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rajadain/wbd.ipynb

Created August 3, 2022 15:58
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Demo of using HyRiver to query Watershed Boundary Data
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wbd.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "d463edaf-cd55-44a3-81a3-442c9d22b357",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Building wheels for collected packages: rioxarray\n",
" Building wheel for rioxarray (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for rioxarray: filename=rioxarray-0.11.1-py2.py3-none-any.whl size=50938 sha256=1226dc9643112c4d3e4de61c8bd69c675f746582f160d50837ce656a9c6b75c8\n",
" Stored in directory: /Users/ttuhinanshu/Library/Caches/pip/wheels/92/59/40/c02b0382117a145e3114b6594d4c10f7bbfcf9532f505b7e19\n",
"Successfully built rioxarray\n",
"Installing collected packages: python-forge, cchardet, Brotli, appdirs, affine, url-normalize, ujson, typing_extensions, toolz, snuggs, shapely, pyyaml, pyproj, pygeos, pyarrow, networkx, munch, multidict, locket, itsdangerous, fsspec, frozenlist, exceptiongroup, cloudpickle, click, cftime, async-timeout, yarl, pydantic, pycares, partd, owslib, netcdf4, cytoolz, cligj, click-plugins, cattrs, aiosqlite, aiosignal, xarray, requests-cache, rasterio, fiona, dask, aiohttp, aiodns, rioxarray, geopandas, aiohttp-client-cache, pygeoutils, async-retriever, pygeoogc, pynhd\n",
"Successfully installed Brotli-1.0.9 affine-2.3.1 aiodns-3.0.0 aiohttp-3.8.1 aiohttp-client-cache-0.7.3 aiosignal-1.2.0 aiosqlite-0.17.0 appdirs-1.4.4 async-retriever-0.3.4 async-timeout-4.0.2 cattrs-22.1.0 cchardet-2.1.7 cftime-1.6.1 click-8.1.3 click-plugins-1.1.1 cligj-0.7.2 cloudpickle-2.1.0 cytoolz-0.12.0 dask-2022.7.1 exceptiongroup-1.0.0rc8 fiona-1.8.21 frozenlist-1.3.1 fsspec-2022.7.1 geopandas-0.11.1 itsdangerous-2.1.2 locket-1.0.0 multidict-6.0.2 munch-2.5.0 netcdf4-1.6.0 networkx-2.8.5 owslib-0.25.0 partd-1.2.0 pyarrow-9.0.0 pycares-4.2.1 pydantic-1.9.1 pygeoogc-0.13.3 pygeos-0.12.0 pygeoutils-0.13.2 pynhd-0.13.3 pyproj-3.3.1 python-forge-18.6.0 pyyaml-6.0 rasterio-1.3.0 requests-cache-0.9.5 rioxarray-0.11.1 shapely-1.8.2 snuggs-1.4.7 toolz-0.12.0 typing_extensions-4.3.0 ujson-5.4.0 url-normalize-1.4.3 xarray-2022.6.0 yarl-1.8.1\n"
]
}
],
"source": [
"!pip install pynhd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9956c0c0-d4d9-47a7-a208-a485694b5569",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ttuhinanshu/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages/geopandas/_compat.py:112: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n",
" warnings.warn(\n"
]
}
],
"source": [
"from pynhd import WaterData"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a978bd23-583d-44eb-a048-585df590abaa",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Fontconfig warning: ignoring UTF-8: not a valid region tag\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"huc12 = WaterData('huc12')\n",
"phila_schuylkill = huc12.byid('huc12', '020402031008')\n",
"phila_schuylkill.plot()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "994e328f-8cec-4fa4-87cf-963edd70f989",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"huc08 = WaterData('huc08')\n",
"schuylkill = huc08.byid('huc8', '02040203')\n",
"schuylkill.plot()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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