09_machine_learning_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine learning models\n",
    "\n",
    "Now, we will train and evaluate two machine learning quantile regression models, XGBoost and a Random Forest from quantile-forest. Our final model will be a hybrid of the two that produces a probabilistic forecast in the form of prediction intervals based on a deterministic forecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xgboost as xgb\n",
    "from quantile_forest import RandomForestQuantileRegressor\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get data ready\n",
    "Read in the DataFrame we created in the last notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle('data_files/full_2021_2023_dataset.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p_ac_all_fcast</th>\n",
       "      <th>p_ac_all_fcast_max</th>\n",
       "      <th>p_ac_all_fcast_min</th>\n",
       "      <th>p_ac_all_cs</th>\n",
       "      <th>level_0</th>\n",
       "      <th>p_ac_all_act</th>\n",
       "      <th>tcc_std</th>\n",
       "      <th>max_min_norm</th>\n",
       "      <th>abs_err</th>\n",
       "      <th>norm_abs_err</th>\n",
       "      <th>csi_norm_abs_err</th>\n",
       "      <th>csi_actual</th>\n",
       "      <th>csi_fcast</th>\n",
       "      <th>hour</th>\n",
       "      <th>month</th>\n",
       "      <th>el</th>\n",
       "      <th>zen</th>\n",
       "      <th>az</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_center_labeled</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-01 09:00:00+00:00</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>-44.725579</td>\n",
       "      <td>134.725579</td>\n",
       "      <td>90.865286</td>\n",
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       "      <th>2021-01-01 10:00:00+00:00</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>-32.169594</td>\n",
       "      <td>122.169594</td>\n",
       "      <td>98.221778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 11:00:00+00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.346134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>-19.832171</td>\n",
       "      <td>109.832171</td>\n",
       "      <td>105.126225</td>\n",
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       "    <tr>\n",
       "      <th>2021-01-01 12:00:00+00:00</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>12</td>\n",
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       "      <td>-7.896291</td>\n",
       "      <td>97.896291</td>\n",
       "      <td>112.278339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 13:00:00+00:00</th>\n",
       "      <td>46.237118</td>\n",
       "      <td>146.468144</td>\n",
       "      <td>4.036621</td>\n",
       "      <td>827.123924</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.438303</td>\n",
       "      <td>4.919961</td>\n",
       "      <td>0.172201</td>\n",
       "      <td>13.798815</td>\n",
       "      <td>0.00372</td>\n",
       "      <td>0.016683</td>\n",
       "      <td>0.039218</td>\n",
       "      <td>0.055901</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>3.619760</td>\n",
       "      <td>86.380240</td>\n",
       "      <td>120.212465</td>\n",
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      "text/plain": [
       "                           p_ac_all_fcast  p_ac_all_fcast_max  \\\n",
       "time_center_labeled                                             \n",
       "2021-01-01 09:00:00+00:00        0.000000            0.000000   \n",
       "2021-01-01 10:00:00+00:00        0.000000            0.000000   \n",
       "2021-01-01 11:00:00+00:00        0.000000            0.000000   \n",
       "2021-01-01 12:00:00+00:00        0.000000            0.000000   \n",
       "2021-01-01 13:00:00+00:00       46.237118          146.468144   \n",
       "\n",
       "                           p_ac_all_fcast_min  p_ac_all_cs  level_0  \\\n",
       "time_center_labeled                                                   \n",
       "2021-01-01 09:00:00+00:00            0.000000     0.000000      0.0   \n",
       "2021-01-01 10:00:00+00:00            0.000000     0.000000      0.0   \n",
       "2021-01-01 11:00:00+00:00            0.000000     0.000000      0.0   \n",
       "2021-01-01 12:00:00+00:00            0.000000     0.000000      0.0   \n",
       "2021-01-01 13:00:00+00:00            4.036621   827.123924      0.0   \n",
       "\n",
       "                           p_ac_all_act   tcc_std  max_min_norm    abs_err  \\\n",
       "time_center_labeled                                                          \n",
       "2021-01-01 09:00:00+00:00      0.000000  0.657303           NaN   0.000000   \n",
       "2021-01-01 10:00:00+00:00      0.000000  1.346134           NaN   0.000000   \n",
       "2021-01-01 11:00:00+00:00      0.000000  1.346134           NaN   0.000000   \n",
       "2021-01-01 12:00:00+00:00      0.000000  1.346134           NaN   0.000000   \n",
       "2021-01-01 13:00:00+00:00     32.438303  4.919961      0.172201  13.798815   \n",
       "\n",
       "                           norm_abs_err  csi_norm_abs_err  csi_actual  \\\n",
       "time_center_labeled                                                     \n",
       "2021-01-01 09:00:00+00:00       0.00000               NaN         NaN   \n",
       "2021-01-01 10:00:00+00:00       0.00000               NaN         NaN   \n",
       "2021-01-01 11:00:00+00:00       0.00000               NaN         NaN   \n",
       "2021-01-01 12:00:00+00:00       0.00000               NaN         NaN   \n",
       "2021-01-01 13:00:00+00:00       0.00372          0.016683    0.039218   \n",
       "\n",
       "                           csi_fcast  hour  month         el         zen  \\\n",
       "time_center_labeled                                                        \n",
       "2021-01-01 09:00:00+00:00        NaN     9      1 -44.725579  134.725579   \n",
       "2021-01-01 10:00:00+00:00        NaN    10      1 -32.169594  122.169594   \n",
       "2021-01-01 11:00:00+00:00        NaN    11      1 -19.832171  109.832171   \n",
       "2021-01-01 12:00:00+00:00        NaN    12      1  -7.896291   97.896291   \n",
       "2021-01-01 13:00:00+00:00   0.055901    13      1   3.619760   86.380240   \n",
       "\n",
       "                                   az  \n",
       "time_center_labeled                    \n",
       "2021-01-01 09:00:00+00:00   90.865286  \n",
       "2021-01-01 10:00:00+00:00   98.221778  \n",
       "2021-01-01 11:00:00+00:00  105.126225  \n",
       "2021-01-01 12:00:00+00:00  112.278339  \n",
       "2021-01-01 13:00:00+00:00  120.212465  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter by dropping nighttime values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup test and training data\n",
    "filtered_df = df.dropna()\n",
    "approx_nameplate = df.p_ac_all_cs.max() # use max of clear sky power as proxy for nameplate rating\n",
    "filtered_df = filtered_df[filtered_df['p_ac_all_cs'] >= approx_nameplate*.001]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the normalized mean absolute error (NMAE) of our input deterministic forecast, just for reference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original NMAE: 8.44%\n"
     ]
    }
   ],
   "source": [
    "orig_nmae = filtered_df['norm_abs_err'].mean()\n",
    "print('original NMAE: ' + \"{:.2f}\".format(orig_nmae*100) + '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the XGBoost quantile regression\n",
    "Let's train an XGBoost quantile regression model with methods based on [1]. \n",
    "\n",
    "We will define a prediction interval (PI) list for training our XGBoost model, naming that variable `pi_list_xgb`. The final PI values we are targetting are `[99.5%, 99%, 98%, 95%, and 50%]`, and the values for `pi_list_xgb`, `[99.95%, 99.9%, 99%, 96%, 57%]` have been manually adjusted to give us results close to those PI values.\n",
    "\n",
    "The features used in the model are:\n",
    "- standard deviation of weighted average TCC from GEFS\n",
    "- normalized max-min power from HRRR\n",
    "- clear sky normalized deterministic power forecast\n",
    "- solar zenith and azimuth\n",
    "- month of the year\n",
    "\n",
    "Solar elevation and hour of day are also available, but were not found to be helpful, so they are commented out below.\n",
    "\n",
    "We will train on 2021 data, test with 2022, and do a final evaluation with 2023. We will exclude 2021-04-09 through 2021-04-12, a time range we looked at early on, so we can revist what our model would have predicted in that time range. \n",
    "\n",
    "[1] https://xgboost.readthedocs.io/en/latest/python/examples/quantile_regression.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "pi_list_xgb = [.9995, .999, .99, .96, .57] \n",
    "pi_list = pi_list_xgb\n",
    "\n",
    "percentile_list = [(1-x)/2 for x in pi_list] + [.5] + [1-(1-x)/2 for x in reversed(pi_list)]\n",
    "percentile_list = [round(elem, 6) for elem in percentile_list]\n",
    "num_intervals = len(pi_list)\n",
    "\n",
    "alpha = percentile_list\n",
    "evals_result = {}\n",
    "\n",
    "# setup test and training data\n",
    "X=filtered_df[['tcc_std']].copy()\n",
    "X['max_min_norm'] = filtered_df['max_min_norm']\n",
    "X['csi_fcast'] = filtered_df['csi_fcast']\n",
    "X['zen'] = filtered_df['zen']\n",
    "X['az'] = filtered_df['az']\n",
    "X['month'] = filtered_df['month']\n",
    "# X['hour'] = filtered_df['hour']\n",
    "# X['el'] = filtered_df['el']\n",
    "\n",
    "y = filtered_df['csi_actual']\n",
    "\n",
    "## year split\n",
    "X_train = X[(X.index.year == 2021) & ((X.index < '2021-04-09') | (X.index > '2021-04-13'))]\n",
    "y_train = y[(X.index.year == 2021) & ((X.index < '2021-04-09') | (X.index > '2021-04-13'))]\n",
    "X_test = X[X.index.year == 2022]\n",
    "y_test = y[X.index.year == 2022]\n",
    "X = X[(X.index.year >= 2021) & (X.index.year <= 2022)]\n",
    "y = y[(y.index.year >= 2021) & (y.index.year <= 2022)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will be using the `hist` tree method, quantile DMatrix can be used to preserve\n",
    "# memory.\n",
    "# Do not use the `exact` tree method for quantile regression, otherwise the\n",
    "# performance might drop.\n",
    "Xy = xgb.QuantileDMatrix(X, y)\n",
    "# use Xy as a reference\n",
    "Xy_test = xgb.QuantileDMatrix(X_test, y_test, ref=Xy)\n",
    "\n",
    "booster = xgb.train(\n",
    "    {\n",
    "        # Use the quantile objective function.\n",
    "        \"objective\": \"reg:quantileerror\",\n",
    "        \"tree_method\": \"hist\",\n",
    "        \"quantile_alpha\": alpha,\n",
    "        # Let's try not to overfit.\n",
    "        \"learning_rate\": 0.005,\n",
    "        \"max_depth\": 5,\n",
    "    },\n",
    "    Xy,\n",
    "    num_boost_round=2000,\n",
    "    early_stopping_rounds=20,\n",
    "    # The evaluation result is a weighted average across multiple quantiles.\n",
    "    evals=[(Xy, \"Train\"), (Xy_test, \"Test\")],\n",
    "    evals_result=evals_result,\n",
    "    verbose_eval=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "booster.save_model(\"regression_models/xgboost_model.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load\n",
    "booster = xgb.Booster()\n",
    "booster.load_model(\"regression_models/xgboost_model.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at some model info:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2522ba26a40>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# from https://mljar.com/blog/xgboost-early-stopping/\n",
    "# plt.figure(figsize=(10,7))\n",
    "plt.plot(evals_result['Train']['quantile'], label=\"Training loss\")\n",
    "plt.plot(evals_result['Test']['quantile'], label=\"Validation loss\")\n",
    "plt.axvline(booster.best_iteration, color=\"gray\", label=\"Optimal tree number\")\n",
    "plt.xlabel(\"Number of trees\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Feature importance'}, xlabel='F score', ylabel='Features'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UCgV27twpWR4YGIgqVarA1NQUVlZWaN26NU6ePCmZM3ToUJQvXx7GxsZwdHTEvHnzcPXqVcmcuXPnokmTJjAxMUGJEiWyrSUuLg4dO3aEqakpSpYsCT8/P6Snp0vmdOjQAfb29jAyMkK5cuUwbdo0qNXqd+7jkydP0K9fP1haWsLS0hL9+vXD06dPc9WfLAYfNJuKvJCQEPj7+3/wPyQAaDj/IDIMTPO/qEJGpS9gUQPALXAf0jIVui5H59iP19gLKfZDiv2QKoz9iF3gDQBISUlBzZo1MXDgQHTv3l1rXqVKlbBs2TKUK1cOL168wJIlS9C2bVtcu3YNtra2AIC6deviyy+/hLOzMx48eIBRo0bB29sbN2/ehL6+PgAgPT0dX3zxBRo3boy1a9dqbSczMxPe3t6wtbXFsWPH8PjxYwwYMACCICA4OFic16tXLzRt2hQlSpTA+fPnMWTIEGg0GsybNy/Hfe3Tpw/u3LmD8PBwAMDXX3+Nfv36Yffu3bnuF0NoAfPw8ECtWrUQFBSk0zpcXV3h7+8Pf39/ndZBRERU1LVv3x7t27fPcXmfPn0kr3/44QesXbsW//zzD1q1agXgVajLUrp0aXz55Zfw9/dHbGwsypcvDwCYNWsWgFcHkLKzf/9+XL58Gbdv34ajoyMA4Pvvv4evry/mzp0rzuvbty8sLCwAAC4uLjh8+DCOHj2aY/1XrlxBeHg4Tpw4gYYNGwIA1qxZg8aNGyMmJgaVK1fO8b1v4ul4IiIiIh1JT0/H6tWrYWlpiZo1a2Y7JyUlBQcPHkTZsmXh5OSU63VHRkbCzc1NDKAA0K5dO6SlpSEqKirb91y7dg3h4eFwd3d/53otLS3FAAoAjRo1gqWlJY4fP57r+hhCC5Cvry+OHDmCpUuXQqFQQKFQIDY2FpcuXYK3tzcsLCxgbm6O5s2b4/r16+L7fvnlF1SrVg0qlQqlSpXCqFGjcrW9wMBAODs7Q6VSwdHREX5+fgBeHY29desWxo4dK9aRJSQkBM7OzjAxMUHXrl3x+PHj/G0CERERadmzZw/MzMxgZGSEJUuWICIiAiVLlpTMWb58OczMzGBlZYVz584hLCwMhoaGud5GQkIC7O3tJWNWVlYwNDREQkKCZLxJkyYwMjJCxYoV0bx5c8yePfud67Wzs9Mat7Oz01rvu/B0fAFaunQp/v33X7i5uYl/mZmZmWjRogU8PDxw6NAhWFhY4O+//0ZGRgYAYMWKFQgICMCCBQvQvn17JCUl4e+//37vtv744w8sWbIEW7ZsQbVq1ZCQkIDz588DAEJDQ1GzZk18/fXXGDJkiPiekydPYtCgQZg3bx66deuG8PBwzJw5873bSktLQ1pamvg6OTkZAKDSE6CvL+S+QUWUSk+Q/Le4Yz9eYy+k2A8p9kOqMPYjp5t5MjIytJY1a9YMp0+fxuPHj7F27Vr4+Pjg2LFjknDn4+MDDw8P3LlzB9OmTUPv3r3x119/wcjISLKuzMzMbLev0WiyHRcEAZmZmZLxrVu34tmzZzh//jwmTJiA7777DhMnTsxxX988oPXmerMbzwlDaAGytLSEoaEhTExM4ODgAAD45ptvYGlpiS1btkCpVAJ4dYFyljlz5mDcuHEYM2aMOFa/fv33bisuLg4ODg5o3bo1lEolnJ2d0aBBAwCAtbU19PX1YW5uLtYBvArJ7dq1w+TJk8U6jh8/Ll5knJP58+eL16G8aVptDUxMMt9ba3HxbT2Nrkv4pLAfr7EXUuyHFPshVZj6ERYWlu14VFSU+JmfnS5dumDfvn2YPHkyevToke2ciRMnom/fvggMDESLFi0ky86fPw+1Wq21/WfPnuG///6TjD9//hxqtRqxsbHYv3+/OJ51mr9q1arIzMzE119/jXHjxok3Qb3JwcEB9+/f1xp/+PCh1pHXd2EIlVl0dDSaN2+e7T/GBw8eID4+Xrwo+UN88cUXCAoKQrly5eDp6QkvLy907NgRBgY5/xVfuXIFXbt2lYw1btz4vSF0ypQpCAgIEF8nJyfDyckJc87pIUOp/Y+1uFHpCfi2ngbTz+ghTVM47ugsSOzHa+yFFPshxX5IFcZ+XAxsl+143bp14eXl9c73mpiYwNXVNdt5WQFTT08PVatW1Zrz6NEjKJVKrXE9PT388ccfqF27NkqVKgUA+P3336FSqTB8+PAcaxEEAWq1GoKQ/VHoxo0bIykpCadOnRIPeJ08eRJJSUlo0qTJO/fzTQyhMjM2Ns7TsvdxcnJCTEwMIiIicODAAYwYMQKLFy/GkSNHcvzpK6d/XO+jUqmgUqm0xtM0CmQUksdoyCFNoyg0jxWRA/vxGnshxX5IsR9ShakfWZ+3z58/x7Vr18Tx27dv49KlS7C2toaNjQ3mzp2LTp06oVSpUnj8+DGWL1+OO3fuoFevXlAqlbhx4wa2bt2Ktm3bwtbWFrdu3cLixYthbGyMjh07ituJi4tDYmIi7t69i8zMTFy6dAkAUKFCBZiZmcHLywtVq1bFoEGDsHjxYiQmJmLy5MkYMmQIbGxsxMvpQkND0bBhQ6hUKkRFRWHKlCno2bOneCDr1KlT6N+/Pw4ePIjSpUvjs88+g6enJ4YMGYJVq1YBeHU3f4cOHXJ9ZzzAEFrgDA0NxWs1AKBGjRpYv3491Gq1Vjg0NzeHq6srDh48iJYtW37wtoyNjdGpUyd06tQJI0eORJUqVXDhwgXUqVNHqw7g1SH3EydOSMbefk1EREQf5syZM5LP8ayzhwMGDMDKlStx9epVrF+/Ho8ePYKNjQ3q16+Po0ePolq1agAAIyMjHD16FEFBQXjy5Ans7e1Rrlw5HDlyRHLN6IwZM7B+/Xrxde3atQEAf/75Jzw8PKCvr4///e9/GDFiBJo2bQpjY2P06dMH3333naTeoKAgXL9+HYIgwMXFBSNHjsTYsWPF5ampqYiJiZFcQ7p582b4+fmhbdu2AIBOnTph2bJlH9QnhtAC5urqipMnTyI2NhZmZmYYNWoUgoOD0atXL0yZMgWWlpY4ceIEGjRogMqVKyMwMBDDhg2DnZ0d2rdvj2fPnuHvv//G6NGj37mdkJAQZGZmomHDhjAxMcHGjRthbGwMFxcXsY6//voLvXr1gkqlEn9rQpMmTbBo0SJ06dIF+/fvf++peCIiIno3Dw+Pd55tDA0Nfef7HR0dJddxZp2Of/soY0hISI7PCM3i7OyMPXv2vHPOX3/9JT4nNDvZ7Y+1tTU2bdr0zvW+l0AFKiYmRmjUqJFgbGwsABBu3rwpnD9/Xmjbtq1gYmIimJubC82bNxeuX78uvmflypVC5cqVBaVSKZQqVUoYPXr0e7ezY8cOoWHDhoKFhYVgamoqNGrUSDhw4IC4PDIyUqhRo4agUqmEN//a165dK5QpU0YwNjYWOnbsKHz33XeCpaXlB+1jUlKSAEB49OjRB72vqEpPTxd27twppKen67qUTwL78Rp7IcV+SLEfUuzHawXVi6zP76SkpHxdb27xSGgBq1SpEiIjI7XG9+3bl+N7hg4diqFDh37Qdrp06YIuXbrkuLxRo0biI5veNGjQIAwaNEgyNm7cuA/aNhEREdGH4sPqiYiIiEh2DKGFxObNm2FmZpbtV9aFzERERESFBU/HFxKdOnWS/I7WN73rAbhEREREnyKG0ELC3Nwc5ubmui6DiIiIKF/wdDwRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2RnougAqOhrOP4gMA1Ndl6FzKn0BixoAboH7kJap0HU5Osd+vMZeSLEfUoW1H7ELvPHXX39h8eLFiIqKwr1797Bjxw506dJFnBMaGopVq1YhKioKjx8/xrlz51CrVi3JeoYOHYoDBw4gPj4eZmZmaNSoETw9PV9vJzYW3377LQ4dOoSEhAQ4Ojqib9++mDp1KgwNDSXrCgkJwQ8//IB///0XJUqUQI8ePbBs2TKt2q9du4batWtDX18fT58+fed+PnnyBH5+fti1axcAoFOnTggODkaJEiU+qF/0Go+E6oBCocDOnTtzNffq1ato1KgRjIyMtP6HJSIi+hSkpKSgZs2a2Qa9rOVNmzbFggULclxH3bp1sW7dOly5cgX79u2DIAgIDAxEZmYmgFefhxqNBqtWrcKlS5ewZMkSrFy5Et98841kPT/88AOmTp2KyZMn49KlSzh48CDatWuntT21Wo3evXujefPmudrHPn36IDo6GuHh4QgPD0d0dDT69euXq/dS9ngkVAfu3bsHKyurXM2dOXMmTE1NERMTAzMzswKuDPDw8ECtWrUQFBRU4NsiIqKioX379mjfvn2Oy7PCWmxsbI5zvv76a/HPrq6umDVrFurVq4fY2FhUqVIFnp6ekiOj5cqVQ0xMDFasWIHvvvsOwKujldOmTcPu3bvRqlUrcW61atW0tjdt2jRUqVIFrVq1wvHjx9+5f1euXEF4eDhOnDiBhg0bAgDWrFmDxo0bIyYmBpUrV37n+yl7PBKqAw4ODlCpVLmae/36dTRr1gwuLi6wsbEp4MqIiIh0LyUlBRs2bIC9vT2cnJxynJeUlARra2vxdUREBDQaDe7evYvPPvsMZcqUgY+PD27fvi1536FDh7Bt2zb89NNPuaonMjISlpaWYgAFgEaNGsHS0vK9AZZyxhD6ETQaDRYuXIgKFSpApVLB2dkZc+fORXp6OkaNGoVSpUrByMgIrq6umD9/vvi+3J6OVygUiIqKwuzZs6FQKBAYGAgAuHPnDnr16gVra2uYmpqiXr16OHnyJIBXobVz586wt7eHmZkZ6tevjwMHDkjWu3z5clSsWBFGRkawt7dHjx49AAC+vr44cuQIli5dCoVCAYVC8c6fWomIiPLT8uXLYWZmBjMzM+zbtw+BgYFa13tmuX79OoKDgzFs2DBx7MaNG9BoNJg3bx6CgoLwxx9/IDExEW3atEF6ejoA4PHjx/D19UVISAgsLCxyVVdCQgLs7Oy0xu3s7JCQkJCHPSWAp+M/ypQpU7BmzRosWbIEzZo1w71793D16lX8+OOP2LVrF37//Xc4Ozvj9u3bWj+F5ca9e/fQunVreHp6Yvz48TAzM8Pz58/h7u6O0qVLY9euXXBwcMDZs2eh0WgAAM+fP4eXlxfmzJkDIyMjrF+/Hh07dkRMTAycnZ1x5swZ+Pn5YePGjWjSpAkSExNx9OhRAMDSpUvx77//ws3NDbNnzwYA2NraatWVlpaGtLQ08XVycjIAQKUnQF9f+OD9LGpUeoLkv8Ud+/EaeyHFfkgV1n6o1WqtsYyMjGzHs8bUanW2y318fODh4YGEhAR8//33WLx4MXx8fGBubi6ZFx8fD09PT3Tv3h0DBgzQWu8PP/yAzz//HACwYcMGODk5ISIiAm3btsXgwYPRs2dPNG7cGGq1WrzmNLt6suQ0RxAEaDSad743P7y5fwWxXl1hCM2jZ8+eYenSpVi2bBkGDBgAAChfvjyaNWsGPz8/VKxYEc2aNYNCoYCLi0uetuHg4AADAwOYmZnBwcEBALB69Wo8fPgQp0+fFk9BVKhQQXxPzZo1UbNmTfH1nDlzsGPHDuzatQujRo1CXFwcTE1N0aFDB5ibm8PFxQW1a9cGAFhaWsLQ0BAmJibi9rIzf/58zJo1S2t8Wm0NTEwy87SvRdG39TS6LuGTwn68xl5IsR9Sha0fYWFhWmNRUVFQKpVa4/fv3wcAHDt2DPHx8e9c78CBA9G3b1/MnTsXLVq0EMcTExMxbdo0VKpUCR07dpRs/+HDhwBeHcR5c9zc3BxhYWHIyMhAREQEdu/ejR9++EFcrtFoYGRkhBEjRqB169ZatTx48AB3797V2tf4+Hjcv38/2x4UhIiIiHxdX2pqar6u70MxhObRlStXkJaWJrnwOYuvry/atGmDypUrw9PTEx06dEDbtm3zZbvR0dGoXbu25BqYN6WkpGDWrFnYs2cP4uPjkZGRgRcvXiAuLg4A0KZNG7i4uKBcuXLiRd5du3aFiYlJrmuYMmUKAgICxNfJyclwcnLCnHN6yFDqf9wOFgEqPQHf1tNg+hk9pGkKz2NWCgr78Rp7IcV+SBXWflwM1L7zvG7duvDy8tIaz7rEq1mzZu994ktKSgoEQUClSpXEdd29exdt2rRBs2bNsH79eujrSz9zKlSogODgYJQpU0Y8EpqYmIhnz57B29sbbdq0QWRkpHhkEwB2796N7777DkeOHEHp0qWzvXG4bNmyWLZsGWxtbVG/fn0AwKlTp5Camoqvv/66wG9MUqvViIiIQJs2bbIN93mVdSZTVxhC88jY2DjHZXXq1MHNmzexd+9eHDhwAD4+PmjdujX++OOPAt0uAEyYMAH79u3Dd999hwoVKsDY2Bg9evQQr4UxNzfH2bNncfjwYezfvx8zZsxAYGAgTp8+netnnalUqmxvrErTKJBRiJ5tV9DSNIpC9ay/gsZ+vMZeSLEfUoWtH0qlEs+fP8e1a9fEsdu3b+PSpUuwtraGs7MzEhMTERcXJx79vHHjBpRKJRwcHODg4IAbN25g69ataNu2LWxtbXH37l3Mnz8fKpUKHTp0gFKpRHx8PNq0aQNnZ2f88MMPkud6Zp29q1atGjp37oxx48Zh9erVsLCwwJQpU1ClShUxwNWoUUNS//nz56GnpyeeFQReBcz+/fvj4MGDKF26NGrUqAFPT08MHz4cq1atAgAMHz4cHTp0gJubW0G1VotSqczXEJqf68oL3piURxUrVoSxsTEOHjyY7XILCwv07NkTa9aswdatW7F9+3YkJiZ+9HZr1KiB6OjoHNd19OhR+Pr6omvXrqhevTocHBy0bi4yMDBA69atsWjRIvzzzz+IjY3FoUOHAACGhoaSnxCJiIje58yZM6hdu7YY5AICAlC7dm3MmDEDALBr1y7Url0b3t7eAIBevXqhdu3aWLlyJQDAyMgIR48ehZeXFypUqAAfHx+YmppiwYIF4g1B+/fvx7Vr13Do0CGUKVMGpUqVEr/etGHDBjRs2BDe3t5wd3eHUqlEeHj4BwWu1NRUxMTESK6Z3Lx5M6pXr462bduibdu2qFGjBjZu3Jj3phGPhOaVkZERJk2ahIkTJ8LQ0BBNmzbFw4cPcenSJSQnJ6NUqVKoVasW9PT0sG3bNjg4OOTLb1Xo3bs35s2bhy5dumD+/PkoVaoUzp07B0dHRzRu3BgVKlRAaGgoOnbsCIVCgenTp4s3LQHAnj17cOPGDbRo0QJWVlYICwuDRqMRTyW4urri5MmTiI2NhZmZGaytraGnx59ViIgoZx4eHhCEnG+o8vX1ha+vb47LHR0dta6rVKvVkrH3rSOLhYUF1q5di7Vr1753bk7rzW5/rK2tsWnTplytk3KHIfQjTJ8+HQYGBpgxYwbi4+NRqlQpDBs2DCVLlsTChQvx33//QV9fH/Xr10dYWFi+hDlDQ0Ps378f48aNg5eXFzIyMlC1alXxWWdLlizBoEGD0KRJE5QsWRKTJk2SXPNRokQJhIaGIjAwEC9fvkTFihXx22+/iQ/yHT9+PAYMGICqVavixYsXuHnzJlxdXXNV28kprfgsU7z+xnkxsJ3OT3V8CtiP19gLKfZDiv2g4oYh9CPo6elh6tSpmDp1qtayIUOG5Pi+d/20+Lbo6GitMRcXlxyvL3V1dRVPrWcZOXKk+OdmzZrh8OHDOW6vUqVKiIyMzHV9RERERHnB86xEREREJDuGUB2aN2+e+Jsh3v561+/gJSIiIirseDpeh4YNGwYfH59sl73vUUxEREREhRlDqA5ZW1vn+NB5IiIioqKMp+OJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyc5A1wVQ0dFw/kFkGJjqugydU+kLWNQAcAvch7RMha7L0Tn24zX2Qor9kPqYfsQu8MZff/2FxYsXIyoqCvfu3cOOHTvQpUsXcY4gCJg1axZWr16NJ0+eoGHDhvjpp59QrVo1rfUJggAvLy+Eh4drrWfu3Ln43//+h+joaBgaGuLp06eS954/fx4LFizAsWPH8OjRI7i6umLYsGEYM2aMOCcmJgbDhg3D5cuXkZSUBEdHR/Tp0wczZ86EUqnMcT+fPHkCPz8/7Nq1CwDQqVMnBAcHo0SJEh/UL/o08EgoERFREZCSkoKaNWti2bJl2S5ftGgRfvjhByxbtgynT5+Gg4MD2rRpg2fPnmnNDQoKgkKRfRBOT0/HF198geHDh2e7PCoqCra2tti0aRMuXbqEqVOnYsqUKZK6lEol+vfvj/379yMmJgZBQUFYs2YNZs6c+c597NOnD6KjoxEeHo7w8HBER0ejX79+73wPfbp4JJSIiKgIaN++Pdq3b5/tMkEQEBQUhKlTp6Jbt24AgPXr18Pe3h6//vorhg4dKs49f/48fvjhB5w+fRqlSpXSWtesWbMAACEhIdlua9CgQZLX5cqVQ2RkJEJDQzFq1ChxrFy5cuIcFxcXHD58GEePHs1x/65cuYLw8HCcOHECDRs2BACsWbMGjRs3RkxMDCpXrpzje+nTxCOhhVxsbCwUCoXWl4eHBwDg+PHjaNGiBYyNjeHk5AQ/Pz+kpKSI73d1dcW8efMwaNAgmJubw9nZGatXr9bR3hARUUG4efMmEhIS0LZtW3FMpVLB3d0dx48fF8dSU1PRu3dvLFu2DA4ODvm2/aSkJFhbW+e4/Nq1awgPD4e7u3uOcyIjI2FpaSkGUABo1KgRLC0tJftAhQdDaCHn5OSEe/fuiV/nzp2DjY0NWrRogQsXLqBdu3bo1q0b/vnnH2zduhXHjh0TfxLN8v3336NevXo4d+4cRowYgeHDh+Pq1as62iMiIspvCQkJAAB7e3vJuL29vbgMAMaOHYsmTZqgc+fO+bbtyMhI/P7775KjrVmaNGkCIyMjVKxYEc2bN8fs2bNzXE9CQgLs7Oy0xu3s7CT7QIVHvp2Of/r0KS8M1gF9fX3xp9WXL1+iS5cuaNy4MQIDA+Hr64s+ffrA398fAFCxYkX8+OOPcHd3x4oVK2BkZAQA8PLywogRIwAAkyZNwpIlS3D48GFUqVIl222mpaUhLS1NfJ2cnAwAUOkJ0NcXCmpXCw2VniD5b3HHfrzGXkixH1If0w+1Wq01lpGRIY5nZGRojQFAZmam+P7du3fj0KFDOHXqlGTO2+/J7r05uXTpEjp37oypU6fCw8NDa+6mTZvw7Nkz/PPPP5gyZQoWLlyI8ePHS9ab9d+cticIAjQazTvrKOze7kV+r1dX8hRCFy5cCFdXV/Ts2RMA4OPjg+3bt8PBwQFhYWGoWbNmvhZJuTN48GA8e/YMERER0NPTQ1RUFK5du4bNmzeLc7L+Z7158yY+++wzAECNGjXE5QqFAg4ODnjw4EGO25k/f754TdCbptXWwMQkMx/3qHD7tp5G1yV8UtiP19gLKfZDKi/9CAsL0xqLiooS7zTPOlK4fft2ybWYFy9ehKmpKcLCwrBu3Tpcv34dJUuWlKynZ8+e+OyzzzB37lzJ+Pnz56FWq7PdNgDcvn0b06ZNQ5s2bVCrVq0c5wGAhYUFvvjiCwQGBqJy5crQ19cXl0VERAAAHjx4gLt372qtJz4+Hvfv33/n+ouKrF7kl9TU1Hxd34fKUwhdtWoVNm3aBOBVQyIiIrB37178/vvvmDBhAvbv35+vRdL7zZkzB+Hh4Th16hTMzc0BABqNBkOHDoWfn5/WfGdnZ/HPbz8OQ6FQQKPJ+ZvglClTEBAQIL5OTk6Gk5MT5pzTQ4ZSP8f3FRcqPQHf1tNg+hk9pGn42Bn24zX2Qor9kPqYflwMbKc1VrduXXh5eQF4dQAiMDAQL1++FMfS09MxYMAAzJs3D15eXqhTpw4ePXokWUedOnXw3XffwdvbG2XLlpUse/ToEZRKpbi+N126dAlff/01Bg8ejAULFuRqHxITE6HRaNC+fXsYGBhArVYjIiICbdq0gVKpRNmyZbFs2TLY2tqifv36AIBTp04hNTUVX3/9dZG+MentXuSXrDOZupKnEHrv3j04OTkBAPbs2QMfHx+0bdsWrq6ukguGSR7bt2/H7NmzsXfvXpQvX14cr1OnDi5duoQKFSrk6/ZUKhVUKpXWeJpGgQw+60+UplHw2YdvYD9eYy+k2A+pvPRDqVTi+fPnuHbtmjh2+/ZtXLp0CdbW1nB2doa/vz/mz5+PKlWqoGLFipg3bx5MTEzQr18/KJVKODk5iZ/tbypbtiwqVaokvo6Li0NiYiLu3r2LzMxMXLp0CQBQoUIFmJmZ4dKlS2jbti3atm2LCRMm4PHjxwBeXT5ma2sLANi8eTOUSiWqV68OlUqFqKgoTJ8+HT179oSxsTEA4PTp0xg5ciSOHTsGV1dX1KhRA56enhg+fDhWrVoFABg+fDg6dOgANze3D+pXYaVUKvM1hObnuvIiTyHUysoKt2/fhpOTE8LDwzFnzhwAr37Syrpmg+Rx8eJF9O/fH5MmTUK1atXEUy6GhoaYNGkSGjVqhJEjR2LIkCEwNTXFlStXEBERgeDgYB1XTkRE+enMmTNo2bKl+DrrjNWAAQMQEhKCiRMn4sWLFxgxYoT4sPr9+/eLZ89ya8aMGVi/fr34unbt2gCAP//8Ex4eHti2bRsePnyIzZs3Sy4Hc3FxQWxsLADAwMAACxcuxL///gtBEODi4oKRI0di7Nix4vzU1FTcvXtXct3i5s2b4efnJ97l36lTpxyfi0qfvjyF0G7duqFPnz6oWLEiHj9+LD6XLDo6Ot+PutG7nTlzBqmpqZgzZ474wwAAuLu74/Dhwzhy5AimTp2K5s2bQxAElC9fXryWl4iIig4PDw8IQs43NSkUCgQGBiIwMDDX68xufSEhITk+IxRArrbRs2fP934Wubu7Y+fOnXB1dRXHrK2txcsBqfBTCO/6F5sDtVqNpUuX4vbt2/D19RV/CgoKCoKZmRm++uqrfC+UPl3JycmwtLTEo0ePYGNjo+tydC7rQn0vLy+dn+r4FLAfr7EXUuyHFPshxX68VlC9yPr8TkpKgoWFRb6tN7fydCRUqVSKj1B4U9ajgIiIiIiI3iXPD6vfuHEjmjVrBkdHR9y6dQvAqyOh//d//5dvxRERERFR0ZSnELpixQoEBASgffv2ePr0qXgzUokSJRAUFJSf9RERERFREZSnEBocHIw1a9Zg6tSpkgfK1qtXDxcuXMi34oiIiIioaMpTCL1586Z4M9KbVCoVUlJSProoIiIiIira8hRCy5Yti+joaK3xvXv3omrVqh9bExEREREVcXm6O37ChAkYOXIkXr58CUEQcOrUKfz222+YP38+fv755/yukYiIiIiKmDyF0IEDByIjIwMTJ05Eamoq+vTpg9KlS2Pp0qXo1atXftdIREREREXMB4fQjIwMbN68GR07dsSQIUPw6NEjaDQa2NnZFUR9RERERFQEffA1oQYGBhg+fDjS0tIAACVLlmQAJSIiIqIPkqcbkxo2bIhz587ldy1EREREVEzk6ZrQESNGYNy4cbhz5w7q1q0LU1NTyfIaNWrkS3FEREREVDTlKYT27NkTAODn5yeOKRQKCIIAhUIh/gYlIiIiIqLs5CmE3rx5M7/rICIiIqJiJE8h1MXFJb/rICIiIqJiJE8hdMOGDe9c3r9//zwVQ0RERETFQ55C6JgxYySv1Wo1UlNTYWhoCBMTE4ZQIiIiInqnPD2i6cmTJ5Kv58+fIyYmBs2aNcNvv/2W3zUSERERURGTpxCanYoVK2LBggVaR0mJiIiIiN6WbyEUAPT19REfH5+fqyQiIiKiIihP14Tu2rVL8loQBNy7dw/Lli1D06ZN86UwIiIiIiq68hRCu3TpInmtUChga2uLzz//HN9//31+1EVERERERVieQqhGo8nvOoiIiIioGMnTNaGzZ89Gamqq1viLFy8we/bsjy6KiIiIiIq2PIXQWbNm4fnz51rjqampmDVr1kcXRURERERFW55CqCAIUCgUWuPnz5+HtbX1RxdFREREREXbB10TamVlBYVCAYVCgUqVKkmCaGZmJp4/f45hw4ble5FEREREVLR8UAgNCgqCIAgYNGgQZs2aBUtLS3GZoaEhXF1d0bhx43wvkoiIiIiKlg8KoQMGDAAAlC1bFk2aNIFSqSyQooiIiIioaMvTI5rc3d3FP7948QJqtVqy3MLC4uOqIiIiIqIiLU83JqWmpmLUqFGws7ODmZkZrKysJF9ERERERO+SpxA6YcIEHDp0CMuXL4dKpcLPP/+MWbNmwdHRERs2bMjvGomIiIioiMnT6fjdu3djw4YN8PDwwKBBg9C8eXNUqFABLi4u2Lx5M7788sv8rpOIiIiIipA8HQlNTExE2bJlAby6/jMxMREA0KxZM/z111/5Vx0RERERFUl5CqHlypVDbGwsAKBq1ar4/fffAbw6QlqiRIn8qo2IiIiIiqg8hdCBAwfi/PnzAIApU6aI14aOHTsWEyZMyNcCiYiIiKjoydM1oWPHjhX/3LJlS1y9ehVnzpxB+fLlUbNmzXwrjoiIiIiKpjyF0De9fPkSzs7OcHZ2zo96iIiIiKgYyNPp+MzMTHz77bcoXbo0zMzMcOPGDQDA9OnTsXbt2nwtkIiIiIiKnjyF0Llz5yIkJASLFi2CoaGhOF69enX8/PPP+VYcERERERVNeQqhGzZswOrVq/Hll19CX19fHK9RowauXr2ab8URERERUdGUp2tC7969iwoVKmiNazQard8jXxy4urrC398f/v7+ui5FpxrOP4gMA1Ndl6FzKn0BixoAboH7kJap0HU5Osd+vMZeSOm6H7ELvJGRkYHAwEBs3rwZCQkJKFWqFHx9fTFt2jTo6b06TnP//n1MmjQJ+/fvx9OnT9GiRQsEBwejYsWK4rrS0tIwfvx4/Pbbb3jx4gVatWqF5cuXo0yZMuKcs2fPYtKkSTh9+jT09fXRvXt3/PDDDzAzMwMAPH78GLNmzcLw4cPx+PFj2NnZoXPnzpg3bx4sLCxy3I/cbJvoU5SnI6HVqlXD0aNHtca3bduG2rVrf3RRhc3p06fx9ddf67oMIiL6QAsXLsTKlSuxbNkyXLlyBYsWLcLixYsRHBwMABAEAV26dMGNGzfwf//3fzh37hxcXFzQunVrpKSkiOvx9/fHjh07sGXLFhw7dgzPnz9Hhw4dkJmZCQCIj49H69atUaFCBZw8eRLh4eG4dOkSfH19xXXo6emhQYMGCA0Nxb///ouQkBAcOHAAw4YNe+c+vG/bRJ+qPB0JnTlzJvr164e7d+9Co9EgNDQUMTEx2LBhA/bs2ZPfNX7ybG1tdV3Ce6nVaiiVSl2XQUT0SYmMjETnzp3h7e0N4NWZrd9++w1nzpwBAPz33384ceIELl68iGrVqgEAli9fDjs7O/z222/46quvkJSUhLVr12Ljxo1o3bo1AGDTpk1wcnLCgQMH0K5dO+zZswdKpRI//fSTeIT1p59+Qu3atXHt2jVUqFABVlZWaN++PerWrQulUgkXFxeMGDECixcvzrH+3Gyb6FP1QUdCb9y4AUEQ0LFjR2zduhVhYWFQKBSYMWMGrly5gt27d6NNmza5Xp+HhwdGjx4Nf39/WFlZwd7eHqtXr0ZKSgoGDhwIc3NzlC9fHnv37gXw6q78wYMHo2zZsjA2NkblypWxdOlScX0vX75EtWrVJEclb968CUtLS6xZs+a99YSEhKBEiRLYs2cPKleuDBMTE/To0QMpKSlYv349XF1dYWVlhdGjR0t+wnR1dUVQUJD4WqFQ4Oeff0bXrl1hYmKCihUrYteuXbnqyeHDh6FQKHDw4EHUq1cPJiYmaNKkCWJiYiTzVqxYgfLly8PQ0BCVK1fGxo0bJcsVCgVWrlyJzp07w9TUFHPmzEFgYCBq1aqFX375Bc7OzjAzM8Pw4cORmZmJRYsWwcHBAXZ2dpg7d26uaiUiKuyaNWuGgwcP4t9//wUAnD9/HseOHYOXlxeAV6e6AcDIyEh8j76+PgwNDXHs2DEAQFRUFNRqNdq2bSvOcXR0hJubG44fPy6ux9DQUAygAGBsbAwA4nreFh8fj9DQULi7u+dYf262TfSp+qAjoRUrVsS9e/dgZ2eHdu3a4ZdffsG1a9fg4OCQ5wLWr1+PiRMn4tSpU9i6dSuGDx+OnTt3omvXrvjmm2+wZMkS9OvXD3FxcVAqlShTpgx+//13lCxZEsePH8fXX3+NUqVKwcfHB0ZGRti8eTMaNmwILy8vdOzYEf369UPLli0xZMiQXNWTmpqKH3/8EVu2bMGzZ8/QrVs3dOvWDSVKlEBYWBhu3LiB7t27o1mzZujZs2eO65k1a5bktM6XX36JW7duwdraOld1TJ06Fd9//z1sbW0xbNgwDBo0CH///TcAYMeOHRgzZgyCgoLQunVr7NmzBwMHDkSZMmXQsmVLcR0zZ87E/PnzsWTJEujr62PdunW4fv069u7di/DwcFy/fh09evTAzZs3UalSJRw5cgTHjx/HoEGD0KpVKzRq1Cjb2tLS0sRvzACQnJwMAFDpCdDXF3K1f0WZSk+Q/Le4Yz9eYy+kdN0PtVqNgIAAJCYmokqVKtDX10dmZiZmz56NHj16QK1Wo3z58nBxccGkSZOwfPlymJqaIigoCAkJCYiPj4darcadO3dgaGgIMzMzyX0RdnZ24pzmzZsjICAACxYswOjRo5GSkoLJkycDAO7cuQO1Wi2+t0+fPvjf//6HFy9ewNvbGytWrMjxfovcbLuwyqq9MO9DfimoXui6twpBEHL9f7+enh4SEhJgZ2cHALCwsEB0dDTKlSuXp417eHggMzNTvL40MzMTlpaW6NatGzZs2AAA4oXikZGR2YaikSNH4v79+/jjjz/EscWLF2PRokXo3bs3tm3bhgsXLqBkyZLvrSckJAQDBw7EtWvXUL58eQDAsGHDsHHjRty/f1+8eNzT0xOurq5YuXIlAO0bkxQKBaZNm4Zvv/0WAJCSkgJzc3OEhYXB09PznTUcPnwYLVu2xIEDB9CqVSsAQFhYGLy9vfHixQsYGRmhadOmqFatGlavXi2+z8fHBykpKfjf//4n1uDv748lS5aIcwIDA7F48WIkJCTA3Nxc3JeYmBhcv35d/Am9SpUq8PX1Fb9Bvi0wMBCzZs3SGv/1119hYmLyzv0jIvqUHD16FCEhIfD19YWTkxNu3ryJX375BQMHDsTnn38OALh27RqWLVuG2NhY6OnpoWbNmlAoXt1INWPGDBw5cgTBwcGSzyHg1YEABwcHDB8+HABw5MgRrFu3DsnJydDT00OHDh1w+PBhdOrUCd26dRPf9+TJE6SkpODu3bvYtGkTqlWrluN1obndNlF2UlNT0adPHyQlJb3z5reC8lG/MekD8muOatSoIf5ZX18fNjY2qF69ujhmb28PAHjw4AEAYOXKlfj5559x69YtvHjxAunp6ahVq5ZknePGjcP//d//ITg4GHv37s1VAM1iYmIiBtCs7bu6uooBNGssq57c7JepqSnMzc3f+56c3l+qVCkAr3rg7OyMK1euaN0I1bRpU8mlCQBQr149rfW6urqKATRrX/T19SWniN63f1OmTEFAQID4Ojk5GU5OTphzTg8ZSv0c31dcqPQEfFtPg+ln9JCm4R3Q7Mdr7IWUrvtxMbAdRo0ahRkzZkjCmpWVFX799Vd899134pifnx+SkpKQnp4OW1tbNG3aFHXr1oWXlxeMjY2xZMkSNG7cGFZWVuJ7pk+fjnr16omn9r28vLBw4ULcv38fpqamUCgUsLGxgaenJ7y8vKBWqxEREQEfHx/xGv62bduiZcuWWLVqlfhZ8KbcbrswyupHmzZtiv09DQXVi6wzmbryQSFUoVCIP/29OfYx3m6mQqGQjGWtX6PR4Pfff8fYsWPx/fffo3HjxjA3N8fixYtx8uRJyToePHiAmJgY6Ovr47///nvv0ccPqSdrTKPRfPB63veenN7/Zg/eHssiCILWmKmp9uOS8mP/VCoVVCqV1niaRoEMPnZGlKZR8DE8b2A/XmMvpHTVD6VSidTUVCiVSsn3QUNDQwiCoPW9MeuAxn///YeoqCjMmTMHSqUSDRs2hFKpxOHDh+Hj4wMAuHfvHi5duoTFixdrrSfr0Um//PILjIyM0L59e8mcN+vJeha3RqPJNnx86LYLo7f/foqz/O6Frvv6QSFUEAT4+vqKAeTly5cYNmyYVtgJDQ3NvwrfcPToUTRp0gQjRowQx65fv641b9CgQXBzc8OQIUMwePBgtGrVClWrVi2QmnThs88+w7Fjx9C/f39x7Pjx4/jss890WBURUeHTsWNHzJ07F87OzqhWrRrOnTuHH374AYMGDRLnbNu2Dba2tnB2dsaFCxcwZswYdOnSRbwZyNLSEoMHD8a4ceNgY2MDa2trjB8/HtWrVxfvWAeAZcuWoUmTJjAzM0NERAQmTJiABQsWoESJEgCAvXv34uDBg3B2doaVlRUuX76MiRMnomnTpnB1dQXw6jndrVq1woYNG9CgQYNcb5voU/RBIXTAgAGS13379s3XYt6nQoUK2LBhA/bt24eyZcti48aNOH36NMqWLSvO+emnnxAZGYl//vkHTk5O2Lt3L7788kucPHlS8itGC7MJEybAx8cHderUQatWrbB7926EhobiwIEDui6NiKhQCQ4OxvTp0zFixAg8ePAAjo6OGDp0KGbMmCHOuXfvHgICAnD//n2UKlUK/fv3x/Tp0yXrWbJkCQwMDODj4yM+MD4kJETyWwVPnTqFmTNn4vnz56hSpQpWrVqFfv36icuNjY2xf/9+bNiwAWlpaXByckK3bt0k1+er1WrExMQgNTX1g7ZN9Cn6oBC6bt26gqojV4YNG4bo6Gj07NkTCoUCvXv3xogRI8RHOF29ehUTJkzA2rVr4eTkBOBVKK1ZsyamT5+OhQsX6rL8fNOlSxcsXboUixcvhp+fH8qWLYt169bBw8ND16URERUq5ubmCAoKkjxm721+fn7w8/N753qMjIwQHBwsPuQ+O1k33ObEw8MDCxcuhJeXV46nSV1dXbXux8jNtok+RR90dzxRdpKTk2FpaYlHjx7BxsZG1+XonFqtRlhY2Ds/SIoT9uM19kKK/ZBiP6TYj9cKqhdZn9+6ujs+T7+2k4iIiIjoYxSrENq+fXuYmZll+zVv3jxZahg2bFiONbzv9wMTERERFRUf9ZzQwubnn3/Gixcvsl2W299k9LFmz56N8ePHZ7tMF4fCiYiIiHShWIXQ0qVL67oE2NnZib9xioiIiKi4Klan44mIiIjo08AQSkRERESyYwglIiIiItkxhBIRERGR7BhCiYiIiEh2DKFEREREJDuGUCIiIiKSHUMoEREREcmOIZSIiIiIZMcQSkRERESyYwglIiIiItkxhBIRERGR7BhCiYiIiEh2DKFEREREJDuGUCIiIiKSHUMoEREREcmOIZSIiIiIZMcQSkRERESyYwglIiIiItkxhBIRERGR7BhCiYiIiEh2DKFEREREJDuGUCIiIiKSHUMoEREREcmOIZSIiIiIZMcQSkRERESyYwglIiIiItkxhBIRERGR7BhCiYiIiEh2DKFEREREJDuGUCIiIiKSHUMoEREREcmOIZSIiIiIZMcQSkRERESyYwglIiIiItkxhBIRERGR7BhCiYhIwtXVFQqFQutr5MiRAIDQ0FC0a9cOJUuWhEKhQHR0dLbriYyMxOeffw5TU1OUKFECHh4eePHihbj83LlzmDlzJmxtbWFjY4Ovv/4az58/F5c/fvwYnp6ecHR0hEqlgpOTE0aNGoXk5OR31p+WlobRo0ejZMmSMDU1RadOnXDnzp2PbwwR5SsDXRdARUfD+QeRYWCq6zJ0TqUvYFEDwC1wH9IyFbouR+fYj9cKQy9iF3jj9OnTyMzMFMcuXryINm3a4IsvvgAApKSkoGnTpvjiiy8wZMiQbNcTGRkJT09PTJkyBcHBwTA0NMT58+ehp/fq2Ed8fDw8PT3RsGFDbNq0CS9evIC/vz98fX3xxx9/AAD09PTQuXNnzJkzB7a2trh27RpGjhyJxMRE/Prrrznug7+/P3bv3o0tW7bAxsYG48aNQ4cOHRAVFQV9ff38ahURfSSGUCIikrC1tZW8XrBgAcqXLw93d3cAQL9+/QAAsbGxOa5j7Nix8PPzw+TJk8WxihUrin/es2cPlEolvv76a1SuXBlKpRI//fQTateujWvXrqFChQqwsrLC8OHDxfe4uLhgxIgRWLx4cY7bTUpKwtq1a7Fx40a0bt0aALBp0yY4OTnhwIEDaNeuXe4bQUQFiqfjiYgoR+np6di0aRMGDRoEhSJ3R28fPHiAkydPws7ODk2aNIG9vT3c3d1x7NgxcU5aWhoMDQ3FI6MAYGxsDACSeW+Kj49HaGioGIazExUVBbVajbZt24pjjo6OcHNzw/Hjx3NVPxHJgyG0mAgPD0ezZs1QokQJ2NjYoEOHDrh+/ToAIDAwMNvrv0JCQnRbNBHp3M6dO/H06VP4+vrm+j03btwA8Op7y5AhQxAeHo46deqgVatW+O+//wAAn3/+ORISErBjxw6kp6fjyZMn+OabbwAA9+7dk6yvd+/eMDExQenSpWFhYYGff/45x20nJCTA0NAQVlZWknF7e3skJCTkeh+IqODxdHwxkZKSgoCAAFSvXh0pKSmYMWMGunbtiujoaIwfPx7Dhg0T527evBkzZsxAvXr1sl1XWloa0tLSxNdZNwmo9ATo6wsFuyOFgEpPkPy3uGM/XisMvVCr1ZLXP//8M9q1awdbW1utZVmv1Wq1ZFl6ejoA4KuvvkLfvn0BAIsWLcKBAwewZs0azJ07F5UqVcLq1asREBCATZs2QV9fH6NGjYK9vb1WHYsWLcI333yDf//9F9OnT4e/vz+Cg4OzrT8jIyPb/dBoNBAEQWv8U/JmP4n9eFNB9ULXvWUILSa6d+8ueb127VrY2dnh8uXLcHNzg5mZGQDgxIkTmDZtGtavXw83N7ds1zV//nzMmjVLa3xabQ1MTDKzeUfx9G09ja5L+KSwH699yr0ICwsT//zgwQMcPHgQkyZNkoxnuX//PoBXp8/j4+O1xtPT0yXvs7S0xMmTJ8Uxa2trhISE4OnTp1CpVFAoFAgKCsKTJ0+y3Z6BgQH69euHb775Bg0bNoS1tbXWnFu3biE9PR2///67+H0NAK5fv46SJUtmu95PTUREhK5L+KSwH6/ldy9SU1PzdX0fiiG0mLh+/TqmT5+OEydO4NGjR9BoXn0IxsXFiWEzLi4OXbp0wfjx4+Hj45PjuqZMmYKAgADxdXJyMpycnDDnnB4ylLzzVKUn4Nt6Gkw/o4c0zad5B7Sc2I/XCkMvLga+vnFn9uzZsLOzw/Tp02FgoP1xkXVjUrNmzVCrVi1xXBAEzJo1C8bGxvDy8hLHZ86ciXbt2oljarUaERER+OKLL6BUKhESEgIjIyNMmDABJUqUyLY+CwsLcZuurq5ay5s2bYpvv/0WCoVC3M69e/cQFxeHZcuWSa4V/dRk9aNNmzZQKpW6Lkfn2I/XCqoX73vcWUFjCC0mOnbsCCcnJ6xZswaOjo7QaDRwc3MTT5ulpKSgU6dOaNy4MWbPnv3OdalUKqhUKq3xNI0CGZ/oY2d0IU2j+GQfw6ML7Mdrn3Ivsj7gNBoNNmzYgAEDBog3DGVJTExEXFycePTzxo0bUCqVcHBwgIODAwBgwoQJmDlzJurUqYNatWph/fr1iImJwfbt28VtLF++HJmZmahQoQIOHz6MCRMmYMGCBeLd+WFhYbh//z7q168PMzMzXL58GRMnTkTTpk3FO+3v3r2LVq1aYcOGDWjQoAFKliyJwYMHY9KkSbC3t4e1tTXGjx+P6tWrw9PTs1A8okmpVBb70PUm9uO1/O6FrvvKEFoMPH78GFeuXMGqVavQvHlzANK7TwVBQN++faHRaLBx48Zc3wFLREXXgQMHEBcXh0GDBmkt27VrFwYOHCi+7tWrF4BXRzoDAwMBvHpW58uXLzF27FgkJiaiZs2aiIiIQPny5cX3nT59Grt27cKUKVNQpUoVrFq1Snz8E/Dqbvk1a9Zg7NixSEtLg5OTE7p16yZ57JNarUZMTIzktOKSJUtgYGAAHx8fvHjxAq1atUJISEihCKBExQlDaDFgZWUFGxsbrF69GqVKlUJcXJzkm3hgYCAOHDiA/fv34/nz5+JvLLG0tNQ6AkJExUPbtm0hCNnfQOXr65uru+UnT54s+V7ztnXr1iEsLAxeXl7ZHpFp2bLlex+r5OrqqlWnkZERgoODc7x5iYg+DQyhxYCenh62bNkCPz8/uLm5oXLlyvjxxx/h4eEBADhy5AieP3+OJk2aSN63bt26D3osy8kprWBjY5OPlRdOarUaYWFhuBjYTuenOj4F7Mdr7AUR0WsMocVE69atcfnyZclY1tGDLl266KAiIiIiKs74sHoiIiIikh1DKBERERHJjiGUiIiIiGTHEEpEREREsmMIJSIiIiLZMYQSERERkewYQomIiIhIdgyhRERERCQ7hlAiIiIikh1DKBERERHJjiGUiIiIiGTHEEpEREREsmMIJSIiIiLZMYQSERERkewYQomIiIhIdgyhRERERCQ7hlAiIiIikh1DKBERERHJjiGUiIiIiGTHEEpEREREsmMIJSIiIiLZMYQSERERkewYQomIiIhIdgyhRERERCQ7hlAiIiIikh1DKBERERHJjiGUiIiIiGTHEEpEREREsmMIJSIiIiLZMYQSERERkewYQomIiIhIdgyhRERERCQ7hlAiIiIikh1DKBERERHJjiGUiIiIiGTHEEpEREREsmMIJSIiIiLZMYQSERERkewYQomIiIhIdgyhRERERCQ7hlAiwooVK1CjRg1YWFjAwsICjRs3xt69ewEAarUakyZNQvXq1WFqagpHR0f0798f8fHxWuuJjIzE559/DlNTU5QoUQIeHh548eKF1ry0tDTUqlULCoUC0dHR4nhISAgUCkW2Xw8ePMix/rS0NIwePRolS5aEqakpOnXqhDt37nx8Y4iIqMAY6LoA0q3AwEDs3LlTEgTyquH8g8gwMP34ogo5lb6ARQ0At8B9SMtU6Lqc94pd4I0yZcpgwYIFqFChAgBg/fr16Ny5M86dO4cyZcrg7NmzmD59OmrWrIknT57A398fnTp1wpkzZ8T1REZGwtPTE1OmTEFwcDAMDQ1x/vx56Olp/6w7ceJEODo64vz585Lxnj17wtPTUzLm6+uLly9fws7OLsd98Pf3x+7du7FlyxbY2Nhg3Lhx6NChA6KioqCvr/8x7SEiogLCEFqMKBQK7NixA126dNF1KfSJ6dixo+T13LlzsWLFCpw4cQKDBw9GRESEZHlwcDAaNGiAuLg4ODs7AwDGjh0LPz8/TJ48WZxXsWJFqNVqyXv37t2L/fv3Y/v27eLR1izGxsYwNjYWXz98+BCHDh3C2rVrc6w9KSkJa9euxcaNG9G6dWsAwKZNm+Dk5IQDBw6gXbt2H9AJIiKSC0/HE5FEZmYmtmzZgpSUFDRu3DjbOUlJSVAoFChRogQA4MGDBzh58iTs7OzQpEkT2Nvbw93dHceOHZO87/79+xgyZAg2btwIExOT99ayYcMGmJiYoEePHjnOiYqKglqtRtu2bcUxR0dHuLm54fjx47nYYyIi0gWGUB3w8PDA6NGj4e/vDysrK9jb22P16tVISUnBwIEDYW5ujvLly0uOEh05cgQNGjSASqVCqVKlMHnyZGRkZEjW6efnh4kTJ8La2hoODg4IDAwUl7u6ugIAunbtCoVCIb7OsnHjRri6usLS0hK9evXCs2fPCrIF9Am6cOECzMzMoFKpMGzYMOzYsQNVq1bVmvfy5UtMnjwZffr0gYWFBQDgxo0bAF5d3jFkyBCEh4ejTp06aNWqFf777z8AgCAI8PX1xbBhw1CvXr1c1fTLL7+gT58+kqOjb0tISIChoSGsrKwk4/b29khISMjVdoiISH48Ha8j69evx8SJE3Hq1Cls3boVw4cPx86dO9G1a1d88803WLJkCfr164e4uDg8efIEXl5e8PX1xYYNG3D16lUMGTIERkZGkqC5fv16BAQE4OTJk4iMjISvry+aNm2KNm3a4PTp07Czs8O6devg6ekpuU7u+vXr2LlzJ/bs2YMnT57Ax8cHCxYswNy5c7OtPS0tDWlpaeLr5ORkAIBKT4C+vlAwDStEVHqC5L+fuqzT5eXKlcPp06eRlJSE0NBQDBgwAAcOHJAEUbVajV69eiEzMxNLly4V35ueng4A+Oqrr9C3b18AwKJFi3DgwAH88ssvaN68OX788UckJSVh/PjxUKvV4nvf/PObTpw4gcuXL+OXX37JdnmWrB/G3p6j0WggCMI73yu3N/eZ2I+3sR9S7MdrBdULXfdWIQhC4fikLEI8PDyQmZmJo0ePAnh1+tPS0hLdunXDhg0bALw6ulOqVClERkZi9+7d2L59O65cuQKF4tWNLsuXL8ekSZOQlJQEPT09rXUCQIMGDfD5559jwYIFALK/JjQwMBCLFy9GQkICzM3NAby6aeSvv/7CiRMnsq0/MDAQs2bN0hr/9ddfc3WKlQqHGTNmwMHBASNGjADwKuwtXrwY9+/fx+zZs8WjoMCr0+xDhw6Fv78/PDw8xPHFixdDX18fAQEBmDdvnuRGJuBVUNTT04O7uzvGjBkjWRYcHIwbN25gyZIl76zzn3/+wYwZM7Bp0yaYmZmJ4/7+/mjYsCF69+6d1xYQERVpqamp6NOnD5KSkiTf0+XCI6E6UqNGDfHP+vr6sLGxQfXq1cUxe3t7AK+utbty5QoaN24sBlAAaNq0KZ4/f447d+6IN4a8uU4AKFWq1Dsfa5PF1dVVDKC5ed+UKVMQEBAgvk5OToaTkxPmnNNDhpJ3Iqv0BHxbT4PpZ/SQpvn0746/GJj9jTtLly6Fvb09vLy8oFar0bt3bzx79gx///03bG1tJXMFQcCsWbNgbGwMLy8vcXzmzJlo06YNgFfXd6amporL7t27B29vb/z6669o0KABypQpIy57/vw5+vbtizlz5kjWl52mTZvi22+/hUKhEOfeu3cPcXFxWLZsmeRaUV1Tq9WIiIhAmzZtoFQqdV2OzrEfUuyHFPvxWkH1IutMpq4whOrI2/+IFAqFZCwrcGadUnwzgAKvPvTfnJfTOjUaTZ5qedf7VCoVVCqV1niaRoGMQvBIIrmkaRSF4hFNSqUS33zzDdq3bw8nJyc8e/YMW7ZswZEjRxAeHg6FQoHevXvj7Nmz2LNnD/T09PD48WMAgLW1NQwNDQEAEyZMwMyZM1GnTh3UqlUL69evR0xMDLZs2YKYmBiUK1dO8m8t6xrOypUro2zZspKaQkNDkZGRgf79+2v9+7x79y5atWqFDRs2oEGDBihZsiQGDx6MSZMmwd7eHtbW1hg/fjyqV6+udenJp0KpVBb7D9U3sR9S7IcU+/FafvdC131lCC0Eqlatiu3bt0vC6PHjx2Fubo7SpUvnej1KpRKZmZkFVSYVYvfv30e/fv1w7949WFpaokaNGggPD0ebNm0QGxuLXbt2AQBq1aoled+ff/4pnn739/fHy5cvMXbsWCQmJqJmzZqIiIhA+fLlERMT80H1rF27Ft26ddO62Qh4dUQgJiZGclR1yZIlMDAwgI+PD168eIFWrVohJCTkkwygRET0CkNoITBixAgEBQVh9OjRGDVqFGJiYjBz5kwEBARk+yDwnLi6uuLgwYNo2rQpVCpVth/wVDy96zmcrq6uyO2l45MnT5Y8JxTI+cL3d633XY9Wyu59RkZGCA4ORnBwcK7qJCIi3WMILQRKly6NsLAwTJgwATVr1oS1tTUGDx6MadOmfdB6vv/+ewQEBGDNmjUoXbo0YmNj87XOk1NawcbGJl/XWRip1WqEhYXhYmA7nZ/qICIi+lQxhOrA4cOHtcayC4RvHu1xd3fHqVOnPmidO3fulLzu2LGj1m/GCQwMlDzmCXh1WtXf3z/HbRERERF9LD6snoiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYGui6ACj9BEAAAz549g1Kp1HE1uqdWq5Gamork5GT2A+zHm9gLKfZDiv2QYj9eK6heJCcnA3j9OS43hlD6aI8fPwYAlC1bVseVEBER0Yd69uwZLC0tZd8uQyh9NGtrawBAXFycTv4Rf2qSk5Ph5OSE27dvw8LCQtfl6Bz78Rp7IcV+SLEfUuzHawXVC0EQ8OzZMzg6OubbOj8EQyh9ND29V5cWW1paFvtvFG+ysLBgP97AfrzGXkixH1LshxT78VpB9EKXB494YxIRERERyY4hlIiIiIhkxxBKH02lUmHmzJlQqVS6LuWTwH5IsR+vsRdS7IcU+yHFfrxWVHuhEHR1Xz4RERERFVs8EkpEREREsmMIJSIiIiLZMYQSERERkewYQomIiIhIdgyh9FGWL1+OsmXLwsjICHXr1sXRo0d1XdIHmz9/PurXrw9zc3PY2dmhS5cuiImJkcwRBAGBgYFwdHSEsbExPDw8cOnSJcmctLQ0jB49GiVLloSpqSk6deqEO3fuSOY8efIE/fr1g6WlJSwtLdGvXz88ffpUMicuLg4dO3aEqakpSpYsCT8/P6SnpxfIvr/P/PnzoVAo4O/vL44Vt17cvXsXffv2hY2NDUxMTFCrVi1ERUWJy4tTPzIyMjBt2jSULVsWxsbGKFeuHGbPng2NRiPOKcr9+Ouvv9CxY0c4OjpCoVBg586dkuWf2r5fuHAB7u7uMDY2RunSpTF79ux8+x3h7+qFWq3GpEmTUL16dZiamsLR0RH9+/dHfHx8kezF+/rxtqFDh0KhUCAoKEgyXpT6kWsCUR5t2bJFUCqVwpo1a4TLly8LY8aMEUxNTYVbt27purQP0q5dO2HdunXCxYsXhejoaMHb21twdnYWnj9/Ls5ZsGCBYG5uLmzfvl24cOGC0LNnT6FUqVJCcnKyOGfYsGFC6dKlhYiICOHs2bNCy5YthZo1awoZGRniHE9PT8HNzU04fvy4cPz4ccHNzU3o0KGDuDwjI0Nwc3MTWrZsKZw9e1aIiIgQHB0dhVGjRsnTjDecOnVKcHV1FWrUqCGMGTNGHC9OvUhMTBRcXFwEX19f4eTJk8LNmzeFAwcOCNeuXRPnFKd+zJkzR7CxsRH27Nkj3Lx5U9i2bZtgZmYmBAUFiXOKcj/CwsKEqVOnCtu3bxcACDt27JAs/5T2PSkpSbC3txd69eolXLhwQdi+fbtgbm4ufPfddwXei6dPnwqtW7cWtm7dKly9elWIjIwUGjZsKNStW1eyjqLSi/f14007duwQatasKTg6OgpLliwpsv3ILYZQyrMGDRoIw4YNk4xVqVJFmDx5so4qyh8PHjwQAAhHjhwRBEEQNBqN4ODgICxYsECc8/LlS8HS0lJYuXKlIAivvukqlUphy5Yt4py7d+8Kenp6Qnh4uCAIgnD58mUBgHDixAlxTmRkpABAuHr1qiAIr76R6enpCXfv3hXn/Pbbb4JKpRKSkpIKbqff8uzZM6FixYpCRESE4O7uLobQ4taLSZMmCc2aNctxeXHrh7e3tzBo0CDJWLdu3YS+ffsKglC8+vF20PjU9n358uWCpaWl8PLlS3HO/PnzBUdHR0Gj0eRjJ7R7kZ1Tp04JAMSDFEW1F4KQcz/u3LkjlC5dWrh48aLg4uIiCaFFuR/vwtPxlCfp6emIiopC27ZtJeNt27bF8ePHdVRV/khKSgIAWFtbAwBu3ryJhIQEyb6qVCq4u7uL+xoVFQW1Wi2Z4+joCDc3N3FOZGQkLC0t0bBhQ3FOo0aNYGlpKZnj5uYGR0dHcU67du2QlpYmOQVc0EaOHAlvb2+0bt1aMl7cerFr1y7Uq1cPX3zxBezs7FC7dm2sWbNGXF7c+tGsWTMcPHgQ//77LwDg/PnzOHbsGLy8vAAUv3686VPb98jISLi7u0sebt6uXTvEx8cjNjY2/xvwHklJSVAoFChRogSA4tcLjUaDfv36YcKECahWrZrW8uLWjywMoZQnjx49QmZmJuzt7SXj9vb2SEhI0FFVH08QBAQEBKBZs2Zwc3MDAHF/3rWvCQkJMDQ0hJWV1Tvn2NnZaW3Tzs5OMuft7VhZWcHQ0FC2vm7ZsgVnz57F/PnztZYVt17cuHEDK1asQMWKFbFv3z4MGzYMfn5+2LBhg1gjUHz6MWnSJPTu3RtVqlSBUqlE7dq14e/vj969e4s1AsWnH2/61PY9uzlZr+Xuz8uXLzF58mT06dMHFhYWYg3FqRcLFy6EgYEB/Pz8sl1e3PqRxUDWrVGRo1AoJK8FQdAaK0xGjRqFf/75B8eOHdNalpd9fXtOdvPzMqeg3L59G2PGjMH+/fthZGSU47zi0Avg1dGLevXqYd68eQCA2rVr49KlS1ixYgX69++fY51FtR9bt27Fpk2b8Ouvv6JatWqIjo6Gv78/HB0dMWDAgBzrLKr9yM6ntO/Z1ZLTewuKWq1Gr169oNFosHz58vfOL4q9iIqKwtKlS3H27NkP3l5R7MebeCSU8qRkyZLQ19fX+qnpwYMHWj9hFRajR4/Grl278Oeff6JMmTLiuIODAwDtnxDf3FcHBwekp6fjyZMn75xz//59re0+fPhQMuft7Tx58gRqtVqWvkZFReHBgweoW7cuDAwMYGBggCNHjuDHH3+EgYFBjj8tF8VeAECpUqVQtWpVydhnn32GuLg4sUag+PRjwoQJmDx5Mnr16oXq1aujX79+GDt2rHjUvLj1402f2r5nN+fBgwcAtI/WFhS1Wg0fHx/cvHkTERER4lHQrPqKSy+OHj2KBw8ewNnZWfy+euvWLYwbNw6urq5ijcWlH29iCKU8MTQ0RN26dRERESEZj4iIQJMmTXRUVd4IgoBRo0YhNDQUhw4dQtmyZSXLy5YtCwcHB8m+pqen48iRI+K+1q1bF0qlUjLn3r17uHjxojincePGSEpKwqlTp8Q5J0+eRFJSkmTOxYsXce/ePXHO/v37oVKpULdu3fzf+be0atUKFy5cQHR0tPhVr149fPnll4iOjka5cuWKTS8AoGnTplqP6/r333/h4uICoHj92wCA1NRU6OlJPzb09fXFRzQVt3686VPb98aNG+Ovv/6SPJpn//79cHR0FINPQcoKoP/99x8OHDgAGxsbyfLi1It+/frhn3/+kXxfdXR0xIQJE7Bv3z4AxasfEgV/7xMVVVmPaFq7dq1w+fJlwd/fXzA1NRViY2N1XdoHGT58uGBpaSkcPnxYuHfvnviVmpoqzlmwYIFgaWkphIaGChcuXBB69+6d7aNXypQpIxw4cEA4e/as8Pnnn2f7eI0aNWoIkZGRQmRkpFC9evVsH6/RqlUr4ezZs8KBAweEMmXK6OQRTVnevDteEIpXL06dOiUYGBgIc+fOFf777z9h8+bNgomJibBp0yZxTnHqx4ABA4TSpUuLj2gKDQ0VSpYsKUycOFGcU5T78ezZM+HcuXPCuXPnBADCDz/8IJw7d0684/tT2venT58K9vb2Qu/evYULFy4IoaGhgoWFRb49huddvVCr1UKnTp2EMmXKCNHR0ZLvq2lpaUWuF+/rR3bevju+qPUjtxhC6aP89NNPgouLi2BoaCjUqVNHfKxRYQIg269169aJczQajTBz5kzBwcFBUKlUQosWLYQLFy5I1vPixQth1KhRgrW1tWBsbCx06NBBiIuLk8x5/Pix8OWXXwrm5uaCubm58OWXXwpPnjyRzLl165bg7e0tGBsbC9bW1sKoUaMkj9KQ29shtLj1Yvfu3YKbm5ugUqmEKlWqCKtXr5YsL079SE5OFsaMGSM4OzsLRkZGQrly5YSpU6dKgkVR7seff/6Z7feKAQMGfJL7/s8//wjNmzcXVCqV4ODgIAQGBubbI3je1YubN2/m+H31zz//LHK9eF8/spNdCC1K/cgthSDo4hH5RERERFSc8ZpQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIqJCztfXFwqFQuvr2rVrui6NiChHBrougIiIPp6npyfWrVsnGbO1tdVRNVJqtRpKpVLXZRDRJ4ZHQomIigCVSgUHBwfJl76+frZzb926hY4dO8LKygqmpqaoVq0awsLCxOWXLl2Ct7c3LCwsYG5ujubNm+P69esAAI1Gg9mzZ6NMmTJQqVSoVasWwsPDxffGxsZCoVDg999/h4eHB4yMjLBp0yYAwLp16/DZZ5/ByMgIVapUwfLlywuwI0T0qeORUCKiYmbkyJFIT0/HX3/9BVNTU1y+fBlmZmYAgLt376JFixbw8PDAoUOHYGFhgb///hsZGRkAgKVLl+L777/HqlWrULt2bfzyyy/o1KkTLl26hIoVK4rbmDRpEr7//nusW7cOKpUKa9aswcyZM7Fs2TLUrl0b586dw5AhQ2BqaooBAwbopA9EpFsKQRAEXRdBRER55+vri02bNsHIyEgca9++PbZt25bt/Bo1aqB79+6YOXOm1rJvvvkGW7ZsQUxMTLan0EuXLo2RI0fim2++EccaNGiA+vXr46effkJsbCzKli2LoKAgjBkzRpzj7OyMhQsXonfv3uLYnDlzEBYWhuPHj+dpv4mocOORUCKiIqBly5ZYsWKF+NrU1DTHuX5+fhg+fDj279+P1q1bo3v37qhRowYAIDo6Gs2bN882gCYnJyM+Ph5NmzaVjDdt2hTnz5+XjNWrV0/888OHD3H79m0MHjwYQ4YMEcczMjJgaWn5YTtKREUGQygRURFgamqKChUq5GruV199hXbt2uF///sf9u/fj/nz5+P777/H6NGjYWxs/N73KxQKyWtBELTG3gzBGo0GALBmzRo0bNhQMi+n61aJqOjjjUlERMWQk5MThg0bhtDQUIwbNw5r1qwB8OpU/dGjR6FWq7XeY2FhAUdHRxw7dkwyfvz4cXz22Wc5bsve3h6lS5fGjRs3UKFCBclX2bJl83fHiKjQ4JFQIqJixt/fH+3bt0elSpXw5MkTHDp0SAyRo0aNQnBwMHr16oUpU6bA0tISJ06cQIMGDVC5cmVMmDABM2fORPny5VGrVi2sW7cO0dHR2Lx58zu3GRgYCD8/P1hYWKB9+/ZIS0vDmTNn8OTJEwQEBMix20T0iWEIJSIqZjIzMzFy5EjcuXMHFhYW8PT0xJIlSwAANjY2OHToECZMmAB3d3fo6+ujVq1a4nWgfn5+SE5Oxrhx4/DgwQNUrVoVu3btktwZn52vvvoKJiYmWLx4MSZOnAhTU1NUr14d/v7+Bb27RPSJ4t3xRERERCQ7XhNKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHYMoUREREQkO4ZQIiIiIpIdQygRERERyY4hlIiIiIhkxxBKRERERLJjCCUiIiIi2TGEEhEREZHsGEKJiIiISHb/D0ly5RD8ReE9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(booster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we can see which features are most important for our XGBoost model. Later we can compare those to the feature importance in the Random Forest model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's run the model using 2022 data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict using model\n",
    "y_pred = booster.inplace_predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a results dataframe\n",
    "result_df = X_test.copy()\n",
    "\n",
    "# merge orginal DF, marking duplicate columns and then dropping them\n",
    "result_df = result_df.merge(df, left_index=True, right_index=True, suffixes=('', '_drop'))\n",
    "result_df.drop(result_df.filter(regex='_drop$').columns, axis=1, inplace=True)\n",
    "\n",
    "# adding CS-normalized quantiles/percentiles, and then un-normalizing them to get power\n",
    "for i in range(len(percentile_list)):\n",
    "    result_df[str(percentile_list[i])+'_csi']=y_pred[:,i]\n",
    "for i in range(len(percentile_list)):\n",
    "    result_df[str(percentile_list[i])+'_p_ac']=y_pred[:,i]*result_df['p_ac_all_cs']\n",
    "\n",
    "# fill in overnight \n",
    "result_df = result_df.reset_index().set_index('time_center_labeled').asfreq('1h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a helper function that calculates coverage fraction, also referred to as prediction interval coverage probability (PICP). It is the fraction of time that observations fall within a prediction interval. \n",
    "\n",
    "We can use this to evaluate how well calibrated our model is. For example, we want 90% of all observations to fall within the 90% PI (a coverage fraction of 90%). If only 85% of observations fall in the 90% PI, a coverage fraction of 85%, then our model is \"underdispersed\" and overconfident. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html\n",
    "def coverage_fraction(y, y_low, y_high):\n",
    "    return np.mean(np.logical_and(y >= y_low, y <= y_high))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate coverage fraction and NMAE of the central forecast:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target prediction intervals:\n",
      "[0.9995, 0.999, 0.99, 0.96, 0.57]\n",
      "resulting coverage fraction (rounded to 3 places):\n",
      "['0.992', '0.994', '0.983', '0.957', '0.560']\n",
      "original NMAE: 8.44%\n",
      "adjusted NMAE: 7.03%\n"
     ]
    }
   ],
   "source": [
    "# coverage fraction and error:\n",
    "cf = {}\n",
    "for i in range(num_intervals):\n",
    "    cf[i] = coverage_fraction(y_test,\n",
    "                              y_pred[:,i],\n",
    "                              y_pred[:,len(percentile_list)-i-1]\n",
    "    )\n",
    "\n",
    "print('target prediction intervals:')\n",
    "print(pi_list)\n",
    "print('resulting coverage fraction (rounded to 3 places):')\n",
    "print([ '%.3f' % elem for elem in list(cf.values())])\n",
    "orig_nmae = filtered_df['norm_abs_err'].mean()\n",
    "print('original NMAE: ' + \"{:.2f}\".format(orig_nmae*100) + '%')\n",
    "adjusted_nmae = (result_df[str(percentile_list[int((len(percentile_list)-1)/2)])+'_p_ac'] - result_df['p_ac_all_act']).abs().mean()/approx_nameplate\n",
    "print('adjusted NMAE: ' + \"{:.2f}\".format(adjusted_nmae*100) + '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a noteable improvement in NMAE, and our coverage fraction is pretty good, at least below the highest PI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also look at the sharpness, also known as the prediction interval normalized average width (PINAW). This gives us an idea of how much skill our forecast has. For example, very good coverage fraction can be achieved be simply summarizing historical statistics and using the same prediction intervals every day, but that would result in a forecast with excessivly wide prediction intervals that are not \"sharp\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target PI : resulting CF : width\n",
      "0.9995 : 0.992 : 0.6745222856713913\n",
      "0.999 : 0.994 : 0.6570725027385858\n",
      "0.99 : 0.983 : 0.48219303075377395\n",
      "0.96 : 0.957 : 0.3767447675847643\n",
      "0.57 : 0.560 : 0.14063316005117266\n",
      "for zenith < 75:\n",
      "0.9995 : 0.997 : 0.7818396873533291\n",
      "0.999 : 0.999 : 0.7611906962148535\n",
      "0.99 : 0.987 : 0.5609222253955345\n",
      "0.96 : 0.958 : 0.43995249034942363\n",
      "0.57 : 0.557 : 0.16381326775495783\n"
     ]
    }
   ],
   "source": [
    "# sharpness\n",
    "print('target PI : resulting CF : width')\n",
    "for i in range(num_intervals):\n",
    "    lower = result_df[str(percentile_list[i])+'_p_ac'] # lower\n",
    "    upper = result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'] # upper\n",
    "    print(str(pi_list[i]), \":\", \"{:.3f}\".format(list(cf.values())[i]), \":\", (upper-lower).mean()/approx_nameplate)\n",
    "\n",
    "print('for zenith < 75:')\n",
    "\n",
    "mask_zen = (result_df['zen'] < 75) \n",
    "\n",
    "cf_mask = {}\n",
    "for i in range(num_intervals):\n",
    "    cf_mask[i] = coverage_fraction(result_df['p_ac_all_act'][mask_zen],\n",
    "                              result_df[str(percentile_list[i])+'_p_ac'][mask_zen],\n",
    "                              result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask_zen]\n",
    "    )\n",
    "\n",
    "for i in range(num_intervals):\n",
    "    lower = result_df[str(percentile_list[i])+'_p_ac'][mask_zen] # lower\n",
    "    upper = result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask_zen] # upper\n",
    "    print(str(pi_list[i]), \":\", \"{:.3f}\".format(list(cf_mask.values())[i]), \":\", (upper-lower).mean()/approx_nameplate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try that XGBoost model but with 2023 test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup 2023 test\n",
    "X=filtered_df[['tcc_std']].copy()\n",
    "X['max_min_norm'] = filtered_df['max_min_norm']\n",
    "X['csi_fcast'] = filtered_df['csi_fcast']\n",
    "X['zen'] = filtered_df['zen']\n",
    "X['az'] = filtered_df['az']\n",
    "X['month'] = filtered_df['month']\n",
    "# X['hour'] = filtered_df['hour']\n",
    "# X['el'] = filtered_df['el']\n",
    "\n",
    "y = filtered_df['csi_actual']\n",
    "\n",
    "X_test = X[X.index.year == 2023]\n",
    "y_test = y[X.index.year == 2023]\n",
    "\n",
    "# predict using model\n",
    "y_pred = booster.inplace_predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a results dataframe\n",
    "result_df = X_test.copy()\n",
    "\n",
    "# merge orginal DF, marking duplicate columns and then dropping them\n",
    "result_df = result_df.merge(df, left_index=True, right_index=True, suffixes=('', '_drop'))\n",
    "result_df.drop(result_df.filter(regex='_drop$').columns, axis=1, inplace=True)\n",
    "\n",
    "# adding CS-normalized quantiles/percentiles, and then un-normalizing them to get power\n",
    "for i in range(len(percentile_list)):\n",
    "    result_df[str(percentile_list[i])+'_csi']=y_pred[:,i]\n",
    "for i in range(len(percentile_list)):\n",
    "    result_df[str(percentile_list[i])+'_p_ac']=y_pred[:,i]*result_df['p_ac_all_cs']\n",
    "\n",
    "# fill in overnight \n",
    "result_df = result_df.reset_index().set_index('time_center_labeled').asfreq('1h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And look at coverage fraction and error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target prediction intervals:\n",
      "[0.9995, 0.999, 0.99, 0.96, 0.57]\n",
      "resulting coverage fraction (rounded to 3 places):\n",
      "['0.968', '0.966', '0.948', '0.904', '0.504']\n",
      "original NMAE: 8.44%\n",
      "adjusted NMAE: 7.96%\n"
     ]
    }
   ],
   "source": [
    "# coverage fraction and error:\n",
    "cf = {}\n",
    "for i in range(num_intervals):\n",
    "    cf[i] = coverage_fraction(y_test,\n",
    "                              y_pred[:,i],\n",
    "                              y_pred[:,len(percentile_list)-i-1]\n",
    "    )\n",
    "\n",
    "print('target prediction intervals:')\n",
    "print(pi_list)\n",
    "print('resulting coverage fraction (rounded to 3 places):')\n",
    "print([ '%.3f' % elem for elem in list(cf.values())])\n",
    "orig_nmae = filtered_df['norm_abs_err'].mean()\n",
    "print('original NMAE: ' + \"{:.2f}\".format(orig_nmae*100) + '%')\n",
    "adjusted_nmae = (result_df[str(percentile_list[int((len(percentile_list)-1)/2)])+'_p_ac'] - result_df['p_ac_all_act']).abs().mean()/approx_nameplate\n",
    "print('adjusted NMAE: ' + \"{:.2f}\".format(adjusted_nmae*100) + '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And look at sharpness:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target PI : resulting CF : width\n",
      "0.9995 : 0.968 : 0.6769859621315966\n",
      "0.999 : 0.966 : 0.6596213092119021\n",
      "0.99 : 0.948 : 0.4955499212228397\n",
      "0.96 : 0.904 : 0.3915847757042059\n",
      "0.57 : 0.504 : 0.14469701104344537\n",
      "for zenith < 75:\n",
      "0.9995 : 0.976 : 0.785043224752403\n",
      "0.999 : 0.974 : 0.7644905438950393\n",
      "0.99 : 0.955 : 0.578042536105144\n",
      "0.96 : 0.907 : 0.4586268620065556\n",
      "0.57 : 0.508 : 0.16906880728973453\n"
     ]
    }
   ],
   "source": [
    "# sharpness\n",
    "print('target PI : resulting CF : width')\n",
    "for i in range(num_intervals):\n",
    "    lower = result_df[str(percentile_list[i])+'_p_ac'] # lower\n",
    "    upper = result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'] # upper\n",
    "    print(str(pi_list[i]), \":\", \"{:.3f}\".format(list(cf.values())[i]), \":\", (upper-lower).mean()/approx_nameplate)\n",
    "\n",
    "print('for zenith < 75:')\n",
    "\n",
    "mask_zen = (result_df['zen'] < 75) \n",
    "\n",
    "cf_mask = {}\n",
    "for i in range(num_intervals):\n",
    "    cf_mask[i] = coverage_fraction(result_df['p_ac_all_act'][mask_zen],\n",
    "                              result_df[str(percentile_list[i])+'_p_ac'][mask_zen],\n",
    "                              result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask_zen]\n",
    "    )\n",
    "\n",
    "for i in range(num_intervals):\n",
    "    lower = result_df[str(percentile_list[i])+'_p_ac'][mask_zen] # lower\n",
    "    upper = result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask_zen] # upper\n",
    "    print(str(pi_list[i]), \":\", \"{:.3f}\".format(list(cf_mask.values())[i]), \":\", (upper-lower).mean()/approx_nameplate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here are some plots of sample time ranges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask_start = '2023-10-13 11:00'\n",
    "mask_end = '2023-10-15'\n",
    "# mask_start = '2022-8-15 6:00'\n",
    "# mask_end = '2022-8-19 6:00'\n",
    "mask = (result_df.index >= mask_start) & (result_df.index <= mask_end)\n",
    "\n",
    "for i in range(num_intervals):\n",
    "    alph=0.1+(i/10.)\n",
    "    plt.fill_between(result_df.index[mask],# x\n",
    "                     result_df[str(percentile_list[i])+'_p_ac'][mask].fillna(0), # lower\n",
    "                     result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask].fillna(0), # upper\n",
    "                     color='blue',\n",
    "                     alpha=alph,\n",
    "                     step='mid',\n",
    "                     label=(str(pi_list[i]*100)+'%'))\n",
    "    \n",
    "plt.plot(result_df.index[mask],\n",
    "         result_df[str(percentile_list[int((len(percentile_list)-1)/2)])+'_p_ac'][mask],\n",
    "         label='XGB expected',color='brown', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index[mask],result_df['p_ac_all_act'][mask],label='actual',color='black', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index[mask],result_df['p_ac_all_fcast'][mask],label='DA fcast (HRRR coarse)',color='orange', drawstyle='steps-mid')\n",
    "plt.ylabel('AC Power (MW)')\n",
    "# plt.ylim([0,4500])\n",
    "plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.xlabel('Time, UTC (mm-dd HH)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask_start = '2023-1-7 11:00'\n",
    "mask_end = '2023-1-10'\n",
    "\n",
    "mask = (result_df.index >= mask_start) & (result_df.index <= mask_end)\n",
    "\n",
    "for i in range(num_intervals):\n",
    "    alph=0.1+(i/10.)\n",
    "    plt.fill_between(result_df.index[mask],# x\n",
    "                     result_df[str(percentile_list[i])+'_p_ac'][mask].fillna(0), # lower\n",
    "                     result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask].fillna(0), # upper\n",
    "                     color='blue',\n",
    "                     alpha=alph,\n",
    "                     step='mid',\n",
    "                     label=(str(pi_list[i]*100)+'%'))\n",
    "    \n",
    "plt.plot(result_df.index[mask],\n",
    "         result_df[str(percentile_list[int((len(percentile_list)-1)/2)])+'_p_ac'][mask],\n",
    "         label='XGB expected',color='brown', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index[mask],result_df['p_ac_all_act'][mask],label='actual',color='black', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index[mask],result_df['p_ac_all_fcast'][mask],label='DA fcast (HRRR coarse)',color='orange', drawstyle='steps-mid')\n",
    "plt.ylabel('AC Power (MW)')\n",
    "# plt.ylim([0,4500])\n",
    "plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.xlabel('Time, UTC (mm-dd HH)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df_XGB_2023 = result_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get slightly worse performance than we did for 2022. The \"boosting\" step may be slightly overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try a Random Forest Quantile Regressor. This one is published by Zillow. There's an alternative version published in Scikit-garden (https://scikit-garden.github.io/examples/QuantileRegressionForests/), but it is outdated and not compatible with some of the other package versions we have.\n",
    "\n",
    "We have a prediction interval list for the Random Forest model, `pi_list_rf`, setup in the same way as the PI list for the XGBoost model earlier, where they have been manually tuned to result in the target PI values that we want. We use the same features as well.\n",
    "\n",
    "Here, we can train with both 2021 and 2022, since we don't need to hold 2022 for the eval/boost step needed in XGBoost. We will exclude 2024-04-09 through 2024-04-12 again, as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\willh\\miniforge3\\envs\\pvlib_herbie_env3\\lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but RandomForestQuantileRegressor was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "pi_list_rf = [.995, .99, .98, .95, .5]\n",
    "pi_list = pi_list_rf\n",
    "\n",
    "percentile_list = [(1-x)/2 for x in pi_list] + [.5] + [1-(1-x)/2 for x in reversed(pi_list)]\n",
    "percentile_list = [round(elem, 6) for elem in percentile_list]\n",
    "num_intervals = len(pi_list)\n",
    "\n",
    "# setup test and training data\n",
    "X=filtered_df[['tcc_std']].copy()\n",
    "X['max_min_norm'] = filtered_df['max_min_norm']\n",
    "X['csi_fcast'] = filtered_df['csi_fcast']\n",
    "X['zen'] = filtered_df['zen']\n",
    "X['az'] = filtered_df['az']\n",
    "X['month'] = filtered_df['month']\n",
    "# X['hour'] = filtered_df['hour']\n",
    "# X['el'] = filtered_df['el']\n",
    "\n",
    "y = filtered_df['csi_actual']\n",
    "\n",
    "## year split\n",
    "X_train = X[(X.index.year >= 2021) & (X.index.year <= 2022) & ((X.index < '2021-04-09') | (X.index > '2021-04-13'))]\n",
    "y_train = y[(y.index.year >= 2021) & (y.index.year <= 2022) & ((X.index < '2021-04-09') | (X.index > '2021-04-13'))]\n",
    "X_test = X[X.index.year == 2023]\n",
    "y_test = y[X.index.year == 2023]\n",
    "\n",
    "qrf = RandomForestQuantileRegressor(n_estimators=200, criterion=\"absolute_error\",\n",
    "                                    min_samples_leaf=15, max_samples_leaf=18, max_features=0.5,\n",
    "                                    random_state=42)\n",
    "qrf.fit(X_train, y_train)\n",
    "y_pred = qrf.predict(X_test, quantiles=percentile_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a results dataframe\n",
    "result_df = X_test.copy()\n",
    "\n",
    "# merge orginal DF, marking duplicate columns and then dropping them\n",
    "result_df = result_df.merge(df, left_index=True, right_index=True, suffixes=('', '_drop'))\n",
    "result_df.drop(result_df.filter(regex='_drop$').columns, axis=1, inplace=True)\n",
    "\n",
    "# adding CS-normalized quantiles/percentiles, and then un-normalizing them to get power\n",
    "for i in range(len(percentile_list)):\n",
    "    result_df[str(percentile_list[i])+'_csi']=y_pred[:,i]\n",
    "for i in range(len(percentile_list)):\n",
    "    result_df[str(percentile_list[i])+'_p_ac']=y_pred[:,i]*result_df['p_ac_all_cs']\n",
    "\n",
    "# fill in overnight \n",
    "result_df = result_df.reset_index().set_index('time_center_labeled').asfreq('1h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target prediction intervals:\n",
      "[0.995, 0.99, 0.98, 0.95, 0.5]\n",
      "resulting coverage fraction (rounded to 3 places):\n",
      "['0.991', '0.989', '0.981', '0.954', '0.510']\n",
      "original NMAE: 8.44%\n",
      "adjusted NMAE: 7.79%\n"
     ]
    }
   ],
   "source": [
    "# coverage fraction and error:\n",
    "cf = {}\n",
    "for i in range(num_intervals):\n",
    "    cf[i] = coverage_fraction(y_test,\n",
    "                              y_pred[:,i],\n",
    "                              y_pred[:,len(percentile_list)-i-1]\n",
    "    )\n",
    "\n",
    "print('target prediction intervals:')\n",
    "print(pi_list)\n",
    "print('resulting coverage fraction (rounded to 3 places):')\n",
    "print([ '%.3f' % elem for elem in list(cf.values())])\n",
    "orig_nmae = filtered_df['norm_abs_err'].mean()\n",
    "print('original NMAE: ' + \"{:.2f}\".format(orig_nmae*100) + '%')\n",
    "adjusted_nmae = (result_df[str(percentile_list[int((len(percentile_list)-1)/2)])+'_p_ac'] - result_df['p_ac_all_act']).abs().mean()/approx_nameplate\n",
    "print('adjusted NMAE: ' + \"{:.2f}\".format(adjusted_nmae*100) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target PI : resulting CF : width\n",
      "0.995 : 0.991 : 0.5843910342961928\n",
      "0.99 : 0.989 : 0.5433779869985661\n",
      "0.98 : 0.981 : 0.49278786321970236\n",
      "0.95 : 0.954 : 0.40857402818441163\n",
      "0.5 : 0.510 : 0.126676315759715\n",
      "for zenith < 75:\n",
      "0.995 : 0.991 : 0.6741405583190401\n",
      "0.99 : 0.988 : 0.6267982838148387\n",
      "0.98 : 0.979 : 0.5685810182629771\n",
      "0.95 : 0.949 : 0.47087702539521986\n",
      "0.5 : 0.485 : 0.1459525204460232\n"
     ]
    }
   ],
   "source": [
    "# sharpness\n",
    "print('target PI : resulting CF : width')\n",
    "for i in range(num_intervals):\n",
    "    lower = result_df[str(percentile_list[i])+'_p_ac'] # lower\n",
    "    upper = result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'] # upper\n",
    "    print(str(pi_list[i]), \":\", \"{:.3f}\".format(list(cf.values())[i]), \":\", (upper-lower).mean()/approx_nameplate)\n",
    "\n",
    "print('for zenith < 75:')\n",
    "\n",
    "mask_zen = (result_df['zen'] < 75) \n",
    "\n",
    "cf_mask = {}\n",
    "for i in range(num_intervals):\n",
    "    cf_mask[i] = coverage_fraction(result_df['p_ac_all_act'][mask_zen],\n",
    "                              result_df[str(percentile_list[i])+'_p_ac'][mask_zen],\n",
    "                              result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask_zen]\n",
    "    )\n",
    "\n",
    "for i in range(num_intervals):\n",
    "    lower = result_df[str(percentile_list[i])+'_p_ac'][mask_zen] # lower\n",
    "    upper = result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask_zen] # upper\n",
    "    print(str(pi_list[i]), \":\", \"{:.3f}\".format(list(cf_mask.values())[i]), \":\", (upper-lower).mean()/approx_nameplate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that we don't _quite_ hit our most extreme PI, but it is very close. \n",
    "\n",
    "Now, let's look at feature importance for the RF model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.04805698 0.09983673 0.65129343 0.15776385 0.03248883 0.01056019]\n",
      "['tcc_std', 'max_min_norm', 'csi_fcast', 'zen', 'az', 'month']\n"
     ]
    }
   ],
   "source": [
    "print(qrf.feature_importances_)\n",
    "print(X_test.columns.to_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then normalize the feature weights from the XGBoost model to compare them. I _think_ that the XGBoost importance type of `gain` is closest to the feature importance metric for the Random Forest. Compare https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.Booster.get_score with https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.feature_importances_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'tcc_std': 0.03666995249029145, 'max_min_norm': 0.024108021278302298, 'csi_fcast': 0.8073030093123457, 'zen': 0.0695509175610769, 'az': 0.03906179433041965, 'month': 0.023306305027564003}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate normalized feature weights for the XGboost model\n",
    "booster_score_dict = booster.get_score(importance_type='gain')\n",
    "sum_of_weights = sum(booster.get_score(importance_type='gain').values())\n",
    "booster_normalized_scores_dict = {k:v/sum_of_weights for (k,v) in booster_score_dict.items()}\n",
    "\n",
    "print(booster_normalized_scores_dict)\n",
    "\n",
    "# Mak a plot\n",
    "width = 0.3\n",
    "x_labels = X_test.columns.to_list()\n",
    "length = len(x_labels)\n",
    "x = np.arange(length)\n",
    "\n",
    "plt.bar(x-.5*width,\n",
    "        qrf.feature_importances_,\n",
    "        width,\n",
    "        label = 'RF')\n",
    "plt.bar(x+.5*width,\n",
    "        booster_normalized_scores_dict.values(),\n",
    "        width,\n",
    "        label='XGB')\n",
    "plt.legend()\n",
    "# plt.set_xticklabels(x_labels)\n",
    "plt.xticks(x, x_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In both cases, the deterministic forecast is most important, followed by solar zenith angle, but for the Random Forest, normalized max-min is third, while for XGBoost standard deviation of weighted average TCC is third. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are plots for the same sample time ranges we used earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask_start = '2023-10-13 11:00'\n",
    "mask_end = '2023-10-15'\n",
    "\n",
    "mask = (result_df.index >= mask_start) & (result_df.index <= mask_end)\n",
    "\n",
    "for i in range(num_intervals):\n",
    "    alph=0.1+(i/10.)\n",
    "    plt.fill_between(result_df.index[mask],# x\n",
    "                     result_df[str(percentile_list[i])+'_p_ac'][mask].fillna(0), # lower\n",
    "                     result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask].fillna(0), # upper\n",
    "                     color='blue',\n",
    "                     alpha=alph,\n",
    "                     step='mid',\n",
    "                     label=(str(pi_list[i]*100)+'%'))\n",
    "    \n",
    "plt.plot(result_df.index[mask],\n",
    "         result_df[str(percentile_list[int((len(percentile_list)-1)/2)])+'_p_ac'][mask],\n",
    "         label='RF expected',color='brown', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index[mask],result_df['p_ac_all_act'][mask],label='actual',color='black', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index[mask],result_df['p_ac_all_fcast'][mask],label='DA fcast (HRRR coarse)',color='orange', drawstyle='steps-mid')\n",
    "plt.ylabel('AC Power (MW)')\n",
    "# plt.ylim([0,4500])\n",
    "plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.xlabel('Time, UTC (mm-dd HH)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask_start = '2023-1-7 11:00'\n",
    "mask_end = '2023-1-10'\n",
    "\n",
    "mask = (result_df.index >= mask_start) & (result_df.index <= mask_end)\n",
    "\n",
    "for i in range(num_intervals):\n",
    "    alph=0.1+(i/10.)\n",
    "    plt.fill_between(result_df.index[mask],# x\n",
    "                     result_df[str(percentile_list[i])+'_p_ac'][mask].fillna(0), # lower\n",
    "                     result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask].fillna(0), # upper\n",
    "                     color='blue',\n",
    "                     alpha=alph,\n",
    "                     step='mid',\n",
    "                     label=(str(pi_list[i]*100)+'%'))\n",
    "    \n",
    "plt.plot(result_df.index[mask],\n",
    "         result_df[str(percentile_list[int((len(percentile_list)-1)/2)])+'_p_ac'][mask],\n",
    "         label='RF expected',color='brown', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index[mask],result_df['p_ac_all_act'][mask],label='actual',color='black', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index[mask],result_df['p_ac_all_fcast'][mask],label='DA fcast (HRRR coarse)',color='orange', drawstyle='steps-mid')\n",
    "plt.ylabel('AC Power (MW)')\n",
    "# plt.ylim([0,4500])\n",
    "plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.xlabel('Time, UTC (mm-dd HH)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get especially good performance on clear days like Jan 7 2023.\n",
    "\n",
    "Let's hold on to the results DataFrame for use in a bit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df_RF_2023 = result_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And export our RF model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['regression_models/qrf_file.joblib']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(qrf, 'regression_models/qrf_file.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "qrf = joblib.load('regression_models/qrf_file.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's look at hybrid of RF and XGB. RF seems much better on clear days, and XGB seems better on overcast/partly cloudy days. Because we have good coverage for both, let's merge them to pick the narrowest of both.\n",
    "\n",
    "Because the RF seems to do better with the extremes, we will set the extreme values from the RF model. We will also pick the central value (P50) from the RF model, although it's a toss-up between the two in terms of NMAE. Other metrics may be useful in determining which is actually best for the central estimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create new hyrbid dataframe, starting with RF because PIs were accurate so we can keep column names\n",
    "result_df_hybrid_2023 = result_df_RF_2023.copy()\n",
    "\n",
    "# use PIs from RF model\n",
    "pi_list = pi_list_rf\n",
    "\n",
    "percentile_list = [(1-x)/2 for x in pi_list] + [.5] + [1-(1-x)/2 for x in reversed(pi_list)]\n",
    "percentile_list = [round(elem, 6) for elem in percentile_list]\n",
    "num_intervals = len(pi_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extreme lower bound from rf 0.0025_p_ac\n",
      "lower bounds 0.005_p_ac 0.0005_p_ac\n",
      "lower bounds 0.01_p_ac 0.005_p_ac\n",
      "lower bounds 0.025_p_ac 0.02_p_ac\n",
      "lower bounds 0.25_p_ac 0.215_p_ac\n",
      "mid 0.5_p_ac 0.5_p_ac\n",
      "upper bounds 0.75_p_ac 0.785_p_ac\n",
      "upper bounds 0.975_p_ac 0.98_p_ac\n",
      "upper bounds 0.99_p_ac 0.995_p_ac\n",
      "upper bounds 0.995_p_ac 0.9995_p_ac\n",
      "extreme upper bound from rf 0.9975_p_ac\n"
     ]
    }
   ],
   "source": [
    "# find columns that end in `_p_ac` (e.g., '0.015_p_ac')\n",
    "rf_columns = result_df_RF_2023.filter(regex='_p_ac$').columns\n",
    "xgb_columns = result_df_XGB_2023.filter(regex='_p_ac$').columns\n",
    "\n",
    "# loop through, selecting max of lower bounds and min of upper bounds\n",
    "for i in range(len(rf_columns)):\n",
    "    if i == 0: # first\n",
    "        print('extreme lower bound from rf', rf_columns[i])\n",
    "        result_df_hybrid_2023[rf_columns[i]] = result_df_RF_2023[rf_columns[i]]\n",
    "    elif i == len(rf_columns)-1: # last\n",
    "        print('extreme upper bound from rf', rf_columns[i])\n",
    "        result_df_hybrid_2023[rf_columns[i]] = result_df_RF_2023[rf_columns[i]]\n",
    "    elif i < (len(rf_columns)-1)/2:\n",
    "        print('lower bounds', rf_columns[i], xgb_columns[i])\n",
    "        result_df_hybrid_2023[rf_columns[i]] = np.maximum(result_df_XGB_2023[xgb_columns[i]], result_df_RF_2023[rf_columns[i]])\n",
    "        # result_df_hybrid_2023[rf_columns[i]] = pd.DataFrame([result_df_XGB_2023[xgb_columns[i]], result_df_RF_2023[rf_columns[i]]]).max(axis=0)\n",
    "    elif i == (len(rf_columns)-1)/2:\n",
    "        print('mid', rf_columns[i], xgb_columns[i])\n",
    "        result_df_hybrid_2023[rf_columns[i]] = result_df_RF_2023[rf_columns[i]]\n",
    "    else:\n",
    "        print('upper bounds', rf_columns[i], xgb_columns[i])\n",
    "        result_df_hybrid_2023[rf_columns[i]] = np.minimum(result_df_XGB_2023[xgb_columns[i]], result_df_RF_2023[rf_columns[i]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df = result_df_hybrid_2023.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target prediction intervals:\n",
      "[0.995, 0.99, 0.98, 0.95, 0.5]\n",
      "resulting coverage fraction (rounded to 3 places):\n",
      "['0.991', '0.989', '0.981', '0.954', '0.510']\n",
      "original NMAE: 8.44%\n",
      "adjusted NMAE: 7.79%\n"
     ]
    }
   ],
   "source": [
    "# coverage fraction and error:\n",
    "cf = {}\n",
    "for i in range(num_intervals):\n",
    "    cf[i] = coverage_fraction(y_test,\n",
    "                              y_pred[:,i],\n",
    "                              y_pred[:,len(percentile_list)-i-1]\n",
    "    )\n",
    "\n",
    "print('target prediction intervals:')\n",
    "print(pi_list)\n",
    "print('resulting coverage fraction (rounded to 3 places):')\n",
    "print([ '%.3f' % elem for elem in list(cf.values())])\n",
    "orig_nmae = filtered_df['norm_abs_err'].mean()\n",
    "print('original NMAE: ' + \"{:.2f}\".format(orig_nmae*100) + '%')\n",
    "adjusted_nmae = (result_df[str(percentile_list[int((len(percentile_list)-1)/2)])+'_p_ac'] - result_df['p_ac_all_act']).abs().mean()/approx_nameplate\n",
    "print('adjusted NMAE: ' + \"{:.2f}\".format(adjusted_nmae*100) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target PI : resulting CF : width\n",
      "0.995 : 0.991 : 0.5843910342961928\n",
      "0.99 : 0.989 : 0.5325690061307813\n",
      "0.98 : 0.981 : 0.4588049516607297\n",
      "0.95 : 0.954 : 0.3694330253826054\n",
      "0.5 : 0.510 : 0.1173568601153544\n",
      "for zenith < 75:\n",
      "0.995 : 0.991 : 0.6741405583190401\n",
      "0.99 : 0.971 : 0.6152362620781178\n",
      "0.98 : 0.951 : 0.5332430190687479\n",
      "0.95 : 0.901 : 0.43113240042485207\n",
      "0.5 : 0.437 : 0.1366625443269991\n"
     ]
    }
   ],
   "source": [
    "# sharpness\n",
    "print('target PI : resulting CF : width')\n",
    "for i in range(num_intervals):\n",
    "    lower = result_df[str(percentile_list[i])+'_p_ac'] # lower\n",
    "    upper = result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'] # upper\n",
    "    print(str(pi_list[i]), \":\", \"{:.3f}\".format(list(cf.values())[i]), \":\", (upper-lower).mean()/approx_nameplate)\n",
    "\n",
    "print('for zenith < 75:')\n",
    "\n",
    "mask_zen = (result_df['zen'] < 75) \n",
    "\n",
    "cf_mask = {}\n",
    "for i in range(num_intervals):\n",
    "    cf_mask[i] = coverage_fraction(result_df['p_ac_all_act'][mask_zen],\n",
    "                              result_df[str(percentile_list[i])+'_p_ac'][mask_zen],\n",
    "                              result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask_zen]\n",
    "    )\n",
    "\n",
    "for i in range(num_intervals):\n",
    "    lower = result_df[str(percentile_list[i])+'_p_ac'][mask_zen] # lower\n",
    "    upper = result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask_zen] # upper\n",
    "    print(str(pi_list[i]), \":\", \"{:.3f}\".format(list(cf_mask.values())[i]), \":\", (upper-lower).mean()/approx_nameplate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we fall just a little short of our target PIs, but that is expected based on the way this hybrid is created. \n",
    "\n",
    "And plots of the sample time ranges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask_start = '2023-10-13 11:00'\n",
    "mask_end = '2023-10-15'\n",
    "\n",
    "mask = (result_df.index >= mask_start) & (result_df.index <= mask_end)\n",
    "\n",
    "for i in range(num_intervals):\n",
    "    alph=0.1+(i/10.)\n",
    "    plt.fill_between(result_df.index[mask],# x\n",
    "                     result_df[str(percentile_list[i])+'_p_ac'][mask].fillna(0), # lower\n",
    "                     result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask].fillna(0), # upper\n",
    "                     color='blue',\n",
    "                     alpha=alph,\n",
    "                     step='mid',\n",
    "                     label=(str(pi_list[i]*100)+'%'))\n",
    "    \n",
    "plt.plot(result_df.index[mask],\n",
    "         result_df[str(percentile_list[int((len(percentile_list)-1)/2)])+'_p_ac'][mask],\n",
    "         label='Hybrid expected',color='brown', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index[mask],result_df['p_ac_all_act'][mask],label='actual',color='black', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index[mask],result_df['p_ac_all_fcast'][mask],label='DA fcast (HRRR coarse)',color='orange', drawstyle='steps-mid')\n",
    "plt.ylabel('AC Power (MW)')\n",
    "# plt.ylim([0,4500])\n",
    "plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.xlabel('Time, UTC (mm-dd HH)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask_start = '2023-1-7 11:00'\n",
    "mask_end = '2023-1-10'\n",
    "\n",
    "mask = (result_df.index >= mask_start) & (result_df.index <= mask_end)\n",
    "\n",
    "for i in range(num_intervals):\n",
    "    alph=0.1+(i/10.)\n",
    "    plt.fill_between(result_df.index[mask],# x\n",
    "                     result_df[str(percentile_list[i])+'_p_ac'][mask].fillna(0), # lower\n",
    "                     result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'][mask].fillna(0), # upper\n",
    "                     color='blue',\n",
    "                     alpha=alph,\n",
    "                     step='mid',\n",
    "                     label=(str(pi_list[i]*100)+'%'))\n",
    "    \n",
    "plt.plot(result_df.index[mask],\n",
    "         result_df[str(percentile_list[int((len(percentile_list)-1)/2)])+'_p_ac'][mask],\n",
    "         label='Hybrid expected',color='brown', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index[mask],result_df['p_ac_all_act'][mask],label='actual',color='black', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index[mask],result_df['p_ac_all_fcast'][mask],label='DA fcast (HRRR coarse)',color='orange', drawstyle='steps-mid')\n",
    "plt.ylabel('AC Power (MW)')\n",
    "# plt.ylim([0,4500])\n",
    "plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.xlabel('Time, UTC (mm-dd HH)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the resulting charts are narrower and appear smoother that the XGboost or Random Forest results by themselves. \n",
    "\n",
    "Let's save our results in a pickle file for use later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df.to_pickle('data_files/hybrid_prob_fcast_results_2023.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "This is the end of this notebook, now let's look at how this could be deployed operationally to create forecasts on demand: [10_operational_forecast_example.ipynb](10_operational_forecast_example.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(here's some sample code that could be used to export results into a csv file for evaluation in other tools:)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# result_df_hybrid_2023.fillna(0).to_csv('result_df_hybrid_2023.csv')\n",
    "\n",
    "# filename = 'result_df_hybrid_2023_with_timestamps.csv'\n",
    "\n",
    "# # make a header        \n",
    "# header1 = ', '.join(['time'] + result_df_hybrid_2023.columns.to_list()) + '\\n'\n",
    "# header2 = ', '.join(['0'] * (len(result_df_hybrid_2023.columns)+1)) + '\\n' \n",
    "# with open(filename, 'w') as fp:\n",
    "#     fp.write(header1)\n",
    "#     fp.write(header2)\n",
    "\n",
    "# # write rest of header and write data\n",
    "# columns = result_df_hybrid_2023.columns\n",
    "# result_df_hybrid_2023.fillna(0).to_csv(filename, columns = columns, mode='a', index=True, header=False)\n",
    "\n",
    "# filename = 'result_df_hybrid_2023.csv'\n",
    "\n",
    "# # make a header        \n",
    "# header1 = ', '.join(result_df_hybrid_2023.columns.to_list()) + '\\n'\n",
    "# header2 = ', '.join(['0'] * len(result_df_hybrid_2023.columns)) + '\\n' \n",
    "# with open(filename, 'w') as fp:\n",
    "#     fp.write(header1)\n",
    "#     fp.write(header2)\n",
    "\n",
    "# # write rest of header and write data\n",
    "# columns = result_df_hybrid_2023.columns\n",
    "# result_df_hybrid_2023.fillna(0).to_csv(filename, columns = columns, mode='a', index=False, header=False)"
   ]
  }
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