path -data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config - 'openfield-Pranav-2018-10-30') # DLC project dir -config_path = (data_dir / 'config.yaml') - -# %% [markdown] -# #### Launching trainig -# -# Pair training files with training parameters, and launch training via `process`. -# -# Note: DLC's model processes (e.g., Training, Evaluation) log a lot of information to the console, to quiet this, pass `verbose=False` to `process` - -# %% -key={'paramset_idx':1,'training_id':1,'video_set_id':1, - 'project_path':'openfield-Pranav-2018-10-30/'} -train.TrainingTask.insert1(key, skip_duplicates=True) -run(verbose=True) -model.RecordingInfo() - -# %% [markdown] -# Now, add to `Model`, including -# - Include a user-friendly `model_name` -# - Include the relative path for the project's `config.yaml` -# - Add `shuffle` and `trainingsetindex` -# - `insert_new_model` will prompt before inserting, but this can be skipped with `prompt=False` - -# %% -model.Model.insert_new_model(model_name='OpenField-5', - dlc_config=config_path, - shuffle=1, - trainingsetindex=0, - paramset_idx=1, - prompt=True, # True is the default behavior - model_description='Open field model trained 5 iterations') -run() - -# %% [markdown] -# Add a pose estimation task, using -# - All primary key information for a given recording -# - Add the model and `task_mode` (i.e., load vs. trigger) to be applied -# - Add any additional analysis parameters for `deeplabcut.analyze_videos` - -# %% -key=(model.VideoRecording & 'recording_id=2').fetch1('KEY') -key.update({'model_name': 'OpenField-5', 'task_mode': 'trigger'}) -analyze_params={'save_as_csv':True} # add any others from deeplabcut.analyze_videos -model.PoseEstimationTask.insert_estimation_task(key,params=analyze_params) -run() - -# %% [markdown] -# Retrieve estimated position data: - -# %% -model.PoseEstimation.get_trajectory(key) - -# %% [markdown] tags=[] -# ## Dropping schemas - -# %% [markdown] -# + Schemas are not typically dropped in a production workflow with real data in it. -# + At the developmental phase, it might be required for the table redesign. -# + When dropping all schemas is needed, drop items starting with the most downstream. - -# %% -from workflow_deeplabcut.pipeline import * -# model.schema.drop() -# train.schema.drop() -# session.schema.drop() -# subject.schema.drop() -# lab.schema.drop() diff --git a/workflow_deeplabcut/__init__.py b/workflow_deeplabcut/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/workflow_deeplabcut/version.py b/workflow_deeplabcut/version.py deleted file mode 100644 index 0822c47..0000000 --- a/workflow_deeplabcut/version.py +++ /dev/null @@ -1,5 +0,0 @@ -""" -Package metadata -Update the Docker image tag in `docker-compose.yaml` to match -""" -__version__ = "0.2.0" From 22cb3794caf9e44fd988bc36e853e95844ec9e62 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Fri, 4 Aug 2023 11:29:22 -0500 Subject: [PATCH 086/176] Rename `03-process` -> `tutorial` --- notebooks/{03-Process.ipynb => tutorial.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename notebooks/{03-Process.ipynb => tutorial.ipynb} (100%) diff --git a/notebooks/03-Process.ipynb b/notebooks/tutorial.ipynb similarity index 100% rename from notebooks/03-Process.ipynb rename to notebooks/tutorial.ipynb From be51282812e58cb96d2bfcc9e7bb34d46ab39475 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Fri, 4 Aug 2023 12:55:32 -0500 Subject: [PATCH 087/176] merge paths and pipeline files, move to notebooks --- element_deeplabcut/paths.py | 24 ----------- element_deeplabcut/pipeline.py | 73 ---------------------------------- notebooks/paths.py | 24 ----------- notebooks/pipeline.py | 28 +++++++++++-- 4 files changed, 24 insertions(+), 125 deletions(-) delete mode 100644 element_deeplabcut/paths.py delete mode 100644 element_deeplabcut/pipeline.py delete mode 100644 notebooks/paths.py diff --git a/element_deeplabcut/paths.py b/element_deeplabcut/paths.py deleted file mode 100644 index b42b264..0000000 --- a/element_deeplabcut/paths.py +++ /dev/null @@ -1,24 +0,0 @@ -import datajoint as dj -from collections import abc - - -def get_dlc_root_data_dir() -> list: - """Returns a list of root directories for Element DeepLabCut""" - dlc_root_dirs = dj.config.get("custom", {}).get("dlc_root_data_dir") - if not dlc_root_dirs: - return None - elif not isinstance(dlc_root_dirs, abc.Sequence): - return list(dlc_root_dirs) - else: - return dlc_root_dirs - - -def get_dlc_processed_data_dir() -> str: - """Returns an output directory relative to custom 'dlc_output_dir' root""" - from pathlib import Path - - dlc_output_dir = dj.config.get("custom", {}).get("dlc_output_dir") - if dlc_output_dir: - return Path(dlc_output_dir) - else: - return None diff --git a/element_deeplabcut/pipeline.py b/element_deeplabcut/pipeline.py deleted file mode 100644 index 148cb70..0000000 --- a/element_deeplabcut/pipeline.py +++ /dev/null @@ -1,73 +0,0 @@ -import datajoint as dj -from element_lab import lab -from element_animal import subject -from element_session import session_with_datetime as session -from element_deeplabcut import train, model - -from element_animal.subject import Subject -from element_lab.lab import Source, Lab, Protocol, User, Project - -from paths import get_dlc_root_data_dir, get_dlc_processed_data_dir - -__all__ = [ - "get_dlc_root_data_dir", - "get_dlc_processed_data_dir", - "Subject", - "Source", - "Lab", - "Protocol", - "User", - "Project", - "Session", -] - -if "custom" not in dj.config: - dj.config["custom"] = {} - -db_prefix = dj.config["custom"].get("database.prefix", "") - -# Activate "lab", "subject", "session" schema ------------- - -lab.activate(db_prefix + "lab") - -subject.activate(db_prefix + "subject", linking_module=__name__) - -Experimenter = lab.User -Session = session.Session -session.activate(db_prefix + "session", linking_module=__name__) - -# Activate equipment table ------------------------------------ - - -@lab.schema -class Device(dj.Lookup): - """Table for managing lab equipment. - - In Element DeepLabCut, this table is referenced by `model.VideoRecording`. - The primary key is also used to generate inferred output directories when - running pose estimation inference. Refer to the `definition` attribute - for the table design. - - Attributes: - device ( varchar(32) ): Device short name. - modality ( varchar(64) ): Modality for which this device is used. - description ( varchar(256) ): Optional. Description of device. - """ - - definition = """ - device : varchar(32) - --- - modality : varchar(64) - description=null : varchar(256) - """ - contents = [ - ["Camera1", "Pose Estimation", "Panasonic HC-V380K"], - ["Camera2", "Pose Estimation", "Panasonic HC-V770K"], - ] - - -# Activate DeepLabCut schema ----------------------------------- - - -train.activate(db_prefix + "train", linking_module=__name__) -model.activate(db_prefix + "model", linking_module=__name__) diff --git a/notebooks/paths.py b/notebooks/paths.py deleted file mode 100644 index b42b264..0000000 --- a/notebooks/paths.py +++ /dev/null @@ -1,24 +0,0 @@ -import datajoint as dj -from collections import abc - - -def get_dlc_root_data_dir() -> list: - """Returns a list of root directories for Element DeepLabCut""" - dlc_root_dirs = dj.config.get("custom", {}).get("dlc_root_data_dir") - if not dlc_root_dirs: - return None - elif not isinstance(dlc_root_dirs, abc.Sequence): - return list(dlc_root_dirs) - else: - return dlc_root_dirs - - -def get_dlc_processed_data_dir() -> str: - """Returns an output directory relative to custom 'dlc_output_dir' root""" - from pathlib import Path - - dlc_output_dir = dj.config.get("custom", {}).get("dlc_output_dir") - if dlc_output_dir: - return Path(dlc_output_dir) - else: - return None diff --git a/notebooks/pipeline.py b/notebooks/pipeline.py index 1ac87c6..3201358 100644 --- a/notebooks/pipeline.py +++ b/notebooks/pipeline.py @@ -1,4 +1,5 @@ import datajoint as dj +from collections import abc from element_lab import lab from element_animal import subject from element_session import session_with_datetime as session @@ -7,11 +8,7 @@ from element_animal.subject import Subject from element_lab.lab import Source, Lab, Protocol, User, Project -from .paths import get_dlc_root_data_dir, get_dlc_processed_data_dir - __all__ = [ - "get_dlc_root_data_dir", - "get_dlc_processed_data_dir", "Subject", "Source", "Lab", @@ -26,6 +23,29 @@ db_prefix = dj.config["custom"].get("database.prefix", "") + +def get_dlc_root_data_dir() -> list: + """Returns a list of root directories for Element DeepLabCut""" + dlc_root_dirs = dj.config.get("custom", {}).get("dlc_root_data_dir") + if not dlc_root_dirs: + return None + elif not isinstance(dlc_root_dirs, abc.Sequence): + return list(dlc_root_dirs) + else: + return dlc_root_dirs + + +def get_dlc_processed_data_dir() -> str: + """Returns an output directory relative to custom 'dlc_output_dir' root""" + from pathlib import Path + + dlc_output_dir = dj.config.get("custom", {}).get("dlc_output_dir") + if dlc_output_dir: + return Path(dlc_output_dir) + else: + return None + + # Activate "lab", "subject", "session" schema ------------- lab.activate(db_prefix + "lab") From ca010719f011ce82b7e5c7d82bb95708784758de Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Fri, 4 Aug 2023 16:35:51 -0500 Subject: [PATCH 088/176] Update requirements configuration --- .gitignore | 2 ++ requirements.txt | 11 ----------- setup.py | 14 ++++++++++++-- 3 files changed, 14 insertions(+), 13 deletions(-) delete mode 100644 requirements.txt diff --git a/.gitignore b/.gitignore index ef9ee79..16d25d8 100644 --- a/.gitignore +++ b/.gitignore @@ -90,3 +90,5 @@ workflow_deeplabcut/ /docs/site/ /docs/src/tutorials/*ipynb /docs/mike-mkdocs* + +*.code-workspace \ No newline at end of file diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 55ef4ac..0000000 --- a/requirements.txt +++ /dev/null @@ -1,11 +0,0 @@ -datajoint>=0.13 -deeplabcut[tf]>=2.2.1.1 -element-interface>=0.3.0 -opencv-python-headless -element-lab>=0.2.0 -element-animal>=0.1.5 -element-session>=0.1.2 -element-deeplabcut>=0.2.2 -element-interface>=0.5.0 -ipykernel>=6.0.1 -pygit2 \ No newline at end of file diff --git a/setup.py b/setup.py index e491a64..5fe474b 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages from os import path -pkg_name = next(p for p in find_packages() if "." not in p) +pkg_name = "element_deeplabcut" here = path.abspath(path.dirname(__file__)) with open(path.join(here, "README.md"), "r") as f: @@ -26,5 +26,15 @@ keywords="neuroscience behavior deeplabcut datajoint", packages=find_packages(exclude=["contrib", "docs", "tests*"]), scripts=[], - install_requires=requirements, + install_requires=[ + "datajoint>=0.13", + "element-interface>=0.3.0", + "opencv-python-headless", + "element-lab>=0.2.0", + "element-animal>=0.1.5", + "element-session>=0.1.2", + "element-interface>=0.5.0", + "ipykernel>=6.0.1", + "pygit2", + ], ) From d6dad5989c36425e67b648968af1185c469f9ff1 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Fri, 4 Aug 2023 16:43:48 -0500 Subject: [PATCH 089/176] [WIP] Update notebook and mac requirements --- notebooks/tutorial.ipynb | 2196 ++++++++++---------------------------- setup.py | 7 + 2 files changed, 593 insertions(+), 1610 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index a7bb68f..3f20105 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -6,7 +6,7 @@ "tags": [] }, "source": [ - "# DataJoint U24 - Workflow DeepLabCut" + "# DataJoint Element DeepLabCut" ] }, { @@ -40,20 +40,204 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-08-04 16:31:58,365][INFO]: Connecting milagrosmarin@rds.datajoint.io:3306\n", + "[2023-08-04 16:31:58,753][INFO]: Connected milagrosmarin@rds.datajoint.io:3306\n", + "[2023-08-04 16:31:58,793][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n" + ] + } + ], "source": [ "import os\n", - "# change to the upper level folder to detect dj_local_conf.json\n", "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", - " + \"workflow directory\")" + "\n", + "import datajoint as dj\n", + "from pathlib import Path\n", + "import yaml\n", + "\n", + "# PATHS OF INPUT FILES: Extract abs and rel paths from .json file\n", + "dj.conn()\n", + "\n", + "### DLC Project\n", + "dlc_project_path_abs = Path(dj.config[\"custom\"][\"dlc_root_data_dir\"]) / Path(\n", + " dj.config[\"custom\"][\"current_project_folder\"]\n", + ") # use pathlib to join; abs path\n", + "dlc_project_folder = Path(\n", + " dj.config[\"custom\"][\"current_project_folder\"]\n", + ") # relative path\n", + "\n", + "### Config file\n", + "config_file_abs = dlc_project_path_abs / \"config.yaml\" # abs path\n", + "assert (\n", + " config_file_abs.exists()\n", + "), \"Please check the that you have the Top_tracking folder\"\n", + "\n", + "### Labeled-data\n", + "labeled_data_path_abs = dlc_project_path_abs / \"labeled-data\"\n", + "labeled_files_abs = list(\n", + " list(labeled_data_path_abs.rglob(\"*\"))[1].rglob(\"*\")\n", + ") # substitute 'training_files'; absolute path\n", + "labeled_files_rel = []\n", + "for file in labeled_files_abs:\n", + " labeled_files_rel.append(\n", + " file.relative_to(dlc_project_path_abs)\n", + " ) # substitute 'training_files'; relative path\n", + "\n", + "\n", + "from pipeline import lab, subject, session, train, model # after creating json file\n", + "\n", + "# Empty the session in case of rerunning\n", + "# session.Session.delete()\n", + "# train.TrainingTask.delete()\n", + "# train.TrainingParamSet.delete()\n", + "# train.VideoSet.delete()\n", + "\n", + "# Insert some data in session and train tables\n", + "# TO-DO: substitute lab.project by project schema." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "`Pipeline.py` activates the DataJoint `elements` and declares other required tables." + "dj.Diagram(subject) + dj.Diagram(lab) + dj.Diagram(session) + dj.Diagram(model) + dj.Diagram(train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Subject and Session tables\n", + "subject.Subject.insert1(\n", + " dict(\n", + " subject=\"subject6\",\n", + " sex=\"F\",\n", + " subject_birth_date=\"2020-01-01\",\n", + " subject_description=\"hneih_E105\",\n", + " ),\n", + " skip_duplicates=True,\n", + ")\n", + "session_keys = [\n", + " dict(subject=\"subject6\", session_datetime=\"2021-06-02 14:04:22\"),\n", + " dict(subject=\"subject6\", session_datetime=\"2021-06-03 14:43:10\"),\n", + "]\n", + "\n", + "session.Session.insert(session_keys, skip_duplicates=True)\n", + "session.Session() & \"session_datetime > '2021-06-01 12:00:00'\" & \"subject='subject6'\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Videoset tabley\n", + "train.VideoSet.insert1({\"video_set_id\": 0}, skip_duplicates=True)\n", + "\n", + "# training_files = #['labeled-data/train1_trimmed/CollectedData_DataJoint.h5',\n", + "#'labeled-data/train1_trimmed/CollectedData_DataJoint.csv']\n", + "#'labeled-data/train1_trimmed/img00674.png'] #TO-DO: CHECK IF ALL THE PNGS ARE NECESSARY FOR TRAINING\n", + "#'videos/train1.mp4']\n", + "# for idx, filename in enumerate(training_files):\n", + "for idx, filename in enumerate(labeled_files_rel):\n", + " train.VideoSet.File.insert1(\n", + " {\"video_set_id\": 0, \"file_id\": idx, \"file_path\": dlc_project_folder / filename},\n", + " skip_duplicates=True,\n", + " ) # Changed from + to /; #relative_path" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.VideoSet.File()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.list_schemas()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.schema.drop()\n", + "train.schema.drop()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Restrict the training interations to 5 modifying the default parameters in config.yaml\n", + "paramset_idx = 0\n", + "paramset_desc = \"First training test with DLC using shuffle 1 and maxiters = 5\"\n", + "\n", + "# default parameters\n", + "with open(config_file_abs, \"rb\") as y:\n", + " config_params = yaml.safe_load(y)\n", + "config_params.keys()\n", + "\n", + "# new parameters\n", + "training_params = {\n", + " \"shuffle\": \"1\",\n", + " \"trainingsetindex\": \"0\",\n", + " \"maxiters\": \"5\",\n", + " \"scorer_legacy\": \"False\", # For DLC ≤ v2.0, include scorer_legacy = True in params\n", + " \"maxiters\": \"5\",\n", + " \"multianimalproject\": \"False\",\n", + "}\n", + "config_params.update(training_params)\n", + "\n", + "train.TrainingParamSet.insert_new_params(\n", + " paramset_idx=paramset_idx, paramset_desc=paramset_desc, params=config_params\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TrainingTask table\n", + "key = {\n", + " \"video_set_id\": 0,\n", + " \"paramset_idx\": 0,\n", + " \"training_id\": 1,\n", + " \"project_path\": dlc_project_folder,\n", + "}\n", + "train.TrainingTask.insert1(key, skip_duplicates=True)\n", + "train.TrainingTask()" ] }, { @@ -61,14 +245,361 @@ "execution_count": 2, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ModelTraining: 0%| | 0/1 [00:00\n", + " app.launch_new_instance()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/traitlets/config/application.py\", line 1043, in launch_instance\n", + " app.start()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelapp.py\", line 736, in start\n", + " self.io_loop.start()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/tornado/platform/asyncio.py\", line 195, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n", + " self._run_once()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/asyncio/base_events.py\", line 1905, in _run_once\n", + " handle._run()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/asyncio/events.py\", line 80, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 516, in dispatch_queue\n", + " await self.process_one()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 505, in process_one\n", + " await dispatch(*args)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 412, in dispatch_shell\n", + " await result\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 740, in execute_request\n", + " reply_content = await reply_content\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/ipkernel.py\", line 422, in do_execute\n", + " res = shell.run_cell(\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/zmqshell.py\", line 546, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3009, in run_cell\n", + " result = self._run_cell(\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3064, in _run_cell\n", + " result = runner(coro)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3269, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3448, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3508, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"/var/folders/h8/_lx50k3d6qx586662kr066h40000gn/T/ipykernel_43888/556392206.py\", line 1, in \n", + " train.ModelTraining.populate(display_progress=True)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/datajoint/autopopulate.py\", line 241, in populate\n", + " error = self._populate1(key, jobs, **populate_kwargs)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/datajoint/autopopulate.py\", line 292, in _populate1\n", + " make(dict(key), **(make_kwargs or {}))\n", + " File \"/Users/milagros/Documents/element-deeplabcut/element_deeplabcut/train.py\", line 312, in make\n", + " train_network(dlc_cfg_filepath, **train_network_kwargs)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 210, in train_network\n", + " train(\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 168, in train\n", + " batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 69, in setup_preloading\n", + " enqueue_op = q.enqueue(placeholders_list)\n", + "Node: 'fifo_queue_enqueue'\n", + "Enqueue operation was cancelled\n", + "\t [[{{node fifo_queue_enqueue}}]]\n", + "\n", + "Original stack trace for 'fifo_queue_enqueue':\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/runpy.py\", line 197, in _run_module_as_main\n", + " return _run_code(code, main_globals, None,\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/runpy.py\", line 87, in _run_code\n", + " exec(code, run_globals)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel_launcher.py\", line 17, in \n", + " app.launch_new_instance()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/traitlets/config/application.py\", line 1043, in launch_instance\n", + " app.start()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelapp.py\", line 736, in start\n", + " self.io_loop.start()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/tornado/platform/asyncio.py\", line 195, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n", + " self._run_once()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/asyncio/base_events.py\", line 1905, in _run_once\n", + " handle._run()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/asyncio/events.py\", line 80, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 516, in dispatch_queue\n", + " await self.process_one()\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 505, in process_one\n", + " await dispatch(*args)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 412, in dispatch_shell\n", + " await result\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 740, in execute_request\n", + " reply_content = await reply_content\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/ipkernel.py\", line 422, in do_execute\n", + " res = shell.run_cell(\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/zmqshell.py\", line 546, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3009, in run_cell\n", + " result = self._run_cell(\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3064, in _run_cell\n", + " result = runner(coro)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3269, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3448, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3508, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"/var/folders/h8/_lx50k3d6qx586662kr066h40000gn/T/ipykernel_43888/556392206.py\", line 1, in \n", + " train.ModelTraining.populate(display_progress=True)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/datajoint/autopopulate.py\", line 241, in populate\n", + " error = self._populate1(key, jobs, **populate_kwargs)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/datajoint/autopopulate.py\", line 292, in _populate1\n", + " make(dict(key), **(make_kwargs or {}))\n", + " File \"/Users/milagros/Documents/element-deeplabcut/element_deeplabcut/train.py\", line 312, in make\n", + " train_network(dlc_cfg_filepath, **train_network_kwargs)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 210, in train_network\n", + " train(\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 168, in train\n", + " batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 69, in setup_preloading\n", + " enqueue_op = q.enqueue(placeholders_list)\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/tensorflow/python/ops/data_flow_ops.py\", line 346, in enqueue\n", + " return gen_data_flow_ops.queue_enqueue_v2(\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/tensorflow/python/ops/gen_data_flow_ops.py\", line 4068, in queue_enqueue_v2\n", + " _, _, _op, _outputs = _op_def_library._apply_op_helper(\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/tensorflow/python/framework/op_def_library.py\", line 795, in _apply_op_helper\n", + " op = g._create_op_internal(op_type_name, inputs, dtypes=None,\n", + " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 3814, in _create_op_internal\n", + " ret = Operation(\n", + "\n", + "ModelTraining: 100%|██████████| 1/1 [00:09<00:00, 9.96s/it]" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Connecting cbroz@dss-db.datajoint.io:3306\n" + "The network is now trained and ready to evaluate. Use the function 'evaluate_network' to evaluate the network.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "array([(0, 0, 1, 5, {'Task': 'Top_tracking', 'scorer': 'DataJoint', 'date': 'Aug3', 'multianimalproject': 'False', 'identity': None, 'project_path': '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03', 'video_sets': {'/Users/milagros/Documents/DeepLabCut_testing/test_data/Top_tracking-DataJoint-2023-08-03/videos/train1_trimmed.mp4': {'crop': '0, 500, 0, 500'}}, 'bodyparts': ['Head', 'Tailbase'], 'start': 0, 'stop': 1, 'numframes2pick': 5, 'skeleton': [['bodypart1', 'bodypart2'], ['objectA', 'bodypart3']], 'skeleton_color': 'black', 'pcutoff': 0.6, 'dotsize': 12, 'alphavalue': 0.7, 'colormap': 'rainbow', 'TrainingFraction': [0.95], 'iteration': 0, 'default_net_type': 'resnet_50', 'default_augmenter': 'default', 'snapshotindex': -1, 'batch_size': 8, 'cropping': False, 'x1': 0, 'x2': 640, 'y1': 277, 'y2': 624, 'corner2move2': [50, 50], 'move2corner': True, 'shuffle': '1', 'trainingsetindex': '0', 'maxiters': '5', 'scorer_legacy': 'False', 'modelprefix': '', 'train_fraction': 0.95, 'training_filelist_datajoint': ['/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/img0446.png', '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/CollectedData_DataJoint.h5', '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/img0482.png', '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/CollectedData_DataJoint.csv', '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/img1420.png', '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/img0822.png', '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/img1723.png']})],\n", + " dtype=[('video_set_id', '\n", - " .Relation{\n", - " border-collapse:collapse;\n", - " }\n", - " .Relation th{\n", - " background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n", - " font-weight: normal; font-family: monospace; font-size: 100%;\n", - " }\n", - " .Relation td{\n", - " padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n", - " }\n", - " .Relation tr:nth-child(odd){\n", - " background: #ffffff;\n", - " }\n", - " .Relation tr:nth-child(even){\n", - " background: #f3f1ff;\n", - " }\n", - " /* Tooltip container */\n", - " .djtooltip {\n", - " }\n", - " /* Tooltip text */\n", - " .djtooltip .djtooltiptext {\n", - " visibility: hidden;\n", - " width: 120px;\n", - " background-color: black;\n", - " color: #fff;\n", - " text-align: center;\n", - " padding: 5px 0;\n", - " border-radius: 6px;\n", - " /* Position the tooltip text - see examples below! */\n", - " position: absolute;\n", - " z-index: 1;\n", - " }\n", - " #primary {\n", - " font-weight: bold;\n", - " color: black;\n", - " }\n", - " #nonprimary {\n", - " font-weight: normal;\n", - " color: white;\n", - " }\n", - "\n", - " /* Show the tooltip text when you mouse over the tooltip container */\n", - " .djtooltip:hover .djtooltiptext {\n", - " visibility: visible;\n", - " }\n", - " \n", - " \n", - " \n", - "
    \n", - "
    \n", - " \n", - " \n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    subject62021-06-02 14:04:22
    subject62021-06-03 14:43:10
    \n", - " \n", - "

    Total: 2

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet\n", - "+----------+ +------------+\n", - "subject6 2021-06-02 14:\n", - "subject6 2021-06-03 14:\n", - " (Total: 2)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "session.Session() & \"session_datetime > '2021-06-01 12:00:00'\" & \"subject='subject6'\"" ] @@ -259,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -277,104 +712,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " Paths of training files (e.g., labeled pngs, CSV or video)\n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    video_set_id

    \n", - " \n", - "
    \n", - "

    file_id

    \n", - " \n", - "
    \n", - "

    file_path

    \n", - " \n", - "
    00from_top_tracking/labeled-data/train1/CollectedData_DJ.h5
    01from_top_tracking/labeled-data/train1/CollectedData_DJ.csv
    02from_top_tracking/labeled-data/train1/img00674.png
    03from_top_tracking/videos/train1.mp4
    \n", - " \n", - "

    Total: 4

    \n", - " " - ], - "text/plain": [ - "*video_set_id *file_id file_path \n", - "+------------+ +---------+ +------------+\n", - "0 0 from_top_track\n", - "0 1 from_top_track\n", - "0 2 from_top_track\n", - "0 3 from_top_track\n", - " (Total: 4)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "train.VideoSet.File()" ] @@ -399,21 +739,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "paramset_idx : smallint # \n", - "---\n", - "paramset_desc : varchar(128) # \n", - "param_set_hash : uuid # hash identifying this parameterset\n", - "params : longblob # dictionary of all applicable parameters" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "train.TrainingParamSet.heading" ] @@ -427,102 +753,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading DLC 2.2.1.1...\n", - "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n", - "Help on function train_network in module deeplabcut.pose_estimation_tensorflow.training:\n", - "\n", - "train_network(config, shuffle=1, trainingsetindex=0, max_snapshots_to_keep=5, displayiters=None, saveiters=None, maxiters=None, allow_growth=True, gputouse=None, autotune=False, keepdeconvweights=True, modelprefix='')\n", - " Trains the network with the labels in the training dataset.\n", - " \n", - " Parameters\n", - " ----------\n", - " config : string\n", - " Full path of the config.yaml file as a string.\n", - " \n", - " shuffle: int, optional, default=1\n", - " Integer value specifying the shuffle index to select for training.\n", - " \n", - " trainingsetindex: int, optional, default=0\n", - " Integer specifying which TrainingsetFraction to use.\n", - " Note that TrainingFraction is a list in config.yaml.\n", - " \n", - " max_snapshots_to_keep: int or None\n", - " Sets how many snapshots are kept, i.e. states of the trained network. Every\n", - " saving interation many times a snapshot is stored, however only the last\n", - " ``max_snapshots_to_keep`` many are kept! If you change this to None, then all\n", - " are kept.\n", - " See: https://github.com/DeepLabCut/DeepLabCut/issues/8#issuecomment-387404835\n", - " \n", - " displayiters: optional, default=None\n", - " This variable is actually set in ``pose_config.yaml``. However, you can\n", - " overwrite it with this hack. Don't use this regularly, just if you are too lazy\n", - " to dig out the ``pose_config.yaml`` file for the corresponding project. If\n", - " ``None``, the value from there is used, otherwise it is overwritten!\n", - " \n", - " saveiters: optional, default=None\n", - " This variable is actually set in ``pose_config.yaml``. However, you can\n", - " overwrite it with this hack. Don't use this regularly, just if you are too lazy\n", - " to dig out the ``pose_config.yaml`` file for the corresponding project.\n", - " If ``None``, the value from there is used, otherwise it is overwritten!\n", - " \n", - " maxiters: optional, default=None\n", - " This variable is actually set in ``pose_config.yaml``. However, you can\n", - " overwrite it with this hack. Don't use this regularly, just if you are too lazy\n", - " to dig out the ``pose_config.yaml`` file for the corresponding project.\n", - " If ``None``, the value from there is used, otherwise it is overwritten!\n", - " \n", - " allow_growth: bool, optional, default=True.\n", - " For some smaller GPUs the memory issues happen. If ``True``, the memory\n", - " allocator does not pre-allocate the entire specified GPU memory region, instead\n", - " starting small and growing as needed.\n", - " See issue: https://forum.image.sc/t/how-to-stop-running-out-of-vram/30551/2\n", - " \n", - " gputouse: optional, default=None\n", - " Natural number indicating the number of your GPU (see number in nvidia-smi).\n", - " If you do not have a GPU put None.\n", - " See: https://nvidia.custhelp.com/app/answers/detail/a_id/3751/~/useful-nvidia-smi-queries\n", - " \n", - " autotune: bool, optional, default=False\n", - " Property of TensorFlow, somehow faster if ``False``\n", - " (as Eldar found out, see https://github.com/tensorflow/tensorflow/issues/13317).\n", - " \n", - " keepdeconvweights: bool, optional, default=True\n", - " Also restores the weights of the deconvolution layers (and the backbone) when\n", - " training from a snapshot. Note that if you change the number of bodyparts, you\n", - " need to set this to false for re-training.\n", - " \n", - " modelprefix: str, optional, default=\"\"\n", - " Directory containing the deeplabcut models to use when evaluating the network.\n", - " By default, the models are assumed to exist in the project folder.\n", - " \n", - " Returns\n", - " -------\n", - " None\n", - " \n", - " Examples\n", - " --------\n", - " To train the network for first shuffle of the training dataset\n", - " \n", - " >>> deeplabcut.train_network('/analysis/project/reaching-task/config.yaml')\n", - " \n", - " To train the network for second shuffle of the training dataset\n", - " \n", - " >>> deeplabcut.train_network(\n", - " '/analysis/project/reaching-task/config.yaml',\n", - " shuffle=2,\n", - " keepdeconvweights=True,\n", - " )\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "from deeplabcut import train_network\n", "help(train_network) # for more information on optional parameters" @@ -537,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -570,125 +803,16 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "video_set_id : int # \n", - "paramset_idx : smallint # \n", - "training_id : int # \n", - "---\n", - "model_prefix=\"\" : varchar(32) # \n", - "project_path=\"\" : varchar(255) # DLC's project_path in config relative to root" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "train.TrainingTask.heading" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " Specification for a DLC model training instance\n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    video_set_id

    \n", - " \n", - "
    \n", - "

    paramset_idx

    \n", - " \n", - "
    \n", - "

    training_id

    \n", - " \n", - "
    \n", - "

    model_prefix

    \n", - " \n", - "
    \n", - "

    project_path

    \n", - " DLC's project_path in config relative to root\n", - "
    001from_top_tracking/
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*video_set_id *paramset_idx *training_id model_prefix project_path \n", - "+------------+ +------------+ +------------+ +------------+ +------------+\n", - "0 0 1 from_top_track\n", - " (Total: 1)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "key={'video_set_id': 0,\n", " 'paramset_idx':0,\n", @@ -721,104 +845,10 @@ ] }, { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    video_set_id

    \n", - " \n", - "
    \n", - "

    paramset_idx

    \n", - " \n", - "
    \n", - "

    training_id

    \n", - " \n", - "
    \n", - "

    latest_snapshot

    \n", - " latest exact snapshot index (i.e., never -1)\n", - "
    \n", - "

    config_template

    \n", - " stored full config file\n", - "
    0015=BLOB=
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*video_set_id *paramset_idx *training_id latest_snapsho config_tem\n", - "+------------+ +------------+ +------------+ +------------+ +--------+\n", - "0 0 1 5 =BLOB= \n", - " (Total: 1)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "train.ModelTraining()" ] @@ -836,7 +866,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -865,20 +895,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# \n", - "body_part : varchar(32) # \n", - "---\n", - "body_part_description=\"\" : varchar(1000) # " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "model.BodyPart.heading" ] @@ -892,47 +909,18 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Existing body parts: ['bodycenter' 'head' 'tailbase']\n", - "New body parts: []\n" - ] - }, - { - "data": { - "text/plain": [ - "array([], dtype='\n", - " .Relation{\n", - " border-collapse:collapse;\n", - " }\n", - " .Relation th{\n", - " background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n", - " font-weight: normal; font-family: monospace; font-size: 100%;\n", - " }\n", - " .Relation td{\n", - " padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n", - " }\n", - " .Relation tr:nth-child(odd){\n", - " background: #ffffff;\n", - " }\n", - " .Relation tr:nth-child(even){\n", - " background: #f3f1ff;\n", - " }\n", - " /* Tooltip container */\n", - " .djtooltip {\n", - " }\n", - " /* Tooltip text */\n", - " .djtooltip .djtooltiptext {\n", - " visibility: hidden;\n", - " width: 120px;\n", - " background-color: black;\n", - " color: #fff;\n", - " text-align: center;\n", - " padding: 5px 0;\n", - " border-radius: 6px;\n", - " /* Position the tooltip text - see examples below! */\n", - " position: absolute;\n", - " z-index: 1;\n", - " }\n", - " #primary {\n", - " font-weight: bold;\n", - " color: black;\n", - " }\n", - " #nonprimary {\n", - " font-weight: normal;\n", - " color: white;\n", - " }\n", - "\n", - " /* Show the tooltip text when you mouse over the tooltip container */\n", - " .djtooltip:hover .djtooltiptext {\n", - " visibility: visible;\n", - " }\n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    model_name

    \n", - " User-friendly model name\n", - "
    \n", - "

    task

    \n", - " Task in the config yaml\n", - "
    \n", - "

    date

    \n", - " Date in the config yaml\n", - "
    \n", - "

    iteration

    \n", - " Iteration/version of this model\n", - "
    \n", - "

    snapshotindex

    \n", - " which snapshot for prediction (if -1, latest)\n", - "
    \n", - "

    shuffle

    \n", - " Shuffle (1) or not (0)\n", - "
    \n", - "

    trainingsetindex

    \n", - " Index of training fraction list in config.yaml\n", - "
    \n", - "

    scorer

    \n", - " Scorer/network name - DLC's GetScorerName()\n", - "
    \n", - "

    config_template

    \n", - " Dictionary of the config for analyze_videos()\n", - "
    \n", - "

    project_path

    \n", - " DLC's project_path in config relative to root\n", - "
    \n", - "

    model_prefix

    \n", - " \n", - "
    \n", - "

    model_description

    \n", - " \n", - "
    \n", - "

    paramset_idx

    \n", - " \n", - "
    FromTop-latestfrom_top_trackingFeb230-110DLCmobnet100fromtoptrackingFeb23shuffle1=BLOB=from_top_trackingFromTop - latest snapshot0
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*model_name task date iteration snapshotindex shuffle trainingsetind scorer config_tem project_path model_prefix model_descript paramset_idx \n", - "+------------+ +------------+ +-------+ +-----------+ +------------+ +---------+ +------------+ +------------+ +--------+ +------------+ +------------+ +------------+ +------------+\n", - "FromTop-latest from_top_track Feb23 0 -1 1 0 DLCmobnet100fr =BLOB= from_top_track FromTop - late 0 \n", - " (Total: 1)" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.Model()" ] @@ -1171,218 +976,25 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "model_name : varchar(64) # user-friendly model name\n", - "---\n", - "train_iterations : int # Training iterations\n", - "train_error=null : float # Train error (px)\n", - "test_error=null : float # Test error (px)\n", - "p_cutoff=null : float # p-cutoff used\n", - "train_error_p=null : float # Train error with p-cutoff\n", - "test_error_p=null : float # Test error with p-cutoff" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "model.ModelEvaluation.heading" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Config:\n", - "{'all_joints': [[0], [1], [2]],\n", - " 'all_joints_names': ['head', 'bodycenter', 'tailbase'],\n", - " 'batch_size': 1,\n", - " 'crop_pad': 0,\n", - " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_from_top_trackingFeb23/from_top_tracking_DJ95shuffle1.mat',\n", - " 'dataset_type': 'imgaug',\n", - " 'deterministic': False,\n", - " 'fg_fraction': 0.25,\n", - " 'global_scale': 0.8,\n", - " 'init_weights': '/Volumes/GoogleDrive/My '\n", - " 'Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/models/pretrained/mobilenet_v2_1.0_224.ckpt',\n", - " 'intermediate_supervision': False,\n", - " 'intermediate_supervision_layer': 12,\n", - " 'location_refinement': True,\n", - " 'locref_huber_loss': True,\n", - " 'locref_loss_weight': 1.0,\n", - " 'locref_stdev': 7.2801,\n", - " 'log_dir': 'log',\n", - " 'mean_pixel': [123.68, 116.779, 103.939],\n", - " 'mirror': False,\n", - " 'net_type': 'mobilenet_v2_1.0',\n", - " 'num_joints': 3,\n", - " 'optimizer': 'sgd',\n", - " 'pairwise_huber_loss': True,\n", - " 'pairwise_predict': False,\n", - " 'partaffinityfield_predict': False,\n", - " 'regularize': False,\n", - " 'scoremap_dir': 'test',\n", - " 'shuffle': True,\n", - " 'snapshot_prefix': '/tmp/test_data/from_top_tracking/dlc-models/iteration-0/from_top_trackingFeb23-trainset95shuffle1/test/snapshot',\n", - " 'stride': 8.0,\n", - " 'weigh_negatives': False,\n", - " 'weigh_only_present_joints': False,\n", - " 'weigh_part_predictions': False,\n", - " 'weight_decay': 0.0001}\n", - "/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n", - " warnings.warn('`layer.apply` is deprecated and '\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running DLC_mobnet_100_from_top_trackingFeb23shuffle1_103000 with # of training iterations: 103000\n", - "Running evaluation ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "20it [00:06, 3.29it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis is done and the results are stored (see evaluation-results) for snapshot: snapshot-103000\n", - "Results for 103000 training iterations: 95 1 train error: 9.28 pixels. Test error: 9.84 pixels.\n", - "With pcutoff of 0.6 train error: 9.28 pixels. Test error: 9.84 pixels\n", - "Thereby, the errors are given by the average distances between the labels by DLC and the scorer.\n", - "The network is evaluated and the results are stored in the subdirectory 'evaluation_results'.\n", - "Please check the results, then choose the best model (snapshot) for prediction. You can update the config.yaml file with the appropriate index for the 'snapshotindex'.\n", - "Use the function 'analyze_video' to make predictions on new videos.\n", - "Otherwise, consider adding more labeled-data and retraining the network (see DeepLabCut workflow Fig 2, Nath 2019)\n" - ] - } - ], + "outputs": [], "source": [ "model.ModelEvaluation.populate()" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    model_name

    \n", - " User-friendly model name\n", - "
    \n", - "

    train_iterations

    \n", - " Training iterations\n", - "
    \n", - "

    train_error

    \n", - " Train error (px)\n", - "
    \n", - "

    test_error

    \n", - " Test error (px)\n", - "
    \n", - "

    p_cutoff

    \n", - " p-cutoff used\n", - "
    \n", - "

    train_error_p

    \n", - " Train error with p-cutoff\n", - "
    \n", - "

    test_error_p

    \n", - " Test error with p-cutoff\n", - "
    FromTop-latest1030009.289.840.69.289.84
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*model_name train_iteratio train_error test_error p_cutoff train_error_p test_error_p \n", - "+------------+ +------------+ +------------+ +------------+ +----------+ +------------+ +------------+\n", - "FromTop-latest 103000 9.28 9.84 0.6 9.28 9.84 \n", - " (Total: 1)" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.ModelEvaluation()" ] @@ -1403,103 +1015,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", - " \n", - "
    \n", - "

    device

    \n", - " \n", - "
    \n", - " \n", - "

    Total: 0

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet *recording_id device \n", - "+---------+ +------------+ +------------+ +--------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.VideoRecording()" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1516,103 +1041,9 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", - " \n", - "
    \n", - "

    file_id

    \n", - " \n", - "
    \n", - "

    file_path

    \n", - " filepath of video, relative to root data directory\n", - "
    subject62021-06-02 14:04:2211from_top_tracking/videos/test-2s.mp4
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet *recording_id *file_id file_path \n", - "+----------+ +------------+ +------------+ +---------+ +------------+\n", - "subject6 2021-06-02 14: 1 1 from_top_track\n", - " (Total: 1)" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.VideoRecording.File()" ] @@ -1626,119 +1057,9 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", - " \n", - "
    \n", - "

    px_height

    \n", - " height in pixels\n", - "
    \n", - "

    px_width

    \n", - " width in pixels\n", - "
    \n", - "

    nframes

    \n", - " number of frames\n", - "
    \n", - "

    fps

    \n", - " (Hz) frames per second\n", - "
    \n", - "

    recording_datetime

    \n", - " Datetime for the start of the recording\n", - "
    \n", - "

    recording_duration

    \n", - " video duration (s) from nframes / fps\n", - "
    subject62021-06-02 14:04:22150050012360None2.05
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet *recording_id px_height px_width nframes fps recording_date recording_dura\n", - "+----------+ +------------+ +------------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n", - "subject6 2021-06-02 14: 1 500 500 123 60 None 2.05 \n", - " (Total: 1)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.RecordingInfo.populate()\n", "model.RecordingInfo()" @@ -1753,24 +1074,9 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'subject': 'subject6',\n", - " 'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n", - " 'recording_id': 1,\n", - " 'model_name': 'FromTop-latest',\n", - " 'task_mode': 'trigger'}" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "key = (model.VideoRecording & {'recording_id': '1'}).fetch1('KEY')\n", "key.update({'model_name': 'FromTop-latest', 'task_mode': 'trigger'})\n", @@ -1779,84 +1085,9 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Config:\n", - "{'all_joints': [[0], [1], [2]],\n", - " 'all_joints_names': ['head', 'bodycenter', 'tailbase'],\n", - " 'batch_size': 1,\n", - " 'crop_pad': 0,\n", - " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_from_top_trackingFeb23/from_top_tracking_DJ95shuffle1.mat',\n", - " 'dataset_type': 'imgaug',\n", - " 'deterministic': False,\n", - " 'fg_fraction': 0.25,\n", - " 'global_scale': 0.8,\n", - " 'init_weights': '/Volumes/GoogleDrive/My '\n", - " 'Drive/ref/DeepLabCut/deeplabcut/pose_estimation_tensorflow/models/pretrained/mobilenet_v2_1.0_224.ckpt',\n", - " 'intermediate_supervision': False,\n", - " 'intermediate_supervision_layer': 12,\n", - " 'location_refinement': True,\n", - " 'locref_huber_loss': True,\n", - " 'locref_loss_weight': 1.0,\n", - " 'locref_stdev': 7.2801,\n", - " 'log_dir': 'log',\n", - " 'mean_pixel': [123.68, 116.779, 103.939],\n", - " 'mirror': False,\n", - " 'net_type': 'mobilenet_v2_1.0',\n", - " 'num_joints': 3,\n", - " 'optimizer': 'sgd',\n", - " 'pairwise_huber_loss': True,\n", - " 'pairwise_predict': False,\n", - " 'partaffinityfield_predict': False,\n", - " 'regularize': False,\n", - " 'scoremap_dir': 'test',\n", - " 'shuffle': True,\n", - " 'snapshot_prefix': '/tmp/test_data/from_top_tracking/dlc-models/iteration-0/from_top_trackingFeb23-trainset95shuffle1/test/snapshot',\n", - " 'stride': 8.0,\n", - " 'weigh_negatives': False,\n", - " 'weigh_only_present_joints': False,\n", - " 'weigh_part_predictions': False,\n", - " 'weight_decay': 0.0001}\n", - "/Users/cb/miniconda3/envs/ele/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n", - " warnings.warn('`layer.apply` is deprecated and '\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using snapshot-103000 for model /tmp/test_data/from_top_tracking/dlc-models/iteration-0/from_top_trackingFeb23-trainset95shuffle1\n", - "Starting to analyze % /tmp/test_data/from_top_tracking/videos/test-2s.mp4\n", - "Loading /tmp/test_data/from_top_tracking/videos/test-2s.mp4\n", - "Duration of video [s]: 2.05 , recorded with 60.0 fps!\n", - "Overall # of frames: 123 found with (before cropping) frame dimensions: 500 500\n", - "Starting to extract posture\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 98%|█████████▊| 120/123 [00:37<00:00, 3.22it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saving results in /tmp/test_data/from_top_tracking/videos/device_Camera1_recording_1_model_FromTop-latest...\n", - "Saving csv poses!\n", - "The videos are analyzed. Now your research can truly start! \n", - " You can create labeled videos with 'create_labeled_video'\n", - "If the tracking is not satisfactory for some videos, consider expanding the training set. You can use the function 'extract_outlier_frames' to extract a few representative outlier frames.\n" - ] - } - ], + "outputs": [], "source": [ "model.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True})\n", "model.PoseEstimation.populate()" @@ -1875,264 +1106,9 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    scorerFromTop-latest
    bodypartsbodycenterheadtailbase
    coordsxyzlikelihoodxyzlikelihoodxyzlikelihood
    0246.782684298.7280880.00.999998241.036957316.3324890.00.999850256.203064278.5533140.00.999998
    1246.217529299.3580630.00.999997239.048737319.1770020.00.999905255.819626280.2007450.00.999996
    2244.459579301.3092350.00.999999240.238800320.5256960.00.999899255.705093280.9390560.00.999995
    3242.014755302.8652040.00.999999238.536774322.3244630.00.999941254.424484282.0157780.00.999990
    4240.900177303.4591670.00.999998237.967987324.0723270.00.999941252.180603280.8992000.00.999977
    .......................................
    118248.682251364.7098690.00.999965270.854980371.8931270.00.999961234.899185356.0355830.00.999996
    119250.326385366.8703610.00.999972271.488495373.0998840.00.999991235.644073356.8151250.00.999989
    120251.634140367.7091980.00.999972272.043884373.4028930.00.999995236.953812358.6514590.00.999977
    121255.393692364.1111450.00.999979273.417572373.9067990.00.999997238.825363361.5617980.00.999885
    122257.736847365.2640080.00.999996276.008667373.9012450.00.999992239.148163364.0292970.00.999962
    \n", - "

    123 rows × 12 columns

    \n", - "
    " - ], - "text/plain": [ - "scorer FromTop-latest \\\n", - "bodyparts bodycenter head \n", - "coords x y z likelihood x y \n", - "0 246.782684 298.728088 0.0 0.999998 241.036957 316.332489 \n", - "1 246.217529 299.358063 0.0 0.999997 239.048737 319.177002 \n", - "2 244.459579 301.309235 0.0 0.999999 240.238800 320.525696 \n", - "3 242.014755 302.865204 0.0 0.999999 238.536774 322.324463 \n", - "4 240.900177 303.459167 0.0 0.999998 237.967987 324.072327 \n", - ".. ... ... ... ... ... ... \n", - "118 248.682251 364.709869 0.0 0.999965 270.854980 371.893127 \n", - "119 250.326385 366.870361 0.0 0.999972 271.488495 373.099884 \n", - "120 251.634140 367.709198 0.0 0.999972 272.043884 373.402893 \n", - "121 255.393692 364.111145 0.0 0.999979 273.417572 373.906799 \n", - "122 257.736847 365.264008 0.0 0.999996 276.008667 373.901245 \n", - "\n", - "scorer \n", - "bodyparts tailbase \n", - "coords z likelihood x y z likelihood \n", - "0 0.0 0.999850 256.203064 278.553314 0.0 0.999998 \n", - "1 0.0 0.999905 255.819626 280.200745 0.0 0.999996 \n", - "2 0.0 0.999899 255.705093 280.939056 0.0 0.999995 \n", - "3 0.0 0.999941 254.424484 282.015778 0.0 0.999990 \n", - "4 0.0 0.999941 252.180603 280.899200 0.0 0.999977 \n", - ".. ... ... ... ... ... ... \n", - "118 0.0 0.999961 234.899185 356.035583 0.0 0.999996 \n", - "119 0.0 0.999991 235.644073 356.815125 0.0 0.999989 \n", - "120 0.0 0.999995 236.953812 358.651459 0.0 0.999977 \n", - "121 0.0 0.999997 238.825363 361.561798 0.0 0.999885 \n", - "122 0.0 0.999992 239.148163 364.029297 0.0 0.999962 \n", - "\n", - "[123 rows x 12 columns]" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.PoseEstimation.get_trajectory(key)" ] @@ -2164,7 +1140,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.9.17" }, "vscode": { "interpreter": { diff --git a/setup.py b/setup.py index 5fe474b..55ed97b 100644 --- a/setup.py +++ b/setup.py @@ -37,4 +37,11 @@ "ipykernel>=6.0.1", "pygit2", ], + extras_requires={ + "default": ["deeplabcut[tf]>=2.2.1.1"], + "apple_mchips": [ + "deeplabcut[apple_mchips]", + "tables=3.7.0", + ], # "tensorflow-deps", + }, ) From 5f50b4b61d05f5e3fb9ea82bde0706c0e031ff5e Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Tue, 8 Aug 2023 15:59:02 +0200 Subject: [PATCH 090/176] Update name from `pipeline` to `tutorial_pipeline` --- notebooks/{pipeline.py => tutorial_pipeline.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename notebooks/{pipeline.py => tutorial_pipeline.py} (100%) diff --git a/notebooks/pipeline.py b/notebooks/tutorial_pipeline.py similarity index 100% rename from notebooks/pipeline.py rename to notebooks/tutorial_pipeline.py From 3a2eba8767cfee13923bb1963be72775a2006d7f Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Sat, 12 Aug 2023 05:17:51 +0200 Subject: [PATCH 091/176] Update intro tutorial notebook --- notebooks/tutorial.ipynb | 31 ++++++++++++++++++++++--------- 1 file changed, 22 insertions(+), 9 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 3f20105..618b707 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -11,29 +11,42 @@ }, { "cell_type": "markdown", - "metadata": { - "tags": [] + "metadata": {}, + "source": [ + "Open-source data pipeline to automate analyses and organize data.\n", + "\n", + "In this tutorial, we will walk through creating, testing, and analyzing a pose estimation model using DeepLabCut.\n", + "\n", + "For detailed documentation and tutorials on general DataJoint principles that support collaboration, automation, reproducibility, and visualizations:\n", + "\n", + "[`DataJoint for Python - Interactive Tutorials`](https://github.com/datajoint/datajoint-tutorials) - Fundamentals including table tiers, query operations, fetch operations, automated computations with the make function, etc.\n", + "\n", + "[`DataJoint for Python - Documentation`](https://datajoint.com/docs/core/datajoint-python/0.14/)\n", + "\n", + "[`DataJoint Element for DeepLabCut - Documentation`](https://datajoint.com/docs/elements/element-deeplabcut/0.2/)" + ] }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "## Interactively run the workflow" + "## 1. Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "The workflow requires a DeepLabCut project with labeled data.\n", - "- If you don't have data, refer to [00-DataDownload](./00-DataDownload_Optional.ipynb) and [01-Configure](./01-Configure.ipynb).\n", - "- For an overview of the schema, refer to [02-WorkflowStructure](02-WorkflowStructure_Optional.ipynb).\n", - "- For a more automated approach, refer to [03-Automate](03-Automate_Optional.ipynb)." + "### 1.1. Sample dataset in a DeepLabCut project" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's change the directory to load the local config, `dj_local_conf.json`." + "These notebooks are built around data provided by DataJoint, including a well-trained model. \n", + "\n", + "We will use the following dataset `DeepLabCut project example` as an example across this tutorial. You can download this project example here:" ] }, { From 4e2a7eb020dde70cdf0db863bcc42b394b6a29a0 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Sat, 12 Aug 2023 05:34:47 +0200 Subject: [PATCH 092/176] Tutorial: major changes & network training tested --- notebooks/tutorial.ipynb | 911 ++++++++++++++++----------------------- 1 file changed, 383 insertions(+), 528 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 618b707..d5ff7ca 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -53,28 +53,181 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2. Configuring DataJoint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to `configure the connection` of the DataJoint schemas to the same database server with the same user credentials. \n", + "\n", + "- If this is the first time that you are running this tutorial, then you will need to specify the connection parameters by input arguments in `1.2.1. Configuration Code for Initiating this Tutorial`. To prevent this to be necessary for further analysis in this tutorial, in the next step we will create a DataJoint configuration file named `dj_local_conf.json` that will save these credentials arguments as environment variables (DJ_HOST, DJ_USER, DJ_PASS). Thus, the configuration file is unique to each machine and will be created in your `Element-Deeplabcut` directory.\n", + "\n", + "- If you have already run this tutorial and created the `.json` file with your credentials info, then you can directly jump to the `1.2.2. Configuration code to configure this tutorial in subsequent restarts`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 1.2.1. Configuration Code for Initiating This Tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### *The configuration file only needs to be set up once. If you already have one, jump to the following subsection `1.2.2. Configuration code to configure this tutorial in subsequent restarts`*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# By convention, we set a local config in the workflow directory.\n", + "import os\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "assert os.path.basename(os.getcwd())=='element-deeplabcut', (\"Please move to the \"\n", + " + \"element directory\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the packages necessary to run this DataJoint pipeline `Element-DeepLabCut`\n", + "import datajoint as dj\n", + "from pathlib import Path\n", + "import yaml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The connection parameters are specified by input arguments:\n", + "- HOST, USER, AND PASSWORD are the fields for the user credentials\n", + "- Configuring a `custom` field helps manage privileges on a server,for instance, teams who work on the same schemas should use the same schema prefix. \n", + " - Setting the prefix to `dlc_` means that every schema we then create will start with `dlc_` (e.g. `dlc_lab`, `dlc_subject`, `dlc_model` etc.)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "dj.config['database.host'] = '{YOUR_HOST}' \n", + "dj.config['database.user'] = '{YOUR_USERNAME}' \n", + "dj.config['database.password'] = getpass.getpass() # enter the password securely\n", + "dj.config['custom']['database.prefix']= '{YOUR_USERNAME_dlc_}' " + ] + }, + { + "cell_type": "code", + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The input arguments are saved in a configuration file named `dj_local_config.json`\n", + "dj.config.save_local() " + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-08-04 16:31:58,365][INFO]: Connecting milagrosmarin@rds.datajoint.io:3306\n", - "[2023-08-04 16:31:58,753][INFO]: Connected milagrosmarin@rds.datajoint.io:3306\n", - "[2023-08-04 16:31:58,793][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n" - ] - } - ], + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.conn()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once set the configuration file, it will be created and saved as `dj_local_conf.json` in the `Element-DeepLabCut directory`. Please, check this file and its content. Remember that this step only needs to be set up once." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.2.2. Configuration code to configure this tutorial in subsequent restarts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you have already created and saved the `dj_local_conf.json` file, then you only need to run the following code lines after restart the kernel of the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ + "# By convention, we set a local config in the workflow directory.\n", "import os\n", "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "\n", + "assert os.path.basename(os.getcwd())=='element-deeplabcut', (\"Please move to the \"\n", + " + \"element directory\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the packages necessary to run this DataJoint pipeline `Element-DeepLabCut`\n", "import datajoint as dj\n", "from pathlib import Path\n", - "import yaml\n", - "\n", - "# PATHS OF INPUT FILES: Extract abs and rel paths from .json file\n", - "dj.conn()\n", - "\n", + "import yaml" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.conn()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Design the DataJoint pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the project path specified in the `.json` file, the paths of the input files are extracted to be used later:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "### DLC Project\n", "dlc_project_path_abs = Path(dj.config[\"custom\"][\"dlc_root_data_dir\"]) / Path(\n", " dj.config[\"custom\"][\"current_project_folder\"]\n", @@ -98,19 +251,29 @@ "for file in labeled_files_abs:\n", " labeled_files_rel.append(\n", " file.relative_to(dlc_project_path_abs)\n", - " ) # substitute 'training_files'; relative path\n", + " ) # substitute 'training_files'; relative path\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combine multiple Elements into a pipeline\n", "\n", + "Each DataJoint Element is a modular set of tables that can be combined into a complete pipeline.\n", "\n", - "from pipeline import lab, subject, session, train, model # after creating json file\n", + "Each Element contains one or more modules, and each module declares its own schema in the database. Schemas are conceptually related sets of tables. \n", "\n", - "# Empty the session in case of rerunning\n", - "# session.Session.delete()\n", - "# train.TrainingTask.delete()\n", - "# train.TrainingParamSet.delete()\n", - "# train.VideoSet.delete()\n", + "This tutorial pipeline is assembled from four DataJoint Elements.\n", + "\n", + "| Element | Source Code | Documentation | Description |\n", + "| -- | -- | -- | -- |\n", + "| Element Lab | [Link](https://github.com/datajoint/element-lab) | [Link](https://datajoint.com/docs/elements/element-lab) | Lab management related information, such as Lab, User, Project, Protocol, Source. |\n", + "| Element Animal | [Link](https://github.com/datajoint/element-animal) | [Link](https://datajoint.com/docs/elements/element-animal) | General subject meta data, genotype, and surgery information. |\n", + "| Element Session | [Link](https://github.com/datajoint/element-session) | [Link](https://datajoint.com/docs/elements/element-session) | General information of experimental sessions. |\n", + "| Element DeepLabCut | [Link](https://github.com/datajoint/element-deeplabcut) | [Link](https://datajoint.com/docs/elements/element-deeplabcut) | DataJoint schemas (Train and Model) for storing and running analysis of markerless pose estimation with DeepLabCut.\n", "\n", - "# Insert some data in session and train tables\n", - "# TO-DO: substitute lab.project by project schema." + "The Elements are imported and activated within the `tutorial_pipeline` python script." ] }, { @@ -119,16 +282,16 @@ "metadata": {}, "outputs": [], "source": [ - "dj.Diagram(subject) + dj.Diagram(lab) + dj.Diagram(session) + dj.Diagram(model) + dj.Diagram(train)" + "from tutorial_pipeline import lab, subject, session, train, model # after creating json file" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "subject.Subject()" + "By importing the modules for the first time, the schemas and tables will be created in the database. \n", + "- Once created, importing modules will not create schemas and tables again, but the existing schemas/tables can be accessed.\n", + "- To empty these schemas and tables for introducing new entries, run (uncomment) the following code lines:" ] }, { @@ -137,44 +300,18 @@ "metadata": {}, "outputs": [], "source": [ - "# Subject and Session tables\n", - "subject.Subject.insert1(\n", - " dict(\n", - " subject=\"subject6\",\n", - " sex=\"F\",\n", - " subject_birth_date=\"2020-01-01\",\n", - " subject_description=\"hneih_E105\",\n", - " ),\n", - " skip_duplicates=True,\n", - ")\n", - "session_keys = [\n", - " dict(subject=\"subject6\", session_datetime=\"2021-06-02 14:04:22\"),\n", - " dict(subject=\"subject6\", session_datetime=\"2021-06-03 14:43:10\"),\n", - "]\n", - "\n", - "session.Session.insert(session_keys, skip_duplicates=True)\n", - "session.Session() & \"session_datetime > '2021-06-01 12:00:00'\" & \"subject='subject6'\"" + "# Empty the session in case of rerunning\n", + "safemode=True # Set to false to turn off confirmation prompts\n", + "(session.Session & 'subject=\"subject6\"').delete(safemode=safemode)\n", + "train.TrainingParamSet.delete(safemode=safemode)\n", + "train.VideoSet.delete(safemode=safemode)" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Videoset tabley\n", - "train.VideoSet.insert1({\"video_set_id\": 0}, skip_duplicates=True)\n", - "\n", - "# training_files = #['labeled-data/train1_trimmed/CollectedData_DataJoint.h5',\n", - "#'labeled-data/train1_trimmed/CollectedData_DataJoint.csv']\n", - "#'labeled-data/train1_trimmed/img00674.png'] #TO-DO: CHECK IF ALL THE PNGS ARE NECESSARY FOR TRAINING\n", - "#'videos/train1.mp4']\n", - "# for idx, filename in enumerate(training_files):\n", - "for idx, filename in enumerate(labeled_files_rel):\n", - " train.VideoSet.File.insert1(\n", - " {\"video_set_id\": 0, \"file_id\": idx, \"file_path\": dlc_project_folder / filename},\n", - " skip_duplicates=True,\n", - " ) # Changed from + to /; #relative_path" + "Each Python module (e.g. `subject`) contains a schema object that enables interaction with the schema in the database." ] }, { @@ -183,16 +320,14 @@ "metadata": {}, "outputs": [], "source": [ - "train.VideoSet.File()" + "subject.schema" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "dj.list_schemas()" + "The Python classes in the module correspond to a table in the database server. We can check also if there is any entry in the table:" ] }, { @@ -201,39 +336,16 @@ "metadata": {}, "outputs": [], "source": [ - "model.schema.drop()\n", - "train.schema.drop()\n" + "subject.Subject()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Restrict the training interations to 5 modifying the default parameters in config.yaml\n", - "paramset_idx = 0\n", - "paramset_desc = \"First training test with DLC using shuffle 1 and maxiters = 5\"\n", - "\n", - "# default parameters\n", - "with open(config_file_abs, \"rb\") as y:\n", - " config_params = yaml.safe_load(y)\n", - "config_params.keys()\n", - "\n", - "# new parameters\n", - "training_params = {\n", - " \"shuffle\": \"1\",\n", - " \"trainingsetindex\": \"0\",\n", - " \"maxiters\": \"5\",\n", - " \"scorer_legacy\": \"False\", # For DLC ≤ v2.0, include scorer_legacy = True in params\n", - " \"maxiters\": \"5\",\n", - " \"multianimalproject\": \"False\",\n", - "}\n", - "config_params.update(training_params)\n", + "Let's plot the diagram of tables within multiple schemas and their dependencies using `dj.Diagram()`. \n", "\n", - "train.TrainingParamSet.insert_new_params(\n", - " paramset_idx=paramset_idx, paramset_desc=paramset_desc, params=config_params\n", - ")" + "This is the diagram of the whole data pipeline for this `Element-DeepLabCut`:" ] }, { @@ -242,350 +354,105 @@ "metadata": {}, "outputs": [], "source": [ - "# TrainingTask table\n", - "key = {\n", - " \"video_set_id\": 0,\n", - " \"paramset_idx\": 0,\n", - " \"training_id\": 1,\n", - " \"project_path\": dlc_project_folder,\n", - "}\n", - "train.TrainingTask.insert1(key, skip_duplicates=True)\n", - "train.TrainingTask()" + "(\n", + " dj.Diagram(subject) \n", + " + dj.Diagram(lab) \n", + " + dj.Diagram(session) \n", + " + dj.Diagram(model) \n", + " + dj.Diagram(train)\n", + ")" ] }, { - "cell_type": "code", - "execution_count": 2, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ModelTraining: 0%| | 0/1 [00:00\n", - " app.launch_new_instance()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/traitlets/config/application.py\", line 1043, in launch_instance\n", - " app.start()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelapp.py\", line 736, in start\n", - " self.io_loop.start()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/tornado/platform/asyncio.py\", line 195, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n", - " self._run_once()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/asyncio/base_events.py\", line 1905, in _run_once\n", - " handle._run()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/asyncio/events.py\", line 80, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 516, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 505, in process_one\n", - " await dispatch(*args)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 412, in dispatch_shell\n", - " await result\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 740, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/ipkernel.py\", line 422, in do_execute\n", - " res = shell.run_cell(\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/zmqshell.py\", line 546, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3009, in run_cell\n", - " result = self._run_cell(\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3064, in _run_cell\n", - " result = runner(coro)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3269, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3448, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3508, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/h8/_lx50k3d6qx586662kr066h40000gn/T/ipykernel_43888/556392206.py\", line 1, in \n", - " train.ModelTraining.populate(display_progress=True)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/datajoint/autopopulate.py\", line 241, in populate\n", - " error = self._populate1(key, jobs, **populate_kwargs)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/datajoint/autopopulate.py\", line 292, in _populate1\n", - " make(dict(key), **(make_kwargs or {}))\n", - " File \"/Users/milagros/Documents/element-deeplabcut/element_deeplabcut/train.py\", line 312, in make\n", - " train_network(dlc_cfg_filepath, **train_network_kwargs)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 210, in train_network\n", - " train(\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 168, in train\n", - " batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 69, in setup_preloading\n", - " enqueue_op = q.enqueue(placeholders_list)\n", - "Node: 'fifo_queue_enqueue'\n", - "Enqueue operation was cancelled\n", - "\t [[{{node fifo_queue_enqueue}}]]\n", + "The diagram becomes clear when it's approached as a hierarchy of tables that define the order in which the pipeline expects to receive data in each of the tables. \n", + "\n", + "The tables higher up in the diagram such as `subject.Subject()` should be the first to receive data.\n", "\n", - "Original stack trace for 'fifo_queue_enqueue':\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/runpy.py\", line 197, in _run_module_as_main\n", - " return _run_code(code, main_globals, None,\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/runpy.py\", line 87, in _run_code\n", - " exec(code, run_globals)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel_launcher.py\", line 17, in \n", - " app.launch_new_instance()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/traitlets/config/application.py\", line 1043, in launch_instance\n", - " app.start()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelapp.py\", line 736, in start\n", - " self.io_loop.start()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/tornado/platform/asyncio.py\", line 195, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n", - " self._run_once()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/asyncio/base_events.py\", line 1905, in _run_once\n", - " handle._run()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/asyncio/events.py\", line 80, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 516, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 505, in process_one\n", - " await dispatch(*args)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 412, in dispatch_shell\n", - " await result\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 740, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/ipkernel.py\", line 422, in do_execute\n", - " res = shell.run_cell(\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/ipykernel/zmqshell.py\", line 546, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3009, in run_cell\n", - " result = self._run_cell(\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3064, in _run_cell\n", - " result = runner(coro)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3269, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3448, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3508, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/h8/_lx50k3d6qx586662kr066h40000gn/T/ipykernel_43888/556392206.py\", line 1, in \n", - " train.ModelTraining.populate(display_progress=True)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/datajoint/autopopulate.py\", line 241, in populate\n", - " error = self._populate1(key, jobs, **populate_kwargs)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/datajoint/autopopulate.py\", line 292, in _populate1\n", - " make(dict(key), **(make_kwargs or {}))\n", - " File \"/Users/milagros/Documents/element-deeplabcut/element_deeplabcut/train.py\", line 312, in make\n", - " train_network(dlc_cfg_filepath, **train_network_kwargs)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 210, in train_network\n", - " train(\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 168, in train\n", - " batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 69, in setup_preloading\n", - " enqueue_op = q.enqueue(placeholders_list)\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/tensorflow/python/ops/data_flow_ops.py\", line 346, in enqueue\n", - " return gen_data_flow_ops.queue_enqueue_v2(\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/tensorflow/python/ops/gen_data_flow_ops.py\", line 4068, in queue_enqueue_v2\n", - " _, _, _op, _outputs = _op_def_library._apply_op_helper(\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/tensorflow/python/framework/op_def_library.py\", line 795, in _apply_op_helper\n", - " op = g._create_op_internal(op_type_name, inputs, dtypes=None,\n", - " File \"/Users/milagros/miniconda3/envs/dlc_pip/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 3814, in _create_op_internal\n", - " ret = Operation(\n", + "Data is manually entered into the green rectangular tables with the `insert1()` method.\n", "\n", - "ModelTraining: 100%|██████████| 1/1 [00:09<00:00, 9.96s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The network is now trained and ready to evaluate. Use the function 'evaluate_network' to evaluate the network.\n" + "Tables connected by a line depend on entries from the table above it.\n", + " \n", + "Tables with a purple oval or red circle will be automatically filled with relevant data\n", + " by calling `populate()`. \n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Table Links\n", + "\n", + "- **One-to-one primary**: thick solid line, share the exact same primary key, meaning the child table inherits all the primary key fields from the parent table as its own primary key\n", + "\n", + "- **One-to-many primary**: thin solid line, inherit the primary key from the parent table, but have additional field(s) as part of the primary key as well\n", + "\n", + "- **Secondary dependency**: dashed line, the child table inherits the primary key fields from parent table as its own secondary attribute" ] }, { - "data": { - "text/plain": [ - "array([(0, 0, 1, 5, {'Task': 'Top_tracking', 'scorer': 'DataJoint', 'date': 'Aug3', 'multianimalproject': 'False', 'identity': None, 'project_path': '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03', 'video_sets': {'/Users/milagros/Documents/DeepLabCut_testing/test_data/Top_tracking-DataJoint-2023-08-03/videos/train1_trimmed.mp4': {'crop': '0, 500, 0, 500'}}, 'bodyparts': ['Head', 'Tailbase'], 'start': 0, 'stop': 1, 'numframes2pick': 5, 'skeleton': [['bodypart1', 'bodypart2'], ['objectA', 'bodypart3']], 'skeleton_color': 'black', 'pcutoff': 0.6, 'dotsize': 12, 'alphavalue': 0.7, 'colormap': 'rainbow', 'TrainingFraction': [0.95], 'iteration': 0, 'default_net_type': 'resnet_50', 'default_augmenter': 'default', 'snapshotindex': -1, 'batch_size': 8, 'cropping': False, 'x1': 0, 'x2': 640, 'y1': 277, 'y2': 624, 'corner2move2': [50, 50], 'move2corner': True, 'shuffle': '1', 'trainingsetindex': '0', 'maxiters': '5', 'scorer_legacy': 'False', 'modelprefix': '', 'train_fraction': 0.95, 'training_filelist_datajoint': ['/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/img0446.png', '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/CollectedData_DataJoint.h5', '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/img0482.png', '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/CollectedData_DataJoint.csv', '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/img1420.png', '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/img0822.png', '/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/labeled-data/train1_trimmed/img1723.png']})],\n", - " dtype=[('video_set_id', '()` show table contents\n", + "- `heading` shows attribute definitions\n", + "- `describe()` show table defintiion with foreign key references\n", + "\n", + "Let's have a look at the `TrainingTask` table.\n", + "\n", + "To know what data to insert into the table, we can view its dependencies and attributes using `.heading` function. Note that `heading` displays all the attributes of the table definition, regardless of whether they are declared in an upstream table." ] }, { @@ -594,18 +461,7 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "# change to the upper level folder to detect dj_local_conf.json\n", - "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", - " + \"workflow directory\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`Pipeline.py` activates the DataJoint `elements` and declares other required tables." + "train.TrainingTask()" ] }, { @@ -614,46 +470,29 @@ "metadata": {}, "outputs": [], "source": [ - "import datajoint as dj\n", - "from workflow_deeplabcut.pipeline import lab, subject, session, train, model\n", - "\n", - "# Directing our pipeline to the appropriate config location\n", - "from element_interface.utils import find_full_path\n", - "from workflow_deeplabcut.paths import get_dlc_root_data_dir\n", - "config_path = find_full_path(get_dlc_root_data_dir(), \n", - " 'from_top_tracking/config.yaml')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Manually Inserting Entries" + "train.TrainingTask.heading" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Upstream tables" + "The cells above show all attributes of the train table.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can insert entries into `dj.Manual` tables (green in diagrams) by providing values as a dictionary or a list of dictionaries. " + "## Insert entries into manual tables" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "session.Session.heading" + "\n", + "We will insert example data into the `subject.Subject (dj.Manual)` table (green in diagrams) by providing values as a dictionary or a list of dictionaries." ] }, { @@ -662,20 +501,28 @@ "metadata": {}, "outputs": [], "source": [ - "subject.Subject.insert1(dict(subject='subject6', \n", - " sex='F', \n", - " subject_birth_date='2020-01-01', \n", - " subject_description='hneih_E105'))\n", - "session_keys = [dict(subject='subject6', session_datetime='2021-06-02 14:04:22'),\n", - " dict(subject='subject6', session_datetime='2021-06-03 14:43:10')]\n", - "session.Session.insert(session_keys)" + "# Subject and Session tables\n", + "subject.Subject.insert1(\n", + " dict(\n", + " subject=\"subject6\",\n", + " sex=\"F\",\n", + " subject_birth_date=\"2020-01-01\",\n", + " subject_description=\"hneih_E105\",\n", + " ),\n", + " skip_duplicates=True,\n", + ")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can look at the contents of this table and restrict by a value." + "Let's repeat the steps above for the `Session` table. \n", + "\n", + "- We can insert in the `session.Session` table by passing a dictionary to the `insert1` method. \n", + "\n", + "- We can look at the contents of this table and restrict by a value.\n", + "\n" ] }, { @@ -684,16 +531,19 @@ "metadata": {}, "outputs": [], "source": [ + "session_keys = [\n", + " dict(subject=\"subject6\", session_datetime=\"2021-06-02 14:04:22\"),\n", + " dict(subject=\"subject6\", session_datetime=\"2021-06-03 14:43:10\"),\n", + "]\n", + "session.Session.insert(session_keys, skip_duplicates=True)\n", "session.Session() & \"session_datetime > '2021-06-01 12:00:00'\" & \"subject='subject6'\"" ] }, { "cell_type": "markdown", - "metadata": { - "tags": [] - }, + "metadata": {}, "source": [ - "#### DeepLabcut Tables" + "## Train schema and VideoSet tables" ] }, { @@ -711,16 +561,17 @@ "metadata": {}, "outputs": [], "source": [ - "train.VideoSet.insert1({'video_set_id': 0})\n", - "project_folder = 'from_top_tracking/'\n", - "training_files = ['labeled-data/train1/CollectedData_DJ.h5',\n", - " 'labeled-data/train1/CollectedData_DJ.csv',\n", - " 'labeled-data/train1/img00674.png',\n", - " 'videos/train1.mp4']\n", - "for idx, filename in enumerate(training_files):\n", - " train.VideoSet.File.insert1({'video_set_id': 0,\n", - " 'file_id': idx,\n", - " 'file_path': (project_folder + filename)})" + "# Videoset table\n", + "train.VideoSet.insert1({\"video_set_id\": 0}, skip_duplicates=True)\n", + "\n", + "for idx, filename in enumerate(labeled_files_rel):\n", + " train.VideoSet.File.insert1(\n", + " {\n", + " \"video_set_id\": 0, \n", + " \"file_id\": idx, \n", + " \"file_path\": dlc_project_folder / filename\n", + " },\n", + " ) " ] }, { @@ -734,18 +585,16 @@ }, { "cell_type": "markdown", - "metadata": { - "tags": [] - }, + "metadata": {}, "source": [ - "### Training a Network" + "## Training a network" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "First, we'll add a `ModelTrainingParamSet`. This is a lookup table that we can reference when training a model." + "To train the network, we need to add the parameter set (`TrainingParamSet`) of the model training (`train`)." ] }, { @@ -754,31 +603,23 @@ "metadata": {}, "outputs": [], "source": [ - "train.TrainingParamSet.heading" + "train.TrainingParamSet()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The `params` longblob should be a dictionary that captures all items for DeepLabCut's `train_network` function. At minimum, this is the contents of the project's config file, as well as `suffle` and `trainingsetindex`, which are not included in the config. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from deeplabcut import train_network\n", - "help(train_network) # for more information on optional parameters" + "The `params` attribute has to be a dictionary that captures all the items for the DeepLabCut's `train_network` function. At minimum, this is the contents of the project's config file, as well as `suffle` and `trainingsetindex`, which are not included in the configuration file." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Here, we give these items, load the config contents, and overwrite some defaults, including `maxiters`, to restrict our training iterations to 5." + "\n", + "We will insert these items, load the config contents, and overwrite some defaults, including `maxiters`, to restrict our training iterations to 5.\n", + "\n" ] }, { @@ -787,22 +628,29 @@ "metadata": {}, "outputs": [], "source": [ - "import yaml\n", - "\n", - "paramset_idx = 0; paramset_desc='from_top_tracking'\n", + "# Restrict the training interations to 5 modifying the default parameters in config.yaml\n", + "paramset_idx = 0\n", + "paramset_desc = \"First training test with DLC using shuffle 1 and maxiters = 5\"\n", "\n", - "with open(config_path, 'rb') as y:\n", + "# default parameters\n", + "with open(config_file_abs, \"rb\") as y:\n", " config_params = yaml.safe_load(y)\n", - "training_params = {'shuffle': '1',\n", - " 'trainingsetindex': '0',\n", - " 'maxiters': '5',\n", - " 'scorer_legacy': 'False',\n", - " 'maxiters': '5', \n", - " 'multianimalproject':'False'}\n", + "config_params.keys()\n", + "\n", + "# new parameters\n", + "training_params = {\n", + " \"shuffle\": \"1\",\n", + " \"trainingsetindex\": \"0\",\n", + " \"maxiters\": \"5\",\n", + " \"scorer_legacy\": \"False\", # For DLC ≤ v2.0, include scorer_legacy = True in params\n", + " \"maxiters\": \"5\",\n", + " \"multianimalproject\": \"False\",\n", + "}\n", "config_params.update(training_params)\n", - "train.TrainingParamSet.insert_new_params(paramset_idx=paramset_idx,\n", - " paramset_desc=paramset_desc,\n", - " params=config_params)" + "\n", + "train.TrainingParamSet.insert_new_params(\n", + " paramset_idx=paramset_idx, paramset_desc=paramset_desc, params=config_params\n", + ")" ] }, { @@ -818,7 +666,7 @@ "metadata": {}, "outputs": [], "source": [ - "train.TrainingTask.heading" + "dj.Diagram(train)" ] }, { @@ -827,34 +675,42 @@ "metadata": {}, "outputs": [], "source": [ - "key={'video_set_id': 0,\n", - " 'paramset_idx':0,\n", - " 'training_id': 1,\n", - " 'project_path':'from_top_tracking/'\n", - " }\n", - "train.TrainingTask.insert1(key, skip_duplicates=True)\n", "train.TrainingTask()" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "tags": [] - }, + "metadata": {}, "outputs": [], "source": [ - "train.ModelTraining.populate()" + "# TrainingTask table\n", + "key = {\n", + " \"video_set_id\": 0,\n", + " \"paramset_idx\": 0,\n", + " \"training_id\": 1,\n", + " \"project_path\": dlc_project_folder,\n", + "}\n", + "train.TrainingTask.insert1(key, skip_duplicates=True)\n", + "train.TrainingTask()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "(Output cleared for brevity)\n", - "```\n", - "The network is now trained and ready to evaluate. Use the function 'evaluate_network' to evaluate the network.\n", - "```" + "After inserting the training parameters and the video recordings, the model training can be run and outputs will be stored in `ModelTraining` table.\n", + "\n", + "*Note that the following code line will run the model training with DeepLabCut. It will take some minutes if you have installed DeepLabCut in the GPU. However, it will take longer if the installation was in CPU*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.ModelTraining.populate(display_progress=True)\n" ] }, { @@ -863,28 +719,22 @@ "metadata": {}, "outputs": [], "source": [ - "train.ModelTraining()" + "train.ModelTraining.fetch()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To resume training from a checkpoint, we would need to \n", - "[edit the relevant config file](https://github.com/DeepLabCut/DeepLabCut/issues/70) (see also `update_pose_cfg` in `workflow_deeplabcut.load_demo_data`).\n", - "Emperical work suggests 200k iterations for any true use-case.\n", "\n", - "For better quality predictions in this demo, we'll revert the checkpoint file and use a pretrained model." + "The network is now trained and ready to evaluate. The next step consists of evaluating the network. \n" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "from workflow_deeplabcut.load_demo_data import revert_checkpoint_file\n", - "revert_checkpoint_file()" + "## Evaluate the network" ] }, { @@ -901,7 +751,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `model` schema uses a lookup table for managing Body Parts tracked across models." + "The `model` schema uses a lookup table for managing the body parts tracked across models." ] }, { @@ -910,14 +760,19 @@ "metadata": {}, "outputs": [], "source": [ - "model.BodyPart.heading" + "model.BodyPart()\n", + "new_body_parts = [\n", + " dict(body_part=\"subject6\", session_datetime=\"2021-06-02 14:04:22\"),\n", + " dict(subject=\"subject6\", session_datetime=\"2021-06-03 14:43:10\"),\n", + "]\n", + "session.Session.insert(session_keys, skip_duplicates=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Helper functions allow us to first, identify all the new body parts from a given config, and, second, insert them with user-friendly descriptions." + "We can also modify the body parts as desired. For that, we can use helper functions to identify and insert the new body parts from a given DeepLabCut configuration file (`config.yaml`) in the data pipeline." ] }, { @@ -926,7 +781,7 @@ "metadata": {}, "outputs": [], "source": [ - "model.BodyPart.extract_new_body_parts(config_path)" + "model.BodyPart.extract_new_body_parts(config_file_abs)" ] }, { @@ -936,7 +791,7 @@ "outputs": [], "source": [ "bp_desc=['Body Center', 'Head', 'Base of Tail']\n", - "model.BodyPart.insert_from_config(config_path,bp_desc)" + "model.BodyPart.insert_from_config(config_file_abs,bp_desc)" ] }, { From b0ea7d451d7bb33bfd4f5b7758346b4d1abdedc2 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Sat, 19 Aug 2023 07:57:39 +0200 Subject: [PATCH 093/176] processed_dir can not exist - bug fixed --- element_deeplabcut/model.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/element_deeplabcut/model.py b/element_deeplabcut/model.py index ef49333..7ba06c0 100644 --- a/element_deeplabcut/model.py +++ b/element_deeplabcut/model.py @@ -628,12 +628,21 @@ def generate( videotype, gputouse, save_as_csv, batchsize, cropping, TFGPUinference, dynamic, robust_nframes, allow_growth, use_shelve """ - processed_dir = get_dlc_processed_data_dir() + output_dir = cls.infer_output_dir( - {**video_recording_key, "model_name": model_name}, relative=False, mkdir=True + {**key, "model_name": model_name}, + relative=False, + mkdir=True, ) - if task_mode is None: + processed_dir = get_dlc_processed_data_dir() + + if processed_dir: + pose_estimation_output_dir = output_dir.relative_to( + processed_dir + ).as_posix() + else: + pose_estimation_output_dir = output_dir.as_posix() try: _ = dlc_reader.PoseEstimation(output_dir) except FileNotFoundError: From 745f5d4e2aab7f630f79390a7b5ed09399088b2c Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Sat, 19 Aug 2023 08:02:01 +0200 Subject: [PATCH 094/176] Fix:generate triggers getattribute with model_name --- element_deeplabcut/model.py | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/element_deeplabcut/model.py b/element_deeplabcut/model.py index 7ba06c0..4088c2d 100644 --- a/element_deeplabcut/model.py +++ b/element_deeplabcut/model.py @@ -609,10 +609,8 @@ def infer_output_dir(cls, key: dict, relative: bool = False, mkdir: bool = False @classmethod def generate( cls, - video_recording_key: dict, + key: dict, model_name: str, - *, - task_mode: str = None, analyze_videos_params: dict = None, ): """Insert PoseEstimationTask in inferred output dir. @@ -643,23 +641,22 @@ def generate( ).as_posix() else: pose_estimation_output_dir = output_dir.as_posix() + + if key["task_mode"] is None: try: _ = dlc_reader.PoseEstimation(output_dir) except FileNotFoundError: - task_mode = "trigger" + key["task_mode"] = "trigger" else: - task_mode = "load" + key["task_mode"] = "load" cls.insert1( { - **video_recording_key, + **key, "model_name": model_name, - "task_mode": task_mode, + "task_mode": key["task_mode"], "pose_estimation_params": analyze_videos_params, - "pose_estimation_output_dir": output_dir.relative_to( - processed_dir - ).as_posix(), - } + "pose_estimation_output_dir": pose_estimation_output_dir, ) insert_estimation_task = generate From f77471395d07096450d17e34ce29ebeaaf328b92 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Sat, 19 Aug 2023 08:02:20 +0200 Subject: [PATCH 095/176] add skip_duplicates to generate function --- element_deeplabcut/model.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/element_deeplabcut/model.py b/element_deeplabcut/model.py index 4088c2d..cb37daa 100644 --- a/element_deeplabcut/model.py +++ b/element_deeplabcut/model.py @@ -657,6 +657,8 @@ def generate( "task_mode": key["task_mode"], "pose_estimation_params": analyze_videos_params, "pose_estimation_output_dir": pose_estimation_output_dir, + }, + skip_duplicates=True, ) insert_estimation_task = generate From 18daef44a12cecbbccdb4a3627c307e8efebe6a4 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Sat, 19 Aug 2023 08:03:10 +0200 Subject: [PATCH 096/176] change name for clarity to get_trajectory function --- element_deeplabcut/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/element_deeplabcut/model.py b/element_deeplabcut/model.py index cb37daa..1084920 100644 --- a/element_deeplabcut/model.py +++ b/element_deeplabcut/model.py @@ -760,7 +760,7 @@ def make(self, key): self.BodyPartPosition.insert(body_parts) @classmethod - def get_trajectory(cls, key: dict, body_parts: list = "all") -> pd.DataFrame: + def coordinates_dataframe(cls, key: dict, body_parts: list = "all") -> pd.DataFrame: """Returns a pandas dataframe of coordinates of the specified body_part(s) Args: From 1ef12f95b9eea0995c093c4662ef93335e0e840f Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Sat, 19 Aug 2023 08:04:15 +0200 Subject: [PATCH 097/176] important change to install dlc[apple_mchips] --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 55ed97b..8cb52b6 100644 --- a/setup.py +++ b/setup.py @@ -40,7 +40,7 @@ extras_requires={ "default": ["deeplabcut[tf]>=2.2.1.1"], "apple_mchips": [ - "deeplabcut[apple_mchips]", + "'deeplabcut[apple_mchips]'", "tables=3.7.0", ], # "tensorflow-deps", }, From 0973437784e5f566dcf2f9e921ba0779f9e8a7bd Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Wed, 6 Sep 2023 20:25:57 +0200 Subject: [PATCH 098/176] dockerfile and devcontainer files for element-dlc --- .devcontainer/Dockerfile | 48 +++++++++++++++++++++++++++++++ .devcontainer/devcontainer.json | 30 +++++++++++++++++++ .devcontainer/docker-compose.yaml | 25 ++++++++++++++++ 3 files changed, 103 insertions(+) create mode 100644 .devcontainer/Dockerfile create mode 100644 .devcontainer/devcontainer.json create mode 100644 .devcontainer/docker-compose.yaml diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile new file mode 100644 index 0000000..9b734bb --- /dev/null +++ b/.devcontainer/Dockerfile @@ -0,0 +1,48 @@ +FROM python:3.9-slim@sha256:5f0192a4f58a6ce99f732fe05e3b3d00f12ae62e183886bca3ebe3d202686c7f + +ENV PATH /usr/local/bin:$PATH +ENV PYTHON_VERSION 3.9.17 + +RUN \ + adduser --system --disabled-password --shell /bin/bash vscode && \ + # install docker + apt-get update && \ + apt-get install ca-certificates curl gnupg lsb-release -y && \ + mkdir -m 0755 -p /etc/apt/keyrings && \ + curl -fsSL https://download.docker.com/linux/debian/gpg | gpg --dearmor -o /etc/apt/keyrings/docker.gpg && \ + echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/debian $(lsb_release -cs) stable" | tee /etc/apt/sources.list.d/docker.list > /dev/null && \ + apt-get update && \ + apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin -y && \ + usermod -aG docker vscode && \ + apt-get clean + +RUN \ + # dev setup + apt update && \ + apt-get install sudo git bash-completion graphviz default-mysql-client s3fs procps -y && \ + usermod -aG sudo vscode && \ + echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers && \ + pip install --no-cache-dir --upgrade black pip nbconvert && \ + echo '. /etc/bash_completion' >> /home/vscode/.bashrc && \ + echo 'export PS1="\[\e[32;1m\]\u\[\e[m\]@\[\e[34;1m\]\H\[\e[m\]:\[\e[33;1m\]\w\[\e[m\]$ "' >> /home/vscode/.bashrc && \ + apt-get clean + +COPY ./ /tmp/element-deeplabcut/ + +RUN \ + # pipeline dependencies + apt-get install gcc g++ ffmpeg libsm6 libxext6 -y && \ + pip install --no-cache-dir -e /tmp/element-deeplabcut[elements] && \ + # clean up + rm -rf /tmp/element-deeplabcut && \ + apt-get clean + +ENV DJ_HOST fakeservices.datajoint.io +ENV DJ_USER root +ENV DJ_PASS simple + +ENV IMAGING_ROOT_DATA_DIR /workspaces/element-deeplabcut/example_data +ENV DATABASE_PREFIX neuro_ + +USER vscode +CMD bash -c "sudo rm /var/run/docker.pid; sudo dockerd" \ No newline at end of file diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json new file mode 100644 index 0000000..89bbe16 --- /dev/null +++ b/.devcontainer/devcontainer.json @@ -0,0 +1,30 @@ +{ + "name": "Environment + Data", + "dockerComposeFile": "docker-compose.yaml", + "service": "app", + "workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}", + "remoteEnv": { + "LOCAL_WORKSPACE_FOLDER": "${localWorkspaceFolder}" + }, + "onCreateCommand": "mkdir -p ${IMAGING_ROOT_DATA_DIR} && pip install -e .", + "postStartCommand": "docker volume prune -f && s3fs ${DJ_PUBLIC_S3_LOCATION} ${IMAGING_ROOT_DATA_DIR} -o nonempty,multipart_size=530,endpoint=us-east-1,url=http://s3.amazonaws.com,public_bucket=1", + "hostRequirements": { + "cpus": 4, + "memory": "8gb", + "storage": "32gb" + }, + "forwardPorts": [ + 3306 + ], + "customizations": { + "settings": { + "python.pythonPath": "/usr/local/bin/python" + }, + "vscode": { + "extensions": [ + "ms-python.python@2023.8.0", + "ms-toolsai.jupyter@2023.3.1201040234" + ] + } + } +} \ No newline at end of file diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml new file mode 100644 index 0000000..0113bce --- /dev/null +++ b/.devcontainer/docker-compose.yaml @@ -0,0 +1,25 @@ +version: "3" +services: + app: + cpus: 4 + mem_limit: 8g + # build: + # context: .. + # dockerfile: ./.devcontainer/Dockerfile + image: datajoint/element_deeplabcut:latest + extra_hosts: + - fakeservices.datajoint.io:127.0.0.1 + environment: + - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/deeplabcut-v1 + devices: + - /dev/fuse + cap_add: + - SYS_ADMIN + security_opt: + - apparmor:unconfined + volumes: + - ..:/workspaces/element-deeplabcut:cached + - docker_data:/var/lib/docker # persist docker images + privileged: true # only because of dind +volumes: + docker_data: From 4820f759ca42e042dad212139e7331b52e9f35a6 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Wed, 6 Sep 2023 21:18:57 +0200 Subject: [PATCH 099/176] comment `image element_dlc` code line before merge --- .devcontainer/docker-compose.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index 0113bce..9c07a5f 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -6,7 +6,7 @@ services: # build: # context: .. # dockerfile: ./.devcontainer/Dockerfile - image: datajoint/element_deeplabcut:latest + # image: datajoint/element_deeplabcut:latest extra_hosts: - fakeservices.datajoint.io:127.0.0.1 environment: From 61aa1800c39e3e90e1d788ab764021e748202443 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Wed, 6 Sep 2023 21:23:38 +0200 Subject: [PATCH 100/176] comment 'S3 location' for DLC-v1 to test dev --- .devcontainer/docker-compose.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index 9c07a5f..ee53ea9 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -9,8 +9,8 @@ services: # image: datajoint/element_deeplabcut:latest extra_hosts: - fakeservices.datajoint.io:127.0.0.1 - environment: - - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/deeplabcut-v1 + environment: # - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/deeplabcut-v1 + devices: - /dev/fuse cap_add: From d0088ed171c5217840ae0f8d60d40fc6fab01c36 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Wed, 6 Sep 2023 21:58:32 +0200 Subject: [PATCH 101/176] comment `environment` line with previous commit --- .devcontainer/docker-compose.yaml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index ee53ea9..80b541e 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -9,7 +9,8 @@ services: # image: datajoint/element_deeplabcut:latest extra_hosts: - fakeservices.datajoint.io:127.0.0.1 - environment: # - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/deeplabcut-v1 + #environment: + # - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/deeplabcut-v1 devices: - /dev/fuse From 2957160cd50e6e5de59df7b32321f7e6f83303be Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Wed, 6 Sep 2023 23:53:40 +0200 Subject: [PATCH 102/176] use build instead of image to test the dev cont --- .devcontainer/docker-compose.yaml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index 80b541e..ee8065f 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -3,9 +3,9 @@ services: app: cpus: 4 mem_limit: 8g - # build: - # context: .. - # dockerfile: ./.devcontainer/Dockerfile + build: + # context: .. + dockerfile: ./.devcontainer/Dockerfile # image: datajoint/element_deeplabcut:latest extra_hosts: - fakeservices.datajoint.io:127.0.0.1 @@ -19,7 +19,7 @@ services: security_opt: - apparmor:unconfined volumes: - - ..:/workspaces/element-deeplabcut:cached + #- ..:/workspaces/element-deeplabcut:cached - docker_data:/var/lib/docker # persist docker images privileged: true # only because of dind volumes: From 6133ca7fd4ed77f25ee320a7c8e79da966e4ddd5 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 00:43:07 +0200 Subject: [PATCH 103/176] add `context` in docker yaml as well --- .devcontainer/docker-compose.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index ee8065f..2486fd4 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -4,7 +4,7 @@ services: cpus: 4 mem_limit: 8g build: - # context: .. + context: .. dockerfile: ./.devcontainer/Dockerfile # image: datajoint/element_deeplabcut:latest extra_hosts: From 8a8d704a324379bc5dc0c98b2467cd81434631d6 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 01:47:56 +0200 Subject: [PATCH 104/176] uncomment volumes in docker config file --- .devcontainer/docker-compose.yaml | 2 +- .gitignore | 1 + cspell.json | 209 +++ dj_dlc_config.yaml | 59 + dj_pose_cfg | 116 ++ docker-compose-db.yaml | 15 + element_deeplabcut/plotting/__init__.py | 0 .../plotting/plot_coordinates.py | 20 + notebooks/testing_merge.py | 186 +++ notebooks/tutorial.ipynb | 497 ++++--- notebooks/tutorial_copy.ipynb | 1190 +++++++++++++++++ setup.py | 20 +- ...C_resnet50_Top_trackingAug3shuffle1_100.h5 | Bin 0 -> 282521 bytes ...0_Top_trackingAug3shuffle1_100_meta.pickle | Bin 0 -> 1661 bytes 14 files changed, 2149 insertions(+), 166 deletions(-) create mode 100644 cspell.json create mode 100644 dj_dlc_config.yaml create mode 100644 dj_pose_cfg create mode 100644 docker-compose-db.yaml create mode 100644 element_deeplabcut/plotting/__init__.py create mode 100644 element_deeplabcut/plotting/plot_coordinates.py create mode 100644 notebooks/testing_merge.py create mode 100644 notebooks/tutorial_copy.ipynb create mode 100644 train1_trimmedDLC_resnet50_Top_trackingAug3shuffle1_100.h5 create mode 100644 train1_trimmedDLC_resnet50_Top_trackingAug3shuffle1_100_meta.pickle diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index 2486fd4..e0321d6 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -19,7 +19,7 @@ services: security_opt: - apparmor:unconfined volumes: - #- ..:/workspaces/element-deeplabcut:cached + - ..:/workspaces/element-deeplabcut:cached - docker_data:/var/lib/docker # persist docker images privileged: true # only because of dind volumes: diff --git a/.gitignore b/.gitignore index 16d25d8..91d8ded 100644 --- a/.gitignore +++ b/.gitignore @@ -81,6 +81,7 @@ ENV/ # datajoint, notes, nwb export dj_local_c*.json +dj_pose*.y*ml temp* temp/* *nwb diff --git a/cspell.json b/cspell.json new file mode 100644 index 0000000..655e189 --- /dev/null +++ b/cspell.json @@ -0,0 +1,209 @@ +// cSpell Settings +//https://github.com/streetsidesoftware/vscode-spell-checker +{ + "version": "0.2", // Version of the setting file. Always 0.2 + "language": "en", // language - current active spelling language + "enabledLanguageIds": [ + "markdown", + "yaml", + "python" + ], + // flagWords - list of words to be always considered incorrect + // This is useful for offensive words and common spelling errors. + // For example "hte" should be "the" + "flagWords": [], + "allowCompoundWords": true, + "ignorePaths": [ + "./images/*" + ], + "words": [ + "acorr", + "aggr", + "Alessio", + "Andreas", + "apmeta", + "arange", + "arithmatex", + "asarray", + "astype", + "autocorrelogram", + "Axona", + "bbins", + "bdist", + "Binarize", + "bouton", + "Brody", + "Bruker", + "bshift", + "Buccino", + "catgt", + "cbar", + "cbin", + "cdat", + "chans", + "Chans", + "chns", + "Clust", + "clusterings", + "cmap", + "cnmf", + "correlogram", + "correlograms", + "curations", + "DANDI", + "decomp", + "deconvolution", + "DISTRO", + "djbase", + "dtype", + "ecephys", + "Eftychios", + "electrophysiogical", + "elif", + "Ephys", + "fluo", + "fneu", + "Fneu", + "gblcar", + "gfix", + "Giovannucci", + "Hakan", + "hdmf", + "HHMI", + "hstack", + "ibllib", + "ifnull", + "imax", + "Imax", + "IMAX", + "imec", + "imread", + "imro", + "imrotbl", + "imshow", + "Inan", + "inlinehilite", + "iplane", + "ipynb", + "ipywidgets", + "iscell", + "Kavli", + "kcoords", + "Klusta", + "Kwik", + "lfmeta", + "linenums", + "masky", + "mathjax", + "mdict", + "Mesoscale", + "mesoscope", + "mkdocs", + "mkdocstrings", + "Moser", + "mtscomp", + "nblocks", + "nchan", + "Nchan", + "nchannels", + "ndarray", + "ndepths", + "ndim", + "ndimage", + "Neuralynx", + "NEURO", + "neuroconv", + "Neurodata", + "Neurolabware", + "neuropil", + "Neuropil", + "Neuropix", + "neuropixel", + "NeuroPixels", + "nfields", + "nframes", + "npix", + "nplanes", + "nrois", + "NTNU", + "nwbfile", + "NWBHDF", + "oebin", + "openephys", + "openpyxl", + "Pachitariu", + "paramsets", + "phylog", + "plotly", + "Pnevmatikakis", + "PSTH", + "pykilosort", + "pymdownx", + "pynwb", + "pyopenephys", + "pyplot", + "pytest", + "quantile", + "Reimer", + "repolarization", + "Roboto", + "roidetect", + "rois", + "ROIs", + "RRID", + "Rxiv", + "Sasaki", + "sbxreader", + "scipy", + "sdist", + "sess", + "SGLX", + "Shen", + "Siegle", + "Sitonic", + "spikeglx", + "spkcount", + "spks", + "Stereotaxic", + "Sutter", + "tcat", + "tickvals", + "tofile", + "Tolias", + "tqdm", + "usecs", + "usedb", + "Vidrio's", + "vline", + "vmax", + "Vmax", + "voxel", + "xanchor", + "xaxes", + "xaxis", + "xblock", + "xcoords", + "xcorr", + "xlabel", + "xlim", + "xoff", + "xpix", + "XPOS", + "xtick", + "yanchor", + "Yatsenko", + "yaxes", + "yaxis", + "yblock", + "ycoord", + "ycoords", + "ylabel", + "ylim", + "yoff", + "ypix", + "YPOS", + "yref", + "yticks", + "zpix" + ] +} \ No newline at end of file diff --git a/dj_dlc_config.yaml b/dj_dlc_config.yaml new file mode 100644 index 0000000..39f19ea --- /dev/null +++ b/dj_dlc_config.yaml @@ -0,0 +1,59 @@ + # Project definitions (do not edit) +Task: Top_tracking +scorer: DataJoint +date: Aug3 +multianimalproject: false +identity: + + # Project path (change when moving around) +project_path: /Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03 + + # Annotation data set configuration (and individual video cropping parameters) +video_sets: + /Users/milagros/Documents/DeepLabCut_testing/test_data/Top_tracking-DataJoint-2023-08-03/videos/train1_trimmed.mp4: + crop: 0, 500, 0, 500 + /Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/videos/train1_trimmed.mp4: + crop: 0, 500, 0, 500 +bodyparts: +- Head +- Tailbase + + # Fraction of video to start/stop when extracting frames for labeling/refinement +start: 0 +stop: 1 +numframes2pick: 5 + + # Plotting configuration +skeleton: +- - bodypart1 + - bodypart2 +- - objectA + - bodypart3 +skeleton_color: black +pcutoff: 0.6 +dotsize: 12 +alphavalue: 0.7 +colormap: rainbow + + # Training,Evaluation and Analysis configuration +TrainingFraction: +- 0.95 +iteration: 0 +default_net_type: resnet_50 +default_augmenter: default +snapshotindex: -1 +batch_size: 8 + + # Cropping Parameters (for analysis and outlier frame detection) +cropping: false + #if cropping is true for analysis, then set the values here: +x1: 0 +x2: 640 +y1: 277 +y2: 624 + + # Refinement configuration (parameters from annotation dataset configuration also relevant in this stage) +corner2move2: +- 50 +- 50 +move2corner: true diff --git a/dj_pose_cfg b/dj_pose_cfg new file mode 100644 index 0000000..95f260d --- /dev/null +++ b/dj_pose_cfg @@ -0,0 +1,116 @@ + # Project definitions (do not edit) +Task: +scorer: +date: +multianimalproject: +identity: + + # Project path (change when moving around) +project_path: + /Users/milagros/Documents/DeepLabCut_testing/test_data/Top_tracking-DataJoint-2023-08-02 + + # Annotation data set configuration (and individual video cropping parameters) +video_sets: +bodyparts: + + # Fraction of video to start/stop when extracting frames for labeling/refinement +start: +stop: +numframes2pick: + + # Plotting configuration +skeleton: [] +skeleton_color: black +pcutoff: +dotsize: +alphavalue: +colormap: + + # Training,Evaluation and Analysis configuration +TrainingFraction: +iteration: +default_net_type: +default_augmenter: +snapshotindex: +batch_size: 1 + + # Cropping Parameters (for analysis and outlier frame detection) +cropping: + #if cropping is true for analysis, then set the values here: +x1: +x2: +y1: +y2: + + # Refinement configuration (parameters from annotation dataset configuration also relevant in this stage) +corner2move2: +move2corner: +all_joints: +- - 0 +- - 1 +- - 2 +all_joints_names: +- Head +- Bodycenter +- Tailbase +alpha_r: 0.02 +apply_prob: 0.5 +contrast: + clahe: true + claheratio: 0.1 + histeq: true + histeqratio: 0.1 +convolution: + edge: false + emboss: + alpha: + - 0.0 + - 1.0 + strength: + - 0.5 + - 1.5 + embossratio: 0.1 + sharpen: false + sharpenratio: 0.3 +cropratio: 0.4 +dataset: + training-datasets/iteration-0/UnaugmentedDataSet_Top_trackingAug2/Top_tracking_DataJoint95shuffle1.mat +dataset_type: imgaug +decay_steps: 30000 +display_iters: 1000 +global_scale: 0.8 +init_weights: + /Users/milagros/miniconda3/envs/DLC/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt +intermediate_supervision: false +intermediate_supervision_layer: 12 +location_refinement: true +locref_huber_loss: true +locref_loss_weight: 0.05 +locref_stdev: 7.2801 +lr_init: 0.0005 +max_input_size: 1500 +metadataset: + training-datasets/iteration-0/UnaugmentedDataSet_Top_trackingAug2/Documentation_data-Top_tracking_95shuffle1.pickle +min_input_size: 64 +mirror: false +multi_stage: false +multi_step: +- - 0.005 + - 10000 +- - 0.02 + - 430000 +- - 0.002 + - 730000 +- - 0.001 + - 1030000 +net_type: resnet_50 +num_joints: 3 +pairwise_huber_loss: false +pairwise_predict: false +partaffinityfield_predict: false +pos_dist_thresh: 17 +rotation: 25 +rotratio: 0.4 +save_iters: 50000 +scale_jitter_lo: 0.5 +scale_jitter_up: 1.25 diff --git a/docker-compose-db.yaml b/docker-compose-db.yaml new file mode 100644 index 0000000..1d453c8 --- /dev/null +++ b/docker-compose-db.yaml @@ -0,0 +1,15 @@ +# MYSQL_VER=8.0 docker compose -f docker-compose-db.yaml up --build +version: "3" +services: + db: + restart: always + image: datajoint/mysql:${MYSQL_VER} + environment: + - MYSQL_ROOT_PASSWORD=${DJ_PASS} + ports: + - "3306:3306" + healthcheck: + test: [ "CMD", "mysqladmin", "ping", "-h", "localhost" ] + timeout: 15s + retries: 10 + interval: 15s diff --git a/element_deeplabcut/plotting/__init__.py b/element_deeplabcut/plotting/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/element_deeplabcut/plotting/plot_coordinates.py b/element_deeplabcut/plotting/plot_coordinates.py new file mode 100644 index 0000000..57feb46 --- /dev/null +++ b/element_deeplabcut/plotting/plot_coordinates.py @@ -0,0 +1,20 @@ +import matplotlib.pyplot as plt +import plotly.graph_objects as go + +from .. import model + + +def plot_xy(imaging, df, model_name) -> go.Figure: + """Prepare plotly trajectory figure. + + Args: + imaging (dj.Table): imaging table. + df (dataframe): Pose Estimation coordinates in a panda's dataframe + model_name(str): name of the model used + + + + """ + + df_xy = df.iloc[:, df.columns.get_level_values(2).isin(["x", "y"])][model_name] + df_xy.plot().legend(loc="right") diff --git a/notebooks/testing_merge.py b/notebooks/testing_merge.py new file mode 100644 index 0000000..cae6052 --- /dev/null +++ b/notebooks/testing_merge.py @@ -0,0 +1,186 @@ +# PATHS OF INPUT FILES: Extract abs and rel paths from .json file +import os + +if os.path.basename(os.getcwd()) == "notebooks": + os.chdir("..") +assert os.path.basename(os.getcwd()) == "element-deeplabcut", "Please move to the " + +import datajoint as dj +from pathlib import Path +import yaml + +dj.conn() + + +### DLC Project +dlc_project_path_abs = Path(dj.config["custom"]["dlc_root_data_dir"]) / Path( + dj.config["custom"]["current_project_folder"] +) # use pathlib to join; abs path +dlc_project_folder = Path( + dj.config["custom"]["current_project_folder"] +) # relative path + +### Config file +config_file_abs = dlc_project_path_abs / "config.yaml" # abs path +assert ( + config_file_abs.exists() +), "Please check the that you have the Top_tracking folder" + +### Labeled-data +labeled_data_path_abs = dlc_project_path_abs / "labeled-data" +labeled_files_abs = list( + list(labeled_data_path_abs.rglob("*"))[1].rglob("*") +) # substitute 'training_files'; absolute path +labeled_files_rel = [] +for file in labeled_files_abs: + labeled_files_rel.append( + file.relative_to(dlc_project_path_abs) + ) # substitute 'training_files'; relative path + + +from tutorial_pipeline import ( + lab, + subject, + session, + train, + model, +) # after creating json file + +# Empty the session in case of rerunning +# session.Session.delete() +# train.TrainingTask.delete() +# train.TrainingParamSet.delete() +# train.VideoSet.delete() + +# Insert some data in session and train tables +# TO-DO: substitute lab.project by project schema. + + +# Subject and Session tables +subject.Subject.insert1( + dict( + subject="subject6", + sex="F", + subject_birth_date="2020-01-01", + subject_description="hneih_E105", + ), + skip_duplicates=True, +) +session_keys = [ + dict(subject="subject6", session_datetime="2021-06-02 14:04:22"), + dict(subject="subject6", session_datetime="2021-06-03 14:43:10"), +] + +session.Session.insert(session_keys, skip_duplicates=True) +session.Session() & "session_datetime > '2021-06-01 12:00:00'" & "subject='subject6'" + +# Videoset tabley +train.VideoSet.insert1({"video_set_id": 0}, skip_duplicates=True) + +# training_files = #['labeled-data/train1_trimmed/CollectedData_DataJoint.h5', +#'labeled-data/train1_trimmed/CollectedData_DataJoint.csv'] +#'labeled-data/train1_trimmed/img00674.png'] #TO-DO: CHECK IF ALL THE PNGS ARE NECESSARY FOR TRAINING +#'videos/train1.mp4'] +# for idx, filename in enumerate(training_files): +for idx, filename in enumerate(labeled_files_rel): + train.VideoSet.File.insert1( + {"video_set_id": 0, "file_id": idx, "file_path": dlc_project_folder / filename}, + skip_duplicates=True, + ) # Changed from + to /; #relative_path + +# Restrict the training interations to 5 modifying the default parameters in config.yaml +paramset_idx = 0 +paramset_desc = "First training test with DLC using shuffle 1 and maxiters = 5" + +# default parameters +with open(config_file_abs, "rb") as y: + config_params = yaml.safe_load(y) +config_params.keys() + +# new parameters +training_params = { + "shuffle": "1", + "trainingsetindex": "0", + "maxiters": "5", + "scorer_legacy": "False", # For DLC ≤ v2.0, include scorer_legacy = True in params + "maxiters": "5", + "multianimalproject": "False", +} +config_params.update(training_params) + +train.TrainingParamSet.insert_new_params( + paramset_idx=paramset_idx, + paramset_desc=paramset_desc, + params=config_params, +) + +# TrainingTask table +key = { + "video_set_id": 0, + "paramset_idx": 0, + "training_id": 1, + "project_path": dlc_project_folder, +} +train.TrainingTask.insert1(key, skip_duplicates=True) +train.TrainingTask() +train.ModelTraining.populate(display_progress=True) +train.ModelTraining.fetch() + +model.BodyPart() +new_body_parts = [ + dict(body_part="subject6", session_datetime="2021-06-02 14:04:22"), + dict(subject="subject6", session_datetime="2021-06-03 14:43:10"), +] +session.Session.insert(session_keys, skip_duplicates=True) +model.BodyPart.extract_new_body_parts(config_file_abs) + +bp_desc = [] +model.BodyPart.insert_from_config(config_file_abs, bp_desc) + +model.BodyPart() +model.Model.insert_new_model( + model_name="FromTop-latest", + dlc_config=config_file_abs, + shuffle=1, + trainingsetindex=0, + model_description="FromTop - latest snapshot", + paramset_idx=0, + params={"snapshotindex": -1}, +) +model.Model() +model.ModelEvaluation.heading +model.ModelEvaluation.populate() +model.ModelEvaluation() +model.VideoRecording() +key = { + "subject": "subject6", + "session_datetime": "2021-06-02 14:04:22", + "recording_id": "1", + "device": "Camera1", +} +model.VideoRecording.insert1(key, skip_duplicates=True) + +_ = key.pop("device") # get rid of secondary key from master table +key.update( + { + "file_id": 1, + "file_path": "/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/videos/train1_trimmed.mp4", + } +) +model.VideoRecording.File.insert1(key, skip_duplicates=True) +model.VideoRecording.File() +# model.RecordingInfo.populate() +model.RecordingInfo() +key = (model.VideoRecording & {"recording_id": "1"}).fetch1("KEY") +key.update({"model_name": "FromTop-latest", "task_mode": "trigger"}) +# videotype, gputouse, save_as_csv, batchsize, cropping, TFGPUinference, dynamic, robust_nframes, allow_growth, use_shelve +analyze_videos_params = {"save_as_csv": True} + +# key.update(analyze_videos_params={"save_as_csv": True}) +# model.PoseEstimationTask.insert_estimation_task(key) +model.PoseEstimationTask.insert_estimation_task( + key, model_name=key["model_name"], analyze_videos_params=analyze_videos_params +) + +model.PoseEstimation.populate() +model.PoseEstimation.coordinates_dataframe(key) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index d5ff7ca..2281782 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -13,9 +13,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Open-source data pipeline to automate analyses and organize data.\n", + "**Open-source Data Pipeline for Markerless Pose Estimation in Neurophysiology**\n", + "\n", + "This tutorial focuses on providing a comprehensive understanding of the open-source data pipeline offered by `Element-DeepLabCut`. The package is designed to facilitate pose estimation analyses and streamline the organization of data using `DataJoint`. By the end of this tutorial, participants will have a clear grasp of how to set up, utilize, ad optimize the package for their specific pose estimation projects. \n", + "\n", + "**Key Components and Objectives**\n", + "\n", + "- 1. Download Sample Data and Context\n", + "\n", + "- 2. Setup\n", + "\n", + "- 3. Design the DataJoint Pipeline\n", + "\n", + "- 4. Enter the Metadata into the Pipeline\n", + "\n", + "- 5. Run the Model Training\n", + "\n", + "- 6. Run the Model Evaluation\n", "\n", - "In this tutorial, we will walk through creating, testing, and analyzing a pose estimation model using DeepLabCut.\n", "\n", "For detailed documentation and tutorials on general DataJoint principles that support collaboration, automation, reproducibility, and visualizations:\n", "\n", @@ -25,63 +40,148 @@ "\n", "[`DataJoint Element for DeepLabCut - Documentation`](https://datajoint.com/docs/elements/element-deeplabcut/0.2/)" ] - }, + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Download Sample Data and Context" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, you will download the sample data that simulates a real research project. By working through this sample data, you will gain valuable insights into the `practical application` of the package's tools and techniques." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Project Context: \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this research project, we are studying the `behavior of a freely-moving mouse in an open-field environment`. The objective is to `extract pose estimations of the animal's head, body, and tail` from video footage. This information can provide valuable insights into the animal's movements, postures, and interactions within the environment." + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. Setup" + "### Downloading Sample Data:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 1.1. Sample dataset in a DeepLabCut project" + "1. Click the following link to download the sample data archive: `##TO-DO`\n", + "\n", + "\n", + "2. Once the download is complete, extract the contents to a `path of your choice on your local machine`.\n", + "\n", + "After running this tutorial, you can try `Element-DeepLabCut` with your own dataset. To do so, create a new `DeepLabCut` folder with your own videos. Then, remember to change the path in the configuration file (`config.yaml`) in your new `DeepLabCut project` folder accordingly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "These notebooks are built around data provided by DataJoint, including a well-trained model. \n", + "### Challenges: " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Complex Background**: The open field environment introduces complex backgrounds and varying lighting conditions, making accurate pose estimation challenging.\n", + "\n", + "**Multiple Body Parts**: Extracting the pose of multiple body parts (head, body, tail) adds complexity to the analysis due to potential occlusions and variations in appearance.\n", "\n", - "We will use the following dataset `DeepLabCut project example` as an example across this tutorial. You can download this project example here:" + "**Data Management**: Managing the large volume of video data generated in the field and ensuring consistent annotation requires an efficient data pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Expected Outcomes:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Upon completing this tutorial, you will have acquired practical proficiency in employing the `Element-DeepLabCut` package to effectively tackle the complexities of pose estimation. \n", + "\n", + "This tutorial and sample dataset will serve as a practical foundation for your learning journey with the Element package, enabling you to apply these techniques to your own research projects. \n", + "\n", + "By integrating this element package with other Elements of DataJoint, you unlock a powerful data pipeline that provides numerous benefits for your research workflow. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Setup" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, + "outputs": [], + "source": [ + "####Explain this part better and include the link to download the project folder" + ] + }, + { "cell_type": "markdown", "metadata": {}, "source": [ - "### 1.2. Configuring DataJoint" + "Before using DataJoint and this tutorial, you need an account to gain access to the database server. \n", + "\n", + "Please, go to ### and create an account. \n", + "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We need to `configure the connection` of the DataJoint schemas to the same database server with the same user credentials. \n", + "Now that you have your credentials (DJ_USER, DJ_PASS), you need to connect to the server. To do so, we need to `configure the connection` with the user credentials. \n", "\n", - "- If this is the first time that you are running this tutorial, then you will need to specify the connection parameters by input arguments in `1.2.1. Configuration Code for Initiating this Tutorial`. To prevent this to be necessary for further analysis in this tutorial, in the next step we will create a DataJoint configuration file named `dj_local_conf.json` that will save these credentials arguments as environment variables (DJ_HOST, DJ_USER, DJ_PASS). Thus, the configuration file is unique to each machine and will be created in your `Element-Deeplabcut` directory.\n", + "- If this is the first time that you are running this tutorial:\n", + " - Then you will need to specify the connection parameters by input arguments as in the next subsection `Configuration Code for Initiating this Tutorial`. This section will create a DataJoint configuration file named `dj_local_conf.json` that will save your credentials as environment variables in your local machine. You can find this file in your `Element-Deeplabcut` folder. This configuration file is unique to each machine and DataJoint user.\n", "\n", - "- If you have already run this tutorial and created the `.json` file with your credentials info, then you can directly jump to the `1.2.2. Configuration code to configure this tutorial in subsequent restarts`." + "- If you have already run this tutorial and created the `.json` file with your credentials info:\n", + " - Then you can directly start from the subsection `Configuration Code to Configure this Tutorial in Subsequent Restarts`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "##### 1.2.1. Configuration Code for Initiating This Tutorial" + "##### Configuration Code for Initiating This Tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "##### *The configuration file only needs to be set up once. If you already have one, jump to the following subsection `1.2.2. Configuration code to configure this tutorial in subsequent restarts`*" + "##### *The configuration file only needs to be set up once. If you already have one, jump to the following subsection `Configuration Code to Configure this Tutorial in Subsequent Restarts`*" ] }, { @@ -90,23 +190,28 @@ "metadata": {}, "outputs": [], "source": [ - "# By convention, we set a local config in the workflow directory.\n", "import os\n", "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", "assert os.path.basename(os.getcwd())=='element-deeplabcut', (\"Please move to the \"\n", " + \"element directory\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by importing the packages necessary to run this pipeline." + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Import the packages necessary to run this DataJoint pipeline `Element-DeepLabCut`\n", "import datajoint as dj\n", "from pathlib import Path\n", - "import yaml" + "import yaml\n" ] }, { @@ -116,7 +221,18 @@ "The connection parameters are specified by input arguments:\n", "- HOST, USER, AND PASSWORD are the fields for the user credentials\n", "- Configuring a `custom` field helps manage privileges on a server,for instance, teams who work on the same schemas should use the same schema prefix. \n", - " - Setting the prefix to `dlc_` means that every schema we then create will start with `dlc_` (e.g. `dlc_lab`, `dlc_subject`, `dlc_model` etc.)\n" + " - Setting the prefix to `dlc_` means that every schema we then create will start with `dlc_` (e.g. `dlc_lab`, `dlc_subject`, `dlc_model` etc.)\n", + "\n", + "Please, substitute the blue text with your personal host, username, and prefix. Also, your password will be asked.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##TO-DO: WHAT HOST IS NECESSARY FOR A NEW USER?" ] }, { @@ -134,17 +250,43 @@ }, { "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### DELETE BEFORE COMMIT TO GITHUB\n", + "\n", + "import getpass\n", + "dj.config['database.host'] = 'rds.datajoint.io' \n", + "dj.config['database.user'] = 'milagrosmarin' \n", + "dj.config['database.password'] = getpass.getpass() # enter the password securely\n", + "dj.config['custom']['database.prefix']= 'milagrosmarin_dlc_' " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Credentials will be saved and the connection to the database server will be run with the next cells." + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# The input arguments are saved in a configuration file named `dj_local_config.json`\n", "dj.config.save_local() " ] }, - { + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make the connection to the database server." + ] + }, + { "cell_type": "code", "execution_count": null, "metadata": {}, @@ -157,21 +299,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Once set the configuration file, it will be created and saved as `dj_local_conf.json` in the `Element-DeepLabCut directory`. Please, check this file and its content. Remember that this step only needs to be set up once." + "Once set the configuration file, it will be created and saved as `dj_local_conf.json` in the `Element-DeepLabCut directory`. Please, you may verify this file and its content. Remember that this step only needs to be set up once." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### 1.2.2. Configuration code to configure this tutorial in subsequent restarts" + "#### Configuration Code to Configure this Tutorial in Subsequent Restarts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If you have already created and saved the `dj_local_conf.json` file, then you only need to run the following code lines after restart the kernel of the notebook." + "If you have already run the previous subsection, the next time you want to run this tutorial (restart the kernel of the notebook) you will only need to start the tutorial from here: " ] }, { @@ -180,25 +322,37 @@ "metadata": {}, "outputs": [], "source": [ - "# By convention, we set a local config in the workflow directory.\n", "import os\n", "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", "assert os.path.basename(os.getcwd())=='element-deeplabcut', (\"Please move to the \"\n", " + \"element directory\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by importing the packages necessary to run this pipeline." + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Import the packages necessary to run this DataJoint pipeline `Element-DeepLabCut`\n", "import datajoint as dj\n", "from pathlib import Path\n", "import yaml" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's connect to the database server to be able to use DataJoint." + ] + }, { "cell_type": "code", "execution_count": null, @@ -212,14 +366,44 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. Design the DataJoint pipeline" + "## 3. Design the DataJoint Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, you need to update the path of your `DeepLabCut project folder` into your configuration file `dj_local_conf.json`. Open the file in your `DeepLabCut-Element` folder, and copy and paste the `DeepLabCut project folder` path in `dlc_root_data_dir`. Also, copy and paste the `DeepLabCut project folder` name in `current_project_folder`:\n", + "\n", + " \"dlc_root_data_dir\": \"{DLC_PROJECT_PATH}\",\n", + " \"current_project_folder\": \"{DLC_PROJECT_NAME}\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Based on the project path specified in the `.json` file, the paths of the input files are extracted to be used later:" + "Or you can run the following lines to automatically change this information in the configuration file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from element_interface.utils import find_full_path\n", + "dj.config.load('dj_local_conf.json')\n", + "data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config\n", + " 'Top_tracking-DataJoint-2023-08-03') \n", + " # DLC project dir" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the project path specified in the `.json` file, the paths of the input files are charged as variables in this tutorial's session:" ] }, { @@ -273,7 +457,7 @@ "| Element Session | [Link](https://github.com/datajoint/element-session) | [Link](https://datajoint.com/docs/elements/element-session) | General information of experimental sessions. |\n", "| Element DeepLabCut | [Link](https://github.com/datajoint/element-deeplabcut) | [Link](https://datajoint.com/docs/elements/element-deeplabcut) | DataJoint schemas (Train and Model) for storing and running analysis of markerless pose estimation with DeepLabCut.\n", "\n", - "The Elements are imported and activated within the `tutorial_pipeline` python script." + "The Elements are imported and activated in the next code cell." ] }, { @@ -289,9 +473,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "By importing the modules for the first time, the schemas and tables will be created in the database. \n", - "- Once created, importing modules will not create schemas and tables again, but the existing schemas/tables can be accessed.\n", - "- To empty these schemas and tables for introducing new entries, run (uncomment) the following code lines:" + "By importing the modules for the first time, the schemas and tables will be created in the database. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.list_schemas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once created, importing modules will not create schemas and tables again, but the existing schemas/tables can be accessed.\n", + "To empty these schemas and tables for introducing new entries, run (uncomment) the following code lines (note that you will have to commit the delete in the prompt by typing \"yes\")" ] }, { @@ -302,9 +501,11 @@ "source": [ "# Empty the session in case of rerunning\n", "safemode=True # Set to false to turn off confirmation prompts\n", - "(session.Session & 'subject=\"subject6\"').delete(safemode=safemode)\n", + "session.Session.delete(safemode=safemode)\n", "train.TrainingParamSet.delete(safemode=safemode)\n", - "train.VideoSet.delete(safemode=safemode)" + "train.VideoSet.delete(safemode=safemode)\n", + "model.BodyPart.delete(safemode=safemode)\n", + "subject.Subject.delete(safemode=safemode)" ] }, { @@ -327,7 +528,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The Python classes in the module correspond to a table in the database server. We can check also if there is any entry in the table:" + "The Python classes in the module correspond to a table in the database server. We can check also if there is any entry in the table." ] }, { @@ -343,9 +544,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's plot the diagram of tables within multiple schemas and their dependencies using `dj.Diagram()`. \n", - "\n", - "This is the diagram of the whole data pipeline for this `Element-DeepLabCut`:" + "Let's plot the diagram of the whole data pipeline for this `Element-DeepLabCut`." ] }, { @@ -367,101 +566,31 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "And this is the main body of this `Element-DeepLabCut`:" - ] - }, - { + "And this is the main body of this `Element-DeepLabCut`." + ] + }, + { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dj.Diagram(model) + dj.Diagram(train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Table Types\n", - "\n", - "There are 5 table types in DataJoint. Each of these appear in the diagram above.\n", - "\n", - "| Table tier | Color and shape | Description |\n", - "| -- | -- | -- |\n", - "| Manual table | Green box | Data entered from outside the pipeline, either by hand or with external helper scripts. |\n", - "| Lookup table | Gray box | Small tables containing general facts and settings of the data pipeline; not specific to any experiment or dataset. | \n", - "| Imported table | Blue oval | Data ingested automatically inside the pipeline but requiring access to data outside the pipeline. |\n", - "| Computed table | Red circle | Data computed automatically entirely inside the pipeline. |\n", - "| Part table | Plain text | Part tables share the same tier as their master table. |\n", - "\n", - "The diagram becomes clear when it's approached as a hierarchy of tables that define the order in which the pipeline expects to receive data in each of the tables. \n", - "\n", - "The tables higher up in the diagram such as `subject.Subject()` should be the first to receive data.\n", - "\n", - "Data is manually entered into the green rectangular tables with the `insert1()` method.\n", - "\n", - "Tables connected by a line depend on entries from the table above it.\n", - " \n", - "Tables with a purple oval or red circle will be automatically filled with relevant data\n", - " by calling `populate()`. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Table Links\n", - "\n", - "- **One-to-one primary**: thick solid line, share the exact same primary key, meaning the child table inherits all the primary key fields from the parent table as its own primary key\n", - "\n", - "- **One-to-many primary**: thin solid line, inherit the primary key from the parent table, but have additional field(s) as part of the primary key as well\n", - "\n", - "- **Secondary dependency**: dashed line, the child table inherits the primary key fields from parent table as its own secondary attribute" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## 3. DataJoint Basics\n", - "\n", - "DataJoint pipelines can be run with four commands:\n", - "\n", - "- `Insert` metadata about a subject, recording session, and \n", - " parameters related to processing markerless pose estimation data through DeepLabCut\n", - "\n", - "- `Populate` tables with outputs of pose estimation including training parameters and video recordings\n", - "\n", - "- `Query` the data from the database\n", - "\n", - "- `Fetch` and plot animal position estimates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Insert entries into manual tables\n", - "\n", - "#### Common Table Functions\n", - "- `()` show table contents\n", - "- `heading` shows attribute definitions\n", - "- `describe()` show table defintiion with foreign key references\n", - "\n", - "Let's have a look at the `TrainingTask` table.\n", - "\n", - "To know what data to insert into the table, we can view its dependencies and attributes using `.heading` function. Note that `heading` displays all the attributes of the table definition, regardless of whether they are declared in an upstream table." + "## 4. Enter the Metadata into the Pipeline" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "train.TrainingTask()" + "In order to run the `Model Training`, we need to start by adding the input data to the `train` module. Let's start having a look at the `TrainingTask` table. This table will pair each video set with their corresponding training parameters.\n", + "\n" ] }, { @@ -470,29 +599,36 @@ "metadata": {}, "outputs": [], "source": [ - "train.TrainingTask.heading" + "train.TrainingTask()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The cells above show all attributes of the train table.\n" + "Let's pair some example data and launch training via `process`. " ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "## Insert entries into manual tables" + "#IS THIS NEEDED???\n", + "\n", + "#key={'paramset_idx':0,'training_id':0,'video_set_id':0, \n", + "# 'project_path':dlc_project_folder}\n", + "#train.TrainingTask.insert1(key, skip_duplicates=True)\n", + "#process.run(verbose=True, display_progress=True)\n", + "#model.RecordingInfo()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "We will insert example data into the `subject.Subject (dj.Manual)` table (green in diagrams) by providing values as a dictionary or a list of dictionaries." + "The `Subject` module corresponds to the table that will contain the subject (e.g., the mouse) information. Let's insert example entries into the `subject.Subject` table." ] }, { @@ -517,12 +653,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's repeat the steps above for the `Session` table. \n", - "\n", - "- We can insert in the `session.Session` table by passing a dictionary to the `insert1` method. \n", - "\n", - "- We can look at the contents of this table and restrict by a value.\n", - "\n" + "Let's repeat the step for the `Session` module. We can also insert in the `Session` table by passing a dictionary to the `insert1` method. \n" ] }, { @@ -531,19 +662,15 @@ "metadata": {}, "outputs": [], "source": [ + "#Definition of the dictionary named \"session_keys\"\n", "session_keys = [\n", " dict(subject=\"subject6\", session_datetime=\"2021-06-02 14:04:22\"),\n", " dict(subject=\"subject6\", session_datetime=\"2021-06-03 14:43:10\"),\n", "]\n", + "\n", + "#Insert this dictionary in the Session table\n", "session.Session.insert(session_keys, skip_duplicates=True)\n", - "session.Session() & \"session_datetime > '2021-06-01 12:00:00'\" & \"subject='subject6'\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train schema and VideoSet tables" + "session.Session()" ] }, { @@ -561,7 +688,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Videoset table\n", + "# Videoset table \n", "train.VideoSet.insert1({\"video_set_id\": 0}, skip_duplicates=True)\n", "\n", "for idx, filename in enumerate(labeled_files_rel):\n", @@ -734,7 +861,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Evaluate the network" + "## 5. Evaluating the network model" ] }, { @@ -790,7 +917,8 @@ "metadata": {}, "outputs": [], "source": [ - "bp_desc=['Body Center', 'Head', 'Base of Tail']\n", + "# Add ONLY if there are new body parts compared to the config.yaml. If the table has already descriptions, then leave it empty.\n", + "bp_desc=[]\n", "model.BodyPart.insert_from_config(config_file_abs,bp_desc)" ] }, @@ -817,8 +945,10 @@ "metadata": {}, "outputs": [], "source": [ - "model.Model.insert_new_model(model_name='FromTop-latest',dlc_config=config_path,\n", - " shuffle=1,trainingsetindex=0,\n", + "model.Model.insert_new_model(model_name='FromTop-latest',\n", + " dlc_config=config_file_abs,\n", + " shuffle=1,\n", + " trainingsetindex=0,\n", " model_description='FromTop - latest snapshot',\n", " paramset_idx=0,\n", " params={\"snapshotindex\":-1})" @@ -830,23 +960,23 @@ "metadata": {}, "outputs": [], "source": [ - "model.Model()" + "model.BodyPart()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "`ModelEvaluation` will reference the `Model` using the `populate` method and insert the output from DeepLabCut's `evaluate_network` function" + "model.Model()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "model.ModelEvaluation.heading" + "`ModelEvaluation` will reference the `Model` using the `populate` method and insert the output from DeepLabCut's `evaluate_network` function" ] }, { @@ -878,7 +1008,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To use our model, we'll first need to insert a session recoring into `VideoRecording`" + "To use our model, we'll first need to insert a session recording into `VideoRecording`." ] }, { @@ -899,12 +1029,12 @@ "key = {'subject': 'subject6',\n", " 'session_datetime': '2021-06-02 14:04:22',\n", " 'recording_id': '1', 'device': 'Camera1'}\n", - "model.VideoRecording.insert1(key)\n", + "model.VideoRecording.insert1(key, skip_duplicates=True)\n", "\n", - "_ = key.pop('device') # get rid of secondary key from master table\n", + "_ = key.pop('device') # get rid of secondary key from master table // why this step???\n", "key.update({'file_id': 1, \n", - " 'file_path': 'from_top_tracking/videos/test-2s.mp4'})\n", - "model.VideoRecording.File.insert1(key)" + " 'file_path': 'Top_tracking-DataJoint-2023-08-03/videos/train1_trimmed.mp4'})\n", + "model.VideoRecording.File.insert1(key, skip_duplicates=True)" ] }, { @@ -946,9 +1076,43 @@ "metadata": {}, "outputs": [], "source": [ - "key = (model.VideoRecording & {'recording_id': '1'}).fetch1('KEY')\n", - "key.update({'model_name': 'FromTop-latest', 'task_mode': 'trigger'})\n", - "key" + "recording_dict = (model.VideoRecording & {\"recording_id\": \"1\"}).fetch1(\"KEY\")\n", + "recording_dict.update({\"model_name\": \"FromTop-latest\", \"task_mode\": \"trigger\"})\n", + "# videotype, gputouse, save_as_csv, batchsize, cropping, TFGPUinference, dynamic, robust_nframes, allow_growth, use_shelve\n", + "analyze_videos_params = {\"save_as_csv\": True}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, DataJoint will store results in a subdirectory\n", + "> / videos / device__recording_<#>_model_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`processed_dir` is optionally specified in the datajoint config, or in the `insert_estimation_task`. If unspecified, this will be the project directory. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.PoseEstimationTask.infer_output_dir(key)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.PoseEstimationTask.insert_estimation_task(recording_dict, model_name = recording_dict[\"model_name\"], analyze_videos_params=analyze_videos_params)" ] }, { @@ -957,19 +1121,15 @@ "metadata": {}, "outputs": [], "source": [ - "model.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True})\n", - "model.PoseEstimation.populate()" + "#model.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True})\n", + "model.PoseEstimation.populate()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "By default, DataJoint will store results in a subdirectory\n", - "> / videos / device__recording_<#>_model_\n", - "where `processed_dir` is optionally specified in the datajoint config. If unspecified, this will be the project directory. The device and model names are specified elsewhere in the schema.\n", - "\n", - "We can get this estimation directly as a pandas dataframe." + "The resulting coordinates of the pose estimation are now available in the corresponding `BodyPartPosition` table, ready to use for visualization, or to combine with other Elements." ] }, { @@ -978,14 +1138,23 @@ "metadata": {}, "outputs": [], "source": [ - "model.PoseEstimation.get_trajectory(key)" + "model.PoseEstimation.BodyPartPosition()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In the [next notebook](./04-Automate_Optional.ipynb), we'll look at additional tools in the workflow for automating these steps." + "We can visualize the pose estimation results directly as a pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.PoseEstimation.coordinates_dataframe(key)" ] } ], diff --git a/notebooks/tutorial_copy.ipynb b/notebooks/tutorial_copy.ipynb new file mode 100644 index 0000000..2281782 --- /dev/null +++ b/notebooks/tutorial_copy.ipynb @@ -0,0 +1,1190 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# DataJoint Element DeepLabCut" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Open-source Data Pipeline for Markerless Pose Estimation in Neurophysiology**\n", + "\n", + "This tutorial focuses on providing a comprehensive understanding of the open-source data pipeline offered by `Element-DeepLabCut`. The package is designed to facilitate pose estimation analyses and streamline the organization of data using `DataJoint`. By the end of this tutorial, participants will have a clear grasp of how to set up, utilize, ad optimize the package for their specific pose estimation projects. \n", + "\n", + "**Key Components and Objectives**\n", + "\n", + "- 1. Download Sample Data and Context\n", + "\n", + "- 2. Setup\n", + "\n", + "- 3. Design the DataJoint Pipeline\n", + "\n", + "- 4. Enter the Metadata into the Pipeline\n", + "\n", + "- 5. Run the Model Training\n", + "\n", + "- 6. Run the Model Evaluation\n", + "\n", + "\n", + "For detailed documentation and tutorials on general DataJoint principles that support collaboration, automation, reproducibility, and visualizations:\n", + "\n", + "[`DataJoint for Python - Interactive Tutorials`](https://github.com/datajoint/datajoint-tutorials) - Fundamentals including table tiers, query operations, fetch operations, automated computations with the make function, etc.\n", + "\n", + "[`DataJoint for Python - Documentation`](https://datajoint.com/docs/core/datajoint-python/0.14/)\n", + "\n", + "[`DataJoint Element for DeepLabCut - Documentation`](https://datajoint.com/docs/elements/element-deeplabcut/0.2/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Download Sample Data and Context" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, you will download the sample data that simulates a real research project. By working through this sample data, you will gain valuable insights into the `practical application` of the package's tools and techniques." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Project Context: \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this research project, we are studying the `behavior of a freely-moving mouse in an open-field environment`. The objective is to `extract pose estimations of the animal's head, body, and tail` from video footage. This information can provide valuable insights into the animal's movements, postures, and interactions within the environment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Downloading Sample Data:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Click the following link to download the sample data archive: `##TO-DO`\n", + "\n", + "\n", + "2. Once the download is complete, extract the contents to a `path of your choice on your local machine`.\n", + "\n", + "After running this tutorial, you can try `Element-DeepLabCut` with your own dataset. To do so, create a new `DeepLabCut` folder with your own videos. Then, remember to change the path in the configuration file (`config.yaml`) in your new `DeepLabCut project` folder accordingly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Challenges: " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Complex Background**: The open field environment introduces complex backgrounds and varying lighting conditions, making accurate pose estimation challenging.\n", + "\n", + "**Multiple Body Parts**: Extracting the pose of multiple body parts (head, body, tail) adds complexity to the analysis due to potential occlusions and variations in appearance.\n", + "\n", + "**Data Management**: Managing the large volume of video data generated in the field and ensuring consistent annotation requires an efficient data pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Expected Outcomes:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Upon completing this tutorial, you will have acquired practical proficiency in employing the `Element-DeepLabCut` package to effectively tackle the complexities of pose estimation. \n", + "\n", + "This tutorial and sample dataset will serve as a practical foundation for your learning journey with the Element package, enabling you to apply these techniques to your own research projects. \n", + "\n", + "By integrating this element package with other Elements of DataJoint, you unlock a powerful data pipeline that provides numerous benefits for your research workflow. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "####Explain this part better and include the link to download the project folder" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before using DataJoint and this tutorial, you need an account to gain access to the database server. \n", + "\n", + "Please, go to ### and create an account. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that you have your credentials (DJ_USER, DJ_PASS), you need to connect to the server. To do so, we need to `configure the connection` with the user credentials. \n", + "\n", + "- If this is the first time that you are running this tutorial:\n", + " - Then you will need to specify the connection parameters by input arguments as in the next subsection `Configuration Code for Initiating this Tutorial`. This section will create a DataJoint configuration file named `dj_local_conf.json` that will save your credentials as environment variables in your local machine. You can find this file in your `Element-Deeplabcut` folder. This configuration file is unique to each machine and DataJoint user.\n", + "\n", + "- If you have already run this tutorial and created the `.json` file with your credentials info:\n", + " - Then you can directly start from the subsection `Configuration Code to Configure this Tutorial in Subsequent Restarts`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Configuration Code for Initiating This Tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### *The configuration file only needs to be set up once. If you already have one, jump to the following subsection `Configuration Code to Configure this Tutorial in Subsequent Restarts`*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "assert os.path.basename(os.getcwd())=='element-deeplabcut', (\"Please move to the \"\n", + " + \"element directory\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by importing the packages necessary to run this pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj\n", + "from pathlib import Path\n", + "import yaml\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The connection parameters are specified by input arguments:\n", + "- HOST, USER, AND PASSWORD are the fields for the user credentials\n", + "- Configuring a `custom` field helps manage privileges on a server,for instance, teams who work on the same schemas should use the same schema prefix. \n", + " - Setting the prefix to `dlc_` means that every schema we then create will start with `dlc_` (e.g. `dlc_lab`, `dlc_subject`, `dlc_model` etc.)\n", + "\n", + "Please, substitute the blue text with your personal host, username, and prefix. Also, your password will be asked.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##TO-DO: WHAT HOST IS NECESSARY FOR A NEW USER?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "dj.config['database.host'] = '{YOUR_HOST}' \n", + "dj.config['database.user'] = '{YOUR_USERNAME}' \n", + "dj.config['database.password'] = getpass.getpass() # enter the password securely\n", + "dj.config['custom']['database.prefix']= '{YOUR_USERNAME_dlc_}' " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### DELETE BEFORE COMMIT TO GITHUB\n", + "\n", + "import getpass\n", + "dj.config['database.host'] = 'rds.datajoint.io' \n", + "dj.config['database.user'] = 'milagrosmarin' \n", + "dj.config['database.password'] = getpass.getpass() # enter the password securely\n", + "dj.config['custom']['database.prefix']= 'milagrosmarin_dlc_' " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Credentials will be saved and the connection to the database server will be run with the next cells." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config.save_local() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make the connection to the database server." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.conn()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once set the configuration file, it will be created and saved as `dj_local_conf.json` in the `Element-DeepLabCut directory`. Please, you may verify this file and its content. Remember that this step only needs to be set up once." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Configuration Code to Configure this Tutorial in Subsequent Restarts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you have already run the previous subsection, the next time you want to run this tutorial (restart the kernel of the notebook) you will only need to start the tutorial from here: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "assert os.path.basename(os.getcwd())=='element-deeplabcut', (\"Please move to the \"\n", + " + \"element directory\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by importing the packages necessary to run this pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj\n", + "from pathlib import Path\n", + "import yaml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's connect to the database server to be able to use DataJoint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.conn()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Design the DataJoint Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, you need to update the path of your `DeepLabCut project folder` into your configuration file `dj_local_conf.json`. Open the file in your `DeepLabCut-Element` folder, and copy and paste the `DeepLabCut project folder` path in `dlc_root_data_dir`. Also, copy and paste the `DeepLabCut project folder` name in `current_project_folder`:\n", + "\n", + " \"dlc_root_data_dir\": \"{DLC_PROJECT_PATH}\",\n", + " \"current_project_folder\": \"{DLC_PROJECT_NAME}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or you can run the following lines to automatically change this information in the configuration file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from element_interface.utils import find_full_path\n", + "dj.config.load('dj_local_conf.json')\n", + "data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config\n", + " 'Top_tracking-DataJoint-2023-08-03') \n", + " # DLC project dir" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the project path specified in the `.json` file, the paths of the input files are charged as variables in this tutorial's session:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### DLC Project\n", + "dlc_project_path_abs = Path(dj.config[\"custom\"][\"dlc_root_data_dir\"]) / Path(\n", + " dj.config[\"custom\"][\"current_project_folder\"]\n", + ") # use pathlib to join; abs path\n", + "dlc_project_folder = Path(\n", + " dj.config[\"custom\"][\"current_project_folder\"]\n", + ") # relative path\n", + "\n", + "### Config file\n", + "config_file_abs = dlc_project_path_abs / \"config.yaml\" # abs path\n", + "assert (\n", + " config_file_abs.exists()\n", + "), \"Please check the that you have the Top_tracking folder\"\n", + "\n", + "### Labeled-data\n", + "labeled_data_path_abs = dlc_project_path_abs / \"labeled-data\"\n", + "labeled_files_abs = list(\n", + " list(labeled_data_path_abs.rglob(\"*\"))[1].rglob(\"*\")\n", + ") # substitute 'training_files'; absolute path\n", + "labeled_files_rel = []\n", + "for file in labeled_files_abs:\n", + " labeled_files_rel.append(\n", + " file.relative_to(dlc_project_path_abs)\n", + " ) # substitute 'training_files'; relative path\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combine multiple Elements into a pipeline\n", + "\n", + "Each DataJoint Element is a modular set of tables that can be combined into a complete pipeline.\n", + "\n", + "Each Element contains one or more modules, and each module declares its own schema in the database. Schemas are conceptually related sets of tables. \n", + "\n", + "This tutorial pipeline is assembled from four DataJoint Elements.\n", + "\n", + "| Element | Source Code | Documentation | Description |\n", + "| -- | -- | -- | -- |\n", + "| Element Lab | [Link](https://github.com/datajoint/element-lab) | [Link](https://datajoint.com/docs/elements/element-lab) | Lab management related information, such as Lab, User, Project, Protocol, Source. |\n", + "| Element Animal | [Link](https://github.com/datajoint/element-animal) | [Link](https://datajoint.com/docs/elements/element-animal) | General subject meta data, genotype, and surgery information. |\n", + "| Element Session | [Link](https://github.com/datajoint/element-session) | [Link](https://datajoint.com/docs/elements/element-session) | General information of experimental sessions. |\n", + "| Element DeepLabCut | [Link](https://github.com/datajoint/element-deeplabcut) | [Link](https://datajoint.com/docs/elements/element-deeplabcut) | DataJoint schemas (Train and Model) for storing and running analysis of markerless pose estimation with DeepLabCut.\n", + "\n", + "The Elements are imported and activated in the next code cell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tutorial_pipeline import lab, subject, session, train, model # after creating json file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By importing the modules for the first time, the schemas and tables will be created in the database. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.list_schemas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once created, importing modules will not create schemas and tables again, but the existing schemas/tables can be accessed.\n", + "To empty these schemas and tables for introducing new entries, run (uncomment) the following code lines (note that you will have to commit the delete in the prompt by typing \"yes\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Empty the session in case of rerunning\n", + "safemode=True # Set to false to turn off confirmation prompts\n", + "session.Session.delete(safemode=safemode)\n", + "train.TrainingParamSet.delete(safemode=safemode)\n", + "train.VideoSet.delete(safemode=safemode)\n", + "model.BodyPart.delete(safemode=safemode)\n", + "subject.Subject.delete(safemode=safemode)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each Python module (e.g. `subject`) contains a schema object that enables interaction with the schema in the database." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.schema" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Python classes in the module correspond to a table in the database server. We can check also if there is any entry in the table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot the diagram of the whole data pipeline for this `Element-DeepLabCut`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(\n", + " dj.Diagram(subject) \n", + " + dj.Diagram(lab) \n", + " + dj.Diagram(session) \n", + " + dj.Diagram(model) \n", + " + dj.Diagram(train)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And this is the main body of this `Element-DeepLabCut`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(model) + dj.Diagram(train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Enter the Metadata into the Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to run the `Model Training`, we need to start by adding the input data to the `train` module. Let's start having a look at the `TrainingTask` table. This table will pair each video set with their corresponding training parameters.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.TrainingTask()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's pair some example data and launch training via `process`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#IS THIS NEEDED???\n", + "\n", + "#key={'paramset_idx':0,'training_id':0,'video_set_id':0, \n", + "# 'project_path':dlc_project_folder}\n", + "#train.TrainingTask.insert1(key, skip_duplicates=True)\n", + "#process.run(verbose=True, display_progress=True)\n", + "#model.RecordingInfo()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Subject` module corresponds to the table that will contain the subject (e.g., the mouse) information. Let's insert example entries into the `subject.Subject` table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Subject and Session tables\n", + "subject.Subject.insert1(\n", + " dict(\n", + " subject=\"subject6\",\n", + " sex=\"F\",\n", + " subject_birth_date=\"2020-01-01\",\n", + " subject_description=\"hneih_E105\",\n", + " ),\n", + " skip_duplicates=True,\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's repeat the step for the `Session` module. We can also insert in the `Session` table by passing a dictionary to the `insert1` method. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Definition of the dictionary named \"session_keys\"\n", + "session_keys = [\n", + " dict(subject=\"subject6\", session_datetime=\"2021-06-02 14:04:22\"),\n", + " dict(subject=\"subject6\", session_datetime=\"2021-06-03 14:43:10\"),\n", + "]\n", + "\n", + "#Insert this dictionary in the Session table\n", + "session.Session.insert(session_keys, skip_duplicates=True)\n", + "session.Session()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `VideoSet` table in the `train` schema retains records of files generated in the video labeling process (e.g., `h5`, `csv`, `png`). DeepLabCut will refer to the `mat` file located under the `training-datasets` directory.\n", + "\n", + "We recommend storing all paths as relative to the root in your config." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Videoset table \n", + "train.VideoSet.insert1({\"video_set_id\": 0}, skip_duplicates=True)\n", + "\n", + "for idx, filename in enumerate(labeled_files_rel):\n", + " train.VideoSet.File.insert1(\n", + " {\n", + " \"video_set_id\": 0, \n", + " \"file_id\": idx, \n", + " \"file_path\": dlc_project_folder / filename\n", + " },\n", + " ) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.VideoSet.File()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training a network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To train the network, we need to add the parameter set (`TrainingParamSet`) of the model training (`train`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.TrainingParamSet()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `params` attribute has to be a dictionary that captures all the items for the DeepLabCut's `train_network` function. At minimum, this is the contents of the project's config file, as well as `suffle` and `trainingsetindex`, which are not included in the configuration file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We will insert these items, load the config contents, and overwrite some defaults, including `maxiters`, to restrict our training iterations to 5.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Restrict the training interations to 5 modifying the default parameters in config.yaml\n", + "paramset_idx = 0\n", + "paramset_desc = \"First training test with DLC using shuffle 1 and maxiters = 5\"\n", + "\n", + "# default parameters\n", + "with open(config_file_abs, \"rb\") as y:\n", + " config_params = yaml.safe_load(y)\n", + "config_params.keys()\n", + "\n", + "# new parameters\n", + "training_params = {\n", + " \"shuffle\": \"1\",\n", + " \"trainingsetindex\": \"0\",\n", + " \"maxiters\": \"5\",\n", + " \"scorer_legacy\": \"False\", # For DLC ≤ v2.0, include scorer_legacy = True in params\n", + " \"maxiters\": \"5\",\n", + " \"multianimalproject\": \"False\",\n", + "}\n", + "config_params.update(training_params)\n", + "\n", + "train.TrainingParamSet.insert_new_params(\n", + " paramset_idx=paramset_idx, paramset_desc=paramset_desc, params=config_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we add a `TrainingTask`. As a computed table, `ModelTraining` will reference this to start training when calling `populate()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.TrainingTask()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TrainingTask table\n", + "key = {\n", + " \"video_set_id\": 0,\n", + " \"paramset_idx\": 0,\n", + " \"training_id\": 1,\n", + " \"project_path\": dlc_project_folder,\n", + "}\n", + "train.TrainingTask.insert1(key, skip_duplicates=True)\n", + "train.TrainingTask()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After inserting the training parameters and the video recordings, the model training can be run and outputs will be stored in `ModelTraining` table.\n", + "\n", + "*Note that the following code line will run the model training with DeepLabCut. It will take some minutes if you have installed DeepLabCut in the GPU. However, it will take longer if the installation was in CPU*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.ModelTraining.populate(display_progress=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.ModelTraining.fetch()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "The network is now trained and ready to evaluate. The next step consists of evaluating the network. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Evaluating the network model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "### Tracking Joints/Body Parts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `model` schema uses a lookup table for managing the body parts tracked across models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.BodyPart()\n", + "new_body_parts = [\n", + " dict(body_part=\"subject6\", session_datetime=\"2021-06-02 14:04:22\"),\n", + " dict(subject=\"subject6\", session_datetime=\"2021-06-03 14:43:10\"),\n", + "]\n", + "session.Session.insert(session_keys, skip_duplicates=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also modify the body parts as desired. For that, we can use helper functions to identify and insert the new body parts from a given DeepLabCut configuration file (`config.yaml`) in the data pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.BodyPart.extract_new_body_parts(config_file_abs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add ONLY if there are new body parts compared to the config.yaml. If the table has already descriptions, then leave it empty.\n", + "bp_desc=[]\n", + "model.BodyPart.insert_from_config(config_file_abs,bp_desc)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "### Declaring/Evaluating a Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can insert into `Model` table for automatic evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.Model.insert_new_model(model_name='FromTop-latest',\n", + " dlc_config=config_file_abs,\n", + " shuffle=1,\n", + " trainingsetindex=0,\n", + " model_description='FromTop - latest snapshot',\n", + " paramset_idx=0,\n", + " params={\"snapshotindex\":-1})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.BodyPart()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.Model()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`ModelEvaluation` will reference the `Model` using the `populate` method and insert the output from DeepLabCut's `evaluate_network` function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.ModelEvaluation.populate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.ModelEvaluation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pose Estimation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use our model, we'll first need to insert a session recording into `VideoRecording`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.VideoRecording()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "key = {'subject': 'subject6',\n", + " 'session_datetime': '2021-06-02 14:04:22',\n", + " 'recording_id': '1', 'device': 'Camera1'}\n", + "model.VideoRecording.insert1(key, skip_duplicates=True)\n", + "\n", + "_ = key.pop('device') # get rid of secondary key from master table // why this step???\n", + "key.update({'file_id': 1, \n", + " 'file_path': 'Top_tracking-DataJoint-2023-08-03/videos/train1_trimmed.mp4'})\n", + "model.VideoRecording.File.insert1(key, skip_duplicates=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.VideoRecording.File()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`RecordingInfo` automatically populates with file information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.RecordingInfo.populate()\n", + "model.RecordingInfo()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we specify if the `PoseEstimation` table should load results from an existing file or trigger the estimation command. Here, we can also specify parameters for DeepLabCut's `analyze_videos` as a dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "recording_dict = (model.VideoRecording & {\"recording_id\": \"1\"}).fetch1(\"KEY\")\n", + "recording_dict.update({\"model_name\": \"FromTop-latest\", \"task_mode\": \"trigger\"})\n", + "# videotype, gputouse, save_as_csv, batchsize, cropping, TFGPUinference, dynamic, robust_nframes, allow_growth, use_shelve\n", + "analyze_videos_params = {\"save_as_csv\": True}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, DataJoint will store results in a subdirectory\n", + "> / videos / device__recording_<#>_model_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`processed_dir` is optionally specified in the datajoint config, or in the `insert_estimation_task`. If unspecified, this will be the project directory. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.PoseEstimationTask.infer_output_dir(key)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.PoseEstimationTask.insert_estimation_task(recording_dict, model_name = recording_dict[\"model_name\"], analyze_videos_params=analyze_videos_params)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#model.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True})\n", + "model.PoseEstimation.populate()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The resulting coordinates of the pose estimation are now available in the corresponding `BodyPartPosition` table, ready to use for visualization, or to combine with other Elements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.PoseEstimation.BodyPartPosition()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualize the pose estimation results directly as a pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.PoseEstimation.coordinates_dataframe(key)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3.9.13 ('ele')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + }, + "vscode": { + "interpreter": { + "hash": "d00c4ad21a7027bf1726d6ae3a9a6ef39c8838928eca5a3d5f51f3eb68720410" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/setup.py b/setup.py index 8cb52b6..e2b1ca7 100644 --- a/setup.py +++ b/setup.py @@ -36,12 +36,30 @@ "element-interface>=0.5.0", "ipykernel>=6.0.1", "pygit2", + "graphviz", # This worked to me installing using conda, not pip! -> pip install anaconda graphviz at the very beginning ], extras_requires={ "default": ["deeplabcut[tf]>=2.2.1.1"], "apple_mchips": [ "'deeplabcut[apple_mchips]'", "tables=3.7.0", - ], # "tensorflow-deps", + "tensorflow-deps", + # "--upgrade tensorflow_macos==2.10.0", ##issue with keras.legacy after installing keras -c apple + # conda install keras -c apple + ], }, ) + +# TO-DO: CHECK THIS FILE TO INSTALL ELEMENT IN ANOTHER CONDA ENVIRONMENT +""" +!!!! For M2 downgrade tensorflow-macos and keras to 2.12.0 +tensorboard 2.12.3 +tensorboard-data-server 0.7.1 +tensorboard-plugin-wit 1.8.1 +tensorflow 2.13.0 +tensorflow-estimator 2.12.0 +tensorflow-macos 2.12.0 +tensorflow-metal 1.0.1 +tensorpack 0.11 +keras 2.12.0 +""" diff --git a/train1_trimmedDLC_resnet50_Top_trackingAug3shuffle1_100.h5 b/train1_trimmedDLC_resnet50_Top_trackingAug3shuffle1_100.h5 new file mode 100644 index 0000000000000000000000000000000000000000..19d68415c30fa5fac3e7d0ad64fe63d4f80f84f9 GIT binary patch literal 282521 zcmeFadAQEi_xQa{88a1`VrM7~j(IHFC_|Y-2xZG0AyZVe4H+AiNMa*O=8~b%Mj=zk ztYKHkEHjP2`|Pz(YpwhH`##_2_t*1W*Y9(#s}AR!*I9e7wf36!`@W59@IbvAvlYrF zQa`d}5gA49@W-_O{S!alX__&7DedoR-`DXE>Eh?H@$-l2;ScG=4I=e>1>)Cp$KNkA zz!!Y{ddubyJ}44DnfCkBKF4nhcLV$>OWGIy^ZEZO4K#SLcC*wCw~4z>^QXZt6F0`e^C~@#nK7es31h>cQqM8a8bb zesAiLN&EToDdiG>Oa1w3;d8pg2}}L?W{xU_W$JbDWd|q$GDxQj{zN@?A0almkvET zr2R2`E6uOLo6~;!fBxK}Nz(_?B|P|Fzl;8w_WMf5zc)x)$N&7B|L^*Vv^bZnumAtW zxf~(R_3hZZbI1N21`O$&>P--LvnM{M*}ZGOj!$Jq+sD zYhaiD_zP2iU!mmR*DC(hlfC+M>e-=O>Mu?Eg*n7;;Wwx0rFWm+9bksv8ftHIg&R!VHFa<-18yH&{6E%B8h+4@#Ve5ERWrB{!h zU3&HC-ltCjsh0Rwb@*1RmK}Tada`5xE{ScF%hoOR*OX7}C;Xn&FQopK@(GRo`-N1t ze8p@X6aTM7_MPSrHwL?Q?l_=hsul-6-8->s*f;fuV3&julJj%#j=dB6@B3f!bK>i% zzb5hXX>zg#IbC{p>eD&3Fo_?j^#ZK}9&K5#Olk~7Ihld$*RM~%RC^?Tp02C^fPOtX z4WM!|C)g~aSC>IuQhh9UA}~U1R6$~zf2V!UA(q6iX9{w9^zPax^$V$XpOE-3?dM8& zPWWZ2**ho9-l})vaP?2MXhJOs+x73%r(c(TsV42%>d8KxhxF~(Z$RSP3G24%)Td9s z&WS%)$=0!dtJKq)En(tTjR*D`(4%4MQAjmy|Nd#tUOoK&a@qR-SC0kJ6|(h*laMa4 z!mzfuz_hw0cdpdc3(+k07a4>OKX-eg8cWq{@a^yuTsQoLfX?np{9LN_QbnYtk391J zfAv`@yQuU}l#{Bx)SpYA0hW~|c03uMr~`uxj{Y;(w3_)BoGOH7f*(FRDijj(?w01iqd|ugsD58~*b-RRgJYboiVO zG?zPP`0KxYDcb4(xjwIuQZ<18@AGG>r=`k=cvP?9gN+{OP_9Fh+KnGf{d#hoNb_T? z|0!3Vaj9EK^MhnRr+jMMNRwNyVWUQ=a{s&dk@~&Vj~qgU&#CLa`Cq<#hcy1bJ1=SL z32^v5Y5S*nGLc9+!%w8{KXc-bXuvIaURTA;8bhq-^5*aKteN|3;B@yP*zCzPi)x@`|!M8~Is-Bwo z5?QHSgc5&G{T-0a62CU__f#-7nWdneQnmbF_W5*|0Ud)LQumqKc&6#8353gpo*?w3 z6Z!skeCM=6Dp}86d-dr!ph~3#pJtyM5`WGjnlx|PxCtP4g7zh0r^4E=Cu=o^{Lgp*3kw0U9ENM*ZtAA zo1}gYAL-KmAcV_%6L813FwRdZ8#uFPIC4-e1Xy0UGjMj`D#n=%4}iRs`N+{KB7IzE zyS0oHi(s90_ax*? zOY2(`tT`zIC-Z;Cr>^c6*#@sB1g(Ne_zSKnRmHf{8PZ0w`L*-7nfnBUVuE?hRc)nYl2?%4CGR0g=rr> zK`-LU8n!>*0raBGROD#8oC0=NrMMkruIjMALBv048S09_{&sRsOsg6=`PrK&FSehl zOSON%{^S zksJLga(U9I#8umC!0FMSGkvO)6~PYA5H8L}Qfc>L)XRC!`LduFf12newa^#n^mXL4NUnJj_LQeF7eB?h_&h6eb?k|PpquY_wLsEh~x1P)M z2O5ArhkkiD6~fjm`{$4@&f=CN5s-rZ?bBJTDDf03Q}I!$fRr~2!C+}}Nf zTMTuGFURAq`|!S?7c*}fa%GT{Ib!{i!q+J#z&g!0WSwz*l}g)Aus@%bb9x_~BRzxU z>9R!L;~-Dw<#xB_L7r$y)(KOt0ISQusjqn5W9CNtHG2>D2TxN3&i06Yom^+_jecEk z0dk2e#rj&(tMpZPvp9iI8ljk-@rP>8iwba8G6ZQ%UL>g4>v^gO^O3)DSs zBClIjEvW0%&7`g)b3FZ8)XT$3{&JHF{^^$xy;R3_y28anoRFnB=N5urt0lzlHIUQK z+ye2X2VbY@0Qo^r9glXumvAM@1kTRqda*t0LtLu36uJA5bJGh4PPgahPYtUA=kNL( z$kjwn7Ke3;UVNRd7_3v}AnUA!oPQqjtvb!)gxGO6$orV&+xw9d-Q&4^neWfkhI1>2 zkaMe$vnBI_9ZGV)7UMyl??d{ye;dIB{}9wKGF_7W8k7m^bmd6>QU~S9fl!C2_B_t} z2choqJzqk7)u%Dill;58NeY)3){pus<78U?MYmubS{7}!^OE06m z2FRJoxdUg9^18sj4D#fQ!;ou;oEQ`R^II;@9*h2YlKAIC$mx#YFTOF)oAOn#yBS3M z$4o0%QBHyra% zG3xffsR09#djvVNC|lsnOIsPIZw7fPbARNTBj+vbPxa;T%;k@D`SH=nrQSWo4_y$> zgKF~?ljlFVGptjOAA(%VG%C^mRfK%2U*hs?BdB}yLQ?lUik$l}_P0rWfNKr?pSgkb zx2=#f6`_An3;6l78(@E~59uFTBPXlm2>R5oCiP?eL7!R=!u_=&+|Dduhg-S*f~J^6WD) zZ{APNdwb+$MVJ%mS9l-bk3(JH`ja`)~ zt)LIrh2KK119JYhIG;GgIkgSqr&>?u3>rC66Y_)2$k!=z#5%bh$qyZob0;BQ%l=7v zd8PuKhuK8llZ3k&@`E4D?I&ZrcI`=i=!Bf#4s%mIbSXQ3`j0vgCpMElt}}A#d#LMl zai0JD_1HfYAolBmoERSC{1$$0RV9e?awCcJU6Ip=;=Jbt9tZ7unD@xf$+_)@oX-pM z9g# z$SbBY&ky1*(2K81>Ykp2TN1ejN&0LD`ADTB^Os)8nZ9rybQ6AV&DXL197pC-Pa~&F z!}-%$_*}{zg1X0^Bk`p-a`GS0r`?ofhgos1Sexjx4|3{3h-dx}t`{HY^X>%cr~4u& zAB*$I&p2maf&JNLWPi^f=U)Z?v_<&-?A7R>D~NygLr&)cJNWi|e`;*xekOH6f8=b3 z%)wvO0X}zgV<67!K~JN@d2-w>}818 zwkY8SA*Y^&Jng3Q`phi=zcwqL!*xE3oX;HRd^@>5%~+W8>9eGc9E_aq8P{2X`==iR z^_h^QUK)a&I|S>L19+TJ(_o#h2dS^0BitOQcU7xREDp*Qah+AjI-f_*SBmE^Kc6f5 z_3`{|B6fcPIX&&xV0U?j>r-8Wdnfq>xpx|hoUNZVaAE+jm(==_kRS3rgLW8(oY)BY zU8iu)jEeE>3aMXSM9z(a`qMPq#`Izyy$j^^AoZs~&QAk*rV#f}Sr7b7rjUAKICAz3 z)II7H*QaR?`?DiS-7^9?-wy1cEAexr=f^&7JF&w^pLQI`6Ge%u|l;bx<$K8;L$&L(WYC`>9$ykGXH- zeN~HzDDQRT0D^cVO#RW691b{2{A6Oj`h&aK_U{Y9^b zJZ9e_=k`tHd}XL3%|c!$i(6wKu$0UP-y&Qum`lm7++WO@IDdJC%%vtFXS+jO5(D{p zH$LXgxg-w0jht&5x#zjRh#J7z9fX^VoGk%;5El&lKe3?-0-AQ+%Di z3*;5imDF|b5bibl=MGpbJ=qHI@rXr_3+{?JnxnJ9k&`--7K14rz7di81*^rN1 zA%6Z`!-t`NIQJ=X(~z^vp?|kY^JK9Y=DX&GxyZeToca^?r(WcC*UO-}en~j`U9sBSCJWiN$U~0nlyU#&Rd{!>_i(bV2Q$7{_W#$T$Hy1fmTtVG4m+M9S**3^? zmA*yp1LS0d>VZq1@A~E~17~mCgxrV7sbe)^oqf5V`TUQ8U$5PP+&tt=yQt3wJPyjU z@!oeF>5D%?&UUUE9P85Q; ztZAyEkRD5g}Iyhp6^erfVrE?Lh9C~$obCD|CwiaA7BSV+?B;h|MxNB zYQ?#CS8jLF7UI0PM$Y>u$cfR=AIX^;S$(Y@i+%q{GPhZVoLd3%^eH_-!KkxQZ?2GRwb-{Av^w;2Lwj4hXawhnh@3aK{a|Lo@DfIomGUtqsdU=!F zKdeMfEdW0grMbW8)er~8W5mx^A!qu<{e8gWpp&pa*O}D2pCMhsZUg!0hn-TA)%aOe4kh8_(bAZ{B zq(-TVvz4)3uKd9dzzQ`-24q1mQLww3Ozge^Iky(( zSz-V`5B?I=ORfN!Cx1z}D{=1q2KN_R5&YUupMduJ3ORix_Bs9e{`@Vmo@hemSsRh_ z7oh)>^>(m0VfMkjp|3a)xIFFpb=V1Ds*cW#o zarYbKd=c<#Gl$1{KR(8@+{CYUB4^@rm2xfDr{4knt-na}`!3{s_PEY}_<0aT;yRxs z>)ef;9d`qq+dp~U6fYMKanN+_h5q?1a@xXq@Hg^vt2CSkv7Xe|dkA+Y^hc@%kArpx zC zjDtUtb94YXu`$+>pYZ(W{{T)6CUyBiRa`H+ zOPsf^C-bwz$my(*AKcsAujR?u$BiTF{0=#}1Lh%eGS`cFKF&i9ko&Rk3D+LxFR}}d zFLo%L2hoY#=lp=2D-`b$lJ+MDf!)PPV)q}B({n(c-^BYyZ$X~?iOBQF$?qVZ`B8j- zvMb2*O-MZZ2|3X%=9Q7$uT9QaFAXMj-Oq%Z0sbk<@p$dpf&Kj3>G=tD5Y}nOlQ@4AIr}~MnZ23EU0oE`Da(>`bPPF_9_|DD zZXO4XhVy57kb8??krR`le{iL^{X`jjeuDH!K`%eWoHpOD9V2V(aV$mzGB z@Av8XI>lD#`$c||S56{lhQ<6>g4<6Gi~ji?dCvG0;i|#;(;x6SA)bT!%(R@1arZQG zdOFx$cI5rLPXW8TMa1rBkaJ~XJ#jzp$7H@Zk9?fi{VZ~_YrM~Sj^`CMKE}^KiT%zY zXWxzboWbuO?0U#!b{v_z{f3-bALD$|>k9lt$SZ0kIq&C@vqvFL*mZoJt_j$|9V7GP z3&`mope{ERc>MJDK%CHhNL_vrIrAOlcbAL%r#b}rR@5T-_7dR=K%c7L&Nqh`X|l{?jrZ^zaytFLw_q4CFx}X zJl|^Olm7M(QhzsLAUIbvx7{`XJfgzi(WS zcs@&kKHY14e_}?o-yt%Wif`AEDpijH|J2ix&e3@|53)7!&-CeVp+Cm?#IL-cmKQ*t z&axE!B?EFUAJkcH56{y!J;YCYo%E?0k<;%)eU4bd`T)5P=AS;xN|cugIolrQ1?CdB zpO!GEbuGxe;0EN>PZdIat%mYE?eA+0_4U%vQC?=`eAhUyJI~{bJQ(K_Z<0BC7UWzC z=tca)^NNlApVVZInH4#kAJy|kR`#m4$f=Og(qJ8~jl zMd&+{p9d=rdXe*qUUDF(o{#zORMI*(!kj_vC3?9LIoBHOAa?M+-#h~Hbmz}-e>n-) z1)dYvNBQdmjzF38hQiG`S_l5oW-HX}&)SsXi_tz7(QAL-_? zUpYzYmtusw7tXuA%J=8iGz$CEnaO#V$f>=Rf?nKvob&aY2F{OJi~G9`IlCg(AqA53 zQW@fe>q_d7;>el%tAhRJ@^fTAXcpwT2IT&z1ac~GeErHeE>EnD^6n&YsU&jd8<>}y z61+Z>2K>|1B=ho8$ccsEpLRE2rx(#bD=b94+>V?Z4ti0olk|BW)+tI5z1)GE7*{#y zMW*ocZmWVFTq%e0N+YL6fjqs6uhVRUIH=nZd3PeGOF`Z0hVc7-)f4t-c9Q+wg`6J+ z>lAuEJAZzC%qz>tI`2l#-Uysp#@FcwHUa?>fbDiNF=}lao9|`-j&yaIe z4mo`e@}K>c=S}%jj4zKZLwV(qOY|jSoi^$9YF0;oX+z{yAlwkhf8zW?W#yuba4 z!tA`MIm60Q9%G%hQ))Jy?j; zpH+}^1?qv_46_{VPMqU;)17Y{IC*j(a@CL%6Tz=d8(zQ2 zJu!ZkA?Kkw;l6@+=IbQIiHuP9=(|YWQ-g5-z?{}Tn^b@Hg+9Q2WpSPNBB%BM=Ns~Q zmKYoRhldDvAK~)Fb#~%*5S!ut&n_kLr6zJ>8*pM8kGr-~^lL@-SBr2a#)${GKJ7u! zrz%eLc|YOaiSm;ARQF?C=W&uB6mss)=x3iKo%eR&XI>FMtBst9uk+Sb`2N(pus>Jh z6SPAe!W{v5eir9!Hi*}%B&l2LBBxhE9&~Vfpg1la)A#&n8)VsD6pMR?RWB=Zd)VmKM zCo{$8i9}MKo)70%oh0%4VdV7JFsF6>d47%ULt8f}nMFTlA~xsv>4 zcAUE{Ci9oakaIuBd{l?ab8X^zUrO>(TjXSQ=!;EtUPs#Rp^r1~lfJke;nu^v)hFMt zbb@)Sd-DZ6f9;X8DY3uZ!{eEI0PN>Z5c@q&xLnXbm>>E1lU1R<_Gd|)e*!t55&Agy z0pFi|7Ur$CI_cv&AScd1UXg$EdP2|GNu`jO6&u!ZKs_45&J@FKB`Ul`- zS)QlGB#2Ae5w0h4_C7dA_8dPCs$sl;7)Ih*FXYVn`21Otf7+KppW+m$vz|uIAA-6- z7T|GSABoS0+(GJs-pGlq5YO~3zCX7%)>)-VUg?9JY!3IuzIf7oK%26me^6hHK)d%v z&TfW0CJQC$s(lUHJjALqFt9&lu0LaJ|UMF`l&`cIZ#I zDse6~kDnvmBi4goPQd*QAl$ha=d|+y?cosmlG@E`>VM z-uwpI?*+pB2G9NKyj-7pMAXYzgWOQ$99_JhOBcG zayq^bh}TJZWe7YcuEvtM^b&GHgMXSY`FU4eV!hjj_~&Tk?8x|B-fG?-sTH6XbCEpf zI0iY{0_5qZll1u>$g?jHd1H|?-@>^SL-{)0KB(*DxM8Tzame`su^zmSbN>3BL7%ew z^T>@STy7Ek#cblwxBB0T1Wwh{$W1`bWPo+*$GH96URbBONb1j*k@G#{>(w6OuNU>h zN`ikj>x1%MA>8*6KSgG)Pw_DHoBBHG-(N+}E{1cfmh$r_)5rUV!X)m#M!0fdcio-O ziQMl{hp1M>?yn=Kr$HSl79{zLigjH^GKYQxIsaRnPgLjf^b?RDbjMdvFB1uO0P0Wu z={zo{L>#M@pCG2;yBdh>JMI*yBV-Q zQ;O8(?;@u<11AP@Khv9GUf>=k^N?xCi4kzW;!AUVio&sfSWV`i?;$5ozhs4nsL$!hiRa^Us;}|=nLlGcy^B02Hv>665$a^|C*PlJ3UeuWBbiHC!u0}qVjq_$ zZ;$ovIU;W+a^hjAC%oqUn5-4!{25YD%p%-CIJd@g&i9OU!A~SE%|_1s2>$7Y@VMkI z!?_g&$^Fs$$cgu$pVpQ5`O}|5KP_J(&t1$x&P;(mPSoLg@lV9Q;}_D$%_ZFXF-|6)=m?acQlOF|rU>&UtN5II{5&bv;&pB6B$6MvHPFb_HLOuRR| zg~t~g=U~1z@#~KW=VQOQgP&V_E!L5XNxwNCIoBNgOsz`t&r{&nW+m~n1;~kt&_B3O zlJxm7^eZ|$=^qvn?$y}GCEc&soghz?Cw<%^< zeEuT7fpyv~Bn~b?&VLf~=7uDFeh>YF&OrLqrO1g=aZcNT_l-Uq*xhSl_m7cNAAz6g zY}^heAI#_VgJh2K339dt%xP^q?w|e`#7~)<+#@VQ&d-5!WajX7x_H0o4v};8DRS~8 z*iXLB^ShW2aY@b~_FImeuN(ck8IQZl!#Ogwh+nTjPCg5qTFdP&uE*yKj*;g8S0d-1 z0e#xiysvVNK%RJ-oZD5%nV;i*)h@12@e;&qcQ?5o`wThxP<$@o5iU=~=kolw#D1S6 zX9mK(wphjOAZN!oc!E3!xEeXp6#Psb=Y6WtF)sZ@^5z=kWD4lT_u%J2c8~XJ^!|1& zayBRIPwn9A^aZ0n_mlm7LAb4P&M=hMyRvkgqZ}o#UvbEZh4KFV8uv531me8^ip-bR zA!i1~`RB`Ao_zrPMP4WCT#uZq2YM0Rlj=wv?^lixf7yVXs2l5vxjY}qTJib8`m3>? z_!2q28tkq!@bfOJfZcTm@;t*=$e9jsZdGn>2iZ2(t%bq_04J zaM{T`YddnTF!)*W`*iz>aQ?&rau2%$xy1WbhyK>z$LH53+oPf16whrzKl=tbHx~ML z_a(3EYzpj8HyMN6PUQ4QF%te%dr4`F$^P zb|=hf>2>8kCEgppMdpM1k<%CBIa<%{VCDhmexS%P;z_$zcz1? zdBI`iWbQcs%*6GnzJR(_-9-A8?~pUUM}2PN{-Sn6e`_|7yz)JA@_mSB=4w(sQ7y)^ z^klC819GZpyhk6-XqRQ*_d-gGR9pC^!WPewb; z<@=Kz!4Cc+vBOE^(&j{ZFRw#$LO9xjl`XI1lGt z_u=_S#rj%LB-|O~Oh$-jrV>97c00sRQ;?k7v&gx7VV(Y69$#ebSnn1j&xf2N+;s34 zKacyFx&h9+KTi6i-;h&p!+n6fpZ5XwBGeOd54jIGkDPlh&Oi6@do@=v-j7uybCe6n zi4%}ld?&t6dvCmlrT^~0Mdai$h}U8af9~ENhxv;=LE`l#!sSunyoe zH=d)*$eA{v7nSs!m47upPqdln)xkk91&_DQ1Jdf#Lp^r=G8TWUcaCb+2Ce5#96POd}g6oj`9XVA7o@@5G`1vzc zTZg>qmTpJx59Dm)8o>_gU2b<*1D=~vpKeC(PvlI?+7Pe%bNk8oT&!=t7rDQX)6J`c z-8b{|Zr%iWV$w$B{wCaLu)Fz;>r>ty&%;Gx_kWPHf5UlKrFg#e)#H3=KdE2BKVA$^ zI@*QMhs&u+_W_e(zAN*P{x4nnw8tY8|L$CjXVsGG@}>~alpubV9y#?F%p=9>r2N+h z=E8P2nMY{H?cmqM`+$-pk7Yv68Q|Peo^M50 zh-YFs$zwMVELsKfPX&Vyd`Ia1eUN6s&-4Cg3m z&Td4kdk#3%OAh4B7SM}7%Jt&k2fdgKB;VeMoNNGnhyOOI->d}npm>w?9XXM+eZkN4 z)0`8nA#b|l%I`TS~r}jhrVono%-h`a)4|)-2xnG+BaQv; zl&6#Gmwd4v+(6cu4>>U?&WTdEJo{1f>v)jL09(gM6OXFKO?)EKZHZ{l$N7 zJvI2M!SNn_AMeA(G`L3>xrQNEn8@oGpHqE<>r*a-{NUD+=TwUzCw~RItGjt#kvZTz z$eJYX-in-G2Y&67{=EtLc#NObh+h{)P8Ylh^fD=Fo+aRU4g2{B++Q)|R2i_lKhEQs z_%+@apBjgpL{5GMeX;n3?@wQdeet8D@3@U{r{n#~^L(8$<_9yH%;$?ECwjy=N`L;G zj$I!8vlp48lt51BiS_PvK1Y#5px!mj$(*4ia)+Mp*!Q0&^NG^PSr7f6 zS<26?o(9je`$tIscPHU0f<8rq?d&-L-v;dN$`O6ug`6k_&u6K(_+}uAAeVxitr6oyNxn`U|D8LjNSr8(oG$|VbDNUR!%cDi*^TV49CGGYs9Qxjo;S6P zbGMzOZY__TcX3{q^zT;7PVs)TGT|y9=Vrk?%P0MNS^8*{*PP7DD-!PKn5Vzs>y&%q zJmgst&nh9OeuR2ZEa!1oJp}g;VitM+r806NJH#_xD#?CTAWrxVB%W14&NYE}ZSp4N zhba)R?Iu#cR7Fnx7Uu;9`+*?#fP8ETCE2r}K zqB22$tMZe4TLU>g5%eN+ad~P*^y@_=zu$|T+7O?&In2+ki09p8ACQxciY4 zbwMxob-q9OLCk+sh`%W0+ z{q%##=^KEvP5JYRDl2fNG`Z)hhnzeHoT$qE#oZ2ik&}tM`pCH|P!F16++W07U_V)b z#Onsg*;Qb7@j=o#Y8bgU$a!c;xDIjd{R5vfsG?Ax*%UIbdk8t-`hga;6LqMbH1z3p z|4suE|88v05p7bxZgw|mmijq-JdB)e_Eg|h_1k+>PF;0@6BTiYr6HAc>q^{~zcMHuG~RC_FSeYVbCHZ6H&5#o#HwDfXxsk0?b}s_!?DPe4k09sI^az~VcnjnFcWZ)P zWM-nz=E$iI?*z`T5sZ_~PDT5@jq+L`r?(dfoXe2Hxz@b{XSbF>t|fBj%HY5WQ;2bX z{Pw`PCx#&RD01$jhXZHZmtvggIRm)gn-6P{0@rgLuJbYEOq1UN=O<=noS0P? z?7rq_A!kukDM?0 zbKrF0&Wy7^)d0Ky_5*T{Bj_JB+Kt>3$jSOD>`#wx%s6#*O5o%j z58(bf5N_1LAkR$e$~e)uLf}mEZ&02_&b@IwaH4cK#;L=#LEgm^$aO?c*T@ytX&=#y zGwpi>PW^oguJcLc{GB<#ezFte{9|1M=hNpyt`lirp)c>M~<%eD*G*%>+2 zYGRNlwpL}FdHiJH#Qs-MUKix-XE%iNpdT&9I5+%>;AiUUW#qadCnuZ^^5iRbF;3Qf zFmSTn56E>xP95wN37q@10iM6^$eF8;2hI=A&N%nSGLW~kE^<8x*XJ;t z_qTVm^KL#Y(>C=S=&Q?+dkQ&m^KXGuyFO){tz0W`>f9#edLm~>uL_)=yODA3OqRfj zdXtgsMYs*~0w>s}?0#~Cea=nqWM;8Xp?fHUn zZrvY&Q|Vtpt`Bmq_oBd=V;dN!5B?cA--2*`kyCwkz&dMu#yCHy2`zBG;dAw|dxLcD_!zCf!$uq@j!v6YfL~bB*?z{bg^Yzy;PCa=?;9P}|ksE}Z{(N=d(z}by$kb53E+h#%F-1P4m=j1A?#oc(?qm$xZ1*rD_!Jdr#oZn=6qs$f?hc2Tm?o#^kvLRbZVjtwU}&a=vqhM^odZ&2Jee z@;3!}2M;1Q0y!o2f?mvg#<@bb11PgsULNH86U9mg&hIDjdK7Y|#NUAv z*WTyyT0aEbg*_ev&xw~-TB!46{05iT#|j=ip2hI%1 z$?9Em^@YIsgL#pA7di7<<-qB8xjb#h!8#jD zAW!Dz{jIGxHpnxd--_IH+|+zztZQ^0+k9=Tb_xlL69rytJ5^r^2; z2%K$o8*;Oe^L?uYPENkg*6Du$&J{0#-1~&9kuPv|37;?7J+Myycn0L=AZLE71@hkH z`joG|3HzH=8o9ZItKSf~%73tR%6jhw&h06W+y}_nJmmxDt7Kt%QE!Y6oGeW2{vmSy z?Yjc!nkUIyJQTR8`BC0H)i|TO7Qq1O1ui|oKXt71<2{*;Mb-PKeuZ4h`{;r#IF}3=XPaK~C1l9OUWI z8JIjhxozOgwJW&JrO1gU(D&=gNqPF+#h{mb58ygKM$X*zcbHGOH3gYGf8(QpQ=`wK zyiW*s!+Dr%)a80H6JTEFo+tC&Wyrau#{*}tW@Pf*+s(q8SEfO{m9O(`txE~r!G1i^|=x`^&RAQ z^W0v}J$ysR?`|x~@2ij#i$I>8#Op|#9^{#}MBZn}sc#`p*slCMh#8O{M0%1}K1a?y zb~5Zw9Qd5A)3mD|)@c{7$NjA)+{$kQr+iZ1|6S}?7A!(;4RRvO#lYFoe9qufRFG#5 zZ$WM?a<2XvSZCM8Y@KpU4Or)@uaNr!Ia%=M!1*J*Uy;Mg0r%GD$T{S6qpN{)ug_%i z^sqXC(Fr|JlE6uXU_hfT<-V_%2;=^s93>y+*CgS--Cf18mLv%t^P`Pqzfe}Ns$ z>sxVuUn8ey?+fze%_|tEs+I(OKC~3MEy($i(5I>*ybo8k^8`*bCVlEw4LcAVF<}cfjb4%iUa5FzgYCxeNPY(PD*SQ@zJz;f_XV!hf_UCuQywx=zeen+D z#JnM=FdA2HtC&2U2l9iu@HTRLk(0$g2%PB5pX;ht}{S}>%UTuQh3FK^h@H2A@kMm}* z0Q==7es&T$S>vT3Pi^G)cxnge#jNU%@=hUVhR6E43b(tNeh&0`Za8wMk<%669?vx9 zdQlglp4a(_9nK)9vM&XFcH;Gf+Yk3~vOKxZIZL<};McYauLu2=3Sft+#IMgGCrkYj zf9(5L-FAaHt;qfY>g7Cgwi4XOxh)?uPX2Uvkmv9E z47m%)`KM!?xQm~6{~W~6#J`V@+(qPccDNVy_b1t*7Tj}*J6}fb5^|z5*ulNZ>mF0& z?_hV?me}Dk;j%)07ae&$Aum9F7X?Xvzk-}U3iD6ji~FZ9wk60Dsw1xRDsp<->%o3z zT9SYE{~>TrkaK$tIX@EWax;zB6aJf9!0zvpy7fA8YI>{(i>+by^S?knC`*%g_B-KT zn*x4zm7iO==_u&)@(|qLAIQ1tH-!5t|HD)!Pw#6M)@dL94!J*(lTD#6_qXx-)Bb%k zaGU2K_ZM<1!)LJ0<=oG7JE(hP{wc`)jhwFzb);Lv`#AmR@vuKNiqw(+AZKcWJb9JZ z6ZR{(Zxp$RJdq(ho|pPR`|Pt|_uDr!y~s^pg1>ZVkLyf_oM{eqt7)1PUot^nk+VtN znjSe-_DGN?j?ZQC)Degi_WT-@mjOBb0_06SjGrUf6XK`)hUCqRgj4&2JpXV~oOn8( z_ZlCgyiCZMl2Et0%1L?iBe=IvPm{X!2IO>|4Y0rLoYMmGn65_Zy3EM=RRe&V%}b;B|o%uuj{S^qX0clR7Kh-|k7ezdh74__aFx1Io)rxC@!# z9Ic){ssMmIm_>TW!ZR7`7+7xIgryOJ`H-&J^B5#=??w0Tm2@= zyAe5m<~-=L1fNgnui&{0S!@e(IgyiP;5i+all!&4DO1>=X-A&Z$wj#IF>n5zq|bSf zH+>tDH*+KBt3dp;y?7nzGeF<(-z4!f4|3|gzry~E;d$B=Z3uB`BDvSji=3Mn=Tgt} zK0x-lANJRU%nNQpPVR%g!%1G3tDE2)>8+#>xEVS90QB$rbDkgUYlVY6cb@cP`H+)^ zP1v8`$>Wk)3iqk%d?&PDe&p0;s9WXA-P}KK{}$qBx^BqbLO9b5M~WHIFIT^j?ZR`Y!8H{}hS-aQNDN#tai z*!S<}=TBUQcx`LUMD8}^)P&6-??Eol-kUGT^XW-lP#ih;HQ3Mh;rr9qVJ_td5&M-u z&KH5_-OTqhxSy4*7v#AnyK$W*kyF$1gm|rs@p$cSYY*J)W5|^v+?h*YhsUNfdD23h zP`_k#pOi5Acil zI{nc&r@fKPz3)U$UH%dFH#4aY=^6Wnill$I3pqXi`@q?@@38&J?Y9Sg%8q0%d^d8k zQNCaY@#t%ebI(5!IQ#nv)aO0Oxy9FmJW-FYQ!jWhaQe_r+n3SJ;cw0 zEDiH(dGpuEl}AoZPY?Rs!t=Yo(ggha*8|8^AYAEKN8ZQ#ID7iH5O;+pb!0{4ToJgJ zabNTJX~x3+k(^5IWhx;jzB>W_Qjy0OwLCsw_W`+gs*IeT0`-}z`X2Kau?*@nU-?Je zUlqa)fVrvseIw({E%3ae$V}#@RgrUDPQyCyUBEbhx*GWBr7uxlHRSx|wqW-oKQT@} zJwNEvb*O_}b>v)1ystXX^O$Q0eSrCloZA|N%a|$HUA0O&@5P{x({~?6dG{hG7ycU7 zDX#E2re6m0IzQDRcOP{Qld<^Vo`tiP4d;xv9t3~YhAaZv1`(d5FJ)bkEHW$EtzmCFn)s^-(Ubke%KZ)`h5bngHz_~Mgo$gnd^QoW7 zbN3CA6Z2qyx_(mMcpUa8W{~|ogq)i(G|1DLw=ln!J+}tE*u!ML^e}RILgT=h3I`Zx zvrY?~`LGJ^uMu)$FU(EF1-?$77viYI=h_A>zW|vzJj@% z-O29}^iwcrw*?5-lyFm^PBxvA;zSd8u2L>1b#gQ0L@Agrsg*oG_+R3D=~YrkK7yP) z4|7E^obS&akM|^Rk-1`X$+PxKWW;@hprZLacuIrs) zf8zWK?UR zASW}751h-qj>Q*c!G1pd(PxTHSS){C#_Dn;T;C*nSO?y@I5 zuP;7(4)xLnIX@WkgXzrg1Ju}<|N4^r(3NoMfO8vo9Mlr-H*IOcbwf_hf;eHW@H{QA z#QAlrDY(w=$fa#C$B4uq@r*6hMzY6LlU4hI)o)7h(_T060;zWgA}9JoyjC-k?*B%^ zJ(vE9#Op!G`Df1tc`}8^Pix}ygUdIf9iBx_PmT9~nYbOy((j=@bN!GTjGW9G@6lWH zb89n09pV>|I%EiPdic(;PUCpLB4)tr+~lSCDDOGseAaou{lxE!<+;mn{^pED?s?=? z%k6=4b$H%X*WtM!mz(&@3&_cPW4%;8DL<5l`KNxI*nKE+=02F~>)Uv|7K`Ct)YT?) z{b9)IRnUj~`8==K_u`yEkU7JP$k`(BoPe9e>la-aUSDNbc0s)u?ay|Hq{*?I32;|h0&>z_kdEY3S!#$81NBX0Y$cY=E|5MYF z^y%R_9o2;Nf1{Ao_dtHvm$;v)$#LGgn&kJFkaOFh9+cnnzQf+JH~5)5Lh8ZM$l2G% z!uiX|{nKdZ`|Y8gsLwITnZ1yw&3tYLHvsaqyzP19#veGR!+kkb_*Z@LvbnO^jD=%;-i(oesNoY@8UN4h1C z*T%y+(%s4Z(QC-*b#VT~7u;Xu)o;KK&vnIhzK)z92>zm`@N?v*gP)mM#9!V(PR)Y( zlFP^Ya5D@1%zR1aOA`tA&@(}wb{~%u^5R;EpF3OOI^RUje$y1}aOe=zr?S)GJUm|w zxwnur>EL+=^(H^>@+v&fAkqy*ZW3~KK#w3#U-*W}b2>iPHKQqVZzCu5Td>Z>T%Nf1 zC)i)}LC8%;&h39CaI!zwi!J|M;C#`($W1{`H{TmLpPu)BVq?4)Ek@?-?;s~DLfz_G z@j6*dg877bfYhy1k@G{~Jjgx#{Q3UzJbXyb!@J1o9^Zv^nwGqev(=z4HY1kf{-z=4 zzJ@;Bj7@qT-ms`+g@l=XXI}X9|(~{^`isZ=jzRSNQ(Kp*U|HP2%+o zgEFiT9CnugB-53h+2+ z{($G}%%~wKZw_*{(Jx_tx+3=%-MSpiiDs=vZZ2|uI6QBoM)La{-LC}n#VtNY?gQjZ zivvNPyu#~iyR;O@TTi$Tk@HJnK4CH?`Ac(neU7wbJ~0nD`OKm--sH1<3i~a33z_@p}^U z7u>6v&$c7C5IM6L?)%ji{v47y1N&27lKcKe$cZKKp7|)(OXAIMbZNYw@96@dga(499pcg%5I^$fiI)O8#zd>#(a_R)cLH82xZ{=}#uF`BI zaqwg0T&E*Jo~q6FCmxD^eQpiP`vf^z2JSayE#5cU%y17Y`;v7oL(bGc737Hvyzkfh z;QmkN`x51Sik!^?=TDtavfov>9}{nq^S2y1eHr>&)0_L5I1K%*T1WcZ70B5wkdNed zyw6eVA+M;9NIqJLoPHC|pYOuYgIxpnGGY@sf2)vl55WG!&?J5Kf&Cf!yzpnp*-T(R zae>><-3Ip4uMzuwj+{RD7x>xZ?=wFWA2$qsCTH$JJFG^|&dvh&jpb)>?)Mgf({0If za%+&&>7eg0mOszuUXOiFX_Cj*BB!JZ>on=kFnO~28&JPoy%X2@1#+_Wqk)snJ>&fS za{{Lxs)?LKPECcnKuKE7vA=vVCemr&kU$cc&Y zoWCsh1JkE^73RsNBzew%BXa8Qhl3r|vXhLHqo)NsnDZ4;-X`Si(OB>P$k*u_uLADN zrpRqZPMmKR4cxv!B^8LGfKUpdRxsoIVKe<^$$a$5-ZMybHLCFdBY zhYkXH?eZeG6*+nG$MAY)wTZu9f;w9qQlV zIg!mt-mmKba^iKUL+r4FY=80@n6vAtWR7wWIY06BAkRJVJ>&ct=u@rehwD5j>+d{WqSYACNP(_duM`*BK{XEg3jhXbW;bBB!_S1@18(Cv25F z0%sa;Mb0B<8*K=jKE>a6#J-s?aC+)z$o+(zc5H4Pk%k5&r%}`kkVIv(J(Jogke2KFE`^v$6NraLvnsyvND@P7?0TwXnZp zxfmxF<_VlDvmDoX3OP{<^eGD_*&$uo!1)^}9{dKmGsxMK zUj|OBSKSggdF6BD&LXFtI21S|xZO=}kS7xVKE*>zSYHynY0)5(#xnA^c@K4o-*zW>zA``sdw&=;v zt-QYw$UCqGT!5 zzD9Z1kdqgVgmwD0{C#mm6^KiEEs0Cl36}xRpE=3(qBFwzb8iv*{f?Z=4(HvC=k=Lf z3-QdQrb5>?qStay^+g`oLvp~o2oSLH_e&-!LLP2a=)1kIlmA3O>zA^ zI}c)ge4qSF`$-b_dq&be7)~ka-VY} za^k(a1E-cHt+NumFPz;vZRM!npMoc}u=_*n`+ zf3{BZAWvVtfLu}JRMwv%Pv`iP+0PV>bE2mEkSm6qoIMKUJ?X4TYj%CH!KS4RK4q={cc0f&)px^X|MA>N4*R6lB~EL*I67n^BdH=CL^y~&8wgn z|17C@OCTq25@DTcEU!ORffnGOmT)DJ^Amvcleu5}V$lv2&*M5vA*UC?c{hW3|KM8Q z5B}1UocG(2Gb7;K+G4zqlVzI(&b>g+?H$OupRC3#o)vvV!I}Esn>nx3&KJx(B zZyon*`_kK>mq$|2es?0L`d1B{yUhJloQ3zNwX!&JcOmEZ76EPrKM!Vh?;uaN&W7CG z$ho1}f$Q-*^J`n>@xa+jzvKSyA>1z|180U^*dy3gleK-)-*^|u6D-&+s|KjSr<9M#V$A8Jr zDzf8}O}2<|vy#16#u-yx-Tl5jao31M&{xKCTitpZgM%C*sb8{p~G`{!#)ta}(mA zNXI!>WL@CoY7z%aBBx3x5At-pm^_`NC9JdG3)JVk$oX=RkK8`)*ZMN#BQr1!uCo+! zrtZC92RSk(KdgiAu!`xVo+wSY9T3I=)R z=pE*t`Y+&AQ^Hk1PE0EuIG2t4i)t`F$P-(0pubc^&K8IJb?$rqoJ7umK1c2$eNH9h z)Y5F=*HyXQedk_5o?k`ktjfsw_iMub6rUFuF+XtjyLWJ%A0VfCL*F60@xDW}g1*BP zB7H{{Q&mB86qWSupTGgs3F zdAsWF5H2>F4t}D0W13WIl3_x)yS}G2F8f6Am)Hm`|<-d2TAX zXH^?Hm3AW7@9h(e)3YytzuY4C4(lMN8+;HrIp7b*sn=%&PK_>v`>TtbFZxyBOqFAd zb4UIPocU}pa`lkYJ*&bxbMoiD{v&v$@U=TmY|=Og4?X1K>G-{sCw|dQ8R=cpxeGF@c%`~1CfzZK;9zC@lz z&JTe1Xyu-tnLM{2z5{6%Ohh}hLe72(@mloZeZO0EB-lY-B=2RlM$UDI`^okOpVxT_ z^DN(^H_B^+oZ7fK*uk~q@mimU=hHIT4CLA(=h8v`Q+s({acLp{=@}$mw?ocf1%2A5 zJnl*h?iV8$`m`PY4tnufNqp&moPG%NNMH09#<}%To^DCj`7v_- zy#+xp`YJyUvf3Sxm!G_M))6`TE}UE6?;Mx6`%&OrS8{GUA!kZ_5AsIw`04jY_XPTp zdjg#aS8FoZuOH7VIu*QsYuofh`*lH1--EoOdUE@zZIGwUkK{abMNVXgdkHcww}VO! zb8l6E+)MZbId`Zj?5_Zi^YV|?;MX(W$8~liTv^Burf^Js_6g(%zo`gv-H}sgiU)b> zOI~NGl+cI!=@PjfgzFy7TbJ?r+D?J|?yJW`?o;HnYZv6%tTFXjt4-i9i>e{l6FHkV zJ#g*#{6&oK0{*g>)Dyjs)BRx%ZT{r*0=E(7(54!hyY)uSb^Z|KUF7w(T{au!H6r@# zgPg3}C~)!spDT(uD*`8`mBsz_Mb7^VoO;UV*KYC3z^P}1>xY~jTLhhL+(orKu*qwdo056y2qv87uIR7H9>g;kyE)whIRT~ z{QQX((R(%*+aos!IUCi3CgyhvW&h(rp6*2I!NJIxLXh8mCLTZ4{OG-jvn0O{K~5fO z4C{Q2_ebts_@0iuQV!QS6gi!1Vc^UNUPsyv_rQL4NgOoD*>0Z&PNe7g)@_gK`S-~^ z$6?61K2Vpdw|Kl3so*;O;Hv%~^bR+DqCXe%~ zIDD7U*PephNaS438G+M%dH-i~KwWNAllN#pLr#@!3+vQ8PS|}LL7#Q1qP$Uro6`-} z+2A7EpDgVHC-&7xZZvXc>af6xYUdf}-UB~V``RKm208r_`Z2ZO4C7=z$eV64c@OM! zC53XelmlN)-nH3~Ij73h)hWoT~8`q~w{y6NFUUhN6zGp^6mG0e`?9D_J0w=_j=GIIWIrNF6!Jids8FduXU3nDiKIn{Jg;A}76=eTwdUvvZV-L$F5 z*_@qVosVPe_iRhhr`$*SziG&swD8q+!77dgMbVc={M zUWfQ8%Ykb}>VmJ3bGf59SdI5b_V~`gnQ|l!&O^?Rg*lOH$?I};5atthFUc$OkyCS_ zo=}e0FRC`^MKmMzghkGEhkjF4=XJ6>4gIEneE{ln0djUjy`UG>G-mGR7l-}Hfu)gK zh@751KX76_kDv11?ZBytLy=pAoEZ=2-DbPY>@HqLbn=VTY;P%4tdk9;(q39Mg3-Bk~dc(C&owd zEH#fWZhEAb=R_~7kTYjse`+3&gKC`*`?KxI{#GMr$MuK(mFM-6odb3k*PG-1)*vVT zDjPUglRw`yYrhQg%(2|aeT|%+9O-2Tw}UtdaY+^@dRdE{JO_QMUd;PqF%|k$zlHRv z>yUHn+k|!6M!a6~8@~mAx$+^db3JmZOB9zh?{j34C@x(ngWNaB`Gl2&y#KxD$j*^m zHVgdhD0yD|EplQM_?Z~P@6U_nI|3&*5kK32oa{Uf_NQa&kjdu)XZv@>b#6q?W`g?D zbm4K{u7>*47bErOcgV@>J;Cn1c$|<0_kg_GjZxkvFZmAx`*R zBu?x=&eZH2_9svA_#%3513z0*8}+giIlBz%WM42w-ffs`=r2f}?2t44pr4lS^8Kl_ z@Lq`cgY?rs5H2p{cmIsnpDs7_IX*MV@4E<>3hba_>H<*=?4U*veg24?{TS+bb%dWI zaUbe=f2b$wWjAvA!q}isF_3fW-?KrVE-^WOdyw4uHLe4b# z81(W9m*>+(?;B>QiQEC?Y$7tQR zoSFlinj5pf2j7SN$-0C)gq(gg%D3@&|E_?aUEePdov_#6;FeF73c zk07T@ZwmU9*SVjWx1;Y*%$thxjv^=XL;llExnA7fXwFcZrw&+KrH ze08;5_eA!?$eLJugCCuLZpRza;{)`+}}y$>`%=C=WFu* zL5u^=kE?*(DdfCM7&tX30lOEZOMVpWuIfF;dl09Q6FYMUPPXLFeNE+offJ8Yqr6{` z(-q)8gFO0x)deoy@W9!v8Ie1KoPReXtTRc>`zzmf1A#i z;QZ*s$ekrzi5!8mAH>YrSM~{<$xQAOokLF5g!#2e5_7Ml65LyJN67s8JmJQbfOW=y z!t|ow8XM$^g5;juZ^+qHkXKam7(eR_dBt=hdF2A(Ze$Dc{Dy~2o|@DPq-J9GW^vKy1DFdg@@Osc?Z4GvK_AmO`CFJBYm>0;{c)b?p^ZFK< z7yOQ#UJ&)~Ie2_giJ?z5tx5fI89CXq9q8papHKMgkl)p7|9XXKz@~!wV#y@8qgLNh$`Su2K<}UO{ zsyJV#y9IrtY1zaAwLFl=nAs z{+GUibKmiPQy<+QII-s=vJg8wK~C+2 zI!hMi=iR-8dcq7ObB%up_XhOgvS>^m>jQIWTcjZR%TvP5hv)q^37<3QUUy-CgNGsa z3_14z<^|$BuS4W$=o?izGRJ&QxD!x++D9?<*#xLt%^Rfte1V*~4e?rB;qt^Yh}Zs0 z60iS7PN$3J4EZ=GD?wkS57)*0y+qD7DHiOfzUB8gWuni6UhJ{V$cgy>|NTEz6P{1o zV=-~Q%aFkN#N_#O9OQHch=YE3jDJ2o6F5`kW0V&cx&QkT9aZqQ1=Lo(6c|M&8InfO60s3~l zK6BHc4pF%Z;W`r|=f2TFo}Ln8hsH2>Qzt7UmjpSxFnTVUp4;7h0?*^rXfl_26*-mR zogmMoPF&A za>D%#^J|lg`-`0h^K17wKgvsioUQ`(wYR)qu^RdSahlZEDUoxJBReRrPqP}HSE*tj zpuE=zSLJ=sOaGYo*>g(Ji#U=Oxz~{s#o-*;)V$9TCB}h&&Lih26>@e({~*u)$m=Cn z_2QqYS`Pv-3D zkyD3YZfeW%`Gjk-K5(urIe!_D6VsqBunqY0Ds>s^WRZ;2bs3Q}(Y znDn=qkQ3cYgP--_{-XDc2Y>mA=<`kF^m{NbuseC)G{e@w{$9S1cF2sJZwqyu`1d&T z7j@x$;Cx#$x5`nPykr{B!)k(qZPzKBI6zDVTU1vu{_)?Yq@^DeTG=T%veGpFF5 zocS;&E@h4Ow}|X78*=KNh4aviKkrxFpnh>b7}U#K$oc&h181J|^KRdq0s6d7)|nkS zlLzL|Y6t(GoZkuaWZAeD%FBVAGi8E2{dbK0ZcPAr^K&7W6FJ!<^6OW)Ukkqu?3bMQ zbuQ#w{6?Uc-#DiWL49UNkoqh)a;6!~mt--%Khp^2OEzZ(TxTBSY)^xu>4NzV_Z(Z*y$mK^)o`U_EZG0Z$ z@`65X1G2xjk#i~GdAQxf`(nRhVX%X(R0`!4K+Y^E8SEhQ@^y+VP)E8iNPZ}YoSP2) zk^7qaiyH&-~w*%-|{*{ywfS@MK2|Jx+rq~YMQ{gjr_i;OW7Ip>BxQ4V#w(m17Mvg`TRv)IT$!G zum#$oICB5{y~x1HRlF|udpiWqOn3#kcaSs5UJIPr$M31Cxora{55B~CRte zrI>l-;Q_GDJ)~|eiJaIC`!f^x99kDX1naC$_V+Gw_C*HJOAX$ysFN^{6kQVG{z?(< zX`#TWt=diI*Y6=Gj>A0%!Y`T6^)^2_Mn z(^3*A6moV9#9cRwpFeRA?hDHsB<{YCoM{IArpU_Yt@h05us@$W3+}Hxa_%$Gr_0Iv z6`5^W;6xIl&kD%t0WjCFvG19F1Np(z=e`w@a~;7Brf|%A?6p1(@??Kvhf2uFa?n5c zO1!S~vu6d)l_UK_W#rsm$Sd**&kz38^uX!lg!=$FodEK!Ss(LVfT)f%8*`#Qs~{&c zABs-vca&wgycQi50SI^^9RnXddcc6d1Pqd?0wSrS4GahnhX5e@%Sn4_5-eC zDqLqZ5HU3tB#z09_e!zKkw=fu!DL;@^lU4R3_M;e!=Crt}xe- zucpIw)hpfg?uO?bb{2V`xh`^Y3!Gb*^B& zTX?_N7U1=#>+x#Pi=MX;^Gbc>#P!92)2m|Y^4|9w|Mwj^)5s#%06BBxPTc@Me~a-!|bAWyuNpFMAs-`xu9bR(zWIvXSB z$7~3kJO2*j?9gN&uik3pnjq&UM|w$6nsIv03y}BVE99CYr&huDSlsS&Cd6 zc|k7~4kFhUIoamhAkTb}itSG?OAt6wgZO7V9pI5#lnyCY{W0ax$~9mz1a!5kCBt}8U;=a ze~Yct%wHKeRjDa*9g(x0CIn8cd_hpDi?+cEMI{~0(LXA*KV?( zkofWma<=Nyz}e-z4=}49!8)Iic-;*--6Hz_?iKzWNVgK=i@!>~+uI#EmGrA1&kg5# z(FdcrYi8p9dLXAKLj1IE@_Z}adjNWwGatE6k@K^c1bJp6uP4M_;Pia5zn;jc&2Vn* z7+x=#C|>IW zzXSP5Cg%5FbftLVyxVqPqrASzsjQ!Y-Tz9-&XNB1bXccrGy=JP$cZlp0ryD^xAIWn z^s0f#^+!(68Xq{hoA){51e^z-o}9k{$Vo8}s`NpU(kzN&>z=10y(n-?5>jzW_7C? z4f(+x8;aaW!sSU&H+-+S$l5MH{Qsw(Lf8#&Hu~@XtKD5{hQI&s?><9Lw#*kez2|Gj z`S=wBC+>cY+$iM4=C?uK)ai^9UzQJ?skQ;R(S%FV2)O){8RtLf5;%YK1#)AMlc_2L zw|_R{RE1_Buk1zSK1a@-ECKsVzKU@+Pwl|D6nl^xi=1g#D{yuMmuLI7gLO8)jodio zTFureM%Rb^WjKi3c*4#9AaH6YU#Gg!Ja9VO z@5oI+PEDyDIQi!cCQp6YK5!z(J>(`LXR1{P?x!(~b6Z*h*YYNEUn2Lv*SP}cri^5q zsL>&C^6EdxO+wD@FAw`$W*BEifL_F&v&c;*+)-GknKhJga!w17H{vhkrXVM#fIMAd z0^{6}hQN*g6}hR%sRva+FH62)oH`EvDU$q&+%&@F2hOdX!Z`mr*w5s>jNEkO#Pa+> zo*grdaU$>gu+DPdAU6X!b@x43=YYA4(|76y&bY(K%|y=4%N96Md=BR>l?a@#WRaVN zoV}MdaK6R@#@XB@11FBoLvA*5rfi|Wshcwyr#e)Eb&mcXxjD#*k+}nBU*r2zSIPuV zez_94xyZR$pci$8`=@$Y6V^HL2y$N$Zh4&`Pxs<}X7;uToU8T_xp~OxwBVm=-9WZK zR|@QI8<2A}A30y9Nswpq^<$jM)hWo+r{mW8|8d?TC*wdolMlE&T}%hgjK72HT!5Tw zS24(Q$N71%W21O>?L2Y|ku(2*JfEmPU*~3!r#qAQvIseKuvCyI?|#NO9lr+HAvcMG zi;>gA;T)OY`TlIzCa}Ldf8aWoAZI=)3;WACgvk@bYJt4?BwjB?PX7jRUJdKZIDZg0 zb@UX6b4)$|v2)7D3F$(lz7msF~O$qTuRy&FERwHNA6%YH< z)A;_(UzNi;?N2`-w+1k?5M9QrNl^IPQf3W&SD@+c-x9R~jtO|~Gnfp7_!ze&R>H# z;al-IZ@z;3t`l#^b#5ixkvw64vI^IWTu}<-O(*sIHp0!4z+LBdu=@)IP9!Jm+>V@l z2ysH_u55p5CDd8|G>P*&kh3bPKTm(mIC&QGpUZO}_qUU9nIJ#-Ed2bLbXCJTRpXzL zbI9rY;Aid;&krgw*iX;gh};jz*@E~J{`_D|FAgd zv&IsX_akyLb%DUC;`|(`g0N11n5=U*a{5jY;O6uAX&*ozr{CX!^7bI-E4>vsUH@~o zPTd~XDa$WJZZC4S0r;m`%FmIX81;W+wj#F=Iguq@kSAO5e#I@%AM_$ejz?}ka;ic+ zSZ9l6Y@Mc9hQPUD9g+J9IeYzPc>ZNF^S(+wjsteMS_ZiT$ce(&0;jgFXY%~Xe;fW+ zU-`@LAonwJatW+cef=Hd%$sipdHM^o&V$IAGv|Ul*JUr`7x(q|^FmkF~G%u()pK;=Bq|Yv0kvoE%+X$R%IFE6@ZARGNluwX5ikz5y zH|)>O=k^nCBn+JGR0X+X$eB9-09R)vlP5-}04{Mutz>0w)F#?gVnW zx(NF8oj7NkzY#cJtqsaMiJTbxYT(p3-cQ?-Spw&3^hNFza^}Y?VSgs;BDPN3?^&?B zTwNTw)5!U9k-x1r&|q1?pNe|C+NrY8lL~$kC2a4$yLamMNV9a^6d)F`A^G%9jcRjdya6e-VN)t zb$A@~ZJ|EX?kALY9y$ACvA~HqoD+kiIA5OBpT80Aat+|h@O*1p!#u>Ny@m2FAm>IT z3!J{miu@zYFZ(yUj*< zmymPW*9HGnS$8werr96-%v?NHA_#R^7*GtTNwPh)CAPa734&c z$Unc~a}8Mu@}E=dkh@B_E-8aN-;2jHry*YJ^FxrkhMd|6^D|p!BGZdW*)gnBeMIJG z*O9Zo4-WENHD1sAtCPTAW|zl#!42fhxIuw4=Xt#=KKwFpVyZ&!CURP~1$hs6-J?(S z37n1l26BHP=aY2~oEpOWa8q<};C!#F$o);Y-M*K)baUwux((Sx=9?|-#^ItV^H;{gW7HvV0sko8~dD z^CfbwOS8baYkQzZEBX;=79t%%O8n8W0`58ym_l?Ayt$m#fS{&cCi zj1$cvo~e9Ek&BC*IuHG=D#`0)^=b#$-GtwuVeD`b(l}+nWS$_jGQ`O0sO^qJJ=J^ zT(}f@ev||`{U`KQa{VSI&&yIR z=*R3wA0hWTa`t%gus`!Yx1XIF)maq>B9{s|nGx!8RfYSR9hE(-Q#I*_Tx#U>?)zb# zzTQ^8&Pqu^pFdYc?hWLW&l}e16Z3jd2*^je&Kl&>5N^lIAWyupfy*0|HpmlWHF9Z@ zbLTRFUJCO5PbMk`T<@vKr9;kFfWA>3=JyNK2hcal%AJr)kDM5g6#T4m%z4NO`ZO6x zUz`Cs86VDr{hQBGPlGZS*U%v)idHZAYN zRhoALrz=?G-bBtd|0BpVXSkn<8_>U-`2~^7jGUbp%^BX~eTV7+_H#8`BbNm^(+)CPR4zkTb2|JqBHve-BVMfcJXL7W)1la>~H_9da@M zE{-Y-?`4@>Sy5ggM$Q(gANJ=eay!VR@Ld3#gnZ|u z2y$X~yujHt{5y%VLA{_CzmfbtdQrj+dpmIYqnP)0$HMz@wi)>zL^0%i{ZfH5uW-HS zeDFS{{G7Z$T^u=mu|04{_A!6)1K@p8xuFK?^Bv^Oa(JIa)aLJfiW)7#{$v~a{vUFF zB>0Q|;XIS)>U9NfBk`A#$jM&to~bFx^az0pNz5y7ZcW?x$i0W0yIwfhLFVM& z1+a7BJt_5SX5`8sXMRr%{<1mdy}=7jL7xZz!TXA3krNea2F{n``V=MLcT`ME^1X|4 z$oblXVVy0HF#G8>KL>fbb}L+`LQXx7?r~1$?_Hbx?*q5}Ph98w$oawR;lAlw{yi48 zPXtb+AooqnBd7ayfcu{h3$uH%qT6=3|M{c?%Bz5!*m(l-N~VsCQ+M*iecDaCv5%`r zxE%0ZIMcTY{sQvj;@cUd{wxB*M6Yj|mffKjVb9oyx z1d2Yd8)2Q@xnG;q(eEnMn1WmlVsU-Yfx*q;(kmt_Bebn%; zHAK#hOakkCk%zA{OXtApVdrswjgT|Hz8N@qgP&VhWl-Q$>RZS)M$Y}%0pz`1gvrxy z9}JvqTM+t${}$SWa2-LP_9TCA&{dokID6+`l-CqFb;<;J?$^3Zo~d~g_LsO4%KHd8 zn}1{A{CRG7)hEYC|M`)=^eJ-9kn`hC22LcLz~z;%8aQ8k0&>lf)06H6&eq<{ID5HU z;9Sz>$hAOD4!IaOIb$W`+_I*y&fjMs*Ah9uY+^|0l3D;S!DsoV~^0x0l~u4xE2b z1Ld_rPJc5baPAzBXFkcvz}cSBdjS8*Ym1yLI~?T2{tjd7)3CqI(LDV>t{rl&X~V$D z*;`l~^rMymw=lZ*{2$jIIo+aK;QXZtoLdTht&(KG{dGW2P5T?-((Fx)vk%`3^2C|F zcpg3`+~FO8GbLiacNq7Lz^P{>&UYl-5Ank~)%OdSJo8uG!1*k{p}bDWxpJ?9UVh!m zIGw+F;M~MZ$aO}}?#~@KJ#rr7?BPCvvmKu!*9AFuJ|nDi-$urXYF$BIALwKMGlS@g zoG+UJarSrIUr*#*?ews}mofUB3wg!oBl_%xoP6_jSmy)I$)at8JbCmg%Il4s zC<*qHtGS=~qhLRqfz$i_O_Oj^Q?L{6Sf0rIMHzczJS1$pw$pD6D$z<>&c2#i};2zaiwjk0#vv$w1x*j?2rY1E;%RMtNh9^FM$c^v)Q4Rsp@JPlz2p zN6vl+=TCL|fyr~PbOgOzA?I%_a`KN%u+HQ>?&_s*-feBT&lvn=9CEr%blx5JYgrQf zS}q~-zCg|nOdaIezC3=KDc~>aHnID7Fx6OmJEfRj&R_SYZIpZbdU*_X)aAEG?1cz##qAfEZJNL-qPoO986c!S3m zu?F%N9&x<*QxHcfOGVM)Pu8-^S>nse*M4toyqeFA+PxAr2d?Z zoV=GPaOT(v#;HW07yl;d`{y8McR+m67r1|lsZo5nNaD*}NaDKxD)aN|pYz>g73hie5bAL1qoJd2s`N+u(al<;*Nxn|i zreWY*`Cn0;!TPkP}(%gTEZ$&t+WEDuGj5e@1R0a{l|< zfm3byeAjfS6gatiKXQwZv$O67PG9BENmS(zVV(I8Ah#GfIq5;*D+2F};#--*%*findNw+uNk7WzMtfj_S@O+N_o zL@Uz&El19mcogK>q0Q?XX9_*! z{xY-ztn=y*C~p;VCg17KAZIRv zpE)0+mtQ0Q976W@HFEANSf?Ayzn^SVK^$}yj^O^*BB#^*1^cTJ!R>IdL80wVTX;cR)?7ALRKuBwl|*xOfl4I#qoBopt%LI&kli z^YATlHsL>kGl}_gQ8^OqXO17m{cS)_zJUG7d@<+!SPhVugm4>?vu}f6n~R)tMPZ%d zbK)=GAty3C3G4K8&T;>I74+#|C-OES=j+9TyfU2YQ>1}7FaAA?`}-a_)dlR}lX1=^ z1v}_}P9nFNaAjeCYVTRT&g`HUJD%)s3v%)l$dfU@$L@B3JXMa^eJkN+J`H-Y)A(~d zU9V2CgJ^vm*SQTjdp^oXMR=T-^B_-)<|H3&M^1N%@7dINB$T&4?B?){$<#o{)XGZtcLvOGm(D9A!p0OJVaOhh07}m^`Iz1>Z~7- zGv7eo)GPV(VxOX3*q{4|#HC$?TNLf@4nOa9ROFwQ?C(e9{Ja}rzoRkF?ejz2mH+;T z_S=n|*bC>@*Wmk;#o^rQy~mK-gPg7W3Y>?2{QQ}XO~7BOlJl?^IrApeSuzjLw`wxz z)4w44+=ra31^#KC^X~*}3;Ogg$U65UXG(xPTabSb-o=CdUBoAK-A~B5WN_Yn&lv6- zsI%0cWPb;c6X&9NNPHe&{P?KPxk=_BKNIdPu!C#N_a_@fdF2j?O9zqD??JpakGY?z z6;ZriOXBq*wtS?Qp`?yHLbyHmAnw-X z^@}?P>r_d}c|VGr+XMBPOULU1Hw4bRJaQ2I^B8h=YZN~Z@VU2)>K^w!iJ!-jGhh81 z)@d^H{rNW_PRLhIqr4NyskcEdA_G51ZhxfDcZgn2B4<8=`o+H$6PMD0U+dRMemI4k zDhl&?bC$GfbgKaH&O7s9=O_-SI}bq?5{iAVh9 z408H+I1lm*?q{|woCkY^oQGeLGtD4=+EM&Gh~|*T#CIegokdO!kLtlm+Ebjk;(yaSLPx9 zc^x@XJkpEdoSOuCaq)>>ZXl;8!Q4i~{C=E!2j)E{JDG>vM9$2L;^#45_m~$^TpC5< z(qG8A8z9eJ;r^mOgn5r{K;-?6oLv^pmtyOlZ=(3wgmAZz)5johnmN2~Rb`;=k>^Pq zyp5bs6xE-<@_eL|LHxAuk-TyTIdu~1WVwRR8T@$2etmmoU8(Ru|M#2 znrL1pt`Prxf}C9#^$$0>JlPMPKiEd3-u(wTZ9$%T{Q`^gW*FGPbR~9pO1Op5yfp>y z8+8__ukBJYZ+(WG*caJhAlK*rK0qGqr~W2(c#fQ(3;9+Q=bUW`^B0qb)GsfP)7zsu zBpY9+y8(7^-w}QOi=6r+szXx7)a7-cKC_CPzn94Q0?=34#k`I*Nuj^B&&YWY2}9ZR z-yg1K6ep(fcrCI+d@=KhpT$8=o`7|_?VOYSqx1d?Iqz|i6ZJqZ?j_fY+7!i?ibOB3 zASa7Jzap0M^C#;-JhLl_zr;h%{229B<@h?if_h1`Cv{eQ`VII$9BY2MIZ;(`KT!=UdFrFqr#lM}eV$c)iBI!5f53ORQUIGcuZW^&{&4T-;`CfxDJemQyHXugGY z>TSfY-$2f{kIwsSUiYXKk)Nd|>r8{3TMKsgukd$PfM?-=F*z z;=F4^@1 zo)<6V`V{A*b(SIP%#55!3D5gQZ~lB!&w+b0R+8u1S&&mpqWHNu#?RJ)pGkTzPa>y} zL;jQTxgC7QsGfh9>@O>F@;=Cujk!D>KZ;8?iCIr{==P|n_n$wOU^+Z18#3#{n*wefYF(;u< zwVTNP@)Pbic%GxrUuSi)(9zs<9GRQGjhu@U>2o0eT|{*a`e~Pw$SXj&JyG5BoS$3S zESg6qAoKZx$l3U?PSc9#Y4akgmr{^*7DCP(kLv3ZT%KwXJwIwn>g&SD`6-dz%W(Vo z@t_x5m)N}sawajHBR`hQb3cOJWjS&liX!KCKwo9Yar?Qc(7%hNBu^JZPV@$Sn#i@=Z&Sr`9(vQUJO2~nh0ke9`@-(wuWyVSjELk@q2TGD~z{ zcpbN&oDcPxT~6xHs>qo&aR1s=;r*ZQ(IeC^n!YDh4LMsG`ae;h$1}GnstXM1|EeQr z4n}?IgP8MBH@aU}i0GvTa^g(XhbQNKmD>XTqIZ%wUlTdoIl8wtiq|jl2*?xnN&ip_ zIWan#pB?6NMUx!rCEbY3Cu$>Sl16<_4el={x(8^M5Uvh#Vk?{n-+;##TOQ(|`h%Q@ zy2#mT(R0|^-0ntzU%Q+nZ`MQ3ybtpwca!^>st$49(|bYnkrS(+9#sGEe#|U|y4-w2 z>cIxc`PNW>>I>ZN_Bq5sN#{fjk(0kczhcjEKl7vE9H|DxevOc`jiR{Qo!db-1bMC& ziMx%FQ-h-Cacg)Ub2%a3DnaU(Cdlb%uIRGz=fx@x#7}dU#OtQWxhv3jsFl3`^Kqf? zP^rm$;v>SXhW*Lkc|3EIqdCJ&vcG1?$%Wu&wgu0B{_iNxQ$K5toH-LcS8K-W3DX4X zU00Vp=W2nR>kRQzX6E)2!@)m&R}#-!BIgrC@wy|gcg;IdoqUPJYmJ=h0O#GF<>%Js zfw&}oCjQb2IhzIi+8*ToY0iRQ+cd9t^uugH6*u4#MZaUaO#5^DK z528M9E;;XQkrRWX{MV4@KQk?wf2Jk+Y=@l96#2_l{+^;e3;yCykoegiIk5`*TYrYz z&y4s4`r9_7zwLmW=^Wh)Y7}$+UWxA44IuXW7&%!N>Rqv%*SoF+oIih+oWG99xu;OK z+7moKh^y{HqUzV3>gObL0@$NWC5J_YkSJ(lzjpAfDnddx zRmkst3AcmF8_oF=lJn3VIkN)JpT8NS&$&@u@GCigJ&q`Y2|drK9)U{vhuM^hC~n9?i2h@i?zG!F*ReA@x!(;K zjhtx?^|gq{`+j*B;;tS;`iDM*I}*)5d-MJIzdWhnt(xJX*M@f1(7b&&D99euR3^KjiyUkKj3i>_X^4yYsb2p+H1)2Q#CMD#hCaFd`8 z(c`(@T~Ej>?kI^%Q;;*SL3~jk^1ku^)~PL7=TyRVg>~BUyzkIEqWbJGS?4t5{OBmY z^yPJdFB8o_^AmZ~k+T({o=_=xJQK^ouiaph|7IX3(!)C4&)n|rSJ0=JM%FnKIXfTx zTF1<2&kW*{mUY?l7>jJ+4-hWU<$UJ!- z;c7?E!(;AeslHH8$l@f<&qq#Y1-QozgCyX{uU8#FznBC<@=MXVSj!e+23Nq zwSxF5$8tZDr=sT%eM$UWf}A}8^^5j=fBNkxPNXAoVkvTZAk3HSe(o>wQ;6559+@vK zL(Zj*>fMu^Q)QxiPu)npyBs;QANn!%4}YHOZbST(v>#i6oSOvk#m?mMOs@tz=(kCH zS&5w91o6c+iy!s<8@RtH1Mx+jCwBN6InzJt#~jzEpB(LPKiS_}=QsU@&KUzWs| zb;y}tqWlnZZ(H7r>VmxF9IYqZNwA-3!s`h+3;F<;hS={L!u0^oy%Li*+kwB>KS+J{ zE#VHpxi#mw{p^p?xvfjM4an&QQJwXe`?bvw&6lo`I%^|xwheq|!EEDxW~=mqIZD?% zSigLSoF5t8LpsOvpN;Mz*+0m=i%rPcebKzN1h1DwT+oYaO5)P@$mze~UWt0j=ezFd z$I*QT@_cMFa%y1IcO2&SGrNH^J4xTM1v#?^_Gk9-cxL0lcO85da*t&za;|cum-#Vy z(}O%Yj_73@ayB!}`Q#tmer5v9HADh3*VvAn+6eJX=H`8ltP;)ZekAd12XeM>bblgi z%sM}V`L6z#JSW+SoLdBSok<*X{(3^*G={`$hn#p5&bw*KIZ-B>+X!;re?U%Gi{={D zd43QZqquv3oWEVj`8eQbrYf)N+{!4wd(uDrh@80)%?pxpfBC=pwdg~*-N?x%Q9rhT z+d+%Sua6PG-h-Sg4Efz<=6#2+8_m7zk+`%MIe$C)zEq-^{iTF{+V3a*=04g;9LDMoxB$`nZ_qB=RxTA+{f>Lk=RRzJWf@mg4qvf5ZDSz8UG`4k73M zk1G^YfA$2<)Fj+t!i|OZdPGWY2iY9n3lU|>dp$>xGaJBP{5t+z#%6>3XDgEPew1*Z zNBS(v&ymX>*`YSk=P~5OU8u7}Gwz=@F36K@Nq#?$oaq_e^El5rKLYAqvyRl|Cyg3=kdv=N|KPIm=SQ+-^gd;M^8DyD z;a-XA*6}>Qn}4B>bng&5{6e@}(H!$I=WM)a9(juNIcErW5AwS!#`A+O1Nq%$Ch_xE z!p(=cacXajL=(*u((jR%`OeN5ZNXP4IKQ}tJKNESEkdrgPKYdbuj$9w`PyaXZ z&)<3@sv4~`@E`Tju8<^ayO<90Vwfs=g+ z_a}0`8N>;-igV%+^zV8Ti4#|lGu42TF>^k*8sv$ogu9BIZ4K+RG3)dPB5oB~=QZTy z0I;7L!q1=kBARPtA%1-wIafBS&z5n{dG%p!5(26A?3RA=Rgsh8G)U;pp9Jl5qm zkyF(nzPQFQb?XzT2VH9tU;aYQyc_k!={cvP=K-3o^Kaz*A?S1TpqRW;8R{OCHdhFa_V=emwX+bSA75IUV3X% zFWp5>7K*-G7@yz2_RV0OY6N+XcMmx;H<}lG&GqSeMRV^fU zho{Jy)$rXrv7P(1pVlSxn`YQGJb%xSv%6r8rze7f(r?cuu%PaQ^&NUcac3 z!0GBFPP{_MzyBvsgI-h~u1}K~<}XT-J|_-xu0T{5D6SX39PB4Ek-8u*a;EeDJ^$l= z?dL(B*6)z#f3F}Xw?lq+zr^I*wdehq*#!C4ULpB5K63gkct6}+ zc+8%Mn@RBe$W9@BKmz2<8&RE=nLoc3TOxhdCGrv?r!PRfmN9cvRUP8B?@Qu!BIHaR zs3+77UKi+kkpEmVA}=v=@_y8hHQ?)%yP`T|4e7^{Am{3U-Q{Q8&%}Z#&eP|VuOjDj z!uN1R4*vd;Thhr)d#`_pb_2emJ{f4!7&$&eF^;Jcc>3YTZo_W*gf zZlS#7$eCjBJ!6&f7?UTr_6(eCc^A19$cc6EoY}A9b+Wt%eZMJ6=2&$J~pL#*^*lWn?dk_cJCLXU{Z_uaROXA?`$f^2 z`(sPyGrhQm-Gg52*N;#ysgYA3X9w<|rHoU5fj-@oC&;~loE`}0NIvELpDz@}-QC2m z(;z3#!~Xo&oRg!Yx%Wb{zqH87Nl?$*L{Hf|#dk1gH@iqZpAI<})z{|Z_)MR!cqH#3 zsdv*OXQo_)@0C2}zaOAyD)7%yn^7+rkdqtW`zUH+awbnyhwrNR&0CSnh@37Dzx&}f zCT5%*nj^^5Ll+~L2|1ere$U2yn1pe*ZI-~PTl0{66FJ{v1MF`}8qN($4cz1@$Yn-O zci9*?cPcgKa=ZcS{CFyISqOIl#N_#xNdjl5k4Nq;!o>qS=$o%HPEH0p zm>a|n*$G!@TUe+0_X+pw5ouw6iRYla9LV`<2S6{wHZxAWni1AHbvbf5k+XSz51e1K zmT~5-B7u|fb|9AvIiJOYK4ZRH?-BuLQfxymH*zK?__a8;oyoKF)4@9TEI}?0;U)m5 zTk^ajt7QPayt)**yoB2bzYpZr{KVwRw&?>WT9R{=4>?hG3+S`mQO4;x$zh$(W}&?N z$mvzzfj&F&=W)73Qs5R%NA7Lp)T=8%pA~qYBksNozqe$nk43Hka$?!Hfs<8uA15zG z=iv!Cw*`?ipZ^p%^Ecn0AC@_+(=T6!@(LlR+fEOhYtQQ}-QW)VF4UO*$Q4G;>qUX{ z7x?{T(dv2NR7P_CiXbQ3eH}Qpir3dZTf(qT`EUZtD@wSPUvRPUu(&Jc6%L$A zMe3|d$ocN4gFIdOAmi-jydckggYqgPCpsU3b#^(zIG-h3;LNd=$bEpE`{6|3d~3cx zF(6mqT%UEwRYA^_f;{an9Afh1mMHF~CwclqCdpgQGA{D7}PIPu0VO!k#qTgb6fWF{au9pxeSD>ft-2{ ze(e)*yZgVO-gSwHztkjL_Fuv})p2fj^JBiSKfCx_TxTuh%!9+QzwF%ZZsS{Ezp|^5 ztBss!aXxU)Y-9U#E#N%pe&ih0A>2xkr`PlS*%jbtIw_G?7did#NRTI{@cr4=aNbS* zHMq`t$oV0#PPODmwobdNAlQ8tS!aFZv;|u)e6q7NJ7qSBjkJ?(5D*A_32B2KJD&RsF%jbiEVJ+#f`&kf378P z@-4zOK~A09AJ%CTaXSbf#p_F?{%nez*aChnyK;HrV~B%hJBimHA!nOJ_562So*Mz@ z$P^-Rq8W0!ADlmvWFOm~-yCtZ$@y!JoSL>5{A?kQFK#CE4|*J_lUpDs20I+8^TWB_zX9#1k@IJd22L;G@kMlo^Wg83^Uw-8of-N7 z@$6@&7keH04l#HU%4?0B91NWOIwlSp=pW=7!nHxpBsdw?sc!Oh%GptVSW4n*+1y`LBk(iv7xA<9$k|8HoZ&gQyOki%?euhmVi7rz$rk@$%CbtmN9 z2=HrFmakJUg1*sDB!1l)IrZVeU_V)#$0avg2LF_kh`cU@y9W74kJ`ojOizG#X3G%2 z?uwl49Odbb+7PU0DqmTMe%%c@cLwb4U_ZiD0y~(d zoKuaXI`THLLx1Gt4X8hLalTG_0_KBy2&q2@AZOo?;{1Fb=f!iV&s+g=9tI+3UIk9Q zx|f|J{~mBY58(zO=ZXO5i*tEy7jTMlgOM}qA+N~g+&Pp~(3|&~MsO{5+_*a2{+~Vh4kqegXNx&*kUA-hy~-`jGrE3^{jmdswG> zo5x+94dx6sH}UJ?$cer%FED9&oY0Sx2fsF+%nL>!=bD0Fo1(lG}g8p5kBJ+aJkW(ALuVokh+)$>B8s#ED;2ltvod0!&um%!Xx_2K=r-d;R#q853cGYL6Y`ET&EKV$T{3!WeO z@Asm-$;j!bud>Ht@_V~zZu9gzIcS|lHRMYzq- zPuuA{ABom55AkbBoiz_R*$nJw6@UI55OHmhkgaedk|+G3d+Yc4JkRZO&Ut-*{(m_4-m}lW&)0iVOg`ETcCC^UyPl1l_;FLTKld`3D+X%?=;uD*#-LZ?-TvaL(VS?^=<{u>C4a` z*?&k~FdsSbD%``W{QRAVy9<4Qxk>7f1<2`C!EXofcK~8-f`jxSq zGdW?tWG@kUOOf+?!n~k2zh9BRfd1TAGA~$$ocR#m7s$8yeVp!^1nR7PqcHCNf}Gz0 zeVoh9_3jUcc>Nuzcb6k42SQygMsoY~XF(6|?LolBZqe+}tg`9OEE}i82bNNHQEllFlYUK3fFz0)j=M^&w?8W9MezXQT z(-6+ne8JzXsmGyjTub7_TI6&Zh!eIQ=fuQ+`y8T&b%e_v`st&5e|kp9e5TY>hu z2{}6t>PUNg1>;oH&^Kly?SJ9KF>&)SBZ=>UC){D;ra z)N9apsJBVqu^ld(l_I988gTwi7v51LVov zJkHB=AWu&r``d+_SRLe5;`0Kt9sFG$BJy@4r;9>gr3&)7jotzGcy1)=tA0h!*9E=X zBg>fHO;NB<{XNn99^{k?{xF#L;bH;Q<+cm)hrNXRI^?6~y#7?ZLOd%%`u=^$$?U&? zeV*ZY%y&%)_F0y^FW8TqnFRfcTN=~f?gTxUt3>YykW;UO{x22x2Q#R6B+vb{3H|mU za^^ntn_@M8zohzt9&{2impX);>KOW*)V%*Q=fb_rYT`$SkrN%kkHj~Q=}(jn_o9!< zy!Z|U)AWAjC~__@%-z%yZlB__(1#x* zefTlte3kQ&-sM+(f4*}O@Z0noP!GqElPlmn#T_1BOjWQ?^$R)A6Udnbp}$?sJaHt<3z`x;JB^&GAJ%EP{`_#T zGgW}dJA<4~4)TQJ`9VDcd1fYg&vzC%_ax}y^O$v}20e&NL=WeX^E>{4{T=51ZYBk8 zCCOvIBWJh5J+u6R`-8BM$5c9U&+L&ii{TxvYY-#vZFqO+7p+Bm`2#us80NI%D(BpE z$hZ16!ktIXOu7L3E5+^8?g-~unVjbZ=AQUPewg1^v0t`TqRa!Vq^G6aD>(ocQBUIF~AX zoiazMh%*JYpdPLeZa(BA`Fe~Vl0Y3|E0BD2m2g#|UUF0UI@LhXyZDOC-L4_0e*?QV zd)Kn~X)}X;`W3{k|03K3kSC_|I>hFIbCL6iyz9t`VWB=d$n%3M1a@uW5dGaC+~iOX z9^~g@DhEA`Ch~3~r+>H>>A`)pn(a@FfH|ffwhi@g3psZg&Ey8WDMSkkiAjMEle8`2O_PlHf`LtH9&&mO*o!K|_ouIbzq>Dqz1%0<4DfgT1Gh7k8vIXwNao28 z2=@VSraGTT`q?4A6eZk4vnVFcXAX7wL{djS zM$XQGx?IQS`}4oRdkxuv)a6f*QyCz>m?bOOI-LuBP8||oo+9U4g#IB7uUkcyFrR2e z>g#97nZH8ax{~WpT@3HK_K>>uIdb{|k9v@&Y-zH27bs<$Qlb!2jH9 z+tL62K~5hH`C%v5pPU@(&!I%`;${5)Z*Mry7F-YFWAIxWpV(&{+2x&P^b zr6WJm%gOyiT;%LmaBrcT^8M)wWgtJ$ztbBJIejF=PtEHrxhKTWW5nNILQZ@V?yDN{ zy13$?$Cq&L33GZ0a zb9r)WsLQ93x;znb{v2?+1n2w};QUg;y^Ngf67IP^<8xuL2I_0qp6o9%a^_dC>;HUj z$?Q|s3+Hm4)R9S$6N%ydnLW?z7gHajgbCI3N^86mi6-o>7IuV(ly@H&{ z3Ug>bmCvC?-_#K&&P_l+N`{;q9_Hnl__>&AkdKrjbE4$Psb%4QEava4Neg|y=t}zV z6v&BVVIK1Cadw`*cgTP7N#Bu@aET#r`fR+vbvIz1wM<`Qyw#)3St zg2;OnIX4J6U7g#fdmrpm?IK({!VO*vc9xHShvH4Jv+m>3UeY5c)S`&9D{nFVnV(?J zV9yUhE(3D%+`@=6)l#zWxpcDdy+u;Oy@s5=KP%!yyDW^eIuXRr75z|NM&$hSWf7+@ z@$cMBzElzC2aP~36LPNIQjm9%e^)1SrHD9JemHWkBWL5zi8$XOBU`869M(CeKXRFo z)8B=A`(rWRDK1C{^173I`z*+bmN4&;%~LaZDh~L&JxTT_30HSH>@OAnzR|UXbFoLr zoG2@D<_FN9=*jgbXTd$Jn=u&omyK{aK%Otlzuz>!fjn)9yzI#7tSjI=w{m%QQkv*o zL?$ss}`^<%$+%y#AeUOdG zlZ)YdF1xio%F9hS4fZ1Ire&PX0`_8Z|Abr~72`y|_}YD{^lkr+33TRhkzpp4sM4 zBF@Dp-yan~PPGTlPv+}%XP-u#HDrGUk&{Ix!TvmtFRD3kvSv?QXCdU=8i<2BK?0@+ zzdT-aF8VNugKr~ej|X}6<1$Vsg#F1(WSxbPa|OUYRjYW6v!`DIJ(M8!Sp+%N4fH3* z@bAM#n>Z0CulL3M6-7?ZnFi;wpWBNQBI11EACN1CoV-6J;?y{<2h;jF=x;C4UvcEb z_cr2uIewn90dV4z-YBmGa`NHSh!am@&hrcSPSI`cgIr1Ee1%|VNx2`Xw?XgbbE1b* z$jK(tBY83rw@(=dzH7Cwkn?;8IrUkv&$~R%i+dnX)F$%YMNX%k3Hy69Mt@I0f9hM} zf2EOA-N!_nuE_JPzXad$$xl1uxs*Z9^Z`2)*LYlVOA~-yHzjsf7CG4({6SXX`%}fi zKGi_t4+=SV8REQ8#`mZ1!hN{PMdILl$ccmy2lWdcU)-0F$7FAkZ_5!byjRp4xIFa? z;8C;+V!Dk%y?=n5I|2DfPvz%gu0TB@j}bjoLQb@U{>ZF( z%;L33815TSl6Ny7BByGsi|o@pjp2TS{psrDJxXQd>~46+B7foE!`frOxl^RC`UpAk za3%Orn@4<|f5ZD1*Or`1735SOs6XW>zD_+noXa>;e^y0Kn()4L7LOC|KHS@@Hst-p z$H?g>P-nT!_xU=v1p9n-H0tjY!hHhg;%@W(>ETefinqwQR71{|fO=3i=KHfvL7s?1 z>bg&n6DIU4Y3{LgIuZJnmBb&aBd5Cu?$B+ zI*|8LpCKnIZ;R|xHR9(fvw*$0hUDE$9pubZ=)+y|8*G27IrOP^HtECbBIh52|B0^G z8E1=!xRjsR%jd|sws22rUgi7K^TRuxwPapU4>|n>oTqw|+ou@<@k| z=&?x%eoa-LrxC%3>ov(uNDJeMoz;W&9u)DSuI zN|^VY;qp`}@IRk~*rz634cMQ`62nyjyOytz{WU^PH{1;IbK*6&PW3gsOR$&7T&gj0 zvQyBzZJ)dx{ZFnD2eG>ZZ zm&mzn5U*WtelC6jFr#KC6B`OMJ2>lJsHJY5p}UAH3r`&Y>6>ab3C=O*J^e6TZF zkgT&ga;hJ2D(79s=_Da8)g)XC-698yrOo%I?c~yovn~FD`Bo^S6^lFpqWhx*g01tt9d~ASWC8 z$dCMm`J6jfAgas7baL<1k#POs9gFVF_ot3Se(;98=lhm$yDmrar^zsyasC>di|cq8xgN+F2fv3a zn*Yc+_04CIJahRXay^mLho6HUIt*Z(aIj8eh#m}beh%zU#~;QxHv>4;hU~8wa`t2R z{Z>DhbFyWXXq{@}NnB@d!p*x1=Tdnjmp8sBa09m^*9STC8T`JYRs9(E@88o9-7X>b z19G-i`snuu_1PYbGYuPq{yN-3t}k+KP5684&jvD1HmMF==iib05ji^zekWKq{)us7 z?Z*+P51v7;A9C_+;OY+I+{8~nUh1>R^+(Ru{~OM;5YNO8B5w$CB2V}`n(4V7T#steI?YX@ zhoQ*1_2KUd6_1IZQ-Bkd2saElb@ZQTf40mJwm;eGBartt@uT6$*>n&GMS1Q=q7=kI zL;sHS2;^i2h)e1g=VY@Gmr9bjG?H+qz&`a;ZZB>V*r!=c?0OX8J`BGPU6}7r--dH> zanGUtMk8m!??apC+>c~_I2XB;oXZ&G#Jl0|-+mGkKNo)*ou|J={B|sI>eul1Y&OUE zd(y!DK-M`9IsZwBgAaIK(I;S?Y6e;7c;v+Upm#T)$7^4%GOTm&2|Smdk(2EqUYo;w ze|jY7UA#x)^#tVX9mo%^3b$)n>cdE$oK5n>MC5!Wkf%Ji7dr{^gYzU$PeRU3dl1>R ze#-NvpIHv}cj+MRZ!&V?i;!=(@O7GQkf#OxyK_^JGe?8pw&a{I0s1qei2f{cKE+dr zFCxZo@56cOJEWeNikvJQ&gI-B?zgXj{?rh1F4K^+=V6^z^L6SEf}L5i&gsaB=U~@j zUd(xR3vpL_JkJ@(`P`s)^&_uC++onWTTbj{CUWM+ZP4H65W8xbCL6R zLGSViKNm4G*!5_lzj?^1p73{YTs1Dw4TZd^N1aAJ%ty|24E~oOCLd)0J@|V>4-1g9 zQ}2Kt9FM#5m7s^#`%vCOkCpZ}8VZ!vN< z9mGL9f!n973-zF`N#fuV|!#e$760}J>c^6L#VSn{d*w}Ih!Bq5P5_9gG&na zi|<3~kj=>H)bP9YreklmPFJXYv_G-DO+lrh%685(! zM(;;LeLaxGvu((k>CiVC+l#GJeGC1xSV{WE?a0Y(51}q79n-JG3w84KL%6>k$cdql z|HMt6S8P^@*RBi6e>;&=J8nku%pc>~I>m8#_bX46__B*|zd@hl_VIec?}I)^bv%IU z+>M<47C4!W`=4nB{g`}0`lDZw^RpjAK57vY&-TN)=q10QygkUdgD_vRyLo(ZOJSY1 zEs?huIk6yf|1mB++TN3OuJ~_|*$cZ*k7l;JB9+V$J z+%@lzy5Ioe5`rJ;bX*TEC)9(kAMvAu$k~HXC;QL%d79tfi`Hp!6FWPEoF4@FR+Q%F zDQ1Bmxv3=I9!5@Vz7P7lAG6Mdpm$xG)aAb+XNrZn+kDQ+I#6fX_eh;}gm9O^Z{^E< zm|eSFp>I4${Prl}z6<$iZcIFD2YOHyNq#tnock5>t^SqgBl$Db6V4HPIgXt88|=)i z;&vvUft}e;2zLTGKMnNY=JPmcKMH!!MdH~>c;*vPTIkO7BJ9AloMSnkwoGlpU z$s4);%(d`+zHyETvO%bKOOm?a59Cx`*q^z}{noV!b$K!p&(0&KP0+(QKKJ%JKo8Ahl|MRrNCLk?aXxr{rPkx4qigeZVGdve0=_5%9W4el7B+xM3<2> z6Ch5gOI&|yc!(4INSyc+Ik5)(T|VcWJ{sa~H{$PCkn=A=+!fWh-^#O4x2kC*?p{Ss z{tkMVL%1HqJcvtvEz$cm!?~zp z#IElkr@Msu>@DuMrXlokssX9b?jq;TgC2ATUKfZSrK0^gs)u{X*?rKz`(=E8x-rB- zxq!sM`^bsJA?~)|b-An;^sdPM9w4U^K%c5-^LQ;20%yM@_VN%pKLg^YnZfT##Ox41 z%aHi_2sxJq&Qnd`eX2SR=b|c*^L&h)nE`Ramf&_Rhe4bWqliB|K~6jib!1Gw74_ge z)n6p;K1EJ;fO`@>pX*(<3E#DLCHExHkh3Kq4!Zp@{=O6Dm~I$}gU^vO0{RD4I!5om zgu1RJ$?q=+mkaDgl#1z(8if6&AolVPaw6`F$lv8&t_PVF;-KF^`hJo4f8YP87DynZ*6bC4>Cu&J+E`BV6-PPweIO zy#FH9JvYd?yo8*&71r6GpNl&Uc}%=Y>?}TVst)97dzjm&DG2&gzmYth068%f^yfBl z{fUFY-&>J&CPYq+5A#UF^R3Gf>YffHuOvdwWrFjxG4~Iq5}b?POV0CU$?yK^)uoRiIFoC!H;}Lu6LUYI5C58Nsx0zV1NEgu0Po_%(KoDJtQSuUYHY!mArot z$piNqvFlfm^A7YUXY+jP=7;(^E%Co($cePU&U*4VVKargj@nsrhkg`9jn+#7z+IkgG= z)|Mf9NR6D?8s-Hhx!=k@;J3Ca={wR8E*abx+i|=<(&=GdrxuWTU0USiZ=o*V!0S4Z zBJ_3kh4F6{&Zc=iFgnv^yfr>8Ie<&!hGU6 zuM2c{i1Tg=IhRbx*`wj_CAZ~qP$Yx>x#r|P^>yU@t1xGP-;5!Cn~iX3!u<0s?(Zf(%xz3PQfFmH&ZP|XSz9ho)dYEV7?GC)Ih_~g z^WrqOPxnT+?`T5i^Er`o6Tz;{W}Y{FYVccMfcR}L!exMUx|%%h%Ie^M;#;!L+{oD@ z;hrQjk1y&X%x%mnGJnZ~oH+?|X!#jG7dfs0^f~#iVZZVQa^g)m7k4ejk8(nMQR&IK z)833J_`Dib4k1| zh@ATp_NRS}oh=CWPI<`w3L$4j_ zVLnlmoM$oQMI!pg+>PNFVnea;j!{XVol5 zo(^@v9a0yRLr!)8|MTx~PWG!E*^3xM{HQ!~Vh_Y!nU`~B5yV~7ipYB(Ib*^9WXwA& zHzw3|RfzvpAlzu+^i5ue_zIz3dXsP!k<+Wf{Q4yxceMn0x(SJ&A0X!!z?{KP;dZS) zg}U7DBl@d^oSYf@)IHp9bso^4*+J^150NujL4WS&m^fG+^k>(R^Q?@VN*nyPCf}bJ z5bXMEaxNbsXIF;0^=HnRg5XEK6S3*FH#0h_v>s?=lIN=MDIPnQ`dKuWKPS4L%Yz+2Um7Hfa$eBk$5A(Rc>l|V37MIv(4Z^j8d8_`7*U7#P ztW)kI_YXCZb1$F{5r6aiC$@)v+LAh?7ILZ;*tOop{lPv7eZVGS*R_!o?ZfY2{V7R?wSI`Z|frG zQ-pii0Ws&981|>TlfL6~#Lo7j$)p|T;EJ1^YVJgiR19@-rcqQT#gopbID2G z-PcFX9|irHVSJtH1k{nT1<_vvWNKSx0N6#nu zy)km8C#+NN;PPZDSf|ZT>WL=EiG0Ct-{bpJW1v1$&&fKQBImP({;wXlPg6AXRRu_& z^CfcPOPCkfq+AbTeV9wlCiPh}!hICt^%p$u+SkH7`5@WfSIF6Hz=?W1kGWkT-;N+$ zbL2#m&_5L7@kM`qvxGW=X*amat>T_Ud~ za^f-61+GgBS3cP1cw(QekaJ%{A7Fd%{2 zYRJ>G_<8D`!QUSdd-)bQTQ>M@IqnboR_Om8kvil%c`oAiVvd3zsYJw&en8Hbg#Gy$Tz@75#Cbo3F>h+9`JXgwjiw27bW}q z5jpt+IGKajcZ+)74o0!N9<)Va^fD;S+*&UpKc2HpZbRQ-w@8-%2g*=bRHo(cw#9l@s=W@Y)mF~dvyG#Q9?r)PkJqkJ1FWd+0=5{8!hdOx|vFp)< zTNUbpDV#GqAYPk+MBW(W^gXaoafRnwHy-TMmnHdVEONdf*o*s`pQq~@>a$zqT*e`% zx5578DPCv!TChKRm+Wsma<(_@&$s9MQ=MRcsxsN%&&ZkhL0)n$PmP8;#C0a}CLm{X zz?@bTiiu0#!JJk+B;O-UL{9Go&UK54gY$v2!^wPi5^_E%e8+B+a!zc3dAWO!e8)Z+ zIh7jh%$?(Xl?dNKi|6EArXXj-ck@0@cOEBx3-ay}c@{ZY0_r*w^ZrwJ40^vt>bj}O z*&^^=f&81-pKdkWBglf}yMk%RnQ@^mFTn4^)z@I3G8xJ5(~)yMLfl=)^=IcoUeOOp z+?_$V>M+j|wPO6?SBSg52g&a<33oGa>A1hU;ZPT-i-ennoNfa9^S|&oFE&CwsOf$G zY~=he;XZs5k4tVo)oUPd7>rJ!+hj)F*q0VGOx4rWH=Ysi^Qb`$f>6gmt+B6x5_@Df0#=0 z!$RczcW|EORLnZJg!uUZInPDNxtm~Tz8gOm(G2Xc_7&%)dCH&4pM{b{TUOkwf z9k_yXwKJmU3> zoDOy-Mw4^-1v#-laEEyRF1o?Fm=eTZmLsPRgnSe;e{oNM(?!U6u0YOh0eQAB*WbV2 z!`iMy-b&N$89H*W9js$8e8OnZ(_-$k~Y?&rRU*OoaCgW-pPqj&R>XzEv%`o#|R3 z-)fRq)+6Tzg*^Q`m#5zfTt~ufAl#pzcU6JQvsXdywi?m6p;(rb~ksbCYx5d=S3BVuN@0DyuPNffiJA=>9-0yHM z?hetz7UaZykSBiQ=OPz|xHO2I=T^ea1bOZz{~p~W5B*9N5+}AH=dVK?w5z$_nu#HA z((fX-BWFg0y5J7r@6U(# zwC)snueb|2aU=M{U9LZQ4bD>^B6`@3oGcvP;a=e1GrRE4%8n*;jbD*7Ti`t9=e%w; zPeUIthQ!Z3$jPZef0y}u!e54U+QCGBdy%snpx)J|cwH`Yf*$1Gq~6_!oZ1L_P|ITM z^R@8%6R(nfY(H{3cbN0VeoxsK;*y$A^nL(2(--2I>d)tAzFn9npCa>#gUE?)VXpBW zKTrQ24(cA&mFwNs5AS9gkvMo1Iaey=&8s}W`&Hq6&j?ay9Yao3 z2zmNrex9N^#0h(k*ynNNWW137^68sl_4g z?&Woe?hSrqhLbpW3OShp`VMu6*I9o2|EKRbjhvef@^pKy2lE@qQ{NJKXOL4*p#D@n zxgJakm`|8dr2agMoShu{)R=dwwiDQkxJK;d9CBhW#258G*Pn?W^3f!szu%El66!(s zg7+P23-qb}FVc^Be>4PU415aRrQ2Ac2v`<2Cc zpJyKsIL{VmpjObr7-_AJ$N2-tzrIY44L!YMNX9s^VXR7 zDP9j;a>Ct1&fbLmnL{!9s~zI*G_t?@$muLW53h4hjS6~LLGNJT_1RP8To=%vSjp|$bOHUjf<%AMkn?3BANlOuk3?O_NA4lX zo6nIGUxWVKavsn8pCOMuCi;7Uoc<)#6KnYVMLvW0BKil=PK*rka}dw(`pZy<(D)f2Ilm(0m8slb^ihaQ zvIX(m1jvcpq3?e;Mt@;W>z0tbl8|sR=>09OKRFrnE?X15Cn8)!I2W5ECJrtPalRAL z!^_CY1Yu58E$06Avrw1UBXgp}$ce;I7pTm9el4bh-hD$-7bHPWZwq!-mixPX66`Ds z@%N<2`P*Z8GGPXXt*TI6JnFlRW&&(qBb{#S~e%d5!Qg`p2G$2>nVSVrS`)(_=!ueaib{ z72>?7?|0K9r`Cl!D`Skj@VhZCE~(ElAop)R@|x#OQ!LaIHAy~t4LQ9N)~PCR|1;yk zUi?@RKQkg{KMmg%T#LzLdEuRaFGlW*GZAi6=>N)dJ5w8Aos#}8$Lq-H@cYpJ=XY&y zLYz<&NLRHKN!p^wykmI_IWY|UNZ#iDZvKIK!d)VMQ~)`@GyGoO4PGy~ z%`gu!56JwwAads4-`D8C{YZTkxS?cTP>67^hIi=?V)T#<-sO4vzWi@7ZN9GS@GHoXs8VWf1oVvlHyawIudZ6gjsI;;tFP z+XV&v_EcTYMAi7zFPbI;*C zRZDJX@+#Pw$U)AtByxHK?9ZL#^5hKIpPoSKiBiakWT9{TlE+Uy3*xoiLgMv1$jO>*=x2t$KQ5^w%OYp1!?~yp+&*2oP?sMj=c16)yTcs1F86nrAL6{Gzk~H2a()QJ zGhdg-UA+eES{x+ttQ>N}fj`)m{9JUSP?s+!{!kt{^CHZJ-{taLG3Xl=eYgESa(a8{ zSK9ITsfR+`RcFXLD-bSas6X5AbJ1-=o%M+H4;2a589397@6Tol^MbOZZv6l`yEgPW zJFJt@henp%B{mDh-olY&}OjD3&+Q!(~Z^3Uz5P7wclheZY z=vnw&!zK>z9LJLW_A}&6%`iV}%-@NtxiCNTKN35ugPgwxao6PGb|$k!+!d`!pHmk( z@hi*=#Cl%$7z1|ZauIv^966shyz?j@bDovq9>Lxr`>ThXO&aP!&GWQB19g_DMCz>i z$jROz4o>F#6Bi&3%4#H@H9*eY3-9DwaldtGVLstTlQ{7Oa_V}xFOJLgAk#x1p!$+? zX^5P76uyi6nfsq8QXKBJC-21jIE|dE3UN>r9LK^&**pI z{3U)bW3CmBI5B4{a$h1R8ixEZji09p-|gCb5`A1#+r$u$MX9|5SZAPg9E6%h$-6 zt>8y8Ma=x{GOSa!BK2-d zGZVfmx1;%7!(1;J>0Q4>eh;Y)a(YA9-x9t*@kg-F%4C0Sk+b*VJjGxhC;SRHPqCGp z=Qqf?H$k5Jisvym8sd^1LE=(7bFIuoug^kb?a_dnAxd{>@>^kZEJR}u29%Nx_bkB5FtrXl&ZD{`VS%=v8Z zn0g}oT@3do>5sZ0r>}?K*6J&B(^kn`0b?%K25 zUbKX~qCOy8Pvm5w(8s;Z`vAQi>Lo+}{+K~dEDQ2lalPBxuuj{O$m@lisRQ#KnVQEX zxjFpq!CK;fy^#~)cVG1jp5H}#m>1Z6WWL*naB*R7s@LN=CzFD~cd%^8fh4($K9O>WtA*UaMeY(rM zF1Nj4onkGq&;H2yrJ?`p&d){kgL+=4BJ2DKIgugssW-Z^?`1@aP+xZ>b=?5u^fTyl z{0RP?jaQ*Q8$|llfynvP5C_#pt_S;Rh=bio+#Q6R%mj6T?9A;`UWYzauOV?_Fmn1) zn1h|-b1?mOsOyf9IoJ^7%xQ>&CJkSwKLT;kF4=+g(on+f40+`}&gqk|KmCmCZy0j+ z0?gfF^QO8PzBjx~{9!n9x*F7xF6MU@d;;)4wSm-2Baridg*nkC9w+Qia1SfGkoY+g zIhz;yM)fu4)G_FD^bFFcjw0Mr*q_|RJ3Y-(99vq9D&Kd5Hvhe-+L*c$^3EAH`V(-qEBO(*M|gq&{#oL$1>l6)m_0|+-6Irk^nwQ0%EMZN|8?v|6hG6gyH zP3XgKb9)gt!LIdl5#7-c>7N*V74CAlOT8u0LHloM(M`2I{Y(7XMa)YmhS6Bi-B zo1NTmO+u*4bv44xLe6)E{kdkmE*JS>onk53-)!XEaOmUISA3oNEaZ3fBk|ig$oUbV zKXshX8PqV)pZPR<>)CKd96J=nXrWLQxOmUd&t2fCy=Od>Z zLcUdb`2PH#FhA2xNWNWwoLCaxdBp5bhu>AvRY*OtkZ}D%y)=^7XYM^X7h8txZxM37 zS(v{#Zr7?qnA{utCDg8n|yQsm_O zAues@`g3o9y$E{0vJ5#_6ZWTL`Z4z_)GzuD+21dO3%_rpD)BjyNeJ^gL-nwnaJ?a3 z>qk7V7z5{GKO^ya1>v5-cN*fQm^iVuNz{+oGq->QX>SIl@~pR18m@gTm4_EoXE@Nxuu|YSC;tSI^g4sv=?>tx?hf|{-y`^KFLEv$ zkQ151ed8KHv73jJmtQrB%F+&TFDI#HR|$vR6Bu$Qgd z&|VyJZVu=n_T7c-5bEo)L=T$@moN11F@Lv3?+bnEEwaBYgv%c0ZuR*+tjYv)TEB_Z z*ISV@MIj%Fg#0{3e6TY;g{*TMa<*uAA32l9UA+S80=bRUyW5e|#o%0AC4Qd%&oEz_ zMD(x&Iq6}YzAleTelV<4)Fl3}6FKo3%xzrEcfx*Q3DDnMGPl`9xJw{U9pL*D6@xtL zf4h~ zhse4hPe|eqhmrG7LqA=V`=4tC`tu!$UH^uh%>{mIipKP}H=!@qcZlB}LC)NOzRFCA z@rMgxKJhlWZ#;^e=o98{=lJhT>{2NwtWcXOW1cTXVakAYo_J2CmF3)r=xe~;)Sax(lKMz^2GGy6)=-;YEOr;rn! z!5>^{-XH1kPF3_K^~7o9Ouo>k-ioo8pTj*Rje}>9^F@MPpNq*y2SPrIL)Li~Ir$>o zSH*m{YyJXz5y!|p`5ba83Cv$q9$sg;2Ea){?Cf{s?Djt*KeE@kzl$AjMt-CxZ$v-x z$mzHsPp9E}knSRRTAIOPu;r{Ox9zR8c@b`0mCH8q9IX43GiaO8F(-i={t5U>o zFCZryg!_O>JT9463q|{LOSa(tE+S`B!W_(wI6rxSuG?%Y@q!{yxx2zCYU%{?3*!K;~|LA}0@oxm)b-C1eG?n_WZ?SCBJ@As<=Kzc)1h zz8|bc`pv7z>0M#&eT4gw%?0;rIu41~*N`(GL7rAmc^~c@L!MSC$(-*mCV#Fy*H*+-yvxr?8tt_pg$-N`-B4dhgRSf_3lGnXnG>WMmJoi~xQ1B1U$ z<>%?kfWONb#NTfrr$4w5?ayT8b|%My|M{LIkNu6Dy$JT{m&e>UegO931`|KJjhxDO zI+CZm@VG0o=8Wu9 zNL}|3IoSZ-3Fs9ubyn+OXMdCZJwnc2hTq%PZ*xD=_uh*3XAY3K^cXoeGuTV382#rE1pLU|=KHf9!H=XNeiR=$ z^Bn3(*^kfJRr!!_SCIN80dnd#*qQv2&tJsca4rpqy(C1==7hYdM#ikO3&d-gndHqx z$hkS8F7L$8(-jTzhyLBh zBkN3poLn7#fBt-oeO`cd>dM4kk|L)^Y>VQ&tjgn(8v9D5clY{CT<0sunf;K*+}e(8 zonj;8cSZmHW-{dLcaV?dA$~5RVYtWZN%B#0}{}X+2cDV&-MYkwxda2Nr#;GVVx(r{`^kJM>a26XL{u1qO)M1^|=0YlN`}H zZ7Omu8IUu7LH%O4@Ob7P1EYj|qxkjhpJTvk%?Y0=?_wL=UecXWE{LRZk9<2=r&~<+ml-+r1)QgQ%Ihp~?8u31;r-cNex71J_`5VDPv<~R_YUt84)Oij z^B_;{C-2X4A}0!kerz50cfB{n>)%Pd&V`)50C`hq;rr7IpiY+CN#4wjoH+vir`mnb z{WkcYPDK1K4|2L~sIRZ^{rS<5r$uE__q>6eFBa;ayd9W4)d=b(xtZk6yo9R-`!g^2 zI^CzRKU0M4FCTJtO{iNl#+=Ky5MRt=Qn$W|oH+>kvv2b}W;=#?d3ur`@+0SS1wHKI z`m+av9$qKwdwS!V&{v=4oMQGPBWC%ij!x5zpR5^gBS6G?de zVy?rSL1!R(FNB;*1aV%x!{d@|^+x3Hays$bw~=!fp&pb+xZk?uq2HWB>cPUu>E^-C zw)6A!alu|Zt*?t9=WLk2bm#UdQoy;W?WCS4ikun(_nS5?muG%{KiZ%Fl-zR_BitTX zr`pEjyl)16>-!UbD2|-J1f1@|?bE#o{=S%SC6IIB-LLD@iusYN2AuW8k4hqE+JasC zd@=ra7UGgvLE=&=Q0izEID%=Y4=yU}t(B(L-6}Y*XkP z{Sn@G_y?iRnnU_Vg`BMKqde`mb50+H{HI?debsw}`vL65m*W2J#|L|tqx{z??krR!=JaP+l-+9PF(r;Ek&X$68 z+BGrys~_%DHdx;M;GYyomlQkignhJJbkb&4f-m%h1~yDLC$Sk2X$maKL1p|z65cw#3ZZ-s}k<<0kG>Ce6DX(ycTgf z@nYmYMovA3xs+bdL6$TULSGt9Ph_ew)n8l zFDD~c7dg4)C~$kZznjaiN1PhI9J$Ytb4fsddOX*k`U~nFHH6eX^^jACL*IChuhYE< z{n!c8AJs=rKimN4@`T?Ts$mJEb-KJ$aGed1bL~KXvP#VRiL)@bF~1Z2eSw_0AM7Pg zjNTh(hI45_?4=>$`c8!Ne0Myn3-r#*k^ao@2G^;P)348uI9r0h6E`RCf!=Quy*ENm zwVe@hs?azlPoBF8+=G57uQ75)!Fj4_{2tw}O%Ux*7a;c11UZ|`MekVxiHFCBx#KG9#@W35 z{Px_>$bE~PZ#Ea?UE`ei1@e&^KM1++kW+OQN1T6!b8hLQNDsmj{dGdlj9(4w%);Ln zco!#Hr^rv%`8{&>+u#pxa?bV%{*Zq>uCp`YMuI%koX7e9{QY`3moh|N7v#j;jj+FK zJdcTf5I@C1l8?F)t||Dto5$DbTfd0TMZQn`y&G~S8SKwwz|Gau|sw zy_e+nBDMmjTa7|_y^u2{Anux%WBQ!tVApa5iMzd#v!%iRe9Ygs7yrQi#7=T9eUS6j z_eAT|Ph##HUrQ6&i+nf_*ZBi-rtRK{6WRHkT~!B8q*{PnU*z2VUxC}n?_qt@)F5vh zk@q8VQtgO1|0A~-buAg#b(2{ruOD(M=WdX(of8{+)(_w#i@*+0~Ie(`f{9ThuUo&}fz_f^S{RuZ1IeFuYh!dq^?#D*V zia3)dKgt_|oLyHt;#_Io$C<~Ifg7C@xuM93vNa=4d)^m|E)ye8^vsUjFyu_zS`lX( z@H)igh5hN5b09YyIeo1va9z25+Fauzc~ZZQ+z8}k-XL#1@599xuuhwV$Qy~A=mqEE z+j4&wIcy})XD8<}3OT#I0-R^rNlbr!&PX`VAJgJGMqXOf^$8#f29V&+0 z80751&w^n4aAe61>zsg`aU~;8#r)2? z{-$rl`6Ee?n~0qHHD|<$mwBD#OnX@8>rZfErD4RuQMVJVb6Xa{4^{ z-jkZg`wm^aF6^%{`TfW#$hlA9-IH0v{mAx*ca?G~xsS8RsRR%|bxpoM*&5#O$}{BN zVk&YzIdCe+3}&CUExfZbKM-yja{KQk{DF;2t- zPH!S}sTs)mYcOXJe=O%*bGT1+tw}#UlW;kq5BH6hGETmL`&55oDXw!Ca&`&iTeoa2 zG$Gu4mbkRCRa#Ck4 zLr!%K`a8?lX}bsgEg|~*1v$SN!bmj#;{=W3^qIDc>k z+Sv-^qytWk;`);d!~U)iZY6SlFxZP%+;8=YFmL^c*vl&9+!l~$25#l+91QE!Yl*zo zgv%f5-C_J(^dR8Wo8(;9AZK^)jLt=N<94mI}ia2s~9b$Scu;!~0S zVIAR4LqFzn@pXzjp?@e%=2GjCQwd<5HV!`*xjn4&64~DdxoaCHX0dYc)BXMFAa`r)ZC%5DPI~TPv*ylcSE)F>}6XqJS=wZ&yNe*^B zhxFl_kyCxbc@F=Tak}fPVAp#{pSp!`?LeM7!q@2@Lx1F35qVpY)19F2x9d+Zd1?XZ z&+R7t<~HQ)vM?tae~58zAFh3LJ`y#g+ zIgv8-{nLNv-0z7Z{r%^6#*zD#a92SOy2=X1ncJX0Glk5r_aJ902YCf}+!g0w-lIy8 z`NUr2{_UsT%bc^1z|M3b;(z;)^V4DeX-oaV)~Sxcyxg}Y^7bQV6G9x6{dqnT2h&F9 zVrCORI)I%28sdb1m*0=sLEuNK+-yA0gUIQTp)RNyGoLsNeZMG2>X$>v$pI%Kd$B5J zerDi4&ZSz3@(v@XcEdf8I?MY;{|@AL`v$oO`i*d<;9Tsfm^pMW*q{55=-~)*;#H_a z)N|f9>ho|PF58p)@T182J+MyQnD5Umfx1;JBr{_Oy>tROd+Q*`8x$jN3fwchKgd1vNy4RtcaA0=pJUnqus^+FE$Z(S za^f3!=ixT;dqdX*-jS)cqz^caoIZ0cTBm%$?~C21MIyV_rOA7SGlUxm>$FSwoXD;N zd-3(jd(5-Qsggn7W&Vyu41sqEVgh-WaE@@L??&rXliM-B6}4fWB{q2|?YhMCA zNbW~^0=%ElCCU2oXj|t zak|4G;I1S??h10M>-!O>+Qh6g#h8e*xzi(e6**JAG|2mIF_Y&qjfgn8AtiFxkdvul zP9#>x^zWHhM4TF52)Vxqx274$tIPX$nQcAnuML@JT}RGTfjO9N$NL;z(M6n?R|4hT zKu%w46Uj3%{gF+yBjW7T(#YK;T;3nxJZJKGfp2;`;>?U{$lXHDR2UBXi^KaBJMvn@ z`PTK3`x`k`t8c{lw|6r=sG}a%nW#E)w~_O+CcyssZ)cqN^FhRkU0))12RWNzM8t`d zn>qKx^@x+-H$d(#a;_*Jmw7nB?Zg>-vcON-_ z5jcI6pQrr(Zp7JCO_6(moE<#{aaiAn6J|(g#D?TMBXFh z)XYhdJin6HXJ*m;Xq_h8S19i>a%RCW;CAu-xp7zFJb$Q%+!N&VMmQJSllQl3>E9r4 zDLK!l$hoBx;an!f_|a0>pDWx93cImiCo>oR;IT!KD|GxiIlR*!%NDO!X zN+i!WBzlO0obNd#+Mh|qOm zU(AS^j8oS^o~T9Q%S*^f59eYI&u5%$bPLwmlblO@L5>-Z#)X;`Qs-j z?`7od3W$R;Kj+L+I8WV)#KFYKnQC()dFCfRpEveNB+oDY2IVC|PCBq_J#jWWPc`{Q z#OcYzu9G6?+bx3qwVum3SLvmC|Gj6_alS)&uOKHk%#S#Aav9@v`gjp12X{a&8FDUt zXV_n&EsS$V4@K(~Ybqj_966t>Y{bbO2N@@ZjgL6_N*d%+5UzZE;Fj!XoK3qb;@nko zPni-q`!R6h^L>nS7gxeMlaTwWRLHrOa4#xrZQ@)t;Piz8xX#qbnFWobb-D~Zp6R@6 zB6+q`VdT;vry70(`@7EP1@f)n54+1EmlioO_WOv_fAcxCzXSf@N`HXdtH?> z^)8B>fpu0R@g*H{K8FE$bNM_=ehzhyURxFArAN;00eex~cz*D04oC9zHL}hOgxePC zrFlHg%dSu_xfdj!y@s4x-w)RLI@g1HaW0bQPt?G5W<<`OofvWE2CqL=t5Ao$Px4qM zOQJBW(={dbk`+09KIow!?*r6B z$YcH);j$qozW}?I=jO0=>i&CRf0ZlXIUsq9evZWX9LVXTp&p#U z=Ne`b)Vr!`bCj18Ibk7QyU+OC#_Ycc^5TKU>yxhq7 zB9M=CTz+padY_Esxp#@)^B|{3!21vt^IpScn*r+_l@H~;ft)PdC0eJB$Jc58*&K2I z`CW13@*-zWLfs?cH)Q)W8(@E;DXDw%A*Yvsy@=DFGtMp93-Xc>zkL%qvk>|?)v*@i zWcy{n-FO$*nIAd%#&^;Fe5Wd$Yq&b%+@kWxy@j040QHj1T!wLe$_bEHhUmQja-zuT zNS^AMhjHpbI>@(uC!)N9$l0=gMV!@7TW$VdRVsvPCLzDSG#|M_$jKG2)&6h(7w_F+ zoc#5Zi1THRBKJ1oCbft--QWb{eBFr=r^aWj5&gga{l5z%=km^vIGb9q{kaL3BhGcL zgmqKG;iAGG{1(oIh3~;>`U@OrAXrb0T||yelb*oH!Ex z-dVDkb$(SblIN#fz`0Z@ z)FJO8C*|gd(^V5OPF#k*%1!Bw=UEy#zj$fHxi2y^PR$SfN}G#5$Dt#

    r$Y3FEe>c5%K{J}gL7u`F#e;T76-b2py z9vs=b9#fBTqV>c&|MeeLYecpG`B6FK$pOl#5~eS(}T4)IeAdB`|jpsv&1j4v75LH~5=z z=HM`pcd!<6pCTu(j{$DepNvy!CPbWgOCwhuInylEgKykroJce?;&gE`ud9Ka-!dqY zCllXhoWB15+B*~QDvE3Y*UbVU1QHO0k*x$35Re2EQN}Ng0VT+05*CGl4p|_OjVyw) zq)}uM6%+(TghoL@1p}xsxV1R$BRhmqfo5OAzAqxLZ=LR(sw?0_XL#>>?_@r6bMNiH zPt{+iPE~hqm=^@6eJ}0nl91#1ZJ;iFt&Sht5-T|SkaSSUs<-y-gu(0njUwR^Ew}ro7;c1adei~;!fc(5o`h9UPxZ1Z4miDz+D_yAKEc^rB~C|9i#sji zX&25bKK13NVSPAo7~>@)rWc9*j#t6)OGA$Jc~Efr>@5ms zY<^I1R<4wv>Bt$s#zLMg_)+2bsaV0;z7`m70CM)EBxq;fZ3<^Ob=N3- zRpsKKe%kGKVZ3bQ%uVj^?N`61aID#0(N1lY6fXxk`xE!`$*0u**&pnJ{_T)HUyzHO zcK1o)#b(bc@pwG^9j%cd{a$+ra;)87VEs!SrEuDjDq>wRTAafEKAeZ52*` zAMP{oTGI7qK61v;cyS$PO{}hPw%tnXul1}TnAiUONda>Fy|JQy+UThYXC#giUi1-p z*v>-a^vdgi%buff`otduXOELUkMbCD`~mp;95W?d;jCErTnVcqU5^b#&S(|_`BL`#JY4@sZXF(mG#P|-hq)l!vfTn_eULDK$uG;&sb_y3pXiTMg=?f_0N zlIqKo62~E5>?-pVPJ13cKgi~mV*j2(&Rzna2jk1s^~fH(L-fz+CXL@1 zrn2ub9?Ssld_2Z`8aZnrd``zc`?=Ci^ECWDx-oAHa$}KWMK;7c`Gvw+r=Sk9S<-dR zIONPey+r-6jrvME&HB1n-_2+G9rJ&C3Jyy?np%LS>){g z_Y2OvSKZf{FSQq(*1aoo&mpIcdP8tL#a70_9`gpotMC?b&r4jb<$^QrQTKI5-p7Ko zMlVNh5^~m$6~wsf7ekf)>9b*akuKz_1XeUY1r zoSt;6;I#I4Dx5YU2I8%1h1^TX@#)~xY@&|4Rd}PAw|sao`kaQG@l;JQ4#xA9)p(!X zCOCUj6mrugu5PhtC;Pm)!s&N`Pn}CXUq((p@+EL9)caCw$wtvX{mHMef3G0N55m4& zs}*p6sZTS}POEY?Z08K*%tcc~JS+BorJdG*dEm44D;V!pen{RRw3fRUNrG$gzf1 zAl}6Ug)@5HEI4N0g52xK8Bewp9DlNd!fEI47o0XJ4!Jjw(@X9VoSCR{h8{0Cvq?O1 zb0zMZ=Fra4hm?5M=dsYvZW?m)kkh6{3eITuh{Ev&TyT1i2FSgMoF3tNY5B0iu`uwW zos_)1g`D+uMd;tp=?Z7nst5gBR1f3Lm$+w-!hNnWZzvqgihy=52t{rIa@xVof-@gq zq;e&vdiw8MTN~`IW%sY&Mvjl&AULhYGL@TiTyXk?&B(nYabKMTE@Oep4X+~FX*aEa z?R;0_CN&jaw0FiSoG}R2KlW-H>W!iv_1=W-6RE;#A#o=z3C`H9j=O2p5}fhT4aj|joMD`X`B$vA)A$1BpBY;bxsQ>v zn?e7~=C3O4)Ve?&)cQ;P`vf_o>Urp&_PWBcdv6r&)CN_?c%LH2^B^8C3g}-o@M+GJ z;(dmkksc<-fp2|5iDwP0FXCCR*1~uea^_v`{A)W&;q1MQ1jklL^Y3%ytdS9rXOo^) zIBo8&!1b2u^-AQ7s<1xr$z|Vs81Gx;tour#ol{>{ zIQ!mE5swe|A@`lc)!ZsLeZSgHKH`j6|LifP*v{{fGmK+`vtCg-ds;>4U$ycW@1Mxo zG2qkOp!UyN5A(>HC5_(?$gvNgp6MxSJpDpt5zh_^$9Sudv&QWZ?bHrUQM_1lE{OhV zt50D6RwHL@fqjToSzR}IYv>=#Du?mbAg2vGB;xT2>Nv28PzU+QVC2>!$A(^jaVV$e zwK3ra7(Xovxpm0tXJ9^9>&7enGgiTTFs4dA*CS`IJ_ug!4w$#)AzxUu)XojaSsTHN z9zIctXMPeU+Q~kaylg~H8{y{Jxq$U=J>;1+^B|7HCgj-Da35ChHCc>bz5x>@DqI zN|3WwL4F#E>V8RkIvnzIo|K=Pk<&s>!8jaJ$ARs0=id-1&;ErRZ+}>Dc8Z$UdUqIi zt#b&D-;c;yOSS{oQ(YfeeI&uz%D zPEgPE9td>;vsIpHk-^TMTtjA11Z)IC6Rn z)Oj{jt=HNj$WLpFv>u&6P8;zrF%EVU)u%Q01mx#}9oWB<$nkI7`Je^FJLm4}N`tWO zonr@sODqWx9!!YaF;qI2$Z zErNZB)ll6Jvgh1=-9l*};zN#ig}TeDtJgW!&EV7cR;p(~$Qi{j-}U$>lsaL)3VCKl zO7kxmIdh6z&xVasIDW+4ms=OmO9*mSJNO*9ecn*HL9nhED}F{URN|_+`PoVJVue9G zzWp?E<&d*4!2HuohpF*S!#+fhldi|gBga}ponYgO6way+b)H|8>O>fFy!j=OFJ^hw zi*ZMFF>mda(*2MM$XV~Nhxs=}^Xm4g(HWy1c&Xn6|z zR~b3$9d|wIF+}04OsLm(w$#o@fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^b$S!Ym7^%4~bp6w(~g|?5zj;-1WqpcTsgAUbMSz zs5CwE40z*nv_s(dzzp{4k6hnwbYRc3sBfaCiOTELxy-_>Y`GpmeU$50sLVt8`ft~- z7I#2CL@=+8g*j{(dmz43l2h_nl#>vU6PoCv*G``geu`jTzaj;fUznSg*FQ5SWpIAR z6KPCT`^)i+fgj|<1uvl@UX=6w@;nsOKt4Y+BP9(&m+=sZi|1bZ8M{26GO#e)ZTi)D zIH}WSum2T9`*U&&GSePQ%M`Hl?MnXqYmWa5dFX#$T54fRK}L=<Ty((dnoExzAn4g2ekimOrpC zJv}qc9V34p`#B#=O-s*AE=UuM|CC|wjx{(a*lU7lpc7RLZ&mjU{uk@qZl_{C2IRZ* zez^0WfSpINT(?a~X#0rs#QB0c7V2g*WBd+xpy2J_d~f8wlH2s^$NBTn|CQVD>c{nh z!`Pa;u6~^69@j1A>c_chlLs`F_bIEb8bv}5b`UmEN zK>uCy;o8T`t7ifGaK}eY;n(@*e03?z>hEm6bJDq}m^>pJHsODE+|1t@H@Ezh?Vr8p zW#i^l6M4P3)^P*&n(CN;jq^7{pnr7S;@ZYJTR*I0*FJ9E`u*E+i*Op}&9j8$>;Y*w z&HnT}8(%hF{_Jq#xcdU}#QEkG`|CeGL0)Lay_9g;^V{)hB%12&%gXju&a3iup*J@F z&T*0JU+1*cjO1*d=O3PbNB#S~c~m5NjpO4zzy9N-><9eK_+LAZBAv#0^XUGZ!tB)K zyvMz%^k?fH%_H~zisSfl*Q@CF>h&$LAUUsq_xF#Ee;QGJ^R5Tn`)b#-zRCOa_Svb@ zsL;6JI`Z4=oBx4zr)} zEcX%kP6^)m-1C&Z;Ej*x&pYl~AHDHt?G^*fCw1P-`4Qy=@#2@|hugp_?Uw`Z>t&~9 zre&pN7l=)XbH2d)D&F~{DnpOT&g(pdLFD3%KYL#P@1K0m8NG`m?=iz`0E935uEj(2 zviHj}3J$5)Dd2stK)Gia-x=P2%saooh)Q1zItHB3FWdPFI1Ri=-^lqg~nHieyj_u=Vk8)EbljU4R}9bp^tOlZ|0ppV84**zVE&5+~5&k0b$DCrXR=v z86X2>fDDj<|GWY3^Syze7sUI@<;3SX+Qla%`9HGbzYhXyHy4fbZWDMd9p+2XQ~ReU zr={$*ckS4-efQ29QPry6Sf%<6(@?`hV1S=P42 z`6Y+%8-C_cZ0^xD+Z#MMsZQ*W;~Vz39I*Pe&vK8g+ufw+dw=_E$casd+6+CirReC< zwL2Sjoqb=?vEv&{@6Orw$)%&m*6(fJ@3WDYj-4nu5?gfX;((f`E)D!;(yo)MqnG%0 zhL!Kspmy}6+~_6wm*yXNFiyWS~V`D`p_Dm z|C22%%`;uIBFk546j3u;w+vr!Irw4pnbAJyCa+H%=r8v^{_E^k?K;o@(imMbS~}AD z!D7`rgusCnoCAZ*eV6u)oqI!OjBXcl>8!aeqi>oVCJu~o4vYvcZhLX~)<+_5ePMD) SaGlml1N1!G_u(()V*U#rh&ih@6YDvIb3qCuiSL5PBqf;YQ+PF&Dubvn(ynSJx#_kG^` zWcCZyoGPE&-Zc_xsK4!3-K;iL8Q;J5^VQa^GoQ0B`72VY(NNfn_67V0@%dkgzy}p#42}zS*Mb&$B+H9acH-Ta-J2Se?&gfsOiA6{m9fo17+e7q*XeM_8e%{jOuYM~3ehfV z{jOxfL|1xOHlp6usJGEgcw7mgyUakPZqz&TVURCEEt$|@-rSrh*$tO;Cjojl$7Mzm zhLxDXm3Qro%OT}N#}uXQZ~gIAc)oT^T*hS)u~;b+5Q4{4+h|4u(I$Z-RR7hLpYA>= z-g3{^b}qFhrQS%%*59?R7wX-S9fBg^47xfe3&V2uaiC&GXNh;qm^G*zYex+bZ3|FG zOb3wext0IhBb<)W#h~q43Np*7LYMeKyNZG&5YsWn^3of>J$hv)a`Ul5=n9(n!IW$3 zfMrgLN|Htp%e&T)ns`KXa5R{A?SMauO3b(b@0u*ns72|XO(2BJ1j91Mcx5wZN=b#Q zrGheb%ruZQDPO8cl!A`~qZ&0OhoRPi$x8uUnxfduj8g_YnQ)Aoi>D52w$aw2oKHSS zS`RQGiht7-+~VUcB501x;{n%r2QY0ne}ghU#Ed*rT<7LpTC#&UMz!fIYBvSu93X%s z5W0_YJc(_f?+lJ4oGLkf%;H*3QQdY(ux3Pi_svt(DxrH$dnGDo&^!*Ry71?!u|+m< zG_{1yC)y95aSP#9T2ojo-j9y2K7IEO@fS{p@0c!2v`)3+|pJ7vq zat|$w&$*XRJG`?RCe#r`9HoBG Date: Thu, 7 Sep 2023 03:21:04 +0200 Subject: [PATCH 105/176] apt-utils and apt-get in dockerfile --- .devcontainer/Dockerfile | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 9b734bb..25b9b3d 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -8,6 +8,7 @@ RUN \ # install docker apt-get update && \ apt-get install ca-certificates curl gnupg lsb-release -y && \ + apt-utils && \ mkdir -m 0755 -p /etc/apt/keyrings && \ curl -fsSL https://download.docker.com/linux/debian/gpg | gpg --dearmor -o /etc/apt/keyrings/docker.gpg && \ echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/debian $(lsb_release -cs) stable" | tee /etc/apt/sources.list.d/docker.list > /dev/null && \ @@ -18,7 +19,7 @@ RUN \ RUN \ # dev setup - apt update && \ + apt-get update && \ apt-get install sudo git bash-completion graphviz default-mysql-client s3fs procps -y && \ usermod -aG sudo vscode && \ echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers && \ From 2a394aee22dab16f2a9f18d5ba3741bc64f999f5 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 14:43:06 +0200 Subject: [PATCH 106/176] test: from python short command in dockerfile --- .devcontainer/Dockerfile | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 25b9b3d..7c6c3ff 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1,4 +1,5 @@ -FROM python:3.9-slim@sha256:5f0192a4f58a6ce99f732fe05e3b3d00f12ae62e183886bca3ebe3d202686c7f +#FROM python:3.9-slim@sha256:5f0192a4f58a6ce99f732fe05e3b3d00f12ae62e183886bca3ebe3d202686c7f +FROM python:3.9-slim ENV PATH /usr/local/bin:$PATH ENV PYTHON_VERSION 3.9.17 From a5a13f97ffe22601d4ab000b5db207a15a105b29 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 15:46:37 +0200 Subject: [PATCH 107/176] setup.py: delete code line about requirements.txt --- setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index e2b1ca7..ba3487f 100644 --- a/setup.py +++ b/setup.py @@ -7,8 +7,8 @@ with open(path.join(here, "README.md"), "r") as f: long_description = f.read() -with open(path.join(here, "requirements.txt")) as f: - requirements = f.read().splitlines() +# with open(path.join(here, "requirements.txt")) as f: +# requirements = f.read().splitlines() with open(path.join(here, pkg_name, "version.py")) as f: exec(f.read()) From 160a32e7a00aa5ddbed0bab3aebac20a2ed6fd53 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 16:01:32 +0200 Subject: [PATCH 108/176] test: Revert two minor comments in dockerfile --- .devcontainer/Dockerfile | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 7c6c3ff..cfc2c4a 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1,5 +1,5 @@ -#FROM python:3.9-slim@sha256:5f0192a4f58a6ce99f732fe05e3b3d00f12ae62e183886bca3ebe3d202686c7f -FROM python:3.9-slim +FROM python:3.9-slim@sha256:5f0192a4f58a6ce99f732fe05e3b3d00f12ae62e183886bca3ebe3d202686c7f +#FROM python:3.9-slim ENV PATH /usr/local/bin:$PATH ENV PYTHON_VERSION 3.9.17 @@ -20,7 +20,7 @@ RUN \ RUN \ # dev setup - apt-get update && \ + apt update && \ apt-get install sudo git bash-completion graphviz default-mysql-client s3fs procps -y && \ usermod -aG sudo vscode && \ echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers && \ From ebfea90edf72c14ce054b4c34a1b1d81eadbc44e Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 16:15:12 +0200 Subject: [PATCH 109/176] revert dockerfile --- .devcontainer/Dockerfile | 1 - 1 file changed, 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index cfc2c4a..a52cf34 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1,5 +1,4 @@ FROM python:3.9-slim@sha256:5f0192a4f58a6ce99f732fe05e3b3d00f12ae62e183886bca3ebe3d202686c7f -#FROM python:3.9-slim ENV PATH /usr/local/bin:$PATH ENV PYTHON_VERSION 3.9.17 From ed8c1ccf77ac1df3a530b68bbc3a366aad14cea1 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 16:16:07 +0200 Subject: [PATCH 110/176] test: no volumes workspaces dlc --- .devcontainer/docker-compose.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index e0321d6..2486fd4 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -19,7 +19,7 @@ services: security_opt: - apparmor:unconfined volumes: - - ..:/workspaces/element-deeplabcut:cached + #- ..:/workspaces/element-deeplabcut:cached - docker_data:/var/lib/docker # persist docker images privileged: true # only because of dind volumes: From 30c3dff84b0a78bcd289815bbab1bec5d49bc898 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 16:23:36 +0200 Subject: [PATCH 111/176] test dockerfile: imaging_root_data_dir not needed --- .devcontainer/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index a52cf34..bcc8410 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -42,7 +42,7 @@ ENV DJ_HOST fakeservices.datajoint.io ENV DJ_USER root ENV DJ_PASS simple -ENV IMAGING_ROOT_DATA_DIR /workspaces/element-deeplabcut/example_data +#ENV ROOT_DATA_DIR /workspaces/element-deeplabcut/ ENV DATABASE_PREFIX neuro_ USER vscode From 42fa38cd1e89acd6480c999f195da7eb59b1f188 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 16:25:54 +0200 Subject: [PATCH 112/176] use FROM with base image without digest --- .devcontainer/Dockerfile | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index bcc8410..725a48e 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1,4 +1,5 @@ -FROM python:3.9-slim@sha256:5f0192a4f58a6ce99f732fe05e3b3d00f12ae62e183886bca3ebe3d202686c7f +#FROM python:3.9-slim@sha256:5f0192a4f58a6ce99f732fe05e3b3d00f12ae62e183886bca3ebe3d202686c7f +FROM python:3.9-slim ENV PATH /usr/local/bin:$PATH ENV PYTHON_VERSION 3.9.17 From f9c8eaa377f80c7b68e4f8c796b01b6d33536028 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 18:21:35 +0200 Subject: [PATCH 113/176] test: try with calcium imaging image --- .devcontainer/Dockerfile | 12 +++++------- .devcontainer/docker-compose.yaml | 15 +++++++-------- cspell.json | 1 + ...LC_resnet50_Top_trackingAug3shuffle1_100.h5 | Bin 282521 -> 0 bytes ...50_Top_trackingAug3shuffle1_100_meta.pickle | Bin 1661 -> 0 bytes 5 files changed, 13 insertions(+), 15 deletions(-) delete mode 100644 train1_trimmedDLC_resnet50_Top_trackingAug3shuffle1_100.h5 delete mode 100644 train1_trimmedDLC_resnet50_Top_trackingAug3shuffle1_100_meta.pickle diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 725a48e..655d0f3 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1,5 +1,4 @@ -#FROM python:3.9-slim@sha256:5f0192a4f58a6ce99f732fe05e3b3d00f12ae62e183886bca3ebe3d202686c7f -FROM python:3.9-slim +FROM python:3.9-slim@sha256:5f0192a4f58a6ce99f732fe05e3b3d00f12ae62e183886bca3ebe3d202686c7f ENV PATH /usr/local/bin:$PATH ENV PYTHON_VERSION 3.9.17 @@ -9,7 +8,6 @@ RUN \ # install docker apt-get update && \ apt-get install ca-certificates curl gnupg lsb-release -y && \ - apt-utils && \ mkdir -m 0755 -p /etc/apt/keyrings && \ curl -fsSL https://download.docker.com/linux/debian/gpg | gpg --dearmor -o /etc/apt/keyrings/docker.gpg && \ echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/debian $(lsb_release -cs) stable" | tee /etc/apt/sources.list.d/docker.list > /dev/null && \ @@ -32,18 +30,18 @@ RUN \ COPY ./ /tmp/element-deeplabcut/ RUN \ - # pipeline dependencies + #pipeline dependencies apt-get install gcc g++ ffmpeg libsm6 libxext6 -y && \ - pip install --no-cache-dir -e /tmp/element-deeplabcut[elements] && \ + pip install --no-cache-dir -e /tmp/element-deeplabcut/ && \ # clean up - rm -rf /tmp/element-deeplabcut && \ + rm -rf /tmp/element-deeplabcut/ && \ apt-get clean ENV DJ_HOST fakeservices.datajoint.io ENV DJ_USER root ENV DJ_PASS simple -#ENV ROOT_DATA_DIR /workspaces/element-deeplabcut/ +ENV IMAGING_ROOT_DATA_DIR /workspaces/element-calcium-imaging/example_data ENV DATABASE_PREFIX neuro_ USER vscode diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index 2486fd4..b9894fa 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -3,15 +3,14 @@ services: app: cpus: 4 mem_limit: 8g - build: - context: .. - dockerfile: ./.devcontainer/Dockerfile - # image: datajoint/element_deeplabcut:latest + #build: + # context: .. + # dockerfile: ./.devcontainer/Dockerfile + image: datajoint/element_calcium_imaging:latest extra_hosts: - fakeservices.datajoint.io:127.0.0.1 - #environment: - # - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/deeplabcut-v1 - + environment: + - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/calcium-imaging-v2 devices: - /dev/fuse cap_add: @@ -19,7 +18,7 @@ services: security_opt: - apparmor:unconfined volumes: - #- ..:/workspaces/element-deeplabcut:cached + - ..:/workspaces/element-calcium-imaging:cached - docker_data:/var/lib/docker # persist docker images privileged: true # only because of dind volumes: diff --git a/cspell.json b/cspell.json index 655e189..6f76707 100644 --- a/cspell.json +++ b/cspell.json @@ -14,6 +14,7 @@ "flagWords": [], "allowCompoundWords": true, "ignorePaths": [ + "./element_deeplabcut.egg-info/*", "./images/*" ], "words": [ diff --git a/train1_trimmedDLC_resnet50_Top_trackingAug3shuffle1_100.h5 b/train1_trimmedDLC_resnet50_Top_trackingAug3shuffle1_100.h5 deleted file mode 100644 index 19d68415c30fa5fac3e7d0ad64fe63d4f80f84f9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 282521 zcmeFadAQEi_xQa{88a1`VrM7~j(IHFC_|Y-2xZG0AyZVe4H+AiNMa*O=8~b%Mj=zk ztYKHkEHjP2`|Pz(YpwhH`##_2_t*1W*Y9(#s}AR!*I9e7wf36!`@W59@IbvAvlYrF zQa`d}5gA49@W-_O{S!alX__&7DedoR-`DXE>Eh?H@$-l2;ScG=4I=e>1>)Cp$KNkA zz!!Y{ddubyJ}44DnfCkBKF4nhcLV$>OWGIy^ZEZO4K#SLcC*wCw~4z>^QXZt6F0`e^C~@#nK7es31h>cQqM8a8bb zesAiLN&EToDdiG>Oa1w3;d8pg2}}L?W{xU_W$JbDWd|q$GDxQj{zN@?A0almkvET zr2R2`E6uOLo6~;!fBxK}Nz(_?B|P|Fzl;8w_WMf5zc)x)$N&7B|L^*Vv^bZnumAtW zxf~(R_3hZZbI1N21`O$&>P--LvnM{M*}ZGOj!$Jq+sD zYhaiD_zP2iU!mmR*DC(hlfC+M>e-=O>Mu?Eg*n7;;Wwx0rFWm+9bksv8ftHIg&R!VHFa<-18yH&{6E%B8h+4@#Ve5ERWrB{!h zU3&HC-ltCjsh0Rwb@*1RmK}Tada`5xE{ScF%hoOR*OX7}C;Xn&FQopK@(GRo`-N1t ze8p@X6aTM7_MPSrHwL?Q?l_=hsul-6-8->s*f;fuV3&julJj%#j=dB6@B3f!bK>i% zzb5hXX>zg#IbC{p>eD&3Fo_?j^#ZK}9&K5#Olk~7Ihld$*RM~%RC^?Tp02C^fPOtX z4WM!|C)g~aSC>IuQhh9UA}~U1R6$~zf2V!UA(q6iX9{w9^zPax^$V$XpOE-3?dM8& zPWWZ2**ho9-l})vaP?2MXhJOs+x73%r(c(TsV42%>d8KxhxF~(Z$RSP3G24%)Td9s z&WS%)$=0!dtJKq)En(tTjR*D`(4%4MQAjmy|Nd#tUOoK&a@qR-SC0kJ6|(h*laMa4 z!mzfuz_hw0cdpdc3(+k07a4>OKX-eg8cWq{@a^yuTsQoLfX?np{9LN_QbnYtk391J zfAv`@yQuU}l#{Bx)SpYA0hW~|c03uMr~`uxj{Y;(w3_)BoGOH7f*(FRDijj(?w01iqd|ugsD58~*b-RRgJYboiVO zG?zPP`0KxYDcb4(xjwIuQZ<18@AGG>r=`k=cvP?9gN+{OP_9Fh+KnGf{d#hoNb_T? z|0!3Vaj9EK^MhnRr+jMMNRwNyVWUQ=a{s&dk@~&Vj~qgU&#CLa`Cq<#hcy1bJ1=SL z32^v5Y5S*nGLc9+!%w8{KXc-bXuvIaURTA;8bhq-^5*aKteN|3;B@yP*zCzPi)x@`|!M8~Is-Bwo z5?QHSgc5&G{T-0a62CU__f#-7nWdneQnmbF_W5*|0Ud)LQumqKc&6#8353gpo*?w3 z6Z!skeCM=6Dp}86d-dr!ph~3#pJtyM5`WGjnlx|PxCtP4g7zh0r^4E=Cu=o^{Lgp*3kw0U9ENM*ZtAA zo1}gYAL-KmAcV_%6L813FwRdZ8#uFPIC4-e1Xy0UGjMj`D#n=%4}iRs`N+{KB7IzE zyS0oHi(s90_ax*? zOY2(`tT`zIC-Z;Cr>^c6*#@sB1g(Ne_zSKnRmHf{8PZ0w`L*-7nfnBUVuE?hRc)nYl2?%4CGR0g=rr> zK`-LU8n!>*0raBGROD#8oC0=NrMMkruIjMALBv048S09_{&sRsOsg6=`PrK&FSehl zOSON%{^S zksJLga(U9I#8umC!0FMSGkvO)6~PYA5H8L}Qfc>L)XRC!`LduFf12newa^#n^mXL4NUnJj_LQeF7eB?h_&h6eb?k|PpquY_wLsEh~x1P)M z2O5ArhkkiD6~fjm`{$4@&f=CN5s-rZ?bBJTDDf03Q}I!$fRr~2!C+}}Nf zTMTuGFURAq`|!S?7c*}fa%GT{Ib!{i!q+J#z&g!0WSwz*l}g)Aus@%bb9x_~BRzxU z>9R!L;~-Dw<#xB_L7r$y)(KOt0ISQusjqn5W9CNtHG2>D2TxN3&i06Yom^+_jecEk z0dk2e#rj&(tMpZPvp9iI8ljk-@rP>8iwba8G6ZQ%UL>g4>v^gO^O3)DSs zBClIjEvW0%&7`g)b3FZ8)XT$3{&JHF{^^$xy;R3_y28anoRFnB=N5urt0lzlHIUQK z+ye2X2VbY@0Qo^r9glXumvAM@1kTRqda*t0LtLu36uJA5bJGh4PPgahPYtUA=kNL( z$kjwn7Ke3;UVNRd7_3v}AnUA!oPQqjtvb!)gxGO6$orV&+xw9d-Q&4^neWfkhI1>2 zkaMe$vnBI_9ZGV)7UMyl??d{ye;dIB{}9wKGF_7W8k7m^bmd6>QU~S9fl!C2_B_t} z2choqJzqk7)u%Dill;58NeY)3){pus<78U?MYmubS{7}!^OE06m z2FRJoxdUg9^18sj4D#fQ!;ou;oEQ`R^II;@9*h2YlKAIC$mx#YFTOF)oAOn#yBS3M z$4o0%QBHyra% zG3xffsR09#djvVNC|lsnOIsPIZw7fPbARNTBj+vbPxa;T%;k@D`SH=nrQSWo4_y$> zgKF~?ljlFVGptjOAA(%VG%C^mRfK%2U*hs?BdB}yLQ?lUik$l}_P0rWfNKr?pSgkb zx2=#f6`_An3;6l78(@E~59uFTBPXlm2>R5oCiP?eL7!R=!u_=&+|Dduhg-S*f~J^6WD) zZ{APNdwb+$MVJ%mS9l-bk3(JH`ja`)~ zt)LIrh2KK119JYhIG;GgIkgSqr&>?u3>rC66Y_)2$k!=z#5%bh$qyZob0;BQ%l=7v zd8PuKhuK8llZ3k&@`E4D?I&ZrcI`=i=!Bf#4s%mIbSXQ3`j0vgCpMElt}}A#d#LMl zai0JD_1HfYAolBmoERSC{1$$0RV9e?awCcJU6Ip=;=Jbt9tZ7unD@xf$+_)@oX-pM z9g# z$SbBY&ky1*(2K81>Ykp2TN1ejN&0LD`ADTB^Os)8nZ9rybQ6AV&DXL197pC-Pa~&F z!}-%$_*}{zg1X0^Bk`p-a`GS0r`?ofhgos1Sexjx4|3{3h-dx}t`{HY^X>%cr~4u& zAB*$I&p2maf&JNLWPi^f=U)Z?v_<&-?A7R>D~NygLr&)cJNWi|e`;*xekOH6f8=b3 z%)wvO0X}zgV<67!K~JN@d2-w>}818 zwkY8SA*Y^&Jng3Q`phi=zcwqL!*xE3oX;HRd^@>5%~+W8>9eGc9E_aq8P{2X`==iR z^_h^QUK)a&I|S>L19+TJ(_o#h2dS^0BitOQcU7xREDp*Qah+AjI-f_*SBmE^Kc6f5 z_3`{|B6fcPIX&&xV0U?j>r-8Wdnfq>xpx|hoUNZVaAE+jm(==_kRS3rgLW8(oY)BY zU8iu)jEeE>3aMXSM9z(a`qMPq#`Izyy$j^^AoZs~&QAk*rV#f}Sr7b7rjUAKICAz3 z)II7H*QaR?`?DiS-7^9?-wy1cEAexr=f^&7JF&w^pLQI`6Ge%u|l;bx<$K8;L$&L(WYC`>9$ykGXH- zeN~HzDDQRT0D^cVO#RW691b{2{A6Oj`h&aK_U{Y9^b zJZ9e_=k`tHd}XL3%|c!$i(6wKu$0UP-y&Qum`lm7++WO@IDdJC%%vtFXS+jO5(D{p zH$LXgxg-w0jht&5x#zjRh#J7z9fX^VoGk%;5El&lKe3?-0-AQ+%Di z3*;5imDF|b5bibl=MGpbJ=qHI@rXr_3+{?JnxnJ9k&`--7K14rz7di81*^rN1 zA%6Z`!-t`NIQJ=X(~z^vp?|kY^JK9Y=DX&GxyZeToca^?r(WcC*UO-}en~j`U9sBSCJWiN$U~0nlyU#&Rd{!>_i(bV2Q$7{_W#$T$Hy1fmTtVG4m+M9S**3^? zmA*yp1LS0d>VZq1@A~E~17~mCgxrV7sbe)^oqf5V`TUQ8U$5PP+&tt=yQt3wJPyjU z@!oeF>5D%?&UUUE9P85Q; ztZAyEkRD5g}Iyhp6^erfVrE?Lh9C~$obCD|CwiaA7BSV+?B;h|MxNB zYQ?#CS8jLF7UI0PM$Y>u$cfR=AIX^;S$(Y@i+%q{GPhZVoLd3%^eH_-!KkxQZ?2GRwb-{Av^w;2Lwj4hXawhnh@3aK{a|Lo@DfIomGUtqsdU=!F zKdeMfEdW0grMbW8)er~8W5mx^A!qu<{e8gWpp&pa*O}D2pCMhsZUg!0hn-TA)%aOe4kh8_(bAZ{B zq(-TVvz4)3uKd9dzzQ`-24q1mQLww3Ozge^Iky(( zSz-V`5B?I=ORfN!Cx1z}D{=1q2KN_R5&YUupMduJ3ORix_Bs9e{`@Vmo@hemSsRh_ z7oh)>^>(m0VfMkjp|3a)xIFFpb=V1Ds*cW#o zarYbKd=c<#Gl$1{KR(8@+{CYUB4^@rm2xfDr{4knt-na}`!3{s_PEY}_<0aT;yRxs z>)ef;9d`qq+dp~U6fYMKanN+_h5q?1a@xXq@Hg^vt2CSkv7Xe|dkA+Y^hc@%kArpx zC zjDtUtb94YXu`$+>pYZ(W{{T)6CUyBiRa`H+ zOPsf^C-bwz$my(*AKcsAujR?u$BiTF{0=#}1Lh%eGS`cFKF&i9ko&Rk3D+LxFR}}d zFLo%L2hoY#=lp=2D-`b$lJ+MDf!)PPV)q}B({n(c-^BYyZ$X~?iOBQF$?qVZ`B8j- zvMb2*O-MZZ2|3X%=9Q7$uT9QaFAXMj-Oq%Z0sbk<@p$dpf&Kj3>G=tD5Y}nOlQ@4AIr}~MnZ23EU0oE`Da(>`bPPF_9_|DD zZXO4XhVy57kb8??krR`le{iL^{X`jjeuDH!K`%eWoHpOD9V2V(aV$mzGB z@Av8XI>lD#`$c||S56{lhQ<6>g4<6Gi~ji?dCvG0;i|#;(;x6SA)bT!%(R@1arZQG zdOFx$cI5rLPXW8TMa1rBkaJ~XJ#jzp$7H@Zk9?fi{VZ~_YrM~Sj^`CMKE}^KiT%zY zXWxzboWbuO?0U#!b{v_z{f3-bALD$|>k9lt$SZ0kIq&C@vqvFL*mZoJt_j$|9V7GP z3&`mope{ERc>MJDK%CHhNL_vrIrAOlcbAL%r#b}rR@5T-_7dR=K%c7L&Nqh`X|l{?jrZ^zaytFLw_q4CFx}X zJl|^Olm7M(QhzsLAUIbvx7{`XJfgzi(WS zcs@&kKHY14e_}?o-yt%Wif`AEDpijH|J2ix&e3@|53)7!&-CeVp+Cm?#IL-cmKQ*t z&axE!B?EFUAJkcH56{y!J;YCYo%E?0k<;%)eU4bd`T)5P=AS;xN|cugIolrQ1?CdB zpO!GEbuGxe;0EN>PZdIat%mYE?eA+0_4U%vQC?=`eAhUyJI~{bJQ(K_Z<0BC7UWzC z=tca)^NNlApVVZInH4#kAJy|kR`#m4$f=Og(qJ8~jl zMd&+{p9d=rdXe*qUUDF(o{#zORMI*(!kj_vC3?9LIoBHOAa?M+-#h~Hbmz}-e>n-) z1)dYvNBQdmjzF38hQiG`S_l5oW-HX}&)SsXi_tz7(QAL-_? zUpYzYmtusw7tXuA%J=8iGz$CEnaO#V$f>=Rf?nKvob&aY2F{OJi~G9`IlCg(AqA53 zQW@fe>q_d7;>el%tAhRJ@^fTAXcpwT2IT&z1ac~GeErHeE>EnD^6n&YsU&jd8<>}y z61+Z>2K>|1B=ho8$ccsEpLRE2rx(#bD=b94+>V?Z4ti0olk|BW)+tI5z1)GE7*{#y zMW*ocZmWVFTq%e0N+YL6fjqs6uhVRUIH=nZd3PeGOF`Z0hVc7-)f4t-c9Q+wg`6J+ z>lAuEJAZzC%qz>tI`2l#-Uysp#@FcwHUa?>fbDiNF=}lao9|`-j&yaIe z4mo`e@}K>c=S}%jj4zKZLwV(qOY|jSoi^$9YF0;oX+z{yAlwkhf8zW?W#yuba4 z!tA`MIm60Q9%G%hQ))Jy?j; zpH+}^1?qv_46_{VPMqU;)17Y{IC*j(a@CL%6Tz=d8(zQ2 zJu!ZkA?Kkw;l6@+=IbQIiHuP9=(|YWQ-g5-z?{}Tn^b@Hg+9Q2WpSPNBB%BM=Ns~Q zmKYoRhldDvAK~)Fb#~%*5S!ut&n_kLr6zJ>8*pM8kGr-~^lL@-SBr2a#)${GKJ7u! zrz%eLc|YOaiSm;ARQF?C=W&uB6mss)=x3iKo%eR&XI>FMtBst9uk+Sb`2N(pus>Jh z6SPAe!W{v5eir9!Hi*}%B&l2LBBxhE9&~Vfpg1la)A#&n8)VsD6pMR?RWB=Zd)VmKM zCo{$8i9}MKo)70%oh0%4VdV7JFsF6>d47%ULt8f}nMFTlA~xsv>4 zcAUE{Ci9oakaIuBd{l?ab8X^zUrO>(TjXSQ=!;EtUPs#Rp^r1~lfJke;nu^v)hFMt zbb@)Sd-DZ6f9;X8DY3uZ!{eEI0PN>Z5c@q&xLnXbm>>E1lU1R<_Gd|)e*!t55&Agy z0pFi|7Ur$CI_cv&AScd1UXg$EdP2|GNu`jO6&u!ZKs_45&J@FKB`Ul`- zS)QlGB#2Ae5w0h4_C7dA_8dPCs$sl;7)Ih*FXYVn`21Otf7+KppW+m$vz|uIAA-6- z7T|GSABoS0+(GJs-pGlq5YO~3zCX7%)>)-VUg?9JY!3IuzIf7oK%26me^6hHK)d%v z&TfW0CJQC$s(lUHJjALqFt9&lu0LaJ|UMF`l&`cIZ#I zDse6~kDnvmBi4goPQd*QAl$ha=d|+y?cosmlG@E`>VM z-uwpI?*+pB2G9NKyj-7pMAXYzgWOQ$99_JhOBcG zayq^bh}TJZWe7YcuEvtM^b&GHgMXSY`FU4eV!hjj_~&Tk?8x|B-fG?-sTH6XbCEpf zI0iY{0_5qZll1u>$g?jHd1H|?-@>^SL-{)0KB(*DxM8Tzame`su^zmSbN>3BL7%ew z^T>@STy7Ek#cblwxBB0T1Wwh{$W1`bWPo+*$GH96URbBONb1j*k@G#{>(w6OuNU>h zN`ikj>x1%MA>8*6KSgG)Pw_DHoBBHG-(N+}E{1cfmh$r_)5rUV!X)m#M!0fdcio-O ziQMl{hp1M>?yn=Kr$HSl79{zLigjH^GKYQxIsaRnPgLjf^b?RDbjMdvFB1uO0P0Wu z={zo{L>#M@pCG2;yBdh>JMI*yBV-Q zQ;O8(?;@u<11AP@Khv9GUf>=k^N?xCi4kzW;!AUVio&sfSWV`i?;$5ozhs4nsL$!hiRa^Us;}|=nLlGcy^B02Hv>665$a^|C*PlJ3UeuWBbiHC!u0}qVjq_$ zZ;$ovIU;W+a^hjAC%oqUn5-4!{25YD%p%-CIJd@g&i9OU!A~SE%|_1s2>$7Y@VMkI z!?_g&$^Fs$$cgu$pVpQ5`O}|5KP_J(&t1$x&P;(mPSoLg@lV9Q;}_D$%_ZFXF-|6)=m?acQlOF|rU>&UtN5II{5&bv;&pB6B$6MvHPFb_HLOuRR| zg~t~g=U~1z@#~KW=VQOQgP&V_E!L5XNxwNCIoBNgOsz`t&r{&nW+m~n1;~kt&_B3O zlJxm7^eZ|$=^qvn?$y}GCEc&soghz?Cw<%^< zeEuT7fpyv~Bn~b?&VLf~=7uDFeh>YF&OrLqrO1g=aZcNT_l-Uq*xhSl_m7cNAAz6g zY}^heAI#_VgJh2K339dt%xP^q?w|e`#7~)<+#@VQ&d-5!WajX7x_H0o4v};8DRS~8 z*iXLB^ShW2aY@b~_FImeuN(ck8IQZl!#Ogwh+nTjPCg5qTFdP&uE*yKj*;g8S0d-1 z0e#xiysvVNK%RJ-oZD5%nV;i*)h@12@e;&qcQ?5o`wThxP<$@o5iU=~=kolw#D1S6 zX9mK(wphjOAZN!oc!E3!xEeXp6#Psb=Y6WtF)sZ@^5z=kWD4lT_u%J2c8~XJ^!|1& zayBRIPwn9A^aZ0n_mlm7LAb4P&M=hMyRvkgqZ}o#UvbEZh4KFV8uv531me8^ip-bR zA!i1~`RB`Ao_zrPMP4WCT#uZq2YM0Rlj=wv?^lixf7yVXs2l5vxjY}qTJib8`m3>? z_!2q28tkq!@bfOJfZcTm@;t*=$e9jsZdGn>2iZ2(t%bq_04J zaM{T`YddnTF!)*W`*iz>aQ?&rau2%$xy1WbhyK>z$LH53+oPf16whrzKl=tbHx~ML z_a(3EYzpj8HyMN6PUQ4QF%te%dr4`F$^P zb|=hf>2>8kCEgppMdpM1k<%CBIa<%{VCDhmexS%P;z_$zcz1? zdBI`iWbQcs%*6GnzJR(_-9-A8?~pUUM}2PN{-Sn6e`_|7yz)JA@_mSB=4w(sQ7y)^ z^klC819GZpyhk6-XqRQ*_d-gGR9pC^!WPewb; z<@=Kz!4Cc+vBOE^(&j{ZFRw#$LO9xjl`XI1lGt z_u=_S#rj%LB-|O~Oh$-jrV>97c00sRQ;?k7v&gx7VV(Y69$#ebSnn1j&xf2N+;s34 zKacyFx&h9+KTi6i-;h&p!+n6fpZ5XwBGeOd54jIGkDPlh&Oi6@do@=v-j7uybCe6n zi4%}ld?&t6dvCmlrT^~0Mdai$h}U8af9~ENhxv;=LE`l#!sSunyoe zH=d)*$eA{v7nSs!m47upPqdln)xkk91&_DQ1Jdf#Lp^r=G8TWUcaCb+2Ce5#96POd}g6oj`9XVA7o@@5G`1vzc zTZg>qmTpJx59Dm)8o>_gU2b<*1D=~vpKeC(PvlI?+7Pe%bNk8oT&!=t7rDQX)6J`c z-8b{|Zr%iWV$w$B{wCaLu)Fz;>r>ty&%;Gx_kWPHf5UlKrFg#e)#H3=KdE2BKVA$^ zI@*QMhs&u+_W_e(zAN*P{x4nnw8tY8|L$CjXVsGG@}>~alpubV9y#?F%p=9>r2N+h z=E8P2nMY{H?cmqM`+$-pk7Yv68Q|Peo^M50 zh-YFs$zwMVELsKfPX&Vyd`Ia1eUN6s&-4Cg3m z&Td4kdk#3%OAh4B7SM}7%Jt&k2fdgKB;VeMoNNGnhyOOI->d}npm>w?9XXM+eZkN4 z)0`8nA#b|l%I`TS~r}jhrVono%-h`a)4|)-2xnG+BaQv; zl&6#Gmwd4v+(6cu4>>U?&WTdEJo{1f>v)jL09(gM6OXFKO?)EKZHZ{l$N7 zJvI2M!SNn_AMeA(G`L3>xrQNEn8@oGpHqE<>r*a-{NUD+=TwUzCw~RItGjt#kvZTz z$eJYX-in-G2Y&67{=EtLc#NObh+h{)P8Ylh^fD=Fo+aRU4g2{B++Q)|R2i_lKhEQs z_%+@apBjgpL{5GMeX;n3?@wQdeet8D@3@U{r{n#~^L(8$<_9yH%;$?ECwjy=N`L;G zj$I!8vlp48lt51BiS_PvK1Y#5px!mj$(*4ia)+Mp*!Q0&^NG^PSr7f6 zS<26?o(9je`$tIscPHU0f<8rq?d&-L-v;dN$`O6ug`6k_&u6K(_+}uAAeVxitr6oyNxn`U|D8LjNSr8(oG$|VbDNUR!%cDi*^TV49CGGYs9Qxjo;S6P zbGMzOZY__TcX3{q^zT;7PVs)TGT|y9=Vrk?%P0MNS^8*{*PP7DD-!PKn5Vzs>y&%q zJmgst&nh9OeuR2ZEa!1oJp}g;VitM+r806NJH#_xD#?CTAWrxVB%W14&NYE}ZSp4N zhba)R?Iu#cR7Fnx7Uu;9`+*?#fP8ETCE2r}K zqB22$tMZe4TLU>g5%eN+ad~P*^y@_=zu$|T+7O?&In2+ki09p8ACQxciY4 zbwMxob-q9OLCk+sh`%W0+ z{q%##=^KEvP5JYRDl2fNG`Z)hhnzeHoT$qE#oZ2ik&}tM`pCH|P!F16++W07U_V)b z#Onsg*;Qb7@j=o#Y8bgU$a!c;xDIjd{R5vfsG?Ax*%UIbdk8t-`hga;6LqMbH1z3p z|4suE|88v05p7bxZgw|mmijq-JdB)e_Eg|h_1k+>PF;0@6BTiYr6HAc>q^{~zcMHuG~RC_FSeYVbCHZ6H&5#o#HwDfXxsk0?b}s_!?DPe4k09sI^az~VcnjnFcWZ)P zWM-nz=E$iI?*z`T5sZ_~PDT5@jq+L`r?(dfoXe2Hxz@b{XSbF>t|fBj%HY5WQ;2bX z{Pw`PCx#&RD01$jhXZHZmtvggIRm)gn-6P{0@rgLuJbYEOq1UN=O<=noS0P? z?7rq_A!kukDM?0 zbKrF0&Wy7^)d0Ky_5*T{Bj_JB+Kt>3$jSOD>`#wx%s6#*O5o%j z58(bf5N_1LAkR$e$~e)uLf}mEZ&02_&b@IwaH4cK#;L=#LEgm^$aO?c*T@ytX&=#y zGwpi>PW^oguJcLc{GB<#ezFte{9|1M=hNpyt`lirp)c>M~<%eD*G*%>+2 zYGRNlwpL}FdHiJH#Qs-MUKix-XE%iNpdT&9I5+%>;AiUUW#qadCnuZ^^5iRbF;3Qf zFmSTn56E>xP95wN37q@10iM6^$eF8;2hI=A&N%nSGLW~kE^<8x*XJ;t z_qTVm^KL#Y(>C=S=&Q?+dkQ&m^KXGuyFO){tz0W`>f9#edLm~>uL_)=yODA3OqRfj zdXtgsMYs*~0w>s}?0#~Cea=nqWM;8Xp?fHUn zZrvY&Q|Vtpt`Bmq_oBd=V;dN!5B?cA--2*`kyCwkz&dMu#yCHy2`zBG;dAw|dxLcD_!zCf!$uq@j!v6YfL~bB*?z{bg^Yzy;PCa=?;9P}|ksE}Z{(N=d(z}by$kb53E+h#%F-1P4m=j1A?#oc(?qm$xZ1*rD_!Jdr#oZn=6qs$f?hc2Tm?o#^kvLRbZVjtwU}&a=vqhM^odZ&2Jee z@;3!}2M;1Q0y!o2f?mvg#<@bb11PgsULNH86U9mg&hIDjdK7Y|#NUAv z*WTyyT0aEbg*_ev&xw~-TB!46{05iT#|j=ip2hI%1 z$?9Em^@YIsgL#pA7di7<<-qB8xjb#h!8#jD zAW!Dz{jIGxHpnxd--_IH+|+zztZQ^0+k9=Tb_xlL69rytJ5^r^2; z2%K$o8*;Oe^L?uYPENkg*6Du$&J{0#-1~&9kuPv|37;?7J+Myycn0L=AZLE71@hkH z`joG|3HzH=8o9ZItKSf~%73tR%6jhw&h06W+y}_nJmmxDt7Kt%QE!Y6oGeW2{vmSy z?Yjc!nkUIyJQTR8`BC0H)i|TO7Qq1O1ui|oKXt71<2{*;Mb-PKeuZ4h`{;r#IF}3=XPaK~C1l9OUWI z8JIjhxozOgwJW&JrO1gU(D&=gNqPF+#h{mb58ygKM$X*zcbHGOH3gYGf8(QpQ=`wK zyiW*s!+Dr%)a80H6JTEFo+tC&Wyrau#{*}tW@Pf*+s(q8SEfO{m9O(`txE~r!G1i^|=x`^&RAQ z^W0v}J$ysR?`|x~@2ij#i$I>8#Op|#9^{#}MBZn}sc#`p*slCMh#8O{M0%1}K1a?y zb~5Zw9Qd5A)3mD|)@c{7$NjA)+{$kQr+iZ1|6S}?7A!(;4RRvO#lYFoe9qufRFG#5 zZ$WM?a<2XvSZCM8Y@KpU4Or)@uaNr!Ia%=M!1*J*Uy;Mg0r%GD$T{S6qpN{)ug_%i z^sqXC(Fr|JlE6uXU_hfT<-V_%2;=^s93>y+*CgS--Cf18mLv%t^P`Pqzfe}Ns$ z>sxVuUn8ey?+fze%_|tEs+I(OKC~3MEy($i(5I>*ybo8k^8`*bCVlEw4LcAVF<}cfjb4%iUa5FzgYCxeNPY(PD*SQ@zJz;f_XV!hf_UCuQywx=zeen+D z#JnM=FdA2HtC&2U2l9iu@HTRLk(0$g2%PB5pX;ht}{S}>%UTuQh3FK^h@H2A@kMm}* z0Q==7es&T$S>vT3Pi^G)cxnge#jNU%@=hUVhR6E43b(tNeh&0`Za8wMk<%669?vx9 zdQlglp4a(_9nK)9vM&XFcH;Gf+Yk3~vOKxZIZL<};McYauLu2=3Sft+#IMgGCrkYj zf9(5L-FAaHt;qfY>g7Cgwi4XOxh)?uPX2Uvkmv9E z47m%)`KM!?xQm~6{~W~6#J`V@+(qPccDNVy_b1t*7Tj}*J6}fb5^|z5*ulNZ>mF0& z?_hV?me}Dk;j%)07ae&$Aum9F7X?Xvzk-}U3iD6ji~FZ9wk60Dsw1xRDsp<->%o3z zT9SYE{~>TrkaK$tIX@EWax;zB6aJf9!0zvpy7fA8YI>{(i>+by^S?knC`*%g_B-KT zn*x4zm7iO==_u&)@(|qLAIQ1tH-!5t|HD)!Pw#6M)@dL94!J*(lTD#6_qXx-)Bb%k zaGU2K_ZM<1!)LJ0<=oG7JE(hP{wc`)jhwFzb);Lv`#AmR@vuKNiqw(+AZKcWJb9JZ z6ZR{(Zxp$RJdq(ho|pPR`|Pt|_uDr!y~s^pg1>ZVkLyf_oM{eqt7)1PUot^nk+VtN znjSe-_DGN?j?ZQC)Degi_WT-@mjOBb0_06SjGrUf6XK`)hUCqRgj4&2JpXV~oOn8( z_ZlCgyiCZMl2Et0%1L?iBe=IvPm{X!2IO>|4Y0rLoYMmGn65_Zy3EM=RRe&V%}b;B|o%uuj{S^qX0clR7Kh-|k7ezdh74__aFx1Io)rxC@!# z9Ic){ssMmIm_>TW!ZR7`7+7xIgryOJ`H-&J^B5#=??w0Tm2@= zyAe5m<~-=L1fNgnui&{0S!@e(IgyiP;5i+all!&4DO1>=X-A&Z$wj#IF>n5zq|bSf zH+>tDH*+KBt3dp;y?7nzGeF<(-z4!f4|3|gzry~E;d$B=Z3uB`BDvSji=3Mn=Tgt} zK0x-lANJRU%nNQpPVR%g!%1G3tDE2)>8+#>xEVS90QB$rbDkgUYlVY6cb@cP`H+)^ zP1v8`$>Wk)3iqk%d?&PDe&p0;s9WXA-P}KK{}$qBx^BqbLO9b5M~WHIFIT^j?ZR`Y!8H{}hS-aQNDN#tai z*!S<}=TBUQcx`LUMD8}^)P&6-??Eol-kUGT^XW-lP#ih;HQ3Mh;rr9qVJ_td5&M-u z&KH5_-OTqhxSy4*7v#AnyK$W*kyF$1gm|rs@p$cSYY*J)W5|^v+?h*YhsUNfdD23h zP`_k#pOi5Acil zI{nc&r@fKPz3)U$UH%dFH#4aY=^6Wnill$I3pqXi`@q?@@38&J?Y9Sg%8q0%d^d8k zQNCaY@#t%ebI(5!IQ#nv)aO0Oxy9FmJW-FYQ!jWhaQe_r+n3SJ;cw0 zEDiH(dGpuEl}AoZPY?Rs!t=Yo(ggha*8|8^AYAEKN8ZQ#ID7iH5O;+pb!0{4ToJgJ zabNTJX~x3+k(^5IWhx;jzB>W_Qjy0OwLCsw_W`+gs*IeT0`-}z`X2Kau?*@nU-?Je zUlqa)fVrvseIw({E%3ae$V}#@RgrUDPQyCyUBEbhx*GWBr7uxlHRSx|wqW-oKQT@} zJwNEvb*O_}b>v)1ystXX^O$Q0eSrCloZA|N%a|$HUA0O&@5P{x({~?6dG{hG7ycU7 zDX#E2re6m0IzQDRcOP{Qld<^Vo`tiP4d;xv9t3~YhAaZv1`(d5FJ)bkEHW$EtzmCFn)s^-(Ubke%KZ)`h5bngHz_~Mgo$gnd^QoW7 zbN3CA6Z2qyx_(mMcpUa8W{~|ogq)i(G|1DLw=ln!J+}tE*u!ML^e}RILgT=h3I`Zx zvrY?~`LGJ^uMu)$FU(EF1-?$77viYI=h_A>zW|vzJj@% z-O29}^iwcrw*?5-lyFm^PBxvA;zSd8u2L>1b#gQ0L@Agrsg*oG_+R3D=~YrkK7yP) z4|7E^obS&akM|^Rk-1`X$+PxKWW;@hprZLacuIrs) zf8zWK?UR zASW}751h-qj>Q*c!G1pd(PxTHSS){C#_Dn;T;C*nSO?y@I5 zuP;7(4)xLnIX@WkgXzrg1Ju}<|N4^r(3NoMfO8vo9Mlr-H*IOcbwf_hf;eHW@H{QA z#QAlrDY(w=$fa#C$B4uq@r*6hMzY6LlU4hI)o)7h(_T060;zWgA}9JoyjC-k?*B%^ zJ(vE9#Op!G`Df1tc`}8^Pix}ygUdIf9iBx_PmT9~nYbOy((j=@bN!GTjGW9G@6lWH zb89n09pV>|I%EiPdic(;PUCpLB4)tr+~lSCDDOGseAaou{lxE!<+;mn{^pED?s?=? z%k6=4b$H%X*WtM!mz(&@3&_cPW4%;8DL<5l`KNxI*nKE+=02F~>)Uv|7K`Ct)YT?) z{b9)IRnUj~`8==K_u`yEkU7JP$k`(BoPe9e>la-aUSDNbc0s)u?ay|Hq{*?I32;|h0&>z_kdEY3S!#$81NBX0Y$cY=E|5MYF z^y%R_9o2;Nf1{Ao_dtHvm$;v)$#LGgn&kJFkaOFh9+cnnzQf+JH~5)5Lh8ZM$l2G% z!uiX|{nKdZ`|Y8gsLwITnZ1yw&3tYLHvsaqyzP19#veGR!+kkb_*Z@LvbnO^jD=%;-i(oesNoY@8UN4h1C z*T%y+(%s4Z(QC-*b#VT~7u;Xu)o;KK&vnIhzK)z92>zm`@N?v*gP)mM#9!V(PR)Y( zlFP^Ya5D@1%zR1aOA`tA&@(}wb{~%u^5R;EpF3OOI^RUje$y1}aOe=zr?S)GJUm|w zxwnur>EL+=^(H^>@+v&fAkqy*ZW3~KK#w3#U-*W}b2>iPHKQqVZzCu5Td>Z>T%Nf1 zC)i)}LC8%;&h39CaI!zwi!J|M;C#`($W1{`H{TmLpPu)BVq?4)Ek@?-?;s~DLfz_G z@j6*dg877bfYhy1k@G{~Jjgx#{Q3UzJbXyb!@J1o9^Zv^nwGqev(=z4HY1kf{-z=4 zzJ@;Bj7@qT-ms`+g@l=XXI}X9|(~{^`isZ=jzRSNQ(Kp*U|HP2%+o zgEFiT9CnugB-53h+2+ z{($G}%%~wKZw_*{(Jx_tx+3=%-MSpiiDs=vZZ2|uI6QBoM)La{-LC}n#VtNY?gQjZ zivvNPyu#~iyR;O@TTi$Tk@HJnK4CH?`Ac(neU7wbJ~0nD`OKm--sH1<3i~a33z_@p}^U z7u>6v&$c7C5IM6L?)%ji{v47y1N&27lKcKe$cZKKp7|)(OXAIMbZNYw@96@dga(499pcg%5I^$fiI)O8#zd>#(a_R)cLH82xZ{=}#uF`BI zaqwg0T&E*Jo~q6FCmxD^eQpiP`vf^z2JSayE#5cU%y17Y`;v7oL(bGc737Hvyzkfh z;QmkN`x51Sik!^?=TDtavfov>9}{nq^S2y1eHr>&)0_L5I1K%*T1WcZ70B5wkdNed zyw6eVA+M;9NIqJLoPHC|pYOuYgIxpnGGY@sf2)vl55WG!&?J5Kf&Cf!yzpnp*-T(R zae>><-3Ip4uMzuwj+{RD7x>xZ?=wFWA2$qsCTH$JJFG^|&dvh&jpb)>?)Mgf({0If za%+&&>7eg0mOszuUXOiFX_Cj*BB!JZ>on=kFnO~28&JPoy%X2@1#+_Wqk)snJ>&fS za{{Lxs)?LKPECcnKuKE7vA=vVCemr&kU$cc&Y zoWCsh1JkE^73RsNBzew%BXa8Qhl3r|vXhLHqo)NsnDZ4;-X`Si(OB>P$k*u_uLADN zrpRqZPMmKR4cxv!B^8LGfKUpdRxsoIVKe<^$$a$5-ZMybHLCFdBY zhYkXH?eZeG6*+nG$MAY)wTZu9f;w9qQlV zIg!mt-mmKba^iKUL+r4FY=80@n6vAtWR7wWIY06BAkRJVJ>&ct=u@rehwD5j>+d{WqSYACNP(_duM`*BK{XEg3jhXbW;bBB!_S1@18(Cv25F z0%sa;Mb0B<8*K=jKE>a6#J-s?aC+)z$o+(zc5H4Pk%k5&r%}`kkVIv(J(Jogke2KFE`^v$6NraLvnsyvND@P7?0TwXnZp zxfmxF<_VlDvmDoX3OP{<^eGD_*&$uo!1)^}9{dKmGsxMK zUj|OBSKSggdF6BD&LXFtI21S|xZO=}kS7xVKE*>zSYHynY0)5(#xnA^c@K4o-*zW>zA``sdw&=;v zt-QYw$UCqGT!5 zzD9Z1kdqgVgmwD0{C#mm6^KiEEs0Cl36}xRpE=3(qBFwzb8iv*{f?Z=4(HvC=k=Lf z3-QdQrb5>?qStay^+g`oLvp~o2oSLH_e&-!LLP2a=)1kIlmA3O>zA^ zI}c)ge4qSF`$-b_dq&be7)~ka-VY} za^k(a1E-cHt+NumFPz;vZRM!npMoc}u=_*n`+ zf3{BZAWvVtfLu}JRMwv%Pv`iP+0PV>bE2mEkSm6qoIMKUJ?X4TYj%CH!KS4RK4q={cc0f&)px^X|MA>N4*R6lB~EL*I67n^BdH=CL^y~&8wgn z|17C@OCTq25@DTcEU!ORffnGOmT)DJ^Amvcleu5}V$lv2&*M5vA*UC?c{hW3|KM8Q z5B}1UocG(2Gb7;K+G4zqlVzI(&b>g+?H$OupRC3#o)vvV!I}Esn>nx3&KJx(B zZyon*`_kK>mq$|2es?0L`d1B{yUhJloQ3zNwX!&JcOmEZ76EPrKM!Vh?;uaN&W7CG z$ho1}f$Q-*^J`n>@xa+jzvKSyA>1z|180U^*dy3gleK-)-*^|u6D-&+s|KjSr<9M#V$A8Jr zDzf8}O}2<|vy#16#u-yx-Tl5jao31M&{xKCTitpZgM%C*sb8{p~G`{!#)ta}(mA zNXI!>WL@CoY7z%aBBx3x5At-pm^_`NC9JdG3)JVk$oX=RkK8`)*ZMN#BQr1!uCo+! zrtZC92RSk(KdgiAu!`xVo+wSY9T3I=)R z=pE*t`Y+&AQ^Hk1PE0EuIG2t4i)t`F$P-(0pubc^&K8IJb?$rqoJ7umK1c2$eNH9h z)Y5F=*HyXQedk_5o?k`ktjfsw_iMub6rUFuF+XtjyLWJ%A0VfCL*F60@xDW}g1*BP zB7H{{Q&mB86qWSupTGgs3F zdAsWF5H2>F4t}D0W13WIl3_x)yS}G2F8f6Am)Hm`|<-d2TAX zXH^?Hm3AW7@9h(e)3YytzuY4C4(lMN8+;HrIp7b*sn=%&PK_>v`>TtbFZxyBOqFAd zb4UIPocU}pa`lkYJ*&bxbMoiD{v&v$@U=TmY|=Og4?X1K>G-{sCw|dQ8R=cpxeGF@c%`~1CfzZK;9zC@lz z&JTe1Xyu-tnLM{2z5{6%Ohh}hLe72(@mloZeZO0EB-lY-B=2RlM$UDI`^okOpVxT_ z^DN(^H_B^+oZ7fK*uk~q@mimU=hHIT4CLA(=h8v`Q+s({acLp{=@}$mw?ocf1%2A5 zJnl*h?iV8$`m`PY4tnufNqp&moPG%NNMH09#<}%To^DCj`7v_- zy#+xp`YJyUvf3Sxm!G_M))6`TE}UE6?;Mx6`%&OrS8{GUA!kZ_5AsIw`04jY_XPTp zdjg#aS8FoZuOH7VIu*QsYuofh`*lH1--EoOdUE@zZIGwUkK{abMNVXgdkHcww}VO! zb8l6E+)MZbId`Zj?5_Zi^YV|?;MX(W$8~liTv^Burf^Js_6g(%zo`gv-H}sgiU)b> zOI~NGl+cI!=@PjfgzFy7TbJ?r+D?J|?yJW`?o;HnYZv6%tTFXjt4-i9i>e{l6FHkV zJ#g*#{6&oK0{*g>)Dyjs)BRx%ZT{r*0=E(7(54!hyY)uSb^Z|KUF7w(T{au!H6r@# zgPg3}C~)!spDT(uD*`8`mBsz_Mb7^VoO;UV*KYC3z^P}1>xY~jTLhhL+(orKu*qwdo056y2qv87uIR7H9>g;kyE)whIRT~ z{QQX((R(%*+aos!IUCi3CgyhvW&h(rp6*2I!NJIxLXh8mCLTZ4{OG-jvn0O{K~5fO z4C{Q2_ebts_@0iuQV!QS6gi!1Vc^UNUPsyv_rQL4NgOoD*>0Z&PNe7g)@_gK`S-~^ z$6?61K2Vpdw|Kl3so*;O;Hv%~^bR+DqCXe%~ zIDD7U*PephNaS438G+M%dH-i~KwWNAllN#pLr#@!3+vQ8PS|}LL7#Q1qP$Uro6`-} z+2A7EpDgVHC-&7xZZvXc>af6xYUdf}-UB~V``RKm208r_`Z2ZO4C7=z$eV64c@OM! zC53XelmlN)-nH3~Ij73h)hWoT~8`q~w{y6NFUUhN6zGp^6mG0e`?9D_J0w=_j=GIIWIrNF6!Jids8FduXU3nDiKIn{Jg;A}76=eTwdUvvZV-L$F5 z*_@qVosVPe_iRhhr`$*SziG&swD8q+!77dgMbVc={M zUWfQ8%Ykb}>VmJ3bGf59SdI5b_V~`gnQ|l!&O^?Rg*lOH$?I};5atthFUc$OkyCS_ zo=}e0FRC`^MKmMzghkGEhkjF4=XJ6>4gIEneE{ln0djUjy`UG>G-mGR7l-}Hfu)gK zh@751KX76_kDv11?ZBytLy=pAoEZ=2-DbPY>@HqLbn=VTY;P%4tdk9;(q39Mg3-Bk~dc(C&owd zEH#fWZhEAb=R_~7kTYjse`+3&gKC`*`?KxI{#GMr$MuK(mFM-6odb3k*PG-1)*vVT zDjPUglRw`yYrhQg%(2|aeT|%+9O-2Tw}UtdaY+^@dRdE{JO_QMUd;PqF%|k$zlHRv z>yUHn+k|!6M!a6~8@~mAx$+^db3JmZOB9zh?{j34C@x(ngWNaB`Gl2&y#KxD$j*^m zHVgdhD0yD|EplQM_?Z~P@6U_nI|3&*5kK32oa{Uf_NQa&kjdu)XZv@>b#6q?W`g?D zbm4K{u7>*47bErOcgV@>J;Cn1c$|<0_kg_GjZxkvFZmAx`*R zBu?x=&eZH2_9svA_#%3513z0*8}+giIlBz%WM42w-ffs`=r2f}?2t44pr4lS^8Kl_ z@Lq`cgY?rs5H2p{cmIsnpDs7_IX*MV@4E<>3hba_>H<*=?4U*veg24?{TS+bb%dWI zaUbe=f2b$wWjAvA!q}isF_3fW-?KrVE-^WOdyw4uHLe4b# z81(W9m*>+(?;B>QiQEC?Y$7tQR zoSFlinj5pf2j7SN$-0C)gq(gg%D3@&|E_?aUEePdov_#6;FeF73c zk07T@ZwmU9*SVjWx1;Y*%$thxjv^=XL;llExnA7fXwFcZrw&+KrH ze08;5_eA!?$eLJugCCuLZpRza;{)`+}}y$>`%=C=WFu* zL5u^=kE?*(DdfCM7&tX30lOEZOMVpWuIfF;dl09Q6FYMUPPXLFeNE+offJ8Yqr6{` z(-q)8gFO0x)deoy@W9!v8Ie1KoPReXtTRc>`zzmf1A#i z;QZ*s$ekrzi5!8mAH>YrSM~{<$xQAOokLF5g!#2e5_7Ml65LyJN67s8JmJQbfOW=y z!t|ow8XM$^g5;juZ^+qHkXKam7(eR_dBt=hdF2A(Ze$Dc{Dy~2o|@DPq-J9GW^vKy1DFdg@@Osc?Z4GvK_AmO`CFJBYm>0;{c)b?p^ZFK< z7yOQ#UJ&)~Ie2_giJ?z5tx5fI89CXq9q8papHKMgkl)p7|9XXKz@~!wV#y@8qgLNh$`Su2K<}UO{ zsyJV#y9IrtY1zaAwLFl=nAs z{+GUibKmiPQy<+QII-s=vJg8wK~C+2 zI!hMi=iR-8dcq7ObB%up_XhOgvS>^m>jQIWTcjZR%TvP5hv)q^37<3QUUy-CgNGsa z3_14z<^|$BuS4W$=o?izGRJ&QxD!x++D9?<*#xLt%^Rfte1V*~4e?rB;qt^Yh}Zs0 z60iS7PN$3J4EZ=GD?wkS57)*0y+qD7DHiOfzUB8gWuni6UhJ{V$cgy>|NTEz6P{1o zV=-~Q%aFkN#N_#O9OQHch=YE3jDJ2o6F5`kW0V&cx&QkT9aZqQ1=Lo(6c|M&8InfO60s3~l zK6BHc4pF%Z;W`r|=f2TFo}Ln8hsH2>Qzt7UmjpSxFnTVUp4;7h0?*^rXfl_26*-mR zogmMoPF&A za>D%#^J|lg`-`0h^K17wKgvsioUQ`(wYR)qu^RdSahlZEDUoxJBReRrPqP}HSE*tj zpuE=zSLJ=sOaGYo*>g(Ji#U=Oxz~{s#o-*;)V$9TCB}h&&Lih26>@e({~*u)$m=Cn z_2QqYS`Pv-3D zkyD3YZfeW%`Gjk-K5(urIe!_D6VsqBunqY0Ds>s^WRZ;2bs3Q}(Y znDn=qkQ3cYgP--_{-XDc2Y>mA=<`kF^m{NbuseC)G{e@w{$9S1cF2sJZwqyu`1d&T z7j@x$;Cx#$x5`nPykr{B!)k(qZPzKBI6zDVTU1vu{_)?Yq@^DeTG=T%veGpFF5 zocS;&E@h4Ow}|X78*=KNh4aviKkrxFpnh>b7}U#K$oc&h181J|^KRdq0s6d7)|nkS zlLzL|Y6t(GoZkuaWZAeD%FBVAGi8E2{dbK0ZcPAr^K&7W6FJ!<^6OW)Ukkqu?3bMQ zbuQ#w{6?Uc-#DiWL49UNkoqh)a;6!~mt--%Khp^2OEzZ(TxTBSY)^xu>4NzV_Z(Z*y$mK^)o`U_EZG0Z$ z@`65X1G2xjk#i~GdAQxf`(nRhVX%X(R0`!4K+Y^E8SEhQ@^y+VP)E8iNPZ}YoSP2) zk^7qaiyH&-~w*%-|{*{ywfS@MK2|Jx+rq~YMQ{gjr_i;OW7Ip>BxQ4V#w(m17Mvg`TRv)IT$!G zum#$oICB5{y~x1HRlF|udpiWqOn3#kcaSs5UJIPr$M31Cxora{55B~CRte zrI>l-;Q_GDJ)~|eiJaIC`!f^x99kDX1naC$_V+Gw_C*HJOAX$ysFN^{6kQVG{z?(< zX`#TWt=diI*Y6=Gj>A0%!Y`T6^)^2_Mn z(^3*A6moV9#9cRwpFeRA?hDHsB<{YCoM{IArpU_Yt@h05us@$W3+}Hxa_%$Gr_0Iv z6`5^W;6xIl&kD%t0WjCFvG19F1Np(z=e`w@a~;7Brf|%A?6p1(@??Kvhf2uFa?n5c zO1!S~vu6d)l_UK_W#rsm$Sd**&kz38^uX!lg!=$FodEK!Ss(LVfT)f%8*`#Qs~{&c zABs-vca&wgycQi50SI^^9RnXddcc6d1Pqd?0wSrS4GahnhX5e@%Sn4_5-eC zDqLqZ5HU3tB#z09_e!zKkw=fu!DL;@^lU4R3_M;e!=Crt}xe- zucpIw)hpfg?uO?bb{2V`xh`^Y3!Gb*^B& zTX?_N7U1=#>+x#Pi=MX;^Gbc>#P!92)2m|Y^4|9w|Mwj^)5s#%06BBxPTc@Me~a-!|bAWyuNpFMAs-`xu9bR(zWIvXSB z$7~3kJO2*j?9gN&uik3pnjq&UM|w$6nsIv03y}BVE99CYr&huDSlsS&Cd6 zc|k7~4kFhUIoamhAkTb}itSG?OAt6wgZO7V9pI5#lnyCY{W0ax$~9mz1a!5kCBt}8U;=a ze~Yct%wHKeRjDa*9g(x0CIn8cd_hpDi?+cEMI{~0(LXA*KV?( zkofWma<=Nyz}e-z4=}49!8)Iic-;*--6Hz_?iKzWNVgK=i@!>~+uI#EmGrA1&kg5# z(FdcrYi8p9dLXAKLj1IE@_Z}adjNWwGatE6k@K^c1bJp6uP4M_;Pia5zn;jc&2Vn* z7+x=#C|>IW zzXSP5Cg%5FbftLVyxVqPqrASzsjQ!Y-Tz9-&XNB1bXccrGy=JP$cZlp0ryD^xAIWn z^s0f#^+!(68Xq{hoA){51e^z-o}9k{$Vo8}s`NpU(kzN&>z=10y(n-?5>jzW_7C? z4f(+x8;aaW!sSU&H+-+S$l5MH{Qsw(Lf8#&Hu~@XtKD5{hQI&s?><9Lw#*kez2|Gj z`S=wBC+>cY+$iM4=C?uK)ai^9UzQJ?skQ;R(S%FV2)O){8RtLf5;%YK1#)AMlc_2L zw|_R{RE1_Buk1zSK1a@-ECKsVzKU@+Pwl|D6nl^xi=1g#D{yuMmuLI7gLO8)jodio zTFureM%Rb^WjKi3c*4#9AaH6YU#Gg!Ja9VO z@5oI+PEDyDIQi!cCQp6YK5!z(J>(`LXR1{P?x!(~b6Z*h*YYNEUn2Lv*SP}cri^5q zsL>&C^6EdxO+wD@FAw`$W*BEifL_F&v&c;*+)-GknKhJga!w17H{vhkrXVM#fIMAd z0^{6}hQN*g6}hR%sRva+FH62)oH`EvDU$q&+%&@F2hOdX!Z`mr*w5s>jNEkO#Pa+> zo*grdaU$>gu+DPdAU6X!b@x43=YYA4(|76y&bY(K%|y=4%N96Md=BR>l?a@#WRaVN zoV}MdaK6R@#@XB@11FBoLvA*5rfi|Wshcwyr#e)Eb&mcXxjD#*k+}nBU*r2zSIPuV zez_94xyZR$pci$8`=@$Y6V^HL2y$N$Zh4&`Pxs<}X7;uToU8T_xp~OxwBVm=-9WZK zR|@QI8<2A}A30y9Nswpq^<$jM)hWo+r{mW8|8d?TC*wdolMlE&T}%hgjK72HT!5Tw zS24(Q$N71%W21O>?L2Y|ku(2*JfEmPU*~3!r#qAQvIseKuvCyI?|#NO9lr+HAvcMG zi;>gA;T)OY`TlIzCa}Ldf8aWoAZI=)3;WACgvk@bYJt4?BwjB?PX7jRUJdKZIDZg0 zb@UX6b4)$|v2)7D3F$(lz7msF~O$qTuRy&FERwHNA6%YH< z)A;_(UzNi;?N2`-w+1k?5M9QrNl^IPQf3W&SD@+c-x9R~jtO|~Gnfp7_!ze&R>H# z;al-IZ@z;3t`l#^b#5ixkvw64vI^IWTu}<-O(*sIHp0!4z+LBdu=@)IP9!Jm+>V@l z2ysH_u55p5CDd8|G>P*&kh3bPKTm(mIC&QGpUZO}_qUU9nIJ#-Ed2bLbXCJTRpXzL zbI9rY;Aid;&krgw*iX;gh};jz*@E~J{`_D|FAgd zv&IsX_akyLb%DUC;`|(`g0N11n5=U*a{5jY;O6uAX&*ozr{CX!^7bI-E4>vsUH@~o zPTd~XDa$WJZZC4S0r;m`%FmIX81;W+wj#F=Iguq@kSAO5e#I@%AM_$ejz?}ka;ic+ zSZ9l6Y@Mc9hQPUD9g+J9IeYzPc>ZNF^S(+wjsteMS_ZiT$ce(&0;jgFXY%~Xe;fW+ zU-`@LAonwJatW+cef=Hd%$sipdHM^o&V$IAGv|Ul*JUr`7x(q|^FmkF~G%u()pK;=Bq|Yv0kvoE%+X$R%IFE6@ZARGNluwX5ikz5y zH|)>O=k^nCBn+JGR0X+X$eB9-09R)vlP5-}04{Mutz>0w)F#?gVnW zx(NF8oj7NkzY#cJtqsaMiJTbxYT(p3-cQ?-Spw&3^hNFza^}Y?VSgs;BDPN3?^&?B zTwNTw)5!U9k-x1r&|q1?pNe|C+NrY8lL~$kC2a4$yLamMNV9a^6d)F`A^G%9jcRjdya6e-VN)t zb$A@~ZJ|EX?kALY9y$ACvA~HqoD+kiIA5OBpT80Aat+|h@O*1p!#u>Ny@m2FAm>IT z3!J{miu@zYFZ(yUj*< zmymPW*9HGnS$8werr96-%v?NHA_#R^7*GtTNwPh)CAPa734&c z$Unc~a}8Mu@}E=dkh@B_E-8aN-;2jHry*YJ^FxrkhMd|6^D|p!BGZdW*)gnBeMIJG z*O9Zo4-WENHD1sAtCPTAW|zl#!42fhxIuw4=Xt#=KKwFpVyZ&!CURP~1$hs6-J?(S z37n1l26BHP=aY2~oEpOWa8q<};C!#F$o);Y-M*K)baUwux((Sx=9?|-#^ItV^H;{gW7HvV0sko8~dD z^CfbwOS8baYkQzZEBX;=79t%%O8n8W0`58ym_l?Ayt$m#fS{&cCi zj1$cvo~e9Ek&BC*IuHG=D#`0)^=b#$-GtwuVeD`b(l}+nWS$_jGQ`O0sO^qJJ=J^ zT(}f@ev||`{U`KQa{VSI&&yIR z=*R3wA0hWTa`t%gus`!Yx1XIF)maq>B9{s|nGx!8RfYSR9hE(-Q#I*_Tx#U>?)zb# zzTQ^8&Pqu^pFdYc?hWLW&l}e16Z3jd2*^je&Kl&>5N^lIAWyupfy*0|HpmlWHF9Z@ zbLTRFUJCO5PbMk`T<@vKr9;kFfWA>3=JyNK2hcal%AJr)kDM5g6#T4m%z4NO`ZO6x zUz`Cs86VDr{hQBGPlGZS*U%v)idHZAYN zRhoALrz=?G-bBtd|0BpVXSkn<8_>U-`2~^7jGUbp%^BX~eTV7+_H#8`BbNm^(+)CPR4zkTb2|JqBHve-BVMfcJXL7W)1la>~H_9da@M zE{-Y-?`4@>Sy5ggM$Q(gANJ=eay!VR@Ld3#gnZ|u z2y$X~yujHt{5y%VLA{_CzmfbtdQrj+dpmIYqnP)0$HMz@wi)>zL^0%i{ZfH5uW-HS zeDFS{{G7Z$T^u=mu|04{_A!6)1K@p8xuFK?^Bv^Oa(JIa)aLJfiW)7#{$v~a{vUFF zB>0Q|;XIS)>U9NfBk`A#$jM&to~bFx^az0pNz5y7ZcW?x$i0W0yIwfhLFVM& z1+a7BJt_5SX5`8sXMRr%{<1mdy}=7jL7xZz!TXA3krNea2F{n``V=MLcT`ME^1X|4 z$oblXVVy0HF#G8>KL>fbb}L+`LQXx7?r~1$?_Hbx?*q5}Ph98w$oawR;lAlw{yi48 zPXtb+AooqnBd7ayfcu{h3$uH%qT6=3|M{c?%Bz5!*m(l-N~VsCQ+M*iecDaCv5%`r zxE%0ZIMcTY{sQvj;@cUd{wxB*M6Yj|mffKjVb9oyx z1d2Yd8)2Q@xnG;q(eEnMn1WmlVsU-Yfx*q;(kmt_Bebn%; zHAK#hOakkCk%zA{OXtApVdrswjgT|Hz8N@qgP&VhWl-Q$>RZS)M$Y}%0pz`1gvrxy z9}JvqTM+t${}$SWa2-LP_9TCA&{dokID6+`l-CqFb;<;J?$^3Zo~d~g_LsO4%KHd8 zn}1{A{CRG7)hEYC|M`)=^eJ-9kn`hC22LcLz~z;%8aQ8k0&>lf)06H6&eq<{ID5HU z;9Sz>$hAOD4!IaOIb$W`+_I*y&fjMs*Ah9uY+^|0l3D;S!DsoV~^0x0l~u4xE2b z1Ld_rPJc5baPAzBXFkcvz}cSBdjS8*Ym1yLI~?T2{tjd7)3CqI(LDV>t{rl&X~V$D z*;`l~^rMymw=lZ*{2$jIIo+aK;QXZtoLdTht&(KG{dGW2P5T?-((Fx)vk%`3^2C|F zcpg3`+~FO8GbLiacNq7Lz^P{>&UYl-5Ank~)%OdSJo8uG!1*k{p}bDWxpJ?9UVh!m zIGw+F;M~MZ$aO}}?#~@KJ#rr7?BPCvvmKu!*9AFuJ|nDi-$urXYF$BIALwKMGlS@g zoG+UJarSrIUr*#*?ews}mofUB3wg!oBl_%xoP6_jSmy)I$)at8JbCmg%Il4s zC<*qHtGS=~qhLRqfz$i_O_Oj^Q?L{6Sf0rIMHzczJS1$pw$pD6D$z<>&c2#i};2zaiwjk0#vv$w1x*j?2rY1E;%RMtNh9^FM$c^v)Q4Rsp@JPlz2p zN6vl+=TCL|fyr~PbOgOzA?I%_a`KN%u+HQ>?&_s*-feBT&lvn=9CEr%blx5JYgrQf zS}q~-zCg|nOdaIezC3=KDc~>aHnID7Fx6OmJEfRj&R_SYZIpZbdU*_X)aAEG?1cz##qAfEZJNL-qPoO986c!S3m zu?F%N9&x<*QxHcfOGVM)Pu8-^S>nse*M4toyqeFA+PxAr2d?Z zoV=GPaOT(v#;HW07yl;d`{y8McR+m67r1|lsZo5nNaD*}NaDKxD)aN|pYz>g73hie5bAL1qoJd2s`N+u(al<;*Nxn|i zreWY*`Cn0;!TPkP}(%gTEZ$&t+WEDuGj5e@1R0a{l|< zfm3byeAjfS6gatiKXQwZv$O67PG9BENmS(zVV(I8Ah#GfIq5;*D+2F};#--*%*findNw+uNk7WzMtfj_S@O+N_o zL@Uz&El19mcogK>q0Q?XX9_*! z{xY-ztn=y*C~p;VCg17KAZIRv zpE)0+mtQ0Q976W@HFEANSf?Ayzn^SVK^$}yj^O^*BB#^*1^cTJ!R>IdL80wVTX;cR)?7ALRKuBwl|*xOfl4I#qoBopt%LI&kli z^YATlHsL>kGl}_gQ8^OqXO17m{cS)_zJUG7d@<+!SPhVugm4>?vu}f6n~R)tMPZ%d zbK)=GAty3C3G4K8&T;>I74+#|C-OES=j+9TyfU2YQ>1}7FaAA?`}-a_)dlR}lX1=^ z1v}_}P9nFNaAjeCYVTRT&g`HUJD%)s3v%)l$dfU@$L@B3JXMa^eJkN+J`H-Y)A(~d zU9V2CgJ^vm*SQTjdp^oXMR=T-^B_-)<|H3&M^1N%@7dINB$T&4?B?){$<#o{)XGZtcLvOGm(D9A!p0OJVaOhh07}m^`Iz1>Z~7- zGv7eo)GPV(VxOX3*q{4|#HC$?TNLf@4nOa9ROFwQ?C(e9{Ja}rzoRkF?ejz2mH+;T z_S=n|*bC>@*Wmk;#o^rQy~mK-gPg7W3Y>?2{QQ}XO~7BOlJl?^IrApeSuzjLw`wxz z)4w44+=ra31^#KC^X~*}3;Ogg$U65UXG(xPTabSb-o=CdUBoAK-A~B5WN_Yn&lv6- zsI%0cWPb;c6X&9NNPHe&{P?KPxk=_BKNIdPu!C#N_a_@fdF2j?O9zqD??JpakGY?z z6;ZriOXBq*wtS?Qp`?yHLbyHmAnw-X z^@}?P>r_d}c|VGr+XMBPOULU1Hw4bRJaQ2I^B8h=YZN~Z@VU2)>K^w!iJ!-jGhh81 z)@d^H{rNW_PRLhIqr4NyskcEdA_G51ZhxfDcZgn2B4<8=`o+H$6PMD0U+dRMemI4k zDhl&?bC$GfbgKaH&O7s9=O_-SI}bq?5{iAVh9 z408H+I1lm*?q{|woCkY^oQGeLGtD4=+EM&Gh~|*T#CIegokdO!kLtlm+Ebjk;(yaSLPx9 zc^x@XJkpEdoSOuCaq)>>ZXl;8!Q4i~{C=E!2j)E{JDG>vM9$2L;^#45_m~$^TpC5< z(qG8A8z9eJ;r^mOgn5r{K;-?6oLv^pmtyOlZ=(3wgmAZz)5johnmN2~Rb`;=k>^Pq zyp5bs6xE-<@_eL|LHxAuk-TyTIdu~1WVwRR8T@$2etmmoU8(Ru|M#2 znrL1pt`Prxf}C9#^$$0>JlPMPKiEd3-u(wTZ9$%T{Q`^gW*FGPbR~9pO1Op5yfp>y z8+8__ukBJYZ+(WG*caJhAlK*rK0qGqr~W2(c#fQ(3;9+Q=bUW`^B0qb)GsfP)7zsu zBpY9+y8(7^-w}QOi=6r+szXx7)a7-cKC_CPzn94Q0?=34#k`I*Nuj^B&&YWY2}9ZR z-yg1K6ep(fcrCI+d@=KhpT$8=o`7|_?VOYSqx1d?Iqz|i6ZJqZ?j_fY+7!i?ibOB3 zASa7Jzap0M^C#;-JhLl_zr;h%{229B<@h?if_h1`Cv{eQ`VII$9BY2MIZ;(`KT!=UdFrFqr#lM}eV$c)iBI!5f53ORQUIGcuZW^&{&4T-;`CfxDJemQyHXugGY z>TSfY-$2f{kIwsSUiYXKk)Nd|>r8{3TMKsgukd$PfM?-=F*z z;=F4^@1 zo)<6V`V{A*b(SIP%#55!3D5gQZ~lB!&w+b0R+8u1S&&mpqWHNu#?RJ)pGkTzPa>y} zL;jQTxgC7QsGfh9>@O>F@;=Cujk!D>KZ;8?iCIr{==P|n_n$wOU^+Z18#3#{n*wefYF(;u< zwVTNP@)Pbic%GxrUuSi)(9zs<9GRQGjhu@U>2o0eT|{*a`e~Pw$SXj&JyG5BoS$3S zESg6qAoKZx$l3U?PSc9#Y4akgmr{^*7DCP(kLv3ZT%KwXJwIwn>g&SD`6-dz%W(Vo z@t_x5m)N}sawajHBR`hQb3cOJWjS&liX!KCKwo9Yar?Qc(7%hNBu^JZPV@$Sn#i@=Z&Sr`9(vQUJO2~nh0ke9`@-(wuWyVSjELk@q2TGD~z{ zcpbN&oDcPxT~6xHs>qo&aR1s=;r*ZQ(IeC^n!YDh4LMsG`ae;h$1}GnstXM1|EeQr z4n}?IgP8MBH@aU}i0GvTa^g(XhbQNKmD>XTqIZ%wUlTdoIl8wtiq|jl2*?xnN&ip_ zIWan#pB?6NMUx!rCEbY3Cu$>Sl16<_4el={x(8^M5Uvh#Vk?{n-+;##TOQ(|`h%Q@ zy2#mT(R0|^-0ntzU%Q+nZ`MQ3ybtpwca!^>st$49(|bYnkrS(+9#sGEe#|U|y4-w2 z>cIxc`PNW>>I>ZN_Bq5sN#{fjk(0kczhcjEKl7vE9H|DxevOc`jiR{Qo!db-1bMC& ziMx%FQ-h-Cacg)Ub2%a3DnaU(Cdlb%uIRGz=fx@x#7}dU#OtQWxhv3jsFl3`^Kqf? zP^rm$;v>SXhW*Lkc|3EIqdCJ&vcG1?$%Wu&wgu0B{_iNxQ$K5toH-LcS8K-W3DX4X zU00Vp=W2nR>kRQzX6E)2!@)m&R}#-!BIgrC@wy|gcg;IdoqUPJYmJ=h0O#GF<>%Js zfw&}oCjQb2IhzIi+8*ToY0iRQ+cd9t^uugH6*u4#MZaUaO#5^DK z528M9E;;XQkrRWX{MV4@KQk?wf2Jk+Y=@l96#2_l{+^;e3;yCykoegiIk5`*TYrYz z&y4s4`r9_7zwLmW=^Wh)Y7}$+UWxA44IuXW7&%!N>Rqv%*SoF+oIih+oWG99xu;OK z+7moKh^y{HqUzV3>gObL0@$NWC5J_YkSJ(lzjpAfDnddx zRmkst3AcmF8_oF=lJn3VIkN)JpT8NS&$&@u@GCigJ&q`Y2|drK9)U{vhuM^hC~n9?i2h@i?zG!F*ReA@x!(;K zjhtx?^|gq{`+j*B;;tS;`iDM*I}*)5d-MJIzdWhnt(xJX*M@f1(7b&&D99euR3^KjiyUkKj3i>_X^4yYsb2p+H1)2Q#CMD#hCaFd`8 z(c`(@T~Ej>?kI^%Q;;*SL3~jk^1ku^)~PL7=TyRVg>~BUyzkIEqWbJGS?4t5{OBmY z^yPJdFB8o_^AmZ~k+T({o=_=xJQK^ouiaph|7IX3(!)C4&)n|rSJ0=JM%FnKIXfTx zTF1<2&kW*{mUY?l7>jJ+4-hWU<$UJ!- z;c7?E!(;AeslHH8$l@f<&qq#Y1-QozgCyX{uU8#FznBC<@=MXVSj!e+23Nq zwSxF5$8tZDr=sT%eM$UWf}A}8^^5j=fBNkxPNXAoVkvTZAk3HSe(o>wQ;6559+@vK zL(Zj*>fMu^Q)QxiPu)npyBs;QANn!%4}YHOZbST(v>#i6oSOvk#m?mMOs@tz=(kCH zS&5w91o6c+iy!s<8@RtH1Mx+jCwBN6InzJt#~jzEpB(LPKiS_}=QsU@&KUzWs| zb;y}tqWlnZZ(H7r>VmxF9IYqZNwA-3!s`h+3;F<;hS={L!u0^oy%Li*+kwB>KS+J{ zE#VHpxi#mw{p^p?xvfjM4an&QQJwXe`?bvw&6lo`I%^|xwheq|!EEDxW~=mqIZD?% zSigLSoF5t8LpsOvpN;Mz*+0m=i%rPcebKzN1h1DwT+oYaO5)P@$mze~UWt0j=ezFd z$I*QT@_cMFa%y1IcO2&SGrNH^J4xTM1v#?^_Gk9-cxL0lcO85da*t&za;|cum-#Vy z(}O%Yj_73@ayB!}`Q#tmer5v9HADh3*VvAn+6eJX=H`8ltP;)ZekAd12XeM>bblgi z%sM}V`L6z#JSW+SoLdBSok<*X{(3^*G={`$hn#p5&bw*KIZ-B>+X!;re?U%Gi{={D zd43QZqquv3oWEVj`8eQbrYf)N+{!4wd(uDrh@80)%?pxpfBC=pwdg~*-N?x%Q9rhT z+d+%Sua6PG-h-Sg4Efz<=6#2+8_m7zk+`%MIe$C)zEq-^{iTF{+V3a*=04g;9LDMoxB$`nZ_qB=RxTA+{f>Lk=RRzJWf@mg4qvf5ZDSz8UG`4k73M zk1G^YfA$2<)Fj+t!i|OZdPGWY2iY9n3lU|>dp$>xGaJBP{5t+z#%6>3XDgEPew1*Z zNBS(v&ymX>*`YSk=P~5OU8u7}Gwz=@F36K@Nq#?$oaq_e^El5rKLYAqvyRl|Cyg3=kdv=N|KPIm=SQ+-^gd;M^8DyD z;a-XA*6}>Qn}4B>bng&5{6e@}(H!$I=WM)a9(juNIcErW5AwS!#`A+O1Nq%$Ch_xE z!p(=cacXajL=(*u((jR%`OeN5ZNXP4IKQ}tJKNESEkdrgPKYdbuj$9w`PyaXZ z&)<3@sv4~`@E`Tju8<^ayO<90Vwfs=g+ z_a}0`8N>;-igV%+^zV8Ti4#|lGu42TF>^k*8sv$ogu9BIZ4K+RG3)dPB5oB~=QZTy z0I;7L!q1=kBARPtA%1-wIafBS&z5n{dG%p!5(26A?3RA=Rgsh8G)U;pp9Jl5qm zkyF(nzPQFQb?XzT2VH9tU;aYQyc_k!={cvP=K-3o^Kaz*A?S1TpqRW;8R{OCHdhFa_V=emwX+bSA75IUV3X% zFWp5>7K*-G7@yz2_RV0OY6N+XcMmx;H<}lG&GqSeMRV^fU zho{Jy)$rXrv7P(1pVlSxn`YQGJb%xSv%6r8rze7f(r?cuu%PaQ^&NUcac3 z!0GBFPP{_MzyBvsgI-h~u1}K~<}XT-J|_-xu0T{5D6SX39PB4Ek-8u*a;EeDJ^$l= z?dL(B*6)z#f3F}Xw?lq+zr^I*wdehq*#!C4ULpB5K63gkct6}+ zc+8%Mn@RBe$W9@BKmz2<8&RE=nLoc3TOxhdCGrv?r!PRfmN9cvRUP8B?@Qu!BIHaR zs3+77UKi+kkpEmVA}=v=@_y8hHQ?)%yP`T|4e7^{Am{3U-Q{Q8&%}Z#&eP|VuOjDj z!uN1R4*vd;Thhr)d#`_pb_2emJ{f4!7&$&eF^;Jcc>3YTZo_W*gf zZlS#7$eCjBJ!6&f7?UTr_6(eCc^A19$cc6EoY}A9b+Wt%eZMJ6=2&$J~pL#*^*lWn?dk_cJCLXU{Z_uaROXA?`$f^2 z`(sPyGrhQm-Gg52*N;#ysgYA3X9w<|rHoU5fj-@oC&;~loE`}0NIvELpDz@}-QC2m z(;z3#!~Xo&oRg!Yx%Wb{zqH87Nl?$*L{Hf|#dk1gH@iqZpAI<})z{|Z_)MR!cqH#3 zsdv*OXQo_)@0C2}zaOAyD)7%yn^7+rkdqtW`zUH+awbnyhwrNR&0CSnh@37Dzx&}f zCT5%*nj^^5Ll+~L2|1ere$U2yn1pe*ZI-~PTl0{66FJ{v1MF`}8qN($4cz1@$Yn-O zci9*?cPcgKa=ZcS{CFyISqOIl#N_#xNdjl5k4Nq;!o>qS=$o%HPEH0p zm>a|n*$G!@TUe+0_X+pw5ouw6iRYla9LV`<2S6{wHZxAWni1AHbvbf5k+XSz51e1K zmT~5-B7u|fb|9AvIiJOYK4ZRH?-BuLQfxymH*zK?__a8;oyoKF)4@9TEI}?0;U)m5 zTk^ajt7QPayt)**yoB2bzYpZr{KVwRw&?>WT9R{=4>?hG3+S`mQO4;x$zh$(W}&?N z$mvzzfj&F&=W)73Qs5R%NA7Lp)T=8%pA~qYBksNozqe$nk43Hka$?!Hfs<8uA15zG z=iv!Cw*`?ipZ^p%^Ecn0AC@_+(=T6!@(LlR+fEOhYtQQ}-QW)VF4UO*$Q4G;>qUX{ z7x?{T(dv2NR7P_CiXbQ3eH}Qpir3dZTf(qT`EUZtD@wSPUvRPUu(&Jc6%L$A zMe3|d$ocN4gFIdOAmi-jydckggYqgPCpsU3b#^(zIG-h3;LNd=$bEpE`{6|3d~3cx zF(6mqT%UEwRYA^_f;{an9Afh1mMHF~CwclqCdpgQGA{D7}PIPu0VO!k#qTgb6fWF{au9pxeSD>ft-2{ ze(e)*yZgVO-gSwHztkjL_Fuv})p2fj^JBiSKfCx_TxTuh%!9+QzwF%ZZsS{Ezp|^5 ztBss!aXxU)Y-9U#E#N%pe&ih0A>2xkr`PlS*%jbtIw_G?7did#NRTI{@cr4=aNbS* zHMq`t$oV0#PPODmwobdNAlQ8tS!aFZv;|u)e6q7NJ7qSBjkJ?(5D*A_32B2KJD&RsF%jbiEVJ+#f`&kf378P z@-4zOK~A09AJ%CTaXSbf#p_F?{%nez*aChnyK;HrV~B%hJBimHA!nOJ_562So*Mz@ z$P^-Rq8W0!ADlmvWFOm~-yCtZ$@y!JoSL>5{A?kQFK#CE4|*J_lUpDs20I+8^TWB_zX9#1k@IJd22L;G@kMlo^Wg83^Uw-8of-N7 z@$6@&7keH04l#HU%4?0B91NWOIwlSp=pW=7!nHxpBsdw?sc!Oh%GptVSW4n*+1y`LBk(iv7xA<9$k|8HoZ&gQyOki%?euhmVi7rz$rk@$%CbtmN9 z2=HrFmakJUg1*sDB!1l)IrZVeU_V)#$0avg2LF_kh`cU@y9W74kJ`ojOizG#X3G%2 z?uwl49Odbb+7PU0DqmTMe%%c@cLwb4U_ZiD0y~(d zoKuaXI`THLLx1Gt4X8hLalTG_0_KBy2&q2@AZOo?;{1Fb=f!iV&s+g=9tI+3UIk9Q zx|f|J{~mBY58(zO=ZXO5i*tEy7jTMlgOM}qA+N~g+&Pp~(3|&~MsO{5+_*a2{+~Vh4kqegXNx&*kUA-hy~-`jGrE3^{jmdswG> zo5x+94dx6sH}UJ?$cer%FED9&oY0Sx2fsF+%nL>!=bD0Fo1(lG}g8p5kBJ+aJkW(ALuVokh+)$>B8s#ED;2ltvod0!&um%!Xx_2K=r-d;R#q853cGYL6Y`ET&EKV$T{3!WeO z@Asm-$;j!bud>Ht@_V~zZu9gzIcS|lHRMYzq- zPuuA{ABom55AkbBoiz_R*$nJw6@UI55OHmhkgaedk|+G3d+Yc4JkRZO&Ut-*{(m_4-m}lW&)0iVOg`ETcCC^UyPl1l_;FLTKld`3D+X%?=;uD*#-LZ?-TvaL(VS?^=<{u>C4a` z*?&k~FdsSbD%``W{QRAVy9<4Qxk>7f1<2`C!EXofcK~8-f`jxSq zGdW?tWG@kUOOf+?!n~k2zh9BRfd1TAGA~$$ocR#m7s$8yeVp!^1nR7PqcHCNf}Gz0 zeVoh9_3jUcc>Nuzcb6k42SQygMsoY~XF(6|?LolBZqe+}tg`9OEE}i82bNNHQEllFlYUK3fFz0)j=M^&w?8W9MezXQT z(-6+ne8JzXsmGyjTub7_TI6&Zh!eIQ=fuQ+`y8T&b%e_v`st&5e|kp9e5TY>hu z2{}6t>PUNg1>;oH&^Kly?SJ9KF>&)SBZ=>UC){D;ra z)N9apsJBVqu^ld(l_I988gTwi7v51LVov zJkHB=AWu&r``d+_SRLe5;`0Kt9sFG$BJy@4r;9>gr3&)7jotzGcy1)=tA0h!*9E=X zBg>fHO;NB<{XNn99^{k?{xF#L;bH;Q<+cm)hrNXRI^?6~y#7?ZLOd%%`u=^$$?U&? zeV*ZY%y&%)_F0y^FW8TqnFRfcTN=~f?gTxUt3>YykW;UO{x22x2Q#R6B+vb{3H|mU za^^ntn_@M8zohzt9&{2impX);>KOW*)V%*Q=fb_rYT`$SkrN%kkHj~Q=}(jn_o9!< zy!Z|U)AWAjC~__@%-z%yZlB__(1#x* zefTlte3kQ&-sM+(f4*}O@Z0noP!GqElPlmn#T_1BOjWQ?^$R)A6Udnbp}$?sJaHt<3z`x;JB^&GAJ%EP{`_#T zGgW}dJA<4~4)TQJ`9VDcd1fYg&vzC%_ax}y^O$v}20e&NL=WeX^E>{4{T=51ZYBk8 zCCOvIBWJh5J+u6R`-8BM$5c9U&+L&ii{TxvYY-#vZFqO+7p+Bm`2#us80NI%D(BpE z$hZ16!ktIXOu7L3E5+^8?g-~unVjbZ=AQUPewg1^v0t`TqRa!Vq^G6aD>(ocQBUIF~AX zoiazMh%*JYpdPLeZa(BA`Fe~Vl0Y3|E0BD2m2g#|UUF0UI@LhXyZDOC-L4_0e*?QV zd)Kn~X)}X;`W3{k|03K3kSC_|I>hFIbCL6iyz9t`VWB=d$n%3M1a@uW5dGaC+~iOX z9^~g@DhEA`Ch~3~r+>H>>A`)pn(a@FfH|ffwhi@g3psZg&Ey8WDMSkkiAjMEle8`2O_PlHf`LtH9&&mO*o!K|_ouIbzq>Dqz1%0<4DfgT1Gh7k8vIXwNao28 z2=@VSraGTT`q?4A6eZk4vnVFcXAX7wL{djS zM$XQGx?IQS`}4oRdkxuv)a6f*QyCz>m?bOOI-LuBP8||oo+9U4g#IB7uUkcyFrR2e z>g#97nZH8ax{~WpT@3HK_K>>uIdb{|k9v@&Y-zH27bs<$Qlb!2jH9 z+tL62K~5hH`C%v5pPU@(&!I%`;${5)Z*Mry7F-YFWAIxWpV(&{+2x&P^b zr6WJm%gOyiT;%LmaBrcT^8M)wWgtJ$ztbBJIejF=PtEHrxhKTWW5nNILQZ@V?yDN{ zy13$?$Cq&L33GZ0a zb9r)WsLQ93x;znb{v2?+1n2w};QUg;y^Ngf67IP^<8xuL2I_0qp6o9%a^_dC>;HUj z$?Q|s3+Hm4)R9S$6N%ydnLW?z7gHajgbCI3N^86mi6-o>7IuV(ly@H&{ z3Ug>bmCvC?-_#K&&P_l+N`{;q9_Hnl__>&AkdKrjbE4$Psb%4QEava4Neg|y=t}zV z6v&BVVIK1Cadw`*cgTP7N#Bu@aET#r`fR+vbvIz1wM<`Qyw#)3St zg2;OnIX4J6U7g#fdmrpm?IK({!VO*vc9xHShvH4Jv+m>3UeY5c)S`&9D{nFVnV(?J zV9yUhE(3D%+`@=6)l#zWxpcDdy+u;Oy@s5=KP%!yyDW^eIuXRr75z|NM&$hSWf7+@ z@$cMBzElzC2aP~36LPNIQjm9%e^)1SrHD9JemHWkBWL5zi8$XOBU`869M(CeKXRFo z)8B=A`(rWRDK1C{^173I`z*+bmN4&;%~LaZDh~L&JxTT_30HSH>@OAnzR|UXbFoLr zoG2@D<_FN9=*jgbXTd$Jn=u&omyK{aK%Otlzuz>!fjn)9yzI#7tSjI=w{m%QQkv*o zL?$ss}`^<%$+%y#AeUOdG zlZ)YdF1xio%F9hS4fZ1Ire&PX0`_8Z|Abr~72`y|_}YD{^lkr+33TRhkzpp4sM4 zBF@Dp-yan~PPGTlPv+}%XP-u#HDrGUk&{Ix!TvmtFRD3kvSv?QXCdU=8i<2BK?0@+ zzdT-aF8VNugKr~ej|X}6<1$Vsg#F1(WSxbPa|OUYRjYW6v!`DIJ(M8!Sp+%N4fH3* z@bAM#n>Z0CulL3M6-7?ZnFi;wpWBNQBI11EACN1CoV-6J;?y{<2h;jF=x;C4UvcEb z_cr2uIewn90dV4z-YBmGa`NHSh!am@&hrcSPSI`cgIr1Ee1%|VNx2`Xw?XgbbE1b* z$jK(tBY83rw@(=dzH7Cwkn?;8IrUkv&$~R%i+dnX)F$%YMNX%k3Hy69Mt@I0f9hM} zf2EOA-N!_nuE_JPzXad$$xl1uxs*Z9^Z`2)*LYlVOA~-yHzjsf7CG4({6SXX`%}fi zKGi_t4+=SV8REQ8#`mZ1!hN{PMdILl$ccmy2lWdcU)-0F$7FAkZ_5!byjRp4xIFa? z;8C;+V!Dk%y?=n5I|2DfPvz%gu0TB@j}bjoLQb@U{>ZF( z%;L33815TSl6Ny7BByGsi|o@pjp2TS{psrDJxXQd>~46+B7foE!`frOxl^RC`UpAk za3%Orn@4<|f5ZD1*Or`1735SOs6XW>zD_+noXa>;e^y0Kn()4L7LOC|KHS@@Hst-p z$H?g>P-nT!_xU=v1p9n-H0tjY!hHhg;%@W(>ETefinqwQR71{|fO=3i=KHfvL7s?1 z>bg&n6DIU4Y3{LgIuZJnmBb&aBd5Cu?$B+ zI*|8LpCKnIZ;R|xHR9(fvw*$0hUDE$9pubZ=)+y|8*G27IrOP^HtECbBIh52|B0^G z8E1=!xRjsR%jd|sws22rUgi7K^TRuxwPapU4>|n>oTqw|+ou@<@k| z=&?x%eoa-LrxC%3>ov(uNDJeMoz;W&9u)DSuI zN|^VY;qp`}@IRk~*rz634cMQ`62nyjyOytz{WU^PH{1;IbK*6&PW3gsOR$&7T&gj0 zvQyBzZJ)dx{ZFnD2eG>ZZ zm&mzn5U*WtelC6jFr#KC6B`OMJ2>lJsHJY5p}UAH3r`&Y>6>ab3C=O*J^e6TZF zkgT&ga;hJ2D(79s=_Da8)g)XC-698yrOo%I?c~yovn~FD`Bo^S6^lFpqWhx*g01tt9d~ASWC8 z$dCMm`J6jfAgas7baL<1k#POs9gFVF_ot3Se(;98=lhm$yDmrar^zsyasC>di|cq8xgN+F2fv3a zn*Yc+_04CIJahRXay^mLho6HUIt*Z(aIj8eh#m}beh%zU#~;QxHv>4;hU~8wa`t2R z{Z>DhbFyWXXq{@}NnB@d!p*x1=Tdnjmp8sBa09m^*9STC8T`JYRs9(E@88o9-7X>b z19G-i`snuu_1PYbGYuPq{yN-3t}k+KP5684&jvD1HmMF==iib05ji^zekWKq{)us7 z?Z*+P51v7;A9C_+;OY+I+{8~nUh1>R^+(Ru{~OM;5YNO8B5w$CB2V}`n(4V7T#steI?YX@ zhoQ*1_2KUd6_1IZQ-Bkd2saElb@ZQTf40mJwm;eGBartt@uT6$*>n&GMS1Q=q7=kI zL;sHS2;^i2h)e1g=VY@Gmr9bjG?H+qz&`a;ZZB>V*r!=c?0OX8J`BGPU6}7r--dH> zanGUtMk8m!??apC+>c~_I2XB;oXZ&G#Jl0|-+mGkKNo)*ou|J={B|sI>eul1Y&OUE zd(y!DK-M`9IsZwBgAaIK(I;S?Y6e;7c;v+Upm#T)$7^4%GOTm&2|Smdk(2EqUYo;w ze|jY7UA#x)^#tVX9mo%^3b$)n>cdE$oK5n>MC5!Wkf%Ji7dr{^gYzU$PeRU3dl1>R ze#-NvpIHv}cj+MRZ!&V?i;!=(@O7GQkf#OxyK_^JGe?8pw&a{I0s1qei2f{cKE+dr zFCxZo@56cOJEWeNikvJQ&gI-B?zgXj{?rh1F4K^+=V6^z^L6SEf}L5i&gsaB=U~@j zUd(xR3vpL_JkJ@(`P`s)^&_uC++onWTTbj{CUWM+ZP4H65W8xbCL6R zLGSViKNm4G*!5_lzj?^1p73{YTs1Dw4TZd^N1aAJ%ty|24E~oOCLd)0J@|V>4-1g9 zQ}2Kt9FM#5m7s^#`%vCOkCpZ}8VZ!vN< z9mGL9f!n973-zF`N#fuV|!#e$760}J>c^6L#VSn{d*w}Ih!Bq5P5_9gG&na zi|<3~kj=>H)bP9YreklmPFJXYv_G-DO+lrh%685(! zM(;;LeLaxGvu((k>CiVC+l#GJeGC1xSV{WE?a0Y(51}q79n-JG3w84KL%6>k$cdql z|HMt6S8P^@*RBi6e>;&=J8nku%pc>~I>m8#_bX46__B*|zd@hl_VIec?}I)^bv%IU z+>M<47C4!W`=4nB{g`}0`lDZw^RpjAK57vY&-TN)=q10QygkUdgD_vRyLo(ZOJSY1 zEs?huIk6yf|1mB++TN3OuJ~_|*$cZ*k7l;JB9+V$J z+%@lzy5Ioe5`rJ;bX*TEC)9(kAMvAu$k~HXC;QL%d79tfi`Hp!6FWPEoF4@FR+Q%F zDQ1Bmxv3=I9!5@Vz7P7lAG6Mdpm$xG)aAb+XNrZn+kDQ+I#6fX_eh;}gm9O^Z{^E< zm|eSFp>I4${Prl}z6<$iZcIFD2YOHyNq#tnock5>t^SqgBl$Db6V4HPIgXt88|=)i z;&vvUft}e;2zLTGKMnNY=JPmcKMH!!MdH~>c;*vPTIkO7BJ9AloMSnkwoGlpU z$s4);%(d`+zHyETvO%bKOOm?a59Cx`*q^z}{noV!b$K!p&(0&KP0+(QKKJ%JKo8Ahl|MRrNCLk?aXxr{rPkx4qigeZVGdve0=_5%9W4el7B+xM3<2> z6Ch5gOI&|yc!(4INSyc+Ik5)(T|VcWJ{sa~H{$PCkn=A=+!fWh-^#O4x2kC*?p{Ss z{tkMVL%1HqJcvtvEz$cm!?~zp z#IElkr@Msu>@DuMrXlokssX9b?jq;TgC2ATUKfZSrK0^gs)u{X*?rKz`(=E8x-rB- zxq!sM`^bsJA?~)|b-An;^sdPM9w4U^K%c5-^LQ;20%yM@_VN%pKLg^YnZfT##Ox41 z%aHi_2sxJq&Qnd`eX2SR=b|c*^L&h)nE`Ramf&_Rhe4bWqliB|K~6jib!1Gw74_ge z)n6p;K1EJ;fO`@>pX*(<3E#DLCHExHkh3Kq4!Zp@{=O6Dm~I$}gU^vO0{RD4I!5om zgu1RJ$?q=+mkaDgl#1z(8if6&AolVPaw6`F$lv8&t_PVF;-KF^`hJo4f8YP87DynZ*6bC4>Cu&J+E`BV6-PPweIO zy#FH9JvYd?yo8*&71r6GpNl&Uc}%=Y>?}TVst)97dzjm&DG2&gzmYth068%f^yfBl z{fUFY-&>J&CPYq+5A#UF^R3Gf>YffHuOvdwWrFjxG4~Iq5}b?POV0CU$?yK^)uoRiIFoC!H;}Lu6LUYI5C58Nsx0zV1NEgu0Po_%(KoDJtQSuUYHY!mArot z$piNqvFlfm^A7YUXY+jP=7;(^E%Co($cePU&U*4VVKargj@nsrhkg`9jn+#7z+IkgG= z)|Mf9NR6D?8s-Hhx!=k@;J3Ca={wR8E*abx+i|=<(&=GdrxuWTU0USiZ=o*V!0S4Z zBJ_3kh4F6{&Zc=iFgnv^yfr>8Ie<&!hGU6 zuM2c{i1Tg=IhRbx*`wj_CAZ~qP$Yx>x#r|P^>yU@t1xGP-;5!Cn~iX3!u<0s?(Zf(%xz3PQfFmH&ZP|XSz9ho)dYEV7?GC)Ih_~g z^WrqOPxnT+?`T5i^Er`o6Tz;{W}Y{FYVccMfcR}L!exMUx|%%h%Ie^M;#;!L+{oD@ z;hrQjk1y&X%x%mnGJnZ~oH+?|X!#jG7dfs0^f~#iVZZVQa^g)m7k4ejk8(nMQR&IK z)833J_`Dib4k1| zh@ATp_NRS}oh=CWPI<`w3L$4j_ zVLnlmoM$oQMI!pg+>PNFVnea;j!{XVol5 zo(^@v9a0yRLr!)8|MTx~PWG!E*^3xM{HQ!~Vh_Y!nU`~B5yV~7ipYB(Ib*^9WXwA& zHzw3|RfzvpAlzu+^i5ue_zIz3dXsP!k<+Wf{Q4yxceMn0x(SJ&A0X!!z?{KP;dZS) zg}U7DBl@d^oSYf@)IHp9bso^4*+J^150NujL4WS&m^fG+^k>(R^Q?@VN*nyPCf}bJ z5bXMEaxNbsXIF;0^=HnRg5XEK6S3*FH#0h_v>s?=lIN=MDIPnQ`dKuWKPS4L%Yz+2Um7Hfa$eBk$5A(Rc>l|V37MIv(4Z^j8d8_`7*U7#P ztW)kI_YXCZb1$F{5r6aiC$@)v+LAh?7ILZ;*tOop{lPv7eZVGS*R_!o?ZfY2{V7R?wSI`Z|frG zQ-pii0Ws&981|>TlfL6~#Lo7j$)p|T;EJ1^YVJgiR19@-rcqQT#gopbID2G z-PcFX9|irHVSJtH1k{nT1<_vvWNKSx0N6#nu zy)km8C#+NN;PPZDSf|ZT>WL=EiG0Ct-{bpJW1v1$&&fKQBImP({;wXlPg6AXRRu_& z^CfcPOPCkfq+AbTeV9wlCiPh}!hICt^%p$u+SkH7`5@WfSIF6Hz=?W1kGWkT-;N+$ zbL2#m&_5L7@kM`qvxGW=X*amat>T_Ud~ za^f-61+GgBS3cP1cw(QekaJ%{A7Fd%{2 zYRJ>G_<8D`!QUSdd-)bQTQ>M@IqnboR_Om8kvil%c`oAiVvd3zsYJw&en8Hbg#Gy$Tz@75#Cbo3F>h+9`JXgwjiw27bW}q z5jpt+IGKajcZ+)74o0!N9<)Va^fD;S+*&UpKc2HpZbRQ-w@8-%2g*=bRHo(cw#9l@s=W@Y)mF~dvyG#Q9?r)PkJqkJ1FWd+0=5{8!hdOx|vFp)< zTNUbpDV#GqAYPk+MBW(W^gXaoafRnwHy-TMmnHdVEONdf*o*s`pQq~@>a$zqT*e`% zx5578DPCv!TChKRm+Wsma<(_@&$s9MQ=MRcsxsN%&&ZkhL0)n$PmP8;#C0a}CLm{X zz?@bTiiu0#!JJk+B;O-UL{9Go&UK54gY$v2!^wPi5^_E%e8+B+a!zc3dAWO!e8)Z+ zIh7jh%$?(Xl?dNKi|6EArXXj-ck@0@cOEBx3-ay}c@{ZY0_r*w^ZrwJ40^vt>bj}O z*&^^=f&81-pKdkWBglf}yMk%RnQ@^mFTn4^)z@I3G8xJ5(~)yMLfl=)^=IcoUeOOp z+?_$V>M+j|wPO6?SBSg52g&a<33oGa>A1hU;ZPT-i-ennoNfa9^S|&oFE&CwsOf$G zY~=he;XZs5k4tVo)oUPd7>rJ!+hj)F*q0VGOx4rWH=Ysi^Qb`$f>6gmt+B6x5_@Df0#=0 z!$RczcW|EORLnZJg!uUZInPDNxtm~Tz8gOm(G2Xc_7&%)dCH&4pM{b{TUOkwf z9k_yXwKJmU3> zoDOy-Mw4^-1v#-laEEyRF1o?Fm=eTZmLsPRgnSe;e{oNM(?!U6u0YOh0eQAB*WbV2 z!`iMy-b&N$89H*W9js$8e8OnZ(_-$k~Y?&rRU*OoaCgW-pPqj&R>XzEv%`o#|R3 z-)fRq)+6Tzg*^Q`m#5zfTt~ufAl#pzcU6JQvsXdywi?m6p;(rb~ksbCYx5d=S3BVuN@0DyuPNffiJA=>9-0yHM z?hetz7UaZykSBiQ=OPz|xHO2I=T^ea1bOZz{~p~W5B*9N5+}AH=dVK?w5z$_nu#HA z((fX-BWFg0y5J7r@6U(# zwC)snueb|2aU=M{U9LZQ4bD>^B6`@3oGcvP;a=e1GrRE4%8n*;jbD*7Ti`t9=e%w; zPeUIthQ!Z3$jPZef0y}u!e54U+QCGBdy%snpx)J|cwH`Yf*$1Gq~6_!oZ1L_P|ITM z^R@8%6R(nfY(H{3cbN0VeoxsK;*y$A^nL(2(--2I>d)tAzFn9npCa>#gUE?)VXpBW zKTrQ24(cA&mFwNs5AS9gkvMo1Iaey=&8s}W`&Hq6&j?ay9Yao3 z2zmNrex9N^#0h(k*ynNNWW137^68sl_4g z?&Woe?hSrqhLbpW3OShp`VMu6*I9o2|EKRbjhvef@^pKy2lE@qQ{NJKXOL4*p#D@n zxgJakm`|8dr2agMoShu{)R=dwwiDQkxJK;d9CBhW#258G*Pn?W^3f!szu%El66!(s zg7+P23-qb}FVc^Be>4PU415aRrQ2Ac2v`<2Cc zpJyKsIL{VmpjObr7-_AJ$N2-tzrIY44L!YMNX9s^VXR7 zDP9j;a>Ct1&fbLmnL{!9s~zI*G_t?@$muLW53h4hjS6~LLGNJT_1RP8To=%vSjp|$bOHUjf<%AMkn?3BANlOuk3?O_NA4lX zo6nIGUxWVKavsn8pCOMuCi;7Uoc<)#6KnYVMLvW0BKil=PK*rka}dw(`pZy<(D)f2Ilm(0m8slb^ihaQ zvIX(m1jvcpq3?e;Mt@;W>z0tbl8|sR=>09OKRFrnE?X15Cn8)!I2W5ECJrtPalRAL z!^_CY1Yu58E$06Avrw1UBXgp}$ce;I7pTm9el4bh-hD$-7bHPWZwq!-mixPX66`Ds z@%N<2`P*Z8GGPXXt*TI6JnFlRW&&(qBb{#S~e%d5!Qg`p2G$2>nVSVrS`)(_=!ueaib{ z72>?7?|0K9r`Cl!D`Skj@VhZCE~(ElAop)R@|x#OQ!LaIHAy~t4LQ9N)~PCR|1;yk zUi?@RKQkg{KMmg%T#LzLdEuRaFGlW*GZAi6=>N)dJ5w8Aos#}8$Lq-H@cYpJ=XY&y zLYz<&NLRHKN!p^wykmI_IWY|UNZ#iDZvKIK!d)VMQ~)`@GyGoO4PGy~ z%`gu!56JwwAads4-`D8C{YZTkxS?cTP>67^hIi=?V)T#<-sO4vzWi@7ZN9GS@GHoXs8VWf1oVvlHyawIudZ6gjsI;;tFP z+XV&v_EcTYMAi7zFPbI;*C zRZDJX@+#Pw$U)AtByxHK?9ZL#^5hKIpPoSKiBiakWT9{TlE+Uy3*xoiLgMv1$jO>*=x2t$KQ5^w%OYp1!?~yp+&*2oP?sMj=c16)yTcs1F86nrAL6{Gzk~H2a()QJ zGhdg-UA+eES{x+ttQ>N}fj`)m{9JUSP?s+!{!kt{^CHZJ-{taLG3Xl=eYgESa(a8{ zSK9ITsfR+`RcFXLD-bSas6X5AbJ1-=o%M+H4;2a589397@6Tol^MbOZZv6l`yEgPW zJFJt@henp%B{mDh-olY&}OjD3&+Q!(~Z^3Uz5P7wclheZY z=vnw&!zK>z9LJLW_A}&6%`iV}%-@NtxiCNTKN35ugPgwxao6PGb|$k!+!d`!pHmk( z@hi*=#Cl%$7z1|ZauIv^966shyz?j@bDovq9>Lxr`>ThXO&aP!&GWQB19g_DMCz>i z$jROz4o>F#6Bi&3%4#H@H9*eY3-9DwaldtGVLstTlQ{7Oa_V}xFOJLgAk#x1p!$+? zX^5P76uyi6nfsq8QXKBJC-21jIE|dE3UN>r9LK^&**pI z{3U)bW3CmBI5B4{a$h1R8ixEZji09p-|gCb5`A1#+r$u$MX9|5SZAPg9E6%h$-6 zt>8y8Ma=x{GOSa!BK2-d zGZVfmx1;%7!(1;J>0Q4>eh;Y)a(YA9-x9t*@kg-F%4C0Sk+b*VJjGxhC;SRHPqCGp z=Qqf?H$k5Jisvym8sd^1LE=(7bFIuoug^kb?a_dnAxd{>@>^kZEJR}u29%Nx_bkB5FtrXl&ZD{`VS%=v8Z zn0g}oT@3do>5sZ0r>}?K*6J&B(^kn`0b?%K25 zUbKX~qCOy8Pvm5w(8s;Z`vAQi>Lo+}{+K~dEDQ2lalPBxuuj{O$m@lisRQ#KnVQEX zxjFpq!CK;fy^#~)cVG1jp5H}#m>1Z6WWL*naB*R7s@LN=CzFD~cd%^8fh4($K9O>WtA*UaMeY(rM zF1Nj4onkGq&;H2yrJ?`p&d){kgL+=4BJ2DKIgugssW-Z^?`1@aP+xZ>b=?5u^fTyl z{0RP?jaQ*Q8$|llfynvP5C_#pt_S;Rh=bio+#Q6R%mj6T?9A;`UWYzauOV?_Fmn1) zn1h|-b1?mOsOyf9IoJ^7%xQ>&CJkSwKLT;kF4=+g(on+f40+`}&gqk|KmCmCZy0j+ z0?gfF^QO8PzBjx~{9!n9x*F7xF6MU@d;;)4wSm-2Baridg*nkC9w+Qia1SfGkoY+g zIhz;yM)fu4)G_FD^bFFcjw0Mr*q_|RJ3Y-(99vq9D&Kd5Hvhe-+L*c$^3EAH`V(-qEBO(*M|gq&{#oL$1>l6)m_0|+-6Irk^nwQ0%EMZN|8?v|6hG6gyH zP3XgKb9)gt!LIdl5#7-c>7N*V74CAlOT8u0LHloM(M`2I{Y(7XMa)YmhS6Bi-B zo1NTmO+u*4bv44xLe6)E{kdkmE*JS>onk53-)!XEaOmUISA3oNEaZ3fBk|ig$oUbV zKXshX8PqV)pZPR<>)CKd96J=nXrWLQxOmUd&t2fCy=Od>Z zLcUdb`2PH#FhA2xNWNWwoLCaxdBp5bhu>AvRY*OtkZ}D%y)=^7XYM^X7h8txZxM37 zS(v{#Zr7?qnA{utCDg8n|yQsm_O zAues@`g3o9y$E{0vJ5#_6ZWTL`Z4z_)GzuD+21dO3%_rpD)BjyNeJ^gL-nwnaJ?a3 z>qk7V7z5{GKO^ya1>v5-cN*fQm^iVuNz{+oGq->QX>SIl@~pR18m@gTm4_EoXE@Nxuu|YSC;tSI^g4sv=?>tx?hf|{-y`^KFLEv$ zkQ151ed8KHv73jJmtQrB%F+&TFDI#HR|$vR6Bu$Qgd z&|VyJZVu=n_T7c-5bEo)L=T$@moN11F@Lv3?+bnEEwaBYgv%c0ZuR*+tjYv)TEB_Z z*ISV@MIj%Fg#0{3e6TY;g{*TMa<*uAA32l9UA+S80=bRUyW5e|#o%0AC4Qd%&oEz_ zMD(x&Iq6}YzAleTelV<4)Fl3}6FKo3%xzrEcfx*Q3DDnMGPl`9xJw{U9pL*D6@xtL zf4h~ zhse4hPe|eqhmrG7LqA=V`=4tC`tu!$UH^uh%>{mIipKP}H=!@qcZlB}LC)NOzRFCA z@rMgxKJhlWZ#;^e=o98{=lJhT>{2NwtWcXOW1cTXVakAYo_J2CmF3)r=xe~;)Sax(lKMz^2GGy6)=-;YEOr;rn! z!5>^{-XH1kPF3_K^~7o9Ouo>k-ioo8pTj*Rje}>9^F@MPpNq*y2SPrIL)Li~Ir$>o zSH*m{YyJXz5y!|p`5ba83Cv$q9$sg;2Ea){?Cf{s?Djt*KeE@kzl$AjMt-CxZ$v-x z$mzHsPp9E}knSRRTAIOPu;r{Ox9zR8c@b`0mCH8q9IX43GiaO8F(-i={t5U>o zFCZryg!_O>JT9463q|{LOSa(tE+S`B!W_(wI6rxSuG?%Y@q!{yxx2zCYU%{?3*!K;~|LA}0@oxm)b-C1eG?n_WZ?SCBJ@As<=Kzc)1h zz8|bc`pv7z>0M#&eT4gw%?0;rIu41~*N`(GL7rAmc^~c@L!MSC$(-*mCV#Fy*H*+-yvxr?8tt_pg$-N`-B4dhgRSf_3lGnXnG>WMmJoi~xQ1B1U$ z<>%?kfWONb#NTfrr$4w5?ayT8b|%My|M{LIkNu6Dy$JT{m&e>UegO931`|KJjhxDO zI+CZm@VG0o=8Wu9 zNL}|3IoSZ-3Fs9ubyn+OXMdCZJwnc2hTq%PZ*xD=_uh*3XAY3K^cXoeGuTV382#rE1pLU|=KHf9!H=XNeiR=$ z^Bn3(*^kfJRr!!_SCIN80dnd#*qQv2&tJsca4rpqy(C1==7hYdM#ikO3&d-gndHqx z$hkS8F7L$8(-jTzhyLBh zBkN3poLn7#fBt-oeO`cd>dM4kk|L)^Y>VQ&tjgn(8v9D5clY{CT<0sunf;K*+}e(8 zonj;8cSZmHW-{dLcaV?dA$~5RVYtWZN%B#0}{}X+2cDV&-MYkwxda2Nr#;GVVx(r{`^kJM>a26XL{u1qO)M1^|=0YlN`}H zZ7Omu8IUu7LH%O4@Ob7P1EYj|qxkjhpJTvk%?Y0=?_wL=UecXWE{LRZk9<2=r&~<+ml-+r1)QgQ%Ihp~?8u31;r-cNex71J_`5VDPv<~R_YUt84)Oij z^B_;{C-2X4A}0!kerz50cfB{n>)%Pd&V`)50C`hq;rr7IpiY+CN#4wjoH+vir`mnb z{WkcYPDK1K4|2L~sIRZ^{rS<5r$uE__q>6eFBa;ayd9W4)d=b(xtZk6yo9R-`!g^2 zI^CzRKU0M4FCTJtO{iNl#+=Ky5MRt=Qn$W|oH+>kvv2b}W;=#?d3ur`@+0SS1wHKI z`m+av9$qKwdwS!V&{v=4oMQGPBWC%ij!x5zpR5^gBS6G?de zVy?rSL1!R(FNB;*1aV%x!{d@|^+x3Hays$bw~=!fp&pb+xZk?uq2HWB>cPUu>E^-C zw)6A!alu|Zt*?t9=WLk2bm#UdQoy;W?WCS4ikun(_nS5?muG%{KiZ%Fl-zR_BitTX zr`pEjyl)16>-!UbD2|-J1f1@|?bE#o{=S%SC6IIB-LLD@iusYN2AuW8k4hqE+JasC zd@=ra7UGgvLE=&=Q0izEID%=Y4=yU}t(B(L-6}Y*XkP z{Sn@G_y?iRnnU_Vg`BMKqde`mb50+H{HI?debsw}`vL65m*W2J#|L|tqx{z??krR!=JaP+l-+9PF(r;Ek&X$68 z+BGrys~_%DHdx;M;GYyomlQkignhJJbkb&4f-m%h1~yDLC$Sk2X$maKL1p|z65cw#3ZZ-s}k<<0kG>Ce6DX(ycTgf z@nYmYMovA3xs+bdL6$TULSGt9Ph_ew)n8l zFDD~c7dg4)C~$kZznjaiN1PhI9J$Ytb4fsddOX*k`U~nFHH6eX^^jACL*IChuhYE< z{n!c8AJs=rKimN4@`T?Ts$mJEb-KJ$aGed1bL~KXvP#VRiL)@bF~1Z2eSw_0AM7Pg zjNTh(hI45_?4=>$`c8!Ne0Myn3-r#*k^ao@2G^;P)348uI9r0h6E`RCf!=Quy*ENm zwVe@hs?azlPoBF8+=G57uQ75)!Fj4_{2tw}O%Ux*7a;c11UZ|`MekVxiHFCBx#KG9#@W35 z{Px_>$bE~PZ#Ea?UE`ei1@e&^KM1++kW+OQN1T6!b8hLQNDsmj{dGdlj9(4w%);Ln zco!#Hr^rv%`8{&>+u#pxa?bV%{*Zq>uCp`YMuI%koX7e9{QY`3moh|N7v#j;jj+FK zJdcTf5I@C1l8?F)t||Dto5$DbTfd0TMZQn`y&G~S8SKwwz|Gau|sw zy_e+nBDMmjTa7|_y^u2{Anux%WBQ!tVApa5iMzd#v!%iRe9Ygs7yrQi#7=T9eUS6j z_eAT|Ph##HUrQ6&i+nf_*ZBi-rtRK{6WRHkT~!B8q*{PnU*z2VUxC}n?_qt@)F5vh zk@q8VQtgO1|0A~-buAg#b(2{ruOD(M=WdX(of8{+)(_w#i@*+0~Ie(`f{9ThuUo&}fz_f^S{RuZ1IeFuYh!dq^?#D*V zia3)dKgt_|oLyHt;#_Io$C<~Ifg7C@xuM93vNa=4d)^m|E)ye8^vsUjFyu_zS`lX( z@H)igh5hN5b09YyIeo1va9z25+Fauzc~ZZQ+z8}k-XL#1@599xuuhwV$Qy~A=mqEE z+j4&wIcy})XD8<}3OT#I0-R^rNlbr!&PX`VAJgJGMqXOf^$8#f29V&+0 z80751&w^n4aAe61>zsg`aU~;8#r)2? z{-$rl`6Ee?n~0qHHD|<$mwBD#OnX@8>rZfErD4RuQMVJVb6Xa{4^{ z-jkZg`wm^aF6^%{`TfW#$hlA9-IH0v{mAx*ca?G~xsS8RsRR%|bxpoM*&5#O$}{BN zVk&YzIdCe+3}&CUExfZbKM-yja{KQk{DF;2t- zPH!S}sTs)mYcOXJe=O%*bGT1+tw}#UlW;kq5BH6hGETmL`&55oDXw!Ca&`&iTeoa2 zG$Gu4mbkRCRa#Ck4 zLr!%K`a8?lX}bsgEg|~*1v$SN!bmj#;{=W3^qIDc>k z+Sv-^qytWk;`);d!~U)iZY6SlFxZP%+;8=YFmL^c*vl&9+!l~$25#l+91QE!Yl*zo zgv%f5-C_J(^dR8Wo8(;9AZK^)jLt=N<94mI}ia2s~9b$Scu;!~0S zVIAR4LqFzn@pXzjp?@e%=2GjCQwd<5HV!`*xjn4&64~DdxoaCHX0dYc)BXMFAa`r)ZC%5DPI~TPv*ylcSE)F>}6XqJS=wZ&yNe*^B zhxFl_kyCxbc@F=Tak}fPVAp#{pSp!`?LeM7!q@2@Lx1F35qVpY)19F2x9d+Zd1?XZ z&+R7t<~HQ)vM?tae~58zAFh3LJ`y#g+ zIgv8-{nLNv-0z7Z{r%^6#*zD#a92SOy2=X1ncJX0Glk5r_aJ902YCf}+!g0w-lIy8 z`NUr2{_UsT%bc^1z|M3b;(z;)^V4DeX-oaV)~Sxcyxg}Y^7bQV6G9x6{dqnT2h&F9 zVrCORI)I%28sdb1m*0=sLEuNK+-yA0gUIQTp)RNyGoLsNeZMG2>X$>v$pI%Kd$B5J zerDi4&ZSz3@(v@XcEdf8I?MY;{|@AL`v$oO`i*d<;9Tsfm^pMW*q{55=-~)*;#H_a z)N|f9>ho|PF58p)@T182J+MyQnD5Umfx1;JBr{_Oy>tROd+Q*`8x$jN3fwchKgd1vNy4RtcaA0=pJUnqus^+FE$Z(S za^f3!=ixT;dqdX*-jS)cqz^caoIZ0cTBm%$?~C21MIyV_rOA7SGlUxm>$FSwoXD;N zd-3(jd(5-Qsggn7W&Vyu41sqEVgh-WaE@@L??&rXliM-B6}4fWB{q2|?YhMCA zNbW~^0=%ElCCU2oXj|t zak|4G;I1S??h10M>-!O>+Qh6g#h8e*xzi(e6**JAG|2mIF_Y&qjfgn8AtiFxkdvul zP9#>x^zWHhM4TF52)Vxqx274$tIPX$nQcAnuML@JT}RGTfjO9N$NL;z(M6n?R|4hT zKu%w46Uj3%{gF+yBjW7T(#YK;T;3nxJZJKGfp2;`;>?U{$lXHDR2UBXi^KaBJMvn@ z`PTK3`x`k`t8c{lw|6r=sG}a%nW#E)w~_O+CcyssZ)cqN^FhRkU0))12RWNzM8t`d zn>qKx^@x+-H$d(#a;_*Jmw7nB?Zg>-vcON-_ z5jcI6pQrr(Zp7JCO_6(moE<#{aaiAn6J|(g#D?TMBXFh z)XYhdJin6HXJ*m;Xq_h8S19i>a%RCW;CAu-xp7zFJb$Q%+!N&VMmQJSllQl3>E9r4 zDLK!l$hoBx;an!f_|a0>pDWx93cImiCo>oR;IT!KD|GxiIlR*!%NDO!X zN+i!WBzlO0obNd#+Mh|qOm zU(AS^j8oS^o~T9Q%S*^f59eYI&u5%$bPLwmlblO@L5>-Z#)X;`Qs-j z?`7od3W$R;Kj+L+I8WV)#KFYKnQC()dFCfRpEveNB+oDY2IVC|PCBq_J#jWWPc`{Q z#OcYzu9G6?+bx3qwVum3SLvmC|Gj6_alS)&uOKHk%#S#Aav9@v`gjp12X{a&8FDUt zXV_n&EsS$V4@K(~Ybqj_966t>Y{bbO2N@@ZjgL6_N*d%+5UzZE;Fj!XoK3qb;@nko zPni-q`!R6h^L>nS7gxeMlaTwWRLHrOa4#xrZQ@)t;Piz8xX#qbnFWobb-D~Zp6R@6 zB6+q`VdT;vry70(`@7EP1@f)n54+1EmlioO_WOv_fAcxCzXSf@N`HXdtH?> z^)8B>fpu0R@g*H{K8FE$bNM_=ehzhyURxFArAN;00eex~cz*D04oC9zHL}hOgxePC zrFlHg%dSu_xfdj!y@s4x-w)RLI@g1HaW0bQPt?G5W<<`OofvWE2CqL=t5Ao$Px4qM zOQJBW(={dbk`+09KIow!?*r6B z$YcH);j$qozW}?I=jO0=>i&CRf0ZlXIUsq9evZWX9LVXTp&p#U z=Ne`b)Vr!`bCj18Ibk7QyU+OC#_Ycc^5TKU>yxhq7 zB9M=CTz+padY_Esxp#@)^B|{3!21vt^IpScn*r+_l@H~;ft)PdC0eJB$Jc58*&K2I z`CW13@*-zWLfs?cH)Q)W8(@E;DXDw%A*Yvsy@=DFGtMp93-Xc>zkL%qvk>|?)v*@i zWcy{n-FO$*nIAd%#&^;Fe5Wd$Yq&b%+@kWxy@j040QHj1T!wLe$_bEHhUmQja-zuT zNS^AMhjHpbI>@(uC!)N9$l0=gMV!@7TW$VdRVsvPCLzDSG#|M_$jKG2)&6h(7w_F+ zoc#5Zi1THRBKJ1oCbft--QWb{eBFr=r^aWj5&gga{l5z%=km^vIGb9q{kaL3BhGcL zgmqKG;iAGG{1(oIh3~;>`U@OrAXrb0T||yelb*oH!Ex z-dVDkb$(SblIN#fz`0Z@ z)FJO8C*|gd(^V5OPF#k*%1!Bw=UEy#zj$fHxi2y^PR$SfN}G#5$Dt#

    r$Y3FEe>c5%K{J}gL7u`F#e;T76-b2py z9vs=b9#fBTqV>c&|MeeLYecpG`B6FK$pOl#5~eS(}T4)IeAdB`|jpsv&1j4v75LH~5=z z=HM`pcd!<6pCTu(j{$DepNvy!CPbWgOCwhuInylEgKykroJce?;&gE`ud9Ka-!dqY zCllXhoWB15+B*~QDvE3Y*UbVU1QHO0k*x$35Re2EQN}Ng0VT+05*CGl4p|_OjVyw) zq)}uM6%+(TghoL@1p}xsxV1R$BRhmqfo5OAzAqxLZ=LR(sw?0_XL#>>?_@r6bMNiH zPt{+iPE~hqm=^@6eJ}0nl91#1ZJ;iFt&Sht5-T|SkaSSUs<-y-gu(0njUwR^Ew}ro7;c1adei~;!fc(5o`h9UPxZ1Z4miDz+D_yAKEc^rB~C|9i#sji zX&25bKK13NVSPAo7~>@)rWc9*j#t6)OGA$Jc~Efr>@5ms zY<^I1R<4wv>Bt$s#zLMg_)+2bsaV0;z7`m70CM)EBxq;fZ3<^Ob=N3- zRpsKKe%kGKVZ3bQ%uVj^?N`61aID#0(N1lY6fXxk`xE!`$*0u**&pnJ{_T)HUyzHO zcK1o)#b(bc@pwG^9j%cd{a$+ra;)87VEs!SrEuDjDq>wRTAafEKAeZ52*` zAMP{oTGI7qK61v;cyS$PO{}hPw%tnXul1}TnAiUONda>Fy|JQy+UThYXC#giUi1-p z*v>-a^vdgi%buff`otduXOELUkMbCD`~mp;95W?d;jCErTnVcqU5^b#&S(|_`BL`#JY4@sZXF(mG#P|-hq)l!vfTn_eULDK$uG;&sb_y3pXiTMg=?f_0N zlIqKo62~E5>?-pVPJ13cKgi~mV*j2(&Rzna2jk1s^~fH(L-fz+CXL@1 zrn2ub9?Ssld_2Z`8aZnrd``zc`?=Ci^ECWDx-oAHa$}KWMK;7c`Gvw+r=Sk9S<-dR zIONPey+r-6jrvME&HB1n-_2+G9rJ&C3Jyy?np%LS>){g z_Y2OvSKZf{FSQq(*1aoo&mpIcdP8tL#a70_9`gpotMC?b&r4jb<$^QrQTKI5-p7Ko zMlVNh5^~m$6~wsf7ekf)>9b*akuKz_1XeUY1r zoSt;6;I#I4Dx5YU2I8%1h1^TX@#)~xY@&|4Rd}PAw|sao`kaQG@l;JQ4#xA9)p(!X zCOCUj6mrugu5PhtC;Pm)!s&N`Pn}CXUq((p@+EL9)caCw$wtvX{mHMef3G0N55m4& zs}*p6sZTS}POEY?Z08K*%tcc~JS+BorJdG*dEm44D;V!pen{RRw3fRUNrG$gzf1 zAl}6Ug)@5HEI4N0g52xK8Bewp9DlNd!fEI47o0XJ4!Jjw(@X9VoSCR{h8{0Cvq?O1 zb0zMZ=Fra4hm?5M=dsYvZW?m)kkh6{3eITuh{Ev&TyT1i2FSgMoF3tNY5B0iu`uwW zos_)1g`D+uMd;tp=?Z7nst5gBR1f3Lm$+w-!hNnWZzvqgihy=52t{rIa@xVof-@gq zq;e&vdiw8MTN~`IW%sY&Mvjl&AULhYGL@TiTyXk?&B(nYabKMTE@Oep4X+~FX*aEa z?R;0_CN&jaw0FiSoG}R2KlW-H>W!iv_1=W-6RE;#A#o=z3C`H9j=O2p5}fhT4aj|joMD`X`B$vA)A$1BpBY;bxsQ>v zn?e7~=C3O4)Ve?&)cQ;P`vf_o>Urp&_PWBcdv6r&)CN_?c%LH2^B^8C3g}-o@M+GJ z;(dmkksc<-fp2|5iDwP0FXCCR*1~uea^_v`{A)W&;q1MQ1jklL^Y3%ytdS9rXOo^) zIBo8&!1b2u^-AQ7s<1xr$z|Vs81Gx;tour#ol{>{ zIQ!mE5swe|A@`lc)!ZsLeZSgHKH`j6|LifP*v{{fGmK+`vtCg-ds;>4U$ycW@1Mxo zG2qkOp!UyN5A(>HC5_(?$gvNgp6MxSJpDpt5zh_^$9Sudv&QWZ?bHrUQM_1lE{OhV zt50D6RwHL@fqjToSzR}IYv>=#Du?mbAg2vGB;xT2>Nv28PzU+QVC2>!$A(^jaVV$e zwK3ra7(Xovxpm0tXJ9^9>&7enGgiTTFs4dA*CS`IJ_ug!4w$#)AzxUu)XojaSsTHN z9zIctXMPeU+Q~kaylg~H8{y{Jxq$U=J>;1+^B|7HCgj-Da35ChHCc>bz5x>@DqI zN|3WwL4F#E>V8RkIvnzIo|K=Pk<&s>!8jaJ$ARs0=id-1&;ErRZ+}>Dc8Z$UdUqIi zt#b&D-;c;yOSS{oQ(YfeeI&uz%D zPEgPE9td>;vsIpHk-^TMTtjA11Z)IC6Rn z)Oj{jt=HNj$WLpFv>u&6P8;zrF%EVU)u%Q01mx#}9oWB<$nkI7`Je^FJLm4}N`tWO zonr@sODqWx9!!YaF;qI2$Z zErNZB)ll6Jvgh1=-9l*};zN#ig}TeDtJgW!&EV7cR;p(~$Qi{j-}U$>lsaL)3VCKl zO7kxmIdh6z&xVasIDW+4ms=OmO9*mSJNO*9ecn*HL9nhED}F{URN|_+`PoVJVue9G zzWp?E<&d*4!2HuohpF*S!#+fhldi|gBga}ponYgO6way+b)H|8>O>fFy!j=OFJ^hw zi*ZMFF>mda(*2MM$XV~Nhxs=}^Xm4g(HWy1c&Xn6|z zR~b3$9d|wIF+}04OsLm(w$#o@fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^fDDiUGC&5%02v?yWPl8i0Wv@a$N(8217v^b$S!Ym7^%4~bp6w(~g|?5zj;-1WqpcTsgAUbMSz zs5CwE40z*nv_s(dzzp{4k6hnwbYRc3sBfaCiOTELxy-_>Y`GpmeU$50sLVt8`ft~- z7I#2CL@=+8g*j{(dmz43l2h_nl#>vU6PoCv*G``geu`jTzaj;fUznSg*FQ5SWpIAR z6KPCT`^)i+fgj|<1uvl@UX=6w@;nsOKt4Y+BP9(&m+=sZi|1bZ8M{26GO#e)ZTi)D zIH}WSum2T9`*U&&GSePQ%M`Hl?MnXqYmWa5dFX#$T54fRK}L=<Ty((dnoExzAn4g2ekimOrpC zJv}qc9V34p`#B#=O-s*AE=UuM|CC|wjx{(a*lU7lpc7RLZ&mjU{uk@qZl_{C2IRZ* zez^0WfSpINT(?a~X#0rs#QB0c7V2g*WBd+xpy2J_d~f8wlH2s^$NBTn|CQVD>c{nh z!`Pa;u6~^69@j1A>c_chlLs`F_bIEb8bv}5b`UmEN zK>uCy;o8T`t7ifGaK}eY;n(@*e03?z>hEm6bJDq}m^>pJHsODE+|1t@H@Ezh?Vr8p zW#i^l6M4P3)^P*&n(CN;jq^7{pnr7S;@ZYJTR*I0*FJ9E`u*E+i*Op}&9j8$>;Y*w z&HnT}8(%hF{_Jq#xcdU}#QEkG`|CeGL0)Lay_9g;^V{)hB%12&%gXju&a3iup*J@F z&T*0JU+1*cjO1*d=O3PbNB#S~c~m5NjpO4zzy9N-><9eK_+LAZBAv#0^XUGZ!tB)K zyvMz%^k?fH%_H~zisSfl*Q@CF>h&$LAUUsq_xF#Ee;QGJ^R5Tn`)b#-zRCOa_Svb@ zsL;6JI`Z4=oBx4zr)} zEcX%kP6^)m-1C&Z;Ej*x&pYl~AHDHt?G^*fCw1P-`4Qy=@#2@|hugp_?Uw`Z>t&~9 zre&pN7l=)XbH2d)D&F~{DnpOT&g(pdLFD3%KYL#P@1K0m8NG`m?=iz`0E935uEj(2 zviHj}3J$5)Dd2stK)Gia-x=P2%saooh)Q1zItHB3FWdPFI1Ri=-^lqg~nHieyj_u=Vk8)EbljU4R}9bp^tOlZ|0ppV84**zVE&5+~5&k0b$DCrXR=v z86X2>fDDj<|GWY3^Syze7sUI@<;3SX+Qla%`9HGbzYhXyHy4fbZWDMd9p+2XQ~ReU zr={$*ckS4-efQ29QPry6Sf%<6(@?`hV1S=P42 z`6Y+%8-C_cZ0^xD+Z#MMsZQ*W;~Vz39I*Pe&vK8g+ufw+dw=_E$casd+6+CirReC< zwL2Sjoqb=?vEv&{@6Orw$)%&m*6(fJ@3WDYj-4nu5?gfX;((f`E)D!;(yo)MqnG%0 zhL!Kspmy}6+~_6wm*yXNFiyWS~V`D`p_Dm z|C22%%`;uIBFk546j3u;w+vr!Irw4pnbAJyCa+H%=r8v^{_E^k?K;o@(imMbS~}AD z!D7`rgusCnoCAZ*eV6u)oqI!OjBXcl>8!aeqi>oVCJu~o4vYvcZhLX~)<+_5ePMD) SaGlml1N1!G_u(()V*U#rh&ih@6YDvIb3qCuiSL5PBqf;YQ+PF&Dubvn(ynSJx#_kG^` zWcCZyoGPE&-Zc_xsK4!3-K;iL8Q;J5^VQa^GoQ0B`72VY(NNfn_67V0@%dkgzy}p#42}zS*Mb&$B+H9acH-Ta-J2Se?&gfsOiA6{m9fo17+e7q*XeM_8e%{jOuYM~3ehfV z{jOxfL|1xOHlp6usJGEgcw7mgyUakPZqz&TVURCEEt$|@-rSrh*$tO;Cjojl$7Mzm zhLxDXm3Qro%OT}N#}uXQZ~gIAc)oT^T*hS)u~;b+5Q4{4+h|4u(I$Z-RR7hLpYA>= z-g3{^b}qFhrQS%%*59?R7wX-S9fBg^47xfe3&V2uaiC&GXNh;qm^G*zYex+bZ3|FG zOb3wext0IhBb<)W#h~q43Np*7LYMeKyNZG&5YsWn^3of>J$hv)a`Ul5=n9(n!IW$3 zfMrgLN|Htp%e&T)ns`KXa5R{A?SMauO3b(b@0u*ns72|XO(2BJ1j91Mcx5wZN=b#Q zrGheb%ruZQDPO8cl!A`~qZ&0OhoRPi$x8uUnxfduj8g_YnQ)Aoi>D52w$aw2oKHSS zS`RQGiht7-+~VUcB501x;{n%r2QY0ne}ghU#Ed*rT<7LpTC#&UMz!fIYBvSu93X%s z5W0_YJc(_f?+lJ4oGLkf%;H*3QQdY(ux3Pi_svt(DxrH$dnGDo&^!*Ry71?!u|+m< zG_{1yC)y95aSP#9T2ojo-j9y2K7IEO@fS{p@0c!2v`)3+|pJ7vq zat|$w&$*XRJG`?RCe#r`9HoBG Date: Thu, 7 Sep 2023 18:33:31 +0200 Subject: [PATCH 114/176] test: change CaImaging image to build --- .devcontainer/docker-compose.yaml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index b9894fa..f45833e 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -3,10 +3,10 @@ services: app: cpus: 4 mem_limit: 8g - #build: - # context: .. - # dockerfile: ./.devcontainer/Dockerfile - image: datajoint/element_calcium_imaging:latest + build: + context: .. + dockerfile: ./.devcontainer/Dockerfile + #image: datajoint/element_calcium_imaging:latest extra_hosts: - fakeservices.datajoint.io:127.0.0.1 environment: From 2480421184aab57bc7a4d50874fb99df0e589107 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 18:46:46 +0200 Subject: [PATCH 115/176] test: environments and volumes commented in yaml --- .devcontainer/docker-compose.yaml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index f45833e..72ab55b 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -6,11 +6,11 @@ services: build: context: .. dockerfile: ./.devcontainer/Dockerfile - #image: datajoint/element_calcium_imaging:latest + #image: datajoint/element_deeplabcut:latest extra_hosts: - fakeservices.datajoint.io:127.0.0.1 - environment: - - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/calcium-imaging-v2 + #environment: + # - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/deeplabcut-v1 devices: - /dev/fuse cap_add: @@ -18,7 +18,7 @@ services: security_opt: - apparmor:unconfined volumes: - - ..:/workspaces/element-calcium-imaging:cached + # - ..:/workspaces/element-deeplabcut:cached - docker_data:/var/lib/docker # persist docker images privileged: true # only because of dind volumes: From da33b010f96aff64ede616b6dd5dfb2dbb53fe62 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 18:55:11 +0200 Subject: [PATCH 116/176] setup comments deleted --- setup.py | 21 +-------------------- 1 file changed, 1 insertion(+), 20 deletions(-) diff --git a/setup.py b/setup.py index ba3487f..b442d83 100644 --- a/setup.py +++ b/setup.py @@ -7,9 +7,6 @@ with open(path.join(here, "README.md"), "r") as f: long_description = f.read() -# with open(path.join(here, "requirements.txt")) as f: -# requirements = f.read().splitlines() - with open(path.join(here, pkg_name, "version.py")) as f: exec(f.read()) @@ -36,7 +33,7 @@ "element-interface>=0.5.0", "ipykernel>=6.0.1", "pygit2", - "graphviz", # This worked to me installing using conda, not pip! -> pip install anaconda graphviz at the very beginning + "graphviz", ], extras_requires={ "default": ["deeplabcut[tf]>=2.2.1.1"], @@ -44,22 +41,6 @@ "'deeplabcut[apple_mchips]'", "tables=3.7.0", "tensorflow-deps", - # "--upgrade tensorflow_macos==2.10.0", ##issue with keras.legacy after installing keras -c apple - # conda install keras -c apple ], }, ) - -# TO-DO: CHECK THIS FILE TO INSTALL ELEMENT IN ANOTHER CONDA ENVIRONMENT -""" -!!!! For M2 downgrade tensorflow-macos and keras to 2.12.0 -tensorboard 2.12.3 -tensorboard-data-server 0.7.1 -tensorboard-plugin-wit 1.8.1 -tensorflow 2.13.0 -tensorflow-estimator 2.12.0 -tensorflow-macos 2.12.0 -tensorflow-metal 1.0.1 -tensorpack 0.11 -keras 2.12.0 -""" From 955172331f11d3d77fe09e4fe624f4501b6d440f Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 19:11:28 +0200 Subject: [PATCH 117/176] from IMAGING_ROOT_DATA_DIR to DLC_ROOT_DATA_DIR --- .devcontainer/Dockerfile | 2 +- .devcontainer/devcontainer.json | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 655d0f3..bae724c 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -41,7 +41,7 @@ ENV DJ_HOST fakeservices.datajoint.io ENV DJ_USER root ENV DJ_PASS simple -ENV IMAGING_ROOT_DATA_DIR /workspaces/element-calcium-imaging/example_data +ENV DLC_ROOT_DATA_DIR /workspaces/element-deeplabcut/example_data ENV DATABASE_PREFIX neuro_ USER vscode diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 89bbe16..cc78b6b 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -6,8 +6,8 @@ "remoteEnv": { "LOCAL_WORKSPACE_FOLDER": "${localWorkspaceFolder}" }, - "onCreateCommand": "mkdir -p ${IMAGING_ROOT_DATA_DIR} && pip install -e .", - "postStartCommand": "docker volume prune -f && s3fs ${DJ_PUBLIC_S3_LOCATION} ${IMAGING_ROOT_DATA_DIR} -o nonempty,multipart_size=530,endpoint=us-east-1,url=http://s3.amazonaws.com,public_bucket=1", + "onCreateCommand": "mkdir -p ${DLC_ROOT_DATA_DIR} && pip install -e .", + "postStartCommand": "docker volume prune -f && s3fs ${DJ_PUBLIC_S3_LOCATION} ${DLC_ROOT_DATA_DIR} -o nonempty,multipart_size=530,endpoint=us-east-1,url=http://s3.amazonaws.com,public_bucket=1", "hostRequirements": { "cpus": 4, "memory": "8gb", From 9354ac1bdcb8e171936e4a0d7910b35db1b839bd Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 19:12:09 +0200 Subject: [PATCH 118/176] uncomment env&volumes but changed to element-dlc --- .devcontainer/docker-compose.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index 72ab55b..de80a01 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -9,8 +9,8 @@ services: #image: datajoint/element_deeplabcut:latest extra_hosts: - fakeservices.datajoint.io:127.0.0.1 - #environment: - # - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/deeplabcut-v1 + environment: + - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/deeplabcut-v1 devices: - /dev/fuse cap_add: @@ -18,7 +18,7 @@ services: security_opt: - apparmor:unconfined volumes: - # - ..:/workspaces/element-deeplabcut:cached + - ..:/workspaces/element-deeplabcut:cached - docker_data:/var/lib/docker # persist docker images privileged: true # only because of dind volumes: From e0fb8d38af32f9af1ce7ea43731a206f9a91a045 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 20:06:37 +0200 Subject: [PATCH 119/176] dockerfile new env for dlc folder --- .devcontainer/Dockerfile | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index bae724c..3a06fe8 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -30,9 +30,9 @@ RUN \ COPY ./ /tmp/element-deeplabcut/ RUN \ - #pipeline dependencies + # pipeline dependencies apt-get install gcc g++ ffmpeg libsm6 libxext6 -y && \ - pip install --no-cache-dir -e /tmp/element-deeplabcut/ && \ + pip install --no-cache-dir -e /tmp/element-deeplabcut[elements] && \ # clean up rm -rf /tmp/element-deeplabcut/ && \ apt-get clean @@ -41,7 +41,8 @@ ENV DJ_HOST fakeservices.datajoint.io ENV DJ_USER root ENV DJ_PASS simple -ENV DLC_ROOT_DATA_DIR /workspaces/element-deeplabcut/example_data +ENV DLC_ROOT_DATA_DIR /workspaces/element-deeplabcut/ +ENV CURRENT_PROJECT_FOLDER Top_tracking-DataJoint-2023-08-03 ENV DATABASE_PREFIX neuro_ USER vscode From da33fd4640ac6a21b5033b27fef1c2e68a7dacd7 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 21:49:26 +0200 Subject: [PATCH 120/176] removed files from git --- notebooks/00-DataDownload_Optional.ipynb | 278 ---- notebooks/01-Configure.ipynb | 220 --- notebooks/02-WorkflowStructure_Optional.ipynb | 457 ------- notebooks/04-Automate_Optional.ipynb | 844 ------------ notebooks/05-Visualization_Optional.ipynb | 392 ------ notebooks/06-Drop_Optional.ipynb | 92 -- notebooks/09-AlternateDataset.ipynb | 1 - notebooks/tutorial_copy.ipynb | 1190 ----------------- 8 files changed, 3474 deletions(-) delete mode 100644 notebooks/00-DataDownload_Optional.ipynb delete mode 100644 notebooks/01-Configure.ipynb delete mode 100644 notebooks/02-WorkflowStructure_Optional.ipynb delete mode 100644 notebooks/04-Automate_Optional.ipynb delete mode 100644 notebooks/05-Visualization_Optional.ipynb delete mode 100644 notebooks/06-Drop_Optional.ipynb delete mode 100644 notebooks/09-AlternateDataset.ipynb delete mode 100644 notebooks/tutorial_copy.ipynb diff --git a/notebooks/00-DataDownload_Optional.ipynb b/notebooks/00-DataDownload_Optional.ipynb deleted file mode 100644 index 2f0b661..0000000 --- a/notebooks/00-DataDownload_Optional.ipynb +++ /dev/null @@ -1,278 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# DataJoint U24 - Workflow DeepLabCut" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download example data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These notebooks are built around data provided by DataJoint, including a well-trained model. For similar content using data from DeepLabCut, see [09-AlternateDataset](./09-AlternateDataset.ipynb).\n", - "\n", - "DataJoint provides various datasets via `djarchive`. To pip install..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, - "outputs": [], - "source": [ - "pip install git+https://github.com/datajoint/djarchive-client.git" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os; import djarchive_client\n", - "client = djarchive_client.client()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can browse available datasets:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['t',\n", - " 'workflow-array-ephys-benchmark',\n", - " 'workflow-calcium-imaging-test-set',\n", - " 'workflow-dlc-data',\n", - " 'workflow-facemap',\n", - " 'workflow-trial']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(client.datasets())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Datasets have different versions available:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('t', '1'),\n", - " ('workflow-array-ephys-benchmark', '0.1.0a4'),\n", - " ('workflow-array-ephys-benchmark', 'v1'),\n", - " ('workflow-calcium-imaging-test-set', '0_1_0a2'),\n", - " ('workflow-dlc-data', 'v1'),\n", - " ('workflow-facemap', '0.0.0'),\n", - " ('workflow-trial', '0.0.0b1')]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(client.revisions())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can make a directory for downloading:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "os.makedirs('/tmp/test_data', exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then run download for a given set and the revision:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(79, 0)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "client.download('workflow-dlc-data',\n", - " target_directory='/tmp/test_data/', \n", - " revision='v1')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Directory organization\n", - "\n", - "After downloading, the directory will be organized as follows:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "/tmp/test_data/from_top_tracking/\n", - "- config.yml\n", - "- dlc-models/iteration-0/from_top_trackingFeb23-trainset95shuffle1/\n", - " - test/pose_cfg.yaml\n", - " - train/\n", - " - checkpoint\n", - " - checkpoint_orig\n", - " ─ learning_stats.csv\n", - " ─ log.txt\n", - " ─ pose_cfg.yaml\n", - " ─ snapshot-10300.data-00000-of-00001\n", - " ─ snapshot-10300.index\n", - " ─ snapshot-10300.meta # same for 103000\n", - "- labeled-data/\n", - " - train1/\n", - " - CollectedData_DJ.csv\n", - " - CollectedData_DJ.h5\n", - " - img00674.png # and others\n", - " - train2/ # similar to above\n", - "- videos/\n", - " - test.mp4\n", - " - train1.mp4\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will use this dataset as an example across this series of notebooks. If you use another dataset, change the path accordingly.\n", - "\n", - "- `config.yaml` contains key parameters of the project\n", - "- `labeled-data` includes pixel coordinates for each body part\n", - "- `videos` includes the full training and inference videos\n", - "\n", - "This workflow contains additional functions for setting up this demo data, including adding absolute paths to config files and shortening the inference video to speed up pose estimation." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading DLC 2.2.1.1...\n", - "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n" - ] - } - ], - "source": [ - "from workflow_deeplabcut.load_demo_data import setup_bare_project, shorten_video\n", - "\n", - "setup_bare_project(project=\"/tmp/test_data/from_top_tracking\", \n", - " net_type = \"mobilenet_v2_1.0\") # sets paths\n", - "shorten_video(\"/tmp/test_data/from_top_tracking/videos/test.mp4\",\n", - " output_path=None,first_n_sec=2) # makes test-2s.mp4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For your own data, we recommend using the DLC gui to intitialize your project and label the data. \n", - "\n", - "In the next notebook, [01-Configure](./01-Configure.ipynb), we'll set up the DataJoint config file with a pointer to your root data directory." - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:percent" - }, - "kernelspec": { - "display_name": "Python 3.8.11 ('ele')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - }, - "toc-autonumbering": false, - "toc-showcode": false, - "toc-showmarkdowntxt": false, - "vscode": { - "interpreter": { - "hash": "61456c693db5d9aa6731701ec9a9b08ab88a172bee0780139a3679beb166da16" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/01-Configure.ipynb b/notebooks/01-Configure.ipynb deleted file mode 100644 index b4a8fcf..0000000 --- a/notebooks/01-Configure.ipynb +++ /dev/null @@ -1,220 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# DataJoint U24 - Workflow DeepLabCut" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Configure DataJoint" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "- To run `workflow-deeplabcut`, we need to set up the DataJoint config file, called `dj_local_conf.json`, unique to each machine.\n", - "\n", - "- The config only needs to be set up once. If you already have one, skip to [02-Workflow-Structure](./02-WorkflowStructure_Optional.ipynb).\n", - "\n", - "- By convention, we set a local config in the workflow directory. You may be interested in [setting a global config](https://docs.datajoint.org/python/setup/01-Install-and-Connect.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "# change to the upper level folder to detect dj_local_conf.json\n", - "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", - " + \"workflow directory\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure database host address and credentials" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can set up credentials following [instructions here](https://tutorials.datajoint.io/setting-up/get-database.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ····\n" - ] - } - ], - "source": [ - "import datajoint as dj\n", - "import getpass\n", - "dj.config['database.host'] = '{YOUR_HOST}'\n", - "dj.config['database.user'] = '{YOUR_USERNAME}'\n", - "dj.config['database.password'] = getpass.getpass() # enter the password securely" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You should be able to connect to the database at this stage." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.conn()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure the `custom` field" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Prefix" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A schema prefix can help manage privelages on a server. Teams who work on the same schemas should use the same prefix\n", - "\n", - "Setting the prefix to `neuro_` means that every schema we then create will start with `neuro_` (e.g. `neuro_lab`, `neuro_subject`, `neuro_model` etc.)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config['custom'] = {'database.prefix': 'neuro_'}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Root directory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`dlc_root_data_dir` sets the root path(s) for the Element. Given multiple, the Element will always figure out which root to use based on the files it expects there. This should be the directory above your DeepLabCut project path." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config['custom'] = {'dlc_root_data_dir' : ['/tmp/test_data/', '/tmp/example/']}\n", - "\n", - "# Check the connection with `find_full_path`\n", - "from element_interface.utils import find_full_path\n", - "data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'],\n", - " 'from_top_tracking')\n", - "assert data_dir.exists(), \"Please check the that you have the from_top_tracking folder\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save the config as a json\n", - "\n", - "Once set, the config can either be saved locally or globally. \n", - "\n", - "- The local config would be saved as `dj_local_conf.json` in the workflow directory. This is usefull for managing multiple (demo) pipelines.\n", - "- A global config would be saved as `datajoint_config.json` in the home directory.\n", - "\n", - "When imported, DataJoint will first check for a local config. If none, it will check for a global config." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config.save_local()\n", - "# dj.config.save_global()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the [next notebook](./02-WorkflowStructure_Optional.ipynb) notebook, we'll explore the workflow structure." - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:percent" - }, - "kernelspec": { - "display_name": "Python 3.8.11 ('ele')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - }, - "vscode": { - "interpreter": { - "hash": "61456c693db5d9aa6731701ec9a9b08ab88a172bee0780139a3679beb166da16" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/02-WorkflowStructure_Optional.ipynb b/notebooks/02-WorkflowStructure_Optional.ipynb deleted file mode 100644 index ec8e84c..0000000 --- a/notebooks/02-WorkflowStructure_Optional.ipynb +++ /dev/null @@ -1,457 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# DataJoint U24 - Workflow DeepLabCut" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook introduces some useful DataJoint for exploring pipelines featuring the DeepLabCut Element.\n", - "\n", - "+ DataJoint needs to be configured before running this notebook (see [01-Configure](./01-Configure.ipynb)).\n", - "+ Those familar with the structure of DataJoint workflows can skip to [03-Process](./03-Process.ipynb).\n", - "+ The playground tutorial on [CodeBook](http://codebook.datajoint.io/) provides a more thorough introduction. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To load the local config, we move to the package root." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", - " + \"workflow directory\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Schemas, Diagrams and Tables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Schemas are conceptually related sets of tables. By importing schemas from `workflow_deeplabcut.pipeline`, we'll declare the tables on the server with the prefix in the config (if we have permission to do so). If these tables are already declared, we'll gain access. \n", - "\n", - "- `dj.list_schemas()` lists all schemas a user has access to in the current database\n", - "- `.schema.list_tables()` will provide names for each table in the format used under the hood." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj\n", - "from workflow_deeplabcut.pipeline import lab, subject, session, train, model\n", - "\n", - "dj.list_schemas()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['#training_param_set',\n", - " 'video_set',\n", - " 'video_set__file',\n", - " 'training_task',\n", - " '__model_training']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train.schema.list_tables()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`dj.Diagram()` plots tables and dependencies in a schema. To see additional upstream or downstream connections, add `- N` or `+ N`.\n", - "\n", - "- `train`: Optional schema to manage model training within DataJoint\n", - "- `model`: Schema to manage pose estimation" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "title": "`dj.Diagram()`: plot tables and dependencies" - }, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\ntrain.TrainingTask\n\n\ntrain.TrainingTask\n\n\n\n\n\ntrain.ModelTraining\n\n\ntrain.ModelTraining\n\n\n\n\n\ntrain.TrainingTask->train.ModelTraining\n\n\n\n\ntrain.VideoSet.File\n\n\ntrain.VideoSet.File\n\n\n\n\n\ntrain.VideoSet\n\n\ntrain.VideoSet\n\n\n\n\n\ntrain.VideoSet->train.TrainingTask\n\n\n\n\ntrain.VideoSet->train.VideoSet.File\n\n\n\n\ntrain.TrainingParamSet\n\n\ntrain.TrainingParamSet\n\n\n\n\n\ntrain.TrainingParamSet->train.TrainingTask\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(train)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\nmodel.ModelEvaluation\n\n\nmodel.ModelEvaluation\n\n\n\n\n\nmodel.PoseEstimationTask\n\n\nmodel.PoseEstimationTask\n\n\n\n\n\nmodel.PoseEstimation\n\n\nmodel.PoseEstimation\n\n\n\n\n\nmodel.PoseEstimationTask->model.PoseEstimation\n\n\n\n\nmodel.VideoRecording.File\n\n\nmodel.VideoRecording.File\n\n\n\n\n\nmodel.Model\n\n\nmodel.Model\n\n\n\n\n\nmodel.Model->model.ModelEvaluation\n\n\n\n\nmodel.Model->model.PoseEstimationTask\n\n\n\n\nmodel.Model.BodyPart\n\n\nmodel.Model.BodyPart\n\n\n\n\n\nmodel.Model->model.Model.BodyPart\n\n\n\n\nmodel.RecordingInfo\n\n\nmodel.RecordingInfo\n\n\n\n\n\nmodel.VideoRecording\n\n\nmodel.VideoRecording\n\n\n\n\n\nmodel.VideoRecording->model.PoseEstimationTask\n\n\n\n\nmodel.VideoRecording->model.VideoRecording.File\n\n\n\n\nmodel.VideoRecording->model.RecordingInfo\n\n\n\n\nmodel.PoseEstimation.BodyPartPosition\n\n\nmodel.PoseEstimation.BodyPartPosition\n\n\n\n\n\nmodel.PoseEstimation->model.PoseEstimation.BodyPartPosition\n\n\n\n\nmodel.BodyPart\n\n\nmodel.BodyPart\n\n\n\n\n\nmodel.BodyPart->model.Model.BodyPart\n\n\n\n\nmodel.Model.BodyPart->model.PoseEstimation.BodyPartPosition\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(model) - 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Table Types\n", - "\n", - "- **Manual table**: green box, manually inserted table, expect new entries daily, e.g. Subject, ProbeInsertion. \n", - "- **Lookup table**: gray box, pre inserted table, commonly used for general facts or parameters. e.g. Strain, ClusteringMethod, ClusteringParamSet. \n", - "- **Imported table**: blue oval, auto-processing table, the processing depends on the importing of external files. e.g. process of Clustering requires output files from kilosort2. \n", - "- **Computed table**: red circle, auto-processing table, the processing does not depend on files external to the database, commonly used for \n", - "- **Part table**: plain text, as an appendix to the master table, all the part entries of a given master entry represent a intact set of the master entry. e.g. Unit of a CuratedClustering.\n", - "\n", - "### Table Links\n", - "\n", - "- **One-to-one primary**: thick solid line, share the exact same primary key, meaning the child table inherits all the primary key fields from the parent table as its own primary key. \n", - "- **One-to-many primary**: thin solid line, inherit the primary key from the parent table, but have additional field(s) as part of the primary key as well\n", - "- **Secondary dependency**: dashed line, the child table inherits the primary key fields from parent table as its own secondary attribute." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Common Table Functions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "- `

    ()` show table contents\n", - "- `heading` shows attribute definitions\n", - "- `describe()` show table defintiion with foreign key references" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "title": "Each datajoint table class inside the module corresponds to a table inside the schema. For example, the class `ephys.EphysRecording` correponds to the table `_ephys_recording` in the schema `neuro_ephys` in the database." - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - "
    \n", - " \n", - " \n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", - " \n", - "
    \n", - "

    file_id

    \n", - " \n", - "
    \n", - "

    file_path

    \n", - " filepath of video, relative to root data directory\n", - "
    \n", - " \n", - "

    Total: 0

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet *recording_id *file_id file_path \n", - "+---------+ +------------+ +------------+ +---------+ +-----------+\n", - "\n", - " (Total: 0)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model.VideoRecording.File()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "title": "`heading`: show table attributes regardless of foreign key references." - }, - "outputs": [ - { - "data": { - "text/plain": [ - "# \n", - "model_name : varchar(64) # user-friendly model name\n", - "---\n", - "task : varchar(32) # task in the config yaml\n", - "date : varchar(16) # date in the config yaml\n", - "iteration : int # iteration/version of this model\n", - "snapshotindex : int # which snapshot for prediction (if -1, latest)\n", - "shuffle : int # which shuffle of the training dataset\n", - "trainingsetindex : int # which training set fraction to generate model\n", - "scorer : varchar(64) # scorer/network name - DLC's GetScorerName()\n", - "config_template : longblob # dictionary of the config for analyze_videos()\n", - "project_path : varchar(255) # DLC's project_path in config relative to root\n", - "model_prefix=\"\" : varchar(32) # \n", - "model_description=\"\" : varchar(1000) # \n", - "paramset_idx=null : smallint # " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model.Model.heading" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# Specification for a DLC model training instance\n", - "-> train.VideoSet\n", - "-> train.TrainingParamSet\n", - "training_id : int \n", - "---\n", - "model_prefix=\"\" : varchar(32) \n", - "project_path=\"\" : varchar(255) # DLC's project_path in config relative to root\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "'# Specification for a DLC model training instance\\n-> train.VideoSet\\n-> train.TrainingParamSet\\ntraining_id : int \\n---\\nmodel_prefix=\"\" : varchar(32) \\nproject_path=\"\" : varchar(255) # DLC\\'s project_path in config relative to root\\n'" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "train.TrainingTask.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "title": "ephys" - }, - "source": [ - "## Other Elements installed with the workflow\n", - "\n", - "- [`lab`](https://github.com/datajoint/element-lab): lab management related information, such as Lab, User, Project, Protocol, Source.\n", - "- [`subject`](https://github.com/datajoint/element-animal): general animal information, User, Genetic background, Death etc.\n", - "- [`session`](https://github.com/datajoint/element-session): General information of experimental sessions.\n", - "\n", - "For more information about these Elements, see [workflow session](https://github.com/datajoint/workflow-session)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(lab) + dj.Diagram(subject) + dj.Diagram(session)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "title": "[session](https://github.com/datajoint/element-session): experimental session information" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> subject.Subject\n", - "session_datetime : datetime \n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "'-> subject.Subject\\nsession_datetime : datetime \\n'" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "session.Session.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary and next step\n", - "\n", - "- This notebook introduced the overall structures of the schemas and tables in the workflow and relevant tools to explore the schema structure and table definitions.\n", - "\n", - "- The [next notebook](./03-Process.ipynb) will introduce the detailed steps to run through `workflow-deeplabcut`." - ] - } - ], - "metadata": { - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:percent" - }, - "kernelspec": { - "display_name": "Python 3.8.11 ('ele')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - }, - "vscode": { - "interpreter": { - "hash": "61456c693db5d9aa6731701ec9a9b08ab88a172bee0780139a3679beb166da16" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/04-Automate_Optional.ipynb b/notebooks/04-Automate_Optional.ipynb deleted file mode 100644 index c2c879d..0000000 --- a/notebooks/04-Automate_Optional.ipynb +++ /dev/null @@ -1,844 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# DataJoint U24 - Workflow DeepLabCut" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Workflow Automation\n", - "\n", - "In the previous notebook [03-Process](./03-Process.ipynb), we ran through the workflow in detailed steps, manually adding each. The current notebook provides a more automated approach.\n", - "\n", - "The commands here run a workflow using example data from the [00-DownloadData](./00-DataDownload_Optional.ipynb) notebook, but note where placeholders could be changed for a different dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting cbroz@dss-db.datajoint.io:3306\n" - ] - } - ], - "source": [ - "import os; from pathlib import Path\n", - "# change to the upper level folder to detect dj_local_conf.json\n", - "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", - " + \"workflow directory\")\n", - "from workflow_deeplabcut.pipeline import lab, subject, session, train, model\n", - "from workflow_deeplabcut import process" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll be using the `process.py` script to automatically loop through all `make` functions, as a shortcut for calling each individually.\n", - "\n", - "If you previously completed the [03-Process notebook](./03-Process.ipynb), you may want to delete the contents ingested there, to avoid duplication errors." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Deleting 4 rows from `u24_dlc_session`.`session_directory`\n", - "Deleting 4 rows from `u24_dlc_session`.`session_note`\n", - "Deleting 4 rows from `u24_dlc_session`.`session`\n", - "Deleting 3 rows from `u24_dlc_train`.`#training_param_set`\n", - "Deleting 0 rows from `u24_dlc_train`.`video_set`\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "safemode=True # Set to false to turn off confirmation prompts\n", - "(session.Session & 'subject=\"subject6\"').delete(safemode=safemode)\n", - "train.TrainingParamSet.delete(safemode=safemode)\n", - "train.VideoSet.delete(safemode=safemode)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Ingestion of subjects, sessions, videos and training parameters\n", - "\n", - "Refer to the `user_data` folder in the workflow.\n", - "\n", - "1. Fill subject and session information in files `subjects.csv` and `sessions.csv`\n", - "2. Fill in recording and parameter information in `recordings.csv` and `config_params.csv`\n", - " + Add both training and estimation videos to the recording list\n", - " + Additional columns in `config_params.csv` will be treated as model training parameters\n", - "3. Run automatic scripts prepared in `workflow_deeplabcut.ingest` for ingestion: \n", - " + `ingest_subjects` for `subject.Subject`\n", - " + `ingest_sessions` - for session tables `Session`, `SessionDirectory`, and `SessionNote`\n", - " + `ingest_dlc_items` - for ...\n", - " - `train.ModelTrainingParamSet`\n", - " - `train.VideoSet` and the corresponding `File` part table\n", - " - `model.VideoRecording` and the corresponding `File` part table" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "---- Inserting 0 entry(s) into subject ----\n", - "\n", - "---- Inserting 4 entry(s) into session ----\n", - "\n", - "---- Inserting 4 entry(s) into session_directory ----\n", - "\n", - "---- Inserting 4 entry(s) into session_note ----\n", - "\n", - "---- Inserting 3 entry(s) into #model_training_param_set ----\n", - "\n", - "---- Inserting 3 entry(s) into video_set ----\n", - "\n", - "---- Inserting 10 entry(s) into video_set__file ----\n", - "\n", - "---- Inserting 1 entry(s) into video_recording ----\n", - "\n", - "---- Inserting 1 entry(s) into video_recording__file ----\n" - ] - } - ], - "source": [ - "from workflow_deeplabcut.ingest import ingest_subjects, ingest_sessions, ingest_dlc_items\n", - "ingest_subjects()\n", - "ingest_sessions()\n", - "ingest_dlc_items()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting project variables\n", - "\n", - "1. Set your root directory in your DataJoint config file, under `custom` as `dlc_root_data_dir`. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj; dj.config.load('dj_local_conf.json')\n", - "from element_interface.utils import find_full_path\n", - "data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config\n", - " 'from_top_tracking') # DLC project dir\n", - "config_path = (data_dir / 'config.yaml')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2. Next, we pair training files with training parameters, and launch training via `process`. \n", - " - Some tables may try to populate without upstream keys. \n", - " - Others, like `RecordingInfo` already have keys loaded.\n", - " - Note: DLC's model processes (e.g., Training, Evaluation) log a lot of information to the console, to quiet this, pass `verbose=False` to `process`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "key={'paramset_idx':0,'training_id':0,'video_set_id':0, \n", - " 'project_path':'from_top_tracking/'}\n", - "train.TrainingTask.insert1(key, skip_duplicates=True)\n", - "process.run(verbose=True, display_progress=True)\n", - "model.RecordingInfo()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the purposes of this demo, we'll want to use an older model, so the folling function will reload the original checkpoint file." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from workflow_deeplabcut.load_demo_data import revert_checkpoint_file\n", - "revert_checkpoint_file()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "3. Now to add such a model upstream key\n", - " - Include a user-friendly `model_name`\n", - " - Include the relative path for the project's `config.yaml`\n", - " - Add `shuffle` and `trainingsetindex`\n", - " - `insert_new_model` will prompt before inserting, but this can be skipped with `prompt=False`" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--- DLC Model specification to be inserted ---\n", - "\tmodel_name: FromTop-latest\n", - "\tmodel_description: FromTop - latest snapshot\n", - "\tscorer: DLCmobnet100fromtoptrackingFeb23shuffle1\n", - "\ttask: from_top_tracking\n", - "\tdate: Feb23\n", - "\titeration: 0\n", - "\tsnapshotindex: -1\n", - "\tshuffle: 1\n", - "\ttrainingsetindex: 0\n", - "\tproject_path: from_top_tracking\n", - "\tparamset_idx: 1\n", - "\t-- Template/Contents of config.yaml --\n", - "\t\tTask: from_top_tracking\n", - "\t\tscorer: DJ\n", - "\t\tdate: Feb23\n", - "\t\tvideo_sets: {'/tmp/test_data/from_top_tracking/videos/train1.mp4': {'crop': '0, 500, 0, 500'}}\n", - "\t\tbodyparts: ['head', 'bodycenter', 'tailbase']\n", - "\t\tstart: 0\n", - "\t\tstop: 1\n", - "\t\tnumframes2pick: 20\n", - "\t\tpcutoff: 0.6\n", - "\t\tdotsize: 3\n", - "\t\talphavalue: 0.7\n", - "\t\tcolormap: viridis\n", - "\t\tTrainingFraction: [0.95]\n", - "\t\titeration: 0\n", - "\t\tdefault_net_type: resnet_50\n", - "\t\tsnapshotindex: -1\n", - "\t\tbatch_size: 8\n", - "\t\tcropping: False\n", - "\t\tx1: 0\n", - "\t\tx2: 640\n", - "\t\ty1: 277\n", - "\t\ty2: 624\n", - "\t\tcorner2move2: [50, 50]\n", - "\t\tmove2corner: True\n", - "\t\tcroppedtraining: None\n", - "\t\tdefault_augmenter: default\n", - "\t\tidentity: None\n", - "\t\tmaxiters: 5\n", - "\t\tmodelprefix: \n", - "\t\tmultianimalproject: False\n", - "\t\tscorer_legacy: False\n", - "\t\tshuffle: 1\n", - "\t\tskeleton: [['bodypart1', 'bodypart2'], ['objectA', 'bodypart3']]\n", - "\t\tskeleton_color: black\n", - "\t\ttrain_fraction: 0.95\n", - "\t\ttrainingsetindex: 0\n", - "\t\tproject_path: /tmp/test_data/from_top_tracking\n", - "\n", - "---- Populating __model_training ----\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ModelTraining: 0it [00:00, ?it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "---- Populating _recording_info ----\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "RecordingInfo: 0it [00:00, ?it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "---- Populating __model_evaluation ----\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ModelEvaluation: 0%| | 0/1 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    scorerFromTop-latest
    bodypartsbodycenterheadtailbase
    coordsxyzlikelihoodxyzlikelihoodxyzlikelihood
    0246.782684298.7280880.00.999998241.036957316.3324890.00.999850256.203064278.5533140.00.999998
    1246.217529299.3580630.00.999997239.048737319.1770020.00.999905255.819626280.2007450.00.999996
    2244.459579301.3092350.00.999999240.238800320.5256960.00.999899255.705093280.9390560.00.999995
    3242.014755302.8652040.00.999999238.536774322.3244630.00.999941254.424484282.0157780.00.999990
    4240.900177303.4591670.00.999998237.967987324.0723270.00.999941252.180603280.8992000.00.999977
    .......................................
    118248.682251364.7098690.00.999965270.854980371.8931270.00.999961234.899185356.0355830.00.999996
    119250.326385366.8703610.00.999972271.488495373.0998840.00.999991235.644073356.8151250.00.999989
    120251.634140367.7091980.00.999972272.043884373.4028930.00.999995236.953812358.6514590.00.999977
    121255.393692364.1111450.00.999979273.417572373.9067990.00.999997238.825363361.5617980.00.999885
    122257.736847365.2640080.00.999996276.008667373.9012450.00.999992239.148163364.0292970.00.999962
    \n", - "

    123 rows × 12 columns

    \n", - "" - ], - "text/plain": [ - "scorer FromTop-latest \\\n", - "bodyparts bodycenter head \n", - "coords x y z likelihood x y \n", - "0 246.782684 298.728088 0.0 0.999998 241.036957 316.332489 \n", - "1 246.217529 299.358063 0.0 0.999997 239.048737 319.177002 \n", - "2 244.459579 301.309235 0.0 0.999999 240.238800 320.525696 \n", - "3 242.014755 302.865204 0.0 0.999999 238.536774 322.324463 \n", - "4 240.900177 303.459167 0.0 0.999998 237.967987 324.072327 \n", - ".. ... ... ... ... ... ... \n", - "118 248.682251 364.709869 0.0 0.999965 270.854980 371.893127 \n", - "119 250.326385 366.870361 0.0 0.999972 271.488495 373.099884 \n", - "120 251.634140 367.709198 0.0 0.999972 272.043884 373.402893 \n", - "121 255.393692 364.111145 0.0 0.999979 273.417572 373.906799 \n", - "122 257.736847 365.264008 0.0 0.999996 276.008667 373.901245 \n", - "\n", - "scorer \n", - "bodyparts tailbase \n", - "coords z likelihood x y z likelihood \n", - "0 0.0 0.999850 256.203064 278.553314 0.0 0.999998 \n", - "1 0.0 0.999905 255.819626 280.200745 0.0 0.999996 \n", - "2 0.0 0.999899 255.705093 280.939056 0.0 0.999995 \n", - "3 0.0 0.999941 254.424484 282.015778 0.0 0.999990 \n", - "4 0.0 0.999941 252.180603 280.899200 0.0 0.999977 \n", - ".. ... ... ... ... ... ... \n", - "118 0.0 0.999961 234.899185 356.035583 0.0 0.999996 \n", - "119 0.0 0.999991 235.644073 356.815125 0.0 0.999989 \n", - "120 0.0 0.999995 236.953812 358.651459 0.0 0.999977 \n", - "121 0.0 0.999997 238.825363 361.561798 0.0 0.999885 \n", - "122 0.0 0.999992 239.148163 364.029297 0.0 0.999962 \n", - "\n", - "[123 rows x 12 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.PoseEstimation.get_trajectory(key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary and next step\n", - "\n", - "+ This notebook runs through the workflow in an automatic manner.\n", - "\n", - "+ The next notebook [05-Visualization](./05-Visualization_Optional.ipynb) demonstrates how to plot this data and label videos on disk." - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:percent" - }, - "kernelspec": { - "display_name": "Python 3.8.11 ('ele')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - }, - "vscode": { - "interpreter": { - "hash": "61456c693db5d9aa6731701ec9a9b08ab88a172bee0780139a3679beb166da16" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/05-Visualization_Optional.ipynb b/notebooks/05-Visualization_Optional.ipynb deleted file mode 100644 index d05518b..0000000 --- a/notebooks/05-Visualization_Optional.ipynb +++ /dev/null @@ -1,392 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# DataJoint U24 - Workflow DeepLabCut" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The notebook requires DeepLabCut pose estimation already processed via DataJoint.\n", - "\n", - "- If you don't have data, refer to [00-DataDownload](./00-DataDownload_Optional.ipynb) and [01-Configure](./01-Configure.ipynb).\n", - "- For an overview of the schema, refer to [02-WorkflowStructure](02-WorkflowStructure_Optional.ipynb).\n", - "- For step-by-step or autmated ingestion, refer to [03-Process](./03-Process.ipynb) or [03-Automate](03-Automate_Optional.ipynb)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's change the directory to load the local config, `dj_local_conf.json` and import the relevant schema." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting cbroz@dss-db.datajoint.io:3306\n" - ] - } - ], - "source": [ - "import os # change to the upper level folder to detect dj_local_conf.json\n", - "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", - " + \"workflow directory\")\n", - "\n", - "import datajoint as dj # Import relevant schema\n", - "from workflow_deeplabcut.pipeline import model\n", - "\n", - "# Directing our pipeline to the appropriate config location\n", - "from element_interface.utils import find_full_path\n", - "from workflow_deeplabcut.paths import get_dlc_root_data_dir\n", - "config_path = find_full_path(get_dlc_root_data_dir(), \n", - " 'from_top_tracking/config.yaml')\n", - "\n", - "# Grabbing the relevant key\n", - "import pandas as pd\n", - "key = (model.PoseEstimation & \"recording_id=1\").fetch('KEY')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Fetching data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the previous notebook, we saw how to fetch data as a pandas dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "bodyparts coords\n", - "bodycenter x 231.709213\n", - " y 351.878936\n", - "head x 234.893540\n", - " y 367.393746\n", - "tailbase x 235.567368\n", - " y 333.615991\n", - "dtype: float64" - ] - }, - "execution_count": 192, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df=model.PoseEstimation.get_trajectory(key)\n", - "df_xy = df.iloc[:,df.columns.get_level_values(2).isin([\"x\",\"y\"])]['FromTop-latest']\n", - "df_xy.mean()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We plot these coordinates over time. " - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df_xy.plot().legend(loc='right')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we'll make a copy of the data for the next plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_flat = df_xy.copy()\n", - "df_flat.columns = df_flat.columns.map('_'.join)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, we can overlay the traces of each point over time." - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 196, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "fig,ax=plt.subplots()\n", - "df_flat.plot(x='bodycenter_x',y='bodycenter_y',ax=ax)\n", - "df_flat.plot(x='head_x',y='head_y', ax=ax)\n", - "df_flat.plot(x='tailbase_x',y='tailbase_y', ax=ax)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our visual check shows that these trajectories are more-or-less aligned." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Video Labeling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This Element adds to the DeepLabCut tree structure by sorting results files into output directories. Let's see where they're stored using `infer_output_dir`." - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PosixPath('/tmp/test_data/from_top_tracking/videos/device_Camera1_recording_1_model_FromTop-latest')" - ] - }, - "execution_count": 199, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "destfolder = model.PoseEstimationTask.infer_output_dir(key)\n", - "destfolder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When labeling videos, we need to provide this as an additional argument. \n", - "\n", - "Note that DataJoint handles paths as `pathlib` objects, while DeepLabCut requires strings." - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading DLC 2.2.1.1...\n", - "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n", - "Loading DLC 2.2.1.1...\n", - "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n", - "Starting to process video: /tmp/test_data/from_top_tracking/videos/test-2s.mp4\n", - "Loading /tmp/test_data/from_top_tracking/videos/test-2s.mp4 and data.\n", - "Duration of video [s]: 2.05, recorded with 60.0 fps!\n", - "Overall # of frames: 123 with cropped frame dimensions: 500 500\n", - "Generating frames and creating video.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 123/123 [00:00<00:00, 236.95it/s]\n" - ] - } - ], - "source": [ - "from deeplabcut.utils.make_labeled_video import create_labeled_video\n", - "\n", - "video_path = find_full_path( # Fetch the full video path\n", - " get_dlc_root_data_dir(), ((model.VideoRecording.File & key).fetch1(\"file_path\"))\n", - ")\n", - "\n", - "config_paths = sorted( # Of configs in the project path, defer to the datajoint-saved\n", - " list(\n", - " find_full_path(\n", - " get_dlc_root_data_dir(), ((model.Model & key).fetch1(\"project_path\"))\n", - " ).glob(\"*.y*ml\")\n", - " )\n", - ")\n", - "\n", - "create_labeled_video( # Pass strings to label the video\n", - " config=str(config_paths[-1]),\n", - " videos=str(video_path),\n", - " destfolder=str(destfolder),\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The video should now be labeled at this path" - ] - }, - { - "cell_type": "code", - "execution_count": 206, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "/tmp/test_data/from_top_tracking/videos/test-2s.mp4
    " - ], - "text/plain": [ - "/tmp/test_data/from_top_tracking/videos/test-2s.mp4" - ] - }, - "execution_count": 206, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import FileLink\n", - "FileLink(path=video_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next notebook, [06-Drop](./06-Drop_Optional.ipynb), we'll demonstrate dropping schemas in this Element." - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:percent" - }, - "kernelspec": { - "display_name": "Python 3.8.11 ('ele')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - }, - "vscode": { - "interpreter": { - "hash": "61456c693db5d9aa6731701ec9a9b08ab88a172bee0780139a3679beb166da16" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/06-Drop_Optional.ipynb b/notebooks/06-Drop_Optional.ipynb deleted file mode 100644 index f55b2f0..0000000 --- a/notebooks/06-Drop_Optional.ipynb +++ /dev/null @@ -1,92 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# DataJoint U24 - Workflow DeepLabCut" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Change into the parent directory to find the `dj_local_conf.json` file. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import os; from pathlib import Path\n", - "# change to the upper level folder to detect dj_local_conf.json\n", - "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n", - " + \"workflow directory\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Drop schemas\n", - "\n", - "+ Schemas are not typically dropped in a production workflow with real data in it. \n", - "+ At the developmental phase, it might be required for the table redesign.\n", - "+ When dropping all schemas is needed, drop items starting with the most downstream." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from workflow_deeplabcut.pipeline import *" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# model.schema.drop()\n", - "# train.schema.drop()\n", - "# session.schema.drop()\n", - "# subject.schema.drop()\n", - "# lab.schema.drop()" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:percent" - }, - "kernelspec": { - "display_name": "venv-dlc", - "language": "python", - "name": "venv-dlc" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/09-AlternateDataset.ipynb b/notebooks/09-AlternateDataset.ipynb deleted file mode 100644 index 93540ad..0000000 --- a/notebooks/09-AlternateDataset.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"markdown","metadata":{"tags":[]},"source":["# Workflow DeepLabCut - Alternate Data"]},{"cell_type":"markdown","metadata":{},"source":["## Introduction"]},{"cell_type":"markdown","metadata":{},"source":["\n","This notebook provides a general introduction to DataJoint use via Element DeepLabcut. It follows the same structure as other notebooks in this directory, but uses data from the DeepLabCut team. \n","\n","We recommend the other notebooks as they provide access to a pretrained model and allow for a more in-depth exploration of the features of the Element."]},{"cell_type":"markdown","metadata":{},"source":["## Example data"]},{"cell_type":"markdown","metadata":{},"source":["\n","### Download\n","\n","If you've already cloned the [main DLC repository](https://github.com/DeepLabCut/DeepLabCut), you already have this folder under `examples/openfield-Pranav-2018-10-30`. [This link](https://downgit.github.io/#/home?url=https://github.com/DeepLabCut/DeepLabCut/tree/master/examples/openfield-Pranav-2018-10-30) via [DownGit](https://downgit.github.io/) will start the single-directory download automatically as a zip. Unpack this zip and place it in a directory we'll refer to as your root."]},{"cell_type":"markdown","metadata":{},"source":["### Structure"]},{"cell_type":"markdown","metadata":{},"source":["The directory will be organized as follows within your chosen root\n","directory.\n","\n","```\n"," /your-root/openfield-Pranav-2018-10-30/\n"," - config.yaml\n"," - labeled-data\n"," - m4s1\n"," - CollectedData_Pranav.csv\n"," - CollectedData_Pranav.h5\n"," - img0000.png\n"," - img0001.png\n"," - img0002.png\n"," - img{...}.png\n"," - img0114.png\n"," - img0115.png\n"," - videos\n"," - m3v1mp4.mp4\n","```"]},{"cell_type":"markdown","metadata":{},"source":["For those unfamiliar with DLC...\n","- `config.yaml` contains all the key parameters of the project, including\n"," - file locations (currently empty)\n"," - body parts\n"," - cropping information\n","- `labeled-data` includes the frames coordinates for each body part in the training video\n","- `videos` includes the full training video for this example\n","\n","Part of the demo setup involves an additional\n","command (as [shown here](https://github.com/DeepLabCut/DeepLabCut/blob/master/examples/JUPYTER/Demo_labeledexample_Openfield.ipynb)) to revise the project path within config file as well as generate the `training-datasets` directory."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["your_root='/fill/in/your/root/with\\ escaped\\ spaces'\n","from deeplabcut.create_project.demo_data import load_demo_data\n","load_demo_data(your_root+'/openfield-Pranav-2018-10-30/config.yaml')"]},{"cell_type":"markdown","metadata":{},"source":["### New video"]},{"cell_type":"markdown","metadata":{},"source":["Later, we'll use the first few seconds of the training video as a 'separate session' to demonstrate pose estimation within the Element. `ffmpeg` is a dependency of DeepLabCut\n","that can splice the training video for a demonstration purposes. The command below saves\n","the first 2 seconds of the training video as a copy.\n","\n","- `-n` do not overwrite\n","- `-hide_banner -loglevel error` less verbose output\n","- `-ss 0 -t 2` start at second 0, add 2 seconds\n","- `-i {vid_path}` input this video\n","- `-{v/a}codec copy` copy the video and audio codecs of the input\n","- `{vid_path}-copy.mp4` output file"]},{"cell_type":"code","execution_count":null,"metadata":{"tags":[]},"outputs":[{"data":{"text/plain":["0"]},"metadata":{},"output_type":"display_data"}],"source":["vid_path = your_root + '/openfield-Pranav-2018-10-30/videos/m3v1mp4'\n","cmd = (f'ffmpeg -n -hide_banner -loglevel error -ss 0 -t 2 -i {vid_path}.mp4 '\n"," + f'-vcodec copy -acodec copy {vid_path}-copy.mp4')\n","import os; os.system(cmd)"]},{"cell_type":"markdown","metadata":{"tags":[]},"source":["## Configuring DataJoint"]},{"cell_type":"markdown","metadata":{},"source":["### DataJoint Local Config"]},{"cell_type":"markdown","metadata":{"tags":[]},"source":["- To run `workflow-deeplabcut`, we need to set up the DataJoint configuration file, called `dj_local_conf.json`, unique to each machine.\n","\n","- The config only needs to be set up once, skip to the next section.\n","\n","- By convention, we set a local config in the workflow directory. You may be interested in [setting a global config](https://docs.datajoint.org/python/setup/01-Install-and-Connect.html)."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import os\n","# change to the upper level folder to detect dj_local_conf.json\n","if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n","assert os.path.basename(os.getcwd())=='workflow-deeplabcut', (\"Please move to the \"\n"," + \"workflow directory\")"]},{"cell_type":"markdown","metadata":{},"source":["### Configure database credentials"]},{"cell_type":"markdown","metadata":{},"source":["Now let's set up the host, user and password in the `dj.config` following [instructions here](https://tutorials.datajoint.io/setting-up/get-database.html)."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":[" ····\n"]}],"source":["import datajoint as dj\n","import getpass\n","dj.config['database.host'] = '{YOUR_HOST}'\n","dj.config['database.user'] = '{YOUR_USERNAME}'\n","dj.config['database.password'] = getpass.getpass() # enter the password securely"]},{"cell_type":"markdown","metadata":{},"source":["You should be able to connect to the database at this stage."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["dj.conn()"]},{"cell_type":"markdown","metadata":{},"source":["### Workflow-specific items"]},{"cell_type":"markdown","metadata":{},"source":["**Prefix:** Giving a prefix to your schema could help manage privelages on a server. \n","- If we set prefix `neuro_`, every schema created with the current workflow will start with `neuro_`, e.g. `neuro_lab`, `neuro_subject`, `neuro_imaging` etc.\n","- Teams who work on the same schemas should use the same prefix, set as follows:"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["dj.config['custom'] = {'database.prefix': 'neuro_'}"]},{"cell_type":"markdown","metadata":{},"source":["**Root dir:** `custom` keeps track of your root directory for this project. With multiple roots the Element will figure out which to use based on the files it expects. \n","\n","- Please set one root to the parent directory of DLC's `openfield-Pranav-2018-10-30` example.\n","- In other cases, this should be the parent of your DLC project path.\n","\n","We can then check that the path connects with a tool from [element-interface](https://github.com/datajoint/element-interface)."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["dj.config['custom'] = {'dlc_root_data_dir' : ['your-root1', 'your-root2']}\n","\n","from element_interface.utils import find_full_path\n","data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'],\n"," 'openfield-Pranav-2018-10-30')\n","assert data_dir.exists(), \"Please check the that you have the folder openfield-Pranav\""]},{"cell_type":"markdown","metadata":{},"source":["### Saving the config"]},{"cell_type":"markdown","metadata":{},"source":["\n","With the proper configurations, we could save this as a file, either as a local json file, or a global file. DataJoint will default to a local file, then check for a global if none is found."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["dj.config.save_local() # saved as dj_local_conf.json in the root workflow dir\n","# dj.config.save_global() # saved as .datajoint_config.json in your home dir"]},{"cell_type":"markdown","metadata":{"tags":[]},"source":["## Workflow Structure"]},{"cell_type":"markdown","metadata":{},"source":["### Schemas, Diagrams and Tables"]},{"cell_type":"markdown","metadata":{},"source":["Schemas are conceptually related sets of tables. By importing schemas from `workflow_deeplabcut.pipeline`, we'll declare the tables on the server with the prefix we set. If these tables are already declared, we'll gain access. For more information about lab, animal and session Elements, see [session workflow](https://github.com/datajoint/workflow-session).\n","\n","- `dj.list_schemas()` lists all schemas a user has access to in the current database\n","- `.schema.list_tables()` will provide names for each table in the format used under the hood."]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Connecting cbroz@dss-db.datajoint.io:3306\n"]},{"data":{"text/plain":["['#training_param_set',\n"," 'video_set',\n"," 'video_set__file',\n"," 'training_task',\n"," '__model_training']"]},"execution_count":1,"metadata":{},"output_type":"execute_result"}],"source":["import datajoint as dj\n","from workflow_deeplabcut.pipeline import lab, subject, session, train, model\n","\n","dj.list_schemas()\n","\n","train.schema.list_tables()"]},{"cell_type":"markdown","metadata":{},"source":["`dj.Diagram()` plots tables and dependencies in a schema. To see additional upstream or downstream connections, add `- N` or `+ N`.\n","\n","- `train`: Optional schema to manage model training within DataJoint\n","- `model`: Schema to manage pose estimation"]},{"cell_type":"markdown","metadata":{},"source":["#### Table Types\n","\n","- **Manual table**: green box, manually inserted table, expect new entries daily, e.g. Subject, ProbeInsertion. \n","- **Lookup table**: gray box, pre inserted table, commonly used for general facts or parameters. e.g. Strain, ClusteringMethod, ClusteringParamSet. \n","- **Imported table**: blue oval, auto-processing table, the processing depends on the importing of external files. e.g. process of Clustering requires output files from kilosort2. \n","- **Computed table**: red circle, auto-processing table, the processing does not depend on files external to the database, commonly used for \n","- **Part table**: plain text, as an appendix to the master table, all the part entries of a given master entry represent a intact set of the master entry. e.g. Unit of a CuratedClustering.\n","\n","#### Table Links\n","\n","- **One-to-one primary**: thick solid line, share the exact same primary key, meaning the child table inherits all the primary key fields from the parent table as its own primary key. \n","- **One-to-many primary**: thin solid line, inherit the primary key from the parent table, but have additional field(s) as part of the primary key as well\n","- **Secondary dependency**: dashed line, the child table inherits the primary key fields from parent table as its own secondary attribute."]},{"cell_type":"code","execution_count":3,"metadata":{"title":"`dj.Diagram()`: plot tables and dependencies"},"outputs":[{"data":{"image/svg+xml":"\n\n\n\n\ntrain.TrainingTask\n\n\ntrain.TrainingTask\n\n\n\n\n\ntrain.ModelTraining\n\n\ntrain.ModelTraining\n\n\n\n\n\ntrain.TrainingTask->train.ModelTraining\n\n\n\n\ntrain.VideoSet.File\n\n\ntrain.VideoSet.File\n\n\n\n\n\ntrain.VideoSet\n\n\ntrain.VideoSet\n\n\n\n\n\ntrain.VideoSet->train.TrainingTask\n\n\n\n\ntrain.VideoSet->train.VideoSet.File\n\n\n\n\ntrain.TrainingParamSet\n\n\ntrain.TrainingParamSet\n\n\n\n\n\ntrain.TrainingParamSet->train.TrainingTask\n\n\n\n","text/plain":[""]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["dj.Diagram(train) #- 1"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"image/svg+xml":"\n\n\n\n\nmodel.BodyPart\n\n\nmodel.BodyPart\n\n\n\n\n\nmodel.PoseEstimation.BodyPartPosition\n\n\nmodel.PoseEstimation.BodyPartPosition\n\n\n\n\n\nmodel.BodyPart->model.PoseEstimation.BodyPartPosition\n\n\n\n\nmodel.Model.BodyPart\n\n\nmodel.Model.BodyPart\n\n\n\n\n\nmodel.BodyPart->model.Model.BodyPart\n\n\n\n\nmodel.ModelEvaluation\n\n\nmodel.ModelEvaluation\n\n\n\n\n\nmodel.VideoRecording\n\n\nmodel.VideoRecording\n\n\n\n\n\nmodel.VideoRecording.File\n\n\nmodel.VideoRecording.File\n\n\n\n\n\nmodel.VideoRecording->model.VideoRecording.File\n\n\n\n\nmodel.PoseEstimationTask\n\n\nmodel.PoseEstimationTask\n\n\n\n\n\nmodel.VideoRecording->model.PoseEstimationTask\n\n\n\n\nmodel.PoseEstimation\n\n\nmodel.PoseEstimation\n\n\n\n\n\nmodel.PoseEstimation->model.PoseEstimation.BodyPartPosition\n\n\n\n\nmodel.Model\n\n\nmodel.Model\n\n\n\n\n\nmodel.Model->model.ModelEvaluation\n\n\n\n\nmodel.Model->model.PoseEstimationTask\n\n\n\n\nmodel.Model->model.Model.BodyPart\n\n\n\n\nmodel.PoseEstimationTask->model.PoseEstimation\n\n\n\n","text/plain":[""]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["dj.Diagram(model)"]},{"cell_type":"markdown","metadata":{},"source":["### Common Table Functions"]},{"cell_type":"markdown","metadata":{},"source":["\n","- `()` show table contents\n","- `heading` shows attribute definitions\n","- `describe()` show table defintiion with foreign key references"]},{"cell_type":"code","execution_count":3,"metadata":{"title":"Each datajoint table class inside the module corresponds to a table inside the schema. For example, the class `ephys.EphysRecording` correponds to the table `_ephys_recording` in the schema `neuro_ephys` in the database."},"outputs":[{"data":{"text/html":["\n"," \n"," \n"," \n"," \n","
    \n","
    \n"," \n"," \n","
    \n","

    subject

    \n"," \n","
    \n","

    session_datetime

    \n"," \n","
    \n","

    recording_id

    \n"," \n","
    \n","

    file_id

    \n"," \n","
    \n","

    file_path

    \n"," filepath of video, relative to root data directory\n","
    \n"," \n","

    Total: 0

    \n"," "],"text/plain":["*subject *session_datet *recording_id *file_id file_path \n","+---------+ +------------+ +------------+ +---------+ +-----------+\n","\n"," (Total: 0)"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["model.VideoRecording.File()"]},{"cell_type":"code","execution_count":8,"metadata":{"title":"`heading`: show table attributes regardless of foreign key references."},"outputs":[{"data":{"text/plain":["# \n","model_name : varchar(64) # user-friendly model name\n","---\n","task : varchar(32) # task in the config yaml\n","date : varchar(16) # date in the config yaml\n","iteration : int # iteration/version of this model\n","snapshotindex : int # which snapshot for prediction (if -1, latest)\n","shuffle : int # which shuffle of the training dataset\n","trainingsetindex : int # which training set fraction to generate model\n","scorer : varchar(64) # scorer/network name - DLC's GetScorerName()\n","config_template : longblob # dictionary of the config for analyze_videos()\n","project_path : varchar(255) # DLC's project_path in config relative to root\n","model_prefix=\"\" : varchar(32) # \n","model_description=\"\" : varchar(1000) # \n","paramset_idx=null : smallint # "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["model.Model.heading"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["# Specification for a DLC model training instance\n","-> train.VideoSet\n","-> train.TrainingParamSet\n","training_id : int \n","---\n","model_prefix=\"\" : varchar(32) \n","project_path=\"\" : varchar(255) # DLC's project_path in config relative to root\n","\n"]},{"data":{"text/plain":["'# Specification for a DLC model training instance\\n-> train.VideoSet\\n-> train.TrainingParamSet\\ntraining_id : int \\n---\\nmodel_prefix=\"\" : varchar(32) \\nproject_path=\"\" : varchar(255) # DLC\\'s project_path in config relative to root\\n'"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["train.TrainingTask.describe()"]},{"cell_type":"markdown","metadata":{"tags":[]},"source":["## Running the Workflow"]},{"cell_type":"markdown","metadata":{},"source":["`Pipeline.py` activates the DataJoint `elements` and declares other required tables."]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Connecting cbroz@dss-db.datajoint.io:3306\n"]}],"source":["import datajoint as dj\n","from workflow_deeplabcut.pipeline import lab, subject, session, train, model\n","\n","# Directing our pipeline to the appropriate config location\n","from element_interface.utils import find_full_path\n","from workflow_deeplabcut.paths import get_dlc_root_data_dir\n","config_path = find_full_path(get_dlc_root_data_dir(), \n"," 'openfield-Pranav-2018-10-30/config.yaml')"]},{"cell_type":"markdown","metadata":{"tags":[]},"source":["### Manually Inserting Entries"]},{"cell_type":"markdown","metadata":{},"source":["#### Upstream tables"]},{"cell_type":"markdown","metadata":{},"source":["We can insert entries into `dj.Manual` tables (green in diagrams) by directly providing values as a dictionary. "]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"text/plain":["# \n","subject : varchar(32) # \n","session_datetime : datetime(3) # "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["session.Session.heading"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["subject.Subject.insert1(dict(subject='subject6', \n"," sex='F', \n"," subject_birth_date='2020-01-01', \n"," subject_description='hneih_E105'))\n","session_keys = [dict(subject='subject6', session_datetime='2021-06-02 14:04:22'),\n"," dict(subject='subject6', session_datetime='2021-06-03 14:43:10')]\n","session.Session.insert(session_keys)"]},{"cell_type":"markdown","metadata":{},"source":["We can look at the contents of this table and restrict by a value."]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"data":{"text/html":["\n"," \n"," \n"," \n"," \n","
    \n"," \n"," \n"," \n","\n","\n","
    \n","

    subject

    \n"," \n","
    \n","

    session_datetime

    \n"," \n","
    subject62021-06-02 14:04:22
    subject62021-06-03 14:43:10
    \n"," \n","

    Total: 2

    \n"," "],"text/plain":["*subject *session_datet\n","+----------+ +------------+\n","subject6 2021-06-02 14:\n","subject6 2021-06-03 14:\n"," (Total: 2)"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["session.Session() & \"session_datetime > '2021-06-01 12:00:00'\" & \"subject='subject6'\""]},{"cell_type":"markdown","metadata":{"tags":[]},"source":["#### DeepLabcut Tables"]},{"cell_type":"markdown","metadata":{},"source":["The `VideoSet` table in the `train` schema retains records of files generated in the video labeling process (e.g., `h5`, `csv`, `png`). DeepLabCut will refer to the `mat` file located under the `training-datasets` directory."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["train.VideoSet.insert1({'video_set_id': 1})\n","labeled_dir = 'openfield-Pranav-2018-10-30/labeled-data/m4s1/'\n","training_files = ['CollectedData_Pranav.h5',\n"," 'CollectedData_Pranav.csv',\n"," 'img0000.png']\n","for idx, filename in training_files:\n"," train.VideoSet.File.insert1({'video_set_id': 1,\n"," 'file_id': idx, \n"," 'file_path': (labeled_dir + file)})\n","train.VideoSet.File.insert1({'video_set_id':1, 'file_id': 4, 'file_path': \n"," 'openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4'})"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"data":{"text/html":["\n"," \n"," \n"," \n"," Paths of training files (e.g., labeled pngs, CSV or video)\n","
    \n"," \n"," \n"," \n","\n","\n","\n","\n","
    \n","

    video_set_id

    \n"," \n","
    \n","

    file_path

    \n"," \n","
    1openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.csv
    1openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.h5
    1openfield-Pranav-2018-10-30/labeled-data/m4s1/img0000.png
    1openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4
    \n"," \n","

    Total: 4

    \n"," "],"text/plain":["*video_set_id *file_path \n","+------------+ +------------+\n","1 openfield-Pran\n","1 openfield-Pran\n","1 openfield-Pran\n","1 openfield-Pran\n"," (Total: 4)"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["train.VideoSet.File()"]},{"cell_type":"markdown","metadata":{"tags":[]},"source":["### Training a Network"]},{"cell_type":"markdown","metadata":{},"source":["First, we'll add a `ModelTrainingParamSet`. This is a lookup table that we can reference when training a model."]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/plain":["paramset_idx : smallint # \n","---\n","paramset_desc : varchar(128) # \n","param_set_hash : uuid # hash identifying this parameterset\n","params : longblob # dictionary of all applicable parameters"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["train.TrainingParamSet.heading"]},{"cell_type":"markdown","metadata":{},"source":["The `params` longblob should be a dictionary that captures all items for DeepLabCut's `train_network` function. At minimum, this is the contents of the project's config file, as well as `suffle` and `trainingsetindex`, which are not included in the config. "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from deeplabcut import train_network\n","help(train_network) # for more information on optional parameters"]},{"cell_type":"markdown","metadata":{},"source":["Here, we give these items, load the config contents, and overwrite some defaults, including `maxiters`, to restrict our training iterations to 5."]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["import yaml\n","\n","paramset_idx = 1; paramset_desc='OpenField'\n","\n","with open(config_path, 'rb') as y:\n"," config_params = yaml.safe_load(y)\n","training_params = {'shuffle': '1',\n"," 'trainingsetindex': '0',\n"," 'maxiters': '5',\n"," 'scorer_legacy': 'False',\n"," 'maxiters': '5', \n"," 'multianimalproject':'False'}\n","config_params.update(training_params)\n","train.TrainingParamSet.insert_new_params(paramset_idx=paramset_idx,\n"," paramset_desc=paramset_desc,\n"," params=config_params)"]},{"cell_type":"markdown","metadata":{},"source":["Now, we add a `TrainingTask`. As a computed table, `ModelTraining` will reference this to start training when calling `populate()`"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[{"data":{"text/plain":["video_set_id : int # \n","paramset_idx : smallint # \n","training_id : int # \n","---\n","model_prefix=\"\" : varchar(32) # \n","project_path=\"\" : varchar(255) # DLC's project_path in config relative to root"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["train.TrainingTask.heading"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"text/html":["\n"," \n"," \n"," \n"," Specification for a DLC model training instance\n","
    \n"," \n"," \n"," \n","\n","\n","\n","\n","
    \n","

    video_set_id

    \n"," \n","
    \n","

    paramset_idx

    \n"," \n","
    \n","

    training_id

    \n"," \n","
    \n","

    model_prefix

    \n"," \n","
    \n","

    project_path

    \n"," DLC's project_path in config relative to root\n","
    111openfield-Pranav-2018-10-30/
    \n"," \n","

    Total: 1

    \n"," "],"text/plain":["*video_set_id *paramset_idx *training_id model_prefix project_path \n","+------------+ +------------+ +------------+ +------------+ +------------+\n","1 1 1 openfield-Pran\n"," (Total: 1)"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["key={'video_set_id': 1, 'paramset_idx':1,'training_id':1,\n"," 'project_path':'openfield-Pranav-2018-10-30/'}\n","train.TrainingTask.insert1(key, skip_duplicates=True)\n","train.TrainingTask()"]},{"cell_type":"code","execution_count":8,"metadata":{"tags":[]},"outputs":[],"source":["train.ModelTraining.populate()"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/html":["\n"," \n"," \n"," \n"," \n","
    \n"," \n"," \n"," \n","\n","\n","\n","\n","
    \n","

    video_set_id

    \n"," \n","
    \n","

    paramset_idx

    \n"," \n","
    \n","

    training_id

    \n"," \n","
    \n","

    latest_snapshot

    \n"," latest exact snapshot index (i.e., never -1)\n","
    \n","

    config_template

    \n"," stored full config file\n","
    1115=BLOB=
    \n"," \n","

    Total: 1

    \n"," "],"text/plain":["*video_set_id *paramset_idx *training_id latest_snapsho config_tem\n","+------------+ +------------+ +------------+ +------------+ +--------+\n","1 1 1 5 =BLOB= \n"," (Total: 1)"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["train.ModelTraining()"]},{"cell_type":"markdown","metadata":{},"source":["To resume training from a checkpoint, we would need to \n","[edit the relevant config file](https://github.com/DeepLabCut/DeepLabCut/issues/70).\n","Emperical work from the Mathis team suggests 200k iterations for any true use-case."]},{"cell_type":"markdown","metadata":{"jp-MarkdownHeadingCollapsed":true,"tags":[]},"source":["### Tracking Joints/Body Parts"]},{"cell_type":"markdown","metadata":{},"source":["The `model` schema uses a lookup table for managing Body Parts tracked across models."]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"data":{"text/plain":["# \n","body_part : varchar(32) # \n","---\n","body_part_description=\"\" : varchar(1000) # "]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["model.BodyPart.heading"]},{"cell_type":"markdown","metadata":{},"source":["Helper functions allow us to first, identify all the new body parts from a given config, and, second, insert them with user-friendly descriptions."]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Existing body parts: ['leftear' 'rightear' 'snout' 'tailbase']\n","New body parts: []\n"]},{"data":{"text/plain":["array([], dtype='\n"," .Relation{\n"," border-collapse:collapse;\n"," }\n"," .Relation th{\n"," background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n"," font-weight: normal; font-family: monospace; font-size: 100%;\n"," }\n"," .Relation td{\n"," padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n"," }\n"," .Relation tr:nth-child(odd){\n"," background: #ffffff;\n"," }\n"," .Relation tr:nth-child(even){\n"," background: #f3f1ff;\n"," }\n"," /* Tooltip container */\n"," .djtooltip {\n"," }\n"," /* Tooltip text */\n"," .djtooltip .djtooltiptext {\n"," visibility: hidden;\n"," width: 120px;\n"," background-color: black;\n"," color: #fff;\n"," text-align: center;\n"," padding: 5px 0;\n"," border-radius: 6px;\n"," /* Position the tooltip text - see examples below! */\n"," position: absolute;\n"," z-index: 1;\n"," }\n"," #primary {\n"," font-weight: bold;\n"," color: black;\n"," }\n"," #nonprimary {\n"," font-weight: normal;\n"," color: white;\n"," }\n","\n"," /* Show the tooltip text when you mouse over the tooltip container */\n"," .djtooltip:hover .djtooltiptext {\n"," visibility: visible;\n"," }\n"," \n"," \n"," \n","
    \n"," \n"," \n"," \n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","
    \n","

    model_name

    \n"," user-friendly model name\n","
    \n","

    task

    \n"," task in the config yaml\n","
    \n","

    date

    \n"," date in the config yaml\n","
    \n","

    iteration

    \n"," iteration/version of this model\n","
    \n","

    snapshotindex

    \n"," which snapshot for prediction (if -1, latest)\n","
    \n","

    shuffle

    \n"," which shuffle of the training dataset\n","
    \n","

    trainingsetindex

    \n"," which training set fraction to generate model\n","
    \n","

    scorer

    \n"," scorer/network name - DLC's GetScorerName()\n","
    \n","

    config_template

    \n"," dictionary of the config for analyze_videos()\n","
    \n","

    project_path

    \n"," DLC's project_path in config relative to root\n","
    \n","

    model_prefix

    \n"," \n","
    \n","

    model_description

    \n"," \n","
    \n","

    paramset_idx

    \n"," \n","
    OpenField-5openfieldOct300-110DLCresnet50openfieldOct30shuffle1=BLOB=openfield-Pranav-2018-10-30Open field model trained 5 iterations1
    \n"," \n","

    Total: 1

    \n"," "],"text/plain":["*model_name task date iteration snapshotindex shuffle trainingsetind scorer config_tem project_path model_prefix model_descript paramset_idx \n","+------------+ +-----------+ +-------+ +-----------+ +------------+ +---------+ +------------+ +------------+ +--------+ +------------+ +------------+ +------------+ +------------+\n","OpenField-5 openfield Oct30 0 -1 1 0 DLCresnet50ope =BLOB= openfield-Pran Open field mod 1 \n"," (Total: 1)"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["model.Model()"]},{"cell_type":"markdown","metadata":{},"source":["`ModelEvaluation` will reference the `Model` using the `populate` method and insert the output from DeepLabCut's `evaluate_network` function"]},{"cell_type":"code","execution_count":47,"metadata":{},"outputs":[{"data":{"text/plain":["model_name : varchar(64) # user-friendly model name\n","---\n","train_iterations : int # Training iterations\n","train_error=null : float # Train error (px)\n","test_error=null : float # Test error (px)\n","p_cutoff=null : float # p-cutoff used\n","train_error_p=null : float # Train error with p-cutoff\n","test_error_p=null : float # Test error with p-cutoff"]},"execution_count":47,"metadata":{},"output_type":"execute_result"}],"source":["model.ModelEvaluation.heading"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Running DLC_resnet50_openfieldOct30shuffle1_5 with # of training iterations: 5\n"]},{"name":"stderr","output_type":"stream","text":["/Users/cb/miniconda3/envs/venv-dlc/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n"," warnings.warn('`layer.apply` is deprecated and '\n"]},{"name":"stdout","output_type":"stream","text":["Running evaluation ...\n"]},{"name":"stderr","output_type":"stream","text":["116it [01:17, 1.50it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Analysis is done and the results are stored (see evaluation-results) for snapshot: snapshot-5\n","Results for 5 training iterations: 95 1 train error: 245.06 pixels. Test error: 247.52 pixels.\n","With pcutoff of 0.4 train error: 239.24 pixels. Test error: 238.07 pixels\n","Thereby, the errors are given by the average distances between the labels by DLC and the scorer.\n","The network is evaluated and the results are stored in the subdirectory 'evaluation_results'.\n","Please check the results, then choose the best model (snapshot) for prediction. You can update the config.yaml file with the appropriate index for the 'snapshotindex'.\n","Use the function 'analyze_video' to make predictions on new videos.\n","Otherwise, consider adding more labeled-data and retraining the network (see DeepLabCut workflow Fig 2, Nath 2019)\n"]}],"source":["model.ModelEvaluation.populate()"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"data":{"text/html":["\n"," \n"," \n"," \n"," \n","
    \n"," \n"," \n"," \n","\n","\n","\n","\n","\n","\n","
    \n","

    model_name

    \n"," user-friendly model name\n","
    \n","

    train_iterations

    \n"," Training iterations\n","
    \n","

    train_error

    \n"," Train error (px)\n","
    \n","

    test_error

    \n"," Test error (px)\n","
    \n","

    p_cutoff

    \n"," p-cutoff used\n","
    \n","

    train_error_p

    \n"," Train error with p-cutoff\n","
    \n","

    test_error_p

    \n"," Test error with p-cutoff\n","
    OpenField-55245.06247.520.4239.24238.07
    \n"," \n","

    Total: 1

    \n"," "],"text/plain":["*model_name train_iteratio train_error test_error p_cutoff train_error_p test_error_p \n","+------------+ +------------+ +------------+ +------------+ +----------+ +------------+ +------------+\n","OpenField-5 5 245.06 247.52 0.4 239.24 238.07 \n"," (Total: 1)"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["model.ModelEvaluation()"]},{"cell_type":"markdown","metadata":{},"source":["### Pose Estimation"]},{"cell_type":"markdown","metadata":{},"source":["To use our model, we'll first need to insert a session recoring into `VideoRecording`"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["key = {'subject': 'subject6',\n"," 'session_datetime': '2021-06-02 14:04:22',\n"," 'recording_id': '1', 'device': 'Camera1'}\n","model.VideoRecording.insert1(key)\n","\n","_ = key.pop('device') # get rid of secondary key from master table\n","key.update({'file_id': 1, \n"," 'file_path': 'openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4'})\n","model.VideoRecording.File.insert1(key)"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"text/html":["\n"," \n"," \n"," \n"," \n","
    \n"," \n"," \n"," \n","\n","\n","\n","\n","
    \n","

    subject

    \n"," \n","
    \n","

    session_datetime

    \n"," \n","
    \n","

    recording_id

    \n"," \n","
    \n","

    file_id

    \n"," \n","
    \n","

    file_path

    \n"," filepath of video, relative to root data directory\n","
    subject62021-06-02 14:04:2211openfield-Pranav-2018-10-30/videos/m3v1mp4-copy.mp4
    \n"," \n","

    Total: 1

    \n"," "],"text/plain":["*subject *session_datet *recording_id *file_id file_path \n","+----------+ +------------+ +------------+ +---------+ +------------+\n","subject6 2021-06-02 14: 1 1 openfield-Pran\n"," (Total: 1)"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["model.VideoRecording.File()"]},{"cell_type":"markdown","metadata":{},"source":["`RecordingInfo` automatically populates with file information"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"data":{"text/html":["\n"," \n"," \n"," \n"," \n","
    \n"," \n"," \n"," \n","\n","\n","\n","\n","\n","\n","\n","\n","
    \n","

    subject

    \n"," \n","
    \n","

    session_datetime

    \n"," \n","
    \n","

    recording_id

    \n"," \n","
    \n","

    px_height

    \n"," height in pixels\n","
    \n","

    px_width

    \n"," width in pixels\n","
    \n","

    nframes

    \n"," number of frames\n","
    \n","

    fps

    \n"," (Hz) frames per second\n","
    \n","

    recording_datetime

    \n"," Datetime for the start of the recording\n","
    \n","

    recording_duration

    \n"," video duration in seconds\n","
    subject62021-06-02 14:04:2214806406330None2.1
    \n"," \n","

    Total: 1

    \n"," "],"text/plain":["*subject *session_datet *recording_id px_height px_width nframes fps recording_date recording_dura\n","+----------+ +------------+ +------------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n","subject6 2021-06-02 14: 1 480 640 63 30 None 2.1 \n"," (Total: 1)"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["model.RecordingInfo.populate()\n","model.RecordingInfo()"]},{"cell_type":"markdown","metadata":{},"source":["Next, we specify if the `PoseEstimation` table should load results from an existing file or trigger the estimation command. Here, we can also specify parameters for DeepLabCut's `analyze_videos` as a dictionary."]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/plain":["{'subject': 'subject6',\n"," 'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n"," 'camera_id': 1,\n"," 'recording_id': 1,\n"," 'model_name': 'OpenField-5',\n"," 'task_mode': 'trigger'}"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["key = (model.VideoRecording & {'recording_id': '1'}).fetch1('KEY')\n","key.update({'model_name': 'OpenField-5', 'task_mode': 'trigger'})\n","key"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["model.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True})\n","model.PoseEstimation.populate()"]},{"cell_type":"markdown","metadata":{},"source":["By default, DataJoint will store results in a subdirectory\n","> / videos / device__recording_<#>_model_\n","where `processed_dir` is optionally specified in the datajoint config. If unspecified, this will be the project directory. The device and model names are specified elsewhere in the schema.\n","\n","We can get this estimation directly as a pandas dataframe."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"text/html":["
    \n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
    scorerOpenField-5
    bodypartsleftearrightearsnouttailbase
    coordsxyzlikelihoodxyzlikelihoodxyzlikelihoodxyzlikelihood
    00.7906777.9657290.00.397091115.835762164.0040280.00.51840558.8182914.8376490.00.5146124.134376463.0094600.00.717231
    12.80712010.9734660.00.43559010.124892470.6539310.00.51464415.192053472.9543760.00.5091284.339864462.9882200.00.711722
    29.41576416.2906190.00.40028210.313096470.7494200.00.51392715.203813473.0462040.00.5096834.241215463.0609440.00.709923
    38.46756215.0726820.00.40727210.299086470.7163090.00.51508514.914599472.9465640.00.5079314.296385463.3855900.00.704007
    41.95269610.8455160.00.38894810.309416470.7199100.00.51184814.834159472.9201660.00.5045384.267960463.3635560.00.702786
    ...................................................
    585.49781812.1814960.00.50396110.725180470.4308470.00.50552615.931270474.6929630.00.5075649.060750481.2784420.00.704268
    594.19278810.0053490.00.45533410.476208470.8465880.00.4990143.50862626.8213390.00.5370643.786860462.7603760.00.689251
    602.21614910.1157280.00.42014110.644203471.0361020.00.4873163.16688726.8353730.00.5481098.188313481.5249020.00.707340
    615.19661010.8389530.00.484508178.00723372.9359130.00.5766884.47888826.5136280.00.5319054.350879462.5533450.00.703052
    622.67855410.2772410.00.42675810.260103471.3215640.00.50259015.026831472.4920650.00.5287008.123420481.6425780.00.707681
    \n","

    63 rows × 16 columns

    \n","
    "],"text/plain":["scorer OpenField-5 \\\n","bodyparts leftear rightear \n","coords x y z likelihood x y z \n","0 0.790677 7.965729 0.0 0.397091 115.835762 164.004028 0.0 \n","1 2.807120 10.973466 0.0 0.435590 10.124892 470.653931 0.0 \n","2 9.415764 16.290619 0.0 0.400282 10.313096 470.749420 0.0 \n","3 8.467562 15.072682 0.0 0.407272 10.299086 470.716309 0.0 \n","4 1.952696 10.845516 0.0 0.388948 10.309416 470.719910 0.0 \n",".. ... ... ... ... ... ... ... \n","58 5.497818 12.181496 0.0 0.503961 10.725180 470.430847 0.0 \n","59 4.192788 10.005349 0.0 0.455334 10.476208 470.846588 0.0 \n","60 2.216149 10.115728 0.0 0.420141 10.644203 471.036102 0.0 \n","61 5.196610 10.838953 0.0 0.484508 178.007233 72.935913 0.0 \n","62 2.678554 10.277241 0.0 0.426758 10.260103 471.321564 0.0 \n","\n","scorer \\\n","bodyparts snout tailbase \n","coords likelihood x y z likelihood x \n","0 0.518405 58.818291 4.837649 0.0 0.514612 4.134376 \n","1 0.514644 15.192053 472.954376 0.0 0.509128 4.339864 \n","2 0.513927 15.203813 473.046204 0.0 0.509683 4.241215 \n","3 0.515085 14.914599 472.946564 0.0 0.507931 4.296385 \n","4 0.511848 14.834159 472.920166 0.0 0.504538 4.267960 \n",".. ... ... ... ... ... ... \n","58 0.505526 15.931270 474.692963 0.0 0.507564 9.060750 \n","59 0.499014 3.508626 26.821339 0.0 0.537064 3.786860 \n","60 0.487316 3.166887 26.835373 0.0 0.548109 8.188313 \n","61 0.576688 4.478888 26.513628 0.0 0.531905 4.350879 \n","62 0.502590 15.026831 472.492065 0.0 0.528700 8.123420 \n","\n","scorer \n","bodyparts \n","coords y z likelihood \n","0 463.009460 0.0 0.717231 \n","1 462.988220 0.0 0.711722 \n","2 463.060944 0.0 0.709923 \n","3 463.385590 0.0 0.704007 \n","4 463.363556 0.0 0.702786 \n",".. ... ... ... \n","58 481.278442 0.0 0.704268 \n","59 462.760376 0.0 0.689251 \n","60 481.524902 0.0 0.707340 \n","61 462.553345 0.0 0.703052 \n","62 481.642578 0.0 0.707681 \n","\n","[63 rows x 16 columns]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["model.PoseEstimation.get_trajectory(key)"]},{"cell_type":"markdown","metadata":{},"source":["\n","."]},{"cell_type":"markdown","metadata":{"pycharm":{"name":"#%% md\n"}},"source":["## Workflow Automation"]},{"cell_type":"markdown","metadata":{},"source":["Below is a more automatic approach to run through the pipeline using some utility functions in the workflow using the `process.py` script to automatically trigger all computed tables.\n","\n","Because we just inserted all the data, we'll delete using the command below to start over."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from workflow_deeplabcut.process import run\n","safemode=None # Set to false to turn off confirmation prompts\n","(session.Session & 'subject=\"subject6\"').delete(safemode=safemode)\n","train.TrainingParamSet.delete(safemode=safemode)\n","train.VideoSet.delete(safemode=safemode)"]},{"cell_type":"markdown","metadata":{},"source":["#### Automated Ingestion\n","\n","Refer to the `user_data` folder in the workflow for CSVs to fill in various tables.\n","\n","1. Upstream tables:\n"," - `subject.Subject` via `subjects.csv` \n"," - `session.Session` via `sessions.csv`\n","2. `train` schema:\n"," - `train.TrainingParamSet` via `config_params.csv`\n"," - `train.VideoSet` via `train_videosets.csv`\n","3. `model` schema:\n"," - `model.VideoRecording` via `model_videos.csv`\n"," - `model.Model` via `model_model.csv`\n"," \n","Run automatic ingestion via functions in `workflow_deeplabcut.ingest` "]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","---- Inserting 0 entry(s) into subject ----\n","\n","---- Inserting 2 entry(s) into session ----\n","\n","---- Inserting 2 entry(s) into session_directory ----\n","\n","---- Inserting 2 entry(s) into session_note ----\n","\n","---- Inserting 3 entry(s) into #model_training_param_set ----\n","\n","---- Inserting 2 entry(s) into video_set ----\n","\n","---- Inserting 8 entry(s) into video_set__file ----\n","\n","---- Inserting 2 entry(s) into video_recording ----\n","\n","---- Inserting 2 entry(s) into video_recording__file ----\n"]}],"source":["from workflow_deeplabcut.ingest import ingest_subjects, ingest_sessions, ingest_dlc_items\n","ingest_subjects(); ingest_sessions(); ingest_dlc_items()"]},{"cell_type":"markdown","metadata":{},"source":["#### Setting project variables\n","\n","Set your root directory in your DataJoint config file, under `custom` as `dlc_root_data_dir`. "]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["import datajoint as dj; dj.config.load('dj_local_conf.json')\n","from element_interface.utils import find_full_path\n","data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config\n"," 'openfield-Pranav-2018-10-30') # DLC project dir\n","config_path = (data_dir / 'config.yaml')"]},{"cell_type":"markdown","metadata":{},"source":["#### Launching trainig\n","\n","Pair training files with training parameters, and launch training via `process`. \n","\n","Note: DLC's model processes (e.g., Training, Evaluation) log a lot of information to the console, to quiet this, pass `verbose=False` to `process`"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["key={'paramset_idx':1,'training_id':1,'video_set_id':1, \n"," 'project_path':'openfield-Pranav-2018-10-30/'}\n","train.TrainingTask.insert1(key, skip_duplicates=True)\n","run(verbose=True)\n","model.RecordingInfo()"]},{"cell_type":"markdown","metadata":{},"source":["Now, add to `Model`, including\n","- Include a user-friendly `model_name`\n","- Include the relative path for the project's `config.yaml`\n","- Add `shuffle` and `trainingsetindex`\n","- `insert_new_model` will prompt before inserting, but this can be skipped with `prompt=False`"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model.Model.insert_new_model(model_name='OpenField-5', \n"," dlc_config=config_path,\n"," shuffle=1,\n"," trainingsetindex=0,\n"," paramset_idx=1, \n"," prompt=True, # True is the default behavior\n"," model_description='Open field model trained 5 iterations')\n","run()"]},{"cell_type":"markdown","metadata":{},"source":["Add a pose estimation task, using\n","- All primary key information for a given recording\n","- Add the model and `task_mode` (i.e., load vs. trigger) to be applied\n","- Add any additional analysis parameters for `deeplabcut.analyze_videos`"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["key=(model.VideoRecording & 'recording_id=2').fetch1('KEY')\n","key.update({'model_name': 'OpenField-5', 'task_mode': 'trigger'})\n","analyze_params={'save_as_csv':True} # add any others from deeplabcut.analyze_videos\n","model.PoseEstimationTask.insert_estimation_task(key,params=analyze_params)\n","run()"]},{"cell_type":"markdown","metadata":{},"source":["Retrieve estimated position data:"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"text/html":["
    \n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
    scorerOpenField-5
    bodypartsleftearrightearsnouttailbase
    coordsxyzlikelihoodxyzlikelihoodxyzlikelihoodxyzlikelihood
    0125.2137680.4644250.00.1428361.902155184.6194310.00.123875-7.28514661.4020880.00.2675322.36050530.9298230.00.132607
    1125.0097581.0589690.00.1361791.532405183.6681210.00.130291-7.26930461.5893970.00.2692699.910207168.7025760.00.140683
    2123.7856981.8012530.00.1509941.467412183.7215420.00.129725-6.98838161.6243170.00.2666202.75398130.9490590.00.136884
    3122.6218802.7299370.00.1508311.424251184.0093230.00.133028-7.05495361.3318480.00.2868762.39993830.4677140.00.146240
    4123.7296452.9010600.00.1634421.417472183.9140780.00.129994-6.63356760.8808900.00.2836612.54470830.3628430.00.139938
    ...................................................
    58240.315948-1.1352410.00.1414772.564324153.4503780.00.108718-6.01461359.2915530.00.2642132.49439730.7135490.00.127640
    59240.919571-1.1040960.00.1228476.900490-0.2430960.00.104687-6.63268959.6834070.00.2367663.03435630.4541170.00.127521
    60255.197067-0.8761620.00.1413313.224912170.1051790.00.102174-6.59783859.6435130.00.2367052.66621630.1858830.00.123881
    61255.0426030.5547000.00.1521196.523534-0.5630770.00.102816-6.13483359.9624900.00.2495652.55579930.3262370.00.130592
    62255.079330-0.3261630.00.1686993.389258170.1414950.00.103460-6.66127659.5938840.00.2363052.97858930.1031780.00.124924
    \n","

    63 rows × 16 columns

    \n","
    "],"text/plain":["scorer OpenField-5 \\\n","bodyparts leftear rightear \n","coords x y z likelihood x y z \n","0 125.213768 0.464425 0.0 0.142836 1.902155 184.619431 0.0 \n","1 125.009758 1.058969 0.0 0.136179 1.532405 183.668121 0.0 \n","2 123.785698 1.801253 0.0 0.150994 1.467412 183.721542 0.0 \n","3 122.621880 2.729937 0.0 0.150831 1.424251 184.009323 0.0 \n","4 123.729645 2.901060 0.0 0.163442 1.417472 183.914078 0.0 \n",".. ... ... ... ... ... ... ... \n","58 240.315948 -1.135241 0.0 0.141477 2.564324 153.450378 0.0 \n","59 240.919571 -1.104096 0.0 0.122847 6.900490 -0.243096 0.0 \n","60 255.197067 -0.876162 0.0 0.141331 3.224912 170.105179 0.0 \n","61 255.042603 0.554700 0.0 0.152119 6.523534 -0.563077 0.0 \n","62 255.079330 -0.326163 0.0 0.168699 3.389258 170.141495 0.0 \n","\n","scorer \\\n","bodyparts snout tailbase \n","coords likelihood x y z likelihood x \n","0 0.123875 -7.285146 61.402088 0.0 0.267532 2.360505 \n","1 0.130291 -7.269304 61.589397 0.0 0.269269 9.910207 \n","2 0.129725 -6.988381 61.624317 0.0 0.266620 2.753981 \n","3 0.133028 -7.054953 61.331848 0.0 0.286876 2.399938 \n","4 0.129994 -6.633567 60.880890 0.0 0.283661 2.544708 \n",".. ... ... ... ... ... ... \n","58 0.108718 -6.014613 59.291553 0.0 0.264213 2.494397 \n","59 0.104687 -6.632689 59.683407 0.0 0.236766 3.034356 \n","60 0.102174 -6.597838 59.643513 0.0 0.236705 2.666216 \n","61 0.102816 -6.134833 59.962490 0.0 0.249565 2.555799 \n","62 0.103460 -6.661276 59.593884 0.0 0.236305 2.978589 \n","\n","scorer \n","bodyparts \n","coords y z likelihood \n","0 30.929823 0.0 0.132607 \n","1 168.702576 0.0 0.140683 \n","2 30.949059 0.0 0.136884 \n","3 30.467714 0.0 0.146240 \n","4 30.362843 0.0 0.139938 \n",".. ... ... ... \n","58 30.713549 0.0 0.127640 \n","59 30.454117 0.0 0.127521 \n","60 30.185883 0.0 0.123881 \n","61 30.326237 0.0 0.130592 \n","62 30.103178 0.0 0.124924 \n","\n","[63 rows x 16 columns]"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["model.PoseEstimation.get_trajectory(key)"]},{"cell_type":"markdown","metadata":{"tags":[]},"source":["## Dropping schemas"]},{"cell_type":"markdown","metadata":{},"source":["+ Schemas are not typically dropped in a production workflow with real data in it. \n","+ At the developmental phase, it might be required for the table redesign.\n","+ When dropping all schemas is needed, drop items starting with the most downstream."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from workflow_deeplabcut.pipeline import *\n","# model.schema.drop()\n","# train.schema.drop()\n","# session.schema.drop()\n","# subject.schema.drop()\n","# lab.schema.drop()"]}],"metadata":{"jupytext":{"encoding":"# -*- coding: utf-8 -*-","formats":"ipynb,py:percent"},"kernelspec":{"display_name":"Python 3.8.11 ('ele')","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.11"},"vscode":{"interpreter":{"hash":"61456c693db5d9aa6731701ec9a9b08ab88a172bee0780139a3679beb166da16"}}},"nbformat":4,"nbformat_minor":4} diff --git a/notebooks/tutorial_copy.ipynb b/notebooks/tutorial_copy.ipynb deleted file mode 100644 index 2281782..0000000 --- a/notebooks/tutorial_copy.ipynb +++ /dev/null @@ -1,1190 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# DataJoint Element DeepLabCut" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Open-source Data Pipeline for Markerless Pose Estimation in Neurophysiology**\n", - "\n", - "This tutorial focuses on providing a comprehensive understanding of the open-source data pipeline offered by `Element-DeepLabCut`. The package is designed to facilitate pose estimation analyses and streamline the organization of data using `DataJoint`. By the end of this tutorial, participants will have a clear grasp of how to set up, utilize, ad optimize the package for their specific pose estimation projects. \n", - "\n", - "**Key Components and Objectives**\n", - "\n", - "- 1. Download Sample Data and Context\n", - "\n", - "- 2. Setup\n", - "\n", - "- 3. Design the DataJoint Pipeline\n", - "\n", - "- 4. Enter the Metadata into the Pipeline\n", - "\n", - "- 5. Run the Model Training\n", - "\n", - "- 6. Run the Model Evaluation\n", - "\n", - "\n", - "For detailed documentation and tutorials on general DataJoint principles that support collaboration, automation, reproducibility, and visualizations:\n", - "\n", - "[`DataJoint for Python - Interactive Tutorials`](https://github.com/datajoint/datajoint-tutorials) - Fundamentals including table tiers, query operations, fetch operations, automated computations with the make function, etc.\n", - "\n", - "[`DataJoint for Python - Documentation`](https://datajoint.com/docs/core/datajoint-python/0.14/)\n", - "\n", - "[`DataJoint Element for DeepLabCut - Documentation`](https://datajoint.com/docs/elements/element-deeplabcut/0.2/)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Download Sample Data and Context" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section, you will download the sample data that simulates a real research project. By working through this sample data, you will gain valuable insights into the `practical application` of the package's tools and techniques." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Project Context: \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this research project, we are studying the `behavior of a freely-moving mouse in an open-field environment`. The objective is to `extract pose estimations of the animal's head, body, and tail` from video footage. This information can provide valuable insights into the animal's movements, postures, and interactions within the environment." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Downloading Sample Data:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. Click the following link to download the sample data archive: `##TO-DO`\n", - "\n", - "\n", - "2. Once the download is complete, extract the contents to a `path of your choice on your local machine`.\n", - "\n", - "After running this tutorial, you can try `Element-DeepLabCut` with your own dataset. To do so, create a new `DeepLabCut` folder with your own videos. Then, remember to change the path in the configuration file (`config.yaml`) in your new `DeepLabCut project` folder accordingly." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Challenges: " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Complex Background**: The open field environment introduces complex backgrounds and varying lighting conditions, making accurate pose estimation challenging.\n", - "\n", - "**Multiple Body Parts**: Extracting the pose of multiple body parts (head, body, tail) adds complexity to the analysis due to potential occlusions and variations in appearance.\n", - "\n", - "**Data Management**: Managing the large volume of video data generated in the field and ensuring consistent annotation requires an efficient data pipeline." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Expected Outcomes:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Upon completing this tutorial, you will have acquired practical proficiency in employing the `Element-DeepLabCut` package to effectively tackle the complexities of pose estimation. \n", - "\n", - "This tutorial and sample dataset will serve as a practical foundation for your learning journey with the Element package, enabling you to apply these techniques to your own research projects. \n", - "\n", - "By integrating this element package with other Elements of DataJoint, you unlock a powerful data pipeline that provides numerous benefits for your research workflow. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "####Explain this part better and include the link to download the project folder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before using DataJoint and this tutorial, you need an account to gain access to the database server. \n", - "\n", - "Please, go to ### and create an account. \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that you have your credentials (DJ_USER, DJ_PASS), you need to connect to the server. To do so, we need to `configure the connection` with the user credentials. \n", - "\n", - "- If this is the first time that you are running this tutorial:\n", - " - Then you will need to specify the connection parameters by input arguments as in the next subsection `Configuration Code for Initiating this Tutorial`. This section will create a DataJoint configuration file named `dj_local_conf.json` that will save your credentials as environment variables in your local machine. You can find this file in your `Element-Deeplabcut` folder. This configuration file is unique to each machine and DataJoint user.\n", - "\n", - "- If you have already run this tutorial and created the `.json` file with your credentials info:\n", - " - Then you can directly start from the subsection `Configuration Code to Configure this Tutorial in Subsequent Restarts`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Configuration Code for Initiating This Tutorial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### *The configuration file only needs to be set up once. If you already have one, jump to the following subsection `Configuration Code to Configure this Tutorial in Subsequent Restarts`*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "assert os.path.basename(os.getcwd())=='element-deeplabcut', (\"Please move to the \"\n", - " + \"element directory\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's start by importing the packages necessary to run this pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj\n", - "from pathlib import Path\n", - "import yaml\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The connection parameters are specified by input arguments:\n", - "- HOST, USER, AND PASSWORD are the fields for the user credentials\n", - "- Configuring a `custom` field helps manage privileges on a server,for instance, teams who work on the same schemas should use the same schema prefix. \n", - " - Setting the prefix to `dlc_` means that every schema we then create will start with `dlc_` (e.g. `dlc_lab`, `dlc_subject`, `dlc_model` etc.)\n", - "\n", - "Please, substitute the blue text with your personal host, username, and prefix. Also, your password will be asked.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##TO-DO: WHAT HOST IS NECESSARY FOR A NEW USER?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import getpass\n", - "dj.config['database.host'] = '{YOUR_HOST}' \n", - "dj.config['database.user'] = '{YOUR_USERNAME}' \n", - "dj.config['database.password'] = getpass.getpass() # enter the password securely\n", - "dj.config['custom']['database.prefix']= '{YOUR_USERNAME_dlc_}' " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "### DELETE BEFORE COMMIT TO GITHUB\n", - "\n", - "import getpass\n", - "dj.config['database.host'] = 'rds.datajoint.io' \n", - "dj.config['database.user'] = 'milagrosmarin' \n", - "dj.config['database.password'] = getpass.getpass() # enter the password securely\n", - "dj.config['custom']['database.prefix']= 'milagrosmarin_dlc_' " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Credentials will be saved and the connection to the database server will be run with the next cells." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config.save_local() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's make the connection to the database server." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.conn()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once set the configuration file, it will be created and saved as `dj_local_conf.json` in the `Element-DeepLabCut directory`. Please, you may verify this file and its content. Remember that this step only needs to be set up once." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Configuration Code to Configure this Tutorial in Subsequent Restarts" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you have already run the previous subsection, the next time you want to run this tutorial (restart the kernel of the notebook) you will only need to start the tutorial from here: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", - "assert os.path.basename(os.getcwd())=='element-deeplabcut', (\"Please move to the \"\n", - " + \"element directory\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's start by importing the packages necessary to run this pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj\n", - "from pathlib import Path\n", - "import yaml" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's connect to the database server to be able to use DataJoint." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.conn()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Design the DataJoint Pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, you need to update the path of your `DeepLabCut project folder` into your configuration file `dj_local_conf.json`. Open the file in your `DeepLabCut-Element` folder, and copy and paste the `DeepLabCut project folder` path in `dlc_root_data_dir`. Also, copy and paste the `DeepLabCut project folder` name in `current_project_folder`:\n", - "\n", - " \"dlc_root_data_dir\": \"{DLC_PROJECT_PATH}\",\n", - " \"current_project_folder\": \"{DLC_PROJECT_NAME}\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or you can run the following lines to automatically change this information in the configuration file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from element_interface.utils import find_full_path\n", - "dj.config.load('dj_local_conf.json')\n", - "data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config\n", - " 'Top_tracking-DataJoint-2023-08-03') \n", - " # DLC project dir" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on the project path specified in the `.json` file, the paths of the input files are charged as variables in this tutorial's session:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "### DLC Project\n", - "dlc_project_path_abs = Path(dj.config[\"custom\"][\"dlc_root_data_dir\"]) / Path(\n", - " dj.config[\"custom\"][\"current_project_folder\"]\n", - ") # use pathlib to join; abs path\n", - "dlc_project_folder = Path(\n", - " dj.config[\"custom\"][\"current_project_folder\"]\n", - ") # relative path\n", - "\n", - "### Config file\n", - "config_file_abs = dlc_project_path_abs / \"config.yaml\" # abs path\n", - "assert (\n", - " config_file_abs.exists()\n", - "), \"Please check the that you have the Top_tracking folder\"\n", - "\n", - "### Labeled-data\n", - "labeled_data_path_abs = dlc_project_path_abs / \"labeled-data\"\n", - "labeled_files_abs = list(\n", - " list(labeled_data_path_abs.rglob(\"*\"))[1].rglob(\"*\")\n", - ") # substitute 'training_files'; absolute path\n", - "labeled_files_rel = []\n", - "for file in labeled_files_abs:\n", - " labeled_files_rel.append(\n", - " file.relative_to(dlc_project_path_abs)\n", - " ) # substitute 'training_files'; relative path\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Combine multiple Elements into a pipeline\n", - "\n", - "Each DataJoint Element is a modular set of tables that can be combined into a complete pipeline.\n", - "\n", - "Each Element contains one or more modules, and each module declares its own schema in the database. Schemas are conceptually related sets of tables. \n", - "\n", - "This tutorial pipeline is assembled from four DataJoint Elements.\n", - "\n", - "| Element | Source Code | Documentation | Description |\n", - "| -- | -- | -- | -- |\n", - "| Element Lab | [Link](https://github.com/datajoint/element-lab) | [Link](https://datajoint.com/docs/elements/element-lab) | Lab management related information, such as Lab, User, Project, Protocol, Source. |\n", - "| Element Animal | [Link](https://github.com/datajoint/element-animal) | [Link](https://datajoint.com/docs/elements/element-animal) | General subject meta data, genotype, and surgery information. |\n", - "| Element Session | [Link](https://github.com/datajoint/element-session) | [Link](https://datajoint.com/docs/elements/element-session) | General information of experimental sessions. |\n", - "| Element DeepLabCut | [Link](https://github.com/datajoint/element-deeplabcut) | [Link](https://datajoint.com/docs/elements/element-deeplabcut) | DataJoint schemas (Train and Model) for storing and running analysis of markerless pose estimation with DeepLabCut.\n", - "\n", - "The Elements are imported and activated in the next code cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from tutorial_pipeline import lab, subject, session, train, model # after creating json file" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By importing the modules for the first time, the schemas and tables will be created in the database. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.list_schemas()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once created, importing modules will not create schemas and tables again, but the existing schemas/tables can be accessed.\n", - "To empty these schemas and tables for introducing new entries, run (uncomment) the following code lines (note that you will have to commit the delete in the prompt by typing \"yes\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Empty the session in case of rerunning\n", - "safemode=True # Set to false to turn off confirmation prompts\n", - "session.Session.delete(safemode=safemode)\n", - "train.TrainingParamSet.delete(safemode=safemode)\n", - "train.VideoSet.delete(safemode=safemode)\n", - "model.BodyPart.delete(safemode=safemode)\n", - "subject.Subject.delete(safemode=safemode)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each Python module (e.g. `subject`) contains a schema object that enables interaction with the schema in the database." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.schema" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Python classes in the module correspond to a table in the database server. We can check also if there is any entry in the table." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subject.Subject()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's plot the diagram of the whole data pipeline for this `Element-DeepLabCut`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(\n", - " dj.Diagram(subject) \n", - " + dj.Diagram(lab) \n", - " + dj.Diagram(session) \n", - " + dj.Diagram(model) \n", - " + dj.Diagram(train)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And this is the main body of this `Element-DeepLabCut`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(model) + dj.Diagram(train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Enter the Metadata into the Pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to run the `Model Training`, we need to start by adding the input data to the `train` module. Let's start having a look at the `TrainingTask` table. This table will pair each video set with their corresponding training parameters.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train.TrainingTask()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's pair some example data and launch training via `process`. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#IS THIS NEEDED???\n", - "\n", - "#key={'paramset_idx':0,'training_id':0,'video_set_id':0, \n", - "# 'project_path':dlc_project_folder}\n", - "#train.TrainingTask.insert1(key, skip_duplicates=True)\n", - "#process.run(verbose=True, display_progress=True)\n", - "#model.RecordingInfo()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `Subject` module corresponds to the table that will contain the subject (e.g., the mouse) information. Let's insert example entries into the `subject.Subject` table." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Subject and Session tables\n", - "subject.Subject.insert1(\n", - " dict(\n", - " subject=\"subject6\",\n", - " sex=\"F\",\n", - " subject_birth_date=\"2020-01-01\",\n", - " subject_description=\"hneih_E105\",\n", - " ),\n", - " skip_duplicates=True,\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's repeat the step for the `Session` module. We can also insert in the `Session` table by passing a dictionary to the `insert1` method. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Definition of the dictionary named \"session_keys\"\n", - "session_keys = [\n", - " dict(subject=\"subject6\", session_datetime=\"2021-06-02 14:04:22\"),\n", - " dict(subject=\"subject6\", session_datetime=\"2021-06-03 14:43:10\"),\n", - "]\n", - "\n", - "#Insert this dictionary in the Session table\n", - "session.Session.insert(session_keys, skip_duplicates=True)\n", - "session.Session()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `VideoSet` table in the `train` schema retains records of files generated in the video labeling process (e.g., `h5`, `csv`, `png`). DeepLabCut will refer to the `mat` file located under the `training-datasets` directory.\n", - "\n", - "We recommend storing all paths as relative to the root in your config." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Videoset table \n", - "train.VideoSet.insert1({\"video_set_id\": 0}, skip_duplicates=True)\n", - "\n", - "for idx, filename in enumerate(labeled_files_rel):\n", - " train.VideoSet.File.insert1(\n", - " {\n", - " \"video_set_id\": 0, \n", - " \"file_id\": idx, \n", - " \"file_path\": dlc_project_folder / filename\n", - " },\n", - " ) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train.VideoSet.File()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training a network" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To train the network, we need to add the parameter set (`TrainingParamSet`) of the model training (`train`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train.TrainingParamSet()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `params` attribute has to be a dictionary that captures all the items for the DeepLabCut's `train_network` function. At minimum, this is the contents of the project's config file, as well as `suffle` and `trainingsetindex`, which are not included in the configuration file." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We will insert these items, load the config contents, and overwrite some defaults, including `maxiters`, to restrict our training iterations to 5.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Restrict the training interations to 5 modifying the default parameters in config.yaml\n", - "paramset_idx = 0\n", - "paramset_desc = \"First training test with DLC using shuffle 1 and maxiters = 5\"\n", - "\n", - "# default parameters\n", - "with open(config_file_abs, \"rb\") as y:\n", - " config_params = yaml.safe_load(y)\n", - "config_params.keys()\n", - "\n", - "# new parameters\n", - "training_params = {\n", - " \"shuffle\": \"1\",\n", - " \"trainingsetindex\": \"0\",\n", - " \"maxiters\": \"5\",\n", - " \"scorer_legacy\": \"False\", # For DLC ≤ v2.0, include scorer_legacy = True in params\n", - " \"maxiters\": \"5\",\n", - " \"multianimalproject\": \"False\",\n", - "}\n", - "config_params.update(training_params)\n", - "\n", - "train.TrainingParamSet.insert_new_params(\n", - " paramset_idx=paramset_idx, paramset_desc=paramset_desc, params=config_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we add a `TrainingTask`. As a computed table, `ModelTraining` will reference this to start training when calling `populate()`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train.TrainingTask()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TrainingTask table\n", - "key = {\n", - " \"video_set_id\": 0,\n", - " \"paramset_idx\": 0,\n", - " \"training_id\": 1,\n", - " \"project_path\": dlc_project_folder,\n", - "}\n", - "train.TrainingTask.insert1(key, skip_duplicates=True)\n", - "train.TrainingTask()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After inserting the training parameters and the video recordings, the model training can be run and outputs will be stored in `ModelTraining` table.\n", - "\n", - "*Note that the following code line will run the model training with DeepLabCut. It will take some minutes if you have installed DeepLabCut in the GPU. However, it will take longer if the installation was in CPU*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train.ModelTraining.populate(display_progress=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train.ModelTraining.fetch()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "The network is now trained and ready to evaluate. The next step consists of evaluating the network. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Evaluating the network model" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "### Tracking Joints/Body Parts" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `model` schema uses a lookup table for managing the body parts tracked across models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.BodyPart()\n", - "new_body_parts = [\n", - " dict(body_part=\"subject6\", session_datetime=\"2021-06-02 14:04:22\"),\n", - " dict(subject=\"subject6\", session_datetime=\"2021-06-03 14:43:10\"),\n", - "]\n", - "session.Session.insert(session_keys, skip_duplicates=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also modify the body parts as desired. For that, we can use helper functions to identify and insert the new body parts from a given DeepLabCut configuration file (`config.yaml`) in the data pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.BodyPart.extract_new_body_parts(config_file_abs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add ONLY if there are new body parts compared to the config.yaml. If the table has already descriptions, then leave it empty.\n", - "bp_desc=[]\n", - "model.BodyPart.insert_from_config(config_file_abs,bp_desc)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "### Declaring/Evaluating a Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can insert into `Model` table for automatic evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.Model.insert_new_model(model_name='FromTop-latest',\n", - " dlc_config=config_file_abs,\n", - " shuffle=1,\n", - " trainingsetindex=0,\n", - " model_description='FromTop - latest snapshot',\n", - " paramset_idx=0,\n", - " params={\"snapshotindex\":-1})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.BodyPart()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.Model()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`ModelEvaluation` will reference the `Model` using the `populate` method and insert the output from DeepLabCut's `evaluate_network` function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.ModelEvaluation.populate()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.ModelEvaluation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pose Estimation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To use our model, we'll first need to insert a session recording into `VideoRecording`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.VideoRecording()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "key = {'subject': 'subject6',\n", - " 'session_datetime': '2021-06-02 14:04:22',\n", - " 'recording_id': '1', 'device': 'Camera1'}\n", - "model.VideoRecording.insert1(key, skip_duplicates=True)\n", - "\n", - "_ = key.pop('device') # get rid of secondary key from master table // why this step???\n", - "key.update({'file_id': 1, \n", - " 'file_path': 'Top_tracking-DataJoint-2023-08-03/videos/train1_trimmed.mp4'})\n", - "model.VideoRecording.File.insert1(key, skip_duplicates=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.VideoRecording.File()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`RecordingInfo` automatically populates with file information" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.RecordingInfo.populate()\n", - "model.RecordingInfo()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we specify if the `PoseEstimation` table should load results from an existing file or trigger the estimation command. Here, we can also specify parameters for DeepLabCut's `analyze_videos` as a dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "recording_dict = (model.VideoRecording & {\"recording_id\": \"1\"}).fetch1(\"KEY\")\n", - "recording_dict.update({\"model_name\": \"FromTop-latest\", \"task_mode\": \"trigger\"})\n", - "# videotype, gputouse, save_as_csv, batchsize, cropping, TFGPUinference, dynamic, robust_nframes, allow_growth, use_shelve\n", - "analyze_videos_params = {\"save_as_csv\": True}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default, DataJoint will store results in a subdirectory\n", - "> / videos / device__recording_<#>_model_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`processed_dir` is optionally specified in the datajoint config, or in the `insert_estimation_task`. If unspecified, this will be the project directory. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.PoseEstimationTask.infer_output_dir(key)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.PoseEstimationTask.insert_estimation_task(recording_dict, model_name = recording_dict[\"model_name\"], analyze_videos_params=analyze_videos_params)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#model.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True})\n", - "model.PoseEstimation.populate()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The resulting coordinates of the pose estimation are now available in the corresponding `BodyPartPosition` table, ready to use for visualization, or to combine with other Elements." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.PoseEstimation.BodyPartPosition()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can visualize the pose estimation results directly as a pandas dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.PoseEstimation.coordinates_dataframe(key)" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:percent" - }, - "kernelspec": { - "display_name": "Python 3.9.13 ('ele')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "vscode": { - "interpreter": { - "hash": "d00c4ad21a7027bf1726d6ae3a9a6ef39c8838928eca5a3d5f51f3eb68720410" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 316683090f0d37f99806fd1aff18a0606a14b317 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 22:58:16 +0200 Subject: [PATCH 121/176] change DLC_ROOT_DATA_DIR to example_data directory --- .devcontainer/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 3a06fe8..6c6c035 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -41,7 +41,7 @@ ENV DJ_HOST fakeservices.datajoint.io ENV DJ_USER root ENV DJ_PASS simple -ENV DLC_ROOT_DATA_DIR /workspaces/element-deeplabcut/ +ENV DLC_ROOT_DATA_DIR /workspaces/element-deeplabcut/example_data ENV CURRENT_PROJECT_FOLDER Top_tracking-DataJoint-2023-08-03 ENV DATABASE_PREFIX neuro_ From 845974af467629120d160db2f8fea29051a99a02 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 7 Sep 2023 23:39:38 +0200 Subject: [PATCH 122/176] removed more files from git --- dj_example_local_conf.json | 26 ------ notebooks/testing_merge.py | 186 ------------------------------------- 2 files changed, 212 deletions(-) delete mode 100644 dj_example_local_conf.json delete mode 100644 notebooks/testing_merge.py diff --git a/dj_example_local_conf.json b/dj_example_local_conf.json deleted file mode 100644 index 19a7612..0000000 --- a/dj_example_local_conf.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "database.host": "", - "database.password": "", - "database.user": "", - "database.port": 3306, - "database.reconnect": true, - "connection.init_function": null, - "connection.charset": "", - "loglevel": "INFO", - "safemode": true, - "fetch_format": "array", - "display.limit": 12, - "display.width": 14, - "display.show_tuple_count": true, - "database.use_tls": null, - "enable_python_native_blobs": true, - "database.ingest_filename_short": "", - "database.ingest_filename_full": "", - "custom": { - "database.prefix": "YourPrefix_", - "dlc_root_data_dir": [ - "/Abolute/Path/Here/", - "/Abolute/Other/Path/" - ] - } -} diff --git a/notebooks/testing_merge.py b/notebooks/testing_merge.py deleted file mode 100644 index cae6052..0000000 --- a/notebooks/testing_merge.py +++ /dev/null @@ -1,186 +0,0 @@ -# PATHS OF INPUT FILES: Extract abs and rel paths from .json file -import os - -if os.path.basename(os.getcwd()) == "notebooks": - os.chdir("..") -assert os.path.basename(os.getcwd()) == "element-deeplabcut", "Please move to the " - -import datajoint as dj -from pathlib import Path -import yaml - -dj.conn() - - -### DLC Project -dlc_project_path_abs = Path(dj.config["custom"]["dlc_root_data_dir"]) / Path( - dj.config["custom"]["current_project_folder"] -) # use pathlib to join; abs path -dlc_project_folder = Path( - dj.config["custom"]["current_project_folder"] -) # relative path - -### Config file -config_file_abs = dlc_project_path_abs / "config.yaml" # abs path -assert ( - config_file_abs.exists() -), "Please check the that you have the Top_tracking folder" - -### Labeled-data -labeled_data_path_abs = dlc_project_path_abs / "labeled-data" -labeled_files_abs = list( - list(labeled_data_path_abs.rglob("*"))[1].rglob("*") -) # substitute 'training_files'; absolute path -labeled_files_rel = [] -for file in labeled_files_abs: - labeled_files_rel.append( - file.relative_to(dlc_project_path_abs) - ) # substitute 'training_files'; relative path - - -from tutorial_pipeline import ( - lab, - subject, - session, - train, - model, -) # after creating json file - -# Empty the session in case of rerunning -# session.Session.delete() -# train.TrainingTask.delete() -# train.TrainingParamSet.delete() -# train.VideoSet.delete() - -# Insert some data in session and train tables -# TO-DO: substitute lab.project by project schema. - - -# Subject and Session tables -subject.Subject.insert1( - dict( - subject="subject6", - sex="F", - subject_birth_date="2020-01-01", - subject_description="hneih_E105", - ), - skip_duplicates=True, -) -session_keys = [ - dict(subject="subject6", session_datetime="2021-06-02 14:04:22"), - dict(subject="subject6", session_datetime="2021-06-03 14:43:10"), -] - -session.Session.insert(session_keys, skip_duplicates=True) -session.Session() & "session_datetime > '2021-06-01 12:00:00'" & "subject='subject6'" - -# Videoset tabley -train.VideoSet.insert1({"video_set_id": 0}, skip_duplicates=True) - -# training_files = #['labeled-data/train1_trimmed/CollectedData_DataJoint.h5', -#'labeled-data/train1_trimmed/CollectedData_DataJoint.csv'] -#'labeled-data/train1_trimmed/img00674.png'] #TO-DO: CHECK IF ALL THE PNGS ARE NECESSARY FOR TRAINING -#'videos/train1.mp4'] -# for idx, filename in enumerate(training_files): -for idx, filename in enumerate(labeled_files_rel): - train.VideoSet.File.insert1( - {"video_set_id": 0, "file_id": idx, "file_path": dlc_project_folder / filename}, - skip_duplicates=True, - ) # Changed from + to /; #relative_path - -# Restrict the training interations to 5 modifying the default parameters in config.yaml -paramset_idx = 0 -paramset_desc = "First training test with DLC using shuffle 1 and maxiters = 5" - -# default parameters -with open(config_file_abs, "rb") as y: - config_params = yaml.safe_load(y) -config_params.keys() - -# new parameters -training_params = { - "shuffle": "1", - "trainingsetindex": "0", - "maxiters": "5", - "scorer_legacy": "False", # For DLC ≤ v2.0, include scorer_legacy = True in params - "maxiters": "5", - "multianimalproject": "False", -} -config_params.update(training_params) - -train.TrainingParamSet.insert_new_params( - paramset_idx=paramset_idx, - paramset_desc=paramset_desc, - params=config_params, -) - -# TrainingTask table -key = { - "video_set_id": 0, - "paramset_idx": 0, - "training_id": 1, - "project_path": dlc_project_folder, -} -train.TrainingTask.insert1(key, skip_duplicates=True) -train.TrainingTask() -train.ModelTraining.populate(display_progress=True) -train.ModelTraining.fetch() - -model.BodyPart() -new_body_parts = [ - dict(body_part="subject6", session_datetime="2021-06-02 14:04:22"), - dict(subject="subject6", session_datetime="2021-06-03 14:43:10"), -] -session.Session.insert(session_keys, skip_duplicates=True) -model.BodyPart.extract_new_body_parts(config_file_abs) - -bp_desc = [] -model.BodyPart.insert_from_config(config_file_abs, bp_desc) - -model.BodyPart() -model.Model.insert_new_model( - model_name="FromTop-latest", - dlc_config=config_file_abs, - shuffle=1, - trainingsetindex=0, - model_description="FromTop - latest snapshot", - paramset_idx=0, - params={"snapshotindex": -1}, -) -model.Model() -model.ModelEvaluation.heading -model.ModelEvaluation.populate() -model.ModelEvaluation() -model.VideoRecording() -key = { - "subject": "subject6", - "session_datetime": "2021-06-02 14:04:22", - "recording_id": "1", - "device": "Camera1", -} -model.VideoRecording.insert1(key, skip_duplicates=True) - -_ = key.pop("device") # get rid of secondary key from master table -key.update( - { - "file_id": 1, - "file_path": "/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/videos/train1_trimmed.mp4", - } -) -model.VideoRecording.File.insert1(key, skip_duplicates=True) -model.VideoRecording.File() -# model.RecordingInfo.populate() -model.RecordingInfo() -key = (model.VideoRecording & {"recording_id": "1"}).fetch1("KEY") -key.update({"model_name": "FromTop-latest", "task_mode": "trigger"}) -# videotype, gputouse, save_as_csv, batchsize, cropping, TFGPUinference, dynamic, robust_nframes, allow_growth, use_shelve -analyze_videos_params = {"save_as_csv": True} - -# key.update(analyze_videos_params={"save_as_csv": True}) -# model.PoseEstimationTask.insert_estimation_task(key) -model.PoseEstimationTask.insert_estimation_task( - key, model_name=key["model_name"], analyze_videos_params=analyze_videos_params -) - -model.PoseEstimation.populate() -model.PoseEstimation.coordinates_dataframe(key) From 276487b91823a31600bf82f184b062517b1c2d7b Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 11 Sep 2023 13:35:00 +0200 Subject: [PATCH 123/176] Recover files from git rm --- .devcontainer/Dockerfile | 4 +- .devcontainer/devcontainer.json | 4 +- .github/ISSUE_TEMPLATE/config.yml | 2 +- .../workflows/u24_element_before_release.yaml | 17 -- .../workflows/u24_element_release_call.yaml | 28 --- .../workflows/u24_element_tag_to_release.yaml | 14 -- .../u24_workflow_before_release.yaml | 18 -- .../workflows/u24_workflow_release_call.yaml | 20 -- .../u24_workflow_tag_to_release.yaml | 15 -- .gitignore | 2 + dj_dlc_config.yaml | 59 ------ dj_example_local_conf.json | 26 --- dj_pose_cfg | 116 ----------- notebooks/testing_merge.py | 186 ------------------ notebooks/tutorial.ipynb | 4 +- 15 files changed, 11 insertions(+), 504 deletions(-) delete mode 100644 .github/workflows/u24_element_before_release.yaml delete mode 100644 .github/workflows/u24_element_release_call.yaml delete mode 100644 .github/workflows/u24_element_tag_to_release.yaml delete mode 100644 .github/workflows/u24_workflow_before_release.yaml delete mode 100644 .github/workflows/u24_workflow_release_call.yaml delete mode 100644 .github/workflows/u24_workflow_tag_to_release.yaml delete mode 100644 dj_dlc_config.yaml delete mode 100644 dj_example_local_conf.json delete mode 100644 dj_pose_cfg delete mode 100644 notebooks/testing_merge.py diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 6c6c035..5039ce1 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -31,7 +31,9 @@ COPY ./ /tmp/element-deeplabcut/ RUN \ # pipeline dependencies - apt-get install gcc g++ ffmpeg libsm6 libxext6 -y && \ + apt-get install gcc && \ + #g++ ffmpeg libsm6 libxext6 -y && \ + pip install numcodecs && \ pip install --no-cache-dir -e /tmp/element-deeplabcut[elements] && \ # clean up rm -rf /tmp/element-deeplabcut/ && \ diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index cc78b6b..ec9b835 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -6,8 +6,8 @@ "remoteEnv": { "LOCAL_WORKSPACE_FOLDER": "${localWorkspaceFolder}" }, - "onCreateCommand": "mkdir -p ${DLC_ROOT_DATA_DIR} && pip install -e .", - "postStartCommand": "docker volume prune -f && s3fs ${DJ_PUBLIC_S3_LOCATION} ${DLC_ROOT_DATA_DIR} -o nonempty,multipart_size=530,endpoint=us-east-1,url=http://s3.amazonaws.com,public_bucket=1", + "onCreateCommand": "pip install -e .", + "postStartCommand": "docker volume prune -f", "hostRequirements": { "cpus": 4, "memory": "8gb", diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index d31fbac..b3d197d 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -1,5 +1,5 @@ blank_issues_enabled: false contact_links: - name: DataJoint Contribution Guideline - url: https://docs.datajoint.org/python/community/02-Contribute.html + url: https://datajoint.com/docs/community/contribute/ about: Please make sure to review the DataJoint Contribution Guidelines \ No newline at end of file diff --git a/.github/workflows/u24_element_before_release.yaml b/.github/workflows/u24_element_before_release.yaml deleted file mode 100644 index 692cf82..0000000 --- a/.github/workflows/u24_element_before_release.yaml +++ /dev/null @@ -1,17 +0,0 @@ -name: u24_element_before_release -on: - pull_request: - push: - branches: - - '**' - tags-ignore: - - '**' - workflow_dispatch: -jobs: - call_context_check: - uses: dj-sciops/djsciops-cicd/.github/workflows/context_check.yaml@main - call_u24_elements_build_alpine: - uses: dj-sciops/djsciops-cicd/.github/workflows/u24_element_build.yaml@main - with: - py_ver: 3.9 - image: djbase diff --git a/.github/workflows/u24_element_release_call.yaml b/.github/workflows/u24_element_release_call.yaml deleted file mode 100644 index 4324cca..0000000 --- a/.github/workflows/u24_element_release_call.yaml +++ /dev/null @@ -1,28 +0,0 @@ -name: u24_element_release_call -on: - workflow_run: - workflows: ["u24_element_tag_to_release"] - types: - - completed -jobs: - call_context_check: - uses: dj-sciops/djsciops-cicd/.github/workflows/context_check.yaml@main - test_call_u24_elements_release_alpine: - if: >- - github.event.workflow_run.conclusion == 'success' && ( contains(github.event.workflow_run.head_branch, 'test') || (github.event.workflow_run.event == 'pull_request')) - uses: dj-sciops/djsciops-cicd/.github/workflows/u24_element_release.yaml@main - with: - py_ver: 3.9 - twine_repo: testpypi - secrets: - TWINE_USERNAME: ${{secrets.TWINE_TEST_USERNAME}} - TWINE_PASSWORD: ${{secrets.TWINE_TEST_PASSWORD}} - call_u24_elements_release_alpine: - if: >- - github.event.workflow_run.conclusion == 'success' && github.repository_owner == 'datajoint' && !contains(github.event.workflow_run.head_branch, 'test') - uses: dj-sciops/djsciops-cicd/.github/workflows/u24_element_release.yaml@main - with: - py_ver: 3.9 - secrets: - TWINE_USERNAME: ${{secrets.TWINE_USERNAME}} - TWINE_PASSWORD: ${{secrets.TWINE_PASSWORD}} diff --git a/.github/workflows/u24_element_tag_to_release.yaml b/.github/workflows/u24_element_tag_to_release.yaml deleted file mode 100644 index 57334e9..0000000 --- a/.github/workflows/u24_element_tag_to_release.yaml +++ /dev/null @@ -1,14 +0,0 @@ -name: u24_element_tag_to_release -on: - push: - tags: - - '*.*.*' - - 'test*.*.*' -jobs: - call_context_check: - uses: dj-sciops/djsciops-cicd/.github/workflows/context_check.yaml@main - call_u24_elements_build_alpine: - uses: dj-sciops/djsciops-cicd/.github/workflows/u24_element_build.yaml@main - with: - py_ver: 3.9 - image: djbase diff --git a/.github/workflows/u24_workflow_before_release.yaml b/.github/workflows/u24_workflow_before_release.yaml deleted file mode 100644 index 28a5ff5..0000000 --- a/.github/workflows/u24_workflow_before_release.yaml +++ /dev/null @@ -1,18 +0,0 @@ -name: u24_workflow_before_release_0.0.1 -on: - pull_request: - push: - branches: - - '**' - tags-ignore: - - '**' - workflow_dispatch: -jobs: - call_context_check: - uses: dj-sciops/djsciops-cicd/.github/workflows/context_check.yaml@main - call_u24_workflow_build_debian: - uses: dj-sciops/djsciops-cicd/.github/workflows/u24_workflow_build.yaml@main - with: - jhub_ver: 1.4.2 - py_ver: 3.9 - dist: debian diff --git a/.github/workflows/u24_workflow_release_call.yaml b/.github/workflows/u24_workflow_release_call.yaml deleted file mode 100644 index 8196673..0000000 --- a/.github/workflows/u24_workflow_release_call.yaml +++ /dev/null @@ -1,20 +0,0 @@ -name: u24_workflow_release_call_0.0.1 -on: - workflow_run: - workflows: ["u24_workflow_tag_to_release_0.0.1"] - types: - - completed -jobs: - call_context_check: - uses: dj-sciops/djsciops-cicd/.github/workflows/context_check.yaml@main - call_u24_workflow_release_debian: - if: >- - github.event.workflow_run.conclusion == 'success' && github.repository_owner == 'datajoint' - uses: dj-sciops/djsciops-cicd/.github/workflows/u24_workflow_release.yaml@main - with: - jhub_ver: 1.4.2 - py_ver: 3.9 - dist: debian - secrets: - REGISTRY_USERNAME: ${{secrets.DOCKER_USERNAME}} - REGISTRY_PASSWORD: ${{secrets.DOCKER_PASSWORD}} diff --git a/.github/workflows/u24_workflow_tag_to_release.yaml b/.github/workflows/u24_workflow_tag_to_release.yaml deleted file mode 100644 index 3a6ce58..0000000 --- a/.github/workflows/u24_workflow_tag_to_release.yaml +++ /dev/null @@ -1,15 +0,0 @@ -name: u24_workflow_tag_to_release_0.0.1 -on: - push: - tags: - - '*.*.*' - - 'test*.*.*' -jobs: - call_context_check: - uses: dj-sciops/djsciops-cicd/.github/workflows/context_check.yaml@main - call_u24_workflow_build_debian: - uses: dj-sciops/djsciops-cicd/.github/workflows/u24_workflow_build.yaml@main - with: - jhub_ver: 1.4.2 - py_ver: 3.9 - dist: debian diff --git a/.gitignore b/.gitignore index 91d8ded..15a4dff 100644 --- a/.gitignore +++ b/.gitignore @@ -81,11 +81,13 @@ ENV/ # datajoint, notes, nwb export dj_local_c*.json +dj_ex*.json dj_pose*.y*ml temp* temp/* *nwb workflow_deeplabcut/ +example_data/ # docs /docs/site/ diff --git a/dj_dlc_config.yaml b/dj_dlc_config.yaml deleted file mode 100644 index 39f19ea..0000000 --- a/dj_dlc_config.yaml +++ /dev/null @@ -1,59 +0,0 @@ - # Project definitions (do not edit) -Task: Top_tracking -scorer: DataJoint -date: Aug3 -multianimalproject: false -identity: - - # Project path (change when moving around) -project_path: /Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03 - - # Annotation data set configuration (and individual video cropping parameters) -video_sets: - /Users/milagros/Documents/DeepLabCut_testing/test_data/Top_tracking-DataJoint-2023-08-03/videos/train1_trimmed.mp4: - crop: 0, 500, 0, 500 - /Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/videos/train1_trimmed.mp4: - crop: 0, 500, 0, 500 -bodyparts: -- Head -- Tailbase - - # Fraction of video to start/stop when extracting frames for labeling/refinement -start: 0 -stop: 1 -numframes2pick: 5 - - # Plotting configuration -skeleton: -- - bodypart1 - - bodypart2 -- - objectA - - bodypart3 -skeleton_color: black -pcutoff: 0.6 -dotsize: 12 -alphavalue: 0.7 -colormap: rainbow - - # Training,Evaluation and Analysis configuration -TrainingFraction: -- 0.95 -iteration: 0 -default_net_type: resnet_50 -default_augmenter: default -snapshotindex: -1 -batch_size: 8 - - # Cropping Parameters (for analysis and outlier frame detection) -cropping: false - #if cropping is true for analysis, then set the values here: -x1: 0 -x2: 640 -y1: 277 -y2: 624 - - # Refinement configuration (parameters from annotation dataset configuration also relevant in this stage) -corner2move2: -- 50 -- 50 -move2corner: true diff --git a/dj_example_local_conf.json b/dj_example_local_conf.json deleted file mode 100644 index 19a7612..0000000 --- a/dj_example_local_conf.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "database.host": "", - "database.password": "", - "database.user": "", - "database.port": 3306, - "database.reconnect": true, - "connection.init_function": null, - "connection.charset": "", - "loglevel": "INFO", - "safemode": true, - "fetch_format": "array", - "display.limit": 12, - "display.width": 14, - "display.show_tuple_count": true, - "database.use_tls": null, - "enable_python_native_blobs": true, - "database.ingest_filename_short": "", - "database.ingest_filename_full": "", - "custom": { - "database.prefix": "YourPrefix_", - "dlc_root_data_dir": [ - "/Abolute/Path/Here/", - "/Abolute/Other/Path/" - ] - } -} diff --git a/dj_pose_cfg b/dj_pose_cfg deleted file mode 100644 index 95f260d..0000000 --- a/dj_pose_cfg +++ /dev/null @@ -1,116 +0,0 @@ - # Project definitions (do not edit) -Task: -scorer: -date: -multianimalproject: -identity: - - # Project path (change when moving around) -project_path: - /Users/milagros/Documents/DeepLabCut_testing/test_data/Top_tracking-DataJoint-2023-08-02 - - # Annotation data set configuration (and individual video cropping parameters) -video_sets: -bodyparts: - - # Fraction of video to start/stop when extracting frames for labeling/refinement -start: -stop: -numframes2pick: - - # Plotting configuration -skeleton: [] -skeleton_color: black -pcutoff: -dotsize: -alphavalue: -colormap: - - # Training,Evaluation and Analysis configuration -TrainingFraction: -iteration: -default_net_type: -default_augmenter: -snapshotindex: -batch_size: 1 - - # Cropping Parameters (for analysis and outlier frame detection) -cropping: - #if cropping is true for analysis, then set the values here: -x1: -x2: -y1: -y2: - - # Refinement configuration (parameters from annotation dataset configuration also relevant in this stage) -corner2move2: -move2corner: -all_joints: -- - 0 -- - 1 -- - 2 -all_joints_names: -- Head -- Bodycenter -- Tailbase -alpha_r: 0.02 -apply_prob: 0.5 -contrast: - clahe: true - claheratio: 0.1 - histeq: true - histeqratio: 0.1 -convolution: - edge: false - emboss: - alpha: - - 0.0 - - 1.0 - strength: - - 0.5 - - 1.5 - embossratio: 0.1 - sharpen: false - sharpenratio: 0.3 -cropratio: 0.4 -dataset: - training-datasets/iteration-0/UnaugmentedDataSet_Top_trackingAug2/Top_tracking_DataJoint95shuffle1.mat -dataset_type: imgaug -decay_steps: 30000 -display_iters: 1000 -global_scale: 0.8 -init_weights: - /Users/milagros/miniconda3/envs/DLC/lib/python3.9/site-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt -intermediate_supervision: false -intermediate_supervision_layer: 12 -location_refinement: true -locref_huber_loss: true -locref_loss_weight: 0.05 -locref_stdev: 7.2801 -lr_init: 0.0005 -max_input_size: 1500 -metadataset: - training-datasets/iteration-0/UnaugmentedDataSet_Top_trackingAug2/Documentation_data-Top_tracking_95shuffle1.pickle -min_input_size: 64 -mirror: false -multi_stage: false -multi_step: -- - 0.005 - - 10000 -- - 0.02 - - 430000 -- - 0.002 - - 730000 -- - 0.001 - - 1030000 -net_type: resnet_50 -num_joints: 3 -pairwise_huber_loss: false -pairwise_predict: false -partaffinityfield_predict: false -pos_dist_thresh: 17 -rotation: 25 -rotratio: 0.4 -save_iters: 50000 -scale_jitter_lo: 0.5 -scale_jitter_up: 1.25 diff --git a/notebooks/testing_merge.py b/notebooks/testing_merge.py deleted file mode 100644 index cae6052..0000000 --- a/notebooks/testing_merge.py +++ /dev/null @@ -1,186 +0,0 @@ -# PATHS OF INPUT FILES: Extract abs and rel paths from .json file -import os - -if os.path.basename(os.getcwd()) == "notebooks": - os.chdir("..") -assert os.path.basename(os.getcwd()) == "element-deeplabcut", "Please move to the " - -import datajoint as dj -from pathlib import Path -import yaml - -dj.conn() - - -### DLC Project -dlc_project_path_abs = Path(dj.config["custom"]["dlc_root_data_dir"]) / Path( - dj.config["custom"]["current_project_folder"] -) # use pathlib to join; abs path -dlc_project_folder = Path( - dj.config["custom"]["current_project_folder"] -) # relative path - -### Config file -config_file_abs = dlc_project_path_abs / "config.yaml" # abs path -assert ( - config_file_abs.exists() -), "Please check the that you have the Top_tracking folder" - -### Labeled-data -labeled_data_path_abs = dlc_project_path_abs / "labeled-data" -labeled_files_abs = list( - list(labeled_data_path_abs.rglob("*"))[1].rglob("*") -) # substitute 'training_files'; absolute path -labeled_files_rel = [] -for file in labeled_files_abs: - labeled_files_rel.append( - file.relative_to(dlc_project_path_abs) - ) # substitute 'training_files'; relative path - - -from tutorial_pipeline import ( - lab, - subject, - session, - train, - model, -) # after creating json file - -# Empty the session in case of rerunning -# session.Session.delete() -# train.TrainingTask.delete() -# train.TrainingParamSet.delete() -# train.VideoSet.delete() - -# Insert some data in session and train tables -# TO-DO: substitute lab.project by project schema. - - -# Subject and Session tables -subject.Subject.insert1( - dict( - subject="subject6", - sex="F", - subject_birth_date="2020-01-01", - subject_description="hneih_E105", - ), - skip_duplicates=True, -) -session_keys = [ - dict(subject="subject6", session_datetime="2021-06-02 14:04:22"), - dict(subject="subject6", session_datetime="2021-06-03 14:43:10"), -] - -session.Session.insert(session_keys, skip_duplicates=True) -session.Session() & "session_datetime > '2021-06-01 12:00:00'" & "subject='subject6'" - -# Videoset tabley -train.VideoSet.insert1({"video_set_id": 0}, skip_duplicates=True) - -# training_files = #['labeled-data/train1_trimmed/CollectedData_DataJoint.h5', -#'labeled-data/train1_trimmed/CollectedData_DataJoint.csv'] -#'labeled-data/train1_trimmed/img00674.png'] #TO-DO: CHECK IF ALL THE PNGS ARE NECESSARY FOR TRAINING -#'videos/train1.mp4'] -# for idx, filename in enumerate(training_files): -for idx, filename in enumerate(labeled_files_rel): - train.VideoSet.File.insert1( - {"video_set_id": 0, "file_id": idx, "file_path": dlc_project_folder / filename}, - skip_duplicates=True, - ) # Changed from + to /; #relative_path - -# Restrict the training interations to 5 modifying the default parameters in config.yaml -paramset_idx = 0 -paramset_desc = "First training test with DLC using shuffle 1 and maxiters = 5" - -# default parameters -with open(config_file_abs, "rb") as y: - config_params = yaml.safe_load(y) -config_params.keys() - -# new parameters -training_params = { - "shuffle": "1", - "trainingsetindex": "0", - "maxiters": "5", - "scorer_legacy": "False", # For DLC ≤ v2.0, include scorer_legacy = True in params - "maxiters": "5", - "multianimalproject": "False", -} -config_params.update(training_params) - -train.TrainingParamSet.insert_new_params( - paramset_idx=paramset_idx, - paramset_desc=paramset_desc, - params=config_params, -) - -# TrainingTask table -key = { - "video_set_id": 0, - "paramset_idx": 0, - "training_id": 1, - "project_path": dlc_project_folder, -} -train.TrainingTask.insert1(key, skip_duplicates=True) -train.TrainingTask() -train.ModelTraining.populate(display_progress=True) -train.ModelTraining.fetch() - -model.BodyPart() -new_body_parts = [ - dict(body_part="subject6", session_datetime="2021-06-02 14:04:22"), - dict(subject="subject6", session_datetime="2021-06-03 14:43:10"), -] -session.Session.insert(session_keys, skip_duplicates=True) -model.BodyPart.extract_new_body_parts(config_file_abs) - -bp_desc = [] -model.BodyPart.insert_from_config(config_file_abs, bp_desc) - -model.BodyPart() -model.Model.insert_new_model( - model_name="FromTop-latest", - dlc_config=config_file_abs, - shuffle=1, - trainingsetindex=0, - model_description="FromTop - latest snapshot", - paramset_idx=0, - params={"snapshotindex": -1}, -) -model.Model() -model.ModelEvaluation.heading -model.ModelEvaluation.populate() -model.ModelEvaluation() -model.VideoRecording() -key = { - "subject": "subject6", - "session_datetime": "2021-06-02 14:04:22", - "recording_id": "1", - "device": "Camera1", -} -model.VideoRecording.insert1(key, skip_duplicates=True) - -_ = key.pop("device") # get rid of secondary key from master table -key.update( - { - "file_id": 1, - "file_path": "/Users/milagros/Documents/DeepLabCut_testing/Top_tracking-DataJoint-2023-08-03/videos/train1_trimmed.mp4", - } -) -model.VideoRecording.File.insert1(key, skip_duplicates=True) -model.VideoRecording.File() -# model.RecordingInfo.populate() -model.RecordingInfo() -key = (model.VideoRecording & {"recording_id": "1"}).fetch1("KEY") -key.update({"model_name": "FromTop-latest", "task_mode": "trigger"}) -# videotype, gputouse, save_as_csv, batchsize, cropping, TFGPUinference, dynamic, robust_nframes, allow_growth, use_shelve -analyze_videos_params = {"save_as_csv": True} - -# key.update(analyze_videos_params={"save_as_csv": True}) -# model.PoseEstimationTask.insert_estimation_task(key) -model.PoseEstimationTask.insert_estimation_task( - key, model_name=key["model_name"], analyze_videos_params=analyze_videos_params -) - -model.PoseEstimation.populate() -model.PoseEstimation.coordinates_dataframe(key) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 2281782..7fe547b 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -394,7 +394,9 @@ "source": [ "from element_interface.utils import find_full_path\n", "dj.config.load('dj_local_conf.json')\n", - "data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config\n", + "data_dir = ./exa \n", + "\n", + "find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config\n", " 'Top_tracking-DataJoint-2023-08-03') \n", " # DLC project dir" ] From 842561f9af1b5ef1629cf5386cd44665395ce98e Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 11 Sep 2023 17:59:06 +0200 Subject: [PATCH 124/176] Major changes for PR codespace environment --- .devcontainer/Dockerfile | 6 +- .devcontainer/devcontainer.json | 2 +- docker/Dockerfile.test | 29 - docker/apt_requirements.txt | 4 - docker/docker-compose-test.yaml | 63 - docker/setup.sh | 37 - docs/docker-compose.yaml | 17 +- docs/src/tutorials/index.md | 114 -- element_deeplabcut/export/__init__.py | 1 - element_deeplabcut/export/nwb.py | 74 - element_deeplabcut/plotting/__init__.py | 0 .../plotting/plot_coordinates.py | 20 - element_deeplabcut/readers/__init__.py | 0 element_deeplabcut/readers/dlc_reader.py | 377 ------ notebooks/ingest.py | 188 --- notebooks/load_demo_data.py | 188 --- notebooks/process.py | 55 - notebooks/tutorial.ipynb | 1192 ----------------- notebooks/tutorial_pipeline.py | 93 -- setup.py | 36 +- tests/__init__.py | 10 - tests/conftest.py | 364 ----- tests/test_ingest.py | 33 - tests/test_pipeline_generation.py | 52 - tests/test_populate.py | 97 -- user_data/config_params.csv | 4 - user_data/model_model.csv | 2 - user_data/model_videos.csv | 2 - user_data/sessions.csv | 5 - user_data/subjects.csv | 2 - user_data/train_videosets.csv | 11 - 31 files changed, 36 insertions(+), 3042 deletions(-) delete mode 100644 docker/Dockerfile.test delete mode 100644 docker/apt_requirements.txt delete mode 100644 docker/docker-compose-test.yaml delete mode 100644 docker/setup.sh delete mode 100644 docs/src/tutorials/index.md delete mode 100644 element_deeplabcut/export/__init__.py delete mode 100644 element_deeplabcut/export/nwb.py delete mode 100644 element_deeplabcut/plotting/__init__.py delete mode 100644 element_deeplabcut/plotting/plot_coordinates.py delete mode 100644 element_deeplabcut/readers/__init__.py delete mode 100644 element_deeplabcut/readers/dlc_reader.py delete mode 100644 notebooks/ingest.py delete mode 100644 notebooks/load_demo_data.py delete mode 100644 notebooks/process.py delete mode 100644 notebooks/tutorial.ipynb delete mode 100644 notebooks/tutorial_pipeline.py delete mode 100644 tests/__init__.py delete mode 100644 tests/conftest.py delete mode 100644 tests/test_ingest.py delete mode 100644 tests/test_pipeline_generation.py delete mode 100644 tests/test_populate.py delete mode 100644 user_data/config_params.csv delete mode 100644 user_data/model_model.csv delete mode 100644 user_data/model_videos.csv delete mode 100644 user_data/sessions.csv delete mode 100644 user_data/subjects.csv delete mode 100644 user_data/train_videosets.csv diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 5039ce1..42d7316 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -31,10 +31,8 @@ COPY ./ /tmp/element-deeplabcut/ RUN \ # pipeline dependencies - apt-get install gcc && \ - #g++ ffmpeg libsm6 libxext6 -y && \ - pip install numcodecs && \ - pip install --no-cache-dir -e /tmp/element-deeplabcut[elements] && \ + apt-get install gcc numcodecs psutils ffmpeg graphviz && \ + pip install --no-cache-dir -e /tmp/element-deeplabcut[elements,dlc_default] && \ # clean up rm -rf /tmp/element-deeplabcut/ && \ apt-get clean diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index ec9b835..17f5e76 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -6,7 +6,7 @@ "remoteEnv": { "LOCAL_WORKSPACE_FOLDER": "${localWorkspaceFolder}" }, - "onCreateCommand": "pip install -e .", + "onCreateCommand": "mkdir -p ${DLC_ROOT_DATA_DIR} && pip install -e .", "postStartCommand": "docker volume prune -f", "hostRequirements": { "cpus": 4, diff --git a/docker/Dockerfile.test b/docker/Dockerfile.test deleted file mode 100644 index 1fa14d7..0000000 --- a/docker/Dockerfile.test +++ /dev/null @@ -1,29 +0,0 @@ -FROM datajoint/djbase:py3.9-debian-8eb1715 - -# ARG GITHUB_USERNAME=datajoint # tried moving to ENV -USER anaconda:anaconda - -COPY ./workflow-deeplabcut/docker/apt_requirements.txt /tmp/ -RUN /entrypoint.sh echo "Installed dependencies." - -WORKDIR /main/workflow-deeplabcut - -# Always get djarchive -RUN pip install --no-deps "djarchive-client@git+https://github.com/datajoint/djarchive-client" - -# Always move local - conditional install in setup.sh -COPY --chown=anaconda:anaconda ./element-lab/ /main/element-lab/ -COPY --chown=anaconda:anaconda ./element-animal/ /main/element-animal/ -COPY --chown=anaconda:anaconda ./element-session/ /main/element-session/ -COPY --chown=anaconda:anaconda ./element-event/ /main/element-event/ -COPY --chown=anaconda:anaconda ./element-interface/ /main/element-interface/ -COPY --chown=anaconda:anaconda ./element-deeplabcut/ /main/element-deeplabcut/ -COPY --chown=anaconda:anaconda ./workflow-deeplabcut/ /main/workflow-deeplabcut/ - -# Conditional install - local-all, local-dlc, or git -COPY --chown=anaconda:anaconda ./workflow-deeplabcut/docker/setup.sh /main/ -COPY --chown=anaconda:anaconda ./workflow-deeplabcut/docker/.env /main/ -RUN chmod 755 /main/setup.sh -RUN chmod 755 /main/.env -RUN /main/setup.sh -RUN rm -f ./dj_local_conf.json diff --git a/docker/apt_requirements.txt b/docker/apt_requirements.txt deleted file mode 100644 index 5392839..0000000 --- a/docker/apt_requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -git -libgl1 -ffmpeg -locales-all \ No newline at end of file diff --git a/docker/docker-compose-test.yaml b/docker/docker-compose-test.yaml deleted file mode 100644 index 5fcd41e..0000000 --- a/docker/docker-compose-test.yaml +++ /dev/null @@ -1,63 +0,0 @@ -# .env file. Careful that vscode black does not add spaces around '=' -# COMPOSE_PROJECT_NAME='dlc' -# TEST_DATA_DIR= -# GITHUB_USERNAME=datajoint -# INSTALL_OPTION=local-all, local-dlc, or git -# TEST_CMD="pytest" # pytest --dj-{verbose,teardown} False # options -# # to do nothing, set as "True" -# export COMPOSE_DOCKER_CLI_BUILD=0 # some machines need for smooth --build -# docker compose --env-file ./docker/.env -f ./docker/docker-compose-test.yaml up --build --force-recreate --detach -# docker exec -it workflow-deeplabcut /bin/bash -# docker compose -f ./docker/docker-compose-test.yaml down --volumes - -version: "2.4" - -services: - db: - networks: - deeplabcut: - image: datajoint/mysql:5.7 - environment: - MYSQL_ROOT_PASSWORD: simple - container_name: workflow-deeplabcut-db - - workflow: - networks: - deeplabcut: - build: - context: ../../ - dockerfile: ./workflow-deeplabcut/docker/Dockerfile.test - args: - - GITHUB_USERNAME=${GITHUB_USERNAME} - image: workflow-deeplabcut:0.1.1 - container_name: workflow-deeplabcut - environment: - - DJ_HOST=db - - DJ_USER=root - - DJ_PASS=simple - - DLC_ROOT_DATA_DIR=/main/test_data/ - - DATABASE_PREFIX=test_ - - COMPOSE_PROJECT_NAME=${COMPOSE_PROJECT_NAME} - - GITHUB_USERNAME=${GITHUB_USERNAME} - - INSTALL_OPTION=${INSTALL_OPTION} - - TEST_CMD=${TEST_CMD} - command: - - bash - - -c - - | - eval ${TEST_CMD} - tail -f /dev/null - volumes: - - ${TEST_DATA_DIR}:/main/test_data/ - - ../../workflow-deeplabcut/docker/apt_requirements.txt:/tmp/apt_requirements.txt - - ../../element-lab:/main/element-lab - - ../../element-animal:/main/element-animal - - ../../element-session:/main/element-session - - ../../element-deeplabcut:/main/element-deeplabcut - - ../../workflow-deeplabcut:/main/workflow-deeplabcut - depends_on: - db: - condition: service_healthy - -networks: - deeplabcut: diff --git a/docker/setup.sh b/docker/setup.sh deleted file mode 100644 index 6f0895e..0000000 --- a/docker/setup.sh +++ /dev/null @@ -1,37 +0,0 @@ -#! /bin/bash -export $(grep -v '^#' /main/.env | xargs) - -echo "INSALL OPTION:" $INSTALL_OPTION -cd /main/ -# all local installs, mapped from host -if [ "$INSTALL_OPTION" == "local-all" ]; then - for f in lab animal session event deeplabcut; do - pip install -e ./element-${f} - done - pip install -e ./workflow-deeplabcut -# all except workflow pip installed -else - pip install git+https://github.com/${GITHUB_USERNAME}/element-lab.git - pip install git+https://github.com/${GITHUB_USERNAME}/element-animal.git - pip install git+https://github.com/${GITHUB_USERNAME}/element-session.git - pip install git+https://github.com/${GITHUB_USERNAME}/element-event.git - # only deeplabcut items from local install - if [ "$INSTALL_OPTION" == "local-dlc" ]; then - pip install -e ./element-deeplabcut - pip install -e ./workflow-deeplabcut - # all from github - elif [ "$INSTALL_OPTION" == "git" ]; then - pip install git+https://github.com/${GITHUB_USERNAME}/element-deeplabcut.git - pip install git+https://github.com/${GITHUB_USERNAME}/workflow-deeplabcut.git - fi -fi - -# If test cmd contains pytest, install -if [[ "$TEST_CMD" == *pytest* ]]; then - pip install pytest - pip install pytest-cov -fi - -# additional installs for running DLC -pip install torch -pip install ffmpeg \ No newline at end of file diff --git a/docs/docker-compose.yaml b/docs/docker-compose.yaml index 1ca7eae..03393e8 100644 --- a/docs/docker-compose.yaml +++ b/docs/docker-compose.yaml @@ -16,10 +16,11 @@ services: - UPSTREAM_REPO - MODE - PATCH_VERSION - - JUPYTER_PLATFORM_DIRS=1 + #- JUPYTER_PLATFORM_DIRS=1 volumes: - ../docs:/main/docs - ../${PACKAGE}:/main/${PACKAGE} + - ../notebooks:/main/notebooks user: ${HOST_UID}:anaconda ports: - 80:80 @@ -29,16 +30,14 @@ services: - | git config --global --add safe.directory /main set -e - export ELEMENT_NAME=$$(echo $${PACKAGE} | sed 's/element_//g') + export ELEMENT_UNDERSCORE=$$(echo $${PACKAGE} | sed 's/element_//g') + export ELEMENT_HYPHEN=$$(echo $${ELEMENT_UNDERSCORE} | sed 's/_/-/g') export PATCH_VERSION=$$(cat /main/$${PACKAGE}/version.py | grep -oE '\d+\.\d+\.[a-z0-9]+') - git clone https://github.com/datajoint/workflow-$${ELEMENT_NAME}.git /main/delete || true - if [ -d /main/delete/ ]; then - mv /main/delete/workflow_$${ELEMENT_NAME} /main/ - mv /main/delete/notebooks/*ipynb /main/docs/src/tutorials/ - rm -fR /main/delete - fi + + cp /main/notebooks/tutorial.ipynb /main/docs/src/tutorials/ + if echo "$${MODE}" | grep -i live &>/dev/null; then - mkdocs serve --config-file ./docs/mkdocs.yaml -a 0.0.0.0:80 + mkdocs serve --config-file ./docs/mkdocs.yaml -a 0.0.0.0:80 2>&1 | tee docs/temp_mkdocs.log elif echo "$${MODE}" | grep -iE "qa|push" &>/dev/null; then echo "INFO::Delete gh-pages branch" git branch -D gh-pages || true diff --git a/docs/src/tutorials/index.md b/docs/src/tutorials/index.md deleted file mode 100644 index 7ccd7d8..0000000 --- a/docs/src/tutorials/index.md +++ /dev/null @@ -1,114 +0,0 @@ -# Tutorials - -## Installation - -Installation of the Element requires an integrated development environment and database. -Instructions to setup each of the components can be found on the -[User Instructions](https://datajoint.com/docs/elements/user-guide) page. These -instructions use the example -[workflow for Element DeepLabCut](https://github.com/datajoint/workflow-deeplabcut), -which can be modified for a user's specific experimental requirements. This example -workflow uses four Elements (Lab, Animal, Session, and DeepLabCut) to construct a -complete pipeline, and is able to ingest experimental metadata and run model training -and inference. - -The [DeepLabCut (DLC) website](https://deeplabcut.github.io/DeepLabCut/README.html) has a -rich library of resources for downloading the software and understanding its various -features. This includes getting started with their software (see -[links below](#steps-to-run-the-element)). - -## Steps to run the Element - -The Element assumes you: - -1. Have a DLC project folder on your machine. You can declare a project either - from the - [DLC GUI](https://deeplabcut.github.io/DeepLabCut/docs/PROJECT_GUI.html#video-demos-how-to-launch-and-run-the-project-manager-gui) - or via a - [terminal](https://deeplabcut.github.io/DeepLabCut/docs/standardDeepLabCut_UserGuide.html#deeplabcut-in-the-terminal). -1. Have labeled data in your DLC project folder. Again, this can be done via - [the GUI](https://youtu.be/JDsa8R5J0nQ?t=94) - or a - [terminal](https://deeplabcut.github.io/DeepLabCut/docs/standardDeepLabCut_UserGuide.html#deeplabcut-in-the-terminal). - -With these steps in place, you can then use the materials below to start training -and pose estimation inferences. Training starts by configuring parameters in the -`train` schema, and launching training in the `ModelTraining` table. When you're happy -with the state of a model, you can insert it into the `Model` table, and pair it with -videos to trigger pose estimation inferences via the `PoseEstimationTask` table -in the `model` schema. See [Element Architecture](./concepts/#element-architecture) -for a full list of table functions. - -### Videos - -The [Element DeepLabCut tutorial](https://www.youtube.com/watch?v=8FDjTuQ52gQ) gives an -overview of the workflow files and notebooks as well as core concepts related to -DeepLabCut. - -[![YouTube tutorial](https://img.youtube.com/vi/8FDjTuQ52gQ/0.jpg)](https://www.youtube.com/watch?v=8FDjTuQ52gQ) - -### Notebooks - -Each of the notebooks in the workflow -([download here](https://github.com/datajoint/workflow-deeplabcut/tree/main/notebooks) -steps through ways to interact with the Element itself. For convenience, these notebooks -are also rendered as part of this site. -To try out Elements -notebooks in an online Jupyter environment with access to example data, visit -[CodeBook](https://codebook.datajoint.io/). (DeepLabCut notebooks coming soon!) - -- [Data Download](./00-DataDownload_Optional.ipynb) - highlights how to use DataJoint tools to download a sample model for trying out the Element. -- [Configure](./01-Configure.ipynb) - helps configure your local DataJoint installation to point to the correct database. -- [Workflow Structure](./02-WorkflowStructure_Optional.ipynb) demonstrates the table - architecture of the Element and key DataJoint basics for interacting with these - tables. -- [Process](./03-Process.ipynb) steps through adding data to these tables and launching - key DeepLabCut features, like model training. -- [Automate](./04-Automate_Optional.ipynb) - highlights the same steps as above, but utilizing all built-in automation tools. -- [Visualization](./05-Visualization_Optional.ipynb) - demonstrates how to fetch data from the Element to generate figures and label data. -- [Drop schemas](./06-Drop_Optional.ipynb) - provides the steps for dropping all the tables to start fresh. -- `07-NWB-Export` (coming soon!) will describe how to export into NWB files. For now, - see [below](./#nwb-export) -- [Alternate Dataset](./09-AlternateDataset.ipynb) - does all of the above, but with a - [dataset from DeepLabCut](https://github.com/DeepLabCut/DeepLabCut/tree/master/examples/openfield-Pranav-2018-10-30). - -## Data Export to Neurodata Without Borders (NWB) - -The `export/nwb.py` module calls [DLC2NWB](https://github.com/DeepLabCut/DLC2NWB/) to -save output generated by Element DeepLabCut as NWB files. -The main function, `dlc_session_to_nwb`, contains a flag to control calling a parallel -function in -[Element Session](https://github.com/datajoint/element-session/blob/main/element_session/export/nwb.py). - -Before using, please install [DLC2NWB](https://github.com/DeepLabCut/DLC2NWB/) - -```console -pip install dlc2nwb -``` - -Then, call the export function using keys from the `PoseEstimation` table. - -```python -from element_deeplabcut import model -from element_session import session -from element_deeplabcut.export import dlc_session_to_nwb - -session_key = (session.Session & CONDITION) -pose_key = (model.PoseEstimation & session_key).fetch1('KEY') -dlc_session_to_nwb(pose_key, use_element_session=True, session_kwargs=SESSION_KWARGS) -``` - -Here, `CONDITION` should uniquely identify a session and `SESSION_KWARGS` can be any of -the items described in the docstring of `element_session.export.nwb.session_to_nwb` -as a dictionary. - -As DLC2NWB does not currently offer a separate function for generating `PoseEstimation` -objects (see [ndx-pose](https://github.com/rly/ndx-pose)), the current solution is to -allow DLC2NWB to write to disk, and optionally rewrite this file using metadata provided -by the export function in Element Session. diff --git a/element_deeplabcut/export/__init__.py b/element_deeplabcut/export/__init__.py deleted file mode 100644 index 90a7d71..0000000 --- a/element_deeplabcut/export/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .nwb import dlc_session_to_nwb diff --git a/element_deeplabcut/export/nwb.py b/element_deeplabcut/export/nwb.py deleted file mode 100644 index 5a67610..0000000 --- a/element_deeplabcut/export/nwb.py +++ /dev/null @@ -1,74 +0,0 @@ -""" -Portions of code adapted from DeepLabCut/DLC2NWB -MIT License Copyright (c) 2022 Alexander Mathis -DataJoint export methods for DeepLabCut 2.x -""" -import logging -import warnings -from pathlib import Path -from collections import abc -from pynwb import NWBHDF5IO -from hdmf.build.warnings import DtypeConversionWarning -from .. import model - -try: # Not all users will want NWB export, so dependency not in requirements. - from dlc2nwb.utils import convert_h5_to_nwb, write_subject_to_nwb -except ImportError: - raise ImportError( - "The package `dlc2nwb` is missing. Please run `pip install dlc2nwb`." - ) - -logger = logging.getLogger("datajoint") - - -def dlc_session_to_nwb( - keys: list, use_element_session: bool = True, session_kwargs: dict = None -) -> str: - """Using keys from PoseEstimation table, save DLC's h5 output to NWB. - - Calls DLC2NWB to export NWB file using current h5 on disk. If use_element_session, - calls NWB export function from Elements for lab, animal and session, passing - session_kwargs. Saves output based on naming convention in DLC2NWB. If output path - already exists, returns output path without making changes to the file. - NOTE: does not support multianimal exports - - Args: - keys: One or more keys from model.PoseEstimation - use_element_session: Optional. If True, call NWB export from Element Session - session_kwargs: Optional. Additional keyword args for Element Session export - - Returns: - Output path of saved file - """ - if not isinstance(keys, abc.Sequence): # Ensure list for following loop - keys = [keys] - - for key in keys: - write_file = True - subject_id = key["subject"] - output_dir = model.PoseEstimationTask.infer_output_dir(key) - config_file = str(output_dir / "dj_dlc_config.yaml") - video_name = Path((model.VideoRecording.File & key).fetch1("file_path")).stem - h5file = next(output_dir.glob(f"{video_name}*h5")) - output_path = h5file.replace(".h5", f"_{subject_id}.nwb") # DLC2NWB convention - - if Path(output_path).exists(): - logger.warning(f"Skipping {subject_id}. NWB already exists.") - write_file = False - - # Use standard DLC2NWB export - if write_file and not use_element_session: - output_path = convert_h5_to_nwb(config_file, h5file, subject_id) - - # Pass Element Session export items in export - if write_file and use_element_session: - from element_session.export.nwb import session_to_nwb - - session_nwb = session_to_nwb(key, **session_kwargs) # call session export - dlc_nwb = write_subject_to_nwb(session_nwb, h5file, subject_id, config_file) - # warnings filter from DLC2NWB - with warnings.catch_warnings(), NWBHDF5IO(output_path, mode="w") as io: - warnings.filterwarnings("ignore", category=DtypeConversionWarning) - io.write(dlc_nwb) - - return output_path diff --git a/element_deeplabcut/plotting/__init__.py b/element_deeplabcut/plotting/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/element_deeplabcut/plotting/plot_coordinates.py b/element_deeplabcut/plotting/plot_coordinates.py deleted file mode 100644 index 57feb46..0000000 --- a/element_deeplabcut/plotting/plot_coordinates.py +++ /dev/null @@ -1,20 +0,0 @@ -import matplotlib.pyplot as plt -import plotly.graph_objects as go - -from .. import model - - -def plot_xy(imaging, df, model_name) -> go.Figure: - """Prepare plotly trajectory figure. - - Args: - imaging (dj.Table): imaging table. - df (dataframe): Pose Estimation coordinates in a panda's dataframe - model_name(str): name of the model used - - - - """ - - df_xy = df.iloc[:, df.columns.get_level_values(2).isin(["x", "y"])][model_name] - df_xy.plot().legend(loc="right") diff --git a/element_deeplabcut/readers/__init__.py b/element_deeplabcut/readers/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/element_deeplabcut/readers/dlc_reader.py b/element_deeplabcut/readers/dlc_reader.py deleted file mode 100644 index 321003c..0000000 --- a/element_deeplabcut/readers/dlc_reader.py +++ /dev/null @@ -1,377 +0,0 @@ -import re -import logging -import numpy as np -import pandas as pd -from pathlib import Path -import pickle -import ruamel.yaml as yaml -from element_interface.utils import find_root_directory, dict_to_uuid -from .. import model -from ..model import get_dlc_root_data_dir -from datajoint.errors import DataJointError - -logger = logging.getLogger("datajoint") - - -class PoseEstimation: - """Class for handling DLC pose estimation files.""" - - def __init__( - self, - dlc_dir: str = None, - pkl_path: str = None, - h5_path: str = None, - yml_path: str = None, - filename_prefix: str = "", - ): - if dlc_dir is None: - assert pkl_path and h5_path and yml_path, ( - 'If "dlc_dir" is not provided, then pkl_path, h5_path, and yml_path ' - + "must be provided" - ) - else: - self.dlc_dir = Path(dlc_dir) - if not self.dlc_dir.exists(): - raise FileNotFoundError(f"Unable to find {dlc_dir}") - - # meta file: pkl - info about this DLC run (input video, configuration, etc.) - if pkl_path is None: - self.pkl_paths = sorted( - self.dlc_dir.rglob(f"{filename_prefix}*meta.pickle") - ) - if not len(self.pkl_paths) > 0: - raise FileNotFoundError( - f"No meta file (.pickle) found in: {self.dlc_dir}" - ) - else: - pkl_path = Path(pkl_path) - if not pkl_path.exists(): - raise FileNotFoundError(f"{pkl_path} not found") - self.pkl_paths = [pkl_path] - - # data file: h5 - body part outputs from the DLC post estimation step - if h5_path is None: - self.h5_paths = sorted(self.dlc_dir.rglob(f"{filename_prefix}*.h5")) - if not len(self.h5_paths) > 0: - raise FileNotFoundError( - f"No DLC output file (.h5) found in: {self.dlc_dir}" - ) - else: - h5_path = Path(h5_path) - if not h5_path.exists(): - raise FileNotFoundError(f"{h5_path} not found") - self.h5_paths = [h5_path] - - # validate number of files - assert len(self.h5_paths) == len( - self.pkl_paths - ), f"Unequal number of .h5 files ({len(self.h5_paths)}) and .pickle files ({len(self.pkl_paths)})" - - assert ( - self.pkl_paths[0].stem == self.h5_paths[0].stem + "_meta" - ), f"Mismatching h5 ({self.h5_paths[0].stem}) and pickle {self.pkl_paths[0].stem}" - - # config file: yaml - configuration for invoking the DLC post estimation step - if yml_path is None: - yml_paths = list(self.dlc_dir.glob(f"{filename_prefix}*.y*ml")) - # If multiple, defer to the one we save. - if len(yml_paths) > 1: - yml_paths = [val for val in yml_paths if val.stem == "dj_dlc_config"] - if len(yml_paths) != 1: - raise FileNotFoundError( - f"Unable to find one unique .yaml file in: {dlc_dir} - Found: {len(yml_paths)}" - ) - self.yml_path = yml_paths[0] - else: - self.yml_path = Path(yml_path) - if not self.yml_path.exists(): - raise FileNotFoundError(f"{self.yml_path} not found") - - self._pkl = None - self._rawdata = None - self._yml = None - self._data = None - - train_idx = np.where( - (np.array(self.yml["TrainingFraction"]) * 100).astype(int) - == int(self.pkl["training set fraction"] * 100) - )[0][0] - train_iter = int(self.pkl["Scorer"].split("_")[-1]) - - self.model = { - "Scorer": self.pkl["Scorer"], - "Task": self.yml["Task"], - "date": self.yml["date"], - "iteration": self.pkl["iteration (active-learning)"], - "shuffle": int(re.search(r"shuffle(\d+)", self.pkl["Scorer"]).groups()[0]), - "snapshotindex": self.yml["snapshotindex"], - "trainingsetindex": train_idx, - "training_iteration": train_iter, - } - - self.fps = self.pkl["fps"] - self.nframes = self.pkl["nframes"] - self.creation_time = self.h5_paths[0].stat().st_mtime - - @property - def pkl(self): - """Pickle file contents""" - if self._pkl is None: - nframes = 0 - meta_hash = None - for fp in self.pkl_paths: - with open(fp, "rb") as f: - meta = pickle.load(f) - nframes += meta["data"].pop("nframes") - - # remove variable fields - for k in ("start", "stop", "run_duration"): - meta["data"].pop(k) - - # confirm identical setting in all .pickle files - if meta_hash is None: - meta_hash = dict_to_uuid(meta) - else: - assert meta_hash == dict_to_uuid( - meta - ), f"Inconsistent DLC-model-config file used: {fp}" - - self._pkl = meta["data"] - self._pkl["nframes"] = nframes - return self._pkl - - @property - def yml(self): - """json-structured config.yaml file contents""" - if self._yml is None: - with open(self.yml_path, "rb") as f: - self._yml = yaml.safe_load(f) - return self._yml - - @property - def rawdata(self): - """Raw data from h5 file""" - if self._rawdata is None: - self._rawdata = pd.concat([pd.read_hdf(fp) for fp in self.h5_paths]) - return self._rawdata - - @property - def data(self): - """Data from the h5 file, restructured as a dict""" - if self._data is None: - self._data = self.reformat_rawdata() - return self._data - - @property - def df(self): - """Data as dataframe""" - top_level = self.rawdata.columns.levels[0][0] - return self.rawdata.get(top_level) - - @property - def body_parts(self): - """Set of body parts present in data file""" - return self.df.columns.levels[0] - - def reformat_rawdata(self): - """Transform raw h5 data into dict""" - error_message = ( - f"Total frames from .h5 file ({len(self.rawdata)}) differs " - + f'from .pickle ({self.pkl["nframes"]})' - ) - assert len(self.rawdata) == self.pkl["nframes"], error_message - - body_parts_position = {} - for body_part in self.body_parts: - body_parts_position[body_part] = { - c: self.df.get(body_part).get(c).values - for c in self.df.get(body_part).columns - } - - return body_parts_position - - -def read_yaml(fullpath: str, filename: str = "dj_dlc_config") -> tuple: - """Return contents of yaml in fullpath. If available, defer to DJ-saved version - - Args: - fullpath (str): String or pathlib path. Directory with yaml files - filename (str, optional): Filename, no extension. Permits wildcards. - - Returns: - Tuple of (a) filepath as pathlib.PosixPath and (b) file contents as dict - """ - from deeplabcut.utils.auxiliaryfunctions import read_config - - # Take the DJ-saved if there. If not, return list of available - yml_paths = list(Path(fullpath).glob(f"{filename}.y*ml")) or sorted( - list(Path(fullpath).glob(f"{filename}.y*ml")) - ) - - assert ( # If more than 1 and not DJ-saved, - len(yml_paths) == 1 - ), f"Found more yaml files than expected: {len(yml_paths)}\n{fullpath}" - - return yml_paths[0], read_config(yml_paths[0]) - - -def save_yaml( - output_dir: str, - config_dict: dict, - filename: str = "dj_dlc_config", - mkdir: bool = True, -) -> str: - """Save config_dict to output_path as filename.yaml. By default, preserves original. - - Args: - output_dir (str): where to save yaml file - config_dict (dict): dict of config params or element-deeplabcut model.Model dict - filename (str, optional): default 'dj_dlc_config' or preserve original 'config' - Set to 'config' to overwrite original file. - If extension is included, removed and replaced with "yaml". - mkdir (bool): Optional, True. Make new directory if output_dir not exist - - Returns: - path of saved file as string - due to DLC func preference for strings - """ - from deeplabcut.utils.auxiliaryfunctions import write_config - - if "config_template" in config_dict: # if passed full model.Model dict - config_dict = config_dict["config_template"] - if mkdir: - output_dir.mkdir(exist_ok=True) - if "." in filename: # if user provided extension, remove - filename = filename.split(".")[0] - - output_filepath = Path(output_dir) / f"{filename}.yaml" - write_config(output_filepath, config_dict) - return str(output_filepath) - - -def do_pose_estimation( - key: dict, - video_filepaths: list, - dlc_model: dict, - project_path: str, - output_dir: str, - videotype="", - gputouse=None, - save_as_csv=False, - batchsize=None, - cropping=None, - TFGPUinference=True, - dynamic=(False, 0.5, 10), - robust_nframes=False, - allow_growth=False, - use_shelve=False, -): - """Launch DLC's analyze_videos within element-deeplabcut. - - Also saves a copy of the current config in the output dir, with ensuring analyzed - videos in the video_set. NOTE: Config-specificed cropping not supported when adding - to config in this manner. - - Args: - video_filepaths (list): list of videos to analyze - dlc_model (dict): element-deeplabcut dlc.Model - project_path (str): path to project config.yml - output_dir (str): where to save output - # BELOW FROM DLC'S DOCSTRING - - videotype (str, optional, default=""): - Checks for the extension of the video in case the input to the video is a - directory. Only videos with this extension are analyzed. If unspecified, - videos with common extensions ('avi', 'mp4', 'mov', 'mpeg', 'mkv') are kept. - gputouse (int or None, optional, default=None): - Indicates the GPU to use (see number in ``nvidia-smi``). If none, ``None``. - See: https://nvidia.custhelp.com/app/answers/detail/a_id/3751/~/useful-nvidia-smi-queries - save_as_csv (bool, optional, default=False): - Saves the predictions in a .csv file. - batchsize (int or None, optional, default=None): - Change batch size for inference; if given overwrites ``pose_cfg.yaml`` - cropping (list or None, optional, default=None): - List of cropping coordinates as [x1, x2, y1, y2]. - Note that the same cropping parameters will then be used for all videos. - If different video crops are desired, run ``analyze_videos`` on individual - videos with the corresponding cropping coordinates. - TFGPUinference (bool, optional, default=True): - Perform inference on GPU with TensorFlow code. Introduced in "Pretraining - boosts out-of-domain robustness for pose estimation" by Alexander Mathis, - Mert Yüksekgönül, Byron Rogers, Matthias Bethge, Mackenzie W. Mathis. - Source https://arxiv.org/abs/1909.11229 - dynamic (tuple(bool, float, int) triple (state, detectiontreshold, margin)): - If the state is true, then dynamic cropping will be performed. That means - that if an object is detected (i.e. any body part > detectiontreshold), - then object boundaries are computed according to the smallest/largest x - position and smallest/largest y position of all body parts. This window is - expanded by the margin and from then on only the posture within this crop - is analyzed (until the object is lost, i.e. / videos / device__recording_<#>_model_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`processed_dir` is optionally specified in the datajoint config, or in the `insert_estimation_task`. If unspecified, this will be the project directory. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.PoseEstimationTask.infer_output_dir(key)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.PoseEstimationTask.insert_estimation_task(recording_dict, model_name = recording_dict[\"model_name\"], analyze_videos_params=analyze_videos_params)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#model.PoseEstimationTask.insert_estimation_task(key,params={'save_as_csv':True})\n", - "model.PoseEstimation.populate()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The resulting coordinates of the pose estimation are now available in the corresponding `BodyPartPosition` table, ready to use for visualization, or to combine with other Elements." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.PoseEstimation.BodyPartPosition()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can visualize the pose estimation results directly as a pandas dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.PoseEstimation.coordinates_dataframe(key)" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:percent" - }, - "kernelspec": { - "display_name": "Python 3.9.13 ('ele')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "vscode": { - "interpreter": { - "hash": "d00c4ad21a7027bf1726d6ae3a9a6ef39c8838928eca5a3d5f51f3eb68720410" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py deleted file mode 100644 index 3201358..0000000 --- a/notebooks/tutorial_pipeline.py +++ /dev/null @@ -1,93 +0,0 @@ -import datajoint as dj -from collections import abc -from element_lab import lab -from element_animal import subject -from element_session import session_with_datetime as session -from element_deeplabcut import train, model - -from element_animal.subject import Subject -from element_lab.lab import Source, Lab, Protocol, User, Project - -__all__ = [ - "Subject", - "Source", - "Lab", - "Protocol", - "User", - "Project", - "Session", -] - -if "custom" not in dj.config: - dj.config["custom"] = {} - -db_prefix = dj.config["custom"].get("database.prefix", "") - - -def get_dlc_root_data_dir() -> list: - """Returns a list of root directories for Element DeepLabCut""" - dlc_root_dirs = dj.config.get("custom", {}).get("dlc_root_data_dir") - if not dlc_root_dirs: - return None - elif not isinstance(dlc_root_dirs, abc.Sequence): - return list(dlc_root_dirs) - else: - return dlc_root_dirs - - -def get_dlc_processed_data_dir() -> str: - """Returns an output directory relative to custom 'dlc_output_dir' root""" - from pathlib import Path - - dlc_output_dir = dj.config.get("custom", {}).get("dlc_output_dir") - if dlc_output_dir: - return Path(dlc_output_dir) - else: - return None - - -# Activate "lab", "subject", "session" schema ------------- - -lab.activate(db_prefix + "lab") - -subject.activate(db_prefix + "subject", linking_module=__name__) - -Experimenter = lab.User -Session = session.Session -session.activate(db_prefix + "session", linking_module=__name__) - -# Activate equipment table ------------------------------------ - - -@lab.schema -class Device(dj.Lookup): - """Table for managing lab equipment. - - In Element DeepLabCut, this table is referenced by `model.VideoRecording`. - The primary key is also used to generate inferred output directories when - running pose estimation inference. Refer to the `definition` attribute - for the table design. - - Attributes: - device ( varchar(32) ): Device short name. - modality ( varchar(64) ): Modality for which this device is used. - description ( varchar(256) ): Optional. Description of device. - """ - - definition = """ - device : varchar(32) - --- - modality : varchar(64) - description=null : varchar(256) - """ - contents = [ - ["Camera1", "Pose Estimation", "Panasonic HC-V380K"], - ["Camera2", "Pose Estimation", "Panasonic HC-V770K"], - ] - - -# Activate DeepLabCut schema ----------------------------------- - - -train.activate(db_prefix + "train", linking_module=__name__) -model.activate(db_prefix + "model", linking_module=__name__) diff --git a/setup.py b/setup.py index b442d83..f49fd9c 100644 --- a/setup.py +++ b/setup.py @@ -1,5 +1,6 @@ from setuptools import setup, find_packages from os import path +import urllib.request pkg_name = "element_deeplabcut" here = path.abspath(path.dirname(__file__)) @@ -10,37 +11,50 @@ with open(path.join(here, pkg_name, "version.py")) as f: exec(f.read()) +with urllib.request.urlopen( + "https://github.com/DeepLabCut/DeepLabCut/blob/main/requirements.txt" +) as f: + dlc_requirements = f.read().decode("UTF-8").split("\n") + +dlc_requirements.remove("") +dlc_requirements.append("future") + setup( name=pkg_name.replace("_", "-"), version=__version__, - description="DataJoint Element for Continuous Behavior Tracking via DeepLabCut", + description="DeepLabCut DataJoint Element", long_description=long_description, long_description_content_type="text/markdown", author="DataJoint", - author_email="info@vathes.com", + author_email="info@datajoint.com", license="MIT", url=f'https://github.com/datajoint/{pkg_name.replace("_", "-")}', - keywords="neuroscience behavior deeplabcut datajoint", + keywords="neuroscience behavior pose-estimation science datajoint", packages=find_packages(exclude=["contrib", "docs", "tests*"]), scripts=[], install_requires=[ "datajoint>=0.13", "element-interface>=0.3.0", "opencv-python-headless", - "element-lab>=0.2.0", - "element-animal>=0.1.5", - "element-session>=0.1.2", - "element-interface>=0.5.0", "ipykernel>=6.0.1", "pygit2", - "graphviz", ], extras_requires={ - "default": ["deeplabcut[tf]>=2.2.1.1"], - "apple_mchips": [ + "dlc_requirements": [dlc_requirements], + # "dlc_default": ["deeplabcut @ git+https://github.com/DeepLabCut/DeepLabCut"] + "dlc_default": ["deeplabcut[tf]>=2.2.1.1"], + "dlc_apple_mchips": [ "'deeplabcut[apple_mchips]'", "tables=3.7.0", - "tensorflow-deps", + "tensorflow-deps>=2.9.0", + "keras >=2.12.0", + ], + "elements": [ + "element-lab>=0.2.0", + "element-animal>=0.1.5", + "element-session>=0.1.2", + "element-interface>=0.5.0", ], + "tests": ["pytest", "pytest-cov", "shutils"], }, ) diff --git a/tests/__init__.py b/tests/__init__.py deleted file mode 100644 index ed47a6e..0000000 --- a/tests/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -""" deeplabcut -fresh docker: - docker run --name wf-dlc -p 3306:3306 -e \ - MYSQL_ROOT_PASSWORD=tutorial datajoint/mysql -dependencies: pip install pytest pytest-cov -run all tests: - pytest tests/ -run one test, debug: - pytest --pdb tests/tests_name.py -k function_name -""" diff --git a/tests/conftest.py b/tests/conftest.py deleted file mode 100644 index ccda057..0000000 --- a/tests/conftest.py +++ /dev/null @@ -1,364 +0,0 @@ -import os -import sys -import pytest -import logging -from pathlib import Path -from contextlib import nullcontext -from element_deeplabcut.model import str_to_bool -import datajoint as dj -from element_interface.utils import find_full_path -from workflow_deeplabcut.paths import get_dlc_root_data_dir -from workflow_deeplabcut.ingest import ( - ingest_subjects, - ingest_sessions, - ingest_train_params, - ingest_train_vids, - ingest_model_vids, - ingest_model, -) - -__all__ = [ - "ingest_subjects", - "ingest_sessions", - "ingest_train_params", - "ingest_train_vids", - "ingest_model_vids", -] - -# ---------------------- CONSTANTS --------------------- - -test_data_project = "from_top_tracking" -inference_vid = f"{test_data_project}/videos/test.mp4" -inf_vid_short = f"{test_data_project}/videos/test-2s.mp4" -model_name = "FromTop-latest" - - -def pytest_addoption(parser): - """ - Permit constants when calling pytest at commandline e.g., pytest --dj-verbose False - - Parameters - ---------- - --dj-verbose (bool): Default True. Pass print statements from Elements. - --dj-teardown (bool): Default True. Delete pipeline on close. - --dj-datadir (str): Default ./tests/user_data. Relative path of test CSV data. - """ - parser.addoption( - "--dj-verbose", - action="store", - default="True", - help="Verbose for dj items: True or False", - choices=("True", "False"), - ) - parser.addoption( - "--dj-teardown", - action="store", - default="True", - help="Verbose for dj items: True or False", - choices=("True", "False"), - ) - parser.addoption( - "--dj-datadir", - action="store", - default="./tests/user_data", - help="Relative path for saving tests data", - ) - - -@pytest.fixture(scope="session") -def setup(request): - """Take passed commandline variables, set as global""" - global verbose, _tear_down, test_user_data_dir, verbose_context - - verbose = str_to_bool(request.config.getoption("--dj-verbose")) - _tear_down = str_to_bool(request.config.getoption("--dj-teardown")) - test_user_data_dir = Path(request.config.getoption("--dj-datadir")) - test_user_data_dir.mkdir(exist_ok=True) - - if not verbose: - logging.getLogger("deeplabcut").setLevel(logging.CRITICAL) - logging.getLogger("torch").setLevel(logging.CRITICAL) - logging.getLogger("tensorflow").setLevel(logging.CRITICAL) - - verbose_context = nullcontext() if verbose else QuietStdOut() - - yield verbose_context, verbose - - -# ------------------ GENERAL FUCNTION ------------------ - - -def write_csv(path, content): - """ - General function for writing strings to lines in CSV - :param path: pathlib PosixPath - :param content: list of strings, each as row of CSV - """ - with open(path, "w") as f: - for line in content: - f.write(line + "\n") - - -class QuietStdOut: - """If verbose set to false, used to quiet tear_down table.delete prints""" - - def __enter__(self): - os.environ["DJ_LOG_LEVEL"] = "WARNING" - self._original_stdout = sys.stdout - sys.stdout = open(os.devnull, "w") - - def __exit__(self, exc_type, exc_val, exc_tb): - os.environ["DJ_LOG_LEVEL"] = "INFO" - sys.stdout.close() - sys.stdout = self._original_stdout - - -# ------------------- FIXTURES ------------------- - - -@pytest.fixture(autouse=True, scope="session") -def dj_config(): - """If dj_local_config exists, load""" - if Path("./dj_local_conf.json").exists(): - dj.config.load("./dj_local_conf.json") - - dj.config.update( - { - "safemode": False, - "database.host": os.environ.get("DJ_HOST") or dj.config["database.host"], - "database.password": os.environ.get("DJ_PASS") - or dj.config["database.password"], - "database.user": os.environ.get("DJ_USER") or dj.config["database.user"], - "custom": { - "database.prefix": os.environ.get("DATABASE_PREFIX") - or dj.config["custom"]["database.prefix"], - "dlc_root_data_dir": os.environ.get("DLC_ROOT_DATA_DIR") - or dj.config["custom"]["dlc_root_data_dir"], - }, - } - ) - - return - - -@pytest.fixture(scope="session") -def test_data(setup, dj_config): - """Load demo data. Try local path. Try DJArchive w/either os environ or config""" - from workflow_deeplabcut.load_demo_data import ( - download_djarchive_dlc_data, - setup_bare_project, - shorten_video, - ) - - verbose_context, _ = setup - try: - _ = find_full_path(get_dlc_root_data_dir(), test_data_project) - - except FileNotFoundError: - with verbose_context: - download_djarchive_dlc_data(target_directory="/main/test_data/") - - with verbose_context: # Setup - expand relative paths, make a shorter video - setup_bare_project(project=test_data_project) - shorten_video(vid_path=inference_vid) - - -@pytest.fixture(scope="session") -def pipeline(setup): - """Loads workflow_deeplabcut.pipeline lab, session, subject, dlc""" - with verbose_context: - from workflow_deeplabcut import pipeline - - yield { - "train": pipeline.train, - "model": pipeline.model, - "subject": pipeline.subject, - "session": pipeline.session, - "lab": pipeline.lab, - "Device": pipeline.Device, - } - if _tear_down: - with verbose_context: - pipeline.model.PoseEstimationTask.delete() - pipeline.model.VideoRecording.delete() - pipeline.model.Model.delete() - pipeline.train.TrainingTask.delete() - pipeline.train.VideoSet.delete() - pipeline.subject.Subject.delete() - pipeline.session.Session.delete() - pipeline.lab.Lab.delete() - pipeline.train.TrainingParamSet.delete() - - -@pytest.fixture(scope="session") -def ingest_csvs(setup, test_data, pipeline): - """For each input, generates csv in test_user_data_dir and ingests in schema""" - # CSV as list of 3: relevant insert func, filename, content - all_csvs = [ - [ # 0 - ingest_subjects, - "subjects.csv", - [ - "subject,sex,subject_birth_date,subject_description," - + "death_date,cull_method", - "subject6,M,2020-01-01 00:00:01,manuel,2020-10-03 00:00:01,natural", - ], - ], - [ # 1 - ingest_sessions, - "sessions.csv", - [ - "subject,session_datetime,session_dir,session_note", - f"subject6,2021-06-01 13:33:33,{test_data_project}/,Model Training", - f"subject6,2021-06-02 14:04:22,{test_data_project}/,Test Session", - ], - ], - [ # 2 - ingest_train_params, - "config_params.csv", - [ - "paramset_idx,paramset_desc,config_path,shuffle," - + "trainingsetindex,filter_type,track_method," - + "scorer_legacy,maxiters", - f"0,{test_data_project},{test_data_project}/config.yaml,1,0,,,False,5", - ], - ], - [ # 3 - ingest_train_vids, - "train_videosets.csv", - [ - "video_set_id,file_id,file_path", - f"0,1,{test_data_project}/labeled-data/train1/CollectedData_DJ.h5", - f"0,2,{test_data_project}/labeled-data/train2/CollectedData_DJ.h5", - "1,1,openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.h5", - "1,2,openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.csv", - "1,3,openfield-Pranav-2018-10-30/labeled-data/m4s1/img0000.png", - "1,4,openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4", - "2,1,Reaching-Mackenzie-2018-08-30/labeled-data/reachingvideo1/CollectedData_Mackenzie.csv", - "2,2,Reaching-Mackenzie-2018-08-30/labeled-data/reachingvideo1/CollectedData_Mackenzie.h5", - "2,3,Reaching-Mackenzie-2018-08-30/labeled-data/reachingvideo1/img005.png", - "2,4,Reaching-Mackenzie-2018-08-30/videos/reachingvideo1.avi", - ], - ], - [ # 4 - ingest_model_vids, - "model_videos.csv", - [ - "recording_id,subject,session_datetime,file_id,file_path,device,paramset_idx", - f"1,subject6,2021-06-02 14:04:22,1,{inf_vid_short},Camera1,0", - ], - ], - [ - ingest_model, - "model_model.csv", - [ - "model_name,config_relative_path,shuffle,trainingsetindex,paramset_idx,prompt,model_description,params", - f"{model_name},{test_data_project}/config.yaml,1,0,0,False,FromTop - latest snapshot,{{'snapshotindex':4}}", - ], - ], - ] - - # If data in last table, presume didn't tear down last time, skip insert - if len(pipeline["model"].Model()) == 0: - for csv_info in all_csvs: - csv_path = test_user_data_dir / csv_info[1] - write_csv(csv_path, csv_info[2]) - csv_info[0](csv_path, skip_duplicates=True, verbose=verbose) - - yield - - if _tear_down: - with verbose_context: - for csv_info in all_csvs: - csv_path = test_user_data_dir / csv_info[1] - csv_path.unlink() - - -@pytest.fixture(scope="session") -def populate_settings(): - yield dict(display_progress=verbose, reserve_jobs=False, suppress_errors=False) - - -@pytest.fixture() -def training_task(pipeline, ingest_csvs): - """Add task to train.TrainingTask""" - if 0 not in pipeline["train"].TrainingTask.fetch("training_id"): - pipeline["train"].TrainingTask.insert1( - { - "paramset_idx": 0, - "training_id": 0, - "video_set_id": 0, - "project_path": test_data_project, - }, - skip_duplicates=True, - ) - with verbose_context: - print("\nAdded training task") - - -@pytest.fixture() -def pose_estim_task(pipeline, ingest_csvs): - """Add model.PoseEstimationTask. Return key and device ID""" - key, device = (pipeline["model"].VideoRecording & "recording_id=1").fetch1( - "KEY", "device" - ) - key.update({"model_name": model_name, "task_mode": "trigger"}) - analyze_params = {"save_as_csv": True} - - if 1 not in pipeline["model"].PoseEstimationTask.fetch("recording_id"): - pipeline["model"].PoseEstimationTask.insert_estimation_task( - key, params=analyze_params - ) - with verbose_context: - print("\nAdded estimation task") - - yield key, device - - -@pytest.fixture() -def revert_checkpoint(setup): - """Reverts checkpoint to included downloaded well-trained model""" - from workflow_deeplabcut.load_demo_data import revert_checkpoint_file - - revert_checkpoint_file() - - -@pytest.fixture() -def run_pose_estim( - setup, pipeline, pose_estim_task, populate_settings, revert_checkpoint -): - """Run pose estimation""" - - verbose_context, _ = setup - with verbose_context: - pipeline["model"].PoseEstimation.populate(**populate_settings) - - -@pytest.fixture() -def pose_output_path(setup, pose_estim_task, run_pose_estim): - """Run model.PoseEstimation populate. Return expected output dir.""" - - verbose_context, _ = setup - _, device = pose_estim_task - - output_path = find_full_path( - get_dlc_root_data_dir(), - ( # essentially tests model.PoseEstim.infer_output_path() is working - f"{test_data_project}/videos/device_{device}_recording_1_model_" - + model_name.replace(" ", "-") - + "/" - ), - ) - yield output_path - - if _tear_down: - with verbose_context: - for results_file in output_path.glob("*"): - results_file.unlink() - - -@pytest.fixture() -def get_trajectory(pipeline, pose_estim_task, run_pose_estim): - """Run model.PoseEstimation.get_trajectory for sample task, return pandas df""" - key, _ = pose_estim_task - yield pipeline["model"].PoseEstimation.get_trajectory(key) diff --git a/tests/test_ingest.py b/tests/test_ingest.py deleted file mode 100644 index 3e3c933..0000000 --- a/tests/test_ingest.py +++ /dev/null @@ -1,33 +0,0 @@ -"""Tests ingestion into schema tables: Lab, Subject, Session - 1. Assert length of populating data conftest - 2. Assert exact matches of inserted data fore key tables -""" - - -def test_ingest(pipeline, ingest_csvs): - """Check successful ingestion of csv data""" - import datetime - - subject = pipeline["subject"] - session = pipeline["session"] - train = pipeline["train"] - model = pipeline["model"] - - table_lengths = [ - (subject.Subject(), 1, "subject6"), - (session.Session(), 2, datetime.datetime(2021, 6, 1, 13, 33, 33)), - (train.TrainingParamSet(), 1, "from_top_tracking"), - (train.VideoSet(), 3, 0), - ( - train.VideoSet.File(), - 10, - "from_top_tracking/labeled-data/train1/CollectedData_DJ.h5", - ), - (model.Model(), 1, "FromTop-latest"), - (model.VideoRecording(), 1, "Camera1"), - (model.VideoRecording.File(), 1, "from_top_tracking/videos/test-2s.mp4"), - ] - - for t in table_lengths: - assert len(t[0]) == t[1], f"Check length of {t[0].full_table_name}" - assert t[2] in t[0].fetch()[0], f"Check contents of {t[0].full_table_name}" diff --git a/tests/test_pipeline_generation.py b/tests/test_pipeline_generation.py deleted file mode 100644 index 753ade9..0000000 --- a/tests/test_pipeline_generation.py +++ /dev/null @@ -1,52 +0,0 @@ -__all__ = ["pipeline"] - - -def test_upstream_pipeline(pipeline): - session = pipeline["session"] - subject = pipeline["subject"] - - # test connection Subject->Session - assert subject.Subject.full_table_name == session.Session.parents()[0] - - -def test_train_pipeline(pipeline): - train = pipeline["train"] - - # test connection train.TrainingTask - traintask_parent_links = train.TrainingTask.parents() - traintask_parent_list = [ - train.VideoSet, - train.TrainingParamSet, - ] - for parent in traintask_parent_list: - assert ( - parent.full_table_name in traintask_parent_links - ), f"train.TrainingTask.parents() did not include {parent.full_table_name}" - - -def test_model_pipeline(pipeline): - model = pipeline["model"] - - # test connection model.VideoRec -> schema children - modelvids_children_links = model.VideoRecording.children() - modelvids_children_list = [ - model.VideoRecording.File, - model.PoseEstimationTask, - model.RecordingInfo, - ] - for child in modelvids_children_list: - assert ( - child.full_table_name in modelvids_children_links - ), f"model.VideoRecording.children() did not include {child.full_table_name}" - - # test connection model.Model -> schema children - model_children_links = model.Model.children() - model_children_list = [ - model.Model.BodyPart, - model.ModelEvaluation, - model.PoseEstimationTask, - ] - for child in model_children_list: - assert ( - child.full_table_name in model_children_links - ), f"model.Model.children() did not include {child.full_table_name}" diff --git a/tests/test_populate.py b/tests/test_populate.py deleted file mode 100644 index e0e6611..0000000 --- a/tests/test_populate.py +++ /dev/null @@ -1,97 +0,0 @@ -"""Run each populate command - for computed/imported tables -""" - -import logging -from .conftest import find_full_path, get_dlc_root_data_dir - -from time import time -import pytest -import logging - - -def test_training(setup, test_data, pipeline, populate_settings, training_task): - verbose_context, verbose = setup - train = pipeline["train"] - - # Run training - with verbose_context: - train.ModelTraining.populate(**populate_settings) - - if not verbose: # train command in DLC resets logger - logging.getLogger("deeplabcut").setLevel(logging.WARNING) - - project_path = find_full_path( - get_dlc_root_data_dir(), train.TrainingTask.fetch("project_path", limit=1)[0] - ) - - # Examine results - snapshot_path = sorted(project_path.rglob("snapshot-5.index")) # 5 bc maxiter - assert snapshot_path, f"Counldn't find trained snapshot-5.index in {project_path}" - snapshot_filetime = snapshot_path[0].stat().st_ctime - assert time() == pytest.approx( # approx equal, within 2nd delta - snapshot_filetime, 1e4 # 1e4s is 2.7 hour delta - ), f"Training file is old: {snapshot_path[0]}" - - -def test_record_info(setup, test_data, pipeline, populate_settings, ingest_csvs): - verbose_context, _ = setup - model = pipeline["model"] - - # Run recording info populate - with verbose_context: - model.RecordingInfo.populate(**populate_settings) - - # Check success - assert len(model.RecordingInfo()), f"Recording info didn't populate" - fps = model.RecordingInfo.fetch("fps", limit=1)[0] - assert fps == 60, f"Test video fps didn't match 60: {fps}" - - -def test_model_eval( - setup, test_data, pipeline, populate_settings, ingest_csvs, revert_checkpoint -): - """Test model evaluation""" - verbose_context, _ = setup - model = pipeline["model"] - - # Run model evaluation - with verbose_context: - model.ModelEvaluation.populate(**populate_settings) - - # Check results. First appropriate number of iters, Next results recent - iter_eval = model.ModelEvaluation.fetch("train_iterations", limit=1)[0] - assert iter_eval > 5, f"Did not eval prev model. Iterations eval'd {iter_eval}" - - eval_file = list( - find_full_path( - get_dlc_root_data_dir(), model.Model.fetch("project_path", limit=1)[0] - ).rglob(f"*{iter_eval}-results.csv") - )[0] - eval_time = eval_file.stat().st_ctime - assert time() == pytest.approx(eval_time, 1e4), f"Eval result is old: {eval_file}" - - -def test_pose_estim(setup, test_data, pipeline, pose_output_path): - """Test pose estimation""" - output_path = pose_output_path - - # Check output path, and that results files exist - assert output_path.exists(), f"Missing output of `infer_output_dir`: {output_path}" - assert ( - len(list(output_path.glob("test-2s*"))) == 3 - ), f"Should be 3 output files in {output_path}" - - -def test_get_trajectory(get_trajectory): - data = get_trajectory - assert data.shape[1] == 12, f"Expected 12 columns. Found\n{data.columns}" - - names = ["x mean", "y mean", "z mean", "liklihood mean"] - means = data.mean(axis=0) - expected = [231, 250, 0, 1] - delta = [10, 10, 0, 0.01] - # averaging across body parts: zip x/y coords, means, and permissible delta - for n, m, e, d in zip(names, means, expected, delta): - assert m == pytest.approx( # assert mean is within delta of expected value - e, d - ), f"Issues with data for {n}. Expected {e} ±{d}, Found {m}" diff --git a/user_data/config_params.csv b/user_data/config_params.csv deleted file mode 100644 index 5603b7d..0000000 --- a/user_data/config_params.csv +++ /dev/null @@ -1,4 +0,0 @@ -paramset_idx,paramset_desc,config_path,shuffle,trainingsetindex,filter_type,track_method,scorer_legacy,maxiters -0,from_top_tracking,from_top_tracking/config.yaml,1,0,,,False,5 -1,OpenField,openfield-Pranav-2018-10-30/config.yaml,1,0,,,False,5 -2,Reaching,Reaching-Mackenzie-2018-08-30/config.yaml,1,0,,,False,5 \ No newline at end of file diff --git a/user_data/model_model.csv b/user_data/model_model.csv deleted file mode 100644 index a25b40d..0000000 --- a/user_data/model_model.csv +++ /dev/null @@ -1,2 +0,0 @@ -model_name,config_relative_path,shuffle,trainingsetindex,paramset_idx,prompt,model_description,params -FromTop-latest,from_top_tracking/config.yaml,1,0,0,False,FromTop - latest snapshot,{"snapshotindex":4} \ No newline at end of file diff --git a/user_data/model_videos.csv b/user_data/model_videos.csv deleted file mode 100644 index 94b263d..0000000 --- a/user_data/model_videos.csv +++ /dev/null @@ -1,2 +0,0 @@ -recording_id,subject,session_datetime,file_id,file_path,device,paramset_idx -1,subject6,2021-06-02 14:04:22,1,from_top_tracking/videos/test-2s.mp4,Camera1,0 \ No newline at end of file diff --git a/user_data/sessions.csv b/user_data/sessions.csv deleted file mode 100644 index 858950b..0000000 --- a/user_data/sessions.csv +++ /dev/null @@ -1,5 +0,0 @@ -subject,session_datetime,session_dir,session_note -subject6,2021-04-01 14:43:10,openfield-Pranav-2018-10-30/,DLC Example -subject6,2020-04-15 11:16:38,Reaching-Mackenzie-2018-08-30/,DLC Example -subject6,2021-06-01 13:33:33,from_top_tracking/,Model Training Session -subject6,2021-06-02 14:04:22,from_top_tracking/,Test Session \ No newline at end of file diff --git a/user_data/subjects.csv b/user_data/subjects.csv deleted file mode 100644 index fdc0292..0000000 --- a/user_data/subjects.csv +++ /dev/null @@ -1,2 +0,0 @@ -subject,sex,subject_birth_date,subject_description,death_date,cull_method -subject6,M,2020-01-01 00:00:01,manuel,2020-10-03 00:00:01,natural causes diff --git a/user_data/train_videosets.csv b/user_data/train_videosets.csv deleted file mode 100644 index eead072..0000000 --- a/user_data/train_videosets.csv +++ /dev/null @@ -1,11 +0,0 @@ -video_set_id,file_id,file_path -0,1,from_top_tracking/labeled-data/train1/CollectedData_DJ.h5 -0,2,from_top_tracking/labeled-data/train2/CollectedData_DJ.h5 -1,1,openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.h5 -1,2,openfield-Pranav-2018-10-30/labeled-data/m4s1/CollectedData_Pranav.csv -1,3,openfield-Pranav-2018-10-30/labeled-data/m4s1/img0000.png -1,4,openfield-Pranav-2018-10-30/videos/m3v1mp4.mp4 -2,1,Reaching-Mackenzie-2018-08-30/labeled-data/reachingvideo1/CollectedData_Mackenzie.csv -2,2,Reaching-Mackenzie-2018-08-30/labeled-data/reachingvideo1/CollectedData_Mackenzie.h5 -2,3,Reaching-Mackenzie-2018-08-30/labeled-data/reachingvideo1/img005.png -2,4,Reaching-Mackenzie-2018-08-30/videos/reachingvideo1.avi From 0b17199d2fabf0bfd3bb3d821691610dac342d24 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 11 Sep 2023 18:04:12 +0200 Subject: [PATCH 125/176] Delete `docs` directory from this PR --- docs/.docker/Dockerfile | 17 -- docs/.docker/apk_requirements.txt | 1 - docs/.docker/pip_requirements.txt | 12 -- docs/docker-compose.yaml | 58 ------ docs/mkdocs.yaml | 178 ------------------ .../.overrides/.icons/main/company-logo.svg | 11 -- docs/src/.overrides/404.html | 19 -- .../assets/images/company-logo-blue.png | Bin 41770 -> 0 bytes .../.overrides/assets/stylesheets/extra.css | 98 ---------- docs/src/.overrides/partials/nav.html | 33 ---- docs/src/api/make_pages.py | 32 ---- docs/src/changelog.md | 1 - docs/src/citation.md | 19 -- docs/src/concepts.md | 162 ---------------- docs/src/index.md | 21 --- 15 files changed, 662 deletions(-) delete mode 100644 docs/.docker/Dockerfile delete mode 100644 docs/.docker/apk_requirements.txt delete mode 100644 docs/.docker/pip_requirements.txt delete mode 100644 docs/docker-compose.yaml delete mode 100644 docs/mkdocs.yaml delete mode 100644 docs/src/.overrides/.icons/main/company-logo.svg delete mode 100644 docs/src/.overrides/404.html delete mode 100644 docs/src/.overrides/assets/images/company-logo-blue.png delete mode 100644 docs/src/.overrides/assets/stylesheets/extra.css delete mode 100644 docs/src/.overrides/partials/nav.html delete mode 100644 docs/src/api/make_pages.py delete mode 120000 docs/src/changelog.md delete mode 100644 docs/src/citation.md delete mode 100644 docs/src/concepts.md delete mode 100644 docs/src/index.md diff --git a/docs/.docker/Dockerfile b/docs/.docker/Dockerfile deleted file mode 100644 index 340dea5..0000000 --- a/docs/.docker/Dockerfile +++ /dev/null @@ -1,17 +0,0 @@ -FROM datajoint/miniconda3:4.10.3-py3.9-alpine -ARG PACKAGE -WORKDIR /main -COPY --chown=anaconda:anaconda ./docs/.docker/apk_requirements.txt ${APK_REQUIREMENTS} -COPY --chown=anaconda:anaconda ./docs/.docker/pip_requirements.txt ${PIP_REQUIREMENTS} -RUN \ - umask u+rwx,g+rwx,o-rwx && \ - /entrypoint.sh echo "Dependencies installed" && \ - rm ${APK_REQUIREMENTS} ${PIP_REQUIREMENTS} && \ - git config --global user.name "GitHub Action" && \ - git config --global user.email "action@github.com"&& \ - git config --global pull.rebase false && \ - git init -COPY --chown=anaconda:anaconda ./${PACKAGE} /main/${PACKAGE} -COPY --chown=anaconda:anaconda ./docs/mkdocs.yaml /main/docs/mkdocs.yaml -COPY --chown=anaconda:anaconda ./docs/src /main/docs/src -COPY --chown=anaconda:anaconda ./CHANGELOG.md /main/ \ No newline at end of file diff --git a/docs/.docker/apk_requirements.txt b/docs/.docker/apk_requirements.txt deleted file mode 100644 index 5664e30..0000000 --- a/docs/.docker/apk_requirements.txt +++ /dev/null @@ -1 +0,0 @@ -git diff --git a/docs/.docker/pip_requirements.txt b/docs/.docker/pip_requirements.txt deleted file mode 100644 index ae44fb5..0000000 --- a/docs/.docker/pip_requirements.txt +++ /dev/null @@ -1,12 +0,0 @@ -mkdocs-material -mkdocs-redirects -mkdocstrings -mkdocstrings-python -mike -mdx-truly-sane-lists -mkdocs-gen-files -mkdocs-literate-nav -mkdocs-exclude-search -mkdocs-markdownextradata-plugin -mkdocs-jupyter -mkdocs-section-index \ No newline at end of file diff --git a/docs/docker-compose.yaml b/docs/docker-compose.yaml deleted file mode 100644 index 03393e8..0000000 --- a/docs/docker-compose.yaml +++ /dev/null @@ -1,58 +0,0 @@ -# MODE="LIVE|QA|PUSH" PACKAGE=element_deeplabcut UPSTREAM_REPO=https://github.com/datajoint/element-deeplabcut.git HOST_UID=$(id -u) docker compose -f docs/docker-compose.yaml up --build -# navigate to http://localhost/ -# -# Check templates: https://github.com/dj-sciops/djsciops-cicd/tree/main/docker-template -version: "2.4" -services: - docs: - build: - dockerfile: docs/.docker/Dockerfile - context: ../ - args: - - PACKAGE - image: ${PACKAGE}-docs - environment: - - PACKAGE - - UPSTREAM_REPO - - MODE - - PATCH_VERSION - #- JUPYTER_PLATFORM_DIRS=1 - volumes: - - ../docs:/main/docs - - ../${PACKAGE}:/main/${PACKAGE} - - ../notebooks:/main/notebooks - user: ${HOST_UID}:anaconda - ports: - - 80:80 - command: - - sh - - -c - - | - git config --global --add safe.directory /main - set -e - export ELEMENT_UNDERSCORE=$$(echo $${PACKAGE} | sed 's/element_//g') - export ELEMENT_HYPHEN=$$(echo $${ELEMENT_UNDERSCORE} | sed 's/_/-/g') - export PATCH_VERSION=$$(cat /main/$${PACKAGE}/version.py | grep -oE '\d+\.\d+\.[a-z0-9]+') - - cp /main/notebooks/tutorial.ipynb /main/docs/src/tutorials/ - - if echo "$${MODE}" | grep -i live &>/dev/null; then - mkdocs serve --config-file ./docs/mkdocs.yaml -a 0.0.0.0:80 2>&1 | tee docs/temp_mkdocs.log - elif echo "$${MODE}" | grep -iE "qa|push" &>/dev/null; then - echo "INFO::Delete gh-pages branch" - git branch -D gh-pages || true - echo "INFO::Fetch upstream gh-pages" - git fetch $${UPSTREAM_REPO} gh-pages:gh-pages && git switch gh-pages || git switch --orphan gh-pages && git commit --allow-empty -m "init commit" - echo "INFO::mike" - mike deploy --config-file ./docs/mkdocs.yaml -u $$(grep -oE '\d+\.\d+' /main/$${PACKAGE}/version.py) latest - mike set-default --config-file ./docs/mkdocs.yaml latest - if echo "$${MODE}" | grep -i qa &>/dev/null; then - mike serve --config-file ./docs/mkdocs.yaml -a 0.0.0.0:80 - elif echo "$${MODE}" | grep -i push &>/dev/null; then - echo "INFO::Push gh-pages to upstream" - git push $${UPSTREAM_REPO} gh-pages - fi - else - echo "Unexpected mode..." - exit 1 - fi diff --git a/docs/mkdocs.yaml b/docs/mkdocs.yaml deleted file mode 100644 index a1533f8..0000000 --- a/docs/mkdocs.yaml +++ /dev/null @@ -1,178 +0,0 @@ -# ---------------------- PROJECT SPECIFIC --------------------------- - -site_name: DataJoint Documentation -site_url: http://localhost/docs/elements/element-deeplabcut -repo_url: https://github.com/datajoint/element-deeplabcut -repo_name: datajoint/element-deeplabcut -nav: - - Element DeepLabCut: index.md - - Concepts: concepts.md - - Tutorials: - - Overview: tutorials/index.md - - Data Download: tutorials/00-DataDownload_Optional.ipynb - - Configure: tutorials/01-Configure.ipynb - - Workflow Structure: tutorials/02-WorkflowStructure_Optional.ipynb - - Process: tutorials/03-Process.ipynb - - Automate: tutorials/04-Automate_Optional.ipynb - - Visualization: tutorials/05-Visualization_Optional.ipynb - - Drop Schemas: tutorials/06-Drop_Optional.ipynb - - Alternate Dataset: tutorials/09-AlternateDataset.ipynb - - Citation: citation.md - - API: api/ # defer to gen-files + literate-nav - - Changelog: changelog.md - -# --------------------- NOTES TO CONTRIBUTORS ----------------------- -# Markdown in mkdocs -# 01. Redering concatenates across single line breaks. This means... -# A. We have to be careful to add extra line breaks around paragraphs, -# including between the end of a pgf and the beginnign of bullets. -# B. We can use hard wrapping to make github reviews easier to read. -# VSCode Rewrap extension offers a keyboard shortcut for hard wrap -# at the ruler, but don't add breaks in [multiword links](example.com) -# 02. Instead of designating codeblocks with bash, use console. For example.. -# ```console -# cd ../my_dir -# ``` -# 03. Links across docs should ... -# A. Not involve line breaks. -# B. Use relative paths to docs in the same repo -# C. Use lowercase and hyphens not spaces: [sub headings](./doc#sub-heading) -# -# Files -# 01. Add a soft link to your changelog with the following -# ```console -# ln -s ../../CHANGELOG.md ./docs/src/changelog.md -# ``` -# -# Site rendering -# 01. Deploy locally to localhost with the command -# ```console -# MODE="LIVE" PACKAGE=element_{ELEMENT} \ -# UPSTREAM_REPO=https://github.com/datajoint/element-{ELEMENT}.git \ -# HOST_UID=$(id -u) docker compose -f docs/docker-compose.yaml up --build -# ``` -# 02. The API section will pull docstrings. -# A. Follow google styleguide e.g., -# https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html -# With typing suggestions: https://docs.python.org/3/library/typing.html -# B. To pull a specific workflow fork, change ./docs/src/api/make_pages.py#L19 -# 03. To see your fork of the workflow-{element} in this render, change the -# URL in ./docs/src/api/make_pages.py#L19 to your fork. -# 04. To deploy this site on your fork, -# A. declare a branch called gh-pages -# B. go to the your fork > settings > pages -# C. direct pages to render from the gh-pages branch at root -# D. push a tag to your fork with the format test*.*.* -# -# ---------------------------- STANDARD ----------------------------- -edit_uri: ./edit/main/docs/src -docs_dir: ./src -theme: - font: - text: Roboto Slab - code: Source Code Pro - name: material - custom_dir: src/.overrides - icon: - logo: main/company-logo - favicon: assets/images/company-logo-blue.png - features: - - toc.integrate - - content.code.annotate - palette: - - media: "(prefers-color-scheme: light)" - scheme: datajoint - toggle: - icon: material/brightness-7 - name: Switch to dark mode - - media: "(prefers-color-scheme: dark)" - scheme: slate - toggle: - icon: material/brightness-4 - name: Switch to light mode - -plugins: - - markdownextradata: {} - - search - - mkdocstrings: - default_handler: python - handlers: - python: - options: - members_order: source - group_by_category: false - line_length: 88 - - gen-files: - scripts: - - ./src/api/make_pages.py - - literate-nav: - nav_file: navigation.md - - exclude-search: - exclude: - - "*/navigation.md" - - mkdocs-jupyter: - ignore_h1_titles: True - ignore: ["*make_pages.py"] - - section-index -markdown_extensions: - - attr_list - - toc: - permalink: true - - pymdownx.emoji: - options: - custom_icons: - - .overrides/.icons - - mdx_truly_sane_lists - - pymdownx.superfences: - custom_fences: - - name: mermaid - class: mermaid - format: !!python/name:pymdownx.superfences.fence_code_format - - pymdownx.tabbed: - alternate_style: true - - pymdownx.highlight: - linenums: true - - pymdownx.inlinehilite - - pymdownx.snippets - - pymdownx.magiclink # Displays bare URLs as links - - pymdownx.tasklist: # Renders check boxes in tasks lists - custom_checkbox: true -extra: - PATCH_VERSION: !ENV PATCH_VERSION - generator: false # Disable watermark - version: - provider: mike - social: - - icon: main/company-logo - link: https://www.datajoint.com - name: DataJoint - - icon: fontawesome/brands/slack - link: https://datajoint.slack.com - name: Slack - - icon: fontawesome/brands/linkedin - link: https://www.linkedin.com/company/datajoint - name: LinkedIn - - icon: fontawesome/brands/twitter - link: https://twitter.com/datajoint - name: Twitter - - icon: fontawesome/brands/github - link: https://github.com/datajoint - name: GitHub - - icon: fontawesome/brands/docker - link: https://hub.docker.com/u/datajoint - name: DockerHub - - icon: fontawesome/brands/python - link: https://pypi.org/user/datajointbot - name: PyPI - - icon: fontawesome/brands/stack-overflow - link: https://stackoverflow.com/questions/tagged/datajoint - name: StackOverflow - - icon: fontawesome/brands/youtube - link: https://www.youtube.com/channel/UCdeCuFOTCXlVMRzh6Wk-lGg - name: YouTube - -extra_css: - - assets/stylesheets/extra.css - -extra_javascript: - - https://js-na1.hs-scripts.com/23133402.js # HubSpot chatbot diff --git a/docs/src/.overrides/.icons/main/company-logo.svg b/docs/src/.overrides/.icons/main/company-logo.svg deleted file mode 100644 index e876313..0000000 --- a/docs/src/.overrides/.icons/main/company-logo.svg +++ /dev/null @@ -1,11 +0,0 @@ - - - - - - - - diff --git a/docs/src/.overrides/404.html b/docs/src/.overrides/404.html deleted file mode 100644 index 0c4e4a6..0000000 --- a/docs/src/.overrides/404.html +++ /dev/null @@ -1,19 +0,0 @@ -{% extends "main.html" %} - - -{% block content %} -

    🚧 Not Found 👷

    -

    - Unfortunately, we could not find what you were looking for. -
    -
    - Usually there are two possibilities for this: -
    -

      -
    • We are currently updating some of our content.
      • -
    • There is a mistake in the address or link you are using.
      • -
    -
    -Please make sure you are navigating to the correct address. -

    -{% endblock %} \ No newline at end of file diff --git a/docs/src/.overrides/assets/images/company-logo-blue.png b/docs/src/.overrides/assets/images/company-logo-blue.png deleted file mode 100644 index d15194b8db09a9fabae8da2cdb2f2a4d3c820a96..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 41770 zcmd43c{~(c`v=T4icxl13S-MwNMvV3wzBWCMkI-n2r-kSM1&Ga_I*oK_Mw|dmMkeL z(}KuWSxV%+&baUU>3QGZ^Z)zDb3Z=KnRBjlt>0@obJf&XZySymM?*ui%|KtroQ7r- zGYt*akbX1#3&ZT;DEL2Ge{;P9G-cg8C(*z8>D&3!&`1lT|Ir5JYM!H^!P6M%Xjuk1 zO}{-~ztbvZ{p;N1or5<{Tk77p7r};~oa$`6l7abfnelT*yHI=j*?@~Daajc!!t|1= z1~y{io%HQ_;vX(DMn`|Z<1U>IU?Z6E5dOL;8vkZuxpsG@XidYwnWF9}P0uN{iC^{i zf=MQ)0?Sy}@kAO7{lEWFr@zgg{+<=fLZG4j=Rbtq%=Os+ETPBuCen0jn;cfV^yfEW z(Wd|3E;>bDr9zq-d=)F3L%o>o4VunPb9Ct9#2pwj&p`DBN?^bbkMICt776Oz)Ax|S zkyT0PhLp>M2kI7%Z;O*y}?zB*@o!TtIN#&*Hc?uu( z96#-h*BZvU>v18U<&iNx=xe5K`!trY zK{iF~A10r|>D(>XKNlE#+_7uxtlvJ7LPT{F-H(ol(eL>oa15D`(edo{8!tJ(Duc?Sl{c0pPY}=R2 zxmmzZm}7%Ew){a`sGE(jA8&osc_KAu2j}J^kGbM?s2Z^Pi4;gxAM!Smc%w-lZvy|z z^*!r>7fw9U8==>wGLR%j2fJ`>?80)qwbNZ;?7pmHU9QJa*qE-YIh8>b|8h#$ubZ!@ zzw#Kf`kr1|T!jA!gFYJ~kaU_h>>D|Rs>AV3gztID9funxoV8-qtrmw_e{K<`MFJuB zqr(KKhkfHZzQ@kw+{sA`t?6Zsm+w-;W!e5;hl%#q2kc9^vE$zMDP~+m^;?_)FO?Z^ zlIlMvRRyUQ{`%;N+=U1=2NtuM{!u2bZG8m*jV!)Nwqs?ye{j!{h3Fr zMBOBIIP7QC#>J2pQ;m?Qih1#Y`Ow=QO{v~kT*RIc1_LU85O@BidEf7<7>%znc-^=H z{=;4W9P^{&KYN&k7UeuiXFpydSERgecor+lfb{_(;qI9Fcmn&hjw5+Dl4LnZve7ubkrm{WjLizF$+>Y;r}nUGMOM?* zj$Eepi$Ft=Ao&}m^|luqn6{s7M!=h-rRDXh3puH6} za9kn=9p9Tska7|!9{#H*)tl4$(Y+ta=^Mu#g5&nsO(8zgFYM}#p1V1CC?x2I04{=S zX?|xMRReQG$lv1Gb{|80#0GO*`B0Yg?stg0?n|2sD?aMh<9n%eT%rs9l^oELY|exn zHx212w);eRronilhWxvH*~cYu6X!Y5CwMKO3%ge$l1f)Tx&Coc^oMj49=`J6`LF5s zCbX#E)&b>a8_VGa+KB{bOxKS9xfQQD&*xUSV%5dpY9>^IMI?Z*oL$va53s_1%cJtZn$vwz3aw-piykD zIrQj2G6C}wA8c%)S8b=kI2p-6y5fY`6 zwHf#&V=&xQ-rp8_aB(XAf9+(py3Ig!`TV)7EiStEJcD*pNvB1uVv(Rm8rcj2hqIX1 zoko*Df%8{Ad{>hjdrng&?II4#GJhyhB`upo;E=f`-!R7;>Q(D2M-83^^c89%nflwP z7j4F7{RlIV;it@tacsWFL)ocErSmd4S#C^RHdVp2=^c*hoelT=N#rcE$;KOIys~b!)XSwpBUxZJ^^4OcV`mryXo6f2ajP29~ z+E>}Zwfc^qMLd5j$Pyr(G@Y(}S()JK*B!5*be*be*?|1wOx3807hgf(2zTzgbGd|9 zVk@uqONtVe>^MaBfv+2C-hwd;eUKA-x%h;yrR|%ek9pJ3Ni3hOfJ-(K=~M~mTW1W` z9C=x$eD+1MrI?rEzWrzUa8xTjEDlT;WM$qkS)tykv&Vce>wJ~vR<^&+OY%rWBbw&7 zlCZyXW07+O5njN_`v#{x9|&1dtFC;?Y{8wDZo20gb%T-v#4*y)CaM;OkjAsN9g}<{ za`r17E~0_G=pYZ$Bm{iW=QOnW0DN{FTZp(Mx5te8xqiX+4Oae(eA3vCOAl-3oiRI_ zQX5yV^FZ=!c}BEp3ZI9b1)dac)tRPUNXX|<8u`tGKSx*=67 z{{=`&f-J2z`oV!`?55R0^d@EeH>skx79pGP2gy+XuH?G%;fsh*;^$V6Z^f3*bKN@L z=3CK06drUC%fAQZCOFExzcvaUZuk4tB&v*pQX#&VZ=wo#3F7-)<;tT=GQaCvC#%k`8R%A7o zh;wo@l>ieQTuPcMFn6h5hsQUc=X|lK891kC?snEyvU)@2_*+|?Fi1|MlXR&wcio@g zlyD3twouZNPFQS*E6qDW#QkLh85lTcHY=t63t z_S8*Sj|bf99KK;W$A6^p*7(Vwz@k7?1O?3V*U#@`_ne;24+GASJOohdsp zAZ@b7FAbN*sv$pcl2UaISo2AeWzWu=yWXaTZT63DnTqObirRrV^QZd$#L2($jo&+z zBhcbhUhyzsW!VoY3MCmZhV%ElBS;3$q?aF-JI~3qi(s2}OS5}O15W$zJ*qwkA6u+B zdEo3L8>L{M{eh>cC@l*15b)1Cgcu3QgoaUHX3C|#kAIZvXdOZYkk_3r;ffLK@tzM?917@k1t2| z$I;8nr%~!>dIB7~d0g^1;!zaVxbB#dy6?BQJYp((u6~5Jz%zVkkv%NxyMC(;EqaPg zJ+wcOr*7{@OTI|0_^)>zqR>g!a8gxvE`lNCy(EtiF&_V8 z4XV=B5S+Pz^LB2ZICbhEeD|3!5R-1#M@>A+$jX6gCY`s`libKs9GmMt1!>wKgUN+} zCf>^Jh4N`Xhm2}>_1E`vmGkOtTq``TFZ(MXCgYGKVs7lBGMO&x;2VRR0uy&CRkh!L zP6*xSr;65`gf&Nd;ug>a_F~5amrRG8lDuu*mMj{AoyvF~Ba;z-3?Z+1i+(7&0Sl(I zJIdXs+u8OZBgab*&X#NONMW+!V%M22BZN*U#BR8XpG)qitZd)Pj&2=6p_2pQf>+;# z)zP_IA>Xj=J5>{P;d3U}m-(*WsUb`l#1%ToZp1%RuAuCO!*C-z|LM7Z&eC?x`nR$^ ztf1$xfQ@eZ3sWz-8#Z$6s~XWh5^|BZ+U-n_7V>x*89)ym-_H9;OAavTe3dh>*x9}+ z9NEmo^a+na6zIdv4_uG!MHG{;-H*TKrdAF&To(zmbfvVgeWc&A7x+f{4MJV{{=p0O zq#=a?DBqQ^X4Lf5Jf!jH!@iQK^*rl)Eog_UfNTxb9PCl3NMpI8G8aE>W$ezw?0WX` zRI^w2CqaZ^O!06gX4|y_^bJ99+bMD$jr(55N?WP7ub0OLY`%m?pUeU64M)C;z&D6w zJf>^uwA}bBt?PMb+NPuxF52Xn-^oJDI?$0CF2m@^GhA0xCiKrWjrgmn6RZ0N3f5j0 z9=?Qp0}f!TDN&G>6$!G6z_D(ca3oy4Xz<+$EEmV{&3UvF6C9|V;V%jYQf?BZn((tc zpFPrBrgoN(6CR)i7+Ao7H>VbFTsSdiYP$+Q++9(nRppX3pua~JQ8;cEY!vDJ5aO|m#@fZqDc8qG$|@h^(V5n~A` zyH!#bUhAv3-^OjFynj;$3Kgc(Ai6VMiKmb;J;K_b*0bI>f?JG{kCNDG9H=Dl0{weF zC`%ge7FVPz2^&f`GV?6V$aO}2c3hYTvG*2u^CRE!+t8N$q?cLM?oxp@Z{r)BU46Da z+LwT`EI&Qq(OtWEERt9<`J(6%?~~IL-HOM=d~(`W81MENATE<3i`cT|G69P?o`mT- z7}9PjeWp>-jmf$}W$0%b7WuWE;3fQA=yiZLtf2%;tH6!3S>GG%9E)@-?tE8fKz>aL z^n*fXxq=KU$vlIzC($aKrUBI(9?&RXMJql@L*ny`o}RkGGJ_}lkgl(cPN3UOv#yjE z(mK`$5SUN~PyFdh&mfXUA(%;N0^c6}xNVVImhmNjAyUqOAlhTouOWj>VAPq}BJ#j~ zZ>>C+L`h>4x19kBU7y6@pnK|y$iIzoD!FwWQaCpKu0{U%+2pBaDO_T52HM>iDQmO; z9>l>DB=L9Vee2tco|a*{m{cO49kvB_-~p0E#WdF-X3ihLM+e?K>y;U-;E^{q_<-?d zN(I`b4R+y)@JGRj5=kgt6SVstd2w;&SY-1S+^v+yXh|L{Njc+B728$9fUmVhJot-6Z#NIRUupo{Q8R_~7OQh*(a zHlU`v?0g&^r~4cWSMW$X@^}P`k^wn3;0=AMH`1YUY*x3k=&!lfFR#;zD{8~Po=N0} zd$dCVMR14bDP+>CthWQdU$aU%6Rx$RQQNiRl-2|G9k52<5!M78{TspDZVIvpsB4lw zHT0GTr~PskuT5L1vls26A3%gn$Tq#AF6zS8(CwEY>vT9h^fHd z@qm`KPgHTB>#0F76wnWL%dER6?^VlWAMTYIf>%R}F2W*QK@!rB`t5-F3?!F)Pdt2k zVSsy0pnO9AJ5WHz0l`6g5Nogr>`Ld}bCfE0*`1;DFLEC+SEu){z3YvL@C0X=#<#5* znH~nG!>E^Q@Myd2&j-R!aS`?mD;ac1FaD?*1WB{(d2Pr0OIL#9dHYI{NmD6DLoaeB z@$o>@f%qiEXqNbash!f^CK8d6RDYS2#g&TaU;}yS(VpsbP=w5p2v za!WVc`(^+1)J*PVlrFl9Al&OR_7gR)B(r0l@*jC&A9DTXvnQ5I<`>!`bEmF)P?rId zE|Wh}GeB}bHtS8^p*U&jn^&tn3(bDH6b5IZ+|Gp_>_Tu}X(MNpC;hzpsMx#7N-psZ z$L2e?O2mfIoi}fXioxyc5!Ac&uv{4yNP5nbYr*rPHkD_@thVtuJ@AE_02Koo`RnK& z^d*E0o_o^S+Rr>K&NsF2aNR7IQ9&4Jg$r`-(HM5>PEr8si$xl;LzR{eM^>{*9~`^G z!6!&0tbmbC?(nCgiHuN{PS zv?&{uf*$GLEKU?3bKmCOW14-gAn`Rl+R7cEV7%ad8)VbDoX6X?@pMV~g!jpCZZ4NN z9bSdf{spL<^|G@wQ}fRfbNq>WyHkV>ORMVZt~2qkITnWGC?jrTz(G50i1$5c0M1~J zzcId(^Ep4Rn5!-={v@Jf7%u0O@AJ23-&B?oE_nI5%VdpYXfAG0ursw$nLx<#O1< z@U<;HV9$YY;Jetg%jjE|=5f5$Pz${Eg|=fCW&L+vkMOj<{T8*8XI?7*{;E$NlU9 zwjg=GxK!R|L`8iUF%*A++TL;U_Sx4L2kRVNE1a6;*tw7ou>~LE zqQZo1HXQ(gp2)t}{&XEvizl#}-`~*!il2vSRr9sI{RjxhSCB6TrM8_7X|ZCo?+!cc zI`e>To5@KyEJFtP)A%?DMM;n{A%-n^YZW^@7KHrW zF>q_*zb;KO!+HAJkf7ivW4)53-HnR(4bZoR^FGl;w=P8BT-_-Zfpc^4eVY2g2Mq_W z*)A#KBD$iv)1IS6nxNNf$G`60P};!vQ@kk)%;M>yF4JvAL#LYQ-~{mpV2UaEhkVg` zUd;F5BLzoK>1UtK^yc-im>R5!oFyR$PsSoE6N0~JFl!9Yy_=jl{k6iQ>VsAwCeJ#Z zHB5wXUkcdsQFi`mq*Me_!J0%z=oxbVF^zMN_<8K~q7FM-kMEj(#cF-6lvdOQRC4t|IAFBRJvIF(36p(H7^pF;TNz&AUhucO;uWkd40f%~W(jJGySC1<5@!Ov)1f z%mS3JJ}j9L?Mp^UjcXG9u7jXn&;!{rwCeyrQnh19;5oi^JNGW1bgaAQG1T3uzJ(T9 zIR|K|IUyHmkEuPW;KC($V`-o8tMVQ_X11#M8|EA+-0cLc<98DO7y3JG`|*J7?ZXji z-M0gkir);3)6fX1TM4u+JmrFY?A{zIk#T!y)v(h4*OVEGiheq9 z-5XjgsJ<$GiNG-`Guay1p6aPz99PV`^PVSm7NMa(@5=-ItCtC-BUWuM6XrTXOAg(1>K^a={H)R0btX$*(t;hxX-6F)ne($KC5qm~(&{hTC2c=F@{PCJlDTDM zcH$`93{MA!#9>M;R3@2&VvBo4nJ?Wkwz%Jtam(1N2-(C>V8o}7-K9uaU$I$BZxieu z1U35~KH23xQ1DysDb|7NfeN?uvW2rLB4njTSZK`LC+Gcyzef);m=n*6{9Ft+mZGKv{yC4b+7nZRE~_nD46P)YEBV}JOG8p|K&hH5j%pGP+pnnn(qB?b7T%73!-TVfh)ra!VDaW#>XEjIL z>o|;?`qt7!5W}ni5s}{@xQds3VZy)tP%2Y@Jf zWbv)%OiFkokxIqD?c_F3s32=Jh3I15F>PP)+&D^M`E#?TNk41(h4#tYmyqYR7J?fV zQ*T%ieNXK#9|KyQtGIec*ZI3TQq;b{v-`68<6 z>;KUVKDZq+$J(O9HKpx)l)S_!5^C22duNfD$dk z1QpPIf*rPzd;Jdo;E&O&zcjrVZGSN;oOqLSVcQ~7_#E){2?;q&a1Yb%Ba!>No}=L9%)9e#*t8(d+q3pg90HkS z3nZg$@D~lHhvSOMcTrXLThFm&rrh&m3(p_dfV000C3kM!I4Vx!fjcfHye*0;RUYmi ztsW^Wj_?jYCoh0z8c5-A&v`j&qNMgkjQQ)6guIr7OyY}EMO%_Rf`qAg#wJ*^^{+NT z#ETqEsWIOBjp$7G*h8U6ttVqdccjT}$nu$~mY>dcMWyc0l2p9AbcL;}Y1gVXwT8U{ z5b!g5%u{qTbLf?^i~M?FzWn)wzvF|u9yULv&N$v>uK8~qoWJ%-u+gwj72lynzWvd% zJWMj`P7Mm&pY5Q+>M6;Oyo4HbO98z^Zn>-SDyzoYH6Ub51S4GH-~2AQn_L#QAsvEZ_e3g@n%-DJ)R_<7;Ng9>s-DE>8h)Q7{qF7ZcI4w; z6&~WC`lk{Iza$%KbCYs)Oz?MXjuEBZk%C6QderHtkbo0yWWTXdjhe0aP8L}e?L~^| zmIM}BxKYq`bfb-#IF8mMhAYnEX)Lw{vai~&id;f2DP0sumD=!_jcS(?QcabFTqKVa zYkUmW>!Zf~Kbi95o8QBnO__cN$^69?OI|j8@rbw}bJQk8#}QmY&r>@4q#L%pbjsJA z36MT0ggQR5$SqUnxqkfPaes7E9M;`B75me+Jxx~mO$95UfPNkvSoHT9Hron!@7UNi zwfr|V=W;P`v!R-UrADR%h*F38@o3-pC_=G+MTz>pxZ)cBn;1E; z14VddFdhfHI$wSp{|PL{E$;e>Ze5==q<1!8Vh$--Wu(;gP^3E0aVmajaik7MI<@TA z#jn1Nx)&$xWwT3*X=5*ou=UW=C33`hhpxNo^xPMYEK>N?)jCihChP*uNRjoYb~y^j z_1zDPUU~Bw>Fq8oo)zv_2>JM?8FBp2u=tJb_&KKJ`}v;&)R&h;dQG`?)YUt*(O%DB zFNsYS{J>N{`wSjueD<>H-5Y*35Hv`L(|%fjAtEK>(ckTd@FxOA_6e7Nsd*$uigFi7 zFKf#SpPuyMM@0*Bup)idd*Zvcf+BC_N;{IVBo4S4u9e7N`3T4K-G4Y9mGZgr;5c zH3E@OJde})+L~~#a~~#fSl{Si{thx&~m zt&SnGgh=+|)uWQ$jZlni+*0<+QK9%;&D_=nD4F~OxAe)grwiRO3{$%F)V=HLPddtY zGjt6YduZplljY*NLtZiEY(etJYn9a-|zCAOFHjzVenCCmEQ{n0DFIS ztUg=dqvw*U&fUuo8lj{04z$i*GUCcY;#o!Y`i%*UM#i*rovFd^KGj|D6hFD%mYy3t z6A{^E;)fa;aM{eKdO^sDXduC6>TpsZ8(dcsbV(L3>GBi82|{Y13c5I_Yy1QNS_5I= zXO6q(Sc7&ZEj5d2IPi!7-xkk-jRc?S9!DF|Ls#!|&fTa2@{4-95_P-S;i&J|n?cBw zdtgQ3`|{l&?QsOkZ!E}-@1J-~6TcWNNJG@u)^QG+|Ujk?|9q)EC zv?vowK9&Yi+Z>}XKOyExDV#VmE^rB^EGaO79AVO*XM-#)x;(LJ8;dBbtlN>|#^y!Q z$;+wM|V2eVBZF>O2ZgKsvSQyi6mCZH2YIY{ADDuB>(y754# z(z3POq0PD8?;Of2Y6B*Rvjo9@pc&iz`UMM`mC(1vp9nXV^AvHodFDLh{EJ+wAq0VE zqzpdxg3$w1$(1=Y-02-5x4%a(`I(Yc@By+N$ih=kTNY943f|oxOQnJLG>OTA3He;mp`-|K&J;}2yet^`PGhJCwR;+J?pQ_ic zw8@3;+fN+O5V}^wM;iwoN{x4asSuI)6kwr6!q1$i7*XH5and(K$yHIreZY_In2XdN zTQtBbJ_FUfpiQ~ow^OnYoi`!=V*R7w5CnPe>cN+d?IBTeo8SOBljL_SerSp@#f$}Y zX7mj*1{vIaRCA}20(9XL%KLB^@uo*XzHH3NFOidciXE?BwrWkgc6kdX^lV3jV80ON zCu);?>6t+$wSwsiN{`{4bpC5w4dUa79)Sk?44l8|5<|ZL=n+e6Q$m$8iw6Dt3sHf* z!*T!RE4&!1PTk&Jw<7H2tbHL0`{Q$h_7gk~GY0k}K6-c(S$U9Q#^Cvek~Ibqeg@bg z?y^xay}Jy!n+9b?1T$X){t-y1c5a_NaO#;e5`^X^HoU15($Vf)-N@-noN$&*am<`Q z44Br94@MO=T~dPpK)ch16)$o5yOqHeC2Yc^^)j9Vmye}OHwY4mqZrS&b(6$J zy(ZSsne;g6pDO29bn!m0LseuUs>yy5?VX+$a%)P3Kyv2+jez5`^E3`|1Z4m_&y;Oy zkweZA*D&kT%b2%B6sQCZ>*5{Z`)4l{qLk#57)$Grez0~J?PFzUT8-MIa?MA=rpB<~ zO!v()B!sW`)MD1pJnk?J+D+C=R^!(w6^@4}udPyMiL$P5oX5{SOt?QZvE$+!e=g92 znm8g2eG-goW}alv6+m9WA!AV4;bl7J@*I-lvLf*}tG+$BPcO461cRkva;ejO(Fxp_ zpeG{XJI!N$(it$SB=7XIDDP2|W@%j)n;i4Q<27pkIt1Tg04)%?VjuzAlNm@a6BgZ{ z-cq>{$-;yrP3`F!q8Wfxvp$q*&R-%7ywUt`4rXA$p=Z|{E3#Q_wuGVuuvN;=L8vez_r~5C2LLF zQHWgI1WsqhN5r2`p1#DA{5df7TR3TH^u22m68|5SN=g=FlNIh7b4TUnltt$?BAbsH ziwZ#vlLM?eQ#M-&HBV$oFwkez7ykYdH_f7u;H8iZE z1{y{hLU2Spg1|8+GkNIP@ko9-7TH>dMVFzir#`?70r3=IRmWySRdDu&)R`25NnQTT zZ@ODveMv=3_RE250%H*Re?ZNW@_k{F4~>qnwE7rmo#)L;8~|0w;H`dO-W0_M46_7= zJcvcVfium74Zs=c-#f(~U*ZbVYpd!RNIMCHQ%6w2&dW{`#oijh^EOX?@ zLL!N9%w4-HHOzan;%Pv~NdUAnMIv%w@^s!dgY%w9akkp_jKyZU>(<<9@;^=+g9G-3 z^BRrP!dM9zq2OvQ49caP-OeVF$UPjJ%X=vNWgwgcU}}1+2-I z53Mh>Ek3#R9y)|S&}jM7v`NTGp1~N_uyN7qyiNvTnW~^?C9eHmOA54I7_75eKso2=ON@0@Q}Cp`Qh0Q@PI+Q1hl|aPY+Pf zyi*3xx{56x&}%gl@E{CD>M$@%0ffh;pz~yFRDinBMbKn6^;M#i^@>qfh8ykrFIw ziH#w{B0zmL7M0#1b2Nl#%u;Y3WfN-E9{&0JDQ3om#pxU9SUZi=HMDV~^Cl|3!-`beL>xN7~NmB@eBDe|t^SSBF zvIcr)tQ<#>rAJQQ%EBAD`ov;zQ?8)$QBm6R=tQ0ks8N8AFC^XbaCNJ;|3^pi1 zHH0#KoE~*G)ehmOb|+15evFU5Pd4G){Kg|xYzTt*ACq0=eSc5?Ygn?hA0*%Qu`2&9 zpIlc16WwXR2fq_LsUt_$LK{&%ni*3=Z|h&lJIFk7Mll(@0;;PR$3}pkB$5qbVqM-~ zwME(~z8-rjd(Jxu>;mrK>1spj08w>_;O zw@UyQ+1>Cc^{K$oHqMd1yuU~sj68u2sbPuS3fV4TcI~jG5maVgfQomW;Rln@kp2&a z3t0XnJ12_^PgMKsE`nB3AWBAjVHes!Tu>O_j3JHC#V}c^D+w>}!~zW?K+l4d?66n&k8LbyeScK21Zdy|n`MW|8 z#l{NQRSpu&c9uIJAsSL6;B7Q;H?XYCgpa>PtQIRW`*qxYtPr&66o~n;x1J3@anW~=gb;{4+e9Y@<0L& z^;EwAxlag7bvxBI-jZ>~U@_ecw*Kk7gKKG>2l0Sq{}>$E%yGPW`KH1IQ&6fKeU8Mu zV#+C_#TV;`zO$WxPHYIAIoowf2MO>a_;pc(Ur$KHM2zkOE9O*V4`E0MbN(|L0@Djw zKbQ=@Y_*I0NS`C>SKZ?Bv?|&Hx_=|U1wVi39H43x28(0eySmWzK4S9fBB!zyRnilJ zK!h0}!i%?)5b1324#bpAfWcsE(t$#H+qFl$&0-dXM$lpPgL+DH$9)#)57fhW_xg>h zN6Sn!kEQ*twYazqX(vFRE&&a+bSD@B)r2!Z^%jG2{WAPb!0=?tb1+6>C1I5hO*kc> zobrlr1)-UO8b7HNgCSuO7-dPfweW2_$lP^MJ_dspyK-O!v?O7${jhcZE=YvX?e68p z#7q$qAjffRR?V@zQwiDyQz;QtfKQLlIR5pV`SMeJTgGry8_ny`P#LODhA#7ySiH4*(Qs=YZmp9ee^lvWUhRRzjEa< z#t^`j_HI6aP+P+Iqd?1{&@GHu+-~@sQB5sNxc3?eP{vS=TK$JtbDF}%Zk3OtRUv;P z${6SC9&-J!`VP#=bjOL-@p030IKbyzp~S!R{!>6CGGa<2%aeAkTfh7^cZ_RX>vA*Q zj}Hr!b7?vmG?-PrA{uQW=$r-Ndt;UpUL>o3PTJnULb=xO6qB;60o3j9_INi#)dzj+ zfQd7#^rU7iY~XS#?Z6-7i}MHYCq^Yx%w&Rd4K(5NDl~gN2Ogk)hw?VC0DnyB!8@JS zVPg)`7(v-I8s{|r?zn!KJ_gJ!Z1^5U^e5sc}Du zGKuiuLQ2f}a*GuD2!aqM^t1}AZuFlL4if+Ssb^E-f*f;Q{@6Fh`PNBPHc&^^k&U_Z z>kK;AMuh^(&+Xu4vorA+%M>3V82hdN9!Z6-i&2Ou1x;cF%>TQ3e4Qw)8e7gVSAI

    0&Ui>991kwaN#WdXyjhZ&mh?gR4=!+Dnv0hPZLd~5eWCtBM} z?~ogCqaOOCLSEVv{%kBHci{I~x$Bl%cpC`N4nJ$qWCQ0Fl{@-Nitm<8KK^EyZ}nRD zFn*c~@)RGl^#Yqu<2oL|bgSA^1wwP*p4VS_>U?TG$=U#HcKYWqLUd06k*38cKh(d`5luLXM7q}yCm0W-``Y|dBhVrX zgIRB8k%kw(*xKbIhzsiBwKA3P@r``FbFe`=Ipynmzk6)>sOy=pEs5MvuK`vZP2xC! zKLV^c*-!(0`B1Ja!y$&_2S#~53($7d+qnj;=KHOJanT_V(pzx_2D?OIwx7{Gz?4BH zlMZ{Cy<#PSx0&>W^SwOu-{3uWe6`XtH8D2}t;_KW6Yf=qtBng9@xHd*Qrg7945Xm! z__W7CbNI8CaQ4fc9M<2_FSB}XtTNzkyK!1;-gVJ36#TD}>yKo@1ONJ%HEtomlsUw! z$9~_xKXpVS8cMqyh)LZ3LNHpKq=i4Ren75VLR)Hq2uy05%@3Zb-)NniLy*$Hnfw9y zN_I?F?v7`>o9KX;poc`=`;FKCnFVODclFV8fNX{vd~54%b_vKXK7$q)yQjRAK-2TP0srT<|9IJi8`2hrxgA^-Ix*8>~@OZme zevTH?fD0wK&W||>j4PwU3Ybrb|0!s{+{6AT?b9&2;}o@BcGTSV!bI;eanQ#?s4wIo z{|R244Ja8xlj^l@Z@iW^YfjFwm_P<+4b<8h_%j0@ks$ltT=JhitNxg1X0hn9M4?jx zo%~OiasCK#RB~o{bu!G9)YP$i)OK;(+|DW*cdslb`6jof-^dSGcJ66)e zq^l@QH07wJ)_?Wx;N3l9$0p1ZoI{~(&``xeB`6bhpmNC<0|hE}Mhko~7zX;7dh7QK z_ry%EumKf+3~iOB3|Zo$Cv0-#ViT2?LIMCNJ2}>mnz5CA$e#Iq0A6 zg-f|aV!nS6wt6wq3*6A5gEr%I!tsOAf38Wr9MnLm9L_zMn8@%C9-{=_!Lzwy8brp??fr4i(JRxTZUcZ3FwjS@Q##Q`?xlIwFPoq4ahMk zGglUNgaqG;0%L#?!l=K*C4gt|pi|S8u>TuGH9b5tEp>wQIP}cKFdBf{f2dsu)}tZ4 zryGCdTwM0^^?YOjsLKW1s#Ry*ohizT(Z%!EpR)efJZ`~j4ePd@QOJkjT^0-mBi6g$ zdz9&r!TWFV)ie2sm<^9Nbo#!q7an8<(o9gv;dhg7(FLd9gzzorc|kbfMY%1Z!vkJ) zRdh#z>O&TKa3&TRs(SA~*Q>GnDPlChZ~Tz=2?p=z4)DOWQbpl)mZdiG9@K%-<-p7N zK-eE6hdN)a65v57(6!rzIwPu_4`ZhFs1kDYxjeX$tBlVZj`~Rzu$3EW0c{p! zhun%v-m^lj?hHg~ zv5u*Nou|g3D)8BkJiuu^vAn7TvxkHpI8)u?D;}~8yhL=ksF(k4YDVu}ZjrsD62;E8 zjno4EgX7#;Ea=(uzuPv)#+T0sLLMOFLVe6Qw~iMB=kjj{o>2aG53fO^8+0T-oA^S} zlKI_T)P~PULbu#0WdxC3aswA`JTr=~Cmj2u9cueUI>qN&3g*nxmmAj!f=BGnWwL4+SrIc_PQWo@Hu_9X-rl#D5 zWHrN*YgdAI1)c^Blq2p^W@GzSrIk@UmdFKsMi~jHvkS@k*I_&VdGdrZ6clIa&(4IU zI;!^EC5UKc1i`Y_J3Lo`vxlGrzTWCLxV!w5H0pd&#BdQa=EcUYf0iRR{rv!SE6UpO zlq{k`NRioE^tBi5mk6wIg=kq(InXN&c!096Qf1e@&KgeWb+gKj|0=Q>O)&I{7NWrT zQp6VRyo2cX06-YYRfEKQWhe}l;Vhqr$QQ3DP^Wv6A^&wqXL=2+(j<+S-sy-Lx$R7I zN*B7_bd9;PL7vfz60rm?SY@bc&kK$v6JC*<$$e1lV~T8d#qQiL`}VG-K*O{5mvpqr z2H@Ppo*obfC*_Era@b?1!FBf+o}Ngs$BcW!IJH5sN&u@k8bPxYJLC+(;xXB7h9}is zC4!400^ssM#?g0Kc7ta2q8bx7Fp^dB9(X;fWPl3bk9*pnDuH zIT?@vS&1Ih5sO)$FBG`hA9Qg6B_~ZPfga?tQ#(?tlWvon4{6gM{-IZB{r3Kzfmf6v zTR;Rwz}X;e6qv;zFz1VWHz0s#(4*aFItBNBVBC@HYBi6r4E63{y)RMt$|EruX1<$q7Y-j=WpuxXVG(}+lb+S?g3*Hz? zrL@1iBCFkAz(4=}y9AUJOtOPq)-T0L{d4Nasf@t>F1VrhsP}p7tQJK4bP4p{Ul_RJ zOZxe>ZeVa|O*aF64l3w6aWD95by~1oIEfFPghr|MbaY>YudxK(BJ-uH1o+Qr&Xl)9 z1hv4xk2pO7~~Ehq(E0#Q2P(j!w&Q~Lu^ZTU;fHPM0*^i4Y|@XoWid8l>N^>NBSN=>yT|m zavufk>^GsOl`gp$Yenzasqx{im`phE@1K?mg}fNeh&sPPKj8COn>=h9YhN74t`-f} z2d_ak((4LCIP<=Hpf>8)e%7KW%B_zWF76SSd2h$Bl`(*vKEF&CC*o{T@>SUHOR7T`vuuH;`qSho8}C9_r{<#595l z7K?u0zQGV~drHq#YN>o(@UPyR;zR;1C?HNrUI-2%V!kJz%$R-ELTloW1{#7K$hQ-% z%EP#su%W0=}6@C?%t=*fJ{w?0uRh!Y}ehFJ3pS7h17kH?iOIb(yH~U zreGP`KYt#etL|qjZ%W^d!9&QgVb4Gu<{m5-`-GY#@mUb=q8}$gL`{R49eAQ614DhN z!^X|^BYT&(6d(5p-4@I-RPyPUpM;JAKfL{RzBw)JhkQ>Bj+^?EOAh<|lraooK_XiX zQANRSV_Ic`!vW1*#4xTNbnAT7m$nV{>+Ap^Jd-n60keNfXMZ>Q*o^0UL{}P1#ge7M z2|XnkO@LQLP_&@_{1I6Yp4>b2Su=?W;h?y%r{b`qPwwmHuCRT*7yh#qt(VrSZtUb1 zsz)?tuzh2m-R4^tctZRV54-_)HC65g9S?!W?J08OF3T5hpd7d0WVv5kwG!SGets`1 z%OrjkLds+ENMy0Md*2VP+;w(TwI-|6{!)mnUVfZ>O^1E~DtW#4xS-xaEQZ%gTimCA zmbD2(LluutQRlFh(Z&4fM)HB7V+ms>i$MU4$&R$%;hDNVI|4T)Y=tz}mVej&4-A0n z99^6otXg*`oNQez_yn`Jb5vJ2+kl~ogb*b@{h1yz?R9w56Q{4j3aJ<&g9g(y_-epM z-gEdZUAv(UDjj?y9|Aws3Zl_Wo_^Wtdx;57WG5wZY}O0ulk&NR+OY1Ma{1ta`terP z_Nl0Mi$+hNGwDt`R_XSp;C^%YwGr&=HRO+=a1%>7zY!I4)V93-YN&7 zCI3BnI(PO}a_0~?yut)js%ulsKmGY&xx5`O@V+l5K^J~01tuwXWiJQ6Qr-hD!VW8` zV*22s%*|ZB74are(x0A(qv(??@U=d*RrPyb-#D{h-v;9TRq`Hq=eD2v&TTSzKO-7w znx|zGp!Ee$X(p%AvGHQ^X@!d)JNsGgCZTcT=l!{g*k`+8pvPc1@A;m-9bntS ze)rmL`DQEckY3Dl$x3aIc#FtP5Jc~J3)-v3j>wWFAAu8?G-soe& z@YCyj^-5q`%&@Sh*3n{KTQ5$TTsV>9bPcVc_U4=D>=jFP{kr(j-Dy%0`xb6FiH(uA zQGZo^_QDI(La+-%1>;mL@+N@lYn9^|W|dsP$Ma_7P|cw`W|r3cl<6@SEbJVV@c{(I5d4&wsw} ze2K@}_gjwZ@NQJY(b|0QFtvMTA<8$tvFZkq`kpV2H*XMwqMeJrjt;zKv?vQ{v<~SI zGWZhYOADUl$FBKvkPX za(v6s82|M=U%67Wh!x+(IM(twDVrCk$vj_4!Uam=ac8YWuh{Ulg+vu!iNEqbQ%xm6 zGTw#d>ShBGas5xM2d>nMisTuK67k=@k+KMvX3H)N6^gJNNGO}t$nSpb86L97#;>yH z-TL}OxX1P;z6@mrcLh$n3u7HsU~hkE3&sof=}VY!z_PrmA*bv(_d@!~HoFFXN$RpanDOM` z=2O3>Ds0rRGeFr7)|yTnhBNIl9?mApMMetCxTE2B%3ZQSQBvcSUuKFR*r%LDxn%JYnBX?I|2_Yi_UKUA6kAlZ)bPYo$>$u{9gQTle& z8pR|hoI{r3WVX{e+)cCrF9OIF!@OK5wpZICjQxMAy6$+Y z|L>n0AEJze7TvNUD?}N&QufT=3JGOoZ?}{RB{N&uTV}Q>dqhSyC3}^X{X6e_)wkd0 zj~<^#_uXrp*E#2P&hv~{DI1KHEMNvmgc(ZQIVRD`spvJNhiu`S_-R0g@@>FqAz^ z>fZ(eV-T7}lWDnmr+a9QmqVuNQ#eb;NAchQ1*#dYujf533^I6*_(tBCdQ`JI+kEd& z3)*u${&k{WL5O~sb7Jc#$BN3q_b8ljb{qIW}RRcX;8i6RGH0Apkk zKCEMJET^W%kza*uTSfG-$EEuZRxb7e1HFbxFX{-uJ+E?zaEu^`>rD404x}D}@~doh zbdK!L{6KVIB77vHzXl(*5k0i2Oso~I@;4V?+3SIB+JZC~FEemWB!m$O~eiCK& zIH#AeOoNwuYWti9V);S&S@rf!?yvU^>DSWhJH*k*i5^X#1IQ|YnxrO2TFAwo?j4|q za0tXG*0h_O>_Q|=XEDyw*Fr_XO|geF$Vl`$J#-@zzc_4lpsVXzcY%x`+6Oa;^6`Ct zqYq;2lB|?IXI`y*@6i8ff!i+?A(u+hib@FOQ(f;R%Hs1|z#LORk;g$BT~l1QFCpZu zeu_VKvyToO7^7n_n2OOm9DP#-J1yr98)+>qTa& zxb-QZdKls@Mrdr%l)gY^@*8Ww79C`StrARIk2(w+XmDgsIHr{?YhI;dQ2T(e=mN%h zv<|+qCww^ZDM<8uOzjnbQ7D%bpuj_RB(2gxq3S1az z!j;D%ij!LPF!KwFz+Y50vhlka#m#GjqiU_kkb1`PoAFDw>MY!Crk4DnsX#gv0|DT6 zvqOXdUznDe)nFv3a;43+G$q|i)7zHl54EY(1wB|iaifsr{RQPJOToT3g+U$xWeUny zfC~ay3Hah=e@=-wT<2~C@&yS_K$-qi!iSPoCNoC3Q#FzHRx)h*%rkiY@zQ8rbm~!p zb)tt$vNDTKRa8Pa6Pn4WOZnC}&&ZsT zxw^kJ-6Qm{hNR31_{hZOe25Qb0S>e;DCSiezP&|5fcH)Ty9b%w7xDlm77zvhumCVK zIBnV@_mamBbqLBDPcgn+(gUZ znv^l3M+|_p>V^EA-1DEMc!B;VdVRn;MI?k{4nf05kThR!Z)((Dw(K;Dl|89JjgW*Q zE!XZmw3Ru`zpE`eL8?@mgp?nnDNs($hv!p%b&ZU_IpO-$5M-;0O&H)wJ|+6LP@-OVd-B(8xD!exTg8E|l&fY7^B#d%mAUp@nMbt40;s|F=^i5vK*xXN zlm%Iv&nkT(3y7+7gQ{*Y3Ou*_KE*m(e`$ z?sYXz`iTc*T?|$5ji;(3ILPs~@g`v?kU{vOyjmqaWa7#Q(UFfp)~5kM0f6zCW6|5O ziS1|vAVWUcxoIh;o_x=ylKSM+8?Zex#wY|sK$!`$SU%Kd%y^cHew={!EnoTwyKZjGW&WsSsl6(eUu8v9;M2y3QapxV8Oc4u*P#$=VPznG3*k z^SD;^)H<)|R+?95&VxbbtoiHSOV#s0p`RpOHGC%a@*+|1kb*5p@gmCP(kMK(6Ui>d z0O~^Iv=ApFWm&cMSVp(yV&Fw?KM!jGq(+ajAs#b!T%9E@^h3>gwp*a#YpUV0RKKiGW_tI#4=JYYh-gr2X6ttuK zcc2L(`Sx9QC7rMhAvPXbSQLlgBtdV` zp+&~M>g=CFqL#Q;u$s)m0Y}`uVESe40M26xCb(pR_PW`A)=Z_xwU>v+$&JhqZh}TcnK4NgBerclN@Kz_Xc3!7z?4%&59@dv-Hlf09v}u5;&2r? z1g5KoId^23DI8@GIdysd9FG+kSe}grR~9_JB4>(0x1$t}Ph-GPB)p=b}*^+??Eml{vE0d2)GGfKWb#9nt4Qb_y_(RX9feK`SMjE*>AU%EbO%{y_BoleuW(|TcmNo3sf zBmYy3JSrvA^Nr&Wv+K;#lh=+WkURmOpCRgFzLzq1>r?PIY(gMjDj26}K&SagUu<`jSWo z@qLmSGA8=SuC+Pjl7jx;)e8OZZM{5xa(b;Le~-Q->)g%1!tQF_l^Z%=m;k=w^B(TGwWY|1~@m6x12WDP$Oy4KIhPBi`b>n&$;Jmu-T+t zwKwuMuWPoC^WcV}Ad;6On$Ow&7Rn&7sMqX6JSi#pfy7>XG+KBhee8{Hsm)O-oMuH+ z9?H~cAKi^CTCcS+0qZSrenXiaHBHiWDy2D+JK64X3eK{0W;0s#P?wAQndjsi$s%%@ zpF{RW`~}qH;LZsDw%W?i%$&IV%GBWabk#gEU8ya2fjB=1N%~Pe1{ncfoAH$^*#S>Y(2Ok$Cb%oQ))rozmxEP;!dSL0{a;(79ROg~aLo>(oYhU_Uq1olZqz?TKbA4KFBY zaa!(i4PCp8_2vG;tWf~d#ZES{qZfFM^+O)%oa;w46MZ5 zAEGlQE%y)GWx@|zj6SSBH;^C|*`exNp?^O7Bu=%L(>dRiJfl`( zAFzIujR5?$hm%@_yuiTn!58iLRLE}NndEas^~ocJSJ1?0m*wf zF%6lCnCwu63Y>gZlD%$T867|9@X&mNt7Crm5XBJfqzGZ&IsBE9f0Nt+KO&D1fv{%i z)k>_Z{oU^d3eLv`&z?eF3;3+X5Y<%7%Jxd$OP@t86|bR%QR3e* zjU26t;%fWccAUt6Bf9Amk#Tusvm`i_$C-~eQ#Cw>p*SL5{@8Eh2F09439c(=6)xz{ z3tt!9WEHWOh~T4a2&%@sk8}OFhi&bYPesZNp->woIeY0YMOTN%Y<)46=1V3RQs!xu z6xte9xa59f$8EU(EzX)iAmr?xIyz5j!+wv1$)nGTl$@nQ!ds~tURPt5@6WswG|gmh zLLddu?+gT%bgBL8CC&oI;q$^lBHRvxsSv1YBwZHlU=$gOUK*VqH6M_`qN6#dwJ3yB zOyL8hyhrEeEJi(9Z^Cf4(NW%6x+i>ys^Lj>b>xtsn1i#60O?y&+=q}g!&9o6N1Q%b z%rX{S-VZ7TN;`XWL0&I4#*W_KI5fxgYc}HR3u>zm_D0C?~UxVcXssnhqFG!6i?*+V#x>txMe2z+iC719ndhAGdXGMfOgVr0LiEJnm?)54{e^*>h7ppJ>< zbV&+`a2~^5JTrNFGbK=~b9o%`dk5B9omG7(i3!|wyVvp-8$92rL8KL!6BACDxl^9* zVdgYlFi0=q+%P0-tYTWe;HQ6Y1<_Hig7}=XC`ea;o(`3ThN)0^*t@ zy&fcJrQ>WXOnemTjuy3>K@Q_uc}`Ty#28171Vgek1nR@EeO|UAF9i$!NS7!^oDLnMy(IE8}?I4_;ATB=xT6I^3*=n35oo#fNGee7C9@Z zOU>|rPxhoZ<|f`8)!HXj^Coym0sTDuF?^o-K^}afIh~mZJ=e$(M zRnR0#uU5`5W*g&PqXFX2>BTEM`)#t0kbkiQYif{&WL!MHat)$1pp3$QyrFl+kB4?3 zST5Jh*e)ej&0#bqqGaF|pllL1k_RjDXWrVZacP(zjw3)ww@};_37W)bL=Ly}fxo%% zAE6%IWv6+$OM`AvnY$lWHBxb01_7Z%uLIJL83txWc4j@n>q{O_tm|@fUf9d9Q&mjD zc)Bd<5oEZjb5@W?Dj>rV66(=v6*#`#qIuZ%Mdpv5QMdzsMA0X_p`%_c?> z`^OOyl+x1i!{?hL(Qk(OkkAdXl~}6xYN4IJ9>ixwetQyst{MPfB|p()(CXm~PVGW< zu~960QbKf^0CX&-I2519jMArioxgCNhIG>Y2M0M`X&k@;aM5zJ&^bEiv=6Z%tJ25< zU>2vCnimaA#bktVM1yRoermP(TAJvhPIokyn67A}OR z)zVG$B*DX_(I5H>Uld6R#~?>-(q#S^()<8sP(oetB!*wV1jy$sz=1X=0s<5uzv_3a3lxllRcvSH4#537f6dQ}3NkrK4aJfhHv;E6EJFo|B0Y~!@k@k12D zgBkoY(U{yZjJb1~or5Zrdf*OePJUECUDnzm^y$**%y_Es9!mT;k?+A}C?Uj|X9$j) z1K{P@ZLn(TQY2e9=zyCH)z5D;?FDo~q8KttEa~xOvrs>7+bp-5N0!tH24bK=>VvL+ zlghUTh!CM>J!;||U3z&tbv7WzdJ)GQHW?{>h^0#h2Fum%vg{+xa%lvFGjW&H!+qnT_AWrPzt%}6;$44%)uS}m^ zU$U!em}oy*3j8sx9OVTr!Ylu)V}{>xPjVm&jv~4Ww}_N>H()moyj?%ys;`V~@n>di zTSmXYPlXxPh7Jc=4{XtX>^{C6ijO0=kEIIIHT5fP_KE1@Z3yq`{DVG<;0zUJrh(Nb zKsEDIHNM9=$G@bmePSAihQfl1xQ*IXl5)i4gkT(nM>^XyI3Ht%`#{W}VH}Av!_sw{ zTAakF7~U&UNrT&EXGn7|Sxo!v{c~-2^Q;<@;}TpQH=ZWX|Fr2R_wg-Yt=<4vf7x_p z5tj&qI|*T`_fn#pPm@Czq*b3S1twoLW8^9R$yM00`oC`&$zBJXwDM5n+q}7@c)q5# zLG?Bfs__tE>oU_;$zK-~-~fo7`=;q^8=Ul@!h`(hu$!C3%|%uRkQOvJC2_n04_Zrk z4gJnWi}<%gle*(&7|PfS(+%&etMMo^_)&%4g=QK3>l;=l0Y^$})dtY;xYm}y>dG)7 z4z|Ln3q`D{MMQcs-6F%}ReF6z=c%X9ZjgNBpyZzoYV8WI{5W`a0Ebw@6}3*dpdx4Z zd8M;6@gZ_-XGu)3TTtorkCuzm%Oy#8Cu}o{M@KR1wEXka7JngQu zEW5$|x1=@lPDmE>jWMG&|9_B3q={>7%eW z*uR}3u+#4EYMM}Gxwqar+w~X{S6sVPqc zES$qx3z`#7sSmS~QvBRK?WK_8==I6Z)XnKjuFE_A@+i^@?j9RU!H6j_QErWlRgn`F zYCYw$-JKu4b9yiF7>H*}#ZyB_MNi{d`TMTMpP7Gdo-Cr4#eOWB^gosJPj^dbcKTph z&=;{rg4fGU`pd{~*1=c^1VIZ|v^lnav%RsPO>Xn}+P)CjVe9*q{(I}S@iLaYAnxdO zEQTVjyMLv<>^1Rf)HeyC4+SE{C+)U;d@p{nJ&U3T?={L~wTN6ZAFo#70==*l;=d7) zS)r7--Wa*nzgH2z#V_I>N2)ZK^6TnLRCqaHjxNTEOWNqWis5tK?Syy~$Qwh9@(L?3 z=oF25_+cUty?ko*@NkN`o7mt729D{62gcrzHJlzxt7gkEOr;ZBl!UuI1yz8*N~(KM zkAk$h3ww(93UqQFYUgOQyQ;5m)2ms5E*yMKtzLRwnc`(=_dPnf6r@=za9tgT(R>B3 z4!7#vrpQ(H!#Uv>XD?-~%wAklaqO6$(CV?h_Ne1hfIsC})2R-oYj2BfEgW+M?{ zw0Y?d_*L>wO5qXFzA}S)dDo+f+u$$%64n)xQ;i?KY|!H_+IsXd((M~=n%9JH>0b3p zM?HGjeNv%|UMO#+_w}0DDPO>_$dCBlq^G*OeyN%oWw|@%7W+9D591e6J1La zQm2Bw(?yo{DRLWHmYyeyKRs$m&&xMPp{i z$WWyu*jT4WPRxZ!0j$Q5BC9lg=y2wus?r4jLf)=CA2q>KB9?hcP^MVAv~VZoVsWb8 z;fvRdH~{*7muhL6sa!8{mQ)T6vL4Vt&P%I|Mvjp?e#8k20tsa-| zX@0D_2w*yV?|i|ir8TPNp*EYYR%0=bn;WZ1x^0a_a-I0aHqx#rGShk1n5?)O+?CTw z!^7g5=8~k7HET-RJDpY|TX~Oj=#|ap8e-X=Ao14(Qz_7X!?}12>Ck)tI@S2QXysiH z6xIGLt1jD-a`xvV%mjQJafXWMjsf~EM`#N+A}1JyAD!BH)8lcCY?@gTyY<&VgNxDr z)nDObGS}Y{$}S^nHLW$ zZfhp;MygtO3$sI?WOuI!?y?!Ls*t1J0Yl%uWkSsjrU+k9<)rB%u9+}8myP4NU6zh$ z#&0GjD-uIstxEaGtKaEJYQW|?u~#>T^P~dBv8}hnGrD*))HsLZKAdDbg#8Fhd`4}D z;ljIRC36AEIQck-9bk5=kUvISAlqH(W(2zB*;2K>H~1j5Suv?nt{@Z19R^J}LSb7- zGhfYd_uu-^ubwVH_9c*4n4MHk6)+CC9?RI2DfWspE9dY0RKrPYu1xi|Mo%^%OcBGg zY7qtGkm-3`8#G!T)dfmt$jO>XyLFGN#-D{zwm`LS$|e+I zgchxPTnVR`w?=mEzUD&CZIW=ebN5nWn0Ci5Nw>vC*PAVnt@aW4?I~4o)&nkOiYn%e zy%v}70tEbg&tj9|um5Ijs`r(Fb5`=+LBqC*e!{d~HiDW*s^8y!?IL`5XQfeAP5$m_Ypn7fO+yB$rwNMTjI7#U&Xk>{m)9l)Ap>w0^k-K$e zwn!}n>^1Kqae6}@Mzs98#+kMU&$f9l(Vt-aB`S?W7M_LpnF%zw24#;aEmPv@wxV=q+gDuKJIOv(l-yQ?n5+! z6`?ch}HSdal($D z2RH5j>FbfEe$$}r&m#g{U+6}Dnfb%2xnb4tXw<+f!nVF8@*@;`xA$#~(E#s@PDKYL z#5M2MJZiT8?di3g>~s=Jc_!gx;3eI!I_SFw=lhhf2ngoPm8+50Y-0bo$)h9qKTqVk zC;}CKiU7-rvg?K5UQd!YvbR{$!A{Q_BiC$YQ(nWb6VnQq0f=hh;jMKk^j)stIpS^e z*NO;BXWgr+GHT^j_Y)jLKu|OJ4Q>jC3|0r>2=>EedSTDAy z`g@Qzdtb9joL=%wl&3EeRHG~gE5S--%}x#=iLe!#;^)ao5Cwb&vEY`Z88!~cY^eH# zfG5&7K@TKyxtW1Y|67u#Ux_|8ZvI*t(#-m+WcBM)-Fe~ZM&W>w{f8ib@etznwUV(@ zTmuJ6Pe$ra4i7uhxS)vDNU*^oO}_nRr7u<(c&8J1IFY%%1OcMg8>iCufoP-aSh0q? z=ED3TesZS%^gl^yk<&Q@m;cd^rn8612*+MBdob0~+=LeQzc27`^d;qq%3T`UkaOr- zc==mS93EE-yaZQY56d;tMzStXhg|t~ieRy)ka&({m~ez&l)J?5oOVaP!cI*y4{1v? z6$GXa0E)q90s0Wq-dZ@Uew_^r{+#NWwH)Rn5ptIjob#&=+k=4p@%-u-l_@gvBB|T& z%ppAHFEc7Q&wAKGKmEZ--o^Q9L|Wh`_wfxGy&^z_-PA8@B$qtXsv|pQ{{n!6DxzI> zb5$ef+r3GfoeQUSCXlpz8+C{ug8S;W0>R3Ryv@2P?r{W#H1db8C&gZz<`2UVe9?ID zaWk@$7GQN=K2lN{wfM@hBYCw5Sj6f|9)SMEpspC(P5qt=D&sm12)^lihnVVlT%S9fN2`mMsSfx^i)-?wMoYLy`;3?J9Fyd8G zJw{{Nj3nlC+QoG$?^x)cfS1m-{DF9vcP13S@?Au?1vA`LbqmAakmH@D3~EmYa5H@p3P(? zhR1eFJEN9IIKYpG1_3?8@ej2#-@IXVpS3$za}R)s7l0L?K6U)Zi)-F5@6Z#`%27cS zogjm_AN$5MJXNRkBwT#3Q!}`%2ajwVdtG*m`v^r&P>0`gA6g&ssx97=v2q&JV!AbuK|0ry&Q=yP5=*F|c^?b!$hN=yTt1Dm|KseU`4yyckzL`Q#?T1neze2Ju%!H|MC5b%;LsB#~?tm88BVvM+vCqb7cw3_^Gu@>)< zz5*q-g%$${+)=ar<1BNTn`b1V8jdGa)3D!ttqzncxTw}JHwfKIo1woO8K zYvtov*I(7Ra_FV_#2f5iZ-BD-!AgKb1R6okf>uq}q1wm1T#6{aT({m9nkt#dqBpso z_;m>vNJ}YrFsX*BAjxlmd~mdF|IOSN7C-J$+KEM46OjC7UGmL}<<*-t+`?fMiHs1} zsgF_pek?U>FIs-K{Bp4gH+bgh-X)3_`iZe`ZbTLd#=Q>dzNiA}%Hv3C-#~|J=U1Xr z2ATCwG-U2@D+whlKt+PHTArWUd5)W2KV{DlOhHE%%7*8_trvphU%Z2;mvzHbhH=?z z=qH07KutVRP&(mCXWpMUa;Hoq9YJol0jvFeanjxKiqlS1w}n3UG||Srz-%Om_3Bv~ z+Cs5Ag;mZaFHtMFe(Yc^A;8N3WScFaWPEGDI(MPWEiSBT$&&Kp2RBB<{yGIBaUj)t>^Op2wzp9Cty!?4J=9KRl=E7>>79&R7pGwj4=#5N799g)NV^iv zUVJe5-DwgL$a^r=fnAwinewFt!2+cYj+~9$d%(du%qq`}Dt=IV4)0RWn$BNaow{b= zy>@4UAH*~k#uK40x4YjP`Nd>TZ*nHti0~I@ZYKRn0|ECp0!4)(AZKG|q6_i!`xb>_kq+02$dZHzA6&3WQ`1Uq8h>0e zA6SRmjS!?P+RJ~cQG)~y3MMycb~8vk+9ln7x5M>lx1Na9H12Odee)oOoPjq1-Fus(NlK z9}+7>7>(@T=?J(IY~BX@4+aIm1rch-D7XQbwCTfJzon95Pm>%M{p?BL`o2&ZjILI3 zsREp;_)G+{M;CbIb_i;9)mqciImv&T{zoVWB%o0d&2gKM(plsdDod^J*na%Mfg2oS zbOE$Wu>`2KiQA2y9Bz+Qs8C#F*B5zL^ip>l#cWH+EToWCNbko^tuH`|NMMJE5r`Bm zR?1`!)mMX3f1Y97_eue`-g2Oqv*@n>B{8qs3oBiahvpt^UnePX<;5W_w-kTv__1N4 z$aj+-FVvMva7S?h{(*_}<>62b>sWTnsyY1;et(QRN>Jtw)*yEPg(PNv9|%M8QW0W+ z@inU0@B}a@gA;ZN0?*%&5=K-1j>y!GxA;lirml#${uO}>qEBkR@H0K*pjkU|WSWVPD48PXh{na1=?Gg4O>`%VN6b@0tZd)hw!oqK z4KG2hh~?Yo2XFTGHmymHFzx6Fo{G1CV4KZLlXlIFT+A0gC#QKea>TBI8yZj7Qh=RB zZC}S)QANMNBokQ$+gA!$>BVzb{%DWL4*7=L)B^}|H|#v+8#JPnT)cdJQD_N@xCbEb zB1xxqGDGNx+bB+1paW^(bTwGUjOlTj)eXt8r~3M=`t4eMmd5k&H~U~g4_s{8QBS#7 zyG|O0T};{R8x6F zsYtWcCdp)YL7|=hIYzgdt8e9~{q#{N;6&(Y1G;zg*NR?71uX+>La^S2C|9cE378d= zF_A*~l_aYUy0gE4YOnH;F=)*lC1&f7f zEw3m;yW3|Q6{*UqXn)=6ottCM*6W6nj zJ*#HHzFCvXI>~4Fqfzi7J_P~z8&cV9%syVA%Kgs*u2ilH zw^MR2;@R{HTqoluff08ThTTgNe?XS9e3t|j$(+{cS#3VU$2IPutV2Z5S_DT;d&r!e zYI&v}L()~tXW}5wDyRJjjBcmms)RS6pBwmi@+6(a17t`t8VN<)3ohfw?MK8i!?+r? z)I%Os(_#zf*H3f_8~zX!A;skx!p{5^04EU;%_f+q`cDKDFmxxbGf|_u&$Yqi8E;+Gb+LiUzsou~v1xRaH z(ye|S*Hko-=t$kpVY;i*2Z2&$p%mE)T?I=|z1H#O)KzmY)t?)A7>HBGOd z#QThnv_0|hU6~saaZ&u9#r}GVd77@I?|`~6i_K87p%S~t$h%TD)s8*3L1os0`_iRj}FAswRO5p0Mo zO%JY`wW3hN;UCbT9gcb<=6-n$k!4E53Rigdg>OTzhbWa-636bG=vHTIt^E`E$pdGj zVc}k$x^~4oW*;M>AqC&0G6;j$%s{ zzgnM{^5{Sud!RVpGg!VHdAMdHi2O`Fx!!Zk%FWu+HNVe~L>=h)re*#JWwio8AyIa- zirs}v$|v+c$JAbCPC+U3Y3OqgT>AW>%(}(=WdY79@doqDhuB(Dv`LbGu2ZJR{iPki|0s)nG{}n89TO=R1d*&x#Uwb zwx3AZ4PThY2dqY8KzEhBa{6rZ`LZ-Gol|ZQGs(T9xuu$QJ>9~3y(+3_u(%W(c#~z8&~$_5gtxuz+VvNj{flz{}>>XB?m$CYE&jM&7}^#{}wr z>uweR+2Gj48F3>WR|fbEtSj|;3>eMXnMm$QRX2SlNaKlraPj(|sMj{|{O>F4la8$^ z7i?MQq^tXhENK4YPjKe@6Y&}al;p$WhV{% zPqJTKa@&+0^%#*{FOqn~a7x)Fj`5Ea_Q`oCx}cPc2f19Of) zzGl=rdA^md+_w0Skky0KzDRpez;l;@rH|;!%u@{^>f`ss%#Smi{Oq6R${=0F)@uisNDBGeQ^G3dyJsHs^>`;MZKM_d$B&oq z11Kbo={9TY)RTiw3u&=$fZ7JZ9i(g*7_g!9ix#QJypFd!;!qjfrbNK~T>4=}QZu!6 zntU}$pJGc7(oZ!?oU?r^z95gkB8H5;3}nzTPpQ2;6uA?Wg(NbvG8rvcc+Jho{YPxS z)Rl5n$mjn4c5v|F*+s!?71}p590L4Z)Q5FfGsKq5*6Op#3;(mXA33u#6JxdiHJTF) zIh|iaLV|a9F?qiBvzOasbgJ)#%z0Xy-bRQDIR!o=|LtV`8mmCyZl;kk!Ym%^vjH{cZg zR0p(M#ow!SVqGjROzwzqS+)hbwkbQbPcdq3>_<_d1uTLdk89OwsZn~~=xjuC6ye<_ zA|dMptQqT9mwD}9_{v%0=lBhVhmFfOGb`tJEaXyEd{r-- z?~fNb!VvR!&G&<}hHwjUDP1M0Vw@)9Dh_P!cR3*xZk)unPs>1@7aJ5f+<| z{BDL6QsYnSYYF^bD4d8h_Oe4`%3R@hC&r|+`@iGkK`M?KEr9fa_bVDdQ1sQ@6{f7TKq7b+HMI_#7}`S$^^-N*W|EOiuS)6bZPUhhG*%8 z>gLZws}o4KWrQ6Xa^doJq`a&7Xyrz=yoQnVO1or*pjB(Oga`YK3?dsySV z8U=T7H*v7AcT5~J3^Yjj=Txg5fuPz|7cItYd4gW zH^wpm`dm#lkG}r$`;t@ML2e1wrsj$LVFFJP>?ZMNBxH{8im%;Jv2k|NkZTdwFFzdf z=xitIk21ie)6RCp978hSo1jF`K?)_QB(r8;$l|WsE$Y?Vaf#!Pgb`AX_TJ)kp&$%6 z$ehm~TzJJWME`um7j?FwZ|)cP5#54UFxuX%6#D<2`T4#-CMV>C0ZX4?!22+w1giH2 z?9tgxP+&|N`sHA=CzzXSFG5lwEfjO_jxnRaQ1n)hPUH>aH}bBh~SqLY7(lMT&dk$3`5O0c;6 zb)Kkr?eKeEw6eAZPs9qu#3AN8Ep(?d_{ACq30~p$kBjJ}{I>B+^A{>3Zh=UaKBMFyrGoUkk1^wSj57Q69xndD$G zcAo5?&WO#SE9WOAUsTK>6l^b?74T{t5D}bB!PQ+Ni$nrn5<2;0k!G@M%jWcp!zF9B zGAWJnBKlzR&lv2eAy^-7mG6=4bK@s=wTxXy_u`fn=d<)b@4hXbP7)TX@NxLF6R1pr z%jcWm(8h~ZhrRQLMQ*f|;OT(C}LUDK^`jU%`_ zqs%s1)lAE+b|U-@_k(n+=SZJQ9N|R}yWe<9{I{yiUS~i;^p)Bv?ArRJ6{V~Cby{XK z$;y9rQ*8`GcGl)xy3_P}Pj>To@mjH2Gy0T|fTP0JwE!4(W z7cTwyi+V))IT=SN-`$@S`U}Ek;-6u^5aVWX_xz?-PcN&3kHpq+<%a3h;uST%Bz)!` z!a(;B0C^^i`grB9E?1W^Fi} zaC1MZTb7?9DwRy>(V;t~*sAs?cM-lrpzFbPsoY(3nuG1&fKYy1p=SDLx93g4Zw>pt z{mq=hprzmpFZ-UzjAEk6URn|L9g%e`V)ronB-3Q@Kep%t$MxeCKlJvy#FrmSl1{lr zT$!Pd%}PifoRzHjXL(5VSFKi8k&^;t?D^@vPlYjzTY8MgAH^0hucDx(+95L7R&mw&{Mqw zCA<>(>cd-Hf-_07e-7{f_B~u|-$M^ahRd!=d>>-j>W!k|y;IZ_>=n7AlLozC|EOaq zjjnZ8vuusvU8}0gzcGYjBBpD~UQI}nOqZ)KJjI>d#rah#t0@j_2f ziAY#?$77a0Eu8m%D@2M={fPv%gm26QRJM;@GAv1xN&)aDxd!`)WceW#?`p;D8aJ7Y96!>0vlVM+6K@7q{3=)VaYJ!y zx(1dx6`}?k{{JE&NN|gP4xh;`Whb-N8+j^8W98b)x4B*bjQ4dE(ghZ38M||auBhL+ z8ibYyTlTNIJIefhr7t+Wr~A>=~9#Cz7f_`Gv%`hN1+ywcR2 zYxYU@es+a_vvT2xkW8L|7paIFGnMv8POL*JTk_3^!cu+SWR1Vmy1}$P#om_O(z>TS zN(S!D_iu4}-03tduZkl0yA4WdHSeX|$Gs(ViLJLI-t@6AIvBroBEdyF_bHPLuwm{G z4VxcC<{3{&Cv@5A=xpfz>Q`ve1wOPy;(Q%hXVK0D*$Rn#hpsNEQkP(xeTWn{3!5bpIy_6X8HXg2Y=S8MjOwVKvp}X)H6z&G|j#FabU4K~MqSn)PvyeYZ z7b$q(rye$w?Dz8cSvzj&sHvDI+UdtM&30JcAWyLVVUlwMUNsVq27#5jM6GIG=2wf+ zf5i;mbvpE$iXrdU!vb{$UOsis;l)mcYgzx}Mv~t@$Ip_%;SCM$<`v6MAGN{-a`va! zqiI+V_U$qZ6oBq>om^!tU1K?WQ5k}V9R4|(z(rOH^D6Xx`x28oX?l8%S-A9=ecOS7 z@YmM(*(|!mEYGS9&5*W++9TJHz#DGc@W(x>1~X^rMWSgklNR|{mhl!^cHO*!|JkGbTImBu8$2>$}7CQ{m&Yw-g*y=y`imC zs^bc6A*1HYkL0$zW8wO9;bWT-Jj3^QGp8wS1Vvhga>I3M<(TJR1uk3kjcKPZ6rO*x z^SAN>`8tJg@G!-4jsB(73rrP!RQD75I*s$yh9^7z)@|YkJ$=lEPoyhK&SYMSdZ|)J z1CCxlrh53^|E^l(UC4#UzVRyI@?PaL6Dr!ma^0PC&g-Uv|19el6_(sBQ~xZ#8a`3~o>g~0QyMwyKwSdC z8i|Sx740&nA~vmVCO%bYHSlh}QSyJMO){>B-Wt2LpdZ|QvRtOq>U-Sd3I*N~XIxD|NH?8uD zjwriy=GiXP{`hA`XU@Q(B}yMUnayHb`Fh#4>ojt#7Giw-FFcU$DE~VhCR08U!hn2c zKI0bZyfxVqZg1ID-?Gx-L&N**iaCtxBcl+vD>Wgm>xuHXroo!t@AT_y_9Xj115ti` zg(<3$z=gzGkJme-J!yRwsZ#FZ-)o19^ZyZxx$9`*u{VyB7fhcpFw_?8o}a$1E^FpL zro;8u`-atF$%*o)86GaJ2N?|C8qy8p40^|!HduTor1u>(EdJl}CD{T4mMHn5Q8`;e z>Q3>z(#`ahR{BquIR09F$Y8c4iDwva4M9@BGi2(cjm^?|O(p7kbN^>qzvYw zu9ew&Y$+)4jC8$9s^a}u@F^R@FzYoEl#)uP9S6j>nv-fc)LHgKbVc^V2K$GkOIS+H zFw=S@&#`?n_mn8J<(;PO)hPBwzJ~p9Fn&UG8LH9OH~uGCW4W>LhcQ6J8q?*qU+FWj z|06>OunQtCBzaviR0j^Af@Q9YtJNe0k-G~N|G5z^{*N4hkqDz2MO{b`*m;EIGRG@= z5$*0Y!4iq%gN z|NFNHUOY(rFRed=QXSDBAm%e<`OR&Ehi^wckJEK(24K=-*ov8Ic|!W}(}+k!O|u{yijI5dZs+ w;t_XCO8y@>1XfmaaQ%-O!u?Borlmasq0#gjfhafK1Mr{B4aMu(*Nh(iACLOL=l}o! diff --git a/docs/src/.overrides/assets/stylesheets/extra.css b/docs/src/.overrides/assets/stylesheets/extra.css deleted file mode 100644 index 0bb7f5b..0000000 --- a/docs/src/.overrides/assets/stylesheets/extra.css +++ /dev/null @@ -1,98 +0,0 @@ -:root { - --dj-primary: #00a0df; - --dj-secondary: #ff5113; - --dj-background: #808285; - --dj-black: #000000; - --dj-white: #ffffff; -} - -/* footer previous/next navigation */ -.md-footer__inner:not([hidden]) { - display: none -} - -/* footer social icons */ -html a[title="DataJoint"].md-social__link svg { - color: var(--dj-primary); -} -html a[title="Slack"].md-social__link svg { - color: var(--dj-primary); -} -html a[title="LinkedIn"].md-social__link svg { - color: var(--dj-primary); -} -html a[title="Twitter"].md-social__link svg { - color: var(--dj-primary); -} -html a[title="GitHub"].md-social__link svg { - color: var(--dj-primary); -} -html a[title="DockerHub"].md-social__link svg { - color: var(--dj-primary); -} -html a[title="PyPI"].md-social__link svg { - color: var(--dj-primary); -} -html a[title="StackOverflow"].md-social__link svg { - color: var(--dj-primary); -} -html a[title="YouTube"].md-social__link svg { - color: var(--dj-primary); -} - -[data-md-color-scheme="datajoint"] { - /* ribbon */ - /* ribbon + markdown heading expansion */ - --md-primary-fg-color: var(--dj-black); - /* ribbon text */ - --md-primary-bg-color: var(--dj-primary); - - /* navigation */ - /* navigation header + links */ - --md-typeset-a-color: var(--dj-primary); - /* navigation on hover + diagram outline */ - --md-accent-fg-color: var(--dj-secondary); - - /* main */ - /* main header + already viewed*/ - --md-default-fg-color--light: var(--dj-background); - /* primary text */ - --md-typeset-color: var(--dj-black); - /* code comments + diagram text */ - --md-code-fg-color: var(--dj-primary); - - /* footer */ - /* previous/next text */ - /* --md-footer-fg-color: var(--dj-primary); */ -} - -[data-md-color-scheme="slate"] { - /* ribbon */ - /* ribbon + markdown heading expansion */ - --md-primary-fg-color: var(--dj-primary); - /* ribbon text */ - --md-primary-bg-color: var(--dj-white); - - /* navigation */ - /* navigation header + links */ - --md-typeset-a-color: var(--dj-primary); - /* navigation on hover + diagram outline */ - --md-accent-fg-color: var(--dj-secondary); - - /* main */ - /* main header + already viewed*/ - /* --md-default-fg-color--light: var(--dj-background); */ - /* primary text */ - --md-typeset-color: var(--dj-white); - /* code comments + diagram text */ - --md-code-fg-color: var(--dj-primary); - - /* footer */ - /* previous/next text */ - /* --md-footer-fg-color: var(--dj-white); */ -} - -[data-md-color-scheme="slate"] td, -th { - color: var(--dj-black) -} \ No newline at end of file diff --git a/docs/src/.overrides/partials/nav.html b/docs/src/.overrides/partials/nav.html deleted file mode 100644 index fea49d7..0000000 --- a/docs/src/.overrides/partials/nav.html +++ /dev/null @@ -1,33 +0,0 @@ -{% set class = "md-nav md-nav--primary" %} -{% if "navigation.tabs" in features %} -{% set class = class ~ " md-nav--lifted" %} -{% endif %} -{% if "toc.integrate" in features %} -{% set class = class ~ " md-nav--integrated" %} -{% endif %} - diff --git a/docs/src/api/make_pages.py b/docs/src/api/make_pages.py deleted file mode 100644 index 9fab8ae..0000000 --- a/docs/src/api/make_pages.py +++ /dev/null @@ -1,32 +0,0 @@ -"""Generate the api pages and navigation. - -NOTE: Works best when following the Google style guide -https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html -https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings -""" - -import mkdocs_gen_files -from pathlib import Path -import os -import subprocess - -package = os.getenv("PACKAGE") - -element = package.split("_", 1)[1] -# Previous git clone feature moved to docker compose - -nav = mkdocs_gen_files.Nav() -for path in sorted(Path(package).glob("**/*.py")) + sorted( - Path(f"workflow_{element}").glob("**/*.py") -): - if path.stem == "__init__": - continue - with mkdocs_gen_files.open(f"api/{path.with_suffix('')}.md", "w") as f: - module_path = ".".join( - [p for p in path.with_suffix("").parts if p != "__init__"] - ) - print(f"::: {module_path}", file=f) - nav[path.parts] = f"{path.with_suffix('')}.md" - -with mkdocs_gen_files.open("api/navigation.md", "w") as nav_file: - nav_file.writelines(nav.build_literate_nav()) diff --git a/docs/src/changelog.md b/docs/src/changelog.md deleted file mode 120000 index 699cc9e..0000000 --- a/docs/src/changelog.md +++ /dev/null @@ -1 +0,0 @@ -../../CHANGELOG.md \ No newline at end of file diff --git a/docs/src/citation.md b/docs/src/citation.md deleted file mode 100644 index 7b28f20..0000000 --- a/docs/src/citation.md +++ /dev/null @@ -1,19 +0,0 @@ -# Citation - -If your work uses the following resources, please cite the respective manuscript and/or Research Resource Identifier (RRID): - -+ DataJoint Element DeepLabCut - Version {{ PATCH_VERSION }} - + Yatsenko D, Nguyen T, Shen S, Gunalan K, Turner CA, Guzman R, Sasaki M, Sitonic D, - Reimer J, Walker EY, Tolias AS. DataJoint Elements: Data Workflows for - Neurophysiology. bioRxiv. 2021 Jan 1. doi: https://doi.org/10.1101/2021.03.30.437358 - - + [RRID:SCR_021894](https://scicrunch.org/resolver/SCR_021894) - -+ DeepLabCut - + [Manuscripts](https://github.com/DeepLabCut/DeepLabCut#references) - -+ NWB - + [Manuscript](https://www.nwb.org/publications/) - -+ DANDI - + [Citation options](https://www.dandiarchive.org/handbook/10_using_dandi/#citing-dandi) diff --git a/docs/src/concepts.md b/docs/src/concepts.md deleted file mode 100644 index 15d055f..0000000 --- a/docs/src/concepts.md +++ /dev/null @@ -1,162 +0,0 @@ -# Concepts - -## Pose Estimation in Neurophysiology - -Studying the inner workings of the brain requires understanding the relationship between -neural activity and environmental stimuli, natural behavior, or inferred cognitive -states. Pose estimation is a computer vision method to track the position, and thereby -behavior, of the subject over the course of an experiment, which can then be paired with -neuronal recordings to answer scientific questions about the brain. - -Previous pose estimation methods required reflective markers placed on a subject, as -well as multiple expensive high-frame-rate infrared cameras to triangulate position -within a limited field. Recent advancements in machine learning have facilitated -dramatic advancements in capturing pose data with a video camera alone. In particular, -[DeepLabCut](http://deeplabcut.org/) (DLC) facilitates the use of pre-trained machine -learning models for 2-D and -3-D non-invasive markerless pose estimation. - -DeepLabCut offers the ability to continue training an exisiting object detection model -to further specialize in videos in the training data set. In other words, researchers -can take a well-known generalizable machine learning model and apply it to their -experimental setup, making it relatively easy to produce pose estimation inferences -for subsequent experimental sessions. - -While some alternative tools are either species-specific (e.g., -[DeepFly3D](https://github.com/NeLy-EPFL/DeepFly3D)) or uniquely 2D (e.g., -[DeepPoseKit](https://github.com/jgraving/DeepPoseKit)), DLC highlights a diversity of -use-cases via a [Model Zoo](http://www.mackenziemathislab.org/dlc-modelzoo). Even -compared to tools with similar functionality (e.g., -[SLEAP](https://github.com/murthylab/sleap) and -[dannce](https://github.com/spoonsso/dannce)), DLC has more users, as measured by either -GitHub forks or more citations (1600 vs. 900). DLC's trajectory toward an industry -standard is attributable to [continued -funding](http://www.mackenziemathislab.org/deeplabcutblog/2020/11/18/czidlc), [extensive -documentation](https://deeplabcut.github.io/DeepLabCut/docs/intro.html) and both -creator- and peer-support. Other comparable tools include -[mmpose](https://github.com/open-mmlab/mmpose), -[idtracker.ai]([idtracker.ai](https://idtrackerai.readthedocs.io/en/latest/)), -[TREBA](https://github.com/neuroethology/TREBA), -[B-KinD](https://github.com/neuroethology/BKinD), -[VAME](https://github.com/LINCellularNeuroscience/VAME), and -[MARS](https://github.com/neuroethology/MARS). - -## Key Partnerships - -[Mackenzie Mathis](http://www.mackenziemathislab.org/) (Swiss Federal Institute of -Technology Lausanne) is both a lead developer of DLC and a key advisor on DataJoint open -source development as a member of the [Scientific Steering -Committee](datajoint.com/docs/elements/management/governance). - -DataJoint is also partnered with a number of groups who use DLC as part of broader -workflows. In these collaborations, members of the DataJoint team have interviewed -the scientists to understand their needs in experimental setup, pipeline design, and -interfaces. - -These teams include: - -- Moser Group (Norwegian University of Science and Technology) - see [pipeline - design](https://moser-pipelines.readthedocs.io/en/latest/imaging/dlc.html) - -- Mesoscale Activity Project (Janelia Research Campus/Baylor College of Medicine/New - York University) - -- Hui-Chen Lu Lab (Indiana University) - -- Tobias Rose Lab (University of Bonn) - -- James Cotton Lab (Northwestern University) - -## Element Features - -Development of the Element began with an -[open source repository](https://github.com/MMathisLab/DataJoint_Demo_DeepLabCut) shared -by the Mathis team. We further identified common needs across our respective -partnerships to offer the following features for single-camera 2D models: - -- Manage training data and configuration parameters -- Launch model training -- Evaluate models automatically and directly compare models -- Manage model metadata -- Launch inference video analysis -- Capture pose estimation output for each session - -## Element Architecture - -Each node in the following diagram represents the analysis code in the workflow and the -corresponding tables in the database. Within the workflow, Element DeepLabCut connects -to upstream Elements including Lab, Animal, and Session. For more detailed -documentation on each table, see the API docs for the respective schemas. - -![element-deeplabcut diagram](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/diagram_dlc.svg) - -### `lab` schema ([API docs](../api/workflow_deeplabcut/pipeline/#workflow_deeplabcut.pipeline.Device)) - -| Table | Description | -| --- | --- | -| Device | Camera metadata | - -### `subject` schema ([API docs](https://datajoint.com/docs/elements/element-animal/api/element_animal/subject)) - -- Although not required, most choose to connect the `Session` table to a `Subject` table. - -| Table | Description | -| --- | --- | -| Subject | Basic information of the research subject | - -### `session` schema ([API docs](https://datajoint.com/docs/elements/element-session/api/element_session/session_with_datetime)) - -| Table | Description | -| --- | --- | -| Session | Unique experimental session identifier | - -### `train` schema ([API docs](../api/element_deeplabcut/train)) - -- Optional tables related to model training. - -| Table | Description | -| --- | --- | -| VideoSet | Set of files corresponding to a training dataset. | -| TrainingParamSet | A collection of model training parameters, represented by an index. | -| TrainingTask | A set of tasks specifying model training methods. | -| ModelTraining | A record of training iterations launched by `TrainingTask`. | - -### `model` schema ([API](../api/element_deeplabcut/model)) - -- Tables related to DeepLabCut models and pose estimation. The `model` schema can be - used without the `train` schema. - -| Table | Description | -| --- | --- | -| VideoRecording | Video(s) from one recording session, for pose estimation. | -| BodyPart | Unique body parts (a.k.a. joints) and descriptions thereof. | -| Model | A central table for storing unique models. | -| ModelEvaluation | Evaluation results for each model. | -| PoseEstimationTask | A series of pose estimation tasks to be completed. Pairings of video recordings with models to be use for pose estimation. | -| PoseEstimation | Results of pose estimation using a given model. | - -## Data Export and Publishing - -Element DeepLabCut includes an export function that saves the outputs as a Neurodata -Without Borders (NWB) file. By running a single command, the data from an experimental -session is saved to a NWB file. - -For more details on the export function, see the [Tutorials page](/tutorials). - -Once NWB files are generated they can be readily shared with collaborators and published -on [DANDI Archive](https://dandiarchive.org/). The DataJoint Elements ecosystem -includes a function to upload the NWB files to DANDI (see [Element -Interface](datajoint.com/docs/elements/element-interface/)). - -```python -dlc_session_to_nwb(pose_key, use_element_session, session_kwargs) -``` - -## Roadmap - -Further development of this Element is community driven. Upon user requests and based -on guidance from the Scientific Steering Group we will add the following features to -this Element: - -- Support for multi-animal or multi-camera models -- Tools to label training data diff --git a/docs/src/index.md b/docs/src/index.md deleted file mode 100644 index 8e493e6..0000000 --- a/docs/src/index.md +++ /dev/null @@ -1,21 +0,0 @@ -# Element DeepLabCut for Pose Estimation - -DataJoint Element for markerless pose estimation with -[DeepLabCut](https://www.deeplabcut.org/). DataJoint Elements collectively standardize -and automate data collection and analysis for neuroscience experiments. Each Element is -a modular pipeline for data storage and processing with corresponding database -tables that can be combined with other Elements to assemble a fully functional pipeline. - -![diagram](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/diagram_flowchart.svg) - -Element DeepLabCut runs DeepLabCut which uses image recognition machine learning models -to generate animal position estimates from consumer grade video equipment. The Element -is composed of two schemas for storing data and running analysis: - -- `train` - Manages model training - -- `model` - Manages models and launches pose estimation - -Visit the [Concepts page](./concepts.md) for more information on pose estimation and -Element DeepLabCut. To get started with building your data pipeline visit the -[Tutorials page](./tutorials/). From ead5b39fd050af7afa2a6cba19021bce7ac59371 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 11 Sep 2023 18:05:10 +0200 Subject: [PATCH 126/176] Delete `.github` directory from this PR --- .github/ISSUE_TEMPLATE/bug_report.md | 39 ---------------- .github/ISSUE_TEMPLATE/config.yml | 5 -- .github/ISSUE_TEMPLATE/feature_request.md | 57 ----------------------- 3 files changed, 101 deletions(-) delete mode 100644 .github/ISSUE_TEMPLATE/bug_report.md delete mode 100644 .github/ISSUE_TEMPLATE/config.yml delete mode 100644 .github/ISSUE_TEMPLATE/feature_request.md diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md deleted file mode 100644 index 31fe9fc..0000000 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ /dev/null @@ -1,39 +0,0 @@ ---- -name: Bug report -about: Create a report to help us improve -title: '' -labels: 'bug' -assignees: '' - ---- - -## Bug Report - -### Description - -A clear and concise description of what is the overall operation that is intended to be -performed that resulted in an error. - -### Reproducibility -Include: -- OS (WIN | MACOS | Linux) -- DataJoint Element Version -- MySQL Version -- MySQL Deployment Strategy (local-native | local-docker | remote) -- Minimum number of steps to reliably reproduce the issue -- Complete error stack as a result of evaluating the above steps - -### Expected Behavior -A clear and concise description of what you expected to happen. - -### Screenshots -If applicable, add screenshots to help explain your problem. - -### Additional Research and Context -Add any additional research or context that was conducted in creating this report. - -For example: -- Related GitHub issues and PR's either within this repository or in other relevant - repositories. -- Specific links to specific lines or a focus within source code. -- Relevant summary of Maintainers development meetings, milestones, projects, etc. diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml deleted file mode 100644 index b3d197d..0000000 --- a/.github/ISSUE_TEMPLATE/config.yml +++ /dev/null @@ -1,5 +0,0 @@ -blank_issues_enabled: false -contact_links: - - name: DataJoint Contribution Guideline - url: https://datajoint.com/docs/community/contribute/ - about: Please make sure to review the DataJoint Contribution Guidelines \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md deleted file mode 100644 index 1f2b784..0000000 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ /dev/null @@ -1,57 +0,0 @@ ---- -name: Feature request -about: Suggest an idea for a new feature -title: '' -labels: 'enhancement' -assignees: '' - ---- - -## Feature Request - -### Problem - -A clear and concise description how this idea has manifested and the context. Elaborate -on the need for this feature and/or what could be improved. Ex. I'm always frustrated -when [...] - -### Requirements - -A clear and concise description of the requirements to satisfy the new feature. Detail -what you expect from a successful implementation of the feature. Ex. When using this -feature, it should [...] - -### Justification - -Provide the key benefits in making this a supported feature. Ex. Adding support for this -feature would ensure [...] - -### Alternative Considerations - -Do you currently have a work-around for this? Provide any alternative solutions or -features you've considered. - -### Related Errors -Add any errors as a direct result of not exposing this feature. - -Please include steps to reproduce provided errors as follows: -- OS (WIN | MACOS | Linux) -- DataJoint Element Version -- MySQL Version -- MySQL Deployment Strategy (local-native | local-docker | remote) -- Minimum number of steps to reliably reproduce the issue -- Complete error stack as a result of evaluating the above steps - -### Screenshots -If applicable, add screenshots to help explain your feature. - -### Additional Research and Context -Add any additional research or context that was conducted in creating this feature request. - -For example: -- Related GitHub issues and PR's either within this repository or in other relevant - repositories. -- Specific links to specific lines or a focus within source code. -- Relevant summary of Maintainers development meetings, milestones, projects, etc. -- Any additional supplemental web references or links that would further justify this - feature request. From c79f3f3acc8ae640493be61bdcd7470b6c02ed7e Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 11 Sep 2023 18:20:51 +0200 Subject: [PATCH 127/176] minor change in dockerfile --- .devcontainer/Dockerfile | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 42d7316..a588fc5 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -31,8 +31,10 @@ COPY ./ /tmp/element-deeplabcut/ RUN \ # pipeline dependencies - apt-get install gcc numcodecs psutils ffmpeg graphviz && \ + apt-get install gcc psutils ffmpeg graphviz && \ + pip install numcodecs && \ pip install --no-cache-dir -e /tmp/element-deeplabcut[elements,dlc_default] && \ + #TO-DO: ADD element-deeplabcut[tests] # clean up rm -rf /tmp/element-deeplabcut/ && \ apt-get clean From 0eb7c3a372623a460f8552970e27660dfdd483bc Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 11 Sep 2023 18:59:38 +0200 Subject: [PATCH 128/176] update`.github` directory --- .github/ISSUE_TEMPLATE/bug_report.md | 39 ++++++++++++++++ .github/ISSUE_TEMPLATE/config.yml | 5 ++ .github/ISSUE_TEMPLATE/feature_request.md | 57 +++++++++++++++++++++++ .github/workflows/release.yaml | 27 +++++++++++ .github/workflows/test.yaml | 37 +++++++++++++++ 5 files changed, 165 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md create mode 100644 .github/ISSUE_TEMPLATE/config.yml create mode 100644 .github/ISSUE_TEMPLATE/feature_request.md create mode 100644 .github/workflows/release.yaml create mode 100644 .github/workflows/test.yaml diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..31fe9fc --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,39 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: 'bug' +assignees: '' + +--- + +## Bug Report + +### Description + +A clear and concise description of what is the overall operation that is intended to be +performed that resulted in an error. + +### Reproducibility +Include: +- OS (WIN | MACOS | Linux) +- DataJoint Element Version +- MySQL Version +- MySQL Deployment Strategy (local-native | local-docker | remote) +- Minimum number of steps to reliably reproduce the issue +- Complete error stack as a result of evaluating the above steps + +### Expected Behavior +A clear and concise description of what you expected to happen. + +### Screenshots +If applicable, add screenshots to help explain your problem. + +### Additional Research and Context +Add any additional research or context that was conducted in creating this report. + +For example: +- Related GitHub issues and PR's either within this repository or in other relevant + repositories. +- Specific links to specific lines or a focus within source code. +- Relevant summary of Maintainers development meetings, milestones, projects, etc. diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000..b3d197d --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,5 @@ +blank_issues_enabled: false +contact_links: + - name: DataJoint Contribution Guideline + url: https://datajoint.com/docs/community/contribute/ + about: Please make sure to review the DataJoint Contribution Guidelines \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..1f2b784 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,57 @@ +--- +name: Feature request +about: Suggest an idea for a new feature +title: '' +labels: 'enhancement' +assignees: '' + +--- + +## Feature Request + +### Problem + +A clear and concise description how this idea has manifested and the context. Elaborate +on the need for this feature and/or what could be improved. Ex. I'm always frustrated +when [...] + +### Requirements + +A clear and concise description of the requirements to satisfy the new feature. Detail +what you expect from a successful implementation of the feature. Ex. When using this +feature, it should [...] + +### Justification + +Provide the key benefits in making this a supported feature. Ex. Adding support for this +feature would ensure [...] + +### Alternative Considerations + +Do you currently have a work-around for this? Provide any alternative solutions or +features you've considered. + +### Related Errors +Add any errors as a direct result of not exposing this feature. + +Please include steps to reproduce provided errors as follows: +- OS (WIN | MACOS | Linux) +- DataJoint Element Version +- MySQL Version +- MySQL Deployment Strategy (local-native | local-docker | remote) +- Minimum number of steps to reliably reproduce the issue +- Complete error stack as a result of evaluating the above steps + +### Screenshots +If applicable, add screenshots to help explain your feature. + +### Additional Research and Context +Add any additional research or context that was conducted in creating this feature request. + +For example: +- Related GitHub issues and PR's either within this repository or in other relevant + repositories. +- Specific links to specific lines or a focus within source code. +- Relevant summary of Maintainers development meetings, milestones, projects, etc. +- Any additional supplemental web references or links that would further justify this + feature request. diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml new file mode 100644 index 0000000..4a5f2cb --- /dev/null +++ b/.github/workflows/release.yaml @@ -0,0 +1,27 @@ +name: Release +on: + workflow_dispatch: +jobs: + make_github_release: + uses: datajoint/.github/.github/workflows/make_github_release.yaml@main + pypi_release: + needs: make_github_release + uses: datajoint/.github/.github/workflows/pypi_release.yaml@main + secrets: + TWINE_USERNAME: ${{secrets.TWINE_USERNAME}} + TWINE_PASSWORD: ${{secrets.TWINE_PASSWORD}} + with: + UPLOAD_URL: ${{needs.make_github_release.outputs.release_upload_url}} + mkdocs_release: + uses: datajoint/.github/.github/workflows/mkdocs_release.yaml@main + permissions: + contents: write + devcontainer-build: + uses: datajoint/.github/.github/workflows/devcontainer-build.yaml@main + devcontainer-publish: + needs: + - devcontainer-build + uses: datajoint/.github/.github/workflows/devcontainer-publish.yaml@main + secrets: + DOCKERHUB_USERNAME: ${{secrets.DOCKERHUB_USERNAME}} + DOCKERHUB_TOKEN: ${{secrets.DOCKERHUB_TOKEN_FOR_ELEMENTS}} \ No newline at end of file diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml new file mode 100644 index 0000000..8a4ec13 --- /dev/null +++ b/.github/workflows/test.yaml @@ -0,0 +1,37 @@ +name: Test +on: + push: + pull_request: + workflow_dispatch: + schedule: + - cron: "0 8 * * 1" +jobs: + devcontainer-build: + uses: datajoint/.github/.github/workflows/devcontainer-build.yaml@main + tests: + runs-on: ubuntu-latest + strategy: + matrix: + py_ver: ["3.9", "3.10"] + mysql_ver: ["8.0", "5.7"] + include: + - py_ver: "3.8" + mysql_ver: "5.7" + - py_ver: "3.7" + mysql_ver: "5.7" + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{matrix.py_ver}} + uses: actions/setup-python@v4 + with: + python-version: ${{matrix.py_ver}} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install flake8 "black[jupyter]" + - name: Run style tests + run: | + python_version=${{matrix.py_ver}} + black element_calcium_imaging --check --verbose --target-version py${python_version//.} + black notebooks --check --verbose --target-version py${python_version//.} + From 421e49dc9ff295357dd978b10b25e968523ed5e5 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 11 Sep 2023 19:01:31 +0200 Subject: [PATCH 129/176] update code_conduct, contrib and license files --- CODE_OF_CONDUCT.md | 1 - CONTRIBUTING.md | 4 +++- LICENSE | 2 +- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 684cf81..0502528 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -1,4 +1,3 @@ - # Contributor Covenant Code of Conduct ## Our Pledge diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 5836c18..e04d170 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,3 +1,5 @@ # Contribution Guidelines -This project follows the [DataJoint Contribution Guidelines](https://docs.datajoint.io/python/community/02-Contribute.html). Please reference the link for more full details. +This project follows the +[DataJoint Contribution Guidelines](https://datajoint.com/docs/community/contribute/). +Please reference the link for more full details. diff --git a/LICENSE b/LICENSE index 2f92789..386e298 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2022 DataJoint +Copyright (c) 2023 DataJoint Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal From ef0099c2f3f36c9f4644ee16a6834c799d4992b3 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 11 Sep 2023 19:01:49 +0200 Subject: [PATCH 130/176] update `.gitignore` --- .gitignore | 91 +++++++++++++++++++++++++++++++++++++----------------- 1 file changed, 63 insertions(+), 28 deletions(-) diff --git a/.gitignore b/.gitignore index 15a4dff..58c8439 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,15 @@ +# User data +.DS_Store + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class -# Distribution, packaging, PyInstaller +# C extensions +*.so + +# Distribution, packaging .Python env/ build/ @@ -21,11 +27,17 @@ wheels/ *.egg-info/ .installed.cfg *.egg +.idea/ + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec + +# Installer logs pip-log.txt pip-delete*.txt -.idea/ # Unit test / coverage reports htmlcov/ @@ -39,59 +51,82 @@ coverage.xml .hypothesis/ .pytest_cache/ -# C extension, Translations -*.so +# Translations *.mo *.pot -# editors: vscode, emacs, Mac -.vscode -**/*~ -**/#*# -**/.#* -.DS_Store - -# Django, Flask, Scrapy, Sphinx, mkdocs: -# PyBuilder, Jupyter, SageMath, celery beat +# Django stuff: *.log local_settings.py + +# Flask stuff: instance/ .webassets-cache + +# Scrapy stuff: .scrapy scratchpaper.* + +# Sphinx documentation docs/_build/ -/site + +# PyBuilder target/ + +# Jupyter Notebook .ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file celerybeat-schedule + +# SageMath parsed files *.sage.py -# dotenv, virtualenv, pyenv, mypy -./.env +# dotenv +.env + +# virtualenv .venv venv/ ENV/ -.python-version -.mypy_cache/ +./.env -# Spyder/Rope project settings +# Spyder project settings .spyderproject .spyproject + +# Rope project settings .ropeproject -# datajoint, notes, nwb export +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +# datajoint dj_local_c*.json -dj_ex*.json -dj_pose*.y*ml +dj_*.y*ml temp* temp/* -*nwb -workflow_deeplabcut/ -example_data/ # docs -/docs/site/ +/docs/site /docs/src/tutorials/*ipynb -/docs/mike-mkdocs* -*.code-workspace \ No newline at end of file +# emacs +**/*~ +**/#*# +**/.#* + +example_data + +#nwb export +*nwb + +# vscode +*.code-workspace +.vscode From 5a01aceb3f2a95b133bf068da0cc357c2191b31a Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 11 Sep 2023 19:02:07 +0200 Subject: [PATCH 131/176] update `.yaml` files --- .markdownlint.yaml | 7 ++--- .pre-commit-config.yaml | 59 +++++++++++++++++++++++++++++++++++++++++ 2 files changed, 63 insertions(+), 3 deletions(-) create mode 100644 .pre-commit-config.yaml diff --git a/.markdownlint.yaml b/.markdownlint.yaml index d4592ed..0e9ceeb 100644 --- a/.markdownlint.yaml +++ b/.markdownlint.yaml @@ -3,14 +3,15 @@ # https://github.com/DavidAnson/markdownlint/blob/main/doc/Rules.md MD009: false # permit trailing spaces MD007: false # List indenting - permit 4 spaces -MD013: +MD013: line_length: "88" # Line length limits tables: false # disable for tables headings: false # disable for headings MD030: false # Number of spaces after a list MD033: # HTML elements allowed - allowed_elements: - - "br" + allowed_elements: + - "figure" + - "figcaption" MD034: false # Permit bare URLs MD031: false # Spacing w/code blocks. Conflicts with `??? Note` and code tab styling MD046: false # Spacing w/code blocks. Conflicts with `??? Note` and code tab styling diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..e991fd6 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,59 @@ +default_stages: [commit, push] +exclude: (^.github/|^docs/|^images/|^notebooks/|^tests/) +# Current tests/__init__ violates many flake8. Excluding pending change to conftest + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-added-large-files # prevent giant files from being committed + - id: requirements-txt-fixer + - id: mixed-line-ending + args: ["--fix=lf"] + description: Forces to replace line ending by the UNIX 'lf' character. + + # black + - repo: https://github.com/psf/black + rev: 22.12.0 + hooks: + - id: black + - id: black-jupyter + args: + - --line-length=88 + + # isort + - repo: https://github.com/pycqa/isort + rev: 5.12.0 + hooks: + - id: isort + args: ["--profile", "black"] + description: Sorts imports in an alphabetical order + + # flake8 + - repo: https://github.com/pycqa/flake8 + rev: 4.0.1 + hooks: + - id: flake8 + args: # arguments to configure flake8 + # making isort line length compatible with black + - "--max-line-length=88" + - "--max-complexity=18" + - "--select=B,C,E,F,W,T4,B9" + + # these are errors that will be ignored by flake8 + # https://www.flake8rules.com/rules/{code}.html + - "--ignore=E203,E501,W503,W605,E402" + # E203 - Colons should not have any space before them. + # Needed for list indexing + # E501 - Line lengths are recommended to be no greater than 79 characters. + # Needed as we conform to 88 + # W503 - Line breaks should occur after the binary operator. + # Needed because not compatible with black + # W605 - a backslash-character pair that is not a valid escape sequence now + # generates a DeprecationWarning. This will eventually become a SyntaxError. + # Needed because we use \d as an escape sequence + # E402 - Place module level import at the top. + # Needed to prevent circular import error From 6359f0681c9ac1c4f10325e7c45ce94b0382a877 Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Mon, 11 Sep 2023 17:42:45 +0000 Subject: [PATCH 132/176] update `dockerfile` and `setup` --- .devcontainer/Dockerfile | 9 +++++---- setup.py | 4 ++-- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index a588fc5..ae8b0e4 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -31,10 +31,11 @@ COPY ./ /tmp/element-deeplabcut/ RUN \ # pipeline dependencies - apt-get install gcc psutils ffmpeg graphviz && \ - pip install numcodecs && \ - pip install --no-cache-dir -e /tmp/element-deeplabcut[elements,dlc_default] && \ - #TO-DO: ADD element-deeplabcut[tests] + pip install ffmpeg graphviz && \ + #apt-get install gcc psutils ffmpeg graphviz && \ + #pip install numcodecs && \ + pip install --no-cache-dir -e /tmp/element-deeplabcut[elements] && \ + #TO-DO: ADD element-deeplabcut[dlc_default,tests] # clean up rm -rf /tmp/element-deeplabcut/ && \ apt-get clean diff --git a/setup.py b/setup.py index f49fd9c..5c549c0 100644 --- a/setup.py +++ b/setup.py @@ -41,8 +41,8 @@ ], extras_requires={ "dlc_requirements": [dlc_requirements], - # "dlc_default": ["deeplabcut @ git+https://github.com/DeepLabCut/DeepLabCut"] - "dlc_default": ["deeplabcut[tf]>=2.2.1.1"], + "dlc_default": ["deeplabcut @ git+https://github.com/DeepLabCut/DeepLabCut"] + #"dlc_default": ["'deeplabcut[tf]'>=2.2.1.1"], "dlc_apple_mchips": [ "'deeplabcut[apple_mchips]'", "tables=3.7.0", From bd8f57c14f1910f5181bc11ca8729ebe7f219a10 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 11 Sep 2023 19:45:53 +0200 Subject: [PATCH 133/176] test `dlc_default` in `Dockerfile` --- .devcontainer/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index ae8b0e4..57b3d5b 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -34,7 +34,7 @@ RUN \ pip install ffmpeg graphviz && \ #apt-get install gcc psutils ffmpeg graphviz && \ #pip install numcodecs && \ - pip install --no-cache-dir -e /tmp/element-deeplabcut[elements] && \ + pip install --no-cache-dir -e /tmp/element-deeplabcut[elements,dlc_default] && \ #TO-DO: ADD element-deeplabcut[dlc_default,tests] # clean up rm -rf /tmp/element-deeplabcut/ && \ From 9f816fbe7e7e1b83d8e0f7deae61d0ac5ffbc9a6 Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Mon, 11 Sep 2023 18:16:55 +0000 Subject: [PATCH 134/176] delete redundancy --- setup.py | 1 - 1 file changed, 1 deletion(-) diff --git a/setup.py b/setup.py index 5c549c0..c5570d1 100644 --- a/setup.py +++ b/setup.py @@ -34,7 +34,6 @@ scripts=[], install_requires=[ "datajoint>=0.13", - "element-interface>=0.3.0", "opencv-python-headless", "ipykernel>=6.0.1", "pygit2", From f74a497315230151f6eb5f4c8ae4a2b1f1f4fe20 Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Mon, 11 Sep 2023 18:36:49 +0000 Subject: [PATCH 135/176] Test codespaces: #depend.reduced, lines commented --- .devcontainer/Dockerfile | 6 +++--- .devcontainer/devcontainer.json | 2 +- setup.py | 20 ++++++++++---------- 3 files changed, 14 insertions(+), 14 deletions(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 57b3d5b..f47800f 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -31,10 +31,10 @@ COPY ./ /tmp/element-deeplabcut/ RUN \ # pipeline dependencies - pip install ffmpeg graphviz && \ - #apt-get install gcc psutils ffmpeg graphviz && \ + #pip install gcc ffmpeg graphviz && \ + apt-get install gcc ffmpeg graphviz && \ #pip install numcodecs && \ - pip install --no-cache-dir -e /tmp/element-deeplabcut[elements,dlc_default] && \ + #pip install --no-cache-dir -e /tmp/element-deeplabcut[elements] && \ #TO-DO: ADD element-deeplabcut[dlc_default,tests] # clean up rm -rf /tmp/element-deeplabcut/ && \ diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 17f5e76..ec9b835 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -6,7 +6,7 @@ "remoteEnv": { "LOCAL_WORKSPACE_FOLDER": "${localWorkspaceFolder}" }, - "onCreateCommand": "mkdir -p ${DLC_ROOT_DATA_DIR} && pip install -e .", + "onCreateCommand": "pip install -e .", "postStartCommand": "docker volume prune -f", "hostRequirements": { "cpus": 4, diff --git a/setup.py b/setup.py index c5570d1..9c328e1 100644 --- a/setup.py +++ b/setup.py @@ -11,13 +11,13 @@ with open(path.join(here, pkg_name, "version.py")) as f: exec(f.read()) -with urllib.request.urlopen( - "https://github.com/DeepLabCut/DeepLabCut/blob/main/requirements.txt" -) as f: - dlc_requirements = f.read().decode("UTF-8").split("\n") +#with urllib.request.urlopen( +# "https://github.com/DeepLabCut/DeepLabCut/blob/main/requirements.txt" +#) as f: +# dlc_requirements = f.read().decode("UTF-8").split("\n") -dlc_requirements.remove("") -dlc_requirements.append("future") +#dlc_requirements.remove("") +#dlc_requirements.append("future") setup( name=pkg_name.replace("_", "-"), @@ -39,9 +39,9 @@ "pygit2", ], extras_requires={ - "dlc_requirements": [dlc_requirements], - "dlc_default": ["deeplabcut @ git+https://github.com/DeepLabCut/DeepLabCut"] - #"dlc_default": ["'deeplabcut[tf]'>=2.2.1.1"], + #"dlc_requirements": [dlc_requirements], + #"dlc_default": ["deeplabcut @ git+https://github.com/DeepLabCut/DeepLabCut"] + "dlc_default": ["'deeplabcut[tf]'>=2.2.1.1"], "dlc_apple_mchips": [ "'deeplabcut[apple_mchips]'", "tables=3.7.0", @@ -54,6 +54,6 @@ "element-session>=0.1.2", "element-interface>=0.5.0", ], - "tests": ["pytest", "pytest-cov", "shutils"], + #"tests": ["pytest", "pytest-cov", "shutils"], }, ) From f5eaae46bd466d35c074945bd106ef80426e9df2 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 11 Sep 2023 20:42:19 +0200 Subject: [PATCH 136/176] test minor change in dependencies in dockerfile --- .devcontainer/Dockerfile | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index f47800f..6c7dc14 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -31,8 +31,9 @@ COPY ./ /tmp/element-deeplabcut/ RUN \ # pipeline dependencies + apt-get update && \ #pip install gcc ffmpeg graphviz && \ - apt-get install gcc ffmpeg graphviz && \ + apt-get install -y gcc ffmpeg graphviz && \ #pip install numcodecs && \ #pip install --no-cache-dir -e /tmp/element-deeplabcut[elements] && \ #TO-DO: ADD element-deeplabcut[dlc_default,tests] From ae8ce7d402173a7aa591e2e3fc1b6dba8ebdea28 Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Mon, 11 Sep 2023 19:15:28 +0000 Subject: [PATCH 137/176] Install deeplabcut --- .devcontainer/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 6c7dc14..4f3cdc7 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -35,7 +35,7 @@ RUN \ #pip install gcc ffmpeg graphviz && \ apt-get install -y gcc ffmpeg graphviz && \ #pip install numcodecs && \ - #pip install --no-cache-dir -e /tmp/element-deeplabcut[elements] && \ + pip install --no-cache-dir -e /tmp/element-deeplabcut[elements,dlc_default] && \ #TO-DO: ADD element-deeplabcut[dlc_default,tests] # clean up rm -rf /tmp/element-deeplabcut/ && \ From f040971843bdabb3cecf098c972bbf76cb15e954 Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Mon, 11 Sep 2023 19:28:25 +0000 Subject: [PATCH 138/176] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 9c328e1..685bde5 100644 --- a/setup.py +++ b/setup.py @@ -38,7 +38,7 @@ "ipykernel>=6.0.1", "pygit2", ], - extras_requires={ + extras_require={ #"dlc_requirements": [dlc_requirements], #"dlc_default": ["deeplabcut @ git+https://github.com/DeepLabCut/DeepLabCut"] "dlc_default": ["'deeplabcut[tf]'>=2.2.1.1"], From 3480270c153ea015cf7bed0013403a96e0bbfbf8 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Wed, 13 Sep 2023 14:37:11 +0200 Subject: [PATCH 139/176] updated libraries in mchips installation --- setup.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/setup.py b/setup.py index 9c328e1..517120c 100644 --- a/setup.py +++ b/setup.py @@ -43,10 +43,10 @@ #"dlc_default": ["deeplabcut @ git+https://github.com/DeepLabCut/DeepLabCut"] "dlc_default": ["'deeplabcut[tf]'>=2.2.1.1"], "dlc_apple_mchips": [ - "'deeplabcut[apple_mchips]'", - "tables=3.7.0", - "tensorflow-deps>=2.9.0", - "keras >=2.12.0", + "tensorflow-macos==2.12.0", + "tensorflow-metal", + "tables==3.7.0", + "'deeplabcut[apple_mchips,gui]'", ], "elements": [ "element-lab>=0.2.0", From 84e09d9919133e58d1ed63442563e3bfd679f491 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Wed, 13 Sep 2023 21:13:00 +0200 Subject: [PATCH 140/176] Update setup.py for DLC in M2 MacOS --- setup.py | 22 ++++++---------------- 1 file changed, 6 insertions(+), 16 deletions(-) diff --git a/setup.py b/setup.py index 0c25a4c..d4c3420 100644 --- a/setup.py +++ b/setup.py @@ -1,6 +1,5 @@ from setuptools import setup, find_packages from os import path -import urllib.request pkg_name = "element_deeplabcut" here = path.abspath(path.dirname(__file__)) @@ -11,14 +10,6 @@ with open(path.join(here, pkg_name, "version.py")) as f: exec(f.read()) -#with urllib.request.urlopen( -# "https://github.com/DeepLabCut/DeepLabCut/blob/main/requirements.txt" -#) as f: -# dlc_requirements = f.read().decode("UTF-8").split("\n") - -#dlc_requirements.remove("") -#dlc_requirements.append("future") - setup( name=pkg_name.replace("_", "-"), version=__version__, @@ -34,26 +25,25 @@ scripts=[], install_requires=[ "datajoint>=0.13", - "opencv-python-headless", - "ipykernel>=6.0.1", - "pygit2", + "graphviz", + "pydot", + "networkx==2.8.2", ], extras_require={ - #"dlc_requirements": [dlc_requirements], - #"dlc_default": ["deeplabcut @ git+https://github.com/DeepLabCut/DeepLabCut"] + # "dlc_default": ["deeplabcut @ git+https://github.com/DeepLabCut/DeepLabCut"] "dlc_default": ["'deeplabcut[tf]'>=2.2.1.1"], "dlc_apple_mchips": [ "tensorflow-macos==2.12.0", "tensorflow-metal", "tables==3.7.0", - "'deeplabcut[apple_mchips,gui]'", + "'deeplabcut'", ], + "dlc_gui": ["'deeplabcut[gui]"], "elements": [ "element-lab>=0.2.0", "element-animal>=0.1.5", "element-session>=0.1.2", "element-interface>=0.5.0", ], - #"tests": ["pytest", "pytest-cov", "shutils"], }, ) From 38587c85c0ca394d5ecd260d0bc5d11bbf317fd3 Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Fri, 22 Sep 2023 21:10:20 +0200 Subject: [PATCH 141/176] Update .github/workflows/test.yaml Co-authored-by: Kabilar Gunalan --- .github/workflows/test.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 8a4ec13..ca2ca14 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -32,6 +32,6 @@ jobs: - name: Run style tests run: | python_version=${{matrix.py_ver}} - black element_calcium_imaging --check --verbose --target-version py${python_version//.} + black element_deeplabcut --check --verbose --target-version py${python_version//.} black notebooks --check --verbose --target-version py${python_version//.} From 735a1cb8c4641e4e319e4e6bc9b12b0cb3441bc1 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Wed, 27 Sep 2023 21:55:50 +0200 Subject: [PATCH 142/176] restore docs/ directory --- docs/.docker/Dockerfile | 17 ++ docs/.docker/apk_requirements.txt | 1 + docs/.docker/pip_requirements.txt | 12 ++ docs/docker-compose.yaml | 59 ++++++ docs/mkdocs.yaml | 178 ++++++++++++++++++ .../.overrides/.icons/main/company-logo.svg | 11 ++ docs/src/.overrides/404.html | 19 ++ .../assets/images/company-logo-blue.png | Bin 0 -> 41770 bytes .../.overrides/assets/stylesheets/extra.css | 98 ++++++++++ docs/src/.overrides/partials/nav.html | 33 ++++ docs/src/api/make_pages.py | 32 ++++ docs/src/changelog.md | 1 + docs/src/citation.md | 19 ++ docs/src/concepts.md | 162 ++++++++++++++++ docs/src/index.md | 21 +++ docs/src/tutorials/index.md | 114 +++++++++++ 16 files changed, 777 insertions(+) create mode 100644 docs/.docker/Dockerfile create mode 100644 docs/.docker/apk_requirements.txt create mode 100644 docs/.docker/pip_requirements.txt create mode 100644 docs/docker-compose.yaml create mode 100644 docs/mkdocs.yaml create mode 100644 docs/src/.overrides/.icons/main/company-logo.svg create mode 100644 docs/src/.overrides/404.html create mode 100644 docs/src/.overrides/assets/images/company-logo-blue.png create mode 100644 docs/src/.overrides/assets/stylesheets/extra.css create mode 100644 docs/src/.overrides/partials/nav.html create mode 100644 docs/src/api/make_pages.py create mode 120000 docs/src/changelog.md create mode 100644 docs/src/citation.md create mode 100644 docs/src/concepts.md create mode 100644 docs/src/index.md create mode 100644 docs/src/tutorials/index.md diff --git a/docs/.docker/Dockerfile b/docs/.docker/Dockerfile new file mode 100644 index 0000000..340dea5 --- /dev/null +++ b/docs/.docker/Dockerfile @@ -0,0 +1,17 @@ +FROM datajoint/miniconda3:4.10.3-py3.9-alpine +ARG PACKAGE +WORKDIR /main +COPY --chown=anaconda:anaconda ./docs/.docker/apk_requirements.txt ${APK_REQUIREMENTS} +COPY --chown=anaconda:anaconda ./docs/.docker/pip_requirements.txt ${PIP_REQUIREMENTS} +RUN \ + umask u+rwx,g+rwx,o-rwx && \ + /entrypoint.sh echo "Dependencies installed" && \ + rm ${APK_REQUIREMENTS} ${PIP_REQUIREMENTS} && \ + git config --global user.name "GitHub Action" && \ + git config --global user.email "action@github.com"&& \ + git config --global pull.rebase false && \ + git init +COPY --chown=anaconda:anaconda ./${PACKAGE} /main/${PACKAGE} +COPY --chown=anaconda:anaconda ./docs/mkdocs.yaml /main/docs/mkdocs.yaml +COPY --chown=anaconda:anaconda ./docs/src /main/docs/src +COPY --chown=anaconda:anaconda ./CHANGELOG.md /main/ \ No newline at end of file diff --git a/docs/.docker/apk_requirements.txt b/docs/.docker/apk_requirements.txt new file mode 100644 index 0000000..5664e30 --- /dev/null +++ b/docs/.docker/apk_requirements.txt @@ -0,0 +1 @@ +git diff --git a/docs/.docker/pip_requirements.txt b/docs/.docker/pip_requirements.txt new file mode 100644 index 0000000..ae44fb5 --- /dev/null +++ b/docs/.docker/pip_requirements.txt @@ -0,0 +1,12 @@ +mkdocs-material +mkdocs-redirects +mkdocstrings +mkdocstrings-python +mike +mdx-truly-sane-lists +mkdocs-gen-files +mkdocs-literate-nav +mkdocs-exclude-search +mkdocs-markdownextradata-plugin +mkdocs-jupyter +mkdocs-section-index \ No newline at end of file diff --git a/docs/docker-compose.yaml b/docs/docker-compose.yaml new file mode 100644 index 0000000..1ca7eae --- /dev/null +++ b/docs/docker-compose.yaml @@ -0,0 +1,59 @@ +# MODE="LIVE|QA|PUSH" PACKAGE=element_deeplabcut UPSTREAM_REPO=https://github.com/datajoint/element-deeplabcut.git HOST_UID=$(id -u) docker compose -f docs/docker-compose.yaml up --build +# navigate to http://localhost/ +# +# Check templates: https://github.com/dj-sciops/djsciops-cicd/tree/main/docker-template +version: "2.4" +services: + docs: + build: + dockerfile: docs/.docker/Dockerfile + context: ../ + args: + - PACKAGE + image: ${PACKAGE}-docs + environment: + - PACKAGE + - UPSTREAM_REPO + - MODE + - PATCH_VERSION + - JUPYTER_PLATFORM_DIRS=1 + volumes: + - ../docs:/main/docs + - ../${PACKAGE}:/main/${PACKAGE} + user: ${HOST_UID}:anaconda + ports: + - 80:80 + command: + - sh + - -c + - | + git config --global --add safe.directory /main + set -e + export ELEMENT_NAME=$$(echo $${PACKAGE} | sed 's/element_//g') + export PATCH_VERSION=$$(cat /main/$${PACKAGE}/version.py | grep -oE '\d+\.\d+\.[a-z0-9]+') + git clone https://github.com/datajoint/workflow-$${ELEMENT_NAME}.git /main/delete || true + if [ -d /main/delete/ ]; then + mv /main/delete/workflow_$${ELEMENT_NAME} /main/ + mv /main/delete/notebooks/*ipynb /main/docs/src/tutorials/ + rm -fR /main/delete + fi + if echo "$${MODE}" | grep -i live &>/dev/null; then + mkdocs serve --config-file ./docs/mkdocs.yaml -a 0.0.0.0:80 + elif echo "$${MODE}" | grep -iE "qa|push" &>/dev/null; then + echo "INFO::Delete gh-pages branch" + git branch -D gh-pages || true + echo "INFO::Fetch upstream gh-pages" + git fetch $${UPSTREAM_REPO} gh-pages:gh-pages && git switch gh-pages || git switch --orphan gh-pages && git commit --allow-empty -m "init commit" + echo "INFO::mike" + mike deploy --config-file ./docs/mkdocs.yaml -u $$(grep -oE '\d+\.\d+' /main/$${PACKAGE}/version.py) latest + mike set-default --config-file ./docs/mkdocs.yaml latest + if echo "$${MODE}" | grep -i qa &>/dev/null; then + mike serve --config-file ./docs/mkdocs.yaml -a 0.0.0.0:80 + elif echo "$${MODE}" | grep -i push &>/dev/null; then + echo "INFO::Push gh-pages to upstream" + git push $${UPSTREAM_REPO} gh-pages + fi + else + echo "Unexpected mode..." + exit 1 + fi diff --git a/docs/mkdocs.yaml b/docs/mkdocs.yaml new file mode 100644 index 0000000..a1533f8 --- /dev/null +++ b/docs/mkdocs.yaml @@ -0,0 +1,178 @@ +# ---------------------- PROJECT SPECIFIC --------------------------- + +site_name: DataJoint Documentation +site_url: http://localhost/docs/elements/element-deeplabcut +repo_url: https://github.com/datajoint/element-deeplabcut +repo_name: datajoint/element-deeplabcut +nav: + - Element DeepLabCut: index.md + - Concepts: concepts.md + - Tutorials: + - Overview: tutorials/index.md + - Data Download: tutorials/00-DataDownload_Optional.ipynb + - Configure: tutorials/01-Configure.ipynb + - Workflow Structure: tutorials/02-WorkflowStructure_Optional.ipynb + - Process: tutorials/03-Process.ipynb + - Automate: tutorials/04-Automate_Optional.ipynb + - Visualization: tutorials/05-Visualization_Optional.ipynb + - Drop Schemas: tutorials/06-Drop_Optional.ipynb + - Alternate Dataset: tutorials/09-AlternateDataset.ipynb + - Citation: citation.md + - API: api/ # defer to gen-files + literate-nav + - Changelog: changelog.md + +# --------------------- NOTES TO CONTRIBUTORS ----------------------- +# Markdown in mkdocs +# 01. Redering concatenates across single line breaks. This means... +# A. We have to be careful to add extra line breaks around paragraphs, +# including between the end of a pgf and the beginnign of bullets. +# B. We can use hard wrapping to make github reviews easier to read. +# VSCode Rewrap extension offers a keyboard shortcut for hard wrap +# at the ruler, but don't add breaks in [multiword links](example.com) +# 02. Instead of designating codeblocks with bash, use console. For example.. +# ```console +# cd ../my_dir +# ``` +# 03. Links across docs should ... +# A. Not involve line breaks. +# B. Use relative paths to docs in the same repo +# C. Use lowercase and hyphens not spaces: [sub headings](./doc#sub-heading) +# +# Files +# 01. Add a soft link to your changelog with the following +# ```console +# ln -s ../../CHANGELOG.md ./docs/src/changelog.md +# ``` +# +# Site rendering +# 01. Deploy locally to localhost with the command +# ```console +# MODE="LIVE" PACKAGE=element_{ELEMENT} \ +# UPSTREAM_REPO=https://github.com/datajoint/element-{ELEMENT}.git \ +# HOST_UID=$(id -u) docker compose -f docs/docker-compose.yaml up --build +# ``` +# 02. The API section will pull docstrings. +# A. Follow google styleguide e.g., +# https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html +# With typing suggestions: https://docs.python.org/3/library/typing.html +# B. To pull a specific workflow fork, change ./docs/src/api/make_pages.py#L19 +# 03. To see your fork of the workflow-{element} in this render, change the +# URL in ./docs/src/api/make_pages.py#L19 to your fork. +# 04. To deploy this site on your fork, +# A. declare a branch called gh-pages +# B. go to the your fork > settings > pages +# C. direct pages to render from the gh-pages branch at root +# D. push a tag to your fork with the format test*.*.* +# +# ---------------------------- STANDARD ----------------------------- +edit_uri: ./edit/main/docs/src +docs_dir: ./src +theme: + font: + text: Roboto Slab + code: Source Code Pro + name: material + custom_dir: src/.overrides + icon: + logo: main/company-logo + favicon: assets/images/company-logo-blue.png + features: + - toc.integrate + - content.code.annotate + palette: + - media: "(prefers-color-scheme: light)" + scheme: datajoint + toggle: + icon: material/brightness-7 + name: Switch to dark mode + - media: "(prefers-color-scheme: dark)" + scheme: slate + toggle: + icon: material/brightness-4 + name: Switch to light mode + +plugins: + - markdownextradata: {} + - search + - mkdocstrings: + default_handler: python + handlers: + python: + options: + members_order: source + group_by_category: false + line_length: 88 + - gen-files: + scripts: + - ./src/api/make_pages.py + - literate-nav: + nav_file: navigation.md + - exclude-search: + exclude: + - "*/navigation.md" + - mkdocs-jupyter: + ignore_h1_titles: True + ignore: ["*make_pages.py"] + - section-index +markdown_extensions: + - attr_list + - toc: + permalink: true + - pymdownx.emoji: + options: + custom_icons: + - .overrides/.icons + - mdx_truly_sane_lists + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + format: !!python/name:pymdownx.superfences.fence_code_format + - pymdownx.tabbed: + alternate_style: true + - pymdownx.highlight: + linenums: true + - pymdownx.inlinehilite + - pymdownx.snippets + - pymdownx.magiclink # Displays bare URLs as links + - pymdownx.tasklist: # Renders check boxes in tasks lists + custom_checkbox: true +extra: + PATCH_VERSION: !ENV PATCH_VERSION + generator: false # Disable watermark + version: + provider: mike + social: + - icon: main/company-logo + link: https://www.datajoint.com + name: DataJoint + - icon: fontawesome/brands/slack + link: https://datajoint.slack.com + name: Slack + - icon: fontawesome/brands/linkedin + link: https://www.linkedin.com/company/datajoint + name: LinkedIn + - icon: fontawesome/brands/twitter + link: https://twitter.com/datajoint + name: Twitter + - icon: fontawesome/brands/github + link: https://github.com/datajoint + name: GitHub + - icon: fontawesome/brands/docker + link: https://hub.docker.com/u/datajoint + name: DockerHub + - icon: fontawesome/brands/python + link: https://pypi.org/user/datajointbot + name: PyPI + - icon: fontawesome/brands/stack-overflow + link: https://stackoverflow.com/questions/tagged/datajoint + name: StackOverflow + - icon: fontawesome/brands/youtube + link: https://www.youtube.com/channel/UCdeCuFOTCXlVMRzh6Wk-lGg + name: YouTube + +extra_css: + - assets/stylesheets/extra.css + +extra_javascript: + - https://js-na1.hs-scripts.com/23133402.js # HubSpot chatbot diff --git a/docs/src/.overrides/.icons/main/company-logo.svg b/docs/src/.overrides/.icons/main/company-logo.svg new file mode 100644 index 0000000..e876313 --- /dev/null +++ b/docs/src/.overrides/.icons/main/company-logo.svg @@ -0,0 +1,11 @@ + + + + + + + + diff --git a/docs/src/.overrides/404.html b/docs/src/.overrides/404.html new file mode 100644 index 0000000..0c4e4a6 --- /dev/null +++ b/docs/src/.overrides/404.html @@ -0,0 +1,19 @@ +{% extends "main.html" %} + + +{% block content %} +

    🚧 Not Found 👷

    +

    + Unfortunately, we could not find what you were looking for. +
    +
    + Usually there are two possibilities for this: +
    +

      +
    • We are currently updating some of our content.
      • +
    • There is a mistake in the address or link you are using.
      • +
    +
    +Please make sure you are navigating to the correct address. +

    +{% endblock %} \ No newline at end of file diff --git a/docs/src/.overrides/assets/images/company-logo-blue.png b/docs/src/.overrides/assets/images/company-logo-blue.png new file mode 100644 index 0000000000000000000000000000000000000000..d15194b8db09a9fabae8da2cdb2f2a4d3c820a96 GIT binary patch literal 41770 zcmd43c{~(c`v=T4icxl13S-MwNMvV3wzBWCMkI-n2r-kSM1&Ga_I*oK_Mw|dmMkeL z(}KuWSxV%+&baUU>3QGZ^Z)zDb3Z=KnRBjlt>0@obJf&XZySymM?*ui%|KtroQ7r- zGYt*akbX1#3&ZT;DEL2Ge{;P9G-cg8C(*z8>D&3!&`1lT|Ir5JYM!H^!P6M%Xjuk1 zO}{-~ztbvZ{p;N1or5<{Tk77p7r};~oa$`6l7abfnelT*yHI=j*?@~Daajc!!t|1= z1~y{io%HQ_;vX(DMn`|Z<1U>IU?Z6E5dOL;8vkZuxpsG@XidYwnWF9}P0uN{iC^{i zf=MQ)0?Sy}@kAO7{lEWFr@zgg{+<=fLZG4j=Rbtq%=Os+ETPBuCen0jn;cfV^yfEW z(Wd|3E;>bDr9zq-d=)F3L%o>o4VunPb9Ct9#2pwj&p`DBN?^bbkMICt776Oz)Ax|S zkyT0PhLp>M2kI7%Z;O*y}?zB*@o!TtIN#&*Hc?uu( z96#-h*BZvU>v18U<&iNx=xe5K`!trY zK{iF~A10r|>D(>XKNlE#+_7uxtlvJ7LPT{F-H(ol(eL>oa15D`(edo{8!tJ(Duc?Sl{c0pPY}=R2 zxmmzZm}7%Ew){a`sGE(jA8&osc_KAu2j}J^kGbM?s2Z^Pi4;gxAM!Smc%w-lZvy|z z^*!r>7fw9U8==>wGLR%j2fJ`>?80)qwbNZ;?7pmHU9QJa*qE-YIh8>b|8h#$ubZ!@ zzw#Kf`kr1|T!jA!gFYJ~kaU_h>>D|Rs>AV3gztID9funxoV8-qtrmw_e{K<`MFJuB zqr(KKhkfHZzQ@kw+{sA`t?6Zsm+w-;W!e5;hl%#q2kc9^vE$zMDP~+m^;?_)FO?Z^ zlIlMvRRyUQ{`%;N+=U1=2NtuM{!u2bZG8m*jV!)Nwqs?ye{j!{h3Fr zMBOBIIP7QC#>J2pQ;m?Qih1#Y`Ow=QO{v~kT*RIc1_LU85O@BidEf7<7>%znc-^=H z{=;4W9P^{&KYN&k7UeuiXFpydSERgecor+lfb{_(;qI9Fcmn&hjw5+Dl4LnZve7ubkrm{WjLizF$+>Y;r}nUGMOM?* zj$Eepi$Ft=Ao&}m^|luqn6{s7M!=h-rRDXh3puH6} za9kn=9p9Tska7|!9{#H*)tl4$(Y+ta=^Mu#g5&nsO(8zgFYM}#p1V1CC?x2I04{=S zX?|xMRReQG$lv1Gb{|80#0GO*`B0Yg?stg0?n|2sD?aMh<9n%eT%rs9l^oELY|exn zHx212w);eRronilhWxvH*~cYu6X!Y5CwMKO3%ge$l1f)Tx&Coc^oMj49=`J6`LF5s zCbX#E)&b>a8_VGa+KB{bOxKS9xfQQD&*xUSV%5dpY9>^IMI?Z*oL$va53s_1%cJtZn$vwz3aw-piykD zIrQj2G6C}wA8c%)S8b=kI2p-6y5fY`6 zwHf#&V=&xQ-rp8_aB(XAf9+(py3Ig!`TV)7EiStEJcD*pNvB1uVv(Rm8rcj2hqIX1 zoko*Df%8{Ad{>hjdrng&?II4#GJhyhB`upo;E=f`-!R7;>Q(D2M-83^^c89%nflwP z7j4F7{RlIV;it@tacsWFL)ocErSmd4S#C^RHdVp2=^c*hoelT=N#rcE$;KOIys~b!)XSwpBUxZJ^^4OcV`mryXo6f2ajP29~ z+E>}Zwfc^qMLd5j$Pyr(G@Y(}S()JK*B!5*be*be*?|1wOx3807hgf(2zTzgbGd|9 zVk@uqONtVe>^MaBfv+2C-hwd;eUKA-x%h;yrR|%ek9pJ3Ni3hOfJ-(K=~M~mTW1W` z9C=x$eD+1MrI?rEzWrzUa8xTjEDlT;WM$qkS)tykv&Vce>wJ~vR<^&+OY%rWBbw&7 zlCZyXW07+O5njN_`v#{x9|&1dtFC;?Y{8wDZo20gb%T-v#4*y)CaM;OkjAsN9g}<{ za`r17E~0_G=pYZ$Bm{iW=QOnW0DN{FTZp(Mx5te8xqiX+4Oae(eA3vCOAl-3oiRI_ zQX5yV^FZ=!c}BEp3ZI9b1)dac)tRPUNXX|<8u`tGKSx*=67 z{{=`&f-J2z`oV!`?55R0^d@EeH>skx79pGP2gy+XuH?G%;fsh*;^$V6Z^f3*bKN@L z=3CK06drUC%fAQZCOFExzcvaUZuk4tB&v*pQX#&VZ=wo#3F7-)<;tT=GQaCvC#%k`8R%A7o zh;wo@l>ieQTuPcMFn6h5hsQUc=X|lK891kC?snEyvU)@2_*+|?Fi1|MlXR&wcio@g zlyD3twouZNPFQS*E6qDW#QkLh85lTcHY=t63t z_S8*Sj|bf99KK;W$A6^p*7(Vwz@k7?1O?3V*U#@`_ne;24+GASJOohdsp zAZ@b7FAbN*sv$pcl2UaISo2AeWzWu=yWXaTZT63DnTqObirRrV^QZd$#L2($jo&+z zBhcbhUhyzsW!VoY3MCmZhV%ElBS;3$q?aF-JI~3qi(s2}OS5}O15W$zJ*qwkA6u+B zdEo3L8>L{M{eh>cC@l*15b)1Cgcu3QgoaUHX3C|#kAIZvXdOZYkk_3r;ffLK@tzM?917@k1t2| z$I;8nr%~!>dIB7~d0g^1;!zaVxbB#dy6?BQJYp((u6~5Jz%zVkkv%NxyMC(;EqaPg zJ+wcOr*7{@OTI|0_^)>zqR>g!a8gxvE`lNCy(EtiF&_V8 z4XV=B5S+Pz^LB2ZICbhEeD|3!5R-1#M@>A+$jX6gCY`s`libKs9GmMt1!>wKgUN+} zCf>^Jh4N`Xhm2}>_1E`vmGkOtTq``TFZ(MXCgYGKVs7lBGMO&x;2VRR0uy&CRkh!L zP6*xSr;65`gf&Nd;ug>a_F~5amrRG8lDuu*mMj{AoyvF~Ba;z-3?Z+1i+(7&0Sl(I zJIdXs+u8OZBgab*&X#NONMW+!V%M22BZN*U#BR8XpG)qitZd)Pj&2=6p_2pQf>+;# z)zP_IA>Xj=J5>{P;d3U}m-(*WsUb`l#1%ToZp1%RuAuCO!*C-z|LM7Z&eC?x`nR$^ ztf1$xfQ@eZ3sWz-8#Z$6s~XWh5^|BZ+U-n_7V>x*89)ym-_H9;OAavTe3dh>*x9}+ z9NEmo^a+na6zIdv4_uG!MHG{;-H*TKrdAF&To(zmbfvVgeWc&A7x+f{4MJV{{=p0O zq#=a?DBqQ^X4Lf5Jf!jH!@iQK^*rl)Eog_UfNTxb9PCl3NMpI8G8aE>W$ezw?0WX` zRI^w2CqaZ^O!06gX4|y_^bJ99+bMD$jr(55N?WP7ub0OLY`%m?pUeU64M)C;z&D6w zJf>^uwA}bBt?PMb+NPuxF52Xn-^oJDI?$0CF2m@^GhA0xCiKrWjrgmn6RZ0N3f5j0 z9=?Qp0}f!TDN&G>6$!G6z_D(ca3oy4Xz<+$EEmV{&3UvF6C9|V;V%jYQf?BZn((tc zpFPrBrgoN(6CR)i7+Ao7H>VbFTsSdiYP$+Q++9(nRppX3pua~JQ8;cEY!vDJ5aO|m#@fZqDc8qG$|@h^(V5n~A` zyH!#bUhAv3-^OjFynj;$3Kgc(Ai6VMiKmb;J;K_b*0bI>f?JG{kCNDG9H=Dl0{weF zC`%ge7FVPz2^&f`GV?6V$aO}2c3hYTvG*2u^CRE!+t8N$q?cLM?oxp@Z{r)BU46Da z+LwT`EI&Qq(OtWEERt9<`J(6%?~~IL-HOM=d~(`W81MENATE<3i`cT|G69P?o`mT- z7}9PjeWp>-jmf$}W$0%b7WuWE;3fQA=yiZLtf2%;tH6!3S>GG%9E)@-?tE8fKz>aL z^n*fXxq=KU$vlIzC($aKrUBI(9?&RXMJql@L*ny`o}RkGGJ_}lkgl(cPN3UOv#yjE z(mK`$5SUN~PyFdh&mfXUA(%;N0^c6}xNVVImhmNjAyUqOAlhTouOWj>VAPq}BJ#j~ zZ>>C+L`h>4x19kBU7y6@pnK|y$iIzoD!FwWQaCpKu0{U%+2pBaDO_T52HM>iDQmO; z9>l>DB=L9Vee2tco|a*{m{cO49kvB_-~p0E#WdF-X3ihLM+e?K>y;U-;E^{q_<-?d zN(I`b4R+y)@JGRj5=kgt6SVstd2w;&SY-1S+^v+yXh|L{Njc+B728$9fUmVhJot-6Z#NIRUupo{Q8R_~7OQh*(a zHlU`v?0g&^r~4cWSMW$X@^}P`k^wn3;0=AMH`1YUY*x3k=&!lfFR#;zD{8~Po=N0} zd$dCVMR14bDP+>CthWQdU$aU%6Rx$RQQNiRl-2|G9k52<5!M78{TspDZVIvpsB4lw zHT0GTr~PskuT5L1vls26A3%gn$Tq#AF6zS8(CwEY>vT9h^fHd z@qm`KPgHTB>#0F76wnWL%dER6?^VlWAMTYIf>%R}F2W*QK@!rB`t5-F3?!F)Pdt2k zVSsy0pnO9AJ5WHz0l`6g5Nogr>`Ld}bCfE0*`1;DFLEC+SEu){z3YvL@C0X=#<#5* znH~nG!>E^Q@Myd2&j-R!aS`?mD;ac1FaD?*1WB{(d2Pr0OIL#9dHYI{NmD6DLoaeB z@$o>@f%qiEXqNbash!f^CK8d6RDYS2#g&TaU;}yS(VpsbP=w5p2v za!WVc`(^+1)J*PVlrFl9Al&OR_7gR)B(r0l@*jC&A9DTXvnQ5I<`>!`bEmF)P?rId zE|Wh}GeB}bHtS8^p*U&jn^&tn3(bDH6b5IZ+|Gp_>_Tu}X(MNpC;hzpsMx#7N-psZ z$L2e?O2mfIoi}fXioxyc5!Ac&uv{4yNP5nbYr*rPHkD_@thVtuJ@AE_02Koo`RnK& z^d*E0o_o^S+Rr>K&NsF2aNR7IQ9&4Jg$r`-(HM5>PEr8si$xl;LzR{eM^>{*9~`^G z!6!&0tbmbC?(nCgiHuN{PS zv?&{uf*$GLEKU?3bKmCOW14-gAn`Rl+R7cEV7%ad8)VbDoX6X?@pMV~g!jpCZZ4NN z9bSdf{spL<^|G@wQ}fRfbNq>WyHkV>ORMVZt~2qkITnWGC?jrTz(G50i1$5c0M1~J zzcId(^Ep4Rn5!-={v@Jf7%u0O@AJ23-&B?oE_nI5%VdpYXfAG0ursw$nLx<#O1< z@U<;HV9$YY;Jetg%jjE|=5f5$Pz${Eg|=fCW&L+vkMOj<{T8*8XI?7*{;E$NlU9 zwjg=GxK!R|L`8iUF%*A++TL;U_Sx4L2kRVNE1a6;*tw7ou>~LE zqQZo1HXQ(gp2)t}{&XEvizl#}-`~*!il2vSRr9sI{RjxhSCB6TrM8_7X|ZCo?+!cc zI`e>To5@KyEJFtP)A%?DMM;n{A%-n^YZW^@7KHrW zF>q_*zb;KO!+HAJkf7ivW4)53-HnR(4bZoR^FGl;w=P8BT-_-Zfpc^4eVY2g2Mq_W z*)A#KBD$iv)1IS6nxNNf$G`60P};!vQ@kk)%;M>yF4JvAL#LYQ-~{mpV2UaEhkVg` zUd;F5BLzoK>1UtK^yc-im>R5!oFyR$PsSoE6N0~JFl!9Yy_=jl{k6iQ>VsAwCeJ#Z zHB5wXUkcdsQFi`mq*Me_!J0%z=oxbVF^zMN_<8K~q7FM-kMEj(#cF-6lvdOQRC4t|IAFBRJvIF(36p(H7^pF;TNz&AUhucO;uWkd40f%~W(jJGySC1<5@!Ov)1f z%mS3JJ}j9L?Mp^UjcXG9u7jXn&;!{rwCeyrQnh19;5oi^JNGW1bgaAQG1T3uzJ(T9 zIR|K|IUyHmkEuPW;KC($V`-o8tMVQ_X11#M8|EA+-0cLc<98DO7y3JG`|*J7?ZXji z-M0gkir);3)6fX1TM4u+JmrFY?A{zIk#T!y)v(h4*OVEGiheq9 z-5XjgsJ<$GiNG-`Guay1p6aPz99PV`^PVSm7NMa(@5=-ItCtC-BUWuM6XrTXOAg(1>K^a={H)R0btX$*(t;hxX-6F)ne($KC5qm~(&{hTC2c=F@{PCJlDTDM zcH$`93{MA!#9>M;R3@2&VvBo4nJ?Wkwz%Jtam(1N2-(C>V8o}7-K9uaU$I$BZxieu z1U35~KH23xQ1DysDb|7NfeN?uvW2rLB4njTSZK`LC+Gcyzef);m=n*6{9Ft+mZGKv{yC4b+7nZRE~_nD46P)YEBV}JOG8p|K&hH5j%pGP+pnnn(qB?b7T%73!-TVfh)ra!VDaW#>XEjIL z>o|;?`qt7!5W}ni5s}{@xQds3VZy)tP%2Y@Jf zWbv)%OiFkokxIqD?c_F3s32=Jh3I15F>PP)+&D^M`E#?TNk41(h4#tYmyqYR7J?fV zQ*T%ieNXK#9|KyQtGIec*ZI3TQq;b{v-`68<6 z>;KUVKDZq+$J(O9HKpx)l)S_!5^C22duNfD$dk z1QpPIf*rPzd;Jdo;E&O&zcjrVZGSN;oOqLSVcQ~7_#E){2?;q&a1Yb%Ba!>No}=L9%)9e#*t8(d+q3pg90HkS z3nZg$@D~lHhvSOMcTrXLThFm&rrh&m3(p_dfV000C3kM!I4Vx!fjcfHye*0;RUYmi ztsW^Wj_?jYCoh0z8c5-A&v`j&qNMgkjQQ)6guIr7OyY}EMO%_Rf`qAg#wJ*^^{+NT z#ETqEsWIOBjp$7G*h8U6ttVqdccjT}$nu$~mY>dcMWyc0l2p9AbcL;}Y1gVXwT8U{ z5b!g5%u{qTbLf?^i~M?FzWn)wzvF|u9yULv&N$v>uK8~qoWJ%-u+gwj72lynzWvd% zJWMj`P7Mm&pY5Q+>M6;Oyo4HbO98z^Zn>-SDyzoYH6Ub51S4GH-~2AQn_L#QAsvEZ_e3g@n%-DJ)R_<7;Ng9>s-DE>8h)Q7{qF7ZcI4w; z6&~WC`lk{Iza$%KbCYs)Oz?MXjuEBZk%C6QderHtkbo0yWWTXdjhe0aP8L}e?L~^| zmIM}BxKYq`bfb-#IF8mMhAYnEX)Lw{vai~&id;f2DP0sumD=!_jcS(?QcabFTqKVa zYkUmW>!Zf~Kbi95o8QBnO__cN$^69?OI|j8@rbw}bJQk8#}QmY&r>@4q#L%pbjsJA z36MT0ggQR5$SqUnxqkfPaes7E9M;`B75me+Jxx~mO$95UfPNkvSoHT9Hron!@7UNi zwfr|V=W;P`v!R-UrADR%h*F38@o3-pC_=G+MTz>pxZ)cBn;1E; z14VddFdhfHI$wSp{|PL{E$;e>Ze5==q<1!8Vh$--Wu(;gP^3E0aVmajaik7MI<@TA z#jn1Nx)&$xWwT3*X=5*ou=UW=C33`hhpxNo^xPMYEK>N?)jCihChP*uNRjoYb~y^j z_1zDPUU~Bw>Fq8oo)zv_2>JM?8FBp2u=tJb_&KKJ`}v;&)R&h;dQG`?)YUt*(O%DB zFNsYS{J>N{`wSjueD<>H-5Y*35Hv`L(|%fjAtEK>(ckTd@FxOA_6e7Nsd*$uigFi7 zFKf#SpPuyMM@0*Bup)idd*Zvcf+BC_N;{IVBo4S4u9e7N`3T4K-G4Y9mGZgr;5c zH3E@OJde})+L~~#a~~#fSl{Si{thx&~m zt&SnGgh=+|)uWQ$jZlni+*0<+QK9%;&D_=nD4F~OxAe)grwiRO3{$%F)V=HLPddtY zGjt6YduZplljY*NLtZiEY(etJYn9a-|zCAOFHjzVenCCmEQ{n0DFIS ztUg=dqvw*U&fUuo8lj{04z$i*GUCcY;#o!Y`i%*UM#i*rovFd^KGj|D6hFD%mYy3t z6A{^E;)fa;aM{eKdO^sDXduC6>TpsZ8(dcsbV(L3>GBi82|{Y13c5I_Yy1QNS_5I= zXO6q(Sc7&ZEj5d2IPi!7-xkk-jRc?S9!DF|Ls#!|&fTa2@{4-95_P-S;i&J|n?cBw zdtgQ3`|{l&?QsOkZ!E}-@1J-~6TcWNNJG@u)^QG+|Ujk?|9q)EC zv?vowK9&Yi+Z>}XKOyExDV#VmE^rB^EGaO79AVO*XM-#)x;(LJ8;dBbtlN>|#^y!Q z$;+wM|V2eVBZF>O2ZgKsvSQyi6mCZH2YIY{ADDuB>(y754# z(z3POq0PD8?;Of2Y6B*Rvjo9@pc&iz`UMM`mC(1vp9nXV^AvHodFDLh{EJ+wAq0VE zqzpdxg3$w1$(1=Y-02-5x4%a(`I(Yc@By+N$ih=kTNY943f|oxOQnJLG>OTA3He;mp`-|K&J;}2yet^`PGhJCwR;+J?pQ_ic zw8@3;+fN+O5V}^wM;iwoN{x4asSuI)6kwr6!q1$i7*XH5and(K$yHIreZY_In2XdN zTQtBbJ_FUfpiQ~ow^OnYoi`!=V*R7w5CnPe>cN+d?IBTeo8SOBljL_SerSp@#f$}Y zX7mj*1{vIaRCA}20(9XL%KLB^@uo*XzHH3NFOidciXE?BwrWkgc6kdX^lV3jV80ON zCu);?>6t+$wSwsiN{`{4bpC5w4dUa79)Sk?44l8|5<|ZL=n+e6Q$m$8iw6Dt3sHf* z!*T!RE4&!1PTk&Jw<7H2tbHL0`{Q$h_7gk~GY0k}K6-c(S$U9Q#^Cvek~Ibqeg@bg z?y^xay}Jy!n+9b?1T$X){t-y1c5a_NaO#;e5`^X^HoU15($Vf)-N@-noN$&*am<`Q z44Br94@MO=T~dPpK)ch16)$o5yOqHeC2Yc^^)j9Vmye}OHwY4mqZrS&b(6$J zy(ZSsne;g6pDO29bn!m0LseuUs>yy5?VX+$a%)P3Kyv2+jez5`^E3`|1Z4m_&y;Oy zkweZA*D&kT%b2%B6sQCZ>*5{Z`)4l{qLk#57)$Grez0~J?PFzUT8-MIa?MA=rpB<~ zO!v()B!sW`)MD1pJnk?J+D+C=R^!(w6^@4}udPyMiL$P5oX5{SOt?QZvE$+!e=g92 znm8g2eG-goW}alv6+m9WA!AV4;bl7J@*I-lvLf*}tG+$BPcO461cRkva;ejO(Fxp_ zpeG{XJI!N$(it$SB=7XIDDP2|W@%j)n;i4Q<27pkIt1Tg04)%?VjuzAlNm@a6BgZ{ z-cq>{$-;yrP3`F!q8Wfxvp$q*&R-%7ywUt`4rXA$p=Z|{E3#Q_wuGVuuvN;=L8vez_r~5C2LLF zQHWgI1WsqhN5r2`p1#DA{5df7TR3TH^u22m68|5SN=g=FlNIh7b4TUnltt$?BAbsH ziwZ#vlLM?eQ#M-&HBV$oFwkez7ykYdH_f7u;H8iZE z1{y{hLU2Spg1|8+GkNIP@ko9-7TH>dMVFzir#`?70r3=IRmWySRdDu&)R`25NnQTT zZ@ODveMv=3_RE250%H*Re?ZNW@_k{F4~>qnwE7rmo#)L;8~|0w;H`dO-W0_M46_7= zJcvcVfium74Zs=c-#f(~U*ZbVYpd!RNIMCHQ%6w2&dW{`#oijh^EOX?@ zLL!N9%w4-HHOzan;%Pv~NdUAnMIv%w@^s!dgY%w9akkp_jKyZU>(<<9@;^=+g9G-3 z^BRrP!dM9zq2OvQ49caP-OeVF$UPjJ%X=vNWgwgcU}}1+2-I z53Mh>Ek3#R9y)|S&}jM7v`NTGp1~N_uyN7qyiNvTnW~^?C9eHmOA54I7_75eKso2=ON@0@Q}Cp`Qh0Q@PI+Q1hl|aPY+Pf zyi*3xx{56x&}%gl@E{CD>M$@%0ffh;pz~yFRDinBMbKn6^;M#i^@>qfh8ykrFIw ziH#w{B0zmL7M0#1b2Nl#%u;Y3WfN-E9{&0JDQ3om#pxU9SUZi=HMDV~^Cl|3!-`beL>xN7~NmB@eBDe|t^SSBF zvIcr)tQ<#>rAJQQ%EBAD`ov;zQ?8)$QBm6R=tQ0ks8N8AFC^XbaCNJ;|3^pi1 zHH0#KoE~*G)ehmOb|+15evFU5Pd4G){Kg|xYzTt*ACq0=eSc5?Ygn?hA0*%Qu`2&9 zpIlc16WwXR2fq_LsUt_$LK{&%ni*3=Z|h&lJIFk7Mll(@0;;PR$3}pkB$5qbVqM-~ zwME(~z8-rjd(Jxu>;mrK>1spj08w>_;O zw@UyQ+1>Cc^{K$oHqMd1yuU~sj68u2sbPuS3fV4TcI~jG5maVgfQomW;Rln@kp2&a z3t0XnJ12_^PgMKsE`nB3AWBAjVHes!Tu>O_j3JHC#V}c^D+w>}!~zW?K+l4d?66n&k8LbyeScK21Zdy|n`MW|8 z#l{NQRSpu&c9uIJAsSL6;B7Q;H?XYCgpa>PtQIRW`*qxYtPr&66o~n;x1J3@anW~=gb;{4+e9Y@<0L& z^;EwAxlag7bvxBI-jZ>~U@_ecw*Kk7gKKG>2l0Sq{}>$E%yGPW`KH1IQ&6fKeU8Mu zV#+C_#TV;`zO$WxPHYIAIoowf2MO>a_;pc(Ur$KHM2zkOE9O*V4`E0MbN(|L0@Djw zKbQ=@Y_*I0NS`C>SKZ?Bv?|&Hx_=|U1wVi39H43x28(0eySmWzK4S9fBB!zyRnilJ zK!h0}!i%?)5b1324#bpAfWcsE(t$#H+qFl$&0-dXM$lpPgL+DH$9)#)57fhW_xg>h zN6Sn!kEQ*twYazqX(vFRE&&a+bSD@B)r2!Z^%jG2{WAPb!0=?tb1+6>C1I5hO*kc> zobrlr1)-UO8b7HNgCSuO7-dPfweW2_$lP^MJ_dspyK-O!v?O7${jhcZE=YvX?e68p z#7q$qAjffRR?V@zQwiDyQz;QtfKQLlIR5pV`SMeJTgGry8_ny`P#LODhA#7ySiH4*(Qs=YZmp9ee^lvWUhRRzjEa< z#t^`j_HI6aP+P+Iqd?1{&@GHu+-~@sQB5sNxc3?eP{vS=TK$JtbDF}%Zk3OtRUv;P z${6SC9&-J!`VP#=bjOL-@p030IKbyzp~S!R{!>6CGGa<2%aeAkTfh7^cZ_RX>vA*Q zj}Hr!b7?vmG?-PrA{uQW=$r-Ndt;UpUL>o3PTJnULb=xO6qB;60o3j9_INi#)dzj+ zfQd7#^rU7iY~XS#?Z6-7i}MHYCq^Yx%w&Rd4K(5NDl~gN2Ogk)hw?VC0DnyB!8@JS zVPg)`7(v-I8s{|r?zn!KJ_gJ!Z1^5U^e5sc}Du zGKuiuLQ2f}a*GuD2!aqM^t1}AZuFlL4if+Ssb^E-f*f;Q{@6Fh`PNBPHc&^^k&U_Z z>kK;AMuh^(&+Xu4vorA+%M>3V82hdN9!Z6-i&2Ou1x;cF%>TQ3e4Qw)8e7gVSAI

    0&Ui>991kwaN#WdXyjhZ&mh?gR4=!+Dnv0hPZLd~5eWCtBM} z?~ogCqaOOCLSEVv{%kBHci{I~x$Bl%cpC`N4nJ$qWCQ0Fl{@-Nitm<8KK^EyZ}nRD zFn*c~@)RGl^#Yqu<2oL|bgSA^1wwP*p4VS_>U?TG$=U#HcKYWqLUd06k*38cKh(d`5luLXM7q}yCm0W-``Y|dBhVrX zgIRB8k%kw(*xKbIhzsiBwKA3P@r``FbFe`=Ipynmzk6)>sOy=pEs5MvuK`vZP2xC! zKLV^c*-!(0`B1Ja!y$&_2S#~53($7d+qnj;=KHOJanT_V(pzx_2D?OIwx7{Gz?4BH zlMZ{Cy<#PSx0&>W^SwOu-{3uWe6`XtH8D2}t;_KW6Yf=qtBng9@xHd*Qrg7945Xm! z__W7CbNI8CaQ4fc9M<2_FSB}XtTNzkyK!1;-gVJ36#TD}>yKo@1ONJ%HEtomlsUw! z$9~_xKXpVS8cMqyh)LZ3LNHpKq=i4Ren75VLR)Hq2uy05%@3Zb-)NniLy*$Hnfw9y zN_I?F?v7`>o9KX;poc`=`;FKCnFVODclFV8fNX{vd~54%b_vKXK7$q)yQjRAK-2TP0srT<|9IJi8`2hrxgA^-Ix*8>~@OZme zevTH?fD0wK&W||>j4PwU3Ybrb|0!s{+{6AT?b9&2;}o@BcGTSV!bI;eanQ#?s4wIo z{|R244Ja8xlj^l@Z@iW^YfjFwm_P<+4b<8h_%j0@ks$ltT=JhitNxg1X0hn9M4?jx zo%~OiasCK#RB~o{bu!G9)YP$i)OK;(+|DW*cdslb`6jof-^dSGcJ66)e zq^l@QH07wJ)_?Wx;N3l9$0p1ZoI{~(&``xeB`6bhpmNC<0|hE}Mhko~7zX;7dh7QK z_ry%EumKf+3~iOB3|Zo$Cv0-#ViT2?LIMCNJ2}>mnz5CA$e#Iq0A6 zg-f|aV!nS6wt6wq3*6A5gEr%I!tsOAf38Wr9MnLm9L_zMn8@%C9-{=_!Lzwy8brp??fr4i(JRxTZUcZ3FwjS@Q##Q`?xlIwFPoq4ahMk zGglUNgaqG;0%L#?!l=K*C4gt|pi|S8u>TuGH9b5tEp>wQIP}cKFdBf{f2dsu)}tZ4 zryGCdTwM0^^?YOjsLKW1s#Ry*ohizT(Z%!EpR)efJZ`~j4ePd@QOJkjT^0-mBi6g$ zdz9&r!TWFV)ie2sm<^9Nbo#!q7an8<(o9gv;dhg7(FLd9gzzorc|kbfMY%1Z!vkJ) zRdh#z>O&TKa3&TRs(SA~*Q>GnDPlChZ~Tz=2?p=z4)DOWQbpl)mZdiG9@K%-<-p7N zK-eE6hdN)a65v57(6!rzIwPu_4`ZhFs1kDYxjeX$tBlVZj`~Rzu$3EW0c{p! zhun%v-m^lj?hHg~ zv5u*Nou|g3D)8BkJiuu^vAn7TvxkHpI8)u?D;}~8yhL=ksF(k4YDVu}ZjrsD62;E8 zjno4EgX7#;Ea=(uzuPv)#+T0sLLMOFLVe6Qw~iMB=kjj{o>2aG53fO^8+0T-oA^S} zlKI_T)P~PULbu#0WdxC3aswA`JTr=~Cmj2u9cueUI>qN&3g*nxmmAj!f=BGnWwL4+SrIc_PQWo@Hu_9X-rl#D5 zWHrN*YgdAI1)c^Blq2p^W@GzSrIk@UmdFKsMi~jHvkS@k*I_&VdGdrZ6clIa&(4IU zI;!^EC5UKc1i`Y_J3Lo`vxlGrzTWCLxV!w5H0pd&#BdQa=EcUYf0iRR{rv!SE6UpO zlq{k`NRioE^tBi5mk6wIg=kq(InXN&c!096Qf1e@&KgeWb+gKj|0=Q>O)&I{7NWrT zQp6VRyo2cX06-YYRfEKQWhe}l;Vhqr$QQ3DP^Wv6A^&wqXL=2+(j<+S-sy-Lx$R7I zN*B7_bd9;PL7vfz60rm?SY@bc&kK$v6JC*<$$e1lV~T8d#qQiL`}VG-K*O{5mvpqr z2H@Ppo*obfC*_Era@b?1!FBf+o}Ngs$BcW!IJH5sN&u@k8bPxYJLC+(;xXB7h9}is zC4!400^ssM#?g0Kc7ta2q8bx7Fp^dB9(X;fWPl3bk9*pnDuH zIT?@vS&1Ih5sO)$FBG`hA9Qg6B_~ZPfga?tQ#(?tlWvon4{6gM{-IZB{r3Kzfmf6v zTR;Rwz}X;e6qv;zFz1VWHz0s#(4*aFItBNBVBC@HYBi6r4E63{y)RMt$|EruX1<$q7Y-j=WpuxXVG(}+lb+S?g3*Hz? zrL@1iBCFkAz(4=}y9AUJOtOPq)-T0L{d4Nasf@t>F1VrhsP}p7tQJK4bP4p{Ul_RJ zOZxe>ZeVa|O*aF64l3w6aWD95by~1oIEfFPghr|MbaY>YudxK(BJ-uH1o+Qr&Xl)9 z1hv4xk2pO7~~Ehq(E0#Q2P(j!w&Q~Lu^ZTU;fHPM0*^i4Y|@XoWid8l>N^>NBSN=>yT|m zavufk>^GsOl`gp$Yenzasqx{im`phE@1K?mg}fNeh&sPPKj8COn>=h9YhN74t`-f} z2d_ak((4LCIP<=Hpf>8)e%7KW%B_zWF76SSd2h$Bl`(*vKEF&CC*o{T@>SUHOR7T`vuuH;`qSho8}C9_r{<#595l z7K?u0zQGV~drHq#YN>o(@UPyR;zR;1C?HNrUI-2%V!kJz%$R-ELTloW1{#7K$hQ-% z%EP#su%W0=}6@C?%t=*fJ{w?0uRh!Y}ehFJ3pS7h17kH?iOIb(yH~U zreGP`KYt#etL|qjZ%W^d!9&QgVb4Gu<{m5-`-GY#@mUb=q8}$gL`{R49eAQ614DhN z!^X|^BYT&(6d(5p-4@I-RPyPUpM;JAKfL{RzBw)JhkQ>Bj+^?EOAh<|lraooK_XiX zQANRSV_Ic`!vW1*#4xTNbnAT7m$nV{>+Ap^Jd-n60keNfXMZ>Q*o^0UL{}P1#ge7M z2|XnkO@LQLP_&@_{1I6Yp4>b2Su=?W;h?y%r{b`qPwwmHuCRT*7yh#qt(VrSZtUb1 zsz)?tuzh2m-R4^tctZRV54-_)HC65g9S?!W?J08OF3T5hpd7d0WVv5kwG!SGets`1 z%OrjkLds+ENMy0Md*2VP+;w(TwI-|6{!)mnUVfZ>O^1E~DtW#4xS-xaEQZ%gTimCA zmbD2(LluutQRlFh(Z&4fM)HB7V+ms>i$MU4$&R$%;hDNVI|4T)Y=tz}mVej&4-A0n z99^6otXg*`oNQez_yn`Jb5vJ2+kl~ogb*b@{h1yz?R9w56Q{4j3aJ<&g9g(y_-epM z-gEdZUAv(UDjj?y9|Aws3Zl_Wo_^Wtdx;57WG5wZY}O0ulk&NR+OY1Ma{1ta`terP z_Nl0Mi$+hNGwDt`R_XSp;C^%YwGr&=HRO+=a1%>7zY!I4)V93-YN&7 zCI3BnI(PO}a_0~?yut)js%ulsKmGY&xx5`O@V+l5K^J~01tuwXWiJQ6Qr-hD!VW8` zV*22s%*|ZB74are(x0A(qv(??@U=d*RrPyb-#D{h-v;9TRq`Hq=eD2v&TTSzKO-7w znx|zGp!Ee$X(p%AvGHQ^X@!d)JNsGgCZTcT=l!{g*k`+8pvPc1@A;m-9bntS ze)rmL`DQEckY3Dl$x3aIc#FtP5Jc~J3)-v3j>wWFAAu8?G-soe& z@YCyj^-5q`%&@Sh*3n{KTQ5$TTsV>9bPcVc_U4=D>=jFP{kr(j-Dy%0`xb6FiH(uA zQGZo^_QDI(La+-%1>;mL@+N@lYn9^|W|dsP$Ma_7P|cw`W|r3cl<6@SEbJVV@c{(I5d4&wsw} ze2K@}_gjwZ@NQJY(b|0QFtvMTA<8$tvFZkq`kpV2H*XMwqMeJrjt;zKv?vQ{v<~SI zGWZhYOADUl$FBKvkPX za(v6s82|M=U%67Wh!x+(IM(twDVrCk$vj_4!Uam=ac8YWuh{Ulg+vu!iNEqbQ%xm6 zGTw#d>ShBGas5xM2d>nMisTuK67k=@k+KMvX3H)N6^gJNNGO}t$nSpb86L97#;>yH z-TL}OxX1P;z6@mrcLh$n3u7HsU~hkE3&sof=}VY!z_PrmA*bv(_d@!~HoFFXN$RpanDOM` z=2O3>Ds0rRGeFr7)|yTnhBNIl9?mApMMetCxTE2B%3ZQSQBvcSUuKFR*r%LDxn%JYnBX?I|2_Yi_UKUA6kAlZ)bPYo$>$u{9gQTle& z8pR|hoI{r3WVX{e+)cCrF9OIF!@OK5wpZICjQxMAy6$+Y z|L>n0AEJze7TvNUD?}N&QufT=3JGOoZ?}{RB{N&uTV}Q>dqhSyC3}^X{X6e_)wkd0 zj~<^#_uXrp*E#2P&hv~{DI1KHEMNvmgc(ZQIVRD`spvJNhiu`S_-R0g@@>FqAz^ z>fZ(eV-T7}lWDnmr+a9QmqVuNQ#eb;NAchQ1*#dYujf533^I6*_(tBCdQ`JI+kEd& z3)*u${&k{WL5O~sb7Jc#$BN3q_b8ljb{qIW}RRcX;8i6RGH0Apkk zKCEMJET^W%kza*uTSfG-$EEuZRxb7e1HFbxFX{-uJ+E?zaEu^`>rD404x}D}@~doh zbdK!L{6KVIB77vHzXl(*5k0i2Oso~I@;4V?+3SIB+JZC~FEemWB!m$O~eiCK& zIH#AeOoNwuYWti9V);S&S@rf!?yvU^>DSWhJH*k*i5^X#1IQ|YnxrO2TFAwo?j4|q za0tXG*0h_O>_Q|=XEDyw*Fr_XO|geF$Vl`$J#-@zzc_4lpsVXzcY%x`+6Oa;^6`Ct zqYq;2lB|?IXI`y*@6i8ff!i+?A(u+hib@FOQ(f;R%Hs1|z#LORk;g$BT~l1QFCpZu zeu_VKvyToO7^7n_n2OOm9DP#-J1yr98)+>qTa& zxb-QZdKls@Mrdr%l)gY^@*8Ww79C`StrARIk2(w+XmDgsIHr{?YhI;dQ2T(e=mN%h zv<|+qCww^ZDM<8uOzjnbQ7D%bpuj_RB(2gxq3S1az z!j;D%ij!LPF!KwFz+Y50vhlka#m#GjqiU_kkb1`PoAFDw>MY!Crk4DnsX#gv0|DT6 zvqOXdUznDe)nFv3a;43+G$q|i)7zHl54EY(1wB|iaifsr{RQPJOToT3g+U$xWeUny zfC~ay3Hah=e@=-wT<2~C@&yS_K$-qi!iSPoCNoC3Q#FzHRx)h*%rkiY@zQ8rbm~!p zb)tt$vNDTKRa8Pa6Pn4WOZnC}&&ZsT zxw^kJ-6Qm{hNR31_{hZOe25Qb0S>e;DCSiezP&|5fcH)Ty9b%w7xDlm77zvhumCVK zIBnV@_mamBbqLBDPcgn+(gUZ znv^l3M+|_p>V^EA-1DEMc!B;VdVRn;MI?k{4nf05kThR!Z)((Dw(K;Dl|89JjgW*Q zE!XZmw3Ru`zpE`eL8?@mgp?nnDNs($hv!p%b&ZU_IpO-$5M-;0O&H)wJ|+6LP@-OVd-B(8xD!exTg8E|l&fY7^B#d%mAUp@nMbt40;s|F=^i5vK*xXN zlm%Iv&nkT(3y7+7gQ{*Y3Ou*_KE*m(e`$ z?sYXz`iTc*T?|$5ji;(3ILPs~@g`v?kU{vOyjmqaWa7#Q(UFfp)~5kM0f6zCW6|5O ziS1|vAVWUcxoIh;o_x=ylKSM+8?Zex#wY|sK$!`$SU%Kd%y^cHew={!EnoTwyKZjGW&WsSsl6(eUu8v9;M2y3QapxV8Oc4u*P#$=VPznG3*k z^SD;^)H<)|R+?95&VxbbtoiHSOV#s0p`RpOHGC%a@*+|1kb*5p@gmCP(kMK(6Ui>d z0O~^Iv=ApFWm&cMSVp(yV&Fw?KM!jGq(+ajAs#b!T%9E@^h3>gwp*a#YpUV0RKKiGW_tI#4=JYYh-gr2X6ttuK zcc2L(`Sx9QC7rMhAvPXbSQLlgBtdV` zp+&~M>g=CFqL#Q;u$s)m0Y}`uVESe40M26xCb(pR_PW`A)=Z_xwU>v+$&JhqZh}TcnK4NgBerclN@Kz_Xc3!7z?4%&59@dv-Hlf09v}u5;&2r? z1g5KoId^23DI8@GIdysd9FG+kSe}grR~9_JB4>(0x1$t}Ph-GPB)p=b}*^+??Eml{vE0d2)GGfKWb#9nt4Qb_y_(RX9feK`SMjE*>AU%EbO%{y_BoleuW(|TcmNo3sf zBmYy3JSrvA^Nr&Wv+K;#lh=+WkURmOpCRgFzLzq1>r?PIY(gMjDj26}K&SagUu<`jSWo z@qLmSGA8=SuC+Pjl7jx;)e8OZZM{5xa(b;Le~-Q->)g%1!tQF_l^Z%=m;k=w^B(TGwWY|1~@m6x12WDP$Oy4KIhPBi`b>n&$;Jmu-T+t zwKwuMuWPoC^WcV}Ad;6On$Ow&7Rn&7sMqX6JSi#pfy7>XG+KBhee8{Hsm)O-oMuH+ z9?H~cAKi^CTCcS+0qZSrenXiaHBHiWDy2D+JK64X3eK{0W;0s#P?wAQndjsi$s%%@ zpF{RW`~}qH;LZsDw%W?i%$&IV%GBWabk#gEU8ya2fjB=1N%~Pe1{ncfoAH$^*#S>Y(2Ok$Cb%oQ))rozmxEP;!dSL0{a;(79ROg~aLo>(oYhU_Uq1olZqz?TKbA4KFBY zaa!(i4PCp8_2vG;tWf~d#ZES{qZfFM^+O)%oa;w46MZ5 zAEGlQE%y)GWx@|zj6SSBH;^C|*`exNp?^O7Bu=%L(>dRiJfl`( zAFzIujR5?$hm%@_yuiTn!58iLRLE}NndEas^~ocJSJ1?0m*wf zF%6lCnCwu63Y>gZlD%$T867|9@X&mNt7Crm5XBJfqzGZ&IsBE9f0Nt+KO&D1fv{%i z)k>_Z{oU^d3eLv`&z?eF3;3+X5Y<%7%Jxd$OP@t86|bR%QR3e* zjU26t;%fWccAUt6Bf9Amk#Tusvm`i_$C-~eQ#Cw>p*SL5{@8Eh2F09439c(=6)xz{ z3tt!9WEHWOh~T4a2&%@sk8}OFhi&bYPesZNp->woIeY0YMOTN%Y<)46=1V3RQs!xu z6xte9xa59f$8EU(EzX)iAmr?xIyz5j!+wv1$)nGTl$@nQ!ds~tURPt5@6WswG|gmh zLLddu?+gT%bgBL8CC&oI;q$^lBHRvxsSv1YBwZHlU=$gOUK*VqH6M_`qN6#dwJ3yB zOyL8hyhrEeEJi(9Z^Cf4(NW%6x+i>ys^Lj>b>xtsn1i#60O?y&+=q}g!&9o6N1Q%b z%rX{S-VZ7TN;`XWL0&I4#*W_KI5fxgYc}HR3u>zm_D0C?~UxVcXssnhqFG!6i?*+V#x>txMe2z+iC719ndhAGdXGMfOgVr0LiEJnm?)54{e^*>h7ppJ>< zbV&+`a2~^5JTrNFGbK=~b9o%`dk5B9omG7(i3!|wyVvp-8$92rL8KL!6BACDxl^9* zVdgYlFi0=q+%P0-tYTWe;HQ6Y1<_Hig7}=XC`ea;o(`3ThN)0^*t@ zy&fcJrQ>WXOnemTjuy3>K@Q_uc}`Ty#28171Vgek1nR@EeO|UAF9i$!NS7!^oDLnMy(IE8}?I4_;ATB=xT6I^3*=n35oo#fNGee7C9@Z zOU>|rPxhoZ<|f`8)!HXj^Coym0sTDuF?^o-K^}afIh~mZJ=e$(M zRnR0#uU5`5W*g&PqXFX2>BTEM`)#t0kbkiQYif{&WL!MHat)$1pp3$QyrFl+kB4?3 zST5Jh*e)ej&0#bqqGaF|pllL1k_RjDXWrVZacP(zjw3)ww@};_37W)bL=Ly}fxo%% zAE6%IWv6+$OM`AvnY$lWHBxb01_7Z%uLIJL83txWc4j@n>q{O_tm|@fUf9d9Q&mjD zc)Bd<5oEZjb5@W?Dj>rV66(=v6*#`#qIuZ%Mdpv5QMdzsMA0X_p`%_c?> z`^OOyl+x1i!{?hL(Qk(OkkAdXl~}6xYN4IJ9>ixwetQyst{MPfB|p()(CXm~PVGW< zu~960QbKf^0CX&-I2519jMArioxgCNhIG>Y2M0M`X&k@;aM5zJ&^bEiv=6Z%tJ25< zU>2vCnimaA#bktVM1yRoermP(TAJvhPIokyn67A}OR z)zVG$B*DX_(I5H>Uld6R#~?>-(q#S^()<8sP(oetB!*wV1jy$sz=1X=0s<5uzv_3a3lxllRcvSH4#537f6dQ}3NkrK4aJfhHv;E6EJFo|B0Y~!@k@k12D zgBkoY(U{yZjJb1~or5Zrdf*OePJUECUDnzm^y$**%y_Es9!mT;k?+A}C?Uj|X9$j) z1K{P@ZLn(TQY2e9=zyCH)z5D;?FDo~q8KttEa~xOvrs>7+bp-5N0!tH24bK=>VvL+ zlghUTh!CM>J!;||U3z&tbv7WzdJ)GQHW?{>h^0#h2Fum%vg{+xa%lvFGjW&H!+qnT_AWrPzt%}6;$44%)uS}m^ zU$U!em}oy*3j8sx9OVTr!Ylu)V}{>xPjVm&jv~4Ww}_N>H()moyj?%ys;`V~@n>di zTSmXYPlXxPh7Jc=4{XtX>^{C6ijO0=kEIIIHT5fP_KE1@Z3yq`{DVG<;0zUJrh(Nb zKsEDIHNM9=$G@bmePSAihQfl1xQ*IXl5)i4gkT(nM>^XyI3Ht%`#{W}VH}Av!_sw{ zTAakF7~U&UNrT&EXGn7|Sxo!v{c~-2^Q;<@;}TpQH=ZWX|Fr2R_wg-Yt=<4vf7x_p z5tj&qI|*T`_fn#pPm@Czq*b3S1twoLW8^9R$yM00`oC`&$zBJXwDM5n+q}7@c)q5# zLG?Bfs__tE>oU_;$zK-~-~fo7`=;q^8=Ul@!h`(hu$!C3%|%uRkQOvJC2_n04_Zrk z4gJnWi}<%gle*(&7|PfS(+%&etMMo^_)&%4g=QK3>l;=l0Y^$})dtY;xYm}y>dG)7 z4z|Ln3q`D{MMQcs-6F%}ReF6z=c%X9ZjgNBpyZzoYV8WI{5W`a0Ebw@6}3*dpdx4Z zd8M;6@gZ_-XGu)3TTtorkCuzm%Oy#8Cu}o{M@KR1wEXka7JngQu zEW5$|x1=@lPDmE>jWMG&|9_B3q={>7%eW z*uR}3u+#4EYMM}Gxwqar+w~X{S6sVPqc zES$qx3z`#7sSmS~QvBRK?WK_8==I6Z)XnKjuFE_A@+i^@?j9RU!H6j_QErWlRgn`F zYCYw$-JKu4b9yiF7>H*}#ZyB_MNi{d`TMTMpP7Gdo-Cr4#eOWB^gosJPj^dbcKTph z&=;{rg4fGU`pd{~*1=c^1VIZ|v^lnav%RsPO>Xn}+P)CjVe9*q{(I}S@iLaYAnxdO zEQTVjyMLv<>^1Rf)HeyC4+SE{C+)U;d@p{nJ&U3T?={L~wTN6ZAFo#70==*l;=d7) zS)r7--Wa*nzgH2z#V_I>N2)ZK^6TnLRCqaHjxNTEOWNqWis5tK?Syy~$Qwh9@(L?3 z=oF25_+cUty?ko*@NkN`o7mt729D{62gcrzHJlzxt7gkEOr;ZBl!UuI1yz8*N~(KM zkAk$h3ww(93UqQFYUgOQyQ;5m)2ms5E*yMKtzLRwnc`(=_dPnf6r@=za9tgT(R>B3 z4!7#vrpQ(H!#Uv>XD?-~%wAklaqO6$(CV?h_Ne1hfIsC})2R-oYj2BfEgW+M?{ zw0Y?d_*L>wO5qXFzA}S)dDo+f+u$$%64n)xQ;i?KY|!H_+IsXd((M~=n%9JH>0b3p zM?HGjeNv%|UMO#+_w}0DDPO>_$dCBlq^G*OeyN%oWw|@%7W+9D591e6J1La zQm2Bw(?yo{DRLWHmYyeyKRs$m&&xMPp{i z$WWyu*jT4WPRxZ!0j$Q5BC9lg=y2wus?r4jLf)=CA2q>KB9?hcP^MVAv~VZoVsWb8 z;fvRdH~{*7muhL6sa!8{mQ)T6vL4Vt&P%I|Mvjp?e#8k20tsa-| zX@0D_2w*yV?|i|ir8TPNp*EYYR%0=bn;WZ1x^0a_a-I0aHqx#rGShk1n5?)O+?CTw z!^7g5=8~k7HET-RJDpY|TX~Oj=#|ap8e-X=Ao14(Qz_7X!?}12>Ck)tI@S2QXysiH z6xIGLt1jD-a`xvV%mjQJafXWMjsf~EM`#N+A}1JyAD!BH)8lcCY?@gTyY<&VgNxDr z)nDObGS}Y{$}S^nHLW$ zZfhp;MygtO3$sI?WOuI!?y?!Ls*t1J0Yl%uWkSsjrU+k9<)rB%u9+}8myP4NU6zh$ z#&0GjD-uIstxEaGtKaEJYQW|?u~#>T^P~dBv8}hnGrD*))HsLZKAdDbg#8Fhd`4}D z;ljIRC36AEIQck-9bk5=kUvISAlqH(W(2zB*;2K>H~1j5Suv?nt{@Z19R^J}LSb7- zGhfYd_uu-^ubwVH_9c*4n4MHk6)+CC9?RI2DfWspE9dY0RKrPYu1xi|Mo%^%OcBGg zY7qtGkm-3`8#G!T)dfmt$jO>XyLFGN#-D{zwm`LS$|e+I zgchxPTnVR`w?=mEzUD&CZIW=ebN5nWn0Ci5Nw>vC*PAVnt@aW4?I~4o)&nkOiYn%e zy%v}70tEbg&tj9|um5Ijs`r(Fb5`=+LBqC*e!{d~HiDW*s^8y!?IL`5XQfeAP5$m_Ypn7fO+yB$rwNMTjI7#U&Xk>{m)9l)Ap>w0^k-K$e zwn!}n>^1Kqae6}@Mzs98#+kMU&$f9l(Vt-aB`S?W7M_LpnF%zw24#;aEmPv@wxV=q+gDuKJIOv(l-yQ?n5+! z6`?ch}HSdal($D z2RH5j>FbfEe$$}r&m#g{U+6}Dnfb%2xnb4tXw<+f!nVF8@*@;`xA$#~(E#s@PDKYL z#5M2MJZiT8?di3g>~s=Jc_!gx;3eI!I_SFw=lhhf2ngoPm8+50Y-0bo$)h9qKTqVk zC;}CKiU7-rvg?K5UQd!YvbR{$!A{Q_BiC$YQ(nWb6VnQq0f=hh;jMKk^j)stIpS^e z*NO;BXWgr+GHT^j_Y)jLKu|OJ4Q>jC3|0r>2=>EedSTDAy z`g@Qzdtb9joL=%wl&3EeRHG~gE5S--%}x#=iLe!#;^)ao5Cwb&vEY`Z88!~cY^eH# zfG5&7K@TKyxtW1Y|67u#Ux_|8ZvI*t(#-m+WcBM)-Fe~ZM&W>w{f8ib@etznwUV(@ zTmuJ6Pe$ra4i7uhxS)vDNU*^oO}_nRr7u<(c&8J1IFY%%1OcMg8>iCufoP-aSh0q? z=ED3TesZS%^gl^yk<&Q@m;cd^rn8612*+MBdob0~+=LeQzc27`^d;qq%3T`UkaOr- zc==mS93EE-yaZQY56d;tMzStXhg|t~ieRy)ka&({m~ez&l)J?5oOVaP!cI*y4{1v? z6$GXa0E)q90s0Wq-dZ@Uew_^r{+#NWwH)Rn5ptIjob#&=+k=4p@%-u-l_@gvBB|T& z%ppAHFEc7Q&wAKGKmEZ--o^Q9L|Wh`_wfxGy&^z_-PA8@B$qtXsv|pQ{{n!6DxzI> zb5$ef+r3GfoeQUSCXlpz8+C{ug8S;W0>R3Ryv@2P?r{W#H1db8C&gZz<`2UVe9?ID zaWk@$7GQN=K2lN{wfM@hBYCw5Sj6f|9)SMEpspC(P5qt=D&sm12)^lihnVVlT%S9fN2`mMsSfx^i)-?wMoYLy`;3?J9Fyd8G zJw{{Nj3nlC+QoG$?^x)cfS1m-{DF9vcP13S@?Au?1vA`LbqmAakmH@D3~EmYa5H@p3P(? zhR1eFJEN9IIKYpG1_3?8@ej2#-@IXVpS3$za}R)s7l0L?K6U)Zi)-F5@6Z#`%27cS zogjm_AN$5MJXNRkBwT#3Q!}`%2ajwVdtG*m`v^r&P>0`gA6g&ssx97=v2q&JV!AbuK|0ry&Q=yP5=*F|c^?b!$hN=yTt1Dm|KseU`4yyckzL`Q#?T1neze2Ju%!H|MC5b%;LsB#~?tm88BVvM+vCqb7cw3_^Gu@>)< zz5*q-g%$${+)=ar<1BNTn`b1V8jdGa)3D!ttqzncxTw}JHwfKIo1woO8K zYvtov*I(7Ra_FV_#2f5iZ-BD-!AgKb1R6okf>uq}q1wm1T#6{aT({m9nkt#dqBpso z_;m>vNJ}YrFsX*BAjxlmd~mdF|IOSN7C-J$+KEM46OjC7UGmL}<<*-t+`?fMiHs1} zsgF_pek?U>FIs-K{Bp4gH+bgh-X)3_`iZe`ZbTLd#=Q>dzNiA}%Hv3C-#~|J=U1Xr z2ATCwG-U2@D+whlKt+PHTArWUd5)W2KV{DlOhHE%%7*8_trvphU%Z2;mvzHbhH=?z z=qH07KutVRP&(mCXWpMUa;Hoq9YJol0jvFeanjxKiqlS1w}n3UG||Srz-%Om_3Bv~ z+Cs5Ag;mZaFHtMFe(Yc^A;8N3WScFaWPEGDI(MPWEiSBT$&&Kp2RBB<{yGIBaUj)t>^Op2wzp9Cty!?4J=9KRl=E7>>79&R7pGwj4=#5N799g)NV^iv zUVJe5-DwgL$a^r=fnAwinewFt!2+cYj+~9$d%(du%qq`}Dt=IV4)0RWn$BNaow{b= zy>@4UAH*~k#uK40x4YjP`Nd>TZ*nHti0~I@ZYKRn0|ECp0!4)(AZKG|q6_i!`xb>_kq+02$dZHzA6&3WQ`1Uq8h>0e zA6SRmjS!?P+RJ~cQG)~y3MMycb~8vk+9ln7x5M>lx1Na9H12Odee)oOoPjq1-Fus(NlK z9}+7>7>(@T=?J(IY~BX@4+aIm1rch-D7XQbwCTfJzon95Pm>%M{p?BL`o2&ZjILI3 zsREp;_)G+{M;CbIb_i;9)mqciImv&T{zoVWB%o0d&2gKM(plsdDod^J*na%Mfg2oS zbOE$Wu>`2KiQA2y9Bz+Qs8C#F*B5zL^ip>l#cWH+EToWCNbko^tuH`|NMMJE5r`Bm zR?1`!)mMX3f1Y97_eue`-g2Oqv*@n>B{8qs3oBiahvpt^UnePX<;5W_w-kTv__1N4 z$aj+-FVvMva7S?h{(*_}<>62b>sWTnsyY1;et(QRN>Jtw)*yEPg(PNv9|%M8QW0W+ z@inU0@B}a@gA;ZN0?*%&5=K-1j>y!GxA;lirml#${uO}>qEBkR@H0K*pjkU|WSWVPD48PXh{na1=?Gg4O>`%VN6b@0tZd)hw!oqK z4KG2hh~?Yo2XFTGHmymHFzx6Fo{G1CV4KZLlXlIFT+A0gC#QKea>TBI8yZj7Qh=RB zZC}S)QANMNBokQ$+gA!$>BVzb{%DWL4*7=L)B^}|H|#v+8#JPnT)cdJQD_N@xCbEb zB1xxqGDGNx+bB+1paW^(bTwGUjOlTj)eXt8r~3M=`t4eMmd5k&H~U~g4_s{8QBS#7 zyG|O0T};{R8x6F zsYtWcCdp)YL7|=hIYzgdt8e9~{q#{N;6&(Y1G;zg*NR?71uX+>La^S2C|9cE378d= zF_A*~l_aYUy0gE4YOnH;F=)*lC1&f7f zEw3m;yW3|Q6{*UqXn)=6ottCM*6W6nj zJ*#HHzFCvXI>~4Fqfzi7J_P~z8&cV9%syVA%Kgs*u2ilH zw^MR2;@R{HTqoluff08ThTTgNe?XS9e3t|j$(+{cS#3VU$2IPutV2Z5S_DT;d&r!e zYI&v}L()~tXW}5wDyRJjjBcmms)RS6pBwmi@+6(a17t`t8VN<)3ohfw?MK8i!?+r? z)I%Os(_#zf*H3f_8~zX!A;skx!p{5^04EU;%_f+q`cDKDFmxxbGf|_u&$Yqi8E;+Gb+LiUzsou~v1xRaH z(ye|S*Hko-=t$kpVY;i*2Z2&$p%mE)T?I=|z1H#O)KzmY)t?)A7>HBGOd z#QThnv_0|hU6~saaZ&u9#r}GVd77@I?|`~6i_K87p%S~t$h%TD)s8*3L1os0`_iRj}FAswRO5p0Mo zO%JY`wW3hN;UCbT9gcb<=6-n$k!4E53Rigdg>OTzhbWa-636bG=vHTIt^E`E$pdGj zVc}k$x^~4oW*;M>AqC&0G6;j$%s{ zzgnM{^5{Sud!RVpGg!VHdAMdHi2O`Fx!!Zk%FWu+HNVe~L>=h)re*#JWwio8AyIa- zirs}v$|v+c$JAbCPC+U3Y3OqgT>AW>%(}(=WdY79@doqDhuB(Dv`LbGu2ZJR{iPki|0s)nG{}n89TO=R1d*&x#Uwb zwx3AZ4PThY2dqY8KzEhBa{6rZ`LZ-Gol|ZQGs(T9xuu$QJ>9~3y(+3_u(%W(c#~z8&~$_5gtxuz+VvNj{flz{}>>XB?m$CYE&jM&7}^#{}wr z>uweR+2Gj48F3>WR|fbEtSj|;3>eMXnMm$QRX2SlNaKlraPj(|sMj{|{O>F4la8$^ z7i?MQq^tXhENK4YPjKe@6Y&}al;p$WhV{% zPqJTKa@&+0^%#*{FOqn~a7x)Fj`5Ea_Q`oCx}cPc2f19Of) zzGl=rdA^md+_w0Skky0KzDRpez;l;@rH|;!%u@{^>f`ss%#Smi{Oq6R${=0F)@uisNDBGeQ^G3dyJsHs^>`;MZKM_d$B&oq z11Kbo={9TY)RTiw3u&=$fZ7JZ9i(g*7_g!9ix#QJypFd!;!qjfrbNK~T>4=}QZu!6 zntU}$pJGc7(oZ!?oU?r^z95gkB8H5;3}nzTPpQ2;6uA?Wg(NbvG8rvcc+Jho{YPxS z)Rl5n$mjn4c5v|F*+s!?71}p590L4Z)Q5FfGsKq5*6Op#3;(mXA33u#6JxdiHJTF) zIh|iaLV|a9F?qiBvzOasbgJ)#%z0Xy-bRQDIR!o=|LtV`8mmCyZl;kk!Ym%^vjH{cZg zR0p(M#ow!SVqGjROzwzqS+)hbwkbQbPcdq3>_<_d1uTLdk89OwsZn~~=xjuC6ye<_ zA|dMptQqT9mwD}9_{v%0=lBhVhmFfOGb`tJEaXyEd{r-- z?~fNb!VvR!&G&<}hHwjUDP1M0Vw@)9Dh_P!cR3*xZk)unPs>1@7aJ5f+<| z{BDL6QsYnSYYF^bD4d8h_Oe4`%3R@hC&r|+`@iGkK`M?KEr9fa_bVDdQ1sQ@6{f7TKq7b+HMI_#7}`S$^^-N*W|EOiuS)6bZPUhhG*%8 z>gLZws}o4KWrQ6Xa^doJq`a&7Xyrz=yoQnVO1or*pjB(Oga`YK3?dsySV z8U=T7H*v7AcT5~J3^Yjj=Txg5fuPz|7cItYd4gW zH^wpm`dm#lkG}r$`;t@ML2e1wrsj$LVFFJP>?ZMNBxH{8im%;Jv2k|NkZTdwFFzdf z=xitIk21ie)6RCp978hSo1jF`K?)_QB(r8;$l|WsE$Y?Vaf#!Pgb`AX_TJ)kp&$%6 z$ehm~TzJJWME`um7j?FwZ|)cP5#54UFxuX%6#D<2`T4#-CMV>C0ZX4?!22+w1giH2 z?9tgxP+&|N`sHA=CzzXSFG5lwEfjO_jxnRaQ1n)hPUH>aH}bBh~SqLY7(lMT&dk$3`5O0c;6 zb)Kkr?eKeEw6eAZPs9qu#3AN8Ep(?d_{ACq30~p$kBjJ}{I>B+^A{>3Zh=UaKBMFyrGoUkk1^wSj57Q69xndD$G zcAo5?&WO#SE9WOAUsTK>6l^b?74T{t5D}bB!PQ+Ni$nrn5<2;0k!G@M%jWcp!zF9B zGAWJnBKlzR&lv2eAy^-7mG6=4bK@s=wTxXy_u`fn=d<)b@4hXbP7)TX@NxLF6R1pr z%jcWm(8h~ZhrRQLMQ*f|;OT(C}LUDK^`jU%`_ zqs%s1)lAE+b|U-@_k(n+=SZJQ9N|R}yWe<9{I{yiUS~i;^p)Bv?ArRJ6{V~Cby{XK z$;y9rQ*8`GcGl)xy3_P}Pj>To@mjH2Gy0T|fTP0JwE!4(W z7cTwyi+V))IT=SN-`$@S`U}Ek;-6u^5aVWX_xz?-PcN&3kHpq+<%a3h;uST%Bz)!` z!a(;B0C^^i`grB9E?1W^Fi} zaC1MZTb7?9DwRy>(V;t~*sAs?cM-lrpzFbPsoY(3nuG1&fKYy1p=SDLx93g4Zw>pt z{mq=hprzmpFZ-UzjAEk6URn|L9g%e`V)ronB-3Q@Kep%t$MxeCKlJvy#FrmSl1{lr zT$!Pd%}PifoRzHjXL(5VSFKi8k&^;t?D^@vPlYjzTY8MgAH^0hucDx(+95L7R&mw&{Mqw zCA<>(>cd-Hf-_07e-7{f_B~u|-$M^ahRd!=d>>-j>W!k|y;IZ_>=n7AlLozC|EOaq zjjnZ8vuusvU8}0gzcGYjBBpD~UQI}nOqZ)KJjI>d#rah#t0@j_2f ziAY#?$77a0Eu8m%D@2M={fPv%gm26QRJM;@GAv1xN&)aDxd!`)WceW#?`p;D8aJ7Y96!>0vlVM+6K@7q{3=)VaYJ!y zx(1dx6`}?k{{JE&NN|gP4xh;`Whb-N8+j^8W98b)x4B*bjQ4dE(ghZ38M||auBhL+ z8ibYyTlTNIJIefhr7t+Wr~A>=~9#Cz7f_`Gv%`hN1+ywcR2 zYxYU@es+a_vvT2xkW8L|7paIFGnMv8POL*JTk_3^!cu+SWR1Vmy1}$P#om_O(z>TS zN(S!D_iu4}-03tduZkl0yA4WdHSeX|$Gs(ViLJLI-t@6AIvBroBEdyF_bHPLuwm{G z4VxcC<{3{&Cv@5A=xpfz>Q`ve1wOPy;(Q%hXVK0D*$Rn#hpsNEQkP(xeTWn{3!5bpIy_6X8HXg2Y=S8MjOwVKvp}X)H6z&G|j#FabU4K~MqSn)PvyeYZ z7b$q(rye$w?Dz8cSvzj&sHvDI+UdtM&30JcAWyLVVUlwMUNsVq27#5jM6GIG=2wf+ zf5i;mbvpE$iXrdU!vb{$UOsis;l)mcYgzx}Mv~t@$Ip_%;SCM$<`v6MAGN{-a`va! zqiI+V_U$qZ6oBq>om^!tU1K?WQ5k}V9R4|(z(rOH^D6Xx`x28oX?l8%S-A9=ecOS7 z@YmM(*(|!mEYGS9&5*W++9TJHz#DGc@W(x>1~X^rMWSgklNR|{mhl!^cHO*!|JkGbTImBu8$2>$}7CQ{m&Yw-g*y=y`imC zs^bc6A*1HYkL0$zW8wO9;bWT-Jj3^QGp8wS1Vvhga>I3M<(TJR1uk3kjcKPZ6rO*x z^SAN>`8tJg@G!-4jsB(73rrP!RQD75I*s$yh9^7z)@|YkJ$=lEPoyhK&SYMSdZ|)J z1CCxlrh53^|E^l(UC4#UzVRyI@?PaL6Dr!ma^0PC&g-Uv|19el6_(sBQ~xZ#8a`3~o>g~0QyMwyKwSdC z8i|Sx740&nA~vmVCO%bYHSlh}QSyJMO){>B-Wt2LpdZ|QvRtOq>U-Sd3I*N~XIxD|NH?8uD zjwriy=GiXP{`hA`XU@Q(B}yMUnayHb`Fh#4>ojt#7Giw-FFcU$DE~VhCR08U!hn2c zKI0bZyfxVqZg1ID-?Gx-L&N**iaCtxBcl+vD>Wgm>xuHXroo!t@AT_y_9Xj115ti` zg(<3$z=gzGkJme-J!yRwsZ#FZ-)o19^ZyZxx$9`*u{VyB7fhcpFw_?8o}a$1E^FpL zro;8u`-atF$%*o)86GaJ2N?|C8qy8p40^|!HduTor1u>(EdJl}CD{T4mMHn5Q8`;e z>Q3>z(#`ahR{BquIR09F$Y8c4iDwva4M9@BGi2(cjm^?|O(p7kbN^>qzvYw zu9ew&Y$+)4jC8$9s^a}u@F^R@FzYoEl#)uP9S6j>nv-fc)LHgKbVc^V2K$GkOIS+H zFw=S@&#`?n_mn8J<(;PO)hPBwzJ~p9Fn&UG8LH9OH~uGCW4W>LhcQ6J8q?*qU+FWj z|06>OunQtCBzaviR0j^Af@Q9YtJNe0k-G~N|G5z^{*N4hkqDz2MO{b`*m;EIGRG@= z5$*0Y!4iq%gN z|NFNHUOY(rFRed=QXSDBAm%e<`OR&Ehi^wckJEK(24K=-*ov8Ic|!W}(}+k!O|u{yijI5dZs+ w;t_XCO8y@>1XfmaaQ%-O!u?Borlmasq0#gjfhafK1Mr{B4aMu(*Nh(iACLOL=l}o! literal 0 HcmV?d00001 diff --git a/docs/src/.overrides/assets/stylesheets/extra.css b/docs/src/.overrides/assets/stylesheets/extra.css new file mode 100644 index 0000000..0bb7f5b --- /dev/null +++ b/docs/src/.overrides/assets/stylesheets/extra.css @@ -0,0 +1,98 @@ +:root { + --dj-primary: #00a0df; + --dj-secondary: #ff5113; + --dj-background: #808285; + --dj-black: #000000; + --dj-white: #ffffff; +} + +/* footer previous/next navigation */ +.md-footer__inner:not([hidden]) { + display: none +} + +/* footer social icons */ +html a[title="DataJoint"].md-social__link svg { + color: var(--dj-primary); +} +html a[title="Slack"].md-social__link svg { + color: var(--dj-primary); +} +html a[title="LinkedIn"].md-social__link svg { + color: var(--dj-primary); +} +html a[title="Twitter"].md-social__link svg { + color: var(--dj-primary); +} +html a[title="GitHub"].md-social__link svg { + color: var(--dj-primary); +} +html a[title="DockerHub"].md-social__link svg { + color: var(--dj-primary); +} +html a[title="PyPI"].md-social__link svg { + color: var(--dj-primary); +} +html a[title="StackOverflow"].md-social__link svg { + color: var(--dj-primary); +} +html a[title="YouTube"].md-social__link svg { + color: var(--dj-primary); +} + +[data-md-color-scheme="datajoint"] { + /* ribbon */ + /* ribbon + markdown heading expansion */ + --md-primary-fg-color: var(--dj-black); + /* ribbon text */ + --md-primary-bg-color: var(--dj-primary); + + /* navigation */ + /* navigation header + links */ + --md-typeset-a-color: var(--dj-primary); + /* navigation on hover + diagram outline */ + --md-accent-fg-color: var(--dj-secondary); + + /* main */ + /* main header + already viewed*/ + --md-default-fg-color--light: var(--dj-background); + /* primary text */ + --md-typeset-color: var(--dj-black); + /* code comments + diagram text */ + --md-code-fg-color: var(--dj-primary); + + /* footer */ + /* previous/next text */ + /* --md-footer-fg-color: var(--dj-primary); */ +} + +[data-md-color-scheme="slate"] { + /* ribbon */ + /* ribbon + markdown heading expansion */ + --md-primary-fg-color: var(--dj-primary); + /* ribbon text */ + --md-primary-bg-color: var(--dj-white); + + /* navigation */ + /* navigation header + links */ + --md-typeset-a-color: var(--dj-primary); + /* navigation on hover + diagram outline */ + --md-accent-fg-color: var(--dj-secondary); + + /* main */ + /* main header + already viewed*/ + /* --md-default-fg-color--light: var(--dj-background); */ + /* primary text */ + --md-typeset-color: var(--dj-white); + /* code comments + diagram text */ + --md-code-fg-color: var(--dj-primary); + + /* footer */ + /* previous/next text */ + /* --md-footer-fg-color: var(--dj-white); */ +} + +[data-md-color-scheme="slate"] td, +th { + color: var(--dj-black) +} \ No newline at end of file diff --git a/docs/src/.overrides/partials/nav.html b/docs/src/.overrides/partials/nav.html new file mode 100644 index 0000000..fea49d7 --- /dev/null +++ b/docs/src/.overrides/partials/nav.html @@ -0,0 +1,33 @@ +{% set class = "md-nav md-nav--primary" %} +{% if "navigation.tabs" in features %} +{% set class = class ~ " md-nav--lifted" %} +{% endif %} +{% if "toc.integrate" in features %} +{% set class = class ~ " md-nav--integrated" %} +{% endif %} + diff --git a/docs/src/api/make_pages.py b/docs/src/api/make_pages.py new file mode 100644 index 0000000..9fab8ae --- /dev/null +++ b/docs/src/api/make_pages.py @@ -0,0 +1,32 @@ +"""Generate the api pages and navigation. + +NOTE: Works best when following the Google style guide +https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html +https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings +""" + +import mkdocs_gen_files +from pathlib import Path +import os +import subprocess + +package = os.getenv("PACKAGE") + +element = package.split("_", 1)[1] +# Previous git clone feature moved to docker compose + +nav = mkdocs_gen_files.Nav() +for path in sorted(Path(package).glob("**/*.py")) + sorted( + Path(f"workflow_{element}").glob("**/*.py") +): + if path.stem == "__init__": + continue + with mkdocs_gen_files.open(f"api/{path.with_suffix('')}.md", "w") as f: + module_path = ".".join( + [p for p in path.with_suffix("").parts if p != "__init__"] + ) + print(f"::: {module_path}", file=f) + nav[path.parts] = f"{path.with_suffix('')}.md" + +with mkdocs_gen_files.open("api/navigation.md", "w") as nav_file: + nav_file.writelines(nav.build_literate_nav()) diff --git a/docs/src/changelog.md b/docs/src/changelog.md new file mode 120000 index 0000000..699cc9e --- /dev/null +++ b/docs/src/changelog.md @@ -0,0 +1 @@ +../../CHANGELOG.md \ No newline at end of file diff --git a/docs/src/citation.md b/docs/src/citation.md new file mode 100644 index 0000000..7b28f20 --- /dev/null +++ b/docs/src/citation.md @@ -0,0 +1,19 @@ +# Citation + +If your work uses the following resources, please cite the respective manuscript and/or Research Resource Identifier (RRID): + ++ DataJoint Element DeepLabCut - Version {{ PATCH_VERSION }} + + Yatsenko D, Nguyen T, Shen S, Gunalan K, Turner CA, Guzman R, Sasaki M, Sitonic D, + Reimer J, Walker EY, Tolias AS. DataJoint Elements: Data Workflows for + Neurophysiology. bioRxiv. 2021 Jan 1. doi: https://doi.org/10.1101/2021.03.30.437358 + + + [RRID:SCR_021894](https://scicrunch.org/resolver/SCR_021894) + ++ DeepLabCut + + [Manuscripts](https://github.com/DeepLabCut/DeepLabCut#references) + ++ NWB + + [Manuscript](https://www.nwb.org/publications/) + ++ DANDI + + [Citation options](https://www.dandiarchive.org/handbook/10_using_dandi/#citing-dandi) diff --git a/docs/src/concepts.md b/docs/src/concepts.md new file mode 100644 index 0000000..02000bd --- /dev/null +++ b/docs/src/concepts.md @@ -0,0 +1,162 @@ +# Concepts + +## Pose Estimation in Neurophysiology + +Studying the inner workings of the brain requires understanding the relationship between +neural activity and environmental stimuli, natural behavior, or inferred cognitive +states. Pose estimation is a computer vision method to track the position, and thereby +behavior, of the subject over the course of an experiment, which can then be paired with +neuronal recordings to answer scientific questions about the brain. + +Previous pose estimation methods required reflective markers placed on a subject, as +well as multiple expensive high-frame-rate infrared cameras to triangulate position +within a limited field. Recent advancements in machine learning have facilitated +dramatic advancements in capturing pose data with a video camera alone. In particular, +[DeepLabCut](http://deeplabcut.org/) (DLC) facilitates the use of pre-trained machine +learning models for 2-D and +3-D non-invasive markerless pose estimation. + +DeepLabCut offers the ability to continue training an exisiting object detection model +to further specialize in videos in the training data set. In other words, researchers +can take a well-known generalizable machine learning model and apply it to their +experimental setup, making it relatively easy to produce pose estimation inferences +for subsequent experimental sessions. + +While some alternative tools are either species-specific (e.g., +[DeepFly3D](https://github.com/NeLy-EPFL/DeepFly3D)) or uniquely 2D (e.g., +[DeepPoseKit](https://github.com/jgraving/DeepPoseKit)), DLC highlights a diversity of +use-cases via a [Model Zoo](http://www.mackenziemathislab.org/dlc-modelzoo). Even +compared to tools with similar functionality (e.g., +[SLEAP](https://github.com/murthylab/sleap) and +[dannce](https://github.com/spoonsso/dannce)), DLC has more users, as measured by either +GitHub forks or more citations (1600 vs. 900). DLC's trajectory toward an industry +standard is attributable to [continued +funding](http://www.mackenziemathislab.org/deeplabcutblog/2020/11/18/czidlc), [extensive +documentation](https://deeplabcut.github.io/DeepLabCut/docs/intro.html) and both +creator- and peer-support. Other comparable tools include +[mmpose](https://github.com/open-mmlab/mmpose), +[idtracker.ai]([idtracker.ai](https://idtrackerai.readthedocs.io/en/latest/)), +[TREBA](https://github.com/neuroethology/TREBA), +[B-KinD](https://github.com/neuroethology/BKinD), +[VAME](https://github.com/LINCellularNeuroscience/VAME), and +[MARS](https://github.com/neuroethology/MARS). + +## Key Partnerships + +[Mackenzie Mathis](http://www.mackenziemathislab.org/) (Swiss Federal Institute of +Technology Lausanne) is both a lead developer of DLC and a key advisor on DataJoint open +source development as a member of the [Scientific Steering +Committee](datajoint.com/docs/elements/management/governance). + +DataJoint is also partnered with a number of groups who use DLC as part of broader +workflows. In these collaborations, members of the DataJoint team have interviewed +the scientists to understand their needs in experimental setup, pipeline design, and +interfaces. + +These teams include: + +- Moser Group (Norwegian University of Science and Technology) - see [pipeline + design](https://moser-pipelines.readthedocs.io/en/latest/imaging/dlc.html) + +- Mesoscale Activity Project (Janelia Research Campus/Baylor College of Medicine/New + York University) + +- Hui-Chen Lu Lab (Indiana University) + +- Tobias Rose Lab (University of Bonn) + +- James Cotton Lab (Northwestern University) + +## Element Features + +Development of the Element began with an +[open source repository](https://github.com/MMathisLab/DataJoint_Demo_DeepLabCut) shared +by the Mathis team. We further identified common needs across our respective +partnerships to offer the following features for single-camera 2D models: + +- Manage training data and configuration parameters +- Launch model training +- Evaluate models automatically and directly compare models +- Manage model metadata +- Launch inference video analysis +- Capture pose estimation output for each session + +## Element Architecture + +Each node in the following diagram represents the analysis code in the workflow and the +corresponding tables in the database. Within the workflow, Element DeepLabCut connects +to upstream Elements including Lab, Animal, and Session. For more detailed +documentation on each table, see the API docs for the respective schemas. + +![pipeline](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/pipeline.svg) + +### `lab` schema ([API docs](../api/workflow_deeplabcut/pipeline/#workflow_deeplabcut.pipeline.Device)) + +| Table | Description | +| --- | --- | +| Device | Camera metadata | + +### `subject` schema ([API docs](https://datajoint.com/docs/elements/element-animal/api/element_animal/subject)) + +- Although not required, most choose to connect the `Session` table to a `Subject` table. + +| Table | Description | +| --- | --- | +| Subject | Basic information of the research subject | + +### `session` schema ([API docs](https://datajoint.com/docs/elements/element-session/api/element_session/session_with_datetime)) + +| Table | Description | +| --- | --- | +| Session | Unique experimental session identifier | + +### `train` schema ([API docs](../api/element_deeplabcut/train)) + +- Optional tables related to model training. + +| Table | Description | +| --- | --- | +| VideoSet | Set of files corresponding to a training dataset. | +| TrainingParamSet | A collection of model training parameters, represented by an index. | +| TrainingTask | A set of tasks specifying model training methods. | +| ModelTraining | A record of training iterations launched by `TrainingTask`. | + +### `model` schema ([API](../api/element_deeplabcut/model)) + +- Tables related to DeepLabCut models and pose estimation. The `model` schema can be + used without the `train` schema. + +| Table | Description | +| --- | --- | +| VideoRecording | Video(s) from one recording session, for pose estimation. | +| BodyPart | Unique body parts (a.k.a. joints) and descriptions thereof. | +| Model | A central table for storing unique models. | +| ModelEvaluation | Evaluation results for each model. | +| PoseEstimationTask | A series of pose estimation tasks to be completed. Pairings of video recordings with models to be use for pose estimation. | +| PoseEstimation | Results of pose estimation using a given model. | + +## Data Export and Publishing + +Element DeepLabCut includes an export function that saves the outputs as a Neurodata +Without Borders (NWB) file. By running a single command, the data from an experimental +session is saved to a NWB file. + +For more details on the export function, see the [Tutorials page](/tutorials). + +Once NWB files are generated they can be readily shared with collaborators and published +on [DANDI Archive](https://dandiarchive.org/). The DataJoint Elements ecosystem +includes a function to upload the NWB files to DANDI (see [Element +Interface](datajoint.com/docs/elements/element-interface/)). + +```python +dlc_session_to_nwb(pose_key, use_element_session, session_kwargs) +``` + +## Roadmap + +Further development of this Element is community driven. Upon user requests and based +on guidance from the Scientific Steering Group we will add the following features to +this Element: + +- Support for multi-animal or multi-camera models +- Tools to label training data diff --git a/docs/src/index.md b/docs/src/index.md new file mode 100644 index 0000000..5732762 --- /dev/null +++ b/docs/src/index.md @@ -0,0 +1,21 @@ +# Element DeepLabCut for Pose Estimation + +DataJoint Element for markerless pose estimation with +[DeepLabCut](https://www.deeplabcut.org/). DataJoint Elements collectively standardize +and automate data collection and analysis for neuroscience experiments. Each Element is +a modular pipeline for data storage and processing with corresponding database +tables that can be combined with other Elements to assemble a fully functional pipeline. + +![flowchart](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/flowchart.svg) + +Element DeepLabCut runs DeepLabCut which uses image recognition machine learning models +to generate animal position estimates from consumer grade video equipment. The Element +is composed of two schemas for storing data and running analysis: + +- `train` - Manages model training + +- `model` - Manages models and launches pose estimation + +Visit the [Concepts page](./concepts.md) for more information on pose estimation and +Element DeepLabCut. To get started with building your data pipeline visit the +[Tutorials page](./tutorials/). diff --git a/docs/src/tutorials/index.md b/docs/src/tutorials/index.md new file mode 100644 index 0000000..7ccd7d8 --- /dev/null +++ b/docs/src/tutorials/index.md @@ -0,0 +1,114 @@ +# Tutorials + +## Installation + +Installation of the Element requires an integrated development environment and database. +Instructions to setup each of the components can be found on the +[User Instructions](https://datajoint.com/docs/elements/user-guide) page. These +instructions use the example +[workflow for Element DeepLabCut](https://github.com/datajoint/workflow-deeplabcut), +which can be modified for a user's specific experimental requirements. This example +workflow uses four Elements (Lab, Animal, Session, and DeepLabCut) to construct a +complete pipeline, and is able to ingest experimental metadata and run model training +and inference. + +The [DeepLabCut (DLC) website](https://deeplabcut.github.io/DeepLabCut/README.html) has a +rich library of resources for downloading the software and understanding its various +features. This includes getting started with their software (see +[links below](#steps-to-run-the-element)). + +## Steps to run the Element + +The Element assumes you: + +1. Have a DLC project folder on your machine. You can declare a project either + from the + [DLC GUI](https://deeplabcut.github.io/DeepLabCut/docs/PROJECT_GUI.html#video-demos-how-to-launch-and-run-the-project-manager-gui) + or via a + [terminal](https://deeplabcut.github.io/DeepLabCut/docs/standardDeepLabCut_UserGuide.html#deeplabcut-in-the-terminal). +1. Have labeled data in your DLC project folder. Again, this can be done via + [the GUI](https://youtu.be/JDsa8R5J0nQ?t=94) + or a + [terminal](https://deeplabcut.github.io/DeepLabCut/docs/standardDeepLabCut_UserGuide.html#deeplabcut-in-the-terminal). + +With these steps in place, you can then use the materials below to start training +and pose estimation inferences. Training starts by configuring parameters in the +`train` schema, and launching training in the `ModelTraining` table. When you're happy +with the state of a model, you can insert it into the `Model` table, and pair it with +videos to trigger pose estimation inferences via the `PoseEstimationTask` table +in the `model` schema. See [Element Architecture](./concepts/#element-architecture) +for a full list of table functions. + +### Videos + +The [Element DeepLabCut tutorial](https://www.youtube.com/watch?v=8FDjTuQ52gQ) gives an +overview of the workflow files and notebooks as well as core concepts related to +DeepLabCut. + +[![YouTube tutorial](https://img.youtube.com/vi/8FDjTuQ52gQ/0.jpg)](https://www.youtube.com/watch?v=8FDjTuQ52gQ) + +### Notebooks + +Each of the notebooks in the workflow +([download here](https://github.com/datajoint/workflow-deeplabcut/tree/main/notebooks) +steps through ways to interact with the Element itself. For convenience, these notebooks +are also rendered as part of this site. +To try out Elements +notebooks in an online Jupyter environment with access to example data, visit +[CodeBook](https://codebook.datajoint.io/). (DeepLabCut notebooks coming soon!) + +- [Data Download](./00-DataDownload_Optional.ipynb) + highlights how to use DataJoint tools to download a sample model for trying out the Element. +- [Configure](./01-Configure.ipynb) + helps configure your local DataJoint installation to point to the correct database. +- [Workflow Structure](./02-WorkflowStructure_Optional.ipynb) demonstrates the table + architecture of the Element and key DataJoint basics for interacting with these + tables. +- [Process](./03-Process.ipynb) steps through adding data to these tables and launching + key DeepLabCut features, like model training. +- [Automate](./04-Automate_Optional.ipynb) + highlights the same steps as above, but utilizing all built-in automation tools. +- [Visualization](./05-Visualization_Optional.ipynb) + demonstrates how to fetch data from the Element to generate figures and label data. +- [Drop schemas](./06-Drop_Optional.ipynb) + provides the steps for dropping all the tables to start fresh. +- `07-NWB-Export` (coming soon!) will describe how to export into NWB files. For now, + see [below](./#nwb-export) +- [Alternate Dataset](./09-AlternateDataset.ipynb) + does all of the above, but with a + [dataset from DeepLabCut](https://github.com/DeepLabCut/DeepLabCut/tree/master/examples/openfield-Pranav-2018-10-30). + +## Data Export to Neurodata Without Borders (NWB) + +The `export/nwb.py` module calls [DLC2NWB](https://github.com/DeepLabCut/DLC2NWB/) to +save output generated by Element DeepLabCut as NWB files. +The main function, `dlc_session_to_nwb`, contains a flag to control calling a parallel +function in +[Element Session](https://github.com/datajoint/element-session/blob/main/element_session/export/nwb.py). + +Before using, please install [DLC2NWB](https://github.com/DeepLabCut/DLC2NWB/) + +```console +pip install dlc2nwb +``` + +Then, call the export function using keys from the `PoseEstimation` table. + +```python +from element_deeplabcut import model +from element_session import session +from element_deeplabcut.export import dlc_session_to_nwb + +session_key = (session.Session & CONDITION) +pose_key = (model.PoseEstimation & session_key).fetch1('KEY') +dlc_session_to_nwb(pose_key, use_element_session=True, session_kwargs=SESSION_KWARGS) +``` + +Here, `CONDITION` should uniquely identify a session and `SESSION_KWARGS` can be any of +the items described in the docstring of `element_session.export.nwb.session_to_nwb` +as a dictionary. + +As DLC2NWB does not currently offer a separate function for generating `PoseEstimation` +objects (see [ndx-pose](https://github.com/rly/ndx-pose)), the current solution is to +allow DLC2NWB to write to disk, and optionally rewrite this file using metadata provided +by the export function in Element Session. From 89fd6746d29d53bf26a6d441e59223351d50143e Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Thu, 28 Sep 2023 23:03:29 +0200 Subject: [PATCH 143/176] fix bug using ' ' in extra_require in setup.py --- setup.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/setup.py b/setup.py index d4c3420..f1ce52c 100644 --- a/setup.py +++ b/setup.py @@ -30,15 +30,14 @@ "networkx==2.8.2", ], extras_require={ - # "dlc_default": ["deeplabcut @ git+https://github.com/DeepLabCut/DeepLabCut"] - "dlc_default": ["'deeplabcut[tf]'>=2.2.1.1"], + "dlc_default": ["deeplabcut[tf]>=2.2.1.1"], "dlc_apple_mchips": [ "tensorflow-macos==2.12.0", "tensorflow-metal", "tables==3.7.0", - "'deeplabcut'", + "deeplabcut", ], - "dlc_gui": ["'deeplabcut[gui]"], + "dlc_gui": ["deeplabcut[gui]"], "elements": [ "element-lab>=0.2.0", "element-animal>=0.1.5", From 80100db00493c4d19dfa806efbd9d8dcb336df8e Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Fri, 29 Sep 2023 01:08:57 +0200 Subject: [PATCH 144/176] Update .github/ISSUE_TEMPLATE/config.yml Co-authored-by: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> --- .github/ISSUE_TEMPLATE/config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index b3d197d..287ae12 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -1,5 +1,5 @@ blank_issues_enabled: false contact_links: - name: DataJoint Contribution Guideline - url: https://datajoint.com/docs/community/contribute/ + url: https://datajoint.com/docs/about/contribute/ about: Please make sure to review the DataJoint Contribution Guidelines \ No newline at end of file From 60f1a814750efd1cac7ac09cddbdfd7db2548169 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Fri, 29 Sep 2023 01:31:14 +0200 Subject: [PATCH 145/176] update dj_public_s3_location in docker-compose --- .devcontainer/docker-compose.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index de80a01..efa55ca 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -10,7 +10,7 @@ services: extra_hosts: - fakeservices.datajoint.io:127.0.0.1 environment: - - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/deeplabcut-v1 + - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/workflow-dlc-data/v1 devices: - /dev/fuse cap_add: From 89df0f4bdaacdf5d59a7a679b65155ac0fa1a5b2 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Fri, 29 Sep 2023 01:42:52 +0200 Subject: [PATCH 146/176] add s3fs postcommand in devcontainer --- .devcontainer/devcontainer.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index ec9b835..d63ea66 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -6,8 +6,8 @@ "remoteEnv": { "LOCAL_WORKSPACE_FOLDER": "${localWorkspaceFolder}" }, - "onCreateCommand": "pip install -e .", - "postStartCommand": "docker volume prune -f", + "onCreateCommand": "mkdir -p ${DLC_ROOT_DATA_DIR} &&pip install -e .", + "postStartCommand": "docker volume prune -f && s3fs ${DJ_PUBLIC_S3_LOCATION} ${DLC_ROOT_DATA_DIR} -o nonempty,multipart_size=530,endpoint=us-east-1,url=http://s3.amazonaws.com,public_bucket=1", "hostRequirements": { "cpus": 4, "memory": "8gb", From 8511b4ea3efea1f0b1d6dc939048cc0c13f5a30d Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Fri, 29 Sep 2023 17:04:44 +0200 Subject: [PATCH 147/176] delete comments in dockerfile --- .devcontainer/Dockerfile | 3 --- 1 file changed, 3 deletions(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 4f3cdc7..3d3f5ef 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -32,11 +32,8 @@ COPY ./ /tmp/element-deeplabcut/ RUN \ # pipeline dependencies apt-get update && \ - #pip install gcc ffmpeg graphviz && \ apt-get install -y gcc ffmpeg graphviz && \ - #pip install numcodecs && \ pip install --no-cache-dir -e /tmp/element-deeplabcut[elements,dlc_default] && \ - #TO-DO: ADD element-deeplabcut[dlc_default,tests] # clean up rm -rf /tmp/element-deeplabcut/ && \ apt-get clean From f30425fbf30eae728f9e8926587a04aaac7d8c41 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Fri, 29 Sep 2023 21:18:31 +0200 Subject: [PATCH 148/176] SCIOPS-59 & add two dependencies in setup --- setup.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/setup.py b/setup.py index f1ce52c..9bd4b8d 100644 --- a/setup.py +++ b/setup.py @@ -28,6 +28,8 @@ "graphviz", "pydot", "networkx==2.8.2", + "ipykernel", + "ipywidgets", ], extras_require={ "dlc_default": ["deeplabcut[tf]>=2.2.1.1"], From 49f2b2f011981db8c345093bef685faf03c1f8c9 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Fri, 29 Sep 2023 22:56:14 +0200 Subject: [PATCH 149/176] revert removing files in `element-deeplabcut` --- element_deeplabcut/export/__init__.py | 1 + element_deeplabcut/export/nwb.py | 74 +++++ element_deeplabcut/readers/__init__.py | 0 element_deeplabcut/readers/dlc_reader.py | 377 +++++++++++++++++++++++ 4 files changed, 452 insertions(+) create mode 100644 element_deeplabcut/export/__init__.py create mode 100644 element_deeplabcut/export/nwb.py create mode 100644 element_deeplabcut/readers/__init__.py create mode 100644 element_deeplabcut/readers/dlc_reader.py diff --git a/element_deeplabcut/export/__init__.py b/element_deeplabcut/export/__init__.py new file mode 100644 index 0000000..90a7d71 --- /dev/null +++ b/element_deeplabcut/export/__init__.py @@ -0,0 +1 @@ +from .nwb import dlc_session_to_nwb diff --git a/element_deeplabcut/export/nwb.py b/element_deeplabcut/export/nwb.py new file mode 100644 index 0000000..5a67610 --- /dev/null +++ b/element_deeplabcut/export/nwb.py @@ -0,0 +1,74 @@ +""" +Portions of code adapted from DeepLabCut/DLC2NWB +MIT License Copyright (c) 2022 Alexander Mathis +DataJoint export methods for DeepLabCut 2.x +""" +import logging +import warnings +from pathlib import Path +from collections import abc +from pynwb import NWBHDF5IO +from hdmf.build.warnings import DtypeConversionWarning +from .. import model + +try: # Not all users will want NWB export, so dependency not in requirements. + from dlc2nwb.utils import convert_h5_to_nwb, write_subject_to_nwb +except ImportError: + raise ImportError( + "The package `dlc2nwb` is missing. Please run `pip install dlc2nwb`." + ) + +logger = logging.getLogger("datajoint") + + +def dlc_session_to_nwb( + keys: list, use_element_session: bool = True, session_kwargs: dict = None +) -> str: + """Using keys from PoseEstimation table, save DLC's h5 output to NWB. + + Calls DLC2NWB to export NWB file using current h5 on disk. If use_element_session, + calls NWB export function from Elements for lab, animal and session, passing + session_kwargs. Saves output based on naming convention in DLC2NWB. If output path + already exists, returns output path without making changes to the file. + NOTE: does not support multianimal exports + + Args: + keys: One or more keys from model.PoseEstimation + use_element_session: Optional. If True, call NWB export from Element Session + session_kwargs: Optional. Additional keyword args for Element Session export + + Returns: + Output path of saved file + """ + if not isinstance(keys, abc.Sequence): # Ensure list for following loop + keys = [keys] + + for key in keys: + write_file = True + subject_id = key["subject"] + output_dir = model.PoseEstimationTask.infer_output_dir(key) + config_file = str(output_dir / "dj_dlc_config.yaml") + video_name = Path((model.VideoRecording.File & key).fetch1("file_path")).stem + h5file = next(output_dir.glob(f"{video_name}*h5")) + output_path = h5file.replace(".h5", f"_{subject_id}.nwb") # DLC2NWB convention + + if Path(output_path).exists(): + logger.warning(f"Skipping {subject_id}. NWB already exists.") + write_file = False + + # Use standard DLC2NWB export + if write_file and not use_element_session: + output_path = convert_h5_to_nwb(config_file, h5file, subject_id) + + # Pass Element Session export items in export + if write_file and use_element_session: + from element_session.export.nwb import session_to_nwb + + session_nwb = session_to_nwb(key, **session_kwargs) # call session export + dlc_nwb = write_subject_to_nwb(session_nwb, h5file, subject_id, config_file) + # warnings filter from DLC2NWB + with warnings.catch_warnings(), NWBHDF5IO(output_path, mode="w") as io: + warnings.filterwarnings("ignore", category=DtypeConversionWarning) + io.write(dlc_nwb) + + return output_path diff --git a/element_deeplabcut/readers/__init__.py b/element_deeplabcut/readers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/element_deeplabcut/readers/dlc_reader.py b/element_deeplabcut/readers/dlc_reader.py new file mode 100644 index 0000000..a7f6a32 --- /dev/null +++ b/element_deeplabcut/readers/dlc_reader.py @@ -0,0 +1,377 @@ +import re +import logging +import numpy as np +import pandas as pd +from pathlib import Path +import pickle +import ruamel.yaml as yaml +from element_interface.utils import find_root_directory, dict_to_uuid +from .. import model +from ..model import get_dlc_root_data_dir +from datajoint.errors import DataJointError + +logger = logging.getLogger("datajoint") + + +class PoseEstimation: + """Class for handling DLC pose estimation files.""" + + def __init__( + self, + dlc_dir: str = None, + pkl_path: str = None, + h5_path: str = None, + yml_path: str = None, + filename_prefix: str = "", + ): + if dlc_dir is None: + assert pkl_path and h5_path and yml_path, ( + 'If "dlc_dir" is not provided, then pkl_path, h5_path, and yml_path ' + + "must be provided" + ) + else: + self.dlc_dir = Path(dlc_dir) + if not self.dlc_dir.exists(): + raise FileNotFoundError(f"Unable to find {dlc_dir}") + + # meta file: pkl - info about this DLC run (input video, configuration, etc.) + if pkl_path is None: + self.pkl_paths = sorted( + self.dlc_dir.rglob(f"{filename_prefix}*meta.pickle") + ) + if not len(self.pkl_paths) > 0: + raise FileNotFoundError( + f"No meta file (.pickle) found in: {self.dlc_dir}" + ) + else: + pkl_path = Path(pkl_path) + if not pkl_path.exists(): + raise FileNotFoundError(f"{pkl_path} not found") + self.pkl_paths = [pkl_path] + + # data file: h5 - body part outputs from the DLC post estimation step + if h5_path is None: + self.h5_paths = sorted(self.dlc_dir.rglob(f"{filename_prefix}*.h5")) + if not len(self.h5_paths) > 0: + raise FileNotFoundError( + f"No DLC output file (.h5) found in: {self.dlc_dir}" + ) + else: + h5_path = Path(h5_path) + if not h5_path.exists(): + raise FileNotFoundError(f"{h5_path} not found") + self.h5_paths = [h5_path] + + # validate number of files + assert len(self.h5_paths) == len( + self.pkl_paths + ), f"Unequal number of .h5 files ({len(self.h5_paths)}) and .pickle files ({len(self.pkl_paths)})" + + assert ( + self.pkl_paths[0].stem == self.h5_paths[0].stem + "_meta" + ), f"Mismatching h5 ({self.h5_paths[0].stem}) and pickle {self.pkl_paths[0].stem}" + + # config file: yaml - configuration for invoking the DLC post estimation step + if yml_path is None: + yml_paths = list(self.dlc_dir.glob(f"{filename_prefix}*.y*ml")) + # If multiple, defer to the one we save. + if len(yml_paths) > 1: + yml_paths = [val for val in yml_paths if val.stem == "dj_dlc_config"] + if len(yml_paths) != 1: + raise FileNotFoundError( + f"Unable to find one unique .yaml file in: {dlc_dir} - Found: {len(yml_paths)}" + ) + self.yml_path = yml_paths[0] + else: + self.yml_path = Path(yml_path) + if not self.yml_path.exists(): + raise FileNotFoundError(f"{self.yml_path} not found") + + self._pkl = None + self._rawdata = None + self._yml = None + self._data = None + + train_idx = np.where( + (np.array(self.yml["TrainingFraction"]) * 100).astype(int) + == int(self.pkl["training set fraction"] * 100) + )[0][0] + train_iter = int(self.pkl["Scorer"].split("_")[-1]) + + self.model = { + "Scorer": self.pkl["Scorer"], + "Task": self.yml["Task"], + "date": self.yml["date"], + "iteration": self.pkl["iteration (active-learning)"], + "shuffle": int(re.search(r"shuffle(\d+)", self.pkl["Scorer"]).groups()[0]), + "snapshotindex": self.yml["snapshotindex"], + "trainingsetindex": train_idx, + "training_iteration": train_iter, + } + + self.fps = self.pkl["fps"] + self.nframes = self.pkl["nframes"] + self.creation_time = self.h5_paths[0].stat().st_mtime + + @property + def pkl(self): + """Pickle file contents""" + if self._pkl is None: + nframes = 0 + meta_hash = None + for fp in self.pkl_paths: + with open(fp, "rb") as f: + meta = pickle.load(f) + nframes += meta["data"].pop("nframes") + + # remove variable fields + for k in ("start", "stop", "run_duration"): + meta["data"].pop(k) + + # confirm identical setting in all .pickle files + if meta_hash is None: + meta_hash = dict_to_uuid(meta) + else: + assert meta_hash == dict_to_uuid( + meta + ), f"Inconsistent DLC-model-config file used: {fp}" + + self._pkl = meta["data"] + self._pkl["nframes"] = nframes + return self._pkl + + @property + def yml(self): + """json-structured config.yaml file contents""" + if self._yml is None: + with open(self.yml_path, "rb") as f: + self._yml = yaml.safe_load(f) + return self._yml + + @property + def rawdata(self): + """Raw data from h5 file""" + if self._rawdata is None: + self._rawdata = pd.concat([pd.read_hdf(fp) for fp in self.h5_paths]) + return self._rawdata + + @property + def data(self): + """Data from the h5 file, restructured as a dict""" + if self._data is None: + self._data = self.reformat_rawdata() + return self._data + + @property + def df(self): + """Data as dataframe""" + top_level = self.rawdata.columns.levels[0][0] + return self.rawdata.get(top_level) + + @property + def body_parts(self): + """Set of body parts present in data file""" + return self.df.columns.levels[0] + + def reformat_rawdata(self): + """Transform raw h5 data into dict""" + error_message = ( + f"Total frames from .h5 file ({len(self.rawdata)}) differs " + + f'from .pickle ({self.pkl["nframes"]})' + ) + assert len(self.rawdata) == self.pkl["nframes"], error_message + + body_parts_position = {} + for body_part in self.body_parts: + body_parts_position[body_part] = { + c: self.df.get(body_part).get(c).values + for c in self.df.get(body_part).columns + } + + return body_parts_position + + +def read_yaml(fullpath: str, filename: str = "*") -> tuple: + """Return contents of yml in fullpath. If available, defer to DJ-saved version + + Args: + fullpath (str): String or pathlib path. Directory with yaml files + filename (str, optional): Filename, no extension. Permits wildcards. + + Returns: + Tuple of (a) filepath as pathlib.PosixPath and (b) file contents as dict + """ + from deeplabcut.utils.auxiliaryfunctions import read_config + + # Take the DJ-saved if there. If not, return list of available + yml_paths = list(Path(fullpath).glob("dj_dlc_config.yaml")) or sorted( + list(Path(fullpath).glob(f"{filename}.y*ml")) + ) + + assert ( # If more than 1 and not DJ-saved, + len(yml_paths) == 1 + ), f"Found more yaml files than expected: {len(yml_paths)}\n{fullpath}" + + return yml_paths[0], read_config(yml_paths[0]) + + +def save_yaml( + output_dir: str, + config_dict: dict, + filename: str = "dj_dlc_config", + mkdir: bool = True, +) -> str: + """Save config_dict to output_path as filename.yaml. By default, preserves original. + + Args: + output_dir (str): where to save yaml file + config_dict (str): dict of config params or element-deeplabcut model.Model dict + filename (str, optional): default 'dj_dlc_config' or preserve original 'config' + Set to 'config' to overwrite original file. + If extension is included, removed and replaced with "yaml". + mkdir (bool): Optional, True. Make new directory if output_dir not exist + + Returns: + path of saved file as string - due to DLC func preference for strings + """ + from deeplabcut.utils.auxiliaryfunctions import write_config + + if "config_template" in config_dict: # if passed full model.Model dict + config_dict = config_dict["config_template"] + if mkdir: + output_dir.mkdir(exist_ok=True) + if "." in filename: # if user provided extension, remove + filename = filename.split(".")[0] + + output_filepath = Path(output_dir) / f"{filename}.yaml" + write_config(output_filepath, config_dict) + return str(output_filepath) + + +def do_pose_estimation( + key: dict, + video_filepaths: list, + dlc_model: dict, + project_path: str, + output_dir: str, + videotype="", + gputouse=None, + save_as_csv=False, + batchsize=None, + cropping=None, + TFGPUinference=True, + dynamic=(False, 0.5, 10), + robust_nframes=False, + allow_growth=False, + use_shelve=False, +): + """Launch DLC's analyze_videos within element-deeplabcut. + + Also saves a copy of the current config in the output dir, with ensuring analyzed + videos in the video_set. NOTE: Config-specificed cropping not supported when adding + to config in this manner. + + Args: + video_filepaths (list): list of videos to analyze + dlc_model (dict): element-deeplabcut dlc.Model + project_path (str): path to project config.yml + output_dir (str): where to save output + # BELOW FROM DLC'S DOCSTRING + + videotype (str, optional, default=""): + Checks for the extension of the video in case the input to the video is a + directory. Only videos with this extension are analyzed. If unspecified, + videos with common extensions ('avi', 'mp4', 'mov', 'mpeg', 'mkv') are kept. + gputouse (int or None, optional, default=None): + Indicates the GPU to use (see number in ``nvidia-smi``). If none, ``None``. + See: https://nvidia.custhelp.com/app/answers/detail/a_id/3751/~/useful-nvidia-smi-queries + save_as_csv (bool, optional, default=False): + Saves the predictions in a .csv file. + batchsize (int or None, optional, default=None): + Change batch size for inference; if given overwrites ``pose_cfg.yaml`` + cropping (list or None, optional, default=None): + List of cropping coordinates as [x1, x2, y1, y2]. + Note that the same cropping parameters will then be used for all videos. + If different video crops are desired, run ``analyze_videos`` on individual + videos with the corresponding cropping coordinates. + TFGPUinference (bool, optional, default=True): + Perform inference on GPU with TensorFlow code. Introduced in "Pretraining + boosts out-of-domain robustness for pose estimation" by Alexander Mathis, + Mert Yüksekgönül, Byron Rogers, Matthias Bethge, Mackenzie W. Mathis. + Source https://arxiv.org/abs/1909.11229 + dynamic (tuple(bool, float, int) triple (state, detectiontreshold, margin)): + If the state is true, then dynamic cropping will be performed. That means + that if an object is detected (i.e. any body part > detectiontreshold), + then object boundaries are computed according to the smallest/largest x + position and smallest/largest y position of all body parts. This window is + expanded by the margin and from then on only the posture within this crop + is analyzed (until the object is lost, i.e. Date: Fri, 29 Sep 2023 22:57:00 +0200 Subject: [PATCH 150/176] Add `env` variables and prefix to dj database --- element_deeplabcut/__init__.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/element_deeplabcut/__init__.py b/element_deeplabcut/__init__.py index e69de29..3ef2702 100644 --- a/element_deeplabcut/__init__.py +++ b/element_deeplabcut/__init__.py @@ -0,0 +1,17 @@ +import os +import datajoint as dj + +if "custom" not in dj.config: + dj.config["custom"] = {} + +# overwrite dj.config['custom'] values with environment variables if available + +dj.config["custom"]["database.prefix"] = os.getenv( + "DATABASE_PREFIX", dj.config["custom"].get("database.prefix", "") +) + +dj.config["custom"]["dlc_root_data_dir"] = os.getenv( + "DLC_ROOT_DATA_DIR", dj.config["custom"].get("dlc_root_data_dir", "") +) + +db_prefix = dj.config["custom"].get("database.prefix", "") \ No newline at end of file From 1e6c27ed0dce5e35ea713d26c64dd2008ba81482 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Fri, 29 Sep 2023 23:21:59 +0200 Subject: [PATCH 151/176] revert model.py and train.py to add in next PR --- element_deeplabcut/model.py | 36 +++++++++++++++--------------------- element_deeplabcut/train.py | 8 +++----- 2 files changed, 18 insertions(+), 26 deletions(-) diff --git a/element_deeplabcut/model.py b/element_deeplabcut/model.py index 1084920..097a5c8 100644 --- a/element_deeplabcut/model.py +++ b/element_deeplabcut/model.py @@ -609,8 +609,10 @@ def infer_output_dir(cls, key: dict, relative: bool = False, mkdir: bool = False @classmethod def generate( cls, - key: dict, + video_recording_key: dict, model_name: str, + *, + task_mode: str = None, analyze_videos_params: dict = None, ): """Insert PoseEstimationTask in inferred output dir. @@ -626,39 +628,31 @@ def generate( videotype, gputouse, save_as_csv, batchsize, cropping, TFGPUinference, dynamic, robust_nframes, allow_growth, use_shelve """ - + processed_dir = get_dlc_processed_data_dir() output_dir = cls.infer_output_dir( - {**key, "model_name": model_name}, + {**video_recording_key, "model_name": model_name}, relative=False, mkdir=True, ) - processed_dir = get_dlc_processed_data_dir() - - if processed_dir: - pose_estimation_output_dir = output_dir.relative_to( - processed_dir - ).as_posix() - else: - pose_estimation_output_dir = output_dir.as_posix() - - if key["task_mode"] is None: + if task_mode is None: try: _ = dlc_reader.PoseEstimation(output_dir) except FileNotFoundError: - key["task_mode"] = "trigger" + task_mode = "trigger" else: - key["task_mode"] = "load" + task_mode = "load" cls.insert1( { - **key, + **video_recording_key, "model_name": model_name, - "task_mode": key["task_mode"], + "task_mode": task_mode, "pose_estimation_params": analyze_videos_params, - "pose_estimation_output_dir": pose_estimation_output_dir, - }, - skip_duplicates=True, + "pose_estimation_output_dir": output_dir.relative_to( + processed_dir + ).as_posix(), + } ) insert_estimation_task = generate @@ -760,7 +754,7 @@ def make(self, key): self.BodyPartPosition.insert(body_parts) @classmethod - def coordinates_dataframe(cls, key: dict, body_parts: list = "all") -> pd.DataFrame: + def get_trajectory(cls, key: dict, body_parts: list = "all") -> pd.DataFrame: """Returns a pandas dataframe of coordinates of the specified body_part(s) Args: diff --git a/element_deeplabcut/train.py b/element_deeplabcut/train.py index f24722f..cfb8f0b 100644 --- a/element_deeplabcut/train.py +++ b/element_deeplabcut/train.py @@ -21,7 +21,7 @@ def activate( *, create_schema: bool = True, create_tables: bool = True, - linking_module: str = None + linking_module: str = None, ): """Activate this schema. @@ -195,9 +195,7 @@ def insert_new_params( if existing_paramset_idx == int(paramset_idx): # If existing_idx same: return # job done else: - cls.insert1( - param_dict, skip_duplicates=True - ) # if duplicate, will raise duplicate error + cls.insert1(param_dict) # if duplicate, will raise duplicate error @schema @@ -263,7 +261,7 @@ def make(self, key): project_path = find_full_path(get_dlc_root_data_dir(), project_path) # ---- Build and save DLC configuration (yaml) file ---- - _, dlc_config = dlc_reader.read_yaml(project_path, "config") # load existing + _, dlc_config = dlc_reader.read_yaml(project_path) # load existing dlc_config.update((TrainingParamSet & key).fetch1("params")) dlc_config.update( { From 5b317a91d72a5548699edde48fb81d134c77b19a Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Sat, 30 Sep 2023 00:01:32 +0200 Subject: [PATCH 152/176] Images from workflow-don't match the `element` --- images/attached_model_only.svg | 149 -------------------- images/attached_train_model.svg | 238 -------------------------------- 2 files changed, 387 deletions(-) delete mode 100644 images/attached_model_only.svg delete mode 100644 images/attached_train_model.svg diff --git a/images/attached_model_only.svg b/images/attached_model_only.svg deleted file mode 100644 index 639079b..0000000 --- a/images/attached_model_only.svg +++ /dev/null @@ -1,149 +0,0 @@ - - - - - - - - - -session.VideoRecording - -session.VideoRecording - - - -model.EstimationTask - - -model.EstimationTask - - - - - -session.VideoRecording->model.EstimationTask - - - - -model.Estimation - - -model.Estimation - - - - - -model.EstimationTask->model.Estimation - - - - -model.Estimation.BodyPartPosition - - -model.Estimation.BodyPartPosition - - - - - -model.Estimation->model.Estimation.BodyPartPosition - - - - -model.Model - - -model.Model - - - - - -model.Model->model.EstimationTask - - - - -model.Model.BodyPart - - -model.Model.BodyPart - - - - - -model.Model->model.Model.BodyPart - - - - -model.ModelEvaluation - - -model.ModelEvaluation - - - - - -model.Model->model.ModelEvaluation - - - - -model.BodyPart - - -model.BodyPart - - - - - -model.BodyPart->model.Model.BodyPart - - - - -model.BodyPart->model.Estimation.BodyPartPosition - - - - -session.Session - - -session.Session - - - - - -session.Session->session.VideoRecording - - - - -subject.Subject - - -subject.Subject - - - - - -subject.Subject->session.Session - - - - diff --git a/images/attached_train_model.svg b/images/attached_train_model.svg deleted file mode 100644 index 601c17c..0000000 --- a/images/attached_train_model.svg +++ /dev/null @@ -1,238 +0,0 @@ - - - - - - - - - -session.VideoRecording - -session.VideoRecording - - - -model.EstimationTask - - -model.EstimationTask - - - - - -session.VideoRecording->model.EstimationTask - - - - -train.VideoSet.VideoRecording - - -train.VideoSet.VideoRecording - - - - - -session.VideoRecording->train.VideoSet.VideoRecording - - - - -model.Estimation - - -model.Estimation - - - - - -model.EstimationTask->model.Estimation - - - - -model.Estimation.BodyPartPosition - - -model.Estimation.BodyPartPosition - - - - - -model.Estimation->model.Estimation.BodyPartPosition - - - - -subject.Subject - - -subject.Subject - - - - - -session.Session - - -session.Session - - - - - -subject.Subject->session.Session - - - - -session.Session->session.VideoRecording - - - - -model.BodyPart - - -model.BodyPart - - - - - -model.BodyPart->model.Estimation.BodyPartPosition - - - - -model.Model.BodyPart - - -model.Model.BodyPart - - - - - -model.BodyPart->model.Model.BodyPart - - - - -train.VideoSet.File - - -train.VideoSet.File - - - - - -model.Model - - -model.Model - - - - - -model.Model->model.EstimationTask - - - - -model.Model->model.Model.BodyPart - - - - -model.ModelEvaluation - - -model.ModelEvaluation - - - - - -model.Model->model.ModelEvaluation - - - - -train.ModelTraining - - -train.ModelTraining - - - - - -train.TrainingParamSet - - -train.TrainingParamSet - - - - - -train.TrainingParamSet->model.Model - - - - -train.TrainingTask - - -train.TrainingTask - - - - - -train.TrainingParamSet->train.TrainingTask - - - - -train.TrainingTask->train.ModelTraining - - - - -train.VideoSet - - -train.VideoSet - - - - - -train.VideoSet->train.VideoSet.VideoRecording - - - - -train.VideoSet->train.VideoSet.File - - - - -train.VideoSet->train.TrainingTask - - - - From 0df510d88c712d779a2b6c1242866cf4b7c898a9 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Sat, 30 Sep 2023 00:09:15 +0200 Subject: [PATCH 153/176] rename image names to mirror ca-imaging --- images/{diagram_flowchart.drawio => flowchart.drawio} | 0 images/{diagram_flowchart.svg => flowchart.svg} | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename images/{diagram_flowchart.drawio => flowchart.drawio} (100%) rename images/{diagram_flowchart.svg => flowchart.svg} (100%) diff --git a/images/diagram_flowchart.drawio b/images/flowchart.drawio similarity index 100% rename from images/diagram_flowchart.drawio rename to images/flowchart.drawio diff --git a/images/diagram_flowchart.svg b/images/flowchart.svg similarity index 100% rename from images/diagram_flowchart.svg rename to images/flowchart.svg From 07522a7a6330268c80371e87a8c7cc8838e8e6fc Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Sat, 30 Sep 2023 00:09:44 +0200 Subject: [PATCH 154/176] update flowchart URL --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c55f8ac..f6146e1 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ and automate data collection and analysis for neuroscience experiments. Each El a modular pipeline for data storage and processing with corresponding database tables that can be combined with other Elements to assemble a fully functional pipeline. -![diagram](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/diagram_flowchart.svg) +![flowchart](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/flowchart.svg) Installation and usage instructions can be found at the [Element documentation](https://datajoint.com/docs/elements/element-deeplabcut). From c0b6ed23e43981d7ac40f7b2aa7c8f695f321354 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 2 Oct 2023 16:04:42 +0000 Subject: [PATCH 155/176] update current_project_folder->from_top_tracking --- .devcontainer/Dockerfile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 3d3f5ef..74fcb03 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -43,8 +43,8 @@ ENV DJ_USER root ENV DJ_PASS simple ENV DLC_ROOT_DATA_DIR /workspaces/element-deeplabcut/example_data -ENV CURRENT_PROJECT_FOLDER Top_tracking-DataJoint-2023-08-03 -ENV DATABASE_PREFIX neuro_ +ENV CURRENT_PROJECT_FOLDER from_top_tracking +ENV DATABASE_PREFIX dlc_ USER vscode CMD bash -c "sudo rm /var/run/docker.pid; sudo dockerd" \ No newline at end of file From cbc6e2724357ecb59a61214edf6ee103ee5111f1 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 2 Oct 2023 18:22:23 +0200 Subject: [PATCH 156/176] update README based on ca-imaging element --- README.md | 76 ++++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 64 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index f6146e1..be39d49 100644 --- a/README.md +++ b/README.md @@ -4,22 +4,74 @@ DataJoint Element for markerless pose estimation with [DeepLabCut](https://www.deeplabcut.org/). DataJoint Elements collectively standardize and automate data collection and analysis for neuroscience experiments. Each Element is a modular pipeline for data storage and processing with corresponding database -tables that can be combined with other Elements to assemble a fully functional pipeline. +tables that can be combined with other Elements to assemble a fully functional pipeline. This repository also provides a tutorial environment and notebooks to learn the pipeline. + +## Experiment Flowchart ![flowchart](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/flowchart.svg) -Installation and usage instructions can be found at the -[Element documentation](https://datajoint.com/docs/elements/element-deeplabcut). +## Data Pipeline Diagram + +![pipeline](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/diagram_dlc.svg) + +## Getting Started + ++ Please fork this repository + ++ Clone the repository to your computer + + ```bash + git clone https://github.com//element-deeplabcut + ``` + ++ Install with `pip` + + ```bash + pip install -e . + ``` + ++ [Interactive tutorial on GitHub Codespaces](#interactive-tutorial) + ++ [Documentation](https://datajoint.com/docs/elements/element-deeplabcut) + +## Support + ++ If you need help getting started or run into any errors, please open a GitHub Issue +or contact our team by email at support@datajoint.com. + +## Interactive Tutorial + ++ The easiest way to learn about DataJoint Elements is to use the tutorial notebooks within the included interactive environment configured using [Dev Container](https://containers.dev/). + +### Launch Environment + +Here are some options that provide a great experience: + +- (*recommended*) Cloud-based Environment + - Launch using [GitHub Codespaces](https://github.com/features/codespaces) using the `+` option which will `Create codespace on main` in the codebase repository on your fork with default options. For more control, see the `...` where you may create `New with options...`. + - Build time for a codespace is a few minutes. This is done infrequently and cached for convenience. + - Start time for a codespace is less than 1 minute. This will pull the built codespace from cache when you need it. + - *Tip*: Each month, GitHub renews a [free-tier](https://docs.github.com/en/billing/managing-billing-for-github-codespaces/about-billing-for-github-codespaces#monthly-included-storage-and-core-hours-for-personal-accounts) quota of compute and storage. Typically we run into the storage limits before anything else since Codespaces consume storage while stopped. It is best to delete Codespaces when not actively in use and recreate when needed. We'll soon be creating prebuilds to avoid larger build times. Once any portion of your quota is reached, you will need to wait for it to be reset at the end of your cycle or add billing info to your GitHub account to handle overages. + - *Tip*: GitHub auto names the codespace but you can rename the codespace so that it is easier to identify later. + +- Local Environment + > *Note: Access to example data is currently limited to MacOS and Linux due to the s3fs utility. Windows users are recommended to use the above environment.* + - Install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) + - Install [Docker](https://docs.docker.com/get-docker/) + - Install [VSCode](https://code.visualstudio.com/) + - Install the VSCode [Dev Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) + - `git clone` the codebase repository and open it in VSCode + - Use the `Dev Containers extension` to `Reopen in Container` (More info is in the `Getting started` included with the extension.) + +You will know your environment has finished loading once you either see a terminal open related to `Running postStartCommand` with a final message of `Done` or the `README.md` is opened in `Preview`. +Once the environment has launched, please run the following command in the terminal: +``` +MYSQL_VER=8.0 docker compose -f docker-compose-db.yaml up --build -d +``` -The DataJoint Workflow for DeepLabCut combines multiple DataJoint Elements for -markerless pose estimation with [DeepLabCut](https://www.deeplabcut.org/). DataJoint -Elements collectively standardize and automate data collection and analysis for -neuroscience experiments. Each Element is a modular pipeline for data storage and -processing with corresponding database tables that can be combined with other Elements -to assemble a fully functional pipeline. +### Instructions -Installation and usage instructions can be found at the -[Element documentation](https://datajoint.com/docs/elements/element-deeplabcut). +1. We recommend you start by navigating to the `notebooks` directory on the left panel and go through the `tutorial.ipynb` Jupyter notebook. Execute the cells in the notebook to begin your walk through of the tutorial. -![element-deeplabcut diagram](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/diagram_dlc.svg) +1. Once you are done, see the options available to you in the menu in the bottom-left corner. For example, in Codespace you will have an option to `Stop Current Codespace` but when running Dev Container on your own machine the equivalent option is `Reopen folder locally`. By default, GitHub will also automatically stop the Codespace after 30 minutes of inactivity. Once the Codespace is no longer being used, we recommend deleting the Codespace. From 7ce600754d6301137e16bbddcd1622204671069a Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 2 Oct 2023 20:08:30 +0200 Subject: [PATCH 157/176] solve readme conflict --- README.md | 25 ------------------------- 1 file changed, 25 deletions(-) diff --git a/README.md b/README.md index 15fe3d8..6768d38 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,6 @@ tables that can be combined with other Elements to assemble a fully functional p + Please fork this repository -<<<<<<< HEAD + Clone the repository to your computer ```bash @@ -34,27 +33,6 @@ tables that can be combined with other Elements to assemble a fully functional p ``` + [Interactive tutorial on GitHub Codespaces](#interactive-tutorial) -======= -![element-deeplabcut diagram](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/diagram_dlc.svg) - -## Experiment Flowchart - -![flowchart](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/flowchart.svg) - -## Data Pipeline Diagram - -![pipeline](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/pipeline.svg) - -## Getting Started - -+ Install from PyPI - - ```bash - pip install element-deeplabcut - ``` - -+ [Interactive tutorial](https://github.com/datajoint/workflow-deeplabcut) ->>>>>>> c94f33679d2837b7f8111816f56f5f199d27d917 + [Documentation](https://datajoint.com/docs/elements/element-deeplabcut) @@ -62,7 +40,6 @@ tables that can be combined with other Elements to assemble a fully functional p + If you need help getting started or run into any errors, please open a GitHub Issue or contact our team by email at support@datajoint.com. -<<<<<<< HEAD ## Interactive Tutorial @@ -100,5 +77,3 @@ MYSQL_VER=8.0 docker compose -f docker-compose-db.yaml up --build -d 1. We recommend you start by navigating to the `notebooks` directory on the left panel and go through the `tutorial.ipynb` Jupyter notebook. Execute the cells in the notebook to begin your walk through of the tutorial. 1. Once you are done, see the options available to you in the menu in the bottom-left corner. For example, in Codespace you will have an option to `Stop Current Codespace` but when running Dev Container on your own machine the equivalent option is `Reopen folder locally`. By default, GitHub will also automatically stop the Codespace after 30 minutes of inactivity. Once the Codespace is no longer being used, we recommend deleting the Codespace. -======= ->>>>>>> c94f33679d2837b7f8111816f56f5f199d27d917 From 7980338699d33bbfcda4c6c6884f4e1404283a68 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 2 Oct 2023 20:10:21 +0200 Subject: [PATCH 158/176] add current_project_folder env variable to __init_ --- element_deeplabcut/__init__.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/element_deeplabcut/__init__.py b/element_deeplabcut/__init__.py index 3ef2702..1dd032b 100644 --- a/element_deeplabcut/__init__.py +++ b/element_deeplabcut/__init__.py @@ -14,4 +14,7 @@ "DLC_ROOT_DATA_DIR", dj.config["custom"].get("dlc_root_data_dir", "") ) +dj.config["custom"]["current_project_folder"] = os.getenv( + "CURRENT_PROJECT_FOLDER", dj.config["custom"].get("current_project_folder", "") +) db_prefix = dj.config["custom"].get("database.prefix", "") \ No newline at end of file From 61fbfea3b78c799d2ca97841ca865430f7708554 Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Tue, 3 Oct 2023 14:06:49 +0200 Subject: [PATCH 159/176] Update .devcontainer/Dockerfile Co-authored-by: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> --- .devcontainer/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 74fcb03..4256845 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -44,7 +44,7 @@ ENV DJ_PASS simple ENV DLC_ROOT_DATA_DIR /workspaces/element-deeplabcut/example_data ENV CURRENT_PROJECT_FOLDER from_top_tracking -ENV DATABASE_PREFIX dlc_ +ENV DATABASE_PREFIX neuro_ USER vscode CMD bash -c "sudo rm /var/run/docker.pid; sudo dockerd" \ No newline at end of file From 61e73bfafd8f4874be990997d15dbbac4f75abf7 Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Tue, 3 Oct 2023 14:06:59 +0200 Subject: [PATCH 160/176] Update .devcontainer/devcontainer.json Co-authored-by: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> --- .devcontainer/devcontainer.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index d63ea66..cc78b6b 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -6,7 +6,7 @@ "remoteEnv": { "LOCAL_WORKSPACE_FOLDER": "${localWorkspaceFolder}" }, - "onCreateCommand": "mkdir -p ${DLC_ROOT_DATA_DIR} &&pip install -e .", + "onCreateCommand": "mkdir -p ${DLC_ROOT_DATA_DIR} && pip install -e .", "postStartCommand": "docker volume prune -f && s3fs ${DJ_PUBLIC_S3_LOCATION} ${DLC_ROOT_DATA_DIR} -o nonempty,multipart_size=530,endpoint=us-east-1,url=http://s3.amazonaws.com,public_bucket=1", "hostRequirements": { "cpus": 4, From 1c7bec4889b47dd00737567a86286c8ea890bb9c Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Tue, 3 Oct 2023 14:07:09 +0200 Subject: [PATCH 161/176] Update CONTRIBUTING.md Co-authored-by: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> --- CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e04d170..2bd0f49 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,5 +1,5 @@ # Contribution Guidelines This project follows the -[DataJoint Contribution Guidelines](https://datajoint.com/docs/community/contribute/). +[DataJoint Contribution Guidelines](https://datajoint.com/docs/about/contribute/). Please reference the link for more full details. From 525924da99aabd040719a74e3633604141426e41 Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Tue, 3 Oct 2023 14:07:22 +0200 Subject: [PATCH 162/176] Update README.md Co-authored-by: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6768d38..58ef41b 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ tables that can be combined with other Elements to assemble a fully functional p ## Data Pipeline Diagram -![pipeline](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/diagram_dlc.svg) +![pipeline](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/pipeline_dlc.svg) ## Getting Started From 2e497081f3e9cc97b746ef67ab38963c476cf55d Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Tue, 3 Oct 2023 14:07:36 +0200 Subject: [PATCH 163/176] Update setup.py Co-authored-by: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 9bd4b8d..8e63b44 100644 --- a/setup.py +++ b/setup.py @@ -13,7 +13,7 @@ setup( name=pkg_name.replace("_", "-"), version=__version__, - description="DeepLabCut DataJoint Element", + description="DataJoint Element for Continuous Behavior Tracking via DeepLabCut", long_description=long_description, long_description_content_type="text/markdown", author="DataJoint", From 19dc567a5d5121707d9f24256cbd26679431c9c6 Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Tue, 3 Oct 2023 14:07:48 +0200 Subject: [PATCH 164/176] Update setup.py Co-authored-by: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 8e63b44..d3e2fc2 100644 --- a/setup.py +++ b/setup.py @@ -20,7 +20,7 @@ author_email="info@datajoint.com", license="MIT", url=f'https://github.com/datajoint/{pkg_name.replace("_", "-")}', - keywords="neuroscience behavior pose-estimation science datajoint", + keywords="neuroscience behavior deeplabcut pose-estimation science datajoint", packages=find_packages(exclude=["contrib", "docs", "tests*"]), scripts=[], install_requires=[ From ed8de3a9ce275d25fc9f680a780fccd000a25dc2 Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Tue, 3 Oct 2023 14:07:58 +0200 Subject: [PATCH 165/176] Update setup.py Co-authored-by: Kushal Bakshi <52367253+kushalbakshi@users.noreply.github.com> --- setup.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/setup.py b/setup.py index d3e2fc2..e501313 100644 --- a/setup.py +++ b/setup.py @@ -41,10 +41,10 @@ ], "dlc_gui": ["deeplabcut[gui]"], "elements": [ - "element-lab>=0.2.0", - "element-animal>=0.1.5", - "element-session>=0.1.2", - "element-interface>=0.5.0", + "element-lab>=0.3.0", + "element-animal>=0.1.8", + "element-session>=0.1.5", + "element-interface>=0.6.0", ], }, ) From 930c6195de33b7403278a05a7b70a1f7391ff9f5 Mon Sep 17 00:00:00 2001 From: Thinh Nguyen Date: Thu, 12 Oct 2023 10:23:13 -0500 Subject: [PATCH 166/176] use `v2` example data --- .devcontainer/docker-compose.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index efa55ca..b2b6b7e 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -10,7 +10,7 @@ services: extra_hosts: - fakeservices.datajoint.io:127.0.0.1 environment: - - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/workflow-dlc-data/v1 + - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/workflow-dlc-data/v2 devices: - /dev/fuse cap_add: From f1fad23923588599750767203538e78fa83cd56b Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Thu, 12 Oct 2023 15:35:03 +0000 Subject: [PATCH 167/176] Add a tutorial quick test --- .devcontainer/Dockerfile | 4 +- .devcontainer/docker-compose.yaml | 2 +- notebooks/tutorial.ipynb | 709 ++++++++++++++++++++++++++++++ notebooks/tutorial_pipeline.py | 93 ++++ 4 files changed, 806 insertions(+), 2 deletions(-) create mode 100644 notebooks/tutorial.ipynb create mode 100644 notebooks/tutorial_pipeline.py diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 4256845..a09b01f 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -47,4 +47,6 @@ ENV CURRENT_PROJECT_FOLDER from_top_tracking ENV DATABASE_PREFIX neuro_ USER vscode -CMD bash -c "sudo rm /var/run/docker.pid; sudo dockerd" \ No newline at end of file +CMD bash -c "sudo rm /var/run/docker.pid; sudo dockerd" + +ENV LD_LIBRARY_PATH="/lib:/opt/conda/lib" \ No newline at end of file diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml index efa55ca..b2b6b7e 100644 --- a/.devcontainer/docker-compose.yaml +++ b/.devcontainer/docker-compose.yaml @@ -10,7 +10,7 @@ services: extra_hosts: - fakeservices.datajoint.io:127.0.0.1 environment: - - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/workflow-dlc-data/v1 + - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/workflow-dlc-data/v2 devices: - /dev/fuse cap_add: diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb new file mode 100644 index 0000000..6d3cd59 --- /dev/null +++ b/notebooks/tutorial.ipynb @@ -0,0 +1,709 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "assert os.path.basename(os.getcwd())=='element-deeplabcut', (\"Please move to the \"\n", + " + \"element directory\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj\n", + "from pathlib import Path\n", + "import yaml" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-10-12 01:17:45,270][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", + "[2023-10-12 01:17:45,278][INFO]: Connected root@fakeservices.datajoint.io:3306\n" + ] + }, + { + "data": { + "text/plain": [ + "DataJoint connection (connected) root@fakeservices.datajoint.io:3306" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.conn()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-10-12 01:17:45,437][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n" + ] + } + ], + "source": [ + "from tutorial_pipeline import lab, subject, session, train, model " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.list_schemas()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from element_interface.utils import find_full_path\n", + "data_dir = find_full_path(dj.config['custom']['dlc_root_data_dir'], # root from config\n", + " dj.config['custom']['current_project_folder']) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PosixPath('/workspaces/element-deeplabcut/example_data/from_top_tracking')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_dir" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "### DLC needs absolute paths\n", + "### DataJoint uses relative paths\n", + "\n", + "#dj_config_file -> modify the config uploaded to have the full paths instead, we don't change raw data. \n", + "\n", + "### DLC Project\n", + "dlc_project_path_abs = Path(dj.config[\"custom\"][\"dlc_root_data_dir\"]) / Path(\n", + " dj.config[\"custom\"][\"current_project_folder\"]\n", + ") # use pathlib to join; abs path\n", + "\n", + "dlc_project_folder = Path(\n", + " dj.config[\"custom\"][\"current_project_folder\"]\n", + ") # relative path\n", + "\n", + "### Config file\n", + "config_file_abs = dlc_project_path_abs / \"config.yaml\" # abs path\n", + "assert (\n", + " config_file_abs.exists()\n", + "), \"Please check the that you have the from_top_tracking folder\"\n", + "\n", + "config_file_rel = config_file_abs.relative_to(Path(dj.config[\"custom\"][\"dlc_root_data_dir\"]).parent)\n", + "\n", + "### Labeled-data\n", + "#labeled_data_path_abs = dlc_project_path_abs / \"labeled-data\" / \"train_video\"\n", + "labeled_data_path_abs = dlc_project_path_abs / \"labeled-data\" \n", + "labeled_files_rel = os.listdir(labeled_data_path_abs / \"train1\") \n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PosixPath('example_data/from_top_tracking/config.yaml')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config_file_rel" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['CollectedData_DJ.csv',\n", + " 'CollectedData_DJ.h5',\n", + " 'img00674.png',\n", + " 'img02507.png',\n", + " 'img03417.png',\n", + " 'img04243.png',\n", + " 'img07087.png',\n", + " 'img10817.png',\n", + " 'img10854.png',\n", + " 'img10997.png',\n", + " 'img13211.png',\n", + " 'img13538.png',\n", + " 'img15154.png',\n", + " 'img15553.png',\n", + " 'img17741.png',\n", + " 'img20640.png',\n", + " 'img20796.png',\n", + " 'img23499.png',\n", + " 'img26252.png',\n", + " 'img26419.png',\n", + " 'img29448.png',\n", + " 'img29883.png',\n", + " 'img32079.png',\n", + " 'img32500.png',\n", + " 'img33628.png',\n", + " 'img35209.png',\n", + " 'img36905.png',\n", + " 'img37720.png',\n", + " 'img39434.png',\n", + " 'img40578.png',\n", + " 'img40733.png',\n", + " 'img41989.png',\n", + " 'img43673.png',\n", + " 'img45967.png',\n", + " 'img46726.png',\n", + " 'img46858.png',\n", + " 'img47791.png',\n", + " 'img51645.png',\n", + " 'img54116.png',\n", + " 'img56508.png',\n", + " 'img57346.png',\n", + " 'img59317.png']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labeled_files_rel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(\n", + " dj.Diagram(subject) \n", + " + dj.Diagram(lab) \n", + " + dj.Diagram(session) \n", + " + dj.Diagram(model) \n", + " + dj.Diagram(train)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(model) + dj.Diagram(train)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    subject_nickname

    \n", + " \n", + "
    \n", + "

    sex

    \n", + " \n", + "
    \n", + "

    subject_birth_date

    \n", + " \n", + "
    \n", + "

    subject_description

    \n", + " \n", + "
    subject6F2020-01-01hneih_E105
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*subject subject_nickna sex subject_birth_ subject_descri\n", + "+----------+ +------------+ +-----+ +------------+ +------------+\n", + "subject6 F 2020-01-01 hneih_E105 \n", + " (Total: 1)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Subject and Session tables\n", + "subject.Subject.insert1(\n", + " dict(\n", + " subject=\"subject6\",\n", + " sex=\"F\",\n", + " subject_birth_date=\"2020-01-01\",\n", + " subject_description=\"hneih_E105\",\n", + " ),\n", + " skip_duplicates=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Definition of the dictionary named \"session_keys\"\n", + "session_keys = [\n", + " dict(subject=\"subject6\", session_datetime=\"2021-06-02 14:04:22\"),\n", + " dict(subject=\"subject6\", session_datetime=\"2021-06-03 14:43:10\"),\n", + "]\n", + "\n", + "#Insert this dictionary in the Session table\n", + "session.Session.insert(session_keys, skip_duplicates=True)\n", + "session.Session()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "session.SessionDirectory() #for the worker? is it needed?" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Videoset table \n", + "train.VideoSet.insert1({\"video_set_id\": 1}, skip_duplicates=True)\n", + "\n", + "for idx, filename in enumerate(labeled_files_rel):\n", + " train.VideoSet.File.insert1(\n", + " {\n", + " \"video_set_id\": 1, \n", + " \"file_id\": idx, \n", + " \"file_path\": (dlc_project_folder / \"labeled-data\" / \"train1\" / filename).as_posix() \n", + " #\"file_path\": dlc_project_folder / \"labeled-data\" / \"train_video\" / filename \n", + " },\n", + " skip_duplicates=True\n", + " ) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vr_file_insert = dict(subject='test_sub',session_id=1,recording_id=0, file_id=0, file_path='')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([], dtype=object)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.VideoRecording.File().fetch('file_path')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vr_file_insert = dict(subject='test_sub',session_id=3,recording_id=0,file_id=0,file_path='from_top_tracking/videos/test_video.mp4')\n", + "model.VideoRecording.File.insert1(vr_file_insert)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    file_id

    \n", + " \n", + "
    \n", + "

    file_path

    \n", + " filepath of video, relative to root data directory\n", + "
    \n", + " \n", + "

    Total: 0

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *file_id file_path \n", + "+---------+ +------------+ +------------+ +---------+ +-----------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.VideoRecording.File()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.BodyPart.heading\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- DLC Model specification to be inserted ---\n", + "\tmodel_name: from_top_tracking_model_test\n", + "\tmodel_description: Model in example data: from_top_tracking model\n", + "\tscorer: DLCmobnet100fromtoptrackingFeb23shuffle1\n", + "\ttask: from_top_tracking\n", + "\tdate: Feb23\n", + "\titeration: 0\n", + "\tsnapshotindex: -1\n", + "\tshuffle: 1\n", + "\ttrainingsetindex: 0\n", + "\tproject_path: from_top_tracking\n", + "\tparamset_idx: 0\n", + "\t-- Template/Contents of config.yaml --\n", + "\t\tTask: from_top_tracking\n", + "\t\tscorer: DJ\n", + "\t\tdate: Feb23\n", + "\t\tvideo_sets: {'videos/train1.mp4': {'crop': '0, 500, 0, 500'}}\n", + "\t\tbodyparts: ['head', 'bodycenter', 'tailbase']\n", + "\t\tstart: 0\n", + "\t\tstop: 1\n", + "\t\tnumframes2pick: 20\n", + "\t\tpcutoff: 0.6\n", + "\t\tdotsize: 3\n", + "\t\talphavalue: 0.7\n", + "\t\tcolormap: viridis\n", + "\t\tTrainingFraction: [0.95]\n", + "\t\titeration: 0\n", + "\t\tdefault_net_type: resnet_50\n", + "\t\tsnapshotindex: -1\n", + "\t\tbatch_size: 8\n", + "\t\tcropping: False\n", + "\t\tx1: 0\n", + "\t\tx2: 640\n", + "\t\ty1: 277\n", + "\t\ty2: 624\n", + "\t\tcorner2move2: [50, 50]\n", + "\t\tmove2corner: True\n", + "\t\tcroppedtraining: None\n", + "\t\tdefault_augmenter: default\n", + "\t\tidentity: None\n", + "\t\tmaxiters: 5\n", + "\t\tmodelprefix: \n", + "\t\tmultianimalproject: False\n", + "\t\tscorer_legacy: False\n", + "\t\tshuffle: 1\n", + "\t\tskeleton: [['bodypart1', 'bodypart2'], ['objectA', 'bodypart3']]\n", + "\t\tskeleton_color: black\n", + "\t\ttrain_fraction: 0.95\n", + "\t\ttrainingsetindex: 0\n", + "\t\tproject_path: /workspaces/element-deeplabcut/example_data/from_top_tracking\n" + ] + }, + { + "ename": "PermissionError", + "evalue": "[Errno 1] Operation not permitted: '/workspaces/element-deeplabcut/example_data/from_top_tracking/dj_dlc_config.yaml'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mPermissionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[27], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model\u001b[39m.\u001b[39;49mModel\u001b[39m.\u001b[39;49minsert_new_model(model_name\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mfrom_top_tracking_model_test\u001b[39;49m\u001b[39m'\u001b[39;49m,dlc_config\u001b[39m=\u001b[39;49mconfig_file_rel,\n\u001b[1;32m 2\u001b[0m shuffle\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m,\n\u001b[1;32m 3\u001b[0m trainingsetindex\u001b[39m=\u001b[39;49m\u001b[39m0\u001b[39;49m,\n\u001b[1;32m 4\u001b[0m model_description\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mModel in example data: from_top_tracking model\u001b[39;49m\u001b[39m'\u001b[39;49m,\n\u001b[1;32m 5\u001b[0m paramset_idx\u001b[39m=\u001b[39;49m\u001b[39m0\u001b[39;49m,\n\u001b[1;32m 6\u001b[0m params\u001b[39m=\u001b[39;49m{\u001b[39m\"\u001b[39;49m\u001b[39msnapshotindex\u001b[39;49m\u001b[39m\"\u001b[39;49m:\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m,\n\u001b[1;32m 7\u001b[0m \u001b[39m\"\u001b[39;49m\u001b[39mproject_path\u001b[39;49m\u001b[39m\"\u001b[39;49m:dlc_project_folder})\n", + "File \u001b[0;32m/workspaces/element-deeplabcut/element_deeplabcut/model.py:456\u001b[0m, in \u001b[0;36mModel.insert_new_model\u001b[0;34m(cls, model_name, dlc_config, shuffle, trainingsetindex, project_path, model_description, model_prefix, paramset_idx, prompt, params)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[39mreturn\u001b[39;00m\n\u001b[1;32m 455\u001b[0m \u001b[39m# ---- Save DJ-managed config ----\u001b[39;00m\n\u001b[0;32m--> 456\u001b[0m _ \u001b[39m=\u001b[39m dlc_reader\u001b[39m.\u001b[39;49msave_yaml(project_path, dlc_config)\n\u001b[1;32m 457\u001b[0m \u001b[39m# ____ Insert into table ----\u001b[39;00m\n\u001b[1;32m 458\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39mconnection\u001b[39m.\u001b[39mtransaction:\n", + "File \u001b[0;32m/workspaces/element-deeplabcut/element_deeplabcut/readers/dlc_reader.py:247\u001b[0m, in \u001b[0;36msave_yaml\u001b[0;34m(output_dir, config_dict, filename, mkdir)\u001b[0m\n\u001b[1;32m 244\u001b[0m filename \u001b[39m=\u001b[39m filename\u001b[39m.\u001b[39msplit(\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m)[\u001b[39m0\u001b[39m]\n\u001b[1;32m 246\u001b[0m output_filepath \u001b[39m=\u001b[39m Path(output_dir) \u001b[39m/\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mfilename\u001b[39m}\u001b[39;00m\u001b[39m.yaml\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m--> 247\u001b[0m write_config(output_filepath, config_dict)\n\u001b[1;32m 248\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mstr\u001b[39m(output_filepath)\n", + "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/deeplabcut/utils/auxiliaryfunctions.py:217\u001b[0m, in \u001b[0;36mwrite_config\u001b[0;34m(configname, cfg)\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mwrite_config\u001b[39m(configname, cfg):\n\u001b[1;32m 214\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 215\u001b[0m \u001b[39m Write structured config file.\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 217\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(configname, \u001b[39m\"\u001b[39;49m\u001b[39mw\u001b[39;49m\u001b[39m\"\u001b[39;49m) \u001b[39mas\u001b[39;00m cf:\n\u001b[1;32m 218\u001b[0m cfg_file, ruamelFile \u001b[39m=\u001b[39m create_config_template(\n\u001b[1;32m 219\u001b[0m cfg\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mmultianimalproject\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m 220\u001b[0m )\n\u001b[1;32m 221\u001b[0m \u001b[39mfor\u001b[39;00m key \u001b[39min\u001b[39;00m cfg\u001b[39m.\u001b[39mkeys():\n", + "\u001b[0;31mPermissionError\u001b[0m: [Errno 1] Operation not permitted: '/workspaces/element-deeplabcut/example_data/from_top_tracking/dj_dlc_config.yaml'" + ] + } + ], + "source": [ + "model.Model.insert_new_model(model_name='from_top_tracking_model_test',dlc_config=config_file_rel,\n", + " shuffle=1,\n", + " trainingsetindex=0,\n", + " model_description='Model in example data: from_top_tracking model',\n", + " paramset_idx=0,\n", + " params={\"snapshotindex\":-1,\n", + " \"project_path\":dlc_project_folder})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.Model()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dlc_project_path_abs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py new file mode 100644 index 0000000..3201358 --- /dev/null +++ b/notebooks/tutorial_pipeline.py @@ -0,0 +1,93 @@ +import datajoint as dj +from collections import abc +from element_lab import lab +from element_animal import subject +from element_session import session_with_datetime as session +from element_deeplabcut import train, model + +from element_animal.subject import Subject +from element_lab.lab import Source, Lab, Protocol, User, Project + +__all__ = [ + "Subject", + "Source", + "Lab", + "Protocol", + "User", + "Project", + "Session", +] + +if "custom" not in dj.config: + dj.config["custom"] = {} + +db_prefix = dj.config["custom"].get("database.prefix", "") + + +def get_dlc_root_data_dir() -> list: + """Returns a list of root directories for Element DeepLabCut""" + dlc_root_dirs = dj.config.get("custom", {}).get("dlc_root_data_dir") + if not dlc_root_dirs: + return None + elif not isinstance(dlc_root_dirs, abc.Sequence): + return list(dlc_root_dirs) + else: + return dlc_root_dirs + + +def get_dlc_processed_data_dir() -> str: + """Returns an output directory relative to custom 'dlc_output_dir' root""" + from pathlib import Path + + dlc_output_dir = dj.config.get("custom", {}).get("dlc_output_dir") + if dlc_output_dir: + return Path(dlc_output_dir) + else: + return None + + +# Activate "lab", "subject", "session" schema ------------- + +lab.activate(db_prefix + "lab") + +subject.activate(db_prefix + "subject", linking_module=__name__) + +Experimenter = lab.User +Session = session.Session +session.activate(db_prefix + "session", linking_module=__name__) + +# Activate equipment table ------------------------------------ + + +@lab.schema +class Device(dj.Lookup): + """Table for managing lab equipment. + + In Element DeepLabCut, this table is referenced by `model.VideoRecording`. + The primary key is also used to generate inferred output directories when + running pose estimation inference. Refer to the `definition` attribute + for the table design. + + Attributes: + device ( varchar(32) ): Device short name. + modality ( varchar(64) ): Modality for which this device is used. + description ( varchar(256) ): Optional. Description of device. + """ + + definition = """ + device : varchar(32) + --- + modality : varchar(64) + description=null : varchar(256) + """ + contents = [ + ["Camera1", "Pose Estimation", "Panasonic HC-V380K"], + ["Camera2", "Pose Estimation", "Panasonic HC-V770K"], + ] + + +# Activate DeepLabCut schema ----------------------------------- + + +train.activate(db_prefix + "train", linking_module=__name__) +model.activate(db_prefix + "model", linking_module=__name__) From 2f41aa3f57a803d33a5695f1d4a1b0fd3f7a35e4 Mon Sep 17 00:00:00 2001 From: Thinh Nguyen Date: Thu, 12 Oct 2023 11:43:52 -0500 Subject: [PATCH 168/176] Update model.py --- element_deeplabcut/model.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/element_deeplabcut/model.py b/element_deeplabcut/model.py index 097a5c8..3a17253 100644 --- a/element_deeplabcut/model.py +++ b/element_deeplabcut/model.py @@ -453,7 +453,10 @@ def insert_new_model( print("Canceled insert.") return # ---- Save DJ-managed config ---- - _ = dlc_reader.save_yaml(project_path, dlc_config) + try: + _ = dlc_reader.save_yaml(project_path, dlc_config) + except PermissionError: + pass # ____ Insert into table ---- with cls.connection.transaction: cls.insert1(model_dict) From 477a355f5c63b88b3f2b19ed7436a8220715dd37 Mon Sep 17 00:00:00 2001 From: Thinh Nguyen Date: Thu, 12 Oct 2023 16:45:23 +0000 Subject: [PATCH 169/176] update codespace and mounting strategy --- .devcontainer/Dockerfile | 5 +++-- .devcontainer/devcontainer.json | 4 ++-- element_deeplabcut/__init__.py | 5 +++-- 3 files changed, 8 insertions(+), 6 deletions(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 4256845..b47bd31 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -42,8 +42,9 @@ ENV DJ_HOST fakeservices.datajoint.io ENV DJ_USER root ENV DJ_PASS simple -ENV DLC_ROOT_DATA_DIR /workspaces/element-deeplabcut/example_data -ENV CURRENT_PROJECT_FOLDER from_top_tracking +ENV DATA_MOUNTPOINT /workspaces/element-deeplabcut/example_data +ENV DLC_ROOT_DATA_DIR $DATA_MOUNTPOINT/inbox +ENV DLC_PROCESSED_DATA_DIR $DATA_MOUNTPOINT/outbox ENV DATABASE_PREFIX neuro_ USER vscode diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index cc78b6b..4a3db1b 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -6,8 +6,8 @@ "remoteEnv": { "LOCAL_WORKSPACE_FOLDER": "${localWorkspaceFolder}" }, - "onCreateCommand": "mkdir -p ${DLC_ROOT_DATA_DIR} && pip install -e .", - "postStartCommand": "docker volume prune -f && s3fs ${DJ_PUBLIC_S3_LOCATION} ${DLC_ROOT_DATA_DIR} -o nonempty,multipart_size=530,endpoint=us-east-1,url=http://s3.amazonaws.com,public_bucket=1", + "onCreateCommand": "mkdir -p ${DATA_MOUNTPOINT} && pip install -e .", + "postStartCommand": "docker volume prune -f && s3fs ${DJ_PUBLIC_S3_LOCATION} ${DATA_MOUNTPOINT} -o nonempty,multipart_size=530,endpoint=us-east-1,url=http://s3.amazonaws.com,public_bucket=1", "hostRequirements": { "cpus": 4, "memory": "8gb", diff --git a/element_deeplabcut/__init__.py b/element_deeplabcut/__init__.py index 1dd032b..80f05d0 100644 --- a/element_deeplabcut/__init__.py +++ b/element_deeplabcut/__init__.py @@ -14,7 +14,8 @@ "DLC_ROOT_DATA_DIR", dj.config["custom"].get("dlc_root_data_dir", "") ) -dj.config["custom"]["current_project_folder"] = os.getenv( - "CURRENT_PROJECT_FOLDER", dj.config["custom"].get("current_project_folder", "") +dj.config["custom"]["dlc_processed_data_dir"] = os.getenv( + "DLC_PROCESSED_DATA_DIR", dj.config["custom"].get("dlc_processed_data_dir", "") ) + db_prefix = dj.config["custom"].get("database.prefix", "") \ No newline at end of file From 94b2b17b47e8f9c1abba0104cbc596c4cf516153 Mon Sep 17 00:00:00 2001 From: Thinh Nguyen Date: Thu, 12 Oct 2023 17:21:50 +0000 Subject: [PATCH 170/176] add notebooks --- notebooks/tutorial.ipynb | 1188 ++++++++++++++++++++++++++++++++ notebooks/tutorial_pipeline.py | 93 +++ 2 files changed, 1281 insertions(+) create mode 100644 notebooks/tutorial.ipynb create mode 100644 notebooks/tutorial_pipeline.py diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb new file mode 100644 index 0000000..89090ba --- /dev/null +++ b/notebooks/tutorial.ipynb @@ -0,0 +1,1188 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "assert os.path.basename(os.getcwd())=='element-deeplabcut', (\"Please move to the \"\n", + " + \"element directory\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj\n", + "from pathlib import Path\n", + "import yaml" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-10-12 16:45:35,059][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", + "[2023-10-12 16:45:35,066][INFO]: Connected root@fakeservices.datajoint.io:3306\n" + ] + }, + { + "data": { + "text/plain": [ + "DataJoint connection (connected) root@fakeservices.datajoint.io:3306" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.conn()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-10-12 16:45:36,295][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n" + ] + } + ], + "source": [ + "from tutorial_pipeline import lab, subject, session, train, model " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['mysql',\n", + " 'performance_schema',\n", + " 'sys',\n", + " 'neuro_lab',\n", + " 'neuro_subject',\n", + " 'neuro_session',\n", + " 'neuro_train',\n", + " 'neuro_model']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.list_schemas()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(\n", + " dj.Diagram(subject) \n", + " + dj.Diagram(lab) \n", + " + dj.Diagram(session) \n", + " + dj.Diagram(model) \n", + " + dj.Diagram(train)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(model) + dj.Diagram(train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2 - Insert Subject, Session, and Behavior Videos" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    subject_nickname

    \n", + " \n", + "
    \n", + "

    sex

    \n", + " \n", + "
    \n", + "

    subject_birth_date

    \n", + " \n", + "
    \n", + "

    subject_description

    \n", + " \n", + "
    \n", + " \n", + "

    Total: 0

    \n", + " " + ], + "text/plain": [ + "*subject subject_nickna sex subject_birth_ subject_descri\n", + "+---------+ +------------+ +-----+ +------------+ +------------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subject.Subject()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Subject and Session tables\n", + "subject.Subject.insert1(\n", + " dict(\n", + " subject=\"subject6\",\n", + " sex=\"F\",\n", + " subject_birth_date=\"2020-01-01\",\n", + " subject_description=\"hneih_E105\",\n", + " ),\n", + " skip_duplicates=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    subject62021-06-02 14:04:22
    subject62021-06-03 14:43:10
    \n", + " \n", + "

    Total: 2

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet\n", + "+----------+ +------------+\n", + "subject6 2021-06-02 14:\n", + "subject6 2021-06-03 14:\n", + " (Total: 2)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Definition of the dictionary named \"session_keys\"\n", + "session_keys = [\n", + " dict(subject=\"subject6\", session_datetime=\"2021-06-02 14:04:22\"),\n", + " dict(subject=\"subject6\", session_datetime=\"2021-06-03 14:43:10\"),\n", + "]\n", + "\n", + "#Insert this dictionary in the Session table\n", + "session.Session.insert(session_keys, skip_duplicates=True)\n", + "session.Session()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "### VideoRecording\n", + "recording_key = {'subject': 'subject6',\n", + " 'session_datetime': '2021-06-02 14:04:22',\n", + " 'recording_id': '1'}\n", + "model.VideoRecording.insert1({**recording_key, 'device': 'Camera1'}, skip_duplicates=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "### VideoRecording.File\n", + "\n", + "video_files = [\"./example_data/inbox/from_top_tracking-DataJoint-2023-10-11/videos/test.mp4\"]\n", + "\n", + "model.VideoRecording.File.insert({\n", + " **recording_key, \n", + " 'file_id': v_idx, \n", + " 'file_path': Path(f)} for v_idx, f in enumerate(video_files))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    px_height

    \n", + " height in pixels\n", + "
    \n", + "

    px_width

    \n", + " width in pixels\n", + "
    \n", + "

    nframes

    \n", + " number of frames\n", + "
    \n", + "

    fps

    \n", + " (Hz) frames per second\n", + "
    \n", + "

    recording_datetime

    \n", + " Datetime for the start of the recording\n", + "
    \n", + "

    recording_duration

    \n", + " video duration (s) from nframes / fps\n", + "
    subject62021-06-02 14:04:2215005001800060None300.0
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id px_height px_width nframes fps recording_date recording_dura\n", + "+----------+ +------------+ +------------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n", + "subject6 2021-06-02 14: 1 500 500 18000 60 None 300.0 \n", + " (Total: 1)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### RecordingInfo\n", + "model.RecordingInfo.populate()\n", + "model.RecordingInfo()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3 - DLC inference task" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'subject': 'subject6',\n", + " 'session_datetime': '2021-06-02 14:04:22',\n", + " 'recording_id': '1'}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recording_key" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "task_key = {**recording_key, 'model_name': 'from_top_tracking_model_test'}" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "model.PoseEstimationTask.insert1(\n", + " {**task_key,\n", + " 'task_mode': 'load',\n", + " 'pose_estimation_output_dir': './example_data/outbox/from_top_tracking-DataJoint-2023-10-11/videos/device_1_recording_1_model_from_top_tracking_100000_maxiters'\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "### PoseEstimation\n", + "model.PoseEstimation.populate()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " uses DeepLabCut h5 output for body part position\n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    model_name

    \n", + " User-friendly model name\n", + "
    \n", + "

    body_part

    \n", + " \n", + "
    \n", + "

    frame_index

    \n", + " frame index in model\n", + "
    \n", + "

    x_pos

    \n", + " \n", + "
    \n", + "

    y_pos

    \n", + " \n", + "
    \n", + "

    z_pos

    \n", + " \n", + "
    \n", + "

    likelihood

    \n", + " \n", + "
    subject62021-06-02 14:04:221from_top_tracking_model_testhead=BLOB==BLOB==BLOB==BLOB==BLOB=
    subject62021-06-02 14:04:221from_top_tracking_model_testtailbase=BLOB==BLOB==BLOB==BLOB==BLOB=
    \n", + " \n", + "

    Total: 2

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *model_name *body_part frame_inde x_pos y_pos z_pos likelihood\n", + "+----------+ +------------+ +------------+ +------------+ +-----------+ +--------+ +--------+ +--------+ +--------+ +--------+\n", + "subject6 2021-06-02 14: 1 from_top_track head =BLOB= =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject6 2021-06-02 14: 1 from_top_track tailbase =BLOB= =BLOB= =BLOB= =BLOB= =BLOB= \n", + " (Total: 2)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### Results\n", + "model.PoseEstimation.BodyPartPosition()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "df = (model.PoseEstimation.BodyPartPosition & task_key).fetch(format='frame').reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    subjectsession_datetimerecording_idmodel_namebody_partframe_indexx_posy_posz_poslikelihood
    0subject62021-06-02 14:04:221from_top_tracking_model_testhead[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...[273.9966125488281, 274.1033630371094, 274.032...[314.97100830078125, 315.1459655761719, 315.13...None[0.999998927116394, 0.999998927116394, 0.99999...
    1subject62021-06-02 14:04:221from_top_tracking_model_testtailbase[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...[254.29002380371094, 254.2755584716797, 254.26...[275.48602294921875, 275.44000244140625, 275.4...None[0.9999996423721313, 0.9999996423721313, 0.999...
    \n", + "
    " + ], + "text/plain": [ + " subject session_datetime recording_id model_name \\\n", + "0 subject6 2021-06-02 14:04:22 1 from_top_tracking_model_test \n", + "1 subject6 2021-06-02 14:04:22 1 from_top_tracking_model_test \n", + "\n", + " body_part frame_index \\\n", + "0 head [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n", + "1 tailbase [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n", + "\n", + " x_pos \\\n", + "0 [273.9966125488281, 274.1033630371094, 274.032... \n", + "1 [254.29002380371094, 254.2755584716797, 254.26... \n", + "\n", + " y_pos z_pos \\\n", + "0 [314.97100830078125, 315.1459655761719, 315.13... None \n", + "1 [275.48602294921875, 275.44000244140625, 275.4... None \n", + "\n", + " likelihood \n", + "0 [0.999998927116394, 0.999998927116394, 0.99999... \n", + "1 [0.9999996423721313, 0.9999996423721313, 0.999... " + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    indexsubjectsession_datetimerecording_idmodel_namebody_partframe_indexx_posy_posz_poslikelihood
    00subject62021-06-02 14:04:221from_top_tracking_model_testhead0273.996613314.971008None0.999999
    10subject62021-06-02 14:04:221from_top_tracking_model_testhead1274.103363315.145966None0.999999
    20subject62021-06-02 14:04:221from_top_tracking_model_testhead2274.032654315.133331None0.999999
    30subject62021-06-02 14:04:221from_top_tracking_model_testhead3274.025238315.152283None0.999999
    40subject62021-06-02 14:04:221from_top_tracking_model_testhead4274.073181315.173248None0.999999
    ....................................
    1199951subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59995323.29388433.214066None1.0
    1199961subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59996321.60226432.794708None1.0
    1199971subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59997320.17398132.857304None1.0
    1199981subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59998318.70861833.147358None0.999999
    1199991subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59999317.67410333.861454None1.0
    \n", + "

    120000 rows × 11 columns

    \n", + "
    " + ], + "text/plain": [ + " index subject session_datetime recording_id \\\n", + "0 0 subject6 2021-06-02 14:04:22 1 \n", + "1 0 subject6 2021-06-02 14:04:22 1 \n", + "2 0 subject6 2021-06-02 14:04:22 1 \n", + "3 0 subject6 2021-06-02 14:04:22 1 \n", + "4 0 subject6 2021-06-02 14:04:22 1 \n", + "... ... ... ... ... \n", + "119995 1 subject6 2021-06-02 14:04:22 1 \n", + "119996 1 subject6 2021-06-02 14:04:22 1 \n", + "119997 1 subject6 2021-06-02 14:04:22 1 \n", + "119998 1 subject6 2021-06-02 14:04:22 1 \n", + "119999 1 subject6 2021-06-02 14:04:22 1 \n", + "\n", + " model_name body_part frame_index x_pos \\\n", + "0 from_top_tracking_model_test head 0 273.996613 \n", + "1 from_top_tracking_model_test head 1 274.103363 \n", + "2 from_top_tracking_model_test head 2 274.032654 \n", + "3 from_top_tracking_model_test head 3 274.025238 \n", + "4 from_top_tracking_model_test head 4 274.073181 \n", + "... ... ... ... ... \n", + "119995 from_top_tracking_model_test tailbase 59995 323.293884 \n", + "119996 from_top_tracking_model_test tailbase 59996 321.602264 \n", + "119997 from_top_tracking_model_test tailbase 59997 320.173981 \n", + "119998 from_top_tracking_model_test tailbase 59998 318.708618 \n", + "119999 from_top_tracking_model_test tailbase 59999 317.674103 \n", + "\n", + " y_pos z_pos likelihood \n", + "0 314.971008 None 0.999999 \n", + "1 315.145966 None 0.999999 \n", + "2 315.133331 None 0.999999 \n", + "3 315.152283 None 0.999999 \n", + "4 315.173248 None 0.999999 \n", + "... ... ... ... \n", + "119995 33.214066 None 1.0 \n", + "119996 32.794708 None 1.0 \n", + "119997 32.857304 None 1.0 \n", + "119998 33.147358 None 0.999999 \n", + "119999 33.861454 None 1.0 \n", + "\n", + "[120000 rows x 11 columns]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.explode(['frame_index', 'x_pos', 'y_pos', 'likelihood']).reset_index()\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1 - Register an existing model in DataJoint pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A DeepLabCut model is defined in a DLC-specific folder structure with a file named `config.yaml` that contains the specifications of a DLC model.\n", + "\n", + "To \"register\" this DLC model with DataJoint, you can just specify this config file. See example below" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "config_file_rel = \"./example_data/inbox/from_top_tracking-DataJoint-2023-10-11/config.yaml\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-10-12 16:45:52.864682: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-10-12 16:45:52.964849: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:\n", + "2023-10-12 16:45:52.964877: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n", + "2023-10-12 16:45:52.985085: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2023-10-12 16:45:53.645277: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:\n", + "2023-10-12 16:45:53.645349: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:\n", + "2023-10-12 16:45:53.645359: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading DLC 2.3.7...\n", + "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n", + "--- DLC Model specification to be inserted ---\n", + "\tmodel_name: from_top_tracking_model_test\n", + "\tmodel_description: Model in example data: from_top_tracking model\n", + "\tscorer: DLCresnet50fromtoptrackingOct11shuffle1\n", + "\ttask: from_top_tracking\n", + "\tdate: Oct11\n", + "\titeration: 0\n", + "\tsnapshotindex: -1\n", + "\tshuffle: 1\n", + "\ttrainingsetindex: 0\n", + "\tproject_path: inbox/from_top_tracking-DataJoint-2023-10-11\n", + "\tparamset_idx: None\n", + "\t-- Template/Contents of config.yaml --\n", + "\t\tTask: from_top_tracking\n", + "\t\tscorer: DataJoint\n", + "\t\tdate: Oct11\n", + "\t\tmultianimalproject: False\n", + "\t\tidentity: None\n", + "\t\tproject_path: /workspaces/element-deeplabcut/example_data/inbox/from_top_tracking-DataJoint-2023-10-11\n", + "\t\tvideo_sets: {'/Users/milagros/Desktop/from_top_tracking-DataJoint-2023-10-11/videos/test.mp4': {'crop': '0, 500, 0, 500'}, '/Users/milagros/Desktop/from_top_tracking-DataJoint-2023-10-11/videos/train1.mp4': {'crop': '0, 500, 0, 500'}}\n", + "\t\tbodyparts: ['head', 'tailbase']\n", + "\t\tstart: 0\n", + "\t\tstop: 1\n", + "\t\tnumframes2pick: 40\n", + "\t\tskeleton: [['bodypart1', 'bodypart2'], ['objectA', 'bodypart3']]\n", + "\t\tskeleton_color: black\n", + "\t\tpcutoff: 0.6\n", + "\t\tdotsize: 12\n", + "\t\talphavalue: 0.7\n", + "\t\tcolormap: rainbow\n", + "\t\tTrainingFraction: [0.95]\n", + "\t\titeration: 0\n", + "\t\tdefault_net_type: resnet_50\n", + "\t\tdefault_augmenter: default\n", + "\t\tsnapshotindex: -1\n", + "\t\tbatch_size: 8\n", + "\t\tcropping: False\n", + "\t\tx1: 0\n", + "\t\tx2: 640\n", + "\t\ty1: 277\n", + "\t\ty2: 624\n", + "\t\tcorner2move2: [50, 50]\n", + "\t\tmove2corner: True\n" + ] + } + ], + "source": [ + "model.Model.insert_new_model(model_name='from_top_tracking_model_test',\n", + " dlc_config=config_file_rel,\n", + " shuffle=1,\n", + " trainingsetindex=0,\n", + " model_description='Model in example data: from_top_tracking model')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py new file mode 100644 index 0000000..861f465 --- /dev/null +++ b/notebooks/tutorial_pipeline.py @@ -0,0 +1,93 @@ +import datajoint as dj +from collections import abc +from element_lab import lab +from element_animal import subject +from element_session import session_with_datetime as session +from element_deeplabcut import train, model + +from element_animal.subject import Subject +from element_lab.lab import Source, Lab, Protocol, User, Project + +__all__ = [ + "Subject", + "Source", + "Lab", + "Protocol", + "User", + "Project", + "Session", +] + +if "custom" not in dj.config: + dj.config["custom"] = {} + +db_prefix = dj.config["custom"].get("database.prefix", "") + + +def get_dlc_root_data_dir() -> list: + """Returns a list of root directories for Element DeepLabCut""" + dlc_root_dirs = dj.config.get("custom", {}).get("dlc_root_data_dir") + if not dlc_root_dirs: + return None + elif not isinstance(dlc_root_dirs, abc.Sequence): + return list(dlc_root_dirs) + else: + return dlc_root_dirs + + +def get_dlc_processed_data_dir() -> str: + """Returns an output directory relative to custom 'dlc_output_dir' root""" + from pathlib import Path + + dlc_output_dir = dj.config.get("custom", {}).get("dlc_processed_data_dir") + if dlc_output_dir: + return Path(dlc_output_dir) + else: + return None + + +# Activate "lab", "subject", "session" schema ------------- + +lab.activate(db_prefix + "lab") + +subject.activate(db_prefix + "subject", linking_module=__name__) + +Experimenter = lab.User +Session = session.Session +session.activate(db_prefix + "session", linking_module=__name__) + +# Activate equipment table ------------------------------------ + + +@lab.schema +class Device(dj.Lookup): + """Table for managing lab equipment. + + In Element DeepLabCut, this table is referenced by `model.VideoRecording`. + The primary key is also used to generate inferred output directories when + running pose estimation inference. Refer to the `definition` attribute + for the table design. + + Attributes: + device ( varchar(32) ): Device short name. + modality ( varchar(64) ): Modality for which this device is used. + description ( varchar(256) ): Optional. Description of device. + """ + + definition = """ + device : varchar(32) + --- + modality : varchar(64) + description=null : varchar(256) + """ + contents = [ + ["Camera1", "Pose Estimation", "Panasonic HC-V380K"], + ["Camera2", "Pose Estimation", "Panasonic HC-V770K"], + ] + + +# Activate DeepLabCut schema ----------------------------------- + + +train.activate(db_prefix + "train", linking_module=__name__) +model.activate(db_prefix + "model", linking_module=__name__) From 8ebd1f13e9ccfb0a1dcc7e017be71d3cc9f3a515 Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Thu, 12 Oct 2023 19:16:17 +0000 Subject: [PATCH 171/176] correct the name of the pipeline figure --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 58ef41b..d7cdd9e 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ tables that can be combined with other Elements to assemble a fully functional p ## Data Pipeline Diagram -![pipeline](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/pipeline_dlc.svg) +![pipeline](https://raw.githubusercontent.com/datajoint/element-deeplabcut/main/images/pipeline.svg) ## Getting Started From 828cd87a377ede7ebd18bcd64cebfb0cf3bbc62e Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Sat, 14 Oct 2023 06:34:22 +0200 Subject: [PATCH 172/176] draft tutorial --- notebooks/tutorial.ipynb | 1183 +++++++++----------------------------- 1 file changed, 274 insertions(+), 909 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 89090ba..6b7bcb0 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -1,8 +1,121 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DataJoint Element for Pose Estimation with DeepLabCut" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Open-source Data Pipeline for Markerless Pose Estimation in Neurophysiology**\n", + "\n", + "This tutorial focuses on providing a comprehensive understanding of the open-source data pipeline offered by `Element-DeepLabCut`. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![pipeline](../images/flowchart.svg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The package is designed to facilitate pose estimation analyses and streamline the organization of data using `DataJoint`. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![pipeline](../images/pipeline.svg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By the end of this tutorial, participants will have a clear grasp of how to set up, utilize, ad optimize the package for their specific pose estimation projects. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Key Components and Objectives**\n", + "\n", + "- Setup\n", + "\n", + "- Design the DataJoint Pipeline\n", + "\n", + "- Step 1 - Register an existing model in DataJoint pipeline\n", + "\n", + "- Step 2 - Insert Subject, Session, and Behavior Videos\n", + "\n", + "- Step 3 - DLC inference task\n", + "\n", + "- Step 4 - Visualization of results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For detailed documentation and tutorials on general DataJoint principles that support collaboration, automation, reproducibility, and visualizations:\n", + "\n", + "[`DataJoint for Python - Interactive Tutorials`](https://github.com/datajoint/datajoint-tutorials) - Fundamentals including table tiers, query operations, fetch operations, automated computations with the make function, etc.\n", + "\n", + "[`DataJoint for Python - Documentation`](https://datajoint.com/docs/core/datajoint-python/0.14/)\n", + "\n", + "[`DataJoint Element for DeepLabCut - Documentation`](https://datajoint.com/docs/elements/element-deeplabcut/0.2/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Steps to run the Element¶\n", + "\n", + "The Element assumes you:\n", + "\n", + "- Have a DLC project folder on your machine\n", + "- Have labeled data in your DLC project folder\n", + "\n", + "This tutorial includes a DLC project folder with example data and its results in `example_data`. In the following tutorial consists of studying the behavior of a freely-moving mouse in an open-field environment. The objective is to extract pose estimations of the animal's head and tail base from video footage. This information can provide valuable insights into the animal's movements, postures, and interactions within the environment. The results of this Element example could be combined with other modalities to assemble a complete pipeline. \n", + "\n", + "After running this tutorial, you can try `Element-DeepLabCut` with your own dataset. To do so, create a new `DeepLabCut` folder with your own videos and a training dataset. Then, remember to change the path in the configuration file (`config.yaml`) in your new `DeepLabCut project` folder accordingly.\n", + "\n", + "#### Challenges\n", + "**Complex Background**: The open field environment introduces complex backgrounds and varying lighting conditions, making accurate pose estimation challenging.\n", + "\n", + "**Multiple Body Parts**: Extracting the pose of multiple body parts (head, tail) adds complexity to the analysis due to potential occlusions and variations in appearance.\n", + "\n", + "**Data Management**: Managing the large volume of video data generated in the field and ensuring consistent annotation requires an efficient data pipeline.\n", + "\n", + "### Expected Outcomes\n", + "Upon completing this tutorial, you will have acquired practical proficiency in employing the `Element-DeepLabCut` package to effectively tackle the complexities of pose estimation. \n", + "\n", + "This tutorial and sample dataset will serve as a practical foundation for your learning journey with the Element package, enabling you to apply these techniques to your own research projects. \n", + "\n", + "By integrating this element package with other Elements of DataJoint, you unlock a powerful data pipeline that provides numerous benefits for your research workflow. " + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -12,9 +125,16 @@ " + \"element directory\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First start by importing the packages necessary to run this pipeline." + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -23,74 +143,72 @@ "import yaml" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's connect to the database server. " + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-10-12 16:45:35,059][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-10-12 16:45:35,066][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - }, - { - "data": { - "text/plain": [ - "DataJoint connection (connected) root@fakeservices.datajoint.io:3306" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dj.conn()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Design the DataJoint Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combine multiple Elements into a pipeline\n", + "\n", + "Each DataJoint Element is a modular set of tables that can be combined into a complete pipeline.\n", + "\n", + "Each Element contains one or more modules, and each module declares its own schema in the database. Schemas are conceptually related sets of tables. \n", + "\n", + "This tutorial pipeline is assembled from four DataJoint Elements.\n", + "\n", + "| Element | Source Code | Documentation | Description |\n", + "| -- | -- | -- | -- |\n", + "| Element Lab | [Link](https://github.com/datajoint/element-lab) | [Link](https://datajoint.com/docs/elements/element-lab) | Lab management related information, such as Lab, User, Project, Protocol, Source. |\n", + "| Element Animal | [Link](https://github.com/datajoint/element-animal) | [Link](https://datajoint.com/docs/elements/element-animal) | General subject meta data, genotype, and surgery information. |\n", + "| Element Session | [Link](https://github.com/datajoint/element-session) | [Link](https://datajoint.com/docs/elements/element-session) | General information of experimental sessions. |\n", + "| Element DeepLabCut | [Link](https://github.com/datajoint/element-deeplabcut) | [Link](https://datajoint.com/docs/elements/element-deeplabcut) | DataJoint schemas (Train and Model) for storing and running analysis of markerless pose estimation with DeepLabCut.\n", + "\n", + "The Elements are imported and activated in the next code cell." + ] + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-10-12 16:45:36,295][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n" - ] - } - ], + "outputs": [], "source": [ "from tutorial_pipeline import lab, subject, session, train, model " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By importing the modules for the first time, the schemas and tables will be created in the database. " + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['mysql',\n", - " 'performance_schema',\n", - " 'sys',\n", - " 'neuro_lab',\n", - " 'neuro_subject',\n", - " 'neuro_session',\n", - " 'neuro_train',\n", - " 'neuro_model']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dj.list_schemas()" ] @@ -128,6 +246,44 @@ "dj.Diagram(model) + dj.Diagram(train)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1 - Register an existing model in DataJoint pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A DeepLabCut model is defined in a DLC-specific folder structure with a file named `config.yaml` that contains the specifications of a DLC model.\n", + "\n", + "To \"register\" this DLC model with DataJoint, you can just specify this config file. See example below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config_file_rel = \"./example_data/inbox/from_top_tracking-DataJoint-2023-10-11/config.yaml\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.Model.insert_new_model(model_name='from_top_tracking_model_test',\n", + " dlc_config=config_file_rel,\n", + " shuffle=1,\n", + " trainingsetindex=0,\n", + " model_description='Model in example data: from_top_tracking model')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -137,106 +293,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    subject_nickname

    \n", - " \n", - "
    \n", - "

    sex

    \n", - " \n", - "
    \n", - "

    subject_birth_date

    \n", - " \n", - "
    \n", - "

    subject_description

    \n", - " \n", - "
    \n", - " \n", - "

    Total: 0

    \n", - " " - ], - "text/plain": [ - "*subject subject_nickna sex subject_birth_ subject_descri\n", - "+---------+ +------------+ +-----+ +------------+ +------------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "subject.Subject()" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -254,93 +320,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    subject62021-06-02 14:04:22
    subject62021-06-03 14:43:10
    \n", - " \n", - "

    Total: 2

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet\n", - "+----------+ +------------+\n", - "subject6 2021-06-02 14:\n", - "subject6 2021-06-03 14:\n", - " (Total: 2)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#Definition of the dictionary named \"session_keys\"\n", "session_keys = [\n", @@ -355,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -368,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -384,125 +366,29 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", - " \n", - "
    \n", - "

    px_height

    \n", - " height in pixels\n", - "
    \n", - "

    px_width

    \n", - " width in pixels\n", - "
    \n", - "

    nframes

    \n", - " number of frames\n", - "
    \n", - "

    fps

    \n", - " (Hz) frames per second\n", - "
    \n", - "

    recording_datetime

    \n", - " Datetime for the start of the recording\n", - "
    \n", - "

    recording_duration

    \n", - " video duration (s) from nframes / fps\n", - "
    subject62021-06-02 14:04:2215005001800060None300.0
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet *recording_id px_height px_width nframes fps recording_date recording_dura\n", - "+----------+ +------------+ +------------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n", - "subject6 2021-06-02 14: 1 500 500 18000 60 None 300.0 \n", - " (Total: 1)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "### RecordingInfo\n", "model.RecordingInfo.populate()\n", "model.RecordingInfo()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Element DeepLabCut has the capability to train a new model as well. To train the network, we need to add the parameter set (`TrainingParamSet`) of the model training (`train`). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "dj.Diagram(train)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -512,29 +398,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'subject': 'subject6',\n", - " 'session_datetime': '2021-06-02 14:04:22',\n", - " 'recording_id': '1'}" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "recording_key" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -543,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -556,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -566,133 +439,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " uses DeepLabCut h5 output for body part position\n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", - " \n", - "
    \n", - "

    model_name

    \n", - " User-friendly model name\n", - "
    \n", - "

    body_part

    \n", - " \n", - "
    \n", - "

    frame_index

    \n", - " frame index in model\n", - "
    \n", - "

    x_pos

    \n", - " \n", - "
    \n", - "

    y_pos

    \n", - " \n", - "
    \n", - "

    z_pos

    \n", - " \n", - "
    \n", - "

    likelihood

    \n", - " \n", - "
    subject62021-06-02 14:04:221from_top_tracking_model_testhead=BLOB==BLOB==BLOB==BLOB==BLOB=
    subject62021-06-02 14:04:221from_top_tracking_model_testtailbase=BLOB==BLOB==BLOB==BLOB==BLOB=
    \n", - " \n", - "

    Total: 2

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet *recording_id *model_name *body_part frame_inde x_pos y_pos z_pos likelihood\n", - "+----------+ +------------+ +------------+ +------------+ +-----------+ +--------+ +--------+ +--------+ +--------+ +--------+\n", - "subject6 2021-06-02 14: 1 from_top_track head =BLOB= =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject6 2021-06-02 14: 1 from_top_track tailbase =BLOB= =BLOB= =BLOB= =BLOB= =BLOB= \n", - " (Total: 2)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "### Results\n", "model.PoseEstimation.BodyPartPosition()" @@ -700,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -709,351 +458,18 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    subjectsession_datetimerecording_idmodel_namebody_partframe_indexx_posy_posz_poslikelihood
    0subject62021-06-02 14:04:221from_top_tracking_model_testhead[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...[273.9966125488281, 274.1033630371094, 274.032...[314.97100830078125, 315.1459655761719, 315.13...None[0.999998927116394, 0.999998927116394, 0.99999...
    1subject62021-06-02 14:04:221from_top_tracking_model_testtailbase[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...[254.29002380371094, 254.2755584716797, 254.26...[275.48602294921875, 275.44000244140625, 275.4...None[0.9999996423721313, 0.9999996423721313, 0.999...
    \n", - "
    " - ], - "text/plain": [ - " subject session_datetime recording_id model_name \\\n", - "0 subject6 2021-06-02 14:04:22 1 from_top_tracking_model_test \n", - "1 subject6 2021-06-02 14:04:22 1 from_top_tracking_model_test \n", - "\n", - " body_part frame_index \\\n", - "0 head [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n", - "1 tailbase [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n", - "\n", - " x_pos \\\n", - "0 [273.9966125488281, 274.1033630371094, 274.032... \n", - "1 [254.29002380371094, 254.2755584716797, 254.26... \n", - "\n", - " y_pos z_pos \\\n", - "0 [314.97100830078125, 315.1459655761719, 315.13... None \n", - "1 [275.48602294921875, 275.44000244140625, 275.4... None \n", - "\n", - " likelihood \n", - "0 [0.999998927116394, 0.999998927116394, 0.99999... \n", - "1 [0.9999996423721313, 0.9999996423721313, 0.999... " - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    indexsubjectsession_datetimerecording_idmodel_namebody_partframe_indexx_posy_posz_poslikelihood
    00subject62021-06-02 14:04:221from_top_tracking_model_testhead0273.996613314.971008None0.999999
    10subject62021-06-02 14:04:221from_top_tracking_model_testhead1274.103363315.145966None0.999999
    20subject62021-06-02 14:04:221from_top_tracking_model_testhead2274.032654315.133331None0.999999
    30subject62021-06-02 14:04:221from_top_tracking_model_testhead3274.025238315.152283None0.999999
    40subject62021-06-02 14:04:221from_top_tracking_model_testhead4274.073181315.173248None0.999999
    ....................................
    1199951subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59995323.29388433.214066None1.0
    1199961subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59996321.60226432.794708None1.0
    1199971subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59997320.17398132.857304None1.0
    1199981subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59998318.70861833.147358None0.999999
    1199991subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59999317.67410333.861454None1.0
    \n", - "

    120000 rows × 11 columns

    \n", - "
    " - ], - "text/plain": [ - " index subject session_datetime recording_id \\\n", - "0 0 subject6 2021-06-02 14:04:22 1 \n", - "1 0 subject6 2021-06-02 14:04:22 1 \n", - "2 0 subject6 2021-06-02 14:04:22 1 \n", - "3 0 subject6 2021-06-02 14:04:22 1 \n", - "4 0 subject6 2021-06-02 14:04:22 1 \n", - "... ... ... ... ... \n", - "119995 1 subject6 2021-06-02 14:04:22 1 \n", - "119996 1 subject6 2021-06-02 14:04:22 1 \n", - "119997 1 subject6 2021-06-02 14:04:22 1 \n", - "119998 1 subject6 2021-06-02 14:04:22 1 \n", - "119999 1 subject6 2021-06-02 14:04:22 1 \n", - "\n", - " model_name body_part frame_index x_pos \\\n", - "0 from_top_tracking_model_test head 0 273.996613 \n", - "1 from_top_tracking_model_test head 1 274.103363 \n", - "2 from_top_tracking_model_test head 2 274.032654 \n", - "3 from_top_tracking_model_test head 3 274.025238 \n", - "4 from_top_tracking_model_test head 4 274.073181 \n", - "... ... ... ... ... \n", - "119995 from_top_tracking_model_test tailbase 59995 323.293884 \n", - "119996 from_top_tracking_model_test tailbase 59996 321.602264 \n", - "119997 from_top_tracking_model_test tailbase 59997 320.173981 \n", - "119998 from_top_tracking_model_test tailbase 59998 318.708618 \n", - "119999 from_top_tracking_model_test tailbase 59999 317.674103 \n", - "\n", - " y_pos z_pos likelihood \n", - "0 314.971008 None 0.999999 \n", - "1 315.145966 None 0.999999 \n", - "2 315.133331 None 0.999999 \n", - "3 315.152283 None 0.999999 \n", - "4 315.173248 None 0.999999 \n", - "... ... ... ... \n", - "119995 33.214066 None 1.0 \n", - "119996 32.794708 None 1.0 \n", - "119997 32.857304 None 1.0 \n", - "119998 33.147358 None 0.999999 \n", - "119999 33.861454 None 1.0 \n", - "\n", - "[120000 rows x 11 columns]" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = df.explode(['frame_index', 'x_pos', 'y_pos', 'likelihood']).reset_index()\n", "df" @@ -1063,104 +479,53 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 1 - Register an existing model in DataJoint pipeline" + "## Step 4 - Visualization of results" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 4, "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'df' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m df_xy \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39miloc[:, df\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mget_level_values(\u001b[39m2\u001b[39m)\u001b[39m.\u001b[39misin([\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m])][model\u001b[39m.\u001b[39mModel\u001b[39m.\u001b[39mfetch1(\u001b[39m\"\u001b[39m\u001b[39mmodel_name\u001b[39m\u001b[39m\"\u001b[39m)]\n\u001b[1;32m 3\u001b[0m df_xy\u001b[39m.\u001b[39mmean()\n\u001b[1;32m 4\u001b[0m df_xy\u001b[39m.\u001b[39mplot()\u001b[39m.\u001b[39mlegend(loc\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mright\u001b[39m\u001b[39m\"\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined" + ] + } + ], "source": [ - "A DeepLabCut model is defined in a DLC-specific folder structure with a file named `config.yaml` that contains the specifications of a DLC model.\n", - "\n", - "To \"register\" this DLC model with DataJoint, you can just specify this config file. See example below" + "import pandas as pd\n", + "df_xy = df.iloc[:, df.columns.get_level_values(2).isin([\"x\", \"y\"])][model.Model.fetch1(\"model_name\")]\n", + "df_xy.mean()\n", + "df_xy.plot().legend(loc=\"right\")" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "config_file_rel = \"./example_data/inbox/from_top_tracking-DataJoint-2023-10-11/config.yaml\"" + "df_flat = df_xy.copy()\n", + "df_flat.columns = df_flat.columns.map('_'.join)\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-10-12 16:45:52.864682: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-10-12 16:45:52.964849: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:\n", - "2023-10-12 16:45:52.964877: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n", - "2023-10-12 16:45:52.985085: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2023-10-12 16:45:53.645277: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:\n", - "2023-10-12 16:45:53.645349: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:\n", - "2023-10-12 16:45:53.645359: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading DLC 2.3.7...\n", - "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n", - "--- DLC Model specification to be inserted ---\n", - "\tmodel_name: from_top_tracking_model_test\n", - "\tmodel_description: Model in example data: from_top_tracking model\n", - "\tscorer: DLCresnet50fromtoptrackingOct11shuffle1\n", - "\ttask: from_top_tracking\n", - "\tdate: Oct11\n", - "\titeration: 0\n", - "\tsnapshotindex: -1\n", - "\tshuffle: 1\n", - "\ttrainingsetindex: 0\n", - "\tproject_path: inbox/from_top_tracking-DataJoint-2023-10-11\n", - "\tparamset_idx: None\n", - "\t-- Template/Contents of config.yaml --\n", - "\t\tTask: from_top_tracking\n", - "\t\tscorer: DataJoint\n", - "\t\tdate: Oct11\n", - "\t\tmultianimalproject: False\n", - "\t\tidentity: None\n", - "\t\tproject_path: /workspaces/element-deeplabcut/example_data/inbox/from_top_tracking-DataJoint-2023-10-11\n", - "\t\tvideo_sets: {'/Users/milagros/Desktop/from_top_tracking-DataJoint-2023-10-11/videos/test.mp4': {'crop': '0, 500, 0, 500'}, '/Users/milagros/Desktop/from_top_tracking-DataJoint-2023-10-11/videos/train1.mp4': {'crop': '0, 500, 0, 500'}}\n", - "\t\tbodyparts: ['head', 'tailbase']\n", - "\t\tstart: 0\n", - "\t\tstop: 1\n", - "\t\tnumframes2pick: 40\n", - "\t\tskeleton: [['bodypart1', 'bodypart2'], ['objectA', 'bodypart3']]\n", - "\t\tskeleton_color: black\n", - "\t\tpcutoff: 0.6\n", - "\t\tdotsize: 12\n", - "\t\talphavalue: 0.7\n", - "\t\tcolormap: rainbow\n", - "\t\tTrainingFraction: [0.95]\n", - "\t\titeration: 0\n", - "\t\tdefault_net_type: resnet_50\n", - "\t\tdefault_augmenter: default\n", - "\t\tsnapshotindex: -1\n", - "\t\tbatch_size: 8\n", - "\t\tcropping: False\n", - "\t\tx1: 0\n", - "\t\tx2: 640\n", - "\t\ty1: 277\n", - "\t\ty2: 624\n", - "\t\tcorner2move2: [50, 50]\n", - "\t\tmove2corner: True\n" - ] - } - ], + "outputs": [], "source": [ - "model.Model.insert_new_model(model_name='from_top_tracking_model_test',\n", - " dlc_config=config_file_rel,\n", - " shuffle=1,\n", - " trainingsetindex=0,\n", - " model_description='Model in example data: from_top_tracking model')" + "import matplotlib.pyplot as plt \n", + "fig,ax=plt.subplots()\n", + "df_flat.plot(x='Head_x',y='Head_y', ax=ax)\n", + "df_flat.plot(x='Tailbase_x',y='Tailbase_y', ax=ax)" ] } ], @@ -1180,7 +545,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.17" + "version": "3.9.18" } }, "nbformat": 4, From 6d5011e174076f4925af2019a539878041de8acb Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Sat, 14 Oct 2023 07:17:40 +0000 Subject: [PATCH 173/176] update tutorial draft --- notebooks/tutorial.ipynb | 2372 ++++++++++++++++++++++++++++++++++++-- 1 file changed, 2251 insertions(+), 121 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 6b7bcb0..80c3f01 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -41,7 +41,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "By the end of this tutorial, participants will have a clear grasp of how to set up, utilize, ad optimize the package for their specific pose estimation projects. " + "By the end of this tutorial, participants will have a clear grasp of how to set up and apply the Element DeepLabCut for their specific pose estimation projects. " ] }, { @@ -87,25 +87,31 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Steps to run the Element¶\n", + "The following tutorial consists of studying the behavior of a freely-moving mouse in an open-field environment. \n", "\n", - "The Element assumes you:\n", + "The objective is to extract pose estimations of the animal's head and tail base from video footage. \n", "\n", - "- Have a DLC project folder on your machine\n", - "- Have labeled data in your DLC project folder\n", + "This information can provide valuable insights into the animal's movements, postures, and interactions within the environment. \n", + "\n", + "\n", + "The results of this Element example could be combined with other modalities to assemble a complete pipeline for your particular lab/study.\n", "\n", - "This tutorial includes a DLC project folder with example data and its results in `example_data`. In the following tutorial consists of studying the behavior of a freely-moving mouse in an open-field environment. The objective is to extract pose estimations of the animal's head and tail base from video footage. This information can provide valuable insights into the animal's movements, postures, and interactions within the environment. The results of this Element example could be combined with other modalities to assemble a complete pipeline. \n", + "#### Steps to run the Element\n", "\n", - "After running this tutorial, you can try `Element-DeepLabCut` with your own dataset. To do so, create a new `DeepLabCut` folder with your own videos and a training dataset. Then, remember to change the path in the configuration file (`config.yaml`) in your new `DeepLabCut project` folder accordingly.\n", + "The Element assumes you:\n", "\n", - "#### Challenges\n", - "**Complex Background**: The open field environment introduces complex backgrounds and varying lighting conditions, making accurate pose estimation challenging.\n", + "- Have a DLC project folder on your machine\n", "\n", - "**Multiple Body Parts**: Extracting the pose of multiple body parts (head, tail) adds complexity to the analysis due to potential occlusions and variations in appearance.\n", + "- Have labeled data in your DLC project folder\n", "\n", - "**Data Management**: Managing the large volume of video data generated in the field and ensuring consistent annotation requires an efficient data pipeline.\n", + "This tutorial includes a DLC project folder with example data and its results in `example_data`. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "\n", - "### Expected Outcomes\n", "Upon completing this tutorial, you will have acquired practical proficiency in employing the `Element-DeepLabCut` package to effectively tackle the complexities of pose estimation. \n", "\n", "This tutorial and sample dataset will serve as a practical foundation for your learning journey with the Element package, enabling you to apply these techniques to your own research projects. \n", @@ -115,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -152,9 +158,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-10-14 07:16:40,528][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", + "[2023-10-14 07:16:40,535][INFO]: Connected root@fakeservices.datajoint.io:3306\n" + ] + }, + { + "data": { + "text/plain": [ + "DataJoint connection (connected) root@fakeservices.datajoint.io:3306" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dj.conn()" ] @@ -170,63 +195,804 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Combine multiple Elements into a pipeline\n", - "\n", - "Each DataJoint Element is a modular set of tables that can be combined into a complete pipeline.\n", - "\n", - "Each Element contains one or more modules, and each module declares its own schema in the database. Schemas are conceptually related sets of tables. \n", - "\n", - "This tutorial pipeline is assembled from four DataJoint Elements.\n", - "\n", - "| Element | Source Code | Documentation | Description |\n", - "| -- | -- | -- | -- |\n", - "| Element Lab | [Link](https://github.com/datajoint/element-lab) | [Link](https://datajoint.com/docs/elements/element-lab) | Lab management related information, such as Lab, User, Project, Protocol, Source. |\n", - "| Element Animal | [Link](https://github.com/datajoint/element-animal) | [Link](https://datajoint.com/docs/elements/element-animal) | General subject meta data, genotype, and surgery information. |\n", - "| Element Session | [Link](https://github.com/datajoint/element-session) | [Link](https://datajoint.com/docs/elements/element-session) | General information of experimental sessions. |\n", - "| Element DeepLabCut | [Link](https://github.com/datajoint/element-deeplabcut) | [Link](https://datajoint.com/docs/elements/element-deeplabcut) | DataJoint schemas (Train and Model) for storing and running analysis of markerless pose estimation with DeepLabCut.\n", - "\n", - "The Elements are imported and activated in the next code cell." + "This tutorial is setup so that the element-deeplabcut is already configured, and instantiated, connected downstream from subject and session.\n", + "And that's what we're doing here, importing the schemas for subject, session, train, model, etc." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-10-14 07:16:40,699][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n" + ] + } + ], "source": [ "from tutorial_pipeline import lab, subject, session, train, model " ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By importing the modules for the first time, the schemas and tables will be created in the database. " - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], - "source": [ - "dj.list_schemas()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.ProjectSession\n", + "\n", + "\n", + "session.ProjectSession\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.ProjectSession\n", + "\n", + "\n", + "\n", + "\n", + "session.Session.Attribute\n", + "\n", + "\n", + "session.Session.Attribute\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.Session.Attribute\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionNote\n", + "\n", + "\n", + "session.SessionNote\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionNote\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionExperimenter\n", + "\n", + "\n", + "session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionDirectory\n", + "\n", + "\n", + "session.SessionDirectory\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionDirectory\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording\n", + "\n", + "\n", + "model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->session.Session\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Source\n", + "\n", + "\n", + "subject.Subject.Source\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Source\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.User\n", + "\n", + "\n", + "subject.Subject.User\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.User\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Line\n", + "\n", + "\n", + "subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "subject.Zygosity\n", + "\n", + "\n", + "subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Strain\n", + "\n", + "\n", + "subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Lab\n", + "\n", + "\n", + "subject.Subject.Lab\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Lab\n", + "\n", + "\n", + "\n", + "\n", + "subject.SubjectDeath\n", + "\n", + "\n", + "subject.SubjectDeath\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.SubjectDeath\n", + "\n", + "\n", + "\n", + "\n", + "model.ModelEvaluation\n", + "\n", + "\n", + "model.ModelEvaluation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation\n", + "\n", + "\n", + "model.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation->model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "lab.User\n", + "\n", + "\n", + "lab.User\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.User->subject.Subject.User\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProjectUser\n", + "\n", + "\n", + "lab.ProjectUser\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.User->lab.ProjectUser\n", + "\n", + "\n", + "\n", + "\n", + "lab.User->session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "\n", + "lab.LabMembership\n", + "\n", + "\n", + "lab.LabMembership\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.User->lab.LabMembership\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProtocolType\n", + "\n", + "\n", + "lab.ProtocolType\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Protocol\n", + "\n", + "\n", + "lab.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProtocolType->lab.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "lab.Source\n", + "\n", + "\n", + "lab.Source\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Source->subject.Subject.Source\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele.Source\n", + "\n", + "\n", + "subject.Allele.Source\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Source->subject.Allele.Source\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab\n", + "\n", + "\n", + "lab.Lab\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Location\n", + "\n", + "\n", + "lab.Location\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab->lab.Location\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab->lab.LabMembership\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab->subject.Subject.Lab\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab.Organization\n", + "\n", + "\n", + "lab.Lab.Organization\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab->lab.Lab.Organization\n", + "\n", + "\n", + "\n", + "\n", + "model.Model.BodyPart\n", + "\n", + "\n", + "model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.Model.BodyPart->model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line.Allele\n", + "\n", + "\n", + "subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.BodyPart\n", + "\n", + "\n", + "model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.BodyPart->model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProjectSourceCode\n", + "\n", + "\n", + "lab.ProjectSourceCode\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimationTask\n", + "\n", + "\n", + "model.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimationTask->model.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele\n", + "\n", + "\n", + "subject.Allele\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Allele.Source\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet\n", + "\n", + "\n", + "train.TrainingParamSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingTask\n", + "\n", + "\n", + "train.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet->train.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "model.Model\n", + "\n", + "\n", + "model.Model\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet->model.Model\n", + "\n", + "\n", + "\n", + "\n", + "train.ModelTraining\n", + "\n", + "\n", + "train.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.SubjectCull\n", + "\n", + "\n", + "subject.SubjectCull\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet\n", + "\n", + "\n", + "train.VideoSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet.File\n", + "\n", + "\n", + "train.VideoSet.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet->train.VideoSet.File\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet->train.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "model.RecordingInfo\n", + "\n", + "\n", + "model.RecordingInfo\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line->subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line->subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProjectKeywords\n", + "\n", + "\n", + "lab.ProjectKeywords\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Protocol->subject.Subject.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording.File\n", + "\n", + "\n", + "model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProjectPublication\n", + "\n", + "\n", + "lab.ProjectPublication\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Organization\n", + "\n", + "\n", + "lab.Organization\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Organization->lab.Lab.Organization\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project\n", + "\n", + "\n", + "lab.Project\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->session.ProjectSession\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->lab.ProjectSourceCode\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->lab.ProjectUser\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->lab.ProjectKeywords\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->lab.ProjectPublication\n", + "\n", + "\n", + "\n", + "\n", + "lab.UserRole\n", + "\n", + "\n", + "lab.UserRole\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.UserRole->lab.LabMembership\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording->model.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording->model.RecordingInfo\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording->model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Strain->subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "lab.Device\n", + "\n", + "\n", + "lab.Device\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Device->model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingTask->train.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "subject.SubjectDeath->subject.SubjectCull\n", + "\n", + "\n", + "\n", + "\n", + "model.Model->model.ModelEvaluation\n", + "\n", + "\n", + "\n", + "\n", + "model.Model->model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "model.Model->model.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " dj.Diagram(subject) \n", @@ -239,9 +1005,238 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "model.RecordingInfo\n", + "\n", + "\n", + "model.RecordingInfo\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimationTask\n", + "\n", + "\n", + "model.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation\n", + "\n", + "\n", + "model.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimationTask->model.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "model.ModelEvaluation\n", + "\n", + "\n", + "model.ModelEvaluation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation->model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet\n", + "\n", + "\n", + "train.TrainingParamSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingTask\n", + "\n", + "\n", + "train.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet->train.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "model.Model\n", + "\n", + "\n", + "model.Model\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet->model.Model\n", + "\n", + "\n", + "\n", + "\n", + "train.ModelTraining\n", + "\n", + "\n", + "train.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording.File\n", + "\n", + "\n", + "model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingTask->train.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet\n", + "\n", + "\n", + "train.VideoSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet->train.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet.File\n", + "\n", + "\n", + "train.VideoSet.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet->train.VideoSet.File\n", + "\n", + "\n", + "\n", + "\n", + "model.Model.BodyPart\n", + "\n", + "\n", + "model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.Model.BodyPart->model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "model.BodyPart\n", + "\n", + "\n", + "model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.BodyPart->model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording\n", + "\n", + "\n", + "model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording->model.RecordingInfo\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording->model.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording->model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "model.Model->model.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "model.Model->model.ModelEvaluation\n", + "\n", + "\n", + "\n", + "\n", + "model.Model->model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dj.Diagram(model) + dj.Diagram(train)" ] @@ -264,7 +1259,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -273,9 +1268,75 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-10-14 07:16:42.318459: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-10-14 07:16:42.442151: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", + "2023-10-14 07:16:42.442185: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n", + "2023-10-14 07:16:42.466555: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2023-10-14 07:16:43.231023: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", + "2023-10-14 07:16:43.231135: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", + "2023-10-14 07:16:43.231151: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading DLC 2.3.7...\n", + "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n", + "--- DLC Model specification to be inserted ---\n", + "\tmodel_name: from_top_tracking_model_test\n", + "\tmodel_description: Model in example data: from_top_tracking model\n", + "\tscorer: DLCresnet50fromtoptrackingOct11shuffle1\n", + "\ttask: from_top_tracking\n", + "\tdate: Oct11\n", + "\titeration: 0\n", + "\tsnapshotindex: -1\n", + "\tshuffle: 1\n", + "\ttrainingsetindex: 0\n", + "\tproject_path: from_top_tracking-DataJoint-2023-10-11\n", + "\tparamset_idx: None\n", + "\t-- Template/Contents of config.yaml --\n", + "\t\tTask: from_top_tracking\n", + "\t\tscorer: DataJoint\n", + "\t\tdate: Oct11\n", + "\t\tmultianimalproject: False\n", + "\t\tidentity: None\n", + "\t\tproject_path: /workspaces/element-deeplabcut/example_data/inbox/from_top_tracking-DataJoint-2023-10-11\n", + "\t\tvideo_sets: {'/Users/milagros/Desktop/from_top_tracking-DataJoint-2023-10-11/videos/test.mp4': {'crop': '0, 500, 0, 500'}, '/Users/milagros/Desktop/from_top_tracking-DataJoint-2023-10-11/videos/train1.mp4': {'crop': '0, 500, 0, 500'}}\n", + "\t\tbodyparts: ['head', 'tailbase']\n", + "\t\tstart: 0\n", + "\t\tstop: 1\n", + "\t\tnumframes2pick: 40\n", + "\t\tskeleton: [['bodypart1', 'bodypart2'], ['objectA', 'bodypart3']]\n", + "\t\tskeleton_color: black\n", + "\t\tpcutoff: 0.6\n", + "\t\tdotsize: 12\n", + "\t\talphavalue: 0.7\n", + "\t\tcolormap: rainbow\n", + "\t\tTrainingFraction: [0.95]\n", + "\t\titeration: 0\n", + "\t\tdefault_net_type: resnet_50\n", + "\t\tdefault_augmenter: default\n", + "\t\tsnapshotindex: -1\n", + "\t\tbatch_size: 8\n", + "\t\tcropping: False\n", + "\t\tx1: 0\n", + "\t\tx2: 640\n", + "\t\ty1: 277\n", + "\t\ty2: 624\n", + "\t\tcorner2move2: [50, 50]\n", + "\t\tmove2corner: True\n" + ] + } + ], "source": [ "model.Model.insert_new_model(model_name='from_top_tracking_model_test',\n", " dlc_config=config_file_rel,\n", @@ -284,6 +1345,141 @@ " model_description='Model in example data: from_top_tracking model')" ] }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    model_name

    \n", + " User-friendly model name\n", + "
    \n", + "

    task

    \n", + " Task in the config yaml\n", + "
    \n", + "

    date

    \n", + " Date in the config yaml\n", + "
    \n", + "

    iteration

    \n", + " Iteration/version of this model\n", + "
    \n", + "

    snapshotindex

    \n", + " which snapshot for prediction (if -1, latest)\n", + "
    \n", + "

    shuffle

    \n", + " Shuffle (1) or not (0)\n", + "
    \n", + "

    trainingsetindex

    \n", + " Index of training fraction list in config.yaml\n", + "
    \n", + "

    scorer

    \n", + " Scorer/network name - DLC's GetScorerName()\n", + "
    \n", + "

    config_template

    \n", + " Dictionary of the config for analyze_videos()\n", + "
    \n", + "

    project_path

    \n", + " DLC's project_path in config relative to root\n", + "
    \n", + "

    model_prefix

    \n", + " \n", + "
    \n", + "

    model_description

    \n", + " \n", + "
    \n", + "

    paramset_idx

    \n", + " \n", + "
    from_top_tracking_model_testfrom_top_trackingOct110-110DLCresnet50fromtoptrackingOct11shuffle1=BLOB=from_top_tracking-DataJoint-2023-10-11Model in example data: from_top_tracking modelNone
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*model_name task date iteration snapshotindex shuffle trainingsetind scorer config_tem project_path model_prefix model_descript paramset_idx \n", + "+------------+ +------------+ +-------+ +-----------+ +------------+ +---------+ +------------+ +------------+ +--------+ +------------+ +------------+ +------------+ +------------+\n", + "from_top_track from_top_track Oct11 0 -1 1 0 DLCresnet50fro =BLOB= from_top_track Model in examp None \n", + " (Total: 1)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.Model()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -293,16 +1489,110 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    subject_nickname

    \n", + " \n", + "
    \n", + "

    sex

    \n", + " \n", + "
    \n", + "

    subject_birth_date

    \n", + " \n", + "
    \n", + "

    subject_description

    \n", + " \n", + "
    subject6F2020-01-01hneih_E105
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*subject subject_nickna sex subject_birth_ subject_descri\n", + "+----------+ +------------+ +-----+ +------------+ +------------+\n", + "subject6 F 2020-01-01 hneih_E105 \n", + " (Total: 1)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "subject.Subject()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -320,9 +1610,93 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    subject62021-06-02 14:04:22
    subject62021-06-03 14:43:10
    \n", + " \n", + "

    Total: 2

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet\n", + "+----------+ +------------+\n", + "subject6 2021-06-02 14:\n", + "subject6 2021-06-03 14:\n", + " (Total: 2)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Definition of the dictionary named \"session_keys\"\n", "session_keys = [\n", @@ -337,7 +1711,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -350,13 +1724,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "### VideoRecording.File\n", "\n", - "video_files = [\"./example_data/inbox/from_top_tracking-DataJoint-2023-10-11/videos/test.mp4\"]\n", + "video_files = [\"./example_data/inbox/from_top_tracking-DataJoint-2023-10-11/videos/train1.mp4\"]\n", "\n", "model.VideoRecording.File.insert({\n", " **recording_key, \n", @@ -366,9 +1740,119 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    px_height

    \n", + " height in pixels\n", + "
    \n", + "

    px_width

    \n", + " width in pixels\n", + "
    \n", + "

    nframes

    \n", + " number of frames\n", + "
    \n", + "

    fps

    \n", + " (Hz) frames per second\n", + "
    \n", + "

    recording_datetime

    \n", + " Datetime for the start of the recording\n", + "
    \n", + "

    recording_duration

    \n", + " video duration (s) from nframes / fps\n", + "
    subject62021-06-02 14:04:2215005006000060None1000.0
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id px_height px_width nframes fps recording_date recording_dura\n", + "+----------+ +------------+ +------------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n", + "subject6 2021-06-02 14: 1 500 500 60000 60 None 1000.0 \n", + " (Total: 1)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "### RecordingInfo\n", "model.RecordingInfo.populate()\n", @@ -379,35 +1863,74 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Element DeepLabCut has the capability to train a new model as well. To train the network, we need to add the parameter set (`TrainingParamSet`) of the model training (`train`). " + "## Step 3 - DLC inference task" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "dj.Diagram(train)" + "{summary about next line}" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 16, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-> model.VideoRecording\n", + "-> model.Model\n", + "---\n", + "task_mode=\"load\" : enum('load','trigger') # load results or trigger computation\n", + "pose_estimation_output_dir=\"\" : varchar(255) # output dir relative to the root dir\n", + "pose_estimation_params=null : longblob # analyze_videos params, if not default\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "'-> model.VideoRecording\\n-> model.Model\\n---\\ntask_mode=\"load\" : enum(\\'load\\',\\'trigger\\') # load results or trigger computation\\npose_estimation_output_dir=\"\" : varchar(255) # output dir relative to the root dir\\npose_estimation_params=null : longblob # analyze_videos params, if not default\\n'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## Step 3 - DLC inference task" + "model.PoseEstimationTask.describe()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'subject': 'subject6',\n", + " 'session_datetime': '2021-06-02 14:04:22',\n", + " 'recording_id': '1'}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "recording_key" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -416,7 +1939,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -429,7 +1952,118 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    model_name

    \n", + " User-friendly model name\n", + "
    \n", + "

    task_mode

    \n", + " load results or trigger computation\n", + "
    \n", + "

    pose_estimation_output_dir

    \n", + " output dir relative to the root dir\n", + "
    \n", + "

    pose_estimation_params

    \n", + " analyze_videos params, if not default\n", + "
    subject62021-06-02 14:04:221from_top_tracking_model_testload./example_data/outbox/from_top_tracking-DataJoint-2023-10-11/videos/device_1_recording_1_model_from_top_tracking_100000_maxiters=BLOB=
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *model_name task_mode pose_estimatio pose_estim\n", + "+----------+ +------------+ +------------+ +------------+ +-----------+ +------------+ +--------+\n", + "subject6 2021-06-02 14: 1 from_top_track load ./example_data =BLOB= \n", + " (Total: 1)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.PoseEstimationTask()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -439,37 +2073,497 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    model_name

    \n", + " User-friendly model name\n", + "
    \n", + "

    pose_estimation_time

    \n", + " time of generation of this set of DLC results\n", + "
    subject62021-06-02 14:04:221from_top_tracking_model_test2023-10-12 15:21:47
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *model_name pose_estimatio\n", + "+----------+ +------------+ +------------+ +------------+ +------------+\n", + "subject6 2021-06-02 14: 1 from_top_track 2023-10-12 15:\n", + " (Total: 1)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### Results\n", - "model.PoseEstimation.BodyPartPosition()" + "model.PoseEstimation()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " uses DeepLabCut h5 output for body part position\n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    model_name

    \n", + " User-friendly model name\n", + "
    \n", + "

    body_part

    \n", + " \n", + "
    \n", + "

    frame_index

    \n", + " frame index in model\n", + "
    \n", + "

    x_pos

    \n", + " \n", + "
    \n", + "

    y_pos

    \n", + " \n", + "
    \n", + "

    z_pos

    \n", + " \n", + "
    \n", + "

    likelihood

    \n", + " \n", + "
    subject62021-06-02 14:04:221from_top_tracking_model_testhead=BLOB==BLOB==BLOB==BLOB==BLOB=
    subject62021-06-02 14:04:221from_top_tracking_model_testtailbase=BLOB==BLOB==BLOB==BLOB==BLOB=
    \n", + " \n", + "

    Total: 2

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *model_name *body_part frame_inde x_pos y_pos z_pos likelihood\n", + "+----------+ +------------+ +------------+ +------------+ +-----------+ +--------+ +--------+ +--------+ +--------+ +--------+\n", + "subject6 2021-06-02 14: 1 from_top_track head =BLOB= =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject6 2021-06-02 14: 1 from_top_track tailbase =BLOB= =BLOB= =BLOB= =BLOB= =BLOB= \n", + " (Total: 2)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "df = (model.PoseEstimation.BodyPartPosition & task_key).fetch(format='frame').reset_index()" + "### Results\n", + "model.PoseEstimation.BodyPartPosition()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ - "df" + "df = (model.PoseEstimation.BodyPartPosition & task_key).fetch(format='frame').reset_index()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    indexsubjectsession_datetimerecording_idmodel_namebody_partframe_indexx_posy_posz_poslikelihood
    00subject62021-06-02 14:04:221from_top_tracking_model_testhead0273.996613314.971008None0.999999
    10subject62021-06-02 14:04:221from_top_tracking_model_testhead1274.103363315.145966None0.999999
    20subject62021-06-02 14:04:221from_top_tracking_model_testhead2274.032654315.133331None0.999999
    30subject62021-06-02 14:04:221from_top_tracking_model_testhead3274.025238315.152283None0.999999
    40subject62021-06-02 14:04:221from_top_tracking_model_testhead4274.073181315.173248None0.999999
    ....................................
    1199951subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59995323.29388433.214066None1.0
    1199961subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59996321.60226432.794708None1.0
    1199971subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59997320.17398132.857304None1.0
    1199981subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59998318.70861833.147358None0.999999
    1199991subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59999317.67410333.861454None1.0
    \n", + "

    120000 rows × 11 columns

    \n", + "
    " + ], + "text/plain": [ + " index subject session_datetime recording_id \\\n", + "0 0 subject6 2021-06-02 14:04:22 1 \n", + "1 0 subject6 2021-06-02 14:04:22 1 \n", + "2 0 subject6 2021-06-02 14:04:22 1 \n", + "3 0 subject6 2021-06-02 14:04:22 1 \n", + "4 0 subject6 2021-06-02 14:04:22 1 \n", + "... ... ... ... ... \n", + "119995 1 subject6 2021-06-02 14:04:22 1 \n", + "119996 1 subject6 2021-06-02 14:04:22 1 \n", + "119997 1 subject6 2021-06-02 14:04:22 1 \n", + "119998 1 subject6 2021-06-02 14:04:22 1 \n", + "119999 1 subject6 2021-06-02 14:04:22 1 \n", + "\n", + " model_name body_part frame_index x_pos \\\n", + "0 from_top_tracking_model_test head 0 273.996613 \n", + "1 from_top_tracking_model_test head 1 274.103363 \n", + "2 from_top_tracking_model_test head 2 274.032654 \n", + "3 from_top_tracking_model_test head 3 274.025238 \n", + "4 from_top_tracking_model_test head 4 274.073181 \n", + "... ... ... ... ... \n", + "119995 from_top_tracking_model_test tailbase 59995 323.293884 \n", + "119996 from_top_tracking_model_test tailbase 59996 321.602264 \n", + "119997 from_top_tracking_model_test tailbase 59997 320.173981 \n", + "119998 from_top_tracking_model_test tailbase 59998 318.708618 \n", + "119999 from_top_tracking_model_test tailbase 59999 317.674103 \n", + "\n", + " y_pos z_pos likelihood \n", + "0 314.971008 None 0.999999 \n", + "1 315.145966 None 0.999999 \n", + "2 315.133331 None 0.999999 \n", + "3 315.152283 None 0.999999 \n", + "4 315.173248 None 0.999999 \n", + "... ... ... ... \n", + "119995 33.214066 None 1.0 \n", + "119996 32.794708 None 1.0 \n", + "119997 32.857304 None 1.0 \n", + "119998 33.147358 None 0.999999 \n", + "119999 33.861454 None 1.0 \n", + "\n", + "[120000 rows x 11 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = df.explode(['frame_index', 'x_pos', 'y_pos', 'likelihood']).reset_index()\n", "df" @@ -484,48 +2578,84 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 26, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'df' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m df_xy \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39miloc[:, df\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mget_level_values(\u001b[39m2\u001b[39m)\u001b[39m.\u001b[39misin([\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m])][model\u001b[39m.\u001b[39mModel\u001b[39m.\u001b[39mfetch1(\u001b[39m\"\u001b[39m\u001b[39mmodel_name\u001b[39m\u001b[39m\"\u001b[39m)]\n\u001b[1;32m 3\u001b[0m df_xy\u001b[39m.\u001b[39mmean()\n\u001b[1;32m 4\u001b[0m df_xy\u001b[39m.\u001b[39mplot()\u001b[39m.\u001b[39mlegend(loc\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mright\u001b[39m\u001b[39m\"\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined" - ] + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAHHCAYAAABZbpmkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOx9d7wdRd3+s+fclt6Tm5BKCIFAIBAEIgF9qUIQkUgRaVL0hyAq5VXe15emFBEBwYiICqiIAgFFaggGDAk9hMQAIYH0Xm/qLeec/f2xOzPfaVvO3T03uezz+ST3nD2zM7O7szPfeb7NcV3XRYYMGTJkyJAhQztFrq07kCFDhgwZMmTIkCYyYSdDhgwZMmTI0K6RCTsZMmTIkCFDhnaNTNjJkCFDhgwZMrRrZMJOhgwZMmTIkKFdIxN2MmTIkCFDhgztGpmwkyFDhgwZMmRo18iEnQwZMmTIkCFDu0Ym7GTIkCFDhgwZ2jUyYSdDhgxGXHDBBRg6dGhbd2O3xUMPPQTHcbB48eK27oqEoUOH4oILLmjrbmTIUFFkwk6GDG0ItiC+8847xt+/+MUvYv/9969wrzLEwS233IK///3vbd0NCTNnzsQNN9yAzZs3t3VXMmTYJZAJOxkyZMjQCtiEnXPPPRc7d+7EkCFDKt6nmTNn4sYbbzQKO/Pnz8cDDzxQ8T5lyNCWqGrrDmTIkCFDe0Q+n0c+n2/rbmiora1t6y5kyFBxZMxOhgy7If785z9j7Nix6NChA3r27ImzzjoLy5Ytk8pMnz4dp59+OgYPHoza2loMGjQIP/jBD7Bz506tvr///e/Yf//9UVdXh/333x9PPfVU5L4MHToUJ598MqZMmYIxY8agrq4Oo0aNwpNPPqmV/fTTT3H66aejZ8+e6NixIw4//HA8++yzWrl7770X++23Hzp27IgePXrgkEMOwV/+8hepzIoVK3DhhReiX79+qK2txX777Yc//OEPkfsd5R4uWLAAEydORH19Perq6jBw4ECcddZZaGhoAAA4joPt27fj4YcfhuM4cByH28OYbHbYvXrllVdwyCGHoEOHDhg9ejReeeUVAMCTTz6J0aNHo66uDmPHjsV7770n9WfOnDm44IILsOeee6Kurg719fW48MILsWHDBl7mhhtuwDXXXAMAGDZsGO8X64fJZifKc3nllVfgOA4ee+wx3HzzzRg4cCDq6upwzDHHYOHChZHve4YMbYGM2cmQYRdAQ0MD1q9frx1vaWnRjt188834v//7P5xxxhm4+OKLsW7dOtx777046qij8N5776F79+4AgMcffxw7duzApZdeil69euGtt97Cvffei+XLl+Pxxx/n9U2ZMgUTJ07EqFGjcOutt2LDhg345je/iYEDB0bu/4IFC3DmmWfi//2//4fzzz8fDz74IE4//XS88MILOO644wAAa9aswec//3ns2LEDV1xxBXr16oWHH34Yp5xyCp544gl89atfBQA88MADuOKKK/C1r30N3/ve99DY2Ig5c+bgzTffxNlnn83rOvzww+E4Di6//HL06dMHzz//PC666CJs2bIF3//+9wP7G+UeNjc344QTTkBTUxO++93vor6+HitWrMAzzzyDzZs3o1u3bvjTn/6Eiy++GIceeii+9a1vAQCGDx8e2PbChQtx9tln49vf/jbOOecc3HHHHfjyl7+M3/zmN/if//kffOc73wEA3HrrrTjjjDMwf/585HLevvSll17Cp59+im9+85uor6/HvHnz8Nvf/hbz5s3DG2+8AcdxcNppp+Hjjz/Go48+irvuugu9e/cGAPTp08fYn6jPheG2225DLpfD1VdfjYaGBtx+++34xje+gTfffDPwujNkaFO4GTJkaDM8+OCDLoDAf/vttx8vv3jxYjefz7s333yzVM/cuXPdqqoq6fiOHTu09m699VbXcRx3yZIl/NiYMWPc/v37u5s3b+bHpkyZ4gJwhwwZEnoNQ4YMcQG4kydP5scaGhrc/v37uwcddBA/9v3vf98F4E6fPp0f27p1qzts2DB36NChbrFYdF3Xdb/yla9I12zCRRdd5Pbv399dv369dPyss85yu3XrZrx2hqj38L333nMBuI8//nhgXzp16uSef/752nH2bBctWsSPsXs1c+ZMfuzFF190AbgdOnSQnsv999/vAnCnTZvGj5mu69FHH3UBuP/+97/5sZ///Oda27QPtL9Rn8u0adNcAO6+++7rNjU18bK//OUvXQDu3LlzjfcnQ4ZdAZkaK0OGXQCTJk3CSy+9pP074IADpHJPPvkkSqUSzjjjDKxfv57/q6+vx4gRIzBt2jRetkOHDvzz9u3bsX79enz+85+H67pcPbJq1SrMnj0b559/Prp168bLH3fccRg1alTk/g8YMEBiALp27YrzzjsP7733HlavXg0AeO6553DooYdi/PjxvFznzp3xrW99C4sXL8YHH3wAAOjevTuWL1+Ot99+29iW67qYPHkyvvzlL8N1Xek+nHDCCWhoaMCsWbOsfY16D9n9ePHFF7Fjx47I9yIMo0aNwrhx4/j3ww47DABw9NFHY/DgwdrxTz/9lB+jz7SxsRHr16/H4YcfDgCB1xyEqM+F4Zvf/CZqamr49yOPPFLrZ4YMuxoyNVaGDLsADj30UBxyyCHa8R49ekjqrQULFsB1XYwYMcJYT3V1Nf+8dOlSXHfddXj66aexadMmqRyzOVmyZAkAGOsbOXJk5AV0r732guM40rG9994bALB48WLU19djyZIlfAGn2HfffXlf9t9/f/zwhz/E1KlTceihh2KvvfbC8ccfj7PPPhtHHHEEAGDdunXYvHkzfvvb3+K3v/2tsT9r16619jXqPRw2bBiuvPJK3HnnnXjkkUdw5JFH4pRTTsE555wjCYZxQQUaQAhVgwYNMh6nz27jxo248cYb8de//lW7RvZM4yLqc7H1v0ePHlo/M2TY1ZAJOxky7EYolUpwHAfPP/+80dOnc+fOAIBisYjjjjsOGzduxA9/+EPss88+6NSpE1asWIELLrgApVKp0l2PjH333Rfz58/HM888gxdeeAGTJ0/Gr3/9a1x33XW48cYbed/POeccnH/++cY6VEaMIuo9BIBf/OIXuOCCC/CPf/wDU6ZMwRVXXIFbb70Vb7zxRiybJgqbh5btuOu6/PMZZ5yBmTNn4pprrsGYMWPQuXNnlEolfOlLX6rYM43SzwwZdjVkwk6GDLsRhg8fDtd1MWzYMM6cmDB37lx8/PHHePjhh3Heeefx4y+99JJUjsWAWbBggVbH/PnzI/dr4cKFcF1XYnc+/vhjAOBRmIcMGWKs86OPPpL6AgCdOnXCmWeeiTPPPBPNzc047bTTcPPNN+Paa69Fnz590KVLFxSLRRx77LGR+8gQ9R4yjB49GqNHj8aPf/xjzJw5E0cccQR+85vf4Kc//SkAaIxWWti0aRNefvll3Hjjjbjuuuv4cdOzi9OnOM8lQ4bdFZnNToYMuxFOO+005PN53HjjjdpO2nVd7oLMdt+0jOu6+OUvfymd079/f4wZMwYPP/ywpAZ56aWXNFuNIKxcuVJyV9+yZQv++Mc/YsyYMaivrwcAnHTSSXjrrbfw+uuv83Lbt2/Hb3/7WwwdOpTbCFE3agCoqanBqFGj4LouWlpakM/nMXHiREyePBn/+c9/tL6sW7cusK9R7+GWLVtQKBSk30ePHo1cLoempiZ+rFOnThWJVGx6pgBw9913a2U7deoEAJH6FfW5ZMiwOyNjdjJk2I0wfPhw/PSnP8W1116LxYsX49RTT0WXLl2waNEiPPXUU/jWt76Fq6++Gvvssw+GDx+Oq6++GitWrEDXrl0xefJko13FrbfeigkTJmD8+PG48MILsXHjRh7nZtu2bZH6tffee+Oiiy7C22+/jX79+uEPf/gD1qxZgwcffJCX+dGPfoRHH30UJ554Iq644gr07NkTDz/8MBYtWoTJkydz9+rjjz8e9fX1OOKII9CvXz98+OGH+NWvfoUJEyagS5cuADz352nTpuGwww7DJZdcglGjRmHjxo2YNWsWpk6dio0bN7b6Hv7rX//C5ZdfjtNPPx177703CoUC/vSnP3Fhi2Hs2LGYOnUq7rzzTgwYMADDhg0z2sC0Fl27dsVRRx2F22+/HS0tLdhjjz0wZcoULFq0SCs7duxYAMD//u//4qyzzkJ1dTW+/OUvcyGIIupzyZBht0al3b8yZMggwNyT3377bePvX/jCF4xu2JMnT3bHjx/vdurUye3UqZO7zz77uJdddpk7f/58XuaDDz5wjz32WLdz585u79693UsuucR9//33XQDugw8+qNW37777urW1te6oUaPcJ5980j3//PMju55PmDDBffHFF90DDjjAra2tdffZZx+jy/Ynn3zifu1rX3O7d+/u1tXVuYceeqj7zDPPSGXuv/9+96ijjnJ79erl1tbWusOHD3evueYat6GhQSq3Zs0a97LLLnMHDRrkVldXu/X19e4xxxzj/va3vw3tc5R7+Omnn7oXXnihO3z4cLeurs7t2bOn+1//9V/u1KlTpXo++ugj96ijjnI7dOjgAuBu3TbX8wkTJmh9AeBedtll0rFFixa5ANyf//zn/Njy5cvdr371q2737t3dbt26uaeffrq7cuVKF4B7/fXXS+f/5Cc/cffYYw83l8tJ/VBdz1032nNhrufqc2X9VMdUhgy7EhzXzazKMmTIUD6GDh2K/fffH88880xbdyVDhgwZjMj4yQwZMmTIkCFDu0Ym7GTIkCFDhgwZ2jUyYSdDhgwZMmTI0K6R2exkyJAhQ4YMGdo1MmYnQ4YMGTJkyNCukQk7GTJkyJAhQ4Z2jSyoILxcOStXrkSXLl0qFvo9Q4YMGTJkyNA6uK6LrVu3YsCAAYEBMDNhB16oezXjcIYMGTJkyJBh98CyZcsCk/Nmwg7AQ9AvW7YMXbt2bePeZMiQIUOGDBmiYMuWLRg0aBBfx23IhB2IDMFdu3bNhJ0MGTJkyJBhN0OYCUpmoJwhQ4YMGTJkaNfIhJ0MGTJkyJAhQ7tGJuxkyJAhQ4YMGdo1MpudiCiVSmhubm7rbrRL1NTUBLoMZsiQIUOGDK1BJuxEQHNzMxYtWoRSqdTWXWmXyOVyGDZsGGpqatq6KxkyZMiQoR0iE3ZC4LouVq1ahXw+j0GDBmUMRMJgAR1XrVqFwYMHZ0EdM2TIkCFD4siEnRAUCgXs2LEDAwYMQMeOHdu6O+0Sffr0wcqVK1EoFFBdXd3W3cmQIUOGDO0MGU0RgmKxCACZiiVFsHvL7nWGDBkyZMiQJNpU2LnhhhvgOI70b5999uG/NzY24rLLLkOvXr3QuXNnTJw4EWvWrJHqWLp0KSZMmICOHTuib9++uOaaa1AoFBLva6ZeSQ/Zvc2QIUOGDGmizdVY++23H6ZOncq/V1WJLv3gBz/As88+i8cffxzdunXD5ZdfjtNOOw0zZswA4DEBEyZMQH19PWbOnIlVq1bhvPPOQ3V1NW655ZaKX0uGDBkyZMiQYddDmws7VVVVqK+v1443NDTg97//Pf7yl7/g6KOPBgA8+OCD2HffffHGG2/g8MMPx5QpU/DBBx9g6tSp6NevH8aMGYOf/OQn+OEPf4gbbrghUz1lyJAhQ4YMGdreZmfBggUYMGAA9txzT3zjG9/A0qVLAQDvvvsuWlpacOyxx/Ky++yzDwYPHozXX38dAPD6669j9OjR6NevHy9zwgknYMuWLZg3b561zaamJmzZskX6lyFDhgwZMmRon2hTYeewww7DQw89hBdeeAH33XcfFi1ahCOPPBJbt27F6tWrUVNTg+7du0vn9OvXD6tXrwYArF69WhJ02O/sNxtuvfVWdOvWjf8bNGhQsheWIUOGRFAquWhsyQzXM2TI0Dq0qbBz4okn4vTTT8cBBxyAE044Ac899xw2b96Mxx57LNV2r732WjQ0NPB/y5YtS7W9SmPdunWor6+X7JZmzpyJmpoavPzyy4Hn3nDDDRgzZgzuv/9+DBo0CB07dsQZZ5yBhoYGXqZUKuGmm27CwIEDUVtbizFjxuCFF17gvzc3N+Pyyy9H//79UVdXhyFDhuDWW29N/kIztHt89b6ZOPgnL2FbU/JOBxkyZPjsoM1tdii6d++OvffeGwsXLsRxxx2H5uZmbN68WWJ31qxZw2186uvr8dZbb0l1MG8tkx0QQ21tLWpra8vqo+u62NlGO80O1flInkt9+vTBH/7wB5x66qk4/vjjMXLkSJx77rm4/PLLccwxx4Sev3DhQjz22GP45z//iS1btuCiiy7Cd77zHTzyyCMAgF/+8pf4xS9+gfvvvx8HHXQQ/vCHP+CUU07BvHnzMGLECNxzzz14+umn8dhjj2Hw4MFYtmxZuxMoM1QG7y/bDAB4e/FG/NfIvm3bmQwZMuy22KWEnW3btuGTTz7Bueeei7Fjx6K6uhovv/wyJk6cCACYP38+li5dinHjxgEAxo0bh5tvvhlr165F377eRPjSSy+ha9euGDVqVCp93NlSxKjrXkyl7jB8cNMJ6FgT7ZGddNJJuOSSS/CNb3wDhxxyCDp16hSZXWlsbMQf//hH7LHHHgCAe++9FxMmTMAvfvEL1NfX44477sAPf/hDnHXWWQCAn/3sZ5g2bRruvvtuTJo0CUuXLsWIESMwfvx4OI6DIUOGlHfBGT7TcF2Xf67OIpdnyJChFWjTGeTqq6/Gq6++isWLF2PmzJn46le/inw+j69//evo1q0bLrroIlx55ZWYNm0a3n33XXzzm9/EuHHjcPjhhwMAjj/+eIwaNQrnnnsu3n//fbz44ov48Y9/jMsuu6xs5qY94Y477kChUMDjjz+ORx55JPI9GTx4MBd0AE+oLJVKmD9/PrZs2YKVK1fiiCOOkM454ogj8OGHHwIALrjgAsyePRsjR47EFVdcgSlTpiR3URk+M2gpCmEnn8tiMWXIYEKx5OKBf3+KOcs3t3VXdmm0KbOzfPlyfP3rX8eGDRvQp08fjB8/Hm+88Qb69OkDALjrrruQy+UwceJENDU14YQTTsCvf/1rfn4+n8czzzyDSy+9FOPGjUOnTp1w/vnn46abbkqtzx2q8/jgphNSqz+s7Tj45JNPsHLlSpRKJSxevBijR49OqWcyDj74YCxatAjPP/88pk6dijPOOAPHHnssnnjiiYq0n6F9oKkg1MU1VZmwkyGDCY+/sww3P+dtNBffNqGNe7Prok2Fnb/+9a+Bv9fV1WHSpEmYNGmStcyQIUPw3HPPJd01KxzHiaxKaks0NzfjnHPOwZlnnomRI0fi4osvxty5c7m6LwhLly7FypUrMWDAAADAG2+8gVwuh5EjR6Jr164YMGAAZsyYgS984Qv8nBkzZuDQQw/l37t27YozzzwTZ555Jr72ta/hS1/6EjZu3IiePXsmf7EZ2iUaW0r8cz5TY2XIYMRHq7e2dRd2C+z6q3aGsvC///u/aGhowD333IPOnTvjueeew4UXXohnnnkm9Ny6ujqcf/75uOOOO7BlyxZcccUVOOOMM7jR9zXXXIPrr78ew4cPx5gxY/Dggw9i9uzZ3ID5zjvvRP/+/XHQQQchl8vh8ccfR319vRZGIEOGIFBmp0TsdzJkyJAhLjJhpx3ilVdewd13341p06aha9euAIA//elPOPDAA3Hffffh0ksvDTx/r732wmmnnYaTTjoJGzduxMknnyypD6+44go0NDTgqquuwtq1azFq1Cg8/fTTGDFiBACgS5cuuP3227FgwQLk83l87nOfw3PPPYdctjvPEAPNBcHsuJmwkyFBPPbOMvzhtUX43fmHYGCPjm3dnVYhezeiIRN22iG++MUvoqWlRTo2dOhQKVZOGC699FKrUJTL5XD99dfj+uuvN/5+ySWX4JJLLone4QwZDKAGysVSQMEMGWLiv5+YAwC4/YX5uOfrB7Vxb1qHUibrREK21c6QYRfFk7OWY+iPnsXQHz3b1l1pE7QQCaeYzegZEgJlQgql3V+Knrcy+ib2s4yM2fmMYb/99sOSJUuMv91///0V7k08FIolbGksoGenyiR4dV03UhDHtHDlY+/zz5t3NKN7x89OYtt1W5vw4jyR8iWj6jMkhX/OWcU/b9reElBy98CspZvbugu7BTJh5zOG5557Di0tLdiysxmO46BLXTX/rV+/fujSpQtuuOGGtutgAM787Rt4d8kmTL3yC9irb+dU2yqVXJxx/+vo1qEav7/gc6m2FQU7W4ro3tadqCBOuPvf2Li9mX8vZsJO6mhsKeIPMxahOpfDeZ8fgtqqeKEudhdM/WAN/zx+RO827EmGSiITdj5jGDJkCIqlEuat9DK9DxvQbbcJ2Pbukk0APPXOf39pn1TbWrRhO97x22spllCdr7zG99BhPfHWoo0AgKaW3Z9ujwMq6ACZXUIlcNXj7+NZn/Vo2NmCq08Y2cY9SgfH7NsXT7+/EgDQrUN1SOkM7QWZzc5nEHSTvDu69FZi4aPiX1vdI6q6aSx8tjN/lzJpJ3U8S9Q7v5q2sA17UjnsjvOfiiMzdioSMmHnM44PV23B9t0so7SL9Ccoyna1FF3866M1mDZ/bertUtB5+LPG7Kgo10D57cUbuZF3Zvdjx2fp3kibvXYgRJfNOrsusGNjsp3ZhZEJOxnw6brtbd2FWEhlXm5pBD59FSh46pMcMUze//oXceFD7+CbD76Nnc2VY1jorrOp8NkWdsrdgZ/+m9f559te+Cip7rQ7nPjL6W3dhYqBbpYWrd+O038zE9M+quxGJkmUPSc9eQlw+zBg0Wfj2WfCToaKMCVJIpVd6PPXAH88BXj5RgD2xJNbmyrnvUE3nU2fdTVWAs/8/lc/TaAn7RNqyoGj9u7TRj1JH9Tb/OHXl+DtxZvwzYfebrsOtRKvf7qhvBPnPu79fe3O5DqzCyMTdjJILMbuAOu6t30DsOT18qifWX/0/r7+KwCA7ZZUUp1EhbpMjRX8++YdzfjKpBn4w2uLpONnHzY4xV61X/z743XtQsVjQipXtfFTYMWsNGqOhbI2gs5nQwz4bFxlBgnq67C7TWnW/v58T+DBLwGv3NqK2j0pxzZnMIahEguBzOx8toWdMGZn0rSFeH/ZZtz0zAfS8U417dN9uhI4ZdJrbd2FVJAKM3zPQcAD/wVsWZl83QFobJEZ350t5TDAu9dmt1xkwk4GuK67Wxkohnb11Z+VX3l1BwD2xbVYcrFkw3aM/elLuPflBeW3EwFUvdjyGc+XECbsLFq/g3+mY5kKjH271Cber/aC3p31gJU92mkQy1Rnuo2VVZWO/9m/pO/3/qsML7qM2cnwWcLuI+oAf5ixCB+u2pJO5VXegqiurd07evE4Sq6Lr/3mdWza0YJfvPRxOn3wQW0LPuvpEsKu/4i9evHPNKcWfY5bGnf/aLlpoV/XOgBy3JkRfbu0VXdShWljd8iQHgnVXlmWZP02OR7V4+8sj1/JbmbGUC4yYacd4o9//CN69eqFpqYm6fipp56Kc88913yS//7fcMMNGDNmDO6//34MGjQIHTt2xBlnnCElES2VSrjpppswcOBA1NbWYsyYMXjhhRf4783Nzbj88svRv39/1NXVYciQIbj11vJUSw07W4z5oQK9R4YfXVZbAICqOu3Qd744HHl/Qii5XiqDSqAkMRSfHWGHLkaj9+gGIDy2Uodqoa7avnQWMPcJry4ixje2lD7zht42MGGyYWf7FwhNr1LnulbE16W7kgqyJAUD29urrFQ6mbCTwQTXBZq3t82/iAve6aefjmKxiKeffpofW7t2LZ599llceOGFxnNKZFFYuHAhHnvsMfzzn//ECy+8gPfeew/f+c53+O+//OUv8Ytf/AJ33HEH5syZgxNOOAGnnHIKFizw1Dr33HMPnn76aTz22GOYP38+HnnkEQwdOrSMmw3839//E/8ktxUqn61eYDX1VrMcWZVkWHb34I/lopEYY+d8r7gwGynqPdfjj8cAky8CFr+mPcetjbtXTKlKgY1rxmACQIea9rk8mEZSq95rlwjQFRR2mg3CTsfaMmzUPiNqrCxdRFy07ABuGdA2bf/PSqCmU2ixDh064Oyzz8aDDz6I008/HQDw5z//GYMHD8YXv/hFFIqGF5scamxsxB//+EfsscceAIB7770XEyZMwC9+8QvU19fjjjvuwA9/+EOcddZZAICf/exnmDZtGu6++25MmjQJS5cuxYgRIzB+/Hg4joMhQ4aUfcknH9Cfh3aPjFIrdu/dPe8dKly4AFjcrkoKO7QPnyWTna0GdVNYbizjr6vnwnWPlOv5jKsDbWD3t4oIje11zJmGUqv2EkUyXiuoEmo2OC3066Iz06HI1FgZdmdccsklmDJlClasWAEAeOihh3DBBRfAcRzjwkBf9sGDB3NBBwDGjRuHUqmE+fPnY8uWLVi5ciWOOOII6fwjjjgCH374IQDgggsuwOzZszFy5EhcccUVmDJlStnX0dWSu2Zor472k1rD7NR4CUbpPSq5Lldj/XvBuvLrjgm1D58V0ASUPYmtVCD8n7uDxIvJVWnqr0zYMYPdFxqGor2OOdN1tWpclNqGLTR5aPYpxwg/LWFn5yZgxbvp1F0GMmYnLqo7egxLW7UdEQcddBAOPPBA/PGPf8Txxx+PefPm4dlnn7WWTzKw4MEHH4xFixbh+eefx9SpU3HGGWfg2GOPxRNPPBG7Lpv6oiooRHqxNXYHzPVcNnJl6pSlG3YYz0oDn1WbHXqtLBR+mBqLjd8RzgrjcVPdGQTYYk9vc3sVDI1qrHLGRakE5HKKsNO2zE554zulPk86DNi2BjjvaWDPL6TTRgxkzE5cOI6nSmqLfzEl8IsvvhgPPfQQHnzwQRx77LEYNGiQtSylrJcuXYqVK4VA98YbbyCXy2HkyJHo2rUrBgwYgBkzZkjnz5gxA6NGjeLfu3btijPPPBMPPPAA/va3v2Hy5MnYuDF+HhbbJBToKr/8rdjtqKDzfK9ONXzHO6hndIGztaCXeN0/5rXbxUeFSdgJu3b2c7VDFh7X1ZidUjtVzbQW7P66kuq0nY43w9wRO/TG6v8Atw8FXp8kq813rG9d32JAjbEDlCnspGWzs22N93fek+nUHxOZsNOOcfbZZ2P58uV44IEHFMNk/YVYv014GNXV1eH888/H+++/j+nTp+OKK67AGWecgfr6egDANddcg5/97Gf429/+hvnz5+NHP/oRZs+eje9973sAgDvvvBOPPvooPvroI3z88cd4/PHHUV9fj+7du8e+Btt8G/pOl7uqcYFSNHD+54dyA1jTbiotqBPXe0s3VazttgR95txAOUyL5f9eB+KK6xa1cVLWDr6do1RysaqhEYAcz6m9CjuJGCg/fTnQ2AC8+D9AYac4/uhZFVPdrNi8UztW1rTXsKz1nQnCp6+kW39EZGqsdoxu3bph4sSJePbZZ3Hqqadqv+cchy+om3Y0c9Zir732wmmnnYaTTjoJGzduxMknn4xf//rX/LwrrrgCDQ0NuOqqq7B27VqMGjUKTz/9NEaMGAEA6NKlC26//XYsWLAA+Xwen/vc5/Dcc88hl4svW9vUF9oORn3LSy1Arvwgcqz67h2rUVedB7PbbKyg67J6jalFUS408fhCuwLYdeccIO/Ix2xg6qorq4iqtFTQduztdQFvDe7/twiEt4V4q7VXwdB0WbGHBc0WPk0Jq/HWA8BXx8buVxxsayoYPQvLYnbSFs72/XK69UdEJuy0c6xYsQLf+MY3UFsbbzG79NJLcemllxp/y+VyuP7663H99dcbf7/kkktwySWXxO6rCbbFSTu8Q0mGt2Ul8OyVwMHnA/udGqNF32bH/8bUV4zZoTmqendOV0BQ5bdUoigvexv4/XHAF68FvvjD5OsvA2zCzucczuxEVWPtn1tMDurMTmazo+Pef5kjgbfX3FimMRB7XGxeIj6//xf5tzXzyuhVdJRKLva//kXzb1EvY1vlHC2Q3zUicWdqrHaKTZs24amnnsIrr7yCyy67rOLtJzVR2iYhzaC6SYmo/NL/AZ/8C3j8/HgNKkwCU2oxoYeyK5VeOFNp7fn/9mp+5ZY0ai8LbOg4jsPve+hwMj0Lx8kMlCOAWgIeMLAb/9xeWTDTEIh8resXADd0Cy5TF/J7KzF/zVbrb5HH9x17JdSbCNhF4vhkzE47xUEHHYRNmzbhZz/7GUaOHMmP77fffliyZIlx8fi/2+5MJEfW5h3NWLpxB/bo3gG9Wsl+cKHDUYLsqSTHTsWeZevqMluUE4EyEx4h7Ag11sbtzXBdlwccTBraxJXG2sOMCAHvoneBmBtMUPbUWEzYicbsSOi5J0qK4+Qus4C7LtCyE6ipnMG7DdubxZj+yVf2x1cmec4H7VaNZTgWeVj86yfhZVJ+h7oZwnFMPHggJs9avmsK85mwkyFNLF682Hj8ueeew/adjfh03XbkHAed66qwxQ8R36tPHwzu1ws33nhjq9peutFzz16xeWerhR2muanJ5yRWRRPKHlb0wq2MfeESdgEgaizFbuZ/nvoPbj1tdKvaMjb+9OX4dssO3ICvicNpSDudegNbfHftYgtQFYFy3rIKWD0HGHF8KhO7sNlxkIsYzNEopLuursbaVbyx/nEZMPsR4PJ3gN4jop1TLHj3O5deJvfuHavx4wn74qfPfrjrCIYJwzRWIjPRC6bK3wccBKx8Tz424oQyexYN6nPZp74L9t+jKybPKsP2qBLYRYSdXaMXGSqGIUOGYPjwvTB42J4YMmxPHDl2fwweticGD9sTnTp3wYbtzeGVVBBs4atW4upo73SLEv+msUEtEQ0Kk8DVWAabHQB49K2l5bUThNVzgff+jAtKsstmKps2ajwYVUD81SHAX84A/jM5hQ6JCTsvqbHCDJQN2LlRW9h2iZ3vyzd5gg4AzLg72jnFAnDPQcCvxyU+EP5rZB/+ueQKwb79Cjv6scjjomW7UplBek6Z2VH7ms9Ff08qBtqPTNjJsCvAcRwcMLA7/963axnhxlOEKnSox63Y+Gnw7xQBdQk1lve3IokkiUquP4ThdSrzWI6Qu1GFneZt3t+PXwguVyao6jIfMTcW+/m14n7i4D+/pwlBu8RiMP0X4nPU7mxZDjQsBdbP99RfCYKyr8WSK+75rnCvUoCJIY2sslMDu5qowo2LyuhVdKivQlXO4fNTEmYIiYDOJbuAahzIhJ3ISHsQlUpumw7U7h099UW+DQZm0HVH9sZqDaRcWrLNDvfGMhgop4adwq316uq/8c+pjA6664qr+kspTD632SE71tDcWP7vOyAL6/13fizXvYusBRxuGcJzOecEgKTDQseavLjnu9zNSgZ0KHX1s51Hnno//12lMsOzePuB8joWEepzyeWc+ImK6w9Iulsy2IYI2GWYncxmJwTV1dVwHAfr1q1Dnz59yjdG3braCy3eqa/2U3NLEYs2bEeX2ioM6JG+wWJTSxFuoRmlnIPGRi+YWLG5CW6hBWs3FdClunWTnFsQqjBWv7Ws62LdunVwHAfV1brhHXt39+3fFW8t3iidlxgME5ZVjVUJYYe40b9UPETrU7Ig4zmu8JKScG5WY0U7V8389t+LL8HLzm2Y73oJXneZnS9DOUlrW5UOxdAF/5b06VKLAd07tH81lv+3vmsdfnHGgfjG796Mfq0desrfS0Wg737A2nTdzaUmlTFcnctFf08atwAr3vFia6WJ/xAVfCbs7B7I5/MYOHAgli9fbjX6DUWxBdi6yvvcfbv28+YdLdjWVMBaAE2bO5Td16hoKZawdksTcg5Qtd1rb82WRrT42dBz21vXh7WbBM1eszO8LsdxMHDgQOTzuuEl2+V3rqvC+9cfj8Xrt+Mrk2bEW2fv3A+4dAbQobv5d7rguCVgyetwnX143wDC7BhCtCeOnZtF14gwkso6TW0OYi+iaQk7TI3l8Gzz4Wosf+wa+vSr6ntxXPPPAaTV41YgKkvTsFx8TphRYwv9JUcOA9D+bXbYWBk/ojc61XpLYORrVW103CKwdn6S3QuF2teqvHhPAoX5YgG4zZ4yqGyUSsD9RwGjvwaM/7537KNnxO+ZsLP7oHPnzhgxYgRaWsrcUa37GHjhKu/zd94ClEjCk6YtxJOz1gIAXr7qi63oaTQs2bAdN/zjbXSurcI/Lh8PALj4F6/w31vbh4ufjFdXdXW1UdABhPoi5zjo1qEaHWu8crFYji3LgTtGAP9nCaRFJ7DVc4AHv4QxAAARLIw9soqkiyA2O5SpSIWVoNcedxH94B/J9oV1g0RQjmygzNSO0J/PIGetqHtXW8BNBq4mdK4Xn8thgwLAFs+8P8jzXHWYaDO7DLinJcS1Rn631Oe14RPxea9jgYVTgf0ntr6TAVCFnTxRYwUO73f+kE6Hburh/V0zFxg6Hhh4CDD0SC/OGRB9jKeMTNiJiHw+b12QQ1HtANv8/CO11UBeVtc0lnJYsdWbwOrqUjIQ3r4e6NgLcBzkqlqwYmsR3Ys53h5rHwCQ91IklAtaV2uvR0TT9b4zLWLsNasY4GUW8DIyIYctujuaK8vsUKYilbgnrRF2UgKz+fRczxnLEHwOuzMmZucjX4UF7AI2O+oznPcUcPpDEc5L7zlxYcd/t6Iahe/ucBwxn0R+tzQmjpzHVEPFFuDDf3pu6d0GtrqfWheMBsoR2LjZf068LxoWT/eEnbqu4ljCwnm52DX4pfYOKRqe/uBzuTLtgKJi7hPAz4d7Lq8Qrydt9Zh9hC3RY++knBiO4v2/Aktet/4sAsx5vXUi7vQ1jPmG/bcAYcfx71KDH4to9ZZgG6REQJgdSdhJY/Gh9zFhW5ByQdNFRA8qyGys9HJ/KhzHP7e5zU65u1x6XsIGyoLZ8e511BQduytcPlYc4nkW9WT/OXQfov/G5vYPnwb+dg5wd8Lxt1gzmut5jhuZB74nq95PpT/YS7xfmHqD95f0o1gstv17h0zYqQyoUbNhV5ZL2wPq2Su9v6/dCUAPmAcAd5x+IP+8bmvKxmsMK2cDT30bePBL1iJF4pkDkIi6cSfioN1FkLDj36I5y81xewb3TMGgXBJ2RN8KaegV6LWnRXPHBHU9jzSJI1iNRc9s8/W7XFZGYnYSFnZc+R2r2kWEnXLDPBRLLs7/w1u45bkPjb+zy8rliJo0rs2Oyf5Ps+dJR32jGSjnhdDWJjJF1/7isyFVxu/+vQDn/eGtCnbIjEzYqQTojtmgTkmb2FEnRxZngjbbvaNQrfXvlr6RNABgc3hAPuqZAxDjybhvdSmAtQioK0wQZQaOiaJxM/9ImYpCGuF/6YT81v3J118G5AjK0RZetnM0qbFyjjjW5rFjAoSdWUs34Z6XF6DRZASforBT4mosn9mJ6O6fJu5/9ROM/PELmLFwfexz31q0Ea9+vA6/JdncKcRlCcPe6GospmPVPUcx9IhY/SwXak9lm50ynlmffVrXITovHXKh95f0o6mlBdMXxH+OSSMTdtLG/OeBB/4rsIijhcxLGDTmAfS8T95n8WV4n07p9kf0JLQENVYFUvIUCVRjBePDVVtCSpQBixpr5icbTKVbh13EeJCCC7gxIsOKuEh6OSow7qrCzrT5a3Har2fizpc+xq9f+UQvkKbNDlEb0r9tyezc+vxHAIDv/2127HPpvGa6Br7Zc8pgdtjCnjcIOybVVgpQVUI0qGDgM+s53Hy8tcKzq3izeh/4obyBbW0LZMJO2nj0rNAiqTI7az/SDtGdDcWIvp0BAD959gNsa6qAsSqdwNd9bCyiqrHKp9gDbnLQgm85rTqf4kMjyTnpRPGP2StNpVsHeu1Dj4x3rhpNNiGwhUeOoBx8jrBDMzA7kkdbIl0sH5aF5ZrHhT3F3OWb9QIVtNnh7v5tfrM8lfq/PloTXpAgTybUFoNlO/XGihvHiT8HVdjpMgCVCmyg9jVH4lEFPrI9xpqPt3Y80THNPrtU2Gn7cQRkwk76GHuB/N002aVps2NwD6Y7G4oqf5b7z4ot+MWUCsSOoOq9gjkEvk2NVXKVHU6HHuX3I8gby2/3n76LPgNLsbF3v87lt2tC8w6pPzkn5V0RvfYRx6fbVkRwmwonegRlEWdHv1/0WJsv4Jaxtn6bUG8bTbNSZHZKCrNj9OzZsVHyEqwkLnzonVjlqW2b6Xlzzz3HiZ8agws7SsJcJ6dLGiltBtRmZi3dFI0Btc1zrR1PrkHYIQIOe//a2rsvE3bSRu+R8vf5z2pFUlViDfqcdojubCg+XSfUXQvXbkNr0SHMfb2JqICqzHZC1H4DkHdt0mTcuV9wW0ECZQQ1Vr9ucvZ2xuzUViWcgXqFPLGbmIpEIV37rrEDk+Ps+MdCbXa8v0abHUmNlUwfy0YEtWGzyTBX8uhMVgBmwgF7x6r8eAv8/So0AbcPA342ZJdxIw7Cz14QbLZRjUUM4LnreVwDZdVmx8lBe3/C5qQyoQo0izfsiGbIbxV2WjmeJGbHF5wkZservyUNm8MYyISdtKFShNW6PcyO5hRVRnTw73Ws9JO6/lckFQLDDd2AZ68S33OWoIKqNxYRdgpsglrwErBOV9fJMAg7az/y+jH3MftZ/mlVSiBIloU9UbuGbWuBh78sHcrBxXe+6Onau9SlYAydouFruaACbtSdN9+tG4SdXcpmJ4Kw88anG/WDlWR2mNEuG9skfQnW/CfRtqPgxP3rwwsRzF62mX82ra9SUMGymR3lXXQc/dmm9D6pXX35qi9EU8fZxl5r1Vgf/F185sIOYaeZsNPGUSozYSdtqAO+x1CtyAPTF/HPm7YHBL8rB4YYP+Jlt7MdZecAk+qIUdgy2ahBBanQwSfjR75WTveAhyZ4f1lsCAPYPcorhlVM2En09X37d9qhHFwcMNBz5+zbpVb7vdWQbEF2DUNCsxor+BwROyVYjdXWso6tAxMO6G88Ls5L32ZHTXrLVYdU3bxgSqJt2zC0l1AB7dkKhwmT+lOo8Wkcp4gVsntvYnbUtlIK0jl/tWDEF982AcP7dOYCaixm5+Dz/JMSHE/8mnVmpyKpdgKQCTtpQ52YQiaqR98Od8eO174+SdpsdiiSUK3FqsOy0KpBBSnBEss11lT/jnB3SHaPVGGnise1SHD1NERbzaHEBbxUVDC0/20uCXgQWc+TV2O1eXAzyzgf2CMk3EOqcXa8v6o3Fr/nNLdRlwGJtm3DgO7ifoRFz1bBHC0Ac7gGOgRiZwu32eyYmOmEhVKGX768QDsWyfVcHXvsGpIUylgbBjVWRTUHBmTCTtpQX7aQ3XNNPuFHYpgkbTY7FKm4VAfBMjHQ3FiAwuzEoUVNL3QEA0KHt6swO1U+s5Pk2mkIyJVDCfl8iq7AuySz47N5sdRYzEB597TZCR3LaaqxuDcW/L/ePedqYtp2dUrpbBTQsV6MaeuxgNgbGtVY/l+qJgUiCsKsjKbGqhyz06NTjXZMBFsNOFHt34Fn+8cTFMp4rjDdQNkYP6qCyISdtKEOpJBdWeJ2GSZhx/8apKpam0AU5ViqMBuzo+w6qcwRi9np3Fc/FmEyYs1paqy4uv4oMKUSgYtq1WA0SeySwo7314kRVJCrvozCzi7kjWVRfIaO5UqqsVRmh7ZXSFjNbgF9TnGZHQqjGoscoq91JOYhyEBZm+vTEXa+cdgQ7Vgsb6yBhwLfe19EgU7ScHjZG35boh9fr5oGIGN22j/UBSxkoqpOk9lhaizLC0HdqFUmIw4+53yEgc7ahNVY3neH7MaMC+CFFpsCU/0RckFxNZYiuFXFtNmJJKhYhJ2qfAqCFcMuKexQb6x4QQXDbHbaUtjZ0VzAlDlmNXXo+EgzgrLN9ZzdKyl/WmVSyRTI/WjNMzOpP6WggmSee2bOqggV+vdejbPj5LyM33LjsfoaFazL1M4rljfWwed5tqNMPZmKuk3vR8bstHdEkPa/OLIP/5yqsOO3LZgdueiksw/mn8s2ClzyOh6vvQmv1X4/uFyVQodbJnDVGwsIifBqimwKWBby8EmU3SM1WWt1DAFk0/ZmHPLTl3Dtk3ODCxrGhoMSbyudiLbUZmcXEXYI0xDVgDSqGqstiZ1v/+ldrJzyS+NvhTYUdkTWc5nZWbOlyXsWtO1CZYSdkqTGKv+hGc+l3lhkEty8IwJrFcTs1I8GvvUqcMm/vGNBKWpaAXZNdEPqRHlPWN+ZkJPztQhJjqchvsBneNHaOtdaJuykDXUgGaT97h3Ei5N4fMEgmx2lLTrhHj8qnrsnx/K3+cfAa4mYNE8NKkg/G18exzKky1jIv1/1hDU3FrcdivD+XvLHd7BpRwsefSvE+Nyww8rBJQbKKUwWdHzG3uGlEyFKJGqMkxvLP4c9kJPvBgYcJB9D2zI70xesx7jcB8bf2spm59N127iNi2qgDADPzl0lj5EKCTtU/RRHXb21URYwzN5YHmi6CCDi2LBFUGb1DBgDdPbnzpRcz6lNG0Mk2zZN2PGNqhM1UNaDCjK0tb1cJuykjQjeWHQQJD4gjGoKn8ZVFqshxN2zbNsh4pUQaLPD6WDf2C7E9ZxOSjt9OnTdNsPEa2uzLGHnSeQtAkCc+BzvLNkUrUHDpJNHKd2Mxq1SY6UzexmDCoaqsRiz419Dn5FA/QEA1Dg7CXc2BgZ0q7PmCQoNuCZ5zSW3iB79i1f5Z5Ows3LzTnlcbFqcWNtBkKIgx3hoi9fvkL4b1Vg8qKAje3dGGf6c2TEYKDOwOTAlbyzWTzq/skcW+Jqowo6TQj8N6SL8Axmz0+6hTmIGaZ/uPhJ3jTWpsSzMTseaKhw3yov6Wfa4JHYwwcyOEq/C5o1lUGMx7Ggypd5IjtkBgDqYd7JMtZTo0zIIOzm4ke1WysIubbMT3RuLGzWTLFlsLOwqNjsda6vQw9lq/K0tbXYYeOBO8uLWVeflttebc9glDdlAuRVqrAADZQflMDv+va9SYl5Jwo4vCLmlVHYoauwxIKILPRfU8vJfIDn7Iguzk4Pb5s4BmbCTNiIwO25CL7a5fUobKd5YhuI9O3pMS9kDk+iprbIOfbEYHWxVY+mULcvKbrShtgk7cV7mHy7hH2tg1ru/v6xB6l8iMPTRIQbKqUwVu2KcHWIwGzUOiqbGcnJE2JE3E3dP/Rjf/+t7KLTGzacMVOdz2OGaXbdDk7xWQNhRbXYAoLZK8TKqUGZvyfU8xrhUGTJz1nMfjjyvRGKQqMBwzmRxnM479HMKz0qNPeZ99n+LpMZi3h6kn+WyO5q7PWN25MN5lDJhp91Ds9kxMDuS50HC7UtJ2lRmR5cWuClKOQNzzQfAv37Kv1rVWLRPEYUdKtjU+PmoWuLY7MQxFqyqRcnx+lVtEXaWbNzuVRuyXsYSXhmzs//X0LzXSQD8ODuqK3CSMCbxa1uUCE0fKX4IiIeNSdhxxH1r2NmCu6cuwN9nr8T7yxuS7XgIavIOWqAEn1MNXQEcPLi7fnKKcXYYTCxahxqF2akQ+yep9mOM+xbFvTk4XYQjsc+RhCqXjK/uQ8VxE7MDpKLKojZtDJFU3fxH5mJKxl4Ez1RznTbNhdyRPIqZGqvdQ2N29LdPttlJUY2luJ4biZG44dMp7hsn12UrRxdVNtnH8MZiKiTjztwm7MR5mZ08Sv5EUAvzwnLpF4ZHqmrCPdOjt8sWsVwVip28uEBUjZW+zc6uYqAsBFxG1Ud1PTcxO9QdfTtRfVY6fH1NVQ4vlcbKB/1AkvsN6MoPjR/RBxpSjLPD0M13lKCMQefaKlliSKltFZIaK8a4b1bmhCABxksEKq61vmuEgImcHcnLenqHCLGSeih5wbRo2ABGUnWT+cX7S4Sdcj3HbKFVXFXYKbU5cZwJO2kjArNDdy7p2ux4nymNqyKqQWgUbLDl+aJ9ovptA9jcRal15nJpdNe1MjsxJp1cHqWcp86jzA7dcbMopmH36aPVZhuNwD7mquASViLJZ6KB3ve3fgus/TDOyYl3B5BtdrgaK6KBssNYHAeSGovdwxayGIa6eyeMkgsUGLMz/Gjvr3//a6vEuDUyGRVgduq7eYv9oJ7CUcFx1LYrL+zEYXYKimRkVmPJm71+XT37m64dLGErpI751+/kZKHGxuykcL/YWKcqOCfKHMHtJP1+E2an8ItRZXYmKrNTypiddo9I3ljUZifp9gMMlA3FI2XPjdO88eUjx1jYdcuO0TXsYlhAP3ViA5AMs0OEHWqzM6JvF/45FQ8paTLy6q9ySoLZSbAp0aYy4H59eBqtxAJNBCrUWCHCjv/XqMaCy58XFXbmxxFEE8CKTTtxWdXT3pdP/Fgs/v0PDaJXAYGDxvg6cFB3r1lXaTslAVcFJZPiLJJqWdO9VA8N690pejvUo8lmp+Oky+yUuGBPbXaYbVvQiWx+YcyO6GdVyzY07CiD3dGEHT3rOeCp42NFvE8Bu4ywc9ttt8FxHHz/+9/nxxobG3HZZZehV69e6Ny5MyZOnIg1a9ZI5y1duhQTJkxAx44d0bdvX1xzzTUoFNLZ+ZQFVWn890u1InTNrogaC/rLwiBUJmX0Y3+RfbzZ9V4kY4jwOMyOwfVcMDsmNZYD/L/XgJPvko/HpGm5sOOK89ZubSR9iBb7xmh/YW1UCDsuWagj7drKhWHhfH/Z5ognV0KNFU345udI3liOf0wIjC3kZbv5uTgsVuvx+b166Qf9fjeT98S4KKQk7PTt4rEaf/2WLOSyJ1tyIW9EKmSzIzltxBj36jti3BD5UJP8xoqz4+RkoYbOpZTxSeF+mdhusfmKoMZyDElLAWyMElRRhW0zb1RjZcIO3n77bdx///044IADpOM/+MEP8M9//hOPP/44Xn31VaxcuRKnnXYa/71YLGLChAlobm7GzJkz8fDDD+Ohhx7CddddV+lLsCOCjpsOgnTVWHJfzDY7ftEy+kF3zWzRMaoKjMKOuT0e3TWn72KMp7BIpodcqFQUUdg55CKveF5XY725aCP/zLOeh1THdsiRQNVY/quZd9zW2VGFwTAZv714o6Fg5UC9Tdh4jOqNFYfZqTQ611ZhpdsTAPCjlou9g/78QPtlJkPTMRJmt7V7R1mFI+K2uBU3UN7RXMDKBrGxiKPGUuctM7PD1Fi+MBwntAP1xrIyOyLsQRrMjontjuWNlUtw2deYHTmWG0MOpeS1FjHR5sLOtm3b8I1vfAMPPPAAevTowY83NDTg97//Pe68804cffTRGDt2LB588EHMnDkTb7zhJRubMmUKPvjgA/z5z3/GmDFjcOKJJ+InP/kJJk2ahObmyiSsCwUbDHv+l7WInOG3AsIOWw+MNjvlL6wv/ke4z1Y5Jb+eiMKOZbeq5u0BwgQyclGDDiMVRZh0eu4JnHynV5zZ7BBm58T9RS4alok8TDiNJTMSmtnlrIQrLzxJQ5ms1rldsf8e3ZJvJwaEt0n0XTf7NUcHt7/DzqPE1WFtKezUVeex0/WYlCZX9kL8ZN12Xs5ss0OPJTcOTEE7AcVRQRJ20t+d/+9T/5G+x2F21Mcb5HrOU8FE9PjzTmY2Ow6sNjuAYE9SUDmanDYibYhUNRaACU23AAB2ujVa/r9IsKqxdGbnM+96ftlll2HChAk49thjpePvvvsuWlpapOP77LMPBg8ejNdffx0A8Prrr2P06NHo168fL3PCCSdgy5YtmDdvXmUuIAxsMPQY6v3t0FMrIhnjJT0eDJFXBdFvUmPpfYrRmPbdnJKKlOPRRi2u54ZooYEvNp10Tn9YfC9GEX5FG0WDzc4bn27gn6si2uyo97E5KPMvp5lzcP3JMu+k7Y2l7sDiNJLO5EVtEvIRd91it06YHd/bpAoFvjAEqTUqgWrfu++gPf2UAm4Jn67bJpUJVWMlOBBswo5YR115wa4As/PUeyuk77FsdpR7ExZUEBDXGtv1XGVzKHLBtoitAbVp481FsW0zqLE2uJ4dYhWK3LwhXmeU8WAJKuggJTV8DLSpsPPXv/4Vs2bNwq233qr9tnr1atTU1KB79+7S8X79+mH16tW8DBV02O/sNxuampqwZcsW6V9q0MKL6w+cjpdUbXaavUlVxNnRi7dmYe2lUuFwwyfuiDY7eenF9k8x1U0noK79gbMf9yuKoMYi55q8sTZsF9GUo0f2lX/f0RzAMFFmh1Hsadvs+JPT7wsn8vba2muiRBajuEEFpQjKXBVZ5M9LdU2uJFzXRbWjpkkp4aZnPlDKmU5Ox0iYswTKXMA2Qm3B7KiIM+6LyuIblPWcvViR7F34yRYDZRVp5J3yYQq0Gk2NpXhjASjAm3+rnSLWbWk0nRUMmzeWqwo7bT+vtJmws2zZMnzve9/DI488grq6CPENEsStt96Kbt268X+DBg1KrzE1RLdhUZfTRaTUPkPT1kAJ3omyQ7CgQ7U8nHI26tIk7DQsM9ZZ4jY75JQwmx0K5u0VZdIh5zJmhQako94q7HO44az8PdDd2aXCjm+zg5K88CQN/1k0wY+xglIMVU86Bsp/fu4V/FfuPTwzZ1VkrzfOBkk2O97H0/LTiYFyWwo7HssEADuJGmvFpp1SOXOm7nQEDlZVXpF2HIf8LrHDlb9/cRZJlTk1qrEUZkcI1BEasAk7zLuOgQs7yd8vU6DVSOYHBjVWhw5i7b3qb+/G74zKXPF5Vhd2PrOJQN99912sXbsWBx98MKqqqlBVVYVXX30V99xzD6qqqtCvXz80Nzdj8+bN0nlr1qxBfb1HAdfX12veWew7K2PCtddei4aGBv5v2TLzQpsI+JvlDf7mQgnLNirJ6sr0PIjWvvKyzX40OIIy3yGU0VTUfCgmKviFHwHz/q4VNbtZyr9JUIUdFjgrErMj2qDMCgMVdqLuBlWhMVCNIsXZYcaTrjSppWXAXoAw5m3rHdi0mu/jwZqf48jcnMgqBvazZLPz3p8BAHVOCxeW21KN5QKo8YWdJgghvFSUF4zQDUJFbHbI79KClv79+8LeclDFOPKCKuwEMR3CZie8rKiQ2ezkZZsdrfL0mR1qs5OLwjTTGEE+mNcsAKzZvE09Ixzq+sK+q8yOU94GOkm0mbBzzDHHYO7cuZg9ezb/d8ghh+Ab3/gG/1xdXY2XX36ZnzN//nwsXboU48Z5kXrHjRuHuXPnYu3atbzMSy+9hK5du2LUKHuQpNraWnTt2lX6lxoUZqeppYAv/HyaVIQOglTVWADQe6/A3FitSTrpGKhL40Rl2x29+L9aUbY2ycZzxBNK7afG7PjCzoYFwIp3RfkQQUkIO+ICTIEN49rsGN3leWEm7OQJs+NKC1FazF/B9RZfj9lp4y2Yj0Ny8/kkHqrG8v9KzA7J0M3GT9uqsTzbCABodIXK9+oBc6RyxktNidlh91UzOaHzQIW9sVjcG4Y4G8AudbIq3fS4xVipgBorBZsddk3xc2PpaqwWIuxUo4y+RgwqiF0gEWhVeJF00KVLF+y///7SsU6dOqFXr178+EUXXYQrr7wSPXv2RNeuXfHd734X48aNw+GHezEhjj/+eIwaNQrnnnsubr/9dqxevRo//vGPcdlll6G2tlZrs03At5y+WgQlbTKj31NXY+Vr4RbMExwQYg8T3phcV5gaS50wtizXigo1luXFVr0d1AmI/v7A0cAp9wIHnwf8Ug5z4J9MPjr+EcLsmOJaGGqRmlcKBC7aNM4Od4t1pedUcl3kklQf+W3uSswOwwnDatAU0YbMGGeHIM9TjLTdteVKjejkeHZfO0tiUd6zcR6AEfx7aATlBNkVmxpLYnroO1SBCMrq3JNa1nOH/Y0mUHsnW+LsqMjFUJ/HhHlOjKLGUtJFAGh2hbq3ypIaJ7gzFjWWyWbns2ygHIa77roLJ598MiZOnIijjjoK9fX1ePLJJ/nv+XwezzzzDPL5PMaNG4dzzjkH5513Hm666aY27LUCxUDZtExVzPXcO6C5XlJEDc9vbiuqGov1yQneHcHsZim92GqwQPWiWrbL35/+LjDrT8DmpXpjErPDFn8BGk5e2OyEqLGU3wNZE6JTLzGbIbiSCi95bz2mxhLtBbJPFcQ+y/5GIsOGXDi3wyArWf8x/Od8nAUtJZz+yY/55x0uzUskLzKhaqwKeGPJaqzKRlBWH1GcjZcWQdlMk0nf8lEEBX5qRGYnTddzwzOLlhuLqOB4XTkUXe/cqiSYnWITM/SSDufgtrkaq82YHRNeeeUV6XtdXR0mTZqESZMmWc8ZMmQInnvuuZR71grQxHGQ1SIMdHFJXY3llsjCYLLZaY0xrD7AaeJFrU9OTu/ftrVA5778q8kYj7/jrqvb4mgTkEGie/pyc/epuojbzIj+Xf/l/fD1B7wYTx2q87wLHG/8xnvZj/ieqCcWsyNcQ6nNkGSzk/Ri43ewCDE+G4MSZFZ4d8bin4Xa7Ph/JTXWKfcA9x/l1+MbKLehIDdo+1z+ebPTTfygLIihHoxJemNxmzj5uEMZtQqrsaK4j0c91+QQYHM9jxdUUMmNpYIN3BTul9lpw2/O9YRDkz0mdYDgdbkuCqhCHi3JqLEAYNG/jcxOWxPGuzSz0z4gq7FMzA6l1tPauYvvLkkXoRdnL1BZaixtgJfw35PnGMoRYadJcfvfvl76anazpMyOQr2qF7XnF5XGA1RAkoGyz+yQaxo3vBeuOHovXP/lUbo7eKEZeOGHwEvXccNYQBduAuPs7PDj+FTV8nQReZLqAEhPzdlChJ3fvPppQPnKzVjNXQZFDoWgqbEcB8h7quyNbmc+ftpSjbWxdg/+ublIJVh5TJhdz5PXdbuuK9RYKrPj/9XSRVRAjaUyAHHMrNRzja7nihorknEvPzkms5NGuogAZgcIGB6cOc5JZdm7X+2UocYyXd+WFdohB23LqgKZsJM+6MsBTwBQ0SyFik94QGhBn0qBiUCF63kZbRnUWMY8S/Se5KrNv/kwRwv1/no2O6qwo3pjqbuvgPtrMlB25PJXHj8S3zximJ6cs5l4MvzjMv5RnUB//5pFkCiVgE98Y3y3xNvPG2x2EoW/kDFmJ++46N8tIBREBV2P3VwNcYEOYXY0NZbYeedR4vYNlc50TvFO76/wz4WSxSYGlfPGorfCFlTQSxeRvKAV3C+5jVhqrAiskJobMFISTQb2njs5eWOlRVBm6SLSi6BsstkBApgwoxrLRdEXA/bp1zl+Z0zzgZO3MDuZsNO+odjsmCLU0t1m8jY7aqI2IeyYqJ3WeGOZ1FiBcHLaOSpM0UIlij1UjRUHBjWWpX/aIty83VhOvY1/n73SWE6yPdqwkNgMycxOWswf290BwBdG9A46IeEOBMCBLlRawH6XE4HqubEKbeiNVfBjGf27OBotkgAhv6PrtjZBg6RKSqY/9B23pYtoEzWW38QYP69cnDlRZXJ+9a+FWhn1nYysxtq6Glj1vveZzTOfu8T7e+nrSqXpeWM9M2cVAGDu8gbRHJn2rNdhUWOV/LmmWGyFgXJ1R3Gstos2TjJh57MAzUDZIOxINjsJt6/tLIKtPloTZyeysMOZHUefeZSXROTGMvXRNRgot2JIGwyUTc/L64Ni21Qg0Uf3PpF/LOsFX/EuSoYIykAKzJ9/v4vEBbUY5EFSQWbHcV2iTgkTitnvwcxOm1Dpi18D/v4ddCxsBgCUkEOLxOzI93vmJxugIRVmhwg7KiHKhHktXUTlvLF6d/bUkPEiKMtlV2zeqZVR93om20Uj5jwmPrO5YsIdwA0NQN995LIpqrEYqMNENDWWCG3hlfPUmKy4Gu8pEtj11XYRx/I1UMeoF0E5fvVJYpcyUG6XUOLs5B19JFI7juQXM5XZcUkeIR3CRiIZmx1zOSrsqHEaZOFFhLPXKVuP2QlRY8WByWYnKrOzbr74seeeXuccR5uo9+proYrpfegxVAkqmCKz4y9klNkpBc1KFd6dSQxDEBgDSG12HCHs8AjKbWGg/NAEAMAJ/j0uwUGLJLvoi0yhWEIVlfCpQJTQM6C3QmV22Oygp4uonIFydT6+s0Qckyye9TyqnSK99iC3cyBVNdbR+/TFvz5ai1EDRHw4eY4wqUFdMu/KzhWM2Snr3eCJUXPAgIOAle95x7S1IAUVfExkzE7a4MYE9OXwjm3c3oxn56zClkYxkSU+INSXzS2FuJ6X3w9HmQjDmR2DkV8xXNhxpF1nG6mx1BQOnUjU13cfBG7sDrz8E2038/VDB5ubpvd76JEiXQVcSShNjdkBZXaCJuhKTljR84JxOwwDs5OjNjvKalhJd9i87+3iCTtkPBsWmYdmLpYPSOkIkmd29Dg7fkuqsFMJA2W/W0zYa40aywTVQBnqu2w9kQo7IfMMl6DSSARqnxMBy/2iz81/L1g9bIZxy6FeTHO5W4KJ2Wlr1/NM2Ekb7M0iPDGbkM/53Zu47C+zpOKJUn0NK4CZ92j9CTJQTtr13FyMGm0rZRRhh3uLUANlOjklabNjMFC2sVNaCoc37xMHWvx0INPviCGckHKjTxcGyqnb7HgVFiRmJ8j1vIJqLBo9OuS6mbwg2+xQZsc7qhoot0WgsxJyKLnEoNTAEmiuw4teFZ+TYnZIPcERlCub9ZwtitVxvKR8RHmeQjD2IOUBCzmTI2ye4Qt/8uNL2DGKY6FzxE96kcJ5qRxTmbvlPFvKFtHYQpnr+WcQWtZzMSF/sErPtp4Ys+O6wF2GlBlE6g7OjdX6fphiCok+wJsQVijJ51Q1ltHNkvTxw6fl8xMWduxqLEVHPu8pY7nIniXKrrFE1Gip2uys8WK/FMhUUAq02amwGos1G1KOLWDce44yO47Lr07d9aZuw7NtrXaopLwZ66v30MqoWchlpK/GktS0FVZjsXemiqmxYjyjaFGQvT/CZocdDjjXdYGXSbBaEy1OkWJQQdcwJ9LNoDZHaFHmZWaHzTVlsVBsEDkO5GTXuhori6Dc3qHoSQG70SuQ4GK20eLiHOJ6zly8y3PGUpgdJ4Kwo0JhaoJCo9c1bQT+/XP5/LBJSAVVPxlcSR2DjZXXB/E56FapNgR2Twm6a3SI63spvQjK24UhbA0JFV8KigUkCWUx73UZiOod6N0X+R7S8VWT9xkshTpNff59+rvaoRKbAcZd7vdBv9/Sc966Wv4xBWZHjbMjecGVKsvsMIGFRSmPs0hGUmMp3yOx2Td2V04KsdnJpWegLJIjk+aovbt6HTsUg3d/4y2EHf9Zl2WzQ2xSHSI0GZidxDdqMZEJO2lDMVAGgl2yE5N+t6+zdShSuohUXc+5tBUu7Ahmh/bR+3vUgtvK6KOCQy4iFRN2g6uRzKAeHKX1unsrA3vBq3JhE6pMkZcUA+nW5SyzYJtYROmzKgZOeqT9CkxeUVUMrqtsIgizAwBVvuCtqrFSN5osNmuHPDWWaJcJO18/dJAoQ/vZqDLA6aux2PAulVz55leQ2REpWaKfG0mNxR00fFU1p3ZiNBRZjZWCzQ5bUgzhOAADu/XpK/L3Kubl5n11E1FjEZudUhGmtSALKtjuwRSsQo0VxOwkNh4a9KSaXncos5OwGktldsJsduAA+5ws/6aosbgxXk5/sXvsWBSvf0d8Xz9WTQLoLZ4uuhhis0PnuvzU66xNqgHA7MwObYcwO1zYITvtJFAqAvd9nn/Nk+ssBQk7Fdyd0fckfDy68ngj3lgAUO0zdKqwk/rVjDhBO1SC491GfxCxxXdIr06YePBArwy93vXz5QoSEjhoqghVpS2Nt4qrsby/fIOQtIGy/1dXY8VAqLCTnhrLls/MuiHqPkR8vn4zv3BuoOxG3FGYQL2x6D3R6mr7RKCZ63naMKhsgtVYCbRZaAYmXyQf6z4E2LzEE3a40lo/tTUGyup15VDCoJ4d9IKU2anrJv+mqbG8v1K6CLioRgEllXc5/SFzx65dAWxY4CWGnHG3/Nuyt4ynCGHHfCNkDyn7AlAizE4TAp6vpMbK8XQRzLrDey4JBubauVn6mid5cQKZnQoaKANErRpSruSqwrUjMTtc2FHUWOm7w+r1l+CHJJC8V7wxYoxz9bdzlCqT6TM3/jdQvOxIW6SL4GqsqvhqrGjMjvw9cngD+azgn1NUY3GfF6UL+ZyDUtFgCJz3l/muAyUKj3UtETWWkyc0rNkbq41lnYzZSR0BBsrG4kmMiPvGyd9vaAC6D2YNhHhjeX9f+mANJr9rYYdsMETNHNqrk72cSY8WwfX8rKXXYU7txagtKPT+fl8196u2sxcDwtTeoMPE53EiQWhYnB2JQjYYoTKwiScftktVbHZUNRZMi2BrkJf3OVWOWMRKFVjQIsEVbvfh6SJcXY1FmB0W30rPqJ1ERwM7ph0qMjWWI4+JfM6JaKOUTKdN7xaDnC6ibQyUq8tgdqJ4swpmR1ZjBd7zasM8FgRp4U8WgpFTjcotY8fgEUzLtcobq2RgdlxXG/cO2j43VsbspA2DzU7qzM4Guw1JeJwdcfCqx9/HiH6dccDA7pGaVauz6mmlrOfK71Y1ljg2evM0wAE6NK+J1C8JYy8A3n3I+3zy3cAh3wS69Af6HwD03Vd0kecyCxd23JqOxjIAcaP17Q8Cnjz57HDXZOYGznf8FZgwAuPsVNBAmbqeh9rsAOgGkp+MeocAqLYay7eyk6HQG3CRk9RYTFWazzkiKSV9zvt/DfjPE6SCZDptercY2jJdhPDGim+zE4epc/jfCOyhllA5ZENQETWWfNxqgmBxCFG9sVpls5OLEGcnM1Bu5zB4YwUxO6kNCCJ1qwZ6FOou798f2wydTVDVWBGEHRWqGssVu95AmOxxjCD1HPJN7++BZ0qCDiDUWDb3eSk5Z489ra2p/Y/qjXXfvxcBAJoLnpeUaffdKigT2zPFw4WAFdVmp4IGylG8sc7Ok8B7ig1BVc58fuoTsKH+kuvb7LCx6D8LxxFqLEkdU6tG3U5WjWUaW3Lgzsqmi2DDj7mex2EEopTl859/jVIARetJaqT3kPuQqjeW91cPBMneX+UEq7Dj/yys0eN3RnI2IePZ4I2VBRVs72DPNyqzk0Sbex0rPu/5X36j7FGLFkzrpypT7NknTiZc3WYnMINzhKCC3MA3bLEffXq0LkYUGkRQP4vNDqmm2Gc/cyW999aNLa02OzJjYjNQTjQOk48bxryKnajjdPaU/6wOUBtRYScNewRlkuSLbvh51Q6ND+QAjsPjB9VamJ30iR29XaHGkgPP5RyLzVxLo1xBQmMgSI0lBe6UmJ0KMItcjcWYnYSFHf8vZ3aieDpamJ3125rQ2GIQfFL0xjLF2QHEHBmV2WH18Dx8ThkCiclA2SDs5HYBA+VM2EkbMdVYiSxm1A7l3Kf8RoXUzYXxAAPlcvrjGKT5txdv0jNNByUCteTGMgVAlNDXEEDR3MtIpdgEYDdQFvWUmD1WlWKMXSpolHyoEOFPGNxmx2EsnF9lUvMF6UehxGh8cc0vzrOoCFNISGnplt+j6MajVZSFU+5jdVsxO0Y1lmKgTARatluXxgl7J/I11jrLgU0dQo95aqx0BVwVPFxDPr6AH6ksn/8c6W88NZaLtVsbcchPp2LcrS/r5SugxlKnRCsLahF2ilzYESr72O+DZKBMx7NJjRWv6qSRCTtpw2igrE8YnJJMYkAwweqAM4l/pZC6Rbh0O33NEM+oTJfmAeD+f6sBDsnCrk4iSlwS7jESpsYyGR6YEJHZ4XF2IgQV5J0sKBmWqbAT2fXcK/el0QO88xx5YktucRb1sMCHNB/Y8k07LKelq8aSYr/AkJYj4DzqUcaNf/0prsYi7LSFgbJwPfcXW5d53InnLL13qio8MZsd72/O8G5Z00VU0BurHJudaMyO/E6VxeyUinjz040AgE07WvTyaaqxDHF2APEctVsQqsbyz0cp/vJjNFDWmR2v3xmz075hjKAMfLpum1SMvaSJLGasTT94lGgVkLyxIjA7cbqjGyh7/fj5i5Y4IU4O6FIv/6ZkMWe7D5Maq+DUaMdCMf4Hnrs78bwyweW7HTMkA2XbPSoVuXdIVVgGZ+WhVFV546V3J09IblVk66D2QKM8C2bHKlymbKwq3R/XjRw92nWBKknY8d2W/edYZRFa04/qqtfvqbGgqbE8mx3DOJHsIpKDLV6L1xfWtJv6M1fBxiPzxiqWXHyizJc2RJk/1SJaUl/zWcrXYnBb/AZWMs5OPDUWEz6o52ns10GKoExt0EzMTibstG8YBloOLn7w2Pvm4km0WdIFLInZCWhEXeSSYHb27K24bdJ7ctQ1wP4TgW6+a7xVjaW3xgP+HXZp9C52Gwj89yLghJsDiwk9driBMvdi6CMbOaPYwhfTfKj9gbygFV3ZZoc1l9jiTJgkJpC5RHVWV22LHe1aPifULcl9PC6zIwdmBASzYxV2yu5pRBiZnZx3LYzZ8fvtAGY1ljI2EvfGMgo7RLiusIGyUGOJOfPUX82IdG4UG1suO/rfQ6N0m34oFYPnRsbkl2P0GwJhWK40GVONxYpRlX1gfjBjZ2gE5WAD5USTXJeBTNhJHWKiooOqWck/dM0JIwEkZJPBJqScRdhhhwyT3KvzZe+reDY7epwdADhq7z5yQWqzU9cV+NofgFGneMdiqLGccne8YXltQIQdq4Gyo9PfvYYDl74OnPcPv5SrqbHsE6qsxmLCjiMFFUzQZkcYLvD+u2TxHdLT4k6f8i5fXuOFzQ4QzuxIwo7jYEtjC6+vOm9mqtIndvR7xG12IK+yOTKmZDWWOs6T6bQwUNZ/E2pTVJ7ZMfTrqJF9LKWVc6MwO2Ts03asCz295i79vb/DjgweO1zYCUiqWyaEzY6F2bF6Y5ntMelcF5/ZscTZ0ZidStjHBSMTdtJG83bvr5OTDECp/vIflx2BTjXeApzIzt0kyYsEMMT1XMf0Ba0QdpTvTI1lDXJFz8hXe39taiyTsMMWt6j2OjEQlvUcEKo1/swcB+g3Cujsq+bI7i9yUEEbs5OaN5Yj8o/lhOqusWDZwVfQZgdw5Qj0IV6M20BSf+TyuOXZD/kZNmGnLQyUi+ypqgbKOZtQKy/OSUdQDgwqiMqrsdi1U4Fvr4heoXHsQjizE2YET6/5/80A/mcl0KFHsMhZAWFHZ3ZsaizzppCniwhxxggErTsgqGAuU2N9BrB1lfe3eRvRjcou2d06VJcZstwCbjQWxuzop35B2UHFYxLMaiyN7jW9fErofIZgNVaZzE4EWIWdDZ8Aj5wOLH2DTC4yK0ONE9mlcmbH2qC8+1KFnUixQOKACMT88ZAUFU0ttkUtXTUWHSoOZAE60H7UdbHU7Scde/4/q/lzrLLYIKWvxtIPleB4izILKkiYHaP7cErMTlAMK9n1nBooV84bq19XIbx2rAlnY4F46SIiGyjTOSlfDdR4avnXyMZQm+PYHJCCsMOFVC3Ojt+keh3UiJge9ovRdalsmx01zo7K7DhZItDPDtbNJxK04nXiIJxKjYMgZoe8uKbpv2cn2eh3xsL1kZtVdwZnHbIHgKg6ZME8MdBdWmCcHSfaRBgHXOWo2no8dj6wYArwhxP0SVLzfBOxJcLdaGW2q0AmIFp14t5YjkPusxifdmZHd8FNEur1RTIE939j4+8lHA4AOHRYT/7O2dbK9D1E9PpFBGX5naRBBY3CTi5Zb6ygjQRfR9VdekVcz72/VTkHXz3Im0OixmiJF2eHveMRNyKANGf9ffZK/rlFNUhpA2bHuw6Di3dYBGXihRl7/YnojZXlxvosobYLce2VXfwcPwAakNDGidvsGIQJ1yXrnD7LfevI4dL3Z+asitOw9I25bevMTpCaTYBO+IGu5xFscOKi6FpyY21eovdJVcsRT4wlGzwX7qpcTi6qQlNjsRrFrj/w/LiQmB2Zzs6hhJaCbZdrYekS7haDZAgeosbialMixoepsVKHMaigHGdH2GXZ1JVpMTveXyOzQ9VpbZQuAg5QV+2/D8Vo1xxlOGp7E7Vd7QSzsENR0JidNIUdvyvKnHl54WHMrP0unJ0b5BNChB0HRNgpl9nJ5WVhx6S+zZiddgw6ckYcB75zduSfkmd22NtgUmMJ6d00/XfrWF12syoJItRYav8CmB1yY+huzhQLRJyaphrLPrkLAUSdPZn9lTh3/bYmAEETqlwHV2PxGCzp2eyIj0J332RzndDaT5HZcV2J2QmaK0uuK8YbU8GUXMHsWIZIm6SLsLmewyFpRQx1JOyNZYvECxB5HW7lvbH8vzTIoiZMWBBtQZXnv3BvrAjCTgWZHVvk668X/o4Bzkb0mfegfILV9dz/S/IAxh5ZJgNluNrNdJBFUG7foA83V028sUqoJ/po75jJMLFMmHS0BtfziPH1YkBldry3SdOFB2U9l9RY4ih/sU3UVwpqLD7haq+/6LPoklLGYH/EaO5w13M2ubPWZFVDctOFaI9NcTS2UEvBJuRZnmWyvTJ/C5gsqTeW67NyhZLL37kqy0yX/vxrEnbYPZcNQ3Nk0yOPqXSYnWB7uLZndhwIRrQQkfIux2aHb1qsJ8hefia0qMxTisIOD2dh6Yt2HSHMDjVQju0gY3M917yxyqg7YWTCTpqgu6CcMODKwcX4Eb35TxKzk8R4MLqei4EomkhW2lFZEL7TtrEBRpsiIuxQNZbB5kg0lPwwtqaLILdMY3a4GsvvD9kR86znobtHX9jharSUbHbIJKV235rTDEhdjRVksxPoeg7xrFggQU/ADBF2yu5pRJiYHVdmdljIBi+cQQQD5YTuOVdjBXhjeRGUKyvssIeSyzncsDwqsxPFBksMd2GjBgQI0xGYHd1mJ810EX5XLNN3SV3WDYFtATqMSFDBuJ2hk4dks6OvBZkaqz1DeUlcQhfSl9IYsyWJdk3MDoIjKCeJWDY7BgNlWY3Ffg5IupcgrDY7pC3NZodvFe2h4qNGULbb7CSvxtK9MgJ095qBcrKLn2S477ryGA0SdlyXC4ZMjdVSLAlvLMtgr5jr+dAjRZvwYxtpubHEmJLWTu19SV+NlRN6dfmds6QCSBKU2cmzzOcRbXaiRVBWWIdQNRb5warGqhyzExQMEgBKKtMdEmeHrUtleWNRRjokqGCWG6s9oySHr6fuzHSnQg0TExkPoa7nss46KagsCBN2tAlo7Ufe3yUkKqqJ2ZHukf970ZCHJhU1loXZIXdN7H4V13ODGmu4HyckaiJQxuyIvEkJGygz+B4ctO1grwwLS5cQ5OtTbXaC1VhMMC359k7NBcFiVrV1UMEaESdG2OzIYz5HvLEqEUGZbSbMaiy/ryqzk2D7NnAx3BGZzyMzOxGK8SLsdQ17tyIIOy2qmq0CBso2QtuFKuwYmHRY4uzEfbSme+O60NVYWVDB9g2J2clLg4p6QNRV5cM9Aspp1+iNlZ7NjioYsNY1ZsfYsIHZMbmelwKS7iUIawRlMmE4KtuiuJ5798P7bdSArl69oVS5zOxwNRbrV1LzBdntsS4J5jFgh1dh1/OIxI5voCx7YzUXhYFytdVmJ+UJmNVPxqieLsIXdqSgggFqrAp6Y7kudDu5lFVZ4nVyCNOVHLPDoBooR/PGMk+alWR2ghg5QBgcixNsNjv+z5E2Odbe+HU7EGuMa2R2MjVWe4aqxiLCTjXJ+9KjU02yO3c3yEBZDGdT1nMAuOkr+5XbsPTN6o3VfYj3d/gxpH8mmx1SF5uQjcxOGjY7ftWqNxaZYHQ7K4XZAQ0KGGKArixonNlRDJRT8cZih/y28yiF95N/T1qNJbdF5/Ogay+WqDeWdx0frtqCyEEFP/gHcN8RwLr5xnLlwyTsOLI3Fhdohc2OvHamw+ww5jTIG0szUAbMquQEIamx4trsxDJQZvNxRAPlgHnGbrNTuTg7/HerzY6Z2WGG/XvmVpbhei7mkeCs51kE5fYNyUA5T3IPAUV/t3TGIQO9Y0kuZiGJQLmrq+VlOW/cUHSwJoK0Q3U9D/fGCgkqyF5G+lZXSI1lz40l+iLSRSg6cUnYETFUvLKWBrUIyv7X1FzPKbPj09n+fWxLNZZqYEpjiQRdOhV2Sq7DXf3ZKXnLTMfv52PnAWv+Azx5SVn9toKvrLKw4/XNnw/I+5g3zQOpMTuMUbIbKGvpIoCKMTtUrRf1miMlAvX/sqqjv5txhB3G7KRpoGwT4CMKO35Fh5TmAACuqPp7+SPLUYQdw8a3AsG3A5EJO2lC0WfSsNzMVZEtYjZKsrx2TQbK4jf+sgc0adsJhzTs/y8LCpo3VmD/dDWW1BUlUahXIAVhhxkoq7eB3DRhZ2VXdeUhCyuhNjtcjaWoOBL3PRcLqKiSPbe2U2MplXv9MdqxyCi6Ls9QX4KDR95Y6tfgMzuW4axVuXNT+d01t+D9YYsfxM573qptUpmc43DBw2izE2D4Xg647YfRZocwzSqT07IzkfZtcInwx5mumMzOMfv0jVQ//RvqjRUg7JQTVHDzjmZ8uGqL9Xcbwg2U46mxpKJx32cTswOzGitjdtozLC6L1A2PTW6JMjsm13PCnPCNYoCJco3NVzcAOWVSzrXWG8tEs5smjxTcyrjrubawiLaqHO8+a67n5L5HVkMpu3c167mkVkgCVI3lf2TMTh6lgIk/bTWWzmhEMd7/dN12yWZHMBM2hs7QXlgj5YBGmGVt+n364xtLAMjsnXFxV12HE+qjLTidd8xvymSg/PbvkumABezyPGYnRP2rnuuXO2rv8CzpIoJyAmosNS5VBGFnzE0v4cRfTsdzc+NEqafMFzlIGKSSZqBs9sYyvePxhxbd0Au7ULUmB9FTfqSFTNhJE3wAOoDj8B2dQ7yx2IBNNBGoKa4CYQaEQa29itoyhB2RmNNfNG3eWBHTRbDTZDWWgdlJQ42lMCscZFc7EH4iwJIymSiC7fnjhsjh901Q1Vj+YRqDxTs/BTUWO8QMFZ0Yrucpe2N5/fG+hV27YBLF/XdZJOqozE7S0o5BjeXyxVVl70ASgZrq0DcEreuaV48xOB3th/rMC02JtG8Dfc5Rn716LvW+Uxd1dbMXmdlRJkyaR7ClFYlAv/PIrNAyFEZmp2kr/1hUl3Xm1EHYRa8eve6ybXZUNZYyZtRwK22BTNhJE8qiLmx2XB5ePMePeUjGZseU5ZYwO8oRE8phdrgaKydsPwATsxOUu0scKZpe6ooZKAuVjoSmBv6xitsk8Y5o/cmjhJNG948wactPpVBirufevYo76YeDqLG4zY5Qs9rnJfVZJizsGOqWVCoBUNNF0Poik39p2aOQse5a3jyaCNS48KYUVNB0bySbHdXuRFk0kwa12cnnoj17BjZnUAcQbfpR2NbQjYhqIM77KU6wp4tIw2ZH7j8AYPYjol+asOP3IS+nAjLNJWV7Y4UYKGdxdto7NHUSEXYUCjlRb6zFr/nNkbfBYOhpM3ADyhN2VGaHG4xqZIBh8jDsWs02O5V1PdeYnXwt/9gVns0FnyD47EnVWCXkcoKOt8+nihqLny9PbInNF6rqjbQdaKCssXRJMzumRd77E5XZKSFH7pdZjWVVKyZNtRuYHf4TddX1+8TGiazGUt+XZPpo3Ez4sKaLAIDOdnuYJECFsLiMJjuXJn5VN1tqVaF8WQSbl8pmPfeboM9t52bxu7r5K9qYHa+iRog5Lb6sY2F2TGqsjNlpx1CZHRKhluV64cyOPO+1DjvWe3/n/d3Qp/SYHV4fF3Z8G4o43likLKfZqbRjirOTghqryBdJBWPP5x97u5sAGGx2FDWWFzQSSlkFimqxWJKfTvIRlIUaS42kmg80UFZ/SJHZ0QyUg881Zz2XmVOGKhtjkLTgzG12dDaEqdjEdQoDZXldUISdhMaA8f3yIdvsKO116JFI+wE9A8CEP+9IdJsdX42Vo8xOiLAT+d20Mztabqytvh3OginGKluz8BtDBmwVdj8ldbRzNZaZ2bmt6//wY1F6tXZrI376zAf4dN02cgZp03WxZaes6swMlNs7NGHHQw4uD0LFXuZEs54zbFpEvhA1lokGVVBbVc6kz7YcihorkrCj1AHDztN1gTfv109JQ42lGAiL7on+HVZ81zsUEDQxj5JkeGp3v5Qn1AJrX7XZSUzLQtVYrN/ec3MCDZTTdUM2EzvRdFBC2BHXxN85JS6CyC6uPrukx5L8TtA+iWVCzAVGdWVazE5AItAcFQZVb6yUXc8pcxE35AK32SECnO1c9k5FTgSq3CdaXktU+lawEffsZZv55zMPGRRYVoUIGUAOEhWV9u4ydimvMDt+l1fnBwAAtrgdIsnRlz/yHn732iJ8/YE3rMzO++T6gEzYaf9Q0jbQ3FhMjSV2VWF64zJQ3UF8NlLVdixevz1aG2s/BKb/AmjZySdtaugK6LFTAg2UyQuheqxh4cvAB3/X+1BJNRb5viQ/1D+iTIbkxfeYHSfc5kZLBMq+yXF6EjdQpt5YIMxO6Hnse9ITmKt9jHrtF1c9DwAY7KzBlsYWvwq2oMll2c7fBeRrSJzZ0dVYYtshj/l8zrK4qx5dCdvsBIW9MKaLSMEOhYJvxhAt7AAF0yZRmx1NjQVRPxCB2TGl34H8GLRNyNE/Duznkg1ifmVjNSqMz42oy1y1M0X/N4say/WPVwUlACZ4a/FGAMCaLU0w2+zobOCuEEE5XUuzzzqUHRn1vhC7KnkyTjR8/Rd+SL6IiTWKzc6G7QavJxN+fbj3t2mbEAxyshorGrOja87ZO8u9RRqWmvuQArPjRmB2Ck61f8hA5To5wC3B4cJOiDCreNw0M1pc1m4laLMj+umqQiqCvLHUhS6gR4Vmb8cZIzSAkdkJsWdT35mT82/i5W3Nfu/MNjvMW8d1XeB58p5s/DRyX6OBvRP6GDUJOwwzFm7Q60gpqKCmxtq2DkfPvxFTnbFw3YG6cJNyBGU+NCMxosq5Bm8sLduF/KqZQnzJKJmFBTrutFO7emwJ+o02VrmqoZF/rrJFvDSAtikJO733JoVU4ZT1X1Vj+cV5yIli/JFlYXbUTaKDgPtbIWTMTppQclRRYaelKO/Ywz0CIqJIDOL2OtbUKW1nY8JXxgyI1+7SN3TXc9YldaIyJirVmR1tMibGwRLSyHpujc8ivrM4O7prsOhTHiVvHjCcb6zXyWHd1iYecE4wOwnb7BApildJhNRIeYJM3xl2bgbu3Ad49Ovl9Er6FhajyPTOsPfLltBVstl5y6AaTQoGZkdVY7H3Mec4mLV0EzmVrUaKsJMUs2NyAACApy/HvqufxlO113v39pOXlRNTThdB1GvlqrEkZke12fH/CtfzYGHaHLdMHqvae8kNlM2szYrN5QVmlFLo0OfWLJgijdmxuJ4LL0zG7BTLmF8osyO2ZJpDANwszk67hpKjihooFxU1lrDZaSUK5CUyqbEIsxMk7RhjbwTA3bxEM1BmC0wkNZYpqKBqW9Sxl7lxfxJKMo6DNc4OeWHzjBHhh6iwIwQHWT1haZColf70xhLePktFETe4WiioGosdIuPT2ozKfNgmsI9fAHZsAD5+Pl63XP1LGKulTtCPFv5LCDNR1FhpIsBAmRQC4M0F1NBVROVVhemU1Vgfv0B6Zmor3bvGZW9J/RvtXFNyU1V9ctaWh/C76p/D8X0eQwN+WtRYtLxu6B7ser58U0RhZ93HwMx7gZZGrU2JmV/6hvisbkCKFpsdJkvnRFw0TVAKg4XZGdijTiqW2ey0dyjB/fhLTGx2eDK6pNRYNOCXxISEcwsUpnw5QXC2rEB/ZyM72fsTGlSQCgcGZke1a6LCG0WpgFLJxVcmzcDE+2YmIvSICMp2Zmf1Zm83ta3J3zkZmB3HcX0DZb/eCB4fecfhizQPWpC4zQ5hC/jApIlALe28dpdSj2VyLLOfrlSfzzwYUyiopQR+VTgVneuqpN+sBsqp2xEwicLgjaXElsrnHHy0WqQP4It0WsyO6gDAUNNF9NEYea4yubE8m514jCb1VLIZoZ+2/a84Nv8e+qyZ4bcTYqDMXLdbdhj76Z2rMjv+m7thgVad67r498fr+PdhvTraWgYmfQ6Y8mNgxt3atUhT9KhTSP1R1VgysyOVjQzK7AhhR0035AUVjFl1wsiEnTShBPfjC6hDgwr6RZKKs8OEnVy10U4AiJYuIkzW2d5UwGPvLDP/6IR5YymTt98bFXyXFrajXTwDG7Y3Y+6KBry7ZBM274xn8GeCMFC22+ywvFcL17DopWY1Vs6JwMwQAXBQzw7EfVROF5Hc5kjsyLjNDsndZp35x11urkervryZzbSohdlUsEmbpdhoQZXYtVpCCHCbnbJ6GQOGsd6jI9uEyOM6n3Pwq7MP5uWEsKNGRE9Y2FFvTnfhHdSrebnhxMoYKEfaJCgQqu9ww/Z8qclvR25Xw3NXe393bpT7KfVZOYeyQJtlW8N/zpHTQ3SsjWA6+8qtWjuygTJ5Juq795/J/gnBBsoAZDOIKDAxO2//DjlDBOW2NlDOhJ00oalrhGqEe2MlHUG54Bu+Vck0oqTGUmwhTAhLTHr90/Pw30/MMf/YGm8sqsZSXWNtC2j/A6RrScKuhQc209RYog89O3j9H9G3s14BYUkcx+ELSpREoH261Gq5uVLLek68seSgghaoqkQrU1Xeglii2z+FebDNla7rCaUsPUkBeXSuZcyOWY1ldT1PHKoKCjh+VB98bmgPcY9dMRf07ixSEIhNQkrMji03FmUwTO9chVzPnSibBOu5QnVsX2S93xeu9ezjpn641lxstXmeCzRQpvf0/b9JP1312GylHkv3DJCZHdIGCbYqzTGuK+KubZevTwg7xJ7MbQWz07yNH61veE8q5aAS71owMmEnTSguo1JuLBZnhycCTYjZYbmjqmqUH4gay1WP6Ajy1AKAZ+cEJK/LiesEAryxTIlKgwyUbZPsQedK15LEK8UNlANyQdV3YbSwKdGeEGzzOQehKQ8IA+DNzfL9SDTCttIeX1IJG2VX79iFP45184Gnv9uqblGIaMgWNZYLVEMIVy2owqAeHaTeqgaTeVGpTO+rm4TWwmAgV9u8CTmiqqTMDrWVK9nUWGnb7LQIT6GSaYlIedGiz5ltEuIyO5IayzJtsPnpwZmLy+una/4MAGgS6kis/1j6SQ1AGCe2Gn0tpce2dh6p0MLyNJI+QdwXSY1VNrMDyUhaRWaz096h2KbQiVdlLRKLo8LUWKrnkoE5CWZ2ojU3q7SXfpCrsTzo6SLiMTs5ujCp6NJfu5Ak3qmSa3k1SN1MjWV1PfdBIyiHejk5DlzXJWosF3Dd0AU/NpT2vM8sW31QnB31F0PJqTe0pmPa5zD3YxcuqiRhJ8/HTqfaar8OUV5TK/Y/UPyYdHh/g31aoaoT8jld2MnlHIthbUrMDhcMlB+2rSZlDCdWKqggTbMSldkhHmZMcLR5AfHNWJnqFXqW9l4TgRHVsgC9b/+ucj225g3Cg5XZee/Poj6JHSWft6w01uWQucptjc1OwR6uJFNjtXewwcp1tmK33+zb7DBDrqhRYkNhZXZ8uCSCcqDNTrT+6K7ZkNQhQEAi0JB0EWySCmR2fH0zbSKJHYTVZodcL1ObCFlHF3ZEUMEwtYng7l3SPmsgtQjKcEi8jQhxdrRqDAUXvhxexlqdXlaIBMpvW1YBbz0At3ErqiEm6RZUQQlTxD3nAG88ScIjHVeJ26MQnYwP5qHHn4B/zVU5x48t4x3ni3RqzI7MLgMAtq+XyjgmtUbKwg6/ZSCCbmRmx/tLU2+0epHt3M/SlqhXa4Gqe6tkx4qDBneXvltV27fo4T/orbduSGl9dDxr6S78enI5tDBXCFPuwSDQua/QaC3mqbHiVZ00MmEnLZRKwB9PkQ6J5UXE2clzzyX/tNYu1JYAWNKSoc+/GsKYHRGQy7A4+eopJ9QbK5jZYbs0YdlvuDd+W1IG4gTeKrZYasKcwUA5iNnJ+XF2IgcVhMe0yJmx3fQiKBM1Fu1zqNeY7TsA1Mk711gCBJ3Nw1R4D50EPHc1ql+9GX2dzfxwEXnCVsnvF6tPUhtL9kVukhIlGbLyC0XVWC45BghGQnRDeWETGgMs/pW0sVEEmZxpp5+2NxbEc4877qkaS1yWjdnxcMqBITHFDv2W95cG7oPyGNT+DToUqPa9rIoy49FckO9fnMdJWSrrhtS1MDtLXpOKCWbHQYEJO7GFfTL3FZuspXJOkGq8MsiEnbTQ1KAdokkJmc0OX8iTmsdswg55MUxEhIowmx2GnMZ8AFrW8yjCDv/NoMaKyews37hDLxcTUZgdLiYEuNLnmM0Of74hQoTjGQzLzE4peZsdmi9HUWPlTc9UdCbkO4B9TlaKRJ9A5fvDJmP2m1LYj/lT9dE/cHjuA+knNnZMBsraIppqhGB9Z8HsuOh3QDCY7C/Pt8S34MmmsuA2cZKOT543cq5hp78bGCjncmR7ZzmX3fd+XS3BShlY3qk9xvJD6nusNeE4wJFX+j/K46laiZhs7N72DaajSpwdS39tNjvnPqXU5f3NOd4GwSveCm+sEHufzGanvaJDD+DH64Cj/w/4gTcRmyIoi6CC8ehaK8KYHZeqKFJSY+XC1Fgmg17+I/+kuZ6b7g0XdsRvP3vho/COh6CkJOIU3aM7K6bGMvSL5EGji6v98QomwnUBl76absmuyikXJF8Oq5HmbgsVymzfAWCPg+XvMXaLmicJor0bC9yB0nfB7rH3S5y7s6VI1FjQ7XSSVGUpOc8AT21FmR0u7DiysMOZHVWNldCiwV286SqwKzA7nHGI4BauQGZ2ZObMr4h/ZPe9c60cf8bQIX6GoRrjd6+4L5yu+QDYuIgfrq0yq5MkNG42doXaWdk2pPI7RJ7VkCOkck0Fb5xvayqgyJxnWmOzE3BuFkG5vaOqBjjqaqDbHgDoS2ey2YFSpkywGdK6C4zqeh7cDN9sBtjscNsDTdiRmQSpMNWDu0o/jcyON1HRhbBZy08RH0WrIEgnS/9IgBrLgWdcHGp7QBbFkquING4puXQiDEQo5l3KCWbH3o5FcA06VjazE9DytFvFZyfHDZQ/LA2W63FkgYKfwr3jXIOwk6CRsmZvA+RQRFVOVwDn8+pcwMVQpY5k1VjSoqnc/xy12Rk5wS+TcroIdrlwYo97KgwEZpCH+L1TbQhjZticqd0xvtdsw7niHeCeMbzthxTvL+MGRp2/fRsgPu+qkze1K7IZKCtM+p1TPC+xmZ9s4MxOXJudGQv84IiOWdgpuGweTFY7XA4yYaeCoGosjdlJIl+E6wKPTPQ+B6mx+GRih5YcUG2KCUxBBsouU2OpJweli6Bt+L8Y7Hk4uM2OOBQ5FHsAShESgXK2wGAE5RI1Vs5xsLXRmwhmLd1sbpAsii4Ul183BZsdElWVT7aMWUDJziBp7RvKqWXiMDuGb8YYRa/eJj4TYafg3ze2kPMzlC5JTJs6Saesxmrq2N9/v2ThhvVJU1mmxOwY1ViKoJqnwg7zKkqR2XEVYSR2bizJg9Og+iV9Z/NKl7qwoH6G91tVY5m6pwosLeZ5yXiuplr1+m2Nei0JdBY1liLsbG0Sz5YLOzHH/jtLWKBFs7BTIuxlxux8hiCrsbwHX6Xs5lq1mL31gPhsy8VDWINATVU0LZbRZqd1BsoCmmtsoM2OaKO+a+tjpRS5Gsu+uOsiGL0GJuyUkHcczF62KbhByRXcbrMTmdnZvgG4oRsw45fm37mwk+fzpEtymkVndkwFI7A/ttoNu3DGwliDCoIKO954KJYEUwYAHaqV8UXvp7qwJKrGEqzfdV1uwq8Lp2D10K8iR72xIC9gepiCdJgdV32/AO1ZSd5YjIlNcdGiVTtO/NxYlPkwar/J9TFWuhOJYGxkFg3MrUZWmzqj5NKyJQU1nmtJuEvtmWy1qKyw6I+83J9Kkj0zA2U3bpwdKggaWCGmjg9UjVcImbBTQdDJraUgR8Y16pfjgirfrcwOXZrtEk23DsF67EhqLF8Q0tRYpWiu55TOVn/jMBgoJxHPQXhjxbDZMbieO/Ce8XGj6gGAR/bVQUaHq3hjuSXy+CJe2z+v8P6+dJ35d/YMqBqLBBW0NhMQZNFaJobwoNlIgS7Glk45OeR9YYfZHrQoq4Ka2FayBUlT2CGLwayag3F74Swgn0fekTc/gGBTNdWNqvZNiFkRiXYlaUcqk6cLNJtT0mR2yGf27gDB6k0KyWaH10kFaF3Yoe+k2ZPTwOyoHKRxblKEnTh55DRmx9842qJe2zywqM2Ycg6L93PawXvw9wYxIyhTkd3kjcXqzQVuoCqDNhV27rvvPhxwwAHo2rUrunbtinHjxuH550WW5MbGRlx22WXo1asXOnfujIkTJ2LNmjVSHUuXLsWECRPQsWNH9O3bF9dccw0KhYQDgyUEuoAJmx3ZvqVVzI60LbIFxXPFjjmAvTlp//6RmjSqsXJKUMFYzA7ZoagGlBGZnSRUPQVb1nODN5bryiyC95G4nueAan8hG9Ddwjopaizd9Twms/PRM8G/l3QDZdmLzkajRGB2tDKt9cYKufZSM4+gLJgdZTeu5erxULt9BbBVDraWqBrLkJbDAXxmR1Vj+WwgFcRoHbmk1VjeX0llrRoo08Uvl6ywZe4T3UzEz41VJHObmdkRz5b9TtVYLSZ7P/GCiEORCE5V6DfftzjMjmCu1PKkFpPNjmE92LTDE2Q3bW8WDhExJRLeDccB9v2y9ntJEejbkt2JkIFMoFQq4dVXX8X06dOxZMkS7NixA3369MFBBx2EY489FoMGDQqvhGDgwIG47bbbMGLECLiui4cffhhf+cpX8N5772G//fbDD37wAzz77LN4/PHH0a1bN1x++eU47bTTMGOGl622WCxiwoQJqK+vx8yZM7Fq1Sqcd955qK6uxi233BKrL5WE0WYnCdfiuu7iszXODuUQ7GD96lBtNt5jdRjVWEQdAgR5YwUzO5oxnmmS9fO+JP0SsVAYem4sKuwoxySbnZwXFA0s63nIgh2ixkpEGKZgO/ZcFb93LhHQ7MaEduFPHFKZvOibD3kXLgvltkvPb1utMztFdYy5oCOejamx0y/SK0zJQFkME8fPbO9/52osuW+aYiLpoIIluV2pvz7yVPALEna2rgYmXwwcciGw/2ll90lqntrsRJSvqABnZK4ps+P/TL2xdjQX0bFGnTsN77emxorCzigCtyPtPZWyMW12LOyVmUX38JtXPwEATJu/Di4zx4opyErMzuevADZ8Asx+RDSv8CmuG2I+kSIiMTs7d+7ET3/6UwwaNAgnnXQSnn/+eWzevBn5fB4LFy7E9ddfj2HDhuGkk07CG2+8EbnxL3/5yzjppJMwYsQI7L333rj55pvRuXNnvPHGG2hoaMDvf/973HnnnTj66KMxduxYPPjgg5g5cyZvY8qUKfjggw/w5z//GWPGjMGJJ56In/zkJ5g0aRKam+2hq9sKdHJjL6XmjdWaeSxnYkr073TSbS3MrudqnB1FGFF3qlL/YjI06z7ibSSJFquwQw0cw3crObjIOxHi7JAFreS6moFy4t5Y//ye97d5m6bGysUxUI5isxPHQFm6QF/YYdVY7l3j3qeg2pGZHRbHyiYpscOdty7SK0xJjUU3GbIDgMxgaZ57KRsoBwUVzLsR1Vgv/wRYPB144put6pOUF4sKOxGvmWZMN3tjGYQdwuxsbzK52uvbQ12NFaVz3rjq1cmLbj9uz17GurxOm1WrIhCgWneIGsvG9LNigocPLKfCoYJgvlqLsVUkNjtA28baiSTs7L333pgzZw4eeOABbNmyBa+//jomT56MP//5z3juueewdOlSfPLJJzjyyCNx1lln4YEHHgivVEGxWMRf//pXbN++HePGjcO7776LlpYWHHvssbzMPvvsg8GDB+P1118HALz++usYPXo0+vUTbncnnHACtmzZgnnz5mltMDQ1NWHLli3Sv0rANahGmLtpXN20EdQgTjMWE5N+nFgt9kXP+xNss0MFF3pugswOr1/bB7cKBVucHaOBst4ijVnjOLSsBVTdoZZzXV21kRQWTxf9Z67nToDNTiRmp3zXc9MzNrKenev5x1J1Rx4IkRlaqnF21H4GCvppMDsQOcgcR1djSUEG2SKtRVBOKRFogBrLoc8uyGbIEhcmLmwGylGHPQ2UZ0yuTDcr/t+OhL1ev820SY7C7Jhg3ihpaXCMpzJGRr7nxmemNmUSdkICUrLT3XLVWOyTIlSpaqy2tNuJJOxMmTIFjz32GE466SRUV5sNV4cMGYJrr70WCxYswNFHHx25A3PnzkXnzp1RW1uL//f//h+eeuopjBo1CqtXr0ZNTQ26d+8ule/Xrx9Wr/YS1a1evVoSdNjv7Dcbbr31VnTr1o3/i6t+Kxeqjh4gzA7fhbSiATqgF0+390N/dzWEkT4iVo9J2GFqLAFjrIuwdBGaN5ahrb1P9MrS+TeBF4oTA4FqLLbz9g9IN42lKXClPD12YocwAAYD5cQjKBuaBtmFhae1sHw3HYsRYEMV8gCYWTGJqi/w3FhFf0wVRBAZY58Ch3eiNin6y+aAqbHEQiBHePb+ps3sFE1qLFUopMKOz6Lik2l6ZdvXic+N5W8e6aVRgSWyzQ5PsKyr7dUG2OxTWy3mof+saAjolKhT7U80mx3vXm72bWWqguYEdpBFb+Y2OwY2TmsrPrMTYTtmOUsZ31Zhx/++qzM7++67b+QKq6urMXz48MjlR44cidmzZ+PNN9/EpZdeivPPPx8ffPBB+ImtwLXXXouGhgb+b9myZam2x2DiHpiXCJ+TW7NSS8KD8miJMCFe3XA1lm1sctbFmC6CDXDxm2S3EzERqKZuMy1Cw44EkDyz08K6GJAuQhjd2Q2U2UIm5LWQ3vHn5Ai7nXJsdmq7RisHsqvLEW8s612MwOxoAmIcA2X9mG7DAnksFAuc2Sm6vhpLW/RUZsf721TTQxxkCRsTZXZkWyzWNk0EKvczYCHjzG0yCwYbi3KcHbnuHH12i171/q6arVe2/0TxOSAhZBikdAiIlxuLvlvWFC2SGss7XlclNolDe3cy1ex3yCJA2fqnbQxKWLJBZDKv8tNGGK+MeEvSflPmythHILKBstQc+z3CxmRkvy78cxiz43JvrDaOKIgyvLFeeOEFvPaaSCg2adIkjBkzBmeffTY2bdoUuwM1NTXYa6+9MHbsWNx666048MAD8ctf/hL19fVobm7G5s2bpfJr1qxBfb1HYdfX12veWew7K2NCbW0t9wBj/yoBE7PD3U1DYolEAtXz/p+aW4WosSIxO9HseQJtdgjrY9Sbh3ljKYabxmnBryPpDUOB3SNtbSc7Q3UyNUyGOZTkmB+2BpUIyuwzO4tH/I16AZ16Rys37nKyGETIeh6J2Snf9Vw2kJTrttpe7NzI4+xwZkfzxlIWcf+BrNzjBO/A6NOB2i6x+xsKqsYiNkhyUEFXEjj0XXA6zI7wzo8o7Bx5lfd3r2Ohg9TRivsnC4AIZ0QJ6NzpsUJ6nbIay59fcg56d67h5+md0jcz0R6BLvTfPXUB/1qdD2J2FGEHLuC6RuZKq8Q410abz90IQoks2KnMjlxW2OyYzq0sYgs711xzDbdxmTt3Lq666iqcdNJJWLRoEa688spWd6hUKqGpqQljx45FdXU1Xn75Zf7b/PnzsXTpUowbNw4AMG7cOMydOxdr167lZV566SV07doVo0aNanVfkkZzkU124oGzoIKJeJWyHemQ8bLxL6AwO2LSDUPY/t5ss+OrscjFyMyOulMlvSHnaJOxidnx60j6JWqxRVAmEGosneYWxr6uP2HHUWN5HymzEzuCsi2oJEOHnt7fg88TT5AblpfCDamt36Ff5JIZwX0h0IM4htte1C37txZUUFdj6Z4w0vHeexNvoxTSIZDnCs7siM0PXWT1ODvsh4TTRUQwUBbCjiPspGo665VJUXvLv39qoss4414+V3hj2cYNZRv26OFlKG9qMbzvps2MpsaKwuy4mDZfrFUs7EiggXKemI24rq7a57+1To3FHCKi2ATK7JtSfhe22Ynleg4AixYt4oLE5MmTcfLJJ+OWW27BrFmzcNJJJ8Wq69prr8WJJ56IwYMHY+vWrfjLX/6CV155BS+++CK6deuGiy66CFdeeSV69uyJrl274rvf/S7GjRuHww8/HABw/PHHY9SoUTj33HNx++23Y/Xq1fjxj3+Myy67DLW1IZls2wBNRRfIyQMkz+Ls8BezFaMhojGagZXVECYIcWNLoxqrHANlHdzzQDRqaEvX6SdhxFsowX9W9qCCumKQFCPCDhBh0ib3hJtqIAegiLJsdmjBUkkXfukzUBZTT40VoV5bh1ShtPvgKD32TjW0HJbjyM1VE9dzb+yzCOW2kawZvTuOEL7TMlDmnxw/Eaj4hQocenoM5YVNSLDnaqygRKC+LRScnFh4TfmTKJtTzv1bvwB450G4n/8RPxQ3NxbdUNHcWFY1FkSf6/zknI0Fk6Cmj6Uor4EmlJaKGL9XbzwzZxUAEh4hkNmhwo5wHFCDZNJKGnY0S+d4XQ8zUGYvWTizIxmR8w8Wmx035xdiwk7bSTuxhZ2amhrs2LEDADB16lScd955AICePXvG9mpau3YtzjvvPKxatQrdunXDAQccgBdffBHHHXccAOCuu+5CLpfDxIkT0dTUhBNOOAG//vWv+fn5fB7PPPMMLr30UowbNw6dOnXC+eefj5tuuinuZVUUdJgyI7UkUmPRFADWVl2aLiICt2Nbm/2/QWosKuwYJ5zQoIJ+dUHMTo4xO6FdjoUiE3a0l5PuHhVmh95PJjiwOSBUWBETqh6E0eULYCnq1qhW6NVRKgC5GqVOIlzxLouQAZF3YGYOXv4aMtHK3TIxO4ZayVho3GMcqj9V1VjKGLPceIcII/y9STJjoWSzw1QQnoBBmR1p6Kgq7Sh65zJgVonI94kzOw7RC81/Vq+MCjjlqLF+dQgAoNOytwD8wGvbied6Tot4NjvBtl7U07LW98gKZnbEfBWB3zRIRCUpgOEe3TsEnOv3I18lHROu53Y11qrNO/R6Ql3P2b0ql9lxlL8eVDVWivEoQxFb2Bk/fjyuvPJKHHHEEXjrrbfwt7/9DQDw8ccfY+DAgbHq+v3vfx/4e11dHSZNmoRJkyZZywwZMgTPPfdcrHbbCmp4eICGiPe+t0ryVY3aKKgaS2VMDAj1xmKCSIAdDZ1MzMwObUTftUbKeu7o9GsSmwcx59mZHRFBmXdGFPM/M6o8dJkiEyqrrsRYF7fEa4h8aXRyM6kVDIswDSpoZccipYtQd7TRMylrQc1cF8awDIp7bd6Ps1NirudqnB11Eed1ksUglwKzA/25OoBio+NKbsg6I6EvtklAGLva1Vg8EaiTA97/m70yW/LJmKha8Tb/HNf1XIu+zLpjmnsA5Kgq1Gd2zEmEdWFTnafN87Yq7BRRTWg0YY9kOJcJ3BKzUxRqLHUoWNRzsb2xIgj6dC6P6o3F+tQqB5xWIvbb86tf/QpVVVV44okncN9992GPPfYAADz//PP40pe+lHgH2wtc14Upzo5wPY+ppjCBTdJhlGUwwy+XDRmcZm8s3fXcbLMjGSrwFhn0ydguWCWlC565cD2G/uhZkghULaHvatwA4Y0xO6FxlGgdjDBqjc0O7Ytpp03obSGr0aCCFpSjxipGD/Cp3243dNGCWxIBLCEzO8LeS67ZUSt1cuK9STRdBHnZ+LrgSHF2oKqxNJsdRRWRmIGySY2l3CdqnLtzU0BlRfPnViAus6Pa+4j9EzmX9I2qqJv8kOl3Tf1Yr9gwYUZSYxmYnSoipTQXAgQL12SzUzILqN6P/JO0AQ2IoCydLWwFAssBKrOjfNK8sRyp3G5lszN48GA884yed+euu+5KpEPtFcWSC/2VEdK9GAytGA1czxtRjRUg7URxS/fKGfpLDD3zOce7dpO9BRXKDOqGaMxOsgbKZ//uTa8+Tr8GMDvaLSKTIRMcHPkawrOJCzWWiGpaRm4s+vwCmZ2cuHf+vcy31kBZPWay8bDVblAbGu02JGHHFSwbj7Ojvm1mZseRmB1/OkyF2SGEvwMlXYSillAXaU2NkpCwY0oqqbxjjU1Nou2h44F1H1oqS8ZAudhlD8D3XHeceLmxKCkhJwIlsDA767eJJJaNLUXUSWlydGZHi6Bs6pDGcBbRrYPIjRdoglWy2ewYnpn/G4Mk7EQOKhjd9Vy22VGZHblfYh71NyNtaLMTidlRow0H/ctgRqGkBIrzIWx2EjDaKQUIO5IaSzkUgLCxabbZYYtGkTM6WxrJghdErRpUX7bdOa1DjrPT+heKekIpHRRN80Mmpko2UA7tm0GN5RIB1RgzJAiRmR1hEM248XOqXkZdYVtwP23fTcfi5MYy7IbD4qXALRKa3LuGd5Zs8r+b+0QUEv6BHBG4k7TZESINVR/LEZTVoIKqcKcIO4kxO3J7Xt3yte9s8lk5xwHGfD2gsoL5c0y0DDxc+h5HyKdzgJeixcAKSayEuFaqXtLsdgz3W5fJDR0cfJhSpsQDGA7s0QG1fnwfs6BkmMtdl8yJ9valDVrEyT4xmx1lTt/tXM+7d++OHj16BP5jZTKYQZkdOrxTsdkxqrEos+PSI0ZEtYUMFHbIxHnHi4QeDjRQJsVYdUY9Bvsxr/20d98uermY4AuRZkOix+kwCSDs/LzC7NgJEz34nEsMs+NHUKYLmEkgMQg7ZNyctv4+W0dDvhvai6HG0gzCXRfvLd0MAHhr0UZyPFiNxbClkQl6irCjsoWSsJPghEyfK2m7pViSNj9rtwpmQc96niyz858VDZi+YJ3FjVmuu4p7LDlA3jdy72yIYeYmo8Yq+fqUmjxjRkPUv9K5ZBF26LxB+0kNlEU/abZzfQ42MDvqMDV1aNhRwDlPQtjDFHlIhPF79Q6eEzgjYzZQjqzGimmgHGXsG9coi82OZqfadrJONDXWtGnT0u5Hu0eBbE1kmx3lpW5NI1ogKkuxOMxOyO9Gm50tK7y/7/wewDEAgI9WE9bP+ALqL5tO2UZjdvbo0SGk13Z0qavC1sYCyTpuZzL4tZuMoByLzY61ZbGgCTWWzuzQSTkyQtRYos9C2BnSNN9SVxRmR7XZiaHG0tSG4vvvXluEH588SjvulAqC2VEG9ZDenYDlep80ttDJiceX6O6TMHa8WgfPzlmF3vyb3F5o1vNW9u/ke72gsGMGdffaC8iN1bNDDmiBouYLcT1vBTPGYv8wBkTYqoWfS1kPKc4OLWTxxtrZIvpv8ob0zxCHlGdm3aTudQzQYyiwaRHgFnlIhKo8VbMZzjU6m1ji7FhZS8QXdso1UA5hdthT2OVtdr7whS+k3Y92jyJRY9HBqCaDax2zE+B6bhAYgm12osFos/ORbtO1eEOIO6TRQFlhoIw2OzrN3Zp7uLWx4PeCMTt2JkOsq4adH8mNRX+K4uVEnxD77V1fLXP/vz/FtSdFSN9C71WoGkvvf9Ex58BrKRYh/2LclspfW2mzYygkH3dLPF2EqzA7TFVgWxAcE7OT5PaTqrEgxsKmHS3oReYD5oZMYc2N1Yr+UUeB2cs2A1DVWHJ5Fr/Io0oCbJoSMlBmsnwH32aGCaUbtzfDdYX9lgkq62FkTizMzs5m8bloe0clbyz5p8Aph3v5FbnhfFUuVwazI0wQ5GemCMuSGiuagTIvHmFs0XeUs9uAYXUTwg5jm6z3tgIoy5dx+vTpOOecc/D5z38eK1Z4u/g//elPUhqJDDIKhLY2eWOx3VWjKc5DVATZ7FA1lmr42woY1VhhiMzs+L8ExdkxGCi35n0a0deLDsvVIQZXaKXHZtdzv8/s/hijuZrqdRz+2SU2JHOWN8S6DtWmRQLdvREbITpuCpZ90AtzV5n7HXQshuu5voqUcOkXlVx7BpsgbgCpTmkWAcEYVNBqp9UaiOfKHzE8g1jqndmzk4iDZPXGSiDM+m9e/UQ7FimCsuNIi7aGhCIoszQtsoGwh38vWB94ri26sKsIxgyU2Tlv3FBRRJO3iUcaLyMXCnwixMuPGc5X553AzaaIoCwLO+Y4O3LrvTrJRs1SHyxwY9irycwOO8aYHfma1DUviYCv5SK2sDN58mSccMIJ6NChA2bNmoUm31q/oaEBt9xyS+IdbC+wqbGYkEODxa3YbIr1EAFBNjsS68D6EY6wwRmW4O1bR+0JADhyBMnVZGR29EVJS3qnCgHkPNrP1lClPfxF5+oT9pH7ymHY1Zj0gjyooMLshHXAZKDcGoES0Bcn+luO2uyI+1qAeXKcu2Kz2lBw20CrXc/Z2GGCqKaScouC2VF2sI5F9WNk5VKx2SHMDhHeu3esJu+hi5oqGn/F+2vNjdUKZudX/1rIPw/01b2ySkSNs0NsdphnkJHZaYWBMhG+VWaHqm3fWezZbDUVinhoxiIs30TYYugG12FpRiizw+Yprx4LbRNksxOR2RFqrJCl1zXN5a4+JwL6+1BGnB1uoBzXNopvMtgBc5ydVDTEMRFb2PnpT3+K3/zmN3jggQdQXS0kyCOOOAKzZs1KtHPtCXNXNBjVWF1qPcmd0suFCHYZMxeux9/eXiofDHQ954XI/Bsg7kRkffJ6IBoJ+w3oCoC6AkPZTSt9kbyxFAbKZQsauT4WQVlyQCj/jWJGsNVc/RHges6N7nTxkRsoQ95thquxHC74uq1iGuj9DhB2DK7nAFCwqLG0YWHm4O3thcDkjcUC8HEKXK2vVNRcz3l/DVaqPTpWG5idtLyx9LHuAPjZxAOkYjVk8eM7fi7rKMJOK8Y3tU1hgkROXjml8jylQphrfmvUWC5VIXl/63ybnUbSX8Z+nXj3dNzwzw8w/mfT5C4orvSCdQhndvI5B7W+wFnUdkuG91sdpkECKAnFwdRY1bas7Go/VW8sY7gARZA3XW9EYUdzyDCA9eG200aLY5Y1ZbdzPaeYP38+jjrqKO14t27dtAzln3W8vXgjhv7oWWza3oxv/+ldMaB8AeEvlxzGJxqqy1TteEw4+3dv4oeT52IuVW9wmx2TCoKosaJ4Y0G3hdFh+bH7EP6RTeIFWRrxG6E9sCuwVQNl1xCfh75ESbxQ89f4rtcRmB2JHeBHVME25H6SBU3U2orFV2J27Ea/croIMR20WIUd20Jgqd/UfiB0YUdjPzUBtMh3s6rNjilsweBenfiDkW120lBj8Y5I6uO9+3WRKH5qwK8HkEyO2fnaWBHlnrEMwRGUibBGhR2DKlHUEVPYIeeyOHtMjdW1gxiHTMX/6frtxmqEPYv/l70+UhkzswOIeVdXY5neb9dYxAiec60kMTuBvK3E0otxGcWDrlXMTiSbHe/v54b1JEIMa9zieu6vebogWTnEFnbq6+uxcOFC7fhrr72GPffc03DGZxObdzTj9N+8DgA46CcvoVenGom2BmS9NFVj6W6Fdrz+KdFjB6qx2IdocXYkBw3Lm2y11znhZv6R0bXNErOjTt60M0RoUXP3mPTPhgjK5co69Dqrq5jAaOerBTVraNCmxgpjdoyu52VcUJDNjirssEWY7CJb0Apmx8DORIXJQDkXwuw4pSJn0FRvLIeMqz9fdBiOHNEb9551EGF2yFhM3UCZffLSINBfNu0Qdk2hWc9bIczv2acT/9ziSxZBEZSlRKASyxAgQMfNLUaYINVmZ3gfkWGdLfQda8zMtZrFXWPIAJSKoq38Jtl+yR6tOQKzE/RMKLPjs2lVeYdPCmZylLD0ZAxzNVaAB520ISkRYTUAnBGNIIyw+1NNjKxtwk6Vb3O0V5/OuO7kUejVqe0SdMcWdi655BJ873vfw5tvvgnHcbBy5Uo88sgjuPrqq3HppZem0cfdEt06yIvFmEHdoQYVrCV6+oE9OvLPcQyHu3cgCR6D1FhkoqSTrg3UAM72HlvtdTr28v52Hei91FBUcxENlLUAWkabHRZnRz8vLlqIQMaFnYBJTGN2JDUW88YqSb/YaxN1lFylPreE40f1C+m9Wp2Zute+OxabHcdsoFylqS2NM7XyNcZO36A2zHNmx1ZGBBUsKbZGDlFNjR/RG3+66DAM7tWRPCk6FitkoOzAz3ou2L8vjuxD+uz3TBXuEhDG6LBo8W9oILPD33FHZoxVVVZrsp6T8uzdpUH+vn7oIO83/8fOteaxKfJGMQbdOy5FOCNjsWbh89L5rLzGPhg2Z6pAFI3ZKXLbzeocZXYMJ9M0D2TuNsbZ0dRYhrk2NIIybziwHCDPy+yOiFAd8poywF/XTh7dDxeOH4ZuHc2bqEogdrqIH/3oRyiVSjjmmGOwY8cOHHXUUaitrcXVV1+N7373u2n0cbeE4zj480WH4Zzfv4mR/brABVVteKOFqqs6WHYrYaCGz9G9sVgf7fVKGwdbGdsvLPhYqcDVWC1hwo5hImeTgDC78FUVdNI1uJ6Xa7ND+xjFZkc3UCbF/L/CZkffZRrrlTIri2d23rihmPLBGozsFzFgomsYF/w3s82OxOw4SpZ0H3oCQhOzo9vURIbB8lNT62jMToG7+KsbirDcWEbX80QNlIWwQBe1vBJBmbqe6wEklbHRiv5RBpkJ906QsOMabHYAX0Ahu/RyvbGatgKTRKRh5qFG2SaN2bNAxOXyu8yPkzKEdXKdvDEMiL5Z0ifMKCI/B2F2WgizE0jcvvuQ9/fDp4VhuFuC68rxh0ytS8xORDUWu1txDJRzOZGolY8rtZ2E87m1BrGFHcdx8L//+7+45pprsHDhQmzbtg2jRo1C586dw0/+jIEtDEVfIlf3/2psjdqqHJoKpVBmQopzQAd9xKzn7OWIYrMDeIM7byhtjLEDiOR1pRbuWm82UDaosSQDZe+vUeXAwAyUE7DZocJOfbeOWn/8A1qXTQbKPF2EI5e19s2oxhICIDdwjrqrj6HGYve5aucGfrjFMjXktWGQsBpLO1BCzlGM+A1MlQgqqDI75lGujSk4xjHYapCdBd1kqHZ5kjeWqvLUxn0rhB1yKruf+QBhJ2eKswPo7E1YXCcbZj8KbFtNK/LaJX3KqzZbFmjzhUFNRG12CiNPlpS11mjNJgcEWxETiK1TQbLZYXYyBqz5j/hMnruWQgfQhf8yhJ049oHUNorNBzZvrFRUw2UithrroYceAgDU1NRg1KhROPTQQ9G5c2cUCgVce+21SfdvtwaLjlwqeWyKyux0qpEXFK4zDnmpZTd2gsCs5/GYHTpmY6uxiItqtT+Jt0gGytHUWDSXkH/E+z/UQNncrTDQ+3oUUysEeWPx30w7PzmYVuDEJtVBIyiTCUjVjYchqus5sdnp9eZt/HDBYrPTUlDrMrWtrgTRFz99rJG8YPyQgdkx2Ox4kXQtruf8k4HZSXRiFs9VLJmOosZyRfBDGGx2DOOrXJiE7UDXc5M3FgCsnitXIkkU5astGfNCbVJUZsd2G5jwxu4fKybF4KLzkBISgTWpM0iG91spE7jB4vYwJRJUMNxMQJwvhPBIEZRNG51QYSdqZyCp0nSbHfnhtMruMGHEFnauuOIKnH766di0aRM/Nn/+fBx22GF49NFHE+3c7g5GxRZKTNcqD4ScsrvLRRwXVKcsx5aKmwjUPnnSnZXtRQ5VYxULqPYFvpYCnQwDmB1Jv670M9BAmZxn7lUo+K4r58Cx0q967WLiIxOY3+fYubGoJRBR38XJESTXB21BsdnsbDrsv/nhJosaSx8xRmlH+RrniRhYG3WHbhCmRCJQwgg4jnFcAWRBNLGFiaqxxNhQmR1qs0Pt9zRbDrWPreifafzILIH8e57mxqI6zIe/rNYsPsYxUFYXYX9sUraJMTuMeLXZGj7+znIAIoO5YEMJSN8cJbI3FzI1Mld/v5sKMa5RUmPROSbgnKo68kVMHsbkrQmoseIFFRRzmpbOw6bG2h2Znffeew/Lly/H6NGj8dJLL2HSpEk4+OCDsc8+++D9999Po4+7LfiOhMdN8WBT/9i9AWTIzA4Z9EHpIgyu50EI2Dhw2IUdocYyqm8iGygrlDZjPOj1+S8TVZOVq8Ziu658ziELi11QyHFjXYMA5F8PZ3bC1FAGdYdgdkh4wciXRhcfG7PjCQOsZMPYy7XuqOjaQVFvRbHZiQHXYBwq5nV2YxRmhxooE0HYWyTNUqYau0kOKpiOgTLIWKCLueMoQQWtzE6yaiwGSaWmumPbFssAxjMWs6Ou+P65tE/ClsZ+3YvXb8cfZixSqtbVWCXSN6fYJJXP2+ZfQ6ykv729TC4ShdlxBbNTTVzPjc9z7De9v+N/ID13PV8g9PfB6HoeZhOqvycNO8yRz6nApQs7KrOz69jsxBZ2hg8fjhkzZuC0007Dl770JfzgBz/A7373OzzyyCPo1q1bGn3cbUFfUs9mR6ZXVXBvgJCBUaT2L7SyUsDANjA7QaAvk22BdmxqLGKgbEyAaQwqKFpj0L2xDHYZjh4IrFwDZRakrKlQChB2qKCp7rzp9fjCDmd21AVMhaiDq7FYfYWdnAWMLutEsNlRWLFcvhqL9jo/sNpI3lixDBq0k5WvJT3mk8H1nCqJGDyh1czsqEbvFTFQZo8YDnI5uUd06PAIyqqNEl84yu+OaW6JpsYKqzlAuI4Dv33Dq0RCUein3fnSx9oxozBB4+wodkd6fCP1fFHjph3NxhJGOMLZoUATgQaxvfSZG9RYMrGjjG0jsxP8ANVo7f+YvQIH3jQFd7woJwSmc2vOcUiaT/98m81OGrGrYiK2sAMAzz77LP76179i3Lhx6N69O37/+99j5cqVSfdtt4egX802Oyr4ghbK7JAXlv4QmPWcMjv+kYDxT3+zLdChzI5b0j2W/D54jRi8sSR1FNvFiCNeWV3YoWxX3DAfRlhfUhOLw08ix5jNjvKL7dFStkWt709fRc7fhUZmraLY7CjCjuOAPBPzTdSGjHmmDvluh2u437rBrmJEW2zEifm3AcRgdtR3Ma2ggmSs0/dO9cZ6cMZirW9WNVarmB2TsGNnCSQ1VhASY3YMaqwI3ljUVf1fV31BqlpidsjkkFv2utIVy4ZESKkcfbrI8WKi2ewI13MvEajOPIk2TSERXD63BcVik2x2osbZ4d5YXvnr/jEPAPCraXJMPXpvpNdLtK50Zjc2UP72t7+N008/HT/84Q8xffp0zJkzBzU1NRg9ejQee+yxNPq424IKO6Z8Ilr50N0/eH0M0ksWMes53WHaIAs7FuHMOoBlih5QXmhlsW3Y0QLXsCgJq395EZLUWDmd2XlhHvXuiI4ONPmghREwGigbmB0Wd4KpV8LVWKxdYTDcr2kJ/7lu6xK1+WDEYHak7qu2LArKiqDcGuHBLelGkIH1iWdQRZkdlQ1iG1ETs5OKgTKJoAw1zo7L81TRvolHoAg7rbLZ0Y9JtoOKYCwZKEdFLGbHIuyY1Fgl13QGAJFeAgA6+XF4jFHg1Z0Q6atdXaYzO0ft3UcuEfRIcpTZYWqsEObcOC6pap+UXSezL2abHbsaK0fee3YhVTnTXZbvjeM4YjPLD6rMThB9VVnEFnZmzJiBN998E1dddRUcx0F9fT2ee+453HTTTbjwwgvT6ONuC7oj8ZgdD7071+DRSw7XyoudRRizI36X0mgFuZ4bbHaChH1JjRWX2aH2CGxzSn8nLMa/P16Hg3/6Ep54dznUkiJ2g3JeCLNTLljunae+83n5pZUlNdG09rvOyjA1lh47RQGhm42X4kcije56bqhba0sWdnLUoNcq7KjtmCZqu4AYBldLbeHyVjVmJ69HY6UBJyVmx2KgLLyx6DY1BWaHLEGeYacjLaEXfH6o1jch3CXI7BgGV5Aaq4pmPQeAEcd7f8ddDhllMjsGtSUQ7I1lArXZY8KOYB3IBkoVxLatIe14f7V7ZKDCVfZ9/uqtcuoeCv5OyYlAB25+C3+ruQm9G5fo50jCjjimbQABYNNiuTkYNjqGDfAhQ3oAAG7/2oEas6M6zzDQtSnnEAGCbNYkhAQzrCRiCzvvvvsuDjzwQO34ZZddhnfffTeRTrUX2NRY3z9mBMYN76WVFy9bcL30xS7SwiUS2l0FZSpcF3+uvhknva9OWKQ4+WxTq1ltdsiLyA1HLQbKP332AxRLLp6cvYo1JoqxOtQFmHqFcJud1i9Q1HhQFnbo5EF3Nnw1YgdEMRZnh5X1/9oE2RY/jH3JNeePyfuTdOTLNFHZ/Lu8W5eF3+AFtSxmJ44ay1CX5lVDo8J+5w25uKrGsghvGnEnqbHSYnZ465oaS04Eyrqhjq8EggqamJ0oaizWdu+95e/8PMowxxB2LCrWQG8swy5t/4Hd+OdOfoBWk3erpiZdLeLZ6Ibh/Cz/r/f70B89iwsfekcq8c6STfjyr14zG/USI12aCPQr71+Kw3If4bxl1+nnWNRYwv2e3IO++8rNmeynDMwOq6JDdV5j1vOWnTC9l5LrObfZsaixdkdmp7bWntti5MiRrepMewOlXyUfKJtaKDKzI15YaWGki4AGMZgHNH6C8fl5GLxxJlA0W9y3itkJU2ORyZumaJB+g4GyDTBQToLZYX2pqcrJL600QRpYHgOzw72xnJL0k62Xz8/1hL05K7aa7SpKzdqxQERRY/GAjP5Xh7rDWoQd1UA50GanDOHBkHFas22gC0FedpGncXby1MPKYqDsmBaVJNVYhMWk49lrX1/Qad+0+cKmWo0B09iSvbEUeyiaLgIQc0uQQBNH2KntKn9nzALpkkjQab/uWl9YPHqfviTOjn+eVL3SN7JxytnUWOT+N6lxphSs2dqoH6Q2O4TZYehS2KCf45J5Qwoq6AsjOUNZ1pxpY6msCaWSi7cXb/J+csh7Y7j/0nmKgbK2+dmFXc8jRVA++OCD8fLLL6NHjx446KCDAuOzzJo1K7HO7e5gAoMXZ0csgLYHX06cHUlWCMp6TibK4Ttmi+MtO4VBsak4Itjs5GsB6sZpYJZc0wvo5NDUUvR7pV+8blvEVmWaLsJ7mYqa0BQfLDFiZGZHHNTqYsxOnpd1MNr5FFUwT5aL1m8FqoAPV29FcZhX31/2+jnOXniNV48v7CRjoCwvniYD5dbZ7LDnlPfGZAy1kKamcwMMlNXklABckhsrn6eqKYXZ4awKVRekwOxQFQi/7Y43hzKBCy7PIcd+97qRPLNjEhiqAoSdvJQ7DLCq0so1UK7pJH11DGoU7qUa4I1lsmVx1HEDA7NDNnuh3lhODs0h8XWMWb2JzQ4Lrio976ANg2I4b4yzo3iVyTY7ZpurtVvFfL2tqYDuyhxrW+Pp5ZEhbLXZiRO/J21EEna+8pWvcEbn1FNPTbM/7QrU4M0ltgdWe4gybHYk/XJQ1nPS9vLa4eKwZRDSwW73xrLE4KC7ax7sjYAsVisbGv02dHsJNdcNu29yItAEmR2uxiKLpNIneiWOeu8kd315Mc2VWvDP2h97v+28AE6H7vKpRN3BqOpPu48Heo8E1s/nzE70dS5g8bEZKMPOhGj95HWZhB1qW1Ww1mXstWFXzRd/tU3H0QV7jdkxbzCMrufh/tVlQPRV5f+YqsABkKcMA50mXGV1oXWWAaPreaCwo9jsWO2aDPNQJKjP24+zE+CNZRZ2WDd1IUmuP0jYYUy8eo54cmUJO3zBF8xONXneRocVyU5LDAhjnB0lErRkXlAKNzA3eS3a+AyV2RHdVcYH78yuo8aKJOxcf/31xs8ZglHFdc2M2WGwMCWM7QwYGNuaCnj143X8uzR5RWR2mh2awM/+8uYc+P0OUWloL5KjfZJtfHUhycR6aRNYgIGycZKJCRFBWWV2XONnLc6OUY3l/+KK3Ze7Y5Mm7PB0B8hxATafc7iaxjs/lxCzI1QTctwMOsnZhB2tIVPjfoV5oIiYE526+JWEFsDI7MhjvUQ088FBBZmgIbNcxj60BtRAWV1IGIsGV7GRYJseud/NJQc1AJoLRZjjW4fD9JoEMTtVquu5jf0ql9lRn4tLxr4P3RtLH4VGZoffRzLzavZrBmHHyuw4BrW7UtL0M4mz00Li7Ih+GuZgi5egMdaQmvbC5I2lMaCiTD7naAyMzbWdDg+hjoVQg+3CubFiJwJleOedd/Dhhx8CAEaNGoWxY8cm1qn2AqED9nasJlWNVN5iILdw7Tbc+68FuGj8MJzyqxnSb7LNTlDWc1ZGCSqoJvRT+lNSyxNoFDeDQ3ctrFnDCygJO6J/vGvq4uD31aVeOAkyOwUqZNjUWEEvrdFA2b8GiUrX65CYHTY/5RzuhZUrFQDURJ8yItrsqAaHQPBOLBqzw67FkjkeAApNwIZPPONKyT5KX2i09TVA2KGsnye0Kuew6zAxO6mosYS3lxB7fEFY0EsWmx0XdLz9dvpiXA5g2cYdINxsvO6E2ewognE1fGGA3Wfr4kU3XXFUFrpwC9i8sfwuGNZh1yAkGR9nELNj22ySzUEYs2OEFGdHdz03qrGM49Kixiqqc3jwXAvIAmPJsDbFtdnh40p5OLbcdG2B2MLO8uXL8fWvfx0zZsxA9+7dAQCbN2/G5z//efz1r3/FwIEDk+7jbgu6W2spkgFltdlRdfUezv39m1jV0Ih/zNYDN0rCTsksxQOQmB0jzWkAt7QPs9lxctjWdyw6r31XaYsyFgSGFyOI2eH3kQlm1MaITYTKBOsS9UcUUHVgoLBDtQr8uJ3ZYbmxaj98glSn33Npn0c9Ivykqjm3BUBNoJGmhCBvLDIBynEzqBqgFcyORMFbyjxyOrDoVeC0B4ADzjD32/+uq7HsNjv0ueWCXM+JJxQ7YivbOlBmx//kN7OzpQTUeq3KNjveX1V+f395A1BjUXtEhEkOqcrZBHugBupCalFjJcbseOeaDJSD4uyY1VhsPiXNqTfApMYKMFBuLgZfm5EFzzF1cQmbfW+tKkmNFcLsEEHEnAhU7pMkPFlMG+g1Fkt0/vXatdvsyPMF74LNGyu36xgox/bGuvjii9HS0oIPP/wQGzduxMaNG/Hhhx+iVCrh4osvTqOPuy3o7qRYCo+QYpvkVjUYLPx9TJu/VnwJynpOJeygXb/UH32yoMiB7lp1Gt775J1sdj03CDsGBoTfR8bsSAbKZmYnVqI+yOrAYGFHZ3kck/Dm67DZPaqd/bDo8oIXtPZp1m4mwHrMjifsOP6kFXmDFLT4kImUVucQG5fIBspBu9JcwK5u0ave39fuCq4u1EBZYXaIgbIcVFC5DlW/qjI7q+YAz14FbFuHVoHUr84A1PU8b2AyvHeVCP+agBYfZm8s2in5vens+HPPGj/LuZX9MiywkWAeT3SjqBoOmxZiLY8ezKKrq74LJjWWNnUIgbW5EHzvg2x2Vm7axg9VV8UQdqTcWHJfpbK8PlqPme2XnFxKJe252raJNIWP44Sni3CD2N0KIzaz8+qrr2LmzJmSm/nIkSNx77334sgjj0y0c7s7qC68pVSKocaKPpm9t3Sz+BKoxhJty+HEA4QdVkTpj4gEq+7gYfjOaE56SLe9cQ07Rl2N5S/4OcrsmG125q1sAOBgQPc69O/WAWEoasyOob/kegCZx1HB1CnGSWPTEsNBMaHKzI73iub9STnyyDAJl+p3hdnxiJAE4uxo4yKg12s/CK7PFbmxoqmxyCIZwOxwWyqjKtYF7vfnsq2rgbMesfc/FGJ1UGViKuxUGdQv3pCkwk7AmIoIcyJQO7OjIWlvLG2uM6ixiO0jEMzshHljdXvl/+QTDd5YmhG3xOyUY6DszXNzl23W2gLiqrEMAl+QsbhFjUVd6D1mR9mYWAaZaiAtQouY14JdSY0Vm9kZNGgQWlr02CzFYhEDBgxIpFPtBXS3VoikxvL+qsKFlMYAwPGj+uHQYT0BACP6dhY/RDRQloWdYJsdQB+nfGKxCjt0V+a/DPT3IGbHoMbiuxi/rx3Wvkc66Wc9VyaZhWu3YeJ9MzHu1n8ZrkyHJOw4jn5NvKNU2FGvTb8eUywit6AzdVRwlKhqxuz4Bs5J58ai1UVjdtR2TBM1u5b4GY/1oi5Z/MOZHfrcqqTs9XLF3EA5zGbno2ci990IaqCstC1+URgJqs4mfenVxR7jLHp39GcRZKCsw6LGKpvZUWoP8MYqBSzEJi8l02ybb9qsnijKW5ls8aRaQoQdowGzPwbnrxZt0w1YILND1auuaxTq1PttDiooz2d3TV3AP/fuLMzdeQRlqxrL0D7IJkPVKuzOaqyf//zn+O53v4t33hERJN955x1873vfwx133JFo53Z30AFTKJaMRrim8urPpxzoCZHXnDASi2+bgN+edwjG79UbAPA5X+gBENn1XA4nHuyNZeoPX4zJAi2psSQDZUXypxUaDZQN7SjMjkOFBcbsKJPM395eZrkqMyKrsQy7JqMaixkoq1GWATgtO7T2adZuSY2Vk4Wd6GqsIANl0V8q5OUcumOMyuwEtJ0zCDulEvD6pICTTTY7So8kAUXxMqHpIgJcz/ncLI1FwwTfoUdAXyOACvbKWk2ZnS07xQZSMKpyv13O7JS/cJhczwMNeBlqu/md8/rwxqcb5LAXtBIlV1McsOcRn9lRWGAoQqMN5Hq1hMP8K2F2ynE937EeAHBS7i3jOXmjsEPmFENQwSA1lpHZUdj+Z+es4p+P3bcfF1bY2LIZKKsRnHnPWJNVikCehtF/mYgt7FxwwQWYPXs2DjvsMNTW1qK2thaHHXYYZs2ahQsvvBA9e/bk/z7rkJidkgsbpc5gi7PDA1FRqpvVZDJGCzNQjqrGsvRHCDsWbyzZ3NbvJ/k5ouu5pp/2mZ1te50i6vJ39iqzM7BHR/75sQiCDxWWpDQDtL/KhYhFx76AiImM1D9vslaeC4XEGytPbHZyxSSDCor7P32BsElxIhjpRmJ2gtRYH/4DePF/7N3WVAgmNRZZCHLy2HOjup4rBplWZqff/ta+RgNldvQFmf2yeosQ4PmmR+lLsSQvSOXAtBa7psVRxcgvsc4CABat24bpC9dLtXB8/Hz0DhmeNyAv5iLODrTfGExeSmKODGpfXK8xcahXiJcIU2M9NHORfnDeUwCAEbkVgeca++XkpAsRdoyGsryXBBHi7HjCpMyA2pJEMwFXqIH9eYtXpjoM7DrMTmybnbvvvjuFbrRPUOm4YNsFGcqrLxvbLeQNux1ZiIhms2M3uFXOUHfUPpgBn2yzY1CWg0j+tIIgYUea3OVdBBN2SjVdRF113f2y8nX0717HP//35Dk443ODEAS64+W32cl5fbUGFWQTg53ZcZSyHLP/Aow5W7RpUGPlHYeosYpq88Ggfd6y0vKbg/XblIBkcQ2UTR1SbbJoXcvf0ctL5+q7atkz3ZVVT+rpZOzLQqtcb+R0EQGLRCRQA2XO7DBhhgkvwOeH9xZN8lfBlfrCXa9b0R2TsLxHd2LTZtv8cHWhUM9SNqp8KEKo/zyo0bSaLsKkYTFHUJbVhaHt20iIGMzO1A/XoqlQRG0VmYNrOgPN2+wnGbtF5laiijV5nZneGfFZt480Nkfsgrz6g7slbHbYL+YTnCAnhQojtrBz/vnnp9GPdgnH8RL+FUsuWorEQNlqs2NmUnjUTZoskAtGJmYnxGYnshorjNkhE7nMH4uPfhlbIlB+yNAfseizA971FboOxsOF49BU1QXfqvL0zSqzEzceBt2xiIlEngC8z5J0qRwT1617zijPvGG59JWVcyxqrJxvoBzdeJ2Um34HcAwxzCT3f2APsdB1qMmb2RhDP0VdJmGHzYhyoDIAwJv3h3Rbr1/N0xYk7NBnIBkoW+LsGIMKUju2AkmDUhZE/XyU8E2EeCdp3BWbOrvgsgWm/IUjNM6OryJ2q+pkdbESZ8eBq8T4KrNPGuOmMzvsY1DgUI0FJudFZXbk+EZSIf6JzSuHDeuJn566P6YvWI+bnpGN7Gcv3YzD9iSJnr8yCXj8fMzJ7wcAGNW/a0CHlH5JruclMTc4hrI+jOkilA3wmEHdMZsYTKubzTDXc5Vd4xHwAaD+AGD1HLndXUDYibRt2b59e6xK45Zvz2AUbKFIgvOFMDvqhMSoU0nYMVGuQa7nNm+sKMxOmBorlzOsjfJiL8sIlBHyDxk4ILFmyswOcnlcX/gmfoWzeFl1IlSNusPAmJ0qQ0Z1o4ADgyAjGVx75+Ztwo6SE4if54h0EXkHfJHhrufhl+JXFCDsGbyxDmBZoxXdvYpWe2OVFDagU9+Q2uSgBi5gFJb57zTOjgPrisd3piR8Ai9LBRzbFjcqqIGywkwM79PZ/0WNu+KhpBgob9ppdyaI3R0fVTlHXtjYtVcpHoxc2PGZHcdVNhjkc6c+cXokfRPMDlFjaTY7VPj1N1MqCwzK0wW8NVHUWGS+YgbKNVU5jOjXRYqPxKAd8/vU2OI9v68fNtjeH7VfVL0KocaSIm77ZSlTyGFRY3Wp857n3WeOYZ306uDzYLCww5rPmeY3OrftQhGUIwk7e+21F2677TasWrXKWsZ1Xbz00ks48cQTcc899yTWwd0dbA7z4uwEMzvcRkZZp1q4sEN3f95fmTGJZrMTVY1VtjcWaU9aqFhFhjYFsyMa09wsXZm5ot1SmZ24EZUZeybpwk1J7EyG1gaIXXzJXFaxW6HZpaV0EbxDcePskD4POsz8m5MT0Zo5LR28HdbWfiOzE6DG0spajKdJXXQXWaJqWKOwQ9RYDmF21EUVymG6gybuyGH0fyj863Gh25Uc6u/+HbjSuJPVL1T4DxZEo0BlBvPqosaYnZqO8nFFjeXAtRsox/HG0saGQdhRGGaJhHPlvybX88CpgKqv+d7Gwl4SNVatHyfHxIAUVI8swoYBwMrNOwM6xNo0xNkhEZRlNZY87xuZe+VdUe+lULvr918+z29Kmd9LdKYn74zTWjVwgoikxnrllVfwP//zP7jhhhtw4IEH4pBDDsGAAQNQV1eHTZs24YMPPsDrr7+OqqoqXHvttfj2t7+ddr93G3g7Ni/brZsPXkhsrud0N8FgNEUIMlCWmB2DgGQ/Q5ssRCRTIey46rvh27s40mTN+m1idvQL0r2xFNd60i+V2aFqrAkH9DdcnXJNJsM8k7Aj3XB1V6MzOwZFnRFC9Z1T1FgqsxNxobOq3gBZ2CGCFejk1Apmh1NyEYwTd2xQTlW9sVxJYvbMWALUWNQTkLI16j3QmB2yg6a5hpa8Zu97JDCRRV+8qeBgyvKtMjtJBBVUb0N1XrmHocyOGB8tJct7ESHOTnOhhB89OQffqN2AsYC3QLpFzuyYIiGbFvpiyQvIaA4qGEGPZWB29OLi/X5z0UYAwJzlDf4RHbq6zSvFoqkfSj1ow/pljaBsYHacPBwUFDWW2RurpGxyePEQZkdLzmy6xaHseNsgkrAzcuRITJ48GUuXLsXjjz+O6dOnY+bMmdi5cyd69+6Ngw46CA888ABOPPFE5POt3Am1M1D3bbLsW8qaadQWP2pnDZmYjPY0Qa7nRDrKgUxGAYNQ7DDlMkZvLK0afWIW7I04r1enGmzY3mw0UNbj7NhtkoIiKHeMoNJavMFzB9/ZQu5NCLPD7WxceJcrGSj7dD8Lv67SdYfKGwKJ2aFUNRN2/AUkEmFlE27U76oxNIHNQDnQGFI9ZoqeWtsNaGqw9RzaQCKu596vwcyOHOOJlpHrDTRQVhIrtgpMzUJtidi7azhG++adahJ2yoc6fmzMjqZmZYslH9dKzjxpHgq3l5s8azmenLUCufxSjK2GN86LRZEI1Hg/2IaEXo8r/WYMKhjYE/I+8/J2Zuep9zyPqrVbm/z29KfRot5kfwzWVeWAJqBWETBLcHQVCw1ZQOZuY5wb7X0wrQlyC0VFaFI3m7nIzI64NxyGRM1hT6ESiGWgPHjwYFx11VW46qqr0upPuwOdTMSCbi5ry41ltNkxCUYFnx41GShL3lgGyd/YH++vliiYDXjK7GjNOXIZsOtypJeTsVUmQVB1c1RtkmibqjdWgbiIRhEQvvuXWfpBxUNB7V9Qbix1F+72HQVn40JRjbKYCGbHkScU/1pjeWPZhBv1u8QisebDJqcIzI7K3EkLYYjdiWFXrRooBwo7MV3PJY9Czuwk4WXEoDM7ottCeJHDSpB5wKVvRuuZHfUGazv4JX6iYTUWFO+06IO1FxGYnQ3bmng9ADxhqkjVKKKsbRMICBbFxPrY1PByX6mw47ejTYn6+y3OMfWpZCzE5gubIGFvU4xhLYUOIDE7Wi9d8wZYJE5lzeSkPobb7ChqLHqTCYvk7G4GyhnKBw3FHsbs2HTM3GZHyqei1NTSCDT6O+YQm50i1alHMFC2pYvgAfNMAdn4y6MzNabFqgSdRdFsdjizk5P6Aeh6ckolR0meuUePjvpB02JqFHz03Y2aCNTdY2xAPfKiK6uxZGEnkhorVNgR/eXMDqc6QqaESMwOMVz3DojfjII4OdXE7KjNGSJwc0gGyvKumCKY2WmtBxYBY3boLVBcorXcWJKMmKywoxkoq8a0GzyBPLdhgXyc9Zkucjb7tdAozAbBxV+Mc4Y4O6qjBNWSlPj9dbXzoJwHAIU64iWl9FV4Yykg78sJ+/UDAIzz7a1MMkGYzY4tOrGxX4qBssluSVVVRVJjKcKhyqzbbXbkDajomY3ZMb9/bYFM2EkZMmMZPMhtrt6RDJRXvR/SEzGYF6wiaoQIBsoq+Isi7eDVsnw1EU2x8gY1Ci9lEI44pc13KbqBsqonp1Tye8TF0oYLPj9EP2hidgwTvJw52/8JCrOlCRyWEO+SAAIxgcVJBKoV0gUIr1IiWHFfbLaYWcZFWN20jEmNlVfTOyiCuUEw0w2UZeZo2YjzRHG6sw9Ig8Avlz47k80OixxcLvx2aetqPC0HrmLoSpgMg81OayZtzUBZfcf7jfbKDT9GOVNeFHMoKU+esnfhzE5JfXe4BKMbyOrCIRkPJVYfK6v2WB6hzV2993wtfLsZSS0t901AtLxHd29TNGZwd60vDLpzhKIi0k4JWBeokb3rQlPt02swBfCzqLE04VCZ6+j9lzeOcvs8N5blGhxuX5kJO+0edDIJTQTqPw1d2PG+SzY7SqAtCSZVAZGwpYUsYGKyCV9c30tsdrQdOUuXAP1FoTtz9h4FGijzedBPF8GZHb1P/DuhkhetDw+F8NIHawAAddUm4zqz95rRrd7H+u3eM1i7ZadUlkOhukUkUkUA4ZNFDNfzMtRYwkA5eIzqgpOpnCyMwHWBl64DbuimGSSr7Iy+zqg2O3L/AaBEU0SQHWyQ67mjCnV0MSjSODt6HrN40BcDsVDJqgDRN3am3GeRCLT8hWPafDmLu6ZS6VLv/VXTZDhM3Sz6bPXGMqixXNfFz1/8CM/MWSkV561z2zSd2WGFTKwGe+9N6h2jqp8Ln/q7Lca+1nm/gJjnOLNhYnYsNjs5kwrKBno/DcyOKTdWHDWWaiLgcmHWA123WiSTAOX62fwtXTL5YmJ32wiZsJMy6MB2DZ+kshYdM/MskuPsQC5LF7TqgCzfrou9+xJ7kTIoZ/6imIzTeAd1yl30lUweAWoRdRfB3Xj5xCvKasyOQiXvaA62FZn64VoAQGMLuR8hwg57jiYjvXeWbPb6xSaKEAGE3UvHUSJmswmMZT2PskOyqa3U300GyobnFliXqdyCKd5fSp3P+KWls+rYCa7fs2ORBRTXEWxRTjPit+w41RfIZqBcbIpkcGtFJANl+Rpl5kdndiIslZGhsbf+OHOqauTjXHAQfbYyO25JGyf/+mgtJk37BJf/5T2viDqOmBrLlPU8wPZG2OzogpB4xErfABQNrGNoUEGHZK5nz8Iw9xXUlBK8iMKmRIaY/7Q5ERDXwFXe+vWqaVV0hsi/HoM3HJ1bNdsok3wo2UHtOt5YmbCTMqokYSd412zLRRVsoOyXpWxOzz0NlZNFANFsdmxxJ/gLx3ataiJQr0Hvf8nA0vVPpsIO+83O7KhxdpjRG+1WkM0OIDza4sGkxiI7Qe3eEXqd78L9yUNjRMxqLEcVQJa/DQDo+P5D3mlB3XVdb5cXxuxI9kHex5zC7NiEHaunignL3gwvo9avStZanB0EMjtVErNDVFNKH4zeJCY1FhBuVB0IxjzQxZv+ot9ryWXaYKCMVkRQVqGRDD6r5eTVhI4ys5NDSARlZcz966O18s+sWoUFZN9N3lgipo4+RxhzYxlkCh6E0JjIxsAESdciAkOqNisUNmZHRIc2nKR31D+XjEvJZscg7Bi9sczG/II19wVJxTmGVm+632xt4C1KsUeosJOkaN46xBZ2XnjhBbz2mog9MWnSJIwZMwZnn302Nm3alGjn2gNyJmHHyux4f20GyjVVht0hK8viY9QfYO4I1clGNCa0RRQV+naqxlIGNaNu6TvpQm5bstnRFyWNslV2KbRbbOJldk0tyu4qcnwawzWE3y+d2WFncMZGXQyI+tB1Sf4n1UD502lyS0GXcWN34KaewMr3lO4FqLEUZscUBGxVw04xCdoEpyC4JaCqzvybNhnqwpSjzqMas0MEnLyixrK8c+xo3qWxm1IQdpjahDSv7uzVO2AzlE1CjaVCZ3b8a1WzVytqDgeK67mmppWF+QHdZbZZt9mR1VjU1lHyToP8DugGyuI3EyPE6i8hgNnRhB3xfvPnyLxNDSuoLc5OPGZH3GnK7BjVWJqBMv3NosbiwqG5j1JZyWRRvgahfjcLvo5jr7fSiC3sXHPNNdiyZQsAYO7cubjqqqtw0kknYdGiRbjyyisT7+DujrxhAbTa7NgMlE1qLD7+/bLM7dyqwqK7RYvBrdYfrecAqM2OWNJtzVH7oJLaNpk8SiZhh73DijeWwyloUVWhJN+joDw6Jozfy0vE+MuzxpD+hdjsNG31/rp0YvKw/x7dAQDdO1TrdSjfiyViR+U4POGjZjzKTg1jSh4+2dqW9N3JydGaQZgdv8xf3lyKcbf+Czc/+6G57SisTfO2ANsX5RoN9cuyjgtN2IGYyPN5ldkx0+jeZbqocf1NQnWHAGanFa7onElQ2ybfVUGMzgMSs8PKJwfNfoRdqyrssD4Rbyw5ersq7MgCYqcaizeQ6Ij/XWcuVCGEvtrsPTepd4wKN6bGMtrssPo1aYfXqNnsmAyUNTWWYrOj2qlpNdBzHUkNpkY8p9fA1LnSeLIEmlWFFhYElauxSFlqD2lsH3QjL19RuA1g5RBb2Fm0aBFGjRoFAJg8eTJOPvlk3HLLLZg0aRKef/75xDu4u8PswhfM7KgLSoshEai2C3nnQe8vUx2ooAbK0uIdxUBZ6b0i7LgGNgAG4cUFlIXX0ZkdgxrLzuzQl1C+R6paK+xdY6pCc24ss7CT37bS77nO7HTu4Nk89OtSrV2X91Xc96aCsN5wcroAooIFNIuMAJsdiUUCiDeTd/ymZ+YBAH732iK/KttCYED/Md7fxdODOqd80+sPVWORibxKNeK37CxzjoNaECGmqg6C2VGEmzjpDzQwYcdnAhyyADhmpkZeH8j7kILVga7G8q/dosZiodJzKClOAfbxbYLG7HDXc+88SdBXhBA6BoU3li4kma0GlDmL/Kh6fYlTdGZH2Ozo16arsWQWxMQGaZA2UILqMwVPtIZ6WL8A+M8TfjVKUEGeS0zuo+l9phvHAp8nVWbH1Hd6f3ZDYaempgY7dngBp6ZOnYrjjz8eANCzZ0/O+GQQMAcVNDz4UglHbX0OA511kFXhLl+I5XQRXl3sN3zyckhPbMxOgPElm2SUl1cEFRRshHZFbCKnrud0oWJl/J9NrJfmHunKzI5ks6MKO4pRadirZnLvDzdQhrxgGm122AUq95n0r7GlyO9lPidUe7mcA3TsrfX1/lc/1S8gSJqzMjs0WjM7JE9i1qSIUb7v91V7nxhUFZFBMJO9bE3MDvHGsqmxDPenDoTBqe4gHp/KQrUmyKAizEsBEm3eWFR9TN8HyM9Gb8rF0B89i6E/elZT49qgq7F8IUU1UGZ94uM6ZAOhCIiSWEQXQ67GkmPEBLmeU+aFCVwm9Y5RDc+ZHV2NpbHlau+JQ0VOeV8obHF2WD02xtbYJu0YsdmRwyrIqip+T5+8RJTZKM8Z6tzKNzmMJTKUBURID7apEA5sNmZnNzZQHj9+PK688kr85Cc/wVtvvYUJEyYAAD7++GMMHDgw8Q7u7jAKO6bJ6unLcd76O/Fa7fekwbW1SSwGlNmZvmC99Bf7ftn722uEsR/3T/cGe8ktwZgozgDbTkdkHxaTgAauDqGSG4CdG0kZg82OxOzI/QiKnMv6VOOv2uUmAqWBG43GrcpLKws7ejF7nB3xnTI7gENczwGc/hAAoNRRZJLWIrQCuku3pS2pc05OmzzF5MSKqgxZiKfXzk3iM7fTCZjc3ZLi7aQLT44kIEAS1gDIyT+JsJOnzI7q/eY4QtjJVQH5atFPXz3JUSp4NnFznwB2brZfixGy0CgLF0wAUlkndqZ5DNuEneWbRILJtxdtNJbR6rJ4Y2k2Vmzjww8EJAIFpPv99uKNeGjmYv7dqGLOySoYkzeWYHZId137/XUC7qNwPaeCl2O8FKOHaE4wdSqsrufMWSGKsEPYJCqwB8XZ0VzPqe3emnnG6sX6JN8Peg9MzA7bFBrDbzST6Nu7s83Or371K1RVVeGJJ57Afffdhz322AMA8Pzzz+NLX/pS4h3c3WE0RjNJubMf4R/pu3LPVBHJlMbZ6d9NmYw6e5E9bbvpd31X6K07W3iUUq8vQcIO64/K7PgTEt3xWAyUHUl4cYHHzpPKiGvVFyUtYio34tHTRXBmxxdWVNfzMDsXzuzkDMKOQefPUEMT71F1SxizQ2j+7U2ijhKU3Fh5b4ftkvQSxsnyzfvtFxdks6NMno7y3GzG6aQy+eurPxOfOTsQMtFRlYdl0ZTsKYiwBgAli7Aju57L9ToOcFz+Xb/SgjgIAOs/lvtQagHu3BeYfBHwsyHB16JCWYwNso4Gmxor5DTB8sZAXDUW600OrpIDSh0oYpN2+m9ex5INYgGkwoCmxoKuxlL3HJJ3ELfZMTE78nneF99A2eC5ZLfZEQXU30w2O/pmRC6j3nNzQD7WjgMjs2NUYwUk3j3mOumrVg9jqhizI9npEGbHn1eFGsuvj1a+/C3+cVey2YmVGwvw8mM988wz2vG77rorkQ61N0Rmdvb7KjDvKe9XMjCYrQQgq1jGDOoOAPjc0B7eASZNqwn8lLZLbilQLUNh2+mwd/nbVf8EAOSWzgT6jtfO9v4nuyKAu1J7P+bIzkx/KbSYDlyNxYIK6i9kDbfZiTfxN5etxmom10iFHX/nJygSr5+u46WQIPW8v7wBdUS4kOxoDGk3jNC8ZwgCbHb0yVNVY4lzVzXs1FkFte55fxef1QXThmKLz6yY5kSX94qv/Zo3ltn13JN1zJNtznHw0+oHo/WvVAxmzoIQoMaSFkHX5X2VcuSR88PSRTST5LdRic24aiyXCPHSO6ZNEnabHapiUyMo5wyLuWrLKLEObP7gZJ/OCEkIVGPZmB1WRsTZyfFnZbo+C7NjiE4cCoXZUduX+qemi9h/IvCfyUDXPYDBh0vV6ukiZGHW5G4OCPMArsZiXaBjecBBnFUKz7VXOcQWdgCgWCzi73//Oz780PPQ2G+//XDKKadkGc8NiGyzQ/NEWWYqWhdTafEXq8WPEmwQdoolV7YPoAt2wKRkE8rZ4B+TE3pgrcdsMqDCjlpRLs9/DTZQVlgfE7OjxCJSqfKwV82sxoog7Dgt4jWXmB12v0vSeQXkkUdBuu8DutVho8TseMfzRNgJDf7Yc1jAjzZhR5+8VVdR+sg+XbfdsAdV6u7UG9i22r+Aaq20uXt0DJr7mvN31eY4O8RAmTy/fAizo8OyCLUqMah4roBt8wNJ2BEGueJ8zy5O3k2raCLCjhpRnKFTTR7bm8X9tnpj5dWggjnSJ1/YCWJ2AgyUC0WX253x1rkaq2ToF9uo+V00bHJMubGMNjhceNTfqyi5sVSGjo6jDtV57Gwp6mo6ZbDpQlgQ+29mdsxxdhQ1FvPM/dxFWvW2NDEqE0nLAmSeVNRYsn+/blu6KyC2GmvhwoXYd999cd555+HJJ5/Ek08+iXPOOQf77bcfPvnkkzT6uFvD6HoesvTSgUaZBjpwqrltSsmbjD/4h9+guiPzdlK8SldJFxHE7Ci6cgbbRCqfLEv+Xj3wdhkM+WqxWyP94+1oWc/lPC8mvTJXYylUcpgNg1mNFS7s1EnMjgDZC0rniR2lWAyKrsuFwpJks0MnOuIFZtoZlmWgnCNeGeZJjyLnOOHMzsiTyBeln0OPBEafwfMvGfsXosYyuZ6XaATliMyOA+CN0r7+SdXkBANKBXv8qjAozKXchKSL4J+k+DCuadYwP+u5KxpEly0bpg6KC7g2lNg7pgk7/lxAGEvGJJVKLrY2qh5s9thEwWosnf1QbZgkEYsLQPr1CLUUOcYjKJtsdtghjdohvZXnJKOBcqjNjnaKAbQOMYY1l33AzuzwKgxbFKoqB2HseFwouukU57WoXqv82RiELyBwPqk0Ygs7V1xxBYYPH45ly5Zh1qxZmDVrFpYuXYphw4bhiiuuSKOPuzWsO7kA0IF29D59jWWYuqal4AKz/yJ+eOcPWllP2BGSuBO0uBDYdjom+xf9iMLGwJ+sRnjee6j3Ew7y91LfgXMjOoXZcQxZ3dkEwwyUiwqVfOkjs7RzKERmeYOawSTs+CoayUBZcpE2qxwKBvrci7MjmBQ5XYR9ByohyD060GaHtcUO2VUltJ+kMvkruwefu1if1as7ABMfAMZ/39p3V01Aquxk3RBmp7pKfH5h3mqyy1SZHQer3R7el+NuZEdhRMMyabcab+JmQqwHOeeTwuwovfCeDV1o7c8GAP7v7//hn232OyrroLEMTAjXWDm2KIo+sHfmiVnLsXj9Nrl4wHgsSdfKpBR5oab94gbKJf18EWdHP4/3WbpkfzE3BhU0lScHCBPKXc9JsZ0t3jXrKnR5boukxqIGypTZMYWlKMneWGLMsDr0ZV5jqETDUvNeWcLsKMFb9a2YvD7ET42RHmILO6+++ipuv/129OzZkx/r1asXbrvtNrz66quJdq49ILIai4D+zNQy1395lFSmiquxSsCGheIHPnELfLxmmyRMGHOnGGBNBOqfwnbGTYdeDm2hIC9oTn7XPezzZanuoAjKqs0ODZDFXiw1zk6LZWdrgyn/WGAEZZ8iltRYBGynpHpjFdgrV1KFHf+waqAcVY1l+p151MQyULZPToVSSb9Wqz2QQaXNGJT9vuoJQ6a+awuNL+D6X6XAlIbcWHmSVX3Zxp3iTJXZcaiBfV4cNOHDZxRhJ4Y9GNsps803aWJo7860IP9kzmgdrsaioPY7FLqwoxTgQejU7PTMpkOcwNr47yfmGMaF/R4ZPSU5s6Mv5qo6ipK2quu5SZY0Rfctmd4rzgRZBHries7qps+K2VHa4uzkDIKcHULIlZkdvw7J9ZwNLoVNV7wWKfSs5zKzY8s7WFCYHW7bR1/h428BANxbOBVOxDWvEogt7NTW1mLr1q3a8W3btqGmRlehBOHWW2/F5z73OXTp0gV9+/bFqaeeivnz50tlGhsbcdlll6FXr17o3LkzJk6ciDVr1khlli5digkTJqBjx47o27cvrrnmGhQKrclnkxxkYUf/ZIIU16AoT/YMTLJuLpbEYD70W8CeX9Tqe3r2CqltJ2JuLKPOm/RvndsNAFDsMgAr6o8BgP/f3nfHWVGd7z8z995twO7Sm1RpKlVRxF5QUOwaibElsaRojJpqiibml+A3xajRGEuiphI10RhbYuwVFAVFFJUiiNJhgQV29945vz9mzjnvaXPnbmOFeT4f2HvnTjnTznnP8z7v+2KTz41g+ZCr1YfV2QbftTgCaY+ZVDDqaIirKdRwMiPPTqkZlCUzlNCNlasCEDI7MpcQYXYE3a9qdqxurICJ9QJG3Fg+YC3uZ4PtPp73kP03YiyY6d9tlqlsp8Uace7bmFFytsDPANN/BTm6xERjaQNEMWYnm9HeFHE66jXwPY/kifK1lTXsdYJ6LqWUj+CDq0WgfNK4/sZ6AHW/0GtBZfDFn21Xnp1tjSrjYhi3OzaFfw1jh77D4XPdaBMac/BK3JbnlrKu4uiC2eEaLbm+ngJDTSrIjR11XbpvpQlicmVGLhVNKgiq2eGTA7lal4rwmuk5vvSkguVZ9b2IZfw1Zsdw7QPGBMPU0diMnfCvqH+ovfeKQWmLxuIMevTbQ/M/kfseOBkjd9yNX+XPNDSAOxMlGzsnnHACLr74YsyePRuMMTDG8Morr+DLX/4yTjrppJL29eyzz+KSSy7BK6+8gieeeAJNTU049thjUV9fL9a54oor8O9//xv33Xcfnn32WXz88cc47bTTxO+FQgHTp09HY2MjXnrpJdxzzz24++67cfXVV5d6am0CWxKxYlYuf7Ya8wH+83Zo2OkKfyVTMB8sHKUiIhNBHNuaQbmxHlj8lCLGdEUn8A5GZs/0sWTAafh847fxw3638Y3lfkQ76KzcUzotmRiOLBMvpDooemRwY9A1ToTxKgHCFx0XjaVQbhGzQzU7VoGy2vE0WejzgKldjXQt2ZkdK/tiE4TyAcvJvnjGjFiWizCf0XzAFMG5dd9UV6W3U3eN+Kbh58oJpKRB0GasqrGjx1zYO1sPQIYbO0J/oLWXJnSkv5VUK0szdqgWRR2xyKHoe2djdooPHDZmZ/3WBoN1UJpAE88ZruKoTWLg1qOqNETXyDbpoMaAnqsrK4wdk9mRLLCEKVAmp2A1XtzMjsIeWrYJ7wHft3oMAKjIZZQ2SejGToJAHsVQocxO+FFJTCg6Sq1cRExeMndSQfV66p/1sjwbtobZ3BmANVt2ROsDDYiIj0+zZuemm27CnnvuicmTJ6OiogIVFRU4+OCDMWzYMNx4440l7evxxx/H5z//eeyzzz4YN24c7r77bixfvhxz54b5L+rq6vD73/8e119/PY466ijst99+uOuuu/DSSy/hlVdeAQD897//xcKFC/HnP/8Z48ePx3HHHYef/OQnuOWWW9DY2Bh3+HZBRrnCSehL+XC9t1oyaH94camyTlmWDOqGz1ZF1vcVQ8vqxvrHhcCfTgWe/pn4yVWYVM7uZOfDPB/PBONRn+2qHZ05Z+VKTjJLu13lIlRmhymdJy+WWgqzwxizluQwjR0ygEQuojLkzfVh0exwZodF94iwGXnCmBTI7FHJE9McNxYf+GOTCtJjwebEF0ik2VEMEW0nvmbseOa1MEANP0TXSmd2SDeW1SNCpS9DbYrnmW4so73UWGymsSMGD35cS9vIerQVam0sy/WMga7ZaSoE2O///c9YT9F+LHpcfna5schzTQ0qU7heiI5rvoeqMcEvTEZZprixoLJKNvGsMTECJUTIteVuxdgMylqDFWZHmToqt4QbO2YG5Xhmxw6yD0s0VnzoOWe/zEkYh7kfdbJJr0Fcnp2qMnkuP4vq59H741s1DDsHJRs7tbW1+Ne//oVFixbhvvvuw/33349FixbhgQceQE1NTYsaU1dXBwBCDzR37lw0NTVhypQpYp1Ro0Zh4MCBePnllwEAL7/8MsaMGYPevXuLdaZOnYrNmzfj7bfVrJE7A7TWkrjdRTU74e9bSfbkT+rUFPb8YWssBLBpWSgmDe1GJGuOaKxFj4Z/X75Z/OSa6RTECx+9GNSXrW9Nxgk9IZzK7JgdvyvPDjy1I6Yvo6s2Vhxoh6waO2p7FKOB+KxNd4iN2dE0O4Ybi3cyrmgs2cZOZZaMETZjx3cZO8Tg1GjxOGaHaovkvhzGjp+xuLG0dluZHfv++OAXBOozBFBWEMgYs2ZHZ+vBet/s7Qs0N1YJtbJEdEvE7FgEtHr7VEZCDrRSHFs6s1O33R4+79SPONxY/FXxPYb/vbOGHMfO8OlRkYAejWUez9eMHeFqEcYOOYyu2aFNhrm+kVSQ/Oa8FtSA14wqhdmJjBhTkyTZsIzvmeH+1mNSQ4UyO0w5Pm2fR9xYSqkRG7Oju8OSMjvCjRVpEsWz6mFFlMGbvsIdqVxEs/LsAMDw4cMxbNgwAA5avUQEQYDLL78cBx98MEaPHg0AWLVqFcrKylBbW6us27t3b6xatUqsQw0d/jv/zYaGhgY0NMhiim1Z08tXNDtxVi5xgUQ/b2t0zyAVd00RZocxmWfHCD3XO25S8dkVnaBnUGa0IxYvjzi6ajMkYXYYAzxt0Kdt9VQDMm8zdmwlFSzIFwKM+IEsYGtNKqjTwgBoTgufDEgcZlJBbuyYBkjoxiLMjhJxIZMKlmd9NOQDoQ1QYBuAOZPidGPFlIsAoOstCgFDWVFmh3Swer+QgNkxorE0wWpBcWNxzY5sc043qBzTdd/ziBvLIVD2qLFDmZ1SCoPqzA59vhzMjmgyU5bHubH0e9WgGTuuscZztcfF7GgsBX93TCM4PH6TxZ1mZYRoFmwE2NFUIOvwfogpfwHZF/E+gBaClQnzTCZJloswmR0zbN/N7NDzFsyOQ6DsgSGJnaMfs7hmR5/shkZRhiRDNPauPY80RALQI97kdtKNxScfgdh+7B41xrYJ4+zbBSUzOwDw+9//HqNHjxZurNGjR+POO+9sUUMuueQSLFiwALNmzWrRfpJg5syZqKmpEf8GDBjQZsfS9ZIAilq5/GHZUO9OZsbdWKFmJzKK9A4qQiGQg6+p2WmGQJm/cBbJpKdbO4zZKVdPTb1uy9Og54LQow6i3SuCR27sJPVirdy0Xfme2I0VdSy+Fxi6A3p8ea2jTtkSjZUvkDw7TDJnGW1WN210n2gdy8nZlvkmVa98t0VjRauI2SFB3ubGMgwpapBqD7+h2bEJwO2GGW9fweLGotc9S1IHDOnRibRB3a9ipDrdWETEqiTibI5AOTqCcggXs0M3jQZ4L97Y0Y0bXbPmqrNlFboCQoCvw5VSwRQoh9fIFnnVYDN2CCvtI1Dar6fAsLlYeJSQLaxbGXv5/fDMSYc0jhw78Oikjk8O5GrcPWWUiyCTppKJAa0P4MfPWJgd1Y1FziRRNJY6KShYrjFA3VjhOXGjlAHoX1up7FvZ/6fRjXX11Vfj61//Ok488UTcd999uO+++3DiiSfiiiuuaLYo+NJLL8XDDz+Mp59+Wikm2qdPHzQ2NmLTpk3K+qtXr0afPn3EOnp0Fv/O19Fx1VVXoa6uTvxbsWJFs9qdBArdmPDGc2Pim/fNF8v0lzhLNAxMhIvab2c+kHl2wALV2InNoGx/+eWzzzti36SRybkqAkPFjUX2aen4lVwQxAetaHbA8PbHkpnLWa1LN/TaNlnlfsUZO2Fn6Xt00KTMjhqaGReNRZkdBi0ai17HuBOxGa2usHUbs6PT2bAxO5bQc6dmx8bs6GyBRbNjGDsqsxME2jGganYy5Bg/OXk0uQba4OxZ3Fiu9rKg+caOYBI85TysxxOLo3PVNDu2q8+hGze6G8tFdCoTESrILu+iNwoAeX2j85o4qKt9x9E9tQmlVdaGfyCMDBjGD5D71ZOb0nPl7RHMji1kXTm67LP0XwW3YYp2RBt1Qf92Et3GmR1TpyQnEomZHarVsjA71gzKPnVjqX2tjoJ2HtAMaUVioLixVGanjOiP+D1Qkjh2IDdWycbOrbfeijvuuAMzZ87ESSedhJNOOgkzZ87E7bffjt/+9rcl7YsxhksvvRQPPPAAnnrqKQwZMkT5fb/99kMul8OTTz4pli1atAjLly/H5MmTAQCTJ0/GW2+9hTVr1oh1nnjiCVRXV2PvvdXcNBzl5eWorq5W/rUV1KgCO6UeLRSfbKyEbuzQsgZrN0fshMONFVA3lqHZKQCfzLdu5yoEWtCYHQZl8hOBMEkQH5WBSp1tmpS+MvuwsCp81duek5m73/mkNJekrilQaf2YaKxoIMyAsB2KZidaZLixbEkF5XoB8xyzN8mQ2Zkdi9HqMq4Jva3T2WLG6mB2zH25jB2bZkd3Y5nGmFlVXWV28kEAw9ihbqycfDYG96hyXgOrG8vF7LBANciSGjvKgCGPK2E+8+bmhFUQrlHTgNDvlW5kuKITVdY1Osdhx5iGmDAsVQZwv8jYcQmUbczOjiYbs0NyJSGw5tnhk0DKNPB3gS+jbiyrG14IlM1nr2hSQXKWfF0aRFImmB1tB568bzZdkN0MsL3j8YVAPc8RjWVBoPcx2rtIz4FmzJfuQvW9opM0ahx9qkPPm5qaMHHiRGP5fvvtV3Jum0suuQR//vOf8de//hVdunTBqlWrsGrVKmzfHg7eNTU1uOCCC3DllVfi6aefxty5c/GFL3wBkydPxoEHhoXNjj32WOy9994499xzMX/+fPznP//BD37wA1xyySUoL09YiLANkbEZO8WYHUvHt0etGlZO88F8sinKXupwYyliXaa5IhY9Btx2mHU7PQpC7oKzOPxB941OgHaWiuDSqdmxMTuE4SCDjee7BcqvLttoPRcX4qO2JBum/A0bEbYNsBs7TGd2wj/C2AmoQFm6wgLIzsUQKMfays1gdvyMmbiRGDu664NGYxWY41lWDBHdeCgees4MCkJjdiyaHSpQpoVAQ6pdu4cRPA/IeEmZHaZun1SzQ3UPJvmnfZHrSqNWabF0FVvHQXXhA2+sVJa5nnNl0FTc4fqgHLVJM7iaCgyHj+hpck5coGwxsrbbmB1y33p3UZ8TPYScJgzl5/XS4rBQq6oHQrQdZW+4QDkuGsvF7NC+LwR1e/PNzHOmzE5CaocQOyrbGv5VQ89NN5aSfdvC7BgZp/kfzXjUP/PJZH1DQVkfxNhRmR2t4TsRJRs75557Lm699VZj+e23346zzz67pH3deuutqKurwxFHHIG+ffuKf3//+9/FOr/+9a9xwgkn4PTTT8dhhx2GPn364J///Kf4PZPJ4OGHH0Ymk8HkyZNxzjnn4LzzzsO1115b6qm1CZRQyNjRSsLWLw3qrvrQ6UvmBbpATd+fZHYYAJ+yAMueV1fuNpS0nTdXG/T4iyIoYZkzx3yX5SCtD1SqZoduor5wGc9TBkRPe/lLTSBIEZuPR6dgrW4s5hAo8yWqoVRwRGNRI0JhtEgb7IMgzLYZ7Xe5sTxD8Oh7ZufNkS9QGaMZzRL+UEKeHZtA2cEUCYGy4sbizzTV7JCoHjpQ6G4sMBzovyPbGi1VIIwx3dghk7o37wMe/CqQt6W5oAxEZCQkYHak+4VprAKPgDEfAP2ZWF/fiK/8+XXx3Zq1WG+PMHYs/YgwLNWWF4IAvavLLZodt7GjurH4gCyPeds5+6qHjv4KlxXZp35a/5q3Um5nu/XRZ/n8mkamcaXEAlvVc3n9BvcI+2gXs+MRl35xKNaOaKt0P1meHRIhqkZjmQfVUyHoSRbpNfvn6/Kazo5qDN790jKllQB1Y3VMZqdZ0Vi///3v8d///lewK7Nnz8by5ctx3nnn4corrxTrXX/99bH7sWXX1FFRUYFbbrkFt9xyi3OdQYMG4dFHH03Y+vZFthmaHX5dDtqzu5ix6MI21cURH42VDxhEfgxq8dtQLl16S9aGyR1vfWYxTh4fZnvd0VTA318NNU58dsdYvEDZ7sbyVF2qhdmR2hVPY3ZoUkGmvJhXHjMC1z/xnvv8NFDWa8ZETaiuC6OtAmXaYctzKDg0O3lmzihpsj6mdKjhHvj6tpmqgI1tSKTZCT9mtOfLg2nsUKOMtsa+b9JJc+jMo0VAbbqxVGPMJlBWo7GIG8OnbVDb2X+ljMBzRmO5NDt0svDPqOzFgAPwwYDT0bWqDN07lyttp0d35tkh7ePP/T9fX4nrjx4lfjUmBFQjZulLH39bRqM6mR063aWlIgw3VmRsaoxlUxC+f6Xk2Wloos8qf9iJG0sz5qR7KawNpTDC2nkP6yVLcHhkO7GMBz9YmB1umG3Ww/RtQRXR5Tl0RA/gUaBXl3ISCaqfszR2Sq4VpbG71mgsnmyVaHbUSD7zmGYBYH4IPumS5/DQ/I9x01kTlOt4zoED5fpeeBe5MJu62jqSZqdkY2fBggXYd9/Q8uZVznv06IEePXpgwQJZiK4jlXbfmVB9z34iAzdg4cPKDZ1i8C1RSsr+Ajo8atFYHNV7AJs/AvIynw/P7fPuqi1gLIwk+PUT7+H599cpmxYTKG/eEc4Y12zegWEOzY4tGksZiJndjcWY+mKO6qMJK4uAd0wDu1Xh/84Yq/5ohJ6Tm8eZHQREoGxGY/kaKyTdWPIeNBak6UAnwroby6kpIPu3tj8mqaCgs7W8ITY3VkO+oAiprY0RlloSzU64jzN/9xLOPr03Th7f3wz7jdq+ODK8F6/disma8FIkiEPI9HIo109rZ9U2md7eNUlQQs/pNbz1oPDvj+rEorr1qzHlvufge8CSmdN548Xv3EhIwuzcE82aAWDd1h3oAYBqdsT6RYwdCmrsnDK+Hx6c9zEAVT+TxI1FeSYAeGjex9jakMeX9UpBscyO7VmVx/O155X/VN9YMAyJgnbeZ08aqLVYv/VuN9ZfZi8HANzz8of48cmjyTb6Wcv7OKpPNf535eHo2aUcrywJ+2ujEKh4p4iRUmzwVwwVwuxE569EjVrdWKTd2nuoFurkZ6j2Fbbn6elFUhd74NDu0bFkfyCYnag54ddPMbPz9NNPt0U7dlmo0QGRCWwVmMpldzy/VCj7Ex3DcxTvi1AI1Dw7NoEjegwPjZ2m7eZvCI2CXMbD31+TkWuS2aGMqcnscHzuztlYNk6+xEpyMEvHr1T/Zi5mB9ijayXmfrgRgKU2UhHkbWUi5JGig7g1OxmP+K2VaCzewfFtwnXylmispjwDr6+Vp/aUr3Z0bk2B1jaj/S72xYwu4b2f7DAltjTkZYi8pbyHcl6eb46XRp6dKFdUPo+vz5qHE8b2Q32D7g5S9/+DBxfgnGm6ZkceKKMIVPmZWNpJXTXFkgqC2a9vQbqz1mwNPwcs1I2UZX3luvOtnXl2SPvWbJE5wNZviYwd6NFY6vnYHomvHLGnbGp0M/vVVKBXdYVYTl1KirHjECjr4e888akr9Ly4G4t/8BAwT3MLq20H1OgnAHjxg3U4YWw/lGV9NOYDVNKEm2IgJ2B8cHY8vzYQ41pEiJKfOZuU0+pFGQ2hzI7C+tn6HtKnkAmPKRCGeDblJJAzO6q7l4M2TwqUtXUsTOC/IgMZAN5aWYcTxvYDvX48BcjGbeQd7kCkR7Py7KRIjgx5KEvxX/7mqQ+U73GPTG0Fp+Fj3Fjim4PZKY/oX4f40kjwB6rZsahWnOdKOw7XC69Sqb7vAXk5AEBhdpjyEpZKE4sCojZWTO+YlLBqYuxYmR3t/A3NjrwHKzZus7iHNM0J9IKqGuKYHUX7oZ6HkWeHXD99htqYD8x2xhhSxlNrJPxTXSJ7fu9RzFu+SV1n0WPAytfVZYYbi75j8h3IZXzzHnJQY4c/T0ndWBxN28ju5P6EAJdG3PHrrDxmdmZnaI9O8hBEcGtjPzn4u1SW8XHhIUOUZYBMBuf7HoYTV49ijMQxO8KNpRo78kx4X6Aa8zY31o68RbNDjDm9f6KZ5NdubVB++9ucFQgCWbqiIkuNXZMJFc+vaGcSxkEaHpIJNdfKRAuNcybMjozItUSkKYeky+QzbM0npBk7Ht+cmf0S4NLUqNdeZ8z0Y1blskozKbOjZnh3vH87Aamx08bI2GZydj9Eyfs+ZFiUF6NIuQid2bG+XGXc2LEnMuQP8mf2k3mQ+JkFIJ2Ap/2agFUwwDgdGjE7up4kJlmFLalYHKwFQMWB4oyd8Lec7xAoc0Gqxgo1WaKx7p/7kbgnNGJYLQTKijw+ccaOthG9B5oGgIvoPY8Ziero9+KaHVueHTuzk0FMx//B/4A7jsSoytBltN+grqaxo7ix5DGzGXL9dCaETgyy3AdjZzOM0HMOwoLSYqQNYjCnxnwIN7Mj8eOT95GHKPCkbdqV0R4CmpNKKa0hfo8YAd/DafvKd1gJUacCZaNtquGgsy9ibW4gRtdL1eeE2N5oud+eZ0R6cfTqIqNqF1pSS9DnkjLion9Snn3d2IkJUNC2gUWgTMFZfJc+SsmgTCMQ45gd2JmdeDcWz7MjwySA8F7/9pkP8OZHdWJTmV9LfU/oPeKgz5PQ7Ag3Viha3tFUENe7c3kW7klv+yM1dtoYhhsLgPXGN8PyFSn+uZPUFXoeqHl2rG6ssmg2SSJN/vnVg+Q+IqOAdyb7D+6KXIY/6GZmUSd9SQcq2gfFMjukXRk1nYB+1XTDqBhEfg6bkeQqF0FYi6zv2ZkdfV/cNWfJswPIwcPQ7BBXGm/iRxu3ma4sq0DZwQRYkwqqzI5e6BEAGpoks+PW7PB92zQ7mrAjGnDowGlUVY/w5y5hzbaencsNY2dDl5F4JxiItyv3R//aSsyYOABfOHiwWl1aH9ToxCCjVWgW6yRndio2vS8+N3BNCnVjBSZz5mJ2Dh3eE0N7hu+juAeG4FszdgjzyvsFOuZyIb7ve8qEQDFolahOu+Gn59mRZ8KtAHnNXl68Hhf/aS50qMyO/CQTcWrGDnG7Xfa3N8z9EYOKFtm0dQV838wSjfXlw0O3X1WZPmkkBllMPy0Tvbo0O9SNRdeJ6bOUPoAJlsvO7Gi1sagrDMAfX16Gnz++CKff+pLY1Aw9D/e1nZQqqqkMJymUsepapYu0II5huMajtu9spMZOGyPj05ev+cp024srBmgWPZgO7YEy02AON5ZgduRDPmFArfishxXu06+GPDyeELOazWR4+GuHkK+mCyVcS315GSOp0T1PmXXSrMeMAYcODxmuM/bboxnMTmTsZGzXTuuYLG6snO9wY2naBjOpoGqcyNpYZJnuxor2ed/cj/Cr/2oRZ7xtVaFwEAdektDYCT/KTk+ew9J19cohqEC54NI8KPXLdDeWndnxPfo82t+NHpvDor7WPDteBsc3/gw397sOAPB/Z4zFNSdG7IiDDlOYnYyD2XHl2eEgYv4BS2S6jAZLcUy+RCEQHZodQA4mjQWSadthHNGvvicNfluuFN2oV6qjFyJW18+ZHY5woca7sYQRGeRx1h2vwIYdjRY3lieNHVtoPUfXqpyxjF/vrO+ptbEEG2WymoyydhEmDKwFAOzVt1o9QEJmJ+NkdqSBKPqnGEZp07ZGBJSVIX3A2kjPpWit6AQjOo6StiA6158++o5xLPO9D/U+NBdSp8j4m7xnd7md9hzx/ru6ImevA9cBkBo7bYxsYs1OvAE0sFsnY5lRINPhxtKZnVg3FhFdep5nvMDc6MkQRiPMsyO3iT5Fh2NiZlCZyzjdWHpHTjuMDA0997PGGMEHhr36Vpds7PBZWCyzE+PGyvqEdleiY3gHpxpKIvQ8Ou6WHU3Reiazo9fGok28+WlV0yXaNvazwLeWANN+phm/5J4Tg0TPyCrdWQzn/WGOcoiH3/yEuC4dhjsVRbqYEg5Ns6N/tsGeVDCcqdvdm47nwSNtaS6z07jNXAbixrJpdlxuLO068ueYGwbhW+swXgGFofMtg66SqJLAqtnRjVKAMDshKrLqfkTvErkq19bZrw2gMTueNCSqvHAgtzLPET57wEBjGR/49aAOYeda2ilqY5FfBTNjiKpNZsc2jvPrvmz9Noz78X8xb8UmpSFKnh2HNnJDfSPGX/sElqzZSrY1DXZFqC3cWDKDcqjZUd1Ytjm2yKVGxofGQqBMRHX9ztR9ZOFtnekNmDQu4/Jc7Qykxk4bwwg9B+w3nizrr2VLHrtHDb565J76FuLl9IRmx9JJIcx/QB/mCjSYK+WiY2qp8GkNrnBfcoboE0pYqWOlfGDC4NNzpPCOozzr4wyiBQLUF0zJs+NnNDKfkcKZ5oyjGGSaeZuxU5zZyfpkCLKWi1CR1wTKV947H4B03yjRWFokRmw6B/EM+ECn7kZ77MwOmalaqp7bYLixYjU72r50Y1xkoabGTjwC2olrAlS9zhldxxQok7Y5y0WQe2U1draYy0CYHYsx757tqu2bEyVv+8V/3hVto8aOno+IDsKC2SHHVyIb6XZ0N0pSQXs7RY0vg/hRmZ0gpqSGWi5C7EAsq1z8uLENZ291N3WXiqwwnipy6vMmnlB6aWOYHc4Krd6s9Y/EaOC7sjI7ZFnd9iac9tsXlZa43Fi0eQujOn+K0S9YNbnNvgO7mu3TNTsWxlmHnvHeY6ZWryAeZ9NgpiWDgLB8hsrsxE3w2xepsdPGsBWmK3bjX/zuUThpXD/x/eaz9kWPzmbpC95xeUWqnivRWIzhWG+OuVIxYyd64t+KxG2+L4eWwOrGkoNMRhhMgTJQcR1BLuMrmXvBGNZvleGLihvLy0DPoByQjtzK0MTAqP5LoWt2GjbLg3LNjueRmaiN2VFZIVoIdEdTAU8s5EVsOXNGOhIlKR7MAZvCFnnhNHb4upZyEeJM7Mei99zaJkYHPe2aLnlW21lkAJCZfNysHrAnFWTS2olpsdZOJXKRd/i+ex2bsdOw1dpGodmhbix9MqAf23FvacRjoBg76vpigPE9cE+OTaCsGzuKy0X0I3FuLP6VYeZpY/DtaSPVYqCcXSDXa/41xyq7UsPHJbPDUbb8Oejg7W7UWJcgYMJ4UjRakM+0Wi4i/GwTKPO+Y9XmHVhHo77Iu6WXi6DQ+xBxaUk0lhQo259zs5Cx7AMKBWqYWVg+6sayTApskG2WfZ1RRJaLui3s5Ijeal6zu19apvYpKbOz+0ApBCozOFmgLuxSIQ2XijL7bfINZsc0djbvaMJtzy6BzI/hADd2WEF5MDMas7N6S6hT2NaQF751NeuvOlMAWFSjSKU44XmqS0yJ5WQ46Lqn1DaQc1SZHTXTcql+Yt5/2LfTZmF/PDn821QvBsaMV4TZ0VghWhvr9ueWiPWFQFlcR3Ofb3600X0iWoenb+vS7BgZWb3454TP4JPl2dH2okf6NYvZYYZhZw6XSoOjdqntZEqeHf2ZjfZWLBqr0W7sbOPiTsqsuJidItEq1JCOY3borNvqxnI85yJJ3/rFwMJ/RSvZQs/5O8wNVIazDhiIrx4xDHd/8QDJ+EV9UD4f3us+1RWoqcxh2XXTxQTOFnpOzz6zhSR85Ncharc+EBcYE26scp3ZiU5BXAYSiWoLPacTJc6sgbbO6q4nbXSOpvIe2/Ls6OdjnITQYMlzt0djyXdSeU9iYLz3LDCYHT7RtbnwqsU4RSd6tE8prT9uS6TGThvj2ocXis+ea5YJGA9md8Lk9OpSoa8NgLqx3DVtfvPk+9ER5Uv2WjDSsjNyDMLucGOHd6a8hMSovtWiI6ah5/JFIMyOZ+mkPV/oZXIZT3U/aR15qNkhAmWF7JCVgDOe59TsUD8zhWvGy9sIAFi7CHjm/4DtG43fwpTo5rTd0CgaAuUAry6THapnGDvmAGwLu5UHpMJguVf9+Mpnm0BZE1bnMh6OGtXLaGez8uwMPEj5KqNviLETI04VhzOYHb5tjMH63mNqW6lmh0ci0vZSY83lxmqwu7Eems/zPsnjCbG98ZzFD35SF6flStEifmitI5sby/Wci8ih3+wLcCMjY0kqyDUZ2vMBRCHGEbixU4iKQpeR6CjeXxVzY2XXvwsd/PnUB+IgkMsqdGaHaFVe/GAdVm8mCVMtbix6B8oUY0Ka07GaHddEy6bZYXbNjijjoJjwpvGqGDvi3aflIujxw3VPGd8PFKEdpb73NmaH78oqPI4OdNK4sJzQjIkD1IlvEWO+PdGs2lgpmgfhqklgcdMQSvf+opcocDM7PBsrzYuyHZawwRzRCQV5IVJ0FZ/sU1OhuKwIYcMXCdDcJ0Ik4PnCjZX1fesLJLb3PFWzQ40nqMyO3pn36lKONVsaFOOR4nfPhCVPnnp3jfnje1H9pJdvNn/jLhgvsAqUjeRrRuh5QS3mGv3Ni0ma2XHWlGeUWbECzQAwPlsiUigtn9GMK97ugd2qrK7BZml2hh6ufF1b34jeUF1XzRUoh9ta0Egiyt7/LzBiqrlyTaQXU8TDpP0uY2fNQnMZgJG8ZImimTEPIRYwehbazyQIICBzU70Loa4DW1SQnk9JtMuS9C8uqaDrWsuoqojZiQIdqI5GiK4thUDXbm2ENKkRniC5WPxV4eLv8qyPhnyQiNl5eP7H+Nuc5fARYEk0p2OWFBDUvVam9L+E2YmWxEVjmZBsqU2zQ59/bmgo9fY0Zsfz7KHnMJIKato2rVU2sbzHGOobVCmDiM9g9mcIAMqiax+Kmy3MTurG2r2wfCOfWRS58YypMwsHTIGyaezI/fAXLrDrIiizU5DuBkkFq20e1rOzyuzILbQdM2WwpMwOTejne54wEPTrowqU1XNkTLIhNmYnK5gpWLFEC69ODCWDsroMkEaLrtlpIrWx1tdLXZLbjSXPhxrA+w/uqrZHEynq7SmWZ8c1Ke1TU6GK7DmzwxydGJ1lGi4bdebNxzzVjdUcZifmHHgoPgDUfSSPE+3jzewY0j7NUFSMHYuR+dofrG2srSwjjQ3hdGM5BoNbz94XADCiF2ed1AxEgeZWowOMzdhxRWNZq6HHlIsQRpvWh4g3N3r+CpGxQ3U0NmOH462Vm9HEyPOhaQf5tpzF4cZIIWB4NXI5vaFl3+Zt2hIN3rTfC3RDXAOzfpElbmyPmpvZkQymzdihzzwP+bbtqSCYcK3tzqSCqmmqh8QrjDvRJ26LjD7ed0rNjpxUkoOH+4pcaG+trCO6ro7F7KTGThvj8csPFZ9jb7c28zaFaiZE8UbmFijzAVXsnTFRnHLLsdfLFemLWmgki1VjgT/nZVmfeqKN32Unrs5CGGV2SDZQ3yOaBJtlQgTKenOpQFmP1pAvZjNetnGfi/kxuvZgVoGyy9ihVc8PJnkrpHuIG1EmRbatURqhRi4PZnFjJcmg7HRjhbjmxH2s0Re8nUZywzg3VueeyleeBE1lduJhY3Zitx03Q34m7KWZL0jbg2LsMPszGdfGcEOyLPxrerHsz2dV5Boa1RjmF6rY9jHU0HN1feo6yGiDFOCOxrJm+7VGdUb7dGj/9KSCQZ4bO8mYHQA4oOEWucO8GhHFn8G6beE7UEvy7bgYFZ0dpeaireo5j/gC9PBzYuGIAd88nq1m3bJ19QpbKm43MVbp87+toSDWFQfl7Do3dvTzJQEHfFtGz01z93JQsbdH+t76SHPWOdLjxAmU+U5nRwbnB2u2EqMoar/t4DsBqbHTxujeSbpPYgdz2tkxhrKsqb/RwTuNpiaeDMzcZvmGbeqxwUQSN6+iWq64aQVQFtHvO+rEYv5eBSzMfcMf+LKMr4QdGgwB6cRdmh3B7EQRJE7XCAA4Is4YGF74YJ04pBlaG+6LV153YXT/anPhXie4NxCRRHaBct7wvYd/aVJB6lrThZq2JH90kDC8D7bIC6VTimd2ROetubGqK3LKoKFrdgq6eycuz44GXk9KzaBsj1Kp7zQo3L2F2RFtcx1v3Fnh362rxSJexoPRLlB3Y1GBekwSOB3CyCDvuSHglwcy1gXkgHbx9jvlfhURqD1ixvekSF9xY7misWx9UVmVk9nRC4FqZyGMCMHsENdSnGaHMQ91kDW76ISLnxcQ5qEBgG6kX62JDJ9TJ/RXttHP1Sti7GQzfliOBFp9K0Wzwz+Zz5qtmvv8jzbB6sZyhOZ/+x9vqgs8T1x77l4yam9p5YI88PtNXGGAwc4ruxDvfSCO00UYO+E6RjADAXX72ULUU2ZnN0CPzlIfEzuYKw8iS1S9+4E3VgKQkQ82Zue8yYPUYzNZi8ijycOGHytnvqTmDy2m16SFPoo8Ox6tDqUxEoxFUVLRYjGj8ZTZpk/84fY8RKobS1wdsuqzi9YaHdzHdWH02CNvmhEeFL/6zHhz4dAj3RtE1yV0Y5mDb1OgDQoi9JzHBReUmaCev8Y0GoEmotcxqhJbo7E8wPbMEYPEDL1X16/MZZTOTWegmNEO3sFq0Vg994KObDa8l7ZorAVdDgEunQtMD9nH+uqh4XEVZkdlHZ3YtDz8+78fkeNIFyw9O/mRao6YMwkc+owFALwWjBCLpJFBjA3OBOojhYPZsWm2FGankAeWPCM0SbZK1kq+QHHJEjA7Yz5jLhOi5+irYexEzy8XKEfGDnXHc1eHLRorQPhM5Vm0vsbs8HMSxg5hdniov86GGzX16P1w6Ce5QaYkW6QaN2iTuiJgTK7se0xOKsjzxPvjNZtlRm6bQHlBFI2ph98L2UGUHNP3eNVz8i4i3tihfRdnlzqX56JzYMpfmxFz1F59AABdyrMqA1Qk+qw9kRo7bQzP80QG4djBXBuMaOXjYsjArdnh/l0ypxFGiu95wPdXA99aDHTpLTPJUjdW9DdgTPHvU79xOInQrH6tE+fh5yLnBREo5zI+fJ+6sSyzaKHZifzfFtHd+vpGw9jhX/foqiZqFOcX/d61k4W6L6uybsPbD0SzNW0Gtb2xgOUbtke/q7P8JiKMpAONbuzI85Dn89n9ZeJFo+MK7GyHkRiRtCUsBMpX89T1I1SVZ5RBQzwP3K2h1wGis0zals69oIOfq02g3BQA6DFMPJNSK8Rg0PN8ADKOwE+im7HIVmXeYMWKRWOd/zCw/4UAgI1MshLS1pHuzbhZcbiuej/trhm5rOyBL4apEH7WLzqmZOhkuQjZZpp4k6JPtSXSM1cJ42oatbEcxk5kbH+03gzL5xM4JShO+yQE/BrzwQfYVZFBQMvwcB2PrmWZ++FG5Xs5OXnGo/G0+8r3kQ8CYPsmvnbURM9pNAJ2YyK873JdyexoaRiYfj35B/kcclZ8YDetXypEhiHRXTKaj4q/q7H2RnRfWSDcWDqzo2hxtHZzI3FAtyoxEQtXi5ngtzNSY6cdwIv6xd54RVPBMHFwN/zijLH4x1cOMteNMHaPGgBAlg8WFmOHd7KUfuaDi+dngVwF0CnyVWctxg6ZPdPIjYwzg7LJ7PD1ASiDsipQJtuQ63PxYUOj7ezMDr1s5x44yHAT7N0vdE8dMMQc8IJAhmhmXUkyug6xL+dGiUfk2dGAcP/rH5F5mc7sSDcW7Xx8zdixhZ5fNW0kvnDw4HA/TmZHN3bMEFubG0ufBXcuz+BPFxwgDFF51hqzE6fZoW05+DLosLlEFGOHtF8aO7DMWMOvzuzZ488J//YcZRyHNdfYGT4VGHKocB3Qyu2CdSMhivZZcdiSaGVlabHcmNmP1LpTdP+C2SG7pLo2ALjvy5NxwJBuuOsL+0fNiJ5L7srW2vnH2Svws0ffIW4cx+Ac6X0Wr94EAHh60Vqxjt2AU+8D05bLbdWt/veOdEnyCC3d2NmmCaFpgJUQ5H40B3jsu2GeIUiDrN+H/wb+bxDw4o2KG4t/sp1Kf8uEKiRXzckCDQIJGxSgiU5+SBkN2ZeGf/QQcuSj/jorvQhWN1actUNy9Pzx5Q8ByFxDhkBZOXdu2MhJtZXZ6QBIjZ12wDeOGYlcxsO0MX3DBUUpvfD3z0wcIHzINlxy5DAAQJlvicThe2Jap86YGFi9jLY+Z3ZWvy0W8XcgYAxNZKYYlouQs2OmdU76Qy6joqQLQgiUo9BzmruFCxvPmzwIqF8nDTBu7Ij3X0au7dWv2giTzsriLwYoU+UMG/3qyyHzRXHq7WKgDQXKtGMKKwbLHDIQ5wSoeXYoKyIGX560zcLsVGR9HLNXmC/ImEXaorGAosaOS1g+rEcnHDq8p/YboA9ODXooPNn3hm1kdt6lL3SIwo82Y0d4O1VmilmYnaKsCR8EyGSAR2MFShdIjR0qUAYMY4dfZ880dqQORj4XzuKIDprfFdkjIxZVWAXKMdFY+w/uhnu/NFkWvewc5aH6wiOizRRPvLMWtz+3BPVN0X6cbix+PcwXzpbCgLqxwr+W5xVxYd1qIVAKPaJVScRI8yzN/6YZ1wAAnLlJREFUvjXMM0T2MWned8Pfnrga1GiQGZTN9vToXI5HLjsE35oq85ipJROALC96S3SRYeMCNGn5bWRbOTPDJ4fasM37RsLsBEFQ1I015/tHy0MIZocZBYBDjxhzP8PhDqJ1mRaibn++dwZSY6cdcMjwHlh47TTsP5hH39huPGV2kokhuY86GxN6LpkdeRyh2dFZAG7sPHIlUBfqgWieHaqx8TxSCBTKJNZ6XiLXDhmouOC2LKtFY0EmCixf9Qbwiz2Be8+NtuMiPMk4NRKhsz6759fITTFDbGtFrjJkvo6+Wi4bN0MaOx51Y6lMAwAjgzKN/inoLiAQ4W+g7jPaiTU7bviTLakg+a4wh2YhUNONZTcE+SfeTqozCDeT93cV/S1j5nYSOpboeezZpVw8U3nt/JUaPI7Qc2foLx8ESJVyYTw5mZ0ioef8N0tEmSFQjg3xt7fZdS7MsT4tR2F7RvhHp9GgM4Pa8fm9WvgxT6ToMHY8rsMK9/fOtdPEOrZjy6dNY3Z0t47Wnm9PkwYFz02T0Xx0ef39IjoZZiuavKPOfn0os2NMDlTs068GR44kSTg1ZkcwqB+/oR0jUHVCYgfiPzE5MvScwtghwTB0UuBwY1WVkfGCvPen7SsTBMr9AdY8O9q7x5iL2UmNnd0GIcUaY+Vqbqwk4A+YH6PZEYy6wuxEmp2Mtj4dkD6ZB4B0RkSzI2py8dIBRKBsCF01ny6fXf322aWiCOaKjdsMgTLvqDu/+QftpPmMOvzz0UYpprZFY0mtkAnaGRatln7IlcBh3wZO/q08GBCVzNDoYsbMQUnPoAxYw095O7kQU4+okmHFWvtsAmXAzuyQBI0mNW0+o3Sg0bUuNRXaM0Ty7DDSFmZ5NnkklCg7wuRV26NbJ7X9MZqdom6sTDQI5KkWzSJQNrJP82thESfz8+H5lqxuLMliukK/XYOB63l0Gzty0LFmUA7UQcncse4G1Y2dcPmcZRvD/TjdWOE9z6IA3wMqy+QzEMfs8L25ypDoblZaOLjRwezs2bOz8p2R960Mlmio//3Y4c6W77cwZGO6i1E8qSSA2irVyBd2Su0AZTm0Mg220HNGmHAFeVOzE7JA8cxOpVIlnjM7gdh/daV8ZwPGzIlR2Cple3O9jsPspBmU2xOxVi5zfHaDv5hCoJwxRbYmoyHdLr4+u6EDYpRCX1jskLkneFisx+TAJx5wvr12rlKYF+7j+Q/WAQip8w/Xb1NCzxkL7GI4QAmvVA4DoGfnciP8c8HHIV1sc1cnYnbo+Rz1ffKdMw6mQJkxm5AzYixI4jRGRJiSMdE7W2rsyKRkbs2Ofh4aowYQZidjKU8BAxmLsZPLZoFAcw0o7VDz7OSRgf506teoqSAN8ZxIvaA9a4F+DJeWgIDPeAmzI59dhxuLCqxtYcL83eFuG09eBzlBtzECrvuj3k+XURIUNXakvkWtjcXf+SLGDt+/dnz+bu7TvxZYJ6+fPIvIaIk0Oz4CQ0OTsRgSunbKxeyYmdEr4Hvhe81dqfr+L58yHB6Ag4f1wBfuflUx0PbYschoC9a8g0xlEWYn+hRXg8/3PYzuX40FKzeHRiZ9f7gWR4/u05gdJYOyMCRczA6PxiJZ4pWCuXyR+5rSpIJCzE6uJ0MRdtCj65H3MWV2dnMUs3ITurF8P5yhipfYyuyonYlH3Fi+rtkhIefIhmI7aaOYzI44LvN0cgN6J+5rD33A1EfPI24sGs5s+MY1zQ7vxCtyPmiqfA4+y7K5sRJpdpzg18AUKFOXnAhL1wXKUM9Tzy8janlpsyhbdlwAsNfGIt+tzI4sF2GOQ6RtlPwQv3LDTje65INQ1rBeLC40bNMPIAZu/jzu2bMTqiImoHvnCrGf8LgJmB3XAMRTKlBjx8rs0M9FjB1P1ezEu7Fiaio52lysqGShcx9lKScpi+bZcT7m6jOsW72F6H21GSyAHMQZ0TCZxo7rbOSlEnmPYoy//rWV0f7CZS5mp0tFDj84YW/sG+keZZZyD/13vG82hhXsk55t6/hJmpM6BzLKPfDIcn4sc7JC8+fQ6YUuMDY1O5zZoTndAvIK8+3jWizfbH6cMvKwUMbG5sbyyMRDNew7DrOTGjvtihgrtxlurIznyUgswCpQlta4FFvKWk7a+nli7EQDik2zw182WqTQmPEYzE60WGglJE4a108xEGiiOpPZ4dFYaofOOxfdaDl8RCiytWU31TVIJSFGs6MkSBS9eHhOTcTYKWvaKHcX/WXwMH5ALW47d6L2S7ivjLgfDiPDJVBW1pX335lnh+zeFo3FNL2M3Lc0umjxyrxnYR2j7qdfTTlOndAfN501AZcfHYruO/HiksLY4QJl2v6Emh1u7DRslp0zYSUldGaHW/oxbixbNFYpAmXHYKC7bThswyEYUwTIWYtOLTGzU0Sz08j34yoXEWl2MgiMd/HdVWbhVDezowuU6edoohG1sbHgYDz09YWB6+P+wT82Vwzy8ZOe+nXyNSsyOeK/B0xldsR5WBhRK7MTfQv3pTLr4XbM7sYKGPRErPF5diSDmrcYVaFmJ9qdxY3lkX4jLmprZyI1dtoTjsiLaKHjsxvZjCddWECsQFm6BZgxOAtsXEaawEXMvMlMyXgMqB2V0Ck4mB1hTFiEuseP6RMZCCGU3C1G9IpaLkIfrPU+iA+aj761SksBD4OpKglCL2VGYymRZRxRx0PPe/j6Z8ju5ODblSRM0+8Rt1kTC5Q5m+HS7BBGgJ4XfQbXbTVrePEZuNON5WcUF1FQOxA6+O971JTj1zPGY4+uVbIjF4Ouej62pILF6nspdd92bArX1c7DOBYVKFvdWLx9JrMj7o1FoGyWi+Af1PvpMr5NN2fYPjnrtzM7Ms9OQmNHe365MSJrZdr7KJaRxo7OksTlDuNXT94PN7PD39dizI5YP9q20guNgpxXQGNZDfCjOmC/z5PGB/HJXFkBccUwbccM77tcWdw9y3tjJAsEQFMgWJmdIA9xrfTQc/7uR89/7Bw6eo4HbH9HPCuUmQsYI/lzzJOngYtqbSzIH3YyUmNnp6DInS9BoJwtYuww48GVbiwbEyQ3LIhj8BYLZkdzY9FCoMLC114IPXMo7bT1aCwlJNuh2eEoBOpAp69P05g/sXC18hvPG+QcAOKgGFeq8agK86JzWfUWAKACMr/GMct/LXdHDEelM3MIlHk1e3z0GvDnM4C1i5Q2CHCKe+27ZD9Ss+POoCzx7/kfk2+c2YmeCwsdL9pBo18s/bjMKURZTU07QoSTQDPdWBW18vOOzdFei7mxihg7nqbZUZgd7Vw8z8hzQ3YUrasxO9F6dSw0EObs88NwNbMlQKFJGO5Knh1i7PCsuM6eRQ+n1I3M6H1tLNjdOHpSwYxnGg66WJdup/0xHhjKpPCP/L0V0VgOFxu/HhdlHpXL+HmWSzExGItneL0M0f4VYXaEwQnluRKnoYveGRPnEe6dTqDUflHJFE3LatCkgoyRKK3wuscxO5X5TeJzrnGLcZyA0feMtpu3Uhp31qrnHQCpsdOeiHuRmuPG8j2lk7UzO+FfzuyExegczA5FNFB5ZIai05vCtUAEmOYp6oOpyeyUZTKqsUPdWFmtg9SSCtojBCQ2ksriq7Uwab5tUXGyFZIa1wXKZRnZVekTxYm+RRgJ2bkF8NTOTDF2VPavccFDwJ1HAx88AWxcGq3vMGAXPSY/E2bHGUrreAblRC1jX48YIoUczSps7k9ki7axmtqgq0TtMPX5lZFG1iarAphIxC/z7NCNyGdqVFuZHT0aS56Dea6eOttVfrJPfflgvBmhsbO+y8hoLYtRWmhUXLKqXiTEzU9/AAC4f66s/K5AF7hr7eTv645oQNarnkMzdnwERmSTbuidTJLjBeKvXbNDJyTCLRX9deXZ4eDLu3mbzbYccqWybmxPQMpFFOsyBAOrMzu87zWYHaa5sfgHyeyIaCw6GaJlNbJUoBxIZicSLlvroPFNA9lP3rDsJABMuZ6qEWOzdjjTq7mVU4Hy7gr7LC5a6PjsRsbXmB2L8SKEiZEY2WOE2YkzdoRmh+8HoryDdGPJ1pqdgHqu/AXxAnOQCZkd4saiQj36AgMyGovOnOB2RW0nWVQ3bVezlhZ1f8SBRGPxgXjd1qZov7LT1jPNuu4szaDszOYMOfs71n8NZfef62yXgaFHkN1IzU5BNxYtnRMXhAJ0Bu9gdohQekvtXrg9Px0/ajrPauzw+kQ+iWTSDRlDoKxEmYTrcGGnIdyk4JW8hcC0hQJlLRor3o3lud1Yjj6BNyUXhUgXIi0Mb6+ydpDHl/88F0Bo0Is8O6WIQouGnofftze5mJ1oN0SgrL+TOoNamcsYmh25ozhmJzKSNTdWserndIIlnndaSqSsU3xfUNM/tlwEhZLYkRpqmjtfwKHZ2dYUQDA7zGLUcfbG85XJLgMjmZXLo+3d7dVPpxr1yNHingFiXXhUVqAyrXFjXvsiNXbaE23J7PhZ6/4NYyexG0vV7ASMCc0L9y1TwZ/o1w03lmZQWDLXlmXV2liKGyur1e7RmJ2i2XMJhmp5N2Q9oWZYO8KNJTU7P3t8kWiTi47/a+FoLJ54jbE72a94Ft2A7DD4+U7037O3S8+dNHBytJzogISuxqYlMTunH56wN2kJH5xcAmUqfgZ+lj8bdxemiQ5wy44mI6u3krPFCIFWo8nCchG6scPp/ZjuTI9KE/tzaHaU0PO4PDumG2stdzESV4RTRO1idqIbwo2dvMgSwicNxABrkMLfDfWNYjCMLQ+gwzAy1WvJ79XaSL9VrBBoBmZkk/6elWd9uV2UGTpJnh1doCxrY7nf48pcRnnOrI/KiKmxIeXoOlieSzFmxxGN5WZ2AjTlzQnvlu1NYnP+rirPOTd2MmXKcYIgACJ3FDd2+DN41KheeOabRyiHX995hPK9HE1KPp+AiOBttbFokldlEpkyO7srYqxcWzr/IshQzY7FhQWYxo4fyomj5sQYO4Gm2WEMD84LsyrzRH4Ks2OwJOq5isXM1Ozw4wTaDAYAvJylUCHZYTE3Vp6wRJ3K1PMtqvWIA3Gv8A7s47oG0SYjGitafwPrgg39DgUA7PDDgn6XTxmOrlGx2ACemTSM5MDg6f23w9Q/hCejPQe5qGhgE3Hh0cR/UfPErNjSOR0xsqdsivjgYHbI+VJmgTGGdz7ZjDE/+i8umzUvbEasGwtiP/RrwcLs8OSQubgRSEtOaC0EqqxPo7Fsmp1ofxZm5yGucVKYnWj1hNFYfD1u7DRBKyZM29Qkw/ofuvSQWGbn4GHdjWXhjotFY/nK8T2tj5LMjjsaSzcwchlfuqP1PenMDtmVMAQz3Nix59mhyPoenizsK/dBz49XefezbjdWeU3YTm1wd0Em/1SZHTGpseTZsQmUG4mxZM2gzNmbTLnKIG3fKNeJJoy87edNHoTBmlh8Tc0YLAgGy+0RIJclxhNjDhY9uh6+7A9SZidFvJWrvNzJmR3hAnC4L/gDyl0jHoKS3Fi0EOjWBrXTl5odEo/kcIcIN5YlGmtE787Ki8Ffas+zGDvz/xYdm59fPLOzeK2svpzXZrp2H3RS8IHaFFMHjJFBlEWObMloBVEYdoaF1zOcqUn9k572XobEBajIZdCpLIN65jACDWOH55ihaQViykVYOvGKXAZLZx6PpTOPx/BeYScpQ88d0Viep1zvgAF3Pr8UgBQ82wqB0gRu9C9/1mzGTmM0I6a0uwHNfWCtjUWfA79YNJa7NpY8F3kt9NpU8piO6KNoMZ/McDeWNKKJ+JvUWaqpzMlIINKk7p1C4/iU8f3NdipttRs7/H21RoPBxuwEeH+NWvlcP/cywuzYMr0r28a4sXgi0TjtXTbjoTFix14u7I1XoiKXvPX8mE73VMSYurWJ2uqOaCxnnp26FVbNTmNeGkt8YqG4uUWOHZXZKd/6oVxH1JALv9r6O9/zcErjteJ71gsU4zFgiBHZA7QQqJK7K2V2dlfEWLlkdlaKG0tV7ZvgD14mw2vWlBqNFX4NGHDg0HBWeNSoXtER5ezYmWxL7+R5np3o+xcPHoKqsqyWQTk6P88DyqvV/VWHnbWndCb2F9jzoGRUpiwPABJ2bTn/YtBqNokDIuxUZKcdKIZsAT4CXzV2fM8TJx0yO/Gz/07lWTQkZXa4G5AmjIwtFwHlWPLUolxEYrnmFhLbSUOqQK53wJgxQHDXhbrY7k4RRrIlGoszO7FCc82NZWd2yOekSQUt0VjGuSBGs+PQcOjMTl7T7HiB1J95dx0nUlDkMjIai5ZD4aUTrOHN9PiOCZCuFXK5sQJLKD6HPkgqxo7Yv/160G0lsxO2VTI7ccaOLwzHJmQw90PCfJAB2WnEaLlqihk7iqaQXFPxjOrvze+PQVWdLDjMr8viddvAn0sWF42VKVMa1VTWVa4TRSPG5aPyELpKOdvM3ZCiuYzZDXat3zeFzCmzs3sizsqlheGSZlD2CJnqePtkIkDqxtIGFI7uw+RnzY0VanbC7XiWWzorKzbjEYRPNBjyjKxfPXLP6HfZfQb82L6nDt7dhwNXLlT2J5kd88AZz8MeXaW4Vi8M2CJmh88uiUCcz3oVMS5jCmUdRPFbAB8QGDI+RJI7I/ScHIuyZG43lmbAcmanycbsJMuzoyJqp63AKCCfXT+j1mayRmPxY1uYHa09ojO1ubGi5zKZZifqnC2FQOd9VKeuH6fZ0d4hRWRtORcR9ZY09Nzz4CFAzosGaKYyO15BNcAO8t8GEA7qSthzhB2RQdC3xsEIGiH/KovDNGMksF4TyexkbcaO9p5lfdMZRIsBu7bVE4g2aYETNuR8DzkvvGYNyOE7pJCowuy43FO+6kYs1meIkh2aG0syO+b1GbLqcdKi8Eg9O0v3lIgeVTQ7vFREDsq94+94tlLQhFKjaLZX9Kcev38FJRs3ZXayVjeWT9bj+zTX25lIjZ12RVIrN9mDoQpZ7S+fyKAchZ77Xkw01rkPkibwGXA4u+y84W3pM9by7NAMys48O9oMltPiMhmgKVD2Pbk+Rp0AfO0142xFfhHLk+x7Hq48VgrvdGZHDEAtYHYUY4fxQYapnTZxORTgiygkILyG4TXgxo4tE6z63GR8D5sZ8bn3HS8/u9xYRZkdfswizyiT9zz8qq0nBkEP2xrzymZ6Rl8ZsWbuH9pzJNxYCrOjMhixySENNxY3uuU2s15bKdf3M7INK+ea+yNGHWAyO+F1kQaRTMjmaJfhxvKQI89WPmJMxHMVqJGFO1ho/FJmh17vHVFUYnnWxeaaEyDaIsG8MPvzYat6Xkyg3Kk8K7bjrybz7PtX3FjcxaftLy4aL5PxRPHPJmTRuZwm7jSZnfpsV20H3I2VjNlRorGoG0tQJaaxmGmSbj+6+6XrQ9Z/SxRNqkZjcWNHZXaEMUwCE+ImhsKogSzkGmaWD3+nqUeU+6j1H4xpeXaaJRFoG6TGTnvCNWs2xGoJ3VgedWPZYZsNZF1urNoBwJDDojZwg8PDb3M34OAnT8OoFX8Pl4nQ82h2zDyL4aB2WiLhrJbfRCQI8+Uy4RsmRoD+0gg3Vlw1Zy8sGMjdbnrW4WLi5nio14Cek6LZoYOzWEdedyHkJMxOnEAZiOQk9L4PPFB+drmxSF0oyowYtHxCZkdnSuTPsijtb5+WtDytmQMA5/5+tpwlWwXKWgh0tHFBoRFVLUK8saO5sbixQyuzu9xYW9VklHQ/Ls1OnjJQaI5AWbqwABmNZdU5QV7DnO9bkwryiKWKnKXLp8c2Kr+HKAiBsqf9wtfkxo68HrxUC4dunIzs0wX8fgfC6HRfDw7djeXaP0XO94nYO6uJpSmzE2J7VnOf+6pmpxizQxlxRaDsisYCsN8nfzOWFQC8+MH6qJVysiNXiNxYvsrsiCLDpI83ghEs4O9DNjJWqVxAZuE2t5NGkX6Nkk7w2x6psdOucNx4OusOV0i0N59odpjj5ZPGjhwEs7wTtfnneecvkgoCx2ReBwCM/uivACSNTOZD5ozHIVDOFLZH7Ypm9TZmhxtaxAgoxlzZOh89jXyT5sYqaIZYSRDMjtxnQzQ9DQItGkt3Y5G2+iJqRbpVynWhrcZKKEZurkp1PzqjsVzMTvhRdoBFDD9hlElmZ9ac5VjE6x6JfWcxun+NPCST2goAeGnxejlw2jIoCzeWjHoDgC078tKFolU9j82EbbixVHdqeE4ENPTcBtGj2zUqBc2362yjw7jM+B6+mv2X+C6jseznmPXyYZ0239NYhRCxzA4deEn7AoXlUY0dxfUI4mbkg6VXMAZVndnJeB7G7REaFTx5cBLNji5Qdu2fope3Adfk/gQAODnzktpfkHvA99HkV6o7iNxYiQuBKgYnZXb4oexShXHeB9H+JeOlvyd2ZidnZ3aIMS81N+ZxhcZIMDt5+JFmpwfq0OMfZ2BC3VPKuUUnomxvZFBOBcq7O7QbT8MEAeeLoMPm89YhqEtS4TwjMihbOj4+E9AKgQKyw+MPu0jM5pE8Ow53iOd56Id1Yl8i7JjkzJBVz4lbQpvFc/CjFHQjywLuFtKZHZZkkHSBG2lkkFu8tl60SXVjaQJlwuz4CAyBcpcKPY2AxpJ5nmRD+u+nMnR6EsacRaBMNDtO0aLTjcUF5uH9WLGhHt/951uYesOzwHO/CIttAoCfwRjF2GF49K1V1kOoeXbsbqwCMVTnfhhVUxcCZcuMV4fO7EQGU55nBm4qaMyOHz+iFWF2qAtp0/Z8TNSgi8nwcEn2IfFdMit25FAQ7C1/nvOKscOZnWLGjkOgLPLgRO+tbrNp0Vg+zHIR+nuWIf2X4cbSmSvKjmih5xxL16rRXxTnNvzduT+d2emFjei77V2oG3BmLdqimGbHwewUM3b6ehuiFvGJk8mmKUZdYNfsgEXLSd8Qp1Hku6SanVCg7OGb2b+j4qMXceHqn5jH5/0v1+wEMvRcLZuTGju7Fxz+aDz3C/V7wgdDqUbNBxkN/MGz+rNt0VhiUJDMjtiXKMIZvXy8g2IAExY+34/O7ADHZKT2QWp2oqbQGWUgGQxXlIigTePcWBF4qGaTHo2VkJK2ghs7pNPas1dYZ6cQIMaN5YMWn+RuLCqY7VyhVQjXr6VPjB3PE7NOAKEgkYI/S/P/Cqx5B1j4Lyuz4zvumwnV+KzfFhp4h/tvAk/9P7man1UE4X98aZllT2onbts//1seDWxZ5LE/T6iolYsoydiBmu9pQ32jauw01hdhdlRXsM7sBAyobwgHnIZ8AZ/UhW7EpOUi9GcyEMawvU1ZFFAWveNqqYLodCLqxGAN6bko7VFZJCNayng+TDeWfg42JsbQ7LjKRViisfT9TR/bDy6Ue9IleGf+OHVb8ZEBHjCn4hJzBxEz7tReaRC5jgJ1ZZHJJ7AbOzMyTytNCpkd0j5oRqMjGqvv4vvCD2RCawQjEIjjeVJgzgXKA7y1Yr1yNFonhzxjEtXmpczObg3HjV/2grZesgcj43nYz5VJNwJ/OXOZGMNGWaa6sRQjJGIkuN0kmR1S+0eco9axeR5+nLtHfN+CKmX/IYnjKW32FGNHf8FUtiZunJPMjtrBzJqzAgDNeFsCROg5yY0RXTslt4YWeh7Ak5FM0fZUm8TgoXO5fq9UI1nRMsEDtpOcIXpeoi2fyM+/PRC49zxgQ6SlIYVAXYycgWjx8O3zAQDH+bMBAP28dep6flYxLu95+UPl5y4VWRlGrTA7djcWv86fjQYD+tvCTzZHTYt7b8j9AGV2uHDfk+JbAFj5Wryxo7nSdGanEDB8vGlb1C55v9yaHXWp/jzz+2ScYVT3KIuCeM5tbqzYLNMOzQ5l3Bo0N5orqWDgxwiUtXP/eNN2cYx8oD9/mhuLun49zuzItnqezCVkw8ZMD/H5I9ZTc3lRZsfRkfBorIQTJJmyw2G0Ref3l/zRmJSRrNORmfnRp8gIVJgdi1HP3VVaBv0+K6Kip1tkEV/hSrV0lvx6CIGyl0c2EwqUHw/2F+tVYYd27Uw3lsoWp8zO7gnXC0LSvQNI/GBkfA8H+wti1+E6lbKcJcOy1djRWAQqwPXkwBD+Fs02iUDZOEWHn3gHypX1w3IR0XGEG0u2w8nsWKjZrx4RhrPfed5EAESzozE7/3g9LIq4pcGSR6UYosylQ9c+KRbJ9lMxrmR2wsHdE9cRiGbA5OIE8NRIEcDQm6jMjg98INsAvbzGyOPc5+BnTGOxKMsVrl9TkAZWDbaaWhI/a0S/UQzsVmUXKBsaLbWz7E+NKk+yMgDw9ko7u0nX1ZMKcoaxqRAYBtu2RjLgDj0C+FEdaacZjTV5qMxOzBjD7CXhNaLXxhV5btPsUHARtnGd+44DEIqZOYMpCoFGejHGaBFfy/1VDAvSVhIx1KgbO1p79WisLNSkdLZzGtitymR2HEwAfSxtzE5NZS5Ws/NJ2UDlu2rryGM6dxFFNYnrWCSEUxeJi6K3Pj/RSDMGXxHJ6wjAoDOgVoFyJqpsHuN7LcSwUpTPA1RmZwdJczHU+0RldoQbixs7GmOeMju7KxxWbmO9+r0EY6ccTbHriCgMfQAFirixwjaMaHxH/CTyw2gPcJi5J3roxX7UdVwzJv7irN/aKPb//Ptr5G+W/B/0W4GyQBG+PW0Ull03HVP27g1AuvB0zU6L8OYsYxFvf56EVVM3lp6rBAgH+owP5TzLigiUfY8MNp4H7He+XNfQ7FS5z4ForczZnovZMZfPr7hYyYgdNjJj5DWiCEPPo05cObRmNRMjpX9tpRh0w9/U53f+R5ucxzONnajAJjF2xvmLlU1+/eQH5Hy090fT7PgIMKi7vNYBUwXxwqhMmGdHd3dJZocsn/4roDxMFhi6sVQjoDPbBlzbDd6Pa8UmRqRftFd5YDO3Dv2stJK0WUx8iBtLNwh0Umloz87iOc7rRm6cQDn6TDMJV+uuXw1ZIqi+uzBVc8VIds0Z3RoxVoUkLlNo0Vggxo72LhvvDYDvTBtF+jfaysgQ8QD8+3LgqZ8axo5LwB62Be62R+3q37gUADDGWxJl6FcnIz/P3W41mAWzRwTK4S5TZmf3hMvKrdLr1SR3Y1WjPnadhkiYWFVm6QwSaBIqIYWtuSCk5XVmh3m+aLIrX4uLMODrv7d6izjrZxaFxo4SjeVgduIiDDh4p5t3ZY9tDhq3mcs8aVRJYicQLg+lvhBxzygCZeaha5WL2Qnbv7G+UWV2aJbpjGbsZNzUfrPy7ETH/aBynLKUJ72T+84aTBog2TamzAAtzI5RtoDhga8ehHpGzk/LNTOsl1roVYFeG0tkZJYG6g4tUaNaEV17V5jUPQEWho7JJJwMJB9UQs2ODu6S2oOyT5tWCCMs6xGBctSOz2dkkjreT9B6R/Jc7AJlmkNKsJaKCNZsM82zo5c90c89NNpDGMxVnGZHuLHkskqb8JqAn/bThXEAPNXoJPcgFzTad8CNHR7dmpDZka7EyEDjySejPoFBSgA4unXKiRpaAUw2LbfpA2DuXcBzP4fI7q0XALYgiUCZYxM6IxMxO5Td7+et17bnRjyPzmyhFrINkRo77QrHQFJRo35PmkHZ93BI5u3YdXi4b4XV2LExO2rnu8OXA8jyLhPC40ariKrnhjVv7sf3PDxfGO083LH79BYvdWNT+AKH0Vh2zQ5nikoRKOu1sVoEyz16L6oFlDeSCnI3luwQaBQPFSgfuVdvDO/dRd2xiJAL7+XHdTtIuLYnEwfSdTl0podC0ezwZUUG32j9l7qeLBYtDvoahgK8jNW4rIr0SAUmozaUMVG/rsTQ61Vdgb49usnf9jhAWfW8yYPtbQYIO8aNHV5NPGxPUyFQSnCsH3wiFDZRv65aaG/GY1GCyHBxEDAUSMqBUqOxAOC/kVYiqOhqLeqJ/S8Q7pUy5MWMmw/k38jdL1Y9JRPqArM2ZschUFZWsbCS4hkhbQtIbSxb9BVFmAE+PHae66U05oOuq++H7q+iLN7YyXnqO2hndhiO/+BHcvHAg+TnxU+GJROcDJ0KkcVa01rpSQUL8EONyxl3AQDeDIZEBVIjxqtAjZ1oH4wY+e8+Ev19NDqO1q4Dvyo+xpbWibZbVBFOYjayLmE+L89T9GghE20avLw2lqrZQerG2m3huvFGfaHkD8b8YM/Y319dFuoGqmxuLCuzo3a+BULfF3gBSyX/DcBU1YV1P74PvMcGAADq9780XOZJuj7r++JF5YOknyAaSxa3s5xKBD4ItLWxkw+Aum1NKASBNRqL6xEYYySKhymC45+eMsY8liYaB+Qsb9mG7dpgpXX6OtNDoURj6cyOY5voWGsrBuF3+RPDz6i1anbufGGpsTkNyeWsinpnNTeW9hxtoZltO3XH4O8+Ir5WxQ14jtBz7kbIFxi2M2nsfLT/VfHMTpc+4V9iBGU8RtwXQP+oNAOD1yxmZw3Cc90y5nxFbCxQO1AYO1kUhIvKFi2zkYUGtB6uHR7a7sZSVrEZO3w7KsAXVc8teXYMZscT914yOyoDx2HLs0MFyhVxRWAh020IVzxdndyDhhyZaIz/nLIP6gZPrtmJ9hwdQy8XEXBmp7IWQJhCIKwZhuh3eSX4O+9Tw5unLREsp9auaTON9tuaLox0bgxGkzDFZQ5gARtsN5b49sxVG8s8ZnsjNXZ2BnRjZo3OziR/MoIYV1RDviDya6zdYqFnrZodvUMibYkoU7UgZJgg0IhSsDA7nAlikQFlm60BctAKF+mDX/Q1+ivz7MQxO9yIajtjh+chWblpezSwkRkq45S1xY3lBZq7ztYTqcwOIF2I76/dBgw6JFxY1cOks7Mxbixbnp2izLNs5xtBmMwwg4IosigbaKfVfULv8yuYiXVjqRoy7o5Y022iocFKVBuLG4xMzbOTD5hC1+c791XDvPk9OPV2YPhU4JAr1P0CyEImpQsYw6DuUYV4eMKAN4Ox7KHWgGQjmJcxXB2yXdzYyYsin/xderggM2t/zLpHx7fcYJrBPc61Dd3Y4SM5mfjEanZs31Xmw6XZoZvy20z3x3NcuZDzubETsV8OZuf97keTg5L+cexnlclSMc2OWbJDM3YCyTQFjIn7uJe/XHmO1Wgsvm9yj7oNCf9Gz6Nyf2iyUcDsownE5FEYOwXixpLn/VRhgral2v9SgbLneaQ/2fnWTnFHX4rWQ1IfZgnMjr3acojVdTKkum5HHgHzVOPF6sZSO98M6XR4dIYSJYVw1mJWA9aME88TAyLTorrCfXqyVlLURsWNZdlf2Ez3bIVDGDtEMPrG8o3uDZJAq23DO9El67ZaamOps0rDjeV5RANii5pTEz0CdLblhdNUGilEUYTZcc/24t1YnucJQyGHgqLvCHdoDppHj+qlMB9Gjh+yf3G/tZkwN4yY5+Otleo5JzJ2ovPqsvhhAESzUwiU4pW0MG24ILoH42aE/zjIgOh7gRZyLPfw+vJNYfudAmXzPebvNvMydjcWIJidHApYuq4+ake4zzWsVqxW5uXd481qGtEZ30dZ3Vhkx5zZ8T1maHbMUHQibObvhnBjQVvXnBhRlmrd1vj0EVygLAZzh2aHG7wfVY7EHrR/HDdDCSMvZuxI5pkbc5GxIx5DKVBmDEoh6DLixjISlAJKglh88FT4tyoKrZf2o9G/x4mrhSxAuPnCSdiqzTuQyVA3VoBn31uLaaMjZtPiGlP7FJWZ3ZlImZ12RcIbn1CzA9irC3PsIOn5vzVtpBmWaDW+1M6XGkcezb1DswIzSrVqIMYIHxA5G6WEk5LBhYlCoB7Zsf1RjStux8GFm1Qwe+pvX3KunwiB3di57G9vRNFYnrGudAUwYQxkEKgGa5yOKqCdTngu/brGRFsBRZgdXxocMvY8/OPMs8MZCl+kls+goBStdIHT4kAUjSWMHXosjeHSjBTO7ATwUa+lDChLyuxsXSMW8/uUDxgypHK573kqs+NiPPQ0AsIIl4Y4RSluLC5mLXgZuxsLEMYxZdZkoV55PrTOloFnf072V8yNRRcy9S/ku11MsxNGJZNyN/QHwOgDbW4sqj/au2814qC7sVyaHRF0AV9ldvxcacyOEY3Fl0cfGA9aiCaKQw4V24ZGe2TsBEXcWA2RwV8mWUR5Wuq9jMs2L7oYEl0oywLJe5HxAqXsi9yeslGkT041O7spXDd+0MHaiskfjKznHmS2NYa/9a+tRM73TQ1C3FMftYG6GLio09OMHcAjYxQfpLTZiOcJw4xHbOhZUWXVcwuz4zJ2YkR3dN+ATCr4u2fVEONvTR3p3NaJPY9SvvK2nzphDxQKWno7php5lNnxwNBlCwlxTujGEh1fEbdDHLOjJDd03DfLVmK9JlIhOe45FE3xZd0mxpjIraKcgcHsqFqbrC+1DnpZjSojGSMB3c/2TXJxdD5NhQALg0Fiue9pA4fN5astzxJjpxBIoaaSrNBsWNQui7Ej6qX57rQJnNnxCjh0eDi794WxI7cx3IwUH+pJTU0wbeYfLgzEr2KRLzVERlVy33ze5NPGjSm7ZsfX+gr6FwDOOXAQ4pAVDFJ0XPqeWZgd5nlqn5MpQ4FMlqxCbwJXNJZoM4nQDBgTKSI2ss7IZaQRmGcWN5ZtglvGA0ncz6woj2Njdvhzq2l2sr7qxvLAcMZ+e9C9hoci11NWRyftMeo/tj9SN1a7wtGx9RkLfPii/F4C5RfH7GyLZr6dyjNR0j4f4J2eM5GV2kblxSKFKJXZHGSeHfkeqfvxPSDjxbux+EtNo1iSlouI8xByuvve1z7CM4vWYo2WMfnUCf3dG7tw5PeAdx6SzYzavnFbI2idr7CR+WgdU7OTQQCPRkyVW2aoMQLlIO7EgdhoLJrc0HXfDJD7IYsGBipz8Jm7AQDdOpWJhH8AosKC0T1mTOic1L6XG1P8fuvPo5yh0/H/vi9Pjndj+aYrEIAo91EIGP5YOBY+Aow46CQMZ5qx43pfKLOjubFszM4jb36C8w8aTLYvwuww4C+vfoT76j9ynFdoXJyVeQonDj8GwKSwLb4aRRPL7CSA6bAi35S+gDADelJB4nbi18kTBp3OLGrMjke3DT9TN1asOB0ymZ/VjUWeMY88X9yQDDfIKsxOEWLHEo0VGTvimsnUBwwQDF0GBeSIQHlrQ0G0zwMLq8NYjR0bs6M2Mo4FtwmUfd/DvgO7IrOCMDsIMKwnSfHAtB1ATiqVSfH2DWEEY4IQ+bZCyuy0JxJHY5WQDyZwd2L1EbNTVaYWsQvb4qLlNUaGbOVHzE6oKyQRGPClh8XBEMz/qE50vjxjqOLGkql6xOv69sebixo7MhrL3fvQ33RDB4CZxC8JtEzFvJN56t01WF/fYHVjSc0OA83P4ome37d3BlaBcjRIxLIGcN9nqLN0k5FzgGh2uN5luL9SMAfPlx0K7HMqADOJIxc8AqFeRzA7CaqeS0PbZDsGdKvE/oNJSLoNDvcIv45NBYYmZHFH4QSs7TQc+UKg3kPXTJ7Mnj0E2LwjfEeOv+l5XPvvhVFbJeZE0ZGkYVG7LC6vaMtNO2ImP9Hz0svbhKFPfUXsp6AJrm8ru8G9jwSwR2MF6l+ooed68jnK9DiZHc9+Peyh5yQaq0ienZxwY/FtyY8Ks0PdWMTY8XOK5qVoIVA9GsvQ7Mg+IWR6pZFBr9MrSzYobizGYLjQAQBlFne2ZqDH5b8Rk0cerRa1o7Iso44BYNb+0srseAB2bJIrbdef/fZFauzsDOgdm2HclMLsuOnpe6Liiys2hAnwrNElOjSB8hsfyiRmXvSC+opwmIvookFQ31+0n7VbGgw3ltqB+aAzGLm9w9gRLFCk74l5kt9dtcX9I+Jr6jihtYcOjG8s3wRYjB3RoRM3lo9AisD1fEviWCor0a+mQg4SxYyTmN9p9tbEAmURfeFjoCe1L/w5XLedDHyWxHC+eLyIZke537oby258B/BQUtV6ajRRoXf0mYrXg4ChqaDprmImB/y+5sh57GgKYMvGe5rOIsYwOyKPlUU0LIyOD19Wf6iX76vu7ugNx2BT7njuCHgJE2vouSJQTlYIlGd2l5qdeGaHvt82gXJlMWaHPzeM6wVdzA655nTikclhR1OUriBBCgvdjWVkUDaisdQCnPya1m1vMtxY1slwLmR2mhR1gbxoQRFWSvSnwtgJ0yg8+95a5Tn2EaA8S6+1zuhDuPtostSwEcVd3W2J1NhpV7iYHe0hKMGNpYo7VbzwQdjxrY9cCYk6b62zoYnheDQWfRkB4Jp/v2vOGiyduBQo84KixNjxPOlPT2LscGYngWYnbij8xRlji87S7DtVt9GrUdvcWEKzAybOJ8yzw8+xiAEadRazLp4cOxAmBVOMHX2wcbmxpDEylw0Xi7lAubxMus10Ua3vebJuU8BQSOLG0pkdMvNOIk4XoPshmZdF1AtpayEIjZ9EbizIe5/11EGokxdWOh/oy6rR188Yrzcsapd5vZvyeWX/6mbRditfU5f/UoYbZ7T25DwHC8zzyYw+w/47gJoqi7Fjc2N5hNnRQ81tjIJwY/EF9j7S5saiMLKOa8jEJRVMwuxkynDf3BWxx6AQNo0mUBakiIjGigTKPnX/efCi7Xp0qZDh8tx41ccLAKhbHh2HspGk4nmRSDLJ7ERGWdQO/llsi0BldrSkgoCWLbyaGPdBfGmjtkZq7LQnXJ1yC5IKxoWe6wiSdN7GTJrOgknoeV51B5naGfNcObNjG+R8H8YM5poT945hdkIkcWPVVLo7QmthxCTwdOMG7u876gAAPQrhoMcYtGgstfSAAV8d8Ad2r8KRI3vyhpTe9gh0EBUdYDGBMuncGqIkfA0sK85hR0CiMgxmR86oldBzZf+aG0szvvnzHni+NHSLCSgAlR0rSB2RFCiTAZsxNBUC58ChgxvvWW3icXHm4QTtItebMaBph/hJnKulmxaG6v4XOXbMcHrmeWXJ1452CfGjdncd5Gxm1ypL7SWLQJkyO0ZenbiaSrAbt2JbRaAc/l2yTubW6VtTiTj4IvScu5NczA5xd1HNjp9V9GfFoBRjBWV2ohUYFSgDNBUFNRK3N6nu1Ge+eYQSlSnQf7/oOPS07O+ibXInBMqc+QIT5+DTKEVoGZQ506todshY0GuUXDVGctEeSI2ddoVjFtdGbiwdauddjNkxjR2p2fGAtx8QywP4aOIPuHFQk9lpslDJoUCZtyA85mkT9oCc6at7FoNmTFbQJGh2/RbN2PG1wVAZoJ76ifKb4cbi5+hIxGcVKLOWMzs0IWWckaqCt9UXpRayKCAXic93FCh1rm5Jo7GCgERjKX2n7sZS3ap80GLwZBHTUpidoCBLPQDwo2eSMpihsSMF1Mr2Guq2NYlSBzqzc2hmgW0TvWHhH8aA+78I/LQ3sHFZ9AsfoGOYnSO/py4fOBkArDXzMi4WOFBLX9hQXWmpqm0NPZfPdZxmh8MIPXf0kWpfEV6PHNlfzy4x+aQgWWluJPpOZodEY9G6cpkcDh3eE0nhrI3Fb6Wu4xNJRpmStXj5hu2yzQhCbZLNjdWpl3Icuk9AvZw2ZkekhGCSzfH9sJYdHQN6dtL6KPFeOpgdAKioVc55ZyE1dtoTiQXKyY2dqgTi9q8dFVLbyfKGqKGfR46QRUp5h+F5HpCXM9AAHlHg8/3YmJ1oYGG8npBcJ2Ams5PNeJaZPpR1krix4mb+xUJIndCuX+fyLM45cKD4rtzBVW8q6zJApa15Z+IabCyRRJJubx1mx7h+RfPsyKSCGY9huBdGCzUE9J6abiwarcQ7Vi82z47d+A7gozHSfawtklAu3I/DjRXtN6+4scIinnE5SzhOuPl55Jm8DnG4/dz9LO3iHxjw9j/Dj6/eCQDoWhne91iDVjeQI8PluFGmYDvjmkTxQchlbAOo7VQetdLWFrlf5slQeHvGZBW6Zodp/Q/HMsLi8H5j3dbkTIuvGY724u8xeXYyZYIhji04y48nmJJo13y54cYKF3D3H6Cy9VsbCjJqESzUKdncWFHUZVW53M/yjbKPLiTU7OQhj5XxPYzuX6No6sxn3Ox/hY6SL+LPVSF1Y+1GcDE7xZgeN/wEzE59g0xgJbBtvX1lYY+FbejVWc5uOJuQ8aBY6QX4wg0gZ2DmuYrIFzG7kocNSNZh3gGGxo6m4dDaKd1n7gEhbuLfWGjubEPbqecrhpMyKIw7S1mVManZyZDZpJNtEwM1jcYKB7VCC7JHNCvPDo3GghwMjs/MAQA0kfaYbiw99DxabtVoacyOLlD2fPzhxaUAkMy9QI0d4sYSSQUJs1OI3FhJ3L4rNmyXOgcvwGn7utMY2BPR8feELGLauTLbcxFtl9FctNF7WeWZBqA1ZBmQzE6M4V8bDfQqs2NGYxWUqunq/bdNLEwRt+q25Fi1mbj3ot3s3c+SpsEBXePm0uwoAmWFCc8KgyE2xYFoo8rs8P5AFJ1nap9MU0hkUFCuS0AMkFzWNxkSTxpmtB9cuUk+Axu3kRQQMdFYQmIQCaU7levRWPbIYY/cW4PZcRiw7Y3U2GlPJGV2SngofJuVr6FXdWj1J3N5qEaKklWWRmOVd1a2KehuLO1cH73sUEHz29xYNVU5w9jJ+X5RzY4sOuc+ozjWZ83mBKyADYbx5SvRIYqxEyUM+0/16eFv1I3lJWB2LG6sTORSLMS4HoqhwKixIw4G2UgLSAblJpjH5q4txpixC9/zSO0oktFV2b/uxlIHP1Eugnl4/v11SAwavk/cWH8PwlpIlNkJo7ECRcCdJKotiwCnjHcbO1aG0dYnLH8FABH026Kx+PPnYHYqYRqATuaJv2MxzI4UABdxY0EaX7pbL47ZEQadI/RclCeAvI6XTxmOw0f0xANfPQjFoJelUO+FPCZn+pg2NF71r3dx7cMLo/MoejjxPvEoVTMaS3VjUZY168lIvtD84jqaIMwSbgS00OtMdDrk86E/f5q0Lc6NJV1mGd9Dp7KsYuAY7KBFoPxGVBrF042dUlKqtAF2qrHz3HPP4cQTT0S/fv3geR4efPBB5XfGGK6++mr07dsXlZWVmDJlCt5//31lnQ0bNuDss89GdXU1amtrccEFF2Dr1q3teBalwMXsNN+NlcTYOXFcv3C3SW53ojw7HtBzlLJZ3mBY1HPdu1+1cGNxY4dO9KorchjaswvdUg1xd2ZQhmyTA50JtfunCw5QfmtWjh1bezxPlKUANGOnEBpUeY9HtDDQPDsZnvCtWJZeyuzwQpZefBRKHCh17sp8bUIybQWLscMNIFt4bsaX0ViBEo0V48ZyaMgCzy/t3tFBdNGjYnEdwpBdxdhhUTmLhAJlkajOY7Gzfru2yNInRBFWuuvFup1h7ITPRYVvugyyrnvKjegYw7mmypaewdxfQK4TL75JcVLUFzn35DB2aLZsfh2ryrK454sHYMLArs52c+jMjl2zQ40ij7YK989bjQ/WhOOKnizRBqkpjE6Ht51fE6beW0aTU2qsDu9L9unbxa3ZEQeWH2kfVEyzwzcUmh0vLBfh+x66VdJrb2d2aH8oa7RB/W13Nnbq6+sxbtw43HLLLdbff/7zn+Omm27C7373O8yePRudOnXC1KlTsWOHpDTPPvtsvP3223jiiSfw8MMP47nnnsPFF1/cXqdQGtqC2XG4sWjYb0WWzx5KYXbCP9R/zDsM3/NEB7koCFOHczeAfMDNc9WZHb3z71KRU44Tbu4KPVdp4rj+5/yDBmPioK743vGj0KOzKmTkKfZLhj5web4imFSMnXw4yy5wY4dBnI+PQLgHi4eekxkWawU3Ft+XbZbr3EhYl3ZmJ9Jj2VKRLFm7VdHs2AuB8mbYo3PoYPTVI/aMbysF3U+V1KHx01HcWAHD3+Ysd2p2GvMBlq/fhlue/iBqC2d2CijLuq+fdZBJkGfHppNhhnHKfwjfy+Hb3jAPVdSN5TZ2aGg3HxBtVc9pVm6d2bGBZ7AW5+hweZQpE4nSwa+lNEwdzI7jmtNnPUkAp0yeqTI7wsha9IiyXNHPFWRpBQbZb4/rH7ntbNFY+rnAZSTbWXC+jGt2PDDBfvXqIu+9b1x9fXJC99mxmJ2dWi7iuOOOw3HHHWf9jTGGG264AT/4wQ9w8sknAwD++Mc/onfv3njwwQfx2c9+Fu+88w4ef/xxvPrqq5g4cSIA4De/+Q2OP/54/PKXv0S/fvZZxM5DUmanBM2OY91G0nmXlWLsaJ0Npb45sxPWqVNp2LzQ7IgdGbvmoef5wBIRQTZW3RrxbqzVkS8/TrPTuTyL+78iqe7vHT8KVWVZHDWqF/rVxoesOmEwTZ4yq1eudSTmFsYO2T4suFfEjWARKGcikW1L3FhWY4PDxS5Gxw2rgpudKWd7dHEyALy4eL3ixsoLfQA9Lj9HhxsL8rnjjJ2LLVBAO9zqcP3HCvuLrlsPPZ//UR36KZWxZSs/e/vLooo5QAfQeD2HlX3Uos2U9WOYHeZgOrnhsqZKhplv8ruiNtgY48ZKIFAmxg7jrIc1qaDch41JuuKYEXho/se4YsoIZVv5vnBrOA/UrQRqQrcgZfGSJPXTIQ1Hi0CZRmPRTMs9wjZuY+WgvVKSoAZp1PM9hwt0W3iC9z7+jiOF2B8Acg2bxOeN6CINL2FcahPcvuPkqZC2VVfZI9TiQ8/DvznyrOh5dhTEMO8ps5MQS5cuxapVqzBlyhSxrKamBpMmTcLLL4dZQ19++WXU1tYKQwcApkyZAt/3MXv2bOe+GxoasHnzZuVfu8A1i9MFZyW4sWxZWoGwsCEH74CVaKxOjjBKbXBR3VgkGitQZ0o8A62nGzk09Dwa1BuFZkc/tOX6FEkq+NqHG8O2lRBCfvFhe+KcAwc139ChDRDffbcbK8/dWFHZDgbFjSUM1nYUKLP+E/HahxvC3TvEmgbWyYKlQcZ+7fjzYBuQfE+9T7xQrco4aDNFl0CZJBVMlCtJCT0PDbYmZEUW5oIWjRUeg14XaVRSQ4e3BQDK/CDejRUrUDYHAhp5Ztmb/SCRscNtg9VVw2UiPSezw91Y7rbXVEo3lny2bZqdeGZnSI9OWHbddHx9SpiQUu4p/CSMuAe/Avx6b+CdfwNQjchExs7Hb4gQ/vA43HC0uLHIJFQxiso6Ad9djn0bfqfsOkkAp/TGcWYnREZz7XWOEk/mA09EXWUK4bIGlkX4dmjXWx8vSOkael5ludBATXK9+FZ5EXpO+n2YfbiArrGj+xTvsL7uzkGHNXZWrVoFAOjdu7eyvHfv3uK3VatWoVevXsrv2WwW3bp1E+vYMHPmTNTU1Ih/AwYMaOXWu5CQ2WkF1ToPywVUClhgjwPMZQBMjYRp7IRpwKPU6drgZoaeEzeW0OxEL5TR+duYHakRUddUt81ZB5I2RFGBMkGk2SlQzQ5Nq59UoPzhS2JRzfYwm2vea56x4618DZf+NXR1UBeO8xkFgPXS2GnM2MNvecesR2IBUELPATJo0nWN+60b31J7wXU2uUSjj5lUsIl06U20XIStA48xBLioM+fZ6wZxWO0g/p589KrxU1x6AWeZkOg8yqJsyXlkpfDfmWenOLNTQ3QbgWGgyf1SximuSDGHNHT5u6+d1xPXhPuyJK1zYuMy4PYjgBsl48EnFMLwU1g7wuzoTFNFDXZAZUjUcgl28IFecl9Rn+cBWL1QrMfdY/lCIMPig4bot6zaFkv0W7gB1e3J8+LbNRWK3wf+CuWJZkf85tkNn7At8n0d2qOTus8O5sbqsMZOW+Kqq65CXV2d+LdiRfI04C2CeA5bz43lAqflsz6NgCEvuLM2lmqkKEkFQTIoa9EESULPeefXxMyq5+EmPOqA4V+XHBxtr7k19GZG6FzRzh5Zi0B5PQmBjtPsBITZ8cGKZ1B+/z/h3zf+JBYNWB8aPkO2vdXME5BQxo44ZocX9Rt6hEKXK/viGgQrs+MpUTCyVpiFydM1HHo4NnzhOrVl5jVgCT1vYiRMnrixCgHDmP41GrMTY+yAGDulurH4MV75rbm+pjOhYC7DOHIz1uTD1BIFLyuSRzqND1bk+YMsJgyQd1xEY0WGBAvFtCI6zSJQ1mEkFdSv84bF6jEBrNmyA7F47S5jkR6Npd4Kyuyo69nw7Htrnb+J4xmanRAZD8DCf4n1+DPWWAik9itidrixI9zFLjcWvW80d1m0XUM+idEZbrct0rUHVB8Y58YiTOw+/WvUZhlurJTZsaJPnzDUcPXq1cry1atXi9/69OmDNWvWKL/n83ls2LBBrGNDeXk5qqurlX/tA/5SaYuL5d1pBm5+OoxaU6JM6O12zuLUjkx5uAOT2eEhh8VCz8N9RRFEosMx/FgAgN9+bgLGDaiNNtfyrjjQpQMYO88sop0gNXYizQ6fgTEGRqueFxMox2DUtteKr1QEiQXKTZFwMtfJ6TbkHbNt8u1pbiy+ir02lqf+NQTKvhhIEmVQ5gNCkNfcWOFiPRqrMa9XPU9QG4u4sfb0Vhrr8YroCnjb8+YAHlsI1GV8Rdfp2CUzAQDVjatluHWxaKwYZoc+I77MjBf9kQM6TQ5aWikbLki33Eut2OnD8z+J39mLNxiL+ERNuIqceXYsLsxmwNDs0OPOpcZYuDxfYLJfzKvGjsHs6G6sAk0zYE4mXvpApmhYOvN4a3tFRLzl3qmFQB0TdY21BUh4f8rsxGPIkCHo06cPnnzySbFs8+bNmD17NiZPDlOiT548GZs2bcLcuXPFOk899RSCIMCkSZPavc3J0XpuLJdA9c+vLDeWKS+wnoyMQ3swqZ6iUCCh53w252maHWntRPuxMTvcb24c3Dim042ldYq8KnP7wWSldBeGYHcizQ4XbzIAqzaHA24GBdmxxAyoCsis67Hu55XW7Ag35k8Tn61uAdsjyAfkbLnT9hRuLMs+mwqBMshww0gt/Kq5kAzNjoyWSZJjSYA/70GeuLHk4E6p/iBgaDRqY5mGwO/O2RcAzbPDhCvzsbLvGuvbDXJ34+NYBmcaCa0vqW1aoySlsyJBuQjVSLW7sUJTQYbs67XCbOCGvmR2LNfj4cuVr5ceNcxch6IzkT7wCZrGkrk1O8WNnT7VFc7fOPj+OWsZiOUBcOBXxHpbWKh9ywfSjZXJh5OKxqRurOXEGCTnxffXqdzCymkQAmVhGJtRuPrn6AT5HoxJhyT5U2MHW7duxbx58zBv3jwAoSh53rx5WL58OTzPw+WXX47/9//+Hx566CG89dZbOO+889CvXz+ccsopAIC99toL06ZNw0UXXYQ5c+bgxRdfxKWXXorPfvazHTASC252ogVurMU9jwEArKyQFahnPvaOdd1qIjJ012HS3FiKsUKYHYcbK0nV83xgVj1XtrG5NRzRWBw73Y0FD2XEnXLVcaNkoi1ds8OA99aGHZoqUHYMNidrLo7FT4mPz9acWlKzt2e64JnCOPw676hwHefG4sZOrlIYGOc3fkdZRYTSWtjJHU2B8grwNTqV0WupGbf0OhMBaYEwO4mq1nNWrdAoXD18MGGMqQJlxtDQVHBODqqjZ2147zAvFH8Hcj4TbXk52Mdowr62fDC87RU1xk9JmB2mX2dL/aHAMoCpOyvuxspY3I96UkFer0xWFnfuTkAvF2F1F449EwCwR9fQMDh1gjtxIwBg4IHy8/v/VY4jDD+XZkfU0HI3/s8XFp9IC6bEKATqAd1lX90YJWFsKjDpYouMHe7uF8auy9hRjyw+8e24kR1XEJlvZVRYNz7HMDtaf97RNDs7NfT8tddew5FHHim+X3nllQCA888/H3fffTe+/e1vo76+HhdffDE2bdqEQw45BI8//jgqKqRl/Ze//AWXXnopjj76aPi+j9NPPx033XRTu59LMlgGc8Bi7JS+59drjwXvAm57dol1ne5dKgGeMNjZsalt9C0hiGHoeTQj89QkcvJxNzsLvVyEc5Bq2h4enzBIrmgsjjWbi/jxWxtWgbJctu+grlH7IaOxfG7sMMkGeAV4XLPjmlkPOSz8y6Mu6qW7rNGPL4Co4/k+5+PixcUzzlpdqbzSfbZc3LvVTB3A45idipxvHTSVK7kyYmm5Pog+R4axEy62Rzlp4EUdC02KGyvarRJ6XggYPq7bgeFUl0QmB1wDUR4xefJeyvf4HTYQh+PN4u3i55etAFCn/RKj2fGl4ax6AQPj3hV3Y7nDhzlcbh/6l0X/qFuvGLwonYVk2Sz3MmrXU984AvUNeXTtZEtwSJAnWdHvPR/44RphxFgFyiVodpZdNz3+2BGEZofbJ9HyjAfF7bQG3cImF2Sf4EXt54aQwaTFFdS0MDv8XaTpA5ztjcT2w3tKsXF8NJZ8dgw3lsHy78bGzhFHHGHOTAg8z8O1116La6+91rlOt27d8Ne//rUtmtf6cCYV1B/e5NaO7PztA+Uhw2TSPJrSOzGzQ6z6Ht5m5JAPB7qCzuzobiy+G5MZCgsnMtP9wNv30KVh5NGptzqNHR35ZuTeaBEsmh1q7ITnpruxZJ6dyopyoCnKoMwNSqdoXJsZBVL7kbfWTXLDlghQPVaM4cA1O9lK2Tlqg1Mcs9O9U7lVs6M87zxceN37ZntYgAwZtEpzY5FihJpAmUG6YWnb6Rksr2vEQISGKs9hxaNyhPsBgejPKizlGqyIud5xoeeV2z4Wbf1p09n4Ye4v4Q+MGYOhZFpcoefR8+RybUMNt3YzOz4YY8mZHcbQee28qI2RC9t2PSLjtCzroyxbxNABgPceJ9s2AFvXQK8gr9o60lXqeW4DsxToAuWA+YAXTRTzMrv131jIzDcFUqCMpm3hH8ONxa93XNZ88n4xdeIRNynQNTtlRFyeiWN2RELKrLH/jsbsdFjNzq4JB7MTaMLFkspFxL+ct3xuX3L4JMaOHv2iPqBvll8YzvI43evpxo77AecvDc+z40oqCACY/1d1+yLMzrenjkS7whJJtq2R3kdPtjmvurECBmSiwTeDALlizI5+LWtlqoQmlKZVarIYR7fZqnFb3ViU2Qk/6saOqPVj6dd6dCkvHno+MGKdBkSuAuU6M8W4l8mcS2B2AmLsEDeWWhtLPRcAeGdVNAAVZM2v8pyvrJfxGLpGZRXKoZZrONGZ+NDT/kr4dKDUsJ3JZ+n3hek4rOHX4Q+sAFrVPWyf6ZpQV7Brdq5tOle2xaFxCf+quhshci02uH0kxfUXZqISHrZJTSGh4ejCmoWGS9BaLoIFMtyfJXimYuDrlyhaHjI70Xs0YhqaED4vTflA1JXjkwpp7BRxY/UYIT/HMDtxQn7+Ey19In5TBMrE0GIMNCGl/h52tNpYO5XZ2e3gYnYKurGT/KHgL3Gjw9ivodRlEmNHa4OetLDSawxneQE3dtTyAOKFEs+9TbPjAyhYXj7by+gQKJN1v3TY0GS6jdaExdjpSuoHeR5ZR4vGYowhl8sBO4CLqp6Fx8aG6yVldqIe9N1gQKy2wAauA+DoXJ7F1H1o5KLDICfngWwFKXSoGzvRbNKy/fVnjoPneaF3j1FDiazLtSvZyD2naHYChe1IUvFeQGh2pBurkRcthVn1PON7ygRiTX347Dbk5Yumu7EyCIRugee54bhxxnh7uxIxO+E6o/tXA2FEOeYVhuJgEOaADoja5Gl7HoCXRLOj9gl/KEzDMG8lPmS9cQV1PzrdWKFoPLAMmI4Di0+LWWQMWpmdFho7Vd2NPDt2Y6f1orGcmh0f8nwyOdlFBNKNhWICZc7cDT8WOOBLqkbJkmeHv4uxzA5PA2FxyToFynSc8jIWZof/5hj32hkps9OuSMjslPBQ5PPhtvM/TlD8lL7giQXKphXlWZgdjowRbhidy3v/RSdw4R3X7MS0jyMBs2OtJt0eUNrk4cyJknFZsLIO4n4X1GgsAOjZEEbLDW5aLAcbl6vOMHZkQkdmeVZ2NBUw+LuP4IcPLjB+awocHZI4VkzHRDI9SzeW2mZds1NdkcWy66Zj2XXTsWfPMBGhNHItvnzjWlAqiImZJdXslBSNRdxYeaLZKSjMTihYpoZhTeewcv2mbZI14Tl1bJqdDNT3pugzanvPNM0OdZNWda4WbQdIDi2SIZpDFMstFo1lGNsevpe/ELcVTtTcj3bjWwqUw3WL5tkpk4kpZ+Y/Fx4xxo3VEnhQjR27ZkemgSi02NjR3FjRct9j8nwy0q3bVAjkPWyKQs8ZN8a194Q/K9lyYPgUoJwk+FSY0xCctYzLNC6Zncgoo/0reT9pwIriLvUzBrNjurFSY2f3gWsg0WjnUh6KrTsiFwkc0RnK8ZMwO6pBZitHkSHRWHpyM9mpa4bdXz8j1mkUoecJmB1Hnh36LVGelbYAvZ6ehyl7y5DXmsqc0eYCEZVmGZmt8s6vGLPDN478LHlkrPlsZs0JDak/vfKh8Vujxuy4sljbn0FzmUuzw2JmkyIs13Ys3bjVmR0yQ5eanSRuLB56bhEogykCZT440ND0PbqHkVe3PyfF/3JA48yOXecQC0EBmDl49EKgOd/HAwOvwrvBAMzd6zvRsVUtSsjs2DU7rZVnR0CrjcUQMrzCVVSMoY4G7bWsGtsQie/bwo1FGJtimp04gfJxo92523TIZzw0nkWeHYAwO2WiHU0FG7PDXZU6s8PfEVt/IU/syPrHotWLu7H0yYs7g7JlYgI4jB3epI7hxkqNnXaFYyDRZy4lPBRl0UOZZxk0FQKl0zYPXwqzE61m6bRpnh2D2UngpxVVz12h5xSupILke4dgdqKO5xdnjMU5Bw7EyeP7Gx03LRfR6JFcHQ0RK5fknhA3RQDfapMsWr3F2eRGTf8Rq5vSQXJqFBMox1HnXOxqFSgbxg7dnmp2SJ6dUqKx8g1KuQh+WlSgzPVn1Nj5xxth+Zl1W0mkTwQ5Gw63+9LhQ1GRSTphIYyM/otmyIzuX4MFvU7CtMb/w/qyvuFmgjngfUvBMJyKhp47mJ0fnrC3+KxeYm3SpjA7TJTPKJpnJ+BJRulzb7mXFkOwJLCCUl3d8zQGSXFjqQYmAFEG4YuHDEl8SJpUsCkIiGbHExnVkcmJ96gQBDLIJKlmxzY5shiL+ZIEyqbh7jFq7FBmh9wXP2uUQ5GaHQuDuxOQanbaE07Njj5zSc7sZEXnHxo7saTQx2/Iz0kFypYHlAX5GGZH24/lXBoDlxvLYns7a2ORY+4kW0dpRXTin5k4AJ8R7iyN2SF5dgY2yjpTIpS8mEAZCDsMRjNRm9f3b3Pc5U+aAs3YcV68eKNZduaaG0uLALG5JWKZnW0bxDHCv+q599/wSrh/ZEqLxuLPe1AQ71sjKReRJ5MEbuw0EPH3mvqwYx8/oBaPLVDr7umFNq86bi+wTT2ARQnaJXIxma4aXbPzjWNH4DdPhc9NUxT+zpk9ZUAk7+xmVpUgqaCd2fn8QYPRuTyD/Qd3U+4jc0xkAs2N5buiv8SO1Pp6gMONFRdqnQRBQXEJGgwH6fOEgUkEykmMBR00qeCClXXoytm5rKcwO7wpCrOjGTviruluLFt/abl+nNmJq9Yu3ueonXuulhFtVJSsGMz0vnhJ3Fgps7MboQizI4oVJjd2MuAF/3w0FZiSCfblq45yb1g0z064H98yO8u9fpfzhcsksObXbgu3bTRqtsQxO27NTsdwYxXveAI/ZBcM19Oc28K/idxYxZkdCt2t2ahdY6dI3LbjT+aFfxu2ynprhmZHjcay3ZuMbuzw7nz12/IYensAYNMKdGoIy8MEDESzk+D+82vLCqYbi6mpCxoLphuLD0T9o8R2Q0kekoJm7ACAFxseTCCm1KaxI1kGHwcP645O5VmRuJK/50xndgBlxn1Z0yXWrLgKSPgwRcb3MGP/gRjaUy36aoae8/2GbJs8XjFmhxts5qRBPWAJg6RN3xPkifvTc7vPSTQWvZ4FYSwk72eoQPlvc1aIa1aeIcZOtly8R00FGnquCpSlRopbtnHRm2YbOcsa/5qEP07wP4i+2dkcz2Xs+FlLUkG+UWrs7H5wMjvRC8ojUEp4KDJCsJlBnrixPK9IWvOEhUA9S1v2f+9XTmbHKVAmePb9cPY+e+kGx7EJEmRQ7khuLPV3zdjxpEbEvr8Exk5QENe+wIoOJ0Zyv8aC7sbSj+V4RgFgVVR09IXrxfU3Q88jIyjGjWWErfNn5FaS7PCt+6KVSXs3fyw+ZlmDGOgT3X5+bQmzQzU7NBqLsyaU+dGF1727yHfLMCa2bwQa6xM0CpBuLJtmR7qx+ADNRcrcIDOYHW1fzwQTSPJGS7+yeiGw6cOoKQmHA30iQ/ISMdKWolXPbcyO1Y1VArNj6zuDvCJQNoS6NM+OJWs1d3EmMqoj0KSCyjvImBKNxdfLK5odLlB2lYuIyXhN2rgotxeCgCVKvsk3O8EnpSei664aPi7NjsmYyTiE1NjZDeFiduTDH62QeI88yVoTMmEESdRp5zK+SQkffY38zAeuIm20dZAZ1kSYnWICZXN7d8Ium7HjcGPtLDZHaQQVfBefZVGB8gV97jNXd9HMdN/ETZEnrhwX9GSLhmanJIEyWSuhZsd2SrzTVaJMXrxJXemD//EDyWX/vkx+DohOs1RmRyu0qDM7Ns3OlL1CcarNiFPcWDvqgP8bDCx7Xh576s/c7YrLfSISKHqCVchGxg5voxF6Doj+ZIcfsk9cQ2M1jW+dLD8XS0cRwWDkSOh5IWDCBWQLblAQGWVKQlTrhCfqa4g43904yzGDvJi0BcyLKVNThNlJUv8iAk0quGlbo3rNbALlIBCFQGVSQV4uwsGk2YxTYhhesPVL2Ovqx/HJpu3Kaca191d5GUjCJ+HOchFafiYzg3LxiW97IjV22hPForEynNlJ/lD4TDI7TdTYsVnxA0hNF5eVrWlteId1d/5Yrc0Rs6ONZkkEyq5U7M1mdnaaG4scN4kby5MakM3orK+dULMjBaguN9befavF53zA8PvgRPG9SbsdpegQKKTuxnRj5QsBFn68WVnPtq0EA574of1ACrMjK4ln2Q5pUCW5/4LZyQtDv0nJs2MaO41Es8PdxZwAotftED8M8c9tWgKs0sL9z/gDMPmSuIY5f6HMDj9eTnNjCWOXXoNokOLamqJJBTkyCbIT0zYbGZRLzLMTmMyOfQCPBtUf1wLXdgXWvhezU4exQzQ7hjuKanYgjSKO5rixaFLBpxetVV1/imYnup95mxsrisbSBcpx5T0I27KRdUFDPsDMx96N2hRjWEd//1Qg/Xy0L0WgTNkcTeulT5xMlj9ldlLobqwSmB2faHYa84HQweSytltL9+t48DWKWk/GJXdlD380HvAE4cqxbUpQLmKnCZQVY6d4ZERAkgpay1vkKl0Hkh9JOYB8cVUECgHDEtZXfK9v0jokZzSWRr2/+6h1NZsb65v3zccPohw/Ns2ObzA7lrMQlavtNzffsA0b6sNBI5lAOboXxL20kXWJDs+0aKywPQ0eSRIZvaOBRaxa5YURWmUL/wGUVanHjakkHv4ex+zIyCB+PD7gfrJpB37z5PtYtyW8Boo7WRg7aiFJr5ixU24xwC1gWh8hmZ0ow3SJoee0b0kkUL5rWqJ20u1lNJYfk24hkCHqERt2y9MfYGOUWykTI/A1wN8Po/QII3l2ysTTrSQVNDQ7JbixyLXKQ/190Sp3lCa/7I00Zik6Hk2TobB1Wjv0d90wrtJorN0Jjs5dj4ZoBrOTZ1kc+ctnxHKr8p7u19nJqm20FiPsPdopkjNmmi1hdhhLJFBeELEI7Q7FjWU7J80Q8O1J7ARyVeYy/ThgyiARm1cJiJLjye0/qlMFnOZjYHlG3/4ncP8XlbX06A2OAB4enCe1NTY9lSv7srUdjud065bNeGpjKFZOxE5ppTsAmbWXQRcoRwY+SQLpBWGHH8cmefWrYRhnRV1DcbNtEkEUnWMmcmPNWbYBc5ZtwP2vfxStTDU7kWEmmJ0iAmWOqu5F2qq32WR28oVACraLJRUMzL7Fs7IVWrvzZvi/XLc4s2O6seTEjEbAvbd6C37xHxlSVxqzI91Y4Z7Je7ViTviZaHZo1XPdzerMoGwzpGOMnTVb3NfNmiQ0KABbVqPPptflevQZ2hJFJUZuN/1dXxm5z1I31u4IlxtLT9feHGNHu5VlNv8y7UhcWUkdAuUAHh4LIjfYiKlwRWOt39qo7sdq7BRhlTiUMFr1Nypk/Pf8j7FTUGI0lhcZRHwG/G4wQF3fxewYSQW51sHuxqI6Ht3Y0Z8Td64jsuNX/2A2yaHZ0e9TXDSWqkVwtMNh7FR6ZLZZihuLDJSKZocmFYyMHZ8YsBle8kMwO47j6M+7M+qRtyvOjWVGEOkD7ofrt0X7sbzbnqr5KMoDOplFFfK+8SVS55IPWMlJBVU3loPZoVqdIYclaqfcPq/0Y8ZEkPRV3GUTwMM2rQZPKYEQ/H4tXhsyiYpmZ8Pi8ONb9wvDP18IBJskMyiHzycvoIyP5oh2hgexPFvEzdScYqbKNiwAXv+j8rtyT//3Y/U37d69x/N9pW6s3RF6JxHBqDpcikDZIvIDkLEZOwMOkJ+dsyN1Zs9zZTD4WIPu8rfATqUO68Wp8BgXhcvY0cWHQYEwO/pgKD+Wktm0VVE0Gov8ftqdMoKJMRSCAK8GWvHSsk6wwkgqyGepGWtkl27s0Mro+nOSKIv1iGONddyaHc3As9ncRp6dAOi/r7mi2MDspu7OTxWfk7mxovOOSneE6gzeCUNJ2cDdWNRQ67s91InYBMr35g8PP0y8wHS5FHNjxTE7QlQrdSYuFktxY0XMDr83fACzRVai9+jw77kPFGknaZdRvkROSPKEoSjqNgtMN9aSddvM9ZiMoAMg+7G6j4Bnfw7Ur6MrW46jRmO5BcpqNFa5JgVojmbHaBXtDz9+XWp2AkYKgYbXgH/fx18mt2nc5pxohg2nz5/aiGNIhnezvZbJS1AwJ2z0nq58TflNnwD0qdGyYqfGzm4E8dxoeghhqXNmJ/lD4WJ2ctYwmBzQfVj4ee+THW3UBMoii6unPrQOzY75gJdAXerht8tfktsbDJI01mxZbdsHpCMoFo3l+7RPVWbAAk5mR9fsxDM7BS2yiNLZRZkdehyxUrn6W8+RRUPP4/Yvo7HEwSzhxZ7jM7A86In5bJj4XpJAmRv5xL20dmuDQvFzNxZt+/uV4wDIa8uPeeUxI7ABkSA8V2k+w8V0HomYHV+wCs4BVxEoh23gAmVRhNJqCBQvFcHxzWNHoE91BbpUapMy4qoJ88Xw45XO7HyyeYe9jQXyjvN7+adTgad/CvzjQvkbncQNOFBsrzI7DgOfaHYC5hmu5lLE/DrbKA1r8pwffY14PPI0z07A80BZ+pT5fyNurGLGjoou5e57LJvrqeUptGM4kwrCfA93NGrtTI2d3QkWtoM+AC1yY6kPsjNM8sIngc8/Aow7K7aJvF29F4YujEP9t1TXlHC9uaKxHC47gosPG6ou0BOxbVnl1OxwehgAzjpgoPMYbYpS3FheRrpvGMOStfWmdmlHjPZIMTSJQLmIsdNINBRAAmYnwX0DPHHbTWOnuDFlaHaYxdiJiXR7IDjEur9Y+Jqxk5GRVqfc8qKyKmd5fA/474SbcXP+ZMztFLpOCppA+dIjh+GMidHzxwIYyQFbxOzIaKxizI7v+XJfnAWJjh2bVFALH47DpUcNxyvfO1q6kC21sUIjPibUXTm2aez062phN4O86nbnz8O6KCprydPyt/f+Iz+LrNl5UuAzTrOjTu4aC+r1agmzIwyXQh6o7h9+HnKYUghUdzvxd7WRyWcVDVsSu7F0JCkEGh7XYpjx9ZSiver10fuScQNqo43cwSrtidTYaU/YBpI8mck0x40FO7PjjByorAUGHxIz47S7n8b6S2WHSFwptJP0POLXTmDNT95TE0Tq6+YqIa5FTDRWRa54R90mKMWN5WfEteEDpmHsvHRT3MHCP6TQY8DsVc8L5N41NOnMjisvknYcCkun56p6rouOraHnxjGZyYjQ/Wj7uCV/irpmImZHEygTJmNrg3psfvkyvofVvQ7FL/MzkGfSUAWkUe/7Hnp0iRg5kt1aoEWanSi9gxKNZX8PPA/yHLkby+cC5XBbq1spLrLH2WbdjaUKlKUbq0g/xuRzzJG1iaFYoJXUidkvfVYzxNgh7qn4PDsyAq5Jy/DeHM0ORzUi99yGxQqbZhUoRxDFnenCHsOdE0AAscxO0vZKhqkALH1WWU99htT7oBtTJ4+PjLoY/WZ7IjV22hUWQ+L56+XnZrixKjN8JqJ2VrkSEmApiJvZU9cUz8FAOknFPkpg7Biutu0b1e8v3Rz/Yke49zV3Lag2RSnRWH5WDJA8PYDhxorLYEtnRwFPN2Cvel4gYtsn31mtdKL67NF4THSXGWAO4GQd3bjRz8k2PshIFfI8xRV7JNelKVej5L8BEroXBLMTTS4yOfe6ZL98gJv7Yfhs8qgtZeDzyOCwYanW9mYY4nufrLxMNIPyxm32CuAN+YDokiI3JzfQYpmduNIDDhh9BDF2qHvWNvAufAiYe3f0O2db5LXMusKp6aQw7lmh7jiSNVu6sWwMrHwOsw2bwt2wglFUubKESZVu7Az1o8il/3wPNCCFrzb/o03GhNVadqPQVCQay31tbH0FB22tUlR2yTPKemWF7fLL+HOU3+h7OLh7VZpnZ7eGzZDoO1Z+5pqNZrixdP9uc5PFuUTUDxYOwhbugyXsQnmZKxGZnSGiMGhVWqgUCKMPEhg7fCBqd5SSVNDLCNF4g8vYidNNWPRSAXyAhf7+z93xCr5x73wAKrOzV9/qeGYniUDZwrq4qp4btbIst1/WxoI8pxiDirZJT2IJJHRj8YEhYgi8BBoV3/Pw7KKwSCvX9CxbZykD4RPG85ErteMWaZzt9/f/B7zxJ/GV6kyWb7AIeBE9UxqzwyciscZOnEvE3ehoW24MSxfR8++vJW4Qy/HuPRf499dDo5AnxyTvTjbjcM28Lq9HLHvRmYhws5F+UHNjmRo/yTx0/fAxAMDnm/6uiNYBWaojCWJvO6kyz9+jN5ZvsrixVGYOAPD8L+PZOEuNNfFTjLVD2dG4+1eRr5P3vapr+Hfypcb+rZOB1NjZnWAxAHhui4paMtiVIuoNH+4C05idUhJgKU1U/atbBx4FAHgxGK3me4heuLX1rhD24g94UR943/GJjJ1Xrjo6fj9thaJJBSmz41uYHe2c4gYcSgU3hNoehtCtcttzS/DS4vX4x+sfYVtjHrSPvvCPr2Fvb5n4vp2pxmksNa8XHiTY3mRG0oTf1f3NW7HJfSo0HNdwldkNSVqNWu6vGcyOX5zZyWV8LNGMm3tfC/Pa3D/3I7N9tme9smuRo1ja3lQPPPQ18ZUKlGMZW62COjccOGPgN241tyEDb2Lo57tpGQBgD28dFq+tJ1os7Z7Sfq9+nXRjEb1hxmZQBAWgxwiyn+i4XPtC950l9QB5klaN2RnYTU/8aGpK/lOYiC/c/arZloTQw9YVcAmAn1UMdZcb6480q/Gad0ifWBpr+MQ7q52/ebZ2uEpzcJdiQY0ifuStT8QqS4imMnVj7Y6wMTu8s6nub3YiS54BFv4rfp/CpaGFSbbUjcU7B0GJ+zLMmDA7Ewb1iN+PZrhd0PgN0sYij1+hKZGx0ykmyqBNUUptLC8j2DaRtK5ZzA4DXvoNAOCc7JMIGFMSn21tyKOgdVINkAbOWqiDrzuDMiDunYV1efjNT6znUIPiBTDN2lg2gbK9TXqoO1BiNJZI85AtSrpkMx627pDn/q95K+0reo7BPVsJ9NqrSLuKd8GU2TlhbD/rOuccONA4Ry/SrJyeeQEA0Omln1t23gpurEe/rfws8sXogxsVGb98s9SeFWN2gjywcZn6HSAZ5wEsfU79rcdIIgsoSM0O8/H68k3282EB6vYMo1TnBKPMdpSArlUxxrQQhftKvjCD2YkmsHXojBemPyW3jYvGisGWHW4XF30V4gTK4QrRfeQsUjRx2K7XohE7J33XTkRq7LQrLMyOMrPSfv/jycC95wG/JcX6KALp0jDy7LTYjRW2oUuZH+3fV0MSoxehfzdXbhh7h3eg/474vHm7m3IND9pIjJ3mnk8bopSq5740dhrydlYE2zcVPxa5nqtYV6zerFLy9Q0FI2SWlovQ8fKS9e5jujQ78HDg0G7iM8WTwb7u/UXwdTeWVaBMD0cGw6xpEJYUjSW+5wxO5dwDBynfM76nuDy+Pmueo33EjUXhSu+gbFu88QF87IgGkr1I3TOK3l0qiBsrvJadyouzV80SKOv91I46rb1cs6MbO0RvVNVNHJsasE431rPXkQNEbS4n14ILz2mCVl8af3X1O9S2Kacj362aCtnftQTDe3fBlL0ceW1IGxfxxHtwMzsAsO/IwfIHwU623vCtCpRNzVUDI+8dv4+a/m1oj9LGgvZGauy0J6zMDuls6O9NRJC3ZiHQYKGgl78kPupajIXNLaFgZFCWA7PixiLRWGXWOlx26vLBwsHi8x5di9SC+lQZOzHCRwA09LwhGriYvs32DXEHC/+wABh2DADgpvxpMiV7hCVrtxo6meeDMfhh0+fx9rR78aXDhuL/nTI65jBJNDvABYcMwYFDu6FvTYWyXBcP2yBrY8UJlO2anbJsFrMuPlBZM9ZlIHahuwyzhvvr+DGqUZjzfVx29PDk+9ZnrokMiOLPdQE+/vTKh+L7/50+xljno43b5eBXkJqdw0f0jN95CXl2BPTBS9OJGIUrxQ9kvdf+II5N3wNrTT8j2ShndsizRyKvAITXXhg7BfToHD6XViNGCbwIrOs9/+0jze2K4KwDZIb0G/OnhR/2Oc3pOsxrUoRxg0KJw18vnISqSmJINPGs2e7nay0zjeL3f3qcc32rG4swO9Ma/0+6kLmx88afw7/1oa7Naewk0G+2B1Jjp11hG0hIZ0Ozzm3RSiD89/vmtndPFx91Y2d9vT1qo3gTtY6bvPxKJ0ZmhEeN7GXZUbR9QzRzyYUvwhZIf7nBPn3mHqDrEOCEKEKt0CjtwhIp2/aBqsmJ/z0rBnkZ6lzCOen1sQA0MNOwWL5hm1LUkrfjT4Vjsc+BU3HV8XuVEKrv0Ox4QHk2g1kXT8bLRC/Fcp0whHR4tVU5vP3jqdAh8+zwDS0CZeV4qn7nwKFqyoL7qH7GBd3wyGSNt7GxEMjcIAifz0uOHIaioNFYxQxgY9vixo4e8TZj/4H48wWTlGVPL1pjCJTh+Thlgt3tJdAcl0iRXExOY6eg3WPuKieDtpPZsX2nTJFwrdD+VBo7WRJSboJO4uys6wBd55MA9cQI38Si98LznQZmQWvbFw7ZE8uum46DhvVQK9JHhULjjOmeIw/GFw4erCyLE1hTZkcRKEcJRY/cZ4CsYl/QxpZIanHRYUNx4SFDcNzoPnj4ayQXVsrs7MawurGyUF46vZzD/Fmxu2wp7SqhMTIk8ddxXC9AOgV4GXt/PeeO8O/bD0TbhOv3rpGDoaG12OcU4OvzgIGR227LJ4k0OzsNzXRjzV4aMjguF7f9WOS+FNwZVvvXVlp1hVcdJzUIroie6EDyo1OgbB+gvUxOSbH/5wsmWfVUMhqLTie1RisGTrxROW6PGmt71P3pzE7OeG4XfrwZ3z9eamxymTAny5s/0stlAI99/VCyL+LGUpKEth6zQ+8fYGryxg+olc8gNyq8DOavqMNrARX3agZKs/LsxM/UldBl5QfNbW1JKmjV8enPH/9O9yeMHdKfkqSCvK39u3XCT0/VmE1qvJFMyxwnjStiMDrwxEIpCFazI/PcYRks/tnxYp3Jw7RJI+1TfF8aPE1agU0bPL+kJIgU4n4EBXEPrz6JsImLn1I3iLwOFbkMfnDC3rj1nP0wuj95J1NjZzdEnEDZy6idel5Lm14R36E3aRmU5/5gSuu0MXrYbzvvAIzuXxstY0onaTV2Grao36Pz9MnMzakrohoAh7HDX+QzJ+7hOpO2R9FZvMpI6J2PmfMjLtKGzj5lnh0d2xoLFmYHWLRK3o//vr2q+HHCg0UNjQsLJ8jklNmjSyTv68ZOCXl2+OdvHCMH8KtP2Nu9rdhOZ3ZyijgUAA4b0UNhpvjzWZFVt+3ZpVzVzmhaGYEkrqFEmh0zEZ4ePn3yeBLgQJidY/fpjcubvkp25jAcSorsiTd2nKHLevFhXi6CRNglYnZ4m+n+eDkJm7HDCmL5zWfvj7MnqdosxXizGGDXnznObFMC5ElYpDCeaJujCdD3j98Lp++7Bwb31FxP+vPDy7Y0RkEAcffM89yJZS2g9fSYhelSjvWRFqVW1Q2xSI2d3RE2gTLV7BAXkk75bnWHDQLqTGRUny7o3rk8Zu0S2kgfdvrQEvrbGvp7xHfCv92GRvvhxo50vTijaLoT14Gj6N1/rzgM35k2Cj86aZ+iZ9RmKBaNpWdQdtXLiduHvi/GgA/D8ga/PXcSLjp0iLLaax9usOa2OWKUnDXSgfM/lx+mH0h+dAqUHfBzSmh0bLZfFBMo2zU7/Dp87ejhWHbddCy7bnrxqD7AIlDOGrblPv1qlPbz51oP9+5SoQ1CupHB0amIXibcuOgaDL7hRlmxQWq1bjt3Pxw/po9sBx9Q/QyCANhA9RsGu8LXLUWzE+/GEoOlYViRe5yrkoYFyaCcy1regSAPjD7d3I/VjWXT7OTjGSyLZicoxjYlABUof/XIEWo7SVsuOmwofnXmODP3k95WHn2WwI0Fz1YDzI2+NVI/Ke5HkIe4x7Rtg9RyLZh2HWKRGju7IWydBI0esOSxKWHn4lOiUFznbuzMjvJizf+rfHD9jL277hQNrt32VFwUfka+NO6KFeSHQpO5DMDQnp3xlSP2RFXZTgo7BzRXSxE3Fgk957hsygh9g5hjRee/YYlclsnh+9P3xrLrpHbrz68st24+cVBX8ZlGa1XktJtge3bicuBQBE3KwODKCWMPPY9hj4q5C5NA385XNTtPXHGY0ja1KSqzUlWm7UsYGeQcBh8qkq3Ftyv+XWWej29NHYljtYrVe/aUDNTUffqEhpkY3OU7M2loN5UBpAYCieYsTbMTH0qciNnJ7xDPVWMxZicIVMNJaHbI9ebnRSePRKCsygWME5Ltjfb9mYkh+3PjZ8db1k+GUyb0x2VHD8dfLpyEvrVVajttbTEM8iLGThE3VikRuWVZH+//9DgsnXk8BvXoYmmrH4bzA9KI53mOaki+I0dbAOx0Y2cnjhS7IzTW5P3/AQ98KfxMBcrWKtAlHKUlgUsOgTI8D1gxR65HGB+rcWWhkMPVM0BU88fI8aJvC5BtW3JSbYRiGZQVgXLGSOBXltNp6rhIpmhbyvCR69SlPIstDW4GhtLUNOS/d3WFbfUI2jMgmuK4Fwd/HWUL5XWwR+nBzL7MCoDBEjg0O83VbumWdUbV7HD3m0vEmfVlFewq/b7Z3FhnzQLKOydoWPxz7XkZq0j6yFG9cPCw7hi3Ry1dOfwrNDs+chkfi352InAt1N8AkZwSQGnMjiPSkiNw/U5ZJaJLbAqosWO5/lofYtfs6MaOKlCOddcppVjC9Sbt2RMLp09t0WQq43u4krtbX+d6KsrsOJ4j8d1l7Gyzr69tS5mdE8a6009wiGfft7TVy8jnWRcoF+ubecHWj18v2oa2RMrstCd01uQvhJrV3VhJXQcWtIjZ4RACZZKtk4qmCeNjnUDYKGQAzCPZUl0zDzqjsRRu7DAoyY2VNWhl3+ZaKXosYhSQpGpGBXkNVMZDjS4zMsvmxioyIzvr78DR1wCTL1UqRZc5DAd5fIuOwYZSI5ys+zDz7OwgCnFumLmeSXrvNm3XOnubGytpO4uWk7DvJ5fx8ZcLD8S3pxHhsmcyO+GfjPzNGKgilHdJ1l4ggRsroWYnErZSl1HGyuwU1H0JY4cyO0UEykGcG4syO5LpalXWWLB/5Pobz2Sx79FkSOTZsZwLZ1v2OUWUpwFKzLtma6ufUa9nKfgomiS/8+/StmtldMARZFeGIVaQUJIKFgnFLXaUFjE70cabVwJ/+Qyw5m3ZPluH49LsiAFAOxcyw3aWKqCDPhcelpT0rJ1QNM+OykjoTJZXkrFj0XuRY5br7igNtDr6iN5dwrwscceJtgr/FHFjjZwW/gMwZ6nMFeRidiqjtooW2er5OGpjNTuRWpGBhLeVGjW0BXSweG+1lvPKNhNO/LwWeVmbU8aBaHbkfrJAoaDeS8oeNyvPToluLL1P+2QeAFVobxXVUmMFIG4sG7PjisYqTbPT6v2NYUB45rNscbVav/OyHzs2w8BXXgTWLgIGTEJ2nXR5O1l0a1sthrEu+Aac97+jImV22hNKiKOegKz13FgtewajNqycC7z/X7I4o3WUMorM+h6JcNyCci5zPpRRQU4Gir70PDKrozM7CdxYxrimbxPnxuLr0utABpNiRQppPaB4FsjC7CR1Y2kot4lNAXTrFIbQCs2OLsbX29EWzI52rbmx46qzFStS5W2KhOPKsqLt0o6nZ10u5Xwpm6pvq/+mfLYMvLFI6MbS+zAjGssUA1sZiEB3Y+XN/RvGTsbeBxXT7DQrOi0BPItBrMNwa+nGj3aP3nvc3EdlV2DggdCjsRLVj9OPo7uxaD4ppZ0dUGJgQWrstCeoUEsPzV72AhRNTzGB8qq3nD+9tbKuBW2McS0VLB2OJcoo3I/FXw41pNOt2bF0NBuXxrV6J0EzZoyfVYHyfxZoId+W3C/uQ9m0DHKwsV3Jnl1CN1fXqpzS2U0a0g1nTxqIH0y31G2yZlBunuHtEihzI0hG7RRhMVtFs+NwCUSwudzyRMhNGZ+Dh6lJDYtmz46Fdo2KDXixu9K0Q57G7ADqvYwV7cYdJ96NlSiDMiCYCdonGGwnb6eNkaL7e/dR9Td9cLZdE3nQ6APpd1s7rxff31peLsdy7YoxvXrbJ34x9pD0mV2zZUfMmhqszA41Hvl9TTirPjBKfdBnbPI2tAFSY6c9EadKr1+rCeWKDAC/k+F/eU+tZP378ye2vI225TT3D38RPB9XHDMC3TuV4bKjiJCSUp6ko6Ih8k4/sufBGAT0JIsdASUmFeyhpwMwjJ24gY2zIKQDGiDLJthmbr85awI6lWVwuRb15XkefnrqGFx4aLzOxylQjnG90PwzrtkkixgjyQAUcWMVi3pLgmLMjsXYoRXbeZV3ADhkmBZSHjuAFmuX9j2jPyPN0VpYIhhts/JmGzvxbqyKsuja6hM2ncGLXNRqQlSbsd1kbzftIz9+PUxgSs+J32PFjWU5V5tmp7XdWEmYM6M/KNI/VNjrpHE8+e4a8fn599cVP77eDtLHw/PUCWwp6L5n+LfroPj12hipsdOeoC+V/mB36qn+XsID9f6hN4jPz3/7SBztKkCXqI2Ol9zPqHlDiC6gf20lXvvBFFx57Eh1fSDyt8tZVSWJZHHN/JXtOQYfYl9vZ6IkN1ZWyQk0Za9els4tgWaHX8vaQUC2zPiZ4sCh3TH/mmNx/kGD3fuNazNzaHZiBuAdTcWf2zFRBBHTBcqea9Brg2gs7fly6sci0IrRxnNbTK8VB33bLtq7W5JmRxMo03O2iUuba+zEuLHmX30spo8fGH7RjRvdqI3q/5XniOFpu26FRk2zE9iDOO77vGrUCEEvmSgV0+y0mRsrwXNbKrNT5L4NcNYeTNgO8V5m1OVJU1GIn/lzmebZ2X2gzIi0WdF+X4DixopjdrSHZq/+3bB05vFYdt30ZtVwUZApsy/3MsC0n8nvwuoPH2RjFs/P9ZP5wIs3hp/9DH7/eck6xfqR9Re7tTuf1kCxaCxl3QwqyzL4/vF7Ya++1fjlZ8aZ2yTR7PDrrnV0+w/uZt2s5IRoukC5sT68hwnxxy8eACAqX+BA/9pKvvcQwnAm5+TMs9NKmp2ta/H0N48AAAzqrr4zPEzXlWPFSNbWorxWMdFwQGnnawk9F7ANVHERSkmOY3Fj1FTlpJGhR37pepWIKT5hfJgF/awDBrjPNxIzAzAFyxRUs5PRopf4cgOU2eHXpI3cWHEo1biJc3sDSsFfV2SkFbobi18zndlJKg413F87Bx1Q9bkLQwxYDcDs29TfFvwDGHRQ9IXFPxgNmibHUsG52Vj7rn25nwWqSWkG/UUwQF6E2b8L/3oZHLRnDyydebx9E+V4GZ6OJ9q2A9rlxbQkdGCJkiledNhQXMQFwiVpdjQWROv49upbjXd/Mg2jfmgRLZYE7Tn6/VRg0/L4dQgGdKtSkhza0JDn10VzY7kMWs0d2Czo2733GIZ8rpO1rTd/bl/c/Dn3rgwDsiUuD/oMHPl9s0xMswTKluvZmsxOsSRx3MhY8A/gKFLAWGd2IsalZzV5Zp79j/x98qXAyzeHn5tIPbegYHd98t8ANXqoiUQexrqbSeBIq2t2EjwjpYaix+blCieTPTqXY93WBgzrlSTnk3YcbULrZHaSpk8oOVFu66IDjiC7MshD8dRP1J+GH5PcjWVEcrUi61G/xr6cCtQA80XQYessfMkCFTXOjARbHfBRLcY4KKG9lo6pFM1OEWYHsOXMaSEYA1ZbhPAtNKx5uQWjrICSkv5gcrw2YHZKxK8+I+sjJXJjJQW959kKoPtwbd8tCD23RmO1gmaHwzWz51GcGxary3W3Fjfs6DnSSzvkcMdxCw7m27NrdhRmJ06z09HdWKUb2bMunoTTJvTHzZ+bUPz4+nH0iZUrGqvo/pqp9WlldMARZBeG64HPVQHH/R8knYoibiztoSlCZ5aEvU62L/ezavsLFl0Aha2jKomSLxJ22RFQjHGg99DmHixJsxOt++z/uY8HYM73jsbp++6Bl686yr2vONjy7HBURq6yo69p3r4j7DuwKwAiUOaZfBu3AKNOCD8f/wvaKEf7SoB+/S97o6TNjyS1xfQCosZ9POBLyXdMt81WAGNnxO87yb6oK0f81orGTrF7sP4D+3JnXS6HMZvJmqH4QHgOthDu/c7XjJ3onhdzY1GmqjlV4JMgyf6KTfCKRBTaMKxXF1w/YzyG9iyB2VkxO/y7NZr48vvT3GisDuLG6oAjyC4M1wM/5cfh36TRWHqn0Zo5aIYfY18uFPm6Uj9G0Py5+8LP2Qq5j6QwEm51wEe1KLNDkynaOtlSBIdFQpQj9KquwK/OHKcU9isNFoEyx7TrgO99DAw+GC0BZ/WYzR322b8AP6rTBuoiUW9JkNOuR4n7ofWwmnShZZFIr1jQc8tVhM89N/iA0ox8PZeLlsEbgObGaq5mp4gbi0LJheNwPSnXT4u827bBWB3LnlddUxxU60gz/jYRY8d232lYfkdmdowq6K04yaXgffurd4R/+bXY+GH4t26FtkHqxkqhw/XA8w5NCYGMeTB0Q6g1jZ1iuW/0DjWuo8xoHWwpBotOeXd0gbKtffQ+WXMRacuSCJQ52irJYqxo3AfKOrl/LwH7DeqadF4IbFvf8gOWacL9Eq9fOckG3ZTXjZ0W3Bud2dGXNYvZsWh2bOHaLY3GaqwvvurDV8jPrlIVimGrsaXLnrdv89sDzWWsIPOPFZrk+yRy2ziSJwrWK9+Ca1IE+nt1wq/NdYoVBi0xGqvFGHqE2g5e2+ql34R/kwqUaTb9nYjU2GlPuAwD8RBbfMc2GMZOOxgC/MVyides25A8F3TbJGjcon7viMxOsaSChrBX37wZbqy447UG6HGM56z17sGfLjgAPzl5TLKVt290t6kUdBsqP5d4/ajGjCYbDH/UrktzmR1u7NjcT0mghwzT+8WTcm76UC5rqRvr4cuB1/8Yv+7r98jPvMyBsT8HixfXLlrElCMIgMVPhZ/n3GYmbnWZ11R4y4XQunHcUuj3sUu/4uvo3+l7ALS9sbPkmWTHKebWdIasty864giy6yLO5QNAiQqwzYJ+VBP+01mP9iilwNvO/8YVo+NoSTZY4/gd8FFtqXC2ObWxkqzbEtDj/LIFYtkiqCrLYmhtwv05SmSUjKQDaREYE9qFD6rfS9HQ0WeAu9pcg38xiIg9t4hdWdbSaCwAeOhrybdzJQZVjt+MBJJHXBXtX3NtffJmsu1pHpj6teHnVmIw5TESTGyK9QcrXlG/t5UbSwe/PpzpAYB7z3MHs7i2TzU7uxHishPTv4zFZwy+ZX/1e2sPfDYBqvB/a9R1XIekv4ytFbXSUaDoLZoxE9TdALFuLN3Y2QnXo7UNTi6ELAbbAN0clJIXKQb9ajX9z0gtlUJzmR0uqKXL1r9fwr70/Cjkuu3JBevkOWquZqeYRuNLz9mX62H1HE43VsLnbVOkIXn7AXX5sf8v2fb8OHWEiS0rQdCb6BhFwsgB830odv7tVS+Qt4NGSC78l/y8+ZP47fnznPR9byOkxk57IrEbK3D7t63bt1KOHY5DrwS+rdWicrU97oVM8oInRUdndnIV7t+reji2L+H6GJEaO8HYaW0D66ASWAGOlhg7zWENCO754gH4+tHDcdzoPuoPe52gfm+2sRNt19zrbISeU9YoMsYp+9EazI4NfccBFz8jv9etDCdvTQ5jx8WQJm3X/L+ayw77dqgZ/FGd+ZsO23F0QXtLkYTZqaiJX+fkW7Tf24jZOUZLiyLy7Djuh8gP58DqBS1vUyugA44guzCSurGe/6UMMU624xY1y4qqbtohXKxUAs0Ohy2CwoWLn012/J2Jj16Vn23MzhULgWOuBS591fwNAEafpn6vjasdo93jJU8namKrorXvgf6MuRgBSn+3Vq6OZsyKDx/RE1ccM8JeWuKEG8i+SzF2yPuj5zMpFUYuJi2HD6AaHG0Veg4AWWIs/Hpv4P/1Ala/HX7fQ2emY6KxkmD06eYy+myd/+/w74GX2Le3HWdnuLFGnahto7VrL+33TBsxO7qxzttqS59x2h3F9U0bP4z/vZ3QAUeQXRjOh9PT/nYQ0IebP/B60rNSNDvbSihG12880EvWkmp19qo1QM8na2F2qvsCB3/dHNQ59E5lyo/cx6Lp8oG29X+f8jugUy9zeVuzSa79t5axo7hIWnmgoMZuSaysxbXW7PpfuhuLMjvc2KGZiFsYjVUqPnwh/Ku7bxWNkib6p0akC4dcYS6j+xxyGPCDtWq5G4r2cAkniaTS2WHduNAnVG3lxuqvFZLWM3NTjPlM8f2tebvlbWoFpMZOR8Cwo8O/s2/due3QQTtt/sDve666TlzH3FIBXQd5SRJhy6qW76O9BIfFMP4s4FsWrUgJNbKaBVfnrRg7jlwtidAKZSdcoLNbV7i0DUqxzuj+Uz1EKVi7KPy7I3LdKMLu6O9TPwmFuK/cCiyPBK/NzbMThx7D3b8NOUz97sqp5GeBLn2LH0t3/wDAiKnq96yj5h/QPi5hI2LP8qzrEyZ9HYNxaaP+op+WbXnNwvDvYgubnGQSeiaJ2NuJ4eepsdMR4Jr5J0bijCXNB+84P9JcMrFurN2o9BoNa24PJBVftgQX/E/93tbkWhJjx1W7rVS0tkuODjy6myZpO/j507DqczXRbRw26jo7cj3fe0x+/scFwOPflZOr1nJjDTqEHDsT6mVOu8Ncj7JLgNaHaPXm9LYJoTWBzYWcxEjiaJfUHQncWJ2jivedegI/dOWWakN2kiOTBfrtay6vrG3e/irIdoWYwJs2Rmrs7GzwsEkA+CaZTfcZCxz3C+Az95jb6GgrC5+Cd0iDY2ZlOvSX8cw/lXbMnnuVtv7OROee7Xesadc1T9xbKmoHqN9tA01LUd1ffnaK3Ykxr1PspUAp3NrKlhudsBxyZfLtiglyawaYy5JizTvyM3WJvP1Pdb3WGjT3PslcZjM6GrR8O9RI012NOrtxjtb2/hPt+ppSzqld3FgJjJ3yzmEZkwufdEseKLvTliV0bGyZnvIkKShjFRdl3MZIjZ32Rt/x6nc6q+ncK5wN/XAd8OXngUkXA71HF99n9z1btYlW8BfrgIuAgy+Xy+OiFuiL2W1Pe2cYh7YYXFsTvUlSvPZkdsac2T7H0XU7vRMmASwFx1wrPzsHKDIAXvRkCw7WhtTUHvsDR/0A+OzfShOO0vfftl2cO6gY6MBi05RxtCQa6/hfys9dh5jr2iJ1uNueY8Aksm/N1ahP5HQj9XP3Atly8xjNKbPRlkiaQLTbUKBrTKBCKXqwloAmAORuLZs2KgnoOLB+sXu9NkZq7LQ36IsNALN/Z65DH444XzNHW4t3D7pMPRZ96Cu7urdTXuhmuNo6YvFPClsnWyo4zV+KW6pT95YfNwl8X00kluRZLBXlXeRn16DTWjlP2vI98TzgsG8Bo44vvq6yXeskOrRiBmFS4yYlJTMb5F3OlgOffxSYOtNeV8+2b/1+Km58LRqrmI6N71+pJ1bidWwXN5aut+mAecNcGH1G+HeA5p698h1zXRvoe6dnxm9H7Eaiig4Cmq4dKB6hlGmFAbWl6K/5bytrw0E6k4sP0aQv9IYlpR+3I9bDomiNwXPsmeG/jgpX8cbWAh34XINUWSegIUG+lGJorbD11gR9R1rT2PnWYtUYj9v3moSDFsc7/5afvUxYGLaU4rDlmrFD+zjdjUWfPxujya/fuw+r25UCnfU69JulbZ/oGNpEoaPrGWma8IlflJ+rustadaXIJ0adEAroB05unfY1Ax38iu+CqB2ofo+jl4G2mU0nxZdfAFa+Dux9ivlbkgG6pS90R5/9dMTcP62Ntvax0+ff9by0VoK3tSUO6u0BhdmJBo+uQ0Idi61+UlJ00hJZdh0ErHKUT9jQAtdCc95x3YCmfRwdZP2Men26D0t2/FInSbrwtk8C6UCp0CetnyZjh0Ya0naXMtmb8edwnzuRrd8NeusOhmN/qn53pVDn2JnMTp8xwH7nN5/BaGliro7O7CSt+vtpBg/hdWWBbimSCC6Ngo67EPTBHQDO+Qew73nA5x+2b+PCqbeHf8++3/xNd5+3FpozIVmtpZSgfRx9HjxfNdriKpZ3I7rFlhoSbdHn6i7vjm7suGQHVLhcSv/uOSrOtyNSY6e9oTM1ttBMimKz2mLMUIq2A6d3achtm2EnJVU8+HLg1NtCwXxbwFYbSsfUaILQEVy6rQ0qBBWJO/cETvpN6YEH42aEAQ427cwBXwImfbn57XRh/Qelb7PPqep3qtvSE5nSsOWVr5v74sbWUT8wl5UCaszT9rQW9Gfb9ax3FLgmcjTMv7VLarQxdhlj55ZbbsHgwYNRUVGBSZMmYc6cOTu7SclQLAOli1W56Clgr5OAC/9n/72j4MtR1tRm1UFqY71ISzHus2GJg3P+0fbH+s7SMHt1exyLIlsWnmd1C1wqcaAzXJcxM+YzwKWvAT9Y3bJjnXRzy7Zva7RlUslsGXDk9+y/DSxS2ygOeyRIBfDF/6rfdSOuolp+Vpi+jJrSYcqPzX1zY5lO+rZvKN4mHV+bGwZbDJhUvNZTc6AzO83NWdNeWP6SffmnzMCh6OhcWiL8/e9/x5VXXonf/e53mDRpEm644QZMnToVixYtQq9elrT3OxvfXQHUrw3DDJO4iH64DnjxBqCsCzDyuFDU2am7Gm3RUdFnTLJifDb0GNG6bWlteF5Y9LA9UNkV+Npr7XOs9kSPEWGKgcqu7pBtz2tZCDbHvueaGcB3NmwlWdoKWcdA5RrYkmDPo4uvM7AEF1rGIqrW+4/Dvg089/PwM+8/eUK+5qKyFvjOspbtIw5UzLvvec3fz+cfAf75JWD6r1repuZg+q+Ae04CDv/Ozjl+C7BLGDvXX389LrroInzhC18AAPzud7/DI488gj/84Q/47ne/u5NbZ0FFtTqbKYZMLgxr3d0wdgawdXU7uYlS7BT4fmlZgnc1KJqdNu6OXczReSWWp7jgf8D/fhS6F5Pq+S5+Frj9cKDr4Pj1qBjWpdk7/DthSY1R0+UyGjFqq+u2s0H1KnptsFIw+BDgyp1YRqf3PsC3PuiYtQqL4FNv7DQ2NmLu3Lm46iqZidj3fUyZMgUvv/zyTmxZihbDzzQ/kVWKFJ8GvPe4/NzWA4i+/177hFoXmkspCQbsD3zhkdK26Tc+GcNLk6i6BK2ZLHCpJlPwPOBbS4D5fwUmXlBa29obrVFHr63xtdeB3+xrv5afQkMH2AWMnXXr1qFQKKB3b5XG7N27N959115Hp6GhAQ0NMqR28+bN1vVSpEiRok1x3M+Bm/fbOcceNb30JIithYO/Drx4o1okEgizyF/4ZPMCLzp1b58yKs1FzQCgboVaIqijovuezZcfdFB86o2d5mDmzJn48Y8tYrcUKVKkaE/0GAZ8e2nbRADZMOEc4I0/h5/3v7B9jmnDlB8Dk79mrymXRPT8acSlr4bRa33aoOxKiqL41Edj9ejRA5lMBqtXq5Eaq1evRp8+fazbXHXVVairqxP/VqxY0R5NTZEiRQoTVd3aNhKL4uRbwhn7j+qALi0U9bYEnte+xXM7AnKVqaGzE/GpN3bKysqw33774cknZYHAIAjw5JNPYvJke2rq8vJyVFdXK/9SpEiRIkWKFLsmdgk31pVXXonzzz8fEydOxAEHHIAbbrgB9fX1IjorRYoUKVKkSLH7YpcwdmbMmIG1a9fi6quvxqpVqzB+/Hg8/vjjhmg5RYoUKVKkSLH7wWNsdyjwE4/NmzejpqYGdXV1qUsrRYoUKVKk+JQg6fj9qdfspEiRIkWKFClSxCE1dlKkSJEiRYoUuzRSYydFihQpUqRIsUsjNXZSpEiRIkWKFLs0UmMnRYoUKVKkSLFLIzV2UqRIkSJFihS7NFJjJ0WKFClSpEixSyM1dlKkSJEiRYoUuzRSYydFihQpUqRIsUsjNXZSpEiRIkWKFLs0donaWC0Fr5ixefPmndySFClSpEiRIkVS8HG7WOWr1NgBsGXLFgDAgAEDdnJLUqRIkSJFihSlYsuWLaipqXH+nhYCBRAEAT7++GN06dIFnue12n43b96MAQMGYMWKFWmB0SJIr1VpSK9XcqTXKjnSa5Uc6bVKjra8VowxbNmyBf369YPvu5U5KbMDwPd97LHHHm22/+rq6vRlSIj0WpWG9HolR3qtkiO9VsmRXqvkaKtrFcfocKQC5RQpUqRIkSLFLo3U2EmRIkWKFClS7NJIjZ02RHl5Oa655hqUl5fv7KZ0eKTXqjSk1ys50muVHOm1So70WiVHR7hWqUA5RYoUKVKkSLFLI2V2UqRIkSJFihS7NFJjJ0WKFClSpEixSyM1dlKkSJEiRYoUuzRSYydFihQpUqRIsUsjNXbaELfccgsGDx6MiooKTJo0CXPmzNnZTWpVPPfcczjxxBPRr18/eJ6HBx98UPmdMYarr74affv2RWVlJaZMmYL3339fWWfDhg04++yzUV1djdraWlxwwQXYunWrss6bb76JQw89FBUVFRgwYAB+/vOfG2257777MGrUKFRUVGDMmDF49NFHW/18W4KZM2di//33R5cuXdCrVy+ccsopWLRokbLOjh07cMkll6B79+7o3LkzTj/9dKxevVpZZ/ny5Zg+fTqqqqrQq1cvfOtb30I+n1fWeeaZZ7DvvvuivLwcw4YNw9133220pyM/m7feeivGjh0rEpBNnjwZjz32mPg9vU5uXHfddfA8D5dffrlYll6vED/60Y/geZ7yb9SoUeL39DqpWLlyJc455xx0794dlZWVGDNmDF577TXx+6euf2cp2gSzZs1iZWVl7A9/+AN7++232UUXXcRqa2vZ6tWrd3bTWg2PPvoo+/73v8/++c9/MgDsgQceUH6/7rrrWE1NDXvwwQfZ/Pnz2UknncSGDBnCtm/fLtaZNm0aGzduHHvllVfY888/z4YNG8bOOuss8XtdXR3r3bs3O/vss9mCBQvY3/72N1ZZWcluu+02sc6LL77IMpkM+/nPf84WLlzIfvCDH7BcLsfeeuutNr8GSTF16lR21113sQULFrB58+ax448/ng0cOJBt3bpVrPPlL3+ZDRgwgD355JPstddeYwceeCA76KCDxO/5fJ6NHj2aTZkyhb3xxhvs0UcfZT169GBXXXWVWGfJkiWsqqqKXXnllWzhwoXsN7/5DctkMuzxxx8X63T0Z/Ohhx5ijzzyCHvvvffYokWL2Pe+9z2Wy+XYggULGGPpdXJhzpw5bPDgwWzs2LHs61//ulieXq8Q11xzDdtnn33YJ598Iv6tXbtW/J5eJ4kNGzawQYMGsc9//vNs9uzZbMmSJew///kP++CDD8Q6n7b+PTV22ggHHHAAu+SSS8T3QqHA+vXrx2bOnLkTW9V20I2dIAhYnz592C9+8QuxbNOmTay8vJz97W9/Y4wxtnDhQgaAvfrqq2Kdxx57jHmex1auXMkYY+y3v/0t69q1K2toaBDrfOc732EjR44U388880w2ffp0pT2TJk1iX/rSl1r1HFsTa9asYQDYs88+yxgLr00ul2P33XefWOedd95hANjLL7/MGAuNS9/32apVq8Q6t956K6uurhbX59vf/jbbZ599lGPNmDGDTZ06VXz/ND6bXbt2ZXfeeWd6nRzYsmULGz58OHviiSfY4YcfLoyd9HpJXHPNNWzcuHHW39LrpOI73/kOO+SQQ5y/fxr799SN1QZobGzE3LlzMWXKFLHM931MmTIFL7/88k5sWfth6dKlWLVqlXINampqMGnSJHENXn75ZdTW1mLixIlinSlTpsD3fcyePVusc9hhh6GsrEysM3XqVCxatAgbN24U69Dj8HU68rWuq6sDAHTr1g0AMHfuXDQ1NSnnMWrUKAwcOFC5XmPGjEHv3r3FOlOnTsXmzZvx9ttvi3XirsWn7dksFAqYNWsW6uvrMXny5PQ6OXDJJZdg+vTpxjml10vF+++/j379+mHo0KE4++yzsXz5cgDpddLx0EMPYeLEifjMZz6DXr16YcKECbjjjjvE75/G/j01dtoA69atQ6FQUF4KAOjduzdWrVq1k1rVvuDnGXcNVq1ahV69eim/Z7NZdOvWTVnHtg96DNc6HfVaB0GAyy+/HAcffDBGjx4NIDyHsrIy1NbWKuvq16u512Lz5s3Yvn37p+bZfOutt9C5c2eUl5fjy1/+Mh544AHsvffe6XWyYNasWXj99dcxc+ZM47f0eklMmjQJd999Nx5//HHceuutWLp0KQ499FBs2bIlvU4alixZgltvvRXDhw/Hf/7zH3zlK1/BZZddhnvuuQfAp7N/T6uep0jRzrjkkkuwYMECvPDCCzu7KR0WI0eOxLx581BXV4f7778f559/Pp599tmd3awOhxUrVuDrX/86nnjiCVRUVOzs5nRoHHfcceLz2LFjMWnSJAwaNAj33nsvKisrd2LLOh6CIMDEiRPxs5/9DAAwYcIELFiwAL/73e9w/vnn7+TWNQ8ps9MG6NGjBzKZjKHkX716Nfr06bOTWtW+4OcZdw369OmDNWvWKL/n83ls2LBBWce2D3oM1zod8VpfeumlePjhh/H0009jjz32EMv79OmDxsZGbNq0SVlfv17NvRbV1dWorKz81DybZWVlGDZsGPbbbz/MnDkT48aNw4033pheJw1z587FmjVrsO+++yKbzSKbzeLZZ5/FTTfdhGw2i969e6fXy4Ha2lqMGDECH3zwQfpcaejbty/23ntvZdlee+0l3H6fxv49NXbaAGVlZdhvv/3w5JNPimVBEODJJ5/E5MmTd2LL2g9DhgxBnz59lGuwefNmzJ49W1yDyZMnY9OmTZg7d65Y56mnnkIQBJg0aZJY57nnnkNTU5NY54knnsDIkSPRtWtXsQ49Dl+nI11rxhguvfRSPPDAA3jqqacwZMgQ5ff99tsPuVxOOY9FixZh+fLlyvV66623lA7kiSeeQHV1teiYil2LT+uzGQQBGhoa0uuk4eijj8Zbb72FefPmiX8TJ07E2WefLT6n18uOrVu3YvHixejbt2/6XGk4+OCDjdQY7733HgYNGgTgU9q/lyRnTpEYs2bNYuXl5ezuu+9mCxcuZBdffDGrra1VlPyfdmzZsoW98cYb7I033mAA2PXXX8/eeOMN9uGHHzLGwtDE2tpa9q9//Yu9+eab7OSTT7aGJk6YMIHNnj2bvfDCC2z48OFKaOKmTZtY79692bnnnssWLFjAZs2axaqqqozQxGw2y375y1+yd955h11zzTUdLvT8K1/5CqupqWHPPPOMEvq6bds2sc6Xv/xlNnDgQPbUU0+x1157jU2ePJlNnjxZ/M5DX4899lg2b9489vjjj7OePXtaQ1+/9a1vsXfeeYfdcsst1tDXjvxsfve732XPPvssW7p0KXvzzTfZd7/7XeZ5Hvvvf//LGEuvUzHQaCzG0uvF8Y1vfIM988wzbOnSpezFF19kU6ZMYT169GBr1qxhjKXXiWLOnDksm82yn/70p+z9999nf/nLX1hVVRX785//LNb5tPXvqbHThvjNb37DBg4cyMrKytgBBxzAXnnllZ3dpFbF008/zQAY/84//3zGWBie+MMf/pD17t2blZeXs6OPPpotWrRI2cf69evZWWedxTp37syqq6vZF77wBbZlyxZlnfnz57NDDjmElZeXs/79+7PrrrvOaMu9997LRowYwcrKytg+++zDHnnkkTY77+bAdp0AsLvuukuss337dvbVr36Vde3alVVVVbFTTz2VffLJJ8p+li1bxo477jhWWVnJevTowb7xjW+wpqYmZZ2nn36ajR8/npWVlbGhQ4cqx+DoyM/mF7/4RTZo0CBWVlbGevbsyY4++mhh6DCWXqdi0I2d9HqFmDFjBuvbty8rKytj/fv3ZzNmzFDyxqTXScW///1vNnr0aFZeXs5GjRrFbr/9duX3T1v/7jHGWGlcUIoUKVKkSJEixacHqWYnRYoUKVKkSLFLIzV2UqRIkSJFihS7NFJjJ0WKFClSpEixSyM1dlKkSJEiRYoUuzRSYydFihQpUqRIsUsjNXZSpEiRIkWKFLs0UmMnRYoUKVKkSLFLIzV2UqRIkSJFihS7NFJjJ0WKFM3GM888A8/zjAKK7YUnn3wSe+21FwqFglh2++23Y8CAAfB9HzfccMNOaVdz0djYiMGDB+O1117b2U1JkWKXQppBOUWKFIlwxBFHYPz48YoB0djYiA0bNqB3797wPK/d27TffvvhyiuvxNlnnw0gLEbYo0cPXH/99Tj99NNRU1ODqqqqdm9XS3DzzTfjgQceMIofpkiRovlImZ0UKVI0G2VlZejTp89OMXReeOEFLF68GKeffrpYtnz5cjQ1NWH69Ono27ev1dBpbGxsz2aWjLPPPhsvvPAC3n777Z3dlBQpdhmkxk6KFCmK4vOf/zyeffZZ3HjjjfA8D57nYdmyZYYb6+6770ZtbS0efvhhjBw5ElVVVTjjjDOwbds23HPPPRg8eDC6du2Kyy67THE9NTQ04Jvf/Cb69++PTp06YdKkSXjmmWdi2zRr1iwcc8wxqKioEMceM2YMAGDo0KGijT/60Y8wfvx43HnnnRgyZIhY//HHH8chhxyC2tpadO/eHSeccAIWL14s9r9s2TJ4nod7770Xhx56KCorK7H//vvjvffew6uvvoqJEyeic+fOOO6447B27VqlbXfeeSf22msvVFRUYNSoUfjtb38rfmtsbMSll16Kvn37oqKiAoMGDcLMmTPF7127dsXBBx+MWbNmlX6jUqRIYUV2ZzcgRYoUHR833ngj3nvvPYwePRrXXnstAKBnz55YtmyZse62bdtw0003YdasWdiyZQtOO+00nHrqqaitrcWjjz6KJUuW4PTTT8fBBx+MGTNmAAAuvfRSLFy4ELNmzUK/fv3wwAMPYNq0aXjrrbcwfPhwa5uef/55fO5znxPfZ8yYgQEDBmDKlCmYM2cOBgwYgJ49ewIAPvjgA/zjH//AP//5T2QyGQBAfX09rrzySowdOxZbt27F1VdfjVNPPRXz5s2D78t54DXXXIMbbrgBAwcOxBe/+EV87nOfQ5cuXXDjjTeiqqoKZ555Jq6++mrceuutAIC//OUvuPrqq3HzzTdjwoQJeOONN3DRRRehU6dOOP/883HTTTfhoYcewr333ouBAwdixYoVWLFihXJuBxxwAJ5//vlm3q0UKVIYKLlOeooUKXZLHH744ezrX/+6suzpp59mANjGjRsZY4zdddddDAD74IMPxDpf+tKXWFVVFduyZYtYNnXqVPalL32JMcbYhx9+yDKZDFu5cqWy76OPPppdddVVzvbU1NSwP/7xj8qyN954gwFgS5cuFcuuueYalsvl2Jo1a2LPb+3atQwAe+uttxhjjC1dupQBYHfeeadY529/+xsDwJ588kmxbObMmWzkyJHi+5577sn++te/Kvv+yU9+wiZPnswYY+xrX/saO+qoo1gQBM623HjjjWzw4MGx7U2RIkVypMxOihQpWhVVVVXYc889xffevXtj8ODB6Ny5s7JszZo1AIC33noLhUIBI0aMUPbT0NCA7t27O4+zfft24ZIqhkGDBgmWh+P999/H1VdfjdmzZ2PdunUIggBAqPsZPXq0WG/s2LFKuwEId5l+LvX19Vi8eDEuuOACXHTRRWKdfD6PmpoaAKFL8JhjjsHIkSMxbdo0nHDCCTj22GOVtlVWVmLbtm2Jzi1FihTFkRo7KVKkaFXkcjnlu+d51mXcuNi6dSsymQzmzp0rXEwc1EDS0aNHD2zcuDFRmzp16mQsO/HEEzFo0CDccccd6NevH4IgwOjRow0BM207F2Lry+i5AMAdd9yBSZMmKfvh57bvvvti6dKleOyxx/C///0PZ555JqZMmYL7779frLthwwbDOEuRIkXzkRo7KVKkSISysjJFVNxamDBhAgqFAtasWYNDDz20pO0WLlzYrGOuX78eixYtwh133CGO+cILLzRrXxS9e/dGv379sGTJEhEOb0N1dTVmzJiBGTNm4IwzzsC0adOwYcMGdOvWDQCwYMECTJgwocXtSZEiRYjU2EmRIkUiDB48GLNnz8ayZcvQuXNnMTC3FCNGjMDZZ5+N8847D7/61a8wYcIErF27Fk8++STGjh2L6dOnW7ebOnUq7rnnnmYds2vXrujevTtuv/129O3bF8uXL8d3v/vdlpyGwI9//GNcdtllqKmpwbRp09DQ0IDXXnsNGzduxJVXXonrr78effv2xYQJE+D7Pu677z706dMHtbW1Yh/PP/88fvKTn7RKe1KkSJGGnqdIkSIhvvnNbyKTyWDvvfdGz549sXz58lbb91133YXzzjsP3/jGNzBy5EiccsopePXVVzFw4EDnNmeffTbefvttLFq0qOTj+b6PWbNmYe7cuRg9ejSuuOIK/OIXv2jJKQhceOGFuPPOO3HXXXdhzJgxOPzww3H33XdjyJAhAIAuXbrg5z//OSZOnIj9998fy5Ytw6OPPioiwF5++WXU1dXhjDPOaJX2pEiRIs2gnCJFik8xvvWtb2Hz5s247bbbdnZTWg0zZszAuHHj8L3vfW9nNyVFil0GKbOTIkWKTy2+//3vY9CgQUIg/GlHY2MjxowZgyuuuGJnNyVFil0KKbOTIkWKFClSpNilkTI7KVKkSJEiRYpdGqmxkyJFihQpUqTYpZEaOylSpEiRIkWKXRqpsZMiRYoUKVKk2KWRGjspUqRIkSJFil0aqbGTIkWKFClSpNilkRo7KVKkSJEiRYpdGqmxkyJFihQpUqTYpZEaOylSpEiRIkWKXRr/H/e83kRsL1J/AAAAAElFTkSuQmCC", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "import pandas as pd\n", - "df_xy = df.iloc[:, df.columns.get_level_values(2).isin([\"x\", \"y\"])][model.Model.fetch1(\"model_name\")]\n", - "df_xy.mean()\n", - "df_xy.plot().legend(loc=\"right\")" + "import matplotlib.pyplot as plt\n", + "\n", + "head_data = df[df['body_part'] == 'head']\n", + "tail_data = df[df['body_part'] == 'tailbase']\n", + "\n", + "plt.title('Head pose estimation')\n", + "plt.plot(head_data['x_pos'],label='x_pos')\n", + "plt.plot(head_data['y_pos'],label='y_pos')\n", + "plt.xlabel('time (frames)')\n", + "plt.ylabel('pos (pixels)')\n", + "plt.legend()\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "df_flat = df_xy.copy()\n", - "df_flat.columns = df_flat.columns.map('_'.join)\n" + "plt.title('Tailbase pose estimation')\n", + "plt.plot(tail_data['x_pos'],label='x_pos')\n", + "plt.plot(tail_data['y_pos'],label='y_pos')\n", + "plt.xlabel('time (frames)')\n", + "plt.ylabel('pos (pixels)')\n", + "plt.legend()\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAGxCAYAAACEFXd4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydZ3gUZReG79ma3jupkEBoIfTeu4CAFEVRQBEFEbFixYaCgAoWBEWlqKCgIErvvfdeEtJI733bzPdjwiZLCiBYv7mvi4vszDvvzG42O8+e95znCJIkSSgoKCgoKCgo/EdR/d0XoKCgoKCgoKDwZ6KIHQUFBQUFBYX/NIrYUVBQUFBQUPhPo4gdBQUFBQUFhf80ithRUFBQUFBQ+E+jiB0FBQUFBQWF/zSK2FFQUFBQUFD4T6OIHQUFBQUFBYX/NJq/+wL+CYiiSHJyMs7OzgiC8HdfjoKCgoKCgsItIEkSBQUFBAQEoFJVH79RxA6QnJxMUFDQ330ZCgoKCgoKCn+AxMREAgMDq92viB3A2dkZkF8sFxeXv/lqFBQUFBQUFG6F/Px8goKCrPfx6lDEDliXrlxcXBSxo6CgoKCg8C/jZikoSoKygoKCgoKCwn8aRewoKCgoKCgo/KdRxI6CgoKCgoLCfxolZ0dBQUFBQQGwWCyYTKa/+zIUKqDValGr1Xc8jyJ2FBQUFBT+r5EkidTUVHJzc//uS1GoAjc3N/z8/O7IB08ROwoKCgoK/9dcFzo+Pj44ODgo5rL/ECRJori4mPT0dAD8/f3/8FyK2FFQUFBQ+L/FYrFYhY6np+fffTkKN2Bvbw9Aeno6Pj4+f3hJS0lQVlBQUFD4v+V6jo6Dg8PffCUK1XH9d3Mn+VSK2FFQUFBQ+L9HWbr653I3fjeK2FFQUFBQUFD4T6OIHQUFBQUFhX8hXbp0YfLkyX/5eUNDQ5kzZ85fft47QRE7CgoKCgoKCv9pFLGjoKDwryYpp5i8EsUITkFBoXoUsaOgoHDXuZpZxNPLjrPncuafep7E7GK6f7iTPnN2kVesCB6F/z9EUeSll17Cw8MDPz8/3nrrLeu+3Nxcxo4di7e3Ny4uLnTr1o2TJ09a98fExDBw4EB8fX1xcnKiZcuWbNmyxWb+9PR0BgwYgL29PWFhYXz//fd/1VO7qyg+OwoKCneNDWdSePK7Y9bHv51MZteLXQn2/HPKeg/EZmEwi6TklfLCypN8+XBzpapG4Y6QJIkSk+VvObe9Vn3b79/Fixfz3HPPcfDgQfbv38/o0aNp3749PXv2ZNiwYdjb27N+/XpcXV1ZsGAB3bt359KlS3h4eFBYWMg999zDe++9h16vZ8mSJQwYMICLFy8SHBwMwOjRo0lOTmb79u1otVomTZpkNfn7N6GIHQUFhbvGFztjK20rMpr/tPP1auDHi5wCYPO5NBbsiqVRgCutwjzQaf79gevsIiPbLqQzuGkt1CpFxP0VlJgsNJi68W8597l3euOgu73bclRUFG+++SYAERERfPbZZ2zduhV7e3sOHTpEeno6er0egNmzZ7N69WpWrlzJuHHjaNKkCU2aNLHO9e6777Jq1SrWrFnDxIkTuXTpEuvXr+fQoUO0bNkSgK+//pr69evfpWf816GIHQUFhbvGycRcm8e+Lnrq+7v8aedzddDSLdKHbRfkb5oz1l8AINLPmfXPdPxXRXkSsor5/XQyRQYzFhHm74yx7ntj9RmmDWrE4Ka1UCmiR6ECUVFRNo/9/f1JT0/n5MmTFBYWVnKFLikpISZGfm8VFhby1ltvsXbtWlJSUjCbzZSUlJCQkADA+fPn0Wg0NG/e3Hp8ZGQkbm5uf+6T+hNQxI6CgsJdwSJKlbalFxiYvv48U3pH/mk3aX9Xu0rbLqQWcDQ+hxahHn/KOe8GkiRx+loeuy5lsP1iBkfjc6odW2Ky8PyKk+SWmHisQ9hfeJX/f9hr1Zx7p/ffdu7bRavV2jwWBAFRFCksLMTf358dO3ZUOua6WHnhhRfYvHkzs2fPJjw8HHt7e4YOHYrRaPwjl/+PRhE7CgoKfxiTRSQmo5Bzyfk2ycg/PdGWl1aeJC6rmAU7Y+lS14e2de5+36GcIiPfH0yoct/5lPx/lNjJKzYRn11EfFYxl9IK+O1kMnFZxdb9KgHah3sR5OHAzosZXMstsTneQaemjrfjX33Z/3cIgnDbS0n/RJo1a0ZqaioajYbQ0NAqx+zdu5fRo0czePBgQI70xMXFWfdHRkZiNps5evSodRnr4sWL/8ru8P/+36iCgsIfJrPQgLOdBr1G/kZZarJwMbWAADd7vJ31lcYXlJp4bNERDBYRiyhy5lp+pTGTuoXz3rrz1ht513reNA12u+vXvi8mkwe/OmizrWcDXzafSwMgJqPorp/zVskrMbHvSiYHr2ZzPCGHuKyqy+PttWo6RnjRMcKLXg398HUpj1JJkkTYK+sAiPBx4teJ7f8TN2GFv4YePXrQtm1bBg0axMyZM6lbty7JycmsXbuWwYMH06JFCyIiIvjll18YMGAAgiDwxhtvIIqidY569erRp08fnnjiCb744gs0Gg2TJ0+2Nuf8N6H85Sgo/B+SU2Tk5V9OsfFsGo46NW3reJKWb+B8Sj5mUUKtEugW6cMTnWrbREcav7XppnM722mtuTsPtQ5m2qBGdz13ZvO5NB5fcsT6uFNdb/QalVXoAKw4ksiELnXwcam8zHW3kSSJtHwDMRmF7IvJZPG+eAoNlROzvZ31hHo6EOzhSLs6nvRp5IejvuqPYUEQGNepNl/uiuVyeiGnkvJoU1vpyq1wawiCwLp163jttdcYM2YMGRkZ+Pn50alTJ3x9fQH46KOPePTRR2nXrh1eXl5MmTKF/HzbLzDffvstY8eOpXPnzvj6+jJt2jTeeOONv+Mp3RGCJEmVF9r/z8jPz8fV1ZW8vDxcXP68ZEoFhX8CBrOFIV/sqzIqA3K04XrprU6j4sI7faz5Nk8vO85vJ5Ornfuh1sG81DuS0YsOcTwhF41KYNqgRjzQKviuXf+ey5k8uugwRkv5N1AHnZpiowWtWmBM+zB+P5lMcl4pHSO8WDym1V3NF0rNK+VEYg5X0guJySgiJqOQmPRCioy25cphXo50jPCiZagHEb5OBHs43FZkJjG7mMHz9pJZaKRzXW++HtUCjfrfX2H2T6O0tJSrV68SFhaGnd2fL4wVbp+afke3ev9WIjsKCv9i0vJLGfT5XvJKTPRu6MfT3cKp7e1U4zELd1/lzLV8PBx1LHm0FRZR4uDVLALc7CksNfPJ1suU5Mk37sc7htkIhbfvbWgVO13redO7oR8tQt0J9XS0uREve7wNL608xZqTybz8y2lKTRZGt//jibXFRjOfbbvChjOpxGZWXp4qNlqICnRl5tAoIv1cGN4ikP6f7mH35UwW7ollXKc6f/jcFfn5aBKv/HLaRmhdR60SCPF0INzbifua1aJXA78/LLLyik2MWXSYzEIj9f1d+PyhZorQUVC4AxSxo6DwL8VoFrln7m6yiuTKiVXHr7Hq+DVGtQ2hf5MAmgS62XjNSJLErI0XmbdDLjt9uW8kjWq5AlDb25G3fzvHyqNJAAR7ODBzaFSlZZOEbDkPx0mv4dsxraq9NjutmrkPROPvaseCXbEsO5R422Kn1GRhf0wWuy5nsOV8GonZJdWO7R/lz5z7o62CINzHman9G/LqqtPM2niRtrW9aBzoelvnv5GMAgOvrz6D0SJSx9uRJkFu1PF2oo63E+E+jgR7ON4Vbx+jWeTJ745yJb0QgNxiI43e3IiXk46vHmlBdJDbv6qkXkHhn4AidhQU/qUUlJqsQqcii/fHs3h/PB6OOlqHedAvyp8e9X25f8F+TiblAfBEp9oMbRZoPea9teetQmdU2xCm9I2scsnl0NUsQK4cuhmCIPBohzAW7IrlcnoBRQZztfkp15EkiXMp+Xy5K5aNZ1MpNVWOoAA82DoYDwcdn22/AsCjHcIqRT5GtApi16UMNpxNZdLy4/z+dIebnr8mfj+VTInJQuNarqyZ2P6uC47zKflcTi/k+wPxHLyabd2eklcKQGahkcHz9lHf34V7GvkR4GZPr4a+ONtpq5tSQUGhDEXsKCj8S/F00tMixJ0j8Tl0refNjCFRzNxwkcScYmIzisgsNLD+TCrrz6RWOvaVe2wdUBsGlK91v9qvvrU6qyLp+aV8uk0WFw+1Cbmla/R1sSPYw4GE7GKmrT2P2SJSbLLQu6Ef9zYJsBlbaDAzcuFBTtxgTFgV3SN9mLTsOAAdwr1oEuhWaYwgCMwY0piTSblczSzizTVnmT2sSaVxN8NsEdlyPo11p1MA2bDwz4isPL7kCEk51UevrnM+JZ/zKXK+1diUMF7v3+CuX4uCwn8NRewoKPyLuf6tf3uZL8uHw+Wbudkiciguu1JpNsDA6IBK21qXLVe52murFDqpeaUM+GwPBaVmGtdy5fmedSuNKTSYyS02EpNRxPYL6eSVmGgW7EZWoQGAZYfK/XDWnkrheEIOz/eqh1NZtGXVsaRbEjoAjy2WK7Hah3vy1SMtrK0UrldFuTvKz8PNQcec+6MZ8dUBVh5NolNd70oiqyZKTRbGf3eU7RczrNtCve6+141FlG5J6DjrNYzvWoeZGy4CsOZkMsUmC7EZhUQHufNgq+A/rQ+ZgsK/GUXsKCj8i+nV0JdF++KQJBg2fz/vDWrE8BZB7I3J4rsD8VUe8+sJ2cyuro8TD7UJoVGAC2n5smjydansrQMw4fujZBTIouX9wY0rLRml5JXQdvq2SsetOn7N5nGEjxOXy3JRvt0bx7d74/h5fDt+PJzAT0eSbu/JA4fjchi+YD9d6nlTZLCw5XwaCdnF+Djrebp7BPe3CKJ1bU8mdg3nk21XeO2X03QI98LDUXfTuUtNFsZ8e5j9sVnYaVU81DqE7vV9aHsXyr+NZpGt59MY//2xmw8Gnu1Rl6/3xJJfaubw1Wx2v9SVB748wLXcEn4oM1U8EJvN/J0xPNg6mFfvqW8VkQoKCkrpOaCUniv8u0nJK2Ha7+dZW7bM4uWkI7OwPJenbW1PRrYJISajkA1nUjmXYlty7mynoaC03BPmvqa1qO3tSJ9G/oR5ObL3SiaPfHMIgCl9Ihnfxbay6dDVbIYv2P9nPb07ItjDgWd7RtA/KoABn+7hQmoB7wxsyCNtQyuNzS02cjwhF0GAuMwifjuVwtH4HJz0Gr4Z3ZJWYXfmxpyWX8r2C+l8vOUSafmGO5pLJYCHo57MwqrnebxjGK/1U5a3bgWl9Pyfj1J6rqCggL+rPZ+OaMq5lHyuZhaRWWjE2U7DkGaBjGwTTLiPs3Xs093COXMtn8+2X2bjWdmAr6LQAfilLBoze9Mlm+0PtQ6uJHSWHojnjdVn/oyn9YfxctIxqXsEn2y9QkJ2Mc/+eJJCg4VhLYJ49/dzLDuUyIOtgm2iU8cTcnh8yREbkQiyz9BXj7T4Q0LHbBE5kZjL9ovpfLMnzupdVB1hXo6EejrYLJlVhyhhFTp2WhUPtAzG2U7Dp9uuoFULNAt2v+3rVVD4L6OIHQWF/wAqlcD6Zzqy5kQyLvYaOtX1rrKaShAESs0WjGaR1U+1p3EtV44l5DBsfnlk5ulu4Xyz52olkzytWkVqXinOdho+236FL3bE3Dj9P4LMQiNxmcW8O7Ahuy5nsOxQIpvOpvLx/dF8vPkS51PymfLzad7oXx83Bx2xGYU88s0hCkrN+Lva4e6gw8tZT5vaHvRp6HdT36KKZBUa2FnW2LMm80WAVmEeXMsp4VpuCaGeDrSr42nT58uPLA7YPQ3AektLxpuerXKeUpPIon1xeDnp+fWp9py+loebg470glK8nfRKmbqCDaNHjyY3N5fVq1cD0KVLF6Kjo5kzZw4AoaGhTJ48mcmTJ/9t1/hnoIgdBYX/CHZaNcNbBt103NM/HCc1v5T0AgNrJ3XkzDW5HF2nVvHjE21oGuyOu4OOd34/Z3Pcon1xLNoX94evb3iLQF7oVQ8fFzuOxGXz8NeHbhrtqA5Xe22Vvaau883eq3yz9yrzRzZn2aFEdl/OZMfFDGYMaczEH47z87Ekfj6WhLuDlpxieR69RsWP49oS5GF/ywJBFCVOXcvjix1XrJGym/HuwIaMaBXMhO+PcehqNq72WgLdHWyEjgOlVqED0Fd9mIkdwknKKWb1iapFVGahgYGf77XZZq9V066OJ2/d25AgDyVx+b/GjULlVpg7dy7/j9krithRUPiPUmqy8MnWyyRkF/PJA01RqQQsokRqWTLy2eR81pxM5v115wEwWkReXHkKB52aU2V+PH8ER52a+v4uHInPsdn+05Eknuhch1krTrLi6O0nI3s66tgzpRv2Orla7PdTyTz74wlMluo/uH85lsQTnWuzYGcsszZeYPp9jWka7MbxhFwAq9ABMJhFOs3ajpeTnvbhnkQHueHppMfdQa7sMltETKJEdpGBbRduHrm5jpuDlil9IukW6WNt9PnOb+fYdC4NQZBbXey5kmlzzETNapvH7Uo/IbnMU+h2KDFZ2HohnQYBLjzfq95tH6/w38PV9c7MNf+tKP7jCgr/UQ5ezWbejhh+P5VC7VfXUWqycPWGVguTlh23EQtXyhpO/hGe6Fyb02/14tRbvWkWUnXOSPcPd1qFzqDoALrU8ybkFkuls4qMWCp8I+0fFcDCUS1rPGbTuTQ6RXjj7qAlLd/Ao4uOWIVORVQC+DjrEQQ5QvLriWTe/u0ck5Yd5+GvDzF8wX4eXHiQUd8c4tkfT95U6PRu6Mvul7oSN6MfJ6b2YkSrYKvQ+bYs6gQgSeX2AZ6OOlSITLVfwQTNGutcC819SebOKsAW7Izlp8OJlP7BSJrCP4/Ro0ezc+dO5s6diyAICIJATEwMjz32GGFhYdjb21OvXj3mzp1b6bhBgwbVOHdBQQEjRozA0dGRWrVq8fnnn9vs/+ijj2jcuDGOjo4EBQUxYcIECgsLrfvj4+MZMGAA7u7uODo60rBhQ9atW2fdf+bMGfr27YuTkxO+vr48/PDDZGbaCv67jRLZUVD4j7LhBjPBFtO2VNmJ+0ZqC8mMVa8jG2cuiMGck0KIlar2pnmmewTDWwZRy80es0XkSHwOX+yIYeclOcm2W6QP2y6kVzpudLtQdl/OICajcp+rmmjz/lbOvN3b+tjFzvYj7OW+kcxYf8Fmm5eTnun3NebJ72zLvJ/sXAcnvZpukb40KDNVNJgtnEjIZdO5NBbvi8Ms3lq4v0s9bwZGB9C9vi8uNTgabz6XVml5UEaiS8kWPrSbDzeccqxmPSM1W9E88gslxYXsPp9IakoyUSWHcC5OZKelMdvFaI6KdTGiRY8RExrECt9ljRaRl34+xUs/n7Ju69XAl1lDm+DqoDgw/xuZO3culy5dolGjRrzzzjsAuLu7ExgYyIoVK/D09GTfvn2MGzcOf39/hg8ffstzz5o1i1dffZW3336bjRs38swzz1C3bl169uwJgEql4pNPPiEsLIzY2FgmTJjASy+9xLx58wB46qmnMBqN7Nq1C0dHR86dO4eTk5z7lpubS7du3Rg7diwff/wxJSUlTJkyheHDh7NtW2X7iruFUnqOUnqu8O/CIkpsOZ9GhI8TiTklbDmXRkGpiUtpheSVmKjv70Jtb0e+3BV7W/O6UMSD6q28rF1e5f7mpV+QRXkI3E6rItLPBZ1GhUWUOJecb5ODc2+TAE4m5RKfVXzL1xDq6UDcTca3q+NJPT9nvt0bZ7Pdw1GHq73WJnqlU6sY1S6E7w8mUHxDwvXpt3pZWy1IksTVzCK2X8xgx8V0DsZmV9ns0zqvRkWXut70i/KnW6TPLbVsOJWUy/0LDlTKU2omXGKZ7j30QvU5SLeCWVJRjB4XoQSLJJCFK1clP86JIcw3DyCNqivK6ng70j8qAJ1GxQ8HE7iWW8K8h5rRua73HbXX+LdQqaxZksB06+/Zu4rWAW4jofxWcnYmTpxIamoqK1euBG4tQbl+/fqsX7/eOscDDzxAfn6+TXSmIitXruTJJ5+0RmeioqIYMmQIb775ZqWx06ZNY/fu3WzcuNG6LSkpiaCgIC5evEjdupUNS5XScwWF/yPMFpGfjyUx5efTNY67llsC5299XlcKeUu7mH6qA+iE6pc5jtqNt/6cLTmRJHlzKCWSrWIzDomyp4uAhFQWUVhzizktOrWKlmHuvNQ7klXHr900CXpfTBb7YrIqbc8uMpJ9Q68wo0Xkq93yklHzEHca13Jl0b44GtdyRatWseNiOjsuZrD9YvpNRZleo6JrPR/uKRM4FU374rOKMJhFInycypObJQmSjsDpnzDG7uFqlh9NLW04RW0KccCDfN7RLqK/+oDtiRoNobAgj/2FfggZ5+ihusF4MKi1fFMMbEmBnT+Jxzfjnb4XbyEPF2QXZrUg4UMuPkIurVUXGKPZyDZLNE+ZJlGC7c0iJqOIuVsv22ybUGZ2OHtYE4Y2D+T/ClMxvH/rLtt3lVeTQXdnDt2ff/4533zzDQkJCZSUlGA0GomOjr6tOdq2bVvpcUVBtWXLFqZPn86FCxfIz8/HbDZTWlpKcXExDg4OTJo0ifHjx7Np0yZ69OjBkCFDiIqKAuDkyZNs377dGumpSExMTJVi526giB0FhX8o13JLWHsqmYW7r+JkpyH2Npd8bpVPtZ/SSS0LqCLXCJbQnw/SWgAQZ/dQlcd4CIV4CIVEqa4ylvJvgDmSEw8YXweVhnSLEznUHCld+WRbGtdyRp96HLI3svPKJV7SXCJCuEaYkMJlqRZfmftxTKr6A/DxjmEVojYSnuTjJeQRKqTSUBVHkK6YDpqzuEiFnCxqzuz97YFITl/LI/qdTdU2Gr2OvVZNt0gf+jb2o1Ndb4xmkdTkJOzPLofMi2QnXeJyXBzZkjMl6HH3t+CtKpJvWAUpkBMHgA4YyDkG6qoP0xvtvPmlw69sjS1h88XrlV33MKSxJ7PVnyFc+A0AQ63W7A6awKbz6aw+kYzRPBJ4CF9ycBJKyJDc0GPCT8imoSqOlzXLcBOK6KY+wTrhFdaI7Tgo1ueYGEEpVTtmX+eTrZf//8TOv5jly5fzwgsv8OGHH9K2bVucnZ2ZNWsWBw9WbhvzR4mLi6N///6MHz+e9957Dw8PD/bs2cNjjz2G0WjEwcGBsWPH0rt3b9auXcumTZuYPn06H374IU8//TSFhYUMGDCADz74oNLc/v7+d+06b0QROwoKfwFXM4vILzHhaq/F383O2n+q1GRBp1ZRUGrmWGIORQYzznZapq87z4XUAuvx6QV35rhbFSpEntH8bBU6jPyFi6oGfPXlDgLI4iPdF5gkNdoqoj2rLO3ZYmnOFM89BBeURx7chUI26l+WH2jhtBhKMXboMPOLpQPLLN0wowEkGglXKV77G5rS3VAgJy1PAptPpXCS6as+zGGxLlstzTgkRnJRCqIIO6I1CfQrOYPaEou9xkhP1VEaqG5okSECZcGeVqZt/KTfxmWxFj9YurHa1J7SKsSYg04WOP0a+5NbYmLd6RSm/nqWgOILPKn5nT6qQ6gFWSR5AK0rlnnckJ5UJOnJxYlaQuVIVEXiRR+G504l7fc4QF7J6NPQj/Fd6qDXqPn60luEZrrRI3Mp+gOfELzvZ/LMwzCKrYgOcmNsxzB+PCyX2F8nQ3LjtKU2GywtGaLezSTNL4Sp0nhGtQpYRZGk517jNGKkWtVel6nCUp4kSfT7ZE8lB+6KvH1vQ0a1C63xuf7j0TrIEZa/69y3gU6nw2Ip//vcu3cv7dq1Y8KECdZtMTG374d14MCBSo/r15ebBx89ehRRFPnwww9RqeQ3/08//VRpjqCgIJ588kmefPJJXnnlFb766iuefvppmjVrxs8//0xoaCgazV8nQRSxo6DwJ3MqKZdBn+/leq6rRiXg62JHcl4Jf1fGXEXDOoBSSYvdd/fRDDh2C475+8SGvK9diGtBzUs/jVVx1p+bqq7wrnaR7YAqzIKT1bUIaNCer86IDBC34ifk0FJ1iZaqS5UHn4HoGtJlzoihrLJ04LQYxiD1Hgaq9xGhusabqqW8pvme38U2vGR6AiPyJCpBTjbu09CH4quHOH9wK/cKV3lTdYVwffkN8JQYxlGxLvGSL1mSC+5CAfYYSZfcyMEJe4yYUXNeCmaPfnKNrxGARrDQ3imFgqBIark7UM/PmZj0Qp5edrzC8lpf7lW5MFP7JXVV11igm8OVbl8S3qkfafmlPPvjCQBWTWjH5bRCazJyLs58bbmHnyxduEd9kDaqcwxW78VRMLBQO5vBxnfIxbnK6/pwWBN2X85gxvoLnE2uXuRc5801Z9FrVDzQKvimY/+xCMIdLyX9VYSGhnLw4EHi4uJwcnIiIiKCJUuWsHHjRsLCwli6dCmHDx8mLCzstubdu3cvM2fOZNCgQWzevJkVK1awdu1aAMLDwzGZTHz66acMGDCAvXv3Mn/+fJvjJ0+eTN++falbty45OTls377dKpaeeuopvvrqK0aMGMFLL72Eh4cHV65cYfny5SxcuBC1unIj4ruBInYUFP5klh1KtAode62aEpNFzqupBgGRfqqDOAvFrLa0r5RjcTO0aoEgdwdiM4sAiTAhlfpCPEFCBmekULzIY65uns0xdreZHDtL++Vtjb8dAizX4PRPPA5wh+a/jVRx1BfiOSLVY5clivfMI2mmukxD4Sr1VYkMUu/DJGl40fwEIBDGNaLO/0CzS/sIELIZcoOQ2mmJYrr5QS5IN7+ZazDzpfajm44zSypqCVl8ZH6PXVfWsMDSn7ViIBm4AQI6jYp2dWTfnyjPOhQ434fdodlwaT3hR96hNLo7r/xyFZNFomWoO031KTSV9lKq2YijUIJB0mFASz6OxIs+vGkaxVJzT5bpphGmSuOA2+s8UzyWjcbGla7twYW3v/zx8i+n/91i51/ECy+8wKhRo2jQoAElJSVcuHCB48ePc//99yMIAiNGjGDChAk2yca3wvPPP8+RI0d4++23cXFx4aOPPqJ3b7kKskmTJnz00Ud88MEHvPLKK3Tq1Inp06fzyCOPWI+3WCw89dRTJCUl4eLiQp8+ffj4448BCAgIYO/evUyZMoVevXphMBgICQmhT58+1kjRn4FSjYVSjaXw53AhNZ+z1/L5eMslknJKeLJzHab0qUdKXimL98exYGfV1VK9VIf5Uvdxpe2bLM35ztKDo2JdirC3bvcjiwaqeCZpfiFaFYtJUrNVbEaEkEQdVcqf9vzuJufFYBwpIVh1875QFTFLKhZbepMoeWOPkQLsKUWHP1lEqK5RX0ggQnWt0nF5kgMCEi6CLDpfNI2jneosg9V7K429kWTJg0Xm3nxp6c+NasyNAtqozhMmpDKlmqo2AFESeNc8km8tfXGjgPGaNYxRb7BJEC+U7Mi2C8InpD52xamQdKjKuX4Q+vFqyUOoEJnr/Rv9C35CuLF+vQI5khOfmwdySQrkDc131tfnB3NXZpgfJJ87j2rMGhrFsBaym3d8VhE/Hk7kgZbBBN+ip9JfidII9J/P3ajGUsQOithRuPv8fiqZScuOU9Gm5elu4VYX26eXHa/WmG6oeieztQtueg6jpOaCFEyU6upduebbId8+EJeSchfkHMEVd6lqM8Ivzf14oFUoWw6f5DtzD/SCiXc0i6oUIX+UONGXZ00TSJK8yqIYcqVHqKcD5ux4eqiO0lgVSy0hi7pCIh5C4U1mlDGr7cgwO2CW1LgKRbgI5ct2+ZID31u609cjGUHnhJsxFdf8i7d8zR+YHiBZ8mCT2IIS7OiiOsE32lmohLv3kVwg2bPJeSApuSV4UEBr1XmrADZLKg6LkYSrkvAW5CWqNMmNqabRbBRb3fG5fxzXBke9htHfHiKz0EiEjxMLR7WgoNRMfokJg1mkXbinNX/t70IRO/98FLFzl1DEjkJFjGaRpJxia6VORqGBX09co7DUTJCHA5O6R+Bqb7u+kV9q4lpOCc52GuIyi3l00WEbn5b24Z58fH80Ps52fL3nKu9WaSwHIHFO/ygOwt1PSP6zmG4awZeWfgSQhaeQzzTtN1YBZpZU/GzpRBvVOUJUlc0FbwufBpBe3etWmTzJgQtSME6UYEJNkuTDZkszdopN+Fg7j67qk1Uel4IXn5sG0KzH/XRoFk2r6XIFlQtFPKtZyRjNxiqP+6eyxNyTqeYxAKixMFy9gxHqbTYi+aRYGydKrEJoo6UFiyy9mf7MWEJ9ZX8eo1nkQGwWn227wqG4bNQqgan9G/BwmxAKSs0Mnb+Py+m3JiKv0yrUgx+faPO3NitVxM4/H0Xs3CUUsaPwxuozLD0Qj5eTnrwSY439lno28OWrR1qQW2zk482XOH0tj2NVtCDoEO5l7Xn0er/6jGwjm9tVL3Rkhql3/Kk5Mf8kxNrdUPlEwoF5Nx/8f0q2TxuO+w4lxq09xZIGx4I4Hj857LbniQkczDZLNOfiU5EQcBcKmBIWi11S9Ut3oqBGVbszh+07MOF0BBkGOQrjpNcw76FmdKrrbR1barIwadlxNp27tYaoFVk0piWd63r/LaJHETv/fBSxc5dQxM7/N5IkEfaKrTOog06No16DySKSWyyXjD/QKogvd8UiSdC5rjcJ2cU2br3uDloKDWZMFomB0QE83CaEofP33+nV0UF1hu900+9wnn8mpYO+wS56CJiNsG8upJ7G5BRI2tE1BFoS/+7L+3PoNQ02TwWpBo8fR2/o9BK0GANq2yhiTEYh/T7ZzTPS94zXyN476Jzk+YJaQ+YlKEgF6e72wcqQXHhe8xq1GrbjsQ61CfepbAoHcDopj9HfHiLrBoPHer7OeDvrMZpFDBaRk4m5lY4d2yGMR9qG/qW5PYrY+eejOCgrKFSgxGhh07lUejf0w05763kAyw6V31RHtQ3h8U61qeVmjyAISJJEeoEBdwcdOo0KdwcdszZetPZ+qkhOsYkAVzuS80r59UQyv564M68Ob3J5QvMbD6u3WLcdoDFzjAM5KEaiQiJauEI39XHGa9ai4t/X5PHpQy580sCCvU4HnV7kWEIOszdeZF9Re+sYDWbO6B+TK8a86kHfD0Clhqu74cpmyI4FUylY/iVLf3nXahY6TUbAgE9Ao6u0y2QRee7HE5SaLPRzPgMmYOA8aHqD+aNogbSzELMNDi+EvDsTjvmSA95CPovV0xBcxsJ5R0jwAv8m4B8NFapoGge6cvSNnlhEiaScYsYsOkxsRhETutZhYLStp8/Hmy/ZuDcv3HOVn48l8d3Y1jQM+P/szq3w56BEdlAiO/8VQl9ea/25f5Q/s4Y2wV5Xs+hZfzqF8WXW+MOaBzJzaNRNQ+lXM4voOnvHHV9vdQQLaTyrWUl/1QGroZ9JUvOlpR+/WtpzRapl0+TRhSJO2T1ufXzBpT2R+TevKvonkCB680GtT/n0/sZ8dSSH6VsSrPsaegqgsWNi9vv0VR+WNz53Hpz9K/cPMhtJPbmZg7vWYp99ATehkHzJgVhVGNFNW5B39Gd6qo/KY+3dIaAZqDRw+W/Iv3ELhtyEyttrtYBrRwABGg+D9pPAz7YcfM6WS6zdup0F+rnUpizBe/Jpec7qyIqBPR/D8aV/+JJLtO7Ym3Kq3hnWSRZnHlV7uTz89UF2X85k+n2NGVFFSbpFlGj53habVh/Odho+Gh5Nzwa+f/iab5XrUYPQ0FDs7e1vfoDCX05JSQlxcXHKMtadooid/wbTfj/Hwj3lSZdBHvY80akO3SJ9CHCz/RArMVqYufEC3x9MwGgWebhNCG/f2xCVqnqhcymtgM1l3bD/DEdjL/KYpPmFEeptVboWXyddcuN904NckQIYqd7CA5odd/1a/vHYu4NHbfAMx+LfjGWp/rx/REWxRYVKgCle+3mi4NOqj9W7gKHcIE+UBM6q69Ewsj5ZuHFEqseC5NrEpeVQV0iitioFewz0VB2lnbo83+ox4/OokKgvJHBBCqrSLuCWcPKDzi+BV11Y+zxkVqjoqt0VtPaQHUsxek6lGWmjuqHxmc4Z6nQBn4by62IxgqCSxdyhLyH79h10QX5dqq0Mi7ofCtMhdnv5tknH5W2BLeXIWxlN39lETrGJXya0o1mwe5XTzVh/gfk7Y2gV5oEkSRyOk4VV30Z+PNg6mA7hXn9aPo/FYuHSpUv4+Pjg6en5p5xD4c7IysoiPT2dunXrVjIdVMTObaCInf8O3x2I5/XVZypt79fYnwdaBdExQk6o3Hg2lSeWyt/06/o6sXJ8O1xu0rm6YuTodujbyI9wHydc7LT8evIaZ65VdqINIJPN7tNxLKnaFydXcsQO422b/ynUgKCipHYvBp3rwsVbMAkEaChc5VXND7RXn6VQsuNJ07PsERtjh4EvtHMqV3j1ng4bX/kTLv4fTpdXwbMO1O1D508OE59VzPM96/J094gqh19OK6Dnx7sAueVEXFaRTVf74S0CmXFfVI1fRu6ElJQUcnNz8fHxwcHB4W+tDlMoR5IkiouLSU9Px83NrcreWYrYuQ0UsfPfwWC20G32zhodire/0IWPNl+y+txcmtYXnaZm584vd8Xw/roLt3QN80c2o2OEN45625S4xOxiOs7cXml8QyGOr3SzCRCyiRX9qK1KrTSmuh5V/0V+tnQgJKQOLcJrQa3m4BHGgi0n+f5ELnZ6O5aN74CnlAfZsfy+dRtO6cdpqrqCq1BFo9T693JaVY8vjhvQObnzbP+WZFvsybLYk2rQcTXHxNd7bH2KBAHeGdiIRgEu1PZ2Ys/lTJ5fccJqRWCHga+0H9JRfQaTpOYF0xP8KnZAp1bhoy7gc6bTRBXLEy7zGNyrG93y16Db9FLla/NpCOln7+zF8gyXl5BSTshl+UlHIKPy+zRTcMdLysEsqdgrNuJXSzs2iS3wFXLorzpAc9Wl8h5pdxOtIwl+PXn4SieSVf5snNyJ2t5VJzZXjMz2bODLuE61+e1kMt8fTMAiSoxqG8Kr/er/Kb48kiSRmppKbm7uXZ9b4c5xc3PDz8+vShGqiJ3bQBE7/y2KDGaOxudwND6HYwk57L+cymztfAokB94wP1pp/O9Pd6BRreqTIROyiuk0q7JIqYpVE9rR9IZQ/bd7r/LdgXhibuha3jFAwi9tJy9rluEpyE0/a1w6+D/iqBjBiSZv8dh9/UAQMJpFBs/by9nkfEa0CmL6fVHEZxXRedYOGguxLNdPw5FS6/Hzzf2ZYR6Bi52W/FLzbZ3bXqtm5fi2Ngmyc7ZcYs6W8kRaHSZma+dzr1qutpNaj0fo+iqlake+3RvHvO1XKDCYaa86zRfaubgIxeTr/dAI4FB6g5jVu0K9vhDSDg4uqFIAXVGFEi7GVXm9G/vs5LNDBTimHmCBbg6uFFIo2bHQcg8LzfdQiFzZpMeITrBQIMlLur0a+DKxWzgrjiSx9EA8IBGjH4lakMjyaIZn9rEqz3cjRR1exXHP++Ub2kyAvCQ4v8a6qUTSMcM8gsHj3iQ6pOqlIoso8eWuWD7efAmjRSTcx4n3BzcmKaeY536SI2Y6jYqoWq40D3Wnc11v2tXxuqVrvFUsFgsmkxI9/Seh1Wpr7JeliJ3bQBE7/20spQWoZwQCkCG50sEwFwOVK13mj2yOg05NsdFMsdFS9s/MpbRCVh5NqjS+Kn4Y25p24bYfwM3f3UxWkRFHSqgnJNJKdYEe6mM0U11GVYOtf02USlrrkta7pod4Ur8Zb/EOTftuQorgwyuG0bRQXWSQei+BQmalMefEEOoI19ALlQXGB6YHqmyhcE3yrLYruEnvjtY9CBw8SNf4sflcBk5CCbXtCrE3ZOIj5Nq4Gl/npFiHj81D2CFG1/icogJdGdk6hP5N/NGoVDy2+DC7L2fiqFPz68QO1vLqjzZd5JNtV2yOFRB5TfM9YzVlfYecfOWy8sbDyC42sX/lx/S+OgMNIkfEujxhfBYjWp7WrOI+9R68hDwMge3QD/8aXALKJzYUynk2mZfh16fAXMpwwxsUOoXy8yAH7FeMsLkOg6RBQrC+H66Kvgw1vkUWslh7uW8kbvZacopNJOUU8/3BBNQqgbNv98ZOq6bQYKbRm3Ki9j79RAKEbAASRW+CbtK+wyBpScKbOkKFykOvenL5e1XvbXt3GLYYaneuds7TSXk8vuQIqfmyeG0Y4IKznYazyfkU3CBchzYP5L5mtYjNKGLtqRT2x2YR6edMy1AP3hnYUFmO+j9AETu3gSJ2/g94q/xbeoLoTSfj3LsybV1fJy6llbvGvjWgAaPb21alfDBvPvemfkZ9Vc3lv1c04YSbr9Q4BiBJG0qxwUTdu9hu4VYw3vsFPxnbs+1COhkFBsJVybS0HKd74W/4Gqt+bgMM03hR82ONSyTdDbN4ZUgHevze9o6vUVJpQRAQLHJlT1arFyhq9RyuDjpWHE1k7ekUSowWErKLKTbKy4JNg93o19gftUpgy/k09l4pF15T+zfg0Q5h1ijSjZx7pzcOCTtg3UvlicAh7eXlpWOLAciNGMIK/xe4nG0iMbuEq5lFpOUX404hRRpXejTww8/FDm9nPVGBrrSt7SnfpCWJ3M+74ZZ5jPnmATQePYf24V7wbT+I31PpWgySlp8snfnUPJh05OjiS33qkZZXypID8VT8pI8KdGXNxA7Wx9fz0Zbr3q2cAH0Dn5vv5SnNmhrHAHLVnMYOKSeucq+uOt2h8xQIbl3loen5pXy85RK/HLuGwSwvH3o66mhUyxVBgJwiIyeTqm5Pch13By0LR7WgeYjHza9V4V+LInZuA0Xs/B9w+GtY+5z1YVjpd0hUnafj66Knrq+zbCyo02CvU5OWX8qW87aRk8a1XOlaz9vmG//jHcN4rV8D+YGpFDa9Jvuc3ISfLR0Yoq58A7tTbiXXZ4W5E8M0u2qeqM8MaDO+6n0WE+z8AHbNqrSrXeknPKX5lYc0W6ud2iIJvGIey1kxlLX616zbD4n1eMf0MJ5CAd5CLoFCBpIkUIQdGZIr6bgzt2UePic/A5UWntwNPvWhMAN2TIcjX8sT1ekGfT4A77rWubMKDUz+8QR7rmRys0/AH8e1oXVtT95ac5ZF++Js9i15tBURvk742guoDnwmvwbmUtsJurwKPpHgFgL27kiO3uyNL+aTrZc5FJeNIyW0V53htFibFDxp4O9ChLc9j+Z9RpO0VQAs9ZhEeL/JpOaX0GTPBGpn7bQ5xWumR9mpas3YPq0pNlmYuaFyj67oIDfqeDsR7uPE4Ka18HMtL+G9LnYivB3Y/GRjUnKL0X3VAU9sBUVhu5d4Ia0Pu87G4a8r4aVe4TQ7+CzeBbau4FK9fgj3fgqOnlCcDTOrLktn8AJo8kC1r31OkZFlhxNYuj+elLzSasdVx3W3c4X/LoqpoIJCRVo+ZiN2ntH8whzz0CqHphcYCPF0RK0SKDIYMIsip274FjmiVTBvDmjAvB22Zb02H8gH51uFzhJzT076D+dDy3TIjiVD8GCDqRlxkh+OlPKcduVdeqK21Dd8y46BRgI3jq12zBTzOPqoD+MsVJ/UjXs1NyuQHX4tVec5LNHNIFxVs7miWpCYqf0Kk2S7Lt9KdZHf9a/zpmkUP1q6UIreZr8bBehOPAcC5Ld9ARef+vIOJ2/o/5FseLf2edlYb34HuPdTCO0ALgF4OulZ+lhr0vNlA8jfT6fgVRY56F7fB4soMXjePgA8neQlz7Z1PCuJnUe+kTuRu+jgjQgH7nMJQp192WYMO963eSgg0ME1iPZeEaQ3cMc39obffQ6cyQqlkSoOiyTwpaU/s5NbYvnqAAALtfnULnupfjB340uXiXRv4M+S1sHU9naqsmpw5pAohrcMqv6XUEaHuj7g6IW/IxicXaHA9n3v5OLB5z2aMXKhif2xWTzxexar7CW8AZOg5WtTH57U/IZwcS3FH+7gXPgT2HeaSFLv7fitf4xIIcF2iXPVEzWKHXdHHT3q+1JqEll/OuW2e29tPpfGm7+e4d7oAH48nEiJSeT5nnUJ9brzzu4K/y4UsaPwf0F8VhH7xB6MUMluxJOrETv1fJ25mFbAoavZ1c41c2gUw1vINw5vJ9vcnxJjhShK4kEAToaPZ9qZNqzJnwWGWJIkLwaVvosJNVv0L1g7TlfkghhEZDXLXlclf7Jbv0CaR2vu2dChyjEV6fCrA7+NPkijn7shmOQk6R/M3XhQIze4bCLEsElsXmNkyXJhHep6fao/yaGvqtx8XehMMk5kndiKFzQ/8aTmd5sxi8096a4+XmUOEMDb2sW8rV1sfXxKDOMF05Pcr96Bm1DEeTGIgdvq0/HaYdqHe9E8xJ2oQFeE5qNkcfP7s3B1J6waJ0+gc5KrvPp/jI9nHR7vVJvHO9WudN7tL3TBIoqE+zgD0LO+L6PbhdoIHn8XPR2LN/GMsJJaMfLyl6TWIdwzG7wj5fdA+nnIOC+3cCjOlp2e8xIQ8hKozjKvkSqOUknLs6YJrBdbY6dVEeJqj7+bHXWyzFACzxuf5GexE1df7GaTm+LlpCezsNwH6ouHmtG3ceWS3aqoVcGPSh/UFM7dYH547lfUTR9m/sPNmb8zhi92xMhLhirQSiacer7Mo1ujeZ4lNCSeFpc+Yu65q3wqDkevmY7BYMBPyGGP/hnrlJIkcTGtgJi0fLztLHi5u+Pj6oCTXsOq40m8uOIUZvGPL0As3h/P4v3x1se/nUwm5v17UP9JZewK/0yUZSyUZaz/B9afTmHi94eJsXvYuq2PYQYXKvirjGgVxPuDG3M1s4i3fjvHripaQrw7sCEPtw21Pi4xWnjn97PWlhPt6njSu6EfuisbGBE7BYDlro8RlH2A9mq5yuYHczf0gokh6t1/+PlICJXzIKqgUelCStHxtXY2ndWnsNi5k/bARo7mOdPwl27UFlIolvQccu5Bl8LKEYGVlk7sskRx2rEtX4/rUm3ZMHs+hi1vVdp8UQxknOk5Fk4ejpeTnh8OJeCx9QVGaMqr274y38N75ofwJ5v9dk/f8mtwnU/Ng/jQPNxmW5MgN6b0qSdX65gNsOkNOcKTHVveM0rvCoO/gMh+t3U+SZJo+u5miouLORS+GLckWTRm48Iv5vb8rupGs5YduDc6gCaBrrZJspIExVlyAm/mZTi9AuKqfh9kSc4s8n2Fx0Y/jqu9Vp4nNwFpThQCEh0NHxNZP6rSMo0oSsRnF+PppKPIYMbftWZXYIPZQr3XNwDw+YPN6Bflf30iViz7ip6X3sbtxrL+cTsgoClzN5/Hc+drjLy+TDl2KyYHb84WOFG861PaxXxMAQ60KJ1nLQpwoZBTduOsU/VxWMbI/IUMVu/BUTCQLzmw3tKKz4X7STCV59qNbheKShBQq0ClElAJAv6udpgtEpvOpXIgtvwLyvguddh2Pp2LaQVVPueX+0byZOc6Nb4uCv8OlJyd20ARO/9tJEni3s/2cvpaHr/avUUTLgFyrkgdw/eA/EH65oAGSBLM3nSx0vIUgJeTjkg/F0pN5dGb5iHurDmZTHpeEU2FyzRVXeE17Q9/zRO7BdZHvIPLhR9prz5LsaTnIdNrNGjZjWmDGpE4vTnBxprddaNKvyKf8pD/pyOaMqCJXDkkihLJeSXo1Cq8nXSYji6F9S+hE+WlvJPaJtR+Zi3OTs7W48cuPkLRxW0s071nc57DHb/l6+QQcs9vY7luGgCfOD9PnmtdcmOP86Fufo3XuTvgMU6Gj+d4Qi77YrIoKfsd3d8iiNf718f5umGk2QhZl+VoT1nkjagHoO8MuVLoFlh+KIGXfznNJM0qntOsALUeur5KfPjDTFp53iZxtr6/CyPbBBPh40xUoCt7LsvRqw4RXnL/tt8mw9Fvaz5h+2eg+5uyK/H26bBzBnssDRlpeo2lj7WyGmX+UcwWkfDX5IqydZM60iCg/DOwyGBm7cdPMLy0imVWZ38oqNoEE3sPqNUMrsiR1HM+/RiV0I8B6v1M1dq2rYgTfQlVVe6UfkSsy1DjW9TzdWbhqBYEeVTfHNRkEXly6VG2XijPq1v/TEdc7bW0m7Gt2uPubRLAzKFRt9VLT+GfhSJ2bgNF7Pz3sIgSWYUGcktMbD6XxqyNcsLmG91r8dje8rLX8NIlmNHwcJsQGtVy4ZOtV2o0JKwKH3L4UvcR0ao/Zst/K8R0+JCJe+x4wbKQ7urjt318iaRjgd9bzIkPBWDlk205vPRVxluW1Xjc0YdOM2HFJdLybdtjRPo5E5dVhMFkprPqFE9rVtFcJeeq7LREsSl6LlMHNrEawEmSxPcHE3h99RlUgshJv/dxzilPajU6+jO/wVLWnU7ll5LHcBDk8y0x9+Qt8yhEVEzsGs4LvetBfjIk7Ae9K5e2fE3dNLlj/a9R87hn4Ahyi018svUy3x2UK5AC3e15qU8kvRr4lt/UzEbYPg32fSo35XT2h+FLIKhVta+FJMk+MNPXXyBQSGeH/RQ0ogGGfA2Nh1rH7LyUwXcHEth7JdMqugA0KsFmOWZeJzP3HB4tn7/Ti+DsT6o2kDFrctCUZPCMxwF6FJUt+XV9DTq/RMHsaJwLrzLJOJFW945jZJuQGn9/t0p8VhGZhYYqK5cyrp7Ge/HNl0svi7VwVBnxU+WiEv+gV039AZBxsax0HSzOtRB6TUNVrw/oau6ELkkSAz7bY+NQHhXoytiOtfl6d2yN1Vs/j2+rVG39S1HEzm2giJ3/FhZR4t7P9nA22TYXZnS7UIY2D6TRV+VLV0vMPZlqHlPjfBE+TnSM8MbPVY+Xkx4HnQZJkpi58SJXMwv5TfcajVVx1R6fKbngVUVezq1SEtqT9641YZpp9h86/ohYl2UBr/DW6AE0fmsTAHZaFZKplOc0K0iX3NE0Gcor5wZVPvitPERRYvnhRF5ddRpvchih3k5T1WU8hXz8hWy8BfkmUiTp+dw8CKntU0zp38Rmmi92xPDBBtnZd2SbYCaHJeG1yjYx9XPzvcwyP0C4kMQWfbnjcJ7Gk4UlXdnk9gAbXuhhsyxkESXSZzTB3xjP66YxHPMZwpwHoqnr68zB2Cye++mkVby62GkY3S6UxzrUxtWhLNKTeAhWPSmXjTt4whO7wDWwytexotfOJr8vqJu7G0I7wqjfKjcmBXKLjSzdH8+Bq1lcTC0ks9CAs15DgUFO0P3c/kv6STugXj9Od/yCbRfS+XiLfJOv5+vMzxPa4XR6Kfw+GQQ1MZ3nErJ9EhpBZF7TNUwYWL1Xzd3GtOxhtBerLzfP6ruAIbt8icsqRouZZxsbGOaXhvfu1+/eRbSfDFHD5VwoVdWRmGu5JQz8bK9NzhJAixB3jsRX08i0At+OaUnXej5342oV/iIUsXMbKGLnv8W13BLal4Wu3R201HK3p39UAOM61ubp5cc5f/oo2/QvWMeHllZedhreIpC6vs50qedtTVC1iBJH4rLZdC6NjWdTScopwQ4DF+xqFktVYZC0TDWPpolwhQcr5K/g5AvOfpBysvqDy5hmeojRmo3VJvZep3bpdzzeOZyXekfS6r0tZJV1l27g70KwhwMbzpY7+j6v+YmnNavLDx4wFw58ARkXMNXpjTam6i7hBknD6+ZH2WRpQR5OPNg6mFf6RlqXj/rM2cWFVDl/YmLXcD7bfpkZmq9smpheEmvRyyiXr3uTw3d1d1EvYxOUyDeprZamBI5fRb0A2+UmaeWjCGd+5kPhET4t6YOLnYZvx7SkeYgHBaUmvtoVy8qjSSSXVco522m4v0UQvRr60SzYDY2lBL7pA6mnoN3TsjHgDfxwMIFXV8leQR90sef+A4PlZpvj98tl5TdBFCWuZBTi62zHDwfjKdjyAS9pfwJgfvBsZlwKqHTM2/c25JG2IQirJ8DJ8veoBRXC6xmoNH9hfcnJ5bDqCYp8mjMo/3mGFn7PE5qyHK8ur0CXl8kpMjLgsz0k5ZRHRu0pZZXuzWqT7W3o+Y6c02QshGNLobBy2xRA9jAathj8GlW5WxQltl5I58WVJ8ktvv0I064XuxLsWXMUSeGfw79S7MyYMYNXXnmFZ555hjlz5gBQWlrK888/z/LlyzEYDPTu3Zt58+bh61tex5CQkMD48ePZvn07Tk5OjBo1iunTp6O5xQ8DRez8t/j1xDWeWX6CIA97dr/UzWbf0C/2cSQ+hzi7B63bhhje5KhUjzHtQ5nav4FN5CC/1MSvx6+x4WwqZ5PzrR+eT6lX82LZzeqPkB/aF5e49bd9XJLkxVTLYwy3O0R3067KHjruobK/T/PRsvcNEqst7TjZ7H3eHNyUr/dc5d3f5eWjb0a3oH24F8/9eJK1p+Xci4v6UejvoNlontab1gUzKUWPt7OeSd0jeLBVMJvOpvLM8hMYLWKF0RLPaVYwqYK4alH6BZllzr9z7o9mUGNvOLvKWkm1suHnDB020vakW9+B3R9S0mQMD6cO50h8DnZaFeM61eHR9qG4OegQRYmNZ1OZs+WyTdKqVi0Q5O7APap9vJD/AdlqL973n8vKK+XvgSHNAll1PAlRgme6R/CswwbY/AaE94CRP9/2a1SydwH2m+XI1VzzYD42D6t27MSu4bzQow65P4zBLUaOrIj2HqimXK32mD+FnDiY2wRUWnKfuYJxTjN8pCw+N9+L0ONNJnQJl69NlDgQm8Wn266wP1auTgsTUtjm9AaC6Qan6+iHICsGEuWSevybwOi1oC/P8cJUCtvehf2f2R6rsYfe06DJg9Uub1XMRbodlo9rQ5vaSvfzfwu3ev+uufvhX8jhw4dZsGABUVFRNtufffZZfvvtN1asWMHOnTtJTk7mvvvus+63WCz069cPo9HIvn37WLx4MYsWLWLq1Kl/9VNQ+Aew5VwaL644BUDnupUTN7Vq+S0/yjjFuu1n/dsMbR7IlD6RNkJnX0wmPT/ayRu/nmXvlSxyi02E2JVwwW70HQkd4A8JnVTJnXihFgs1s+hj3l61WeCQr+GFi9D1FbjvK0RBwyD1PvrGTgNJ4qHW5Ut413JK0GvU9G7kZ91msL95suss03DGGZ9lgnESz5kncDD4caSI3gC4mjI4GLKAru6yy/Ibq88wfMF+YjOLcHe8sau8wEfm4WRL5RVeG4MW8dmIKJaPa8PA6ADQ6OTlpTJOxmdAYQVzR7MRdn8IgH1hPEsea0WXet6UmkQ+2XqZDh9s57NtlzFaRPo29mf9Mx356pEWDG5aC1d7LSaLRGxmEV+n1yNB9MbDkskHiSOZr/2YQEGuxvv5mCx0HmodzOQeEZBbVo7tb7tUd0skH7cKnaXqQXxsHkbLUHdmDo2icVl/tqn9G/DqPXK0aN6OK2y5mMWXzhOtU6hKsmXjxL8StxD5n2jCbedUPB3lyqoYMYCZGy6ysSw6qFIJtAv34ofHy52Rr0r+pHre4JTc9XW5geljG+HJPeDgJUczvx8GcXtALBPFWjvo/R5MOg5hncqPN5fIHkrv+8u2B2ajzfSlJgvjv7+13l430jpMyd35L/KPiOwUFhbSrFkz5s2bx7Rp04iOjmbOnDnk5eXh7e3NDz/8wNChcgLghQsXqF+/Pvv376dNmzasX7+e/v37k5ycbI32zJ8/nylTppCRkYFOV7kH0o0okZ3/BhvPpvLU98cwixJ9G/kx94GmNt3MLaJEnVfXWR9XjO7wajLoyquOruWW0HnmdsyihBsFfNw4nrYZK7DLvcEwrgoO6tvS2rD/7jypWyBZ8kDr6Il38WVwDYaxm+WlMGDrb9/R+cjTaAQRurxKTstnafruZgC61PNm0ZhWhL68Fi1mXtV8zxhN1ctUAL+3+4lphwVSC8ojPw+3CeHdQWXLCZc3w0+joMzL51idp3jwUkdrt/DqeFGz3Lb9QPep0PF5+ee4vbDonsoHOfmCV93KZdsRvRBbjmODoRGfbL1sXTrzd7Xjyc51uL9lkDVJWRQlUvJLic8sotBgRpsbQ+Ojb+CVfRSQc5Bmm4ez2NKbqQMaMapdqCyGv+oG147e1P23SnZ/KEeiarXAMmYDRWZwsdNal14FAfa/3B0/VzueXnac304m40Qxs7QL6Ks+XD6P1gGaPQJtnwK34OrPdzcpW8qyIqhYUvdTpp50p3EtV357ujyJOTG7mC6zd2ApS8g+6DAZXzEderwlRx1vrHy7dgwWD5CXsEAWVk1GyK+vR5mhpSjC4a9g0+tgsRU3eNSG+76CQLkM/+PNl5i79TI6jYpZQ6Po28ifJfvjmLa25lYYB1/tjq+LXY1jFP5Z/KsiO0899RT9+vWjR48eNtuPHj2KyWSy2R4ZGUlwcDD798s3k/3799O4cWObZa3evXuTn5/P2bOVuwcr/DdJzC7m+Z9OYhYlBkYH8OkIW6EjSRIPLTxgc8yHpgqmgvPa2OxzsdNQz8+ZWmRwwu4Jul5+v1qhk622DXkXlRiqHHe3kQQVp917MsDwHr2ynyceP8hLgB9HQkEafNyYgKureP16p/cd07Ff3JMNuimMUa9nx8V0dl3KIEJIkrfdKHSiH7L+mIQfE7eZSS0wEehuT5CH7N2y7JBcdQRARE8Yt12uqAGaxXzOln7F1Pd3oZ6vM5N7RDD3gWjmPdSMPg3Lo0n7xIa2590125qnQ9oZm125kiMiAhSmVe1Pc3kTquUPcE8DL9ZN6sjcB6IJcLUjJa+UN9ecpcMH2/l062UupRUgCLKJXrtwL3o19KNr+/Z4TdrGpSGbOShG4igYeFO7lCO+0xntcQZrzC+nzKDOK+KWfk82JJ+Q/6/fH7VGi0tZTtNvJ2XzxVahHtY2Dh/eE8DS4LXs00+ir/owBknD8aBHwC8KTMWyQ/fcaLmMPr+aEvC7iXuo7eOxW+jQYxAAF1PLlwYPxmbZCB2AeHOZuDn9s7wEdSO1msFjm2UBp3OG3HjYOQM+iYZv74EL60ClgtZPwHPn4d7PILBl+fHZsbCwO2yfTn52Ot/slZf5Zg2NYmB0LXQaFWM71ubZHnUrnxt4qmsd4mb0U4TOf5i/3UF5+fLlHDt2jMOHD1fal5qaik6nw83NzWa7r68vqamp1jEVhc71/df3VYXBYMBgKL8h5ef/8UoZhb8fSZJ4ceVJCsuqXD4aHl3JHXXmxos2pmPzRzZjwnciz19v03B9aaIMZzstayZ24Oo3X0MVDc+3WppaS8A9LLYdu7up/lj4/FYwSmoMOg8cou5F3f5pGnuE8dqxJOZsucwjOS+xRvc6rkmH4UP5Q70+CXRUtWKbthPdTLuwSz9BpAreVC2lr/oQcd8HsllfRd+qSSfAI4x99p3ZtGsPP1m64Odix4SucnREq1Ix+ccTrDmZzBNLjzK2YxjDWgRRy7se3P8drHsRDn1J4PZnWT9uO3jaGrjd09if11ad5vuDCeRJckQtX+eDi7uPLHA2vQ73zIaWY8GvMfg34ceTWXyy9Qo5uTk0EOJ4wnEXPc07Kl+7aIYj36IKbc/AxvXo3dCPlUeT+GJHDNdyS/hw8yU+3HyJQHd7ukf60DXSh6hANzwcdZgtIvPOavnV+DoPqrcxTfstHnlnZQEJcjSp+HpC+G068FpMEFvW0yqkffnlihI/H5XfZAOjvOH0Sjj+HbqkI3Q0FoAAMaI/L5qe4NjlukzqNpFnuieh3vWB7BV05Bs48q3cSbzDc/Jyz610+zYWA5JNRBOzQe7tJVrkZGHJIgvLi+vlMv2KZFwk2VEuffdwLI+gS2AVOm1qezC8RRBvrhjND7r3cE87LRspNnuYSvg2kFt69PkALqyFE99D7A6I3yv/u/cz+ThHL/n/6IfgwOfye+U6O2fgsGs2U83t2Ok5kAFRtlHBh9uGsGBXjLUJ7HU+3x5DXV9nBkbXuvnrpvCv5G8VO4mJiTzzzDNs3rwZO7u/TlFPnz6dt99++y87n8KfS16JyUbIFJaay0uLkcXQFxVMAvdM6YqjToOIikTRmyCVnP8gHZjPueARbDufzk9HE0nMLiFE6MhO/Sqb85kk9R/yuvmjlKociCxeiF4t8dUjreh0Q2nsfc0CZXO0jX48sec5qynfdfqpD5FqdK90b26lukgryhtGpktu+Ai5HHTpTUaSnvcWbCUlzwmQ20QU55eiVavILDRSy82eWcOiSC8o5UBsNnO2XGbu1st0jPBmeItAGjR7heBrx9FcO4xlYS9y/Nphr9fjENgYodnD4ODBG/0bkJhTQupluVLHYCglseGTBKVNhOPfQdxepHs/IVlVi7S9G+lz7VeGm9Yj2JV9UTFTPetflP/X2GHn04CRHrUZ0cCJmDyBs5kWjmXrOJsbyLYDrqzbr6cEHcXYIVqD3SpWWjrxnvYb23nL/F/wDAf/6BouoAoS9oMhT85PqdXcunnz+TRKMmKZot/NA3v3QWGFKI1/EzKaP0uPlRpr49pPtl1hZ5AbU/v/SPOcDbLYSTokC4PYHeDkJ7tCN3lAjn5cFz6mUrlbeuwOiN8nR5lUavBpIOdBFWXAzfxxQjvKc+75CNa+gK7ec0AkqfmlHIzNolWYB63DPNBrVBjMIv0a+3Nfs0BiMrqxcvduHtesY+eurURHPmDzN2qDzgGihsn/8q7BrplwdBGseRpMJdDqcfk5qVRy9VxAU1j+EJTmAqCRzAzT7GJY0S4454GlwWDrlx8PRx21vR1tvHiu88zyEzyz/ATRQW7MGhpFhK9zpTEK/17+1pyd1atXM3jwYNTqcs8Ei8WCIAioVCo2btxIjx49yMnJsYnuhISEMHnyZJ599lmmTp3KmjVrOHHihHX/1atXqV27NseOHaNp06aVzltVZCcoKEjJ2fkXIkkSszaWOx4/2j6MqQMa2IzZeSmDUWUNG78d3ZKukbJYCH15LQ6Ucs7uUevYimXoAWSyz27Sn/0UMEga2+aIVRBa+kOlVhVV8f6681zcs4rFug8goBlEDcey+yPURek1HleRlqXzyMCtxjEdI7xYOKoFAgLrz6Tw4+FE9sXYRrh8yGGpbjr1VLahMQM64rR1yNd44iAY8Cy6gp9wcw+UmkiX3EgJ7EOxeyRuUh7+WYdwyTqO6sYKoBowSmrOSSGcEcOwFww1d6F3qQWTT1fr91IlG16BA/PkiMSgeQBI8fs4tXQKTcynysfpnMErHEuzMSwzdeKjLVfILjLiYqdhbMfaLNgZQ1FZZCLMy5FeDX2Z2FSH87H5csm2uQpTTP8mkHGp6n03Q2MvR43qD4DGw+R8mWUjrMuIuyyNeck0jlQ8aR3mQdNgd+bvjEGnUbF3Sje8nfVIksTuH6bT6fIHAEx1/4BnHh2Np/MtfMmVJFj3grWpLoEt5XL3Ot3KhVzeNYp2z8N4ZAnulAuZPLtaNMmdxYQudXipj5z03WLalko+PDdi0zZD4R/Nv6L0vKCggPj4eJttY8aMITIykilTphAUFIS3tzfLli1jyJAhAFy8eJHIyMhKCcopKSn4+Mg3sS+//JIXX3yR9PR09Hp9pfPeiJKg/O/EIkq8+stpfjwiRwZe7F2PCV3q2PYiAvrO3c35FPkD8Pw7fbDXyTeo0d8eYsfFDJtE5eeMTxKmSrX1mrnLLDT3JVoVQwvVpVsav91lEKr+s6usLruR8yn59J0r34R61PehZagHvpoSTFun0cmyH18ht8bji9zq0TB1KreyRLNqQjuaBpcnmiZkFfPTkUTWn0khPd9AgcGMDhN9VQfxEvLRY+Ie9UEa1WDAeDc4LwZxUqzDRSmIYuwIdLNjSCNXAuwtYCjAWJLP5UsXcCuKwY1C7DGiEm7xY9AtBAz5ck5Rz3eg3aRbWzKSJLl0OzdeXuaL6A073kfaMwcBCVESsIR2RtvyEYjsz56rBbz7+zlrmXxdXyfmPdSccB8n0vNLmbXxIqtPXMNkka+7RbAbC/o645m2F3bOhJKqG9lmqzzZSzSntY256tiEjsF2dPMtwD+wDmpnX9A7yeJGpQZBLUdPqkIU4dACLJveRC3K/azeNI1ildiB6++d6wnwVtLOYfm6N2qj/LeYghdunSdg3+kZUN9kkUGS5PLzbdPkZTaA8J7w0AokYMOZVN79/RyZeQXMdP6RQSa5EMEgaahnWIKbg5YTU3sB0P/T3VVGdiqiNAr99/CvEDtV0aVLF2s1FsD48eNZt24dixYtwsXFhaeflhsF7tu3D5AjQdHR0QQEBDBz5kxSU1N5+OGHGTt2LO+///4tnVMRO/8OTibm8tQPx9BrVDSu5crqE3JSpyDAjPsac3/LylUppSYLkW9ssD4+9kZPjsXnsHh/HLvL+hTVFpJtTAb/SvK0PuykGSUlpdxfwWDPhkHzIXrELc0nSRLT1p7n6z1V+7AseLg5vRv6sfrzKQzKqNxvqm3pp6RQnnDdIdyLIA8HVh5NtN5Yr9Ojvg+xGUV4OOoIcLPHw1GHk15DqJcjSTnFxGcVs+r4NZtj3hvUkDpiHNq8ONRFaeSLetQF12if+CUgNw69MRIEQLNHsPT/hMvpBZy5ls+LK47jRw4eQgFuQgEDwgQa5e+mfsE+1FQuyU+SvDjr2YsXkrtRgK0vi14j8LzXIcblflz1iwpss0QzmRd5snsk48QVaHbNkHe4hUB4dzmnxWyQow5BrSCotZx0ez0fJv28nASv1sOjG+C3Z2QTQ2CFuRM5rZ5n3L1dOBqfw4z15zkcJ0e63By0PNezLg+2CkajriA8RAtFiSe5enQzSSe20EK4cEsu3WfFED4zD2K9aFsKrhLkbukNA1yo6+eMXqNGANqHe9GqmlLsjAID05eu4eHUGTRVyc7Sqy3tmGx6iuuC59w7vXHQVRAyhRlk/PYGDhd+wbGsJQj3LZSXrG6FgjTYNUuuygKkKfE8vSqW30/JS39BHvb8eK8TAct7ApAiedDWIHv0XHdHPp+Sz4ivDtRoODigSQADovy5mllEdJAbrcI8Kn2JUvhn8J8RO9dNBZctW2ZjKujnV17NER8fz/jx49mxYweOjo6MGjWKGTNmKKaC/wJEUSKryIinow5VDd+kpq87z4JdsVXuc7XXcvLNXpW2p+aVsutSBi/9fKqKo2QChQyW6969qQvxn8EuS2OeMD1LCXY0FOJYq3+16oFv5tYYPTBbxPIbocUEF9aSc3YzcfmQUGpPTKGeC/laEiUfip2C2flUFObP26ExVe4IHVr6PQ46DSNaBdMh3IvXV5+57V5hNzKuU22u5ZSw9nQKvRv6suBh2y7dHF4oe6aUIUoCH5qHscjSm612U/AjEx5cAXXl3/HMDReYtyMGJ72G9+9rzL1NKrgPF2XJyytpZ5BST2NJOY2moFxwZUnOrLB0Zp/YkAtiMJ3VJ3lS/Rt1VOV5Mj+ZO3NZU4c2mit0N++yudRkyYNEyYfWqgs3fd4iarKd65Ll3oSg9O04lKYhaR0RJBHMJRi0rkwqGsMJp478/nRH5u24wqJ9cUgS6NQqHizz9XGz00BeotwlPe00Yvw+xLj9lX5/pZKWY2IEh6RIDor1OSHWoQQ7vMhjoW4W0aryv5+MiOHsjpjCuvM57LqUeYPZoy2TuoXzXK961seSJPH2b+dYvF++VjUWpnps5qHi79Fgob9hGmek2gDsf6Vbpa7rG86k8Mx3B/hV94bsrDxsMTQcdNPX00reNfhYXqo+1Wcl9642olULjO9chwmNwe6HgdYGpYmiN/cYp1sFbj1fZ17rV59a7vb0+niXTcVYTTSq5cL9LYJYdfwa9zT2Z2zH2rd+vQp/Kv9asfN3oIidv48PNlywJg+H+zjRPdKH53vVsykbB/h271Xe/u1cVVMAcr+lNwc0JDG7mB8OJvDdwfga/V00mDmnH4OuKmO+P4vAlpB0GNGnIX0SH+GSFASAJ3kctRtfeXzU/XDfl9VOJ0kSYa+U+wbNHBLFsGvvI5z4413X65UuopaXO0vHtubNX8+y5bxtN2q9RkWTIDdahrpjp1HTsJYLJUaRuKwi0vJLySgwsONiBvYaiXubBtOjvi8dIry4mFpA37m7uH5v8SaHJ+tkM8IvGYejX1jnNwh6njZMYJPYkgghic36l+RllSlXQWtPXomJNu9vpcRk4cuHm9OrQgl7Vaw7ncLsXw9Sr/gYz2tWEK5K/sOvTU28bBrLeTGYZqrLNFddpqnqMrWErBqPGW98hquSPy1UF2mrvUxD8TJqRDT2Tnh6eKDDDKV5UJBavnRTgQLJniNiXQ6J9Tmvb8S+4iCM2Cb93tPYjwBXe5oFu9E70Ij62GLY8zEgyZVu/T7EbBHJKjKSnFvC0fgcknNLMVlElh4oTzH4dnRLruWWkJxbwrmUfHZclJP6owJdGdk6hOEtgzB81Rv9tQNMMj7FGrE9k7pH8FzPyqXecZlFdPtwBxu0L1JXdQ0GzoOmD1UaVyUWk5xg/f1Qm82n3LoT1XsMbHoDcq5i0HugLc1BJUic9+xF32ujrWMndg2nfxN/+sypwrrgFvn4/ibU8XYiwM0eL6ebp0oo/Hnc6v37by89V/j/JTm3xKZK6kp6IVfSCyk0mJk2qJFN2Hho88Aaxc53BxL47kBCtfsBBESaClcY63aUe0p+u/MnUIZB0nJGCrV2/a4WvyiKH9nAW2vOcilBXqppLZznE51tSa9J44TWXAgNB1c7lShK9JlrG3V49+cDdLNbhxdA7S7g15jczFSOX4zBXcolREjHXSis8RLrOZVwKlPHK7+cZt8V22jX4Ka1eL5XXQLda+gbdO5XpOy3EbJj4IInZDeAixHUE1Ss8ImnIDuduqpEAoRsuIb8rwL6J7ayaY78e9SWlVsVWwSy880EekJafiklJgvOdhp6NvClKvKKTZxNyWPp/njWn0kFtJg8unC44yhqHx6OKvtK1dfuEggaHaJaR4nOi1zX+qQ41CMNTxwPzqGLurxf2ZeW/uyxNOQ5zUqiVTF0Vp0ktNcEnPQarhQaOFJswq44Gb/809QtOEC7gspmjV/o5tpuuK7vDRmQYrsMaZA0xEl+XJECOCrW47yuEXWbtKFthB+jgtys3jylJgsfb7nEgp1yFMdkkXi5b2R55K/Hm7Lx3vIH5YhafjKa7lPx9amPr4udTQ5WbW9H69/cmEWVrUFGtgnmvmaBOOo0FBrMxJj9aQL0cL3G3JfuqXbZJ9TLkX5RAaw/24q6qlWw5U25eszererfiyRBcRZkX4Utb8kVZTcQlbsVfiy3UEg2uzLbNJJPdZ9RP2sTbVStOCDK0aBIf2eGfVHZ9PO5nnW5mFpgbZ1SE8/+KL8XIv2c2TC5001GK/wTUCI7KJGdv4u8YhP3fLK72mWS3yZ2QKdRUWw0M/nHE8Rn3XplzY20FC4wR/f5Tb9t3yrnxWBWWjpxWKxHluTC69rvbB1uq2Cl37O8ca0NJSYLIDFQtZeZ2i/RC2ZrRVahZMfPYidGqTchRT+IMOiLKucaufAge8rEiKu9FkNJIYt1H9BadYE8tQeuL53GonVk2Px9HEvIBUCPkYt2o61zHBPDiWjUEuf0Y5Apl6AnPrCN7kvTKy1rvN6v/s1D9/H74ds+NY8pwyIJXJICOS6Gc0yqy2ztAnnHI79SGtSRyDc2oMbCSf3jOAmlPKSexRcvjiG3yESnWdtx0Kk5904fJEni9LU8Np9L48y1PC6kFpCSVx4FUasEnupSh6c6BaFfNxlO/SjvaPGonODqU1/ucq6upgy6jIISIz/Ne4OR+V+jF0yY7b2I7zib9bEmJl55nALJnlbi17zctyEPtwmxXZI9uhh+K6/q+0IaSm0xjmaqS9hj5JgYwRGxHkelCAwqB1oE6PDVm4nPNXMqUyIDVzJUvnSJ9KNNbQ9ahXlSz8+5xgTazefSeOqHYxjNYqVlKEAWDXvngiTKkbORP0Noe5shMRmFdP9wZ42vS0X6q/bzme5T8l3r4fLsoRrHbr+QzhOL9rHJ7mVCSYFBX0D0g7aDchNg/RS5fYShcj7SMft2iEWZ1Sb6i5JgTTxPldwZxxs8OfQe3v7tLGn5BlqFevBYxzCeWHqUQHd79kyR++idSsrl3s/2AhDh48TLfSOx06oRJYlNZ9NsIl5Ngtz49an2lU+u8JehRHYU/vG4OmjZM6Urg+bt42RibqX9Az6rofS3BtZN6kgdH0dWHEni9dVniHbKZYlpBvaC8eYH3yK/WtrRUXWaIerd1BUS5XYMNyE4eR0L2IJaKxKiSrPmCa2ztCK29Ts8daw/TmIp96nk8PrRi/F4ZxUR4ulYaa5TSbnWn59t506T3a/QVHWFQhzIvW8ZzlpHJv5wzCp0APqq5BtQsuRBV8NHGNAS53sOzi2zjgkyxtIhIpRtF2xL1W/Mu6gSO9fyn8dukyt50s5BzlVAADsX9iWLzDlq4owURjHlZcdvNC3B9cwSOLwQu7DOnHm7N43e3MhesRG91UdoZdjP6aT7iPSTvU+KjRZ+OpLIkv1xVVbW1HKzp0WoO493rE0jH51sCnhli1xh1P9jaD7q5s+nAs72Oh6Y+B4vL4jiiczpRJYkUmfTaCa2HCvvF0rwMqfx5hq5Ee1r/RrQPMRdrlqqIHR667/jYl75Eu2P49rgqFZhOJvKtbOpxGUVc6RCgFKjEugf5c8LvevVHFG7gZ4NfJk5JIrJP57gk21XKDZamNyzLk76so/8Hm/JTTTXPifnOP0wHB5eJSdXI0dZ719QHv2YPawJwR4OnE3O41hCLjsupFNgMOProqfUJJJXYuKEJDcDdc6vJnJWgU51vfF0debXojY8o1kFlzbYip20c/DdECi4vuwoyOX+HmHg15gdgU8y+rszqFUCW57rTFjxGbmvliHPOkXFCjs/IYc1PMeGnafIye9KmJcbix9txfcHZeGir7Bs3ijAlYWPtMBOq6ZDhJfNdTvpNTZip9MN+xX+uShiR+FvRRAEGgW4cDIxF5UAt5gvaOX3pzvQqJYrZ67l0f/TPdhr1YT7OKHTqAjxdECLmbeMH2KvuntCB+Bl7fLbPqaV6qLNY1HryCJLX6aVDuJnr1IEUV62cRZKKJF0TM3tR8zHu3i+V13GdqhtEy3oFxXAskPxtFedoevurwlRpZMrOSKNWE5IZBt+PJxQtoRTTk/1EQBWWLpgQMcbfgdg+ye2F7npddo0+p5tFfJv+zX2p3fDqpeMbPBtACqN7GAsmiCwjWz4VoF2QLuhcmXdwM/lb88zh0ThGhQCZ5bA+d9g2QicynxoNlpa0Ft9hD6qQ/ReeIDX7in3UHpppZx4bq9V062+D21qexLp50xdX2dc7csiNaX5sOwBOc9DYw8PfCd3K/8DOOo1vDl2GIPnevBI0bdye43r3i/A1PYOPHNIzbGEXIZ8sY++jfyY7LGf6zGVyZrXbISOo05NsxB3tGoVzUPceblvJJfSCjl4NYv8EhP+rvZ0quuNt/MfywkZ1LQWsRmFfLLtCgv3XGXV8WsMaxFEv8b+1Pd3RuNdFx5aAT/cD1d3wpKB0OkFaPs0r646TVaRkUa1XFg8phWeZXkprcI8GNMecoqMFJss1HKTRXChwYwx7RJ8A4L25qIsvaCUwlIz2yxNZbFzZZvczFOjg4QDsvgqzQPvSBj4Ofg2kpuCAkUGM6/PkZdwH+sQRpiXI9AaRvwAi/rVeN4+mYtYp9tETttPsNepWVnmXD2yTYh1jEol0KOaJdK6NxgNBrrfwpcAhX8EithR+NspKTNIG9IskPr+Lrzze/W5OTey+VwaBrPIt2W9cEpMFr7aHctTXcMJdHfg2bKciuqwCFp2mxvY5GPcCZ+ZBzJR86vNttP3HyDUWWLu0p/ILDBQy92esX1a4x7RBnH5SpbETKfpBts+brkDF+NxzJNzVzJ5f90F9sdk8cmIpjjbacFYzCOO+xmk+9paFZQgejPaNIXYRQXAWpu51CqB0e1CaXMyEUxwTfIkVEhhZL5cvku316HNUzC/PWTHEpq1C8pu0V5Oej57sOnNy27TzsmGeWWCjaX3yd3XcxNlt2GNbUPeRrXKo0DNQtzBJwh6vis3yby0Hr7ty+cDv+SVX5thkLTUUyXRSLjKe+tsr6NXA19mDImyaVdgJTtWNr/LuABaR/nGHnpnSw5uDjq+HtuR0d/asTM3ig91X+KJHE3o6ZXDjhcG8eGmS/x0NJH1Z1J5WTfXmouzurAhrvZaHmgVxIKdsbQK80BboZxcEATq+TlTz+/uOfc+16seUYFuTFt7jrisYubvjGH+zhgcdGqig9xo4O+C3uMt7st6mTr5B2HrOySe2smRxMdRq9R89UgLq9CpiLujjoqtPJ30GnD3AAQwFsi/x25vVFlFaDSLTPj+GAUGMxb/aCSjD0JROrznJ7enuE5QaxixHBzKS99LjBYeW3yYpJwSAlzteKZ7hf5koR2YonqeD8QPa3xNwlXJsHko+VfuoU1eGK6CBx1dvSHfTm5Qqq3e6LDUZFvQsPlcepWWFwr/PJScHZScnb+bfVcyeXDhwT98vCDIOYzX0aoFLr93D9svphP2fQdCVWlVHrfU3IMPzcMYrt7Bq9plVY6pSKFnY5w0QNrpKvcXqN1oXDSP+0KNTNMtxqF2G2j/DMdTSpn661lOX8ujlps9v09sj3vcWrkqpsxrxSypEBveh+5cWa+ucTuQ/KNZfjiRt9acxWAWae2n4utGp3E6tsDan8kgafje0oNPzIPJpfJNsnNdb17vV5+IklM23cNNkhqtYKHIrxXrW3zNxnPpjM+eSbPcjVwUA9kktmCPpTHvPPNE1TdfiwkubZT9Y67urLopZ0We2CW7+JZhNIs0eXsTJSYLT3Suzct9ImVBlVq2HFGQTLF3U3oljWaKZhkD1AdYYu7JVPMYm2nreDuyYXIntCpBjggUpMh5KCkn5FwZQz44+8MD39u0aLhTMgsNjF18hJTEWA7aTSzf0XcmNBrChQIde7f8ymNXyvc9ZZzEWrG84ewDLYOYfl/jv8S/xWQR2Xo+jZVHr3HwahYFpTc6dksMVu1hhnYhesHEEnNP3jc/yGsDmzGkeSAOOg2peaUUGkzU8Xaq/pr3fQabXpN/7v2+3JX9BracS2PsEjnKuOvFrgQffBMO3VB12GCg7C+lK48SlRgtPL7kCHuuZMrLSY+1skmotogSEa+tY67mEwaoD0D7Z8AvCtPqiWgtt2Gf4OAll8JHP1jpPXM9ggzylwiLKLHiyba0DK3ai0jhz0cpPb8NFLHz91FkMLP2VEqNXji3gx0GRrudILjwNA9qtlU5xiDY8aLhMdaI7XGmmNN2Y2867+/BU+if8EGNY+abBzDDPIJH24ehUQtcSivgclqhNQHbWa9h6aMtiD4+FU58B4CkdWC10IPZ+d0ZP6grIw/fB1lXZO+R4LaQfo60M9tIP76eBtIV1NfzENyCSQ27j8H769iYAFZk8aOtyl2Xr5vaVcAsqfi541pe25aLWZQYp/6tatHX9TXZHE+SoCgdUk7JYuJ6Z/Jbodvr0OlFm01L98fxxq9yRGtw01q81q++XMabeRkW9rD2OrpOnuRAK8M8DMhRHI1KwE4sYlbEOfqU/I6QWUWiamArGL4EXO6+9X+pycK7P2zhvavDbXcIanAPkSNLFTgvBtPXOMNmW6SfMw+2DmZQ01rWDuh/NqIocTm9kCPx2cRmFKFWCagEgfxSE63iFjAobykA2ZITSy29+NLcDztHV7KK5KXg6CA35o9sbq0Aq8TeT2DzG+DkC5PPVIrq7bqUwSPfHCLMVc32lgfKSuFvoIKvEkBWoYHHFh/hRGIuDjo1Sx5tRYsbBIZFlIh6ayPjxR/k6GrrJ6HvB8z97meGXX6BACEbSeuAsd696M/YLkNnSi64C0WVDSmjR0Lnl+TfJ5CSV0Lb6dtQCbLh4u7LmfRt5McXI++ekFa4PRSxcxsoYuevQ5IkJAkyiwz8dDiRVcevEZNRdMfzqhDppjrOQl3NIex1llbMMQ/BjJrB6j233BYiVXKvsX/TCONr7BcbVrlPEOC+poFM6VsPn/NL5T4/ggo6vgBNR/LT2g0UX9hCL9dEAorO13gd58QQlggD6HP/U2y6mMUPB6svt/92dAu6+hTJEY+EA3BscaUxWyxN2SVG0V1zmg6q06ilmzSCrIijt9wM07cBtBoHuz+Um1J61YXOU0DvDF4Rck+m0A5yK4IbWHognqm/nkGSwNlOw/T7GtM/KgASD8OGl5GSTyBI5VGIScaJrBHbEeGp56PgvYSdm4eTIFdfGdUOiL5RFBtMaLzDcWnUGxoMrr7lwV2gJCsJ+0/l3/s75ocZ63KEgGLb32Gu5IRbWcn/y+ZxbDU3IQN3a7NMADutit4N/RgUXYsOEV42y1t/KaKFU6s/xO3ElwSXNcjdZGnOONPzNsPCfZz4eXy78tyoilhMMKexHGWrwh05Ja+EdjO2MVm9Qs7XqQr3MJh4GNRa4rOKGPXNIeKyinFz0LLwkRaVhM51xn93lPDz83heuxKajoSBn7PxbCqzv/uVVfo3caIE9C6Ig78iY80b+BZfQpQE5prv4zPLIKZ0D2Zc7Ww48YPcfZ6y26NnOGgdEJ18GXq2Pcekcv+g/lH+fPZgs1t7fRXuOorYuQ0UsfPXMHvjRT7bfvNKjapQIdJLdYRaQgY7xGhipFq4UUBf9SHe0HyHw3Xr+TvkmuTJZvt+jC5dclvHzTQNZ55lECD7wzwW7UBDHz3B3m6EBIfgVnQVtr8Pl8v8VjR2slBIOmybp1ARQSV/6Ac0RardhRiXlvRYWH3+kZ1WZTVS7K/az7v2P+Buqb7UvmJp7nUy9MH8VBRNuuTO29oK4qjRULlfkt5ZThb1byL/r6kiV+Y2ORqfzdRfz3I2Wa6qGtepNk91CcfFXsPucwk0/Kk9noLsFvyNuQ8Jkg/j7Tbja5H9UK5IASw292KVpQOFFVpBjGwTzLRBje/4+mrEbIRpcvSsWel8snHmgPPL+JkSqz3EggrCe2KIvI/f8uvw9cliLqWV+x95OupoH+5Fy1B3mod43LTM/G4jihIrDsdhOr2KB5PeRYVo44qsU6swWkTGd6nDlLLmmpXY9p7crbzRUBj6daXdk5Ydp+D0Wr7Sf4xGunFJrYze0zkR+CCPLTpMVpGRQHd7Fo1pRbhPZdF8nTe+/Y1340fKD1o9AffMxGgWaf/BNuwKE9jsPhO74hTZh+qBZZz86gmaZKwB4BdLB16yPMWBV7vLEcb4/bD9PaS4PQiU/52IksC75pF8a+kLyBYZjQNdb7wUhb8IRezcBorY+XNJyCqm06ztf+hYNRYGqPbztGaVjaX/n0Vnw0c4YGC9/pVK+zIkV06JtemuPl7lsT+Yu9JcdZnaQgra23BmztDWYkNJfZwj2jMo7XNrPs6Vpq+wxn4wxxJyOZOcV2Mvn4r0Uh3mC+2c8iWvKnjC+CwXpUB+bXYC16I4COtMjHdXui8pz29yJ5+jduNRIcFTh8C7XrXz3SkWUeLd38+xaF+czfbH1Gt5Q/t91Qc5eELPdznq3pffTqVw+loeR+Nto28LH2lRbWXNXWNWOBRlsLfHr+zeuIKX1d9hFrQ8Ir2DwVDKc96HaZ8vO12flCJoItiaT0ruYRTaBRBrcOJ4jh0JRhfSJTfSJHdScSdP50+TYA9ahHjQItSd6CA3HPV/UW3JL+Pg1I+YfZswsOgVzmaKtArz4NDVbFzsNCwc1bLq3lnHlsKaiXLD04d+qrR7+8V0xnx7mBB3HTuH28Hi/pXGGO28aFr0KUUmiUa1XPhmdEt8KnRJF0WJw3HZJOeVkFVgIPzy13RJ/Lx8goaDYdgiAF7++RSbD59hu/NUXEwZ4BYMk09TYrTw2luvMku7ALUg8bjmPT584Qlc7LScTMxl6YF49p88R7CYiB4TA9T7GKLeg0lS08LwBXk4MbV/Ax7tEHbHL7XCH0Px2VH4RyCK0h8WOgBztJ/LyYZ/ETv1z9HJYJtD0MXwIUu0MwhWZZAt2Sbr/mTuzHCNbLz2oKb8eRrRyHb/VRHRW84B8G1Inn8H2s+7jNEsMsI5mFN2Lel6+X06mvcRdGw2nxkaIpaV86gE2X02toZlvxdqnWFilu31p0luzDUPwZtcntX+TKLozWaxOV0j/XAdJucrSZLEpz+eAGSPlr6N/Hjup5Nc0DWmgfEULOoPD/8Cfn9OpKSg1FTpG7sfWVUKnQTRm0XCQMaOeZUAb0+aA83LljWOxmczpII77qX0gj9f7LgEQFEG7Uu20VazDCR4yziSfZYQGnjraRt6CU7JS5CHe/5Ek0gznPxB9v1JPYOQcxVnrtIEaALc0PGBLMmZQ3GRHIhtwOtiFAlCAA38Xehaz5t7owMI97l71VuV6PoqXN6MJu0kU0L38UhmG4I9HMgrNnExrYDhC/bzSNsQ3hnYyPa464nFxqodu1v6CozUbKFn4RGkpeexxq18G8vLXpunoivNpKXlGFLdXsx7qJmNwDNZRMZ/d5Qt52U/qB6qo4zVfW57krOrIKIXRD3AM2mvM8NuJ1z/vtBM9lnKKzGxWuzA2yzGmRKmN8lkw5lUvjsQz6mk6549Lrj4t6VfYz/Cwycg/dIZbW48syIvMe5CM348nKiInX8BithR+NMwW0TCX1v/h461w8AD6u03FTorLZ2YahpNa9V5vtXNqnHsGOOLbBeb0ly4SCau9FIdoZ/6ABOMk9lnV278Nka9wea4TMmVBMmHYDIYpilv0VDqFs7wXFuH2enmEezQdCS/1MJO/WR0goWf7IcT0mUMrVu1tSnFPZecz8DP91i7iS87JOfffMcTXLLbh14wMSLKjcjawUQHuhHh64SdVk3UWxvJr1RNA75k83jmrOsNpxElgdnmYXxj6YuHuoQd+hdAhLmW+xBRsbWCceAHGy6y+kQyKgHGtAu1+h29rX+BH91nyRVoX/eGwfOhwb01vs63iihKHIrLZsWRJH4/lWzNXwHopjrGN7rZlY/pM5Pnj9bjcGIRTieyeK6nbXJ28xAP1j/Tkb5z5eow9V/RqdqrLqSchL1zUQFbNZ34rlT28vna63tUp35BlAS+tvTlpSYB4GInm/r1eEtO8k45Jfe/KkiBwjT5/wL5fyk/GU9LAX3Vh+mrPoyIwHJzV2Zeu59PruXxybYr1Pd34d4mATzUJvjuJzm7h0LH52DT63hlHwfa4GqvZenYVszdcpkfDiWwZH88D7UOsa3acyrrWZZ/Qy8y0QI7P8Bp/zymacoamYrIOTFtJ0KzR0Cl5tjJEzRL/4XPdJ+j69Eb3Q2RrN9PJVuFTpvaHjRSh0BVK4cbXoGkI/in3+AEXSbCzqXk0Vl1EmdBLiIYtc+bs5JcLKFTq+gX5c/INiE0C3Yrr0BrORY2v0HPhLkECrO5mAaJ2cUEedy66aPCX48idhTuOjc2qLwZ6yZ1pEGAC/cv2E9y3HkeVG/jQfVWXIXq20PssTRktvl+fIVsztk9etNz7Ks1hk4NHsI+Lhuj2ZdoTweig+/hksFM2qrTdDF8yA69nIQ5RlPex8ji6Meb/dsQ/ntqpTntcivnH50SazO2fyca7H8OXZaFg2IkL+UMglU5zHeUHXIvpORz6lpepQhNixB3WoR60NEtCzYCKi3vDW1u/ZZsESXWnEyuJHTGtA9lZJsQfvl5GfrU8qWu98wP8rWlHyAxTTUXnVjMGSGCny0dbY5fdzqF+TvlXKAZ90XRLtyL4wnyctCFQntKH/8Vu9WPysZ8Pz0Cg+ZVtva/DfKKTSzYFcPq49dIrtDaIdLPmUFNazFU2oz7jo8qH9h3JqrWT9A0+zyHE2MreZ5cp76/C8NbBPLTkaQam8HeNfwaw+kV8s+eEWz3fhVOZAPgkS6L9edNT5IcMhBflxsqmOzdoXbnaqcWzEa58i1uN8TuRHV1Jw9qtjHE/ijLXcYwI6U551PyOZ+Sz6J9V3lvUOO7H8kKbgtA7fyDePMArcKa4+Nsx3uDG5NTbGTd6VTeXHOG7x5rXd6Dy63MeyYvSRY4KrX8+Ogi2ClXNcapQ1le2oZ6ne9ncK9u1tPFZhQy6tq9LFSfk32kfhgCo34H/yhAbmMxZ90JuqmO81TgVZprU2UvpaoozYUjlXOG2PMxXN5CWMQoJpUlSR8R63JWCgXg5b6RDGseWKXHEO2ehjMrEVJO8qB/MjOTvZmz5TIfDm9SeazCPwZF7CjcFQxmC5IkfxD1++T22jw8vXQfzwdeZFLSj7TXn73p+HzJgYaqeFbrp97S/GOML7IztimRxUn0aujLiFbB+LrYceZaHg8tPIhFlGjatAVi+CeoKlj7A6i9whmm2Q3cvKdWiuDDaTGMnoe+o2HWJkQE3jE9bN3/5HfHqj3216fa0yTITX6wo6zEvXYXq9ApMVoY+fVBa06Ks52GnvV9ebB1sLUy5cVBbWB++Zz5AZ1wSFMz3LKWbuoTGCQtzxseQ6K80ic5t4SXy8r+n+xch+Et5U7sDQNcqeVmz7XcEhYfz+WJh36Gdc/LN6vVE+Ty6ib33/Q1uZG9VzJ57qcTpOXLCeXOeg19G/txf8tg+dvzlS3w/RQAroq+hFX0SCrrX3U9zbCmoI2xLEpktPwFXe2965f/3HwU8efl11eHCVNRDnrgLGG8VdEA71bR6OQWDkGtoOPzEL8P1r6APv0so7Lm8LCzK1e9urIgM4qf8yIYu+QIQ5oFMv2+xug0d6miq1ZzCGyFXdIhlug+wDM1B0IfBEdPnutZj50XMzgQm82CXbKZJyB7G6k0spN2boLc5gGsfkx7vO7n4aQBaNRqVjcqL9suNJhlw0GLjm9rf0Br6T1IOiSL7Cd3k73hfUqOb2aTFIdeZ4L0Gy8W8Gkg5+uoNHJlmCGfwsPf42S+oZoy7TRhaS+ASrZhmG8eYN3VPMTdKnRKjBYupOZzJjmf7/bHo9Oo+Cm4JfYpJxnmncjM5Cb8fCyJQHd7JveI+Et8kxRuH0XsKNwxKXkl9P9kj9WH43boojrOO0WLCL6cAWp56SVW8pddTqvBpYaIT0WMkpr2hk/JU3sgiiLnUvI5l5LPnC2XcbbTWI3VooPcmH5fY1TaaDmMvmYiHJd9cIjfU2WX5SNiXZwpJlDIQHDwYFzeaI6L4UQI13go7UMQYJ75Xs5Kldfy6/u78EKvuuSVmHjup5P4uuiJul7NkXIS9pRFNRrdB8g395d/OcXR+BzstCrGdarDhC51sNOqbSdOPlH+c2ArZo19gHeunUH/9TIQIaPd61zcXu72Oqx5IMMX7Ce/1EyTQFee71VeTqvTqHimRwQvrTzFR5sv0b2+D+H954BaJxvAbXpdvr6bNNCsyIYzqTy97Bgmi0RtL0de6F2PbpE+5c9DkmDHdAAKGo7kvuOdOaR90prsXWCUcKa8pYiqhpvKibJea3+J2Vtsea6W4fxGDsTWAWC8eg1OlJAhuTCkewfahd+FPkoh7WSDxsNfwZ6PURWmUefaamaymtddfPiquAs/HOvK+GIjnz/UrPJ75I8gCJR2ewdp8b3UVyXA7tfh0Ezo/R7hTR/m3UGNeO6nk3yy9TKDm9YiwM0e1BoIbAkJ++HMStljyWyQK5yAL5JrIwgq5tzflIYB8nvfZBGZvPw4F1IL8HbWM3VIa7BbCZ+2gJyriDPD8bCU4gEgQJrgwxnH1nQv/M3mco0lhVx070GefTClJgslJgsvFTbjZc0yRmk2V3p6RknNAON7OAU3QZuUi8kiMWx+ed5XVW1sNpTmMxjwJpdX74nk/XUXmLv1MmeT85g5tEnVjt4KfyuK2FG4Y/JLzLctdJY8FEnI7hcJSd8KyEm0e10H4OnpReerVZiMAbGiH8elcA6K9TkjhvG5dq7tN/8bWNrtAIc6yd+00vJL2Xslk5X7zpOefBU/Yzbe6jya+6oYFOGI3bZ1UJIL6WchtWqH5OskuLdmaMoz5RvKqt59yWaB7iP0golzLh3QN5/Kl15OeDvr2X4xg+8PxJNVZOR8Sj52WjW/HL8GQL/GAfK3wczLsHwkmEutiZVFBjOv/HKatScTqa3K4qMhjYgOUkNhouz0et275tJGuanjdYrlSJT9jrdBNGGu05MO28NtnseKsr5A7g5a5j7QtJK3y9Bmgfx2MpndlzP5eMtlPn+wmeyKe3aVbC54eRNE1tyL6DrnkvOZ+MMxzKJE/yh/Zg1tgr3uhhvxuV/h2lHQ2OHc9016CikcPBlJB7Uc7TueVEAnQKOWRc7Z5HwkSarym/R1/5eswrvbE61Kzv9u/VGbuAc3ywP4C9lMLPNw+sV3EuO6Najm4D+AWgNtxsveRgn75d/H2VW4FKfzvOYnnlb/wvqYVnz1RVceG/0YDi5Vm07eFkGt6G6eQ1/2McXvKLqs87DmaUg8yOD+c1l+KJFDcdnM23GlvNy/2Sj5+o4ugQ7PyVHBwlQKdd4cKa1H3yh/+kXJZo8FpSYmfH+M3Zcz0WlUfPlwc1k0YY/Y9mlUW95AZZGXPN82PcwOMZqrkh/6EhOH9LZL3rqCBH758RtrabiMHW+ax7BJbEFv1REeqSB6dIIFCaFSJd91rgudznW92XkpAz1GOhaVHd9gEOOa1MFBp+Gd38+x5Xw63T7cwfO96vFgq+C/1DJAoWYUsaNwx9xuL5/vxzSj/bHnIH0rFlQsNPdlrnkIWzsI+K8bU/mAvjNJ8e7AtIW/0FJ1kWHqnczUflXjObZbmrBiw1b27NpM71pG7nc6wX0x27nPkAcVv3RlA/tucsE6Z7nfTxnBOQd5OyqLN0+V30TChSQWaD/GV8hF8mlAg8eW00Bf/ro0DXZnQpc6vPzzKdacSOLHTXtIycqjjmCgt48rrJ8CB8vXoC57dSfhi0mUpMcySkrnA3283LV99U2u1fq8YmRjvsubkAQ1/S/1g/KaF4I87OkU4U3bOp50CPfCzaHyN1GVSmBCl3B2X87kYGwWafmlcs5J42FyH6xLG29Z7Kw4mohZlOhU15s590eX53ZcpyAV1pYZ17V7Gpx8eLqbE6+dGEQHZLGTf3odayPvp2mQOxqVwK5LGRyOy6lU+pyUU0xSzm20B7gTijIhT04svyAGEalKZLzmN7qoTqAVLPxuaUP/EU/9OUsbKrVs1hjaoUyEroZDX6K7doSB6n2QvQ/zRzMwB7VGU/8eaD5a9kn6A9hp1YSE1OHrWDcOicP4NfRDVHG74Ph3CDE7mF37fvrHRfLj4UTGtA+jjreT3HJhw8vy67P7Q2tLiOX2IzDk62hT9ntLzStl9LeHuJBagINOzecPNaNpsDt5JSZm/biFMbHzqFPh7fKtpS+13Ox5pW0I7g46FsUuJC3mNO8bp1vHxNhHQRWFYHvFxsRLvjZiB2CTfgorLZ3Y7f0ABwp8SCuoLJJ3XZZNFoerd+BFmTAyyN5QI9uE0DTYjed/OsmF1ALeWH2GrefT+HpUS0Xw/ENQfHZQfHbuhBVHEtl+MZ3YjCIupBbcdHwH1WkWeq/ALu8KqPXs6/Atb25K4mX9Crpz2GasQeXAdPtnaV2yh57iHjQ3Wrn/UfSucrmwk4+cIGrnWvbPRTbxC2wp5xlc9/5o9ohclbL1HesUl93a0zNV7vtTV0hkpe4tXIQS8rTeuE7YIo+vSFEmXFhL3qm1ELe7xuTratHYy8tISNYP2Rpx8oXCNOJ8etAlQU7i1mtUrJ3Uoeb+RhUoMVroMns7afkGfF30fD+2DeHZO2H5g+AaDJOOy5GGGhBFiXYztpGaX1q1743ZKL/WiQdlo8LHt4FGzpf48XAC968tL3e/z/CWjXuts17DkOaB9G7oR+swD1T/Y++sw6M6t+//OaNxTyDEgBBBg7u700KhFKlBgVJKqRttaakLVbQtFChW3N3dLUhCSEJCQtxt9Pz+eCczGZIA7e299/u7ZT1PHphz3iNzZuac/e699loKiWd/O82+6xnCh+zFjnj+O0sK0etgzTOkKAP4qHQ48zXfWVelyl701X3O8hf72hmf/tuRcpb04ysojt5GXVJsy139RVDU8NF7E56qwe6r6Txn8bT6/rFIhhSvgxOzrbYhxQoX/tB3IMe3Fa+MGYrkEQLnl8KWadZ9lLmG0ChzJkZUHHy9KzXcHHh0zjGu3SnAx0XLoqdbWQX63l52gHExz1cqaV9tMZPw/sJzLD6rmCup+cRlFNE8bg49MoUQZg/9LG6aa1Z6D00C3dk0oTl8HW43gbGDTzhbTK35Ia0xsXIQzholxXrbvedZ5XbeVy+1jX/povX3bjSZWX4qic+2XafUYOKVXuFM/StcrYd4YDwUFfwTeBjs/HUM+OGwVfn2fnhUcZhvNXNtC4LbI6ecRTJVrX583NSANoprVpXfm2Z/TpsjuC378pp69X2Ply27Uiw7kI8zR8yN2WFqRaIiiCc6NuD1PhH3n3FdXAkbngfZTFmPjym6tBmfTGFYutPUkomGV/Ahnx3OM/AxpXPWHMYE/Su8+VgnRkRqITNGPAiubhClBtnWGaRHjUrjiEJvf+22mNrgIxXgpSghz6MhNUOjCKodhlSzsWjPVSgEN2f541BUoUOsThcI6wXJp+DaJrt9vqZ4nTUlzQAIr+GCWqmgVW0vpg+oXznDUgWSsksYt/g0NzKKiKjhysYJUTjMbiZKZSOX3ze7s/bsbV5dfRFXBxVnpvdEq7qrfHX2N9j8kghCJ+wH71C71XJJLtKXta2vI00rq+yyCvR0ZGizABYdTaRQZ+SPie2qFrz7u1CchXlOexTF6cwzDmStsh+7JZvx5Uj9dE6YG7DhhQ40LSef/wcRnZLP279uprnuFM+odlBbEiVf2dUfKbQHhHaDut3A+cHLXJ9uu8aCQ/F0jfDlt2dag6FMfL+PfFu5I0qhEpOHbJuI4rGGMxh1NhxXBxWXZ/ThvQ3RLD1xC29nDRte6GBt39538iy+W5+lsSKRVNmL97y/ZX6ja6gOfY5ZUjLH533mZdSnSFexM1HmmPZFakk5fGN4jK3OQxkfkkEv5xvo0mNJTUkiUF1ILXVJJd+16rDW1ImT9d/hrSEtMZllXlp5nmM3s2gg3WKb9h0ATAo1ytbPCYsURw+xneU7r5BgzfPtaV7BsPQh/l48DHb+BB4GO38d2y/f4fll1XcZlaOH4iwL1LPuqepbEUWyAxrJJDpatJ4YzTKOhrwHPq/ssftZctOZ7/fZ2sMr2imE+bkwuVsofRv623FHDCYzpxNzuJJSwLW0AqKSf+epwp/Jll3Jl52pqxABxkIGs13XlNVaW7ZnjvZZHIpTaahIpJXyBoq7bCDiVaGsK23OYXNjunbtRaTuEv3OTQRgqv4FLnn24pNHGxPq60INN23lzIvJCJdWwrbXwVAiuoCGLrC25ALoozehWTPWbrOZhtGWFnR7/DSqmfChegBkFuro9/1hsop0jGoTzKclHwnOzuAfRearGsiyTMcv9pOSV8pb/SKZ1CW08qCd78Lxn4TOSp9PqtxPztFFeO2eBoBp9HqG7tRw0SL6NrJVEFsv3aFQZ9+Sf+TNbgR6/pu0T2QZecUTSLHbuWEO4EnpE3Z4f4N7jo3v9b7hKVYr+nPhg16VA7z/EJJzSvh02zX2RSfxvGoTE5VbRDnUAhkF5jqdUTYdBfUHii67ghTRMl6QInR/XGqCbzh4hXL7diKfL1pDA8UtHg8uxFtjBLWjsD9JvUCq5EtuThZ1FXdwpHIpSOfXlI5Jz5GJJ+1DvTl2U3DLfnumFV0j/MBsJnbHHMJPvWvdJsOxLgfrvsZPCf5MLZzFMOVhzLLEJ8bRrFQOopG/M0McL9C09CQN0iuQlSVl9VYsfwJxcgBxXX6iR5euvLHmEuvPp1DPz4WxtQuoe/5zOimjxUC3QCHLYJESmLbyPBsupNIixJOVE9r+9/zO/sfxMNj5E3gY7Px1JGYV0/XrA/cc00y6wXrtB9UPCOnA5Bst6K68wGPKQ9WPuw+mG55lpakrx97tY5WVL3cpBqHnE5dZxLvrL1s7sTQqBW3qeNGmjhd5JQY2XEghqwKpVYOBaO2zaP6E/UM5sjUBePjWwuwWyAfZPVmeZJvdKTGxQvMxrRUxLDX2ZGftN/jhiWZVd3EYSkV32LEfRHkNxIx8xGJRfkOkz7/dE4v22LdMlVZW2sV4/asclFoyrWc4J+KzOXwji/AaLnw4uBFt63o9UEnryI0sxi48iSzDzjoribizScxmu71T7Ta3c0vo+MV+VAqJ6A/7VN0dtHa80Knp/bHg61SB+Mwi6s4OEC9aPUdyu4/o9vUBjGaZNZPa0SjAnZ1X0lh7LoUjNzJxdVBz8p0ef083UlW4thlWjUEnqxhh/oRFUbF4Rf9KoeTC74ZuPK/aTJHswLpO23iy53/fEftMYg7f773B9eQMIvRX6KS4RGfFZdFdZYEsqexMV/8KZCSOqtqyviSKXp070Pf2j6J1vALyVL6MKX7J6rX1ROtgPhvaWIgrzu9U1W4ByJA9cKaUXI0/gYZEAMztpqK4thHyblW9kXsQ1O7ESV0Qiy+VUq9uHV4Z0kFkXf8Qk4Ksjh/ic+Qe9yegRNbyvuY1zju05mZmMY+1COSLYU14YsEJtEkH+NJhMf5mi53N2PUQ2p3UvFK6f3OAMoOZ3g1q8NVjUbg7/Wec7f9JeBjs/Ak8DHb+GhKyiul2j0Bn3yudcfy9P/4Fl6oeENYHWo2D5SP++kk4+8Iz28EnjK5f7Scxu4Sl41rTKczXOqTD5/tIySslsqYrqye1QwaWHr/F8pNJpORVJrL6uGhoGezJI+rjNM/djl/G0XueQpHsgNY7CLWrH3LNxmxI8+HHWA/i5VrUdHMgp0Rv1X0B0KJnrvYnuktn0Mlqvghfzvuje1fesSyLktT2N4WqLogOrHYviKBAqUaWZfbHZPDdnhtcuZ3Des37NFEkWHeRJzvjIRVzyyWKkNdEIJlRUEb3bw5aSwBj24Yw85FGlQ5fFb7bE8t3e27wtHIHM9RL0Nfri2bMqmrHb710hxeWn6NRgBtbXqzmQbZ4ECQcgqE/Q5OqvwunE3No9VuFNv7Ob/Bedi+Wns2kYz0ffh/fxroqq0iURX2qEoT7O2Aow/hjK1QFSfxgfISo9v3ockpk6MbpX2WfuRkXtBNwl0owjduLMqjlv+c8/gJkWSarSM/NzCKO3czm9LkztC7Yw6PKI9S2dDbqFQ6UOdXC7FoLs5Mv2tI0tHk3UZVkYFY5UuIRweZ0b66ag8iVXXmnVwi1nGShoXNXCbU6GGQlrxqe54A5igKcWNBNpvfxMXZjrpmDOWcOw00qoZ/yNKrq7FfuRvsXocnjwjvN1R8kiTVnb/Pa6ot0Cfdl8WNB8EtPKLgNdTpzouMikhc9w3DVIYyd3uDt6Fq8lPMxgVJWpV1P1k9lm7ktNdy0HHurB9Ep+YxccALJUMwah5k0IIGC7p/h1nkyIDzAJi45i95kxsdFy4zBDRjQ2P+hFs/fiIfeWA/xb0NhmdCH2X21+rZvCTMu68fid3egM34vBFpu/pf++NcCnb5fQOvnBHckJ4EuNQ0YczKJvXySTm51QKGG4gy2d4xj4b7LkFXExh9W06VxCC/4+TC5vxNpJRIX0gzcyDNjUjjQIcSR5n56VJe+E7P3e2CBcQC/GvuRjhe7Hu9MeA1XJOBRwC8ui3fXXyYx256I7CDpORHyMx5pItB5X/saHwzvbn0IJeUUcyu7hKKMW3SI+YzQXCHClquuwfEao0mv9xhlJgcKdt8ks1DHuVu5xGcVo8HA99qfaSLZAh0TSobrP2Cb5m1Cii4KBeS6XfFzc2DDC+2ZueUaB2MziU2/P7G8HC/1CMPDUc2BXbEA5Nw8x9r9cXQO86W+vysJWcVcvVNAsJcTUYEeJOWI9x92L/+mIosynLNvtUOcNEqMsgKVZAkaD33JGPVmNvEyR+JEBs/f3RH4NwY5Flw+vo3GBUlkyu4crzmGF2+JrMARz6HsvdMCF0qsBHSlTxVlu/8iJEnC11WLr6uWtnW9kXuGcS6pHz+euMWFSxfIMjmSjzOUSHDXs96ZUkrRYi6yL8dssQiO+7g8hb+pE/3kw0xW3TvoUUsmftD8ZFtw3H79SXMkaTW60rheJCGh9VFJpULbKT26+p26+ov7i3tA5eNZ5AokfSEsHy4CHe8wGL6Y39Ym8KJCZIaOFtdi9R0/9vIxe5vsx/P2HquMA8AczQ80KGtKegGcTMimfagPm1/swEsrLxCTWYsGygRu75nHorhQpj/RnW4RfqyY0IY3114mLqOIKcvPs6F+Cj880QwnzcPH738SDzM7PMzs/Fkcis3kyYWnKi0vF+pzoqyyhcP4fRBoSedn30Te8DxS8knr6vPmeqwydUVC5jN1FfLud8M3UnQ4lWQD/52vcLhhJQaTkUGKE3wVfAKtRiP4K1EjhRCbwcSs3bEsOBQPiJLYfPUsuikvUiQ7ME7/Oifl+tT2diKzUGft+PAlj23at/CVCtDLSuaaBjPHOAQdVXcV+WtKWeX+I8GFFwRPoc1EODGHHMmD5qWz+UC1RFhg+ETA+N3W0tdHm6+y8GjCn8rslONmUjKhC8U2Tcp+pgBnVAoJYwX1NW9njVV/qVOYD7NHN6/k3SQb9cif1kJhNpA45gS169WnKsiyzPefvsY0wy8AmFROKI0lXDcHMUb/Drj4MiiqFv0b+9Mi2BPFv6HdN7tIx/QN0ZiubmaB5lsuKSKp0X0yNfZMBa07wzRzOJspESXFsVH7PkYnP1Rv3Lj/jv+PIKtIx+EbmVy6nU90Sj45xXrKDGbKDCbKDCaMZhknjRJHtRJHjZKCMiOZhZWbCwKlDA5oXkElmdHJKk6oW9POeBqN1YXz34SWz8LAqjW6fjkcz2dbo9nk9RMNS06KwHrcbvCqw8AfD7M46wm8pUL66r/gujnI1kVlNsGZhbDtNeu+MmU3Vpi6s9TYi/0fjsRFq8Jkltl58AjtDo3FU86lQHZigfYpHp84nSBvF3RGE/MOxDN7fxx6k/kvdWnJskxuiQFPJ/XDzFAFPMzsPMS/DXe30aqVEgMa+7PlQhLjlDsrO1Vr3WyBTlEmxt8Goyq8bV09XPc+nXoOZtexRIqLix4s2LHr/JBA7Yhs1KE3KyjEEU8nNUrZJFqYazUDBw8KzBqup2STnpWDA3p8tEaiaqhRGErBUCy4MWoncPISLemeteH0L9ajmNyDUebbOA6nXV6msMwg0t3ljVFJx2DDJG769OC3gA+4nVf+MJD5Xv0T3ZQXKZG1PKN/g9NyJIA1+yNJUMvNgdksxFdXQIa2NhvCP8PkGspUtZKCMgMpuaVoVUrcHFV4OGpo7GWgy4kJKDMui26mEb9BcDvk07/iZcqjrnSH74zDGOl4CsesGFj6KIxZB44eHI8XM9ZWf6FjKTQ4CJNbEMqCZMbWKWRxqjtFOiOOaiURNV25mVFkJzR5+EYWTWbsItDTkdZ1vJjWI5wgL0d+X/U7Y80GsmVXRq5K4ejbkVV2yUmSRGGDUaRfWEMNKQ9lcGt0d64SWZrM19qfebroVRYdTWTR0UQ8ndQ0DvTg/YENKjmp/1WkF5Qx6ucT3Mwspp+FBtSghiOq08KstLj1FM7vEeftLYkOO6X7g5G//6/Ax0XLo80CebRZ4ANvk5hVzOhfTlrLwa/3ieApH1CtNZOqrEWn4i9o6OvFkpFhaOI3kXvtAJ4JW+65z/2Bk+kWrBRmqPnJgqemK4RaTSGojeCr/da/8oZnFooOsD6fVZJDMMsyrRXXRaAD8MQqq4VFUakeD4soT7bZhb4Na/Jid4sAp0Ipssd1OsPs1gD4SgVMVW1gqmoDh385TKdnPkPp5EX/bp2g8W5KVj6LW9YlXtPPJe6XIzBpJVr3QF7qGUZtHydeWnmBX48k8EyH2rjex7jVbJb540wyWy7d4WRCNgaTTGRNV57vGsqAxv4P1E35EAIPg52H+NNItdzYVBgxouSnx5uwb/Vs9mrWEqKowqzG0q2jN5pJWDqNiAqBzhj923jU78KL3euRmZnBzOuP3f8E+n4OvhFidubsJ2rzShUS8Pjso1xIzuPzAY0Z2TrYbjM3oDUQl1HE8HnHyC0y4Ctp6deoJm/3q2+v6Gs2wdlFdtt3KfoUXVk+px1Ee7G77g7uEhTIjmzSDmCMfo11bGjWXo6n9CFOFg+OhlIi/ZSn0ckqxhte5bQcydPta9O7YQ3USgVezhoCHfVot70MV8+AygG/cauY4BdZ/XUoTIPFgyErRlyHJzdAjYYAlPm3wvH2ETooollq6s35rotof+QZoVA8tz3mHjO4nuYESDQN9Lj/Na8CylpRUJDM60ExTBv/DCm5pQR6OqJSKjCYzBy7mc1Td2UAb+eWcjs3hc0XUzGYZL5RrwMlbDO1oUhvwmAyo1RUTSoO8PXiA8PTzNN8B/EH0LaeCGcX0dV0ji1d7rCwoBW7r6WTW2LgUGwmcw/c/FvMGfNK9IxccIKErGL83R14u0tD2AWqO+fFAJea/Groi1kWttvFsiinSfri6nb5fxZms8yllHz0RjNhfi731Siq7ePMnle6MGX5OfZez+CrnTE4NcrmGcDX3QUXswOXbucz7uf9/Bh4Gt/47RW1LSvhgjmUN+Ia0tqpAW/3i6y+m+79HPjIEqS/dBEur4F9M4VwYUk2DPvVpiVk1OOReohRyr227X1sauLuyjJrl2gBznQK96mcOfGNgEE/wF3eeZ0yV8KXK2HsBtHK71oTpzHLyDixEufjX1Ov9DLmn3ugGL0a/JswsEktfth7g5uZxSw+lsiU7tVnd3ZE3+GrnTHcvMsw+HpaIS+tvMCP++JY9HSrh27rD4iHwc5D/CkcvpHJpKVniZLiWKP5UPgWrYM+dz2fJuqn8YPDArTmEqjbjbiMQj5ctoeFefY3u3SnMDZ7rkL6sAczH+QEvOsJqfxq0CPSjwvJeSw8msCwFoGoJdl2U3w7BbQu1PNz4ZenWvHckjNkFupYcvwWqXllfPVYEzw1Zri4HI7+ALk2/sus8GXcviTh6eTH+f7baLZFzCxvmv0ZrP+YYp0jexR1+E3zFQBHPB+hb0Rn/D2deHd9NBGSeBCeM4dzzCzKP9N6htmUi5NPwbJxYharUIl0/L0CHX0x/D5MBDquteCpTeBju3HGq8NoyBFaKWI45PEIrdt2gdDNsGIU5CehWP8cBx1qsVLfifR4T4I929icqR8U9QfB9S1waj5qSaJ274/BMtNUKxUYTTZS9ql3eqBVKYlOzWfewZscvpFFV8UFHlEI37Fdqi58/3jTe3ZP1fFx5iNza+Yrn2CiaYXIuoW0h8TDNDr/IbPGrkM/rBebL6by6uqLHLqRWa2dxIPCbJZ5edUFErKKCfBwZOWEtgQV2Est6Dq9wcKdIrUXWdOVwnQR7FTkevz/gMIyA+MWn+FUgnBsVykkejWoQe+GNRgcFVCtLpWjRsmCJ1vy3Z5YftwXx4YruTyjBbk0l7Wjg7m88gMGlO1Gc9MEEhw0NeFtw3jejUhlwK3P7fbVVHGTQ9qX+e1qH0Zd7o5nYASPtwxiRMtA+yyGQgkz8m2vO78GPuGw5lmIXita58N6CU2qxCOMKMuD8q9W1Cihim5BvzpquCQ6rnRoCKouwGrxlMjwHPwSw82DqIsqCDYufcRuqK9CRanlgIqiNMFNnHgYpYsvU3uE8dLKC8w9cJMWIV60C7XXOUrMKmbZyVv8ciQBWQZHtZIp3evRu0EN3J3U/HE6mfmH4onLKOKbXTF8N7JZ1ef7EHZ4yNnhIWfnQXEhOY+RC45TZjDTQ3GWXzXfWNfl4IojehzRscHUHhdK6ak8j+zflMUNF/L5zlhekFfyosUv6IHR8FFxg/EKhSWDBd9k1GoIblPl8LwSPd2/OUhOsZ53+kcyoXSh0HApR88Z0PFlAEp0Bs4f20nS/kW4yQUUSc70VV/A3VTZI2ew9xYupYjyhFaloI35PEs0wp18h6kVLo/9SEQtT3xnRwAgv32bSxkmfj2SwKaLqUxWbuAN9R/sMLVikkEc31GtZFSbYN52341q34dCE8SzNgxbaCv7VQVZhnXPiXbtCtwD6zW4fR3Fz11xk0r5wjCSTs9+QvtQiwmloRSO/SRE4Ay2GaOscUHybypmvF51IeoJoTB9L5jNcPALOGh5YAW0FO3jIe04ciOLZxefRm80M6JlIF8+Zp9huX5qF4Fbn8RFKmWVsSsOw2Yz5D7lkzKDibaf7SWvRM+x8JXUStosSKleocKsVeMKY9ZS5t+SyPd2AHDx/d7/UrvvilNJvL3uMlqVgnWT2wvTyvQrMLe9GOATwW9Ry5ixNda6jSNlXHF8TugsvXJNqHX/H4csy0xedo7t0WminOruaNepGF7DhZd7htOrQY3qSydmE0cuXmPZxq3M5dNKq4+YGvKt8THcwjvy2dAm1HTTIv/WH+mW8GvJ9m2Lt9Zs16p+wVyXLaZ2XPXsznMDO9M1wrfq4FW2dIMtHlR5HaJtfb+pKV0GP0XNVvYK0kn7fyX44CtcNwcxwPglFz/ojYv2/nmAp+fuxjX5AD9WJFrfDwEtMY1Zx1PLrnMkTviA7Xm5C8HeThTpjHy+/RorTiVjsnDfHm8ZxNv9IyvZuZTzJl0dVBx9q3slLtw/CQ85Ow/xtyI5p4Txi09TZjDTJdyX6Fh7N+9TA/fQKmsDjic+owZ5tFNexSxL9EkcxY2E67SQYnhRu+GBj3do4EE6t2xqW1BWILg/ZfmwsLeQvW/3QqXtPJw0vN0vktfXXOK7PTcY0SsEj4oD9n4ENRrDrSM4Xd9Kh6xYOlS8d1chp7NePcAa6ADojGZKa3dlM8X0vTOXvsrTyDsHILUW7cd6B2+GLbjI5RTbzFNkc/6gh/IcfoZcMvCk1GAi6dhqVJpyl/NhIqPjcB9rgRu7RKAjKWH4YrtAx2yWSVj6As2kUk6ZI8hs8pwt0AEhANfldWg7ibyzq7m+61eayDdw0hfZO7zv/0yk5Ws0As8Q8AgGvwbgXGFfCgV0extqNob1EyHlDCzqi75uLz6OG4je6EvvBjX45FGb3QMmIxz6ishDX4JkJs6pGdNznkVefQmVUmk1hqwKDmolI1sFM+/gTcZmjGaP9iBS4R1RXpAk8bD7fSja0autTtU6own46w+CDRaz1pd6hlnduUk4bF2vH/AdP68SWbtAT0du55bSMMQfiUgRFKWc+z8f7OiMJp5bcpZDsZmoFBKrJ7WjWbAn55Ny2Xstg6UnbhGbXsTzy87h46KlSS1nBtdV8oh3EiQegdTzoqRanElH2UTHu/Z/wlyfbw2PcVIW5POBWhWezmqQJFJCRxFoCXY8HFXw+BJhG3JqAXLCQZoq4mmqiIeiZexfFsXrNZ+nZ5eu9KxvCbp0hbD7fbixW/B77kbzJ/k+qyXfx3oR7O3C4627WVeVGUzEphfilihawSIVychm0wMFOgBzx3Un6kMjtcvaMUm5mUmqzXhItgmEQVZiRGkn4kjKGZSHvqRvowkcicvCZJaRkTGZZaYsP8eBGOG/1SnMh8daBDI4qlaVwV3but7U9nYiMbuEL7Zft/+NPUSVeBjsPMQ9Icsyy04m8cWO6xSWGanv78bs0c1p9EGm3bi+zcNhsdBxaae8CsARcyPyZGe+Uc9hmPLIAx/za/Monq1/l0u0gxtMOgIHPoOLK2DnOyApKpe04g/w2I6RXPCfSVJ6FpdPR2On7iKbYdkw22u1k8ge1WxCZkYqO9NdqZW0he7KCwDMNQ7ii7InrMP7NKzBE62D6RLuS6GuFSM/DWQJH+Bcko188AskQFmawxNFX+Om6oRfw64MbBrIxKUSp8wRtFbEMEa1m8TGLzMgTEvjjUKPY6/ncLo8Mh/V/ZR2S3KEuSKIYK92B/G2ZJkT8TksPJrA+6W3QAFfGh5nVFhlfyAAtK54tH+WIo8BdPjjLL66JBpLCTRxyWO0dyyqO+cgZpv4qwjXWiLT1vBRCO0OKo1Q3g04LbI855aiid/Neukgqz2GM7LvNJtyrCzDmqdtLf1NRhLc50u6rb3BrqvpvLb6IiqlRJ+G1ZwzMKV7PTZfTOVmXikXg7rQNHMzXN8Mo/6AFSMh4SDSiidoqJ3B5TI/rqcV4ufmcO9rWg1yivWcThQlnUEVlaZ3vGn978yLbqTk5eLjouWTRxvz1MJT3MopgUbNLcHOWXF9/o/CbJaZsekqh2LF7/mzoY1pZrE2aBbsSbNgTybVy+X2tq9xyL6Cu6EAz6QiSKpmf0hkyu44oLe234/Uv2c3ZsulO+y8ksa56T0xnLM1MyiTjsDPXYUFyZMbkIoy4eoGjJfXoko+TjflRTpnPM/Kld0Z7DSGV7uH0P3cFKQMcb9B4yJMaiP6id9ITjxc3YhHSBvMKPBw0rDlUio30os4nZjDmVu56I1mWkn1WW1RK2jl8+AdY44aJVundmTE/OPMKxnMPNNg/Mnmh5o7aJW3FbVkYpepBQOUlkxVWB+4sRNOzOWoayTgxZg2wYR4OzPnQBwHYjJxUCv45clWdAzzueexNSoFnz7amFG/nGTZySS6RvjR627PuYeww8MyFg/LWPfC93tu8O0ekaJvHODOT6OaMX/3JT693tc6Zor+RX5qkiD4GxWw1NiTR5RHcZUe3IH6Vf0kFM1G8dXwaoilsgz7PobDoguGZ7YL3gaAUQcf36f0AuDgAZED0Ad1ICuwF5l6DTczi9h3PYPt0WlEyTGs084AYK+pGeMMrwPw/sAGPNuxQkbLqOPyT6NonLcHgDnOz9O44LBNPh7ALZAY3958fs2bga43GKbbwDpTR863+IKZ5h/h0kpuyrXor/uUfk1r882IptV7dpUViFJe6nkRdLxwEhzc0BlNvLHmEhsvCMPEM9pJ+EgF9NF9zgfjh9tndqpAYZmB5SeT+HzHdWQZ3h9Qn2fr5MDt05BxFfKSBX8pN9F+Q0dPGP4b1O1qW5YVx9Wfn6WB7qJtmU8ERPaHoky48LswMx0yB5oMF5fRZObpRadFWl+p4I9J7e7pJfXKqgusO5/CB/VTeSbhNVF2m3pelOgWD4Lbp7nh1pZeGVNpGeLJ6knt/hJv55fD8Xy89RoN/N3Y9pIlZC5IhVkiQ3E7cAAd40YDsOjpVjQN8qDZTOGmHTcsHdXWl8W1eXLjnz72fwpf74zhp/3CUmXmkIaMbVdbrDCbRAbx2E+2bF8FmGSJa3IIp+QGXFU34FqpJ5myB9m4YULJHPV39FeeIsW7HaqnNtDm072V9lFbusMB7asYZCWnW82i/c1vxXdM7QRDZkOjobbB2Tcp2f4eTnFbK+3H7OyH3PVtrnn3xqxxwdtFi7koE89NT+OScZZSWcNY/VuckStz4LydNdT1cWJ1uuDgfRO1lVcfvTs3dW/czCxi3G+n7TS1Lg/Nw3XbZPuB0zNh7bNwbTMp7s3pkP4qgZ5OLB/flt7fHaTMYObLx5owomXQAx/7k61X+flwAn6uWo682R2N6p/XnfWwjPUQ/zI2XkixBjqv94lgXMc6jFt8mk6Js+2+OS+r1sD1O5gUaj7QjWGGajEqycxY1Z4HP5hSwzzfd1mbGMbrPs7Vj5Mk6D7dFuzsfh9GLLGUCu7/QLsi12W0/guM52WKThjhLqd1AHWdtmxx/4KB198kRJ2PwggTu4TyTIfa9gP3f0LjvD0YUPKafiIbyzriqunMmj5mIjK2w9VNUHCbiIKFLNIAli50P3KJ1EfD1ZUgKcjq/i2mHWY2XEhFrVTwxbAmlXVi9CXC/DP1vOg+G7teZLuAqSvOs/NKOiqFxLDmgXjGmMAApWi5mlpw32DH1UHNxC6hOGqUvL/xCj/sj6NhQAvatL1L+VdXKAif17fAlQ3CiHTteJhyWgQ+QJFrbcYaptNZv4+Pal/CNe2kIFEfibHtx6QXs+6Y7VCnMyqNM78904rJy86x62o6k38/y5apnaq0zijWGdlxRZCBm7XpBgmIfZXl29zrgYA69dHmKDhzK5fDN7LoHF69YGFVyC3W88NeoZEztl2IbcXR763/Lbf/mNw1lG6RfmQUlFnXKQOai/+knBfcJsX/zYfQmVsic9U73INRtQtFV1P6FVEmLS8LKVRCkbjxcHCtidnRh4/33WHRccv6u5IhbRXX6K88BZKCgBGzOJJeVMWRZas1TIwcRIPuo6D7YEEwjt8Pa54RJdM6ncVw71CcxiyHW8dgUT+7PaUUyuzYtJcTptucMUeQj5Ab0PIi89Tf0k15kYWarxmpn45jcDNCfZ1pHOBO+3o+1PVxJuHkZtghCMpD2lTTFKArFKWyc4shM1Y0CJh04B9FqG8Euzo05Ndbfnx1UY0ZBY3XebCx8YtE3fjRto+Ly0X5/dpmAvLP0dS1gAu5kjXQaRHiyfAWD972D/Banwg2Xkglo1DHvusZ9G1UfVb0n46Hwc5DVIkjN7J4fY1QP57QuS7jOtbh2z2xHI3LZozWJq2aI7sQqrhDrsqPp4uncFGuxweqJX/uYDUasbv2q3x+UOSSm93PIVqSoMNL4sFz+zT80FyUs1pPuOdm3xuH8oPxUUwV7s5qpYSPixZ/dwda1fFiYONaNA50h3g9XIdQdRbnX+qEu3sVMwajiF7i/AexMUHMBns3CiCiXRQwEPp/Q86FzRze/BuNuEmoQlg+dFReoeNVwe+hwSO06dyX7z3u8OKKc6w+e5szt3IZ2iyAR5oFiLZSow5WjRYaPlp3oZNj6dTKKtKx84pQsv75yZZ0i/BF/khk0kplDXklD56Wf6J1MKvP3OZySj5P/HyCl3qEM7VHPVtWROsKdTqJv54fwpy2IuMTfwAaPsq5pFxeWHaO7BIDu7Xd+Pipj8FUAAv7QFas/cEOWAisaieIHICq48t8PSKKIT8dJSGrmJdWnmfJs60rZWS2Xr5Did5EXR9noiJChfliwW1IixYlvRzRQecU3IzRqhAWHk3g8+3XaVXby15a4D5YeTqZgjIjkTVdbTPtwjTh0G5BQzmW9qFjeaVXOAC5lmvt6aRG0lq6fYylFrf7/4PBTmkeo/Vr+ESzlTpJ6Sjm3+Uk7+gJzcZAm+eR3WpxM7OYUwk5nEq4zenEyiT+oc0CmDEoErclnwndqZbPEm0MYNoqUcbpWM+H80m56PQ6ZqoW8YRqPwDrTJ0YlFVMgKcTikeW4bXrJRTRq2H3+5jG7eN6eiHnk/I4n5RHbFIOSy0WKOUIUmTyHFt4Tikyy9flII7RlFWqwfziMwOnO2/TRnGdNd7zcRp3AjT23Vbmw0KI8LTXQLrUqjAxyLgujHdjd4kMZ1XipcknIfkkGuB5YIzWkQvmUGLkIBZfCSFIGsbL6rVi7OaXRDMBgMaVVIMIzMvNicd3rPOnM5A7otPIsAg76k3m+4z+Z+NhsPMQdjCbZdadT+Gd9Zetfk6Xb+cT9eEudEYzbhTT3SEWdJDlHIZP8Q3KZDU9iz4kF1feVi0T7eh/Am94/8T2kxmAkWk9w2hf796ZCAB6fSTExQ5+AUnH4cgs8VcNPjSMZZGpH61qe/LZ0MYoJAlvZy1ujqqqbzC1O4KzH1JxBu4FMeDeqvKYoDZwch6BRdGAsL1Ye+429f1dGd+pLqgdmBkfxnr9ZNrU9mRl6QSku0mUV9aBZ20GdHoV08hmvLX2EglZxXyzO5ZvdsfSJ8jMx4r5+KYfBrUzjF4txNUs+Ha3CCIa+LvRLdIPjDokWXxuJpUjj7d68JS4Wqlg5YS2vL/xCmvP3ebbPbFoVAqe71qF5YHaQXB2zvwKN/eTU3sAk5aeJaNQR7CXE58Pa4yzwgjrXrAFOs3GitJExnVIuyyCt9xEkUW4sgG3Hu8zb/TTDJlzlMM3sjiZkEPbuvZtuWvOCI2mYS0CxefmU08EO3m3gA7i4bz3QzjyLZOeOc6as8lcvVPAC8vPMX9siwd2nj5ryXgMbxlkKyse/QGMZegkLVpZR0/ledoPq2ftTsq2eHJ5u2jFdxIgoEUlgbv/OkrzxG/l9EIG6QttcZiDh1Am9w2HkI7QYAglsooN51NZfOwwMXfZiigVEo1qufFIswCeaB0sZAPOLRGfrdadY0ETGD//OCV6E/X93VjwZAv2nL+Bx5bn6Ky8jEmW+ND4JEtMfVg455h1v4GaXuxWbsEx9TzT3p/BZpPovFRh5Gf1N3goRaAzxfw6BqOBUCmVrsqLtFaI7GGklEwkyTxr2gzpWN+fU9Et+NRftJ771AOfcGSziXrFQkrAtfVouLYFkk9A0gkxkaoI92CIehzC+4msakGqsK8ozYOUM5iTT+GqL6KTMppOVGNr8WsvAPIDu1BONQJ4tVf4X8rK7Kpg2dPlT2Yv/2n4P/YrfIj/Jkr0Rp789RRnbtnP2pITrvGWcjulzt6MUh9EWyb0Q26WuuADnDJHko07U5TrmaiqXFO/Fw6bGiFfXM48xVFmqMYzpVu/+29UjtBulBXn4VD+YKkKH+SBJNH5egZLlpzhdGIuGYW6+5Z2UCgFF6Q4AwpSgCqCndBuIClxLYwjQkoiRhYihh9vvYajRkmXcF/Wn09BkuC9QQ0pvP4Mboc/qryfI7Pg0h8M7vcF3d/ty47oNNafv41PwiY+zliIq1SKUdKgeHw5igot9xeS81h+SjBFPxhkIXQbbPyo4W3q/WnBMWetim9GRNGglhszt1zlix3XqePjRN9GVXRJhXYTwc65xZyNKyKr8FFCfV3ZNKUjzloVHJ8jCM5KLQycJQIREEESCP5VyjlRkozZBrvfI6K3xMAmnVlz9jbrzt22C3YSsoo5lZiDQoJhzS3pflcLcbjcKLXVOBHs5Cbgp9bx69OtGD7vOPuuZ7DrSvo9u70q4mqq6L6LCrR0YBVlCIVe4DndNKarfidckYI2eS94PW63rQSQaDGPLeeT/V/CuucEHwco8Qjn3YwexLm2YvObQ60t2ekFZSzcncCKU0kUlAkDTq1KQbNgD1rX8aZ1bS+aBXuIz7kcsgxHvgPA1Pl1nl9/ixK9iQ71vJk7pgVOGhWDEj9FUl6mRNbyomEKe802iQVJEru4rXdhrnIAr6jX8KpyJXtMUZShYaH6KzorL1vH/6T4imocVO6Ni8ttx6ywuPnOoXcNlCCiv03+wvUuArBPGNTtYn2pMJuIiz7FL6vWECal0EIRQxMpAYVUOSOUEXceiZHIKFj7fHtahHj+hTcCAR5C06ln/Rq4O/5z288fBA+DnYdAlmXGLz7D3uuV1Y87hfkw3biKiDs7RVt2haRNK9MFkGCjqQMg89yfDHQA8uoM4KskoVfzsmkFzT4KwEGjZEhULaYPbFD1RrKMIe4A6Zs/ItAi8HZH9sKEkkDJ0iU24BtoNd66SbdIP0a3CWbJ8Vt8uu0am17oeH//JEuGBGU1NxFHT8z1eqG4sYO56u8oGruDPQl6ftgXx/QN0ZRT/2UZgryciFeF0rR8W4UaXr4iZo873xZigqtG4zLwWx5r+SyPhcrIP/6CZNJx3lyPdwzjCD7uwrfBRpw0wovn3fWXkWUY2jyANpagIPfSVjyBYlnLqHZ/3YRyXMc6JGUXs/j4LV5bfYkmgR7UstxYrYgcCN3fg30z6VWwlo/UhTR74hfbA/DqBvFvrw9tgU5FSJLQExq5XOj+7P0Qdk1nYs+FrMGBdedSmNy1HrUtHK6NF0QbeOdwX2q6WzqsXC2z4UKLX4eywtNPoSS8ho3/5aR98DJWoeUB711uKnrsBzCWEquK4FBZE2779yI8/Tfh8h0lgp1yPZ/cEj1kC9IvNf6c59i/C2UGEwlZxZQknKTFjV2YUfKm6jVWpzUBJBxLlMjA7ZwS5hyIY+3ZFGtZJNjLiSfbhTC8ZdC9H6ip5yDnJqgcSQh+DHPpEUI1OhYNrIsm+5LIOmbfBOCQuYldoANC26fMYCK7WM8vpv6MUu2ltiKdvdrXqCXl2I29bg7CRSrFk0Ic0VOII3myC7m4cFMO4Jo5mAApiydVu1FyV3nHpaYIXrJvCA5cRfjWFxpeQW1FdtfjwTOjKJTUbdSWi/uNrLwjguVAKZPRyj08r7I3FQ5TpNBWcY0Yx2Z/OdAB0R264FA8B2IyuJKab5NHeIhKeBjsPATxWcVVBjpuDipkGTaluPJ6Fdl/hSSz1NiTteZONJPirK2mfwbBOptRYn/lKe4YF/FJ4Wh+OZLAk+1qE+xdITNh1GGOXkfRoTm45VwiENDLSlZpHkXbdjyPXX4e8jLFDatCoFOOl3qEse5cCtEpBWy8mHJ/D6A8S3+tc/UdXpeaf4RP7BnqKtKQD4yn8ag/yCjUsfK0fblqw/kUVKVetmCn4SNiplh/oMh07JkBp+bD1ldFtiLhEJJJByEduRU1j5tro7l2JZ3H559gybOtmbn1KldSC3BzUPF2P9EdZMhJQr3jDQC2uTzGcJ9/zRfqvYENuJSSz/mkPKZviObXp1ral/wkic3uo9hryGGWag5jlLsgbSPUelKQXMuNXhs8cu8DSZIQeixIgdO/EHbyXfqH/sS2mzqeW3KG5c+1xddVy7E4kVG0a02XLF9MsyUKN1XgKClUuKptt7iDMZl0i3iAbj2gzCj256BWiH1fWAHAFyUDcVSraNpzNCz7DeL2CvK4xskaILk6qAW/Cewybf9JyLLMldQCdl5JY/fVdGLSC/GS81mh+RgUsM7UgdVlto7Hfo1q8um2ayw+dssa5LSq7cmEzqF0j/SrvkMQICsOEg6I7y6AsZS6Cxtz2cHyWcyrvElf5Wmuq54ly+xCruxCgeyMqVhBGRoyHWpQo0EnfJW94fKqSoHOllZLmHpERbnnbJcwH1rV8SI5p5RVZ+x/d58bn+Bz9c88qjxqW1iUBv2+oDQnGcdfOmOQlfzYZC2vDGpTic/zZ6FQSMwaEcUTP58gr8TAbdmXL4xPUCg78YZ6ld3YV1SreV5q/i8dr0WIF30a1mDnlXQmLj3LT6OaW7ORr6+5RHpBWZXct38iHgY7D0Ft76q7nwrKjByJy0JSBFrTxfPDf0adeppnixaQKbvzofFJZqgW87Rq1wMdyygr+Ng4hhlqQWKOSl9nt36cajsGB28+L+rPomMJtKvrTWZeAcHXf6Vp6ipcTbm4AWWymnVST7z7vs5o7VUUm2yt8HSYKkiF+iIxg7MI4Xm7aJncLZQvd8Tw1Y4Y+jXyr96eoCBV3BSRwK9qJ26AM1lq/tC/zlqHmbhaRPU+H7OWF7rVo9OX+63jooI8mLH0BKPLF7Ss4AqvcYJ+XwhF4/O/i06U8od42+d5pH4QgV7OTFh6lssp+db2ZqVC4svHmuDrqgWzmeRFT1NXLuKyHErT0R/f76O4L1RKBV8Oa8KAH46w73oGM7dc442+EdZrtuDQTT7ddh3oQN8APX0zfhYZmqZjYJto16f+YHB7gNKRJAn15fgDkB3Ht44fccP1VW5kFPHBpmhmjWjK+WRRXm1XkceTIxzlrcKKpZYSrMoBVA5W6wNJEqn+B0GZwYTBJJ6kTmqVsPIoyaJYcuGgOYpxnULwqhcpMgRFaYKjEtyGaIuIZICHo7U7jbK8Bzrm34WMgjIWHIpne3SanQKyJwWscPiMcFLIVnhzJngSC9u3xN/dkRPx2fxyOME6vn2oNy/3CqdV7bsMYk1GwY8qyYbibMi8JqwZ7lzkbihkEegYJTUqBxfBNyu4bTfGQS4jUCoTRrp34+q2SosuBzzOApfn2X0iDbNsxkGtYO6YFnYB7Dv96xP1ke1epEPDy4bJxJiDeEu90rrc+GUYjojg9Ji5Ic0aNbQLdDIKy1h95jYJWcWE+rpQ19eZ/FID2UV6WoR40voe5rn1/d048mZ3tl26wxtrRZOHn1SZ0N1KEQtFGeSXGB5Y5dtklolNLySrSEc9Pxf83R359NHGxKQVkphdwiOzjxLg4UhdX2cO3xDXtc7b20j4rP8/PuB5GOw8BEqFROLnA6j9VtVlKG14T0gUlgBPxEzFzaKb85PxEY5qp1JDynvgY6kkM3vNzZhB9R1bk4y/M4fOFhfrBOK0Y1FJYrZ5R/ZitdQbY9MnGd2jBTXcHGDdfPsdXFwJCQctb04rFIM7vQaSxLMd6vD78Vuk5pex8GgCk7vWo0okiLZY/KNAW32GpGmQBx/LQQwte591rt/gmhWLfkFPPlR9iLAehe6Rfiw+lkh6gR7K9e0c70pdSxIM/A5unxUPkXJYSmgta3uxdFxrBvxg0zx5u1+klUtzed0XNC48S4msJa/fbDrV+vNO5lUhrIYrr/eJ4JNt11h4NIFDNzIZ2SqI+Kxilp8Uma+n29emV+8ZMGuFCD5meosSoMpRtNo+KNSOQkZg8SC0GRfZ5P0F7QtfZHs0PNMhH4NJxkmjJKRits8a7NQV/5YHO0oNGErYYyFwyjI0CngwDa14i/Giu6MaN0cVxIjfxR5jFEZUPN4ySHxeLn4i2NEVkFFYxo/7ROmqR30/yLdc/7wqVH3/DSgzmFh+Molvd8dSqBMPcQe1gu5hHoyolUXXI5OsY737vs3nvm4UeKqYtTeG306nAxIBHo58/Ggjuob7IskyZFnKPCnnxL9pl8BQRfZWoYKQDrbfXKNhvJo3jC1xeqYPacbYdrXJ3zsL98MfYpQVvGscxzWPruTnpONFIR5SER0V0Tyj3FElv6Uc52/lsNkotKSiAt35aEgjou7q3JSr6phCYp5pMLWlNEaqDgCC7FyOLspLyLGfgdck8I3gQEwGL644b83UVYUZgxrwdIc61a530aoY0SqIEa2CeGPNRYIuZlY57ozD88R+8w1pPT8kvO0AzLLQvSosM5JfKv4tKDOQX2LgREI2B2IyySm2KTI3CnDDw1GD0Wx73yl5pXaBLoiAJ/HzAdWe7z8BD4OdhwBg7oGb1a47fv2W9SHtVkEg8EP1Yrtx1+UQIqVb9z3WYe3L9x1zyWEC5831cKHUGuiUyWrOPHKA8Q0DcNJU+Or2/hguVUgRJxwUM3vPOiJw2PexKA01G42DWsnrfSN4edVF5uy/yeMtg2y8jHLIMlywkBhDu3EvtAjxtKSRoVfBdBZrviCi5DYz5OnkSFM4J4cxtUcYk5aeJZ8KGbQbuytnjJRqCO9tH+zE7oDwPgDkFtu3kX+zKxaTWeaZsBIiooVP2dHQafRq2+6e5/xn8VznutT1deatdZeJyyji463X7Na/O6A+SqVCmDHumWHjOsmm+/tr3Y0aDeHprbB4MI7ZV1jn9BmflD3G2Hk6QIujWmmbocqytdXcGux41xM6RCXZsHY8LRp+xSKFhMks0+e7Q3w9PIpOYffuWikv2ZhlWXTbnP4VgJ2mFjSs5UZdX0vwW16q0hXw6+EE8ksN1Pd3Y2zbEIjpKBy4r6wX38+/qSNLbzSTXlCGzmimzGBCZzRzNC6LxccSyS7W40UBr/lcYbjrFXxL41HEJ0L8XTvZ9hogQvEZwHNab2QnX/xdVSiPecCebNElZ6yiBKfUCj82Z2+R2QrvI8qUzt6wboL4HdZqTnKWFzpyuJ1XSum5lbgc+ggk+Mg4llWmbpANPo7+fNfSTIPkTWjTzlb7np/Sv8lizRcMUx3mSO0XiAwJoIG/G1dSCzgSl4VCknBUKwjycmLc4jPV7sfVUmbPlN1ZZurBNJUtqyydXQhnF1JcsxW3U714xOSP0ace/o26cCPXRFJOCc4aJSazzMmEHGZsvsrK08l0CvPh8VZB1PNzre6wfPlYFIW3CqCw6vXhpjjYORp2wkZTRz43PEEG1fN4XLUqfN20JGQVE13BxuZ+iM8ssn13/4F4GOz8g6Ezmhj180nO3qqcYlVaHhAAxTgyTPcBa7UfVruvOmW/I6MAZLorzrNQ8/W/fH7NFHHW/x8yNeYb97fY2Cyk8kAXP6jXC+J225Y9tQUCWwp7iYNfwL6ZgiejcWZIVAC/HkkgOqWA2ftv8v6gu4jQ1zaLgEmpheZP3fMcJUli7ugWrD13m9fXXOIJ/XTWaj6gjiKdddoZlHjWR33nOcpKvKgr3bFtGLtTlNvuRt2udsJ11swFNgG4Bv5uuGhVnErM4evt0XTa+x4NFAYOys3p8Pjr9zzfv4oe9Wuwa5oni44mEJteZBX2A2g8YyfhNVwJ8uxCvzrvMTDB4l9v0gvDzAHf2Kss3w9+9S0BzyDqFN3iF803ZMruDNe/T2KxP7uupNG7YU2RxdFZ/Mc8a4t/tS4wcoVQUo7ZRr+ajVn3/GRe/uMC8ZnFPLXwFJumdKRRQPVEzhBvJ1QKCaksD/2y19AaSoh3a82OjNaMqUgmVVhunyYjgZ6CvG0ym0WwFN4PHL1E5ufmPhHE/gso1Zv4/cQt5h28SXaFmX056kh3eM95J4PkAyiL9FCFjl+qOph4nRve5OMqlVrLRwFSNpRmw92xjcoBajaBgOZQq5n48w6rXiDRbMmEKJSM61SHU4nZ6I/ORaNcglKSWWbswRJTbzQqBSOa+vF+4YdoTh+sclc3zf5WXapOikvEmWtRT5GKX8JGfojrdc9r1VSKY7xqG0YU3DTX4qZci9PmSH4yPkqklEyo4o5doLNc7ou3OYteirM4p51mjALRrl4EXPIW+l2DnwNnb2RZ5tvdscw9eJPraYVcTyvk58MJ9Kxfg/cG1iekKkqAUY9roW0yuc7UkaHV2OcMVR5hqPIIn5ifZqN2EG6OalwdVLg6qImo4UKP+jVoEeKJWqkgs1DH0bgszLKMSqngVlYxNzOLuJlZTG6Jntu59h9o92/EtT72VvfKzQb/ADy0i+CfaReRXaSjz3eHybJog9wPTpRx1eHZatfXLitv55Rpp7jKCs0nf8NZ2pBorsFBcxM6t21DnRqeIhBx8hJBjkojiKCfWIirod2FwjCAoQxmtxY6LN2mi5IWcDA2k6cWnsJJo+TYW91trsL6EpjdBvKToPPrQq35AVFeBqxJNq+o1vCo+jhqWTyYsmVXrinC6ShXmMFW5YhtNokAIfO6eK11E11bDm48s+gU+2My+XBwQ55sF8Lacymc2TibzxVzyJZdmR2xhPdHdf9zF/YvoNxGAcDVQWWX7h+v3Mp0tfA7ysYTbyyBdKPHREnr7vbdeyEvCY79hPnqBhRF6awwduNt43MAvNE3gsn18uCXHiJr96p9tolTP4sMRs0mMOkwpXoTzy87y4GYTPo0rMH8sS0rH68CvlvyB4Pi3hcPXI9gprp+y6YbOvvyxdwOIvMzZi0FgV1o88leSg0m/pjYTnA6tr8JJ+cJ5eFhvzz4+66A6JR81py9zaaLqdbyhUapwFGjRKtS4KHS8Yq0gr4lm6vdxzqXJ3g7qw86C/GuSaA7fRrWpF89R+p4apBunxYcMaVKaMY4eoisqEdw9Z2IdyMtGpaPECTzAbMwNxjKrs+H01cptGpWGLvxgfFpvm+VSxeXZJyOf1PlbgyykgWmAXxjHMEX4TEMTxKB83FVG9oZTxKjCGWi4ze4O2nwcdbg7aLBLIO+rITGyctoXXaUKMXdqSzIl50YqP8EBTLLa60mIOeUyDwC+ifWMu92CH/sOUo7xRXqSndo4pBBS3Ui2lKLjo2TN4zfa+WGZRfpOB6fzaYLqey9noHJLKNRKXipRxgTO9e1OcMbSjEvH4ki4YD1XIwoUVXlOHw3ntpsU5D+i/hh7w1m7Y6ttPx/qaT10C7iIe6JvFJDtYFOLXcH6vg6U8fHmTKDmXO3ctlXNKrafUWba9NKuk5f5Wl6Ks4Soqjc2VUdEsw1CFIXojKJFLOMhBTRD9pOFjPFC8sxX91IbdKprdgNZ3bb76BGYxi+SGhevJEgJO79K/hqqR3AN0IEOxW8nTqH+VDf341rdwpYevwWL/YIEyuOzxaBjlsgdHyl2vOWZZkinVF03gCz99uyUGl484ZxIr2m/YznjTUUHp6Ld0myfaCjqNCxUxEKJXR9G1ZbMkq6AmF82mYiVyzaL40C3JEkiWHNA2iyYz8Y4Fdjfy7lO2A2y/dvqf8XkF5QZhUy/HJYEx5rEUh8lphN3s4tpevJb6EQvpSeZmlpR15RreYp1W4U0WtEtuypLVb15/vCIxj6f4kioDmsn0gjRYJ11Te7YpngUSRuYJ5VZPs0lnS9SpQoHTVK3uwbyYGYTHZeSWfnlbSqDUdNRjj6LS8lfI6kMJKBJ34jV+B7RgE3EuyF9YotPAxHL9wc1AxpWouVp5OZcyCO1nVaC5d4EFYWfwKyLHM0Lps5B+I4djPbujzQ05Gp3cN4tHmAEEc0m+GrUGEOexf0rsFsljvyfXYrkspqoFZKjGgWwMQuoYTeXcqI7P+nzq8SLq6EzdNE2cs9mLgyV1y+bk1fZQY6WcVvpj4YFA7E+LyBdDm90uZGSUOW0pe9ZZHMNQ3mtuzLj080Y1CjfrBkP9w6QjuXNMiTiDDf5MBYH6jZuPxiCVL7/k9BfwoUImDaY27OVXMIwVIG7ZRXCZSy2KZ5R/j0ZcMth/qElIkAecvRc/yabCZf9mW1qavYbxEoMTFUe4ZXNeuoWZKMac04lON2glKNt4uWgU1qMbBJLeIyipix6QpH4rL4amcMx25mMXtUczwMGbByNIo7F9DLSjQWsdW7A52J+pcZojwqLDYqYvEg4uuOYV+dVzGZZbwswZ2zRoXa4oFVojOhN5lwUClpFOiOm4N9cDq1RxhPtA6m1Sd/wrrnfxQPg51/ILKKdPT4pur0MUBqfhmFZUaSckowGGUUpZlwD4mSMCmF1doqxPIeAHUU6UK7xy0A6g9GajVeqJuWI7QbioGzmPjRt7SVovGSChnY0AulyQC3T0H6Zdg0FZ7dLjI9TncRcw2lVgG1ig7pkiQxqUtdXlp5geWnkkSwYzIKkTyAHu9X24ZqNsu8tOoCmy+mElnTlQAPxypb9z19aoDPC+zNrcMjp56wXxnctupgB0QHU2Arm4KrQklmoY6MQh2SBJE1xXZb9uxhkOEaBlnJalMXMm/l8suReCZ0/uv6OvfDJ1uvUaw30TTIg8daBKJQSNTzcxWcBV0R7BedOS9OeB4uwocHnFhr6sRcl18JKk4Q5aV+n0N4X9DcwwOtIiyikWfMEXQJ9+XYzSwMJplkoxt1ADJjxGdXzouRZZFRAahjE32r72+b9U1cepabn/a3b6nOSYD1EyH5JBKw1dSaNf6vs6hmIwI8RKBV3t0FiGCsKF2o7QY0Z1KXUFafvc2BmEzO3sqlRZHlO+Fiy2bllxqISSvkTn4pkiShlCQUEuiMZjILdVxLK+DwjSwyLRYAKoVE30Y1Gdo8gE5hviLIKUwTZdDNd5VBnX0x1n+UBXkt+DLaBZDQqBQ83TqYCZ3r/v2lC7NZOMCfWgBAVs1OHC8JpO+eSVYVdQMqm9DoXaW1JcZe/Gh8hCzcLSVwGy7dzmNQVC14bCF8H2VRyLbg3BJRFr11THDfsoRysqzUsFA7lnk5LcjEg/ah3lxRK1mTdIHlptftDInLAx2A5om/4Gp4G/+aofz8ZEtUSom5B26y9dIdVhe34ZiuLtu0b+Oeepbds1+Enh/SJdzXarpZz8+FpeNas/ZcCu9vjOZsXCqbZi3gCXk7alMJObILz+tfZqHP7zgX2gJ2gNXGzuw0t2SnuRWJysoTyrrxv7MgxoE1ps4Y7/O4VikkBjTx56UeYVZuzpwDcczeF1dprMks31tO4H8QD4OdfyA+vYtgWhUKdUZrV0eiw8R7jtVKD+6/VI4T5vpMNzzDqrGReHt6iHJDda2RWld2mlqwEyFCFtjOojiafkWUfJJPQG4iskdI5fZKlYMoBekKbO3cFnSLFOTZO/llFOmMuCTsEkq8Tt6C31MNFh5NYPNF0RVSXrcHUR64dNs2iy9vKe0SW6Ejya8hZFy5N/FZoRDKreXBTtJJrroPAaCOtzPOWhUXk3JxOfwJKCDZrxvTWnbk3fXRfLkjhla1vWgW/NeFyqrD8ZvZbLqYiiTBx480qpxBSjwieDoeITjWjOANf4kAT0c+3KRgYOHbrNJ+QmTxLWH2qHYSxNaWz4jA7l5tsWUio5Ume5FXoqdtXW8O38hixDY4qHTDqTSH/Xs2keDcjOYhnjRVJcGdC6IbrK298/SzHeqw8Kh44Dy96BQ/P9kSB5UCLiwTZSd9EWhc2VhrGi9dj+SZoACOxmXx+XZRVmxe8bo2eVx8RhdXQLvJ1PZx5rHmgaw6k8yP+27wm5/IYhid/Ji79wYrTydX6pKpDk4aJSNaBjGhrR+1sk9CylY4e0m0eRdVzo4wZi3R2ma8uf6qNQP4SNNavNYngkDPf007piJMZpk7+aUk55Tif3kutS+IQCdTdsM37TCDwE6W2EUqq7QPI0p+85pGtN8gBjhpUCok/Fy1dIv0Iym7hPFLzrDwaCJDmgbQKKAGDP9NeMOVc4JOLbAGWIDI4jUdxWHPoczcmIObg4qdk9oTYZkUmMwtuXO5HikpSeTHn6F3lq0TtAhHIVzo/gnKEetQWRTHPxrSiBmDGnI5JZ8919L55uwUPtJ9Sa+cFUz+3ZfXtJ0Y2zaEKd3r4WAhzT9W34leMashdgfuRhHZXTDXZYrhJRo2aIQxe63ddXjHMI5btYcTklfGrewSfjH2Y7xqOyCyU+UB4+fqX3hPtZRZxsdYZupJGbaGCh8XLbU8HMgrMZCUU8LGC6lsvJBKgIcjBZaurqow+pcTrJzw9zYy/F/Hw2DnH4YinZF151MeaOz6ye3xu/EHHK56fams4Yi5EXvMLfhC/fMD7XOfqSndlRdoLMUT5a7Du0GX+2+E0P4oT+l/sCmaj4Y0ollQA7JrdMAn/SgLv3mTj4xPMmd0c4xmmUBPRxr4uwlNGNeaItgpsdfzcHNQ4+OiIatIT0xaIS3OWbrLop6wlj/uxspTSXy5Q8wk3+4XSQ03B84l5dIs2IO1Z+2v6+wDcbzTPQDPPJvEvZybKJ4FAfbqsXaIXivUhK2v15Ct6g9oaFDLjbwSPXuWfMKrivMYJDV1HvuIOn7BHIvLZuvlO0xZfp4/JrWzSsn/XVh9VrRRP94yqGqCb/IJ8W/drtbgZXSbENrV9ead9ZcZEf8uE1RbGeV0Gi99qpDtv7hcBIAtnoYmIwRf5G4ENIcr63hUeYRfUwey55VuPPvbaeKzitmmbspjykPEH/6DmUbxmX0fdIghIKT8ne29td4bWJ/Imq68sfYSh29k0f3TLRyrvxaubgRAH9AW5bD5nDpYCCQhIfHKHxfQm8z0bViTT4c2tu2s/iDBC0q7JFywNc5M6FKXVWeSORCTSamHDkfgxJU4vkmz8SYCPBwJtjxUTbKM2cL38HXVEujpSLeaeqJKT6C+sQDmHxYBZDUw1OnGtiY/sfZQKodixfX3ctbww8hmdAx7AI+5B0BGQRkbLqTw9a5Yi1+ezDTVWjuSr6/0YF1BpoiBqHp9yPiK2dsKCK/hyoDG/my9fIfXVl9k05SOaCL6wrBfRdbNWCF4ajkOglpDRD9wcOeb2UI4cFSbEGugA6LZIjCqG4FRQPFA+MoW7Jw1h9NFcRFtWSYsHgj9vxQcK4UShUIiKsiDqCAP5F7vkLkuF9/LPzNLM49ndK78tN/Atst3+HxYE1qHuMOSwbinid96rjaAtwqHs9PcCpC4fSUdP2VrZqqvUiareVT/EdfkELhpyxR+ZhxlDXbUkonDpkZ0UgqPLWdJx3vqZYxUHeRTwxPsNzcFJPJL9TQOcCMq0IPdV9NJKxDX535B9Yn4HIp1Rnu7j/9x/HPe6T8c6QVl7L+ewVvrLt9/MOBIGRcWTOQZ1c4q1882Dma+cSD1pWRWaWdWWm+SJZR3aWbMMw7kW+Nj/MLXdFJG83XZ+7ArQ1gOVBNclGPBky05HJvJy39cIDqlgKEW48ABihbM1hyli0KUTyYvO2fdRqWQqOvrzIrCbLyBbw6m0agkjW4RftYUdNMgT/ZcS+d6zFValJe7Wjxd5TlEp+Rbr1+3CF+e61QXhULikWYBnErIsbTBwozBDXl/4xVWn0nmLZ+jdgl6yWBxay7nc9yN0jyR+ShHw6FwZR0tr36KxPs0qOXG5t37eMHwG0hg6vEh6hoNAfhsWGMup+STlFPC0DlH+e2Z1nalm38VZQYx06z4ILFD+czbwf6YdX1dWD6+Lb8c8eOz7S58XTCCaRF5vOB2BPW1DSLTtf112P2+MAptOgqC2wn+EggD0QNfEKlP5kXFWm7ntmXHtM4cicskZ39zSD9Ec9dcuvr5cjA2k8w7t0AFxwt9iCjW4+Vss5CQJInhLQN5Y+0lapLNL6av4eotjKj43jyc2TcH0HL1HcL8RBmgPAtUx8eZ70Y2tTcS1VnqMmpnZJUjO6Pv8OXOGOvqo6p29GQJjXN3o+VRZg5rSZ9GNStbLsiyECaM2Sb+jt8l0ucVKjy2/KOQazbBuGY86oIkDjr2ZHzMMxiuifEKCQY2qcVb/SL/lpLV2rO3eXW17VyCpHS6Ky8wVHm4ShIwCHmIMjR2ruRICtFI0OkVlMFt73vcD4c05Hh8NtfTCnlr7SW+Gh6FsuEj4jux5lm4Zelk6j7dWrbOLtJxMTkPEFYn1UJnH5SV3zfEunwRUB36Grq+JTyxFEqyi3QoFRK+j34BpQk4xO1hheYT4glkcW4Pvv35FL9rPkdZzsUZuYIfYwLYeVyIKEYFeXAxOY/D7kPYX3iebsqL/Kz5hiG6mWRjmzSYUHLOXI/mli7UTsporvv2o07+CbR6QfQPk26zSPMVmR5RTJemsPOOM/tjqtbwKceYtsHU9nauJBnxT8PDYOcfgIzCMtp8urfKdXV9nYnPLLZrNQfYonnX2vp5N1JkbwY+O53ndzyDIuOK/cqQjhh6fUyOcz1qfG9vx7DB1BEdGsYbXuNn91V0LtoOx36Em/th6AKhsVINXLQq+jX2p1GAO2N+PcmtbEForq8QtfwY2eZh07qOFzczisgu1hObXoSDtkh4eMUU8eP1swR5OfLJI43pHO5L0yB39lxLxyf2D6EPE9JBkJ3vQkGZge/2CGuLtnW9+PWpVtYyTk6xnqkrhMfOsOaBPNE6mC93xGAsyce879O72AiAk0/1+jPlysMuNUVHmbMv3NhFsC6WwYpjNKjRnMBDb+MgGbjj2xH/DrYyjZuDmpUT2vLUwlPcyChixLzjzH+yxf1NTx8Q5WJ71WaMJEtwYjZXWqVQSEzoHIqnk4Y3117iuxhP1nqN4Othb9CmYDecWST0hS4sE39OPoI4W38w1O4keD4bX+Al1TpOn49EE/Ym3SNrwB0HSIdmDSL5bVBrLt3O4/KKnVAMjinHaPHxLup4u9A02IOxbUOs5b16bmZml31BhOI2mbI7E/Uvc04OBwQvJ7/EVppVSPD50MaV1bbzEgHI1dTk6TnHuFihhAnw4kl3zrj64156h+d8rzCi1aP225vNohxz/CdBrLddSAhqIzIWEf3BNxyzWWbHlTQOrFrDl8VJFMtapuSOxIBERA1Xutf3Y2SroKpbn/8EMgt1/HIknvkH4y1nYqaf4hRTVBtpoKheQ0sO7UGuaxgl59dYW9oNHnVRt3wSmox8MBVtC3xctHwxrAmTfj/LuvPCo+vbx5uidq0hOpTmdxZcvYNfiu8FwvIGBInb1/UeE6cKmUP90EXs2L6RgSUb7cUMs2/A2nGwdhz5Tcbx8hkfIqRkXmxsxK2C/UddblfSGgPIObqIyzfbAhEsfrYNXcJ90RvNfLnjOi8deYEN0vvUVaRxwPcrGLWKC0WebLucxopTSZw017cGOwCRmdsxNx0jrFeyLdY6amd88y4yXz2N1D4fspFupBfqCPR0rBTQNKzlxrG4bDZfrHwvzynW/6MyOw9bz/nfbT03m2XqvlNZdv1++FX9FT2U5+8/8C6sN3XgaONP+XRoE9ILynBYOxbfFFsXQEfdd9yWxUO+ZYgna7rlCnJxSZZQvO38OrR/USjp3gMV39cuzeuEK1L42jCcn0yP0jTIgw0vdECWZVLzy4hPz6PTCtH9M6vpdpZHF5NVpEchCbG8rEI9G87d4pTTNLzN2Xzr9ianXbtjNMkYzGYMJjNGk0xyTgnFejFz2/KivU7La6svsubsber6OrPZ4vg9bO4xgpM38a1mrlBLLlf2BcFVGVH5JklpLnxZVwRd4/ZAkMVp/eCXsP8TCmRH8usOJihhFbmyC8oXTuLmV9nfK7/EwHNLznAqMQe1UuLjRxrxeKvg+35+94Isy0S8twO90cyh17vZe5aVY+urcPoXaDNJ2F9Ug1MJOby86oI11d62rhfjOtShvfYm5rOLUd/YjoPRNgM3S0pMXvVQZ8fY70ipsZV4Or0GPd4T51qYjvm7KJSmUt4xjGO5qYd1k/ljW9An0pv8RcNxv72fIrUPvzf6BZNbEN0i/Ji1O5Y91+w5MW/1i2RSl1DySwyUGIzIMhyNy6Lw6AKezf2BPaZmjDe8jkalYGLnumy5dIcEy8N3pvsmxupWku1YB+8X9wkOWfwBoQl1Y4/o/APBYQrtLgKcsD7g4mu97vtjMvhmVyyxqTn8ofmIZoo4drsMJrX9TLpH+v1pZ/uKMJtl4jKLOBGfzc4raRyNK+/+kumquMirqj9orEgUSyQlUkh78f28VcFryr8p5CZYu85SZG82+01i0uTX783Fug92RN/hxRXnMZhkejeowezRzUVm7cZuWPaYGDT0Z2gygm2X7zB52TlxX3n+Hk7zsgyfB4sMzwunSNeG8O1P3zNNN5eaVVg6/Cs449GPllOWgErD3AM3+WKH4H3VlVJZrvmUmlKOuDc8/jvU7ojJLDNj1vfMLPqAAtmJzaZ2jFaJSaohrB/qG9u55NaVL+SxfGD4gfAykZXaamrD24ZxFPDnBAOn9gjjlV7hf+t7/m/gYev5Q/DRlqt/epsQKa3aQGeVsSuPW+TWK2KJsRefGZ9AjYl65/fzwcUlyEicMfdmj9YW7GTINnLnmVu5XHbpSOPJx2HTi0IpeP8ncHm1cMGuIrtSojdy7lYe3+y2PfRuyrUIJ4VWTmkoCs2UpVwm+U49gvxrEODhSIAzIuMgm3glPItJ/fvz4aarrDqTbJm9ykxWbsHbnE227MrcjAboM7IrHRsgvIYLr/SKqMRXibW0I7/SK9w6U+ronMoA1SYAivxa4nKrQst8g8FV7p9bx8WDxCfcFugAZW1e5PLe1bRSxOKWIJSiV3lOYFIVgQ4I9+0l41rzyh8X2HY5jTfXXubsrVzeG9jA2ir/Z5FTrEdvNCNJ2BzH7QYkCF8vENmxe6B1HS+2T+vEF9uv88eZZE7E53Aivpy7MAQVA2ituE5fxWl6K89Qk1wUdwc6YM9lOfw1N2MukhD2NFLNKBq2eJmapz7lE4elDO/Rj9k3RLnyjdXnCfZZRP2s/aBywOWpP5gUaONP5RRXlmOY2Lkuq04nMX1DtNUzC+AT1RVQQaFLHd7v0ICBUf74uTqgViqs2iY/5Hemr3YbvqUJ8GUV5RWNC/ScIVzhqwjyZ+2OZfa+WLopzvOhdhvNpDhktTO9JnxRWaPpHjCZZRKyikjL15FeUEZaQRmXbudxKiGH3ApZLAkzvRVneEG1kSblrf4aF2g3BantJPFwPvyNfbBz5wIAt2Uflht7sNDUl686tv2XAh2Avo38mT9WwaTfz7Hrajpvrr3EN8OjkMJ6QYdpcPQ72PgCuAdRqheZ3ft2GEkSeISIzFDySWo0C2fq5KmMW9CQlvm7CFOm0dNfR83SuEpeXt+bHmPqI52RnH2FnIVnHbi+RfC2qiCNt8zbDh+LoPV5oKcmgK+NgsezrsUSJqe9J2w4lgyB/l+jbP4ULz43nqR5qwguvUr3Cvdh9Q3B5YnIP0K8bhh9eZ2Jyi28olrNAOVJmilu8Kz+Da7LDz6p2XUl7X8i2HlQPMzs8L+b2anO6+pemKWeY1X3jHHrgDrvJnUVaffcJsYciL+UbWclUSUGzELX7Gkipu+wLlr7fHtaBHsIUu7Od6EoDdktkMQRuzCqBcG4zGDicko+n267Xkkb6BX/y0zN/QyAQskVV7mQUpUbjgO/hKiR4ua2/S04OVeYEY7bhVyjIbuupvPiivO8y688pRKByFeGEdyMnES/xjVRKRSolBIapfjXzUFNk0D3Ks30Rsw/zqmEHGaPas6Auio48yvmg1+hkI1ky668Jr3Kr/KHtlT5i+fAu4r28CPfwZ4PKonQmc0yvWf+wR55gnXZzlqT6fP09Hu2b5vNMnMP3uSbXTGYZdCoFHSL8GVgk1r0qO9nb7lxH1xIzuOR2UfxddVy+t2elQesHC1u/HU6w5ObHvhBl5pXys+H49l7LYOknBJUCom2db3pWd8PSZK4lJxH6u14nHKuEsEtAqRsvKUCuiou4FBNF6BOVnNZrkNLhQg4dApHTjaYzrRrEYwsW80b6lWYUKIctdJO2fhkfDaPLzhht69a7g4ce7sHXb/aT6KldKqQINTXhVWGqXiVJorgPNIm0haTksPbPy2mpSKGVopYeinvskJw9oUGQwSPpU6nqj9Ds4ncmCP8vmIJj0oHbGaZSo1QiA6r4jOogIyCMmLShTnkzYwidl5J405+5c4oEB5aZQYTAxUnmKpaR7jCQrRXOwnD2o4vC05d9Do4txhS7no/DYdyyW8wQ7arkFGgUSq4+EFvHDX30Kv4E9h3PZ3nlpzFZJaZ2KUub/erL0qAq58UaucO7iR1+IzOWz1wdVBx6YPe9za9XDUWromJCF3fhq5vkV2k46WVFzgSJ67z4KhaIMv4XFnIdNXvKCSZg6Ym7G/yFe8ObW3P3SrJEfeui8urONg9UK+X6P5MFyRkvEKh1TgI7YF+2Ug0+QlVbqZTu7O2zR+ky17U0cfQ5/p0HAsTSTTXYKD+EwJq1EBnNFGkM1KqN1kz0ndDpZCI/rBP9WbI/5/gQZ/fD4Md/tnBTpNAd94b2IDh84SWyTHtFGpJlUXKHhSpshex5iC8pALbzLAi3rnDxqu5vLfyCL2VZwmSMujZvTcNuwyH0lz0szugKc3gI8NYFpr6VXmMES0DcdGqWXg0gWB3NTOM39HddLTywJCOMHCWuIksfQQSDwsOyONLwWwmdde31DrxEWZZYqZxDItMfXmpRzgv/8nZzphfTuIcv41Z7qtxLrV1ZB1Rt+OlwifJxp3PVD/zhMrigv5anLVMYYfyYCegJYzfYxcwzFi6gxk3H7cf7+QDHV4ScvbqKrIt5bu9kcUHm6K5mWkjjbo5qJjQuS7PdKjzQHX730/cYvqGaDqF+bB0XBv7lcXZQtwOGSaffHDRwLuQXaRDo1JUmX2SZRlJkigzmHht9UW2XLqDG0VccphQxZ6qxllzBErJRFMpjgUOz9L1mY8Ir2EjWz//+1m2R4vAvlxGQKmQWPFcW0bMF7+P/o1rMmd0CyjKhK8tHUVvJAiirL5EaMAc+0EoCVeH9i8Kv6xyGHXCUuLmPsgSTtjmgjQUZbbfodnBk9u1h3HYYzDpSn9CfZ0J9XWxCgTGpBdy/U4BV1ILOB6fTVxGZb8IJ42SAA9HErOL7TJUDTTpvC3/au38kbWuSK0nCl0qow4OfQWX/gBDcaV94lkbXrrIvIM3re35QV6OHH7j71XyXn0mmdfXCAfxp9vX5uVe4bgrDeJ3nXwSgE3mDkzXP8Wm1wZS2+ce3KWMa7DlFUg6JrJWb8SDSovJLPPdnliroWs5einO8L16Nk6SjuvmILZHfc/Lw3pU3m9OAvNWrmNSxl/THLNC4yIsVooyKL2yDcfU45WGGFyDUb8qmiVWHLxIp31DCZSyMNZ/FNWIRXb3jmKdkZS8Unp/e6jKw8V90s+m+Pz/IR6Wsf7hKHd7vh/GtA2xBjo+5P+lQGes/i1KHPz5bdoQ2n8quqSaS7Gs086oPPhTf3o3Gk1v5004mixqtIfWU3hyBomaCBqXCiG2mpbzUEjg5qimppsDbet6M7lbKH6uDly+nc/CowmUmCQutf2WOfu2UMvDkWMlgQw3buUV7QbUt44ISf/2L9rEBj1ro8uII3/VJGplCx2bBaaBLLIEVv5VlWjugXNJuRyPz2KXahXOpXcASXg7dXqVxnUHkz1TZI1S5AokYYdqfpBNRwkvr5QzYvYcaLM0GKcR5cCT5kjWmDoz02snDoW3YPd7guTa/T1Ly2zlm1bHMB/2vNKFa3cK2XIplc2XUknOKeXrXbEsOprI7NHNaVvXu9J2FXHkhpjxNgmsouX85j5AFmrWfzHQASobslZA+UzdQa3k06GN2XLpDt0VF8RK30gYvQa+ayReTzmLnHySkqs70cbvtqpzt1DYSmHHCnz49NtD+Lho8HHR0ijA3RroABSWGYmo4UpMeqE10Ims6cpbfS3mrYVCZwknb9E1dvgbOD7HKm9gdvBkX2k9ThrDOGsO55Jcl+ujTajWPiMUh3vMECKIyadFh1E5d8cCBcLi4LC5MftNzdhS1hbdBQ1Cme9GhesiZG3Md01ZFZLoIKvj40xtb2eaBXvSJNCdL3fGcMMSCEVJcUxUbaGvdBqFQsao0KLo9DKKts8Lm4ijPwiftnJDUO96gix+dlGFD00EfNfu2DhWPvf4HP8qhrcMIqtIzxc7rvPbsUQ2XkjhpR5hjB67CfWRr+HwLAYrjtJKe43bV1yo3eUedgjl3msfeQpdpbJ8cPFDqZB4tXcE3SL9WH3mNitOic9kt7klI/TvsVDzNZGKZLwuPcv1yA3UrNMQJ43KasyqN3hiSr1Q6alapvFiddt1bI4t43ZiLK+q/2BYNd5YgDin9RNhyBwcJ+wQljeGErsyqLowibyEC3jUacqmmFL+0L/IWoeZqK6th8Rn7WwmnLWqe7ai13t3+//3Ac+D4GGw8z+IwjID45dU7/4LgpszUrmfU+sPMlih4gXVRiIUt++5TVWYZxzIYXMTlGUSLq4enHynB6M+W8IcjTCzXG/qwFFzI75Wz7du4xgtvJNumAO4LNehk+ISvrp0GutEgHbL7McSkzD7M8uQV2Lg8Bvd7Gb8idlilhni7czQ5kF8tzcSRR683DOcb3Yr2aFrz6Z6m3C9tQeOzLJud+fo73gcnYefpKdE1vKFcSTuXSbTOj6XU4k51pb0B0FhmYFpKy8QKScQqriDrHJAevkKOIvAxh2I/rAPjT7YyePKA7YNLTfXSnDxE5mny38I9+jyYEdXRGDCHwDMNw5kn7k5X077VIzZ94no5Fk/AU7Mhl4zhb7MXZAkiQa13GhQy41Xe0ew5VIq3+25QUJWMROWnGHd5A7U86ua4Hgru5hdV0UgMDgqoPKALIuGTAWe0b8TGstNuY/Fd4n6g+z5OylnkJqNxrnZaDDq4cZOWDXGbh+DlMc5am5EVhFkFemtwpDlSMgqpl2FADCypiurJrTD3cnyHXS3dP+VZMOXoWC2lNQ8QqDjNBRRo9izKZaVp21dVued2tLKyUfYTMTtFiavu98XLfsuNcgK7sdv8a5cKHChQHbimhyCwXKLViokwnyciajpiquDipsZxdzIKCS3xIAM+LhoqO/vRmRNV1qEeNGurrf1XMsMJhYdTeTNtZco0hlxk0qY77+ZdjkbredmDO2Nqv/n4B4ogrEDn4kSC0BQW9HmXbsjLL8ru9jiGWRZtjMTblX7LhXzvwnPdw2lvr8rn267Rmx6ETM2X2XJ8Vt8MHgiXcb1Je23J/E3puB5dAq07lC1ZlM5cizGnCoHwUOqgObBnjQP9uSjIQ2Ze+Ams3bHEi3X5RHdRyzUfEWkIpldK2Yy3TjOuo0vubyvXsoLKlEGLZIdeN1hBh9NeRZfVy1jgbHd4WBsPaYsq8mrZaKLMlJKYnvwUqT0u7paATZOFpO0iH4ic/tWkiBXW3Buy1zaT5rD2aRc9HIYBQ1H4xG9GI79ZA129EYzq84k31dItt672/+n/LKqwsNg538QjWfsqnadM6X8oP7pL3VbVYULZjGzM5ll9Jnx1Dgzj63axTigI9YcwPqaL+Pk5sWAuPpslaZZt9sid2SqfhJmFCQ62MukTzFMtXZtleN6WqHdTbRcTt/NQUWwtxMtQzw5cysXdyc1WpWCBKMPfdIm80pYbx678YZ1O39LxuiYqQFvGp9jZO/OvNCtHoN/EjOtP9OK+dHmq6TmFLDW8XeQQQrvaw10yuGiVfFVFw1BJytoYex4S8jgV4XwPiLYuXPJtixuD1JZPonmGhYxMSjQgXuzMUKH58QcUQK7cxGWDBYB06DvK1tnWKBUSAxpGkCfhjUZ9fMJziXlMfinI3QJ9yXE25ku4b60qu1pnektO5mEWYYu4b5Va+yUd5o5eDzAVfv7UKO8e0ZSQMx224oLywRfC4RJbP1BwrKhAol0mPIwUdJNdppbcs0cghYDblIxrpRa/9Um6+mtciVbdmNweBTuySXgESQCnXJ1axCBjm+k8FJrNMxqW5FeYM+RySg2C+HEE3NgxUjr8hu+vXg0+XGKsuy7qjqH+zI4qhYN/N0I9XNGq6rMrcgu0mGWqbLdWjab2XEhnp93nqO0IIsOUga9PW8zWHEEdY4lkGnyOHR8GZVbLdH+f2KucGoHYYfR80OhNyNJELtLBI7lcA+GiH7EZxXbOWw/2qyKgPhvQtcIPzrW82HVmWRm7YolPquYZ387zYKxLfjR5Xu+zplKPX0q7PsYBnxd9U7ykmGDxTrGv2m1ZqdqpYKpPcLoGuHLY/OOk2r04Uvj4yzUfE031WUwyiiQeVWzlmelrThKeoyygjcNE1hr7swrncMrfS5dwn3ZMKUDTy86RXJOKdflYDoVfMzO11vjfHYe7LtLs2zFSHh8GdQfCA72WdX8jGTeWX8ZvdGMr6sW924vQfQS8RllxpCuDWHkghPWzsD7YX9MBt0iqpHE+B/Aw2DnfwDFOiMZhTrq3KtObcEk1eY/FeisNXVimNImofy7sQeDlcdwk0qJcevA7gzRyfKGaiWauVtANuMAnDJHMFH/MrnJes6/1xhP5xbw5UwxEwYGSkdo16ktnx0rrOqwKCRoGeLFqcQcnmgdTMuQqu0PymeU5UGKSqGgVW0vjsRlkZpfxmuXA3msQmXqN2NvNpo6cF6uR5dwP57rVBeA5BxR6gh6QGn9M4k5rD57mw9Uy4iSrwlLiu7vVTm2VbIIbJLNvgQpMgXRs+vbVXacWYOlsjzbsluiNHjAHGX1EFp26haTu9YT/l2dXxNCiAe/gDMLBfky9bwIqIJaV/seHNRKFjzZkueWnOF8Up61jDPv4E08nNRM7R7Gsx3rcD5JXOMhTavp/invCrJ05fy7YbTUbH4z9qG5Jk6873LUagbD7goks27YBTqGJqMpu7KVeqRST7HpwQ56ajWcqmZdk8fhkXl2JcQyg4mjN+27+grLDNxqNAX3K7vxKBSlqGSzL72Sn6aix0INNy17XunyQJ1z3s4a4Q5/86boiMtNhNxEStPjMOcm0k8uoR9gdRgoj0k8a8PgH0UZ6sRcEejoLb9FV39o9wK0es7GBTMZYdd0+4N3fQsUSqJTbPpCjQLc/lYhy6qgUioY3SaEwVG1mL4hmo0XUpmwVBCY31M8wwrNJ8LjrtkYqNXUfuPodULuQl8ogoeeH9z3eE0CPVg9sR1DZh/lhLkBellJgJTBpRfqoojZgsuR9QCcNYfxk+opuvUdxDBfF9qFVl0aDvV1Ye8rXQmfvh2QMebe5v0fL/N8twbUUznayoblWDUaXrleSavICR3rzgluWLu63kjeoYIof30L5uOzmZY2ioSsYnxctEzpFkqLEC9GzD9OqaFqwvIzi07/T2d3HgY7/wN4c+0lwWGI9OP7kU2rHafBwEjlPgBe0k8mVfZhjua7e0q9Vwx0ALKaT8XpkiDazspqhQklTyj3Mlm1CWSQQ3tiajeF19bK5OrFj/bpRadYMq4N7k1GilKLBd6nv+brCvdzo6xAJZnZ6Pwxhc8cwT2gimDAgt0WTlK5c3X5LNosy3wzIooPNl7heloBzUM8oUIHfkbjiZw/r8PbWcO8MS3QqBSYzLK1/dbP7f58A7NZ5sPNV2ki3bQpTD86397AtBz5twm+I8ZMMkxjmnodvRRnBc+m/1eVx5ssKsRShVl8kgh2TpltfJhlJ5KY2DnU1mrr7CP213QUrH5G6J4s7Cu0Z9q/VCWXBwS/Yt3z7Tl2M5uYtEKupBaw73o6uSUGZm69SosQT67fEQ/BBrWqeYiVP1AKqhah/LthNAnRwk3m9nzvsAypPDBs8QwM/LZyJ9jpX+xeFl/ezFTd8wQocnkmOJ0wZTqSxhkc3DFqXNmfqON8hhk9ajylQrwpwEfKx1/KoZaUjadUZOdd9PZZF1ac2o4kgbNGRYNabhSUGizWCjaUq2/7MY1TDi8AEKTIxJVSCnGia4Qv7/Svb0eargR9sQhoMq5B/H64eaBSizRAxSZ2k6RE4eiF5FoTAppBYGuhVB27E5YNt1kw+EZC+6mC+6XS2O/w3G9Ww01AdGk1Gw3Ajgpcp08fbcx/Cq4Oar4eHkV+qYEDFhXh4+aGyI2GIUWvFcKAI5YIsVKzCba8LLrJAPyjxDrP2g90rKggD355siXjl5zhhLkBnZWXcfvV5i31qfwMC/Q9WfV0O9rch/8GoClMZle9dXgm78ZXyocyYPs9NpgVCf2/hgkHYYEoU/dRnqG5MZZzcrgtsGozCa5vwXBxLWeKe+Ck0bJqYlsrkf338a0ZNtdGeP728SheXnWx0uH+F/Ew2PkfwJZL4iGz73rGPUtYXRUX8JUKSJM9CVWk8q1yrr1yaDnC+nDaeyCtTrxov9zZj2lhWXBJ3MTna76zW33Y1Ij4sg6Mid3Jmz5O7MjXccDclIu3oetX+3m6fhde0C5Hpass3nVL9uMl/RR+0vxAoDEL92OfwGOLqm1hvmXh7AxpKlLmAxr7cz2tkC93XKdn/RrMG1vBe2qG7b89zMeZQ3Oah3haW2OLKpjluTrc/yex8WIKl1Py+UprcY5vPFyo/VaFE3NRyCaOmRpwRa7DdmMremnOimxDVfCykBCzYkV3j9kAaaJL5rQ5ksFRtTh8I5OUvFL2XkuntyXYs6JWM5h4CLZME+38e2ZAwmERjFXVAYbg83So50OHeiKrZDSZeeWPi2y6mMrkZeeshrDVZg5vWDSE7irh/btg41VJGEM6o46xZGci+lX+vpQVwHnBEbvQZhaqEz/RiHjma74jsf9ywlvbt3CrAN/kPObMrtzd5+OipUhnQGkoRkbiXdUyRqv2WsXoZFl4z9k5o1eBnspzdq+XRhwjeMQXdrYWgOj4unNBZOlSz0PqBRsxugJkpYYy1xBu48exHDcSTL4kyX6E12/MM307UsPH2/66yLJQLt9tyUQGthbZwXq9qg6Kywpg/2e21/5Noa8tm3aggl1Bk0CPe773vxtqpYI5o5vT4H1bea2g0wzcE49Adhws6AbNnxS+bWkVrHKe3Xlf8dK70aq2F9PbKOh80d5yZ6N2EAvye9IixEsYFFcFQxnE7RF2IAmHID+ZcBCWLyjIUteiSGciTfaig7IK/g4IPZ8RS60WMgC/az5jmH4GvUuT4WAB+IRhdvZDW5xBB0U0A4c8ZQ10AJoFedKxno+1xb480FEqJBY9/Z/h3P238DDY+R9ALXcHUqvR0KiIhhYlVB9nNVNLNgBCKDBEkU5bhYXApnGF3h/TqqI/UzmKM8RsqRp0UkbTKSUaUmAAMMBy775iDuGN0ol8e7Y2S/mUp32vM6Xwe7ttQ6QM2iiuMUU/lfUOHyJdWS9u9gO+qbLDx8dVS2p+GSV68SCe1DWU3dfSuXQ7n/c3RrPgSQu5V29fr/bMOgM0x7GCtkRBmcjqaFSKKnkRd2PJ8Vs4oGOI6jiYgOZPVT3QbILzSwH42STSw1nlXjjFWVVv41UX3ALFbD3puOgcQcaEggKciKjpiqeTmsXHb3EiPqdysAOi22vYr1Cni3DyvrkX5rSFwT/Y6cFUB5VSwfQB9TlkCarKUaVIReIRUQYBkRX4D8BJo8LLWUNOsZ5SnQFrcjDtkuA8lUOWhaqzvpAy93qMOFIDteldVrj9RBP9eeqfeQ9adrN5cFnQoIoyjJ+rlpPv9ECSJPRGM6V6E5qjMXB0LxObOTGqd09kZHKLDVxOyee11VXPlrXoeU0lhCGzvJrhk3OeptlbweErcb4p50QZ8voW8bCuCg4eGDzqEu/UmD36RixN8SctzRaktKrtyXsDG1QOPPQl4iF5+hcRPIHIBPT5tNI1sMORb+1NdAfMsmZ+ygwma1nE5b9kPeCkUdElXPiiATy/KZXFzx1CvWWq4K+crmBS3O9LYSCq/HPnmpZfxuAfD7FG/yIV/V8OmprwRv4w3BzUzB7VvOqOJl0R/NpbeMCVQ1KS492cqak9ua5pyL5X+3L4Sjofb71KYGkMW7TTK+8HhOho3a7WYMdJ0rFd+zbstw0pP4NHnaMZ3FxMBu/kl7LsRBIrTydX0ip7uWc4g6L8qVshKPpfxP92r9k/BPdrGy6HThaPBVWJaO/+xdiPN40TCJcq+PKE9YTfhwqF0b8JDRW32Kp9h0SHUehUznyd2YZsh9rW9cYGwwDorjzPBbkemd2+ApWjMPyb1wH2fChu1BWQUSB+sDXcBKdArVTw9fAolAqJXVfTOXzDMtuM3WG3Xc3c04BMpL+tVFBkyVy4PUBW50Z6IeeT8uihvIjGVCxInNUpBqddgrJ8ShXOHDRHifMuV5HOia864JEk0fUCQhfI1R/ZyQclZhpIt+hQz4dAC68ouwq1X7v9tHgKJuwXpqMlWbByFGyYbJX1vxf83BxYNt5eT2f6hmg7/zR0hWJ/yGL2XEGg79+NIE8xK48Om2RbuO9j+2t6+he4/AeypORN/bPoTRLtG9SmwdS1gkydcVWUcu6CWlk5m5hRqLMSPTUqBe5Oahy9BFfJQZeFr6sWP1cHwvxcuJFuz0Mb0ERwLVwdVMwfEoCXVIRRVtA9fRoFCg8oSsfwXVPkbxvCL92FMnB2HCAJNe0mj0PfzzE+tY2Dj5ziuVpriUx6nT5X+/BVXABppQpcHVT0a1STeWNa8MfEdvaBjskIp36GbxsKxeHU80KcsM9nwtbjXoFOXrIgVJejwRCooDi9+qythPZCt6qdzP/dKCwz2GXTjt3M5s2daRgfXyFKPxWhcRGE9j+Jt9ZdQlt8m2CFuK9sdnoUsyzRRXmJ3zWfEuYp2ZTFzSZB5t41HX7uDp8F2AKd1hOF591bSXhM3k26TzuyyhQsPprIYy0COfRGN55rIgLJHNmF5cZu9iey+inYXMWkom43QcRX2HgBQwzbOJWQw+RlZ+n4xX5+2h9XKdA59U4PXuoZ9j8f6MB/OdiZO3cuTZo0wc3NDTc3N9q1a8f27bbCZVlZGS+88ALe3t64uLgwbNgw0tPt9WOSkpIYMGAATk5O+Pn58frrr2M0Gu8+1P801p2/h4BZBew1N7d7vdgkHk5ekk2ATL6ywc6U8LipGnfuB4SstNetOej/EyBTXGrLGKgyRGBVA1EOaL2tJplPHYLwfqIt98gsmNPGouciUD6bTK2QeQiv4cqT7UIA+HDzVQwms/AfqgAnczEL1LPYHX3Hyv0o39d9peaBNZab+9MeF8SCBo9Uy4chUZRCjhrCQVIQ6OlIrBxImmOYEGjbOAUMpZW3K+dLHPkWMq6S4So+g86uKUQFuuPtItbfybt/Ng+/+jDhgBAfRBKdSt9EwopRcHYxFFavjh18l+fSmrO3mXfwJlYd0l3vQd4t0ZXT+5P7n8vfiPKA76o52E5ThCVDRGCcf1u0dQNba05mY25taro58OWwJqhcvKH5WDH+zK+V9j17vy2j8nqfCFrXEV1t8w7etDq/A4KUDqArpLDMwLbLd3j6t9PMP2RzBL/wfi++HdEUNwcVhWVGlp0XwZhKMvMYe3Az5wGgLkxGKkihTHIkJbAft7rPxvxGIkw5DUMXsEY9iI4rynhqZRy7r6ZjMss0DnBnavd6rJnUjvPv9WLumBb0bVTTXj04/oCYMGx7DUpzLB1WM+CVa9DOZiJbLU7Nt3F6QHRmWVCsMzLvwE3r61a1qynh/Jsx/2A8pQYTYX4u/PxkSxQSrDuXwrglZykOuUvccONk+K0/VNXqXQ2yi3QcjM0kXfbCrBZBwaCuHYjrs5h82YlWilhezprBnkuJcHUjzGkHy4eLUmG52rRHsMhS9/9SeKBpXVAoJF7sIXiJ8w/F89vRBDRKBUMGDEZWqPGSihil2l/NWd2FMeuEx9YLJylwtNnIvP7zRrZdTsNklmlTx0soQ1vwaLMA/Nz+nK7Y/8/4rwY7gYGBfP7555w9e5YzZ87QvXt3hgwZwpUr4ov48ssvs3nzZlavXs3BgwdJTU1l6NCh1u1NJhMDBgxAr9dz7NgxFi9ezG+//cb777//33pL/6dR0Rn8juxFslyj0hiJCjP34Pa0U97HX+vdNOQP8hijqoJsC0jvpXPa5xHra6/MUyQ6jCZYqhC0WnRaVE628sHT69MxPr5cSPG7BYqOk+WPW+vuo9sIvYmvdsZYgxaAaT3D8XLWEJdRxKrTyaIUdBc6Ky5x4XYen2wTpbt6fi4oFRLpBTprV1Z1KOcnROktZYCIarg6IEo8wElzfUa1CeaDQQ0xo+Bd/dPICjXEboffBlQOONwqtO7ObU+NdKF82sDfDUmSrM7dZ27lcCf/PhYdIKT+e30Ez2wX3TeGEojZKmaI30TAov5QUJkLsvKUCHqDvZyYYpm1f7Uzht7fHmL26m02cblHZlcvlPhvQqCXyOzczi0V349ypEeL7MWWV8BQQo53C6YktKWB4ha/dirCs5wXU156jNsjSkcWJGQV8/WuWOvrF7rVY3gLsf8/ztym//eHWX8mkd17tnP9lODH3Um5RfOZu5m87ByHYm38la6hbngk70WzZQpHfD7jmHYqP2c8YV3/vnqp3Xsap3+VqNK5dIgbS5dtnoz6/Rr5JQY+336d11ZfJK2gDE8nNeM71mH3y53Z/GJHXukdQcvaXlWXT2K2w9JHIfM6OHqJh+2L54X9w4PyqyrWLpVaweux4LPt1+zKnHd7xv0nkJhVzPxDIuB6rU8EvRrUYP7YljioFRyMzeTN3y0NFo5eQoNK7SzuCfM6wYHPq6nN2mN/TCayDPX8vVG0Hi8WbnsN056PWWTqC0BH5RV6rouCP54URG5HT/EdG/ozTIuGaZeh1fhK+x7Q2J/mwR4U6YzM2HyVrl8dYGO8Ganb23/uQnzkSfytREauzaBHrm3bdzUreKJ1MDumdWLRM63YdNH2O3+tT8SfO8b/5/ivBjuDBg2if//+hIWFER4ezieffIKLiwsnTpwgPz+fX3/9lVmzZtG9e3datGjBokWLOHbsGCdOCOGmXbt2cfXqVX7//XeaNm1Kv379mDlzJrNnz0av19/n6P9E2GZ8/hWUki+ba1c93NIFVAkNLQGnxgXUjkiSRNv23Riss9eIGKD7hIVHEghv0w+jfP+vWmCZjbR7JbWAsb+eYqu+OfILJyCstxCOWzseDKVM7lYPL2cN8VnF1vZLAHdHtfXB/NuxRGT/qErHcZAMhEkp1oe5m4OaZkEeAFbiXlXILtIRYylRKDwt4l65VfvXUFYACYLAfNzcgPjMYrqE++LrqmVvcR12t5wvbogpZ2FeR+HxU44ub8JTm0XJQGErrfW59TUUplHHx5k2dbwwy/DH6T8hBBnSDqacEQTmbu9CgKUccesoLHlE2D5UwIXkPABGtQnmlV7hvNUvEke1khsZRew+Jz4rk0tN+8zKfwiFFlK5u1wgiNgA3aYDkuAzWPRgvs9pw27NG2zTvE3DvU/CxzUFf+JX20Obn7uBrpAyg4kXltkCn6k9wiA/hWFFyznk9QkL1V/ycv5ndN3cgV5HRhKZtAIAf+NtflV8ygztcl5XreQt1QrW+C1iYeYTQiflwjLcss5TS6r+uwXwZfh13o0qoUmAGw5qBSfic4j6aBfzDt60ns+Jd3owfWADwu7VsQVCc2nNs4Lj0WgYTD0nHrZ/kqtCfoXvV+vnQCsyG4diM/n9hE31uUmg+3/FY2njhVQMJpkO9bzp3UBM3no1qMGqCe3wcdFwLlPc88y6Qmg3BaacEjpUskkIJ657TpCHq0B+qYFPt13jnfVigtU90g+6vQPtp2JSOlDfHMs01bpK26Uq/IkZcVBw5JqMENpM1UCpkFg5oR0fP9IIf3cH0grKeGnlBX4q6oZc8891ttVdFMWJ+By0kq2y0Uc6xWc1DxLh40DnLw9Yl389PIoAjz9H0P7/HX8q2Ll27RoffPAB3bt3JzQ0FH9/f5o0acJTTz3F8uXL0enuwSG4D0wmEytXrqS4uJh27dpx9uxZDAYDPXvauiUiIyMJDg7m+HExWz9+/DiNGzemRg1bhqJPnz4UFBRYs0P/BJx4uwqflmqgk+1vds5KM648QHagHJNPgM7Sqt5omG1x13oENOxA7bLl1r8rch0+2nKV/nv9ONJn2wPtvmLp5Hh8Ni8sP0fXH84w3+s1jE5+YpZ6agEuWhXPdxFmmt/vvYHOaCsvDG8ZiLNGSVxGIcWZt6o8zmbNu9QyJlm3K+9EOp1YfSdNuTN3RA1XVI6WWay5as0KLq8GQwkGrzCi5Tocj88mp1jPi91FIDb9vDu6p3cLPk1xplD4XTNOZHkkSQQQI5aIGWFFLB4MJgOjLJmtVaeT7Hk094MkibbbLm/Ac/uEKalrLTEbXfaY4OFYEJ0quD2NA9xRKCQmdQnl2Fvd+WlUM5x8RLZDUZQuTBn/wyjnaPQ3HwCTTnQIdX4NxqyxG/ehPIcwRYUyr7FUeCmVCyFaoP+6AUtnvYYy7QJtFVcZrjzAc0lvYv62EYoDnxJccoXuygsMUp7AUyqiSOGKXrJ1T3VWXuZpaQsvqDYxSbWZlgW7UejtuTuVMH6fEO2zwDtxK0/GTGad6UXmuy3iddVKnlbuYKDiOB/Xvcor7gfRHv9OlEfykqrfb048/PGUyOCFdhedeI5/ocR0Yw9c3WB73UtMZvJLDby59pLd0KaWycJ/GgdiBf9wYJNaduW7qCAP1k/ugLNPICZZQmE28NHK/WQofIQ33uCfxETi8mrhr1Uh0DeazPxxJpmesw6y4FA8eqOZ9qHePNe5rsiSdn8PQ7tp1Z5TLfMdNL/1ZteqnzA/wG9To1Iwpm0IB17vyuSu4p729cE7rGy6VGTjPOuAS007Pk45LpjtDYV7Kc5QE/tJC7vepfS3R8kust3nH2sRyD8NDxTsnDt3jp49e9KsWTOOHDlCmzZtmDZtGjNnzmTMmDHIssy7775LrVq1+OKLL/5U0HP58mVcXFzQarVMmjSJ9evX06BBA9LS0tBoNHh4eNiNr1GjBmlpIu2flpZmF+iUry9fVx10Oh0FBQV2f/8/4kZ6IS+tPE/bz/bed+w76hUsVX9qF/V/rlrAFfUYaisezEcLIDPunEj9K9QWHoiAQiExa0RTtJa24HEdbT4uKXmlZB/+udK+JumnVVrmnhtt/f+YtsE4a5Tcyi7hs4NZvJUv+AJlB74FfQlj24VQw01LSl4pC48kCmuA1Au4Ju7i/Vqn+F49G5fMuwQUR/2Bya8hWslIR0U0my+Ktv3yIKtcmbkqbLakgLuFedpq8dWJ9mWI8p+6TgdahHghy0IaYGSrYAI9Hcko1LEqXi34NB1fEaTJ6DXwYws4PEsYMAK6/Ls+m6wYSD5Jn4Y1USkkUvPLSCt4AO5OdfAOhSc3iDR/6jnYJOQGygwmbllcviuqJns6axjYpBavKBh9AAABAABJREFUtBFt6JmyBzml/1mOXGahzmp0Gexoee8BLUTgeatyNjLVu63gqAz6Hgb9INp3x++l7C3btdUYCv4fe2cdHtW5dv3fHo07SYgjgeDu7u5StECBQqlQqNKWGlUoVaxUsFLciru7ewIhnhB3m4zs749nMpMhSYG273nP+d6zrisXZLJnZs/Mnr3Xc9/rXoupRb+yU/se6zWfsEC9HOf4Iygwcc5Uhw+Yzhz9ZL7Sj+BMh1U4vRuD5t0EywUoreF0fjX25RdDH8KrPQsdXoeaZcbafRvC+O38UFv4vBgkjYhmaP8qVBF5W7JbMCVKB1TZ0XQq2M+Lqj/4UL2aRZofGJf0idDdHP5YCF+/bQi/Dbe+Xl0+3N4u2ig/NBMVR9cgYSxZiUPwn+LmZlhvbbmhsgOFgoN3Uuj3/Uke5hRTzcvRYiDYJMjt6Z/jbyIpu4ircdlIEhU6/wZ6OLB5RkeLIDnu1mm6fnWc5SceYGo8DsZtAa2raGv90h1yH3IxJpO+35/kzc03SMvTUd3LkRWTWrB2Sitc7dVC3P1TF+xOfWHzXPKA7+HtePLbzyFf4Uw1KZmed99lzaL3yS58si6DVqXkzd5hvGFuL3154D5ZdZ+Fmdfg9Qh4I1KIysugseKBze8/ab62VO0vmGrznn4SAA4Jp+iuEFXLaR2rP9H+/P+GJyI7w4YNY+jQoSQnJ3P48GE+//xzXn75ZaZMmcKbb77J6tWriY6OZteuXVy9epWFCxc+8Q7Url2ba9eucf78eV544QUmTJjAnTuP0Yn8TXz++ee4urpafgIDKy8z/ruiqMTI0KVn2HGtvNbCnVzeUa1lg+ZjDmteI8ZuDM8rd1pSjUsxSnXsiZ7rSLXXiDWJk4nLmc/EjVVqiwtlGZhkGZ3ZSM1okrnwrqg4tZLuMqxoi822n7p+QH/luXLP9arKul2IpyMX3+vON880okOoF7voQJLsgZ0+i9/WryG3WM+UtoGMUh6h9ckJ8EWgMNxaP4ZnkhcySCkuBFm1RooHVNlDrV4og1oD4CHl8vv5WLILS/Ay27qn51d8YsouLOFwuLg4jvaJFxUCOzfwrMT4sLp5iuLeftpXdwNE1UijUjDFTARXn43FpNAIF9cph0XaeUk+HP5IGAJmxZJ6Ubwfx6SWyGpz1UvrjJ1aidqs03iS1eOfokptGC1aMoTvhpJC4szaJWc7lXDpfQTN7ARJDDcFsOPakwnk/yksOiJaaA0DXLH3Nh+DUUfFJNNJ23NPiuxG4bC1wuW52URoNoHi0H78EuNJh4WnKJDF577M0J9TxnqkyS5EmXw5aazPEsNAZrgvJ37gJt5+5zOOOfVjkXEIDrU6i3aQSgtVGwKwOsaVj/XjuFj7DWr3nCIuoJFmcXyLqSLFvkYXoqVgwk2BqOQSWDtMVNLMraGfHabSqGARL5W8zG8OE4irOR592CCuKepyyliPvcYWbDR04rxcD5BFvtaKPmSunYI8v7qY1LmzQ7SuQnvC+K1PX9GRZRH+uWWyTd5YUdWWTF55kamrL5GQVYSfqx0/jG5iGRD4n3ZNrgh7bopjsEWwh3US6hG4akGJqL7+rFlIvs7AZ3vCWXIsUoxwTz4gSGFmFFcXjWHEsjPcS8nH3UHNO33D2DOzA11qeyOZDHB4HvzUTejCHDxJazOXeFl4Vx2+GQt2Ljh1fxvHN+8QUe1ZAMZl/MCn33xj4zL9OEzrWJ3aPs5kF+pZeFAYORZnJhCz52sKDn/5mHuDnyQqO9XsCthvsi7GksxhxMP+D1Z14Al9du7du4da/fjVQZs2bWjTpg16vf6Jd0Cj0VCzpijtN2vWjIsXL/Ldd9/xzDPPUFJSQnZ2tk11JyUlBV9f4S3i6+vLhQu2Hu6l01ql21SEOXPmMHv2bMvvubm5/3GEJyGr0KJbeBRz1b8x9M9SdZ8SS8Id+VUjVtLafHMPPyMSzi8HV3+RO6RQosjLYrz9aeJ1jpyLtOeNXrU5+WYXwr+xjn8+o5vLebkOC41LKyQ73ZRX+aqzB68fzORYRBpTOlRnSJMAhjQJQG80cevHrfilbiUs8me6f1GFxYqFTFXfgtJukr07eNTA5ODFvgQVq7KbUP9+KnPB6tfjKr7sDRSxfBOXTd/vTjK6pWgL5esqPnZXnYlFb5Sp6+tE8FWzxXy9IZVPYoX2EHb0eQ/pZn+f71BZWi/DmgXw1YF7RKbms+ZcLBPahoB/U5h8UORi7XtbVFl+7ECgeUw83qcbUrq5pWXOoCodHjM9gcjysQhsVcbf5wzROpEgHuLpaDvdY4ZkrlzdlYO4Y9b2/CtwKzGHNedEa/Lt3mGgMH/wmVEVbj/D+QfW+3hwNS6Lq3HZIqX+QQYZBSVImLDXigv6L4a+pOFmc9/N09swo0weW7bZZTuvuMwx4t8MEi/jmnkDD6oxT7kT6cf1gCzIdZ8vhQWAGYV6E9P0s9jn/An2yTfh4AcUK+yxA27FJKFX1KD1wKmMbRVked+rZBfx5q/nyUpNIFBKI8CURqzCi5EqoQnzuL9JvAXaAOwaDMKh2SgLCXsqmIyw902r43SVMEgLR5aUjIodwPWSVNRKiSkdqvNy15poVUqLR5Wn4z+fdP44lJqp9m9UtfKNylgLmAb/yLu5dfh0z12+OXSf1tU9aR4Sxt7Gi+hybBhNSq4wQX0YXeNJvN0nDDcHM8kvzoVNE4VfFYjvfd+vqHJ7G0hp5Mt2zA0PoWFeMd7Odkh2LtR+9nuy1utwj9jAvJIFTP9RycSxz9L5CbKnVEoFb/cNY9KKi2w5dw/p7k7eKP6BEOnPhydKMU4pSHaVkgQ+C4uGaIg2+XBbDgGg5zcnmNg2hDl9w57IV+z/FzwR2Xkc0XmUkDwJMaoMJpMJnU5Hs2bNUKvVHD58mGHDhDYkIiKCuLg42rQRNt1t2rTh008/JTU1FW9vcRAdPHgQFxcX6tatfGRaq9Wi1f7rv5z/JCobNw+Wki1EJ9rkw2eGsfyk+brCbUsRZfKluqLytt9m7cflbzQUw943bG6yB+YBaKA4R03kwjrUeHYJ3ZrUghuihHperkM9KbpcDIXNazj5GvAupyLTySnSi/IxwkunyYi3MSw/QHP9PY5LL+NuHpv/XD+aBx4dwDOU7CIDl25m0b6mF4UOelqnzgUlENZfPEHtPnD4IzqrblHPycTtzGIWHhQTOO1qlJ9Sicso5OdT4mL6ac07cOmqMF/s8k6lrwGVVpwUL6+kTvo+JKm/aOfl6/B00vJW79rM3XGbL/aG06lWFUK8HAVxajQKgtuKyIdEa3K9S91ucMRcwtaKtlLpxfCf4DpIkljpXvsNoo5xQxaEsNLoglQxyRYp+3PoTgqRqXnU9H6MaPZvwmSSmbvjFiYZBjTyo21NLwjPL7fd+iozqZ+yg/qKGGa5naDzAiebqaFSeJBncRBXOrgxsm4APer68vaWG2QUlFgu5KUY0KgqGy8l8N72W+yd2QEHjUpU41jOFNVehilP4n7PvD/1hooJuEfEqYV6I7GyL2ebLKDr+cnIl34lEX9qALW0GYwa10pY/xsNQp+WeAn/mFPsl08j2ZWv4pZiYskbHCtuDKckqkdk0jz4Ok2C3Knv50otXydKDCaSsovJyNfh62pHsKejrdWCQSfE/3f/ACTo8RFc/Q2Araq+XC/yp3GgG1+NaERNb6tIWZZBq1Lg5vDXz/l/Bdfjs7kWn41Cgt71fEnIKuRBWgEFOgNZhSVk5pcgA/5Z2QwDjAo119x7Mq6uK7eTcth+LYnxv1zgjV61+exgAeOlUXygXsOH2nVI7adCKdFJvCy8pNLCQe0AgxZD/aEY7x2CvW+jBL4yjCQwJJQqTmWuKZKE+8jF6NdlYxe5n8XMZ/Rqe+L792Nc6+AKFxBGk8yF6Ey2XElg780klqi/pa/yAugoO1tig23GdszXj+KsndXtPlBhnQjsET0fAHdnR8pKeVaeieFqfDbLxjWlquv/DaHyU1tefvnll4SEhPDMM88AMHLkSLZs2YKvry979uyhUaPy0y+VYc6cOfTp04egoCDy8vL4/fffOXbsGPv378fV1ZXJkycze/ZsPDw8cHFx4eWXX6ZNmza0bi3aED179qRu3bqMHz+e+fPnk5yczHvvvceLL774H09m/gxnItP5+WTFK9mXlNsB0MtKXta/TJCU+tjHuylXpzqVk52KYFBoUNXqCbmJwshNNomTgas/+qRb2BWnU7/kBoafO6NAVKDkgJZMdXXmmZsiH+uOKZi6ilgKcOCtksks0vwAQAtuM0O5nSXGwcxcf5WVk8roYqrURvXsNljRB3eTuLCEmwJZrRhMUZoR0qxf9FOR6fzxUjvylwv9SxJV8APhPVMlDEVaOGu7FtNqu5Ol/bbtaiJtangyqLE/ucV6Fh+JZMXpGEqMJhr5qGkcYXZ+7vgaOD1mlVZnAFxeiTrhPO4OQ8ksKCE9vwRPJy1jWwWz91YyZx5kMHfHLdZMLmPg5xYEz+3nzA8TaZstprT6FYqwQTTOltZEiXnkXlWBCd5fQo0uguzc2EiUfTNAQ6tqFSenl47HD3K+x6bszjy/5jKLRjclJa8Ye7WSliEeKJ7At+hpsPlyAlfjsnHUKHm3dy3RJlo/ptx2SxJCaKXoyQLFcpzijpBY0gVXezXNg93JLdZzMUaIk5srRIsg37kGp2f1tVz8j0b48vv5OI6Ep9I1zKoJfK9/XU7eTyc2o5BlB28yu24eMdePEWL+u7uUDz71hUtvSMVGk9W9HDlxL43l8f5Uc2pCtfyrpBhdqKGEF+UNcNsOjtwWrZIy/jYSCO2JS4A4PtyDhWeSGb/afcsFVTN+z29GTLov59MdOXjJgVwcMUnKcoRYo1Lg5ajBxV6Nt52Rt3I+pV7RRUwKNYphPwvRfPo98pRufJQ3kKqudvw6sYUl0uJWYo5lSmlMqyBLS/WfhskkE5mWz63EHB6k5fMgtYDo9ALLVKRJhk4LjlUabtlXEcEwDcQbPBi29CwqhWQJki3SG/l4l6hQZjeahKyPR4o6BkvbiNZjdqw4xmSTqF6PXg/+TTHEnse4bixa2cAuY2tOuQ/mp+ENyxMYpRr1M6sxrR2JY8xxJij28tqOEHbdeMhnQxtQ3cuRB2n5nI/O5HRkOqfup5NrrtY7U0hfu8qSZwW+1g9nl/t4vLQqTqfWqzxmAihs+RKv6kP59tB9qrraUVhi5Hp8NgMXnWblpBbU8/vX2wb8q/HUZGfZsmWsXSu+ZAcPHuTgwYPs3buXjRs38sYbb3DgQOXZTI8iNTWVZ599locPH+Lq6krDhg3Zv38/PXqIsdBvvvkGhULBsGHD0Ol09OrViyVLrG6eSqWSXbt28cILL9CmTRscHR2ZMGECH39cQSXi/xNk5OuYtuYyeqNM1zBvzkdlUFAivugqDPRWXgRggv4tbsnVGakUpe6Txvq8pX+eM3bl3TdLtS1PA5WphOR2H+EbWN41VW0ycvP6RVK2vUN3xWXL7ZJnDd6R1iMpkkiSPfjSMIpVmi9BNrLH1IpRJa6s13wCwJvqjRw3Naaqq2gvmUwyJlkWfiKBLQWxMk+FhSofcu7lpux/oMMoy6gUEm9sFtMi3xy8x0suVSAfroQ/wK90SjqoDaSF45Z1k971h1i0TzqDiZnrr7HqTAxR6QWW1kX7ml4s8tuHdOEhuAVDqxce/yb5mlsJmVFUdZbJLBCfHzijUEh8MbQhnb86ysn76ey+8dDitAtwKCKDb1NasUsryI6qNEDVwQMkCVmWLUGT1qyov4nafcCrNqRH8EbeHM7zgcVQrxzazIAb62mnO0Fzl5FcSoO+31urdWG+zrzUtSb9GlStcBX7tMguLOGLfeHYU8wvtW7hu/J1G/PLUhw0NiVO9sbBJAhukCKdDwbUZXTLIDZdiueDP8QFoWWIB5+4pUM4ONXpbu0JAj3r+vD7+Tj23kzmw/51UZXkQN5DXDIesC74CNnhJ6h3IQYuGi1EBxBi16lHRFWvEjzXrhorz8RwLiqTYk0GKKBtWS+r0qBKEMTWr7Go9IW0h4AWtnlOfebD+WVwczOKtLu0LjlHa0351nCubM9D2ZMURRWy1L7c0PmwuaQtSTlO5ORk8ZFmAfUU4RTKWqYWz6b6LR8+jn0dCZhfPJhChRMrxjTBw1GDLMusOhPDZ3vCKTGaCHC3t1g9/JNIz9fx2e67HLyTYslmqwxFeiNqpUSIpyNuDmqc7dR4O2vFBOH9T6EQQhQpeDtrSX1kAMGDXLq4pfJl1TtId8uUPspGTDQYCb2/AEdP5Ox4dKuG4ygXc8LUkMJ+S9jXolrFPkcAajsUrZ6HmON0c4pDm1XC+ehMui08XuHmLnYqXg+OZHTKQhEOWglOuQ3i++ShvNU8EEmCsXvfIUY5tsJtIwihZofnUB0TZpmdalVhRueaTF19iYiUPEb9eI4N09pUHvT7/wmemuwkJydb9C27du1i5MiR9OzZk5CQEFq1avWYe9vil1/Ku5eWhZ2dHYsXL2bx4sWVbhMcHMyePU821vyfjuvx2SzYH0GezkAtHydm96jFG5tvcPehuOj7kIWzJMr1F80J2S0V4QAYGo9nuZezTYbKeVMYrcx//yvw/aUZt4YfB/dq/H4hjsjoGNoXH6OlMgIPb3+2eE7DO+NzGirMXjTX11mqsbNKXuSaXINs2RE3qYABijPsMLWnk+5rjmuFnmq39h06XPyG5ndSyCkqQatSsmJSC1qE2F6AlbIB1+SzjGwxyHKbi72aaWsucyoynY/DqkI+pKeVqV75NRGmePEXeaHv6xwNT7WsqgCuxGUDIvxx/vAGdHFKQFppzoDqOQ/UT+A86uQNDl5QmE4tRSK38bakqwMEeTowsJEf268l8eLvVzgVGcQng+uTrzPw7vabVJMqONvlPRRxD1oXnM2uvIlZRXg7/wNOqBpHGL8N3fLuVC9I4ne7Lwm26w5UEABatRHU7I4UeYhlNc7R9W5/jCaZQA8HErKKCE/O46XfrxLbq/AfiRH4fs9lRhVv5Hn7fbhFVjw9edMUwkv6VwAJO6VYvbs72jGpXTVWno7mw52CVAxvFsBnA2qh+UGISKlha8nfPlBLD/VNOusuwNczoNBaHQ0By1hHkuzBRVMYHdR38TBlgW+DPyU6AD6u4u/PKvdTR1HJ+PiwX8QovUf1yjVhIITNHV8XPyl3hN9Q5EExRl2cLcTugItUhIuUQG0SQA+DFPCu0wZygnrgHiVCVEtUzqypNp8zt1zpevM7JFUa0SYf1hm7MrS5P852aq7EZrDq8DVu3I+mPnm0q+bE5NHP4Ob0z1XRZVnmj+tJfPjHbct3xUGjpL6/K7V8nKju5cTGS/GEJ+dRxVnLNyMbU9XNjiAPh4qrSx9aqx3nX21Ckt4Bo64Iz4jfMZxahGvJQ0EqjlWyQ1OPCj2d2DmS175AVVMuN0zVMY5YzcgG1Sq5I+gMRm4m5HAjrgpjJS1uRbGsVM9nmv5VcrHGMygkeLlrKB1DPWn6Rw+k2AeVPmYp8mXxfXe1V9Omhidf7A3npqkaDUrPtWVQmxhQSJQYxXdCpZQI8nRg4/Q2TFl1kYsxWUxedZENz7chyNOh3P3/f8FTkx13d3fi4+MJDAxk3759fPKJWInLsozRWInnyH/xt7H4aCQL9kdYfr+Xkk//H2xFyC+rRKsjTXbFgIIqZBOmEKvf5ZEu/C5Zc6Kummoyq2QGR7SvYSf9uaDcJKm4PuoSnhcWEPRgrc3f6m/uxEFjU2Yo4ggoa5oWC9/KW9AqKn7sDdp56GUlakkcMx+pVxFV4sdNuTrbjO0YohRRCye1syjRK9GojTQsXs68XXf446X2ol1QxvTQWJBBWald6Siq3ihjZ36O1EKZvGI9znZqsxGeBHFnCDM94PgbXZi44gLXE2ynJlQKiWbKB0hrRokJrBpdhSnZk8I9GArTMaZFopC8qVPVVtcyf3gjqrrZs+z4A9ZdiEMhgd5oIiVXx0DXAtGvBwhsDYUZkHEfrq9HajWN9jW92HsrmRP30i2uyn8brv5sCPuBPpcmESZFw6+9xFSPe0j5bRuPhchDeOWFc+3975EkoSPKKdSz5HgkPx6PYsH+CHKK9MzuUeuvmc5lx5F68Ftm3fodZ3URyAjPkXzbtqtOVvOCfhY6NHw2pAEjSYa9IHmEkJBVyJf7xHdnZrdQXu0einRjgwi2dfGHGt0g7rzQq8ScQpV8g5+UZu+gUk2ovbsQtge04J5dAyYeUpKE0HedmVIbVrYUWW7Jt8C3foUvxWSSmb3xOn0U5/lYXaaC02e+EIj/3F2k3FepDV5PSRB96oqfbnOttxkNovpZkC6qYDnxwp/n3gEUKTctRAdAY8hjmmInUxyPozSIDLB7ciCzVZupdv0hphvJ1JOS+U7SQym3eQjy8oVifL7ZhKdOEX8UqbnFvLv9FgfviCGTOlVdmDeoHo0D3WwqJ7+dFwL1DwbUpX3oEzpBA9La4fir7IT+pkgMC5iQiDV5Y/CuT2jLPmKKsjgbVvYX3/era8TCSJLIPLuaqmkn0clqbrdawOhHiI7eaOJ8VCYXojO4EJPJ1bhsS3v8gOINflIvpI3yDpeVL3JN3YjNhU04ZGxKhuxKSmY2oRGbkTIrJzoPFCGs0HXhE/UKgnIvA2JxV83LkcaBbryWMJ2F6mUVEh5SwykqEftSGoLsaq/m5wktGLrkNA/SCujxzXFmdK7JtE7V/1cMIv+n8dRkZ+jQoYwZM4bQ0FAyMjLo06cPAFevXrVMVf0X/zzOPsj40783l8Ito+Sz9DOQUZCDIzpZjVbSU5idhunGJgshqCY9ZFfQ79il6oky+TJNP5vGikgWqJeXe2yFbMD39642rstl0UN5xeb3NNkFDQZcHzM9UEp0ANykAnZq32NSyRu8pX+eelIMtcxmcBrzdjfsnudwShNuHphIg7C+NmRHuXsW1B1oscEv2zlxksUKNxdH7j7ME60Zj2rQYISYfjr2Be5jNvDj+OaPeBbJNMg/hePG5aDPh6C2wujvSdsyecmQdA2A23Iwg5v4lwvc06gUvNU7jPp+rry07gprz1tX+5Nr5EBphyO+THvi4AdQbwgda1Vh761kjt9LZWb3Skbg/wLS7YIYWfIBGx3mUyXzgXAcHv6rNaC0FM7micfCDBt9jquDmrd7h5GWp2PrlUSWn4giNqOAz4c2tGg+Hovok7BKCMq9ASTIVXpQ2PBZiq9vsW0fAd8YhvHttIE0C3YXbbPt5vZpSHs+/OMORXojLat5CKIjSZbYEXIT4fsmYgqtDFIUPhwuqUvtLmNo1qE/aKwr3pLEHJIOWRcahfa+Qp91ZzucXypErBVg/v4Ikm6eYJv2O8ttE01z+ajmOII9HSGsrxgdv7Ia+lYcv/JUUKpE29PBA6rUst7eda6waHj4SDJ7xB6bBUMv5SUqQq5sT5bsjINUTJX8h7DvLYoOf46izkC0DQdDUGtRJXwK3ErMYezP58kp0qNWSrzcNZQXOtcoV62JTM0nKq0AjVJBp1pVbB9EX2wmnDfhxFeWypYFSWXOU85+0OlN9krteXHTPaREGP8wmNcbBuPiVROG/QQbxsOlX8E9BLnVdNSHRBL5RqexjOlja+SqL87nzZ93Y0q6RhNFJHMUkVRTPiRVVYVcxxAU3rXJ9XsPp4jVqNMjaKG/RAv1JSjVdd+t4E0J6SAmOsN3Qa3eBD2zDo8fvoJsKDCK96WqeeT+5wnNmbfLgbHXPLhhN7X8Yy1phaLaKkBNFWdrJc7VXs3qya14feN1zkZl8M2he+y+mcSP45tTzevpPsN/dzw12fnmm28ICQkhPj6e+fPn4+QkTt4PHz5kxownCJb7L/4SWlbz+NMog1KX2BzZgeumGqgUEj+Mbc2+9S0YpDzDUOVJUmQ3/MyExU0qgFRxAV3m/hr3U/wJkNMqffzKiE5FqCKJNsM6YzdGK63kIbvZK7iFrxPix0qwQrOAoboPGVLyMas1X9BMcd/m792UV+HMVfRO/qidfCC/jOneghpifDuwJbtuCA2Os1aFVi0Oc09yLToXQMQy3NosktETLvP7XWvVxQ4d36sX0VN5GfRAcHsYs8HiifJEuL4OZCM3pNo8kP35tHnl9gb9GlYlPb+eRVOiwoBr1G7rBpJCCCVBrDi/qU+7F8QK7npCDiaT/I8Jgoc3DeCHI1XpV/A+J/0Xoc24KzK8avURIZKlI/wOnuLfwvJEXJIkvhreiM61vXl943X2305h/+2DNPB35c3etekQWqXcfTAZRZ7T8S+sZKQMXIyZuFz9ttztBf4deHvyUtuWT4bQJ0RH3efQgxTUSolPB9dH0heKANSzi6zb5iYIfUxYX+FPE9SGoUsjSCwsYmtoWxuiA0LEXha/nYvlw9YvCLJz9TeoP7xcawxg+8UHnNN+YPn9O7e3OJZch6XHHvDFsIYiOf7ODrixQcR52LuVf4/+CWTHWabpGL1BxIjc3SVCMs1IsqtBVb8gHqr8WRGu5J7BF6/gOozu3oY7qcWcj87kyoMUuhQfZIZqBwH6dLixWvyA0C+5+IFLVfGvs5/4N6RDuaqV0SQzZ+tNcor01PNzYeHIRoT5VqwfKTX2bFvDA+fsCHEuOb9MfIcfh24fCHG3W7AYzVdp6SvLjI0rZu35OFafjWXfrWQ+HFiPPvX7I/X6DPbPgYPvE6t3x9Eo4SxBzw5thZjdaIDjX2K4sgZ1/kO+AXiEy7sQBwVxEH0CohHmnR414E8qOKUo0Ms4+gYL88OQjqiVCmoEVIVsqC49pLEUiYNGDOt4OWn5blQTzjZ0g43Wx9DJKouR7JzoCfzI2nIBv/5u9vw+tRW7bjzko513uJeSz7ifz3P4tU7/X1V4nlrZqFaref311/nuu+9o0qSJ5fZZs2YxZUr5oLP/4p/By+Z047Ii1rLYauwAgKtUSG0pDoNJZtqay+wwtgWgp/IyiwxDKrzv1N6taK+4yQrNP7CaNOOBsjo3ymRuzSyZwY/qsfDcfpGp9SdY6LGN2kFVGVbyEb8bbC8aiw0DyZYdUecn2hKdUqzoS96VTXx/WFzspneugWS+8DRT3KOqWxlti1dNIT4EUo4tt5jVjWvmzU/qhfRUXsYkqUVw4thNT0d0AOKFWPyKs3j+pArGn8viQZp1JdpHcQH74hQyJDd2DriG/F6qCBUshVFH0CI/Wkl3MZpksoue3NvqcQjydKBnXR9Sceczn6+h+XMgKUVw6dI2sPt1caIvJTvF2eL3R6BQSAxs5Mey8U2p5SPeu5uJOYz/5QIz1l62jnYbDYIkLGoBG8baEJ2jxkZ8qH+WFNmtwn2VHbxwHPWzLdGJOS0iIYAdsYLozurgS+i95fBtA3EBK4tnfoM37sPQ5dBgOLj6W4p3hbryrfmELFGx9DKnz0ck54lqRvPnxAbbppfLGQM4Ypxk/aX9bFoPnAYIv5jCEgNU7yoE4sU5cGReufv/IzCUCC8dYwlU6wS1esGdP8RtQJ5sz0D1MpxfPc/Zdj/T+U5/firphapWDz59biDNa/jybJsQFo9pys5ZXZGaT6Kz7mvGlcxhraEbabJ5qkeXA2l34cER8dmemA+7XoVFzWDVAOH2bNRToDPwwm+XuZmYg7NWxcpJLSslOpkFJRw4dY5XlFtZkjZBpLmvGVw50fFpAGM2wdwM+DAHOswWn29gC4u2SpIkPh3SgN+ntKKalyOpeTpmrL3CO9tuYmr1ArSaDoD/8deIksW51+feOijMRL96KJyYjypf+P3ky/ZkezQSwwvDfoEXzsDYzdDrMxGto3YQ7bMnIDoAjomn4Nxi+G0YXBL61vZd+pCCJ55SHhs0H3P+vK2Uoc3tj2x+/8EwhCsmK7ns4xxtM2FYCkmSGNDIjz2vtMfP1Y7E7CI+2X0H+R/xtfj3wBNVdv7444/Hb2TGwIFPoWf4L54YkiTRPMSDxoFu7DabaZVFkEJUfYyyxC3Z2ku+aAojV7anqpTJZ+qKBeGhidv4ym23VZ/wOJSaz5WFnas4SQOMXs/3V30Zdlt4P9yt/wY7LjVBeSKKzrVa0WrCHyK7Jyceoyzxs7Ev01TWKka1guts7XqfkLgqvGOYylLjQE5qZwGgc/SjVc5i1rZPp3nmbuGcWxYmPc5/TMFF9zFuDnWEp8U960rmXFQGNcq2khoMhxvrUUXuwyQPYFQTX7rfeoMOylsUyFocnvtDXMj+CoxCcOPo6gmpWCIOKkJMegGrzwotwpKxTQk+sRbSYL2+Iws23WHHrXQ+HjQAvzmJ8Lk1FX2Ddh6vlLxIZkHHJ28RPQEmt6/O/tsprLuRwytvf4Fn6xlw6ENRUr/4k6g09foMMRQti6wppwqqNUDXMB+6hvmQnq9j8dFIVp+NZc/NZCJT89n0bG1ctz9rISdlMVA3jxuycEj+w9iWK3bTbTeoOxip+wfWdhqAUS8iFYCtxvZ8axjGwrAIhl5/0ZqH5R4itDqxpwXxrt0XFLYr2ObB7iRkFXEhJrOcLqRlNU/2306xuG3fTzVnYPX8VBCt9AjYPRtGWnU5ut9G4SCJ48HgVBVVt/dpCQR7OhCbUcjem8nC2bbfV4IMXPwFGo2BgGYVf0B/BUXZsHG8qDIoVMIH6OLPIoYCuCzX4u2SKcwc1omUXB3T11ymxGCiR10fFo9pSmZBCYuO3uFecj56k4mrZhE/qDhlasApUwPeNUzGiUJ8pUz8pCz6BMv0DjLhbkxHzngAMSeRok9A9AnyFK5clurhWxxKfWU9XhzW36bFYoPMaApXT2WvdFG0fipaN3hUFxWxGl1F6+4p0LamF3tndmDJ0UgWHY1k3YV4tColH/T7FFNWHOp7e6zDHFFHyfu6Gc6GTApkLR8YJpIX0os3BrUs7zXlU08YjII4NpOuCe8eXa74PJJviNidtAg48K7lbpecunAh25kZKnHtvXliO55hE/Hz9OLGmINE/zaW1oq71LrzAwbjAFSGApHifnubzdO/rt5k8/tMxwNoVC9TGbxd7Hh/QF2m/3aF387F0TjQ/f+bHK0nIjuDBw9+ogeTJOm/IuX/QSTnFHOhkqDKPpJoSaXgTjHWE0YeDpiG/AzbKx5LBODUN1TuN10BHiU6YCU6AOtGkeK8wiKUC2vWkSFGF7ZdTWTq6ks82yaEcWMO4HvqA5Q3N9oQHQt2zuSXfruYvDsXU5nE9CD5ITo0LElvzK8Tp4oqwLL25e6+Q/s+uYE9cckPtuRMGVCy+EgkfetXxd1RAwYd+lPfowb0skSIhz0dH/5CZ+kKxbKayfo3+Na18dO9N2Vhfl4PF0GuErIqr+yUmkR2rFWFvg2qwtUCSIOadZuhvi1x6G4qJ+8f4/mO1Xlp2lm0P7ax3Pcj9SqiUseJUNF/CC1C3Gng78rNxBzWno8TCeCj1oqJn83PwYUfhZDW3k2QiMKMSslOKbyctHwwoB5DmvgzdfUl8lJiyV/2Eq5626kkk9qB4flvWogOwHvOO0U70cETnt0hjBUfFU2XFAin2/hz6GQVKwy92evzI3ViTph3oDZ0eE2sso06+MxP6DoMxeU0Ji2rebL9WhLno8pXaCa2DeHk/TSORYh2bHp+Cen5OrycHGDoj7C8sxA764tAbU/iqTX4R+613N848xYqSUICRpjdtFefjWFgYz/U1TqKauPNjYKI/FNkR5Zh/VihadE4weClcHmlmEgEdjuP4KW0QbQP9aZNdU+GLDlDbrGBpkFu/DC6Cfk6A6OWnyUmo/yKqF1NT2r7uJCer+OP60nk40Ck7ECkHMCJaHgvVrxWg6k7/gxllOooo5RHqWLKoTNn6KwW2rvcbZ9w5UATYr06o6jdiw4NQwWBv38QecsUAoqzbZ9Y6yKqNbX7imOxEpQYTJx+kM6So5HEZRZSVGLESati1XMtbdLj7dRKZvesTbCnI69tus7KMzE4apUM7vgd6vDLNhmCzoZMYk3ePK+fTYQcxN7+Laju9ZjKr1ItqkqBLcTv2XFwfL4I4C2L9rNo3v1DfDIL+WVfXybfm07Ngit0+Ho/U7o1YHL7asxyf4nm2S8Lq5F5jwwnhPVnf24IvZIW8SjCsk9gMhhQqCq/9PeuX5UXu9Rg8dEHLD0WSf+GVf+/aGc9Edkx/S+kGv8XAiaTzKG7Kfx2Po6zD9LRGysuK7ZUiB68n5RJFbIt1vfTOlXHrUFN2P4v2mEz1udNIss8XintnMlnU04TlZbP9YQcFh2NZMVpJd+P/oRudQdSvOUF7AzlE6K76o8DTZAk2TK5JRdmocTIuagMoVPxbVDpPrjEHoCl1spPtp0/STnFzN54jV8mtKBoy8s4xp4gX7ZjJm/RziWVHg83gCRE3udMdRm+7Axrp7QSAtKnhVG0adQa0TqrzPjMZJLZekUQyGFNzVWbPDFt1Kt1Y3Z1bcbc7be4EJPJD0ci2XXdgd2e9XHIEFln7lI+jbZ3g+Tp0PZl66rWZMI8IvXUuy5JEpPbV+PVDddYfTaWaZ2qC2v5+sMgK1bkd+1/x5r6fm+vVcvzGDQMcOO3ga44bpqKn96WvBcqnZmQP4srslVQe7RbAtVOmwnxwB/EiHdF2P063D9Akaxhr6klGxwW4JCTI8I6O78F7V61hmJKEii1gvTkJJS7WLatIVp0l2OzbFy8AZQKiWXjmjFj7RWOhIux9O1XE5nSoboYGTfbDZBym6hzf1D9llWQHDUtkupq62l3eLNAlhx7wPWEHN7ddpMvhzVEqjNAkJ106/Tl38a9fYLoqOzE+3D4I4uu6X7tabx4vSMalZK3+4QxdfUl4jILCfSwZ/mzzbFTK/lsz11iMgrxdxMxMCZZuP12q+NDj7rWtsibvWvT7/tT5Jjbqu1qenI60koYE6nCQsNIvjMMpYkUSSvFXVoqwmmuuIcLuTTNP07T/OMYoz9G3ieBJK49ZY9gk6TkTvB4IsJm0KVBNUtF02A0cT46k+xCPbnFei5EZ3IxJpOk7CIejY/LLTbwzaF7LBlbnkwOaxZAYYmBuTtus+bodfJPnOYjtW3L/FP9GNYau1OI+G73+e4kHo4aOteuwpu9wirN6sJoENquM9/bCsQDW0Od/tDwGYtZaaCHA5NHj8Iw/z3si9Jpq7/IF3vV/HIqGq/8XFTaCq7LI9eAkw8tV1lDXHcZW9tE89xLyyesqlvF+2fG+NYhrDgdw4O0Aj7aeYdPBte3ddz+D4Qk/42mXHFxMXZ2/4C/x/8ycnNzcXV1JScnBxeXfx9jpV9PRfPTySge5lj9Vur5uXA7qbzPSAfFDdZoRBLviyWvsNtkbb0c63iPkAsf/o/v76PYaOhEb9UlXCjgRq+NhDbrzrGIVJadiOJ6vEgrntMnjKkNlMT++AwhxY94/tQbQna/5TT++CCjlYf53NyGe0P/PJuMndn+YjsaB7rBin7iRF4GB41NaVfdHYdYq0A6oesPdDtQBZ3BxNvNYPrtMZhkidma95k+oh/K34cTShxJfj0xDl/F+F/OE5NRSBVnLVumt316D4qV/SHmJJebfMqws9XoEOpl65RsxvmoDJ5Zfg4nrYqL73bHXinDF0GgL4AXL0CV2siyLMSTO2+TkqtjsPoc3yq/B+CBqSo1FI+0Nh28RLVFqRaeOIGtxAo4uO0Tk58Sg4kO84+Qkqtj4YhG1gBBWYY1Q2xbiAqVCFX0f4JKRPwFWDtCaH3K4IGpKpP1rxMjW3Vpm3ubaH7yOaEx6TwHOr9d4UMab/+BctN4ADJkZzwlM3n2bQCDl9mOgyddFc64R4RtBoOXQePRPIoeXx/nfmo+3z7TmMFN/Mv9XWcwUvs9q14kfF5vsQJePbh8exXIf2aLMDB8BIfvpjB19SVMMrzWoxYvNzDAktaicvF23F8iqzYwmUT1M/URh11nP3J7f0f37QpS83RM71SD6/HZnI3KwMVOxZYX2hLq40xRiZGWnx0ir9jAmsktKxaXl8Gvp6ItzsS+LnYk54rzl7NWxSdD6jOwkR+SJFGsN5JTpCenSE92XgElcZepGrGaGil/Ljb+QD+BA8bmZOCCSaGhQ6gXDQPc+O7w/T+9n4NaYlCIkayMFFKy8lFh5L2BDWnUsrNNMrzOYOTU9XskbXuPkcrjaB+x5uhl+Jpt709AIUn8ciqa89GZXIrJpNBs7uqsVfFi15pMbBtiWxG5vR0Ovi+cmUHo4AJbQbf3hUi8ItzcLEJZgav13+HZW43JKzYQY2frHB6nqob0wikCPZ3g5x6QULn78oWOq2jZdfCfvlcgglZnrBUTbE2DRFTIo9Ok/w540uv3UwuUjUYj8+bNw9/fHycnJ6KiRGzB3LlzH2sS+F88OdLydMzbfYeHOcU4apRM61SdvTM7sPuVDiwfX/aCIlNbiqO+FGO55bipIQMb+QHQQ3HpX0t0xmwUq2+gu/o6F41ihZ6+5zNafHqQqPQCNk9vw5hWQcgyfLYnnI2RCqq8Wj4ryxh1AjcHDUvGNmWdsSt3TMEA1JaEd9DgxafFdNWwn8vdd6eyG/pn1kNN68UloGFnPh5UDwDn6+JYPaZoydzBTam2tS+hxJGJC94jvyPQQ5hueTtrScvTcfx+5RNklcJHPJdnnsjdKj0ZPoqtV0QLq28DX+w1SpGJpS8Q3i6eQlwoSRJ9GlTl4OxOdK/jzU59C0vi8teGEayv/rkQZJaiMB2QBUlIuCimj1b2FRe9O0+mwdOoFDzbJgSAX05FW8WKkgSDFomJm1KYDLBzpriw/hnu7YcVfcsRnT+MbRhS8pEN0enrkUizM9PFa6gzADq+We7hIlPzmbXhGnc2fWi5zVPKQ1aooNPbMOWILdGRZVg10Ep0oNIWSK96ooG5/3bFUSpalZKZ3awj/6+su4rOYLR8ZmVh7DSnQqID0K2ODx8NEvu48OA9Tme6CE2ULtdS4ftbOPO9LdHRukDbV0gYdYhBezWk5ulwtVeLkNSoDJy0KlZPbkWojzOyLLP4aCR5xQYCPezLZcfpjSbS8nQcCU/ho5236fXNCQvRASxE58thDbj6fg8GNfa3OGrbqZX4uNhRy8eZloFOtC868liiA6Jte9buZe7ZTeCKejLvRE+kzokZvKFazxDFSRpIUdSQEumluMCLyu18q17ELs07XFZM4PP4cSwrfI1t2g/YpP2YRvuHo5sfhrzvXW5ePcfnG47w9UczafpHd8arDqGV9Nw1BXLYaB3G2T8xGAeNCju1khe71GT1cy25/kFPNjzfmkaBbuTpDHyxN5xuC4+z7WoCJpMMafdE+zc7VrRiu7wLr9+H5/ZWTnSSrlqIDsHtaDL4VX6d2IJaUnnn8FPFIYxZclSI50sn7SpB7fNvi5bvY9C3QVW+HtkIZ62KK3HZDFt6xlKx+0/EU4+ef/rpp6xatYr58+czdap1nr9+/fp8++23TJ48+R/dwf+LMJlkPt97F1kWxlrbZrTFTq1ElmV+ORXNl/tKKyAyX6uX2iScH9d0JL9YOPNWsZeYe618dtA/jtl34fiXQgNw6huYsBOOz8cjJ56atetjeHCLrsprtCy5yIL9RgI9HPh0cH2ctSp+PBHF7pvJPNMiiMVV3ufFNGvUh7IoA93FVbSrO5Iv1D9TVyFWRCdN1kTnET+eZfWklpwaeI3uO1pYVmHfswBTwSiR3BwpUoBxDWRkczjzIINGd8REhMpQiPvmYShkA3dNQRxosJCZboIoutqrLcnyzf6KaV9VkRNnl34L6IVbmVZIKYr1RnbfFFWZoU3NlZMHR8S/1TuXE8662KlZNq4Zc3fc4sCV5kxW7aWd4ib7jQMYNW06HPsMTiyANi9By+cFCUm4KHxrbm8VmUsbx4t2VN+vHivkHNsqiB+O3OfOw1zORWWKkEoQBnsDv4etUwUZAaGfOrsI2pWPJEGWbcSwpUiUPZmrn8QRU1Ob2xtJkXxW8CWSVCAMIIfaTlzFZxby3eH7bL2SgD8pfKO1TrgUutXGYeRyEbVQERo+YxsHULXiPL9e9XxZdDSSYxFpFOuNFeoWetbzsVQUDtxJYdaqEyyJ/8lmG31AW9Sd3ih337IY3zqYuw9z+f18HLO33uWMZy2U6eEiq6zjn9+3UphMcGqhLbFrPwvaz+ZWhszEFRdJzxe6spwi0faxUyv4dWILGge6IcsyszZcY7s5SqW2jzPfHb7P5dgs4rMKySoosXEdrwxalQKDSSazsITLMVkUG4z0qW/WgaRFwObJkFLeasAY2ofN+jZ8HuFLDRKZHJBAX49kIerNSwaTHhepEBepkFAqDkZ+FAZJTaHKjQKDBCYDVaVMtLp0OLeIBiyiAVDWbOj5klkcMDWnh+KysL0AMW03/TQ4W9t3aqWCVtU92fpCW7ZeSWDhgXskZhcxa8N1Du3fyef6L3GRjdx3bsnGGl9gzLVHeSwFpSINZzsVXk4anLRqsotKuJ+ST1qejveix1NK/RPzTRxb/SPtYxdzQFueAI9RHaWD/hbf//wqIxt/TPMLsyp9D1x1D+HCT8IM8jEY2jSAVtU9GbX8LPGZRRwNT62wyvmfgKcmO6tXr2b58uV069aN6dOt0xGNGjUiPPyvRw/8F1YsPhppWe1Pbl/NcpJddyGeeWVWTY2lBxaic9FUi1rth7I1pRPcziI2s5BRravBtb+3LwaXIFTd54qJl1UDKt7IyUesuq+tg7izEHVcXGz3vUVw6hGoNxBubeEd7zMcSW7K21tuEOhuz4BGfvx4IoobCdkAxHl35U7KKgupAdDufgXt7lcYpRSTZu8aJnPcZL04XY/P5pnlZ1k2rhmt5V9ZJb9viaco+bkndjU7Wvcz5TY6zzrcT8lHiaiydFTeBBl2Glvzpv551rZqYdn8ckwWRXojXk7acs7HTwTzRdQ1NxwJE40C3cptcuBOCvk6AwHu9rQM8RAamBtmo4yaFVcCVEoFnw1pwOK0DvBwLx2VN3knIlWQga7viZ+y8Kwh0tR7fQJnfoBT3wqhcexZMVJfieMvgJuDhmFNA1h7Po6vDkSwcVoba+++3mDwqiX8WZLMF4KDc4UWodv7wj0aoDgXvq4LJVZdVo7swHeGYaw1dkOHhj71ffl0SAPORWWwcd0vLFF/j4Ok44qpJlGhCxhujufIKdKz8EAE6y7EoTeaGKg4y/caWyGmw0snK49tkCQx8dRsojBL1BeI6aQKfHHq+7sQ6GFPfGYRO64l8kyLoHLb1PZxtmQueZDLC7HvlKuXq0etLkdaK8LcfnU5F5VBVFoBK9yGMoXP4OTXYirL9SkvMCYT7HxFOACXYtJeCG7Lmch0pq6+ZMnUK0WotxNfj2xMgwBRsdt/O9lCdAAO3U3l0N3ywcKSBIHuDrSr6UWHUC8aB7pR1dWOB2kFzN54jRsJOby77RbvbhMaMy9ySDx6n5dyv674fdBPZLOxI0U3rRKJKvU60WZoAyidOJRl5OJs5m85zc07t6inecjLDWSc8qIgLQJZXwheoUhVwsArFAozIS0Clb4Ql6xYnAvSkGRdhc9fFss13xBl8hUhr6UoSKNgw2Qe9FqNt4uDjT5HqZAY0TyQ/g39+PV0NMnHfuK9op/RSmIxNSltPMlpj6/W1ZASqaq1DoL4Z5xlbMZZG/HSfMfXebPgK8vvgYo05he8y66zrXnVOINvNdYcSZPKAYWhjLj87GJoPQNUj5/g1CgVGMxa0bjMJx3Z/ffDU5OdxMTECp2STSYTev1/bonr3wVrzsWy8KBoe3w6pL7N2N/vFwQJ8HHRkpKrw1TmyP9EP46AtA4EejkAWcRnFlLT4e9n1qhy49hz9DjeAz+ieWUbKZTiZNxyqljZH/5InFjPL4OsaGGUBtTIPkv/oBfYFQcjfzzLCLPJntJc1p7aKZQvro3gZ8VXFT7NUuNA1hu7lrs9PDmP0T+dQ2vnyITct7hqHlG202XYjmLufIUNduPIfqjE3c76pf1KP4JFxsGARAN/a2umtHXVMdTrr4VZetVCVmqxNxYQJKXSOLD8CPt28xTW0Cb+whTwzi7xntm5Qd3BlT60JElMGjMO/cL3CZDSqS3FM2frDT4cWE8IiSuCvbsgIWH9YOs0ET3xa294bt+fEp4Xu9Rk+9VELsdm8fPJKKZ1sk5J4VNXtIpubYGtZp+tW5vFNFLdweK1JFy0ebyv9cP5xdiHAuwJ8XTg7T5h9K4v1rDRh3/lZ/VCVJKJCKeWjE+fRsEfDwjx88bVXs3zay4TnV5AbSmORS6rCC15pGT/3IHH5lMB4vU2GiX8S84vE1W0Rz5jSZIY3zqYz/aE88upaEY2Dyx3HKiUCr4c1pBJKy/yvno1DRQx5Z/rEVPCymCvUfL9qCYMWXKaT+Lq0dW9AdWLbsKhDyps1VYKkxF2vChMLUtRZyAEt+ViTCZjfrYd87dXK5nSoRovda1pOXZScouZ/putM3qAuz3Ng91pHuJBbV9nPBw1uDtocLVXVyherentxOIxTen5zQkc9ZmMVx2kr+K8MEB9RHaYJbkyuXgWtxSh+Lk5IucWg96ERqVg3qB65YmmJPHViRSW3pJQSA2Y9MwknOpYKy0SQHok3FgvKs7ZthN/pXsrK9TkSk5cLQnipKk+ybInflI6DRVRNJSiCFakUl1hS06KZTWOCSfZs2wOy4wDaRrkRt8GVWlZTbwvWpUSe7mIF7MXgvQ7AFFVunG05gcMx474rEKi0wvIKdJjr1ZiNMmk5esoKjHi7qChcaAbDdzcSL9XF6+8OzyKLI/G3O2+glo6LX02erJXa+sb1V95zkaQDJDm0x6fxAPsM7agieI+PgWp4jva4JFJsArw2qbrPMwpprqXIxPMbe3/RDw12albty4nT54kODjY5vbNmzfbmAz+F08Pk0lm/l5RHZvYNoSxrazv8c7rSdxKzEWSYFSLIL47fJ+bcjX2G5vTS3mJr9VLGXWrCnV7tgTEmLN0dOk/sl99s36jZOU625GIUqjKCNTbzxbutMk3RCDhsztgRR9hyQ9IyHzjvAa7Oi+w+W4Rv5ujETIKSth4KZ5qXo44BTcB62KSHLU3rnqxmpym3EWO7MjPxr7I5uVzowBXcor0ZUZiXVhkGMRLqh3l9zXxMhO4zIRHNPX35QDqSrEkyx78tO8CM7rVRdY6c9w8WtzxUVv6J4VSTZI6CH/jfTq6ptKqevmWUWyG6J23ruEpWj2nheiYFlMea2Lo6OyKoWY3iNzPQvUyhl3w5UpsNm/2rk3XMO/KCZp/M5hyCNaNFpEbWybD+O3C7bYC+LnZM6dvHd7bfovP94YTk1HI+/3rCn0RiIpSwxHCAK20wmMsERNFZXDBVJvnS2aTjTM1vZ14pVso/RpUtVwo485uZVrWV6gkE4V1RlBr2BJ6brnDtquJDF921vI4fVyi+UH+HFVJPqgdocMsqN1PhLN6VP/T98wGzSeJlPF7+4RHSZc55TZ5pkUQ3x66z72UfE5Fplcozu0S5s2ENsHUulxJK2XXbBiy7ImExvX9XflkcH3e2XaLl7NHs1N7C8XNTdBwFIRWXOmzgdEA26fDzU1CAOvfFBIuIis17LyexCvrrlo21SgVTO1Yjcntq9t4NMWkF9D5q2OW39tU92RO3zAaBrg9/vkfwbaDRziomEmAXcXu74k+XZlVOIELaWqc7VTsfbEdNao4IcsyGQUl2KuVOGptL1N5xXo+3X2X9ReFduWzIQ3oFiAL88K4c5AZLYz78sqI9jXOUG8QBLcTmipnX7B3R9I4sWRfOD8ej2JAIz+61KrCrhtJnMnTkVdsIDczhWaKezyrPEgn5Q0AS5bg2+r1+EvpLIkbxCdmzyGVQqKPZwrvFi3A15CICQXbXcezVTOK+BuZJGYVYXh0NKwMknOL2Xc7mX3AAt5joXoJw8rIFACaJL0Jq0tJfrDN+S4BXwKwJWd6WclJUwOGcwAvKYffDd2Ypd4iWlmPITvhybmcuJeGUiGx/NnmuDqUb8X/p+Cpyc7777/PhAkTSExMxGQysXXrViIiIli9ejW7du36n9jH/zMwyVbL/6IyJea4jEK+MJMgWcaiEZBR8IZ+Gk0UkdRQPOSk5hX2356AKy15PmkRRJ8p/yR/ERqpYnFtml0wD6IyaBrkjsbRE9q+BMc+hzOLYOphod/5bShkxQCgfnCABY7XaNF8Hm9dsraG3twsTiR1pDjK2AThYsyy/F8tGXlX/TsuUiELDcL5uDS400mrIl8n9AMbjJ2ZofwDhVT+pJIn2+OgKEEpW1/Pj5pvrBtcBPmixHWfocQl90OttH+qsMGyiM/Ix7HoIUgwumvLyisumKtbMaeEOFmphVbTnug5VAO+Jv/7ttQnhg9Vq5iTMpXJqy7RKNCNhSMaljc5K4W9GzyzBpa2FcGI39QVgZhNxkKdQeXStse0DCIpu4il5sDSy7GZ/DallW3aun8zK9mpAE4U86ZqPVJAM54ZOw2Fs7f1j/EX8T0wHZVk4pxLb1qP/Akkiff61bGJZ+isuMoS0yIkQ5G4cA3/1dZU8Gng2wD6fS3aPce/FNWdR8SirvZqRjYPZOWZGH45FV0h2ZFlmb4NquJyxSr6vG/y5yPDs/ymnS+qC951nkgjAYJgBbg78OLvKtaVdGWs6jDy+tFIw34R+W+VwWQU1bXb28R03PBfhbYl4SIXHqTyyiXrZ9O9jg9fjWiIm4NtG8Nkkm2IzoQ2wRbx9GORFiGOJXsP0T67sYFXoMJF0il1G76VR3MpVny3XOxU/DKxhcXwU5IkvCpIUz/zIJ03Nt0gLzudocorTA5Kpd7lz2FPec0PkhJqdhMarbB+lQaVppinXev7uTC8WYBNNf351Zc4cMeZ1r3G0ulo03L3Ha86xHjVIVKVvqw3dUVfUsLLudvQSEYSZU9eLXmRiylhkJKFAhPu5OGq0uPjao/RwZvsEsk6uGAy4a5PJkAfQ4zBg7tyMK/pX2CRaQRH1TMtzzm5ajRqXSZRBVoO6erylWGkheyUJTpLDQN4QbWTcDmQ7bEahmvAhyx+N3ZlpmYHivhz6OKvog1sYn56mfisQhKyighwtyfA3YHvzdeaXvV8qOn97zeJ9TR4arIzaNAgdu7cyccff4yjoyPvv/8+TZs2ZefOnfTo0eN/Yh//z0ClVPDJ4Pq8vO4qW64k8OHAeuQV6xm69IxFSPgocnFkSslr/OryE166OHqkreGS0kBL3d8nOp/qxzCmXS2qVTDNdclUi0JZy9vpU0lafg57tZKW1Tzo5VyNMZRJIfesAVMOiwkY80SIVJDKyPRFVJm4hedWWsMG/d3suZdt69Ypmcq3Rl9U7uCksQEX5DqW20qJDkC87MNGYydLMCpAoazFQdKx3W4Q46a/A99ZRc7XTdUJkNIs48oSMo1TtrBdc45rvbdWeNJ9EqiyIvGQ8imSNVRv2PbPNzYZ4ZC5stB0vMVr47FwDeBi0/l0ujCd0aqj2Nfuypx7tbgen82AH04zb3D9yh1QHb1g9HrhlxN3VlTjIg9C7y+g9Qs2myoUEm/2DqNtDS9mbbzGvZR8pqy6xPqpLXEoSjZf6Gx9YfQqJ1bYTyAk+xzdFFeoq4gVeqzko/D9ElG96vgGyEaMG59FI+s4YmyM29AfQJKISS/gxxNW4fEAxRm+Vi9FMhghtJdwKP6bSds0myBG4a/9JmIknj9WbpNJ7UJYdTaGYxFpRKbmWQikySQGBtaci0XOiuak1lq9iJADOUtD6POlEGWf/FpED6ifzKqjXU0vdr7Unok/mfDMz6U3F5E3TUDq97WoSFWEQx+aiY4aRq7GULMXKYt74w/czbM+r4+Llp+ebVau8nc1LoshS6znjQ8H1GViO9tkbxukhgsxfWmr8k+IbowymHeLxnDLVI0cHKFYPLevix0T2oYwumVgOeJVFhn5Or4/fJ+75/YxV7WPbnZXUWMAS/FGEunkNbpAlTqiwudZ47H5YrIsE2mOaanIG6fU0TmvxAgvX4EfBOG53/gtQq99adnO25jMK/xuDfYEXB3t+cVpDSpjIWpDASpDARLmBVgBUCCBc1XwChKj70lXwZQvBNJKMNTogcm7Hpqz39rs09ysdy0vuSSgHpGdvudW9DzqX5lrs90Lqp2AcNFvZfZhuyyHkoY7ewzN6K88z+pfvuVg1ekYjCbup+STV+Y8WhYSEnEZhU9vv/FvhKcmOwAdOnTg4MGD5W6XZfmvaRv+CwtKDboMJpnsohJmrrtGer6OQA97uoX5sPJMTLn73JBroHzhFHwbhIOkQ0X5KsxuY0t6q66glM0Hc8NnhDlc2TTtR/Cu+ncSz3uWW5mZ7D3JHbSL8OQ8miTlUhKVQXp+CcfvpWFQRDNGAw+yDGzZc5cpHapTxdkLxm0W/g9m92Up+RZdg9R0r+PNobupzOkTxrRONdh6JYHex7axL7fiHK+Nhk6MVB3nU/Wv9Cz50tLOKosQ6SEdzSVngCiTLz8aB/Cl+ica6W8gxVpP6OsNnYls/Tktq3mw+WIMB8PTaKu4zbfqJdRSJFLLsBN4MrO8R+GriwEgXA5CTi2maZDthbmoxEhaniCxVaM2iGkmO1fo/M5TPU/L7sNZcXEPk9lB+8JDHH/jeWZvvM6pyHRe33SdMw/S+WxIg4pdUP2bCs1OxgOhtbqzA2LPlCM7pWjva2RXt3QO7d1GvdR7yJ8lglQxEX+pcCr781ugVLTHQ59FM8U9+ngkMcgpXLQ6z3wPD44g27mizEsiyuTLS/pX6H0hiYWHojjzIIPSafef6l6nW9RiFMjsNLWjVudl1P67RKcU3d4XZCfpKuSl2EzZAAR7OtK9jg8H76Sw7kI8c/vXJadQz6sbrnIiIpkJygPM1Gy1uc89UwBGk4y+6STUp74Rrdz7+6HuoCferUAPBxZPaMuwxbPJNCxnjOqoyJeyc4X6Q203vrZOvJ8AQ5Zx17U9G77/gQ9zL6CTVVz2G4Nfjh1JOcWMaxVMQlYRe289JLfIQEGJgfjMQhvxsUapqJjoZMfB9fWCVKWW15M8inf0k1ln7FLh9/S5dtV4u08YGlXlDihpeTp+OhnF72ejeFVew0daqxM13nWFiN+/majyPcbBuyKsPhvLrcRcy3NFpeXbeMn4uAgClJpbDCbzokfrQujgd0jo9CrLV/zCyznzLeHHZeFUWIHTPJJo/ctG0erNSxI/pVBqLNONqgcH4UH56ywgXm/KLTTpt6m7c7A4v+qGionLR/BQ9qCPUvjunDaJKp2TkwsUQbZew4Voq7GnRqnA392exOwim9Dk3TcfkpJbzOYXHrNo+zfGU5OdiRMnsnjxYhwdbR1lY2JiGD9+PCdPlvdL+S+eHLFm7Ykkwb5byVyIycRJq2J2j1rM2nDdZltJwnIxOBtfSC/fRiiTr/O6ylYroVM68WLxTNZ5rqdNtmD73NjwRPvjL5W3y1cUZdD13GS65j2E4lxktQGDh5piWYVzkWg5ZMpO/Hgiip9ORlHPz5VW1Tzo3vJ7Wh0aZl7dyJTcO8SZB8IEql1NUc4e2jSAoU0D2L/idXrFlhcq/2LsQy/lRUIViXRTXOWQydbEzo901mk+tUlpvyFX54xJRCnUNYaTeW4NpeoZHw8XhvUJQ61U4GSn4kB4BqdNDZinHy+mfE5/D61ffGKRaVlIZlFkvFyFjPhsmj4yvr7pcjy5xQZqu8sEXl0obuzyHjh6PtXzOGpVKOsOhDs70KZcwdlZy6rnWrL0WCRfH7zH1iuJRKcX8NOzzSuvUnnWEBNKd3aI8fSySL6J8dJKDPcOo82NxgcYK2EhwSWyEhMKi5ahFJ3tI6nXdizPtAjk7S032BfhRuMWYdCxOtzbj/GPl1Gm3LJw6Vn6GRRiZ5lEBBhcQ8Ebbkfwv70cgENOA3gl/RlqbrjJHy+1t+qG/g5KRewa50p1UsHmpOhivZH0fB3jfj5PUcp9NmuX0kQym9n51MeQEo4KAxGyqKYl55YQ2GA4nP5OTNk9BdkBCPN1YcHIJsxYO4VitDyn2gc7XhIXeu8wcQKI2CNacYCh3Wt8k1Sfjev2sEm1BBQQVX0cM3p2o8934tyckFVE96+PoytzMbN9TufyF7WCDDj5lbAPKLUaqAgtp0H9oWxLr8qsjbcq3w4I8rCvlOik5hbz44ko1p6PpVhvYr7qR0aqjos/Nn1WhG36/P14lBP3rP5Zn+y+yxd7w3m2jag0hfo44+Mivi8pucXgICwp0OVCQToB7l68+8oMPt/VgQ+vdbJ94JbTBBHTOIofrbP4sfcApUp8bgXpgjxmx4i4nYAWkJ8q2v6laDFV2CRUbSwq5EadNdokLxk2TRK6uzVDRKW2Aryp2mCpKJ0x1kNLCY1Moso+pFt76ng1QUKihrcjNao4oVYqMJpkvth7l59ORlse51JsFluvJDCkif9/ZFHjqU0Fr1+/TsOGDTl71ioYXLVqFY0aNcLL669pG/4LK/zd7XHWqpBl+GinWDnl6wwcrmDk86UuNZlunoxZeToGZbIgQ46SDqNsPRi1xnxuaKdYic4/gZiTwm6+IBWpKBN1YYqF6AC0VtylSZAbJlkkXf98KppRu4oplK2l6ugcYbRXxVlLPT9b58ueE9/DKJW/kA1SnuGUeXUSINka/WkpYZXmS6pKmdw3+bPVKDKzJERr65qpBirJhEfyact92vUYilopvgaLjkRabt9pas1DhY84sd2pQOz8BDDoRHk8X7bD/pGqisFoYvkJYcj5QfX7SEVZ4FHDmpz9lOjRrQfFshpnUx63bl5GqZB4qWsov01phau9mqtx2Qz84RT7biVXnmRsNiWUM6PZdf4221d/w/3PWsOy9igv/Yw2NxqTLHHHFMxKQ09eKnmZ3rov2OM7HaNKVFlytH7cChQ5bKOq6XilWyg5RXqOmy8q3cK8eZhbzIcRgfTMedfm6e/JAUxoE8w7vWqwqm0at8NW8G3SWAvRoeMbNJ72M17O9txPzWfhgX8gTiEnQVS0AHp8VC4jqxSXYoV2LMjDgUk/HqNt2kb2aecIoqN1gf7fwuClqDBgkBVcMQmzQZ3BKLKuAO4fAF3lYbCVoW+DqnSs5c2nhrEkubcQ4/IbxokW0sr+sH4MGEvIDOpJr2vt+fXobZarFhCiSMHoEkidkR/y6ynrRWvDpXh0BhMtQzyY2DaE4DKtiWeaB7Lr5fY4lYqCs+Ng3zvwXSM4t0QQneD20HSCNfUeoN5QmHkd+s6HoNYMaBRY6etxc1Azu0ctRrUsP8qfmlvMh3/cpv38o/xyKppivYnZVS4wUnUcWVLA8BXCtPQfIDoAz3esTqMAVxw0SoI8HDCYZH49Hc2ARafQGYx4l1Z2copEmxCEHspM+LQqJR/6Wqvj2bI4fuSLP4uE85B2wu/Js4ZoTSvN76skiUpUQDPhedX8OaEhc/KxmnW+lyZsEj7MgWnHxX3LHp/OvjBuiwg+1ReWmzgrhVoyopJMPDBVJZEqTFXuxl2XRJ7aiwy/znSp7U2/hlUJ83WxnAuVCon65snUNtU9GWA2qZ298Tpf/RPfu/8FPHVl58KFC7zzzjt07tyZ1157jcjISPbu3cvXX39tYzL4X/w1OGlVLBjRiOm/Xba5fVcFSedjWgWhkCRWnHpAcPxWm36x8hFxrotUeQjln0FftRnqh5cr/uOzO8QJT6EWKw6DToyem8nBtlb3SR47ivPRGZyLyuTMg3SU+dbV5DSzo76Hg6bcSkGSJKS34uALW3+RccqD6MwvtDSXphSve54htCCRFNmN8SVv4ydlMFR5iv6KsyySBnPXFERjxQOb+zyzW0+nlHtMbBvCmQfWKpaMgrW6jiI1+MJyMab8lKuZ82ka2gFB6lyaP2LEtfvmQxKyivBw1NAqz2x62GSs9WT4lPD3dOWBQxg1im7yx5bfyLIPpkNoFdrW8GLrjLZMXnmRmIxCpv92mVBvJ7rV8aGenwtalYJ8nYFVZ2O5Hp9NjJ3QLPXfa13Z62Ul+00t2CN1pMS/FWF+HgTqo/G+fJS3NesJSDGTzqqNcR2zEdeM+7ByLVLyDW7FZzJx1WVMMlT3cmTFmRg2X0qgxGiihZRhcwb6wmsfg1wi4NIKKChD7gNbCcF2/WF4Ae/2rcOrG65xtoKQzqfG3Z3iQuHfHJpVrIXRG03cSsyhClkoD73Pb4ojuKrN038hHUSoplugcJAG9puak4ao4tWo4gRSfXANgpw4ke5es9tT72a7Gp6cuJfGfKc3+TZrhLANWCNavSalluNuQ3jhXi/05LPKfjFN5Eiwd0c5fis77xWx6bK1peLtrGVu/7r0qOvDjmuJrDknLC2eaR7I50MbiCGJnAQRbXB7u2i5gLgYt3sVEq8I4oMs0uMH/lDuNamUCsLn9Wbn9STS8nUEuDvQuXYVXOwqnuYpKjHy08kolh1/YBHsNgt2Z04zE80O/AiA1OWd8u27v4lW1T3Z8ZI1SLj/Dye5lZiLk1aNSqHAxyzAr5l7VoiuJQUMXQ4u5irPvf1C6wXcavAWY6+E8bHiJwYpzyBvn4GkdYGwvk++Q771YU7FpKVCaBxg1O/wqRDpnzPVobWiYgdlqUYXfO9m8JJqOwDvFIxi58pbKKRb1PNzpUOoF02C3Gka5Iank5ab5uEPd0c1C4Y3JKughFOR6aw+G8sbvf5aa/9/E099ZlWr1SxYsAAHBwfmzZuHSqXi+PHjtGlTieX1f/HU0JYp7S4Z29SST1IWR1/vTFVXsZpe5H+IHqm//qP7MF//DG+qN1ROdADd7T1oB8y3vdF7ibUSsnMmvj3zGdTmRQY19keWZWIPzSPktMg3etm4mtd4gYiU8iGgAAq78i0FQdoEcZvUtQEbzS1tLSUMzN8IEnxrGEb31k347Vwce40t6KO8yIeuu9EXZJd7PN/cW3x7yJFfypRrS/G7sRszNTtQJ10RrYKwfpW+F4+iQGdgQ4SRdkBdp4JyepnVZ8UFZnJLH5RnzZWm+o/3vPgz+LcbDYduMkA+ypAVPZk/vCFDmwZQo4oTe2d2ZPHRSJafiOJ+aj73U8tWGGRaSBEsUu+3ebw02ZVVhp5sMHYhD3vGqo/zbNprBD6MQ4HJQq5Njj4ourwNTcYLoaWdi/AJyk9m6U9LSS9pDEBUegFR6WJiqVU1D752T4Yyso9B+RvB3KnAsQo0Gg1NxpWLcyidCrn7MJcfjz9gUrtqf6r7+FPEmMd6w/qVm0ArRXZqAnMUqxmrPmRp1eldQ1C3f0UQJIVCJL+bzSBXG3pZ7muSQSkhVvjX4yD29F8iO21reNFCCmdk3PZyGrp2BV/xsMATkNlU9XdaZF0GlT2M2ciOREdmrrcKh/s1qErHWl7sv53M21tuWIwFhzb1txKdm5th92zRWgExpdb0WXh4QxC6EvOx03gs9PqsUhGwnVpp8dL6M1yIzmTWhmskZovvdZMgN17vUYu2+fuR9r0DhiIxKdj+tad4x/4aWlXz5FZiLm1reKJUSJY2VrDuvjjeQ3uJSgyIEfdNk0A2QZPx1B84h5+aZjHjN2dKdCpGqE5g3DgB5bPbIKR95U/6d1FGu/Yo0YmXqhIoi4Xywfs5fFHzFnYJei6ZarHTJK7ZpdX3m4ni81YpJMa2CmK1mQQPbxaAnVpJ7/q+nIpMJ6/YUE7b9J+Apz5D6PV6XnvtNb788kvmzJlDmzZtGDp0KHv27Pmf2L//k9h02Zp9UhHRAThVJqupo+F0hdv8VXTUfcMS4yDe01cy9WGG9vKPRO6zda9F8Qh/PvAubHwW0iKQJImQHlbh6zDlSSYrxXHz8c47NuP2pZA1lX+hki9ZM57eUG3AR8omQfZis7ETv50Tq6NFBrH6bVd8nM7KMpqnemKF+K3TGnpWyapwCiETF37R9wQgb+N0YqP/PGiwLHbfeEiaeeLE/RGZTEa+jitxoi0yoqE7lE5ouJUv6z8N7JqORlZqaKCIIVSOYfbG61yMEdole42S13vV5uK73fn2mcaolRIKTPRSXGC75n02aT+mv9JqNne/2fucG3gMj07TeMv7PGfsXmGuciXBhhgUmEiTXTlibMw8/Ti+rrMRY9NJ1jBFtT2RgeL9fUa2CkolCbrX8WbD1FZs6GXE/87yil/I8BUigqTnvApzq+r5uTC6ZRAmGT7fG07zTw4ye+M1Dt1JobiSZPkKkfEAIsz79ygBkWVIvELu1lk4LWvGZNVe7CQ9Eeo6ZAxcjXrmFWgx2UqQ7u0XFaIqdSio2tLyMKtKBwpCOoh/b29/fH7Yo/vx4Aj1D4xik/Zj2ko3MJY5bWfJThgUGvo1qMq5VudokbUbJAWZfZcx+bDEzPXXLNtqlArOR2fw1pab7LrxkIISI74udrzctSYLhjdCUZwFW6YK36XiHCH8nXZSZIwd/BBOfyuIjm9DGLsZBi957LTTnyE1t5iFByIYtfwsidlF+LvZ8/3oJmwdE0S7Cy8i7XgRdDmi6jb0p0rJ6D+JhzmCcJUSag9HDT7KPF5RmYW/wWW0TGcXi5ZiYGvo/w1IEi2rebB7Zgc2+b3JQWMzlKYSSn57BhIuPfpU/yjiWn1U4e2lRAfAgJJ6aeJ8u87QFZBw1CgJ83XG0ax983bWYjDJrDoba9GDhifnsf5CnKXSA9B14XHi/8PclJ+6stO8eXMKCws5duwYrVu3RpZl5s+fz9ChQ3nuuedYsmTJ4x/kv6gUWQUl7Ln5eDvxuTtuY69RYTSZ6GByxu8pn+eksT4dlOUFhLWLV6JD6Gp+M/ageVg1BkfPgwpGwAFqnnuXyKYTrH4uajtw8oX8ZGgwQog/7/4hfkJ7QpsXhbPune0AvKtaS7rswq+n4VhEKl+NbGQj5DWp7FGWVKxz6FJ0EIkJ9FRcZopKXLQ+0E9AX+awvi0Hc58gQrGWhvOUrjj3/xoyItEm3+BH9UccG7CCSTtzyj3H14YRtFfcoj4xJK8djenV/SicHi8gvp6QjTNmsbmdrdfNsYg0ZBnqVnXB28G8TFdq/n66tYMHUq1ecHcnz/tEMCs5mJP302kRYjUzzCwsYevlWAbKx3lRs93iDisrtUj1hghPGCA0fguh+nvi8zOKaSu9SxAXqo5l8cMwzqSWaUecSuSPO1k837E6nWpVYdqayxQl1+OoFtopbiFhYmzrEKY29yA44Q/YOwPS79nsugGlmCIMavvYVoUkSXw2pD51/Vz47tB90vN1bL2SyNYriYR6O7H9xXbljOgqxMmvRYsmtKc1H6swU7Qtb2yEzAe4AEhwxVSTbwzDWTn3LZTKCi66ZuNM/JqQFWElzl8diKBnPR8C6g6EfXOE2V3EHqjT/8/3zaATGp9T30LiJSRAJ6vYbOzEUuMAkmUPdtvNpbYUy/FGB3EISIGDIoB3m//rzNqoBmx1fiVGE+n5Jfi4aBncxJ/e9XxpFOCGAhmurhaalKJM0arp+KbwBTr5tRAmyyZwDxG2BLV6/+Vj9WFOEftuJbP3ZjIXYzMtF9RhTQP4uHcQjld+hJ3fimqOUgNd3oE2L//l9u7TwlEjnsdgFIRUkiTaOiSh0psJaunnVpQF14Q7Ml3esUlN93Gx47fn2/H+li9wujWTNtyheMVANBP/QBFYqQ/938IdnwEE8cGfbvOi6g/QQYmkZZ9JxOIUlBgJT7ZW1ov1RpoEuXHVbJIIMH9fxRqdDvOPsueVDtR9RG/574q/RHa+//57yzSWJEm89dZb9OzZk/Hjx//jO/h/DU3mVTJqaIYnOUxX7aSFIhy7HXo06PErY2f+pX4U1+QatFbcZWbpaqQCVER0ACLsJnLeFMYBYzNWGntzxaUbg59rD5snViqAe23TDdZMbmntxwc0h/BdIixy8kE4uRDCd4uT9/0DNvdVSDLfaZbQUhHPB+kjGL70DM93rMGsHqFoVUqMCg3KCp6zFOOUh3jDPH223NCPw49MZ4HEHkNzZqqs+56uDcbZ3l1ojlYNQEq5RecrryLxATIKwnydCU/OY1qn6rzSNZQHEYFkbRlEqOE+ect74TzjiGjV/AluJ+VS3dxuQ2tLdo5GiItQ1zBvcVEDYST4T8CnPtzdSR0HMQobkSz+LdYbmb83nPjz23hHuZ4wjageynauSC2mIrWaJgSUtXrB5knCE6k0KduvCbR+EXW9IbRTqmgry8RmFDJq+TlLqnVcZiHvbbceUxrEsIJSkjnXJwWflM2wch8Yisvtcq3iVbzW1pVpVwYLK4SCjMdOpJVGOYxpGcTl2Cz23HzItquJ3E/NZ+uVBMY/ztZeX2wd0+3wmnAevrISjnwqLvhAkazhkKkpG42dOWlqwIk3ulZMdEBM1gAFGg9LOybU24n7qfm8sekGPz7bDJcWk+HU12IyqyKyo8sXPkd3d8K9A5YssRJJw2/6riw39CMZ8b64O6gJHr8cVvXC4e5muLsZgG/0w/gusvGfvnQ3ew0eDhpq+TijSL4uWlaJ5na1d13ouwDS78PiViKlG0Q+V9/55Y7lJ0F2YQmbLyew5+ZDrpS5iIJoWc1ooqFHwTZY/Kuo5IAgvf0W/mNC5CeBwWiyVFyDPc1C4KRrfGpYIP5bpQN+biHi9kMfiqqOT30RVPsINCoFn49swTL3ZShOPU8rwin8dQCZfZYR0PLpJvKeBB5R259422tOHSgossderWTJ2Kak5hUTm1HI4bupRKTk2RCdx6Hv9yeJ+qyvxQz33xlPXRf85Zdfyo2dAzRp0oTLlyvXd/wXfx/z2mm5bPcCU1V7aKyIIkwRXy635YSpAWdN9bDj8SF3pRiq+5B9RmsAZitFOHPVa1mn+YRL545T5N1YlLMrwZcp0/hy0WKSs8wOsg1GiH9vbhYXylFr4eXLIoVbXfEI91jTTs64vMt4xX5+O36Trl8d58M/bqOTrCLkN+0/5IK9be97nnolLlIhiZpqzDc8Y7m9UaAbKvMX8K7Jtj2kMpj308FDEB6tK1LGfUa6iTZVqYYoyMMBR62Khg2bsaXRcpJld5xz78Petyp9LwCMJpnw5Fy6lqYkO9hOKV6LzwagbQ1Pa4XjaYMeK4OziHzwRlywI5LzeJhTxISlh+l6cSo/qRcQpojHqHWF7h8izboN3eYKohN5uPxr6/SWMNprOMKyupYkiRAvR2b3qFXhLigw0Uxhrdz4HH1NVPIMxeBdT1zEen4KQLrsQglqurdpIWz8ZRMkX6/wcSuCUiFaBx8OrMes7mICqlRw+6eIPiHaTnZuQnvxY0fY/RoUZZKgDmFWyQs01y3lZf0rnDQ1pFc93z83VNMJUnkqSlysGwW6sWx8M7QqBWejMuj9zQku+owU1YqECyLWoBRJ12D7DFhQAzZNFDljJXkYHX3ZoB1G26Lv+NjwLHXD6vBsm2BAprvuIHaretnswnz9SL4z/nlVTIGJlJQktu47wLIvZmH4qYcgOhpn6PaB0F1tfV74+WTHimN3+K8wZOlTE50CnYFFR+7TYf5RPtl9lytx2UgStAhx5/3+dbk0VsM27Uf02N8dTn0jiI5nKIxYCZP2/EuJDsDeW8k8SCvAzUFN97o+kHYPfhuKg1zIeVMYv/p9KFppsWdE3hZAn/mVVrkkSeKFno1IHbCGC6Y6OMiFBOx5lkvfjSY1NeUf3fdaCVsA2OdQSVhzGRwqDsPP1Y4ivZEv94XTwN+NN3uHsWdmBxaOaCQWYU+BBf8h01lPVNnJzc3FxcXF8v8/g1b7D61Q/4/ih9FN2HAxnhc61yApu4g3NlvN8fKvbrLZ9m39FKJkP15Rb6e9JLbTo+ID1SomqWzFpmWxztCFqJYfMyByLg1zj9FVeZXp+lmglwmRkummuMps1SZaKiLYpXmXHZ8fJrvNW1Sm4AlTxPNp/gfw3SNl1Jx44awa2FKMXvZdIEq+e94sl5sE4F0Sz0fqVbyp3sCivMEsPTOAYRojDcyU/Gq2AxvlGXgzmi2aDwlUWHVL/iXR9FFcQI+KKLkqN+JNyChoWc2D4txawrHUjMCSB0QkZVPbz014UzQeA+eXMkZxgA1YNSLbriQypmUQkiTRu0tnZl5+hXWqj1Fc/12suju/U+EJOSotnx7GU/TXnEeWlEhlDPpyCvUkZImVfz0/Vzhr7uX7/0PlbRdBmlyK4gCZmIxChi46wbziz2mvvI1RqUXZejrK9rNEMCgIXcj5ZcJJWTaJC45CKaz/LywXwukqtsQmq6CEN7fcsLmtjhTLVJdzDFCeRV34iFVC25cFCfZtKPQLK8WEykpDL7qFeYuppSphws4g4ZIYp31KtKwmqh6lRo2VwmSCzeYR/+JskSWFGBteaBjB78XdMKLkpS41ic8qZMe1pMfmQhmrNkF59Tfc0i6ikPowvWN1alRx4veprZm14RpxmYWMWBvF1oDeNE3/Q8RTNBkH55fbGHua3KsTU6UrS1PqsDnFx8aM79q9aJw1EaxQH6SLsjwhfFO9kTGqI0Sa/Lkv+xMtV8Xbw40Jzbxwzr6L6eENVOl3UZjbkqVSsXv2janVpCOc/1G0n0G0otvNFN5LT+kxFZ9ZyMZL8ay7EEd6vhjRDvN1ZkyrIHrV8xVGfTkJsHiSuXolCQFv6xmiRfYv0OZUhNKJtWdbB+Nqr4b1s6Awgyy3+kxOfpU6KWZ/ofvm6nu9IUJ4/hgMaFGLaP+dHN7wFl2yt9I8aw8pi89yrtk7tO43SbQqU++ICa+wfqIirnESi7EnQdx53LJFFfaz/H60buaP29VllW7+Vslipmm3cdk+iE9SRtL3+zye71idtjU88XDUUN/PhSPh5a1OKsPSYw8o1hvpVc+XVtU8/m09eCS5UtMNK5RKJQ8fPsTb2xuFQlHhiyl1TzYan0Ig+G+C3NxcXF1dycnJsZC6fwcU642Ezd1n+b2mlMCG4J14JosqS7FfK4zjtuOQFYH0U+cnftx+2pXsnjNEOKFum4bOpwnT7eZzNMJKHvxI5231OgYqhZ9SnmyP818cX6f3F1YPCccqwgE26Sr81OVP73ZT3ZAGeusFdYjuI/Kwp1C2IxkPDmleL1fZKkU2zsQ1nk2Dga8y/pdzLEkYhotkFdSddB1Ih1dXi1VZ+n1Y1BwTEq2KF1nGhgGWjWtG7/pirHPu9ls4XvyBN9UbhM4BSTgN9/rMZnV35NQpmh4ciZtUAJ3nQOe3LX/bfzuZaWsuE+zpwPE3usBvwyDykKh2tJjyVG9rhSjMRF5YG8lYQn/dJ8TIvnygWs0I1QlMSjsUz+0RwtNSpEUIXYZZq0PjsSIvSpLgl57w8JrQcIT1h7avUFK1GXO332LDpVIRvUxbxW2mK3fSUWnNJypRu5Ks0xCkSBMZRS9dpMg5hENX7tL96CDsdensN7VgeslMtr/YgUaBbnBlNfzxsqj+Tdwt3J2fAkfCU3hu5SXq+bmw+5UO5TdIvCKqJmdtRfWpqqqsLmrPb8buuHr6MKChH4Ma+xHq48xHO2+z4nQM0zpWZ07fOuUfE3Hu2338DP2P9cUgKzg35jbta1tVdAU6A5/uucuW85E8p9zHW+r1Nvc3SiqivXuwWdWX3xK8ydfZnkPbKG7zvmoNdRRPMZL8GMgaZwxGI2pjITpZhVYy64xcAoRWp8n4J462KEV6vo4P/7htY5MR7OnA7B61GNDQz9rqiD0DG8ZDYTr4NRWj05WE0P6rUKw30vjjAxTrTRyc1ZFQZQosagYKFfdGnabnrw9wd1Bz9f2ecOIrODIPGo+DwYuf6nnCL+zHZf8s/IyVhMaWhW9DQTYbjqy8qmYywse2pChN7UcVfVLF2z+CpYYBfGkY/UTbAnQI9WJc62CORaSx7kL543F4swAWDG/4LyU8T3r9fqLKzpEjR/DwEG/o0aNH/5k9/C/+FMV6I69ttF29RcoBNIt5gf3dh1H79Gzsks7DtomiTdTjY+GL8QSYrFsNDKF0hlXr4MyXQxrS8rPDlm2S8OIV/cusMvTkA/VqGirKj2Y/Mfa9bfOrUWlPjlsdqDkMj8gtltuLJEfsZWsJpizRAdimtVaOkmQP/jC2Y7qiYqNEN/Jwu/YRxKzkrUJ3G6ID0CHnD3RbX0Q7dDF4hYJfUxRJV+iquskGg7UHP39fON3qeKNWKnilWyh9bg3nSEETPnPfRfPCk8JvxLuuyLMCSA2n7clnsZMKeKAJo0YH23HZA7dF+dpSKi5t6/0Fs7myyC3Ws/uG0K2MLWnOIOUZftYsREayuEkrBi2yJTqpd2F5Z9FekhTiGGrzkpW4jVorRo0jD1lE5q+XvMgfJrGa9SWDZdrvaCwJM0ZZUiDVGQCNRhPp0Jy+iy/wu/182srXuL3mNUZnv8Bo/TYGqNN5YKrK7JLptA/1FkQHhC7k9HeiurNvDkyuvDpZERKzhR7Iz+2RGImU2+K7EXmo3H1GlbzH+eIwFAol341pTL8GVW1O1G72QqyfVVixa3BOkZ5nf71AZkIE/bUgK1S0r2V74XZUK3jP7wqvOXyEp8nWG+hbw1DWGrqRFltKsAXRUWCiq+IqH/ufwy/NOm0ZbfKhmkIcQ0myBwN0nxIgpbFD+z46Wc24kjmEKhKpo0piXC2TyJZTasQxWrWhuICq7JCOfIL6uhDYaiUD0bIv9p1m4dvxOVBVnlFVGXbdSOL9HbfJLChBkqB9TS9GNg+kd31fi1EdRr1oH26dCoUZYp+G/fy/SnRMJpkd1xNZeOAexXoTVZy1YhLr6naxQWArdA5VgQdW+4hSj51SPdNTIKxlL+TGFzn8y3t0S3nELqRqI8iMMXuWFYtIld2zxfnTxU/8ffBSW2NBqXwV7EmJDoD+TxWRVng5aTg8u7Ml9byOr4sN2enfsCq7bjxk8+UEgj0ceLlb6BPvw78KT0R2OnXqVOH//4v/Oay/EMfum+WNBAEmXw7m6Ig1qDeOFXk7d3YIPUwZsnPC2MBmpV0WbaVbonVxz1w1cg2yOIU+ighNPS523cDpqxt5Idsa39C0eBmZuAAyAVIaraRwXlD9QU1FEkZZ4gvDaN5V/17hYyqNRXhkXIEM27H6bfpWZOHEFOVetI9EDwAkyp44oMOJIvykTKarnsAROjuWBlR8UtLeXAs31wqNQM1ukHSFWdXi2HAfPB3FCT8qvYD1F+IY3yaEKs5alj/bnFHLDQzPfIFfa9Sna+JS2PumML4zGWD1QOx0GdwxBfOd58f8WGZKw2A0cThcXKh61TMndfs3E0QiqWKLgSdBfGYhI388y0NzerNa0YlByjP4SkJsGWPy4VKD9xnecIT1TrIs0ukNxeBeDQYtKu8F4hoA47Ygp9wmacdH+Cft5xP1r1wpCUWBzAGPr7ArSKRI1rDB2BnfXq+Ra+9PXpqBebtEFs9HxaPYo7lOveyj+Oj6ME4jCPUy4wAKsOfk/XSGLT1Dz7o+9KrnS4hnqCA7qqdvhyeYR2H93eyFYPj+QTH5FL5LtOcUKuEZYyY9k0reINWjBVPqeNOrni/NQ8q3DZLMYuNyBApR0Xl3202ux2fTSSPea6V7oIUsGnRF3Dm1A7cLXxGku48DgqBEmvwt303Hen1Ju27bJtJSwkbNPBopHkAamGSJdcau7HAdy/sehyF+LUZJzQzdq2TgigPiczei4KIcxkVjGGfe6Ir06D7r8kTcw8mvLRojU6PRfPSwDaviPJmYX40Pn4LoyLJMap6Oj3betkyQhvk689WIRhb3XQuu/S4IbHG2+L1qI5i07y/FsPxTuJOUy+yN1yzTSN7OWhaMaCTIbqK5vRzQnOwiQXRd7c3f5ZTbf+t5JY0jXaZ9zTvrh/HZvTLeXXkp8PxR0fIvzBSV90u/CgPJrBjIiqGk1gAuOXfhQnQml2OzKCwx8oMqAD+D1TTygLEZnxrG0kVxjemqnZbzQFlcMNWmpSKClq45UIZ/B3s6MKpFEO1qejJwkZVkr3++tUW8fTUuiyXHrOasGqWCr0Y0olV1T+Zuv8XCg/coMZp4rWd524j/Tfyleb6srCx++eUX7t4VBkZ169Zl0qRJlurPf1E59EYT8ZmFVPNy/NNS3x/Xbdm5RqXATqUgt9hAQlYR67dsxjL7VpyNrLKz8RqrjOgA3Db4Y7/zXVxvbwVJQVbNIby9pmIfiA3TWgttScuX4Esr2RFEB0AiQfamQb2GLGIwIyJeo53yNk5SMTHdlhJy2KpXuaZsyGuad6ilyaKO/ADfvFuMlK1tujGqIwAY3GtCcYYY7zTjrimQ50reJBtHpnUO5dVz1jbFUWMjPKQ8GilE/EJpWT5PtueEqQE3TdV5u0zr4IqpJk0V1mgINk0UFQ3APes6MA47tZJpnarz/o7bfHvoPkOaBuCkVdE0yJ2FIxrx8rqrTH7QjnP+V/HJOCdacuYR+RzXOoxJeZVasm1J9cyDDLIL9bg7qGkebF7Je1QT/+Y9vWCxQGfgWEQaX+4L52FOMfXdSpjrd5nmaVvBPE2a5t2WXnHPw3U72vQQXiYUZgoBaqn546i14FOvwucwGE0M2pBJeNI41muiaaG4xyntqxRpPLEryCBJ6c+IgjdJpArszsDmzAlEyEGk44YPWXzXSUHguVR0soqdxjZU83IkOr2Ay7FZXI7N4uu9N4iwM/veRB9Hzk9DetJwR1kmM/ICryhPMCEqAq7cwiJKAag7CLnVdNI3zqQKQhitrtGRHWPb4FyJq2+BzmDxKarmVX4oY8uVRHbdeIhSIbEwLALugULjQOrRpWRf301g9gUamgcFcmUHNjiMIiF0HKsuJPMVyxiuPEHDuwuB9yitsioxskD9I40UD9DJalYYe7GJ7kxsFcDahHmo40W1972SZ7km1wQgxxxR4CDpUGOgSTVvW3JWmCk0Wed/tJINvybQ9ysUAc3pFJ7CqpWX+ON6Eu/0rVOhQWO+zsCxiFQO3E7hQnQmecV6CvVGy+i4SiExo0tNXupSU9y/KFtUcWJOQdxZUaUAoROr3Q96ffK/SnSO30tjxm+XKSgx4myn4oXONZjUtpo1a63U3ys7jqxCsfByc1DD9Q3CXwdEu++vwGhAIRuZF3gJyjow5CfD2hHw4nlw8MDU6gUSa00gPjqcZnsHoDUWEL/tfcboFlDWWfKuugp+SivZ2WFsR6zsy2pTbwLslUwpXmH5m6HNK6jOfk9LhRAV18q/iJpJ6FExs1soM7uFsv92sg3R6VHXhyGLz5TzIqvt40yv+r50rl0FO7WS8a2DmWueyPzhSKQlguLfBU9Ndk6cOMGAAQNwdXWleXMhqvz+++/5+OOP2blzJx07lh/D+y+smLfrDqvPxtIixJ01k1tVnESNyMMpO6bZoaYXP4xpwtpzcfx8KoqGxddBAXnV+uDcbBLLjkcRrZ/KfPVPNo+TJTvhLtm2SLopr8IVMSl0u9k8Rm82kltc/mJb3ctREB0QEyNl8GCyHcvuOfHdqWRKZJXIKhrRiICgiXDkDaYrdxKdUsPmPo2J4HDzi9B+luhB56XAQiF8faFkJrNC4qiVtA1VVqQQ2pYhO3UU8Zy1e1n8ctN2WqCL8jof6CdYyM40/SzmqVYSqEijn/IC/cyJvwDL5cF8VjISkNnm/gNNisziULOOQ5ufiDu5pOUpGNUiiBWnY4hOL6D1Z4dpEuTGK91CGdDIjwdp+Xx76D5DkydwuGoBdulWcumUH0U2zuiMtuZxW66IE1L/hn6oSkv7pVcLSUFOkZ57D7PQXl+NMTuBBNmbYjtvfJ2UeGiN2MklqEzFGHQF3IpJ4WFGFvboeIkSqjoW0l5/AynKVpzr9fwOGv9ymfPRmSw5Gsmn7TWwerBIWlaooMc88KlHvs7AnaRcUnKLyS7Sk1NYwt3kPHZb9BdKPtRPYLdW5FnZl2Rw1xTI+OJ3SEccI6XkpRRqpcSCfkH4HBCfo9FM6CIJYMsr3ajn58rDnCIO3klh/+1kzkelc8ZYl7ZKYauc+1UTjtZ8E9dmz1C7qgtVXe0qXiAUZGD642UWZO4WLrfZ5tt9GwoPnboDSXGsTcayftQtvEea7Mrqmt/w8cCW5BTpScvTUWI0UWIwoVYqqOpqh5NWxUu/XyEqvQAPR40lqLYUucV65u26gxIjq2tfxOueOVg3+SbeyW9TeoSm4sED7x64957D1OrVOB+VwaoLySzUj2CA4iytFHfpqrjKEVNTVBj4Vr2E/spzGFCxKvhzSoI6sbRoFyFX3kIt68iWHXlXP5ndpta81KUmo1sFMeSH45gMEgpJJkCRyVu9zefggnRhBHjxVzEmDWLircNr0PAZIUIHOoZWoYqzlrQ8HUfCU+hd39pWupeSx+Kjkey7lVxpcGgDf1c+H9rAWs25s0OEleoeGWZp+zJ0+/Bf5plTEZJzill7PpYlxx5gNMm0qe7J4rFN8XB8pKLVcKQ4J4TvpshnNqFSAmOL9sP2NYAMzSeLoM8nhD4vnYIDn6CKP4tDXjSSyYBStmqzOum+ZrPmQ6pkPmDlT1+zQdeW6PR8is3+PkHSJxzSvE4NKYlPHdYTGTya6rXrU8VJS53TnlCmCfCs6gC7S1rxdp86TLn9GZRxe1A1GQtnv7f87iHlEySlMKpPd6Z2rM7Gi/HlBg8O3hHf2wB3e9wdNKiUEhPbhjCose0EaWQZV3Y/VzsC3P/3yGxFeCKBclk0aNCANm3asHTpUpRK8WUxGo3MmDGDM2fOcPNm5RWFf1f8KwXKLT49ZDMtsnVGW5oGuRORnIfeaLIp/16Jy2LokjOW39/sXZsp7atTVGLkxA/PMaDoD+5J1XB/9QwtPj+KAhPXtVOfWEh8LHAGE++L1kUDf1eLXXgpFo9pSr+G5hOfyQTLO0Jy+c+3EDuyZQdyZUfs3XwIzq2gSqSyF0ZhAB7VRevIoINfepAge9Fe9z0bp7WhZc4+2Pmqxcjur+KBqSo+dkacSmynCnImnqDRMkE6NOg5on2NACndZpsBuk+4KVfn2vs9eGPzDcuXvRQ7XmxHA39XJq+6yNGINEY09WNBwyTYOMFivrjF2J5vnWZz8i3hzJtXrKfFp4co1pvY/mI7GpfqVPbNMecMwRLTEJpzx7Lq+iuI1oSidnAlIPsSpoCWKKYctIiiW/vIrJfegexYil2rc7DOpxzN9eNmQg6RaflUfiaQ6a84xwfqVVSRrBewBaFr8Qyux8e7BDm5OrcHLT49hMEk88ng+oxrHSzEqCv6AFCAHY4UcyFoKi2fK59on1VQwuG7KSRc/IPeycsIk4QmYIOhM28bpuBspyGsqgt1fJ2p7euCn5sdQZlnCD75BsrCVEpkJcfkpjTvMYoHrm2I1rnwIC2fH09E0VS6x1bth5TISvqUfMED+clH/YM8HOhVzwcfFztCfZyFAPrIMfQXVjBSdRIXrCd5kyxxTa5JrGcHvJsNpH7TdiRmF7PpcjwrTsfYPO5bqnW8oNpJguzFFLtv+NJrD40S14msuWfWCL3IxsloY0TF84SxAW/op5GCqKC7O6jpXNubk/fT+UE3lzbKO5xz60frmWtFC2T/HOuCwbeBIDl1BlpITlm8s+0mv5+P4/mO1XnHLMROzS2m93cnySwQbZxqXo70rOdD19re+LjY4aBV4qhR4aBRWknotXWW6Tbcq4n2cFAbITb3qP7E7/k/jbsPc/nu0H0O3k3BaBIH+tAm/nwxrGHFUSOyDMvaQ0oFfmRNxsGAH2ymxmRZJi1PR3R6Acm5xSTnFJOSqyMxu5DI1HxGZP/CdOUf5R7qnsmfiSVvkYQXLym38bp6EzEmH/qUfE4RdqiVEsGejjQOdGN6/hJqxpYRt9fsIbSCmyeDSc/b+im8r1qDg6Rjesmr3HTpxGmH14WRZSkm7oaV1tZZsuxOR923bHqxM+suxLH+YjyPwtfFjnmD69MtzLucn86hOynsuSmqm/tvJ5NbLKo/R1/vXGE19H8CT3r9fmqyY29vz7Vr16hd27YfFxERQePGjSkq+osTO/+L+FeSnc2XE3h9U+U+Ii1DPFg4shGBHoIVf3voHt8essYUVHHW0r2ON+2qSnTc3wsXCnhD/zybjJ0BOPVCbQJWPGqsVx6rDT143zARkJjSvhrTO9eg+SdWAadGqeD2x72s4kIQZm8L/toJK7LBbGrWay58XHLikRVqTC7+KLNjOGeqw6iSuVYSUJAO19Y+seD6qRDWnxV5rfjhQRUycaGVdJcN2nk2m3ylH8Ei45A/fZglY5vi62rH0CVn0KgUHHmtEwGOMnxmncJZYejN2A9+R6NWWlZMNao4cmh2JyRJIvXmIby3DCv32AZUXHLtiRc5OJSkk29UklokUYSWYjQUyxqK0FKEBr3CjgKjmiI0XDPV5Jpcg1dVW3hVtZVtpg6s9H4bZzs1DyIjWKL5jiaKSGJlHwbrPiIL22Pd1V5NTpGtVsqPdOapV4hqIIix9Azz8RjYmozhm2n2+QkA3utXh092i9b2ikktQAZtbgxt9/SwPN4ZY13W1vqWjCLh5quQoLqXEzW8Hc3/OlG9iiNKk57EnZ9S4+5SlBhZZ+zGHP1zgESAlEZXxRW6K65Y2rX3TP68qn+RO3JIhZ/XBOV+PlKv4qCxGVP1VtG4Wilhp1KiUSnQqBQU642WtsWj0FJCP8U5RquO0EJxr9zfP9aPZ5uxHbKDJxqlguwiPSWVVEMAnChkl+ZdQhQpyLX7IUUdFd4/3T+CFlPIXtYHt6wb6GQ1nxtGs8rYkxc6h1K9ihOf7blrISGAhcwBoipa6ujs0wC6vQ+hPf7U9Xjm+qvsuJbEO33DeL5jDTILShjz0znCk/Oo5uXIt880pmGA659P2cSeEeGkhmKhIez1+f9qFacUGy7GMXfHbctn0SLEnfFtQhjQsGrlr+fBEUvQKogw3Fj31ni3HMYNz/6EpxYQmZrPw5xiUnKLScgqIr+CyJlS1JNi2K19B4BPVS9ypLAmSrmEBHUwLnYaknOLcaaQ/do38ZMyia0+BrnvVwS421urwEYDROyGy6vE/pVp0yZ5tqZt4svMddjGZNNmLptCeZZPuKUcg1SmgnSyxmt0eLAQgHzZjpEl71f6nQGoXsWRNZNbifb3I1h2/AFf7A23uc1OrWDJ2KZ0DfOp9DH/afyj01hl0bRpU+7evVuO7Ny9e5dGjRo9/Z7+H8PwZgF0rOXFd4fus/Z8+dG9CzGZHLyTwnPthZYjNkOILt0d1BhMYvWw7kI864BZ2gHMlNYzVHGKTcZOBHk4EhBck2KlI3bGgnKPXRafGsZS2vdVKRVkPzJtMrZ1kC3RAeFqO2YT/G4Wuo7dIlZsxdlQlM2Fu1EcPX2GeoZbtFbcxatMFaDmza85fz2Mxa4fMM60jJ5cQpkdA0B9KRqQKSw9WTh6CW+XY1+AvhAjSpQ83tLggLEZPZWX2WzsyBVTKL0VF2ioiBIj4KUI38UkdjFBKxFrH4ZjvT7sSJvDoLjPLZu8rt7EGVM9rsiixfZKt1Dq+bkwbY3VNHPG2ivc/LAnrat7cC4qk4923uHHcc1QtHtVtA6ASap95G2agmbQAksLa2jTACSTgZgtcwm5s9TyeNkqL/TVu+NVrSGqGt1o7R1GUYmRrVcTWHE6hshcUT1QSDCudTCze9TCzUGU3ov1RrIL9TzMKeJqXDb621HwcCsNieRuQjpjlIdZpt2Ik1RMjuzAcyWvo3D0okuAKw0D3Aj2dOBqXDa7btjqxAYpTvGp+lecpGJBTtu/xrXgiVy/fYtnrk7AMf4cexZMBIRnTSnRAZi04iIgqmf3ymjfNxo7s/u2bSXtXko+PKL5rOKspb5nFyZ459A5dQ2jlYcZrTxMrlN1XPKjbLZdYejFF4bRlpiTilCaJ6XAlnyYZPB3t6dhgCvDmwXSspoHcRmFdFwgpk7f7hOGSiGheniZgffewcMgKoUGWUGs7EMNhegfzNFPZp3RnK9VCVkqfV2OGiV1/Vy4FpfNy7kvs1P7HlLEbrGBpIS6gyj5fSxuWTfIkp0YU/Iud+Vg3utXhykdxGLD01HDpJUXLY+bLJfRS+YmgspOWB60eckmyqAyPDRPslV1tefgnRTmbL1Jer6OKs5aVk5qYXUUrgip4WIUO3yX+L1mD+j95f+aX04pTCaZz/fe5SdzyG/n2lV4u0/Yn+tIUsPh4NxyTu861HhnXeXBvhTumg5x2VSL+7I/SkwYUaCQ3VBIDgR6OOLnao+PixYfFzt8XOyo7Wqg+fnFkAA4VuHdF19njp07Jlm2Ehlg361k5qz/f+ydd3hUVdv1f2dqeieNhISQkBB6J/TeBKQJiCAoSFVEUBQr2EFERBBQEUWkSe+99xpaaAkkISG992nn+2NPZhISmg/P4/O+37uuay7InDPnnDkzs/e973vda43nd9UX+N9ZRXHeK6g8rD5rKFUQ/rx4ZN6BXe/DLcFx8804RajUl4P2PRmVt44GUjTKkhwue7ahfu4hyyHsbm+xSAlvNLYuF+h4OGhJzy8p9/e6cS0rlvgQZdy5e0XA369hVUK8HHC2VdOjjk+l+/834KkzO2vWrGHatGm88cYbtGjRAoBTp06xcOFCvv76a2rVsmpR1KtX79le7b8J/5TOTmpucbl2bxCLr/1T2uHrYsvvJ2KZv/82BTojP7zYEBDEumv3c7melEsNKZG9mmkoJJmVhg6EjlpC4+pe3J3VkupFj+4YCCyuvFOqFPumtMNWo0RvMBHgbld+BbRjGpxZIjgRYw6DQkGJwcj5uCwuxGUxZ88tQCZISuKA9u0Kxy6RVaw2dWSE0jqgXDAFU/OluTiEmrv91o4Qqrt+zXhF8RlVYtaZ+UgSPDdHqN0Cl0xBFq5OKb7Uv8hPxlIlUZnL2tFmt3SBHKeaOOdWXJk/iFWGDnxkeAUDKupUdcLX2ZY9ZUpa/RtVpUV1d0uNu3d9X+Y2SEa9ZgggShoKScaodeHj/P6sNnXkzEADmt3v4qizHiel3Sy82o62rIKTcor442QcK8/Ek22eOB20KgY18Wdky0CquduRX2IgIasQRxs17vaa8tyvomzk2dWR5PIT+zlTTRY4TOKLMQPxdRYRyPf7b7Pk8B2KyhloyoxTbrWQuvM9G7PY6U1+v22DrqSQloprTFRtpkmZ7MYCw/OcNIVz3lSTYrTUqeoEBh3vZ35o4eCU4nD4p3hIObiaslEWplFcmEeOTiJHJyEX51DFmEpVKb2CXEApjLLEOTmUfcZG7DM15q78+Nblbg53WWL4gDTZhXaGHyg0VizlKBXCc6tQZ2Tm1igaVXNhw/iWcOZnIbho0pMku7PC0Ilo9w7Mlr7HOecGh431GKF/l1bBHpToTZyLK9/90jXci3e6hRLiVV4rpcRg5LfN+xh7ZZDlubywF3BUA1f+okDW8pLuAy4RzJyB9RnQ2A+90cQX26/zm9lgtK67zGj9KrqX7LJq5QAMWi4mxSdEq68PkJhdxIT2NVh0OAZZFmaYP77UiJoPXLcFJhPsfEd0DMkm0QZdbwh0/+pfMgh9FtAbTUxbd5mNF0WGa2qXmkzsEFyprUGx3kjctVM4nZ6Ld9J+JJ5qSrRAVtshOXoLPTFZFhkufRGklylLj9zxSCHC49HppP3xCn2lI9xUBKMae4AaXs4P3Z8Z1m2FspaXde/yhfpXQhUJfKEf+tCOWICFhj58YxBjVUSQOym5xdwpw7n7fkiDCrwcWZa5dj+Xeftuse96arlM9T+Ff1sZS/GYaF2SpP9xAoP/VLCTkV9Cq1kHLCQ0EAPMnBfq03dheSdzDweNRY20LF5QHmKW6mcUkswdVQ3knnOI2b2IriV7KuxbFmUNPx8HT0ctB99ubzVXLMiA+Q2gJJf02q/wdt6LnI/LrtQ5fIJyM9PUa57oPAByk9FIoT3gzwEgKdCPPkSLX1PJKNBxptEePKN+s+xrkiVqlSzjW/Wico7dJlkiouQHUnCjmpTCEe1blm36+sPoeHsA+swE2ikv00ERSXPF9Qok7rI4ZarFZmNL7so+3DL5lelEqwgnCrhkMwYJmdG6qXzushXvwocHVmvC5lPT35tqLirOZ9rw1y0Te29bM2JVHLV0CK1CkwA3UvOKuZ6cR9T9XGIzCspxbOw1SrycbAiqYk9QFQemXuyKVm9uL3bwZrPTS0y50xAZBdXc7FAppHIDW1m8p1rJOJVYpf9Ob2YWD6an4jRfqJfi/JAA5N+FTNmBeNmLBgor76B58QILb6Us/FxtaeDvQgN/F6o4anGx0xDkYY+viy1KQxHMqyP0XVpNxthpBjqDicxCHVcTc9gSeb+C1MOn3QN5Of1bIUQIHFBE8EbhaJoEVeE31VdI8SdJl53oVfIF1YNqcvFeluW37KhVMaCxH8NaBFgctCsgKw5WDoa06xU26WQlo/TvcNRUj19ebkLncC+KdEYmrrxgUbd9v14Bo1M+Q5EjMsQnjOEEKZLwlrIoqdkb7dAVT3aPC3Q0M3OtSjGwsR9f9KuDVvUIHZbTP4lgB6BWb+jwIXiGPdE5/90Y98d5dl1LRqmQmD2gHgMa+5XbXlBiYMeVJNadu0ethDV8rPwdhfTwqXCibhKD6jrRMsARdVY0xB2HnESRNTPphUv84zDwV6hTsWz9IK7duk3AyrY4UMhMeQwtB79Nl/DyZaFivZFDl2PotKUFaslIguyBn5ROPnbE2dWlduHphxzdik/1w/nV2OOh28+836mcJMmuq8ksOhTNJbP7uUYpSladw/9zJavK8G8LduLinlxIKSAg4GkO/Y/hnwh2tl66z9S1l9AZH17Tf1J0VFxgnvpHnKRCSmQ1Pxmf4w3VJgCKXMOwzbpR4TWFspZhuumWUs3j4GSj4oPnatGnflVsNUrkS6uRNo4FRFZmmaE7x23b08DfhVrGmyiSLrA8vzkrNF9SVxELQKSpRrlJ60nwQ4OtfHsqD2dbNaend8Bm+xtwaZVl+5dNT7Lr3HX2m15DLVmD62JZzUZja3ylDNopy3cXtC35jnjZ+gOVMBEkJfGtevETXd8Nkz/HTXXI9mrBwUx3rhc5YywjznVYM5kARSqjdVMJku7zvnrVI45WEZmyA/myLSrJiBojCkyky87cl91Jkt1JlD2IlqtyVVOfVL0WvbH8T9idHM7bWFv+h+re54SpzmPOKuNEAS8r9/K2WtiS3DJVxU4qqUDgflbIChlAhl0NcoxaCoqLKS4qIlOv4q7encg8R67kO1GIGGyVGDmlfYMqUjbz/ecR2qIH762/TFahnhca+/H54yZmEAaba4YBEkw4CZ7WLLQsy8zde4sfDkTjSCE9laf5wvOg6AxUqDhZYzIvXmlImJuSLd5L0dzZS7HCnv5FH5QrBYR5OzI8IoC+DapiLxcKgmtWrAhscu6JDqWSfLhTXpy1qPZgbK+VXxRM1E2ibYfuDO7ckpwiA6N+P8u5uCyqqApZW/sk1W//LrSdXAL4w/NtPrrsTk3pHnu0Zn+zsUeFkOAjYDTJjFx2hqO3rZ9xRJA7y0c1q1jGLovMu7CopeAY9ZgNzcc+8jz/SUTey6bvwuOolRI/DW9CB7OAp8kkcyY2k7/OJbDzahI2uiwOaKeWL3MDRpTcr9IG/7RDAHxmGslSXVcOv9P+4eU8XaFoHc9LhvwUkeVS2Qq9KPsqogvuKTSM8g7/gOPBD8mSHWhRsoCxHcOZ3LkmCoXEsuN3+XLHdZrIV1ml+YIE2YPOJd9wyv0zXAruPP7gZgwo+YTzcuVaOAMa+fHtICstZdfVZMatEGV8jUpBl3AvxrWtQV2/R2Sd/kP4twU7/xvxTwQ73ecdsYhZ9azrbRHl+rt4qY4dPe9+SSvjmcfvXAbrPcYxNeHp5AJqVLEnJq2AIcoDfKpahkb692XwTLJE65LveWdwJ/o19AOjgezF3XBJEx1f6YG9uNxiLpuXz+N7zY9PdMwP9a+wwtilwvNTVWt5Q7XJwv15UsiSkgJbX67mO2CQFbRWPp3oWILsgU5W4S1lYSc9RReaQoXs35ySwA7k2viRYnImPl+iceSHeBdZdYQiTUFMcZyLRpeFb8E16kixBChScKIQb1U+LsZMPKXsSoUcK8A9BCImgF8zsHNHLspC/3NXNAbxXf5EP4KZ6t8tu5e4haGt2UnwSKI2lTuUUZY4Y6rFdbka2bIDWThwW/bjlKkWIOFoo6JHHW+6hHtT29cJn92vIV3fCl0+5W7oaDrPPYzRJLNjUhvCfZ/wd/tTe2FV8sLvwgIj+bLI9hTnYMpP4/LOnysGvP4t+CvFi4LCIkaqrBnTd/RjuGIKwo5i3KVcXqpjTzs/kDJihLlmuijn/qsw2lVhlbEzn+V0Y5TNAaZoNqPSmTMJ4X2hz3wMakdeX3mRXdeSWaD+nl7K0+gcq6EZewAeoVW0/GQsH2+2fl/9XG3Z+nprXB/Fu8hJEN5i905DYBt4ecs/zs8pi7fWRLLxYiI96ngzuk0Q1xMzyYo5T+a9KFQFKQRL92miuGnhW1lg5w6NX4Emrwpj3nl1ITueQSUfcZZa3PysR+WdW/8OGA3I39dFyr3PWN1b7DY1pUNoFUa2qs6rv53FaJLprzrBXNUC4hVVaVv4DSvVn1coGT8KVmFYK56r68O3g+pXkERp8vleS2Vhz1ttH17a/AfwTIOdU6dOWfg5j0NhYSF3796ldu3KRcr+G/FPBDvPzT/KtfuizPD7q81w0CoJ9XZi26X7vLfhydr31UqJNzuFmDkyoMLADNXvDFPtf8wr4bapKiGKRGRJySvabziU7f233kcVsllTYzdBiZsfuZ8hqBOxqupEp+aTkp7Bi8oD5YKkbcYW9FKeqvS1eqUtqjp9kUJ7QmBrNt3Ip++Wutbt488Q8l00sTZDASEy6CllU1tReRbyV0N3PjW8TJCHPT3r+rDgoAgM5qkX0Fd5gtX2w3iuYCOOFPCy7l1SZFfeUa2hc2lH0t/AVmML0mXnSg1ab5mq8q78OovffgUHCjFlJ2AqzsGo16FBj4NUItpHs+JE0JCTIEw6/1NwCRD+Rd7ls0OyLDNzaxQbT1xlp/Y9fM22FCACGcXzPyA1Gk6Jwci68wl8s/M6602TK04yDyDbJZzU+hMJaD0YrboMufbYd7BvBnL48wzNnsDJOxm0q1mF319tJjgSWXfh3lkoyhTZk5Jcocaceh2y40H+B8rqzv5CEdclQDidl+FvyO4hFNToiS4jFreYR/9+KqBKmLD3COlq6bLSGUy8tSaSk1duslHzMQGKVIy+jVGO3FapgF+JwUirrw+WI6WWk5t4EPmpQn353FIw6kBtD+OPW4UxnzX0xcIhPvaYUC1WaYXYn9bR+jD/XSjZcjtbYmNUDsfuZFNdSiZYuk9DxW1aKK4/lP9lQf9fRCmu1A+sJA++EqWvBsVL8PTyYc9b/2H3gF3vw6mF6JV2vKkbzw69tcu2d31f5vfyQfpWZGamhuyg1c2v6a84Yn29kx/kJjx4VABOm8IYrPsYpUKytOIDRH/RoxxpuhSB7223/P/yjK44PUSI85/AMw12QkJCCAoKYvTo0fTs2RN7+4qpvKioKFasWMGyZcuYNWsWL7/88r/2Dv6D+CeCnRPR6Qz9xVpX9XDQMLNPHT7cdKVC62uIpwN2WhWX7mVXOE5VF1sSs63kWy06ftfMooWiIg+gLPYaG9G0qhaX5JP8Ri9mFA+1bKvmZsdbXUJwtlUTk1rAmqNXaVu4h1aKq2jRIQEGlOhRYkCFu5RTvhV38lVRy/6+gdDWaTQC+gghK1mW+XDTVTacvsVs9U/0LhPgzLD/kBkFnz/B3SsPXbMJND9Sj4s24yps22lsynlTTT5U/1nu+dd0Uzilbk6Il6NFvHGz5kNBdq7aGBLPY7L3pFbGbErQIGEiQhHFROVmWj1F5maRoTfzDAMoQcO7/tcZn/YZSEpu1n+XuEuHaGc6i1bSk+HWEPcJu4U+yskF5qzAP4ROH4N/CzGJOfpU2rJsNMl8ti2K307EosZAlO1o1LKVUzZSN41xo8dx7HY6q87Ek2Fuk27qpeAP03vY5MWCR6gwP827L/RgCtJFS63ePDF51xNEW7fqIpjZ/QGcEuq1UaYA7KVivKpWx8aYX7keyr+AY8ba5GJPgWzDC6ojFbbLCjWS2hZZbUtasYJEnT05khPtG4aLrEDVxsLk0qGKCBK2vWXpVpJ9G7E+4ENmnjRQWFzCEvVcOisvki3bM1D3CdGyH+0VF5mhWk6gohJl7R7fiOxDJW3dRpPM+BXnib5+kQ2aT3CRCsip3hPnEeVLqYnZRczZfdNC4AVhFXBwavuKJF59sQg0T8y3fjaBbaDrZ0KJ+V9BSZ743EtyoThXBKX3L4pH8pV/WW/LchqlPQWutXHyCkDlFiA0fzZPEBtfWg8hDwgEFufArOogG+lb8im1mnbkq/51Kx7434miLGGYGnsUo8qOOvkLKDKXdT99vjYvtwiAma6ADFNvcTdPovpPVk+qOWGrGV20DJe4igusGsV/lCu9Aywb2ZRaPk54O1e0Dur5/VGiksTiPNjTgW9fqG/1tPuH8UyDHb1ez6JFi1i4cCF37tyhZs2a+Pr6YmNjQ1ZWFjdu3CA/P59+/frx/vvvU7fuf/hL8S/inyIoH7yZamnRfRTc7DVc+KgLsiyTkFXEC4tPkpxbXGG/boqz/KCe/0RlJYOs4K7sQ4gikR8NfZhtZuWDaGs9M7UpipvbSDu9Fsfkk9hSuRHigzjh8zItx/4gJqfv6oiVxYtrILS79dxGE8Ef7ARklqi/o5uycquKr/QvssTYi57OcXxf9y7qu4esGi9loNO6cqKwGu2V5fWLhummc8wkvotVyOKszcRy2yNNNfja8CKnTOGAzFXtKBykYm4rgwkxRnPCGM4MwwheVB6gn/JYhdp+WeR4Nsexbg8UmTFw8Q/iTJ60182lR11fdlxJpipp7NBOx1kqZJf7cMYlCmJgG88i/sgd9fAbKilB6yBWsArVY80Hb/n2ZVNJE/YkO6A0FbNbK0xYf6n+HV1at+Sjg1kcic7EniJm2K7hBdlcllHbCe5Fg5ceW5LIKtAxafVFjt5OF1oxQRsJvG9d+X2nH8D3xvJETG8nG8a0DeLliABU6TdEOclYAn0XQ4MyrssFGVZrg5IcsHERzvG73n3kNf1dnPAexg2DL0fSHbhe7EYqLsgoBMFZA+Nzv6O/8hgAf9oNI6neBCJqeNI40A0btZLtl5OYuFL4mk3rHsqE9sHlT3BtkzBzLMwAhZrURpMZe7cNFxMFIf4t1TreVG2gWFbzku79chwKRwq5YjO64kVXaynIrg8x0JRlmUFLTiLFneBPzZeoJSPzwlby5uCeSJKEySTTee5hC0Hdy0lLSm4J3Wt7s3j4AxpdsceEyGfp765qY+j4kfAY+zsdOClRQkMr9brITuYmPnJ32cGbQt8Ikh3rkJlfTEFeNsX52RTl56DQ52NPEQ5SMQ4Umf9fhBYDsmsgWu8wbPzqQ/U24F2/fHC4b4YI4HwawJhDlb+XTRMhcgX7jQ1Jeu53IZL5n4bRgPyVP5KhsBzXUMLEav9NNE9bJ0Qo34kWIq1mRfo7Jm866uZast0PomXxfO7jUem26h72tAhyo0WQOx3DPHG0UZNVoOP9jVfYEyVEGYM87Nn9VttH87r+Q3imOjtqtZpJkyYxadIkzp07x7Fjx4iLi6OoqIj69evz1ltv0aFDh//zxnpKdAj1pGUNd07ECD8hjVLB8IgA/jp3z6JECVgEBiVJwt/Njv6NqpYzYitFR8XFcoFOlCmA8IeUclSSiRApEZ2s5FeDmHgHNfFj7bkEzhsHwmyxX6ns/Q2TP38Z25EmC0KaGiMqyYizBjrWcKBF9HcALI2rQt61ZLq5pYpAR20HQeXTvyqlwnKut/QTiFK+Wuk1Tlev4jdjN3bkBLLjWCDQgTrSHbZpPyy3n6Yki/bKimZ3Z03WiUNDxU6xBooYVms+Z6exKcsM3XGQitHLSr4t6sVizTxaKqPYo6x8kk2QPUiS3SwZrbO136d6WBMC1dkUX1hLgCKV15xOE9FkEm62SvpFfoKzVEikqQbTEtvQQXGR8X5xNDE+UBpz9IGWk0TXho2zSN1Lklhdn/wBDpTJfCm1UHcgtBhPkm0w41dcIPJOtmVzS5dMKBZZiNGD+nMtS+Jo9G2eU5xhunolfrKZlNrkVSFkZ/P4QP/SvWzeX3GQ2vnH+U17jjaKqyjv6zGi5BI1acR1AhQppebdtAhyY0REIJ3DvawDo1e40IDZPxN2vCNKIg2HiyDL3h06fgCNR8La4YL78rSBjkJtUbJ+GIplNTaSHp/7e1lmeIljpkAM5uGwfWgVlg6tw7Xv+1FPeQqDrGCqfhybi1vDoVgWHIpFo1Tgaq8mJdeaedAoFWyOTMTLyQYfdSE+xz9Cc2MjAEVutfhMNYmVx5zBrLhcQ0pkvEqo6k7Tj7EEOt5ONngW3GaWckHlFx9/An5oJFTIa3arsFmSJP4a15Ke3xu5mBFMM+kmCVcO82egULW+dj/XEuj8/mozFh6MJiW3hFbB7taDFGUJUc8Ly8XfDt7Q42vBEfo7QY6uEA7PEllL0wO/RbUdaJ2QbZwp1roRp6nJFTmIw3l+HEyzpyC98iYOrUpByxruwq/OrPnSq54P3w9piLKSFnMLsuPhlFnjqt27D38/baZgjFxJJ+VFLqiTgP98sJOUr0dhdMCLQgIVqQzq1AZdiY7qJ96hedpxTEjounyJjY0zfGqdf/N6/shrN/PgbuXHlXn4/bmbXsDd9AJWnblHTS8Hdr7ZFld7DYuGNSY1r5hmX+znTnoB9zILCarykE7D/0I8tahgkyZNLJ5Y/4d/HRll2snbhHjwUa9w3u9Zixrv77A8b6tWWNr59UZTpYEOiNJSWZwxhfKdYQA/a+ZW2DdXtsVJKiIVV9JxZlATP4ban+dt5++hTOb4W/1A9pkac12uBpX9QIrAcHM3LZSQo/HiaHE9Tq2JZJ7fIboAOb6tScsycDslCZ3RRHUPe7IK9aw9J2rJhdiw29jkodmd2eqfeFP/uuXvjopIAA4Z63Ndrsb4Rziff6H+FZMsMd/YnwTZg73GRnRRVnQX76E8Sw+lyLCpJSOLNfMs2wyygr2mxqw2diTfsxG3k3MBmVzsGdYigLTzU+ipPINi3yd02zkFAyrGKPvzvnoVI0v+pOWyCForrtBYI1bGHlIO57TjRVCaWuFS4M1LIrWflwxJkYKnE3sUovdb/Y3qvwh1XxAy/Bo7TsSk88bPx8go0OFsq+aNjsF0DPOkupsWFv2AlH4Tw8EvWX/Dl22aXy1cpmIHf2z6L6wQjFYGWZb568QNsnd9wUbFDjRqczQjC2sOkRmUWaK5Tl3pLhImZBS80y2MxqWmp2XRcpJ4T3HHYOsk8WjztjlbIYkJyd6z4uueBOZAx2TrRoHanUhjdQpys+iuPEua7ESnkjnUtcvme8W3VDek8LNmLlk4sd/YgCs2TZjebyzpPz1PvcIzFMtqkrv/xPu1e9LqVhqnYjI4HpNOSm5JuUAHrKKK7RUXma3+GY2UjUFW8KOxDz/c74++3HAr86V6KRoMHDA1JMn/OWY18adeVSeurfucPiW/opGMpMtOzNFO5LNp76A+swj2CG8y9IWwchC0mCDKjuqKCrevtArkwqYQmilu0ki6xYxtUQRVsUdbhmR7MiaDM3cF16ptTTOR+X6kaInPNzdNNH4FOs/4e9o5GTGidf/CH2BukadmDxGkedYiSRPA+VSZU3cyOHQzjYSEBxX4TWiUCqq52xHobkeguz2BHvYEVbGnUTVXbNRKPtgoOI7VPeyZNaDeowMdgL2fCA2cwDYQ+vDWa51zdY6aGtBJcYHg5O3Ak/FWnwUMRhMLDkaz+HAMn8i1eVGVyo9BJ3FoOwV2TgPlcfSykqn68dw9F85voZm4l+pqedelfotO1M/7uNJg56wpjCTcKzyvVkoVOjtvpeQz9OdTvNMtlCaBbuW4Og8z0P1vxT+v5f3/Oe7nWH/cpZoG+gfa0U/dyWT5yTg2RyaWMwctha+zDfdzirknl++6GNGxISOPzKp4UpUt9t71IOE0B4yi5p51YRMNHgiKahX/aqkRPwqDJUGI/stmILpcNTqdkZJ7F0AJP0a7s2RuRc5DWfxs6PnQYOd55Qne0Y9Fh/hhlWaqjprqsNPYvEKwkyB7sMTQi8/UvzFQKc47SHWYwOKV/GrsQRflBTJkR342PFfOCf1hmGEYYe3cSjYCVr5aQ39XwoM/x7SuBx2Vkfyu+IZxJW+y1tie99Wr8JUyedXxNB/rv7e8prSN+56pCqeVDej43BDcVHrhJ6RQwZY34PJDdIkcfURGpPFIy1PLT8YyY8s1TDKE+zixeFhjqrmXIaN2/xJWDEB1ZgkfAyigSLJjka4nhUFj+TCo2YNnqYCiEgMbls+jc8IPeCmzAbhmCmCHsTm75aa4B9SlfagnLb2NyBt+IUSXyCvK3fxq7MGARSc480EnPB3LfI8y78Khr0QwVxZHK/plPQwFruHYe/iDoxc4eKOzrUK83onbBXZEZttyPFnBtdRiZFmk/A9rhNZSbK1xrO/Qk2BPB6TCgXD8O4rOrcRVl8FA5REG6o/AvLmWb32cT3dCq4eAvRB1HNTEH1mW2XAhkalm25fn6vng42RDWm4BnRIX06dgHSCaAKbqx3FZLm+IG+7jRH/VMZqn3cCosqXlmGV09KwOugLu/vQSAzMPggQ3nNsyNmc4cbn2+By6w5udXwef+rD6JVHiA+Grdm0jtH1HZMfKtDe3q1mFD02Cw9HK5i66fBOvLDvL3EENLF54iw+LhZNCQrRVxxwU7fm6fNF512c+BLR84s8FgNwkuLYBrvwluDdmyI6+3Iv4lD3Gxly8mc35PVkk55ZffGiUChoFuFC3qjO1fZ0J93UiyMO+UtIsCM+5v8wLpy/71bVqgT0M986Ka0MS4oePyFJdT8plg6E1nTQXcLy1EXrM/I90ncmyzDtlBBEvVB3AkMwjONw7BJ9bx/jErks4dsCFzMQcxv9yiLWlG9oLWwoyKl8Uxz8wT8R82RO90YTBJLPhQoIl01eK03czGbj4JCtGNcfHxQZJMmsmmv512ZT/JP4v2PkHYTCayCtTrmpeXaQht1mcpq34ZMvDSbEvtwzk6503yMe6usuSHXA9+1Ol+8tqW5QJghy9xtie7ooz5bIZfUo+w0XK58eQcySlZvBjbksSHviBlCJQSiJUkYBBVjA/Vegy2FBiMbOMkh+f+n1wMvjR0IcJKqtp3gbNJ4zQvUsGztSWYs3HDaSgkkBstn4wW0wt0aFmltkB/qBRXFepMqq7lEfPMiKED8NOl6G8N34Oo/NKWHU2niWH7+BMPjZm/tLCLUc5MLUd9J4LW9+klXSFszYTWGKwGu2VDXQA6PYVudU6Mm5dKhlJsej27GSw8qDIyZkM5QMd73rgUg28aosVqE+DcoPzwoPRfLNb3OcBjYQInKVl1Kgn68xq8vbPoVqZ02dW7ci15l8zf2U0HlG5vNPH+Eh9mvNxWRz86R2hvSNBrMmLTw3DKQjoTK96Pqys410+kOn6GWx7i3c1azlY3IC7sg9vrLzI6jEtkAoz4PBsob79tFCoYdwx6i+KJ6dIz9iQIDQqBdGp+dy8ncfd9LIii9ZsabCnAyOq3qfa9TTQONK0/1vWziR7d2IavkevIy1oaLrGu8EJ1E/eICZ6M0KTt8LircJ+wbsehHRBajqaywnZAAxu4s+sgfWEwNy6yVAgvleXfIcwLL4nebIYYoe3CGBa91CxGpZlWCy4VMq2b6P0rA7Z8cirh1I9/QolsorIeh/SvP9kply6z5urI/nhwG1aBbvTpHoboRH01whIMPP98pIEL+j499BzDtTsCojFU45HI0w5EgGGWEZVz2TpXTfeWhPJH6OakZxbzJurIwHwkLPI2/U5jmfmicxYYBvRgfcEpU3A7Nu0Q3Rq3TlMacu9LCnJ8IzggLotP6aEE7tFAVibJ5QKiVo+jjSu5krbmlWIqOGOnebJp6VdV5PRGU3UqGJPRI2K2YoKKLW0qDtQmKM+Ahfjs9hnakShZIddzj24fwH8/r1VDVmWWXLkDhsvJqJSSMweWI9+DasiRfsIodUyCJQTWT+6A8N+v8z1zEKsEfpxCOvJRbkmldHH1RiY1DGY+QeiCfF0QKmQUJrNYV+OCKRLuBcLD0ZzIymvnBr4sKWn6VHHG1mGzrW88HGumE38b8b/BTv/IFRKoWYbnyk6HMpmeR6H+n7OaFQKzsZmccwsCFboFATmQ8zUv8y8osp1Z6Qia4vwdu0HFbZv0X4k/mM2wO2k2U3fks8qTX12UoiV2ylTLXIR9dvxqq14StkkyB6cMYXhbKumnp8zarMH14PZKR1qNhhbW4igw5V7McoSSrOiaR1FLPPVC/jQ8Cr+ijSMssQ1UyC5VOwK/L6tzLyuz7H2XD3O7b9Mk5LTnDYJ8bguCqt2Tj3FQ4rZZdAjeyV8tRIH34ZMV9nwtstN1MWZ5Xd6oEJog443VRsrP+BHGaBU4QT81uY26i3v46LLe2AnSTgZd/pEeIQ9BHP33mL+flEae7NTCJM7hwjJdqMezi2j+Mg8XAsScYVy99It7xYt/GwtPjgfbbrKrAH1ysm9Zxfq+GDTVbZfTuJF5X6+MosM/mDoS2ajScxoV6t89qgsGr8CUZvR3jnEN+olDNJ9zNW7iaQv7kWVlGMPfT/loLbHULUxqliRmctTujDBZw13l923GJUuOVJRPM3TUUttXyfCfZ2o4+tMk0A3qjhqYf1rYofw5yu0YP94MIYig4QypD31RjYld2kcTomHAbhcbQT1FHcg6ZLoFko4Ix7Hv6eRuju7aUfzoPqQlyII1wWpyFonfnKZwld3BFG0gb8LH/cOp1G1MqW82GOie0xtB01HQdQW2PI6UnEO6bITb0nv8FPviSBJ9Knvy/7rqWy5dJ/xf15g2xut8XKuKtyrV70IMWaZCfsqgry+8gVoNha6zAS1La90bcrGNa0ZoDzKCznLiA2bxf4bqYxbcZ5NE1oy0PkmXQq300lxAdUp80q9dj/ot0TwxR6HvGRhTHn+N9FVZ0aiY332KFqzOL0uKXHWgMlWrSSihjuNA1xpHOBKPT/npwpuyuJeZqGlpN+/kd9j9jajVLLBv/ljd80s1FOChmS7mgQVRIr7+28MdvRGE59sucZKs2fi1K6h1vcV0lkoVB8sw9nb9wnVTy5gU+gL9D5XJnA7uYBlDqPZddmeNZV8hApM1PQWOjkudhVLUT7Otnzety65xXpeXnqGyDJdwDuvJqOQYGrXJxOj/W/C/wU7/zDGt6/BdLOuTkKWiFRaBLlhq1ZSbDBaVquLhzXG1U7N4J9O4WyrZsXo5gz9Wawij0WLYMc59xbmag96Gw94RllGLymbL22WM1maRk6RnvpSNMNsjtPcIYVqeZEAVHOzhRTwl1IYpxSlpYPVJrGxR6cKgm8ztlyzePuAKDP8buhqCXYcpYpBXyvlNeYofxHv11SXXOzxk9Iq7Ced+hHJ2Y8htq4YEQPbe+rVT1SyeijMqfjKKtR6WYlKYc4ayY+44Y1fsXaDnPiBKnsEyfqmIojFxd143WYnNUyxYhDuOeeRE83OK0mWQOe9HmGMa2fOjOUkwl8jIeEMNkCa7MR2+350HPYe1Zy18Gs3yLiNeuVA5j+/gmErb7P2XAJ1qjozvEUAp+5k8uLPVimAroqzfK76FYAj3iN4cdg3eDg8ZgKUJOjzA8yrSxPFLe7YDBPPP9hB7RoIAa2EkJvajuKSImxOiSzYHJsJrLgVyhn1cTSSkf6F73M7OqPcy6u52dEq2J0aVRwI8XIk3MdJBDYP4u5RuGJO8DcpT4RPzC5ic6QoFUzsEMyKNSsZnniYElnNSO13/PzSi6BVCQ+ozBihk3N6CaRc4XndOp7TbqDgSjfYstNyzDNOXclJiGKa+hJdQhwJdlUinSuCk4XCJ0k2WQMUfSHMCix3TVGmAMZXvYntmflg64pk58HX3RpwMzmPmyl5TPjzAqtea4FGpYXe82Bhc3Gc9u9B+m3RyXZmichgdPyI7vUGs7/nJ+h2dSOs8DxhhedJ9A4nPyWGewt6MYeLlFL9zslhNOo/FUXdgY8v16TdghPfw6U1Fo5UocqVFYb2LC9pT0KxNRMc4G5Hh1BPOoZ50qy6WwXBuqeFySTz5+k4vtp5g0KdETd7DcOaPwF5uDhXBJogyoGPgaf5+5QtmxdVRdl/84ofD1mWeX3lBXZfS0GS4IOetRjV+gH9olJz0s4zwcFTNCvkJlLl0o8ctbGlrFfyzK1R9FFUbNoA6KU8zRlzNrayrt7S6xm7/Hy5QKcUb3QMoZbPf65r+VnhmQQ72dnZuLi4PItD/X+HdjWtg4LJLO7k52rHmQ86YTLBzK3X2HAxkZi0fMa2DSLQ3Y7YjEKGLz3DlcTyfixJsjXzMsf4daV84oehlLD8MHTgLJfkFzBqzVkCGSiTlKiWc47Yr3qSuXQg2gQ9x4y1+eh2Dbh9lJcjAvjguVpoVUpMJpmjt0WQ4kYu8z230lJ/GkXR4y0JGpvT3wc07UEvM0X1V+U77hY160cNqZdN1YkyBdBFeR536cHsypPBKEtk4ESa0ZmA0AY4dphMw/k3Oa6dhP2DSsjnlwlRO6UWzoryGk1H4xLxMccXneVyfhDbbGdge++UKEn0WVApn+B+dpFFdHJsuyBroHPnEKwbBYXp5GHHLP1gskJeYO6wFtYy1fCNIuBJv0nLs5N4r/sPfLnzFh9vvlZORRcgQErmW/VilJKM3GgEbXt//3B+g0EndIGSrwgeTvS+h9+03t9DUHsK7Pw4E5vJieh0jl/PICoplymqvkxSbWJM7gLOyG+jkYyYUDCsa0vsHF0AeGfdZVQKiUNvV6IH8yD0xbBtsvh/k1fBr3xb9cKD0RY/qDdXX2RK4V+gggsuXVg4ZjAOpfwPhQI8QsSj4TDyr2zn6rrPaaG4jvPdneWO2TxtHc1Lo+InV+63oK3yiriPZQTV7YAtHrWZYtOL7XH1eGtNJHNeqI+tSzXRTbTvE9j/KXT9Qsg8bJ8qOiE3jYPYY3Tqu5C4mKEERP9Bv+Tv8bJtxyDtemzQUSKrWGnsxJ/GTkTLftyp21MQeB6Ge2fg2DwodWkHkp3qMTe7HZvym6BDjb+bLQOru1Pf34VWNdyfacdOsd7I2D/Oc/iWGEOaVXdj9oB6OFeSoaiAyJWiROkRCn5NH7u7l5lDmaI3l2se5Jg9Q+y8mszuaylolAoWvtQIFzs1h26mWawuSL8tsoqSEuoPAUdv0aRwaxcc+w51YnnF91iboezn4Xw8W5OoJhgeICSXYk9UCifvZGCrVrJmbAv6LLB6NY5vX6PS1/y346mDnVmzZhEYGMjgwYMBGDRoEOvXr8fb25sdO3ZQv/7jI+b/gxW+Lta6Z1nn6VKmu51WTFTf7L6JySQTmyG+pJVF3MoyqRxb6cl0cUrhJBVhkBXkOQTi+gh/FeUjzPKY6WKxZ6zn58LryizWREucPRXH69dP8arzORT5KbyXm02qypWuynNUyTUHbBpHZP9mSKWr3kfgLWkl72h/wkGqfFXyJKinuPtEpSwAnKtRMnA5M3/dgLc+nhcDC3DJuoK6IBlvsvCWsuB2LNzeRKS5bq5TO6HR55Y/Tmkbbyl0BXhd/YkNtXI4ePEGcQY3whQFcHEFIEGveeW0QYwmmSlrI0V2zc+Zt7uGim6tA58JMigQowzilcI3sPMKZv2LzcvzcVz8RcDzc0eIP0nf8PN8SUXZ9+F1bfk0dxlSShFUi0B6bq4IdArSBS8j8w7kp0FBKuTeh7Sbj231/t7Qj+8ML/B6ejCXIlM4dSeqQufHDpeX6GOKIbDwCr/Vj4LkABTZcYzwS4aQuhTpjHy0+SrFehPXk3Op7fsYX56j3woFZQcvURYsgzVn4y3lAoD83GyesxFWKxH9JsLD7BIkCX2NrgzRSdSWYtmufd+yaY+xMUVoCfP3JNTPS3RIqe3M/9qKzM7ejyoc8o57e75PCsdVWczYpq74aIpF63dRllDLTrmKNv0aC7nGAE1DJl+ZwID0ApYMb4x/xETh+ZV4Dra8Lngovb8XNhj7P4XIFdBiPAH9ZsA3fxCsuE9wiRAYPC3XZrruFe7IvgB4OGgrDyCLcyD2OJz4QbS9m5Hh15mZmV3YkuoPQG1fJ97oGEzXcO/HB6J/A7Is8/Zflzh8Kw1btZJ3u4fyckTgk5/rotkYtdlrT9Q+3zTQFbVSYmVBE3po9onfb+hz5TTDnhVKM7Xj29cgqIo9nb4VpdRVr7UQXKRzIsNKcGcR6IAQbq3VG8J6Ybyxg8y1b1BFtmZAO/Fw66Cg7YNQMB29UbZ0+pbi4M1Uxv4hgqdXWwdSz8+l3Gv/1czcP4WnDnYWL17Mn38KNdq9e/eyd+9edu7cydq1a3nnnXfYs+fRbtv/h4ejlLsjyzKzd99k0QMt5qVaEg/DTfkJ69aVYLz2K07luDKvXyNCEtbje66SLq6ngFPScd7mOG+XkuaKzQ+okHKRq7dDCuuFZCgS0vpnKidWl8LFkP5UWavHYaxuMk5SId+oH3LenHiytn3M6eLeFDt3YOKrHVAqJO7F3eXtZbtw1KXxufpXEfSYUSHQqQxmQ1M/YPiD48fFPwSRte9CZFnm6O10Zu++wdXEXOw0Sr4f0hD12SWijdasMhvtP5DnbvfCzs6eP19uUnlnSpVQkmq/hs/F78jbMQMlsy1KqrZqJQdfcsV7x6siM2DrBj2/EaTpq+tF9uhvWi6M1uzjnCmUBWX8L6u62NI62IOWwe60rOEhylD3q8BP7bG7tRlqdhc8iT0fQdXG2Nq50TakCnuiUth9NfnRwU7qDSEaB0IssUzb9Jm7mby73mrJ0rO2Jx8XzcLhfqEor1V7dPeRg40KR62KtgZhMJsmO9FN/y3t69fktbZBhD6Y4i/OhYNfiBJTKSQFdJ7JPuf+vPbnJWQZvu5bF59m1aiAwkxBPj71Ix25yF6bd/kh9XkG/JDLopERNH5lh/jNHP5GZIX+HADNx4lAS18Ai1tVOORfhrbMNgzGS8qineISDhThWFSI8VgUSl2eID1nxIhgscCqkWCS1Nz07skq5fMsjxY/bnd7DdN71qJ/w6r/liCnFCdjMth2OQlJgqUjm9CyxsM5bZUiz9z48YTdZS52GjqFebHrWj1Oew2mecoa2DReWGQ4+T7l1T8cidlFFp/EzrW8LIGOuAa1UFU/ZeZfNh5R4fUGk8zUS75sLvqBZspbrFXPeOw57TOv8bZmPbPzXyAmLZ9gT7HouRCfZRG6tVErGN8+uJydSM+6f89W6L8BTx3sJCcn4+8vIvlt27YxaNAgunbtSmBgIM2bP5709X8oj22XraS+UouSrZeTKgQ6T4KPVCue+jUlow6h8K2P29rjHL0xEIdNxZi0TmDrKlaWD6BPyWdclmtwbYQa+7+GPnZF/ySQ7h6Gu4cr3bZR2Y0a+lsVsjBz9QOJ6D6EiP0DAZikm8h8zcJKj5EgezBWNwUHimilvMIksyP8QkMfjprq4dugCyGXhYriKkMHTFpnXjJuKncM75Qj7NYc47Jjb5Q38sFQgn/ieX53Pwzpt7F5AhPNTOdw5PpDsa3RErv8e0KVNuOO8OOxc+fs7URKEi5ZjUQjV5BSYyBTTmo5fkd8FvYaJXNeqE9g5jHYJTp6qN6WzFYf8dLaXEoo4ZWm1SxClKW4m17Atkv32Xr5PvdTanNE60ANRRL9lUf5y9gegOltXPHe8qJQ+wXRCr+kbXkukk8DqNZCEGKLsoRI3INQqMDZn1xbP9bH29FQcZsG3OE39SxmGl5mubEb+6a0o0YV+3IrSkDYD3jVhZQrIkuReB7SrgtNmZe30LW2N3uiUjganc6UrpU7NmMyifKVSS8CpvDnLffg2z03y3U77nyzDbVu/giH9oJSIzySHsNXMZpknEqSmKTdAMAq5zFse7V3uSytBXkp8OdAkWkphX9zCl/cwKy9sfy+VbSvD2nqz5DKAh0AOzdBOK7TH1YPwysnns/Vy5ho2szhpQ1RN+9KvdBagreze7p4TdnAqhK8oDpSqQ0GD6lApsgubDS25ldDD1LvCrK1QoJhLQKY2iX0ycpI/yJS88Sk62yrpkX1J+i8KgtZtlpdqB4vp1GKgY392HUtmddTn+e09w0UyZfE727Q8se/+AlgMslMXRtp+bv3AiuJ39VOTa3807DZrPwe8TqE9iz3er3RxOQ1kWy/nIRKITGtQ1V4wj6ACYqN7JEaciKmtiXYsS9DFm8c4MrVxBw+3my1YilHtP8fhqcOdlxdXbl37x7+/v7s2rWLzz8X7HBZljEa/wGjvf/BuJ9dxOsrrToUpd5Xe6Mq8cN5DCYoN9NOefnxO5aB3tGPOgvj8ZIu8YpyFw4qkXpRlIishKxQARJSmYBmnWYGr+qnUfv3upyaEoW3i70gbe6fKXZwDxHKrkmRYpUZvR88a1Hs35omW9zQYCBMEc/LTbzZdu42gVIyY2sbcZSKRYnkAfQz7iam0bsQWT7TNEW9jmOu75Mt2+MiFTw00Ikx+TBAN4NsHPGT0izaO9dN1fjGMAQbtYKvOwRTdFWs9E+aanOyIJyBDvvRGspzeVSSiUZpm2Gt1bTRBipkmSJNQTRQVCwF7srwZt4eV1JJpmmgJ+1D69ChuSe1fByRJImG7UwMWXwMTcrbltZ9r/V9mW4KZK7qRQKb9WZix2CK02IpWjEKW+BMlQHMKhjF+V+sZG3Z3PabmF3E9sv32XopqRy/S6N0YL/HcF7IWMRk1Xq2GiMoRkPzc1NAV4YIXLqi96wtOnTq9BeZt/RowUE6bW4h1ziI1vhqEeLhUROUKk5HpTBz+Tk06PlCtZQXVEeYrFrPcmM3jt1OI9jzIVwOjxAR7GidYNgGkZlIOAvR+2heXXgYXU3MoVhvrJhS1xeLsl78SWFU2XMOhXojX++8wZ+n48uZHnar7UWt3ONC8weg13fg/2guhyzLTFp1kU/Uy7GVdJwy1cK5+bDKA52MGPi9T3kzxqAOnGvzC28vPGspSQ9tXo1Peoc/8ryAINW+fhYuLMd09Ft88pMZIu2Hs/vh8a4zj8QpUy3yZFshX6F1IsXgQJTOk7uyD3dlb/Kxw91eQzVfOyLc7Ahws6NbHe/HlxKfITrV8sRBqyK7UM+VxJyn82bKTxXBjqQA5yfPgHcI88TfzZZ7mUVct2tMbS7xrNLKOYV66n/68EpIHadCileOxUY2cFjbgXT3sfQymsqVpj/Zco3tl5NQKyUWDm1Ek1szn+oanlceR2fobfk71NuRkS0D+e1ELMejMzj+QGPAzeS/x2/8b8BTBzv9+/dn6NChhISEkJGRQY8eQoHy4sWLBAcHP+bV/4eyuJyQU+G5su6yT4oaUiLT1FZ9llO27WhRVHmmpCzUeQksUM+ni+I8CjMX57CxHpdqTyM/K4Xf49wxoERGooviHNNVqwhUpDBT9RtddbP5cGccv4xoCsfnWQ86dI2YEB9wyL6Xkkc+ItA4YapDX/96ZKSHsu1OBl5BdRhe1nfmzxesnQdAjTKBziFjfYsHVut1jR477kzVjycbR9QYWKCej6+UyR2TN5PMqszFehMuSUcJVMRTIqs4bqpNBs58XDTEotMDUCKrWGbsgbeUQd8AvVgdugdDUDt2X08n4spHOElFzJZf5pKhKn9qvqIYLS/xBS8atzBQeYShqgMMVB5mj6kpm+Nbsj7Ol3m7PXF3cqBfo6qMbBnIdy82ofPs6dy0GWk5dx1FLL8qvuLW9Z2cjGtEw/Qt2Ep5RJt8+SExBEcOUl9yJAMnsmRH9lxN5uCNVG6lWPVilAqJVsEe9K7nQ9NAN95ba0uE/Bd+Ujqb6p3C5sYmAnVlguxqESLACe4sPHdSrgnOQvxJuFdGo6hmD3hujmUCSc8vYeWhu2y5dJ/oVHF+HWqWG7vyguoIGht7KBGEzJGtKnHLLsiAODMvxNFLdEHJJhFQBbTE19YWO42SQp2RWyl55fkEcSdgyySrj1PnGdwsdmHir8ct1+Joo7JoWw0JATaazWObjYGGwx79ZQJWnY4n5OYSuqrPo5eVfKh/hTfK8ntkWShen/8drq4r91o54nXmml5kwU+nLV2WLzarRvPqbmy9lITeaMJBq8LXxRZHGxU6g8io+bvZ4WxrzpyobaD5GBSNhmO8vZ8TB7dhm3IeB4oIU9x79MX7N4eGw9CrHVCvf8XytF5Wst7Yhi3GlpSgAfPaxtNRS7+GVfm8rg81PB2shO1/CI42asJ9nThzN5PYjIKnC3ZumMVHXao9WUu9GUqFxLDmAXy18wYu98x8wvA+T37eR6DUWPNhaJq+ERtVMZGmIEbnvIJ+3RU+23GDljXciajhQbHOyMrT8UiScKvv6lMEf618spPbOENxDj2VpzluV37B8F6PsHLdst5ONrjYqbmRnIen05Pfu/82PPW397vvviMwMJB79+4xe/ZsHBzE6iwpKYkJEyY88wv834yyXjQ+zjYk5fw9wm2GXJ4j8CSBTilKlYujTAHckX2YZRjMvUgFYDUZtNMocajTn8+v2PALX1NDkcQy9WzevT6GXVf96F5sDtpUNiLQeQBnYzN5fWV5pdRp6y/zXD1xju/33aZDaBX8XO3EZFGqutro5Qqk3gfNPksRK3sTKCUTY/LBrv8P+GwS5a1N2o8JLF7Je6pVNFDEkC3bM1w3nUREF5wCE0nr3sNFAcuNXcnAmRERAWTkv8iBe7F0LN4LgFYyME61lZU2Q+DVH8EswhV3+wpNr7yBk1REhks9Lij7cDqxmMwqzXBLO8P64N2Yhm7BEHcCed9MNImn6aU8RS+z23uRrOHHgj6sPdS+TOlSwzFjbVorr/G9oT/2FDFcuZeaRZHULIq0BHjBivv8ofm6wr1IyPdgvbEN/soaqKs2pHXDOvSo64O7g5bT0alMXrIZ24IELmnC8OMYYbeWQJnKTZ5XUxyd/cW93/1BxVKlpBBBUJNRULMbeSUGjl9NYm9UKlsv37dM0iqFRLfa3my/koST2URVY+8COXAxPpsinRFbTZmBVpYFyTY/WWQIa/YQJSCAFuPB3oM/jt+lUGfE0UZFgFsZnaVj3wlzRwAHL/TdZrEytwFfLTxGsd5EFUctYd6OFh+62p42tLv8DhRnC3PLrl9U+r0qi+iUPPJ3fMg7aiF4OdswmBj8CPdxEpmDyD/FPcusmNUzDPqTXnucuJFc3qtu1Zl4Vp2Jr7D/g/Bw0FDF0QZZlpFlMMkyksmGBoaaNDIl0fUhCuRlUev2WIpuixJOkDSHrZoPsJdKUEtGvlH/xHuqVaw0duIPQxdScbWUja4n5WKSZWp6OT5eofjfDH9XO87czeSemd/4RIg5CDvNJd9GFTkvj0Op3YnBYBaA1Tyb7rKtZSgMZVHb1wmlSc+o/MNgANv2U5gs1Wb5yVhSckvYcSWZHVes7XqyDDXj18BfHz/xuYsbjsbm5Ld4Sdlo8hMo9f2SZZkPN10tt++IloHM2nUDpULipSdp8f8vxVN/c9VqNW+//XaF5996661nckH/P0GrUlr8SMa0DWLm1qi/dZxsHDlnqkkTxaMJzA9Dz5IvCaobQftQTyYYTczceo26VZ35qn89/FxtUSsVZBXqaHIhgSnGcXymXkZb5RVOKt8g6q/q1uyKe0iFY2+OTGTK2ksYTTI1qtgz54X69PtRrNwNRhO1fJy4npTLqN/OsXFiS+yybkFBmmjR7jmnYgfTQ3DaGEagKpkqUg6DDmn5xBhOhFLcz89UvzJcJcgIMa2+IfGAi+V1NaUEainiKZbVLDQIbsfM5+sgyzJR934kYd0L+OVGWvYfWrwa1pfAgF8xIpH21xQCpDzuakIIHLsF7arbyOhY6fk2E9KHoojeywdzF/BXZg0cbN6mJnfpY9hNI0U04Yo4bCUdU9XrmKpeR4LsQazJiyK0Ft7Om6oNfKIfgVaqaGRaihTZBVlS4yrlopVL8JPSrcKGqcBeNRxQo5MVNNQXsUkywkOajQAcU85CSpm6iMZRqDh7hYNXHVGycvLlTlo+3/x5gb1RKZYWbhCClyNaBtKplhfOtmoUqy5SdEV0d6hVSktgfz4ui9YhZUim17eKUqZSI1y91baiFApQuz8grFMAuoR7WXki55ZZAh1jg+Fs8BjLvO1pJGaLz79NiAdVXWxZfVZkPp5v4Ms37ttRHL8gVrgDl5WzWagMeoOBG7+OZYxCZF6/Mg7nZ2MPxrUJIOTmYjg0q1L+ms6jNkurfsas5cWU02pABDCB7vZoVArUSgVqpURukYHE7CIKdQa0KiUGk0x6fgnp+TrSzT56EibeUG7iVdVOXKSCJx7FZ6iW865hDAB3ZF/662ayVfOBxTzYXcrjDdUm3lBtYqxuMrtNzcqJN0qS0DdytdMgSSKY7RDmyajW1R+pwv0skWLWhbF9EiFCQwkc/FKQu5EFd6vV5Kc+Z2nr/Cl9CAGqRJHdrMSA9WlhydaVgZu9Bn9XO3o7XMf+UhY4eBPa7kVClSrGtg3iUkI2x25n8N0+61g/SHmQwNM/VzjWo2Bz8lsAVhvaE1/oRi/gdkoe3+y+yZ4yNIpAdzsO3RTl7Ocb+FZerv0fgr8VpsfExDBv3jyuXxe6J+Hh4UyePJmgoKBnenH/26FRKXipeQC/nYh9qkBnUsdgwnycWHAg2pIK1ZaRyH8qdP2CHS0nlntqQCM/NGXMAuMyCmj3zSEANpjaIjkH843jakyJFwiXyhCHU64IcqiZ4CnLMt/svonRJNOrng8z+9Rm33XrD2n3tRT+GNWMqWsvcTMlj/ofb+dWjR9E7FS9rRBxqwR6Wckd2YdQhZULMVglrs9JKsQt9SRf8yKblaLNtzTQocOHNG73EssD03j5V9GWWZoVU2OgCJGi/XRrFPuupxCfWYgDr/ObZnb5QPLaRnCqyq6S+jynO4NBVhAVMZeVB5Is+h9zzhuorm7Ec8ozBGSfwmAKIrtQzxn8uKR6jWoudqRmZTPCtIWOyovUk+7gJ6Xjp6yoNzRT/Xul96EUXlK24NWEvgwNXhJlpjuHhfJvurkt3KQX8c1jyn5XNA24W2RLrmMwL/XpgeRVR6T+yxCJMwt0zN9yjRWn4ixBTnUPe9rVrELv+j40quZajng8smUg4y4FYZIlFKlR9KxpZOllWHIkhpY13K0dPOnme1y7H/jUE+q8Jbkik+QuSuRDmvmz61oyO68k82U/M2fH3NV2NWgUY2/0IjFbZEqqOGp5vUMwgR72jDB/3jN6hzMiuAhpyTxxrl7fgetjVquyzO3lk+hVsh0TEj/aT2RJRkv615CZlvIOnDWX3ao2ESWO0z9BbgLp2mr0TJpEakL5jO38FxvSOMAVX2ebigTtSpBfYuBuWgHZuTk4Zl+n/t4XkZ6yK84kSwxWHaLYtxmOTYbQvngf8pVNaNIqP8407Qb2FzWyOMGbbwNxGYXEZVizKmdjs1h3PoGFQxv924Xm8or1nL4rMnMdQiu3r7EgJQo2jBFjEkD9ofDct3/L26pQJxYaFwljMIcg7uRTH6MyTOlSk/XnE8oRrzMLdOy6lkwd1R5QwZ6SWlzdH0OfBlUJ9nSgcYAbuUXWhc93bSV6n1lm+fu6yZ9s2dGy0HsUDhgb8J7hNTok53ExPoshP52ixGCyeF8B3M8uJjajEI1KwZQu//NUk8viqYOd3bt306dPHxo0aECrVqKl8fjx44SHh7N161a6dOnyzC/yfzM6hHmWq48+Dne/6oksw8dbrpar+VaXkh/xqkdgzwdQo4NYuZtRNtABePGn8kFHqnM90gcN5+UfdrJLN7L88T51FSvlOv2JvJdtUYUu0hlpM/sghbryg+vwpVYtiPdVfyIlnkXWOiP1/AbmN6hwuWccOtIs/wChUkKFbaVYrJ7HW3ZfWawzAFFia/cOUMbdGUjDWfiISfnUkO4TJQfy63ERwGlVClqFVueY22L8zgzAW8rC6FUPZcplOLmAUgesVcaOfLQ7j7IrdwkTLdR3wATNWnflcNP2FOtNIsPlaW9ZCZtMffjzdBwvbz5NsHSfIEUSNaUE2iiuUEtRsbzRu+RzrsmBjG9bnSkuh1FeWSuCmtRr4nHyR0yNR5LUdDp3ix2IvHOfTSeuUKzT0V5xiYmaHfjIDyHAuwbiM2ILg+eepDDTiJepCV1cvSybbyTnsvFiIitPx1t4Lx3DPHmnW+gjJ7pG1Vzw8QvkbGoozaUbjKtyjT/VYRy9nc7vJ2N5pZS7o1CV/1cyfw9l2RJstatZBU9HLal5JZyPyyJIuo9nwnmUwOTroSTKRVRx1DK+XQ2GNq9GbrGent8fBeCl5tUYGWqAP1+0dmqZM0aPQvGR7wmPF3IbC52nsjCjEUOV+/ksdQ0Kfb4oa/ScIwL0lYMhN4Fk3Omf8zapZXSMPn2+Ni9HBD72fOWQn4bDpZXUvb1XcIEeg0JZy5/GTmwztuCONpR+NRT0drlL0/PTABiROht2zH7scWrI8dy2GUG8VJXj+hB2yy2o0bw3ncK9KNQZkWWZlNxivt8fzZ20AgYuOsHcwQ3oVvvf15p8IiYDvVGmuof9o4UKb++FtSNE272du9AdqtX74fs/BqVjmMLOWdiu6Qr+9rEeROl4uGZMC+r7uxB5L5vLCdm0Ox4DJbC3MIS/DkTzw8FoetfzZXhEAPPMWZ3Rrfzpl/gWYA1+aj2Ot2VGhuzE+/pRgMS5uCxWHr/FYHkn0b49+OiFVlxNzOGddZfRmU2pdQaToBn8D8ZTBzvvvfceb731Fl9//XWF5999993/C3aeEhllNAyeBAsPRjNnT8VyVS52OPDknJ84VXUCDOaszE8d4PkFUG+Q+NtkBCTLKsjXxZb7ZfhER2+n0/P7YxTqbNkhN6On8gHxqnWvwO29RGqHWp7af0OkQqu62DKoiT9tanrQ/0erQFl3xRleUe0GYFzBayjnr6YyZ6+dWVWJlJ5jjKo8kfu+7IavJEocTlIhnxZ9QbTJl2CFuS5uKIYzPwtBMWBYi2qsOBUPSNyS/Wgu3aCmlECUHGg55qwB9egc7sWpmAzmnBjEHPUSFNmx7HMeQOec9dZrMjWjoZueoKrebLiagSzDZ71q4r5PrEIbNWoG7hV9vEAQdb/aeYNCHLhITRxrtGJzTDpf6mSeVxzje035u/CFeiljdFNYeETBQgIZ334JfiEFBOWeIfjuSqrkXEZxehG2p/5ghX40udjxguISHdSR1FQkggwGVBS5huKQd0doGzUbI0pIWbF4XFrCyJZ9+PFQDN/uuUmnME8kCd7feIVVZ6wDaS0fJz58rhatgh+vdSJJEq+0CmTHuuY0V9zA9cJCvuiwgql7Mvhq5w1aBXtQ08vRGuSYzIO3nbtQjJWNQtDQyQdJkmhbswrrzifw0i+nWKn5Eh+FgQPGBhQ7B/Nx6yCGNq9m6dKavuEK6fk6Qr0c+bheDvzSS/B0XAKgVCzxUbiyDpuDQpDwc/1L7E31ZZnqcyJUUYLI69cM+i8RWjq/dIK8JNJkJ17Svcd9rPdmbNugpwt0CjPNooArLTpKZSGr7ZH0FSddJUaKzDXKi80Po7r4B8RU3kFzzlSTP7WD6Go8Sg9TRZ6fhEyAnECAKoGhHCTq3Er2xw5l8MhJeDqJia93fV8m/HmBEzEZjP3jPD3qePNlv7q4PkyU8V/AqTvi91SW61gBl9cK0rlshOrtoP/Pguj+L6BUwLWdXZwIdqpWZq/59LiTVkB+iQFHrYomgW4oFRItgtxpEeQOV4yQBv1b1yMjxZMDN4Q/2pZLYjyTMDG54AchJqm2F4HdU8DgWYfAxBSyTI5cYSjcRPjhZP4OdtcIbeTHO+us3b2PvOf/Q/DUOb3r168zatSoCs+/+uqrREX9Pc7J/89wtXu6QeHBQEejUtCnvi/2nk8n4b2zuA7y62aJcWMJbHgNZjiLx6du8G1Ni86O3hzd//BiQ5a90hRvJxvS80so1BkpkAXh8a7TAwZ5l1Yy5MJQi0s5wCe9wzn2bgfe7BxCo2qu7JrcxrLtFdUuAJYYnmO3oRFe+sRKr/sT9R90UVQkY14wlU+xViXVGuiUYsfbcEV0yLSr6Wl5+pZJdBKFPrAqmrwmkgYz9zB6+TnWG9twzRSAVJJbLtABWKn5ko2FI5hzpzdr7GYzRbWWurlHrZP3jy3EfU0pb8lwIT6LyWsuUqgz4qBV0biaK2fuZmIyGnhFuZM5atHafd6lO4ulQeTKdtRT3GWT9mPqSIJLsehQDAv2XGPNmXj+zAjmnklkrdykfBZr5rFS8yVjVdtFoAMcMDViqaEbNpk3kAxFxKiC+bq4P6eCBedOPvot4+oL0bwbyXksOhzDggPRrDpzD4UEXcO9WDysEdveaP1EgU4petfzJcavH9dN1VAVpdH31jQi/G3QGUxWJeMHgx2FUpTQwOJWLcsy7uaJtL4UQ0vFNXSo0fT+lsPTOvJq6+qWQCfqfi6bzN5XPze6i3ZlPzMhuQmM3g/OVR990clXkTeNB2CZoRtGFOzSvEeEMgpZbQfdv4ZXdwkBw2U9IC+JaNmPfrrPaNSoOVXN/IbOtbyY1j3sie8Vt/fBjxGivf/BQCe4M2cGnKaP01o+0Vck22olA5NUm9is/RjV6R9Blye4dA2HQZdPhdAgkC/bsN1hIPGaELoYRcYoxuRDoay1nIfxJ2DIKuQmozAobQlXxPFG5lekzG3L8hW/sSXyHgU6I7+/2owxbYNQKiR2Xk2m94JjnI/LRLba0D8TnLkrFjPNH6axc20jbBwrAp16g+Gldf9yoAPiewRQRzaPvX4Pt2F4GtxIFscN9LBH+aAYY9VGAERoYvl1ZFN+frn8+DpWuQ2HG2vFYuApustK4ZV2gtWaz8t1fVqw5Q0uJWSXe2p6j1pPfY7/Njx1ZqdKlSpERkYSElKejBoZGYmnp+dDXvV/eBgCPcqv+J1sVOQWP5yMWooedbyJqOFOn/q+uNhpYNnTfeELZC3XNs2hzkN3SAOFmiKdkWvmH3sDfxf83ezYPbktLy09xdXEXC7JNXiBI1TPfSAA8amPbdIl/tR8wUu6D7gmB/Ld3lt4OGjpXV+oj4Z5O7F5Yivm779N0F0RmHR6YQI9/RvhcPJcOe2QDc1Wkx65nSEl66iuqFiGKe1ueizWj4bMO9Su97rlqZuyEMkMqaQ0VspJkVHwuWEYqzTWrp2vpFFMq5WF8sYWMBmQjDqaEUkzVSSc2VTx3ItaCjG+5xeS6tOO8SvOWywT8ksMXIxLp63iMu/b/kWIHCvO6xFK477TCHEKYtIv3fgo+0NqKJLYpv3wyd7vA+iouEDHMkucuBIHqlz4ntPY0kIFkqEY7ZKWNFJ9SmSJK9/tvoYSE3aY+OS5MAZHhJazsHhSqJQK5g6LYMz86fyqewe35EvMME1iojSJFkFmzypzhxvGMvyziIkiSD08m7ywgUzfdtciCjhEKeSYdaF9aN2sohv1byfuIssy8/0OUu2gMJElrJdY7WueICV/ejGSUcdpUxhV1Xm8IovMozGwLco+88GpqiBGn/gBkDkl1ee14jeoVd2PQA97/jqfgKejlnlDGlSczCqDrkCoRZ9bWnGbUkNSs+m8ey+CI3+Krr3XtTfK76NQQ40OZKUlYshK4IbJnw3avvTqNJxO4aK8ZGo6hrNfdqG5dIVPCr/kqLEOKqVYzOwOep/TtxJYqp2LKnqfIIf3XYTUay6qjh+ScWghtmcWUpfb1I1+k+TbrmwxtuSMW28Gdu1An4kiyxOfWciARScJ93FiSDN/+jasipPNvyY6aDCauG2WUmhQWct57DHx25ZN0HA49J7/t/g5laGUs+NoEMEWbs+Gm3raHLy5O2jKWTacupPBiRsuTAHOH9/FhNMRpORag14n8nlLu1kYPfs1hXtPOPY9IRJ6LudegjUb6O9mS5h3RVuZ/2l46lHrtddeY8yYMdy5c4eWLYXs9vHjx5k1axZTpkx55hf4vx3F+vIcltxiA4Oa+LH23MM5KSDSxz3rWtvD5azYJ5K6Wmdsy0DlEaaq13E7vqo1t+fTwGp0F/688BLSOpCfV2KZ8EuZ+M52ajZOaMXlhGz0huYU6Hpif/hTa+cMIHf4kBtrPqSW8Sa/OyzgFbsfuJJSwnvrL9Ml3Muy+q7v78LS3m7wgwiogsPqg9YO5PIaRPWy99MttysLlB35qfF9Wlz+4AnebWWQ4eAX+LiH4G7vQEaBjmhZrPBrPoIHBPBarzZQRgPMIagFykHm0p8ss//YMY7uWksr5zS6uGcJcq3KxtxKb17lFqTBykEs0g8nxSg0quwpYqJ6K0O1x4QVRpkFsZR+E37piBPwq6MvCoVV/fdBrDW0I1H2wNsvEOcqftSwyaWqKg/7u7uQUq5W+pqOykggstxzWkr43fiuWTGxDPaZHypb0DqC1kFMsCD4NVpHYcvgEiBUlgNalpPV93S04ePhPZj4Szrf8y2higS2aj4kIVkNdV617pt63XrORiOESnNWLIfmj2Vb/ghUColBDTx4PkoQRadG12dOsd7iJ1eK5Njr/KheSs90c5m1xUTo+pk1qHoUSvIxXt2AEmiuuAGyIMb/7vQao0d8LWwUlnax/GY2qnvyTt4Qani58kXfOpaOw2ndw55Mnybtlmizz46rsEnnHsZch7dZfNAOyEStlBjWIoB2hjC4dBpCugmfpBvbIOYArpMucjzdjg82XCE+s5CNy8/Tq54Pn/SujYzE8OK3eVO1nnHKrbRRWr8XE2LfoJ/ajTsmb2pK9yA/BVb0h04fQ5upuPf8iOIWrxK77XO84rbibcxijGo7o3N2sHdNY35yHcabnbpwPCadbZeSiErK5ePN15i//zYf9QqnT33fJyJkV4ZLCTnojCZs1UpLxqwcYg6KjKDaDrp+/swCHYASs5SCSWnOwldSVvw7aF7djZWn4zl0M42ZW6P4pHc4MWn5vPzrGZqZnEADdoYcUgrLn29JHy+0e4pEl2S7aeIzeoa4M68Hjh6h9Fd4sM0UQXKOhEr57O7nP4WnDnY++ugjHB0d+fbbb5k+XUiT+/r6MmPGDCZNmvRUx/rqq6/YsGEDN27cwNbWlpYtWzJr1ixCQ60y8MXFxUydOpXVq1dTUlJCt27d+PHHH/HysqYn4+PjGT9+PAcPHsTBwYERI0bw1VdfoVL9s5oQT4LcoortqoonGBAm/HmB2QPqMaipP8gyhdoq2PPoyRrAkUKMPo1QJl0gRFGmVNRwmOBuOPuJoOWHRhDeF4fnravMQp3BMqGolQoaB5TafnaF0M5wZa1IIwPSyhf4VPcB89Q/4qW/z+bGl2h9vAH3c4rZEnlfXHcpjs0V/4Z0ExOmLFdY3Qbf+pktmj3s8H2DP4pbctXQg9Gq8o7TTwMpei+96k3g95Nx1DZ3lGWVIZM+V9cHb2cblh4T22wppuah8jpSXRxjyxxQ4kqJF78Zu1MU5E+XgfXKn3DDGOExZcZHqhXckX05ZqrDJo9FhOSfK8sztEJtJwiwBako8kT2S6+y567eHRdycaTQYvoaKQez0tgJ4oA40OLKh6oVvKS6hgToFDbcDBxOeo2+uChLcDWk4ViSSnHmPbKzs1HJOkLvb3r8zTMUiUcZz6QKKHV2d/A2t62LRyP3EBa8PoBucz2Yq/6RtsorBB57mxSfYLyCWomgKSNaGGA6+5FVAoeqvsPzma/T27Cbcw616DN8Mo21iRBVTC727C6owYtxWbQPNWeWCzLQH/yaX/KWolEakSUFUvevofnYx783M67s/Y26ZXgQabITE3VvMjSiq+DSnF4M+kJkW1d+c5vCzJga+DjbsHh4Y95cfZH8EgP1/Jzp3/AxpTIQ/JyFDyg3K7UU13yO1fr2fBHljt4kxoS+DXyZ0iWUau52EDsYLv0ufq9TomB5H7h7BI7Pp9Vzc9g9uS3z9t/il6MiG3bwRioFOiOg5hvDEPYZG7PY4Re89NbyrY+UiQ+Z5a9l/6cQ2Bb8m2LjVpXAlxeBYR7c3ov+3HLUMbvppjxH55zzLN3YkyTf0fwyogkxafksPxnH3fQC3lwdybrzCXzcK5wQr6fPEvxsboHvWdencv+tiImC35R3X3i41e771Od4GEqDHVlpDrL0f9+AuCyeb1CVYr2R9zZc4bcTsVR1seVcXCY6g4l6PhrIgmqebkyqGcz8A9EAzB1Un4ha9mLRpcsT4p/O1SDn8VpNOlmF5hESFqVoq7wCWVfooIGXTXvpq/uMpcfuMrJl4JNlKP9L8dTRgCRJvPXWW7z11lvk5YlUl6Pj30txHT58mIkTJ9K0aVMMBgPvv/8+Xbt2JSoqCnt7Ud5566232L59O3/99RfOzs68/vrr9O/fn+PHheW80Wjkueeew9vbmxMnTpCUlMTLL7+MWq3myy+//FvX9Z/E7dT8Cs+V6mk8DtPWX+bjDedYX/sktdMuPv4FmEUEK0sO7KioncT9C+QVi2BMIYHdo7QtFAoLH6AUX6l+4ZjfawxInI3i6BxebbCOz48W8+HmqzjYqERmKu6EMLoDaCu6pcoq9O41NmK7sQUz1b9RWxFH7eS3OZ0UzkJTb0ZjDXaOGuuwzNidXzVznug+4BLAsFoBLD95l+FK0Zq+2tjBsrlAZ6BdzSqWYOct1Xr8dHdIk53ZYmzJKNVOasavBvkdC8k1Nl1Mjg+WJrMLdbyd9TKfmA7gr0gz30+ZGQ4b8AzPwD6yEkE41+rQdLQIQm1dxISYEQ02Lqjdg8lPyKGrOXswUrmLGerlfOG4jjF1q7LTpgcJ9+8zLHYGtYyCZ7DR2IpZxUNIjnKHqNKsmaP5YeV7jVHa8r56FRdNwbyo+wANeowoMZjNQh0UOqqodTgpiqEkFwViIpCQcaIQZymfMOkezRQ3qaWIQ5mfLEQCy7jZuwMnHezZWtKQGyZ/whT3kNa/Ssmbx9C6VofMGNJvneL7xDD+On+PYr0rJnUrBiiP8bHjFpQBM+GaINSma6tBsWQx0eXeWVg1BHWhMIs9JTWgxdgFwmfrCVCsN/LBnO/5pPgbS5t+lCmATdWms8D1Ip4Hu1vJoNXbMsv2LRZfEN06n/QO59Ot17ickIOrnZp5gxtUPjHrCiHhjPjuxx6HuAfMjDp+xGGn3ry+KdbS9da2ZhWmdQulTtUy9gylGSpDsfgORrwhgp3bu4E52GqUTO9Ri971fHlvw2WuJpZX7H2hb3/c64+GQ1/AqUWUphTXG1vTUXMDV2MZGYSlncU9bPO2yPyqtFCrF+pavSD1BiUHvkJ7YxNjVNvplnyW9357jaoNu7F6TAvWnr3HDwejOXo7nS7fHaFjmCeda3kR6G5HqLcj7g6PLsGfj8tk17VkJAnGtH1ICcnOTVxf3n0oeXaWBjlFehLM3y0l5iy88cnG5yfB4KbVyCs28Pn263yxw5rRPJUsgRZUBcmEels7HXvX94VMEfigUAuem53bEwU7ZQOdRNkduUo4funmDr/ANuyoM5ef1+8gRJHAbLN6fANFDEqMfLYtitbBHoT+Dy5n/e3UR2pqKjdvCv+esLAwqlR5jO5BJdi1a1e5v3/77Tc8PT05f/48bdu2JScnh6VLl7Jy5Uo6duwIwLJly6hVqxanTp2iRYsW7Nmzh6ioKPbt24eXlxcNGjTgs88+491332XGjBloNM++K+BZIbtQx3cPOJmHeDpUmu15EN2rFtE8ZS39lUdxjn4KNdGHIM41ggAXtVhRlyrANhlFWoH4YbvZax8d1affFjVzoLBmX3JuHiFQkYKr3W3wDIfUKF51u8T5Os3ZeTWZ11deYHenFEJOvivSzzV7CF8iWYatky2H/dLwEndlH5JdWrE0YA+2V1fS3BRFc42VDG+QFbyln4ir9BSDXO1+hFRxZGiVuwTmpZAr27LJaHWHPnQzjZY1BBGyR4DM6PR9YIRp+jGcMYUxSrUTRWaMKDu4BgLCaBJEh93yk7FE3c/lXFyWxaqgr7oG/lg9rKrrbkPkbes1KdTQ+RPBK3ENLN8pZOcGdlZiZClpEmClsROD1ceoVXyHwLOfMp5PLdtkGxfiOizAqGpIr6Rc4jILySnSk1ukJ8f8cLXTULeqMwoFXEvtiTFnDQ0V0TSpIpOu9LE4MgOUmDRkWLLqvng72RDiJb6zt7OLyC7UYzDzkOwoJlS6Rwv7ZF7wz6G6MRYpKxbyklAbCuivtE7ynqY0+M6a0X1pUxbxxpsYUTG4SiJ9C86ACZR1+okdzKrdSXoRYDtoVXBtk8gsGopJsanO1Nwh+DTsQYsnCHSScopYcfIuyqPf8o1qvcU+BaBaSB3ej3sLEsxaBj4NoN27FAd1ZfnnIlDuWdeb2btvcietABu1gqUjm1rbo0vyIP60CGriTkDihcoNdGv2oKTvEn48nsL3q8X3oravE+/3rKTrLf4UrDM3iviZ+Uql6uXZ8WA0WLhV4T5O2D7oIQYsPxlL4wBXQrt/Jdrw174Mxdk0lm4zuPh91o9rjuOKnlBiDo6Tr8BfIyD0Oeg8A6qYmwI8w9AO+R1u7ca4ZTIB+fdZpfmCNZeO88Gdvsx4bRC96vvy9c7r7IlK4cCNVA6YuzPVSol3u4cxqnX1SktcRpPMJ1sEsX9wE/+HT7ZGvRi7QBCUnwGK9UZe+/0c93OKCbUvxDHL3GBQtfEzOX4pRrWuzvHodA7etI4NpTxCbVEKX68/BojveWJmIYF7hX4Y1duIMSLdPIaobETg+xhcMQUSaFuMY2mgAyCbCHJTc1EO4aIxhOumALaaeYH9lUdxbPEKNb2ejXL0P4WnDnby8vKYMGECq1atwmQSqzqlUsngwYNZuHAhzs5/3xguJ0f8qNzcRHnk/Pnz6PV6OnfubNknLCyMatWqcfLkSVq0aMHJkyepW7duubJWt27dGD9+PNeuXaNhw4ptgiUlJZSUWOugubmP9ij5d2HWrhtkFJRfJXSq5cWfpyrW7bXoLMz5XNkW20wDatW/7jgOMF0/itXJndjQMYCGh0ZaN9ToQEaeuD4Ph8cEjfEnRS3bux7LvKZz/mowv2rm4Hx7o2UXRfQ+Frz4Gh+sKSDs+g+EHDMTYGr1gf4/if+f+lG4XJsxSbWBt/QTWTqhO/baXvyheo4m56aW05NQSSZmq5cgP4VBn7Eom5O302mUvQeUsMXYkqIHSCqrza3Wb9lsQ2EsIc2lAQeTG1BLsq6iZKOeO2n5HLyRyiWz19kvx8o7tAOMUW59NIna3lMYqAa2evg+Ziw/GcvHm8XA+1Lzapy+m0nv1BlMdj/D6wVlXMirhCENWUmgew0CH3vUUjSGXxpDwllWtM+HxgOQZZnXlp9n3/UUXOzUrB0bgQS42mvweGBVLssyRXojuUUGribmMGPrNRZlhbDoOgR52DO0eTUiAhzwL75F8dEf8IjfXS6wKMVu9duiFRbKCw+nXheifeYSmqQvwtNBQ+/8v2DLDLFPze6MTBzJdZOJpXUerftiNMn8fPQOS/ZcYq5iHh3UFe1IHGLMMgdVG0O7dyGkK0gSGdlFFp2UUvl+D3sNi3q60Chzl+DTJJyF1KjyzvEgyM0BrUTpFzC0msIqhxEsnHeOZLNK8PAWAXzUK7y87pXRAEe+gSOzxTFdAgRHBYR8AAjD0DIk8sVHYjgbm2X5+93uYfx89A43kvPoveAY73UPY2TLtihG7YU/BxCYHc9q6WPO315K+3FHYEFTkc3wqS86Cm9uF0rX4X2g9RTwbWC+791Qvn5amAKf/YXBqkMMLjpE/PxZuDQZxJK+E7nboxarz8ZzKzmPmLQC4jML+Xz7dVafvUerGu40D3KneXU3S7Znzdl7XE3MxdFGxdvdHuJyD4IonnoNtM4Q/K/Ln+QU6pmyNpIzsZk4alUsi0hFOiaDbyNw8X/8AZ4CkiRR18+lXLBTgC3pKi88DCl46u5xD/He129cxdT7u8TCqNtXgg+oLwBbN3hbqM/fP7kG9YnvqCJV9F7UyUqOVxlE3Qyz1lKzMXDmJ4g7TsjqtnyiiiAfW66YqvOFfigfqFfyjft26P7Z42Ua/svx1MHO6NGjuXjxItu3byciIgKAkydP8uabbzJ27FhWr179ty7EZDIxefJkWrVqRZ06okcoOTkZjUaDi4tLuX29vLxITk627FM20CndXrqtMnz11VfMnPl07rDPGsk5xRb5+q7hXhaJ7qqutmJwe4ADt0Mz3fJ/J6kIZLiqbcjsvK4s15R3BH8abDFGcMEUgq1cTP7RH8uYKM4En/qkJ4nV0oOTWgVozalWjQMbL6UQbWrEkWZLaHt9pkgtA8TsR/m5B18pVEgqkVK9Hz4a34HfiIF778dmaXe46tGDOuk76aM4wXRG89m2KNRKBX+cMtBIGsUG7YxypxdE2yfHbz/N5RvDIM5pRcvXBmMbPBy0vNcjjLf/EhPenfQCXDVGQhI3icuv8yYkS7RVCP2JKLsmvLw4jvT88tk5Vzs1WYVlA1GZ99WrHn1BE0+L7M1jsDky0RLojGkbxPQeYUSn5vPakr145Fwt/4seusaSdXoqBHexuIzTeASSJPH1gLp0+y6LjAIdkfeyGdSk8gFfkiTsNCrsNCq8nW2IqOHOsuN3+enIHe6kF/D59jLkY14mQOpKf+UxXlHuwkl6ggzlzR3iYUZLZRRHeRX1AfOCpWYP5Hbv4rV4PzUU2TS8fxvuZQg1Zl0+BLYR7u2O3sRlFDB17SXOxWXxlWo5HR7iu1baQVca5ACYTDIrT4tFiQY9LRVXmeh9ncbFp1FsTat4DJdqENBaBLMBrcTnIssYb+5EqctjxGlfjueLz9XH2YZp3UPp1/ABd+74U7DnQ/HZANQbAj2/ARsnURo7MV8833yc5SWpecXM3nXT8vfmia2o7+/CgMZVeXfdZQ7eTOPTbVEcvJnKnBfq4zV6P/cX9cW34BrNj42C0C3ieCfmC/uFsUeEBcONbRC1WTyCO0ObqYKQbuMklIrrvkDx4XkoYvZRjSQ49z1E/kT1pqOY3noSONZClmVWnI7n821RRKfmE52az+8nxT0N9nSgioOWk2Ztnd71fS2SAxUQc0CQ2AH6LXq8pMAjYDTJrD4bz5zdN8kq1KNRKfh5RBN8rwg/NII7P/oAfxM5heUXvXe/6on06/dwL4WPO3jw/AHxvC7uPKjBFNYLhWcYnFwoNgS0FCR1J18yar/KrWP7GFAmc1qKGYaRNMk0Ux5UttDlM6jVG3nrZJSZMRatM4AbJvNvPDdRcPBavvHM3/d/Ek8d7Gzbto3du3fTunVry3PdunXj559/pnv37n/7QiZOnMjVq1c5dqziB/SsMX369HKdY7m5ufj7P9to/VEwmmSmb7iMLEPdqs6E+ThZgp2PHjBhK8UOU3PeUGyy/D1SNw3bgPZ85LMCYv/+tfRRnqSP0ix/Xso3bTsNWk8GINs8aT+oqlwBZq2HkpIiolPzUSkkGnQcAB26Y1zWC2WqtVNLMuuovKSbzoDgYfQvzBBChGaF2M0Og3gz4XmuaA/hKBVRVUpn9VlrsDVJZc0WJVZpx4T73fhA+TvNFNZB/XF4QXmIncamOEjFpMguXJBDIL8Elwf8asYFpSPFFoGDN9H2jYBr2EoiEr2c60C6oQSNUmFRGgUYHhEo2uml+6xwXICvLvbxF7T8eei7CDxrPbRbqFhv5Oudot341VbVmd4jDAkISdnJPs17qEwZ5V9wfevfG6CCO8OhLwXR06gHpRoPBy2vtq7ON7tvsuFCwkODnQdhr1XxescQRraqzubIRDZfvM+d9ALSzWKacbI33xkGssjQmxs2VifuXNlWBPXAJVVdNvq/TysfEw2NV7BNPI59gjUFrzWUycze2ol0aye/KQEl8KDo8M0dyLvfJ9GlCZ+md+KcoR5tFJd5UXWw8jdQtTEM+qPcBHr6Tgafb7+O8f4lZqn20EN5WlxrKa9XqRXZDr+m4N9M6LI4WTsnc4r0bDsTz7HTZ1mky8MkS5zK98LbyYYJHWowuKl/ea+pe2dEgHHHfI1aJ2FzUXdgufdFYYYgq9YVHYJpeSU0+8LKlZo7qL7FKdzT0YZfRzZlxak4Pt9+3SwUepTfXmlGQa9VJKx6gWbcRF7eF2nwH4LTk3ZDNBAM+VPYMRz7Tji7R+8TjxodhXq6rQtUa4HN8NUkJKcyd8H3DFfsoqEhWgQlZ38B/+ZI3nUZ7lWbPmPqcTLHjVN3szh1J4MbyXmW4KcUK0/HszcqhZY13Ole25vudbytZa9Ti8S/TUZB2HP8XaTllTBp1UVLgBXi6cAX/erSrLoblA5f6n+PN1RpkAcwe0A98d4cRVayvkMOvs5VuZ9TRGuFuJAjma60B6uas721zCllxlQa6AB8qV5q7faUTYJCUL0t31ZbiD7ld1ylPFzIZ4jqEGFldcfO/vL/X7Dj7u5eaanK2dkZV1fXv3URr7/+Otu2bePIkSP4+VlXM97e3uh0OrKzs8tld1JSUvD29rbsc+ZMeQXflJQUy7bKoNVq0Wr/Gav6ghIDH2y8wsGbaWhVCr7sV5epf0VW2G9gYz/Wnbd2V+0xNuEN1SYAYkee5/5P+1ly95VKNWf+ZRyZLRzHXfxpZHb8PXIrjcTsosrbPsEiBJddIn5JTQPdcLJRE52qJD+lmAYPZECNKPEgh/37ttPv4HdIeUnolba8WfQaO9JbAJAge1BLukc1ZQb9OnbgelIe8rWN5ZzP19yvQn1u0kh5p1zL9uPgJBXRXXnWfJ4qlLJR31lXfnUfrhPBpzGwDR+aMyrRJjHxhSgSebVVdaZ1D2XsH+ctvljz999Gg54D2rep1LKsamPo+CFUbw9nlsDhWZB8GRa3EiaYbjXAt6EQFgvpavFuWnEqjqScYrHy71Id6dJqOLUQkq+gAopdazImaxje+nvMVv+M8fh8lC0mPFmrdVn4NhDqxYUZcP43i+p034ZVmbPnJqfuCNdpf7cnl4930Kp4qXmAxTW5oMRA7U+sq8hitGw3NuM5sxp3aaBzwRTMwPx3MV2X+e26hCPBjFbd5M2HjFwlspps7MmR7cnCEaO9N43Cw7Bxq4pJhuwL63HLjMQv+yxLVWcfPQI2HC6yFOZAPja9gFk7rmG4sZP3lbuI0JYRUXXwhlq9hC1BtYgKQm9Gk8zx6HTWnU9g97Vk9AYDo5T7RalOkvhhaGM6hXuXD3LSbsHu6SKQAEFGbfCSaDd2fiDrUxoI1ekPShUnYtIZ+rOV6N/A34X+jcq/RpIkhkcEElHDnddXXuRGch5DfjrJ/BcbMk2ezmLTlzQtuSW4PKUco5QokaXyCocBP0OH6SIbG7lSZFiWPw/DN1qylH7enjg2fZF+JyPoqLzMN1V24p59Ge4eFg/AGehu60b3ai2gSXPynIKJKnLh2zNFnEkUPyCtSkFaXgmbI++zOfI+jQNcmdmnNnUcC6z3J6K8x9/T4HxcJhP+vEBKbgn2GiXvdAtlWIsAa8u1xbrE9PCDPCM0DjTPoz71IWoz8r0z2OTZEmszFYASWcXMuNq0MckoSzk6CjV30ws4fDOV09E6Fj3Jibp/BVoHTCaZ3yNzyTP2ppF0i/maBRX3fcYCkf8EnjrY+fDDD5kyZQp//PGHJZhITk7mnXfe4aOPPnqqY8myzBtvvMHGjRs5dOgQ1atXL7e9cePGqNVq9u/fz4ABAwC4efMm8fHxlhJaREQEX3zxBampqRZRw7179+Lk5ER4ePjTvr1/K+IzChm57Ax30gtQSDB7YD00KgW3Usp3ZKkUElO61CwT7Mg8V4bvEagtZLXdt7gZKgY6ZSeMx2LiWd7dmcDaqAJ8yOSETRnpgN96wvBNNPCvQUSQOyfvZPDzkTvM6FO78mMZxWCYaw52Iszk3j1nrzNBspJwX9W9zcvaw7SXzworBHPlIlr2ZWzhW8TI1hV0sZ0vFN9jbD0V9jU9mbPnJrE288ud9mXVHjykXJDB6FkXZeoVDChoWvwjMhKvaXYzUbGh0ksubV0vNQMFHig/wem4XNqoYX2ktc06SSMm7BApgRXHb7Ht8n2LmR+AWjJywXk6Fdw7bF2hxzdiRV66Km0xXqxGt00RE5ZRJzhLadfh0kqxj2dt4j1aseRSE1wxsbjaSWwWjBc6KCDS0W2nYtPyTd5MzOeVX04wU/4d24JUzkdeoHGjB9qaHweFUmT3dr0ryibV20KVUKq62NI62IOjt9OZt+823w6q/3THLYOyCq2Nqrnw17iWKFMDMP3UHkUZ8m5dNxNLuzTgWkIWDpd+pV/hWpyxtoS/q5jCJcf2+GnykWyc0NraW0QHAdBBwA07vu5fjyVHYjh0vwZ+Uhofqf4QnYmVwdFH6EzVHwImI4XxkRw6sJOCO6d4n2v4a0RQK0tKthqascLYhZWT36xU6iImLZ/15xPYcCGR5Nxi1BjoqzzGZLutVDWJ61TUe4Ge9czf++IciN4Pt3YJwrWxRKjkNhgKbd9+eFky27wK96xFal5xuUCnRZAbq15rUfnrgGBPR/4aF8G4Fec5Hp3BuBXn0ShtGal7l03O3xJSXCaoS74CoWUy+G5Bwnuq6WhY3lfoDv3eG17ebMk2vP9cLbKL9GyOlGiSUo/9Q90I0sdAylVxvMQLUJRpKVE6As2BtUCG1pHr9s1oOnAq5001OXw7nT9OxnE+LoveC47xkcdhXpVNFHg1xdY16KktAWRZ5vcTsXy+/ToGk0ywpwOLhzUi2PMBInRZn7Z/AxxtVJbOu5MxGdSo4gDVhI6ddH0LBzRbLPseMjXgruzDiF/PsMJNzBF7E1WMm3sYo1kTrYJGVmU4PBt8GxKvDSWv2IAWHfM1C/CTHjAjVmpEmfJ/OJ462Fm0aBHR0dFUq1aNatWElHt8fDxarZa0tDSWLFli2ffChQuPPNbEiRNZuXIlmzdvxtHR0cKxcXZ2xtbWFmdnZ0aNGsWUKVNwc3PDycmJN954g4iICFq0ED/erl27Eh4ezvDhw5k9ezbJycl8+OGHTJw48R/L3jwM8w/cFjwQOzWLhjWmRZA7Gy5U1Map7evEZ9usA4wjRYxTbbPuELnSEujcMlW1WAEAjw10dqo64aOLpYEiBjaNp0OzX1kTdZWqD37Bs+OF1k63r5jQ4QVO3slg9dl43ugYXHmrqDmzk2Oe80tVTm2TrEFadI9VXNmn4ZW8Bty1GWZ9qSzxfMlnFGBree368S3Z9+kP4pov3WP3hc3E2lRMo3pIuaBQca/BVLZevMsErnDOFEq+0pkG/i406ToH1uy3dpRUglTZhQB3u3JuzqXQSmLi9ZSyLc+t+3AEpnnf4JSfwnztIj7KGw6UZjVlLrq8h0PRA1YVjV8RgnbaSrpJXKrBsHXCkywnQRBaEy+Izp2445B6jWqp1zir/klkAkpjR0cfkXVp/IplJd04wI11r7cj4Sd/QowxrN+0Dq1XzfIty0+CZmNEC3PMAWElMmofqDS83TWUo7fTWX8hgREtA6jn5/J0xwVSc4sZ98d5y9+tgz1Ep593HRQdpgtdFzPU2Xfo8FcdOti6iQmxDO6ogpj14SflnivUGTh8M428EgMf9wpn6bG7xGUU8uLP1u9hI+mWpRxQKfKSYO9HyEfnYMxOxM5YRE+wCHAatc4om75KpNdAJv0Zh4udGkWZ7FlOoZ7tV5JYd/4eF+KzLc8PsD3PR6oVuOhThPqtrStEvA6t3oTr2wQvIvaY1S4DoEYneG7O41V7TeYOJKWac2XIyH0b+PLd4AaPFfNztFHz68imTFl7ie2Xk9AbjYAt/XKmcDrwZ+yTzcHTwc+hwYsVM0vedWHkdqH1k3IVfntOBDyOIls1b3ADCkqM7LuewtJoR77oN9z6WoNOZDbjT4qSXdZdMf4U5+Au5dG6cD8s30/LKmG0bDqakW8O4Ks9cWy5dB+v7IughIUJQaz+Yh9tQzxoH+pJmxCPx7a0R6fm8c3um+y+JsbS5+r6MGtgvcpFIC3BzrPp9HoQg5r4W2QuPtx0FQetiufrNEBSaiq0uqvMLfDHotO5pDlLfQVsiNVgNMlEBLnTzTPrQZ3QypGfLDJxYaMZrszkM/VvFXa5Klfnbp1p9Gr04lO0f/x34qmDnb59+z6zky9aJJJt7du3L/f8smXLGDlyJADfffcdCoWCAQMGlBMVLIVSqWTbtm2MHz+eiIgI7O3tGTFiBJ9++in/bTgZI2rBnz5fR5i9AZfMJnNlcSkhx9LZA5CHrcXoUi8rKdJLlOYiygY6T4Iehv201n/PIcePUCWeo8v9RbQI6MXUJEEsX29swwBlGaLD7um0YToO/EK+3o7+i06wb0o71A8qapozO3nmRfmRW2lM++sSp/TWbF9wsx4cbmjk9qWTmHaoUMhiUC9Eiwv5lmAn8l42Nd7fwTXtBZBgpvp3Zqp/r/BeSpyD0Hb9hByfVoxYdJpNph9AAnXzUVzp2s2i0kyDF4UI3EOQKFepNNDpqjjLBOVmAPaZGlmev5ulI6jHLPhrJN2lU7TXnidZdsNOKhFBUVGFQxGtrcWlqzncz07GyVZNdQ97qnvY4+tia23pVyhFyco1AEKFunJiQjxLly7kY/mB66/aBPr8IDg+D0xkNb0c0bcZCoc+4yV2Mnp5VzZPbIWn05Ms98xQKOD5H2FRhHBVP/QVdP6E+v4u9G9YlQ0XE/l0axR/jYt4alXcP0/Hl7NEmX8gGjd7DSNbVYdWk8sFOxYUZQrDw+e+5cTNBFpe/5xCbUV7mq2X7pNXYiDA3Y6RLQMZ0NiP+jNF158GPR+qVvCyai8Asl0VpMJKyMQABWlIBWmWAfKCoi5uoS0JrN8OZVB70Niza6cgW7evWYXYjAL2X09l3/UUzsVlWVbYSoVEnyAl0+Sl+CTuFuahpajZXXzmnz3QVu4eIrInoT1FSexJ7m+p+vSej7jp/C6YTUjzig3sv55Kp1qej/2ctColPwxpiK+zDb8cu4ssQz521It9nVPNG1Hlkrk48l1taP2WKLV61RGaUAoFeIbByB0is5N2wxrwOPshSRKvtg5k3/UUNl1MZHrPWtagQqUR7fOlLfRmnLh2h29XbGKo9ij9VaeQ0m7AjrfxsZ/N/IbDmNE3GLddYnE3WbWenGJ7/ozszKbI+0gS9Kzjw3s9wizl1qwCHUdup+GgVbHhYiI7riQhyyKTPr1nLV5tFfjwe2Rv/q5d+UtwVzSVG/v+XXQM87QEOyB8+VaccmWNxgHlA0G+yRx1a9BTSxJcn6tyIF9392FI1s+ipPgIHDHWpUaz7lS98C2U5BJ4aS6fVeLmMdJ/F4duZ8JpmHltH73r+/JOt9BH6639F0OSn7VbmxmrVq2iT58+FnHA/2bk5ubi7OxMTk4OTk5Oj3/B30T3eUe4kZzHrAF1GdxUZMWem3/U4j31KHysWs6rql0sN3SxDNZ/F1NMk/mifz1sN71aYdtvhq6MVO2p8HyC7EHrEmsJaXATf6q529Eh1JNaPo5IpxfDrvfYa2zEa3ohUDhBuYlpatFaWxg+BLtBS0RQ9FMHSLkCNXuQnJyAd+4VfjT0YbZhSLlzxtoM5WEwejcgqtcW7qTn8+bqSBpI0WzSfkym5MJU/zV4Odvh6WSDt5MN9bP2UPvUo9OwfUs+JVIOtvzdXhHJT+pv0UhGVhva855B8FaqS8m0UVymnfIqnRTnH3a4CvhaP4TFxj4VnrfTKGkc4EpEDXeaBbpRp6qzJUgzmWSG/3yUSYnvCMuCylCtJby4SpBCy6IwE/mbYCTZSOuS7/HwC2H1mBbWAPBJEbUF1g4HJBi1B/ybkZRTRIc5hyjWm5jZpzYjWgY+8eEMRhOtZh0gJbeE7wbX50JcNn+cisPfzZaj0zpC7n2Y+wjTQQdvYuwbUCNlF7dUoYR8cLrcBDXi1zMcvpXGtO6hjGkTxLLjsczadQNXUxY/a+bQQHHnoYeWkZAeQvwy+TRC0Ww0usIc8tITic8uYdKNcO7JlRtNhnk7MrSuA0NiP0Rz7/iT3ZyI16HJq1a9nKdBwjnhug4USHbUK1qCEetn3bmWJ5/1rYOP85MRbCPvZTP693MWIjnAafeZeBVU0gSgcRBBT63eQqU6JwF+7yOE7hy8YchK8GuMLMu0mX2QhKwiAt3tWPhSI2r7PjzjaDTJPL/wGFcTcxlaz5kva1wTPmQ59yrdv8ipOvPDV3PoZhrXk8SYqlEqGNLMn+EtAlh/IZHFh2PKvaZruBeTO9ck3Pcx435hJixqJTpLm7wqCOLPGIHvbS/3tw0lFtJ+jsIFZ1O2dd/ilYRLsezQvo9RlmhZ8gPrPH7GP/8hHYUPg40z+U7BOKSWH8sm6iYx//NPWXkmvlzTTAN/FzaMb1m5WOY/hCedv/9twY6TkxORkZEEBT0b07R/J/5Twc6MLdf47UQsw1sE8FnfOhiMJsI+2mXxniqL2r5OXLufi1opoTfKbNB8TCNFNIsNvRmn2vpsLqhUY+EJcNznZUYl9KRYX5Gg500G67Qz8ZPSmaN/gQXGfrRUXGWlxqpg3bx4AW0a1+OF4nU0j5lPsdqFb4OXo0w8xXu5X5Isu9Ky5AfLqkWBiTtlSl0PQgQgYwCxwlmgnk9XpfjBfql/ESMKZBTYUkIb5RVaKK4/9FgP4rCxHq0VV1BKMjuNTVlvbEtHxUXaKi9XrGc/AgaUqDCSLTkzxvY71G5V8XW2JadIz930AuIyCst1cYFYZXat7cW0bmGsORuP//H3Gao6gEnjiGL4BtGJc2unKC/FnxIp7qqNBSnU5oGJ49ceEH+CT6Wx/FrU7olLGhWwcRxcWiW6tIYJx/dFh2KYtUsEYHNeqM/Axn6POoIFq8/E896GK7jZazg5vSPFepMl83LpRRPOW0ZV6j0kq+2Q9EWUZaHHmTyJGXqMjmEi4JBlmcaf7yOzQMeX/eqy6kw8VxJzUGBipeaLh34HDLKCbrpZ3JV9MKFAkgQ1w4l8XlQe5A31JhwqSdcZZYltpgiWGnpwXVGD5tU96FTLk05hXlRzs4UlbUV5piy0ToKzZV9FCMBFbRFlm0YjoM/8Cud4YpxcCLvfB2CnqRnjdZOZ80J9ou7n8sepWPRGGQ8HDfumtBPGwU8Ao0lm8eEYvtktApzSxYusUCPVHyL4NqnXy39eXnXF+7CvAisHiZKsygYGLYea3Zi27pLF98/JRsX68S0faR9xMT6Lfj+eQCFB1KfdsZGMcHW9KO9mRAtBvcIyv8lxx8G7DjeSc/nC3GVWGYa3CGBIM/9HBlsVcOeQKPsADN8ENTo8au+nxu2UPLp8d8Tyd1Pb+/wlv41BZYfKYM08XzQFc9OhGXVcDdRJXPvwA36SLXyzYg5YnkrDjSoP2IGY1A7k6mRcJMGFm2fozzzDAJ5vUJXm1d15f6O15Otoo+LsB52fftH0b8STzt//tnzUvymG+h+N+9liwEw0/2swyZUGOk42Kvo2qMq1+7nUrerMxfhM6kliRbrFGIGEibGq7RVe99R4SKBz1lSTMOkejpJ1gG/VsB5nRnTm/Q1XLARQe4r4ULXC0rabIHvwi7EnAJ8sGGMAAQAASURBVM8rTlhee85UkxTcuH7hGGGaX0CCDwqGsP5iPmrCGKN1wFvKopPiAuNUW2msKKMqjCit7ZObskg11/JcU8VNS7brQTxW0+YxaKe0TlA9lGfpoTxbbrtBVvCdYSCnTWGUoKGx4hYz1MsB2OAykudz/kApG1FhRHbyw2XoatZWouJrNMncSsnj1J0MTt3J4HxcNun5Jey4ksyOK8l0U5zhXc0BZCQUA38VbcwgygWt34Lkq6JkkHgeVgyEEVtBXaZU5d8U4k8wJiSf5VclNkXep38jP9rWfEq183bThKVH9D5IuwlVQhnXLoiU3GJ+OxHLtHWX0KoUFjf7yiDLMj8esk6cw5pXQ6tSolUpCfVyxC71Ag5bvhITp40LFGcjq2yJMlWjtukm5zXNaPL2n+L93hdcwCtydUoKrHWhxOwiMs0inaUDtL1GyffKH2ghVx7oxLi2YYbDR2Qk5mAyk9NLh65cHFhi7M0GYxveUG2kpiKBdNmZNNmZICmJdsrLPK88wfPKE2Tb+JMitcQQ64pzTALc2VbxZO3egxbjkG1cKDGYyC3Sk1v/HXKKDOQV6ym4nESotyM1qtg/WUAqy+jjzpB7/Cfcb68D4ICxAVP144gIchcBaGMY0syfkb+e4X5OMbN23eCLvnWfaGWuVEhM7BDMC439aPblfhwkwbjfqexAtSZfCB6Y0SC0uWKPwcEvRMb2l07QYoIICLa8IbhfW9+ESRf5uHdtWgV7sOx4LJH3shm57CwbJrTE6yEl1v/H3lmHR3F2bfw3a3FXLME9ENzdpTjF3aFoS1taalAoLaVIgWLF3aG4u0NwTwgWQpz46nx/PJvdbATp1/bt27f3dfUqO7azk5lnznPOfe67pNkqwSSL5wU7jShLB3ezbnRxCew2Z26Xt4SRVyjp783K/lU5ExrLijPhHLrzkoyhtlZRLya3K/vm65sVhetDuS7C4y7s2B8e7BTzc6GglyPh5pJ6L1lMasMcylE8yco5q6B4SIXUh5bmjhzxRYwof5psOUZZAx3sXFFoE3E33w73S49i1hXBh91xNYKncdYvGVC7EH1qFPxbBTrvgv/O4tt/CUKexDPr0AP8Xe35tn1ZrjwRxME8buLBzu2maROc1+L1IwiOCu7KAZSVwmmjPMslUwmGYA12pui706ucMwF33i5L8yZUUdiK5N1zqkyPPf7EbLWWt/JLUZyyG2OzXR/dJ+hRUUR6ThfVMctyZzsV8+VZtDSTp++YCrDNJHSa9KjYZqzDANVeFmt+Iid0HD6Vjgvr2CwronhBkZwcwAvXF6lzZNEmqrSDq6tz/a1njKWpqbyd6/qcoJJM9FXtx98Yx0lTEH2UooV6i7EOH0Y2RaG+TzulCPZGJPbkQ2UhcipMKBUSpfK4UiqPK/1qFUKWZW5FJNL651PYoWOiag0A8wxtSA4N4NPiWQ7gX1ZwIla8J7yWbm6BCj2s672F6qq/MZKuVQuw+twTtl99/u7Bjmdh0TF2d5fIILSZgyRJfPVeabQGI+suPGXMhqs426loUDI7j0aWZT7adJ0tZjJ+35oFGdWomGV9q7xJ9EyYjtKYDgWqiYVPzyP5lcax+mTY0pLKKceIODyfvKVaW4KdY6Zg2rqaSahGA8eOHqSR4hr+Ujy+UjxlnFOplX4MBzlnl+qwgl352X4o4U/iLXpSOSEad7409Mu2vLQhnMGqXTRTXMI9/SnuTzfksDdsrbSSJw6leB6TxsNltwmNSrbhLGVFfg8H6hX3oU4xHwK9HPFxscPFXkVMso7IV+m8TEznbkQCwde+omHqfrzM+80xtGODU28+rlvYUiYHweHqVjWAGQfvs+7CU6oU9MzWhv46+Lras3ZgNTYtFVy+RrojdJ+/gnoNWzCiQVGUvqUEd6x0O9Eqf2OT0NO5uws6LBbZnVdP4eISnGuOpG1wPuoU86HjL2d4FJPC0NWX2TqsZo4Bnvw2ehIOmcQ401/B9Y1QYziSJFGrqDe1inrzNC6VOj+ISdnph7HcfP7q3Un7YM2eKv6cV2eyNuO+kGkqnwFJZFoc9eX5Ub3wtfvaYGo+Eey8xj6iUPpqHvTxRLVCTFCXGFpwW+4IWHmgGQT7svlcaRnkLwxos8Bkktly5RlFfZ2pEPD75Gf+Cvwb7PyJ2HT5mUV7ZcMla50580AzokER5h0Nzbbv9hBb4nGMLB6ynEpYO4y1qKqLICDbGjPqjhcS8++ASNkDfykeoyzRK7YfMebykpuDGqe0F9kCHYB56jkUliLQSLaziZL625Q0x3WPZT9+MnQmD7HE4IYWDT8b2tHJ4RJu+lzIolkCnaxI8KqIe/cluXIdku8ewTnd2h2VJmssTuHvGuhkwEd6RS/VIXpxyLLsG31vAOIytbKfTS9IoxnH8Xa2Y0mfypTP75brrF2SJO6/FN4Ig5S7KaCIJkryYp6hLWnHQ6lTzDu7R1KecmIWfWyq0Osp0sBKVnU0vwbT4mhbJx+rzz3h0O2XGE3yu7sX1/hAvLyurbPovEiSxJR2QWj1JraGPGfi9psc+aierVYMsPNaBFuuPEOpkPimTRl6Vg+0rkx4wsDHH+EoJfNS8sEvOUqUdexcoekUCgXWIHGHJ66GONwuzQaltezXRHFZZASi7pC+vg894+7RM3OF5jU2QZ/pB7D2biMgIsf1Bb0c2TmyNq72aowmmfOPYm3auQGqVK/HeWNdLuiSKfbqDHnSHtA8wZpV3GKsw4f6oXBawtpCZ4VCAlcHNS72Klzt1aiVCm5HJPIsPo0155+w5nzO5o6FpBfMUP9CRcVDjLLEPqk29/1aUbj6exwvl9eqDZMJwxsUZe/NSG6/SLSapr4Divo5s81Um6bGS7RQXmSuaiatD3pzNjSWWV2Dxd/B2Qc6LhGihrvGQny4ICn7lRXBzqmZUKkv2Lng6aRhRb+qNJt1gpAnCZwNi6VmEe/XnoMpt2rBTVFaRVKKbqmQ1YI7lKlDztVBjYNaSZpejE3dF59j9cBq795NmPFMXd8gnjsnr9dv/5ZI1RnYFvLcYgLtwyvszZ2gjxP0jFHZZpefmHw4JlWlV75IpIgcuIM5lIKzwp1kDqVUpvkHl7h55QzfHfHAGJJzw8vN54nMPfKQZf2q2ixPSNVRa9oRUsy2KZ+2KMnQer+Dc/YX4N9g50/EsHpFWJtlwJrQoiSVAq3R7/hmJbMFO9eeviJJa535lc7jiloOgAQr+SxadkOFkSS/KkQ9cedq7AtydYTJX8U6ELwFjhrL862hJ4ftxmNCQemiBZnfuDSl87riGHkJ09IuOe5no7iZCwKll5YMjkmWeCT7c0suhOxfDp4efsPe2THG7ht+GjFavDlyQFjoPQpnCnSeB7Rh5IOK2ewmMuOZ7M0n+kGEmIpRR3GddsrTNFKGoCH32TjA5Ur7uevZkKCT1tJavLlvLiZZS7t5pyni48R75fPi6aTBQa00WysocdAo0RpMjNt4jTqK64zRbAcZfDv8QMfQ4qw+94Qhqy4zt3sF6pfIkj2pMkBo8sSHC62TfnvMGifml4NsokIBd5ztVCSmG7gbmfhuXAWAwBrCaiH8pHhptZoBgEIhMbVDEKdDY3iekMaac0/oX9uql5WiNTDV7OY8tnEx20An7Dhs7odjaiyPTH5E4YFf/F3R+dJziwjkAAcnV3gVh5MpGUwgu+ZHSnxGM+Ul9AcGYAw/jr0xnSTZgaeyL6UV2b3lMqOX7lNOmspRKo8rdYp5cy8yyTIpsVMpmNyurEUhOl1vZPW5x/xyTDyjGpWCEfWLMqReYdvMbGoJwW1KEB+jCrblvs94Wr3SY69SYqdW4OtiR1FfZ4r4OJPfwwFnO1W2wDdVZ+BsaCzH70dzMTyeqMR04lJ1yLIwzSzrnMwi/TR8TNEYlPbE1J9Oi1q9aPWG4FWpkPBxsYMXoivpXeHjbIervZqP0ofS0CuBPAkPGGu3g4lhvWk5+yQz3i9vvS+LN4Vhp2D7cKGdY87EkRor1I7rfQxAgJcjHSuJIPzXk49yDHYc1Ep8XeyIStJyLiyOJqWzEMLTX1ntQ/rtEfd/1C3Y1FdkldT2PHiZxLe775CmN+LhqMbfzYE7LxIZtPISu0bWEdflbVF9mAh04sJg22Dosfn/7RmVmK7n/QVnbQx3O5g7Yu+YAvhWvYwKCuF0/iigIwHvfcbSeYsZYNqCFJHzBPFZwY48tStGjXvTsq908mVUfGficWXz5ec071OZi04qjLx+4pe1EiHLMp9tu2EJdACm7b1LqTyu1HvX7PFfgH+DnT8RBTwd2Ta8Ju3nW/krrcrlybbdwl6VGJJJd+TGc1tNmNsvEvFs3AROWbM6102FWWJsSc/qneHJbdZH5mN8bp3Fa99/63N+ZPJjpH4kyThYJPtTnt/hVWpR1MubQcSlbMJdQ3RjySvF8GmP1tjlKcXexRNpkboz27HjZGdSsUeJCS8S0UgGikgvKMILeHOclA1f6vsQWKtlrvwDrcHI5Q1TyKDIm5p8S1T+Hty///qgqrZ2Dm4OalLT9Ow3VWW/qSqu+mTmV3iG55P9lE45n+N+6lubCWKzdUGVgdxp0pz2809bBrLQ6BRmHco+y89AdcVtFql/QiXrOaqowanwUjQq5cP9l8lceBRH/+UXmd21gi0/xskbeu+EZS0g5p6YTffcahWacyuASqmgUqAHx+9HczY09t2DHRAmmOEn4cpKwRkya63Yq5WMblScz7bdYO7RhzQr608+dwfiU3R8u/sOLxO1BHg6MrCO+S8hy6IcdvALUWr0L8fypLZ8kzJZdET12ip0WzKu66twy7/DVEVRFmpD4DURMKtDRQnxuLEcm4z1GKexCkg+MvllUxgfaP8jTZs1YXppf2KStXyy5bqlG7JGYS+mdgiikLfoIL3/MomRa0O4Z862FfZ2Yk63CjmXP/ZNENwUpR00/w7fyv2Z8Dtego4aFY1K+dGolPWlrjeaSNEacNUoUCxtAhHR4FUMVZ+d+LvmzpPKimK+zhy/H83OaxFMaFnqnbgXkiRR0t+VC+EG9uQfQ/uEEXRXH2OXey/OvdTRd9lFhtQrzCfNSorn0cFDdGGdmQOHvrFOtI5OERkRO+Gg3bdmIdaef8Lhu1FsufyMjlmI7pIk8V75vPx66hHbrz7PHuwoNaB2FL5nJgN0WAhbBsKdnZiWv2Bz0e+YcDAao0lGpZBY1q8qRX2daTfvNA+jkhm57gqrB1TLMRuWI+zdoMtqWNxQcNgeHBTB3e+EwWhiwpYb3I1MwlGjtBjLNlKKAPGoKZjhKutYago/Q/jPrfla8YLcFBS76T4n7/1YPlQvIkdxnHF32Pm5eG5OPMglm25GBlkfoGkZ22u//uJT9tyIRKmQWNW/Kt2XiHHx483XaFLaz2Lg2rSMP+9Xzp8t4/tX410FJ98agYGBqNU5NO//j6Gwj7PN595Ls4v+NSvjz8mPG3DlC9vczJB6hWkXLAazQRfz8bhAW8u6RsoQ1mmm0Py3Slx3HMZl+2F/yPn20X9KMo4U9nHG5CZmt/m0YTTaWAJ1hK3ibDIO1NXOZL+pCofcOmBXujl4BFKvrK0SdqTsQfX0n6moXURt7RxqaOdSXLuCyum/0Ef3CcsMzd7pHGW1Ez2Ywkpjs+yDXyZcfBRPZ90Oy2dFrZEU9nbke/XruU3VA114lWbL4UjEmZ4hJVmW8A6qwVUG4qBRsm9MXWZ3Dc62upC3k7ndXGR/Kkr3+VU9HQdJx2FjBQanDuPX0+H0W3aRu+ZWWpMMI9eFsOnSU9smAI9As4hbXqFxsrSZmH0CaMXLuk4xMXPefSMHrtPboFAdkd0x6oT6aiZ0rpyfEn4uxKXoqPvDUYavuUzNaUcsPJ2Jrcwv17R42DIADnwuAp3y3WDAAbqphXXALmN1JpwRBpbIssj+ZEJhw0NLoJOBCfoBTDT040f1AgrznBjZlen697MFOnsa7OGX8QPoUS2QlWfDaTP3FLciEnFzUPNDx3KsHVTNEuhsufyM934+xb2XSXg7a5jWIYj9Y+vmHOjIsrXjpcsqkWn7Ax2i1UoF7o4aFOd+FlkSOzfotdVarnxLZDwr8an6dy9jAj1riKzcxOte6P0roDCks7rsFfqYly88HsZHm65hyOgwlCQhmNhrq9BIysB1K7epqK8zoxsJMtpn227w04F7JKbbPnvtgoW69KHbL0nVZcmuqh0gqLP496VlULot9NyCUeOG4vlF6h3rRBn5IXWL+7BmYDWCzRnOBT0r4qRRci4sji923Hq3hhq/MqKTFeDIJDD9PguJQ7dfUm/6MXbfeIFKIVm0mVxIpaJZdb630lYGJFeuYias00xhhmYBeaW4bOvSsGPb9UjymnmjOoM499Do5Gzb9qwegIM5IO5YMb/l7wBCmuCrncI+56OmJahZ1JsjH9bD00nDy0Qtq8894erTBE4+iOGL7TdZcy7nkuxfiXcOdvr06cOJEyfeuN3Nmzf/UnPNvyvcHNSc/6yR5bM2h9ZtEFkgTycNvTKl+RceD8NgkvFxsePZKy31H3Rml9FW9l0pG3E15a4O/K44YTeWcPvu7EvsgHuiICrP0sy32aa19lsKpq+lbPqvPDHrjOwfU9ey3vHCbMu/TxrL0lT7A5FkrW1LxODGcVN5ouV3yzLEtl/H6fRCKBUSxfycc90u+lGmtt+28yHhCW7T/d6oMt3++Qycs7Q62KFjmmoR0zMCpdJtudvjIqnya1LgZ+eJNDvQNjgfe0fXoaiv9XwfxaTg6qCmfH53ghWhLNd8j5OkhcINqDFhFz/3rEaHCvlwtVdlI7SO33ydQhP2MHbDVU4/jBGDtXcxGLAfvIoKjsR5IQInPz3P0RvhFsfxkCcJnA/LYhr6tmg4Ufw/ZLXozDJDrVTwQydRdjKaZPbciCRNb6R0Hld+6VGRpmX8hc/P3KqCY6FQQYsfhPlpzANKJogx5Y6pAB6X53B7Zlvkb/2EIm8mGGQFp4xlmKK3ajDtM1bhc9Va7CU9500lGaYbw3i1tSXXJCkxjA+nZb1aqJUKpu27y/xjoZhk4aZ9aFw93q9SAEmSkGWZX46F8uGma2gNJuoV92Hv6Lp0rRqQXUgzA/HhkBIFCrWw1vgzYNDCse/Fv5t9K1S33xGFfJywMxv6Hrv3+hl9TnivXB7K53cjRWdis117AFS3t/JN27LM7hqMUiGxNeQ5YzZcRZ9ZUqFwfeifqWty9zibe+eDhkVpXMoPrcHEnCMPqT71MM1mnmDchqvEpegom88VO5UCrcFEbHIOJbii5vE1zkwHKFSXD5xncN+UDz8pgY3OM1jRzodqha1jUFFfF2a8H4wkwboLT5h9OPdsa46oPVbwyiJvwO1tb94+E2RZ5ufDDxi48pKlM3dml2C05sCjpuIWKkn8O6MDLgO/GN7jtyzvgGzHV+fuWTdIN46xG67ZdAFHJKTxIiE7wW31uSek6oyUL+DO5HZlLCXXqCShgK4zmGhcyo8hdUXGtrCPM+sHV6daIU/K5Xdjavsgi5fipF23Ofd7x5w/CO+ss9OuXTv27NlDYGAg/fr1o0+fPuTLl+/NO/6N8Vfo7Oy7GcnQ1Zcpn9+NHR/UznW7jRef8vEW60tarZTI4+ZgQypspLjMr5oZ2fYNM/lTWBFps+yQsQKNlSF/wC8QeCm7E2rKixEFRpQYUWCSlNQo4oNGo0Gd8gLpmTWYiCnZg9uRKYTGpGEwb2/5Txafh6h22bS5vwkXK35P5zMFKOnvwr4xub9cHk2vR6GUq+JD8ebCbygLqqu3sFU3KNssKE52ZoHhPfzqD6Zk3FH8by2miOIFJllihqEzXiVr8/7jr3E2ZJ89ZcOws+BdHJQqTCaZ4/ejWXE23PLC8eYVB+zG4yklIwfWQuqxGTTWAUtvNHHxURwnHsRw7F6UTW0/Mya2KkW94j4UdUpHWtsZIqx/dyEKOZuMvHYeN3v2jq7z1porNljXHe7thhKtoJtQa73/MsnS3pwZoVNbcvHGbdT7P6ZSqnBifmjKyyTlcJ46BZHXXsvMhNH4Gt6cbfpMGsXetNIWHtQRzTgKKyLZaKjH+6rjGGQF8+WOjFJssu7kURA+uARKkWVedCKUqXuEPlBmcc8MnAuLpesi0eY7pG5hPmle8vVt2rIsjDLv7BRqx/2z32N/CAw6+C6/IJ4OOWnhM70rpuy+zeKTj2gbnJfZXSu88/7nw2LpsugcLgot1+0HI5n0MPIKeBVh381IRq67gt4o06S0Hz92Ko+bY6bs/vmFsFfwdbB3F5wacwlIlmX23YzkxwP3CI22ep8V9HJkTrcKdFl4jjS9kZMfN8hmQCufX4S0dzwUbwHd15OUrqfcNwdwlNO4XGAO9tHXwLeMEMW0s50crTr3mC+230SSYMuwmlQM8LBked7Y/n/8B1GW8ygEQ09lO3ZuWHQilA17D9NbeQAPRzUNGzRBXbwRJaYL8b5vVb/SU3UY2aMQUrxVVZky7UlNTsTx8Ru4jXZuOdrjpHwaw8KTj5iTQ2BXOo8rt19kF7ed1LYMPaoFWjKB6XojfZZe4PyjOIr6OrNteE1c7HOv4IxcF8Jv16ycyV0ja/++LrjX4E8VFYyOjmbVqlWsWLGC27dv07hxYwYMGEDbtm3/K0tXf0Wwsy3kGWM3CIJx7xqBfNOmTI4P041nr3hv7qnXHkuFgYf2vW2WNddO464cgDevuPSWJa0k2QEXKQ2DrKCD7hv8pHgqKh4w7I8SLfwT0U33OdUbtmd042I5b6BLgak5p/nTZTX2kp6zxtJ0009kiupXeqjeTI6OkV05FfQdR6/cYrp6IRrJSKgpD2cDh1HzyQIKS+aH2jU/JGb3PLOsc/AARw+OaUvw1aNSjFdtoLXyPLdNgczIP4fe9ctQp6h3ri/ZmGQtWy4/47u9uagqA0VcZRa4r6RY1H6b5R/ph3DZsS6PkiRqFvFice/KOOXkBfQ6RN+D+dVFGarXNs5J5Rm88hKJ6QYKezsxqlExJmy9QZreSPs8sXwd9wlukgjWdxhrssNYEz8pnjJSOO2Up21mr9dMhQmT89Beaas6vMlQl/GGoTbLPlWttfWMywrf0jDoiCh1AFuvPGPcRvEMTmhRkiE5dI1kiCXWLOLF2teYZ1pwZaXQklGoYOAhYaHwZ2FDT7jzm7DUaPLN7zpExsu9RVl/fulZ6XcdY8iqS+y/9ZLNjt9R2XQDao6CJpNAkjhy9yVDV19BZzDh5aThs5al6FAxnxjrtMnwXZaJcbku0Ow7S1eT0SQTGp3M07hUvtxxy5L5yMDsrsE4alTEJGuJSdIy4+B9Vqq/o67yBjT4HOp9zE8H7zPn8AN8XOw4N6IkysUNROat1HvQeYVNlxbAuI1X2XrlOcV8nelfuxBTdt8hWWugZ/UAWgXlJbiAOw6aHPgm2iSYW1WoKgf3gHbzs29jxotXaTjOKYObMZY42RlPybZsZJJUrNQ35CdDZz5XrbGR7ngTtLKaNrrJ7Lf71LrQzlX83vyVRWccEnz6GOzdLOK2b4Pwaa0s/05I1dF5wVkeRCXjqFGya2TtbDSNzLgbmUjnX87aNNtcntj4jZ5l74q/TEH5ypUrLFu2jCVLluDs7EzPnj0ZPnw4xYrl8hL6G+KvCHbS9Ua+3X2bNeefIMuCj/Np85LZAh6twUiL2ScJyzS7yQk5WSncNRWgqPTckgJ9E77R96Kr+gQleMwjkx9b/Ebx0LUGUbdPvLZbKSvumApwzlSap7IvX6pX2axba2hINO4oMaLElOk/8bmcIozyr5Hwzw3nTKXwG3XYwrHIwLaQZ9w/sZHh8T/gkoPqbV3tTDxJYrvdlxhliZa67/igTj7eu9Ar27ZZ0V/3Ed8Xv4dPuAgG9xir8pF+KL+qf6SG8jbPZS8+cJzO1o87iBnZgS9Eu/Zb4itDP44ayxEju+Hv7UWvGoF0rJQf19fMnGRZ5osdN1mdS028sBTBEbuPsi2faerKXF0ryub3ZGnfKu8+AO0aB5d+xaBxoX3qF9ww5KdyoAeLe1fGw0nDugtP+HnrUc7Yj3rrQ8bZFWBasTWUevkb/aKnY5AVpDv645wWQVKh5jxqMI82863k8DGqzYxR5exoj3cJGHjQoovy4GUSrX8+hdZgYlCdQnzeqnSOu/X69TwnH8TQMsif+T3eEAykxsHs8qBNhMZfi9LGn4mbW2FzP+Gd9cHF38UL2nktglHrQggu4M72EbV+12nEp+jo8MsZKsfvtpZ1qwyC5tNAqeLy4zg+2XKDh1HihV61oCeT25WlhL+L0IR6lIUG4eAB1YYKHoyjVTMnJlnLlN132JZLOzQIlesQuyEoJRl55BUkryJM2HqddRee0rVKAaZ1LCfMRZe3Elyz0u2gwyJQWe/32GQtTWaesAhSZoWdSoFSIVEp0IPvO5bDYJT5ft9dZGR+qJSE84b2IvBvO99W5yozvn5NNiN/VaGTBaTIdqKU/Y4IN/lRMBNHLXXMAxzdzd1xP1cSatP1J0D9T5FlmY2XnvLJlteY4WYc1xzs3HmRSIf5Z0jTG3FzUPNLj4rUzCqDgdDcSUzXs/LsY34+8gC9UcbVXsUXrUvTsWL+P8Vm4i8Jdl68eMHKlStZtmwZz549o2PHjjx//pzjx4/zww8/MHbsn/zw/0H4q+wiADZcfGK5yfrWLMhX75XOFvDEp+iYvOs2W1/zkJeUnjBetYFG71iiemLyIUAhyidTyh+mV8FEvPcNwlErZNUXK95nW1owe+w+s+xjRIESE+P1g1EgU0dxg5qKm9lmJ1lhkBVU1c4njtyv6QbNpNx9n4AW2u/Yazch2/KLpuIEf3XewqNI0xmZu/0I5W98Z7GNyIxJ+l6sMTZCiyjbzFPPsnB3XhRqT55H2evuD0z58PFwwT0pFEy2pElThd4UP9uYHsrDfKNeQZLsQGfdV9yVAzj6UX1rEBZ9D25shnt7hbpsJhhlCaWU8+OXKtsRI7tyXypIQmBTarXsRR7/7J18Gbjx7BU/HbzH0Vy4GIOVv2VTljbIClYZm7DXqx/rRjd/N9KqPp30ZW2wjzhPpOzBD/nn8l3LQOyenYEX15AjQpCicmhlVdmDe6AoL3kUhOLNrKTlgJrQfy+mJY1RPLvIDH0n+rWsg+fB0WLfMu0peFmQUfNL0RzWfIidlIscQKYXj9ZgpP28M9x+kUjd4j4s71sl10E3o8yjUSlYN6galQI9c9wOgP2fC/E8vyAYcjxbxuAPR/or+LEEGNKg0zIo2+GdD/EwKonGP51Ao1Jwb3Lzd7cOMeNRTArt552ik24Hn6nXokCGYk1F5kTjiM5g4tdTomSSpjeiVEh0rpSfcUVf4ruzuwg8nP1F6SdWtFWjdhS2GTVGgLuV7znr0H1LB2NBL0fcHDX4OGs4dCeKgcrdTFSvwWTnhmLCEwxGE61/PsXdyCQmtS1D7xoFxUFubRddWia94FV1XWdTdnocm8LkXbc5dCfKsqxigDsRCelEJlozj2qlhCRJFmIvwJ7ypyl9b57gbPXYlLOycm7BTuH60Phr0tb2xiE55wnLUkPzHJXic8MxY3n2V5jLdx3Mpc6MIFlpB2Ouk27vw8xD91l4/M2TzIW9KtGsjD9dFp7l/KM47NUKlvWtSo0i2fWFVp97zMRMPloAjUv5MaV92VwVsv8I/GnBjl6vZ+fOnSxbtowDBw5Qrlw5Bg4cSPfu3S1ftG3bNvr37098fPz/71f8Rfgrgx2AZacf8c1v4kWwtG9li7dPZpx8EE2vX19PpAWYUMlE5yeT8UzKwaAvB6wxNKKz8hgayUi8Y0HcilRFARhvbkH5Gh2eUwQTUmcJBTwdGbPhKhImSkuPqaO4QS3FTeoob+a43yJDK06bynLeVJJ0bLMHLqRyw34gALW1s/latdyGX1Q2fQnJOHLC7SsCtLZ15sumYlT85iKPYlJYdzYMuyuLGCZvzHFW1E83nqOmCniQSDXFXWoobtHT7qRQ7M0F0wvMY94Ddya2Ks3AIomwqJ5lnc7OA80H5/hh11WG3u2Hq5TK5/r+rDE2BuCHjuV4v0p2cn54xEvub5lM3Zj1FsGwt4UBJfqAOjiUawdl2onZcA6ITtLiYq/CXq3kZWI6H2++btGQcSCdVsrz2ZRYo2VXntT4lkrN+7z1+TyMSmbksiPMTvmU4orcg3KAe6b82NUfR8EKjcAtQDhkZ8ajk7CitdCCqjEczgiPtGrpP9O9UVXG5r0Fm/oBMlXS5xONO5NUy15viPvxI0uWYOqeOyw6EYank4Z9o+u81v1dbzQxaOUljt2LxlGjZFGvytQuloPYXcITMWM26oQmUNHGr70GfxgyAiyAfvuE/tE7YOOlp3y8+ToFvRw5Nv7/Z3dwLiyWXr+ep6F8gbn281GbtILH9f5KUIrS6LP4VCb9dpsDt0XWQa2U+Kx4BH2ffI5kTBfdeMWaCO2mSPNkQKESQqh1PwaFgi+232TVucf0qBbAlPZWSYJDmxfS4MYnKCWZ2Mpj8Wr9NacexNDzV5H9Wzuomq12T9gxWN9DtKkH1oLuG20CHr3RRLHP91o+F/dzZtOQmoTGJHM2NJb1F5/wNC57tljCRFilLUi3tglT1H57c+RUXbl8noq/ZWpTV6hJ67YV5caeaPR/XJPJZH0Pdjp24OLnjZFlGUmWYVoA6JJYWXYpP950fq2Cd1bUKOzFWTO5eP+YuiJDh8jiRLxK4+SDGPbceGHjQ1bcz5kRDYrSpnze3x1Qvy3e9v39zt1YefLkYdCgQQQGBnLhwgUuXbrE0KFDbb6kQYMGuLu7/64T/19Av1qFLHo74zdd5+bz7Df6y8ScU5kejrYljT1RntSI/4b3tV8QZvJ/43e3VZ5mlH4k0bIbHqnhKG5shBsbcwx0zhqtqf6gxr0Y2agYrg5iEJNRcEsuxOUCfVhvapRt3wwMVu1mheZ7rtkNYq36W4Yrt1NOCkWBiTQ0xMlisAmUIjlqsuU7aJWO3Pi6KQGVWmQ7biXFAwpN2MPnM+fT4VIPPmJVrunfST5HuZNnEiH2Q1mgmUUf1cFsgU4n7Zc2n+0LVgMkjl2+gXzoK8tyg6xAo42HGSUYe687rlIqMa5lMAZbOVQH79i2PJtMMt/8dotGcy8z+HkLmuhs27ZtoHaEMu2hwxLkvrt5EjSSR4oAVBhxeHIMdo2BGaVgxwh4fiXb7j4udhb9FD9Xe1b0r8qDKS3Y+UEtggrl5bxbc9ppJ9nuIyVS4dxouLw89/PKhHNhsXT85Qx34pV86/SpzbpHrlVZI1ulBGYZOtBM9wNTnpUXmZysgQ5YSwqyUbhaA88LtCYaD9EmX6Y9KR4lAaiquIs3r2wCnTiVH2sNWV7c5kDn9MMYFp0QM9jvO5Z7baADoqtsfo+K1CnmTarOSP/lF3nwMgdC+KmZItApVBeK5H7//+GoORKLeMqK90D/9sR+wBL4tnmNj9nbonphL77rUI79pir0SPsYo0IjiOt7x1vEWfJ7OLKod2U2D61BraJe6I0y39zJQ3+tyPrL1zdA3oqCdN1zq7ieJgMc+w429kLWJnHkrsi2NMxsRXJrG41uTUApyWww1KfHgwak642U8HehgKfgaHVffJ5DtzM9i4XrCz0qO1dhJLriPWHEmvAUZBlZFi7pGbj/Mpnhay9jNMmEx6QQ+SrnyZGMgjvVfxCyDLpk2D5MeIZlQdnyVeiqm2j5vKvODqr9+sIS6NyVA0kNzH4vGXhNxrDNz2xpYstv22BsgN5oouCnuyk0YQ8NP/8VdEmkyRomX8re2fkmnM3URbX63GM+3nyN5rNOUPqrfdT+/igTtt6wBDrezhp+7VOZA2Pr0TY4358e6LwL3jnYmTlzJhEREcybN4/g4OAct3F3d+fRo0c5rvsXAp+1LEVxP2diU3QMXX2ZmGTbF/XFR6LLp3u1AAbVserWxGfx8EnRGpjWMYgLciluyG92mHeW0nnf/S47qm/kU/1ArpsK5bptjUxWCm4J4t/JWmtQ5KFIZa3vKuapZ2fb966pACN1H7DeUJ9nsjd2koGaytt8rN7ITrsvuGI3hDnquZZSmD/xFJKsHTm3TIFUKeiFi70ao1vOEgYnNKNZp5lCKcXrNRwKvLqEQ/xdy3ltU7fC2Gklg4yiPKaTldyRA4n3tRJSe5WSKKh5xffxY5HCjoHKnviOG+lhshJD1RiJl9xx67+ZL9pYZ5wHb7+0/D0NRhNT9txh2elwjCaZ0QHhHPSdZ3uCtcYIoqZXMdCnwq1tsHUg0r4JBJSpgdOYC/RxnMf3+q48lAJFGSNkNSxuAIsbCSPQ10CtVFAuvzsbh9Rg7+i6XJWLstAgavEvZXdumgqKMsRvo+He69Pl20Oe0+vX8ySlaRntG8Iy+5k260dEt8POJF7Aae7FaT3iRxSSuCYZ3nDZcCrTMezdoOWPpLQQ91RCqo5zYbFsjxYv54qKB7YEfDs3PD296G42o83A6jXLiU/R8aGZkNy9WsBrNZkyw1GjYkmfytQp5o3OaGLSrtuYMhv2yrIoS4LQkfkrB3QXf+j0q/i3SQ/Gt88Q3o1MZN9N0a1Z5w9SuO1UKT9D6xXhglyKaY4fARJcWgqhtoT/ygU9WTOwOhuH1KBGYS+OGoI4YQxCkk2YFjeCS79CoXrCzLbtPCEWeHcX2oWNkV49xk6lsGZpbu+AzQOQZCNppd/nR80w7r5MZt2FJ/i42LFzhLXbdeDKS3y35w5nQmOIS9FhzFsRU4+tyCoHoVm0sRfMKgvTi6LZPoh+HtdwJN3i3Xb6YSydF5xl0+Vn6I25F0EexOq5VftnDBo3eHmTpFMLeGUeq00mmW0hQrepimQt2S8+cIkDdp9YPsdKHtyot5iDpio2x5Ze5w8WeoSOB225VxoMNl5vRcyNEw/lvOj/nzrCq849ZuOlZ9yNTCJdb0KtlAjK58b4ZiU48mE9Lk1sYiOI+XfC/5ug/E/AX13Gsnxvup6Ws0/yLD4NB7WSBiV9GNekBEV9nfnpwD3mHHn4VscJn9aKK0/iebS4Jx2VopPLJEsocuGDAIwtdZzDIfe5YDfcpqTSTjuJKNmduZo5VFRk+f4KvdjsOYSPdj+hmPSMlY4zyWO0BiizDB34QLkdlWSik/ZLLsklzWtkCkmRtHa+xyifENQRtj4vAJuNdemktCUudtd9xkOnSnRPW5M7EfVtUGUQFKzNvEe+TD8Vz/uV8/NDp/LU+eEIu1N64CqlsrvuTloWtUda+ho1VO/ipMdHYG+0cpXSXAvhMPwE2Lty+XE8HX85k+Ou1aQ7zM27D59Y82939IZGX0CFXlauhyzDk7NweQXc3m418ctbgbiq4+l40JFHsSnUsw/jp8KX8Hq8V2QXJIXgOdT/zKZlPSdkGCI6kk6I10TsUmy9oYx2biiHnhQihZkgyzJzjzxk1sE7tFGc4VOn3/DTi46zaDwwyuAvWYMZGQlpwEEoUIXxm66x6fIzahT2Yu2garazvaxdc2NugnsBy3naqRQU8naidcwSPlBZBSKzQWWPrtEknp7bSpFX5zDKErMNHZlvbEOAtxu7RtXGUfNuA/2jmBQa/3Qco0m2bVOPvAkLaoHKAT4Jt3Wa/ysgyzDFX9wfo66CZ+4Tlsz4YO0Vdl1/QfMy/izo9fs6sXJCdJKWKlOER9ztmsdwvLJIZGsGHck1ENx46SnLtu/nZ8UMiirM96BPSWg6BYo1FqTi9T0gJYo42ZnFAdP5ZIC5KWNmELx6Ijqg2vzMqvNP+WLHLYr4OHH4w/oAzDv6kOn7cy/tB0sP6aE+RlW7x+Q3hNtkttNlNSkF6jElrCj7TVVIweGtr0VP5UG+VS8jTdbQUzeBF67lbeQY6iuuslyTc2Y3o0TrTyzn7Ee+9XdmRnfdZ5wxWR3dKwa487HbYao/mEFswdaYOi5FlmVOPohh+9XnNqWnzJjfoyItg/IQn6Lj6rME4pJ1JKTpeRqXSprOSIOSviKL5uHw9urTfxLe9v39r13EfxCu9mpW9q/KkFWXeRCVzJ4bkRy6HcWoRkVzDHSCC7jzY+fyNP7JVlVWlmW+33uXSqa8ZGQ8XxfoAJwMuU1r5SWbQOehKS9X5aIAdNBN4rDmQ1u1zpBVlHZ7TDNFRX5S/4KTUSvKLnrRVjy6pjfSBRNHjeUzBTpiplFSekJw+kWkCKu/V2ZkBDrf67uSR4qlt+og01SLaZZUlHDFm8tzmWEo1BCViy9cXy84Ii2ngyQR/+g2EI+TnYroJC1RiVqiFO64Sql4RRxBaviGdt6Y+9ibr9NJUxDdlUdwSHwEy1pCj01UCsxDo5K+HL5rJTnmJYZp6sWiNTYWMWOtOlj4A2U4KGdAkiCwpviv+XeipHN+IUSE4Lm9OwfyVmayugMrIwtT434xZrQcR+uIuUg3N4lt7+6GNj9Dwdx1nDI0etzc3NH0343x+A/or2223AdK7SvuL+qN+7B9+LqKQV5vNPHF1hAMVzdwSLNdKBPrIVXpytz05qw0NqWop4rtqX2tP6X6MCggZqhjmhRnx9UIzobFcvphrC0HJjRTRsanlIWY6m3uDtMaTMRGPuUD+9cEOnnKQ7sFaPxKU6RKX8KWD6bws+2MU2+mqfISmqaL3jnQAaFw3aCEL4fuvOTq01d0yZhw3zRbghRp8NcHOgAxD0SQC2/tvh2RkMZec1Yns+P8HwEfFzuC8rlx4/krDnn1oI16tcia3N8HJbKXoAHer1yAMnk7M3h1ILVe7WKcagse0XdhTUfBf2r6LaZBR7k3uw2lCGV09FeQ1Eg8M6/Mmdym34JCSWmz9Ulspm6qa08TXnvOV+WiXNUVBZ0QDA2SwmisvEILxQUCFVHYPzvET5pDvJA38ov7h9Rp1hmtwUh0kpZrTxOIT9XzPCENhSQyp2k6I6k6I2fUbTibcoUa0jXWa75la0odriqLUESKoKwinAqS7bieJmsYpx/GXlM1QHRPdlbaju/bjTVpp8x5EpX5OMHaRZYGjImtStGlSgGhgXNgHzwAL/9AcLFDlmUalfJl/cXcM+ItgwTNwsNJQ4OsXnz/pfg32PkPo7CPM/vH1OXG81fMOHifE/ej+fHA/Ry3vfo0gZ5LsvsyFZogjPCeUIfRqm3YZSG/GlCiwpaTk5MWj49Gh48unmg88CCRglJktm1KvzrBQo0ITAyBdVB1WAAzywAgmQdeDQZ8iKeO4gb1VDdpZX8Dlc7KS3pIAPnK1sbBwREuLrEsn6TvxVJjc+orrtKbgwQoorlj3z/Ha5EbvtL34YXUm5EV8hJ0a6sYGGNDwbsoRczqxaFRyRxeOYUtih2WWWX1h7Pg2XvZjrfI0IozptLYuflRs4AD++7EcFFfCAMqthlrs9NzDry8gXZBA6b4zeD4fetMtq7iGrPU8/CUkjFKKpSV+kCdcRZPqdfC0RMafyV8hE7PgotLUEdcYhKX6OFenomv2jJyp4k5vr2YXL0B1W59ixQXJlpsK/aGRl/n6Mh8K0L8HfK42SN5FUbZYQFxNT4jdkF98kmiNl887SpfzZ/J8OHjcFCa2LDkB4bFriFQLYI4vZ0nS02tmZNUj1TJgeENijC6sgP8nOmLfKzBbj53B3pUD2DZ6XA+2nSNdYOri241WYYNmVp1K1p5Tw4aJf6u9sQkJnPRfnjO18g1PzT6UtgFmLlATxONrPQZT1xYfr5RL6esIhzTjvdA+x1U7v/OJaempf04dOclIRklOH0aXDPbHZTv+k7H+sNwepZodS7RyqZr6XU4aHa7L+jlSOm8f3z2ulKgBzeev+JWoh1tqg4W53h0qhDzzOWal8nrxraR9Rm/yYN6t2vygWoH/VX7UT08BKFHiCvZg55pH7HR7luKpD8XHUV1zTIKjl4Wkn6oucU9yCxWJ8syV54kvPW5l8zvw6VnGi4ZSnKn/IeMD9aTP/IISedXkic9gkmvPic97Bn2LSZbxClfh5i48pxa2Jfa2hN0UR2jC8ds1mfuwvzW0JO9pmpUle4wVrXFhjqQgS/0/bMFOz/ou1BbeZOaCmHZMFw/2hLoAHy7+w4FPB1pXNKX9AcncQI2h8osmXWCZ/FpJGtz5+0c+bBeruv+m/FvsPM3gEIhUb6AO8v6VuHrnbdYdU64Nn/SvCQ9qgfQYtZJi7hW5jbIrHiBF98aejBZvdxmudLOSWiBvAZGFLgZYtiq+Zo++k+oID1EKcncNgXip0jEC1u+xcOC3Sna62fRdeGSB5JeIL96hgTUUt7ionKEdWPzhCut2miG3SzBsVh3hjgWZpS0ngyVnHG6oaRgz07NRMop3o3v1VI7lW7KI/RSHSKPFMuK2y85fDeK055F8U++DXMrYVI5ULDaz/iTRofwuWLwyJp9XZKdHDjX0JZEnCEe9scDeFAuvxvXn73iulyEO6234rypGwVSn1H14WxWmkZTJcCN4Yqt1HuxFIUkc8NUkA/0o9hYt8e7t2A6+0CzKYKYevInuLyMEunX2GR3jasUZ1lMY3ofq0Yxz+nMCdhOkSebhNDdnd+E9kuF3jak4PL53QHRTZWYrsfVXo1PngD21piNz9k+aCQRFA9K+5XZ38cwQr2TQUSDAhKV7hzy6MLEZ9VIxR4fFzvmdgyigf4kLLUlKrNvArjmEyUJYHSjYpx8EMPDqGS6LjrL2kHVKfI4k9JxwTrCUToTIhPT+TCn8qXGRQSN1YdZBANvRbxi0Ykwdl1/YfYYqslZbWmmqRfTiBBhUfDoOLw3Bxzc3/ryNzCTYu+9TCJNZ8ThyGQhIufsL17k/wlk+IVVG5zjapNJtrTWxyRruR+ZxObLouSYmG4QHTp/MM/I2SxMqdWboN5oOL8AIq+LDE++3Etmbg5qFvaqxKITHny/34XV2sZMcdpIHcNZvO+sYqnmNNvzjeXDmG8EqXiVmYybKYjK8NLyMKuBJ6YZbDiQKoXEx81LUCnQgzSdiYiENB5EJfHbtRf0r12QwXWLZFdNLlUNTbUR/DZzEO/p92F/6ReIuyM6zbJmZM2IfJXO+wvP8iQuFYnBjFN5UkYKR0biiezLbTmQS6YSPJL9+VC1iZGq7XRRHqWZ4qLI/AKypOCIoTy1FDct2daMjtXM+Ei9EQUyybI94/TDsjV3gBB/7K08wCR1CGmyhu+fliWanNXXM1Auv9trhQL/m/EvZ4f/HGcnN2TMIoMLuCNJEi8T02k15yQxOfnC5IBxqs2MekeOy0f6IYxQihJFnOxMnOxKUUUEsw0dGOT/AMeYLAJUZTuKIEehErO4t0HzaexzbsfQ1aKLKLMw4mFjBYtmkFFpz3ZdZQv/KCe00k5lomo1NZS3majvhxEl36mXkJq/DiuNzQh+vobqijvvdA1ywlFNffolipdK35oFqRDgTsugPDT48RjP4kUAWkJ6YlEvfdxqHYF3FkGYKM+sNjRisqEXWjRULeTJxiHv1iqcDa+ew8kf4coqi/ZPHK6sMTRkoaE1dVxfMtVuBR5J5uxgvkqCB2FuUU7XG2k68wRP4lKpWcSLhb0q4WKvRmswsvi70XxgXJXtK6NkdxYaWrPG2MgiH9CreiCfVLfH+eDHVjKqTynosRF+GyOWSQqReak1BiSJ6CQtPZac4/7LZALsUzmmGIYiQ78oU6s4wJG7L9m0ch6/aGzJ78bKA1E2mABO3siyzJnQWBYcD7XhHtQp5s3guoUp5O1Ex/mnaZ26jU/VG1BjACcfaPCZCAKVbzfXqzT5ILEpOo50UFB4jzmb033T/8vt+ncjKRJmlBDX9tOn2SwK1l14woSt4lkt4edicWzPwPD6Rfi4eUn+aGRwZNpXyMfMLsGweYAo91UeAK1/euP+IKwoPlgXQnSSllqKG/ys/hlPKZl0e1/sK/cUpdrMeldd1kCJlgxZc4X9t14yokERxjcTv+3Q7ZdcCI+jXnEfqhf2+l3GpwAXw+NYsmi2KNtLWpvfk5CqY/3Fp1Qp6EFJf1fKfLX/DUez4ivVCvqprNvrZSVhAR0p0Xosp3avoPaTBW88hs6jGK0jB3Jfts3uKRUSHqYEvlMvoYlZc2yR3IHIyuNZejr3iWS/WgUZXLcwedzenqP0d8BfpqD8T8DfLdjJCZnFCN8GOSksvw6zS65i5dVklmu+J0gRblneSjuFqYFXKB+55Z2OlxsG593KgbB07NBxz76vzTo9atQVuhKVYsT3/tpcj9FGO5nrchGOVb1IwesziZLduVuwN3Ufz3nj9+tlJWopdz2hnDBZ3wOHOqP4KNNL4v2FZ7lg7pgr4OnAQf8F2IdlGuzUjsitfuKwpiEDV1rd4g9/WI8if8TMKemlyOBcXgaJQucmGg8m63qwx1SVEU5HGcFGNEazEneRRtDwc8hXiZAn8fRYcp5UnZGqhTxZO7Aa914m8d6cE2zSfEMlhVXT6Bt9L9ZmEmMEKO3nyMduh6j9fAkqY7rgIdX9WHQmqTTCtHLPR+L8QLTSt50HGidikrUMWnmJThEzLBYdsksepA9Fl0rkq3SWnAzj4umD7LCzlQMAhDVB429I0hn5YG2IpZVaIUHrcnkZXLewjffOo5gUui06h2/SLeY5LKCAyawJ5F1ClAmLt8i5HT4Tui06x9mwWM4ELCRv1HFBKm879+3/Vn8k7uwSpT/fMjDctrSRrDVQNocXbkEvR4r7uVC9sBe9awT+KYTSw3deMmDFJQp7O3Hko/qCi7WqnciCfHj/rblNUUnpfL/3HluuPKOA9JKl6h8ppniOSVJB3mAUzy/ZbB+r9mdHWjAHTZX4esRASuR7jQhkLkjRGvhky3V2XX9BszJ+tAvOR82i3rg5iJLV8DWXSbx1kNWa74Qo5pib4Oxj47P2OtirFaRnMoCuID1gm51VzuKEMQi31pMon3JWcPRy8LXKDYN1YzmQpXurqPSMTXbf4kEiWlnFTEMnFhlbY3rL5uu6xX34uVsFy+//u+PfYOcd8N8Q7AD0XHKeUw9zZs9nRahdj1zVeXNDO+0kikgRzNCIWUWs7EJl7S+0U5xmpuaXdz7fnBBm8qedbjIjVdsYpNpjs+6q93sEJR5HqXt9yY1Pn4C9G3JyFKE/1LN2c2RGtaFiFn9kMgTWRu67i+FrrrD35gv8iOfLGmpaFVaBTwlYkDuhNwPXVUEEDVuB5FWEqKR0qk4RL+pi0jPmFw+h2GNbdWKGnwdfERytPf+Ez7aJQLVb1QJWZdM/AkaDsKQ4/A3ECT2ZdYr3mJDaDV/i+dRhO+04ikI21+iLNYPg7tx0qkHXZVdJ1hroUrkAV0OfUTfxNz5X2waZX+r7sNJo1c0pJ4XynXoJZRSi1HpLHURqsxlUrlTVtjQiy6IFee/HQjfFryx0XQMeBdEajKR9XxJ3veAA7fToh6nueLZffc6J+9EU4jmH7cbbnMedkh9Q6q4IMNKLtqRrTH+uRuqwUynoWqUAA+sUzmYQmYHwmBS6LT5HzKtkxrifYpi0GUWa2cDVqxhUGwKV+ubKx8jwEtqbZzGl4o8KL6caufCI/mwc+lq06lfsLcjoZhy9G8X4zdcs2d+GJX3pUDEfFQM8yOv+58/UX6XqKT/pAACXJjbG21ENs8vBq6fC3b7akHc63m/XIhi5LgRnUpmmXkxrpZWraPQsirJ0G7RnF2KXEcyDMBctXB8K1YGCdcG72FtxtM48jKF7Fi6kRqmgd41APm1RkmfxaTSZeYxNyokEK8JIqzSEZS6D+WHf64VcWwXloWf1QGoU8SIqMY1HZ7eT/9Yi8iXa6mPtNVahjuKGxSNOlpRIuQm8VuwtJg+r2lsWFUpfjYwCD0c1wV4GJkeNIr8UTYRdITSdf2X6VTUnH0TzIjGdt33b964RyKS2Zd+84d8A/wY774D/lmBn4vYbuXogZUY+dwdWpI7IOQh4AzJMMjOwztCAblk0TLKig/ZrQuSiDFfuZLx6I3GyM09l39/leZUZ500lc7aSaDYVXPxJf3qVvWcuZzONBOCrBKGYuqod+JbhTLOddF9sHdAefdcSSZLYe+MFH2+5TlK6IZvBaprShdnprRit2oqDpEOLhsXKLsxMEeWLD5TbGanals2L7L5TZTomf0xRP2fmdK1AeGyKRQ1bo1Jw+pOG+Lj8sWZ46NPh9Gw4NhWAo5UX8MUtP57FpxEgveQL599obDiGJJvPVePMc/+GTHxYAh8pgY9Um/CVErIfFiX3m64mPW813M//QKE7i1BgIlXpwhR9D9bo6gAShX2c6FQpP50rFbD9bY/PClfwlCgRfPbYJMwyM8nnl09fxCtEtquY9IyDdh/bnMMGYwPafbUFu9tbMW0fjsKk47qpECPV3zC3X32C8r/ZRflxbArvLzzLy0QtNfIqWV78NHYhy61ctlxMIsFaGprjs5M2SeuFlEGrH9/4nX8K1nWDe3tEabLmB4Dolqs+9TCxKTryezgwtX0Qdf8gHZ13QbOZJ7j3MokFPSvSvGweEezuGiuIxCOv2JQp34Tv9t5h4fEwyud3471yeUg/PosPjCst648W+ohfEmvg/uIUnZxv0Eh5BWVGAJsBZ38R/NT7GLyym75m4HlCGrWmHbF8LuzjZPEmDPRypHW5PFx5nIBd+CGWa6YD0Ev3KSdNuU9a2pTPy5xuFcRk5OYWUerPyT4lE6Jkd77S96GcXSTD5A05b+RXFoadFm35Zt+9YbrR7DVVY0HPSjS/9RHc+Y1w2Y/22m9oUyOIb8xBi85g4sKjOL7fd5cbOQjZZkCtlFjUu/J/TRfWn6ag/C/+c5jYqjTjmhSndg4GbJmRx83+dwU6elmZzcbgTYEOQCr2yChYZGwNgKeU/M6BzjM5+2+yJxeO0v7PYHN/7M/PyTnQKVAdJImYJDOpOy7BJtDZMLg6kiSx7+YLhq25QlK6gYoB7oxrXsbmMA7GJMpVqklT3fecMpbBDh0fGFfxm2YiWzRfM1a9BZVk4pCxAr10n9JNPRujLFE85RKBuvuEPEmgzdxTXAq3krt1BhMr3tJx+J2gtof6n4i2dqDB3a85OrwcX71XmgS7fAxKGkij9B844NENk6MP6JLJ92QnyzTT+UG9GF8pgXCTn0U1+7SxDCfs6qPGSJlTI6n0aCFF7ixAgQmCOuM4NoRhY7+iX61COGmUhEWn8MO+e9SbfpS5Rx6QrjfPTANrwOBj4B8EKdGwrBU8OGQ5bRMSr3DGhVQ+Va1ltya7D5rS3hk7lZLzzg3pY/qCWNmFcopHbK8V/laBDkCglxOrB1TD00nD2QgjI162xTTmltm8UiMI3RcW5bivg1mR2tesK/Sne2C9Dn7m2fb9fRaV4rXnnxCbosPVXsXBsfX+I4EOQJVCojvq9EOz4m6F3sJ5Pi0ejr9GNTwLYpO1rDwjMoejGhVjYN0iDAu25Vc1ePQjHi9OccBUhaARq1GOfwj9Dwjn84J1hA9UcqSQn1hQGy4sBlN2g+SoxHSGrxa8lmK+ztz4uilHPqzP5HZlUUjwODaVeUdDORsWyzFTBQ4YBdn6feWx1/4Go0km5dp2mOwF2wa/MdABWO/ci/1yNTz0QvV5l7Fatm3k2FCICCEu6pllmZckAvbl23aJ+xiJB/V/IR5XVpx9THSSIGtrVApqF/Nmy7CaDKufc/BX1NeZ0580/K8JdN4F/wY7/0WwVysZ1agYqwdW4/63OetXAFx6bNs5FdXnlJhVvwETDAM5aMy5c+KAsRIT9ANyXNdeeZJaihs8yJQVeRdoZTWttFMxyOJ2vGYqTDyutgFTZdvvNvqWZR3NmKHvxA5TljJU9aEcuBXJ9i2rATiZXgRVJoKizmhiyckwRqwVhOiuVQqwYUgNqhXy5JTRNuBp+HIFT2U/Buk/tKwrrXhMsCKURNmRUboRDNSP56SpHGeTfNhlqgnAL8WvEJTPjfhUPbMP2/p6LToR9kYdkN+Nxt+Ad3FIjkS9fTD98r/gXKNQduRZwS/q2VSL24EiNWez0IumEswztgWEUvEm72HIrvkhNQaOfy82ajYVOi4BZx/yezjy1XtlOPdZI77vGES5/G6k6oz8eOA+Nb47zLgNV3kalwpu+aDvHjHL1qcILRUzomU3uiqPcNRuHENVuyzdYACbDHUByO/hwK7rEfT69QIn04tw0Fmco0fi2/nBZaCYnwvL+lZBo1Rw6M5LFp6PFh1dTaeIDc4vzPZCvP8yial77hAoRVI13cyRqfj77vM/BBV6ihd5+EkIO0pSup7v9goi/odNS+Cg+c8FYvWLixfkkbtRortJqRKdhAAXF2d3O88Fi08+Ik1vJCifm7CICD+N8vJSAOReO3ievyUAFczcshVnHpNuBAKqiSxO312i1N3nN2E/oU8VHLK174Mu1fI9Px28T7XvDnPt2SvcHdX82qeK0KVBEPBPfdKQWV2CaV7GH41SgR06y3fmNk6C0MqZ96ABTtty8JrzLwfBPaH59+KZmBglJASAUSk/c2dUIVr6iOfzkauVi/PAlI9I2QPJkAaL6uMZdxUQz0i3YV9SzNeZQukioErMW4smDRrh5SR4drEpItgJj0lhzPoQKn17kF+OhWY7tSH1CrNrZO03Wqr8t+LfYOe/FBpV7n86CdsBO9ougOiind54zDJSOEP1Y5io72ez/KKpOCP1Ixmg3Guz/LFJDG5DVLtZo/nO9mC1xsBH2YURDQoN8bItQTcaNwKll6gkE3GyMyV87PEgE2/nvdmiA6Lx1+Jz/qqcarydCel92KFuTlPNdZvjhSUYGbPhKlUl8fCnBTbg4Lh6lC/gDoiZ8Le772A0ybQNzsvkdmVRKxVoIi5SW3nL5lj2Ly8zVz2HI3YfZVvn0u1XuvQfZ/lct7gPlTqKzwVeHGTTgGA6V7LV1Cnp74LOaGLY6svEZrEI+UNg1IKzWa499DAsa4Hj4QmUj99PCcVT3CQx2KfJGo4ay/ODvguHjaJttbPqBD9qRHbDQdIhPTpBQnom7kDlAUL3Jwtc7NV0qRLA9uG1mN01mLxu9sSn6tka8pz2888Ibyl7V9HBlAV+UgLT1EvwlhIJNVld3U8YgyyaIxdS8/DB2hB0RhPNyvjRoXkTsdHzKznO1l+H8gXc+bqNCFqn77/LmdAY4Y5u5wbxjyBUlDNkWWbDxSe0nXuKailH2WH/tchqFWkIfmVe9xV/LjwCraajsaHojbKF/NquQr7/3HkBtYp6o1EpeJ6QxuNYc1BRpKHgmJgMsOb9NwY8eqOJVWfDARjTuJjggUWYzYF9SiIVqU++oPoAlJPEZGjB8VBazD7Jb9cirLYeansMAbWh1w7BGVLZw8ODoqRq0HHifjRzDj9AloXK8LK+VQjwsuV85XV3oF2FfCzoVYlNQ2vQQnEBHymRZ7I3e8wigEqMFJee0lFxgh9UCwm3784Ru49sf1SND2DAQRHYDD0J7eZB9aFQsJbwhatnLdvaLayBa7wYZ0b2tjaZfGHoxzPZOmE1yRIttVMZbxjCjYhkJrQsKSxfgLNP05mw9YZFZLH5rJM0nXmc+j8eY/vVCJJy8cb6tHlJi6/ePxH/Bjv/QKizCAi2nXOMGZfe7KHTT7WfLZqvsokJDtGNI1gKpagigkTZgeUGwVkJVETldBiB07OERsxnL0THixkbi89Al0XeKUL2ws9sNeApJWMfa035mioPEOlwEB00ALKR0nlcsVMpGGpYg4MhkTumAhxxFjMk1cHPMOrS8FeLMlbv5nUo5O1EQqp4+DOUZIfXL8KsLsGolQp4fpkSJ4Q20DapEabm31vOobXyHHmkOF7InixRvi8UewHp2FRqBLpS0DxIKiTIE9RAqDbrkrAPO8D0zuVZ2b+q5Vh3I5Mo7O1ExKt0Rq0P4Q+jzMmyUNb9tZmY9WfBc/tiDNCPp5F2OuXSF1NKu4x++k+Yb2xLzS8OC6dmzyL4YzX9m6OZh4cuk4J22DHYMlCUJG7vgBjbYFahkGgbnI/jHzdgw+DqlMrjSkyyliGrL4uyVnzOba8psh0z9R1Za2xoWVZXeYP8UgwxsisLYsT1HlK3MPN7VEJTsIbIbkTfEYaR74huVQvQsWJ+TDKM23CNdMkegs0vlvO/8CpNzwdrQ5ix5QRzmM7Pmrm4y4miA6rlf4irkwF9Gjw1l2Rd8+LppMHFrHHzNC71NTv++XDQKCnsLZSzHmc+l3YLoFhT4em25n3hcp8LTLJMik6MX5UDzRyfoM6gUEP0XXh2mag8QvSuuuIOv7T2wdfFjkcxKYxcF0Lhz/ZQ/PO91J9+lKKf72XcpuuCHN1ru7D3eHgQ0/oeTN0uurr61Ahk6/BaVAjwyPWcUnUGvt19G39JcIJcSGWcajPrNZO5YTeQA3afMEOzgPdVtsrHiwytKJi+loJHaxLvGWw1vM2KvMFCDTorfqlp+WcNxS0qK6xisxfkktyWCwISn269Qf/llwiQRPkrSnZn3QVbbuf9l8nkBGc7Fac+aUD4tFZ/K9POPwP/Bjv/QOhQEyu7WD5XkB6yxVj3rfYNVoQxUGWbwSmjCKdNIfFSVvmXpdj7k9/qWJFxiay4FEX35NGW86l8exp+WYiwDmhZrMmuxTHE8CGK1j9ZW4MzBovEF/g4qanoHEtX5TEAvtT347PkziSqvQngJf29buFjZw76zH5RcZnk5Iv7OfNR0xLiAb+7B5a1QpMWzX0CmJzWmSOPsrtJt9FOZkpqOxI7rBOky8gbKM7MZm73itirFRy7F82sww+hXGexw9U1gMj4lMmkWruwVyXsVApOP4wl5I8oZz27DPOqwdzKEGMu7Tj5Ett5K8+dBccjX/oDqkq3cctXgkScsDhnI2QNjAVqwJDjyEHv5/49caFCO+XoFDFDnlsJFtaDS8tAa9VzUSsVVCvsxZqB1fBxsSMsOoWDV+7BvKo5HvakqRwDVXv4Qr3GsswoS2wz1qKj7mvSJHuW9avChJalhF6Ki5/I9gGc+OGN5qVZIUkS37YrS143eyIT08WLoeogkJTw8BAfzvyVazevs9PuC5ooLyMr1MJ3bPCx1xJd/xJc3yjKim4FkIs1ZcnJMJLMarimv0GvSUbnV0RCpudHbQ/vr7IGPFsGCk+0HKBRKiyaOKl6cwbCxQ+CzJnpCwuJUubhtikQhSTjlXiHtYNsuS06o4lwc2Zpa8hztAYjT5zLc6bKbPSSBsXDA0xL/pzKTtGMf4Pm0I7j55n2zTh6PJ/Mp+r1ALhJqQxX7aS64g6OUvbs7M+GdhROX81Ug1UdvMLkg6/9HmqOhK9fCc5RDvDLIup6wFiZyxMbM6FFSUrlccXFXkWQJCYT119jCJ1R2spAvRKiHP2/gH+Dnf9inPm0Ya7rthutPJZ6ymvoUVkyMjnhmLF8rutWaabRI0LMPBxfXqTWqbezcPCfU4A+B8qT/OgiXpJ4GRZXPM+2XWZdHwC9T1kKpq/loLGSlegKEFhLeHElRaCLuk+VlBMoJJm0AvW4pS5DZLqG8+nCwLJDGTcknfkFrHZEbzTZpG/L5HUTKrMxD2HLADEIF23CmtKLiMOVIzef2p5k5xW4+ORHluF8tEqkxgGOf09Z1XO+7yg6M+YdfcjdPG0BCR4esmQ/Vg2oRuNSvnzRujTF/FxoUVb4fW27kv16vBOSXoo21Jh7gmgbWJvnNSfxYb5VVFujpX7MxywxiMzaENVuFhi+wp9YnDRKKgS4A/D1b7dpOvM4u+8lI5frZnP4M8bSXPLpYF1QexyU7y6MHpUaeHEVdo2BH0vAzlFw/wCkiOyQp5OG3tUDcSWZJvsa5PoTmisv4iLZBpeNdT8yVj+Cx7I/XSoXyE6YDO4GVc3tzIe+eudyloNGyYiGwgduyclHGD0K8zCPyAxO1X7HVvtJYibvXRxpyAlB/lZpXnfIPx9pCaLjDqDaUM4+esW3uwVfp3W5PBa7hP8k8roLvseTrFmmjIDHPVAQh8/krFMUn6o3K2CL+8eCjGDnxTVK5XHFUyXatKedjKPxT68vjZWYuI+604/S/agzXdI/I152JlgRyibjGJx3DRUE6gwkPIWTPyHf2EzI7PdpdaQ5k9Qrstk17DdWtvkcJbvzkX4IpfRrmGF4H28XB5b1s9W/eZBF4DFHPLuU4+Irsq2f2VlTaZzsVAypV4S9o+tw46umVHMQpOWbppyNYRWSKA1mRpO/qUP5n4F/g53/YrxOP8MF62BTXyHMNw+ZcifV1VfmbNCZI6JuvXmbTNhp98Vr1x81lkeHVeNE1eEXPBzVmGRBELVAbQ9OomvrTvgzmkginW8f3JHmZTLMQsVAWdRFb3UNd/EnMpPzMJhnOEY9bB0oCIwF60C39aQpHFFh4CvVCtuTLNGCYmZvrWfxqSK1XryFUHTdMZy2QX60KZ8XkwxjDrzCVMwcWJo7fDydNCzpU4UBtcVA1L6i4PLsuPqcVF3uPjVvxIHPhQiZXxAXO52nq34itY4UZcv1WAwmmYqFfAnsPhtT55XIdi74JoRwyG48y0qcZ+vgKnzZujTujmpCo5O5umEScibyMIgs4ejY9uBRUCyIfwTt5sPgozDurki/exUVxOMrK2BtZ5heGGaXh/U9GPJwGNftB2MvW69/iKkoIaails+XTMXpqxtv4YAN143ikWzl7+y9GUlSeg5l2IafC65N9F1LG+67oGPF/Lg7qnmekEaTmcfpESb+Zr5SAr5yrPjNvXeCX+l3PvYfDl2KCGrjQkWzQcVepJrLPYV9nPi5W4W/RRmirNmU81xYbPaVansr7+70bKEGnQXP4sW45etih50qE3/EyRzspsWjVEj4Oojn3N5JZExL+ruw84NahE9rxcLXOLoHVW/ChUYbSS3cAgkZbmyC5a3FpCElFmaVhcPfIG0ZQIX4/agkE+dNJflJ34lbha2TvMzq7KsNjWisnc5mYz3SjOJvEJWkpd+yizbfPXjVZSunKCdcWw8PclZhrixZS1ivZEfuygVsM3lxYSh1iWhR80AW3K0yeV2paJ7QAJhk+GKH7dj9n+Z5/ZX4N9j5L8fuUTkL4iViTU2WVYRTRHrOC/nd1UUBoUHS+zWu0/8PjNcPpp/+EzZUWm9ZJkWEUCqPGMQyXLotMPvSRFzYTpAiHBMKpJKtqBBoW3M3xZn5Ic7+oHbg8J2XNus9nDRicIkIEcdsv4DD92PZcukxs9TzsZMyBSANJoLKzlIG83a2E2JlrX8SL9uIEDi/gK/eK42Ho5q7kUkcdzdnQ66ugfTsIom1i3oT6OVIYrqBTZeeZVv/Vgg7Bjc2IUsKPjMOofOKO5wLi0OlkGhfIR+7RtZmw5AaNCnth6JMW47U3cwVU1GcpXSqPvgJaWEd+ud7yomx1dhXYCWfq9eixMQGQ336OQjBurqK65AaR2jd2cIa5NY2WNkW4h8Lo9GaI+GDS9B3t+gU8jLPHOPD4e4uNC9sB/wE2YkzptJUUFj5PisNTVFjJFARxSvZkYBqbS2ZL4BXaXreX3iOpace2RoY2rtZ/aFO/shbK6Zl7K5W0rVKgLiU0SmkkKULxbc0uPjnsOdfDFmG7cOEz5SDp+Cf2LtRMo8LSoVEWHQKl7N0YP6nkNH2HvIkweLxZ4My7SF/FREcH52SbfVzswVLfo8sE7kMjZ7UOJBlFJJ4da3sW4kjH9Zj18jalDP7vuV9jd1B6/J5aVa3Fo6918PAI4LM//ImzCgugvQsWBe8igpfnmXc5MWUKRxoWe4mpWDyK0tSj93crfwNBk3O+i5tg/Ny9KP6uNireBSTkq1TFhCK4/s/h22ZhBeDOttskpkPFC+7IKPAUWPlPj4+KUrAt02BeLo4MbV9EDtG1GLtoOq42udsi/JgSu4dvf9E/Bvs/JejTN6cU9dxsu3D10Z5xkKwe2fEPBRtwyVavnHTy6ZiXDIVf+tDj1ZtxZNEyhazDiTc2kpCqpjJO2myPKil2wHQIk54OKXmqUa6xoPFJ0RnxlNZzACVIWYBMjPHYt8t21lkUR8Hq6dX3fGE6dz5YPVFZqrn01p5zvY7q4i29yizXoVvhmiea15hOQBwaSleTho+aChe9t/c8kX2LgG6ZAt3JzOUComB5izPklNhltT9W0Ofjuk30fm1XN+Etc88USslelQL4MTHDZjZJdjGNuFVqp5p57V01H3NgaJfCNfo6Luw4j1cZwRQMno/skLF7gIf8olhEEfjvThjLI1Ckhms2kWvfUZiG/4oSJ6PjsP8GsKryKATgV/B2sISovl3gvuSC9ylFEaodgKglcXf9j3lGQarRGbGrsYgPm1bmWkdyuGYqY36zotEJu26TaMZx7j8ONN9XG0YqJ3gxTWrR9c7oFcNcd/Zo+VXTRby8b09Qpn6P42IK4IQrlBDt3XgL3hY+T0ceb+yyBB+v+/uH0d2/38gr7sDHzQQWbsvtt9k48Us5WBJsrb6h6yGdFtxu4xslbN9FjVrB3OwY9KLZ8pejG8qfSKFfZxRKRUYTTLzjj6k/Xyr9parvcqGp9J5wVlqTTvC7usvMPiXf+2YdrXwYLq1bobm+mqYX12USzNQoReKISdwKVabb9sFcWli42z7z+xSntldK1DI24lgcydotvJe9H1hQHw2U1mvdDvku7tzPa8wc9Zz2elHyLLM84fX8A0Rk5Pz3h04+lF9ulcLQKVUYK9WsnJAdr2e9hXyicaM/yH8b/3a/yFkrfG6kMZozwu/61iyNpHnCWnc82v12u2OG8vRUfcNnXRfUzB9LR399gqdi9cgvxTDSNU20pWZgrOwY7i8FOcanCkNC2CqZtv67BzcgRkH7vEkLhU/VzvyVmlj+wWl2nA+LJZzYdYXZGEfJxprbkPsQyExX6kvO06FMFvxE22UZ7OfpL0bdyMTLcafNuXDcu+LzqC4UHh5k25VC+DhqCY8Lo0b+c2mkadm5UjI7FRJbPs0Lo31F9+sjJ0BWZa5uXMmivhQXsru/GToTOdK+Tn6UX2mtA/KVt6MSkyny6KzPIhKxt3RjirtR8HIy8K4MxOkEi1o1f8LFvYSfISfjUKSvrvyCK6J92l+vAAhrXcL7pQ+BQ5MhF9qwAMz+fLlLVjTGWQj5AlG220b573aWY6fItt2o3yqHwRAE+UVqijug0KNfS3x93VzVNPRXOrzcbGjV/VAAjwdeZmope/Si9x4Zn5JOnlBWXMW7bEtr+JNeJ6QxsAVl9CgZ6F6JtUUdzHZucKQE1YC9KmZcHb+Ox33D8c1c9azTDsIqG6zqnNlYQJ5MTyeV2lv7rj8K/Bh0+L0q1UQgE+2XudieJZJVkA1sHMF2QQptvY3Ga3PUYnptsGbyg4LqV6XYpVXeCWyokaTzPhN15i+/x4Gk0yLsv5c/bIJ179uxqWJjW1KW88T0pi49hjnpzYV3nK5IDhsEXzrCztHCk6cXaYxytnPRlzyRZYyOcCGTIGeqzl4S8mcmUxLgBXvQeQNMfnIwO3tSHprUHTDtZ7l3y8lb8brRQbom99us/DQDeQNvXGQdNzQBNN36Cc42dlOEIMLuFsC0AyMb1Yi19/9T8W/wc4/AIfG1cu27KapoM1nB7S8eJW9w+htcC3BnlrTjtD2gCMmOXdeQD3ldYpKz5Aw0VhxmU7Pf3grITETCjO/xnrsj1VigM86W921daXtZ2UDFp8UJatv2pSlcfMONuvPOjWgyyJrpqZaIU9W9KuK0mgenJx9Mdzdz8Br79PU7BCc7fxSE/h483WMJpkmpf1sPZjsXKCYWffl1jYcNSr61hQZm4nhwcgeBXMlZDpolIwwD0Lf7rrDw6ic20MzIzwmhR/mzKLQ9ZkArNF0ZsHABkzvXD7HrorHsSl0WnCWu5FJ+LrYsW5wdTwc1XBtA8Tct934zm+wrhvNCmkY3agYZ01lOGishFoyskTzIyRF0n59JIXuDWO63Qe8UriLgHFNJ6FQ+/Q8IEO+ytxqsYm+O6OpFrvdcvjrJttOpknq5dw3ZeIMFKhmUzbqV6sg9moF0Ula1l98Qt3i3pT0dyFJa6D30vNWPpeXeSBPyJJFeA2uP0ug/c8nKPVyF0ftx1NPeZ1U2Y45flOR/csJr6xGZiPS/RPg0DdgejcD2T8MGa3mRRplW/XtLiHTUL6AO+6O/2ECtRmSJPFl69K0C86LLMMnm6/bNhqApRxNeoLN4ioFPbBXK7gbmcShO5mkLV49A2RBinfyEZYjAA8OYDLJfL7tBltDnqNUSEzrEMT8HhUt1yMxzUCK1sCPnUUThhIjazVTqWW6jBY1h4tMoB6/stTQPPcfFfQ+jL0F7c0q25eXQZTVysZgzJ5VOxcWZ+HjZZCtD9yOFFnc9FewuqMYG9wDoNe23L860VrCWm5oRixutCmfF5DxO/Ep+fXhRMnuOHVbin3WTLgZXatandEntCj5l/il/d3wb7DzD0BRX1uRPgfS2ayxTb93Ux0lUbZ9GUbInryU3d94/GBFKO1UZyjg5UKoXanXbrte8y0/qX9hiWbGW1lNAPxiaINP8n0yyMUAQYpHKDFy87nguxiMJrZtXk3ru5/a7Ltux2+A0MxpXtYfldo2e9BtjZUbUjafK+sGVRfBSsYsLeY+0rZBuJDKLck6+znh0kpkbICYmEiumzMJk9rmIChnLq3xUNgg9KkZiJNGyfXING6VMosO5kLI7F+rELWKepGmNzJ6fUiu5axXaXoWH3/IgZ+H80n81zhJWmIcizJs1OfUysU+5PqzBDotOMuTuFQCPB3ZPLQmJf1d4cZm2PeJyMCU6yK0kFr+KH7v/b2woA5jCj+nfnFvxusHE2byJ78Uwxz1PABkFMx7VZPaqT+yTWl+QewZDyGiXBeS7susRYuYnvyZ5VwMsoIaytukyHa00k7ljLE0LlKabXfey5uiLGZGYR9nfvugNvWK+6A3yqw+98TiWRSfqqfP0gskpuvB3TyQR1tJo6/D7YhEPlmykxX6D/lJs4B8RKFz8GGw4SNm3fdk2elwsWHtcVB7rPj3qZ8EQTg5Z/XpPxfmScAL2yaCrVeeceVJAoCNtMHfAZIk8U3bsvi62BEWk8KsQ7Yq4tZgx7aM5etqT/9aYrIwbe8dDEZzl12s+Tn2KCQyKmXaAWC4u4/Wc06w/uJTFBLM7hpM16oBFrL2wdsvaTLzOOM2XuOjTeL6dVMeoZTiCXGyM+20kxhwK4jH6Q6s9RzOhT6PRAt4j8225xt1R2SXSrcR1iepsbC8lVgOBORiQmtvJlk3KiXK66cfxnLyfhRs6AUZDu4OnrDEtgy2zNCMnLBCLwLemkW8+CIogfbK0xhkBSN0oyhUMPeW89mZrn+fmgVz3e6fjH+DnX8A5mSxI6ijuEFJxVPSZA3ttd9YWo97qQ5ZOl4A8kpxNpo3IfWWovfKOb05SzWXgymdKFa5MTK5Z3e8pUTaK09jlKVs2aWsmFpsPUHyBmJwxeeZcEymeHNQO6FBT0EpkjXnH7PvZiSdZh+g7PWpKCSZOz4teZBfZHAmSktpVNydD5uazztklc132GXy16pS0FO0m7+4LgwKzVBiYqepNs877iRWIQKH5GLtyAi+vFwcLcJtsck5+HVlzDJjHoDJhLujhu7VBPH1m9CiVkLmsWnZdlUoJH56PxhXexW3IhItRGpZlnkcm8KmC4/4aO05Ok9dTfFD/RgsbQcgqcJgvMeexsEluxiaLMusOf+YTr+cJTpJS0l/FzYPrSEUYo0GKzG05ihov1DoEFUdBAMPgWcRSHyGtKodvySPprnyIp8rxwBQQ3mb4bXzWr4nCUfGpvRiv30Lca3Mg3eF2D0sVk0nv2QtUWSYpa4wNuOWXJB+eluzT0DM8h/a6pEU83NhRf+qLO9XhWK+zujMLz8HtZIXr9KZvu+e6KRTqEU54PmV7MfNhNDoZKb8uo6V8kRKKZ4i23tA42/QjL1OwxaCFDplzx3RTSRJonuo46+CF/ToOKxu/85E6P83Mkqg/kE2i8dttAY/oxvZlq3/DnBzUPNtO8Ev+vVUGI9jsziUgyAcZ8HQ+kXwcFQTGp3C5su25H05KYKLN++y9rLI+mgNJm5HJuGoUTKzSzCty4n783lCGsPXXGbQyktEJWkp4OlAufxuVHKIZIJqLQCzDB25I1u5gk52KiIS0tDf+g02m+1pgjqLYOTlDTjwBagdRIeefzmhd7RPeLlFnFlHuH13ZqrnWcacq182QaGQ0BqMfLVTdEEFumuo9nihuJcy8OIqGK3jylxTJ1YYs8uErDY0ItVMoo9K0tIudQsgjHIvyiVZd+Fptkz4nReJtJ17ik3m61jCz+UfrZL8Ovwb7PwDkDXYyWjjTkfDVbkI3xp6sc1YCxCqx1/qc/BsAfLdW4HWteDrv+zMHHYaa7zxnJSSTNks+jlZUbtKZdK1WqaqluB2TaSH77rV5T4iUKipuMXJB9EcWDuTRa+GUEzxnHSNB9v9R9HlYVNiZRdKKZ6yoPQdIUSmS83W4dFJaS2jhZw7jry5PyyqLzg2ZlySgig8YDk1uI6XKYYU2Y5KhbzFAKRxQekRSKWCIqg4G5pDS61HQZFe16fCK8G9GVinMBqlgouPE1juZG5ZvboGEl9k293P1Z7u1cSg+9XOW3y27QY9p/zKlVmdabW7Gj/eb8YB5RjqKa9jlNSY2v6CS9vpopU3CwxGEx9vvs7n226iM5poUtqPjUNrWP1urq8X7eOO3lDvE/FCz0CecjDkuHD1VjngEHeHaeolLDBNsmzycaFwLnzWgDrFMrJJEsMTunPIbDmRFXpZyWf6ATwyCY5FvOyMhIlxquzWEQDc3pnj4volfNk7ug6VzF13LuYOk82Xn5Gi9hRdPpCrmScIUclZixazwPAVPtIrjL5lkYafhdpjQONIv1oFaRecF6NJ5pMt19EZzFmFoE4w6IgIeCJv5KqF8qdAnwax5ue7iFVXKzPxt3EpX/z+pn5GTcv4U9ecmfthfyYvM2fzpCuHbKervZqh9UTJc/tVkfnblVKc+4oiSNok0jYMpM6lkQDcUxRmfLOSnPm0IW2D81lIyo1mHGPPjUgUkvB8Oji2HjsHlGWLxzycJC0XKMsaY2PqFPPmvfJ5USkkbj6NJXLLJ6g39QTtK4z5q8F7c6D9AnFiFxbC4UnCeuK9WWLZy1to9QaKHBPq6+2Vp7ln35cr46tZymg7rkbwODaV7xzXcjy9Ew5nZ+R6vRId8jFX14qG3gnZ1k00WFvfjx7Zh9fzI5hkiSVGQbL+bNsNlmZkJYFHMSm0mH2Sa+asdOk8ruwcWSvX7/6n499g5x+Aellcjh/KYnbjISXzo3oBSow2xnWXTCXoqxuf7Ti+kcfRh+XgIp4FbZXvRgTNCSM0k6kZ6Mx6x+l0Vx1FRmKDx1CanwxkdapQ2h2h2sFP6l/4SbMAXykBvVth7tdbwMKLccThSniA0IRRR98UB318BpJfgkteaCJUngcrd+HNK5aop7NdPQHp5hZRvjHjrro0BUdso2ygD+lHRDfOPvsW+JmtJvAIBIXCImqXMfjaQKkSM72Mc0AEMJ+1FOqsX19z45FjORE8nZuXbXdZlinhL0qRL16l8/LiNtYYPqS98rSNQqtcpBHKEedQVOie7RhgJmluvs6my89QSPBpi5Is6lXJQo4k4QkcM9tg1BoNds7ZD2LnAq1+hA/vQNMpxKjzWvy0ANjUF99fSrPCYSYLi5whH9EYUbLJmJ03BjBA/xGVFfcopBAZq3x+vvyins0Qleg2uVBwKHEdNlp3uL4+p8MAoFIq+Oo9oXkTlaRFo1SQpjey72YkVBsqNrq5RRA/c8DF3UuZoZuMi5SGvkAtlP33gKtVz0eSJKa0D8Lb2Y7HsalsyEwa9y0pJBjecI5/OFT21k6kJGug/PEWqx9cBqH874oJLUoiSbD7+gtri7yrOUOYlD34B6htDqZvRSSy81oEo9Zf55M0YRtTV3mDAopokh3yU3bYSkY0KIq7o4bEdD2DVl5i+v57pOtNVC3kye5RdZjQohT2shY29YW4UPTO+Ria/gGSQsXU9kH83K0C54cEcNxvJkNVoiz+q6EFNSLGMv9MBGkFG0OdD8WJnZwBi+qZy2kqSIli//yPsp2/p1FkNWVZZvGJMDooTtDN9GYtqEmqUaRjR3tP24aFFYr2gEQR6TlfqFaxVCWyxGecG7P7m76MMmf2Zh26T2yylthkLW3mnrLsv3tUbXZ+UMtWu+h/DP8GO/8AWGfZAs9kXz7X98cgK+ioPMUg5W72mqpy3CheyDPV87lmKmKxcLhkZ21N9JDeTJL9vVjvZzXN7Fc0DdWNdVQy3SBZtmeA7kM+eVEXjUpJnkqi68tfiqeD8hQGWcH3+q5UiptEm99EoNKhQj4qVTFrDF1dA+Gn4bE5UCvSEKoMIE52JlARxSX7YTRWhmCUJZJkKzHvtmttCo87hLe3D7y4hk/cJXSykpsBPcHHLCMffQ+0ybQpnxeNUsGtiEQexeQgdV+4vvh/2DHLor61CjG7azCSBJMSzDX48wtFdgAwGo0cO3OWT2b/ym+bltFPuZdJqmX8qsk08xtwCCY8g08eI/XaCt62XRUZyCBpbjOTNOf3qMTQekWsQnO3tsMvtUXmySWvpZ0+Vzh4QM0PeNH7DF11E0nN3EmVFo/i/l6aPZ/LYfuP6aU8wELNrBwPs1LzPR2UYtC9oqpAhZidNFdeRCurGKUbwft361JxrYEJpmGWfbZtWcvzhJzJ9OXyu1OtkHj5q5Tit20NeSaCNBBCkcbsXUlybCj1b3+BRjLyJE9T1H22WXkjmeBkp2JUI3GN5xx5aEusLWe20ri6TgSOfwUkyXovZiWUA1ULelrsFf6uKJXHlU7mzroPN14VWkku5iAzh98EUNzPBY1SQVK6gVHrQjDJULxSQ7RB1kDfuWpPNL5C5uJhVDLt553myN0o7FQKfuhUzuLPRlo8rGoHYUdB5cCTJguJwxVvRyUFIg/BijZ4LatF/ldXkDXOnAz+keWug4lKNfHDvns0+PEYG137Ymo733rOkkJkQIG6cRuznr6FXxUem0qxmEP8pFlgXedTiqeDbnOz6DCi7a1qx0eMwWyOLoAdOoIe24qa5tOH01Jxjn2aTxmg2ouHlMxj8lC21wwcNSrGNCpG6Tyu4nqtD2HU+hCS0g3kc3fgtw9qUyavG6r/sVbzrPjf/vX/EASZxbQyY42xsSXtOVa1BQ0GxumHESO7UkLxjM7K48wxCN5LWUKRnfNkO0ZWxKjzME3f9Xed4zJDM6Y8tirRVvDUoz0m/LB+NLzPEVNFGpfyY/vwWgzzsnUxDy0xiOO+PUnUW29Xfzd7KNtRzLaNOljfHS4vFysDa4LGiYWG92yOM9PQySIc9zBfO0qP3oHGQRgXkiw4AKFyXp4ZPcCzsJC2N+nh8Rk8nDQU8xOZkNCcuqYyWuyf2Gr0tA3OR9cqARw1BXNeXVWc64LaRM9vSfLkQOofaM4PCR+yVPMjX6lX0VslOCuypBDZqQJVxIvcwf2113fy7tsWkuasLsE0zxDlM5lg32ewqY9QWs5XGfrtAY3Ta4+XgaACHugL1KKadh5pKhEcyCVbE1JyHDek4tijZbJ6uc0+A3UfZjvObPuhJBsVlFeEkapwIqnLFup3Gk5JfyGMt05nFcdsf2MYo35YQP/lF1l17jH3XyaRmK638BHam1VfMzRZzoTGknxmsdi5REthQJsZskziphHYoeOsqQzuvVfnbsoIdK0SQD53B6KTtGy5kokzUrgBBNQU/KvdH/113J1E8zm4it8976iVdL+49987q5OBia1Kk9fNnvDYVL7acctaknt4CDIEQDNBpZAs/CwQf/OpHYKwK9/JutHd3aTpjCw5GUarOScJjU7B39WeTUNr8H7lAkhGPVz8VWhCPT0vgtveO7ALqERdxTW26ofBxl6CPyMpoERLpMHHqNNuEEc/rM+MzuXJ5+5AZGI6H2+5wXfHzN1hrvnF81j/UxIkd9ylHCY/2wbD127Yr23LfM0cy+JH/i34wG029eZepfXNOvR7ZZ105JNiqK24wT37vtkO11gZwnzNHNSSkZPGsvTVjedCy324+4vyt0Ih0c3MEzz9MJbTD0W5vVkZf4Ly/+dtRP4O+DfY+QegbL6cOzG2GWvzSnbETtIzTz2bWFyZZhDeR6NVWzlsqsAjkx/22hhWJpR94/d4619QUIqks/bLdzq/g8ZK7PAbwaTyCZZll84exi75KbGyC+uNwjfppy7lKZ16wepkrRQ17xL5vNk9qjY/dCpn2X/+sVC+23+PpJbzBQE4PQHSBNkxxrsStaYdYbHRVheoj+oA/lI84SY/1G1+EuWnDJjT6r5SgnBHlySR3QCL7UQeszJrZGJ2TQ1Ly3QmQ8wMfNSkGPXUt7HXWgm7PlGncSOJdNQk2OVD71sOSrXhpF8vxusH09t5IfrqH7zmqlpx5mGMpYPoh07lea+8uURg0MHWQdbSWe2x0H8feObsnZMb+tQsSBKOzDCJe8d0dw8/XdcwIb13tm3vmgqwUC2C2HCTHzpZpM1Hpy+grnwZLRqUPTfhXbo+HSrmZ9+YutyZ1JzDH9bnQXkraXmL5mv6hI5j0Y7DNJ15gnJfH6Dq1MMsPB5Kk9J++LhYgxVZhrAos0p1DkGM6fJK3CLPkiZrOF36S1wdcg90ADQqhcXWY96Rh1ZTS4VCcDWUGiHrf2vr213A/y/U5k4fo45TD2KYbua+fN6yFG6O6tfs+PeBm6OaWV0roJBgy5Vn7HjhJgIe2SSynZmQkKpj0EpbGYjpncqJDJZvpm7QlzfY8G0vfth9Ha3BRJ1i3uwcWYty+dyEYeq8KrB7nCiVuQdAv72QpzxuJ75mpeZ78kqxyI7eokQ1+poQbPQW5SCVUkHHSvk5/GE9PmtZEmc7FcoYc6t54jPks/PAwZ25JlFKT3YuSJQii38bkCfOqiAebvKjcXh3dt2MoSyh7HP5ll12Ey3rSyiesVrz3Wuv4yFjBfroP8WpbEs6Vg60WZd1ElarqNf/pJ5Obvg32PkHwE6lpEZhr2zLtWiYqBfZncbKEPZqPuWMsQxXTYVxkrS0UFyw+GVp0VAhfQHzDW2yHSczuqqOsclu0mu3yYwUvypUGzSL7bFtaH/PWtuuZhRdM4sMrXFASx3FdXQHJ8ParoJTU7YT5DPzjFz8kSSJZqVtpfsXHg+jxfxL3KhrO1jWnneb5wmpjFLZvox8JEHUG60fwdJzWbgC5mDHS0pCg15kRKLNhEpfkZHKyCyociobmAMzDFlckFNi8NrwHiuUUyivCLNZla5wRB5xCfcJt1EPPwldVlGuz0wO2zflZLQTq87mILefBQajiUlmrZXeNQLpVEmUC9CnwbouwqVcoYIOi0VnkfINL0dZFpyXyBvCzXzbUN472Y7r9oOYaBKpeCUmVmmm2QzUGSipeIpSktllrEZj3XQqaW3/Ng+rT8WusC1JUqNSUMTHmWLtPxf2HmbUU15nn/1E2mkEITg6Sct3e+/SdOYJopNsr/O8eHMp9s5vtu3h6a8wHhTKtz/L79O3de6GpJnRtWoBCng6EPEqnc4LzvIwyhzE+pSw8je2Dxf6Qn92hsfN3Fr/6imbLwtictVCngyqm3ur8d8RVQt5MtKsMP75tps8LzVQrLiy0pJZlWWZYauvcCiLvcusQw9I1Rn4+micTSm6r2I3+x0msrHiTVZ28MdX9xxWthFBfny48NSq96noPDw6FaYXwSVE3JNblC2Qxt4UekruATmes71ayeC6Rdg3pg6FPKzPjrT/MyJOrmCdtibpshrn5HB8TVE5HiMDvfSfYpKUfFnXje1OUympv/3Ga7bfWJkK6Qv43pxRd5HS6FI1kDldK4jOUvM1M5lklp8Jt35X9UBW9a+Gg+Z/l6OTFf8GO/8Q5Jaq3GWqzq+GFiTL9pRSPGWcejOPZfFC0WAg3Pzv9spTKJH5wdCVyfoef9h5abwK4Lq0Tq7rJ6jXEWI/lFWaaXhfni3KRmU7QuuZ8Nw8uwuoCYgOnIxAY0r7shTwdOBZfBrtl9/jocKarbhr349w+x6MUeU88/aX4tl46SmvUjNxO+zd0Utixl/NMx1SooUTOoCbKB8kmdVP7dSveWzkTO7bKbGwog08u4BWsmeFoQn1tTMYrx+MTmGPvSkVh2UN4d5eyy5ujmrLbGzmofvEJGuzfoMN1l18yt3IJNwc1IxtbLbpMBlh62AINXcQdd+AHNSZmGQtz+JT0RrMPJT0RKGNc2CiCDJ/rgxT88L3gbCgtnAzv7YOKeYeruSQqn8NghWhbKzykAlBtr5g5x9GsfJsOGHRuXDDemwUbeRmOMkpzFL8xMNKW1nU1I5AL0diU7K3/++P8+O+qjiY9MSfyyQ/cGoWam08D015eVq8r/A1ews4alSsH1yDwt5OPE9Io/28Mxy7Z36Z1R4LxZqKjN+ej2BpMzgx3aK58ofDbI1AWgLF/AQ36ebzV9mVif8LMLJhUaoW9CRZa6DrYXv0/hVtZBn233rJ2bBY7FQKfvugNl+bCelzjz6k5rQjLD/7mPuyCOhveTbFYOdBIfkpVW9PRZpdDn6uaBUybThRdNqdmy/+Tnd3gS6ZdAc/Bug+ZI3XSNFK/hbI7+FIp4q2ppl5D4/ilv0A7KW3U67+RrUCVzkZ18f7URhy5qRlRtX0eQzRjyMBZyorxMRLgYlOlQpYeFpRSekUmrCHwp/tsdl3UtsylmDoXwj8G+z8Q5CRds8KGQWTDb3opRN6EJ2UJ6ilEN1L9pKOzca63DPlx0d6xQ67iTRQhLDJWP8POy/17bdL9YeZ/Ekv0U7I9HdYIgTmjDoxMzP7WykUkkWJtHQeV3aPqsMHRWP4WrGEoqbsdf8MhMp5kf3KWLIvCzUzWcgUvvstk4ifJPFCKbI7lRwjhUprBony0Ul2XY/gwiPxcinik0MXk7mEZpF9T42DVW2FQ7yzH53laXxl6Ee4nIe89QehHnbCKk62rqsQ5TOKYOr9ygUom0+QDafvu2fzNbIsEx6TwskH0ey+/oIvtou/5djGxYS5KQjPrzs7xe/tvoFLqoo0nXmCyt8eovb3R2nx9WoO/NQP/Y8lYcdw4XF1f69occ6Qqbd3F/o1dcejfX8DkwOWMVQ3hnum/Nl++mwz9+uiqTgdtV/x2ORLfimGijcm0f3BOJttg6K2s3bnXhrOOE7vpRcsLtcW5CkPDcS9ispB6C4BqlubaXqiI0d9Z7G1QTyj6xWgvNlvKAORWhHILD56h+azTrDotxOYzooS3jRDN6oWzV5meB3yuTuweVhNqhb0JElroP/yiyw4HopJoYFuG6D5NHGNn56HI98iL6hNzJG5PHwp7FUSUnXW9vXfi8QXVh6YTwn61ypEzSJepOqMjFl/9d091f7DUCkV/NKzIgGejjyNT+frtC5ixeXl6J9cZtpeETAOrFOIoPxuNgJ4Cal6fF3syFs0GIAy5SqhGnPVKuqZFUe+hf2fgTZRZGcbfA6Dj7Gxzj4Omyrh6/L27fohT+JZdz78nX9vZjRUXuWa/WA6vbRyeFA7ElV+uMUnDuCOqQAV0xcQhQd26PhRvYBGyhC0soov9P05G2othzefdTLb99Qr7mNtTPgXFuSsLf0v/uvg52rPyY8bUOeHnFWLQ+RiLDM0o59qP96SmG33Ve4nRnZjuH40y9Q/EKCI5lf1j+w1VfkrT52x0kds01WkgyIfP1UKFgsz/JYCa9powZTN58aRu1GsOnKNn5yW89Gzbbnexca6n1LpUFESTI6c694If3s9nJ6N8fTP1OUGMTe/YaxxMjO6BKNWKtArRLCgSYsS/IySreHiYgy3dvBJiHhpDalX2OKubIMMcTR9qjA4PLdACJE5+UKf37g+Q5BKne1UjG1izsAMPCx0O87OhQuLeP4yiuU+44lJMfAiQfCCNlx6Sj4PBwr7OHEpPJ5j96IIj03N9vWOGhU7rj7nfmQiw6//ihNwJ3giSy66cvbKb7RUnme65ixlpXAh8GdOuDw05eWRW1Xq1KhJhDIfV5PdsHfPS4C/NzqjidvPYgndtooO2u0EacJzvM6jzRk0dYFKrOk7mpXHG5NydBZj1VuybVtFcZ99dp/yhaE/q+43ptnME3zashQ9qgZYZ6K1xsD9/SKICD0KxVsI1dx7e1A8OkbFR8eoaOfK2JKtCanag/ZbXuHNK2qbg/g9ck3CI5PwiZ2HQqnlvKkkZ0xl6JR0m6uHz6LGRLpPEC5+Bcnn7pDNSygzPJ00zO4WTM1pRzDJMG3vXabtFdyNvG7FKeo4l6r6S1RKP0sN0w28T3zOsaP7+Vrfm2QE18bHxY4AT0cCPR0J8HIk0MuRAE8nCno54vW6TFP4KdEunRItAu/AWjholPzapwo1px3meUIaR+9G0bi0X+7H+BvCy9mO5f2q0OGXM6x5WYAuntUpl3oOeWkz6uh7kOzUimH1RUfc2TCrrlWlQA8W966M57mzEIawBzn2nTBJBUCCvMEQEWL7hY2/gZojLV5WUTfEBMLX9e2yfClaA32XXaS+1pOe5vnEpAK/8uXTN3Q0mrHc0JS+qgM5rttXbQXG49NppbR6Zi0sOAvF/Sh6K/fTW3mQoooIjEisz/85d0MD8A6L44OGcDE8jrgsWc6v3ytN87Jvbjb5X8S/wc4/CAU8HVnYqxJDVuXs8TTN0I0KiocEK4SgnouUxjfqFew1VqGtbjIfqzbQTXWUVsrfZxj6ezGueBzbrsHWkOdMaR+Egwq4vkGsNMvCZ+Dj5iW4fj+UAWETQJE7p0VWqFE2nIDzxSMkxKfxLD4V/4Ke0HAiykJ1Ma1sRwflKW7fWsKw1f2Y16EwRXRiEIxPN8/GS70HFxejuLeHdF0zQMlHTXMh/GXI3qfFwQ4hMIajN8beO5mZSdQ3o6NLlmUexes55z6EBB9PBkd9S77H2ykQmsJiQ18y+4T9dDDn9lyb62LWXamnuIaTRmgBRV3YzDjFbPLZZxdCfOJRnaXGFqyIKoIcrQCLll8ikIgj6fRUHqSfaj89pbhsOeDlhqYsNLzHWfuRlmXBNZuBWsngesVIvPcIzNSZG6aClFY+QylbB/TJqqU0cH7KqsRgvthu4LdrEUzrEERhH2fxUnp/peDEhB4WWSefksKXKPK60NJJfA7X1lLh2lq2aIrx0JQPhSSyHPsrnCX1+U08EoRqbTXFXW7b94dMElJaWUV//XhOm4LwcFST38ORwj5OlPR3pVQeFxSSxKXH8Zx+GMOVJ/E50nIiXqUTgTMnqA/UY7ByF5+o1tNJeYLqituM1o3gslyC6CQt0Ulaq8ZMJpTN58p75fLSqlweq7eZLIsA+OBXgr/mVVRcD3M5y0Gj5P3KBVh4IoxV5x7/1wU7IKxAFvWqzPsLz9I7rh8/qdNpqLzKZPVyhhdIwFnThKikdMasv2rZZ2r7IJHZzWgcuLraesCSrcEtv8WyxAYvbwoVavP1i0sVAYKbg7lc+rWZAvDZC6EmngXT9t7lVZqefM4GMN/CX+YPgdLfIR/4HEk20Vv3CSs139vslyrbMTnffPI/y9nBfLOxLvVPDsBbaS31Jg44S4WTV/jW7kOcJTHhiZbdiGsym1rFG6OYeYJTD2P4cf89fjkemu2YRjnT7/oXNpDkrPrS/4NITEzEzc2NV69e4er69/KY+T04ei+Kfssu5rjODh3z1bNppLSd/cR7lKPRi+Es1Uy3BEN/FeSu6yiyQsYkw7kJjfBPvA6/ms01P39pqxScEsuz2Y3Ir3tEtOzKLEMnpqiXZjum3s4D9YRwuiw8y/lHcczuGkzb4Ew19/OLYO94jLJEB903NG/WmsBT42lpOIxR6YByxBlwC4DvC4IuiWbaadyTA7g8sXH22XhKDMytLPQ8gFSXQlx3qcMWGrMpLPt8IsDTkWStwWZW1kZxmlma+SiQuRrQl5ASY7jyJIHfrkVYtqlayJOqBT1ZdvoRKToj3s52zOtegVMPYzhy9BCrNVNz1EmSkSCgBlLZDsK01N7N8sI48zCGHr+eR5bBSaOkRoATtRN20CZ5A54kZjuWHjWf6fuxyVifytJdNmcmq/fYLI5/fDoc/ZY0pQsdUj/jjhxIGTcd2xvEoL6/y0aLCGCBqT3TdJ2RJKhbzIduVQvQqJQfaoUkApt9n4rsBghycP0Jgs91fiHyrW1IvN0QlqBwJ0byIq8xAkfSeIEXnbVf8kz2eeO+ZfO50qikHy72Kn45FmrDG5rfoyLFfJ3xd7PHJfICbB8KCU+QFSrSGk4hNLArj+NTeRybypPYVB7HpfAkNpUXiek2QVTFAHdq5ZHpGDGDgtFHxMJyXaD1rGwv4bDoZBrOOI5KIXH966Y45mIA+XfG3cjETGUYmb7K/XxltxbJZCCt0VQ6XinH7RfWe3DVgKrUKeYDW4fYCjt6FhYEZ5353vcvJ8j4cWGCqwPC2NXsZL/geCjT9t6laWk/FtXTw7JMBqBf23p17bj6nNHrr1JVcZeNmkz3erku0GERLGsJj0+zwX0gXRKW/L+uR7hzMHblO+J66lucJC13TAFsNtZFV7ozk3vUB+CL7TdZde7NjQvh01q9cZt/Ct72/f3f94T8izciQ+03J2jRMEr/AfOYQ32l1VvHI/46++w+ZbWh8V8a7MQ5F+PH2/kxyU/RKBViVhKZiXiZFg9qa1r28fZvCNQ94qXsTnfd51QsHwx3swc7am083PmNfB4F4BE8i89CCKw6CJ6eR3lzM5+q1tNtXxFU9KGAJowgHonySZUBQmVYl4SngwJShaJr3cyK1Ua9eBmnWWftH8a2YW90NXLDkzhRhtKoFJTL50b9Ej40Ll0H6Vkx2DWG4CfLCa7din61mvJj53L0WHyeS4/jufAozsIbKu7nzNK+Vcjv4Ui1/A6MPfc1ClOmlHb+qsK0MG8FJP9yVpJrFtQs6s3yflVJjXpE47R9qK+tFirUOeCWKZDx+iFESL5MtltNN/bZbrCuG9pGk+H4T9gBn6T1sXgP3XqlYVFaA0b0HgTrusM962x3qGIbvv7ujItswvH70Ry/H423s4bOlQswpG4b3Ec0hINfiPLgyRnw7CJ0XAqdl7HMoT93zu6kjc9LKuqv4pRi+yL4wtCf7m1aUqpsJdwdPXEHUXJc0pg8caGcdPmciDrfcdurGfdfJnH7RSJ3XiRiMslUCPCgckEPGpb0tcgOYDIxINiRA7df8t2euyRpjczddZ4h9YpQqFxe8CsN/fbBb6ORHh7E8dAnBJU8SVDZjpDfBSqVAZcgkCRik7XsuxXJb9ci8Hm8i/ovrtHo5RXcpRT0spIZir5gP4jW0XrK5JVteBghZgNQhUJC8V/Iz7gYHseA5ZknZBLLjc1pUzIvFW9PQ334C+y1X+DtHGQh6TtqlPD4bHYF6zhzl6N3Caj/qeDwKBQgNxTifiGrrKRloJafgUmqZfQOOyjKYZmxa5wwxVUoeBiVxIStQgB0sctiyNwrEFBdNAIkiixqsbjjr2XA7vXoSYv41blvABRMvgqnr4IEJ41lGaj/iBL5fVjbqTpGk8zB25GczsTXeR1GrLnCnG4V/vaCk38l/s3s8M/L7AB0mH/a4oicM2Q6K48zXrURX+l12/25+Ew/gLVG4eRbNp8rI+oXpU6gPc7zygsRvGrDoIXo1Hh26wxuGzvgIqXRTzeeo6YKKDESat/L5phLDc3pr9oH/kH8VGQpcw4/oFvVAL7rYGukSMJT5J8rIhl19NJ9yklTOX7xXE+L1J1QoRe0nQszSkFSBHOLLeXHG/aUyuPK7pG1MZmMJJ5bieb0jziniQHvqqkIwYpQ1hga8bnhzfX8E+MbCHPODBgNcPBLoYvjmh9GnAM7F16l6ik/yVrzrxDgzor+Va02ELPKQYL5Ja9QwQcXxWz3TTAZ4cEB5EvL4OFBJHMnWbJDXsLty1A6/hAKZFJlO+Ya/o+98w6vouq6+G9uTe+NdDoJvffeexcVkCKKIKKgKKKIYgGx0BQUkCq9CCi9915CDYQWkpDe+23z/XGSm1xSAMVX3/fLep48kJkz586dezNnz95rr9WH1cpetDGcYKr6VzPva5uxGdP0w1jrvYGgpAPmqa+ZAulj+JIp3apjZ6Xi/U1XqOJpx94JrYWa88ZhQrSx0euw9yMAklp8ymJDVzaejzQvcG52Wja90ZRAN1vh1r59vOjesS8HL61l9AEje67H8kmPYEY2D4TPnMzn0Dl3JkN6d2NoE0s9EgBSI2HjCIg8K67Z64eLGG1aQJYhdAfsn1bgwP1nYeMqyN9Vugjl7du7LIxpY9W+vCe/zbGMgkykj5M1bau507SCG3X8nXjhp1NEpWTzfpeqjG1TvKr2vxGyLLPi5AO+2HETg0mmfoAzS4c15JcT95l3IIyqHnZ8YfyehpmHScKB5EG/039jHClZOs52eoj7salIhcqhRllin6kB4RVfZvSwEZZebyDkI35sJLKZkx+CPgd5YTOkpFIe6OqPgO7f8e6ma2y+GEmPACM/xFreYwjuIzS17h4odoq/ih65X5DtVpMNo5ty/I7QVirywPYETOhQhbc7/PsMYp83yjI7/8/RrprHE4IdiY3GNvxhbMIryn28q9qARjKWMv7vwWFjbfP/r0WlMWb1RTQqBW94TWRi7jRMt3Zi6vglhohzuG7qj7WUzR1NNb6dOIHGM48wVCpK/LtiylvoMxPwdRJP5MXaDzj5ITUUontTVGvooatOduWeELId09XN/KgdxQs5SjyBe/duA7W4GZ1GzSmbmaNeQEel4EbFy47MMgwiUXZgqeZbOluH0nx0G/xcbJBkmQfhd3lwLwx3EnExJrD3zBUUunSu//gznoFWaLOixRNiVmKBXk9aJMzwJb72GHbeM9JNYUW07Mod2YccvQMGowxxobDgsQzS+EslaoYApOXouXTtBtorq6kW/RtO+jgzO+iEsTprjO15lOvKKs0MFJLMMbkuF2pNo0OwPyNOT8U9XGRkwkw+fGp4hRMmESB0ezSCQ5prBOZ5YBmajudUi46422uJzRNhvBOXQbbOiHXljiLASAkXnCy9MHB1Of4pH3RUMnHymxwIjePr3be4n5DJtO3XWTGykTDk9KoJ64dCwi1Y3gM/+ylAIG72Wnh4yvw+42VHbsn+XIlIwVSY/JwPR18hsLhusAg2to8Xcgcu5UHrULBoGnTw6CIc+BzCjxeaQIKnLJ+Z4R4krAayEuHGVvHzOKp2x7P7tyyz9WL/zTi2XoriyO14olKy+fX0Q349bWlTMWv3LWbtvsWEDlV4tWV57EohW//TyNIZ+HDLVbZdFqXZHrXKMWtALWw0Kl5tUZ5FR+9yKy6D4QxlvTacGtJ9XHYMonrua/RQHcfjaNHmi1a5c4jCHUJhdHEZrnzdK2Ve6fnSqtIDHYALy5Az4zh9XzywDGviB9seG/PwNGQUNTHNx4KAuSy+pWW843FG5Jae0cnHAWNdKklRBCjiWK/9kjsNf2bMrxc5mycx4Gyj5uXG/vx4qPTzH9e2Ej8cusPCI3doVcWNuv7OT/X6/+v49/5llOEvYVBDf9aejbBY5BUSrBzZmE+2X+NevNBNycaKn409+dnYA1fS+EC1jhdUR/5j53nSajzLDJ056T0MvbU79+IzeZiUxdJIH8ZoNVinhDP6ky9oobjKMJUo/8i9f8TVwZaBzneYlrmqyJxzNHkeNiorfJ1FsFOkxTkfrd4j69xKgnjIS8qDrI4eSAPZA39DHCFHt2OnCGaEKoJ2ukNkKHJporhJR8UF/BTx5MpqvjMMYKWxEx4uznzUwRf5jzm46aNwiz8IV67Aja1USLhN4TzLCBB/eSaKptGNlt0V7iELGQYMy9csRMGlxEqYvk0A+TGdlY/jilUQfpSSzdqzD7kbGkLr+NX0VRwzB7ZJsh2bjK1Zb2pHkpU/1ewSWZ47GTtTDnFujak3ZDUtryyHra9Abhp6WclcQz9+MvbEUOj2IaPgruxNICLYqRPxK9gOB8DDXouXgxUxaTkcDYunc3UvoYQbcxWiLkKrSSLgOT4b9k1FHXmOLr1/wLlfTQYtOs21qFR0BhMalUKI+o3aD+sHw/2jfJD4CVGKN3GVA8lcPZR8E4xttgNQ6MgzRZWY0a9m0YBHoYT2U4Ue0aOLwuARhM6PjavgiqVEFBjHqqyg6ZuiWyy/LCjLIMtk5OpZduIeCw6FYTDK9K3txdeDGiLtnwYn54HWEV5eJ0QTY66IbsPbuwV5trA2060dcGc/qopt6VL7Rbq82JVsWc3xOwmcuJPAqbuJ3IotqtI9e/9tVp0O591OVRhY3/df54OUnKlj6NIzXItKQ6WQmNItiBHNA82lOUdrNY3Lu3LkdjyZWHOhxRJqXB0GaZGsVn9e7Jxy3aFUTa5OVGic2QizCPJd1W3z/APP/FT8uDzk+rdC+/AoUugO/HR1iVVUp3rgY91NHT8XZdUS8LrLL5y/pedV1S765uwv3GtQLPRB/VieWpcv71XAiQx+sZpHfa5T+cBrxOm+wlrty5ttK/JqiwpYa5SlBjvVvR2Y2LEKe67HEBaXQd8FJxnaJIAXGviRkJmLn7MNFdxs0RlNWKn/fwkOlpWx+N8oYxlNMqvPhHP6XiKyLNRmcwxGrkVZkkxbVnZj1auNCZxcfIcAyPysnk1n5fm//6QLQ20DbT5EbjKGu4m5bLschduxTxim3M1pUxDzDX1Y/QQp9eIQNfgIzX+JQqtSEPp5lyL6E7kGI19Mm8jn6uWkyLa0yf2eT9Ur6KM8yXxDHxSYeFO1vci8CZIzo3ImcFmuyE/tlHRRnofk+4JQ+zgkpSi7OJQT/9p7kWi0Zum5BJKNVrRpUJtOTeuBnRdyZhz7T5yh45WCssYF+7bUsMtAmxEF6Y+Kzt/qfWj3UbHvPzVbzxuz1zI4ezVdFWdQ5nUs3beuSajfQAxVe1LN153ybrZigVzZWxCIHf2EuOPl1WZy8A1TAJP1o7giC90jW42SqT2CiUzOZvmhK5zWvoWdVCiD1ucnqCMsJr7aeZNFR+/RIciDJcMawrZxgkvRerLQ1ZFlOLsI9nwkhCWdy5PRaxGNliWRpTPSpboX81+uizp/ETfkCuHEG1vRyUpSVW64GwtxjfotYbvcnHfWCRPJFxv68VXfYgIegEeX4fe3RdZFX0xQrLGHoB5Cp8XJr9jrnI8912N4c/VFDCaZn4bUo0uQGyzrKnhGXjVh2B+WPme6LPgqbzH1rie6+ZIfFOy3coJ6Q6HJm2aH9sxcA9Wn7TEPsVYr8XAQTu0gSl5tqrrzUiN/avj8875IyZk6Bi85w43oNFxtNSwcUp9GeWau+TCZZJp/fZDo1ByhLeN1kJ4pK4uf0NEPUiOgWg+W+HzOFztu0ri8C+tHNy069sRcURqu3heu/1biOZ43VaGBwrLrsW/uZ9Ru2pFPW9jAvDpPfJ96WcmXhsFUlB7RW3kCB6n0stN+Y11G6ydixDLo6Kc8xvfqhQBEq/0xjj1j7tSzJHVbwkqtYOmwhjSr5EZUSjbNZx4s8bVdbDWcnNzufyLgKStj/T/CjUdpvLcxxKJzoSQcC0vgq52lKb1KvK1/k5+ZTWvllVLGPWfos2DfVKTrv1Gp9w+Ma1eN5Zf8IBtsyOGEqSYjde+xVPPtM03rvaYtv6hrs9PYmMT4Orh5FDylfbMnlB8P3UVJe15WHiRI8ZCv1YupnUfQfku1tch8Kw0dOWMKIkSuSDdtCCsd1+BwMrT4F6/SVdxkq3Yp4rDtCni5h/Pj1mscuWVN+941UCok5p1OYfZZT2pL09mm/QRZUlH/1fkFi+zuKWavqxTJEaex+0S2oxhkpCZx5KcJrMz9HbVSZCeyy3fEuu0kyvs3pogMpSxDVF6XXmqEECcEcC5PeO2JdN/tjFyIhTn/5bq0qybant2uLcEuPZsEq0DcGg0UisJ39pmDnRca+LLo6D3234zjyO14Wuc/aee37EsSNB4tjEo3Dofk+9it7MLu6iPpfqUlu6/HMHFDCHMG1RGkS5UWBizl4vfJ1Ms4YhnoAHjXoZebN7IsM2H9Zdadi8DZVsMHXaoVvVDedWB0XjZTlyXKTFkJ4v8u5UWA+pQk4M7VvRjbpiLzDt5h+u83qO3XjHIDlsGiNiKT9Ws/eGW7IL6D2XcNEGq/Qb0gPhSuboSQdaK8eXK+8I/q+jU0GMnxOwUk1SndqjGyeXlMMvx6Opx5B8OISslm9ZmHrD37kGHNAnm9VYUCgvV/GNk6oznQcbPTsva1xmYV6MJYdvIB0ak5VJceMFv9I1VSooqfsP008X1f9zKkPULlJz6X1OwSVIzz3elLKe0C2DR4ibCQ1VQ2FvCx0rAhPj2XpadTGfnkt4paMvKpuoQA7THoZCUzDC9jRIkWHTpU5r+t/EAHoFytduBcwOub/nvxFhPB5Rz4dVRjs+iqj5M1u99pyem7iZx7kMyOq5b2OEmZOt5cfZHqPo6MblWhVK2p/xWUZXb4783s5OiNTN58ha2Xi3na/4tQYqSP4gTjVVsIUJTu+fK8IStUHHAdwoYoZxZpZgOQPeQP7qx9n5rGJ/vJlDgvEpKVI7K1E/fSVdzNdWSNsR1HTbVpKN1ivbb4dHk+bpl8uSEH0ExxHc/CpG6lFqp1F15eGpsCsumoA+Bbsit1jt5IkxkHSMnS882AWqRm6/lihwhEJ3etxuj77yA9OAp1h0DvH4WtxFrhkbPV2Iwp+lH88lobmlYs6osWG3YBzZp+OMviPNP9O2Df7TPwKsXwNfoK/FzI2sOjOjR/G2r04/CdZIYXkjOw1Si5MLWjeDLMTkE3py6a3CQ+43U+6FkPq9/fEE7ww343H5PfNutmp+VI8yvYHvlMWIGM2GkZTGQlwY53zUabWXaBvJYylBOGIPrX8+WbAbVQKCSuRaUyYP4B1ms+t/Qdcy4vuEt5c248H8GkTVew1Sg5/3HHv90vKFtnpMvco4QnZuHvYsPa15vgk3MXVvQQXXuNRkO3WWKwyQjT87IclTvB4I0FE5mMotx1eAZEXxbb2k/jvEd/XlxxHUOeevJrLcvzUXdhq5ClM3DqbiJbLkaZFzilQqJzdU9eaRpIo0CX/6iNwG+XIpmwPgRXWw3rRzehkkfRQOdmdBq9fzjOCLabuYPJsh25Gie89JGWg/0aC1L75ldJdqlD0/jJ5OhNvNuxCm8VV8o6Ma/kktPUBGHn8vBksbuNssRXhpf5w9iUM1alm/LeNPlhL2UTKbsTZvIhUnZnsmYDUn4JtBhM0b9KZ8U5WiquEir700c3HZNCw3m7iTjposGzBow5gc5gYt+NWE7fSyy27Xzbm82LqIk/jiydgYxcA2N/vcj5x/SevhlQi4ENSs9Y/pvxtOt3WbDDf2ewYzLJ1P18X5EnmgputjjZqJ9AToafh9Zn4/kI9t8sPZBRY2CqahWvqPb91VN+7rhkqkRdRcGTWJgmiIFp73DZajQA8c51cR+8mA3Lvqdm+nGCFA9LmupPw6jUouy/RIiaKfKyHlvHivJPpY4wZFOpx/946I7ZxTof5hv3w9PCdwlESWjPFFHmaPQ6k7OGsO58JF4OVux6u2WBVQRwOSKFzKV9aC5f4gHemLrMpEKT3qW/EVm26GTCvykM3WrWOAqauptsfcGN216r4tzHHUSws2synFlIuMKX9llfsdx/Fy3i1kKdIdDnR/MxOXojvX84wa3YdLr4GViYNArJqBMmja3fB+1jC2HozgLXauC8qSpLDF1xa9CXyd1r0HXuUSKSsmmhuGrpFm3nCe8VlCRkWabF14eISslmeu/qvNI0sPRrkYc7cen8cSUaHydrfJ1tsNOq8HexeSqn8aiUbF5adJqHSVlU8rBj0xtNcYo+Aav6AJJokW7+tvBmOjlf+JN5BMMbJwq+R/kwmYQWTMQZ8buVE6m1RzH2Vm1OxCgZ2iSAz/sUDWKP3I5nwaE7nLlfwO3ydrSiR21vetQqR00fx7/dVmDs6gvsvBrDuLaVeK+QA7fOYGLegTB+OCT+fnsrjjM3j2t30+SPHdn4KUT5NK58H14Kbc4O7UdYoUNfqQvqO7tZa2zLh/rX6BDkwU9D6hfPU8qIg2+LCYLyS7/3j4nS7WNBiUFlg8pQAs8P6KeYyxbT2wC8px/NJmNr87460h22aj+xGH/ZZzB1oooRPCyEXfYDqPLKXCoeeVsY+LabyiGPV/h467XiGyzI0yVzfHrbi1yDkWFLz3L6XsF3oogG2X8ZyoKdZ8B/Y7BzKyadznMKtCM0KgU6g4llwxtS09eRBl/sL/X45SMa4manpcf846WOA5AwcUH7Bi7FCNb9UxirG88FUxWOaiegzTPii5TdaJE7jwdWLxcM/CSZtzeEsO3yI0bWsSM5IYbwqEc4SJks13zz/E7IuTz41BMci6Ce8Gt/cQMddRB865d4mNEkM3jJafPNp46fE7+NbVawCO14D84tLjigXG14dR9ZJiU95h/nXnwmHYM9WTS0PpIkYTCaGDbjF1Yb3sOIgvgRp/AKKKZ08zh+aCQ6nEC8h/GXwEZkHEwmuYjRIMCA+r5808YaaUETkI2car6Elw7YsFj9LR2VF9F3+Q51k1EWx9yOTaf/gpOk5xr4xm0HAzPyFgBbDxHw1B1iac6Ykwr7PxPO2CbxOUeY3PnDtg9zk5tR0yGLb3OnEyA9VsbqMhOajDH/OnvfbeYeCAOgfz1fPukZXKLSrCzLbA95xKSNV9AZLb2tbDRKFg1tQIvKbqVeThDE8P4LTxKdmkNtX0d+Gd4QtyNT4Fye+JxTgBC6cw6E+fXF96V6X+FQ/7g7vdEgSlvHvhMeZnkIN3ng5uKEbW68yASptIJInf+vUkOWSUVEpsSm1CDW6FqixIQd2VR2kmlezZcWDeoT5OP03AOfXIORetP3kakzsmVsM2w0Sm7FpJOSpWfa9uvmcV4ksls7GScpk5OaZjRU3EKdk0iKbIuh6/fE+nel+7zj/Kb9jLpSwYPBJ/phKBqP5uPuQaUTsj8thrc0ZAtUEpIXpEahu7oFzf6PzbsTuy9m86FzvJ61qMihabI1Pxt6Mkm9AYCBuZ9wThZ/Yz2trzDL9D3WUkGjwTllXSoY7uAqpXNLWxOPBv1wujifcJM7mzNqYlKomaRcKwY7BQjCemoEmc0/oPqB2kVePx+PB5ClQZZlNpyP4Pt9t4lNEx1qLSq5MbixP11qeP1Xe2mVBTvPgP/GYCc5U0erWYfMTtz5aBjozMY3mjFn/23m7A8r4WiB+zO60XTGQWLSckoc0ynYk703YumoOM9izffP5dz/KtYY2jHFIBbRj1S/8ppKLMR6WUnl3JUsV88yCybOcfmYOY+Ci8yhQc9tq2FPfjGF2rzI3jF500EnOEMHRwex+/f1eMafoKvqAjZyoadAG1fB+wDwbSi6h0rB4Vtx5hJRBXdbDkxsXXDzyU2HGYXMN8ecEsJ1COfrfgtOojOaWDGyEa2ruBOfnsuZWT3poTyDPqgv6kHLn/we904VHUP5mPIINLbmXwufH8DwZoEsP/kAgKP1DuN/Y5G5BLPlYiSBW/tQTxHGEvfJjHrzwyIvd+lhMq/8cpb0XD1jvG7zLitRpdwvuHYNXhVBj3MhfZy0aDj/CxknFmFnTC0yZ6TsxgjTJ+xTji/YOOhXEXgiMgnf77vNoqN3MclQ08eR11pVIDwhk0ydkYrutqiVCm5Gp3HpYYq53beKpx1ONhoSMnJ5mJiFwSTjbKPm6Pttsbd6cobndmw6L/x8ipQsPf4uNqwa2ZCAmL0ik5MWJVrw+y0GZKEMbNILjade84vnCZmMcGMruUfmoI1/Ppy6bFlDhMKXh6oArkuVCdPWIMelKr6u9tT1d6Kqlz1O1hrsrVRC2A8hjGk0yQS62hZbFotPz2Xa9mvsvCq6oWw0SrJ0RUs69aTbLLaej6spEYPSCoXaGkVOMuHqCgxKn0DbRnX4ok9N6n2+j/mG6bRSXjUfO0Y7k4UfjikypxmyLGxnfhttuf0xQn94Yianfv2UF5MLAptkp5rcSjLQRPFkJ/tjxhpclSsw2C8R20enhPdcMUiwqYDrq5vYH2vHp9uvE5WSjUalYMkrDWgVs1w4v5vE/VxW2/BJ4GpWXS2a0anh40Dbqh6Mb1+5gLBfAowmmR1Xo1lzJtz8QOXlYMWnvarTpYbXE9/bfwPKgp1nwH9jsANCM2XHlWiWnbjP7ViRdant68i2cS0AcYOfvPkKWy4VT/Zb9WojWlRyY/iycxy5Hf+EV5NZo/6SZso/z5l5nvhG/wKrjB3QYOS8VcENr2HOAs5ZjbUYe8oYTDQuxMrOZMlarsnlaaq4weuqkjrSBFJ82uA0eBknbkXTfFszTLJEvdyfSMGecW0r8XJjf5rNPIiNlMOpvrk4mlJFBiLusWtUtZvQcLEv/uby9rpLZu0ReExs8N4RWNmrYPBbF80u8AAfb73Kr6cfMqiBH18PqMXt/UupcnwCJlnC9PoRVD4lPxkCQrPmZqFus2kpFouswWiix/zjhMaIdmeFBKentOe1lRe4FpHIFecPsM1+BANXmH3MjF+UQ2nIon3uN0x/tR/NKxXNghQEPAZcrGR+rX2D4PBVBYRSEJyfOkNEwKKxITEjlxd+PMgnGV8VIc/PNfRjnqEvw5V7mKoWuiYGSc3B/lep5eeMp4OWbL2RZSceFCkbFgeNUsEbrSswrl1l0fKOyFR0m3uMu/GZzyTYdjc+g+HLzhKRlE1VT3u2jWuOlSkb/nhHZGskheBkWTsL4q1sEnYHLSaUOGdEUhbdZ/1BXfUDVgyrL0jUKq0gPBtyRLda4X9To0S3W574pKxQo1PZodBnoJaLknszZCsumiqzzNiFQ6Y65PdPKxUSNmql+SErv7TnbKvGyUYDMoREpjyVAN7cmvfpdXcakskggr68hR7Pmlxqu4K+y0NRKSQOvdeGL3bcYFzYq9RUPDAfXzFnFc0qezKyeXnaVntMNf7RJdj2ljDjLYxWk0RXnSSRkqVj3oE7rDr9AGtjBru0H+IjPZ1C8bPgtkMTjAGtiK02jAXHIsyBtI+TNbMG1Cr4+8hNh/CTZN45zjehriyPs/x+Xfus8zPpKMmyzOTNV1l/PsK87d2OVRjduqL5O/2/gLJg5xnw3xrs5ENvNDFkyRnOPUhiybAG5g4ZEF/4znOOmoOhwrBSK5gzqC72VioGLzlT7Nx+LtZEJBXcuLZoPqFeIZ7MPwm9rOSW7EeNQjfAfGf354H99n2IbTGdj367xlntWDykFI62Wssre2UcrFQcntSW11ae50J4MtW87Bnc2J9gZxM1wn5Cm/UIbhaQc/GuC68dKvK0fjUylV4/HrfwSPppSH3x1BV1QRAodYU+u+E7IbC5+deTdxJ4eckZnG3UnBvhin5pd6zlbA54DKf92LklvzmTCb6rCpmFOFuT7nIzTYNJlgmNTufcgyTWnYuwOGxc20qMbVuRBl/sp6vhEN9pfgJrF5hwvcC/6Utv0GfSNvc7bL2rsv3NFsU+/d+OTWfSxhBCIlPRqhTsGNeESomH4cKKPP8scVFkKyduBr7C5DtBvJn7S4myCGEmH46YajFKtcti+3lTFXQKK5KNVpw3VeWAqR4PZU9cbDV0CPLASq0kNDqdjFwDDQOdqeBuR5uq7gS42hZ5jT+uPGLcmkvYa1Uc+6CtWOCfAnFpOXSbd5yEjFxeaODL1/1rCcXqPybAxRVi0PAdEHsDdk0Sv7+4RhDfi0Hh1uKzH7XHw/4peBsmkyBIa+0K9JhMRjJiwnhw8yL2KaE4JV7ENu4iKkOm+bCLVONr/SDOGAtKJhqlAoUCcvTFZzEKI6icAzfzOkVr+Djwac/q1Fc/QFrWVQRifo0hIUzw0TxrwrDtYOPC0F/OcCwsgZca+RGfnovjrU3i+5aH4NylZMnifQ9u7E+3muVoWsEVhS4N5ta2sHDJx6EXw7j4MJlLD1O4+DDZnG1qXcWdqa0cqbS6iaXu0V/EGfcB5HScyYydN80PDGqlxKiWFXirXaUinmZ34jIYtvRsEY7Oz0PrC32qEhCZnMWF8GTUSgX3EzLpVrMc8w+GseViFApJlG7bB3n8Tzqi/1cEO0ePHuWbb77hwoULREdH89tvv9GnTx/zflmWmTZtGosXLyYlJYXmzZuzcOFCKlcuiHiTkpJ46623+P3331EoFPTv35+5c+diZ2f31Ofx3x7s5MNokot4oaRm62n37WEL48LHUT/AuVhHZoCB9X1pWcWdmTtv8ig1h8pSJBs1n+EkZRY7/t+Kb/Qv0FF54cm+X9V6EBfQHY89YzDJEn11n3FTDuCmdjhKSUb3TihN5l8jKVNHv7o+dKruyRu/XiwyzSCrs3zNHIttJ4KnUbnLWDwcxA1almUG/HSKC+HJ9K7jjbudliXH71PR3ZZdgz3RrOgmFoDyrYRacmZcEQ6QwWii0VcHaJF9iO+0i1HLOk6ZgvF+aw8B7iV8l+NuwoZhBRwd4MHoO3y5L5x9N4r3xQLoV9eHrwfU4pNt19ly9g6HrD/AW46FjtMF2TYfP7WAmKuMl99je249oTdTwk3WaJIZsfwcR2/HU93bgTWvNRFcmpQICFmL7vxKNOmWAZesUCG1mCAyH0oNXFiOfOgrpOykYl+jONwxeWOqMZAqvd4tIgtQGkwmmW7zjhEak27RGfY0OBYWz7ClZzHJ8HH3IEa1rCBKLVvHQsgaQVB+dR/MyCOL5ptNFoO912N4fZVQ8H7qYOdpYTKK78iVdXB2sbk93lipM8lNPiDFoQreTtZolAruxmcSnZpNSpae5CwdnxVqjZ7QoQqJmbmsPCWyST1rezOrfy2sVZL4jsRdF1IDOamCh+RSEUbuATvhPXfybgIvLz6Di60GTwcrbkanccV7Jg5JIqt3f+RVfrmYalaVljCxV/MBlRUltK0Dw3WTOGyqa/69mpc9U7oF0aqKO2dDrtPot2bP7TI+sq7CqirzWHhGfC9tNUoGNwlgZPPyRUjFsizz2e83zOXhwviybw0GNy4o6cam5fAgIZOKHnbYalTM2X+bRcfuUdJKPmtALV74L+62ehL+K3R2MjMzqV27NiNHjqRfv35F9s+aNYt58+axYsUKypcvz9SpU+ncuTM3btzAyiovoh88mOjoaPbt24der2fEiBG8/vrrrFmz5j/9dv5xFGf69nvIIxIzdfi72LDr7ZasOPWAWbst0/glBTogFGi1agUH32vD2rMPWX3GjlZxcxim3MO7astOoxumAF7WTeEz9Qp6K4tv53xeCDFVYJp+eJGuBxDZnWXGLmzWTMM9z8Mpn0xYGJuNLbhiqkh5JwW9m9bEuVprZNeKrNp3mwrG5vRVnmCG+hfG699EKclkylqyFM5mt/Itl6KY3qcGy0Y05PTdRMLiMrgVk05cSjpjTGtBAd/qB6LExAT1Zppcn857IfGcd+xIPX9nyjlacyE8GRuNkg+7BmGtUbLk+H3i4uORV40RgY53PfGEPz8vwHmMuKrCyFzX32gZtxpkITl/ovYMPiku0DHqRRvzibkFJQPg2MthvPrjeXSGgidaNzsNSoVkJjMuG9GQtlU92HElmrVnw/lWvVQEOnae0PA1y9fxrgcxVxlrd5TtuXW5HZtBlxI63pUKiW8G1KLLnKNcf5RGy68P8kabigxvFkicT3cOHrzKSKkg2DG5VUUxcLmZtwRAo9eQag6Ec4uJiIpk6TUj09QFytr6FpOI1/jiZEjE5uEhjA9OUknxCG7MhXsroPl40c78eDdYMVDkqf8OW3aWzRcjiUvPoWctb/xdbWgQ4FwqUbZlZXc+6FKNGbtC+WLHTTZfjKJ9NQ8MpqG8o96JVdyNgkDHyhFavldkjti0HByt1aTniM9PkiiRaP2noVAKiQKvL4Q33ZGv4dKvKO/swe3OXtxqvSCc513KU9XLnqpe9siyzKxC5cGPuwdxMDSOk3cFd+29TlV4s20lwUW7skEEOiCEOLMSwd4bXtlqDnQA6vk7o1RIJGXqzH9zJo/qkBfslPf24gt/f3Ow005xqdRAB2C55huW+05HVaMPdf2dCPJyQKGQSL78B422Dn4uly9F4Uxs668ZecKFqLxAZ0gTfyZ1qlZiJ9+3e28VG+i81MifVpXdWXj4LpceJhMSmWL+mwSRYXucRJ8PrUrB4lcaWBoX/z/GPxrsdO3ala5duxa7T5Zl5syZw8cff0zv3qJtduXKlXh6erJ161ZefPFFbt68ye7duzl37hwNGgg9k/nz59OtWze+/fZbvL29/2Pv5d8KVV4A5GyjxlarYmybSkQkZbH2bMECkt/JVRJ+Pf0QbydrxrapxPBmgXyy7TrzT9uSjRY1Rj5QCxfiYEU4jlImn+iH01Vx5rl7bY3XjWOe5gcAaivu8aOm+DLNMVNNHsqezDYM5Cv1L8WOmawfxTpjO7rXLMdng+uZtw9YeJIL4cm4MoQ2ihCCFeGMUIqymATciLGU6rdSKWhb1cPCaT7z7klsV8Vi0joybNzXxGQrCN1poFr0NmarFzAnLYY5l/uTz4N4s20lvByt+OOK4O28otqLNvOR6NIZvEkswvkdSoUVfiPPw/bxtMxbOH409GKL43B29SnGcT05HDa/KpR8CyG84xLe+PUiOoOJZhVdaVnZHWcbNX3q+jBuzSVi02J5u31l2lb1IDI5i2nbr/OqchcDlEeFMnTfnwvKV/lo9haErKNa5lnqSWHYaqsX+xnkw9PBiiXDGvLhlivcjs1g9u7rWB2YynDFLkZKlo+rClv34k1OrZ2g1ST8gIfLzzH+tgPzNKLtXX1jC97j87Nv7xIRGcW8BfMYq9pOpZxHcGA6nPpRtMA3es2CnF0cWlVxZ86gOkzaeIVjYQkcCxM8j+reDnzdv1YR1eIsnYEz95M4HpbA7msFfko38xzWAVKUA5ipXlJwUN9F4F7F/Kssy8w/eIfv91mq/HarWQ6t6m/UDXL0gV7zxLU59IVQIb6yXiiFV+kC9V6Biu354fB9Fh4WWdPhzQJZdTqc8MQsbDRK5gyqQ6fCJZjCZPisRHD0F4HOY8J/VmolFdxsCYsrKONqgrtC6NqCYx3K4WanJSEjlwOmepSEnrlf8LXTFoKzLzI84Tuo0gtcHEGfA1/642zMLfHYp0GoyY9txuZsNzZlfP92rDgZTlRaGj5O1nzSM7jYElSuwcjZ+0lsvhBZRCutWUVXPuhSjVWnw2n1zSGLrI1CEiTjR6k56Iwm/FysmdS5GnX9nHh73SUuPkzB00HL1jeb/2Nikv9G/GtlE+/fv09MTAwdOnQwb3N0dKRx48acOnWKF198kVOnTuHk5GQOdAA6dOiAQqHgzJkz9O3b95849X8V2gd5AlcJiUwlJCKF2n5ONKngahHseDta8SCxZE0JEIaD/ev54ulgxed9arDqdDhLjIJPcFP2Z7lGiKQd0U4kMGcNC429eVu15bm+l/xAJx8+UmKx435Uz2OeoR/jVJby8O/rX6O2dI/BqgOMUO7GuvFwpvWydLrOz40l4sh+v7cYGDmDwSrhbGwj5RLkUeA9FVzOodgneVsHIfKnQMbd1QV3SYLXlsPB6XB8Nu+otqBVmPhaNxCQ6FGrHBFJWUzaeAVrcnjTag8YgLYfg22eYKCNm7ARyEwQ5pT7pgpVXWTSFQ58kDOcnaYmtHWztyQfZibCmYVibK6lwna6UxB999uTqTPSrKIry0c0Mh+bkJHL0TBBWu9WsxwrTz3gk23XqSHdY4o2L2va+Uuo2LboB+BWGYJ7wdWNdFReICWrW7GfU2HUD3Bm19utmPHbGXqEjDWXG88qalOt5wQc3P2FHkr4cfjtdUGILqFd9r3OVeka2pxJ8gb8pHhIugt3D5nPNdDXh+TK/el0qwUzKt9iUOYaMWb/NDj1g3C9rje0VNXd3nV8qOHjyIqTD3iQmMWlh8lcf5RGzx+O07SCq1nD5nJEMhfDUyyevu2tVObMDECfOt7sDu3ITAoFO4U8zg6FxvHNnlvFKqR/0PkpZAWeB9wqwcDloly5/zO4dwhC/4DQP8hROdBI580XKh+UAY2Zd0FHdK4GX2drlgxrQDWvQlnGxLtCSTof7tVg6G/gUPyDaVA5B4tgJ9a9eYHy965JJHT/hfQcQbDe+XotKEbAOEF2oHmrjlTz9YHNI0TZbHkPYbb7FzEw9xM8K9ZiQMvaHNl9i6joND7YLN6fvVbFshENqVJIMTo+PZf9N2P5cMvVkqbk/oxu5BpMvLbyvDmQblHJjTZV3anl60QNHwdsNCrScvTEpeVQwc3OXErdMrZ5ifP+f8e/NtiJiRFPQJ6enhbbPT09zftiYmLw8LBk4atUKlxcXMxjikNubi65uQWRfFrak20W/lsx6OcCJ+gHiZnU9nMyPwmqlRKO1mqLQGfTG035cudNLhUjStj4qwNM7lqN2r5ODG7sz+ozIn182FTHYtwLykPMNgzglsmXBZp5Reb5u2Et6czZpsIYrfyDAbpp9FKepKoikmnVi3agrXq1MXqTCQcrNcjdyPj5EHYxpwEh8e7hYMfI5uVZeuI+9xIySM/RF21Bzl8kc9MESdLGRQjFdfhUGEHu/oAxiq1cU/izw9SEMb9eRGc0ka03Mt39FDbpqYL0W6GN0FdRqoQvUhSCtHzpV+HUDVx168Yrkb1IRiwoh27FcyE8mfq+dqKV9fSCYv2eIq2D6B0zniSM1PRx5Keh9c2BTkhECmNXi2xPFU87vByt+GTbdUBmqvpXlJiEHkzjN0r+ECQxV46sISSyaKt4YaRk6Vh/LoIN5x4yIXUGdZR3SZFteU//Bl6N+tKobl5A+tIaWNETbmwThNZCmY/CCCrnQD1/Jzo+nEWo1Yi8D7aPRUv96NYVOXQrng/CgnB6eTudTUdFuSb5ARydJewuKraDmgOhWrdieT0V3e2Y3lvU5+LTc5n+xw1+D3nEybuJ5vJNPnycrGlZ2Y0Wld3oEORJts5Iv4UnuZ+Qye3YDBZ31kAhXn2mzoAtomttxHLLbFw+JnWuWtC195+Cd12RhYm9QeyRxVjf3ISDIY3GijQaK0Ih6gD9UBHi0ICgtq9g7/xYQHrgs4L/+zSAl9fndWIZRfmsMHIzGFzbgT+uPCJPLJrzUdkFwc7N31nqeJ9cg4lAF2uCV1o+uEzRv8pX6l/QugUyWbEKNhd6WHoOgc4H+tcYN+IVWldxx2A0MTy64HPqXcebj7oF4WSjYd3Zhyw/+cBMUC4Jthol1z7rTI7exOhfL3AsLAEbjZIlrzSgWTEdjQ5WanGfKsNT4V8b7PydmDFjBp999tmTB/6XIz49l3sJgkgcVM6BHrXE01O+E7jeKJOQYUlcTs8xsGZUE77aebNYafKZu4r3gaqWs8y8sMxSLyZH1nBf/ueZ/5dNFaiTZyVQURFNa8UVNhlbMUK1B/n0QqQKbS0yBNYaJdb5xnyShJ27P+QFO9GyK55GmY+7B7HlUiQpWXruxWcWlWpPLxRomx4r5TV5g6ykKGzOzuNHzTxmyouRk0CJCVurXMi/H2YnwbeVxP+1jpCbFzAcL9A6mu02nbmRYsy0nsHcjE5jw/lIFmw/yhLrH5Aiz4qB7tVEyj/P0POksiGvJo8hGytGtSjPpC5Vyco1suNKNN/uvcX9hALyudEk02m28I3qoLgoFjSVNXT6onS/qESRmQmV/UguhRwfkZTFS4tPE5WcyRTVGnqozmBEyZKAb9l/2xH7S48Y376yIOD61BeEZKPOrH1UEl5s5M/7D1N4z24m32ZMFhu/8ja31jep4MrrrSqw6Og9pv4eSvP3XsCu5kDRhn9hOdw/CncPiB+lFip3FCrZlTpY8Ery4W6vZf5LdZnQoTLHwhIIT8zCJMtUdLelRWV3Al1tLITbrNRKVoxoRL+FJ4iIjqHi7ncs3LF1W97EZuJZYlILNLBq+jhyNaogcLzxKI0cvRErtZK0HD0XwpOp4+tkoab9dyA0Jo1v92Sy/2YH1LQhWBVFR7cUvHLuUCvrNFUUUTTSnYadp2HvRHHtqvcVAfCNbQUTlW8FC5tBRh4pXmUtSrYaW5F9yU6iMXDBrTKbkytxzlSNVb+FMzC4E4TtBaBq0mHqSdk45mqLnOdbXjcgEewTr8Cp5+/zt9vYkPVLz3JmSnvOP7DkPW67/MhCTuJpcOXTztyNz2DMrxcJi8vAWq1k6fCGNKlQ1A6mDM+Of22w4+UlapyxsbGUK1ewaMbGxlKnTh3zmLg4S7sDg8FAUlKS+fji8OGHHzJx4kTz72lpafj5/e+x1Z0LkeFGNAs0E5hr+Dji42RdrAT5iOXnCCrnwButK3A5IsXi5loactDSIncOx7XvAJj5Ev806hT2TALmaBbwjf4FAKSwvbD9LdFNtG+qsHfI04rJhynmmtn6MgcNK089YFSLCqjyJP2Ts4pZyM/n8YQqti92YdxkM5Cmps1UVkRh/wRnZKAg0HkMZx/lolZKfN2/Fv3q+RKblsPeK+FMiP8ESfFABEk9Zwvtn/hQZCQWm3rxdc4A3Bxs+KxjVTJ1BoYvPcfZB0kYTUXbOe7Gi8DHwUrFd/4h8BDBa3H0LTLWAnkGn45SJpfScortFAxPzOSlRadJTE1jme3PtDGKLKSy29dMaPASxxacICQylddXXmDlq41wCN0mAh3nQNG5VAo6BHmikGBTgj/Ta/fD5lZeSXXnJOguhCHf7VSFPddjCE/MYsGhO7zfpZpweq/RXwRrVzcJ2f6E2+aSDSCyG5U6isyPbwML0ngFdzsquD9dJ6i/qw1rXmvC9TVTcEnN4I7Jm5d1H7FR8xkB+lhOzhmM24srzOMf/1vccTWa8+FJFoRVhQR3v+r2tyji5uiNTN16jU0XI80cEj0q7igqEBJjBGpjpx3EHy+4EBizV3ibJd4REgyFZRjyUShwB8CQLX4ea/R0Tg9jlCqMUeRJChTSSu19+wN6a4Fi6IHlEk8V3fickCOrSUV8zo2/OvCX59v2ZnOUConPfr9BWFwGzjZqfh7aoIg7fBn+PP61wU758uXx8vLiwIED5uAmLS2NM2fOMGaMEJFr2rQpKSkpXLhwgfr1RbfKwYMHMZlMNG5cDEkzD1qtFq226JPA/xpUSgUdgjzZfzOWXdei6VfPx8wxScsp+mT8aovyrD37kJvRaby97jJ+Ls9GbouUPeidO50hyv0EKR7iIqXhLT19O/B/ChadWZdWiR8Q5aGDlcQTZ7Px4FIeQ1oM+c/JgVIs/fbdwiQLTouNRknDwMduRvpsMQ9AQ0urhHxsuJrKJ7pZzOjmz0vBogwxe9HPTNCJNuOXdB9xziQ0TRzIwlHKJMBGx3KjpRrxRNVGbjSsh7VayR9XHhGTks0k03JqqB6QKNsT2m4TnvePUeneYYwqa4ZlvcNxk0j1ezpY8f7mkp92a/g4UMHNjpo+jlT3caCmiwn7+XnO4HVeLvE4M4J7wbHv+Ey9knlZ6Zy4WZlW1QvaZyOTsxj082l0aXFssZlNdeMtkbXpsxBqDkAJzBpQm0GLTnE5IoVXlpxms+l7kXOrO/SJLuQuthrq+TtzPjyZzYHTGJof7JxbLALawBZoVUqmdAti9KoLLD52DxdbDe72WhoEuuDjWhHafCAsLGKvC2Ju2F6IuSIE6x5dEuUujR0ENBclx4ptRRbtGQKNKp72VJGFXs4hbVvidM5M0I9lneZzmuUeZ/4v7wKC21UY73Wqwrd7b1sEOgAmGWbuDuXDrkFPfQ5PA1mW+WDzlWKzFZk6I/ZaFQMb+DGieSB+LjZQqbpQwN72ZsmT2nmK61vrRSF+qEuH3Awhrqe1Byc/0UF4/yjGu4e5ceEI5aQk3KT/DO3gc/1gpqqL97Oa4PIjRBe7608h12DiwM1YTuQ52mfrjdTyfXpJhDI8Gf9osJORkcGdOwUCdffv3+fy5cu4uLjg7+/PO++8wxdffEHlypXNrefe3t5mLZ6goCC6dOnCa6+9xk8//YRer2fcuHG8+OKLZZ1Yeajl68j+m7EcuhXPjF2hTO0hnoiretqb3W83vtHUvGi/1a4Sq06Fs+zkAwsxwadFiFwJ3+CWTLoi7gRadLyl+o2K0iO6KovnHvyrkHhH/JxfCm9d5LZvf2rcFd5UWkmPsyGWmbvEY21NH0dsH1c0vbZF8HQc/aFK5yLTX3+UyrWoNFQKBZ3rVwNbDcgyb1gfBB0sNnTjlKk6o1qUx0qtJDo1h9P3EolNDYXH4vOGitvUOz+AYacmEyM7M021kpaqawBM1I8lbut5tmhmggTTsl80BzoAVyJTUUjQINCFBgHOLMjrpHm1RXnzd8QCl1aL0pFHdfB4ioW0zRSIDsH6zn4+UK8jecs+sF4GFYRh4pz9YSjTIthuMxNfU7TgxLy41kIwsaqXPatHNWbIkjPcj3yEZJWnkZRRunltPjoEe3I+PJl15yIYMvEm0vd55728O0xNBKWKTsGe9KrtzfaQR2bHeYUE7ap5MrRpAC0ruaHwqiFasdtPFSXKOwfgzn64f0SUB8P2iB8QC3hQL9HKXgKnqAiqdYezP/Oq9Du7ndpwIaUKnxhHMVP1M2+pttJIEcp3+oGclQuu+7h2lanobseY1UU1nvycnz+PZ+6BsGIDnfJutgxvFkj/+r5C3VeXVdR+pDCajBX+T2prqDmgoPNNa1dAxn8cNfqhrNGPrJqJNFh02izw+XfhgcmTDrpv6Ku09A38wdCby6ZKLP58CmOjMzmx5DRphYjmfwUv/GyZhQosRtCyDH8N/6io4OHDh2nbtmg3x7Bhw1i+fLlZVHDRokWkpKTQokULFixYQJUqBTeRpKQkxo0bZyEqOG/evP+XooLFwWiS+fyPArGqz/vUYGiTANJz9Oy6GkOzSq74FnNzzNYZ2XA+gp+O3CW6EG/gz8DLwYqYtBw8SOasVSlPev82jL/EzkgNtTa3xjdPRv4l3UecMlVHpZA4+G6bogTRRW3EU3/7adByYpEpx/x6gV3XYuhZ25v5L+WJm0VdhMVtkVXW1Mv+kWSjFZ/2DGZ4c0HFTLt3DsOKvrhIBQTHK6byeCuScEOUNkxIKJDRS1qm6YYQYqrIas1XOEkZXFLVoV/Ge8h5BblG5V2o4e1ofgr/YNMV1p+PoLKHHb+/1UI4mReGQSeIwRGnBSm569dPd/1MRuJPrSZn73T8pHhkSYnU6Qviq4+k+dcHWa74XNiPOPmLFnv34k0Nb0anMWDhSfoad/OFepnY+Oa5JwYTSZk6ms88SLbeyMqRjWiV/FuBOnEhJWqTSeaX4/fZfDESpULi+qOCzEHzSq4sHd6w+PZukwlirwm153uHIPykWYAPEIaqnjWgwQgI7l3U3DMfBh381BwSbpPcaS79TgZyPyGTiTa7GMd6FCZRKt1vrMs4/XiMSivebl+ZFxr64WStocrHBYrRT1La/TPYcC6iSBbQXqvi+0F1aF/No0BUMeYabBohyn6FobGHFm8L6w+Hv8bjC5y8g/JSNJ+qVlhYhiTLdmwxtuTVx9Sz/wwa5vxIouTMH83vEXy+wBz0Fd0HHDXVprybrQWv7XnBx8maphVdaVLBlV61vf+nLB3+TvxXKCj/W/C/HOxAUXVOTwctKoUCrVqBUpIIKufA9y/ULraNOiPXwOe/37DwV3kW+DpbE52aY+aD1JHuFCsE+Fdh8q6H4lHRp9y/gkeKcqQYNFSTHqLI03sZqpvMMVMtyjlasW9ia0uvmpSHMKem0J957zbYWnZQXItKpcf840gS7HmnlbklVd41GenMQo5oWjEs7Q2UComtY5tT08cBLv2KvOsDJH0mV0zl2WFswofqtcICwSGAZhn7Cl6gWg/oOJ2PVuzig9QvcJCyyfWsyzSH6ay7mo6VWsGx99vhbl+QIrobn0Gn2UcxmmQ2j2lK/YDHynIpEcJIMfyEeF/DtkNgi2e6ju+tOU3Tm5/TP+9J+YZ7N3575MRH6jWClDr2FLiUL3WOn47cZeauUC5avYELafDGCZFteQI++/06y048oHUVd1a8Uge+yONQ+TeDkcUvjHfiMlh9Jpx1ZyPI1hvpV9eH716o/WQejD4HHhwXWcHbuyxtB5wDocNnIugpbp6FLYSP08sbiSvXiqFLznIrNp1K2hRGGDfzgvIwasnIKuvBTE0u3kKiU7Ani15pQEaugdux6ZRztMLLwarY887RG9GqFE98T39cecT4tZcoTOfSqhSsHNmIxoWJsze2w4ahlgf7NoTWHwhS91/kEBmMJk7eTeSVpWcLbZWZpFrPm6rtJR73rBirG89OUxPxUCjvgD0FpeO3dOP43SQUlrUqQRFoXsmNKb+V3Eb+JGiUCl5pGsDbHSo/lbFsGYriv0JBuQz/GUiSxLSewaTnGNh8MbJInT8sLoPKHna81b6osaGdVsXXA2rRMdiTUSuL9yQqDfmGgFqVAn8XG2o7+GCKFFmI5wnFo4tcMwVa+GQVxllTVdYY2jNHs+Cp5/Q2ReP9WPz3XuB9wlOa8DApix8O3mFy12rciUvnUGg8dRK20xDI9aqH1taN5EwdZ+4nolIouBKZwryDomQry7Dk2D361PGhnk0cynNLUQMrMhohSTD3xTrUdDbA+iEQ+gcScNxYnTH6CbygPAxAA8VtyCh4gh6m/5DkmEa8t3Een6WtQiUZOWOqxqjwN0nPa/Ga2LGKRaADMHvfbYwmmfbVPCwDHZNREJv3fSJa6DV2QmflGQMdgPd71KHb3be5llOeqerVBMfvJDj/vu5SQZR+noARzQNZe/oBjlnpgr5iW7QVtzh0DPJk2YkHgoyvKtSl9LBkhe9KHnZM61mdtlU9GLH8HFsuRZFrMDGzf83SFyS1FVTuIH5y04WQY+gfcG6JaGnfOKx4R/NrmwsMK21c8bC3Yt3rTRi18jwXwuEjXuWebR2m5n7HEOM2Att34ptbblx5rKXfzV7L2+suseNKNIa86CSonANDmwSQkq0jJCKFxAyduXxdwd2WOr5O1PR15JWmgUUI5MfC4nln3WWLQEeS4IeX6xUEOpkJsG6wyPoVRucZ0GTMXw5yAGbtDjWXWfOhwMQw5Z6nCnSOGmtauKWXhHd0Y9lpasKbbSsyuJE/p1aE07TQfsc8i5zZg2rTMdiLSw+TGVmCLMDTYsf4FlQupMNThr8PZZkd/vczO/mQZZk7cRlm87vwpCzGr71k3l/ezZamFV1pU8Wd9kGeFje/6NRsLj9MKZYjUBpmD6pN84puuNtrC54ib+1Gf+QbMqJCcZaKGpT+HYgwueOneJKz+9Mhv5QFoiR09n4StmSzWzMZP0U8S1QvktnkPZYcv2chHvc4gqRwVmi+xkNK4YixFsP0H3Dw3TZUyL4unpIzYkGhZm+513njblNMKNhq9Sl1uF1krvf0o3lJeZD6CtGqssXYgsn619AhFud1rzcp0sJaONO0c3xLgsrlffcfHIddkwsWYN9G0PcnC7f1Z8XxsASGLz3JIfXEop+D1hEajoTWk0XAUALW7z/FoONdxC9TE0ouCxXC4VtxDF92jureDuzobhB6O/kYfQzK1Sr1+I3nI/hwy1Vz8DCjX01ealSy2GCxyM0QFh3HvgPZKIKdeq+Ifce+L9Ce8aopvKHyeCw5eiPvb7rC9pBHqBUy53zn4hR3VujSdP2atBqvUOuzfcW+pJudlpQsnfm8n4TO1T35aUh9899oXFoOXeYeIylTZ2FJ8EmPYEa2yMvChe2H1f0tJ/IIhh6zwb/JM1ygkrH7Wgxv/HrBYlt5KZol6m+pqHgyQ3idoQ0vqg4/cdwJY3UG6z9izqA6tK3mwfi1l2h8bz5jCwVT3+hfQNlmEhM7ViEuPYduc48Vke4ojPx7A4hS/onJ7ag4Zad5/8WpHXH5m2UC/j+grIz1DPj/EuwUh6uRqXyz9xYn7iRYtB57Omip6+eMnZWKSw+TzS3IfwYjm5dnVMvyeDsVdHdN3nyFdeci8JXi+E79k9Bv+ZciXnbEXSp4itbJSj7Qv85vppbmbV+pFvOy6hCRshtdcmeSgeDyPF7fb1fNg+reDhiTwxlz4xXspWzuSAHsrruQbk1ri7bln1tBdAi4VyOt6wLarUkmISOXmtI9ftcWcAh4dR9sHw/xN82bMhV2TNGPYpu+kcV7EP5b1XixkT/qvHLliGVnOXQrnt51vJn7Yh5/6Oxi2JnnyWTlKDyQGr4mhA3/In764k3eMIhOtRw7P6xq9RHlj5Q8PSf3ajBgKXgWby+h2zIWzZXVXDZVxP6to1R8ihbvfMPMuv5O/OazpqBTDiCwJQz7/YnZhwvhyfRfWJAJuj/jT7Z2H/4aDn8lOrde2QYZ8fBdFVHuajFRXGuV5eJnNMlMWH+Z7SGP8LeHA5U3o76xGYCIgH50vNWTnMeY69vHNaeWrxPJmTp+OX6fq1GpGEwmmlV0o7ybLVqVAnd7LVejUnmQkMmKk+HojCbGtKnI+52rIsswbNlZs3qvQhJdXsOaBvBpr+pIsiy6rEIK+Q8qVPDSuudSssrHodC4YgUVH1g9RTfgMyIwZzWPd7x9qlrOcNVe8++LDN0Jrfk+0/vUoMa0PZSGdztWoWvNcnT4/ghalYKQaZ3MXLjtIY8ILmdPJY+yjM7zQFkZqwxPhZq+jqwc2YjUbD1n7ydx4k4CWy9HEZuWy+7rBeJ4SoVEFU974tNzSch4Nh+ZpSfus/TEfYLKOfBpz2AaV3A1LxaRsgcv6j5miHI/76g241qIhPt34YKpMtuMzZiuXvHkwWAOdBYbuuEtJdBdeZbv1D8RrvPkolyFbZ0yqH30EAAfGMegU9rRvLwzA+r70rVGOfouOMnN6DQ+61WdYc0CRR3rt+9AyibHozYVR2xjnLWzeLF7h0WrM/C923TmLRKfQStFCHPUhbSLXCqCX6MiC0ti5wXcOuUMj6m1ZumMTN0m+CurRjXmUUo2h27Fo1JITOhQiOj7sFBXyFuXSu6QeUZkRt9ihGE9AJP0r1Op4RuMbl0ROkyHWzvgj4kQHwq/9oc3z4JV0ZuW5r5o0Z5n6EvrsISnCnYydSKzZqNRgjqvw6V6X7i1Cx4cE1o6tQaWOkf9AGcuTu1Ivc9FFqXr3GMsHFK/iFDgE1G9rwh2wk+JrqWHp0Sg41weOkwr9hClQmLWgFpcjUrlfkImLyW8ymA3V3rFL8YvfAu/ac7xiu5D4nECYNnwhtTyFf93ttXwXufiSd+AeVyAqy0fb73GwsN3yco1UNnT3hzogAh02lZ1Z2qPYKTMBNjymiBk56PnPFGeUzwfQm2uwci7G0L440rRzI09pdvaPCt0spIauUt5PNABCLAHCjWkOpLJlktRbLlUutkoQP1AZ77cIRzg6/k7W5D+e9Uu6xT+J1AW7JQBEM7JHYM96RjsyQddquUJCqagM5io4G5Hi8puOFipkWWZ8h/ufPKExeBmdBqDFp0usl1GwSpjJ7YYW9JRcYFOyvN0UpxHJZVsTvpXcMhYh5XGzqTIdnxrvxZN7tNpAaV7Nab72PE8WDKEwEc7+Eb9M0N0U6hwKq8M0WQsC9u8jUapwEqt5GZ0mjnQ0agU9My/ye2ZIswUAaue30J+oBN7HXnNICSTgb3G+sy7pAckpqlWMEL12JNk0l34tKgOx6zfLxNqqI+rrZaZ/WuhVMDI5QVcq3sJmXy6/TqpWUJn6YWGfgS65QUBqVEQllcW8an/3AIdjHpS1ryGD3qOGWuw0diaqhejeLVFeUGKD+oJ/k3hl46QdE9YNXT6vOg8zuUhPRpXKY3wJ3i55SMpU7xPZxtNQfeWPlt4PB35Gra+Icphj4lJPo7C5YbQmHTafnu4iJ/YE+FWGRz9IDVCtK3vel9sr9yxyFCjSeZ2bDrnw5O5/DDFnB08/zCF87Rhg8KdeeofCFJEMEm1nvcNowGoF+D8dOdSCEOaBCADn2y7xopTRVXTg8o5MP/leqgenoDNoyCjkEJ4z3lQf9gzv2ZhxKXlsPjYPW7FZpCjN5pLP4+jqocN07PnQOni2U8Ng6xgkPViKji7FGvlkJmRBgUxipmz8zR4efEZQGTFPu7xfDWPyvDnUBbslKEIrDVKmlZ0pWnFooudJEn4OlubiccgnNWndAti+h83/tLrZmLNVlMLtppa4EkSg1X7Ga/a+pfmLA4PZUGI3W5qTu2mrzDS7SbS7V3mAKQkeLm7g0KBqcvXxPxykoqKaE5ZvQV6yHasiHX7T3BQqzEYTSw4fIfZ+26jN8q42Gr4ZkAtXBRZsOsTYcyJBF1niewMgEFH5sYx2BpyOGqsyTj9eEBimHJP0UCnGOTa+aHNiOAH1fd8aB2AY+s3savUDFltQx0/Jy5HpNCrtjc7r0az74aQ51cqJMa3yyOlp8eI9vLcNBFUDPq1lFd7Nhj3TMUnPYQ02ZrktrPQHkzjVmw6I5af44eX6uFooxaE49YfiM6vh0UDYgACmsLDk3ygWsfwCzWIbBFYrGxCYeSbRFqrlUK0MB+t3hcqydc2iXZpe68n8kwOvdeGb/fc4kFiJtcfpXHybiJbLkby4tNyeCRJqC5fXIHx9E8o06Mxqmw5W2E8OaFx5BqMhMVmcD48mYsPk0vle50yVSei8y+47x1Af9UxVhg707hZWxyt/1xHz9AmAVipFEzaZNli7mqrYenwBtiFH4R1L4Gp0DmpbUXZ6k8iNi2HnVej+fnIPWLSipe3GN4skImdqnD28hWq7RuGr/HPdYU+js3GFnypH0JSrhUFHi2WsMHynBwfl3Z+CswaUJvq3mXigP8GlAU7ZXhqzDsQxvf7ipJjDSYZB2s1O8e35K21F/8SvycfsbjwveEFImQPvlEv+svzFYYyT1veV4qD/as4Ij2iufIaT1omXr45Fu56U6FiW2b7T2dCxFvmfROyRzHLqCIhJYN3N4aYjVQ7BnvyVa+quN9cBfNmCcFBgFaToPHr6I0mlp24j3LvR7yqCiFNtuE9/RvoULN3ZAWqrClUamv/ici4GHSwplDpxc6LN50WUidlMaPUe/HRh8P+9+HSIqTXDjGooR+XI1KITM5iaNMAlp14AAhhSS9HK8hKgpV9RLbI0V/wWEpwoX5mnPsF5dmFAExXjWdm66ZoPeN4Z91ljoUl8OLi02x8o6lo4bfJ67DSlyBm2fwdTGH7cIu5wo+mL5iyyoVl43oU6SIqjEoeotR1NSoVKufd7gw5goPUb5EgDF//TWRZXjtcaimmvJstPw6uB8Dc/WHM3n+bg6FxRYKda1GpLD/5gH03YtEZTDjbqHG21eBso6Fuhh/vAsr7hwHI0pt4aXkIxZVRSsPyEQ3x9Xbg0MHmtDWc4DvrX/Bs+2rBAEMuRJyFiDPCPDYnVXTYySZhTGtfTnzG9l5g741s78mdB6mAbHEunap7Us5OBT+9LgIdjR3o8poKOn4Gjj7PdN4mk8yS4/dYdy6C+wmZZtsJHydrhjcL5MudBfyzM1Pa42an5adffmJ05JTnlukNMVXgXf3YEvd/0KUa3+29ha1kGey4FeLtFYfChOR8NK9U5mv1b0FZsFOGJ8JokolOzS4S6FT3djALsH218yYz+9Vk59st2X75UZEnxD+LjcbWdFWcpZ3y8nOZD2C2ZiE1DA8YqDyMQyFvqoumSqwztmWXsTH9mwXzqcMfgmNRGA+OQcW21GneBQoZq3fK3kH9T31QqjXk6E3Ya1V83MGHF3S/Ic1pXzDQvZrw4qrciZCIFHr/eIKXlAeYoRaaLxP1Y8jSunN3WieU050Kjuv9I9QdUvD7m+dgUWvhaF7rBdLuq/jWMAinDu8xxOqE6PJJvAMrexNo3RpfKYByTuUY1NDPHOxUK2cvAp01LwiSs305GLZNyPQ/D9z8HdOO91AAs/X9qdHxJVRKBZ2re7F5TDNeWXqGm9FpfLXzJl/1rSk4O1Dy61s5oBiyGcPiDgSkhjMp4WOu3a9D7Yoln2/TvA600Jh0Uqx8BLMl8rzI6rhWhK7fCEXk6BBhG/KUJZnGFUSb/t4bsWy+EElajp7kTNHW/bjjeXaqkUd5wpyXKM9orRV2eQupvZRNC/dsUjReaJQKyjlZU8/fmYvhyRy9HU96rsik1PRxpF89H0IiUth6+RHvb7qCm52WuIwhHLa6RDXTXTjyEbhWEiam9w4X63ZfEiTgQ2CiVkU8ToSYKnDEVJurNxtC4OWCID1fNLFGf2jwagmzlYx3N4bwWyHOS/0AZ7pU96JvPR86fXcILTpy0bD7nZZ4mOLI+bIpbxoznjUWLECNASJ7Vwi1FfcYq9zKAmOfYg/5erf4HlpjyU2sIJVs7LloaH1eX3WhyPamMw7yac9gXmkaWCC+WIZ/BGXdWPz/7sYqDe9tDGHThchi920e04yw2HQmb7HUr3i9VQWmdAsiKVNnJnT+VWjR8YN6Ph2VRW8mfxVpsjWzDQM4YapBJSmKxoqb2Eq5DFAeLf6Aj+NApcVk0JP9hS+2hVLdawxtmWJ4jRaV3Kimimbo/UkESMLaIEvjhk3nqUJFVqkyc5+CpHB2aKagkGTmGvoy2yAyNnV9bPktsXfB6xanGHxtiyjBAFvKf8bEm5WRJNj+ZgtqGq7Cqr7COBPIlVVcrvgGdV+cRpVP9iFhYpj2KJ/abBIO61ZOMHL301lBPA3CT2Jc0RulSccaQ1sims/g/S7VLAi9+2/EmrWbWlZ2Y4brLnwvzxbiiC8W70kEQOJd0n5sh4MphWjXJpQb83uRLqbC6DLnKKEx6Sx8uTZdz70qNGE8guH1I+K4k/Nh78dCNLHFBFFOK2a+mNQcVp8JZ8fVaO6Vkr1UKiR61CrHy4388XK0IjlLT3KWjuRMHUqFRIPIlficn2l5kHc96L8EXCtyMDTWzLOq6G7Lp72q07KyEEPM1hnpMf8YEfEpdFWcoYniJi+pDj1+CgJ2nhDQTEgHOJQT70+ShDZOejSkPcKUHkN05AO02bFP7TkV4dqcuG5LqeLj+lRCeEmZOrZfjmLH1WjO5bmDe5PA9nbxuGXeJS3yOsaEuziSiUKSiZPc8LDTQPqzuYY/CV1yZ9JZcY4JatHNtsDQi59VQ0gtoVx4QPNukfb2wJw1xY6VJChtJZ01oBYvNPjfM5v+N6CsG6sMfxkpxTl652HEsrNM6FgFNzutRXfW0dvxTOkWhEKCPnW82VqMn86zIhcNr+nfpaEhlI3a6X95vsJwkLLJwJrKUhQ/akrw8ymMe0egSicUKeHYkkOWrGW8fhxLNN/ltZ67M9rLC6tzC9BK4iY6z9CHn3J68eKjIF6vZMDJRuKnI3cJlKJZoJ6DQpK5aKrEbMMAFJhwJAOv6DPkO5BmyVpeXvsIZ/sUTDLEpedSztGKg6FaJql68aZqO13vf8lP0ufclv1Eg1ZgC3jzDNzazaW9K6nLTRrf+wF5+UmCpQG8q9pIe+mS6DZxDxI6Os8r0DHoMKwbisqkY5+xPncafsbUxwIdgJZV3Agu58CN6DSOhSUw656eeWowZSZQKuXXtSLbqs+h35XRlEs8DYe+FCWVElAvwJnQmHQuR6bT9YUV8ENDiLshsjl+DYUFRvQVuLoBjn0Lt/dAv5/Bszqp2Xr2Xo9h97UYjtyOL1G3pnvNcjjbqinnaE3vOt4WXKKAxysZdT6Em79AZr7ekASPLsKyrtBqEmcja+QdZ8PeCa0LynRGPdZRp9lSaRe6tLUWcggWaDcVKncSuj3FdIvFpOZwJTKFcGUWe6NjOJeSjEKCgXU8uXDjFq66aJoqr9NKcYV6ijtFjn/7UUcuLT5nth8pDIUkurxq+zpSy9eJ8u629FtQ0LbvQCbvqTYwRHUAxUlT3jYsMjceckJJNJo/jTmGfoTK/oQa/clCy0fqNYxVbWdsY2/u1f+Y/aFxfLXTUv7CRiradarEiLEwazkPsiy0it5uX4WQyBSWHLtnUc5/ms7BMvy9KMvsUJbZKQlGk8yBm7HFpmdLw9wX6/D2ust/yzmpMHBQ8y7+z0kg8GmwROrPKHlzwYZPkiHlAcyri0ltS4X0xWadncI4aqzJVMMIwmVLryJrtZJ2xhN8rV6EnZRDhmzFKP17VJYimaxai+1jN9lU2YbauYspLpevwMQy9SxaK69w1FiTj+0/5+j7ln5zX+24QcKJFXyqWYVDIZJlrqzmUpXxNHnxw6cS6HtapN06hsPaHiTK9kz2W8NPI1uWyKvJF7pceOQu6pBf+Vq9mLOq+qhe2Uw9/5I7i3Zfi+G3NT/xs2a2yFg0HCW6miq2A4XlYrTxfASTNl2hYaAzG99oBrNriI6oUQfBt37BwOtbYcdEyErEpNSyzWciU+7XIVtvNA9pVN6FIU0CaFLBhU0XIpm1+xZONmouf9JJDNDniFKPpMgLNCRRTspKhPhbgkMTe02Ump6AB1VfI7ByMFi7CKHHa5sKyklAtOzCNmNzBvXpi7ObF6zoITg571wVfmPFXOvlJx/w1c6b6I0Ft30rtYLgcg5ciUzFYJJpXN6FbwfW5s01F+kes4DRqh1F5oqXHTlpqs4dkzf35XLsM9Unl5KzawDtFBeZqV5iNvE8ZQzmpCmYu7I3o1V/UFtxD8O4y1xY/wWN4wtKT+fVDWigf3b19sLIkrVM1I9ht0k0BAxR7jP7rOlrD0Xdey4olAROLnivIdpROEqWpcC6OT+RTNE1YkgTf77oU2C0my/YmY/fx7WgZpmL+d+CMlHBZ0BZsPNkyLLM4VvxZpGvhoHO3E/ILFVB9O+CFh2HtBPxlp6uZfy5w7cRpEZC+iP0CisqZy1lsHI/X6qXmof8rB3G16kdMT329BsoRfOeagM9lKI19bQpiHd1b/CBeh29lAUaN+mSHfZygbp0o5wfiaP4xd9PiuWA5j00kpE99X6ic6+XLPZn6Qx0mn0UOfkhJ6zeNm//TD+U9coenPqw/Z/u4nkcNx6lEb7yDbrm7OCwshl1390uuq2eAmGrJ1I57BdWGDryqXEEU7oG8VqrCsWODU/MpPU3h5mnWUAvRSF36rYfQev3LcYuOnqXr3aGUsfPia1vNodZFSErAUYdAN8GpOXouRaVytXIVO4/uE/PB1/QXBbK4puMrVjhNI72tcvTrWY5s58ZwM1HqXwwfwUtNGG8XzsXHl2GxDBLT6znDRs3sgPbMeGyN/tN9TCgIricA+tHN8F+WVuhel3MNZBlmbfXCXFCgCqedlT2sOdmTBpRydnkGsQ5d6vpxcz+tXCwUot0xeoBwuG9EDLkAs5RPm6pqtArYwq5aHAhLS8rFIYBJVloaaa4XiRLlCrb8FD2wF1KxUtK5u/AYWNtVBhooRTaVXMNfZlj6I+Mgn6Ko3yj/hmlJPO7sQkf6F8niwIF79vaoWgko8V8bXK/44FcYGYaVM4BLwctP7xcD9tCPnnZOiO1p+9Fl3dd6/g5sWVMszLezt+AsjJWGZ4rJEmibTUPHswsMCFMzdbz6fbrFoTD/wRy0TBIN5U56gVme4T/KCILzAhjjXaATF/lcYshcmYiJhT4uQjC6bbLjwiSwtlu8zlqY8HTojPp/KCZT93CC8HHcWiPfA/HCngdCZT8VBghe7La2IERqj10il0CWAY7NhoVP7xcj9U/WRpfTlJv4mJuZXZeDX52C4THEJmcxfd7b3Mt5Aw71btAggrd3n7qQAegslaUZZy8KyM/hC933qRlFTeqeRW9gfk4WaNWSryje4PmA0biev930VV1cRW0fM/cVaUzmPjl+H0ABjbw5WpEMjWzhGDeqV8/ZbzxHeLTLTNp63mXceo/mKDcwADlUfpbJyA13wXWeYFOeiycW4zf+bVs1+Zx2krl40tg7QQOvqK93TkQ9n4kdg1YBuVqcycyhm827MOHBMZb78LJkCfqZ+UIrpWFUWrtF6FCWzaeiWD3xeu42mqQJLgRncboVRdY6d8UVexVuLC8SLDz26UoYTuhlPioWxBu9lrm7A8zc4/stSo+612dvnV9RLkxOxm+Diz23dTNXUQDxS3qSmEESrF0Vp6jquE2Jz2/5U6GmvqGkKfqnHKUsqgpPXjiuD+LHcZGvKl/ByVGPpTXMEq1i7dVv1FNimCifgxbTK3I1muZp/6BnsrT1FXcYbRuItflQEAuEuiAZfv5lU87iaCwGFhrlBx7vy1JmToG/nSKyxEpbLwQwaCGf+3vrAx/HmXBThn+NByt1Xz/Qu3/eLADYoEfqJvGy8oDdFaco6Xy2n/8HAB8pQQ+Vy2jgeI2ubKazcaWvKw6SCvFVQIG16NrzXIcvhXHocthLNZ8ZxHoAFRVPEYAr9IVNryC5vZui81qDKWWCX409GG4eh9S1HnhVP5YR1Md+RbV89L2cw196WT3gKCcS6zXfM6Rs3ehzkzQlK5ZUxyMJpnZ+26z6Og9MOayRrMElWQiq0JX/Ot3ebbJ8ko0vZvVYrVStPGee5BcbLCjUgpj2bvxmSxPqkG7+k2pdXsfytSHZM5rSmijL0h2qsXBW3Fm49vp26/yi+Irs1BcdKZMvF7s83GyplYez6SevxN1/LuhiBgCm0Yixd2AnZNEq/qNbfDHO5CdjB2QKWs5TU0uGwLRe9Tk/eEvoLB1FVkR2QTIoLKyLK1F5pdkJOEM7uTH5A2xnDc2pFOwJ05D55PyfSOc0m+zIKM1Ad1m0b2WyCYYjCbWnHkIwJg2FWlSwZVBP5/i5N1EzuUmCeNKa8sMYHqOnq/yWrondKxCeo6BT38XmlgOViqGNy/PiGaBOOcLJ5YS6Mj9lnDQtyO3YppwKzadI4/S2B26n58VM3FNvYZr3tu6bgrghKkGRhT0VJ7CVxLB23ybN3kr68di536eWGbozPd5ZH8jSr4wDCXXNZixGfPpzHmue33LxeYL6LeuMUP19nyj+hk/RTw/qufSTTcDqQSj4qoOBkLyqFLx6bklBjsAng5WeDpY8U6Hynyx4yYzd4XSuboXTjZlflj/BMqCnTL8JZQml/9epyp8u7eoLs/zggkFvxo78quxI1b6XF5WHuQT9aq/7fVKwlCVSPMvM3Zmt7ERL6sOYkcWh2/FU9nTnlq+TnyuXma+4ecj3ORBpOxONUUErvmdMLd3PT49ABt7WVOpQQdSs/V8/scNkjJ1NCrvyrwDIrPVv1VdpOiGghMSthcaWrYFy3s/Qo2ePcYGzDH057yDLZ9qv6Zi6mk6J6yApZfgtYPPxN3J0hl4e91lDtyIprPiHJPtthNguA9aB2x6zHjqecxIz+t8sXE1LyKFs/4pWTqOhiVwIiyBK1GpZgLo/IN3mH8QBigH86lqBXYpodTcM4iphpGsNxbwlxyMqbRQi3LGkcDxqGqNYruHI4FutsUvWuVbwotr4JcOgrx8dYN5V5xtFb5I7sQ+Uz2y80sf0fCBQ7nSvaGMBtg6Rvy/5gBzUJqUKcrBmToDM3aFkpXUjM/Vt6kr3WHr7XhzsPPL8fuExqTjaK2mT10f3Oy0fD+oDqNXXWBvorsIdmKvCSf1GsKkc/25CBIydAS42pCQrmPpCZHpGt2qAuPaVbLsqDIaSgx0AKRaA/ED/FxsaFPVnQeJWay009DxtAvNFdexIYejUgPq1m3I+fBkEuMe0U1xBiTYr2xJ/8y1f76N/Eno/SM5FTrTfuFV4XL/GKp0Ho3k0E24tMffpN6eftwdtYyfIvrSfU8Au7STCVTE8qFqDQsNvYp9CbW+oGPt3P2kpyIeD2sWyMbzkdyKTWfmrlBm9i/dfLYMfw+ej5lJGf5fY1S+C/JjcLTR8GBmd/rX8/3bzyEHLUuNXblrKvfkwX8DFhm68/K7s5EU4vlBIxlYfz6C0avO4xS+m97Kk0WOCVDE0Vx5vSDQAdJkGzYbWzJaN6FgoKSgVtPO2GhUlHO0ZsHg+qx7vak50AGEB1KVzuKX678Vea1UtWhdvmKqgIyCmhW8WeL/DW/o3kGv0ELMFUh4+sD0QngSY2evpvbteZzUjmehZq4IdKydhfqyS/HfiRKRmUfgBfCszp040Y4Tm5rDipMPGL/2Eo2+OsD4tZdYfz6Cm9FF26QvOnfjFYdfOKZujkYy8rV6Mb0UBdf917c6Ief5Y7W2j6FXrXLU8nUqPtDJThYt6fmmqHkwKbX8ZOpL88SP2W5qVhDoALV9HZ/slXV1g7jO1i7Q7Vvz5gkdqyBJcOJOIj8fvUeGLExzjUi83FiUPgxGEz8fvQfAR92DcLMTBqAtKwsxxmXZLcit/oKY8MRcQHxOc/aL70l4YpY50PmoWxAfdguyDHR0mfB5KSJ4b13kwM1Yxq25SOfZRwn+ZA8dvj/CylPhPJDLcdtvIJ1e+5I5bw7k4sNklPE32KaZSoBCyC90MB77W3h2Z0zVADDlpDN0bVixgQ5AhyAP8G0Arx8W4pzZyShX96dD6hbSsGGafjggHl70KMmwLdoqLuUWdMBN3nKV5jMPMv9AGJHJJWsaqZUKpvcW5rbrzkXww8F/oPRehrLMThn+Oj7qHkSHYE+m/HbVQn9kYH0R5IxvX4nMXIOFsejfhSG6KcLC4T+M11U7SN2YzsreL8JO0ekE4JhwCcWGT4s9Jkm247QpGJVXMHti7Lhu8OW27Evbal587rwDLuUN1NoL9VqlGp3BRGhMGlU87fm0Z7C5HKFSSFDzBTj4hRA+jAsFj2rm1wrV1KQJO6mnCAOjMCe8G5/BWlMjYtmAL48sOn1Kgi76Omf/WIpHxC6WK6IK7iDWLtDoNWg0+pk9tUwmmazza7CTjWS61mBrqIEHed5X8w5aklqreNrRqrI7jcq7UN7NlsO34vly50087LUceLe1CDbk7kIR+ewi5lgv5mhmTbJVjni7OiH1mA3bxoqgw8oRun9b6ESMEH5S2IZc21ysKF+9zHmkULxbdUhkKjl6o4XpowWMejicx8Nq/rbg8eShZ21vnG00/Hz0LsfCEszGnvW1kVg7ZgNOHL4VT1KmDhdbDf3qFigX22hUeDlYEZOWQ0TgQCpd34ApLZr3i9HJsteq+KJvDXrXeUz5OP42/Niw+PPOw8/XYMYuy64oa7WSGj4ODGzgx4B6vkQmZzNk4QFeylnPKO1O1BTlvZSKkXsg/AQceHqJicYK0TK+59w1zj0qnhNjb6UqCEQdysHwnaIcGbKWqpe/ZJaqNbdkEdzcMvkSjxNNEqfxpmob1aUHtFIKPTEJmSFN/Pn1tCglRqVk892+23y37zY1fRwZ2iSASp52uNpqcLHVYKcVr9u4gitj21RkweG7fLv3NstPhnP0/TbYaMqW4P8Uyq50Gf4yJEmiSQVXpnQNYtTK82iUCna909J80w9wtWVcu0rmYGdAfV9GNA/kh4N32HXt+QZA0bhglCWU0n++yfCHhwEMTF+BPRAlu+FAJnOsl0AxXM2fXT9gVlQNjCjpYOfJfr3wqzrwbmuRGv+6kB1ETiqpDy6xKtyZlafCiUvPRamQMBbSfIlMzsbPxQ+qdoPQP+DcEvNCnpiRy5nwVJogSn8alYKGgS40CHTmh0NhOBsTRWnBvpSsWMpDTHumorm5lRYACjBIauQKbVHXGwxVu4JK+0zXKyIpi/XnIth2/i4bdN9jJ8GMmIb8+lsB/8pWo6RpRTcqedjRtYYXtR7Lnvi72vD9vtvEpedyLSpNtPdKEnT5GsJPoYi9ykjHC3yf2oYFh+/yQZdBoLWDdS/D+aVQ+yXwDIbjs+HiyoJSGoBnDWj4KjGOdfBaLcphquI+zDxUcLMtOdABuPQrpISDrYcIDB9Di8puOFqrOffgJGf11QhXlRfZslV90Q/5jZm7ReZtQH1fYaBaCB4OWlzTQ6m0YwogtIoeD3QaBDgz96W6+DhZF2xMugeHv4Yr6ygNyeV7MmOXCCpeauRPp2BPKnnY4eNkbe4wMhlNrF8xj6OGGX9uZanarSBY/xPYEFP0+zusaQArToWTnmMgPUdPtt7IzF2hbL/8CIOpB68qlUxRreYF1RHzMeW9PbB5pCJDZ8PXBkH2H2rai5uUxjZjMzTXYvisV3VSsvRk6QxcfJjMxYcpXI1K5f3Nlkx1jVKBs60arUrJw6SC4DkhI5fgT/Zw58uuRT7LMvw9KAt2yvDc4OEgFjsXW41FLTs5U8foPK2eyh52zOhXE7VSwcIh9Xl58eki8vp/DRIPZC8qStFPHvqccdIYxEfpgjPUTHmDK8rXig101tZfx4KLSox59s01fRzZfzMWO62q4LoVyrLoUNN+8R2LjqzCgY63oxWudnmkx4ajRLATsg46TCPJoOXlxWd4N/McKOGe7EXfOj7mLqklnbTY7s3FJEtcTrGiXnFJmbsHkdcPRaHLwChLHJHr4tZ4ELXavySyI8+IG4/S+HbvLQ6GivLGSOUuyqmTiMWFM47dIKnA1vrUlPalkkC1KiXtqnmw42o0cw/c5ruBdcR7UygguBfEXqW/Zyzfp8KiI2F08DFS3zHPg0s2wpJ2lhNaOUJwb6j1olAfliSGzznKbJM/QYqHNFHc4A9T0yLn4WqrYfGwBiW/aZMJTswR/285ETS2RYbsuhrN+5uukKM30bqKN+V6bIZV3SH+JqkLOxOe8jkutja82bZSkWO97LUs0k4x/67ChI+TNTFpORhNMnX8nFj5aqOCTII+R4gxnixGSNOlovBJy0f/XxhzygdIol9dH2b0q2k53qiHy6tR/P42k0q+Ak/GrZ3PNv79+xjmN0KVHU+87MgFU+UiQ/Jb6gFqfrr3sb0Svxi7cUv24wf1PJzyXM01MRdx0MWRRcEfwypjp4LDMnRM236d3e+0NJPnkzJ1rD37kL03YklIzyUpU0e23ojOaDIT5EEExDFpOWTpjFirlRhMMqpS4uMyPD+UBTtleG5Q5bX7GgtJN12NTGXsmgtEpWQT4GrDpjHNUBd6klk9qjGXIlKYsz+Mo7efj1DgWVO1IjLv/wnsKLTYlIbdDxWkZhcs6LP3iyd2c8DymPRVhMmNBByp4ePAqy3K07m6F/fiM3GyUaOQJFxsNQUZhQptRKtyYhiErGNWRGNuxaYToBGZo9GqHaQG9gUESbJxrHB632lqzLFLidSrmFfeyM0QT/tXNkDEGSTgvKkK04wjeW9of2pV83j2C4QIdPr8eAKd0YQkwQsBWXwUtw5M4Nn9I/Y17MTnf9zgl+P3Gd26QqmBTj7GNXPFeH0bAbdjmT9DQ0NPaOulQ3NtLQA+D7dx0vEKzjlRWG8uRReq/y8Q1LNIhspOq+KUKZggxUM62txGqtgfpQSJmTri03NxsdXwcffgksmqt/fC+sFm6w6q9yM+PZe0HD0KSUICjobF88k2QZ5uGOjM/JfrorFSw4idMK8ubjnh1JTuMaznC5aaSAl34If6FGeV+0brCkzddh0rtYIFg+tho1aKoOvaJthSNLOExg76LBSeagfyFKkdfAn37srpe4dRKSTe7Vy1YHx6DGx5He4fKTrXfwKzypsXMHcplY6Ki2w2tbIYsu7ck13Sj5tqMsD4JYvV31JeFtmwhZq59NWVXkrrMueYOTPjYqvhzbaVLALRbJ2RpCwdSRk6MnINVPG0wzWPZ5WZa0BnMJWeCSzDc0VZsFOG5wZT3iKdmWtg7v4wnG3V5hs4wGstK2CvtfzKSZJEPX9nVo5sRHx6LuceJDF29cW/dB7fGl4o2S/oX4CZcW+wU9WY2Yb+ZFDQ7h2U95R44todmhcaX1ERzdy+lejVqIq5hFPDp4SMiiSJTqzdk9GdXsyJGA3W2KEPbAMPfwXA8eBkqN1TdF5FCHHDTcaWnAl5RLsgDzpXcYTFbS0Iy9uNTflMOY4vX2hA2z8R6MiyzKm7iXyz9xY6o4kGAc58192HgN8HgUkPlTpA/ZGA6PISxxQ7EWTECjLzg+Nw7xBBURf4SVMohZaQ91MI3rn3QQKDrCBadkXt5IWXh6elYN6VDYJY7VPf4tgFQ+rx4YzqjGQ3Xa1v0PvFOqV3XOVDlwUha0TbeiGhwXHbwtlx/Xyx769jsCcLB9dDZcyBhxd5uHc++SyUsY2caF2zHJcjUjgYGoch6jLvPygmaAGSA7ubOyHHtK6Ed/ZtmN+hIOAqDLcqQsOn5guiOyykUEkrLZI9Z0RZsVF5l4ISWEIY/FBKJus/jCxZy+/FZNyeBpIElarWouf1T7lmNQqAuoo7/Kb5hPmGPhw01aW4FrL6Ac4lKoOD0Nrx0Vhblg3zYKtVYftsVd8y/EWUKShTpqD8vPAgIZM23x4udUx5N1t+f6sFdtqS4+xsnZGgT3aXuP9p4Ewa+7WTcJUKTHZm6V8gRK7IIvX3RSwZ/inkGwvWl27x3aiuHIy2YvofN6gj3WGr9pOCgZ8kFbFBKBayLDyXvi2a0i+MLprlvGu1nY5pW5BV1rzhvoI99w1olAqOtA6j3Mmp5CismZPbm6OmWlSo0YRpvWrgbv/sd+j0HD2jV10wlys1SgUb32hK7SufC26RfTmRwchNxxR/my+2XSI3J5vhjb2p7KqB3HTBLUm8I9zKdRlFX8StKrJ3beKSM9h/P5doXBnUrjF+R98V+yu2R+72LXPOZTP38AP8XWyErYZBBxuGglnXSBImpNW6W0w/ZulR5oT3FX5nY89YkL+LIDcdjn0nxP0eI31P0r/ORmMbQGjcyIiPzMVKYkTFDLqlb8A+7gI2uXEWx11RVudAre9YfTXDrFpeT7rNFu2n5jFz/OYx9uEE5hj685OxFyYU1PFzYv1AL7QL6hU9z47Thb2GR3WzCCMgylKfu1kMvWnyw1B9IDXdlRCyHlIfFpnOpFCjMOmLbH8e2Gndk6ZZh3CWivns81AjZ4nFw0Nx8HOxJiKp+G4tgIZSUf+9N3TvsJ/GHHy3DUZZxtvJCm1Z7elfgzK7iGdAWbDz/LDjSjRXo1JJzdaRnKnnUWo2VyItDQsdrFRc+bTzE+fKd6v+s+ijOM4czQKLbYNyp3JGDmJkYy8+CWlXwpH/OawxtCVYEU4dhWgpfkn3ERWlR3xgvQ17Q0GbblK7b3Gp2ky4dT+eVchOhm3jRAnrsVbpkrDR0IqBqjx/poHL0VfrzehVF/AKW8tn6hWoMfCxfgRr5U583ruGuf35WRGXnsPI5ee4FpWGtVpJ//o+DGvkTeVrs0Vrdz6snCAn5ekmlRTC+8m7nlisK7YFxwJ5g7fWXuL3kEcElXNgZ51TSIe+hDqDoc8CUrP01J4uuBsWCrjxt4WqcdheKFcHRluWZjaci8Br+0uiK6cYOwYzjs+G/Z+afzXY+7Dfvg8T79UnCytcbTW0quLO6NYVzHwP+e5hdBtGoM21bMtOkW05aKrLMkMXrsoFthmO1mrc7DTcjc9klfors6DmGkM7phhGmcc5WKk49IobrisLfc8dfKHrTFGuKwnJD2Bu7ZL3P4YzVi2oVckf62vFO4L/FUzSv44Cma/Vi5849nP9YH4xdmdS56qcuZ/0p8rie8bUpeoyS0PcT/TDMDZ4jS/71izhqDL8kygLdp4BZcHO3wuTSabCFEvy4YWPO5jr1yVBlmW2XX7EnP23za3IzwaZpepvaKe8/CeO/e/AeusXGZRdeidNaYiqMwGfPp8CEPIwmUq/VLXIeiV5NMFl2GqwdSthBsg1GLkamUpCRi7ZeiPZOhOZuQZO3E3geFgCBpOMq62GFSMbifLbkVmCHPsY9EobbisqEJujQo+KTrX8kVRaUFsLiwXXSoKP5BwIqpJVaBMzcmnx9SGy9UZCqq3C8cEuaPAq9Piee/EZtPvuCFqVguufdbbshDm3BHa8K8o6485ZzHnjURpLfviS7zU/IVs7I70dUkDOjr0OC5sVey5zDX2ZnafkO6SJP0OaBJCUoeNGdBq3YtJJzshmesQwvE0xZMsajlObHTZ9OZTsRiqCA2SlVlDJw45AV1t61fambTUPXlx0mgvhybR2iGGp4UOUJvGZNc75gVhcKEci41VbeEl9VBCxQTihD95Y4nVDluHGVuRdk5Ey/n6ZiCfhnsmLRBxoqCgop55U1KeZSTQ7yF2/Ieg3N/oqjzND/QsRJnda62YX8aN7GthbqVj/elOCvR0IfxTHg4X9aa0UnVWzFa/w0tvf4OVo9YRZyvBPoMwbqwz/OFKydMzac8ssb18YdlaWX72hv5zhWFgC1bzs6Vzdi47BnjjZqOldx5setcrR/6dThESkPOMZSIzTj2eFNNPihlka4mQnsyvzvwVZshabEspuTwp0fjZ0Z4ZhMH5SLMe0E1hi6EqwFE4zpdDnKXd5Lpl3tnBHUYG5CfUZqKxFV2XBQu8SdxpSHhYJdsJi09l1LYbT9xK5EJ5s0fXyOGr6ODLnxTqCwHttc5FAx1StJx/EtGVLjDtGlCgVEkMa+9O5d41S31tJcLXT0qi8C2G3b4pAB6DR6wDcjBaZwqpe9kVbfkMEWZs6g4vMuS0kiq2mFryj3IF/dgRcWMHVgGFsuRTJtAvFBzoAcbhSxdMOL0drtl9+ZNZnyceryh14q2NIku1Y3Xg7dSr7c37LVVLJxtVWw5axzQhwLdq5dTdelHMmDRuA0nMoD2c1wV93l7e1v7NF15hfNTOwkvRYuB60/qDki6bLgk0j4PZuJOCOyZtKikclj39G6K3dUGcnPHFcrqwSpUKggiKGCsRgkBXMMgyiQvf3eLFZJfimEmTGExUVQQ4+/GZswfuq9fgp4umguMBek6VeUM/a3qRl6zlSSqZn+YhGBHs7cOZeIq+vukyqfjKu+lT6Ot5m2IhxZYHO/wDKgp0yPHeERKTw5pqLRCYXrY2PbVORiR2rWCw0WToDx8LEjTA0Jp3QmHTmFlIHdrJRk5IluABudppnclrPwoo3dBO4YDXmqcafNAVzzlTNwsH8n8YNOYAbxgDKa1NoaTxXZP/Pihd4fcx7SEdmiU6bQgiSxOIaIXua+UEAY01beVu1Ba1kwDYjnNqEs1RTAql7cVv0dYdzq8F0joUlsO9GDBcfplgMcbPTEOhqi7VGiZVaibVaSWUPO7rVKkdFVxu4exD2LCzioE3371ljaM/Gy9ext1Ixtk0l+tf3wcP+ry0uga42KBV5gYWdp5lj45knj/AgIRO90WTRGWgW+XusnJaWo2fVqXBMKMis+zqcn8qDfQvpmVMBkKijbmahkB1hVRXfd49i1GXzsdqBiRsum/WkbDRKPB2sqOppT7Vy9gy7ch7SILXeODKwYcSyc2hM2dR0VrKsSRRuWY7g2qjI+8vWiWyNVqVgU0gchzO68YNmPi9Le3jR6gAK2cBlU0XqKPJayKv3K0K8NsNoEN1idw+il9T8oOvFclNnQrSvP/0FLwV/GBvTI/tMke0njcG8qx/DJPV6+uUZ6SbgSIucucxpmEJvryR2hUTwQ0R54u2qsqK8B2ExaVTOFEHLx+fFdyQHLWuN7Rir2s5I1W726hoS4GrDjL41aVbJjZN3Evhoa8neeaNbV6B+gDN/XHnEO+suY8iTdUjEkTFvffjEDHQZ/jtQFuyU4bkgW2dk3bmHfJan6Ps4lg5vQNuqHsXK6Wfmlq6ympKlx9lGzZAmAbzdvjIPEjPp8P3Rpz63RBzpnDuTPdrJTxzbR3mStYb2fKcfwLvqTU8c/59AA8VtGihuU5IY7WjTBvhxQ7H7LsnFE5UXGPuwyNgDLykZPymOV5U76aC8VOxYAPWl5fQ4VaA1olRItK3qTusq7jSt6EpFdzvLz1aWhUdT6M9wea1ohQfBt1FqwJADgOnwTHZmGwBf3utUlWHNAks8h2dBgKstu0zl0aNGnRELURfApz51/Z3NAfO5B0k0q5ivuSNDTp4FRXaKxVxXI1PJygsuBhz35opWIlB6hDsp1AmuRkzADzTbdZyTVuMB8Mu5xd3YZDzd3Xl9xTlO3k1Eo1QwrVcwLzb0L+jgkWXk4yLjOOq0K3fleziRzmWr0ZANHMr78awBY05YnFP9AGdO3k2k4+z8v4OmvOJ8j0bJO1DIIjMSJbtSh7vkSlq03b+z4HrFp+cSkZxFbGoOdiFLaXn3ICZZYomhC57KVELUzyfQAeihLBroLDN0ZpOxNdPVy+ioLOi+9JESkVHw9jkX3sYFyGvlTs+l69xjVJSiOJAXe4xTbeWwrg4AKw0deV35B00UN1nT04ZmzdsiyzIbzkcwZctVDCYZNzstEztWYcpvVy3OJTwhi7j0HD77/YY50AGY/1LdskDnfwhlwU4Z/hJO3k1g5PJz5OiLljGCyznww8t1qfAEszx3ey0bRjclOjUbTwcrTt5JKGITsGF0UwLdbFEpFVTysOfnofXNQoVPg1uyP1VyVtBbeYKKUjR7jfV5X72eJoqbRcau137OcN37fKYfyrR/wFj0eaLRgAlI68KxJYdyUhIOZBIpu2NCwQTVRoIV4ZwzVSs10MnH51a/cihwIq2ruNOlhheeWj2obQs6eWRZdDWF7hAZnMJqxFoHqDsUGo0C5/Ji3P7PUMTf5GvTN4xwns/gP0mCLg4BrjbE4cxFdR0a689BpAh2lAqJZhXd2B7yiHP3kwuCneT7EHEaFGpoZSmN1yDQmWpe9oTGpJOJNTG44EMi++ocw8m4DY4eYbRVQZlxun4oG5cIvkd6jgFbjZLFrzSgWaWCUmBmroEfD95ijGyFvZRNRUUM/pXqMNXqENx67M3EXiu4vnkBS+PyrubuNq1Kwbi2lajdcC58v8N8WHflWQA+yH2V762cMRpN7D57g7snt3AxUcUZUxCDlIeYrl4BgEKSGaP6/YnXVkYq0RX8SZiqH06c7MR41W+MUO0psr9mzpJSj4+SC65hA8VtqkkPCZX9icGVXaZG9FSepmHsBg7cDOarnTfNZrG963jzZd+aJGcWzQrvvh7DiTsJpOcaLLYHe5fxN/+XUEZQpoyg/Gex+kw4H/1WND3sZqdh8SsNqOvv/KfnNhhNrD37kK92hpKtF0/VKoWEn4sNFdxsqRfgzPKTD4hP/yst5DK9FSd4TbWTGooHRfY+kl3+FuPCvxPLDJ2LXUSeGyp1EF5d0VeEyq5SC7bugCy6wgr7SaltoHwrqNIFag4UNg2FkZNK1pxG2OTE8IfDIHpMLE4a78/hTlwGHb4/wpfaFQyW9kDV7vCSKOOtPPWAT7Zdp2VlN1a92lgccO8wrOwtsk4fxxXpeLvxKI2ZP/zAVNWvVFZEFfuasbiwzdCUrwwvU1iX5fdxLYSNBYJ0/8eVaL7ccZOemZv4SP2MHUxOAdDtG3qvjYGcFELkihyd1A5/VxtIjYLZwUUO6Z87jSCHXEbnLsePf554XBg7jI341jCI+3LJViVvtatETGoOG/PsLxpJN1mlmYlW0pMm2zBYN4WrcgVzK75RUtMoex6JOGKrUTKqZQXebl+Zcw+SGLXivEVQs3JkI77de6tIx6hCgtNT2v/lcmoZ/n6UEZTL8LcjI6fgptGovAuf965BFU+7Jzs/PwVUSgVDmwYS7O3AZ7/f4FZMOrkGE/cTMrmfkMmB0LgnT/JESGwztWCbrgUtFFf5VTPDYu9/W6AD0FlZlNPzXPE458aYC2mFPJgUKqg3DIJ6gH8zUJeyWFg5crLqZDqEvEPXtE0Q/y64Vy15/DPAz8UaSQI7UzoosbC1aBDgAsClhykYjCbBH0vNC2CMOqFW4XrcAAAuyklEQVTjo7U0+wxOO85Kq++FAOLj8K4L/ZZwN9WJ75ef43GPEFut0GRJztQxadMVYkJPMUm1h97qk0XnysfUBKFM/NsYyCz0XU8JhzUvsA1ACx/rR+Dv2kNkffZNLXaqzdrP4N8hK2VGmmxDd92XRMieTxw7/7Es71k5iCa58/lZM5tGilvMUv9MD91XOFVpTsiDitTmLi8rD/CH81B8na2ZeyCMrZejeJiUZSHkOLpVBVpVcad5JTd+OX6Pr3aGmvctGdagLND5H0NZsFOGP43RrSsyqmWFUlVE/yrqB7iwfVwLTCaZmLQcHiRkcjMmnXP3kzj7IImkYtLSfwbHTTWpkrOCroozzH1Mm+e/CY8HaDnl22N1/0CxYxcZuvO9YQD2ZNNbeYKP1atLn7zTF5CZIDI5rhXBrzHkpkFWkuDiSBLYe4Od+1Ofb6hjC2yMwaI77O6h5xbsaFVKvB2tyc3I09Ep5MRe1cseZxs1yVl6Fhy+y/j2lSGhUO1I81gGymSCPyaIQMc9COJvQvtpec7cssheOfrQzM2aNa81od8CyyCm73c76Kk+h0ohMUY+RH1tGCXCykkIGirVIos28QakRcHVTXD0G1Fm0xVoT72l+QN+vgTRl//chfoHcMlUiVmGQaUGOq62GgJcbYoQ4fORjAOv6d7lsHYiQYoIBiqPsO5WO+wUXZin+ZEhqv3MT+jH/QRRxgrPk67oX88XN3sNPx+5R1rew1pUcjZLjz8wz334vTYEuhXtgCvDfzfKgp0y/CX8nYFOYSgUEt5O1ng7WdOskhuvtiiPLMuExWVw4k5CicToZ4EONdtMLTiUU4eD2vdwk9Kew5n/sygp0NHJSr4yiBbrHLQsMXYnBTu+Vf9c8mR7Pxb/Nn8H6g4Ga2ewcRG6N38SPs7WhMueNOMG5KQ++YB8RJyDXzqI/zv6i/Op1AF8GwgF4Nt7eNXqDC/k5IkDKgpudUqFxNQewUzcEMKCw3cY17YSitovwYm5YkD4CQhsUfBaD09Cvu5MfB7H68BnxNZ4Hc9ri+DEXDIub2FJuenMuWZJaK0n3Wa+Zj4+Up7Zbf6fi0sFYTR66deCwSprKFdbWHh8WS5PW6iiEPmLvy2yaORYzO8px0P08/GUe17YZmzGMVNN+qlP04wQ8/ZTxmAm6McQU8hgs4KbLffyApJ8uNhqGN++MtO2X6c0pGLHD4beTFWv5iPVao6bapCEKGNYk4tGpaBdVQ+0agXeTta0r+ZBZQ97Nl4Qflmp2Tr2XI/h463XiE/PNXOrygKd/02UcXYo4+z8r2DLxUgmbgh58sCnQAfFBZZovgNgrqEfRllBQ0UotRV3cZBKlpv/b8FH+pGsNnaw2FaORE5ZvfX0k2gdYcgm8CvaGv20WH/uIaZt44WXWWBLGP7Hkw+6fwxW9Ch+n6MfpBZj/th5BjQda/41R2+k2lRhEWFWUl7eAx4cg36LodYLwgz17CKhipybBgHNRSCUh7mGvlw1VeBz9TLKSUlcNlWgj+5zREQjM1h5gM9Uy1FJJWsQPRPUNkSpfPHJfjrNqL8b8bIj7pJlgLrM0JkY2YV3VJuxlgqyrh/pR7LW2K5EwT8vByuUComolGf721Jj4IR2PB5SCsmyHfZkoZJMnHLtT8DQH3G10/DJ1utsuhiJ0VTyUufvYsOqVxsVq2lUhn83yjg7Zfh/h371fKnr70zbJ/hzPQ32m+qz1diMPsqT9FMc41PDK+wz1MeAkspSJPUUYSTKjlyTA2mtCGGUatdffwP/QUxWreWUKZh7srd5Wwvl1VKOKAa5qfBLR/H/Tl9AkzctPZYex4JmEHcdpqWYCcD3EjIpn9/Z85iPlAVMRmGmuW8anP6x5HHFBToAez4UxqY95wBgpVaiUSrQGU2k5xhEsJPvdp72CE7+IIKcLKH/lOFSnUmZo1hIQbDztuo3pumH8YOhD1+ql1JHcY9f1N9CuTq0sb6DMvxYyedZGl5YKWxBYq6Klnm/RuBVi91RGt5YfZkRyl3/aJfg78YmnDYFM1B5xCLYiZcdLMjx6V6NSav/Fl23mUgzqnCx1aCQKFYnKyYtp8i2p4EeFfNcP2Fy6nScjSnm7U0TN/PNgRFsDdM/VQBVFuj876Mss0NZZud/DY2/2k9sWi7ONmrScgylPtGVBl8pnn2aSRZPqIWhl5V00c3kruzDOe2YIk+5/0akyTbclP1prAglzOTDEN2HxCIIu9s0H1M7z6OrVNQbJsizyQ8st1fvBwOXFR2fES9c1AsHIp+mkqM30vbbw6zPHo2/Ih66fgON8/RdTCbxGjau8HPLP/dmi4NXTdEZ5teYpkujic5Rs++dVlROOQ7rXioyPFHry2qrl5gTW5uWiqus0HyNSZa4K3uX2JX1l/DuLdGmf3QWnFsqAkrAZO1KWLYdpw1VGKQ9iZUx8wkT/T2omrOcXNSc044t9vsua+yQ/JtA5c5cLTeAESsvkpCRS+PyLrzRuiKrzzxk/83Y535eTqSzV/uBhfr5bmND3tBPeOKxswfVpm9d3yeOK8O/E2WZnTL8v8Qvx+8TmyZaT1a92hhbrYpPt18vVSq+JETK7ryim8wI1W78pDjKSUkoMWFCwlVKRy0Z8ZESiJA9Sg109LIStVS6cOKzIE22wUEShMvSrCSKg4OUxU/6nlRQR1NZEcUZq3F0z/2S63J55hj6s0zzTbHH3TT5cW/APrpXdxPkWYDrv8HG4QWDrm8RP74N4dEl4cPkUR2+q1J0wu+rY8rJ5cPsSvgr8z6bat0K9h+dBYdnFD3ucdQYAC7l4dFlQdLN/L/27js8qir9A/j33qlpk957SAIkkEJJDLCigAUBQWVBxBVBZRVQZJUV15UiK+CCKKgo6C6sBXDBH7CLwoKU0BFCQmgJEBJaGiGklyn3/f0RGBiSkAlMMmTyfp5nnoe555x7zz1E8nruueet//d8TN4FXfXXt0jIP1b3AbAfANQAvqp/2lxywyL9M1hb8yAMpXVvU70m/w8AIIUisLHzx3jPcQOU6asAXfMCj0a3NHj667qs7hsm1Askxeqr6Iir6Cg/3+jmki0tXQqFK8qxQPFVvZ/3U1IgLkc8jwEjJgEqR5zILcWQxXuM5Qezi3Ewu+XebiyBEwbUzsdsxXLjbtYrDeYl+uVAp33gmR3wzI4teXrJXhy5UAI/ZzX2TusHQRBQqzdg1LIDjb7ZcTcyVGPqcg8BMJAAmdB2/zP62ZCAibrJAASkBX0Cl8L6r69P0r6Ox90LMLhibd3MQ/RTQOp3gKSvf8Ib3COAQQvq9q9pyoNTgX7XF0BrK4E5/sDtG9eFPQycq0tp8b2+P86RH1z6voaBsUEI93KEAKDy6iWcT9sBV5TBY/+HUBiq8D/lADz24l+B5YPMCkxqSY4lvXbDXWOPS1fKkJ6Tj9O5V7FF9eebi9YdfW4uWrYUpZPJm1Ztwaf6p7HTEIc06oC/PNEZ4x/sAAAImfZzEy3vjqu9Aiq5DHpJMnkcppKLeCEpGK89FA69JMHZToFD2deQWVAOTycV3ljV+KaZOfMGtUhfWevgmR3WLgW51b2uGh/satzvRyWXYdGz8Xhi0e56u6Terd+kTnjw+hoXmUANLtZsKwbJfsMg2WiM105Bts4N8Q3U+Vz5GXDj93BNCZBy/XFV1DDggQnAPx+t30hfC5Q0sobmuk90z+Dlx3vCydUL2DCpLoBqiL07rlVUwxU3k5sCALbnYOH2HIS5qTDY7QIiL/2EJ2gvxFuCz8e0vwLLfkVZ9PPQnPi+4fPfQiXoMergU/CiImPqBdy+5YqFAp31skcxzLCl7ksbC3S2GrrhU/1wAHU5v17sFYrCshokzGn4DcB7tWR0NzzR1XTzQSLCxeJqeGlUUCtkJmV9IjzQJ8IDpdU6dPV3xrHL9f/7HJVguV272f2NZ3bAMzu25GRuGZ5YXLcwdOPrfdDF/+ZmcutTL+PNH9Msch1XlKGPeBxFcMZZyQ9X4AIRhHPq5y1y/jah6++Bp5YCogz4dphx1qU57uYR31zPj/DHF8fh11MF2JmehQ5Z3+F5+a/wvmW9xkV4IxCWXxtyNwbVzsHPqr9YuxsWN6T2bzhGYRY/r0YtN+6Bc8OzPQMx75mYuz4nEeHHQxfx8dbTeCjS07gbc9acJ1pt+wzWMsz9/c3BDjjYsTW3TqHfPkW9PaMA41YcrtdmSKwfeoa4YvqGO+/t0RQREhYqliBYKES8eLbpBm2Ywa87KoavhrObV91ak8+6Wf4iU04i49fl6HTs5lqi2rG/QuXZAdi1ADi60vgWVznscTXgEQT1Gg7h+FoIJzdYvj8MALDe0Atv6iZZ7HyPR/ugtFqH/eeu1isb2zsE0wZ2gkoua6Dl3TFIBAF1+3exto2DnWbgYMe2PPPlPqScr/sF2NDz+KwrFej/cXK941Mf64iHOnpi/LcpJq+rPtMtAD8duVSvvrkcUI1YMQuJYgb6iMfQXbzDDrr3sQwpEO5CGTSogkpoIG1CSxj5A/Dj6CarSa5hMPh1h/zk/0EgK63gbYMKyAWXyBMBwhWTmTEASJPC8B9Db5yiIMxXLEWAUGQsOy6FYIR2OqrqPd+zrFWvPICkDu5NV2TtFgc7zcDBjm15YM42474dn46Mw7B4fwB1U9lpF0swdW06zhZWmH2+aD8Npj7WEa9+n9JgdvfmckA1BssO4CPF1/d8Lsaaq5Ts4SxUNVhmIAHrpN/hS/0QZJG/8bg7SrFEuQiJ4s38UTsMsXhZ9zYMMH/Gxd/Fzqx9bx6L9saQWD8MjvFrsi5r3zjYaQYOdmzLutRLmPLjzZ2Ul/6hO0qrdXjnp3Tcbz/tHYTLeERMwTTFamt3pV05IoVjof73CHO3w0NiGrpX7oKzvqjphjbqN6kjNhoewCZDIq7ApcE6cugxXf4dXpBvNR5brX8I0/Sv4NYs7/fidxEeeLZnEAbFNJ4FnbFbcbDTDBzs2B69QULcB1tR0cDbV94aFd7oH4GHOnphxoYTxk3OHoz0xK672I/HEjSoRLr6Fatcuz05LEXi34a+8EQp+suOIEY4d+/pHEL7Ar3fAApPAdUlwO4FFulrS9huiEM1lCgkV/wmdYKrUIEdhjjkwfxHRaNlv+JDxT+N3z/VP218K+teLPh9LIZ35z1vWPNwsNMMHOzYpj1nirB4+xlUaw3IzC+H1iBh5cuJ6BXuYayjM0iIeK8u1YO3RoVwL0fsPVt/keQNEV6OeP6BYJRW63Dg3FXsy2q8bnPlqJ+z2LmYqRzJG8coFP5CEbrZ8MJxEuU4ZgjBOckLx6VQOArVcEcZeoiZeE/3Eo5QAxs83oVHxUNYpvzE+L2hXGsA8PvuAfB1scPm43mQiyLKanQoq9bVe9tq3tNd8Sy/Bs7uAu+zw9q9G/ts3GCQqN5rpgIAZzsFSqt1KCirNe6+3BgPRxX6dfJCoJs9PJ1UFg12PtD9AdOtmPPIloWIBQi5T15FtzRJbofcYWuwqcgTH27OapVrbpF6YmTt+/hRNRsA8KHinyglB+QFPmF8OQCA8RXv2yllIh7o4G6cSX0gjBchs5bFMzvgmZ327nBOMXZkFqJKa4BSJmJUQhBq9RLG/PO3BhMU2itlWPtqL3yx4yx+PpZn0b48IJ7En+RrkCBmWvS8llZLcqgE0/871/b4I5SHl1qpR7anlhT13nrbFLcEG9NzkVHtbLKA2FqihRyTPYRGad/Dfim6Xr0pAyLh46zCwexiXK3Q4mJxFXKuVkIiINDNDjveeghy2R2SyDLWCH6M1Qwc7LA7Wf3bBUz7P9OM4P07eWHCw+F45su6PDxje4dg+d4ci13z9nURjSkkF+yRuuBp2Z5G6yQbYpAiReJPirUW6x9rGfnkilpSoBAuiBGyoRJ0kAKTsNxtMmYfNKA5C4E9nVTwcFThVF7ZXfVFo5YjIdQNeomwM7NuBsZbo8Lj0T54Z2AnHDh3Fd/szsaVc0exVfVnAMBBtyeRHjcLQe72+ON3KU1eI8zDAfN/H4PuwW531UfGONhpBg522J1Uaw1ImPMrymsaTzXx/uAodPJxQkZ+OeyVMvi72CHaT4OSap3Jnj7mvnoLAM+Iu/Cxsi5LZVLNZ8iDOwRIIIjoIpzDRfJCKRxN2kQKF/GJYgmixfN3cafMWvQkNrhQeo8hGq/qpqAC9q3WF6VMhFwmoEprul/RlAGRmDwgAgCwI6MQY1fczKEWKuRhrP0+XAx4AnLfLgj1cMBDkZ6Npo5QykQk//kh+DrbtdyNsHaBg51m4GCHNeVwTjG2nCzAqbwy7D7T+CvKfx8eA4VMwD/2ZCO3pAbFlVqT8s9GxaOzrxMGLNxl1nVFSJDQ/Ol9NWoxRrYF7ypWNbsta74qhSuO6MOQp3OAXDDgKdleY1mx6IoaUsJZKoHD9Qz1ZWSH8+SN/xl64nPDMHiiFMNluxAuXkYNKZFBgUiXwnCUOsBSr3U3RBSAYfH++L8jlwEAkd6O2DKlL/QGCSdyy/BbdjFSzl9DqKcD/vRIJBTXHzUVltWg90fboTPc3a+P2UOj8YekEEvdBmvHONhpBg52mLnSL5Xgyc/3Nl3xPiFAwpPiPjwn326yIVxzfawbjidl+xEhXja7zVTdeKwxPHT9G6GnkIn+slTEi2fuqS/34vYM9cekENhBCx3kOElBOC0Fohz2qCUFSuGAWigQLlzG48qjSKT0euc7JEXin/qBUEKHKPE8XFGB7uJpdBDzcI0c8X2Hj3GUwrHr9BXoDHp4oQRVUKO8FWdqGjLhoQ7wdbHDjoxCbM8oNB6/dRNOc9xY9F9cqUX6pRKcLazAxeIq/Gt/wzOLiaFumPN0V3TwdGywnLHm4mCnGTjYYeaauPIIfk6/uShZLgog1P2jf6swDweMfzAMvcM9EOBqh5IqHeb8cgq/nirAtSrTRacDOnvhmzE9AQBavYTnvj6Aw7e80WJJcujRQchFX/Eo/IUi1EAJV1QgQcxAiFiAVCkcp6QgiJDQRcxBFzEHAFBJKmyRepjMWDRlrHYqdkj1c6jLocdrsv/grWauIXqk9u/4t/IDuAp33v36f4YemKcfhWzyAQCIIAggSBBAdzFLdsON5K8BQhESxFPoK6abZFe/VSG54HntuzhNgXd9vZYU5umAc1cq6x0/++FAiywUzrpSgWeXHUBsgAvmD4/BL8fzEOrugIRQN16IzCyKg51m4GCHmeuVbw9j68kCdPJxwh+SgjGgszec7RTo9P7mJtu6OSjRM8QV/ztx8xXoUQlBGJUQiJgAF+MxIkLou7/Uax/u5disNBeW0FU4hw8UK0ySmt7+ltBWQ3cECoXoJF6s115LMiivZzWvIDVK4GiSYymfXDFFNwGpUjgWKJZisOwA3tBOxG6pK8KFXKxRfQAAeLj2Y2STL4KFfKxTTofbLQGPRAIm6V5HmhSOcti3+KyJh6MSHo4qVBacxWjZdjypPASZoyeUQd2RXavB+oxKbNJ1wxW4tmg/7pZcFKC/LTgflRCEGUOioFZYLtkmY62Bg51m4GCHmYuIUKuX6v1SWHP4Iqaurf+YoylPx/ujZ6gbVv92AXKZiBB3B8QEOOPhjl5Iu1SCBf/LxIXiujxGzVncbFmEBCEDz8p3YJB4sNEkoDqSQSHcfRLOyJp/QQuF8fsixecYKtt3PSXBeOPxYCEfr8n+AzvRgNMUgC1ST5wj33qza+1dlK8GJ5t4E+vBSE9MH9wZ4V5OrdQrxiyLg51m4GCHWUJptQ47Mwvx36N5xhQUDeke7Gqy8VpjIr0d8WCEJ77Zk91onc+fi8fKgxcsurnhnWhQgTgxCwIAX+EqIoTLECHhGjlhg9QL42SbMOaW3EmNOSKF19vJeEjt33CMwgDUJZ7cr5oEpWDAoNoPcYJCW+J2bNLBv/SHt0YNSSLM25yBZbvONVjv5T6hePeJzvU22mSsLeEdlBlrZc52CgyN88fQOH+cu1KByloD/FzUkIsitp4qwNtr6pKTRng5NhrsjE4MwtnCChw+fw2nCypwuqDxx1ZD4/wwaWWqybFBMb4Y0NkLGrUCuSXVWLrrHC5ds9xsUBkcsUuKbbT8F8MD6CxegC+KsUOKw2EpEkcoArnkgYHibygiZ/xGHUEQsUzxMR6V1e3FUkUqHKcQBLrZwdlOgWHlP0OpNSBN6sCBjhkej/bBB0Oj4aRWQK0QkXK+GHN/yTCu/RoU4wsnlRyrD9U9anwuMQh/HRxlzS4z1qp4Zgc8s8Nax8ItmVi83by8TAGudgh2t79jni5zJIS6YUisH95ff/yeztMSksQTWKX8EAYS0Ff7CS6RFwBCsFCA/ynfgVrQ4W3dH7HW0NfaXb3vLHo2DoVltXC2UyDc2xHbTxXiH3uyUaM3wNlOgZLri+AdlDLMGBKNfp29MOXHNOO2CT+8nIjet+SIY6yt4sdYzcDBDmsNRITk01ew5WQBiiu0KKnW4sC5YoudP9jdHp18nFBeo8fVCi3OFJbjfl7GIkLCHtUb8BPqxiBDCoQ9ahAk3sw8H17zLfQ8AV2Pk1qOJ2P9cDC7uNFF6z4aNb59KQGR3k6YvDoVG9JyoZKLeG9QZ7zAe9wwG8HBTjNwsMOsRW+QMHbFoTtuVHi7hzt6Ysf17fuf6OqDvw3rCp1BgrdGbVLvYnEV/rUvB2kXSyAR4ciFEkt23SKihWxMV3yHnkKmyWvcEgmYrn8R3xseMftcrvYKVNTq73qju7shCIC1/wUVBMBeIUNSBw84qeVYn3bZ2CdBACK9nIyB76pXHkBSB066yWwHBzvNwMEOs7aeH/6KK+W1WPRsHIbG1W3qdq1Si/3nrmLCD0eM9QLd7DAkxg9LdtZltx4a54dFz9bfy6Yhn207g4+3nrZ85y3AFWXoLZ6AAnrkwR2XyOP6Yy3zhbjbQxQEnCuqv39MS8iZNwhAXcC652wRvtyZhYPZlpupM8dHz3SFIAiY9lN6k7N4w+L88MnIOAgCL0hmtqPdBTtffPEF5s+fj/z8fMTGxuKzzz5DQkKCWW052GHWVlKlxYXiKpP9dm51IrcUY5cfQmF5rcnxuU93xaiEILOuMeyLvUi7WGJyrLOv5q4TRdqSz0bFY3CMLwRBQMi0n5us/0a/cEzqFwGl3HSDvPIaHa6U1+Lb/eexYl9Oo+2Hxfmhf2dvhHk6QK2QYcuJAuQUVWJbRiGKKkz/jvuEe+CbMT2gM0h4b91x/OdoLoCbMzqV13NYdfV3RrSfBkkd3BHu5YhOPhoUlNXg8Plr6OKnQRjvWsxsULsKdn788Ue88MIL+Oqrr5CYmIhPP/0Ua9asQWZmJry8mv6/Qw52WFuQXVSJFXuzoZMIUb4aPBDm1qz9UWb+58QdfwGbKzHUDSfzyu6YGLWt6NXBHZP7RyAx7Oajnc3H8/Dq90fu0KrOyB6B+Gh4DABAZ5BwrVKLlPPX8FVyFo5eKgVQt9dNYpgbuvo740RuGeKDXPBYtI8xx1RRRS1e+z4Fh3Juvp3nYq8AUd1WBn3CPfD1Cz1gp6zb14mIsOVkAf6xOxu/5dycRXokyhtfPNetXvDFmK1rV8FOYmIievbsic8//xwAIEkSAgMD8frrr2PatGlNtudgh7UHBokw+psDOHCuGOMfDMO1Si2uVenuuCfQvXq9XzgivJ2wP+sqDuU0vpjWGrZMeRCR3g0Hi/89movXV6U2WGauV34XivggVxRV1KKovBZ6iRDsbg8ndV0wc/FaFb7dl4Pc0hrYK2UIdndAjL8z3hvcGWq5DGcKy9HJR9PoPjhnC8tRrZXQwcsB9kpexM3ap3YT7Gi1Wtjb22Pt2rUYNmyY8fiYMWNQUlKCDRs2NHkODnZYe0JEJus2fk7Pw8SVTc9kWNvLfULh46yGXBQgigJKq3S4WqmFg0qGDp6OcFIrcCinGEFu9vB3tcNn287ccVF230hPRPlp4KiSw1Elh71SBkeVHDJRwO4zRfjuQMPJLC0tzMMBy17ogXAvfszEWHO1m00Fi4qKYDAY4O3tbXLc29sbGRkNZ1aura1Fbe3N5+JlZbxmgbUfty9QHRDlhQGdvfDrqcJGWrSe2EAXVNXqUaU1oFKrR7XWgD7hHlg4Mg7Odoom2z8SdfPfgYciPZFbWoO8kmpcLqlGXmkNckuqsT2jEJeuVSP59BUkn75yh7M1bcqASAS72yPC2xEu9kqcuFyKJTuzcLawAu7Xc2jdyKUlEwWcv1qFaq0BEhF8XewQ7afBqIQgs+6NMXb32nywczfmzp2LWbNmWbsbjN0XVHKZMev68culuFhcBULdK9UEgrdGjR7BrqjUGjD3l1PYc7YIclGAQiZCJgqQy0Q4qeRwsVdArZBBrRChlsugUohQyER4a9SICXBGR2+nVs14LQgC/F3s4O9ihx63HJ82UI89Z4pw/moVckurUVmrR2VtXXBVeT3Q6ujthGe6B6BXB/dmvb3k72KHR6N9LH8zjLF70i4fYzU0sxMYGMiPsRhjjLE2xNzHWG1+6b5SqUT37t2xbds24zFJkrBt2zYkJSU12EalUkGj0Zh8GGOMMWabbOIx1p/+9CeMGTMGPXr0QEJCAj799FNUVlZi7Nix1u4aY4wxxqzMJoKdkSNH4sqVK5g+fTry8/MRFxeHzZs311u0zBhjjLH2p82v2bEEfvWcMcYYa3vazZodxhhjjLE74WCHMcYYYzaNgx3GGGOM2TQOdhhjjDFm0zjYYYwxxphN42CHMcYYYzaNgx3GGGOM2TQOdhhjjDFm0zjYYYwxxphN42CHMcYYYzbNJnJj3asbGTPKysqs3BPGGGOMmevG7+2mMl9xsAOgvLwcABAYGGjlnjDGGGOsucrLy+Hs7NxoOScCBSBJEnJzc+Hk5ARBEKzdnXajrKwMgYGBuHjxIidgbWU89tbF4289PPbWZenxJyKUl5fDz88Potj4yhye2QEgiiICAgKs3Y12S6PR8D86VsJjb108/tbDY29dlhz/O83o3MALlBljjDFm0zjYYYwxxphN42CHWY1KpcKMGTOgUqms3ZV2h8feunj8rYfH3rqsNf68QJkxxhhjNo1ndhhjjDFm0zjYYYwxxphN42CHMcYYYzaNgx1mUbt27cKQIUPg5+cHQRCwfv16k3IiwvTp0+Hr6ws7OzsMGDAAZ86cMalTXFyM0aNHQ6PRwMXFBS+99BIqKipa8S7aprlz56Jnz55wcnKCl5cXhg0bhszMTJM6NTU1mDhxItzd3eHo6IhnnnkGBQUFJnUuXLiAQYMGwd7eHl5eXpg6dSr0en1r3kqb9OWXXyImJsa4f0hSUhI2bdpkLOexbz3z5s2DIAh48803jcd4/FvOzJkzIQiCyadTp07G8vth7DnYYRZVWVmJ2NhYfPHFFw2W//3vf8fixYvx1Vdf4eDBg3BwcMBjjz2GmpoaY53Ro0fjxIkT2Lp1KzZu3Ihdu3Zh/PjxrXULbVZycjImTpyIAwcOYOvWrdDpdHj00UdRWVlprDNlyhT897//xZo1a5CcnIzc3Fw8/fTTxnKDwYBBgwZBq9Vi3759+Ne//oUVK1Zg+vTp1rilNiUgIADz5s1DSkoKDh8+jH79+mHo0KE4ceIEAB771nLo0CEsXboUMTExJsd5/FtWdHQ08vLyjJ89e/YYy+6LsSfGWggAWrdunfG7JEnk4+ND8+fPNx4rKSkhlUpFq1atIiKikydPEgA6dOiQsc6mTZtIEAS6fPlyq/XdFhQWFhIASk5OJqK6sVYoFLRmzRpjnVOnThEA2r9/PxER/fLLLySKIuXn5xvrfPnll6TRaKi2trZ1b8AGuLq60jfffMNj30rKy8spIiKCtm7dSn379qXJkycTEf/st7QZM2ZQbGxsg2X3y9jzzA5rNdnZ2cjPz8eAAQOMx5ydnZGYmIj9+/cDAPbv3w8XFxf06NHDWGfAgAEQRREHDx5s9T63ZaWlpQAANzc3AEBKSgp0Op3J+Hfq1AlBQUEm49+1a1d4e3sb6zz22GMoKyszzlCwphkMBqxevRqVlZVISkrisW8lEydOxKBBg0zGGeCf/dZw5swZ+Pn5ISwsDKNHj8aFCxcA3D9jz7mxWKvJz88HAJMf6Bvfb5Tl5+fDy8vLpFwul8PNzc1YhzVNkiS8+eab6N27N7p06QKgbmyVSiVcXFxM6t4+/g39/dwoY3d27NgxJCUloaamBo6Ojli3bh2ioqKQlpbGY9/CVq9ejSNHjuDQoUP1yvhnv2UlJiZixYoV6NixI/Ly8jBr1iz87ne/w/Hjx++bsedghzEbNHHiRBw/ftzkuTlreR07dkRaWhpKS0uxdu1ajBkzBsnJydbuls27ePEiJk+ejK1bt0KtVlu7O+3OwIEDjX+OiYlBYmIigoOD8e9//xt2dnZW7NlN/BiLtRofHx8AqLcKv6CgwFjm4+ODwsJCk3K9Xo/i4mJjHXZnkyZNwsaNG7Fjxw4EBAQYj/v4+ECr1aKkpMSk/u3j39Dfz40ydmdKpRLh4eHo3r075s6di9jYWCxatIjHvoWlpKSgsLAQ3bp1g1wuh1wuR3JyMhYvXgy5XA5vb28e/1bk4uKCyMhInD179r752edgh7Wa0NBQ+Pj4YNu2bcZjZWVlOHjwIJKSkgAASUlJKCkpQUpKirHO9u3bIUkSEhMTW73PbQkRYdKkSVi3bh22b9+O0NBQk/Lu3btDoVCYjH9mZiYuXLhgMv7Hjh0zCTi3bt0KjUaDqKio1rkRGyJJEmpra3nsW1j//v1x7NgxpKWlGT89evTA6NGjjX/m8W89FRUVyMrKgq+v7/3zs2+RZc6MXVdeXk6pqamUmppKAGjhwoWUmppK58+fJyKiefPmkYuLC23YsIHS09Np6NChFBoaStXV1cZzPP744xQfH08HDx6kPXv2UEREBI0aNcpat9RmvPbaa+Ts7Ew7d+6kvLw846eqqspY59VXX6WgoCDavn07HT58mJKSkigpKclYrtfrqUuXLvToo49SWloabd68mTw9Pendd9+1xi21KdOmTaPk5GTKzs6m9PR0mjZtGgmCQFu2bCEiHvvWduvbWEQ8/i3prbfeop07d1J2djbt3buXBgwYQB4eHlRYWEhE98fYc7DDLGrHjh0EoN5nzJgxRFT3+vn7779P3t7epFKpqH///pSZmWlyjqtXr9KoUaPI0dGRNBoNjR07lsrLy61wN21LQ+MOgJYvX26sU11dTRMmTCBXV1eyt7enp556ivLy8kzOk5OTQwMHDiQ7Ozvy8PCgt956i3Q6XSvfTdszbtw4Cg4OJqVSSZ6entS/f39joEPEY9/abg92ePxbzsiRI8nX15eUSiX5+/vTyJEj6ezZs8by+2HsOes5Y4wxxmwar9lhjDHGmE3jYIcxxhhjNo2DHcYYY4zZNA52GGOMMWbTONhhjDHGmE3jYIcxxhhjNo2DHcYYY4zZNA52GGOMMWbTONhhjNksrVaL8PBw7Nu3z+w2O3fuhCAI9RIX3gtBELB+/fom62m1WoSEhODw4cMWuzZjjIMdxpgN++qrrxAaGopevXqZ3aZXr17Iy8uDs7NzC/asYUqlEm+//TbeeeedVr82Y7aMgx3GmE0iInz++ed46aWXmtVOqVTCx8cHgiC0UM/ubPTo0dizZw9OnDhhleszZos42GGMNenKlSvw8fHBnDlzjMf27dsHpVKJbdu2Ndl+5syZiIuLw9KlSxEYGAh7e3uMGDECpaWlxjqSJOGDDz5AQEAAVCoV4uLisHnzZmO5VqvFpEmT4OvrC7VajeDgYMydO7fRa6akpCArKwuDBg0yHsvJyYEgCFi9ejV69eoFtVqNLl26IDk52Vjn9sdY48aNQ0xMDGpra439iI+PxwsvvGBss2HDBnTr1g1qtRphYWGYNWsW9Hp9g/1q6j5cXV3Ru3dvrF69uslxZYyZyWIpRRljNu3nn38mhUJBhw4dorKyMgoLC6MpU6aY1XbGjBnk4OBA/fr1o9TUVEpOTqbw8HB67rnnjHUWLlxIGo2GVq1aRRkZGfTnP/+ZFAoFnT59moiI5s+fT4GBgbRr1y7Kycmh3bt308qVKxu95sKFC6lTp04mx7KzswkABQQE0Nq1a+nkyZP08ssvk5OTExUVFRER0Y4dOwgAXbt2jYiIysvLKSwsjN58800iInr77bcpJCSESktLiYho165dpNFoaMWKFZSVlUVbtmyhkJAQmjlzpvG6AGjdunVm38c777xDffv2NWtsGWNN42CHMWa2CRMmUGRkJD333HPUtWtXqqmpMavdjBkzSCaT0aVLl4zHNm3aRKIoUl5eHhER+fn50YcffmjSrmfPnjRhwgQiInr99depX79+JEmSWdecPHky9evXz+TYjWBn3rx5xmM6nY4CAgLoo48+IqL6wQ4R0b59+0ihUND7779Pcrmcdu/ebSzr378/zZkzx+Q63333Hfn6+hq/3xrsmHMfixYtopCQELPukzHWNH6MxRgz24IFC6DX67FmzRr88MMPUKlUZrcNCgqCv7+/8XtSUhIkSUJmZibKysqQm5uL3r17m7Tp3bs3Tp06BQB48cUXkZaWho4dO+KNN97Ali1b7ni96upqqNXqBsuSkpKMf5bL5ejRo4fxOo3Vf/vttzF79my89dZb6NOnj7Hs6NGj+OCDD+Do6Gj8vPLKK8jLy0NVVVW9c5lzH3Z2dg22ZYzdHQ52GGNmy8rKQm5uLiRJQk5OTqteu1u3bsjOzsbs2bNRXV2NESNGYPjw4Y3W9/DwwLVr1yxybUmSsHfvXshkMpw9e9akrKKiArNmzUJaWprxc+zYMZw5c6bBYMuc+yguLoanp6dF+s4Y42CHMWYmrVaL559/HiNHjsTs2bPx8ssvo7Cw0Oz2Fy5cQG5urvH7gQMHIIoiOnbsCI1GAz8/P+zdu9ekzd69exEVFWX8rtFoMHLkSHz99df48ccf8dNPP6G4uLjB68XHxyMjIwNEVK/swIEDxj/r9XqkpKSgc+fOjfZ9/vz5yMjIQHJyMjZv3ozly5cby7p164bMzEyEh4fX+4hiw//ENnUfx48fR3x8fKP9YYw1j9zaHWCMtQ3vvfceSktLsXjxYjg6OuKXX37BuHHjsHHjRrPaq9VqjBkzBgsWLEBZWRneeOMNjBgxAj4+PgCAqVOnYsaMGejQoQPi4uKwfPlypKWl4YcffgAALFy4EL6+voiPj4coilizZg18fHzg4uLS4PUefvhhVFRU4MSJE+jSpYtJ2RdffIGIiAh07twZn3zyCa5du4Zx48Y1eJ7U1FRMnz4da9euRe/evbFw4UJMnjwZffv2RVhYGKZPn47BgwcjKCgIw4cPhyiKOHr0KI4fP46//e1v9c5nzn3s3r0bs2fPNmtcGWNmsPaiIcbY/W/Hjh31FuZmZ2eTRqOhJUuWNNl+xowZFBsbS0uWLCE/Pz9Sq9U0fPhwKi4uNtYxGAw0c+ZM8vf3J4VCQbGxsbRp0yZj+bJlyyguLo4cHBxIo9FQ//796ciRI3e87ogRI2jatGkmfQZAK1eupISEBFIqlRQVFUXbt283uVdcX6BcXV1NUVFRNH78eJPzPvnkk9SrVy/S6/VERLR582bq1asX2dnZkUajoYSEBFq2bJmxPm5ZoNzUfezbt49cXFyoqqqqyXFljJlHIGpgjpcxxixo5syZWL9+PdLS0lr1uunp6XjkkUeQlZUFR0dH5OTkIDQ0FKmpqYiLi2vVvphr5MiRiI2NxV/+8hdrd4Uxm8FrdhhjNismJgYfffQRsrOzrd0Vs2i1WnTt2hVTpkyxdlcYsym8Zocxds+io6Nx/vz5BsuWLl3ayr0x9eKLL1r1+s2hVCrx17/+1drdYMzm8GMsxtg9O3/+PHQ6XYNl3t7ecHJyauUeMcbYTRzsMMYYY8ym8ZodxhhjjNk0DnYYY4wxZtM42GGMMcaYTeNghzHGGGM2jYMdxhhjjNk0DnYYY4wxZtM42GGMMcaYTeNghzHGGGM27f8BjGkQ2VthMO0AAAAASUVORK5CYII=", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "import matplotlib.pyplot as plt \n", - "fig,ax=plt.subplots()\n", - "df_flat.plot(x='Head_x',y='Head_y', ax=ax)\n", - "df_flat.plot(x='Tailbase_x',y='Tailbase_y', ax=ax)" + "plt.plot(head_data['x_pos'], head_data['y_pos'], label='head')\n", + "plt.plot(tail_data['x_pos'], tail_data['y_pos'], label='tailbase')\n", + "plt.xlabel('x_pos (pixels)')\n", + "plt.ylabel('y_pos (pixels)')\n", + "plt.legend()\n", + "plt.show()\n" ] } ], @@ -545,7 +2675,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.17" } }, "nbformat": 4, From 5e942b609970c5687b0ea609d9d5674e72adb525 Mon Sep 17 00:00:00 2001 From: Milagros Marin Date: Mon, 16 Oct 2023 20:58:36 -0400 Subject: [PATCH 174/176] final draft without output --- notebooks/tutorial.ipynb | 2552 +++++--------------------------------- 1 file changed, 305 insertions(+), 2247 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 80c3f01..c1b4953 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -13,7 +13,7 @@ "source": [ "**Open-source Data Pipeline for Markerless Pose Estimation in Neurophysiology**\n", "\n", - "This tutorial focuses on providing a comprehensive understanding of the open-source data pipeline offered by `Element-DeepLabCut`. " + "This tutorial aims to provide a comprehensive understanding of the open-source data pipeline by `Element-DeepLabCut`." ] }, { @@ -27,7 +27,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The package is designed to facilitate pose estimation analyses and streamline the organization of data using `DataJoint`. " + "The package is designed to simplify pose estimation analyses and streamline data organization using `DataJoint`. " ] }, { @@ -41,7 +41,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "By the end of this tutorial, participants will have a clear grasp of how to set up and apply the Element DeepLabCut for their specific pose estimation projects. " + "By the end of this tutorial, participants will have a clear grasp of how to set up and apply the `Element DeepLabCut` for their specific pose estimation projects. " ] }, { @@ -50,17 +50,17 @@ "source": [ "**Key Components and Objectives**\n", "\n", - "- Setup\n", + "**- Setup**\n", "\n", - "- Design the DataJoint Pipeline\n", + "**- Designing the DataJoint Pipeline**\n", "\n", - "- Step 1 - Register an existing model in DataJoint pipeline\n", + "**- Step 1: Register an Existing Model in the DataJoint Pipeline**\n", "\n", - "- Step 2 - Insert Subject, Session, and Behavior Videos\n", + "**- Step 2: Insert Subject, Session, and Behavior Videos**\n", "\n", - "- Step 3 - DLC inference task\n", + "**- Step 3: DeepLabCut Inference Task**\n", "\n", - "- Step 4 - Visualization of results" + "**- Step 4: Visualization of Results**" ] }, { @@ -69,7 +69,7 @@ "source": [ "For detailed documentation and tutorials on general DataJoint principles that support collaboration, automation, reproducibility, and visualizations:\n", "\n", - "[`DataJoint for Python - Interactive Tutorials`](https://github.com/datajoint/datajoint-tutorials) - Fundamentals including table tiers, query operations, fetch operations, automated computations with the make function, etc.\n", + "[`DataJoint for Python - Interactive Tutorials`](https://github.com/datajoint/datajoint-tutorials) covers fundamentals, including table tiers, query operations, fetch operations, automated computations with the make function, and more.\n", "\n", "[`DataJoint for Python - Documentation`](https://datajoint.com/docs/core/datajoint-python/0.14/)\n", "\n", @@ -87,41 +87,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The following tutorial consists of studying the behavior of a freely-moving mouse in an open-field environment. \n", + "This tutorial examines the behavior of a freely-moving mouse in an open-field environment. \n", "\n", - "The objective is to extract pose estimations of the animal's head and tail base from video footage. \n", + "The goal is to extract pose estimations of the animal's head and tail base from video footage. \n", "\n", - "This information can provide valuable insights into the animal's movements, postures, and interactions within the environment. \n", + "This information offers valuable insights into the animal's movements, postures, and interactions within the environment. \n", "\n", + "The results of this Element example can be combined with other modalities to create a complete data pipeline for your specific lab or study.\n", "\n", - "The results of this Element example could be combined with other modalities to assemble a complete pipeline for your particular lab/study.\n", + "#### Steps to Run the Element-DeepLabCut\n", "\n", - "#### Steps to run the Element\n", + "To run the Element, ensure that you have:\n", "\n", - "The Element assumes you:\n", + "- A DeepLabCut (DLC) project folder on your machine.\n", "\n", - "- Have a DLC project folder on your machine\n", - "\n", - "- Have labeled data in your DLC project folder\n", + "- Labeled data in your DLC project folder.\n", "\n", "This tutorial includes a DLC project folder with example data and its results in `example_data`. " ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Upon completing this tutorial, you will have acquired practical proficiency in employing the `Element-DeepLabCut` package to effectively tackle the complexities of pose estimation. \n", - "\n", - "This tutorial and sample dataset will serve as a practical foundation for your learning journey with the Element package, enabling you to apply these techniques to your own research projects. \n", - "\n", - "By integrating this element package with other Elements of DataJoint, you unlock a powerful data pipeline that provides numerous benefits for your research workflow. " - ] - }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -140,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -153,33 +140,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's connect to the database server. " + "This codespace provides a local database private to you for experimentation. Let's connect to the database server:" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-10-14 07:16:40,528][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-10-14 07:16:40,535][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - }, - { - "data": { - "text/plain": [ - "DataJoint connection (connected) root@fakeservices.datajoint.io:3306" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dj.conn()" ] @@ -195,804 +163,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This tutorial is setup so that the element-deeplabcut is already configured, and instantiated, connected downstream from subject and session.\n", - "And that's what we're doing here, importing the schemas for subject, session, train, model, etc." + "This tutorial assumes that `element-deeplabcut` is already configured and instantiated, with the database connected downstream from existing subject and session tables. Import schemas for subject, session, train, model, etc.:" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-10-14 07:16:40,699][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from tutorial_pipeline import lab, subject, session, train, model " ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "session.Session\n", - "\n", - "\n", - "session.Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.ProjectSession\n", - "\n", - "\n", - "session.ProjectSession\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.ProjectSession\n", - "\n", - "\n", - "\n", - "\n", - "session.Session.Attribute\n", - "\n", - "\n", - "session.Session.Attribute\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.Session.Attribute\n", - "\n", - "\n", - "\n", - "\n", - "session.SessionNote\n", - "\n", - "\n", - "session.SessionNote\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.SessionNote\n", - "\n", - "\n", - "\n", - "\n", - "session.SessionExperimenter\n", - "\n", - "\n", - "session.SessionExperimenter\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.SessionExperimenter\n", - "\n", - "\n", - "\n", - "\n", - "session.SessionDirectory\n", - "\n", - "\n", - "session.SessionDirectory\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->session.SessionDirectory\n", - "\n", - "\n", - "\n", - "\n", - "model.VideoRecording\n", - "\n", - "\n", - "model.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "session.Session->model.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject\n", - "\n", - "\n", - "subject.Subject\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->session.Session\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Source\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.User\n", - "\n", - "\n", - "subject.Subject.User\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.User\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Protocol\n", - "\n", - "\n", - "subject.Subject.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Line\n", - "\n", - "\n", - "subject.Subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "subject.Zygosity\n", - "\n", - "\n", - "subject.Zygosity\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Zygosity\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Strain\n", - "\n", - "\n", - "subject.Subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject.Lab\n", - "\n", - "\n", - "subject.Subject.Lab\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Lab\n", - "\n", - "\n", - "\n", - "\n", - "subject.SubjectDeath\n", - "\n", - "\n", - "subject.SubjectDeath\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject->subject.SubjectDeath\n", - "\n", - "\n", - "\n", - "\n", - "model.ModelEvaluation\n", - "\n", - "\n", - "model.ModelEvaluation\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.PoseEstimation\n", - "\n", - "\n", - "model.PoseEstimation\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "model.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.PoseEstimation->model.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "lab.User\n", - "\n", - "\n", - "lab.User\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.User->subject.Subject.User\n", - "\n", - "\n", - "\n", - "\n", - "lab.ProjectUser\n", - "\n", - "\n", - "lab.ProjectUser\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.User->lab.ProjectUser\n", - "\n", - "\n", - "\n", - "\n", - "lab.User->session.SessionExperimenter\n", - "\n", - "\n", - "\n", - "\n", - "lab.LabMembership\n", - "\n", - "\n", - "lab.LabMembership\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.User->lab.LabMembership\n", - "\n", - "\n", - "\n", - "\n", - "lab.ProtocolType\n", - "\n", - "\n", - "lab.ProtocolType\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Protocol\n", - "\n", - "\n", - "lab.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.ProtocolType->lab.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "lab.Source\n", - "\n", - "\n", - "lab.Source\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Source->subject.Subject.Source\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele.Source\n", - "\n", - "\n", - "subject.Allele.Source\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Source->subject.Allele.Source\n", - "\n", - "\n", - "\n", - "\n", - "lab.Lab\n", - "\n", - "\n", - "lab.Lab\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Location\n", - "\n", - "\n", - "lab.Location\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Lab->lab.Location\n", - "\n", - "\n", - "\n", - "\n", - "lab.Lab->lab.LabMembership\n", - "\n", - "\n", - "\n", - "\n", - "lab.Lab->subject.Subject.Lab\n", - "\n", - "\n", - "\n", - "\n", - "lab.Lab.Organization\n", - "\n", - "\n", - "lab.Lab.Organization\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Lab->lab.Lab.Organization\n", - "\n", - "\n", - "\n", - "\n", - "model.Model.BodyPart\n", - "\n", - "\n", - "model.Model.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.Model.BodyPart->model.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line.Allele\n", - "\n", - "\n", - "subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.BodyPart\n", - "\n", - "\n", - "model.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.BodyPart->model.Model.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "lab.ProjectSourceCode\n", - "\n", - "\n", - "lab.ProjectSourceCode\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.PoseEstimationTask\n", - "\n", - "\n", - "model.PoseEstimationTask\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.PoseEstimationTask->model.PoseEstimation\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele\n", - "\n", - "\n", - "subject.Allele\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele->subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele->subject.Allele.Source\n", - "\n", - "\n", - "\n", - "\n", - "subject.Allele->subject.Zygosity\n", - "\n", - "\n", - "\n", - "\n", - "train.TrainingParamSet\n", - "\n", - "\n", - "train.TrainingParamSet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "train.TrainingTask\n", - "\n", - "\n", - "train.TrainingTask\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "train.TrainingParamSet->train.TrainingTask\n", - "\n", - "\n", - "\n", - "\n", - "model.Model\n", - "\n", - "\n", - "model.Model\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "train.TrainingParamSet->model.Model\n", - "\n", - "\n", - "\n", - "\n", - "train.ModelTraining\n", - "\n", - "\n", - "train.ModelTraining\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.SubjectCull\n", - "\n", - "\n", - "subject.SubjectCull\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "train.VideoSet\n", - "\n", - "\n", - "train.VideoSet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "train.VideoSet.File\n", - "\n", - "\n", - "train.VideoSet.File\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "train.VideoSet->train.VideoSet.File\n", - "\n", - "\n", - "\n", - "\n", - "train.VideoSet->train.TrainingTask\n", - "\n", - "\n", - "\n", - "\n", - "model.RecordingInfo\n", - "\n", - "\n", - "model.RecordingInfo\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line\n", - "\n", - "\n", - "subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line->subject.Line.Allele\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line->subject.Subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "lab.ProjectKeywords\n", - "\n", - "\n", - "lab.ProjectKeywords\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Protocol->subject.Subject.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "model.VideoRecording.File\n", - "\n", - "\n", - "model.VideoRecording.File\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.ProjectPublication\n", - "\n", - "\n", - "lab.ProjectPublication\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Organization\n", - "\n", - "\n", - "lab.Organization\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Organization->lab.Lab.Organization\n", - "\n", - "\n", - "\n", - "\n", - "lab.Project\n", - "\n", - "\n", - "lab.Project\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Project->session.ProjectSession\n", - "\n", - "\n", - "\n", - "\n", - "lab.Project->lab.ProjectSourceCode\n", - "\n", - "\n", - "\n", - "\n", - "lab.Project->lab.ProjectUser\n", - "\n", - "\n", - "\n", - "\n", - "lab.Project->lab.ProjectKeywords\n", - "\n", - "\n", - "\n", - "\n", - "lab.Project->lab.ProjectPublication\n", - "\n", - "\n", - "\n", - "\n", - "lab.UserRole\n", - "\n", - "\n", - "lab.UserRole\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.UserRole->lab.LabMembership\n", - "\n", - "\n", - "\n", - "\n", - "model.VideoRecording->model.PoseEstimationTask\n", - "\n", - "\n", - "\n", - "\n", - "model.VideoRecording->model.RecordingInfo\n", - "\n", - "\n", - "\n", - "\n", - "model.VideoRecording->model.VideoRecording.File\n", - "\n", - "\n", - "\n", - "\n", - "subject.Strain\n", - "\n", - "\n", - "subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "subject.Strain->subject.Subject.Strain\n", - "\n", - "\n", - "\n", - "\n", - "lab.Device\n", - "\n", - "\n", - "lab.Device\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "lab.Device->model.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "train.TrainingTask->train.ModelTraining\n", - "\n", - "\n", - "\n", - "\n", - "subject.SubjectDeath->subject.SubjectCull\n", - "\n", - "\n", - "\n", - "\n", - "model.Model->model.ModelEvaluation\n", - "\n", - "\n", - "\n", - "\n", - "model.Model->model.Model.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "model.Model->model.PoseEstimationTask\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "(\n", " dj.Diagram(subject) \n", @@ -1003,240 +190,18 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, this data pipeline is quite extensive, with various tables related to other components like models, training, and evaluation in DLC. Some, such as the `Subject` table, are not relevant to this tutorial and are upstream." + ] + }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "model.RecordingInfo\n", - "\n", - "\n", - "model.RecordingInfo\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.PoseEstimationTask\n", - "\n", - "\n", - "model.PoseEstimationTask\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.PoseEstimation\n", - "\n", - "\n", - "model.PoseEstimation\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.PoseEstimationTask->model.PoseEstimation\n", - "\n", - "\n", - "\n", - "\n", - "model.ModelEvaluation\n", - "\n", - "\n", - "model.ModelEvaluation\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "model.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.PoseEstimation->model.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "train.TrainingParamSet\n", - "\n", - "\n", - "train.TrainingParamSet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "train.TrainingTask\n", - "\n", - "\n", - "train.TrainingTask\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "train.TrainingParamSet->train.TrainingTask\n", - "\n", - "\n", - "\n", - "\n", - "model.Model\n", - "\n", - "\n", - "model.Model\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "train.TrainingParamSet->model.Model\n", - "\n", - "\n", - "\n", - "\n", - "train.ModelTraining\n", - "\n", - "\n", - "train.ModelTraining\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.VideoRecording.File\n", - "\n", - "\n", - "model.VideoRecording.File\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "train.TrainingTask->train.ModelTraining\n", - "\n", - "\n", - "\n", - "\n", - "train.VideoSet\n", - "\n", - "\n", - "train.VideoSet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "train.VideoSet->train.TrainingTask\n", - "\n", - "\n", - "\n", - "\n", - "train.VideoSet.File\n", - "\n", - "\n", - "train.VideoSet.File\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "train.VideoSet->train.VideoSet.File\n", - "\n", - "\n", - "\n", - "\n", - "model.Model.BodyPart\n", - "\n", - "\n", - "model.Model.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.Model.BodyPart->model.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "model.BodyPart\n", - "\n", - "\n", - "model.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.BodyPart->model.Model.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "model.VideoRecording\n", - "\n", - "\n", - "model.VideoRecording\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.VideoRecording->model.RecordingInfo\n", - "\n", - "\n", - "\n", - "\n", - "model.VideoRecording->model.PoseEstimationTask\n", - "\n", - "\n", - "\n", - "\n", - "model.VideoRecording->model.VideoRecording.File\n", - "\n", - "\n", - "\n", - "\n", - "model.Model->model.PoseEstimationTask\n", - "\n", - "\n", - "\n", - "\n", - "model.Model->model.ModelEvaluation\n", - "\n", - "\n", - "\n", - "\n", - "model.Model->model.Model.BodyPart\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dj.Diagram(model) + dj.Diagram(train)" ] @@ -1245,98 +210,48 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 1 - Register an existing model in DataJoint pipeline" + "This diagram represents the `element-deeplabcut` pipeline." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "A DeepLabCut model is defined in a DLC-specific folder structure with a file named `config.yaml` that contains the specifications of a DLC model.\n", - "\n", - "To \"register\" this DLC model with DataJoint, you can just specify this config file. See example below" + "## Step 1 - Register an Existing Model in the DataJoint Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A DeepLabCut model is defined in a specific folder structure with a `config.yaml` file that contains the model's specifications (see folder `example_data/inbox`). To \"register\" this DLC model with DataJoint, you can specify this config file:" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "config_file_rel = \"./example_data/inbox/from_top_tracking-DataJoint-2023-10-11/config.yaml\"" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `insert_new_model` function is a helper function provided in `element-deeplacut` for convenient model registration.\n", + "\n", + "This function prints out the essential information, like the `model_name` and the `model_description`, together with other relevant information from the config file. \n", + "\n", + "If all the information is correct, you can confirm the insertion by typing 'yes,' which will insert the new model and its two body parts, `head` and `tailbase`:" + ] + }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-10-14 07:16:42.318459: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-10-14 07:16:42.442151: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", - "2023-10-14 07:16:42.442185: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n", - "2023-10-14 07:16:42.466555: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2023-10-14 07:16:43.231023: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", - "2023-10-14 07:16:43.231135: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", - "2023-10-14 07:16:43.231151: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading DLC 2.3.7...\n", - "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n", - "--- DLC Model specification to be inserted ---\n", - "\tmodel_name: from_top_tracking_model_test\n", - "\tmodel_description: Model in example data: from_top_tracking model\n", - "\tscorer: DLCresnet50fromtoptrackingOct11shuffle1\n", - "\ttask: from_top_tracking\n", - "\tdate: Oct11\n", - "\titeration: 0\n", - "\tsnapshotindex: -1\n", - "\tshuffle: 1\n", - "\ttrainingsetindex: 0\n", - "\tproject_path: from_top_tracking-DataJoint-2023-10-11\n", - "\tparamset_idx: None\n", - "\t-- Template/Contents of config.yaml --\n", - "\t\tTask: from_top_tracking\n", - "\t\tscorer: DataJoint\n", - "\t\tdate: Oct11\n", - "\t\tmultianimalproject: False\n", - "\t\tidentity: None\n", - "\t\tproject_path: /workspaces/element-deeplabcut/example_data/inbox/from_top_tracking-DataJoint-2023-10-11\n", - "\t\tvideo_sets: {'/Users/milagros/Desktop/from_top_tracking-DataJoint-2023-10-11/videos/test.mp4': {'crop': '0, 500, 0, 500'}, '/Users/milagros/Desktop/from_top_tracking-DataJoint-2023-10-11/videos/train1.mp4': {'crop': '0, 500, 0, 500'}}\n", - "\t\tbodyparts: ['head', 'tailbase']\n", - "\t\tstart: 0\n", - "\t\tstop: 1\n", - "\t\tnumframes2pick: 40\n", - "\t\tskeleton: [['bodypart1', 'bodypart2'], ['objectA', 'bodypart3']]\n", - "\t\tskeleton_color: black\n", - "\t\tpcutoff: 0.6\n", - "\t\tdotsize: 12\n", - "\t\talphavalue: 0.7\n", - "\t\tcolormap: rainbow\n", - "\t\tTrainingFraction: [0.95]\n", - "\t\titeration: 0\n", - "\t\tdefault_net_type: resnet_50\n", - "\t\tdefault_augmenter: default\n", - "\t\tsnapshotindex: -1\n", - "\t\tbatch_size: 8\n", - "\t\tcropping: False\n", - "\t\tx1: 0\n", - "\t\tx2: 640\n", - "\t\ty1: 277\n", - "\t\ty2: 624\n", - "\t\tcorner2move2: [50, 50]\n", - "\t\tmove2corner: True\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "model.Model.insert_new_model(model_name='from_top_tracking_model_test',\n", " dlc_config=config_file_rel,\n", @@ -1345,141 +260,31 @@ " model_description='Model in example data: from_top_tracking model')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check the `Model` table to confirm that the new model has been added:" + ] + }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    model_name

    \n", - " User-friendly model name\n", - "
    \n", - "

    task

    \n", - " Task in the config yaml\n", - "
    \n", - "

    date

    \n", - " Date in the config yaml\n", - "
    \n", - "

    iteration

    \n", - " Iteration/version of this model\n", - "
    \n", - "

    snapshotindex

    \n", - " which snapshot for prediction (if -1, latest)\n", - "
    \n", - "

    shuffle

    \n", - " Shuffle (1) or not (0)\n", - "
    \n", - "

    trainingsetindex

    \n", - " Index of training fraction list in config.yaml\n", - "
    \n", - "

    scorer

    \n", - " Scorer/network name - DLC's GetScorerName()\n", - "
    \n", - "

    config_template

    \n", - " Dictionary of the config for analyze_videos()\n", - "
    \n", - "

    project_path

    \n", - " DLC's project_path in config relative to root\n", - "
    \n", - "

    model_prefix

    \n", - " \n", - "
    \n", - "

    model_description

    \n", - " \n", - "
    \n", - "

    paramset_idx

    \n", - " \n", - "
    from_top_tracking_model_testfrom_top_trackingOct110-110DLCresnet50fromtoptrackingOct11shuffle1=BLOB=from_top_tracking-DataJoint-2023-10-11Model in example data: from_top_tracking modelNone
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*model_name task date iteration snapshotindex shuffle trainingsetind scorer config_tem project_path model_prefix model_descript paramset_idx \n", - "+------------+ +------------+ +-------+ +-----------+ +------------+ +---------+ +------------+ +------------+ +--------+ +------------+ +------------+ +------------+ +------------+\n", - "from_top_track from_top_track Oct11 0 -1 1 0 DLCresnet50fro =BLOB= from_top_track Model in examp None \n", - " (Total: 1)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "model.Model()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Much of this information is directly sourced from the `config` file. However, it's worth noting that this model is currently distinct and singular. \n", + "\n", + "If you wish to incorporate another model, you must specify a new `model_name`; duplication of an existing model is not permitted—it must be an entirely new model." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1487,112 +292,32 @@ "## Step 2 - Insert Subject, Session, and Behavior Videos" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Confirm the availability of data in the `Subject` and `Session` tables:" + ] + }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    subject_nickname

    \n", - " \n", - "
    \n", - "

    sex

    \n", - " \n", - "
    \n", - "

    subject_birth_date

    \n", - " \n", - "
    \n", - "

    subject_description

    \n", - " \n", - "
    subject6F2020-01-01hneih_E105
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*subject subject_nickna sex subject_birth_ subject_descri\n", - "+----------+ +------------+ +-----+ +------------+ +------------+\n", - "subject6 F 2020-01-01 hneih_E105 \n", - " (Total: 1)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "subject.Subject()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Insert a subject into the `Subject` table:" + ] + }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1608,95 +333,19 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define session keys and insert them into the `Session` table:\n", + "\n" + ] + }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    subject62021-06-02 14:04:22
    subject62021-06-03 14:43:10
    \n", - " \n", - "

    Total: 2

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet\n", - "+----------+ +------------+\n", - "subject6 2021-06-02 14:\n", - "subject6 2021-06-03 14:\n", - " (Total: 2)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "#Definition of the dictionary named \"session_keys\"\n", "session_keys = [\n", @@ -1705,13 +354,35 @@ "]\n", "\n", "#Insert this dictionary in the Session table\n", - "session.Session.insert(session_keys, skip_duplicates=True)\n", + "session.Session.insert(session_keys, skip_duplicates=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Confirm the inserted data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "session.Session()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Insert data into the `VideoRecording` table:" + ] + }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1722,9 +393,16 @@ "model.VideoRecording.insert1({**recording_key, 'device': 'Camera1'}, skip_duplicates=True)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Insert video files into the `VideoRecording.File` table:" + ] + }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1738,121 +416,18 @@ " 'file_path': Path(f)} for v_idx, f in enumerate(video_files))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Populate the `RecordingInfo` table:" + ] + }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", - " \n", - "
    \n", - "

    px_height

    \n", - " height in pixels\n", - "
    \n", - "

    px_width

    \n", - " width in pixels\n", - "
    \n", - "

    nframes

    \n", - " number of frames\n", - "
    \n", - "

    fps

    \n", - " (Hz) frames per second\n", - "
    \n", - "

    recording_datetime

    \n", - " Datetime for the start of the recording\n", - "
    \n", - "

    recording_duration

    \n", - " video duration (s) from nframes / fps\n", - "
    subject62021-06-02 14:04:2215005006000060None1000.0
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet *recording_id px_height px_width nframes fps recording_date recording_dura\n", - "+----------+ +------------+ +------------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n", - "subject6 2021-06-02 14: 1 500 500 60000 60 None 1000.0 \n", - " (Total: 1)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "### RecordingInfo\n", "model.RecordingInfo.populate()\n", @@ -1863,83 +438,85 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 3 - DLC inference task" + "Recording info extracts metadata from the video and validates the number of frames (n_frames), which will correspond to the number of entries for each body part in the pose estimation results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "{summary about next line}" + "## Step 3 - DeepLabCut Inference Task" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `PoseEstimationTask` table is used for defining an inference task. Let's explore the table description:" ] }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> model.VideoRecording\n", - "-> model.Model\n", - "---\n", - "task_mode=\"load\" : enum('load','trigger') # load results or trigger computation\n", - "pose_estimation_output_dir=\"\" : varchar(255) # output dir relative to the root dir\n", - "pose_estimation_params=null : longblob # analyze_videos params, if not default\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "'-> model.VideoRecording\\n-> model.Model\\n---\\ntask_mode=\"load\" : enum(\\'load\\',\\'trigger\\') # load results or trigger computation\\npose_estimation_output_dir=\"\" : varchar(255) # output dir relative to the root dir\\npose_estimation_params=null : longblob # analyze_videos params, if not default\\n'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "model.PoseEstimationTask.describe()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To define and insert a task, you need to:\n", + "\n", + "1. Define a video recording.\n", + "2. Select a model.\n", + "3. Choose the task mode (load or trigger).\n", + "4. Specify the output directory and optional parameters.\n", + "\n", + "When the task mode is \"trigger,\" DataJoint triggers the inference, running the DeepLabCut model. This might take a long time, depending on the hardware. If the hardware lacks GPU support, it's not recommended.\n", + "\n", + "For this exercise, we are choosing the **\"load\" task** mode because the server does not have the necessary GPU for inference. The results have already been prepared. The results of this inference are generated in `example_data\\outbox`. \n", + "\n", + "If you select the **\"trigger\" task**, DataJoint will perform the entire inference process and generate these file sets." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define the keys for recording and task:" + ] + }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'subject': 'subject6',\n", - " 'session_datetime': '2021-06-02 14:04:22',\n", - " 'recording_id': '1'}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "recording_key" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "task_key = {**recording_key, 'model_name': 'from_top_tracking_model_test'}" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results are located in the `pose_estimation_output_dir` location." + ] + }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1950,120 +527,25 @@ " })" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display the `PoseEstimationTask` table:" + ] + }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", - " \n", - "
    \n", - "

    model_name

    \n", - " User-friendly model name\n", - "
    \n", - "

    task_mode

    \n", - " load results or trigger computation\n", - "
    \n", - "

    pose_estimation_output_dir

    \n", - " output dir relative to the root dir\n", - "
    \n", - "

    pose_estimation_params

    \n", - " analyze_videos params, if not default\n", - "
    subject62021-06-02 14:04:221from_top_tracking_model_testload./example_data/outbox/from_top_tracking-DataJoint-2023-10-11/videos/device_1_recording_1_model_from_top_tracking_100000_maxiters=BLOB=
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet *recording_id *model_name task_mode pose_estimatio pose_estim\n", - "+----------+ +------------+ +------------+ +------------+ +-----------+ +------------+ +--------+\n", - "subject6 2021-06-02 14: 1 from_top_track load ./example_data =BLOB= \n", - " (Total: 1)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "model.PoseEstimationTask()" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2071,246 +553,53 @@ "model.PoseEstimation.populate()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look into the `PoseEstimation` table." + ] + }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", - " \n", - "
    \n", - "

    model_name

    \n", - " User-friendly model name\n", - "
    \n", - "

    pose_estimation_time

    \n", - " time of generation of this set of DLC results\n", - "
    subject62021-06-02 14:04:221from_top_tracking_model_test2023-10-12 15:21:47
    \n", - " \n", - "

    Total: 1

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet *recording_id *model_name pose_estimatio\n", - "+----------+ +------------+ +------------+ +------------+ +------------+\n", - "subject6 2021-06-02 14: 1 from_top_track 2023-10-12 15:\n", - " (Total: 1)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "model.PoseEstimation()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The most critical table is the `PoseEstimation.BodyPartPosition`. " + ] + }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " uses DeepLabCut h5 output for body part position\n", - "
    \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    \n", - "

    subject

    \n", - " \n", - "
    \n", - "

    session_datetime

    \n", - " \n", - "
    \n", - "

    recording_id

    \n", - " \n", - "
    \n", - "

    model_name

    \n", - " User-friendly model name\n", - "
    \n", - "

    body_part

    \n", - " \n", - "
    \n", - "

    frame_index

    \n", - " frame index in model\n", - "
    \n", - "

    x_pos

    \n", - " \n", - "
    \n", - "

    y_pos

    \n", - " \n", - "
    \n", - "

    z_pos

    \n", - " \n", - "
    \n", - "

    likelihood

    \n", - " \n", - "
    subject62021-06-02 14:04:221from_top_tracking_model_testhead=BLOB==BLOB==BLOB==BLOB==BLOB=
    subject62021-06-02 14:04:221from_top_tracking_model_testtailbase=BLOB==BLOB==BLOB==BLOB==BLOB=
    \n", - " \n", - "

    Total: 2

    \n", - " " - ], - "text/plain": [ - "*subject *session_datet *recording_id *model_name *body_part frame_inde x_pos y_pos z_pos likelihood\n", - "+----------+ +------------+ +------------+ +------------+ +-----------+ +--------+ +--------+ +--------+ +--------+ +--------+\n", - "subject6 2021-06-02 14: 1 from_top_track head =BLOB= =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject6 2021-06-02 14: 1 from_top_track tailbase =BLOB= =BLOB= =BLOB= =BLOB= =BLOB= \n", - " (Total: 2)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "### Results\n", "model.PoseEstimation.BodyPartPosition()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After pose estimation, entries related to the task include `subject`, `session`, `recording_id`, `model name`, and each detected `body_part` (two entries in this case).\n", + "\n", + "Entries contain `frame_index`, `x_pos` and `y_pos` positions, and `likelihood` (`z_pos` is zero). This structure is familiar to DeepLabCut users.\n", + "\n", + "These results can be fetched in a Pandas DataFrame structure: " + ] + }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2319,256 +608,44 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    indexsubjectsession_datetimerecording_idmodel_namebody_partframe_indexx_posy_posz_poslikelihood
    00subject62021-06-02 14:04:221from_top_tracking_model_testhead0273.996613314.971008None0.999999
    10subject62021-06-02 14:04:221from_top_tracking_model_testhead1274.103363315.145966None0.999999
    20subject62021-06-02 14:04:221from_top_tracking_model_testhead2274.032654315.133331None0.999999
    30subject62021-06-02 14:04:221from_top_tracking_model_testhead3274.025238315.152283None0.999999
    40subject62021-06-02 14:04:221from_top_tracking_model_testhead4274.073181315.173248None0.999999
    ....................................
    1199951subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59995323.29388433.214066None1.0
    1199961subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59996321.60226432.794708None1.0
    1199971subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59997320.17398132.857304None1.0
    1199981subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59998318.70861833.147358None0.999999
    1199991subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59999317.67410333.861454None1.0
    \n", - "

    120000 rows × 11 columns

    \n", - "
    " - ], - "text/plain": [ - " index subject session_datetime recording_id \\\n", - "0 0 subject6 2021-06-02 14:04:22 1 \n", - "1 0 subject6 2021-06-02 14:04:22 1 \n", - "2 0 subject6 2021-06-02 14:04:22 1 \n", - "3 0 subject6 2021-06-02 14:04:22 1 \n", - "4 0 subject6 2021-06-02 14:04:22 1 \n", - "... ... ... ... ... \n", - "119995 1 subject6 2021-06-02 14:04:22 1 \n", - "119996 1 subject6 2021-06-02 14:04:22 1 \n", - "119997 1 subject6 2021-06-02 14:04:22 1 \n", - "119998 1 subject6 2021-06-02 14:04:22 1 \n", - "119999 1 subject6 2021-06-02 14:04:22 1 \n", - "\n", - " model_name body_part frame_index x_pos \\\n", - "0 from_top_tracking_model_test head 0 273.996613 \n", - "1 from_top_tracking_model_test head 1 274.103363 \n", - "2 from_top_tracking_model_test head 2 274.032654 \n", - "3 from_top_tracking_model_test head 3 274.025238 \n", - "4 from_top_tracking_model_test head 4 274.073181 \n", - "... ... ... ... ... \n", - "119995 from_top_tracking_model_test tailbase 59995 323.293884 \n", - "119996 from_top_tracking_model_test tailbase 59996 321.602264 \n", - "119997 from_top_tracking_model_test tailbase 59997 320.173981 \n", - "119998 from_top_tracking_model_test tailbase 59998 318.708618 \n", - "119999 from_top_tracking_model_test tailbase 59999 317.674103 \n", - "\n", - " y_pos z_pos likelihood \n", - "0 314.971008 None 0.999999 \n", - "1 315.145966 None 0.999999 \n", - "2 315.133331 None 0.999999 \n", - "3 315.152283 None 0.999999 \n", - "4 315.173248 None 0.999999 \n", - "... ... ... ... \n", - "119995 33.214066 None 1.0 \n", - "119996 32.794708 None 1.0 \n", - "119997 32.857304 None 1.0 \n", - "119998 33.147358 None 0.999999 \n", - "119999 33.861454 None 1.0 \n", - "\n", - "[120000 rows x 11 columns]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`frame_index` is an array of frame numbers, `x_pos` is a NumPy array of x positions, and `likelihood` is also a NumPy array.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use DataJoint `fetch` as a Pandas DataFrame and utilize the `explode` function to expand `x` and `y` positions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df = df.explode(['frame_index', 'x_pos', 'y_pos', 'likelihood']).reset_index()\n", "df" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned earlier, you can confirm these results by the number of entries. There are 66000 frames for each body part, matching the `n_frames` from the `RecordingInfo` table." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2576,22 +653,18 @@ "## Step 4 - Visualization of results" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, separate the data for the head and tailbase and then plot the head pose estimation and tailbase pose estimation." + ] + }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -2609,20 +682,9 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj8AAAHHCAYAAABQhTneAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOx9d7wVxd3+s+ecW2iXXkSagqIoimKi2KMGVNTktRtjNMUYe6Lxl2gKanw1GnuN8Y3YY9Q0u6JBo4BRURQBQZQmXcqlXG47u78/dmfmO23P7rm7915gn494z9kzOzO7OzvznefbHM/zPGTIkCFDhgwZMmwnyLV1BzJkyJAhQ4YMGVoTmfCTIUOGDBkyZNiukAk/GTJkyJAhQ4btCpnwkyFDhgwZMmTYrpAJPxkyZMiQIUOG7QqZ8JMhQ4YMGTJk2K6QCT8ZMmTIkCFDhu0KmfCTIUOGDBkyZNiukAk/GTJkyJAhQ4btCpnwkyFDO8Thhx+Oww8/nH9fuHAhHMfBQw89xI+dc8456Ny5c+t3LkPieOONN+A4Dt5444227ooEdRxmyLCtIBN+MmRICI7jRPrX3ha4DK2He++9VxJg2wNmz56Nq6++GgsXLmzrrmTI0GootHUHMmTYVvDoo49K3x955BFMmjRJO7777ruXrOvVV19NtG8Z2gfuvfde9OrVC+ecc450/NBDD8WWLVtQWVnZ6n2aPXs2rrnmGhx++OEYMmSI9Fs2DjNsq8iEnwwZEsJ3v/td6fs777yDSZMmacejoC0WwQxth1wuh+rq6rbuhoZsHGbYVpGpvTJkaEVMnDgRRxxxBPr06YOqqiqMGDEC9913n1Yujq3FF198gXHjxqFTp07o378/rr32WnieJ5W5+eabceCBB6Jnz57o0KEDRo8ejWeeeUara9KkSTj44IPRrVs3dO7cGcOHD8dVV10llWloaMCECRMwbNgwVFVVYeDAgfh//+//oaGhoWRfDz/8cOy5556YPn06DjzwQHTo0AE77bQT/vjHP2plV61ahR/+8Ifo27cvqqursffee+Phhx/Wyj355JMYPXo0unTpgpqaGowcORJ33HGHVGb9+vX46U9/ioEDB6KqqgrDhg3DjTfeCNd1S/YZAF566SUccsgh6NSpE7p06YLx48dj1qxZUpkVK1bg+9//PgYMGICqqirssMMO+Na3vsXVSUOGDMGsWbPw5ptvchUoe8Ymmx92rz7++GMcdthh6NixI4YNG8af25tvvon9998fHTp0wPDhw/Haa69J/Vm0aBEuuOACDB8+HB06dEDPnj1xyimnSOqthx56CKeccgoA4Bvf+IammjWNwyjPhdmo3XzzzfjTn/6EoUOHoqqqCl/72tfw3nvvRbrnGTKkiYz5yZChFXHfffdhjz32wAknnIBCoYDnnnsOF1xwAVzXxYUXXhi7vmKxiKOPPhoHHHAAbrrpJrz88suYMGECmpubce211/Jyd9xxB0444QSceeaZaGxsxJNPPolTTjkFzz//PMaPHw8AmDVrFo477jjstddeuPbaa1FVVYX58+djypQpvB7XdXHCCSfg7bffxo9//GPsvvvumDlzJm677TbMmzcP//znP0v2ed26dTj22GNx6qmn4owzzsBTTz2F888/H5WVlfjBD34AANiyZQsOP/xwzJ8/HxdddBF22mknPP300zjnnHOwfv16XHrppQB8Ye2MM87AkUceiRtvvBEAMGfOHEyZMoWXqaurw2GHHYalS5fivPPOw6BBgzB16lRceeWVWL58OW6//fbQ/j766KM4++yzMW7cONx4442oq6vDfffdh4MPPhgffvghVxWddNJJmDVrFi6++GIMGTIEq1atwqRJk7B48WIMGTIEt99+Oy6++GJ07twZv/rVrwAAffv2LXmvjjvuOJx++uk45ZRTcN999+H000/H448/jp/+9Kf4yU9+gu985zv4wx/+gJNPPhlLlixBly5dAADvvfcepk6ditNPPx0DBgzAwoULcd999+Hwww/H7Nmz0bFjRxx66KG45JJLcOedd+Kqq67iKlmbajbqc2F44oknsHHjRpx33nlwHAc33XQTTjzxRHzxxReoqKgIvfYMGVKFlyFDhlRw4YUXeuorVldXp5UbN26ct/POO0vHDjvsMO+www7j3xcsWOAB8CZOnMiPnX322R4A7+KLL+bHXNf1xo8f71VWVnqrV6+2ttvY2Ojtueee3hFHHMGP3XbbbR4A6TwVjz76qJfL5by33npLOv7HP/7RA+BNmTLFei67LgDeLbfcwo81NDR4o0aN8vr06eM1NjZ6nud5t99+uwfAe+yxx6Q+jxkzxuvcubO3YcMGz/M879JLL/Vqamq85uZma5u/+93vvE6dOnnz5s2Tjv/yl7/08vm8t3jxYuu5Gzdu9Lp16+ade+650vEVK1Z4Xbt25cfXrVvnAfD+8Ic/hF7/HnvsIT1XhsmTJ3sAvMmTJ/Nj7F498cQT/Ninn37qAfByuZz3zjvv8OOvvPKKNj5MY23atGkeAO+RRx7hx55++mmtbdoH2t+oz4WN1549e3pr167lZf/1r395ALznnnvOeH8yZGgtZGqvDBlaER06dOCfa2tr8dVXX+Gwww7DF198gdra2rLqvOiii/hnx3Fw0UUXobGxUVKD0HbXrVuH2tpaHHLIIfjggw/48W7dugEA/vWvf1nVQU8//TR233137Lbbbvjqq6/4vyOOOAIAMHny5JL9LRQKOO+88/j3yspKnHfeeVi1ahWmT58OAHjxxRfRr18/nHHGGbxcRUUFLrnkEmzatAlvvvkm7/PmzZsxadIka3tPP/00DjnkEHTv3l3q81FHHYVisYj//Oc/1nMnTZqE9evX44wzzpDOzefz2H///fn1dujQAZWVlXjjjTewbt26kvcgKjp37ozTTz+dfx8+fDi6deuG3XffHfvvvz8/zj5/8cUX/Bh95k1NTVizZg2GDRuGbt26Sc89DqI+F4bTTjsN3bt3598POeQQrZ8ZMrQFMrVXhgytiClTpmDChAmYNm0a6urqpN9qa2vRtWvXWPXlcjnsvPPO0rFdd90VACTbjueffx7XXXcdZsyYIdnmOI7DP5922mn4v//7P/zoRz/CL3/5Sxx55JE48cQTcfLJJyOX8/dJn332GebMmYPevXsb+7Nq1aqSfe7fvz86depk7fMBBxyARYsWYZddduHtMjB1zKJFiwAAF1xwAZ566ikcc8wx2HHHHTF27FiceuqpOProo/k5n332GT7++OOy+vzZZ58BABfuVNTU1AAAqqqqcOONN+Lyyy9H3759ccABB+C4447D9773PfTr189afykMGDBAekYA0LVrVwwcOFA7BkASvLZs2YIbbrgBEydOxNKlSyU7sHIF7ajPhWHQoEHSdyYIJSkgZshQDjLhJ0OGVsLnn3+OI488ErvtthtuvfVWDBw4EJWVlXjxxRdx2223RTa+jYu33noLJ5xwAg499FDce++92GGHHVBRUYGJEyfiiSee4OU6dOiA//znP5g8eTJeeOEFvPzyy/jrX/+KI444Aq+++iry+Txc18XIkSNx6623GttSF+W00adPH8yYMQOvvPIKXnrpJbz00kuYOHEivve973EjXNd18c1vfhP/7//9P2MdTPAygT2TRx991CjEFApiCv3pT3+K448/Hv/85z/xyiuv4De/+Q1uuOEG/Pvf/8Y+++xT1vXl8/lYx6mAc/HFF2PixIn46U9/ijFjxqBr165wHAenn356amOtnH5myNAWyISfDBlaCc899xwaGhrw7LPPSjviKKoiG1zXxRdffCEt4PPmzQMAboj7t7/9DdXV1XjllVdQVVXFy02cOFGrL5fL4cgjj8SRRx6JW2+9Fddffz1+9atfYfLkyTjqqKMwdOhQfPTRRzjyyCM1RiIqli1bhs2bN0vsj9rnwYMH4+OPP4bruhLL8Omnn/LfGSorK3H88cfj+OOPh+u6uOCCC3D//ffjN7/5DYYNG4ahQ4di06ZNOOqoo2L3dejQoQB8ISvK+UOHDsXll1+Oyy+/HJ999hlGjRqFW265BY899hgAlH3PysEzzzyDs88+G7fccgs/Vl9fj/Xr10vl4vQpznPJkKE9I7P5yZChlcB2war6wSSExMHdd9/NP3ueh7vvvhsVFRU48sgjebuO46BYLPJyCxcu1Dyz1q5dq9U9atQoAOCqslNPPRVLly7FAw88oJXdsmULNm/eXLK/zc3NuP/++/n3xsZG3H///ejduzdGjx4NADj22GOxYsUK/PWvf5XOu+uuu9C5c2ccdthhAIA1a9ZIdedyOey1115an6dNm4ZXXnlF68v69evR3Nxs7eu4ceNQU1OD66+/Hk1NTdrvq1evBuB7lNXX10u/DR06FF26dJHUjJ06ddKEj7SQz+c1huWuu+6SxgHrE4BI/Yr6XDJkaO/ImJ8MGVoJY8eO5SzFeeedh02bNuGBBx5Anz59sHz58rLqrK6uxssvv4yzzz4b+++/P1566SW88MILuOqqq7iNy/jx43Hrrbfi6KOPxne+8x2sWrUK99xzD4YNG4aPP/6Y13XttdfiP//5D8aPH4/Bgwdj1apVuPfeezFgwAAcfPDBAICzzjoLTz31FH7yk59g8uTJOOigg1AsFvHpp5/iqaeewiuvvIL99tsvtM/9+/fHjTfeiIULF2LXXXfFX//6V8yYMQN/+tOfuPvzj3/8Y9x///0455xzMH36dAwZMgTPPPMMpkyZgttvv527c//oRz/C2rVrccQRR2DAgAFYtGgR7rrrLowaNYrboVxxxRV49tlncdxxx+Gcc87B6NGjsXnzZsycORPPPPMMFi5ciF69ehn7WlNTg/vuuw9nnXUW9t13X5x++uno3bs3Fi9ejBdeeAEHHXQQ7r77bsybNw9HHnkkTj31VIwYMQKFQgH/+Mc/sHLlSslgefTo0bjvvvtw3XXXYdiwYejTp4/VnqilOO644/Doo4+ia9euGDFiBKZNm4bXXnsNPXv2lMqNGjUK+XweN954I2pra1FVVcVjUamI+lwyZGj3aEtXswwZtmWYXN2fffZZb6+99vKqq6u9IUOGeDfeeKP34IMPegC8BQsW8HJRXd07derkff75597YsWO9jh07en379vUmTJjgFYtFqd0///nP3i677OJVVVV5u+22mzdx4kRvwoQJUv9ef/1171vf+pbXv39/r7Ky0uvfv793xhlnaC7ijY2N3o033ujtscceXlVVlde9e3dv9OjR3jXXXOPV1taG3pPDDjvM22OPPbz333/fGzNmjFddXe0NHjzYu/vuu7WyK1eu9L7//e97vXr18iorK72RI0dK1+95nvfMM894Y8eO9fr06eNVVlZ6gwYN8s477zxv+fLlUrmNGzd6V155pTds2DCvsrLS69Wrl3fggQd6N998M3evD8PkyZO9cePGeV27dvWqq6u9oUOHeuecc473/vvve57neV999ZV34YUXervttpvXqVMnr2vXrt7+++/vPfXUU1I9K1as8MaPH+916dLFA8Cfsc3VfY899tD6MnjwYG/8+PHacQDehRdeyL+vW7eO37/OnTt748aN8z799FNv8ODB3tlnny2d+8ADD3g777yzl8/npX6o49Dzoj0XNl5Nrv8AvAkTJmjHM2RoTTiel1meZciQoXVw+OGH46uvvsInn3zS1l3JkCHDdozM5idDhgwZMmTIsF0hE34yZMiQIUOGDNsVMuEnQ4YMGTJkyLBdIbP5yZAhQ4YMGTJsV8iYnwwZMmTIkCHDdoU2FX6uvvpqOI4j/dttt9347/X19bjwwgvRs2dPdO7cGSeddBJWrlwp1bF48WKMHz8eHTt2RJ8+fXDFFVeEBi3LkCFDhgwZMmzfaPMgh3vssYeUfZrmyvnZz36GF154AU8//TS6du2Kiy66CCeeeCKmTJkCACgWixg/fjz69euHqVOnYvny5fje976HiooKXH/99ZH74Louli1bhi5durRq+PkMGTJkyJAhQ/nwPA8bN25E//79tYS7pU5sM0yYMMHbe++9jb+tX7/eq6io8J5++ml+bM6cOR4Ab9q0aZ7ned6LL77o5XI5b8WKFbzMfffd59XU1HgNDQ2R+7FkyRIPQPYv+5f9y/5l/7J/2b+t8N+SJUtiyR9tzvx89tln6N+/P6qrqzFmzBjccMMNGDRoEKZPn46mpiYpmeBuu+2GQYMGYdq0aTjggAMwbdo0jBw5En379uVlxo0bh/PPPx+zZs2yZlJuaGiQ8u14gc33kiVLUFNTk9KVZsiQIUOGDBmSxIYNGzBw4MDYqVXaVPjZf//98dBDD2H48OFYvnw5rrnmGhxyyCH45JNPsGLFClRWVqJbt27SOX379sWKFSsAACtWrJAEH/Y7+82GG264Addcc412vKamJhN+MmTIkCFDhq0McU1W2lT4OeaYY/jnvfbaC/vvvz8GDx6Mp556Ch06dEit3SuvvBKXXXYZ/84kxwwZMmTIkCHDto925ererVs37Lrrrpg/fz769euHxsZGrF+/XiqzcuVK9OvXDwDQr18/zfuLfWdlTKiqquIsT8b2ZMiQIUOGDNsX2pXws2nTJnz++efYYYcdMHr0aFRUVOD111/nv8+dOxeLFy/GmDFjAABjxozBzJkzsWrVKl5m0qRJqKmpwYgRI1q9/xkyZMiQIUOG9o82VXv9/Oc/x/HHH4/Bgwdj2bJlmDBhAvL5PM444wx07doVP/zhD3HZZZehR48eqKmpwcUXX4wxY8bggAMOAACMHTsWI0aMwFlnnYWbbroJK1aswK9//WtceOGFqKqqastLy5AhQ4YM2xGKxSKamprauhvbHCoqKpDP5xOvt02Fny+//BJnnHEG1qxZg969e+Pggw/GO++8g969ewMAbrvtNuRyOZx00kloaGjAuHHjcO+99/Lz8/k8nn/+eZx//vkYM2YMOnXqhLPPPhvXXnttW11ShgwZMmTYjuB5HlasWKGZaGRIDt26dUO/fv0SjcOX5faCb/DctWtX1NbWZvY/GTJkyJAhMpYvX47169ejT58+6NixYxYoN0F4noe6ujqsWrUK3bp1ww477KCVKXf9bvM4PxkyZMiQIcPWiGKxyAWfnj17tnV3tkkwz+9Vq1ahT58+ianA2pXBc4YMGTJkyLC1gNn4dOzYsY17sm2D3d8kbaoy4SdDhgwZMmRoATJVV7pI4/5mwk+GDBkyZMiQYbtCJvxkyJAhQ4YMGbYrZMJPhgwZMmTIkGG7Qib8ZMiQoV1jS2OxrbuQIUOGbQyZ8JMhQ4Z2iwffXoDdf/syXv5keVt3JUOGbQarV69Gv379cP311/NjU6dORWVlpZRSyoSrr74ao0aNwv3334+BAweiY8eOOPXUU1FbW8vLuK6La6+9FgMGDEBVVRVGjRqFl19+mf/e2NiIiy66CDvssAOqq6sxePBg3HDDDclfaAiyOD8ZMmRot7j2+dkAgJ/+dQY+3VMPcJYhQ3uD53nY0tT6bGWHinxkr6jevXvjwQcfxLe//W2MHTsWw4cPx1lnnYWLLroIRx55ZMnz58+fj6eeegrPPfccNmzYgB/+8Ie44IIL8PjjjwMA7rjjDtxyyy24//77sc8+++DBBx/ECSecgFmzZmGXXXbBnXfeiWeffRZPPfUUBg0ahCVLlmDJkiUtuv64yISfDBkytHvUN7lt3YUMGSJhS1MRI377Squ3O/vacehYGX1JP/bYY3HuuefizDPPxH777YdOnTpFZl/q6+vxyCOPYMcddwQA3HXXXRg/fjxuueUW9OvXDzfffDN+8Ytf4PTTTwcA3HjjjZg8eTJuv/123HPPPVi8eDF22WUXHHzwwXAcB4MHD45/wS1EpvbKkCFDhgwZtkPcfPPNaG5uxtNPP43HH388ckLwQYMGccEHAMaMGQPXdTF37lxs2LABy5Ytw0EHHSSdc9BBB2HOnDkAgHPOOQczZszA8OHDcckll+DVV19N7qIiImN+tnNsbmjGHhPELuXJHx+AA3bOwrRnyJAhQznoUJHH7GvHtUm7cfH5559j2bJlcF0XCxcuxMiRI1PomY59990XCxYswEsvvYTXXnsNp556Ko466ig888wzrdI+kAk/2z2ufnaW9P30P72Dhb8f30a9yZAhQ4atG47jxFI/tRUaGxvx3e9+F6eddhqGDx+OH/3oR5g5cyb69OlT8tzFixdj2bJl6N+/PwDgnXfeQS6Xw/Dhw1FTU4P+/ftjypQpOOyww/g5U6ZMwde//nX+vaamBqeddhpOO+00nHzyyTj66KOxdu1a9OjRI/mLNaD9P6EMqeLp6V9K388e0/q61wwZMrQPfLK0Fsfd9TYu++auuOTIXdq6OxlSxK9+9SvU1tbizjvvROfOnfHiiy/iBz/4AZ5//vmS51ZXV+Pss8/GzTffjA0bNuCSSy7Bqaeein79+gEArrjiCkyYMAFDhw7FqFGjMHHiRMyYMYMbRN96663YYYcdsM8++yCXy+Hpp59Gv3790K1btzQvWUJm87Od42tDukvfH562qI160v6wbP0W/PeLNdpxz/Nw5C1v4OK/fNgGvdo+8N7CtTjjT++0dTe2Oxx319sAgFsnzWvjnmRIE2+88QZuv/12PProo6ipqUEul8Ojjz6Kt956C/fdd1/J84cNG4YTTzwRxx57LMaOHYu99toL9957L//9kksuwWWXXYbLL78cI0eOxMsvv4xnn30Wu+ziC9RdunTBTTfdhP322w9f+9rXsHDhQrz44ovI5VpPJMmYn+0cQ3p2wnsL17V1NyKh6HqYubQWe/SvQUU+/ZfkwN//GwDw9wsOxL6DhJD41mdf4fPVm/H56s248/RRWVLDFHDKH6e1dRcyZNhmcfjhh2sZ0ocMGSLF6imF888/H+eff77xt1wuhwkTJmDChAnG388991yce+650TucAjLmZztHfbPvQnzU7n35saLrtVV3QnHivVPw7Xum4Khb32zVdqcrwmFlQbw2TcX2ea8yZMiw/aDoumh2s3AQcZAJP9s5WOqAA4cKD6+2CNAVBR996e9KFq2pa/W2Nzc0Y+rnX8HzPIl1qmtsbvW+bI/4+k4tM4JsKrpYt7kxod5kyNB+4HkeZi3bgNnLNsBNYOO6xx57oHPnzsZ/zGZnW0Cm9tpO4XkePl+9mS/e3TpW8N+2NBbRuSobGgxTPv8KS9bV4ZHAHuqRHwiPhav+MRP3njk61fbrm4qoLsONdVtCx8ryr9/zPOzyq5cAAFN/eQT6d+uQVLe2acxaVos9+ndt626kgs9WbsT/vjgHPz1qV4wa2K2tu9MiUHGnqeiiKteyueLFF1/UVGIMffv2RZcuXXD11Ve3qI32gGyF204xccpCnjoAAPI5Bx0q8tjSVER9O2V+2gpvzF0tff/reyIMe4eKdF+hf3z4JX7214/wh5P3win7DUy1rfaMQq58u6r5qzbxz5Nmr8TZBw5JoEfbPsbf+fY2G/binInvYen6LXhj7uqt/ho9Iv1saSqiqoUbpbaIttwWyNRe2ylueuVT6XtDs4sOwe66vaq92gtGDxbGz5+v3hRSsuX42V8/AgBc8czHqbbT3uGVyeZvqG/CN2/7D/9O7bUybL9Yun5LW3chMazYUM8/L17b+iYBWyuymWA7hQN5J73f4O48QmhdYyb8UIzcsSsqiZ0PXYeH9+3S+h3ajsDUXcUypZ8Db/i39D2feeZl2MawZlNDW3dhq0Qm/GynqMjLi0CnqoJgfjLhR8LMpbXo3UXkvKFqwZ17d2qLLm3zYDZn5x6yM4DyPRA3NcgG6b26VLasYxm2OcxYsh6zlkV38c6wbSATfrZTbKiXF4WqQo4zP5nNjw5Kk9duEcaAze00LMDWDibsMCHdjcj8qN4uF31jmPQ934pB1DJsHfj2PVMw/s63E/GUyrD1IJsJtlNQ7y4AqCrkOfOTqb18DyEbHpm2kH9uKmaxNdIAE3ZYWIHmCPGUJn+6Cntf8ypenLmcH8sphtJRhagM2x8at9J3uTUCvm6LyO7adoojhovkdWN27onqCsH8XPyXD9DQvH0LQGFrJFXBZMJPOmBCCjNQjiK0fP+h97CxoRkXPP6BOKicl+3uw1FTvX04AHcxXOfWyuLSjezWkFC1vSATfrZTNAUv+m+PG4Enzt0fjuNw4cf1/OzuLUHaUaLf+mx16UItAO39B7/5JrqTCYYG3Ltn8uep9mN7hVB75aTvcaGetpWub62G7WXxzBtCJ7TXyPYlsZV2u62RCT/bKZqCtBYVeYfnpnp/0Vr++4eL15dd99L1WzDqmldxHYkjlDTO+vO7qUZXpkxDzgHeuOIb2DsIhtaaBuE79dr+DKo9z+NCCrP5KTeLiKesDJnaKxxbK/uRBLZW4afd9rrYBCz7ENi0qq17YkQm/GynYOoaGvfkq00i/P8NJ44su+773piPjQ3N+L+3F5TfwQhIUwiha6TjOOjaoQJ9A4+v1rSJ2h4Xa7oGFZiBcpn3QWN+ttIFrrVQ3I7zQ01ftHUkeFbhSZ+jje9HHnkEPXv2REOD7Cb/7W9/G2eddVbouVdffTVGjRqF+++/HwMHDkTHjh1x6qmnSklRXdfFtb+8FANGH42qngMwatQovPzyy/z3xsZGXHTRRdhhhx1QXV2NwYMH44YbbojU96SQCT/bKZhxHzWWG7FDDf/ckjVXjSGUFtJcx6jQwULDsBQTrRUE0vM8KY/Z3BUbW6XdtgbdgRcC5qfcR62O40z2Cce6OuHJuC2HRFpfp6dveHNeQgyF5wGNm1v1n9NU57cbcXyfcsopKBaLePbZZ/mxVatW4YUXXsAPfvCDkufPnz8fTz31FJ577jm8/PLL+PDDD3HBBRfw3++44w7ccv+juPm3P8PHk/6KcePG4YQTTsBnn30GALjzzjvx7LPP4qmnnsLcuXPx+OOPY8iQIbFuc0uxfSh4M2hobNaZnwfO3g8H/d4PClduUDnAVxO1BlqLps4Fq0BVcK9ai/mZOGWh9H3c7f/Z6kPxRwEVPJltRrkMmLoTbsm43tahGu97nv+Omexjtnbs0LUay2vrpWNVhYTy5zXVAdf3T6auCNgx+Dfz7DnwKqMFXe3QoQO+853vYOLEiTjllFMAAI899hgGDRqEww8/vOT59fX1eOSRR7DjjjsCAO666y6MHz8et9xyC/r164ebb74Zv7j4XJz+rXEAgBt//21MnjwZt99+O+655x4sXrwYu+yyCw4++GA4jtMmKTUy5mc7RZOB+dmxWwccO7IfgJapB5xW2jKW9LSqrwU2riirbon5Cf5y5qc1hJ9Vc1Dx8s/RF2tLl93GQO89y+lVrsyinhcWwmB7xvsL1/LkrxSL1mxug96kD8pyM4zccdtM4mrDueeei1dffRVLly4FADz00EM455xzIs3fgwYN4oIPAIwZMwau62Lu3LnYsGEDli1bhoP221uc4Lk46KCDMGfOHADAOeecgxkzZmD48OG45JJL8OqrryZ7cRGQMT/bKRoNNj+AYDm2BluTksLP7wf5fy/+AOg5NFbd9PJ15kcYWqfmGvzAETirUIfhuSU4tXFCOm20U0hqrxxzdS+vLlXY2VqNWtPGyX+cZjz+57cX4H//p3z7v/aKekMoj8TGRkVH4KplydRVAms2N2DZep/B8godYumH99lnH+y999545JFHMHbsWMyaNQsvvPBCcp0rkGjqynu47777YsGCBXjppZfw2muv4dRTT8VRRx2FZ555Jrn2S3Wv1VrK0K7Q1OwPxqq8Tfhp9S6VRFUhh4ZmIfBE9kyZeCzw87mx2gqz+aHNpuYd0+Tb+uztfJFO/e0Y1OY2z2x+ylV7ZTY/sTG0dyd8vtpnfIb32/Zy1xVdD1PmrwEA/OHkvfDyJyvw+qerkhN+HAeobB0vzaWrmnxhK0CsK/Bc/OjMk3H7Hx/E0qVLcdRRR2HgwIGRTl28eDGWLVuG/v199d4777yDXC6H4cOHo6amBv3798eU92fisNG78Z5NmTIFX//613kdNTU1OO2003Daaafh5JNPxtFHH421a9eiR48ehhaTR6b22k7BDZ4V5iefa9liA6RjKOm6niT4ACWYH5fs7DZFUH3NfRm4uivw7gMAzJMIi4BNkbZrcA7bn/cNtcupCJifcodj5u0VHxX5HA4c2hMA0LVDRYnSWx/e+WIN/7xjtw58ztvu3Pxrv8R3jj0IX375JR544IFIhs4M1dXVOPvss/HRRx/hrbfewiWXXIJTTz0V/fr5ZhNXXHEFbrz9bvz1X69g7vyF+OWVV2HGjBm49NJLAQC33nor/vKXv+DTTz/FvHnz8PTTT6Nfv37o1q1bGldqRCb8bKfgBs8K88MEl5aovdLw9qI0dY9OPp3apAZ/WTkLmDTBt/Vplo0ZS+Ivp/l/X/w5AMAjMgdjwxjzIyHl+TIPFwcN8xeivjVVJUqXCc+ThcU2hhRjiXm6l7jRtl17FucnPvI5h88D2+Ltoqr+A4f14h6FzduAm39UV3cAQN0adK3pgpOOPRKdO3fGt7/97cinDhs2DCeeeCKOPfZYjB07FnvttRfuvfde/vsll1yCyy48D5dfextGHnUqXn7lVTz77LPYZZddAABdunTBTTfdhP322w9f+9rXsHDhQrz44ovItWLuvUzttZ3C5OoOiIW+JVkb0mB+qJFxl+oC1m5uRLPayfsO9P/WrQG+eW2L2qOTiFB76S9mrMmmrH4AJ+4zAFPmr9GYr8Rw99eANZ8BVy4Fqjqn00YMMAHFcYQgXWpTPmX+V/zzgO4dgEVTgUIVPK9aKpd5e5VGIZ/j80Da47stwIYACyDKkt1GyR/X7lHGJSxdsQpnnnkmqqriba7OP/98nH/++cbfcrkcJlx5GSZc9B3/QJ8RQEHUf+655+Lcc8+N39kEkTE/2ylMQQ4B4abeMuYneVCWhwlsGvPDsOzD+MyPApPBsynOTmsw5YxxamhKSfhZ48fewEu/SKf+uAjuac5x+HgspYalRug7F1YDE48BHjhCO6/daDaatgCfvQY0tWycpoGKnIj6vg2QIRo8IlwD/vUC24YxfJwrWLd+A/7x0r/xxrTpuPDCC1PuTPu7txnzs53CpvZqbzY/jc0u7n/zc2xqEIvb/FWbAAAzlqzHmMA2QcLKT4B1C+Vjc54Ddj8+WqOeZ3R1P2Hv/nhk2iKlaLovtQPyTNKeQHrvGr1swybfqDMFmo+tQQ5E2IRSt5kG57tmy+/5515bFrCa/LrbywL3zA+BuYFnzdW14WVbGZsamtE58GLcFtWEdHwBQK5cm58pdwLVvYGOoxLrWxy0aO7xPOwz7gysq92IG391CYYPH85/2mOPPbBo0SLjaffff3+cRowf2wsy4ac1UWwG8u3jltuYHycBb68k4/z87YMvccukecbfbnz5U5x/eODCXq8sIM8oxnt//W70RaZpCzwI+x52OXsa4oC0xjstAv2l3BDxGglF7ZfAbXsAux4NfOeviXeDCXkS81PinB26CvXWTs3CQ+7iT7+LXOEE/KH5dADtaDGfm6BLccL4dMVG9O/WAcC2afPDxhebpxzleCSsXwxM+g3QeSAw/umEexgNpmcT+Qrq1mDhf81j8MUXX0RTkx4BGwD69u2LLl264Oqrr47Sw3J61mpoHyvx9oAX/x/w4aPAhe8C3aK5E6YF1/W4yogljmRgi01LKGBao+d5LRKGlq3fEq3g3xT98cblZbeJV65Er+kPYWE1MKT+cd7/qoLB5ifld9oDDfSXQmPl1PnRX/y/814OL1cm+NBzohvgs2c00Fmp/XZh4Vki/CTWzdbHgv8Ab94EjL8F6D28dPkWIAn1d7sFV6uyv2UE0mwgKnDP8ze2zQ2SXUvaMAprUa+hdon1p8SiLbfzoZPZ/LQW3r3fj90y9c627omUm8oW5LBFCy2RdUoKUe8+APzpcGDzGuPPvbvYJ5PzDttZfFk0JUYHS2D6Q/zj+Py7/LPjOGaj56QXCIXFyqXJ/FAvLy+igUfaAp/HmJ/oai+2SA91woPLtRu1V/9945/z8PHAwreAp89JvDsU++/UA+wlbid3K1EItZd/jSJ3bpyrJZNccwOwZh6warafybyVYB7K7emJtae+6MiEn9ZGO3ApnrVsA//cQXHfTiLIIXV1L6lHf/HnvoHyf24y/tyrsy78fHuUH1irN/2tUQnD37FXtM6WwArINkUVBlfMxDfH/5X16oUW5rcKhUsm66j1169Pvh8EHlmc2Egqee3Bz/kScZHaDZNRradXiIxNOruVJHbt22WbZn6E2osdKWPO47EAXGDzSnFuU0SmOgG0SO3V6mhZz9wULO8ztVdrI+WJKwr6EDalYI3wnMxrFFl9Vr+hdBkAD33/a3h2hr+7l7qYrwCKjeK7m8wOrMKRhdWNxPCaN+V5yCXp40YmUA+ORMu3VI2ogd6zqBPUtLuTa98A9lxzTnSVBBuvpYSfbcLV3UkoASdBr85V+GpTAwDg4iOG4bf/mgVgK1cTWsCF62BsCY/CGJXk/KWzsm4lclvWYtm6GvTuWo3KLfVwUFni5GTQ2FSE19woHXMdB/X1ETwIm+nFOkCUc+KioUm0s6UBKMbnWjzPQ2NjI1avXo1cLofKyuTubSb8tDZSspOIAzbsu1Tpj5/b/CTk7RU5doZF5cK6sf9OPfDX88YAAJ77yLfnkQS0w38JvE5i+1R01I2gy0A1SgtRaa4PBcflQdgA/34k6mBVJMJcOxEMRJwfGmwvvG9skc6VeBrt5BJbhhTsStg9f/Vnh6JPTXWZqqCtA3x8Bd9bEtg15zVjp3d/g+W7/QDLeo8C1je1jNWLgaaii1UbGuT+OEChrkPpk9evJl8cYHO1tWjZqFsLNPqeudiQB/LlCy4dO3bEoEGDEg2CmAk/rY0R327rHogJzbCI5lqYRVutNnLUVM+sDqQB7xiMAlp1N/nEboNbZvQcoKPTWLJM4qqBt2+VvubIxSfOMk36jfg8+1/AmAuSq7tMcHtnyvyUOicq87MtUBkVERa3mGABQ5lnoYjzsw3cLwXsitg6WpadIzFfqKz/CoNm/AHNlTUodhkE/GhSOpFeFbz8yXL8YbJsuFxZyOGlSw8tffKTvwa+IvkOL3wv+T6//hgw51/+51MfAfrsVFY1+XwehUIhWcYbmfDT+gixsm8tEGcaDXwX1IJJr6zEnxZbKCZYUAHAGHp/7kvyiUveidYuQ69dga90l/rqCMJPmpvjN4p7o3tOXHvR85J9aWc8Lj7HvWcpQd2Z02P2c/y/W43NT0sQNSRBDDChkNmXRRU6t0bwIIdowbUqmzUHHioaa1GxZibwt7OA76afnfyrLR6WbpT7UZn3UF0dgcVprgU2kbWoIi9nYU8Cn78o2sgDiNKvVkRm8NzaWPJf4+G3PluNsbe9iQ8Xr0u9C6rOmyKfgMFzkbA9kRebEp5GtKvG3GHzJ0VrxwaL8FUF3canNVFEju/GgRQErf77xD+nU++EOyGD2/zknMg2P9xDzCD81HsiOec2wWRUJp+ChG1S2P0WhuaJN9XmoDZlFLEE4zWf239r6VwUEde9MEc7FtlcQZ3vNq9KoEcK1n5BvrS/gZQJP+0EZ/35XcxbuQln/fnd0oVbDF2VxJCEwTNNuRV58iyh9jIzPy18oTwP+OTv/kTG2j/xAeDUR1A3+Ai/3QhZ1dNkE/Jw0xV+9jnL/DkMPXdJuBMyPML8CHuM8HPCmJ9KIsBuE4t5ZafEq2RjmNmXRU0rsjWCX5KjMD9xLrXuq9Jl2uDeRd9spuB1vGk10GxhytvhMMqEn3aGTQZvoqQRYvKTiIsrPTfyTttiG2TqhlHt1atEagbHMNRn/xN45vvAXfuK9nvsDIz4Fi+fN92kCH1MCjlF+Elc0KL3ZfG0ZOsuE9x42XGI0FvCkJlHhdbHUc7x+PntxturJf1IweCZMT95Re21TagJFdgNnmNUUlFCAL3vYOCabr7Rbyuge0ef3WQeoSWhzrdRY3zZsOZz4OZhwAPfMP78q79/hNotrRcDKQoy4ae1kKsoXSZNeJ7mTm5SezmJMD9E+Gmh2osuhAIGHX2/vcz19t1T1K9SvUsIy8baZ7vAwJ3YtJiqSHx56D6Ef8zB1QyeEwW972vmRz0p2T5otQtmMi7zY/P2qgieY7tZzOl9j9InWiZh4cd1PV49U3snkeamvYIbPDvy38gs18pZwD9+XKLMTP/vh4/F7l8UsLAEAPCtUf3x78sP598jPTOV+WlpcMZZ//D/rvzE+POc5bW4740QVWEbIBN+WgvUzS/EA0pNNJoYrukG/H4gsGxG6NKVRB6pZkn4iXiShYZVMzDTz9JcZYvrQ1UEn09WfiSVsvYDoccLXo0oEVUSX1DJ+HAgjFD9tpJtSlqEcxFNqVMWIFwuh0bP6l7K24sF5m43Nj90sYmy66bxmJIWfsi9FcyP/tu2AjGnlGHw/NVnwH0HRm8sBc+8+qYi9rvuNf79t8eN4F66QESPRnUj2FLhJ8IY3twKWo04yISf1kKeMD9FOTYDndgbiy2kH0vh7Vujqb1a4u1VDvNjMTgWxonE5of9RqerKJGzmzbbf2Pn5wLhh0+MEZifpNcHV0wSOXiKzU+KzM/QI5Ktu0xw5of8v9RwZLdloGM23CzkvEj1tBpcPb7Stc/NxpBfvoBPVxgCftLIwYVkvWZmLhXxsHKqt1d7uV8JQjN4jjPnLfswXmPVXeOVj4DVG+X1o1NVIb5qXGN+Snu1hsJVBBtX94ZrBe//WMiEn9YC3a01y9E0X5uTgqW9DV0HGsK7CySi9iLnRl6sbcyPtBD6MDI/tp1LzY7is7po0BvA1V4B18NsfiIYPCeuBSITieN40q4uVeanucFeTj4p4U4otUs2P+xYeJtsvJ5feM74e1VOLtfmkIQf/xk8OGUBAODo29/Sy9M5I2EV+v/cO5V/ZmqvbTnIIbd3LsfVvaOc7gYDDxCfR56ql0+B+alQtANVhZzkuRZpjCfN/KjC02evSl+ddmjxnAk/rQUakl4ZaP/+VAg/ew1IfqcgoaIjERp06SeJ3F5uVLUXfUmtcX78v47E/BgmK3XnwdCpN1BVE14GIGqvXFA3E37MF7BzL6FOS94OR9wLBx5fkICUmZ+ouz9LuIakIEIxxElsGv57IQFGMzF8NglYPkN8j6L2knJGpXcNItfftmvz4yrUt6MeDz2ZzFNDjwS6DhDfo6qNW4i84qPvSI4BZdr8TL2jZZ1ShScl12I7I30AZMJPK4KMSOUl+8ZwETdlj/4pCD+0vYpqaXFRkU9gh9wcVe1FJ5ISai+TzY8sPFl2Lrm8MHoO290wQxOu9vJvhO8ppKNvjWCREl8fKPMDL8huHvzULpifdEHDG3Dmp8Q5pYTCyvak9nr8ZPl7FLdj+mwSFoB37SviBuW2AZufdZsbJYNgFaraK5aKj84zJ/+ZzxcAgC59DeWTdymn6v77ztwXgGwWEM3mRxG4Z/8LWGBgHKNCiukDzbu2VNqZtkAm/LQWJO8OeeDJNGYKg4Tu6Cs6GlVJDJz5acEqQdVeoS8infQtrIxYCMUxo/Mzm2RGnSlX4OSAfMHchlHtJTM/tpc2VfdzV2Z+4JQZgj9mW1j2AfDpC8nWXwboFTqcgShl8Oz//VeRGKMOPoh/rAiEnzZ3dTc5O0RhfiThPtlrOHQXf/N13mE782NJMMBtAdf1sM/vJmG/617DRU98gC9Wb9LK6Gqv4HiUscE2UIMOBDp0lxn9fCVwda1cPoV4OtwzL+fgmJE78M/i9zJsfgA52ntczH1R/q7MtY5lE9mWyISf1gIdkMpkJ7mGp2HvTCnIQjjzk4SLKxWcQt9D+oJYhB91ogIsqpDNQdCx4ccCux4jjufygo7W2jB5ewXCD7N9sCw0OxG1V/IGz1T4kRmQVJkfAHjyO6UZoB47h//eQnCBNxc96SQ/h9loFaqBkx/kv1e0FxsWk6ATpo41nZfwNTCBkKpX+Vraxvfry3V1aIrhBEJZ5+c/Xo5zJr6nlfHI+ALAB1mkd4s9K8b40ESbK2bq5VO4f27Y80KZ3l4AUNWlpV0TUK47s/nZnhHC/NDdaCo0cyPZ/RA60pQmIgm6O3KcH7c086NNVCAGz+yFWrtAJOnLFWTj8lxBGIiqai8q/SneXuw+FRRvr2d+MgZnHTAYVxw9nKhkkmZjqLeXG0Q6TinonGkxbqqLcX7y41WYZDhm43bTOcFfLvyMvQ7o0o//XsgHzE9bUxmmHbey49lzR0NWcOk5JXsNbLNC2YP2EOdn2udrcPCNkzH+zujqGPVdXLxWH8t0fAGI9x6zd5N571Lmh82nv1hIyifP/Ag7SNKyI96VSOymab5NNG2N3oe6xhSiSrcAWWLT1kKI8CMxJWm0TZmfEjQsmwBbsqYVoxo8GzxeVKgTFf3M637iNPkk6tXlhDE/tCE1zo+Z+dlvSA/sN6SHX9RxAM9LgfmhNj8q85O08GOYpOrr0TGqk4rnJZ4Nmgu81OC5xDl8Nwx5ZXCRQw4uKtBObH4szA9lpIyxvlqB+clJTIJB2N64EujYQw7bkSLOeMBPtDtvpa66siHSuq/EDtPmkzCwDRTbUK2cJX7b80T/b4fuwLCjgPmvRVNpxgRbL3LKe5dzHBRLzUefTQI+eBjYYog83alXeR367DX9mIH5GdIz+YS8LUG7YX5+//vfw3Ec/PSnP+XH6uvrceGFF6Jnz57o3LkzTjrpJKxcuVI6b/HixRg/fjw6duyIPn364IorrkBzc/sKpuTDs3xWXcNTaFoSftxIaq+W7JAjM1k0i7rFGFmdqOhnXjVjfQA/hpLE/OTtNj+S2kux+eERnu39T0Uz4Hmg48OBC0ey+UmwLcA4OX/7zjdKnGS4bwmCqzodIvKWuG4R4VkOWeAGNbSbIIem++UVpfhelQWT8GOfP1oKUxR1zcB++UfALbsCDx6daNs2LDEwNlEQJ1i2CHLIjpfB/CydLn6jHnmMYU/R5kdNzJqPMnc/fjIwh4SDIKphdN+pvA59+ryhk/I4d+ChW8eEs8a3EO1C+Hnvvfdw//33Y6+95BQFP/vZz/Dcc8/h6aefxptvvolly5bhxBNP5L8Xi0WMHz8ejY2NmDp1Kh5++GE89NBD+O1vf9val1AaYcwPNQdKg/uRhB+vhMEz61NCaq+wF5FOHF/NBYq60KpOVIAhyGGH7uKE5kY78xOm9mLPJBfO/FCI+CAJPjPVUBCyK2tqbvVf+xHPfr55SxyvrxRsGlwh8GoqTms3FJsfxXC9gnt7tUPhx21GQ7M4rsZx0c5L+BqE2ksc0wSCGU/4f5e+n2jbNjwz/cuyzlOfb0GVEEDtCH3k4rDdqs3PjvuS38j8wtRhqai92PuhMD+lPHWnP6QfI3GKvFyUePYG0Fhq3KxC7kMOXtu/ewraXPjZtGkTzjzzTDzwwAPo3l0sYrW1tfjzn/+MW2+9FUcccQRGjx6NiRMnYurUqXjnHZ8OffXVVzF79mw89thjGDVqFI455hj87ne/wz333IPGxhZGrEwSX80H6okXgDIIIhsIlwtV+DEIFAxJeHlEVnvtMEr+/tj/aEWM3l6qu9fXzxM/NtfLAkQuLyjqKLswR3Z1Dw1ymIYRsiUyauqu7k4ORebhFiGqtXZ+kl0K/jKVH1D6vdByewUTucttt/zDxbaef6V0IsG4dIuobxLPXY3jop2XsMDJ3lcaTFMP/Ne6kVp26FpeFGv1zlQZWDTTnEKPh0JVe9HnQt9dzvykoPYysOEA9dS1nPjcpfqxXAHNvXYDAJz5QJmJjamNILteg9qrze3tFLS58HPhhRdi/PjxOOqoo6Tj06dPR1NTk3R8t912w6BBgzBtmv+Qpk2bhpEjR6JvXxFfYdy4cdiwYQNmzZoFGxoaGrBhwwbpX2pobgTuHi0fC/H2SsUbRQo4Fb6Hjp3kz4DIBs9qpnUqIKpF6Wd1YqZpK7oPkYPw5UKYHxNyjDGIwvz4f6Pcqy2NRWyKkttGYX5y8JAj6p/UDJ6dPBd+hvUqoZs3MWYJgsb5iXrdwuZHVnux51iRb4fMD8v35xbR0CSOG9eIFO4zg8nbixs8m6xrWwHlpvlRn+9mk5GtpvaKwfx4imMEFXhGkvhNufTUXuZkz+L5xRrj+Qp8scaPHl52LB7VnEBR3QP+/J0JPwRPPvkkPvjgA9xwww3abytWrEBlZSW6desmHe/bty9WrFjBy1DBh/3OfrPhhhtuQNeuXfm/gQMHtvBKQrBoin4sxNsrleGh5LQyJQtlYC9US+Kh0Jcv3NtLeWnqdCM8uhAyCFuboG4WwRkAdjpEzizsUJsfdSIyGT0xm5+A+Qm1+Yk2aXqeh1HXvoo9J7wi7fDNhQ3MD2JS83FAmB8mKHQoxFjo0liU6XrrSIes4Gs0P9l/fm4wxRWC423u6k635ey93LIWDc1FUsTQx1ZRe+mu07wr6kYlZZR8TyxQb03PTrqdCWdOgu+xWFVXdoyQngtVv/Pfkx9v1CGAoqS31z5n6cdyeTD5MFIqHxNMeb0MNj9tvvFQ0GbCz5IlS3DppZfi8ccfR3V1son6SuHKK69EbW0t/7dkyZL0Gpv9L/1YiLdXKsKxEh1WGJTqRZNWe0WO8wPIk4d6vkT9KL+xCWm/H+hthMX5MVp8y1ndw3ZDUd2wm4oet+n4YnVIclXAoPZi3l5M+EmL+XE48+OWslOQYlalYPNDdrZxBEyATOA5+Tkyg+c2332ahMU/fxP1hPkx9jFNtZeBSUjNxiwiKBPWwyDA2KC+HybjcTH/lWHwzJmfEmotJiym6OquMT+5EnOE6XiuQqi7kxJ+PNfQloe0c3bHRZsJP9OnT8eqVauw7777olAooFAo4M0338Sdd96JQqGAvn37orGxEevXr5fOW7lyJfr18+N39OvXT/P+Yt9ZGROqqqpQU1Mj/UsNfXbXj4UxP2lMNi/9P9q40X2cIYmEhnTyDl1s1JfGEDzP6Imi5vYqBsIdM3Q++veiAsnVPYraS01vYX9joxo8N5Pd/hPvLgpvXxN+mLdX8HPSw4PvZHOcJfli1YYSzz/dBZHez6jaFtbdvCOEOQBwg78Fh6m9kulj2VC8Chkk5sd07+m4aFXmp42EH2IAHifIodrdfQfbN1RsbMVKbMre5VIGzUwtloraizH3uqs7ALuQYepLvoK/92UzP6o5gefCpPbKmJ8ARx55JGbOnIkZM2bwf/vttx/OPPNM/rmiogKvv/46P2fu3LlYvHgxxowZAwAYM2YMZs6ciVWrRGLQSZMmoaamBiNGjGj1azLC6Noa5u2VMBoVl1EvfGFJYscX2dVdo0t1mxiVogYMjEtzYNzObCiGHCwKhzE/DYb4IWp6ixC1jrBHsRYBADQ1iwKPvbM4vLDR2yvNIIdCTUQnwWuem136HP9Lsv2BLPBSoTdMIBPjhF0Pc3UPmB9m89PW0g8fTw7QL/Bu3fd7EvNjfMZpBjk0qVHSCq0QEbOWCfu/5hhW6uq9U9kRWqYsg2fV5scm3JjUYgnB1n+mGrdeh6kvxNavfLWXKvwUtYGTg9v2rKuCNgty2KVLF+y5557SsU6dOqFnz578+A9/+ENcdtll6NGjB2pqanDxxRdjzJgxOOAA3z1v7NixGDFiBM466yzcdNNNWLFiBX7961/jwgsvRFVVldZmm4DtDEae4ieO27QCG+ubQAOJu2lKP1quJt0YjULsHlrC/JDW4qi9QgySjTY/7DqafYM9Ht+H2gDlCmbjRAB49369Ie7txSjxkAuISJdT480z9x8UWladTP0Iz2kGORRqIhYTJwcPvbtEfH9StGlwHFnodT0gb2GCWC9saq9CWvcvJuobG1ENwHXyyO1+PLDiY8DJK8yP4UQvvTnC5O2lvWOtfN8mz13NP8exP1TvnUnYFZr0MgyeVZsfG/OTotpLxPlRmR//r3XuNvUll0N1ZQXQ7G8cPM8zegGHQq3Xcw02P+1A5aygzb29wnDbbbfhuOOOw0knnYRDDz0U/fr1w9///nf+ez6fx/PPP498Po8xY8bgu9/9Lr73ve/h2muvbcNeK+A7hQKag1H7g4felYpE9o4qB531kOVC7aUjCZsfN+r1qMKOifkhMV8YNOaHq72CBbtDN1G4uYG4FEfwtlIWzXwEQbHUraK0/d4Du9kLAkbmJ+e0QpBDJ8cn9Bxc9O4cdfOQhvDj/82RkP3+8dLMjxrnhwl0TO3FSIRHpy3E2Q++iy2tHHL/wbf97NcNRUBIzy6mL1rHyyxfv0U/sRWYH9nbK2iJNZWC+iYq4rB1qgrazKLJc0rU/HEAgJlP+38XB27htjmllE1QC2BjfvKl5gjWl50OVerzz8vDLW9+0eZxfay0R4PndpXe4o033pC+V1dX45577sE999xjPWfw4MF48cUXrb+3OchOob7ZQ2cAdQ3y4KCDIvHxkVcWMcng2WDzk4Cr+3e3PIo9Kz/A6Y2/tgtRn/wdeOb78jGDTY6pr5qtEld7Bdda3VX8tmllPFd3LtCUdnV3It4rKvyElvU84I0b5TbgpRzkUI/zk4eLusYwQZGyEGmovSjzQ9ReYT1iApPm6h4wP0GQQ3b/f/MvPxTGMx98ibMOGJxU10viL/9dgAuqAqGMBKy6Z/LnvMyy2nrc+upcXDZ2uDgxzfQWRuZHtatrvbhpKkMQh/lRi5reF9WOMNbGYt0C/y/zKLXNKSlGeBbRB+R5kEfnt6q9gr7sfgJw0oPcwYS+967nIRc3plMEg+cszs/2CNU7wD8oFZG8o1JMkslbCGF+kkhoeFbjU9gnNx/fzk+xD3hV8AGMOwZTQC9NQFPVXgDQfx//7z7ftdv8hMANFs+wbMRRJ00q/ITe17kvAR89IR1y4AU5riKcXw5INnsh8LmoC3M1TnkH59HJ3dGPm89hzA9b2ZQ4P4z5cT1JsGttGyCmlisiFxoI785/z5cPtIK3lylLuGBXW0/4UQ2cPS/6ZkwtZjL+9ZQJsEUJigfs5/8tKMnwuFosReZHWb2ZwXpJtZeT8zUCQQgQ6u1V1shSBTxPN61w0LLwKWmgXTE/2yQkHbE/ONUFVTYQTrp9PQCV+vJTlHyBYqCAIv754VIcvafd806CYRclVCDkoCqgsYmZCj8/fkN8ZjY/pmR+FrBFMx/i7RXV4HnZ+nr+OfS+blhqaKP1mZ9cSfrbs3xOBtTInT73sMWJ3VYR5FBm8PJcePTw6izhIdqtY+sk6WRgwpkHhwg/HgZ074Av1wl11w8O2kk+sZXj/GisZsPGRNsMQ7PhHSm6Hgo2gy8C9f0wCU2aB2mpyMgUe54EfPI34PCr/O/H3wH0HAqM+q5cLpeewbOI8yPfj3xJg2c278tSE2V+yhpa6kl1Xxmu28OXaw3q3DZExvykDVfY/Hhc+FGKpJnewkC7clWSoXgSai8GFw5enmUPNsmxTzBxGJgZbvwqZXUPflMNnlUVHwMLNEkT+pUAN3gO8YCImnfqf1+YI+oNu68Veip1JihHVbHFBhF+qMFzZEYkDYPn4C/N6l6qKd3mhzE/Pli4F9cF7vr3Z/y8qkKZ+YzKRM7C/Izfawe5nPpyphnh2aT2UllNU7yylNBsoGuisgZqKZMgoM5/sZgfZj/I3tVOvYBvXgv03lUul6Lai3m/5RXhhztF2N5d1VMtQNFj6j+3PPZLZez//mPtZc3Bwwszl8evO0Vkwk/aMAw4lfmRx2rSai/15Us/t5doKaLuuLqb/7dRdz2nCyGDZozJsilXWtIybIj40g0fT9qNEuQwmtprzx2FDVLofe06gH/c8M0/+O078i4vTYPnoseiWrvh/UyZvRbeXrJ1VylzKYAwK6rtFmF+vjakBz+vMk406xZi8txVvH8ucmQgu9qCpaV3aG1Xd96SJ2crbwWYxl5U7ZF6H01e8mqEezbKIs15ljhNGlJMbMrGhhrAsRDowazssidvDBhcMteVx/woD2eHvaGrvdqXygvIhJ/0QdVejkXtlWaEZ80NMTyreyzPhxJoRsiuupI4+zPvrK/mAWsXSMVMAb00Y0yWWM/AnAAATvyT/N00IQ08ADj9cVEkisGz0kcb/vaByFAdHvco6FffPdHcc4TURno2P4IKF9fsRn/+qbi6+3+prRMQvjNXIzx7kG1+WMbyoudhtCHwXWvgnn/P5/1zFeZHlXW0wH5pqr1KeXulsIBH6Q9FufYi/5m3WjumuorHi/Ash1KwIsUghys3+N6tn66QVZGMuTOpDQHINj8ERfaOlKv2+uwV+Xu/kQaDZ2DPHVMMJlwGMuEnbRCDZxsTInt7Jc386AbPDKG5vcpdZckWrdGLYFJ24v/JiVfvHCX9LFgqcczK/FRYmJ+qQNAqdADWLQKu7QHcqgTBzFdIjbDFMyzwV9T0FhThjIrYVTZ7QhABUkw3QCbEorQDbDvqR+TpciR1Z7jai50TMCtKGII8YTS3EGPu1rTB/MZuffjzdOkWyBNuwIVgAWtsVjrWRlndXQ+pLOBhMI1xkyos6rkq1M1fvMSmUZmf9Fzd750833i8UMpe0yK4McbXN3iOObaMoQRMEZ49DOvdOV7dKSMTftIGX72p2kuG7O2VdPv2rMYmiPwwZbTVVA9cK3bVocwPexEHfg045HJxfNCBUjG6EDJQEcVvlwk/FuYnH+jpm7cAdwRRdVXjYkUS9CwsHYUpeqwJ1JU61JbGFSpSocZhbQVFWsnVPVxIoz+mYfMj1BIy82OH3eZHDnLoeR7qSGyf1nT26lSZ50xiETkeXwXEDbhDhd9vnfnxzJ8TgMnbS7Kra2Xmx3R5D769QD8Y8VxbGUfovQBEfLc8M3uiwSFGZgnj2JE7GI+XdFax9D26o4OpTovwY0hsGiNQd6sgE37ShrRTaAtvL5PaK+iSgYlilOnclWV4d/zpMOlrA0I8aeh9qeoCDPia/33wGLkYdHsEnflhai8L85OL4NFj1YNH8fYyPLRNq3iWerqbCp1gPaEiZUaIDr/+lG1+cnm+GMcKSJZKnB//ryb8hLTFfhHeVDnpeD6I8+O7ulPmp/VmZNejajmZ+Xnsv4sAAFWB8NPYHKL2StrmJ8TbCx70Z5zyPTOpbbpUR/PKizJu6fgCogcr9QvJ3oRWpKj2YtHXxytCUL6U2svi7eVStVfczhg316Y4P60fVqIUMuEnbUh6VrPwI3t7paTWEC0YVUkMX6wWRscb6yMEBaTYsExuOmx40RxHgBB+lP6a1V6KILBlnf/XxvyU0s/TOli73FDW/jysBs9NW4CbdwFu2glwXSm3V+j7T5gfkWmbsSApqb3KYX5SdnWnHn5UQA8lzZQ4P6rBs3B1B+qp2iuxXpeGH0Au8PbyBPOzrq6Rj6HKoKOajQtdZJJmfkoFOTTGcUkP7y8UISlYKII+NdEijpt6ps6pqtpLbGKiNKAz+UakmNvL9LyAmHF+aH0S8xNX7UWur++e5Jgq/LhSguf2gEz4SRtkZ+1ZNgupCsSmOD8hUz5V0azbHFP4GXmy9DUHFzv37mQprOxCLDslW0wLvwaPsysAgEqLTrlDBAPX5TOUuqN4e/l/NYFk81fi85a1aCIvfVTmx+WCso/Usrp7gomQdoCh/Ux3EmsKJL+KQk4W0MO6xIVklflRhB8lyGHrMj8e+jjrAQCDc6v45dST/jQFD1jbJafI/LBFqcIW50djj9NVg906aR7/PCpIB9OgMmEWhMX1EWX8v9zgmYdbiqL2imnzk4LKUBioy8cLnPmx3CuLzU8Hzw8VUuNsiT+y6PX12CloxzMyPxHNtloNmfCTNqKovdLM7aVNVOGu7oV8Djt0rQYArN8SL6prQ5MsaOUQEtJcnUQsrqH8dJvaixpLdxQuzBIcB/hlkE29zx7AhPVAr+Hmsqxdp3SmYxHnJ6yiZr6Y+30Ooy905kdVe6U1PjzC/JRUe6VofwKAJ/msKsiB9qN4ezFmxXMUtRcRVOtaOZ8Xg+sB5xSEZwzPO0Zm4dUbfU8enflJz9uLxY0p5EVHpEjvqrCbsg3QojV1/HNF0CfNBsqCJYZAeuocpLq6l5fYtMTSmaLaqxTzYw9yaO77N/AeAODSwt/j72voCSySvsXmp73l9sqEn7RhEH5UpJrbyxQ4MPhr01p37eBTzbVb4jE//5m3Svqeg8cnVr0TivBjybyu7tIAhZJn11dhY5gCVHcFfrUSOH+KP+v13UP+/aLpcrsWQZXCOmnSe+55aCQZu0PncBpwkF136kEOA6bBcwRLUtLgOT0WAgBeff9T/KPytzi89p8xghz6f4XND+udLGAUPU9KZtqa87HreXi9uI/Wdt4wC2ubhpTuued5WFfnb3JoBGVq8qOrvdLdwn9vjM8+7zWgKyqZ8BOR+enRqVI7pkd99v+qm79oBs9tz/wwwVgNcshtfqLOuQGWwk9+vcrrVoa3F7k+Jvz4hmJSsSy31/YILm3TicXO/CQv/KgTl77zUVEVrBQNTfEmua+CXSuDAw9L12/BnOUb9MKa8MN2Deakr7SrEvMTNe4GAFRUi5PViLXdh8jtOtENnjWBRBJ+XMW7KCrzIwtffCee9LrjCaaEuryGLwTpMj8HLX8Y++Tm48w1dynpLewQ44SVkqc2zpy5Hl76REQdb1WbH9dDY+AE8HzxAD6y6Pj5zv6D/LKtxPwM//XLWFfnb3IqcpT5IX1rZbUX23ztO6g7KgKBrCmmqxAVgtRF12rwHMnmJ+J8Q1KXJA2TgToQgR22BDl8AP/jHy7L24syPxXimFKPE9avNkIm/KQNssgL92l5wnMtnxOBweCZwS78BB4nMZW0qn0MExyOueMtpQukXAm1F6/L5oYb1ftCxfF3yN/zckyiaFndLV4iivCzoT6ijQk7L1cAuwuqzU/i00dw/9w4ub1SdnXvDKG6oLvzsHdDhAYImCwWtZdndfd/V9VJrcv8AFXwWZYGVAhX96ATu/btjDE79wRg2L2nxLbQd7xvV2FULDM/YZ5nyYM+Z6b2ijoXsedbQVgs7ZmrBs9puLqnqvYKmlCEH7EZs5xoUdn19tb5f50NZdj8UOGHxjZS1gLHzZif7Q6SdwB1JRZFUo3zYzR4DgcLm97QHO/F7VsjU85Wexk6efLtFxN+5P6as7oTScCymymJfc8CjrsN2PkbwG/X6V2M4uour10CNEGr52LJWmHDEK5O0l3dUw9ySAyeqdqrLQ2erc2GmiHJzI9q88PWiboGRa3aityP63mohD++G7yCUM0FfS/kcna7jZQFTgDo06Waf87xeF+e/rxTtvmhqu6KYC7SXP+t54p7yaAaj6uJTdMJcpheegub2qukDSK/sXLfp3h7kSJx1V50LmcCn2HMIJlk2UkiE37ShtHgWR4I0qYm6fEREuTQFOcHEMJP1AmHoaDsRL7zNT9X1YFDeyrtU+ZHEX6Ul0bkHdI9UVz6ksVlfgBgvx8A3/unNhkANM5PCPOj9FGcLISff3+6Ems3C8PxyGovdlnBT0kmnDW1eeuk+cLV3SkV7yNdtVeXalmQjZJAVtj8MGFO8fYKzt3UIAvXrcn89Ng4Dz+veBoAsHtuCe8zX7Dzjj3Cekpqr70GdAUAPHjOftJxiUUwxApLE1QtVVHKfVsBe29o3iv9XsobqliJTT3SuTCkmNXdpvZiT836eAyC271vzMeaJl9dtdrrGt+blDJhOSr8yBVlBs/bI6jaKzjkOPJAsKnAEoEhvQWNoGsCMzL803++wKoN9ZGbcpQXvVOVXw9NJ+B3ge4WwtVenOI1urrD+EInAeFqHsb82AyexTVc8+xM6afQHEV0rHAB1ZXaSsvVfdWmJtD0K+HeXukaPJvcZEs1xYNhcoNnR/rLjIpV9UmrTcd1a/G9j87kX/fJzedqL56XLOeIWC0hYypJsA1OZV5hTunY1tReaTM/bMMjPNCaIhq7uZ4uGOhqLwT1c8VXcG6UzkVkmlPM6l40bAiBCJsEg8ruppfnogm+yr8CzWUYPBsceoxxfjLmZ/sD1bNaPFfoy5m82ksPky8WVjOqKvxh8fnqzTjxvqmRm1JZicpgAtqiuhabhB9m8OyqO3M2mZFTpImZXUyyQzlKnB/rjpGovQZ3r5Z+CpVtCfPDcnupu9O01F4uckTgK5HdWdLAJD+hqTVGicDL5tUOgU2Np5zDTEB0bVIrTchfvid9/XXT94VYHTyDvONw9lR1HkiL+eHCj5IhXLara11vLyqcMA80qweTAsaK5Bx7qh71HYrFqkZ1dU8zzo+rz4lABJsfi+DWFKQhqkBz/AVIStxNbH4MzE+W3mJ7g8E7wB8IVO1lZoESgYn5ERSU8ZRK8lZ9uU6Pm2GDypLknWBhDfNc4cyPORGgOat7UJSWT4n5Cff2sjE/4p4ftmtv+aew3Q+ZnITBs7zLS4v5cYnNT67U/q+1mZ8Igp/neahCI6ocX/B0NeanjWfejr2kr597/Y3Mz4YgqvrS9cp7JwkgyV0LCx5YoUTME/cc+gKess0Pf0cc4YEWPbGp/zfnONwmRo/zE1TvsL/lMD9RDZ5bL8JzSZsfi+DmemKjF3tk0fvB6/W063bgZekttjtIai+zWkH+nHD7IQbPNuanolDmsDBI+0AJ+wUu/EgiDQeb80x5h2RjzDJsfkLAmJ+wWq0Gz8Tmx9Nc98MaFWOlyEggRfhJLc4PEX5KMj+pK4vUcVTaINXzgN2cxaSGnHSOGg2Xntc6kBtq8vL8CLX5oZsNmoYjLeaHBQ9UmZ9cW6q9SB94kMOoNj9EJcRM+TThh9Tv/w2OR/L2ihnnJw3hx2bwDNuEBLkvips+tW+M7+ou2GoxIRq8vbI4P9shJINc4u0lGTynqPYyTFSl4vzEje9Dapa+MW8vbcxLE4Ksd1ffPqr/V8/wyP+TV3uxtuxPxJpvq0hc210zk2VulBg8K1cZyx03DoI2XeTQOUgeWVLJRPuQhvSgVllqRwvTMwgYIBavyTLYW83bS1kEG1Eh3guLDYcU1dhNh/lhNlBVFrWXaRffWq7uDoBZy2oBAE/8d3HIGfq5+ZxgftSxocYOKy+xadt5e9kMnkt7e+l979W5UrJvjD2/ULODEmqvzOB5e4NECxLhh4wD2durFdRewScbq3Ha1wbyzx0rY7iQq7p0FnNFuybyXWV+lInVZNzn0Lc8LbUX2xVG8PbSSihxfqSfIho8C+ZfVvulFuQQDioKvt1V6Tg/6aq9+uAr6buwZbC35XpCxQUAbqFDcI7/vc2ZH2URbEJBqOaCTlTkc+hJgvNJdi5p2/zkLR52baD2oq7u1UGW+6jOnNTmJ2fzFGP1M+EhzsaCMh1hSDPOTymDZ9tlGDxjXY+q+MvYCkg2rTSwo15TxvxsbzB5e8Fu55O82ks3eGYw5fYCgK/v1ANP/2QMAKBvTbWxjAkqS8JsgFqi9lJjctCivjEmlxISheuJ3ZANPCaLen1U7RWL+SFqr+BQTmG+0jJ49t3czQb5hpPIx+QntAO8j6XvJSd1+PdFhIYE3IIfsI/bMbW18KMsgo0o8La9YIzlcw5OGj2Al2mm4yolgZOxSxUFswrF09pG6jeNMtMHDfPDZHxjeJ9I5/L5gnjORWZ+olxW1NAaqWZ19/9qzI8iTGswGDy7nic5d5Qd54duPq0Gz5nws32BDDjZpkIMhNc/FTmxEqfhwwyeQ1BOXBlPedG527FG/BiYH4vay1UWf1KyVby9wtRePJeOJvxYbDVQQrhd87n/t/ZLEf1XtfkJ73Z8UINnznaVoL9bOchhpMXJk5+V51SwwwCAvGM+udWmY1cXfrQ4PznfxoUZH0ubhhTYFs/zeNqICovrkB9Lq3XTW4g4P46INh8x5hg3BpYMnuUywuFD+hPR4FmebxavqcPLnyzX58lW8fZSBLAy1F6u6wnmx/Hgxd19eybmx9XmiMzgeXuEJbFp0fNw/5uf457J8+XiSY8P40Ql73xMKCeujGNRe4UzP478VwGnsSXpx2CMmZLNT5jaSyxSyuxapAbPSlyZsAc843H/7xeTtUSdqQU5JMKPsEkrIRRIfUh/QhNiYJjay8N6dObfi5VdgnMCby8r89NKE7KyCdnsdSCu7vJiJoRqqupKXu1FX0s1QKkkcGpBDtN2dRdjPkqAS+lcYgws0s/I5/J3W4vwHEXtJc83J/1xKn7y2Ad47uPlcjkp1UOysKq9WBdtl2Hw9vKI2is4Eq8zJm8vg9rLATLmZ7uDUe3lob7JxQ0vfYo/vDIXO3brwIsnLhwXG+XvHonzEyL9lJdOQRV+ApWK1dWdzG4lmR+i9uKteXJdCYKlR4jC/GhJF105vQUta7XZUX4oKsxPakEOgwnRQ45fc0mbn5TVXiqswSQJXML8rPU6C08qLvy08cRLhJ8mL4+1qCG5vfxnzwQQo4t2Cq7u9N1Wo71Lo09dwJfLasmkIezdRMTrqLZubK5xyNSinqurvfy/kYYyjWsDYHUQj+ntz1bL5VJUe1nj/JRihw3eXq7nScKPqqYvCbqYUNMFbSPsJm+v2EIUShfJ0CIYpG3V5mcliaKc+E70g0eUA9Tg2S4wxJoQSN0UzF5GuyYTW1OOzY9HyifM/KhZ1U1g+YOKQn8BXNNNKuO6YmEruiF6bzpJXjID7px1Uvvp2fwE7BzEzs1nftqP2stqWE7gQXgXFpHjY07s8u3ntQoIG/iiuz8AaAbP+WA85XIGQVdSpSbTa6kaVYNCmUaVPV75CYDTEulDWL8o8xN13NP5wsYYeuqcEsvg2cw0FzRJJD21V2nmxzbH6GuR6wE5UDVY3P4SHSJVeyn3x4EhdlUbI2N+0oYhzo/q9qfZjKTaHxrk0F6MTR1xFtvYai/p5bUwP4ZdjtEYM3G1V0CJhyz0LPosd0let8BQkbyrt99PcrxDN75Laosghw7cEpJG6zI/oAuxBa7nkdQWIj2I58lsiobWevUoG8gOcXpKFnCN+b1SMHimY1EzH6Gvo7pl7zoQaYIaPMcyRibn5nOO1VBeDfWRhKu7ZpOUalZ3WU3KUNIjzmLwLKm94m5sJOYn3OanvSETftIGpxppkEN7wKfUYiF06C66FMnmp4w2LMKPHufHwNZYmR9Z7QMorFRLEpuGgKZ6sIEJNPxZmnhdJvzk5aB7tnIIWuXeXqrBc0o2Px6x+Snt8tq6E1mUxckjwo8Lh99nasxpPK+1roXEfuLBPy1G7SItg0X4SWGOUD0/xQbD0xfEopJ6I2HQdz5W0lHp3LDYTj4UhXs8V3dF+KkJYmRxpBnnR7ER400Gf+1zDBFUSF00RERstZd0N8nErHu5oFOcsCmtgEz4SRtkoafDxCb8JDqvbRJeZNjpMNZCJJuf8mBWe2lW/sbdk3mbxsxpzGovD0ZBKgEIFsQOpvbi0Wfnv6YXCn4ryfxIHnCOnAfH84j9QjrCj+vlJDsnO3WuT2ppQ3reFngeGW9w+H1mZ6haCXpeq4AwP0z4eW3OSqkTzEaWrWlFm/CT0D2n1YcyPyp7odoRJgyqlopr68YZU6L2Ut85wXwHgrElB5i5c5YoyerJrZHeQovzQwRWEwwmGDTODwDNQSMyVObHYPDczpy9MuEndVi8vWyLYKLjY8YT4nPXIH5IiJFjS8FtfLjKiO1wIwg/FubHmNgUDhy4qaq9WL6b0Dg/zNurGMS1ePkXWhmW3iJvsuOQS4qPTo6HnA8qSVHtxSI8CwOLHDx7O60c8wWIsKOFrPZyg0+e54EFKy9YhnqrzcdFXe01d+XmoBMyu2kMaJmCzU+owXOYt5fhWpKEcHWP73hBQ2PYDOVV5jteegvzzlF3dWeVpqH28v/GZ37MBs8S8xN3bHnyvMXbUerJIjxvjzDQpE5rqb2WfeD/7dxPEi7SGoJ1DT617wUZ2pnyxm7zU5r5MXl7DVj+Cj6qOhd71L1LFuNkBTmPCAI2FLhLsgc0brZU5J/P4qjYmR9Z7SWzz4T5SUntVUQOUrAza/m2YH5Kq71cj6ZTceB5HlZuaOA2P03N5kWoLZgf3rbyidklGdMypMH8kM8qCyyxJq2c3oK6ugtD/4jnGphibcRq8kuMjQW14STPR9vgcbVXCt5enjxeRJtBF20nKmuRF9h/ShutFhk8kw2sIeZbJvxsb7AEObQvggm2vWW9/3fMBaDCRancXuWCGzgjL323ZnWPwPyYKN4DP7gcNc4WXL7qKlE+4Ysp8mcVYvDMMk67HvDe/5kLaa7uEdVeSh02+4UWg9r8kPQr9h2g+iyTndBM7UZhfny1l2B+XM9XI5Gp2Xxea3A/tV8C7z3Ivw7v1wWjBnaTbAABavDs/5WFn5SZH5vaS20bSMWOhYILJ8TVPSojQZkfHmqnxLmx7IqI6oi+yhqL3hZZ3bn3oOVEZd5V7eIAwAuZ78x1ssaJ8GNUe4WwyW2EzNU9bVjTW1iKJ9k2iy3SbTBQt0b7Oen1lOehyuUBN4LBszRh2Jgf/6918U/L5scT9i82SAbPkydY6mEGz6Xoe0Xt5ZHr9dJnfvwIz8Tmp0T5tFB0PW1SKmnLAAQGzzRgoxf8V8LguTUm5PsPA+pEvjIHQM9OlUL4UdReJV3dE4Js8yO/Xzkq/WgpctIdA9RoOZYbOuha7BCPVUsZtsGJE0uIbGbDvOWECihNg2f5eMlwGGwMBcw8KyfH+Yn7QlDmJ0ztlaIzT5nImJ+0YXF1txs8JzhAFk3x/xbk/Fx0Z5UkqllmaK5TDpggXfoJOkDaL2HzY4vTkpa3VxTmZ81m3/Bzee0WYPQ54oeCCFpJUxcAwJpNFmNR1dtLug1p2vwItoStNLlAcAgrT/uWaHcMx2zB6ig0mx8PaGh2eX1DenaM3F7iqPtKO5TLSck4/GNKxGGZ+Um+p3Su0dZu3jOT2qv1EpvGDXJIAxiKKUGdU/y/wtWdHS9xj5sbgM2r+Ml0XtPyJKbo7WUzeA5lh13CxnDhx/9KbX5iP1uxmACSMK8zP75/SvsRgDLhJ21YhJ9my9ucCjVYqIKk9mIMTeLMjxxHIkcEB0ndE8fby0LxiorNrqctR7AQhbyszFvnif8uBpoDoeaoq4EfviIKBZPJvJWbAADvL1pnrkxRe8nMj5tekENXGDzzCM+Oa2dZUjZ4NlUXxVrN9YC8Q729fOHH5XZMMRpMGQ6YvYY85jW1ly3Cc0J9DmN+Qr29WpH5iTvujUEOtVOFgMTKiqMh+Piv4nO+MrxPbRDnp+A0YydnuVm1TmOQBX0T/RfzTYtc3Wl6C22MePyn9oJM+EkbrpmZaFZTIgRIxQah+xDZ4DlGE3HK8isMXi6HvABGt13J5secC6ek2qs5iI5N2JYkIELs22/A0Xv0AwAct1d/oCkweK7oBOywN7DDKP975IVCVns102aJt1fik4chzo8TOkbSZn70+mzB6tReUJsfDx7+/sGXUonBBvanLeZiB/7CpbbNFjOj2ivlIIf660XYJ5W9SDlPAesVZX6iYsMW37B8+YZ6q1BjcdgqLWA11onP+Urp+Wj9tMxnScBm8HzakuswuepyDF/xnH7SXfuKz7kg6S/pv4g2Xi7zU1rtRfveHpAJP2mDuBcSGVlEBQbwyk8PxR2nj/KLJzU2PE8MxooOoMS2GK9JG9EG0r2B+TFGqy3T1V1CUzAhVZrVGuXCjZDba3Avv83enSuBzybJ/eDh7SM+UOnBi2jLwY/ELiHhyaPZDzlfj0re51Dhp02Yn9LjlAY59OAHOezfrYNkV2OK8tw2c7EXqL0sNj8mtVca6S3IZy3IoWTw3MreXoT5iWvr9sc3PgcAfLRkvbAXctU5BUH98v0u2cTbt4rPhSpF7aWU5e9/6xk877X+dQDAfksmhlcQzFH0njLv1vg2PwQhBs/cHq/9yD6Z8JM6jHF+PCmlxa59O2NIz07+L0kNDrdZtF2oIv3xJCEsSbABLlzdxcUY3XZNqipV7aUsDBqagnwxFQkzP2E2Pys+AR4+AQM2zQIA7LLuTSGEsX7EjfOhqL2kaPmS2iviBURts8FXx232qvnzCHVLbQs1UQTmxyUGz0Xk4HoeCpKA4RpVp21hg+CrvaitRUy1V8LMj+nVkgQCdQyzEBopgQYqjGvrNm5Pn40d0L2DcnftcKK+W5tWis8VHaTno91Cpvba8GXidj9MaWBN2aIeXzXHUg+ZnwNRwIkt2BrUXvAy5icDLDY/gvlhwbiiRLGNhWaRLBWFaiOzkpb3NGcQyItkpPCNBs8y2ERopb+50JEw88MSm5qexyPfAha8ibNm/RAAMLT2HfHbhmX+XxJLAwCO37t/iRZltZcc5sczswEtxXOX8oVtM4RRfHh6i5TVXobqoly750Hy9vI8PwQB2dvaF4s0sXGl4aAnGzxzmx+V+aGnpJDeIkSlLM0W6uK95L/JtG8BDUIYd9HcsZu/+Th8eG8ro6OO7kju9Fd3lb9Xdw1XG9Lo+stmlOx3HLgWmx8GTxXF1nwuPl8hPktDiqm9WpTVPSzCc6CSzoSf7QhGV3eP2/yoE15iQ6OZeBXlFYPnlAYgG+Am5kdWe7EXJnqQQ+vCNem3/t+khR9i/6JB8d7ZlCcT46AD/b/U+A9A1w7+PWGTs4Ywg2d48aLQRsUHD/OPdagWBs9tqfaKIXZRyN5eQliiqiUj80O/vPob4E+HCzYxCTx9jnaIGTzbmR//g2Qn56bB/Ij+aH2k4019xt0GJdJ+qX7lHCde6gmAO5IUcjnrhtLq7RWzn/T5aP2jws/ffxSz5nCEMXbGznTsIT5Xi7mKCiKM+fEQl6WiegSi9lKZH4cJPzGrTxGZ8JM2SFRNk7eXOiEnJhnXLhafcznZ4Dn4lPQ+mLMkgc2PE8fby2LzQ4OWhWLdwpi9DQeLdhzm6s6wppKwOjvs5f/l1+Y/fxYQMVqEZ6DoOtJvcXMcxYULkdvLfwZR1V7pMz9RWFHXA7H58dVeRdeTBAzTGOJVeh4w9U5g2YfArH+UfwEqeg7VjzlOYPAsC/xsLhCpUMj10nxaidn8yBswpYuiKVXt1cNwTQmCXZ4c4TnaNTNzgkJOxPmxjVgR5ydeG7jSN6R3JeZEwd6ni8+9do1Wb0SwOcDG/PSsXyQfqOoiPudFAlbJ5kcEVIrXGSpJVvqmG2jYlKm9MoAs9HlQOrKJMz/BzxHsGiJj+cf+DlYCXUxZm8mKP4L5Yd5ehPmRbH6UrRftn8b8ROzrruNi9zcMbF4LS2/B0IDApqrXrmJyUZif0kaVMhtWhHy/hAFq8pPHlzX7BG0HLGQo85Pu5GWqXTK+DTmR2vx4ns82umRcmRYLXufyGeKg26yVKxt99/D/duguHQ6P8xMcpff6s1fJ2ckyP6ZdkJTVXQmOl+j9MUA2eI6n7mWMeiGfs86p6vRjywEmobqb//ei97kwEcr85PLAN6/1P3fogSThKXNK6ROC2ayLrHqncg739mpJeotOvf2P8ydBT+fiSV1pD8iEn7RBWQ5CMzdzm58U1F6fPBPSHxLnJ4m25Mr9P06g9qKGuuUyPyX02xz7n1dOh60QNj+l31a+SHXpJw4qrq4swrM9fo68EjUrEZ5TCXIYqApf3mVC0DJTe7kh49CykiQEE7vDc12FXLzrecTV3c/q7i+YlPkxCD+sveaUMpWzxWTYN/khh9sfKcyPshhzh9Ba6rKfHMICiMrMD3OcCOzCUs7qLtQ68YMcsnm1Ii8ywqvvnDr/cVYi7OUyqOppeaNwxu4Xtb8k2NzQjOc/XoZNDfGEyZJqLxUWBxPhSetwpjt+egsiSTZsEMdnPi0Vs6Y6akNkwk/aIINDLG/C24tN7IkaPLMYMwBwdS1vP2jBGufChDhMA2dJSIAvJrSUjPNTwuYnVPbZ+fDIfYyKIqgKKBweF5BIJyW3T4sRq1SJbATuWWx+Ep08PDYhBW1KzE809Vziai/DsUIQ56DJEhsL8O8LS2zKxKCiS+qzMD/mTiS4PTVk0nbgSWovnhbGkdVe3E7utj2UOhNSe/HNmEntRcYrU3sxr9GUs7rTbUDUR8bQxNVeOaJqsTQQoKrCfzYbG5pDcu/pThrNpXYi7H41Nxh/vvypj3DREx/i5099FF6PAjfkuRlhEX6EbRW5JWXvrhxg5Ckhv2bCz/YHEoGYLS4OPD6Rc+qVB5lKoE02Oe38DXJQCBeeesyA8jRiyu7IZqsSJixoNj/+X2uEZ4DbGCWJUINntWyIGo95T1TkSz1fRe0l3a+UghySvF607dAkhDYdQlJdMm2gg2dvi4oO+HePJ9b1fLWXZPBs8fYShFvOcDABeOYI5DnHrvZylOO62iRZ4cfI/PAyRO3FmZ90hR/J4Dmm2ouyWaU2lOz3zpUim9y9b8y3VKwLEMVSzE+eCT9m5uflWSukv1FRMuWPfoL/Vxn/lGHzuChQrqt7UH++ylhKCD8xq08RmfCTNqyJTeVs3+V6HBhRDHYa+UpxjAz8OMxPHKjeXvBcs8qinPQWYZ3NJS/88F15FLWXaxLm/Gv7fNVGAEBFXnZ91yuR1V6+2kawR6kEOQwRfiKrvZK2QdKq9/i9s0VFB2SDZ9/VXTF49lye5VtujkvX4iDxiGkxDOlXfOaHjjGmfgh+V1+F0Wf7fys6KT+0sGtk8VMhHWLtMeFHs+dIFnxxJ48kcnoLRrTlSPgQtf7gL9twdqoS88fazZZrM8xZksGwkbIMF37KBRdaLdLPvJoxygk25kcIUa6yWYvdGXazz/qH/DMJ7+IXbz/STyb8pA0a4ZmoMmqDMOzr6vy/sT0OwsB08oVKw4/p2fxw8Y4xMUTVICc31fXnZSc2VetJCLGYH1eZAEifmCrGfHUUch2eR8tStVfJ7kQHE374uCxH7ZUsNLHL87i9FI2Krp3nedwzzw1GYpHG+bEZPLMCOeEFoxontwh8paICuiNHeFaZH9X+jxkYdxsolW9x11hvjMwPYRo9lflJ1+aHquPi2rrRvIU5i/RDDaoBoVYFgN126AIjSjA/xu0C23wmzJSVMgXw1GXdJvyQGGrM07NFub0A4fHFflUi5WfMz/YE48Dz8JWW3TsFtRdlfgzMShiZwoqt3GDWV1vO8v/PbX6EwXO5Nj9FT14YjEhB7VVkBs8qDbxhOf9YX+mrI1yjGi8nHalr8heQkkKFI1zipRg1aQQ5VIUfHucnZALU1F7JdcdUPeChIqAAwmwsPA+yzY+m9jKPIV4jje2TqM2PWe2Vp2ovhYERtipBCR7yOC+VbyloBnQVgokmaq+K1lJ7EQGGO01GZH6I4KTdxwCmmg7btXdwngUl1V6GczgDnmyE51IesPoGohTzI0dCjwWV+ckVlALymC4mOX+1EJnwkzYsWd07VPoTGQt6l2gQO7Yzo7tZg8FzGBatEUn8ovZJGDwTtReLWVJ2nJ+gylC1VxrMD4tSrVw7CXG/utf+flnTfMG9+PwfG5tL5LYxqb2oyibJ8SEa9dtyZOHHQdj6GkPttWkVsHR6GT2iB6IxP67nIccCqQXeXrGYny1rw3pRPrjaSzd4dhWjemvYC81oOiHmJ2wRpUwj9/YKAnS2FvPjlMH88PkCEotqKkPBbPKsAnYp5idM+Ek4NAAVWsTBkKBDJYQfh6q9WpLewtCGp8R8Szw3YQuQCT9pQwpyGHwEUN/oH+/awRdQ2AS0oT6BF4W9bJIUrplRhnoL0IUm+nqrqr3cMry95JePumNakYbBM99BKpNB/XrRLF+kTOk65FeL3U+78GJSewmBMPEgh6QfLKCiMMh37VY/cSI837wL8MARwNLouaC0++O5XC1RyuaHcSkszo/rURWAxeCZXWfdWnowObD3n3p7OfLC5UBezDSWTxV+EvP2sqtPhNrLg+7t1Tqu7uUEOZRiBLFjapngLxX6WBDSZpuAbZizltcKWx7je82eV8K5vYyG6mG530p4e1FX9xYzP+q8x5pWircHZMJP2uCDIwdKwW8J1CCVBf8RfLmuTju1bLjqTlHpj6LzLlldxBHLWRKi9hKGurQPIbm9rAbPIQ2nafCsTiRznucfhR7boPYi9jMAMLyfb0tgl33EOPHrJMKP5yZrExbUyVBUdm3xghxG6M8Xb0TvluFIZUSbHxrh2YMXqL14AUti0+BDE3n/0nB1VxaFj79cT56v/4f1T3svPSXIYOI2P/p9ydHXkau9GPOTcpBD1i84iGvoz0r5nmLBMZu3F/n8ymzf4+qFmcv1gr7hU3CSeI4/eWy6XEQFF35ag/kRwo/OnobH+ck5jsQyR8V7C9fij29+EXxTaMsATZ5q89N+pJ9M+EkbhoHnwOPCT1Ug/PToZDJOLhOc+SFCAR+TJL1FZOEnWjnN4NktCm8viflRdgsAuT9mtVdohOc0bH4UTxyODUv5R+4cGmLw7MDDt0f1x0FDewEIWbYUtZdnsflJbOogk5yoU+jn7cJPGYJBS7xdPBcdA1fksGBw1OaHJTZV01sUwiRoyY4lwQna8v5PnruaOEDIAr4W1oBEiU+ye0YGgfWRjjeu9mod5kdyV2fHIp4rJxs1s6WqwbN/zP/7zhdroYG+DBbnCiNTmrLaS+4AYX40djZCnJ8yDJ5P+eM0TP18tXxQFbCUTWQm/GxPsCQ2rVeYH6H+SqJNdafot+r/RoIcRvT3ij5gg523weanxYlNwxauFJgfl+9YlMlgyCH8I0vW5ymCS/Cj3zV4+J99B0QwWOYPJahTVnsx1uPLdQkl3SQTJLc9IQbPJfvJv0YYGw0bo3dLI5Y8/m6EXbuf2FRPb0EFSOrV06WqEBwOGpRyZyXJ/JgWKmCP/jWSNx+g27UJg2dddZYEXNO4ZUcoa2KK8JziIkY3PHHjWwmBjs5u8sn2q/axpVFRU9HxQJ7RPoO6aX2WwIWfdAyeZeaHCFjaO2Rg2qHG+YnP/ADkHuof/DbIhuov5x6AHbpaEju3ATLhJ20YDJ4BmttL1vNXJGG8a7L5kQye5YW2FKJOPFxFRIQfdjklbX4IM0XhqmovU2dSCXIYVK3ZoIiJjMeVcQ2TCwloKQdcszRo8PaiVPRDUxYCAP4zb7Xh5DJAbX74ahAIP05InJ9y1F7v3Be9WwZ7hb9/4Kd3eGjqQut5anoLXe3looII0BUF5T2jKWFSMCqnY8OBhxtP2gtqnB81vYXO/ESPOh6pZyHMjxRaQQ1ySD3AUgBlZuJ6OVIjXus7Z2CT/3b+gfzzV5sUD1dJ+LEwP6Fqr5RsfmhXSBuu2kfjZlO2rSrX5sdRRUlFwGKR8h14GDO0J3f0aQ/IhJ+0QSM8E5mDGdblFT1/IokrDYHVYNgHRSWZTBPP5oZm/PCh9/D0+0tIC8pb6blYstbfrS9fT1QfoQbPattBlY4QBDQkHa0RxNtLZX7IDosHdTTuoKnw45R+vqraC4T58TycNHpAGVcRAsr8eHJ0PQeefX2N4jqjou+eMfqlH9hjx66RTqOu7q5J7ZWnBq6KgFEgO9JUmB8qGPu3WmV+HL4RokdJHQkbPIfliMo11eGA3Gx/vLL7UUHuUYqqL/rOx7V1Y6X8c82qYtM7OHpwd1RXWEIqWIQfmcyOp/ai5fcaUHp8m8612/woD9Rq8wNeTzk2P6w1v27ZbpB3iwg/7Q2Z8JM2JE8NJuh4aLQwP4l486hZmKX+iI9Rs7qbuvTQ1IV4/dNVuOKZj0V9/EUQzA/DhU8Qj58Yru48zg/fihp2UWmovVi34MmLjST8+DBOfESF5O9CSz1feRJRY9QcuXsfAMBu/SxB2OJCUnvJffZ5k4hqryjos1v0bmkHXPziaP/8zlWG8cyKeTTCcw56hGdZ7cUjbrMDA78e1ouWw3HwZcVgAMCSHY+Tcnux9niev+Co8PZKx+CZwRRGYp/H9sCTldfhjMZ/QPP2ApBmlGcqLrL3PrqrO2F+gmNanB+djAMAVAc5voqq3YtF+KHvvbF7jt3guZEY7/eMae9pDHIo2fxYhLfQOD8sAn004Ycx8pTP9P/IN1UTxNoRMuEnbVhsfgTzExyjOvYWt2mwEYgZ54fCtOuSjE+b6oHpD6Mf1vgtEF13F9OCFSPIoeaOa2R+0rD5MVhDApLbmmbEZzR4lj1P7DKF7DHmUfKFxktKw9tLC3KYsLdXjAnQZPPDWJqaarvw46u9ZJufpqLM/FQS4YfZ2vH23JDFoyUgzM81fe7AtxquxdIdjwmCHMrCTo7PBQr1oxk8J8z8hJQ5qPiuQe2FVAMd0vQWceNbCQ20Y1d7BVCvu8DtE9UO2ZgfIvyEMT+GIIc0bEO3jnGFn6ArNpsfg+rYP0Fe7pkdpuOAh7kwBy3TsfNVL/rnlmR+HLlcO0Im/KQJxUXSJa8bk/zTYX5C4vwQO4ioS5JJ3pA8Z978PfDcJejkBLpyktX9/87eDwAwpGdHQ4W6jYz64vJYFGFqrxSYn6Ik/JA2DcyPpvcG+PWwp87ML63Ci6KXV7292PWXzCQdFZK3l7gS1mc7QRUjzg9DDLWkyeYniqdb0aWu7j531VR0ZeaHjFmu9mK1UiYj0RQeYnGoz3XER94wODlHdrtX1BhSdGXAYPCczBjwTIso/QHAzNxuMnvN5pRU1V5CDVh2egvAOm6EPCpfN9tgaAl0bcIPnRZM/WP3qm4NcOsI4CuRNHXK/K/451AvRADYsAxYu4C0VULtpV1wKW+vFkR45siEnwwUmouk+N4URPzlsT2k01o4UNgC7ZiYH5kajlSdoT/SqXNfkn/MB5Gl3SI6B7v1zZIHhbzQSzUqbYndCfvdoPZKMat70Cj5gQo/SuBCG/MTkmRRa4NM9lQPz+xVikkJP6QnPJVHjqi9onp7lbRhgjYhhvbKpEGMYPfhup5wdfecgPlxSX2y2quQV5mfkJ1zS0CYH2Hz50jMD2tPCCHKgp9SkEOrzQ+5F5vQUV48eb6q9IQfahsZqlo2wGQvpJ5ry23IAh1q71gU5sfUGbop27AUmD6Rf7362Vn8c6gnKwDcujtw5yhgy3q/LX6NpEwZzA8NIBtH+NncoNs9igM2m5/2h0z4SRN0oZZobmHzk1eYHyCBuS3UNVZUHjYg6Q68pKpFtcNhaTVKprcIZ37opBWq9kqF+SGvBm2TPFPyxAw1sGftKt5epYQFsVuli2PeNjGXC4O3l4jw7EVIw2H5zkDHRCzhR69fCD/282hW9yJycJnwQ4TqCmLwzD7zKmngvpSYHylnlTIfANTbi3dZ7o+TLPNjTR3TLLydinAgpehgG5sU1V6yACMEwSgCEN2HSMlZLWUomNoxqsGzpA0PU3sxVHbmH1muP1P/JND3aM3nUlvSc5PGrE34kS+YhieLY/Pzvy/O4Z95jYrgzpv2wlaZtkWbCj/33Xcf9tprL9TU1KCmpgZjxozBSy8JFqG+vh4XXnghevbsic6dO+Okk07CypUrpToWL16M8ePHo2PHjujTpw+uuOIKNDenG4E0MqSXJi8ZfzF3SiYc0IHcYrsOo8EzUXvZ6G6CAd2Fmqrkeqsa9BEXTyP1bHS91Jkf2i7fHRltflLI7SURP5TfpsIPkxrCmR/HEWkW7UKF7upuUnslJ/wY4vzAf265EHPnyIKBZBAbQ+1lCNAWJdbLig31squ7528wqABZYTB45pVKcVJSZn4cB7kceSdKqb1Ug+eE+sfGkqZ2ocKPl5OZp1ZhfqiQKI5f+/zsyOdKGw5lNO9Z9w6eqLgOHerltcTO/JhZTDlwq6kzyqawQzf+kcYSCvXwNQTfNK4PYeO3hM2Pn9U9unrqif8u5p91V/dM7RUJAwYMwO9//3tMnz4d77//Po444gh861vfwqxZPiX4s5/9DM899xyefvppvPnmm1i2bBlOPPFEfn6xWMT48ePR2NiIqVOn4uGHH8ZDDz2E3/72t211STLUHQOhc/lhw5hp8fpWyuDZQvtSfG1ID1Gd4WWb9sUa8UVjfoShH2tDniiieXvRScjRtsO0vfSCHPptuubPzMZEYW38j8J+RjJ4hvl+qmov2eaHxEtKRfgJmmaCuOrhZuqn9TurlEzGcWx+tOsj0a1LLPrC1d3ngJqaie0SUR0C1ObH0N8khR/yXFV7FM3gmS9AqtqLUSHJMj9sLGlql6IQfnJwIWWm3xzEmWpqQdTuEhBqHUfaoK2vK8028TeRurort+uSFb/CgfnZOHrqd6TjxoCsgDKPmzepRgGm22D5e14kmt6jf404N+xxNpPAnsH5/P7Q50bm4IYmZT42bjaJ2ssRSXbjRHgGiFCTGTzHw/HHH49jjz0Wu+yyC3bddVf87//+Lzp37ox33nkHtbW1+POf/4xbb70VRxxxBEaPHo2JEydi6tSpeOeddwAAr776KmbPno3HHnsMo0aNwjHHHIPf/e53uOeee9DYmG4I9khQhB+yB+SHTWqvljM/JQyeDWu1CWyBMK23fNeIZqB2sfxjTtj8GCegiN5e9D60OvNj6i8g9U9jfgx98hc6mWUzyz6K2suT1V6ppbcg8adY2+HMT1S1FxEmYgmn+sITNbllzhHpLTbWN2PaF2uIAAnJ26tCs/lJK72FifmB0dVdVXvxEzS1VzKwCj8kHUnBa5bVXgybVyXaFwrqhUbT/uzUq1Osc8XGy1y2Y4McMLRQSvixxMkBLE5SjgNcXQuM+LZW6KtNEdenwM4HAGfdStn81DUoQmIJg2df7aVvPm0Y3leE23DUT5nNT3wUi0U8+eST2Lx5M8aMGYPp06ejqakJRx11FC+z2267YdCgQZg2bRoAYNq0aRg5ciT69u3Ly4wbNw4bNmzg7JEJDQ0N2LBhg/QvFSh2D6ZkmXmjwXML22WUtM3gmR0qMSTDoqvuM6g7AOCiwj8NJxLmx7RwhSY2pcH3xDmt7epehIX5oTZTQf+E7GBifjwl1L5lelF2Z3KEZ2H3kkgoBKU9XicPGRvm6q75AZvLlWvzY1A5RMloX1Nd4O9VETl8uHhd0DsxqVdViHEibH6Y2iskN1KLQJmf4CMcKcghM5xXPT8FK8WYXEVV10IwryZd+BELcx7NRO2VA2qCYJs05k/CoOwNAByzZz8AwD8/XGo5g5wrsUbsWLT7Jby9ogk/JZkfBkOwQ5o9PrR3W9aRfshqL9nmJySKdIw4P1GiUe83pDutPKhbldzZr+1R7PHR5sLPzJkz0blzZ1RVVeEnP/kJ/vGPf2DEiBFYsWIFKisr0a1bN6l83759sWKFn4F3xYoVkuDDfme/2XDDDTega9eu/N/AgQOTvSgGhflxDQMhZ7D5aXGU5znP+X9XfGTqlOhSiXFZ2kMJ6IN1+sG8iPPDm5CYFBMNq+883l2wln/m9yckyOHsZRuw//WvYd7K6LmkbJCeVQnmR9ipSArN4P8syCGtIkztFbRPmR/PtRpvlg3SZz7XB8+jgGLIGCyD+YnTLQPzE8XbywMkV/cu1RX8M+tnFUlpoXl7UduK1Gx+AlbCgRznR5U92fWyB5OSwbPd5kcwPznPlRfPzn2CMum7urNu1QTPkqotS53rGzz7iHq3BPNjcXXXknbSdkMqNgg/3xwh1q3Q4fbVPK1Fo5demNBiMXgWAWQh4vxEuFvsWnfq1cnA/MhtZGqvEAwfPhwzZszAf//7X5x//vk4++yzMXt2acO2luDKK69EbW0t/7dkyZLSJ5UDumjm8mADiw4PjepGQrF+AKCOCiYGtVcJcObH0CHW3Y3oqP0m5fYyGjyH2PyQzlEdPzdQNWUIDxaFY+98Cys3NGDsbf8xXk8cFGmHZZ2dXlhsVUmf5CCH9jSLahtU7SXOiCKIxoIh+GZDRTcAQA9nYwzmx4IybWh0wVCo/IwXv24R0LgZ8GhW9xxfwDpWiajI1Ubmx9DfRCdqwvwQdYXPMsgLDmM6tGUoZYNn3eaHCjaerPbimd2V/FdJgt8yv1977ujbx+zcq7PtDHEqYX5Cx40BbCPaXFROiML8xBR+GiXmJ+Tkd+4l/WDCT9AdupIQ4adLtcKER7L54RK3vS+kxwDwdWIXWiq9RbLvVDKwh0w1wHVdvPnmm3jrrbewaNEi1NXVoXfv3thnn31w1FFHlcWgVFZWYtiwYQCA0aNH47333sMdd9yB0047DY2NjVi/fr3E/qxcuRL9+vk0aL9+/fDuu+9K9TFvMFbGhKqqKlRVpUfbciheAsztL3WbH4YjfkXaF1NqFINnv0/BGSHd6ekYWBbJ5if4WNLgWX9JhvURkx2foBs3GdpLXob3JINn8w0Qz9EetJEZPEtEkpH4ke9J0XXFxOER5U1iai/RHns29VW9AQB9nPXR7Z2jGDzHmPjMQQ6DKtVOrZoD3HtAcN5Tkqs7X1uIUC0xP7kQm5+UcntRztBx6HexCAFE7cUKqHF+Emd+lPeHbDAcuHL7IfmqkoLK/LBo3I1a6GUdfG4jbGvU+ZTdf628TfiRuhPSRl6/Zw1NEcfYsKOA5YzBl+Nv2dJbaCxLif47VO2l5jI0gJ1XWciVjPOz1au9tmzZguuuuw4DBw7Esccei5deegnr169HPp/H/PnzMWHCBOy000449thjuTFyuXBdFw0NDRg9ejQqKirw+uuv89/mzp2LxYsXY8yYMQCAMWPGYObMmVi1ShjfTZo0CTU1NRgxYkSL+pEIFC8B0+vBgxxatCxlIR8IdlU15KDO/JRSe0XJqFyDzYYThc2P0U05osEz+9ivploc+OPBenspZnX32y1l8MwO2Jgf5fkaR4L8UJro7pPYvcRa9lbOBua+bP5NMngO1EXBrr4CzSHPPKraq0wbGk3l4JFxqJSddg8pJtJbuHA48+MQob9SCnKoDP600ltIzA9Re+Wotxdb7OW5QOT2UtReidn8CLWH/INgfhzPg+Tt5QiBPC24yvwUJ7M7XdA5ixqxXatDaYkggbTPRvDNIBF+qCAXdm4VyeWnsPZyhOcyXN2JkMmEHyeCzQ97DhV5IfzwFq3eXu0PkZifXXfdFWPGjMEDDzyAb37zm6ioqNDKLFq0CE888QROP/10/OpXv8K5555bst4rr7wSxxxzDAYNGoSNGzfiiSeewBtvvIFXXnkFXbt2xQ9/+ENcdtll6NGjB2pqanDxxRdjzJgxOOAAf7c3duxYjBgxAmeddRZuuukmrFixAr/+9a9x4YUXtg6zUwp00gBZI0swPy22+WG7WOJaaZJ0Shk8h+2cOMtp6iuJ88O7FNXgmdTHDDL5QrXyE3NHU3B19zw/8nHe8ZTF23Ad5MkKsOfqBnF+5LqNDZLzlqyr85lCx6+fP4s4OtH7/E0CznkBGKIKjbo6xg0m6UqE7OojGzwn5TpOFxilnr57kN+AnR3fzi8HjxitCrspKvBU8sSm7OJTCnJoYH6YQOwpAj9Xgat1uIR58U9IpGsrN/gMzztfrJV/UJkfqvbiwk+SRuEyBHvjSH+jDH3KaodFxzDBusEw2vTJ/QllZHlKEMEuvvDxctLnEEjCiKc4gZjVXhoiRHgWTpGlbxYrUVGg85pN7dV+bX4iCT+vvvoqdt9999AygwcPxpVXXomf//znWLx4cWhZhlWrVuF73/seli9fjq5du2KvvfbCK6+8gm9+85sAgNtuuw25XA4nnXQSGhoaMG7cONx7r9CB5vN5PP/88zj//PMxZswYdOrUCWeffTauvfbaSO2nDmXHZnp5hcGzONYimx+X0tS6kEqp09IGz6UnHeOgZkKXV+TXJ5UKMRCmBTWDzAqDfRGQDvPjeXCRQx5F2DImC1WUSZijQQ4pA2EBVUO5HhatqYNbKRZuXTSMgS/e1IUfYgfAJlQv57vS1jh1IWovdVdpKZek2ss0hgCgogM5z8U389MBAKfk38SN7i+DX8QKSNU7XBBilRqCySUDdp/JLQ/sUVS1l6NshKxqr4S69+W6LeYfJOGHMD+5FIWfYjOwdDrQf5SQ9Rx5boyi8qXRobVgkSXA3jFd7SXeFbmtiMwPV3uVERVbYSSldhxzObvayyy8OcT4PoqkyK67UmJ+AkFoK3J1jyT8lBJ8KCoqKjB06NBIZf/85z+H/l5dXY177rkH99xzj7XM4MGD8eKLL0buX6tCi9obfCWDM6fsbvxyLZjd6AuWLxXhObyqsEmHT9SGicXhdgHEZ0pSexkmE0PBZtUg0zbhpsH80J5YDJ4ltZd6L7nxqqu7uhsfr3gos5dvCI6IZ6Zl+m4pyITInYpI1u4dija34ogdkJiU+N0SBzz7OCTj55Pcmfxzd2eTYH4IoygHOZTZ2FZhfgjDYw5yyIv6XeJqLzViezKDYNcgXst+g7vLPxSp2suV39e0hJ/nLgFmPA502QGAv8EV98WgOreBPPaSUdUVWNspITyQZi0VC+9XAFi0RjYVCBXqPJn58aS1w1wuqtqO2lZ5iKHODIpQtZcLxyjmbPU2PxQvv/wy3n77bf79nnvuwahRo/Cd73wH69atS7RzWz1UI1ZDnhNihhA5mFso6A42Z1J7edLyHYYoGZVLMT9m1ZlpJ6XvPDSDTJuRZd7EcLUMfoRlg6eC4TrMEZ5j2vyQOjpWBkwh4XtaxPyYYAhy2FDdi/88sPhl+HnigLlc2cJEDJsfMn4KjjhvUnFfrh6kkzr1auLeXuziDfFUkoFYjfkTDmx+VD5PxPxSmK6UbH4YaCBBADrz4xL1fW3gGbtuYaJ9wIzH/b8bl2txbITcH52VkN65iPfLquaPYPMTrvaS86H97vk50s+hvVOiy0s+NBabH535McxPkNNbkIrCeuOXkGx+giZ441uP2iu28HPFFVfwoIAzZ87E5ZdfjmOPPRYLFizAZZddlngHt2ooL82Gen/wm2x+AKJzbpHaiwo/BmIvBvMj1F72DpmYH5H/pym+q7uB+eFh3K3CT6X5eAvgeUT4sAU55J8NNzS4thxnfsRvpZgf9nOOMF5xjD41mB402c1TRmLNwLEAgD6eJYKvTSWgYs7ztFDkrpp2rVZ1hEXd+YumHxuZn516igjBXJXmAfj832JRj9nfkhCrgrjlcCSbHxYsU6i9IPoG6GqvhGAMlgdIBs85avCcywOrP/U/v3ljon0x9YvbFfKQG6XPpW+nce4JSchqnQ8ZE6bMM5Fd3fOywfMHi2WSIPRcSe0ll5UjPMsMkVyHKeK/qCufc+DGMGRnJWiiYL49s6q92p/wE8vVHQAWLFjAPan+9re/4bjjjsP111+PDz74AMcee2ziHdyq4coGz+BSsACV3nMOUEQLhR+andpo8By98ihMlNngOWh3yzrzwhU1vQVXe7ED0YWfouvp8UtiQE4savH2YrYyxp2VUAtSt1vA8gTI7p57X0lqL635lkEKchgsNnBQrOoGAOjoGrz4gl6Ffw/w9q2kSAzhx1C/dVNgSa65Fl34rpbu/rt3qsTrlx+GjpV5PDx1kej9o/+jNJkW88OlH/+aFFWvGvOLvzNaeolk+seFH83bK8TguRWgbs6swq8Bkps8u4+UMmy2xyeybjDYOCuowg/pc9gz4Q4gvuA1enB3TJq90l6eQtl40b5JynQi1Dlq/00OMKBCJrlZce2quMxk3lGbAvu2F8RmfiorK1FXVwcAeO211zB2rL9T7NGjR3ppIrZWsAGh2BfUdBAyJ12gozAtJcEEBCevDERi80M8IsIQRdduVnsJAz8RBZn8HpH58fiiwGYxi0eDQfhZs6llQdg8UObHU36RP2vJ/chnX+2l6MFNN5TYFXBTD4OyK7InoNSGifnRgxw6DvjuMGejv8ux9Yhxjp7ewpV2uNK9s2YWd0h+Jll1ObR3Z+zQlRhKm25nksKPZPPDPrG/spWEmthU1EHSSyTYP9eo9oAiIHhAU2AYncsDowLbqtHfT6QPHF36i34Rhoz2L8pVU6Ny83YvArNhE7JDmJ9QVorEPQOAA4f2jN4jxdlCeqvpY6MJUNUamWCkMD9C7UU3WhFiKREBs8BVklRy1xmhUlqGtkBs4efggw/GZZddht/97nd49913MX78eADAvHnzMGDAgMQ7uFVDWeQ7BBFmN24RUjqd2BOx+bFI+XT0xTd41n8TE7j+o9dZhG53XLFA8YUrMvMjt2VlfgzqgH/NWGYuGxGuZ1F7STsvTzlmEn5cKc8QYGN+xEPhBsik/djMjyUTtfa7k5N2cl5wL/O2XEFR1V5yoQhlGOw2P4Bi92NQYbxb8XUA0NVeSj8lO5JdxiptJmnMqz9XNYcXG0fMIFvbTXPhJ1mDZ7U/HCR6swMXWBXkSazfANT0V/pC4HnAptX68UjQNxXsPsTx9qILszmpsr0O62aPCYN5OXyKRGaHMj+yq3uTEqzRel3NDZogKjE/9LE1GSLfM7iqwTxr1/+bp9R0pHvM2negOk2qHXO99qv2ii383H333SgUCnjmmWdw3333YccddwQAvPTSSzj66KMT7+BWDdXby2D8dfOrIndLIvmbuJSvGgET5sem61fPKNPmxyFJDx0isGg2DCXi/AhGogTzs+NoLf5N/24dzGUjwq+vFPPDjpiYH8XguZTND7XBEVfOf4ttDxZi2+BXpAc59DdtvvCTgy1uiNqBCB2Ko/bSqnel2yqNRYPrMMtRVHSVMaYINEQbZhB20mJ+yH0mfWXtFZTkXq72viRr8KxGUuYgC66kQlk9h2xYDH2Y9Bvg5mHAzGda2C8E/ZJZgyiXbXZ1JwgRbK0GzyzCMhMCoZeLZvPDhJ8IXFRTPXBdH+DdP5GCnpwxib4YYczPsg/kfij9l1zdYxqVF7igrs99tLZRA7uWrLe1EdvmZ9CgQXj++ee147fddlsiHdqmoAY5DEbC98YMxsS39eJR0kmUBBM28sqjpcwPO1SiKu5kFUL9GOsgTAyVrnktRurJwPwoi4U1o3tNfzQ1y4t1RYQkiGEoup7mjaN+ZH11jM8r2MUjCHJIu2MUfmicH/8jZZ7ixiyRBQPTvRDPwKOLTY4JPxHVXlEGaxy1l8YsufYwEAYBj3l3ifXFPA5kBxdF0EuJ+RE98j83Fj2gIHrImB9tsdeY0mSEH+m5U0y7m3+UNjd7nQZ88nelcwRT7/L/vvIrYOTJ5XUG5L0PvscxB5DsqnjV4ZsXBuvy/9oEY/noub2C+TCwx1SZHyPWm2Llqa7u5LmtmEmLyZgXRHmf/Sxw7B/4YTnCcxy1V/DBcXzhx1X4WrLh7detI7ARGBohL1trIxLzs2HDhsj/MhBYmJ+OlXn83/f2g+MAb/2/b/DiLfLoYbDod0mnJNoyDFHiZOQc/WVxiGGk49AJQqHxjYlNXVKetRHyYga7GXU31dLksEWbwbPkTxKkUDCIk57G/NAaDJ2TbHCU+l78OXJ1a/xiUa+rVO4lQ24vxwE8xx83kdVepmtZ+0XpMrZuacKVavNDfjNdI8mNRr9rai/OsnpikxLD6DMyTDY/ip0EGz/sMjVBl78IyTA/nudhRW29YvBqRs7x4DFbl8pOViZNQgsNPNT5KY7NDw2QWL7aSykz9IjQfvp9C5skg7l47gsAROJUPq5Np5o8+zw5yKF0l2c+TY5b+rL/j6WvEsMWg16jzE9OGcta2YQF9iQRifnp1q1byYXSC6j5YrF0bpDtBipdTSa5o0b0xYIbxkvFrbRrHLgR1F4RB2IcXbvUUp4IP6QtQeMLFY/WP6r20mh5Qz+CiaWpOVnVhUuZH2mWs3wGlNlIMD90Ijad5h/U4+50QKDHXzMfNW9dA+Db0a+qWEr4ESuxtAjmSqi9oowFNZ9YS9RekG1+pN+NzI9flmfmtowdIYBArJj5Ct+4NSXmR3U0IGIRAMp4qJsO9X1p2di+5rnZeGjqQozc0VdFhDlF+olNSftR+rBxuf23CKgNbCJ1b6/S59Jtg7jPIe8sgXX93228Hw5hp8Pktki50L41BMmYe+0KAGgKxltFPoeGZjd8M1TdFejUG1gz378Sm82PfLL8taIj0FQH7HmS0n+/nJ9nLicdC4O4x47vsFMMu63p54IrF5GEn8mTJ6fdj20PTVuA+w/1P2/0jW9djy2I5oHAYo+0LL1FsGhZ1V7lJDYNKWNQj0jMj8lTKZT50YUlbi9jWpSCxVqlkpNgfmLH+VG8HJygjBbh2dQgcT1nt2AAhDtsYd3nfrGok4gUZNAgyBiELX/4mQwlpBOVr4aCHbqb24oEtX4XssEzXXFMNj85pZx5RZNtfohBaLFR70NLQPpB0wkAlPnxMaB7x+B35VyNMmpZ/x6auhAAMHNpLYBw278cPGlsIuxdTBh886MagEc5x8r8hNn8WOY7VkF1jXRYVnuF9K1PkCEh2JA2NftlKwuB8BO6GcqDjmGJ+aHPbcDXgC/f84+r45d5qykbYmYn6VC1V4Qgh3Kk8uCYtXQYvdW2iCT8HHbYYaULZZBhyKvFh5VlrjHrqGMiisEzP1JC7RXCRPEF08jGCKFGEn64rBDi7WUQlrTFQOqkOW9ai9gzqAbPZm8v3gX+waT28nz7A0l1Y5ztgjpyxr67Hf3oy5GFOioYvHkj8I2rlOZ0YYsuGFbqPIraqyUGxIb0FkaDZ88Dln2on8/VXmzsWJgKLoB4ujdMortUwvwoNmxH7NYXWODf63411agOvEHtap50FhIpzo/is+2/30T4SkgA06HXt6WRqZUNQoytFkNXpXcmTPjhPbE1JM+XnuWzBsXgmSVsrsyHWJ3QGHF04woheEjo0MNcj+cRO1DZVb9I3nsGLUaQpUrA7xad6Y1lnUiWNW2Csnr21ltv4bvf/S4OPPBALF3q5wB69NFHpbQX2z3yBeDcgDEbd4P/lwsMZsTRbVtR0tW9HObHJPwEE7mpt47F4FkVfiTXcJ0e1YP0mgStgrGPLZ2bm20Gzwbmx2jzw7oHT3K7VWsTB/W4O38sfFf8bkvqakNJmx9d2HKA0rFkIhk8R2CHbN3SXN1lby9e0zXdgAX/MZzvFy4K2tDYB2noq7mzWiGrOwAM6tGJ/7Jig+6qLMZ0xBe2TEgMguYV50nXkJ7wo2NA9w5B//zvcYIcOo5NmLHXYWW6LROmZ5irzJDv2SPTFgEANjc2S9XLlZOo2pKnLuuKYy6vVkjngbzq6u6Xy+ccIaREGPtUTW60+Rl5imgjRuTo1kZs4edvf/sbxo0bhw4dOuCDDz5AQ4PvFllbW4vrr78+8Q5u1dhxX+DqWmDMBQDEptY2hSUS5DAS8+PRI1YIQ2P9N24sZxR+6H6AGDCraqJSQQ4JhU1/k160YMFSe9FS5sf39jJMCJLNj2LwLAk4gvnRgxwaGjQYIM/L7Sz603M4OT/CtZW0+TEFORRqDcdCfzc0qyo0E/NT/r3XiSXF5qfE3Mwm2yKvyMz8SGs43xlXGMu2DGRsKAtXjrKDtr7RDyktJLYEmYD/fjv0fW1FA9buQc6xOIlNhZjmmOU0dQCtF2lN7JssfXMDqIxSSOcMcxsA1De5wdEQ9tTJS/dcVlEbyquggUBVtZfEREZnFendyPE5maDEvN5eEFv4ue666/DHP/4RDzzwACoqxM086KCD8MEHHyTauW0NLBaCTaXAgxy2ZOPJ87go3gJ0EEbcSBppY1YLPxYMfirwEB5dYtRV5scY5FBvQ3N1p2H2GfOjdLKl64NL1IP2FdcuRDIBLUeEHyFLhk12Qg01PT/K3GqUa6M7vhpD8FGDsEUZKtvQeO4jW7Z3Q92272GnxrH5MYGrvdh3cx+k5KEsrk1Fh9j9LQlbJG2AvxzqfKAzwOpmIWnhh+oVVeFHDZaZEvMTwRA5bnoLoy2TWscfD9La0VqJQJXHYX4iwaT28ly7hx4Zs9J4ok4BijaAvSM5h8T5iXSPxXmq56LfATE/e+Upl1oFsXs2d+5cHHroodrxrl27Yv369Un0aZsFn49tNj8xXnIriha1l8nmp4T0E6b24pMMn5ipe3sOMOwkIrm6G2x+csoWzqOCXfBZ7WKyzI+ZNnfcMFd3cT2q14px7SITLL9PuRyw3w/9w2Ryi3Rt1OanhMEzNcQtFezs0+Xrzf2WD0YoY4Eq+XtydiB+7d0G+39P/4tcnhk8q+ktVLUXHVIsfQNTLSZK/FDViCwsU0Hzf/bZUfRNKZ8+82NXe+UlBtAhfUjX4HlQD6HmLRWMlYKqhfh8KiXhUvpdX8s/igSqpZkflX0N9aBV7lnfGj8I7MmjB0h9litkkgmZSz1PG0Naeam/kDdBFuYn55D5KkZ6C8fxQyFA7VEJR5b2gtjCT79+/TB//nzt+Ntvv42dd97ZcEYGFXbmJwGdvs3VXbL5sTMWplMi2fxQ5scRk6TZ1d0Q4VlULMrzYsqLaXi51Mmn5cyPpTKp4kD4cfiMK4oZmR91R29ogzAEOek+UvVhBFC1lykyNn0GZDw4BiGUYsmaOrUiQ922xSMKdObHaPPTMciPpDCc3OZHpw2lcvRJceanUF1Gf0uBCrXsIxsP4h1hxs4hXUYc1UQcyMJPCeanldQYBRKklDVZjGDtL4fHML1vIQyTrYSB+VGHeOh8o9yzfPCdBWI1zwdkrjPYa2prhWthfui7r2SwpTY/cVzSaR84g0r7Izl+JJuMN0nEFn7OPfdcXHrppfjvf/8Lx3GwbNkyPP744/j5z3+O888/P40+bjNwudrLjESCHNpc3WF4cUtIP2G6drGvNgk/4oWVbII04Sec+dF3OQHzQ9VeQUC9pL29iq7Hn5d9V6UKFbrw40CoM/kVhu30nJxwQQ2+qyfFVnuFqtlIbq8c+HOwe31EmfV1ASYqDGK2eRdP+l/c/VuiuML82Oh8aUPKsphz5ifBiVoyeBY7Zv+vEH7+8q6I6FtS7ZXwOiLZjyhjukDjPUlqL+WZNitJZlt4D6knVKwgh0ROiWTzc9BPtXZ0GUufMNX5JY7ai+WdY+lMjLeKq71sBs9qF6ldoqHvBq8rifENpaXV8yjzw7pGzpM2JO2X+Ymd3uKXv/wlXNfFkUceibq6Ohx66KGoqqrCz3/+c1x88cVp9HGbQXS1V5kNFJuAv/lqEpOrPQDEcXUPS7SqCSaSzY+YJM1xfkxvsP6SCAaEHTDY/LCfVBq6he+apPYyGSLBV0VJ0ZsNBs8syCH9uXSEZ1KdkfmJcHGS2stQni34hWp4zexZOtKCbIItRpWEclJg8KJmwSnnOEHUbaUNx5GFYYX5sTFZ7Hj/urlA/Xr/YEUazA9vUAz74JAqFIuy/h8h6KkLWMLMjzWEdpjaS+nD7H/K3z3X+J5GRYVB+ImT3kL2RKLvb1icn6CIjdaR3m+1SEjflDHIriMfFl2Sq73yZIBQV3fHXF7tndG+0gfN6h5HSJFUiwbBUN7Utl+bn9jCj+M4+NWvfoUrrrgC8+fPx6ZNmzBixAh07tz+cne0J/hh9P3PqTE/NO7J56/Lv5EXUMuZZYE16BfICxBMjp6TI7YMlPkRCI3wbLL5URcwbvNj38WI7y1bIOwGz/LE4j8zfQIQghu1+fHLlmR+6HUbbCwiXdrn/zb3mYHbuXSAx3IiEmHLtsDqQlHI4AgrY4NV+AGKsNjB0J2mEuenlM3BBV8QtpobPKfE/JBFw/8bCMiOh+oK6iFZivlJVviR5gFF7SU9b4lOUYQI1cHCLerHYoDm5jPlPFy1sR6NzS4PDMmbZa8RxH0M98oimxlbEaPNj1IiEvPj/xHMT4gKUcoLKc5XIziI8hahzhRWhPc5EMIcEeE5lqs7hM2P1eB5W/L2euihhwAAlZWVGDFiBL7+9a+jc+fOaG5uxpVXXpl0/7YZ+LKPQRVEYN15REXoTstAnZaoLpz5UepQbX4Y8xM1t5eJ+fHkfhiZn//5k1w3K2q6oBhothk8S5+D1BXEroP/RBgUR0g/9r7RSYpeNzeKjSn8vHWLuc8MjZv9v5Wd5PHA2zM30qFCmTKidCaOcWwI8wMQoYYyZY7Yw7HJVhithk++n3Q5RHzhkamTnKjF2FBt7ajDwYWHD+OftcVeogJb1j/dmDfc28vK/Kh90AzVY6Q5am4ENq+SDlHmx1GfPYCv/+/rOPjGyait06N8A7Inkt1mD9LYFOq10syPFlcs7JkoKiV2HYV8RLWXyV5TU3uJ+y29u6aNJr+G4CeqzowwtsTGzhHcuETi07a2oTg/l1xyCU455RSsW7eOH5s7dy72339//OUvfwk5c/tEQ3MxsB/R0lVqiBPPwghq53H8nfJvhomzlIG1NdEfOcYNInOKtxdb/KkBM5/Mo9r8sJ+U3xx9p6/2MIkIz6USmzJjXHEXKfOjC7piLjbt9AzeV2SxccjiEjmzu6nPDJT5Yf1ziCLUcv++PqRbhOZaoPayHNDeDTqGTMyPhTXkxYLDayv7+R/2P19EwE3N5scHf+8Ig1kgKhCro0ECnjNFw7lynB/F24smLpZYCFX4UYQQk5G9DXfuox2iwg9TD7H7salBzHP/UkIvCA8mx2IrFELZWG9vqZk7KvOjCD85U/9YhTZvL3bYrvZylDnKP2hQe5F7VY63V86h03Yptdc2IPx8+OGH+PLLLzFy5EhMmjQJ99xzD/bdd1/stttu+Oijj9Lo41aL+qYiRv/uNRxzx3/gEubH9g612OaHCT89dwFGn20u44XsHhSE5fbS2SNlwBOBRbN1CWV+9EVeLMhCxSZOM6sKU8vqHsb8EHDhh84JoWuX2KFJhrHBwu6QnV2ktW+XseY+M7z8C/9v42aZkSjB/Oh0e8jgEAdK9ZacamN+/K9C7UWZH5Pai323CD/qSkcj6abG/Mhdou9fBb0E7S6XXnyjwuQxFebtlbd5e6mLpJpkNg7zs+FL7VCY2mvK/K/4b8N6y+YWJnNCV3lnjScgZL4z2fyosl8Mmx/B/IQ8T6u3lzInquVJO3I9elvU1T0W80NuhwhySOrPmdRe7Q+xbX6GDh2KKVOm4Kc//SmOPvpo5PN5PPzwwzjjjDPS6N9WjU+W1mJTQzPmrdwkvRylXN3LZi3UMP0UBmO9UsMybLHmuwZm85NTBRJxssPaDGF+PHaGgSlS4/yYdhYa697C3XsxQnoLuEV/92M0iDQxP1EmOyUBJlvYJaEwAqQ8PiFnfPYq0O1i1jRKGamaEtnqMAswUeBZAiRqC1MJ5oe9Q57JaB1EAKEqywSYFQ0S8yPXmyP3mmoTNQ8abdy3gPkxCD9h6S10tZdlkWwJ82OAyeCZ9b1nJzG21VhlUnoL0+MMyTtHRAxLmTC1VwiI3V5T0UVDs98HzvyYxpvk7cUaCYnRRpkfqTrDnCmqA+Aza66BqbfBnN6CgPZtW0pvAQAvvPACnnzySYwZMwbdunXDn//8Zyxbtizpvm1TKLpU7RW+qy5b+LFFd6a1e9TgNrydKEEOSWnSlLxbsS5cQZ8enroQY29/S++TylBJu3RInxOP8+NasrobmR8GMjkavN1C11bKZNAdGbtWotKMPT7Cih9+JWHxxIphS28RvuNkx5QJN05/LcyPpgqy2vzIwo+d+VHbIwJ7qzE/4m7mDRShq76nCSwkcdVeVSBCjWSAr9SjMT8x7Ly67KAdqijYvb3ovVIFB6oy1g3HDf0yMD86cWna3FirMUCMwWdniHUyz1zdTadI3l5C6KVqPQm2tABhNj88+S/drEZQewV/pWFjEniAbSux6XnnnYdTTjkFv/jFL/DWW2/h448/RmVlJUaOHImnnnoqjT5utTj5j9P45w8XrwcbLrbIyi23+dHtbzgodQoPVxUex3nvHQNsXGGtzmp7QPposvmB4hJbSu014dlZ2FBflCuGgflhFKuB+YlFQ0dAsy2rO4VXVLz1pS8ALDY/Ru8OMUlJc+3nkwEAhQ8e1IqGQhLYDP1nKS92GSup2UpldY/m7WW3MygFm70Ft/swGDxL0cUhe3vZXd3l+uU0AukwP5ItFyClgaHMjzZOlAUsvs2XQLGonyurveTfu4AEtQyz+VGFnzjMj2GcVBABh90mdv8am8NYUI+fY/IS0/pFGROVcdNasQtdUV3dV21s4Id5kEPjZsiS3oJNt5pmtITay8A6C5sf489W0HmZPzl6DQY1dHtE7J5NmTIF//3vf3H55ZfDcRz069cPL774Iq699lr84Ac/SKOP2wTW1okgYLZxFmfuXbWhHj96+H28MZd4STB2wKi6kJmfHxdeQKemtcDUu6xthAljfHHh1asCCVF7afWI1X3+qo3BEX2RErt39pPB20vd6fNzrZcVCZLaS2J7FOYnZ451wc7NEQ5avw8ENG4Nm8AdB/jyXUPZCBdgmwy19oiwBbEw29rIOcoP5osJKtRVdqVgyurut2tXe9F0J15OVpHYFmtHFU5jerxEB61fLMz+ITFeCpKNi3mcLK31YzM1F8tXKZmYH4l1UoUfh0b0dqSFWIKq9opj8+PqSXjzOZ35YQJGY1G0bXvv5cSmJmGAH+CfrDaXuoFjlC0AgRiDVHCrLIQwP5Ygh1bmx2rwrLNW6k/5HHV1L/3cqM2ow13dCbZVb6/p06dj77331o5feOGFmD59eiKd2hZRdF2IqL/mgRDH5ud3L8zBa3NW4pyJ74mDXO0Vbsol1W6YeBjCmB+R2yuw+aEzg+QCJX4xqSwmzV4llyJtcXZJlQoNNh6atqSlNj+eZ+wTlIklT9VelBZnMVyMzI8BZHdPY5XgG7/WitZuMbv3GuvT+s+OmTzuQNReFubHwswY22bPKZbay9xPR303yKQuBzm0xPmxMT90Z5y2zY/C/NAFrNLo7cUrAQC8ErwrJnf1qCjp6q7cJ93g2WJ3VFTmkTjMj1H4of0LmmDCTzMVfuTzZE+kKGovyvzYNicG5keV/SIyP69/upIfruSu7iHvZ051dQ8OhzA/jum40dWdsqPhmx6pSgPzYzN43qa8vaqqqqy/DR8+vEWd2daw8Pfj+ecuVRUlbX4EvVt6oKzcUK8f5AbPJdRetPoQQSnM20vsQAwCCbWfoGovvmsXC/1+Q7oHvbIzP6FxfoLPmgFiC9411/XvkdHgmVbsFoOJQ29MTBDkNyutDpnJYMUdB9jlm/7PNSLx5YNTFpS+iDjMD++eY1UTMThRmB9two3+MPR1p7S3lzSGFSZQ8+qywZKMt+UgQhr4x+CvxdVdPTfoe5Gnxym/f1TtwiDb/Mh1S+ktlPdaQkLMz8aqvkGfdCaMCW6y8GNhfsj+K5T5Ib9pmzRehhWgTybOfCPu2cgdu/KjVWrMLKlC6qWlMz+6wbMc50ePqWZQe5EIz3Fc3Skjbw5yaAhh0g6Zn0jeXvvuuy9ef/11dO/eHfvss4/VZgUAPvjgg8Q6ty1g516d8MVXm9EsMT9mcAO9COPEGBk9lPmhg5A0ULALs6FBDtU5RBJIyC6aGDwL4UcsXE1FxhzZoS0GJpsftX8teNlem+PvzowGz4o+3U9vYRB+HKZWiQhjhGeQyUP0IRrzYzHSNrQnhT4o5fURojbQjjllMD/6Vh6AUM2YxpDJBkyP8Cz3W0+W2xrMDxPIWFfFyJaYDvVaIS8w9tm3NJbX6psmWe0l3yc5samI31XS1T0W80M9lYIFmdr8KPMHVXvpBs8eP8fI5ISMX3sOMQPzE2EPwEHmwp16dQIAfHtU/3DvT0uQQ3596sSiqL08j51mmDNZE8FPfqLV6BsVyvyInpmFn/Zs8BxJ+PnWt77FGZ9vf/vbafZnmwObWJqIoaFtyBtz0VhQMKR4EPoSw2/kBXIozVzVxdpGWJBDVe0luzcqzA9vWd+1s/sivTzBm6vpt1mcn5wiaBn6WK5mwHU9/PhRpr41LJzSLrIY2PzIffGLMeFHf+6mrq2orUM/AF9tbiLUPVEzePokHQ7P8pkdojZGpH8l1F6RZn0+O1oWyhCUtvnRx5BnZH7k7+o94AKIYWFLi/lR5TG6iTSRAKqKj0UcbwnzYxo58mZWrtsa50ftg6q6iuPtRc6tb/KFqCf+uxjX/89IAPqzb6DMj4XIoRGe5fQWYWovpRK1UnKfdKY5wvbN8/h8VyDSbqgNYE61+QkOh9j8AOTphKi9+DyTo0EOS48tmt6ipM1PO1Z7RRJ+JkyYYPycoTSE8OPCpNqh4HYNEeaNt0mgL46IzE/nxjXicFWNtY2w3F5qYlNPNbKOxPw4aDYxP4HwQzbNkCowGjzLzZfrEfP+onX8s9HgOZT5IZMjY1AiGjxPfPsLXFkBvDHvK3Qa5R+jwcccMrkN6tFRO19DKeaH7gj53C4WN9sCqzNqJeq2lrHAsvCw4WRObxESVM0yqWs2P63g7aXmqrMZPNu70XK1V7NhcpFi7SmLaMFRhZ80mB8SxsEYhyhowiT8KDdJqHIcfdNl6pdR7aX2wMD8WEoYQe4Zm+8q8jndC1aq0ODthTCDZ5Mw5pDnpIu9co5HNrbEva1vKmJ9XRP6da3WzmV9EOEELGqvqGrnNkDsIIcM77//PubMmQMAGDFiBEaPHp1Yp7YlsEHaXCy9HIepmSIhos2PpI8PYRGi5PYSzI+6syCDXq2H2PwYmZ/gTulBDu3eXrFo6BD84CFhQL7v4J7Akvn2XaznQspsLDE/Qd8N3iSmqTLHr1lxiSYCxMgdu2Lm0lp07RDhtbUJbKTvfiNyFvlSNj+aLUcUm58YD0NfdyJ4exkMngXM1+Mof2VvryQhxoa6hObIO1NBmtbVL4bddZloNggXYd5eOiyLWUtsfkjZ6uBRHjSsp9Y/1vWmEINnqhYyCpFRDJ7VO21gfrTLD7tv5J1q4sKPvS6/Qou3l5SJ3VAegdpL67vO/DDtYc4y9o+85U0sXb8Fb15xOAb37CSaIkKTsGkkF7EtMT8UX375Jc444wxMmTIF3bp1AwCsX78eBx54IJ588kkMGDAg6T5u1WA7Oqqntk0wYUxLJERkfvIemahCXtqwjbDm6q4uOmTQa8uP0eZHn4BVdonXQFV+ObPBc7keMY7pW2iQQ08vD8DlNj/6ztJ0P9nS6JKpS7X5GdyzI2YurY02Pkra/IhJkdqiiDg/lmqtu2LDoXJc3S02GcLmR2d+YFCDiq825kc97oQP+HIhjRelbbKCFRx9nOhqr/BnEwUlIzzbFin2PtuYH00aiCj8uK5UF9MG7UkMg1X1O33X9Xg74hyjergcV3cj82Nu1wxO8aApqNxnfuxMsC29RRS1F7P50epRm2D2dI6e28t1PSxd7+f/mzR7JX50yM7kPHFVJb29TOYZ7QSxe/ajH/0ITU1NmDNnDtauXYu1a9dizpw5cF0XP/rRj9Lo41YNwfyUVntpYe3jwiVUqQryAuXpLi1kYYpi8+OY0lvQ9oJYOFI95IXcWN8c9IpC/qYxPxLFahF+yryFBwz1d5w3nDjSsmuRK+7kNBhtfmCw+fmR9zc8Wfk7oFk3OqXCj2QbQtgTu0GmAYqQpv9usPlxAIBNXJZWbEYW8kH/T65EXSZoFB5zdQ+aNzE/EJPtJ8s3SqdXFMx94HY3EnNZgvVqCUowP3mD2ovsFoL/B+ET9HgDkdFsCHIYZvAswG6Y7R6pzy2i8KOU4wbP5F3iru7Bw6dDhL7nS9bW8U0mVXvJub1U5lJ8jBXhWWN+YAdlflhqi7xFLaf2k86rnvDiChd+zCEhVJjUXqz8ms0iLl3vLrJTDO+tI+YtzzAnAxAGz9uC2uvNN9/E1KlTJbf24cOH46677sIhhxySaOe2BbCJpdn1kC+h/zTGpYgAzwsCCTLhpyTzQ9xdQwZl2YlNlfY0xoMsvFf9faZcnhTkFG9OPi4Jd81BdnJ17i1zAWNutBV5i2eLcr+Oa37dbPMDXfi50HsSyAEr5v0d2PE8qR4RLymnTHBC8BPERCTqx/KZHaLeXuygwz2Qcrb7F6I2MNUddDhCfy2wGjwT5oosEHWNcltfrtsil9cbCOqBzvx4nm/LUqg0nhmt/2JsyIuNLPxQ7zpNyA1+SyJJpDmxqam/CkrYmOjSQES2TzGUZsIo7VNOUXvRd5vd0+aii0NumkzOEX2OrPZih6x2bWGqqghj3PO42rEix2x+PPMtp/aNZJxo4at4eZn50fplGDtMDvazujMvT7+eVRvFBq2mQ4XSNbbxJYEkbcxPmhuKFiI28zNw4EA0NemutsViEf3790+kU9sSmPAjqb0sKNfmh3uSRbL5AQqukOrDmB8npD/csJAt2lEMntU2pcSm+u6TENLB8UAokoy0zQJaucyPpJPXGCe5VwBQ4TQZDVCFwbOhkaIh1gpVe5mYHxiEyDDEcXWnarawc2ASKsMk4/jeXnr1/rnzV20CQGK8SMyVGHs1HeVd6phhvYwV80ul21h1or6mG3Bdb2Dtguj91yAeJlXJALIMv+CrTdqZevJMfYMQFyabn7AghwKM+fE7PWdZLTbU03WgTOZHU4+ZmB8qdHjSu83u0eP/XSzV4qu9WI2U+bGPX85Qq300MT9x1F4Gm59C3kGPNR/iw6rzcGjda/o5rsHg2XNDDJ7VOD/g5/B61CaY2iunj/0NW4RQqqZE4aKgI+Yt6fqpUN+OXd1j9+wPf/gDLr74Yrz//vv82Pvvv49LL70UN998c6Kd2xaQJwbPAuY3JYxpCUNDczDwI0V49lDwQiatiP3R7HG0VZ4wP6oQRXbDO3brYOiFJ5XPqSsVzVi+aIpcNytZ5uLAJqeqgsViUqm3ySGLrdHg2bDwG3bFNFiYlN7CofeRTc5Rdpl2gU36XUlvUdrVPV21l83VneHp6V/Kx5U4P/sO7iGV79e1o7mf/DojeHu993+R+6+BjHX9uYnxUlWgai+VsRBjQ683Hoomb68oai9u8+OX3VDfiFtemWvvT1SbnwZZTcmYB0cSfki1nmrz4/99/L+L5OY9j3siSfNXWG4vXmdp5sdmaG2GGFdsHajI57DvO5eiu7MJF9Ya1k1pIyvOjxLnx++xpxzXd2E0GraqnqLXownMZL4QzA8BTTdTTqyvVkJs4eecc87BjBkzsP/++6OqqgpVVVXYf//98cEHH+AHP/gBevTowf9lIGovavNjVXuxn+MNlPqmYIBLHgJa7bztro0kH1iozY+9P+riHsb8sLbVyRyOg30GdQuO6BO7tuGSYl8E2HE0rVHrX1w0kslJZZykvgdYk+tlVHt5PCaLjoqNXxqOCuZHWp/Iri+WPW6pQnRR5vfZgVMq2FmUxjXmp/Qp4lR1opW/H8y8gCzCj7rTzOXkSZ2X0/pqYH4YalrCaOvMj1jXxegYs7PwbsqpCwpbkKTpOiXmx0r8yMyPAw8PT6MCR5nMz3qZsWHvkpzkXnwpurKaiC3S1CD3xH12RO/OVRG9vciPfJMGcxmFgZKKIATkHW4KXu6ClBPQAOrZSuZSlT3U+hhchmB+lHeRQE/+CyCYz6nwo6pKBfNjjnEmMz/tV+0V2+bn9ttvT6Eb2y6Y8PP8zOU4s4SfRrlZ3T9fvck3SuPMT7ir+wG1L4jjEQyejWovjfmx2/zYUxPkhXmFwdVdM+4jL/LfiodgZ2c59tllnLGP5YYLaDLZ/ITQ5rnAP8vvF9kZ8t/1fuTr12rHOH1MpkTZ5seyk7XBNMlLi5xOhzt+o+KzqdpSth6mumMFvFPrl899aOoinPa1QbLwQ4QCR9kS5yyTrzB4JoIrHWc0bk1lJ5QNiflhbQfPkaqiqXdw8FeM4SSZH4Pwk9PfvWAJJccd6S+7mncXrMXXd+qhr21RmZ+qztJXk8EzZaZcz5M8OdltqKn2l7L9BnfHraeNknosv792by/7/GvY3KjMj+vhi9WbcOfrn+GqY3dHnxoSG4fMI80kyGFo9GPJeUVnfvT0FtRl3zPMtfYIz/I84/+h40SNDSXnT+NdEzBldW+HzE9s4efss89Oox/bLNiL+8XqzeRu25gfu7ARhhUsZL2JGRG1B2U8TOl6PHbe8ol8Tmh/9N+E2iuYmNWXiyw62gtiSOUgNcGp1+ByFNsbBw4ubzofOQf4ImdmF8q1+Zm9fAOA4LkZF2+54oJDlyQ63QaLhMEzxyz8CJd/Ob0Fsfnh81Mk6Uf56sqTkiW9hUN29tyQnsDR1HimvjBhsBy1l3pAbm9O8HzMrsDQ9AGOZfKlvja8HnqMqmMq5QU6Hijzo2wYqLuAiemQZZ8UmR9aLbmvpnhgfHz4OPX+aVIOQ62eUtCei8HgWZH5TDY/JhdwPU0IdEbKoPbSc3uZmB+5yIb6Zhxxy5sAgH/OWKbcE6L2coVNYajwQ729yFwqrlMprwibgjUUtnFaE9zmR8zfjoH5UT0EF66p41WWcnVPwkg/LURSe23evDlWpXHLb8uQjPX4B/PEJRiS8DqbFOPpTlWBVBVm8yN4fjQ5xGamTINntvtypMXD0KBn2FGRF1IIPwbmR1dm+f83xI5IyuaH4b8L1kp0NalYKpdXI+DynvqfOfNDdk/NHXpBBTV4Bp3ISR+EGjLCBYR5ZXke6KIv9rUiH1IOnnkcao/ExPywC4iv79fi/ATnHhwYLu++Q2DsbglyqKq9RP4sC/NDFzYqpTdsoJVE7r8Go3qR/UQWaukUv+ALM5dLCYzld6Q8mJifPF2g1GfHoNj86LZsqsAQNcihKvzozAadQ4uE/aDdpck2Vciu7na1F7s1a4mbt9xH+n7HmF+I8MIMiQu5HMzmCUo/lSCHNIK1sTzYxkXpewjzI2d1D+YhcpvumTzf2MXFa+vE5pf+YEg8Xa6wniYivdXDhg3D73//eyxfvtxaxvM8TJo0CccccwzuvPPOxDq4tYNmay4d5yca86NK4ixkejSbH0ipEsIWprDFVkR4tjE/vAGeTPH4u9+WK3RyxH1Vn4CtEZ6ZDZGhP6JVH5+t3IgtjTGizQY4Zs9+kSjbAlV7UYNIRT1AXXrreu2l1UN4MtmY3BTnJ5LdTSnhhzWckxdlYvBstvWKwPxou82WTHz+uWP38LN979SLGTBT4YdwKcqinbMyP+wvHVOkv/VE+ImjtrP0n8b54c9RYjdE/25+dR7//M4Xa3gdUi/KVesavE6Nai/bZkZhfqz9ierqrl2HvrjTOfTLdXXSuOSbJwMjEs3mR3yfOGUBAODZj5aZ+xjC/ITDP6+p6PK0RIW8E86K0DyNks1PwNaUEH4Ea2g3eJYEKWWOLZIL/OIrM5kxqEdHYp9mZn5sQUbbAyKpvd544w1cddVVuPrqq7H33ntjv/32Q//+/VFdXY1169Zh9uzZmDZtGgqFAq688kqcd955pSvdTpA3Cj9m8CFeSvhRJhZOZUe0+ZFdP+2CQbQgh0zyt0R4LmETwuo22/yoXWe7wpxWtZbY1PXwxtxVOGfiexixQw1evDRaDKrKQg6NzS66VBfMai8D82NMb6ExPzSeiZ1SoTY/8o7MJUxchAvRqHvX/NmRvZAcbqht2dvG8fYqK8Kzud98Y+CyNilzVT7zIy32EvND1F4tEX4k5ocItZBvHV20fzV+d1wZxL/K5xxe0PSOxIU5zg/deFhsRHgZJhz75Zi3ZmLMD1HFMNAkoICq9mJ/daFJ5J2iJ9uF90E9OuKzVXrIAdO9Xr9FD/fCIO4J64guAHy6fCOOC1sPrN5ecpWifAlvr1BXd1EhD7YaYZIZ2KMjVpnMFraS9BaRmJ/hw4fjb3/7G+bNm4dTTz0VS5cuxTPPPIMHHngAb7zxBnbccUc88MADWLhwIS644ALk8yF03naGnKachVUKDg13TqBOYMK4LSTODzWcpS9KyA5tfZ3/gtOdqNoHK/NDFu1HfvB1AGQHR3YjYcwPNayTzjPsmExxfp5+3/eqYnY8UUATD9JrIJ2TyhdE+lO5fU34EZPljlN/C3wmx/ag9LHd5ifa+DAWsgo/ZubH3k5IveqJZRk7hgs/Rc/TmStqtK0aPFvoS4cv4uyAwvw0pMf80AHjBvQPfWNP/9pAdKjwj/gsr2GDUeZO2pzbi3a3FPMj3zeWAkFnfqJGeJbbEzY/8lvVLzAgbi7Kai+d+aHqMkPXQoT3S47cBQCw14Culj6Kuv/89gLrJXXrWKEcUVhg0m8rLN5e9jg/su2SMDGwPU/5nqlaCTpMBvZQhDmI84R5mpn5aZHKOGXEMngeNGgQLr/8clx++eVp9Webg6z20j9RRLX5UScwLgyFqb2oCkJaCO2T1MuzVkhtSCwWe6dsiU1Je/2DnRC3TTIYgcpXJB/T3CVpcERulKsLhFua4qm7XFfsrKJ6e+Wtai/BoPgHlL48fhJwdS3/StVeEoVP6GhWJpJBfGgkZkV44H0Qk5mjMoSGUy0HxLFy4vxYhDa2QLuuMn4dxXBUZX5s7BO7TqoWoDv0pNReJpsfwkiIMU7YN8fB/jv3wBtzV/tqquBEWdudHPNjzO1le5+D49TmZ8gvX8CCryubgLjMj5MHPDfYLOiG9jRHoiu9jmyxJhsGpcvSmAoxeO5Y6Y8VOx8jftkQwvxo91hhVSJBCnIoNhHGOD+UCQ3a0SOh2/vp0HeIjzVRXyNJJEvvpbRdoDpcOj+3Y2+v9iuWbSOQDZ7D1V5RbX7Ul0sTfsLSW6iIuENTbQXUIId25sfk6i4m2FCbH1eZ0AzMjzB4lFuftawW//50FeKgibBghYgRnvMOmXgM/eKiRdE+WQKQBCgaPl6+jwYa34qozI85orRsNGmpJ+iXXoYsaKZzGFbPAz6bpFSv9lve6frMj8JckWksp9r82NReogFej8T8vH0b7ZS5/5GgC/d0gXYt8aAKwermb3QSZH5Mub3KUnvJ+OeHS+UDhnnlg8XrMOFfn8gqFXYdecGW5OFCJcwrA+m3uSjbornK+0+FJqPaK+RZVgbxBhqaLWM8qHvNpga8OW+1tR59/g7YPSLgqvmyNBjVXq6R4VKvSVZZ25kfOYiswvxQV3cyZmRVLY2iTTtgV0O3J7Tfnm0jKMRSe/l/Sxo8q0Gn2Ncwmx9S2KG7n5Ad2sdXj+Wf1fQcbGcQJcghY4z4C1XS28uHbvAcnEaGrcruMry3cB323LHGdmlGNJGXvIK6mJpsfgIBs4Ai6HTL+y6oheCAnMNIBVV7sV74+YkMru4tZX4U4YG1SF3dbbm99N1rmPBj2fW5LvDxU8A9XwMePxlY8q69PiXir7/JVYQfuuApjIHV4Fm1w3Acuo0FVs8h/Y1vMC/6r49vakvnKeUYKgKmo5m8d6pVRzkwRXiWF1JdkA8OSMfVcaCNC4OQceK9U/HwtEX42VMzAADrNjfipU8CJxqyYfOFHzPz01x0pfnxyfeWBN2mC7l8CTLzY38vKgIBS09FJDYms5dtwOjrDOkoCHTmR19mf3DQTqF1mEM5WOL8aMIPGU4hNj9c7nSE8TVjQuk1mNSMQKD2Mgj3WDpdfN6WsrpniAfZ5ica81PS5kfZvXGK0gthfmyeNyETe6dKUU+TshsSL04w+DVVm85YiIzcYnHkXTdUznNO8eMm5kcuS9G/q9BV6+6rOuhC4+f2Mi2csvAjqb2kNcT/YrL5MYGqvUQYAUe6Vi4GRVn3NPWRZQGQ0lsIV3cH5nYiBTkspfa6ax/g7+eK76tFmgStOm7zQ2pShbdQmx+zwaXgMTzD0dILeXTowo+sZDK3uXqjn/9t7sqN/KYUPWmAldUb5nlJIatQSjA/JBTCSfsOED+rlYbMK/+a4XtT7fO7SfjjG4EbNdmw5QzMDxVMqGzB4j6ZGBFjomgtvQXZ8BB2yVjGcXDH67r9owpNs6ioFe84fRQ6VObDVwPX8Bw8S5wf5ZoklXWIt5csSCnMj4FdUz87OXne4lg+Q5TJ1F7bL6gxoW2XxxCd+VFYGNXby0g1CmHEsbEACvI5QWua7IykgHdhiU15VmaV+clZmB+PdRWAIUqvozM/6oSzW78ukufGkrV1pkuUQJkfKdmfJDgowg+N82Ngfqw2P4qAyr0sJG8vyAu7YWKyIkw9pamNRHt0R2luJ46ru8FbDgDWLZS/V3a018eZn+CrQe1Fg/9Rmt36DEHXIzKmFIZRu55yYLiHMqlh7t/7i9YBAB57ZzHvkOxRWd5i8ursldqxQs5Qr/Y+B98JE/nTo3bBvOuO8Q9rz80u/By5Wx9RLfugMD+6zY8QTIxxx0zMD/+N9ss+fk1sm1zGUe4V8IeT9bAVul0VFXw93VjZBKr2KmXwvOBNw/nsrz5n8n7Se8bU3cH9kYQfCwvkGzwbmJ+9ThPd2Nrj/GQoH/RliW7zE16n1duLCUUlmR8qyodT+nzHpTA/rudJqpF4Nj+68CPTJoEqTTViDLH5UcsWXQ/7De7Oy7HFJAzsvhZyAQNidNOUF4ccfapUcFC9vVSbHyVqsKCPhQecrsIxTDQ2xFB7SS7YfINvdnV31IUn1OaHCT8l+koS1driCEmsqCa8ERsDwiDkHQc2xpMadosD6TE/rrQA+p/nLN+o22YYqwjqMKmnEoCc2NSyWPL7Ksa14wg7GU34CZlXOleL+YmfV0LtVRkIJk1FV7p01r6YEinzwy7JIvgr39lc3WSxO4Pj4LDhvaX66XcGTTiT7JAMnlomSBsIMZea1HtYu0A9mWy0w1zd/b9yVncfVP4zBZVEcIbI6k7OH3ygKJ+r0E9sJ4gt/Lz88st4++23+fd77rkHo0aNwne+8x2sW1d6gdneYB7o5oEQlkiUwu7tFWbzY2N+woUfbmhosDOShZ8Qmx9N7aW7unftQNxD2WSvCgEhuxj2U0UwgRU9TxISozA/z0z37Qf4tYblpgrayTmePvFDCLqmIIemOiWbH8OODBAva7T0JyELuDSDycwPSL+N41DXS9nbjuztpQuztgOuyeCZqgbJZkP3iDG0TIVvOs66DiLntoT5kf7wpgBg596dhOis9O+KccMBAL06V/Kz01o/GOMhNaLOWwrzozIY2iwX0dtLCD/i/TepvahgQsd/Y7PrC0Qg74zSJ5n4CVN7lWZ+WJ3dOlbgs+uOMc7vYTFy/PumdNAE6rlL2pgebOJem0OcOfKya70D00ZTb0zKnajYN9rUXnRL5Nv8sOMEBZHXzMurbv/tB7GFnyuuuAIbNvh61pkzZ+Lyyy/HscceiwULFuCyyy5LvINbO+KovaJ4e7muJ3J5BeCLfMI2P4AwNFS9vYquJ7m7hjE/THgRwogQLtgLWMg5POaJoPk9WpOZ+VHKsv66ricJbDt0JYkGLdDiGYUZPBPD4GgRnhXmxyr8UJdoSNfKVWyRZJ84zI/ohSPdWxMS8PaqCjNEN/c7lPmh3iVE4slL7J2Mkrm9JKeABOL8SAbP/t8OFXmrzc9u/boAkAPmJRHk0ASZ+WFziLJosftKbH7k9TT6vGKc3shzK8QweAaALU3F0NxetGueysBS5qekzY94V0YP6o5CPmeUX4rqBUpComeO/abCwvy88LEhy0KPneXmEC29hXB1J7/zjae4hqJFEPJ9BMS8xdGpFx7s9CP8pukceAVLEMx2gNjCz4IFCzBixAgAwN/+9jccd9xxuP7663HPPffgpZdeSryDWzvyMdReYYlEGXa+6kV8/6H3pGNC7cVsfsKZn6hxfoBwtRdlPPT0FoI1keMDedKLza61kCe1KcyPavND7TqE2iuoJ2ir2fUk26iN9eHeVhvrDQbJYQbPXO1F5jZDWAOh9lLa14wUxSexi5UX76g2YX4HwoQfeQaTVIaGeyvXY1kYTG3ZjB21RKFkHKnVvXEDsOZz+do15ooueMRwVko7rQibTO1FBWrH9o4kYPNjMMTda0BXXT1B+w45qKObgMHzaKIKZqigOzSXulgTOLLaizI///s/e+oNhdwzKtdIGwcSQ0glKhgD3aQYPAP+RoezGDTkE/udqm2afUPyBq+g9ZPNHU22KNAOTTocvN8GRkXPINJCtReZ/y49yg/EOGpgN/0ch8Upiqb2ouktxFPwpN8AwRABJpuf4LhS9786nohHi2PJxnUbEH4qKytRV+erEF577TWMHeu7Q/fo0YMzQhkE8sY7bGN+/L+mQGRh4C+bbeICFOaHboXCJ3abB5pq81O1YaG1PbrRKdIgdWQyKeToIibT/HEiPLOJ3HU9yYB5c2O48HPtc7P55+cuOjhox6D2isj8FMkiAQBo9iPhbvaC+B6e7qEB+FfuStWRhT2O+kMtY1zMHako1fxL+YGkerSK7Y3bQturEzGt0zT2/3I6pJgtSpwil6oGyQsXZvDMT5c+kfHnJsv8UJsI9mlwz06oZh6VSv/YQuy/28GClADzYxKcZc+h4D1R1RU51eBZvIY9OlbGsvlxQFgHanNFPChtzE9TUVfHFl2PxASjkpVh7mr2vT7rEdiZkfewpLcXUdOze2aSY8JsfnJwLWuCCon+5f1gfRzQnURdZuMzJ4I08ntk8fbyPA+frtjIW2LG9A4XtOn16E0B/j0Qwo3Se9Vec1tgfg4++GBcdtll+N3vfod3330X48ePBwDMmzcPAwYMKHH29gcaQMym3wcA1NfiD7MOxdtVl1h1xp8srTUeF8xPGekt1nwOzPiLdbISzLHcJ9eDpPZyHVXVpqu9ACU9geMQocXR7o9u8BzG/Pgf8oT5oaq6UgLD09O/5J/ruKBkYg3Y7BcwP44rjhkMsbmA2BQIP+hgqFPeAQuhT2Z+2KdVgRt0KKKovRx5sncch+fCskV41r16TMwPuwAL86NF2RXfXdMkWb+BCMB6/21BDvOOI3J0LZ4q9cMYNVxifkgfE4jzI/kEknUob1HLsXmj6LqkjmjMz8oN9fjNPz/h7vKG7kjYgYSEEHaDyvscsArs9BwRUPI5B5oMEMIoO47D301JJAzayDuuFh6GxwrzPC0jT5FEZqf9MM1dzqK3AABdoKfl4DY/ripg6cwPu3bDles2Q1rU8SjMD2XExFg1qfc0NTM93WLDtZkke15ZW69sju0eXq7yDhnVXlL3269PVeye3X333SgUCnjmmWdw3333YccddwQAvPTSSzj66KMT7+DWDlntpX/ieN63lxrgfGXMvwMAz5AFesENx+KMrw8EENHmx4ZFU4B//gSYeqfxZ5sqzmd+xEu+4LA7AACPVJwSnMh+MaTFkHToAfOTz2n3RwvoFWLzw8py5sfz8MZcEYU1ynzDsC9TDYSlt2ATtfQsRSNioZIF00aWUcai9tKzuos612/2F7NnP1qGb939NsKhChx24YelAWlsdqXJ3DQMdSNoUyFWv8XmRzX+llgWQ329dlHUXgqV75iFH0ntBQDPXcI/CoaLjinyzJJSexkEY2nBVI35A3C1F4nw7Hqlp+uFX23G/te/jkffWYSv/a8ejM80s8g2PzKLIDrEhB9m8CwW4ELe0YViZXzTxdQBiPBDmR+mStaZH5HYVnd1L3pCvDElNqXjOLd4alBO2TBCTqC6aA1xkCCCiKpeM63tTVoUbdonaigeNimR+0LUx0ZvL435Mc1XckfrSeqf8XvtAC4KsCCHVFXoifdene2MWd1hmru3AeZn0KBBeP755/HRRx/hhz/8IT9+22234c47zQvo9gzZ4DlksG8WC/Vbn5lDpz80dSH/7DiOCPmvxfmJkduL4bWrgau7ajFYjO6iYAbP4lhzRQ2G1D+BByvPZGfy5uiLKnvrULWXiflRum4w3mNl2IRTXZHj/SsX3AZCVbdJfXD4H949MvG6apDDoI5mj3hA0V2U0TDWkb6/PkfkWvvoSzMLKLoZjfmprRO2ThV5mttLPPNXZq3ADx96D2s3N8KJYvBcSu2lCT/ke1Dfmnxv4PS/+MfWLZID1oUyP4rB816ni7o/eIR/FDY/hh2250GiFyLnqTJAGcu0bd6uX0I6rZBT3m0odhWG+768dgsOv/mN0O6owUoBhUXgzI+i9qpdKvWBemTlcwbDX3LPlq3fgp2velH85oj31cT8FFC0Cz+eLpQXXbNQEGndldRe4uR/zqDpOijzw3rrl6006LC0CNGKwXPe0e6WoV/iDMpAG+P8sGsgNj+lvL22BMxPdUXOz7moMD96OJXgV4n5Ec9Ps5KS+i9dULtBrMSmDMViEf/85z8xZ44fAn6PPfbACSeckGVzNyByVvf+o3iwqg8Wr8cLHy/H3ZPn48eH7oSB3Tvi5D9O006hVDAAEUvG6F5I1FBhA/Gh44GfzRRn8R23fglU+OE5iviqIgZ9ThIKIC1ebI2RY014oizIi67uJiBeKfayVhWCbNi2FCBx8FUQffb1a32D5VyeqHRonB+9cs3bi9ttKMaleTnZqwfIOc2IoLdH/xos+2JLtL6HsRXk/k/9/Ct+2HEcrlKkRpPnPeqHq//DK3Pxdd2YyFC/fI90g7EwJoiUrQjUMbWL0WP1ewByRuZHdnUnaq+cQ+6vDE1dEertZawiIjytj9I6xJu0MD/U4LmEzc9J906Vvu/Uq5NWZlmtPn6kFDw2tVdDbdCqYH7Ye1jIhTM/B/7+31qbTDXEE7o6jggfYWR+gmo9i82PQSjQZwmg2H8/5Je9jxnuzhiV+0LqZ3VBjJ1hfYhRvmTzQ95NANUV+ppXdD08M/1LnDx6AD+P9imKs5fM/IhxaYwBxpkf8WzELTIzPze94kdVr2+yeJ0q99j1POSJzRPrAys/c+kGnP7LF/DjQ3fGVcfuLrZyMRj31kZs5mf+/PnYfffd8b3vfQ9///vf8fe//x3f/e53sccee+Dzzz9Po49bNeTcXiFScO/dpa8XPvEB5izfgJ/99SNc9tRHxro15idM+CEvkBO2MNYuNrah9pkaPJtNfoXAQickSuPTIIeFnCMmd06xKrs5A4VL2QkAmB2Euw+LtREZS94Rn9+4Hvj374BNQYRcxyT8kOnWU4Sf4J430f0GeQ5EVCQThyPNHr/71u7cBdqYM45Cs7MxMz/U06dHp0rlGuQqVm+s14duKPMTX+0l7RgrROTnIXP/LH5XhB+XBjmUBKHgw9d/rPVQMFwRvL1aIv144h3hbaP0nMCer0sMnk3JfymWKSEwdumjetX9//a+O96K4nz/2T3n3Ar3XvqlVwHpCIrYCxEVu4kNuzHNEjWaaIomMYn+TDWJ0aiJmmJMzFdNYiFR7IqgCCoWUJGiUoV7qffec87O74/dmXmn7J49twP7fD5wz9kzOzs7OzvzzvM2oG6H6dWobNAikyPLHb7jMML8WISfiDnGdRwRSNChi7Ni8GyeAwQ2P1bhh7dLZ07VrvK69AEAbGTVRjtd1xH5AFXhS9A9qj1eBK5+8A389L/v8UaJ4w6kDWQ8uYAyP1TIo83T1MyweHtpV/vPG59ql1Ht83Tyiq8xIvyIeH/UsXnn88uDani5ELu/ToCihZ/LL78cw4cPx+rVq/H666/j9ddfx6pVqzB06FBcfvnlhSvYw6BmdecfbLvl8Ml2TF81LsrC784AIJkfoZ/lsWRIxFwJ/gLJ+lcNOAG47mPgnEeArn0j22+jmuXCQTggfrsh3l66q7u0jSVqL7H74FVK2le9iMQqLYghNegT1y0Wp/7BPLbjs6DB0j5BtIYyXDrzw9VeZKGm7IIQJB2ou0si6NV2LcUPThgLABjcg6aEsCBK7UUWnIpS2R4aaJIbPNN+y3s2wTmC+Ymt9pILsrAtcBzJ/EDbkRrMD/FA0g2eAWBioPqqMnNRqYIrGX+t5e3Fo5VT4UeRfey6Gf7e0XANahEL87OP6nDSLPlfeHtpwk//KcFV5bgWNj/U4Jk/C9J/vS0ZzJcFnkYSDqQdnZneQqbIsc9FOiPDq/TPISfwxZ2/h5pKk78DSlwzwvzMW+6////WhQcLbnuGkwGq8CNtrCIekFVPyuzCVxxvL82G69jxtQCA8w8YolxDuLpr41EaUKt3ZM2fRsrb2LfOgqKFn+eeew633HILunfvLo716NEDN998M5577rmi6rrpppuw7777omvXrujduzdOOukkLF26VCnT0NCASy65BD169ECXLl1w6qmnYt06NT/NqlWrMGvWLFRUVKB379645pprkMtFuza3F1LxOE4F+i6qXxDo7KuHDceKm2ehR5dSpW7BcuSD5J1W5od/YNLOwXGB0q7A8MOB40MMnvlZ+saO0QXbtQx2OanTPlDVXrrNj3qxUHdJi0B5zDj/ZT54r57W+7hv3krr8UiM/7wpAGn503x3T5P5Ea7uYhLy/+Z0tZc4k7BoYoJT6wSTAdIKynKGR5XdXZ8bPk4YEOyE6a6UAY3ERsSPEWMZCMa1NeHHGDxmlN03P67D6k07wEAEXML8qLJ7uM2Po0R45tREsBnIy+S2BqFpMD8FjLBjg49lmzqGflOvITY2xHjWg7Qlswnz/F3iKptmCfxhzE9Fz6CVpvDjt5Wp55H++9yYPkpVDiBilYUbPKuXp2ovq8EzU8vx6wBaz4pNCBfSVMHWHtdMtvFJS240ir9cNM08SNkobTMYDjrXEeaHx+ahK7fG/Chxft7/n//3YzU2HO+v4b181ajYQGiphThkDkbeLG1zF6BXIOiKZ7Q7GTyXlpZi61Zdage2bduGkhIb4xCO5557DpdccgleeeUVPPnkk8hmszjqqKOwfft2UebKK6/Ef/7zHzz44IN47rnn8Omnn+KUU04Rv+fzecyaNQtNTU14+eWXcd999+Hee+/F9ddfX+yttQmoeoIx+0SnH/voJ8figS/tD8AfnHz3l9HeGoOVyYczP/9Y6Bvw7WzKkZeFFBgwVX6mi7Jt9wR/wklB7molHcr5ULnrV1zdPaYsjrzelIX5Majs4P4cMjHrBs9K/IvWwPjPA9+vB2oGq8fFROOpE7jWLuERx70oQpgf1dU96EtqhOufYOZJC0Msg2dgZ5P/Wdo7yEmNAdhBGLSyTMqcxKysiKDzgq/RzM/i1Ztxwm9fwjG3vqCWTZvj2Fd7qf1Nc165hLEQzA833lWuy++zjb299EUA2q49ZHEQai9ynApQtufP2YoS4vGoQ0llYW1viPDDVYxiXMsIz2nXlePXNYMH6nFzKKugBjmUzE+Ut5d+W6rBM2XYLBsFg/mxCz+Kx5b46OD4if0AAAO7y3nmlMm+x/MXpgzAQWTzJaNzq+9wS1zddeFDuQfC7ojT3/mX/3fnZqV6wxtLm3t1g2f+zPRNrq726l5RQmqBCJ3RGVG08HPcccfhS1/6EubPnw8WGJ+98sor+MpXvoITTjihqLrmzJmD888/H2PHjsXEiRNx7733YtWqVVi40DewrK+vxx/+8Af84he/wBFHHIEpU6bgnnvuwcsvv4xXXvHtMf73v//hnXfewV/+8hdMmjQJxxxzDG688UbcdtttaGpqirp8u2DjNtmGyDg/2s6cenvwFzGteRbwr4bNj+6pAeBPAfNRv7MJYjdKjeBIPhYekwYImUAQ2PwIY0XXoEPlpO7fl5K3zKL2UoQfbuCp67c9i01TcD4XEEvTpgFiqyCtUffCOFO15uDwiOGw307O/FDhh9j8yI4TE4dvA65OciJVSEHhJ0JIIf3P+40HkVM91hi2N+oMahy1l2mDIE83BYmHXvdDOGxrzImSDI4ixCsZ7aOYHyOrO+SCbBHqFZuTtrD5EYuDRBxvL+nqrtpDif6x9CMXMsxknxIFvSDDvL248GPJ7aXE+bH0NY+YzNVftA3KfBHYG/6j5EYjzo+q9tIW5lCbH/+vKvto6meNheR915Sjx6UAy+2oDt5LJjT9ySnjcc8F++LGk/xI15z96coTuGpG2PG0ASHMj83mx1Pfh9C8fARcqyeERZHVPRB+dLUX38NBFTL1uY/PJzJcR9SGv2NRtPDz61//GsOHD8f06dNRVlaGsrIyHHjggRgxYgRuvfXWFjWmvr4eAIRKbeHChchms5gxY4YoM3r0aAwaNAjz5vneT/PmzcP48ePRp4+kVmfOnIktW7bg7bfftl6nsbERW7ZsUf61FaYO6SY+Rz5+dXui7EC4Z0Ra27UZucAimB9KV9vUNIrwk5PB0WzJVllAM/NdM3NktBu6iNP7UuyTLK7uNMS6bLPWyrzpicJfxpxwdW8j4SelCT/E4FkeM5kf3dsry0j7yAImdlCOGUKfqo/kDjiirTs3A43amA4RfqjwqZ0AxmQMIIDb/OhClU34IQKFXka394GmBqJlqfAj6Hi1/QA0mx+i9uL9x9kgYltEnpTZiijWbOP7BTpfg1AXmIxE8EUpx5GybDo8R9ZjC0DJFx4p/JhlCtoBcdWgzrpp7aHqGyXOj2sKFVyFVF7i/6YKP2b/lzpZOFofR6q9PLtQ4FjmLrEJ4e+hFo/IauNIWBglBleAskwKh4/qLeae8hI1fpYSFTuu2kthfsTBkCCHFpsf/ltgq4VBMtO6Xz2fl/ll5PqgXD4Av29Pe1w0Mr1yni6M7g5qr5qaGvzrX//C0qVL8eCDD+Kf//wnli5diocffhjV1dXNbojnebjiiitw4IEHYtw4X4Jeu3YtSkpKUFNTo5Tt06cP1q5dK8pQwYf/zn+z4aabbkJ1dbX4N3DgwGa3uxCsOVgKScGMoTSIV9OYy4vJQvfw4d+E8V2UzY9mO+IfIo+fbrXyUvhR0goEoNS3XqXcSagSvxosUV6f15BWUhGoemdhuxEsXg65P13tVd5Wwo+xGEjmx7aA5kO8vTz6ygULhH+fsg5TUyL7xSaMGnjldvNYiPATRn878BcVyvzkmc2t39YOfSEk17YKP6pgDfhCIBV+XM8f23qcKP9qROChai+d+clKo3hhs0A7W+h4QzJ/L/sf8NupwO/2N+4hHMGiEfq7fWecsjA/HpPsqGdjfkS4B1dpNocidIQtwDk+h+jCvt7XOvPDnzkXNGX7uDqOq1aV/FAGZeyjJL9N+a4GOTTvyzPeGTJ3UdmHb5ZEUL+wYKMUsnJjnrOgqsyfn7bs5MI25UdY5Lm2a9KNJLMIX1abH34DPUf6f0cdo9Suv/dSOA/U86FqL/X6epBDfpY5r3Q+NDv29F577YXjjz8exx13HEaMGNHihlxyySVYsmQJHnjggRbXVQjXXXcd6uvrxb/Vq1e32bWoGiZS7aWSs+K8xqwn3ELTGhf866f9ODTCfdWmFtLgQEZmDh2YxDjOZvMjjJThTxyem7HsRDXmh0xedHWXNj9ElNL1y5rND70/fk4+mGx5kMNWh74YkGiqseL8EOHHc9Rdp8fUyUwPpEbtp8K87xTsrDOPhQg/Mrlh8BsZE79//kO8SYIpKs9O1BWD+aH9QxebUX5qHDUibTA2AVX4IcbKOvOT2UreXyXaM+8/IhAH7KEU9Whb+WDXBDR+vfuD6OUbVaeMSIidcDAe9FcuzNvLNY9TEdkm/Aq1V4jNTyNR5WTCEkzxjY+u5hUqxqB9is2PRe1FnnNOC0BKbYAU5udLz4rjaU+NR0THvS3OD+8n1eYnaIplfHkhYRismwvCwphOGCaqAo+xzTuyfqocTe0VL7EpZX6kSGYVKiwRnsU9CzWmuik0GSR1M2AGOdTnZATluN2nVo6faHgWdB40a6X4wx/+gHHjxgm117hx43D33Xc3uxGXXnopHn30UTzzzDNKfrDa2lo0NTWhrq5OKb9u3TrU1taKMrr3F//Oy+goLS1FVVWV8q+tQA0MdYPeUDBP7N4acnnsCHbfukrnxEn91PMi1F58MVaZipDHryQvtNDvXGChai9dUNEGvWKoaw1yaPaPQTEHL7LK/AQ7ObLrbZPNhsH80Dg//Ji8sBRgVOGHAXD5wrDDDzDohw0wJ1hDIGGeVRg1QDugOmA1bZM5VXtpneaA4S+vrMIN/5aq47xnU7fYhB/N5idM7RWwNEooBPHXUYRcK/MTjNPGChmmwSWBVsWrV0ri3WxXo6erBrchwk9r2PwYNhKyBbZr2AzbGWF+bG0SBs8haq9GEtCuyRLpGYBkfkKEHyrUWzObW2x+eMTj0ozJ/CgMR7/J2Alf/Z7SnoEa50dtWp7Z1UG8fW9/ukUKfnxx5zY/m1cAa94wzlG7jjA/WntsoCEjnn5vvSb8MJkaI7QG9ZpSfWwTWmBhfkj7+XNwdOGHbzp5w9TNmi5g6tONTB3ChR2133bL9BbXX389vv71r+P444/Hgw8+iAcffBDHH388rrzyyqI9rBhjuPTSS/Hwww/j6aefxtChQ5Xfp0yZgkwmg7lz54pjS5cuxapVqzB9+nQAwPTp0/HWW29h/fr1osyTTz6JqqoqjBkzptjba3U4joOXrz0CQKEplA5mJgSdbJ5h7nv+venxMs7cbxAAYFjgrhil9uKRhV14KtVPUe3Xh7Iaccg2CXOBhTM/is2Pfj/ajkx3dRe5vWhWd22X4WqLkmNlfoKXOeUWDgBoQU2FX+cfz59qL2CoAbiru535oTZW/oFAoKOv3B1+9niPqJMYmWBt9lOxmJ8VL/l/D/kmrFGWY6m9LGyWTe0VxWLyOnNkF//J6/KzYAlo2/gHRxmfnPmx2fzkMzKSsUsmeXFPpV1J/XmlaaqnnqOUked4xdn5KOfysWwRFGhD4tj8MCbayCwDgG8AwgyeGwIBINLjS8whdjUvH9dU5e04pB8tNj+6PV7eY6gokQs1yKdskCDZZbrwEzSvgM0P7d7N2/17qduRxajvzgnaZYm39ftDgJUvK+1R2SI5uxX0soS6Se1Xo3qfOjA3GlZQ5oeovSODHNI8kmIXYU92bUTE1oMc2tg1cp6wZ9K6Q2eISCP1Ax2OotNb3H777bjrrrtw5plnimMnnHACJkyYgMsuuww//OEPY9d1ySWX4P7778e//vUvdO3aVdjoVFdXo7y8HNXV1bjoootw1VVXoXv37qiqqsJll12G6dOnY//9fb37UUcdhTFjxuCcc87BLbfcgrVr1+K73/0uLrnkEpSWmsG1OgKVJVo3x1J7mXJplzK1HjPOTzjzI4Ufyfwo3l4AUFYF1EM1DBXUsYSu9mJu2qSDdeaH5ipSJhN5L2SPG1xHq8oSg4RXlROqQQdp10U2X1w+Jl5Pn6oyewE90SPZKdmYHxnnR3V1V1MU+PCFiqAYXBnLQ5+YiNor1OYnnwPWBelJGuqBzSuDUy1B+xxXTHI2tZetnbF2cDabslyTz569/bA8ZlGL5UMyqHPmh2ntB1SBkgoXSlbzdLkvhHG1WlDMpa7dUTY/nhkZOR74uwblugIhgSAlAyGFrjwjzJhV7aW6uutleCqDsnQK2bxpe+VXEgiqGS1khHB1Nxks1yE2P5pKF6A2P4Hay2OorSrD8o3bNeETyAVLUoqp/c3nOsZMoY7a/NDn318TPLY35pDmTDG0DeKivwCDD9BsEzkI82NjXiwY3KMCKz/bQZ6BA190NAM42kGZHyKS8esrdKnO7hABUTA/6lwvHMRCmHo9wjOv74klmh2tFsSTX1b2UxuZIbQCim5ZNpvF1Knm7njKlClFBxa8/fbbUV9fj8MOOwx9+/YV//7+97+LMr/85S9x3HHH4dRTT8UhhxyC2tpaPPTQQ+L3VCqFRx99FKlUCtOnT8fZZ5+Nc889tyghrK1RKuxQ4qq9mNVwV2c0RHoLxvxBLiZyk/mROzZPYV7UCs2Jy7bY5nXhJ4a3l9XV3U3Z4/xoxnUyIKp8kfWmC+bHdZrF/PDdsG5XJRCS5TosvQXPwC2ORESn9pPEchUiFfq0iYl5MrxBmBDytnw3MPIoiLG2cZk8bhE+9cl8fL8qnLmf6gigRPU26lIO+n9yJN3C5o/8v4v+TMqZC6nQToQsEIoAZsntVV4qBeOPN5Oo36QP/Wv630sRMB2ZctkSi1G29VgcaGoBI6dYiB2gkdIFfjJbuciY/Z4t4OrOVT+lUXZx2eCZhQk/FmZQ8RS02Pxwm0Wq9uLjV393OCPjasKmEEo8e24vmyHw4aN748cnjxPf73juQ/H838Ng7b53BNcJmq/sRc33pZD8wvtE2DcRtWfR3l5kjCh5/0RZ0+YHgO/1+d6jym8cvP8lC8U3a8HGMyQ3YljSbX00imdk2z13EhQt/Jxzzjm4/XbTm+TOO+/E7NmzLWeEg8cJ0v+df/75okxZWRluu+02bNq0Cdu3b8dDDz1k2PIMHjwYjz/+OHbs2IENGzbgZz/7GdLpZuVsbROInVgBLa8EsyZE9SPsSkjmB5L1AQqqvWBZrP0fg/PypkuwkuSa2+kQmx+xURFGuhrzY1N7GcyPeo5spbpwUf01p6eXrfMDb6ZdxwgJEAd84QiNweHozI820QDKjETuULTUP+5g6YRvKlV5nuxnB64wIhTqCUJJ00XACh7RFQBKiK1LuYzIblM7SplPPoMfnTReqZom2TTqKnRMH2tOSmHPOLyQgIJb+h3Mm2UI74yoMFLbpfpb6SLd7d4BUsijlAWLfbo8gvnxmi/8CBsK3g7t5xCDUBuzwAjPaFO/FHJ158xPZCwsLrASVaLfTi78BO0LVXtZvL1yKvOjqKloJYDI05Zy9P7w/4apvfgROm+mXAezp0kh5zdPf6B6E579f+S+G5X2qLZthPmBKWTZIOK0aQ9eTW8RBTvzY7f5UVk3hx/6f0NkmU8XqbXrm1KNgbT1MWAZOxrzo7vEh+WG7AxokcHzF7/4RXzxi1/E+PHjcdddd8F1XVx11VXiXwIfhiBj3Swz++cA3StLDLpUeFDptHyE2iul2Pxoj58LTYraiy8wEnyAH5x6EwBQuW2FnBR09YlYXIPJQEtsKm1+zPQWhh5fULhk/xycxF2y63ZkjWCQcRAWTkAglPnxYLePCZqqGTx7cPDJoBPAf4WYzPmkLFV4cpKUwl9Bmx8qBDspmc+KGvoqBue8j7mgxcuYgiAVuLyoaOV8fPUaRdrVpI7ro28GXRBEvXkpNgIADr1W+WrP6k6ed8+9zPbQCoJzU7kGzCu9DH28QFiKYn4YkzGmioXB/IS1Sz0aJvzIas1+52M4kwphfrIm89OjUpsreIDTjKb+1QJK0memqL1sNj/CE1Pa/Ai1i7YRIxyLcnmapFQ3v/LCGBELcoE63IEDjJgBnBI46ix93LiOABFgrGonC4yk05bgkJEJppUGyLnUSPYMWJkfwzGh32Tlq84uy3QVgau7Nr74XKyHbiEisNJsfn19Tu9MKJoeWbJkCfbZZx8AEFnce/bsiZ49e2LJkiWiXGf27+8oMMsne0HzpbAtyny3nvdYQeZHqmGImsZQewXDIW/a/NCdAH8xLk8/IusXk5k4M7gX9WVVFy8S5JDG+dF2y64mSPlqLyeYDHwM6VmJzavq0L2ypFlqLz5Bh+7KQsL9h0V4FoudpgRncOAJtSQDy2fxz4Ufox+Z9jlVLtyRqft2cInGXIhN0zuPkDa7wBY/ejJe+R0w9YLgsiTIoahXE7QsoDGaPDj+7j9qUnNcX/ja8rHvQk1D7E86E/j0deOKOX1lC/rdCQRfprUf8G1hBMLCPGiCfvVni9DbqZMHMpT5sbi6t5D54XdpGjyTa1iaqwvWUYsJPyTtY9Qyi1bXAQCWb5Dpg4x5Wgg/euJcdXEzhZ+gDW7a/6zY/Phluau7yvyoc5FUq6mQEZ5N5icXYvNjQz7PmYqggxvq1LsUU00I82NRr9nA2eecpidz6DUiQpKEMT9Wm6OwOD8HXQW8+AugdgIw5XyldmpuENQY1BW834aru/+XG3CLZNtiXlPrlc+j89r8FC38PPPMM23Rjj0CkXF+VCWu8bMtLocS4ZnGQdFZCshJJQWP7Kq0V1gwP3Kit+2EbJS7OBJiQKe0VfE28j+mXUcaA4cxP2SHIw6JydhHeUmqWWovwfyEnRup9jKFyTzTDFn5PfnEtyj38gcb8OPH38XveNczR6QDEBMTuf9U0L5snqExl49WYdA2l5JwDpb+t/EMOmiEZ2m4HcH8wJHRlfM5YAlRM5R0EVelCyl/DuJd4bvZwPvHJ8k05oe2Pl2Grx42HLc/+yF6diEOD5rNj/E8KfPTtF39DUwVfiw2daHQmR+jo+39KNMHqFsmYfNj8T7jm5IMERQobn7iPfF5/2Hd8cryTThjXy3Aa6jBsy6cUOHHovZipsEzH6tWtZdgfgIh0egPiHvSp5+wCM8cb9xwFCb+wFcHe4Q9BgAMO8x2mzFsfqLnmFSwM5VChOw3g9G1IczmR8yJFuGHenvR40MPMaqXThXq/cg0Mnpz/ANcJd+vhjODfKOqMney+RHzRAej8xjG7OY4YWI/dP0gA4Q5IRVQe9kYiRRVJdkC2xEoNj9iu68JVEJfLyd6KWTINvF599H8NByXmo8lE79r2RHpzA+fkEE2w9GJTUWzxIxkGjwz7WVNOU640XIETFWTBkPtRZkfdYIDqM0PF36kbtwju6EP19cr5TwAD73uJ6FdvyXwViL2KnTNe3fNVpWG1seNmwIOvhp44Wca7S0ZtPCYQuYYVNRzcAHkQ5gfumslapDybrII2dHSHs9LN5TgHgLXZxEQUhWeASCfrsAvs6eib9c0zujSG1fM6IEB3cpx6EiZf8kQfnQmjzI/uvCjMz9e1r/vIrx2QoXMkP7W5V6/JvmO2NJbCC9MrvaK0Krcfd6+WLhyMw4Y3kP9IabBs0tschyF+UkZzI+0F+FMHSPeRHbmxxR++Fxnt0dRkgFrqC7PoH9NOT6p2ylsyoSRPL/PwFQgrrdXQYPn4HeT+SEem5QV8TxzPubnKTY/FiFPi+XjwGTYdRhMmTYODVf34LsxT2rlhE2VNu93RrVX5+WkdjPcesYkfO1wHgm7+IFgYyQUvbKxW1XhUZsfHuFZnygsBs/iRaV1aZS1Is/oiyhnflxyLqH4BVXvhCc2NZgfx4Ueul6qzyLsdkJAM0WHCk6awaAIckgNM6nBs1jsVOHHg6u4ZudzasRhOkc8/R4P3ikXbqoWMu7SWLSZDPBHktXacnvFUXvRMAXRzA8RTmiKC36xoYeGXiuv2/xwtVcU88MYbs2fij9XnAPAZxhmTxuMAd2I6kYzeDaunI5gfpjG/ABK/juOue+uw5f+9JqIL0OvJ4IcGgtRMcwPyRRnE065I0II88Px5UOGoUtpGoeO7GUyyjwFiK72En3NW60xPw6/TzOru7BFEgbPCN0shTE/VJUXafAcMnz5/CnjI3G9ourhKp+GfTNqtbmxXS+M+aGyDB2FRmypMOYnaL2V+ZFzku09oVDNDWgZprU7KB9UxVXycp7ka4A6H4uzO7H5S8L8tBMcx4kf7dLy+7YG0+aAxr4QXho97AafNL6MSBJpGDxbmB9CN4u6tImLwSWTojhTuRclvQVhHlTmh4O/gLwN2q7dSQF8stXa5DpO0QbPOfKihwYgW7dE/S7UMbbIPdIgWBo1yvZ65Bo1QewmSjcP7F6O1Zt24iuHDvcLkYmJRo817Btu6q9+d5xgUYeS18oW5NC8iXBmAYCholTQyPMyObLtXh6Y91v/c0mlck1V7RVi80PzoBlqr+Br5DyrjqF8SVf153SprCBbgPkBfBsmzSj4ovteAwB47E3cfR4PB8J3wkorSLPs/Rh+L/oiTprJmR9XXYh0nKarujiyO4G6lf7nkCCHNpscq8GzkpPM/417veY9z3R1F4yLQ7/Ky5N5SL91agcUpo6STIVmnEjVdIyFPA4L81PA6ofLBnxuYQE75sCTc4zO3ig2QKRfyPsfJ7eXOF801pwPw9La8I2xLmDy7yKeGhcmNZsfXTiXe8mE+dmzETk7M+PzU1dJXe36reZOk79EeUaYn5JKoxwAhW1IQaqPFNhc3S2TAV+fXNCBr9GcoTY/UBYvamtjxPnRd1kkYJdkSvwynDBwneLj/CiJFuO+ESLCM1285XVFkEML88OYvEhpWl0EPDgYGDAWPAs2Vdn0IHYsinFwjrANHIxJWp/G3KGu7sGhMPr7jrP3Eaf5l5P3ERRUr+l5wMoX/c9bPpUT8p9OANYs9j9XcLd7k/XIaztlKWSGGzyLdTfq/dKDCepj3yE7bAMFmJ+3/gmslcLxU++SdDsG82M0TG1XABvzQ0sxS6pUGjbC/25nT7qUhux5P/uAVKbdryH8yOvTfpfMj6n2ylBXd08d94WYH5vaK0OMigvF30kbwk/w/Kk6m3hTFsrtFZf50cezA9hZVp35oSDvv5h7lSCHKvPjHzI3CRQG46u9S2FBDvO6PaJm02Zkf4/aJHUwEuGnQ2AZCMrL5g+wEb27muUIFG8vvls1vDR8KMIP04z+OLg7NMl1oxgq87p0tRdRnxiUrqa6yisvpfT2sqW3kKojc+LSm84n01Qz4vzQewu1+eEu1xwOZX7MmTdM+GFw/LhIATLaJtBjNFq1FhwzaOeQHv4zpowVlj5mtjldJoUfK/NDvO1sVDyAo8f1xbeOHu3fE6NWWZZFAgDqSZLR+XfYpcnxXxDX168oEiVqO3MnWIzVIIfq2IwmftQxpLhSD9hPLaPDxvxwYfK9x4H/uwi448CQC3Pmhws/IZSGYfNj9g21+Qm3yZILvZoXjJHdfoz3Y8C+WjvDmR+HbgCszI//tyQlN0CGDZRQr9uFvhQRSoT3GM8ST2x+wphbboCsRlyW98XbbBdFZRulLBPdh4o9JimvZHXXrq1Ab2dwzJbGw8bwMDBQG0kdenoL3dXdZH78vzSSvn9pVfgRai+DiUuEnz0cUVKwbacRDWWHx206SsKEH/m2uMLqWnv8Hz3n/33zAdliyzsuXhwysctXVX+xpVDil1XpWOrtJcGUPzLarcWrgaltcpyYuXMIaAbj0IXhME34Ifp1eYb8NP+jTcERMRv4f+AELEDw7IStgYwDJAU5Xq3al64+sQJSvQUAex3le3j0GEGEH2rzI/s/nManfYKgrfLZeVAnP4H7T5efD/4GDK+qy14nHjbmQle/o0n5zVR7wVR7xVmQomx+Bk9Xr8kxhAdWZMAjl6i/cebnnxeEX5NcL7yJYcwP/5UIMET4sQW5FMxPSusX8hsQYRPH2UOa4V40UwougAxw6reVMD8Wb6/6nT6TXCLSW3jGBkq8DyHMD1V7ZbVEqTmPCHYh9yYXa83VXWF+8jKuGb08ZX4QQ9BGAeFHvNekFiJcb97ehI3bGmQZxeA5aHaEzY9ocphnI2Buenj7BMOjCz/+95wIBqvb/Kjl5P6k84oYnbdluyOipGD1bROfZk8bFFqdUHt5jBgqllvLUuGHq70i3TW1nYGN+REDn+jCob1M/AWUtDXZRTspkpDUEbvjYgyeOWi49tdWbg6/Lwuo9ihU+HEcn0mh3+FPFjbm550122RZYqfisYA65zvpvGnwnNf6Xu9L1X4Kym/oPxWY/SBw3n98IdEq/FCDZ75oqPdFxyOfyH3VazA5BlOH4XK993Hy87Qvm15yPYbLzxbVjmGupRs8a+0Xx1CI+eHCT8D8UEN1EUhRq4GzqIzJfGkcnPmh6kQr1J2xKfvYN0S+jaA8n39Sv2lXimB+FAE/TPj5z+VBRZ7ZF1qQQ9rmlCttfpglt5e4rhi3Jnsc5UpPz80zmcNMjRukltNheCeJd4uMTy9vtXEEaWPceEIivUVstZd8jybf+CTeCGIyAXKjBBYW5NDM3O55nlXt1ZDNY8i1j+HDINYTHwtcSAllfoL70JkfY+JX963x7Vw7AInw064oLGzon3988nisuHkWVtw8y6yN7MbYzvrQa/zu2Q/U5I8RumABPhFamicmADKxGzsifact9F5N4mX1iDCRcV1jYjdfILmT0d8p/jde6HgVis1P1Ok0szufLJQw/PLkIT2J7RWTE5EQQgOhIC8iznKhwlFUePRadLHx200uHZK9WRo825kfQzdvYSL4hPfiBxvFwiOZH034qejp/x17im/ASRcX/puAKRBIWUrtJxHkkAiS1NsLKKSJ0MZQcM5Gp1soWyrd4Rkw8Uz1t7hBD8ViwBcZvZHhGyJ9gS0U4VkPXOcx8zf6u4L176lG/SHCj/K0CXsg7sJVvb1yxHikexBNOudJ2xU5XwTjiX/V1V5EoONqr1KSxiMqzg9gs/lRx5f/W15sqpSrKzY/6ulhMNJbEEFf9j99QKqwqAiFlFmLzfww814BLNQ2hrIedS0IU3tx1o2zeHIbrArcpio6EX4SACFSsJ35iYIyiS3+q//33X8b5W6Zs1QTfkJsfmgsmA1L/bKOOZHyXaTY7THbpKAzP/7XoYtulvUQVY3N4Nl4gWwGz1xo0NM0WKC4IBPkCy0MHNQLiLu6gxkTNQCM7d9dfmFyz874BMoXE08VfhTmx5LeApD3qMTi8MzdH4CCzE8Y/U1x9wvLxWdXM3jeslPLdq4LYXRxMdynzV1+U54ny+WLUxDnJ6hXtRlrBvPDhXSbTYV+78JN3/I+RgXRAdCUM738AMviHLEzpkIFR1RuLxrrSi9D7cOsaq/P3jfro8uDqFOhHIKfKPNDvPsANOZkP1WU+M9SjdWjPgceid4gAEU3MSNdRo4YUBdkfnSWNoT5UbtXnhPX4FmoprXQDQ6kR5mS3oKFCD8a8xMZ5FB590loCTKKZt8939penaEJU3vtbPLbKUIkaONQPFW5c7WW6wxIhJ/2RFy1V1ybH7oj6DbE/2CJ5glINQVAvb20N/gL98nPY05UiihBDnWbH8dm86PeK5+U0k1bZZsc6dqZtgQ5NChmxeCZMw9qm1Kug1Mmay7fAZ58Z531OFWvRaoClSi/3OCZPCtq8Kw8TzkRCZYsmKheXe4bmfMzPeYITwvpEqvbTwVlFbVXCPMjvL3ixvnhZWTdG7dJoVG0MxhPTXqaDV0IoxNyulQtK9gQIvxkVTUgd//lBs+5vI35Cb5G2vyo9yXUG6rprnoOZTEMg9TgO0keS8MQ7GjKKdeTdxif+aFChV8i2uCZkXdAL1LQro2ymoGbuxI5WzN41i9gqL2CZ0SFn8rSiKzuBvOjCpdC4PcszA9Ve4XZ/KTUOcRu8+MpQpZ5n068sQYpYApXdyL8WJN9akyiwohZbX5IYQvzo0STjWhrQ1ZdC6TaSy3H55p7X14BAHjwtcCxQRvfBvNTpP1leyIRftoVEQNB2QVE7yo5qCMNq+ztfxh+hLVsLIPnboPl57Jqv4TFAFBMAGRXa6geDJsf/2tDedDOaV9VdqbpFFF7abosaVsnX3J9+qDh2ntXSXUaffe660kcxblQ7jUWBPNDE5sS4cfTdshE7cUYxET1/LL1wZmyL021F68mOE5tvfSb0FWZMW1+DMaOTMy/PYswglww5Yapjj5LBpM4FxzoIDWEH/WKAPBp3c7gKjrz49fblLfZMsTYjes2P5Ded7JMCPMDZuzM0bg1eB7ynK5l0oV8J19URH/ZL1GY+aHCD8x3hEBX/Sp2ejSWla2j6OJ75Tt+fbSxunE5oMxT4knQiN6Qi2sm5Qi2IJf3yBwiLuA3Q4wrtXmUgc7mTeZH2smZtwZEeXs5cmzks3JTRU+2eEcWmiqMZyCEC7KpofM8VesigvmxMVwWMwZG5hzaWJ31EwwlSbwKmAb1HgO2NpgJrwf38OcXbnwupu6g3G6X1T1BC9HKaq+hzhqk3vybf7CiR0hpOeiFq7ttphh5dNAEvkjwFsk28d2cjE0jh5EUfkJsfjwpwOQ1Kj6M+ZGu7sR1U9uhUZaI66MB4OixteJzWMTbvCZUxAKx+bEZPOeYNjkx2Vf+mun/zr1mpPAjc3uZMTi4MGgubqExPbiqKbuTzEySig8Nc09w3IR+ePnaI9ClNK20EwDK0tr1xNgKjkcxPxEToziixfnJWoQfY5xYoQoZ+n0oZcS1I5ifv5wC/KAGoEwmGc9SqxNch7u6h7UrxOZHFaHlQuhZNkhRru45hfkxTpXPbeA0oEuv4HpU7RUYF2stknfBmR8eKNWvb1ujL1R1KU2L94u2JdTgWRM2pZaFCY8jLvworu4h0o+IfkHs3QS47WG+UTKbqkGd3toCYw2We5XMj13t5Snl7TY/zJ6AOdTbi3+TZT83po/STp4g2eGGz1ztZdj8MPEsAWDedf4mu2+w0Txn+tCghfz62pyYqL32cLSy2osvWL/L3CoP9hodWj4X6NPFgmszeDa8YuSOi4PrfelUIO1zhPQT1KPuyBhZuBT324ggh44xcTnGkkE9pErJglyeSWHq4G5+GYt7MEBYo2LeBpvai0wySgweYvAs5qRAKBjVWw1KyeBID7gQmx+bTUe4wTNJQMjds5Ws7iE7WW0M8mzOkiDSd9IBdLUXbU8qRO1lEfw/2x7sMjWbH48Fniyk0TbzHQO6zU8xzA+zMD8W0GceZtNSHPPjaH1DcntF2fxEGDynXBJpXjlZY+yg940qaPoF5OItbkNjfviCWVmaFu3KEiPosCCHZjfJe+ICAM0VZkSD18CvbduoCE/Z7RtDmB95TiHDag6uZtMZFNdhpgMHIN4bc45SmR8RMJLGMhM3T7y9qPRD7jWrRS8cP6CGtyz4q24mOfKMoSHrn9ulNE3SovgFhQG0EPaDWpuRZ7G90HlbtlsiQgpWdnLFCT8veuPkwYH7hZYXlDLjruZRwo+6KNImbw/sGRRvL/09M1QSweRDjJZVKp62hVOvvE7OGsmFVbf5oTs/Kvy4riNjboQIlXrAr1BQg/BAkHDUJUL+rDA/DFLt5Squ7lwFSdVeMpZGcH5IXyrzWCGDZ0BO8gqNH3zUhdaQMSjbySc7jYHg7eCLKG1PSsuGTlQB1eUZDOlRIa4unoVgX+SuM5dXg7fFir2iLThcWxef+SmsiqaeTQbzI9Q5xrLOC4Y2mUKUsgo/AfOjpR4AwgV/ebIZEE9Nf8OFD9PV3f81uE8R5NBDQzaPcwID24837yTCDz2Pf3Dw9HvrSKRmXWjgY54EOSRqrzlvrwUANGbtQqrpMWrp3IX3RBs8w7HJE1boru68L6nMogi23KbNyvzI918mFqXPwWLzw5h1V7B0nc9UfnfW3pj7jUPRP9jUGDY/2nhhjAk7NhF5nl8HgK4WNVn2hPnZsxH5xhTP/PAXrImnaJv2VbNWUhdfsCTzE9FGzVaHDmb+EjgW4Se8Hj4hE7UXtflxbcxPUJW4AWrwrF6O5qrh+mcgyPKecpQyOgTTUmhGO59EUW7c4l8PIRGeFVnWE3cjlGQhru4Mjm/XAjLB6X3JDZ7jMD80e7muuonp7SUuwYhiUjxPrU/1dtD69EzqhPmp35nFs9ccLlWpTD3HIW3P51Xh3RC8bTCEenWMWSugru425mfmTcpXyvzIbgkZy9AOWIamrvbyi1lYP+2afNzQ4b6m3relChWCxHsZzfyozl6UwfHhEebnxkffwfYm2W82lZQUchxceO9r4ppprSjfCDQRA2pq8Mzx8/8ts90dYWrUxVrBoOnyPqxGjsWrvfT0Fil6nsXbK5/XhB84yvsv0gHZ1F4OVXvJOQekLas3+eOgpqIEw3tJY32HTyrBKYa3lyftt8oz2hwDysxp75XO2nciJMJPh8DG/LDo3y3g4z/NDZhT+uIC1O2QRmrc4yvU4BmArmLR416s39KAK//+RnD9YKEik0KYnYpUXRG1FxFYXIcaZXPqVLI5+rkSPhW8ObhPx7ExP9zQMoz5gdrGMJRU+vFeeo4S0X9DmR/lm2R+GN89BhNVluvcRUnHIoypQqE1d5Nl5y5uSjyLoAztx7B7t0xW25vyhp2XEeSQBLAEACybQ37T3OLFbpPJJK68KP+gBTkEAC+vjoNYC5KuzmXqffAaFETZ/ABAeY3ylY4v8WzEdYJ7LYL5oQbPQt0VwR5H2fys2rTDKK+ezNVedGGzCD8WV3eAen76fca8PP46f5VyCavwE2LzU6KlqOHzShPZVXDmh2407r1AS8uhnS/tbEj9Qw8NGlgawv7KERbf4DlQyekRnimjZVF7Tfzh/9TWOQ7o+y9ZYYvwQ21+QJif4Nl96U+vid9H9pGCT3Ayv0Mg12QI13nGsCMQZCso86ONCbIUDgAAmBNJREFUWwZ/3haCuJ5TrxMhEX46AoXUXjGlZB4FNsOFGTdjlHl/vYw0zCcW4equswSAIbSIDUHw/SePvyuLCiFFxr8wvIY05kdJaqrZIYTG+RHrg2w33VAs3yDvkTGmCD8plwQcC9n1bgiSxsYKkHjyHcAl84U6yTdgjMH8UG8vMNHPuawvEFAVItfLy6jL6sLtGrtKWCdAAT3qrjXOjyZoxVZ7aeW8iLH10fNGbfz/fYd0U+oX7eC2VfmcEPbzmqDXnCCH1rsMY36I2lKBFk19J1G5GFHQw15p7X1TLu84soWGLUq48COEYzI+egYJcXt3tXvcGepKQFF7bWvK47Q75uE/b66V51C1F+/eYKwxIiz2rynHezcebWVWw2x+yjTqh4/PRhJaoVREePbEO89t03QYQo3CSEq2Sqq9Qpgf8bEA88MdyDRXd+ofEC/OD20rK8D8EMcTGg8ruPbc99aL3ycIWx/1Er1za4CbB2JIbrnyOyM2P5RZ1x1aFG0bYLcr7STovC3bHdHKai8gUOsI5scUfmoq5DG+y+XeXnaDZ3VZ0G1raiqku7ji6i6O8Q+anYrurk4yunN6X/f2MhIx2uL8QI0lkmdMEWJcxzH07xTrtzbgzLteAQDBHhUEYVPUeCTyujmFyJMTkXR1D+LkcLWXIydJY3fnqkakRt4gIIQVg/V8W5wfMZkX8M7gd9jk8WKaUBAlhBmVSeaHZxovDVYN4bFHBD9+u+u37FR/C1BMVndbeAKT+SExa2xqL5ruRIN4NFwNIHb+ersimB+XclnqiVFBDmUeK/kbH/u9q8KEH42xA30fgaeXbsSCFZvw4OufyHMUtRe/T22sAXj88oNRlklZY/CoDEc4s8XHw/ZGIvykpc0PnwPKLCoZUr0QOBRPNi7weTmi9qJnm8xPoX2SYJt1tZdCnFmYW6U0b7jsk2xMb69sPi/bXZDShvou5Rpw4c57lZ89Jm3aMpY8jFQYpOYMcu4u3IT2RiL8dAhszA+L/j0EruMgzZUsFuZHoeL57gMhKhL/oNIeORn43wcHGcWPHV+L7oFgxUDVJ/oiGs78cI1JynXgEtWZUBEx+bt+LmV+thMXTMaA11ZsFt9dxyHMj7l7p2WLAhd+mKfu0gKozI/cDgmbn+D8xkY/gCAVJA21l8bc2GIvhRo8A8rk7p9oetwZjJ0FviDNWTm/3MebNXWKZRENh2R+ugQxcq471vdWHMbtESy0+V3Pf6j8FksVoRs8k0SyRhkOm70UfceMuEWElzGYnxDhR2OklMs7Zjmh/rIYpIrzbK7uec4YhEz5FpsxyvzUNQTedjAXPoq85uoOABVBcENbZGk+7ut25hTGVq+bCzVbGwJnC0cuwjuJXRHP96VDj/CsMDfk/ZKbqhDmR7Q7GgbbLKZF5aUlHzXhh9hCUQF50ao6v34qRVlyezXmKAXjl+V5Ii86aKjR3oy3U/m+AyqD1pjLK0y9vLaN9SaMoC2VRydBIvy0K6J21c1jflyXqL0sNj9KoLPg+sLDyDYRajtRfaHlO6zyDIn54hSR28sSMMx1nIAIUfvH2GV5lPnhrVSZnqE9K/H5KQPE92eXro9kfsJo8oIIouCmqB2LMgFoOzMq/DCABkmkUOL8COYnRO0Vx+AZIJM7p2tsz0A/ybKw5WVMIz6WNm1rVAtp6pOhDX8x2yPaJcda11JfqBjYzX8eZRk+lk3hQPSZLfBeoWvx8cj4lSOYHxqtmF8/RQJlasyPmZ2ONI4/yjD1S4jaSxesZXu1cUOK6dGFAemkQFlSBRZ1Je2btVuajGOqt1dwyKL24u2Jsvl5dcVmuI4jvcm0/uCC0bZG/33LpFzxfjQQVVjGyIwb3FaUzY9V7WWtRgiZoclheZWa8GMz5NbVXmz9u+iFuqB1VKgwG6Xk6LIwP37kdVUw4f1fWWLOESVNW5Tv9U5X5ftfX1klVW4qfRVcgrDetmjiicHzHo6orWkLmJ+UY3pqcNCByA2e01Gu7jrzE3zli2Sj0Pu6wmjU6upu2PxA+U5tflwHqs2PYBd4nSbzI3bCTLrO9q8pR1kmhbH9qsTdrPhsR6TNT1jgw4JI+4ug6+WsKpQsYyQPEoOi9gITE5UrhEazL8OYn8Wr/YnvpQ82khuJYn4iDJ55EYOxM6sZ0aeLuEM+lob00IRHIoR5HgODizyzj3thpA6GbpUBo6I/DzJ5Xn3USL9q3Q5GHydW6ONaXZSUa3FQmx/PssHQmB8qyJo2P5LlsrbLavBMc2apZ5oReOV3LgDQYz/7n5+r79016iInK7CNHzKeg1sLS2/Bn4nnmCpaR1t8OSpL1EjteUb4Fo0J48wPtzvJpFzxXvNj/LgNsvt4f1K1F2F+wFkzcrLN5sd6FQlTAA02norcQC21Z8H53f54texresvJxWSjpg7pbmkfFX5yBvMT9Z5kS7sp31c6/ZTvB4zoSdTxZrgDh4gSeRrKIInzk8BHTOGnSJuflNgJmwufEnXWcHWPz/xw7AhyL5WkXGUBMfaAYfUQDwSaxsEBebWDD9xDRZ5LEpuS6njQM55eQH+5Tf27RMH4J2EIGACXNWm7NFmvOpHrzA+P++G3vTpoO21NmM0Pz7X16JtrZGE9sjKFYfBMnoGm3owao785czL6Vvn3XVrqsx4VKa3/PClY8/5+1pvkH+uuenTtEMEyga5lXJ2kCZLE5ufcA4YEvzDlN1P0tEBXnxFhlBRSz7HZ/ChqL5X5SVHhh38s5O0VsTN2HPO5MKa+V+J65GvGEtqBuziHwhbkUEmXwK+rMZqkrf456lijqi7d4DmTdjWhn8HuOwlNJeazD/z94EbQrhPutCCZH/Ndpe+H4RJPP9P3paDBcyBEaXF+lHQwceb5EOZn71q5wZPMj+wjP96R+p5ExTP7rOe++HH2LNS7Nf4pwQAe1tMPwlpVlrYbWyvt9JEj5gX2/uwcSISfjkBBtVcINW3B1sacnHQtKg8+YLtXlhjeXo51olBfNB6hlRsa/v453wvg3pdXRDM/hs2Pdm+at5erMT+n3v4yuQdNXaPk9pLun+mUeT8VJSkr8/P8sg048bcv4p1PQ3bChcCFHy9rFRyyeSbdqHWDZ0BMuA4YBveoIK6nsg5xP2Jy9p8FT9lx3IS+sj3r31XLUrjq+fZcRXrfmWN0QLcKjOnr0+E5hyccbVILWdKXfCP7FXww/irgvH8rRVPBLr1rqU1Vpy+2DFVlGUwZ3M2i9lJZSisMmx/OyNiuGYDG+bF4QyFdpsT6iWJ+TCFTXFQpp1zeqvbitxGH+ZF9w231QmGz+SGttQsldMujMT+eZGjE/biO0sUlKVX4yXtMCqPaHKkbMqddF++t9QP2vfTBZ/6xENYHMBdhZeMnGL4QtRdhUGyykw2mqp3PvWS2iJjneb/kPUYYTvqMLUIoefcXrdysvOdAdEgPx3VwV/44vFk2NWi3f25l4Ijwo8feFfeiCk/mu0fV8TJ+UCL87NmI8OxoToRnUa22GFDQcOh5wfxExPnR2sgzof/yKTN4GF1Awm1+/D/8xdsR6Ox1by/V4JkpOu3RfJcTEuTQFvW0KmBSJg2sQSqYKKjx97l/XIA3Pq7HDf9+Wxz72RcmGvcYCmHzQxZ/hfnxVOaHepkwJpkfeEg5DhEkySV05idYgLlaTyTS/GQh8NaDQRts3l5ychftCcryOUmqvXibQ8ZgcDzv+PfvaNmoqfEln0Dr0BUrx3wFqB6gFBUG+Iqdaxjz4x8f1L2CqL1U5ieWt5eujopkfqire3Bf1Marojsw9QJZXDF4VqsS19GbGGnzY5YL84ZSbH6IEMDfsRl7+zmdLjxwqHEdANY4P5T5oQyZUGMqNj+a8BP0l26AnCHvKM3BxxAIayEG4Drzs3FbI/pWq8xbJsIORzA/YuzQH+XmQA/WJ1uHYLPAT4+Wfmyxlvx20Gr9e9zIqoGvzhOHU5DxtLY15UVjaT63DO0Pi83P3PfWWcYIn2/N9orEseBzpf/83vqkXpT53ztrlXsLKgWgbp7oJrOoZNHtjET4aVe0vtprSI8KwwaCQgo/rphYhKu71R05fDKm+NvF+wuhy1NcpvV6/DJPBzEm3l8XvExOSvH2chy6EDGUBBP4ISN7id2HNQQ/s7tgPvS1A3D+AUPwy9Mnibb98qllwvBTx959qxRD6YJI8cU/a+zOAV8gk+60TKo/GI/wLIUfv8/MxVgsFHqcH92Vee6N8sJ1K822GgbPlMYPPurPLRT+CTkReVkXfuRETJ3rbMNJCl5m/bLt6nh0HJjCTyirolSktE8Km65RRMCW3iJP7reki7LbpmqvNz6uU266OektogyemWbwrDI/VFDxj/PIyF1sLBsAW34oOhZpMEibgMJLeprai7ujc9AghTrzo0K9bxurO5MkLfbLhC9nfHGWai/SLsXbK7h6CPPD21XI1Z0/Z5neIph7lSb6fXG6dyNQ3V8czbhyYfaYI9VWRKgooRWF2vupL3cc2ziuVuWBRLnaCwCeXbrBv4dCBs9kk5movRKoKKT2KmKgVJVnjMWAgk9+JSkXeZHYNMo+RJ2MT93HFwhOmuQbwPFgaTUVGUISWMK+h0T2pG2lyUgV4YcxQdN/lUf+ZUzW5aSUnVfW4oUwondXfP+EsehTVYZl62Tm7THX/xdDriVpKgJEzJt2kMnUZvCcy2s2PyTCM/X2chB4q2m2IQDZGbuq8Mcf0Vc+/Crw/WrgYxm5FR8+Y7Y1wuA5fDdYiPnx1V5dt34IbNtAfpd1U92/fcQHAoGlfhmYRR1HKccxmM5YqoiQOD9qu3Tmhy8mhPmhZ7hpZbdNBZXvPrJEE36sV4hkfhyH/CyEHl5cLa8IP+S95psfLvyUpEMGusXmB4rwY/tsMj96nJ8w13PeFv05hDFbNhd9sSkKEGbsDMj329GERgBWg2dd5PRPdiJVR+r1dOaHq73ojiDYPBIBBwBKUsSrksmLecSDTrlXC/PjkA2XZI7kBkKHYH4EQ+bXef/F+xtlbcwPfYvpdYR6MVvA5qwDkAg/7Ym4aq8imB/XcaKFH6J7jxXnR2MZBnb3vXn4RKNkh+aZsR25eIfZ/Ij2WowHXReq2gtM8QQz6tENnvPyHm3I5S0Tnoai6VmymErSRKV+Q21+NLWX6ziKcMSrEmHkefBKj8c48csM2bHEP94khTvr2NJshloS5BDaIlfWtAn42QjJiFiCWPL+0CGcjh3btbTnQdz8w9RekdxPmM0PPSfU28szjcV5efIOpYyF1WR+zGEWzfzoYpPM6q6WVQye0xbhJ19A+Mk1BBe1q72s9j8RzI8jhJ/weE8laRcje/u2bt0r/U2VTbACTEPmZ68+TEuzAOwMYXUB8/1mlCWxuLpbc3vxdxfR7Il/PX6qOs7obXAWKg91HJW5spz/+LjwI+dENdaOyfw4YFJA1mx+bHOdvG8X8or2iOD2HG1k40dtg3gbGuqApgIpVtoZifDTrohYWGw65higQohNmKGqJb4YZ4KgiI7FNV5vY0q8OEz56+cd4kUd88UKye0l2+pIby+H77fktaknmH+MCj8OmSJD8t0QvPFxvfW4ctdF66Z5i+3MTzav2fzwnRwvLVzdvaDd6iTJGGmTFqQwknKfeKZ5TLMZomHvzd1ghIDOGwagyVXtLbyGLdi8vUkVfsiYzlkCTHKKXR21OvOjjkfXtam91KJW6OORtzvS5ocvioz0mdYvxFDfEH4o8yNc3bVrRNr8ELWXvniH2HMA6s7cYH7C2JGXf+3/3fKprJNc0672ImKnwxdy4iGHaOEnk3IxONhclWS4t6N900RVecN7VWJIz0pjYaZJVHXweYkbpSvdSZhVe0w++QxCGTwNwnZIGxK2yMyMuYrgUpKSz90jjeXP2GDBxCQv53MHQF4LacG56CibH8nGShX7UWN8e7E+QXRwK/NjsflJOQ7QQOZem0q+A5EIP+2JyNmZ7jTie3ul3Gjmhy9umZQUfjjzY13wxSG+2PgH+IBWU1Jw5odMvvoiGtzLT04e759H1V6evIZLs7qDSWHKKvyQ9BYMeHet77H1aV1havWsIMqpjjhpvRTYFq1Q5scDFTh8tVfg+qqpvawihxBegjxgwXUaHS3GzuRzgGP+n+X8cINnU2gt1BH+CZ+V9FeOfusfr2LyjU+ifkeTqIfq/m3MjyDMo9x/dXWVlflRBRo7Wsj8MAvzw1sWLFx79a4Q43yv3l1An6ZYNItgfmjZQolNVVd3k/lpFGqvApG3q2h8F7vBcxzmxw36SxfSRtfK4Hl+uAy1/WH3R4UGLlAVs2GRpDsfOzabnxxknB878xPFnlAINZLG/DiWeT4PmGqvoPo8YX54KhlDAyg89ajww5DPyTmH3oaV+Qn+8jOo8X5pJoUqbMeJ2Tnohi1qnJ8Imx/HgWSbyf12FiTCT4fAxvw0T+2VKqD2okKEUHs5fPdT2OBZvsRBfVpsHr+ofFWk7KOqz44Y3ds/j7BU592zQJzjOqRXGFOELABAnnjZkBxmDMA9L60AACxbJxOcUtx44ljyeRyOHV9rZDUu3iuBTmbm8/INns0dku7q7hs8O6QO/5x9BtWQxnG1ly60Budcvhi4oQ448bdAWbWlqf61Fq/6TD3PccMFhwLeXpszfZTDLy31PUFWbtwq6qYCTzZv1udZA//pI0ldaFWbH3VSL8rmR6gZlULqObb0Fla1YsD8OAyjav1xVbczizlLJIvSnDg/9B3ToedUU9JbOLJa/gyyQjUcUuOg6f7fwQfKOgupvSw2Px6o6sVDlzI15c4YEoC0JO3K4IiMC3fyKhTpEC+x86YPtt+PBt3bS3kMZK6SNlYUhPmJsJtRr+f/FcKPiOtFqw2EH93mx5XlsnmGxuDd4Wo9GtTRv4hN7QV4nvqeRAV0FUIoz+7uqOPpPyXfwbfZXbi/5MdW+0jaH8rcTcepF87MdQQS4addET7RNVftlU45RtwTrWIA/qJheFRYUyGoExsf6FzooTsfvoB4Dl2AdAbB/6FPla8m4ZPPZzty4iX+cMN2+FOlXKCEbZF4K4nw42bImlG4r86aNhi3nDoBz159GFKug9/NnoL/XXmoUqZ45odMmPKg+JTT1V5U+GFMbN9ceP5Ep+0QH/qaXITC1F5CCHBT0bNxcK1f/e89P8JvlKt7IbUXadNJjT8UX9MOD1goBSslurhF7SV2mbamh6hPXcei9hL1RPSBbvPjyOchsHWNeo4S5FDzlCPgzE/G8QTrsmFrIy7/2yJSxiboRSM6vYWd+fGNTGU8mvfX+xsC6vVphcWTkrbWavBsSWzqaTZQPSpJOhCoLuuZlMzRJ5WKekgCH3SM6LGD4kC+M/w9o8yP7FM9kbP6xYlkT5TrCYNncREARPghjhJ55ihzcWnaEXaKDVlPhBtRIrpTKGEKZLuyeS5sxGB+NLWXA4ZDRvYK2uxgsOt76+7trlbVbkIYlMdyNPnqwP3MdnYSJMJPeyK22it+lapdQATz48g4P+RkWyOVNuh5pIQhsktC78dgfgA/nD0vuXG7mkGdMj9NuTw2BjmjxHtGXYxTGWWHdtpU3yPt8iP3styP/xKetu9ADCFum8ZdF23wLBczm11GLsTgWcZ7IcKPYvNjgSH8aBS6VeilbeUsUx6vrthEhB+LzU+hfiAGn4vZCDSmfaZDqFJJwDW607QxP0JosdQv264LLRa1V5zduM6w6GoWAOg3WTuH2PxEMD9c+Ek5TFmYqeAS6iUUafNDFmsh6FkWZ8g+0Bc2ntaCMz9hdnE2j6GwIIdhqikA8CDZshQ8bNmpvuebtsu4WHnPk0KozvxozBZ9P6kAFZqrTEM08yPvRwhjts2oQ3IYFmR+VLaFiE/iWhw55igVlroyWfL8FZvx9pqt6rk6QtRe7/FUJlpbrAGag795Yr9WkjLZyi2sXBtDxswvBG3Hgc9El3cPiiZqrwRWtVczmZ+YNj9wVBobAFyb2stgfgLhhzM/ipEyn5gjFiBKx7uSpfrjSyvF8XH9q5QIz5f/bZFYLMVkzpkfx2c5aMJVfonyCOPKQtBD78eH3MHRp5bT01so3l4QC6sLFtDDsjYDfEHKc5sf/6vDzInHCleq2O57eYWV+TGEvwLeXnyn5wVZvLk6k47FQjY/fMFzrd5eEPXQ6/rRwENc3SP7QV2whb0aPafXKO0UInjp9lIEnNVJI1z4QaGs7labH/JuC5sffht6nB//r76wbQ6EDRrs1ApmMj9KkEOSn802RuXiqd7/SZNV2zAqnD23bAMRNvS6w8cE7eOGbDxVCmdidGFSaT3zpJcWPZna/MRcv1VvLXlfkvmRFXE2mLcpQzaWI/tUIZOSYTEA4PwgzYto22cfBs1zlfl7Ow8oqwnMto2eNHiWtoic4aFkYRbpkCCH8tC7QeRt8axLuyplOwsS4addEb5jUo4V4+quCD/moGZkUvQM5qewzQ8Xfng9kvkBtjX4L9efX1mtn21lfqigRtXWj152sCL80DQBYpfBbX4Cex/JHkv7IGvOmbaCJY7RY2+tVdpE1XiS2QnsbEhuL8r8fPvYvbHkBzPVa6UK2PwUYn6CHaELhmnDetBZ0LIbLLD46LZgXOUDzkrJNqk2PxahgTfPZvCsq+FImhSd8YqKXyIQEufH0xfyXnvbz7G5ugv4fZB2PMWbSmF+uMCqXy8m8wOd+aHC5MJ7Ubbo7qA6tf7eXX11c1bY6hVSe1FXd1mX6u3FP5hqL0bmlNouGRw/UU2QSYUfj0lhWaq9IubIALSPd0Z4eKnXVdup9BP5LNVe0cxPXINnpm1QbMIPg78J4QxiSUpmXk+T7PX81EHdSaqSv50JbPkkuGhKjBMHJMaSkdvLbC+/nZwQfjxxvn6vNuaHqr2u/9cStZxlLegMSISf9oS+aCmgC0AR3l6OuROmoMns9B2bNeOu1kY+GeQ93/2cv8tpVxorqrm9HK0e0layo+E7xB8GxsjKXETOkcwP12tnlOMMJK5EC4SfnTF3kLKRwYREbH7++frHAGlPJPNDXN1pPIy9+1ajixa8TVd7rfxsu7g2bUt4WyXzU5ZOKQudFEDCn5sKLk37dRrMT4ir+48eexdbGlQViOqLYjRabQ8ZxzrTGcsDJ2wC1jPOT6dZtS3Mj1XtxQVZhkyaLKS0jGC5jIaF1qtkdRelBFfi/8k1Af/5Omqe/Q56oU7U37OLb2szKTCc5zZXtkjJQQOD5ihvn/ik2vyYdjnyvZZjd9qQavOeSAdUlaUN5kdXc9rwxsd14nNz1V7KXEnsylx1uGlf4gc5FNOWpvYSgr7G/HhMOkiUpeRTzjMz9pHC3i17gtxkGnw8ufDQvTytNCaK+ZHtlR7BtdW+N6k+rxZydacbbv+3RPhJEEXLNzO3FxUobMIPNXJj2uO2Cj8680NsfuhilnIclKa5AEJ04bLyoB56X1LtVVXuT877DOrm/+LIiZ3umMULxIWfVFqUB3w1XHOZn737Ss+TOG7yKuSEJG2fpKDo/2K3+WGAIpC4hPmJzM0VqP6eX7YRAEPKiThHOT9YnJH3nxNJYhlHXakeV+OkcO8eyfzQIIfqZHfp/Yu0S/CJWjmqXs/q6q6rvVSW0grddkgIoxpKiF0YNbbWU4PQFouAleFqL5GNXe/oSObHMd4nIQTxD3lpQ1Pp7BSL/Bn7+iEdeHwfkfw31ObHktg0JNWFnfnx4dF4NbahTC7fo0spsfkxrxOGwwPPUQC4+iipqtSDHlI4mvCjCgBybETn9pIf4zI/eU1akntLXfiR4yjtyHJ5j2FrQ5C1Xqh+Q953Rzo+yK2h/BaV1Z3fdw5yXuJClj5kVKN59RqA9OyVG+FE+EnA0Ypqr0JxfqiBnqf/HsPmh0YqVRLWuUC3Cp+FGdCtwrKrMCd16qmT46k2XP6COZCB4IiQpau9AuZHTlIkqFaRws8Ro3uJz+u3NhZ1LrW4ltR9MIF46nfT20ue74IF3l4RLM6qIOnhi78EAIys7YoMCFOVKjHPUdoqJzTG6LVk/i3juRVUe/nPL68xP3QnmNOMnBs0FYVwdY+j9gp+S7mmt1dRai9h8yNZSwU9RpBzSF/EYn68cINn8260I2a9jiNVwLKd6i6eCj85pGQwP81WLyc2CCFTviUzePNc3YnLdsq8J2pbl/eogXH4vMFx21n74IIDh4hYSoDvOv/KdUfi3OmD8a9LDjTO4dDt5NScbmQjIxgQcrJY32VU+kIzjUxvIWr2j1uFHzdgfvw2laQJQ+5JlSU/FirAEm8vJQSHo7bF9p4Ipkkkv/bEWKLPjMGx51BzHBw73s+1xj38Up1c+LGF+E3QVoir9iqS+ZnqBhnXIyI8O45j2De4MZgfOolS4SftumL3nIPlxbIMeD97O6fHZfv5eeYewmLwnOJqr6B6MJLVvTjhx2KDGx8KI6EJP3ltwdJze4HBiPAcZbz8qcqYuI5kWgAUFH6Ym4IDf3Fm1HPJFucngomQ9ws4fHJ3uL1LHmBAnnvlaTY/ADCkZ4XyXQoEtuemTZxBm2xRj+MFntOYH9EG7Zy+E4ETfusH++MCt5LewmbwLJ8lVUnQmmWcH71Z0czPQGeDcZzX6P+h7sOOqI4ytgBNARPSRxZXd7pZKuzqLn9j8J9RxjX7iqpQPOLuLWx+IubIWRP6YtaEvsbx2uoy/PDEcfb74teNxfwwxZFCgAYo5Z8KMj+8SlXItqm9hB1X0N+lrhQhaaZ7fsWGXIiKfutawvwwyD2FfUOrtjd4l4jwkxZzs3qCMoaoNiBYT2R6C375zin8JMxPuyLihWlmkMOBDctI9VE2P9puB4jF/KTIAkPVXv44DyYuTzJMcsCbkxg14OT2r2Knqtgk0etw5kdNvCgSEDJpz1C88COv86OToidPE8HEwDzF9gmwMD9gqvDDAMPbS1vQFZz5gHGoGOEHZHFmAFFxWCI8F9rTanE9OPPDXd1L+cSo2fwAwMBumvAjrm1TMTjqX0CoJcKYn8jHbxg8m95eDdk83vq4Hmzy2cCII1W2KGLi5nWkI1zdw0Xb8MXedYG7S34OACht/Ey5ljCkJcwP3d7o8bkKbhCsru7ys+osYQps0oVbCk0lEQwD4Ku86SLvFzAFq9YAv22rcwhZnHWhBU9cC6x/W5SLxTJCZaYBIvzwAhE2PxlHsqE5RfjhcycD1r3jJzWmGDRd3It/+TDmxzIKg0M5Ifzkxdyrjxk1P5wc2fzV52MtUXslMFFI7VUE8zOk4V35xar2Cn6CqfaKx/z4Xz3GkCdqDJX5IS7T+o6d7g4dR6hHTLWXbCtVR4lraswP3TDzIsXa/NAuP3v/wUWdq1DlRPjZ2ZTHn+et5IWCIupE5/8kd1gFmZ/ybv7f7sNEu3s4W+TvqWgClxlqL/6wpM2P2XXRzI/cKcoYQgCQdqVgktOYn741ajoOW7LH0Dg/wRm+6lS1+ZEJHyOev7Go6gIqcO4fFuD4376IBxd+rJ6zcVlkgDbav+oYtLM51nZZYLsfXiNjDHj2ZuCXMnq5CyYWLEPtFbwkoUEOC7i6UyHRnnxUMjiMeL+FtR/wnR2U7OUg80cru0WLfnE4cxkS5DC4/rCGt4FfjAHm366UozaUkdczbIcCQUJcSmV+fJufwH4uJblQn/nhNRDh57FvmBftOUJcxwmeBL2/KJVdFPOjzw22IIc0/EhOjymVCD8J2sLby1FGpmWyVLy9Yri6GzY/8sVRmB9H7p5zniuvIy5h2R2ShSvH+GQgGQc+wdaUycW8inss7AhSMxg2P0xEPrXFkolCseVVEKqcCD97Xz8Ht859P2gbLwPxTD0EfaV7e0UxP9rkwQB0w1azXAgYEbQ8Xe2lX7aQ2otPoK7K/AgbJFGhqwjLgJwUOeSCZxP8LUwU86zpXIw8cDaE2fyQSy9YsQkA8IcXPvIPfLrY/5vdIYUDC6i3F91V09Ys3+hntDYfb3h/23boUuWUB569SflNsoim2qupUJBDoR+3GzzHzu3FHLHJSlviN3E7QQCoqSiRCzpnSCwhJFoDyjtmQD4D3uWXrr9BupCLYpZgrqHX8/8azI9F7ZUXNj9+mRJH9naeAWP7VSvXzHsMmPCFkFtxRFmHqOuC2wvaEM4/8k2pK0JwmO+VqjqVPcLHq4zRpraps8X5SWx+OgJW5qd5ai9lgrQwOZTqbI63l+s46IYtOGbLXGxa11O9rscFGWo/Ey7t+27K8hzaZJ/58c9Np4B/XXIgGrJ51FSUqPTuhndFecB/9bgQ46fJiI+D9uqJP7z4UWHvbhvIJC13r2pFoksUmx8oai/h7RXF/IhIw2ZclQ0lA9DLPENtB7lWFgD17JE0vkXYsNfmn8onOsdfzMrRpLbRYvOjx/rRbRkUWNVeTIksXpTaS9TBvb2kwKpj6bpAsOw/OUaFEO+VSDcSgAp1qzc3BMfCmB/znbeZ5zQhEP6JuovDF6SDc4MPWxty2LC1UURWDg9yyNVedubHbvNDNjZcmHRkf7iOKTBeevheWLZuG07Zxw9+KA2MtfHXBmqvR0q+Jw8oiU2p2su/fhmzeX8Wk9sr2JxpYS+k96rfN3nm+PV6st8yKZXlKc3wZZqyZGEN4O8Uk1fVmJ+oaZ8bPKfhKc4oFIrai+yeOKnIzRBk+IzOyfwkwk97IuqNaabaiwaXstnw0J09c1ylamtiU93mx3Xw88wdOGLLYqz6+3MAfiiLBotojlE62F4P/43vnfKc+bG8YCnHwcSBNf6XrevMNoJOmrL+g0b0tJYNw2Eje+H+i6dhRO8uhQuHNMCP82NfSJUgh4are7BAIPD2imR+1MWGMWodVXjFp8xPE4MS5yfcWLgA8xPU2eT6QfSuSP8T/246gMQesgk/6ndrVGJdCFRUtUxhD5sV5ye4r/R2PyDlOGe5UVS4TJcGQndlb2D7+tCqhYuy1mf9nM/EZ2n/YDQsqMTsb9v9NDFf+Cnb9onxmwvJPP0zUN09+c46nDYqjWnOu5jP9g53k7aoveDYbX7szI9k0jwhbJv3VF2RwX0X7mecZ2yeWpklSLE8JrnkWSuEOXmbxGOyXL9uJRjzQwjEz+2lzg2iFwM1KnctZ2CCMcu4wPbGHOAAzy3biClVapsYmKmGPe6Xyr04YMa7ZJgm0PZyppCovfjcrEe/Vz0G5TWE2kuPu+aam7fOgETt1WlA1V7xX3zlJSxo8KwOYnuQQ1U94DrAYe4bAIBBTR/gsZLrZNFgEcoyiyGgxXCRBqjLeuqL5ZD2KZtTPdnk9EvV+2ZA/8CWpGtZcbK84zg4YHhPEQW3ODjkUzCBa/2rMj98kg9UTTmfCbg0/XAM5ocbXvkLVFkmFSpw2cAcHuE5sAMgxq3GVUm/htTmnxpMbOU53/ZomLtW3mtQt27zozM/pg+N7cKU+bGrvcLyWqnVqItq15d+AgDo5mwzigrDbP5+5EkYBJtdHREuKWamXjPuw5R9wpkfm9qrKdivluwkXmCpUr+5hPmpIeqlw56chb+X3ojp7tt2Ve+OTfI9a7aruw9/hEn2oBAM4Yffc+NWn/H9frXh7dgcVGQ3Kd+ZsvGTY4OPoTRTA3ICANa/q6QLikJBtVcgvHCh0mNy/si4TIzpVZt3EvWwj2E9K03hp9fooJAcZ/o7HZWXjB/KExs+m9rLAbOzh44jyvH3PPH2SkAQsatpZpBDlfmxTMxkYdieVet1rOkt1Pa4joNGyIl0rLsS04f1UMrkmVy+DK8heq+OXCD4DsMlzI9Qe9HbyAULT/dhwHUfA0f9iNYOjzFsb/IngqggZ60O0tf8nkKZH13tBQYs+ScAoNJpDOxlopgfdfK4ZuYoMYnGEX6EDQbyvqZS7PIljW/YaoVBlPdPeL7H6QCALNN2d45jBDnkwtBHG7fj/XVbo5kfnTIPfrNFeFYSKYZBE+rzVQNDi8oFLrinXAHhR6h5dGaLbkwc+oc2TLYruxNokqpbXZgCpNqrdAdhfspr/OsTm5+bT5kgfs7kfAHvcHextKGjeOHnpDny/nbkbAIPZMBGyzzGiM2Prf06BBPMz+fXX3CnLHTnYQXrKQRdmOEbAgDKRo1/dG1z8PgvFG3wzLQ+EnKDxvx4TKbDKXHVwKk82WjvriX47qy9MXNsrQzDIO6Bz32cYSIGz8Ex/jra2s7n4Srmb2aOSb0qNqb0/UzBQ8am9iLl+PvY2dVeifDTnohUe9HPRQg/dGRahBkqlOi5vRzLRG4zeM5BrVfE5vGo2itYgGTDgmroy2Gmt6BB2Qy9OKDS8aVdySIiqdztjf5EUqmnhWhLkGcZJogoeYoUg2e1qnLWEM38aLRxn6oynDd9sPWaNqTXLgYAHOC+7RvAEuPW8KivYWOQCxp++YE7/Dw+GWHfIQUTPZ1XNu8h7zEc/rNn8blfPo/GwPDLutAItZfK/DiOZBx1tVd0clqVidw57iwAwL/yB4TcIWS/ByxdWCRtEesIqo2LEUMIEd5eLA/8vyHAT/r7KSsAlMG06+Ebkb6rH5cHg/bVONtE/fxdoJfz4NpZzkZiPO/amR/V4Jl/MBk7D3KesT9XHbraK0AVyQnWd1KMeqKhPxuEeXvZxtAVbwHnPwb0nRA7yKGulhf2beJS6iaQBjnMuEDPLj6bN2lQjeibbhUZfPHgYYG9pcb88PvRbOT8Y6ptXBTzMyy/Qhyzqb1cMGRCVKfCDlBPSp0IPwkkbJNCG6m9CGeqT8ZOKl5iU134kTQoV3tF5fZSdwZ6xFr+YqVdOWmqzgSmFwqtPs+YeNnKWpDVvSXg92SqveiCKxcHBgAXPSXKlaDJmKgUiMlDTuDSw6ww0p8tBQAcknrL35VRtZehZYpgJ8kF+XPu3qjanqg2P+pkl817ShbubY1BolbbBUSFqn2Byvyou+tY3l5cqGf+4lHPKo2iBvPD4aalvRQtH3ggpjSPMMU9PILYA+AzPrkGv33bfDu3UjSIn+ft9xsAQKPNTLOhHgDwt5Ifi/orS007iwFVIZuDEDufmkoZmsCa20tRe0khRga+jMP8BHV62rxBF/c1iwvWUwg685N3aGwsPublxmtBaSAUT/sqUDMIGHKQcn5sg2eu9uKbPN5nGvMDovZKO0zZAOZF5HvSn7rwI54hL8vIJoG3JZjTLY3n7/PvM+eIY3wejlR72VzducGzaFIi/CRoE7UXFX7MxZ8ag2a03U808xOc56oWGfWsQoaKDwZ53iO5vSL0vK4j4/yI1AZBsTS5ToqqDyyRZ3ld/rVN2rVdQA20iUA3a0JfjO3nWyjaghwCjv/8B+6LXOApVYIspFRhu5atL7kwVdwrnM17CpvWXObHFbtJ7Twi/Og2P7k8E7mmgkL+/7HTW3jWdC7x0luozA9dfHTVhKQgdeEnA3QbbFStZ7YXx5WHyRmDEOaHqjGC65YGzE/WLcOa2iMASIPnXCoQTEYdqzYx+FuW9uu4LfNr2YKwHHBl1UY7AXVzVDirO/9NepXGUXvx2vK62jcirlIsvPoH4M7DgW2+bVSaPJsTGm9UGXMyNgQTzefK3qOVaqPyY1GY3l58s8crstn8BGov4u1FPWmVp1eA+Qm2BfwGxTUA+yaB3049ZCDSlMuZWXIZeFp6C3kNGedHE7IS4SdBZJwfZlkAYlUZzfxQVdLnUguV31JWmx9VQPPj+agQgzpYRH2DZ362tmhp9xKm9kqnXEkNKyyYquLQWqkssC3J6l40SHso8/NjEilaYX64oEiUAbnAU6oE2Wi1l1X44dcojK0TLwIALPUG+MKixdvLNFQPY34CV3Ee50dnE4lg4mnCT1PeU4ye+Sf1yWr9oNH4rkNsa5rj7cUFpWDxyMJkv/gCt7quQf0hux0460Fg5NHAxU/L8jy/meHaLdvD2clQmx+L63oZ86+fT5WLNnKD53Q+cMWuUYWxHzT9FIB8F45NLRC/dcEO4xr+DzJRKGW2XCXas4X5sbi600XcjSH8CDsRxkcBN+4ni/uEMwrWY+Cxq4BPXwde+BkAIBUwPyu93niTDQ+ZN+VdClf3EtUT1JDNQyANngPhhyfx1QyeqdpLGDw7JHyGFxIPK8zmh0d4BiOvkrpRjIrzk2fymXPmjgqOKXgoocKPwvz4H80Iz9rGo5MgEX7aFVFvjKk/j1UjFQpsNj9RUT1tkYE1AS3lOCJ6L69HCBk8wrPV1T2a+dGj+/pUqkU4tMQf4XUBauC8aJuP1obJ/HzlsBF+XKIAqs1PENeD2PzkA3WJby8TMasKby/Sl5Z2hKGhp58Icg3r4efeCoyt4abEZc30FmHCj6piymvMT9+mlaLNNuankTA/eTGpW2AzeIY/oeo2P3JStzdZuQo35PcCVgVpLYO3vPNLH3jTrKbXSOCsvwP9p4hDYTY/3QO7DcAM8EhuKChABC3u1cd8Q+tcqky0sYk4HwAAMqoNz+H5l5XvT+T3FZ/XZwbY20CfALXvIPODwfABVraaBjlMxVB7GS7lgvkhfRkRYLIgAk8xrvbKgqfHUVvhX8e3+emFOoxrCp59YEzOIW1+CjA/RmLT4LioKAg6KfqNSTdzh2wSWYjwE8b8CINnJlV9QVR8vhbYgnzLCM/yvvjTH7L5JXlZuMikTUYTcISNUC7x9koQilYMcqgM5FTG+D2SprXS4OrO33EclGh0fsrxVTeUmjWCf9lsflzq3aExPy6JnqrIgXbmhzdTYX7aU/ixqL14U3T3VmtiU0CovTKsOcwPp9MLg9ukZJDDtz+5RKnXCBBoMVRXwZ+zX25O5YnilxHOx0rdtiCHInUEiPBjU3vJishv0WqvSOFXjxycJ2ovrSiv77Md2qL7/Xpr1Z7DbX78Oof29O2I9h8u404ZRqCyYf4f6lEWLGylgfCTdctEGxt14SddDgwyjbbFdYmNUDZVbi9E20SYDpeovU6YLAWnKJsf5sjf46i9qLpMOUIXd4udVSToGBroxxTiz4b3RzpM7QWGV8u+RurSqtZOCYOe3mJLg3/9T+sC9u2zDwAAvZ3Nosm83zKuvGiehWwSdKbQ0dVeDJWe77nFc//lyZyuQ8jgRMgVNltk7m1ERovzIyvQIzwn6S0SSMRVexXB/Ch2Pq7J5FBD5Pf2viykPeFtTLmq8FPl7EA5digTUmPe4uoewvw4mq0KL55JkcSmymJoN3jW3SoBgxxqYzjkk9+Gj4MovlwQVGx+PAvz40ihJBbzQ/sSaj9GwQt2hRknhyFN75N6HSP3UEGKWnN1XwN/gW9gGTxV+k1ZLp+1xPlheHapDBbIbQMi01vY1F6a8MONqEujDN61+3KCXHFZljbkLfF8mOzbN9heoVXrzM/9F0/DH86birH9akQZnl7ClH3CmZ/SQO2VS5WJbuE2PwKZMuDUu63t+vyUASiFVI9kwoQRKlxU9xcfXTKfPLdso/gsuissvUUzvL3kux/0OVXrFMv8ZIl6r9oPSiiFnyCtjCL8SGGu6xbyfgDA8CNszY1h8xMUD8p/tt2/n3X13HNQPd8Xfqjay0eeSdWy8p5ktQjUTTxelV/21NTz6JkPYkFx4Sd4XLZNgrChJGOe215S7UIKeTVcQoSre5LYNAFBTLVXEYNEofotwg+16djWa7J2cjybHz1+yRnrb1Xa6MHFzqa8crpej98GBwOcIA+Xxvz46TeCz3Q1CjF45rQzXWDbVe1lifPTo6u/s+Y7f7lIaGqv4DAXftKFmB9LhFRz7x0O7tmyn7tU/YExIrTygwXUXsLg2S/XFDSpzNFsEPKNBvOT8zy8+bFkT0SKE61NfjNswo+f1T2lqb1eeN8fUyVhSTtpPXz3S0IPGGovLvyQlulJgTkasnm8tdZfbLnw07e6HEfu3QdOVV95r1z4MRsWFCDMz6evAwAGB0mLe22VwfUaDLVXBVBqj1Cedh1V+HFDhAjOskw8SzmcIszPxu1yA8Rs7zbxPiyG+TG8Fm1qr2KZH9qXKRrgU7ZtRxNls+X9KHZK3683dlRxs7rrzI8R5DCYqxewsaIcFzz8nGhcHSbZGMXb6/X71Avu3AzasC+mnyC3F0SRJh68YciRV4HnZqPqyzQ8LVyCRe0l0lvwnxLhJwFHK6q9FAozUu0F5NxS9cdYcX7MtgxpfE/ZjXlwsOKz7UH5cGl/nyYZ8XYHStXykK+R8nKGqL1E8N28yiy1G6zeXj5+cOJYnDy5P4b18r2+fINnv79onJ9c0cwPsb3iai+bLYYGLyyLjZc3jYULGjwHk3/wAJxcg73ckEMK5vbi5j+OZYwpIDtz13EC7zgAaV+oWxPsph9989PCdQTjKfPW/QCACe6HRlH+zlBWzYOdVbrv5RXIBf2b0mPJdBsqPkqDZ+158e91q+Wxhy4GABy14V5x6JHFfkiB2kBNIrBtnWGUy5FOOTgs9Yb8Hsr8cNsQdZy4aTmfnDp1sJRLrAIyH48oKshhm6i96JjkNl6CKfXrf/yttaQRRO0VjMWNbg9r1eZmwQ7RV8FY6lbpj9XPBznNRNwvIiQJV/eUNCnIe0zY1bl8Lmw0o5KjNJhrbHN68HyjgoEKp0OYzI/LqPCjPQuyWUnUXgnC0QZqLyVFhYX5ET85Dhas1qjSGDY/6Zw9wR+dkOgLIwkE815vqL9efF7J+qjlAfQN0lQM7UFsE0hMGrUFJvPTrt5epOX6rrJ31zL88vRJqCwLFg8m1V450ld9dy4DAAze8lrxNj/6jjkC+bBI3ixPbLu0axVgfvjEVu9Z7EhuqAPSJeLZ8Lggem4vLgvZvb0o5OKUcolLeUoV5gf3MGP2yIsF51BWAP5iGObtRT3ZWIhg/UndTvFM08zunr3S6y0Ev1DmJ2B7KOZ3PxEAsI7V4JXlmwAA+7jL1EJ1q0IYXD0Hk2Xh4uB946qbJxrOpVuXMkv0dpvaizA/RRg8yyCAvO4WGDwr9lP+uZGhIZQxH61ONtTEIZChOILzhGCjzq/8vhnkeMuQdyDPHDvzo6P7MHu7aicAA/ZT2h6t9iKbOkcVHAGLgE+ZnzCvskT4SWBTBUlQtVd84UdhO1wL80P0r9NH9VN/jJPY1PbCOY6h9ipNq2osm+3IS6WHiM8iEzZp/qSB3fxrKqqOELUX1xZ4hancNgH1iuEsmWF0RPogsEPYyUrBtAV+zMY5KNrmR+zCC7/CeV1VwmFjfiwLmwIhLAX2Np56/RxS4h742OMxZ558R01SK1xibWNfcUUmagnHIcIPDVQHnDY1PGUF3vCZHjx/CwAgP/oEAMB8b+9Yaq8w755XV2xGjoUwPwRC+AljfgZNN86pL/Fd0Ofmpbr6PzQitZsBTvgtAODFk16Rx7knkevgQ0+q3jJhwoinegVxlC/+oywCacBuy+ouhYbiXN25OpDXxOcFxeanWOZHOdcfK3paHRXyflKaMKYjrqs7n490+z+uSpIOEK4o5zE+h6qu7lwdJt4TKgxe9jpw6WtAZQ97w744VzB6ceL8+KERg7YKj1/qUcsUr1Obq7sx1hPhJ0HkG9NM5keJKmtVe8lL7ztCF34KMz8pi0qCwdHUXmSRkNSPUg8ArMr4aoBlA04VZdXFwMKMhcX5MXYZlltpS5D2cK+IkX2qQspQ5ke6l2/J+MbCr/Y/j0witp0pEVK5Pl3WXBChzM+WT81EjYXUXpqg16CRHXmiHsp5Wt0acpE/U+GHxGFxHJQ69sX6zU/qQtpsAU03ov0kvPas41rFu2u2iCi9aejMDyPltobUY1HzBJDsnmzHn/JH4Ybsefhm7d3A9RuF6i9HDFX5u5lOOahwJAsSyvzwa0cwx/as7vL+aFZ3Yc8XYxO3aXujWmdrMD+eKfxEJgImG76waO2Ab9/18WafCY9r8CxUqEFXpLRNIW+PR/qNbjjzAFF7Bf1AhcGaQUBPaowv2+XBUd6RqDg/IhcZaVNaBDnUhBZlrErmxxU2P3zt4D8V2FB1EDpU+Hn++edx/PHHo1+/fnAcB4888ojyO2MM119/Pfr27Yvy8nLMmDED77+vWuNv2rQJs2fPRlVVFWpqanDRRRdh2zaLTrRTofVsfko8opbKVBi/c5bBdQBkNBVFHJsfy+7NhSr95+FKmwa9bnIvfPLNix1OyC5YMXgOsfnhzE+oC3Fbw7zePkM0OwH60pOgZvzuVlf4xo7Mccnzj5qcIW0YikhsSpkfZef76SIzUWMhtZfmGt+kDY8sudY7a3xX2626hMTbEtfmh7BRpdl67MtVPxrzc9CInogNxfvOfn2rOtcC7kGkp7fgYHCwM2tL5QFiHGKeq9up+NdK4778TKyAGrNHMcgO6kq7Dioh54fQdBN5u/Dj9Rwl74EI4Habn+A3xdursNCi97A1t1axzA+1nwreOyHU2GzkyHvqMFPgBIAh1z6G0d+bo7U2HPw+ZPgLVaDQmR9q86MyP4yovdRz/YPaxoY0LIuMMncYYS0sp9F4Q9zWJ6ULLVT4UZifQmqv+Otae6BDhZ/t27dj4sSJuO2226y/33LLLfj1r3+NO+64A/Pnz0dlZSVmzpyJhgZp0DZ79my8/fbbePLJJ/Hoo4/i+eefx5e+9KX2uoUiUWhXHaAICbmxvJf8ki4xflfUGmktqWEzmR+X5TW1lyMs/KNye3G1AJ+ozZewCOYn+JsnFH+7Io6wRV96Rg2eVbsCl9gaRKq9ANEfjmVhDENOWbhkXSv2/Z5lQqSCVrh6ltuaNeTV55Il2bKfX7Yhul1C7UWrtzxv0o/d65bI4z1GAABKApVrzy6aQT/FQVf5f6dc4FdJnoeRVDOAyvzY+3loz0rB/KQM5sdEMcxPysJCcPVyo2Y8rkTaDu4t5TiocqTwE6qWC2F+8odcK4vAwefG+HZ6as664C4EW1aczQ8EI8MrsvRzsYzBRuLVKJjSqPeFqFUjmB+K2OktNObH1VgQbvBMGbM0ubof5ycQfng/0P7QVe3kvdEDYnoRJgI0UTR/fjyWoatHLg9jfoy9bOdWe7VjGmwTxxxzDI455hjrb4wx/OpXv8J3v/tdnHjiiQCAP/3pT+jTpw8eeeQRnHHGGXj33XcxZ84cvPrqq5g6dSoA4De/+Q2OPfZY/OxnP0O/fv2sdXcYwl6Y7Z8B7z1KDsSXkLNl0btdJcKzzgyFBKuibbDt3lLIgbpuA44Zvt8y4KXePRWUjcH8hBg8iwzCEVRumyIqAak84P8hzE+OpFNQXILjGDzzukDjiMRgfhw5CeaYi1OafoQ+zmacUzYKjL0bXFUXWuG3Se9XISy5ADw0avOZEYFYw7s/PBqzfv0Clm/cLry9FMWTTf1HFoxcpqs83mskAGljkE5F9AVnPcV4JIuc8br5B+IwPzTxbzrG5B6a28si/DiijbIdJWkXjTkP2Zx6rS0NlC31382a3GdKmVDmJ+wdI9GjPcdFeRBHSVzJoqpncCRzEGMe69W1RJwHwP5eFcv80E0eZ36Y2ZcCZIzzTUVjLrrthV3d/b/iXRdqrwjmh6u3yGYoz4h6WOSlk+lpLC0TnxqRAXlbItPAUO80sSkLhB7jOdryrhHmh0NEnuikwk+ntfn56KOPsHbtWsyYMUMcq66uxrRp0zBv3jwAwLx581BTUyMEHwCYMWMGXNfF/Pnz273N8aENplc05qsZBs9r03ZBjwY5NJihKLUXX+SCtmaJLUeK5cQLyCctGdJcSD/GvejCTzzmJ9rgueNsfgqopwCrzY/I6g45EfuLXATzQxclpnqvxLL5IXucHFJ4iw3DU94UVJSkVLWo0uaw2rnNj39Cg7Yu9WEyGN7gHr6wfcnhw/HdWXvju7P2RnlJStoG2Gx+BPNDjpJ+5OzFpyl/vOc9JoZYJirKJWc1+MLBc62xeHF+wqSfD9ZvI4t94UW6eTY/8iQugHA1Gse1j7wjvwR92GenGtcp1OYn5B1zyXzBnJSIECxUQoq3l2Q4RBLPGAbPfatKgzplTaHtiwuLwbNNkJSQwvULy9aJcq+u2CRK9KhU587Cwo+qAuI9IVgXzeaHBjmkm6G8x4Q9l/D24v1hs+UjDdM3InmxcbEIP/yDhfkxnqNN7UVsfjgSb69mYu1aPw5Dnz59lON9+vQRv61duxa9e/dWfk+n0+jevbsoY0NjYyO2bNmi/GsfhKi9NPfbYuAKrwL722iktzj4G6Q50RMBII0WlcifRO3FJ/7XV9XRs0OYH84WceEnRFhQ7J/4Yqi5ujtc6AqnctsdRgoO0pc8D5rC/PCdnkd5/+h6NbVXHJufHFFFUTr/mffWY9m6bUFTY6obRJwf/3lk847djgLAsCDY4+AelfjiwcPwxYN9d1xu9Gn19hJCIO1L+d6kNFsnGjsok44j/PgLo2MJOqmDxWB+eB1+8wpP7oanTZE2P7ytH23cjtdXbTba4Bfy29F3y1tKfekwISLsHdsp60/ldqIxyzc8emvU8SiFwThMGK8pUO20hs0PTf3AmR/H7EvZCKlWHdu3iyj3hTvmYcVGP35ZXpuzRVDXEPDnvDObx3trt6Bv3l+PutYFAqkm5Cu2Ng6TmztG01vwiSMe80M3rP7thc+VVE3nacJrNPMjf9OrTYSfToibbroJ1dXV4t/AgREusq2JsDg/uiFyEcwPT17XN/+J9Xcja/em5aQ9hdVefLFRgl+xrGLDQiEmd8u97mjwhbwNQaj3WAbPBbO664xTe0K/ZsT9KAbPwULBbZ+aYfPDj+hZ1W2gzA99jr97Vgb4k4SdpvbSoU2gOY8psYtWe73w2JtrxG+AlkcJcozk+BxOf7ba/Mh+FMJ4cE0q/OjXUcC9XvjEbbHB4vhsexOGXPuYen7EOynclfVF2nJOaBRyK/NjumfTKk/53cvYsNV/p7qWEWYiaEdTaXf12qE2P7YHAYVZyKXK0ZDV2BPL/VGvJTeOGlA33LfNSUV7e+WMz+vqfNsnu/Aj56pDRvRQyj24cDWGXPsY6nZk7aeEgA7Fo3/1AgYzP6dd7Xv3BpdSx3k2T9VekvlRIjzzfojJ/OQ8ByuD4LNANEsuXd2J15klyKHfqGiDZw65FpgR6jsDOq3wU1tbCwBYt06NDbJu3TrxW21tLdavX6/8nsvlsGnTJlHGhuuuuw719fXi3+rVq0PLti5C3pgqTWVVxCApa9oc+Ttj2oDvNZo0Jyq9BYLzTLo4n5dqLz2rt6zGlPa58LN0w056JfPaYERZbp+YXY09aN8AhwEMpifkOwly6DGXMD9khxwl8Fo8eWwqkTDQ5JZh5Q3GLihtgtt2yXJ0bHyGrrjk/td9uj7Ysqa1tBP8q4jwbLP5UVzdKT+gCt05EjgxE5XegjM/gUrE8aRg4TFpDErRp0oaUO/M5vDeWjtDLJivkPeW1mzuuvm55gLPjfkpA3XE6F5KmeUbfOZuRzZP2sENntX6QtVQYWEWukhWfWt5f5FDTdr8WNReKM7gWXpX8QM25qfIRZMyP1y9FCzYhdReui3Obc+oEcC/ethwXHTQUOwzqFtkE6gg0LU0wrQ26POmnIccYVz42b7Nj1+mrH458NHzxAPWJvwQQRkODv3ps+J7PmKuFK7uTLL8POaRoc6lakWLq7usU2tTIvzEw9ChQ1FbW4u5c+eKY1u2bMH8+fMxfbofEGz69Omoq6vDwoULRZmnn34anudh2rRpoXWXlpaiqqpK+dcu2BnokNe8oTVIv3585idXWgMAqHeqrb/zmsTLKCKBIhbz06UkoEPJYvRifpzcmejMT6TNTxDrhnGD55Brv/5n4Ac1wJzr5AujG2MGl81ztVdHMD+hNj7igP+HeWJByoF6XpGdXiTzQ21+VFusOCQh9fZyQsZWozCgpcxPuNrrrU+kIEDviad62NaQE5Ot/mxSwcPLCWP8Qkyf7EddFUSZn0jVp27zo6i9mKHWAFTBCvB38DYINUFBl33gjY/r1AO8yRbmpy6IgUPfvZTr4qObjkXfat+oN5tn+Ov8lcjmpdqE3+O27duV+jL5HbAijEkYchD+mjsSV2e/DABoyOlsr6muZMwpjvkRZ0epvQp70SmwqL24ICgEM1tiUyY3XaUldoFl9rRB+N5xY0K9/0SV5OdeVaW4JXsaAKBu5BeCa3HvWP/aZ971CnZm6fvAbX4ARoW/+44HtgQsv9XGTV5Yf0eMsBYW7Mzmicu9xbsM0JLOysuGq72iNwcdhQ4VfrZt24bFixdj8eLFAHwj58WLF2PVqlVwHAdXXHEFfvSjH+Hf//433nrrLZx77rno168fTjrpJADA3nvvjaOPPhoXX3wxFixYgJdeegmXXnopzjjjjM7n6QUAy+bYj69bon4vRu0VuNducO1eX2JHK2YZMgAtQRF1oaVLJvBGgIs/5o4GAKxhPaB6e5GzI6R9vpMQNj/621ISpCf4LIjl9MrvItReGvPTEVqvMDWX+E4NnuXO03B1Z4W8vUyBpBibH6r2CrNwqd+ZtVwrnPnpQdzKuzgy9AQfD/U7s0IlqQslMsJ/sOBZ6rcaPDMm2ASuhdjvJ3MRC7rai+RaA4ORhwyQmdgBYJr7nl/eUs4WnI6ivEQKFcs3bNd+5auyucCvq98RtFH2heP4AgJ366/fmcV3Hl4i7wUQ72aaqbaEwzeG9FXIOwYA38ldhH/mD8Xm7U2G6kd1dee2gRqjWRD6uLeM51YweN53SI3/Nai/gjwTZcMn7sm+NMa1LaTJP/t0LRMbBO4Ywq9TRzwG8gpjJtVe5UwTWvl6ERmhXz6HtfUN2NGUw9ZGvy9s6mE6F8vx7LdhwxYtxVGWtkc+v3Vb1PGW2PxE4LXXXsPkyZMxefJkAMBVV12FyZMn4/rrrwcAfPOb38Rll12GL33pS9h3332xbds2zJkzB2VlcmD99a9/xejRo3HkkUfi2GOPxUEHHYQ777yzQ+6nICafbT/+4i+1A/GFn5RgFOw7FcPCn7q7R3osBW0ghs1b4Z976uRaxXuJQlKdWj0AylLc6Jcb9mnXL6sx2xNq8Oz/FTY/HaL2KsD8WNyY84Tn2EF3ehELkF+PqtLgt7utycOQax/DkGsfw4cb7ME9mwgzEyb8mEEOAes4DMbT0eP64vwDhhg/8xD92xpzwlAzbMGQ06aN+bGovZiH8uBW8nDxXIE4Qgo0g2cqvHtMzREH+MbaeiJWwCzn3wdfLAgW3AX88SgAQC8iKA7opgca5WPEXOB7B27g9B27f/4qADJf2tYGudCLjUjQh/sPsiQ8tQm0kQa0PrKeJ6Ib2yI8i+pJO+IwP9DVXjY2oxUMnstTKoPdRVFFmWqvMKOeYhjmn35+gt8c4j6e5QElg+tUV8ixIZlgJoTJHANeYhPVivk8aY3cbgo/m7Y3Ycz1/xXHqTDO0b2ihJwn1W8AMGWgppmoJ2YixOanfkeTUsxUe8Vf19oDHSr8HHbYYWCMGf/uvfdeAP4O54c//CHWrl2LhoYGPPXUUxg5cqRSR/fu3XH//fdj69atqK+vxx//+Ed06WLPctzh4BFpe6r3gOFHqN+LYn5MdQqFpxud9hplLSehTWzEpZ3rgruWuOLl1dVeRrwYci9H7d1TOcdYE7doRtsVPcOZH83mp0O8vcLUXPrvebpApYQH3if1/k7pk03boo0YaV1cVWRRsRz58+esp1JPvZK0Wf/+w7pj5ljuVVlA7UWYmRuOHwMA+G72AvFrxgkYLsZIUDW1hiVEZaZdMYQBk4J0/2oZF+bD9VLYm/uNQy1tJRDCj8n8MDChPuXwGFMSsf4vPwWAFLaVskJNEJRv2g48frX4PeU6ePU7M3DH2ftY2hnO/Mwc49v36NGGAWlHRV3eFbXXljXIzLsVAPCf/P6ksRYVUpTgHWBwd5k01mbjJJlIIvwUE+RQsICtwPy8/bD8rKk5+dzDQwb4F6Vqr0BFFNIXxWyyeNypBR9tkmo9Pr6D60wf3gu/On2S0jYqNOY9IAsXQxruR7ZLkBGeC3eRZgtAJnjX312jvm9U0OEoL0mhqoznAOPMnd9nFbqCQAmGKd9X3bYvYX4SSDhkcqLosZf6vYhBkgoySetujRwG89NrFHDG/X7CO2sbw5kfZWcZTCbdupRjZJ8u5umW3WF5KtjNhAU53E+LzJ1rJLtS/cUK7q/D0lsAocKO/rvC/Lgk07NfviSFUIbLqFuovYKvWhsWrtxsnHr/glXic1nGrP+3Z+0jJ66Y3l6AI57fX/NHip+5GvalDzbirU/qAUgbH44DA48aaedhMXi2Rnj2UBosKHm4imv70KiM7gAxeFZ33n56C1OoyeaZogp7wxsOwM786DtlLLzXKNOraymOHtcXpbrwGRHkMMN/sjgVlATPi3tg0XaAecAcGZ15PesmT8yru3O/fLjg/cvTJ+Lkyf3x+SkynYZN1Urj/Hh6ROIoCOZH2zRRFGvwvIMEd/TU5837qISGRbCpvUKEn0iPQg10TuLX1VNUlGbSOGlyf9x/8TSyKZQ2P015T6ob+fPh91TI4Dn4TEes64QLcPOu899jI1SB3v90/SLMz9n7D1aKSW+vRPhJIAaB7hKrD4oimB9HRg62gS+0CjMyehYwYKq1vCG0MKneknR3XrwA6VQKJ07qL8+OkvaJuzdgYX76TVa/Z3eETsz81A61+Qlz1RffubArF7YcUjIlR8q/p7F9u4YKeUZdXPjRgqRxXHTfq8apCz7aFN5G6FQ+/Rxu80ProcxERSDg3vTEeyH1A/0D1Y8QfgrUr7CIRF3VlONhDmLsyPn40aLk+movZtj8NGhBBHlbl3xcj6/+ZSE+WL9V/GYYiAZpNwS2b0Q4QoQfL28s2ADw27P8d4Srvd4kBtQlmRJ57napEtwBkvbDKvyEq3pOnjwAvzx9kiIs2LK6S+HHkYk447yTYhzziizjv1jmh0IwPypTrW4CbGqvljM/SjN4QFM9RUVwnYHdKuQ4Yp59c8OFHc4kR8VpI+dS9e3bPzg6tI2VpWnsM6hGzM/WdBpAyPNwMLRnJR74kmQZE2+vBBJ88OqStDG44g8SR6RNsNv8ROVzsVdoZ348yvx4xEDXSSk7HEn8aPUAYiLKhQY5dICLnwG+cF9wal4GgAxxde+w3F4ATOYnTO0lFxwPrvAiymR8PjlDDBwLq738+90W2HroNleHjVRdoQGgf025cYxCeQ6WmEIKogyzAWQssWR0M46qMv++o21+LMwPmKKu4sJPOiqys14HM9UgzGLzows/S5nPfJx193w8sWQtzvnDAvGbmqMNvtqLolFVO6jt4sKP1tdzrjUW7MXXfw7HTfAdOThT98QSGczVTXEBL6+0YScjao68ZrRMrx1h8wMAUwf7DFKX4PnZxofv6s5tV+KrvYRw0BpBDpVzdebHv06pjfkhaq+tet6WAMXY/HxaJx0BeJ/UlKeV9vBrp1MkIawj48Aro5KPc8/inCDuRX7km5LGgB3sXllitfeheH1VnZifmWBJ9dxe9Lu6WcmQ+AqJ2iuBhKtNwBytIPzoXlccL7zv7wDjq4V0m59g0iAurGB5wsi4ChUcKe2T3XZom/rvA4wkuxPuWRCSvfjNj+vD62prFLT5sau9+EJbH3h6OIzs+mMyP2P6+ll7GBxcM1Pace3Vp6tx6iFUILKoshTZIaa3V5hBaNqS3FNfMLoK4UdTBSjXtLBRZGfuMUcIP7EEX7HxyCt/uT2UzvxwW5pZjT+Gd/yv8QzbR/l9TT1d2LiXTlBHNsSl3N6woBKt3+o/IayIX4ayFSWWmEaCgfPyQI0M3FrpNKCRcbVfFPMTvRzcfvYUfOvo0Rjbv0Zc0aiKWVQ8UdAMnu2RxosUfvpOkp914YdFqb1kuca8ve3FbLIac7Ld/BmWaFndeZ+nXVdRF1q9OYtlfoL74u2Iq7ITAmJKZUllxWRO195XGmsrpau9WiLEtgES4ac9oU/AHC0RfoLd1RbLnNaU8/BpMEk/sWRNzArDmR+PTq5ETUOpYDl5mdS4UHuxAsPOEtTPtPnx6/9su3/jwlW7PRGRUdn/HvRBXi5sHhzkPYbXVmwS/bmDxmOJKfy4ZGf4hSkDcFSQcVvG9GAYet1jGHP9HGUSRkMdyjIhhon+hezX5yjA/NjyR+kLhtwdWtReUcwP2Znn4aIpX8Skrqu9iPos7zGD+eFf38VQuFPOM+yWlLKC6QigMz9RCLP5qaWxtBylKGBP4sroGOk9Vhz/Suo/MtBlkTY/FL26luKrhw2XRvOiL2XfPfbWWqn2iqW+V8eTNXRDsYsm7ShN2OV9eY5in2IK12EhJIoRfmhYBBmGQOuz4JllUg5RN8n7PXI0Sd9k2PxEGzyzYL2RDGm8tnNv3BKxFOjMjyW9BWewyHtiRo1PvL32XLiElqYwvscfJGsjwraP/O4T4nN1eXS2bQlNaKGLhGLwLKny+MyPap8Uup7Qlzpkl6PfLd2JtxsKRXjWdvX+hOog53mY/9Em8cwG1MsgnUiXwgrDXdT/u+/QHuhdVYY+VX74Bz7hrvhsBxgDdjTl8aEWW+abM0cr360B35RrUaiT3Y9OGodjxslo6jbhR7eTmBOoaqxqr0I2P0Ttxen8VFQ2d6MOdTHMIYVFqzcL9akOvpPVmUWFLAieY7fyoO+48DNpNnDi74CvzotqWNAe3eYnB2pvB6gMmi2aNXPI/FJWLY5/oekGsnGxqTLjMT9Gm8UcIZ/fhm1NgtGMpfbiVQR1vql5Avo/Fin8WDZcuiDJ2UcAmtpLLaejGLUXDZxp2Elpfe66joh/5uXzolyKPmf+fLk91yY1+jStD5B92pgv4j2BjMOms2byZiwGz4L5iVB7JczPHoxQ5kdbZIpgfng+IQZH7Pobsnnc+tT7SrnfnrmPca69jXbmh8EhLzBVe6W0xdOx1wMQtVeIzY+ogzI/OXEdio7x7tIQxvTov3PhJ3j+eY+hS2laCJPp6r7yHD3Pm1438/x+fOMBAEB1uS8s8WfAJ1xq5JjNqeMpkwpfyG0qAAXaZHf2/oNx+9lTxM9x1F5n7DfIryou80PMP6lAwIMQxtrRCsGAq3O5uti3WbN5cQFSyDDZK9k+/hx7dQkW1Hm/9f/urAMmzwb6jIloV8iu2MsprCugjnn9GQKA4xIBJ+inbaNOxetspKqy1hEjzo92oaAuPj5k2xkcoX6L94qqai9rktxivb3ouP3gSSC70+hLJZ8btSnjzGIIO12MwTMNE6V44tG/nPlxXRn/jOXAe4QyjjyZMF69O+KqlPlRbX5i2caBhkwIEX6UMaSO20i1VxLnZw9GKPPTfLVXCR+ncLE5UAH9bcEq/PKpZaLML06biGojWEMY7DuUfITaq3jmJ8LmB1ApIS+E+dFOnTSwJvSO2gwFc3tpzA+JTbRlZ1ZS65zdquyNUND+fO2PwKp5yjWE8GNqIrC9SRVIdBVOysay+LWY7QjLAxXAKvxoC0b3St3mx1I/LG0ijGOeMj+xhB9t97k1SL4KF5UlaSOVBQcXMnQBrokIlNKTJ6iDu1ov1ZKj2htmP0y8vWRIANou2f8z9u6Nz43pg7LSwLCZkXMDgSYH7f4pimV+jA2SKvwINV3YYpfPAU07lHP5PdrVXkWmt1j7pvr9tT8aws/A7iTYaxFqr2Jw3gGDhW0WKyD8lJekMLJfdwBAlwxE13qKjBZDOFWeYSD8FK320pmfiM05cXUHVHXsnc8vV9uUGDzvwWgDby8eWM2Dg3+85kfebNR2+n2ro7191Aq1gU905aFqLzIRyyCH4cJPXsT5iWiHvlhpu9Jnl6rRfR+55MCIytoIsYMc+kJpKhDktjf6oebF7jIfeLRZ043wusjkufIl4+e0EH64YaecsLY35qxlOUwhVBOAFRQyeLaovQyVEV/otDoB+0IcpvYShpwxpjG68WioF4fL0ITtTTkc95sXracJtVfEwmHs6jkO/VbhdoW9BET4EbYgNq9KAD/7wkTcde5UOJRZ5glwg77x9N08BW93TGYgTAXLP/GFPlTtdWMP4Cd9gS1rxLm8hpKMpQ3FqL22WGwbG+pFHcdP6o+/Xby/KvxYvL3CghwWg77V5Vjyg5kAIAIHhgk/ADB9RLD58STzQ9kjh3vzjTrW/9trb/OiZA7hm61iHAOqytJyXhKekRFqL81my2aIb9jbdRIkwk97Iszbq5ABdBRILBAeP6WqTF1EKwq4NyrQcyAF9XerLMOFnL1f+rgyYUYyP1TQ0/KBRe5E9Bg52iKxdksH2PjoiGvzM/8O5eirKzbjzueXywWJu/NH7ewoNW8RDFyN+aHeS9sb82hkckzoxrLGwu6ELOZAQeo6xczFVb8ef+5Wby+rzQ9VS0jh55HFnwZNikGnO2QCJkHw1rNu+PoDi0NPC1N77dVbBvYUu3r+HnNvowH7Fm5XKPOTVd5tHdSeRHiBUQEvOLeyrAQXHDgE5ZQV0tFsmx8LzUiZHxtzuHOz/LzwHgvzY2kDnwPeewz4fjXw3uPhTbN52pHNWrfKMkwf3kO7HVPtNWFgN/SvKcc9F8hnOK5/Vfh1Q8C9ygqpvQBASb4rbI9oM7nResAUdxtiXjAlwxrsaPLr+L/XPwZgN5LX8cglB6JPty6yHYCF+bF4Zwrmx2aEHaFy7UAkwk97IszmJ6sljivG5kfQtK5IdthEvHsuPXwEJgyotp5rBX95uFdIUH9tTQVGbn/dbKPjKguDWLP4i7ruLbkb09zyK0IyJ/N6/cJ2tdcVM7So2B0Bw/0+hPkJgRB+eF+7UcwP3TmaggFXy3iM4eoH38BfXlkpiuzM5nFm03ewvcsQ4KwHledllz/D7FA8oClIKVFiTyETh/nheZXsru58XCmGSPI3j7Mh8vdP4xi7UyaRCORr0CPkBB988YoyeM7ru3r+fsdSUxRmfvQUMoAM8QCQmDX0Hsn7ecPxY1FVHuRDtNnPFG3zo48P3eYngvnJkeSXfSeJcz0h/Fj6gz/3B87yvz9wJmm7B3z2oVyErWo9RvojIh8WEZK6V5bhpWuPwOGjpCqapvgoFuHCD7lfPQULAEriS2YvwtU9LfNe7sip72+UxyLHsF5dMLx3ldqOmEEO/WvI+xFRwRO1VwKrzc8Hc03bgKIMnrmaQxptckPQUyb3x9UzR9ljZ4SBMz9c6KCTxil3yXJkwkwrwk/wmagWcMdByjlcp2wz2pQVacyPtpBMCQKudSgK2vwUEn6C+89x4SeGMMg8qzEwZ2/ufXkF/rnwYzzw6mrl9NfZSLx2wlPAyKPsRonWa2nCT2M9xEJXXqP+1tU32t7sdjeq06/BA61ZneatNj+kPRECQSQo9R6oGbe4NQVP42Nb39DaUheId4WP/ajnKRDyDlC7HUsZql6QUdXJPeqLfZi9IW13sTY/FuaHgdhA2Zgf6mqfbyRzHb+HiP6w4d+XAr/ZB/hBDbB1rcx4rl/TKlTzS9vU+TYGw96EQqitKpOMVhzmZ8GdQJMfQVwxYeDCC5+bbcJMmiYoVX/PxDXW1oWwOGqvoF9pzrQbTxwX/JYIPwlszM/fzjDLFTFIxvT1d+AeHJHniut4be6wBSGYn6zaVscFygjtS7y9VJufADTz744gvL+I8+P3w6srCAWuw1B7qfdi5EjqCITZyoT+rkLYUOUC5iLS5ocKP6YxcBwXXGG8a43LRK8VovZaL1NWGC75X7gXGDgNP+95o1Gdrt6UC7eFYbLm9qI7c1V1Ghu0joB9yLlmgkcdQu0V0b/Gwla/yv9LWY5C7dJBoqjb1F6DbbnMXNIOPT6WbsvHkc8CHy9QyxZudHAdO/Mjs7pbBBYaYTrXZFF7hfTHR8/Lzxlir7P4r/LzP84FVsvI2wIrXzb7g8Il83KEkDSmb/FqLwB48CvTcdS4wKMzJM6P0g6CZetkGpVYQQ5TZpZ4jtcsef+soOo32maOiCCHJWkXT111KJ666hAZTTpKjd6BSISf9oQY3EE8Cc+LjrgaA2X8vQVlfvy/ShTT2G3kzI+q9oKbUney5AW0Mj+EfsXex/t/N/oeaNzz5Kxpg8LbYai91IlBD9TXIYjr6h6gaeI5ynfp7VWEzY+u9hJ0c+HmcvUIfV72RT1E7dVQ5/+tNFNoYND+wEX/w8dlI4yfdJui0kwE82M1qCbtIQbPRYFuPIKxnXMKe0Dy5Kn6PThhzA/d2Kx/t7g2UhBX930G91CieAPAF6YOMM9xLIs4H1NhKvcbe8rPcYMz6sygxhAauaEotq4lBRuhq71C6ZXHviE/67nTOFbPB/pNMo/33MsubHBYmEVa7j+XHoSrPjcSXzx4qP26BTCwe4VISxIW5weAVe3drZJsMvizjMrqTjYlzfZYMwRl3eYnnPkBgBG9u2BEbxJp3mb/2QnQCVaQPQh0AffywJrFegH/TzHxEMTOyRWZqTnz0yzhh7MPOzcF9ZMdkyL88BcwZbch6TOOlM0Ca94QX/nCFUnDFmB+bNnJ2x2FhB9t8il5489K4j/BXvAI0JFqLzI2LNGP48QfKQm8RShTtzNr2Z07IeOQP4vuw0KvYdNkhjE/0TY/5j1StUTL1F7+2M2G5MNT2+q3cZ1mYM/vqEdliSr8ULZn3CmF2xXGthC11xemDsIlh6sL/v7DeuC5aw7D0h+RVDCKwXMI8xNldLrq5cLtBQra/EQyP/93kfyck2ovVkj4oUmPw9iymsFAlz7m8QH7RquzlH4zhaTxA6px+ZF7tYxt1lU/UWovgrH9agAAXUvTRICNyOqepsxPM5d3XVCOUnuJRx8x//D7Sgye92BQHS3LS3UHBxc8muntxT18WiT88Jdx8wplcoKjMT/CQ8lVvAjEvDRoGjD6OFl2szTA5TY/Vs8AvaIw4adTqL0sC7TyuzkhUEFRLJpRBox6/S1Qe8XO8ROmo/cKC2kNWXPs6sbCfFzGzu2luCIHaq9CKVJ0UI+TwMZqc6PZD89dc5jyfVMQOyvM4DnPmBoCIk+Eny61KIiwZ15ABQP4qi9lQaaLFrcj42rsMLWXUmHMcBERzA8DCWVhZX6IK7qXI5u34PywoTlgKm2AvcywQ0PmTjlurOxqXJuflkD3eIoyeCa46JBh+NbRo/H41w8mzE88tVdVRWG1rhWFbH4UdS4XFiPq04W2ToJE+GlP6MyPTjPrqqY4IMIPD9TGcx5ZYy4Ug23riK7cCWF+XCXOirJIjDs1KJu1pm0oytVdM+6jaq/pw6I9dtoMdCKNI/wM2FfxrjJir8QVfqxqr8LCD1eLFg52FqL2irKbEGeYrKV+vVLN/dehXkFRzA+YoM7D0g+EQlF7+ZN3ZUWFUuSUyf0xsJt6jKcG0e9BCD8eUwPY8YXBSQGpGAbPYapORoWfmII+ZTBygQcpjxgeFmOsdoL8PDYGU+VXFlzHnKcYHCGYOqxAvr18FlLt5Z9TmtFUP+XdgrIW8wAdLokOqDQ3Fd2XNnVha0eQ12Pd2DYSlrHQtTSNrx423I9LpMUNs76HG2Vw232H9lR+uvOcKXrpkLZqTI3+nJ+/RX4ukOtPqS8RfvZg0MHN8tJtWEezhB8Xjyz+BAf9v6fx+so6AM1kfoYdJj/ns3JwGzY/drWXAi7w5JsUY95S+OdGxp3QbX60l6uG7GrmLf8MHQJrCoaw3wGcerfmJaQxLC1gfuKk++jd1X8eBeN9hKq9+O45fFG3ZYnQVXJcKOcMoKOEeoiw+SEGz8Xb/JD+CwSU2u6qAetBe/UMVR/qhuE8mKfnMfU5cjY3LEdbWLt0aFHUi6rLy8vwGdz2Lkztxd/jc/9dfJDDELVXmePX2WXJX6LrIbGMZk8bjL37VuG0fQeqZXj7s4Qlj7JNomO2JLA7IfZThdVeRQqccaELAMJjK22WUUCZIY35sQnOxNEk896/lJ/61cQMdutqm7JIK4zooKd+fSH2Zh2MRPhpT+jMT8MWe7lmCD8MDl54fyM+3rwT76zx620W8+M4QEXApKx9U7P5cSFeRhKYTw1ySF9WzXg6wE4Ei3DUZFvA1Z0KdpXFBHFsTRRSe33yuvw85Xyg2xC72ktWUvhahvDjH4/D/Azr5XsGFjQpC3N1j6H28izST6kmhHMBg2f9Lt28TI6nSG8viPFetLeXxeantEyyPGUZFydN6h96+jYtSjZv0vamvHyOH78q7yO28BMydm1Gy4VA75ELP9wzSvfg4eDvZtz2AuR5mK7uADDVXSq/bN+onls7Xn4mwfz27luFJ75+MGqrtAWatytLBJ7P3gduP8gcn8xTj3Hmzeb9ptyPjflpbbWXJgDY3iUbM0Lb8U4gzGz5OPjN8s7TcCQaYttJ6mOF239y7DVTfo7F/CTCTwKF+fGAxtYTfmxqgGYxP4CMgPvg+dKVVMQL4VFGidrLZvMDqBGt8/LFfpsNAVAozk8M474AB+3VM/S3NkVoTqwANMdQ0H4qHLr6lqoFaq9XimC/CkfHDlF7RdlNBKDB9zh04YfjQ0YSunIvID3uC6AutiTdSlGgrEjAzjgZueA3ZD0r63P3uVONY4B/n1sa/B14JUh/vvxr/y+NZBynXTpYM1QwdIF95xH/M2Fo/d+0BVa3DYp3oaCNduZnJyOC1E+H288F1M2BkRA5QDoQhpq0yM3r3lJjiQFqnwEq2yIEySjmpw1tfmgYAt4mQA1v8Z4lF1xYomPA3ka+cQWAr76Mg8ncWB53k0g3rStf9iP6U3Sh3p4xnHMSg+cEBvMTpgNtptpLx6f1O41jRYPH1xBeI8FAJrYNlMFxFJqW7CA+nAsAWOX1grRTiRh+24LFkActs7zoj19+MM7ZfzB+fPJ447d2QSHmZ8Tn5Oe3HgSgCj/KDhkosMgRAcDC/Oj53KJQ2OA5+MtYkBGbR88tzPw05dV2HDSiZ2iQzdWMeOZwN3pYmB8qjFnUXrobuBW8zdntQoXi0HAMIehSFn6vE77/PwBAP4cInosKqHqMdkUZPEe4Z1vrsqi2+AYrVO3F88oVIfwYnkuq8FPqaLY+2zfKMjTOz7InzHvU75UH7aMu8hyv/E797nmFhZ/IOD8FyrUEOpsiVFdE+LG56VPh59x/q7/ZNoQ1A4H9vwYcfDXQZyxmjZcbjLK4m2E+Rp7/KXDPMebvNpf1SLVX57T5iROCNEFrQff2yrem8GMOvh6VzbT2t0Hf7XPbBtdFdbl8gee+tw6zJgQvHH3hg/xWg1yZkDSS+dFhmYzG9KvCjSeNsxRuJxSy+TnlTuCWof7nniMBqOopIxVELOaHqdcKPhay+aHsS8Ew93xHfRvJTfX9emLzE28HOaxnJf580X6RZd73+mMv9xN5TbH4kEKWOCxc7bXg20eid1VhIUbZofKwCzGEnzjsaW1pFmE5PAsikvkp1ubHol7gMbbCYq20RO1ltfkBSqAJP5z9+X69qgLvMcLC9GnjmHsv6VHwAT+uDwUVXgAp0Hk5oDEIFmgVfmhOrTYyeNaZN8H8kCXYxhbSMdp9qFZnyLg4+ibxcdUmyZiVxlV7vfZH+bmki2mbSoWYOGqvxNsrAQB1gvJCvCGaFefHHHwHjmhFdRB/0bgXCd91OS5qq+ULqkRBpYM+40ekfdOTL/C/gsSURV2/M4HuvKwUdHf5OWDK6Jy6wBut1RfX4NkUumgWd9uCTZmhroTN6FcdR3CAylTGNAZdvnF7wdQq9QgiFe+s8/8WivDsqRGeK0pj7t9yZNHl6khyH6Nru8IGbjcXlh/v4a8dgAu+/sN4bbBBf+ZlwXWUhbgZ3l6lwXto2PyEqb0KB3yUbQ63+WFw0MAiNl2U+ena12Ra9PHC21WlBXV0UsDeJ6jH1iyGIuTyFCxvPgi8+295ng46TxXLtsWFzry992hwnPT7+NMsbaMbHa3tMdp49DgZbqGozSYHFXwmnO7/LRDk0EBi85MAgPoS5EOEn4X3xa9PSxZKEWZrURAHXWUeC3vRghfyjrP3wRn7DsQ50wfL3+iEO8oPxvZw/iDx81ufmPYhpOJ41+9I2BboMARMWZaohR7IHx5en3GtELVXoC46e38/Wvb0YT2sMX8uOVzaXuw3RApl/+/zE4yyViz7byxvr2Ixxlnpf+DGnFG5vUjWbS7sxzbqryHRxLkqdbFUUQ3tKdNF3HH2Psbp91+8v3Hsq4cNx+RB3YDq/sDUC9Ufz7cwFTboC9pRP/L/Nsf4lto1cXVXQW8vrvYqgvmJCMbK4OA7uYuM43zzo+b2ysrvnKUx1F5Bu/SNIsub6UM2vKd+rwnmog0k0raNVbd6e7W2q3uIwfmqV+TnIQcC5/1H9bil0PsmBgNL06Bk4nrz8fhsOvoH9m/FMj98nVvxQrzrtxM64YqymyOM+SnvLlQjaNoKfL9aUrVRCBZV226LR/QtGjNuAGb/n3os7EULjh89ri9uPnWCGnSNGhJGCGlWXKklKIz74rYnihF+qv2dK2Vgckgj25fE3oha5JTdE7nWu/8BABwxug+eufow3HfhflabniNGS/saathrJRlP8lWUihC8dU0smx+KAd0Ku9ZWOMECtuSfvEX+H5tK0cL8xN7NpkvsaTkC7EsEwlG1kr3s2cVffLtYGKbLjiBRl6mqAIjP1tD7nHimjGvDmiH88DFCQwcIocKy+6YCVlFqL83VXQty+BHri5mNN6vndA0YCCr8eFm5MArmSWd+gvZnLUb6YaFCOKos3nu2fGvi3WqHIIdeHnjzH/L4Ns2WaeghQNd+9jriev0RVJdn8OhlB+HJKw+JFQUeAHD8rdHXX/sWORiD+Xnjb/Gu287ohCvKbg4qEHzwtP9534uBq983J6CbBgC3ToquL3iZmyzmW2UlLXi8Og3OJ08u/YvjUQs2YX7EohXzBdYXqs7I/BQKcmgpS1mZSw8fgYxHFoM4fanHh5rxffFxaM9KlKRd6yQ3vJclEWYYJp3p22fMuEHu2NOloQEnKTgDBcTbPP85N8P/0Hei/9e2846w+SmkVlNQ0kX9fuzP8OzVh+GmU8YrjCV9RmEGzy9883BUlJDfRsxQC8Rlx+gYSpVom6Nm2vw8+T15jCcjtjE/lHkuhs0z4kCpai9AxnCS12oyr/nJQtPmyFB7ceEnsF0ZOE3+ZhN++PgZdIBdWNCj6gOaTUo7qL2obc/kc+zlrdD65tU/xDprXP9q7NXHrta1otJiLjH9Uunyvmm5LwA1bo3H/HQyWx+OTrii7OagO/j1b/ufX73LN3w7519m+c0fRdcXvMyThpqh9FuU/yqMYt3rKK1cxDWoF0UwkX9+qlwczz9gSMS5uvDVCYeqMrkW8qDyy04YUI1T9umPK2bshatnjvJddjm2ry94PrwcsIa40B94hVGUMj8/PHEsXvjm4UpQSAA4cVI/jK7tiv0LRcfm8VX+dQlxFw5fKL9//FjxOU7gxcVewJ5wYTdmVvdmJW3UF8NNyzGkZyXO3G+QyN5OLwfY04YcOrKXH3GXYsoF0dcKg57byaqCieulY/PC4SEqgr+PXukLHfwaxbaXtsea2NTvrw9ZP9/bsU/giZlr8NkbPVKzrvYKY344S54ulffUaBF+qMAYV/hpSZ/HBVV7USF8zIlm2aq+5jEAKNHGXJjNaGuAq4l5W3uNAj5dLH+/4yB/c74jiOMUx9urk6ETrii7OfiibpOGK3v4O+4jviuPddfjZGiICKpWVVaEEaMOfeLQJ1FRLg7zIz1XJg+RjM4hIyMMsl23sEFxR4MKaIUWD0cyFb84bRKumDHSLKPHLVGuRfpy8AGkXnPSoczPwG4V5kIN4NYzJuOJrx9cXCwoEdgyfDJLp1wcO94XxL98SPjYPSYwxDxsbH+1blucH0tW96JzewFmu6tC1AsEtnQd/W3qPH1Rb47wk8rI758ukkJFXJXv2w+Zx/Ss7gBw1xH+XzoHFbVAaQbP1lgvDnD2P4Ev3ON/3b4B+ElfNfcZIJM76wlYOXSPPDcj5zrb+8JVfno6Hg5bmoy4LvEtAd28cBZr7+PtQsM+5/nRqadfqh4vKYK9bSm4wTyPpu2mI9XGkZu/MBuiDkYnXFF2c9i8LojqAgBwyDXAyb/3P3cbjEgEi0bWaUW3dsBkdPikpAtZsdReqqfQHWfvg68dNhyHj+od3QYae6QzCj9UNVjIbTqOeiayL4m3Fxd+QhJnUuanNBNeZyyVETW+3B6EKShgz/LrMybjySsPwZn7DQwt88vTJ+FvF++PYyYG41uoQ2zMD1V7+cJP326V+PaxmrdcIejtDum//jXlmDCgGvsO6YZyC3u6mrgPC+g7+LjChM78fPaB/M6DjbZk7PNnbBPGqP1PMekcdFd3wvx8+dBhalkqoESF8OAqLL0daW1ec9NyDuLqoynnm/UAwOL7zevYhB9+zUKRoFsCyi6J6Nshwky3wcC3Vkjjdwqai60tIcZv8GzTZdHPL2ouGX5EqzWrNdE5+ajdGVT46TkK2LjUtKPRy0UhEH7+t7ROOfyjlsa/0V9+vuvQ7SaiJk2aooJ4Ch09ri+OHhdC7VKkMtK1vrVz7bQG6AJX0FW4hcIP3TlyobDHCGtRqm5qkeoTUBdIsUBFLwzplFvQxqAsk8L04T2AZYHQmNeYH1v0bJK+4KxpQ4CDC7CiOnQGhYYiUIo5eORrB8Jx7ALiC+9vtJyU8qMRi/FapIcW4I8nmv5h66fF1RV5Hcs4UAICNkftpS+GDq4+ahTW1jfIZMP9TM85K0bNCtqh3avuheam5EaD26BQw+Ynvun/DfMsytmYHxp8NkaS4eaAGlUL4SfCISAsKS7dENJozq0NfT5Ll5meghRxzB/0taOD0Qm307s5+ECoX+0LPoDd0yLMNVJHoMN2S1Tm4ez9CzBGhaBPQjxeyKeLtHJRg54Y6Qrhp4ghR1/Azsj8KMJPCPN24Nf9iYGqMsMQOYHEHA9QE5eWpVso/FDPIe5x05rPgj9jzvyInTdVeRIBxJYWIC50NiZi8XBdJ5QZ+82Zk+0njSQ5j7IWdsh6IeodmQYGmi71saMvcy8d2zha8aJ5jC70Rbl2hxg8Ow4yKRe3njEZZ+wX2IzEed8zFXKx15+R/pxTGdkfnPnJmGpdAP67p8Oq9iL9xcdhWwY5zMUQfsJA14pZv2h5u8KgP4dMGdB7THj5MGGN1tXJDJ874Yqym4MPhH+cK4/ZJnIRjn8ncNMg4LFv2OsLmJ/T97ezAM2GwfwEEwwPdBVWjkLRpTcjRozbyYUf+tzCFuPP/RD4zlqgT8TEwRHL1b3wBLLyM7nwlkWovWKBugbniD1Fa4Hv7HnsHVvkW8XVvTh3ewW64F7kzvndHx6Np79xKI6fGGIrNOvn8jO3mSgE3ebHtojoxv9hmHK+bzM48QzzN5E+hMAmaMZBqKt7M8eFssnR2qILfm5GXnfzCv9vJkTlfOi1fviA0/4sj9nYC9q/wiapteP8ULVXsIkoJrwAB+2rtmRSDCG01PdKDi0fMUaLmLvaE51wRdnNYZu0bQHG+ID59HWgsR549W57UMSA+Zm2Vz8M7uELKN+dtXcrNDQkzPyQA4Fjf0aKFeftVdREq9j8tPJk1BpQDJ4jXn7dbiEMcV3di0D3lqY4yVHmpw2EH2orteYNkvOIvCfU5qclwo8OHlMnbvGSFIb1ilhwKnsCZ9zvvx8994pXKX0fQmNpFTtNx3g+nieNjYuNH6NHeI4T68WoQ3Px5yhk8JzKAHWrtGMldqPakgrg5DuAMScAk2b7xyzekT4DE7S9MZ5qt2go9o88tlEz3s2ovmpN2Bi4dImMQG60K0r4ic9atycSm5/2hlX4sbwEtnINW3yPMIpgp5IprcBz10wBY6y42CdxQdtTTQxZ46i9AEk3F7NopSwLYGeCHqOlpYh6bDRIWhFRaHUX96JBg8tli7RniQO6+129gCTipGwAWWx5JGib+qJYhKlLWoLRs4orr9j8BPdc0tUPdCqOt8I0XVKpGgP/kAh+xdYf6upexLyTLpWqQW5Ib2uLrhqyLbKpEvO4HijwxNuAY/4fUGqxRXMcCAGOG2i3lbcXywNLn/A/N+e5KsJPGy7fen/yuW7UsfaghVFtEYIu84XuThKwtnO0Yk+CTViwHbO9fDY7AuHq7r8UbSL4AJptQog9RtQ5QvhpLvPTCYdqHLVXMYjrOWfERlHxwY+Pwevf+xxW3FzkQmwDNQrmTMHWdS2vl4Pu7B+/Glg1z/+sBLDTvIuA1qHQO8MkrNv8AH6UX4rWMPbfFvHMmlt/XOaHMsXyoiFtKRQsNMREQF98p31Zq9exCz46Pns/XjuKBX3OWz4JjjVH+KFscxsKP3rdfIzs/7V45ZXfLAblnQCd4O3fw2AbJFGZhimswg/XH8dMUNlchEUzjvT2Ir9xIa2YSYVOdMufjX9ee8Ft5YkojgqR5jQKsRlIp9yWq7s4Tr3bPPbmA61TNxBur/ESCbGvMw1Ap3WfLRpUYOBjyNh1F8vMFNmGxqgce7b6w1zdQy6838XA6X8NqUODvjnqrrnOhzE/hQylC4Gfnw6YprYUfjjWvV18PXSeb0vhRxdSRP+E2CnFUXsBRavt2xKJ8NPesAo/lonAVo4HnKIosBC2GsJsE9pU7UVeKG7c2JmgMD+tofaKaTy+5g3/M4/U25boNQoYcrB6jKYYaCnCxsOZRMDi78fKl+SxtnTzbU/QZ85ZL8PIt1hmpo3t4/ics3ROcCCGzc+II9XvYXm56Hg49Q/xBEGaFoQeKwZ9gsjkW1oxvACFrd3NidBM5/m2tPmh8abotcI2aHEMnoGE+dmjYZ3sbcKPZZDpEwZj7cj8UPubmJGX6Tm8nUWpvcgLNf2S+Oe1FxSbn2aqvWi6kFhxfvLAwiBqbpQqozWhLyR6KoeWQFdFpMuBb34E1JI4Ve//z/+7mLAHMaIz7xKg79I7j/h/Wyr82ISQuFnm42DBnf5friKKY/MT163b0d4p3UjWyvxkzD4q9n3kGwrOglE7pNaAzall6kXF16MwP20o/Oj9LiL8h8xRcTfBncjoORF+2htxB6xNSNJddan3V2swD5HtaYbai55Dw6THvmaIkXVnQWuovY4gSShjRXjugMlDv7fWZBn1kP0n3x4afFBgxOda7/odDdszD0sq3BIMOQi4flPL6wHUmFV1q9Asb68wKPNMymRHbAxDKmMawLd0PtTn2pbCFsKgxd5ebaj2qlupfhfMT8gcFfXs6fhNhJ89GOvfjVfONsj0XTJN0tfazI+eN6c5ai/6QnB7pWImcqrmaMtdTnPRGgbPdEcc1+CZg7vvtjX0iW1nKy2iHAP2k5/TMRiCs//ZutfvSNBxzdMdGMxPsYtcTHua5mICiSP0xLdaHueHQjcAN4y/bcJiiakCjhsbKQzc67At0Zw5o72EHx3C5tCWx63QueSZtWUy1iKRCD/tDZ4FV4FlQFl1xGThq/8Y+Pdl8ntr2/xsXaO1x+KVAhS/22vuBNwZhR+lT5o52SquzjGMx+kY6FVkbqvmYtkc9Tt1f28NFJMmpCSGx86uBPr8uwS57sLcjGPX2cY2PzWEhV36uDTcbQ17DtofuZ1+XBkaPNLWF6lMdFLg5sCWBqO10Zm9vXQhk7qrtwTr32nZ+a2IRPjprLAmIiSTywOziY1AadtPeGExbYql5Jst/HTCkFQ76+Tn5hpk6xF+w8CF0edvkcd6t0YwyyJROwHY/6utW2eYYM1xBklQ2Rnc01sT9P3pGuS7azHz0w7gyURHHQv8PWAg9YztzQEVOrYHG0Uq8FqdQzKm/RsNzhkH315TuExroznPtYEwUm0Z/kPP2yaCrLZQ+Ikb+bwdsJvNJLsobAPK9mJQGx8ecwVoe2NnAMgTwaslHgfNnsg7YYTneb+Vn1e93Lw6Ci38HO89ah4bclDzrtkSfOWF1mfhChmOU3uvzpjgtiWgi3l5TXBM33W38TTdbUjx5/Ckuksfb941z/qH/bhnsWOk42PNm+Y51k1DkfNFiRbwcvLZxZ3fHDRH7fXKbS07v7ngz6K5GoYZP/AdJfpPab02tRCJ8NNZYVsIn/yeXVCKmz6hGOjX2fSh/FxMwlE9wFmzA6p1HkM5gW5D5eexJzevDj2rdzFoD6G3PUDv29YHirDdAhbkrAebf25bgb5nww7z/75+n1qmrV3dezWDQVw9v/hzKGgSWAo6pvm7Qeebd/9tnmM1om0hQ3HwVS07Pw5ayui11K4pCpW91O9c61A9QB678h3gpNuBb2nG0TYcdAVw/K86VZqiRPhpb1zwBDDmJOCw6+Qx24AIC98/51rzWLssgqSNdMIuRIPq4f6b+8LrNGxnwMwfy888J1CxoMKgLXcbhy0PVSeaSFoERfix2XS0Uj6jkSSsABVcOxLUaWHsKf5f3c262Hema21x5Zujrso3w75n2OH+3z7jw8v020d+5s+60CLvpoCDQxI/Nxftwap0RnUmxzkPq9/LiLrq+/XADXVAdX9g0lmSsdzFkAg/7Y3BBwCn3afFrbEsYmECDQ+NrpRtiwCHmlBD6Whum6Aft0GPb9Fcm422zGDcXNBAex882bw6qAdfVL6qz9/TvPp3BVABerNlF9lazA/FPue2Tj0tBY3dVdnT/9tnnFqm2Hs+4DJfkPrCfYXLAsC0ZthwnWnJ71TwnAf8eENfeia8DJ0fOPtAhfwxJwIX/k89p7w7cOT1wHfaKe5Va6E5Y5kmcG1LdWitJqDq69FusPFKhJ+OAp3wbQOpSx/7ee/+B3hbk8r1aJytAV3YoC8DXYyi2ArAVMkV88JPPFN+5p4wnQmtsRCXkn4edmh4OSpwAruXMPT+f+Vn24TeFsxmZ/EepDYQfB6o6i+Pnfan4lmIkkrgC/cAY08KL1Na7cf9uWKJyojFheMUHzgxU+bbqRW6n5N/D+x/CTBihv+denKd8Btg0DQ5H02/FOjaR9bP0XtMcW1rD3zuRvV7cwQImoyXC8ttjbGn7BbCjo5OzLvtSbAMLN0Aj+LB89usJQJUXZWpBPY+QX6nC0e2gFeFzvwUY/Mz8yf+tSa1g/Fhc0AnhNoJLa/PxnpwUCFy2leBcae0/HqdEbacXVTtVb+6da7TWQyne+4FfOk5NbDjcb/0w1hM+0rzBJM4GDnTf7dqWhA8dMhBvgrknX8B/2hFJm3iGf4/jkaS4b6s2v/7xbm+epDaoADAV14CNi0HBkxtvfa0Fg683Lfb5NADfMYB9Wpra4Gkz3hg3Vvqs9iNkAg/HYXSrn5qg3xTvFD9PUcCG5e1fbs4qIBzwePhL1pB4acFMUsqugMn3la4XEeBuuWe8dfwcnGx38Xhv1G7h/m3A8fc3PLrxUVJF189M+iAtr+WLeptW6h1O5O9Rb9J6vfq/sA5D3VIU5qFkcf4Y6Pf5Lapf9B0/y99B9KlpuAD+GlRaseZxzsjuCBXDFY206u0Objof34Ijz6dkEVrBXSiGWAPg+MAswt4n3yf0L07NwP/b4i9HI2Q25q4ZrlvY9Q3gtUotPvQf2/rNBztCRpLpMoyEcfFpQt9RkPXsyvXauXAgsXg4meA1/7oe2y0NWzCcWsKKvt+EXj/SWDSmYXL7s5oDusQhnQJcOETrVefjtpxwJefV9WBbYm2Di3QEpzzEHDf8e1zrZKK3VbwARKbn10H5d2Ab62w/3bcL9vmmpU9ogUfINpIl4MHKRt1bOG8TbsSqMdLS4Lv9RwBDD88ugz14OExVtoLvUb6TFOxXkRxQcME2OxBWpPen/Vz4OtvNG/XvTvguF8CfScBh3+7o1ui2q8UQt+JbW/jcs4jvq3Qhf8tWLTDMPQQ4Gvzges+7uiW7PJImJ9dCTZ3Z6Bjad44YeCvXuqnRKjsUbjsroTWCOcfF9RG5aJmepZ1VpxyN7D8ufYL2rgbGm/GxtQL/X+dAef9B3jsKt+2rzNg+OHA1+a17TWO+xXw6BX+mG8uerdTWpvdHInws6vhsteB3+xTuFx7IRbzU9m6NHtnQZ+x7Xetkgrg9L8AcHYv9gzwM15/c/meLZTsiRgw1Vdn7UmYegEw4fRoh5YE7YJE7bWrocdw4KttvDspBjajwz0F3QYDX34BuPLt9rne3scDex9XuNyuiEKCz+jd9L4T7HlIBJ9OgYT52RVBdd/Dj+yYNpz7L+DtR1o/suquhkI2UQlaBzN+4Hu6HHBpR7ckQYIEuwES4WdXBI0KXBcjr0pbYNhhMhdRggRtjZ4jgGs+3P2yuidIkKBDkMwkuyIy5fJzexrdJkjQkUgEnwQJErQSktlkV0dnCtaWIEGCBAkS7AJIhJ9dHWU1Hd2CBAkSJEiQYJdCIvzsqjjpdqD7MOCk33V0SxIkSJAgQYJdConOZFfFpLP8fwkSJEiQIEGCorDbMD+33XYbhgwZgrKyMkybNg0LFizo6CYlSJAgQYIECTohdgvh5+9//zuuuuoq3HDDDXj99dcxceJEzJw5E+vXr+/opiVIkCBBggQJOhl2C+HnF7/4BS6++GJccMEFGDNmDO644w5UVFTgj3/8Y0c3LUGCBAkSJEjQybDLCz9NTU1YuHAhZsyYIY65rosZM2Zg3jx7GojGxkZs2bJF+ZcgQYIECRIk2DOwyws/GzduRD6fR58+fZTjffr0wdq1a63n3HTTTaiurhb/Bg4c2B5NTZAgQYIECRJ0Auzywk9zcN1116G+vl78W716dUc3KUGCBAkSJEjQTtjlXd179uyJVCqFdevWKcfXrVuH2tpa6zmlpaUoLS1tj+YlSJAgQYIECToZdnnmp6SkBFOmTMHcuXPFMc/zMHfuXEyfPr0DW5YgQYIECRIk6IzY5ZkfALjqqqtw3nnnYerUqdhvv/3wq1/9Ctu3b8cFF1zQ0U1LkCBBggQJEnQy7BbCz+mnn44NGzbg+uuvx9q1azFp0iTMmTPHMIJOkCBBggQJEiRwGGOsoxvR0diyZQuqq6tRX1+Pqqqqjm5OggQJEiRIkCAGmrt+7/I2PwkSJEiQIEGCBMUgEX4SJEiQIEGCBHsUEuEnQYIECRIkSLBHYbcweG4puNlTkuYiQYIECRIk2HXA1+1izZcT4QfA1q1bASBJc5EgQYIECRLsgti6dSuqq6tjl0+8veAHRfz000/RtWtXOI7TavVu2bIFAwcOxOrVqxMvsgJI+qo4JP0VH0lfxUfSV/GR9FV8tGVfMcawdetW9OvXD64b35InYX7gZ4EfMGBAm9VfVVWVvBwxkfRVcUj6Kz6SvoqPpK/iI+mr+GirviqG8eFIDJ4TJEiQIEGCBHsUEuEnQYIECRIkSLBHIRF+2hClpaW44YYbkgzyMZD0VXFI+is+kr6Kj6Sv4iPpq/jojH2VGDwnSJAgQYIECfYoJMxPggQJEiRIkGCPQiL8JEiQIEGCBAn2KCTCT4IECRIkSJBgj0Ii/CRIkCBBggQJ9igkwo+GTz75BGeffTZ69OiB8vJyjB8/Hq+99pr4nTGG66+/Hn379kV5eTlmzJiB999/X6lj06ZNmD17NqqqqlBTU4OLLroI27ZtU8q8+eabOPjgg1FWVoaBAwfilltuMdry4IMPYvTo0SgrK8P48ePx+OOPt81NNxNDhgyB4zjGv0suuQQA0NDQgEsuuQQ9evRAly5dcOqpp2LdunVKHatWrcKsWbNQUVGB3r1745prrkEul1PKPPvss9hnn31QWlqKESNG4N577zXactttt2HIkCEoKyvDtGnTsGDBgja77+Ygn8/je9/7HoYOHYry8nIMHz4cN954o5KPJhlbElu3bsUVV1yBwYMHo7y8HAcccABeffVV8fue2lfPP/88jj/+ePTr1w+O4+CRRx5Rfu9M/RKnLW2JQn310EMP4aijjkKPHj3gOA4WL15s1LGnzGFRfZXNZvGtb30L48ePR2VlJfr164dzzz0Xn376qVLHLjeuWAKBTZs2scGDB7Pzzz+fzZ8/ny1fvpz997//ZR988IEoc/PNN7Pq6mr2yCOPsDfeeIOdcMIJbOjQoWznzp2izNFHH80mTpzIXnnlFfbCCy+wESNGsDPPPFP8Xl9fz/r06cNmz57NlixZwv72t7+x8vJy9vvf/16Ueemll1gqlWK33HILe+edd9h3v/tdlslk2FtvvdU+nRED69evZ2vWrBH/nnzySQaAPfPMM4wxxr7yla+wgQMHsrlz57LXXnuN7b///uyAAw4Q5+dyOTZu3Dg2Y8YMtmjRIvb444+znj17suuuu06UWb58OauoqGBXXXUVe+edd9hvfvMblkql2Jw5c0SZBx54gJWUlLA//vGP7O2332YXX3wxq6mpYevWrWu3viiEH//4x6xHjx7s0UcfZR999BF78MEHWZcuXditt94qyiRjS+K0005jY8aMYc899xx7//332Q033MCqqqrYxx9/zBjbc/vq8ccfZ9/5znfYQw89xACwhx9+WPm9M/VLnLa0JQr11Z/+9Cf2gx/8gN11110MAFu0aJFRx54yh0X1VV1dHZsxYwb7+9//zt577z02b948tt9++7EpU6Yodexq4yoRfgi+9a1vsYMOOij0d8/zWG1tLfvpT38qjtXV1bHS0lL2t7/9jTHG2DvvvMMAsFdffVWUeeKJJ5jjOOyTTz5hjDH2u9/9jnXr1o01NjYq1x41apT4ftppp7FZs2Yp1582bRr78pe/3LKbbEN8/etfZ8OHD2ee57G6ujqWyWTYgw8+KH5/9913GQA2b948xpj/wrmuy9auXSvK3H777ayqqkr0zTe/+U02duxY5Tqnn346mzlzpvi+3377sUsuuUR8z+fzrF+/fuymm25qk/tsDmbNmsUuvPBC5dgpp5zCZs+ezRhLxhbFjh07WCqVYo8++qhyfJ999mHf+c53kr4KoC9Snalf4rSlPWETfjg++ugjq/Czp85hUX3FsWDBAgaArVy5kjG2a46rRO1F8O9//xtTp07FF77wBfTu3RuTJ0/GXXfdJX7/6KOPsHbtWsyYMUMcq66uxrRp0zBv3jwAwLx581BTU4OpU6eKMjNmzIDrupg/f74oc8ghh6CkpESUmTlzJpYuXYrNmzeLMvQ6vAy/TmdDU1MT/vKXv+DCCy+E4zhYuHAhstmscg+jR4/GoEGDlL4aP348+vTpI8rMnDkTW7Zswdtvvy3KRPVDU1MTFi5cqJRxXRczZszoVH11wAEHYO7cuVi2bBkA4I033sCLL76IY445BkAytihyuRzy+TzKysqU4+Xl5XjxxReTvgpBZ+qXOG3p7EjmsHDU19fDcRzU1NQA2DXHVSL8ECxfvhy333479tprL/z3v//FV7/6VVx++eW47777AABr164FAGWg8+/8t7Vr16J3797K7+l0Gt27d1fK2Oqg1wgrw3/vbHjkkUdQV1eH888/H4Df/pKSEvFycOh91dx+2LJlC3bu3ImNGzcin893+r669tprccYZZ2D06NHIZDKYPHkyrrjiCsyePRtAMrYounbtiunTp+PGG2/Ep59+inw+j7/85S+YN28e1qxZk/RVCDpTv8RpS2dHMofZ0dDQgG9961s488wzRZLSXXFcJVndCTzPw9SpU/GTn/wEADB58mQsWbIEd9xxB84777wObl3nxh/+8Accc8wx6NevX0c3pVPiH//4B/7617/i/vvvx9ixY7F48WJcccUV6NevXzK2LPjzn/+MCy+8EP3790cqlcI+++yDM888EwsXLuzopiVIsMcim83itNNOA2MMt99+e0c3p0VImB+Cvn37YsyYMcqxvffeG6tWrQIA1NbWAoBh7b9u3TrxW21tLdavX6/8nsvlsGnTJqWMrQ56jbAy/PfOhJUrV+Kpp57CF7/4RXGstrYWTU1NqKurU8rqfdXcfqiqqkJ5eTl69uyJVCrV6fvqmmuuEezP+PHjcc455+DKK6/ETTfdBCAZWzqGDx+O5557Dtu2bcPq1auxYMECZLNZDBs2LOmrEHSmfonTls6OZA5TwQWflStX4sknnxSsD7BrjqtE+CE48MADsXTpUuXYsmXLMHjwYADA0KFDUVtbi7lz54rft2zZgvnz52P69OkAgOnTp6Ourk7ZoT799NPwPA/Tpk0TZZ5//nlks1lR5sknn8SoUaPQrVs3UYZeh5fh1+lMuOeee9C7d2/MmjVLHJsyZQoymYxyD0uXLsWqVauUvnrrrbeUl4a/VFwILdQPJSUlmDJlilLG8zzMnTu3U/XVjh074Lrq65ZKpeB5HoBkbIWhsrISffv2xebNm/Hf//4XJ554YtJXIehM/RKnLZ0dyRwmwQWf999/H0899RR69Oih/L5LjquizKN3cyxYsICl02n24x//mL3//vvsr3/9K6uoqGB/+ctfRJmbb76Z1dTUsH/961/szTffZCeeeKLVlXTy5Mls/vz57MUXX2R77bWX4vJXV1fH+vTpw8455xy2ZMkS9sADD7CKigrD5S+dTrOf/exn7N1332U33HBDp3NHZsz3Shg0aBD71re+Zfz2la98hQ0aNIg9/fTT7LXXXmPTp09n06dPF79zN9GjjjqKLV68mM2ZM4f16tXL6iZ6zTXXsHfffZfddtttVjfR0tJSdu+997J33nmHfelLX2I1NTWKB0ZH47zzzmP9+/cXru4PPfQQ69mzJ/vmN78pyiRjS2LOnDnsiSeeYMuXL2f/+9//2MSJE9m0adNYU1MTY2zP7autW7eyRYsWsUWLFjEA7Be/+AVbtGiR8LrpTP0Spy1tiUJ99dlnn7FFixaxxx57jAFgDzzwAFu0aBFbs2aNqGNPmcOi+qqpqYmdcMIJbMCAAWzx4sVKeBPqubWrjatE+NHwn//8h40bN46Vlpay0aNHszvvvFP53fM89r3vfY/16dOHlZaWsiOPPJItXbpUKfPZZ5+xM888k3Xp0oVVVVWxCy64gG3dulUp88Ybb7CDDjqIlZaWsv79+7Obb77ZaMs//vEPNnLkSFZSUsLGjh3LHnvssda/4Rbiv//9LwNg9AFjjO3cuZN97WtfY926dWMVFRXs5JNPViYWxhhbsWIFO+aYY1h5eTnr2bMn+8Y3vsGy2axS5plnnmGTJk1iJSUlbNiwYeyee+4xrvWb3/yGDRo0iJWUlLD99tuPvfLKK616ny3Fli1b2Ne//nU2aNAgVlZWxoYNG8a+853vKJNHMrYk/v73v7Nhw4axkpISVltbyy655BJWV1cnft9T++qZZ55hAIx/5513HmOsc/VLnLa0JQr11T333GP9/YYbbhB17ClzWFRf8VAAtn88phtju964chgjIWYTJEiQIEGCBAl2cyQ2PwkSJEiQIEGCPQqJ8JMgQYIECRIk2KOQCD8JEiRIkCBBgj0KifCTIEGCBAkSJNijkAg/CRIkSJAgQYI9ConwkyBBggQJEiTYo5AIPwkSJEiQIEGCPQqJ8JMgQYIECRIk2KOQCD8JEiRoNp599lk4jmMkf2wvzJ07F3vvvTfy+bw4duedd2LgwIFwXRe/+tWvOqRdzUVTUxOGDBmC1157raObkiDBbo0kwnOCBAli4bDDDsOkSZMUgaKpqQmbNm1Cnz594DhOu7dpypQpuOqqqzB79mwAfpLDnj174he/+AVOPfVUVFdXo6Kiot3b1RL89re/xcMPP2wkeEyQIEHrIWF+EiRI0GyUlJSgtra2QwSfF198ER9++CFOPfVUcWzVqlXIZrOYNWsW+vbtaxV8mpqa2rOZRWP27Nl48cUX8fbbb3d0UxIk2G2RCD8JEiQoiPPPPx/PPfccbr31VjiOA8dxsGLFCkPtde+996KmpgaPPvooRo0ahYqKCnz+85/Hjh07cN9992HIkCHo1q0bLr/8ckVV1djYiKuvvhr9+/dHZWUlpk2bhmeffTayTQ888AA+97nPoaysTFx7/PjxAIBhw4aJNn7/+9/HpEmTcPfdd2Po0KGi/Jw5c3DQQQehpqYGPXr0wHHHHYcPP/xQ1L9ixQo4joN//OMfOPjgg1FeXo59990Xy5Ytw6uvvoqpU6eiS5cuOOaYY7BhwwalbXfffTf23ntvlJWVYfTo0fjd734nfmtqasKll16Kvn37oqysDIMHD8ZNN90kfu/WrRsOPPBAPPDAA8U/qAQJEsRCuqMbkCBBgs6PW2+9FcuWLcO4cePwwx/+EADQq1cvrFixwii7Y8cO/PrXv8YDDzyArVu34pRTTsHJJ5+MmpoaPP7441i+fDlOPfVUHHjggTj99NMBAJdeeineeecdPPDAA+jXrx8efvhhHH300Xjrrbew1157Wdv0wgsv4KyzzhLfTz/9dAwcOBAzZszAggULMHDgQPTq1QsA8MEHH+D//u//8NBDDyGVSgEAtm/fjquuugoTJkzAtm3bcP311+Pkk0/G4sWL4bpyX3jDDTfgV7/6FQYNGoQLL7wQZ511Frp27Ypbb70VFRUVOO2003D99dfj9ttvBwD89a9/xfXXX4/f/va3mDx5MhYtWoSLL74YlZWVOO+88/DrX/8a//73v/GPf/wDgwYNwurVq7F69Wrl3vbbbz+88MILzXxaCRIkKIii88AnSJBgj8Shhx7Kvv71ryvHnnnmGQaAbd68mTHG2D333MMAsA8++ECU+fKXv8wqKirY1q1bxbGZM2eyL3/5y4wxxlauXMlSqRT75JNPlLqPPPJIdt1114W2p7q6mv3pT39Sji1atIgBYB999JE4dsMNN7BMJsPWr18feX8bNmxgANhbb73FGGPso48+YgDY3XffLcr87W9/YwDY3LlzxbGbbrqJjRo1SnwfPnw4u//++5W6b7zxRjZ9+nTGGGOXXXYZO+KII5jneaFtufXWW9mQIUMi25sgQYLmI2F+EiRI0KqoqKjA8OHDxfc+ffpgyJAh6NKli3Js/fr1AIC33noL+XweI0eOVOppbGxEjx49Qq+zc+dOocIqhMGDBwsWiOP999/H9ddfj/nz52Pjxo3wPA+Abzc0btw4UW7ChAlKuwEI9Zp+L9u3b8eHH36Iiy66CBdffLEok8vlUF1dDcBXIX7uc5/DqFGjcPTRR+O4447DUUcdpbStvLwcO3bsiHVvCRIkKB6J8JMgQYJWRSaTUb47jmM9xoWNbdu2IZVKYeHChUIlxUEFJh09e/bE5s2bY7WpsrLSOHb88cdj8ODBuOuuu9CvXz94nodx48YZBtG07dywWz9G7wUA7rrrLkybNk2ph9/bPvvsg48++ghPPPEEnnrqKZx22mmYMWMG/vnPf4qymzZtMoS1BAkStB4S4SdBggSxUFJSohgptxYmT56MfD6P9evX4+CDDy7qvHfeeadZ1/zss8+wdOlS3HXXXeKaL774YrPqoujTpw/69euH5cuXC/d7G6qqqnD66afj9NNPx+c//3kcffTR2LRpE7p37w4AWLJkCSZPntzi9iRIkMCORPhJkCBBLAwZMgTz58/HihUr0KVLF7FQtxQjR47E7Nmzce655+LnP/85Jk+ejA0bNmDu3LmYMGECZs2aZT1v5syZuO+++5p1zW7duqFHjx6488470bdvX6xatQrXXnttS25D4Ac/+AEuv/xyVFdX4+ijj0ZjYyNee+01bN68GVdddRV+8YtfoG/fvpg8eTJc18WDDz6I2tpa1NTUiDpeeOEF3Hjjja3SngQJEphIXN0TJEgQC1dffTVSqRTGjBmDXr16YdWqVa1W9z333INzzz0X3/jGNzBq1CicdNJJePXVVzFo0KDQc2bPno23334bS5cuLfp6ruvigQcewMKFCzFu3DhceeWV+OlPf9qSWxD44he/iLvvvhv33HMPxo8fj0MPPRT33nsvhg4dCgDo2rUrbrnlFkydOhX77rsvVqxYgccff1x4mM2bNw/19fX4/Oc/3yrtSZAggYkkwnOCBAl2WVxzzTXYsmULfv/733d0U1oNp59+OiZOnIhvf/vbHd2UBAl2WyTMT4IECXZZfOc738HgwYOFwfGujqamJowfPx5XXnllRzclQYLdGgnzkyBBggQJEiTYo5AwPwkSJEiQIEGCPQqJ8JMgQYIECRIk2KOQCD8JEiRIkCBBgj0KifCTIEGCBAkSJNijkAg/CRIkSJAgQYI9ConwkyBBggQJEiTYo5AIPwkSJEiQIEGCPQqJ8JMgQYIECRIk2KOQCD8JEiRIkCBBgj0K/x8oL4HTEZyrWQAAAABJRU5ErkJggg==", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.title('Tailbase pose estimation')\n", "plt.plot(tail_data['x_pos'],label='x_pos')\n", @@ -2633,29 +695,25 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's plot the head and tailbase positions on the same graph." + ] + }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.plot(head_data['x_pos'], head_data['y_pos'], label='head')\n", "plt.plot(tail_data['x_pos'], tail_data['y_pos'], label='tailbase')\n", "plt.xlabel('x_pos (pixels)')\n", "plt.ylabel('y_pos (pixels)')\n", "plt.legend()\n", - "plt.show()\n" + "plt.show()" ] } ], @@ -2675,7 +733,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.17" + "version": "3.9.18" } }, "nbformat": 4, From bb2514088aab291abbd027017456e95602894c4d Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Tue, 17 Oct 2023 01:16:52 +0000 Subject: [PATCH 175/176] final draft with all outputs and minor changes --- notebooks/tutorial.ipynb | 2380 ++++++++++++++++++++++++++++++++++++-- 1 file changed, 2304 insertions(+), 76 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index c1b4953..a7ebecf 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -145,9 +145,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-10-17 01:10:16,318][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", + "[2023-10-17 01:10:16,333][INFO]: Connected root@fakeservices.datajoint.io:3306\n" + ] + }, + { + "data": { + "text/plain": [ + "DataJoint connection (connected) root@fakeservices.datajoint.io:3306" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dj.conn()" ] @@ -168,18 +187,798 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-10-17 01:10:16,453][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n" + ] + } + ], "source": [ "from tutorial_pipeline import lab, subject, session, train, model " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "lab.Source\n", + "\n", + "\n", + "lab.Source\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele.Source\n", + "\n", + "\n", + "subject.Allele.Source\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Source->subject.Allele.Source\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Source\n", + "\n", + "\n", + "subject.Subject.Source\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Source->subject.Subject.Source\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Line\n", + "\n", + "\n", + "subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line->subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line.Allele\n", + "\n", + "\n", + "subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Line->subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele\n", + "\n", + "\n", + "subject.Allele\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Zygosity\n", + "\n", + "\n", + "subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Line.Allele\n", + "\n", + "\n", + "\n", + "\n", + "subject.Allele->subject.Allele.Source\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionDirectory\n", + "\n", + "\n", + "session.SessionDirectory\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project\n", + "\n", + "\n", + "lab.Project\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProjectPublication\n", + "\n", + "\n", + "lab.ProjectPublication\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->lab.ProjectPublication\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProjectKeywords\n", + "\n", + "\n", + "lab.ProjectKeywords\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->lab.ProjectKeywords\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProjectSourceCode\n", + "\n", + "\n", + "lab.ProjectSourceCode\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->lab.ProjectSourceCode\n", + "\n", + "\n", + "\n", + "\n", + "session.ProjectSession\n", + "\n", + "\n", + "session.ProjectSession\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->session.ProjectSession\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProjectUser\n", + "\n", + "\n", + "lab.ProjectUser\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project->lab.ProjectUser\n", + "\n", + "\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Strain\n", + "\n", + "\n", + "subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Strain->subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Organization\n", + "\n", + "\n", + "lab.Organization\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab.Organization\n", + "\n", + "\n", + "lab.Lab.Organization\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Organization->lab.Lab.Organization\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Strain\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.User\n", + "\n", + "\n", + "subject.Subject.User\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.User\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject.Lab\n", + "\n", + "\n", + "subject.Subject.Lab\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Lab\n", + "\n", + "\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->session.Session\n", + "\n", + "\n", + "\n", + "\n", + "subject.SubjectDeath\n", + "\n", + "\n", + "subject.SubjectDeath\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.SubjectDeath\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Source\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet.File\n", + "\n", + "\n", + "train.VideoSet.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.LabMembership\n", + "\n", + "\n", + "lab.LabMembership\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording.File\n", + "\n", + "\n", + "model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet\n", + "\n", + "\n", + "train.TrainingParamSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.Model\n", + "\n", + "\n", + "model.Model\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet->model.Model\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingTask\n", + "\n", + "\n", + "train.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet->train.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "train.ModelTraining\n", + "\n", + "\n", + "train.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.BodyPart\n", + "\n", + "\n", + "model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.Model.BodyPart\n", + "\n", + "\n", + "model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.BodyPart->model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "lab.Location\n", + "\n", + "\n", + "lab.Location\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionNote\n", + "\n", + "\n", + "session.SessionNote\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Protocol\n", + "\n", + "\n", + "lab.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Protocol->subject.Subject.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation\n", + "\n", + "\n", + "model.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation->model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "lab.Device\n", + "\n", + "\n", + "lab.Device\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording\n", + "\n", + "\n", + "model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Device->model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionDirectory\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.ProjectSession\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionNote\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "session.SessionExperimenter\n", + "\n", + "\n", + "session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "\n", + "session.Session.Attribute\n", + "\n", + "\n", + "session.Session.Attribute\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.Session.Attribute\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimationTask\n", + "\n", + "\n", + "model.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.Model->model.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "model.ModelEvaluation\n", + "\n", + "\n", + "model.ModelEvaluation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.Model->model.ModelEvaluation\n", + "\n", + "\n", + "\n", + "\n", + "model.Model->model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording->model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording->model.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "model.RecordingInfo\n", + "\n", + "\n", + "model.RecordingInfo\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording->model.RecordingInfo\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimationTask->model.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingTask->train.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "lab.User\n", + "\n", + "\n", + "lab.User\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.User->lab.LabMembership\n", + "\n", + "\n", + "\n", + "\n", + "lab.User->subject.Subject.User\n", + "\n", + "\n", + "\n", + "\n", + "lab.User->session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "\n", + "lab.User->lab.ProjectUser\n", + "\n", + "\n", + "\n", + "\n", + "lab.UserRole\n", + "\n", + "\n", + "lab.UserRole\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.UserRole->lab.LabMembership\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab\n", + "\n", + "\n", + "lab.Lab\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab->lab.LabMembership\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab->subject.Subject.Lab\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab->lab.Location\n", + "\n", + "\n", + "\n", + "\n", + "lab.Lab->lab.Lab.Organization\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProtocolType\n", + "\n", + "\n", + "lab.ProtocolType\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lab.ProtocolType->lab.Protocol\n", + "\n", + "\n", + "\n", + "\n", + "model.Model.BodyPart->model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet\n", + "\n", + "\n", + "train.VideoSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet->train.VideoSet.File\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet->train.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "subject.SubjectCull\n", + "\n", + "\n", + "subject.SubjectCull\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.SubjectDeath->subject.SubjectCull\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " dj.Diagram(subject) \n", @@ -199,9 +998,238 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "train.VideoSet.File\n", + "\n", + "\n", + "train.VideoSet.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation\n", + "\n", + "\n", + "model.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation->model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "model.Model.BodyPart\n", + "\n", + "\n", + "model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.Model.BodyPart->model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording.File\n", + "\n", + "\n", + "model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.Model\n", + "\n", + "\n", + "model.Model\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.Model->model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimationTask\n", + "\n", + "\n", + "model.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.Model->model.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "model.ModelEvaluation\n", + "\n", + "\n", + "model.ModelEvaluation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.Model->model.ModelEvaluation\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet\n", + "\n", + "\n", + "train.TrainingParamSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet->model.Model\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingTask\n", + "\n", + "\n", + "train.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet->train.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet\n", + "\n", + "\n", + "train.VideoSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet->train.VideoSet.File\n", + "\n", + "\n", + "\n", + "\n", + "train.VideoSet->train.TrainingTask\n", + "\n", + "\n", + "\n", + "\n", + "train.ModelTraining\n", + "\n", + "\n", + "train.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.RecordingInfo\n", + "\n", + "\n", + "model.RecordingInfo\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingTask->train.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording\n", + "\n", + "\n", + "model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording->model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording->model.RecordingInfo\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording->model.PoseEstimationTask\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimationTask->model.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "model.BodyPart\n", + "\n", + "\n", + "model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.BodyPart->model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dj.Diagram(model) + dj.Diagram(train)" ] @@ -229,7 +1257,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -249,9 +1277,75 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-10-17 01:10:44.124153: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-10-17 01:10:44.239990: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", + "2023-10-17 01:10:44.240025: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n", + "2023-10-17 01:10:44.263054: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2023-10-17 01:10:45.358939: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", + "2023-10-17 01:10:45.359109: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", + "2023-10-17 01:10:45.359123: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading DLC 2.3.7...\n", + "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n", + "--- DLC Model specification to be inserted ---\n", + "\tmodel_name: from_top_tracking_model_test\n", + "\tmodel_description: Model in example data: from_top_tracking model\n", + "\tscorer: DLCresnet50fromtoptrackingOct11shuffle1\n", + "\ttask: from_top_tracking\n", + "\tdate: Oct11\n", + "\titeration: 0\n", + "\tsnapshotindex: -1\n", + "\tshuffle: 1\n", + "\ttrainingsetindex: 0\n", + "\tproject_path: from_top_tracking-DataJoint-2023-10-11\n", + "\tparamset_idx: None\n", + "\t-- Template/Contents of config.yaml --\n", + "\t\tTask: from_top_tracking\n", + "\t\tscorer: DataJoint\n", + "\t\tdate: Oct11\n", + "\t\tmultianimalproject: False\n", + "\t\tidentity: None\n", + "\t\tproject_path: /workspaces/element-deeplabcut/example_data/inbox/from_top_tracking-DataJoint-2023-10-11\n", + "\t\tvideo_sets: {'/Users/milagros/Desktop/from_top_tracking-DataJoint-2023-10-11/videos/test.mp4': {'crop': '0, 500, 0, 500'}, '/Users/milagros/Desktop/from_top_tracking-DataJoint-2023-10-11/videos/train1.mp4': {'crop': '0, 500, 0, 500'}}\n", + "\t\tbodyparts: ['head', 'tailbase']\n", + "\t\tstart: 0\n", + "\t\tstop: 1\n", + "\t\tnumframes2pick: 40\n", + "\t\tskeleton: [['bodypart1', 'bodypart2'], ['objectA', 'bodypart3']]\n", + "\t\tskeleton_color: black\n", + "\t\tpcutoff: 0.6\n", + "\t\tdotsize: 12\n", + "\t\talphavalue: 0.7\n", + "\t\tcolormap: rainbow\n", + "\t\tTrainingFraction: [0.95]\n", + "\t\titeration: 0\n", + "\t\tdefault_net_type: resnet_50\n", + "\t\tdefault_augmenter: default\n", + "\t\tsnapshotindex: -1\n", + "\t\tbatch_size: 8\n", + "\t\tcropping: False\n", + "\t\tx1: 0\n", + "\t\tx2: 640\n", + "\t\ty1: 277\n", + "\t\ty2: 624\n", + "\t\tcorner2move2: [50, 50]\n", + "\t\tmove2corner: True\n" + ] + } + ], "source": [ "model.Model.insert_new_model(model_name='from_top_tracking_model_test',\n", " dlc_config=config_file_rel,\n", @@ -269,9 +1363,135 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    model_name

    \n", + " User-friendly model name\n", + "
    \n", + "

    task

    \n", + " Task in the config yaml\n", + "
    \n", + "

    date

    \n", + " Date in the config yaml\n", + "
    \n", + "

    iteration

    \n", + " Iteration/version of this model\n", + "
    \n", + "

    snapshotindex

    \n", + " which snapshot for prediction (if -1, latest)\n", + "
    \n", + "

    shuffle

    \n", + " Shuffle (1) or not (0)\n", + "
    \n", + "

    trainingsetindex

    \n", + " Index of training fraction list in config.yaml\n", + "
    \n", + "

    scorer

    \n", + " Scorer/network name - DLC's GetScorerName()\n", + "
    \n", + "

    config_template

    \n", + " Dictionary of the config for analyze_videos()\n", + "
    \n", + "

    project_path

    \n", + " DLC's project_path in config relative to root\n", + "
    \n", + "

    model_prefix

    \n", + " \n", + "
    \n", + "

    model_description

    \n", + " \n", + "
    \n", + "

    paramset_idx

    \n", + " \n", + "
    from_top_tracking_model_testfrom_top_trackingOct110-110DLCresnet50fromtoptrackingOct11shuffle1=BLOB=from_top_tracking-DataJoint-2023-10-11Model in example data: from_top_tracking modelNone
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*model_name task date iteration snapshotindex shuffle trainingsetind scorer config_tem project_path model_prefix model_descript paramset_idx \n", + "+------------+ +------------+ +-------+ +-----------+ +------------+ +---------+ +------------+ +------------+ +--------+ +------------+ +------------+ +------------+ +------------+\n", + "from_top_track from_top_track Oct11 0 -1 1 0 DLCresnet50fro =BLOB= from_top_track Model in examp None \n", + " (Total: 1)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model.Model()" ] @@ -301,9 +1521,99 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    subject_nickname

    \n", + " \n", + "
    \n", + "

    sex

    \n", + " \n", + "
    \n", + "

    subject_birth_date

    \n", + " \n", + "
    \n", + "

    subject_description

    \n", + " \n", + "
    \n", + " \n", + "

    Total: 0

    \n", + " " + ], + "text/plain": [ + "*subject subject_nickna sex subject_birth_ subject_descri\n", + "+---------+ +------------+ +-----+ +------------+ +------------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "subject.Subject()" ] @@ -317,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -343,7 +1653,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -366,9 +1676,93 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    subject62021-06-02 14:04:22
    subject62021-06-03 14:43:10
    \n", + " \n", + "

    Total: 2

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet\n", + "+----------+ +------------+\n", + "subject6 2021-06-02 14:\n", + "subject6 2021-06-03 14:\n", + " (Total: 2)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "session.Session()" ] @@ -382,7 +1776,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -402,7 +1796,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -425,9 +1819,119 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    px_height

    \n", + " height in pixels\n", + "
    \n", + "

    px_width

    \n", + " width in pixels\n", + "
    \n", + "

    nframes

    \n", + " number of frames\n", + "
    \n", + "

    fps

    \n", + " (Hz) frames per second\n", + "
    \n", + "

    recording_datetime

    \n", + " Datetime for the start of the recording\n", + "
    \n", + "

    recording_duration

    \n", + " video duration (s) from nframes / fps\n", + "
    subject62021-06-02 14:04:2215005006000060None1000.0
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id px_height px_width nframes fps recording_date recording_dura\n", + "+----------+ +------------+ +------------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n", + "subject6 2021-06-02 14: 1 500 500 60000 60 None 1000.0 \n", + " (Total: 1)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "### RecordingInfo\n", "model.RecordingInfo.populate()\n", @@ -457,9 +1961,33 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-> model.VideoRecording\n", + "-> model.Model\n", + "---\n", + "task_mode=\"load\" : enum('load','trigger') # load results or trigger computation\n", + "pose_estimation_output_dir=\"\" : varchar(255) # output dir relative to the root dir\n", + "pose_estimation_params=null : longblob # analyze_videos params, if not default\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "'-> model.VideoRecording\\n-> model.Model\\n---\\ntask_mode=\"load\" : enum(\\'load\\',\\'trigger\\') # load results or trigger computation\\npose_estimation_output_dir=\"\" : varchar(255) # output dir relative to the root dir\\npose_estimation_params=null : longblob # analyze_videos params, if not default\\n'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model.PoseEstimationTask.describe()" ] @@ -491,16 +2019,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'subject': 'subject6',\n", + " 'session_datetime': '2021-06-02 14:04:22',\n", + " 'recording_id': '1'}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "recording_key" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -516,7 +2057,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -536,16 +2077,118 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    model_name

    \n", + " User-friendly model name\n", + "
    \n", + "

    task_mode

    \n", + " load results or trigger computation\n", + "
    \n", + "

    pose_estimation_output_dir

    \n", + " output dir relative to the root dir\n", + "
    \n", + "

    pose_estimation_params

    \n", + " analyze_videos params, if not default\n", + "
    subject62021-06-02 14:04:221from_top_tracking_model_testload./example_data/outbox/from_top_tracking-DataJoint-2023-10-11/videos/device_1_recording_1_model_from_top_tracking_100000_maxiters=BLOB=
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *model_name task_mode pose_estimatio pose_estim\n", + "+----------+ +------------+ +------------+ +------------+ +-----------+ +------------+ +--------+\n", + "subject6 2021-06-02 14: 1 from_top_track load ./example_data =BLOB= \n", + " (Total: 1)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model.PoseEstimationTask()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -562,9 +2205,103 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    model_name

    \n", + " User-friendly model name\n", + "
    \n", + "

    pose_estimation_time

    \n", + " time of generation of this set of DLC results\n", + "
    subject62021-06-02 14:04:221from_top_tracking_model_test2023-10-12 15:21:47
    \n", + " \n", + "

    Total: 1

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *model_name pose_estimatio\n", + "+----------+ +------------+ +------------+ +------------+ +------------+\n", + "subject6 2021-06-02 14: 1 from_top_track 2023-10-12 15:\n", + " (Total: 1)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model.PoseEstimation()" ] @@ -573,16 +2310,139 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The most critical table is the `PoseEstimation.BodyPartPosition`. " + "The most critical table is the `PoseEstimation.BodyPartPosition` because it will contain the results of the inference. " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " uses DeepLabCut h5 output for body part position\n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    subject

    \n", + " \n", + "
    \n", + "

    session_datetime

    \n", + " \n", + "
    \n", + "

    recording_id

    \n", + " \n", + "
    \n", + "

    model_name

    \n", + " User-friendly model name\n", + "
    \n", + "

    body_part

    \n", + " \n", + "
    \n", + "

    frame_index

    \n", + " frame index in model\n", + "
    \n", + "

    x_pos

    \n", + " \n", + "
    \n", + "

    y_pos

    \n", + " \n", + "
    \n", + "

    z_pos

    \n", + " \n", + "
    \n", + "

    likelihood

    \n", + " \n", + "
    subject62021-06-02 14:04:221from_top_tracking_model_testhead=BLOB==BLOB==BLOB==BLOB==BLOB=
    subject62021-06-02 14:04:221from_top_tracking_model_testtailbase=BLOB==BLOB==BLOB==BLOB==BLOB=
    \n", + " \n", + "

    Total: 2

    \n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *model_name *body_part frame_inde x_pos y_pos z_pos likelihood\n", + "+----------+ +------------+ +------------+ +------------+ +-----------+ +--------+ +--------+ +--------+ +--------+ +--------+\n", + "subject6 2021-06-02 14: 1 from_top_track head =BLOB= =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject6 2021-06-02 14: 1 from_top_track tailbase =BLOB= =BLOB= =BLOB= =BLOB= =BLOB= \n", + " (Total: 2)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### Results\n", "model.PoseEstimation.BodyPartPosition()" ] }, @@ -590,16 +2450,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "After pose estimation, entries related to the task include `subject`, `session`, `recording_id`, `model name`, and each detected `body_part` (two entries in this case).\n", + "After pose estimation, entries related to the task include: `subject`, `session`, `recording_id`, `model name`, and each detected `body_part` (two entries in this case).\n", "\n", - "Entries contain `frame_index`, `x_pos` and `y_pos` positions, and `likelihood` (`z_pos` is zero). This structure is familiar to DeepLabCut users.\n", + "Entries also contain `frame_index`, `x_pos` and `y_pos` positions, and `likelihood` (`z_pos` is zero). This structure is familiar to DeepLabCut users.\n", "\n", - "These results can be fetched in a Pandas DataFrame structure: " + "Finally, these results can be fetched in a Pandas DataFrame structure: " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -608,9 +2468,100 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    subjectsession_datetimerecording_idmodel_namebody_partframe_indexx_posy_posz_poslikelihood
    0subject62021-06-02 14:04:221from_top_tracking_model_testhead[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...[273.9966125488281, 274.1033630371094, 274.032...[314.97100830078125, 315.1459655761719, 315.13...None[0.999998927116394, 0.999998927116394, 0.99999...
    1subject62021-06-02 14:04:221from_top_tracking_model_testtailbase[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...[254.29002380371094, 254.2755584716797, 254.26...[275.48602294921875, 275.44000244140625, 275.4...None[0.9999996423721313, 0.9999996423721313, 0.999...
    \n", + "
    " + ], + "text/plain": [ + " subject session_datetime recording_id model_name \\\n", + "0 subject6 2021-06-02 14:04:22 1 from_top_tracking_model_test \n", + "1 subject6 2021-06-02 14:04:22 1 from_top_tracking_model_test \n", + "\n", + " body_part frame_index \\\n", + "0 head [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n", + "1 tailbase [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n", + "\n", + " x_pos \\\n", + "0 [273.9966125488281, 274.1033630371094, 274.032... \n", + "1 [254.29002380371094, 254.2755584716797, 254.26... \n", + "\n", + " y_pos z_pos \\\n", + "0 [314.97100830078125, 315.1459655761719, 315.13... None \n", + "1 [275.48602294921875, 275.44000244140625, 275.4... None \n", + "\n", + " likelihood \n", + "0 [0.999998927116394, 0.999998927116394, 0.99999... \n", + "1 [0.9999996423721313, 0.9999996423721313, 0.999... " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df" ] @@ -631,9 +2582,251 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    indexsubjectsession_datetimerecording_idmodel_namebody_partframe_indexx_posy_posz_poslikelihood
    00subject62021-06-02 14:04:221from_top_tracking_model_testhead0273.996613314.971008None0.999999
    10subject62021-06-02 14:04:221from_top_tracking_model_testhead1274.103363315.145966None0.999999
    20subject62021-06-02 14:04:221from_top_tracking_model_testhead2274.032654315.133331None0.999999
    30subject62021-06-02 14:04:221from_top_tracking_model_testhead3274.025238315.152283None0.999999
    40subject62021-06-02 14:04:221from_top_tracking_model_testhead4274.073181315.173248None0.999999
    ....................................
    1199951subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59995323.29388433.214066None1.0
    1199961subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59996321.60226432.794708None1.0
    1199971subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59997320.17398132.857304None1.0
    1199981subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59998318.70861833.147358None0.999999
    1199991subject62021-06-02 14:04:221from_top_tracking_model_testtailbase59999317.67410333.861454None1.0
    \n", + "

    120000 rows × 11 columns

    \n", + "
    " + ], + "text/plain": [ + " index subject session_datetime recording_id \\\n", + "0 0 subject6 2021-06-02 14:04:22 1 \n", + "1 0 subject6 2021-06-02 14:04:22 1 \n", + "2 0 subject6 2021-06-02 14:04:22 1 \n", + "3 0 subject6 2021-06-02 14:04:22 1 \n", + "4 0 subject6 2021-06-02 14:04:22 1 \n", + "... ... ... ... ... \n", + "119995 1 subject6 2021-06-02 14:04:22 1 \n", + "119996 1 subject6 2021-06-02 14:04:22 1 \n", + "119997 1 subject6 2021-06-02 14:04:22 1 \n", + "119998 1 subject6 2021-06-02 14:04:22 1 \n", + "119999 1 subject6 2021-06-02 14:04:22 1 \n", + "\n", + " model_name body_part frame_index x_pos \\\n", + "0 from_top_tracking_model_test head 0 273.996613 \n", + "1 from_top_tracking_model_test head 1 274.103363 \n", + "2 from_top_tracking_model_test head 2 274.032654 \n", + "3 from_top_tracking_model_test head 3 274.025238 \n", + "4 from_top_tracking_model_test head 4 274.073181 \n", + "... ... ... ... ... \n", + "119995 from_top_tracking_model_test tailbase 59995 323.293884 \n", + "119996 from_top_tracking_model_test tailbase 59996 321.602264 \n", + "119997 from_top_tracking_model_test tailbase 59997 320.173981 \n", + "119998 from_top_tracking_model_test tailbase 59998 318.708618 \n", + "119999 from_top_tracking_model_test tailbase 59999 317.674103 \n", + "\n", + " y_pos z_pos likelihood \n", + "0 314.971008 None 0.999999 \n", + "1 315.145966 None 0.999999 \n", + "2 315.133331 None 0.999999 \n", + "3 315.152283 None 0.999999 \n", + "4 315.173248 None 0.999999 \n", + "... ... ... ... \n", + "119995 33.214066 None 1.0 \n", + "119996 32.794708 None 1.0 \n", + "119997 32.857304 None 1.0 \n", + "119998 33.147358 None 0.999999 \n", + "119999 33.861454 None 1.0 \n", + "\n", + "[120000 rows x 11 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = df.explode(['frame_index', 'x_pos', 'y_pos', 'likelihood']).reset_index()\n", "df" @@ -643,7 +2836,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As mentioned earlier, you can confirm these results by the number of entries. There are 66000 frames for each body part, matching the `n_frames` from the `RecordingInfo` table." + "As mentioned earlier, you can confirm these results by the number of entries. There are 60000 frames for each body part, matching the `nframes` from the `RecordingInfo` table." ] }, { @@ -657,14 +2850,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First, separate the data for the head and tailbase and then plot the head pose estimation and tailbase pose estimation." + "First, separate the data for the head and tailbase. \n", + "\n", + "Then plot (1) the head pose estimation, and (2) the tailbase pose estimation." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAHHCAYAAABZbpmkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOx9d7wdRd3+s+fclt6Tm5BKCIFAIBAEIgF9qUIQkUgRaVL0hyAq5VXe15emFBEBwYiICqiIAgFFaggGDAk9hMQAIYH0Xm/qLeec/f2xOzPfaVvO3T03uezz+ST3nD2zM7O7szPfeb7NcV3XRYYMGTJkyJAhQztFrq07kCFDhgwZMmTIkCYyYSdDhgwZMmTI0K6RCTsZMmTIkCFDhnaNTNjJkCFDhgwZMrRrZMJOhgwZMmTIkKFdIxN2MmTIkCFDhgztGpmwkyFDhgwZMmRo18iEnQwZMmTIkCFDu0Ym7GTIkCFDhgwZ2jUyYSdDhgxGXHDBBRg6dGhbd2O3xUMPPQTHcbB48eK27oqEoUOH4oILLmjrbmTIUFFkwk6GDG0ItiC+8847xt+/+MUvYv/9969wrzLEwS233IK///3vbd0NCTNnzsQNN9yAzZs3t3VXMmTYJZAJOxkyZMjQCtiEnXPPPRc7d+7EkCFDKt6nmTNn4sYbbzQKO/Pnz8cDDzxQ8T5lyNCWqGrrDmTIkCFDe0Q+n0c+n2/rbmiora1t6y5kyFBxZMxOhgy7If785z9j7Nix6NChA3r27ImzzjoLy5Ytk8pMnz4dp59+OgYPHoza2loMGjQIP/jBD7Bz506tvr///e/Yf//9UVdXh/333x9PPfVU5L4MHToUJ598MqZMmYIxY8agrq4Oo0aNwpNPPqmV/fTTT3H66aejZ8+e6NixIw4//HA8++yzWrl7770X++23Hzp27IgePXrgkEMOwV/+8hepzIoVK3DhhReiX79+qK2txX777Yc//OEPkfsd5R4uWLAAEydORH19Perq6jBw4ECcddZZaGhoAAA4joPt27fj4YcfhuM4cByH28OYbHbYvXrllVdwyCGHoEOHDhg9ejReeeUVAMCTTz6J0aNHo66uDmPHjsV7770n9WfOnDm44IILsOeee6Kurg719fW48MILsWHDBl7mhhtuwDXXXAMAGDZsGO8X64fJZifKc3nllVfgOA4ee+wx3HzzzRg4cCDq6upwzDHHYOHChZHve4YMbYGM2cmQYRdAQ0MD1q9frx1vaWnRjt188834v//7P5xxxhm4+OKLsW7dOtx777046qij8N5776F79+4AgMcffxw7duzApZdeil69euGtt97Cvffei+XLl+Pxxx/n9U2ZMgUTJ07EqFGjcOutt2LDhg345je/iYEDB0bu/4IFC3DmmWfi//2//4fzzz8fDz74IE4//XS88MILOO644wAAa9aswec//3ns2LEDV1xxBXr16oWHH34Yp5xyCp544gl89atfBQA88MADuOKKK/C1r30N3/ve99DY2Ig5c+bgzTffxNlnn83rOvzww+E4Di6//HL06dMHzz//PC666CJs2bIF3//+9wP7G+UeNjc344QTTkBTUxO++93vor6+HitWrMAzzzyDzZs3o1u3bvjTn/6Eiy++GIceeii+9a1vAQCGDx8e2PbChQtx9tln49vf/jbOOecc3HHHHfjyl7+M3/zmN/if//kffOc73wEA3HrrrTjjjDMwf/585HLevvSll17Cp59+im9+85uor6/HvHnz8Nvf/hbz5s3DG2+8AcdxcNppp+Hjjz/Go48+irvuugu9e/cGAPTp08fYn6jPheG2225DLpfD1VdfjYaGBtx+++34xje+gTfffDPwujNkaFO4GTJkaDM8+OCDLoDAf/vttx8vv3jxYjefz7s333yzVM/cuXPdqqoq6fiOHTu09m699VbXcRx3yZIl/NiYMWPc/v37u5s3b+bHpkyZ4gJwhwwZEnoNQ4YMcQG4kydP5scaGhrc/v37uwcddBA/9v3vf98F4E6fPp0f27p1qzts2DB36NChbrFYdF3Xdb/yla9I12zCRRdd5Pbv399dv369dPyss85yu3XrZrx2hqj38L333nMBuI8//nhgXzp16uSef/752nH2bBctWsSPsXs1c+ZMfuzFF190AbgdOnSQnsv999/vAnCnTZvGj5mu69FHH3UBuP/+97/5sZ///Oda27QPtL9Rn8u0adNcAO6+++7rNjU18bK//OUvXQDu3LlzjfcnQ4ZdAZkaK0OGXQCTJk3CSy+9pP074IADpHJPPvkkSqUSzjjjDKxfv57/q6+vx4gRIzBt2jRetkOHDvzz9u3bsX79enz+85+H67pcPbJq1SrMnj0b559/Prp168bLH3fccRg1alTk/g8YMEBiALp27YrzzjsP7733HlavXg0AeO6553DooYdi/PjxvFznzp3xrW99C4sXL8YHH3wAAOjevTuWL1+Ot99+29iW67qYPHkyvvzlL8N1Xek+nHDCCWhoaMCsWbOsfY16D9n9ePHFF7Fjx47I9yIMo0aNwrhx4/j3ww47DABw9NFHY/DgwdrxTz/9lB+jz7SxsRHr16/H4YcfDgCB1xyEqM+F4Zvf/CZqamr49yOPPFLrZ4YMuxoyNVaGDLsADj30UBxyyCHa8R49ekjqrQULFsB1XYwYMcJYT3V1Nf+8dOlSXHfddXj66aexadMmqRyzOVmyZAkAGOsbOXJk5AV0r732guM40rG9994bALB48WLU19djyZIlfAGn2HfffXlf9t9/f/zwhz/E1KlTceihh2KvvfbC8ccfj7PPPhtHHHEEAGDdunXYvHkzfvvb3+K3v/2tsT9r16619jXqPRw2bBiuvPJK3HnnnXjkkUdw5JFH4pRTTsE555wjCYZxQQUaQAhVgwYNMh6nz27jxo248cYb8de//lW7RvZM4yLqc7H1v0ePHlo/M2TY1ZAJOxky7EYolUpwHAfPP/+80dOnc+fOAIBisYjjjjsOGzduxA9/+EPss88+6NSpE1asWIELLrgApVKp0l2PjH333Rfz58/HM888gxdeeAGTJ0/Gr3/9a1x33XW48cYbed/POeccnH/++cY6VEaMIuo9BIBf/OIXuOCCC/CPf/wDU6ZMwRVXXIFbb70Vb7zxRiybJgqbh5btuOu6/PMZZ5yBmTNn4pprrsGYMWPQuXNnlEolfOlLX6rYM43SzwwZdjVkwk6GDLsRhg8fDtd1MWzYMM6cmDB37lx8/PHHePjhh3Heeefx4y+99JJUjsWAWbBggVbH/PnzI/dr4cKFcF1XYnc+/vhjAOBRmIcMGWKs86OPPpL6AgCdOnXCmWeeiTPPPBPNzc047bTTcPPNN+Paa69Fnz590KVLFxSLRRx77LGR+8gQ9R4yjB49GqNHj8aPf/xjzJw5E0cccQR+85vf4Kc//SkAaIxWWti0aRNefvll3Hjjjbjuuuv4cdOzi9OnOM8lQ4bdFZnNToYMuxFOO+005PN53HjjjdpO2nVd7oLMdt+0jOu6+OUvfymd079/f4wZMwYPP/ywpAZ56aWXNFuNIKxcuVJyV9+yZQv++Mc/YsyYMaivrwcAnHTSSXjrrbfw+uuv83Lbt2/Hb3/7WwwdOpTbCFE3agCoqanBqFGj4LouWlpakM/nMXHiREyePBn/+c9/tL6sW7cusK9R7+GWLVtQKBSk30ePHo1cLoempiZ+rFOnThWJVGx6pgBw9913a2U7deoEAJH6FfW5ZMiwOyNjdjJk2I0wfPhw/PSnP8W1116LxYsX49RTT0WXLl2waNEiPPXUU/jWt76Fq6++Gvvssw+GDx+Oq6++GitWrEDXrl0xefJko13FrbfeigkTJmD8+PG48MILsXHjRh7nZtu2bZH6tffee+Oiiy7C22+/jX79+uEPf/gD1qxZgwcffJCX+dGPfoRHH30UJ554Iq644gr07NkTDz/8MBYtWoTJkydz9+rjjz8e9fX1OOKII9CvXz98+OGH+NWvfoUJEyagS5cuADz352nTpuGwww7DJZdcglGjRmHjxo2YNWsWpk6dio0bN7b6Hv7rX//C5ZdfjtNPPx177703CoUC/vSnP3Fhi2Hs2LGYOnUq7rzzTgwYMADDhg0z2sC0Fl27dsVRRx2F22+/HS0tLdhjjz0wZcoULFq0SCs7duxYAMD//u//4qyzzkJ1dTW+/OUvcyGIIupzyZBht0al3b8yZMggwNyT3377bePvX/jCF4xu2JMnT3bHjx/vdurUye3UqZO7zz77uJdddpk7f/58XuaDDz5wjz32WLdz585u79693UsuucR9//33XQDugw8+qNW37777urW1te6oUaPcJ5980j3//PMju55PmDDBffHFF90DDjjAra2tdffZZx+jy/Ynn3zifu1rX3O7d+/u1tXVuYceeqj7zDPPSGXuv/9+96ijjnJ79erl1tbWusOHD3evueYat6GhQSq3Zs0a97LLLnMHDRrkVldXu/X19e4xxxzj/va3vw3tc5R7+Omnn7oXXnihO3z4cLeurs7t2bOn+1//9V/u1KlTpXo++ugj96ijjnI7dOjgAuBu3TbX8wkTJmh9AeBedtll0rFFixa5ANyf//zn/Njy5cvdr371q2737t3dbt26uaeffrq7cuVKF4B7/fXXS+f/5Cc/cffYYw83l8tJ/VBdz1032nNhrufqc2X9VMdUhgy7EhzXzazKMmTIUD6GDh2K/fffH88880xbdyVDhgwZjMj4yQwZMmTIkCFDu0Ym7GTIkCFDhgwZ2jUyYSdDhgwZMmTI0K6R2exkyJAhQ4YMGdo1MmYnQ4YMGTJkyNCukQk7GTJkyJAhQ4Z2jSyoILxcOStXrkSXLl0qFvo9Q4YMGTJkyNA6uK6LrVu3YsCAAYEBMDNhB16oezXjcIYMGTJkyJBh98CyZcsCk/Nmwg7AQ9AvW7YMXbt2bePeZMiQIUOGDBmiYMuWLRg0aBBfx23IhB2IDMFdu3bNhJ0MGTJkyJBhN0OYCUpmoJwhQ4YMGTJkaNfIhJ0MGTJkyJAhQ7tGJuxkyJAhQ4YMGdo1MpudiCiVSmhubm7rbrRL1NTUBLoMZsiQIUOGDK1BJuxEQHNzMxYtWoRSqdTWXWmXyOVyGDZsGGpqatq6KxkyZMiQoR0iE3ZC4LouVq1ahXw+j0GDBmUMRMJgAR1XrVqFwYMHZ0EdM2TIkCFD4siEnRAUCgXs2LEDAwYMQMeOHdu6O+0Sffr0wcqVK1EoFFBdXd3W3cmQIUOGDO0MGU0RgmKxCACZiiVFsHvL7nWGDBkyZMiQJNpU2LnhhhvgOI70b5999uG/NzY24rLLLkOvXr3QuXNnTJw4EWvWrJHqWLp0KSZMmICOHTuib9++uOaaa1AoFBLva6ZeSQ/Zvc2QIUOGDGmizdVY++23H6ZOncq/V1WJLv3gBz/As88+i8cffxzdunXD5ZdfjtNOOw0zZswA4DEBEyZMQH19PWbOnIlVq1bhvPPOQ3V1NW655ZaKX0uGDBkyZMiQYddDmws7VVVVqK+v1443NDTg97//Pf7yl7/g6KOPBgA8+OCD2HffffHGG2/g8MMPx5QpU/DBBx9g6tSp6NevH8aMGYOf/OQn+OEPf4gbbrghUz1lyJAhQ4YMGdreZmfBggUYMGAA9txzT3zjG9/A0qVLAQDvvvsuWlpacOyxx/Ky++yzDwYPHozXX38dAPD6669j9OjR6NevHy9zwgknYMuWLZg3b561zaamJmzZskX6lyFDhgwZMmRon2hTYeewww7DQw89hBdeeAH33XcfFi1ahCOPPBJbt27F6tWrUVNTg+7du0vn9OvXD6tXrwYArF69WhJ02O/sNxtuvfVWdOvWjf8bNGhQsheWIUOGRFAquWhsyQzXM2TI0Dq0qbBz4okn4vTTT8cBBxyAE044Ac899xw2b96Mxx57LNV2r732WjQ0NPB/y5YtS7W9SmPdunWor6+X7JZmzpyJmpoavPzyy4Hn3nDDDRgzZgzuv/9+DBo0CB07dsQZZ5yBhoYGXqZUKuGmm27CwIEDUVtbizFjxuCFF17gvzc3N+Pyyy9H//79UVdXhyFDhuDWW29N/kIztHt89b6ZOPgnL2FbU/JOBxkyZPjsoM1tdii6d++OvffeGwsXLsRxxx2H5uZmbN68WWJ31qxZw2186uvr8dZbb0l1MG8tkx0QQ21tLWpra8vqo+u62NlGO80O1flInkt9+vTBH/7wB5x66qk4/vjjMXLkSJx77rm4/PLLccwxx4Sev3DhQjz22GP45z//iS1btuCiiy7Cd77zHTzyyCMAgF/+8pf4xS9+gfvvvx8HHXQQ/vCHP+CUU07BvHnzMGLECNxzzz14+umn8dhjj2Hw4MFYtmxZuxMoM1QG7y/bDAB4e/FG/NfIvm3bmQwZMuy22KWEnW3btuGTTz7Bueeei7Fjx6K6uhovv/wyJk6cCACYP38+li5dinHjxgEAxo0bh5tvvhlr165F377eRPjSSy+ha9euGDVqVCp93NlSxKjrXkyl7jB8cNMJ6FgT7ZGddNJJuOSSS/CNb3wDhxxyCDp16hSZXWlsbMQf//hH7LHHHgCAe++9FxMmTMAvfvEL1NfX44477sAPf/hDnHXWWQCAn/3sZ5g2bRruvvtuTJo0CUuXLsWIESMwfvx4OI6DIUOGlHfBGT7TcF2Xf67OIpdnyJChFWjTGeTqq6/Gq6++isWLF2PmzJn46le/inw+j69//evo1q0bLrroIlx55ZWYNm0a3n33XXzzm9/EuHHjcPjhhwMAjj/+eIwaNQrnnnsu3n//fbz44ov48Y9/jMsuu6xs5qY94Y477kChUMDjjz+ORx55JPI9GTx4MBd0AE+oLJVKmD9/PrZs2YKVK1fiiCOOkM454ogj8OGHHwIALrjgAsyePRsjR47EFVdcgSlTpiR3URk+M2gpCmEnn8tiMWXIYEKx5OKBf3+KOcs3t3VXdmm0KbOzfPlyfP3rX8eGDRvQp08fjB8/Hm+88Qb69OkDALjrrruQy+UwceJENDU14YQTTsCvf/1rfn4+n8czzzyDSy+9FOPGjUOnTp1w/vnn46abbkqtzx2q8/jgphNSqz+s7Tj45JNPsHLlSpRKJSxevBijR49OqWcyDj74YCxatAjPP/88pk6dijPOOAPHHnssnnjiiYq0n6F9oKkg1MU1VZmwkyGDCY+/sww3P+dtNBffNqGNe7Prok2Fnb/+9a+Bv9fV1WHSpEmYNGmStcyQIUPw3HPPJd01KxzHiaxKaks0NzfjnHPOwZlnnomRI0fi4osvxty5c7m6LwhLly7FypUrMWDAAADAG2+8gVwuh5EjR6Jr164YMGAAZsyYgS984Qv8nBkzZuDQQw/l37t27YozzzwTZ555Jr72ta/hS1/6EjZu3IiePXsmf7EZ2iUaW0r8cz5TY2XIYMRHq7e2dRd2C+z6q3aGsvC///u/aGhowD333IPOnTvjueeew4UXXohnnnkm9Ny6ujqcf/75uOOOO7BlyxZcccUVOOOMM7jR9zXXXIPrr78ew4cPx5gxY/Dggw9i9uzZ3ID5zjvvRP/+/XHQQQchl8vh8ccfR319vRZGIEOGIFBmp0TsdzJkyJAhLjJhpx3ilVdewd13341p06aha9euAIA//elPOPDAA3Hffffh0ksvDTx/r732wmmnnYaTTjoJGzduxMknnyypD6+44go0NDTgqquuwtq1azFq1Cg8/fTTGDFiBACgS5cuuP3227FgwQLk83l87nOfw3PPPYdctjvPEAPNBcHsuJmwkyFBPPbOMvzhtUX43fmHYGCPjm3dnVYhezeiIRN22iG++MUvoqWlRTo2dOhQKVZOGC699FKrUJTL5XD99dfj+uuvN/5+ySWX4JJLLone4QwZDKAGysVSQMEMGWLiv5+YAwC4/YX5uOfrB7Vxb1qHUibrREK21c6QYRfFk7OWY+iPnsXQHz3b1l1pE7QQCaeYzegZEgJlQgql3V+Knrcy+ib2s4yM2fmMYb/99sOSJUuMv91///0V7k08FIolbGksoGenyiR4dV03UhDHtHDlY+/zz5t3NKN7x89OYtt1W5vw4jyR8iWj6jMkhX/OWcU/b9reElBy98CspZvbugu7BTJh5zOG5557Di0tLdiysxmO46BLXTX/rV+/fujSpQtuuOGGtutgAM787Rt4d8kmTL3yC9irb+dU2yqVXJxx/+vo1qEav7/gc6m2FQU7W4ro3tadqCBOuPvf2Li9mX8vZsJO6mhsKeIPMxahOpfDeZ8fgtqqeKEudhdM/WAN/zx+RO827EmGSiITdj5jGDJkCIqlEuat9DK9DxvQbbcJ2Pbukk0APPXOf39pn1TbWrRhO97x22spllCdr7zG99BhPfHWoo0AgKaW3Z9ujwMq6ACZXUIlcNXj7+NZn/Vo2NmCq08Y2cY9SgfH7NsXT7+/EgDQrUN1SOkM7QWZzc5nEHSTvDu69FZi4aPiX1vdI6q6aSx8tjN/lzJpJ3U8S9Q7v5q2sA17UjnsjvOfiiMzdioSMmHnM44PV23B9t0so7SL9Ccoyna1FF3866M1mDZ/bertUtB5+LPG7Kgo10D57cUbuZF3Zvdjx2fp3kibvXYgRJfNOrsusGNjsp3ZhZEJOxnw6brtbd2FWEhlXm5pBD59FSh46pMcMUze//oXceFD7+CbD76Nnc2VY1jorrOp8NkWdsrdgZ/+m9f559te+Cip7rQ7nPjL6W3dhYqBbpYWrd+O038zE9M+quxGJkmUPSc9eQlw+zBg0Wfj2WfCToaKMCVJIpVd6PPXAH88BXj5RgD2xJNbmyrnvUE3nU2fdTVWAs/8/lc/TaAn7RNqyoGj9u7TRj1JH9Tb/OHXl+DtxZvwzYfebrsOtRKvf7qhvBPnPu79fe3O5DqzCyMTdjJILMbuAOu6t30DsOT18qifWX/0/r7+KwCA7ZZUUp1EhbpMjRX8++YdzfjKpBn4w2uLpONnHzY4xV61X/z743XtQsVjQipXtfFTYMWsNGqOhbI2gs5nQwz4bFxlBgnq67C7TWnW/v58T+DBLwGv3NqK2j0pxzZnMIahEguBzOx8toWdMGZn0rSFeH/ZZtz0zAfS8U417dN9uhI4ZdJrbd2FVJAKM3zPQcAD/wVsWZl83QFobJEZ350t5TDAu9dmt1xkwk4GuK67Wxkohnb11Z+VX3l1BwD2xbVYcrFkw3aM/elLuPflBeW3EwFUvdjyGc+XECbsLFq/g3+mY5kKjH271Cber/aC3p31gJU92mkQy1Rnuo2VVZWO/9m/pO/3/qsML7qM2cnwWcLuI+oAf5ixCB+u2pJO5VXegqiurd07evE4Sq6Lr/3mdWza0YJfvPRxOn3wQW0LPuvpEsKu/4i9evHPNKcWfY5bGnf/aLlpoV/XOgBy3JkRfbu0VXdShWljd8iQHgnVXlmWZP02OR7V4+8sj1/JbmbGUC4yYacd4o9//CN69eqFpqYm6fipp56Kc88913yS//7fcMMNGDNmDO6//34MGjQIHTt2xBlnnCElES2VSrjpppswcOBA1NbWYsyYMXjhhRf4783Nzbj88svRv39/1NXVYciQIbj11vJUSw07W4z5oQK9R4YfXVZbAICqOu3Qd744HHl/Qii5XiqDSqAkMRSfHWGHLkaj9+gGIDy2Uodqoa7avnQWMPcJry4ixje2lD7zht42MGGyYWf7FwhNr1LnulbE16W7kgqyJAUD29urrFQ6mbCTwQTXBZq3t82/iAve6aefjmKxiKeffpofW7t2LZ599llceOGFxnNKZFFYuHAhHnvsMfzzn//ECy+8gPfeew/f+c53+O+//OUv8Ytf/AJ33HEH5syZgxNOOAGnnHIKFizw1Dr33HMPnn76aTz22GOYP38+HnnkEQwdOrSMmw3839//E/8ktxUqn61eYDX1VrMcWZVkWHb34I/lopEYY+d8r7gwGynqPdfjj8cAky8CFr+mPcetjbtXTKlKgY1rxmACQIea9rk8mEZSq95rlwjQFRR2mg3CTsfaMmzUPiNqrCxdRFy07ABuGdA2bf/PSqCmU2ixDh064Oyzz8aDDz6I008/HQDw5z//GYMHD8YXv/hFFIqGF5scamxsxB//+EfsscceAIB7770XEyZMwC9+8QvU19fjjjvuwA9/+EOcddZZAICf/exnmDZtGu6++25MmjQJS5cuxYgRIzB+/Hg4joMhQ4aUfcknH9Cfh3aPjFIrdu/dPe8dKly4AFjcrkoKO7QPnyWTna0GdVNYbizjr6vnwnWPlOv5jKsDbWD3t4oIje11zJmGUqv2EkUyXiuoEmo2OC3066Iz06HI1FgZdmdccsklmDJlClasWAEAeOihh3DBBRfAcRzjwkBf9sGDB3NBBwDGjRuHUqmE+fPnY8uWLVi5ciWOOOII6fwjjjgCH374IQDgggsuwOzZszFy5EhcccUVmDJlStnX0dWSu2Zor472k1rD7NR4CUbpPSq5Lldj/XvBuvLrjgm1D58V0ASUPYmtVCD8n7uDxIvJVWnqr0zYMYPdFxqGor2OOdN1tWpclNqGLTR5aPYpxwg/LWFn5yZgxbvp1F0GMmYnLqo7egxLW7UdEQcddBAOPPBA/PGPf8Txxx+PefPm4dlnn7WWTzKw4MEHH4xFixbh+eefx9SpU3HGGWfg2GOPxRNPPBG7Lpv6oiooRHqxNXYHzPVcNnJl6pSlG3YYz0oDn1WbHXqtLBR+mBqLjd8RzgrjcVPdGQTYYk9vc3sVDI1qrHLGRakE5HKKsNO2zE554zulPk86DNi2BjjvaWDPL6TTRgxkzE5cOI6nSmqLfzEl8IsvvhgPPfQQHnzwQRx77LEYNGiQtSylrJcuXYqVK4VA98YbbyCXy2HkyJHo2rUrBgwYgBkzZkjnz5gxA6NGjeLfu3btijPPPBMPPPAA/va3v2Hy5MnYuDF+HhbbJBToKr/8rdjtqKDzfK9ONXzHO6hndIGztaCXeN0/5rXbxUeFSdgJu3b2c7VDFh7X1ZidUjtVzbQW7P66kuq0nY43w9wRO/TG6v8Atw8FXp8kq813rG9d32JAjbEDlCnspGWzs22N93fek+nUHxOZsNOOcfbZZ2P58uV44IEHFMNk/YVYv014GNXV1eH888/H+++/j+nTp+OKK67AGWecgfr6egDANddcg5/97Gf429/+hvnz5+NHP/oRZs+eje9973sAgDvvvBOPPvooPvroI3z88cd4/PHHUV9fj+7du8e+Btt8G/pOl7uqcYFSNHD+54dyA1jTbiotqBPXe0s3VazttgR95txAOUyL5f9eB+KK6xa1cVLWDr6do1RysaqhEYAcz6m9CjuJGCg/fTnQ2AC8+D9AYac4/uhZFVPdrNi8UztW1rTXsKz1nQnCp6+kW39EZGqsdoxu3bph4sSJePbZZ3Hqqadqv+cchy+om3Y0c9Zir732wmmnnYaTTjoJGzduxMknn4xf//rX/LwrrrgCDQ0NuOqqq7B27VqMGjUKTz/9NEaMGAEA6NKlC26//XYsWLAA+Xwen/vc5/Dcc88hl4svW9vUF9oORn3LSy1Arvwgcqz67h2rUVedB7PbbKyg67J6jalFUS408fhCuwLYdeccIO/Ix2xg6qorq4iqtFTQduztdQFvDe7/twiEt4V4q7VXwdB0WbGHBc0WPk0Jq/HWA8BXx8buVxxsayoYPQvLYnbSFs72/XK69UdEJuy0c6xYsQLf+MY3UFsbbzG79NJLcemllxp/y+VyuP7663H99dcbf7/kkktwySWXxO6rCbbFSTu8Q0mGt2Ul8OyVwMHnA/udGqNF32bH/8bUV4zZoTmqendOV0BQ5bdUoigvexv4/XHAF68FvvjD5OsvA2zCzucczuxEVWPtn1tMDurMTmazo+Pef5kjgbfX3FimMRB7XGxeIj6//xf5tzXzyuhVdJRKLva//kXzb1EvY1vlHC2Q3zUicWdqrHaKTZs24amnnsIrr7yCyy67rOLtJzVR2iYhzaC6SYmo/NL/AZ/8C3j8/HgNKkwCU2oxoYeyK5VeOFNp7fn/9mp+5ZY0ai8LbOg4jsPve+hwMj0Lx8kMlCOAWgIeMLAb/9xeWTDTEIh8resXADd0Cy5TF/J7KzF/zVbrb5HH9x17JdSbCNhF4vhkzE47xUEHHYRNmzbhZz/7GUaOHMmP77fffliyZIlx8fi/2+5MJEfW5h3NWLpxB/bo3gG9Wsl+cKHDUYLsqSTHTsWeZevqMluUE4EyEx4h7Ag11sbtzXBdlwccTBraxJXG2sOMCAHvoneBmBtMUPbUWEzYicbsSOi5J0qK4+Qus4C7LtCyE6ipnMG7DdubxZj+yVf2x1cmec4H7VaNZTgWeVj86yfhZVJ+h7oZwnFMPHggJs9avmsK85mwkyFNLF682Hj8ueeew/adjfh03XbkHAed66qwxQ8R36tPHwzu1ws33nhjq9peutFzz16xeWerhR2muanJ5yRWRRPKHlb0wq2MfeESdgEgaizFbuZ/nvoPbj1tdKvaMjb+9OX4dssO3ICvicNpSDudegNbfHftYgtQFYFy3rIKWD0HGHF8KhO7sNlxkIsYzNEopLuursbaVbyx/nEZMPsR4PJ3gN4jop1TLHj3O5deJvfuHavx4wn74qfPfrjrCIYJwzRWIjPRC6bK3wccBKx8Tz424oQyexYN6nPZp74L9t+jKybPKsP2qBLYRYSdXaMXGSqGIUOGYPjwvTB42J4YMmxPHDl2fwweticGD9sTnTp3wYbtzeGVVBBs4atW4upo73SLEv+msUEtEQ0Kk8DVWAabHQB49K2l5bUThNVzgff+jAtKsstmKps2ajwYVUD81SHAX84A/jM5hQ6JCTsvqbHCDJQN2LlRW9h2iZ3vyzd5gg4AzLg72jnFAnDPQcCvxyU+EP5rZB/+ueQKwb79Cjv6scjjomW7UplBek6Z2VH7ms9Ff08qBtqPTNjJsCvAcRwcMLA7/963axnhxlOEKnSox63Y+Gnw7xQBdQk1lve3IokkiUquP4ThdSrzWI6Qu1GFneZt3t+PXwguVyao6jIfMTcW+/m14n7i4D+/pwlBu8RiMP0X4nPU7mxZDjQsBdbP99RfCYKyr8WSK+75rnCvUoCJIY2sslMDu5qowo2LyuhVdKivQlXO4fNTEmYIiYDOJbuAahzIhJ3ISHsQlUpumw7U7h099UW+DQZm0HVH9sZqDaRcWrLNDvfGMhgop4adwq316uq/8c+pjA6664qr+kspTD632SE71tDcWP7vOyAL6/13fizXvYusBRxuGcJzOecEgKTDQseavLjnu9zNSgZ0KHX1s51Hnno//12lMsOzePuB8joWEepzyeWc+ImK6w9Iulsy2IYI2GWYncxmJwTV1dVwHAfr1q1Dnz59yjdG3braCy3eqa/2U3NLEYs2bEeX2ioM6JG+wWJTSxFuoRmlnIPGRi+YWLG5CW6hBWs3FdClunWTnFsQqjBWv7Ws62LdunVwHAfV1brhHXt39+3fFW8t3iidlxgME5ZVjVUJYYe40b9UPETrU7Ig4zmu8JKScG5WY0U7V8389t+LL8HLzm2Y73oJXneZnS9DOUlrW5UOxdAF/5b06VKLAd07tH81lv+3vmsdfnHGgfjG796Mfq0desrfS0Wg737A2nTdzaUmlTFcnctFf08atwAr3vFia6WJ/xAVfCbs7B7I5/MYOHAgli9fbjX6DUWxBdi6yvvcfbv28+YdLdjWVMBaAE2bO5Td16hoKZawdksTcg5Qtd1rb82WRrT42dBz21vXh7WbBM1eszO8LsdxMHDgQOTzuuEl2+V3rqvC+9cfj8Xrt+Mrk2bEW2fv3A+4dAbQobv5d7rguCVgyetwnX143wDC7BhCtCeOnZtF14gwkso6TW0OYi+iaQk7TI3l8Gzz4Wosf+wa+vSr6ntxXPPPAaTV41YgKkvTsFx8TphRYwv9JUcOA9D+bXbYWBk/ojc61XpLYORrVW103CKwdn6S3QuF2teqvHhPAoX5YgG4zZ4yqGyUSsD9RwGjvwaM/7537KNnxO+ZsLP7oHPnzhgxYgRaWsrcUa37GHjhKu/zd94ClEjCk6YtxJOz1gIAXr7qi63oaTQs2bAdN/zjbXSurcI/Lh8PALj4F6/w31vbh4ufjFdXdXW1UdABhPoi5zjo1qEaHWu8crFYji3LgTtGAP9nCaRFJ7DVc4AHv4QxAAARLIw9soqkiyA2O5SpSIWVoNcedxH94B/J9oV1g0RQjmygzNSO0J/PIGetqHtXW8BNBq4mdK4Xn8thgwLAFs+8P8jzXHWYaDO7DLinJcS1Rn631Oe14RPxea9jgYVTgf0ntr6TAVCFnTxRYwUO73f+kE6Hburh/V0zFxg6Hhh4CDD0SC/OGRB9jKeMTNiJiHw+b12QQ1HtANv8/CO11UBeVtc0lnJYsdWbwOrqUjIQ3r4e6NgLcBzkqlqwYmsR3Ys53h5rHwCQ91IklAtaV2uvR0TT9b4zLWLsNasY4GUW8DIyIYctujuaK8vsUKYilbgnrRF2UgKz+fRczxnLEHwOuzMmZucjX4UF7AI2O+oznPcUcPpDEc5L7zlxYcd/t6Iahe/ucBwxn0R+tzQmjpzHVEPFFuDDf3pu6d0GtrqfWheMBsoR2LjZf068LxoWT/eEnbqu4ljCwnm52DX4pfYOKRqe/uBzuTLtgKJi7hPAz4d7Lq8Qrydt9Zh9hC3RY++knBiO4v2/Aktet/4sAsx5vXUi7vQ1jPmG/bcAYcfx71KDH4to9ZZgG6REQJgdSdhJY/Gh9zFhW5ByQdNFRA8qyGys9HJ/KhzHP7e5zU65u1x6XsIGyoLZ8e511BQduytcPlYc4nkW9WT/OXQfov/G5vYPnwb+dg5wd8Lxt1gzmut5jhuZB74nq95PpT/YS7xfmHqD95f0o1gstv17h0zYqQyoUbNhV5ZL2wPq2Su9v6/dCUAPmAcAd5x+IP+8bmvKxmsMK2cDT30bePBL1iJF4pkDkIi6cSfioN1FkLDj36I5y81xewb3TMGgXBJ2RN8KaegV6LWnRXPHBHU9jzSJI1iNRc9s8/W7XFZGYnYSFnZc+R2r2kWEnXLDPBRLLs7/w1u45bkPjb+zy8rliJo0rs2Oyf5Ps+dJR32jGSjnhdDWJjJF1/7isyFVxu/+vQDn/eGtCnbIjEzYqQTojtmgTkmb2FEnRxZngjbbvaNQrfXvlr6RNABgc3hAPuqZAxDjybhvdSmAtQioK0wQZQaOiaJxM/9ImYpCGuF/6YT81v3J118G5AjK0RZetnM0qbFyjjjW5rFjAoSdWUs34Z6XF6DRZASforBT4mosn9mJ6O6fJu5/9ROM/PELmLFwfexz31q0Ea9+vA6/JdncKcRlCcPe6GospmPVPUcx9IhY/SwXak9lm50ynlmffVrXITovHXKh95f0o6mlBdMXxH+OSSMTdtLG/OeBB/4rsIijhcxLGDTmAfS8T95n8WV4n07p9kf0JLQENVYFUvIUCVRjBePDVVtCSpQBixpr5icbTKVbh13EeJCCC7gxIsOKuEh6OSow7qrCzrT5a3Har2fizpc+xq9f+UQvkKbNDlEb0r9tyezc+vxHAIDv/2127HPpvGa6Br7Zc8pgdtjCnjcIOybVVgpQVUI0qGDgM+s53Hy8tcKzq3izeh/4obyBbW0LZMJO2nj0rNAiqTI7az/SDtGdDcWIvp0BAD959gNsa6qAsSqdwNd9bCyiqrHKp9gDbnLQgm85rTqf4kMjyTnpRPGP2StNpVsHeu1Dj4x3rhpNNiGwhUeOoBx8jrBDMzA7kkdbIl0sH5aF5ZrHhT3F3OWb9QIVtNnh7v5tfrM8lfq/PloTXpAgTybUFoNlO/XGihvHiT8HVdjpMgCVCmyg9jVH4lEFPrI9xpqPt3Y80THNPrtU2Gn7cQRkwk76GHuB/N002aVps2NwD6Y7G4oqf5b7z4ot+MWUCsSOoOq9gjkEvk2NVXKVHU6HHuX3I8gby2/3n76LPgNLsbF3v87lt2tC8w6pPzkn5V0RvfYRx6fbVkRwmwonegRlEWdHv1/0WJsv4Jaxtn6bUG8bTbNSZHZKCrNj9OzZsVHyEqwkLnzonVjlqW2b6Xlzzz3HiZ8agws7SsJcJ6dLGiltBtRmZi3dFI0Btc1zrR1PrkHYIQIOe//a2rsvE3bSRu+R8vf5z2pFUlViDfqcdojubCg+XSfUXQvXbkNr0SHMfb2JqICqzHZC1H4DkHdt0mTcuV9wW0ECZQQ1Vr9ucvZ2xuzUViWcgXqFPLGbmIpEIV37rrEDk+Ps+MdCbXa8v0abHUmNlUwfy0YEtWGzyTBX8uhMVgBmwgF7x6r8eAv8/So0AbcPA342ZJdxIw7Cz14QbLZRjUUM4LnreVwDZdVmx8lBe3/C5qQyoQo0izfsiGbIbxV2WjmeJGbHF5wkZservyUNm8MYyISdtKFShNW6PcyO5hRVRnTw73Ws9JO6/lckFQLDDd2AZ68S33OWoIKqNxYRdgpsglrwErBOV9fJMAg7az/y+jH3MftZ/mlVSiBIloU9UbuGbWuBh78sHcrBxXe+6Onau9SlYAydouFruaACbtSdN9+tG4SdXcpmJ4Kw88anG/WDlWR2mNEuG9skfQnW/CfRtqPgxP3rwwsRzF62mX82ra9SUMGymR3lXXQc/dmm9D6pXX35qi9EU8fZxl5r1Vgf/F185sIOYaeZsNPGUSozYSdtqAO+x1CtyAPTF/HPm7YHBL8rB4YYP+Jlt7MdZecAk+qIUdgy2ahBBanQwSfjR75WTveAhyZ4f1lsCAPYPcorhlVM2En09X37d9qhHFwcMNBz5+zbpVb7vdWQbEF2DUNCsxor+BwROyVYjdXWso6tAxMO6G88Ls5L32ZHTXrLVYdU3bxgSqJt2zC0l1AB7dkKhwmT+lOo8Wkcp4gVsntvYnbUtlIK0jl/tWDEF982AcP7dOYCaixm5+Dz/JMSHE/8mnVmpyKpdgKQCTtpQ52YQiaqR98Od8eO174+SdpsdiiSUK3FqsOy0KpBBSnBEss11lT/jnB3SHaPVGGnise1SHD1NERbzaHEBbxUVDC0/20uCXgQWc+TV2O1eXAzyzgf2CMk3EOqcXa8v6o3Fr/nNLdRlwGJtm3DgO7ifoRFz1bBHC0Ac7gGOgRiZwu32eyYmOmEhVKGX768QDsWyfVcHXvsGpIUylgbBjVWRTUHBmTCTtpQX7aQ3XNNPuFHYpgkbTY7FKm4VAfBMjHQ3FiAwuzEoUVNL3QEA0KHt6swO1U+s5Pk2mkIyJVDCfl8iq7AuySz47N5sdRYzEB597TZCR3LaaqxuDcW/L/ePedqYtp2dUrpbBTQsV6MaeuxgNgbGtVY/l+qJgUiCsKsjKbGqhyz06NTjXZMBFsNOFHt34Fn+8cTFMp4rjDdQNkYP6qCyISdtKEOpJBdWeJ2GSZhx/8apKpam0AU5ViqMBuzo+w6qcwRi9np3Fc/FmEyYs1paqy4uv4oMKUSgYtq1WA0SeySwo7314kRVJCrvozCzi7kjWVRfIaO5UqqsVRmh7ZXSFjNbgF9TnGZHQqjGoscoq91JOYhyEBZm+vTEXa+cdgQ7Vgsb6yBhwLfe19EgU7ScHjZG35boh9fr5oGIGN22j/UBSxkoqpOk9lhaizLC0HdqFUmIw4+53yEgc7ahNVY3neH7MaMC+CFFpsCU/0RckFxNZYiuFXFtNmJJKhYhJ2qfAqCFcMuKexQb6x4QQXDbHbaUtjZ0VzAlDlmNXXo+EgzgrLN9ZzdKyl/WmVSyRTI/WjNMzOpP6WggmSee2bOqggV+vdejbPj5LyM33LjsfoaFazL1M4rljfWwed5tqNMPZmKuk3vR8bstHdEkPa/OLIP/5yqsOO3LZgdueiksw/mn8s2ClzyOh6vvQmv1X4/uFyVQodbJnDVGwsIifBqimwKWBby8EmU3SM1WWt1DAFk0/ZmHPLTl3Dtk3ODCxrGhoMSbyudiLbUZmcXEXYI0xDVgDSqGqstiZ1v/+ldrJzyS+NvhTYUdkTWc5nZWbOlyXsWtO1CZYSdkqTGKv+hGc+l3lhkEty8IwJrFcTs1I8GvvUqcMm/vGNBKWpaAXZNdEPqRHlPWN+ZkJPztQhJjqchvsBneNHaOtdaJuykDXUgGaT97h3Ei5N4fMEgmx2lLTrhHj8qnrsnx/K3+cfAa4mYNE8NKkg/G18exzKky1jIv1/1hDU3FrcdivD+XvLHd7BpRwsefSvE+Nyww8rBJQbKKUwWdHzG3uGlEyFKJGqMkxvLP4c9kJPvBgYcJB9D2zI70xesx7jcB8bf2spm59N127iNi2qgDADPzl0lj5EKCTtU/RRHXb21URYwzN5YHmi6CCDi2LBFUGb1DBgDdPbnzpRcz6lNG0Mk2zZN2PGNqhM1UNaDCjK0tb1cJuykjQjeWHQQJD4gjGoKn8ZVFqshxN2zbNsh4pUQaLPD6WDf2C7E9ZxOSjt9OnTdNsPEa2uzLGHnSeQtAkCc+BzvLNkUrUHDpJNHKd2Mxq1SY6UzexmDCoaqsRiz419Dn5FA/QEA1Dg7CXc2BgZ0q7PmCQoNuCZ5zSW3iB79i1f5Z5Ows3LzTnlcbFqcWNtBkKIgx3hoi9fvkL4b1Vg8qKAje3dGGf6c2TEYKDOwOTAlbyzWTzq/skcW+Jqowo6TQj8N6SL8Axmz0+6hTmIGaZ/uPhJ3jTWpsSzMTseaKhw3yov6Wfa4JHYwwcyOEq/C5o1lUGMx7Ggypd5IjtkBgDqYd7JMtZTo0zIIOzm4ke1WysIubbMT3RuLGzWTLFlsLOwqNjsda6vQw9lq/K0tbXYYeOBO8uLWVeflttebc9glDdlAuRVqrAADZQflMDv+va9SYl5Jwo4vCLmlVHYoauwxIKILPRfU8vJfIDn7Iguzk4Pb5s4BmbCTNiIwO25CL7a5fUobKd5YhuI9O3pMS9kDk+iprbIOfbEYHWxVY+mULcvKbrShtgk7cV7mHy7hH2tg1ru/v6xB6l8iMPTRIQbKqUwVu2KcHWIwGzUOiqbGcnJE2JE3E3dP/Rjf/+t7KLTGzacMVOdz2OGaXbdDk7xWQNhRbXYAoLZK8TKqUGZvyfU8xrhUGTJz1nMfjjyvRGKQqMBwzmRxnM479HMKz0qNPeZ99n+LpMZi3h6kn+WyO5q7PWN25MN5lDJhp91Ds9kxMDuS50HC7UtJ2lRmR5cWuClKOQNzzQfAv37Kv1rVWLRPEYUdKtjU+PmoWuLY7MQxFqyqRcnx+lVtEXaWbNzuVRuyXsYSXhmzs//X0LzXSQD8ODuqK3CSMCbxa1uUCE0fKX4IiIeNSdhxxH1r2NmCu6cuwN9nr8T7yxuS7XgIavIOWqAEn1MNXQEcPLi7fnKKcXYYTCxahxqF2akQ+yep9mOM+xbFvTk4XYQjsc+RhCqXjK/uQ8VxE7MDpKLKojZtDJFU3fxH5mJKxl4Ez1RznTbNhdyRPIqZGqvdQ2N29LdPttlJUY2luJ4biZG44dMp7hsn12UrRxdVNtnH8MZiKiTjztwm7MR5mZ08Sv5EUAvzwnLpF4ZHqmrCPdOjt8sWsVwVip28uEBUjZW+zc6uYqAsBFxG1Ud1PTcxO9QdfTtRfVY6fH1NVQ4vlcbKB/1AkvsN6MoPjR/RBxpSjLPD0M13lKCMQefaKlliSKltFZIaK8a4b1bmhCABxksEKq61vmuEgImcHcnLenqHCLGSeih5wbRo2ABGUnWT+cX7S4Sdcj3HbKFVXFXYKbU5cZwJO2kjArNDdy7p2ux4nymNqyKqQWgUbLDl+aJ9ovptA9jcRal15nJpdNe1MjsxJp1cHqWcp86jzA7dcbMopmH36aPVZhuNwD7mquASViLJZ6KB3ve3fgus/TDOyYl3B5BtdrgaK6KBssNYHAeSGovdwxayGIa6eyeMkgsUGLMz/Gjvr3//a6vEuDUyGRVgduq7eYv9oJ7CUcFx1LYrL+zEYXYKimRkVmPJm71+XT37m64dLGErpI751+/kZKHGxuykcL/YWKcqOCfKHMHtJP1+E2an8ItRZXYmKrNTypiddo9I3ljUZifp9gMMlA3FI2XPjdO88eUjx1jYdcuO0TXsYlhAP3ViA5AMs0OEHWqzM6JvF/45FQ8paTLy6q9ySoLZSbAp0aYy4H59eBqtxAJNBCrUWCHCjv/XqMaCy58XFXbmxxFEE8CKTTtxWdXT3pdP/Fgs/v0PDaJXAYGDxvg6cFB3r1lXaTslAVcFJZPiLJJqWdO9VA8N690pejvUo8lmp+Oky+yUuGBPbXaYbVvQiWx+YcyO6GdVyzY07CiD3dGEHT3rOeCp42NFvE8Bu4ywc9ttt8FxHHz/+9/nxxobG3HZZZehV69e6Ny5MyZOnIg1a9ZI5y1duhQTJkxAx44d0bdvX1xzzTUoFNLZ+ZQFVWn890u1InTNrogaC/rLwiBUJmX0Y3+RfbzZ9V4kY4jwOMyOwfVcMDsmNZYD/L/XgJPvko/HpGm5sOOK89ZubSR9iBb7xmh/YW1UCDsuWagj7drKhWHhfH/Z5ognV0KNFU345udI3liOf0wIjC3kZbv5uTgsVuvx+b166Qf9fjeT98S4KKQk7PTt4rEaf/2WLOSyJ1tyIW9EKmSzIzltxBj36jti3BD5UJP8xoqz4+RkoYbOpZTxSeF+mdhusfmKoMZyDElLAWyMElRRhW0zb1RjZcIO3n77bdx///044IADpOM/+MEP8M9//hOPP/44Xn31VaxcuRKnnXYa/71YLGLChAlobm7GzJkz8fDDD+Ohhx7CddddV+lLsCOCjpsOgnTVWHJfzDY7ftEy+kF3zWzRMaoKjMKOuT0e3TWn72KMp7BIpodcqFQUUdg55CKveF5XY725aCP/zLOeh1THdsiRQNVY/quZd9zW2VGFwTAZv714o6Fg5UC9Tdh4jOqNFYfZqTQ611ZhpdsTAPCjlou9g/78QPtlJkPTMRJmt7V7R1mFI+K2uBU3UN7RXMDKBrGxiKPGUuctM7PD1Fi+MBwntAP1xrIyOyLsQRrMjontjuWNlUtw2deYHTmWG0MOpeS1FjHR5sLOtm3b8I1vfAMPPPAAevTowY83NDTg97//Pe68804cffTRGDt2LB588EHMnDkTb7zhJRubMmUKPvjgA/z5z3/GmDFjcOKJJ+InP/kJJk2ahObmyiSsCwUbDHv+l7WInOG3AsIOWw+MNjvlL6wv/ke4z1Y5Jb+eiMKOZbeq5u0BwgQyclGDDiMVRZh0eu4JnHynV5zZ7BBm58T9RS4alok8TDiNJTMSmtnlrIQrLzxJQ5ms1rldsf8e3ZJvJwaEt0n0XTf7NUcHt7/DzqPE1WFtKezUVeex0/WYlCZX9kL8ZN12Xs5ss0OPJTcOTEE7AcVRQRJ20t+d/+9T/5G+x2F21Mcb5HrOU8FE9PjzTmY2Ow6sNjuAYE9SUDmanDYibYhUNRaACU23AAB2ujVa/r9IsKqxdGbnM+96ftlll2HChAk49thjpePvvvsuWlpapOP77LMPBg8ejNdffx0A8Prrr2P06NHo168fL3PCCSdgy5YtmDdvXmUuIAxsMPQY6v3t0FMrIhnjJT0eDJFXBdFvUmPpfYrRmPbdnJKKlOPRRi2u54ZooYEvNp10Tn9YfC9GEX5FG0WDzc4bn27gn6si2uyo97E5KPMvp5lzcP3JMu+k7Y2l7sDiNJLO5EVtEvIRd91it06YHd/bpAoFvjAEqTUqgWrfu++gPf2UAm4Jn67bJpUJVWMlOBBswo5YR115wa4As/PUeyuk77FsdpR7ExZUEBDXGtv1XGVzKHLBtoitAbVp481FsW0zqLE2uJ4dYhWK3LwhXmeU8WAJKuggJTV8DLSpsPPXv/4Vs2bNwq233qr9tnr1atTU1KB79+7S8X79+mH16tW8DBV02O/sNxuampqwZcsW6V9q0MKL6w+cjpdUbXaavUlVxNnRi7dmYe2lUuFwwyfuiDY7eenF9k8x1U0noK79gbMf9yuKoMYi55q8sTZsF9GUo0f2lX/f0RzAMFFmh1Hsadvs+JPT7wsn8vba2muiRBajuEEFpQjKXBVZ5M9LdU2uJFzXRbWjpkkp4aZnPlDKmU5Ox0iYswTKXMA2Qm3B7KiIM+6LyuIblPWcvViR7F34yRYDZRVp5J3yYQq0Gk2NpXhjASjAm3+rnSLWbWk0nRUMmzeWqwo7bT+vtJmws2zZMnzve9/DI488grq6CPENEsStt96Kbt268X+DBg1KrzE1RLdhUZfTRaTUPkPT1kAJ3omyQ7CgQ7U8nHI26tIk7DQsM9ZZ4jY75JQwmx0K5u0VZdIh5zJmhQako94q7HO44az8PdDd2aXCjm+zg5K88CQN/1k0wY+xglIMVU86Bsp/fu4V/FfuPTwzZ1VkrzfOBkk2O97H0/LTiYFyWwo7HssEADuJGmvFpp1SOXOm7nQEDlZVXpF2HIf8LrHDlb9/cRZJlTk1qrEUZkcI1BEasAk7zLuOgQs7yd8vU6DVSOYHBjVWhw5i7b3qb+/G74zKXPF5Vhd2PrOJQN99912sXbsWBx98MKqqqlBVVYVXX30V99xzD6qqqtCvXz80Nzdj8+bN0nlr1qxBfb1HAdfX12veWew7K2PCtddei4aGBv5v2TLzQpsI+JvlDf7mQgnLNirJ6sr0PIjWvvKyzX40OIIy3yGU0VTUfCgmKviFHwHz/q4VNbtZyr9JUIUdFjgrErMj2qDMCgMVdqLuBlWhMVCNIsXZYcaTrjSppWXAXoAw5m3rHdi0mu/jwZqf48jcnMgqBvazZLPz3p8BAHVOCxeW21KN5QKo8YWdJgghvFSUF4zQDUJFbHbI79KClv79+8LeclDFOPKCKuwEMR3CZie8rKiQ2ezkZZsdrfL0mR1qs5OLwjTTGEE+mNcsAKzZvE09Ixzq+sK+q8yOU94GOkm0mbBzzDHHYO7cuZg9ezb/d8ghh+Ab3/gG/1xdXY2XX36ZnzN//nwsXboU48Z5kXrHjRuHuXPnYu3atbzMSy+9hK5du2LUKHuQpNraWnTt2lX6lxoUZqeppYAv/HyaVIQOglTVWADQe6/A3FitSTrpGKhL40Rl2x29+L9aUbY2ycZzxBNK7afG7PjCzoYFwIp3RfkQQUkIO+ICTIEN49rsGN3leWEm7OQJs+NKC1FazF/B9RZfj9lp4y2Yj0Ny8/kkHqrG8v9KzA7J0M3GT9uqsTzbCABodIXK9+oBc6RyxktNidlh91UzOaHzQIW9sVjcG4Y4G8AudbIq3fS4xVipgBorBZsddk3xc2PpaqwWIuxUo4y+RgwqiF0gEWhVeJF00KVLF+y///7SsU6dOqFXr178+EUXXYQrr7wSPXv2RNeuXfHd734X48aNw+GHezEhjj/+eIwaNQrnnnsubr/9dqxevRo//vGPcdlll6G2tlZrs03At5y+WgQlbTKj31NXY+Vr4RbMExwQYg8T3phcV5gaS50wtizXigo1luXFVr0d1AmI/v7A0cAp9wIHnwf8Ug5z4J9MPjr+EcLsmOJaGGqRmlcKBC7aNM4Od4t1pedUcl3kklQf+W3uSswOwwnDatAU0YbMGGeHIM9TjLTdteVKjejkeHZfO0tiUd6zcR6AEfx7aATlBNkVmxpLYnroO1SBCMrq3JNa1nOH/Y0mUHsnW+LsqMjFUJ/HhHlOjKLGUtJFAGh2hbq3ypIaJ7gzFjWWyWbns2ygHIa77roLJ598MiZOnIijjjoK9fX1ePLJJ/nv+XwezzzzDPL5PMaNG4dzzjkH5513Hm666aY27LUCxUDZtExVzPXcO6C5XlJEDc9vbiuqGov1yQneHcHsZim92GqwQPWiWrbL35/+LjDrT8DmpXpjErPDFn8BGk5e2OyEqLGU3wNZE6JTLzGbIbiSCi95bz2mxhLtBbJPFcQ+y/5GIsOGXDi3wyArWf8x/Od8nAUtJZz+yY/55x0uzUskLzKhaqwKeGPJaqzKRlBWH1GcjZcWQdlMk0nf8lEEBX5qRGYnTddzwzOLlhuLqOB4XTkUXe/cqiSYnWITM/SSDufgtrkaq82YHRNeeeUV6XtdXR0mTZqESZMmWc8ZMmQInnvuuZR71grQxHGQ1SIMdHFJXY3llsjCYLLZaY0xrD7AaeJFrU9OTu/ftrVA5778q8kYj7/jrqvb4mgTkEGie/pyc/epuojbzIj+Xf/l/fD1B7wYTx2q87wLHG/8xnvZj/ieqCcWsyNcQ6nNkGSzk/Ri43ewCDE+G4MSZFZ4d8bin4Xa7Ph/JTXWKfcA9x/l1+MbKLehIDdo+1z+ebPTTfygLIihHoxJemNxmzj5uEMZtQqrsaK4j0c91+QQYHM9jxdUUMmNpYIN3BTul9lpw2/O9YRDkz0mdYDgdbkuCqhCHi3JqLEAYNG/jcxOWxPGuzSz0z4gq7FMzA6l1tPauYvvLkkXoRdnL1BZaixtgJfw35PnGMoRYadJcfvfvl76anazpMyOQr2qF7XnF5XGA1RAkoGyz+yQaxo3vBeuOHovXP/lUbo7eKEZeOGHwEvXccNYQBduAuPs7PDj+FTV8nQReZLqAEhPzdlChJ3fvPppQPnKzVjNXQZFDoWgqbEcB8h7quyNbmc+ftpSjbWxdg/+ublIJVh5TJhdz5PXdbuuK9RYKrPj/9XSRVRAjaUyAHHMrNRzja7nihorknEvPzkms5NGuogAZgcIGB6cOc5JZdm7X+2UocYyXd+WFdohB23LqgKZsJM+6MsBTwBQ0SyFik94QGhBn0qBiUCF63kZbRnUWMY8S/Se5KrNv/kwRwv1/no2O6qwo3pjqbuvgPtrMlB25PJXHj8S3zximJ6cs5l4MvzjMv5RnUB//5pFkCiVgE98Y3y3xNvPG2x2EoW/kDFmJ++46N8tIBREBV2P3VwNcYEOYXY0NZbYeedR4vYNlc50TvFO76/wz4WSxSYGlfPGorfCFlTQSxeRvKAV3C+5jVhqrAiskJobMFISTQb2njs5eWOlRVBm6SLSi6BsstkBApgwoxrLRdEXA/bp1zl+Z0zzgZO3MDuZsNO+odjsmCLU0t1m8jY7aqI2IeyYqJ3WeGOZ1FiBcHLaOSpM0UIlij1UjRUHBjWWpX/aIty83VhOvY1/n73SWE6yPdqwkNgMycxOWswf290BwBdG9A46IeEOBMCBLlRawH6XE4HqubEKbeiNVfBjGf27OBotkgAhv6PrtjZBg6RKSqY/9B23pYtoEzWW38QYP69cnDlRZXJ+9a+FWhn1nYysxtq6Glj1vveZzTOfu8T7e+nrSqXpeWM9M2cVAGDu8gbRHJn2rNdhUWOV/LmmWGyFgXJ1R3Gstos2TjJh57MAzUDZIOxINjsJt6/tLIKtPloTZyeysMOZHUefeZSXROTGMvXRNRgot2JIGwyUTc/L64Ni21Qg0Uf3PpF/LOsFX/EuSoYIykAKzJ9/v4vEBbUY5EFSQWbHcV2iTgkTitnvwcxOm1Dpi18D/v4ddCxsBgCUkEOLxOzI93vmJxugIRVmhwg7KiHKhHktXUTlvLF6d/bUkPEiKMtlV2zeqZVR93om20Uj5jwmPrO5YsIdwA0NQN995LIpqrEYqMNENDWWCG3hlfPUmKy4Gu8pEtj11XYRx/I1UMeoF0E5fvVJYpcyUG6XUOLs5B19JFI7juQXM5XZcUkeIR3CRiIZmx1zOSrsqHEaZOFFhLPXKVuP2QlRY8WByWYnKrOzbr74seeeXuccR5uo9+proYrpfegxVAkqmCKz4y9klNkpBc1KFd6dSQxDEBgDSG12HCHs8AjKbWGg/NAEAMAJ/j0uwUGLJLvoi0yhWEIVlfCpQJTQM6C3QmV22Oygp4uonIFydT6+s0Qckyye9TyqnSK99iC3cyBVNdbR+/TFvz5ai1EDRHw4eY4wqUFdMu/KzhWM2Snr3eCJUXPAgIOAle95x7S1IAUVfExkzE7a4MYE9OXwjm3c3oxn56zClkYxkSU+INSXzS2FuJ6X3w9HmQjDmR2DkV8xXNhxpF1nG6mx1BQOnUjU13cfBG7sDrz8E2038/VDB5ubpvd76JEiXQVcSShNjdkBZXaCJuhKTljR84JxOwwDs5OjNjvKalhJd9i87+3iCTtkPBsWmYdmLpYPSOkIkmd29Dg7fkuqsFMJA2W/W0zYa40aywTVQBnqu2w9kQo7IfMMl6DSSARqnxMBy/2iz81/L1g9bIZxy6FeTHO5W4KJ2Wlr1/NM2Ekb7M0iPDGbkM/53Zu47C+zpOKJUn0NK4CZ92j9CTJQTtr13FyMGm0rZRRhh3uLUANlOjklabNjMFC2sVNaCoc37xMHWvx0INPviCGckHKjTxcGyqnb7HgVFiRmJ8j1vIJqLBo9OuS6mbwg2+xQZsc7qhoot0WgsxJyKLnEoNTAEmiuw4teFZ+TYnZIPcERlCub9ZwtitVxvKR8RHmeQjD2IOUBCzmTI2ye4Qt/8uNL2DGKY6FzxE96kcJ5qRxTmbvlPFvKFtHYQpnr+WcQWtZzMSF/sErPtp4Ys+O6wF2GlBlE6g7OjdX6fphiCok+wJsQVijJ51Q1ltHNkvTxw6fl8xMWduxqLEVHPu8pY7nIniXKrrFE1Gip2uys8WK/FMhUUAq02amwGos1G1KOLWDce44yO47Lr07d9aZuw7NtrXaopLwZ66v30MqoWchlpK/GktS0FVZjsXemiqmxYjyjaFGQvT/CZocdDjjXdYGXSbBaEy1OkWJQQdcwJ9LNoDZHaFHmZWaHzTVlsVBsEDkO5GTXuhori6Dc3qHoSQG70SuQ4GK20eLiHOJ6zly8y3PGUpgdJ4Kwo0JhaoJCo9c1bQT+/XP5/LBJSAVVPxlcSR2DjZXXB/E56FapNgR2Twm6a3SI63spvQjK24UhbA0JFV8KigUkCWUx73UZiOod6N0X+R7S8VWT9xkshTpNff59+rvaoRKbAcZd7vdBv9/Sc966Wv4xBWZHjbMjecGVKsvsMIGFRSmPs0hGUmMp3yOx2Td2V04KsdnJpWegLJIjk+aovbt6HTsUg3d/4y2EHf9Zl2WzQ2xSHSI0GZidxDdqMZEJO2lDMVAGgl2yE5N+t6+zdShSuohUXc+5tBUu7Ahmh/bR+3vUgtvK6KOCQy4iFRN2g6uRzKAeHKX1unsrA3vBq3JhE6pMkZcUA+nW5SyzYJtYROmzKgZOeqT9CkxeUVUMrqtsIgizAwBVvuCtqrFSN5osNmuHPDWWaJcJO18/dJAoQ/vZqDLA6aux2PAulVz55leQ2REpWaKfG0mNxR00fFU1p3ZiNBRZjZWCzQ5bUgzhOAADu/XpK/L3Kubl5n11E1FjEZudUhGmtSALKtjuwRSsQo0VxOwkNh4a9KSaXncos5OwGktldsJsduAA+5ws/6aosbgxXk5/sXvsWBSvf0d8Xz9WTQLoLZ4uuhhis0PnuvzU66xNqgHA7MwObYcwO1zYITvtJFAqAvd9nn/Nk+ssBQk7Fdyd0fckfDy68ngj3lgAUO0zdKqwk/rVjDhBO1SC491GfxCxxXdIr06YePBArwy93vXz5QoSEjhoqghVpS2Nt4qrsby/fIOQtIGy/1dXY8VAqLCTnhrLls/MuiHqPkR8vn4zv3BuoOxG3FGYQL2x6D3R6mr7RKCZ63naMKhsgtVYCbRZaAYmXyQf6z4E2LzEE3a40lo/tTUGyup15VDCoJ4d9IKU2anrJv+mqbG8v1K6CLioRgEllXc5/SFzx65dAWxY4CWGnHG3/Nuyt4ynCGHHfCNkDyn7AlAizE4TAp6vpMbK8XQRzLrDey4JBubauVn6mid5cQKZnQoaKANErRpSruSqwrUjMTtc2FHUWOm7w+r1l+CHJJC8V7wxYoxz9bdzlCqT6TM3/jdQvOxIW6SL4GqsqvhqrGjMjvw9cngD+azgn1NUY3GfF6UL+ZyDUtFgCJz3l/muAyUKj3UtETWWkyc0rNkbq41lnYzZSR0BBsrG4kmMiPvGyd9vaAC6D2YNhHhjeX9f+mANJr9rYYdsMETNHNqrk72cSY8WwfX8rKXXYU7txagtKPT+fl8196u2sxcDwtTeoMPE53EiQWhYnB2JQjYYoTKwiScftktVbHZUNRZMi2BrkJf3OVWOWMRKFVjQIsEVbvfh6SJcXY1FmB0W30rPqJ1ERwM7ph0qMjWWI4+JfM6JaKOUTKdN7xaDnC6ibQyUq8tgdqJ4swpmR1ZjBd7zasM8FgRp4U8WgpFTjcotY8fgEUzLtcobq2RgdlxXG/cO2j43VsbspA2DzU7qzM4Guw1JeJwdcfCqx9/HiH6dccDA7pGaVauz6mmlrOfK71Y1ljg2evM0wAE6NK+J1C8JYy8A3n3I+3zy3cAh3wS69Af6HwD03Vd0kecyCxd23JqOxjIAcaP17Q8Cnjz57HDXZOYGznf8FZgwAuPsVNBAmbqeh9rsAOgGkp+MeocAqLYay7eyk6HQG3CRk9RYTFWazzkiKSV9zvt/DfjPE6SCZDptercY2jJdhPDGim+zE4epc/jfCOyhllA5ZENQETWWfNxqgmBxCFG9sVpls5OLEGcnM1Bu5zB4YwUxO6kNCCJ1qwZ6FOou798f2wydTVDVWBGEHRWqGssVu95AmOxxjCD1HPJN7++BZ0qCDiDUWDb3eSk5Z489ra2p/Y/qjXXfvxcBAJoLnpeUaffdKigT2zPFw4WAFdVmp4IGylG8sc7Ok8B7ig1BVc58fuoTsKH+kuvb7LCx6D8LxxFqLEkdU6tG3U5WjWUaW3Lgzsqmi2DDj7mex2EEopTl859/jVIARetJaqT3kPuQqjeW91cPBMneX+UEq7Dj/yys0eN3RnI2IePZ4I2VBRVs72DPNyqzk0Sbex0rPu/5X36j7FGLFkzrpypT7NknTiZc3WYnMINzhKCC3MA3bLEffXq0LkYUGkRQP4vNDqmm2Gc/cyW999aNLa02OzJjYjNQTjQOk48bxryKnajjdPaU/6wOUBtRYScNewRlkuSLbvh51Q6ND+QAjsPjB9VamJ30iR29XaHGkgPP5RyLzVxLo1xBQmMgSI0lBe6UmJ0KMItcjcWYnYSFHf8vZ3aieDpamJ3125rQ2GIQfFL0xjLF2QHEHBmV2WH18Dx8ThkCiclA2SDs5HYBA+VM2EkbMdVYiSxm1A7l3Kf8RoXUzYXxAAPlcvrjGKT5txdv0jNNByUCteTGMgVAlNDXEEDR3MtIpdgEYDdQFvWUmD1WlWKMXSpolHyoEOFPGNxmx2EsnF9lUvMF6UehxGh8cc0vzrOoCFNISGnplt+j6MajVZSFU+5jdVsxO0Y1lmKgTARatluXxgl7J/I11jrLgU0dQo95aqx0BVwVPFxDPr6AH6ksn/8c6W88NZaLtVsbcchPp2LcrS/r5SugxlKnRCsLahF2ilzYESr72O+DZKBMx7NJjRWv6qSRCTtpw2igrE8YnJJMYkAwweqAM4l/pZC6Rbh0O33NEM+oTJfmAeD+f6sBDsnCrk4iSlwS7jESpsYyGR6YEJHZ4XF2IgQV5J0sKBmWqbAT2fXcK/el0QO88xx5YktucRb1sMCHNB/Y8k07LKelq8aSYr/AkJYj4DzqUcaNf/0prsYi7LSFgbJwPfcXW5d53InnLL13qio8MZsd72/O8G5Z00VU0BurHJudaMyO/E6VxeyUinjz040AgE07WvTyaaqxDHF2APEctVsQqsbyz0cp/vJjNFDWmR2v3xmz075hjKAMfLpum1SMvaSJLGasTT94lGgVkLyxIjA7cbqjGyh7/fj5i5Y4IU4O6FIv/6ZkMWe7D5Maq+DUaMdCMf4Hnrs78bwyweW7HTMkA2XbPSoVuXdIVVgGZ+WhVFV546V3J09IblVk66D2QKM8C2bHKlymbKwq3R/XjRw92nWBKknY8d2W/edYZRFa04/qqtfvqbGgqbE8mx3DOJHsIpKDLV6L1xfWtJv6M1fBxiPzxiqWXHyizJc2RJk/1SJaUl/zWcrXYnBb/AZWMs5OPDUWEz6o52ns10GKoExt0EzMTibstG8YBloOLn7w2Pvm4km0WdIFLInZCWhEXeSSYHb27K24bdJ7ctQ1wP4TgW6+a7xVjaW3xgP+HXZp9C52Gwj89yLghJsDiwk9driBMvdi6CMbOaPYwhfTfKj9gbygFV3ZZoc1l9jiTJgkJpC5RHVWV22LHe1aPifULcl9PC6zIwdmBASzYxV2yu5pRBiZnZx3LYzZ8fvtAGY1ljI2EvfGMgo7RLiusIGyUGOJOfPUX82IdG4UG1suO/rfQ6N0m34oFYPnRsbkl2P0GwJhWK40GVONxYpRlX1gfjBjZ2gE5WAD5USTXJeBTNhJHWKiooOqWck/dM0JIwEkZJPBJqScRdhhhwyT3KvzZe+reDY7epwdADhq7z5yQWqzU9cV+NofgFGneMdiqLGccne8YXltQIQdq4Gyo9PfvYYDl74OnPcPv5SrqbHsE6qsxmLCjiMFFUzQZkcYLvD+u2TxHdLT4k6f8i5fXuOFzQ4QzuxIwo7jYEtjC6+vOm9mqtIndvR7xG12IK+yOTKmZDWWOs6T6bQwUNZ/E2pTVJ7ZMfTrqJF9LKWVc6MwO2Ts03asCz295i79vb/DjgweO1zYCUiqWyaEzY6F2bF6Y5ntMelcF5/ZscTZ0ZidStjHBSMTdtJG83bvr5OTDECp/vIflx2BTjXeApzIzt0kyYsEMMT1XMf0Ba0QdpTvTI1lDXJFz8hXe39taiyTsMMWt6j2OjEQlvUcEKo1/swcB+g3Cujsq+bI7i9yUEEbs5OaN5Yj8o/lhOqusWDZwVfQZgdw5Qj0IV6M20BSf+TyuOXZD/kZNmGnLQyUi+ypqgbKOZtQKy/OSUdQDgwqiMqrsdi1U4Fvr4heoXHsQjizE2YET6/5/80A/mcl0KFHsMhZAWFHZ3ZsaizzppCniwhxxggErTsgqGAuU2N9BrB1lfe3eRvRjcou2d06VJcZstwCbjQWxuzop35B2UHFYxLMaiyN7jW9fErofIZgNVaZzE4EWIWdDZ8Aj5wOLH2DTC4yK0ONE9mlcmbH2qC8+1KFnUixQOKACMT88ZAUFU0ttkUtXTUWHSoOZAE60H7UdbHU7Scde/4/q/lzrLLYIKWvxtIPleB4izILKkiYHaP7cErMTlAMK9n1nBooV84bq19XIbx2rAlnY4F46SIiGyjTOSlfDdR4avnXyMZQm+PYHJCCsMOFVC3Ojt+keh3UiJge9ovRdalsmx01zo7K7DhZItDPDtbNJxK04nXiIJxKjYMgZoe8uKbpv2cn2eh3xsL1kZtVdwZnHbIHgKg6ZME8MdBdWmCcHSfaRBgHXOWo2no8dj6wYArwhxP0SVLzfBOxJcLdaGW2q0AmIFp14t5YjkPusxifdmZHd8FNEur1RTIE939j4+8lHA4AOHRYT/7O2dbK9D1E9PpFBGX5naRBBY3CTi5Zb6ygjQRfR9VdekVcz72/VTkHXz3Im0OixmiJF2eHveMRNyKANGf9ffZK/rlFNUhpA2bHuw6Di3dYBGXihRl7/YnojZXlxvosobYLce2VXfwcPwAakNDGidvsGIQJ1yXrnD7LfevI4dL3Z+asitOw9I25bevMTpCaTYBO+IGu5xFscOKi6FpyY21eovdJVcsRT4wlGzwX7qpcTi6qQlNjsRrFrj/w/LiQmB2Zzs6hhJaCbZdrYekS7haDZAgeosbialMixoepsVKHMaigHGdH2GXZ1JVpMTveXyOzQ9VpbZQuAg5QV+2/D8Vo1xxlOGp7E7Vd7QSzsENR0JidNIUdvyvKnHl54WHMrP0unJ0b5BNChB0HRNgpl9nJ5WVhx6S+zZiddgw6ckYcB75zduSfkmd22NtgUmMJ6d00/XfrWF12syoJItRYav8CmB1yY+huzhQLRJyaphrLPrkLAUSdPZn9lTh3/bYmAEETqlwHV2PxGCzp2eyIj0J332RzndDaT5HZcV2J2QmaK0uuK8YbU8GUXMHsWIZIm6SLsLmewyFpRQx1JOyNZYvECxB5HW7lvbH8vzTIoiZMWBBtQZXnv3BvrAjCTgWZHVvk668X/o4Bzkb0mfegfILV9dz/S/IAxh5ZJgNluNrNdJBFUG7foA83V028sUqoJ/po75jJMLFMmHS0BtfziPH1YkBldry3SdOFB2U9l9RY4ih/sU3UVwpqLD7haq+/6LPoklLGYH/EaO5w13M2ubPWZFVDctOFaI9NcTS2UEvBJuRZnmWyvTJ/C5gsqTeW67NyhZLL37kqy0yX/vxrEnbYPZcNQ3Nk0yOPqXSYnWB7uLZndhwIRrQQkfIux2aHb1qsJ8hefia0qMxTisIOD2dh6Yt2HSHMDjVQju0gY3M917yxyqg7YWTCTpqgu6CcMODKwcX4Eb35TxKzk8R4MLqei4EomkhW2lFZEL7TtrEBRpsiIuxQNZbB5kg0lPwwtqaLILdMY3a4GsvvD9kR86znobtHX9jharSUbHbIJKV235rTDEhdjRVksxPoeg7xrFggQU/ADBF2yu5pRJiYHVdmdljIBi+cQQQD5YTuOVdjBXhjeRGUKyvssIeSyzncsDwqsxPFBksMd2GjBgQI0xGYHd1mJ810EX5XLNN3SV3WDYFtATqMSFDBuJ2hk4dks6OvBZkaqz1DeUlcQhfSl9IYsyWJdk3MDoIjKCeJWDY7BgNlWY3Ffg5IupcgrDY7pC3NZodvFe2h4qNGULbb7CSvxtK9MgJ095qBcrKLn2S477ryGA0SdlyXC4ZMjdVSLAlvLMtgr5jr+dAjRZvwYxtpubHEmJLWTu19SV+NlRN6dfmds6QCSBKU2cmzzOcRbXaiRVBWWIdQNRb5warGqhyzExQMEgBKKtMdEmeHrUtleWNRRjokqGCWG6s9oySHr6fuzHSnQg0TExkPoa7nss46KagsCBN2tAlo7Ufe3yUkKqqJ2ZHukf970ZCHJhU1loXZIXdN7H4V13ODGmu4HyckaiJQxuyIvEkJGygz+B4ctO1grwwLS5cQ5OtTbXaC1VhMMC359k7NBcFiVrV1UMEaESdG2OzIYz5HvLEqEUGZbSbMaiy/ryqzk2D7NnAx3BGZzyMzOxGK8SLsdQ17tyIIOy2qmq0CBso2QtuFKuwYmHRY4uzEfbSme+O60NVYWVDB9g2J2clLg4p6QNRV5cM9Aspp1+iNlZ7NjioYsNY1ZsfYsIHZMbmelwKS7iUIawRlMmE4KtuiuJ5798P7bdSArl69oVS5zOxwNRbrV1LzBdntsS4J5jFgh1dh1/OIxI5voCx7YzUXhYFytdVmJ+UJmNVPxqieLsIXdqSgggFqrAp6Y7kudDu5lFVZ4nVyCNOVHLPDoBooR/PGMk+alWR2ghg5QBgcixNsNjv+z5E2Odbe+HU7EGuMa2R2MjVWe4aqxiLCTjXJ+9KjU02yO3c3yEBZDGdT1nMAuOkr+5XbsPTN6o3VfYj3d/gxpH8mmx1SF5uQjcxOGjY7ftWqNxaZYHQ7K4XZAQ0KGGKArixonNlRDJRT8cZih/y28yiF95N/T1qNJbdF5/Ogay+WqDeWdx0frtqCyEEFP/gHcN8RwLr5xnLlwyTsOLI3Fhdohc2OvHamw+ww5jTIG0szUAbMquQEIamx4trsxDJQZvNxRAPlgHnGbrNTuTg7/HerzY6Z2WGG/XvmVpbhei7mkeCs51kE5fYNyUA5T3IPAUV/t3TGIQO9Y0kuZiGJQLmrq+VlOW/cUHSwJoK0Q3U9D/fGCgkqyF5G+lZXSI1lz40l+iLSRSg6cUnYETFUvLKWBrUIyv7X1FzPKbPj09n+fWxLNZZqYEpjiQRdOhV2Sq7DXf3ZKXnLTMfv52PnAWv+Azx5SVn9toKvrLKw4/XNnw/I+5g3zQOpMTuMUbIbKGvpIoCKMTtUrRf1miMlAvX/sqqjv5txhB3G7KRpoGwT4CMKO35Fh5TmAACuqPp7+SPLUYQdw8a3AsG3A5EJO2lC0WfSsNzMVZEtYjZKsrx2TQbK4jf+sgc0adsJhzTs/y8LCpo3VmD/dDWW1BUlUahXIAVhhxkoq7eB3DRhZ2VXdeUhCyuhNjtcjaWoOBL3PRcLqKiSPbe2U2MplXv9MdqxyCi6Ls9QX4KDR95Y6tfgMzuW4axVuXNT+d01t+D9YYsfxM573qptUpmc43DBw2izE2D4Xg647YfRZocwzSqT07IzkfZtcInwx5mumMzOMfv0jVQ//RvqjRUg7JQTVHDzjmZ8uGqL9Xcbwg2U46mxpKJx32cTswOzGitjdtozLC6L1A2PTW6JMjsm13PCnPCNYoCJco3NVzcAOWVSzrXWG8tEs5smjxTcyrjrubawiLaqHO8+a67n5L5HVkMpu3c167mkVkgCVI3lf2TMTh6lgIk/bTWWzmhEMd7/dN12yWZHMBM2hs7QXlgj5YBGmGVt+n364xtLAMjsnXFxV12HE+qjLTidd8xvymSg/PbvkumABezyPGYnRP2rnuuXO2rv8CzpIoJyAmosNS5VBGFnzE0v4cRfTsdzc+NEqafMFzlIGKSSZqBs9sYyvePxhxbd0Au7ULUmB9FTfqSFTNhJE3wAOoDj8B2dQ7yx2IBNNBGoKa4CYQaEQa29itoyhB2RmNNfNG3eWBHTRbDTZDWWgdlJQ42lMCscZFc7EH4iwJIymSiC7fnjhsjh901Q1Vj+YRqDxTs/BTUWO8QMFZ0Yrucpe2N5/fG+hV27YBLF/XdZJOqozE7S0o5BjeXyxVVl70ASgZrq0DcEreuaV48xOB3th/rMC02JtG8Dfc5Rn716LvW+Uxd1dbMXmdlRJkyaR7ClFYlAv/PIrNAyFEZmp2kr/1hUl3Xm1EHYRa8eve6ybXZUNZYyZtRwK22BTNhJE8qiLmx2XB5ePMePeUjGZseU5ZYwO8oRE8phdrgaKydsPwATsxOUu0scKZpe6ooZKAuVjoSmBv6xitsk8Y5o/cmjhJNG948wactPpVBirufevYo76YeDqLG4zY5Qs9rnJfVZJizsGOqWVCoBUNNF0Poik39p2aOQse5a3jyaCNS48KYUVNB0bySbHdXuRFk0kwa12cnnoj17BjZnUAcQbfpR2NbQjYhqIM77KU6wp4tIw2ZH7j8AYPYjol+asOP3IS+nAjLNJWV7Y4UYKGdxdto7NHUSEXYUCjlRb6zFr/nNkbfBYOhpM3ADyhN2VGaHG4xqZIBh8jDsWs02O5V1PdeYnXwt/9gVns0FnyD47EnVWCXkcoKOt8+nihqLny9PbInNF6rqjbQdaKCssXRJMzumRd77E5XZKSFH7pdZjWVVKyZNtRuYHf4TddX1+8TGiazGUt+XZPpo3Ez4sKaLAIDOdnuYJECFsLiMJjuXJn5VN1tqVaF8WQSbl8pmPfeboM9t52bxu7r5K9qYHa+iRog5Lb6sY2F2TGqsjNlpx1CZHRKhluV64cyOPO+1DjvWe3/n/d3Qp/SYHV4fF3Z8G4o43likLKfZqbRjirOTghqryBdJBWPP5x97u5sAGGx2FDWWFzQSSlkFimqxWJKfTvIRlIUaS42kmg80UFZ/SJHZ0QyUg881Zz2XmVOGKhtjkLTgzG12dDaEqdjEdQoDZXldUISdhMaA8f3yIdvsKO116JFI+wE9A8CEP+9IdJsdX42Vo8xOiLAT+d20Mztabqytvh3OginGKluz8BtDBmwVdj8ldbRzNZaZ2bmt6//wY1F6tXZrI376zAf4dN02cgZp03WxZaes6swMlNs7NGHHQw4uD0LFXuZEs54zbFpEvhA1lokGVVBbVc6kz7YcihorkrCj1AHDztN1gTfv109JQ42lGAiL7on+HVZ81zsUEDQxj5JkeGp3v5Qn1AJrX7XZSUzLQtVYrN/ec3MCDZTTdUM2EzvRdFBC2BHXxN85JS6CyC6uPrukx5L8TtA+iWVCzAVGdWVazE5AItAcFQZVb6yUXc8pcxE35AK32SECnO1c9k5FTgSq3CdaXktU+lawEffsZZv55zMPGRRYVoUIGUAOEhWV9u4ydimvMDt+l1fnBwAAtrgdIsnRlz/yHn732iJ8/YE3rMzO++T6gEzYaf9Q0jbQ3FhMjSV2VWF64zJQ3UF8NlLVdixevz1aG2s/BKb/AmjZySdtaugK6LFTAg2UyQuheqxh4cvAB3/X+1BJNRb5viQ/1D+iTIbkxfeYHSfc5kZLBMq+yXF6EjdQpt5YIMxO6Hnse9ITmKt9jHrtF1c9DwAY7KzBlsYWvwq2oMll2c7fBeRrSJzZ0dVYYtshj/l8zrK4qx5dCdvsBIW9MKaLSMEOhYJvxhAt7AAF0yZRmx1NjQVRPxCB2TGl34H8GLRNyNE/Duznkg1ifmVjNSqMz42oy1y1M0X/N4say/WPVwUlACZ4a/FGAMCaLU0w2+zobOCuEEE5XUuzzzqUHRn1vhC7KnkyTjR8/Rd+SL6IiTWKzc6G7QavJxN+fbj3t2mbEAxyshorGrOja87ZO8u9RRqWmvuQArPjRmB2Ck61f8hA5To5wC3B4cJOiDCreNw0M1pc1m4laLMj+umqQiqCvLHUhS6gR4Vmb8cZIzSAkdkJsWdT35mT82/i5W3Nfu/MNjvMW8d1XeB58p5s/DRyX6OBvRP6GDUJOwwzFm7Q60gpqKCmxtq2DkfPvxFTnbFw3YG6cJNyBGU+NCMxosq5Bm8sLduF/KqZQnzJKJmFBTrutFO7emwJ+o02VrmqoZF/rrJFvDSAtikJO733JoVU4ZT1X1Vj+cV5yIli/JFlYXbUTaKDgPtbIWTMTppQclRRYaelKO/Ywz0CIqJIDOL2OtbUKW1nY8JXxgyI1+7SN3TXc9YldaIyJirVmR1tMibGwRLSyHpujc8ivrM4O7prsOhTHiVvHjCcb6zXyWHd1iYecE4wOwnb7BApildJhNRIeYJM3xl2bgbu3Ad49Ovl9Er6FhajyPTOsPfLltBVstl5y6AaTQoGZkdVY7H3Mec4mLV0EzmVrUaKsJMUs2NyAACApy/HvqufxlO113v39pOXlRNTThdB1GvlqrEkZke12fH/CtfzYGHaHLdMHqvae8kNlM2szYrN5QVmlFLo0OfWLJgijdmxuJ4LL0zG7BTLmF8osyO2ZJpDANwszk67hpKjihooFxU1lrDZaSUK5CUyqbEIsxMk7RhjbwTA3bxEM1BmC0wkNZYpqKBqW9Sxl7lxfxJKMo6DNc4OeWHzjBHhh6iwIwQHWT1haZColf70xhLePktFETe4WiioGosdIuPT2ozKfNgmsI9fAHZsAD5+Pl63XP1LGKulTtCPFv5LCDNR1FhpIsBAmRQC4M0F1NBVROVVhemU1Vgfv0B6Zmor3bvGZW9J/RvtXFNyU1V9ctaWh/C76p/D8X0eQwN+WtRYtLxu6B7ser58U0RhZ93HwMx7gZZGrU2JmV/6hvisbkCKFpsdJkvnRFw0TVAKg4XZGdijTiqW2ey0dyjB/fhLTGx2eDK6pNRYNOCXxISEcwsUpnw5QXC2rEB/ZyM72fsTGlSQCgcGZke1a6LCG0WpgFLJxVcmzcDE+2YmIvSICMp2Zmf1Zm83ta3J3zkZmB3HcX0DZb/eCB4fecfhizQPWpC4zQ5hC/jApIlALe28dpdSj2VyLLOfrlSfzzwYUyiopQR+VTgVneuqpN+sBsqp2xEwicLgjaXElsrnHHy0WqQP4It0WsyO6gDAUNNF9NEYea4yubE8m514jCb1VLIZoZ+2/a84Nv8e+qyZ4bcTYqDMXLdbdhj76Z2rMjv+m7thgVad67r498fr+PdhvTraWgYmfQ6Y8mNgxt3atUhT9KhTSP1R1VgysyOVjQzK7AhhR0035AUVjFl1wsiEnTShBPfjC6hDgwr6RZKKs8OEnVy10U4AiJYuIkzW2d5UwGPvLDP/6IR5YymTt98bFXyXFrajXTwDG7Y3Y+6KBry7ZBM274xn8GeCMFC22+ywvFcL17DopWY1Vs6JwMwQAXBQzw7EfVROF5Hc5kjsyLjNDsndZp35x11urkervryZzbSohdlUsEmbpdhoQZXYtVpCCHCbnbJ6GQOGsd6jI9uEyOM6n3Pwq7MP5uWEsKNGRE9Y2FFvTnfhHdSrebnhxMoYKEfaJCgQqu9ww/Z8qclvR25Xw3NXe393bpT7KfVZOYeyQJtlW8N/zpHTQ3SsjWA6+8qtWjuygTJ5Juq795/J/gnBBsoAZDOIKDAxO2//DjlDBOW2NlDOhJ00oalrhGqEe2MlHUG54Bu+Vck0oqTGUmwhTAhLTHr90/Pw30/MMf/YGm8sqsZSXWNtC2j/A6RrScKuhQc209RYog89O3j9H9G3s14BYUkcx+ELSpREoH261Gq5uVLLek68seSgghaoqkQrU1Xeglii2z+FebDNla7rCaUsPUkBeXSuZcyOWY1ldT1PHKoKCjh+VB98bmgPcY9dMRf07ixSEIhNQkrMji03FmUwTO9chVzPnSibBOu5QnVsX2S93xeu9ezjpn641lxstXmeCzRQpvf0/b9JP1312GylHkv3DJCZHdIGCbYqzTGuK+KubZevTwg7xJ7MbQWz07yNH61veE8q5aAS71owMmEnTSguo1JuLBZnhycCTYjZYbmjqmqUH4gay1WP6Ajy1AKAZ+cEJK/LiesEAryxTIlKgwyUbZPsQedK15LEK8UNlANyQdV3YbSwKdGeEGzzOQehKQ8IA+DNzfL9SDTCttIeX1IJG2VX79iFP45184Gnv9uqblGIaMgWNZYLVEMIVy2owqAeHaTeqgaTeVGpTO+rm4TWwmAgV9u8CTmiqqTMDrWVK9nUWGnb7LQIT6GSaYlIedGiz5ltEuIyO5IayzJtsPnpwZmLy+una/4MAGgS6kis/1j6SQ1AGCe2Gn0tpce2dh6p0MLyNJI+QdwXSY1VNrMDyUhaRWaz096h2KbQiVdlLRKLo8LUWKrnkoE5CWZ2ojU3q7SXfpCrsTzo6SLiMTs5ujCp6NJfu5Ak3qmSa3k1SN1MjWV1PfdBIyiHejk5DlzXJWosF3Dd0AU/NpT2vM8sW31QnB31F0PJqTe0pmPa5zD3YxcuqiRhJ8/HTqfaar8OUV5TK/Y/UPyYdHh/g31aoaoT8jld2MnlHIthbUrMDhcMlB+2rSZlDCdWKqggTbMSldkhHmZMcLR5AfHNWJnqFXqW9l4TgRHVsgC9b/+ucj225g3Cg5XZee/Poj6JHSWft6w01uWQucptjc1OwR6uJFNjtXewwcp1tmK33+zb7DBDrqhRYkNhZXZ8uCSCcqDNTrT+6K7ZkNQhQEAi0JB0EWySCmR2fH0zbSKJHYTVZodcL1ObCFlHF3ZEUMEwtYng7l3SPmsgtQjKcEi8jQhxdrRqDAUXvhxexlqdXlaIBMpvW1YBbz0At3ErqiEm6RZUQQlTxD3nAG88ScIjHVeJ26MQnYwP5qHHn4B/zVU5x48t4x3ni3RqzI7MLgMAtq+XyjgmtUbKwg6/ZSCCbmRmx/tLU2+0epHt3M/SlqhXa4Gqe6tkx4qDBneXvltV27fo4T/orbduSGl9dDxr6S78enI5tDBXCFPuwSDQua/QaC3mqbHiVZ00MmEnLZRKwB9PkQ6J5UXE2clzzyX/tNYu1JYAWNKSoc+/GsKYHRGQy7A4+eopJ9QbK5jZYbs0YdlvuDd+W1IG4gTeKrZYasKcwUA5iNnJ+XF2IgcVhMe0yJmx3fQiKBM1Fu1zqNeY7TsA1Mk711gCBJ3Nw1R4D50EPHc1ql+9GX2dzfxwEXnCVsnvF6tPUhtL9kVukhIlGbLyC0XVWC45BghGQnRDeWETGgMs/pW0sVEEmZxpp5+2NxbEc4877qkaS1yWjdnxcMqBITHFDv2W95cG7oPyGNT+DToUqPa9rIoy49FckO9fnMdJWSrrhtS1MDtLXpOKCWbHQYEJO7GFfTL3FZuspXJOkGq8MsiEnbTQ1KAdokkJmc0OX8iTmsdswg55MUxEhIowmx2GnMZ8AFrW8yjCDv/NoMaKyews37hDLxcTUZgdLiYEuNLnmM0Of74hQoTjGQzLzE4peZsdmi9HUWPlTc9UdCbkO4B9TlaKRJ9A5fvDJmP2m1LYj/lT9dE/cHjuA+knNnZMBsraIppqhGB9Z8HsuOh3QDCY7C/Pt8S34MmmsuA2cZKOT543cq5hp78bGCjncmR7ZzmX3fd+XS3BShlY3qk9xvJD6nusNeE4wJFX+j/K46laiZhs7N72DaajSpwdS39tNjvnPqXU5f3NOd4GwSveCm+sEHufzGanvaJDD+DH64Cj/w/4gTcRmyIoi6CC8ehaK8KYHZeqKFJSY+XC1Fgmg17+I/+kuZ6b7g0XdsRvP3vho/COh6CkJOIU3aM7K6bGMvSL5EGji6v98QomwnUBl76absmuyikXJF8Oq5HmbgsVymzfAWCPg+XvMXaLmicJor0bC9yB0nfB7rH3S5y7s6VI1FjQ7XSSVGUpOc8AT21FmR0u7DiysMOZHVWNldCiwV286SqwKzA7nHGI4BauQGZ2ZObMr4h/ZPe9c60cf8bQIX6GoRrjd6+4L5yu+QDYuIgfrq0yq5MkNG42doXaWdk2pPI7RJ7VkCOkck0Fb5xvayqgyJxnWmOzE3BuFkG5vaOqBjjqaqDbHgDoS2ey2YFSpkywGdK6C4zqeh7cDN9sBtjscNsDTdiRmQSpMNWDu0o/jcyON1HRhbBZy08RH0WrIEgnS/9IgBrLgWdcHGp7QBbFkquING4puXQiDEQo5l3KCWbH3o5FcA06VjazE9DytFvFZyfHDZQ/LA2W63FkgYKfwr3jXIOwk6CRsmZvA+RQRFVOVwDn8+pcwMVQpY5k1VjSoqnc/xy12Rk5wS+TcroIdrlwYo97KgwEZpCH+L1TbQhjZticqd0xvtdsw7niHeCeMbzthxTvL+MGRp2/fRsgPu+qkze1K7IZKCtM+p1TPC+xmZ9s4MxOXJudGQv84IiOWdgpuGweTFY7XA4yYaeCoGosjdlJIl+E6wKPTPQ+B6mx+GRih5YcUG2KCUxBBsouU2OpJweli6Bt+L8Y7Hk4uM2OOBQ5FHsAShESgXK2wGAE5RI1Vs5xsLXRmwhmLd1sbpAsii4Ul183BZsdElWVT7aMWUDJziBp7RvKqWXiMDuGb8YYRa/eJj4TYafg3ze2kPMzlC5JTJs6Saesxmrq2N9/v2ThhvVJU1mmxOwY1ViKoJqnwg7zKkqR2XEVYSR2bizJg9Og+iV9Z/NKl7qwoH6G91tVY5m6pwosLeZ5yXiuplr1+m2Nei0JdBY1liLsbG0Sz5YLOzHH/jtLWKBFs7BTIuxlxux8hiCrsbwHX6Xs5lq1mL31gPhsy8VDWINATVU0LZbRZqd1BsoCmmtsoM2OaKO+a+tjpRS5Gsu+uOsiGL0GJuyUkHcczF62KbhByRXcbrMTmdnZvgG4oRsw45fm37mwk+fzpEtymkVndkwFI7A/ttoNu3DGwliDCoIKO954KJYEUwYAHaqV8UXvp7qwJKrGEqzfdV1uwq8Lp2D10K8iR72xIC9gepiCdJgdV32/AO1ZSd5YjIlNcdGiVTtO/NxYlPkwar/J9TFWuhOJYGxkFg3MrUZWmzqj5NKyJQU1nmtJuEvtmWy1qKyw6I+83J9Kkj0zA2U3bpwdKggaWCGmjg9UjVcImbBTQdDJraUgR8Y16pfjgirfrcwOXZrtEk23DsF67EhqLF8Q0tRYpWiu55TOVn/jMBgoJxHPQXhjxbDZMbieO/Ce8XGj6gGAR/bVQUaHq3hjuSXy+CJe2z+v8P6+dJ35d/YMqBqLBBW0NhMQZNFaJobwoNlIgS7Glk45OeR9YYfZHrQoq4Ka2FayBUlT2CGLwayag3F74Swgn0fekTc/gGBTNdWNqvZNiFkRiXYlaUcqk6cLNJtT0mR2yGf27gDB6k0KyWaH10kFaF3Yoe+k2ZPTwOyoHKRxblKEnTh55DRmx9842qJe2zywqM2Ycg6L93PawXvw9wYxIyhTkd3kjcXqzQVuoCqDNhV27rvvPhxwwAHo2rUrunbtinHjxuH550WW5MbGRlx22WXo1asXOnfujIkTJ2LNmjVSHUuXLsWECRPQsWNH9O3bF9dccw0KhYQDgyUEuoAJmx3ZvqVVzI60LbIFxXPFjjmAvTlp//6RmjSqsXJKUMFYzA7ZoagGlBGZnSRUPQVb1nODN5bryiyC95G4nueAan8hG9Ddwjopaizd9Twms/PRM8G/l3QDZdmLzkajRGB2tDKt9cYKufZSM4+gLJgdZTeu5erxULt9BbBVDraWqBrLkJbDAXxmR1Vj+WwgFcRoHbmk1VjeX0llrRoo08Uvl6ywZe4T3UzEz41VJHObmdkRz5b9TtVYLSZ7P/GCiEORCE5V6DfftzjMjmCu1PKkFpPNjmE92LTDE2Q3bW8WDhExJRLeDccB9v2y9ntJEejbkt2JkIFMoFQq4dVXX8X06dOxZMkS7NixA3369MFBBx2EY489FoMGDQqvhGDgwIG47bbbMGLECLiui4cffhhf+cpX8N5772G//fbDD37wAzz77LN4/PHH0a1bN1x++eU47bTTMGOGl622WCxiwoQJqK+vx8yZM7Fq1Sqcd955qK6uxi233BKrL5WE0WYnCdfiuu7iszXODuUQ7GD96lBtNt5jdRjVWEQdAgR5YwUzO5oxnmmS9fO+JP0SsVAYem4sKuwoxySbnZwXFA0s63nIgh2ixkpEGKZgO/ZcFb93LhHQ7MaEduFPHFKZvOibD3kXLgvltkvPb1utMztFdYy5oCOejamx0y/SK0zJQFkME8fPbO9/52osuW+aYiLpoIIluV2pvz7yVPALEna2rgYmXwwcciGw/2ll90lqntrsRJSvqABnZK4ps+P/TL2xdjQX0bFGnTsN77emxorCzigCtyPtPZWyMW12LOyVmUX38JtXPwEATJu/Di4zx4opyErMzuevADZ8Asx+RDSv8CmuG2I+kSIiMTs7d+7ET3/6UwwaNAgnnXQSnn/+eWzevBn5fB4LFy7E9ddfj2HDhuGkk07CG2+8EbnxL3/5yzjppJMwYsQI7L333rj55pvRuXNnvPHGG2hoaMDvf/973HnnnTj66KMxduxYPPjgg5g5cyZvY8qUKfjggw/w5z//GWPGjMGJJ56In/zkJ5g0aRKam+2hq9sKdHJjL6XmjdWaeSxnYkr073TSbS3MrudqnB1FGFF3qlL/YjI06z7ibSSJFquwQw0cw3crObjIOxHi7JAFreS6moFy4t5Y//ye97d5m6bGysUxUI5isxPHQFm6QF/YYdVY7l3j3qeg2pGZHRbHyiYpscOdty7SK0xJjUU3GbIDgMxgaZ57KRsoBwUVzLsR1Vgv/wRYPB144put6pOUF4sKOxGvmWZMN3tjGYQdwuxsbzK52uvbQ12NFaVz3rjq1cmLbj9uz17GurxOm1WrIhCgWneIGsvG9LNigocPLKfCoYJgvlqLsVUkNjtA28baiSTs7L333pgzZw4eeOABbNmyBa+//jomT56MP//5z3juueewdOlSfPLJJzjyyCNx1lln4YEHHgivVEGxWMRf//pXbN++HePGjcO7776LlpYWHHvssbzMPvvsg8GDB+P1118HALz++usYPXo0+vUTbncnnHACtmzZgnnz5mltMDQ1NWHLli3Sv0rANahGmLtpXN20EdQgTjMWE5N+nFgt9kXP+xNss0MFF3pugswOr1/bB7cKBVucHaOBst4ijVnjOLSsBVTdoZZzXV21kRQWTxf9Z67nToDNTiRmp3zXc9MzNrKenev5x1J1Rx4IkRlaqnF21H4GCvppMDsQOcgcR1djSUEG2SKtRVBOKRFogBrLoc8uyGbIEhcmLmwGylGHPQ2UZ0yuTDcr/t+OhL1ev820SY7C7Jhg3ihpaXCMpzJGRr7nxmemNmUSdkICUrLT3XLVWOyTIlSpaqy2tNuJJOxMmTIFjz32GE466SRUV5sNV4cMGYJrr70WCxYswNFHHx25A3PnzkXnzp1RW1uL//f//h+eeuopjBo1CqtXr0ZNTQ26d+8ule/Xrx9Wr/YS1a1evVoSdNjv7Dcbbr31VnTr1o3/i6t+Kxeqjh4gzA7fhbSiATqgF0+390N/dzWEkT4iVo9J2GFqLAFjrIuwdBGaN5ahrb1P9MrS+TeBF4oTA4FqLLbz9g9IN42lKXClPD12YocwAAYD5cQjKBuaBtmFhae1sHw3HYsRYEMV8gCYWTGJqi/w3FhFf0wVRBAZY58Ch3eiNin6y+aAqbHEQiBHePb+ps3sFE1qLFUopMKOz6Lik2l6ZdvXic+N5W8e6aVRgSWyzQ5PsKyr7dUG2OxTWy3mof+saAjolKhT7U80mx3vXm72bWWqguYEdpBFb+Y2OwY2TmsrPrMTYTtmOUsZ31Zhx/++qzM7++67b+QKq6urMXz48MjlR44cidmzZ+PNN9/EpZdeivPPPx8ffPBB+ImtwLXXXouGhgb+b9myZam2x2DiHpiXCJ+TW7NSS8KD8miJMCFe3XA1lm1sctbFmC6CDXDxm2S3EzERqKZuMy1Cw44EkDyz08K6GJAuQhjd2Q2U2UIm5LWQ3vHn5Ai7nXJsdmq7RisHsqvLEW8s612MwOxoAmIcA2X9mG7DAnksFAuc2Sm6vhpLW/RUZsf721TTQxxkCRsTZXZkWyzWNk0EKvczYCHjzG0yCwYbi3KcHbnuHH12i171/q6arVe2/0TxOSAhZBikdAiIlxuLvlvWFC2SGss7XlclNolDe3cy1ex3yCJA2fqnbQxKWLJBZDKv8tNGGK+MeEvSflPmythHILKBstQc+z3CxmRkvy78cxiz43JvrDaOKIgyvLFeeOEFvPaaSCg2adIkjBkzBmeffTY2bdoUuwM1NTXYa6+9MHbsWNx666048MAD8ctf/hL19fVobm7G5s2bpfJr1qxBfb1HYdfX12veWew7K2NCbW0t9wBj/yoBE7PD3U1DYolEAtXz/p+aW4WosSIxO9HseQJtdgjrY9Sbh3ljKYabxmnBryPpDUOB3SNtbSc7Q3UyNUyGOZTkmB+2BpUIyuwzO4tH/I16AZ16Rys37nKyGETIeh6J2Snf9Vw2kJTrttpe7NzI4+xwZkfzxlIWcf+BrNzjBO/A6NOB2i6x+xsKqsYiNkhyUEFXEjj0XXA6zI7wzo8o7Bx5lfd3r2Ohg9TRivsnC4AIZ0QJ6NzpsUJ6nbIay59fcg56d67h5+md0jcz0R6BLvTfPXUB/1qdD2J2FGEHLuC6RuZKq8Q410abz90IQoks2KnMjlxW2OyYzq0sYgs711xzDbdxmTt3Lq666iqcdNJJWLRoEa688spWd6hUKqGpqQljx45FdXU1Xn75Zf7b/PnzsXTpUowbNw4AMG7cOMydOxdr167lZV566SV07doVo0aNanVfkkZzkU124oGzoIKJeJWyHemQ8bLxL6AwO2LSDUPY/t5ss+OrscjFyMyOulMlvSHnaJOxidnx60j6JWqxRVAmEGosneYWxr6uP2HHUWN5HymzEzuCsi2oJEOHnt7fg88TT5AblpfCDamt36Ff5JIZwX0h0IM4htte1C37txZUUFdj6Z4w0vHeexNvoxTSIZDnCs7siM0PXWT1ODvsh4TTRUQwUBbCjiPspGo665VJUXvLv39qoss4414+V3hj2cYNZRv26OFlKG9qMbzvps2MpsaKwuy4mDZfrFUs7EiggXKemI24rq7a57+1To3FHCKi2ATK7JtSfhe22Ynleg4AixYt4oLE5MmTcfLJJ+OWW27BrFmzcNJJJ8Wq69prr8WJJ56IwYMHY+vWrfjLX/6CV155BS+++CK6deuGiy66CFdeeSV69uyJrl274rvf/S7GjRuHww8/HABw/PHHY9SoUTj33HNx++23Y/Xq1fjxj3+Myy67DLW1IZls2wBNRRfIyQMkz+Ls8BezFaMhojGagZXVECYIcWNLoxqrHANlHdzzQDRqaEvX6SdhxFsowX9W9qCCumKQFCPCDhBh0ib3hJtqIAegiLJsdmjBUkkXfukzUBZTT40VoV5bh1ShtPvgKD32TjW0HJbjyM1VE9dzb+yzCOW2kawZvTuOEL7TMlDmnxw/Eaj4hQocenoM5YVNSLDnaqygRKC+LRScnFh4TfmTKJtTzv1bvwB450G4n/8RPxQ3NxbdUNHcWFY1FkSf6/zknI0Fk6Cmj6Uor4EmlJaKGL9XbzwzZxUAEh4hkNmhwo5wHFCDZNJKGnY0S+d4XQ8zUGYvWTizIxmR8w8Wmx035xdiwk7bSTuxhZ2amhrs2LEDADB16lScd955AICePXvG9mpau3YtzjvvPKxatQrdunXDAQccgBdffBHHHXccAOCuu+5CLpfDxIkT0dTUhBNOOAG//vWv+fn5fB7PPPMMLr30UowbNw6dOnXC+eefj5tuuinuZVUUdJgyI7UkUmPRFADWVl2aLiICt2Nbm/2/QWosKuwYJ5zQoIJ+dUHMTo4xO6FdjoUiE3a0l5PuHhVmh95PJjiwOSBUWBETqh6E0eULYCnq1qhW6NVRKgC5GqVOIlzxLouQAZF3YGYOXv4aMtHK3TIxO4ZayVho3GMcqj9V1VjKGLPceIcII/y9STJjoWSzw1QQnoBBmR1p6Kgq7Sh65zJgVonI94kzOw7RC81/Vq+MCjjlqLF+dQgAoNOytwD8wGvbied6Tot4NjvBtl7U07LW98gKZnbEfBWB3zRIRCUpgOEe3TsEnOv3I18lHROu53Y11qrNO/R6Ql3P2b0ql9lxlL8eVDVWivEoQxFb2Bk/fjyuvPJKHHHEEXjrrbfwt7/9DQDw8ccfY+DAgbHq+v3vfx/4e11dHSZNmoRJkyZZywwZMgTPPfdcrHbbCmp4eICGiPe+t0ryVY3aKKgaS2VMDAj1xmKCSIAdDZ1MzMwObUTftUbKeu7o9GsSmwcx59mZHRFBmXdGFPM/M6o8dJkiEyqrrsRYF7fEa4h8aXRyM6kVDIswDSpoZccipYtQd7TRMylrQc1cF8awDIp7bd6Ps1NirudqnB11Eed1ksUglwKzA/25OoBio+NKbsg6I6EvtklAGLva1Vg8EaiTA97/m70yW/LJmKha8Tb/HNf1XIu+zLpjmnsA5Kgq1Gd2zEmEdWFTnafN87Yq7BRRTWg0YY9kOJcJ3BKzUxRqLHUoWNRzsb2xIgj6dC6P6o3F+tQqB5xWIvbb86tf/QpVVVV44okncN9992GPPfYAADz//PP40pe+lHgH2wtc14Upzo5wPY+ppjCBTdJhlGUwwy+XDRmcZm8s3fXcbLMjGSrwFhn0ydguWCWlC565cD2G/uhZkghULaHvatwA4Y0xO6FxlGgdjDBqjc0O7Ytpp03obSGr0aCCFpSjxipGD/Cp3243dNGCWxIBLCEzO8LeS67ZUSt1cuK9STRdBHnZ+LrgSHF2oKqxNJsdRRWRmIGySY2l3CdqnLtzU0BlRfPnViAus6Pa+4j9EzmX9I2qqJv8kOl3Tf1Yr9gwYUZSYxmYnSoipTQXAgQL12SzUzILqN6P/JO0AQ2IoCydLWwFAssBKrOjfNK8sRyp3G5lszN48GA884yed+euu+5KpEPtFcWSC/2VEdK9GAytGA1czxtRjRUg7URxS/fKGfpLDD3zOce7dpO9BRXKDOqGaMxOsgbKZ//uTa8+Tr8GMDvaLSKTIRMcHPkawrOJCzWWiGpaRm4s+vwCmZ2cuHf+vcy31kBZPWay8bDVblAbGu02JGHHFSwbj7Ojvm1mZseRmB1/OkyF2SGEvwMlXYSillAXaU2NkpCwY0oqqbxjjU1Nou2h44F1H1oqS8ZAudhlD8D3XHeceLmxKCkhJwIlsDA767eJJJaNLUXUSWlydGZHi6Bs6pDGcBbRrYPIjRdoglWy2ewYnpn/G4Mk7EQOKhjd9Vy22VGZHblfYh71NyNtaLMTidlRow0H/ctgRqGkBIrzIWx2EjDaKQUIO5IaSzkUgLCxabbZYYtGkTM6WxrJghdErRpUX7bdOa1DjrPT+heKekIpHRRN80Mmpko2UA7tm0GN5RIB1RgzJAiRmR1hEM248XOqXkZdYVtwP23fTcfi5MYy7IbD4qXALRKa3LuGd5Zs8r+b+0QUEv6BHBG4k7TZESINVR/LEZTVoIKqcKcIO4kxO3J7Xt3yte9s8lk5xwHGfD2gsoL5c0y0DDxc+h5HyKdzgJeixcAKSayEuFaqXtLsdgz3W5fJDR0cfJhSpsQDGA7s0QG1fnwfs6BkmMtdl8yJ9valDVrEyT4xmx1lTt/tXM+7d++OHj16BP5jZTKYQZkdOrxTsdkxqrEos+PSI0ZEtYUMFHbIxHnHi4QeDjRQJsVYdUY9Bvsxr/20d98uermY4AuRZkOix+kwCSDs/LzC7NgJEz34nEsMs+NHUKYLmEkgMQg7ZNyctv4+W0dDvhvai6HG0gzCXRfvLd0MAHhr0UZyPFiNxbClkQl6irCjsoWSsJPghEyfK2m7pViSNj9rtwpmQc96niyz858VDZi+YJ3FjVmuu4p7LDlA3jdy72yIYeYmo8Yq+fqUmjxjRkPUv9K5ZBF26LxB+0kNlEU/abZzfQ42MDvqMDV1aNhRwDlPQtjDFHlIhPF79Q6eEzgjYzZQjqzGimmgHGXsG9coi82OZqfadrJONDXWtGnT0u5Hu0eBbE1kmx3lpW5NI1ogKkuxOMxOyO9Gm50tK7y/7/wewDEAgI9WE9bP+ALqL5tO2UZjdvbo0SGk13Z0qavC1sYCyTpuZzL4tZuMoByLzY61ZbGgCTWWzuzQSTkyQtRYos9C2BnSNN9SVxRmR7XZiaHG0tSG4vvvXluEH588SjvulAqC2VEG9ZDenYDlep80ttDJiceX6O6TMHa8WgfPzlmF3vyb3F5o1vNW9u/ke72gsGMGdffaC8iN1bNDDmiBouYLcT1vBTPGYv8wBkTYqoWfS1kPKc4OLWTxxtrZIvpv8ob0zxCHlGdm3aTudQzQYyiwaRHgFnlIhKo8VbMZzjU6m1ji7FhZS8QXdso1UA5hdthT2OVtdr7whS+k3Y92jyJRY9HBqCaDax2zE+B6bhAYgm12osFos/ORbtO1eEOIO6TRQFlhoIw2OzrN3Zp7uLWx4PeCMTt2JkOsq4adH8mNRX+K4uVEnxD77V1fLXP/vz/FtSdFSN9C71WoGkvvf9Ex58BrKRYh/2LclspfW2mzYygkH3dLPF2EqzA7TFVgWxAcE7OT5PaTqrEgxsKmHS3oReYD5oZMYc2N1Yr+UUeB2cs2A1DVWHJ5Fr/Io0oCbJoSMlBmsnwH32aGCaUbtzfDdYX9lgkq62FkTizMzs5m8bloe0clbyz5p8Aph3v5FbnhfFUuVwazI0wQ5GemCMuSGiuagTIvHmFs0XeUs9uAYXUTwg5jm6z3tgIoy5dx+vTpOOecc/D5z38eK1Z4u/g//elPUhqJDDIKhLY2eWOx3VWjKc5DVATZ7FA1lmr42woY1VhhiMzs+L8ExdkxGCi35n0a0deLDsvVIQZXaKXHZtdzv8/s/hijuZrqdRz+2SU2JHOWN8S6DtWmRQLdvREbITpuCpZ90AtzV5n7HXQshuu5voqUcOkXlVx7BpsgbgCpTmkWAcEYVNBqp9UaiOfKHzE8g1jqndmzk4iDZPXGSiDM+m9e/UQ7FimCsuNIi7aGhCIoszQtsoGwh38vWB94ri26sKsIxgyU2Tlv3FBRRJO3iUcaLyMXCnwixMuPGc5X553AzaaIoCwLO+Y4O3LrvTrJRs1SHyxwY9irycwOO8aYHfma1DUviYCv5SK2sDN58mSccMIJ6NChA2bNmoUm31q/oaEBt9xyS+IdbC+wqbGYkEODxa3YbIr1EAFBNjsS68D6EY6wwRmW4O1bR+0JADhyBMnVZGR29EVJS3qnCgHkPNrP1lClPfxF5+oT9pH7ymHY1Zj0gjyooMLshHXAZKDcGoES0Bcn+luO2uyI+1qAeXKcu2Kz2lBw20CrXc/Z2GGCqKaScouC2VF2sI5F9WNk5VKx2SHMDhHeu3esJu+hi5oqGn/F+2vNjdUKZudX/1rIPw/01b2ySkSNs0NsdphnkJHZaYWBMhG+VWaHqm3fWezZbDUVinhoxiIs30TYYugG12FpRiizw+Yprx4LbRNksxOR2RFqrJCl1zXN5a4+JwL6+1BGnB1uoBzXNopvMtgBc5ydVDTEMRFb2PnpT3+K3/zmN3jggQdQXS0kyCOOOAKzZs1KtHPtCXNXNBjVWF1qPcmd0suFCHYZMxeux9/eXiofDHQ954XI/Bsg7kRkffJ6IBoJ+w3oCoC6AkPZTSt9kbyxFAbKZQsauT4WQVlyQCj/jWJGsNVc/RHges6N7nTxkRsoQ95thquxHC74uq1iGuj9DhB2DK7nAFCwqLG0YWHm4O3thcDkjcUC8HEKXK2vVNRcz3l/DVaqPTpWG5idtLyx9LHuAPjZxAOkYjVk8eM7fi7rKMJOK8Y3tU1hgkROXjml8jylQphrfmvUWC5VIXl/63ybnUbSX8Z+nXj3dNzwzw8w/mfT5C4orvSCdQhndvI5B7W+wFnUdkuG91sdpkECKAnFwdRY1bas7Go/VW8sY7gARZA3XW9EYUdzyDCA9eG200aLY5Y1ZbdzPaeYP38+jjrqKO14t27dtAzln3W8vXgjhv7oWWza3oxv/+ldMaB8AeEvlxzGJxqqy1TteEw4+3dv4oeT52IuVW9wmx2TCoKosaJ4Y0G3hdFh+bH7EP6RTeIFWRrxG6E9sCuwVQNl1xCfh75ESbxQ89f4rtcRmB2JHeBHVME25H6SBU3U2orFV2J27Ea/croIMR20WIUd20Jgqd/UfiB0YUdjPzUBtMh3s6rNjilsweBenfiDkW120lBj8Y5I6uO9+3WRKH5qwK8HkEyO2fnaWBHlnrEMwRGUibBGhR2DKlHUEVPYIeeyOHtMjdW1gxiHTMX/6frtxmqEPYv/l70+UhkzswOIeVdXY5neb9dYxAiec60kMTuBvK3E0otxGcWDrlXMTiSbHe/v54b1JEIMa9zieu6vebogWTnEFnbq6+uxcOFC7fhrr72GPffc03DGZxObdzTj9N+8DgA46CcvoVenGom2BmS9NFVj6W6Fdrz+KdFjB6qx2IdocXYkBw3Lm2y11znhZv6R0bXNErOjTt60M0RoUXP3mPTPhgjK5co69Dqrq5jAaOerBTVraNCmxgpjdoyu52VcUJDNjirssEWY7CJb0Apmx8DORIXJQDkXwuw4pSJn0FRvLIeMqz9fdBiOHNEb9551EGF2yFhM3UCZffLSINBfNu0Qdk2hWc9bIczv2acT/9ziSxZBEZSlRKASyxAgQMfNLUaYINVmZ3gfkWGdLfQda8zMtZrFXWPIAJSKoq38Jtl+yR6tOQKzE/RMKLPjs2lVeYdPCmZylLD0ZAxzNVaAB520ISkRYTUAnBGNIIyw+1NNjKxtwk6Vb3O0V5/OuO7kUejVqe0SdMcWdi655BJ873vfw5tvvgnHcbBy5Uo88sgjuPrqq3HppZem0cfdEt06yIvFmEHdoQYVrCV6+oE9OvLPcQyHu3cgCR6D1FhkoqSTrg3UAM72HlvtdTr28v52Hei91FBUcxENlLUAWkabHRZnRz8vLlqIQMaFnYBJTGN2JDUW88YqSb/YaxN1lFylPreE40f1C+m9Wp2Zute+OxabHcdsoFylqS2NM7XyNcZO36A2zHNmx1ZGBBUsKbZGDlFNjR/RG3+66DAM7tWRPCk6FitkoOzAz3ou2L8vjuxD+uz3TBXuEhDG6LBo8W9oILPD33FHZoxVVVZrsp6T8uzdpUH+vn7oIO83/8fOteaxKfJGMQbdOy5FOCNjsWbh89L5rLzGPhg2Z6pAFI3ZKXLbzeocZXYMJ9M0D2TuNsbZ0dRYhrk2NIIybziwHCDPy+yOiFAd8poywF/XTh7dDxeOH4ZuHc2bqEogdrqIH/3oRyiVSjjmmGOwY8cOHHXUUaitrcXVV1+N7373u2n0cbeE4zj480WH4Zzfv4mR/brABVVteKOFqqs6WHYrYaCGz9G9sVgf7fVKGwdbGdsvLPhYqcDVWC1hwo5hImeTgDC78FUVdNI1uJ6Xa7ND+xjFZkc3UCbF/L/CZkffZRrrlTIri2d23rihmPLBGozsFzFgomsYF/w3s82OxOw4SpZ0H3oCQhOzo9vURIbB8lNT62jMToG7+KsbirDcWEbX80QNlIWwQBe1vBJBmbqe6wEklbHRiv5RBpkJ906QsOMabHYAX0Ahu/RyvbGatgKTRKRh5qFG2SaN2bNAxOXyu8yPkzKEdXKdvDEMiL5Z0ifMKCI/B2F2WgizE0jcvvuQ9/fDp4VhuFuC68rxh0ytS8xORDUWu1txDJRzOZGolY8rtZ2E87m1BrGFHcdx8L//+7+45pprsHDhQmzbtg2jRo1C586dw0/+jIEtDEVfIlf3/2psjdqqHJoKpVBmQopzQAd9xKzn7OWIYrMDeIM7byhtjLEDiOR1pRbuWm82UDaosSQDZe+vUeXAwAyUE7DZocJOfbeOWn/8A1qXTQbKPF2EI5e19s2oxhICIDdwjrqrj6HGYve5aucGfrjFMjXktWGQsBpLO1BCzlGM+A1MlQgqqDI75lGujSk4xjHYapCdBd1kqHZ5kjeWqvLUxn0rhB1yKruf+QBhJ2eKswPo7E1YXCcbZj8KbFtNK/LaJX3KqzZbFmjzhUFNRG12CiNPlpS11mjNJgcEWxETiK1TQbLZYXYyBqz5j/hMnruWQgfQhf8yhJ049oHUNorNBzZvrFRUw2UithrroYceAgDU1NRg1KhROPTQQ9G5c2cUCgVce+21SfdvtwaLjlwqeWyKyux0qpEXFK4zDnmpZTd2gsCs5/GYHTpmY6uxiItqtT+Jt0gGytHUWDSXkH/E+z/UQNncrTDQ+3oUUysEeWPx30w7PzmYVuDEJtVBIyiTCUjVjYchqus5sdnp9eZt/HDBYrPTUlDrMrWtrgTRFz99rJG8YPyQgdkx2Ox4kXQtruf8k4HZSXRiFs9VLJmOosZyRfBDGGx2DOOrXJiE7UDXc5M3FgCsnitXIkkU5astGfNCbVJUZsd2G5jwxu4fKybF4KLzkBISgTWpM0iG91spE7jB4vYwJRJUMNxMQJwvhPBIEZRNG51QYSdqZyCp0nSbHfnhtMruMGHEFnauuOIKnH766di0aRM/Nn/+fBx22GF49NFHE+3c7g5GxRZKTNcqD4ScsrvLRRwXVKcsx5aKmwjUPnnSnZXtRQ5VYxULqPYFvpYCnQwDmB1Jv670M9BAmZxn7lUo+K4r58Cx0q967WLiIxOY3+fYubGoJRBR38XJESTXB21BsdnsbDrsv/nhJosaSx8xRmlH+RrniRhYG3WHbhCmRCJQwgg4jnFcAWRBNLGFiaqxxNhQmR1qs0Pt9zRbDrWPreifafzILIH8e57mxqI6zIe/rNYsPsYxUFYXYX9sUraJMTuMeLXZGj7+znIAIoO5YEMJSN8cJbI3FzI1Mld/v5sKMa5RUmPROSbgnKo68kVMHsbkrQmoseIFFRRzmpbOw6bG2h2Znffeew/Lly/H6NGj8dJLL2HSpEk4+OCDsc8+++D9999Po4+7LfiOhMdN8WBT/9i9AWTIzA4Z9EHpIgyu50EI2Dhw2IUdocYyqm8iGygrlDZjPOj1+S8TVZOVq8Ziu658ziELi11QyHFjXYMA5F8PZ3bC1FAGdYdgdkh4wciXRhcfG7PjCQOsZMPYy7XuqOjaQVFvRbHZiQHXYBwq5nV2YxRmhxooE0HYWyTNUqYau0kOKpiOgTLIWKCLueMoQQWtzE6yaiwGSaWmumPbFssAxjMWs6Ou+P65tE/ClsZ+3YvXb8cfZixSqtbVWCXSN6fYJJXP2+ZfQ6ykv729TC4ShdlxBbNTTVzPjc9z7De9v+N/ID13PV8g9PfB6HoeZhOqvycNO8yRz6nApQs7KrOz69jsxBZ2hg8fjhkzZuC0007Dl770JfzgBz/A7373OzzyyCPo1q1bGn3cbUFfUs9mR6ZXVXBvgJCBUaT2L7SyUsDANjA7QaAvk22BdmxqLGKgbEyAaQwqKFpj0L2xDHYZjh4IrFwDZRakrKlQChB2qKCp7rzp9fjCDmd21AVMhaiDq7FYfYWdnAWMLutEsNlRWLFcvhqL9jo/sNpI3lixDBq0k5WvJT3mk8H1nCqJGDyh1czsqEbvFTFQZo8YDnI5uUd06PAIyqqNEl84yu+OaW6JpsYKqzlAuI4Dv33Dq0RCUein3fnSx9oxozBB4+wodkd6fCP1fFHjph3NxhJGOMLZoUATgQaxvfSZG9RYMrGjjG0jsxP8ANVo7f+YvQIH3jQFd7woJwSmc2vOcUiaT/98m81OGrGrYiK2sAMAzz77LP76179i3Lhx6N69O37/+99j5cqVSfdtt4egX802Oyr4ghbK7JAXlv4QmPWcMjv+kYDxT3+zLdChzI5b0j2W/D54jRi8sSR1FNvFiCNeWV3YoWxX3DAfRlhfUhOLw08ix5jNjvKL7dFStkWt709fRc7fhUZmraLY7CjCjuOAPBPzTdSGjHmmDvluh2u437rBrmJEW2zEifm3AcRgdtR3Ma2ggmSs0/dO9cZ6cMZirW9WNVarmB2TsGNnCSQ1VhASY3YMaqwI3ljUVf1fV31BqlpidsjkkFv2utIVy4ZESKkcfbrI8WKi2ewI13MvEajOPIk2TSERXD63BcVik2x2osbZ4d5YXvnr/jEPAPCraXJMPXpvpNdLtK50Zjc2UP72t7+N008/HT/84Q8xffp0zJkzBzU1NRg9ejQee+yxNPq424IKO6Z8Ilr50N0/eH0M0ksWMes53WHaIAs7FuHMOoBlih5QXmhlsW3Y0QLXsCgJq395EZLUWDmd2XlhHvXuiI4ONPmghREwGigbmB0Wd4KpV8LVWKxdYTDcr2kJ/7lu6xK1+WDEYHak7qu2LArKiqDcGuHBLelGkIH1iWdQRZkdlQ1iG1ETs5OKgTKJoAw1zo7L81TRvolHoAg7rbLZ0Y9JtoOKYCwZKEdFLGbHIuyY1Fgl13QGAJFeAgA6+XF4jFHg1Z0Q6atdXaYzO0ft3UcuEfRIcpTZYWqsEObcOC6pap+UXSezL2abHbsaK0fee3YhVTnTXZbvjeM4YjPLD6rMThB9VVnEFnZmzJiBN998E1dddRUcx0F9fT2ee+453HTTTbjwwgvT6ONuC7oj8ZgdD7071+DRSw7XyoudRRizI36X0mgFuZ4bbHaChH1JjRWX2aH2CGxzSn8nLMa/P16Hg3/6Ep54dznUkiJ2g3JeCLNTLljunae+83n5pZUlNdG09rvOyjA1lh47RQGhm42X4kcije56bqhba0sWdnLUoNcq7KjtmCZqu4AYBldLbeHyVjVmJ69HY6UBJyVmx2KgLLyx6DY1BWaHLEGeYacjLaEXfH6o1jch3CXI7BgGV5Aaq4pmPQeAEcd7f8ddDhllMjsGtSUQ7I1lArXZY8KOYB3IBkoVxLatIe14f7V7ZKDCVfZ9/uqtcuoeCv5OyYlAB25+C3+ruQm9G5fo50jCjjimbQABYNNiuTkYNjqGDfAhQ3oAAG7/2oEas6M6zzDQtSnnEAGCbNYkhAQzrCRiCzvvvvsuDjzwQO34ZZddhnfffTeRTrUX2NRY3z9mBMYN76WVFy9bcL30xS7SwiUS2l0FZSpcF3+uvhknva9OWKQ4+WxTq1ltdsiLyA1HLQbKP332AxRLLp6cvYo1JoqxOtQFmHqFcJud1i9Q1HhQFnbo5EF3Nnw1YgdEMRZnh5X1/9oE2RY/jH3JNeePyfuTdOTLNFHZ/Lu8W5eF3+AFtSxmJ44ay1CX5lVDo8J+5w25uKrGsghvGnEnqbHSYnZ465oaS04Eyrqhjq8EggqamJ0oaizWdu+95e/8PMowxxB2LCrWQG8swy5t/4Hd+OdOfoBWk3erpiZdLeLZ6Ibh/Cz/r/f70B89iwsfekcq8c6STfjyr14zG/USI12aCPQr71+Kw3If4bxl1+nnWNRYwv2e3IO++8rNmeynDMwOq6JDdV5j1vOWnTC9l5LrObfZsaixdkdmp7bWntti5MiRrepMewOlXyUfKJtaKDKzI15YaWGki4AGMZgHNH6C8fl5GLxxJlA0W9y3itkJU2ORyZumaJB+g4GyDTBQToLZYX2pqcrJL600QRpYHgOzw72xnJL0k62Xz8/1hL05K7aa7SpKzdqxQERRY/GAjP5Xh7rDWoQd1UA50GanDOHBkHFas22gC0FedpGncXby1MPKYqDsmBaVJNVYhMWk49lrX1/Qad+0+cKmWo0B09iSvbEUeyiaLgIQc0uQQBNH2KntKn9nzALpkkjQab/uWl9YPHqfviTOjn+eVL3SN7JxytnUWOT+N6lxphSs2dqoH6Q2O4TZYehS2KCf45J5Qwoq6AsjOUNZ1pxpY6msCaWSi7cXb/J+csh7Y7j/0nmKgbK2+dmFXc8jRVA++OCD8fLLL6NHjx446KCDAuOzzJo1K7HO7e5gAoMXZ0csgLYHX06cHUlWCMp6TibK4Ttmi+MtO4VBsak4Itjs5GsB6sZpYJZc0wvo5NDUUvR7pV+8blvEVmWaLsJ7mYqa0BQfLDFiZGZHHNTqYsxOnpd1MNr5FFUwT5aL1m8FqoAPV29FcZhX31/2+jnOXniNV48v7CRjoCwvniYD5dbZ7LDnlPfGZAy1kKamcwMMlNXklABckhsrn6eqKYXZ4awKVRekwOxQFQi/7Y43hzKBCy7PIcd+97qRPLNjEhiqAoSdvJQ7DLCq0so1UK7pJH11DGoU7qUa4I1lsmVx1HEDA7NDNnuh3lhODs0h8XWMWb2JzQ4Lrio976ANg2I4b4yzo3iVyTY7ZpurtVvFfL2tqYDuyhxrW+Pp5ZEhbLXZiRO/J21EEna+8pWvcEbn1FNPTbM/7QrU4M0ltgdWe4gybHYk/XJQ1nPS9vLa4eKwZRDSwW73xrLE4KC7ax7sjYAsVisbGv02dHsJNdcNu29yItAEmR2uxiKLpNIneiWOeu8kd315Mc2VWvDP2h97v+28AE6H7vKpRN3BqOpPu48Heo8E1s/nzE70dS5g8bEZKMPOhGj95HWZhB1qW1Ww1mXstWFXzRd/tU3H0QV7jdkxbzCMrufh/tVlQPRV5f+YqsABkKcMA50mXGV1oXWWAaPreaCwo9jsWO2aDPNQJKjP24+zE+CNZRZ2WDd1IUmuP0jYYUy8eo54cmUJO3zBF8xONXneRocVyU5LDAhjnB0lErRkXlAKNzA3eS3a+AyV2RHdVcYH78yuo8aKJOxcf/31xs8ZglHFdc2M2WGwMCWM7QwYGNuaCnj143X8uzR5RWR2mh2awM/+8uYc+P0OUWloL5KjfZJtfHUhycR6aRNYgIGycZKJCRFBWWV2XONnLc6OUY3l/+KK3Ze7Y5Mm7PB0B8hxATafc7iaxjs/lxCzI1QTctwMOsnZhB2tIVPjfoV5oIiYE526+JWEFsDI7MhjvUQ088FBBZmgIbNcxj60BtRAWV1IGIsGV7GRYJseud/NJQc1AJoLRZjjW4fD9JoEMTtVquu5jf0ql9lRn4tLxr4P3RtLH4VGZoffRzLzavZrBmHHyuw4BrW7UtL0M4mz00Li7Ih+GuZgi5egMdaQmvbC5I2lMaCiTD7naAyMzbWdDg+hjoVQg+3CubFiJwJleOedd/Dhhx8CAEaNGoWxY8cm1qn2AqED9nasJlWNVN5iILdw7Tbc+68FuGj8MJzyqxnSb7LNTlDWc1ZGCSqoJvRT+lNSyxNoFDeDQ3ctrFnDCygJO6J/vGvq4uD31aVeOAkyOwUqZNjUWEEvrdFA2b8GiUrX65CYHTY/5RzuhZUrFQDURJ8yItrsqAaHQPBOLBqzw67FkjkeAApNwIZPPONKyT5KX2i09TVA2KGsnye0Kuew6zAxO6mosYS3lxB7fEFY0EsWmx0XdLz9dvpiXA5g2cYdINxsvO6E2ewognE1fGGA3Wfr4kU3XXFUFrpwC9i8sfwuGNZh1yAkGR9nELNj22ySzUEYs2OEFGdHdz03qrGM49Kixiqqc3jwXAvIAmPJsDbFtdnh40p5OLbcdG2B2MLO8uXL8fWvfx0zZsxA9+7dAQCbN2/G5z//efz1r3/FwIEDk+7jbgu6W2spkgFltdlRdfUezv39m1jV0Ih/zNYDN0rCTsksxQOQmB0jzWkAt7QPs9lxctjWdyw6r31XaYsyFgSGFyOI2eH3kQlm1MaITYTKBOsS9UcUUHVgoLBDtQr8uJ3ZYbmxaj98glSn33Npn0c9Ivykqjm3BUBNoJGmhCBvLDIBynEzqBqgFcyORMFbyjxyOrDoVeC0B4ADzjD32/+uq7HsNjv0ueWCXM+JJxQ7YivbOlBmx//kN7OzpQTUeq3KNjveX1V+f395A1BjUXtEhEkOqcrZBHugBupCalFjJcbseOeaDJSD4uyY1VhsPiXNqTfApMYKMFBuLgZfm5EFzzF1cQmbfW+tKkmNFcLsEEHEnAhU7pMkPFlMG+g1Fkt0/vXatdvsyPMF74LNGyu36xgox/bGuvjii9HS0oIPP/wQGzduxMaNG/Hhhx+iVCrh4osvTqOPuy3o7qRYCo+QYpvkVjUYLPx9TJu/VnwJynpOJeygXb/UH32yoMiB7lp1Gt775J1sdj03CDsGBoTfR8bsSAbKZmYnVqI+yOrAYGFHZ3kck/Dm67DZPaqd/bDo8oIXtPZp1m4mwHrMjifsOP6kFXmDFLT4kImUVucQG5fIBspBu9JcwK5u0ave39fuCq4u1EBZYXaIgbIcVFC5DlW/qjI7q+YAz14FbFuHVoHUr84A1PU8b2AyvHeVCP+agBYfZm8s2in5vens+HPPGj/LuZX9MiywkWAeT3SjqBoOmxZiLY8ezKKrq74LJjWWNnUIgbW5EHzvg2x2Vm7axg9VV8UQdqTcWHJfpbK8PlqPme2XnFxKJe252raJNIWP44Sni3CD2N0KIzaz8+qrr2LmzJmSm/nIkSNx77334sgjj0y0c7s7qC68pVSKocaKPpm9t3Sz+BKoxhJty+HEA4QdVkTpj4gEq+7gYfjOaE56SLe9cQ07Rl2N5S/4OcrsmG125q1sAOBgQPc69O/WAWEoasyOob/kegCZx1HB1CnGSWPTEsNBMaHKzI73iub9STnyyDAJl+p3hdnxiJAE4uxo4yKg12s/CK7PFbmxoqmxyCIZwOxwWyqjKtYF7vfnsq2rgbMesfc/FGJ1UGViKuxUGdQv3pCkwk7AmIoIcyJQO7OjIWlvLG2uM6ixiO0jEMzshHljdXvl/+QTDd5YmhG3xOyUY6DszXNzl23W2gLiqrEMAl+QsbhFjUVd6D1mR9mYWAaZaiAtQouY14JdSY0Vm9kZNGgQWlr02CzFYhEDBgxIpFPtBXS3VoikxvL+qsKFlMYAwPGj+uHQYT0BACP6dhY/RDRQloWdYJsdQB+nfGKxCjt0V+a/DPT3IGbHoMbiuxi/rx3Wvkc66Wc9VyaZhWu3YeJ9MzHu1n8ZrkyHJOw4jn5NvKNU2FGvTb8eUywit6AzdVRwlKhqxuz4Bs5J58ai1UVjdtR2TBM1u5b4GY/1oi5Z/MOZHfrcqqTs9XLF3EA5zGbno2ci990IaqCstC1+URgJqs4mfenVxR7jLHp39GcRZKCsw6LGKpvZUWoP8MYqBSzEJi8l02ybb9qsnijKW5ls8aRaQoQdowGzPwbnrxZt0w1YILND1auuaxTq1PttDiooz2d3TV3AP/fuLMzdeQRlqxrL0D7IJkPVKuzOaqyf//zn+O53v4t33hERJN955x1873vfwx133JFo53Z30AFTKJaMRrim8urPpxzoCZHXnDASi2+bgN+edwjG79UbAPA5X+gBENn1XA4nHuyNZeoPX4zJAi2psSQDZUXypxUaDZQN7SjMjkOFBcbsKJPM395eZrkqMyKrsQy7JqMaixkoq1GWATgtO7T2adZuSY2Vk4Wd6GqsIANl0V8q5OUcumOMyuwEtJ0zCDulEvD6pICTTTY7So8kAUXxMqHpIgJcz/ncLI1FwwTfoUdAXyOACvbKWk2ZnS07xQZSMKpyv13O7JS/cJhczwMNeBlqu/md8/rwxqcb5LAXtBIlV1McsOcRn9lRWGAoQqMN5Hq1hMP8K2F2ynE937EeAHBS7i3jOXmjsEPmFENQwSA1lpHZUdj+Z+es4p+P3bcfF1bY2LIZKKsRnHnPWJNVikCehtF/mYgt7FxwwQWYPXs2DjvsMNTW1qK2thaHHXYYZs2ahQsvvBA9e/bk/z7rkJidkgsbpc5gi7PDA1FRqpvVZDJGCzNQjqrGsvRHCDsWbyzZ3NbvJ/k5ouu5pp/2mZ1te50i6vJ39iqzM7BHR/75sQiCDxWWpDQDtL/KhYhFx76AiImM1D9vslaeC4XEGytPbHZyxSSDCor7P32BsElxIhjpRmJ2gtRYH/4DePF/7N3WVAgmNRZZCHLy2HOjup4rBplWZqff/ta+RgNldvQFmf2yeosQ4PmmR+lLsSQvSOXAtBa7psVRxcgvsc4CABat24bpC9dLtXB8/Hz0DhmeNyAv5iLODrTfGExeSmKODGpfXK8xcahXiJcIU2M9NHORfnDeUwCAEbkVgeca++XkpAsRdoyGsryXBBHi7HjCpMyA2pJEMwFXqIH9eYtXpjoM7DrMTmybnbvvvjuFbrRPUOm4YNsFGcqrLxvbLeQNux1ZiIhms2M3uFXOUHfUPpgBn2yzY1CWg0j+tIIgYUea3OVdBBN2SjVdRF113f2y8nX0717HP//35Dk443ODEAS64+W32cl5fbUGFWQTg53ZcZSyHLP/Aow5W7RpUGPlHYeosYpq88Ggfd6y0vKbg/XblIBkcQ2UTR1SbbJoXcvf0ctL5+q7atkz3ZVVT+rpZOzLQqtcb+R0EQGLRCRQA2XO7DBhhgkvwOeH9xZN8lfBlfrCXa9b0R2TsLxHd2LTZtv8cHWhUM9SNqp8KEKo/zyo0bSaLsKkYTFHUJbVhaHt20iIGMzO1A/XoqlQRG0VmYNrOgPN2+wnGbtF5laiijV5nZneGfFZt480Nkfsgrz6g7slbHbYL+YTnCAnhQojtrBz/vnnp9GPdgnH8RL+FUsuWorEQNlqs2NmUnjUTZoskAtGJmYnxGYnshorjNkhE7nMH4uPfhlbIlB+yNAfseizA971FboOxsOF49BU1QXfqvL0zSqzEzceBt2xiIlEngC8z5J0qRwT1617zijPvGG59JWVcyxqrJxvoBzdeJ2Um34HcAwxzCT3f2APsdB1qMmb2RhDP0VdJmGHzYhyoDIAwJv3h3Rbr1/N0xYk7NBnIBkoW+LsGIMKUju2AkmDUhZE/XyU8E2EeCdp3BWbOrvgsgWm/IUjNM6OryJ2q+pkdbESZ8eBq8T4KrNPGuOmMzvsY1DgUI0FJudFZXbk+EZSIf6JzSuHDeuJn566P6YvWI+bnpGN7Gcv3YzD9iSJnr8yCXj8fMzJ7wcAGNW/a0CHlH5JruclMTc4hrI+jOkilA3wmEHdMZsYTKubzTDXc5Vd4xHwAaD+AGD1HLndXUDYibRt2b59e6xK45Zvz2AUbKFIgvOFMDvqhMSoU0nYMVGuQa7nNm+sKMxOmBorlzOsjfJiL8sIlBHyDxk4ILFmyswOcnlcX/gmfoWzeFl1IlSNusPAmJ0qQ0Z1o4ADgyAjGVx75+Ztwo6SE4if54h0EXkHfJHhrufhl+JXFCDsGbyxDmBZoxXdvYpWe2OVFDagU9+Q2uSgBi5gFJb57zTOjgPrisd3piR8Ai9LBRzbFjcqqIGywkwM79PZ/0WNu+KhpBgob9ppdyaI3R0fVTlHXtjYtVcpHoxc2PGZHcdVNhjkc6c+cXokfRPMDlFjaTY7VPj1N1MqCwzK0wW8NVHUWGS+YgbKNVU5jOjXRYqPxKAd8/vU2OI9v68fNtjeH7VfVL0KocaSIm77ZSlTyGFRY3Wp857n3WeOYZ306uDzYLCww5rPmeY3OrftQhGUIwk7e+21F2677TasWrXKWsZ1Xbz00ks48cQTcc899yTWwd0dbA7z4uwEMzvcRkZZp1q4sEN3f95fmTGJZrMTVY1VtjcWaU9aqFhFhjYFsyMa09wsXZm5ot1SmZ24EZUZeybpwk1J7EyG1gaIXXzJXFaxW6HZpaV0EbxDcePskD4POsz8m5MT0Zo5LR28HdbWfiOzE6DG0spajKdJXXQXWaJqWKOwQ9RYDmF21EUVymG6gybuyGH0fyj863Gh25Uc6u/+HbjSuJPVL1T4DxZEo0BlBvPqosaYnZqO8nFFjeXAtRsox/HG0saGQdhRGGaJhHPlvybX88CpgKqv+d7Gwl4SNVatHyfHxIAUVI8swoYBwMrNOwM6xNo0xNkhEZRlNZY87xuZe+VdUe+lULvr918+z29Kmd9LdKYn74zTWjVwgoikxnrllVfwP//zP7jhhhtw4IEH4pBDDsGAAQNQV1eHTZs24YMPPsDrr7+OqqoqXHvttfj2t7+ddr93G3g7Ni/brZsPXkhsrud0N8FgNEUIMlCWmB2DgGQ/Q5ssRCRTIey46rvh27s40mTN+m1idvQL0r2xFNd60i+V2aFqrAkH9DdcnXJNJsM8k7Aj3XB1V6MzOwZFnRFC9Z1T1FgqsxNxobOq3gBZ2CGCFejk1Apmh1NyEYwTd2xQTlW9sVxJYvbMWALUWNQTkLI16j3QmB2yg6a5hpa8Zu97JDCRRV+8qeBgyvKtMjtJBBVUb0N1XrmHocyOGB8tJct7ESHOTnOhhB89OQffqN2AsYC3QLpFzuyYIiGbFvpiyQvIaA4qGEGPZWB29OLi/X5z0UYAwJzlDf4RHbq6zSvFoqkfSj1ow/pljaBsYHacPBwUFDWW2RurpGxyePEQZkdLzmy6xaHseNsgkrAzcuRITJ48GUuXLsXjjz+O6dOnY+bMmdi5cyd69+6Ngw46CA888ABOPPFE5POt3Am1M1D3bbLsW8qaadQWP2pnDZmYjPY0Qa7nRDrKgUxGAYNQ7DDlMkZvLK0afWIW7I04r1enGmzY3mw0UNbj7NhtkoIiKHeMoNJavMFzB9/ZQu5NCLPD7WxceJcrGSj7dD8Lv67SdYfKGwKJ2aFUNRN2/AUkEmFlE27U76oxNIHNQDnQGFI9ZoqeWtsNaGqw9RzaQCKu596vwcyOHOOJlpHrDTRQVhIrtgpMzUJtidi7azhG++adahJ2yoc6fmzMjqZmZYslH9dKzjxpHgq3l5s8azmenLUCufxSjK2GN86LRZEI1Hg/2IaEXo8r/WYMKhjYE/I+8/J2Zuep9zyPqrVbm/z29KfRot5kfwzWVeWAJqBWETBLcHQVCw1ZQOZuY5wb7X0wrQlyC0VFaFI3m7nIzI64NxyGRM1hT6ESiGWgPHjwYFx11VW46qqr0upPuwOdTMSCbi5ry41ltNkxCUYFnx41GShL3lgGyd/YH++vliiYDXjK7GjNOXIZsOtypJeTsVUmQVB1c1RtkmibqjdWgbiIRhEQvvuXWfpBxUNB7V9Qbix1F+72HQVn40JRjbKYCGbHkScU/1pjeWPZhBv1u8QisebDJqcIzI7K3EkLYYjdiWFXrRooBwo7MV3PJY9Czuwk4WXEoDM7ottCeJHDSpB5wKVvRuuZHfUGazv4JX6iYTUWFO+06IO1FxGYnQ3bmng9ADxhqkjVKKKsbRMICBbFxPrY1PByX6mw47ejTYn6+y3OMfWpZCzE5gubIGFvU4xhLYUOIDE7Wi9d8wZYJE5lzeSkPobb7ChqLHqTCYvk7G4GyhnKBw3FHsbs2HTM3GZHyqei1NTSCDT6O+YQm50i1alHMFC2pYvgAfNMAdn4y6MzNabFqgSdRdFsdjizk5P6Aeh6ckolR0meuUePjvpB02JqFHz03Y2aCNTdY2xAPfKiK6uxZGEnkhorVNgR/eXMDqc6QqaESMwOMVz3DojfjII4OdXE7KjNGSJwc0gGyvKumCKY2WmtBxYBY3boLVBcorXcWJKMmKywoxkoq8a0GzyBPLdhgXyc9Zkucjb7tdAozAbBxV+Mc4Y4O6qjBNWSlPj9dbXzoJwHAIU64iWl9FV4Yykg78sJ+/UDAIzz7a1MMkGYzY4tOrGxX4qBssluSVVVRVJjKcKhyqzbbXbkDajomY3ZMb9/bYFM2EkZMmMZPMhtrt6RDJRXvR/SEzGYF6wiaoQIBsoq+Isi7eDVsnw1EU2x8gY1Ci9lEI44pc13KbqBsqonp1Tye8TF0oYLPj9EP2hidgwTvJw52/8JCrOlCRyWEO+SAAIxgcVJBKoV0gUIr1IiWHFfbLaYWcZFWN20jEmNlVfTOyiCuUEw0w2UZeZo2YjzRHG6sw9Ig8Avlz47k80OixxcLvx2aetqPC0HrmLoSpgMg81OayZtzUBZfcf7jfbKDT9GOVNeFHMoKU+esnfhzE5JfXe4BKMbyOrCIRkPJVYfK6v2WB6hzV2993wtfLsZSS0t901AtLxHd29TNGZwd60vDLpzhKIi0k4JWBeokb3rQlPt02swBfCzqLE04VCZ6+j9lzeOcvs8N5blGhxuX5kJO+0edDIJTQTqPw1d2PG+SzY7SqAtCSZVAZGwpYUsYGKyCV9c30tsdrQdOUuXAP1FoTtz9h4FGijzedBPF8GZHb1P/DuhkhetDw+F8NIHawAAddUm4zqz95rRrd7H+u3eM1i7ZadUlkOhukUkUkUA4ZNFDNfzMtRYwkA5eIzqgpOpnCyMwHWBl64DbuimGSSr7Iy+zqg2O3L/AaBEU0SQHWyQ67mjCnV0MSjSODt6HrN40BcDsVDJqgDRN3am3GeRCLT8hWPafDmLu6ZS6VLv/VXTZDhM3Sz6bPXGMqixXNfFz1/8CM/MWSkV561z2zSd2WGFTKwGe+9N6h2jqp8Ln/q7Lca+1nm/gJjnOLNhYnYsNjs5kwrKBno/DcyOKTdWHDWWaiLgcmHWA123WiSTAOX62fwtXTL5YmJ32wiZsJMy6MB2DZ+kshYdM/MskuPsQC5LF7TqgCzfrou9+xJ7kTIoZ/6imIzTeAd1yl30lUweAWoRdRfB3Xj5xCvKasyOQiXvaA62FZn64VoAQGMLuR8hwg57jiYjvXeWbPb6xSaKEAGE3UvHUSJmswmMZT2PskOyqa3U300GyobnFliXqdyCKd5fSp3P+KWls+rYCa7fs2ORBRTXEWxRTjPit+w41RfIZqBcbIpkcGtFJANl+Rpl5kdndiIslZGhsbf+OHOqauTjXHAQfbYyO25JGyf/+mgtJk37BJf/5T2viDqOmBrLlPU8wPZG2OzogpB4xErfABQNrGNoUEGHZK5nz8Iw9xXUlBK8iMKmRIaY/7Q5ERDXwFXe+vWqaVV0hsi/HoM3HJ1bNdsok3wo2UHtOt5YmbCTMqokYSd412zLRRVsoOyXpWxOzz0NlZNFANFsdmxxJ/gLx3ataiJQr0Hvf8nA0vVPpsIO+83O7KhxdpjRG+1WkM0OIDza4sGkxiI7Qe3eEXqd78L9yUNjRMxqLEcVQJa/DQDo+P5D3mlB3XVdb5cXxuxI9kHex5zC7NiEHaunignL3gwvo9avStZanB0EMjtVErNDVFNKH4zeJCY1FhBuVB0IxjzQxZv+ot9ryWXaYKCMVkRQVqGRDD6r5eTVhI4ys5NDSARlZcz966O18s+sWoUFZN9N3lgipo4+RxhzYxlkCh6E0JjIxsAESdciAkOqNisUNmZHRIc2nKR31D+XjEvJZscg7Bi9sczG/II19wVJxTmGVm+632xt4C1KsUeosJOkaN46xBZ2XnjhBbz2mog9MWnSJIwZMwZnn302Nm3alGjn2gNyJmHHyux4f20GyjVVht0hK8viY9QfYO4I1clGNCa0RRQV+naqxlIGNaNu6TvpQm5bstnRFyWNslV2KbRbbOJldk0tyu4qcnwawzWE3y+d2WFncMZGXQyI+tB1Sf4n1UD502lyS0GXcWN34KaewMr3lO4FqLEUZscUBGxVw04xCdoEpyC4JaCqzvybNhnqwpSjzqMas0MEnLyixrK8c+xo3qWxm1IQdpjahDSv7uzVO2AzlE1CjaVCZ3b8a1WzVytqDgeK67mmppWF+QHdZbZZt9mR1VjU1lHyToP8DugGyuI3EyPE6i8hgNnRhB3xfvPnyLxNDSuoLc5OPGZH3GnK7BjVWJqBMv3NosbiwqG5j1JZyWRRvgahfjcLvo5jr7fSiC3sXHPNNdiyZQsAYO7cubjqqqtw0kknYdGiRbjyyisT7+DujrxhAbTa7NgMlE1qLD7+/bLM7dyqwqK7RYvBrdYfrecAqM2OWNJtzVH7oJLaNpk8SiZhh73DijeWwyloUVWhJN+joDw6Jozfy0vE+MuzxpD+hdjsNG31/rp0YvKw/x7dAQDdO1TrdSjfiyViR+U4POGjZjzKTg1jSh4+2dqW9N3JydGaQZgdv8xf3lyKcbf+Czc/+6G57SisTfO2ANsX5RoN9cuyjgtN2IGYyPN5ldkx0+jeZbqocf1NQnWHAGanFa7onElQ2ybfVUGMzgMSs8PKJwfNfoRdqyrssD4Rbyw5ersq7MgCYqcaizeQ6Ij/XWcuVCGEvtrsPTepd4wKN6bGMtrssPo1aYfXqNnsmAyUNTWWYrOj2qlpNdBzHUkNpkY8p9fA1LnSeLIEmlWFFhYElauxSFlqD2lsH3QjL19RuA1g5RBb2Fm0aBFGjRoFAJg8eTJOPvlk3HLLLZg0aRKef/75xDu4u8PswhfM7KgLSoshEai2C3nnQe8vUx2ooAbK0uIdxUBZ6b0i7LgGNgAG4cUFlIXX0ZkdgxrLzuzQl1C+R6paK+xdY6pCc24ss7CT37bS77nO7HTu4Nk89OtSrV2X91Xc96aCsN5wcroAooIFNIuMAJsdiUUCiDeTd/ymZ+YBAH732iK/KttCYED/Md7fxdODOqd80+sPVWORibxKNeK37CxzjoNaECGmqg6C2VGEmzjpDzQwYcdnAhyyADhmpkZeH8j7kILVga7G8q/dosZiodJzKClOAfbxbYLG7HDXc+88SdBXhBA6BoU3li4kma0GlDmL/Kh6fYlTdGZH2Ozo16arsWQWxMQGaZA2UILqMwVPtIZ6WL8A+M8TfjVKUEGeS0zuo+l9phvHAp8nVWbH1Hd6f3ZDYaempgY7dngBp6ZOnYrjjz8eANCzZ0/O+GQQMAcVNDz4UglHbX0OA511kFXhLl+I5XQRXl3sN3zyckhPbMxOgPElm2SUl1cEFRRshHZFbCKnrud0oWJl/J9NrJfmHunKzI5ks6MKO4pRadirZnLvDzdQhrxgGm122AUq95n0r7GlyO9lPidUe7mcA3TsrfX1/lc/1S8gSJqzMjs0WjM7JE9i1qSIUb7v91V7nxhUFZFBMJO9bE3MDvHGsqmxDPenDoTBqe4gHp/KQrUmyKAizEsBEm3eWFR9TN8HyM9Gb8rF0B89i6E/elZT49qgq7F8IUU1UGZ94uM6ZAOhCIiSWEQXQ67GkmPEBLmeU+aFCVwm9Y5RDc+ZHV2NpbHlau+JQ0VOeV8obHF2WD02xtbYJu0YsdmRwyrIqip+T5+8RJTZKM8Z6tzKNzmMJTKUBURID7apEA5sNmZnNzZQHj9+PK688kr85Cc/wVtvvYUJEyYAAD7++GMMHDgw8Q7u7jAKO6bJ6unLcd76O/Fa7fekwbW1SSwGlNmZvmC99Bf7ftn722uEsR/3T/cGe8ktwZgozgDbTkdkHxaTgAauDqGSG4CdG0kZg82OxOzI/QiKnMv6VOOv2uUmAqWBG43GrcpLKws7ejF7nB3xnTI7gENczwGc/hAAoNRRZJLWIrQCuku3pS2pc05OmzzF5MSKqgxZiKfXzk3iM7fTCZjc3ZLi7aQLT44kIEAS1gDIyT+JsJOnzI7q/eY4QtjJVQH5atFPXz3JUSp4NnFznwB2brZfixGy0CgLF0wAUlkndqZ5DNuEneWbRILJtxdtNJbR6rJ4Y2k2Vmzjww8EJAIFpPv99uKNeGjmYv7dqGLOySoYkzeWYHZId137/XUC7qNwPaeCl2O8FKOHaE4wdSqsrufMWSGKsEPYJCqwB8XZ0VzPqe3emnnG6sX6JN8Peg9MzA7bFBrDbzST6Nu7s83Or371K1RVVeGJJ57Afffdhz322AMA8Pzzz+NLX/pS4h3c3WE0RjNJubMf4R/pu3LPVBHJlMbZ6d9NmYw6e5E9bbvpd31X6K07W3iUUq8vQcIO64/K7PgTEt3xWAyUHUl4cYHHzpPKiGvVFyUtYio34tHTRXBmxxdWVNfzMDsXzuzkDMKOQefPUEMT71F1SxizQ2j+7U2ijhKU3Fh5b4ftkvQSxsnyzfvtFxdks6NMno7y3GzG6aQy+eurPxOfOTsQMtFRlYdl0ZTsKYiwBgAli7Aju57L9ToOcFz+Xb/SgjgIAOs/lvtQagHu3BeYfBHwsyHB16JCWYwNso4Gmxor5DTB8sZAXDUW600OrpIDSh0oYpN2+m9ex5INYgGkwoCmxoKuxlL3HJJ3ELfZMTE78nneF99A2eC5ZLfZEQXU30w2O/pmRC6j3nNzQD7WjgMjs2NUYwUk3j3mOumrVg9jqhizI9npEGbHn1eFGsuvj1a+/C3+cVey2YmVGwvw8mM988wz2vG77rorkQ61N0Rmdvb7KjDvKe9XMjCYrQQgq1jGDOoOAPjc0B7eASZNqwn8lLZLbilQLUNh2+mwd/nbVf8EAOSWzgT6jtfO9v4nuyKAu1J7P+bIzkx/KbSYDlyNxYIK6i9kDbfZiTfxN5etxmom10iFHX/nJygSr5+u46WQIPW8v7wBdUS4kOxoDGk3jNC8ZwgCbHb0yVNVY4lzVzXs1FkFte55fxef1QXThmKLz6yY5kSX94qv/Zo3ltn13JN1zJNtznHw0+oHo/WvVAxmzoIQoMaSFkHX5X2VcuSR88PSRTST5LdRic24aiyXCPHSO6ZNEnabHapiUyMo5wyLuWrLKLEObP7gZJ/OCEkIVGPZmB1WRsTZyfFnZbo+C7NjiE4cCoXZUduX+qemi9h/IvCfyUDXPYDBh0vV6ukiZGHW5G4OCPMArsZiXaBjecBBnFUKz7VXOcQWdgCgWCzi73//Oz780PPQ2G+//XDKKadkGc8NiGyzQ/NEWWYqWhdTafEXq8WPEmwQdoolV7YPoAt2wKRkE8rZ4B+TE3pgrcdsMqDCjlpRLs9/DTZQVlgfE7OjxCJSqfKwV82sxoog7Dgt4jWXmB12v0vSeQXkkUdBuu8DutVho8TseMfzRNgJDf7Yc1jAjzZhR5+8VVdR+sg+XbfdsAdV6u7UG9i22r+Aaq20uXt0DJr7mvN31eY4O8RAmTy/fAizo8OyCLUqMah4roBt8wNJ2BEGueJ8zy5O3k2raCLCjhpRnKFTTR7bm8X9tnpj5dWggjnSJ1/YCWJ2AgyUC0WX253x1rkaq2ToF9uo+V00bHJMubGMNjhceNTfqyi5sVSGjo6jDtV57Gwp6mo6ZbDpQlgQ+29mdsxxdhQ1FvPM/dxFWvW2NDEqE0nLAmSeVNRYsn+/blu6KyC2GmvhwoXYd999cd555+HJJ5/Ek08+iXPOOQf77bcfPvnkkzT6uFvD6HoesvTSgUaZBjpwqrltSsmbjD/4h9+guiPzdlK8SldJFxHE7Ci6cgbbRCqfLEv+Xj3wdhkM+WqxWyP94+1oWc/lPC8mvTJXYylUcpgNg1mNFS7s1EnMjgDZC0rniR2lWAyKrsuFwpJks0MnOuIFZtoZlmWgnCNeGeZJjyLnOOHMzsiTyBeln0OPBEafwfMvGfsXosYyuZ6XaATliMyOA+CN0r7+SdXkBANKBXv8qjAozKXchKSL4J+k+DCuadYwP+u5KxpEly0bpg6KC7g2lNg7pgk7/lxAGEvGJJVKLrY2qh5s9thEwWosnf1QbZgkEYsLQPr1CLUUOcYjKJtsdtghjdohvZXnJKOBcqjNjnaKAbQOMYY1l33AzuzwKgxbFKoqB2HseFwouukU57WoXqv82RiELyBwPqk0Ygs7V1xxBYYPH45ly5Zh1qxZmDVrFpYuXYphw4bhiiuuSKOPuzWsO7kA0IF29D59jWWYuqal4AKz/yJ+eOcPWllP2BGSuBO0uBDYdjom+xf9iMLGwJ+sRnjee6j3Ew7y91LfgXMjOoXZcQxZ3dkEwwyUiwqVfOkjs7RzKERmeYOawSTs+CoayUBZcpE2qxwKBvrci7MjmBQ5XYR9ByohyD060GaHtcUO2VUltJ+kMvkruwefu1if1as7ABMfAMZ/39p3V01Aquxk3RBmp7pKfH5h3mqyy1SZHQer3R7el+NuZEdhRMMyabcab+JmQqwHOeeTwuwovfCeDV1o7c8GAP7v7//hn232OyrroLEMTAjXWDm2KIo+sHfmiVnLsXj9Nrl4wHgsSdfKpBR5oab94gbKJf18EWdHP4/3WbpkfzE3BhU0lScHCBPKXc9JsZ0t3jXrKnR5boukxqIGypTZMYWlKMneWGLMsDr0ZV5jqETDUvNeWcLsKMFb9a2YvD7ET42RHmILO6+++ipuv/129OzZkx/r1asXbrvtNrz66quJdq49ILIai4D+zNQy1395lFSmiquxSsCGheIHPnELfLxmmyRMGHOnGGBNBOqfwnbGTYdeDm2hIC9oTn7XPezzZanuoAjKqs0ODZDFXiw1zk6LZWdrgyn/WGAEZZ8iltRYBGynpHpjFdgrV1KFHf+waqAcVY1l+p151MQyULZPToVSSb9Wqz2QQaXNGJT9vuoJQ6a+awuNL+D6X6XAlIbcWHmSVX3Zxp3iTJXZcaiBfV4cNOHDZxRhJ4Y9GNsps803aWJo7860IP9kzmgdrsaioPY7FLqwoxTgQejU7PTMpkOcwNr47yfmGMaF/R4ZPSU5s6Mv5qo6ipK2quu5SZY0Rfctmd4rzgRZBHries7qps+K2VHa4uzkDIKcHULIlZkdvw7J9ZwNLoVNV7wWKfSs5zKzY8s7WFCYHW7bR1/h428BANxbOBVOxDWvEogt7NTW1mLr1q3a8W3btqGmRlehBOHWW2/F5z73OXTp0gV9+/bFqaeeivnz50tlGhsbcdlll6FXr17o3LkzJk6ciDVr1khlli5digkTJqBjx47o27cvrrnmGhQKrclnkxxkYUf/ZIIU16AoT/YMTLJuLpbEYD70W8CeX9Tqe3r2CqltJ2JuLKPOm/RvndsNAFDsMgAr6o8BgP/f3nfHWVGd7z8z995twO7Sm1RpKlVRxF5QUOwaibElsaRojJpqiibml+A3xajRGEuiphI10RhbYuwVFAVFFJUiiNJhgQV29945vz9mzjnvaXPnbmOFeT4f2HvnTjnTznnP8z7v+2KTz41g+ZCr1YfV2QbftTgCaY+ZVDDqaIirKdRwMiPPTqkZlCUzlNCNlasCEDI7MpcQYXYE3a9qdqxurICJ9QJG3Fg+YC3uZ4PtPp73kP03YiyY6d9tlqlsp8Uace7bmFFytsDPANN/BTm6xERjaQNEMWYnm9HeFHE66jXwPY/kifK1lTXsdYJ6LqWUj+CDq0WgfNK4/sZ6AHW/0GtBZfDFn21Xnp1tjSrjYhi3OzaFfw1jh77D4XPdaBMac/BK3JbnlrKu4uiC2eEaLbm+ngJDTSrIjR11XbpvpQlicmVGLhVNKgiq2eGTA7lal4rwmuk5vvSkguVZ9b2IZfw1Zsdw7QPGBMPU0diMnfCvqH+ovfeKQWmLxuIMevTbQ/M/kfseOBkjd9yNX+XPNDSAOxMlGzsnnHACLr74YsyePRuMMTDG8Morr+DLX/4yTjrppJL29eyzz+KSSy7BK6+8gieeeAJNTU049thjUV9fL9a54oor8O9//xv33Xcfnn32WXz88cc47bTTxO+FQgHTp09HY2MjXnrpJdxzzz24++67cfXVV5d6am0CWxKxYlYuf7Ya8wH+83Zo2OkKfyVTMB8sHKUiIhNBHNuaQbmxHlj8lCLGdEUn8A5GZs/0sWTAafh847fxw3638Y3lfkQ76KzcUzotmRiOLBMvpDooemRwY9A1ToTxKgHCFx0XjaVQbhGzQzU7VoGy2vE0WejzgKldjXQt2ZkdK/tiE4TyAcvJvnjGjFiWizCf0XzAFMG5dd9UV6W3U3eN+Kbh58oJpKRB0GasqrGjx1zYO1sPQIYbO0J/oLWXJnSkv5VUK0szdqgWRR2xyKHoe2djdooPHDZmZ/3WBoN1UJpAE88ZruKoTWLg1qOqNETXyDbpoMaAnqsrK4wdk9mRLLCEKVAmp2A1XtzMjsIeWrYJ7wHft3oMAKjIZZQ2SejGToJAHsVQocxO+FFJTCg6Sq1cRExeMndSQfV66p/1sjwbtobZ3BmANVt2ROsDDYiIj0+zZuemm27CnnvuicmTJ6OiogIVFRU4+OCDMWzYMNx4440l7evxxx/H5z//eeyzzz4YN24c7r77bixfvhxz54b5L+rq6vD73/8e119/PY466ijst99+uOuuu/DSSy/hlVdeAQD897//xcKFC/HnP/8Z48ePx3HHHYef/OQnuOWWW9DY2Bh3+HZBRrnCSehL+XC9t1oyaH94camyTlmWDOqGz1ZF1vcVQ8vqxvrHhcCfTgWe/pn4yVWYVM7uZOfDPB/PBONRn+2qHZ05Z+VKTjJLu13lIlRmhymdJy+WWgqzwxizluQwjR0ygEQuojLkzfVh0exwZodF94iwGXnCmBTI7FHJE9McNxYf+GOTCtJjwebEF0ik2VEMEW0nvmbseOa1MEANP0TXSmd2SDeW1SNCpS9DbYrnmW4so73UWGymsSMGD35cS9vIerQVam0sy/WMga7ZaSoE2O///c9YT9F+LHpcfna5schzTQ0qU7heiI5rvoeqMcEvTEZZprixoLJKNvGsMTECJUTIteVuxdgMylqDFWZHmToqt4QbO2YG5Xhmxw6yD0s0VnzoOWe/zEkYh7kfdbJJr0Fcnp2qMnkuP4vq59H741s1DDsHJRs7tbW1+Ne//oVFixbhvvvuw/33349FixbhgQceQE1NTYsaU1dXBwBCDzR37lw0NTVhypQpYp1Ro0Zh4MCBePnllwEAL7/8MsaMGYPevXuLdaZOnYrNmzfj7bfVrJE7A7TWkrjdRTU74e9bSfbkT+rUFPb8YWssBLBpWSgmDe1GJGuOaKxFj4Z/X75Z/OSa6RTECx+9GNSXrW9Nxgk9IZzK7JgdvyvPDjy1I6Yvo6s2Vhxoh6waO2p7FKOB+KxNd4iN2dE0O4Ybi3cyrmgs2cZOZZaMETZjx3cZO8Tg1GjxOGaHaovkvhzGjp+xuLG0dluZHfv++OAXBOozBFBWEMgYs2ZHZ+vBet/s7Qs0N1YJtbJEdEvE7FgEtHr7VEZCDrRSHFs6s1O33R4+79SPONxY/FXxPYb/vbOGHMfO8OlRkYAejWUez9eMHeFqEcYOOYyu2aFNhrm+kVSQ/Oa8FtSA14wqhdmJjBhTkyTZsIzvmeH+1mNSQ4UyO0w5Pm2fR9xYSqkRG7Oju8OSMjvCjRVpEsWz6mFFlMGbvsIdqVxEs/LsAMDw4cMxbNgwAA5avUQEQYDLL78cBx98MEaPHg0AWLVqFcrKylBbW6us27t3b6xatUqsQw0d/jv/zYaGhgY0NMhiim1Z08tXNDtxVi5xgUQ/b2t0zyAVd00RZocxmWfHCD3XO25S8dkVnaBnUGa0IxYvjzi6ajMkYXYYAzxt0Kdt9VQDMm8zdmwlFSzIFwKM+IEsYGtNKqjTwgBoTgufDEgcZlJBbuyYBkjoxiLMjhJxIZMKlmd9NOQDoQ1QYBuAOZPidGPFlIsAoOstCgFDWVFmh3Swer+QgNkxorE0wWpBcWNxzY5sc043qBzTdd/ziBvLIVD2qLFDmZ1SCoPqzA59vhzMjmgyU5bHubH0e9WgGTuuscZztcfF7GgsBX93TCM4PH6TxZ1mZYRoFmwE2NFUIOvwfogpfwHZF/E+gBaClQnzTCZJloswmR0zbN/N7NDzFsyOQ6DsgSGJnaMfs7hmR5/shkZRhiRDNPauPY80RALQI97kdtKNxScfgdh+7B41xrYJ4+zbBSUzOwDw+9//HqNHjxZurNGjR+POO+9sUUMuueQSLFiwALNmzWrRfpJg5syZqKmpEf8GDBjQZsfS9ZIAilq5/GHZUO9OZsbdWKFmJzKK9A4qQiGQg6+p2WmGQJm/cBbJpKdbO4zZKVdPTb1uy9Og54LQow6i3SuCR27sJPVirdy0Xfme2I0VdSy+Fxi6A3p8ea2jTtkSjZUvkDw7TDJnGW1WN210n2gdy8nZlvkmVa98t0VjRauI2SFB3ubGMgwpapBqD7+h2bEJwO2GGW9fweLGotc9S1IHDOnRibRB3a9ipDrdWETEqiTibI5AOTqCcggXs0M3jQZ4L97Y0Y0bXbPmqrNlFboCQoCvw5VSwRQoh9fIFnnVYDN2CCvtI1Dar6fAsLlYeJSQLaxbGXv5/fDMSYc0jhw78Oikjk8O5GrcPWWUiyCTppKJAa0P4MfPWJgd1Y1FziRRNJY6KShYrjFA3VjhOXGjlAHoX1up7FvZ/6fRjXX11Vfj61//Ok488UTcd999uO+++3DiiSfiiiuuaLYo+NJLL8XDDz+Mp59+Wikm2qdPHzQ2NmLTpk3K+qtXr0afPn3EOnp0Fv/O19Fx1VVXoa6uTvxbsWJFs9qdBArdmPDGc2Pim/fNF8v0lzhLNAxMhIvab2c+kHl2wALV2InNoGx/+eWzzzti36SRybkqAkPFjUX2aen4lVwQxAetaHbA8PbHkpnLWa1LN/TaNlnlfsUZO2Fn6Xt00KTMjhqaGReNRZkdBi0ai17HuBOxGa2usHUbs6PT2bAxO5bQc6dmx8bs6GyBRbNjGDsqsxME2jGganYy5Bg/OXk0uQba4OxZ3Fiu9rKg+caOYBI85TysxxOLo3PVNDu2q8+hGze6G8tFdCoTESrILu+iNwoAeX2j85o4qKt9x9E9tQmlVdaGfyCMDBjGD5D71ZOb0nPl7RHMji1kXTm67LP0XwW3YYp2RBt1Qf92Et3GmR1TpyQnEomZHarVsjA71gzKPnVjqX2tjoJ2HtAMaUVioLixVGanjOiP+D1Qkjh2IDdWycbOrbfeijvuuAMzZ87ESSedhJNOOgkzZ87E7bffjt/+9rcl7YsxhksvvRQPPPAAnnrqKQwZMkT5fb/99kMul8OTTz4pli1atAjLly/H5MmTAQCTJ0/GW2+9hTVr1oh1nnjiCVRXV2PvvdXcNBzl5eWorq5W/rUV1KgCO6UeLRSfbKyEbuzQsgZrN0fshMONFVA3lqHZKQCfzLdu5yoEWtCYHQZl8hOBMEkQH5WBSp1tmpS+MvuwsCp81duek5m73/mkNJekrilQaf2YaKxoIMyAsB2KZidaZLixbEkF5XoB8xyzN8mQ2Zkdi9HqMq4Jva3T2WLG6mB2zH25jB2bZkd3Y5nGmFlVXWV28kEAw9ihbqycfDYG96hyXgOrG8vF7LBANciSGjvKgCGPK2E+8+bmhFUQrlHTgNDvlW5kuKITVdY1Osdhx5iGmDAsVQZwv8jYcQmUbczOjiYbs0NyJSGw5tnhk0DKNPB3gS+jbiyrG14IlM1nr2hSQXKWfF0aRFImmB1tB568bzZdkN0MsL3j8YVAPc8RjWVBoPcx2rtIz4FmzJfuQvW9opM0ahx9qkPPm5qaMHHiRGP5fvvtV3Jum0suuQR//vOf8de//hVdunTBqlWrsGrVKmzfHg7eNTU1uOCCC3DllVfi6aefxty5c/GFL3wBkydPxoEHhoXNjj32WOy9994499xzMX/+fPznP//BD37wA1xyySUoL09YiLANkbEZO8WYHUvHt0etGlZO88F8sinKXupwYyliXaa5IhY9Btx2mHU7PQpC7oKzOPxB941OgHaWiuDSqdmxMTuE4SCDjee7BcqvLttoPRcX4qO2JBum/A0bEbYNsBs7TGd2wj/C2AmoQFm6wgLIzsUQKMfays1gdvyMmbiRGDu664NGYxWY41lWDBHdeCgees4MCkJjdiyaHSpQpoVAQ6pdu4cRPA/IeEmZHaZun1SzQ3UPJvmnfZHrSqNWabF0FVvHQXXhA2+sVJa5nnNl0FTc4fqgHLVJM7iaCgyHj+hpck5coGwxsrbbmB1y33p3UZ8TPYScJgzl5/XS4rBQq6oHQrQdZW+4QDkuGsvF7NC+LwR1e/PNzHOmzE5CaocQOyrbGv5VQ89NN5aSfdvC7BgZp/kfzXjUP/PJZH1DQVkfxNhRmR2t4TsRJRs75557Lm699VZj+e23346zzz67pH3deuutqKurwxFHHIG+ffuKf3//+9/FOr/+9a9xwgkn4PTTT8dhhx2GPn364J///Kf4PZPJ4OGHH0Ymk8HkyZNxzjnn4LzzzsO1115b6qm1CZRQyNjRSsLWLw3qrvrQ6UvmBbpATd+fZHYYAJ+yAMueV1fuNpS0nTdXG/T4iyIoYZkzx3yX5SCtD1SqZoduor5wGc9TBkRPe/lLTSBIEZuPR6dgrW4s5hAo8yWqoVRwRGNRI0JhtEgb7IMgzLYZ7Xe5sTxD8Oh7ZufNkS9QGaMZzRL+UEKeHZtA2cEUCYGy4sbizzTV7JCoHjpQ6G4sMBzovyPbGi1VIIwx3dghk7o37wMe/CqQt6W5oAxEZCQkYHak+4VprAKPgDEfAP2ZWF/fiK/8+XXx3Zq1WG+PMHYs/YgwLNWWF4IAvavLLZodt7GjurH4gCyPeds5+6qHjv4KlxXZp35a/5q3Um5nu/XRZ/n8mkamcaXEAlvVc3n9BvcI+2gXs+MRl35xKNaOaKt0P1meHRIhqkZjmQfVUyHoSRbpNfvn6/Kazo5qDN790jKllQB1Y3VMZqdZ0Vi///3v8d///lewK7Nnz8by5ctx3nnn4corrxTrXX/99bH7sWXX1FFRUYFbbrkFt9xyi3OdQYMG4dFHH03Y+vZFthmaHX5dDtqzu5ix6MI21cURH42VDxhEfgxq8dtQLl16S9aGyR1vfWYxTh4fZnvd0VTA318NNU58dsdYvEDZ7sbyVF2qhdmR2hVPY3ZoUkGmvJhXHjMC1z/xnvv8NFDWa8ZETaiuC6OtAmXaYctzKDg0O3lmzihpsj6mdKjhHvj6tpmqgI1tSKTZCT9mtOfLg2nsUKOMtsa+b9JJc+jMo0VAbbqxVGPMJlBWo7GIG8OnbVDb2X+ljMBzRmO5NDt0svDPqOzFgAPwwYDT0bWqDN07lyttp0d35tkh7ePP/T9fX4nrjx4lfjUmBFQjZulLH39bRqM6mR063aWlIgw3VmRsaoxlUxC+f6Xk2Wloos8qf9iJG0sz5qR7KawNpTDC2nkP6yVLcHhkO7GMBz9YmB1umG3Ww/RtQRXR5Tl0RA/gUaBXl3ISCaqfszR2Sq4VpbG71mgsnmyVaHbUSD7zmGYBYH4IPumS5/DQ/I9x01kTlOt4zoED5fpeeBe5MJu62jqSZqdkY2fBggXYd9/Q8uZVznv06IEePXpgwQJZiK4jlXbfmVB9z34iAzdg4cPKDZ1i8C1RSsr+Ajo8atFYHNV7AJs/AvIynw/P7fPuqi1gLIwk+PUT7+H599cpmxYTKG/eEc4Y12zegWEOzY4tGksZiJndjcWY+mKO6qMJK4uAd0wDu1Xh/84Yq/5ohJ6Tm8eZHQREoGxGY/kaKyTdWPIeNBak6UAnwroby6kpIPu3tj8mqaCgs7W8ITY3VkO+oAiprY0RlloSzU64jzN/9xLOPr03Th7f3wz7jdq+ODK8F6/disma8FIkiEPI9HIo109rZ9U2md7eNUlQQs/pNbz1oPDvj+rEorr1qzHlvufge8CSmdN548Xv3EhIwuzcE82aAWDd1h3oAYBqdsT6RYwdCmrsnDK+Hx6c9zEAVT+TxI1FeSYAeGjex9jakMeX9UpBscyO7VmVx/O155X/VN9YMAyJgnbeZ08aqLVYv/VuN9ZfZi8HANzz8of48cmjyTb6Wcv7OKpPNf535eHo2aUcrywJ+2ujEKh4p4iRUmzwVwwVwuxE569EjVrdWKTd2nuoFurkZ6j2Fbbn6elFUhd74NDu0bFkfyCYnag54ddPMbPz9NNPt0U7dlmo0QGRCWwVmMpldzy/VCj7Ex3DcxTvi1AI1Dw7NoEjegwPjZ2m7eZvCI2CXMbD31+TkWuS2aGMqcnscHzuztlYNk6+xEpyMEvHr1T/Zi5mB9ijayXmfrgRgKU2UhHkbWUi5JGig7g1OxmP+K2VaCzewfFtwnXylmispjwDr6+Vp/aUr3Z0bk2B1jaj/S72xYwu4b2f7DAltjTkZYi8pbyHcl6eb46XRp6dKFdUPo+vz5qHE8b2Q32D7g5S9/+DBxfgnGm6ZkceKKMIVPmZWNpJXTXFkgqC2a9vQbqz1mwNPwcs1I2UZX3luvOtnXl2SPvWbJE5wNZviYwd6NFY6vnYHomvHLGnbGp0M/vVVKBXdYVYTl1KirHjECjr4e888akr9Ly4G4t/8BAwT3MLq20H1OgnAHjxg3U4YWw/lGV9NOYDVNKEm2IgJ2B8cHY8vzYQ41pEiJKfOZuU0+pFGQ2hzI7C+tn6HtKnkAmPKRCGeDblJJAzO6q7l4M2TwqUtXUsTOC/IgMZAN5aWYcTxvYDvX48BcjGbeQd7kCkR7Py7KRIjgx5KEvxX/7mqQ+U73GPTG0Fp+Fj3Fjim4PZKY/oX4f40kjwB6rZsahWnOdKOw7XC69Sqb7vAXk5AEBhdpjyEpZKE4sCojZWTO+YlLBqYuxYmR3t/A3NjrwHKzZus7iHNM0J9IKqGuKYHUX7oZ6HkWeHXD99htqYD8x2xhhSxlNrJPxTXSJ7fu9RzFu+SV1n0WPAytfVZYYbi75j8h3IZXzzHnJQY4c/T0ndWBxN28ju5P6EAJdG3PHrrDxmdmZnaI9O8hBEcGtjPzn4u1SW8XHhIUOUZYBMBuf7HoYTV49ijMQxO8KNpRo78kx4X6Aa8zY31o68RbNDjDm9f6KZ5NdubVB++9ucFQgCWbqiIkuNXZMJFc+vaGcSxkEaHpIJNdfKRAuNcybMjozItUSkKYeky+QzbM0npBk7Ht+cmf0S4NLUqNdeZ8z0Y1blskozKbOjZnh3vH87Aamx08bI2GZydj9Eyfs+ZFiUF6NIuQid2bG+XGXc2LEnMuQP8mf2k3mQ+JkFIJ2Ap/2agFUwwDgdGjE7up4kJlmFLalYHKwFQMWB4oyd8Lec7xAoc0Gqxgo1WaKx7p/7kbgnNGJYLQTKijw+ccaOthG9B5oGgIvoPY8Ziero9+KaHVueHTuzk0FMx//B/4A7jsSoytBltN+grqaxo7ix5DGzGXL9dCaETgyy3AdjZzOM0HMOwoLSYqQNYjCnxnwIN7Mj8eOT95GHKPCkbdqV0R4CmpNKKa0hfo8YAd/DafvKd1gJUacCZaNtquGgsy9ibW4gRtdL1eeE2N5oud+eZ0R6cfTqIqNqF1pSS9DnkjLion9Snn3d2IkJUNC2gUWgTMFZfJc+SsmgTCMQ45gd2JmdeDcWz7MjwySA8F7/9pkP8OZHdWJTmV9LfU/oPeKgz5PQ7Ag3Viha3tFUENe7c3kW7klv+yM1dtoYhhsLgPXGN8PyFSn+uZPUFXoeqHl2rG6ssmg2SSJN/vnVg+Q+IqOAdyb7D+6KXIY/6GZmUSd9SQcq2gfFMjukXRk1nYB+1XTDqBhEfg6bkeQqF0FYi6zv2ZkdfV/cNWfJswPIwcPQ7BBXGm/iRxu3ma4sq0DZwQRYkwqqzI5e6BEAGpoks+PW7PB92zQ7mrAjGnDowGlUVY/w5y5hzbaencsNY2dDl5F4JxiItyv3R//aSsyYOABfOHiwWl1aH9ToxCCjVWgW6yRndio2vS8+N3BNCnVjBSZz5mJ2Dh3eE0N7hu+juAeG4FszdgjzyvsFOuZyIb7ve8qEQDFolahOu+Gn59mRZ8KtAHnNXl68Hhf/aS50qMyO/CQTcWrGDnG7Xfa3N8z9EYOKFtm0dQV838wSjfXlw0O3X1WZPmkkBllMPy0Tvbo0O9SNRdeJ6bOUPoAJlsvO7Gi1sagrDMAfX16Gnz++CKff+pLY1Aw9D/e1nZQqqqkMJymUsepapYu0II5huMajtu9spMZOGyPj05ev+cp024srBmgWPZgO7YEy02AON5ZgduRDPmFArfishxXu06+GPDyeELOazWR4+GuHkK+mCyVcS315GSOp0T1PmXXSrMeMAYcODxmuM/bboxnMTmTsZGzXTuuYLG6snO9wY2naBjOpoGqcyNpYZJnuxor2ed/cj/Cr/2oRZ7xtVaFwEAdektDYCT/KTk+ew9J19cohqEC54NI8KPXLdDeWndnxPfo82t+NHpvDor7WPDteBsc3/gw397sOAPB/Z4zFNSdG7IiDDlOYnYyD2XHl2eEgYv4BS2S6jAZLcUy+RCEQHZodQA4mjQWSadthHNGvvicNfluuFN2oV6qjFyJW18+ZHY5woca7sYQRGeRx1h2vwIYdjRY3lieNHVtoPUfXqpyxjF/vrO+ptbEEG2WymoyydhEmDKwFAOzVt1o9QEJmJ+NkdqSBKPqnGEZp07ZGBJSVIX3A2kjPpWit6AQjOo6StiA6158++o5xLPO9D/U+NBdSp8j4m7xnd7md9hzx/ru6ImevA9cBkBo7bYxsYs1OvAE0sFsnY5lRINPhxtKZnVg3FhFdep5nvMDc6MkQRiPMsyO3iT5Fh2NiZlCZyzjdWHpHTjuMDA0997PGGMEHhr36Vpds7PBZWCyzE+PGyvqEdleiY3gHpxpKIvQ8Ou6WHU3Reiazo9fGok28+WlV0yXaNvazwLeWANN+phm/5J4Tg0TPyCrdWQzn/WGOcoiH3/yEuC4dhjsVRbqYEg5Ns6N/tsGeVDCcqdvdm47nwSNtaS6z07jNXAbixrJpdlxuLO068ueYGwbhW+swXgGFofMtg66SqJLAqtnRjVKAMDshKrLqfkTvErkq19bZrw2gMTueNCSqvHAgtzLPET57wEBjGR/49aAOYeda2ilqY5FfBTNjiKpNZsc2jvPrvmz9Noz78X8xb8UmpSFKnh2HNnJDfSPGX/sElqzZSrY1DXZFqC3cWDKDcqjZUd1Ytjm2yKVGxofGQqBMRHX9ztR9ZOFtnekNmDQu4/Jc7Qykxk4bwwg9B+w3nizrr2VLHrtHDb565J76FuLl9IRmx9JJIcx/QB/mCjSYK+WiY2qp8GkNrnBfcoboE0pYqWOlfGDC4NNzpPCOozzr4wyiBQLUF0zJs+NnNDKfkcKZ5oyjGGSaeZuxU5zZyfpkCLKWi1CR1wTKV947H4B03yjRWFokRmw6B/EM+ECn7kZ77MwOmalaqp7bYLixYjU72r50Y1xkoabGTjwC2olrAlS9zhldxxQok7Y5y0WQe2U1draYy0CYHYsx757tqu2bEyVv+8V/3hVto8aOno+IDsKC2SHHVyIb6XZ0N0pSQXs7RY0vg/hRmZ0gpqSGWi5C7EAsq1z8uLENZ291N3WXiqwwnipy6vMmnlB6aWOYHc4Krd6s9Y/EaOC7sjI7ZFnd9iac9tsXlZa43Fi0eQujOn+K0S9YNbnNvgO7mu3TNTsWxlmHnvHeY6ZWryAeZ9NgpiWDgLB8hsrsxE3w2xepsdPGsBWmK3bjX/zuUThpXD/x/eaz9kWPzmbpC95xeUWqnivRWIzhWG+OuVIxYyd64t+KxG2+L4eWwOrGkoNMRhhMgTJQcR1BLuMrmXvBGNZvleGLihvLy0DPoByQjtzK0MTAqP5LoWt2GjbLg3LNjueRmaiN2VFZIVoIdEdTAU8s5EVsOXNGOhIlKR7MAZvCFnnhNHb4upZyEeJM7Mei99zaJkYHPe2aLnlW21lkAJCZfNysHrAnFWTS2olpsdZOJXKRd/i+ex2bsdOw1dpGodmhbix9MqAf23FvacRjoBg76vpigPE9cE+OTaCsGzuKy0X0I3FuLP6VYeZpY/DtaSPVYqCcXSDXa/41xyq7UsPHJbPDUbb8Oejg7W7UWJcgYMJ4UjRakM+0Wi4i/GwTKPO+Y9XmHVhHo77Iu6WXi6DQ+xBxaUk0lhQo259zs5Cx7AMKBWqYWVg+6sayTApskG2WfZ1RRJaLui3s5Ijeal6zu19apvYpKbOz+0ApBCozOFmgLuxSIQ2XijL7bfINZsc0djbvaMJtzy6BzI/hADd2WEF5MDMas7N6S6hT2NaQF751NeuvOlMAWFSjSKU44XmqS0yJ5WQ46Lqn1DaQc1SZHTXTcql+Yt5/2LfTZmF/PDn821QvBsaMV4TZ0VghWhvr9ueWiPWFQFlcR3Ofb3600X0iWoenb+vS7BgZWb3454TP4JPl2dH2okf6NYvZYYZhZw6XSoOjdqntZEqeHf2ZjfZWLBqr0W7sbOPiTsqsuJidItEq1JCOY3borNvqxnI85yJJ3/rFwMJ/RSvZQs/5O8wNVIazDhiIrx4xDHd/8QDJ+EV9UD4f3us+1RWoqcxh2XXTxQTOFnpOzz6zhSR85Ncharc+EBcYE26scp3ZiU5BXAYSiWoLPacTJc6sgbbO6q4nbXSOpvIe2/Ls6OdjnITQYMlzt0djyXdSeU9iYLz3LDCYHT7RtbnwqsU4RSd6tE8prT9uS6TGThvj2ocXis+ea5YJGA9md8Lk9OpSoa8NgLqx3DVtfvPk+9ER5Uv2WjDSsjNyDMLucGOHd6a8hMSovtWiI6ah5/JFIMyOZ+mkPV/oZXIZT3U/aR15qNkhAmWF7JCVgDOe59TsUD8zhWvGy9sIAFi7CHjm/4DtG43fwpTo5rTd0CgaAuUAry6THapnGDvmAGwLu5UHpMJguVf9+Mpnm0BZE1bnMh6OGtXLaGez8uwMPEj5KqNviLETI04VhzOYHb5tjMH63mNqW6lmh0ci0vZSY83lxmqwu7Eems/zPsnjCbG98ZzFD35SF6flStEifmitI5sby/Wci8ih3+wLcCMjY0kqyDUZ2vMBRCHGEbixU4iKQpeR6CjeXxVzY2XXvwsd/PnUB+IgkMsqdGaHaFVe/GAdVm8mCVMtbix6B8oUY0Ka07GaHddEy6bZYXbNjijjoJjwpvGqGDvi3aflIujxw3VPGd8PFKEdpb73NmaH78oqPI4OdNK4sJzQjIkD1IlvEWO+PdGs2lgpmgfhqklgcdMQSvf+opcocDM7PBsrzYuyHZawwRzRCQV5IVJ0FZ/sU1OhuKwIYcMXCdDcJ0Ik4PnCjZX1fesLJLb3PFWzQ40nqMyO3pn36lKONVsaFOOR4nfPhCVPnnp3jfnje1H9pJdvNn/jLhgvsAqUjeRrRuh5QS3mGv3Ni0ma2XHWlGeUWbECzQAwPlsiUigtn9GMK97ugd2qrK7BZml2hh6ufF1b34jeUF1XzRUoh9ta0Egiyt7/LzBiqrlyTaQXU8TDpP0uY2fNQnMZgJG8ZImimTEPIRYwehbazyQIICBzU70Loa4DW1SQnk9JtMuS9C8uqaDrWsuoqojZiQIdqI5GiK4thUDXbm2ENKkRniC5WPxV4eLv8qyPhnyQiNl5eP7H+Nuc5fARYEk0p2OWFBDUvVam9L+E2YmWxEVjmZBsqU2zQ59/bmgo9fY0Zsfz7KHnMJIKato2rVU2sbzHGOobVCmDiM9g9mcIAMqiax+Kmy3MTurG2r2wfCOfWRS58YypMwsHTIGyaezI/fAXLrDrIiizU5DuBkkFq20e1rOzyuzILbQdM2WwpMwOTejne54wEPTrowqU1XNkTLIhNmYnK5gpWLFEC69ODCWDsroMkEaLrtlpIrWx1tdLXZLbjSXPhxrA+w/uqrZHEynq7SmWZ8c1Ke1TU6GK7DmzwxydGJ1lGi4bdebNxzzVjdUcZifmHHgoPgDUfSSPE+3jzewY0j7NUFSMHYuR+dofrG2srSwjjQ3hdGM5BoNbz94XADCiF2ed1AxEgeZWowOMzdhxRWNZq6HHlIsQRpvWh4g3N3r+CpGxQ3U0NmOH462Vm9HEyPOhaQf5tpzF4cZIIWB4NXI5vaFl3+Zt2hIN3rTfC3RDXAOzfpElbmyPmpvZkQymzdihzzwP+bbtqSCYcK3tzqSCqmmqh8QrjDvRJ26LjD7ed0rNjpxUkoOH+4pcaG+trCO6ro7F7KTGThvj8csPFZ9jb7c28zaFaiZE8UbmFijzAVXsnTFRnHLLsdfLFemLWmgki1VjgT/nZVmfeqKN32Unrs5CGGV2SDZQ3yOaBJtlQgTKenOpQFmP1pAvZjNetnGfi/kxuvZgVoGyy9ihVc8PJnkrpHuIG1EmRbatURqhRi4PZnFjJcmg7HRjhbjmxH2s0Re8nUZywzg3VueeyleeBE1lduJhY3Zitx03Q34m7KWZL0jbg2LsMPszGdfGcEOyLPxrerHsz2dV5Boa1RjmF6rY9jHU0HN1feo6yGiDFOCOxrJm+7VGdUb7dGj/9KSCQZ4bO8mYHQA4oOEWucO8GhHFn8G6beE7UEvy7bgYFZ0dpeaireo5j/gC9PBzYuGIAd88nq1m3bJ19QpbKm43MVbp87+toSDWFQfl7Do3dvTzJQEHfFtGz01z93JQsbdH+t76SHPWOdLjxAmU+U5nRwbnB2u2EqMoar/t4DsBqbHTxujeSbpPYgdz2tkxhrKsqb/RwTuNpiaeDMzcZvmGbeqxwUQSN6+iWq64aQVQFtHvO+rEYv5eBSzMfcMf+LKMr4QdGgwB6cRdmh3B7EQRJE7XCAA4Is4YGF74YJ04pBlaG+6LV153YXT/anPhXie4NxCRRHaBct7wvYd/aVJB6lrThZq2JH90kDC8D7bIC6VTimd2ROetubGqK3LKoKFrdgq6eycuz44GXk9KzaBsj1Kp7zQo3L2F2RFtcx1v3Fnh362rxSJexoPRLlB3Y1GBekwSOB3CyCDvuSHglwcy1gXkgHbx9jvlfhURqD1ixvekSF9xY7misWx9UVmVk9nRC4FqZyGMCMHsENdSnGaHMQ91kDW76ISLnxcQ5qEBgG6kX62JDJ9TJ/RXttHP1Sti7GQzfliOBFp9K0Wzwz+Zz5qtmvv8jzbB6sZyhOZ/+x9vqgs8T1x77l4yam9p5YI88PtNXGGAwc4ruxDvfSCO00UYO+E6RjADAXX72ULUU2ZnN0CPzlIfEzuYKw8iS1S9+4E3VgKQkQ82Zue8yYPUYzNZi8ijycOGHytnvqTmDy2m16SFPoo8Ox6tDqUxEoxFUVLRYjGj8ZTZpk/84fY8RKobS1wdsuqzi9YaHdzHdWH02CNvmhEeFL/6zHhz4dAj3RtE1yV0Y5mDb1OgDQoi9JzHBReUmaCev8Y0GoEmotcxqhJbo7E8wPbMEYPEDL1X16/MZZTOTWegmNEO3sFq0Vg994KObDa8l7ZorAVdDgEunQtMD9nH+uqh4XEVZkdlHZ3YtDz8+78fkeNIFyw9O/mRao6YMwkc+owFALwWjBCLpJFBjA3OBOojhYPZsWm2FGankAeWPCM0SbZK1kq+QHHJEjA7Yz5jLhOi5+irYexEzy8XKEfGDnXHc1eHLRorQPhM5Vm0vsbs8HMSxg5hdniov86GGzX16P1w6Ce5QaYkW6QaN2iTuiJgTK7se0xOKsjzxPvjNZtlRm6bQHlBFI2ph98L2UGUHNP3eNVz8i4i3tihfRdnlzqX56JzYMpfmxFz1F59AABdyrMqA1Qk+qw9kRo7bQzP80QG4djBXBuMaOXjYsjArdnh/l0ypxFGiu95wPdXA99aDHTpLTPJUjdW9DdgTPHvU79xOInQrH6tE+fh5yLnBREo5zI+fJ+6sSyzaKHZifzfFtHd+vpGw9jhX/foqiZqFOcX/d61k4W6L6uybsPbD0SzNW0Gtb2xgOUbtke/q7P8JiKMpAONbuzI85Dn89n9ZeJFo+MK7GyHkRiRtCUsBMpX89T1I1SVZ5RBQzwP3K2h1wGis0zals69oIOfq02g3BQA6DFMPJNSK8Rg0PN8ADKOwE+im7HIVmXeYMWKRWOd/zCw/4UAgI1MshLS1pHuzbhZcbiuej/trhm5rOyBL4apEH7WLzqmZOhkuQjZZpp4k6JPtSXSM1cJ42oatbEcxk5kbH+03gzL5xM4JShO+yQE/BrzwQfYVZFBQMvwcB2PrmWZ++FG5Xs5OXnGo/G0+8r3kQ8CYPsmvnbURM9pNAJ2YyK873JdyexoaRiYfj35B/kcclZ8YDetXypEhiHRXTKaj4q/q7H2RnRfWSDcWDqzo2hxtHZzI3FAtyoxEQtXi5ngtzNSY6cdwIv6xd54RVPBMHFwN/zijLH4x1cOMteNMHaPGgBAlg8WFmOHd7KUfuaDi+dngVwF0CnyVWctxg6ZPdPIjYwzg7LJ7PD1ASiDsipQJtuQ63PxYUOj7ezMDr1s5x44yHAT7N0vdE8dMMQc8IJAhmhmXUkyug6xL+dGiUfk2dGAcP/rH5F5mc7sSDcW7Xx8zdixhZ5fNW0kvnDw4HA/TmZHN3bMEFubG0ufBXcuz+BPFxwgDFF51hqzE6fZoW05+DLosLlEFGOHtF8aO7DMWMOvzuzZ488J//YcZRyHNdfYGT4VGHKocB3Qyu2CdSMhivZZcdiSaGVlabHcmNmP1LpTdP+C2SG7pLo2ALjvy5NxwJBuuOsL+0fNiJ5L7srW2vnH2Svws0ffIW4cx+Ac6X0Wr94EAHh60Vqxjt2AU+8D05bLbdWt/veOdEnyCC3d2NmmCaFpgJUQ5H40B3jsu2GeIUiDrN+H/wb+bxDw4o2KG4t/sp1Kf8uEKiRXzckCDQIJGxSgiU5+SBkN2ZeGf/QQcuSj/jorvQhWN1actUNy9Pzx5Q8ByFxDhkBZOXdu2MhJtZXZ6QBIjZ12wDeOGYlcxsO0MX3DBUUpvfD3z0wcIHzINlxy5DAAQJlvicThe2Jap86YGFi9jLY+Z3ZWvy0W8XcgYAxNZKYYlouQs2OmdU76Qy6joqQLQgiUo9BzmruFCxvPmzwIqF8nDTBu7Ij3X0au7dWv2giTzsriLwYoU+UMG/3qyyHzRXHq7WKgDQXKtGMKKwbLHDIQ5wSoeXYoKyIGX560zcLsVGR9HLNXmC/ImEXaorGAosaOS1g+rEcnHDq8p/YboA9ODXooPNn3hm1kdt6lL3SIwo82Y0d4O1VmilmYnaKsCR8EyGSAR2MFShdIjR0qUAYMY4dfZ880dqQORj4XzuKIDprfFdkjIxZVWAXKMdFY+w/uhnu/NFkWvewc5aH6wiOizRRPvLMWtz+3BPVN0X6cbix+PcwXzpbCgLqxwr+W5xVxYd1qIVAKPaJVScRI8yzN/6YZ1wAAnLlJREFUvjXMM0T2MWned8Pfnrga1GiQGZTN9vToXI5HLjsE35oq85ipJROALC96S3SRYeMCNGn5bWRbOTPDJ4fasM37RsLsBEFQ1I015/tHy0MIZocZBYBDjxhzP8PhDqJ1mRaibn++dwZSY6cdcMjwHlh47TTsP5hH39huPGV2kokhuY86GxN6LpkdeRyh2dFZAG7sPHIlUBfqgWieHaqx8TxSCBTKJNZ6XiLXDhmouOC2LKtFY0EmCixf9Qbwiz2Be8+NtuMiPMk4NRKhsz6759fITTFDbGtFrjJkvo6+Wi4bN0MaOx51Y6lMAwAjgzKN/inoLiAQ4W+g7jPaiTU7bviTLakg+a4wh2YhUNONZTcE+SfeTqozCDeT93cV/S1j5nYSOpboeezZpVw8U3nt/JUaPI7Qc2foLx8ESJVyYTw5mZ0ioef8N0tEmSFQjg3xt7fZdS7MsT4tR2F7RvhHp9GgM4Pa8fm9WvgxT6ToMHY8rsMK9/fOtdPEOrZjy6dNY3Z0t47Wnm9PkwYFz02T0Xx0ef39IjoZZiuavKPOfn0os2NMDlTs068GR44kSTg1ZkcwqB+/oR0jUHVCYgfiPzE5MvScwtghwTB0UuBwY1WVkfGCvPen7SsTBMr9AdY8O9q7x5iL2UmNnd0GIcUaY+Vqbqwk4A+YH6PZEYy6wuxEmp2Mtj4dkD6ZB4B0RkSzI2py8dIBRKBsCF01ny6fXf322aWiCOaKjdsMgTLvqDu/+QftpPmMOvzz0UYpprZFY0mtkAnaGRatln7IlcBh3wZO/q08GBCVzNDoYsbMQUnPoAxYw095O7kQU4+okmHFWvtsAmXAzuyQBI0mNW0+o3Sg0bUuNRXaM0Ty7DDSFmZ5NnkklCg7wuRV26NbJ7X9MZqdom6sTDQI5KkWzSJQNrJP82thESfz8+H5lqxuLMliukK/XYOB63l0Gzty0LFmUA7UQcncse4G1Y2dcPmcZRvD/TjdWOE9z6IA3wMqy+QzEMfs8L25ypDoblZaOLjRwezs2bOz8p2R960Mlmio//3Y4c6W77cwZGO6i1E8qSSA2irVyBd2Su0AZTm0Mg220HNGmHAFeVOzE7JA8cxOpVIlnjM7gdh/daV8ZwPGzIlR2Cple3O9jsPspBmU2xOxVi5zfHaDv5hCoJwxRbYmoyHdLr4+u6EDYpRCX1jskLkneFisx+TAJx5wvr12rlKYF+7j+Q/WAQip8w/Xb1NCzxkL7GI4QAmvVA4DoGfnciP8c8HHIV1sc1cnYnbo+Rz1ffKdMw6mQJkxm5AzYixI4jRGRJiSMdE7W2rsyKRkbs2Ofh4aowYQZidjKU8BAxmLsZPLZoFAcw0o7VDz7OSRgf506teoqSAN8ZxIvaA9a4F+DJeWgIDPeAmzI59dhxuLCqxtYcL83eFuG09eBzlBtzECrvuj3k+XURIUNXakvkWtjcXf+SLGDt+/dnz+bu7TvxZYJ6+fPIvIaIk0Oz4CQ0OTsRgSunbKxeyYmdEr4Hvhe81dqfr+L58yHB6Ag4f1wBfuflUx0PbYschoC9a8g0xlEWYn+hRXg8/3PYzuX40FKzeHRiZ9f7gWR4/u05gdJYOyMCRczA6PxiJZ4pWCuXyR+5rSpIJCzE6uJ0MRdtCj65H3MWV2dnMUs3ITurF8P5yhipfYyuyonYlH3Fi+rtkhIefIhmI7aaOYzI44LvN0cgN6J+5rD33A1EfPI24sGs5s+MY1zQ7vxCtyPmiqfA4+y7K5sRJpdpzg18AUKFOXnAhL1wXKUM9Tzy8janlpsyhbdlwAsNfGIt+tzI4sF2GOQ6RtlPwQv3LDTje65INQ1rBeLC40bNMPIAZu/jzu2bMTqiImoHvnCrGf8LgJmB3XAMRTKlBjx8rs0M9FjB1P1ezEu7Fiaio52lysqGShcx9lKScpi+bZcT7m6jOsW72F6H21GSyAHMQZ0TCZxo7rbOSlEnmPYoy//rWV0f7CZS5mp0tFDj84YW/sG+keZZZyD/13vG82hhXsk55t6/hJmpM6BzLKPfDIcn4sc7JC8+fQ6YUuMDY1O5zZoTndAvIK8+3jWizfbH6cMvKwUMbG5sbyyMRDNew7DrOTGjvtihgrtxlurIznyUgswCpQlta4FFvKWk7a+nli7EQDik2zw182WqTQmPEYzE60WGglJE4a108xEGiiOpPZ4dFYaofOOxfdaDl8RCiytWU31TVIJSFGs6MkSBS9eHhOTcTYKWvaKHcX/WXwMH5ALW47d6L2S7ivjLgfDiPDJVBW1pX335lnh+zeFo3FNL2M3Lc0umjxyrxnYR2j7qdfTTlOndAfN501AZcfHYruO/HiksLY4QJl2v6Emh1u7DRslp0zYSUldGaHW/oxbixbNFYpAmXHYKC7bThswyEYUwTIWYtOLTGzU0Sz08j34yoXEWl2MgiMd/HdVWbhVDezowuU6edoohG1sbHgYDz09YWB6+P+wT82Vwzy8ZOe+nXyNSsyOeK/B0xldsR5WBhRK7MTfQv3pTLr4XbM7sYKGPRErPF5diSDmrcYVaFmJ9qdxY3lkX4jLmprZyI1dtoTjsiLaKHjsxvZjCddWECsQFm6BZgxOAtsXEaawEXMvMlMyXgMqB2V0Ck4mB1hTFiEuseP6RMZCCGU3C1G9IpaLkIfrPU+iA+aj761SksBD4OpKglCL2VGYymRZRxRx0PPe/j6Z8ju5ODblSRM0+8Rt1kTC5Q5m+HS7BBGgJ4XfQbXbTVrePEZuNON5WcUF1FQOxA6+O971JTj1zPGY4+uVbIjF4Ouej62pILF6nspdd92bArX1c7DOBYVKFvdWLx9JrMj7o1FoGyWi+Af1PvpMr5NN2fYPjnrtzM7Ms9OQmNHe365MSJrZdr7KJaRxo7OksTlDuNXT94PN7PD39dizI5YP9q20guNgpxXQGNZDfCjOmC/z5PGB/HJXFkBccUwbccM77tcWdw9y3tjJAsEQFMgWJmdIA9xrfTQc/7uR89/7Bw6eo4HbH9HPCuUmQsYI/lzzJOngYtqbSzIH3YyUmNnp6DInS9BoJwtYuww48GVbiwbEyQ3LIhj8BYLZkdzY9FCoMLC114IPXMo7bT1aCwlJNuh2eEoBOpAp69P05g/sXC18hvPG+QcAOKgGFeq8agK86JzWfUWAKACMr/GMct/LXdHDEelM3MIlHk1e3z0GvDnM4C1i5Q2CHCKe+27ZD9Ss+POoCzx7/kfk2+c2YmeCwsdL9pBo18s/bjMKURZTU07QoSTQDPdWBW18vOOzdFei7mxihg7nqbZUZgd7Vw8z8hzQ3YUrasxO9F6dSw0EObs88NwNbMlQKFJGO5Knh1i7PCsuM6eRQ+n1I3M6H1tLNjdOHpSwYxnGg66WJdup/0xHhjKpPCP/L0V0VgOFxu/HhdlHpXL+HmWSzExGItneL0M0f4VYXaEwQnluRKnoYveGRPnEe6dTqDUflHJFE3LatCkgoyRKK3wuscxO5X5TeJzrnGLcZyA0feMtpu3Uhp31qrnHQCpsdOeiHuRmuPG8j2lk7UzO+FfzuyExegczA5FNFB5ZIai05vCtUAEmOYp6oOpyeyUZTKqsUPdWFmtg9SSCtojBCQ2ksriq7Uwab5tUXGyFZIa1wXKZRnZVekTxYm+RRgJ2bkF8NTOTDF2VPavccFDwJ1HAx88AWxcGq3vMGAXPSY/E2bHGUrreAblRC1jX48YIoUczSps7k9ki7axmtqgq0TtMPX5lZFG1iarAphIxC/z7NCNyGdqVFuZHT0aS56Dea6eOttVfrJPfflgvBmhsbO+y8hoLYtRWmhUXLKqXiTEzU9/AAC4f66s/K5AF7hr7eTv645oQNarnkMzdnwERmSTbuidTJLjBeKvXbNDJyTCLRX9deXZ4eDLu3mbzbYccqWybmxPQMpFFOsyBAOrMzu87zWYHaa5sfgHyeyIaCw6GaJlNbJUoBxIZicSLlvroPFNA9lP3rDsJABMuZ6qEWOzdjjTq7mVU4Hy7gr7LC5a6PjsRsbXmB2L8SKEiZEY2WOE2YkzdoRmh+8HoryDdGPJ1pqdgHqu/AXxAnOQCZkd4saiQj36AgMyGovOnOB2RW0nWVQ3bVezlhZ1f8SBRGPxgXjd1qZov7LT1jPNuu4szaDszOYMOfs71n8NZfef62yXgaFHkN1IzU5BNxYtnRMXhAJ0Bu9gdohQekvtXrg9Px0/ajrPauzw+kQ+iWTSDRlDoKxEmYTrcGGnIdyk4JW8hcC0hQJlLRor3o3lud1Yjj6BNyUXhUgXIi0Mb6+ydpDHl/88F0Bo0Is8O6WIQouGnofftze5mJ1oN0SgrL+TOoNamcsYmh25ozhmJzKSNTdWserndIIlnndaSqSsU3xfUNM/tlwEhZLYkRpqmjtfwKHZ2dYUQDA7zGLUcfbG85XJLgMjmZXLo+3d7dVPpxr1yNHingFiXXhUVqAyrXFjXvsiNXbaE23J7PhZ6/4NYyexG0vV7ASMCc0L9y1TwZ/o1w03lmZQWDLXlmXV2liKGyur1e7RmJ2i2XMJhmp5N2Q9oWZYO8KNJTU7P3t8kWiTi47/a+FoLJ54jbE72a94Ft2A7DD4+U7037O3S8+dNHBytJzogISuxqYlMTunH56wN2kJH5xcAmUqfgZ+lj8bdxemiQ5wy44mI6u3krPFCIFWo8nCchG6scPp/ZjuTI9KE/tzaHaU0PO4PDumG2stdzESV4RTRO1idqIbwo2dvMgSwicNxABrkMLfDfWNYjCMLQ+gwzAy1WvJ79XaSL9VrBBoBmZkk/6elWd9uV2UGTpJnh1doCxrY7nf48pcRnnOrI/KiKmxIeXoOlieSzFmxxGN5WZ2AjTlzQnvlu1NYnP+rirPOTd2MmXKcYIgACJ3FDd2+DN41KheeOabRyiHX995hPK9HE1KPp+AiOBttbFokldlEpkyO7srYqxcWzr/IshQzY7FhQWYxo4fyomj5sQYO4Gm2WEMD84LsyrzRH4Ks2OwJOq5isXM1Ozw4wTaDAYAvJylUCHZYTE3Vp6wRJ3K1PMtqvWIA3Gv8A7s47oG0SYjGitafwPrgg39DgUA7PDDgn6XTxmOrlGx2ACemTSM5MDg6f23w9Q/hCejPQe5qGhgE3Hh0cR/UfPErNjSOR0xsqdsivjgYHbI+VJmgTGGdz7ZjDE/+i8umzUvbEasGwtiP/RrwcLs8OSQubgRSEtOaC0EqqxPo7Fsmp1ofxZm5yGucVKYnWj1hNFYfD1u7DRBKyZM29Qkw/ofuvSQWGbn4GHdjWXhjotFY/nK8T2tj5LMjjsaSzcwchlfuqP1PenMDtmVMAQz3Nix59mhyPoenizsK/dBz49XefezbjdWeU3YTm1wd0Em/1SZHTGpseTZsQmUG4mxZM2gzNmbTLnKIG3fKNeJJoy87edNHoTBmlh8Tc0YLAgGy+0RIJclxhNjDhY9uh6+7A9SZidFvJWrvNzJmR3hAnC4L/gDyl0jHoKS3Fi0EOjWBrXTl5odEo/kcIcIN5YlGmtE787Ki8Ffas+zGDvz/xYdm59fPLOzeK2svpzXZrp2H3RS8IHaFFMHjJFBlEWObMloBVEYdoaF1zOcqUn9k572XobEBajIZdCpLIN65jACDWOH55ihaQViykVYOvGKXAZLZx6PpTOPx/BeYScpQ88d0Viep1zvgAF3Pr8UgBQ82wqB0gRu9C9/1mzGTmM0I6a0uwHNfWCtjUWfA79YNJa7NpY8F3kt9NpU8piO6KNoMZ/McDeWNKKJ+JvUWaqpzMlIINKk7p1C4/iU8f3NdipttRs7/H21RoPBxuwEeH+NWvlcP/cywuzYMr0r28a4sXgi0TjtXTbjoTFix14u7I1XoiKXvPX8mE73VMSYurWJ2uqOaCxnnp26FVbNTmNeGkt8YqG4uUWOHZXZKd/6oVxH1JALv9r6O9/zcErjteJ71gsU4zFgiBHZA7QQqJK7K2V2dlfEWLlkdlaKG0tV7ZvgD14mw2vWlBqNFX4NGHDg0HBWeNSoXtER5ezYmWxL7+R5np3o+xcPHoKqsqyWQTk6P88DyqvV/VWHnbWndCb2F9jzoGRUpiwPABJ2bTn/YtBqNokDIuxUZKcdKIZsAT4CXzV2fM8TJx0yO/Gz/07lWTQkZXa4G5AmjIwtFwHlWPLUolxEYrnmFhLbSUOqQK53wJgxQHDXhbrY7k4RRrIlGoszO7FCc82NZWd2yOekSQUt0VjGuSBGs+PQcOjMTl7T7HiB1J95dx0nUlDkMjIai5ZD4aUTrOHN9PiOCZCuFXK5sQJLKD6HPkgqxo7Yv/160G0lsxO2VTI7ccaOLwzHJmQw90PCfJAB2WnEaLlqihk7iqaQXFPxjOrvze+PQVWdLDjMr8viddvAn0sWF42VKVMa1VTWVa4TRSPG5aPyELpKOdvM3ZCiuYzZDXat3zeFzCmzs3sizsqlheGSZlD2CJnqePtkIkDqxtIGFI7uw+RnzY0VanbC7XiWWzorKzbjEYRPNBjyjKxfPXLP6HfZfQb82L6nDt7dhwNXLlT2J5kd88AZz8MeXaW4Vi8M2CJmh88uiUCcz3oVMS5jCmUdRPFbAB8QGDI+RJI7I/ScHIuyZG43lmbAcmanycbsJMuzoyJqp63AKCCfXT+j1mayRmPxY1uYHa09ojO1ubGi5zKZZifqnC2FQOd9VKeuH6fZ0d4hRWRtORcR9ZY09Nzz4CFAzosGaKYyO15BNcAO8t8GEA7qSthzhB2RQdC3xsEIGiH/KovDNGMksF4TyexkbcaO9p5lfdMZRIsBu7bVE4g2aYETNuR8DzkvvGYNyOE7pJCowuy43FO+6kYs1meIkh2aG0syO+b1GbLqcdKi8Eg9O0v3lIgeVTQ7vFREDsq94+94tlLQhFKjaLZX9Kcev38FJRs3ZXayVjeWT9bj+zTX25lIjZ12RVIrN9mDoQpZ7S+fyKAchZ77Xkw01rkPkibwGXA4u+y84W3pM9by7NAMys48O9oMltPiMhmgKVD2Pbk+Rp0AfO0142xFfhHLk+x7Hq48VgrvdGZHDEAtYHYUY4fxQYapnTZxORTgiygkILyG4TXgxo4tE6z63GR8D5sZ8bn3HS8/u9xYRZkdfswizyiT9zz8qq0nBkEP2xrzymZ6Rl8ZsWbuH9pzJNxYCrOjMhixySENNxY3uuU2s15bKdf3M7INK+ea+yNGHWAyO+F1kQaRTMjmaJfhxvKQI89WPmJMxHMVqJGFO1ho/FJmh17vHVFUYnnWxeaaEyDaIsG8MPvzYat6Xkyg3Kk8K7bjrybz7PtX3FjcxaftLy4aL5PxRPHPJmTRuZwm7jSZnfpsV20H3I2VjNlRorGoG0tQJaaxmGmSbj+6+6XrQ9Z/SxRNqkZjcWNHZXaEMUwCE+ImhsKogSzkGmaWD3+nqUeU+6j1H4xpeXaaJRFoG6TGTnvCNWs2xGoJ3VgedWPZYZsNZF1urNoBwJDDojZwg8PDb3M34OAnT8OoFX8Pl4nQ82h2zDyL4aB2WiLhrJbfRCQI8+Uy4RsmRoD+0gg3Vlw1Zy8sGMjdbnrW4WLi5nio14Cek6LZoYOzWEdedyHkJMxOnEAZiOQk9L4PPFB+drmxSF0oyowYtHxCZkdnSuTPsijtb5+WtDytmQMA5/5+tpwlWwXKWgh0tHFBoRFVLUK8saO5sbixQyuzu9xYW9VklHQ/Ls1OnjJQaI5AWbqwABmNZdU5QV7DnO9bkwryiKWKnKXLp8c2Kr+HKAiBsqf9wtfkxo68HrxUC4dunIzs0wX8fgfC6HRfDw7djeXaP0XO94nYO6uJpSmzE2J7VnOf+6pmpxizQxlxRaDsisYCsN8nfzOWFQC8+MH6qJVysiNXiNxYvsrsiCLDpI83ghEs4O9DNjJWqVxAZuE2t5NGkX6Nkk7w2x6psdOucNx4OusOV0i0N59odpjj5ZPGjhwEs7wTtfnneecvkgoCx2ReBwCM/uivACSNTOZD5ozHIVDOFLZH7Ypm9TZmhxtaxAgoxlzZOh89jXyT5sYqaIZYSRDMjtxnQzQ9DQItGkt3Y5G2+iJqRbpVynWhrcZKKEZurkp1PzqjsVzMTvhRdoBFDD9hlElmZ9ac5VjE6x6JfWcxun+NPCST2goAeGnxejlw2jIoCzeWjHoDgC078tKFolU9j82EbbixVHdqeE4ENPTcBtGj2zUqBc2362yjw7jM+B6+mv2X+C6jseznmPXyYZ0239NYhRCxzA4deEn7AoXlUY0dxfUI4mbkg6VXMAZVndnJeB7G7REaFTx5cBLNji5Qdu2fope3Adfk/gQAODnzktpfkHvA99HkV6o7iNxYiQuBKgYnZXb4oexShXHeB9H+JeOlvyd2ZidnZ3aIMS81N+ZxhcZIMDt5+JFmpwfq0OMfZ2BC3VPKuUUnomxvZFBOBcq7O7QbT8MEAeeLoMPm89YhqEtS4TwjMihbOj4+E9AKgQKyw+MPu0jM5pE8Ow53iOd56Id1Yl8i7JjkzJBVz4lbQpvFc/CjFHQjywLuFtKZHZZkkHSBG2lkkFu8tl60SXVjaQJlwuz4CAyBcpcKPY2AxpJ5nmRD+u+nMnR6EsacRaBMNDtO0aLTjcUF5uH9WLGhHt/951uYesOzwHO/CIttAoCfwRjF2GF49K1V1kOoeXbsbqwCMVTnfhhVUxcCZcuMV4fO7EQGU55nBm4qaMyOHz+iFWF2qAtp0/Z8TNSgi8nwcEn2IfFdMit25FAQ7C1/nvOKscOZnWLGjkOgLPLgRO+tbrNp0Vg+zHIR+nuWIf2X4cbSmSvKjmih5xxL16rRXxTnNvzduT+d2emFjei77V2oG3BmLdqimGbHwewUM3b6ehuiFvGJk8mmKUZdYNfsgEXLSd8Qp1Hku6SanVCg7OGb2b+j4qMXceHqn5jH5/0v1+wEMvRcLZuTGju7Fxz+aDz3C/V7wgdDqUbNBxkN/MGz+rNt0VhiUJDMjtiXKMIZvXy8g2IAExY+34/O7ADHZKT2QWp2oqbQGWUgGQxXlIigTePcWBF4qGaTHo2VkJK2ghs7pNPas1dYZ6cQIMaN5YMWn+RuLCqY7VyhVQjXr6VPjB3PE7NOAKEgkYI/S/P/Cqx5B1j4Lyuz4zvumwnV+KzfFhp4h/tvAk/9P7man1UE4X98aZllT2onbts//1seDWxZ5LE/T6iolYsoydiBmu9pQ32jauw01hdhdlRXsM7sBAyobwgHnIZ8AZ/UhW7EpOUi9GcyEMawvU1ZFFAWveNqqYLodCLqxGAN6bko7VFZJCNayng+TDeWfg42JsbQ7LjKRViisfT9TR/bDy6Ue9IleGf+OHVb8ZEBHjCn4hJzBxEz7tReaRC5jgJ1ZZHJJ7AbOzMyTytNCpkd0j5oRqMjGqvv4vvCD2RCawQjEIjjeVJgzgXKA7y1Yr1yNFonhzxjEtXmpczObg3HjV/2grZesgcj43nYz5VJNwJ/OXOZGMNGWaa6sRQjJGIkuN0kmR1S+0eco9axeR5+nLtHfN+CKmX/IYnjKW32FGNHf8FUtiZunJPMjtrBzJqzAgDNeFsCROg5yY0RXTslt4YWeh7Ak5FM0fZUm8TgoXO5fq9UI1nRMsEDtpOcIXpeoi2fyM+/PRC49zxgQ6SlIYVAXYycgWjx8O3zAQDH+bMBAP28dep6flYxLu95+UPl5y4VWRlGrTA7djcWv86fjQYD+tvCTzZHTYt7b8j9AGV2uHDfk+JbAFj5Wryxo7nSdGanEDB8vGlb1C55v9yaHXWp/jzz+2ScYVT3KIuCeM5tbqzYLNMOzQ5l3Bo0N5orqWDgxwiUtXP/eNN2cYx8oD9/mhuLun49zuzItnqezCVkw8ZMD/H5I9ZTc3lRZsfRkfBorIQTJJmyw2G0Ref3l/zRmJSRrNORmfnRp8gIVJgdi1HP3VVaBv0+K6Kip1tkEV/hSrV0lvx6CIGyl0c2EwqUHw/2F+tVYYd27Uw3lsoWp8zO7gnXC0LSvQNI/GBkfA8H+wti1+E6lbKcJcOy1djRWAQqwPXkwBD+Fs02iUDZOEWHn3gHypX1w3IR0XGEG0u2w8nsWKjZrx4RhrPfed5EAESzozE7/3g9LIq4pcGSR6UYosylQ9c+KRbJ9lMxrmR2wsHdE9cRiGbA5OIE8NRIEcDQm6jMjg98INsAvbzGyOPc5+BnTGOxKMsVrl9TkAZWDbaaWhI/a0S/UQzsVmUXKBsaLbWz7E+NKk+yMgDw9ko7u0nX1ZMKcoaxqRAYBtu2RjLgDj0C+FEdaacZjTV5qMxOzBjD7CXhNaLXxhV5btPsUHARtnGd+44DEIqZOYMpCoFGejHGaBFfy/1VDAvSVhIx1KgbO1p79WisLNSkdLZzGtitymR2HEwAfSxtzE5NZS5Ws/NJ2UDlu2rryGM6dxFFNYnrWCSEUxeJi6K3Pj/RSDMGXxHJ6wjAoDOgVoFyJqpsHuN7LcSwUpTPA1RmZwdJczHU+0RldoQbixs7GmOeMju7KxxWbmO9+r0EY6ccTbHriCgMfQAFirixwjaMaHxH/CTyw2gPcJi5J3roxX7UdVwzJv7irN/aKPb//Ptr5G+W/B/0W4GyQBG+PW0Ull03HVP27g1AuvB0zU6L8OYsYxFvf56EVVM3lp6rBAgH+owP5TzLigiUfY8MNp4H7He+XNfQ7FS5z4ForczZnovZMZfPr7hYyYgdNjJj5DWiCEPPo05cObRmNRMjpX9tpRh0w9/U53f+R5ucxzONnajAJjF2xvmLlU1+/eQH5Hy090fT7PgIMKi7vNYBUwXxwqhMmGdHd3dJZocsn/4roDxMFhi6sVQjoDPbBlzbDd6Pa8UmRqRftFd5YDO3Dv2stJK0WUx8iBtLNwh0Umloz87iOc7rRm6cQDn6TDMJV+uuXw1ZIqi+uzBVc8VIds0Z3RoxVoUkLlNo0Vggxo72LhvvDYDvTBtF+jfaysgQ8QD8+3LgqZ8axo5LwB62Be62R+3q37gUADDGWxJl6FcnIz/P3W41mAWzRwTK4S5TZmf3hMvKrdLr1SR3Y1WjPnadhkiYWFVm6QwSaBIqIYWtuSCk5XVmh3m+aLIrX4uLMODrv7d6izjrZxaFxo4SjeVgduIiDDh4p5t3ZY9tDhq3mcs8aVRJYicQLg+lvhBxzygCZeaha5WL2Qnbv7G+UWV2aJbpjGbsZNzUfrPy7ETH/aBynLKUJ72T+84aTBog2TamzAAtzI5RtoDhga8ehHpGzk/LNTOsl1roVYFeG0tkZJYG6g4tUaNaEV17V5jUPQEWho7JJJwMJB9UQs2ODu6S2oOyT5tWCCMs6xGBctSOz2dkkjreT9B6R/Jc7AJlmkNKsJaKCNZsM82zo5c90c89NNpDGMxVnGZHuLHkskqb8JqAn/bThXEAPNXoJPcgFzTad8CNHR7dmpDZka7EyEDjySejPoFBSgA4unXKiRpaAUw2LbfpA2DuXcBzP4fI7q0XALYgiUCZYxM6IxMxO5Td7+et17bnRjyPzmyhFrINkRo77QrHQFJRo35PmkHZ93BI5u3YdXi4b4XV2LExO2rnu8OXA8jyLhPC40ariKrnhjVv7sf3PDxfGO083LH79BYvdWNT+AKH0Vh2zQ5nikoRKOu1sVoEyz16L6oFlDeSCnI3luwQaBQPFSgfuVdvDO/dRd2xiJAL7+XHdTtIuLYnEwfSdTl0podC0ezwZUUG32j9l7qeLBYtDvoahgK8jNW4rIr0SAUmozaUMVG/rsTQ61Vdgb49usnf9jhAWfW8yYPtbQYIO8aNHV5NPGxPUyFQSnCsH3wiFDZRv65aaG/GY1GCyHBxEDAUSMqBUqOxAOC/kVYiqOhqLeqJ/S8Q7pUy5MWMmw/k38jdL1Y9JRPqArM2ZschUFZWsbCS4hkhbQtIbSxb9BVFmAE+PHae66U05oOuq++H7q+iLN7YyXnqO2hndhiO/+BHcvHAg+TnxU+GJROcDJ0KkcVa01rpSQUL8EONyxl3AQDeDIZEBVIjxqtAjZ1oH4wY+e8+Ev19NDqO1q4Dvyo+xpbWibZbVBFOYjayLmE+L89T9GghE20avLw2lqrZQerG2m3huvFGfaHkD8b8YM/Y319dFuoGqmxuLCuzo3a+BULfF3gBSyX/DcBU1YV1P74PvMcGAADq9780XOZJuj7r++JF5YOknyAaSxa3s5xKBD4ItLWxkw+Aum1NKASBNRqL6xEYYySKhymC45+eMsY8liYaB+Qsb9mG7dpgpXX6OtNDoURj6cyOY5voWGsrBuF3+RPDz6i1anbufGGpsTkNyeWsinpnNTeW9hxtoZltO3XH4O8+Ir5WxQ14jtBz7kbIFxi2M2nsfLT/VfHMTpc+4V9iBGU8RtwXQP+oNAOD1yxmZw3Cc90y5nxFbCxQO1AYO1kUhIvKFi2zkYUGtB6uHR7a7sZSVrEZO3w7KsAXVc8teXYMZscT914yOyoDx2HLs0MFyhVxRWAh020IVzxdndyDhhyZaIz/nLIP6gZPrtmJ9hwdQy8XEXBmp7IWQJhCIKwZhuh3eSX4O+9Tw5unLREsp9auaTON9tuaLox0bgxGkzDFZQ5gARtsN5b49sxVG8s8ZnsjNXZ2BnRjZo3OziR/MoIYV1RDviDya6zdYqFnrZodvUMibYkoU7UgZJgg0IhSsDA7nAlikQFlm60BctAKF+mDX/Q1+ivz7MQxO9yIajtjh+chWblpezSwkRkq45S1xY3lBZq7ztYTqcwOIF2I76/dBgw6JFxY1cOks7Mxbixbnp2izLNs5xtBmMwwg4IosigbaKfVfULv8yuYiXVjqRoy7o5Y022iocFKVBuLG4xMzbOTD5hC1+c791XDvPk9OPV2YPhU4JAr1P0CyEImpQsYw6DuUYV4eMKAN4Ox7KHWgGQjmJcxXB2yXdzYyYsin/xderggM2t/zLpHx7fcYJrBPc61Dd3Y4SM5mfjEanZs31Xmw6XZoZvy20z3x3NcuZDzubETsV8OZuf97keTg5L+cexnlclSMc2OWbJDM3YCyTQFjIn7uJe/XHmO1Wgsvm9yj7oNCf9Gz6Nyf2iyUcDsownE5FEYOwXixpLn/VRhgral2v9SgbLneaQ/2fnWTnFHX4rWQ1IfZgnMjr3acojVdTKkum5HHgHzVOPF6sZSO98M6XR4dIYSJYVw1mJWA9aME88TAyLTorrCfXqyVlLURsWNZdlf2Ez3bIVDGDtEMPrG8o3uDZJAq23DO9El67ZaamOps0rDjeV5RANii5pTEz0CdLblhdNUGilEUYTZcc/24t1YnucJQyGHgqLvCHdoDppHj+qlMB9Gjh+yf3G/tZkwN4yY5+Otleo5JzJ2ovPqsvhhAESzUwiU4pW0MG24ILoH42aE/zjIgOh7gRZyLPfw+vJNYfudAmXzPebvNvMydjcWIJidHApYuq4+ake4zzWsVqxW5uXd481qGtEZ30dZ3Vhkx5zZ8T1maHbMUHQibObvhnBjQVvXnBhRlmrd1vj0EVygLAZzh2aHG7wfVY7EHrR/HDdDCSMvZuxI5pkbc5GxIx5DKVBmDEoh6DLixjISlAJKglh88FT4tyoKrZf2o9G/x4mrhSxAuPnCSdiqzTuQyVA3VoBn31uLaaMjZtPiGlP7FJWZ3ZlImZ12RcIbn1CzA9irC3PsIOn5vzVtpBmWaDW+1M6XGkcezb1DswIzSrVqIMYIHxA5G6WEk5LBhYlCoB7Zsf1RjStux8GFm1Qwe+pvX3KunwiB3di57G9vRNFYnrGudAUwYQxkEKgGa5yOKqCdTngu/brGRFsBRZgdXxocMvY8/OPMs8MZCl+kls+goBStdIHT4kAUjSWMHXosjeHSjBTO7ATwUa+lDChLyuxsXSMW8/uUDxgypHK573kqs+NiPPQ0AsIIl4Y4RSluLC5mLXgZuxsLEMYxZdZkoV55PrTOloFnf072V8yNRRcy9S/ku11MsxNGJZNyN/QHwOgDbW4sqj/au2814qC7sVyaHRF0AV9ldvxcacyOEY3Fl0cfGA9aiCaKQw4V24ZGe2TsBEXcWA2RwV8mWUR5Wuq9jMs2L7oYEl0oywLJe5HxAqXsi9yeslGkT041O7spXDd+0MHaiskfjKznHmS2NYa/9a+tRM73TQ1C3FMftYG6GLio09OMHcAjYxQfpLTZiOcJw4xHbOhZUWXVcwuz4zJ2YkR3dN+ATCr4u2fVEONvTR3p3NaJPY9SvvK2nzphDxQKWno7php5lNnxwNBlCwlxTujGEh1fEbdDHLOjJDd03DfLVmK9JlIhOe45FE3xZd0mxpjIraKcgcHsqFqbrC+1DnpZjSojGSMB3c/2TXJxdD5NhQALg0Fiue9pA4fN5astzxJjpxBIoaaSrNBsWNQui7Ej6qX57rQJnNnxCjh0eDi794WxI7cx3IwUH+pJTU0wbeYfLgzEr2KRLzVERlVy33ze5NPGjSm7ZsfX+gr6FwDOOXAQ4pAVDFJ0XPqeWZgd5nlqn5MpQ4FMlqxCbwJXNJZoM4nQDBgTKSI2ss7IZaQRmGcWN5ZtglvGA0ncz6woj2Njdvhzq2l2sr7qxvLAcMZ+e9C9hoci11NWRyftMeo/tj9SN1a7wtGx9RkLfPii/F4C5RfH7GyLZr6dyjNR0j4f4J2eM5GV2kblxSKFKJXZHGSeHfkeqfvxPSDjxbux+EtNo1iSlouI8xByuvve1z7CM4vWYo2WMfnUCf3dG7tw5PeAdx6SzYzavnFbI2idr7CR+WgdU7OTQQCPRkyVW2aoMQLlIO7EgdhoLJrc0HXfDJD7IYsGBipz8Jm7AQDdOpWJhH8AosKC0T1mTOic1L6XG1P8fuvPo5yh0/H/vi9Pjndj+aYrEIAo91EIGP5YOBY+Aow46CQMZ5qx43pfKLOjubFszM4jb36C8w8aTLYvwuww4C+vfoT76j9ynFdoXJyVeQonDj8GwKSwLb4aRRPL7CSA6bAi35S+gDADelJB4nbi18kTBp3OLGrMjke3DT9TN1asOB0ymZ/VjUWeMY88X9yQDDfIKsxOEWLHEo0VGTvimsnUBwwQDF0GBeSIQHlrQ0G0zwMLq8NYjR0bs6M2Mo4FtwmUfd/DvgO7IrOCMDsIMKwnSfHAtB1ATiqVSfH2DWEEY4IQ+bZCyuy0JxJHY5WQDyZwd2L1EbNTVaYWsQvb4qLlNUaGbOVHzE6oKyQRGPClh8XBEMz/qE50vjxjqOLGkql6xOv69sebixo7MhrL3fvQ33RDB4CZxC8JtEzFvJN56t01WF/fYHVjSc0OA83P4ome37d3BlaBcjRIxLIGcN9nqLN0k5FzgGh2uN5luL9SMAfPlx0K7HMqADOJIxc8AqFeRzA7CaqeS0PbZDsGdKvE/oNJSLoNDvcIv45NBYYmZHFH4QSs7TQc+UKg3kPXTJ7Mnj0E2LwjfEeOv+l5XPvvhVFbJeZE0ZGkYVG7LC6vaMtNO2ImP9Hz0svbhKFPfUXsp6AJrm8ru8G9jwSwR2MF6l+ooed68jnK9DiZHc9+Peyh5yQaq0ienZxwY/FtyY8Ks0PdWMTY8XOK5qVoIVA9GsvQ7Mg+IWR6pZFBr9MrSzYobizGYLjQAQBlFne2ZqDH5b8Rk0cerRa1o7Iso44BYNb+0srseAB2bJIrbdef/fZFauzsDOgdm2HclMLsuOnpe6Liiys2hAnwrNElOjSB8hsfyiRmXvSC+opwmIvookFQ31+0n7VbGgw3ltqB+aAzGLm9w9gRLFCk74l5kt9dtcX9I+Jr6jihtYcOjG8s3wRYjB3RoRM3lo9AisD1fEviWCor0a+mQg4SxYyTmN9p9tbEAmURfeFjoCe1L/w5XLedDHyWxHC+eLyIZke537oby258B/BQUtV6ajRRoXf0mYrXg4ChqaDprmImB/y+5sh57GgKYMvGe5rOIsYwOyKPlUU0LIyOD19Wf6iX76vu7ugNx2BT7njuCHgJE2vouSJQTlYIlGd2l5qdeGaHvt82gXJlMWaHPzeM6wVdzA655nTikclhR1OUriBBCgvdjWVkUDaisdQCnPya1m1vMtxY1slwLmR2mhR1gbxoQRFWSvSnwtgJ0yg8+95a5Tn2EaA8S6+1zuhDuPtostSwEcVd3W2J1NhpV7iYHe0hKMGNpYo7VbzwQdjxrY9cCYk6b62zoYnheDQWfRkB4Jp/v2vOGiyduBQo84KixNjxPOlPT2LscGYngWYnbij8xRlji87S7DtVt9GrUdvcWEKzAybOJ8yzw8+xiAEadRazLp4cOxAmBVOMHX2wcbmxpDEylw0Xi7lAubxMus10Ua3vebJuU8BQSOLG0pkdMvNOIk4XoPshmZdF1AtpayEIjZ9EbizIe5/11EGokxdWOh/oy6rR188Yrzcsapd5vZvyeWX/6mbRditfU5f/UoYbZ7T25DwHC8zzyYw+w/47gJoqi7Fjc2N5hNnRQ81tjIJwY/EF9j7S5saiMLKOa8jEJRVMwuxkynDf3BWxx6AQNo0mUBakiIjGigTKPnX/efCi7Xp0qZDh8tx41ccLAKhbHh2HspGk4nmRSDLJ7ERGWdQO/llsi0BldrSkgoCWLbyaGPdBfGmjtkZq7LQnXJ1yC5IKxoWe6wiSdN7GTJrOgknoeV51B5naGfNcObNjG+R8H8YM5poT945hdkIkcWPVVLo7QmthxCTwdOMG7u876gAAPQrhoMcYtGgstfSAAV8d8Ad2r8KRI3vyhpTe9gh0EBUdYDGBMuncGqIkfA0sK85hR0CiMgxmR86oldBzZf+aG0szvvnzHni+NHSLCSgAlR0rSB2RFCiTAZsxNBUC58ChgxvvWW3icXHm4QTtItebMaBph/hJnKulmxaG6v4XOXbMcHrmeWXJ1452CfGjdncd5Gxm1ypL7SWLQJkyO0ZenbiaSrAbt2JbRaAc/l2yTubW6VtTiTj4IvScu5NczA5xd1HNjp9V9GfFoBRjBWV2ohUYFSgDNBUFNRK3N6nu1Ge+eYQSlSnQf7/oOPS07O+ibXInBMqc+QIT5+DTKEVoGZQ506todshY0GuUXDVGctEeSI2ddoVjFtdGbiwdauddjNkxjR2p2fGAtx8QywP4aOIPuHFQk9lpslDJoUCZtyA85mkT9oCc6at7FoNmTFbQJGh2/RbN2PG1wVAZoJ76ifKb4cbi5+hIxGcVKLOWMzs0IWWckaqCt9UXpRayKCAXic93FCh1rm5Jo7GCgERjKX2n7sZS3ap80GLwZBHTUpidoCBLPQDwo2eSMpihsSMF1Mr2Guq2NYlSBzqzc2hmgW0TvWHhH8aA+78I/LQ3sHFZ9AsfoGOYnSO/py4fOBkArDXzMi4WOFBLX9hQXWmpqm0NPZfPdZxmh8MIPXf0kWpfEV6PHNlfzy4x+aQgWWluJPpOZodEY9G6cpkcDh3eE0nhrI3Fb6Wu4xNJRpmStXj5hu2yzQhCbZLNjdWpl3Icuk9AvZw2ZkekhGCSzfH9sJYdHQN6dtL6KPFeOpgdAKioVc55ZyE1dtoTiQXKyY2dqgTi9q8dFVLbyfKGqKGfR46QRUp5h+F5HpCXM9AAHlHg8/3YmJ1oYGG8npBcJ2Ams5PNeJaZPpR1krix4mb+xUJIndCuX+fyLM45cKD4rtzBVW8q6zJApa15Z+IabCyRRJJubx1mx7h+RfPsyKSCGY9huBdGCzUE9J6abiwarcQ7Vi82z47d+A7gozHSfawtklAu3I/DjRXtN6+4scIinnE5SzhOuPl55Jm8DnG4/dz9LO3iHxjw9j/Dj6/eCQDoWhne91iDVjeQI8PluFGmYDvjmkTxQchlbAOo7VQetdLWFrlf5slQeHvGZBW6Zodp/Q/HMsLi8H5j3dbkTIuvGY724u8xeXYyZYIhji04y48nmJJo13y54cYKF3D3H6Cy9VsbCjJqESzUKdncWFHUZVW53M/yjbKPLiTU7OQhj5XxPYzuX6No6sxn3Ox/hY6SL+LPVSF1Y+1GcDE7xZgeN/wEzE59g0xgJbBtvX1lYY+FbejVWc5uOJuQ8aBY6QX4wg0gZ2DmuYrIFzG7kocNSNZh3gGGxo6m4dDaKd1n7gEhbuLfWGjubEPbqecrhpMyKIw7S1mVManZyZDZpJNtEwM1jcYKB7VCC7JHNCvPDo3GghwMjs/MAQA0kfaYbiw99DxabtVoacyOLlD2fPzhxaUAkMy9QI0d4sYSSQUJs1OI3FhJ3L4rNmyXOgcvwGn7utMY2BPR8feELGLauTLbcxFtl9FctNF7WeWZBqA1ZBmQzE6M4V8bDfQqs2NGYxWUqunq/bdNLEwRt+q25Fi1mbj3ot3s3c+SpsEBXePm0uwoAmWFCc8KgyE2xYFoo8rs8P5AFJ1nap9MU0hkUFCuS0AMkFzWNxkSTxpmtB9cuUk+Axu3kRQQMdFYQmIQCaU7levRWPbIYY/cW4PZcRiw7Y3U2GlPJGV2SngofJuVr6FXdWj1J3N5qEaKklWWRmOVd1a2KehuLO1cH73sUEHz29xYNVU5w9jJ+X5RzY4sOuc+ozjWZ83mBKyADYbx5SvRIYqxEyUM+0/16eFv1I3lJWB2LG6sTORSLMS4HoqhwKixIw4G2UgLSAblJpjH5q4txpixC9/zSO0oktFV2b/uxlIHP1Eugnl4/v11SAwavk/cWH8PwlpIlNkJo7ECRcCdJKotiwCnjHcbO1aG0dYnLH8FABH026Kx+PPnYHYqYRqATuaJv2MxzI4UABdxY0EaX7pbL47ZEQadI/RclCeAvI6XTxmOw0f0xANfPQjFoJelUO+FPCZn+pg2NF71r3dx7cMLo/MoejjxPvEoVTMaS3VjUZY168lIvtD84jqaIMwSbgS00OtMdDrk86E/f5q0Lc6NJV1mGd9Dp7KsYuAY7KBFoPxGVBrF042dUlKqtAF2qrHz3HPP4cQTT0S/fv3geR4efPBB5XfGGK6++mr07dsXlZWVmDJlCt5//31lnQ0bNuDss89GdXU1amtrccEFF2Dr1q3teBalwMXsNN+NlcTYOXFcv3C3SW53ojw7HtBzlLJZ3mBY1HPdu1+1cGNxY4dO9KorchjaswvdUg1xd2ZQhmyTA50JtfunCw5QfmtWjh1bezxPlKUANGOnEBpUeY9HtDDQPDsZnvCtWJZeyuzwQpZefBRKHCh17sp8bUIybQWLscMNIFt4bsaX0ViBEo0V48ZyaMgCzy/t3tFBdNGjYnEdwpBdxdhhUTmLhAJlkajOY7Gzfru2yNInRBFWuuvFup1h7ITPRYVvugyyrnvKjegYw7mmypaewdxfQK4TL75JcVLUFzn35DB2aLZsfh2ryrK454sHYMLArs52c+jMjl2zQ40ij7YK989bjQ/WhOOKnizRBqkpjE6Ht51fE6beW0aTU2qsDu9L9unbxa3ZEQeWH2kfVEyzwzcUmh0vLBfh+x66VdJrb2d2aH8oa7RB/W13Nnbq6+sxbtw43HLLLdbff/7zn+Omm27C7373O8yePRudOnXC1KlTsWOHpDTPPvtsvP3223jiiSfw8MMP47nnnsPFF1/cXqdQGtqC2XG4sWjYb0WWzx5KYXbCP9R/zDsM3/NEB7koCFOHczeAfMDNc9WZHb3z71KRU44Tbu4KPVdp4rj+5/yDBmPioK743vGj0KOzKmTkKfZLhj5web4imFSMnXw4yy5wY4dBnI+PQLgHi4eekxkWawU3Ft+XbZbr3EhYl3ZmJ9Jj2VKRLFm7VdHs2AuB8mbYo3PoYPTVI/aMbysF3U+V1KHx01HcWAHD3+Ysd2p2GvMBlq/fhlue/iBqC2d2CijLuq+fdZBJkGfHppNhhnHKfwjfy+Hb3jAPVdSN5TZ2aGg3HxBtVc9pVm6d2bGBZ7AW5+hweZQpE4nSwa+lNEwdzI7jmtNnPUkAp0yeqTI7wsha9IiyXNHPFWRpBQbZb4/rH7ntbNFY+rnAZSTbWXC+jGt2PDDBfvXqIu+9b1x9fXJC99mxmJ2dWi7iuOOOw3HHHWf9jTGGG264AT/4wQ9w8sknAwD++Mc/onfv3njwwQfx2c9+Fu+88w4ef/xxvPrqq5g4cSIA4De/+Q2OP/54/PKXv0S/fvZZxM5DUmanBM2OY91G0nmXlWLsaJ0Npb45sxPWqVNp2LzQ7IgdGbvmoef5wBIRQTZW3RrxbqzVkS8/TrPTuTyL+78iqe7vHT8KVWVZHDWqF/rVxoesOmEwTZ4yq1eudSTmFsYO2T4suFfEjWARKGcikW1L3FhWY4PDxS5Gxw2rgpudKWd7dHEyALy4eL3ixsoLfQA9Lj9HhxsL8rnjjJ2LLVBAO9zqcP3HCvuLrlsPPZ//UR36KZWxZSs/e/vLooo5QAfQeD2HlX3Uos2U9WOYHeZgOrnhsqZKhplv8ruiNtgY48ZKIFAmxg7jrIc1qaDch41JuuKYEXho/se4YsoIZVv5vnBrOA/UrQRqQrcgZfGSJPXTIQ1Hi0CZRmPRTMs9wjZuY+WgvVKSoAZp1PM9hwt0W3iC9z7+jiOF2B8Acg2bxOeN6CINL2FcahPcvuPkqZC2VVfZI9TiQ8/DvznyrOh5dhTEMO8ps5MQS5cuxapVqzBlyhSxrKamBpMmTcLLL4dZQ19++WXU1tYKQwcApkyZAt/3MXv2bOe+GxoasHnzZuVfu8A1i9MFZyW4sWxZWoGwsCEH74CVaKxOjjBKbXBR3VgkGitQZ0o8A62nGzk09Dwa1BuFZkc/tOX6FEkq+NqHG8O2lRBCfvFhe+KcAwc139ChDRDffbcbK8/dWFHZDgbFjSUM1nYUKLP+E/HahxvC3TvEmgbWyYKlQcZ+7fjzYBuQfE+9T7xQrco4aDNFl0CZJBVMlCtJCT0PDbYmZEUW5oIWjRUeg14XaVRSQ4e3BQDK/CDejRUrUDYHAhp5Ztmb/SCRscNtg9VVw2UiPSezw91Y7rbXVEo3lny2bZqdeGZnSI9OWHbddHx9SpiQUu4p/CSMuAe/Avx6b+CdfwNQjchExs7Hb4gQ/vA43HC0uLHIJFQxiso6Ad9djn0bfqfsOkkAp/TGcWYnREZz7XWOEk/mA09EXWUK4bIGlkX4dmjXWx8vSOkael5ludBATXK9+FZ5EXpO+n2YfbiArrGj+xTvsL7uzkGHNXZWrVoFAOjdu7eyvHfv3uK3VatWoVevXsrv2WwW3bp1E+vYMHPmTNTU1Ih/AwYMaOXWu5CQ2WkF1ToPywVUClhgjwPMZQBMjYRp7IRpwKPU6drgZoaeEzeW0OxEL5TR+duYHakRUddUt81ZB5I2RFGBMkGk2SlQzQ5Nq59UoPzhS2JRzfYwm2vea56x4618DZf+NXR1UBeO8xkFgPXS2GnM2MNvecesR2IBUELPATJo0nWN+60b31J7wXU2uUSjj5lUsIl06U20XIStA48xBLioM+fZ6wZxWO0g/p589KrxU1x6AWeZkOg8yqJsyXlkpfDfmWenOLNTQ3QbgWGgyf1SximuSDGHNHT5u6+d1xPXhPuyJK1zYuMy4PYjgBsl48EnFMLwU1g7wuzoTFNFDXZAZUjUcgl28IFecl9Rn+cBWL1QrMfdY/lCIMPig4bot6zaFkv0W7gB1e3J8+LbNRWK3wf+CuWJZkf85tkNn7At8n0d2qOTus8O5sbqsMZOW+Kqq65CXV2d+LdiRfI04C2CeA5bz43lAqflsz6NgCEvuLM2lmqkKEkFQTIoa9EESULPeefXxMyq5+EmPOqA4V+XHBxtr7k19GZG6FzRzh5Zi0B5PQmBjtPsBITZ8cGKZ1B+/z/h3zf+JBYNWB8aPkO2vdXME5BQxo44ZocX9Rt6hEKXK/viGgQrs+MpUTCyVpiFydM1HHo4NnzhOrVl5jVgCT1vYiRMnrixCgHDmP41GrMTY+yAGDulurH4MV75rbm+pjOhYC7DOHIz1uTD1BIFLyuSRzqND1bk+YMsJgyQd1xEY0WGBAvFtCI6zSJQ1mEkFdSv84bF6jEBrNmyA7F47S5jkR6Npd4Kyuyo69nw7Htrnb+J4xmanRAZD8DCf4n1+DPWWAik9itidrixI9zFLjcWvW80d1m0XUM+idEZbrct0rUHVB8Y58YiTOw+/WvUZhlurJTZsaJPnzDUcPXq1cry1atXi9/69OmDNWvWKL/n83ls2LBBrGNDeXk5qqurlX/tA/5SaYuL5d1pBm5+OoxaU6JM6O12zuLUjkx5uAOT2eEhh8VCz8N9RRFEosMx/FgAgN9+bgLGDaiNNtfyrjjQpQMYO88sop0gNXYizQ6fgTEGRqueFxMox2DUtteKr1QEiQXKTZFwMtfJ6TbkHbNt8u1pbiy+ir02lqf+NQTKvhhIEmVQ5gNCkNfcWOFiPRqrMa9XPU9QG4u4sfb0Vhrr8YroCnjb8+YAHlsI1GV8Rdfp2CUzAQDVjatluHWxaKwYZoc+I77MjBf9kQM6TQ5aWikbLki33Eut2OnD8z+J39mLNxiL+ERNuIqceXYsLsxmwNDs0OPOpcZYuDxfYLJfzKvGjsHs6G6sAk0zYE4mXvpApmhYOvN4a3tFRLzl3qmFQB0TdY21BUh4f8rsxGPIkCHo06cPnnzySbFs8+bNmD17NiZPDlOiT548GZs2bcLcuXPFOk899RSCIMCkSZPavc3J0XpuLJdA9c+vLDeWKS+wnoyMQ3swqZ6iUCCh53w252maHWntRPuxMTvcb24c3Dim042ldYq8KnP7wWSldBeGYHcizQ4XbzIAqzaHA24GBdmxxAyoCsis67Hu55XW7Ag35k8Tn61uAdsjyAfkbLnT9hRuLMs+mwqBMshww0gt/Kq5kAzNjoyWSZJjSYA/70GeuLHk4E6p/iBgaDRqY5mGwO/O2RcAzbPDhCvzsbLvGuvbDXJ34+NYBmcaCa0vqW1aoySlsyJBuQjVSLW7sUJTQYbs67XCbOCGvmR2LNfj4cuVr5ceNcxch6IzkT7wCZrGkrk1O8WNnT7VFc7fOPj+OWsZiOUBcOBXxHpbWKh9ywfSjZXJh5OKxqRurOXEGCTnxffXqdzCymkQAmVhGJtRuPrn6AT5HoxJhyT5U2MHW7duxbx58zBv3jwAoSh53rx5WL58OTzPw+WXX47/9//+Hx566CG89dZbOO+889CvXz+ccsopAIC99toL06ZNw0UXXYQ5c+bgxRdfxKWXXorPfvazHTASC252ogVurMU9jwEArKyQFahnPvaOdd1qIjJ012HS3FiKsUKYHYcbK0nV83xgVj1XtrG5NRzRWBw73Y0FD2XEnXLVcaNkoi1ds8OA99aGHZoqUHYMNidrLo7FT4mPz9acWlKzt2e64JnCOPw676hwHefG4sZOrlIYGOc3fkdZRYTSWtjJHU2B8grwNTqV0WupGbf0OhMBaYEwO4mq1nNWrdAoXD18MGGMqQJlxtDQVHBODqqjZ2147zAvFH8Hcj4TbXk52Mdowr62fDC87RU1xk9JmB2mX2dL/aHAMoCpOyvuxspY3I96UkFer0xWFnfuTkAvF2F1F449EwCwR9fQMDh1gjtxIwBg4IHy8/v/VY4jDD+XZkfU0HI3/s8XFp9IC6bEKATqAd1lX90YJWFsKjDpYouMHe7uF8auy9hRjyw+8e24kR1XEJlvZVRYNz7HMDtaf97RNDs7NfT8tddew5FHHim+X3nllQCA888/H3fffTe+/e1vo76+HhdffDE2bdqEQw45BI8//jgqKqRl/Ze//AWXXnopjj76aPi+j9NPPx033XRTu59LMlgGc8Bi7JS+59drjwXvAm57dol1ne5dKgGeMNjZsalt9C0hiGHoeTQj89QkcvJxNzsLvVyEc5Bq2h4enzBIrmgsjjWbi/jxWxtWgbJctu+grlH7IaOxfG7sMMkGeAV4XLPjmlkPOSz8y6Mu6qW7rNGPL4Co4/k+5+PixcUzzlpdqbzSfbZc3LvVTB3A45idipxvHTSVK7kyYmm5Pog+R4axEy62Rzlp4EUdC02KGyvarRJ6XggYPq7bgeFUl0QmB1wDUR4xefJeyvf4HTYQh+PN4u3i55etAFCn/RKj2fGl4ax6AQPj3hV3Y7nDhzlcbh/6l0X/qFuvGLwonYVk2Sz3MmrXU984AvUNeXTtZEtwSJAnWdHvPR/44RphxFgFyiVodpZdNz3+2BGEZofbJ9HyjAfF7bQG3cImF2Sf4EXt54aQwaTFFdS0MDv8XaTpA5ztjcT2w3tKsXF8NJZ8dgw3lsHy78bGzhFHHGHOTAg8z8O1116La6+91rlOt27d8Ne//rUtmtf6cCYV1B/e5NaO7PztA+Uhw2TSPJrSOzGzQ6z6Ht5m5JAPB7qCzuzobiy+G5MZCgsnMtP9wNv30KVh5NGptzqNHR35ZuTeaBEsmh1q7ITnpruxZJ6dyopyoCnKoMwNSqdoXJsZBVL7kbfWTXLDlghQPVaM4cA1O9lK2Tlqg1Mcs9O9U7lVs6M87zxceN37ZntYgAwZtEpzY5FihJpAmUG6YWnb6Rksr2vEQISGKs9hxaNyhPsBgejPKizlGqyIud5xoeeV2z4Wbf1p09n4Ye4v4Q+MGYOhZFpcoefR8+RybUMNt3YzOz4YY8mZHcbQee28qI2RC9t2PSLjtCzroyxbxNABgPceJ9s2AFvXQK8gr9o60lXqeW4DsxToAuWA+YAXTRTzMrv131jIzDcFUqCMpm3hH8ONxa93XNZ88n4xdeIRNynQNTtlRFyeiWN2RELKrLH/jsbsdFjNzq4JB7MTaMLFkspFxL+ct3xuX3L4JMaOHv2iPqBvll8YzvI43evpxo77AecvDc+z40oqCACY/1d1+yLMzrenjkS7whJJtq2R3kdPtjmvurECBmSiwTeDALlizI5+LWtlqoQmlKZVarIYR7fZqnFb3ViU2Qk/6saOqPVj6dd6dCkvHno+MGKdBkSuAuU6M8W4l8mcS2B2AmLsEDeWWhtLPRcAeGdVNAAVZM2v8pyvrJfxGLpGZRXKoZZrONGZ+NDT/kr4dKDUsJ3JZ+n3hek4rOHX4Q+sAFrVPWyf6ZpQV7Brdq5tOle2xaFxCf+quhshci02uH0kxfUXZqISHrZJTSGh4ejCmoWGS9BaLoIFMtyfJXimYuDrlyhaHjI70Xs0YhqaED4vTflA1JXjkwpp7BRxY/UYIT/HMDtxQn7+Ey19In5TBMrE0GIMNCGl/h52tNpYO5XZ2e3gYnYKurGT/KHgL3Gjw9ivodRlEmNHa4OetLDSawxneQE3dtTyAOKFEs+9TbPjAyhYXj7by+gQKJN1v3TY0GS6jdaExdjpSuoHeR5ZR4vGYowhl8sBO4CLqp6Fx8aG6yVldqIe9N1gQKy2wAauA+DoXJ7F1H1o5KLDICfngWwFKXSoGzvRbNKy/fVnjoPneaF3j1FDiazLtSvZyD2naHYChe1IUvFeQGh2pBurkRcthVn1PON7ygRiTX347Dbk5Yumu7EyCIRugee54bhxxnh7uxIxO+E6o/tXA2FEOeYVhuJgEOaADoja5Gl7HoCXRLOj9gl/KEzDMG8lPmS9cQV1PzrdWKFoPLAMmI4Di0+LWWQMWpmdFho7Vd2NPDt2Y6f1orGcmh0f8nwyOdlFBNKNhWICZc7cDT8WOOBLqkbJkmeHv4uxzA5PA2FxyToFynSc8jIWZof/5hj32hkps9OuSMjslPBQ5PPhtvM/TlD8lL7giQXKphXlWZgdjowRbhidy3v/RSdw4R3X7MS0jyMBs2OtJt0eUNrk4cyJknFZsLIO4n4X1GgsAOjZEEbLDW5aLAcbl6vOMHZkQkdmeVZ2NBUw+LuP4IcPLjB+awocHZI4VkzHRDI9SzeW2mZds1NdkcWy66Zj2XXTsWfPMBGhNHItvnzjWlAqiImZJdXslBSNRdxYeaLZKSjMTihYpoZhTeewcv2mbZI14Tl1bJqdDNT3pugzanvPNM0OdZNWda4WbQdIDi2SIZpDFMstFo1lGNsevpe/ELcVTtTcj3bjWwqUw3WL5tkpk4kpZ+Y/Fx4xxo3VEnhQjR27ZkemgSi02NjR3FjRct9j8nwy0q3bVAjkPWyKQs8ZN8a194Q/K9lyYPgUoJwk+FSY0xCctYzLNC6Zncgoo/0reT9pwIriLvUzBrNjurFSY2f3gWsg0WjnUh6KrTsiFwkc0RnK8ZMwO6pBZitHkSHRWHpyM9mpa4bdXz8j1mkUoecJmB1Hnh36LVGelbYAvZ6ehyl7y5DXmsqc0eYCEZVmGZmt8s6vGLPDN478LHlkrPlsZs0JDak/vfKh8Vujxuy4sljbn0FzmUuzw2JmkyIs13Ys3bjVmR0yQ5eanSRuLB56bhEogykCZT440ND0PbqHkVe3PyfF/3JA48yOXecQC0EBmDl49EKgOd/HAwOvwrvBAMzd6zvRsVUtSsjs2DU7rZVnR0CrjcUQMrzCVVSMoY4G7bWsGtsQie/bwo1FGJtimp04gfJxo92523TIZzw0nkWeHYAwO2WiHU0FG7PDXZU6s8PfEVt/IU/syPrHotWLu7H0yYs7g7JlYgI4jB3epI7hxkqNnXaFYyDRZy4lPBRl0UOZZxk0FQKl0zYPXwqzE61m6bRpnh2D2UngpxVVz12h5xSupILke4dgdqKO5xdnjMU5Bw7EyeP7Gx03LRfR6JFcHQ0RK5fknhA3RQDfapMsWr3F2eRGTf8Rq5vSQXJqFBMox1HnXOxqFSgbxg7dnmp2SJ6dUqKx8g1KuQh+WlSgzPVn1Nj5xxth+Zl1W0mkTwQ5Gw63+9LhQ1GRSTphIYyM/otmyIzuX4MFvU7CtMb/w/qyvuFmgjngfUvBMJyKhp47mJ0fnrC3+KxeYm3SpjA7TJTPKJpnJ+BJRulzb7mXFkOwJLCCUl3d8zQGSXFjqQYmAFEG4YuHDEl8SJpUsCkIiGbHExnVkcmJ96gQBDLIJKlmxzY5shiL+ZIEyqbh7jFq7FBmh9wXP2uUQ5GaHQuDuxOQanbaE07Njj5zSc7sZEXnHxo7saTQx2/Iz0kFypYHlAX5GGZH24/lXBoDlxvLYns7a2ORY+4kW0dpRXTin5k4AJ8R7iyN2SF5dgY2yjpTIpS8mEAZCDsMRjNRm9f3b3Pc5U+aAs3YcV68eKNZduaaG0uLALG5JWKZnW0bxDHCv+q599/wSrh/ZEqLxuLPe1AQ71sjKReRJ5MEbuw0EPH3mvqwYx8/oBaPLVDr7umFNq86bi+wTT2ARQnaJXIxma4aXbPzjWNH4DdPhc9NUxT+zpk9ZUAk7+xmVpUgqaCd2fn8QYPRuTyD/Qd3U+4jc0xkAs2N5buiv8SO1Pp6gMONFRdqnQRBQXEJGgwH6fOEgUkEykmMBR00qeCClXXoytm5rKcwO7wpCrOjGTviruluLFt/abl+nNmJq9Yu3ueonXuulhFtVJSsGMz0vnhJ3Fgps7MboQizI4oVJjd2MuAF/3w0FZiSCfblq45yb1g0z064H98yO8u9fpfzhcsksObXbgu3bTRqtsQxO27NTsdwYxXveAI/ZBcM19Oc28K/idxYxZkdCt2t2ahdY6dI3LbjT+aFfxu2ynprhmZHjcay3ZuMbuzw7nz12/IYensAYNMKdGoIy8MEDESzk+D+82vLCqYbi6mpCxoLphuLD0T9o8R2Q0kekoJm7ACAFxseTCCm1KaxI1kGHwcP645O5VmRuJK/50xndgBlxn1Z0yXWrLgKSPgwRcb3MGP/gRjaUy36aoae8/2GbJs8XjFmhxts5qRBPWAJg6RN3xPkifvTc7vPSTQWvZ4FYSwk72eoQPlvc1aIa1aeIcZOtly8R00FGnquCpSlRopbtnHRm2YbOcsa/5qEP07wP4i+2dkcz2Xs+FlLUkG+UWrs7H5wMjvRC8ojUEp4KDJCsJlBnrixPK9IWvOEhUA9S1v2f+9XTmbHKVAmePb9cPY+e+kGx7EJEmRQ7khuLPV3zdjxpEbEvr8Exk5QENe+wIoOJ0Zyv8aC7sbSj+V4RgFgVVR09IXrxfU3Q88jIyjGjWWErfNn5FaS7PCt+6KVSXs3fyw+ZlmDGOgT3X5+bQmzQzU7NBqLsyaU+dGF1727yHfLMCa2bwQa6xM0CpBuLJtmR7qx+ADNRcrcIDOYHW1fzwQTSPJGS7+yeiGw6cOoKQmHA30iQ/ISMdKWolXPbcyO1Y1VArNj6zuDvCJQNoS6NM+OJWs1d3EmMqoj0KSCyjvImBKNxdfLK5odLlB2lYuIyXhN2rgotxeCgCVKvsk3O8EnpSei664aPi7NjsmYyTiE1NjZDeFiduTDH62QeI88yVoTMmEESdRp5zK+SQkffY38zAeuIm20dZAZ1kSYnWICZXN7d8Ium7HjcGPtLDZHaQQVfBefZVGB8gV97jNXd9HMdN/ETZEnrhwX9GSLhmanJIEyWSuhZsd2SrzTVaJMXrxJXemD//EDyWX/vkx+DohOs1RmRyu0qDM7Ns3OlL1CcarNiFPcWDvqgP8bDCx7Xh576s/c7YrLfSISKHqCVchGxg5voxF6Doj+ZIcfsk9cQ2M1jW+dLD8XS0cRwWDkSOh5IWDCBWQLblAQGWVKQlTrhCfqa4g43904yzGDvJi0BcyLKVNThNlJUv8iAk0quGlbo3rNbALlIBCFQGVSQV4uwsGk2YxTYhhesPVL2Ovqx/HJpu3Kaca191d5GUjCJ+HOchFafiYzg3LxiW97IjV22hPForEynNlJ/lD4TDI7TdTYsVnxA0hNF5eVrWlteId1d/5Yrc0Rs6ONZkkEyq5U7M1mdnaaG4scN4kby5MakM3orK+dULMjBaguN9befavF53zA8PvgRPG9SbsdpegQKKTuxnRj5QsBFn68WVnPtq0EA574of1ACrMjK4ln2Q5pUCW5/4LZyQtDv0nJs2MaO41Es8PdxZwAotftED8M8c9tWgKs0sL9z/gDMPmSuIY5f6HMDj9eTnNjCWOXXoNokOLamqJJBTkyCbIT0zYbGZRLzLMTmMyOfQCPBtUf1wLXdgXWvhezU4exQzQ7hjuKanYgjSKO5rixaFLBpxetVV1/imYnup95mxsrisbSBcpx5T0I27KRdUFDPsDMx96N2hRjWEd//1Qg/Xy0L0WgTNkcTeulT5xMlj9ldlLobqwSmB2faHYa84HQweSytltL9+t48DWKWk/GJXdlD380HvAE4cqxbUpQLmKnCZQVY6d4ZERAkgpay1vkKl0Hkh9JOYB8cVUECgHDEtZXfK9v0jokZzSWRr2/+6h1NZsb65v3zccPohw/Ns2ObzA7lrMQlavtNzffsA0b6sNBI5lAOboXxL20kXWJDs+0aKywPQ0eSRIZvaOBRaxa5YURWmUL/wGUVanHjakkHv4ex+zIyCB+PD7gfrJpB37z5PtYtyW8Boo7WRg7aiFJr5ixU24xwC1gWh8hmZ0ow3SJoee0b0kkUL5rWqJ20u1lNJYfk24hkCHqERt2y9MfYGOUWykTI/A1wN8Po/QII3l2ysTTrSQVNDQ7JbixyLXKQ/190Sp3lCa/7I00Zik6Hk2TobB1Wjv0d90wrtJorN0Jjs5dj4ZoBrOTZ1kc+ctnxHKr8p7u19nJqm20FiPsPdopkjNmmi1hdhhLJFBeELEI7Q7FjWU7J80Q8O1J7ARyVeYy/ThgyiARm1cJiJLjye0/qlMFnOZjYHlG3/4ncP8XlbX06A2OAB4enCe1NTY9lSv7srUdjud065bNeGpjKFZOxE5ppTsAmbWXQRcoRwY+SQLpBWGHH8cmefWrYRhnRV1DcbNtEkEUnWMmcmPNWbYBc5ZtwP2vfxStTDU7kWEmmJ0iAmWOqu5F2qq32WR28oVACraLJRUMzL7Fs7IVWrvzZvi/XLc4s2O6seTEjEbAvbd6C37xHxlSVxqzI91Y4Z7Je7ViTviZaHZo1XPdzerMoGwzpGOMnTVb3NfNmiQ0KABbVqPPptflevQZ2hJFJUZuN/1dXxm5z1I31u4IlxtLT9feHGNHu5VlNv8y7UhcWUkdAuUAHh4LIjfYiKlwRWOt39qo7sdq7BRhlTiUMFr1Nypk/Pf8j7FTUGI0lhcZRHwG/G4wQF3fxewYSQW51sHuxqI6Ht3Y0Z8Td64jsuNX/2A2yaHZ0e9TXDSWqkVwtMNh7FR6ZLZZihuLDJSKZocmFYyMHZ8YsBle8kMwO47j6M+7M+qRtyvOjWVGEOkD7ofrt0X7sbzbnqr5KMoDOplFFfK+8SVS55IPWMlJBVU3loPZoVqdIYclaqfcPq/0Y8ZEkPRV3GUTwMM2rQZPKYEQ/H4tXhsyiYpmZ8Pi8ONb9wvDP18IBJskMyiHzycvoIyP5oh2hgexPFvEzdScYqbKNiwAXv+j8rtyT//3Y/U37d69x/N9pW6s3RF6JxHBqDpcikDZIvIDkLEZOwMOkJ+dsyN1Zs9zZTD4WIPu8rfATqUO68Wp8BgXhcvY0cWHQYEwO/pgKD+Wktm0VVE0Gov8ftqdMoKJMRSCAK8GWvHSsk6wwkgqyGepGWtkl27s0Mro+nOSKIv1iGONddyaHc3As9ncRp6dAOi/r7mi2MDspu7OTxWfk7mxovOOSneE6gzeCUNJ2cDdWNRQ67s91InYBMr35g8PP0y8wHS5FHNjxTE7QlQrdSYuFktxY0XMDr83fACzRVai9+jw77kPFGknaZdRvkROSPKEoSjqNgtMN9aSddvM9ZiMoAMg+7G6j4Bnfw7Ur6MrW46jRmO5BcpqNFa5JgVojmbHaBXtDz9+XWp2AkYKgYbXgH/fx18mt2nc5pxohg2nz5/aiGNIhnezvZbJS1AwJ2z0nq58TflNnwD0qdGyYqfGzm4E8dxoeghhqXNmJ/lD4WJ2ctYwmBzQfVj4ee+THW3UBMoii6unPrQOzY75gJdAXerht8tfktsbDJI01mxZbdsHpCMoFo3l+7RPVWbAAk5mR9fsxDM7BS2yiNLZRZkdehyxUrn6W8+RRUPP4/Yvo7HEwSzhxZ7jM7A86In5bJj4XpJAmRv5xL20dmuDQvFzNxZt+/uV4wDIa8uPeeUxI7ABkSA8V2k+w8V0HomYHV+wCs4BVxEoh23gAmVRhNJqCBQvFcHxzWNHoE91BbpUapMy4qoJ88Xw45XO7HyyeYe9jQXyjvN7+adTgad/CvzjQvkbncQNOFBsrzI7DgOfaHYC5hmu5lLE/DrbKA1r8pwffY14PPI0z07A80BZ+pT5fyNurGLGjoou5e57LJvrqeUptGM4kwrCfA93NGrtTI2d3QkWtoM+AC1yY6kPsjNM8sIngc8/Aow7K7aJvF29F4YujEP9t1TXlHC9uaKxHC47gosPG6ou0BOxbVnl1OxwehgAzjpgoPMYbYpS3FheRrpvGMOStfWmdmlHjPZIMTSJQLmIsdNINBRAAmYnwX0DPHHbTWOnuDFlaHaYxdiJiXR7IDjEur9Y+Jqxk5GRVqfc8qKyKmd5fA/474SbcXP+ZMztFLpOCppA+dIjh+GMidHzxwIYyQFbxOzIaKxizI7v+XJfnAWJjh2bVFALH47DpUcNxyvfO1q6kC21sUIjPibUXTm2aez062phN4O86nbnz8O6KCprydPyt/f+Iz+LrNl5UuAzTrOjTu4aC+r1agmzIwyXQh6o7h9+HnKYUghUdzvxd7WRyWcVDVsSu7F0JCkEGh7XYpjx9ZSiver10fuScQNqo43cwSrtidTYaU/YBpI8mck0x40FO7PjjByorAUGHxIz47S7n8b6S2WHSFwptJP0POLXTmDNT95TE0Tq6+YqIa5FTDRWRa54R90mKMWN5WfEteEDpmHsvHRT3MHCP6TQY8DsVc8L5N41NOnMjisvknYcCkun56p6rouOraHnxjGZyYjQ/Wj7uCV/irpmImZHEygTJmNrg3psfvkyvofVvQ7FL/MzkGfSUAWkUe/7Hnp0iRg5kt1aoEWanSi9gxKNZX8PPA/yHLkby+cC5XBbq1spLrLH2WbdjaUKlKUbq0g/xuRzzJG1iaFYoJXUidkvfVYzxNgh7qn4PDsyAq5Jy/DeHM0ORzUi99yGxQqbZhUoRxDFnenCHsOdE0AAscxO0vZKhqkALH1WWU99htT7oBtTJ4+PjLoY/WZ7IjV22hUWQ+L56+XnZrixKjN8JqJ2VrkSEmApiJvZU9cUz8FAOknFPkpg7Biutu0b1e8v3Rz/Yke49zV3Lag2RSnRWH5WDJA8PYDhxorLYEtnRwFPN2Cvel4gYtsn31mtdKL67NF4THSXGWAO4GQd3bjRz8k2PshIFfI8xRV7JNelKVej5L8BEroXBLMTTS4yOfe6ZL98gJv7Yfhs8qgtZeDzyOCwYanW9mYY4nufrLxMNIPyxm32CuAN+YDokiI3JzfQYpmduNIDDhh9BDF2qHvWNvAufAiYe3f0O2db5LXMusKp6aQw7lmh7jiSNVu6sWwMrHwOsw2bwt2wglFUubKESZVu7Az1o8il/3wPNCCFrzb/o03GhNVadqPQVCQay31tbH0FB22tUlR2yTPKemWF7fLL+HOU3+h7OLh7VZpnZ7eGzZDoO1Z+5pqNZrixdP9uc5PFuUTUDxYOwhbugyXsQnmZKxGZnSGiMGhVWqgUCKMPEhg7fCBqd5SSVNDLCNF4g8vYidNNWPRSAXyAhf7+z93xCr5x73wAKrOzV9/qeGYniUDZwrq4qp4btbIst1/WxoI8pxiDirZJT2IJJHRj8YEhYgi8BBoV3/Pw7KKwSCvX9CxbZykD4RPG85ErteMWaZzt9/f/B7zxJ/GV6kyWb7AIeBE9UxqzwyciscZOnEvE3ehoW24MSxfR8++vJW4Qy/HuPRf499dDo5AnxyTvTjbjcM28Lq9HLHvRmYhws5F+UHNjmRo/yTx0/fAxAMDnm/6uiNYBWaojCWJvO6kyz9+jN5ZvsrixVGYOAPD8L+PZOEuNNfFTjLVD2dG4+1eRr5P3vapr+Hfypcb+rZOB1NjZnWAxAHhui4paMtiVIuoNH+4C05idUhJgKU1U/atbBx4FAHgxGK3me4heuLX1rhD24g94UR943/GJjJ1Xrjo6fj9thaJJBSmz41uYHe2c4gYcSgU3hNoehtCtcttzS/DS4vX4x+sfYVtjHrSPvvCPr2Fvb5n4vp2pxmksNa8XHiTY3mRG0oTf1f3NW7HJfSo0HNdwldkNSVqNWu6vGcyOX5zZyWV8LNGMm3tfC/Pa3D/3I7N9tme9smuRo1ja3lQPPPQ18ZUKlGMZW62COjccOGPgN241tyEDb2Lo57tpGQBgD28dFq+tJ1os7Z7Sfq9+nXRjEb1hxmZQBAWgxwiyn+i4XPtC950l9QB5klaN2RnYTU/8aGpK/lOYiC/c/arZloTQw9YVcAmAn1UMdZcb6480q/Gad0ifWBpr+MQ7q52/ebZ2uEpzcJdiQY0ifuStT8QqS4imMnVj7Y6wMTu8s6nub3YiS54BFv4rfp/CpaGFSbbUjcU7B0GJ+zLMmDA7Ewb1iN+PZrhd0PgN0sYij1+hKZGx0ykmyqBNUUptLC8j2DaRtK5ZzA4DXvoNAOCc7JMIGFMSn21tyKOgdVINkAbOWqiDrzuDMiDunYV1efjNT6znUIPiBTDN2lg2gbK9TXqoO1BiNJZI85AtSrpkMx627pDn/q95K+0reo7BPVsJ9NqrSLuKd8GU2TlhbD/rOuccONA4Ry/SrJyeeQEA0Omln1t23gpurEe/rfws8sXogxsVGb98s9SeFWN2gjywcZn6HSAZ5wEsfU79rcdIIgsoSM0O8/H68k3282EB6vYMo1TnBKPMdpSArlUxxrQQhftKvjCD2YkmsHXojBemPyW3jYvGisGWHW4XF30V4gTK4QrRfeQsUjRx2K7XohE7J33XTkRq7LQrLMyOMrPSfv/jycC95wG/JcX6KALp0jDy7LTYjRW2oUuZH+3fV0MSoxehfzdXbhh7h3eg/474vHm7m3IND9pIjJ3mnk8bopSq5740dhrydlYE2zcVPxa5nqtYV6zerFLy9Q0FI2SWlovQ8fKS9e5jujQ78HDg0G7iM8WTwb7u/UXwdTeWVaBMD0cGw6xpEJYUjSW+5wxO5dwDBynfM76nuDy+Pmueo33EjUXhSu+gbFu88QF87IgGkr1I3TOK3l0qiBsrvJadyouzV80SKOv91I46rb1cs6MbO0RvVNVNHJsasE431rPXkQNEbS4n14ILz2mCVl8af3X1O9S2Kacj362aCtnftQTDe3fBlL0ceW1IGxfxxHtwMzsAsO/IwfIHwU623vCtCpRNzVUDI+8dv4+a/m1oj9LGgvZGauy0J6zMDuls6O9NRJC3ZiHQYKGgl78kPupajIXNLaFgZFCWA7PixiLRWGXWOlx26vLBwsHi8x5di9SC+lQZOzHCRwA09LwhGriYvs32DXEHC/+wABh2DADgpvxpMiV7hCVrtxo6meeDMfhh0+fx9rR78aXDhuL/nTI65jBJNDvABYcMwYFDu6FvTYWyXBcP2yBrY8UJlO2anbJsFrMuPlBZM9ZlIHahuwyzhvvr+DGqUZjzfVx29PDk+9ZnrokMiOLPdQE+/vTKh+L7/50+xljno43b5eBXkJqdw0f0jN95CXl2BPTBS9OJGIUrxQ9kvdf+II5N3wNrTT8j2ShndsizRyKvAITXXhg7BfToHD6XViNGCbwIrOs9/+0jze2K4KwDZIb0G/OnhR/2Oc3pOsxrUoRxg0KJw18vnISqSmJINPGs2e7nay0zjeL3f3qcc32rG4swO9Ma/0+6kLmx88afw7/1oa7Naewk0G+2B1Jjp11hG0hIZ0Ozzm3RSiD89/vmtndPFx91Y2d9vT1qo3gTtY6bvPxKJ0ZmhEeN7GXZUbR9QzRzyYUvwhZIf7nBPn3mHqDrEOCEKEKt0CjtwhIp2/aBqsmJ/z0rBnkZ6lzCOen1sQA0MNOwWL5hm1LUkrfjT4Vjsc+BU3HV8XuVEKrv0Ox4QHk2g1kXT8bLRC/Fcp0whHR4tVU5vP3jqdAh8+zwDS0CZeV4qn7nwKFqyoL7qH7GBd3wyGSNt7GxEMjcIAifz0uOHIaioNFYxQxgY9vixo4e8TZj/4H48wWTlGVPL1pjCJTh+Thlgt3tJdAcl0iRXExOY6eg3WPuKieDtpPZsX2nTJFwrdD+VBo7WRJSboJO4uys6wBd55MA9cQI38Si98LznQZmQWvbFw7ZE8uum46DhvVQK9JHhULjjOmeIw/GFw4erCyLE1hTZkcRKEcJRY/cZ4CsYl/QxpZIanHRYUNx4SFDcNzoPnj4ayQXVsrs7MawurGyUF46vZzD/Fmxu2wp7SqhMTIk8ddxXC9AOgV4GXt/PeeO8O/bD0TbhOv3rpGDoaG12OcU4OvzgIGR227LJ4k0OzsNzXRjzV4aMjguF7f9WOS+FNwZVvvXVlp1hVcdJzUIroie6EDyo1OgbB+gvUxOSbH/5wsmWfVUMhqLTie1RisGTrxROW6PGmt71P3pzE7OeG4XfrwZ3z9eamxymTAny5s/0stlAI99/VCyL+LGUpKEth6zQ+8fYGryxg+olc8gNyq8DOavqMNrARX3agZKs/LsxM/UldBl5QfNbW1JKmjV8enPH/9O9yeMHdKfkqSCvK39u3XCT0/VmE1qvJFMyxwnjStiMDrwxEIpCFazI/PcYRks/tnxYp3Jw7RJI+1TfF8aPE1agU0bPL+kJIgU4n4EBXEPrz6JsImLn1I3iLwOFbkMfnDC3rj1nP0wuj95J1NjZzdEnEDZy6idel5Lm14R36E3aRmU5/5gSuu0MXrYbzvvAIzuXxstY0onaTV2Grao36Pz9MnMzakrohoAh7HDX+QzJ+7hOpO2R9FZvMpI6J2PmfMjLtKGzj5lnh0d2xoLFmYHWLRK3o//vr2q+HHCg0UNjQsLJ8jklNmjSyTv68ZOCXl2+OdvHCMH8KtP2Nu9rdhOZ3ZyijgUAA4b0UNhpvjzWZFVt+3ZpVzVzmhaGYEkrqFEmh0zEZ4ePn3yeBLgQJidY/fpjcubvkp25jAcSorsiTd2nKHLevFhXi6CRNglYnZ4m+n+eDkJm7HDCmL5zWfvj7MnqdosxXizGGDXnznObFMC5ElYpDCeaJujCdD3j98Lp++7Bwb31FxP+vPDy7Y0RkEAcffM89yJZS2g9fSYhelSjvWRFqVW1Q2xSI2d3RE2gTLV7BAXkk75bnWHDQLqTGRUny7o3rk8Zu0S2kgfdvrQEvrbGvp7xHfCv92GRvvhxo50vTijaLoT14Gj6N1/rzgM35k2Cj86aZ+iZ9RmKBaNpWdQdtXLiduHvi/GgA/D8ga/PXcSLjp0iLLaax9usOa2OWKUnDXSgfM/lx+mH0h+dAqUHfBzSmh0bLZfFBMo2zU7/Dp87ejhWHbddCy7bnrxqD7AIlDOGrblPv1qlPbz51oP9+5SoQ1CupHB0amIXibcuOgaDL7hRlmxQWq1bjt3Pxw/po9sBx9Q/QyCANhA9RsGu8LXLUWzE+/GEoOlYViRe5yrkoYFyaCcy1regSAPjD7d3I/VjWXT7OTjGSyLZicoxjYlABUof/XIEWo7SVsuOmwofnXmODP3k95WHn2WwI0Fz1YDzI2+NVI/Ke5HkIe4x7Rtg9RyLZh2HWKRGju7IWydBI0esOSxKWHn4lOiUFznbuzMjvJizf+rfHD9jL277hQNrt32VFwUfka+NO6KFeSHQpO5DMDQnp3xlSP2RFXZTgo7BzRXSxE3Fgk957hsygh9g5hjRee/YYlclsnh+9P3xrLrpHbrz68st24+cVBX8ZlGa1XktJtge3bicuBQBE3KwODKCWMPPY9hj4q5C5NA385XNTtPXHGY0ja1KSqzUlWm7UsYGeQcBh8qkq3Ftyv+XWWej29NHYljtYrVe/aUDNTUffqEhpkY3OU7M2loN5UBpAYCieYsTbMTH0qciNnJ7xDPVWMxZicIVMNJaHbI9ebnRSePRKCsygWME5Ltjfb9mYkh+3PjZ8db1k+GUyb0x2VHD8dfLpyEvrVVajttbTEM8iLGThE3VikRuWVZH+//9DgsnXk8BvXoYmmrH4bzA9KI53mOaki+I0dbAOx0Y2cnjhS7IzTW5P3/AQ98KfxMBcrWKtAlHKUlgUsOgTI8D1gxR65HGB+rcWWhkMPVM0BU88fI8aJvC5BtW3JSbYRiGZQVgXLGSOBXltNp6rhIpmhbyvCR69SlPIstDW4GhtLUNOS/d3WFbfUI2jMgmuK4Fwd/HWUL5XWwR+nBzL7MCoDBEjg0O83VbumWdUbV7HD3m0vEmfVlFewq/b7Z3FhnzQLKOydoWPxz7XkZq0j6yFG9cPCw7hi3Ry1dOfwrNDs+chkfi352InAt1N8AkZwSQGnMjiPSkiNw/U5ZJaJLbAqosWO5/lofYtfs6MaOKlCOddcppVjC9Sbt2RMLp09t0WQq43u4krtbX+d6KsrsOJ4j8d1l7Gyzr69tS5mdE8a6009wiGfft7TVy8jnWRcoF+ubecHWj18v2oa2RMrstCd01uQvhJrV3VhJXQcWtIjZ4RACZZKtk4qmCeNjnUDYKGQAzCPZUl0zDzqjsRRu7DAoyY2VNWhl3+ZaKXosYhSQpGpGBXkNVMZDjS4zMsvmxioyIzvr78DR1wCTL1UqRZc5DAd5fIuOwYZSI5ys+zDz7OwgCnFumLmeSXrvNm3XOnubGytpO4uWk7DvJ5fx8ZcLD8S3pxHhsmcyO+GfjPzNGKgilHdJ1l4ggRsroWYnErZSl1HGyuwU1H0JY4cyO0UEykGcG4syO5LpalXWWLB/5Pobz2Sx79FkSOTZsZwLZ1v2OUWUpwFKzLtma6ufUa9nKfgomiS/8+/StmtldMARZFeGIVaQUJIKFgnFLXaUFjE70cabVwJ/+Qyw5m3ZPluH49LsiAFAOxcyw3aWKqCDPhcelpT0rJ1QNM+OykjoTJZXkrFj0XuRY5br7igNtDr6iN5dwrwscceJtgr/FHFjjZwW/gMwZ6nMFeRidiqjtooW2er5OGpjNTuRWpGBhLeVGjW0BXSweG+1lvPKNhNO/LwWeVmbU8aBaHbkfrJAoaDeS8oeNyvPToluLL1P+2QeAFVobxXVUmMFIG4sG7PjisYqTbPT6v2NYUB45rNscbVav/OyHzs2w8BXXgTWLgIGTEJ2nXR5O1l0a1sthrEu+Aac97+jImV22hNKiKOegKz13FgtewajNqycC7z/X7I4o3WUMorM+h6JcNyCci5zPpRRQU4Gir70PDKrozM7CdxYxrimbxPnxuLr0utABpNiRQppPaB4FsjC7CR1Y2kot4lNAXTrFIbQCs2OLsbX29EWzI52rbmx46qzFStS5W2KhOPKsqLt0o6nZ10u5Xwpm6pvq/+mfLYMvLFI6MbS+zAjGssUA1sZiEB3Y+XN/RvGTsbeBxXT7DQrOi0BPItBrMNwa+nGj3aP3nvc3EdlV2DggdCjsRLVj9OPo7uxaD4ppZ0dUGJgQWrstCeoUEsPzV72AhRNTzGB8qq3nD+9tbKuBW2McS0VLB2OJcoo3I/FXw41pNOt2bF0NBuXxrV6J0EzZoyfVYHyfxZoId+W3C/uQ9m0DHKwsV3Jnl1CN1fXqpzS2U0a0g1nTxqIH0y31G2yZlBunuHtEihzI0hG7RRhMVtFs+NwCUSwudzyRMhNGZ+Dh6lJDYtmz46Fdo2KDXixu9K0Q57G7ADqvYwV7cYdJ96NlSiDMiCYCdonGGwnb6eNkaL7e/dR9Td9cLZdE3nQ6APpd1s7rxff31peLsdy7YoxvXrbJ34x9pD0mV2zZUfMmhqszA41Hvl9TTirPjBKfdBnbPI2tAFSY6c9EadKr1+rCeWKDAC/k+F/eU+tZP378ye2vI225TT3D38RPB9XHDMC3TuV4bKjiJCSUp6ko6Ih8k4/sufBGAT0JIsdASUmFeyhpwMwjJ24gY2zIKQDGiDLJthmbr85awI6lWVwuRb15XkefnrqGFx4aLzOxylQjnG90PwzrtkkixgjyQAUcWMVi3pLgmLMjsXYoRXbeZV3ADhkmBZSHjuAFmuX9j2jPyPN0VpYIhhts/JmGzvxbqyKsuja6hM2ncGLXNRqQlSbsd1kbzftIz9+PUxgSs+J32PFjWU5V5tmp7XdWEmYM6M/KNI/VNjrpHE8+e4a8fn599cVP77eDtLHw/PUCWwp6L5n+LfroPj12hipsdOeoC+V/mB36qn+XsID9f6hN4jPz3/7SBztKkCXqI2Ol9zPqHlDiC6gf20lXvvBFFx57Eh1fSDyt8tZVSWJZHHN/JXtOQYfYl9vZ6IkN1ZWyQk0Za9els4tgWaHX8vaQUC2zPiZ4sCh3TH/mmNx/kGD3fuNazNzaHZiBuAdTcWf2zFRBBHTBcqea9Brg2gs7fly6sci0IrRxnNbTK8VB33bLtq7W5JmRxMo03O2iUuba+zEuLHmX30spo8fGH7RjRvdqI3q/5XniOFpu26FRk2zE9iDOO77vGrUCEEvmSgV0+y0mRsrwXNbKrNT5L4NcNYeTNgO8V5m1OVJU1GIn/lzmebZ2X2gzIi0WdF+X4DixopjdrSHZq/+3bB05vFYdt30ZtVwUZApsy/3MsC0n8nvwuoPH2RjFs/P9ZP5wIs3hp/9DH7/eck6xfqR9Re7tTuf1kCxaCxl3QwqyzL4/vF7Ya++1fjlZ8aZ2yTR7PDrrnV0+w/uZt2s5IRoukC5sT68hwnxxy8eACAqX+BA/9pKvvcQwnAm5+TMs9NKmp2ta/H0N48AAAzqrr4zPEzXlWPFSNbWorxWMdFwQGnnawk9F7ANVHERSkmOY3Fj1FTlpJGhR37pepWIKT5hfJgF/awDBrjPNxIzAzAFyxRUs5PRopf4cgOU2eHXpI3cWHEo1biJc3sDSsFfV2SkFbobi18zndlJKg413F87Bx1Q9bkLQwxYDcDs29TfFvwDGHRQ9IXFPxgNmibHUsG52Vj7rn25nwWqSWkG/UUwQF6E2b8L/3oZHLRnDyydebx9E+V4GZ6OJ9q2A9rlxbQkdGCJkiledNhQXMQFwiVpdjQWROv49upbjXd/Mg2jfmgRLZYE7Tn6/VRg0/L4dQgGdKtSkhza0JDn10VzY7kMWs0d2Czo2733GIZ8rpO1rTd/bl/c/Dn3rgwDsiUuD/oMHPl9s0xMswTKluvZmsxOsSRx3MhY8A/gKFLAWGd2IsalZzV5Zp79j/x98qXAyzeHn5tIPbegYHd98t8ANXqoiUQexrqbSeBIq2t2EjwjpYaix+blCieTPTqXY93WBgzrlSTnk3YcbULrZHaSpk8oOVFu66IDjiC7MshD8dRP1J+GH5PcjWVEcrUi61G/xr6cCtQA80XQYessfMkCFTXOjARbHfBRLcY4KKG9lo6pFM1OEWYHsOXMaSEYA1ZbhPAtNKx5uQWjrICSkv5gcrw2YHZKxK8+I+sjJXJjJQW959kKoPtwbd8tCD23RmO1gmaHwzWz51GcGxary3W3Fjfs6DnSSzvkcMdxCw7m27NrdhRmJ06z09HdWKUb2bMunoTTJvTHzZ+bUPz4+nH0iZUrGqvo/pqp9WlldMARZBeG64HPVQHH/R8knYoibiztoSlCZ5aEvU62L/ezavsLFl0Aha2jKomSLxJ22RFQjHGg99DmHixJsxOt++z/uY8HYM73jsbp++6Bl686yr2vONjy7HBURq6yo69p3r4j7DuwKwAiUOaZfBu3AKNOCD8f/wvaKEf7SoB+/S97o6TNjyS1xfQCosZ9POBLyXdMt81WAGNnxO87yb6oK0f81orGTrF7sP4D+3JnXS6HMZvJmqH4QHgOthDu/c7XjJ3onhdzY1GmqjlV4JMgyf6KTfCKRBTaMKxXF1w/YzyG9iyB2VkxO/y7NZr48vvT3GisDuLG6oAjyC4M1wM/5cfh36TRWHqn0Zo5aIYfY18uFPm6Uj9G0Py5+8LP2Qq5j6QwEm51wEe1KLNDkynaOtlSBIdFQpQj9KquwK/OHKcU9isNFoEyx7TrgO99DAw+GC0BZ/WYzR322b8AP6rTBuoiUW9JkNOuR4n7ofWwmnShZZFIr1jQc8tVhM89N/iA0ox8PZeLlsEbgObGaq5mp4gbi0LJheNwPSnXT4u827bBWB3LnlddUxxU60gz/jYRY8d232lYfkdmdowq6K04yaXgffurd4R/+bXY+GH4t26FtkHqxkqhw/XA8w5NCYGMeTB0Q6g1jZ1iuW/0DjWuo8xoHWwpBotOeXd0gbKtffQ+WXMRacuSCJQ52irJYqxo3AfKOrl/LwH7DeqadF4IbFvf8gOWacL9Eq9fOckG3ZTXjZ0W3Bud2dGXNYvZsWh2bOHaLY3GaqwvvurDV8jPrlIVimGrsaXLnrdv89sDzWWsIPOPFZrk+yRy2ziSJwrWK9+Ca1IE+nt1wq/NdYoVBi0xGqvFGHqE2g5e2+ql34R/kwqUaTb9nYjU2GlPuAwD8RBbfMc2GMZOOxgC/MVyides25A8F3TbJGjcon7viMxOsaSChrBX37wZbqy447UG6HGM56z17sGfLjgAPzl5TLKVt290t6kUdBsqP5d4/ajGjCYbDH/UrktzmR1u7NjcT0mghwzT+8WTcm76UC5rqRvr4cuB1/8Yv+7r98jPvMyBsT8HixfXLlrElCMIgMVPhZ/n3GYmbnWZ11R4y4XQunHcUuj3sUu/4uvo3+l7ALS9sbPkmWTHKebWdIasty864giy6yLO5QNAiQqwzYJ+VBP+01mP9iilwNvO/8YVo+NoSTZY4/gd8FFtqXC2ObWxkqzbEtDj/LIFYtkiqCrLYmhtwv05SmSUjKQDaREYE9qFD6rfS9HQ0WeAu9pcg38xiIg9t4hdWdbSaCwAeOhrybdzJQZVjt+MBJJHXBXtX3NtffJmsu1pHpj6teHnVmIw5TESTGyK9QcrXlG/t5UbSwe/PpzpAYB7z3MHs7i2TzU7uxHishPTv4zFZwy+ZX/1e2sPfDYBqvB/a9R1XIekv4ytFbXSUaDoLZoxE9TdALFuLN3Y2QnXo7UNTi6ELAbbAN0clJIXKQb9ajX9z0gtlUJzmR0uqKXL1r9fwr70/Cjkuu3JBevkOWquZqeYRuNLz9mX62H1HE43VsLnbVOkIXn7AXX5sf8v2fb8OHWEiS0rQdCb6BhFwsgB830odv7tVS+Qt4NGSC78l/y8+ZP47fnznPR9byOkxk57IrEbK3D7t63bt1KOHY5DrwS+rdWicrU97oVM8oInRUdndnIV7t+reji2L+H6GJEaO8HYaW0D66ASWAGOlhg7zWENCO754gH4+tHDcdzoPuoPe52gfm+2sRNt19zrbISeU9YoMsYp+9EazI4NfccBFz8jv9etDCdvTQ5jx8WQJm3X/L+ayw77dqgZ/FGd+ZsO23F0QXtLkYTZqaiJX+fkW7Tf24jZOUZLiyLy7Djuh8gP58DqBS1vUyugA44guzCSurGe/6UMMU624xY1y4qqbtohXKxUAs0Ohy2CwoWLn012/J2Jj16Vn23MzhULgWOuBS591fwNAEafpn6vjasdo93jJU8namKrorXvgf6MuRgBSn+3Vq6OZsyKDx/RE1ccM8JeWuKEG8i+SzF2yPuj5zMpFUYuJi2HD6AaHG0Veg4AWWIs/Hpv4P/1Ala/HX7fQ2emY6KxkmD06eYy+myd/+/w74GX2Le3HWdnuLFGnahto7VrL+33TBsxO7qxzttqS59x2h3F9U0bP4z/vZ3QAUeQXRjOh9PT/nYQ0IebP/B60rNSNDvbSihG12880EvWkmp19qo1QM8na2F2qvsCB3/dHNQ59E5lyo/cx6Lp8oG29X+f8jugUy9zeVuzSa79t5axo7hIWnmgoMZuSaysxbXW7PpfuhuLMjvc2KGZiFsYjVUqPnwh/Ku7bxWNkib6p0akC4dcYS6j+xxyGPCDtWq5G4r2cAkniaTS2WHduNAnVG3lxuqvFZLWM3NTjPlM8f2tebvlbWoFpMZOR8Cwo8O/s2/due3QQTtt/sDve666TlzH3FIBXQd5SRJhy6qW76O9BIfFMP4s4FsWrUgJNbKaBVfnrRg7jlwtidAKZSdcoLNbV7i0DUqxzuj+Uz1EKVi7KPy7I3LdKMLu6O9TPwmFuK/cCiyPBK/NzbMThx7D3b8NOUz97sqp5GeBLn2LH0t3/wDAiKnq96yj5h/QPi5hI2LP8qzrEyZ9HYNxaaP+op+WbXnNwvDvYgubnGQSeiaJ2NuJ4eepsdMR4Jr5J0bijCXNB+84P9JcMrFurN2o9BoNa24PJBVftgQX/E/93tbkWhJjx1W7rVS0tkuODjy6myZpO/j507DqczXRbRw26jo7cj3fe0x+/scFwOPflZOr1nJjDTqEHDsT6mVOu8Ncj7JLgNaHaPXm9LYJoTWBzYWcxEjiaJfUHQncWJ2jivedegI/dOWWakN2kiOTBfrtay6vrG3e/irIdoWYwJs2Rmrs7GzwsEkA+CaZTfcZCxz3C+Az95jb6GgrC5+Cd0iDY2ZlOvSX8cw/lXbMnnuVtv7OROee7Xesadc1T9xbKmoHqN9tA01LUd1ffnaK3Ykxr1PspUAp3NrKlhudsBxyZfLtiglyawaYy5JizTvyM3WJvP1Pdb3WGjT3PslcZjM6GrR8O9RI012NOrtxjtb2/hPt+ppSzqld3FgJjJ3yzmEZkwufdEseKLvTliV0bGyZnvIkKShjFRdl3MZIjZ32Rt/x6nc6q+ncK5wN/XAd8OXngUkXA71HF99n9z1btYlW8BfrgIuAgy+Xy+OiFuiL2W1Pe2cYh7YYXFsTvUlSvPZkdsac2T7H0XU7vRMmASwFx1wrPzsHKDIAXvRkCw7WhtTUHvsDR/0A+OzfShOO0vfftl2cO6gY6MBi05RxtCQa6/hfys9dh5jr2iJ1uNueY8Aksm/N1ahP5HQj9XP3Atly8xjNKbPRlkiaQLTbUKBrTKBCKXqwloAmAORuLZs2KgnoOLB+sXu9NkZq7LQ36IsNALN/Z65DH444XzNHW4t3D7pMPRZ96Cu7urdTXuhmuNo6YvFPClsnWyo4zV+KW6pT95YfNwl8X00kluRZLBXlXeRn16DTWjlP2vI98TzgsG8Bo44vvq6yXeskOrRiBmFS4yYlJTMb5F3OlgOffxSYOtNeV8+2b/1+Km58LRqrmI6N71+pJ1bidWwXN5aut+mAecNcGH1G+HeA5p698h1zXRvoe6dnxm9H7Eaiig4Cmq4dKB6hlGmFAbWl6K/5bytrw0E6k4sP0aQv9IYlpR+3I9bDomiNwXPsmeG/jgpX8cbWAh34XINUWSegIUG+lGJorbD11gR9R1rT2PnWYtUYj9v3moSDFsc7/5afvUxYGLaU4rDlmrFD+zjdjUWfPxujya/fuw+r25UCnfU69JulbZ/oGNpEoaPrGWma8IlflJ+rustadaXIJ0adEAroB05unfY1Ax38iu+CqB2ofo+jl4G2mU0nxZdfAFa+Dux9ivlbkgG6pS90R5/9dMTcP62Ntvax0+ff9by0VoK3tSUO6u0BhdmJBo+uQ0Idi61+UlJ00hJZdh0ErHKUT9jQAtdCc95x3YCmfRwdZP2Men26D0t2/FInSbrwtk8C6UCp0CetnyZjh0Ya0naXMtmb8edwnzuRrd8NeusOhmN/qn53pVDn2JnMTp8xwH7nN5/BaGliro7O7CSt+vtpBg/hdWWBbimSCC6Ngo67EPTBHQDO+Qew73nA5x+2b+PCqbeHf8++3/xNd5+3FpozIVmtpZSgfRx9HjxfNdriKpZ3I7rFlhoSbdHn6i7vjm7suGQHVLhcSv/uOSrOtyNSY6e9oTM1ttBMimKz2mLMUIq2A6d3achtm2EnJVU8+HLg1NtCwXxbwFYbSsfUaILQEVy6rQ0qBBWJO/cETvpN6YEH42aEAQ427cwBXwImfbn57XRh/Qelb7PPqep3qtvSE5nSsOWVr5v74sbWUT8wl5UCaszT9rQW9Gfb9ax3FLgmcjTMv7VLarQxdhlj55ZbbsHgwYNRUVGBSZMmYc6cOTu7SclQLAOli1W56Clgr5OAC/9n/72j4MtR1tRm1UFqY71ISzHus2GJg3P+0fbH+s7SMHt1exyLIlsWnmd1C1wqcaAzXJcxM+YzwKWvAT9Y3bJjnXRzy7Zva7RlUslsGXDk9+y/DSxS2ygOeyRIBfDF/6rfdSOuolp+Vpi+jJrSYcqPzX1zY5lO+rZvKN4mHV+bGwZbDJhUvNZTc6AzO83NWdNeWP6SffmnzMCh6OhcWiL8/e9/x5VXXonf/e53mDRpEm644QZMnToVixYtQq9elrT3OxvfXQHUrw3DDJO4iH64DnjxBqCsCzDyuFDU2am7Gm3RUdFnTLJifDb0GNG6bWlteF5Y9LA9UNkV+Npr7XOs9kSPEWGKgcqu7pBtz2tZCDbHvueaGcB3NmwlWdoKWcdA5RrYkmDPo4uvM7AEF1rGIqrW+4/Dvg089/PwM+8/eUK+5qKyFvjOspbtIw5UzLvvec3fz+cfAf75JWD6r1repuZg+q+Ae04CDv/Ozjl+C7BLGDvXX389LrroInzhC18AAPzud7/DI488gj/84Q/47ne/u5NbZ0FFtTqbKYZMLgxr3d0wdgawdXU7uYlS7BT4fmlZgnc1KJqdNu6OXczReSWWp7jgf8D/fhS6F5Pq+S5+Frj9cKDr4Pj1qBjWpdk7/DthSY1R0+UyGjFqq+u2s0H1KnptsFIw+BDgyp1YRqf3PsC3PuiYtQqL4FNv7DQ2NmLu3Lm46iqZidj3fUyZMgUvv/zyTmxZihbDzzQ/kVWKFJ8GvPe4/NzWA4i+/177hFoXmkspCQbsD3zhkdK26Tc+GcNLk6i6BK2ZLHCpJlPwPOBbS4D5fwUmXlBa29obrVFHr63xtdeB3+xrv5afQkMH2AWMnXXr1qFQKKB3b5XG7N27N959115Hp6GhAQ0NMqR28+bN1vVSpEiRok1x3M+Bm/fbOcceNb30JIithYO/Drx4o1okEgizyF/4ZPMCLzp1b58yKs1FzQCgboVaIqijovuezZcfdFB86o2d5mDmzJn48Y8tYrcUKVKkaE/0GAZ8e2nbRADZMOEc4I0/h5/3v7B9jmnDlB8Dk79mrymXRPT8acSlr4bRa33aoOxKiqL41Edj9ejRA5lMBqtXq5Eaq1evRp8+fazbXHXVVairqxP/VqxY0R5NTZEiRQoTVd3aNhKL4uRbwhn7j+qALi0U9bYEnte+xXM7AnKVqaGzE/GpN3bKysqw33774cknZYHAIAjw5JNPYvJke2rq8vJyVFdXK/9SpEiRIkWKFLsmdgk31pVXXonzzz8fEydOxAEHHIAbbrgB9fX1IjorRYoUKVKkSLH7YpcwdmbMmIG1a9fi6quvxqpVqzB+/Hg8/vjjhmg5RYoUKVKkSLH7wWNsdyjwE4/NmzejpqYGdXV1qUsrRYoUKVKk+JQg6fj9qdfspEiRIkWKFClSxCE1dlKkSJEiRYoUuzRSYydFihQpUqRIsUsjNXZSpEiRIkWKFLs0UmMnRYoUKVKkSLFLIzV2UqRIkSJFihS7NFJjJ0WKFClSpEixSyM1dlKkSJEiRYoUuzRSYydFihQpUqRIsUsjNXZSpEiRIkWKFLs0donaWC0Fr5ixefPmndySFClSpEiRIkVS8HG7WOWr1NgBsGXLFgDAgAEDdnJLUqRIkSJFihSlYsuWLaipqXH+nhYCBRAEAT7++GN06dIFnue12n43b96MAQMGYMWKFWmB0SJIr1VpSK9XcqTXKjnSa5Uc6bVKjra8VowxbNmyBf369YPvu5U5KbMDwPd97LHHHm22/+rq6vRlSIj0WpWG9HolR3qtkiO9VsmRXqvkaKtrFcfocKQC5RQpUqRIkSLFLo3U2EmRIkWKFClS7NJIjZ02RHl5Oa655hqUl5fv7KZ0eKTXqjSk1ys50muVHOm1So70WiVHR7hWqUA5RYoUKVKkSLFLI2V2UqRIkSJFihS7NFJjJ0WKFClSpEixSyM1dlKkSJEiRYoUuzRSYydFihQpUqRIsUsjNXbaELfccgsGDx6MiooKTJo0CXPmzNnZTWpVPPfcczjxxBPRr18/eJ6HBx98UPmdMYarr74affv2RWVlJaZMmYL3339fWWfDhg04++yzUV1djdraWlxwwQXYunWrss6bb76JQw89FBUVFRgwYAB+/vOfG2257777MGrUKFRUVGDMmDF49NFHW/18W4KZM2di//33R5cuXdCrVy+ccsopWLRokbLOjh07cMkll6B79+7o3LkzTj/9dKxevVpZZ/ny5Zg+fTqqqqrQq1cvfOtb30I+n1fWeeaZZ7DvvvuivLwcw4YNw9133220pyM/m7feeivGjh0rEpBNnjwZjz32mPg9vU5uXHfddfA8D5dffrlYll6vED/60Y/geZ7yb9SoUeL39DqpWLlyJc455xx0794dlZWVGDNmDF577TXx+6euf2cp2gSzZs1iZWVl7A9/+AN7++232UUXXcRqa2vZ6tWrd3bTWg2PPvoo+/73v8/++c9/MgDsgQceUH6/7rrrWE1NDXvwwQfZ/Pnz2UknncSGDBnCtm/fLtaZNm0aGzduHHvllVfY888/z4YNG8bOOuss8XtdXR3r3bs3O/vss9mCBQvY3/72N1ZZWcluu+02sc6LL77IMpkM+/nPf84WLlzIfvCDH7BcLsfeeuutNr8GSTF16lR21113sQULFrB58+ax448/ng0cOJBt3bpVrPPlL3+ZDRgwgD355JPstddeYwceeCA76KCDxO/5fJ6NHj2aTZkyhb3xxhvs0UcfZT169GBXXXWVWGfJkiWsqqqKXXnllWzhwoXsN7/5DctkMuzxxx8X63T0Z/Ohhx5ijzzyCHvvvffYokWL2Pe+9z2Wy+XYggULGGPpdXJhzpw5bPDgwWzs2LHs61//ulieXq8Q11xzDdtnn33YJ598Iv6tXbtW/J5eJ4kNGzawQYMGsc9//vNs9uzZbMmSJew///kP++CDD8Q6n7b+PTV22ggHHHAAu+SSS8T3QqHA+vXrx2bOnLkTW9V20I2dIAhYnz592C9+8QuxbNOmTay8vJz97W9/Y4wxtnDhQgaAvfrqq2Kdxx57jHmex1auXMkYY+y3v/0t69q1K2toaBDrfOc732EjR44U388880w2ffp0pT2TJk1iX/rSl1r1HFsTa9asYQDYs88+yxgLr00ul2P33XefWOedd95hANjLL7/MGAuNS9/32apVq8Q6t956K6uurhbX59vf/jbbZ599lGPNmDGDTZ06VXz/ND6bXbt2ZXfeeWd6nRzYsmULGz58OHviiSfY4YcfLoyd9HpJXHPNNWzcuHHW39LrpOI73/kOO+SQQ5y/fxr799SN1QZobGzE3LlzMWXKFLHM931MmTIFL7/88k5sWfth6dKlWLVqlXINampqMGnSJHENXn75ZdTW1mLixIlinSlTpsD3fcyePVusc9hhh6GsrEysM3XqVCxatAgbN24U69Dj8HU68rWuq6sDAHTr1g0AMHfuXDQ1NSnnMWrUKAwcOFC5XmPGjEHv3r3FOlOnTsXmzZvx9ttvi3XirsWn7dksFAqYNWsW6uvrMXny5PQ6OXDJJZdg+vTpxjml10vF+++/j379+mHo0KE4++yzsXz5cgDpddLx0EMPYeLEifjMZz6DXr16YcKECbjjjjvE75/G/j01dtoA69atQ6FQUF4KAOjduzdWrVq1k1rVvuDnGXcNVq1ahV69eim/Z7NZdOvWTVnHtg96DNc6HfVaB0GAyy+/HAcffDBGjx4NIDyHsrIy1NbWKuvq16u512Lz5s3Yvn37p+bZfOutt9C5c2eUl5fjy1/+Mh544AHsvffe6XWyYNasWXj99dcxc+ZM47f0eklMmjQJd999Nx5//HHceuutWLp0KQ499FBs2bIlvU4alixZgltvvRXDhw/Hf/7zH3zlK1/BZZddhnvuuQfAp7N/T6uep0jRzrjkkkuwYMECvPDCCzu7KR0WI0eOxLx581BXV4f7778f559/Pp599tmd3awOhxUrVuDrX/86nnjiCVRUVOzs5nRoHHfcceLz2LFjMWnSJAwaNAj33nsvKisrd2LLOh6CIMDEiRPxs5/9DAAwYcIELFiwAL/73e9w/vnn7+TWNQ8ps9MG6NGjBzKZjKHkX716Nfr06bOTWtW+4OcZdw369OmDNWvWKL/n83ls2LBBWce2D3oM1zod8VpfeumlePjhh/H0009jjz32EMv79OmDxsZGbNq0SVlfv17NvRbV1dWorKz81DybZWVlGDZsGPbbbz/MnDkT48aNw4033pheJw1z587FmjVrsO+++yKbzSKbzeLZZ5/FTTfdhGw2i969e6fXy4Ha2lqMGDECH3zwQfpcaejbty/23ntvZdlee+0l3H6fxv49NXbaAGVlZdhvv/3w5JNPimVBEODJJ5/E5MmTd2LL2g9DhgxBnz59lGuwefNmzJ49W1yDyZMnY9OmTZg7d65Y56mnnkIQBJg0aZJY57nnnkNTU5NY54knnsDIkSPRtWtXsQ49Dl+nI11rxhguvfRSPPDAA3jqqacwZMgQ5ff99tsPuVxOOY9FixZh+fLlyvV66623lA7kiSeeQHV1teiYil2LT+uzGQQBGhoa0uuk4eijj8Zbb72FefPmiX8TJ07E2WefLT6n18uOrVu3YvHixejbt2/6XGk4+OCDjdQY7733HgYNGgTgU9q/lyRnTpEYs2bNYuXl5ezuu+9mCxcuZBdffDGrra1VlPyfdmzZsoW98cYb7I033mAA2PXXX8/eeOMN9uGHHzLGwtDE2tpa9q9//Yu9+eab7OSTT7aGJk6YMIHNnj2bvfDCC2z48OFKaOKmTZtY79692bnnnssWLFjAZs2axaqqqozQxGw2y375y1+yd955h11zzTUdLvT8K1/5CqupqWHPPPOMEvq6bds2sc6Xv/xlNnDgQPbUU0+x1157jU2ePJlNnjxZ/M5DX4899lg2b9489vjjj7OePXtaQ1+/9a1vsXfeeYfdcsst1tDXjvxsfve732XPPvssW7p0KXvzzTfZd7/7XeZ5Hvvvf//LGEuvUzHQaCzG0uvF8Y1vfIM988wzbOnSpezFF19kU6ZMYT169GBr1qxhjKXXiWLOnDksm82yn/70p+z9999nf/nLX1hVVRX785//LNb5tPXvqbHThvjNb37DBg4cyMrKytgBBxzAXnnllZ3dpFbF008/zQAY/84//3zGWBie+MMf/pD17t2blZeXs6OPPpotWrRI2cf69evZWWedxTp37syqq6vZF77wBbZlyxZlnfnz57NDDjmElZeXs/79+7PrrrvOaMu9997LRowYwcrKytg+++zDHnnkkTY77+bAdp0AsLvuukuss337dvbVr36Vde3alVVVVbFTTz2VffLJJ8p+li1bxo477jhWWVnJevTowb7xjW+wpqYmZZ2nn36ajR8/npWVlbGhQ4cqx+DoyM/mF7/4RTZo0CBWVlbGevbsyY4++mhh6DCWXqdi0I2d9HqFmDFjBuvbty8rKytj/fv3ZzNmzFDyxqTXScW///1vNnr0aFZeXs5GjRrFbr/9duX3T1v/7jHGWGlcUIoUKVKkSJEixacHqWYnRYoUKVKkSLFLIzV2UqRIkSJFihS7NFJjJ0WKFClSpEixSyM1dlKkSJEiRYoUuzRSYydFihQpUqRIsUsjNXZSpEiRIkWKFLs0UmMnRYoUKVKkSLFLIzV2UqRIkSJFihS7NFJjJ0WKFM3GM888A8/zjAKK7YUnn3wSe+21FwqFglh2++23Y8CAAfB9HzfccMNOaVdz0djYiMGDB+O1117b2U1JkWKXQppBOUWKFIlwxBFHYPz48YoB0djYiA0bNqB3797wPK/d27TffvvhyiuvxNlnnw0gLEbYo0cPXH/99Tj99NNRU1ODqqqqdm9XS3DzzTfjgQceMIofpkiRovlImZ0UKVI0G2VlZejTp89OMXReeOEFLF68GKeffrpYtnz5cjQ1NWH69Ono27ev1dBpbGxsz2aWjLPPPhsvvPAC3n777Z3dlBQpdhmkxk6KFCmK4vOf/zyeffZZ3HjjjfA8D57nYdmyZYYb6+6770ZtbS0efvhhjBw5ElVVVTjjjDOwbds23HPPPRg8eDC6du2Kyy67THE9NTQ04Jvf/Cb69++PTp06YdKkSXjmmWdi2zRr1iwcc8wxqKioEMceM2YMAGDo0KGijT/60Y8wfvx43HnnnRgyZIhY//HHH8chhxyC2tpadO/eHSeccAIWL14s9r9s2TJ4nod7770Xhx56KCorK7H//vvjvffew6uvvoqJEyeic+fOOO6447B27VqlbXfeeSf22msvVFRUYNSoUfjtb38rfmtsbMSll16Kvn37oqKiAoMGDcLMmTPF7127dsXBBx+MWbNmlX6jUqRIYUV2ZzcgRYoUHR833ngj3nvvPYwePRrXXnstAKBnz55YtmyZse62bdtw0003YdasWdiyZQtOO+00nHrqqaitrcWjjz6KJUuW4PTTT8fBBx+MGTNmAAAuvfRSLFy4ELNmzUK/fv3wwAMPYNq0aXjrrbcwfPhwa5uef/55fO5znxPfZ8yYgQEDBmDKlCmYM2cOBgwYgJ49ewIAPvjgA/zjH//AP//5T2QyGQBAfX09rrzySowdOxZbt27F1VdfjVNPPRXz5s2D78t54DXXXIMbbrgBAwcOxBe/+EV87nOfQ5cuXXDjjTeiqqoKZ555Jq6++mrceuutAIC//OUvuPrqq3HzzTdjwoQJeOONN3DRRRehU6dOOP/883HTTTfhoYcewr333ouBAwdixYoVWLFihXJuBxxwAJ5//vlm3q0UKVIYKLlOeooUKXZLHH744ezrX/+6suzpp59mANjGjRsZY4zdddddDAD74IMPxDpf+tKXWFVVFduyZYtYNnXqVPalL32JMcbYhx9+yDKZDFu5cqWy76OPPppdddVVzvbU1NSwP/7xj8qyN954gwFgS5cuFcuuueYalsvl2Jo1a2LPb+3atQwAe+uttxhjjC1dupQBYHfeeadY529/+xsDwJ588kmxbObMmWzkyJHi+5577sn++te/Kvv+yU9+wiZPnswYY+xrX/saO+qoo1gQBM623HjjjWzw4MGx7U2RIkVypMxOihQpWhVVVVXYc889xffevXtj8ODB6Ny5s7JszZo1AIC33noLhUIBI0aMUPbT0NCA7t27O4+zfft24ZIqhkGDBgmWh+P999/H1VdfjdmzZ2PdunUIggBAqPsZPXq0WG/s2LFKuwEId5l+LvX19Vi8eDEuuOACXHTRRWKdfD6PmpoaAKFL8JhjjsHIkSMxbdo0nHDCCTj22GOVtlVWVmLbtm2Jzi1FihTFkRo7KVKkaFXkcjnlu+d51mXcuNi6dSsymQzmzp0rXEwc1EDS0aNHD2zcuDFRmzp16mQsO/HEEzFo0CDccccd6NevH4IgwOjRow0BM207F2Lry+i5AMAdd9yBSZMmKfvh57bvvvti6dKleOyxx/C///0PZ555JqZMmYL7779frLthwwbDOEuRIkXzkRo7KVKkSISysjJFVNxamDBhAgqFAtasWYNDDz20pO0WLlzYrGOuX78eixYtwh133CGO+cILLzRrXxS9e/dGv379sGTJEhEOb0N1dTVmzJiBGTNm4IwzzsC0adOwYcMGdOvWDQCwYMECTJgwocXtSZEiRYjU2EmRIkUiDB48GLNnz8ayZcvQuXNnMTC3FCNGjMDZZ5+N8847D7/61a8wYcIErF27Fk8++STGjh2L6dOnW7ebOnUq7rnnnmYds2vXrujevTtuv/129O3bF8uXL8d3v/vdlpyGwI9//GNcdtllqKmpwbRp09DQ0IDXXnsNGzduxJVXXonrr78effv2xYQJE+D7Pu677z706dMHtbW1Yh/PP/88fvKTn7RKe1KkSJGGnqdIkSIhvvnNbyKTyWDvvfdGz549sXz58lbb91133YXzzjsP3/jGNzBy5EiccsopePXVVzFw4EDnNmeffTbefvttLFq0qOTj+b6PWbNmYe7cuRg9ejSuuOIK/OIXv2jJKQhceOGFuPPOO3HXXXdhzJgxOPzww3H33XdjyJAhAIAuXbrg5z//OSZOnIj9998fy5Ytw6OPPioiwF5++WXU1dXhjDPOaJX2pEiRIs2gnCJFik8xvvWtb2Hz5s247bbbdnZTWg0zZszAuHHj8L3vfW9nNyVFil0GKbOTIkWKTy2+//3vY9CgQUIg/GlHY2MjxowZgyuuuGJnNyVFil0KKbOTIkWKFClSpNilkTI7KVKkSJEiRYpdGqmxkyJFihQpUqTYpZEaOylSpEiRIkWKXRqpsZMiRYoUKVKk2KWRGjspUqRIkSJFil0aqbGTIkWKFClSpNilkRo7KVKkSJEiRYpdGqmxkyJFihQpUqTYpZEaOylSpEiRIkWKXRr/H/e83kRsL1J/AAAAAElFTkSuQmCC", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -682,9 +2888,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.title('Tailbase pose estimation')\n", "plt.plot(tail_data['x_pos'],label='x_pos')\n", @@ -704,9 +2921,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.plot(head_data['x_pos'], head_data['y_pos'], label='head')\n", "plt.plot(tail_data['x_pos'], tail_data['y_pos'], label='tailbase')\n", @@ -733,7 +2961,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.17" } }, "nbformat": 4, From 0fb7c0515aab665e108c70d9174ac1810b8f7235 Mon Sep 17 00:00:00 2001 From: MilagrosMarin Date: Tue, 17 Oct 2023 15:45:30 +0000 Subject: [PATCH 176/176] change in visualization plots --- notebooks/tutorial.ipynb | 1499 ++++++++++++++++++++------------------ 1 file changed, 770 insertions(+), 729 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index a7ebecf..5a6b16e 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -87,13 +87,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This tutorial examines the behavior of a freely-moving mouse in an open-field environment. \n", + "This tutorial examines the **behavior of a freely moving mouse** in an open-field environment. \n", "\n", - "The goal is to extract pose estimations of the animal's head and tail base from video footage. \n", + "The goal is to extract pose estimations of the animal's **head and tail base** from video footage. \n", "\n", - "This information offers valuable insights into the animal's movements, postures, and interactions within the environment. \n", + "This information offers valuable insights into the **animal's movements, postures, and interactions** within the environment. \n", "\n", - "The results of this Element example can be combined with other modalities to create a complete data pipeline for your specific lab or study.\n", + "The results of this Element example can be **combined with other modalities** to create a complete data pipeline for your specific lab or study.\n", "\n", "#### Steps to Run the Element-DeepLabCut\n", "\n", @@ -152,8 +152,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2023-10-17 01:10:16,318][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-10-17 01:10:16,333][INFO]: Connected root@fakeservices.datajoint.io:3306\n" + "[2023-10-17 15:33:12,400][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", + "[2023-10-17 15:33:12,407][INFO]: Connected root@fakeservices.datajoint.io:3306\n" ] }, { @@ -194,7 +194,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2023-10-17 01:10:16,453][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n" + "[2023-10-17 15:33:14,915][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n" ] } ], @@ -210,768 +210,768 @@ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", "%3\n", - "\n", - "\n", + "\n", + "\n", "\n", - "lab.Source\n", - "\n", - "\n", - "lab.Source\n", + "lab.UserRole\n", + "\n", + "\n", + "lab.UserRole\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Allele.Source\n", - "\n", - "\n", - "subject.Allele.Source\n", + "\n", + "\n", + "lab.LabMembership\n", + "\n", + "\n", + "lab.LabMembership\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "lab.Source->subject.Allele.Source\n", - "\n", + "lab.UserRole->lab.LabMembership\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject.Source\n", - "\n", - "\n", - "subject.Subject.Source\n", + "\n", + "\n", + "session.SessionDirectory\n", + "\n", + "\n", + "session.SessionDirectory\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.Source->subject.Subject.Source\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line\n", - "\n", - "\n", - "subject.Line\n", + "\n", + "\n", + "model.VideoRecording.File\n", + "\n", + "\n", + "model.VideoRecording.File\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Subject.Line\n", - "\n", - "\n", - "subject.Subject.Line\n", + "\n", + "\n", + "subject.Strain\n", + "\n", + "\n", + "subject.Strain\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Line->subject.Subject.Line\n", - "\n", - "\n", - "\n", - "\n", - "subject.Line.Allele\n", - "\n", - "\n", - "subject.Line.Allele\n", + "\n", + "\n", + "subject.Subject.Strain\n", + "\n", + "\n", + "subject.Subject.Strain\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Line->subject.Line.Allele\n", - "\n", + "\n", + "\n", + "subject.Strain->subject.Subject.Strain\n", + "\n", "\n", - "\n", - "\n", - "subject.Allele\n", - "\n", - "\n", - "subject.Allele\n", + "\n", + "\n", + "lab.ProtocolType\n", + "\n", + "\n", + "lab.ProtocolType\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Zygosity\n", - "\n", - "\n", - "subject.Zygosity\n", + "\n", + "\n", + "lab.Protocol\n", + "\n", + "\n", + "lab.Protocol\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Allele->subject.Zygosity\n", - "\n", + "\n", + "\n", + "lab.ProtocolType->lab.Protocol\n", + "\n", "\n", - "\n", - "\n", - "subject.Allele->subject.Line.Allele\n", - "\n", + "\n", + "\n", + "lab.ProjectPublication\n", + "\n", + "\n", + "lab.ProjectPublication\n", + "\n", "\n", - "\n", - "\n", - "subject.Allele->subject.Allele.Source\n", - "\n", "\n", - "\n", - "\n", - "session.SessionDirectory\n", - "\n", - "\n", - "session.SessionDirectory\n", + "\n", + "\n", + "subject.SubjectDeath\n", + "\n", + "\n", + "subject.SubjectDeath\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.Project\n", - "\n", - "\n", - "lab.Project\n", + "\n", + "\n", + "subject.SubjectCull\n", + "\n", + "\n", + "subject.SubjectCull\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.ProjectPublication\n", - "\n", - "\n", - "lab.ProjectPublication\n", - "\n", + "\n", + "\n", + "subject.SubjectDeath->subject.SubjectCull\n", + "\n", "\n", + "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", + "\n", + "subject.Subject.Protocol\n", + "\n", "\n", - "\n", - "\n", - "lab.Project->lab.ProjectPublication\n", - "\n", "\n", - "\n", + "\n", "\n", - "lab.ProjectKeywords\n", - "\n", - "\n", - "lab.ProjectKeywords\n", + "train.TrainingParamSet\n", + "\n", + "\n", + "train.TrainingParamSet\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.Project->lab.ProjectKeywords\n", - "\n", - "\n", - "\n", - "\n", - "lab.ProjectSourceCode\n", - "\n", - "\n", - "lab.ProjectSourceCode\n", + "\n", + "\n", + "train.TrainingTask\n", + "\n", + "\n", + "train.TrainingTask\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.Project->lab.ProjectSourceCode\n", - "\n", + "\n", + "\n", + "train.TrainingParamSet->train.TrainingTask\n", + "\n", "\n", - "\n", - "\n", - "session.ProjectSession\n", - "\n", - "\n", - "session.ProjectSession\n", + "\n", + "\n", + "model.Model\n", + "\n", + "\n", + "model.Model\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.Project->session.ProjectSession\n", - "\n", + "\n", + "\n", + "train.TrainingParamSet->model.Model\n", + "\n", "\n", - "\n", - "\n", - "lab.ProjectUser\n", - "\n", - "\n", - "lab.ProjectUser\n", + "\n", + "\n", + "subject.Allele.Source\n", + "\n", + "\n", + "subject.Allele.Source\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.Project->lab.ProjectUser\n", - "\n", - "\n", - "\n", - "\n", - "subject.Strain\n", - "\n", - "\n", - "subject.Strain\n", + "\n", + "\n", + "lab.Device\n", + "\n", + "\n", + "lab.Device\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Subject.Strain\n", - "\n", - "\n", - "subject.Subject.Strain\n", + "\n", + "\n", + "model.VideoRecording\n", + "\n", + "\n", + "model.VideoRecording\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Strain->subject.Subject.Strain\n", - "\n", + "\n", + "\n", + "lab.Device->model.VideoRecording\n", + "\n", "\n", - "\n", + "\n", "\n", - "subject.Subject.Protocol\n", - "\n", - "\n", - "subject.Subject.Protocol\n", + "session.ProjectSession\n", + "\n", + "\n", + "session.ProjectSession\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "lab.Organization\n", - "\n", - "\n", - "lab.Organization\n", + "subject.Line.Allele\n", + "\n", + "\n", + "subject.Line.Allele\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.Lab.Organization\n", - "\n", - "\n", - "lab.Lab.Organization\n", + "\n", + "\n", + "train.VideoSet.File\n", + "\n", + "\n", + "train.VideoSet.File\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.Organization->lab.Lab.Organization\n", - "\n", - "\n", - "\n", - "\n", - "subject.Subject\n", - "\n", - "\n", - "subject.Subject\n", + "\n", + "\n", + "subject.Allele\n", + "\n", + "\n", + "subject.Allele\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.Zygosity\n", - "\n", + "\n", + "\n", + "subject.Allele->subject.Allele.Source\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Line\n", - "\n", + "\n", + "\n", + "subject.Allele->subject.Line.Allele\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Protocol\n", - "\n", + "\n", + "\n", + "subject.Zygosity\n", + "\n", + "\n", + "subject.Zygosity\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Strain\n", - "\n", "\n", - "\n", - "\n", - "subject.Subject.User\n", - "\n", - "\n", - "subject.Subject.User\n", + "\n", + "\n", + "subject.Allele->subject.Zygosity\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimationTask\n", + "\n", + "\n", + "model.PoseEstimationTask\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.User\n", - "\n", + "\n", + "\n", + "model.PoseEstimation\n", + "\n", + "\n", + "model.PoseEstimation\n", + "\n", "\n", - "\n", - "\n", - "subject.Subject.Lab\n", - "\n", - "\n", - "subject.Subject.Lab\n", + "\n", + "\n", + "\n", + "model.PoseEstimationTask->model.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "subject.Subject\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.Subject.Lab\n", - "\n", + "\n", + "\n", + "subject.Subject->subject.SubjectDeath\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Protocol\n", + "\n", "\n", "\n", - "\n", + "\n", "session.Session\n", - "\n", - "\n", - "session.Session\n", + "\n", + "\n", + "session.Session\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "subject.Subject->session.Session\n", - "\n", + "\n", "\n", - "\n", - "\n", - "subject.SubjectDeath\n", - "\n", - "\n", - "subject.SubjectDeath\n", + "\n", + "\n", + "subject.Subject.Source\n", + "\n", + "\n", + "subject.Subject.Source\n", "\n", "\n", "\n", - "\n", - "\n", - "subject.Subject->subject.SubjectDeath\n", - "\n", - "\n", "\n", - "\n", + "\n", "subject.Subject->subject.Subject.Source\n", - "\n", - "\n", - "\n", - "\n", - "train.VideoSet.File\n", - "\n", - "\n", - "train.VideoSet.File\n", - "\n", + "\n", "\n", + "\n", + "\n", + "subject.Subject->subject.Zygosity\n", + "\n", "\n", - "\n", - "\n", - "lab.LabMembership\n", - "\n", - "\n", - "lab.LabMembership\n", - "\n", - "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Strain\n", + "\n", "\n", - "\n", - "\n", - "model.VideoRecording.File\n", - "\n", - "\n", - "model.VideoRecording.File\n", + "\n", + "\n", + "subject.Subject.Line\n", + "\n", + "\n", + "subject.Subject.Line\n", "\n", "\n", "\n", - "\n", - "\n", - "train.TrainingParamSet\n", - "\n", - "\n", - "train.TrainingParamSet\n", - "\n", - "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Line\n", + "\n", "\n", - "\n", - "\n", - "model.Model\n", - "\n", - "\n", - "model.Model\n", + "\n", + "\n", + "subject.Subject.User\n", + "\n", + "\n", + "subject.Subject.User\n", "\n", "\n", "\n", - "\n", - "\n", - "train.TrainingParamSet->model.Model\n", - "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.User\n", + "\n", "\n", - "\n", - "\n", - "train.TrainingTask\n", - "\n", - "\n", - "train.TrainingTask\n", + "\n", + "\n", + "subject.Subject.Lab\n", + "\n", + "\n", + "subject.Subject.Lab\n", "\n", "\n", "\n", - "\n", - "\n", - "train.TrainingParamSet->train.TrainingTask\n", - "\n", + "\n", + "\n", + "subject.Subject->subject.Subject.Lab\n", + "\n", "\n", "\n", - "\n", + "\n", "train.ModelTraining\n", - "\n", - "\n", - "train.ModelTraining\n", + "\n", + "\n", + "train.ModelTraining\n", "\n", "\n", "\n", - "\n", - "\n", - "model.BodyPart\n", - "\n", - "\n", - "model.BodyPart\n", + "\n", + "\n", + "lab.ProjectSourceCode\n", + "\n", + "\n", + "lab.ProjectSourceCode\n", "\n", "\n", "\n", - "\n", - "\n", - "model.Model.BodyPart\n", - "\n", - "\n", - "model.Model.BodyPart\n", + "\n", + "\n", + "model.ModelEvaluation\n", + "\n", + "\n", + "model.ModelEvaluation\n", "\n", "\n", "\n", - "\n", - "\n", - "model.BodyPart->model.Model.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "lab.Location\n", - "\n", - "\n", - "lab.Location\n", + "\n", + "\n", + "model.RecordingInfo\n", + "\n", + "\n", + "model.RecordingInfo\n", "\n", "\n", "\n", + "\n", + "\n", + "session.Session->session.SessionDirectory\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->session.ProjectSession\n", + "\n", + "\n", "\n", - "\n", + "\n", "session.SessionNote\n", - "\n", - "\n", - "session.SessionNote\n", + "\n", + "\n", + "session.SessionNote\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.Protocol\n", - "\n", - "\n", - "lab.Protocol\n", + "\n", + "\n", + "session.Session->session.SessionNote\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "session.Session.Attribute\n", + "\n", + "\n", + "session.Session.Attribute\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.Protocol->subject.Subject.Protocol\n", - "\n", + "\n", + "\n", + "session.Session->session.Session.Attribute\n", + "\n", "\n", - "\n", - "\n", - "model.PoseEstimation\n", - "\n", - "\n", - "model.PoseEstimation\n", + "\n", + "\n", + "session.SessionExperimenter\n", + "\n", + "\n", + "session.SessionExperimenter\n", "\n", "\n", "\n", - "\n", - "\n", - "model.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "session.Session->session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingTask->train.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "lab.Project\n", + "\n", + "\n", + "lab.Project\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "model.PoseEstimation->model.PoseEstimation.BodyPartPosition\n", - "\n", + "lab.Project->lab.ProjectPublication\n", + "\n", "\n", - "\n", - "\n", - "lab.Device\n", - "\n", - "\n", - "lab.Device\n", - "\n", + "\n", + "\n", + "lab.Project->session.ProjectSession\n", + "\n", "\n", + "\n", + "\n", + "lab.Project->lab.ProjectSourceCode\n", + "\n", "\n", - "\n", - "\n", - "model.VideoRecording\n", - "\n", - "\n", - "model.VideoRecording\n", + "\n", + "\n", + "lab.ProjectKeywords\n", + "\n", + "\n", + "lab.ProjectKeywords\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.Device->model.VideoRecording\n", - "\n", + "\n", + "\n", + "lab.Project->lab.ProjectKeywords\n", + "\n", "\n", - "\n", - "\n", - "session.Session->session.SessionDirectory\n", - "\n", + "\n", + "\n", + "lab.ProjectUser\n", + "\n", + "\n", + "lab.ProjectUser\n", + "\n", "\n", - "\n", - "\n", - "session.Session->session.ProjectSession\n", - "\n", "\n", - "\n", + "\n", "\n", - "session.Session->session.SessionNote\n", - "\n", + "lab.Project->lab.ProjectUser\n", + "\n", "\n", - "\n", - "\n", - "session.Session->model.VideoRecording\n", - "\n", + "\n", + "\n", + "lab.Location\n", + "\n", + "\n", + "lab.Location\n", + "\n", "\n", - "\n", - "\n", - "session.SessionExperimenter\n", - "\n", - "\n", - "session.SessionExperimenter\n", + "\n", + "\n", + "\n", + "lab.Source\n", + "\n", + "\n", + "lab.Source\n", "\n", "\n", "\n", - "\n", + "\n", + "\n", + "lab.Source->subject.Allele.Source\n", + "\n", + "\n", + "\n", "\n", - "session.Session->session.SessionExperimenter\n", - "\n", + "lab.Source->subject.Subject.Source\n", + "\n", "\n", - "\n", - "\n", - "session.Session.Attribute\n", - "\n", - "\n", - "session.Session.Attribute\n", + "\n", + "\n", + "model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "model.PoseEstimation.BodyPartPosition\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "session.Session->session.Session.Attribute\n", - "\n", + "model.PoseEstimation->model.PoseEstimation.BodyPartPosition\n", + "\n", "\n", - "\n", - "\n", - "model.PoseEstimationTask\n", - "\n", - "\n", - "model.PoseEstimationTask\n", + "\n", + "\n", + "lab.Lab.Organization\n", + "\n", + "\n", + "lab.Lab.Organization\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "model.Model->model.PoseEstimationTask\n", - "\n", + "lab.Protocol->subject.Subject.Protocol\n", + "\n", "\n", - "\n", - "\n", - "model.ModelEvaluation\n", - "\n", - "\n", - "model.ModelEvaluation\n", + "\n", + "\n", + "lab.User\n", + "\n", + "\n", + "lab.User\n", "\n", "\n", "\n", - "\n", + "\n", "\n", - "model.Model->model.ModelEvaluation\n", - "\n", + "lab.User->lab.ProjectUser\n", + "\n", "\n", - "\n", + "\n", "\n", - "model.Model->model.Model.BodyPart\n", - "\n", + "lab.User->lab.LabMembership\n", + "\n", "\n", - "\n", + "\n", "\n", - "model.VideoRecording->model.VideoRecording.File\n", - "\n", + "lab.User->subject.Subject.User\n", + "\n", "\n", - "\n", + "\n", "\n", - "model.VideoRecording->model.PoseEstimationTask\n", - "\n", + "lab.User->session.SessionExperimenter\n", + "\n", "\n", - "\n", - "\n", - "model.RecordingInfo\n", - "\n", - "\n", - "model.RecordingInfo\n", - "\n", + "\n", + "\n", + "model.VideoRecording->model.VideoRecording.File\n", + "\n", "\n", + "\n", + "\n", + "model.VideoRecording->model.PoseEstimationTask\n", + "\n", "\n", "\n", - "\n", + "\n", "model.VideoRecording->model.RecordingInfo\n", - "\n", + "\n", "\n", - "\n", - "\n", - "model.PoseEstimationTask->model.PoseEstimation\n", - "\n", + "\n", + "\n", + "model.Model->model.PoseEstimationTask\n", + "\n", "\n", - "\n", - "\n", - "train.TrainingTask->train.ModelTraining\n", - "\n", + "\n", + "\n", + "model.Model->model.ModelEvaluation\n", + "\n", "\n", - "\n", - "\n", - "lab.User\n", - "\n", - "\n", - "lab.User\n", + "\n", + "\n", + "model.Model.BodyPart\n", + "\n", + "\n", + "model.Model.BodyPart\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.User->lab.LabMembership\n", - "\n", + "\n", + "\n", + "model.Model->model.Model.BodyPart\n", + "\n", "\n", - "\n", - "\n", - "lab.User->subject.Subject.User\n", - "\n", + "\n", + "\n", + "subject.Line\n", + "\n", + "\n", + "subject.Line\n", + "\n", "\n", - "\n", - "\n", - "lab.User->session.SessionExperimenter\n", - "\n", "\n", - "\n", + "\n", "\n", - "lab.User->lab.ProjectUser\n", - "\n", + "subject.Line->subject.Line.Allele\n", + "\n", "\n", - "\n", - "\n", - "lab.UserRole\n", - "\n", - "\n", - "lab.UserRole\n", + "\n", + "\n", + "subject.Line->subject.Subject.Line\n", + "\n", + "\n", + "\n", + "\n", + "model.BodyPart\n", + "\n", + "\n", + "model.BodyPart\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.UserRole->lab.LabMembership\n", - "\n", + "\n", + "\n", + "model.BodyPart->model.Model.BodyPart\n", + "\n", "\n", "\n", - "\n", + "\n", "lab.Lab\n", - "\n", - "\n", - "lab.Lab\n", + "\n", + "\n", + "lab.Lab\n", "\n", "\n", "\n", - "\n", - "\n", - "lab.Lab->lab.LabMembership\n", - "\n", - "\n", - "\n", - "\n", - "lab.Lab->subject.Subject.Lab\n", - "\n", - "\n", "\n", - "\n", + "\n", "lab.Lab->lab.Location\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "lab.Lab->lab.Lab.Organization\n", - "\n", - "\n", - "\n", - "\n", - "lab.ProtocolType\n", - "\n", - "\n", - "lab.ProtocolType\n", - "\n", + "\n", "\n", + "\n", + "\n", + "lab.Lab->lab.LabMembership\n", + "\n", "\n", - "\n", + "\n", "\n", - "lab.ProtocolType->lab.Protocol\n", - "\n", - "\n", - "\n", - "\n", - "model.Model.BodyPart->model.PoseEstimation.BodyPartPosition\n", - "\n", + "lab.Lab->subject.Subject.Lab\n", + "\n", "\n", "\n", - "\n", + "\n", "train.VideoSet\n", - "\n", - "\n", - "train.VideoSet\n", + "\n", + "\n", + "train.VideoSet\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "train.VideoSet->train.VideoSet.File\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "train.VideoSet->train.TrainingTask\n", - "\n", + "\n", "\n", - "\n", - "\n", - "subject.SubjectCull\n", - "\n", - "\n", - "subject.SubjectCull\n", + "\n", + "\n", + "lab.Organization\n", + "\n", + "\n", + "lab.Organization\n", "\n", "\n", "\n", - "\n", + "\n", + "\n", + "lab.Organization->lab.Lab.Organization\n", + "\n", + "\n", + "\n", "\n", - "subject.SubjectDeath->subject.SubjectCull\n", - "\n", + "model.Model.BodyPart->model.PoseEstimation.BodyPartPosition\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -998,7 +998,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1012,226 +1012,226 @@ "\n", "train.VideoSet.File\n", "\n", - "\n", - "train.VideoSet.File\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.PoseEstimation\n", - "\n", - "\n", - "model.PoseEstimation\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "model.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.PoseEstimation->model.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "model.Model.BodyPart\n", - "\n", - "\n", - "model.Model.BodyPart\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model.Model.BodyPart->model.PoseEstimation.BodyPartPosition\n", - "\n", - "\n", - "\n", - "\n", - "model.VideoRecording.File\n", - "\n", - "\n", - "model.VideoRecording.File\n", + "\n", + "train.VideoSet.File\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "model.Model\n", - "\n", + "\n", "\n", "model.Model\n", "\n", "\n", "\n", - "\n", - "\n", - "model.Model->model.Model.BodyPart\n", - "\n", - "\n", "\n", - "\n", + "\n", "model.PoseEstimationTask\n", - "\n", + "\n", "\n", "model.PoseEstimationTask\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "model.Model->model.PoseEstimationTask\n", "\n", "\n", "\n", - "\n", + "\n", "model.ModelEvaluation\n", - "\n", + "\n", "\n", "model.ModelEvaluation\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "model.Model->model.ModelEvaluation\n", "\n", "\n", - "\n", + "\n", + "\n", + "model.Model.BodyPart\n", + "\n", + "\n", + "model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.Model->model.Model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "model.VideoRecording.File\n", + "\n", + "\n", + "model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.PoseEstimation\n", + "\n", + "\n", + "model.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", "\n", - "train.TrainingParamSet\n", - "\n", - "\n", - "train.TrainingParamSet\n", + "model.PoseEstimation.BodyPartPosition\n", + "\n", + "\n", + "model.PoseEstimation.BodyPartPosition\n", "\n", "\n", "\n", - "\n", - "\n", - "train.TrainingParamSet->model.Model\n", - "\n", + "\n", + "\n", + "model.PoseEstimation->model.PoseEstimation.BodyPartPosition\n", + "\n", "\n", - "\n", - "\n", - "train.TrainingTask\n", - "\n", - "\n", - "train.TrainingTask\n", + "\n", + "\n", + "model.PoseEstimationTask->model.PoseEstimation\n", + "\n", + "\n", + "\n", + "\n", + "train.ModelTraining\n", + "\n", + "\n", + "train.ModelTraining\n", "\n", "\n", "\n", - "\n", - "\n", - "train.TrainingParamSet->train.TrainingTask\n", - "\n", + "\n", + "\n", + "model.BodyPart\n", + "\n", + "\n", + "model.BodyPart\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "model.BodyPart->model.Model.BodyPart\n", + "\n", "\n", "\n", - "\n", + "\n", "train.VideoSet\n", - "\n", + "\n", "\n", "train.VideoSet\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "train.VideoSet->train.VideoSet.File\n", "\n", "\n", + "\n", + "\n", + "train.TrainingTask\n", + "\n", + "\n", + "train.TrainingTask\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "train.VideoSet->train.TrainingTask\n", "\n", "\n", - "\n", - "\n", - "train.ModelTraining\n", - "\n", - "\n", - "train.ModelTraining\n", - "\n", - "\n", - "\n", "\n", - "\n", + "\n", "model.RecordingInfo\n", - "\n", + "\n", "\n", "model.RecordingInfo\n", "\n", "\n", "\n", - "\n", - "\n", - "train.TrainingTask->train.ModelTraining\n", - "\n", - "\n", "\n", - "\n", + "\n", "model.VideoRecording\n", - "\n", + "\n", "\n", "model.VideoRecording\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "model.VideoRecording->model.VideoRecording.File\n", "\n", "\n", - "\n", - "\n", - "model.VideoRecording->model.RecordingInfo\n", - "\n", - "\n", "\n", - "\n", + "\n", "model.VideoRecording->model.PoseEstimationTask\n", "\n", "\n", - "\n", - "\n", - "model.PoseEstimationTask->model.PoseEstimation\n", - "\n", + "\n", + "\n", + "model.VideoRecording->model.RecordingInfo\n", + "\n", "\n", - "\n", - "\n", - "model.BodyPart\n", - "\n", - "\n", - "model.BodyPart\n", + "\n", + "\n", + "train.TrainingTask->train.ModelTraining\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet\n", + "\n", + "\n", + "train.TrainingParamSet\n", "\n", "\n", "\n", - "\n", + "\n", + "\n", + "train.TrainingParamSet->model.Model\n", + "\n", + "\n", + "\n", + "\n", + "train.TrainingParamSet->train.TrainingTask\n", + "\n", + "\n", + "\n", "\n", - "model.BodyPart->model.Model.BodyPart\n", - "\n", + "model.Model.BodyPart->model.PoseEstimation.BodyPartPosition\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dj.Diagram(model) + dj.Diagram(train)" + "dj.Diagram(train) + dj.Diagram(model)" ] }, { @@ -1257,7 +1257,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -1277,21 +1277,21 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2023-10-17 01:10:44.124153: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "2023-10-17 15:35:14.274368: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-10-17 01:10:44.239990: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", - "2023-10-17 01:10:44.240025: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n", - "2023-10-17 01:10:44.263054: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2023-10-17 01:10:45.358939: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", - "2023-10-17 01:10:45.359109: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", - "2023-10-17 01:10:45.359123: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" + "2023-10-17 15:35:18.481329: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", + "2023-10-17 15:35:18.481376: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n", + "2023-10-17 15:35:18.859072: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2023-10-17 15:35:24.333497: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", + "2023-10-17 15:35:24.333709: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/site-packages/cv2/../../lib64:/lib:/opt/conda/lib\n", + "2023-10-17 15:35:24.333725: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" ] }, { @@ -1363,7 +1363,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1487,7 +1487,7 @@ " (Total: 1)" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1521,7 +1521,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1609,7 +1609,7 @@ " (Total: 0)" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1627,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -1653,7 +1653,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1676,7 +1676,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1758,7 +1758,7 @@ " (Total: 2)" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1776,11 +1776,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "### VideoRecording\n", "recording_key = {'subject': 'subject6',\n", " 'session_datetime': '2021-06-02 14:04:22',\n", " 'recording_id': '1'}\n", @@ -1796,12 +1795,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "### VideoRecording.File\n", - "\n", "video_files = [\"./example_data/inbox/from_top_tracking-DataJoint-2023-10-11/videos/train1.mp4\"]\n", "\n", "model.VideoRecording.File.insert({\n", @@ -1819,7 +1816,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1927,13 +1924,12 @@ " (Total: 1)" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "### RecordingInfo\n", "model.RecordingInfo.populate()\n", "model.RecordingInfo()" ] @@ -1961,7 +1957,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1983,7 +1979,7 @@ "'-> model.VideoRecording\\n-> model.Model\\n---\\ntask_mode=\"load\" : enum(\\'load\\',\\'trigger\\') # load results or trigger computation\\npose_estimation_output_dir=\"\" : varchar(255) # output dir relative to the root dir\\npose_estimation_params=null : longblob # analyze_videos params, if not default\\n'" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2019,7 +2015,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -2030,7 +2026,7 @@ " 'recording_id': '1'}" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -2041,7 +2037,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -2057,7 +2053,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -2077,7 +2073,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -2177,7 +2173,7 @@ " (Total: 1)" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2188,11 +2184,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "### PoseEstimation\n", "model.PoseEstimation.populate()" ] }, @@ -2205,7 +2200,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -2297,7 +2292,7 @@ " (Total: 1)" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -2315,7 +2310,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -2437,7 +2432,7 @@ " (Total: 2)" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2459,7 +2454,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -2468,7 +2463,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2557,7 +2552,7 @@ "1 [0.9999996423721313, 0.9999996423721313, 0.999... " ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2582,7 +2577,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -2822,7 +2817,7 @@ "[120000 rows x 11 columns]" ] }, - "execution_count": 27, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -2857,14 +2852,26 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "head_data = df[df['body_part'] == 'head']\n", + "tail_data = df[df['body_part'] == 'tailbase']" + ] + }, + { + "cell_type": "code", + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
    " + "
    " ] }, "metadata": {}, @@ -2872,30 +2879,34 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", + "fig, axs = plt.subplots(2,1, figsize=(12, 4))\n", "\n", - "head_data = df[df['body_part'] == 'head']\n", - "tail_data = df[df['body_part'] == 'tailbase']\n", + "axs[0].set_title('x position - Head pose estimation')\n", + "axs[0].plot(head_data['x_pos'], label='x_pos')\n", + "axs[0].set_xlabel('time (frames)')\n", + "axs[0].set_ylabel('pos (pixels)')\n", + "axs[0].legend()\n", + "\n", + "axs[1].set_title('y position - Head pose estimation')\n", + "axs[1].plot(head_data['y_pos'], label='y_pos')\n", + "axs[1].set_xlabel('time (frames)')\n", + "axs[1].set_ylabel('pos (pixels)')\n", + "axs[1].legend()\n", "\n", - "plt.title('Head pose estimation')\n", - "plt.plot(head_data['x_pos'],label='x_pos')\n", - "plt.plot(head_data['y_pos'],label='y_pos')\n", - "plt.xlabel('time (frames)')\n", - "plt.ylabel('pos (pixels)')\n", - "plt.legend()\n", + "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
    " + "
    " ] }, "metadata": {}, @@ -2903,12 +2914,20 @@ } ], "source": [ - "plt.title('Tailbase pose estimation')\n", - "plt.plot(tail_data['x_pos'],label='x_pos')\n", - "plt.plot(tail_data['y_pos'],label='y_pos')\n", - "plt.xlabel('time (frames)')\n", - "plt.ylabel('pos (pixels)')\n", - "plt.legend()\n", + "fig, axs = plt.subplots(2,1, figsize=(12, 4))\n", + "axs[0].set_title('x position - Tailbase pose estimation')\n", + "axs[0].plot(head_data['x_pos'], label='x_pos',color='orange')\n", + "axs[0].set_xlabel('time (frames)')\n", + "axs[0].set_ylabel('pos (pixels)')\n", + "axs[0].legend()\n", + "\n", + "axs[1].set_title('y position - Tailbase pose estimation')\n", + "axs[1].plot(head_data['y_pos'], label='y_pos',color='orange')\n", + "axs[1].set_xlabel('time (frames)')\n", + "axs[1].set_ylabel('pos (pixels)')\n", + "axs[1].legend()\n", + "\n", + "plt.tight_layout()\n", "plt.show()" ] }, @@ -2921,14 +2940,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
    " + "
    " ] }, "metadata": {}, @@ -2936,13 +2955,35 @@ } ], "source": [ - "plt.plot(head_data['x_pos'], head_data['y_pos'], label='head')\n", - "plt.plot(tail_data['x_pos'], tail_data['y_pos'], label='tailbase')\n", - "plt.xlabel('x_pos (pixels)')\n", - "plt.ylabel('y_pos (pixels)')\n", - "plt.legend()\n", + "fig, axs = plt.subplots(2,1, figsize=(6,10))\n", + "\n", + "axs[0].set_title('Head pose estimation')\n", + "axs[0].plot(head_data['x_pos'], head_data['y_pos'],label='head',color='blue')\n", + "axs[0].set_xlabel('x position (pixels)')\n", + "axs[0].set_ylabel('y position (pixels)')\n", + "axs[0].legend()\n", + "\n", + "axs[1].set_title('Tailbase pose estimation')\n", + "axs[1].plot(tail_data['x_pos'], tail_data['y_pos'], label='tailbase',color='orange')\n", + "axs[1].set_xlabel('x position (pixels)')\n", + "axs[1].set_ylabel('y position (pixels)')\n", + "axs[1].legend()\n", + "\n", + "plt.tight_layout()\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can plot the spatial mapping of both body parts, head and tail base." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": {