example/example_xgb_decision_tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import subprocess\n",
    "import os\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "import xarray as xr\n",
    "\n",
    "from pycdoexpr import cdoexpr\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Generate Tabular Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>x2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>576</td>\n",
       "      <td>577.254764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>3364</td>\n",
       "      <td>3363.026159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>1089</td>\n",
       "      <td>1088.811888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600.484216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80</td>\n",
       "      <td>6400</td>\n",
       "      <td>6401.753342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>94</td>\n",
       "      <td>8836</td>\n",
       "      <td>8836.374103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>14</td>\n",
       "      <td>196</td>\n",
       "      <td>195.814676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>69</td>\n",
       "      <td>4761</td>\n",
       "      <td>4760.803047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>59</td>\n",
       "      <td>3481</td>\n",
       "      <td>3480.895665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>32</td>\n",
       "      <td>1024</td>\n",
       "      <td>1023.552991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       x    x2            y\n",
       "0     24   576   577.254764\n",
       "1     58  3364  3363.026159\n",
       "2     33  1089  1088.811888\n",
       "3     40  1600  1600.484216\n",
       "4     80  6400  6401.753342\n",
       "...   ..   ...          ...\n",
       "2995  94  8836  8836.374103\n",
       "2996  14   196   195.814676\n",
       "2997  69  4761  4760.803047\n",
       "2998  59  3481  3480.895665\n",
       "2999  32  1024  1023.552991\n",
       "\n",
       "[3000 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers = 3000\n",
    "std = 2\n",
    "data = (100*np.random.random(numbers)).astype(int)\n",
    "label = data**2+np.sign(np.random.random(numbers) - 0.5)*std*np.random.random(numbers)\n",
    "df = pd.DataFrame({'x':data, 'y':label})\n",
    "df['x2'] = df['x']**2\n",
    "train_X, test_X, train_y, test_y = train_test_split(df[['x','x2']], df['y'],\n",
    "                      test_size = 0.3, random_state = 123)\n",
    "df[['x', 'x2', 'y']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Train with XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False, eta=0.1,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.100000001, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=5, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints=&#x27;()&#x27;, n_estimators=30, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False, eta=0.1,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.100000001, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=5, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints=&#x27;()&#x27;, n_estimators=30, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False, eta=0.1,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy='depthwise', importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.100000001, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=5, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=30, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor='auto', ...)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBRegressor(n_estimators=30, max_depth=5, eta=0.1)\n",
    "model.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predict</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>5680.811523</td>\n",
       "      <td>5930.738248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1798</th>\n",
       "      <td>2487.634766</td>\n",
       "      <td>2600.121517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>9260.208008</td>\n",
       "      <td>9802.111714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1599</th>\n",
       "      <td>45.400089</td>\n",
       "      <td>47.971651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>8.728511</td>\n",
       "      <td>0.679747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>6266.856445</td>\n",
       "      <td>6562.886114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2135</th>\n",
       "      <td>2889.613770</td>\n",
       "      <td>3025.452967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2278</th>\n",
       "      <td>1041.927734</td>\n",
       "      <td>1088.507665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2876</th>\n",
       "      <td>9166.683594</td>\n",
       "      <td>9603.485690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>2786.264893</td>\n",
       "      <td>2915.021965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          predict        label\n",
       "1690  5680.811523  5930.738248\n",
       "1798  2487.634766  2600.121517\n",
       "417   9260.208008  9802.111714\n",
       "1599    45.400089    47.971651\n",
       "1206     8.728511     0.679747\n",
       "...           ...          ...\n",
       "1253  6266.856445  6562.886114\n",
       "2135  2889.613770  3025.452967\n",
       "2278  1041.927734  1088.507665\n",
       "2876  9166.683594  9603.485690\n",
       "643   2786.264893  2915.021965\n",
       "\n",
       "[900 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df = pd.DataFrame({'predict':model.predict(test_X), 'label':test_y})\n",
    "eval_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(model, open('../static/test.pkl','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Generate 2 dimensional netCDF4 file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "shape = (20,20)\n",
    "numbers = np.product(shape)\n",
    "std = 4\n",
    "data = (100*np.random.random(numbers)).astype(int)\n",
    "data2 = data**2\n",
    "label = data**2+np.sign(np.random.random(numbers) - 0.5)*std*np.random.random(numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lon = np.arange(100,120,1)\n",
    "lat = np.arange(20,40,1)\n",
    "lon,lat = np.meshgrid(lon,lat)\n",
    "\n",
    "input_ds = xr.Dataset(\n",
    "    data_vars=dict(\n",
    "        x=(['xlon','xlat'], data.reshape(shape)),\n",
    "        x2=(['xlon', 'xlat'], data2.reshape(shape)),\n",
    "    ),\n",
    "    coords=dict(\n",
    "        lon=(['xlon','xlat'], lon),\n",
    "        lat=(['xlon','xlat'], lat),\n",
    "    )\n",
    ")\n",
    "input_ds.to_netcdf('../static/test_input.nc')\n",
    "\n",
    "label_ds = xr.Dataset(\n",
    "    data_vars=dict(\n",
    "        label=(['xlon','xlat'], label.reshape(shape)),\n",
    "    ),\n",
    "    coords=dict(\n",
    "        lon=(['xlon','xlat'], lon),\n",
    "        lat=(['xlon','xlat'], lat),\n",
    "    )\n",
    ")\n",
    "label_ds.to_netcdf('../static/test_label.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Model predict with pycdoexpr and cdo command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning (cdfInqContents): Coordinates variable lon can't be assigned!\n",
      "Warning (cdfInqContents): Coordinates variable lat can't be assigned!\n",
      "Warning (cdfInqContents): Coordinates variable lon can't be assigned!\n",
      "Warning (cdfInqContents): Coordinates variable lat can't be assigned!\n",
      "cdo    expr: Processed 2 variables over 1 timestep [0.06s 14MB].\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cexpr = cdoexpr()\n",
    "expr = cexpr.xgb_decision_trees('../static/test.pkl', 'boosting')\n",
    "cmd = f\"cdo -f nc4 -expr,'{expr}' ../static/test_input.nc ../static/test_output.nc\"\n",
    "subprocess.check_call(cmd, shell=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset('../static/test_output.nc')\n",
    "label = xr.open_dataset('../static/test_label.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x156c94310>"
      ]
     },
     "execution_count": 13,
     "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['VALUE'].plot(vmin=0,vmax=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x156e149d0>"
      ]
     },
     "execution_count": 14,
     "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": [
    "label['label'].plot(vmin=0,vmax=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\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",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".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-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",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray ()&gt;\n",
       "array(189.63122398)</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'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f4c51648-1e3b-457e-a5a8-a2e2903244aa' class='xr-array-in' type='checkbox' checked><label for='section-f4c51648-1e3b-457e-a5a8-a2e2903244aa' 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>189.6</span></div><div class='xr-array-data'><pre>array(189.63122398)</pre></div></div></li><li class='xr-section-item'><input id='section-9fac32e8-6636-4597-83cd-19569b80b784' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9fac32e8-6636-4597-83cd-19569b80b784' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <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-92d765b6-4594-4e1e-b3a6-b0c5a823a2a3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-92d765b6-4594-4e1e-b3a6-b0c5a823a2a3' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <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-ec7e926a-1a11-4303-af2e-40c52d106935' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ec7e926a-1a11-4303-af2e-40c52d106935' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(189.63122398)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(((label['label'] - ds['VALUE'])**2).mean())"
   ]
  }
 ],
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