datasets/gridmet/gridmet-example.ipynb

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    "## Accessing gridMET data with the Planetary Computer STAC API\n",
    "\n",
    "gridMET is a dataset of daily high-spatial resolution (~4-km, 1/24th degree) surface meteorological data covering the contiguous US from 1979. These data can provide important inputs for ecological, agricultural, and hydrological models.\n",
    "\n",
    "This example will show you how to create an animation of air temperature for a year's worth of data.\n",
    "\n",
    "## Data Access\n",
    "\n",
    "The datasets hosted by the Planetary Computer are available from [Azure Blob Storage](https://docs.microsoft.com/en-us/azure/storage/blobs/). We'll use [pystac-client](https://pystac-client.readthedocs.io/) to search the Planetary Computer's [STAC API](https://planetarycomputer.microsoft.com/api/stac/v1/docs) for the subset of the data that we care about, and then we'll load the data directly from Azure Blob Storage. We'll specify a `modifier` so that we can access the data stored in the Planetary Computer's private Blob Storage Containers. See [Reading from the STAC API](https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac/) and [Using tokens for data access](https://planetarycomputer.microsoft.com/docs/concepts/sas/) for more."
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       "<Asset href=abfs://gridmet/gridmet.zarr>"
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   "source": [
    "import pystac_client\n",
    "import planetary_computer\n",
    "\n",
    "catalog = pystac_client.Client.open(\n",
    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
    "    modifier=planetary_computer.sign_inplace,\n",
    ")\n",
    "\n",
    "asset = catalog.get_collection(\"gridmet\").assets[\"zarr-abfs\"]\n",
    "asset"
   ]
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    "Notice that we signed the asset using `planetary_computer`. This places a read-only SAS token in the `xarray:storage_options` dictionary. See more at [Using tokens for data access](https://planetarycomputer.microsoft.com/docs/concepts/sas/).\n",
    "\n",
    "Now this asset can be opened with xarray."
   ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                    (time: 15341, lat: 585,\n",
       "                                                lon: 1386, crs: 1)\n",
       "Coordinates:\n",
       "  * crs                                        (crs) uint16 3\n",
       "  * lat                                        (lat) float64 49.4 ... 25.07\n",
       "  * lon                                        (lon) float64 -124.8 ... -67.06\n",
       "  * time                                       (time) datetime64[ns] 1979-01-...\n",
       "Data variables:\n",
       "    air_temperature                            (time, lat, lon) float32 dask.array&lt;chunksize=(30, 585, 1386), meta=np.ndarray&gt;\n",
       "    burning_index_g                            (time, lat, lon) float32 dask.array&lt;chunksize=(30, 585, 1386), meta=np.ndarray&gt;\n",
       "    dead_fuel_moisture_1000hr                  (time, lat, lon) float32 dask.array&lt;chunksize=(30, 585, 1386), meta=np.ndarray&gt;\n",
       "    dead_fuel_moisture_100hr                   (time, lat, lon) float32 dask.array&lt;chunksize=(30, 585, 1386), meta=np.ndarray&gt;\n",
       "    mean_vapor_pressure_deficit                (time, lat, lon) float32 dask.array&lt;chunksize=(30, 585, 1386), meta=np.ndarray&gt;\n",
       "    potential_evapotranspiration               (time, lat, lon) float32 dask.array&lt;chunksize=(30, 585, 1386), meta=np.ndarray&gt;\n",
       "    precipitation_amount                       (time, lat, lon) float32 dask.array&lt;chunksize=(30, 585, 1386), meta=np.ndarray&gt;\n",
       "    relative_humidity                          (time, lat, lon) float32 dask.array&lt;chunksize=(30, 585, 1386), meta=np.ndarray&gt;\n",
       "    specific_humidity                          (time, lat, lon) float32 dask.array&lt;chunksize=(30, 585, 1386), meta=np.ndarray&gt;\n",
       "    surface_downwelling_shortwave_flux_in_air  (time, lat, lon) float32 dask.array&lt;chunksize=(30, 585, 1386), meta=np.ndarray&gt;\n",
       "    wind_from_direction                        (time, lat, lon) float32 dask.array&lt;chunksize=(30, 585, 1386), meta=np.ndarray&gt;\n",
       "    wind_speed                                 (time, lat, lon) float32 dask.array&lt;chunksize=(30, 585, 1386), meta=np.ndarray&gt;\n",
       "Attributes: (12/19)\n",
       "    Conventions:                CF-1.6\n",
       "    author:                     John Abatzoglou - University of Idaho, jabatz...\n",
       "    coordinate_system:          EPSG:4326\n",
       "    date:                       02 July 2019\n",
       "    geospatial_bounds:          POLYGON((-124.7666666333333 49.40000000000000...\n",
       "    geospatial_bounds_crs:      EPSG:4326\n",
       "    ...                         ...\n",
       "    geospatial_lon_units:       decimal_degrees east\n",
       "    note1:                      The projection information for this file is: ...\n",
       "    note2:                      Citation: Abatzoglou, J.T., 2013, Development...\n",
       "    note3:                      Data in slices after last_permanent_slice (1-...\n",
       "    note4:                      Data in slices after last_provisional_slice (...\n",
       "    note5:                      Days correspond approximately to calendar day...</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-a3011128-59a2-45d5-bd0d-e66041f37081' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3011128-59a2-45d5-bd0d-e66041f37081' 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>: 15341</li><li><span class='xr-has-index'>lat</span>: 585</li><li><span class='xr-has-index'>lon</span>: 1386</li><li><span class='xr-has-index'>crs</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a6ad1338-e0d8-4e7e-a24e-7030bae229bd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a6ad1338-e0d8-4e7e-a24e-7030bae229bd' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>crs</span></div><div class='xr-var-dims'>(crs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>3</div><input id='attrs-adf0cf75-32c9-4b3a-a5a9-883be892a64a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-adf0cf75-32c9-4b3a-a5a9-883be892a64a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f139961-d866-4058-ad5b-bcbca29795d1' class='xr-var-data-in' type='checkbox'><label for='data-3f139961-d866-4058-ad5b-bcbca29795d1' 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>-124.7666666333333 0.041666666666666 0  49.400000000000000 -0.041666666666666</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>long_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array([3], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.4 49.36 49.32 ... 25.11 25.07</div><input id='attrs-3caa3ce0-2f4f-43d4-9b14-50343de444b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3caa3ce0-2f4f-43d4-9b14-50343de444b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0df5ccf7-4c20-4b52-996b-34b45b0392b1' class='xr-var-data-in' type='checkbox'><label for='data-0df5ccf7-4c20-4b52-996b-34b45b0392b1' 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>axis :</span></dt><dd>Y</dd><dt><span>description :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([49.4     , 49.358333, 49.316667, ..., 25.15    , 25.108333, 25.066667])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-124.8 -124.7 ... -67.1 -67.06</div><input id='attrs-6e9437c8-8314-4d94-b345-21f70ce0ebd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e9437c8-8314-4d94-b345-21f70ce0ebd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5f7b608-27c8-42e3-a7dc-a1341acb54bf' class='xr-var-data-in' type='checkbox'><label for='data-a5f7b608-27c8-42e3-a7dc-a1341acb54bf' 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>axis :</span></dt><dd>X</dd><dt><span>description :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-124.766667, -124.725   , -124.683333, ...,  -67.141667,  -67.1     ,\n",
       "        -67.058333])</pre></div></li><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'>1979-01-01 ... 2020-12-31</div><input id='attrs-568f0da5-63b5-4133-b106-c39f3017f5bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-568f0da5-63b5-4133-b106-c39f3017f5bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca97b31d-ee1a-498c-9ef2-dde2890915be' class='xr-var-data-in' type='checkbox'><label for='data-ca97b31d-ee1a-498c-9ef2-dde2890915be' 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>description :</span></dt><dd>days since 1900-01-01</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-03T00:00:00.000000000&#x27;, ..., &#x27;2020-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-30T00:00:00.000000000&#x27;, &#x27;2020-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d345fe2-7673-40f4-80c3-3ab18069509b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d345fe2-7673-40f4-80c3-3ab18069509b' class='xr-section-summary' >Data variables: <span>(12)</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>air_temperature</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30, 585, 1386), meta=np.ndarray&gt;</div><input id='attrs-d9d9bb8d-2c52-4ce8-9fb6-a6cb0fb9efda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d9bb8d-2c52-4ce8-9fb6-a6cb0fb9efda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-442a1a39-a8a6-487c-b857-78211e8a8ca5' class='xr-var-data-in' type='checkbox'><label for='data-442a1a39-a8a6-487c-b857-78211e8a8ca5' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Daily Minimum Temperature</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>tmmn</dd><dt><span>standard_name :</span></dt><dd>tmmn</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-73e93cb8-4029-4110-9bca-a3392534c919' class='xr-section-summary-in' type='checkbox'  ><label for='section-73e93cb8-4029-4110-9bca-a3392534c919' class='xr-section-summary' >Attributes: <span>(19)</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>author :</span></dt><dd>John Abatzoglou - University of Idaho, jabatzoglou@uidaho.edu</dd><dt><span>coordinate_system :</span></dt><dd>EPSG:4326</dd><dt><span>date :</span></dt><dd>02 July 2019</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON((-124.7666666333333 49.400000000000000, -124.7666666333333 25.066666666666666, -67.058333300000015 25.066666666666666, -67.058333300000015 49.400000000000000, -124.7666666333333 49.400000000000000))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>49.40000000000000</dd><dt><span>geospatial_lat_min :</span></dt><dd>25.066666666666666</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.041666666666666</dd><dt><span>geospatial_lat_units :</span></dt><dd>decimal_degrees north</dd><dt><span>geospatial_lon_max :</span></dt><dd>-67.058333300000015</dd><dt><span>geospatial_lon_min :</span></dt><dd>-124.7666666333333</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.041666666666666</dd><dt><span>geospatial_lon_units :</span></dt><dd>decimal_degrees east</dd><dt><span>note1 :</span></dt><dd>The projection information for this file is: GCS WGS 1984.</dd><dt><span>note2 :</span></dt><dd>Citation: Abatzoglou, J.T., 2013, Development of gridded surface meteorological data for ecological applications and modeling, International Journal of Climatology, DOI: 10.1002/joc.3413</dd><dt><span>note3 :</span></dt><dd>Data in slices after last_permanent_slice (1-based) are considered provisional and subject to change with subsequent updates</dd><dt><span>note4 :</span></dt><dd>Data in slices after last_provisional_slice (1-based) are considered early and subject to change with subsequent updates</dd><dt><span>note5 :</span></dt><dd>Days correspond approximately to calendar days ending at midnight, Mountain Standard Time (7 UTC the next calendar day)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                    (time: 15341, lat: 585,\n",
       "                                                lon: 1386, crs: 1)\n",
       "Coordinates:\n",
       "  * crs                                        (crs) uint16 3\n",
       "  * lat                                        (lat) float64 49.4 ... 25.07\n",
       "  * lon                                        (lon) float64 -124.8 ... -67.06\n",
       "  * time                                       (time) datetime64[ns] 1979-01-...\n",
       "Data variables:\n",
       "    air_temperature                            (time, lat, lon) float32 dask.array<chunksize=(30, 585, 1386), meta=np.ndarray>\n",
       "    burning_index_g                            (time, lat, lon) float32 dask.array<chunksize=(30, 585, 1386), meta=np.ndarray>\n",
       "    dead_fuel_moisture_1000hr                  (time, lat, lon) float32 dask.array<chunksize=(30, 585, 1386), meta=np.ndarray>\n",
       "    dead_fuel_moisture_100hr                   (time, lat, lon) float32 dask.array<chunksize=(30, 585, 1386), meta=np.ndarray>\n",
       "    mean_vapor_pressure_deficit                (time, lat, lon) float32 dask.array<chunksize=(30, 585, 1386), meta=np.ndarray>\n",
       "    potential_evapotranspiration               (time, lat, lon) float32 dask.array<chunksize=(30, 585, 1386), meta=np.ndarray>\n",
       "    precipitation_amount                       (time, lat, lon) float32 dask.array<chunksize=(30, 585, 1386), meta=np.ndarray>\n",
       "    relative_humidity                          (time, lat, lon) float32 dask.array<chunksize=(30, 585, 1386), meta=np.ndarray>\n",
       "    specific_humidity                          (time, lat, lon) float32 dask.array<chunksize=(30, 585, 1386), meta=np.ndarray>\n",
       "    surface_downwelling_shortwave_flux_in_air  (time, lat, lon) float32 dask.array<chunksize=(30, 585, 1386), meta=np.ndarray>\n",
       "    wind_from_direction                        (time, lat, lon) float32 dask.array<chunksize=(30, 585, 1386), meta=np.ndarray>\n",
       "    wind_speed                                 (time, lat, lon) float32 dask.array<chunksize=(30, 585, 1386), meta=np.ndarray>\n",
       "Attributes: (12/19)\n",
       "    Conventions:                CF-1.6\n",
       "    author:                     John Abatzoglou - University of Idaho, jabatz...\n",
       "    coordinate_system:          EPSG:4326\n",
       "    date:                       02 July 2019\n",
       "    geospatial_bounds:          POLYGON((-124.7666666333333 49.40000000000000...\n",
       "    geospatial_bounds_crs:      EPSG:4326\n",
       "    ...                         ...\n",
       "    geospatial_lon_units:       decimal_degrees east\n",
       "    note1:                      The projection information for this file is: ...\n",
       "    note2:                      Citation: Abatzoglou, J.T., 2013, Development...\n",
       "    note3:                      Data in slices after last_permanent_slice (1-...\n",
       "    note4:                      Data in slices after last_provisional_slice (...\n",
       "    note5:                      Days correspond approximately to calendar day..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "ds = xr.open_zarr(\n",
    "    asset.href,\n",
    "    storage_options=asset.extra_fields[\"xarray:storage_options\"],\n",
    "    **asset.extra_fields[\"xarray:open_kwargs\"]\n",
    ")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ea6b0c",
   "metadata": {},
   "source": [
    "### Animating air temperature\n",
    "\n",
    "The dataset contains many variable indexed by `(time, lat, lon)`. We'll use matplotlib to create an animation of air temperature over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db6e0dbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation\n",
    "import cartopy.crs as ccrs\n",
    "import numpy as np\n",
    "import numpy.ma\n",
    "\n",
    "plt.style.use(\"dark_background\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c221c94",
   "metadata": {},
   "source": [
    "Let's load a year's worth of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e43815a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "chunk = ds[\"air_temperature\"][:30].compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1903e33",
   "metadata": {},
   "source": [
    "And now we can make the animation using [`matplotlib.animation.FuncAnimation`](https://matplotlib.org/stable/api/_as_gen/matplotlib.animation.FuncAnimation.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "066b435f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-gridmet-gridmet-example.ipynb/5.png\"/>"
      ],
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map_proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=45)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 9), subplot_kw=dict(projection=map_proj))\n",
    "ax.set_axis_off()\n",
    "quadmesh = chunk[0].plot(\n",
    "    ax=ax, add_colorbar=False, add_labels=False, transform=ccrs.PlateCarree()\n",
    ")\n",
    "\n",
    "\n",
    "def animate(i):\n",
    "    a = chunk[i].data.ravel()\n",
    "    a2 = numpy.ma.array(a, mask=np.isnan(a))\n",
    "    quadmesh.set_array(a2)\n",
    "    return [quadmesh]\n",
    "\n",
    "\n",
    "anim = matplotlib.animation.FuncAnimation(fig, animate, frames=len(chunk), interval=120)\n",
    "anim.save(\"anim.mp4\", fps=15, extra_args=[\"-vcodec\", \"libx264\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70a7fd02",
   "metadata": {},
   "source": [
    "We can use IPython to display the video in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e26b6611-59e4-4867-b8ce-5afbb3056d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Video\n",
    "\n",
    "Video(\"anim.mp4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50bb30f",
   "metadata": {},
   "source": [
    "The video will only be embedded if you're running the notebook interactively. If you're just reading this example, you can see the output at\n",
    "\n",
    "<video src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_video/pc-examples-gridmet-air-temperature.webm\" controls>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "567b60d5",
   "metadata": {},
   "source": [
    "### Next steps\n",
    "\n",
    "* Learn more about [Reading Zarr data](https://planetarycomputer.microsoft.com/docs/quickstarts/reading-zarr-data/)\n",
    "* Learn more about [Using tokens for data access](https://planetarycomputer.microsoft.com/docs/concepts/sas/)"
   ]
  }
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