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ScottWales/season_year.ipynb

Created July 7, 2020 03:30
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season_year.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xarray"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
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"</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60265</li><li><span class='xr-has-index'>lat</span>: 144</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8ee5fc12-1f51-4d70-812c-8b6e5f8f8a5e' class='xr-array-in' type='checkbox' ><label for='section-8ee5fc12-1f51-4d70-812c-8b6e5f8f8a5e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(18262, 144, 192), meta=np.ndarray&gt;</span></div><pre class='xr-array-data'><table>\n",
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" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 6.66 GB </td> <td> 2.02 GB </td></tr>\n",
" <tr><th> Shape </th><td> (60265, 144, 192) </td> <td> (18262, 144, 192) </td></tr>\n",
" <tr><th> Count </th><td> 12 Tasks </td><td> 4 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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"</table></pre></div></li><li class='xr-section-item'><input id='section-da9e01d4-84a6-4c44-842f-cb2db0734616' class='xr-section-summary-in' type='checkbox' checked><label for='section-da9e01d4-84a6-4c44-842f-cb2db0734616' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-56170eb4-e77f-4973-b0e1-eeacdb189762' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56170eb4-e77f-4973-b0e1-eeacdb189762' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62071f16-e7df-4156-9fc4-7d9c8c6f0769' class='xr-var-data-in' type='checkbox'><label for='data-62071f16-e7df-4156-9fc4-7d9c8c6f0769' 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>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.38 -88.12 ... 88.12 89.38</div><input id='attrs-f4dd07f2-6675-4459-8748-33da5ad39dc5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4dd07f2-6675-4459-8748-33da5ad39dc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33463b23-6b5c-4898-b6cc-437f5fb468c3' class='xr-var-data-in' type='checkbox'><label for='data-33463b23-6b5c-4898-b6cc-437f5fb468c3' 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>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><pre class='xr-var-data'>array([-89.375, -88.125, -86.875, -85.625, -84.375, -83.125, -81.875, -80.625,\n",
" -79.375, -78.125, -76.875, -75.625, -74.375, -73.125, -71.875, -70.625,\n",
" -69.375, -68.125, -66.875, -65.625, -64.375, -63.125, -61.875, -60.625,\n",
" -59.375, -58.125, -56.875, -55.625, -54.375, -53.125, -51.875, -50.625,\n",
" -49.375, -48.125, -46.875, -45.625, -44.375, -43.125, -41.875, -40.625,\n",
" -39.375, -38.125, -36.875, -35.625, -34.375, -33.125, -31.875, -30.625,\n",
" -29.375, -28.125, -26.875, -25.625, -24.375, -23.125, -21.875, -20.625,\n",
" -19.375, -18.125, -16.875, -15.625, -14.375, -13.125, -11.875, -10.625,\n",
" -9.375, -8.125, -6.875, -5.625, -4.375, -3.125, -1.875, -0.625,\n",
" 0.625, 1.875, 3.125, 4.375, 5.625, 6.875, 8.125, 9.375,\n",
" 10.625, 11.875, 13.125, 14.375, 15.625, 16.875, 18.125, 19.375,\n",
" 20.625, 21.875, 23.125, 24.375, 25.625, 26.875, 28.125, 29.375,\n",
" 30.625, 31.875, 33.125, 34.375, 35.625, 36.875, 38.125, 39.375,\n",
" 40.625, 41.875, 43.125, 44.375, 45.625, 46.875, 48.125, 49.375,\n",
" 50.625, 51.875, 53.125, 54.375, 55.625, 56.875, 58.125, 59.375,\n",
" 60.625, 61.875, 63.125, 64.375, 65.625, 66.875, 68.125, 69.375,\n",
" 70.625, 71.875, 73.125, 74.375, 75.625, 76.875, 78.125, 79.375,\n",
" 80.625, 81.875, 83.125, 84.375, 85.625, 86.875, 88.125, 89.375])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9375 2.812 4.688 ... 357.2 359.1</div><input id='attrs-07ef6529-f96a-44f7-8596-82e92f8b290f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07ef6529-f96a-44f7-8596-82e92f8b290f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-489a658c-ecd6-407c-868e-255a280d1159' class='xr-var-data-in' type='checkbox'><label for='data-489a658c-ecd6-407c-868e-255a280d1159' 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>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><pre class='xr-var-data'>array([ 0.9375, 2.8125, 4.6875, 6.5625, 8.4375, 10.3125, 12.1875,\n",
" 14.0625, 15.9375, 17.8125, 19.6875, 21.5625, 23.4375, 25.3125,\n",
" 27.1875, 29.0625, 30.9375, 32.8125, 34.6875, 36.5625, 38.4375,\n",
" 40.3125, 42.1875, 44.0625, 45.9375, 47.8125, 49.6875, 51.5625,\n",
" 53.4375, 55.3125, 57.1875, 59.0625, 60.9375, 62.8125, 64.6875,\n",
" 66.5625, 68.4375, 70.3125, 72.1875, 74.0625, 75.9375, 77.8125,\n",
" 79.6875, 81.5625, 83.4375, 85.3125, 87.1875, 89.0625, 90.9375,\n",
" 92.8125, 94.6875, 96.5625, 98.4375, 100.3125, 102.1875, 104.0625,\n",
" 105.9375, 107.8125, 109.6875, 111.5625, 113.4375, 115.3125, 117.1875,\n",
" 119.0625, 120.9375, 122.8125, 124.6875, 126.5625, 128.4375, 130.3125,\n",
" 132.1875, 134.0625, 135.9375, 137.8125, 139.6875, 141.5625, 143.4375,\n",
" 145.3125, 147.1875, 149.0625, 150.9375, 152.8125, 154.6875, 156.5625,\n",
" 158.4375, 160.3125, 162.1875, 164.0625, 165.9375, 167.8125, 169.6875,\n",
" 171.5625, 173.4375, 175.3125, 177.1875, 179.0625, 180.9375, 182.8125,\n",
" 184.6875, 186.5625, 188.4375, 190.3125, 192.1875, 194.0625, 195.9375,\n",
" 197.8125, 199.6875, 201.5625, 203.4375, 205.3125, 207.1875, 209.0625,\n",
" 210.9375, 212.8125, 214.6875, 216.5625, 218.4375, 220.3125, 222.1875,\n",
" 224.0625, 225.9375, 227.8125, 229.6875, 231.5625, 233.4375, 235.3125,\n",
" 237.1875, 239.0625, 240.9375, 242.8125, 244.6875, 246.5625, 248.4375,\n",
" 250.3125, 252.1875, 254.0625, 255.9375, 257.8125, 259.6875, 261.5625,\n",
" 263.4375, 265.3125, 267.1875, 269.0625, 270.9375, 272.8125, 274.6875,\n",
" 276.5625, 278.4375, 280.3125, 282.1875, 284.0625, 285.9375, 287.8125,\n",
" 289.6875, 291.5625, 293.4375, 295.3125, 297.1875, 299.0625, 300.9375,\n",
" 302.8125, 304.6875, 306.5625, 308.4375, 310.3125, 312.1875, 314.0625,\n",
" 315.9375, 317.8125, 319.6875, 321.5625, 323.4375, 325.3125, 327.1875,\n",
" 329.0625, 330.9375, 332.8125, 334.6875, 336.5625, 338.4375, 340.3125,\n",
" 342.1875, 344.0625, 345.9375, 347.8125, 349.6875, 351.5625, 353.4375,\n",
" 355.3125, 357.1875, 359.0625])</pre></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'>1850-01-01T12:00:00 ... 2014-12-31T12:00:00</div><input id='attrs-3a139c13-e622-477c-b68c-d3e66d45b0f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a139c13-e622-477c-b68c-d3e66d45b0f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b7808d7-0fa7-4576-8603-f0f0fbfc9588' class='xr-var-data-in' type='checkbox'><label for='data-1b7808d7-0fa7-4576-8603-f0f0fbfc9588' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><pre class='xr-var-data'>array([&#x27;1850-01-01T12:00:00.000000000&#x27;, &#x27;1850-01-02T12:00:00.000000000&#x27;,\n",
" &#x27;1850-01-03T12:00:00.000000000&#x27;, ..., &#x27;2014-12-29T12:00:00.000000000&#x27;,\n",
" &#x27;2014-12-30T12:00:00.000000000&#x27;, &#x27;2014-12-31T12:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-73f5bc87-6f41-4a44-b76e-1ea2c9660fd6' class='xr-section-summary-in' type='checkbox' checked><label for='section-73f5bc87-6f41-4a44-b76e-1ea2c9660fd6' 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>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2019-11-09T02:13:27Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-11-09T02:13:27Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'tas' (time: 60265, lat: 144, lon: 192)>\n",
"dask.array<concatenate, shape=(60265, 144, 192), dtype=float32, chunksize=(18262, 144, 192), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" height float64 2.0\n",
" * lat (lat) float64 -89.38 -88.12 -86.88 -85.62 ... 86.88 88.12 89.38\n",
" * lon (lon) float64 0.9375 2.812 4.688 6.562 ... 353.4 355.3 357.2 359.1\n",
" * time (time) datetime64[ns] 1850-01-01T12:00:00 ... 2014-12-31T12:00:00\n",
"Attributes:\n",
" standard_name: air_temperature\n",
" long_name: Near-Surface Air Temperature\n",
" comment: near-surface (usually, 2 meter) air temperature\n",
" units: K\n",
" cell_methods: area: time: mean\n",
" cell_measures: area: areacella\n",
" history: 2019-11-09T02:13:27Z altered by CMOR: Treated scalar dime..."
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load some sample CMIP6 data\n",
"ds = xarray.open_mfdataset('/g/data/fs38/publications/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/historical/r1i1p1f1/day/tas/gn/latest/*.nc',\n",
" combine='nested', concat_dim='time')\n",
"tas = ds.tas\n",
"tas"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DJF\n",
"JJA\n",
"MAM\n",
"SON\n"
]
}
],
"source": [
"# We can groupby season and loop over each season's data\n",
"\n",
"seasons = tas.groupby('time.season')\n",
"\n",
"for name, data in seasons:\n",
" print(name)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60265</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a184f721-ffad-42d4-b8e5-d06ae452304c' class='xr-array-in' type='checkbox' ><label for='section-a184f721-ffad-42d4-b8e5-d06ae452304c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1850 1850 1850 1850 1850 1850 1850 ... 2015 2015 2015 2015 2015 2015</span></div><pre class='xr-array-data'>array([1850, 1850, 1850, ..., 2015, 2015, 2015])</pre></div></li><li class='xr-section-item'><input id='section-3ee2ba8b-3380-4215-8830-0c207763689e' class='xr-section-summary-in' type='checkbox' checked><label for='section-3ee2ba8b-3380-4215-8830-0c207763689e' class='xr-section-summary' >Coordinates: <span>(2)</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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-864668d5-c9ff-4f9c-8cfd-519442ee14b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-864668d5-c9ff-4f9c-8cfd-519442ee14b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d468aa1c-8092-46aa-a034-a714b42789fa' class='xr-var-data-in' type='checkbox'><label for='data-d468aa1c-8092-46aa-a034-a714b42789fa' 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>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></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'>1850-01-01T12:00:00 ... 2014-12-31T12:00:00</div><input id='attrs-2fe82cf8-c642-4140-9acf-85fae38216ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fe82cf8-c642-4140-9acf-85fae38216ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2c9ca18-2163-46fb-83bb-e4f805ec0eaf' class='xr-var-data-in' type='checkbox'><label for='data-e2c9ca18-2163-46fb-83bb-e4f805ec0eaf' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><pre class='xr-var-data'>array([&#x27;1850-01-01T12:00:00.000000000&#x27;, &#x27;1850-01-02T12:00:00.000000000&#x27;,\n",
" &#x27;1850-01-03T12:00:00.000000000&#x27;, ..., &#x27;2014-12-29T12:00:00.000000000&#x27;,\n",
" &#x27;2014-12-30T12:00:00.000000000&#x27;, &#x27;2014-12-31T12:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-038701e9-8a40-4a0a-8bc9-4a4c3defd8d6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-038701e9-8a40-4a0a-8bc9-4a4c3defd8d6' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray (time: 60265)>\n",
"array([1850, 1850, 1850, ..., 2015, 2015, 2015])\n",
"Coordinates:\n",
" height float64 2.0\n",
" * time (time) datetime64[ns] 1850-01-01T12:00:00 ... 2014-12-31T12:00:00"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# But the DJF season is split over consecutive years, so we can't just group by year\n",
"# We can make a new 'season_year' coordinate that is the year that that month should\n",
"# be grouped into when calculating seasons\n",
"\n",
"# ds.time.dt.month // 12 will be 1 for december, 0 for all other months\n",
"\n",
"season_year = ds.time.dt.year + (ds.time.dt.month // 12)\n",
"\n",
"season_year"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(<xarray.DataArray (time: 1)>\n",
" array([1850])\n",
" Coordinates:\n",
" height float64 2.0\n",
" * time (time) datetime64[ns] 1850-11-01T12:00:00,\n",
" <xarray.DataArray (time: 1)>\n",
" array([1851])\n",
" Coordinates:\n",
" height float64 2.0\n",
" * time (time) datetime64[ns] 1850-12-01T12:00:00)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Checking that the calculation is correct - current year for november, next year for december\n",
"\n",
"season_year.sel(time='18501101'), season_year.sel(time='18501201')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
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" <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
" <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"80.588235,70.588235 106.000852,70.588235 106.000852,96.000852 80.588235,96.000852\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >192</text>\n",
" <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">144</text>\n",
" <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">60265</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></pre></div></li><li class='xr-section-item'><input id='section-9b1fee98-393a-4be6-998a-83331202167a' class='xr-section-summary-in' type='checkbox' checked><label for='section-9b1fee98-393a-4be6-998a-83331202167a' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-32c251ff-9cd2-433b-a433-dbd831e5083a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32c251ff-9cd2-433b-a433-dbd831e5083a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-528d93b5-3262-4cf7-bca3-29c1f5e26a75' class='xr-var-data-in' type='checkbox'><label for='data-528d93b5-3262-4cf7-bca3-29c1f5e26a75' 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>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.38 -88.12 ... 88.12 89.38</div><input id='attrs-cbe084b1-dd7e-4e11-aca7-824339d57a95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbe084b1-dd7e-4e11-aca7-824339d57a95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7aa5497d-6fca-4298-961a-41b22329088c' class='xr-var-data-in' type='checkbox'><label for='data-7aa5497d-6fca-4298-961a-41b22329088c' 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>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><pre class='xr-var-data'>array([-89.375, -88.125, -86.875, -85.625, -84.375, -83.125, -81.875, -80.625,\n",
" -79.375, -78.125, -76.875, -75.625, -74.375, -73.125, -71.875, -70.625,\n",
" -69.375, -68.125, -66.875, -65.625, -64.375, -63.125, -61.875, -60.625,\n",
" -59.375, -58.125, -56.875, -55.625, -54.375, -53.125, -51.875, -50.625,\n",
" -49.375, -48.125, -46.875, -45.625, -44.375, -43.125, -41.875, -40.625,\n",
" -39.375, -38.125, -36.875, -35.625, -34.375, -33.125, -31.875, -30.625,\n",
" -29.375, -28.125, -26.875, -25.625, -24.375, -23.125, -21.875, -20.625,\n",
" -19.375, -18.125, -16.875, -15.625, -14.375, -13.125, -11.875, -10.625,\n",
" -9.375, -8.125, -6.875, -5.625, -4.375, -3.125, -1.875, -0.625,\n",
" 0.625, 1.875, 3.125, 4.375, 5.625, 6.875, 8.125, 9.375,\n",
" 10.625, 11.875, 13.125, 14.375, 15.625, 16.875, 18.125, 19.375,\n",
" 20.625, 21.875, 23.125, 24.375, 25.625, 26.875, 28.125, 29.375,\n",
" 30.625, 31.875, 33.125, 34.375, 35.625, 36.875, 38.125, 39.375,\n",
" 40.625, 41.875, 43.125, 44.375, 45.625, 46.875, 48.125, 49.375,\n",
" 50.625, 51.875, 53.125, 54.375, 55.625, 56.875, 58.125, 59.375,\n",
" 60.625, 61.875, 63.125, 64.375, 65.625, 66.875, 68.125, 69.375,\n",
" 70.625, 71.875, 73.125, 74.375, 75.625, 76.875, 78.125, 79.375,\n",
" 80.625, 81.875, 83.125, 84.375, 85.625, 86.875, 88.125, 89.375])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9375 2.812 4.688 ... 357.2 359.1</div><input id='attrs-73949230-2f9c-4f9e-ba26-0fcca4decd0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73949230-2f9c-4f9e-ba26-0fcca4decd0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d8bdb58-6eba-4f53-b722-863862418be2' class='xr-var-data-in' type='checkbox'><label for='data-3d8bdb58-6eba-4f53-b722-863862418be2' 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>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><pre class='xr-var-data'>array([ 0.9375, 2.8125, 4.6875, 6.5625, 8.4375, 10.3125, 12.1875,\n",
" 14.0625, 15.9375, 17.8125, 19.6875, 21.5625, 23.4375, 25.3125,\n",
" 27.1875, 29.0625, 30.9375, 32.8125, 34.6875, 36.5625, 38.4375,\n",
" 40.3125, 42.1875, 44.0625, 45.9375, 47.8125, 49.6875, 51.5625,\n",
" 53.4375, 55.3125, 57.1875, 59.0625, 60.9375, 62.8125, 64.6875,\n",
" 66.5625, 68.4375, 70.3125, 72.1875, 74.0625, 75.9375, 77.8125,\n",
" 79.6875, 81.5625, 83.4375, 85.3125, 87.1875, 89.0625, 90.9375,\n",
" 92.8125, 94.6875, 96.5625, 98.4375, 100.3125, 102.1875, 104.0625,\n",
" 105.9375, 107.8125, 109.6875, 111.5625, 113.4375, 115.3125, 117.1875,\n",
" 119.0625, 120.9375, 122.8125, 124.6875, 126.5625, 128.4375, 130.3125,\n",
" 132.1875, 134.0625, 135.9375, 137.8125, 139.6875, 141.5625, 143.4375,\n",
" 145.3125, 147.1875, 149.0625, 150.9375, 152.8125, 154.6875, 156.5625,\n",
" 158.4375, 160.3125, 162.1875, 164.0625, 165.9375, 167.8125, 169.6875,\n",
" 171.5625, 173.4375, 175.3125, 177.1875, 179.0625, 180.9375, 182.8125,\n",
" 184.6875, 186.5625, 188.4375, 190.3125, 192.1875, 194.0625, 195.9375,\n",
" 197.8125, 199.6875, 201.5625, 203.4375, 205.3125, 207.1875, 209.0625,\n",
" 210.9375, 212.8125, 214.6875, 216.5625, 218.4375, 220.3125, 222.1875,\n",
" 224.0625, 225.9375, 227.8125, 229.6875, 231.5625, 233.4375, 235.3125,\n",
" 237.1875, 239.0625, 240.9375, 242.8125, 244.6875, 246.5625, 248.4375,\n",
" 250.3125, 252.1875, 254.0625, 255.9375, 257.8125, 259.6875, 261.5625,\n",
" 263.4375, 265.3125, 267.1875, 269.0625, 270.9375, 272.8125, 274.6875,\n",
" 276.5625, 278.4375, 280.3125, 282.1875, 284.0625, 285.9375, 287.8125,\n",
" 289.6875, 291.5625, 293.4375, 295.3125, 297.1875, 299.0625, 300.9375,\n",
" 302.8125, 304.6875, 306.5625, 308.4375, 310.3125, 312.1875, 314.0625,\n",
" 315.9375, 317.8125, 319.6875, 321.5625, 323.4375, 325.3125, 327.1875,\n",
" 329.0625, 330.9375, 332.8125, 334.6875, 336.5625, 338.4375, 340.3125,\n",
" 342.1875, 344.0625, 345.9375, 347.8125, 349.6875, 351.5625, 353.4375,\n",
" 355.3125, 357.1875, 359.0625])</pre></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'>1850-01-01T12:00:00 ... 2014-12-31T12:00:00</div><input id='attrs-70721475-77c4-4ea1-b44f-62ea8bd63a7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70721475-77c4-4ea1-b44f-62ea8bd63a7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59b731b6-8af8-4b5d-bedf-386453cf30a4' class='xr-var-data-in' type='checkbox'><label for='data-59b731b6-8af8-4b5d-bedf-386453cf30a4' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><pre class='xr-var-data'>array([&#x27;1850-01-01T12:00:00.000000000&#x27;, &#x27;1850-01-02T12:00:00.000000000&#x27;,\n",
" &#x27;1850-01-03T12:00:00.000000000&#x27;, ..., &#x27;2014-12-29T12:00:00.000000000&#x27;,\n",
" &#x27;2014-12-30T12:00:00.000000000&#x27;, &#x27;2014-12-31T12:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>season_year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1850 1850 1850 ... 2015 2015 2015</div><input id='attrs-29e10f9d-17bc-4f29-8fec-6a465ddf207d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29e10f9d-17bc-4f29-8fec-6a465ddf207d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04682a43-0f6d-4e61-93f4-cebb2ef1f9a5' class='xr-var-data-in' type='checkbox'><label for='data-04682a43-0f6d-4e61-93f4-cebb2ef1f9a5' 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><pre class='xr-var-data'>array([1850, 1850, 1850, ..., 2015, 2015, 2015])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-34a5e0ac-0b54-4dc3-848a-370dc27d87c4' class='xr-section-summary-in' type='checkbox' checked><label for='section-34a5e0ac-0b54-4dc3-848a-370dc27d87c4' 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>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2019-11-09T02:13:27Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-11-09T02:13:27Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'tas' (time: 60265, lat: 144, lon: 192)>\n",
"dask.array<concatenate, shape=(60265, 144, 192), dtype=float32, chunksize=(18262, 144, 192), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" height float64 2.0\n",
" * lat (lat) float64 -89.38 -88.12 -86.88 -85.62 ... 86.88 88.12 89.38\n",
" * lon (lon) float64 0.9375 2.812 4.688 6.562 ... 355.3 357.2 359.1\n",
" * time (time) datetime64[ns] 1850-01-01T12:00:00 ... 2014-12-31T12:00:00\n",
" season_year (time) int64 1850 1850 1850 1850 1850 ... 2015 2015 2015 2015\n",
"Attributes:\n",
" standard_name: air_temperature\n",
" long_name: Near-Surface Air Temperature\n",
" comment: near-surface (usually, 2 meter) air temperature\n",
" units: K\n",
" cell_methods: area: time: mean\n",
" cell_measures: area: areacella\n",
" history: 2019-11-09T02:13:27Z altered by CMOR: Treated scalar dime..."
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Add the new coordinate to the data\n",
"\n",
"tas.coords['season_year'] = season_year\n",
"tas"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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".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",
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" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
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"\n",
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".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
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"\n",
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" display: block;\n",
"}\n",
"\n",
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" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
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" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
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" 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, 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",
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"</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>season_year</span>: 166</li><li><span class='xr-has-index'>lat</span>: 144</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c1b33a18-444b-4849-8d99-bb00defa3e85' class='xr-array-in' type='checkbox' ><label for='section-c1b33a18-444b-4849-8d99-bb00defa3e85' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 144, 192), meta=np.ndarray&gt;</span></div><pre class='xr-array-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> 18.36 MB </td> <td> 110.59 kB </td></tr>\n",
" <tr><th> Shape </th><td> (166, 144, 192) </td> <td> (1, 144, 192) </td></tr>\n",
" <tr><th> Count </th><td> 686 Tasks </td><td> 166 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></pre></div></li><li class='xr-section-item'><input id='section-0b2dc611-7eac-45d6-8f02-61a7d0ef142e' class='xr-section-summary-in' type='checkbox' checked><label for='section-0b2dc611-7eac-45d6-8f02-61a7d0ef142e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-28aace34-24bf-4082-9b91-09b5ab0d2568' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28aace34-24bf-4082-9b91-09b5ab0d2568' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adfa3a45-8351-4208-8b5f-4f9bcb65a9ce' class='xr-var-data-in' type='checkbox'><label for='data-adfa3a45-8351-4208-8b5f-4f9bcb65a9ce' 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>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.38 -88.12 ... 88.12 89.38</div><input id='attrs-42b1beb6-96a9-4107-9efa-2dcae99f338f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42b1beb6-96a9-4107-9efa-2dcae99f338f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-939b7003-f8f7-4aa1-9eb2-69f93e9c63be' class='xr-var-data-in' type='checkbox'><label for='data-939b7003-f8f7-4aa1-9eb2-69f93e9c63be' 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>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><pre class='xr-var-data'>array([-89.375, -88.125, -86.875, -85.625, -84.375, -83.125, -81.875, -80.625,\n",
" -79.375, -78.125, -76.875, -75.625, -74.375, -73.125, -71.875, -70.625,\n",
" -69.375, -68.125, -66.875, -65.625, -64.375, -63.125, -61.875, -60.625,\n",
" -59.375, -58.125, -56.875, -55.625, -54.375, -53.125, -51.875, -50.625,\n",
" -49.375, -48.125, -46.875, -45.625, -44.375, -43.125, -41.875, -40.625,\n",
" -39.375, -38.125, -36.875, -35.625, -34.375, -33.125, -31.875, -30.625,\n",
" -29.375, -28.125, -26.875, -25.625, -24.375, -23.125, -21.875, -20.625,\n",
" -19.375, -18.125, -16.875, -15.625, -14.375, -13.125, -11.875, -10.625,\n",
" -9.375, -8.125, -6.875, -5.625, -4.375, -3.125, -1.875, -0.625,\n",
" 0.625, 1.875, 3.125, 4.375, 5.625, 6.875, 8.125, 9.375,\n",
" 10.625, 11.875, 13.125, 14.375, 15.625, 16.875, 18.125, 19.375,\n",
" 20.625, 21.875, 23.125, 24.375, 25.625, 26.875, 28.125, 29.375,\n",
" 30.625, 31.875, 33.125, 34.375, 35.625, 36.875, 38.125, 39.375,\n",
" 40.625, 41.875, 43.125, 44.375, 45.625, 46.875, 48.125, 49.375,\n",
" 50.625, 51.875, 53.125, 54.375, 55.625, 56.875, 58.125, 59.375,\n",
" 60.625, 61.875, 63.125, 64.375, 65.625, 66.875, 68.125, 69.375,\n",
" 70.625, 71.875, 73.125, 74.375, 75.625, 76.875, 78.125, 79.375,\n",
" 80.625, 81.875, 83.125, 84.375, 85.625, 86.875, 88.125, 89.375])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9375 2.812 4.688 ... 357.2 359.1</div><input id='attrs-c8ff6fd4-1c1e-4e51-97f5-f71d5fdf906e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8ff6fd4-1c1e-4e51-97f5-f71d5fdf906e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-deea072d-d816-4fc9-83f5-2284359dbd2e' class='xr-var-data-in' type='checkbox'><label for='data-deea072d-d816-4fc9-83f5-2284359dbd2e' 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>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><pre class='xr-var-data'>array([ 0.9375, 2.8125, 4.6875, 6.5625, 8.4375, 10.3125, 12.1875,\n",
" 14.0625, 15.9375, 17.8125, 19.6875, 21.5625, 23.4375, 25.3125,\n",
" 27.1875, 29.0625, 30.9375, 32.8125, 34.6875, 36.5625, 38.4375,\n",
" 40.3125, 42.1875, 44.0625, 45.9375, 47.8125, 49.6875, 51.5625,\n",
" 53.4375, 55.3125, 57.1875, 59.0625, 60.9375, 62.8125, 64.6875,\n",
" 66.5625, 68.4375, 70.3125, 72.1875, 74.0625, 75.9375, 77.8125,\n",
" 79.6875, 81.5625, 83.4375, 85.3125, 87.1875, 89.0625, 90.9375,\n",
" 92.8125, 94.6875, 96.5625, 98.4375, 100.3125, 102.1875, 104.0625,\n",
" 105.9375, 107.8125, 109.6875, 111.5625, 113.4375, 115.3125, 117.1875,\n",
" 119.0625, 120.9375, 122.8125, 124.6875, 126.5625, 128.4375, 130.3125,\n",
" 132.1875, 134.0625, 135.9375, 137.8125, 139.6875, 141.5625, 143.4375,\n",
" 145.3125, 147.1875, 149.0625, 150.9375, 152.8125, 154.6875, 156.5625,\n",
" 158.4375, 160.3125, 162.1875, 164.0625, 165.9375, 167.8125, 169.6875,\n",
" 171.5625, 173.4375, 175.3125, 177.1875, 179.0625, 180.9375, 182.8125,\n",
" 184.6875, 186.5625, 188.4375, 190.3125, 192.1875, 194.0625, 195.9375,\n",
" 197.8125, 199.6875, 201.5625, 203.4375, 205.3125, 207.1875, 209.0625,\n",
" 210.9375, 212.8125, 214.6875, 216.5625, 218.4375, 220.3125, 222.1875,\n",
" 224.0625, 225.9375, 227.8125, 229.6875, 231.5625, 233.4375, 235.3125,\n",
" 237.1875, 239.0625, 240.9375, 242.8125, 244.6875, 246.5625, 248.4375,\n",
" 250.3125, 252.1875, 254.0625, 255.9375, 257.8125, 259.6875, 261.5625,\n",
" 263.4375, 265.3125, 267.1875, 269.0625, 270.9375, 272.8125, 274.6875,\n",
" 276.5625, 278.4375, 280.3125, 282.1875, 284.0625, 285.9375, 287.8125,\n",
" 289.6875, 291.5625, 293.4375, 295.3125, 297.1875, 299.0625, 300.9375,\n",
" 302.8125, 304.6875, 306.5625, 308.4375, 310.3125, 312.1875, 314.0625,\n",
" 315.9375, 317.8125, 319.6875, 321.5625, 323.4375, 325.3125, 327.1875,\n",
" 329.0625, 330.9375, 332.8125, 334.6875, 336.5625, 338.4375, 340.3125,\n",
" 342.1875, 344.0625, 345.9375, 347.8125, 349.6875, 351.5625, 353.4375,\n",
" 355.3125, 357.1875, 359.0625])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>season_year</span></div><div class='xr-var-dims'>(season_year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1850 1851 1852 ... 2013 2014 2015</div><input id='attrs-865eabbe-44e2-42ab-88d1-60bad33fb3a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-865eabbe-44e2-42ab-88d1-60bad33fb3a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87429176-96a2-4fe1-b889-81d967a5efef' class='xr-var-data-in' type='checkbox'><label for='data-87429176-96a2-4fe1-b889-81d967a5efef' 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><pre class='xr-var-data'>array([1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861,\n",
" 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873,\n",
" 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885,\n",
" 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897,\n",
" 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909,\n",
" 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921,\n",
" 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933,\n",
" 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945,\n",
" 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957,\n",
" 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969,\n",
" 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n",
" 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993,\n",
" 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,\n",
" 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-31485387-3d6e-43cf-925b-1a46cb7dea21' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-31485387-3d6e-43cf-925b-1a46cb7dea21' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'tas' (season_year: 166, lat: 144, lon: 192)>\n",
"dask.array<stack, shape=(166, 144, 192), dtype=float32, chunksize=(1, 144, 192), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" height float64 2.0\n",
" * lat (lat) float64 -89.38 -88.12 -86.88 -85.62 ... 86.88 88.12 89.38\n",
" * lon (lon) float64 0.9375 2.812 4.688 6.562 ... 355.3 357.2 359.1\n",
" * season_year (season_year) int64 1850 1851 1852 1853 ... 2012 2013 2014 2015"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Then we can do the mean in the season loop\n",
"\n",
"seasons = tas.groupby('time.season')\n",
"\n",
"means = {}\n",
"\n",
"for name, data in seasons:\n",
" means[name] = data.groupby('season_year').mean()\n",
" \n",
"means['DJF']"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x14adc7b9f810>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# And grab the data for whatever year\n",
"means['DJF'].sel(season_year=2012).plot()"
]
},
{
"cell_type": "code",
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"</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>season</span>: 4</li><li><span class='xr-has-index'>lat</span>: 144</li><li><span class='xr-has-index'>lon</span>: 192</li><li><span class='xr-has-index'>season_year</span>: 166</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e59a7948-c7d3-4c97-b0b8-334c07e6d079' class='xr-array-in' type='checkbox' ><label for='section-e59a7948-c7d3-4c97-b0b8-334c07e6d079' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 144, 192, 1), meta=np.ndarray&gt;</span></div><pre class='xr-array-data'><table>\n",
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"</td>\n",
"</tr>\n",
"</table></pre></div></li><li class='xr-section-item'><input id='section-f10ce452-b6be-4859-9251-a7964ccb06fd' class='xr-section-summary-in' type='checkbox' checked><label for='section-f10ce452-b6be-4859-9251-a7964ccb06fd' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-a2dd11fc-7cf7-4eb3-be37-5c005c7c6f40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2dd11fc-7cf7-4eb3-be37-5c005c7c6f40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09a84e15-e2ba-4b98-ae65-058ec71e150c' class='xr-var-data-in' type='checkbox'><label for='data-09a84e15-e2ba-4b98-ae65-058ec71e150c' 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>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9375 2.812 4.688 ... 357.2 359.1</div><input id='attrs-391e97df-aa0f-429b-aa9c-dc240f804b25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-391e97df-aa0f-429b-aa9c-dc240f804b25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5ca19d2-70b1-47aa-8c2e-706e6de61c6f' class='xr-var-data-in' type='checkbox'><label for='data-c5ca19d2-70b1-47aa-8c2e-706e6de61c6f' 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>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><pre class='xr-var-data'>array([ 0.9375, 2.8125, 4.6875, 6.5625, 8.4375, 10.3125, 12.1875,\n",
" 14.0625, 15.9375, 17.8125, 19.6875, 21.5625, 23.4375, 25.3125,\n",
" 27.1875, 29.0625, 30.9375, 32.8125, 34.6875, 36.5625, 38.4375,\n",
" 40.3125, 42.1875, 44.0625, 45.9375, 47.8125, 49.6875, 51.5625,\n",
" 53.4375, 55.3125, 57.1875, 59.0625, 60.9375, 62.8125, 64.6875,\n",
" 66.5625, 68.4375, 70.3125, 72.1875, 74.0625, 75.9375, 77.8125,\n",
" 79.6875, 81.5625, 83.4375, 85.3125, 87.1875, 89.0625, 90.9375,\n",
" 92.8125, 94.6875, 96.5625, 98.4375, 100.3125, 102.1875, 104.0625,\n",
" 105.9375, 107.8125, 109.6875, 111.5625, 113.4375, 115.3125, 117.1875,\n",
" 119.0625, 120.9375, 122.8125, 124.6875, 126.5625, 128.4375, 130.3125,\n",
" 132.1875, 134.0625, 135.9375, 137.8125, 139.6875, 141.5625, 143.4375,\n",
" 145.3125, 147.1875, 149.0625, 150.9375, 152.8125, 154.6875, 156.5625,\n",
" 158.4375, 160.3125, 162.1875, 164.0625, 165.9375, 167.8125, 169.6875,\n",
" 171.5625, 173.4375, 175.3125, 177.1875, 179.0625, 180.9375, 182.8125,\n",
" 184.6875, 186.5625, 188.4375, 190.3125, 192.1875, 194.0625, 195.9375,\n",
" 197.8125, 199.6875, 201.5625, 203.4375, 205.3125, 207.1875, 209.0625,\n",
" 210.9375, 212.8125, 214.6875, 216.5625, 218.4375, 220.3125, 222.1875,\n",
" 224.0625, 225.9375, 227.8125, 229.6875, 231.5625, 233.4375, 235.3125,\n",
" 237.1875, 239.0625, 240.9375, 242.8125, 244.6875, 246.5625, 248.4375,\n",
" 250.3125, 252.1875, 254.0625, 255.9375, 257.8125, 259.6875, 261.5625,\n",
" 263.4375, 265.3125, 267.1875, 269.0625, 270.9375, 272.8125, 274.6875,\n",
" 276.5625, 278.4375, 280.3125, 282.1875, 284.0625, 285.9375, 287.8125,\n",
" 289.6875, 291.5625, 293.4375, 295.3125, 297.1875, 299.0625, 300.9375,\n",
" 302.8125, 304.6875, 306.5625, 308.4375, 310.3125, 312.1875, 314.0625,\n",
" 315.9375, 317.8125, 319.6875, 321.5625, 323.4375, 325.3125, 327.1875,\n",
" 329.0625, 330.9375, 332.8125, 334.6875, 336.5625, 338.4375, 340.3125,\n",
" 342.1875, 344.0625, 345.9375, 347.8125, 349.6875, 351.5625, 353.4375,\n",
" 355.3125, 357.1875, 359.0625])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.38 -88.12 ... 88.12 89.38</div><input id='attrs-2e3de809-9212-4dda-9260-8fb594729d93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e3de809-9212-4dda-9260-8fb594729d93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ba5df6b-a58a-41cb-a7d5-bab5d61e53fb' class='xr-var-data-in' type='checkbox'><label for='data-5ba5df6b-a58a-41cb-a7d5-bab5d61e53fb' 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>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><pre class='xr-var-data'>array([-89.375, -88.125, -86.875, -85.625, -84.375, -83.125, -81.875, -80.625,\n",
" -79.375, -78.125, -76.875, -75.625, -74.375, -73.125, -71.875, -70.625,\n",
" -69.375, -68.125, -66.875, -65.625, -64.375, -63.125, -61.875, -60.625,\n",
" -59.375, -58.125, -56.875, -55.625, -54.375, -53.125, -51.875, -50.625,\n",
" -49.375, -48.125, -46.875, -45.625, -44.375, -43.125, -41.875, -40.625,\n",
" -39.375, -38.125, -36.875, -35.625, -34.375, -33.125, -31.875, -30.625,\n",
" -29.375, -28.125, -26.875, -25.625, -24.375, -23.125, -21.875, -20.625,\n",
" -19.375, -18.125, -16.875, -15.625, -14.375, -13.125, -11.875, -10.625,\n",
" -9.375, -8.125, -6.875, -5.625, -4.375, -3.125, -1.875, -0.625,\n",
" 0.625, 1.875, 3.125, 4.375, 5.625, 6.875, 8.125, 9.375,\n",
" 10.625, 11.875, 13.125, 14.375, 15.625, 16.875, 18.125, 19.375,\n",
" 20.625, 21.875, 23.125, 24.375, 25.625, 26.875, 28.125, 29.375,\n",
" 30.625, 31.875, 33.125, 34.375, 35.625, 36.875, 38.125, 39.375,\n",
" 40.625, 41.875, 43.125, 44.375, 45.625, 46.875, 48.125, 49.375,\n",
" 50.625, 51.875, 53.125, 54.375, 55.625, 56.875, 58.125, 59.375,\n",
" 60.625, 61.875, 63.125, 64.375, 65.625, 66.875, 68.125, 69.375,\n",
" 70.625, 71.875, 73.125, 74.375, 75.625, 76.875, 78.125, 79.375,\n",
" 80.625, 81.875, 83.125, 84.375, 85.625, 86.875, 88.125, 89.375])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>season_year</span></div><div class='xr-var-dims'>(season_year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1850 1851 1852 ... 2013 2014 2015</div><input id='attrs-9014cdc7-64a8-40e8-9c30-715b6b7d67f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9014cdc7-64a8-40e8-9c30-715b6b7d67f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05bcba54-4086-4e73-8e37-b30d592badab' class='xr-var-data-in' type='checkbox'><label for='data-05bcba54-4086-4e73-8e37-b30d592badab' 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><pre class='xr-var-data'>array([1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861,\n",
" 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873,\n",
" 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885,\n",
" 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897,\n",
" 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909,\n",
" 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921,\n",
" 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933,\n",
" 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945,\n",
" 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957,\n",
" 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969,\n",
" 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n",
" 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993,\n",
" 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,\n",
" 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>season</span></div><div class='xr-var-dims'>(season)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;DJF&#x27; &#x27;JJA&#x27; &#x27;MAM&#x27; &#x27;SON&#x27;</div><input id='attrs-adbbe966-6dda-45d5-be43-28d5b28c39eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-adbbe966-6dda-45d5-be43-28d5b28c39eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50a556cd-6983-422e-9f91-00130258fcdc' class='xr-var-data-in' type='checkbox'><label for='data-50a556cd-6983-422e-9f91-00130258fcdc' 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><pre class='xr-var-data'>array([&#x27;DJF&#x27;, &#x27;JJA&#x27;, &#x27;MAM&#x27;, &#x27;SON&#x27;], dtype=object)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-a13ea72f-1ee5-4627-8184-1c4ccf4475f0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a13ea72f-1ee5-4627-8184-1c4ccf4475f0' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'tas' (season: 4, lat: 144, lon: 192, season_year: 166)>\n",
"dask.array<transpose, shape=(4, 144, 192, 166), dtype=float32, chunksize=(1, 144, 192, 1), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" height float64 2.0\n",
" * lon (lon) float64 0.9375 2.812 4.688 6.562 ... 355.3 357.2 359.1\n",
" * lat (lat) float64 -89.38 -88.12 -86.88 -85.62 ... 86.88 88.12 89.38\n",
" * season_year (season_year) int64 1850 1851 1852 1853 ... 2012 2013 2014 2015\n",
" * season (season) object 'DJF' 'JJA' 'MAM' 'SON'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# You can also do this in one operation using '.groupby().apply()' instead of a loop\n",
"tas.groupby('time.season').apply(lambda x: x.groupby('season_year').mean())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:analysis3-20.04] *",
"language": "python",
"name": "conda-env-analysis3-20.04-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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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