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Created September 19, 2023 14:38
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Hurricane Ike Maximum Water Levels\n",
"Compute the maximum water level during Hurricane Ike on a 18 million element triangular mesh storm surge model. Plot the results using [HoloViz](https://holoviz.org/) TriMesh rendering with Datashader. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import numpy as np\n",
"import pandas as pd\n",
"import fsspec"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read the data using the cloud-friendly zarr data format"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"fs = fsspec.filesystem('s3', anon=False, requester_pays=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"fs_osn = fsspec.filesystem('s3', anon=True, client_kwargs=dict(endpoint_url='https://ncsa.osn.xsede.org'))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"ncfile_on_s3 = 's3://floodid-louisiana-model-data/gstofs/sample_run/fort.63.nc'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.781632181"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fs.size(ncfile_on_s3)/1e9 #GB"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.88 s, sys: 196 ms, total: 3.07 s\n",
"Wall time: 5.12 s\n"
]
}
],
"source": [
"%%time\n",
"ds = xr.open_dataset(fs.open(ncfile_on_s3), drop_variables=['nvel'], chunks={'time':1, 'node':511400})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'chunksizes': (1, 511400),\n",
" 'fletcher32': False,\n",
" 'shuffle': True,\n",
" 'zlib': True,\n",
" 'complevel': 2,\n",
" 'source': '<File-like object S3FileSystem, floodid-louisiana-model-data/gstofs/sample_run/fort.63.nc>',\n",
" 'original_shape': (40, 12784991),\n",
" 'dtype': dtype('<f8'),\n",
" '_FillValue': -99999.0,\n",
" 'coordinates': 'time y x'}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.zeta.encoding"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;zeta&#x27; (time: 40, node: 12784991)&gt;\n",
"dask.array&lt;open_dataset-9d8ed5545d2239c017b6d200f9ff4ff0zeta, shape=(40, 12784991), dtype=float64, chunksize=(1, 511400), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2016-09-01T12:00:00 2016-09-02 ... 2016-09-21\n",
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" y (node) float64 dask.array&lt;chunksize=(511400,), meta=np.ndarray&gt;\n",
"Dimensions without coordinates: node\n",
"Attributes:\n",
" long_name: water surface elevation above geoid\n",
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" location: node\n",
" mesh: adcirc_mesh\n",
" units: m</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'zeta'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 40</li><li><span>node</span>: 12784991</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2e2bcd26-e105-48b4-9173-ffde19a2f5c9' class='xr-array-in' type='checkbox' checked><label for='section-2e2bcd26-e105-48b4-9173-ffde19a2f5c9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 511400), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 3.81 GiB </td>\n",
" <td> 3.90 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (40, 12784991) </td>\n",
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" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1000 chunks in 2 graph layers </td>\n",
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"</table></div></div></li><li class='xr-section-item'><input id='section-de83a7e5-f36d-4f81-8d8e-27b7fe982bd8' class='xr-section-summary-in' type='checkbox' checked><label for='section-de83a7e5-f36d-4f81-8d8e-27b7fe982bd8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-09-01T12:00:00 ... 2016-09-21</div><input id='attrs-c7dcf30a-061c-44c5-a8e9-a5a9c8ce5f54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7dcf30a-061c-44c5-a8e9-a5a9c8ce5f54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-281d6788-672d-4c1e-b913-3a4d369f468e' class='xr-var-data-in' type='checkbox'><label for='data-281d6788-672d-4c1e-b913-3a4d369f468e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>model time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>base_date :</span></dt><dd>2016-09-01 00:00:00 UTC</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-09-01T12:00:00.000000000&#x27;, &#x27;2016-09-02T00:00:00.000000000&#x27;,\n",
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" &#x27;2016-09-03T12:00:00.000000000&#x27;, &#x27;2016-09-04T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-04T12:00:00.000000000&#x27;, &#x27;2016-09-05T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-05T12:00:00.000000000&#x27;, &#x27;2016-09-06T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-06T12:00:00.000000000&#x27;, &#x27;2016-09-07T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-07T12:00:00.000000000&#x27;, &#x27;2016-09-08T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-08T12:00:00.000000000&#x27;, &#x27;2016-09-09T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-09T12:00:00.000000000&#x27;, &#x27;2016-09-10T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-10T12:00:00.000000000&#x27;, &#x27;2016-09-11T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-11T12:00:00.000000000&#x27;, &#x27;2016-09-12T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-12T12:00:00.000000000&#x27;, &#x27;2016-09-13T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-13T12:00:00.000000000&#x27;, &#x27;2016-09-14T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-14T12:00:00.000000000&#x27;, &#x27;2016-09-15T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-15T12:00:00.000000000&#x27;, &#x27;2016-09-16T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-16T12:00:00.000000000&#x27;, &#x27;2016-09-17T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-17T12:00:00.000000000&#x27;, &#x27;2016-09-18T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-18T12:00:00.000000000&#x27;, &#x27;2016-09-19T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-19T12:00:00.000000000&#x27;, &#x27;2016-09-20T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-20T12:00:00.000000000&#x27;, &#x27;2016-09-21T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(511400,), meta=np.ndarray&gt;</div><input id='attrs-16662a93-073a-4139-bfd6-50aab40ee743' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16662a93-073a-4139-bfd6-50aab40ee743' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d61b667-5b37-4345-856e-7a2eca40624f' class='xr-var-data-in' type='checkbox'><label for='data-9d61b667-5b37-4345-856e-7a2eca40624f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>positive :</span></dt><dd>east</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
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" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 97.54 MiB </td>\n",
" <td> 3.90 MiB </td>\n",
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" \n",
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" <th> Shape </th>\n",
" <td> (12784991,) </td>\n",
" <td> (511400,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
" </tr>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(511400,), meta=np.ndarray&gt;</div><input id='attrs-a72db894-163d-49cd-a6a0-2ef0706c2233' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a72db894-163d-49cd-a6a0-2ef0706c2233' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-706cc58d-88f8-4000-982c-1d68a2c34ba3' class='xr-var-data-in' type='checkbox'><label for='data-706cc58d-88f8-4000-982c-1d68a2c34ba3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>positive :</span></dt><dd>north</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 97.54 MiB </td>\n",
" <td> 3.90 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (12784991,) </td>\n",
" <td> (511400,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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"\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-97f30d1c-68f0-43a2-9714-b0e883405683' class='xr-section-summary-in' type='checkbox' ><label for='section-97f30d1c-68f0-43a2-9714-b0e883405683' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eff741e0-3a47-4aab-8f82-bca89d9a938b' class='xr-index-data-in' type='checkbox'/><label for='index-eff741e0-3a47-4aab-8f82-bca89d9a938b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2016-09-01 12:00:00&#x27;, &#x27;2016-09-02 00:00:00&#x27;,\n",
" &#x27;2016-09-02 12:00:00&#x27;, &#x27;2016-09-03 00:00:00&#x27;,\n",
" &#x27;2016-09-03 12:00:00&#x27;, &#x27;2016-09-04 00:00:00&#x27;,\n",
" &#x27;2016-09-04 12:00:00&#x27;, &#x27;2016-09-05 00:00:00&#x27;,\n",
" &#x27;2016-09-05 12:00:00&#x27;, &#x27;2016-09-06 00:00:00&#x27;,\n",
" &#x27;2016-09-06 12:00:00&#x27;, &#x27;2016-09-07 00:00:00&#x27;,\n",
" &#x27;2016-09-07 12:00:00&#x27;, &#x27;2016-09-08 00:00:00&#x27;,\n",
" &#x27;2016-09-08 12:00:00&#x27;, &#x27;2016-09-09 00:00:00&#x27;,\n",
" &#x27;2016-09-09 12:00:00&#x27;, &#x27;2016-09-10 00:00:00&#x27;,\n",
" &#x27;2016-09-10 12:00:00&#x27;, &#x27;2016-09-11 00:00:00&#x27;,\n",
" &#x27;2016-09-11 12:00:00&#x27;, &#x27;2016-09-12 00:00:00&#x27;,\n",
" &#x27;2016-09-12 12:00:00&#x27;, &#x27;2016-09-13 00:00:00&#x27;,\n",
" &#x27;2016-09-13 12:00:00&#x27;, &#x27;2016-09-14 00:00:00&#x27;,\n",
" &#x27;2016-09-14 12:00:00&#x27;, &#x27;2016-09-15 00:00:00&#x27;,\n",
" &#x27;2016-09-15 12:00:00&#x27;, &#x27;2016-09-16 00:00:00&#x27;,\n",
" &#x27;2016-09-16 12:00:00&#x27;, &#x27;2016-09-17 00:00:00&#x27;,\n",
" &#x27;2016-09-17 12:00:00&#x27;, &#x27;2016-09-18 00:00:00&#x27;,\n",
" &#x27;2016-09-18 12:00:00&#x27;, &#x27;2016-09-19 00:00:00&#x27;,\n",
" &#x27;2016-09-19 12:00:00&#x27;, &#x27;2016-09-20 00:00:00&#x27;,\n",
" &#x27;2016-09-20 12:00:00&#x27;, &#x27;2016-09-21 00:00:00&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-be4405c8-bf4a-4f8c-be5b-49064392c12a' class='xr-section-summary-in' type='checkbox' checked><label for='section-be4405c8-bf4a-4f8c-be5b-49064392c12a' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>water surface elevation above geoid</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>location :</span></dt><dd>node</dd><dt><span>mesh :</span></dt><dd>adcirc_mesh</dd><dt><span>units :</span></dt><dd>m</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'zeta' (time: 40, node: 12784991)>\n",
"dask.array<open_dataset-9d8ed5545d2239c017b6d200f9ff4ff0zeta, shape=(40, 12784991), dtype=float64, chunksize=(1, 511400), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2016-09-01T12:00:00 2016-09-02 ... 2016-09-21\n",
" x (node) float64 dask.array<chunksize=(511400,), meta=np.ndarray>\n",
" y (node) float64 dask.array<chunksize=(511400,), meta=np.ndarray>\n",
"Dimensions without coordinates: node\n",
"Attributes:\n",
" long_name: water surface elevation above geoid\n",
" standard_name: sea_surface_height_above_geoid\n",
" location: node\n",
" mesh: adcirc_mesh\n",
" units: m"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.zeta"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#fs = fsspec.filesystem('s3', anon=True, skip_instance_cache=True, client_kwargs={'endpoint_url': 'https://renc.osn.xsede.org'})\n",
"#ds = xr.open_dataset(fs.get_mapper('s3://rsignellbucket2/esip/adcirc/ike'), engine='zarr',\n",
"# backend_kwargs=dict(consolidated=False), chunks={'time':90})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#fs = fsspec.filesystem('s3', anon=True, skip_instance_cache=True, client_kwargs={'endpoint_url': 'https://mghp.osn.xsede.org'})\n",
"#ds = xr.open_dataset(fs.get_mapper('s3://rsignellbucket1/esip/adcirc/ike'), engine='zarr',\n",
"# backend_kwargs=dict(consolidated=False), chunks={'time':90})"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;zeta&#x27; (time: 40, node: 12784991)&gt;\n",
"dask.array&lt;open_dataset-9d8ed5545d2239c017b6d200f9ff4ff0zeta, shape=(40, 12784991), dtype=float64, chunksize=(1, 511400), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2016-09-01T12:00:00 2016-09-02 ... 2016-09-21\n",
" x (node) float64 dask.array&lt;chunksize=(511400,), meta=np.ndarray&gt;\n",
" y (node) float64 dask.array&lt;chunksize=(511400,), meta=np.ndarray&gt;\n",
"Dimensions without coordinates: node\n",
"Attributes:\n",
" long_name: water surface elevation above geoid\n",
" standard_name: sea_surface_height_above_geoid\n",
" location: node\n",
" mesh: adcirc_mesh\n",
" units: m</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'zeta'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 40</li><li><span>node</span>: 12784991</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7003663c-0614-43fb-b7f1-922cbe2a228f' class='xr-array-in' type='checkbox' checked><label for='section-7003663c-0614-43fb-b7f1-922cbe2a228f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 511400), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
" <tr>\n",
" <td>\n",
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" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 3.81 GiB </td>\n",
" <td> 3.90 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (40, 12784991) </td>\n",
" <td> (1, 511400) </td>\n",
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" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1000 chunks in 2 graph layers </td>\n",
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" <tr>\n",
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" &#x27;2016-09-02T12:00:00.000000000&#x27;, &#x27;2016-09-03T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-03T12:00:00.000000000&#x27;, &#x27;2016-09-04T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-04T12:00:00.000000000&#x27;, &#x27;2016-09-05T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-05T12:00:00.000000000&#x27;, &#x27;2016-09-06T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-06T12:00:00.000000000&#x27;, &#x27;2016-09-07T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-07T12:00:00.000000000&#x27;, &#x27;2016-09-08T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-08T12:00:00.000000000&#x27;, &#x27;2016-09-09T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-09T12:00:00.000000000&#x27;, &#x27;2016-09-10T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-10T12:00:00.000000000&#x27;, &#x27;2016-09-11T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-11T12:00:00.000000000&#x27;, &#x27;2016-09-12T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-12T12:00:00.000000000&#x27;, &#x27;2016-09-13T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-13T12:00:00.000000000&#x27;, &#x27;2016-09-14T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-14T12:00:00.000000000&#x27;, &#x27;2016-09-15T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-15T12:00:00.000000000&#x27;, &#x27;2016-09-16T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-16T12:00:00.000000000&#x27;, &#x27;2016-09-17T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-17T12:00:00.000000000&#x27;, &#x27;2016-09-18T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-18T12:00:00.000000000&#x27;, &#x27;2016-09-19T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-19T12:00:00.000000000&#x27;, &#x27;2016-09-20T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-20T12:00:00.000000000&#x27;, &#x27;2016-09-21T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(511400,), meta=np.ndarray&gt;</div><input id='attrs-35b19260-55d0-4dd0-ac35-30ac200b6ae6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35b19260-55d0-4dd0-ac35-30ac200b6ae6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45a71996-d4d2-4b85-a472-0e75147310ad' class='xr-var-data-in' type='checkbox'><label for='data-45a71996-d4d2-4b85-a472-0e75147310ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>positive :</span></dt><dd>east</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 97.54 MiB </td>\n",
" <td> 3.90 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (12784991,) </td>\n",
" <td> (511400,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(511400,), meta=np.ndarray&gt;</div><input id='attrs-2a05b1fd-8543-4bee-b4b5-53d128d6bb75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a05b1fd-8543-4bee-b4b5-53d128d6bb75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a6ce7e5-40ce-48d9-8f59-ea119ed0e809' class='xr-var-data-in' type='checkbox'><label for='data-3a6ce7e5-40ce-48d9-8f59-ea119ed0e809' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>positive :</span></dt><dd>north</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 97.54 MiB </td>\n",
" <td> 3.90 MiB </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (12784991,) </td>\n",
" <td> (511400,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
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" <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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"\n",
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"\n",
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"</svg>\n",
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" </tr>\n",
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-790bd809-bfca-4beb-a073-b5356a0dfd47' class='xr-section-summary-in' type='checkbox' ><label for='section-790bd809-bfca-4beb-a073-b5356a0dfd47' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-30649ebd-2ab3-4013-bfae-08ac9f295976' class='xr-index-data-in' type='checkbox'/><label for='index-30649ebd-2ab3-4013-bfae-08ac9f295976' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2016-09-01 12:00:00&#x27;, &#x27;2016-09-02 00:00:00&#x27;,\n",
" &#x27;2016-09-02 12:00:00&#x27;, &#x27;2016-09-03 00:00:00&#x27;,\n",
" &#x27;2016-09-03 12:00:00&#x27;, &#x27;2016-09-04 00:00:00&#x27;,\n",
" &#x27;2016-09-04 12:00:00&#x27;, &#x27;2016-09-05 00:00:00&#x27;,\n",
" &#x27;2016-09-05 12:00:00&#x27;, &#x27;2016-09-06 00:00:00&#x27;,\n",
" &#x27;2016-09-06 12:00:00&#x27;, &#x27;2016-09-07 00:00:00&#x27;,\n",
" &#x27;2016-09-07 12:00:00&#x27;, &#x27;2016-09-08 00:00:00&#x27;,\n",
" &#x27;2016-09-08 12:00:00&#x27;, &#x27;2016-09-09 00:00:00&#x27;,\n",
" &#x27;2016-09-09 12:00:00&#x27;, &#x27;2016-09-10 00:00:00&#x27;,\n",
" &#x27;2016-09-10 12:00:00&#x27;, &#x27;2016-09-11 00:00:00&#x27;,\n",
" &#x27;2016-09-11 12:00:00&#x27;, &#x27;2016-09-12 00:00:00&#x27;,\n",
" &#x27;2016-09-12 12:00:00&#x27;, &#x27;2016-09-13 00:00:00&#x27;,\n",
" &#x27;2016-09-13 12:00:00&#x27;, &#x27;2016-09-14 00:00:00&#x27;,\n",
" &#x27;2016-09-14 12:00:00&#x27;, &#x27;2016-09-15 00:00:00&#x27;,\n",
" &#x27;2016-09-15 12:00:00&#x27;, &#x27;2016-09-16 00:00:00&#x27;,\n",
" &#x27;2016-09-16 12:00:00&#x27;, &#x27;2016-09-17 00:00:00&#x27;,\n",
" &#x27;2016-09-17 12:00:00&#x27;, &#x27;2016-09-18 00:00:00&#x27;,\n",
" &#x27;2016-09-18 12:00:00&#x27;, &#x27;2016-09-19 00:00:00&#x27;,\n",
" &#x27;2016-09-19 12:00:00&#x27;, &#x27;2016-09-20 00:00:00&#x27;,\n",
" &#x27;2016-09-20 12:00:00&#x27;, &#x27;2016-09-21 00:00:00&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-755a25cb-8996-40c4-b180-38bbd77ceeb6' class='xr-section-summary-in' type='checkbox' checked><label for='section-755a25cb-8996-40c4-b180-38bbd77ceeb6' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>water surface elevation above geoid</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>location :</span></dt><dd>node</dd><dt><span>mesh :</span></dt><dd>adcirc_mesh</dd><dt><span>units :</span></dt><dd>m</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'zeta' (time: 40, node: 12784991)>\n",
"dask.array<open_dataset-9d8ed5545d2239c017b6d200f9ff4ff0zeta, shape=(40, 12784991), dtype=float64, chunksize=(1, 511400), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2016-09-01T12:00:00 2016-09-02 ... 2016-09-21\n",
" x (node) float64 dask.array<chunksize=(511400,), meta=np.ndarray>\n",
" y (node) float64 dask.array<chunksize=(511400,), meta=np.ndarray>\n",
"Dimensions without coordinates: node\n",
"Attributes:\n",
" long_name: water surface elevation above geoid\n",
" standard_name: sea_surface_height_above_geoid\n",
" location: node\n",
" mesh: adcirc_mesh\n",
" units: m"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds['zeta']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many GB of sea surface height data do we have?"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.09119712"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds['zeta'].nbytes/1.e9"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use a Dask Cluster to speed up calculations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use a Dask Distributed LocalCluster"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#from dask.distributed import LocalCluster, Client"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"#cluster = LocalCluster()\n",
"#client = Client(cluster)\n",
"#client"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"#client.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We want to take the maximum over the time dimension. Let's use a Dask cluster to distribute the memory and compute load, getting our work done faster!"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import configparser\n",
"\n",
"def set_credentials(profile='default', region='us-west-2', endpoint=None, cfile=None):\n",
" # Set AWS environment variables based on profile from the user credentials file\n",
" cp = configparser.ConfigParser()\n",
" if not cfile:\n",
" cfile=os.path.expanduser('~/.aws/credentials')\n",
" if not endpoint:\n",
" endpoint=f's3.{region}.amazonaws.com'\n",
" cp.read(cfile)\n",
" os.environ['aws_access_key_id'.upper()] = cp[profile]['aws_access_key_id'] \n",
" os.environ['aws_secret_access_key'.upper()] = cp[profile]['aws_secret_access_key'] \n",
" os.environ['aws_s3_endpoint'.upper()] = endpoint\n",
" os.environ['aws_region'.upper()] = region"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"set_credentials()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"os.environ.update()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"from dask_gateway import Gateway\n",
"import os\n",
"# instantiate dask gateway\n",
"gateway = Gateway()\n",
"\n",
"# specify cluster options\n",
"options = gateway.cluster_options()\n",
"options.conda_environment='users/users-pangeo'\n",
"options.profile = 'Medium Worker' # 2 threads/worker\n",
"\n",
"# set environment variables for cluster workers (including AWS credential environment variables)\n",
"options.environment_vars = dict(os.environ)\n",
"\n",
"# create new cluster\n",
"cluster = gateway.new_cluster(options)\n",
"\n",
"# get the client for the cluster\n",
"client = cluster.get_client()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.43 ms, sys: 0 ns, total: 2.43 ms\n",
"Wall time: 6.34 ms\n"
]
}
],
"source": [
"%%time\n",
"n_workers=30\n",
"cluster.scale(n_workers)\n",
"#client.wait_for_workers(n_workers=n_workers)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-81ec0e33-56f8-11ee-828e-b6b13b65153d</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
"\n",
" <tr>\n",
" \n",
" <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
" <td style=\"text-align: left;\"><strong>Cluster type:</strong> dask_gateway.GatewayCluster</td>\n",
" \n",
" </tr>\n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"https://nebari.esipfed.org/gateway/clusters/dev.bdfaccdf504b4547bb4a802489537821/status\" target=\"_blank\">https://nebari.esipfed.org/gateway/clusters/dev.bdfaccdf504b4547bb4a802489537821/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
"\n",
" \n",
"\n",
" \n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
" <div style='background-color: #f2f2f2; display: inline-block; padding: 10px; border: 1px solid #999999;'>\n",
" <h3>GatewayCluster</h3>\n",
" <ul>\n",
" <li><b>Name: </b>dev.bdfaccdf504b4547bb4a802489537821\n",
" <li><b>Dashboard: </b><a href='https://nebari.esipfed.org/gateway/clusters/dev.bdfaccdf504b4547bb4a802489537821/status' target='_blank'>https://nebari.esipfed.org/gateway/clusters/dev.bdfaccdf504b4547bb4a802489537821/status</a>\n",
" </ul>\n",
"</div>\n",
"\n",
" </details>\n",
" \n",
"\n",
" </div>\n",
"</div>"
],
"text/plain": [
"<Client: 'tls://10.10.106.121:8786' processes=0 threads=0, memory=0 B>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"client"
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},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"#client.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Find the maximum water level at each grid cell over the entire storm\n",
"This is the compute intensive step, reading all the elevation data"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 609 ms, sys: 416 ms, total: 1.02 s\n",
"Wall time: 2min\n"
]
},
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;zeta&#x27; (node: 12784991)&gt;\n",
"array([nan, nan, nan, ..., nan, nan, nan])\n",
"Coordinates:\n",
" x (node) float64 -73.68 -73.68 -73.68 -73.68 ... -90.07 -90.07 -90.07\n",
" y (node) float64 42.75 42.75 42.75 42.75 ... 30.03 30.03 30.03 30.03\n",
"Dimensions without coordinates: node</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'zeta'</div><ul class='xr-dim-list'><li><span>node</span>: 12784991</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d1cbe03c-a07c-4f1b-94c7-efd24809c1a7' class='xr-array-in' type='checkbox' checked><label for='section-d1cbe03c-a07c-4f1b-94c7-efd24809c1a7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></div></li><li class='xr-section-item'><input id='section-debe042d-d0b8-4481-942b-dd5190ae8e44' class='xr-section-summary-in' type='checkbox' checked><label for='section-debe042d-d0b8-4481-942b-dd5190ae8e44' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-73.68 -73.68 ... -90.07 -90.07</div><input id='attrs-fa29bf63-5290-40a8-985e-e655f11d9845' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa29bf63-5290-40a8-985e-e655f11d9845' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f690758c-3805-4749-a4fa-a52c0aafe92c' class='xr-var-data-in' type='checkbox'><label for='data-f690758c-3805-4749-a4fa-a52c0aafe92c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>positive :</span></dt><dd>east</dd></dl></div><div class='xr-var-data'><pre>array([-73.68031409, -73.67945506, -73.6817845 , ..., -90.07315326,\n",
" -90.07274919, -90.07417811])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>42.75 42.75 42.75 ... 30.03 30.03</div><input id='attrs-33a16573-6ba2-457d-9419-774d99bdb14c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33a16573-6ba2-457d-9419-774d99bdb14c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a64cb1ef-a00e-4700-a9c0-c6739e4bd249' class='xr-var-data-in' type='checkbox'><label for='data-a64cb1ef-a00e-4700-a9c0-c6739e4bd249' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>positive :</span></dt><dd>north</dd></dl></div><div class='xr-var-data'><pre>array([42.75262564, 42.75110441, 42.75268602, ..., 30.02664339,\n",
" 30.0281029 , 30.02739188])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4f04ba80-6750-4955-8801-2f884bdbc117' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4f04ba80-6750-4955-8801-2f884bdbc117' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-227a81c4-2be8-4604-88dc-bc36f1bb6710' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-227a81c4-2be8-4604-88dc-bc36f1bb6710' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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"execution_count": 23,
"metadata": {},
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],
"source": [
"%%time\n",
"max_var = ds['zeta'].max(dim='time').compute()\n",
"max_var"
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize data on mesh using HoloViz.org tools"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
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" if (js_modules == null) js_modules = [];\n",
" if (js_exports == null) js_exports = {};\n",
"\n",
" root._bokeh_onload_callbacks.push(callback);\n",
"\n",
" if (root._bokeh_is_loading > 0) {\n",
" console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" if (!reloading) {\n",
" console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" }\n",
"\n",
" function on_load() {\n",
" root._bokeh_is_loading--;\n",
" if (root._bokeh_is_loading === 0) {\n",
" console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
" run_callbacks()\n",
" }\n",
" }\n",
" window._bokeh_on_load = on_load\n",
"\n",
" function on_error() {\n",
" console.error(\"failed to load \" + url);\n",
" }\n",
"\n",
" var skip = [];\n",
" if (window.requirejs) {\n",
" window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n",
" require([\"jspanel\"], function(jsPanel) {\n",
"\twindow.jsPanel = jsPanel\n",
"\ton_load()\n",
" })\n",
" require([\"jspanel-modal\"], function() {\n",
"\ton_load()\n",
" })\n",
" require([\"jspanel-tooltip\"], function() {\n",
"\ton_load()\n",
" })\n",
" require([\"jspanel-hint\"], function() {\n",
"\ton_load()\n",
" })\n",
" require([\"jspanel-layout\"], function() {\n",
"\ton_load()\n",
" })\n",
" require([\"jspanel-contextmenu\"], function() {\n",
"\ton_load()\n",
" })\n",
" require([\"jspanel-dock\"], function() {\n",
"\ton_load()\n",
" })\n",
" require([\"gridstack\"], function(GridStack) {\n",
"\twindow.GridStack = GridStack\n",
"\ton_load()\n",
" })\n",
" require([\"notyf\"], function() {\n",
"\ton_load()\n",
" })\n",
" root._bokeh_is_loading = css_urls.length + 9;\n",
" } else {\n",
" root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
" }\n",
"\n",
" var existing_stylesheets = []\n",
" var links = document.getElementsByTagName('link')\n",
" for (var i = 0; i < links.length; i++) {\n",
" var link = links[i]\n",
" if (link.href != null) {\n",
"\texisting_stylesheets.push(link.href)\n",
" }\n",
" }\n",
" for (var i = 0; i < css_urls.length; i++) {\n",
" var url = css_urls[i];\n",
" if (existing_stylesheets.indexOf(url) !== -1) {\n",
"\ton_load()\n",
"\tcontinue;\n",
" }\n",
" const element = document.createElement(\"link\");\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.rel = \"stylesheet\";\n",
" element.type = \"text/css\";\n",
" element.href = url;\n",
" console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
" document.body.appendChild(element);\n",
" } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
" var urls = ['https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/extensions/dock/jspanel.dock.js'];\n",
" for (var i = 0; i < urls.length; i++) {\n",
" skip.push(urls[i])\n",
" }\n",
" } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n",
" var urls = ['https://cdn.holoviz.org/panel/1.2.1/dist/bundled/gridstack/[email protected]/dist/gridstack-all.js'];\n",
" for (var i = 0; i < urls.length; i++) {\n",
" skip.push(urls[i])\n",
" }\n",
" } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n",
" var urls = ['https://cdn.holoviz.org/panel/1.2.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n",
" for (var i = 0; i < urls.length; i++) {\n",
" skip.push(urls[i])\n",
" }\n",
" } var existing_scripts = []\n",
" var scripts = document.getElementsByTagName('script')\n",
" for (var i = 0; i < scripts.length; i++) {\n",
" var script = scripts[i]\n",
" if (script.src != null) {\n",
"\texisting_scripts.push(script.src)\n",
" }\n",
" }\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
"\tif (!window.requirejs) {\n",
"\t on_load();\n",
"\t}\n",
"\tcontinue;\n",
" }\n",
" var element = document.createElement('script');\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.async = false;\n",
" element.src = url;\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
" for (var i = 0; i < js_modules.length; i++) {\n",
" var url = js_modules[i];\n",
" if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
"\tif (!window.requirejs) {\n",
"\t on_load();\n",
"\t}\n",
"\tcontinue;\n",
" }\n",
" var element = document.createElement('script');\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.async = false;\n",
" element.src = url;\n",
" element.type = \"module\";\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
" for (const name in js_exports) {\n",
" var url = js_exports[name];\n",
" if (skip.indexOf(url) >= 0 || root[name] != null) {\n",
"\tif (!window.requirejs) {\n",
"\t on_load();\n",
"\t}\n",
"\tcontinue;\n",
" }\n",
" var element = document.createElement('script');\n",
" element.onerror = on_error;\n",
" element.async = false;\n",
" element.type = \"module\";\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" element.textContent = `\n",
" import ${name} from \"${url}\"\n",
" window.${name} = ${name}\n",
" window._bokeh_on_load()\n",
" `\n",
" document.head.appendChild(element);\n",
" }\n",
" if (!js_urls.length && !js_modules.length) {\n",
" on_load()\n",
" }\n",
" };\n",
"\n",
" function inject_raw_css(css) {\n",
" const element = document.createElement(\"style\");\n",
" element.appendChild(document.createTextNode(css));\n",
" document.body.appendChild(element);\n",
" }\n",
"\n",
" var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.1/dist/panel.min.js\", \"https://cdn.jsdelivr.net/npm/@holoviz/[email protected]/dist/geoviews.min.js\"];\n",
" var js_modules = [];\n",
" var js_exports = {};\n",
" var css_urls = [];\n",
" var inline_js = [ function(Bokeh) {\n",
" Bokeh.set_log_level(\"info\");\n",
" },\n",
"function(Bokeh) {} // ensure no trailing comma for IE\n",
" ];\n",
"\n",
" function run_inline_js() {\n",
" if ((root.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i].call(root, root.Bokeh);\n",
" }\n",
" // Cache old bokeh versions\n",
" if (Bokeh != undefined && !reloading) {\n",
"\tvar NewBokeh = root.Bokeh;\n",
"\tif (Bokeh.versions === undefined) {\n",
"\t Bokeh.versions = new Map();\n",
"\t}\n",
"\tif (NewBokeh.version !== Bokeh.version) {\n",
"\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
"\t}\n",
"\troot.Bokeh = Bokeh;\n",
" }} else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!root._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" root._bokeh_failed_load = true;\n",
" }\n",
" root._bokeh_is_initializing = false\n",
" }\n",
"\n",
" function load_or_wait() {\n",
" // Implement a backoff loop that tries to ensure we do not load multiple\n",
" // versions of Bokeh and its dependencies at the same time.\n",
" // In recent versions we use the root._bokeh_is_initializing flag\n",
" // to determine whether there is an ongoing attempt to initialize\n",
" // bokeh, however for backward compatibility we also try to ensure\n",
" // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
" // before older versions are fully initialized.\n",
" if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
" root._bokeh_is_initializing = false;\n",
" root._bokeh_onload_callbacks = undefined;\n",
" console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
" load_or_wait();\n",
" } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
" setTimeout(load_or_wait, 100);\n",
" } else {\n",
" Bokeh = root.Bokeh;\n",
" bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
" root._bokeh_is_initializing = true\n",
" root._bokeh_onload_callbacks = []\n",
" if (!reloading && (!bokeh_loaded || is_dev)) {\n",
"\troot.Bokeh = undefined;\n",
" }\n",
" load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
"\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
"\trun_inline_js();\n",
" });\n",
" }\n",
" }\n",
" // Give older versions of the autoload script a head-start to ensure\n",
" // they initialize before we start loading newer version.\n",
" setTimeout(load_or_wait, 100)\n",
"}(window));"
],
"application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n var reloading = false;\n var Bokeh = root.Bokeh;\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/[email protected]/dist/extensions/dock/jspanel.dock.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.1/dist/bundled/gridstack/[email protected]/dist/gridstack-all.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.1/dist/panel.min.js\", \"https://cdn.jsdelivr.net/npm/@holoviz/[email protected]/dist/geoviews.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; 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]
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"name": "stdout",
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}
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"source": [
"%%time\n",
"v = np.vstack((ds['x'], ds['y'], max_var)).T\n",
"verts = pd.DataFrame(v, columns=['x','y','vmax'])\n",
"points = gv.operation.project_points(gv.Points(verts, vdims=['vmax']))\n",
"tris = pd.DataFrame(ds['element'].values.astype('int')-1, columns=['v0','v1','v2'])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
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"source": [
"tiles = gv.tile_sources.OSM"
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"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"trimesh = gv.TriMesh((tris, points), label='ADCIRC Global Water Level (m)')\n",
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"cell_type": "code",
"execution_count": null,
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"source": [
"### Extract a time series at a specified lon, lat location"
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"Because Xarray does not yet understand that `x` and `y` are coordinate variables on this triangular mesh, we create our own simple function to find the closest point. If we had a lot of these, we could use a more fancy tree algorithm."
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"cell_type": "code",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;zeta&#x27; (time: 40, node: 12784991)&gt;\n",
"dask.array&lt;open_dataset-9d8ed5545d2239c017b6d200f9ff4ff0zeta, shape=(40, 12784991), dtype=float64, chunksize=(1, 511400), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2016-09-01T12:00:00 2016-09-02 ... 2016-09-21\n",
" x (node) float64 dask.array&lt;chunksize=(511400,), meta=np.ndarray&gt;\n",
" y (node) float64 dask.array&lt;chunksize=(511400,), meta=np.ndarray&gt;\n",
"Dimensions without coordinates: node\n",
"Attributes:\n",
" long_name: water surface elevation above geoid\n",
" standard_name: sea_surface_height_above_geoid\n",
" location: node\n",
" mesh: adcirc_mesh\n",
" units: m</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'zeta'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 40</li><li><span>node</span>: 12784991</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c72b11d9-c526-431c-b3c9-ad1711c46199' class='xr-array-in' type='checkbox' checked><label for='section-c72b11d9-c526-431c-b3c9-ad1711c46199' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 511400), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 3.81 GiB </td>\n",
" <td> 3.90 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (40, 12784991) </td>\n",
" <td> (1, 511400) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1000 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
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"</table></div></div></li><li class='xr-section-item'><input id='section-1c291efb-ccaa-43ec-a43e-af848ed20587' class='xr-section-summary-in' type='checkbox' checked><label for='section-1c291efb-ccaa-43ec-a43e-af848ed20587' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-09-01T12:00:00 ... 2016-09-21</div><input id='attrs-0dc8c673-ab96-49a6-bdcc-6e33ec2a110e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dc8c673-ab96-49a6-bdcc-6e33ec2a110e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5360892-03dc-496d-8394-cc5d2c86f113' class='xr-var-data-in' type='checkbox'><label for='data-f5360892-03dc-496d-8394-cc5d2c86f113' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>model time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>base_date :</span></dt><dd>2016-09-01 00:00:00 UTC</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-09-01T12:00:00.000000000&#x27;, &#x27;2016-09-02T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-02T12:00:00.000000000&#x27;, &#x27;2016-09-03T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-03T12:00:00.000000000&#x27;, &#x27;2016-09-04T00:00:00.000000000&#x27;,\n",
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" &#x27;2016-09-11T12:00:00.000000000&#x27;, &#x27;2016-09-12T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-12T12:00:00.000000000&#x27;, &#x27;2016-09-13T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-13T12:00:00.000000000&#x27;, &#x27;2016-09-14T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-14T12:00:00.000000000&#x27;, &#x27;2016-09-15T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-15T12:00:00.000000000&#x27;, &#x27;2016-09-16T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-16T12:00:00.000000000&#x27;, &#x27;2016-09-17T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-17T12:00:00.000000000&#x27;, &#x27;2016-09-18T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-18T12:00:00.000000000&#x27;, &#x27;2016-09-19T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-19T12:00:00.000000000&#x27;, &#x27;2016-09-20T00:00:00.000000000&#x27;,\n",
" &#x27;2016-09-20T12:00:00.000000000&#x27;, &#x27;2016-09-21T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(511400,), meta=np.ndarray&gt;</div><input id='attrs-ff916769-f8f7-403d-ae33-b02137b98c2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff916769-f8f7-403d-ae33-b02137b98c2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77f5191d-e8db-4d41-9eb0-29c52f7561d1' class='xr-var-data-in' type='checkbox'><label for='data-77f5191d-e8db-4d41-9eb0-29c52f7561d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>positive :</span></dt><dd>east</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 97.54 MiB </td>\n",
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" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (12784991,) </td>\n",
" <td> (511400,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(node)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(511400,), meta=np.ndarray&gt;</div><input id='attrs-2efffb62-a94c-400f-8c01-55619492fb47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2efffb62-a94c-400f-8c01-55619492fb47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d53e88c8-ee07-46ad-9b49-c61840a35c67' class='xr-var-data-in' type='checkbox'><label for='data-d53e88c8-ee07-46ad-9b49-c61840a35c67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>positive :</span></dt><dd>north</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 97.54 MiB </td>\n",
" <td> 3.90 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (12784991,) </td>\n",
" <td> (511400,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
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"\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8995d556-edf4-48d8-b231-0c0e36cee7b6' class='xr-section-summary-in' type='checkbox' ><label for='section-8995d556-edf4-48d8-b231-0c0e36cee7b6' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2fa909a5-85c2-49e5-b1aa-df5c687aed37' class='xr-index-data-in' type='checkbox'/><label for='index-2fa909a5-85c2-49e5-b1aa-df5c687aed37' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2016-09-01 12:00:00&#x27;, &#x27;2016-09-02 00:00:00&#x27;,\n",
" &#x27;2016-09-02 12:00:00&#x27;, &#x27;2016-09-03 00:00:00&#x27;,\n",
" &#x27;2016-09-03 12:00:00&#x27;, &#x27;2016-09-04 00:00:00&#x27;,\n",
" &#x27;2016-09-04 12:00:00&#x27;, &#x27;2016-09-05 00:00:00&#x27;,\n",
" &#x27;2016-09-05 12:00:00&#x27;, &#x27;2016-09-06 00:00:00&#x27;,\n",
" &#x27;2016-09-06 12:00:00&#x27;, &#x27;2016-09-07 00:00:00&#x27;,\n",
" &#x27;2016-09-07 12:00:00&#x27;, &#x27;2016-09-08 00:00:00&#x27;,\n",
" &#x27;2016-09-08 12:00:00&#x27;, &#x27;2016-09-09 00:00:00&#x27;,\n",
" &#x27;2016-09-09 12:00:00&#x27;, &#x27;2016-09-10 00:00:00&#x27;,\n",
" &#x27;2016-09-10 12:00:00&#x27;, &#x27;2016-09-11 00:00:00&#x27;,\n",
" &#x27;2016-09-11 12:00:00&#x27;, &#x27;2016-09-12 00:00:00&#x27;,\n",
" &#x27;2016-09-12 12:00:00&#x27;, &#x27;2016-09-13 00:00:00&#x27;,\n",
" &#x27;2016-09-13 12:00:00&#x27;, &#x27;2016-09-14 00:00:00&#x27;,\n",
" &#x27;2016-09-14 12:00:00&#x27;, &#x27;2016-09-15 00:00:00&#x27;,\n",
" &#x27;2016-09-15 12:00:00&#x27;, &#x27;2016-09-16 00:00:00&#x27;,\n",
" &#x27;2016-09-16 12:00:00&#x27;, &#x27;2016-09-17 00:00:00&#x27;,\n",
" &#x27;2016-09-17 12:00:00&#x27;, &#x27;2016-09-18 00:00:00&#x27;,\n",
" &#x27;2016-09-18 12:00:00&#x27;, &#x27;2016-09-19 00:00:00&#x27;,\n",
" &#x27;2016-09-19 12:00:00&#x27;, &#x27;2016-09-20 00:00:00&#x27;,\n",
" &#x27;2016-09-20 12:00:00&#x27;, &#x27;2016-09-21 00:00:00&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5918acfc-9e31-47d6-9eeb-6b1e80cb3670' class='xr-section-summary-in' type='checkbox' checked><label for='section-5918acfc-9e31-47d6-9eeb-6b1e80cb3670' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>water surface elevation above geoid</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>location :</span></dt><dd>node</dd><dt><span>mesh :</span></dt><dd>adcirc_mesh</dd><dt><span>units :</span></dt><dd>m</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'zeta' (time: 40, node: 12784991)>\n",
"dask.array<open_dataset-9d8ed5545d2239c017b6d200f9ff4ff0zeta, shape=(40, 12784991), dtype=float64, chunksize=(1, 511400), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2016-09-01T12:00:00 2016-09-02 ... 2016-09-21\n",
" x (node) float64 dask.array<chunksize=(511400,), meta=np.ndarray>\n",
" y (node) float64 dask.array<chunksize=(511400,), meta=np.ndarray>\n",
"Dimensions without coordinates: node\n",
"Attributes:\n",
" long_name: water surface elevation above geoid\n",
" standard_name: sea_surface_height_above_geoid\n",
" location: node\n",
" mesh: adcirc_mesh\n",
" units: m"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds['zeta']"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# find the indices of the points in (x,y) closest to the points in (xi,yi)\n",
"def nearxy(x,y,xi,yi):\n",
" ind = np.ones(len(xi),dtype=int)\n",
" for i in range(len(xi)):\n",
" dist = np.sqrt((x-xi[i])**2+(y-yi[i])**2)\n",
" ind[i] = dist.argmin()\n",
" return ind"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds['x'].min().values"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"#just offshore of Galveston\n",
"lat = 29.0329856\n",
"lon = -95.1535041"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"ind = nearxy(ds['x'].values,ds['y'].values,[lon], [lat])"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {},
"metadata": {},
"output_type": "display_data"
},
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"\t root_el.id = root_el.id + '-rendered'\n",
"\t}\n",
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" }\n",
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" if (root.Bokeh === undefined) {\n",
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" } else if (root.Bokeh.version === py_version) {\n",
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" }\n",
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" embed_document(root);\n",
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" clearInterval(timer);\n",
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"\t if (Bokeh == null || Bokeh.Panel == null) {\n",
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"\t embed_document(root)\n",
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"})(window);</script>"
],
"text/plain": [
":Curve [time] (zeta)"
]
},
"execution_count": 42,
"metadata": {
"application/vnd.holoviews_exec.v0+json": {
"id": "p1166"
}
},
"output_type": "execute_result"
}
],
"source": [
"ds['zeta'][:,ind].hvplot(x='time', grid=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Be a good citizen and shutdown your cluster if you are done using it"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/conda/users/226e91a965d185c2173ace6d0daf5747b886d5f2b5299f8dbce39e2e7fb456a3-20230830-192612-585397-241-pangeo/lib/python3.10/site-packages/dask_gateway/client.py:1014: RuntimeWarning: coroutine 'rpc.close_rpc' was never awaited\n",
" self.scheduler_comm.close_rpc()\n"
]
}
],
"source": [
"cluster.shutdown()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "users-users-pangeo",
"language": "python",
"name": "conda-env-users-users-pangeo-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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