{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "title: Basic Example\n",
    "toc: true\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will see how to use Polire for spatial interpolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.core.options.set_options at 0x7fc3b0198c40>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import polire\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from sklearn.metrics import (\n",
    "    mean_squared_error,\n",
    "    mean_absolute_error,\n",
    "    mean_absolute_percentage_error,\n",
    "    r2_score,\n",
    ")\n",
    "\n",
    "# set default cmap\n",
    "plt.rcParams[\"image.cmap\"] = \"RdYlGn_r\"\n",
    "xr.set_options(cmap_sequential=\"RdYlGn_r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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-cfc2e349-7f19-4121-a071-0e870e16af64' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cfc2e349-7f19-4121-a071-0e870e16af64' 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-6355f394-ba58-4cab-b415-d270a696c923' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6355f394-ba58-4cab-b415-d270a696c923' 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-696a123e-5b8f-4a74-95a0-da82f2e200c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-696a123e-5b8f-4a74-95a0-da82f2e200c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8b8f5c8-adbf-4977-9c76-62bd296a82f0' class='xr-var-data-in' type='checkbox'><label for='data-c8b8f5c8-adbf-4977-9c76-62bd296a82f0' 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-e80d0179-fa1f-4929-bd39-af2cc2d4202e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e80d0179-fa1f-4929-bd39-af2cc2d4202e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73f515d1-b13c-452b-8f23-23470d0a8b7f' class='xr-var-data-in' type='checkbox'><label for='data-73f515d1-b13c-452b-8f23-23470d0a8b7f' 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-83f52ba1-b472-4957-a4b2-56219a896e62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83f52ba1-b472-4957-a4b2-56219a896e62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc75263b-7d87-4597-9e16-fe563a462562' class='xr-var-data-in' type='checkbox'><label for='data-cc75263b-7d87-4597-9e16-fe563a462562' 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-4940db08-9df3-43e2-8f3f-2b2e2570db1d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4940db08-9df3-43e2-8f3f-2b2e2570db1d' 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-1fb1c6e5-9dfc-4467-b64e-a3cfb77bbf63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fb1c6e5-9dfc-4467-b64e-a3cfb77bbf63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d43b876-1fd4-4985-93e2-e0c2d307e46e' class='xr-var-data-in' type='checkbox'><label for='data-2d43b876-1fd4-4985-93e2-e0c2d307e46e' 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-46841388-31b9-40ed-bd6b-a2372877f766' class='xr-section-summary-in' type='checkbox'  ><label for='section-46841388-31b9-40ed-bd6b-a2372877f766' class='xr-section-summary' >Indexes: <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-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fc442c31-1183-44ff-9f0a-571cc137953c' class='xr-index-data-in' type='checkbox'/><label for='index-fc442c31-1183-44ff-9f0a-571cc137953c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,\n",
       "       45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,\n",
       "       15.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d3e596fd-cf8d-4eee-960b-ac6b607f152d' class='xr-index-data-in' type='checkbox'/><label for='index-d3e596fd-cf8d-4eee-960b-ac6b607f152d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,\n",
       "       225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,\n",
       "       250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,\n",
       "       275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,\n",
       "       300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,\n",
       "       325.0, 327.5, 330.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-21bf2639-f44a-4872-a20c-3c0dc74234cb' class='xr-index-data-in' type='checkbox'/><label for='index-21bf2639-f44a-4872-a20c-3c0dc74234cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2013-01-01 00:00:00&#x27;, &#x27;2013-01-01 06:00:00&#x27;,\n",
       "               &#x27;2013-01-01 12:00:00&#x27;, &#x27;2013-01-01 18:00:00&#x27;,\n",
       "               &#x27;2013-01-02 00:00:00&#x27;, &#x27;2013-01-02 06:00:00&#x27;,\n",
       "               &#x27;2013-01-02 12:00:00&#x27;, &#x27;2013-01-02 18:00:00&#x27;,\n",
       "               &#x27;2013-01-03 00:00:00&#x27;, &#x27;2013-01-03 06:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2014-12-29 12:00:00&#x27;, &#x27;2014-12-29 18:00:00&#x27;,\n",
       "               &#x27;2014-12-30 00:00:00&#x27;, &#x27;2014-12-30 06:00:00&#x27;,\n",
       "               &#x27;2014-12-30 12:00:00&#x27;, &#x27;2014-12-30 18:00:00&#x27;,\n",
       "               &#x27;2014-12-31 00:00:00&#x27;, &#x27;2014-12-31 06:00:00&#x27;,\n",
       "               &#x27;2014-12-31 12:00:00&#x27;, &#x27;2014-12-31 18:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2920, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-83479ca4-0457-4ded-a4bc-213c200423b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83479ca4-0457-4ded-a4bc-213c200423b1' 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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.tutorial.open_dataset(\"air_temperature\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the spatial data from a time-stamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_spatial = ds.isel(time=0)\n",
    "ds_spatial[\"air\"].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's assume that only 10% of the data is available and try to interpolate the rest of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_spatial_train = ds_spatial.copy()\n",
    "\n",
    "np.random.seed(42)\n",
    "ds_spatial_train[\"air\"].values = np.where(\n",
    "    np.random.rand(*ds_spatial_train[\"air\"].shape) > 0.1,\n",
    "    np.nan,\n",
    "    ds_spatial_train[\"air\"].values,\n",
    ")\n",
    "\n",
    "\n",
    "ds_spatial_train[\"air\"].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(146, 2) (146,) (1325, 2) (1325,)\n"
     ]
    }
   ],
   "source": [
    "train_df = ds_spatial_train[\"air\"].to_dataframe().reset_index()\n",
    "train_df = train_df.dropna()\n",
    "\n",
    "X_train = train_df[[\"lat\", \"lon\"]].values\n",
    "y_train = train_df[\"air\"].values\n",
    "\n",
    "test_df = ds_spatial[\"air\"].to_dataframe().reset_index()\n",
    "X_test = test_df[[\"lat\", \"lon\"]].values\n",
    "y_test = test_df[\"air\"].values\n",
    "\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Useful plotting function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_predictions(y_pred, model_name):\n",
    "    fig, axes = plt.subplots(\n",
    "        1, 3, figsize=(15, 4), gridspec_kw={\"width_ratios\": [1, 1, 0.05]}\n",
    "    )\n",
    "\n",
    "    ax = axes[0]\n",
    "    ds_spatial[\"air\"].plot(ax=ax, add_colorbar=False)\n",
    "    ax.set_title(\"True values\")\n",
    "\n",
    "    ax = axes[1]\n",
    "    ds_spatial_pred = ds_spatial_train.copy()\n",
    "    ds_spatial_pred[\"air\"].values = y_pred\n",
    "    mappable = ds_spatial_pred[\"air\"].plot(ax=ax, add_colorbar=False)\n",
    "    ax.set_title(f\"{model_name}\")\n",
    "\n",
    "    ax = axes[2]\n",
    "    fig.colorbar(mappable, cax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IDW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/patel_zeel/polire/polire/idw/idw.py:82: RuntimeWarning: divide by zero encountered in divide\n",
      "  weights = 1 / np.power(dist, self.exponent)\n",
      "/home/patel_zeel/polire/polire/idw/idw.py:83: RuntimeWarning: invalid value encountered in divide\n",
      "  result = (weights * self.y[:, None]).sum(axis=0) / weights.sum(axis=0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idw = polire.IDW(exponent=2)\n",
    "idw.fit(X_train, y_train)\n",
    "idw_pred = idw.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
    "\n",
    "plot_predictions(idw_pred, \"IDW\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kriging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kriging = polire.Kriging(variogram_model=\"spherical\")\n",
    "kriging.fit(X_train, y_train)\n",
    "kriging_pred = kriging.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
    "\n",
    "plot_predictions(kriging_pred, \"Kriging\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/patel_zeel/miniconda3/envs/polire/lib/python3.10/site-packages/scipy/interpolate/_fitpack_impl.py:593: RuntimeWarning: The required storage space exceeds the available storage space.\n",
      "Probable causes: nxest or nyest too small or s is too small. (fp>s)\n",
      "\tkx,ky=3,3 nx,ny=11,11 m=146 fp=1143.783495 s=128.911993\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spline = polire.Spline()\n",
    "spline.fit(X_train, y_train)\n",
    "spline_pred = spline.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
    "\n",
    "plot_predictions(spline_pred, \"Spline\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pe/ZdkmTQnCSptMw2xCoqsY1xabtRc97EXrYxrZJaGuWq8Nu/P1V+n1EutytyFyU1adPnMtzmDfgt+3EwvUt5QkxcRGIkqSpgPw6l1fafRV/A7DNm8h4mxxUa5UqISbaNSU/ItI2RJJevyjbGMsokyZNkHxNvcPFZT7F9jCQVFtrHGOxDJMmqtB8HlZUb5ZLBPtyqMNtuTD4crnibkqTMYN0OUmOtcZIUpyTVUVOYfu9F8rrRpm3WV54wWBEcB8uK3PeQiUAE27NE301FtO/1vM1IUqCexyuS6xjJsTcRyW2r/vtu+J1toN77Hsltpp63d8m+//FVhrVZU1QPR+Lt2LFD3l/MI9gdhZebm6uNGzfqvffeC1l+0003qbCwUG+++aZatGihRYsW6dxzz9WKFSvUq5f9v89NhT2JFx8fr5KSmpMNpaWl8ngMJqUAAAAciBoHAADAQephEs/r9YZM4tVl4sSJWrx4sd599121bds2uHzr1q36xz/+oY0bN+rII4+UJB111FFasWKFHnroIT3yyCPKysrSrl27QvL5fD4VFBQoKyvLvNvGkf/zm9/8RpdeeqnWrFkjy7JkWZZWr16tyy+/XGeddVa46QAAAByBGgcAAAC/ZlmWJk6cqIULF2rZsmXq0KFDyPNlZXvPmnH/asIxJiZGgf+dEZmTk6PCwkKtW7cu+PyyZcsUCATUr18/476EPYn3wAMPqFOnTsrJyVFCQoISEhI0YMAAde7cWffff3+46QAAAByBGgcAAMBBonU9vH0PQ7m5uXrqqac0b948paamKj8/X/n5+Sov33sqdLdu3dS5c2dddtll+uCDD7R161bdc889Wrp0qUaOHClJ6t69u04//XRdcskl+uCDD7Ry5UpNnDhRv//9743vTCsdwOm06enpevHFF/XVV1/piy++CHZm38X6AAAAGiNqHAAAAAdxu6J4Oq35JN7s2bMlKXin2X3mzJmj8ePHKy4uTq+++qquv/56nXnmmSotLVXnzp31xBNP6IwzzgjGP/3005o4caIGDx4st9ut0aNH64EHHgir22FP4u3TpUsXdenS5UBfDgAA4EjUOAAAAA5QD9fEM2FZ9rfI69Kli55//vk6YzIyMjRv3jzjdvcn7Ek8v9+vuXPn6q233tKuXbuC5/fus2zZsoPqEAAAQEOgxgEAAHCQME97DTt3IxT2JN6f/vQnzZ07VyNGjFDPnj3lcjXOFQcAAPglahwAAAAHcciReE4S9iTeM888o/nz54ec1wsAANDYUeMAAADAycKexPN4PFzgGQAAHHKocQAAAByEI/FqCLvXV199te6//36jC/sBAAA0FtQ4AAAAzuFyuaL6aIzCPhLvvffe09tvv63XXntNRx55pOLi4kKef+GFFyLWOQAAgPpCjQMAAOAgrigeiedqnEfihT2Jl56ernPOOScafYkqq7paVnUdb5LPb5boV3eqO6hcJnE+X+RyGbL8BkcgGMQEKqqN2nNVFNgHVVSa5WplEJfZ0iiXUlvYt+dJNstVXWEfU/qDfUxFuVFzVtlPtjEZ3tZGufZUl9jGFFSUGuWqCthvz2XVVUa5Sg3G1GfweTXZ3CUpYHBkTpzhF0xSnMc2Jj0+ySiXSb9MYoqrzLatKr/9e1jpM3sPq/z2bVb5DT47kuINxsv4tza/ff+tgEG/qsz6rgqD9irNxtRoX2nQniQFCu1zBUrNclkVBt9RMXW/Q9WG3ykHo7HWONrzkxRj9j1Zp/ouZh3aXiR/l3fV86k6boeOqSPbjOh7Y5jLpO+R3QAjmMyB7TVAm1Y9H7hjWQb/9jQUiGAuU5bqt836XsdIrV9sVVlE8hySOJ22hrAn8ebMmWMUt3LlSh133HGKj48Pu1MAAAD1jRoHAADAQdyuvY9o5W6Eojb1OHz4cH333XfRSg8AANAgqHEAAADQEMI+Es8UF4UGAACHImocAACAesDptDVEbRIPAAAAAAAAOCCcTlsDk3gAAAAAAABwFrcrikfiMYkHAAAAAAAAHDxOp60hapN4LlfjnNUEAACoCzUOAABAPeB02hqiNvXIRZ8BAMChiBoHAAAADSHsI/HKy8tlWZaSkpIkSd9++60WLlyoHj16aOjQocG4kpKSyPUSAAAgyqhxAAAAHITTaWsIu9dnn322nnzySUlSYWGh+vXrp3vuuUdnn322Zs+eHfEOAgAA1AdqHAAAAAdxuX+eyIv0w9VEJvE++ugjnXTSSZKk5557Tq1atdK3336rJ598Ug888EDEOwgAAFAfqHEAAAAcZN818aL1aITCPp22rKxMqampkqQ33nhDo0aNktvt1gknnKBvv/024h0EAACoD9Q4AAAADsLptDWEPYnXuXNnLVq0SOecc45ef/11TZkyRZK0a9cueb3eiHcwYnz+vY/6EAiYxfl8BjFmfbaqDdr0R/BC3DH2s9auCN43xSopNwv0FNmGuJISzHIlptvHxKcYpfIl2MfFpmbaJyr90ag9q2qPbUxyRZVRrnap3WxjPDHbjHLtKiuwjSmtrjDK5TP4nLkN7iCZkZBk1F6Kx367ccvs15ykuHjbmGYJLY1ypXns4w5LKbON+aF8h1F7Ju9PUpzZmDZPOMw2JqZ4p1Euq6LUKM5IwGDfXG7/GVOp/bhLklVZaR9UYRAjyfrJfhz8P5jtTwNF9m0GSsz2I6o2+C6Lqfs7w19p8L4cpMZa41hlBbJiDL8nD1Z9n3rSEKe6OLWoP9TH3qmnNdV3vxpg+3O5Yuq9TVsO3R5c9dyvSLYX0Z7X93baENtDPbZZZfJveeB/wt4yb775Zl1zzTU6/PDDdfzxxysnJ0fS3l+sjznmmIh3EAAAoD5Q4wAAADiIyx3dRyMUdq9/+9vfavv27frwww/1+uuvB5cPHjxYs2bNCivXjBkz5HK5Qh7duv181E9FRYVyc3PVvHlzpaSkaPTo0dq50+yoDAAAgHBQ4wAAADgIk3g1HFCvs7KylJqaqqVLl6q8fO/pG3379g0pTk0deeSRysvLCz7ee++94HNTpkzRyy+/rAULFmj58uX6/vvvNWrUqAPpMgAAgC1qHAAAAIeI1p1po3mtvSgL+5p4u3fv1rnnnqu3335bLpdLX331lTp27KgJEyaoWbNmuueee8LrQGyssrKyaiwvKirS448/rnnz5unUU0+VJM2ZM0fdu3fX6tWrdcIJJ4TbdQAAgFpR4wAAADiIyxW9I+YMrp/uRGGPxpQpUxQXF6ft27crKenni5efd955WrJkSdgd+Oqrr9SmTRt17NhRY8aM0fbt2yVJ69atU3V1tYYMGRKM7datm9q1a6dVq1bVmq+yslLFxcUhDwAAADvUOAAAAA7C6bQ1hN3rN954Q3//+9/Vtm3bkOVdunTRt99+G1aufv36ae7cuVqyZIlmz56tbdu26aSTTlJJSYny8/Pl8XiUnp4e8ppWrVopPz+/1pwzZ85UWlpa8JGdnR1WnwAAQNNEjQMAAAAnC/t02j179oT8Or1PQUGB4uPjw8o1fPjw4P/37t1b/fr1U/v27TV//nwlJiaG2zVJ0g033KCpU6cG/y4uLqbIBQAAtqhxAAAAHCSaR8w1lSPxTjrpJD355JPBv10ulwKBgO68806dcsopB9WZ9PR0HXHEEdqyZYuysrJUVVWlwsLCkJidO3fu9/oy+8THx8vr9YY8AAAA7FDjAAAAOAg3tqgh7CPx7rzzTg0ePFgffvihqqqq9Oc//1mfffaZCgoKtHLlyoPqTGlpqbZu3aqLLrpIffr0UVxcnN566y2NHj1akrR582Zt375dOTk5B9UOAADAr1HjAAAAOAhH4tUQ9iRez5499eWXX+rBBx9UamqqSktLNWrUKOXm5qp169Zh5brmmmt05plnqn379vr+++81ffp0xcTE6Pzzz1daWpomTJigqVOnKiMjQ16vV5MmTVJOTg53bQMAABFHjQMAAOAgTOLVEPYkniSlpaVp2rRpB934f//7X51//vnavXu3WrZsqRNPPFGrV69Wy5YtJUmzZs2S2+3W6NGjVVlZqWHDhunhhx8+6HYBAAD2hxoHAADAIZjEq+GAJvFWrFihf/7zn/r666+1YMECHXbYYfrPf/6jDh066MQTTzTO88wzz9T5fEJCgh566CE99NBDB9LNUBVVUkwdz/v8B9/GPhE8t9ryW2Zx1QH7mD3VEcvlinHZx8TXNeC/4DGIK6kySmWVFdjGuKvMxsF+DSXL8L2OTTvMNmaPr9g2xrWfC67vT1JSun1QuX17kpRk2e8mMuLNjlBxG+wok+LMLh5fHF9uGxOw7Lfl1skZRu21TLC/eHyc22OUy0RyTIpRnFWcZxuTFGM/pp7kLkbt7Sy3v0On6TiYbA+qKjPKZbQ9B8z2p/L5DGIi953hirHfB1qG7QUKK21j/Dv3mOUqss8VMPi+MOWOq3t7sKoM3pcIaJQ1TnGRFLB/v2y5Tb756plTr1fj1H6ZcOL7LDl3TJ36j7wI9svw29Ee72F46Je5Q3zbskrt/10D7BP2Vvf8889r2LBhSkxM1EcffaTKyr1FY1FRkW6//faIdxAAAKA+UOMAAAA4ifvno/Ei/Qh/OswRwu71bbfdpkceeUSPPfaY4uLigssHDBigjz76KKKdAwAAqC/UOAAAAA7C3WlrCPt02s2bN2vgwIE1lqelpamwsDASfQIAAKh31DgAAADO4XK55XIZXqbrAHI3RmH3OisrS1u2bKmx/L333lPHjh0j0ikAAID6Ro0DAADgINE6lTaaN8yIsrB7fckll+hPf/qT1qxZI5fLpe+//15PP/20rrnmGl1xxRXR6CMAAEDUUeMAAAA4CJN4NYTd6+uvv14XXHCBBg8erNLSUg0cOFB//OMfddlll2nSpEnR6CMAAEDUUeMAAADg12bOnKm+ffsqNTVVmZmZGjlypDZv3hx8/ptvvpHL5drvY8GCBcG47du3a8SIEUpKSlJmZqauvfZa+Xy+sPoS1jXx/H6/Vq5cqdzcXF177bXasmWLSktL1aNHD6WkpITVMAAAgFNQ4wAAADhMNI+YCyPv8uXLlZubq759+8rn8+nGG2/U0KFD9fnnnys5OVnZ2dnKy8sLec2jjz6qu+66S8OHD5e0t9YcMWKEsrKy9P777ysvL09jx45VXFycbr/9duO+hDWJFxMTo6FDh2rTpk1KT09Xjx49wnk5AACAI1HjAAAAOEw07yIbRt4lS5aE/D137lxlZmZq3bp1GjhwoGJiYpSVlRUSs3DhQp177rnBH4PfeOMNff7553rzzTfVqlUrHX300frrX/+q6667TjNmzJDH4zHrtnGv/6dnz576+uuvw30ZAACAo1HjAAAAOEg9XBOvuLg45FFZWWnbraKiIklSRkbGfp9ft26d1q9frwkTJgSXrVq1Sr169VKrVq2Cy4YNG6bi4mJ99tlnxkMS9iTebbfdpmuuuUaLFy9WXl5ejRUGAABojKhxAAAAHKQeJvGys7OVlpYWfMycObPOLgUCAU2ePFkDBgxQz5499xvz+OOPq3v37urfv39wWX5+fsgEnqTg3/n5+cZDEtbptJJ0xhlnSJLOOussuVyu4HLLsuRyueT3+8NNCQAA0OCocQAAABykHq6Jt2PHDnm93uDi+Pj4Ol+Wm5urjRs36r333tvv8+Xl5Zo3b55uuummyPX1F8KexHv77bej0Y/oK6+QXIFan7aqa38uhNtlG+KKizFKZVXb/2MgUGR/KKckWWXV9kF+yyiXUXsGzZlyxdh/KK2AYd8rDTpWZdh5T5xtiCs20SyXZb99BWQfU1Sx06i5Erf9+fQtU7KNcrkrSm1j0uO8tjGSlJhkf3H41LhCo1zJsfZjUe6rMMhj1vd0Twv7oJJdRrms6nL7mModRrnkt7+bkZVgP+7JsS2NmuuY2ss2xmRbliS3Sd9jzK4NYXRNi1L7bVmSVGa/3ViWwT4pYPi9UlhiG+L/vsgolX/nHtuYQGmVWa5Kg+8ow+8Vd4z9d6fdft74e+AgNNYaxyouleWP4BdzHX45udnoROuaOvWlMfffoH52LMbd2Rr1+9OI+26qMW+D9fn+lNrXnoger9cbMolXl4kTJ2rx4sV699131bZt2/3GPPfccyorK9PYsWNDlmdlZemDDz4IWbZz587gc6bCnsQ7+eSTw30JAACA41HjAAAAOIhDbmxhWZYmTZqkhQsX6p133lGHDh1qjX388cd11llnqWXL0IMkcnJy9Le//U27du1SZmamJGnp0qXyer1h3VAt7Em8Tz/9dL/LXS6XEhIS1K5dO9vDDwEAAJyGGgcAAMBBXK4onk5rfqRobm6u5s2bpxdffFGpqanBa9ilpaUpMfHns/O2bNmid999V6+++mqNHEOHDlWPHj100UUX6c4771R+fr6mTZum3NzcsOrLsCfxjj766DpPpYiLi9N5552nf/7zn0pISAg3PQAAQIOgxgEAAHCQergmnonZs2dLkgYNGhSyfM6cORo/fnzw73//+99q27athg4dWiNHTEyMFi9erCuuuEI5OTlKTk7WuHHjdOutt4bV7bBHY+HCherSpYseffRRrV+/XuvXr9ejjz6qrl27at68eXr88ce1bNkyTZs2LdzUAAAADYYaBwAAwEHq4e60JizL2u/jlxN4knT77bdr+/btctdyqm779u316quvqqysTD/88IPuvvtuxcaGd2xd2Efi/e1vf9P999+vYcOGBZf16tVLbdu21U033aQPPvhAycnJuvrqq3X33XeHmx4AAKBBUOMAAAA4iEOOxHOSsHu9YcMGtW/fvsby9u3ba8OGDZL2no6Sl5d38L0DAACoJ9Q4AAAAcLKwJ/G6deumO+64Q1VVVcFl1dXVuuOOO9StWzdJ0nfffadWrVpFrpcAAABRRo0DAADgHJYruo/GKOzTaR966CGdddZZatu2rXr37i1p7y/Xfr9fixcvliR9/fXXuvLKKyPbUwAAgCiixgEAAHAOywrIsgJRy90YhT2J179/f23btk1PP/20vvzyS0nS7373O11wwQVKTU2VJF100UWR7SUAAECUUeMAAAA4R8AKKBClybZo5Y22sCfxJCk1NVWXX355pPsCAADQoKhxAAAAnMFSQJaidCRelPJG2wHdjuM///mPTjzxRLVp00bffvutJGnWrFl68cUXI9o5AACA+kSNAwAA4AwBywoejRf5h9XQq3dAwp7Emz17tqZOnarhw4frp59+kt/vlyQ1a9ZM9913X6T7BwAAUC+ocQAAAOBkYZ9O++CDD+qxxx7TyJEjdccddwSXH3fccbrmmmsi2rlI8u8qlz/RV+vzVrXfKI8rLsY+Jt4+RpKsgP3Mb6Co0ihXoNAgzqA9SZLb4DYtMfYxrrLaxzukuQr7OJOxkiT57eNiTHPFeuxj4pPMcrns58vj3AbtGdrjK7aNSawuNMrldSfYB5XbtydJ8R778fIkZBnlSoxNsY0xuVhpcox9Hkmyir6zD9pTYJRLFeX2MVXVZrk8cWZxNqzi3WaBxaW2IaY3ejL5JFqVVfZBktl4+cz2SWa5DL4zDN9D339LDGLsx12SfAbfGb5Ks3HwV9ivo8vgu0CS3HH2+0BXdd0x1VWG799BaKw1jor3SH7DfcZBqvffrN0HdNLIocmkPoM5tq2fMRZ78Rnbi+1hr3ocB2uP2b/5myJOp60p7Em8bdu26ZhjjqmxPD4+Xnv27IlIpwAAAOobNQ4AAIBzcGOLmsKeXu7QoYPWr19fY/mSJUvUvXv3SPQJAACg3lHjAAAAOIdlBaL6aIzCPhJv6tSpys3NVUVFhSzL0gcffKD/+7//08yZM/Wvf/0rGn0EAACIOmocAAAA5wj8779o5W6Mwp7E++Mf/6jExERNmzZNZWVluuCCC9SmTRvdf//9+v3vfx+NPgIAAEQdNQ4AAIBzRPOIuSZzJJ4kjRkzRmPGjFFZWZlKS0uVmZkZ6X4BAADUO2ocAAAAONUBTeLtk5SUpKQkw7tzAgAANBLUOAAAAA2LG1vUZDSJd8wxx8jlMrvl9kcffXRQHQIAAKgv1DgAAADOZCkgK0rXrotW3mgzmsQbOXJk8P8rKir08MMPq0ePHsrJyZEkrV69Wp999pmuvPLKqHQSAAAgGqhxAAAAnMmK4pF4h/Q18aZPnx78/z/+8Y+66qqr9Ne//rVGzI4dOyLbOwAAgCiixgEAAHCmvUfi+aOWuzFyh/uCBQsWaOzYsTWWX3jhhXr++ecj0ikAAID6Ro0DAADgHPuuiRetR2MU9iReYmKiVq5cWWP5ypUrlZCQEJFOAQAA1DdqHAAAADhZ2HennTx5sq644gp99NFHOv744yVJa9as0b///W/ddNNNEe9gpPh/qpC/vI7DMP1ms7CuBPshc1WYzY1a1fZtBkqqjHIFiiptY/yVZoehutz2F/h2xxmsY4zZhcIDcTH2QQZ9MuX6b4lRXNxhu+yD0lob5fKl2G83/oDPNsbraW7UnglPjNk/SKtd9n2P8WQY5XJb9jGu8mKjXInlRfZBvgrbEKtym1F7KttjHxMwWEFTAcNfhopL7WOqfrINsX4yGE9JVlGZfYzBvk2SrD3VtjGBUrN9oCveYN+cYLCvkSSD/lsV9p9Xq8Jsn+v/0X5MK3+y38dLkq/Mfkx9ht8FAZ/9OLhjzb7vYuPtx94dV/fnx6qOzqkUv9RYaxyrsFRWlaehu3HoiGDNgchzGdaXaADusI8PQX1i3+ZcZWb1blNkWYGoXbvukL4m3i9df/316tixo+6//3499dRTkqTu3btrzpw5OvfccyPeQQAAgPpAjQMAAOAcgf/9F63cjdEB/Vxy7rnnauXKlSooKFBBQYFWrlwZkeL2jjvukMvl0uTJk4PLKioqlJubq+bNmyslJUWjR4/Wzp07D7otAACAX4tGjUN9AwAAEL59R+JF69EYOeaY57Vr1+qf//ynevfuHbJ8ypQpevnll7VgwQItX75c33//vUaNGtVAvQQAADBHfQMAAHBguLFFTUaTeBkZGfrxxx+Nk7Zr107ffvutcXxpaanGjBmjxx57TM2aNQsuLyoq0uOPP657771Xp556qvr06aM5c+bo/fff1+rVq43zAwAA7E80axzqGwAAgANnKRDVR2NkdE28wsJCvfbaa0pLSzNKunv3bvn95hegzs3N1YgRIzRkyBDddtttweXr1q1TdXW1hgwZElzWrVs3tWvXTqtWrdIJJ5xQI1dlZaUqK3++AHhxsdlF8gEAQNMTzRonkvWNRI0DAADQ1Bnf2GLcuHFR6cAzzzyjjz76SGvXrq3xXH5+vjwej9LT00OWt2rVSvn5+fvNN3PmTN1yyy3R6CoAADgERaPGiXR9I1HjAACApiWap7021tNpjSbxAoHorNyOHTv0pz/9SUuXLlVCQkJEct5www2aOnVq8O/i4mJlZ2dHJDcAADi0RKPGiUZ9I1HjAACApiWap70e0qfTRsu6deu0a9cuHXvsscFlfr9f7777rv7xj3/o9ddfV1VVlQoLC0N+rd65c6eysrL2mzM+Pl7x8fHR7joAAMB+RaO+kahxAABA0xKwrCgeiWdFJW+0Negk3uDBg7Vhw4aQZRdffLG6deum6667TtnZ2YqLi9Nbb72l0aNHS5I2b96s7du3KycnpyG6DAAAUCfqGwAAgINnWQFZUZrEi1beaGvQSbzU1FT17NkzZFlycrKaN28eXD5hwgRNnTpVGRkZ8nq9mjRpknJycmq96DMAAEBDor4BAAA4eJYCCnA6bYgGncQzMWvWLLndbo0ePVqVlZUaNmyYHn744YbuFgAAwAGjvgEAAEC4HDeJ984774T8nZCQoIceekgPPfTQQeUNFFcpUHnwM62uavsclkGMJPmLKm1jfJV+s1yVvojliol128a44+xjTLnjYmxjYuPtY/bmsu+Xb3uxUS5X3Ne2MaYfoJguPWxjklNaGmQy3IZ9VfYxfvttRpLkMmnToD1JqiqzDbH27DbLVVFq0F61fYzP7HOhsgr7GNML5McabDmGuawS+3EIfP+TbYx/R4lReyb7LVWbjanpvtKEO9VjH2S63/IbXB8jYB8TKDPY/iRV/mS/bVUWmX3GLJN+RXDc/X6z9zrgs28zxmY/X11tuM9yqGjVN5IU+KlSgYrG+Wsymgi3q6F7AKApiGmc+xp/uVnN2BRxOm1NBzQTs3XrVk2bNk3nn3++du3aJUl67bXX9Nlnn0W0cwAAAPWJGgcAAMAZ9t7YInqPxijsSbzly5erV69eWrNmjV544QWVlu49CuSTTz7R9OnTI95BAACA+kCNAwAA4BwBKxDVR2MU9iTe9ddfr9tuu01Lly6Vx/Pz6UunnnqqVq9eHdHOAQAA1BdqHAAAAOcIKIpH4qlxHokX9jXxNmzYoHnz5tVYnpmZqR9//DEinQIAAKhv1DgAAADOEVD0Jtsa6yRe2EfipaenKy8vr8byjz/+WIcddlhEOgUAAFDfqHEAAADwazNnzlTfvn2VmpqqzMxMjRw5Ups3b64Rt2rVKp166qlKTk6W1+vVwIEDVV5eHny+oKBAY8aMkdfrVXp6uiZMmBC8fIupsCfxfv/73+u6665Tfn6+XC6XAoGAVq5cqWuuuUZjx44NNx0AAIAjUOMAAAA4h1NubLF8+XLl5uZq9erVWrp0qaqrqzV06FDt2bMnGLNq1SqdfvrpGjp0qD744AOtXbtWEydOlNv987TbmDFj9Nlnn2np0qVavHix3n33XV166aVhjUnYp9Pefvvtys3NVXZ2tvx+v3r06CG/368LLrhA06ZNCzcdAACAI1DjAAAAOEc0b0ARTt4lS5aE/D137lxlZmZq3bp1GjhwoCRpypQpuuqqq3T99dcH47p27Rr8/02bNmnJkiVau3atjjvuOEnSgw8+qDPOOEN333232rRpY9SXsI/E83g8euyxx7R161YtXrxYTz31lL744gv95z//UUxMTLjpAAAAHIEaBwAAwDnq40i84uLikEdlZaVtv4qKiiRJGRkZkqRdu3ZpzZo1yszMVP/+/dWqVSudfPLJeu+994KvWbVqldLT04MTeJI0ZMgQud1urVmzxnhMwj4Sb5927dqpXbt2B/pyAAAAR6LGAQAAaHjhnvYabm5Jys7ODlk+ffp0zZgxo/bXBQKaPHmyBgwYoJ49e0qSvv76a0nSjBkzdPfdd+voo4/Wk08+qcGDB2vjxo3q0qWL8vPzlZmZGZIrNjZWGRkZys/PN+630STe1KlTjRPee++9xrEAAAANiRoHAADAmSzLitrptNb/JvF27Nghr9cbXB4fH1/n63Jzc7Vx48aQo+wCgb19vOyyy3TxxRdLko455hi99dZb+ve//62ZM2dGrN9Gk3gff/xxyN8fffSRfD5f8PzeL7/8UjExMerTp0/EOgYAABBt1DgAAABNl9frDZnEq8vEiRODN6Ro27ZtcHnr1q0lST169AiJ7969u7Zv3y5JysrK0q5du0Ke9/l8KigoUFZWlnF/jSbx3n777eD/33vvvUpNTdUTTzyhZs2aSZJ++uknXXzxxTrppJOMGwYAAGho1DgAAADOVB+n05qwLEuTJk3SwoUL9c4776hDhw4hzx9++OFq06aNNm/eHLL8yy+/1PDhwyVJOTk5Kiws1Lp164I/Di9btkyBQED9+vUz7kvY18S755579MYbbwSLW0lq1qyZbrvtNg0dOlRXX311uCnrhb+0Sv6qgz8M0yqyf6Ory6qNclWV2sf5K/1GuQLV9nFWwGwjdcfZ3+/EHWd/gW+X22XUXmy8ycXCPUa5PAZ9N1W9rcg2JlD6hVGuuB15tjFWi2a2Ma7m6UbtqaXBTL7b8ONf9INtiPVDgVmuwmL7mIoqs1yeOPuYlCT7GJ/PrD2TfgUid6i3VW42DoEfy21jfDv32MYECiqM2jPbbxmOqYGYeLPtNLbafuxdMWb7JKN9l0Euf5nZONT3d4HfZ7adug3GwXQ/L4OvRctf93eUz2DdDlZjrXECRZUKVEbnVJMDZVpzAADQ1PkrzOYPmqKALAUUpUm8MPLm5uZq3rx5evHFF5Wamhq8hl1aWpoSExPlcrl07bXXavr06TrqqKN09NFH64knntAXX3yh5557TtLeo/JOP/10XXLJJXrkkUdUXV2tiRMn6ve//73xnWmlA5jEKy4u1g8/1PxH/Q8//KCSkpJw0wEAADgCNQ4AAIBzBKxA1K6JF07e2bNnS5IGDRoUsnzOnDkaP368JGny5MmqqKjQlClTVFBQoKOOOkpLly5Vp06dgvFPP/20Jk6cqMGDB8vtdmv06NF64IEHwup32JN455xzji6++GLdc889Ov744yVJa9as0bXXXqtRo0aFmw4AAMARqHEAAACcw0mn05q4/vrrdf3119f6fEZGhubNm2fc7v6EPYn3yCOP6JprrtEFF1yg6uq9h33GxsZqwoQJuuuuuw6qMwAAAA2FGgcAAABOFvYkXlJSkh5++GHddddd2rp1qySpU6dOSk5OjnjnAAAA6gs1DgAAgHM45Ug8Jwl7Em+f5ORk9e7dO5J9AQAAaHDUOAAAAA2PSbyawp7EO+WUU+Ry1X43umXLlh1UhwAAABoCNQ4AAIBzOOXGFk4S9iTe0UcfHfJ3dXW11q9fr40bN2rcuHGR6hcAAEC9osYBAABwDo7EqynsSbxZs2btd/mMGTNUWlp60B0CAABoCNQ4AAAAzmFFcRLP9I6zTuOOVKILL7xQ//73vyOVDgAAwBGocQAAAOAEB3xji19btWqVEhISIpUOAADAEahxAAAA6l/gf/9FK3djFPYk3qhRo0L+tixLeXl5+vDDD3XTTTdFrGMAAAD1iRoHAADAObgmXk1hT+J5vd6QO7e53W517dpVt956q4YOHRrRzkVSRUG54uJqX11/tdksbMBnHxeo9hvl8lXax/krzHK5Ymq/m94+sfExRrlMWH77Dd7ltu9TOHERY9pewH4d/TvLzFKVVNnGuJMKbWNi26Yatefuat+eUhKNclk78m1j/N/8aJTLZLwCBeVGuVwJ9ruvmNYpRrmMmHyu48w+Y1alzzbGdNvyFVXa5zLcv0WK6Wc6YNCv6lKDbVlStcHlygIGn2lJckdon1Rdbv8+S5KvrNo2xrTvJmNqsv+WpIDB94o7LmJX5ZDf5vu12mf2fXgwGmuN4y+slD8++uODKPE3ziMBED7LcF8OBBl+Z6Nx81eZ1YxNEZN4NYU9iTd37twodAMAAKBhUeMAAAA4B5N4NYX9E3rHjh21e/fuGssLCwvVsWPHiHQKAACgvlHjAAAAOMfeSbxAlB5NZBLvm2++kd9f85SNyspKfffddxHpFAAAQH2jxgEAAICTGZ9O+9JLLwX///XXX1daWlrwb7/fr7feekuHH354RDsHAAAQbdQ4AAAAzhNQFE+nVeM8Es94Em/kyJGSJJfLpXHjxoU8FxcXp8MPP1z33HNPRDsHAAAQbdQ4AAAAzsM18WoynsQLBPbeOatDhw5au3atWrRoEbVOAQAA1BdqHAAAAOdhEq+msO9Ou23btmj0AwAAoEFR4wAAADhHwJL8UZprCzTOOTyzSbwHHnhAl156qRISEvTAAw/UGXvVVVdFpGMAAADRRo0DAADgTByJV5PRJN6sWbM0ZswYJSQkaNasWbXGuVwuClwAANBoUOMAAACgsTCaxPvl6SWcagIAAA4V1DgAAADO5I/i6bTRyhtt7nBfcOutt6qsrKzG8vLyct16660R6RQAAEB9o8YBAABwjoAV3Udj5LKs8E4EjomJUV5enjIzM0OW7969W5mZmfL7/RHt4MEqLi5WWlqaPjnxSKXGxtQaZ0VwGtYy3Br8lfZjZdqv2CT7gyrjvfFGuVwxLqM4O+5YszniOIO+x8Sb3YMlJs6+zZj42reDcJm+1y6DfhnlMRwHT/fmtjHuVqlGufzf/mQbU/1lgVmuMp9tjM/gcyFJgWr7ONPtJlJiE8y2rUB1wDambHd5xHLFGmzzbsNt1KS9gOHnwmT/ZvoZM4kz3Z9aAft1NNlO/RWG27LPvj3TvpuMvcl+UjLbh0fq+8JEic+vY9ZsUlFRkbxeb1TaaKw1zo7xJ8rrqd/93aEs0Fh/mm8Apvto7GXy/YK9Ivlvs6aAz6I5J25bJdU+dXphbVRrnMZmX40za9UflJjiiUob5aVVmpLz70Y37mFXfJZlyeWqWbR/8sknysjIiEinAAAA6hs1DgAAgHNE84i5xjr3bTyJ16xZM7lcLrlcLh1xxBEhRa7f71dpaakuv/zyqHQSAAAgWqhxAAAAnIdr4tVkPIl33333ybIs/eEPf9Att9yitLS04HMej0eHH364cnJywmp89uzZmj17tr755htJ0pFHHqmbb75Zw4cPlyRVVFTo6quv1jPPPKPKykoNGzZMDz/8sFq1ahVWOwAAALWhxgEAAEBjYDyJN27cOElShw4d1L9/f8XFxR10423bttUdd9yhLl26yLIsPfHEEzr77LP18ccf68gjj9SUKVP0yiuvaMGCBUpLS9PEiRM1atQorVy58qDbBgAAkKhxAAAAnMiK4um04d0dwjnCvibeySefHPz/iooKVVVVhTwfzgUBzzzzzJC///a3v2n27NlavXq12rZtq8cff1zz5s3TqaeeKkmaM2eOunfvrtWrV+uEE04It+sAAAC1osYBAABwDr9lyR+l2bZo5Y22sG+ZWVZWpokTJyozM1PJyclq1qxZyONA+f1+PfPMM9qzZ49ycnK0bt06VVdXa8iQIcGYbt26qV27dlq1alWteSorK1VcXBzyAAAAsEONAwAA4ByBQHQfjVHYk3jXXnutli1bptmzZys+Pl7/+te/dMstt6hNmzZ68sknw+7Ahg0blJKSovj4eF1++eVauHChevToofz8fHk8HqWnp4fEt2rVSvn5+bXmmzlzptLS0oKP7OzssPsEAACaHmocAAAA59h3Y4toPRqjsCfxXn75ZT388MM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      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trend = polire.Trend(order=2)\n",
    "trend.fit(X_train, y_train)\n",
    "trend_pred = trend.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
    "\n",
    "plot_predictions(trend_pred, \"Trend\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SpatialAverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spatial_average = polire.SpatialAverage(radius=15)\n",
    "spatial_average.fit(X_train, y_train)\n",
    "spatial_average_pred = spatial_average.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
    "\n",
    "plot_predictions(spatial_average_pred, \"SpatialAverage\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = polire.CustomInterpolator(LinearRegression())\n",
    "lr.fit(X_train, y_train)\n",
    "lr_pred = lr.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
    "\n",
    "plot_predictions(lr_pred, \"Linear Regression\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NearestNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "K = 3\n",
    "knn = polire.CustomInterpolator(KNeighborsRegressor(n_neighbors=K))\n",
    "knn.fit(X_train, y_train)\n",
    "knn_pred = knn.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
    "\n",
    "plot_predictions(knn_pred, \"KNN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = polire.CustomInterpolator(RandomForestRegressor(random_state=42))\n",
    "rf.fit(X_train, y_train)\n",
    "rf_pred = rf.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
    "\n",
    "plot_predictions(rf_pred, \"Random Forest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_15362_row0_col0, #T_15362_row0_col1, #T_15362_row0_col2, #T_15362_row0_col3 {\n",
       "  background-color: green;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_15362\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_15362_level0_col0\" class=\"col_heading level0 col0\" >RMSE</th>\n",
       "      <th id=\"T_15362_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_15362_level0_col2\" class=\"col_heading level0 col2\" >MAPE</th>\n",
       "      <th id=\"T_15362_level0_col3\" class=\"col_heading level0 col3\" >Neg R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_15362_level0_row0\" class=\"row_heading level0 row0\" >Kriging (spherical)</th>\n",
       "      <td id=\"T_15362_row0_col0\" class=\"data row0 col0\" >3.10</td>\n",
       "      <td id=\"T_15362_row0_col1\" class=\"data row0 col1\" >1.95</td>\n",
       "      <td id=\"T_15362_row0_col2\" class=\"data row0 col2\" >0.01</td>\n",
       "      <td id=\"T_15362_row0_col3\" class=\"data row0 col3\" >-0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15362_level0_row1\" class=\"row_heading level0 row1\" >Random Forest</th>\n",
       "      <td id=\"T_15362_row1_col0\" class=\"data row1 col0\" >3.96</td>\n",
       "      <td id=\"T_15362_row1_col1\" class=\"data row1 col1\" >2.74</td>\n",
       "      <td id=\"T_15362_row1_col2\" class=\"data row1 col2\" >0.01</td>\n",
       "      <td id=\"T_15362_row1_col3\" class=\"data row1 col3\" >-0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15362_level0_row2\" class=\"row_heading level0 row2\" >Spline</th>\n",
       "      <td id=\"T_15362_row2_col0\" class=\"data row2 col0\" >4.43</td>\n",
       "      <td id=\"T_15362_row2_col1\" class=\"data row2 col1\" >2.85</td>\n",
       "      <td id=\"T_15362_row2_col2\" class=\"data row2 col2\" >0.01</td>\n",
       "      <td id=\"T_15362_row2_col3\" class=\"data row2 col3\" >-0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15362_level0_row3\" class=\"row_heading level0 row3\" >3-Nearest Neighbors</th>\n",
       "      <td id=\"T_15362_row3_col0\" class=\"data row3 col0\" >4.60</td>\n",
       "      <td id=\"T_15362_row3_col1\" class=\"data row3 col1\" >3.19</td>\n",
       "      <td id=\"T_15362_row3_col2\" class=\"data row3 col2\" >0.01</td>\n",
       "      <td id=\"T_15362_row3_col3\" class=\"data row3 col3\" >-0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15362_level0_row4\" class=\"row_heading level0 row4\" >Spatial Average</th>\n",
       "      <td id=\"T_15362_row4_col0\" class=\"data row4 col0\" >6.04</td>\n",
       "      <td id=\"T_15362_row4_col1\" class=\"data row4 col1\" >4.32</td>\n",
       "      <td id=\"T_15362_row4_col2\" class=\"data row4 col2\" >0.02</td>\n",
       "      <td id=\"T_15362_row4_col3\" class=\"data row4 col3\" >-0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15362_level0_row5\" class=\"row_heading level0 row5\" >Inverse Distance Weighting</th>\n",
       "      <td id=\"T_15362_row5_col0\" class=\"data row5 col0\" >6.54</td>\n",
       "      <td id=\"T_15362_row5_col1\" class=\"data row5 col1\" >5.04</td>\n",
       "      <td id=\"T_15362_row5_col2\" class=\"data row5 col2\" >0.02</td>\n",
       "      <td id=\"T_15362_row5_col3\" class=\"data row5 col3\" >-0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15362_level0_row6\" class=\"row_heading level0 row6\" >Trend</th>\n",
       "      <td id=\"T_15362_row6_col0\" class=\"data row6 col0\" >8.02</td>\n",
       "      <td id=\"T_15362_row6_col1\" class=\"data row6 col1\" >6.30</td>\n",
       "      <td id=\"T_15362_row6_col2\" class=\"data row6 col2\" >0.02</td>\n",
       "      <td id=\"T_15362_row6_col3\" class=\"data row6 col3\" >-0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15362_level0_row7\" class=\"row_heading level0 row7\" >Linear Regression</th>\n",
       "      <td id=\"T_15362_row7_col0\" class=\"data row7 col0\" >8.46</td>\n",
       "      <td id=\"T_15362_row7_col1\" class=\"data row7 col1\" >6.98</td>\n",
       "      <td id=\"T_15362_row7_col2\" class=\"data row7 col2\" >0.03</td>\n",
       "      <td id=\"T_15362_row7_col3\" class=\"data row7 col3\" >-0.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc23fb54580>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = {\n",
    "    \"Inverse Distance Weighting\": idw_pred,\n",
    "    \"Kriging (spherical)\": kriging_pred,\n",
    "    \"Spline\": spline_pred,\n",
    "    \"Trend\": trend_pred,\n",
    "    \"Linear Regression\": lr_pred,\n",
    "    f\"{K}-Nearest Neighbors\": knn_pred,\n",
    "    \"Random Forest\": rf_pred,\n",
    "    \"Spatial Average\": spatial_average_pred,\n",
    "}\n",
    "\n",
    "result = pd.DataFrame(columns=[\"RMSE\", \"MAE\", \"MAPE\", \"Neg R2\"], index=models.keys())\n",
    "\n",
    "for model_name, y_pred in models.items():\n",
    "    result.loc[model_name, \"RMSE\"] = mean_squared_error(\n",
    "        y_test.ravel(), y_pred.ravel(), squared=False\n",
    "    )\n",
    "    result.loc[model_name, \"MAE\"] = mean_absolute_error(y_test.ravel(), y_pred.ravel())\n",
    "    result.loc[model_name, \"MAPE\"] = mean_absolute_percentage_error(\n",
    "        y_test.ravel(), y_pred.ravel()\n",
    "    )\n",
    "    result.loc[model_name, \"Neg R2\"] = -r2_score(y_test.ravel(), y_pred.ravel())\n",
    "\n",
    "result.sort_values(\"RMSE\").style.highlight_min(axis=0, color=\"green\").format(\"{:.2f}\")"
   ]
  }
 ],
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