forest-disturbance-daymet-na.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Daymet data\n",
    "Calculate mean and stdev by month of monthly daymet summaries for North America over 40 years (1980-2019 inclusive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import os\n",
    "import time\n",
    "\n",
    "# pip/conda installed\n",
    "import dask.array as da\n",
    "import fsspec\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "from netCDF4 import Dataset\n",
    "from dask.distributed import as_completed\n",
    "from dask.distributed import Client\n",
    "from dask.distributed import performance_report\n",
    "from dask_gateway import GatewayCluster\n",
    "\n",
    "from utils import get_logger\n",
    "from utils.dask import create_cluster\n",
    "from utils.dask import upload_source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill with your account key\n",
    "os.environ['AZURE_ACCOUNT_KEY'] = \"\""
   ]
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   "execution_count": 3,
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    "num_workers = 64\n",
    "cluster = create_cluster(\n",
    "    workers=num_workers,\n",
    "    worker_threads=1,\n",
    "    worker_memory=8,\n",
    "    scheduler_threads=4,\n",
    "    scheduler_memory=8\n",
    ")\n",
    "client = cluster.get_client()\n",
    "upload_source('./utils', client)\n",
    "cluster"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "text": [
      "606.246131796\n"
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       "Dimensions:  (time: 480, x: 7814, y: 8075)\n",
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       "    lon      (y, x) float32 dask.array&lt;chunksize=(505, 977), meta=np.ndarray&gt;\n",
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       "Data variables:\n",
       "    prcp     (time, y, x) float32 dask.array&lt;chunksize=(12, 1250, 1250), meta=np.ndarray&gt;\n",
       "    swe      (time, y, x) float32 dask.array&lt;chunksize=(12, 1250, 1250), meta=np.ndarray&gt;\n",
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       "Attributes:\n",
       "    Conventions:       CF-1.6\n",
       "    NCO:               netCDF Operators version 4.9.3 (Homepage = http://nco....\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-18c1bdf2-a4e7-4fbd-b9d3-7eb760ba9bfe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-18c1bdf2-a4e7-4fbd-b9d3-7eb760ba9bfe' 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>: 480</li><li><span class='xr-has-index'>x</span>: 7814</li><li><span class='xr-has-index'>y</span>: 8075</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5c45d1c1-7ca2-457b-8ad6-30366f8a7a21' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c45d1c1-7ca2-457b-8ad6-30366f8a7a21' 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=(505, 977), meta=np.ndarray&gt;</div><input id='attrs-04ddd38b-73f9-4a18-bd8c-8fa535b9004c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04ddd38b-73f9-4a18-bd8c-8fa535b9004c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d57c5832-0cd3-48f0-afbb-04855debdd4b' class='xr-var-data-in' type='checkbox'><label for='data-d57c5832-0cd3-48f0-afbb-04855debdd4b' 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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       "</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=(505, 977), meta=np.ndarray&gt;</div><input id='attrs-d883d83a-1005-4c9d-9807-f403f73f8adc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d883d83a-1005-4c9d-9807-f403f73f8adc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a360627-f7e3-4cf5-8eae-efe443cf9e3b' class='xr-var-data-in' type='checkbox'><label for='data-5a360627-f7e3-4cf5-8eae-efe443cf9e3b' 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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       "</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'>1980-01-16T12:00:00 ... 2019-12-...</div><input id='attrs-68d3c6cf-6a00-4831-9b1a-23184b4e6a5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68d3c6cf-6a00-4831-9b1a-23184b4e6a5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf283399-69e6-480f-800a-c6653223e9d4' class='xr-var-data-in' type='checkbox'><label for='data-cf283399-69e6-480f-800a-c6653223e9d4' 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;1980-01-16T12:00:00.000000000&#x27;, &#x27;1980-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;1980-03-16T12:00:00.000000000&#x27;, ..., &#x27;2019-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2019-11-16T00:00:00.000000000&#x27;, &#x27;2019-12-16T12:00:00.000000000&#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'>-4.56e+06 -4.559e+06 ... 3.253e+06</div><input id='attrs-12d8f872-33ad-4758-9280-1e4181f3ff61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12d8f872-33ad-4758-9280-1e4181f3ff61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6b22cd3-b50d-4031-b1ee-3c6a845b080d' class='xr-var-data-in' type='checkbox'><label for='data-e6b22cd3-b50d-4031-b1ee-3c6a845b080d' 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([-4560250., -4559250., -4558250., ...,  3250750.,  3251750.,  3252750.],\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'>4.984e+06 4.983e+06 ... -3.09e+06</div><input id='attrs-94cf9cbd-76a0-4d2f-bac4-5a1ec6504cd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94cf9cbd-76a0-4d2f-bac4-5a1ec6504cd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-973b4faa-83b1-4788-8d31-20d23d8f521b' class='xr-var-data-in' type='checkbox'><label for='data-973b4faa-83b1-4788-8d31-20d23d8f521b' 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([ 4984000.,  4983000.,  4982000., ..., -3088000., -3089000., -3090000.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76d3bd73-9af1-4a7a-962e-4dd649d1a109' class='xr-section-summary-in' type='checkbox'  checked><label for='section-76d3bd73-9af1-4a7a-962e-4dd649d1a109' class='xr-section-summary' >Data variables: <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>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=(12, 1250, 1250), meta=np.ndarray&gt;</div><input id='attrs-82f36f68-96f5-437b-97ea-0d7a47441a81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82f36f68-96f5-437b-97ea-0d7a47441a81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6abe63f-1810-450c-b8f7-796b7721b459' class='xr-var-data-in' type='checkbox'><label for='data-c6abe63f-1810-450c-b8f7-796b7721b459' 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 within days time: sum over days</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>long_name :</span></dt><dd>monthly total precipitation</dd><dt><span>units :</span></dt><dd>mm</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>swe</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=(12, 1250, 1250), meta=np.ndarray&gt;</div><input id='attrs-a1a459fd-7007-47f1-a877-8a893c78807a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1a459fd-7007-47f1-a877-8a893c78807a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-baed3766-c435-4e10-a2c7-58273feba009' class='xr-var-data-in' type='checkbox'><label for='data-baed3766-c435-4e10-a2c7-58273feba009' 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 within days time: sum over days</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>long_name :</span></dt><dd>monthly total snow water equivalent</dd><dt><span>units :</span></dt><dd>mm</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>tmax</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=(12, 1250, 1250), meta=np.ndarray&gt;</div><input id='attrs-2d727a49-4101-4b2d-adc6-da6830895f68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d727a49-4101-4b2d-adc6-da6830895f68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e39e9dd9-b9f8-4b82-b29a-073d1ead7a44' class='xr-var-data-in' type='checkbox'><label for='data-e39e9dd9-b9f8-4b82-b29a-073d1ead7a44' 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: maximum within days time: mean over days</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>long_name :</span></dt><dd>monthly average of daily maximum temperature</dd><dt><span>units :</span></dt><dd>degrees C</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>tmin</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=(12, 1250, 1250), meta=np.ndarray&gt;</div><input id='attrs-8f7a2e00-fe28-47ba-a69f-bb76defa7b9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f7a2e00-fe28-47ba-a69f-bb76defa7b9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63ce38c1-c99b-4edd-aa00-aafea9c75d03' class='xr-var-data-in' type='checkbox'><label for='data-63ce38c1-c99b-4edd-aa00-aafea9c75d03' 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: minimum within days time: mean over days</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>long_name :</span></dt><dd>monthly average of daily minimum temperature</dd><dt><span>units :</span></dt><dd>degrees C</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=(12, 1250, 1250), meta=np.ndarray&gt;</div><input id='attrs-b96a079e-93ca-43c9-832d-db2adf241879' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b96a079e-93ca-43c9-832d-db2adf241879' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d66e85e9-d229-43db-a1fd-dd32ab16d221' class='xr-var-data-in' type='checkbox'><label for='data-d66e85e9-d229-43db-a1fd-dd32ab16d221' 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 within days time: mean over days</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>long_name :</span></dt><dd>monthly average of 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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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f1c213f7-73f3-4798-9485-248ce8ee4597' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f1c213f7-73f3-4798-9485-248ce8ee4597' class='xr-section-summary' >Attributes: <span>(8)</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>NCO :</span></dt><dd>netCDF Operators version 4.9.3 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</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>"
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       "    lon      (y, x) float32 dask.array<chunksize=(505, 977), meta=np.ndarray>\n",
       "  * time     (time) datetime64[ns] 1980-01-16T12:00:00 ... 2019-12-16T12:00:00\n",
       "  * x        (x) float32 -4.56e+06 -4.559e+06 -4.558e+06 ... 3.252e+06 3.253e+06\n",
       "  * y        (y) float32 4.984e+06 4.983e+06 4.982e+06 ... -3.089e+06 -3.09e+06\n",
       "Data variables:\n",
       "    prcp     (time, y, x) float32 dask.array<chunksize=(12, 1250, 1250), meta=np.ndarray>\n",
       "    swe      (time, y, x) float32 dask.array<chunksize=(12, 1250, 1250), meta=np.ndarray>\n",
       "    tmax     (time, y, x) float32 dask.array<chunksize=(12, 1250, 1250), meta=np.ndarray>\n",
       "    tmin     (time, y, x) float32 dask.array<chunksize=(12, 1250, 1250), meta=np.ndarray>\n",
       "    vp       (time, y, x) float32 dask.array<chunksize=(12, 1250, 1250), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:       CF-1.6\n",
       "    NCO:               netCDF Operators version 4.9.3 (Homepage = http://nco....\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": [
    "src = fsspec.get_mapper(\n",
    "    \"az://daymet-zarr/monthly/na.zarr\",\n",
    "    account_name=\"daymeteuwest\",\n",
    ")\n",
    "\n",
    "ds = xr.open_zarr(src, consolidated=True, chunks={'time': 1, 'x': 1250, 'y': 1250})[[\"prcp\", \"swe\", \"tmax\", \"tmin\", \"vp\"]]\n",
    "print(ds.nbytes / 1000000000)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_dest = fsspec.get_mapper(\n",
    "    f\"az://fia/daymet/na_40yr_monthly_means.zarr\",\n",
    "    account_name=\"usfs\",\n",
    "    account_key=os.environ['AZURE_ACCOUNT_KEY']\n",
    ")\n",
    "std_dest = fsspec.get_mapper(\n",
    "    f\"az://fia/daymet/na_40yr_monthly_std_devs.zarr\",\n",
    "    account_name=\"usfs\",\n",
    "    account_key=os.environ['AZURE_ACCOUNT_KEY']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.648380052\n"
     ]
    },
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (month: 12, x: 7814, y: 8075)\n",
       "Coordinates:\n",
       "    lat      (y, x) float32 dask.array&lt;chunksize=(505, 977), meta=np.ndarray&gt;\n",
       "    lon      (y, x) float32 dask.array&lt;chunksize=(505, 977), meta=np.ndarray&gt;\n",
       "  * x        (x) float32 -4.56e+06 -4.559e+06 -4.558e+06 ... 3.252e+06 3.253e+06\n",
       "  * y        (y) float32 4.984e+06 4.983e+06 4.982e+06 ... -3.089e+06 -3.09e+06\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    prcp     (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;\n",
       "    swe      (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;\n",
       "    tmax     (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;\n",
       "    tmin     (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;\n",
       "    vp       (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;</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-321c53fe-2804-44d8-b6a6-682f307d0cc3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-321c53fe-2804-44d8-b6a6-682f307d0cc3' 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'>month</span>: 12</li><li><span class='xr-has-index'>x</span>: 7814</li><li><span class='xr-has-index'>y</span>: 8075</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ecd2910a-2b57-4aa7-b86f-cc9b4154444f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ecd2910a-2b57-4aa7-b86f-cc9b4154444f' 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=(505, 977), meta=np.ndarray&gt;</div><input id='attrs-e59a17c8-8952-4670-a962-f1047cb49c4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e59a17c8-8952-4670-a962-f1047cb49c4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c95f2780-e84e-405a-bcce-f1c64a096d02' class='xr-var-data-in' type='checkbox'><label for='data-c95f2780-e84e-405a-bcce-f1c64a096d02' 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",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 252.39 MB </td> <td> 1.97 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (8075, 7814) </td> <td> (505, 977) </td></tr>\n",
       "    <tr><th> Count </th><td> 129 Tasks </td><td> 128 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>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=(505, 977), meta=np.ndarray&gt;</div><input id='attrs-c47a6ecb-b886-4783-8cf0-b72569352798' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c47a6ecb-b886-4783-8cf0-b72569352798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-781b262e-d829-4f9c-80f8-46a5080bd049' class='xr-var-data-in' type='checkbox'><label for='data-781b262e-d829-4f9c-80f8-46a5080bd049' 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",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "    <tr><th> Shape </th><td> (8075, 7814) </td> <td> (505, 977) </td></tr>\n",
       "    <tr><th> Count </th><td> 129 Tasks </td><td> 128 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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-4.56e+06 -4.559e+06 ... 3.253e+06</div><input id='attrs-d8bdc55a-0b93-47f0-bdce-1e1cf16dc2ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8bdc55a-0b93-47f0-bdce-1e1cf16dc2ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c88be8a-37bb-4d22-a4ee-1078cf0ddf35' class='xr-var-data-in' type='checkbox'><label for='data-9c88be8a-37bb-4d22-a4ee-1078cf0ddf35' 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([-4560250., -4559250., -4558250., ...,  3250750.,  3251750.,  3252750.],\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'>4.984e+06 4.983e+06 ... -3.09e+06</div><input id='attrs-0322478c-6fc2-4a4f-8ffd-657fc2a5b111' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0322478c-6fc2-4a4f-8ffd-657fc2a5b111' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34a799f7-dc4c-410d-ba59-60e6bc2248fc' class='xr-var-data-in' type='checkbox'><label for='data-34a799f7-dc4c-410d-ba59-60e6bc2248fc' 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([ 4984000.,  4983000.,  4982000., ..., -3088000., -3089000., -3090000.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-a2450b57-5b9f-4589-8df5-0ef80fc9553d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2450b57-5b9f-4589-8df5-0ef80fc9553d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1814797a-0482-479d-8225-66d34d64fde6' class='xr-var-data-in' type='checkbox'><label for='data-1814797a-0482-479d-8225-66d34d64fde6' 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'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bba06214-56ae-427b-b2ab-7168ca17a0c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bba06214-56ae-427b-b2ab-7168ca17a0c9' class='xr-section-summary' >Data variables: <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>prcp</span></div><div class='xr-var-dims'>(month, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;</div><input id='attrs-c44bd532-a4f3-464a-9336-d3e03f87e64d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c44bd532-a4f3-464a-9336-d3e03f87e64d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-093767f0-7b2c-44d7-bccb-ecab940ad458' class='xr-var-data-in' type='checkbox'><label for='data-093767f0-7b2c-44d7-bccb-ecab940ad458' 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'></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>swe</span></div><div class='xr-var-dims'>(month, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;</div><input id='attrs-d62a84dd-969f-44dd-a79f-26c258fc2a5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d62a84dd-969f-44dd-a79f-26c258fc2a5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1692eda0-b85b-4c59-8097-8c0b6bc63046' class='xr-var-data-in' type='checkbox'><label for='data-1692eda0-b85b-4c59-8097-8c0b6bc63046' 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'></dl></div><div class='xr-var-data'><table>\n",
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       "<xarray.Dataset>\n",
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       "    lat      (y, x) float32 dask.array<chunksize=(505, 977), meta=np.ndarray>\n",
       "    lon      (y, x) float32 dask.array<chunksize=(505, 977), meta=np.ndarray>\n",
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       "    vp       (month, y, x) float32 dask.array<chunksize=(1, 1250, 1250), meta=np.ndarray>"
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "mean = ds.groupby('time.month').mean()\n",
    "print(mean.nbytes/1000000000)\n",
    "mean"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "text": [
      "15.648380052\n"
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       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (month: 12, x: 7814, y: 8075)\n",
       "Coordinates:\n",
       "    lat      (y, x) float32 dask.array&lt;chunksize=(505, 977), meta=np.ndarray&gt;\n",
       "    lon      (y, x) float32 dask.array&lt;chunksize=(505, 977), meta=np.ndarray&gt;\n",
       "  * x        (x) float32 -4.56e+06 -4.559e+06 -4.558e+06 ... 3.252e+06 3.253e+06\n",
       "  * y        (y) float32 4.984e+06 4.983e+06 4.982e+06 ... -3.089e+06 -3.09e+06\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    prcp     (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;\n",
       "    swe      (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;\n",
       "    tmax     (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;\n",
       "    tmin     (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;\n",
       "    vp       (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;</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-bb61eced-d8ed-4523-9601-393c7f16be15' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bb61eced-d8ed-4523-9601-393c7f16be15' 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'>month</span>: 12</li><li><span class='xr-has-index'>x</span>: 7814</li><li><span class='xr-has-index'>y</span>: 8075</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-680bb30f-e450-426f-ae3e-4f087c47f35e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-680bb30f-e450-426f-ae3e-4f087c47f35e' 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=(505, 977), meta=np.ndarray&gt;</div><input id='attrs-91d9ab7c-dfd6-4a83-8f74-e1c134522c31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91d9ab7c-dfd6-4a83-8f74-e1c134522c31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-622f09bd-9f70-4b03-8e36-318882c350f1' class='xr-var-data-in' type='checkbox'><label for='data-622f09bd-9f70-4b03-8e36-318882c350f1' 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",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 252.39 MB </td> <td> 1.97 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (8075, 7814) </td> <td> (505, 977) </td></tr>\n",
       "    <tr><th> Count </th><td> 129 Tasks </td><td> 128 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"166\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "  <text x=\"58.060681\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7814</text>\n",
       "  <text x=\"136.121362\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,136.121362,60.000000)\">8075</text>\n",
       "</svg>\n",
       "</td>\n",
       "</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=(505, 977), meta=np.ndarray&gt;</div><input id='attrs-2d75c1a7-11aa-4e8f-bf3e-37f276f1ec4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d75c1a7-11aa-4e8f-bf3e-37f276f1ec4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c721b2e-10bf-4839-9ae6-80abeed918ca' class='xr-var-data-in' type='checkbox'><label for='data-6c721b2e-10bf-4839-9ae6-80abeed918ca' 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",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 252.39 MB </td> <td> 1.97 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (8075, 7814) </td> <td> (505, 977) </td></tr>\n",
       "    <tr><th> Count </th><td> 129 Tasks </td><td> 128 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"166\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <line x1=\"58\" y1=\"0\" x2=\"58\" y2=\"120\" />\n",
       "  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"120\" />\n",
       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"120\" />\n",
       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"120\" />\n",
       "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 116.12136222910216,0.0 116.12136222910216,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"58.060681\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7814</text>\n",
       "  <text x=\"136.121362\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,136.121362,60.000000)\">8075</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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'>-4.56e+06 -4.559e+06 ... 3.253e+06</div><input id='attrs-992d6c9f-8044-43c9-b7c1-19972432b395' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-992d6c9f-8044-43c9-b7c1-19972432b395' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df6f19da-8660-44c4-9fa2-915536caf7bd' class='xr-var-data-in' type='checkbox'><label for='data-df6f19da-8660-44c4-9fa2-915536caf7bd' 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([-4560250., -4559250., -4558250., ...,  3250750.,  3251750.,  3252750.],\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'>4.984e+06 4.983e+06 ... -3.09e+06</div><input id='attrs-51fb6fd9-2ad7-4c3b-9a79-b1c7531a53cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51fb6fd9-2ad7-4c3b-9a79-b1c7531a53cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5b45ec9-2e58-4018-8a10-aaef2e4bfba3' class='xr-var-data-in' type='checkbox'><label for='data-d5b45ec9-2e58-4018-8a10-aaef2e4bfba3' 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([ 4984000.,  4983000.,  4982000., ..., -3088000., -3089000., -3090000.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-4926478e-9dce-47f9-8a22-db8c9aef3a81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4926478e-9dce-47f9-8a22-db8c9aef3a81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3de9aaa8-8d7b-4b8c-9020-243531681f78' class='xr-var-data-in' type='checkbox'><label for='data-3de9aaa8-8d7b-4b8c-9020-243531681f78' 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'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-65537721-259b-4507-a5ba-62d847380335' class='xr-section-summary-in' type='checkbox'  checked><label for='section-65537721-259b-4507-a5ba-62d847380335' class='xr-section-summary' >Data variables: <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>prcp</span></div><div class='xr-var-dims'>(month, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;</div><input id='attrs-493ea4a6-a1fb-4bbd-9719-4cd692048bbc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-493ea4a6-a1fb-4bbd-9719-4cd692048bbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7692c62d-c55a-44cf-b475-50b63fbc27fd' class='xr-var-data-in' type='checkbox'><label for='data-7692c62d-c55a-44cf-b475-50b63fbc27fd' 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'></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
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       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 3.03 GB </td> <td> 6.25 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (12, 8075, 7814) </td> <td> (1, 1250, 1250) </td></tr>\n",
       "    <tr><th> Count </th><td> 58997 Tasks </td><td> 588 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swe</span></div><div class='xr-var-dims'>(month, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;</div><input id='attrs-5dd2fafa-11e2-4598-9e0a-1cc785b04c23' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5dd2fafa-11e2-4598-9e0a-1cc785b04c23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31ea3c54-fa4c-4a02-b63e-7855e23c04bd' class='xr-var-data-in' type='checkbox'><label for='data-31ea3c54-fa4c-4a02-b63e-7855e23c04bd' 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'></dl></div><div class='xr-var-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (month: 12, x: 7814, y: 8075)\n",
       "Coordinates:\n",
       "    lat      (y, x) float32 dask.array<chunksize=(505, 977), meta=np.ndarray>\n",
       "    lon      (y, x) float32 dask.array<chunksize=(505, 977), meta=np.ndarray>\n",
       "  * x        (x) float32 -4.56e+06 -4.559e+06 -4.558e+06 ... 3.252e+06 3.253e+06\n",
       "  * y        (y) float32 4.984e+06 4.983e+06 4.982e+06 ... -3.089e+06 -3.09e+06\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    prcp     (month, y, x) float32 dask.array<chunksize=(1, 1250, 1250), meta=np.ndarray>\n",
       "    swe      (month, y, x) float32 dask.array<chunksize=(1, 1250, 1250), meta=np.ndarray>\n",
       "    tmax     (month, y, x) float32 dask.array<chunksize=(1, 1250, 1250), meta=np.ndarray>\n",
       "    tmin     (month, y, x) float32 dask.array<chunksize=(1, 1250, 1250), meta=np.ndarray>\n",
       "    vp       (month, y, x) float32 dask.array<chunksize=(1, 1250, 1250), meta=np.ndarray>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std = ds.groupby('time.month').std()\n",
    "print(mean.nbytes/1000000000)\n",
    "std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x7f8290edeac0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean.to_zarr(mean_dest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x7f827adfefa0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std.to_zarr(std_dest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
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       "\n",
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       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
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       "\n",
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       "  color: var(--xr-font-color2);\n",
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       "  width: 15px;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
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       "  padding-top: 4px;\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
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       "  vertical-align: middle;\n",
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       "  fill: currentColor;\n",
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       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "  * x        (x) float32 -4.56e+06 -4.559e+06 -4.558e+06 ... 3.252e+06 3.253e+06\n",
       "  * y        (y) float32 4.984e+06 4.983e+06 4.982e+06 ... -3.089e+06 -3.09e+06\n",
       "Data variables:\n",
       "    prcp     (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;\n",
       "    swe      (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;\n",
       "    tmax     (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;\n",
       "    tmin     (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;\n",
       "    vp       (month, y, x) float32 dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;</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-7b6565ab-a217-4bc1-adfc-a5ee608e734b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7b6565ab-a217-4bc1-adfc-a5ee608e734b' 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'>month</span>: 12</li><li><span class='xr-has-index'>x</span>: 7814</li><li><span class='xr-has-index'>y</span>: 8075</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5a3a68f2-c174-423b-9fb8-5f21a2844217' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5a3a68f2-c174-423b-9fb8-5f21a2844217' 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=(505, 977), meta=np.ndarray&gt;</div><input id='attrs-f64e4dd6-10d2-4365-8bfc-fc1928dde529' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f64e4dd6-10d2-4365-8bfc-fc1928dde529' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b3cf36d-203d-4491-a7a2-7dc9ca2af405' class='xr-var-data-in' type='checkbox'><label for='data-4b3cf36d-203d-4491-a7a2-7dc9ca2af405' 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",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 252.39 MB </td> <td> 1.97 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (8075, 7814) </td> <td> (505, 977) </td></tr>\n",
       "    <tr><th> Count </th><td> 129 Tasks </td><td> 128 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</td>\n",
       "</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=(505, 977), meta=np.ndarray&gt;</div><input id='attrs-2f8dd378-7a1f-4283-ac8f-68428fa948a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f8dd378-7a1f-4283-ac8f-68428fa948a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b05c67bd-cfc0-4f2e-9937-ec792b869523' class='xr-var-data-in' type='checkbox'><label for='data-b05c67bd-cfc0-4f2e-9937-ec792b869523' 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",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 252.39 MB </td> <td> 1.97 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (8075, 7814) </td> <td> (505, 977) </td></tr>\n",
       "    <tr><th> Count </th><td> 129 Tasks </td><td> 128 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'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-e4d7fcb6-4f09-41f8-854c-3a2406dcd90f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4d7fcb6-4f09-41f8-854c-3a2406dcd90f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ede47b6-8d3f-4259-8e84-66d97e5d0bf4' class='xr-var-data-in' type='checkbox'><label for='data-7ede47b6-8d3f-4259-8e84-66d97e5d0bf4' 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'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</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'>-4.56e+06 -4.559e+06 ... 3.253e+06</div><input id='attrs-957d9e2a-72a0-4153-9162-1bf6e87db433' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-957d9e2a-72a0-4153-9162-1bf6e87db433' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-046269eb-60e3-42da-bd94-825d1c7bec46' class='xr-var-data-in' type='checkbox'><label for='data-046269eb-60e3-42da-bd94-825d1c7bec46' 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([-4560250., -4559250., -4558250., ...,  3250750.,  3251750.,  3252750.],\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'>4.984e+06 4.983e+06 ... -3.09e+06</div><input id='attrs-d48695d4-9954-4995-86c3-303bc474cb82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d48695d4-9954-4995-86c3-303bc474cb82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad654fe5-832f-4766-a5be-a2dc8ca4566a' class='xr-var-data-in' type='checkbox'><label for='data-ad654fe5-832f-4766-a5be-a2dc8ca4566a' 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([ 4984000.,  4983000.,  4982000., ..., -3088000., -3089000., -3090000.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c8be63d6-949f-4e89-ae28-e2c7e541fedc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8be63d6-949f-4e89-ae28-e2c7e541fedc' class='xr-section-summary' >Data variables: <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>prcp</span></div><div class='xr-var-dims'>(month, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1250, 1250), meta=np.ndarray&gt;</div><input id='attrs-40d48b0f-89ce-44df-a358-6165cc1b5640' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-40d48b0f-89ce-44df-a358-6165cc1b5640' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6da391d5-179c-4421-98f7-abc1eb00d365' class='xr-var-data-in' type='checkbox'><label for='data-6da391d5-179c-4421-98f7-abc1eb00d365' 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'></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 3.03 GB </td> <td> 6.25 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (12, 8075, 7814) </td> <td> (1, 1250, 1250) </td></tr>\n",
       "    <tr><th> Count </th><td> 589 Tasks </td><td> 588 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "  <tbody>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (month: 12, x: 7814, y: 8075)\n",
       "Coordinates:\n",
       "    lat      (y, x) float32 dask.array<chunksize=(505, 977), meta=np.ndarray>\n",
       "    lon      (y, x) float32 dask.array<chunksize=(505, 977), meta=np.ndarray>\n",
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       "  * x        (x) float32 -4.56e+06 -4.559e+06 -4.558e+06 ... 3.252e+06 3.253e+06\n",
       "  * y        (y) float32 4.984e+06 4.983e+06 4.982e+06 ... -3.089e+06 -3.09e+06\n",
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       "    swe      (month, y, x) float32 dask.array<chunksize=(1, 1250, 1250), meta=np.ndarray>\n",
       "    tmax     (month, y, x) float32 dask.array<chunksize=(1, 1250, 1250), meta=np.ndarray>\n",
       "    tmin     (month, y, x) float32 dask.array<chunksize=(1, 1250, 1250), meta=np.ndarray>\n",
       "    vp       (month, y, x) float32 dask.array<chunksize=(1, 1250, 1250), meta=np.ndarray>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mn = xr.open_zarr(mean_dest)\n",
    "mn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f82597574c0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mn['prcp'].sel(month=10).plot.imshow(size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.shutdown()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:notebook] *",
   "language": "python",
   "name": "conda-env-notebook-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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}