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Created February 14, 2023 19:50
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NWM_1km_DFReferenceFileSystem.ipynb
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"# Open NWM 1km dataset as DFReferenceFileSystem \n",
"\n",
"Open dataset as a fsspec `DFReferenceFileSystem` filesystem by reading references from a collection of Parquet files: one file containing global metadata and coordinate variable references, and one file for each of the data variables. \n",
"\n",
"The big wins here are lazy-loading of the references for each variable, and the more efficient construction of the virtual fsspec filesystem from the Parquet files (JSON is slow to decode)."
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"source": [
"import fsspec\n",
"from fsspec.implementations.reference import DFReferenceFileSystem\n",
"import xarray as xr\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "732722cb-4430-4359-9014-eb9c551e7873",
"metadata": {},
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"source": [
"fs = fsspec.filesystem('s3', anon=True, \n",
" client_kwargs={'endpoint_url':'https://ncsa.osn.xsede.org'})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c6f5f293-ced5-44c2-8231-a77e22c451e1",
"metadata": {},
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"source": [
"s3_lazy_refs = 's3://esip/noaa/nwm/lazy_refs'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "691c9634-cc24-407f-ae56-50d4247960a5",
"metadata": {},
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"name": "stdout",
"output_type": "stream",
"text": [
"Number of Parquet files: 21\n",
"Total size of Parquet references: 0.492091486 GB\n"
]
}
],
"source": [
"lazy_refs_size = [fs.size(f) for f in fs.ls(s3_lazy_refs)]\n",
"print(f'Number of Parquet files: {len(lazy_refs_size)}')\n",
"print(f'Total size of Parquet references: {np.array(lazy_refs_size).sum()/1e9} GB')"
]
},
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"execution_count": 5,
"id": "b5f4c4da-27c1-4374-ba60-61315cd965b1",
"metadata": {},
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"source": [
"r_opts = {'anon': True}\n",
"t_opts = {'anon': True, 'client_kwargs':{'endpoint_url':'https://ncsa.osn.xsede.org'}}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "69e11199-c729-42a0-ad25-0cbbf97088df",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 4.94 s, sys: 353 ms, total: 5.3 s\n",
"Wall time: 8.62 s\n"
]
}
],
"source": [
"%%time\n",
"fs2 = DFReferenceFileSystem(s3_lazy_refs, lazy=True, target_options=t_opts,\n",
" remote_protocol='s3', remote_options=r_opts)\n",
"m = fs2.get_mapper(\"\")\n",
"ds = xr.open_dataset(m, engine=\"zarr\", chunks={}, backend_kwargs=dict(consolidated=False))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "cd8ddfb0-7b57-43aa-98dc-b9a837e57542",
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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",
" * 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&lt;chunksize=(1, 768, 922), meta=np.ndarray&gt;\n",
" ACSNOM (time, y, x) float64 dask.array&lt;chunksize=(1, 768, 922), meta=np.ndarray&gt;\n",
" ALBEDO (time, y, x) float64 dask.array&lt;chunksize=(1, 768, 922), meta=np.ndarray&gt;\n",
" ALBSND (time, y, vis_nir, x) float64 dask.array&lt;chunksize=(1, 960, 1, 1152), meta=np.ndarray&gt;\n",
" ALBSNI (time, y, vis_nir, x) float64 dask.array&lt;chunksize=(1, 960, 1, 1152), meta=np.ndarray&gt;\n",
" COSZ (time, y, x) float64 dask.array&lt;chunksize=(1, 768, 922), meta=np.ndarray&gt;\n",
" ... ...\n",
" SNOWH (time, y, x) float64 dask.array&lt;chunksize=(1, 768, 922), meta=np.ndarray&gt;\n",
" SOIL_M (time, y, soil_layers_stag, x) float64 dask.array&lt;chunksize=(1, 768, 1, 922), meta=np.ndarray&gt;\n",
" SOIL_W (time, y, soil_layers_stag, x) float64 dask.array&lt;chunksize=(1, 768, 1, 922), meta=np.ndarray&gt;\n",
" TRAD (time, y, x) float64 dask.array&lt;chunksize=(1, 768, 922), meta=np.ndarray&gt;\n",
" UGDRNOFF (time, y, x) float64 dask.array&lt;chunksize=(1, 768, 922), 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-3e951f09-0ad5-45a3-8b60-86891dddac2c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3e951f09-0ad5-45a3-8b60-86891dddac2c' 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-541e2ad7-dd8b-459f-9b28-6320531b6a54' class='xr-section-summary-in' type='checkbox' checked><label for='section-541e2ad7-dd8b-459f-9b28-6320531b6a54' 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-8fbc21ea-6d35-4735-af71-97be64d729c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fbc21ea-6d35-4735-af71-97be64d729c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3217c5bb-afed-442c-bc20-e927a7e3b092' class='xr-var-data-in' type='checkbox'><label for='data-3217c5bb-afed-442c-bc20-e927a7e3b092' 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-b78b9fe5-a9a5-4317-9a48-293d82e826e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b78b9fe5-a9a5-4317-9a48-293d82e826e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a9b6f4d-a0f1-4cb1-b04d-26b5d68db1c7' class='xr-var-data-in' type='checkbox'><label for='data-9a9b6f4d-a0f1-4cb1-b04d-26b5d68db1c7' 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-dfb5e7e7-a7cf-484e-bc11-13ba0dbb824d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfb5e7e7-a7cf-484e-bc11-13ba0dbb824d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c57c0807-0f12-4661-9508-e6ab15e54526' class='xr-var-data-in' type='checkbox'><label for='data-c57c0807-0f12-4661-9508-e6ab15e54526' 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-e73c36f4-7314-4736-8d3b-516c08432fe7' class='xr-section-summary-in' type='checkbox' ><label for='section-e73c36f4-7314-4736-8d3b-516c08432fe7' 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, 768, 922), meta=np.ndarray&gt;</div><input id='attrs-8805b883-7e45-4091-89de-eadc4d953fd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8805b883-7e45-4091-89de-eadc4d953fd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27946bf3-7898-441c-ab7d-9a0eb77629c9' class='xr-var-data-in' type='checkbox'><label for='data-27946bf3-7898-441c-ab7d-9a0eb77629c9' 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> 5.40 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (116631, 3840, 4608) </td>\n",
" <td> (1, 768, 922) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 2915775 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></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, 768, 922), meta=np.ndarray&gt;</div><input id='attrs-aabcb7e1-ad5d-471c-bc72-4af0ac27baac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aabcb7e1-ad5d-471c-bc72-4af0ac27baac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dac66b6-319c-4f34-aecd-d24d194b65cd' class='xr-var-data-in' type='checkbox'><label for='data-7dac66b6-319c-4f34-aecd-d24d194b65cd' 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",
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" <table style=\"border-collapse: collapse;\">\n",
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" <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> 5.40 MiB </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (116631, 3840, 4608) </td>\n",
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" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALBEDO</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, 768, 922), meta=np.ndarray&gt;</div><input id='attrs-183cc4a6-09c3-49c9-994e-391cf0a388c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-183cc4a6-09c3-49c9-994e-391cf0a388c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c40e9c84-65cd-40fc-8088-e2e2276cd46a' class='xr-var-data-in' type='checkbox'><label for='data-c40e9c84-65cd-40fc-8088-e2e2276cd46a' 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>Surface albedo</dd><dt><span>units :</span></dt><dd>-</dd><dt><span>valid_range :</span></dt><dd>[0, 100]</dd></dl></div><div class='xr-var-data'><table>\n",
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" <td> (116631, 3840, 4608) </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALBSNI</span></div><div class='xr-var-dims'>(time, y, vis_nir, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 960, 1, 1152), meta=np.ndarray&gt;</div><input id='attrs-cbfc540c-e8f1-41e6-9d7e-2b78a0cc3f22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbfc540c-e8f1-41e6-9d7e-2b78a0cc3f22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ec0a107-e833-4fa0-b1c0-cde3be5c4a7b' class='xr-var-data-in' type='checkbox'><label for='data-5ec0a107-e833-4fa0-b1c0-cde3be5c4a7b' 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>snowpack albedo, diffuse</dd><dt><span>units :</span></dt><dd>-</dd><dt><span>valid_range :</span></dt><dd>[0, 100]</dd></dl></div><div class='xr-var-data'><table>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EDIR</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, 768, 922), meta=np.ndarray&gt;</div><input id='attrs-14474e53-e8db-456f-a460-d757a1ba88d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14474e53-e8db-456f-a460-d757a1ba88d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20d14364-7077-415b-a027-f5f51fbcea0e' class='xr-var-data-in' type='checkbox'><label for='data-20d14364-7077-415b-a027-f5f51fbcea0e' 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>Direct from soil evaporation rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>valid_range :</span></dt><dd>[-10000000, 10000000]</dd></dl></div><div class='xr-var-data'><table>\n",
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" <td>\n",
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" <td> 15.02 TiB </td>\n",
" <td> 5.40 MiB </td>\n",
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" <tr>\n",
" <th> Shape </th>\n",
" <td> (116631, 3840, 4608) </td>\n",
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" <td>\n",
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" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 15.02 TiB </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FSA</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, 768, 922), meta=np.ndarray&gt;</div><input id='attrs-f72641ac-92c8-4512-a5e0-bcf048353df3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f72641ac-92c8-4512-a5e0-bcf048353df3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d131d4d6-5043-46c1-ac88-197b3cbc88c1' class='xr-var-data-in' type='checkbox'><label for='data-d131d4d6-5043-46c1-ac88-197b3cbc88c1' 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>Total absorbed SW radiation</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>valid_range :</span></dt><dd>[-15000, 15000]</dd></dl></div><div class='xr-var-data'><table>\n",
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" <td>\n",
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" <tr>\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-4c9e3051-18d2-472f-be0d-ce15cac24a31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c9e3051-18d2-472f-be0d-ce15cac24a31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3007a9f-d49c-4bf3-9d98-1aaf407a1b0d' class='xr-var-data-in' type='checkbox'><label for='data-f3007a9f-d49c-4bf3-9d98-1aaf407a1b0d' 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-e97cd9b6-62d6-4de7-a45a-4920c072cc40' class='xr-section-summary-in' type='checkbox' ><label for='section-e97cd9b6-62d6-4de7-a45a-4920c072cc40' 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-2e1a5990-b04c-4053-aaed-2989c4ab6903' class='xr-index-data-in' type='checkbox'/><label for='index-2e1a5990-b04c-4053-aaed-2989c4ab6903' 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-4c5a705d-fa52-4ea5-9bcc-2f41f383ebe8' class='xr-index-data-in' type='checkbox'/><label for='index-4c5a705d-fa52-4ea5-9bcc-2f41f383ebe8' 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-9b3148ec-6ef6-408a-a701-ea74013f1436' class='xr-index-data-in' type='checkbox'/><label for='index-9b3148ec-6ef6-408a-a701-ea74013f1436' 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-b53e1727-ee2d-4429-ad60-135d5e20094e' class='xr-section-summary-in' type='checkbox' ><label for='section-b53e1727-ee2d-4429-ad60-135d5e20094e' 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>"
],
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"<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",
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"Dimensions without coordinates: vis_nir, soil_layers_stag\n",
"Data variables: (12/21)\n",
" ACCET (time, y, x) float64 dask.array<chunksize=(1, 768, 922), meta=np.ndarray>\n",
" ACSNOM (time, y, x) float64 dask.array<chunksize=(1, 768, 922), meta=np.ndarray>\n",
" ALBEDO (time, y, x) float64 dask.array<chunksize=(1, 768, 922), meta=np.ndarray>\n",
" ALBSND (time, y, vis_nir, x) float64 dask.array<chunksize=(1, 960, 1, 1152), meta=np.ndarray>\n",
" ALBSNI (time, y, vis_nir, x) float64 dask.array<chunksize=(1, 960, 1, 1152), meta=np.ndarray>\n",
" COSZ (time, y, x) float64 dask.array<chunksize=(1, 768, 922), meta=np.ndarray>\n",
" ... ...\n",
" SNOWH (time, y, x) float64 dask.array<chunksize=(1, 768, 922), meta=np.ndarray>\n",
" SOIL_M (time, y, soil_layers_stag, x) float64 dask.array<chunksize=(1, 768, 1, 922), meta=np.ndarray>\n",
" SOIL_W (time, y, soil_layers_stag, x) float64 dask.array<chunksize=(1, 768, 1, 922), meta=np.ndarray>\n",
" TRAD (time, y, x) float64 dask.array<chunksize=(1, 768, 922), meta=np.ndarray>\n",
" UGDRNOFF (time, y, x) float64 dask.array<chunksize=(1, 768, 922), 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 ..."
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},
"execution_count": 7,
"metadata": {},
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{
"cell_type": "markdown",
"id": "04d02447-a134-45eb-978f-e0d392435dbb",
"metadata": {},
"source": [
"Examine a specific variable:"
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},
{
"cell_type": "code",
"execution_count": 8,
"id": "ce2ec93e-a879-459f-804c-66a9f977db3f",
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".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.DataArray &#x27;TRAD&#x27; (time: 116631, y: 3840, x: 4608)&gt;\n",
"dask.array&lt;open_dataset-a244431a3fb8f9922040249c7a6bd104TRAD, shape=(116631, 3840, 4608), dtype=float64, chunksize=(1, 768, 922), chunktype=numpy.ndarray&gt;\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",
"Attributes:\n",
" esri_pe_string: PROJCS[&quot;Lambert_Conformal_Conic&quot;,GEOGCS[&quot;GCS_Sphere&quot;,DAT...\n",
" grid_mapping: crs\n",
" long_name: Surface radiative temperature\n",
" units: K\n",
" valid_range: [0, 4000]</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'>'TRAD'</div><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><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f40dab2b-b840-4b92-a5c6-148301971f41' class='xr-array-in' type='checkbox' checked><label for='section-f40dab2b-b840-4b92-a5c6-148301971f41' 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, 768, 922), 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> 15.02 TiB </td>\n",
" <td> 5.40 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (116631, 3840, 4608) </td>\n",
" <td> (1, 768, 922) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 2915775 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></div></li><li class='xr-section-item'><input id='section-15fb55e2-eb03-442b-8b9b-200e630d7d45' class='xr-section-summary-in' type='checkbox' checked><label for='section-15fb55e2-eb03-442b-8b9b-200e630d7d45' 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-54acd7b7-e9cf-4903-92c3-575bdd08f528' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54acd7b7-e9cf-4903-92c3-575bdd08f528' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71b00cff-ad03-4895-9f66-b9d9cf64806c' class='xr-var-data-in' type='checkbox'><label for='data-71b00cff-ad03-4895-9f66-b9d9cf64806c' 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-1b2dae9a-38b1-4134-86aa-4a20e280395d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b2dae9a-38b1-4134-86aa-4a20e280395d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3a16454-af60-4445-bcd7-cced1da4ccb9' class='xr-var-data-in' type='checkbox'><label for='data-d3a16454-af60-4445-bcd7-cced1da4ccb9' 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-9ae74a8e-3d26-4e91-938a-ac3c2968c63c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ae74a8e-3d26-4e91-938a-ac3c2968c63c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32045d41-3587-4344-b707-06ae6f0d82c9' class='xr-var-data-in' type='checkbox'><label for='data-32045d41-3587-4344-b707-06ae6f0d82c9' 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-630afa37-bc56-45a5-a4b5-ef44f9914093' class='xr-section-summary-in' type='checkbox' ><label for='section-630afa37-bc56-45a5-a4b5-ef44f9914093' 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-82407f85-2117-4765-bbdb-ab8698d8bea4' class='xr-index-data-in' type='checkbox'/><label for='index-82407f85-2117-4765-bbdb-ab8698d8bea4' 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-9dec4b4c-cb35-44ef-9c7b-38d7d2abf9d0' class='xr-index-data-in' type='checkbox'/><label for='index-9dec4b4c-cb35-44ef-9c7b-38d7d2abf9d0' 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-ca2a7a1f-0a0d-4d48-89a7-a4ea152b4174' class='xr-index-data-in' type='checkbox'/><label for='index-ca2a7a1f-0a0d-4d48-89a7-a4ea152b4174' 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-7399989b-8aee-47ca-9905-fee825130d3a' class='xr-section-summary-in' type='checkbox' checked><label for='section-7399989b-8aee-47ca-9905-fee825130d3a' 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>Surface radiative temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>valid_range :</span></dt><dd>[0, 4000]</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'TRAD' (time: 116631, y: 3840, x: 4608)>\n",
"dask.array<open_dataset-a244431a3fb8f9922040249c7a6bd104TRAD, shape=(116631, 3840, 4608), dtype=float64, chunksize=(1, 768, 922), chunktype=numpy.ndarray>\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",
"Attributes:\n",
" esri_pe_string: PROJCS[\"Lambert_Conformal_Conic\",GEOGCS[\"GCS_Sphere\",DAT...\n",
" grid_mapping: crs\n",
" long_name: Surface radiative temperature\n",
" units: K\n",
" valid_range: [0, 4000]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.TRAD"
]
},
{
"cell_type": "markdown",
"id": "151133ab-e6ee-4903-b23a-36bfef0286d9",
"metadata": {},
"source": [
"Compute the uncompressed size of the whole dataset in TB:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8dd5b70a-2585-4dce-b0b1-a2cd915c7952",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"462.28064798432"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.nbytes/1e12 "
]
},
{
"cell_type": "markdown",
"id": "7496c3b2-ae11-4af4-952f-18c3f3a3b73c",
"metadata": {},
"source": [
"Load some data at a specific time step. The first time a variable is accessed it will take longer as the references need to be loaded."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7b0a2164-053c-4f84-a9b7-172bf3cd83a5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 39.9 s, sys: 9.35 s, total: 49.3 s\n",
"Wall time: 54.9 s\n"
]
}
],
"source": [
"%%time \n",
"da = ds.TRAD.sel(time='1990-01-01 00:00').load()"
]
},
{
"cell_type": "markdown",
"id": "26510252-7ec2-4e21-8ddf-aff2eca56793",
"metadata": {},
"source": [
"Loading data for another time step is much faster as the references are already loaded:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ef01e203-f6cd-4c4e-ac3d-410afd5a4524",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 4.15 s, sys: 490 ms, total: 4.64 s\n",
"Wall time: 4.96 s\n"
]
}
],
"source": [
"%%time\n",
"da = ds.TRAD.sel(time='2015-01-01 00:00').load()"
]
},
{
"cell_type": "markdown",
"id": "1f41f59f-12aa-4e4c-bf64-c61ecfe50db5",
"metadata": {},
"source": [
"Compute the mean over the domain:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "bd30a9a2-2264-4628-8826-8b965fe55ded",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(266.92635398)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"da.mean().data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "73fe200b-ee97-4f49-8d42-60d53d0a7160",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x7f8ad7141700>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"da.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "570bf4f1-5f9e-4cf9-8fc8-d0c8b1356e14",
"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": "3.9.16"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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