From 25782a0be0c3aaa5d3ed171b37898d25b8ff21e3 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Fri, 12 Nov 2021 14:29:00 -0600 Subject: [PATCH 1/3] Changed to imply order, ERD->Diagram --- .../00-Getting started.ipynb | 2 +- .../01-DataJoint Basics - Interactive.ipynb | 6 +-- .../01-DataJoint Basics - Interactive.py | 28 +++++----- .../02-Imported Tables - Interactive.ipynb | 14 ++--- ... Table, and Part Table - Interactive.ipynb | 8 +-- .../data/example_scan_01.tif | Bin .../data/example_scan_02.tif | Bin .../data/example_scan_03.tif | Bin .../02-Imported Tables - Interactive.ipynb | 10 ++-- .../02-Imported Tables - Interactive.py | 24 ++++----- ... Table, and Part Table - Interactive.ipynb | 14 ++--- ...kup Table, and Part Table - Interactive.py | 48 +++++++++--------- .../data/data_0_2017-05-15.npy | Bin .../data/data_0_2017-05-19.npy | Bin .../data/data_100_2017-05-25.npy | Bin .../data/data_100_2017-06-01.npy | Bin .../data/data_5_2017-01-05.npy | Bin .../01-DataJoint Basics - Completed.ipynb | 4 +- ...maging - Imported Tables - Completed.ipynb | 14 ++--- ...iology - Imported Tables - Completed.ipynb | 12 ++--- ...up Table, and Part Table - Completed.ipynb | 8 +-- ...up Table, and Part Table - Completed.ipynb | 20 ++++---- 22 files changed, 106 insertions(+), 106 deletions(-) rename 00-Getting started.ipynb => 00-Getting_started/00-Getting started.ipynb (95%) rename 01-DataJoint Basics - Interactive.ipynb => 00-Getting_started/01-DataJoint Basics - Interactive.ipynb (99%) rename 01-DataJoint Basics - Interactive.py => 00-Getting_started/01-DataJoint Basics - Interactive.py (97%) rename {calcium_imaging => 01-Calcium_Imaging_Data}/02-Imported Tables - Interactive.ipynb (98%) rename {calcium_imaging => 01-Calcium_Imaging_Data}/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb (99%) rename {calcium_imaging => 01-Calcium_Imaging_Data}/data/example_scan_01.tif (100%) rename {calcium_imaging => 01-Calcium_Imaging_Data}/data/example_scan_02.tif (100%) rename {calcium_imaging => 01-Calcium_Imaging_Data}/data/example_scan_03.tif (100%) rename {electrophysiology => 02-Electrophysiology}/02-Imported Tables - Interactive.ipynb (98%) rename {electrophysiology => 02-Electrophysiology}/02-Imported Tables - Interactive.py (95%) rename {electrophysiology => 02-Electrophysiology}/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb (98%) rename {electrophysiology => 02-Electrophysiology}/03-Computed Table, Lookup Table, and Part Table - Interactive.py (94%) rename {electrophysiology => 02-Electrophysiology}/data/data_0_2017-05-15.npy (100%) rename {electrophysiology => 02-Electrophysiology}/data/data_0_2017-05-19.npy (100%) rename {electrophysiology => 02-Electrophysiology}/data/data_100_2017-05-25.npy (100%) rename {electrophysiology => 02-Electrophysiology}/data/data_100_2017-06-01.npy (100%) rename {electrophysiology => 02-Electrophysiology}/data/data_5_2017-01-05.npy (100%) rename {completed => 03-Completed_Exercises}/01-DataJoint Basics - Completed.ipynb (99%) rename {completed => 03-Completed_Exercises}/02-calcium_imaging - Imported Tables - Completed.ipynb (99%) rename {completed => 03-Completed_Exercises}/02-electrophysiology - Imported Tables - Completed.ipynb (99%) rename {completed => 03-Completed_Exercises}/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb (99%) rename {completed => 03-Completed_Exercises}/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb (77%) diff --git a/00-Getting started.ipynb b/00-Getting_started/00-Getting started.ipynb similarity index 95% rename from 00-Getting started.ipynb rename to 00-Getting_started/00-Getting started.ipynb index acfd0ff..316c227 100644 --- a/00-Getting started.ipynb +++ b/00-Getting_started/00-Getting started.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Congratulations! If you are reading this, then you have successfully connected to the workshop JupyterHub and opened up your very first workshop notebook!\n", + "Congratulations! If you are reading this, then you have successfully connected to the workshop JupyterHub and opened up your very first workshop notebook! These notebooks are oranized with numbers to highlight what should come next. First, we'll walk through the startup process. Next, you'll see an introduction to DataJoint, and how to structure a data pipeline. The Calcium Imaging and Electrophysiology folders show example data structures and analysis techniques from common experimental techniques. We've included some fill-in-the-blank sections for you to code yourself. If you want to see completed notebooks, see the folder of completed exercises.\n", "\n", "Before we start, remember that all of the edits you make in these tutorial notebooks are ***not persistent*** - they will be reset to the original content everytime you restart the server. However, you can easily download the notebooks that you're interested in keeping the changes." ] diff --git a/01-DataJoint Basics - Interactive.ipynb b/00-Getting_started/01-DataJoint Basics - Interactive.ipynb similarity index 99% rename from 01-DataJoint Basics - Interactive.ipynb rename to 00-Getting_started/01-DataJoint Basics - Interactive.ipynb index 69cf4ee..616f22a 100644 --- a/01-DataJoint Basics - Interactive.ipynb +++ b/00-Getting_started/01-DataJoint Basics - Interactive.ipynb @@ -555,7 +555,7 @@ "source": [ "Note that, to uniquely identify an experimental session (or simply a **session**), we need to know the mouse that the session was about. In other words, a session cannot existing without a corresponding mouse! \n", "\n", - "With **mouse** already represented as a table in our pipeline, we say that the session **depends on** the mouse! We would graphically represent this in an **entity relationship diagram (ERD)** by drawing the line between two tables, with the one below (**session**) dependeing on the one above (**mouse**)." + "With **mouse** already represented as a table in our pipeline, we say that the session **depends on** the mouse! We could graphically represent this in an **entity relationship diagram (ERD)** by drawing the line between two tables, with the one below (**session**) dependeing on the one above (**mouse**)." ] }, { @@ -590,7 +590,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can actually generate the entity relationship diagram (ERD) on the fly by calling `dj.ERD` with the schema object" + "You can actually generate something similar to an entity relationship diagram (ERD) on the fly by calling `dj.Diagram` with the schema object. Many of the symbols and features are the same as the ERD standard." ] }, { @@ -599,7 +599,7 @@ "metadata": {}, "outputs": [], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { diff --git a/01-DataJoint Basics - Interactive.py b/00-Getting_started/01-DataJoint Basics - Interactive.py similarity index 97% rename from 01-DataJoint Basics - Interactive.py rename to 00-Getting_started/01-DataJoint Basics - Interactive.py index d8d173b..f7cef77 100644 --- a/01-DataJoint Basics - Interactive.py +++ b/00-Getting_started/01-DataJoint Basics - Interactive.py @@ -42,13 +42,13 @@ # # Let's go ahead and build together a pipeline from scratch to better understand what a data pipeline is all about. -# # Building our first pipeline: +# # Building our first pipeline: # Let's build a pipeline to collect, store and process data and analysis for our hypothetical single electrode recording or calcium imaging recording in mice. To help us understand the project better, here is a brief description: # > * Your lab houses many mice, and each mouse is identified by a unique ID. You also want to keep track of information about each mouse such as their date of birth, and gender. # > * As a hard working neuroscientist, you perform experiments every day, sometimes working with more than one mouse in a day! However, on any given day, a mouse undergoes at most one recording session. -# > * For each experimental session, you would like to record what mouse you worked with and when you performed the experiment. You would also like to keep track of other helpful information such as the experimental setup you worked on. +# > * For each experimental session, you would like to record what mouse you worked with and when you performed the experiment. You would also like to keep track of other helpful information such as the experimental setup you worked on. # # > * In a session of electrophysiology # >> * you record electrical activity from a single neuron. You use recording equipment that produces separate data files for each neuron you recorded. @@ -64,7 +64,7 @@ # > * Your lab houses many **mice**, and each mouse is identified by a unique ID. You also want to keep track of information about each mouse such as their date of birth, and gender. # > * As a hard working neuroscientist, you perform experiments every day, sometimes working with more than one mouse in a day! However, on an any given day, a mouse undergoes at most one recording session. -# > * For each **experimental session**, you would like to record what mouse you worked with and when you performed the experiment. You would also like to keep track of other helpful information such as the experimental setup you worked on. +# > * For each **experimental session**, you would like to record what mouse you worked with and when you performed the experiment. You would also like to keep track of other helpful information such as the experimental setup you worked on. # # > * In a session of electrophysiology # >> * you record electrical activity from a **single neuron**. You use recording equipment that produces separate data files for each neuron you recorded. @@ -92,7 +92,7 @@ # In the current notebook, we will design the tables for mouse and experimental sessions, the rest of the pipeline will be designed in the subdirectory `electrophysiology` and `calcium_imaging` -# In DataJoint data pipeline, we represent these **entities** as **tables**. Different *kinds* of entities become distinct tables, and each row of the table is a single example (instance) of the category of entity. +# In DataJoint data pipeline, we represent these **entities** as **tables**. Different *kinds* of entities become distinct tables, and each row of the table is a single example (instance) of the category of entity. # # For example, if we have a `Mouse` table, then each row in the mouse table represents a single mouse! @@ -229,7 +229,7 @@ class Mouse(dj.Manual): # ## Create tables with dependencies -# Congratulations! We have successfully created your first table! We are now ready to tackle and include other **entities** in the project into our data pipeline. +# Congratulations! We have successfully created your first table! We are now ready to tackle and include other **entities** in the project into our data pipeline. # # Let's now take a look at representing an **experimental session**. @@ -245,11 +245,11 @@ class Mouse(dj.Manual): # # to uniquely identify a single experimental session. -# Note that, to uniquely identify an experimental session (or simply a **session**), we need to know the mouse that the session was about. In other words, a session cannot existing without a corresponding mouse! +# Note that, to uniquely identify an experimental session (or simply a **session**), we need to know the mouse that the session was about. In other words, a session cannot existing without a corresponding mouse! # -# With **mouse** already represented as a table in our pipeline, we say that the session **depends on** the mouse! We would graphically represent this in an **entity relationship diagram (ERD)** by drawing the line between two tables, with the one below (**session**) dependeing on the one above (**mouse**). +# With **mouse** already represented as a table in our pipeline, we say that the session **depends on** the mouse! We could graphically represent this in an **entity relationship diagram (ERD)** by drawing the line between two tables, with the one below (**session**) dependeing on the one above (**mouse**). -# Thus we will need both **mouse** and a new attribute **session_date** to uniquely identify a single session. +# Thus we will need both **mouse** and a new attribute **session_date** to uniquely identify a single session. # # Remember that a **mouse** is already uniquely identified by its primary key - **mouse_id**. In DataJoint, you can declare that **session** depends on the mouse, and DataJoint will automatically include the mouse's primary key (`mouse_id`) as part of the session's primary key, along side any additional attribute(s) you specificy. @@ -266,9 +266,9 @@ class Session(dj.Manual): """ -# You can actually generate the entity relationship diagram (ERD) on the fly by calling `dj.ERD` with the schema object +# You can actually generate something similar to an entity relationship diagram (ERD) on the fly by calling `dj.Diagram` with the schema object. Many of the symbols and features are the same as the ERD standard. -dj.ERD(schema) +dj.Diagram(schema) # Let's try inserting a few sessions manually. @@ -397,17 +397,17 @@ class Session(dj.Manual): # ENTER YOUR CODE -# In computer science/math lingo, DataJoint operations are said to **satisfy closure property**. Practically speaking, this means that the result of a query can immediately be used in another query, allowing you to build more complex queries from simpler ones. +# In computer science/math lingo, DataJoint operations are said to **satisfy closure property**. Practically speaking, this means that the result of a query can immediately be used in another query, allowing you to build more complex queries from simpler ones. # ### Restriction one table with another # All mice that have a session -Mouse & Session +Mouse & Session # ### Combining restrictions -# All the above queries could be combined +# All the above queries could be combined # Male mice that had a session @@ -452,7 +452,7 @@ class Session(dj.Manual): Mouse * Session & 'session_date > "2017-05-19"' # ## Projection .proj(): focus on attributes of interest -# Beside restriction (`&`) and join (`*`) operations, DataJoint offers another type of operation: projection (`.proj()`). Projection is used to select attributes (columns) from a table, to rename them, or to create new calculated attributes. +# Beside restriction (`&`) and join (`*`) operations, DataJoint offers another type of operation: projection (`.proj()`). Projection is used to select attributes (columns) from a table, to rename them, or to create new calculated attributes. # From the ***Mouse*** table, suppose we want to focus only on the `sex` attribute and ignore the others, this can be done as: diff --git a/calcium_imaging/02-Imported Tables - Interactive.ipynb b/01-Calcium_Imaging_Data/02-Imported Tables - Interactive.ipynb similarity index 98% rename from calcium_imaging/02-Imported Tables - Interactive.ipynb rename to 01-Calcium_Imaging_Data/02-Imported Tables - Interactive.ipynb index 49786a1..d362c7d 100644 --- a/calcium_imaging/02-Imported Tables - Interactive.ipynb +++ b/01-Calcium_Imaging_Data/02-Imported Tables - Interactive.ipynb @@ -142,7 +142,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This table is dependent on the table `Session`, inheriting its primary key attributes, with an additional primary key attribute `scan_idx`. One session could contain multiple scans, which is another example of **one-to-many** relationship. We could take a look at the ERD again." + "This table is dependent on the table `Session`, inheriting its primary key attributes, with an additional primary key attribute `scan_idx`. One session could contain multiple scans, which is another example of **one-to-many** relationship. We could take a look at the Diagram again." ] }, { @@ -151,7 +151,7 @@ "metadata": {}, "outputs": [], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -296,7 +296,7 @@ "metadata": {}, "outputs": [], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -333,9 +333,9 @@ "source": [ "In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. \n", "\n", - "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the ERD, `Manual` tables are depicted by green rectangles.\n", + "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n", "\n", - "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the ERD, `Imported` tables are depicted by blue ellipses." + "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses." ] }, { @@ -344,7 +344,7 @@ "metadata": {}, "outputs": [], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -676,7 +676,7 @@ "metadata": {}, "outputs": [], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { diff --git a/calcium_imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb b/01-Calcium_Imaging_Data/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb similarity index 99% rename from calcium_imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb rename to 01-Calcium_Imaging_Data/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb index 66e55f9..22281ce 100644 --- a/calcium_imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb +++ b/01-Calcium_Imaging_Data/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb @@ -406,7 +406,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's define `Param` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the ERD.\n", + "Let's define `Param` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the Diagram.\n", "\n", "This tier indicates that the table will contain information:\n", "* that will be referenced by other tables\n", @@ -435,7 +435,7 @@ "metadata": {}, "outputs": [], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -501,7 +501,7 @@ "metadata": {}, "outputs": [], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -1006,7 +1006,7 @@ "metadata": {}, "outputs": [], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { diff --git a/calcium_imaging/data/example_scan_01.tif b/01-Calcium_Imaging_Data/data/example_scan_01.tif similarity index 100% rename from calcium_imaging/data/example_scan_01.tif rename to 01-Calcium_Imaging_Data/data/example_scan_01.tif diff --git a/calcium_imaging/data/example_scan_02.tif b/01-Calcium_Imaging_Data/data/example_scan_02.tif similarity index 100% rename from calcium_imaging/data/example_scan_02.tif rename to 01-Calcium_Imaging_Data/data/example_scan_02.tif diff --git a/calcium_imaging/data/example_scan_03.tif b/01-Calcium_Imaging_Data/data/example_scan_03.tif similarity index 100% rename from calcium_imaging/data/example_scan_03.tif rename to 01-Calcium_Imaging_Data/data/example_scan_03.tif diff --git a/electrophysiology/02-Imported Tables - Interactive.ipynb b/02-Electrophysiology/02-Imported Tables - Interactive.ipynb similarity index 98% rename from electrophysiology/02-Imported Tables - Interactive.ipynb rename to 02-Electrophysiology/02-Imported Tables - Interactive.ipynb index aace0ab..0fbfa86 100644 --- a/electrophysiology/02-Imported Tables - Interactive.ipynb +++ b/02-Electrophysiology/02-Imported Tables - Interactive.ipynb @@ -305,7 +305,7 @@ "metadata": {}, "outputs": [], "source": [ - "# ENTER YOUR CODE! - plot ERD of the schema\n" + "# ENTER YOUR CODE! - plot a Diagram of the schema\n" ] }, { @@ -335,9 +335,9 @@ "source": [ "In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. \n", "\n", - "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the ERD, `Manual` tables are depicted by green rectangles.\n", + "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n", "\n", - "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the ERD, `Imported` tables are depicted by blue ellipses." + "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses." ] }, { @@ -346,7 +346,7 @@ "metadata": {}, "outputs": [], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -710,7 +710,7 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { diff --git a/electrophysiology/02-Imported Tables - Interactive.py b/02-Electrophysiology/02-Imported Tables - Interactive.py similarity index 95% rename from electrophysiology/02-Imported Tables - Interactive.py rename to 02-Electrophysiology/02-Imported Tables - Interactive.py index c203da5..0d600d7 100644 --- a/electrophysiology/02-Imported Tables - Interactive.py +++ b/02-Electrophysiology/02-Imported Tables - Interactive.py @@ -15,7 +15,7 @@ # # Working with automated computations: Imported tables -# Welcome back! In this session, we are going to continue working with the pipeline for the mouse electrophysiology example. +# Welcome back! In this session, we are going to continue working with the pipeline for the mouse electrophysiology example. # # In this session, we will learn to: # @@ -57,7 +57,7 @@ # # So `data_100_2017-05-25.npy` is the data for session identified by `mouse_id = 100` and `session_date = "2017-05-25"`. -# ## Looking at the data +# ## Looking at the data # Let's take a quick peak at the data file content. @@ -68,7 +68,7 @@ # Any item in this list of keys can be used to uniquely identify a single session! -# ENTER YOUR CODE! - restrict session using keys and any element inside keys. +# ENTER YOUR CODE! - restrict session using keys and any element inside keys. # Let's take the first key, and generate the file name that corresponds to this session. Remember the `data_{mouse_id}_{session_date}.npy` filename convetion! @@ -113,7 +113,7 @@ class Neuron(dj.Imported): # Let's check the state of our pipeline. -# ENTER YOUR CODE! - plot ERD of the schema +# ENTER YOUR CODE! - plot a Diagram of the schema # We defined `activity` as a `longblob` so that it can store a NumPy array holding the electric activity over time. This NumPy array will be imported from the file corresponding to each neuron. @@ -122,13 +122,13 @@ class Neuron(dj.Imported): # ## DataJoint table tiers -# In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. +# In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. # -# DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the ERD, `Manual` tables are depicted by green rectangles. +# DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles. # -# On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the ERD, `Imported` tables are depicted by blue ellipses. +# On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses. -dj.ERD(schema) +dj.Diagram(schema) # ## Importing data into the `Imported` table @@ -188,11 +188,11 @@ def make(self, key): # load the data data = np.load(data_file) - + for idx, d in enumerate(data): # add the index of the 1st dimension as neuron_id key['neuron_id'] = idx - + # add the loaded data as the "activity" column key['activity'] = d @@ -210,7 +210,7 @@ def make(self, key): Neuron() -# As you can obviously see, in these example datasets, we only have data for one neuron per session. +# As you can obviously see, in these example datasets, we only have data for one neuron per session. # What happens if we call `Neuron.populate` again? @@ -240,7 +240,7 @@ def make(self, key): # Congratulations! You have successfully extended your pipeline with a table to represent recorded data (`Neuron` as `Imported` table), learned and implemented the `make()` and `populate()` call to load external data to your tables. -dj.ERD(schema) +dj.Diagram(schema) # At this point, our pipeline contains the core elements with data populated, ready for further downstream analysis. # diff --git a/electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb b/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb similarity index 98% rename from electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb rename to 02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb index f67d6ba..a974b69 100644 --- a/electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb +++ b/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb @@ -559,7 +559,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Did you notice that we are now inheriting from `dj.Computed`? `Computed` is yet another table tier that signifies that **the entries of this table are computed using data in other tables**. `Computed` tables are represented as red circles in the ERD." + "Did you notice that we are now inheriting from `dj.Computed`? `Computed` is yet another table tier that signifies that **the entries of this table are computed using data in other tables**. `Computed` tables are represented as red circles in the Diagram." ] }, { @@ -568,7 +568,7 @@ "metadata": {}, "outputs": [], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -842,7 +842,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's define `SpikeDetectionParam` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the ERD.\n", + "Let's define `SpikeDetectionParam` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the Diagram.\n", "\n", "This tier indicates that the table will contain information:\n", "* that will be referenced by other tables\n", @@ -870,7 +870,7 @@ "metadata": {}, "outputs": [], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -959,14 +959,14 @@ "metadata": {}, "outputs": [], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In the ERD, we see that `Spikes` is a computed table (red circle) that depends on **both Neuron and SpikeDetectionParam**. Finally, let's go ahead and implement the `make` method for the `Spikes` table. " + "In the Diagram, we see that `Spikes` is a computed table (red circle) that depends on **both Neuron and SpikeDetectionParam**. Finally, let's go ahead and implement the `make` method for the `Spikes` table. " ] }, { @@ -1324,7 +1324,7 @@ "metadata": {}, "outputs": [], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { diff --git a/electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py b/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py similarity index 94% rename from electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py rename to 02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py index e6bd377..c41bf1e 100644 --- a/electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py +++ b/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py @@ -15,7 +15,7 @@ # # Working with automated computations: Computed tables -# Welcome back! In this session, we are going to continue working with the pipeline for the mouse electrophysiology example. +# Welcome back! In this session, we are going to continue working with the pipeline for the mouse electrophysiology example. # # In this session, we will learn to: # @@ -35,7 +35,7 @@ import matplotlib.pyplot as plt # %matplotlib inline -# Similarly as before, to continue working with the tables we defined in the previous notebook, we can either redefine the classes for each table `Mouse`, `Session`, `Neuron` and populate them. Or, again for your convenience, we can import them from the `tutorial_pipeline.ephys_cell_activity` module. +# Similarly as before, to continue working with the tables we defined in the previous notebook, we can either redefine the classes for each table `Mouse`, `Session`, `Neuron` and populate them. Or, again for your convenience, we can import them from the `tutorial_pipeline.ephys_cell_activity` module. import sys sys.path.append("..") @@ -49,7 +49,7 @@ # # Computations in data pipeline -# Now we have successfully imported all data we have into our pipeline, it's time for us to start analyzing them! +# Now we have successfully imported all data we have into our pipeline, it's time for us to start analyzing them! # # When you perform computations in the DataJoint data pipeline, you focus and design tables in terms of **what** is it that you are computing rather than the **how**. You should think in terms of the "things" that you are computing! @@ -120,9 +120,9 @@ class ActivityStatistics(dj.Computed): """ -# Did you notice that we are now inheriting from `dj.Computed`? `Computed` is yet another table tier that signifies that **the entries of this table are computed using data in other tables**. `Computed` tables are represented as red circles in the ERD. +# Did you notice that we are now inheriting from `dj.Computed`? `Computed` is yet another table tier that signifies that **the entries of this table are computed using data in other tables**. `Computed` tables are represented as red circles in the Diagram . -dj.ERD(schema) +dj.Diagram (schema) # Just like the `Imported` tables, `Computed` tables make use of the same `make` and `populate` logic for defining new entries in the table. Let's go ahead and implement `make` method. @@ -139,7 +139,7 @@ class ActivityStatistics(dj.Computed): stdev: float # standard deviation of activity max: float # maximum activity """ - + def make(self, key): activity = (Neuron() & key).fetch1('activity') # fetch activity as NumPy array @@ -198,7 +198,7 @@ def make(self, key): threshold = 0.5 # find activity above threshold -above_thrs = (activity > threshold).astype(int) +above_thrs = (activity > threshold).astype(int) plt.plot(activity) plt.plot(above_thrs) @@ -232,7 +232,7 @@ def make(self, key): threshold = # enter different threshold values here # find activity above threshold -above_thrs = (activity > threshold).astype(int) +above_thrs = (activity > threshold).astype(int) rising = (np.diff(above_thrs) > 0).astype(int) # find rising edge of crossing threshold spikes = np.hstack((0, rising)) # prepend 0 to account for shortening due to np.diff @@ -255,7 +255,7 @@ def make(self, key): # ## Parameter `Lookup` table -# Let's define `SpikeDetectionParam` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the ERD. +# Let's define `SpikeDetectionParam` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the Diagram . # # This tier indicates that the table will contain information: # * that will be referenced by other tables @@ -270,7 +270,7 @@ class SpikeDetectionParam(dj.Lookup): """ -dj.ERD(schema) +dj.Diagram (schema) # ### Defining `Spikes` table @@ -292,7 +292,7 @@ class Spikes(dj.Computed): # ### Define the `Waveform` part table -# We would also like to store the waveform at each detected spike, for a particular neuron. For simplicity, let's define the waveform to be 40 sample points before and after the onset of the detected spike. +# We would also like to store the waveform at each detected spike, for a particular neuron. For simplicity, let's define the waveform to be 40 sample points before and after the onset of the detected spike. # To accomplish this, we will define a `dj.Part` table `Waveform` which will contain the waveform per spike. The table definition is something like: @schema @@ -304,7 +304,7 @@ class Spikes(dj.Computed): spikes: longblob # detected spikes count: int # total number of detected spikes """ - + class Waveform(dj.Part): definition = """ -> master @@ -316,10 +316,10 @@ class Waveform(dj.Part): # The `-> master` syntax denotes that the `Waveform` part table is foreign key constrained by `Spike` table - i.e. the master table. The master table drives the ***populate*** logic, and the content of the part table is generally ingested together with the content of the master table, all in one step (i.e. one `make()` call). -dj.ERD(schema) +dj.Diagram (schema) -# In the ERD, we see that `Spikes` is a computed table (red circle) that depends on **both Neuron and SpikeDetectionParam**. Finally, let's go ahead and implement the `make` method for the `Spikes` table. +# In the Diagram , we see that `Spikes` is a computed table (red circle) that depends on **both Neuron and SpikeDetectionParam**. Finally, let's go ahead and implement the `make` method for the `Spikes` table. @schema class Spikes(dj.Computed): @@ -330,7 +330,7 @@ class Spikes(dj.Computed): spikes: longblob # detected spikes count: int # total number of detected spikes """ - + class Waveform(dj.Part): definition = """ -> master @@ -338,7 +338,7 @@ class Waveform(dj.Part): --- waveform: longblob # waveform extracted from this spike """ - + def make(self, key): print('Populating for: ', key) @@ -353,18 +353,18 @@ def make(self, key): print('Detected {} spikes!\n'.format(count)) # create and insert a new dictionary containing `key` and additionally `spikes` and `count` - self.insert1(dict(key, spikes=spikes, count=count)) - + self.insert1(dict(key, spikes=spikes, count=count)) + # extract waveform for the `Waveform` part-table before_spk, after_spk = 40, 40 # extract 40 sample points before and after a spike as the waveform for spk_id, spk in enumerate(np.where(spikes==1)[0]): - + # For simplicity, skip the spikes too close to the beginning or the end - if spk - before_spk < 0 or spk + after_spk > len(activity) + 1: + if spk - before_spk < 0 or spk + after_spk > len(activity) + 1: continue - - wf = activity[spk - before_spk: spk + after_spk] - + + wf = activity[spk - before_spk: spk + after_spk] + # create and insert a new dictionary containing `key` and additionally `spike_id` and `waveform` self.Waveform.insert1(dict(key, spike_id=spk_id, waveform=wf)) @@ -449,6 +449,6 @@ def make(self, key): # Congratulations! You have successfully extended your pipeline with a table to represent recorded data (`Neuron` as `Imported` table), tables that performs and represents computation results (`ActivityStatistics` and `Spikes` as `Computed` tables) and a table to hold computation parameters (`SpikeDetectionParam` as `Lookup` table). -dj.ERD(schema) +dj.Diagram (schema) # Our pipeline is still fairly simple but completely capable of handling analysis! diff --git a/electrophysiology/data/data_0_2017-05-15.npy b/02-Electrophysiology/data/data_0_2017-05-15.npy similarity index 100% rename from electrophysiology/data/data_0_2017-05-15.npy rename to 02-Electrophysiology/data/data_0_2017-05-15.npy diff --git a/electrophysiology/data/data_0_2017-05-19.npy b/02-Electrophysiology/data/data_0_2017-05-19.npy similarity index 100% rename from electrophysiology/data/data_0_2017-05-19.npy rename to 02-Electrophysiology/data/data_0_2017-05-19.npy diff --git a/electrophysiology/data/data_100_2017-05-25.npy b/02-Electrophysiology/data/data_100_2017-05-25.npy similarity index 100% rename from electrophysiology/data/data_100_2017-05-25.npy rename to 02-Electrophysiology/data/data_100_2017-05-25.npy diff --git a/electrophysiology/data/data_100_2017-06-01.npy b/02-Electrophysiology/data/data_100_2017-06-01.npy similarity index 100% rename from electrophysiology/data/data_100_2017-06-01.npy rename to 02-Electrophysiology/data/data_100_2017-06-01.npy diff --git a/electrophysiology/data/data_5_2017-01-05.npy b/02-Electrophysiology/data/data_5_2017-01-05.npy similarity index 100% rename from electrophysiology/data/data_5_2017-01-05.npy rename to 02-Electrophysiology/data/data_5_2017-01-05.npy diff --git a/completed/01-DataJoint Basics - Completed.ipynb b/03-Completed_Exercises/01-DataJoint Basics - Completed.ipynb similarity index 99% rename from completed/01-DataJoint Basics - Completed.ipynb rename to 03-Completed_Exercises/01-DataJoint Basics - Completed.ipynb index 9f8588f..e64ba17 100644 --- a/completed/01-DataJoint Basics - Completed.ipynb +++ b/03-Completed_Exercises/01-DataJoint Basics - Completed.ipynb @@ -1064,7 +1064,7 @@ "source": [ "Note that, to uniquely identify an experimental session (or simply a **session**), we need to know the mouse that the session was about. In other words, a session cannot existing without a corresponding mouse! \n", "\n", - "With **mouse** already represented as a table in our pipeline, we say that the session **depends on** the mouse! We would graphically represent this in an **entity relationship diagram (ERD)** by drawing the line between two tables, with the one below (**session**) dependeing on the one above (**mouse**)." + "With **mouse** already represented as a table in our pipeline, we say that the session **depends on** the mouse! We could graphically represent this in an **entity relationship diagram (ERD)** by drawing the line between two tables, with the one below (**session**) dependeing on the one above (**mouse**)." ] }, { @@ -1099,7 +1099,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can actually generate the entity relationship diagram (ERD) on the fly by calling `dj.ERD` with the schema object" + "You can actually generate something similar to an entity relationship diagram (ERD) on the fly by calling `dj.Diagram` with the schema object. Many of the symbols and features are the same as the ERD standard." ] }, { diff --git a/completed/02-calcium_imaging - Imported Tables - Completed.ipynb b/03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb similarity index 99% rename from completed/02-calcium_imaging - Imported Tables - Completed.ipynb rename to 03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb index 85315e8..963d86d 100644 --- a/completed/02-calcium_imaging - Imported Tables - Completed.ipynb +++ b/03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb @@ -365,7 +365,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This table is dependent on the table `Session`, inheriting its primary key attributes, with an additional primary key attribute `scan_idx`. One session could contain multiple scans, which is another example of **one-to-many** relationship. We could take a look at the ERD again." + "This table is dependent on the table `Session`, inheriting its primary key attributes, with an additional primary key attribute `scan_idx`. One session could contain multiple scans, which is another example of **one-to-many** relationship. We could take a look at the Diagram again." ] }, { @@ -448,7 +448,7 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -838,7 +838,7 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -875,9 +875,9 @@ "source": [ "In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. \n", "\n", - "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the ERD, `Manual` tables are depicted by green rectangles.\n", + "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n", "\n", - "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the ERD, `Imported` tables are depicted by blue ellipses." + "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses." ] }, { @@ -977,7 +977,7 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -1879,7 +1879,7 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { diff --git a/completed/02-electrophysiology - Imported Tables - Completed.ipynb b/03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb similarity index 99% rename from completed/02-electrophysiology - Imported Tables - Completed.ipynb rename to 03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb index b3b0e07..4be4994 100644 --- a/completed/02-electrophysiology - Imported Tables - Completed.ipynb +++ b/03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb @@ -1082,8 +1082,8 @@ } ], "source": [ - "# ENTER YOUR CODE! - plot ERD of the schema\n", - "dj.ERD(schema)" + "# ENTER YOUR CODE! - plot Diagram of the schema\n", + "dj.Diagram(schema)" ] }, { @@ -1113,9 +1113,9 @@ "source": [ "In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. \n", "\n", - "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the ERD, `Manual` tables are depicted by green rectangles.\n", + "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n", "\n", - "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the ERD, `Imported` tables are depicted by blue ellipses." + "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses." ] }, { @@ -1189,7 +1189,7 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -2020,7 +2020,7 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { diff --git a/completed/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb b/03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb similarity index 99% rename from completed/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb rename to 03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb index 4cb9b6e..3d00d84 100644 --- a/completed/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb +++ b/03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb @@ -1036,7 +1036,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's define `Param` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the ERD.\n", + "Let's define `Param` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the Diagram.\n", "\n", "This tier indicates that the table will contain information:\n", "* that will be referenced by other tables\n", @@ -1169,7 +1169,7 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -1380,7 +1380,7 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -3051,7 +3051,7 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { diff --git a/completed/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb b/03-Completed_Exercises/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb similarity index 77% rename from completed/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb rename to 03-Completed_Exercises/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb index a78ad1f..064ab9a 100644 --- a/completed/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb +++ b/03-Completed_Exercises/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb @@ -1470,7 +1470,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Did you notice that we are now inheriting from `dj.Computed`? `Computed` is yet another table tier that signifies that **the entries of this table are computed using data in other tables**. `Computed` tables are represented as red circles in the ERD." + "Did you notice that we are now inheriting from `dj.Computed`? `Computed` is yet another table tier that signifies that **the entries of this table are computed using data in other tables**. `Computed` tables are represented as red circles in the Diagram." ] }, { @@ -1561,7 +1561,7 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -1808,7 +1808,7 @@ "outputs": [ { "data": { - "image/png": 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FZu7H1wLxvlWiRSz4pWhfd6EoWlmDaNVgbVDPVZcwyGwZGcWSjXus7h2fmsbBiWl88pZauvgMUZaAp3Qny0R0OIAbAfwtM0fD2eiE1do3xcxXArgSAObPny9DnyBkoAmOI5kZ+8encMTcWbH3BYNLWYNvU8gr3xM1ZSGgd9pv+ryN7y77f65fhAVPPIMnP3sO5gwPpX7+7//fYgDAR885NfWzeifL5s8EocqUOc9mF/BUV7u3yhG+hHRUsX355A1fvBvTM4z1X/iTxHtbBfTBzXtGceyRc52aPusYOBMtACCiWWgLd37AzDdpbtkI4ATl7+PRjtzjrgwuExO8c2B8CiOjk2UXQ6gw37p/HV7+qduxac9o/I0VXsTVAXJs31pTRTDBJQlNynWbM3HfU23NlvGpmULyA5K0k9j4iSBUmor64JmansFJF/0MV9+/ru+zSptoSfTPxjBoh1tZfL75aufrdhzA737hLlz+S//O32cGzclyx4/HtwCsYOavGm67BcBfdqJpvQ7ACDNv8VGenyzejP3jUz6SFhwy/7O/wOmfvr3sYggV5rZlzwAANu6KN7GIM4kQ7HHmg8dROkIDSGgMvtvK0FB7dJia9pPTNQ+sw/0R86g47aSeBo/0EqFeBCfxD6/bhYXrd3nPT+0hcf1lstO3v6AxpUyK5lcFBk04YAsRXU1E24hoqeFzL9GZ//2Op3Dniq2pnnE5nO85OOEusQrhq51v6RwAR+dhHwyiD57XA3gvgLcQ0aLOv7dF/H0sALAWwGoAVwH4oK/CrHxmHz56wxJfyQuOGJ2cLrsIQsWxXZxJyOF8uHp9smcVApJ8XxRlojXcUdmemvGjwfOpnyzH//hW2OVgnHaSmJEKdUVt1Z/92YpC847rNUF3m9Bo6VXZmTuJ3UES1wA4J+ZzL9GZv3bnKnzgOwtTPeOqeT28difOuPgO3LE8nYCpynjXVOtGyvPfycuKVlmaDx5mvh8Je4SO89YP+SyHuqZKNOkQBKHy2C7OqEzd8Sbh6PVJNQhJphFFCWWHOgIeT/IdLbFOljsLROkjQt1QBZdFa6Blza7n56a6HU7GAj3MfG8ncI+JbnRmAA8R0dFE9Hxf1iFxJPWH9TsOYHiIcPyzDo29b3HHcfEj63biD097nrPyqdy3ajve+61HcNffvwkvnHe4lzxUglfjT75TnJZeWX21ElG0qoLLOti0ZxQbd2ePwiEIQj6SFmfUleAXUJgGIhpQgmtsu6LvjeJwq700mpwu3gePzl7f1df91wUr8L1frXeTmCBYoM4TRcy14bEhY5j0Cp/9VLlsNcE6OrNvkqrw7C/fgzd88e7EdAKBpM/+dcuitvvbhet3+8tEg6+5nuJOVBwzsFG0msrrv3AXAFh5CxcEwR22A3fvNlkp5SHv+5O3L0Qxm2gVc+o23PHBU6Rqtc13y7tOvPLetQCA9551Ur6EBMES9Ryg6Lk2rr/Y9CURojQS6+jMRHQB2mZcOPHEE40JTmU8CHDdvnwKEnpCpGI7ha8puEgtvekB9MFTCUL2rDKaC0Ltsd0EyklYPkSBxy15nEMS0TlE9GTns4uKK7UfdH3y3+94Co90nLT67rNDXR88BQ4OceNR1zeRDFZCvQhp8BRo8ghkFwT3tHur19+oBuZjFcc6OjMzX8nM85l5/rx584wJTmZ1xu+oCosIKd40jfciv89AhkmvGg1pt4Iw0NgKbuoQKaMOVHANXFeuQQbnkEQ0BOCyzuenATifiE7zWlLP6DYvX7tzVeznLvHtZDkO3XerstNXQQjYuX8cSzeNhK6pPniKEJiEo2hlS6PKjoxlLMhNYdGZk3A1j7UKEEhSww4Zkvz9uaSsMOlioqUgA6Yg1J+0E5H0+2wEC/c9BydzpSOhn9tkdQ4J4CQAq5l5LQAQ0XWde5f7LbF7rJuC5yZTxIlof56dX6Q7CDXlTy65H8/sHQu5JsgqKmHm2MhyVmnEdCab9UEVpybxfRcPEV0L4GwAxxDRRgCfBDALAJj5CrSjM78N7ejMBwG8v5ySumtfrSAogFcBT/Fzok+KPOAduChaVaGMwVI2NILgj66JVkI3+9JtT7bvkx1VJtZu3w8A+IcbFuNdrz6+5NIMBCbnkLrrrzUlYutXoEyS+m6RPXbdjgN4wbMP7S6ifRGMW3FrQRmphCrzzN6xvmtZNXiY86/P8y61q7w2kG2EHmY+P+Fz59GZs7YTVwKZIoQvra5mfDMaXrEmWuKDp3SqPJgLgmBHT4PHEun2mdgz2tbcach8XwdMziGtnUYC9n4FyqQqTWrt9gN485fvwX8o5mG+0ZpodRfwVXkzgmCHOjil2UxlbelqF8nqsDzog2p5l2/ei58s1rpqKRTR4KkP+8Ym8e7LH8S6HQe0n7sazVsFCCtsDiBc4nuq63ajIsxGDVnsHZvEyGg+Dfg4RMBTArJGc8fpn74dF924pOxiCBXEdjMk3TEbY5PTTtKR8dAak3NIa6eRtSFJg6egNrPzwDgA4IHVO7znZeM7zMfXXrxhT+YoMIKQhCqQSCOgdCHMzJuG+vzbLrkPf3Ptr/MWyRlyIF197lq5DQt/sxtfuf1J7eeu5rGe1rrPKFrwnkeRFGqiZXhnr/jU7Tj907d7y3fgBTyhEI4FtduxKTcbIwEYGZ3EdY9uSL5RGBhsBm51kmrKhFU0zz1iLgDg+UfNLbkkA4PJOeSjAE4hopOJaDaA8zr31g/Lruh7cxOMIXNnDQEARif8z9lxmoc+I/6de9kD+I9fFKehJAwWqsPiNM23zFk56GdVXBnYmqALxWGqi1bCWtS1k2W/UbSK1eDxTRHvLEBMtCpAUXXwzz/SRsEVBMEBPdVLu/tloZSNt7/i+QCA95xZTT8udaPjHPJXAH6HiDYS0QeI6EIiurBzywIAa9F2DnkVgA8CADNPAfgwgNsArABwPTMvK/wLOCRp4VtUn50z3F4iudJWi8PuFNbPF39q6z4v6QpCWIPH/rnMfVx5Lm5jZZV8BdcGqU3QhdLotn2zhMcJra7whfGxHz2B6z0cehfd7rwf4gTzbQHfSKJoVYCipGzRMJKCILgjdRQtj2VpMuILwC15nEMy8wK0BUDO+fkTW/DpnyzHfR99M2YNFXMmVBWh63Cr/X3Hp/ybMMVq8HjOe/awnPUJ/kllouVgZs7vZLl6FBneWchHkhDBWQ0qPnh++PDT+OHDT+O/v+aE+GdSZ9EsP3A+tWKjlKX1JLN6Rg//giBUk94CyHxPyBGjdPtMJKkfC83gX368FM/sHcPugxOF5ZnUpny3ud7BazunIhe1cXn5Ksac4SE/CQsDj3oQkMrJsoO2nlvAU8XFgcy7lcNUF13nx4bzAWdh0sm/NkqRJk1FUGQUrbLCpIuAR6GoSpCTb0HwB6XsYOKsMBs2gjSh/lCButm2fdH3YUz0dM/3V3947U7ct2pHKE8dvsoxa0gWJYIfWsp8XPRca+uHr/+z9s8q+huRnlofkrTJXfWHItZirVbPDKwI/EfRKk4jqSxBsQh4FGSjIgjNwXajJP0+I45OjeT9V5tg8VjkZqcqJ+dBKbIWx/Z7fOv+dcoz/Z/3wqRnK0cSFXndQgMJafCksHTM3OeU+Sh3FK0KH/5In60D8eO2Mw2eVpCev0aR5E6obhSpaGGKouWbgRfwqHVcVCWQyOAFwRut4hQOvPPmL9+D93/7kbKLoaWMjb9QPGl9WrkgMSffp3uRkLBZTy1tH1PTj/PB46sOxDxdKILCffBk/iwwzcxdBOeIBUB90JkBbdx9sPu7q+bVKiLCVUNNtJrsg0ecLCvIIkcQmkA61cuqaAvoWLfjANbtOFB2MbSQ2GgNBGWE5U3Kq0qmHnHMMKNlcaCjLgDL8MEjPVgogjTtzMmmKLcPHgdl8EalCzdQmMZs1Tgx4A1fvDvxuaz43MOqkbqKwL+fveK+T1lRtAZeg0cljfpoHkQCLwj+sbW/l2VSNlw5Wa6yGrxQTY24ojZeXR88GfOzXdep/v+0GjyeTxvlcEsogjTtLOvmV30sr+ZdFQ9/5FylPrQSTGvdO1n2Ryui1Vp3igz7Xtb8OvACHlXY0pSGK2TnmZGxsosg5ET19j8xNYOP3rAktl6l22dDFpqDQc//SxHOCL1nYUV/eNtsBbNd2IVMtEp4CVV570LzCAtcUjyXMb+xqWmrNGzafBW7BRVhjiM4oWei5beyWgXM0T2NF29ZFEqRAqvpgpRHogy8gEelLEdIQjW4e+U2vO7zd+LOFVvLLoqQg57QlnHPk9vwXws34OM3P2G8XzRIslGUb5bJsmZHIUSVpkfvETaiUbQ6P7eMjOL6hRus08km4Old3z8+hcvvWdNdVPvqa3K4JRRBmmaWtUlecufq3Gl0xboV7Ba9g5UKFm5AMdVEkpaIqyrsCpI8LpWK9FnTzse/kVY7H8/ZQDR4KoHsIwabJRtHAACLN+wpuSSCC5jN0Wc4cp+QHZ8nOqu37ccp//xz/GzJFn+ZCLEUvbCzoZilHxTBSpv/8c2H8Y83LMHesUmrdGz7hrowVx/57E+X44u3rsSuAxPtz7yZaPlJVxDUNptq05axTe5T+masQNRKg6d6HaNI0xIhH0n+61y1r1YBh23kKGpqdWDl/55zEgFPOagRrYqSspE44akkOo/3Qv3omVfYhXaU6s5H3mEz7vmlm9pC1zuWP5MvEyEzZUTRqgrRKFo7O4KWqen0mjlxTBs0eIL8fDN4NSuUQSofPA5aZda1XNDvqyTUDljcOYisYtmEMMVp8Pg3n2pq1NQihC9lvbOBF/CoFCbgKSQXIS2+Q9EKxaBqHJhkqeFTRf9laiJd8xWP/SVwPttqyahZFkVG0bLNwvuiLOK0MshuqHN92nLFxpZawWqUDXUdEjVP9BZFSwZBoQDStDInTTJnIlXrFWOT05iYao8Jsk6tDqZmluS/zlUNFmG21ypYlbeo1l2IBk9JfXXgBTzqBnDvqJ3atdBMqmiKIKRH1Tjo1ano8HjD4+sLNBuGROuxNKoYRauwE7GuD55OO2ylC62ayQePcr1PwOPNB4+XZAUh1GbThAt20STjsovrSz3BbrU6xpT6hSJFW71tHz51y7LSQjIL/SQFonDVvpKidbnJo/2zKc0rb4TMLHkVzcALeFRmGLj7yW3e85G9SjWhyKmtUE9UjQMbz/8VW8PVBo78zJuOjkBTYnhIBs2y6Kl/V6mjFHWKGDbVGO6scqcsV7m270w121YX/ZNTxX5PodpMzzC+8POV2LavntE+02nw5G+TedOo1JCH3iYb6H+X77/mUVzz4Hps3D1aaJkEM60EvzXq1TxttdXZyfuco5tqqt2076MiAp4ID63Z6T0PGwFP1U4OBgl59TVH1TgwmWgpg3pT6/vWpVvwoo8twOjEdPLNOfA5VgUb6SEx0SqNpFPIJhL9zsFXD0wFbU/JbU87Ta17oiATLZ/RVwR3/GrNTlzxyzW46EZzVMgqU4CP5RBZD3ai/T5T3jOMr97xFHbuH8+RShjVZ6hvx711hYjOIaIniWg1EV2k+fwoIvoJES0momVE9H5/ZWn/NGvw6H9PnU8BIcxNAUt8UVQ+Rcx9ZfXIgRfwlLFtIItcB2kxXRWSpO1CPdDZI8cbaDWzvr98+1OYnmFs3H3QS/pFqLhOdza4YqJVIt1X77+f2AoLiwqT3o2iFTHRsvbBY63Boz4Tk55VaumplnaWYCKYqwI/LHVAbVpJ7Wz9jgPa5zLnnff5HIV4aO1OXHLnKlx0kx9hXPRdJvlKG5ucxpaRZmv3ENEQgMsAvBXAaQDOJ6LTIrd9CMByZj4dwNkAvkJEs3NlbPLBE3xsbEaK+WKOtlZEZLWmBaEp8muIiZYQQhZcxdNdZMurrzWquUMrwckd0JwJK0pRNtM+F9FBsCJxslweVdTgKbooQR8aSm2iZZd+6GQ+zjeIp0qoUNUKMfRO6utZY0nF/tb963r3OmiV2ftL+7k8c2cwRrjUoA0JgmM+03HB9x7DWZ+/y1lZKsqZAFYz81pmngBwHYBzI/cwgCOovVA8HMAuAFNeStNdgxlMtMwulVJhs87NSxF5lEEx36ecd1aqgIeIriaibUS01PD52UQ0QqaX2h8AACAASURBVESLOv8+4btMVWm6Td10VhmR7zQDdUNqWvM0bI7S4nszwN1FsEe7b28pC7YMsm+yrg+ezs+0UbSs+4ZBgye6afNVB01btDeVugeCSOoPofae8TuG/VmZ77NJPk+/8OGzJGzSk+zXReXep7Y7K0eFOQ7ABuXvjZ1rKpcCeAmAzQCeAPARZn28QyK6gIgWEtHC7dvTv7+uVpXhc/W6Cw2eItZihQmXizLRqulYakPZGjzXADgn4Z77mPmMzr+LXRfAheb/yMFJrN2+32medT2hqTPdQbLJPX4A0PUvG3v7plHUZsBn+nXf0DSBSmrw+DbR6vzsmWi1f6Y10bJ2sqz8XsZYJVNePai7o9Ok9utAvhMSfGRdR7vwwWPjiiEP0bI1VcMiJbqXHn0hfwxgEYDfAnAGgEuJ6EhdYsx8JTPPZ+b58+bNM2Zq6o+thFNjVz54XKZhwmWkriq00V4fL8L03HsWWkoV8DDzvWirx1WGLA3vHZfej7d85ZdOy/H4b3Y7TU9IJknaLtSD3pzKVgviui6Wk6AcPqW27h3D1HS8nwdXk1ZcMjp/SkKxFOmbzDYH32WhiGQxyC14F7YbxyzNVn2kf7fiSRtP+lctKMKZqk/S9FsXh5xxKdi0+QPjU/inm5Zg79hk6rx9HE6E3l8kXVsN9Ib39Y0ATlD+Ph5tTR2V9wO4idusBrAOwKk+CpMUgdJ1sI8iomi5GHuq1AQlTHq5nNXxdv5zInpp2YXR8fSudE5MbeT67/nmw9kKI2RGtAWaQXdSnUl2PNj+sIBClUBWzYu9Y5N47b/eiU/esszqfp8LxkE2D6oKVRwXC9cS96zB0wqZlfSeoYg6oq8QyHUVGAwadfETGApwoJQ1qZ3ZmlfZppFZg6f7PHDtIxtw+T1r0pcjZxmSMIbeTsiuSuO4Bx4FcAoRndxxnHwegFsi9zwN4PcBgIieB+B3AKz1UZgk06mwD57sFROkU8Q47sQ3VkH52NDk7lB1Ac/jAF7Q8Xb+dQA3m27MaisZXUAVgkSEqSR5NB6E6qD2rqCrrdtxALcsjh7ktGmqOWSrM7qn/XoHx9tOIX+xYmvsfeoi2BetCgoXBpUq1YHvonQ3ZzOBBk/7Z+Dse9ryZdgKguKcpwLA7OF2Zx73FD2pQlUrxKBq0zEzNu8JC/yYGQ+u2VG6loYp+zRzrQsTLVevIancE1Mz/Rqvnpf5fUWyjHbY1PUOADDzFIAPA7gNwAoA1zPzMiK6kIgu7Nz2GQC/S0RPALgTwEeZeUe+fPXXhxOc8qcRftoVxEEaBlyaaFWJIsbKsvaUlRbwMPNeZt7f+X0BgFlEdIzhXitbSc1zkb9zFFioNVX0NTFI/HjRJmxIqQ2nRQkZGdTplpEx/O9rf929ZRDqOKuT5bTRt/JOXrHFS2kSI7gnSc28DIrewHajaHUFjrYaPBky0zzTSplv6iwrVLeCGXVs/uZ96/C7X7gLq7bu635+69Jn8J6rHsb3H/pNSSWMJ00zc9Em49L41dqdMc+ly+fFH/85/uzyB52kFUecQMBWntR0bT1mXsDML2bmFzHz5zrXrmDmKzq/b2bmP2LmlzPzy5j5+77KEsydEwbBfNhEK4cGT+enzzna5RxUhfmmG0BBTLTKgYiO7YSyAxGdiXZ5zaNyTRD9nWpCnhfRQjwfuW4R3vmNB3KnExJsWHS2LLV9y+LNWLppJMOTxdFSBF2psO0Hnc99LhhrYpHgDCI6h4ieJKLVRHSR5vP/q0SVXEpE00T07M5n64noic5nC12VqcgI9VUbeoPiBH2hZ6Jl93wmEy1Na7cyNc1BlYR3ghnV5CMQUKguAjaPjAEA1u44UHjZVNTWtPKZvdbPhTTZHJhoxaXx4R/+2vxhFIuyLNkYXg/49ukYnZ/JUsNC+nrxGAU8jjV4fNZsoL1aVDmbFKyrrB43XFK+AAAiuhbA2QCOIaKNAD4JYBbQlrgCeDeAvyaiKQCjAM5jx7tvGeuEgEHbTFaRHfsncqehLhJtIllkmbACbaD1X/iT9A8XRFbNi7SquH598AR5eMuiMhDREIDLAPwh2s4iHyWiW5h5eXAPM38JwJc6978DwN8xsxqo4M151c37yxXk7TLVahN1KBl89aBvTM3YSXiso2iZwqT3pWeVXGoGqW5dQURXA3g7gG3M/DLN5wTgawDeBuAggPcx8+M5cwXQri+dpmWgYWZrGuiL9pzQLsz/vWGJ9XOuI09lfQ19mv0Z0vDtL8lkodV0DZ0qkvTKrUxrHdRbEWHSnfjgqVAbbfKBfqkCHmY+P+HzSwFc6rUMCX/7QFzw5GfLyCief9QhTtO0PQERagKb+5o6STX1RCurRpqtc8iedkO6cplT0pVloMSuZwJYzcxrAYCIrgNwLoDlhvvPB3Ct70L1TqKrUwe+u2xPYyasxh1o8NjmbykHCqEmHfThJGedeWnqGOiZa9Ben37X8PlbAZzS+fdaAJd3fmYmrJXZL8AfamUT6o+MTuLwOcPd523ZdWACw0OEI+fOSvWcDU7CMTsas7JsAl1uiLvlUH+PmmglRAwlaj8jfb04gnZjEvCENXhymGgVoE0Nh/ujKqwlgu9RiAaPmGgJQrnYhNQWqk8oTLrF/dPTzazvrD6leo487bBdmEza2rWEytL+OSBr0uMAbFD+3ti51gcRHQrgHAA3KpcZwO1E9BgRXWDKJG1AApfhUV1RmtNC8hNFS0X3iG8xZ5Xqti4w870AdsXcci6A73bCMT8E4Ggien6ePHuHUKwdG6nbPu3THJ+axumfvh2f+PHS1OV51WfuwPzP/qLvetbmFHY2nr9RZtbgif6dIR3/B4YRE62E1U6rq9HrqzyCGf1LV+cHJ9VSgA+eosKkF9VMC/HBI06WyyFauUVUtijwVBNxstxWOyeibUSkXe1Rm0s6PkKWENGrfJbnJ4s3Y8tIutDAqkmJTZQ8W3OLupE1xHh3Q58wk6cJzfno+l045Z9/jgfXpLMe8q25UDF0jdX0xd8B4IGIedbrmflVaGsOfIiIfk/3YNqABL1xsTp14L0oBsFiy1JDIm27DfkNUao8umnzVgfVqdomkUZgayV0VdcoLUXYE9DV4EmxCwv8g9z8603Wz+ieV7FtpltGRvGWr9zTjQamtnYnTb3EMUtdekzPMJZtduuzz/TVTNdbgzWXVgpTd1TrwkW9eFXgQf94kxVTVLEyKKQ/iAZPOZQhWSslNHvD8DE+ZN0QN4xr0NYMMKGqnV+Attq5F6amZ/A31/4af/6fD6V6Ls65YdTkAqjWZOOSvNov9j54ku95uOMQ9P5V/QKeuOcHzGxyI4ATlL+PB7DZcO95iJhnMfPmzs9tAH6EtslXfgocF6umPRktz5DlJimtPwzjxjayVBAny7XCWmBrK3RVw6TrNOuGMvhdG3LoPDUt1z2yAWu3H8B1j27o+8xFcbL74In8nSENVevukjtX4U8uuT9XYIZNe0bDIeCj+SXM9911UTPPs0rFJPQIrpr6o9o+8wzBwaNqGtv2jWVPUINLbeqfLTEta4qj+z0aPPUNvIBHqCc+TjIH0ZlolDLUzjv59l/r/AxO92wJa/BE8+m/v2yHlL7IG9462QdPICxLTj/QfJjW3HuvRugTMDVdgG15dXgUwClEdDIRzUZbiHNL9CYiOgrAmwD8WLl2GBEdEfwO4I8ApLe50FBFMzn/Pnj0+QSb4akEs860plxh0xRzer4EMRWq2iaRRmBrRU+7MizsiX4ejLMfvWEJfrUmPvAsaXz55MVWUBvVOAo7G89WnpCwNEXL3hSzzshmotX+OcPcFexsGcm26b5v1Xa8/gt3YcETW7rXovVFmvZgKo9QLOZXrgjsnGjwmLU/8+Ky/diYkBalMewrlzhhbFEMvICnX1LvtiqqpNbeJHy81gFz6JoV52rnSWQ1MdL54AkmJzXNpM1aXbF1lmzCVqhic9tQjCbOZ36q9yG8fPNefOxHT3TyaGYdqTDzFIAPA7gNwAoA1zPzMiK6kIguVG59J4DbmVmNhfw8APcT0WIAjwD4GTPf6qJcqvFQVcjizykL0W9sK2hpdZ0xWwp41DxjnPD4EnTKps8LtwD4y45Z8+sAjDDzlqSHbGBA2yaiApP/WrgB518VrwHbmy+LZygi+A+bKmZDfc62v9y9chte/4W7cNuyZzppuHgbve/SPeDI2IFXbGmHml+0YU/3WnoTLb9CYsGMaR4IafA4yaf3+3BKh+lJuLRwcFy0XPjqD2OTvTVKWXIAEfD4Tt+8VhNy8LU7VznvNOrpmGDEudp5EunruSdMiGrw6NZXTdXgaWVcufd864QfnJyewRv/7S7cuvSZ0HWbCTKtRgMALNnYW8xWSLbgFWZewMwvZuYXMfPnOteuYOYrlHuuYebzIs+tZebTO/9eGjzrgiqayY1NTntN33Ra2WvH8c+ndUgZ2tgqz0QHW18LxYYOgV4homsB/ArA7xDRRiL6QEQYuwDAWgCrAVwF4IN581T97sT54Jnm9G0ljd+eJGyz7godNHnHpTEzw1j5zN7E9G2/U+AfZ7EiQAmVJccE1PaXFPyeVSspeE9qmaL3JKRRQWf5TcH0SruRmgw3qO0zl6ChgDrNe2CoMnu4fNFDTwPdT/oTBR1CxVFqmPRBQNd2xAVPfm54bCPOP/OE5BtT4COsZQNxrnZuIo0TX5WwXCPc2XSTU1OdLLc6c6irSCK7D05gw65RfPzmpTjnZcemqp8sJ5gt5ZhHemR5pPUnkwvLPMYMYWddQdALtaIaByZSm2gpv+s2gd4DAFRJelcTmPn8hM8ZwIdc5qmaH+tMJ+MEJkmUodkx1Jmjgn4ScSlufO5b96/D5xaswI1/fRb2jk7hJc8/EsceNbcvDdv+12dCHNXsz/Bq1JD2eSNY6SK8bts7pr3HmAb60xCKwfTGXfng0aXhupazHhiqzJ3VwtjkDI46ZFbivUW1Um/9IUVdMLMX37zli9HKJtKrXM9xog7pD9emNeKDxwovaucu33l3mGQ2LnrUTVRDLbQy+1YI7o5uNE1RFGxOJYe6fSuFgCek1dDQSqoBOk2Bshmd8KvBY6JlGaUo7Tsz+eDp8yHmaTEqp/r1IORkuXNNHd+HMgjSsx6kuKBP8KG097jyLO1o3GzYNYr3X/Mo3nHp/d3P1MdsAygM5RCMmQiFtLcUDCelpRZv+/7xyD3o5KdPo1VBTcxBweyE2U6D5+DElF0+FnlmxYUPnuOOPgSAPvJeWfjqD2rdJuXhqwwDL+DxPdbpTbREhccFrusuLvrSoFCG2rlr1E2RyQfP136xynhPU8h74BIdu6LpseE+HVkWuC3Lxb7gmRJ9dJgozAdPpL0GgsqkjWPU2W0yJhMt6qSXTwMgCTmIqgeqmY1uw66Gwi6zSv/l5qU497IHEu/rRfBKZ6IV1ZDbvm9ce5+toKsnGGv/fXHEL1yWzbK6rhjKKSTXCfPmDg9F7kk40BEny95IcqJsaoYhjZuYaklylK4T/PuaK/IkO9xRK69C5FqDsp7z9G3y8FUGMdHyjHYwbeqOsmCcz1OiwVOK2rkxr4zDniqoi6o9znD7NOSb969T8qk+eVQ4sy4qTf5Houml88Fjn39Ig8f+McEx3s2DMuC7KKbTcFsNnrShp5OiaAV4i6JVoboVzPT6IitCv17luXSEmof/99hGq/u6Uek6ZtLq4Wfc/B+nTRAy0VI+331gAs86bLY2vagD4pXP7EsuvCWqOV1Wf3/d8SFm05h0oJPXTEzIjqktq+03bgy29VkTjtxU3bmiUk2wAoKwGWYMeRAMiAaP55Ym8h1/uB7AdGFHhWJw+cZ7zrL1UbRaGQUlZZLNB0C2xX6wSIguBKO+WJIcCKoMpYwqBLgJmSu447pHn/aeh20tF3UKHc2lK6hMyD+tQ0qTD54+Ey3R4BGAUAABnVNuZvtVTJnrnajQITzmJz+X1GynO/bXP160Ca/8zB1GJ8pJpm1Z3pC6nswbRUsj3+n78kmrmpayLhKKxfTK1etxY/DsIVsBj/qH1SOpyTNXpFl6FzUl+Ts0Ye3v+nu9FCFZwENEL/OTdTXwPbnl874vA3GR9Hy3lFkKNzSh32Zt/uoc0rdB0mmQ1KCfZZmE8kbu6E8vjwZP+2d0gbth18HE/IBqdckm9K00BOrUP17kxZd6Jnx3WZOGwBDZbdTyOLuNK4+sCdxTp/7Mys+eE99+DZ4yWL1tf+pnskbRGooRtKpXgrHrwdVtE5cg3HhfORJMiLPMv2GH2Pn83+i0tUxJmcaIKkVDrFOfs8G0z2O1w2qwqU8gWYNHV6e+qtmJBk8VGmEHb+8pRR6+5BA2YsEriOgRIvogER3tpRQVwnXDy7O+E0F7Ao7fjwMn8VVioPqtiqqmHvV3VdeT6iyl7i4KU7orMeYVOTEOJiWrKFqaBfnG3Qfxxn+72/jMkBpFq1r1NrB9qyr4PoU2+clrxfgM0d9nl58qzNx9cMJ4n2jweKF2/VkNk662MdWcssgxc2p6Bn/w1V+mfm44otkS0mSLmfV6ESL77wmZaAWmX4rvIh1JTpa//1BPezHte1VNtLL2M135+5JK0NhNq1Xomdr1uTwY27KlBs+ciL8lm3xc13OQ3M+eyB1TpVL4GifTJFuaBg8zvwHAX6AdGnkhEf2QiP7QT3GKx/dYp52ALA9ZKjIQVxbXb8eFl/iqULd+62qQHZ+axjUPrgfQXgz1R6HR5O0kZ7/k0uBxXJb4M1M9PROt3rWd+80bWSDsZLlKXbJufSsvFROuASju8CP61QNN+aQIjqlNtJS2ftnda/quB/m5+t79Wnhu0q0jdezP6twW9sHTfy0JF907q9PUrjPyQMATM+b/bMmWbvQ8W6fjQTfVhRlXGYoRGOWFoZiA5XSyHOdjJclXWlX8MwH17HN5MJtoKfUZUzG2Gh62TpvrQFGmo/40eNTKSLi3LAFPO3NeBeDjAD4K4E0ALiGilUT0Z36KVRy+m1CeimuCoMEnrl9PLwy023TLosn91sSyzXoV7IA69akP/eDx7u9Ziq1T63ZBd5HZ+ZFKg0e5eXgoXtKtmhtUrd4GqW8V+eZtq9l3e4huPAOGLPuUrYlWkvBsfLKtgbB/vB0m19WiN5ptFYV4RVKX/qxqT+pMblS/a3WoUVs/cYs27MGHfvg4PnnLUgD2Ual6Gjzxa7s0QQBsu4pqVpnXwbGu/NFy9KZLfSZVO8D01eeI6BwiepKIVhPRRYZ7ziaiRUS0jIjSq55ZErxpUzsNa2SZ6yWpyoo00Woa3rpDSL6TsA4oy0SLiF5BRP8OYAWAtwB4BzO/pPP7v3spVYnkec26Dqq7ZhsmvSLj8MDQJBOtQeu3ASG/LZoOlDYca5moqrCZfAB0fmZRKdd/EJQlfNnKB4/GtCUImWl8JlSXiVkUxqD1rSpqd/gWSMzqHOlPRuwbbZ2l2kbRCj43OX6fjCTgTIMn+ncF67go6tifVbOfUF/QOF6uMlFTq1AULeU77B9rCzg37RltP2fpMDjQfEuaC6NRtOKwfbXqd8nrk0ungRRNSafBMzk9g58u2dwRMgWfl984fPU5IhoCcBmAtwI4DcD5RHRa5J6jAXwDwP/HzC8F8N+y5tclSQBjuG7rgydLMVzXs8vUKtAE/QdYSpGXr7LYhEm/FMBVAD7GzKO9AvFmIvq4n2IVh8sXy9xvEqIbz5975Byr9Koiaa8K+8YmQ3/7CwPYiPdeq36re+NZqqGVIDtt+yYIX6tDUK0s60JTqOe8BP0uSNambLrFZ5IGT8hEy754RVCrvpWbCo6HvoVOgW+QqClWoDmQZJLS1QBKeHfd8KiWY5CruSm6thjwtUbt+jNzT4Cg9cED+25bZs1HnQ+HTLRCzqMRuc9OgBp8nBhCvBWk7+5thJ0st3/P7IMnqGtF3ty/jukfRC69azW+ducqzHpvq2ph0n31uTMBrGbmtQBARNcBOBfAcuWe9wC4iZmf7uS5LUd+fRycmMKhs8Nba1O1q9fj2kYWzdYqD+lWe7cKl9+GNO/f1/xrY6J1EzN/T+2ERPQRAGDm73kpVYFEG1qe96x7VFdxJz77UADAUYfMCl3/4q0rI89mL4sNX79zFZZuGvGbiSOmZxgv/9TtoWuu+0TeSAcVo9H91kRU6yNal3XdyGTT4HFrc29yqmyzKFZDxgYMJ0jjqqrBgwHrW9V69W189+OuBk/EZqOrmWMZRSupbyR9j2g+rr52NN8q1nGB1K4/mzQyyLLduS9PtueC8mqjYWnSDK7ZatwE7yHJ/D7NXBmkuXb7fnyn4+9Pm6YyvaV1um5KK6zxoU9Mvbp17xiAtr+7ijlZ9tXnjgOwQfl7Y+eayosBPIuI7iGix4joL02JEdEFRLSQiBZu377dqgCBthlgo7lhJ5BJbOfatOPzFvwS0rbLqOGVFxsBj67xv89xORqB3kRLd1/7Z3R/c/k9a0J/J6mB5+UrdzyFt3/9fufpHhifcr7A0L0L128nyRFfzah9v81SD6YTwIAZ7r9eAwWebNpM3VPJlHlZ6hsHfTxrmHSTWUpA+ONK9cna9600FLkhsO3zvg8/ZnW0y6ICnrgNqYrtiX1XI8HweXTec1UXTRF8O6I2/VkVsuu0WGyjUFWFqJAqXP4e0bWZbf8K3g1phGEqWbRd33X5g/jkLcv6xogoDM5votVNK5RwiJbmOwTf6wcP/yZzVE1P+OpzuqE0+tKHAbwawJ8A+GMA/0JEL9YlxsxXMvN8Zp4/b948Y6ZZe5raHGI1eGwTZPXX6vX/JF9YJnwIrH2/H05RF4WbaBHR+Wirsp1MRLcoHx0BYKef4pRA5MU+sm6Xq6Ta12JqLlG9tIaLrnU7DuDNX74Hn/+zl+P8M0/0mpfr99MEJ8sD028NJGl9+A6v7Issbd21k+UgmT7zDosFo61Kve6ZtM/5YlD7VkU2BCF8z43DXQ2ecD46QaUOW6etUZOTKL40bUZGI+bOFehfRVPn/tx2shz83iOLoMJFX8q6WerTmjb40AvWZg+tba/P4zRiwpG4AoGQnYaOlQZP5+e+jqaGjX+N3FG0NPUaTam3fu19EoxTyzbvxfHPOgRAucLcAvrcRrQjcwUcD2Cz5p4dzHwAwAEiuhfA6QCecpB/qhND6yhalppq4Wv25bChcK3AUN71cKWgEi2/9c0OifPB8yCALQCOAfAV5fo+AEv8FKd4ou/1ya37sqel21DqNHgCHxaWpw9p+eodT2H1tn34xl+8OlsCOVi7fT8A4I7lW/0LeByksWVkFMccPgezhlrefJYUTC37bZymWxpCAp4UeVUd3ViwdNMI3vftR3D7370Jzz5sdt/nmVWyLdVJTQIfHbrTxVRFqkal1bJv5aUSbz6CfxMtvQZPkGuSDx6dU3EdPeeyenxp8Fx579rQ39XoXoVT2/7c9uvSv6Hv+eXhWtRpnCZOXPFtTbT6AwLo70tj2hb1F2QqQ3CZLe5NQie8MZXVtLGsyAbZd597FMApRHQygE0AzkNboKTyYwCXEtEwgNkAXgtPztQTBTOhe+3us07P8pkycLwkrSRp1qy+1jNGAQ8z/wbAbwCc5SXnEtm0ZxSX3rUKF5/7Mqfp6k1CYtTuEuo0a6VfcueqTM+5wFcoRh/qdHvHJnHW5+/Ce157Iv71nS9X1GDrOJy0aXK/tSHkmJf761LbLiuy8olDV+7/vHctduyfwH2rtuPcM6Jm5skmUFmJTlw2XT2Lbwi19FXQ4BnUvlUR4VoI3+1hyOBkOSBJgycpak9AV8CjNPbZQz3L+eiJv6uqiI4Mg2iiVcf+3BOq6wX4ZR1SmfJL1pgNa+KETLQ03yvA9sAg2r+MJlpBngmlDT1DBCBZkKaGSc9aLzptrb60Et5JmkhhvvDd55h5iog+DOA2AEMArmbmZUR0YefzK5h5BRHdirZAaQbAN5l5ab58M5e393tM67Odg9X7mjSmd4MROMT364nTtuu711MZ4ky07mfmNxDRvkj+BICZ+UhPZfLORTcuwX2rduCclz3f6eJVr8FjVp2zPd2rE0WaOY1PTud6/uB4+/k7V2wF3vny7vUavvYuTe63NlBIg0cncC2yNO7QjQVzh9sbwTFDP3AtbA1SCd5h7297DZ6sZalCtQ1q3ypyPIzLiyid1lgehjsOrKYM9mkmwU+ArYmWbmN71oue0/u8z8myn+9d5zkvK/Xuz9zVEgtpaXQ/rUaF2vqq6vrgiRzQBES3drY+bbraNtZh6mxuCWvd2TjAHeqOBxk1eDTCGYN8Rxt9DFAFPJmK4IQi+hwzLwCwIHLtisjfXwLwpbx55WUmJJAx35coRIz8tHkmLS6Ts0krJCCpxnCWGRshsA/iNHje0Pl5hJecS0QdLIuU4nWvRX6mebbyaE4aoqzeth+7D07gNSc9O1dWP3vimVzP9zad4et1fO0Bde23ugVplnqIavBEWbNtP+YdMSdDyiWj+S6HzB4CAIxN6neRWU8N0753OwFP/+Iy6TGdL4UyqWvfyktVNovt8/I2vptDy7AhC/KdTnBMZGui1dvYqv6mzIt+V5uzqEZEHQ+T8lLn/qz6pNC6AUhRnS5q3pRGkkBjKMaXzs4DE93foz6qur6wLA9Jkw4YolEe/3z+CfivhRu090afMZWhe5ntxwMTPWGSJv3gHo0aks4Bd8kaPLXtc7aowsSkNx0Oex+jwWOZdziJ6o3pWXVwqrIGSUOabuZL6JoYRYuIvkZEtVFhtWEoRl3ziLlxbonSE1fJzdTgaRM3WP3BV3+J/3bFr1Klq7WsSZWC5vmI2Uhv81C/9x6lif3WhqhZUrQq//d1v+57pvoGWvoJYO6stoBn1KTBE/NsFkymWQnKDO2yeDLdLINB61tV0XpTi+G7HQXt1bRBTfLBY2+iFb6//Uzvd5OAKS/RDXNV6rgM6tSfVQ02K4kRNAAAIABJREFU1d9O93PlZxWG2qR+Gp0X1M3x7pCAJ/pcSg2eBPOl6OetmJ1RX5pJjtShCJgya/AEeat1HU5LF+o9pAWVYKZWJHXqczZkFUCE57SY+yyTTxOau0zStsEqfxcT4TaRME55EmDZhEl/HO0wcquJ6EtENN9LSQpEVZ+OvtYXHnNY5nTHJqdxz5PbQtfiTLSSGq3PMOm+Bnlffj98YNTgqeFgoqH2/TZLGw1H0TKdrNWvgnXjSKLQpKAFnU36uihBSZNa6CSsWlVW+76Vht95XvuwdfawzXLBH2obKEogERXk9MJUxxcg2CAmzeHddJTuoT7TF7HOW0eoVgcrmNr1Z1VoYLyhQExzQLIgNKxlqk4Tk8qzRhOthO/Z88HTL/wIlwOhcth0syQNngBm7ploZdXg0dS1SYMn7Puj90cVTLQUatfnspBU3eHx3Hxzsiao7pn4vMtA50vKhG9hle/Xk8rErCwNHmb+DjO/DcCZaIeR+yIRlefF1wGqumT0xefpFJ+8ZRne9+1HsWzziJJejNpdQl4V29RYUeRJfZwsaXqG8bmfLce2fWPGe6JO57qTu7MSlkfd+q2XAdyD1ldZaAU8Cf6ubEPDRjGlF71su9lVyTouVEnzp259Ky8ndw49/vSM3yq5JD18t4eeKZZh45qgtjZkuZnS+QhRv9s5Lzs2cr+f713FzUBR1LE/z8xwrB8a7vxng8+uZKuxErT5IUVqNa04sIqu9QI/5MnClc7zkb+j9Dae9i8jjQ+euLDudnklm3hp18MaDZ6sWkQuqWOfsyVNGwo7RY67zza9bOWwS9xtcumyLr/NpiXOX1b/vX7KkOZI7rcBnArgJAArvZSmIKKO3VTyLBzX7TgAABgZnVTS67+vGyY9odqrtKmxpUgny3Hcv3oHrrpvHT52k9k5vmnCq6OGRwy17bcuakGXRvRaHZTOtMKqhHLHjXMuSXBHEipD2OY8/hmTw80KUdu+lYUi9gW2WRQ1Rps0EJJy1zlF1abT1TBQr/V+nzM8lCpfW6JDRx3XGh6ofH/urR31PnjSaKAUga0WelDew2b32nuc9o9t/4pqyJnX3PHr1t9+7uF9ZU0SmHTrivMffAb+9g6M98yxzWHS9RtLnQlXBah8n0tNihds65Nw/c4Ddlmn0RrJyNxZDjR5U5atAjJJr5RmokVEgWT1YgDLALyamd/hInMiupqIthGRdhdObS7pqPEtIaJXuchXVVUMXmzQaJ10ilAni1O7691zzQPrjJ/7wFfn9xWmU5eeKXpQ+/72A79YsRUnXfQz7T1RYZS6eKo7PvttlTEtbupOFm2k3qlfyrwMby5aBtUfhC3Zo2hVpzY9z4nnENGTnTnvIs3nZxPRCBEt6vz7hO2zWcmiqeUbG6GiC/o2qJZt3jZqnM4HT5xWgrM6iAweFarawqnlXMmq2ZFOg6fYOjVlZSvgCdr1obN7PjDDAp6ok2W7g8Sek+X4+6OmI/1znbquCISyllp66JloZdWeObQj4Nl5YFxJN4zuO+pCzVdhHK9ln4vBZD6c5hA/rl7+6aYnkkoQWyaXFNV8TO3YXfp+v4iaelJevopi41F4HYCzmHmHh/yvAXApgO8aPn8rgFM6/14L4PLOz1yoETKY246Vj5gzjM0jY7kGP51TU+14rgh2AOCB1TvxqZ8s77vNpw8eX2RRdc3Kbcu2Gj8bijVQb2PauFRg/nOBz35bCHnrQev/Kl+SpRHng8c0eQR+QIoyZ0l7b6IWRMY8CsBL3yKiIQCXAfhDABsBPEpEtzBzdHK4j5nfnvHZzFSpDorapBi1CBKyT+sjJOlaN1tHX/vk54R9DVZh01citZsrZ5iNPgSDz63xWPWJJlSRNdgcRTtAXf9GtVW7UbSSnCwHzwd/G8qTZMKlu9x1xG6xeQvWo0k+iZKYnFIETSmHJrIUihVE7fqcLelMtPS/Z87bUmCUKW2HB+Bp94fVaLLpUF//3U9uj7/XUxmMAh4iOpWZVwJ4BMCJRHRiqEDMj+fNnJnvJaKTYm45F8B3ud1qHyKio4no+cy8JU++upCFLga/IJpNWIvAvMkMxvpJ43Gkv2btK+WsWgNJpB0Q0jh77mlSBXnVlyL6bZWJO3kz3VMDC61MPnhsTxjzYrOQ6I15KQpTMROtAvrWmQBWM/PaTn7XoT0H2ghp8jxrRZWEAL7bdHfjacgoWYOnd4gUR1TDoH2tvxy6z/Jw5CGzQn/vG5tyk3CNqPNcyVDH//71JnOx6xhTd7DX4Gn/VJ3xT6o+eCL32/evIN34cvbyTZaaPL3rIE499shYH0jRvGZ1nAZlPbSNaplHfw/f27t+2m8diZsXbQZgr1Xokzr3OVvSHXix9vesbB4x+xx1Ro5iZnWHkBSproqkqU9ffrHiNHj+D4ALAHxF8xkDeIuXEoU5DsAG5e+NnWt9Ah4iugDt8uLEE0+MfhxiKDLQEdyoLz61dX8njd61JFVyZsacIb2l3HQNG3XSPFkU/eGyuS+Sj8nEpOY+eKrQbytBvasxjG78T4pIkNUHj/lkMNtm13RvmnJVxETLd9/SzXc6jdWziGgxgM0A/oGZl6V4NtVcCajjZOKtuYkueHXR16L3+aQ/ilb4p4lAe85WNVv9mnGLPVffW9dnt4yM4vlHHeIk/ZpQu7lSXbO0YoQWlRgtYW9KGbRrtX2HNXjC40Cc9mpY8zMsQDVqt3Tv7/yEvt8DwJINIzj12CMTtX6gfD7c2XRM5lzUx/ls0UUKO+7oQwEAbzn1udi+b7wvjRKoXZ+zgQ2/J3XEsDA/R/66McBTPbtYi9mUTb1lyoNNdlXGSJ8YBTzMfEHn17cyc0gsSERzvZZKyUpzTVsvzHwlgCsBYP78+bF115O8dzIhcmqfmqQmF17EArMM4WerdGJqS0++47bsaV9F1EJrhnuCPVPaHPm7jlSk37ohZz0YF381rN+4haypvSadMLoiTX9JNFmZ4a6GZTiyUJaSuaWAvmUz3z0O4AXMvJ+I3gbgZrRNmL3MlSpFz0fM5hO/osoyrSws1X6UlL2tJmtZJlq6dPYcnBwoAU+d58qZmYRDSWZrYaCTtZohiaSNWZzwWBWuRoeBwORJZx6lXlEPcdW/o6QJ33zcsw7pPGMuQ6g83It4NjGV712HtbXCkOYDtW6r4IOnzn3OFqPza82BhVoXec33+vIreQ8Wm1bK+ycTolZWkTQl9tUlbdxhP2h5zQcbAZyg/H082qeWuVA98PdJ+B3PdTZe/mcZNHh8DsS+TkDLsvONfp9WyzyQRunT4KmjBKCfMvttapxtXAp6pmi0ZYxxsgnY+wGxykvzQdDnkha46rNJ485EPdQWffWtxPmOmfcy8/7O7wsAzCKiY2yezUvRmo3R3F5x/FHd372baHXSVxfd6noh0UQrCOOc0oQEAJZt3ovRiU4AAY7e7+aLx/n0GkBqNVcC7XFSp7FRlpl5Xu1O3dgyFRMmvWeiFZ/u0k1728E4LNelcZvzI+e2z8Nndw5ke36A7OesrJoIusPHfg2emOeZE/0QFUzt+pwtptd769JnYu8djwka47IcZaZLGR0i5NV8K4M0c/Wo47oPiPPBcyzaat+HENEr0RMQHwngUC+l6ecWAB/u+BN4LYCRvP53gLAtKqM9MLYUoU9e1AFUJ3kMDdIAZhlUS6oxDqejleIkJA1J6UVPe6MaPNMzjFlDkWc6P4M676kJZy9n2VSk3zohr6CN0d9uWJNuHeo7y6l+USd2adJnw+/6e1WNifIrqYC+9SiAU4joZACbAJwH4D2aMmxlZiaiM9E+pNkJYE/Ss1npjpMFr7FmmDGkLAjV08/iNHhUAU/v+t0rt8U+p/OPoqMrJI0IgrbvG8eJz+lvUq6+ta5YafzWNYG6z5XDnUXO5FR/x2QuVshjkmPa7st0B/RhDZ5oFK32T13/OmJueFuzdNMI5nSEMuOad6WycfdoJ93wdYaiNdQp17MOnY1t+8axY/9EbJpqUnk3qtGjSJU47fn2PidsuVAGde9zJswh63vsPDDRd586j40ltM3Y/DXXfM2RbvbINveo+2gPJloFHRLZ8MEfPIY7//5s52WI88HzxwDeh/ZJ4FfQ64h7AXzMReZEdC2AswEcQ0QbAXwSwCwAYOYrACwA8DYAqwEcBPB+F/kOdZ0sd8oBfQQsW2YNESanufszJJWdipfMzTAbIz7VOoqWw96zf3xKG0ZeJZpbdLEar8ETSat+r13Fe7+tMlHzR6tnPJXFJbpFWZK7qyHH2nQ6YRmQbsGYGPXE8HFFhkKvfYuZp4jowwBuAzAE4GpmXkZEF3Y+vwLAuwH8NRFNARgFcF4nCIH22bxlUilCqBJ3Qm26zydTys5T/f4HJuLn9V50oPj0g8+HI1q8XR8+ffe7+eK6TeBgiXcA1HyuPKQTOvug5vS3aC1kU7u0NdHSa/CYv0MrImxRefULnoXvP/R06NpRHafiew7qhTFB9qu27TfmOdTplIGp5pGHtLdPqoNyk6/HIP2sJlo6zcF+DZ5+AU5wzz1PbscrTzy6fS1TCZxR6z5nw7Z9Yzjh2XayKrUOx1xr8DhNzX+6cdTRRCsNa7Yf8JJunA+e7wD4DhG9i5lv9JE5M5+f8DkD+JDrfLu2szOMiakZDA+1eiEPM+wknnvEXGzaM9pdrKlJTOhOV9Tf2ay2lmYxd/2jGzA1w3jPa5OdZkbL4JZ4Z3ZZ+MTNS3HTrzf1XX/x8w5XHFuHT3ujQjNdvZql7vUdTIrotz7QnjrlrAZdmrN1/q5qINHLYlKhixZoQ3qnzMn36za8usdCZdX/WhoFzYkL0D7YUK9dofx+KYBLbZ91SdFCtmi7pZjPiihD20Qr3fNJ5ouPrNuF337u4Thsdli91KhN4+hrawXGAybhqetcGRCsY0cVYWPQPFShQhIuupKpP9o7We6/FtLgibTNNNFaiYDD57S3OgcTBLPd8mjKF8hgg3Jpo5hxf8QudR2S11lsrIlWwrMurRSyUvc+Z8O/3LwMCz7yxr7ruvE1pMFTcRMtl8ml3WPV0USrCtsKGx88ryaio4M/iOhZRPRZj2XyjqreuX98CofPGe4OflkG4OC0LWrqA+hVQqODtNl+2b4M/3jjEnzsR0/YP+CJnlmIuzTX7kiWbkYnrT4NnhTVWoWO6YDG9du06Ba57//dk7Xq11VH1ya7gmFDg/XRF3VlSpN+oslKxucKZiD7Vtl1oA7pD67ZiZHRSe95ThlMtGxJ2kxd/svVAPq7sEmr16cPnoHU4WlTm/6sq7aDE/0h7ovuqqb1ldYJsuZab+2sPBuzaOs5Rbb7oknmykmptKOW6Z0qc+S+vrSVS/mjaCnCJEOp2fB7xXzw1KbP2aC+0jgtK93aMzh0zCPg0Vepn3rO03zSHCKo+bh2QF0E0bHg8ad3F14GGwHPW5l5T/AHM+9G22yqtqjORw+MT+GwOUM9AU8GVbCoKYSaQpLNb9wE5XMg9pV0z5O//w6pZvHIul2hz9KYaEXTq99QoqVx/dYG0+ImQKfBU431Tjxak4oEf1euT+zM5lP26YdvNS/0o59WrI4Gqm/1BHnFVkKcBg8ArLcQ/Ocl7IMnvW5nUgS7IcOKtxf0Ify8q3WuVmA8sPKdmvbnmP64fMveQhcyprFBpzm9ff949/fgU20ULWUt3q/B03ne6ju68d1likoZEkrpBDzozd+TWU20+n7RmWgF1/V5ZA264Il69jnHzDBjbmdNOjrRv1ecd8QcAMBfn/2iDGnnK5tP0nZDP3vhYl9Q4N+rSGwEPENENCf4g4gOATAn5v7KozpLm5phzFZMtNJKCp/aug/rdx4MXQuFSdeZBikNKy67Ovrg6S5MHaY5bDjNVPPYsCvceVqRlp0UTtPug1pR+36btxp0cwJrRCV1MMnTDQVJi9w4R5RxWKv2W/oZUdNMWmCbPq5YHdW+b2Wh6OmoCkK9kIAnxQuw1W6LRnsM8GyhJT54wtS6P+c3Zc5PGgHP+KROq73/vjgny/ERo8yfmdbU0fx1mhdRJ8vKp9rndH1sMqeJlukApI0uqlq/kKxMEy2FWve5LOhccTD3/K+Nafy1BgcAh0YjxFjg3ESrxGbjP2qm+wz6x5DiX2Cck+WA7wO4k4i+3fn7/QC+469I/lFPtqPvPM0iDgD+6nuP9V0zqevp4Bi7/hrKd/rsj11gUlc/bE6v+UY1M6KDaZp6rcgEmJda9VtXr7x/gRVOWHWI6Dpvn8T54HEdJt2EUSU8xQuMU1+OphXnb6BkatW3XFH0uBjNLeq81Gvene9qiqJlS9I7Mx5eGNcEbupAKzAeXBWexvTn0PhZoFDc1Cx1AhX1WtR5sMlfjVHgmbI/5JkLTQKepPW+ExMtDv3oSxdIXnv7NtlOibc+R0TnAPga2sEGvsnMXzDc9xoADwH4c2a+wUXeQLyQL/rqJ6dnum1YZ6KVpKVtyqddjmpUtI70JfP7XWYYMASzzky0TsrYVyYKeJj534hoCYA/QFtEfCuAF/gumE/CYdLbXu+DzVBaCfscncmH8rveuW/v97jB1quJlqcO03U85zB9k4BHvap1nqugtwWP/I1gkVF/mtBvc7d/zeOX37MG/+uNL4zkky+bItC9C4qYhpo+9zWxZDHdSbq1DsLuJvStNHTHxYLrIMlEqwjUTWYWJ8uJJlodVVPTXb5OAbUCYycp14869Wd1XVWhIdGoGaNrZzZrsbg0AXVuSy6bKhwy9UebdxksQ+N98GjSVsaNrCZavbTUfE3joz4PnVPosvDV54hoCMBlAP4QwEYAjxLRLcy8XHPfF9GOOlkIOiHlf/xiFZ51aDvCm9ZsNkd+5ddyP1ldePhosiHTyhlzNGsX6ev+LgIbEy0AeAbADIB3Afh9ACu8lagAWpow6YFJT1qzqENn96vOJZ1Sh+B6bGps6Z4SOHR6bup46kR13NFzY9Owqdd7n9rRl27NaVS/Tcvty7dqr/epYxdRmJzECoIN14Ne49NvB2DXt3TCIN1TJieSFeyTA9e3CtfgMZxQF0nUB48twa1JJt9/esZv6Z83BV5wNK/WcW3hmdr2Z1vhoPF5B/3aFJxE1/51QhZboU9A1zxZ81nfuJGQj8qRc4c76Ua1dLhfg0dj8hTWTupPP+8YaooyCSRr8AR7nApNpT763JkAVjPzWmaeAHAdgHM19/0NgBsBbHOQZ4i0h9u7D7aDBcS1jSwHYw2xRADgf77y8a6iKZYx5xo1eIjoxQDOA3A+gJ0A/gsAMfObCyqbN1RbVO6O1UEUrXS1oFNr3nOwF90jqeHEfZ40Gemw9cTuu++7TN6kxh4Xcrl/gk7O50edUOx1Hhbr2m+1C7WcKa3bccCq3isoPOgjThMgqfhpzU5tCVJN8/qSipJ3s+KTuvYtVxSxYAyb5ZVX6UHOoShaGYQrOxSHsjoOnWNYgpmEqT5sWeOzbCxN6c8xe/7C+PyCldrrScKc6Bxiq93efd6yP/QOGOI/N84/MActSBqzwho+WVc13J9X5B7S+ODRfV7mxr+APnccgA3K3xsBvDZShuMAvBPAWwC8JqG8FwC4AABOPPHE1IVRXzUhvr3GCniy9GzH1RzyHTvDRv9xdmlZ3FPgWsCLgCeSpq91eBxxGjwr0ZaqvoOZ38DMXweQPY5bhVCjXjG3BT6tBOm3CV0TV8OhJYWJZMSc1mVodElRu3yT5Mk/C0NRj8kdkrQBVLSmckafItZFqyKN7beuqGP16sqc5IMnuJp2HElthpLigSTtxoqfOg1m30rYHHnLtu8kvngVnqhJRNqFts7nVzj9/o1b6PPI364WiXrT8Ur3PR/Urj+HNz0J9/otSog7V+oVIZJ88ARoBSMWmvA2TVY9hE3sPzH59DR4zI8YTbTik7dGHX+CA8mA4Gs+sWlEybv/85K193z3Od0kEf3G/wHgo8ycmC8zX8nM85l5/rx582LuUwuQznF+QJxftCxDs89q3js2mXyTjoxquD6+i5qmj4BG1y/cGPq7jLVtnIDnXWir0N1NRFcR0e+jIWbagSR+eqbjgweU2cGgGo772CPbZkK/ddQhip+f+Ofj1fLSN4iyF2k+sjdVTVyEk2g50px8lv0Oc9LYfmtDlqpLq7UXzq+YtqL3mWFXra5O/aOpJJ2K6p61MVmNpt/OoxJ9csD7VrF10JdbyW860yI7af5P6RPEp5PlSvSwYhno/hzgot6NazRL0ytde4zXaAh+xmvM9JXHOFlxOM/IbXvHJhUBT1vC04ti2bs5ZKKlK0/Gl63TcDLdc/k9a7SfmzSQCsZ3n9sI4ATl7+MBbI7cMx/AdUS0HsC7AXyDiP7UVQGy+h/NI7zXPemzmvOm7fMQMQsuXYoEXPvI0+E8Suh2RgEPM/+Imf8cwKkA7gHwdwCeR0SXE9EfFVQ+L/R88HTeuKLBkxrluWCSm2buDaZazZEecY4bszS6qoRWd9kfTVWTRp1XO3hanprWibr2W/0Jnq+8wn/n6TNFdTfdu+guqo1r1njtAFek800Sf2/88rtc6tq3XFH01FLmRkSXNRuua5/vOuyPfyDJZCTKtKNXkhTtZxCoe3/WjqWc8LknTGu05Cha7Z8zmrlKfTKPtjUpz5vGlCQTrYMT08rBsDmvpHWMUx88ESY0BVPfmw/t+rQU0OceBXAKEZ1MRLPRNge7JVKGk5n5JGY+CcANAD7IzDc7yLuPvkOxmHttBJppcD1/Fm0KGurznjN0ZvocQ9U0eAAAzHyAmX/AzG9HWxq6CMBF3kvmka52zYzig8ehB8fpGbaXlsd8nKXRFdFQbbBpzLYSa1PVmByyAnk1eKxvrSx17rd5FiDqk6efcLTVvGByEGlDUYN2XNSbpBKkFWDZh0NPL0CajumzgNnsskpadXXuW3koxAdPyLF2+LOyVSt++eT21M8kavAEG1vLDWxeE634ZU51+liR1Kk/G1pJwaXox7R+1gp4cmj19B4wPxeHUcATU44AYxSthINGZu7elLX76gRQL3n+kaF7JhLcM/T2JNnK4BJffY6ZpwB8GO3oWCsAXM/My4joQiK6MG/6xnwd9MH49p4+fZ/VnHc9lvZ5H99FLUIRihGVFPCoMPMuZv5PZn6LrwIVwZAy0DHaC8cXP+/wTGnpprXpGY61d7XVPMlmouX2vqzYJK87cdBhMkVJE60szR6+KlpQrqhbv53pLmby1cOUZfuaynEsXpyAp/+a7Ymcd/VWi/TZcoGbx9dCGdStb2UhePXF++AJZ1hGFC2VL9/+ZOpnkl6Zsb0bnsw7N6nm6VEaNu1loo792dyG8j2fBlPX1M0NOiGlXqMs+aBBa6IVI0AytfFAeNsTuPZDEa18NVhLgFZ4Ffo978a493v0PSYJeHpRNavV0V33OWZewMwvZuYXMfPnOteuYOYrNPe+j5lvcJFvEgSK7Wt5Au4k+bByTdaUbQ8lo1RhDZs7j4o5WW4sURMtIuCT73gpnnXoLLxw3mGp0tItOkMaPAmVGjfgZ2kPVRFO2Awu45P5DB9DKoOR7KLvVbuAMKRbtQlwEFDfuKs2PGkQ8KhtY6hFOX3wZH40ZT79GQWCT9PCzuZUUp+X3fWoer1d2vFC2bDQVl3IC2VTtg+eMpwsq0QFxlbvI+GWX6zYqn/MIOTOOzf1NnnmPAV7iOgcInqSiFYTUZ8GAhGdTUQjRLSo8+8TrvLWVZcLTYIsmISvusOT8PweCP7DgpW5s1rGAzzTAampP6plM60tNuw+GMpfm07nZ3S9YBK66Byou/T1ER0LJjWJq7ckhVEX3PDsw2Z3f1fbZNLYrbW47Fy87G69X6XY9FI/kSLtAtpQ3P7ONUXsm8vodgMp4FF95QRvfe6sIbzxlHmppWyqk+WgkUzNzChOlnWCBe57RkeWxVx1BDzJ94xP5XOeP5Ni8EzzXqryDsuizEUrYHZ0aIPaDJ7aut+qHdpq+iTl5xNdPnNmtYfvlc/si33Wd3ueYWDV1vgyqPfGYRYuDXafrAJFC76j+RWpwaM3H4zcE/M6TAKaKA+v26VNy9fhQ1zkvbKEA3WFiIYAXAbgrQBOA3A+EZ2mufU+Zj6j8+/iosoX3iD5rVujiZYmXxsTraMPmW0UhsxwL5pd2k1g4ntg831djZ2ogCeUfn+S7WAulvnHFytEdCx43QufAwB45yuP06bRi8gk/dwnf/CS52mvJx0kOp9fPSbnUhMtbd6uMDlH94Uui8NmDwHoBWhyzUAKeEJh0sHdk8HhofSn+brTgekZRZ0z4aRsatrsZDlLo7MdJHwt5oLs1+44kHjvmKUGj02EhkQBj8WionvvAAt4qrBodfn+kxxRTs9wLg2eMn3wBMLl7fvGtc9k0bCJYhJ+qf7LAOB93340Np3g1iSzytCCXfO8UDxd8zoPkSbi8w3/XbaJVtRXl02/enT9bqeq2XnHxqiZiYrs+1JzJoDVzLyWmScAXAfgXJ8Zak0xLJ7zvaT545ceq89XZ46ldCOT6VSLzGvU8L3KJs1gXqWaxiRHSU9+Uf0+ePrLoKKOGy6rIfpdhjunys89Yo5Stt7ntpF9hfSYBI3qq1YFkzpc10uTLBF8CyUL8S+oyeO4Zx0CADjnZfrxMy8DKeAJhUnn3sJxVqtlNOswoaqNT3UFPDNa+1wdcX5osrS5Og3ets5tjU6W1YVC5LPou0uzyN66d8z63gZS+KI1Sh5H4X3Otm3yq4WAp/+aTmiifzZdGdW7xw3mX/1q6nZ5JL3rHzz8G+31Ji1W6koxi6De7+sjhwRlm2jNzGQzGzwwMZU6L515B5A/ipaYaDnlOAAblL83dq5FOYuIFhPRz4nopa4yTxaQ251Q5znsC06ej5g7rP1cN5fH+dsJfhKRcS0b1txW8rKYx033xPnM7N4Dvf+q6CY+el2dK7P2Md38Gn2PSQc6QfllLvWLqT+P4ni0AAAgAElEQVQlzV5JEZdTl8NnNedMO+3jTTDRKkNxYCAFPCbhy6xhwmTKFZRJg8c2TPrE1EzMSUX6BqE2or1jk6mfz0uaxYLtnaaFfdifR9Im17ZUwIGJfKZjNcfZopWILiCihUS0cPv2+Ag0Ojt2F8NhggIPgLxOljM/mjIfc0ZJArE8mhcmAU/0NMrW9Mq0AQl47De71Rv6nhfKY+Uz+wpV73/PNx8uLK8oWlOLyEXbsLa6u1oJq31T0os37MFDa3fGPxxD3MGTmGilRleL0Zf4OIAXMPPpAL4OQBuOOc1c2ZehRZ/0talPcvSv29To7oyWb6hFsd+rN5+oc1DyOjrZD4p57dF17dBnq9n/fLSsLrRpo5g2jCYNklYrKIOzIggaTO933hHxZjjOw5o7TQ2hhpU17aAPjcYcejAzfrxoU1gw6nluKqJPxB2qXPPgei95DqSApxUy0VI0eIbSa/CoBBop04oPnqTNV5wGT5ZGpw4Sew6YBTxV2DDl3SyEHezF36tfaFTgJVQPZ4tWZr6Smecz8/x58+ZZF2D9zoPW9yZh08byhEkvqglpv0aC6UzQvtNqRKnvTPWTpSbTf4ppl0fSIqZl2PlWYbwaVNR3/xuHfTMtZZtoqb40APs2qbuvlePLnHflQ5mfVdc+UQ6MD/TBRhY2AjhB+ft4AJvVG5h5LzPv7/y+AMAsIjommpDtXKlW28HOJsnYDNVDE88DqEnYoI/W1r9RjN5GZF7f6Z4P5aWRv/TyyX8Q2A3O0lkqbdvX0/g2Led37h/XFS0Xpq9iymPV1v2d52QydY36RmdMEjaY3XH0PZc2f63wwF895036y7c/Zfzs5kWb8JHrFuGqe9c6y0+Hsc48Ufkw6U1BdYDM3PPBMzuDgEd1LhdstKZm2NoHz+TUjLHxZrHdTwrZWCWsv57JRMugqpt0rxCLs0VrVn6z80Ann/TPRp+xSWKGs4cwLKpdxU3Wrk20VEyR7qY5vFixzeLghF5gFDCkjKfqdloWpdWgSnNK2WO6vYCnOu8sLlTyf//PXxVbmPrzKIBTiOhkIpoN4DwAt6g3ENGx1FkMEtGZaK+5s6tgKeg2SaamFmse4KB5mpLX5qu5FI2i1SKzBo+apOn3UHbqmthw07zD52ivh9IJ8omkodaDycfcto6fPFdOlltkHv9MWazatr+vjIJ7jPu5xD2KfVo2+NRK2T/uzzpk5/4JAMD2/T3fkr41bAqJoiUCnmIJKjWswZOuElTZQ6AJYAr1qyMuv7wmWrYq5C5JU2Tb72c66wxP7hGNgkjSWg0emed0lLNoVepi3hHJiy3rZCN1fOKzDw1dC/p+VkfLtfDBk2PyGlM1eFSTrEiayVnYjYlDJg2epOSFQvDd3OOSj0bq+cY96UPHusQ6oIHFbdE+4es99w6ekjNgZlx9/7qu9oEQhpmnAHwYwG0AVgC4npmXEdGFRHRh57Z3A1hKRIsBXALgPC5opR86obY8t8xatDjzqEM7kWLi7o1eakU1eELmwL2/tin+Ek3zXPikXntL39ii15iNTyMom45AM9XVPrJFZNSiTdp/yLrXL6Y+lPTe3Q8L/ir6D756b+40koQqaVxw+C6LC8owjRxoAY86UQDtzUXb8bJ9TajzQlCB7chY7T/0DUc/WQU8+7DZofTSoE7kLqN3+CBvVBZ1Ivv+Q3rnrAFVOnmuMlVYtAYpZTGhi5YiuuA67uhDtM9lNdOqhA8ek4mWxYJU+5yaryHtfoGYfSZxwjTVRCuLhpDgm+pUxB3Lt3pL2/Qtjdr3MQlUce6xGROWbd6Li3+6HH93/WL/BaopzLyAmV/MzC9i5s91rl3BzFd0fr+UmV/KzKcz8+uY+cF8+dldi2IrjEw9VwRzjOFB3VivG9ej5WsRYdXWfbF5Am2/YAHRfvZXb3ph9/5uJEDlnmFlrrE54AmejevPpo+CrDJr8EQFYC2zE+okKr4tqCVhX1DK9cheL4489aJbKz+ybrfmzjx55EcVo37tF2YzLR9596UZoyDgA73fO78MpICne/I9E67kOPt0E7rz5iTTITV93WTxnK6AJ331l22ilSZH2w189HQlQP2u963aEZ9XBRfZVaXoRWuUuCgbadGLV3tXgz6fVYOnqHYVd+qZ7GA8exmNJ7MRfyRpXl/cacncYf2UJP6yyiNrPbumZBc8fXz0xiW43EKLKMtBS9DeXY8vuo2uicBcfWS0+GANQjbUWlWrOG5+Uz/J6oPSGPEqwQdPtwzBpc7PVdv248DENH6yeLPmXvOc1E6i/fPk5xwWThThuef3Xjyv79k4gvV03L3hua1/neFq/BwiijHR6l3X3RHn4FbIj7FeEp5zrUVy9QPrnKbnmsef3mN9r2jwZGMgBTwBXWdpncFX9c1ji074MKVsfmy89kcbr22IdR2qUCdOKaEKAg/bIhhNtFKE/tStW8p/A0JAUkSmrM21z/QhUutB25pOYZqpphnYtZdB0mbNdgyKz0P/e5qIQtFn4zYRf/TSYxPzbjJEdA4RPUlEq4noIs3nf0H0/7N33fFSFFn31MyLhEfOOSM5iSBZUBAMrDm7q65Z1yzmrBhWXdc167drzhlEBUEwACISBUmSc84vTX1/zFRPdXVVd3Wa9x702Z/Lm57u6pruCrdunXsumZf67ydCSFfuu5WEkPmEkDmEkFlh1E98D7NX78A5L0/3lZzAK8padHncvA14dMJix/OcZEjW7thvmYvCbu9Tl9hviESoONBpKht3HXQ+CcDnc6wOFR2oNhNLE9a2LOsPqusXb9wNQNxtl/cP0RHJO1UM/5FwXW5qQ8Eu9blYb7u5ThmiZWweB9Ox4zGrg0eXsfvMd8sCqUMEOVSvOLnWU1+X6cyGxaUJbC3H4bcqZ3UYyASDJ9LgyRD4gZB/5F7iZGU2pkkHx4GiKrOLmeizJ5FlTQ2e8gBtDR6FIe/m1+l4aDs1KgCQZlBFKBsYO3H8Al/zWnFCFJsYFYxD1raKPYZo/fObPzxd5xb2IVoODB6XP02HupoMZeXvod8b2WJDVrRqze4n61BFASEkDuA/AI4H0AHA2YSQDsJpfwIYRCntAuABAC8J3w+hlHajlPYKo45iW7vpg7n4afk2QxjdL+yMoEw2AaWR7sHgdmLS9n90suQ+4WLRht0h3yFCWYNvq/s0WRsHit1lUnNimpVKJx+eYWJ/PZHMCKo5iQkZcxenyubrY76Wab7pMHjTsgup4iXjkaoYw8HjeBfl3YXy7DSHnA2n8rDBeyjB7Izg2reLTSo/r8TLtWM+mo9eD05EUYmegRhEk+EJEW7m8zBaK99PMrE/JdeBDbcfHpYOHoZEarXH2pkX5oyskfKLH6d54/4vF9rUT7sa0mvKRmRZv2S/lDVbBo9l18j5ZgV52QDKp2bC4YQgQrRa1aksLUsshU04biiaZiaLq2p5hsxOVu1KivDTnqnib7E+bu5w8f/U5BJVOYeBfwcAegNYRildQSktAvAugJP5EyilP1FKWXD9dCSz3IUKc6iHyhIK/wXFdYRQQ4buMGEnSG6Hk7o2FMqJEIGHfYtwCl+SX5P+2+s4q0yTLqmP7NTilG6lyoGqs5tfImwMyVgzpabFNzUcPDr2IfuNugwe2WlBjVl2Gjw694h0eMKD8r049N1Mb8iPn78BgHf9yUfGL8L+DIX7hf1odJ1cflAWLOfD0sHD2goTOGaTmicNHkWIFoMT7W7N9gPKbu83i1Ym4gr9IMgsWv1b22fpduM9Le/i1Ic62OPn30KR5uDIXikTKtftQiUePTWZaiteaOHssPtdAvvxC0gaBTo7hbKvN6Uyn8gMHpOBzF9/eHTJRgDWcJ/Xpo6pcDGAr7jPFMA3hJBfCSGXhlA/fDF3vbnNHx7vxYCXdqiXsQqoUSkbx7Svm/oczoPVKbWwpDQwRlaE8KGTtUd3mvLqplWnSXdm1TL8slJPFFblCCoRbAT2W6jxf9ZnpWLwyJ6pToiWyuZm13hdxIuXyTR4qPCvHco7u78iQ61baO/k0ckup4KX1+k3/OvFqSvw3OTwMlnyv2nS4mASKjw6YTHOe2WG5fjaHfsDKd8OB4sjB09GYaRJT31m/7oZ/BjrQyzXjZihCD8aPE4Cz+UJfueYQi6Fc6dG1cxlC+fqGtnJc/3VK4J7OIUFvT1jtavyGLXbUhI1H2N9vrynSbfbDXSquh9HrypVpRMzyg52OxnKxcrh4UmQra+kP5wQMgRJB8+t3OF+lNIeSIZ4XUUIGai49lJCyCxCyKwtW7a4quDL0/7Eh7PXGp9XbN2XKtNVMZ6Q2RagWjT723SxK4cQYjxHY7FWBs3+to/nY9DjU7DnYCTGWhHglGACcMHidNmRjTnIwbGhOmYOYZH1CVmZ8rqIc7jRl7jDW/cW4UBR2m5krECdjRrRppf9NtVcy64Nqj9v31+E/UWl+NxBhFo1b0YOnvCg2phyeuJBrzvys+O237O6ycIgdeFV2sAtPp69LpBynp+yHD8sS42X3PPORNQGv15lCPuuh6WDhx9s+ffqJU62RiW5g4dBxyuralteOrzTvZ3u6RduitU1ltVZtPj72pc1e5V1d8jyHuBvlyVCMEhr8KTfw95Cd4sNlZNUbCcxI0RLf6LiS8iUM9CufTtNTn7qaLczy1fJTZ8xGFo2Tqvk3xXHWR0Q1gJown1uDMBiwRNCugB4BcDJlNJt7DildH3q380APkEy5MsCSulLlNJelNJederUkZ1iPl9oe7slWZW27S3CQZf6HfJ7lW942VE95p/fB/JsMoFlm/caDgNGvz88oiMrBpzaH1X8feFrM7XKr5ZvtWd1oBr/ZeLOqnOz4jFbgVqn6xkLl32btufN56cZpDyDxzz/y+5gpElPnSozGXj7W/YugrItWTFvTV+l/E7n+gjBwGmD0u648X3ARg5zKExatAlnvfSzcr1128fztOYn6fUuq1xe55KgHr3dmrbQIQyMdzwHhTJ18GhkDBlMCNmVygoyhxByd5D3T6SonsyB4IU5E49bm2wpl0XLS/amJjUrua4HQ4JS43eU1xCt9HPWPF/jHKdH9b+frROhqozy+twOF8gev9sQKtnuHfvMD8KGyHK5D9GyHkvv8tuzXtyOIyZjRaWtYKG065cpQ8xhTNiyp1C6A3KI4RcAbQghLQghOQDOAvA5fwIhpCmAjwGcTyldwh2vTAipyv4GcByABWFUslJOluXYGS/+rL2IVGH1tv3Yxzly29StIj3vwr7NfN3HD2au3O7pOicHtW4Yi184lbtzf1E6tCWaBis0Ji/erHUe3/Zy4u6WBIadq2grn89db9uO+K+yYlZLT0VplJX52RzzLr+xL0jNv5EX7Dc0eDT2d9IbE+4ZPOxcr31KdZnsuHmTRH5dtIkZHpSPltq/f6d38ovN3CN3SAJT/tiMK96cjekrtlscDOyaT+est/QdXbw4dYWn6wBn5mFGW6jw7PcXlWD1NndhW3d9ugDDnvxe+b3TuvKIuye4up8OyszBo5kxBACmpbKCdKOU3h9kHcQQLcPj74Z1JnlnJi++1kBqPuexU7ukrnVRjxQSlHLpITVn1gChU2cvoXBOEBeifjQMorkv8+Af+eY91ixLbgXKiDkC38AsgcnFzpq7Zqd2m1GJNoYJO+FpJx+TrzTpir+Zfpmfe/BXyHZbxRIP9ZARSmkJgKsBfA1gEYD3KaULCSGXE0IuT512N4BaAJ4T0qHXA/ADIWQugJkAxlFKfVkMuw4UY97anZbj2ZJNDQCY8ac35wfDwMcn4+yXphufj2hQYHHqdW5UDfed3Mn4/L+fVvq6p1v8udWqTfP8lOW47A37rPTi4lXWWwh4tipjI8j71Zrt+7Fmezi6AU5ROokExfZ9RY7l7NxfhOZjxmHqEndhgBGCwVcLNri+xuvmlrvkGvJz4xIHj1E+f72ijt/8vglz1qTHK2LYwebz+PbN5h0dodk0gyf1rysHT/LfwEONJc4cN3IEhyI0iAPnEkLmpf77iRDSNcj7J5QsLvNDP7mbWVB/9mqrDcp/8jLXLdu8t/zSZsoRxG57waszMfDxycrzt+0ttMyBb0xfheVbypd2XVkyeBwzhoSNhOBRTe8i649+FEBedszI2gOkBnkbNohd8c1rVULVvCzltU4oTXC/Q3NNvHzLXtf38QPiIu45eYHzKUEQKdK7UYfw7FcBcN8Xv1uO6TJs2KuLpUY2KfOFO8ba4piP5+Ptme50fpLllx2Dh0E5TjCDz0eYtFpkWW2IeAErz+53ZiLTQVmDUjqeUtqWUtqKUvpQ6tgLlNIXUn9fQimtwW169EodX0Ep7Zr6ryO71g8ueHUGTnr2R7/F2OJgcSl+X59O2b2Poyn/uGwr2t05wdi5lC0i/xuSg8dNt350wmJ8vdBeBFJbAkXzngMem4wBj6kNUBF3fDIfH8xa43wiB1WVn/9+OXo88C3W7zxge/2Cdcn3+uLU8IQ4Dze4GWe92I9eM+qo5ol+rWtZjqn6QnY8Zvl9TgwVEfsKS4zvc1KO6OJEwnQNcyRRCmSlzrGYFzZ2A7MPc7OsyyeVlt/E3zc51t0OytA1SUVVmzI8DlUGjyZx4E8AgyilXQA8AOAl/3dWb0wx8E3j9pHtMaitNTzaLown5kHoLptj5H36mzuWDqVU6azyCj9afW4zdt328XwjU5gMpn4i9AdxI5jHgnW70PPBiejxwLeu6iN9fiF3w7J08OhmDOlLCJlLCPmKENJRVZgX4cgETaYzN7JouUiZyEApBQExdT5+kpz8h3NdxNu5Sd3IwDuntBg8HL6y6QRhgBke/5q0VOt8HREwC8XdfbVsnXIRyhZuDU9DZFnyKvl+wbesPzbu0SrbPDG4qpYr8JOhH0aaW4el6vfxf+8+UGxmMjn0Gakh6iDCI34tM36270uyBD75ba3luwj+MHftLgCSMMcA73HTB3Mx8plp2LnfygjZltohm77ckBmyGIhhZZvyC7FWTnOxqFUS9M96a8Zq3PzhPC0GgTjfildMXJRcrG6Q6Kv885s/8Mq0JG2fjTteFiYRvMGsBeL+Greh0E5h7XsOlljGbZN2InfzLXsKLderRJp1apmTcsAUCtlr+OYYd6HBJ4ZZVU0lWOnbspblHP48ANic+m1BO1Zkz/17DcbcIWziOhIHKKU/UUrZCn46klp3gYG944m/b8LL09IhTJQ6z51iW+VhQ3BTzoM8K27Mx/Oxaz+nn+dQmVd/+BMtbx+PrXsLcdZLP2OWZpa7oCD+plUuQ6bembkaV741W+vceyWbyiK+/X0Tvl64ESf8+wdX9TBQBn2uLB08OhlDZgNoRintCuDfAD5VFeZGOJKnMlKuKipKp1NZhJg7Ekfgwda91knLbnpKZtJwX48Yx4phf1/w2kytrDQqEWNv0K+0LqVfp3pO8+aoLg1cXVNeFw6HKpyet+7rMHo0MX/mwe+y+W36YToD47zT2KNGEAATbd0tVAbpBYLeSlDPQVZMhwYFAORZCFjIzOsaGlsRvMHSNwNs8sxoPGAj8mh3uwQF1u08gJd9aAHIMC/l3AoKTt1j4qLNIIQft1L/Bjy86PjJVfplDHZ1+vd3y/DguEWpe0UOnrKEF304zwwexWWyfqSa62VhjuxUs+NK73flZiWzCBVZ0qen2yPb1NWJADdCs4wkEKn7ZMcs56gQtLXAmwXM1pE5yiz1OHTtW13iAMPFAL5SfemFOMAe7SWvz8JMbo2z52AxvkhlPSMgUtvzoGDjyNjmbiCGBvObfXy/kDWH935JPsZZK3dg+ortGJdhIoCI+QHPyW7x99dn4bI3fvV8fVlkgi1LB49jxhBK6W5K6d7U3+MBZBNCagdVATYWGwwew7BxweBB0j3EGzFu9UJkd4sRd/UwJipKTZ7eQ9hTb4IlW5Lwu51SBgLmDhixeDKHzbsPYovEEcrD7eBo5yRVOUvKm82jYgUyONWXfe02tEkt0hgAS87h+i17CnHxf3/BrgPFxhkjO9cHcHiEaJVHiO/JbV98/eeV0mw62vdnizzZd6C4+L+/4KHxixxDhpzQfMw4/DvFKv19w26Hs93Boq2geIRs8fnGz6uweGOwdQCsi13L/Qk49TJ/PZzNyXbaKhHCg5dQc6/JBpzu1bNZDQxokzTdVafuKyrVYgs61ZC1WxZCVVSSkGqhUKQXwDyD52BxqW322/R3yX95LQ5HB4/HLqVMd+7xfoeweatDHEieSMgQJB08t6oK0yUO6DggH/lqMW77eD5XUWtV7bJZefGTr9i6z2Q3uVlTEg/rYRme/W4p2t6h9KFp45aP5vkug4dlrAlpAVCzcg4Ac7iccc9Q7phGWTp4dDKG1CeplRohpDeS9d1mKUkTPy/fhu8Wb0oP8JSaXmrMA3OGwcTgSaTLbVgtz3oyVXdWXvDZjZOBz0LDLwx1Gm1YG2xBOUl0quc3BeH2fUWe6M0R/KP3w5Mw4ulpluNqR4MzWJuRpRHlHbBHNq/prmChLp0bV3N9vS5i3Ojs1fBm8Dp5BZ3W1QnPf78ckxZvxvu/pDfh2E6sU5rJCOHAyXluh/U7D+Duzxbiktd/kX6fnovVZZjYpql/G9fIN65j4tu+xMRT1/7z2yUOZ3qDttxc6ge+MX2VdEy0w6pt+wJxgjLnuNPjdBRjTl0f+XeCg3MTT5+gy+Dhz3JvsyXPd+p7repUxqOp5CFu+ik7dwWnE0kp1RqEcjgHj7nM9N/MVuZ/d/u7JuCrBRuVdRFDtXiWkk6CFT/ObhFebexDVYMHGsQBACCEdAHwCoCTKaWe15Qy6Dxanq3JQ6XhBNhLVaiueklgtrp56+x+ftdCT3yzxHFjoTygNEHR95FJuOOT+c4nC7Czr68a0hqNa+SbGPmZQpk5eDQzhpwGYEEqM8gzAM6iPtxsZ788HRf9N00DFbNoecnulAzRIgaDBgCKuR6xftdBC7WMIm2gqhCLEc8hWnw7Uuqv8tS/AGXW+XJXBCTerNMvrL/TfEC2w8QvHMRMH4fwBFjucX6fZpZjum+DvTbWHWVhgGwSvXJwK1wztI2XKhpoXUeeyjkI8I7abXutGiV8+5WxBvmh0quDyM7JFkYXMcZkYqXA28WnRwgP4tjq5rWz97kzFfu/bucBPDZhMUpTmyDs+6PHfqe+f4LiqrdmYy4XasgLpfLt/Ib352DIE1Nc1DB1j4DbsmimsPlk5dZ9eFPidE7COtHpVmv7viIMenwK7vl8geW7//74p2Yp8nt7tQ7Ybw42BDyCLkSbR8d0dss+Z3ByJvE6lV/OcxHqkSr25g/Tu/e6fTUuYecA5ufAzpFlvxEdk2ysYsXJqqHjcHlswmLHc0SoXp3pfpJzlMyfQ9e+1SEONAXwMYDzKaWBePR5B0aCUrz3i3PCDplNZu2j3OYGAZ6ZtNTIMqs4zRZeXrsTk/OuTxfgto+d2TXvz1qDXwQdnx+X2adKzyRKKcWGXQfx1gz3yVZk2TV5EBI+W0eGsmTw6GQMeTaVEaQrpbQPpfSnIO/PLyYAuBYnBpKNnwDgM8eWJsxd4ucV1kZMQHBGr6S2F3+7HSmxSbchWnFuJ4JnE5VF3J8T2tZLLop7NqsRWJlOj8pp4g0i7XOEYGFKme2WwWOzqChJTcat6lQxpXzW7SvmeoXXTniP/2uSRRp/6zYOFFiZfo0K/O8Lsx/oFp1naCno/4YIwcGNY2/hevs4+Ts/mY/npizHzR/OxRPf/IGtEseliPW7DmLc/A3YwQlEynbDrnt3Dj6evc7R2JKB78dh9GnWj055/ifc+anVCcOwbLO3TZHdB5LP5sdl1s3of37jbQ0jy7q1bucBqSC2DEyvIGLwhI/r35tjOSb6anSatZOdtHjjbgx6fLKlDeg4XViX/WHZVi1tyORnK5zmpPQmDzHKkG1UUEptwwdF7Sj2G5njTPYblmzSSNQQYH/wqplUDpcFgUCTOHA3gFoAniOEzCGEWMWfXOKmD8wOyFs/cmaBTF9hHavt+tHcNTvx5LdLTPdKX2e+sGpulrQM3YzJiQTFH6m27NS335i+Cu/MNM8VizfutoSb3fKhtd6P2jg7Z6ywbs6uE8Kwn5+yXKoxKdO+5XHnp/MxRnBK+cs2q/6OIFgShRuUqYOnrMALt5lFrMzfa5cliCyLuyDiRMEmhpqVc5EjxOXxFD27VG0i2C34LFpG/WT15u8ZIH1O59HVqJSMSXRiMTHIYhct93UK0bKJqWZ/u8kKFCE8yA04XedLEnYiqfzOCd+2vLzyUJtJgHOC1/Amu9/n5qd7eUzsmojBU7aw6MfYvM1Rz/xgGybEwiY+nr1Oe6dMNhzEDAaPt/mSx7SlW/AiR2W/TyOjhluwavJ6HTJs2GU2YD/8VS87nMG2CWDMYGVMW5rcmJqzZic27zmIlVv3od/Y77DSZTaTSGQ5OKjsnE9SKZBNYeYJsd8mMWPFNrzwfTp1PV+mXYgIkBTRXrVtP6am2ga7dP46ZwFUuZPF6Rq53aYzn6jseZnNb3c9dyUAe7bSKz+kN2JUZwW52Fu+ZZ+J2Wjc29EeDqwK5Q4axIFLKKU1KKXdUv/18nvPRZxmm+4GgWyTYs129djK7OIDknTh4vusXTVXWsZZL03Hss1WJ6RY4//+tDL9ncsNj137izHi6Wm46YO5jucyW+GdmatxxZtmAWNZWFc/gen76ITFGP2fHy3njREcbIUlpXhpanrMe3P6aqzdYZ5r35i+0lJOkJs98rHMfOycl6cHGsJ5WDp4GGau3J5i4CQ7muHxd8sWgNmISdLPue8lHZmQpMBbqaADxP48WJzAr6t2YIbEyysDM3hv/nCe6X46vyUs3QEnOBkTDDoOnnd/MXuQxd+tw+Axf9aqWoQQcLA4gb6PTMLERZuNY277pJ0Hn9/1ErWz3OLZycuwafdBnP/qDExfsQ3PfrfUdRlzI7QAACAASURBVBkqOJmBj3xlT/Xmn5kb6r3ZEKdYumkP5qzZGcKmn7pEfgxjTvCKEMd9KMKtBg8ba7+Yux4DHpsMIK2Twy9uPMWkEzZXp+rivgQLzn91Jh7/+g/jM2/ceoVYL50NA9njcNLUaT5mHPYXlRg2RBBLR1k9znppOga7CH3jx5vFGzVYDREChypE68yXpmOsYu5wysCUtpHd9TxCgEq56UQXulfLuo0uq5SNNW4c1LLrxboYGjxapVjhhdEm3ou3WyYu2mQ5h9VV9ai27i0MdGM3QhriWkSFmKQhXPw/NZmIX5+WJijOf3UGflq2FSWlCdwu6MbYsViHPTnVMSEBf71b23tvygE1W2PDhc0Tt308X6p7ZQe7MYhnD038fRNemLICD4+3t5dl3ztN2zd9MBcPj18Ep9FAN0Trp+XbAk0mclg6eH5bnfZ4HyxOpEO0Uk/DbVgCIUTIeGO+XuzG7NtYzCqkLFIuN2ukPATMDiZzFq2y81So7syOl3rUBenqQdj264Wb8L6Ecm7USdgV+nWVXgr3CP6wQLLzt2HXAWzYdRAPfJneSa+ioJx6AcuiRQiQHeMZPJosIeG0p75dgmlLt+Ksl6bjiW+WYL9kh8UL+Nvost1U2F/kLbwpQYFjn5oq3SWhlKJl7cq+6qUCAUe1j9mFzkae2LDx1Xyz4eWkIcHeE681sysVQsQvrnSzK30028piWbIpSTXf4RAudOuH83D+qzOMzweLS7VDjIJEmPMwzwoKS+9mhUSjxA68XeMlZC6Cf6gYPHaYu9bKBuHhp3UV5GWn62I4Seydx7J+48SCYyCcE9gcdsydo3E9A2vTew6WYNeBYs8adEF0USfn+Mbd9iyAE/79g22oaITwwe9b52WnPwx/aqrxt4pttnN/EaYt3Yqr3p4tzfjYwsEu28mFOwPA5t2FJjKBH5kPNu7ozEXFpdQ0Hx8oKk05TJyhy0q/5PVZWLXN2xxEKcWyzXuV2c0+/HUtXpq6Aut22vc33p4FgP1FJdhXWCJlwwY5hR+WDp4JC9MGK7/TFE8t9tztdtPUtZyDpzRh6hQyO5aAT0eaxkEhDIHXCLEDfw9TiJbi/LC0Q+xEWUXoMnjEuvZtVRvXD2trf43k2LPfLVOek5MVM9X349nrtOoWwR9klFRZu6hVRU45FeFWSDLO9S+vG1pifcPoWmt3HJBSa3XBp+d0Axm70ATNyUhFtdcBM2b9xEhH8I49hWaH5e6D9g7MUkpRUprABgnVmO8qQaTPPlicsDU/35u1xgg1AoAzX/wZ3e7/1vd9ZVi7w45eH8otDbDit+wpxCvTVmDPwWLLd7rQDSFhZ708dQVWbNmLeZxzIJKwCwdOj5X/3srgUVzDHec3P2UQw550X7O4aNHtD//340rLJtCN78+1bV/GBqoiRGuCJlPAqsGTLGjWqh3oet83zu9CcYKXEC1x/nQaO99LsUjs6jhuvgux6whK2KU2V4EQYnLS8X//odBx2lcov48swqF/69qu6vPUxCU486XpXP3S37kdyxkpQWd+Ly5NoO8j6bCrI+6eoDU2JBLUNmRfdEptkglTa2BvYQmGPfk9ujvYDPsK1TZRMmMaMWr0xs8r0eHur3H8v9xlyfSCw9LBw6OoJM3gqZSdpJAecNFhKZIvkKfbJaj9YkQUgbPb3cuK6b0i3ltqCjspQ0tLee/UYTG7gS7ysmPQiNpyBZFCFyRNLoIa1fKzLcdk4QxBtuMSY4chGSbp9x5eQru0IBQ77Mmp8vM0Lp/553as23kAzceMw7SlW5TXAIKOg81P8yoIq8KpPRobf/MGBq8vZkWk71EWmPj7Jjw3ZZn0u0SC4pGvFmO1xHnLv0Iv2ix+3/ZcG20uv5htszguKkk4Zlbx89vYonVvYQkeHLcID36Z3gW1c3pfNrAlnjm7u+f7HiwuxUPjF+G0F37Gz8vTO8CyrJURMgtxHhUXPc3HjJPqt9iBtVH3c6WgQ+nC7XjlW7NNn51Ye8YdFSLLTNjV6Sc4haZ63SQNhMHDMyyirlam2LxbL8pCBL9mFJ0hv67abmGqMYFhivT4umO/nEnmt43xDqcb3nfW0uFx9stJR5FOHy9JUFfrbYaBj09G1/u/MR07WJzU2QlSP3VfivnuVEenPniwuBRfzF2P0gTFXZ8tBACpfQREDJ5AcbC41PCosxjh9Q50Kx6UwpJFCwCKOeeFLM0sIURL1DlLk8HDd0gd76t4mN/x8wM3QsXaDB7hc352XBq/aq6H9ZjYcezqGul9ZAYFEgcP74Bh2GvjIeeh06J4Bg/v4PE6MYS5mLmoX4vAynr955UA9GPEAeA/k9OLeHHCXrxxj69FqfjUejSrbvxNuPvx2VAiZAZOi5dLXp+Fxyb8If2uJEFt0p+myxUzYlR0FHObApQCtavkGJ9v+mCuVmYVL26elVv3mzSEAGAex3qwG9aq5GbhpK4Njc+bdxe6MjBZM9lbWGLSBIuSFJQ9RLHO1ZJwgO+X2Dv7RTCnSXFpwtcmmEpkWafVOE23u1IOIIMhb3OBXRiJGAJSnjKryqot/Z3lqM4RzOBtT3E9c+rzP+OMF3+W9ocDRaXo/dAk4/N4CRPLr4/AK7t20YbdxriwZntyQ9EObqJlGLbtLbQIJI+fvwFPT1yKh8cvxqe/WaMvvHYD3c1bJ2cWYzN/OW+9Y1lBhlkf9g6ewpKE0Rsq5yR1PiYs0KcuUlAQQjD5D/NEad4Ft778pDCz+nsGHYFhwNwhzVm09BpoGLR1lQOHdYZpS9XpMu0wunsjaQyyk/aJXbcpTVDTS4sM1MxAxuAxYni5Y89PWW45zysMDR4QE0NO952LTVbM2OVUyt7CEtz/xe9YqpNS1QfEeuamxhI304csvjssxExOaqvxE2ZK+ghmvP7zKueTFLjwtZnK7zI5rP6y0qyjNujxyaHebyrHjJuzZqcpBXyY/Ui2u8hndXHjgL7irdmuxgc2l4uGcGisxghKvPfLatOYL9pfxz6lxwC1mwdZ27j1o/loe+dXnsdkN5fJFk/3fL5Qeu6nv63DE98kk4boJk05s1cTrXqIOnZO5dot+uau2Ykflqqc4M7g+6hsPajThyPuazD4bE7aoSCzZ2UgMDt1ZG1p2ea9Ur0pUZf12clWJq0fJ8H6nQewz4OO5Jrt+12HHYlaQDr4VSLefOVbs4334IURpIKTGDWD7nh2QEMLM4DI9XRZwRVVMcFPgkc0KAAAtK5bxVUZbt+HkfHCCNFyWYCsDiYNHv5eeteH4dDQCcHS6Yz8bxjQpjbqFeRJnUezV7mjG4v34EsMQh8igjNkDB62OPHSJHXaOy9knmXS4LFevK+wBAvX78Kvq3bgZS6VMg9RRFTlsC0uTWDVtn2469MFeO3HPx0N7qB7ZLGm+J0b+rwfQ0J8TCILkWVeSjvCPd8qgkt8Ose7BtnC9WpnRqacdEUlCZz+ws+mY6sEBsOYj+bh/gBTon82x3l3zg5eu5Lqmf6eeg9unS26fZoiPWaK83EUohUO7B6rjs6aU3guYL+rHtTusps5xk0kP88cTIssy+/GjmZnyX+Tk1CtW/FZ4zoKnPyfH3EeJ/7uFk7voU095zVMWILshxv4LMRuHqmnLJIBQZa4AAAe+PJ3HD32O7w53T6cWAZd8XO/UEW1bNsb/P15XSI72GXe5GurM2J40ehS4bB38ADpF8AEjd1QT3XsGOmCjzhpSySh63hRZfFSlR2W/cUXW6LIkuVGiFkEYzR9IqHhXf/+HK5c54L5M275aJ7pu2Y1K7mrWARPkImI64bueUUx1y6d9KqufGs2Rj3zA059/ic8pKnuz4rZsOuAyWP/0LhFGPT4FEznshWIEAX7grQBWFpUr0U6dalFG3Zjs6aY3SWSdKD8b/111Q5jwaK7ExshOPh91qrU2H67tqw/yMWczTeaJ8kO9O4va/Daj3/6q1DgcP+ALn3jV+lxlo3Ezr6QfaOb9YpS9eL7CSFkLGLfhQ+dvnX+q2Z2nWwuKCxJ4N7PF2KwhPEW1HykCtFiB07u1pA7ZD7J7mfyTot0mnSb86FeaOdm2S+PvDZpnfD/3QeLMXt1mqkg3ovff9y6txClCSrVzot6XWah2z0IMfeloH09TuW9+oN83lMd14FuViu/UGn3sX5FSOZtRRmrSAadMM9IgydgsEmBEILcrJirhkrh/ELEBslecXoCCsLBk/67fkGe5V5lAZ266+z08TslLG6Vn5SZTskWh5TyjswFCvRuXhMA0LhG5ODJBHSFVoe0qxPYPUu4icBJZFkcuItKEo47d6w/933kO1z4f2mD+qflyd1F2YIUSFLs2981wdBK8LsoEutZzKWHt8OoZ37Qvgdf1PH/mmaKDTfVRfgpExdtMn7fLSPa4cXze5raAi/+qeMIj1D+0XzMOBSWBEefdoOTnv0x0PJ6t6gpPT7o8clK8emygt007KdLTV2yBaP+LafkizpfUdfNDH5c7i7sJ0FhcXIWlybw359WylP4Cp+9vtaXp8nZsMxm/OvRzZX3qFEpBzpg88bz39uHd8c1E5mIcGzTiu93HbCGpYyfv8GkHfL3/83CKc/9pMzQxNuy78xcYwhHM+iw5yMCT/DQZUXFY8TzmLh1r7Ooc5AsEF3ohjP5hdMzJrA+2/Iy/+jUI8g3Fzl4BLh28FBA9Ura168KAGhWy0r1NGnw2NxO5SShlJqo16qFslpk2frFhAUbcdTDEwPLIKXW4EmjVMHyMZ3PnZKd2lXJ4XZX6hVYU2jr9GerwB8Fm+ujxWRmoGtkbNtXhN9W63jJnZ0YfLuMuxRZ1hF75ouZ+WdaB8TOmfXRr2vx/qwkbXbZljT7QefxnNK9kcmpq0KJzzzjXrrEtr2F+GKufehKt8bVMbxjfdO74BcXaQZP1CczhbCetJeYey9wGr/7PiJ3RKrACyYD6t3KVdv2K8WnnRC0Uf6DUug6DT/z3L8mLbWIXYZxnwhpOG0uvD3DXWjFG9NX4f9+XGk6xr+rTbvNmxHenQLmej89can0LDZ38os4semceWRjqMD7algRO/cXo1RgzTQfM87IfqSbyESE7F1UzctyvK5hdetcfeVbs3Hde2kGOtP1SzJzKPYIdodY44m/bzJ9fv+XpC3hxF6KECx0lR0IzD0i6HdRFs673QEl6nHCNW/Ptv0+mRHZ3PDLcv7Z6jJ0LBJZDhj848yOx1wqe1NlZ6qXWnRZGpcxiSX/XbHVmm44JxWKpHKS3PjBXLS8fbzx2STYxd+Ku/e2vYXKHQEAuOfzBdi0u9BXLCX/U2XsnFs/nGdiRfC/Tyftcnbqd/KhPUEOZmyRGel9ZAa6DJ55a3fhL8/9FEi5D45Lh1rxg+naHQfwt/+biZ02aVhLShPO4orCCT8t34o1ipSIxaUJzF69Azd+MNfUL1rdPt5I0eiEWIxoMeGKS5LnfDZnvVSX40BRaeC7MH9/fRaueec3R3YdoO7HfBateWt34slvvC2gI7hASAZRpuysDnd/bfu9ikWnhrlxuk0vfbgjmk/LJ2RsgA1cFtm//t8vpu/EedXvZqDYLN6euTp1H/4cIUTLpi3x9eP/fnmaNfRk+Za9IEStt+i00BKzlAFJ3bjmY8bZhmzo6Jvwt35uynLc9ekC27olF7Vp/GwTBq4qI4J/VNdklwk5XQJHWbzZYo3N+iCw+6BzMh1L5uoynH/+3JYOeX50wmK0dND2ikK0AoY5FpK4NkZU74PNGzKmCCEE61K7Xw+PXwwRn1/TD4Da8/jxbLMGjUnIibuGv7rngxPxN2HCNtUr4E4gY+e8N8tM3Wa/b/IfmzHsye/lKe64v7NSji8+u5hs51P2W3htgaKSBJ6ZZN5FojRtFEQ7jpmBroNHF+y1ndFLvcunwtLNezH5jy2GkZksz9wOdPSBrn9/Lt7n2vk5L8/AgMcmSw2qBKUmg5qBsYnkmiNmJ0x2nEgdNmIT5h3X63cdwIJ1u0zO3Atfm4mjx34n/1GQ71g6vb41qTFO1p/EI6q2wJy5n/y2Dic9+yOe+W5ZlOUuZNgJJWcCqkXXb6uTjhU+hCMTyNxaKLOmeaZ+VzSfln/0alYDQFIAmGGjMNeI7UXMLOWEgW3TodZ7Dhbjpg/mmr5nm3y8TScuHHWyfAHm+WSdZOOCjTFZirFmkUPmO1kIG8O4eelMvHZ9bN3OAyaWlGwe//BXuSAuD5WzJmK9ZhZVcp0ZXED446GYdv1wAhHpUSjb+Yfvg3sOlmCLQ4hdFKIVMMyTgrtBsaiEKlOZx23S+xLYLxbZpONFcJafD8WGbXj2JcWy9Ht+jD5+EcjX/fZP5qP5mHGW89k5yzYlJ/YF63ZZzuHBnotKoGy1zaTL491fVuNzIXSEd/C8NHWFknURITi4nYdUfXP1tv2mdz+ycwPHslRG0f7CpNE6YcEGC4umNOGcO2Pqki14/Gsry0T2WxMJayY5u+FnwoIN6PvId5i6ZAua1MzHCV0aIDseMxg8lFJ8MXe9lIVYLIwlJ/z7B5z477TezkwhrbRXLFy/y0gBy5g7Py5Pjjv52XHrBannolrUs/GVLe4BcFoukQEbBsISOtcV8HVy4NWpag3LrehwmnfzsoMx107qmhawLQuthgjeQCnFPZ/J04MHAVmo0r6iUoGp4jGcKdWdeS29+WvVth7fF0RGuV2YtElk2aGqrO2HkTE1xjFq7ObzfmO/w1EPp8NFW94+Hq9xQrcU6WySdtBhv4uIen7wcBLmZkjakfKN+CBQFu92r0Y7zQSKSymKBTmCsnTwiLaMU3+OQrRCRDxGXDWGvYXFSq8tnwb9p2VbsXYHE09F6rj6PgaTxIOhzdPHvbRrSpON8tp3fnN0uNiBb9iq2PBSQfhVVl03Wbeuefc3AMndIRkYpVhGLaZIT/a7DhTjgtdmWs6JECzcDmYvSlKVL9+yFwMfn4yBkqwfOhh7SmfT5xgB3p25Gpe/aY31LUlQz+wR2U8tpdQyDti18d9SfXvh+t2gNKlFFSNpBs+EBRtxzTu/4br35mDxRvMOZAnn9GHPXbaz6QayBeKoZ37Aea/OwMX/TbMFpy5JpufljWm7zCA8ZAuPwmJz/42M1YoBnQwyOsh0dEGmbndRv+bS463rVsFNx7UL5B41K6fDCCIGT8XB/qJSLPWwkNeFTB+rqCSBPo9MMjZW/LYXnlXDhy6IsLvPWzY6Q6aNP4e6EJKcv1jCjkbV8x2u0IcfBsX9X/5u/J2gVCFMr9efflmpDhWLIrSCwZWDWxl/5+dINrAkSFAayt7UyM71k3+Uwbt9auIS55MygDs/XWDaDATKNkQ4qIggL4gcPDAvMmMuQ7T2Fpagcq68UzOK560fzcM5r8zAMU98z93TXlw5K6UW9/PybVqp61RVvuLNXy26Gjd/MNdg68hQSilWbduHz+euxzXvJB0ma7bvR7+x37nS6NARdRXPcdQ3Sf3Sc49qpjgh+b0qdSwzNEXWVYcGBQDMBojKSRSh7DD2q8VIJCjW7zyAZ79bCkophv4z3a+8jOOn9UyGc8UNdhjBmFSKbhGliQTOfWW6Y5m6g3RRScI2DaydA4wxzvgxa2cqQ8e4eRswfYWZkeNOW0x+PxF2RuKkxZstx3i7980Zq5JlpJ6WKkQrS5Ll5GBJKbbuLcQHs5zp6xEi+MWwDvVMn48VPgeBDbsOorkiPn/iDYOMOcoNZJp7bhbBQSGKqPSPkpA1LlZsUTtc1qdYPF7bC5s7+LZ3xycL5CfDe+h2zMTgsS+Dff3+r8lwatlmh1enT4wQbN7jVueLq1vq3+nLt0l3/GVzsezYuPkbrActd4ngB/ymVWXNEK3ShNnOa1O3SiB1kdlK5REsHDRTKMu1nNswySBlKypGawgZ/OMkxN1uU3EpNWV04sEyTDBKKdu91GLwpIp8b9YaPPDl7yguTdgKJKswd+0ujP3KrPHzwa9rcddnyclVFn9cWkqNRvbn1n1Yu2M/3p65Gut2HsAjXy2WitD+Z/KypDOI+0k6TIeEsTPEhFQplm/Zi8cmLMbm3QdTzASOypj687iOCgPboXOwOom03FpVckCpWTA72nQsnyilFFe8+Sue+GYJlm+R72jqhB6wM1hbt9O9YSgupViwzlmfRNZ09hVa++8zk5ZKBOGcGx5FkvlDAMRjybrvLyrBbQrHFGDWMuhno7XjBm4nI77fMZ0Cln3BSYOHR2FxAle/PdtIx0wBW3HsCBUL/VvXLusq4IGTOxp/X9y/hem7fwxt47v86pWyLcdse5MHu2+bJGGCeRHsvkwviBg8/hEU+00Fu2xSLEzLb3vRZaR7vQ+fgtypjIPFCRSVJrBmu3rT8t1L++C8Pk3Rqo69MKqIeAy4/r25zic6QExhzxD1p/IDfjzVnbcSlJrsvL4tawVap/IeetulcXXtczs2dL+xIWK5jfM6DAxtX5f75PJdBPjqDisHj47waowQVwt7SqknjxuBPVNI9MSe+O8f0P6uCdL78//KIGOLsrhmWfxxKaWYxqVYnb92l9Hmvpi7XprN6PGv/7CkQ9bZcWJaD6x8SoG//d8veG7KcvR+eBKe/365OUTL+E3yZ751TyHG2+xabNlTiMUbd1sWjdOWbsXijXvwrZBuMkLZo1sT82RQmqCYm4rftwiYe7B7xKakSuEKAOe/qhe2J6uHzChbv/OAxRHKZwkQWzk/cSdSYw/LomVnqALA96kwKa94TcIkdJtiVjberEzpsqg2n7IkGmcJSrGNSz/52+qd6Hb/t0qHX0UBIWQEIeQPQsgyQsgYyfeEEPJM6vt5hJAeutdWJNQtKHuNnU6Nqhl/i63Wa2plHjUkGVfsbAkvRrtK/8/4O0MeHhqub+KwgF8GphPstGjY3OV3d1mXyaXStXQCn61Rp6ZzHLLhNalZCQ+O7oycLL3QG4a4z+fE+qXIwmWQPUaVM0h9D7e1iiAD329yNNttaYKGm0WrnL9bN/W78bi24VUkJPC2vltnbJRFyyNuONYcw87SlfHxsjGXDJ4EdTfpNR8zDhMWbgTg4JQR3szijXsAWEWIdSbMWMyaZYfdWsrgSSRMaRnv++J30/faQpkaz3HE09NwoKjUaNRTl27Bak7ceMof5jAPmaHBG6nrdh7AlW9ZtVMYjnt6KkY8PQ1xDSqjWPv+j36H4U9NdbwuQrBoWrOS6TOfCU5coLjRCmDnuFnkyNLKyiDr26sUAuDiuWJmEb56b/y8EkDS8EvQ5DgRJwSUetcG0sXHkgx3fhg8DIUpPSwlg0dyDYXc8bNSc2wqjyCExAH8B8DxADoAOJsQ0kE47XgAbVL/XQrgeRfXVhg4OTMysUPZvWmaRi6OEUFQ4WX91a47eZH1kIUwlkWGlYhx4B9hO3hU2aQAYGJq48tziFbKmtJtB7oLZTsEGepg92xkCNtxqvMYZUlNeJRzH4Bn+Nkk8QK+aeg6/i3JOmzai4zpKQPPMi3v79ZN/Sri1MFX2a1dHmnweET9anmmz0wQqyF3XKbBM2fNTtz/xe+WhdgPS7fi11U7sHD9LtcxlLsOFNtOdioDUkyZyMqwa0Lb9hZZ7rUmJfgsWySJGVQ27j6I56Yst5y3c38RDghZhnjnzP9+WmlTqzT2HCw2GrUYB56gQgdP/e3VRmXiym4nbCAZcvfHpj244xN1GEyE4CFOmkYmOFjnxYfHL8pElQAANWwmXrdOYiUI8MJ5PY2PLKvX1CVbsGVPIUhKg6e4lGLkM9O07xkU3GYgke1sMhFJpQaPwpGTIzGmdlds3azeAJZRSldQSosAvAvgZOGckwG8TpOYDqA6IaSB5rW+8MoFvYIsrkJBbGmysEG3kDt41OV6ccz8a5KVjajKQBkmIgePf8gSQwQJ2TjLwJIbeHVczEsxbnXbQW4AGeN0q/rYqV0cz3HL2OPnMi9ZuuwyhQHq5+jm9ZR3locX+Nkk8Qp+XB5iCs1RQ3x/dq+iThU9NuslA1oY68Dy/m7d1C/sqeMYzXfmBvzc7nbuizR4AsLC9UktDd7Okmnw/OW5H/Haj38ajo99hSW489P5OO/VGQCAHfuL8eL5PeEGewtLbBd2uhRP1pDsBPK+X7LFwqZhH/mYZbFMJ3S7/1vLonL26rRq/+KNe/DQuN/FyyxYvmWf0nAQnWqiZg8Qnrea3Xvr3kJTJgO7LA4Rgsd+iXYNAy+wDMAI3dKB34ljRKf6yu90+9DO/cWOxtzwjur7xIj9wq92FWsYiIjmY8bhqrfVrDc7xGMEPZrqx1PHJYZyrcpJA0bt4LEev/h/s6QLkscnWNPTVyA0ArCG+7w2dUznHJ1rAQCEkEsJIbMIIbO2bNEP2xOFhsOEY4rjDBuw4v3sFsMMTps+KsPvxmPllPSgfnIuV/dMaTVEIsv+URi2gydEZhdjoGuHaGVQLLZprUqO57h9NvycxWdZCgoq26Wcr+szAT+bJJ7A2y0FeXpsm6QGj/nY2b2bSs/VdRDmcWGE5VmD57bj27tyFIc9dbRQJDbwA/7duhXHj0K0AgZPfV2+ZR/GzdtgyhYldsT//rQSb043L/Lzsq0xuqr06Qw6IstOoBTYX2S/QGTn6cINpUwM13pfoIS/PM05LvjRCYuVjXrH/mLwZMagOvt1781xPGfH/mJQStHrwYlod6dV/yhCZqCTjU2Ezhjp12C287TrdqGZK7fj8a+9OyVihNg6g3dLMnDIwASPdfDoqem08nFCXE3WMsP9lB5JP4RqzFMZ+zKjW2fhXY4he5BiS1Kdo3Nt8iClL1FKe1FKe9WpU8dlFTOD8mueJiELGxRxvcJRw6CaZ8/t00x6PCjDz7Q5kqEHTUM30w99eBVZFjXs/MDv7rKubVneGAhuWTi87R9GuJaqSDeO1M6cxtghBD+bJBbobIZ40VtKZtFKvyxCktIYMuiyx3jWW3nrPzw6NCxwyeAJd+4Io3j+LzXvKQAAIABJREFU3boP0YoYPIFCtgN+/L+s4Q7MISNbGMocPHaxkwT2DUsVoiU6hRKUmsKkqiqcSoXF+sZB2FoeIuas2Yl7P18o/W7djgMmp5Gsan43nuwoelEa5rJHkJRFHmFOHF6cUjI4DfYxQrDJJh1rGJuyTWpWMrIEuA7RkpzPxl83DB4AmPGnVYAyzF3oDGAtgCbc58YA1mueo3PtIYNMvWXWzsV+qONIdOobqmm2ZmU56y6ohWItjtWXqecYRWj5R7HHDYmWmjvUOu8oU1m0VBCz2blFS0k2LJ3f7Vb0mck/AEBJCNpJukwRO1zQt7n/ipQ/+NkksR7U2Azx0icSlKI2F3pll3RHV+8tx8TMLL8gqf+VF4Sx+cCvoXW0aHlEDJ6AIVsU7DpQbGHGGOnNJT2xUo47lX1IQsF45CpSr7/+8yr8vDytQXLisz9gw670Au8IRUq5BzVCpRhEDR4Z3Aj+bdhln90HUBu74q6V7Jm5zXAgwk5P4ful/jIPRfAPLzHsOvOH32Hd7vqDLhyqdnDqO4QAb9uEDAYhBisiTggmLU6Kn+8+WIyqefZMRdO1MgcPsXfwuNkh89RWyg9+AdCGENKCEJID4CwAnwvnfA7ggpRQZB8AuyilGzSvrTAoLyFaL13QC4sfGCEJ0XKugJOz0XV2DVdny9GmbhUc3SqdkjdTz1HltIqgD68MHrcLDDv4bS4qO0+XTcKHygPAyd0aAgAGtrUuvmWLyGNtwkyb1MxXfud2XuH1ksLYdLg8gLCvINtFOYKfTRJP8GJzJBIUlw5siUbVk21OJg3CUJCv58zLisfKkdtEDUJcavCEV5Vk+SHcwOTg0VhPn97TOcO3F0QOHqh3xq59Zw7+M3mZ8Zk1BNnAqHLIKO8J+xdvp6sxbn56LFqxZR/e+HlV+jrFZb85pIPkMV9Dx2QPF/px7ivTbc/t+8h32vd2hOSRuX32Ivjd2NqCoNna7XqZjyKEh7Cyvvh9h5loAnsdQqyc2E1hPDreoNm466CrjCeyZ8ZKUxXj5v1X5BAtSmkJgKsBfA1gEYD3KaULCSGXE0IuT502HsAKAMsAvAzgSrtrg6rb1UNaB1WUFsrLDl88RqTsXB2NEKd265YpWylH35GqQowQtK5b1WBCZOo5e017HSENryLLOht2AFBPSEIi4rM567RDgN68+Ch8cuXRluOqRay4i66aW8UNi8/mJG3hn5Zt1aqXrL2zKbRBNbWDx62T5k4uC22jGupyvSI3K+Z7M8Mvm6qcws8miSdU03TA8MjNjiE7HsNfuqcjw9Rt3ssGZ3jj+rhr+/u6nsBtFi2qzUL0gjDWcsWl7hw8PEEkElnOECYu2mTSx0hQiqlLtuDrBRst53qhTzvt4J17lFx0S9T/eW/WGul5XnH/l85sn92cOPOPy7bZnBksLjy6ueVYtk8HD6+nUCCwEVSivd+k0oYeish0mklHeBh/RUPuvUv7YMF9wwOqUBqyncMgwcQpVXCa+/04x8T09EGUKXOO60xoOplOgGCyG5UlKKXjKaVtKaWtKKUPpY69QCl9IfU3pZRelfq+M6V0lt21QeGm4e2CLM4RGdRY1YIXBo9Tu3a7wGpXv6qr82VgfZd1w/Ks1RDBjBwXdg4/XuouEM9ViLwy/OPdOXhzxirbcxj6t6mN7k1rWI4rsz8J87WKXSI6NW5I6VzJQq9kq8ilm6zzqc76LgwmrB8E0W0zLcWQCfjZJPGK0d2k8j0miO326iFtkvVNGbcE6r7hZsHPSgjTede+vjxKRBeEENdZtEZ3d37GXhHGk3LL4Fmyaa/xdxSiFTB0n2eCUlzw2kys2KrOWKWL3QdLHONo61a131GRQbUjF7Qdt0dTvDVIrBw7Cr1b1LQctxO8fPrMblh0/wjbcvldf93F61wXjKiKhLJIM+mEOR6e9a4DRabP+Tlxi+h5eWEJ2GHdTqcQLfvfsHO/97ThKpF4PmQq5nKylhke7Hq32QZkqOAaPBFSUC26Zt05DIBc8y5M8P3s8kGttNqZ0xl9W9XC4HaZFblmU10kelzxMKCNfls5krOTqmsyDL5eaN24FOE3VbtuWKLqPLHf1bAJ/ZMxXFhosVsc19F7BsEwmL6EEFfMg3b1rM5htyGiFQV+Nkm8QGfNwG88dWhQYNJoAuwX9V7Ij7qsPTsMaFNbetyvhUWINVLCDhU9RGvM8e0dz+dJCkFasJGDB/oes/nr9FMw6+Dekzvafu93rTL9tqHG30s377U50z3enG6/k9O1ceYU+vNs9I+y4gT5OXFc1E8tzscPviKDR4XnpizXr2DFQsbTTDpBjLvXwWpFaJ0KXTy118wZSF0VmVAI0We3uIWKDcMbzrWr5rhylMkMD7ZDFYQmQHnbaT0UcF4f+539MKBqCiw06qwjzXXKpGOvX+taWoxdp1OeObs7ejWzshzs8NlV/XyFy7G+xpypjBXyz9O7ei7zcEZ5YLtec4x9e/j7wJao7FYjMiToDvGq8xoL4U52+my62m06p53Sw7tGRhhkCgL/1kcI2s8RFFAlcuHb+R2jjpCe4yVkR5WRyw0u8ilorgKBnsD3kc2TcyOl4YpG+3F0/uusbo5lOrH8G1XPNzlrg8y6V6bWcHmYHN3gnJdnBFqeyOC5dGBLTLxhkPHZSygE3zbyQ9zldAoLCyM1pAp2GiBscm1VVx3DyS8K3dCgD1EElmZSJ8UkAMy95ziHKrlvS8UBsEGckMkNsLpV5TseMULQpl4V22vvd3Akq6DSzeC7dl5W3BWDR0ZXZdcH8TyzsyIGT5B47tweeHB057KuhgGSapLiOO2Uktwt8rLN5fOtirVTmcaI+Rr7tiiykPhNkXtPFEmTqXOaVMdNw9vhvpO89WlWI8bEYH087FDTQxHlhe3qNOJVzcvG97cMCfq2nqDLvFMtuvJzskysNzsTWdfXH/Y8zv+Wvi1r2ZypDz/m9aguyb24Q1RkuVzijlHy8Zy9AUKIMmLDyzowCPu3YwN5KJbfpR0hREs/iq0hw2ab+ildpb80yAUz95OrjjaNQUHuVZXZara8TI5A+QnV6N6kOlrXTS/WJi5yr/PCdz5Shr4KnQ7877O7+75PCwfxLeYZtXvHvJ5C7+bWELDDDLIH5SnNpE6KScDaVm46zrxYe0bhJbdDSSk1Db7S988dCisVe1C46Ti5BkqMOLNWvKZDVfVh/rjbjAiy9PEGg8duq1PzHhGDJ1iM7GxPzAuL3aMy6vh+yutuBJ09bcpN5gUx38ZZzVQhjLJrRDx9pnVM4xcBvNbcY6dZGXoF+d4El9mijmVkYpsjudnB9ZuOikyehyDKBdtV1lP4phePES0hWD9ToOgQVcGpzzCo5mPR8WO3+HWTfTFM8LvzfvUiGdz+tPVcNs4qKbH28vJ8DgeoZCRYyLqd/emNwWPvtvirRMvUcl9Fnf1u3utO1ey8sP2QuqGOVw2xZq5TPYtbhrdH/YI8nKaRHatSTpZpXDtUGDxlMjmytHQ8+Oe54L7hqFVGKT3FZvbbavfaI7znVmdgKMsx/sSuDX1dP+2WIfj86n625+jQ73imwj+GBbsTXAGR+TSTXCMc3a0hrj6mjel7VXiSHUoSCQ1mUBp26VPtEHY2tWFHJOulWtDFCAlNkLab4rnz74uk/qcLmeHBJvIgRB8jDZ7MIojMTjKouhX/dsdfOwCnpMQXg16s1HfIKASkd++uUKQsJgDm3XucdBwSNRgAKI08NgbwGNXZ29zJtA+Yg4ctOCsFyPa9/vCZQzPOdpXBaQrKihHNLGbe+1CzmmlnqypsAQDOdhByZqhXIO9/ooYbs3Gr51ttdl2nb9g2MF9lO71INyAgrha+vGbmHSccgWuPaY0RneoHUpcIGlC89v1FSfkBlkXphC7W5a1Ok/lpzDGmz5UdHKnn9WnmWGZYm566xcYMBk+4GNHJu0tB9VPiMYLptw/FExqhz1Vys0LTwypLB09gkyOgP0H+OOYYtBeyUfAvqUpuFirlBmPsuO0g4gLnAQ+hFbNWbjf+1rn78SEN8kGIfDmhSc1KqOogVM0IA3avgmmNNKmZH/hOcAVExtNMynbHeXh5J0UuA8yvVCzSyhr/OTfJclM9g427DobWZhsrsmiZFqfEnXNMNi4QHQaPJnSyG0VwxgeX98VL5/e0PWfl2FFG2/OmYZXGLSPMDDWdlpCXHTcM2bDHbb5pMqdu3YI8zLx9KG5OsesszBWSDMPWTaOrynwi+2l+Q4mPTGn/tEqxoLICTGNeWxFOeggi42xXGZwWB8wO9cM4063D+GsH4GSbrEIyxyagv6svhhWxZlu9krWPhSUTcLFCm6SDIqSFfz9+56frhrVBj6bVMbJzA4seEcObFx9lW0ZBXjZuOK5dZOuWAzAHD2PAnS9xvMQJQZ+W9pEFDQXiwhEN/GdcjBMS0vpQr90ZDp6QN1Gb1MjHyrGjQr2HCmzzJqzlclk6eAKbHAF3E+S7l/bBR1eo4+f9ei6Z5kU8RlztZokTdRuJ8r0TGlRLd3SdNhNW3wlC5CsIJIwQLTVYWIfbKoc98JQFyiLNJL+7KHukXlgZo4TQElmXJqbv3d+D0vAzUeRmJQ3ibAVNZ+W2faFRrVXPXVwojO7eCIsfGKHMusDDLquXk5aQDoJcqB7OOLJ5TRzXUd+4G96xvjTsSAZZs7pysFko9pQejaRsW5GJwPofX2aXxtWkhjLD/Hv1mX3GfTgLjO/xdQvyEIsRzLn7WItNYWdHyL5RGXlhhJBfeHRz/DjmGHRsmHbMtUmFh9eROGikaagVOBTTLyuQcbarDLLHzTc9No6P7m7P+vLTylg/VDkwnBySqs0E633Mn1kfS1Bg2BFWIdswFm5uzRFeEF6PSaXGJQNa4uMr+yEnK4aaikiD/jbz8D0Kba8I4YIfw28bmc6qxPSkujVJOtxldmgsRhyddq7ro9GGSQx4/ryemHP3sYHeWxeZii7x4wj2W0fm8A5rLVmW1nCZTY7VK+WgZ7MauGxQSwDWl+TXwTMwlc6yNEHxj2FtlAKpTvCysDULoDq/3rAWqE7F/qW7epdHBtWuiW49dBg8zDDV3dk4VA3ZTKeZjMeIERYZFIOnWS3zgkRWht8dPgqaETFnAIgrDOfseCy0nThVuTL6b152HFU1M9Cp0KxWZfzx4Ajpd7q/MArRChdi+AU/zuuKIeqcdXSr2pYxe9H9IywLRTYExwUnsZ0miO+dfckPqF4pxyIgy99FZQPwIW4qdkOQ/h1WFCHE4kC764Tk4u8ICRPh1hHOqV4ZRGM1zGQPZYyMs11lcGTwpMZEJ0ehH7uXVUFVxl2KDEEMd0q+f/2i3pZjiQQ1/Qp+lz9T5pg4ftRPhZNVVjD/+X7t18HDT2+nesjsdYiarOUCx9kwmQkBbjy2LepUzcWANmkCwoldG2LhfcPRLhVVwpoWP8/FiL4NzJz0qhBHoz7C586N1Azc6pVysOyh47XurwO7YYZn4qX7dtlKiahQVVNPzA7sZ7F++eHlfX2XyaMsHTxlPjmyBiQO2DIhUF00r1XJ6IwGe0SzcYoTtRdDlJ9gs+IxDY0a17fQgpOD58wjm9ifIMDOiFHtZPDX2YssJ7tBiSF4plenTIShHS64J5UVRvaevfQD8ZIwnCAFedkoyVCuURVLJytEB4/KWWIyUgPuAoyx5BWRcGS4EMMvmFNH9djFcGhAnzUqLhblDpCUU547l4Li+mPbYszxcqeElxbCj0u6jiw+fLiKwvl5ft9muHZoGzx2Whf0VKRMz5TPcmDbOhh3bX9c2NfKfjpWogMkgs3DYrjA+H8MCKaC5QxlwXaVQRXaZ6mvQ7sNIkRLORdxhdeQhFPJnIAD29axZKoTi08zeKgyEcFQRYpqwD4Dqwric2KsGJ1QTKdQGyfwY+IFfZtJBdhl+PuA5AZppuyVww0rHh6JF85ThzQTANcMbYNf7hhm+Y7fMCPCv0CyTxFCQIjcEcrjqiGt8f5lfdHHIVubW5s6bGY06ztD26fnGUNk2aOReVSLcBPmjOrSQJtd+8OtQ/DlNf0t9lBaSNqdr0AXZebgKQ+To4qxcbDY3SB4ZPOkYbb4gRH49oZBxktiE69uIxD9Sl4GY7dspLAiTIIu9vJBao2UIxoU4AOF51PH9kkvZJMnXz2kjfpkU9mRgycoqFqpnfPOTXlhLJJuPK5dqE6+ywa2NP5WUdxz4iQ0MTydSd1tH/D6HnQNkrB0FyIowBiSkh4cjxF0stkZdIJOW2FzJn8upUlmjGrOULXYU2xYpW6H+hfO64EOnCaP2EeHpBad2fEYbji2Lc7opd7wcNOm3/67Pyp/x4bVMLhdXVx7jDlkLhYjjgK5lw1siQX3Dbc4eJwyXVZkZJrtKoM0RCvDmWFLJaGSPPjDVw1Jtq2L+qVZ2aqMPXxWxCY183GGsDHIpqhSCrRVhPja9Z/PHDZAedw56gjcfUIHy7Nl86TOGHFazyZ4+YJepmNudE5MWXIJsR03eMRj5k3MCMEiFiO2Gd10x3D2dvjT2dzx5yOjcMmAltaLOMRjBL1Tjg3ROWqqj/C5hmBnXzG4FQoc9E29wi4ciXfm8AweL1AlCQkKbjLINq5RCZ0aVcNwIeydGA5q40hwFUTZMnjKfHIsSYVXiDvVdaroh1RdMbgV3v57HwDJMAU+ZIK9tFpV9Bap4mLJrVAsYDUknUXd7HtPZRVt3KlUSa/kU8C7bcZOlMMjFenN2TO182iz98Ucfv8Y1gYPju7kWKdosgwORrMVHqmOrou8vGSBLY3FRXAD59fXDcQvdwxDfk481BCtJpwugYranRWLhSYsrJPxo2dzOetAhQJNwVkRlTTHoUiCJ3zImF2EWA2xOCE4WFxqfB7Z2Z1go45RnNbgSZ/rpHvnJd7dxODRuFw05MRH5iZUQ7d3P39uDxzdyn681Hmm8RjBDRwbYvJNgwE4O9zqV8uz6HNNvGGg4/0i+ENQG02+GDyGo1VeCH84bW+l7VvVdfzm612jOiA3K25qw8RYBFJl27b7XW7Cii8Z0BIX9W9h6QduHhuBNSnBRS7kB2QM1Vxu84dpuohgY7Yu2ytCsPBjoXndwOvetIZ2AgxRP+90jfTeXiFb1srsf8I5eDxt3JXDvT7R9mA/619ndcPZvZuiq89kFSIOa3NYRSu1o9qJuHpIa4uxxjok29WoUUnPwSMOvV40XsQ27RSyQKn9DhtbZM64fairerB23L912uAcf23mqdrs5zetpRbxYzsw/OPWSSMYTZbBge2KqYzVSTcOclleErIdEfEct2hQPc8QIn3mrG7orXAu8ji7d1MsvG+4q/vozmlhhSXJFqGiwXAztxCMa+Rr97oW0c1EFBabKUIa028fiils0Z+aO2WLfwpqYsOawodtvAUsXEPnVRpDMHfuMK6Ndmpk1ZNROVfsmqY5i5ZzvUSD1E+71L1W3IUNCjoMnOfO7YGTulpFfFvX9Z/NJYI9dB08TuYKAfEUsvS3fs3TjlZFv+ZZL6zv8xtk6hDk9HFZv2XXJSjVYg9ZyteYs0So+qNXa9CNbpzs3vx9VWUd1zE5Jg5upw5Xi1D2kHVlVfN44bwejuUVK0gCYjMSmfJhZljTXdemQ7TsIbJNGarnW+fDRtXzcYWLjLn2Dpf0qHZUi5o49yh7hisAXDygpckmYf25Wa3KeOSUzoGHwh3WDh6mtSMOinUL9Bk8so5gl96tYTU1E6W2wPThnSO6cKs9kqDUVtjp9Yt64+kzu0nTUDK8/fej8P5l5jLYgozfUcjmJmuVR/a3u4JTbL+oXwucpuGJNnY3XK4+RVHNCN6hWsOww63quMuwxMozYls91ksG3shqU68qHjm1s8ZVVCpObAeRCs6cK7xWR4JSW2oww7PndHd1byDZdy/q18Jk9IvCrPyEpLM48JotwCmmnCEK0Qoftavkonlq0X/l4Fa4oG8znN+nudQ4vYDTc2Hv5pQejUxGm2hcMq0dnTdJJQweHu9e2hffXG9mkajGbbvxv2H19Lzd1QP1O6xMHfU5ZmtZtfw6VXMxsnODqO+VAVrVqSxttzLBX9ZXbjxWwXAjwNfXD8QtI+RaNipUz89Jb5ZqtIGYxN5SXcbbrzKmKvPP2O3ys+v+1q+59Du3U5J4G7ufLIrSy851s5h2erwqh1WXxtWxcuwodA6YIRBBD7pDY9peTV+gsu+cMtMBQPNaVuf80PZ1TeXXl0RHOG0q/PdvR5o+s3Tf1w51lrdg6+42da02Pd8VT+qW3DDo0dR+vj075VgR1/Gy/v7jmGOMEFER717ax/R5YFv7bNw8+Md11RC1A6lafjaeOiM9JoQ9Yx7eDp5SxuAxPwY3doqsI7B2JvNUVpeweepUzcVrf+2FIYJ3PSseQwdJRgs7iJNcrcppZ9X/CZ0SSHaoWlVylVlH6hbkYXT3RrYT99Gtahtxnwxn9W6CB0d3wl+Pbi6tmyrjAL8L6ScjTqWcOO4+sYNJtJUJzYnYdSCZullk5BxjI853dKtakYMnQBiMm9QrUL0rXRhOVuGzHXT1K8S+EESKQ1kazJO7mXfEXzyvJ5Y/PNI0EVFJfWQ4oYt9ilyG2lx4alY8hrtP7IAala3O3UdO6WxhLomZH2T0d/FJyVIyf3lNf9wrpHONx4h6YcLhumF6+lkRgkHVvGzcf3InZQaogW3rGPRvAuD3+4fj8dO6Gt9PvmkwvhPYeU+x8zXa9XkpB1K/1rVw9ZDWFiOtSm4W2tZLs0jsMlqy4b+XROy4Uk4WVo4dhZVjR0nbrBPC2BCdcftQfHPDQKO+7HmJYVJhYspNg/Ht9VEYVlnhm+sHSZk5BfnZuH1ke5MOBYuIUvVVgiRby22GUyAddiG2c2a/8scNBg8X3qyan7M521zmCOndohbO69MUj5/e1fIdw70ndsRZRzbBmOPb40SBZebFxuTHEyeIovQyuGH3ScdE7v2fk1rsfnWICptXVOhqYsksyU4N5U45nTL5lOwMf+vXQpmVlcHJ6SgywdjmfeMa+bLTTWDMvb/1s9r4vC09pF1drBw7Ci0dNnfrF+Th2qFt8OlVZj0t1Yaj+MvYPfu0rIX3L+uLp8/shjN6Ncbz5/awDaUmJLnBCwDnHtXMeGZ1q9rLifBlhr0nclg7eJgIYss6Zi+n3YD7sYOqP5De1WaZM3jhKDFD18QbBuKb6wbimPb1pIO326WjWJ+C/LSxxzuQmKeV9afRqYnoyTPkE6VbinmMEJzXp5mFcnZEgwIc074uOioGLR7LHh7p6p4rHh6Jj65Ivh/Zmrt3CzkLYOmmvQCsFObX/nokLhskFzXzm+4yghmGMHnqxbFUvX53he0y2Ym6LnaibDzLQCyrVZ0qOLqVE8PE/nc0rWkOITypa0ML4ycWI4jHiDljEJWnUBcdJLr469HW0EQZy/fs3k3xvsD8y+Hq0a1Jddw1qgPiMYKbjuMcM0Ifk2WV6NSoGv4qmfyP79zAofbOWl0RMgfZGFwpJwvxGDGMqiY18i2bHiz7hU7P79G0BlaOHYUG1fJx0/B2GtlD7OpLMfXmIfifJD2zX/TVZKDJoJp76xXkoSAv2xKG+ssdw5ThoEHZk8zOaF67snTTKkJmEI8R1JaE5mXHY7h0YCvToscx01UKbm29JAtGHqLFwhHMGjxJ26mUGyDiMSJlksclIVq8KDEB8ODozhZmKY+6BXkYe2oX5GbFLbv6yQxF6c8zbh/qqPcmOokY7DZ6nj2nO7oo2DO1XWh+ynAUl5mLsQ6OcLkxHCFkaDN4Uqdz55/Sw73DlSE3K25xfPRvUxuNqufb6uy4DdEyMhFraFKWps45wOnzycA/AxnbJ30ewQ3HtkXHhgUmooEyKsDmp/VuUROjuzfCY6d1ReXcLMd1Xp2quVg5dhRO7NowLc3iEILWnAt5DlsM/7BepZ7fpxkmXDfAYhSyBdQFknShPZqad/dkk2G1/Gzcc2IHvHVxH8t3RSUJvHBeT8NR1LpuVdvYebfsANZgWIYCQggW3jccP445xnQeM6IZc+fB0Z3w213HKnccdMJAeKga+Vf/GIDX/ppmEj1+WhcMckGFs0MsRgzDUzZ4qJ4lYxpIU3QrOmB2SMK2hy/MjBu/pJh0iFbyX1k/tSjaC+/6oyvSBudsLnRQnPwIITi9V7CidHaTEN8XswSHD4PMQaKD5hK9DRnVVQY+w8ktI9rjjCObYPnDI3EZl82I9TG7UFUVWtetgpVjR7m+LkJmoOqyslTqI1POOrvFZJAKZ4bDWOjjVXOzMOb49ujapDquG9YWTWtVch1KqYMxx7fHdzcOwgV9m+Gl8/U1/gDnXT62U8mGhfycuOU39GhaHZ0aFeBml+E3PHgj+4tr+uPm4d7LiuAPM24faohfX32MmbWoYjEyk0y1eJPp1clY33wI18X9W+Cifi24LFoiu9V6n1GdG+DErg1x64g0uyBGgF4SLbssYa4DgNN7NTHsL7dh9eIvFxdw9Qry8Pv9I1yV2SalMzWCczx9e/1AE7PthC4N8fnV/S0bViM61kf9anl4+C86Yd5yOGmG+nUgRfAO1nz97FP63eRUbVzaZeRy7eSVCKfzmHBdmlHGGDwndLFu2PG9mZ+rj+tY31ELjhBikgpRPTc3v80uDG5kJ3P908mV9MckDxJgrnBYO3gIIWhf3+rpjsUIFj8wAvee2NE41qpOZbxxsXVnT9VW/tavhSHsyzfU4lKKEZ3qWxxFQYHV59SeaUdN5dwsyw5H35a1cN2wNsbEkhWPoUblHF9U8vb109RV3UZ+eq8mRpr5K12IX6mwevt+5XfKBUiqrrI6D0xlcWIpd3OzYujapDpuGWGlPkbwDtEhI9FOdVcecxgpXvop3RtZnJZ8X55917Ho2UwuniwVOuTuk6sRG82jck7covs1oI2e07NJzfxABfFkzqIrB7cyMhPZzY36YMCsAAAbzElEQVQdG1ZD91S8dLaDOOYnV/VzLdxuh0dO6Ywvr+kfWHkR3EN0oItdj286T57RDTPvGCrdOAiDtvx6ipUzrIOZWj777mNx+aBW+OyqfqYsj0EjKx5DyzpVcP/JnXBcR5fZxBy+f+rMbrhicCt0byK3KRY/MALvX9YXX14zQGrvqHD3CR1MRvWFXLh1x4bVlFoGgLNuQgRv+Ev3RrjnxA6oV5BnvBtxEaKa85z0qhj474e0q2vRyGKZ2uoV5OKuEzogPydu6EWqQv15Gzg/J45/n93dxLZki7G/Ht3cxPg0C7Ony35odGfkZsUMQXZdtBHCq4IQ5W9aqxIWPzACZx6ZFlltU6+q5V4ysOiBczQEWlWbG84O6SgZSFmBtS/dVkY13pWxOeaz6aZFjJP3vHPUEcZ3bm1KprmqyirLJ8loXju5Lq5XkId7BKY5P3aJXTOo+dnNb+OlRPj5D0gyoWTlukmOFLZu3WHt4LFDXnbcZHyOu3aAsejiU8rpvCC+0+qKhepC1O5ICzyrr6lVOQexGMF1w9qiluDd99PgJlw30KD9ufFiMo+uH80dhr42oTKqKlXNSw4+dSQ7HUe3ro0lDx5vsBiq5Wfjs6v6uYrDjuAMLujI/JlrEg+O7oRzjmqKBfcNx3uXWtlx/E6FSghxaEpXqW199fuLx4jJqG1S0+wclTXTopL0zsUobmeCUdQH2qR7X3j/CJOe07Aj6mqJgwPADce2s52wROPcCYUlCfytX3MTnZwQotTMUsNcpyk3Dca4a/sbI2GV3KxAw6nO7t0UnRpFApJlCXF4ZQtK2bibkxVTxqqzxSDfgh76SydfdatTNRfTbxtq2rQBKkaordMitH61PNw6or2SZZuXHfeUneOi/i0MlgjgzjZ459I+mHfvca7vGcEeT53ZTapdwaOZImMo64YqO6tStlzcnBcYf+WCXujWpDpm3jEUE29Ia2c9cXpXTLlpMCrlmJ0NLNxJRxAWAO49qaOJkcQ3OV5k+Ywjm+CPB4933a5FfSqvTOxTeyTn53HXJjcVvOoxqubus3s3kR73ggAkAiN4hOHg0VZZdj6ldWrt4aXl8uLI4nxxyYCWqJzqr24ys1bLzzY29nScMPwmg3gXu5/vtx13TEmyuFln3n9y0u5oWaeyo7aOmD1bB2cf6ezY9YPMqfFVcPBG1ujujXDde3M8lfPwKe4MVTtHicyjz2cVkGHyTYMdRSLHXdsfo575wbFut45ob0mbnPZiOl5ugC3Me0jELT++8mhs3l2oXVZBnl065fRDuWpIK0xatBkHi0tx9TGt0apuZRypSHedkxUznFBhpg88nCE6Jkd1aYCZf243hRTwqet7t6iJs3s3wTsz1xjH/ndRb3S97xsA6YkjIexavvrXIzF9xTbb1Ob85DbtliGoJmSQk03W/CF+t3JAmzp499K+SuFT2U73KxdaafEq5OfELeLgDDNuH+poeI7u1hCfzllvfN5fVIp7hEUwD6eYYdW4w0K/7DSRIhxasDB4NF+6eFq7elVx7lHWcGm3qO8hLLAs0bFhARau3+25r+TEYyhyMxEHiNysuCnBQYRw8dL5PbG/qBSNa+SbsizycEplzvqraOPcMbIDzn55OgBgWMreExc7edlxaXjvzSPao2blXKVujRsEHRb/5yMjQQgxmFCjNQSRGR47rQseHK0Wl9eF6hfZ27HucLj4dwghNQG8B6A5gJUAzqCU7hDOaQLgdQD1ASQAvEQp/Vd4dXJ3PntXtavkKqMR2tStgqlLtpgS6OiCF0eWkQGYKekmdCg7HsPJ3Rqha+Pq0jEASNuNtVxsOIrPzm8yE0Y+cLNRUbtKLn69c5jFcS0DYxPuKyzRLj/srHblf/uqnCCodb1bo8dtm85zKL9F7cqOWTZUAsgfXXE0/sGlwWtbr4rFY8tS2/FiV9NuGWLSMxExuF1dzLpzmEWZHUhqHvGxzU6w2+1sXCO5s3Xb8e1x8/D2mHDdQEy5eQjysuP4S/fGxvcy1Egt8i8f5D+MLIIVRohW6nNedhyPntZFGT9OUiLesjLY94DcodCnZS1bTSk+xKpJzUpaxtZfujc2HKc8Yy9G1Flt5t5zHN7+u5WJpAt2P/ZbalcxT571CvJM1FgZzjmqGVZwYubt6st3YEZ2boCCvCycc5T9zqJMx4EHy4qiIy73xdX9TbHbImbeEVyIV4QAENBKgrUM1oaeOrOb8txDGW9dchQ+uLyvZ1btzDuGBhoGCSQz3PllU0UIHsd1rI/R3RuhV/OayvZiaPBw3/MhVWyuFBczdqxoJ1TJzcI/hrXxvDHGZ2IMIpyKB3tO9QrysPzhka5YM/EY8e3cAaxhHwxZceJbb+7r65IaQEFk+awgGANgEqW0DYBJqc8iSgDcSCk9AkAfAFcRQrxlpNCA6xCt1Kuyy0Y15vj2eOfvfdC5cTW8cF5PnNfHGwskJtjcgHVD1A5Xp0J0GSNG5dwB0nO5lVlvPsC3VdFGdKu3BQAL7xtusO28olaVXEtff/RUq2YWGyfbaYRBf33dQBM7NixEDB5NiA3xkv4tfE18uqgiSTUMmOMleTxzdne8OX2VQUfzivcv62vJJtCzWQ30bFYD/5q0FIA8jfjRrWpbJqYmNSuhSU218wQITgjOzo7o1KgaJt04CC0dxLpkYGlyI4QLN8aIeCr/6lk7uO+kjrj7s4Vaux152XHceGxbDHfhUGTIyYrh1hHtcdMHc03H7SZKJ+eLHT66oq+pTz1zdnd0b1IdAx6b7LqsWCxpTG7fV6QM6WpYPR/z7pVn5jHBwUD44Iq+GD9/o5Zx7LS74USZjVC28LquEOfaw5U1Wb1SjpJVqnt90OjUqFoUCllBwRZIfKjT7/eNwEez1+LmD+cZKz3dcKogMLJzfYyfv1H5fW5WHE1q5mPN9gOhjgNlNcaIEgkMQfhkGGOxfjXn1NWHCE4GMDj19/8ATAFwK38CpXQDgA2pv/cQQhYBaATg9zAqJHOi2EGWkEBEdjxmrD1HdKqvtQF+es/Gpk33ZN0kWpKpf3X6w9XHtMazk5fhJoXY/nXD2uDpicm1Yro0BwY497dYPUaOuLBvM/RrXRuNa1QyOYBlqJyb5UFewBlnSkKrereoiZ9vOwb1NaQH2tlIRASJyMGjCbG933lCaE5fE54/tyf6PDIJAPD9zYPxr0lL8clv66QK6E+f2Q0Nq+cHIgAsDgY8Pr7yaDSqnh+6QJQXOHmeW9UJT0gzgncYdFEX17SuWwUt61TGii37AJgXhuzvEZ0aYEQn5/TaDNcMlWcgAZLidut3HVR+P7xjPXz4a01cP6wtCAg+mr02tD4iCkCf5IICf/Pwdnj86z8AmCdRt3o9MrBdYtWvbl+/wJXQa4SKD7eLlfI3q0SIUPHBNhQqcwydWIwYizm+m9YryMXZvdOLmMsGtbRNRe4Vz5zVHf883X6AYIl5gmLwfH/zYEcm/dfXDUT1SsGFSYn46h8DMGvVDucTU3hXojnohGr52Xjm7O7o09K7k7iCoV7KgQNK6QZCiHUHmgMhpDmA7gBm2JxzKYBLAaBpU/dMGZFJrn1dwLPg46d3tRxjzG++arpC7EByQ9Ru0/u6YW0NBw+7hbiONpg9kuvFYw/9pRNa162CG45ta8vAv2xgS7w4dYWynDDRoJw5UyMHjyaCWKidq6GUL4LXDWhWqzKePKMbnjzDTFl/YHQnZMcIRnfXjyH2g7AygDF8c/1ArNiy19O15dDnFEEDLIsUS2esg7zsOL67cTDa3DEexaU09IH8s6v7Y9W2fcrvq+Zl491Lk6GIjVIU2/JIPrhqSGvDwRM0dHagIljhVz+AEHIvgL8D2JI6/XZK6fhM1J3BKQOIbpOI2k6ECMHjjpFHoG3dKhh6hHndy/obvwidcfsw0zm3HS9njPtFVjwGJ9UCVq+gWDbNajkzuMPeYT+iQYHF1rlqSCv8Z/JyANYxUCc5S45EcNrNxk9FACFkIpLzn4g7XJZTBcBHAK6jlO5WnUcpfQnASwDQq1cv17yq+tXysGzzXtchWpmYA2XdqXmtyli6eW/gdqu29qIpi5b55FpVcpVsIR63jTwCt40MZ7y6uH8LvPrDn6GUHQYiB08GcbwLJoEbnC9okVR0tK1X1XOWqvLIKorgjI4Nq+HTq/qhk4fQwuRuBw19UqxTNddRoJyhV0ro0k+IRUXEkc1rYsG63YGwgQ4zMP2AsYSQManPtwrnMP2A2YSQqgB+JYR8Syll9PKnKKVPZLDOWnBrFVvi8iuQTOiX1/THb2t2lnU1IkSwoHJuFv7KZeBioerHd2qAqUu2Yszx/pnfYYClHQ5ag6e84ebh7ZGfHccT3yxxfe0XV/c3NskOZVBKh6m+I4RsIoQ0SLF3GgDYrDgvG0nnzluU0o9DqiqOblULT57RDT8u22pk6nVCJme6dH9K3/Xtv/fBgnW7PGVdtIPhuBJcXdYaWL8LA/EYcZXOnOGuEzrgrgxF7wSByMHjgIf+0gnPpbzqXnH4aJxFiOAdfJpzVyiHdt/AtnUw957jXOvs9FJkQAkC/zmnR+iMottHHoFzeje1FSyPIEW50w9wi74ta0uPs9CO9g3cOe2DpqlnApFGTYSKgLcvOQptUyyVvOx4uRYyb1KzEjbvKTQlPyjPCCOUzQlhZ+OpIPgcwIUAxqb+/Uw8gSR3D14FsIhS+mSYlWEJNE7tqS/ym0lBbFkWrTpVczFEoq3qF6rkG21SG/ndmlTHlD+2mDZzwvTnLn94JIY8MQV/blUz8g8FRA4eB5x7VLNAUrQCEfU8QoQwUd4WhG6dOzPvGBpoelQRo7qkGYQ9m9XAry40AHSRHY8Zk3YEVwhCP+BqQsgFAGYhyfSRvmC/ugIqNK1VCSvHjkLzMeNMx/u2qoVPr+qHLg6OD5YWXEQYNm+1/GzsOlAcfMERIlQAHN1a7owtj3j5gl6Y+ed21HDBCl38wIgQa6TGHw+O8GWHjOrSEE98s8RI2X7ZoJZ4e8bqoKp3qGMsgPcJIRcDWA3gdAAghDQE8AqldCSAfgDOB/6/vfuPvauu7zj+fFP7Q75tCS30h7WWArWzopRSlR/GlEEt7R+rxG3BKGKU4EQmJsOkhpDg2BJGdMuWJWawLUEzx1xwsdHN4MiGMZpRaIBWsUCxRGxXKjOhjQsM+OyP77n93rb3+/3e+733nPM59z4fyck993zPvZ/398P31XP53HM+hz0R8XjxuoFezvyNG97X099qu4mBkPI/y1b5/6OtgauTm7zk3MU8/IVN7P3ly+MDPFNcojVTrc+jb1vc+UvHYT7/wgGeCs30g+ru2zfzymuvD7YYacg0fQC1yrtCNbyrjrvh/as5fPSVusvoSsnzB3wVuJPxzyt3Al8BPtnp9f3OKzAT3Zyd940bLuG5X81s7rVe/WjHb/PaDE7RllStRWNzurpTULt5swd/55xuTDd583RWnzV2wsS1X9z6jtLmPxo2KaWXgCs7bD8IbCvWf0jJH38GMXha5eezKo6CE3MLnfqbrVo8dvyLnTK+zPno+97GBSvOmPkVAg3mAE8DOJ+FNByufc9K7t/1i7rLGBpV3c1wEMqcPyCldLhtn3uB7wyu8mqccfpsLip5Av+Wsbl+9JEkZaTCSZaPz39T4WVhk/1eZf66EdFxcGfH1t/ij775RC2XVFalGRe1NlxrotX2O2JJGowmnY1y14ffXXobn7jsnGn38dyF7LTmD4AZzB9QDAq1XAPsLanOaf3NdRcDJ14SOBNNPyNPkqRuvTHJpUxlqPKGNN3eRSuRWFvRJf5b3rmMvV/aUtvZflXwa6wKfO7KNWxf/xbOPXt+3aVIqtlpAWVdHdJ+evdUVp75Zh57/tfM90yGXPQ7f8DdEbGe8bG7A8CnK67/uC3vXMbu2zezYJ5/W5Ik9aLKwZcqvuxrzUn0uxtWdvx569dNCe6/sbpLtYedn8AqMOu0cHBHKsktV63h7u/tY/aAb+1Yll23XcVvXq13Tq0/veZdXH3BMt6xvPfb0tft3o9vZE5D7qjSrX7nD0gpXVdqgT0a5GXF3oVSkjTsqjzWnT5n/MyV3+vhLl9nzZ/bUxtvXzqfpw8fY+G82Tz9J1uZPWuygauJ7WeOzeHisUU9taPOHOCR1Gg3bTqfmzadX3cZXVs8fy6La65hbO6buPqC/i6hqcvmdUvrLkGSJGngqjh/Z97sWfzszquZ0+UXow9/YVNXd4b91k2XHX/PBz5zGS8dexWgqy/l/C5nsBzgkSRJkiSpBhO3SYfrL13FkoXlztvay/wzqxaPdbXfhrYbJSyYN5sF86YfFGq/REuDM1znuUuMXx7QzUSzGj43X3F+199ISMrXZzadB8DKRcN7lwtJg+NnPzXZhW89A4CPXbKKL22/gM9e0Zwz0/txUXGXq+suXVVzJcPFM3g0dHbfvrnuElSTW7es5dYta+suQ1Kftq9fwfb1K+ouQ1JD+NlPTbZk4byub5QxTEb19y6bX3VLkiRJkiQ1nAM8kiRJkiRJDecAjyRJkiRJUsM5wCNJkiRJktRwtUyyHBGLgH8CzgEOAL+fUvp1h/0OAEeB14HXUkobq6tSkiRJkiSpGeo6g2cH8FBKaQ3wUPF8MleklNY7uCNJkiRJktRZXQM824H7ivX7gA/VVIckSZIkSVLj1TXAszSldAigeFwyyX4JeDAiHouIG6d6w4i4MSIejYhHjxw5MuByJUmSJEmS8lXaHDwR8e/Asg4/uq2Ht7k8pXQwIpYA34+In6WUftBpx5TSPcA9ABs3bkw9FyxJkiRJktRQkVL1YyERsQ/YlFI6FBHLgf9MKa2d5jV3AMdSSl/u4v2PAM9P8uOzgF/1WHLZcqspt3ogv5qmq2dVSunsqorJnZkciNxqyq0emLomM3kSc9m33OqB5tVkLtuYyYHIrabc6gEz2RNz2bfc6oHm1dRTLmu5ixawE7geuKt4/PbJO0TEGHBaSulosf5B4I+7efOpOiAiHs1twubcasqtHsivptzqyZ2Z7F9uNeVWD+RZU87MZX9yqwesqenMZP9yqym3eiDPmnJmLvuTWz0w/DXVNQfPXcDmiHgG2Fw8JyLeEhH/WuyzFPhhRDwBPAJ8N6X0vVqqlSRJkiRJylgtZ/CklF4Cruyw/SCwrVh/Driw4tIkSZIkSZIap64zeOp0T90FdJBbTbnVA/nVlFs9TZZjX1rT9HKrB/Ksqaly7MvcasqtHrCmYZZjP1rT9HKrB/Ksqaly7MvcasqtHhjymmqZZFmSJEmSJEmDM4pn8EiSJEmSJA0VB3gkSZIkSZIabmQGeCLi6ojYFxHPRsSOCttdGRH/ERFPRcRPIuKWYvsdEfHLiHi8WLa1veaLRZ37ImJLCTUdiIg9RbuPFtsWRcT3I+KZ4vHMCutZ29YPj0fEyxHx+ar7KCL+PiJejIi9bdt67peIuLjo32cj4q8iIgZR3zCqI5c5ZrJow1yeWoOZrJjHyhNqMpOd6zCXFfNYeUJd5vLUGsxkxTxWnlCTmexcR325TCkN/QLMAvYD5wJzgCeAdRW1vRzYUKwvAJ4G1gF3ALd22H9dUd9cYHVR96wB13QAOOukbXcDO4r1HcCfVVVPh/9W/w2sqrqPgA8AG4C9/fQL8AhwKRDAvwFbq/x7b8pSVy5zzGTRjrk89T3NZIVLXZks2s4ul2Zy0rbNZYVLXbnMMZNFO+by1Pc0kxUudWWyaDu7XJrJSduuLZejcgbPe4FnU0rPpZReBe4HtlfRcErpUEppd7F+FHgKWDHFS7YD96eUXkkp/Rx4lvH6y7YduK9Yvw/4UE31XAnsTyk9P8U+pdSUUvoB8D8d2uq6XyJiObAwpfTjNJ7Kr7W9RieqJZcNymSr7ZHNpZmsnMfK6Y10JsFc1sBj5fRGOpdmsnIeK6c30pmEenM5KgM8K4BftD1/ganDUIqIOAe4CPivYtPNEfFkcQpX6xStKmpNwIMR8VhE3FhsW5pSOgTj/3gASyqsp921wD+2Pa+rj1p67ZcVxXoVtTVd7bnMKJNgLrtlJstTeyYhq1yaye6Zy/LUnsuMMgnmsltmsjy1ZxKyyqWZ7F4luRyVAZ5O16pVen/4iJgPPAB8PqX0MvBV4DxgPXAI+Epr1w4vH3Stl6eUNgBbgc9GxAem2LeyvouIOcDvAP9cbKqzj6YzWQ051NYUtfZVZpkEc9kvM9m/2vsqs1yayf6Zy/55rDyRueyPmexf7X2VWS7NZP8GmstRGeB5AVjZ9vytwMGqGo+I2YyH8B9SSt8CSCkdTim9nlJ6A7iXiVPBSq81pXSweHwR+Jei7cPFaWAUjy9WVU+brcDulNLhor7a+qhNr/3yQrFeRW1NV1suc8tk0b657I6ZLI/HyjZmsifmsjweK9uYy66ZyfJ4rGxjJntSSS5HZYBnF7AmIlYXo3nXAjuraLiY6frvgKdSSn/etn15227XAK0ZtncC10bE3IhYDaxhfHKlQdUzFhELWuvAB4u2dwLXF7tdD3y7inpO8hHaTqOrq49O0lO/FKfbHY2IS4r/9h9ve41OVEsuc8tk0ba57J6ZLI/Hyol2zWRvzGV5PFZOtG0uu2cmy+OxcqJdM9mbanKZKpjxO4cF2Mb4TOP7gdsqbPf9jJ9K9STweLFsA74O7Cm27wSWt73mtqLOfQx4BnvGZ3x/olh+0uoLYDHwEPBM8bioinra2jgdeAk4o21bpX3E+D8Ch4D/Y3zE9FMz6RdgI+P/aOwH/hqIuv/+c13qyGVumSze31x2bt9MVrzUkcmi3axyaSanrMFcVrzUkcvcMlm8v7ns3L6ZrHipI5NFu1nl0kxOWUNtuYzihZIkSZIkSWqoUblES5IkSZIkaWg5wCNJkiRJktRwDvBIkiRJkiQ1nAM8kiRJkiRJDecAjyRJkiRJUsO9qe4CVL6IaN2SDWAZ8DpwpHj+m5TSZbUUJo0oMynlx1xKeTGTUn7MZf68TfqIiYg7gGMppS/XXYskMynlyFxKeTGTUn7MZZ68RGvERcSx4nFTRDwcEd+MiKcj4q6I+GhEPBIReyLivGK/syPigYjYVSyX1/sbSMPFTEr5MZdSXsyklB9zmQcHeNTuQuAW4F3AdcDbU0rvBf4W+MNin78E/iKl9B7gw8XPJJXDTEr5MZdSXsyklB9zWRPn4FG7XSmlQwARsR94sNi+B7iiWL8KWBcRrdcsjIgFKaWjlVYqjQYzKeXHXEp5MZNSfsxlTRzgUbtX2tbfaHv+BhN/K6cBl6aU/rfKwqQRZSal/JhLKS9mUsqPuayJl2ipVw8CN7eeRMT6GmuRZCalHJlLKS9mUsqPuSyBAzzq1eeAjRHxZET8FPiDuguSRpyZlPJjLqW8mEkpP+ayBN4mXZIkSZIkqeE8g0eSJEmSJKnhHOCRJEmSJElqOAd4JEmSJEmSGs4BHkmSJEmSpIZzgEeSJEmSJKnhHOCRJEmSJElqOAd4JEmSJEmSGu7/AZLExHvxr5KVAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -2076,7 +2076,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's define `SpikeDetectionParam` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the ERD.\n", + "Let's define `SpikeDetectionParam` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the Diagram.\n", "\n", "This tier indicates that the table will contain information:\n", "* that will be referenced by other tables\n", @@ -2197,7 +2197,7 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { @@ -2416,14 +2416,14 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In the ERD, we see that `Spikes` is a computed table (red circle) that depends on **both Neuron and SpikeDetectionParam**. Finally, let's go ahead and implement the `make` method for the `Spikes` table. " + "In the Diagram, we see that `Spikes` is a computed table (red circle) that depends on **both Neuron and SpikeDetectionParam**. Finally, let's go ahead and implement the `make` method for the `Spikes` table. " ] }, { @@ -4001,7 +4001,7 @@ } ], "source": [ - "dj.ERD(schema)" + "dj.Diagram(schema)" ] }, { From 1438b8162d5b6257708f64126357b9a5a3171cdd Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Fri, 12 Nov 2021 16:08:13 -0600 Subject: [PATCH 2/3] Update links for new folder structure --- 00-Getting_started/00-Getting started.ipynb | 10 +++++----- .../01-DataJoint Basics - Interactive.ipynb | 4 ++-- .../01-DataJoint Basics - Interactive.py | 4 ++-- .../02-Imported Tables - Interactive.ipynb | 0 ...Lookup Table, and Part Table - Interactive.ipynb | 4 ++-- .../data/example_scan_01.tif | Bin .../data/example_scan_02.tif | Bin .../data/example_scan_03.tif | Bin ...cium_imaging - Imported Tables - Completed.ipynb | 8 ++++---- ...rophysiology - Imported Tables - Completed.ipynb | 4 ++-- ..., Lookup Table, and Part Table - Completed.ipynb | 2 +- 11 files changed, 18 insertions(+), 18 deletions(-) rename {01-Calcium_Imaging_Data => 01-Calcium_Imaging}/02-Imported Tables - Interactive.ipynb (100%) rename {01-Calcium_Imaging_Data => 01-Calcium_Imaging}/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb (99%) rename {01-Calcium_Imaging_Data => 01-Calcium_Imaging}/data/example_scan_01.tif (100%) rename {01-Calcium_Imaging_Data => 01-Calcium_Imaging}/data/example_scan_02.tif (100%) rename {01-Calcium_Imaging_Data => 01-Calcium_Imaging}/data/example_scan_03.tif (100%) diff --git a/00-Getting_started/00-Getting started.ipynb b/00-Getting_started/00-Getting started.ipynb index 316c227..ab1a38b 100644 --- a/00-Getting_started/00-Getting started.ipynb +++ b/00-Getting_started/00-Getting started.ipynb @@ -316,18 +316,18 @@ "The next step in the tutorial is to:\n", "1. learn the basic of the DataJoint framework with [01-DataJoint Basics](./01-DataJoint%20Basics%20-%20Interactive.ipynb)\n", "2. data ingestion in a pipeline, with practical examples in 2 domains:\n", - " + [02-electrophysiology](./electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb)\n", - " + [02-calcium imaging](./calcium_imaging/02-Imported%20Tables%20-%20Interactive.ipynb)\n", + " + [02-electrophysiology](/02-Electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb)\n", + " + [02-calcium imaging](./01-Calcium_Imaging/02-Imported%20Tables%20-%20Interactive.ipynb)\n", "3. more advanced topics on built-in automatic computation of the pipeline with DataJoint, also with practical examples from:\n", - " + [03-electrophysiology](./electrophysiology/03-Computed%20Table%2C%20Lookup%20Table%2C%20and%20Part%20Table%20-%20Completed.ipynb)\n", - " + [03-calcium imaging](./calcium_imaging/03-Computed%20Table%2C%20Lookup%20Table%2C%20and%20Part%20Table%20-%20Completed.ipynb)" + " + [03-electrophysiology](/02-Electrophysiology/03-Computed%20Table%2C%20Lookup%20Table%2C%20and%20Part%20Table%20-%20Completed.ipynb)\n", + " + [03-calcium imaging](./01-Calcium_Imaging/03-Computed%20Table%2C%20Lookup%20Table%2C%20and%20Part%20Table%20-%20Completed.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As you will find out that these tutorial notebooks are meant to be interactive. There will be empty cells meant for you to write the code yourself, as mini exercises. To access the notebooks with complete solutions, visit this [folder](./completed)." + "As you will find out that these tutorial notebooks are meant to be interactive. There will be empty cells meant for you to write the code yourself, as mini exercises. To access the notebooks with complete solutions, visit this [folder](../03-Completed_Exercises)." ] } ], diff --git a/00-Getting_started/01-DataJoint Basics - Interactive.ipynb b/00-Getting_started/01-DataJoint Basics - Interactive.ipynb index 616f22a..6482646 100644 --- a/00-Getting_started/01-DataJoint Basics - Interactive.ipynb +++ b/00-Getting_started/01-DataJoint Basics - Interactive.ipynb @@ -1535,8 +1535,8 @@ "In the next session, we are going to extend our data pipeline with tables to represent **imported data** and define new tables to **compute and hold analysis results**.\n", "\n", "We will use both ephys and calcium imaging as example pipelines:\n", - "+ [02-electrophysiology](./electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb)\n", - "+ [02-calcium imaging](./calcium_imaging/02-Imported%20Tables%20-%20Interactive.ipynb)" + "+ [02-electrophysiology](../02-Electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb)\n", + "+ [02-calcium imaging](../01-Calcium_Imaging/02-Imported%20Tables%20-%20Interactive.ipynb)" ] }, { diff --git a/00-Getting_started/01-DataJoint Basics - Interactive.py b/00-Getting_started/01-DataJoint Basics - Interactive.py index f7cef77..298a7f1 100644 --- a/00-Getting_started/01-DataJoint Basics - Interactive.py +++ b/00-Getting_started/01-DataJoint Basics - Interactive.py @@ -556,7 +556,7 @@ class Session(dj.Manual): # In the next session, we are going to extend our data pipeline with tables to represent **imported data** and define new tables to **compute and hold analysis results**. # # We will use both ephys and calcium imaging as example pipelines: -# + [02-electrophysiology](./electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb) -# + [02-calcium imaging](./calcium_imaging/02-Imported%20Tables%20-%20Interactive.ipynb) +# + [02-calcium imaging](.../01-Calcium_Imaging/02-Imported%20Tables%20-%20Interactive.ipynb) +# + [02-electrophysiology](../02-Electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb) diff --git a/01-Calcium_Imaging_Data/02-Imported Tables - Interactive.ipynb b/01-Calcium_Imaging/02-Imported Tables - Interactive.ipynb similarity index 100% rename from 01-Calcium_Imaging_Data/02-Imported Tables - Interactive.ipynb rename to 01-Calcium_Imaging/02-Imported Tables - Interactive.ipynb diff --git a/01-Calcium_Imaging_Data/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb b/01-Calcium_Imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb similarity index 99% rename from 01-Calcium_Imaging_Data/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb rename to 01-Calcium_Imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb index 22281ce..291ee51 100644 --- a/01-Calcium_Imaging_Data/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb +++ b/01-Calcium_Imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Welcome back! In this session, we are going to continue working with the pipeline for the mouse electrophysiology example. \n", + "Welcome back! In this session, we are going to continue working with the pipeline for the mouse calcium imaging example. \n", "\n", "In this session, we will learn to:\n", "\n", @@ -1013,7 +1013,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We have covered most of the building elements of data pipeline design. Using these elements, we could design more sophiscated pipelines that facillitates your experimental recordings and data analyses." + "We have covered most of the building elements of data pipeline design. Using these elements, we could design more sophiscated pipelines that facillitates your experimental recordings and data analyses. To see these concepts in the context of another methodology, see the next folder on [Electrophysiology](../02-Electrophysiology)" ] } ], diff --git a/01-Calcium_Imaging_Data/data/example_scan_01.tif b/01-Calcium_Imaging/data/example_scan_01.tif similarity index 100% rename from 01-Calcium_Imaging_Data/data/example_scan_01.tif rename to 01-Calcium_Imaging/data/example_scan_01.tif diff --git a/01-Calcium_Imaging_Data/data/example_scan_02.tif b/01-Calcium_Imaging/data/example_scan_02.tif similarity index 100% rename from 01-Calcium_Imaging_Data/data/example_scan_02.tif rename to 01-Calcium_Imaging/data/example_scan_02.tif diff --git a/01-Calcium_Imaging_Data/data/example_scan_03.tif b/01-Calcium_Imaging/data/example_scan_03.tif similarity index 100% rename from 01-Calcium_Imaging_Data/data/example_scan_03.tif rename to 01-Calcium_Imaging/data/example_scan_03.tif diff --git a/03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb b/03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb index 963d86d..61d46bf 100644 --- a/03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb +++ b/03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb @@ -464,7 +464,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here we have prepared two tif files of scanning in the [data](../data/calcium_imaging) folder `example_scan_01.tif` and `example_scan_02.tif` " + "Here we have prepared two tif files of scanning in the [data](../data/01-Calcium_Imaging) folder `example_scan_01.tif` and `example_scan_02.tif` " ] }, { @@ -481,7 +481,7 @@ } ], "source": [ - "ls ../calcium_imaging/data" + "ls ../01-Calcium_Imaging/data" ] }, { @@ -658,7 +658,7 @@ ], "source": [ "from skimage import io\n", - "im = io.imread('../calcium_imaging/data/example_scan_01.tif')\n", + "im = io.imread('../01-Calcium_Imaging/data/example_scan_01.tif')\n", "print(im.shape)" ] }, @@ -1261,7 +1261,7 @@ " \"\"\"\n", " def make(self, key): # key is the primary key of one of the entries in the table `Scan`\n", " # fetch data directory from table Session\n", - " data_path = '../calcium_imaging/' + (Session & key).fetch1('data_path')\n", + " data_path = '../01-Calcium_Imaging/' + (Session & key).fetch1('data_path')\n", " \n", " # fetch data file name from table Scan\n", " file_name = (Scan & key).fetch1('file_name')\n", diff --git a/03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb b/03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb index 4be4994..fab1cb6 100644 --- a/03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb +++ b/03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb @@ -556,7 +556,7 @@ } ], "source": [ - "filename = '../electrophysiology/data/data_{mouse_id}_{session_date}.npy'.format(**key)\n", + "filename = '../02-Electrophysiology/data/data_{mouse_id}_{session_date}.npy'.format(**key)\n", "filename" ] }, @@ -1469,7 +1469,7 @@ " \"\"\"\n", " def make(self, key):\n", " # use key dictionary to determine the data file path\n", - " data_file = \"../electrophysiology/data/data_{mouse_id}_{session_date}.npy\".format(**key)\n", + " data_file = \"../02-Electrophysiology/data/data_{mouse_id}_{session_date}.npy\".format(**key)\n", "\n", " # load the data\n", " data = np.load(data_file)\n", diff --git a/03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb b/03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb index 3d00d84..aaed9e9 100644 --- a/03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb +++ b/03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb @@ -2732,7 +2732,7 @@ " \n", " print('Populating: {}'.format(key))\n", " # fetch data directory from table Session\n", - " data_path = '../calcium_imaging/' + (Session & key).fetch1('data_path')\n", + " data_path = '../01-Calcium_Imaging/' + (Session & key).fetch1('data_path')\n", " \n", " # fetch data file name from table Scan\n", " file_name = (Scan & key).fetch1('file_name')\n", From 0dd1faceb69adf4145e730589a6ed457ec2a0ef2 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Thu, 13 Jan 2022 14:00:54 -0600 Subject: [PATCH 3/3] Set NB wd as /home/notebooks/. Move data to wd. See details - Added changelog for internal update notes - Recent server updates change the default user working directory - Set each notebook to move to /home/notebooks/ if not in a NB dir already - Purged outputs from Interactive notebooks - moved data to /notebooks/ for both modalities --- .gitignore | 10 +- .../02-Imported Tables - Interactive.ipynb | 4 +- ... Table, and Part Table - Interactive.ipynb | 4 +- .../02-Imported Tables - Interactive.ipynb | 81 +---- .../02-Imported Tables - Interactive.py | 4 +- ... Table, and Part Table - Interactive.ipynb | 284 ++---------------- ...kup Table, and Part Table - Interactive.py | 4 +- .../01-DataJoint Basics - Completed.ipynb | 6 +- ...maging - Imported Tables - Completed.ipynb | 17 +- ...iology - Imported Tables - Completed.ipynb | 14 +- ...up Table, and Part Table - Completed.ipynb | 10 +- ...up Table, and Part Table - Completed.ipynb | 10 +- CHANGELOG.md | 21 ++ .../data => data}/data_0_2017-05-15.npy | Bin .../data => data}/data_0_2017-05-19.npy | Bin .../data => data}/data_100_2017-05-25.npy | Bin .../data => data}/data_100_2017-06-01.npy | Bin .../data => data}/data_5_2017-01-05.npy | Bin .../data => data}/example_scan_01.tif | Bin .../data => data}/example_scan_02.tif | Bin .../data => data}/example_scan_03.tif | Bin 21 files changed, 93 insertions(+), 376 deletions(-) create mode 100644 CHANGELOG.md rename {02-Electrophysiology/data => data}/data_0_2017-05-15.npy (100%) rename {02-Electrophysiology/data => data}/data_0_2017-05-19.npy (100%) rename {02-Electrophysiology/data => data}/data_100_2017-05-25.npy (100%) rename {02-Electrophysiology/data => data}/data_100_2017-06-01.npy (100%) rename {02-Electrophysiology/data => data}/data_5_2017-01-05.npy (100%) rename {01-Calcium_Imaging/data => data}/example_scan_01.tif (100%) rename {01-Calcium_Imaging/data => data}/example_scan_02.tif (100%) rename {01-Calcium_Imaging/data => data}/example_scan_03.tif (100%) diff --git a/.gitignore b/.gitignore index 525c175..3a32f66 100644 --- a/.gitignore +++ b/.gitignore @@ -1,12 +1,14 @@ +# OSX and python intermediate files __pycache__/ .DS_Store ~* *~ *.pyc .ipynb_checkpoints/ -dj_local_conf.json - -.idea/ +debug.log +# DataJoint and setup files +dj_local_con*.json julia/setup.jl -debug.log +.idea/ +temp* diff --git a/01-Calcium_Imaging/02-Imported Tables - Interactive.ipynb b/01-Calcium_Imaging/02-Imported Tables - Interactive.ipynb index d362c7d..cc8d3fd 100644 --- a/01-Calcium_Imaging/02-Imported Tables - Interactive.ipynb +++ b/01-Calcium_Imaging/02-Imported Tables - Interactive.ipynb @@ -67,8 +67,8 @@ "metadata": {}, "outputs": [], "source": [ - "import sys\n", - "sys.path.append(\"..\")\n", + "import os\n", + "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n", "from tutorial_pipeline.mouse_session import schema, Mouse, Session" ] }, diff --git a/01-Calcium_Imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb b/01-Calcium_Imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb index 291ee51..8756843 100644 --- a/01-Calcium_Imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb +++ b/01-Calcium_Imaging/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb @@ -69,8 +69,8 @@ "metadata": {}, "outputs": [], "source": [ - "import sys\n", - "sys.path.append(\"..\")\n", + "import os\n", + "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n", "from tutorial_pipeline.imaging import schema, Mouse, Session, Scan, AverageFrame" ] }, diff --git a/02-Electrophysiology/02-Imported Tables - Interactive.ipynb b/02-Electrophysiology/02-Imported Tables - Interactive.ipynb index 0fbfa86..e1619c1 100644 --- a/02-Electrophysiology/02-Imported Tables - Interactive.ipynb +++ b/02-Electrophysiology/02-Imported Tables - Interactive.ipynb @@ -66,8 +66,8 @@ "metadata": {}, "outputs": [], "source": [ - "import sys\n", - "sys.path.append(\"..\")\n", + "import os\n", + "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n", "from tutorial_pipeline.mouse_session import schema, Mouse, Session" ] }, @@ -641,74 +641,9 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "Mouse\n", - "\n", - "\n", - "Mouse\n", - "\n", - "\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "\n", - "\n", - "Mouse->Session\n", - "\n", - "\n", - "\n", - "Neuron\n", - "\n", - "\n", - "Neuron\n", - "\n", - "\n", - "\n", - "\n", - "Session->Neuron\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dj.Diagram(schema)" ] @@ -728,7 +663,7 @@ "formats": "ipynb,py:light" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -742,9 +677,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.6" + "version": "3.8.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/02-Electrophysiology/02-Imported Tables - Interactive.py b/02-Electrophysiology/02-Imported Tables - Interactive.py index 0d600d7..93c9d54 100644 --- a/02-Electrophysiology/02-Imported Tables - Interactive.py +++ b/02-Electrophysiology/02-Imported Tables - Interactive.py @@ -34,8 +34,8 @@ # Now we would like to continue working with the tables we defined in the previous notebook. To do so, we would need the classes for each table: `Mouse` and `Session`. We can either redefine it here, but for your convenience, we have included the schema and table class definitions in a package called `tutorial_pipeline.mouse_session`, from which you can import the classes as well as the schema object. We will use the schema object again to define more tables. -import sys -sys.path.append("..") +import os +if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks') from tutorial_pipeline.mouse_session import schema, Mouse, Session Mouse() diff --git a/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb b/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb index a974b69..c086bde 100644 --- a/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb +++ b/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -65,271 +65,29 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please enter DataJoint username: root\n", - "Please enter DataJoint password: ········\n", - "Connecting root@localhost:3306\n", - "Populating neuron(s) for mouse_id=0 on session_date=2017-05-15\n", - "\tPopulated neuron 0\n", - "Populating neuron(s) for mouse_id=0 on session_date=2017-05-19\n", - "\tPopulated neuron 0\n", - "Populating neuron(s) for mouse_id=5 on session_date=2017-01-05\n", - "\tPopulated neuron 0\n", - "Populating neuron(s) for mouse_id=100 on session_date=2017-05-25\n", - "\tPopulated neuron 0\n", - "Populating neuron(s) for mouse_id=100 on session_date=2017-06-01\n", - "\tPopulated neuron 0\n" - ] - } - ], - "source": [ - "import sys\n", - "sys.path.append(\"..\")\n", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n", "from tutorial_pipeline.ephys_cell_activity import schema, Mouse, Session, Neuron" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " Experiment session\n", - "
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\n", - " " - ], - "text/plain": [ - "*mouse_id *session_date *neuron_id activity \n", - "+----------+ +------------+ +-----------+ +--------+\n", - "0 2017-05-15 0 =BLOB= \n", - "0 2017-05-19 0 =BLOB= \n", - "5 2017-01-05 0 =BLOB= \n", - "100 2017-05-25 0 =BLOB= \n", - "100 2017-06-01 0 =BLOB= \n", - " (Total: 5)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Neuron()" ] @@ -923,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1158,7 +916,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1167,7 +925,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1176,7 +934,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1340,7 +1098,7 @@ "formats": "ipynb,py:light" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1354,9 +1112,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.6" + "version": "3.8.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py b/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py index c41bf1e..29442a9 100644 --- a/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py +++ b/02-Electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.py @@ -37,8 +37,8 @@ # Similarly as before, to continue working with the tables we defined in the previous notebook, we can either redefine the classes for each table `Mouse`, `Session`, `Neuron` and populate them. Or, again for your convenience, we can import them from the `tutorial_pipeline.ephys_cell_activity` module. -import sys -sys.path.append("..") +import os +if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks') from tutorial_pipeline.ephys_cell_activity import schema, Mouse, Session, Neuron Session() diff --git a/03-Completed_Exercises/01-DataJoint Basics - Completed.ipynb b/03-Completed_Exercises/01-DataJoint Basics - Completed.ipynb index e64ba17..51adf41 100644 --- a/03-Completed_Exercises/01-DataJoint Basics - Completed.ipynb +++ b/03-Completed_Exercises/01-DataJoint Basics - Completed.ipynb @@ -5078,7 +5078,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -5092,9 +5092,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb b/03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb index 61d46bf..110991d 100644 --- a/03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb +++ b/03-Completed_Exercises/02-calcium_imaging - Imported Tables - Completed.ipynb @@ -75,8 +75,8 @@ } ], "source": [ - "import sys\n", - "sys.path.append(\"..\")\n", + "import os\n", + "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n", "from tutorial_pipeline.mouse_session import schema, Mouse, Session" ] }, @@ -464,7 +464,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here we have prepared two tif files of scanning in the [data](../data/01-Calcium_Imaging) folder `example_scan_01.tif` and `example_scan_02.tif` " + "Here we have prepared two tif files of scanning in the [data](./data/) folder `example_scan_01.tif` and `example_scan_02.tif` " ] }, { @@ -476,12 +476,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "example_scan_01.tif example_scan_02.tif example_scan_03.tif\r\n" + "\u001b[31m./data/example_scan_01.tif\u001b[m\u001b[m* \u001b[31m./data/example_scan_03.tif\u001b[m\u001b[m*\n", + "\u001b[31m./data/example_scan_02.tif\u001b[m\u001b[m*\n" ] } ], "source": [ - "ls ../01-Calcium_Imaging/data" + "ls ./data/*tif" ] }, { @@ -1894,7 +1895,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1908,9 +1909,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb b/03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb index fab1cb6..7abc090 100644 --- a/03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb +++ b/03-Completed_Exercises/02-electrophysiology - Imported Tables - Completed.ipynb @@ -76,8 +76,8 @@ } ], "source": [ - "import sys\n", - "sys.path.append(\"..\")\n", + "import os\n", + "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n", "from tutorial_pipeline.mouse_session import schema, Mouse, Session" ] }, @@ -556,7 +556,7 @@ } ], "source": [ - "filename = '../02-Electrophysiology/data/data_{mouse_id}_{session_date}.npy'.format(**key)\n", + "filename = './data/data_{mouse_id}_{session_date}.npy'.format(**key)\n", "filename" ] }, @@ -1469,7 +1469,7 @@ " \"\"\"\n", " def make(self, key):\n", " # use key dictionary to determine the data file path\n", - " data_file = \"../02-Electrophysiology/data/data_{mouse_id}_{session_date}.npy\".format(**key)\n", + " data_file = \"./data/data_{mouse_id}_{session_date}.npy\".format(**key)\n", "\n", " # load the data\n", " data = np.load(data_file)\n", @@ -2035,7 +2035,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2049,9 +2049,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb b/03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb index aaed9e9..5419379 100644 --- a/03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb +++ b/03-Completed_Exercises/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb @@ -77,8 +77,8 @@ } ], "source": [ - "import sys\n", - "sys.path.append(\"..\")\n", + "import os\n", + "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n", "from tutorial_pipeline.imaging import schema, Mouse, Session, Scan, AverageFrame" ] }, @@ -3064,7 +3064,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -3078,9 +3078,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/03-Completed_Exercises/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb b/03-Completed_Exercises/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb index 064ab9a..26fb81c 100644 --- a/03-Completed_Exercises/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb +++ b/03-Completed_Exercises/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb @@ -89,8 +89,8 @@ } ], "source": [ - "import sys\n", - "sys.path.append(\"..\")\n", + "import os\n", + "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n", "from tutorial_pipeline.ephys_cell_activity import schema, Mouse, Session, Neuron" ] }, @@ -4014,7 +4014,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -4028,9 +4028,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..f01e87f --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,21 @@ +# Changelog + +## [unreleased] +### Needs ++ Calcium Imaging .py files to mirror Electrophysiology + +## [0.0.0b2] - 2022-05-07 +### Added ++ Folder structure to to imply NB order ++ ERD->Diagram ++ Change working directory to /home/notebooks ++ Per above, move data to /home/notebooks, collapse modalities + +## [0.0.0b1] - 2021-06-11 +### Fixed ++ Notebook structure, polish + + +## [0.0.0a] - 2020-04-13 +### Added ++ Major initial commits diff --git a/02-Electrophysiology/data/data_0_2017-05-15.npy b/data/data_0_2017-05-15.npy similarity index 100% rename from 02-Electrophysiology/data/data_0_2017-05-15.npy rename to data/data_0_2017-05-15.npy diff --git a/02-Electrophysiology/data/data_0_2017-05-19.npy b/data/data_0_2017-05-19.npy similarity index 100% rename from 02-Electrophysiology/data/data_0_2017-05-19.npy rename to data/data_0_2017-05-19.npy diff --git a/02-Electrophysiology/data/data_100_2017-05-25.npy b/data/data_100_2017-05-25.npy similarity index 100% rename from 02-Electrophysiology/data/data_100_2017-05-25.npy rename to data/data_100_2017-05-25.npy diff --git a/02-Electrophysiology/data/data_100_2017-06-01.npy b/data/data_100_2017-06-01.npy similarity index 100% rename from 02-Electrophysiology/data/data_100_2017-06-01.npy rename to data/data_100_2017-06-01.npy diff --git a/02-Electrophysiology/data/data_5_2017-01-05.npy b/data/data_5_2017-01-05.npy similarity index 100% rename from 02-Electrophysiology/data/data_5_2017-01-05.npy rename to data/data_5_2017-01-05.npy diff --git a/01-Calcium_Imaging/data/example_scan_01.tif b/data/example_scan_01.tif similarity index 100% rename from 01-Calcium_Imaging/data/example_scan_01.tif rename to data/example_scan_01.tif diff --git a/01-Calcium_Imaging/data/example_scan_02.tif b/data/example_scan_02.tif similarity index 100% rename from 01-Calcium_Imaging/data/example_scan_02.tif rename to data/example_scan_02.tif diff --git a/01-Calcium_Imaging/data/example_scan_03.tif b/data/example_scan_03.tif similarity index 100% rename from 01-Calcium_Imaging/data/example_scan_03.tif rename to data/example_scan_03.tif