From 61c36100307e781aec6b33544bc10ea12a2506c1 Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 7 Jul 2021 18:26:44 -0400 Subject: [PATCH 01/13] remove 3.6 support --- ci/36.yaml | 18 ------------------ 1 file changed, 18 deletions(-) delete mode 100644 ci/36.yaml diff --git a/ci/36.yaml b/ci/36.yaml deleted file mode 100644 index c112b11..0000000 --- a/ci/36.yaml +++ /dev/null @@ -1,18 +0,0 @@ -# This file is part of the Minnesota Population Center's NHGISXWALK. -# For copyright and licensing information, see the NOTICE and LICENSE files -# in this project's top-level directory, and also on-line at: -# https://github.com/ipums/nhgisxwalk - -name: test -channels: - - conda-forge -dependencies: - - python=3.6 - # required - - numpy>=1.3 - - pandas>=1.0 - - watermark - # testing - - pytest - - pytest-cov - - codecov From 1e2060093645d187759f672c5bb8a54cc7426dbc Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 7 Jul 2021 18:28:25 -0400 Subject: [PATCH 02/13] bump min geopandas verion --- environment.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/environment.yml b/environment.yml index 295e412..39a03c2 100644 --- a/environment.yml +++ b/environment.yml @@ -8,13 +8,13 @@ - conda-forge dependencies: - python=3.9 - - geopandas - - descartes + - geopandas>=0.9.0 - ipython - jupyter - matplotlib - nb_conda_kernels - pandas + - pygeos - pip - shapely - watermark From 2f0401c46fb3887038fb7ead2c53044908e9dd28 Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 7 Jul 2021 18:40:49 -0400 Subject: [PATCH 03/13] remove 36 support from setup --- setup.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 53fd423..ac04141 100644 --- a/setup.py +++ b/setup.py @@ -82,7 +82,6 @@ def setup_package(): "Topic :: Scientific/Engineering :: GIS", "License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)", "Programming Language :: Python", - "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", @@ -93,7 +92,7 @@ def setup_package(): install_requires=reqs, zip_safe=False, cmdclass={"build.py": build_py}, - python_requires=">3.5", + python_requires=">=3.7", ) From caf42575b9cc7cca21d45d5d77df115e6fbe2949 Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 7 Jul 2021 18:41:27 -0400 Subject: [PATCH 04/13] bump preconfig to 3.9 --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0621395..8c88988 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,4 +3,4 @@ repos: rev: stable hooks: - id: black - language_version: python3.8 + language_version: python3.9 From 8b73e1e14fff3fe8af33fe0bf0c036269fc1c503 Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 7 Jul 2021 18:42:20 -0400 Subject: [PATCH 05/13] update preconfig --- .pre-commit-config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8c88988..40e3f4f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: -- repo: https://github.com/ambv/black - rev: stable +- repo: https://github.com/psf/black + rev: 20.8b1 hooks: - id: black language_version: python3.9 From ae7e9fae68e065f43693d87553629d3736626fa5 Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 7 Jul 2021 18:44:39 -0400 Subject: [PATCH 06/13] update requirements --- requirements_conda.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements_conda.txt b/requirements_conda.txt index 76ed554..c348f27 100644 --- a/requirements_conda.txt +++ b/requirements_conda.txt @@ -1,11 +1,11 @@ codecov -geopandas -descartes +geopandas>=0.9 nbsphinx numpy>=1.3 pandas>=1.0 pip pre-commit +pygeos pytest pytest-cov sphinx>=1.4.3 From b1324ca22a9fd01179f97884161d90e46da470f1 Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 7 Jul 2021 18:51:37 -0400 Subject: [PATCH 07/13] update README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 58cbac1..323b800 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ This pacakge allows for the generation of temporal crosswalks of census geograph ## Installation -Currently `nhgisxwalk` officially supports Python [3.6](https://docs.python.org/3.6/), [3.7](https://docs.python.org/3.7/), [3.8](https://docs.python.org/3.8/), and [3.9](https://docs.python.org/3.9/). Please make sure that you are operating in a Python >= 3.6 environment. Install the most current development version of `nhgisxwalk` by running: +Currently `nhgisxwalk` officially supports Python [3.7](https://docs.python.org/3.7/), [3.8](https://docs.python.org/3.8/), and [3.9](https://docs.python.org/3.9/). Please make sure that you are operating in a Python >= 3.7 environment. Install the most current development version of `nhgisxwalk` by running: ``` $ pip install git+https://github.com/ipums/nhgisxwalk From 2229e1520f15c7fd2e1cdee6ee411ad2b299b336 Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 7 Jul 2021 18:53:09 -0400 Subject: [PATCH 08/13] remove 36 unittests --- .github/workflows/unittests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml index 921dd68..cc252b9 100644 --- a/.github/workflows/unittests.yml +++ b/.github/workflows/unittests.yml @@ -21,7 +21,7 @@ strategy: matrix: os: ['macos-latest', 'ubuntu-latest', 'windows-latest'] - environment-file: [ci/36.yaml, ci/37.yaml, ci/38.yaml, ci/39.yaml] + environment-file: [ci/37.yaml, ci/38.yaml, ci/39.yaml] steps: - name: checkout repo uses: actions/checkout@v2 From f7756ae4b2181bdbb410234a602ffcb83abea0d7 Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 7 Jul 2021 18:54:37 -0400 Subject: [PATCH 09/13] add manual trigger to CI --- .github/workflows/unittests.yml | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml index cc252b9..200fb45 100644 --- a/.github/workflows/unittests.yml +++ b/.github/workflows/unittests.yml @@ -12,6 +12,12 @@ - '*' schedule: - cron: '59 23 * * *' + workflow_dispatch: + inputs: + version: + description: Manual CI Reason + default: test + required: false jobs: unittests: From e33962d892ca72c20eb27b17c91b38533a42c76c Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 7 Jul 2021 19:41:47 -0400 Subject: [PATCH 10/13] update release workflow --- .github/workflows/release_and_publish.yml | 105 +++++++++++----------- 1 file changed, 52 insertions(+), 53 deletions(-) diff --git a/.github/workflows/release_and_publish.yml b/.github/workflows/release_and_publish.yml index 01fbbcf..4b48bbe 100644 --- a/.github/workflows/release_and_publish.yml +++ b/.github/workflows/release_and_publish.yml @@ -3,66 +3,65 @@ # in this project's top-level directory, and also on-line at: # https://github.com/ipums/nhgisxwalk -name: release_and_publish +name: Release & Publish on: push: # Sequence of patterns matched against refs/tags tags: - - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10 + - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10 + workflow_dispatch: + inputs: + version: + description: Manual CI Reason + default: test + required: false jobs: build: + name: Create release & publish to PyPI runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: '3.x' - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install setuptools wheel twine jupyter urllib3 pandas pyyaml - python setup.py sdist bdist_wheel - - name: Run Changelog - run: | - jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout=-1 --ExecutePreprocessor.kernel_name=python3 tools/gitcount.ipynb - - name: Cat Changelog - uses: pCYSl5EDgo/cat@master - id: changetxt - with: - path: ./tools/changelog.md - env: - TEXT: ${{ steps.changetxt.outputs.text }} - - name: Create Release - id: create_release - uses: actions/create-release@v1 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # token is provided by GHA, DO NOT create - with: - tag_name: ${{ github.ref }} - release_name: Release ${{ github.ref }} - body: ${{ steps.changetxt.outputs.text }} - draft: false - prerelease: false - - name: Get Asset name - run: | - export PKG=$(ls dist/) - set -- $PKG - echo "whl_path=1" >> $GITHUB_ENV - - name: Upload Release Asset - id: upload-release-asset - uses: actions/upload-release-asset@v1 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - with: - upload_url: ${{ steps.create_release.outputs.upload_url }} - asset_path: dist/${{ env.whl_path }} - asset_name: ${{ env.whl_path }} - asset_content_type: application/zip - - name: Publish distribution 📦 to PyPI - uses: pypa/gh-action-pypi-publish@master - with: - user: __token__ - password: ${{ secrets.PYPI_PASSWORD }} + - name: Checkout repo + uses: actions/checkout@v2 + + - name: Set up python + uses: actions/setup-python@v2 + with: + python-version: "3.x" + + - name: Install Dependencies + run: | + python -m pip install --upgrade pip + pip install setuptools wheel twine jupyter urllib3 pandas pyyaml + python setup.py sdist bdist_wheel + + - name: run Changelog + run: | + jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout=-1 --ExecutePreprocessor.kernel_name=python3 tools/gitcount.ipynb + + - name: cat Changelog + uses: pCYSl5EDgo/cat@master + id: changetxt + with: + path: ./tools/changelog.md + env: + TEXT: ${{ steps.changetxt.outputs.text }} + + - name: Get the tag name + run: echo "TAG=${GITHUB_REF/refs\/tags\//}" >> $GITHUB_ENV + + - name: Release + uses: softprops/action-gh-release@v1 + with: + body: ${{ steps.changetxt.outputs.text }} + body_path: ${{ steps.changetxt.outputs.path }} + name: Release ${{ env.TAG }} + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + + - name: Publish distribution 📦 to PyPI + uses: pypa/gh-action-pypi-publish@master + with: + user: __token__ + password: ${{ secrets.PYPI_PASSWORD }} From 6a55b88fd10ea5addc116119c81d279866a9edba Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 7 Jul 2021 19:54:00 -0400 Subject: [PATCH 11/13] bump minor version --- nhgisxwalk/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nhgisxwalk/__init__.py b/nhgisxwalk/__init__.py index 2289d91..a701d87 100644 --- a/nhgisxwalk/__init__.py +++ b/nhgisxwalk/__init__.py @@ -1,4 +1,4 @@ -__version__ = "0.1.0" +__version__ = "0.1.1" """ # `nhgisxwalk` --- IPUMS/NHGIS Census Crosswalk and Atom Generator From be788f8f1cd7c450416df960930ff501f1755569 Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 7 Jul 2021 19:54:43 -0400 Subject: [PATCH 12/13] rerun select notebooks --- notebooks/synthetic-example.ipynb | 369 ++++++++++-------- notebooks/test_subset_1990.ipynb | 169 ++++---- notebooks/test_subset_2000.ipynb | 160 ++++---- .../weighted-portion-synthetic-atoms.ipynb | 75 ++-- 4 files changed, 399 insertions(+), 374 deletions(-) diff --git a/notebooks/synthetic-example.ipynb b/notebooks/synthetic-example.ipynb index b763faa..e05c545 100644 --- a/notebooks/synthetic-example.ipynb +++ b/notebooks/synthetic-example.ipynb @@ -2,8 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2021-07-07T23:48:56.489073Z", + "start_time": "2021-07-07T23:48:56.486679Z" + } + }, "outputs": [], "source": [ "# This file is part of the Minnesota Population Center's NHGISXWALK.\n", @@ -25,11 +30,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:00.079639Z", - "start_time": "2020-10-01T21:07:59.915471Z" + "end_time": "2021-07-07T23:48:56.528446Z", + "start_time": "2021-07-07T23:48:56.490457Z" } }, "outputs": [ @@ -37,33 +42,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-10-01T17:08:00-04:00\n", + "Last updated: 2021-07-07T19:48:56.511674-04:00\n", "\n", - "CPython 3.8.5\n", - "IPython 7.18.1\n", + "Python implementation: CPython\n", + "Python version : 3.9.6\n", + "IPython version : 7.25.0\n", "\n", - "compiler : Clang 10.0.1 \n", - "system : Darwin\n", - "release : 19.6.0\n", - "machine : x86_64\n", - "processor : i386\n", - "CPU cores : 8\n", - "interpreter: 64bit\n" + "Compiler : Clang 11.1.0 \n", + "OS : Darwin\n", + "Release : 20.5.0\n", + "Machine : x86_64\n", + "Processor : i386\n", + "CPU cores : 8\n", + "Architecture: 64bit\n", + "\n" ] } ], "source": [ + "%config InlineBackend.figure_format = \"retina\"\n", "%load_ext watermark\n", "%watermark" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.625121Z", - "start_time": "2020-10-01T21:08:00.081585Z" + "end_time": "2021-07-07T23:48:57.187858Z", + "start_time": "2021-07-07T23:48:56.531129Z" } }, "outputs": [ @@ -71,14 +79,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "watermark 2.0.2\n", - "pandas 1.1.1\n", - "geopandas 0.8.1\n", - "shapely 1.7.1\n", - "nhgisxwalk 0.0.9post1\n", - "handcalcs 0.8.1\n", - "numpy 1.19.1\n", - "matplotlib 3.3.0\n", + "Watermark: 2.2.0\n", + "\n", + "numpy : 1.21.0\n", + "matplotlib: 3.4.2\n", + "nhgisxwalk: 0.1.1\n", + "json : 2.0.9\n", + "shapely : 1.7.1\n", + "pandas : 1.3.0\n", + "geopandas : 0.9.0\n", + "handcalcs : 1.4.1\n", "\n" ] } @@ -101,24 +111,6 @@ "%watermark -iv" ] }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.629891Z", - "start_time": "2020-10-01T21:08:01.627787Z" - } - }, - "outputs": [], - "source": [ - "try:\n", - " from IPython.display import set_matplotlib_formats\n", - " set_matplotlib_formats(\"retina\")\n", - "except ImportError:\n", - " pass" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -133,8 +125,8 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.678363Z", - "start_time": "2020-10-01T21:08:01.631773Z" + "end_time": "2021-07-07T23:48:57.238405Z", + "start_time": "2021-07-07T23:48:57.189364Z" } }, "outputs": [ @@ -257,8 +249,8 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.685858Z", - "start_time": "2020-10-01T21:08:01.679793Z" + "end_time": "2021-07-07T23:48:57.250029Z", + "start_time": "2021-07-07T23:48:57.241094Z" } }, "outputs": [ @@ -378,8 +370,8 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.701575Z", - "start_time": "2020-10-01T21:08:01.687224Z" + "end_time": "2021-07-07T23:48:57.268827Z", + "start_time": "2021-07-07T23:48:57.252060Z" } }, "outputs": [ @@ -504,8 +496,8 @@ "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.710341Z", - "start_time": "2020-10-01T21:08:01.703054Z" + "end_time": "2021-07-07T23:48:57.277885Z", + "start_time": "2021-07-07T23:48:57.270091Z" } }, "outputs": [ @@ -624,8 +616,8 @@ "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.717400Z", - "start_time": "2020-10-01T21:08:01.713250Z" + "end_time": "2021-07-07T23:48:57.284362Z", + "start_time": "2021-07-07T23:48:57.279437Z" } }, "outputs": [], @@ -653,8 +645,8 @@ "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.726754Z", - "start_time": "2020-10-01T21:08:01.719685Z" + "end_time": "2021-07-07T23:48:57.296072Z", + "start_time": "2021-07-07T23:48:57.288117Z" } }, "outputs": [ @@ -741,8 +733,8 @@ "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.734956Z", - "start_time": "2020-10-01T21:08:01.728123Z" + "end_time": "2021-07-07T23:48:57.305139Z", + "start_time": "2021-07-07T23:48:57.297399Z" } }, "outputs": [ @@ -818,8 +810,8 @@ "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.743035Z", - "start_time": "2020-10-01T21:08:01.736303Z" + "end_time": "2021-07-07T23:48:57.310786Z", + "start_time": "2021-07-07T23:48:57.306703Z" } }, "outputs": [], @@ -840,8 +832,8 @@ "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.747641Z", - "start_time": "2020-10-01T21:08:01.744504Z" + "end_time": "2021-07-07T23:48:57.315685Z", + "start_time": "2021-07-07T23:48:57.311939Z" } }, "outputs": [ @@ -849,7 +841,7 @@ "name": "stderr", "output_type": "stream", "text": [ - ":1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/var/folders/71/bk36jbtj26n_v5fhw9tnzzl00000gn/T/ipykernel_26404/2325306026.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " A = blk1990_unique[A_condition][\"pop_1990\"].sum()\n" ] } @@ -870,8 +862,8 @@ "execution_count": 13, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.758252Z", - "start_time": "2020-10-01T21:08:01.749152Z" + "end_time": "2021-07-07T23:48:57.325181Z", + "start_time": "2021-07-07T23:48:57.317108Z" } }, "outputs": [ @@ -880,13 +872,13 @@ "text/latex": [ "\\[\n", "\\begin{aligned}\n", - "rg_{i} &= 30.0\\;\\;\\textrm{(the population of a specific racial group)}\n", + "\\mathrm{rg}_{i} &= 30.0 \\; \\;\\textrm{(the population of a specific racial group)}\n", "\\\\[10pt]\n", - "AX &= 90.0\\;\\;\\textrm{(the atomic population of the block group part)}\n", + "\\mathrm{AX} &= 90.0 \\; \\;\\textrm{(the atomic population of the block group part)}\n", "\\\\[10pt]\n", - "AY &= 70.0\\;\\;\\textrm{(the atomic population of the block group part)}\n", + "\\mathrm{AY} &= 70.0 \\; \\;\\textrm{(the atomic population of the block group part)}\n", "\\\\[10pt]\n", - "A &= 160.0\\;\\;\\textrm{(the total population of the block group part)}\n", + "A &= 160.0 \\; \\;\\textrm{(the total population of the block group part)}\n", "\\end{aligned}\n", "\\]" ], @@ -918,8 +910,8 @@ "execution_count": 14, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.774026Z", - "start_time": "2020-10-01T21:08:01.759546Z" + "end_time": "2021-07-07T23:48:57.340990Z", + "start_time": "2021-07-07T23:48:57.326312Z" } }, "outputs": [ @@ -928,7 +920,7 @@ "text/latex": [ "\\[\n", "\\begin{aligned}\n", - "atom_{rg_{i_{AX}}} &= rg_{i} \\cdot \\left( \\frac{ AX }{ A } \\right) = 30.0 \\cdot \\left( \\frac{ 90.0 }{ 160.0 } \\right) &= 16.875\\;\\;\\textrm{(the atomic subgroup population)}\n", + "\\mathrm{atom}_{rg_{i_{AX}}} &= \\mathrm{rg}_{i} \\cdot \\left( \\frac{ \\mathrm{AX} }{ A } \\right) = 30.0 \\cdot \\left( \\frac{ 90.0 }{ 160.0 } \\right) &= 16.875 \\; \\;\\textrm{(the atomic subgroup population)}\n", "\\end{aligned}\n", "\\]" ], @@ -957,8 +949,8 @@ "execution_count": 15, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.788884Z", - "start_time": "2020-10-01T21:08:01.775554Z" + "end_time": "2021-07-07T23:48:57.362542Z", + "start_time": "2021-07-07T23:48:57.342205Z" } }, "outputs": [ @@ -967,7 +959,7 @@ "text/latex": [ "\\[\n", "\\begin{aligned}\n", - "atom_{rg_{i_{AY}}} &= rg_{i} \\cdot \\left( \\frac{ AY }{ A } \\right) = 30.0 \\cdot \\left( \\frac{ 70.0 }{ 160.0 } \\right) &= 13.125\\;\\;\\textrm{(the atomic subgroup population)}\n", + "\\mathrm{atom}_{rg_{i_{AY}}} &= \\mathrm{rg}_{i} \\cdot \\left( \\frac{ \\mathrm{AY} }{ A } \\right) = 30.0 \\cdot \\left( \\frac{ 70.0 }{ 160.0 } \\right) &= 13.125 \\; \\;\\textrm{(the atomic subgroup population)}\n", "\\end{aligned}\n", "\\]" ], @@ -1010,8 +1002,8 @@ "execution_count": 16, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.795431Z", - "start_time": "2020-10-01T21:08:01.790607Z" + "end_time": "2021-07-07T23:48:57.370061Z", + "start_time": "2021-07-07T23:48:57.364416Z" } }, "outputs": [], @@ -1048,8 +1040,8 @@ "execution_count": 17, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:01.803414Z", - "start_time": "2020-10-01T21:08:01.796936Z" + "end_time": "2021-07-07T23:48:57.381596Z", + "start_time": "2021-07-07T23:48:57.371577Z" } }, "outputs": [], @@ -1080,14 +1072,14 @@ "execution_count": 18, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.406172Z", - "start_time": "2020-10-01T21:08:01.804946Z" + "end_time": "2021-07-07T23:48:57.788943Z", + "start_time": "2021-07-07T23:48:57.382980Z" } }, "outputs": [ { "data": { - "image/png": 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DZ0kD0SYzMzNwxhlnNIwZM8YzZMgQX15enj89PT24Z88ex6pVqxJfe+21jL179zo++uijlMsvvzzvs88+22WztT1I/80330y54YYb8rxer+Fyuczrr7+++uKLL64bMGCAv6qqyrZz586ElStXJn3wwQdxc+ZDNLPiXXyqpOb5SjyG0ero9JcMw3hJTRfSu7+7CouEr3qdoDxPBSOQY0WvXk0jqZpHUxlG02m3fAiOPvRlzKg2UpUo7+ERyEHDUEA2NRIcRyf2zdhBX8Ysr+FQncOlPYm9DwXHAHqSO++8s2bRokWpS5YsSV2yZEnq/Pnz0++8886a1treeuutfWtra22S9Pzzz+/Lzc2NmysN5+Xl+X/zm9/s/8lPflKdlpZ2xLedZ5xxRuONN97onjFjRubjjz+e4/P5jHvuuSd38+bNpRaVC3SbgQMH+g8cOLDV3voUY42TJ0+ue+SRRyrPO++8AcXFxa4vvvgi+e2330656qqrWj3dc8uWLY4pU6b083q9xqBBg3wffvjhrlGjRvmOatbw4x//uCYQCOwzGYDZ41nxTr5R0h/auO80Nc2D/LmkjZKickqLPa4s7XFltXn/4il9w1pPoQo7XMP06dM7vCwOKSwMr11RUVdW0aXuLwyvXRQ/xSZx0Jc9wf3hvs6tKC4oDqvdX+f2C6vdDycf4/T5QwqKC8Jq15oitpfOi4N9k+PsUaL+iVprQSfe3xWpKKx2ExdkdPgxAFjnD3/4w77vfOc7SZWVlfaHHnqoz8UXX1w3aNCgI6avmD17dq9PPvkkWZImT55cc91118XFdBXN3njjjbJjtfnv//7vyoULF6avW7cuccuWLc6vvvoq8fTTT2/sjvoAq7QRGh8hLS3N/M///M/K22+/vZ8kffzxx8lthcc//elP+9TV1dkSEhLMRYsW7W4lOD6ux4b1uj08PnRxvKmt3WcYxnQ1hcevmab5cnfWBQAAAABoYcsWh4qKkrR7t0Nud3xcRCw1Naj8fL8KCxs0fHjUzMGZn58f+N3vfrf/1ltv7VddXW27/fbb+y5fvnx38/1bt251/OIXv8iRpNzcXP+8efP2t7e+LVu2OJ577rmMoqKilG+//Tahrq7OlpGRETj55JM9V199de3UqVNrEhIS2ly+tLTUsXDhwtRPP/00af369YllZWUJPp/PSE9PD5xwwgmNF198cd29995blZGR0eqQwzlz5qTffffdfSXpgw8++PaSSy5pePnll9Nef/31XiUlJYkVFRX2kSNHNq5fv35Hh16wdpx77rn169atS5SkkpKShFgMj/ft22d79tlnM5YuXZq6bds2p9vttqWkpAQzMzMD+fn5vnPPPbd+0qRJdd/73veOeO6t9ctLL72UtmDBgl4bNmxIbGhosA0cONB33XXXVT/44INVKSkph/v3vffeS37++eczvv76a9fBgwftffr08V922WXuJ554oiInJ6fNEfCd3ZYQOenp6Yf7yePxtDqNwIYNGxKWL1+eKklXXnll7WmnncY8VzGAcwgBAAAAAEdaujRJy5cntzmXeaxyu23auNGpTZucGjeuXhddFDVz3k6ZMqV20aJFqe+++27aRx99lPLCCy+k33PPPTXBYFBTpkzp6z70BcCcOXP2ZWVltRnWzZgxI/OJJ57I9vl8R4RDBw4csK9YsSJlxYoVKXPnzs185513dg8ePDgkYD9w4IBt6NChQ1s7Fb2iosL+xRdfJH/xxRfJc+fOzVy8ePHusWPHthvOejweY9y4cfkfffRRStgvRid4vd7Dz7u9OV2j1aeffuqaOHFifmVl5RFDPqurq23V1dW20tLShJUrVyZ/9tlnyZ9//vmuttbj8/mM8ePH573//vtHzFm7ceNG5y9/+cucpUuXpi5dunRXcnKyee+99+a8+OKLmS3b7dq1K2H+/PmZS5cuTV25cuXO1i70GOltCZ3zxz/+8fAFJ0ePHt1qKPz666+nNffXj370o8PT57jdbmPnzp2OlJSUYH5+fsDBlGZRhd4CAAAAAPzbli2OuAyOWzJNafnyZA0d6oumEcgvvfTSvpNPPjm5vLzc/vOf/7zPZZddVr948eKUzz//PFmSrr/++uorr7yy1VPNJenBBx/M+u1vf5slSQMHDvRNnTq16qSTTvL269fPv3v3bsebb76ZtnDhwvRvvvkm8ZJLLun/1Vdf7Th6/uBAIGDYbDadeeaZ9RdeeGHdKaec4s3NzfV7PB5j+/btCQsXLkxbtmxZallZmWPixIn5X3/9dWl7I08feeSRnJKSksTzzjuv/tZbb60eOXKkt7Ky0l5SUuKM1OvWUvNrJUmnnHJKTI2a9Hq9uv766/MqKyvtdrtd1157bfX48ePr8vPz/QkJCebevXsdq1evTly+fHlKG9enOuzxxx/PXrNmjevyyy+vvemmm2oGDBjg37ZtW8LMmTN7r1271vX3v/89acaMGb3T09ODL774YubZZ59df8cdd1SNHDnSV1ZWZp89e3bvTz75JLm0tDThvvvuy1m4cGHItCKR3pZwfPx+v3bv3m1fvXp14osvvpjZ/AVOVlZW4Pbbb291XvV//OMfSZJkGIYKCwsb3nvvveQnn3wy68svv0xqDpVTU1ODF1xwQd1jjz12MBZH9seiHhUem6Y5XdJ0i8sAAAAAgPhVVJQU18FxM9Nsei2GD6+1upRw5ebmBmfPnr3vhhtuyHO73bZrrrkmb8OGDYlS0wXjXnzxxTav7P7xxx+7Zs2alSVJt99+e9XcuXP3HzU1RePkyZPrJkyY4L7pppvyNm7c6HzyySczn3rqqYMtG2VkZAQ2bNiwbXjrobtn6tSptW+99VbKNddck19WVuaYNWtW5tNPP13RVl0lJSWJP/nJTyrnzJlzRO3jx4+vD+MlOS6vv/566saNG52SdOKJJzbGWni8fPnypD17/v/27j3Orrq+F/7nNzOZmdwTCIRLCCQihFYlx4hgqRApRagoKNh6qaI9+LR9bAGPl/qqx2PUx+p5fKi3WvElKu3j8YpWRDxy02ilD9VG0kpFECEiV7kFcplJMjPr+WPPYMhKwmRmz+y5vN+85rWZvX5r7e/Ob681e3/2b/3WvR1J8u53v/vBd7zjHY/u0mTbOeecs+V973vfIw888MBeh13fdNNN3e985zsfes973vNE/z/vec/b9uIXv3jLMcccs+zee+/t+MQnPrFw+/bt5bzzztt42WWXPWmqlDPPPHPrcccdt/Tf//3fu6+44op5DzzwwK93vYBjs19LPLWf//znHUcdddTyPS0/8MAD+7/0pS/ds3Dhwt2G9EP7z3777dd/8cUXL3jve997wK4jxzdv3tx25ZVXzv32t78990Mf+tD9b3zjG3cbRDNxTL1zMAAAABi5e+6ZUIOMWmoS/lu86lWv2nzOOec8niTr1q3r3rJlSyml5JJLLrl/T4FPkrzvfe/bv6qqHHnkkdsvueSSXYPjJ7z61a/efMYZZ2xOkn/4h39YsOvy7u7u7CHse8I555yz5dRTT92cJN/4xjfm7K3t4YcfvuMjH/nIHkPvZvnVr37V/uY3v3nx0O/vf//7x/wxx9tQcJwkp5122l7D912D3F0961nP6t05OB4yb9686pWvfOVjSSMk3G+//fp3N8f2jBkzcv75529Mkh07duR73/vezF3bNPu1xMh1dHTkrW9968P/+Z//eefq1av3eHXyjRs3tifJpk2b2t7znvcc0NHRkbe97W0P33bbbXf09PTcdsstt9z5pje96ZH29vbs2LEjF1544UHf/va3a33PxDLp/hACAAAAe3bJJZf8+gc/+MGsBx54oCNJXvva12588YtfvMew8PHHHy/f//73ZyfJWWedtemp5iM9+eSTt37rW9+ac99993XccccdHcuXL99jwNff35977723/bHHHmvbtm3bE3MhHHDAAf1Jctttt3Vt3749nZ27n4XiZS972aa9XZyvGbZs2VJe8pKXHPrrX/+6PUne8IY3PPqSl7yk6SObW+2www57op8+9alPzVu1atWDI53X+eUvf/keR+SvXLnyiakIzjzzzM3d3d27bbdq1aon2t1+++1P2cmjfS3x1A4//PC+f/u3f9uQJH19feX+++9v/973vjfrsssum//hD394v1tvvbXz0ksvfWBP04Ns3bq1LfnN3OGXXHLJfX/yJ3/yxGtlxYoVO/72b//2oSOOOGLHhRdeuLi/vz9vfetbDzz99NObfvFLmkd4DAAAwG8cemhfBk89nvYOPXTSzHe8s0WLFg2cdNJJW7/yla/MS5JXvOIVe51648Ybb+zu62s81Ysvvnj/oekrhuPuu++uhcf9/f355Cc/Oe9zn/vc/PXr13f39PTscQLd/v7+PPzww+0HH3xw7YJpSbJy5co9jnJshu3bt+ess846ZP369d1JIxj/u7/7uyk36jhJTjnllJ7ly5dvv+OOOzo/9alPLbzuuuvmvPjFL9500kkn9Zx88sk9ixYtGvZ8wXu6YFqS7DzC/eijjx5Wu8cff7x9d22a+VriqXV2dmbVqlVP6rOzzjpr61vf+tZHf+/3fm/JN77xjbnPec5zum+44Ya7lixZUvt37urqqrZu3VqS5NnPfnbvzsHxzi644ILHLrnkkgW33HJL180339y1fv36zpUrV06paWKmEuExAAAAv7F6dU9uu61z2s97XErj32IauP/++3cb3A3HUFA05JFHHmk7/fTTD/3Rj3407FPRt2zZssdAcL/99huzC6D19fXlZS972SFDFwJ73vOe13PVVVfdM9qRqxdeeOGiL3/5y/OaUuRefOADH/j1eeedt3m47WfMmJErr7zynle84hWH/OQnP+m68847Z3z0ox/d76Mf/WhKKVmxYsW2F73oRZsvuOCCjYcddtheA9jZs2fvsV/a29ufOHjMmjVrWO36++sP1+zX0liZqP3dTAcffHD/P/7jP9533HHHHXHXXXfNeNOb3rTbixzOnj17YOvWre1J8sIXvnCvtb7whS/ccsstt3QlyQ033NAtPJ64hMcAAAD8xpFH9uXUU7fmuutmTdsAuZTk1FO35inmW50q+vr6ngjc3v72tz907rnnDjugOuqoo3bs/Psb3/jGA4bCvuc85zk9f/qnf7rx+OOP7z3ssMP65syZUw1NifGWt7zliRHOu15Qa2cdHR1j8iLs7+/PH/7hHx581VVXzRmq9eqrr7579uzZo368Rx99tP3+++8f87xly5Yt+zznxG/91m/tWL9+/S+vuuqqWV//+tfn3HjjjTNvvfXWrv7+/txyyy1dt9xyS9fHP/7x/T7ykY/c/4Y3vKGlF4ts9mtprEzk/m6m5zznOduPOOKIHRs2bJjxrW99a+6OHTvu33VKmUMPPXTHgw8+2J4kS5cu3evxc+nSpU8cO4am2GFi0jkAAAA82Wmn9WT58h1Zu3Zm7rmnI5s3T4+Lrc+ZM5BDD+3L6tU90yU4TpIDDzzwiWGf27Zta9v1tPXh2rhxY/nqV786L2mEfTfeeOOv2tt3P6j5kUceGfFo59Hq7+/PK1/5yoP+6Z/+aW7SOL3+2muvvXvu3LlNSR4/+MEPPvjOd77z4WZsa28WL148oukZ2tra8uIXv3jr0DzYGzduLNddd92sz3/+8/OuuOKKuT09PeXP//zPDz7uuOO2tWo06GR5LSUTv7+bab/99uvfsGHDjK1bt5b777+/fdcR6sccc8y2oSlgdjeafGf9/f1PfGm18yh0Jh7hMQAAAHVHHtmXI49s6chDxsfxxx/f29bWloGBgVx77bWzkzw0ku389Kc/7dyxY0dJknPOOWfTnsK+JLnpppt2fxW1MTYwMJA//uM/Xjw0H/TKlSt7r7vuursXLFjQtPBq8eLFA4sXLx6z6TaabcGCBdW555675dxzz92yZs2a3ne/+90H9Pf35/Of//zclStXjnkoujuT4bU0ZLL192jsPMJ6/vz5tee8evXqni984Qvzk+T222/f6/wvO18kceeLOTLxTI9vjwEAAIDdOuCAAwZOOOGErUly8803d33ta1+bPZLtDIV9SbJ5L6PVb7jhhq6h0YnjaWBgIOedd97iL37xi/OT5Nhjj+29/vrr7975wm3T3Yte9KKtQ///0EMPtWxE70R/LU1H3/3ud7vvvffejiQ57LDDdsybN6/2hcvLX/7yzV1dXVWSXHnllXMGBna/a+3YsSPf/OY35yZJKSW///u/v3W3DZkQhMcAAAAwzb3rXe96uJRGXnf++ecfdMMNN3Ttrf3NN98847Of/ezcne9bsWLF9ra2Rsxw+eWXz9vdxct++ctftr/2ta89uHmVD9/5559/4Oc+97n5SfLMZz5z2/XXX3/3WF6Qb6K59tprZ/7Hf/zHXkeDfvOb35w19P/Lli3bsbe2Y2miv5amkksvvXTugw8+uNd88Lbbbpvx+te//ol/61e96lWP767dwoULB84///yNSfKLX/yi8x3veMd+u2v35je/edE999zTkSRnnHHG5qe6QCOtZdoKAAAAmOZOO+20nre//e0Pvf/971/06KOPtr/gBS84/Oyzz378zDPP3Py0pz1tR9I4Zf2mm27quvrqq2evW7du5ktf+tLHX//61z8xtcnixYsHTj311M3XXHPNnFtvvbXzuOOOW3rhhRc+eswxx2zftm1b+c53vjPzk5/85MLHHnusfdWqVb3r1q0btxGjf/EXf3HAZz/72QWDdfZdfPHFv96wYUPHhg0b9rjOwQcf3H/IIYdMmVDrmmuumXXxxRfvv3Llyt7TTjtty7Of/ezegw8+uD9JfvWrX3VcccUVcy6//PJ5SWNKgj/5kz/ZbUA4Hibya2mq+fSnP73gL//yLw869dRTN5900kk9v/3bv71tv/32G9i+fXvZsGFDx/XXXz/78ssvn7d169aSNEbsv+td73pkT9t773vf+/A111wz++c//3nnBz7wgUU333xz13nnnff4kiVL+u66666Oz3zmM/OvvvrqOUmy//7793/sYx/79Xg9V0ZGeAwAAADkb/7mbx458MAD+9/xjnccuHXr1vKVr3xl3tDcwLszb9682qjdSy+99IGTTjqpa8OGDTNuueWWrj/7sz87aOflM2bMyAc/+MEH7rvvvo7xDPy+/vWvPzFK+oEHHug47bTTDnuqdd7ylrc8/MEPfrAlc/6OlaqqctNNN3XvbZ7gRYsW9X/xi1+859BDD21pcD5RX0tTUW9vb/nmN785d2gqiT156UtfuunTn/70AzNnztzjHOELFy4cuPrqq+9+yUtecujNN9/ctaftLl26dMfXvva1e5YvX26+4wlOeAwAAAAkSS666KLHXvnKV276+Mc/vuD666+f9fOf/7xr48aNbW1tbVm4cGH/8uXLtx9//PG9Z5111uaTTz65d9f1DzvssP4f//jHv/ybv/mbhVdeeeWcX/7yl52llBx44IF9z3/+87decMEFG4877rhtb33rW/dvxfObzt71rnc9cuKJJ/Zcf/31s374wx/OvP/++zseeuihjh07dpR58+b1H3300dtOP/30LX/xF3/x2ESYB9praXx84QtfuO/aa6+duXbt2lk333xz14MPPtjxyCOPtFdVlXnz5g0M7vM9r3nNazYdd9xx24azzWXLlvX9+Mc//uUll1wy/8tf/vLcW2+9tWvjxo3tc+bM6V+xYsX2s846a9Nf/uVfPjZr1qymXaiSsVOqaur1Uyll3bOf/exnr1u37on71qxZM+Ltrc3aYbU7+7IFw2r39ddtHFa71Vk9rHa7M5rny6DVq4fXbu3asaxiTE2Dp9gwbZ5oa60e7r/zbqxfuX5Y7a74xLJhtTvrz+8cVruV61cOq93urPV6Gb1psG9Og6fYMG2eaGtN9/ezq1atyo9//OMfV1W1asQbHQPr1q27tbOzc9kzn/nM4f3xAYBx9Pd///fz3vjGNx6UJFVV3dbqeoZrstY9Uf3kJz9Ztn379jtXrVp19L6u64J5AAAAAADUCI8BAAAAAKgRHgMAAAAAUCM8BgAAAACgRngMAAAAAEBNR6sLAAAAAACab9WqVdve8pa3PNzqOvbVZK17KhIeAwAAAMAUdPzxx287/vjjt7W6jn01WeueikxbAQAAAABAjfAYAAAAAIAa4TEAAAAAADXCYwAAAAAAaoTHAAAAAADUCI8BAAAAAKgRHgMAAAAAUCM8BgAAAACgRngMAAAAAECN8BgAAAAAgBrhMQAAAAAANcJjAAAAAABqhMcAAAAAANQIjwEAAAAAqBEeAwAAAABQIzwGAAAAAKBGeAwAAAAAQI3wGAAAAACAGuExAAAAAAA1wmMAAAAAAGqExwAAAAAA1AiPAQAAAACoER4DAAAAAFDT0eoCpqKBvhnp652dgf4ZSdVeW77+sjXD2s6GLBhxDWvXjnhVBn14/fDaXbR6LKsYWxs2NG77+pLe3qS/v/F7e3vS3Z10DB4hVq9uRXXNMx36ciJYv/7DI1538+2bh9Wu6nvL4P+VpGpLqt/8mjKQoTs2X/7NYW1v/eY5+1Tnzib7fjERTId903H2ySZzX04EGza8bsTrbszZw2p3+sZL6neW/rS170hH95a0dewYcQ0AAEw+wuMm6+udnb7eOWkkGTDx9fY2fnbW15ds3twINrq7W1MX7FHV1vh50n1pfFlXBgZDZJg4HGeZ9Kr2DPS1Z/vmrnR0b05H95ZWVwQAwDgxbUUTNUYcC46ZPIZGwu1Jb2+jDUwcpR4c76xqi2MwE4njLFNLSV/vnAz0zWh1IQAAjBPhcRP19c6O0ILJZG+Bxr60gXGzt+B4X9rAOHGcZeopg+95AQCYDnzCbqKBfqMwmFyG5t4cbRsYN9VTNxlWGxgnjrNMRd7zAgBMH8LjZtrNxfFgIquEbEw6zu5gcnGcBQAAJjPhMbBX7b4TYbKRLzPJOM4y2bS172h1CQAAjBPhcRN1dG9qdQmwT7q7m9MGxk0ZaE4bGCeOs0w9VTq6t7S6CAAAxonwuIkab6Sdn8rk0d2dlL2M0uzuTjo6xq8eeEpPFQyXgTgOM5E4zjK1VOno3py2DiOPAQCmCx9Xmqx7wQPp652dvt65rS4FhmX+/KS3t/GTNEKO9naBBhNYW19StTV+kiRVY6oKwTETlOMsk17pT1v7jnR0bxEcAwBMMz6yjIGO7i17PZ1v5esuGtZ2Vmf1iGtYs2bNiNdl0OrhNVu7diyLGFurVw+v3WR+jkmmRV9OBKtXXzTiddevXD+sduUTy4bVbs65Zw6r3cr1K4fVbnfWesGM3urhNZvM/9SOs0826Z9ni61Zc9mI112btcNq133ZghE/BgAAU49pKwAAAAAAqBEeAwAAAABQIzwGAAAAAKBGeAwAAAAAQI3wGAAAAACAGuExAAAAAAA1wmMAAAAAAGqExwAAAAAA1AiPAQAAAACoER4DAAAAAFAjPAYAAAAAoEZ4DAAAAABAjfAYAAAAAIAa4TEAAAAAADXCYwAAAAAAaoTHAAAAAADUCI8BAAAAAKagqqpGtb7wGAAAYOT6klQDAwOtrgMAoKaqqpKkSuM9yz4THgMAAIzcxiT927dv72h1IQAAuxp8j9Kf5NGRrC88BgAAGLkfDQwM9GzatGlmqwsBANjVpk2bZg4MDPQk+dFI1hceAwAAjNw/DwwMbH300Ufn9ff3l1YXAwAwpL+/vzz66KNzBwYGtib5wUi2ITwGAAAYuasHBgZu7unp6b3zzjsPeuyxx2b19/eX0V6cBgBgJKqqSn9/f3nsscdm3XnnnQf19PT0DgwM3Jzk6pFsz7xcAAAAI7Rq1arN69at+7O+vr6vbNq06dCtW7fOL6UcEAN1AIDWGaiqant/f/+mgYGBe5L86apVqzaPZEPCYwAAgFFYtWrVL9etW3f6wMDA2QMDAy9LsjRJV6vrAgCmrW1J7krytSRfX7Vq1SMj3ZDwGAAAYJQGP5R9ZvAHAGBKcCoVAAAAAAA1wmMAAAAAAGqExwAAAAAA1AiPAQAAAACoER4DAAAAAFAjPAYAAAAAoEZ4DAAAAABAjfAYAAAAAIAa4TEAAAAAADXCYwAAAAAAaoTHAAAAAADUCI8BAAAAAKgRHgMAAAAAUCM8BgAAAACgRngMAAAAAECN8BgAAAAAgBrhMQAAAAAANcJjAAAAAABqhMcAAAAAANQIjwEAAAAAqBEeAwAAAABQ05LwuJSyfynl/FLKP5VSbi+l9JRSHiul/KCU8l9LKUJtAAAAAIAW6mjR4748ySeS3Jfku0nuSrI4ycuSXJrkjFLKy6uqqlpU36jM7duaQ3ofzuz+3nRW/bXlqy+7f1jbWZANIy9i7dqRr0vDhg2N276+pLc36R/sy/b2pLs76RjcfVavbkV1TfHh9cNsuHoMixgP06AvJ4IPr18/4nU33755WO2W9N2XJOlIf7qqHWlPf1Il/aUt20pn+tKeJPnm5cPb3pzN60dUbxKvl2aYBvum4+zU6cuJ4HVD/84jcHY2Dqtd78aDavdtL+3Z0t6de7v3z6aOWSOuAQCAyadV4fFtSV6S5KqqqgaG7iyl/HWSHyY5J40g+autKW/kDu19KEt6H0ppdSE0R29v42dnfX3J5s2ND8Pd3a2pi32nL6eM7mp7urM92enrxbaqPzOqnvSWzvSWztYVx76zb04d+nLK6qz609m3JQs2b8nd3YtyT/eiVpcEAMA4acn0EFVVfaeqqit3Do4H778/ySWDv64e98JGaW7fVsHxVDI0empPensbbZj49OWU0ZH+WnC8s+5qezpSP+ODCcq+OXXoy2mhJFnS+1Dm9m1tdSkAAIyTiTi38I7B20n3CeOQ3ocFx1PJ3j4E70sbWk9fThld1Y49Bse/abN9fIph9OybU4e+nDZKGu95AQCYHlo1bcVulVI6krx28NdvD6P9uj0sWtG0ovbB7H4fiqaU/mGMXhxOG1pPX04Z7cMYVdz+5JNamMjsm1OHvpxWGu9557a6DAAAxsFEG3n8gSTPSPKtqqqubnUx+2p3F8djEpuc12tkd/TllNE2nL50CsjkYd+cOvTlNONACwAwXUyYkcellAuSvDnJz5K8ZjjrVFW1ag/bWpfk2c2rDvagvb3VFdAs+nLK6J9w34syKvbNqUNfThlbOlz8EABgupgQn7BLKW9M8pEkP03ygqqqHmlxSSNyV/cBrS6BZhrOVeFdOX5y0JdTRk/pfMo224bRhgnCvjl16Mtpo0pyb9d+rS4DAIBx0vLwuJRyUZK/S3JzGsHx/a2taOTu7d4//cVpfFNGd3eyt/7s7k46JszgffZGX04Z20pnqr10ZW/pTF+Mbpw07JtTh76cFqokd3cvyqaOWa0uBQCAcdLSd/GllL9KY57j9Ul+v6qqh1pZTzP8aP7ROaT34SztfbDVpdAM8+c3rg4/dIX4Uhqn3foQPPnoyynjsTInXdmemdX2JMlAKelPW7YJjicn++bUoS+nrO2lI1s6unNv136CYwCAaaZl7+RLKe9M8p4k65KcNlmnqtide7v3z73d++9x+ddfd9CwtrM6q0dcw5o1a0a8LoNWrx5eu7Vrx7KKMXXR6uG1m8RPsWEa9OVEcNFw/513Y/3K9cNqd8UnDh5Wu7PO7R1Wu5XrVw6r3e6s9XoZvWmwbzrO7mLSP9HWumwU7+/WZu2w2p192YIRPwYAAFNPS8LjUsp5aQTH/Un+OckFpX6q44aqqi4b59IAAAAAAEjrRh4vG7xtT3LRHtp8L8ll41EMAAAAAABP1pIL5lVVtaaqqvIUP6tbURsAAAAAAC0KjwEAAAAAmNiExwAAAAAA1AiPAQAAAACoER4DAAAAAFAjPAYAAAAAoEZ4DAAAAABAjfAYAAAAAIAa4TEAAAAAADXCYwAAAAAAaoTHAAAAAADUCI8BAAAAAKgRHgMAAAAAUCM8BgAAAACgRngMAAAAAECN8BgAAAAAgBrhMQAAAAAANcJjAAAAAABqhMcAAAAAANQIjwEAAAAAqBEeAwAAAABQIzwGAAAAAKBGeAwAAAAAQI3wGAAAAACAGuExAAAAAAA1wmMAAAAAAGqExwAAAAAA1AiPAQAAAACoER4DAAAAAFAjPAYAAAAAoEZ4DAAAAABAjfAYAAAAAIAa4TEAAAAAADXCYwAAAAAAaoTHAAAAAADUCI8BAAAAAKgRHgMAAAAAUCM8BgAAAACgRngMAAAAAECN8BgAAAAAgBrhMQAAAAAANcJjAAAAAABqhMcAAAAAANQIjwEAAAAAqBEeAwAAAABQIzwGAAAAAKBGeAwAAAAAQI3wGAAAAACAGuExAAAAAAA1wmMAAAAAAGqExwAAAAAA1AiPAQAAAACoER4DAAAAAFAjPAYAAAAAoEZ4DAAAAABAjfAYAAAAAIAa4TEAAAAAADXCYwAAAAAAaoTHAAAAAADUCI8BAAAAAKgRHgMAAAAAUCM8BgAAAACgRngMAAAAAECN8BgAAAAAgBrhMQAAAAAANcJjAAAAAABqhMcAAAAAANQIjwEAAAAAqBEeAwAAAABQIzwGAAAAAKBGeAwAAAAAQI3wGAAAAACAGuExAAAAAAA1wmMAAAAAAGqExwAAAAAA1AiPAQAAAACoER4DAAAAAFDT0eoCGBtr1qxpdQmT3us2bBhWu8sm8b/1hg2vG1a7NWsuG9M6xtp06EvGn+Ps6E2HfdNx9skmc18CAMB0ZOQxAAAAAAA1wmMAAAAAAGqExwAAAAAA1AiPAQAAAACoER4DAAAAAFAjPAYAAAAAoEZ4DAAAAABAjfAYAAAAAIAa4TEAAAAAADXCYwAAAAAAaoTHAAAAAADUCI8BAAAAAKgRHgMAAAAAUCM8BgAAAACgpqPVBbBna7O21SVMa2dn47DaTeZ+2pizh9VuMj/HZHr05USw4YgNrS5hn42mZq+X0ZsO+6bj7JNN9ucJAADTjZHHAAAAAADUCI8BAAAAAKgRHgMAAAAAUCM8BgAAAACgRngMAAAAAECN8BgAAAAAgBrhMQAAAAAANcJjAAAAAABqWhoel1KWlFI+U0q5t5SyrZSyoZTy4VLKwlbWBQAAAAAw3XW06oFLKU9L8i9JDkxyRZKfJXlukguTnF5KObGqqodbVd9YOvuyBa0ugWHo3XjQsNpN5v48feMlw2rXPYmfYzI9+nIi6O1dMeJ1+zr6htlyeH8W3nL5c4bVrqNv5H8Gu7u7R7wuDdNh33ScfbLJ3JcAADAdtXLk8d+nERxfUFXV2VVVvb2qqlOSfCjJ0Une18LaAAAAAACmtZaEx6WU5UlOS7Ihycd3WfyuJFuSvKaUMnucSwMAAAAAIK0beXzK4O01VVUN7LygqqpNSW5IMivJCeNdGAAAAAAArZvz+OjB29v2sPznaYxMPirJ9XvaSCll3R4WjXziTQAAAAAAWjbyeP7g7WN7WD50/4KxLwVGbntpb3UJY6/0t7qCcTEt+nKaGHZfVmNbB80xLfZNx1kmmZ7O4X2EaM/I+3zGjBkjXhcAgOZp1cjjp1IGb/f60b6qqlW7XbkxIvnZzS4KdrWlvbvVJYy5tvYdrS5hXEyHvpwuNrWPfeBQSnnqRjTFdNg3HWeZbB5eMLxQuCtdI36MOXPmjHhdAACap1Ujj4dGFs/fw/J5u7SbFP79t4yomUru6j5gr8urJPd27z8+xYyRju5NT9GiSkf3lnGpZSxNh76cLu6cOXevy6skd88aXkBVIgButemwbzrONkyFvqShSnL7M3qG1bY7I//CYPZs180GAJgIWhUe3zp4e9Qelj998HZPcyJPSHc+9+H0tQkjpop7u/dP/x5GF1ZJ7u5elE0ds8a3qCZrBBZ7GuBfpaN7c9o6Jv+IuOnQl9PF3XM607+H0LdK8suZc/N419hPW2HkcXNMh33TcXbq9CWNvvzJMe3ZuHTzsNrPysj7XHgMADAxtGraiu8O3p5WSmmrqmpgaEEpZW6SE5P0JLmxFcXt6ml5Wn6RXwyr7Tdf+2iW/XD/HPvT6TF/4VT3o/lH55Deh7O098Ekjfkat7R3597u/afMh+DuBQ+kr3d2+noHR3SW/rS170hH95YpEWgMmQ59OZm19bdloH3gqRsm+ZcD9suSzduzrKcxonN7ac+m9hm5e1b38IPjJG3VyL8/FR43z3TYNx1np05fTlc9nW15eEF7bn9GTzYu3Tjs9Q7MgSN+TOExAMDE0JLwuKqqX5RSrklyWpI3JvnYTovfnWR2kk9WVdW08zhnzpyZnp7hnWK3q0Nz6LDD46QxAvnO547ooZiQ2pMctMt92wd/poqNrS5gnEyHvmydu+66KwMDwwuAd1WlymMLxm+moraBtsx9fO6Ip65YuHBh5s/f08xL7LvpsG9ubHUB42Q69CXD0Z3uzM3epxram/33N80JAMBE0KppK5Lk/0zy6yQfLaV8vZTy/lLKd5K8KY3pKt7RzAc79dRTR7xuW9pGNWcbwHSwcOHCEa9bUtI2MH5/kjq3d45qzuPumf4mAOzNwTl4VMfZ5cuXN7EaAABGqmXhcVVVv0jynCSXJTk+yZuTPC3JR5M8r6qqh5v5eCtXrhzV+quyqjmFAExRc+bMGd36m0a3/nC1DbSla1vXyNdvb0tnZ2cTKwKYWmZmZpZkyYjXnzVrVg46aNcR7AAAtEKr5jxOklRV9askrx+Px2pvH/48mLszIzOyJEtyd+5uUkUAU8to5wFuqxqh7raubU2qaPdmbp2ZUo1i1HF396hG0wFMdUflqLRn5O+9ly1bZm55AIAJopXTVoy7Qw45ZFTrH5kjm1QJwNTU2TW6Ebkze2Y2qZLd69rWlRl9M0a1jZndY1sjwGS2JEuyMCOfxigxZQUAwEQyrcLjM888c9TbODknN6ESgKmpGRc4mr9xbC5E17mtM909o5yruCQzZwmPAXbn0Byap+Vpo9pGe3t7jjrqqCZVBADAaE2r8Hi0I4+TxkWdBMgAu9fVOfK5hIeUlKYHyF3bujKzZ+aop5uYM3tOOtpbOuMTwIS0JEtyZI4c9XH2Wc96VubOndukqgAAGK1pFR4nyR/90R+NehslJauzelQXAgGYqg448IBRb6OkZMHGBaO6sF3SuDje7M2zmxIcJ8n8+WMzKhpgspqZmTk2xzYlOC6l5MQTT2xSZQAANMO0Gz51zDHHNG1bR+bIHJ7Dsy7r0pvepm0XYDKbPWt2HsyDTdnWzJ6Z6ertyua5mzPQNjDs9doG2tK5vTNd27pGdXG8nc2aNSszZoxuvmSAqaI73Tk4B2dJlozq4ng7W7FiRRYtWtSUbQEA0BzTLjxOktNPPz3f/va3m7KtGZmRE3JCBjKQe3JPfpFfNGW7AJPZfvvtl0ceeaQp22qr2jLv8XmpUmV71/b0zOx50vJSlZSqpG2gLe397ZmxY0ba+9qbMtJ4Z0YdA9NVe9rTla50pzuzMisH5sDMzdymH2eNOgYAmHimZXh8wgknNC08HtKWthw2+B/AtDcvWfvjta2uomkWL16cY7qad+YKAE927LHHZskSU8IBAEw0027O4yFvetObWl0CwJR2wgkntLqEpuju7s7Tn/70VpcBMGUtWLAgZ5xxRqvLAABgN6ZteDx//vyccsoprS4DYMrq7u7OsmXLWl3GqK1YsSIdHdPyRB2AMVdKyUtf+tJ0d3e3uhQAAHZj2obHSXLSSSe1ugSAKe3www9vdQmjsnTp0ixYsKDVZQBMWSeeeOKk/1sBADCVTevwOEn++q//utUlAExpz3/+81tdwogsXLgwRxxxRKvLAJiyli9fnhe84AWtLgMAgL2Y9uFxZ2dn3va2t7W6DIApq729PSeeeGKry9gnCxcuzDOe8Yy0tU37P5MAY2L58uV55Stfmfb29laXAgDAXvhUnGTWrFkCZIAxNGPGjEkTIA8FxwINgLExFBzPmDGj1aUAAPAUhMeDBMgAY2syBMiCY4CxJTgGAJhchMc7mTVrljmQAcbQjBkzJuwcyEuXLs0zn/lMwTHAGCil5Hd/93fz6le/WnAMADCJCI930dnZmTVr1uSUU05pdSkAU1J7e3tWr16dZcuWtbqUJEl3d3dWrlyZ5cuXm+MYYAwsWLAgr3vd63Lqqaf6gg4AYJLpaHUBE9VJJ52UY489Nh/60IdaXQrAlHT44Ydn8eLFufHGG1tWw+LFi/P0pz89HR3+HAKMhWOPPTZnnHFGuru7W10KAAAj4NPyXsyfPz9r1qzJv/7rv+Z//+//3epyAKac7u7urF69OnfffXduv/32cXvcRYsWZenSpZk3b964PSbAdFFKyYoVK3LiiSdmyZIlrS4HAIBREB4Pw/HHH5/jjz8+P/vZz/LFL36x1eUATDlLlizJkiVL8tBDD+Xmm28ek8copWTx4sVZunRpZs2aNSaPATCdtbe351nPelZOPPHELFq0qNXlAADQBMLjfbBixYqsWbMm9957b6666qrcc889rS4JYEpZtGhRVq9enU2bNuW2227Lpk2bRrW9GTNmZMGCBVm4cGH233//dHV1NalSAJLGBaeXLVuW5cuX56ijjsrcuXNbXRIAAE0kPB6BQw45JG94wxuSJAMDA7npppty3XXXpaenp8WVAUwNc+fOzapVq5IkVVXlvvvuyx133JG+vr5a27a2tnR2dqazszMzZsxIZ2dnZs6cmYULF2bOnDkppYx3+QBTyowZMzJnzpzMnj37iZ/9998/y5cvz0EHHeQ4CwAwhQmPR6mtrS2rVq16IuQAAAAAAJgK2lpdAAAAAAAAE4/wGAAAAACAGuExAAAAAAA1paqqVtfQdKWUh2fOnLnfMccc0+pSAACAJrjlllvS09PzSFVV+7e6FgCA6WKqhsd3JpmXZEOLS1kxePuzllZBs+jPqUNfTh36cmrRn1OHvpw6JlJfHpHk8aqqlrW6EACA6WJKhscTRSllXZJUVbWq1bUwevpz6tCXU4e+nFr059ShL6cOfQkAML2Z8xgAAAAAgBrhMQAAAAAANcJjAAAAAABqhMcAAAAAANQIjwEAAAAAqClVVbW6BgAAAAAAJhgjjwEAAAAAqBEeAwAAAABQIzwGAAAAAKBGeAwAAAAAQI3wGAAAAACAGuExAAAAAAA1wmMAAAAAAGqEx2OglLKklPKZUsq9pZRtpZQNpZQPl1IWtro2hq+Usn8p5fxSyj+VUm4vpfSUUh4rpfyglPJfSyn2n0mulPKaUko1+HN+q+th35RSnl9K+Wop5b7BY+19pZRrSil/0Ora2DellBcN9t3dg8faO0opXymlPK/VtfFkpZRzSykfK6X8cynl8cHj5+eeYp3fKaV8q5TySCllaynlP0opF5VS2serbnZvX/qzlPL0UspflVK+U0r5VSlleynlgVLKFaWUF4x37QAAjI+OVhcw1ZRSnpbkX5IcmOSKJD9L8twkFyY5vZRyYlVVD7ewRIbv5Uk+keS+JN9NcleSxUleluTSJGeUUl5eVVXVuhIZqVLKYUk+lmRzkjktLod9VEr570nem+ShJN9MYz9dlOS/JFmd5FstK459Ukr5n0neluThJF9Po0+PTHJWknNKKa+tqmqv4STj6r8nOTaNY+fdSVbsrXEp5awkX03Sm+RLSR5J8uIkH0pyYhp/a2mdfenP9yb5oyQ/TeMY+0iSo5O8JMlLSikXVlX10bEtFwCA8VbkXs1VSrk6yWlJLqiq6mM73f+3Sd6U5JNVVf1Zq+pj+EoppySZneSqqqoGdrr/oCQ/THJYknOrqvpqi0pkhEopJcm1SZYl+VqStyR5Q1VVl7a0MIallPLyJF9Ocl2Sl1VVtWmX5TOqqtrRkuLYJ4PH03uSPJjkWVVV/XqnZS9I8p0kd1ZVtbxFJbKLwX65O8ntSU5O48vV/1VV1R/vpu28wXbzk5xYVdW/Dd7fnUbfPi/JK6uq+uI4lc8u9rE/X5fk36uqummX+09O429qleSIqqruG+u6AQAYP067b6JSyvI0guMNST6+y+J3JdmS5DWllNnjXBojUFXVd6qqunLn4Hjw/vuTXDL46+pxL4xmuCDJKUlen8Z+ySQxOF3M/0yyNcmrdg2Ok0RwPKkcnsZ7kX/dOThOkqqqvptkU5IDWlEYu1dV1Xerqvr5MM+6OTeN/vviUHA8uI3eNEa8Jsmfj0GZDNO+9GdVVZftGhwP3v+9JGuTdCb5neZXCQBAKwmPm+uUwdtrdhM4bkpyQ5JZSU4Y78JouqFwqq+lVbDPSinHJPlAko9UVfX9VtfDPvudNEaMfyvJo4Nz5f5VKeVC8+NOSj9Psj3Jc0spi3ZeUEo5KcncNEaYMzkNvS/69m6WfT+NL4F+p5TSNX4lMUa8LwIAmKLMedxcRw/e3raH5T9PY2TyUUmuH5eKaLpSSkeS1w7+ursPxExQg333/6Yxf/Vft7gcRua4wdsHkvw4yTN3XlhK+X4a08k8ON6Fse+qqnqklPJXSf42yU9LKV9PY+7jp6Uxj+q1Sf60dRUySnt8X1RVVV8p5c4kv51keZJbxrMwmqeUcniS30vjywBfygIATDHC4+aaP3j72B6WD92/YOxLYQx9IMkzknyrqqqrW10M++R/pHFBtd+tqqqn1cUwIgcO3v5ZkjuTnJrkX9OY/uDiJC9M8pWYUmbSqKrqw6WUDUk+k+QNOy26Pcllu05nwaTifdEUNzhq/H8l6UrytqqqHm1xSQAANJlpK8ZXGbx1lcJJqpRyQZI3J/lZkte0uBz2QSnluWmMNr64qqr/r9X1MGLtg7cljRHG11dVtbmqqv9M8tI0Lvx0siksJo9SytuSXJ7ksjRGHM9OsirJHUn+Vynl/25ddYwx74smsVJKexpn85yY5EtJ/p/WVgQAwFgQHjfX0Aia+XtYPm+XdkwipZQ3JvlIkp8meUFVVY+0uCSGaafpKm5L8s4Wl8PoDI1qu6Oqqn/fecHgaPKhswGeO65VMSKllNVpXADxG1VV/beqqu6oqmprVVU/TuPLgHuSvHnwgrRMPt4XTVGDwfHnkrw8yZeT/PEwL6IIAMAkIzxurlsHb4/aw/KnD97uaU5kJqhSykVJ/i7JzWkEx/e3tiL20Zw09stjkvSWUqqhnyTvGmzzqcH7PtyqIhmWoePsxj0sHwqXZ459KTTBmYO33911QVVVW5P8MI33Kv9lPIuiafb4vmjwS71laVxg7Y7xLIrRGey7LyR5RZLPJ3lVVVUulAcAMEWZ87i5hj78nlZKaauqamBoQSllbhqn9fUkubEVxTEygxdz+kCS9Ul+v6qqh1pbESOwLcmn97Ds2WkEUz9II+gwpcXE9v00wqanl1I6q6ravsvyZwzebhjXqhiprsHbA/awfOj+XfuZyeE7SV6d5PQ0wsadnZRkVpLvV1W1bbwLY2RKKZ1pjDQ+K8k/Jnn9zu93AQCYeow8bqKqqn6R5JokRyR54y6L353GPI7/WFXVlnEujREqpbwzjeB4XZLfExxPTlVV9VRVdf7ufpJ8Y7DZPwze96VW1sreDe6DX0rjNPj/sfOyUsrvp3HBvMeSfHv8q2ME/nnw9v8opRy684JSyhlpfOnam+RfxrswmuLyJA8leUUp5TlDd5ZSupP8X4O/fqIVhbHvBi+O909pBMefjuAYAGBaKKYna65SytPS+JB7YJIrktyS5PgkL0hjuorfqarq4dZVyHCVUs5L4wJO/Uk+lt3PybihqqrLxrEsmqyUsiaNqSveUFXVpS0uh2EopRyY5IYkR6YRPv4wyeFpzJFbpXEK9VdaVyHDVUppS2Oe6lOTbEojmLo/jSlmzkzjgmoXVVX1kZYVyZOUUs5Ocvbgrwel8YXNHfnNFwEPVVX1ll3aX57GlwBfTPJIkpckOXrw/j80V27r7Et/llI+m+R1aXwh8PfZ/YUO11ZVtXbMCgYAYNyZtqLJqqr6xeDomvekcZrmHyS5L8lHk7zbRdYmlWWDt+1JLtpDm++lETAD46Sqql+XUo5P8t/TCIxPSCN4vCrJ+6uqMjXQJFFV1UAp5Q/SOFvnFWn056w0AsZvJfloVVXXtLBE6lYmOW+X+5YP/iTJL5M8ER5XVfX1UsrJSd6R5Jwk3UluT/Lf0uhfwXFrrczw+3PofdGi7HLmxy7WNqk2AAAmACOPAQAAAACoMecxAAAAAAA1wmMAAAAAAGqExwAAAAAA1AiPAQAAAACoER4DAAAAAFAjPAYAAAAAoEZ4DAAAAABAjfAYAAAAAIAa4TEAAAAAADXCYwAAAAAAaoTHAAAAAADUCI8BAAAAAKgRHgMAAAAAUCM8BgAAAACgRngMAAAAAECN8BgAAAAAgBrhMQAAAAAANf8/Tk6f075qzgYAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -1144,8 +1136,8 @@ "execution_count": 19, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.412354Z", - "start_time": "2020-10-01T21:08:02.408104Z" + "end_time": "2021-07-07T23:48:57.795028Z", + "start_time": "2021-07-07T23:48:57.791005Z" } }, "outputs": [], @@ -1175,11 +1167,19 @@ "execution_count": 20, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.451504Z", - "start_time": "2020-10-01T21:08:02.415130Z" + "end_time": "2021-07-07T23:48:57.848706Z", + "start_time": "2021-07-07T23:48:57.796422Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/71/bk36jbtj26n_v5fhw9tnzzl00000gn/T/ipykernel_26404/2402654510.py:7: UserWarning: `keep_geom_type=True` in overlay resulted in 20 dropped geometries of different geometry types than df1 has. Set `keep_geom_type=False` to retain all geometries\n", + " s_df = geopandas.overlay(s_df, l_df)\n" + ] + }, { "data": { "text/html": [ @@ -1253,8 +1253,8 @@ "execution_count": 21, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.464305Z", - "start_time": "2020-10-01T21:08:02.453828Z" + "end_time": "2021-07-07T23:48:57.860773Z", + "start_time": "2021-07-07T23:48:57.850899Z" } }, "outputs": [ @@ -1292,7 +1292,7 @@ " \n", " 1\n", " y1_M000.s100\n", - " POLYGON ((0.00000 3.00000, 0.00000 6.00000, 3....\n", + " POLYGON ((3.00000 3.00000, 0.00000 3.00000, 0....\n", " \n", " \n", " 2\n", @@ -1312,7 +1312,7 @@ " \n", " 5\n", " y1_M010.s100\n", - " POLYGON ((0.00000 9.00000, 0.00000 12.00000, 3...\n", + " POLYGON ((3.00000 9.00000, 0.00000 9.00000, 0....\n", " \n", " \n", " 6\n", @@ -1332,7 +1332,7 @@ " \n", " 9\n", " y1_M020.s100\n", - " POLYGON ((6.00000 3.00000, 6.00000 6.00000, 9....\n", + " POLYGON ((9.00000 3.00000, 6.00000 3.00000, 6....\n", " \n", " \n", "\n", @@ -1341,15 +1341,15 @@ "text/plain": [ " ID geometry\n", "0 y1_M000.s000 POLYGON ((0.00000 0.00000, 0.00000 3.00000, 3....\n", - "1 y1_M000.s100 POLYGON ((0.00000 3.00000, 0.00000 6.00000, 3....\n", + "1 y1_M000.s100 POLYGON ((3.00000 3.00000, 0.00000 3.00000, 0....\n", "2 y1_M000.s200 POLYGON ((3.00000 0.00000, 3.00000 3.00000, 6....\n", "3 y1_M000.s300 POLYGON ((3.00000 3.00000, 3.00000 6.00000, 6....\n", "4 y1_M010.s000 POLYGON ((0.00000 6.00000, 0.00000 9.00000, 3....\n", - "5 y1_M010.s100 POLYGON ((0.00000 9.00000, 0.00000 12.00000, 3...\n", + "5 y1_M010.s100 POLYGON ((3.00000 9.00000, 0.00000 9.00000, 0....\n", "6 y1_M010.s200 POLYGON ((3.00000 6.00000, 3.00000 9.00000, 6....\n", "7 y1_M010.s300 POLYGON ((3.00000 9.00000, 3.00000 12.00000, 6...\n", "8 y1_M020.s000 POLYGON ((6.00000 0.00000, 6.00000 3.00000, 9....\n", - "9 y1_M020.s100 POLYGON ((6.00000 3.00000, 6.00000 6.00000, 9...." + "9 y1_M020.s100 POLYGON ((9.00000 3.00000, 6.00000 3.00000, 6...." ] }, "execution_count": 21, @@ -1373,11 +1373,19 @@ "execution_count": 22, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.498502Z", - "start_time": "2020-10-01T21:08:02.466318Z" + "end_time": "2021-07-07T23:48:57.912737Z", + "start_time": "2021-07-07T23:48:57.862113Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/71/bk36jbtj26n_v5fhw9tnzzl00000gn/T/ipykernel_26404/2402654510.py:7: UserWarning: `keep_geom_type=True` in overlay resulted in 12 dropped geometries of different geometry types than df1 has. Set `keep_geom_type=False` to retain all geometries\n", + " s_df = geopandas.overlay(s_df, l_df)\n" + ] + }, { "data": { "text/html": [ @@ -1439,8 +1447,8 @@ "execution_count": 23, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.514068Z", - "start_time": "2020-10-01T21:08:02.503842Z" + "end_time": "2021-07-07T23:48:57.922350Z", + "start_time": "2021-07-07T23:48:57.914263Z" } }, "outputs": [ @@ -1508,12 +1516,12 @@ " \n", " 19\n", " y2_L010.s100\n", - " POLYGON ((6.00000 2.00000, 6.00000 4.00000, 8....\n", + " POLYGON ((8.00000 4.00000, 8.00000 2.00000, 6....\n", " \n", " \n", " 20\n", " y2_L010.s200\n", - " POLYGON ((6.00000 4.00000, 6.00000 6.00000, 8....\n", + " POLYGON ((8.00000 6.00000, 8.00000 4.00000, 6....\n", " \n", " \n", "\n", @@ -1528,8 +1536,8 @@ "16 y2_L000.s160 POLYGON ((4.00000 8.00000, 4.00000 10.00000, 6...\n", "17 y2_L000.s170 POLYGON ((4.00000 10.00000, 4.00000 12.00000, ...\n", "18 y2_L010.s000 POLYGON ((6.00000 0.00000, 6.00000 2.00000, 8....\n", - "19 y2_L010.s100 POLYGON ((6.00000 2.00000, 6.00000 4.00000, 8....\n", - "20 y2_L010.s200 POLYGON ((6.00000 4.00000, 6.00000 6.00000, 8...." + "19 y2_L010.s100 POLYGON ((8.00000 4.00000, 8.00000 2.00000, 6....\n", + "20 y2_L010.s200 POLYGON ((8.00000 6.00000, 8.00000 4.00000, 6...." ] }, "execution_count": 23, @@ -1553,8 +1561,8 @@ "execution_count": 24, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.522390Z", - "start_time": "2020-10-01T21:08:02.518185Z" + "end_time": "2021-07-07T23:48:57.927812Z", + "start_time": "2021-07-07T23:48:57.923736Z" } }, "outputs": [ @@ -1586,8 +1594,8 @@ "execution_count": 25, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.529212Z", - "start_time": "2020-10-01T21:08:02.524137Z" + "end_time": "2021-07-07T23:48:57.934307Z", + "start_time": "2021-07-07T23:48:57.929780Z" } }, "outputs": [], @@ -1616,11 +1624,19 @@ "execution_count": 26, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.602343Z", - "start_time": "2020-10-01T21:08:02.531151Z" + "end_time": "2021-07-07T23:48:58.023425Z", + "start_time": "2021-07-07T23:48:57.935610Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/71/bk36jbtj26n_v5fhw9tnzzl00000gn/T/ipykernel_26404/3583996068.py:7: UserWarning: `keep_geom_type=True` in overlay resulted in 36 dropped geometries of different geometry types than df1 has. Set `keep_geom_type=False` to retain all geometries\n", + " xw = geopandas.overlay(df1, df2)\n" + ] + }, { "data": { "text/html": [ @@ -1655,7 +1671,7 @@ " 0\n", " y1_M000.s000\n", " y2_L000.s000\n", - " POLYGON ((0.00000 0.00000, 0.00000 2.00000, 2....\n", + " POLYGON ((0.00000 2.00000, 2.00000 2.00000, 2....\n", " 0.444444\n", " y1_M000\n", " y2_L000\n", @@ -1664,7 +1680,7 @@ " 1\n", " y1_M000.s000\n", " y2_L000.s100\n", - " POLYGON ((0.00000 2.00000, 0.00000 3.00000, 2....\n", + " POLYGON ((0.00000 3.00000, 2.00000 3.00000, 2....\n", " 0.222222\n", " y1_M000\n", " y2_L000\n", @@ -1682,7 +1698,7 @@ " 3\n", " y1_M000.s000\n", " y2_L000.s600\n", - " POLYGON ((3.00000 2.00000, 3.00000 0.00000, 2....\n", + " POLYGON ((3.00000 0.00000, 2.00000 0.00000, 2....\n", " 0.222222\n", " y1_M000\n", " y2_L000\n", @@ -1691,7 +1707,7 @@ " 4\n", " y1_M000.s200\n", " y2_L000.s600\n", - " POLYGON ((3.00000 0.00000, 3.00000 2.00000, 4....\n", + " POLYGON ((3.00000 2.00000, 4.00000 2.00000, 4....\n", " 0.222222\n", " y1_M000\n", " y2_L000\n", @@ -1709,11 +1725,11 @@ "4 y1_M000.s200 y2_L000.s600 \n", "\n", " geometry wt _id_1 \\\n", - "0 POLYGON ((0.00000 0.00000, 0.00000 2.00000, 2.... 0.444444 y1_M000 \n", - "1 POLYGON ((0.00000 2.00000, 0.00000 3.00000, 2.... 0.222222 y1_M000 \n", + "0 POLYGON ((0.00000 2.00000, 2.00000 2.00000, 2.... 0.444444 y1_M000 \n", + "1 POLYGON ((0.00000 3.00000, 2.00000 3.00000, 2.... 0.222222 y1_M000 \n", "2 POLYGON ((0.00000 3.00000, 0.00000 4.00000, 2.... 0.222222 y1_M000 \n", - "3 POLYGON ((3.00000 2.00000, 3.00000 0.00000, 2.... 0.222222 y1_M000 \n", - "4 POLYGON ((3.00000 0.00000, 3.00000 2.00000, 4.... 0.222222 y1_M000 \n", + "3 POLYGON ((3.00000 0.00000, 2.00000 0.00000, 2.... 0.222222 y1_M000 \n", + "4 POLYGON ((3.00000 2.00000, 4.00000 2.00000, 4.... 0.222222 y1_M000 \n", "\n", " _id_2 \n", "0 y2_L000 \n", @@ -1745,14 +1761,14 @@ "execution_count": 27, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.821942Z", - "start_time": "2020-10-01T21:08:02.604232Z" + "end_time": "2021-07-07T23:48:58.141695Z", + "start_time": "2021-07-07T23:48:58.028577Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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aEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQ2uTRU1U/VFXnVdWVVXXjfHlBVZ009WwAAMD2t2PKO6+q5yd5UZKrkvxpkiuT3DXJA5KckOT8yYYDAABamCx6qurUzILnz5M8doxx7br13zLJYAAAQCuTHN5WVbdL8pIk1yV5/PrgSZIxxte2fDAAAKCdqfb0HJvkiCRvT/KFqvqxJPdLckOSj4wxPjzRXAAAQDM1xtj6O616ZpKXJfntJA9O8j3rrnJJklPGGP9+C79nZYNVRy0tLR28srLR6q1RVZPePwAALMIUzbDe8vJyVldXV8cYy5u97VRnb7vbfPnzSQ5K8sNJDslsb897Mwuht00zGgAA0MlUh7fdfr6szPbo/N3853+sqsck+USS46vqQTd3qNtGlTffA7S0yIFvjUMOPGvqEViAa284I0my84yTJ55k3zn8rPOS9H6MyU2P03Nz+1t7XtqWPXid7cPrbB9rz8vtbqo9PV+YLy/bJXiSJGOM6zPb25Mkx2zpVAAAQDtTRc/H58svbrB+LYoO2vejAAAAnU0VPZck+XqSe1fVHXaz/n7z5RVbNhEAANDSJNEzxrgqyblJDkvywl3XVdWPJPnRJNckec/WTwcAAHQy1YkMkuRZSR6Y5HlV9eAkH0lyjySPSfKNJE8dY3xxuvEAAIAOJoueMcbnq+qBSZ6fWej8QJJrk7w7yVljjL+cajYAAKCPKff0ZIxxdWZ7fJ415RwAAEBfU53IAAAAYEuIHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQ2n4TPVX1xKoa839PmXoeAACgh/0ieqrqu5K8KsmXp54FAADoZfLoqapK8ntJdiZ57cTjAAAAzUwePUmenuQhSZ6c5CsTzwIAADRTY4zp7rzq6CSrSV47xnhmVZ2Z5FeSPHWM8bo9uP3KBquOWlpaOnhlZaPVW2O2EwsAALa3KZthzfLyclZXV1fHGMubve1ke3qqakeSNyX5lyS/PNUcAABAbzsmvO8XJnlAkh8cY1y/N79go8qb7wFauhWzLdTOM06eegQW4PCzzkuSfPHGIyaeZN+58wGXJ+n9GJObHqfn5va39ry0LXvwOtuH19k+1p6X290ke3qq6pjM9u68dIzx4SlmAAAAbhu2PHp2OaztE0lesNX3DwAA3LZMsafnTknuk+ToJDfs8oWkI7OTGCTJ784ve8UE8wEAAI1M8ZmeG5O8foN1S5l9zucDST6exKFvAADArbLl0TM/acFTdrdufsrqByT5/T05ZTUAAMAt2R++nBQAAGCfET0AAEBr+1X0jDHOHGOUQ9sAAIBF2a+iBwAAYNFEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKC1GmNMPcPCVdXK0tLS0srKytRzTHr/AACwCPtDMywvL2d1dXV1jLG82dva0wMAALS2Y+oBbgt2nnHy1COwAIefdV6S5Is3HjHxJPvOnQ+4PEnvx5jc9Dg9N7e/teelbdmD19k+vM72sfa83O7s6QEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuTRE9VHV5VT6mqd1TVp6rq+qq6pqo+UFU/W1ViDAAAWIgdE93vqUlek+TKJBcl+Zck357ksUlel+QRVXXqGGNMNB8AANDEVNHziSSPTPLuMcY31y6sql9O8pEkJ2cWQOdNMx4AANDFJIeRjTEuHGP8ya7BM7/8s0leO//xhC0fDAAAaGd//OzM1+bLr086BQAA0ELtTx+bqaodSf4myf2SPHyM8d5buP7KBquOWlpaOnhlZaPVW6OqJr1/AABYhP2hGZaXl7O6uro6xlje7G33tz09L84seM6/peABAADYE1OdyOA/qaqnJ3l2ko8leeKe3GajypvvAVpa3HS3ziEHnjX1CCzAtTeckSTZecbJE0+y7xx+1uzcIZ0fY3LT4/Tc3P7Wnpe2ZQ9eZ/vwOtvH2vNyu9sv9vRU1dOSvDLJR5OcOMa4euKRAACAJiaPnqo6Lcmrk/xDZsHz2WknAgAAOpk0eqrquUlenuRvMwuez085DwAA0M9k0VNVL8jsxAUrSR46xrhqqlkAAIC+JjmRQVU9KcmvJflGkvcnefpuTu98xRjjnC0eDQAAaGaqs7cdMV/ePslpG1znfUnO2YphAACAviY5vG2MceYYo27h3wlTzAYAAPQy+dnbAAAA9iXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQ2qTRU1XfWVVvqKp/q6obq+qKqnpFVX3rlHMBAAB97JjqjqvqXkk+lORuSd6V5GNJjknyjCQPr6rjxhg7p5oPAADoYco9Pb+TWfA8fYzx6DHGL40xHpLk5Unum+R/TTgbAADQxCTRU1VHJnlYkiuS/Pa61b+S5CtJnlhVd9zi0QAAgGam2tPzkPnygjHGN3ddMca4NskHkxyc5Ae2ejAAAKCXGmNs/Z1W/WaS05OcPsZ46W7WvzrJ05L84hjjNTfze1Y2WHXU0tLSwSsrG63eGlU16f0DAMAiTNEM6y0vL2d1dXV1jLG82dtOtafnsPnymg3Wr11+530/yr5z0kknTT0CAADcKvtD8Nxak5297Ras7SK52f+HN6q8+R6gpUUPtVnvfve7px4BAABu86ba07O2J+ewDdYfuu56AAAAe2Wq6Pn4fHmfDdbfe778xBbMAgAANDZV9Fw0Xz6sqv7DDFV1SJLjklyf5C+3ejAAAKCXSaJnjPFPSS5Ics/MztK2q19NcsckbxxjfGWLRwMAAJqZ8kQGv5jkQ0nOrqqHJrk0yQOTnJjZYW3Pm3A2AACgiakOb1vb2/P9Sc7JLHaeneReSc5O8qAxxs6pZgMAAPqY9JTVY4x/TfLkKWcAAAB6m2xPDwAAwFYQPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFqrMcbUMyxcVe086KCD7nL00UdPPQoAALAAl156aa6//vqrxxiHb/a2XaPn8iSHJrli4lGOmi8/NukULIrt2Ydt2Ydt2Yvt2Ydt2cf+tC3vmeRLY4wjNnvDltGzv6iqlSQZYyxPPQu3nu3Zh23Zh23Zi+3Zh23ZR5dt6TM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALTm7G0AAEBr9vQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6JnH6iq76yqN1TVv1XVjVV1RVW9oqq+derZ2HNVdXhVPaWq3lFVn6qq66vqmqr6QFX9bFV5/mxzVfXEqhrzf0+Zeh42p6p+qKrOq6or56+1V1bVBVV10tSzsTlV9WPzbffp+WvtZVX1tqp60NSz8R9V1SlV9aqqen9VfWn++vnmW7jNsVV1flVdXVXXVdXfV9VpVXX7rZqb3dvM9qyqe1fVc6vqwqr616r6alV9rqreVVUnbvXsm7Vj6gG6qap7JflQkrsleVeSjyU5Jskzkjy8qo4bY+yccET23KlJXpPkyiQXJfmXJN+e5LFJXpfkEVV16vANv9tSVX1Xklcl+XKSO008DptUVc9P8qIkVyX508yep3dN8oAkJyQ5f7Lh2JSqekmS5yTZmeSdmW3T707yqCQnV9VPjzFu9k01W+r5Sb4vs9fOTyc56uauXFWPSnJekhuSnJvk6iQ/keTlSY7L7L+1TGcz2/NFSX4yyUcze429Osl9kzwyySOr6hljjLP37bh7r7xfW6yqem+ShyV5+hjjVbtc/rIkz0zyf8YYPz/VfOy5qnpIkjsmefcY45u7XH73JB9J8l1JThljnDfRiOylqqokf5bkiCT/N8npSZ46xnjdpIOxR6rq1CR/lOTPkzx2jHHtuvXfMsb42iTDsSnz19PPJPn3JN87xvj8LutOTHJhksvHGEdONCLrzLfLp5N8Ksnxmf1R8C1jjCfs5rqHzq93WJLjxhh/Pb/8wMy27YOS/Pcxxlu3aHzW2eT2/JkkfzfG+Jt1lx+f2X9TR5J7jjGu3Ndz7w2H5yxQVR2ZWfBckeS3163+lSRfSfLEqrrjFo/GXhhjXDjG+JNdg2d++WeTvHb+4wlbPhiL8PQkD0ny5Myel2wT88NKX5LkuiSPXx88SSJ4tpV7ZPZe5K92DZ4kGWNclOTaJN82xWDs3hjjojHGJ/fwKIdTMtt+b10LnvnvuCGzPQxJ8gv7YEz20Ga25xjjnPXBM7/8fUkuTnKHJMcufsrFED2L9ZD58oLdvFG+NskHkxyc5Ae2ejAWbu1N1dcnnYJNq6qjk7w4ySvHGJdMPQ+bdmxme+jOT/KF+WdBnltVz/D5j23pk0m+muSYqrrrriuq6sFJDslsjx7b09r7ovfsZt0lmf3x4tiqOmDrRmIf2e/fF/lMz2Ldd778xAbrP5nZnqD7JPmLLZmIhauqHUl+ev7j7l7I2U/Nt92bMvt81i9PPA5757/Ol59Lsprke3ZdWVWXZHbY6b9v9WBs3hjj6qp6bpKXJfloVb0zs8/23Cuzzwn8WZL/Md2E3Eobvi8aY3y9qi5P8l+SHJnk0q0cjMWpqnskeWhmEbvf/jFR9CzWYfPlNRusX7v8zvt+FPahFye5X5LzxxjvnXoYNuWFmX3Q/QfHGNdPPQx75W7z5c8nuTzJDyf5q8wOk3ppkh9N8rY49HTbGGO8oqquSPKGJE/dZdWnkpyz/rA3thXvi5qb76V7S5IDkjxnjPGFiUfakMPbtlbNl84esU1V1dOTPDuzs/I9ceJx2ISqOiazvTsvHWN8eOp52Gtrp7itzPbo/MUY48tjjH9M8pjMPpB7vEPdto+qek6Styc5J7M9PHdMspzksiRvqar/Pd107GPeF21j81OOvymzs/Cdm+S3pp3o5omexVr7i8VhG6w/dN312Eaq6mlJXpnZqRpPHGNcPfFI7KFdDmv7RJIXTDwOt87aXxEvG2P83a4r5nvv1va+HrOlU7FXquqEzE5M8cdjjGeNMS4bY1w3xljNLGI/k+TZ8xMFsf14X9TUPHjenNkpx/8oyRP296/wED2L9fH58j4brL/3fLnRZ37YT1XVaUleneQfMguez047EZt0p8yel0cnuWGXLyQdmZ1ZMUl+d37ZK6Yakj2y9jr7xQ3Wr0XRQft+FBbgx+fLi9avGGNcl9nXA9wus8NS2X42fF80/2PUEZl98P2yrRyKW2e+7f4wyeOS/EFmZ9Lcb09gsMZnehZr7UX7YVV1u3Xf7XJIZrv/rk/yl1MMx96Zf8j2xUn+NsmPjDGumnYi9sKNSV6/wbqlzN5QfSCz/0A79G3/dklmb5LuXVV3GGN8dd36+82XV2zpVOyttbN2bXRa6rXL129ntocLk/xUkodn9iZ5Vw/O7Iy2l4wxbtzqwdg7VXWHzPbsPCrJG5M8ef0Zi/dX9vQs0Bjjn5JckOSeSZ62bvWvZnac8hvHGL4XZJuoqhdkFjwrSR4qeLanMcb1Y4yn7O5fkj+eX+3355edO+Ws3Lz5c/DczA6XeeGu66rqRzI7kcE1cWbF7eL98+XPVdV37Lqiqh6R2R8Lb0jyoa0ejIV4e5Krkjyuqr5/7cL5l5P++vzH10wxGJs3P2nBOzILntdnGwVPktR+fvjdtlNV98rsxfluSd6V2SkYH5jkxMwOazt2jLFzugnZU1X1pMw+WPuNJK/K7o85vmKMcc4WjsWCVdWZmR3i9tQxxusmHoc9UFV3y+x7z747szfNH8ns7G2PyewD0Y8fY7xtugnZU/Mvm31vZmfhuzazN1SfzexQ1B/P7IPup40xXjnZkPwHVfXoJI+e/3j3zP7QcFluCtirxhinr7v+2zOL17cmuTqz05Hfd375f9vfPwvS2Wa2Z1X9XpKfySxkfye7PwHFxWOMi/fZwLeCw9sWbIzxT/O/ZvxaZrtzT0pyZZKzk/yqD79vK0fMl7dPctoG13lfZmEEbJExxuer6oGZfaP7YzL7wudrk7w7yVljDIcQbxNjjG9W1UmZHR3xuMy258GZvTE+P8nZY4wLJhyR/+z+SZ607rIj5/+S5J+T/P/oGWO8s6qOT/K8JCcnOTCz05E/K7PtK3imdf/s+fZce19016zb077OxQuabaHs6QEAAFrzmR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKC1/wcinFrmhF4GkwAAAABJRU5ErkJggg==\n", 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" ] @@ -1776,8 +1792,8 @@ "execution_count": 28, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.829125Z", - "start_time": "2020-10-01T21:08:02.823942Z" + "end_time": "2021-07-07T23:48:58.148353Z", + "start_time": "2021-07-07T23:48:58.143108Z" } }, "outputs": [], @@ -1797,8 +1813,8 @@ "execution_count": 29, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.844963Z", - "start_time": "2020-10-01T21:08:02.830907Z" + "end_time": "2021-07-07T23:48:58.163336Z", + "start_time": "2021-07-07T23:48:58.150330Z" } }, "outputs": [ @@ -1963,8 +1979,8 @@ "execution_count": 30, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.861474Z", - "start_time": "2020-10-01T21:08:02.847041Z" + "end_time": "2021-07-07T23:48:58.176486Z", + "start_time": "2021-07-07T23:48:58.165034Z" } }, "outputs": [ @@ -2053,8 +2069,8 @@ "execution_count": 31, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.868503Z", - "start_time": "2020-10-01T21:08:02.863783Z" + "end_time": "2021-07-07T23:48:58.181510Z", + "start_time": "2021-07-07T23:48:58.177999Z" } }, "outputs": [ @@ -2089,8 +2105,8 @@ "execution_count": 32, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.875782Z", - "start_time": "2020-10-01T21:08:02.870628Z" + "end_time": "2021-07-07T23:48:58.187511Z", + "start_time": "2021-07-07T23:48:58.182892Z" } }, "outputs": [ @@ -2117,8 +2133,8 @@ "execution_count": 33, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:02.881826Z", - "start_time": "2020-10-01T21:08:02.877585Z" + "end_time": "2021-07-07T23:48:58.193796Z", + "start_time": "2021-07-07T23:48:58.189095Z" } }, "outputs": [], @@ -2144,14 +2160,14 @@ "execution_count": 34, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:03.186944Z", - "start_time": "2020-10-01T21:08:02.883670Z" + "end_time": "2021-07-07T23:48:58.373520Z", + "start_time": "2021-07-07T23:48:58.195270Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2194,14 +2210,14 @@ "execution_count": 35, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:03.612313Z", - "start_time": "2020-10-01T21:08:03.189358Z" + "end_time": "2021-07-07T23:48:58.630626Z", + "start_time": "2021-07-07T23:48:58.375457Z" } }, "outputs": [ { "data": { - "image/png": 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" ] @@ -2244,11 +2260,20 @@ "execution_count": 36, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:03.655284Z", - "start_time": "2020-10-01T21:08:03.614148Z" + "end_time": "2021-07-07T23:48:58.712697Z", + "start_time": "2021-07-07T23:48:58.632654Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/71/bk36jbtj26n_v5fhw9tnzzl00000gn/T/ipykernel_26404/2402654510.py:7: UserWarning: `keep_geom_type=True` in overlay resulted in 4 dropped geometries of different geometry types than df1 has. Set `keep_geom_type=False` to retain all geometries\n", + " s_df = geopandas.overlay(s_df, l_df)\n" + ] + } + ], "source": [ "year1_medium, year1_small = hiearchy_ids(year1_medium, year1_small, \"y1_M\")\n", "year2_large, year2_small = hiearchy_ids(year2_large, year2_small, \"y2_L\")" @@ -2259,8 +2284,8 @@ "execution_count": 37, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:03.662271Z", - "start_time": "2020-10-01T21:08:03.657446Z" + "end_time": "2021-07-07T23:48:58.719244Z", + "start_time": "2021-07-07T23:48:58.714678Z" } }, "outputs": [ @@ -2285,11 +2310,19 @@ "execution_count": 38, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:03.705692Z", - "start_time": "2020-10-01T21:08:03.663657Z" + "end_time": "2021-07-07T23:48:58.789759Z", + "start_time": "2021-07-07T23:48:58.720651Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/71/bk36jbtj26n_v5fhw9tnzzl00000gn/T/ipykernel_26404/3583996068.py:7: UserWarning: `keep_geom_type=True` in overlay resulted in 28 dropped geometries of different geometry types than df1 has. Set `keep_geom_type=False` to retain all geometries\n", + " xw = geopandas.overlay(df1, df2)\n" + ] + }, { "data": { "text/html": [ @@ -2324,7 +2357,7 @@ " 0\n", " y1_M000.s000\n", " y2_L000.s000\n", - " POLYGON ((0.00000 2.00000, 0.00000 3.00000, 3....\n", + " POLYGON ((0.00000 3.00000, 3.00000 3.00000, 3....\n", " 0.333333\n", " y1_M000\n", " y2_L000\n", @@ -2342,7 +2375,7 @@ " 2\n", " y1_M000.s200\n", " y2_L000.s000\n", - " POLYGON ((3.00000 2.00000, 3.00000 3.00000, 4....\n", + " POLYGON ((3.00000 3.00000, 4.00000 3.00000, 4....\n", " 0.333333\n", " y1_M000\n", " y2_L000\n", @@ -2351,7 +2384,7 @@ " 3\n", " y1_M000.s300\n", " y2_L000.s000\n", - " POLYGON ((3.00000 3.00000, 3.00000 4.00000, 4....\n", + " POLYGON ((3.00000 4.00000, 4.00000 4.00000, 4....\n", " 1.000000\n", " y1_M000\n", " y2_L000\n", @@ -2360,7 +2393,7 @@ " 4\n", " y1_M010.s000\n", " y2_L000.s000\n", - " POLYGON ((0.00000 4.00000, 0.00000 6.00000, 3....\n", + " POLYGON ((3.00000 4.00000, 0.00000 4.00000, 0....\n", " 1.000000\n", " y1_M010\n", " y2_L000\n", @@ -2378,11 +2411,11 @@ "4 y1_M010.s000 y2_L000.s000 \n", "\n", " geometry wt _id_1 \\\n", - "0 POLYGON ((0.00000 2.00000, 0.00000 3.00000, 3.... 0.333333 y1_M000 \n", + "0 POLYGON ((0.00000 3.00000, 3.00000 3.00000, 3.... 0.333333 y1_M000 \n", "1 POLYGON ((0.00000 3.00000, 0.00000 4.00000, 3.... 1.000000 y1_M000 \n", - "2 POLYGON ((3.00000 2.00000, 3.00000 3.00000, 4.... 0.333333 y1_M000 \n", - "3 POLYGON ((3.00000 3.00000, 3.00000 4.00000, 4.... 1.000000 y1_M000 \n", - "4 POLYGON ((0.00000 4.00000, 0.00000 6.00000, 3.... 1.000000 y1_M010 \n", + "2 POLYGON ((3.00000 3.00000, 4.00000 3.00000, 4.... 0.333333 y1_M000 \n", + "3 POLYGON ((3.00000 4.00000, 4.00000 4.00000, 4.... 1.000000 y1_M000 \n", + "4 POLYGON ((3.00000 4.00000, 0.00000 4.00000, 0.... 1.000000 y1_M010 \n", "\n", " _id_2 \n", "0 y2_L000 \n", @@ -2407,14 +2440,14 @@ "execution_count": 39, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:03.937804Z", - "start_time": "2020-10-01T21:08:03.707429Z" + "end_time": "2021-07-07T23:48:58.933641Z", + "start_time": "2021-07-07T23:48:58.791679Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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gNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaG3y6KmqH6yqt1TVtVV1y3x5SVWdPfVsAADA9rdjyh9eVc9N8oIk1yX54yTXJrlzkvskOSPJxZMNBwAAtDBZ9FTVuZkFz58meeQY48ZV679hksEAAIBWJjm8rapuk+TFSW5K8pjVwZMkY4yvbPpgAABAO1Pt6Tk1yXFJ3pzks1X1kCT3SnJzkg+OMT4w0VwAAEAzU0XP982Xn06yJ8l37buyqq5Ics4Y418P9E2qavcaq0465AkX6Mabnz31CLAhy/KcXZbHuQxsy16WYXsuw2OErWSqq7fdZb78mSRHJfmhJEdntrfnXUnun+RN04wGAAB0MtWentvOl5XZHp2/mX/+91X1iCQfTXJ6Vd3vQIe6jTF27e/2+R6gnYsc+FAcfeQLpx4B1mXlL4/dn7PL8jiXgW3ZyzJsz2V4jPTSZa/kVHt6PjtfXrVP8CRJxhh7M9vbkySnbOpUAABAO1NFz0fmy8+tsX4lio46/KMAAACdTRU9VyT5apJ7VtXt9rP+XvPlNZs2EQAA0NIk0TPGuC7JRUmOTfL8fddV1Q8n+ZEkn0/yzs2fDgAA6GSqCxkkydOS3DfJc6rq/kk+mORuSR6R5GtJnjTG+Nx04wEAAB1MFj1jjM9U1X2TPDez0Pn+JDcmeUeSF44x/nyq2QAAgD6m3NOTMcYNme3xedqUcwAAAH1NdSEDAACATSF6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrWyZ6qupxVTXmH0+ceh4AAKCHLRE9VfUdSV6e5ItTzwIAAPQyefRUVSX5nSTXJ3nlxOMAAADNTB49SZ6S5KwkT0jypYlnAQAAmtkx5Q+vqpOTvCjJy8YYV1TVWRv8+t1rrDrpkIdboBtvfvbUI8CGLMtzdlke5zKwLXtZhu25DI8RtpLJ9vRU1Y4kr0vyT0l+Yao5AACA3qbc0/P8JPdJ8gNjjL0H8w3GGLv2d/t8D9DOQ5htoY4+8oVTjwDrsvKXx+7P2WV5nMvAtuxlGbbnMjxGeumyV3KSPT1VdUpme3d+bYzxgSlmAAAAlsOmR88+h7V9NMnzNvvnAwAAy2WKPT3fmOSEJCcnuXmfNyQdSX5xfp/fnt/20gnmAwAAGpninJ5bkrx6jXU7MzvP571JPpLEoW8AAMAh2fTomV+04In7W1dV52cWPb87xnjVZs4FAAD0tBXenBQAAOCwET0AAEBrWyp6xhjnjzHKoW0AAMCibKnoAQAAWDTRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGitxhhTz7BwVbV7586dO3fv3j31HJP+fAAAWISt0Ay7du3Knj179owxdm30a+3pAQAAWtsx9QDL4HO3HDf1CLAudzzi6iT9n7PL8jiXgW3ZyzJsz2V4jPSy8pzd7uzpAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa5NET1V9c1U9sareWlUfr6q9VfX5qnpvVf1UVYkxAABgIXZM9HPPTfKKJNcmuSzJPyX51iSPTPKqJA+uqnPHGGOi+QAAgCamip6PJnlokneMMb6+cmNV/UKSDyZ5VGYB9JZpxgMAALqY5DCyMcalY4w/2jd45rd/Kskr55+esemDAQAA7WzFc2e+Ml9+ddIpAACAFqY6vG2/qmpHkp+Yf/rOddx/9xqrTlrYUAtwxyOunnoE2JBlec4uy+NcBrZlL8uwPZfhMcJWstX29Lwoyb2SXDzGeNfUwwAAANvfltnTU1VPSfL0JB9O8rj1fM0YY9ca32t3kp2Lm+7QuAgd20VVJen/nF2Wx7kMbMtelmF7LsNjpJeV5+x2tyX29FTVk5O8LMmHkpw5xrhh4pEAAIAmJo+eqjovyW8k+bvMgudT004EAAB0Mmn0VNWzkrwkyV9nFjyfmXIeAACgn8mip6qel9mFC3YnecAY47qpZgEAAPqa5EIGVfX4JL+c5GtJ3pPkKfs5SeqaMcaFmzwaAADQzFRXbztuvrxtkvPWuM+7k1y4GcMAAAB9TXJ42xjj/DFG3crHGVPMBgAA9DL51dsAAAAOJ9EDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANCa6AEAAFoTPQAAQGuiBwAAaE30AAAArYkeAACgNdEDAAC0JnoAAIDWRA8AANDapNFTVd9eVa+pqn+pqluq6pqqemlVfdOUcwEAAH3smOoHV9U9krw/yV2SvD3Jh5OckuSpSR5UVaeNMa6faj4AAKCHKff0/FZmwfOUMcbDxxg/P8Y4K8lLkpyY5L9POBsAANDEJNFTVccneWCSa5L85qrVv5jkS0keV1V32OTRAACAZqba03PWfHnJGOPr+64YY9yY5H1Jbp/k+zd7MAAAoJepzuk5cb786BrrP5bZnqATkvzZWt+kqnavseqkgx9t8apq6hFgQ5blObssj3MZ2Ja9LMP2XIbHCFvJVHt6jp0vP7/G+pXb73j4Rzl8xhhTjwAAAIfk7LPPnnqEQzbZ1dtuxcqfPw5YDWOMXfv94tkeoJ2LHupgCB8AAJjWVHt6VvbkHLvG+mNW3Q8AAOCgTBU9H5kvT1hj/T3ny7XO+QEAAFiXqaLnsvnygVX172aoqqOTnJZkb5I/3+zBAACAXiaJnjHGPyS5JMndkzx51epfSnKHJK8dY3xpk0cDAACamfJCBv81yfuTXFBVD0hyZZL7Jjkzs8PanjPhbAAAQBNTHd62srfne5NcmFnsPD3JPZJckOR+Y4zrp5oNAADoY9JLVo8x/jnJE6acAQAA6G2yPT0AAACbQfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABoTfQAAACtiR4AAKA10QMAALQmegAAgNZEDwAA0JroAQAAWhM9AABAa6IHAABorcYYU8+wcFV1/VFHHXWnk08+eepRAACABbjyyiuzd+/eG8YY37zRr+0aPVcnOSbJNROPctJ8+eFJp2BRbM8+bMs+bMtebM8+bMs+ttK2vHuSL4wxjtvoF7aMnq2iqnYnyRhj19SzcOhszz5syz5sy15szz5syz66bEvn9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmqu3AQAArdnTAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JnsOgqr69ql5TVf9SVbdU1TVV9dKq+qapZ2P9quqbq+qJVfXWqvp4Ve2tqs9X1Xur6qeqyr+fba6qHldVY/7xxKnnYWOq6ger6i1Vde38d+21VXVJVZ099WxsTFU9ZL7tPjH/XXtVVb2pqu439Wz8e1V1TlW9vKreU1VfmP/+fP2tfM2pVXVxVd1QVTdV1d9W1XlVddvNmpv928j2rKp7VtWzqurSqvrnqvpyVX26qt5eVWdu9uwbtWPqAbqpqnskeX+SuyR5e5IPJzklyVOTPKiqThtjXD/hiKzfuUlekeTaJJcl+ack35rkkUleleTBVXXu8A6/21JVfUeSlyf5YpJvnHgcNqiqnpvkBUmuS/LHmf07vXOS+yQ5I8nFkw3HhlTVi5M8M8n1Sd6W2Tb9ziQPS/KoqvqJMcYBX1SzqZ6b5Hsy+935iSQnHejOVfWwJG9JcnOSi5LckOTHkrwkyWmZ/b+W6Wxke74gyY8n+VBmv2NvSHJikocmeWhVPXWMccHhHffglddri1VV70rywCRPGWO8fJ/bfz3JzyX532OMn5lqPtavqs5Kcock7xhjfH2f2++a5INJviPJOWOMt0w0IgepqirJnyQ5Lsn/SfKMJE8aY7xq0sFYl6o6N8kfJPnTJI8cY9y4av03jDG+MslwbMj89+knk/xrku8eY3xmn3VnJrk0ydVjjOMnGpFV5tvlE0k+nuT0zP4o+IYxxmP3c99j5vc7NslpY4y/nN9+ZGbb9n5J/tMY442bND6rbHB7/mSSvxlj/NWq20/P7P+pI8ndxxjXHu65D4bDcxaoqo7PLHiuSfKbq1b/YpIvJXlcVd1hk0fjIIwxLh1j/NG+wTO//VNJXjn/9IxNH4xFeEqSs5I8IbN/l2wT88NKX5zkpiSPWR08SSJ4tpW7ZfZa5C/2DZ4kGWNcluTGJN8yxWDs3xjjsjHGx9Z5lMM5mW2/N64Ez/x73JzZHoYk+dnDMCbrtJHtOca4cHXwzG9/d5LLk9wuyamLn3IxRM9inTVfXrKfF8o3Jnlfktsn+f7NHoyFW3lR9dVJp2DDqurkJC9K8rIxxhVTz8OGnZrZHrqLk3x2fi7Is6rqqc7/2JY+luTLSU6pqjvvu6Kq7p/k6Mz26LE9rbwueud+1l2R2R8vTq2qIzZvJA6TLf+6yDk9i3XifPnRNdZ/LLM9QSck+bNNmYiFq6odSX5i/un+fpGzRc233esyOz/rFyYeh4PzffPlp5PsSfJd+66sqisyO+z0Xzd7MDZujHFDVT0rya8n+VBVvS2zc3vukdl5An+S5L9MNyGHaM3XRWOMr1bV1Un+Q5Ljk1y5mYOxOFV1tyQPyCxit+wfE0XPYh07X35+jfUrt9/x8I/CYfSiJPdKcvEY411TD8OGPD+zE91/YIyxd+phOCh3mS9/JsnVSX4oyV9kdpjUryX5kSRvikNPt40xxkur6pokr0nypH1WfTzJhasPe2Nb8bqoufleujckOSLJM8cYn514pDU5vG1z1Xzp6hHbVFU9JcnTM7sq3+MmHocNqKpTMtu782tjjA9MPQ8HbeUSt5XZHp0/G2N8cYzx90kekdkJuac71G37qKpnJnlzkgsz28NzhyS7klyV5A1V9T+nm47DzOuibWx+yfHXZXYVvouS/Oq0Ex2Y6Fmslb9YHLvG+mNW3Y9tpKqenORlmV2q8cwxxg0Tj8Q67XNY20eTPG/icTg0K39FvGqM8Tf7rpjvvVvZ+3rKpk7FQamqMzK7MMUfjjGeNsa4aoxx0xhjT2YR+8kkT59fKIjtx+uipubB8/rMLjn+B0keu9XfwkP0LNZH5ssT1lh/z/lyrXN+2KKq6rwkv5Hk7zILnk9NOxEb9I2Z/bs8OcnN+7wh6cjsyopJ8tvz21461ZCsy8rv2c+tsX4lio46/KOwAD86X162esUY46bM3h7gNpkdlsr2s+brovkfo47L7MT3qzZzKA7NfNv9fpJHJ/m9zK6kuWUvYLDCOT2LtfJL+4FVdZtV7+1ydGa7//Ym+fMphuPgzE+yfVGSv07yw2OM66adiINwS5JXr7FuZ2YvqN6b2f+gHfq2tV2R2Yuke1bV7cYYX161/l7z5TWbOhUHa+WqXWtdlnrl9tXbme3h0iT/OcmDMnuRvK/7Z3ZF2yvGGLds9mAcnKq6XWZ7dh6W5LVJnrD6isVblT09CzTG+IcklyS5e5Inr1r9S5kdp/zaMYb3Bdkmqup5mQXP7iQPEDzb0xhj7xjjifv7SPKH87v97vy2i6aclQOb/xu8KLPDZZ6/77qq+uHMLmTw+biy4nbxnvnyp6vq2/ZdUVUPzuyPhTcnef9mD8ZCvDnJdUkeXVXfu3Lj/M1Jf2X+6SumGIyNm1+04K2ZBc+rs42CJ0lqix9+t+1U1T0y++V8lyRvz+wSjPdNcmZmh7WdOsa4froJWa+qenxmJ9Z+LcnLs/9jjq8ZY1y4iWOxYFV1fmaHuD1pjPGqicdhHarqLpm979l3Zvai+YOZXb3tEZmdEP2YMcabppuQ9Zq/2ey7MrsK342ZvaD6VGaHov5oZie6nzfGeNlkQ/LvVNXDkzx8/uldM/tDw1X5t4C9bozxjFX3f3Nm8frGJDdkdjnyE+e3/8etfi5IZxvZnlX1O0l+MrOQ/a3s/wIUl48xLj9sAx8Ch7ct2BjjH+Z/zfjlzHbnnp3k2iQXJPklJ79vK8fNl7dNct4a93l3ZmEEbJIxxmeq6r6ZvaP7IzJ7w+cbk7wjyQvHGA4h3ibGGF+vqrMzOzri0Zltz9tn9sL44iQXjDEumXBE/n/3TvL4VbcdP/9Ikn9M8v+iZ4zxtqo6PclzkjwqyZGZXY78aZltX8EzrXtn/dtz5XXRnbNqT/sqly9otoWypwcAAGjNOT0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBrogcAAGhN9AAAAK2JHgAAoDXRAwAAtCZ6AACA1kQPAADQmugBAABaEz0AAEBr/xcd1t+/8ZNrcQAAAABJRU5ErkJggg==\n", 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" ] @@ -2439,8 +2472,8 @@ "execution_count": 40, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:03.944280Z", - "start_time": "2020-10-01T21:08:03.939660Z" + "end_time": "2021-07-07T23:48:58.939019Z", + "start_time": "2021-07-07T23:48:58.935136Z" } }, "outputs": [], @@ -2453,8 +2486,8 @@ "execution_count": 41, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:03.959895Z", - "start_time": "2020-10-01T21:08:03.946304Z" + "end_time": "2021-07-07T23:48:58.951657Z", + "start_time": "2021-07-07T23:48:58.940697Z" } }, "outputs": [ @@ -2612,8 +2645,8 @@ "execution_count": 42, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:03.977762Z", - "start_time": "2020-10-01T21:08:03.961779Z" + "end_time": "2021-07-07T23:48:58.966713Z", + "start_time": "2021-07-07T23:48:58.953223Z" } }, "outputs": [ @@ -2758,8 +2791,8 @@ "execution_count": 43, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:03.984525Z", - "start_time": "2020-10-01T21:08:03.980056Z" + "end_time": "2021-07-07T23:48:58.972566Z", + "start_time": "2021-07-07T23:48:58.968668Z" } }, "outputs": [ @@ -2783,14 +2816,14 @@ "execution_count": 44, "metadata": { "ExecuteTime": { - "end_time": "2020-10-01T21:08:04.211138Z", - "start_time": "2020-10-01T21:08:03.986153Z" + "end_time": "2021-07-07T23:48:59.113234Z", + "start_time": "2021-07-07T23:48:58.974216Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -2830,7 +2863,7 @@ "id": "9f032b7713179dec292def31fe34954d" }, "kernelspec": { - "display_name": "Python [conda env:nhgis] *", + "display_name": "Python [conda env:nhgis]", "language": "python", "name": "conda-env-nhgis-py" }, @@ -2844,7 +2877,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/notebooks/test_subset_1990.ipynb b/notebooks/test_subset_1990.ipynb index 557cb4f..5084612 100644 --- a/notebooks/test_subset_1990.ipynb +++ b/notebooks/test_subset_1990.ipynb @@ -2,8 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2021-07-07T23:49:42.739638Z", + "start_time": "2021-07-07T23:49:42.736613Z" + } + }, "outputs": [], "source": [ "# This file is part of the Minnesota Population Center's NHGISXWALK.\n", @@ -35,11 +40,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:32.645310Z", - "start_time": "2020-08-19T20:35:32.535866Z" + "end_time": "2021-07-07T23:49:42.775311Z", + "start_time": "2021-07-07T23:49:42.742265Z" } }, "outputs": [ @@ -47,18 +52,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-08-19T16:35:32-04:00\n", + "Last updated: 2021-07-07T19:49:42.757472-04:00\n", "\n", - "CPython 3.8.5\n", - "IPython 7.16.1\n", + "Python implementation: CPython\n", + "Python version : 3.9.6\n", + "IPython version : 7.25.0\n", "\n", - "compiler : Clang 10.0.1 \n", - "system : Darwin\n", - "release : 19.6.0\n", - "machine : x86_64\n", - "processor : i386\n", - "CPU cores : 8\n", - "interpreter: 64bit\n" + "Compiler : Clang 11.1.0 \n", + "OS : Darwin\n", + "Release : 20.5.0\n", + "Machine : x86_64\n", + "Processor : i386\n", + "CPU cores : 8\n", + "Architecture: 64bit\n", + "\n" ] } ], @@ -69,11 +76,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:32.927586Z", - "start_time": "2020-08-19T20:35:32.647276Z" + "end_time": "2021-07-07T23:49:43.102934Z", + "start_time": "2021-07-07T23:49:42.779295Z" } }, "outputs": [ @@ -81,10 +88,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "watermark 2.0.2\n", - "numpy 1.19.1\n", - "nhgisxwalk 0.0.9\n", - "pandas 1.1.0\n", + "Watermark: 2.2.0\n", + "\n", + "json : 2.0.9\n", + "numpy : 1.21.0\n", + "pandas : 1.3.0\n", + "nhgisxwalk: 0.1.1\n", "\n" ] } @@ -110,11 +119,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:32.947834Z", - "start_time": "2020-08-19T20:35:32.929843Z" + "end_time": "2021-07-07T23:49:43.128747Z", + "start_time": "2021-07-07T23:49:43.105050Z" } }, "outputs": [], @@ -125,11 +134,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:32.967724Z", - "start_time": "2020-08-19T20:35:32.949626Z" + "end_time": "2021-07-07T23:49:43.153433Z", + "start_time": "2021-07-07T23:49:43.131013Z" } }, "outputs": [], @@ -148,11 +157,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:32.991035Z", - "start_time": "2020-08-19T20:35:32.969748Z" + "end_time": "2021-07-07T23:49:43.179773Z", + "start_time": "2021-07-07T23:49:43.155331Z" } }, "outputs": [ @@ -162,7 +171,7 @@ "'nhgis_blk1990_blk2010_gj'" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -181,11 +190,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:33.027884Z", - "start_time": "2020-08-19T20:35:32.992470Z" + "end_time": "2021-07-07T23:49:43.202464Z", + "start_time": "2021-07-07T23:49:43.181315Z" } }, "outputs": [ @@ -195,7 +204,7 @@ "'../testing_data_subsets/1990_block.csv.zip'" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -215,11 +224,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:33.056686Z", - "start_time": "2020-08-19T20:35:33.030825Z" + "end_time": "2021-07-07T23:49:43.233569Z", + "start_time": "2021-07-07T23:49:43.207738Z" } }, "outputs": [ @@ -229,7 +238,7 @@ "'../testing_data_subsets/1990_blck_grp_598.csv.zip'" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -249,11 +258,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:33.152009Z", - "start_time": "2020-08-19T20:35:33.061336Z" + "end_time": "2021-07-07T23:49:43.320083Z", + "start_time": "2021-07-07T23:49:43.235448Z" } }, "outputs": [ @@ -333,7 +342,7 @@ "4 G10000100401103 G10000100401001003 1.000000 1.000000" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -358,11 +367,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:33.179395Z", - "start_time": "2020-08-19T20:35:33.155093Z" + "end_time": "2021-07-07T23:49:43.341375Z", + "start_time": "2021-07-07T23:49:43.321761Z" } }, "outputs": [], @@ -385,11 +394,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:33.661572Z", - "start_time": "2020-08-19T20:35:33.181408Z" + "end_time": "2021-07-07T23:49:43.813431Z", + "start_time": "2021-07-07T23:49:43.343282Z" } }, "outputs": [ @@ -569,7 +578,7 @@ "[1063 rows x 7 columns]" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -600,11 +609,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:33.690715Z", - "start_time": "2020-08-19T20:35:33.663546Z" + "end_time": "2021-07-07T23:49:43.848026Z", + "start_time": "2021-07-07T23:49:43.814899Z" } }, "outputs": [ @@ -784,7 +793,7 @@ "[1063 rows x 7 columns]" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -795,11 +804,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:33.716867Z", - "start_time": "2020-08-19T20:35:33.692741Z" + "end_time": "2021-07-07T23:49:43.880522Z", + "start_time": "2021-07-07T23:49:43.849275Z" } }, "outputs": [ @@ -892,7 +901,7 @@ "16 1.000000 1.000000 1.000000 1.000000 " ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -904,11 +913,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:33.738361Z", - "start_time": "2020-08-19T20:35:33.718599Z" + "end_time": "2021-07-07T23:49:43.905399Z", + "start_time": "2021-07-07T23:49:43.882102Z" } }, "outputs": [ @@ -925,7 +934,7 @@ " '10001042100']], dtype=object)" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -938,11 +947,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:33.761182Z", - "start_time": "2020-08-19T20:35:33.740232Z" + "end_time": "2021-07-07T23:49:43.932269Z", + "start_time": "2021-07-07T23:49:43.907185Z" } }, "outputs": [ @@ -955,7 +964,7 @@ " [1. , 1. , 1. , 1. ]])" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -968,11 +977,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T20:35:33.787744Z", - "start_time": "2020-08-19T20:35:33.762770Z" + "end_time": "2021-07-07T23:49:43.959830Z", + "start_time": "2021-07-07T23:49:43.933753Z" } }, "outputs": [ @@ -1044,7 +1053,7 @@ "16 1.000000 1.000000 1.000000 1.000000" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1053,20 +1062,6 @@ "state_bgp1990tr2010.xwalk[wgt_cols][ix1:ix2]" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -1087,7 +1082,7 @@ "id": "9f47e4ec2cc37bce83acf20abfca69d2" }, "kernelspec": { - "display_name": "Python [conda env:nhgis] *", + "display_name": "Python [conda env:nhgis]", "language": "python", "name": "conda-env-nhgis-py" }, @@ -1101,7 +1096,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/notebooks/test_subset_2000.ipynb b/notebooks/test_subset_2000.ipynb index e3d8dd4..1ef233e 100644 --- a/notebooks/test_subset_2000.ipynb +++ b/notebooks/test_subset_2000.ipynb @@ -2,8 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2021-07-07T23:50:02.070813Z", + "start_time": "2021-07-07T23:50:02.068077Z" + } + }, "outputs": [], "source": [ "# This file is part of the Minnesota Population Center's NHGISXWALK.\n", @@ -35,11 +40,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T22:03:31.431420Z", - "start_time": "2020-08-19T22:03:31.309999Z" + "end_time": "2021-07-07T23:50:02.106345Z", + "start_time": "2021-07-07T23:50:02.072493Z" } }, "outputs": [ @@ -47,18 +52,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-08-19T18:03:31-04:00\n", + "Last updated: 2021-07-07T19:50:02.088520-04:00\n", "\n", - "CPython 3.8.5\n", - "IPython 7.16.1\n", + "Python implementation: CPython\n", + "Python version : 3.9.6\n", + "IPython version : 7.25.0\n", "\n", - "compiler : Clang 10.0.1 \n", - "system : Darwin\n", - "release : 19.6.0\n", - "machine : x86_64\n", - "processor : i386\n", - "CPU cores : 8\n", - "interpreter: 64bit\n" + "Compiler : Clang 11.1.0 \n", + "OS : Darwin\n", + "Release : 20.5.0\n", + "Machine : x86_64\n", + "Processor : i386\n", + "CPU cores : 8\n", + "Architecture: 64bit\n", + "\n" ] } ], @@ -69,11 +76,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T22:03:31.725113Z", - "start_time": "2020-08-19T22:03:31.433924Z" + "end_time": "2021-07-07T23:50:02.421721Z", + "start_time": "2021-07-07T23:50:02.109447Z" } }, "outputs": [ @@ -81,10 +88,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "watermark 2.0.2\n", - "numpy 1.19.1\n", - "pandas 1.1.0\n", - "nhgisxwalk 0.0.9\n", + "Watermark: 2.2.0\n", + "\n", + "nhgisxwalk: 0.1.1\n", + "json : 2.0.9\n", + "numpy : 1.21.0\n", + "pandas : 1.3.0\n", "\n" ] } @@ -110,11 +119,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T22:03:31.752185Z", - "start_time": "2020-08-19T22:03:31.732593Z" + "end_time": "2021-07-07T23:50:02.449884Z", + "start_time": "2021-07-07T23:50:02.424112Z" } }, "outputs": [], @@ -126,11 +135,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T22:03:31.772182Z", - "start_time": "2020-08-19T22:03:31.753707Z" + "end_time": "2021-07-07T23:50:02.474549Z", + "start_time": "2021-07-07T23:50:02.452731Z" } }, "outputs": [], @@ -148,11 +157,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T22:03:31.796510Z", - "start_time": "2020-08-19T22:03:31.773663Z" + "end_time": "2021-07-07T23:50:02.503005Z", + "start_time": "2021-07-07T23:50:02.476142Z" } }, "outputs": [ @@ -162,7 +171,7 @@ "'nhgis_blk2000_blk2010_gj'" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -181,11 +190,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T22:03:31.819218Z", - "start_time": "2020-08-19T22:03:31.798153Z" + "end_time": "2021-07-07T23:50:02.528614Z", + "start_time": "2021-07-07T23:50:02.505325Z" } }, "outputs": [ @@ -195,7 +204,7 @@ "'../testing_data_subsets/2000_block.csv.zip'" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -215,11 +224,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T22:03:31.882568Z", - "start_time": "2020-08-19T22:03:31.822446Z" + "end_time": "2021-07-07T23:50:02.601518Z", + "start_time": "2021-07-07T23:50:02.531406Z" } }, "outputs": [ @@ -299,7 +308,7 @@ "4 G10000100401001003 G10000100401001003 1.000000 1.000000" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -324,11 +333,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T22:03:31.906203Z", - "start_time": "2020-08-19T22:03:31.885205Z" + "end_time": "2021-07-07T23:50:02.623579Z", + "start_time": "2021-07-07T23:50:02.603010Z" } }, "outputs": [], @@ -351,11 +360,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T22:03:32.199916Z", - "start_time": "2020-08-19T22:03:31.908939Z" + "end_time": "2021-07-07T23:50:02.936278Z", + "start_time": "2021-07-07T23:50:02.625525Z" } }, "outputs": [ @@ -535,7 +544,7 @@ "[1043 rows x 7 columns]" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -566,11 +575,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T22:03:32.233672Z", - "start_time": "2020-08-19T22:03:32.203311Z" + "end_time": "2021-07-07T23:50:02.962495Z", + "start_time": "2021-07-07T23:50:02.937960Z" } }, "outputs": [ @@ -723,7 +732,7 @@ "1030 0.557464 " ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -741,11 +750,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T22:03:32.268622Z", - "start_time": "2020-08-19T22:03:32.236095Z" + "end_time": "2021-07-07T23:50:02.996956Z", + "start_time": "2021-07-07T23:50:02.963898Z" } }, "outputs": [ @@ -925,7 +934,7 @@ "[1043 rows x 7 columns]" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -936,11 +945,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T22:03:32.296527Z", - "start_time": "2020-08-19T22:03:32.271603Z" + "end_time": "2021-07-07T23:50:03.019026Z", + "start_time": "2021-07-07T23:50:02.998767Z" } }, "outputs": [ @@ -954,7 +963,7 @@ " dtype=object)" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -968,11 +977,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "ExecuteTime": { - "end_time": "2020-08-19T22:03:32.321429Z", - "start_time": "2020-08-19T22:03:32.298121Z" + "end_time": "2021-07-07T23:50:03.043296Z", + "start_time": "2021-07-07T23:50:03.020442Z" } }, "outputs": [ @@ -985,7 +994,7 @@ " [8.02660754e-01, 8.17567568e-01, 8.20895522e-01, 8.36236934e-01]])" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -996,27 +1005,6 @@ "obs_num_vals" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -1037,7 +1025,7 @@ "id": "9f47e4ec2cc37bce83acf20abfca69d2" }, "kernelspec": { - "display_name": "Python [conda env:nhgis] *", + "display_name": "Python [conda env:nhgis]", "language": "python", "name": "conda-env-nhgis-py" }, @@ -1051,7 +1039,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/notebooks/weighted-portion-synthetic-atoms.ipynb b/notebooks/weighted-portion-synthetic-atoms.ipynb index e6ae2ae..70d861e 100644 --- a/notebooks/weighted-portion-synthetic-atoms.ipynb +++ b/notebooks/weighted-portion-synthetic-atoms.ipynb @@ -2,8 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2021-07-07T23:48:45.196667Z", + "start_time": "2021-07-07T23:48:45.194194Z" + } + }, "outputs": [], "source": [ "# This file is part of the Minnesota Population Center's NHGISXWALK.\n", @@ -23,11 +28,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-08-10T19:36:26.872828Z", - "start_time": "2020-08-10T19:36:26.718850Z" + "end_time": "2021-07-07T23:48:45.240674Z", + "start_time": "2021-07-07T23:48:45.202446Z" } }, "outputs": [ @@ -35,18 +40,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-08-10T15:36:26-04:00\n", + "Last updated: 2021-07-07T19:48:45.218298-04:00\n", "\n", - "CPython 3.8.5\n", - "IPython 7.16.1\n", + "Python implementation: CPython\n", + "Python version : 3.9.6\n", + "IPython version : 7.25.0\n", "\n", - "compiler : Clang 10.0.1 \n", - "system : Darwin\n", - "release : 19.6.0\n", - "machine : x86_64\n", - "processor : i386\n", - "CPU cores : 8\n", - "interpreter: 64bit\n" + "Compiler : Clang 11.1.0 \n", + "OS : Darwin\n", + "Release : 20.5.0\n", + "Machine : x86_64\n", + "Processor : i386\n", + "CPU cores : 8\n", + "Architecture: 64bit\n", + "\n" ] } ], @@ -57,11 +64,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-08-10T19:36:27.379418Z", - "start_time": "2020-08-10T19:36:26.875192Z" + "end_time": "2021-07-07T23:48:45.574629Z", + "start_time": "2021-07-07T23:48:45.243003Z" } }, "outputs": [ @@ -69,8 +76,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "watermark 2.0.2\n", - "nhgisxwalk 0.0.8\n", + "Watermark: 2.2.0\n", + "\n", + "nhgisxwalk: 0.1.1\n", + "json : 2.0.9\n", "\n" ] } @@ -87,11 +96,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-08-10T19:36:27.410167Z", - "start_time": "2020-08-10T19:36:27.382018Z" + "end_time": "2021-07-07T23:48:45.600451Z", + "start_time": "2021-07-07T23:48:45.576228Z" } }, "outputs": [ @@ -215,11 +224,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-08-10T19:36:27.453438Z", - "start_time": "2020-08-10T19:36:27.412881Z" + "end_time": "2021-07-07T23:48:45.650984Z", + "start_time": "2021-07-07T23:48:45.601760Z" } }, "outputs": [ @@ -317,7 +326,7 @@ "4 B B.2 Y.2 Y 1.0 80.0 30.0" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -336,11 +345,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-08-10T19:36:27.490828Z", - "start_time": "2020-08-10T19:36:27.454914Z" + "end_time": "2021-07-07T23:48:45.692119Z", + "start_time": "2021-07-07T23:48:45.653905Z" } }, "outputs": [ @@ -412,7 +421,7 @@ "3 B Y 0.615385 0.600000" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -450,7 +459,7 @@ "id": "ee43d7cfc266d2bfcba379cb572107f4" }, "kernelspec": { - "display_name": "Python [conda env:nhgis] *", + "display_name": "Python [conda env:nhgis]", "language": "python", "name": "conda-env-nhgis-py" }, @@ -464,7 +473,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.6" } }, "nbformat": 4, From 8bf75eac45bfdb05cfc04147c1b6c28b2e9f520f Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 7 Jul 2021 20:02:27 -0400 Subject: [PATCH 13/13] reduce builds from codecov report --- codecov.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/codecov.yml b/codecov.yml index 6d1c1e1..c7b918e 100644 --- a/codecov.yml +++ b/codecov.yml @@ -4,7 +4,7 @@ # https://github.com/ipums/nhgisxwalk codecov: notify: - after_n_builds: 9 + after_n_builds: 3 coverage: range: 50...90 round: nearest @@ -22,5 +22,5 @@ coverage: comment: layout: "reach, diff, files" behavior: once - after_n_builds: 9 + after_n_builds: 3 require_changes: true