{ "cells": [ { "cell_type": "markdown", "id": "58052860-217f-45d9-87c3-938b2416cd7e", "metadata": {}, "source": [ "# Introducing the New CyberGISX Python 3 0.9.4 Kernel\n", "\n", "**Authors:** Alex Michels\n", "\n", "**Last Updated:** July 25th, 2023\n", "\n", "This notebook introduces the brand new Python 3-0.9.4 Kernel!\n", "\n", "## Summary:\n", "\n", "* **Tag notebooks that rely on the \"Python 3\" kernel.** The unversioned \"Python 3\" kernel will be updated to use the latest 0.9.4 kernel on release. Please save the notebooks with the \"Python 3-0.9.0\" kernel if you would like to preserve current behavior. Due to updated package versions and new packages, we cannot guaranttee that all notebooks that work with the \"Python 3-0.9.0\" kernel will work with the Python 3-0.9.4 kernel.\n", "* **Python 3-0.9.4 has updated and new packages.** The kernel has lots of updates and new packages like census, gtfspy, and pyrosm!\n", "* **Java now loaded in Python 3 0.9.4 Kernel.** The \"Python 3-0.9.4\" kernel now uses our new `cybergisx-0.9.0` metamodule which loads Java into the environment. This was required for a variety of packages." ] }, { "attachments": { "58e6d45e-3630-407e-994a-30bb9fd9a6f3.png": { "image/png": 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" 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" } }, "cell_type": "markdown", "id": "45fa7405-31cd-4d0e-a6bb-cb607de1f3be", "metadata": {}, "source": [ "### Tag notebooks that rely on the \"Python 3\" kernel.\n", "\n", "The unversioned \"Python 3\" kernel will be updated to use the latest 0.9.4 kernel on release. This is done so that our \"Python 3\" kernel is always using our latest version. We feel this is important so that new users can jump right in and not get confused with our versioned kernel system. By the \"unversioned\" Python 3 kernel, we are referring to the kernel in the image below:\n", "\n", "![Python3Kernel.png](attachment:58e6d45e-3630-407e-994a-30bb9fd9a6f3.png)\n", "\n", "Please save the notebooks with the \"Python 3-0.9.0\" kernel if you would like to preserve current behavior. You can tell which kernel a notebook is using by checking the top-right corner. Notebooks using the Python 3 kernel will look like:\n", "\n", "![Python3KernelNotebook.png](attachment:81dcd3dd-8445-4e53-ba80-c1dd486f716d.png)\n", "\n", "To change the notebook to use the \"Python 3-0.9.0\" kernel, simply click the kernel name in the top-right (the area circled in the image above) to bring up a \"Select Kernel\" dropdown menu. Then, select \"Python3-0.9.0\" and save the notebook.\n", "\n", "Due to updated package versions and new packages, we cannot guaranttee that all notebooks that work with the \"Python 3-0.9.0\" will work with the \"Python 3-0.9.4\" kernel. There are a variety of updated and new packages which we will discuss further in the next section, so some code may work with both kernels and other code may not." ] }, { "cell_type": "markdown", "id": "581f3e9e-4feb-44b7-a7d5-60ad4c371250", "metadata": { "tags": [] }, "source": [ "## Python 3-0.9.4 has updated and new packages\n", "\n", "The Python 3-0.9.4 kernel has a variety of new and updated packages! A few of the new packages:\n", "\n", "* census\n", "* dgl & dglgo\n", "* googlemaps\n", "* gtfspy\n", "* movingpandas\n", "* plotnine\n", "* r5py\n", "* spatial_access\n", "* torch & torch_geometric\n", "* urbanpy\n", "\n", "A few of the notable updates/upgrades are:\n", "\n", "* geopandas 0.10.2 -> 0.13.2\n", "* osmnx 1.1.2 -> 1.2.2\n", "* pandas 1.3.5-> 2.0.0\n", "* scikit-learn 1.0.2 -> 1.2.2\n", "\n", "\n", "For a full read-out of the kernel we can use conda:" ] }, { "cell_type": "code", "execution_count": 1, "id": "f0b25730-e58e-428f-a29a-96c3ca894d01", "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# packages in environment at /cvmfs/cybergis.illinois.edu/software/conda/cybergisx/python3-0.9.4:\n", "#\n", "# Name Version Build Channel\n", "_libgcc_mutex 0.1 conda_forge conda-forge\n", "_openmp_mutex 4.5 2_kmp_llvm conda-forge\n", "_tflow_select 2.3.0 mkl defaults\n", "abseil-cpp 20210324.2 h9c3ff4c_0 conda-forge\n", "absl-py 1.4.0 pyhd8ed1ab_0 conda-forge\n", "access 1.1.9 pypi_0 pypi\n", "affine 2.4.0 pyhd8ed1ab_0 conda-forge\n", "aiobotocore 2.5.0 pyhd8ed1ab_0 conda-forge\n", "aiohttp 3.8.4 py38h01eb140_1 conda-forge\n", "aioitertools 0.11.0 pyhd8ed1ab_0 conda-forge\n", "aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge\n", "alabaster 0.7.13 pypi_0 pypi\n", "alsa-lib 1.2.3.2 h166bdaf_0 conda-forge\n", "amply 0.1.5 pyhd8ed1ab_0 conda-forge\n", "annotated-types 0.6.0 pypi_0 pypi\n", "anyio 3.6.1 pypi_0 pypi\n", "anyjson 0.3.3 py38h578d9bd_1003 conda-forge\n", "appdirs 1.4.4 pyh9f0ad1d_0 conda-forge\n", "arcgis 1.9.1 py38_2328 Esri\n", "argon2-cffi 21.3.0 pyhd8ed1ab_0 conda-forge\n", "argon2-cffi-bindings 21.2.0 py38h0a891b7_3 conda-forge\n", "argparse 1.4.0 pypi_0 pypi\n", "arpack 3.7.0 hdefa2d7_2 conda-forge\n", "arrow 1.2.3 pyhd8ed1ab_0 conda-forge\n", "arrow-cpp 6.0.1 py38h87e2ab4_3_cpu conda-forge\n", "astor 0.8.1 pyh9f0ad1d_0 conda-forge\n", "asttokens 2.2.1 pyhd8ed1ab_0 conda-forge\n", "astunparse 1.6.3 pyhd8ed1ab_0 conda-forge\n", "async-lru 2.0.4 pypi_0 pypi\n", "async-timeout 4.0.2 pyhd8ed1ab_0 conda-forge\n", "at-spi2-atk 2.38.0 h0630a04_3 conda-forge\n", "at-spi2-core 2.40.3 h0630a04_0 conda-forge\n", "atk-1.0 2.36.0 h3371d22_4 conda-forge\n", "atlas 0.27.0 pypi_0 pypi\n", "attrs 23.1.0 pyh71513ae_1 conda-forge\n", "autopep8 2.0.4 pypi_0 pypi\n", "aws-c-auth 0.6.8 hfef2836_0 conda-forge\n", "aws-c-cal 0.5.12 h70efedd_7 conda-forge\n", "aws-c-common 0.6.17 h7f98852_0 conda-forge\n", "aws-c-compression 0.2.14 h7c7754b_7 conda-forge\n", "aws-c-event-stream 0.2.7 hb80ed28_31 conda-forge\n", "aws-c-http 0.6.10 h58a30cf_2 conda-forge\n", "aws-c-io 0.10.13 he836878_5 conda-forge\n", "aws-c-mqtt 0.7.9 h042a236_0 conda-forge\n", "aws-c-s3 0.1.27 h4f4cd48_12 conda-forge\n", "aws-c-sdkutils 0.1.1 h7c7754b_4 conda-forge\n", "aws-checksums 0.1.12 h7c7754b_6 conda-forge\n", "aws-crt-cpp 0.17.9 hc7d31a4_1 conda-forge\n", "aws-requests-auth 0.4.3 py_0 defaults\n", "aws-sdk-cpp 1.9.154 h77f1c7e_0 conda-forge\n", "babel 2.12.1 pyhd8ed1ab_1 conda-forge\n", "backcall 0.2.0 pyh9f0ad1d_0 conda-forge\n", "backports 1.1 pyhd3eb1b0_0 defaults\n", "backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge\n", "backports.zoneinfo 0.2.1 py38h0a891b7_7 conda-forge\n", "bcrypt 3.2.2 py38h0a891b7_1 conda-forge\n", "beautifulsoup4 4.12.2 pyha770c72_0 conda-forge\n", "black 21.12b0 pyhd8ed1ab_0 conda-forge\n", "blas 2.117 openblas conda-forge\n", "blas-devel 3.9.0 17_linux64_openblas conda-forge\n", "bleach 6.0.0 pyhd8ed1ab_0 conda-forge\n", "blinker 1.6.2 pyhd8ed1ab_0 conda-forge\n", "blis 0.7.11 pypi_0 pypi\n", "blosc 1.21.4 h0f2a231_0 conda-forge\n", "blosc2 2.0.0 pypi_0 pypi\n", "bokeh 3.1.1 pyhd8ed1ab_0 conda-forge\n", "boost 1.74.0 py38h2b96118_5 conda-forge\n", "boost-cpp 1.74.0 h312852a_4 conda-forge\n", "boto3 1.26.76 pyhd8ed1ab_0 conda-forge\n", "botocore 1.29.76 pyhd8ed1ab_0 conda-forge\n", "branca 0.6.0 pyhd8ed1ab_0 conda-forge\n", 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pypi\n", "sphinxcontrib-applehelp 1.0.4 pypi_0 pypi\n", "sphinxcontrib-devhelp 1.0.2 pypi_0 pypi\n", "sphinxcontrib-htmlhelp 2.0.1 pypi_0 pypi\n", "sphinxcontrib-jsmath 1.0.1 pypi_0 pypi\n", "sphinxcontrib-qthelp 1.0.3 pypi_0 pypi\n", "sphinxcontrib-serializinghtml 1.1.5 pypi_0 pypi\n", "spint 1.0.7 pyhd8ed1ab_0 conda-forge\n", "splot 1.1.5.post1 pyhd8ed1ab_0 conda-forge\n", "spopt 0.5.0 pyhd8ed1ab_0 conda-forge\n", "spreg 1.3.2 pyhd8ed1ab_0 conda-forge\n", "spvcm 0.3.0 py_0 defaults\n", "sql 2022.4.0 pypi_0 pypi\n", "sqlalchemy 2.0.16 py38h01eb140_0 conda-forge\n", "sqlalchemy-utils 0.41.1 pyhd8ed1ab_0 conda-forge\n", "sqlalchemy-utils-arrow 0.41.1 pyhd8ed1ab_0 conda-forge\n", "sqlalchemy-utils-babel 0.41.1 pyhd8ed1ab_0 conda-forge\n", "sqlalchemy-utils-base 0.41.1 pyhd8ed1ab_0 conda-forge\n", "sqlalchemy-utils-color 0.41.1 pyhd8ed1ab_0 conda-forge\n", "sqlalchemy-utils-encrypted 0.41.1 pyhd8ed1ab_0 conda-forge\n", "sqlalchemy-utils-intervals 0.41.1 pyhd8ed1ab_0 conda-forge\n", "sqlalchemy-utils-password 0.41.1 pyhd8ed1ab_0 conda-forge\n", "sqlalchemy-utils-pendulum 0.41.1 pyhd8ed1ab_0 conda-forge\n", "sqlalchemy-utils-phone 0.41.1 pyhd8ed1ab_0 conda-forge\n", "sqlalchemy-utils-timezone 0.41.1 pyhd8ed1ab_0 conda-forge\n", "sqlalchemy-utils-url 0.41.1 pyhd8ed1ab_0 conda-forge\n", "sqlite 3.42.0 h2c6b66d_0 conda-forge\n", "sqlparse 0.4.4 pyhd8ed1ab_0 conda-forge\n", "srsly 2.4.8 pypi_0 pypi\n", "stack_data 0.6.2 pyhd8ed1ab_0 conda-forge\n", "statsmodels 0.13.5 py38h26c90d9_2 conda-forge\n", "suitesparse 5.10.1 h9e50725_1 conda-forge\n", "sympy 1.12 pypyh9d50eac_103 conda-forge\n", "tabulate 0.9.0 pypi_0 pypi\n", "tbb 2021.8.0 hdb19cb5_0 defaults\n", "tblib 1.7.0 pyhd8ed1ab_0 conda-forge\n", "tenacity 8.2.2 pyhd8ed1ab_0 conda-forge\n", "tensorboard 2.13.0 pypi_0 pypi\n", "tensorboard-data-server 0.7.1 pypi_0 pypi\n", "tensorboard-plugin-wit 1.8.1 pyhd8ed1ab_0 conda-forge\n", "tensorflow 2.13.1 pypi_0 pypi\n", "tensorflow-estimator 2.13.0 pypi_0 pypi\n", "tensorflow-io-gcs-filesystem 0.34.0 pypi_0 pypi\n", "termcolor 2.3.0 pyhd8ed1ab_0 conda-forge\n", "terminado 0.17.1 pyh41d4057_0 conda-forge\n", "testpath 0.6.0 pyhd8ed1ab_0 conda-forge\n", "texttable 1.6.7 pyhd8ed1ab_0 conda-forge\n", "thinc 8.2.1 pypi_0 pypi\n", "threadpool 1.3.2 pypi_0 pypi\n", "threadpoolctl 3.1.0 pyh8a188c0_0 conda-forge\n", "tifffile 2021.11.2 pyhd8ed1ab_0 conda-forge\n", "tiktoken 0.4.0 pypi_0 pypi\n", "tiledb 2.3.4 he87e0bf_0 conda-forge\n", "tinycss2 1.1.1 pypi_0 pypi\n", "tk 8.6.12 h27826a3_0 conda-forge\n", "tobler 0.10 pyhd8ed1ab_0 conda-forge\n", "tokenizers 0.14.1 pypi_0 pypi\n", "toml 0.10.2 pyhd8ed1ab_0 conda-forge\n", "tomli 1.2.2 pyhd8ed1ab_0 conda-forge\n", "toolz 0.12.0 pyhd8ed1ab_0 conda-forge\n", "torch 2.1.0 pypi_0 pypi\n", "torch-geometric 2.3.1 pypi_0 pypi\n", "torchvision 0.16.0 pypi_0 pypi\n", "tornado 6.3.2 py38h01eb140_0 conda-forge\n", "tqdm 4.65.0 pyhd8ed1ab_1 conda-forge\n", "traitlets 5.9.0 pyhd8ed1ab_0 conda-forge\n", "traittypes 0.2.1 pyh9f0ad1d_2 conda-forge\n", "transformers 4.34.0 pypi_0 pypi\n", "triton 2.1.0 pypi_0 pypi\n", "typed-ast 1.5.4 py38h0a891b7_1 conda-forge\n", "typer 0.9.0 pypi_0 pypi\n", "typing-extensions 4.8.0 pypi_0 pypi\n", "typing_utils 0.1.0 pyhd8ed1ab_0 conda-forge\n", "tzcode 2023c h0b41bf4_0 conda-forge\n", "tzdata 2023c h71feb2d_0 conda-forge\n", "uc-micro-py 1.0.1 pyhd8ed1ab_0 conda-forge\n", "ujson 5.7.0 py38h8dc9893_0 conda-forge\n", "unicodedata2 15.0.0 py38h0a891b7_0 conda-forge\n", "urbanaccess 0.2.2 pyhd3deb0d_0 conda-forge\n", "urbanpy 0.2.1 pypi_0 pypi\n", "uri-template 1.3.0 pypi_0 pypi\n", "uritemplate 4.1.1 pyhd8ed1ab_0 conda-forge\n", "urllib3 1.26.16 py38h06a4308_0 defaults\n", "us 3.1.1 pypi_0 pypi\n", "versioneer 0.28 pyhd8ed1ab_0 conda-forge\n", "vincent 0.4.4 py_1 Esri\n", "wasabi 1.1.2 pypi_0 pypi\n", "wcwidth 0.2.6 pyhd8ed1ab_0 conda-forge\n", "weasel 0.3.2 pypi_0 pypi\n", "webcolors 1.13 pypi_0 pypi\n", "webencodings 0.5.1 py_1 conda-forge\n", "websocket-client 1.4.0 pypi_0 pypi\n", "werkzeug 2.3.6 pyhd8ed1ab_0 conda-forge\n", "wget 1.21.4 h91b91d3_1 defaults\n", "wheel 0.40.0 pyhd8ed1ab_0 conda-forge\n", "whitebox 2.3.1 pyhd8ed1ab_0 conda-forge\n", "widgetsnbextension 3.6.4 pypi_0 pypi\n", "wrapt 1.15.0 py38h1de0b5d_0 conda-forge\n", "xarray 2022.11.0 py38h06a4308_0 defaults\n", "xerces-c 3.2.3 h9d8b166_3 conda-forge\n", "xlrd 2.0.1 pyhd8ed1ab_3 conda-forge\n", "xlsxwriter 3.1.2 pyhd8ed1ab_0 conda-forge\n", "xorg-fixesproto 5.0 h7f98852_1002 conda-forge\n", "xorg-inputproto 2.3.2 h7f98852_1002 conda-forge\n", "xorg-kbproto 1.0.7 h7f98852_1002 conda-forge\n", "xorg-libice 1.1.1 hd590300_0 conda-forge\n", "xorg-libsm 1.2.4 h7391055_0 conda-forge\n", "xorg-libx11 1.8.6 h8ee46fc_0 conda-forge\n", "xorg-libxau 1.0.11 hd590300_0 conda-forge\n", "xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge\n", "xorg-libxext 1.3.4 h0b41bf4_2 conda-forge\n", "xorg-libxfixes 5.0.3 h7f98852_1004 conda-forge\n", "xorg-libxi 1.7.10 h7f98852_0 conda-forge\n", "xorg-libxrender 0.9.10 h7f98852_1003 conda-forge\n", "xorg-libxtst 1.2.3 h7f98852_1002 conda-forge\n", "xorg-recordproto 1.14.2 h7f98852_1002 conda-forge\n", "xorg-renderproto 0.11.1 h7f98852_1002 conda-forge\n", "xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge\n", "xorg-xproto 7.0.31 h7f98852_1007 conda-forge\n", "xyzservices 2023.5.0 pyhd8ed1ab_1 conda-forge\n", "xz 5.2.10 h5eee18b_1 defaults\n", "yaml 0.2.5 h7f98852_2 conda-forge\n", "yarl 1.9.2 py38h01eb140_0 conda-forge\n", "zeromq 4.3.4 h9c3ff4c_1 conda-forge\n", "zfp 0.5.5 h9c3ff4c_8 conda-forge\n", "zict 3.0.0 pyhd8ed1ab_0 conda-forge\n", "zipp 3.15.0 pyhd8ed1ab_0 conda-forge\n", "zlib 1.2.13 hd590300_5 conda-forge\n", "zlib-ng 2.0.7 h0b41bf4_0 conda-forge\n", "zstandard 0.19.0 py38h5945529_1 conda-forge\n", "zstd 1.5.2 h3eb15da_6 conda-forge\n" ] } ], "source": [ "!conda list -p /cvmfs/cybergis.illinois.edu/software/conda/cybergisx/python3-0.9.4" ] }, { "cell_type": "markdown", "id": "c8bf9cd5-1c62-4c85-b223-f0bf0b43cd68", "metadata": {}, "source": [ "Let's briefly splotlight two new packages: `us` and `urbanpy`!\n", "\n", "#### **us**\n", "\n", "The `us` package is very useful for quickly retrieving data our US states and territories. [For more information check out their README](https://github.com/unitedstates/python-us). For example, we can easily loop through the states and their FIPS codes:" ] }, { "cell_type": "code", "execution_count": 2, "id": "70ce7b95-7432-4fa3-96b5-300c1f762b7b", "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Alabama 01\n", "Alaska 02\n", "Arizona 04\n", "Arkansas 05\n", "California 06\n", "Colorado 08\n", "Connecticut 09\n", "Delaware 10\n", "Florida 12\n", "Georgia 13\n", "Hawaii 15\n", "Idaho 16\n", "Illinois 17\n", "Indiana 18\n", "Iowa 19\n", "Kansas 20\n", "Kentucky 21\n", "Louisiana 22\n", "Maine 23\n", "Maryland 24\n", "Massachusetts 25\n", "Michigan 26\n", "Minnesota 27\n", "Mississippi 28\n", "Missouri 29\n", "Montana 30\n", "Nebraska 31\n", "Nevada 32\n", "New Hampshire 33\n", "New Jersey 34\n", "New Mexico 35\n", "New York 36\n", "North Carolina 37\n", "North Dakota 38\n", "Ohio 39\n", "Oklahoma 40\n", "Oregon 41\n", "Pennsylvania 42\n", "Rhode Island 44\n", "South Carolina 45\n", "South Dakota 46\n", "Tennessee 47\n", "Texas 48\n", "Utah 49\n", "Vermont 50\n", "Virginia 51\n", "Washington 53\n", "West Virginia 54\n", "Wisconsin 55\n", "Wyoming 56\n" ] } ], "source": [ "import us\n", "\n", "for state in us.states.STATES:\n", " print(state, state.fips)" ] }, { "cell_type": "markdown", "id": "747402c0-8b4c-4786-9506-dcdecf5fab07", "metadata": {}, "source": [ "The package can also easily grab shapefiles for us. Let's use `us` to get the URL to Illinois counties and then plot that data with geopandas:" ] }, { "cell_type": "code", "execution_count": 3, "id": "a40eeebd-ee39-4ed4-9191-100f49af204a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'tract': 'https://www2.census.gov/geo/tiger/TIGER2010/TRACT/2010/tl_2010_17_tract10.zip',\n", " 'cd': 'https://www2.census.gov/geo/tiger/TIGER2010/CD/111/tl_2010_17_cd111.zip',\n", " 'county': 'https://www2.census.gov/geo/tiger/TIGER2010/COUNTY/2010/tl_2010_17_county10.zip',\n", " 'state': 'https://www2.census.gov/geo/tiger/TIGER2010/STATE/2010/tl_2010_17_state10.zip',\n", " 'zcta': 'https://www2.census.gov/geo/tiger/TIGER2010/ZCTA5/2010/tl_2010_17_zcta510.zip',\n", " 'block': 'https://www2.census.gov/geo/tiger/TIGER2010/TABBLOCK/2010/tl_2010_17_tabblock10.zip',\n", " 'blockgroup': 'https://www2.census.gov/geo/tiger/TIGER2010/BG/2010/tl_2010_17_bg10.zip'}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "us.states.IL.shapefile_urls()" ] }, { "cell_type": "code", "execution_count": 4, "id": "9f4cd6f0-9beb-4318-9d7c-db5546d34393", "metadata": {}, "outputs": [ { "data": { "text/html": [ "