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    "# Comparison of seven regridding algorithms\n",
    "\n",
    "xESMF exposes seven different regridding algorithms from the ESMF library:\n",
    "\n",
    "- `bilinear`: `ESMF.RegridMethod.BILINEAR`\n",
    "- `conservative`: `ESMF.RegridMethod.CONSERVE`\n",
    "- `conservative_2nd`: `ESMF.RegridMethod.CONSERVE_2ND`\n",
    "- `conservative_normed`: `ESMF.RegridMethod.CONSERVE`\n",
    "- `patch`: `ESMF.RegridMethod.PATCH`\n",
    "- `nearest_s2d`: `ESMF.RegridMethod.NEAREST_STOD`\n",
    "- `nearest_d2s`: `ESMF.RegridMethod.NEAREST_DTOS`\n",
    "\n",
    "where `conservative_normed` is just the `conservative` method with the\n",
    "normalization set to `ESMF.NormType.FRACAREA` instead of the default\n",
    "`norm_type=ESMF.NormType.DSTAREA`.\n",
    "\n",
    "This notebook demonstrates how these algorithms behave in different situations.\n",
    "\n",
    "## Notes\n",
    "\n",
    "- `bilinear` and `conservative` should be the most commonly used methods. They\n",
    "  are both monotonic (i.e. will not create new maximum/minimum).\n",
    "- `conservative_2nd` also requires cell bounds, but may overshoot source\n",
    "  minimum/maximum values.\n",
    "- Nearest neighbour methods, either source to destination (s2d) or destination\n",
    "  to source (d2s), could be useful in special cases. Keep in mind that d2s is\n",
    "  highly non-monotonic.\n",
    "- Patch is ESMF's unique method, producing highly smooth results but quite slow.\n",
    "- From the ESMF documentation:\n",
    "\n",
    "  > The weight $w_{ij}$ for a particular source cell $i$ and destination cell\n",
    "  > $j$ are calculated as $w_{ij}=f_{ij} * A_{si}/A_{dj}$. In this equation\n",
    "  > $f_{ij}$ is the fraction of the source cell $i$ contributing to destination\n",
    "  > cell $j$, and $A_{si}$ and $A_{dj}$ are the areas of the source and\n",
    "  > destination cells.\n",
    "\n",
    "  For `conservative_normed`,\n",
    "\n",
    "  > ... then the weights are further divided by the destination fraction. In\n",
    "  > other words, in that case $w_{ij}=f_{ij} * A_{si}/(A_{dj}*D_j)$ where $D_j$\n",
    "  > is fraction of the destination cell that intersects the unmasked source\n",
    "  > grid.\n",
    "\n",
    "Detailed explanations are available on\n",
    "[ESMPy documentation](http://www.earthsystemmodeling.org/esmf_releases/last_built/esmpy_doc/html/api.html#regridding).\n",
    "\n",
    "## Preparation\n"
   ]
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  {
   "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\n",
    "\n",
    "method_list = [\n",
    "    \"bilinear\",\n",
    "    \"conservative\",\n",
    "    \"conservative_2nd\",\n",
    "    \"conservative_normed\",\n",
    "    \"nearest_s2d\",\n",
    "    \"nearest_d2s\",\n",
    "    \"patch\",\n",
    "]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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   "source": [
    "ds_in = xe.util.grid_global(20, 15)  # input grid\n",
    "ds_fine = xe.util.grid_global(4, 4)  # high-resolution target grid\n",
    "ds_coarse = xe.util.grid_global(30, 20)  # low-resolution target grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a wave field that is widely used in regridding benchmarks.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  padding: 0 1px;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 9kB\n",
       "Dimensions:  (y: 12, x: 18, y_b: 13, x_b: 19)\n",
       "Coordinates:\n",
       "    lon      (y, x) float64 2kB -170.0 -150.0 -130.0 ... 130.0 150.0 170.0\n",
       "    lat      (y, x) float64 2kB -82.5 -82.5 -82.5 -82.5 ... 82.5 82.5 82.5 82.5\n",
       "    lon_b    (y_b, x_b) float64 2kB -180.0 -160.0 -140.0 ... 140.0 160.0 180.0\n",
       "    lat_b    (y_b, x_b) float64 2kB -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0\n",
       "Dimensions without coordinates: y, x, y_b, x_b\n",
       "Data variables:\n",
       "    data     (y, x) float64 2kB 2.016 2.009 1.997 1.987 ... 1.997 2.009 2.016</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-b20cff40-8d66-4ab9-a72d-3fcc2ceb8cd2' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-b20cff40-8d66-4ab9-a72d-3fcc2ceb8cd2' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>y</span>: 12</li><li><span>x</span>: 18</li><li><span>y_b</span>: 13</li><li><span>x_b</span>: 19</li></ul></div></li><li class='xr-section-item'><input id='section-1c9614c9-c400-43ba-aedb-ae59e3c4cbc9' class='xr-section-summary-in' type='checkbox' checked /><label for='section-1c9614c9-c400-43ba-aedb-ae59e3c4cbc9' class='xr-section-summary' title='Expand/collapse section'>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>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-170.0 -150.0 ... 150.0 170.0</div><input id='attrs-c3f9df9d-863c-4b1b-91c7-9d6247731ee8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3f9df9d-863c-4b1b-91c7-9d6247731ee8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12a008fc-942c-428e-be40-555daff90e55' class='xr-var-data-in' type='checkbox'><label for='data-12a008fc-942c-428e-be40-555daff90e55' 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></dl></div><div class='xr-var-data'><pre>array([[-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.],\n",
       "       [-170., -150., -130., -110.,  -90.,  -70.,  -50.,  -30.,  -10.,\n",
       "          10.,   30.,   50.,   70.,   90.,  110.,  130.,  150.,  170.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-82.5 -82.5 -82.5 ... 82.5 82.5</div><input id='attrs-b1b4e5f2-d64a-45e4-a62b-7cde311dca09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1b4e5f2-d64a-45e4-a62b-7cde311dca09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24e0f6b0-29bb-406f-9b6a-134a0f589298' class='xr-var-data-in' type='checkbox'><label for='data-24e0f6b0-29bb-406f-9b6a-134a0f589298' 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></dl></div><div class='xr-var-data'><pre>array([[-82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5,\n",
       "        -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5, -82.5],\n",
       "       [-67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5,\n",
       "        -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5, -67.5],\n",
       "       [-52.5, -52.5, -52.5, -52.5, -52.5, -52.5, -52.5, -52.5, -52.5,\n",
       "        -52.5, -52.5, -52.5, -52.5, -52.5, -52.5, -52.5, -52.5, -52.5],\n",
       "       [-37.5, -37.5, -37.5, -37.5, -37.5, -37.5, -37.5, -37.5, -37.5,\n",
       "        -37.5, -37.5, -37.5, -37.5, -37.5, -37.5, -37.5, -37.5, -37.5],\n",
       "       [-22.5, -22.5, -22.5, -22.5, -22.5, -22.5, -22.5, -22.5, -22.5,\n",
       "        -22.5, -22.5, -22.5, -22.5, -22.5, -22.5, -22.5, -22.5, -22.5],\n",
       "       [ -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,\n",
       "         -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5,  -7.5],\n",
       "       [  7.5,   7.5,   7.5,   7.5,   7.5,   7.5,   7.5,   7.5,   7.5,\n",
       "          7.5,   7.5,   7.5,   7.5,   7.5,   7.5,   7.5,   7.5,   7.5],\n",
       "       [ 22.5,  22.5,  22.5,  22.5,  22.5,  22.5,  22.5,  22.5,  22.5,\n",
       "         22.5,  22.5,  22.5,  22.5,  22.5,  22.5,  22.5,  22.5,  22.5],\n",
       "       [ 37.5,  37.5,  37.5,  37.5,  37.5,  37.5,  37.5,  37.5,  37.5,\n",
       "         37.5,  37.5,  37.5,  37.5,  37.5,  37.5,  37.5,  37.5,  37.5],\n",
       "       [ 52.5,  52.5,  52.5,  52.5,  52.5,  52.5,  52.5,  52.5,  52.5,\n",
       "         52.5,  52.5,  52.5,  52.5,  52.5,  52.5,  52.5,  52.5,  52.5],\n",
       "       [ 67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,\n",
       "         67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5,  67.5],\n",
       "       [ 82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,\n",
       "         82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5,  82.5]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -160.0 ... 160.0 180.0</div><input id='attrs-5708dc31-3225-4402-b805-c4535b508071' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5708dc31-3225-4402-b805-c4535b508071' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba1049e7-e773-4fda-a2ef-859981301d0d' class='xr-var-data-in' type='checkbox'><label for='data-ba1049e7-e773-4fda-a2ef-859981301d0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-180., -160., -140., -120., -100.,  -80.,  -60.,  -40.,  -20.,\n",
       "           0.,   20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,\n",
       "         180.],\n",
       "       [-180., -160., -140., -120., -100.,  -80.,  -60.,  -40.,  -20.,\n",
       "           0.,   20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,\n",
       "         180.],\n",
       "       [-180., -160., -140., -120., -100.,  -80.,  -60.,  -40.,  -20.,\n",
       "           0.,   20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,\n",
       "         180.],\n",
       "       [-180., -160., -140., -120., -100.,  -80.,  -60.,  -40.,  -20.,\n",
       "           0.,   20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,\n",
       "         180.],\n",
       "       [-180., -160., -140., -120., -100.,  -80.,  -60.,  -40.,  -20.,\n",
       "           0.,   20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,\n",
       "         180.],\n",
       "       [-180., -160., -140., -120., -100.,  -80.,  -60.,  -40.,  -20.,\n",
       "           0.,   20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,\n",
       "         180.],\n",
       "       [-180., -160., -140., -120., -100.,  -80.,  -60.,  -40.,  -20.,\n",
       "           0.,   20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,\n",
       "         180.],\n",
       "       [-180., -160., -140., -120., -100.,  -80.,  -60.,  -40.,  -20.,\n",
       "           0.,   20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,\n",
       "         180.],\n",
       "       [-180., -160., -140., -120., -100.,  -80.,  -60.,  -40.,  -20.,\n",
       "           0.,   20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,\n",
       "         180.],\n",
       "       [-180., -160., -140., -120., -100.,  -80.,  -60.,  -40.,  -20.,\n",
       "           0.,   20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,\n",
       "         180.],\n",
       "       [-180., -160., -140., -120., -100.,  -80.,  -60.,  -40.,  -20.,\n",
       "           0.,   20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,\n",
       "         180.],\n",
       "       [-180., -160., -140., -120., -100.,  -80.,  -60.,  -40.,  -20.,\n",
       "           0.,   20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,\n",
       "         180.],\n",
       "       [-180., -160., -140., -120., -100.,  -80.,  -60.,  -40.,  -20.,\n",
       "           0.,   20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,\n",
       "         180.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_b</span></div><div class='xr-var-dims'>(y_b, x_b)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -90.0 -90.0 ... 90.0 90.0</div><input id='attrs-cbbe82a8-dc7a-48b0-bc95-0c42ca66cc2c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cbbe82a8-dc7a-48b0-bc95-0c42ca66cc2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a81d742-9bbf-4308-92bb-02fe08e514ba' class='xr-var-data-in' type='checkbox'><label for='data-0a81d742-9bbf-4308-92bb-02fe08e514ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-90., -90., -90., -90., -90., -90., -90., -90., -90., -90., -90.,\n",
       "        -90., -90., -90., -90., -90., -90., -90., -90.],\n",
       "       [-75., -75., -75., -75., -75., -75., -75., -75., -75., -75., -75.,\n",
       "        -75., -75., -75., -75., -75., -75., -75., -75.],\n",
       "       [-60., -60., -60., -60., -60., -60., -60., -60., -60., -60., -60.,\n",
       "        -60., -60., -60., -60., -60., -60., -60., -60.],\n",
       "       [-45., -45., -45., -45., -45., -45., -45., -45., -45., -45., -45.,\n",
       "        -45., -45., -45., -45., -45., -45., -45., -45.],\n",
       "       [-30., -30., -30., -30., -30., -30., -30., -30., -30., -30., -30.,\n",
       "        -30., -30., -30., -30., -30., -30., -30., -30.],\n",
       "       [-15., -15., -15., -15., -15., -15., -15., -15., -15., -15., -15.,\n",
       "        -15., -15., -15., -15., -15., -15., -15., -15.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "       [ 15.,  15.,  15.,  15.,  15.,  15.,  15.,  15.,  15.,  15.,  15.,\n",
       "         15.,  15.,  15.,  15.,  15.,  15.,  15.,  15.],\n",
       "       [ 30.,  30.,  30.,  30.,  30.,  30.,  30.,  30.,  30.,  30.,  30.,\n",
       "         30.,  30.,  30.,  30.,  30.,  30.,  30.,  30.],\n",
       "       [ 45.,  45.,  45.,  45.,  45.,  45.,  45.,  45.,  45.,  45.,  45.,\n",
       "         45.,  45.,  45.,  45.,  45.,  45.,  45.,  45.],\n",
       "       [ 60.,  60.,  60.,  60.,  60.,  60.,  60.,  60.,  60.,  60.,  60.,\n",
       "         60.,  60.,  60.,  60.,  60.,  60.,  60.,  60.],\n",
       "       [ 75.,  75.,  75.,  75.,  75.,  75.,  75.,  75.,  75.,  75.,  75.,\n",
       "         75.,  75.,  75.,  75.,  75.,  75.,  75.,  75.],\n",
       "       [ 90.,  90.,  90.,  90.,  90.,  90.,  90.,  90.,  90.,  90.,  90.,\n",
       "         90.,  90.,  90.,  90.,  90.,  90.,  90.,  90.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c397400b-e2a4-43b6-964a-8eec3da1bc1f' class='xr-section-summary-in' type='checkbox' checked /><label for='section-c397400b-e2a4-43b6-964a-8eec3da1bc1f' class='xr-section-summary' title='Expand/collapse section'>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>data</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.016 2.009 1.997 ... 2.009 2.016</div><input id='attrs-f9dd47dd-139c-4d2e-9b33-3eb8363d727d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9dd47dd-139c-4d2e-9b33-3eb8363d727d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c84333d9-3e5f-44db-b464-79f6f04a67d1' class='xr-var-data-in' type='checkbox'><label for='data-c84333d9-3e5f-44db-b464-79f6f04a67d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.01600962, 2.00851854, 1.99704154, 1.98694883, 1.98296291,\n",
       "        1.98694883, 1.99704154, 2.00851854, 2.01600962, 2.01600962,\n",
       "        2.00851854, 1.99704154, 1.98694883, 1.98296291, 1.98694883,\n",
       "        1.99704154, 2.00851854, 2.01600962],\n",
       "       [2.1376148 , 2.0732233 , 1.97456981, 1.88781539, 1.85355339,\n",
       "        1.88781539, 1.97456981, 2.0732233 , 2.1376148 , 2.1376148 ,\n",
       "        2.0732233 , 1.97456981, 1.88781539, 1.85355339, 1.88781539,\n",
       "        1.97456981, 2.0732233 , 2.1376148 ],\n",
       "       [2.34824114, 2.18529524, 1.93564764, 1.71611122, 1.62940952,\n",
       "        1.71611122, 1.93564764, 2.18529524, 2.34824114, 2.34824114,\n",
       "        2.18529524, 1.93564764, 1.71611122, 1.62940952, 1.71611122,\n",
       "        1.93564764, 2.18529524, 2.34824114],\n",
       "       [2.59145148, 2.31470476, 1.89070418, 1.51784433, 1.37059048,\n",
       "        1.51784433, 1.89070418, 2.31470476, 2.59145148, 2.59145148,\n",
       "        2.31470476, 1.89070418, 1.51784433, 1.37059048, 1.51784433,\n",
       "        1.89070418, 2.31470476, 2.59145148],\n",
       "       [2.80207782, 2.4267767 , 1.85178201, 1.34614017, 1.14644661,\n",
       "        1.34614017, 1.85178201, 2.4267767 , 2.80207782, 2.80207782,\n",
       "        2.4267767 , 1.85178201, 1.34614017, 1.14644661, 1.34614017,\n",
       "        1.85178201, 2.4267767 , 2.80207782],\n",
       "...\n",
       "       [2.80207782, 2.4267767 , 1.85178201, 1.34614017, 1.14644661,\n",
       "        1.34614017, 1.85178201, 2.4267767 , 2.80207782, 2.80207782,\n",
       "        2.4267767 , 1.85178201, 1.34614017, 1.14644661, 1.34614017,\n",
       "        1.85178201, 2.4267767 , 2.80207782],\n",
       "       [2.59145148, 2.31470476, 1.89070418, 1.51784433, 1.37059048,\n",
       "        1.51784433, 1.89070418, 2.31470476, 2.59145148, 2.59145148,\n",
       "        2.31470476, 1.89070418, 1.51784433, 1.37059048, 1.51784433,\n",
       "        1.89070418, 2.31470476, 2.59145148],\n",
       "       [2.34824114, 2.18529524, 1.93564764, 1.71611122, 1.62940952,\n",
       "        1.71611122, 1.93564764, 2.18529524, 2.34824114, 2.34824114,\n",
       "        2.18529524, 1.93564764, 1.71611122, 1.62940952, 1.71611122,\n",
       "        1.93564764, 2.18529524, 2.34824114],\n",
       "       [2.1376148 , 2.0732233 , 1.97456981, 1.88781539, 1.85355339,\n",
       "        1.88781539, 1.97456981, 2.0732233 , 2.1376148 , 2.1376148 ,\n",
       "        2.0732233 , 1.97456981, 1.88781539, 1.85355339, 1.88781539,\n",
       "        1.97456981, 2.0732233 , 2.1376148 ],\n",
       "       [2.01600962, 2.00851854, 1.99704154, 1.98694883, 1.98296291,\n",
       "        1.98694883, 1.99704154, 2.00851854, 2.01600962, 2.01600962,\n",
       "        2.00851854, 1.99704154, 1.98694883, 1.98296291, 1.98694883,\n",
       "        1.99704154, 2.00851854, 2.01600962]])</pre></div></li></ul></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 9kB\n",
       "Dimensions:  (y: 12, x: 18, y_b: 13, x_b: 19)\n",
       "Coordinates:\n",
       "    lon      (y, x) float64 2kB -170.0 -150.0 -130.0 ... 130.0 150.0 170.0\n",
       "    lat      (y, x) float64 2kB -82.5 -82.5 -82.5 -82.5 ... 82.5 82.5 82.5 82.5\n",
       "    lon_b    (y_b, x_b) float64 2kB -180.0 -160.0 -140.0 ... 140.0 160.0 180.0\n",
       "    lat_b    (y_b, x_b) float64 2kB -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0\n",
       "Dimensions without coordinates: y, x, y_b, x_b\n",
       "Data variables:\n",
       "    data     (y, x) float64 2kB 2.016 2.009 1.997 1.987 ... 1.997 2.009 2.016"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_in[\"data\"] = xe.data.wave_smooth(ds_in[\"lon\"], ds_in[\"lat\"])\n",
    "ds_in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1497f41aba10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_in[\"data\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regrid(ds_in, ds_out, dr_in, method):\n",
    "    \"\"\"Convenience function for one-time regridding\"\"\"\n",
    "    regridder = xe.Regridder(ds_in, ds_out, method, periodic=True)\n",
    "    dr_out = regridder(dr_in)\n",
    "    return dr_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When dealing with global grids, we need to set `periodic=True`, otherwise data\n",
    "along the meridian line will be missing.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing resolution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bilinear\n",
      "CPU times: user 411 ms, sys: 131 ms, total: 542 ms\n",
      "Wall time: 884 ms\n",
      "\n",
      "conservative\n",
      "CPU times: user 57.5 ms, sys: 3.2 ms, total: 60.7 ms\n",
      "Wall time: 60.8 ms\n",
      "\n",
      "conservative_2nd\n",
      "CPU times: user 105 ms, sys: 3.43 ms, total: 108 ms\n",
      "Wall time: 109 ms\n",
      "\n",
      "conservative_normed\n",
      "CPU times: user 53.6 ms, sys: 1.26 ms, total: 54.9 ms\n",
      "Wall time: 53.9 ms\n",
      "\n",
      "nearest_s2d\n",
      "CPU times: user 9.94 ms, sys: 1.01 ms, total: 11 ms\n",
      "Wall time: 10.9 ms\n",
      "\n",
      "nearest_d2s\n",
      "CPU times: user 5.04 ms, sys: 0 ns, total: 5.04 ms\n",
      "Wall time: 5.07 ms\n",
      "\n",
      "patch\n",
      "CPU times: user 188 ms, sys: 1.93 ms, total: 190 ms\n",
      "Wall time: 190 ms\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for method in method_list:\n",
    "    print(method)\n",
    "    %time ds_fine[method] = regrid(ds_in, ds_fine, ds_in['data'], method)\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nearest neighbour algorithms are very fast while the patch method is quite slow.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 2, figsize=[8, 10])\n",
    "\n",
    "for i, method in enumerate(method_list):\n",
    "    ax = axes.flatten()[i]\n",
    "    ds_fine[method].plot.pcolormesh(ax=ax)\n",
    "    ax.set_title(method, fontsize=15)\n",
    "\n",
    "for ax in axes.flatten()[len(method_list) :]:\n",
    "    ax.set_visible(False)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When regridding from low-resolution to high-resolution, `bilinear` and `patch`\n",
    "will produce smooth results, while `conservative` and `nearest_s2d` will\n",
    "preserve the original coarse grid structure (although the data is now defined on\n",
    "a finer grid.).\n",
    "\n",
    "`nearest_d2s` is quite different from others: One source point can be mapped to\n",
    "**only one destination point**. Because we have far less source points (on a\n",
    "low-resolution grid) than destination points (on a high-resolution grid), most\n",
    "destination points cannot receive any data so they just have zero values. Only\n",
    "the destination points that are closest to source points can receive data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decreasing resolution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for method in method_list:\n",
    "    ds_coarse[method] = regrid(ds_in, ds_coarse, ds_in[\"data\"], method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 2, figsize=[8, 10])\n",
    "\n",
    "for i, method in enumerate(method_list):\n",
    "    ax = axes.flatten()[i]\n",
    "    ds_coarse[method].plot.pcolormesh(ax=ax)\n",
    "    ax.set_title(method, fontsize=15)\n",
    "for ax in axes.flatten()[len(method_list) :]:\n",
    "    ax.set_visible(False)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When regridding from high-resolution to low-resolution, all methods except\n",
    "`nearest_d2s` produce similar results here. But that's largely because the input\n",
    "data is smooth. For real-world data, it is generally recommended to use\n",
    "`conservative` for decreasing the resolution, because it takes average over\n",
    "small source grid boxes, while `bilinear` and `nearest_s2d` effectively throw\n",
    "away most of source grid boxes.\n",
    "\n",
    "`nearest_d2s` is again different: **Every** source point **has to be** mapped to\n",
    "a destination point. Because we have far more source points (on a\n",
    "high-resolution grid) than destination points (on a low-resolution grid), a\n",
    "single destination point will receive data from multiple source points, which\n",
    "can accumulate to a large value (notice the colorbar range).\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "default",
   "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": "3.14.4"
  },
  "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": 2
}