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rsignell-usgs/03_rechunk.ipynb

Created March 14, 2023 21:29
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03_rechunk.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "a4108776-2095-4696-a4b6-fef7c2496fce",
"metadata": {},
"source": [
"# Rechunk the kerchunked dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "720e3f30-0708-48c0-bbbb-696383c2b2f1",
"metadata": {},
"outputs": [],
"source": [
"import fsspec\n",
"import fsspec.implementations.reference\n",
"import zarr\n",
"import xarray as xr\n",
"from pathlib import Path\n",
"\n",
"from rechunker import rechunk"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "4c5f1ee6-fc59-4b3f-8d9e-0d618bd8dd75",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'0.5.1'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import rechunker\n",
"rechunker.__version__"
]
},
{
"cell_type": "markdown",
"id": "1a0babee-2b88-4713-abf6-32ff6ca7862a",
"metadata": {},
"source": [
"#### Start a Dask Gateway cluster\n",
"Use a custom helper function `ebd.start_dask_cluster` to set options on this cluster. We don't have to use this helper, it just cuts down on lines of code in notebooks."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca9dd82d-0281-498b-8a28-61d8267d6a50",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"sys.path.append('/shared/users/rsignell/lib')\n",
"import ebdpy as ebd\n",
"os.environ['AWS_PROFILE'] = 'esip-qhub' # use env vars for AWS credentials to write\n",
"\n",
"client, cluster, gateway = ebd.start_dask_cluster(\n",
" profile=os.environ['AWS_PROFILE'],\n",
" worker_max=30,\n",
" region='us-west-2', \n",
" use_existing_cluster=True,\n",
" adaptive_scaling=False, \n",
" wait_for_cluster=True, \n",
" propagate_env=True)"
]
},
{
"cell_type": "markdown",
"id": "d2a80a71-afdd-4a14-96b6-505d8ed41af4",
"metadata": {},
"source": [
"#### Open Kerchunked Dataset JSON for one year"
]
},
{
"cell_type": "raw",
"id": "3ffc9c71-aa44-4102-99ca-9dd70c07b28a",
"metadata": {},
"source": [
"year = '1984'\n",
"combined_json = f's3://esip-qhub/noaa/nwm/grid1km/combined_{year}.json'\n",
"\n",
"s_opts = {'requester_pays':True, 'skip_instance_cache':True}\n",
"r_opts = {'anon':True}\n",
"fs = fsspec.filesystem(\"reference\", fo=combined_json, ref_storage_args=s_opts,\n",
" remote_protocol='s3', remote_options=r_opts)\n",
"m = fs.get_mapper(\"\")\n",
"ds = xr.open_dataset(m, engine=\"zarr\", chunks={'time':1, 'y':3840, 'x':4608}, \n",
" backend_kwargs=dict(consolidated=False))\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "de1aafae-7b66-4895-8d36-9beddbbb9aec",
"metadata": {},
"source": [
"#### Open Entire Kerchunked Dataset, lazy loaded from parquet"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "44f9fa14-658e-479f-a485-ac87d775f8a8",
"metadata": {},
"outputs": [],
"source": [
"s3_lazy_refs = 's3://esip-qhub-public/nwm/LDAS-1k/lazyrefs'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "391492ac-706e-471f-8b94-8b9f7c35b65b",
"metadata": {},
"outputs": [],
"source": [
"fs = fsspec.implementations.reference.DFReferenceFileSystem(s3_lazy_refs, lazy=True, target_options={\"anon\": True},\n",
" remote_protocol=\"s3\", remote_options={\"anon\": True})\n",
"m = fs.get_mapper(\"\")\n",
"ds = xr.open_dataset(m, engine=\"zarr\", chunks={'time':1, 'y':3840, 'x':4608}, \n",
" backend_kwargs=dict(consolidated=False))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5c1df56b-d237-498b-8f2c-8272471dd24c",
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (time: 116631, y: 3840, x: 4608, vis_nir: 2, soil_layers_stag: 4)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 1979-02-01T03:00:00 ... 2020-12-31T21:00:00\n",
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" * y (y) float64 -1.92e+06 -1.919e+06 ... 1.918e+06 1.919e+06\n",
"Dimensions without coordinates: vis_nir, soil_layers_stag\n",
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" COSZ (time, y, x) float64 dask.array&lt;chunksize=(1, 3840, 4608), meta=np.ndarray&gt;\n",
" ... ...\n",
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" SOIL_M (time, y, soil_layers_stag, x) float64 dask.array&lt;chunksize=(1, 3840, 1, 4608), meta=np.ndarray&gt;\n",
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" crs object ...\n",
"Attributes:\n",
" Conventions: CF-1.6\n",
" GDAL_DataType: Generic\n",
" TITLE: OUTPUT FROM WRF-Hydro v5.2.0-beta2\n",
" code_version: v5.2.0-beta2\n",
" model_configuration: retrospective\n",
" model_initialization_time: 1979-02-01_00:00:00\n",
" model_output_type: land\n",
" model_output_valid_time: 1979-02-01_03:00:00\n",
" model_total_valid_times: 472\n",
" proj4: +proj=lcc +units=m +a=6370000.0 +b=6370000.0 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-27432d6c-614c-4616-b5a2-136d0947eccc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-27432d6c-614c-4616-b5a2-136d0947eccc' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 116631</li><li><span class='xr-has-index'>y</span>: 3840</li><li><span class='xr-has-index'>x</span>: 4608</li><li><span>vis_nir</span>: 2</li><li><span>soil_layers_stag</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6b055626-4312-4d1d-b7fe-e79bced0b37f' class='xr-section-summary-in' type='checkbox' checked><label for='section-6b055626-4312-4d1d-b7fe-e79bced0b37f' 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'>1979-02-01T03:00:00 ... 2020-12-...</div><input id='attrs-3977b22d-5764-4e58-8c10-7158a4233a74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3977b22d-5764-4e58-8c10-7158a4233a74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e1e924e-9d32-4a74-a97b-bda9cbb69971' class='xr-var-data-in' type='checkbox'><label for='data-5e1e924e-9d32-4a74-a97b-bda9cbb69971' 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>valid output time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>valid_max :</span></dt><dd>4862880</dd><dt><span>valid_min :</span></dt><dd>4778100</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-02-01T03:00:00.000000000&#x27;, &#x27;1979-02-01T06:00:00.000000000&#x27;,\n",
" &#x27;1979-02-01T09:00:00.000000000&#x27;, ..., &#x27;2020-12-31T15:00:00.000000000&#x27;,\n",
" &#x27;2020-12-31T18:00:00.000000000&#x27;, &#x27;2020-12-31T21:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.303e+06 -2.302e+06 ... 2.304e+06</div><input id='attrs-5fced94b-d881-439a-8000-de8708653e7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5fced94b-d881-439a-8000-de8708653e7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df37a2b9-e883-4dbf-b7b1-c63887014a18' class='xr-var-data-in' type='checkbox'><label for='data-df37a2b9-e883-4dbf-b7b1-c63887014a18' 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>_CoordinateAxisType :</span></dt><dd>GeoX</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>resolution :</span></dt><dd>1000.0</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-2303499.25, -2302499.25, -2301499.25, ..., 2301500.75, 2302500.75,\n",
" 2303500.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.92e+06 -1.919e+06 ... 1.919e+06</div><input id='attrs-951b4c11-df39-47c5-9c31-7c073fb8dd13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-951b4c11-df39-47c5-9c31-7c073fb8dd13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b2d5943-fd00-4b71-aaa0-829260f08958' class='xr-var-data-in' type='checkbox'><label for='data-7b2d5943-fd00-4b71-aaa0-829260f08958' 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>_CoordinateAxisType :</span></dt><dd>GeoY</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>resolution :</span></dt><dd>1000.0</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-1919500.375, -1918500.375, -1917500.375, ..., 1917499.625,\n",
" 1918499.625, 1919499.625])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-06834ec5-ad36-40a2-9057-9826c0d08e8b' class='xr-section-summary-in' type='checkbox' ><label for='section-06834ec5-ad36-40a2-9057-9826c0d08e8b' class='xr-section-summary' >Data variables: <span>(21)</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>ACCET</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 3840, 4608), meta=np.ndarray&gt;</div><input id='attrs-ae7791f6-b0bd-4225-ad8c-1a99c1c626b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae7791f6-b0bd-4225-ad8c-1a99c1c626b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63b2f67a-2d21-4b88-bf3b-421f67ad21b7' class='xr-var-data-in' type='checkbox'><label for='data-63b2f67a-2d21-4b88-bf3b-421f67ad21b7' 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>esri_pe_string :</span></dt><dd>PROJCS[&quot;Lambert_Conformal_Conic&quot;,GEOGCS[&quot;GCS_Sphere&quot;,DATUM[&quot;D_Sphere&quot;,SPHEROID[&quot;Sphere&quot;,6370000.0,0.0]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Lambert_Conformal_Conic_2SP&quot;],PARAMETER[&quot;false_easting&quot;,0.0],PARAMETER[&quot;false_northing&quot;,0.0],PARAMETER[&quot;central_meridian&quot;,-97.0],PARAMETER[&quot;standard_parallel_1&quot;,30.0],PARAMETER[&quot;standard_parallel_2&quot;,60.0],PARAMETER[&quot;latitude_of_origin&quot;,40.0],UNIT[&quot;Meter&quot;,1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Accumulated total ET</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_range :</span></dt><dd>[-100000, 100000000]</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> 15.02 TiB </td>\n",
" <td> 135.00 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (116631, 3840, 4608) </td>\n",
" <td> (1, 3840, 4608) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 116631 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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" </table>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSNOM</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 3840, 4608), meta=np.ndarray&gt;</div><input id='attrs-8cfff11f-122e-44db-b47f-afe41093c4b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cfff11f-122e-44db-b47f-afe41093c4b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f495f317-bfe9-42e9-b79a-4072f28529da' class='xr-var-data-in' type='checkbox'><label for='data-f495f317-bfe9-42e9-b79a-4072f28529da' 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>esri_pe_string :</span></dt><dd>PROJCS[&quot;Lambert_Conformal_Conic&quot;,GEOGCS[&quot;GCS_Sphere&quot;,DATUM[&quot;D_Sphere&quot;,SPHEROID[&quot;Sphere&quot;,6370000.0,0.0]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Lambert_Conformal_Conic_2SP&quot;],PARAMETER[&quot;false_easting&quot;,0.0],PARAMETER[&quot;false_northing&quot;,0.0],PARAMETER[&quot;central_meridian&quot;,-97.0],PARAMETER[&quot;standard_parallel_1&quot;,30.0],PARAMETER[&quot;standard_parallel_2&quot;,60.0],PARAMETER[&quot;latitude_of_origin&quot;,40.0],UNIT[&quot;Meter&quot;,1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>accumulated melting water out of snow bottom</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_range :</span></dt><dd>[0, 10000000]</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> 15.02 TiB </td>\n",
" <td> 135.00 MiB </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (116631, 3840, 4608) </td>\n",
" <td> (1, 3840, 4608) </td>\n",
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" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 116631 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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" <td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dfc124a7-c0b8-438d-948c-9f30beb839ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfc124a7-c0b8-438d-948c-9f30beb839ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77222665-788c-4321-b75e-64606bd28b1f' class='xr-var-data-in' type='checkbox'><label for='data-77222665-788c-4321-b75e-64606bd28b1f' 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>GeoTransform :</span></dt><dd>-2303999.17655 1000.0 0 1919999.66329 0 -1000.0</dd><dt><span>_CoordinateAxes :</span></dt><dd>y x</dd><dt><span>_CoordinateTransformType :</span></dt><dd>Projection</dd><dt><span>earth_radius :</span></dt><dd>6370000.0</dd><dt><span>esri_pe_string :</span></dt><dd>PROJCS[&quot;Lambert_Conformal_Conic&quot;,GEOGCS[&quot;GCS_Sphere&quot;,DATUM[&quot;D_Sphere&quot;,SPHEROID[&quot;Sphere&quot;,6370000.0,0.0]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Lambert_Conformal_Conic_2SP&quot;],PARAMETER[&quot;false_easting&quot;,0.0],PARAMETER[&quot;false_northing&quot;,0.0],PARAMETER[&quot;central_meridian&quot;,-97.0],PARAMETER[&quot;standard_parallel_1&quot;,30.0],PARAMETER[&quot;standard_parallel_2&quot;,60.0],PARAMETER[&quot;latitude_of_origin&quot;,40.0],UNIT[&quot;Meter&quot;,1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>40.0</dd><dt><span>long_name :</span></dt><dd>CRS definition</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-97.0</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6370000.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;Lambert_Conformal_Conic&quot;,GEOGCS[&quot;GCS_Sphere&quot;,DATUM[&quot;D_Sphere&quot;,SPHEROID[&quot;Sphere&quot;,6370000.0,0.0]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Lambert_Conformal_Conic_2SP&quot;],PARAMETER[&quot;false_easting&quot;,0.0],PARAMETER[&quot;false_northing&quot;,0.0],PARAMETER[&quot;central_meridian&quot;,-97.0],PARAMETER[&quot;standard_parallel_1&quot;,30.0],PARAMETER[&quot;standard_parallel_2&quot;,60.0],PARAMETER[&quot;latitude_of_origin&quot;,40.0],UNIT[&quot;Meter&quot;,1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision</dd><dt><span>standard_parallel :</span></dt><dd>[30.0, 60.0]</dd><dt><span>transform_name :</span></dt><dd>lambert_conformal_conic</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=object]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae02d87d-1566-4b0f-9c7d-3fa261b30a85' class='xr-section-summary-in' type='checkbox' ><label for='section-ae02d87d-1566-4b0f-9c7d-3fa261b30a85' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fb712636-b3ad-4db3-9361-bbbd1b276539' class='xr-index-data-in' type='checkbox'/><label for='index-fb712636-b3ad-4db3-9361-bbbd1b276539' 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;1979-02-01 03:00:00&#x27;, &#x27;1979-02-01 06:00:00&#x27;,\n",
" &#x27;1979-02-01 09:00:00&#x27;, &#x27;1979-02-01 12:00:00&#x27;,\n",
" &#x27;1979-02-01 15:00:00&#x27;, &#x27;1979-02-01 18:00:00&#x27;,\n",
" &#x27;1979-02-01 21:00:00&#x27;, &#x27;1979-02-02 00:00:00&#x27;,\n",
" &#x27;1979-02-02 03:00:00&#x27;, &#x27;1979-02-02 06:00:00&#x27;,\n",
" ...\n",
" &#x27;2020-12-30 18:00:00&#x27;, &#x27;2020-12-30 21:00:00&#x27;,\n",
" &#x27;2020-12-31 00:00:00&#x27;, &#x27;2020-12-31 03:00:00&#x27;,\n",
" &#x27;2020-12-31 06:00:00&#x27;, &#x27;2020-12-31 09:00:00&#x27;,\n",
" &#x27;2020-12-31 12:00:00&#x27;, &#x27;2020-12-31 15:00:00&#x27;,\n",
" &#x27;2020-12-31 18:00:00&#x27;, &#x27;2020-12-31 21:00:00&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=116631, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a672519c-2db4-4ae0-bfec-66a3ace7fb18' class='xr-index-data-in' type='checkbox'/><label for='index-a672519c-2db4-4ae0-bfec-66a3ace7fb18' 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(Float64Index([-2303499.25, -2302499.25, -2301499.25, -2300499.25, -2299499.25,\n",
" -2298499.25, -2297499.25, -2296499.25, -2295499.25, -2294499.25,\n",
" ...\n",
" 2294500.75, 2295500.75, 2296500.75, 2297500.75, 2298500.75,\n",
" 2299500.75, 2300500.75, 2301500.75, 2302500.75, 2303500.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=4608))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-169a180e-2f32-4f6a-bf41-4ef2737a033d' class='xr-index-data-in' type='checkbox'/><label for='index-169a180e-2f32-4f6a-bf41-4ef2737a033d' 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(Float64Index([-1919500.375, -1918500.375, -1917500.375, -1916500.375,\n",
" -1915500.375, -1914500.375, -1913500.375, -1912500.375,\n",
" -1911500.375, -1910500.375,\n",
" ...\n",
" 1910499.625, 1911499.625, 1912499.625, 1913499.625,\n",
" 1914499.625, 1915499.625, 1916499.625, 1917499.625,\n",
" 1918499.625, 1919499.625],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=3840))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2b865048-7b27-48d2-bdde-0005e5335ff1' class='xr-section-summary-in' type='checkbox' ><label for='section-2b865048-7b27-48d2-bdde-0005e5335ff1' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>GDAL_DataType :</span></dt><dd>Generic</dd><dt><span>TITLE :</span></dt><dd>OUTPUT FROM WRF-Hydro v5.2.0-beta2</dd><dt><span>code_version :</span></dt><dd>v5.2.0-beta2</dd><dt><span>model_configuration :</span></dt><dd>retrospective</dd><dt><span>model_initialization_time :</span></dt><dd>1979-02-01_00:00:00</dd><dt><span>model_output_type :</span></dt><dd>land</dd><dt><span>model_output_valid_time :</span></dt><dd>1979-02-01_03:00:00</dd><dt><span>model_total_valid_times :</span></dt><dd>472</dd><dt><span>proj4 :</span></dt><dd>+proj=lcc +units=m +a=6370000.0 +b=6370000.0 +lat_1=30.0 +lat_2=60.0 +lat_0=40.0 +lon_0=-97.0 +x_0=0 +y_0=0 +k_0=1.0 +nadgrids=@null +wktext +no_defs</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (time: 116631, y: 3840, x: 4608, vis_nir: 2, soil_layers_stag: 4)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 1979-02-01T03:00:00 ... 2020-12-31T21:00:00\n",
" * x (x) float64 -2.303e+06 -2.302e+06 ... 2.303e+06 2.304e+06\n",
" * y (y) float64 -1.92e+06 -1.919e+06 ... 1.918e+06 1.919e+06\n",
"Dimensions without coordinates: vis_nir, soil_layers_stag\n",
"Data variables: (12/21)\n",
" ACCET (time, y, x) float64 dask.array<chunksize=(1, 3840, 4608), meta=np.ndarray>\n",
" ACSNOM (time, y, x) float64 dask.array<chunksize=(1, 3840, 4608), meta=np.ndarray>\n",
" ALBEDO (time, y, x) float64 dask.array<chunksize=(1, 3840, 4608), meta=np.ndarray>\n",
" ALBSND (time, y, vis_nir, x) float64 dask.array<chunksize=(1, 3840, 1, 4608), meta=np.ndarray>\n",
" ALBSNI (time, y, vis_nir, x) float64 dask.array<chunksize=(1, 3840, 1, 4608), meta=np.ndarray>\n",
" COSZ (time, y, x) float64 dask.array<chunksize=(1, 3840, 4608), meta=np.ndarray>\n",
" ... ...\n",
" SNOWH (time, y, x) float64 dask.array<chunksize=(1, 3840, 4608), meta=np.ndarray>\n",
" SOIL_M (time, y, soil_layers_stag, x) float64 dask.array<chunksize=(1, 3840, 1, 4608), meta=np.ndarray>\n",
" SOIL_W (time, y, soil_layers_stag, x) float64 dask.array<chunksize=(1, 3840, 1, 4608), meta=np.ndarray>\n",
" TRAD (time, y, x) float64 dask.array<chunksize=(1, 3840, 4608), meta=np.ndarray>\n",
" UGDRNOFF (time, y, x) float64 dask.array<chunksize=(1, 3840, 4608), meta=np.ndarray>\n",
" crs object ...\n",
"Attributes:\n",
" Conventions: CF-1.6\n",
" GDAL_DataType: Generic\n",
" TITLE: OUTPUT FROM WRF-Hydro v5.2.0-beta2\n",
" code_version: v5.2.0-beta2\n",
" model_configuration: retrospective\n",
" model_initialization_time: 1979-02-01_00:00:00\n",
" model_output_type: land\n",
" model_output_valid_time: 1979-02-01_03:00:00\n",
" model_total_valid_times: 472\n",
" proj4: +proj=lcc +units=m +a=6370000.0 +b=6370000.0 ..."
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds"
]
},
{
"cell_type": "markdown",
"id": "aaf145c1-e623-4b0e-9a2b-d77e0fcc505b",
"metadata": {},
"source": [
"#### Select high-priority vars only"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3b973279-f918-4f88-b9c5-290a47d5a76f",
"metadata": {},
"outputs": [],
"source": [
"ds = ds[['ACCET', 'SNEQV', 'FSNO', 'crs']]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f22e4b63-fb26-4284-8fe2-a1bf36e2e0ac",
"metadata": {},
"outputs": [
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".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (time: 116631, y: 3840, x: 4608)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 1979-02-01T03:00:00 ... 2020-12-31T21:00:00\n",
" * x (x) float64 -2.303e+06 -2.302e+06 ... 2.303e+06 2.304e+06\n",
" * y (y) float64 -1.92e+06 -1.919e+06 -1.918e+06 ... 1.918e+06 1.919e+06\n",
"Data variables:\n",
" ACCET (time, y, x) float64 dask.array&lt;chunksize=(1, 3840, 4608), meta=np.ndarray&gt;\n",
" SNEQV (time, y, x) float64 dask.array&lt;chunksize=(1, 3840, 4608), meta=np.ndarray&gt;\n",
" FSNO (time, y, x) float64 dask.array&lt;chunksize=(1, 3840, 4608), meta=np.ndarray&gt;\n",
" crs object ...\n",
"Attributes:\n",
" Conventions: CF-1.6\n",
" GDAL_DataType: Generic\n",
" TITLE: OUTPUT FROM WRF-Hydro v5.2.0-beta2\n",
" code_version: v5.2.0-beta2\n",
" model_configuration: retrospective\n",
" model_initialization_time: 1979-02-01_00:00:00\n",
" model_output_type: land\n",
" model_output_valid_time: 1979-02-01_03:00:00\n",
" model_total_valid_times: 472\n",
" proj4: +proj=lcc +units=m +a=6370000.0 +b=6370000.0 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fd47cb80-5d9d-4fef-b9c2-ee37443dfb07' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fd47cb80-5d9d-4fef-b9c2-ee37443dfb07' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 116631</li><li><span class='xr-has-index'>y</span>: 3840</li><li><span class='xr-has-index'>x</span>: 4608</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c5fef774-dfc1-4aa8-9950-dd0441b4a027' class='xr-section-summary-in' type='checkbox' checked><label for='section-c5fef774-dfc1-4aa8-9950-dd0441b4a027' 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'>1979-02-01T03:00:00 ... 2020-12-...</div><input id='attrs-68475e65-dc2d-42c3-b51d-74da040f2695' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68475e65-dc2d-42c3-b51d-74da040f2695' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9e5717e-2064-44a8-9785-63450c9938af' class='xr-var-data-in' type='checkbox'><label for='data-c9e5717e-2064-44a8-9785-63450c9938af' 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>valid output time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>valid_max :</span></dt><dd>4862880</dd><dt><span>valid_min :</span></dt><dd>4778100</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-02-01T03:00:00.000000000&#x27;, &#x27;1979-02-01T06:00:00.000000000&#x27;,\n",
" &#x27;1979-02-01T09:00:00.000000000&#x27;, ..., &#x27;2020-12-31T15:00:00.000000000&#x27;,\n",
" &#x27;2020-12-31T18:00:00.000000000&#x27;, &#x27;2020-12-31T21:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.303e+06 -2.302e+06 ... 2.304e+06</div><input id='attrs-300d2341-7df2-439d-ae73-71f903c87203' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-300d2341-7df2-439d-ae73-71f903c87203' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fe285ec-e51e-42b6-97d1-ccdc5fd055d1' class='xr-var-data-in' type='checkbox'><label for='data-1fe285ec-e51e-42b6-97d1-ccdc5fd055d1' 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>_CoordinateAxisType :</span></dt><dd>GeoX</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>resolution :</span></dt><dd>1000.0</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-2303499.25, -2302499.25, -2301499.25, ..., 2301500.75, 2302500.75,\n",
" 2303500.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.92e+06 -1.919e+06 ... 1.919e+06</div><input id='attrs-4fd3e583-4da5-41c4-b8c7-5571ab0abc18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fd3e583-4da5-41c4-b8c7-5571ab0abc18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bd60d9a-ad6d-4c7d-ad81-40f5d98b267b' class='xr-var-data-in' type='checkbox'><label for='data-6bd60d9a-ad6d-4c7d-ad81-40f5d98b267b' 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>_CoordinateAxisType :</span></dt><dd>GeoY</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>resolution :</span></dt><dd>1000.0</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-1919500.375, -1918500.375, -1917500.375, ..., 1917499.625,\n",
" 1918499.625, 1919499.625])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f93ce710-de77-41f0-962f-c08a50197cfa' class='xr-section-summary-in' type='checkbox' checked><label for='section-f93ce710-de77-41f0-962f-c08a50197cfa' class='xr-section-summary' >Data variables: <span>(4)</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>ACCET</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 3840, 4608), meta=np.ndarray&gt;</div><input id='attrs-51db91a8-f0ec-4e21-85de-2d05a51443f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51db91a8-f0ec-4e21-85de-2d05a51443f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfe3ae58-d981-482e-9ab8-15ed5fde42f4' class='xr-var-data-in' type='checkbox'><label for='data-cfe3ae58-d981-482e-9ab8-15ed5fde42f4' 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>esri_pe_string :</span></dt><dd>PROJCS[&quot;Lambert_Conformal_Conic&quot;,GEOGCS[&quot;GCS_Sphere&quot;,DATUM[&quot;D_Sphere&quot;,SPHEROID[&quot;Sphere&quot;,6370000.0,0.0]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Lambert_Conformal_Conic_2SP&quot;],PARAMETER[&quot;false_easting&quot;,0.0],PARAMETER[&quot;false_northing&quot;,0.0],PARAMETER[&quot;central_meridian&quot;,-97.0],PARAMETER[&quot;standard_parallel_1&quot;,30.0],PARAMETER[&quot;standard_parallel_2&quot;,60.0],PARAMETER[&quot;latitude_of_origin&quot;,40.0],UNIT[&quot;Meter&quot;,1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Accumulated total ET</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_range :</span></dt><dd>[-100000, 100000000]</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> 15.02 TiB </td>\n",
" <td> 135.00 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (116631, 3840, 4608) </td>\n",
" <td> (1, 3840, 4608) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 116631 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>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-11e48325-725b-4ebf-9afd-0de29b3a4f86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11e48325-725b-4ebf-9afd-0de29b3a4f86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09f2fbc6-b76d-4438-9a12-c0f8e2e12186' class='xr-var-data-in' type='checkbox'><label for='data-09f2fbc6-b76d-4438-9a12-c0f8e2e12186' 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>GeoTransform :</span></dt><dd>-2303999.17655 1000.0 0 1919999.66329 0 -1000.0</dd><dt><span>_CoordinateAxes :</span></dt><dd>y x</dd><dt><span>_CoordinateTransformType :</span></dt><dd>Projection</dd><dt><span>earth_radius :</span></dt><dd>6370000.0</dd><dt><span>esri_pe_string :</span></dt><dd>PROJCS[&quot;Lambert_Conformal_Conic&quot;,GEOGCS[&quot;GCS_Sphere&quot;,DATUM[&quot;D_Sphere&quot;,SPHEROID[&quot;Sphere&quot;,6370000.0,0.0]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Lambert_Conformal_Conic_2SP&quot;],PARAMETER[&quot;false_easting&quot;,0.0],PARAMETER[&quot;false_northing&quot;,0.0],PARAMETER[&quot;central_meridian&quot;,-97.0],PARAMETER[&quot;standard_parallel_1&quot;,30.0],PARAMETER[&quot;standard_parallel_2&quot;,60.0],PARAMETER[&quot;latitude_of_origin&quot;,40.0],UNIT[&quot;Meter&quot;,1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>inverse_flattening :</span></dt><dd>0.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>40.0</dd><dt><span>long_name :</span></dt><dd>CRS definition</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-97.0</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6370000.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;Lambert_Conformal_Conic&quot;,GEOGCS[&quot;GCS_Sphere&quot;,DATUM[&quot;D_Sphere&quot;,SPHEROID[&quot;Sphere&quot;,6370000.0,0.0]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Lambert_Conformal_Conic_2SP&quot;],PARAMETER[&quot;false_easting&quot;,0.0],PARAMETER[&quot;false_northing&quot;,0.0],PARAMETER[&quot;central_meridian&quot;,-97.0],PARAMETER[&quot;standard_parallel_1&quot;,30.0],PARAMETER[&quot;standard_parallel_2&quot;,60.0],PARAMETER[&quot;latitude_of_origin&quot;,40.0],UNIT[&quot;Meter&quot;,1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision</dd><dt><span>standard_parallel :</span></dt><dd>[30.0, 60.0]</dd><dt><span>transform_name :</span></dt><dd>lambert_conformal_conic</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=object]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-472dbc17-d537-45b7-9cda-3dbb888d6994' class='xr-section-summary-in' type='checkbox' ><label for='section-472dbc17-d537-45b7-9cda-3dbb888d6994' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-74795272-3ba8-4855-87f5-9c00f666f9d8' class='xr-index-data-in' type='checkbox'/><label for='index-74795272-3ba8-4855-87f5-9c00f666f9d8' 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;1979-02-01 03:00:00&#x27;, &#x27;1979-02-01 06:00:00&#x27;,\n",
" &#x27;1979-02-01 09:00:00&#x27;, &#x27;1979-02-01 12:00:00&#x27;,\n",
" &#x27;1979-02-01 15:00:00&#x27;, &#x27;1979-02-01 18:00:00&#x27;,\n",
" &#x27;1979-02-01 21:00:00&#x27;, &#x27;1979-02-02 00:00:00&#x27;,\n",
" &#x27;1979-02-02 03:00:00&#x27;, &#x27;1979-02-02 06:00:00&#x27;,\n",
" ...\n",
" &#x27;2020-12-30 18:00:00&#x27;, &#x27;2020-12-30 21:00:00&#x27;,\n",
" &#x27;2020-12-31 00:00:00&#x27;, &#x27;2020-12-31 03:00:00&#x27;,\n",
" &#x27;2020-12-31 06:00:00&#x27;, &#x27;2020-12-31 09:00:00&#x27;,\n",
" &#x27;2020-12-31 12:00:00&#x27;, &#x27;2020-12-31 15:00:00&#x27;,\n",
" &#x27;2020-12-31 18:00:00&#x27;, &#x27;2020-12-31 21:00:00&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=116631, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-781b48d0-5f82-4ab8-9481-f54534abccf4' class='xr-index-data-in' type='checkbox'/><label for='index-781b48d0-5f82-4ab8-9481-f54534abccf4' 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(Float64Index([-2303499.25, -2302499.25, -2301499.25, -2300499.25, -2299499.25,\n",
" -2298499.25, -2297499.25, -2296499.25, -2295499.25, -2294499.25,\n",
" ...\n",
" 2294500.75, 2295500.75, 2296500.75, 2297500.75, 2298500.75,\n",
" 2299500.75, 2300500.75, 2301500.75, 2302500.75, 2303500.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=4608))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-627a0783-55a2-45f6-8206-2ad5c4468d3f' class='xr-index-data-in' type='checkbox'/><label for='index-627a0783-55a2-45f6-8206-2ad5c4468d3f' 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(Float64Index([-1919500.375, -1918500.375, -1917500.375, -1916500.375,\n",
" -1915500.375, -1914500.375, -1913500.375, -1912500.375,\n",
" -1911500.375, -1910500.375,\n",
" ...\n",
" 1910499.625, 1911499.625, 1912499.625, 1913499.625,\n",
" 1914499.625, 1915499.625, 1916499.625, 1917499.625,\n",
" 1918499.625, 1919499.625],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=3840))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c74b0f61-4e0f-47f0-9cd5-b21240369e21' class='xr-section-summary-in' type='checkbox' ><label for='section-c74b0f61-4e0f-47f0-9cd5-b21240369e21' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>GDAL_DataType :</span></dt><dd>Generic</dd><dt><span>TITLE :</span></dt><dd>OUTPUT FROM WRF-Hydro v5.2.0-beta2</dd><dt><span>code_version :</span></dt><dd>v5.2.0-beta2</dd><dt><span>model_configuration :</span></dt><dd>retrospective</dd><dt><span>model_initialization_time :</span></dt><dd>1979-02-01_00:00:00</dd><dt><span>model_output_type :</span></dt><dd>land</dd><dt><span>model_output_valid_time :</span></dt><dd>1979-02-01_03:00:00</dd><dt><span>model_total_valid_times :</span></dt><dd>472</dd><dt><span>proj4 :</span></dt><dd>+proj=lcc +units=m +a=6370000.0 +b=6370000.0 +lat_1=30.0 +lat_2=60.0 +lat_0=40.0 +lon_0=-97.0 +x_0=0 +y_0=0 +k_0=1.0 +nadgrids=@null +wktext +no_defs</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (time: 116631, y: 3840, x: 4608)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 1979-02-01T03:00:00 ... 2020-12-31T21:00:00\n",
" * x (x) float64 -2.303e+06 -2.302e+06 ... 2.303e+06 2.304e+06\n",
" * y (y) float64 -1.92e+06 -1.919e+06 -1.918e+06 ... 1.918e+06 1.919e+06\n",
"Data variables:\n",
" ACCET (time, y, x) float64 dask.array<chunksize=(1, 3840, 4608), meta=np.ndarray>\n",
" SNEQV (time, y, x) float64 dask.array<chunksize=(1, 3840, 4608), meta=np.ndarray>\n",
" FSNO (time, y, x) float64 dask.array<chunksize=(1, 3840, 4608), meta=np.ndarray>\n",
" crs object ...\n",
"Attributes:\n",
" Conventions: CF-1.6\n",
" GDAL_DataType: Generic\n",
" TITLE: OUTPUT FROM WRF-Hydro v5.2.0-beta2\n",
" code_version: v5.2.0-beta2\n",
" model_configuration: retrospective\n",
" model_initialization_time: 1979-02-01_00:00:00\n",
" model_output_type: land\n",
" model_output_valid_time: 1979-02-01_03:00:00\n",
" model_total_valid_times: 472\n",
" proj4: +proj=lcc +units=m +a=6370000.0 +b=6370000.0 ..."
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d34eca30-bf72-4a8a-8dfd-66f235c191da",
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;ACCET&#x27; (time: 144, y: 3840, x: 4608)&gt;\n",
"dask.array&lt;getitem, shape=(144, 3840, 4608), dtype=float64, chunksize=(1, 3840, 4608), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 1979-02-01T03:00:00 ... 1979-02-19\n",
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" esri_pe_string: PROJCS[&quot;Lambert_Conformal_Conic&quot;,GEOGCS[&quot;GCS_Sphere&quot;,DAT...\n",
" grid_mapping: crs\n",
" long_name: Accumulated total ET\n",
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" valid_range: [-100000, 100000000]</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'>'ACCET'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 144</li><li><span class='xr-has-index'>y</span>: 3840</li><li><span class='xr-has-index'>x</span>: 4608</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-659487c2-80b1-40e5-ba23-3d37673b0925' class='xr-array-in' type='checkbox' checked><label for='section-659487c2-80b1-40e5-ba23-3d37673b0925' 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, 3840, 4608), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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" &#x27;1979-02-18T15:00:00.000000000&#x27;, &#x27;1979-02-18T18:00:00.000000000&#x27;,\n",
" &#x27;1979-02-18T21:00:00.000000000&#x27;, &#x27;1979-02-19T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.303e+06 -2.302e+06 ... 2.304e+06</div><input id='attrs-5978e85a-a990-4e73-a4ac-1677d5f4c70a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5978e85a-a990-4e73-a4ac-1677d5f4c70a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84f348a3-c317-49ae-b58b-3e0b4971cc36' class='xr-var-data-in' type='checkbox'><label for='data-84f348a3-c317-49ae-b58b-3e0b4971cc36' 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>_CoordinateAxisType :</span></dt><dd>GeoX</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>resolution :</span></dt><dd>1000.0</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-2303499.25, -2302499.25, -2301499.25, ..., 2301500.75, 2302500.75,\n",
" 2303500.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.92e+06 -1.919e+06 ... 1.919e+06</div><input id='attrs-eacdc27b-01d0-467e-a330-488f91689666' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eacdc27b-01d0-467e-a330-488f91689666' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5110c532-22fc-47dd-8368-5bc15f4c3675' class='xr-var-data-in' type='checkbox'><label for='data-5110c532-22fc-47dd-8368-5bc15f4c3675' 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>_CoordinateAxisType :</span></dt><dd>GeoY</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>resolution :</span></dt><dd>1000.0</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-1919500.375, -1918500.375, -1917500.375, ..., 1917499.625,\n",
" 1918499.625, 1919499.625])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1151a293-a152-4d4a-9ef4-ecb732c7ad8d' class='xr-section-summary-in' type='checkbox' ><label for='section-1151a293-a152-4d4a-9ef4-ecb732c7ad8d' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d679adbe-7634-45f6-8233-9afd4c45b83a' class='xr-index-data-in' type='checkbox'/><label for='index-d679adbe-7634-45f6-8233-9afd4c45b83a' 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;1979-02-01 03:00:00&#x27;, &#x27;1979-02-01 06:00:00&#x27;,\n",
" &#x27;1979-02-01 09:00:00&#x27;, &#x27;1979-02-01 12:00:00&#x27;,\n",
" &#x27;1979-02-01 15:00:00&#x27;, &#x27;1979-02-01 18:00:00&#x27;,\n",
" &#x27;1979-02-01 21:00:00&#x27;, &#x27;1979-02-02 00:00:00&#x27;,\n",
" &#x27;1979-02-02 03:00:00&#x27;, &#x27;1979-02-02 06:00:00&#x27;,\n",
" ...\n",
" &#x27;1979-02-17 21:00:00&#x27;, &#x27;1979-02-18 00:00:00&#x27;,\n",
" &#x27;1979-02-18 03:00:00&#x27;, &#x27;1979-02-18 06:00:00&#x27;,\n",
" &#x27;1979-02-18 09:00:00&#x27;, &#x27;1979-02-18 12:00:00&#x27;,\n",
" &#x27;1979-02-18 15:00:00&#x27;, &#x27;1979-02-18 18:00:00&#x27;,\n",
" &#x27;1979-02-18 21:00:00&#x27;, &#x27;1979-02-19 00:00:00&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=144, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4b3d1123-95ca-48be-8212-8ef6415bf86d' class='xr-index-data-in' type='checkbox'/><label for='index-4b3d1123-95ca-48be-8212-8ef6415bf86d' 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(Float64Index([-2303499.25, -2302499.25, -2301499.25, -2300499.25, -2299499.25,\n",
" -2298499.25, -2297499.25, -2296499.25, -2295499.25, -2294499.25,\n",
" ...\n",
" 2294500.75, 2295500.75, 2296500.75, 2297500.75, 2298500.75,\n",
" 2299500.75, 2300500.75, 2301500.75, 2302500.75, 2303500.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=4608))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bb161be5-8ab8-47c5-96e0-1faff3fb01cc' class='xr-index-data-in' type='checkbox'/><label for='index-bb161be5-8ab8-47c5-96e0-1faff3fb01cc' 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(Float64Index([-1919500.375, -1918500.375, -1917500.375, -1916500.375,\n",
" -1915500.375, -1914500.375, -1913500.375, -1912500.375,\n",
" -1911500.375, -1910500.375,\n",
" ...\n",
" 1910499.625, 1911499.625, 1912499.625, 1913499.625,\n",
" 1914499.625, 1915499.625, 1916499.625, 1917499.625,\n",
" 1918499.625, 1919499.625],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=3840))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-34e2f771-9ab2-43e8-a9a2-bf86095c4cde' class='xr-section-summary-in' type='checkbox' checked><label for='section-34e2f771-9ab2-43e8-a9a2-bf86095c4cde' 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>esri_pe_string :</span></dt><dd>PROJCS[&quot;Lambert_Conformal_Conic&quot;,GEOGCS[&quot;GCS_Sphere&quot;,DATUM[&quot;D_Sphere&quot;,SPHEROID[&quot;Sphere&quot;,6370000.0,0.0]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Lambert_Conformal_Conic_2SP&quot;],PARAMETER[&quot;false_easting&quot;,0.0],PARAMETER[&quot;false_northing&quot;,0.0],PARAMETER[&quot;central_meridian&quot;,-97.0],PARAMETER[&quot;standard_parallel_1&quot;,30.0],PARAMETER[&quot;standard_parallel_2&quot;,60.0],PARAMETER[&quot;latitude_of_origin&quot;,40.0],UNIT[&quot;Meter&quot;,1.0]];-35691800 -29075200 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Accumulated total ET</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_range :</span></dt><dd>[-100000, 100000000]</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'ACCET' (time: 144, y: 3840, x: 4608)>\n",
"dask.array<getitem, shape=(144, 3840, 4608), dtype=float64, chunksize=(1, 3840, 4608), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 1979-02-01T03:00:00 ... 1979-02-19\n",
" * x (x) float64 -2.303e+06 -2.302e+06 ... 2.303e+06 2.304e+06\n",
" * y (y) float64 -1.92e+06 -1.919e+06 -1.918e+06 ... 1.918e+06 1.919e+06\n",
"Attributes:\n",
" esri_pe_string: PROJCS[\"Lambert_Conformal_Conic\",GEOGCS[\"GCS_Sphere\",DAT...\n",
" grid_mapping: crs\n",
" long_name: Accumulated total ET\n",
" units: mm\n",
" valid_range: [-100000, 100000000]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds['ACCET'].isel(time=slice(0,144))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "15d511f0-f82e-4250-930c-145feff08d27",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Conventions': 'CF-1.6',\n",
" 'GDAL_DataType': 'Generic',\n",
" 'TITLE': 'OUTPUT FROM WRF-Hydro v5.2.0-beta2',\n",
" 'code_version': 'v5.2.0-beta2',\n",
" 'model_configuration': 'retrospective',\n",
" 'model_initialization_time': '1979-02-01_00:00:00',\n",
" 'model_output_type': 'land',\n",
" 'model_output_valid_time': '1979-02-01_03:00:00',\n",
" 'model_total_valid_times': 472,\n",
" 'proj4': '+proj=lcc +units=m +a=6370000.0 +b=6370000.0 +lat_1=30.0 +lat_2=60.0 +lat_0=40.0 +lon_0=-97.0 +x_0=0 +y_0=0 +k_0=1.0 +nadgrids=@null +wktext +no_defs'}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.attrs"
]
},
{
"cell_type": "markdown",
"id": "e2c8e98a-1569-46a0-b85c-2e4d20916b4d",
"metadata": {},
"source": [
"#### set up zarr stores for temporary and final zarr stores on S3"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "86da688a-14bc-486b-87c0-c1d11a28de3f",
"metadata": {},
"outputs": [],
"source": [
"fs_write = fsspec.filesystem('s3', anon=False, skip_instance_cache=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a53b62f7-0df4-44b0-a03d-5dee35f2a00e",
"metadata": {},
"outputs": [],
"source": [
"temp_name = 'esip-qhub/testing/usgs/nwm1km.tmp'\n",
"target_name = 'esip-qhub/testing/usgs/nwm1km.zarr'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "61d64b30-f089-4200-8e98-56dd5d80bc48",
"metadata": {},
"outputs": [],
"source": [
"fs_write.rm(temp_name, recursive=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99febcd6-6e57-4997-a296-56e6e047fa0e",
"metadata": {},
"outputs": [],
"source": [
"fs_write.rm(target_name, recursive=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "94ffca87-dbb5-4a35-9dab-89270429cd9c",
"metadata": {},
"outputs": [],
"source": [
"temp_store = fs_write.get_mapper(temp_name)\n",
"target_store = fs_write.get_mapper(target_name)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "9be12a7f-5737-4c63-b0e1-f80aaf4504ad",
"metadata": {},
"outputs": [],
"source": [
"ds = ds.drop('crs')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "a9989b3c-f8ea-4ec0-8781-1505842e0835",
"metadata": {},
"outputs": [],
"source": [
"a = len(ds.time)/(144/2)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "5a945faa-d66c-44d1-a6ae-099e9ed53adf",
"metadata": {},
"outputs": [],
"source": [
"b = (len(ds.x) * len(ds.y))/((96*2)*(132*2))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "9cdfac4b-65f3-4f5c-8319-962d527cd32f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.640266927083334"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a/b"
]
},
{
"cell_type": "markdown",
"id": "5ee745ca-7648-48b1-abc3-6c10992e1fbc",
"metadata": {},
"source": [
"#### Rechunk!"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "36b812ea-01ae-461f-b8b3-1eb7b3ea9711",
"metadata": {},
"outputs": [
{
"ename": "ReadOnlyError",
"evalue": "object is read-only",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mReadOnlyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m rechunked \u001b[38;5;241m=\u001b[39m \u001b[43mrechunk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mslice\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m144\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_chunks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43my\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m96\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m132\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m144\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_store\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtarget_store\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemp_store\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtemp_store\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_mem\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m3.5GiB\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/home/conda/users/6da996e8bac98e9914efe8740ae916e58e2ced6a618659b47fccce8f5bc9cd6a-20230314-182641-028005-117-pangeo/lib/python3.10/site-packages/rechunker/api.py:297\u001b[0m, in \u001b[0;36mrechunk\u001b[0;34m(source, target_chunks, max_mem, target_store, target_options, temp_store, temp_options, executor)\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(executor, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 295\u001b[0m executor \u001b[38;5;241m=\u001b[39m _get_executor(executor)\n\u001b[0;32m--> 297\u001b[0m copy_spec, intermediate, target \u001b[38;5;241m=\u001b[39m \u001b[43m_setup_rechunk\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 298\u001b[0m \u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 299\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_chunks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtarget_chunks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 300\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_mem\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_mem\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 301\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_store\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtarget_store\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 302\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtarget_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 303\u001b[0m \u001b[43m \u001b[49m\u001b[43mtemp_store\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtemp_store\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 304\u001b[0m \u001b[43m \u001b[49m\u001b[43mtemp_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtemp_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 305\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 306\u001b[0m plan \u001b[38;5;241m=\u001b[39m executor\u001b[38;5;241m.\u001b[39mprepare_plan(copy_spec)\n\u001b[1;32m 307\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Rechunked(executor, plan, source, intermediate, target)\n",
"File \u001b[0;32m/home/conda/users/6da996e8bac98e9914efe8740ae916e58e2ced6a618659b47fccce8f5bc9cd6a-20230314-182641-028005-117-pangeo/lib/python3.10/site-packages/rechunker/api.py:395\u001b[0m, in \u001b[0;36m_setup_rechunk\u001b[0;34m(source, target_chunks, max_mem, target_store, target_options, temp_store, temp_options)\u001b[0m\n\u001b[1;32m 392\u001b[0m attrs \u001b[38;5;241m=\u001b[39m _encode_zarr_attributes(attrs)\n\u001b[1;32m 394\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m temp_store \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 395\u001b[0m temp_group \u001b[38;5;241m=\u001b[39m \u001b[43mzarr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtemp_store\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 396\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 397\u001b[0m temp_group \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m/home/conda/users/6da996e8bac98e9914efe8740ae916e58e2ced6a618659b47fccce8f5bc9cd6a-20230314-182641-028005-117-pangeo/lib/python3.10/site-packages/zarr/hierarchy.py:1355\u001b[0m, in \u001b[0;36mgroup\u001b[0;34m(store, overwrite, chunk_store, cache_attrs, synchronizer, path, zarr_version)\u001b[0m\n\u001b[1;32m 1352\u001b[0m requires_init \u001b[38;5;241m=\u001b[39m overwrite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m contains_group(store, path)\n\u001b[1;32m 1354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m requires_init:\n\u001b[0;32m-> 1355\u001b[0m \u001b[43minit_group\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverwrite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverwrite\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunk_store\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunk_store\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1356\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1358\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Group(store, read_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, chunk_store\u001b[38;5;241m=\u001b[39mchunk_store,\n\u001b[1;32m 1359\u001b[0m cache_attrs\u001b[38;5;241m=\u001b[39mcache_attrs, synchronizer\u001b[38;5;241m=\u001b[39msynchronizer, path\u001b[38;5;241m=\u001b[39mpath,\n\u001b[1;32m 1360\u001b[0m zarr_version\u001b[38;5;241m=\u001b[39mzarr_version)\n",
"File \u001b[0;32m/home/conda/users/6da996e8bac98e9914efe8740ae916e58e2ced6a618659b47fccce8f5bc9cd6a-20230314-182641-028005-117-pangeo/lib/python3.10/site-packages/zarr/storage.py:643\u001b[0m, in \u001b[0;36minit_group\u001b[0;34m(store, overwrite, path, chunk_store)\u001b[0m\n\u001b[1;32m 640\u001b[0m store[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzarr.json\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m store\u001b[38;5;241m.\u001b[39m_metadata_class\u001b[38;5;241m.\u001b[39mencode_hierarchy_metadata(\u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 642\u001b[0m \u001b[38;5;66;03m# initialise metadata\u001b[39;00m\n\u001b[0;32m--> 643\u001b[0m \u001b[43m_init_group_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverwrite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverwrite\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 644\u001b[0m \u001b[43m \u001b[49m\u001b[43mchunk_store\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunk_store\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 646\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m store_version \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m:\n\u001b[1;32m 647\u001b[0m \u001b[38;5;66;03m# TODO: Should initializing a v3 group also create a corresponding\u001b[39;00m\n\u001b[1;32m 648\u001b[0m \u001b[38;5;66;03m# empty folder under data/root/? I think probably not until there\u001b[39;00m\n\u001b[1;32m 649\u001b[0m \u001b[38;5;66;03m# is actual data written there.\u001b[39;00m\n\u001b[1;32m 650\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n",
"File \u001b[0;32m/home/conda/users/6da996e8bac98e9914efe8740ae916e58e2ced6a618659b47fccce8f5bc9cd6a-20230314-182641-028005-117-pangeo/lib/python3.10/site-packages/zarr/storage.py:706\u001b[0m, in \u001b[0;36m_init_group_metadata\u001b[0;34m(store, overwrite, path, chunk_store)\u001b[0m\n\u001b[1;32m 704\u001b[0m key \u001b[38;5;241m=\u001b[39m _prefix_to_group_key(store, _path_to_prefix(path))\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(store, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_metadata_class\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 706\u001b[0m \u001b[43mstore\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m store\u001b[38;5;241m.\u001b[39m_metadata_class\u001b[38;5;241m.\u001b[39mencode_group_metadata(meta) \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 707\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 708\u001b[0m store[key] \u001b[38;5;241m=\u001b[39m encode_group_metadata(meta)\n",
"File \u001b[0;32m/home/conda/users/6da996e8bac98e9914efe8740ae916e58e2ced6a618659b47fccce8f5bc9cd6a-20230314-182641-028005-117-pangeo/lib/python3.10/site-packages/zarr/storage.py:1398\u001b[0m, in \u001b[0;36mFSStore.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 1396\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__setitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key, value):\n\u001b[1;32m 1397\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m-> 1398\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ReadOnlyError()\n\u001b[1;32m 1399\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_normalize_key(key)\n\u001b[1;32m 1400\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdir_path(key)\n",
"\u001b[0;31mReadOnlyError\u001b[0m: object is read-only"
]
}
],
"source": [
"rechunked = rechunk(ds.isel(time=slice(0,144)), target_chunks={'y':96*2, 'x':132*2, 'time':144/2},\n",
" target_store=target_store, temp_store=temp_store, max_mem='3.5GiB')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e86843c4-4f9e-4ec3-b1b6-f9969d22b3a4",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"rechunked.execute(retries=10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3a211f08-4c4f-46fa-8a9f-5231ec17d183",
"metadata": {},
"outputs": [],
"source": [
"zarr.convenience.consolidate_metadata(target_store)"
]
},
{
"cell_type": "markdown",
"id": "8ba15656-c501-4aae-b327-c6cb0d24dce7",
"metadata": {},
"source": [
"#### Explore the rechunked dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15466b5d-13f3-4d45-a09d-136e6ddcb907",
"metadata": {},
"outputs": [],
"source": [
"ds2 = xr.open_dataset(target_store, engine='zarr', chunks={})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "91c1a356-5ad9-4f72-ae3e-85bb29f0f20b",
"metadata": {},
"outputs": [],
"source": [
"ds2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cee33374-49d0-4607-a812-c9ed98eefb48",
"metadata": {},
"outputs": [],
"source": [
"ds2.ACCET"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "91926a5d-0e86-4b57-b5ef-53b9c3b51c03",
"metadata": {},
"outputs": [],
"source": [
"import hvplot.xarray\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c23b9064-8f13-432d-85b4-cbe8ec5384a3",
"metadata": {},
"outputs": [],
"source": [
"ds2.ACCET[:,2000,2000].hvplot(x='time')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6d4f1a5a-62b3-44f8-8c68-e5a3fd5d1c44",
"metadata": {},
"outputs": [],
"source": [
"import rechunker"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5912a9a3-c954-47ae-bb6f-6963a6398a63",
"metadata": {},
"outputs": [],
"source": [
"rechunker.__version__"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb7cc405-309f-4eab-9b46-c22d3235e34c",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.10.9"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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