quickstarts/reading-zarr-data.ipynb

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    "## Reading Zarr Data\n",
    "\n",
    "[Zarr](https://zarr.readthedocs.io/en/stable/) is a storage format for chunked, N-dimensional arrays. It works well with object storage systems like [Azure Blob Storage](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction) and open-source libraries like [xarray](https://xarray.pydata.org/en/stable/). It's widely used in the geosciences, especially within the [Pangeo](https://pangeo.io/) community.\n",
    "\n",
    "This example loads [Daymet](https://aka.ms/ai4edata-daymet) data that are stored in Zarr format into an xarray Dataset.  Daymet provides gridded weather data for North America. We'll look at [daily frequency data covering Hawaii](https://planetarycomputer.microsoft.com/dataset/daymet-daily-hi).\n",
    "\n",
    "The [STAC Collections](../reading-stac) provided by the Planetary Computer contain assets with links to the root of the Zarr store."
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       "{'thumbnail': <Asset href=https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/daymet-daily-hi.png>,\n",
       " 'zarr-abfs': <Asset href=abfs://daymet-zarr/daily/hi.zarr>,\n",
       " 'zarr-https': <Asset href=https://daymeteuwest.blob.core.windows.net/daymet-zarr/daily/hi.zarr>}"
      ]
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     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pystac\n",
    "\n",
    "collection = pystac.Collection.from_file(\n",
    "    \"https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-daily-hi\"\n",
    ")\n",
    "collection.assets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll access the data through HTTPs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "asset = collection.assets[\"zarr-https\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Zarr assets provided by the Planetary Computer implement the [xarray-assets](https://github.com/stac-extensions/xarray-assets) extension. These specify the necessary and recommended keywords when loading data from [`fssspec`-based filesystems](https://filesystem-spec.readthedocs.io/en/latest/) and xarray."
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "{'xarray:open_kwargs': {'consolidated': True}}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "asset.extra_fields"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the dataset should be opened with consolidated metadata."
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                  (nv: 2, time: 14965, x: 284, y: 584)\n",
       "Coordinates:\n",
       "    lat                      (y, x) float32 dask.array&lt;chunksize=(584, 284), meta=np.ndarray&gt;\n",
       "    lon                      (y, x) float32 dask.array&lt;chunksize=(584, 284), meta=np.ndarray&gt;\n",
       "  * time                     (time) datetime64[ns] 1980-01-01T12:00:00 ... 20...\n",
       "  * x                        (x) float32 -5.802e+06 -5.801e+06 ... -5.519e+06\n",
       "  * y                        (y) float32 -3.9e+04 -4e+04 ... -6.21e+05 -6.22e+05\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    dayl                     (time, y, x) float32 dask.array&lt;chunksize=(365, 584, 284), meta=np.ndarray&gt;\n",
       "    lambert_conformal_conic  int16 ...\n",
       "    prcp                     (time, y, x) float32 dask.array&lt;chunksize=(365, 584, 284), meta=np.ndarray&gt;\n",
       "    srad                     (time, y, x) float32 dask.array&lt;chunksize=(365, 584, 284), meta=np.ndarray&gt;\n",
       "    swe                      (time, y, x) float32 dask.array&lt;chunksize=(365, 584, 284), meta=np.ndarray&gt;\n",
       "    time_bnds                (time, nv) datetime64[ns] dask.array&lt;chunksize=(365, 2), meta=np.ndarray&gt;\n",
       "    tmax                     (time, y, x) float32 dask.array&lt;chunksize=(365, 584, 284), meta=np.ndarray&gt;\n",
       "    tmin                     (time, y, x) float32 dask.array&lt;chunksize=(365, 584, 284), meta=np.ndarray&gt;\n",
       "    vp                       (time, y, x) float32 dask.array&lt;chunksize=(365, 584, 284), meta=np.ndarray&gt;\n",
       "    yearday                  (time) int16 dask.array&lt;chunksize=(365,), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:       CF-1.6\n",
       "    Version_data:      Daymet Data Version 4.0\n",
       "    Version_software:  Daymet Software Version 4.0\n",
       "    citation:          Please see http://daymet.ornl.gov/ for current Daymet ...\n",
       "    references:        Please see http://daymet.ornl.gov/ for current informa...\n",
       "    source:            Daymet Software Version 4.0\n",
       "    start_year:        1980</pre><div class='xr-wrap' hidden><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-d6f4807c-5e51-4a7e-873c-9dd7a43816e2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d6f4807c-5e51-4a7e-873c-9dd7a43816e2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nv</span>: 2</li><li><span class='xr-has-index'>time</span>: 14965</li><li><span class='xr-has-index'>x</span>: 284</li><li><span class='xr-has-index'>y</span>: 584</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9c09476f-d9c7-4866-becd-d1347c96ac92' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9c09476f-d9c7-4866-becd-d1347c96ac92' class='xr-section-summary' >Coordinates: <span>(5)</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>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(584, 284), meta=np.ndarray&gt;</div><input id='attrs-37838a1c-fbf1-41f3-bfd7-8aec49fe88d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37838a1c-fbf1-41f3-bfd7-8aec49fe88d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3caf761-be2b-4f09-887f-8bb2e21f7c24' class='xr-var-data-in' type='checkbox'><label for='data-b3caf761-be2b-4f09-887f-8bb2e21f7c24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude coordinate</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'><table>\n",
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       "    <tr><th> Bytes </th><td> 647.88 kiB </td> <td> 647.88 kiB </td></tr>\n",
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       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(584, 284), meta=np.ndarray&gt;</div><input id='attrs-3113622d-2fef-4b4b-9e54-2bee33ea695b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3113622d-2fef-4b4b-9e54-2bee33ea695b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8ae3d34-f968-4472-a5f7-99b98359a2f1' class='xr-var-data-in' type='checkbox'><label for='data-e8ae3d34-f968-4472-a5f7-99b98359a2f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude coordinate</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'><table>\n",
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       "<td>\n",
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       "  <tbody>\n",
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       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></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'>1980-01-01T12:00:00 ... 2020-12-...</div><input id='attrs-ec36183e-5c98-4c69-973d-dffe55553257' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec36183e-5c98-4c69-973d-dffe55553257' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0ca6bb6-80d3-4243-ad3f-be67c2cc58ce' class='xr-var-data-in' type='checkbox'><label for='data-f0ca6bb6-80d3-4243-ad3f-be67c2cc58ce' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>24-hour day based on local time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T12:00:00.000000000&#x27;, &#x27;1980-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-03T12:00:00.000000000&#x27;, ..., &#x27;2020-12-28T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-29T12:00:00.000000000&#x27;, &#x27;2020-12-30T12:00:00.000000000&#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'>float32</div><div class='xr-var-preview xr-preview'>-5.802e+06 ... -5.519e+06</div><input id='attrs-f4d1eef9-23c7-4021-9d59-594ad9dc4fef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4d1eef9-23c7-4021-9d59-594ad9dc4fef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b92147b8-32e0-4351-b8df-0b9a66feefaf' class='xr-var-data-in' type='checkbox'><label for='data-b92147b8-32e0-4351-b8df-0b9a66feefaf' 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>x coordinate of projection</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([-5802250., -5801250., -5800250., ..., -5521250., -5520250., -5519250.],\n",
       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-3.9e+04 -4e+04 ... -6.22e+05</div><input id='attrs-c4ef7d6b-fc88-4c44-9ab8-55949ab2e431' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4ef7d6b-fc88-4c44-9ab8-55949ab2e431' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d6e3af0-712c-4234-b173-82a7a2e80c58' class='xr-var-data-in' type='checkbox'><label for='data-7d6e3af0-712c-4234-b173-82a7a2e80c58' 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>y coordinate of projection</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([ -39000.,  -40000.,  -41000., ..., -620000., -621000., -622000.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-58061d13-267c-494b-865b-1c1c77a574bd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-58061d13-267c-494b-865b-1c1c77a574bd' class='xr-section-summary' >Data variables: <span>(10)</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>dayl</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(365, 584, 284), meta=np.ndarray&gt;</div><input id='attrs-ce383048-a3b7-4fe3-ab29-a5b62903fdd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce383048-a3b7-4fe3-ab29-a5b62903fdd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4bdc0ce-3352-496a-b360-e9d5e0e9b34e' class='xr-var-data-in' type='checkbox'><label for='data-c4bdc0ce-3352-496a-b360-e9d5e0e9b34e' 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>cell_methods :</span></dt><dd>area: mean</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>long_name :</span></dt><dd>daylength</dd><dt><span>units :</span></dt><dd>s</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>lambert_conformal_conic</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a3eddaf9-b488-4284-acac-4a7df4a40e84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3eddaf9-b488-4284-acac-4a7df4a40e84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4027040d-9944-4a76-8170-03177fb5d21a' class='xr-var-data-in' type='checkbox'><label for='data-4027040d-9944-4a76-8170-03177fb5d21a' 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>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>298.257223563</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>42.5</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-100.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>standard_parallel :</span></dt><dd>[25.0, 60.0]</dd></dl></div><div class='xr-var-data'><pre>array(-32767, dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcp</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(365, 584, 284), meta=np.ndarray&gt;</div><input id='attrs-12f6b366-dcf7-4bf5-9c14-37059e719a50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12f6b366-dcf7-4bf5-9c14-37059e719a50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48024f4b-30d8-4de1-b800-53cacbaf853e' class='xr-var-data-in' type='checkbox'><label for='data-48024f4b-30d8-4de1-b800-53cacbaf853e' 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>cell_methods :</span></dt><dd>area: mean time: sum</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>long_name :</span></dt><dd>daily total precipitation</dd><dt><span>units :</span></dt><dd>mm/day</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>vp</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(365, 584, 284), meta=np.ndarray&gt;</div><input id='attrs-ddd8912d-f5e7-4516-aee9-48a3d3e3d15f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddd8912d-f5e7-4516-aee9-48a3d3e3d15f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a0ab27b-ce4c-452c-8fd6-8abb40b2f1b7' class='xr-var-data-in' type='checkbox'><label for='data-3a0ab27b-ce4c-452c-8fd6-8abb40b2f1b7' 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>cell_methods :</span></dt><dd>area: mean time: mean</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>long_name :</span></dt><dd>daily average vapor pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
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       "    <tr><th> Shape </th><td> (14965, 584, 284) </td> <td> (365, 584, 284) </td></tr>\n",
       "    <tr><th> Count </th><td> 42 Tasks </td><td> 41 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\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>yearday</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(365,), meta=np.ndarray&gt;</div><input id='attrs-bfa391ed-beb1-46bc-8d5b-2949f4693632' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfa391ed-beb1-46bc-8d5b-2949f4693632' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-546972f7-4652-4044-8844-bdb2b35a0e2e' class='xr-var-data-in' type='checkbox'><label for='data-546972f7-4652-4044-8844-bdb2b35a0e2e' 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>day of year (DOY) starting with day 1 on Januaray 1st</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-4aad7325-1a34-41bc-ac1f-7881f0958eca' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4aad7325-1a34-41bc-ac1f-7881f0958eca' class='xr-section-summary' >Attributes: <span>(7)</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>Version_data :</span></dt><dd>Daymet Data Version 4.0</dd><dt><span>Version_software :</span></dt><dd>Daymet Software Version 4.0</dd><dt><span>citation :</span></dt><dd>Please see http://daymet.ornl.gov/ for current Daymet data citation information</dd><dt><span>references :</span></dt><dd>Please see http://daymet.ornl.gov/ for current information on Daymet references</dd><dt><span>source :</span></dt><dd>Daymet Software Version 4.0</dd><dt><span>start_year :</span></dt><dd>1980</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                  (nv: 2, time: 14965, x: 284, y: 584)\n",
       "Coordinates:\n",
       "    lat                      (y, x) float32 dask.array<chunksize=(584, 284), meta=np.ndarray>\n",
       "    lon                      (y, x) float32 dask.array<chunksize=(584, 284), meta=np.ndarray>\n",
       "  * time                     (time) datetime64[ns] 1980-01-01T12:00:00 ... 20...\n",
       "  * x                        (x) float32 -5.802e+06 -5.801e+06 ... -5.519e+06\n",
       "  * y                        (y) float32 -3.9e+04 -4e+04 ... -6.21e+05 -6.22e+05\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    dayl                     (time, y, x) float32 dask.array<chunksize=(365, 584, 284), meta=np.ndarray>\n",
       "    lambert_conformal_conic  int16 ...\n",
       "    prcp                     (time, y, x) float32 dask.array<chunksize=(365, 584, 284), meta=np.ndarray>\n",
       "    srad                     (time, y, x) float32 dask.array<chunksize=(365, 584, 284), meta=np.ndarray>\n",
       "    swe                      (time, y, x) float32 dask.array<chunksize=(365, 584, 284), meta=np.ndarray>\n",
       "    time_bnds                (time, nv) datetime64[ns] dask.array<chunksize=(365, 2), meta=np.ndarray>\n",
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       "    tmin                     (time, y, x) float32 dask.array<chunksize=(365, 584, 284), meta=np.ndarray>\n",
       "    vp                       (time, y, x) float32 dask.array<chunksize=(365, 584, 284), meta=np.ndarray>\n",
       "    yearday                  (time) int16 dask.array<chunksize=(365,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:       CF-1.6\n",
       "    Version_data:      Daymet Data Version 4.0\n",
       "    Version_software:  Daymet Software Version 4.0\n",
       "    citation:          Please see http://daymet.ornl.gov/ for current Daymet ...\n",
       "    references:        Please see http://daymet.ornl.gov/ for current informa...\n",
       "    source:            Daymet Software Version 4.0\n",
       "    start_year:        1980"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import fsspec\n",
    "import xarray as xr\n",
    "\n",
    "store = fsspec.get_mapper(asset.href)\n",
    "ds = xr.open_zarr(store, **asset.extra_fields[\"xarray:open_kwargs\"])\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point we can load the data, aggregate it, and plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "warnings.simplefilter(\"ignore\", RuntimeWarning)\n",
    "fig, ax = plt.subplots(figsize=(12, 12))\n",
    "ds.sel(time=\"2009\")[\"tmax\"].mean(dim=\"time\").plot.imshow(ax=ax, cmap=\"inferno\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learn more\n",
    "\n",
    "The xarray [User Guide](https://xarray.pydata.org/en/stable/io.html#zarr) has more information on reading Zarr data. For more about the Daymet dataset, see [here](http://aka.ms/ai4edata-daymet)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}