{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regrid between rectilinear grids\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import xesmf as xe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We regrid xarray's built-in demo data. This data is also used by\n",
    "[xarray plotting tutorial](http://xarray.pydata.org/en/stable/plotting.html).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "Data variables:\n",
       "    air      (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  COARDS\n",
       "    title:        4x daily NMC reanalysis (1948)\n",
       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
       "    platform:     Model\n",
       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4d8fe4cf-1164-4b53-96c1-66a1a733cc13' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4d8fe4cf-1164-4b53-96c1-66a1a733cc13' 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'>lat</span>: 25</li><li><span class='xr-has-index'>time</span>: 2920</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d0fb2ebe-c7c6-4ca8-ad8e-f87c0ffdd669' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0fb2ebe-c7c6-4ca8-ad8e-f87c0ffdd669' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-3642de40-e003-46ce-bc5f-054bfba29e68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3642de40-e003-46ce-bc5f-054bfba29e68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0d96ddd-b153-4d68-b2b6-08dac0009c85' class='xr-var-data-in' type='checkbox'><label for='data-f0d96ddd-b153-4d68-b2b6-08dac0009c85' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
       "       15. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-b3ec4e3e-1600-4373-b1a9-d4e67fb7b95d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3ec4e3e-1600-4373-b1a9-d4e67fb7b95d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d9eda57-ddfb-4b01-8657-b3036fb3e3ec' class='xr-var-data-in' type='checkbox'><label for='data-0d9eda57-ddfb-4b01-8657-b3036fb3e3ec' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
       "       325. , 327.5, 330. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-a9b55b8b-e971-4e4b-817c-9acdee2dd0ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9b55b8b-e971-4e4b-817c-9acdee2dd0ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f414ec9e-b7de-46f3-9a7e-a2e9348db3e8' class='xr-var-data-in' type='checkbox'><label for='data-f414ec9e-b7de-46f3-9a7e-a2e9348db3e8' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-324bcfdb-fda3-4732-85e0-7dc2cf4cc9dd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-324bcfdb-fda3-4732-85e0-7dc2cf4cc9dd' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-316f7188-7be5-4827-b37a-59f100b624fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-316f7188-7be5-4827-b37a-59f100b624fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9caecfc-430f-4b26-8c78-17acf26adb18' class='xr-var-data-in' type='checkbox'><label for='data-c9caecfc-430f-4b26-8c78-17acf26adb18' 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>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><div class='xr-var-data'><pre>[3869000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-31f7cca6-9bb0-4624-a33d-1264135729a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-31f7cca6-9bb0-4624-a33d-1264135729a7' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (1948)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
       "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "Data variables:\n",
       "    air      (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  COARDS\n",
       "    title:        4x daily NMC reanalysis (1948)\n",
       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
       "    platform:     Model\n",
       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.tutorial.open_dataset(\n",
    "    \"air_temperature\"\n",
    ")  # use xr.tutorial.load_dataset() for xarray<v0.11.0\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is the air temperature data over US with 2920 time frames. Let's plot the\n",
    "first frame:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dr = ds[\"air\"]  # get a DataArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f3923d7f400>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "dr.isel(time=0).plot.pcolormesh(ax=ax, vmin=230, vmax=300)\n",
    "ax.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input grid\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its grid resolution is $2.5^\\circ \\times 2.5^\\circ$:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. ,\n",
       "        47.5, 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5,\n",
       "        20. , 17.5, 15. ], dtype=float32),\n",
       " array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. ,\n",
       "        222.5, 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5,\n",
       "        245. , 247.5, 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. ,\n",
       "        267.5, 270. , 272.5, 275. , 277.5, 280. , 282.5, 285. , 287.5,\n",
       "        290. , 292.5, 295. , 297.5, 300. , 302.5, 305. , 307.5, 310. ,\n",
       "        312.5, 315. , 317.5, 320. , 322.5, 325. , 327.5, 330. ],\n",
       "       dtype=float32))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[\"lat\"].values, ds[\"lon\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output grid\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Say we want to downsample it to $1.0^\\circ \\times 1.5^\\circ$. Just define the\n",
    "output grid as an xarray `Dataset`. Notice here that we take care of passing\n",
    "some attributes to the coordinate variables. This ensures xESMF and it's\n",
    "underlying helper, cf-xarray, understand which is which.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 59, lon: 87)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\n",
       "  * lon      (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\n",
       "Data variables:\n",
       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b9cf90ee-6667-42a1-9e37-f0a3b1a2ce4c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b9cf90ee-6667-42a1-9e37-f0a3b1a2ce4c' 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'>lat</span>: 59</li><li><span class='xr-has-index'>lon</span>: 87</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1072712a-e224-4ac4-923c-095e5a92100e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1072712a-e224-4ac4-923c-095e5a92100e' 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 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'>16.0 17.0 18.0 ... 72.0 73.0 74.0</div><input id='attrs-b56a0f0c-8598-4eb0-9d1f-2eb5d892ea46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b56a0f0c-8598-4eb0-9d1f-2eb5d892ea46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91d770ef-1f48-4895-a0bf-24375fd30df6' class='xr-var-data-in' type='checkbox'><label for='data-91d770ef-1f48-4895-a0bf-24375fd30df6' 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>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,\n",
       "       30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.,\n",
       "       44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57.,\n",
       "       58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n",
       "       72., 73., 74.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>200.0 201.5 203.0 ... 327.5 329.0</div><input id='attrs-c797adaa-b575-4f39-a0e0-19975890342d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c797adaa-b575-4f39-a0e0-19975890342d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bbec387-f6d9-49d7-90c4-1e37f3c3a0c2' class='xr-var-data-in' type='checkbox'><label for='data-3bbec387-f6d9-49d7-90c4-1e37f3c3a0c2' 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>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([200. , 201.5, 203. , 204.5, 206. , 207.5, 209. , 210.5, 212. , 213.5,\n",
       "       215. , 216.5, 218. , 219.5, 221. , 222.5, 224. , 225.5, 227. , 228.5,\n",
       "       230. , 231.5, 233. , 234.5, 236. , 237.5, 239. , 240.5, 242. , 243.5,\n",
       "       245. , 246.5, 248. , 249.5, 251. , 252.5, 254. , 255.5, 257. , 258.5,\n",
       "       260. , 261.5, 263. , 264.5, 266. , 267.5, 269. , 270.5, 272. , 273.5,\n",
       "       275. , 276.5, 278. , 279.5, 281. , 282.5, 284. , 285.5, 287. , 288.5,\n",
       "       290. , 291.5, 293. , 294.5, 296. , 297.5, 299. , 300.5, 302. , 303.5,\n",
       "       305. , 306.5, 308. , 309.5, 311. , 312.5, 314. , 315.5, 317. , 318.5,\n",
       "       320. , 321.5, 323. , 324.5, 326. , 327.5, 329. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f86d0086-15bb-4385-8f51-3b9adf344e4c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f86d0086-15bb-4385-8f51-3b9adf344e4c' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-1a6bcf96-526d-4f77-b7d4-a9c00c299b0a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1a6bcf96-526d-4f77-b7d4-a9c00c299b0a' 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.Dataset>\n",
       "Dimensions:  (lat: 59, lon: 87)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\n",
       "  * lon      (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\n",
       "Data variables:\n",
       "    *empty*"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_out = xr.Dataset(\n",
    "    {\n",
    "        \"lat\": ([\"lat\"], np.arange(16, 75, 1.0), {\"units\": \"degrees_north\"}),\n",
    "        \"lon\": ([\"lon\"], np.arange(200, 330, 1.5), {\"units\": \"degrees_east\"}),\n",
    "    }\n",
    ")\n",
    "ds_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform regridding\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a regridder by `xe.Regridder(grid_in, grid_out, method)`. `grid` is just an\n",
    "xarray `Dataset` containing `lat` and `lon` values. In most cases, `'bilinear'`\n",
    "should be good enough. For other methods see\n",
    "[Comparison of 5 regridding algorithms](./Compare_algorithms.ipynb).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xESMF Regridder \n",
       "Regridding algorithm:       conservative \n",
       "Weight filename:            conservative_25x53_59x87.nc \n",
       "Reuse pre-computed weights? False \n",
       "Input grid shape:           (25, 53) \n",
       "Output grid shape:          (59, 87) \n",
       "Periodic in longitude?      False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regridder = xe.Regridder(ds, ds_out, \"conservative\")\n",
    "regridder  # print basic regridder information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The regridder says it can transform data from shape `(25, 53)` to shape\n",
    "`(59, 87)`.\n",
    "\n",
    "Regrid the `DataArray` is straightforward:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
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       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
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       "\n",
       ".xr-section-details {\n",
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       "  margin-bottom: 5px;\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-array-preview,\n",
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       "  display: none;\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
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       "\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",
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       ".xr-var-dims {\n",
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       "\n",
       ".xr-var-dtype {\n",
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       ".xr-attrs dt:hover {\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
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       ".xr-var-data-in:checked ~ .xr-var-data {\n",
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       "\n",
       ".xr-var-data > table {\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
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       "\n",
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       ".xr-var-attrs,\n",
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       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
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       "\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",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;air&#x27; (time: 2920, lat: 59, lon: 87)&gt;\n",
       "array([[[296.1936 , 296.4933 , 296.64383, ..., 296.6239 , 296.57   ,\n",
       "         296.35767],\n",
       "        [295.9    , 296.09998, 296.19998, ..., 295.9    , 295.9    ,\n",
       "         295.43332],\n",
       "        [295.9    , 296.09998, 296.19998, ..., 295.9    , 295.9    ,\n",
       "         295.43332],\n",
       "        ...,\n",
       "        [243.79999, 244.26666, 244.5    , ..., 233.63335, 235.29999,\n",
       "         237.96663],\n",
       "        [243.79999, 244.26666, 244.5    , ..., 233.63335, 235.29999,\n",
       "         237.96663],\n",
       "        [241.87102, 242.6313 , 243.01498, ..., 233.68292, 235.44838,\n",
       "         237.66943]],\n",
       "\n",
       "       [[296.26776, 296.8064 , 297.0761 , ..., 296.19287, 296.17752,\n",
       "         296.2103 ],\n",
       "        [296.19998, 296.53333, 296.69998, ..., 295.56668, 295.5    ,\n",
       "         295.23334],\n",
       "        [296.19998, 296.53333, 296.69998, ..., 295.56668, 295.5    ,\n",
       "         295.23334],\n",
       "...\n",
       "        [249.89   , 249.49   , 249.29   , ..., 241.69   , 242.48999,\n",
       "         243.68997],\n",
       "        [249.89   , 249.49   , 249.29   , ..., 241.69   , 242.48999,\n",
       "         243.68997],\n",
       "        [246.84814, 246.2443 , 245.9487 , ..., 243.05266, 243.60286,\n",
       "         244.30954]],\n",
       "\n",
       "       [[297.2945 , 297.6252 , 297.79266, ..., 296.21606, 296.0664 ,\n",
       "         295.7324 ],\n",
       "        [296.09   , 296.62332, 296.88998, ..., 295.69   , 295.69   ,\n",
       "         295.35666],\n",
       "        [296.09   , 296.62332, 296.88998, ..., 295.69   , 295.69   ,\n",
       "         295.35666],\n",
       "        ...,\n",
       "        [249.89   , 249.49   , 249.29   , ..., 239.82333, 240.29   ,\n",
       "         241.22331],\n",
       "        [249.89   , 249.49   , 249.29   , ..., 239.82333, 240.29   ,\n",
       "         241.22331],\n",
       "        [246.3288 , 245.82306, 245.57744, ..., 241.16115, 241.18028,\n",
       "         241.57034]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "  * lon      (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\n",
       "  * lat      (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\n",
       "Attributes:\n",
       "    long_name:      4xDaily Air temperature at sigma level 995\n",
       "    units:          degK\n",
       "    precision:      2\n",
       "    GRIB_id:        11\n",
       "    GRIB_name:      TMP\n",
       "    var_desc:       Air temperature\n",
       "    dataset:        NMC Reanalysis\n",
       "    level_desc:     Surface\n",
       "    statistic:      Individual Obs\n",
       "    parent_stat:    Other\n",
       "    actual_range:   [185.16 322.1 ]\n",
       "    regrid_method:  conservative</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2920</li><li><span class='xr-has-index'>lat</span>: 59</li><li><span class='xr-has-index'>lon</span>: 87</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0e788505-7a14-4674-b9a9-59e290fd95d4' class='xr-array-in' type='checkbox' checked><label for='section-0e788505-7a14-4674-b9a9-59e290fd95d4' 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>296.2 296.5 296.6 297.0 297.0 296.9 ... 241.8 241.1 241.2 241.2 241.6</span></div><div class='xr-array-data'><pre>array([[[296.1936 , 296.4933 , 296.64383, ..., 296.6239 , 296.57   ,\n",
       "         296.35767],\n",
       "        [295.9    , 296.09998, 296.19998, ..., 295.9    , 295.9    ,\n",
       "         295.43332],\n",
       "        [295.9    , 296.09998, 296.19998, ..., 295.9    , 295.9    ,\n",
       "         295.43332],\n",
       "        ...,\n",
       "        [243.79999, 244.26666, 244.5    , ..., 233.63335, 235.29999,\n",
       "         237.96663],\n",
       "        [243.79999, 244.26666, 244.5    , ..., 233.63335, 235.29999,\n",
       "         237.96663],\n",
       "        [241.87102, 242.6313 , 243.01498, ..., 233.68292, 235.44838,\n",
       "         237.66943]],\n",
       "\n",
       "       [[296.26776, 296.8064 , 297.0761 , ..., 296.19287, 296.17752,\n",
       "         296.2103 ],\n",
       "        [296.19998, 296.53333, 296.69998, ..., 295.56668, 295.5    ,\n",
       "         295.23334],\n",
       "        [296.19998, 296.53333, 296.69998, ..., 295.56668, 295.5    ,\n",
       "         295.23334],\n",
       "...\n",
       "        [249.89   , 249.49   , 249.29   , ..., 241.69   , 242.48999,\n",
       "         243.68997],\n",
       "        [249.89   , 249.49   , 249.29   , ..., 241.69   , 242.48999,\n",
       "         243.68997],\n",
       "        [246.84814, 246.2443 , 245.9487 , ..., 243.05266, 243.60286,\n",
       "         244.30954]],\n",
       "\n",
       "       [[297.2945 , 297.6252 , 297.79266, ..., 296.21606, 296.0664 ,\n",
       "         295.7324 ],\n",
       "        [296.09   , 296.62332, 296.88998, ..., 295.69   , 295.69   ,\n",
       "         295.35666],\n",
       "        [296.09   , 296.62332, 296.88998, ..., 295.69   , 295.69   ,\n",
       "         295.35666],\n",
       "        ...,\n",
       "        [249.89   , 249.49   , 249.29   , ..., 239.82333, 240.29   ,\n",
       "         241.22331],\n",
       "        [249.89   , 249.49   , 249.29   , ..., 239.82333, 240.29   ,\n",
       "         241.22331],\n",
       "        [246.3288 , 245.82306, 245.57744, ..., 241.16115, 241.18028,\n",
       "         241.57034]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-ec9284f1-e56a-41ee-b1b3-9b19c6c88b7b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ec9284f1-e56a-41ee-b1b3-9b19c6c88b7b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-7e730633-61d4-4a24-9f16-59948c54348f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e730633-61d4-4a24-9f16-59948c54348f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccb577b1-ee3a-434a-aa3a-7ae55c2c3fc9' class='xr-var-data-in' type='checkbox'><label for='data-ccb577b1-ee3a-434a-aa3a-7ae55c2c3fc9' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>200.0 201.5 203.0 ... 327.5 329.0</div><input id='attrs-02e5145c-5ea7-4bf8-9477-588bf8382048' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02e5145c-5ea7-4bf8-9477-588bf8382048' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d635f231-9a04-4151-b8ef-a9808be02ea4' class='xr-var-data-in' type='checkbox'><label for='data-d635f231-9a04-4151-b8ef-a9808be02ea4' 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>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([200. , 201.5, 203. , 204.5, 206. , 207.5, 209. , 210.5, 212. , 213.5,\n",
       "       215. , 216.5, 218. , 219.5, 221. , 222.5, 224. , 225.5, 227. , 228.5,\n",
       "       230. , 231.5, 233. , 234.5, 236. , 237.5, 239. , 240.5, 242. , 243.5,\n",
       "       245. , 246.5, 248. , 249.5, 251. , 252.5, 254. , 255.5, 257. , 258.5,\n",
       "       260. , 261.5, 263. , 264.5, 266. , 267.5, 269. , 270.5, 272. , 273.5,\n",
       "       275. , 276.5, 278. , 279.5, 281. , 282.5, 284. , 285.5, 287. , 288.5,\n",
       "       290. , 291.5, 293. , 294.5, 296. , 297.5, 299. , 300.5, 302. , 303.5,\n",
       "       305. , 306.5, 308. , 309.5, 311. , 312.5, 314. , 315.5, 317. , 318.5,\n",
       "       320. , 321.5, 323. , 324.5, 326. , 327.5, 329. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.0 17.0 18.0 ... 72.0 73.0 74.0</div><input id='attrs-d8f756cd-52ec-4374-9148-291593a9e8e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8f756cd-52ec-4374-9148-291593a9e8e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ed9bcfb-cf23-4109-8a2f-6df47d7b7304' class='xr-var-data-in' type='checkbox'><label for='data-3ed9bcfb-cf23-4109-8a2f-6df47d7b7304' 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>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,\n",
       "       30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.,\n",
       "       44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57.,\n",
       "       58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n",
       "       72., 73., 74.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-106c3583-698b-44e3-88bc-3901603cd3c4' class='xr-section-summary-in' type='checkbox'  ><label for='section-106c3583-698b-44e3-88bc-3901603cd3c4' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd><dt><span>regrid_method :</span></dt><dd>conservative</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'air' (time: 2920, lat: 59, lon: 87)>\n",
       "array([[[296.1936 , 296.4933 , 296.64383, ..., 296.6239 , 296.57   ,\n",
       "         296.35767],\n",
       "        [295.9    , 296.09998, 296.19998, ..., 295.9    , 295.9    ,\n",
       "         295.43332],\n",
       "        [295.9    , 296.09998, 296.19998, ..., 295.9    , 295.9    ,\n",
       "         295.43332],\n",
       "        ...,\n",
       "        [243.79999, 244.26666, 244.5    , ..., 233.63335, 235.29999,\n",
       "         237.96663],\n",
       "        [243.79999, 244.26666, 244.5    , ..., 233.63335, 235.29999,\n",
       "         237.96663],\n",
       "        [241.87102, 242.6313 , 243.01498, ..., 233.68292, 235.44838,\n",
       "         237.66943]],\n",
       "\n",
       "       [[296.26776, 296.8064 , 297.0761 , ..., 296.19287, 296.17752,\n",
       "         296.2103 ],\n",
       "        [296.19998, 296.53333, 296.69998, ..., 295.56668, 295.5    ,\n",
       "         295.23334],\n",
       "        [296.19998, 296.53333, 296.69998, ..., 295.56668, 295.5    ,\n",
       "         295.23334],\n",
       "...\n",
       "        [249.89   , 249.49   , 249.29   , ..., 241.69   , 242.48999,\n",
       "         243.68997],\n",
       "        [249.89   , 249.49   , 249.29   , ..., 241.69   , 242.48999,\n",
       "         243.68997],\n",
       "        [246.84814, 246.2443 , 245.9487 , ..., 243.05266, 243.60286,\n",
       "         244.30954]],\n",
       "\n",
       "       [[297.2945 , 297.6252 , 297.79266, ..., 296.21606, 296.0664 ,\n",
       "         295.7324 ],\n",
       "        [296.09   , 296.62332, 296.88998, ..., 295.69   , 295.69   ,\n",
       "         295.35666],\n",
       "        [296.09   , 296.62332, 296.88998, ..., 295.69   , 295.69   ,\n",
       "         295.35666],\n",
       "        ...,\n",
       "        [249.89   , 249.49   , 249.29   , ..., 239.82333, 240.29   ,\n",
       "         241.22331],\n",
       "        [249.89   , 249.49   , 249.29   , ..., 239.82333, 240.29   ,\n",
       "         241.22331],\n",
       "        [246.3288 , 245.82306, 245.57744, ..., 241.16115, 241.18028,\n",
       "         241.57034]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "  * lon      (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\n",
       "  * lat      (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\n",
       "Attributes:\n",
       "    long_name:      4xDaily Air temperature at sigma level 995\n",
       "    units:          degK\n",
       "    precision:      2\n",
       "    GRIB_id:        11\n",
       "    GRIB_name:      TMP\n",
       "    var_desc:       Air temperature\n",
       "    dataset:        NMC Reanalysis\n",
       "    level_desc:     Surface\n",
       "    statistic:      Individual Obs\n",
       "    parent_stat:    Other\n",
       "    actual_range:   [185.16 322.1 ]\n",
       "    regrid_method:  conservative"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dr_out = regridder(dr, keep_attrs=True)\n",
    "dr_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The horizontal shape is now `(59, 87)`, as expected. The regridding operation\n",
    "broadcasts over extra dimensions (`time` here), so there are still 2920 time\n",
    "frames. `lon` and `lat` coordinate values are updated accordingly, and the value\n",
    "of the extra dimension `time` is kept the same as input.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Important note:** Extra dimensions must be on the left, i.e.\n",
    "`(time, lev, lat, lon)` is correct but `(lat, lon, time, lev)` would not work.\n",
    "Most data sets should have `(lat, lon)` on the right (being the fastest changing\n",
    "dimension in the memory). If not, use `DataArray.transpose` or `numpy.transpose`\n",
    "to preprocess the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check results on 2D map\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The regridding result is consistent with the original data, with a much finer\n",
    "resolution:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f391ace9eb0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "dr_out.isel(time=0).plot.pcolormesh(ax=ax, vmin=230, vmax=300)\n",
    "ax.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check broadcasting over extra dimensions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "xESMF tracks coordinate values over extra dimensions, since horizontal\n",
    "regridding should not affect them.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 2920)&gt;\n",
       "array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    long_name:      Time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2920</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a2992492-39c0-4549-bc6e-fbad35b77c92' class='xr-array-in' type='checkbox' checked><label for='section-a2992492-39c0-4549-bc6e-fbad35b77c92' 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>2013-01-01 2013-01-01T06:00:00 ... 2014-12-31T18:00:00</span></div><div class='xr-array-data'><pre>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-f5577479-d4bf-4efc-8437-96f6df1bb918' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f5577479-d4bf-4efc-8437-96f6df1bb918' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-2c1cdf06-6ae3-4011-82fc-add40ba6bb41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c1cdf06-6ae3-4011-82fc-add40ba6bb41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92508f06-73ce-4955-8d9f-2c34e3f13423' class='xr-var-data-in' type='checkbox'><label for='data-92508f06-73ce-4955-8d9f-2c34e3f13423' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15b5b59b-ecd0-4ea6-9ada-6523c0da116c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15b5b59b-ecd0-4ea6-9ada-6523c0da116c' class='xr-section-summary' >Attributes: <span>(2)</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>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 2920)>\n",
       "array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n",
       "       '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n",
       "       '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    long_name:      Time"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dr_out[\"time\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# exactly the same as input\n",
    "xr.testing.assert_identical(dr_out[\"time\"], ds[\"time\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the time series at a specific location, to make sure the\n",
    "broadcasting is correct:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f391ac417c0>]"
      ]
     },
     "execution_count": 12,
     "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": [
    "plt.subplot(2, 1, 1)\n",
    "dr.sel(lon=260, lat=40).plot()  # input data\n",
    "plt.subplot(2, 1, 2)\n",
    "dr_out.sel(lon=260, lat=40).plot()  # output data"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}