{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `GPflow` Gaussian Process Model of the Polydome Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Base math/data packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gaussian Process Modeling packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gpflow\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gpflow.utilities import print_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print summary in notebook format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpflow.config.set_default_summary_fmt(\"notebook\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tf.config.set_visible_devices([], 'GPU')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebook output parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enable horizontal scroll for print output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>pre { white-space: pre !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "display(HTML(\"<style>pre { white-space: pre !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Experimental measurements to fit a GP model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiments used for identification: 7 6 4 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiments used for validation: 3 5 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_id = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copy the corresponding WDB to the model input location:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "copyfile(f\"../Data/Experimental_data_WDB/{exp_id}_WDB.mat\", \"../Data/input_WDB.mat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>time</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>zenith</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dni</th>\n",
       "      <th>dhi</th>\n",
       "      <th>OutsideTemp</th>\n",
       "      <th>Tsky_rad</th>\n",
       "      <th>relative_humidity</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>cloud_index</th>\n",
       "      <th>pressure</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>aoi</th>\n",
       "      <th>incidence_main</th>\n",
       "      <th>incidence_second</th>\n",
       "      <th>poa_direct</th>\n",
       "      <th>poa_diffuse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>201706012000</td>\n",
       "      <td>78.691622</td>\n",
       "      <td>290.430819</td>\n",
       "      <td>7.251337</td>\n",
       "      <td>59.908644</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>78.691622</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1.421911</td>\n",
       "      <td>59.908644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300</td>\n",
       "      <td>201706012005</td>\n",
       "      <td>79.489651</td>\n",
       "      <td>291.279501</td>\n",
       "      <td>7.672114</td>\n",
       "      <td>56.537088</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>79.489651</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1.399494</td>\n",
       "      <td>56.537088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600</td>\n",
       "      <td>201706012010</td>\n",
       "      <td>80.282334</td>\n",
       "      <td>292.130503</td>\n",
       "      <td>8.423139</td>\n",
       "      <td>53.492674</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>80.282334</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1.421769</td>\n",
       "      <td>53.492674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>900</td>\n",
       "      <td>201706012015</td>\n",
       "      <td>81.069332</td>\n",
       "      <td>292.984123</td>\n",
       "      <td>52.657244</td>\n",
       "      <td>65.770239</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>81.069332</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>8.174467</td>\n",
       "      <td>65.770239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1200</td>\n",
       "      <td>201706012020</td>\n",
       "      <td>81.850261</td>\n",
       "      <td>293.840653</td>\n",
       "      <td>94.364403</td>\n",
       "      <td>62.829177</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>81.850261</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>13.377157</td>\n",
       "      <td>62.829177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>160500</td>\n",
       "      <td>201706031635</td>\n",
       "      <td>43.923091</td>\n",
       "      <td>252.722275</td>\n",
       "      <td>64.970386</td>\n",
       "      <td>314.462614</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>43.923091</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>46.796324</td>\n",
       "      <td>314.462614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>160800</td>\n",
       "      <td>201706031640</td>\n",
       "      <td>44.746130</td>\n",
       "      <td>253.882437</td>\n",
       "      <td>530.910153</td>\n",
       "      <td>219.485890</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>44.746130</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>377.069871</td>\n",
       "      <td>219.485890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>161100</td>\n",
       "      <td>201706031645</td>\n",
       "      <td>45.573942</td>\n",
       "      <td>255.018953</td>\n",
       "      <td>428.243363</td>\n",
       "      <td>250.653973</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>45.573942</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>299.765305</td>\n",
       "      <td>250.653973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>161400</td>\n",
       "      <td>201706031650</td>\n",
       "      <td>46.406107</td>\n",
       "      <td>256.133161</td>\n",
       "      <td>667.400308</td>\n",
       "      <td>167.328816</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>46.406107</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>460.200780</td>\n",
       "      <td>167.328816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>161700</td>\n",
       "      <td>201706031655</td>\n",
       "      <td>47.242228</td>\n",
       "      <td>257.226338</td>\n",
       "      <td>514.333795</td>\n",
       "      <td>215.275641</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>47.242228</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>349.181391</td>\n",
       "      <td>215.275641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>540 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       time     timestamp     zenith     azimuth         dni         dhi  \\\n",
       "0         0  201706012000  78.691622  290.430819    7.251337   59.908644   \n",
       "1       300  201706012005  79.489651  291.279501    7.672114   56.537088   \n",
       "2       600  201706012010  80.282334  292.130503    8.423139   53.492674   \n",
       "3       900  201706012015  81.069332  292.984123   52.657244   65.770239   \n",
       "4      1200  201706012020  81.850261  293.840653   94.364403   62.829177   \n",
       "..      ...           ...        ...         ...         ...         ...   \n",
       "535  160500  201706031635  43.923091  252.722275   64.970386  314.462614   \n",
       "536  160800  201706031640  44.746130  253.882437  530.910153  219.485890   \n",
       "537  161100  201706031645  45.573942  255.018953  428.243363  250.653973   \n",
       "538  161400  201706031650  46.406107  256.133161  667.400308  167.328816   \n",
       "539  161700  201706031655  47.242228  257.226338  514.333795  215.275641   \n",
       "\n",
       "     OutsideTemp  Tsky_rad  relative_humidity  precipitation  cloud_index  \\\n",
       "0           22.0      16.0                 50          -9999          0.5   \n",
       "1           22.0      16.0                 50          -9999          0.5   \n",
       "2           22.0      16.0                 50          -9999          0.5   \n",
       "3           22.0      16.0                 50          -9999          0.5   \n",
       "4           22.0      16.0                 50          -9999          0.5   \n",
       "..           ...       ...                ...            ...          ...   \n",
       "535         24.0      18.0                 50          -9999          0.5   \n",
       "536         24.0      18.0                 50          -9999          0.5   \n",
       "537         24.0      18.0                 50          -9999          0.5   \n",
       "538         24.0      18.0                 50          -9999          0.5   \n",
       "539         24.0      18.0                 50          -9999          0.5   \n",
       "\n",
       "     pressure  wind_speed  wind_direction        aoi  incidence_main  \\\n",
       "0       96300           0           -9999  78.691622           -9999   \n",
       "1       96300           0           -9999  79.489651           -9999   \n",
       "2       96300           0           -9999  80.282334           -9999   \n",
       "3       96300           0           -9999  81.069332           -9999   \n",
       "4       96300           0           -9999  81.850261           -9999   \n",
       "..        ...         ...             ...        ...             ...   \n",
       "535     96300           0           -9999  43.923091           -9999   \n",
       "536     96300           0           -9999  44.746130           -9999   \n",
       "537     96300           0           -9999  45.573942           -9999   \n",
       "538     96300           0           -9999  46.406107           -9999   \n",
       "539     96300           0           -9999  47.242228           -9999   \n",
       "\n",
       "     incidence_second  poa_direct  poa_diffuse  \n",
       "0               -9999    1.421911    59.908644  \n",
       "1               -9999    1.399494    56.537088  \n",
       "2               -9999    1.421769    53.492674  \n",
       "3               -9999    8.174467    65.770239  \n",
       "4               -9999   13.377157    62.829177  \n",
       "..                ...         ...          ...  \n",
       "535             -9999   46.796324   314.462614  \n",
       "536             -9999  377.069871   219.485890  \n",
       "537             -9999  299.765305   250.653973  \n",
       "538             -9999  460.200780   167.328816  \n",
       "539             -9999  349.181391   215.275641  \n",
       "\n",
       "[540 rows x 19 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wdb = pd.read_pickle(f\"../Data/Experimental_python/Exp{exp_id}_WDB.pkl\")\n",
    "df_wdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_carnot = pd.read_pickle(f\"../Data/CARNOT_output/Exp{exp_id}_full.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data = df_carnot.loc[:, ['Power', 'Setpoint', 'OutsideTemp', 'SupplyTemp', 'InsideTemp', 'SolRad']]\n",
    "df_simulated = df_carnot.loc[:, 'SimulatedTemp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(df_data.index, df_data['OutsideTemp'], label = 'Outside Temperature')\n",
    "plt.plot(df_data.index, df_data['InsideTemp'], label = 'Inside Temperature')\n",
    "plt.plot(df_simulated.index, df_simulated, label = 'Simulated Temperature')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine the different dataframes into one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df_wdb, df_data.reset_index(), df_simulated.reset_index()], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop duplicated columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[:,~df.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>time</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>zenith</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dni</th>\n",
       "      <th>dhi</th>\n",
       "      <th>OutsideTemp</th>\n",
       "      <th>Tsky_rad</th>\n",
       "      <th>relative_humidity</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>...</th>\n",
       "      <th>incidence_main</th>\n",
       "      <th>incidence_second</th>\n",
       "      <th>poa_direct</th>\n",
       "      <th>poa_diffuse</th>\n",
       "      <th>Power</th>\n",
       "      <th>Setpoint</th>\n",
       "      <th>SupplyTemp</th>\n",
       "      <th>InsideTemp</th>\n",
       "      <th>SolRad</th>\n",
       "      <th>SimulatedTemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>201706012000</td>\n",
       "      <td>78.691622</td>\n",
       "      <td>290.430819</td>\n",
       "      <td>7.251337</td>\n",
       "      <td>59.908644</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1.421911</td>\n",
       "      <td>59.908644</td>\n",
       "      <td>4325.034483</td>\n",
       "      <td>23.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>24.300000</td>\n",
       "      <td>61.321333</td>\n",
       "      <td>23.324679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300</td>\n",
       "      <td>201706012005</td>\n",
       "      <td>79.489651</td>\n",
       "      <td>291.279501</td>\n",
       "      <td>7.672114</td>\n",
       "      <td>56.537088</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1.399494</td>\n",
       "      <td>56.537088</td>\n",
       "      <td>4287.000000</td>\n",
       "      <td>23.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>24.283333</td>\n",
       "      <td>57.926100</td>\n",
       "      <td>22.632962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600</td>\n",
       "      <td>201706012010</td>\n",
       "      <td>80.282334</td>\n",
       "      <td>292.130503</td>\n",
       "      <td>8.423139</td>\n",
       "      <td>53.492674</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1.421769</td>\n",
       "      <td>53.492674</td>\n",
       "      <td>4319.766667</td>\n",
       "      <td>23.5</td>\n",
       "      <td>15.2</td>\n",
       "      <td>24.083333</td>\n",
       "      <td>54.902033</td>\n",
       "      <td>22.696056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>900</td>\n",
       "      <td>201706012015</td>\n",
       "      <td>81.069332</td>\n",
       "      <td>292.984123</td>\n",
       "      <td>52.657244</td>\n",
       "      <td>65.770239</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>8.174467</td>\n",
       "      <td>65.770239</td>\n",
       "      <td>2893.344828</td>\n",
       "      <td>23.5</td>\n",
       "      <td>14.9</td>\n",
       "      <td>23.933333</td>\n",
       "      <td>73.860700</td>\n",
       "      <td>23.299014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1200</td>\n",
       "      <td>201706012020</td>\n",
       "      <td>81.850261</td>\n",
       "      <td>293.840653</td>\n",
       "      <td>94.364403</td>\n",
       "      <td>62.829177</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>13.377157</td>\n",
       "      <td>62.829177</td>\n",
       "      <td>59.137931</td>\n",
       "      <td>23.5</td>\n",
       "      <td>18.2</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>76.042533</td>\n",
       "      <td>23.778789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>160500</td>\n",
       "      <td>201706031635</td>\n",
       "      <td>43.923091</td>\n",
       "      <td>252.722275</td>\n",
       "      <td>64.970386</td>\n",
       "      <td>314.462614</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>46.796324</td>\n",
       "      <td>314.462614</td>\n",
       "      <td>62.137931</td>\n",
       "      <td>24.5</td>\n",
       "      <td>16.4</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>361.247267</td>\n",
       "      <td>20.947152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>160800</td>\n",
       "      <td>201706031640</td>\n",
       "      <td>44.746130</td>\n",
       "      <td>253.882437</td>\n",
       "      <td>530.910153</td>\n",
       "      <td>219.485890</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>377.069871</td>\n",
       "      <td>219.485890</td>\n",
       "      <td>57.482759</td>\n",
       "      <td>24.5</td>\n",
       "      <td>17.6</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>596.456167</td>\n",
       "      <td>21.039538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>161100</td>\n",
       "      <td>201706031645</td>\n",
       "      <td>45.573942</td>\n",
       "      <td>255.018953</td>\n",
       "      <td>428.243363</td>\n",
       "      <td>250.653973</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>299.765305</td>\n",
       "      <td>250.653973</td>\n",
       "      <td>56.233333</td>\n",
       "      <td>24.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.316667</td>\n",
       "      <td>550.335400</td>\n",
       "      <td>21.153586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>161400</td>\n",
       "      <td>201706031650</td>\n",
       "      <td>46.406107</td>\n",
       "      <td>256.133161</td>\n",
       "      <td>667.400308</td>\n",
       "      <td>167.328816</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>460.200780</td>\n",
       "      <td>167.328816</td>\n",
       "      <td>53.379310</td>\n",
       "      <td>24.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.450000</td>\n",
       "      <td>627.393133</td>\n",
       "      <td>21.322202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>161700</td>\n",
       "      <td>201706031655</td>\n",
       "      <td>47.242228</td>\n",
       "      <td>257.226338</td>\n",
       "      <td>514.333795</td>\n",
       "      <td>215.275641</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>...</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>349.181391</td>\n",
       "      <td>215.275641</td>\n",
       "      <td>58.379310</td>\n",
       "      <td>24.5</td>\n",
       "      <td>23.3</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>564.347267</td>\n",
       "      <td>21.371962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>540 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       time     timestamp     zenith     azimuth         dni         dhi  \\\n",
       "0         0  201706012000  78.691622  290.430819    7.251337   59.908644   \n",
       "1       300  201706012005  79.489651  291.279501    7.672114   56.537088   \n",
       "2       600  201706012010  80.282334  292.130503    8.423139   53.492674   \n",
       "3       900  201706012015  81.069332  292.984123   52.657244   65.770239   \n",
       "4      1200  201706012020  81.850261  293.840653   94.364403   62.829177   \n",
       "..      ...           ...        ...         ...         ...         ...   \n",
       "535  160500  201706031635  43.923091  252.722275   64.970386  314.462614   \n",
       "536  160800  201706031640  44.746130  253.882437  530.910153  219.485890   \n",
       "537  161100  201706031645  45.573942  255.018953  428.243363  250.653973   \n",
       "538  161400  201706031650  46.406107  256.133161  667.400308  167.328816   \n",
       "539  161700  201706031655  47.242228  257.226338  514.333795  215.275641   \n",
       "\n",
       "     OutsideTemp  Tsky_rad  relative_humidity  precipitation  ...  \\\n",
       "0           22.0      16.0                 50          -9999  ...   \n",
       "1           22.0      16.0                 50          -9999  ...   \n",
       "2           22.0      16.0                 50          -9999  ...   \n",
       "3           22.0      16.0                 50          -9999  ...   \n",
       "4           22.0      16.0                 50          -9999  ...   \n",
       "..           ...       ...                ...            ...  ...   \n",
       "535         24.0      18.0                 50          -9999  ...   \n",
       "536         24.0      18.0                 50          -9999  ...   \n",
       "537         24.0      18.0                 50          -9999  ...   \n",
       "538         24.0      18.0                 50          -9999  ...   \n",
       "539         24.0      18.0                 50          -9999  ...   \n",
       "\n",
       "     incidence_main  incidence_second  poa_direct  poa_diffuse        Power  \\\n",
       "0             -9999             -9999    1.421911    59.908644  4325.034483   \n",
       "1             -9999             -9999    1.399494    56.537088  4287.000000   \n",
       "2             -9999             -9999    1.421769    53.492674  4319.766667   \n",
       "3             -9999             -9999    8.174467    65.770239  2893.344828   \n",
       "4             -9999             -9999   13.377157    62.829177    59.137931   \n",
       "..              ...               ...         ...          ...          ...   \n",
       "535           -9999             -9999   46.796324   314.462614    62.137931   \n",
       "536           -9999             -9999  377.069871   219.485890    57.482759   \n",
       "537           -9999             -9999  299.765305   250.653973    56.233333   \n",
       "538           -9999             -9999  460.200780   167.328816    53.379310   \n",
       "539           -9999             -9999  349.181391   215.275641    58.379310   \n",
       "\n",
       "     Setpoint  SupplyTemp  InsideTemp      SolRad  SimulatedTemp  \n",
       "0        23.5        24.5   24.300000   61.321333      23.324679  \n",
       "1        23.5        15.5   24.283333   57.926100      22.632962  \n",
       "2        23.5        15.2   24.083333   54.902033      22.696056  \n",
       "3        23.5        14.9   23.933333   73.860700      23.299014  \n",
       "4        23.5        18.2   23.666667   76.042533      23.778789  \n",
       "..        ...         ...         ...         ...            ...  \n",
       "535      24.5        16.4   22.300000  361.247267      20.947152  \n",
       "536      24.5        17.6   22.300000  596.456167      21.039538  \n",
       "537      24.5        18.5   22.316667  550.335400      21.153586  \n",
       "538      24.5        20.0   22.450000  627.393133      21.322202  \n",
       "539      24.5        23.3   22.700000  564.347267      21.371962  \n",
       "\n",
       "[540 rows x 25 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selected the potentially useful columns: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[:, ['timestamp', 'zenith', 'azimuth', 'dni', 'dhi', 'OutsideTemp', 'Power', 'InsideTemp', 'SolRad', 'SimulatedTemp']]\n",
    "df.rename(columns = {'timestamp': 'timestamp_int'}, inplace = True)\n",
    "df.loc[:, 'timestamp'] = df_data.index\n",
    "df.set_index('timestamp', drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
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       "        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>timestamp_int</th>\n",
       "      <th>zenith</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dni</th>\n",
       "      <th>dhi</th>\n",
       "      <th>OutsideTemp</th>\n",
       "      <th>Power</th>\n",
       "      <th>InsideTemp</th>\n",
       "      <th>SolRad</th>\n",
       "      <th>SimulatedTemp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:00:00+02:00</th>\n",
       "      <td>201706012000</td>\n",
       "      <td>78.691622</td>\n",
       "      <td>290.430819</td>\n",
       "      <td>7.251337</td>\n",
       "      <td>59.908644</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4325.034483</td>\n",
       "      <td>24.300000</td>\n",
       "      <td>61.321333</td>\n",
       "      <td>23.324679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:05:00+02:00</th>\n",
       "      <td>201706012005</td>\n",
       "      <td>79.489651</td>\n",
       "      <td>291.279501</td>\n",
       "      <td>7.672114</td>\n",
       "      <td>56.537088</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4287.000000</td>\n",
       "      <td>24.283333</td>\n",
       "      <td>57.926100</td>\n",
       "      <td>22.632962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:10:00+02:00</th>\n",
       "      <td>201706012010</td>\n",
       "      <td>80.282334</td>\n",
       "      <td>292.130503</td>\n",
       "      <td>8.423139</td>\n",
       "      <td>53.492674</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4319.766667</td>\n",
       "      <td>24.083333</td>\n",
       "      <td>54.902033</td>\n",
       "      <td>22.696056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:15:00+02:00</th>\n",
       "      <td>201706012015</td>\n",
       "      <td>81.069332</td>\n",
       "      <td>292.984123</td>\n",
       "      <td>52.657244</td>\n",
       "      <td>65.770239</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2893.344828</td>\n",
       "      <td>23.933333</td>\n",
       "      <td>73.860700</td>\n",
       "      <td>23.299014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:20:00+02:00</th>\n",
       "      <td>201706012020</td>\n",
       "      <td>81.850261</td>\n",
       "      <td>293.840653</td>\n",
       "      <td>94.364403</td>\n",
       "      <td>62.829177</td>\n",
       "      <td>22.0</td>\n",
       "      <td>59.137931</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>76.042533</td>\n",
       "      <td>23.778789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:35:00+02:00</th>\n",
       "      <td>201706031635</td>\n",
       "      <td>43.923091</td>\n",
       "      <td>252.722275</td>\n",
       "      <td>64.970386</td>\n",
       "      <td>314.462614</td>\n",
       "      <td>24.0</td>\n",
       "      <td>62.137931</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>361.247267</td>\n",
       "      <td>20.947152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:40:00+02:00</th>\n",
       "      <td>201706031640</td>\n",
       "      <td>44.746130</td>\n",
       "      <td>253.882437</td>\n",
       "      <td>530.910153</td>\n",
       "      <td>219.485890</td>\n",
       "      <td>24.0</td>\n",
       "      <td>57.482759</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>596.456167</td>\n",
       "      <td>21.039538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:45:00+02:00</th>\n",
       "      <td>201706031645</td>\n",
       "      <td>45.573942</td>\n",
       "      <td>255.018953</td>\n",
       "      <td>428.243363</td>\n",
       "      <td>250.653973</td>\n",
       "      <td>24.0</td>\n",
       "      <td>56.233333</td>\n",
       "      <td>22.316667</td>\n",
       "      <td>550.335400</td>\n",
       "      <td>21.153586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:50:00+02:00</th>\n",
       "      <td>201706031650</td>\n",
       "      <td>46.406107</td>\n",
       "      <td>256.133161</td>\n",
       "      <td>667.400308</td>\n",
       "      <td>167.328816</td>\n",
       "      <td>24.0</td>\n",
       "      <td>53.379310</td>\n",
       "      <td>22.450000</td>\n",
       "      <td>627.393133</td>\n",
       "      <td>21.322202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:55:00+02:00</th>\n",
       "      <td>201706031655</td>\n",
       "      <td>47.242228</td>\n",
       "      <td>257.226338</td>\n",
       "      <td>514.333795</td>\n",
       "      <td>215.275641</td>\n",
       "      <td>24.0</td>\n",
       "      <td>58.379310</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>564.347267</td>\n",
       "      <td>21.371962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>540 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           timestamp_int     zenith     azimuth         dni  \\\n",
       "timestamp                                                                     \n",
       "2017-06-01 20:00:00+02:00   201706012000  78.691622  290.430819    7.251337   \n",
       "2017-06-01 20:05:00+02:00   201706012005  79.489651  291.279501    7.672114   \n",
       "2017-06-01 20:10:00+02:00   201706012010  80.282334  292.130503    8.423139   \n",
       "2017-06-01 20:15:00+02:00   201706012015  81.069332  292.984123   52.657244   \n",
       "2017-06-01 20:20:00+02:00   201706012020  81.850261  293.840653   94.364403   \n",
       "...                                  ...        ...         ...         ...   \n",
       "2017-06-03 16:35:00+02:00   201706031635  43.923091  252.722275   64.970386   \n",
       "2017-06-03 16:40:00+02:00   201706031640  44.746130  253.882437  530.910153   \n",
       "2017-06-03 16:45:00+02:00   201706031645  45.573942  255.018953  428.243363   \n",
       "2017-06-03 16:50:00+02:00   201706031650  46.406107  256.133161  667.400308   \n",
       "2017-06-03 16:55:00+02:00   201706031655  47.242228  257.226338  514.333795   \n",
       "\n",
       "                                  dhi  OutsideTemp        Power  InsideTemp  \\\n",
       "timestamp                                                                     \n",
       "2017-06-01 20:00:00+02:00   59.908644         22.0  4325.034483   24.300000   \n",
       "2017-06-01 20:05:00+02:00   56.537088         22.0  4287.000000   24.283333   \n",
       "2017-06-01 20:10:00+02:00   53.492674         22.0  4319.766667   24.083333   \n",
       "2017-06-01 20:15:00+02:00   65.770239         22.0  2893.344828   23.933333   \n",
       "2017-06-01 20:20:00+02:00   62.829177         22.0    59.137931   23.666667   \n",
       "...                               ...          ...          ...         ...   \n",
       "2017-06-03 16:35:00+02:00  314.462614         24.0    62.137931   22.300000   \n",
       "2017-06-03 16:40:00+02:00  219.485890         24.0    57.482759   22.300000   \n",
       "2017-06-03 16:45:00+02:00  250.653973         24.0    56.233333   22.316667   \n",
       "2017-06-03 16:50:00+02:00  167.328816         24.0    53.379310   22.450000   \n",
       "2017-06-03 16:55:00+02:00  215.275641         24.0    58.379310   22.700000   \n",
       "\n",
       "                               SolRad  SimulatedTemp  \n",
       "timestamp                                             \n",
       "2017-06-01 20:00:00+02:00   61.321333      23.324679  \n",
       "2017-06-01 20:05:00+02:00   57.926100      22.632962  \n",
       "2017-06-01 20:10:00+02:00   54.902033      22.696056  \n",
       "2017-06-01 20:15:00+02:00   73.860700      23.299014  \n",
       "2017-06-01 20:20:00+02:00   76.042533      23.778789  \n",
       "...                               ...            ...  \n",
       "2017-06-03 16:35:00+02:00  361.247267      20.947152  \n",
       "2017-06-03 16:40:00+02:00  596.456167      21.039538  \n",
       "2017-06-03 16:45:00+02:00  550.335400      21.153586  \n",
       "2017-06-03 16:50:00+02:00  627.393133      21.322202  \n",
       "2017-06-03 16:55:00+02:00  564.347267      21.371962  \n",
       "\n",
       "[540 rows x 10 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autoregressive inputs/outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>timestamp</th>\n",
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       "      <th>2017-06-01 20:00:00+02:00</th>\n",
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       "      <th>2017-06-01 20:05:00+02:00</th>\n",
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       "      <td>291.279501</td>\n",
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       "      <th>2017-06-01 20:10:00+02:00</th>\n",
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       "      <td>53.492674</td>\n",
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       "      <th>2017-06-01 20:20:00+02:00</th>\n",
       "      <td>201706012020</td>\n",
       "      <td>81.850261</td>\n",
       "      <td>293.840653</td>\n",
       "      <td>94.364403</td>\n",
       "      <td>62.829177</td>\n",
       "      <td>22.0</td>\n",
       "      <td>59.137931</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>76.042533</td>\n",
       "      <td>23.778789</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>2017-06-03 16:35:00+02:00</th>\n",
       "      <td>201706031635</td>\n",
       "      <td>43.923091</td>\n",
       "      <td>252.722275</td>\n",
       "      <td>64.970386</td>\n",
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       "      <td>20.947152</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-03 16:40:00+02:00</th>\n",
       "      <td>201706031640</td>\n",
       "      <td>44.746130</td>\n",
       "      <td>253.882437</td>\n",
       "      <td>530.910153</td>\n",
       "      <td>219.485890</td>\n",
       "      <td>24.0</td>\n",
       "      <td>57.482759</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>596.456167</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-03 16:45:00+02:00</th>\n",
       "      <td>201706031645</td>\n",
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       "      <th>2017-06-03 16:55:00+02:00</th>\n",
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      "text/plain": [
       "                           timestamp_int     zenith     azimuth         dni  \\\n",
       "timestamp                                                                     \n",
       "2017-06-01 20:00:00+02:00   201706012000  78.691622  290.430819    7.251337   \n",
       "2017-06-01 20:05:00+02:00   201706012005  79.489651  291.279501    7.672114   \n",
       "2017-06-01 20:10:00+02:00   201706012010  80.282334  292.130503    8.423139   \n",
       "2017-06-01 20:15:00+02:00   201706012015  81.069332  292.984123   52.657244   \n",
       "2017-06-01 20:20:00+02:00   201706012020  81.850261  293.840653   94.364403   \n",
       "...                                  ...        ...         ...         ...   \n",
       "2017-06-03 16:35:00+02:00   201706031635  43.923091  252.722275   64.970386   \n",
       "2017-06-03 16:40:00+02:00   201706031640  44.746130  253.882437  530.910153   \n",
       "2017-06-03 16:45:00+02:00   201706031645  45.573942  255.018953  428.243363   \n",
       "2017-06-03 16:50:00+02:00   201706031650  46.406107  256.133161  667.400308   \n",
       "2017-06-03 16:55:00+02:00   201706031655  47.242228  257.226338  514.333795   \n",
       "\n",
       "                                  dhi  OutsideTemp            u          y  \\\n",
       "timestamp                                                                    \n",
       "2017-06-01 20:00:00+02:00   59.908644         22.0  4325.034483  24.300000   \n",
       "2017-06-01 20:05:00+02:00   56.537088         22.0  4287.000000  24.283333   \n",
       "2017-06-01 20:10:00+02:00   53.492674         22.0  4319.766667  24.083333   \n",
       "2017-06-01 20:15:00+02:00   65.770239         22.0  2893.344828  23.933333   \n",
       "2017-06-01 20:20:00+02:00   62.829177         22.0    59.137931  23.666667   \n",
       "...                               ...          ...          ...        ...   \n",
       "2017-06-03 16:35:00+02:00  314.462614         24.0    62.137931  22.300000   \n",
       "2017-06-03 16:40:00+02:00  219.485890         24.0    57.482759  22.300000   \n",
       "2017-06-03 16:45:00+02:00  250.653973         24.0    56.233333  22.316667   \n",
       "2017-06-03 16:50:00+02:00  167.328816         24.0    53.379310  22.450000   \n",
       "2017-06-03 16:55:00+02:00  215.275641         24.0    58.379310  22.700000   \n",
       "\n",
       "                               SolRad  SimulatedTemp  \n",
       "timestamp                                             \n",
       "2017-06-01 20:00:00+02:00   61.321333      23.324679  \n",
       "2017-06-01 20:05:00+02:00   57.926100      22.632962  \n",
       "2017-06-01 20:10:00+02:00   54.902033      22.696056  \n",
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       "2017-06-01 20:20:00+02:00   76.042533      23.778789  \n",
       "...                               ...            ...  \n",
       "2017-06-03 16:35:00+02:00  361.247267      20.947152  \n",
       "2017-06-03 16:40:00+02:00  596.456167      21.039538  \n",
       "2017-06-03 16:45:00+02:00  550.335400      21.153586  \n",
       "2017-06-03 16:50:00+02:00  627.393133      21.322202  \n",
       "2017-06-03 16:55:00+02:00  564.347267      21.371962  \n",
       "\n",
       "[540 rows x 10 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns = {'Power': 'u', 'InsideTemp': 'y'}, inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2017-06-01 20:00:00+02:00</th>\n",
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       "      <th>2017-06-01 20:15:00+02:00</th>\n",
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       "      <th>2017-06-01 20:20:00+02:00</th>\n",
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       "      <td>22.0</td>\n",
       "      <td>59.137931</td>\n",
       "      <td>23.666667</td>\n",
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       "      <th>2017-06-03 16:35:00+02:00</th>\n",
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       "    <tr>\n",
       "      <th>2017-06-03 16:40:00+02:00</th>\n",
       "      <td>201706031640</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-03 16:45:00+02:00</th>\n",
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       "    <tr>\n",
       "      <th>2017-06-03 16:50:00+02:00</th>\n",
       "      <td>201706031650</td>\n",
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       "      <th>2017-06-03 16:55:00+02:00</th>\n",
       "      <td>201706031655</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>540 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           timestamp_int     zenith     azimuth         dni  \\\n",
       "timestamp                                                                     \n",
       "2017-06-01 20:00:00+02:00   201706012000  78.691622  290.430819    7.251337   \n",
       "2017-06-01 20:05:00+02:00   201706012005  79.489651  291.279501    7.672114   \n",
       "2017-06-01 20:10:00+02:00   201706012010  80.282334  292.130503    8.423139   \n",
       "2017-06-01 20:15:00+02:00   201706012015  81.069332  292.984123   52.657244   \n",
       "2017-06-01 20:20:00+02:00   201706012020  81.850261  293.840653   94.364403   \n",
       "...                                  ...        ...         ...         ...   \n",
       "2017-06-03 16:35:00+02:00   201706031635  43.923091  252.722275   64.970386   \n",
       "2017-06-03 16:40:00+02:00   201706031640  44.746130  253.882437  530.910153   \n",
       "2017-06-03 16:45:00+02:00   201706031645  45.573942  255.018953  428.243363   \n",
       "2017-06-03 16:50:00+02:00   201706031650  46.406107  256.133161  667.400308   \n",
       "2017-06-03 16:55:00+02:00   201706031655  47.242228  257.226338  514.333795   \n",
       "\n",
       "                                  dhi  OutsideTemp            u          y  \\\n",
       "timestamp                                                                    \n",
       "2017-06-01 20:00:00+02:00   59.908644         22.0  4325.034483  24.300000   \n",
       "2017-06-01 20:05:00+02:00   56.537088         22.0  4287.000000  24.283333   \n",
       "2017-06-01 20:10:00+02:00   53.492674         22.0  4319.766667  24.083333   \n",
       "2017-06-01 20:15:00+02:00   65.770239         22.0  2893.344828  23.933333   \n",
       "2017-06-01 20:20:00+02:00   62.829177         22.0    59.137931  23.666667   \n",
       "...                               ...          ...          ...        ...   \n",
       "2017-06-03 16:35:00+02:00  314.462614         24.0    62.137931  22.300000   \n",
       "2017-06-03 16:40:00+02:00  219.485890         24.0    57.482759  22.300000   \n",
       "2017-06-03 16:45:00+02:00  250.653973         24.0    56.233333  22.316667   \n",
       "2017-06-03 16:50:00+02:00  167.328816         24.0    53.379310  22.450000   \n",
       "2017-06-03 16:55:00+02:00  215.275641         24.0    58.379310  22.700000   \n",
       "\n",
       "                               SolRad  SimulatedTemp  \n",
       "timestamp                                             \n",
       "2017-06-01 20:00:00+02:00   61.321333      23.324679  \n",
       "2017-06-01 20:05:00+02:00   57.926100      22.632962  \n",
       "2017-06-01 20:10:00+02:00   54.902033      22.696056  \n",
       "2017-06-01 20:15:00+02:00   73.860700      23.299014  \n",
       "2017-06-01 20:20:00+02:00   76.042533      23.778789  \n",
       "...                               ...            ...  \n",
       "2017-06-03 16:35:00+02:00  361.247267      20.947152  \n",
       "2017-06-03 16:40:00+02:00  596.456167      21.039538  \n",
       "2017-06-03 16:45:00+02:00  550.335400      21.153586  \n",
       "2017-06-03 16:50:00+02:00  627.393133      21.322202  \n",
       "2017-06-03 16:55:00+02:00  564.347267      21.371962  \n",
       "\n",
       "[540 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add input lags:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "lu = 1\n",
    "\n",
    "for idx in range(1, lu + 1):\n",
    "    df[f\"u_{idx}\"] = df['u'].shift(idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add output lags:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ly = 3\n",
    "\n",
    "for idx in range(1, ly + 1):\n",
    "    df[f\"y_{idx}\"] = df['y'].shift(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp_int</th>\n",
       "      <th>zenith</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dni</th>\n",
       "      <th>dhi</th>\n",
       "      <th>OutsideTemp</th>\n",
       "      <th>u</th>\n",
       "      <th>y</th>\n",
       "      <th>SolRad</th>\n",
       "      <th>SimulatedTemp</th>\n",
       "      <th>u_1</th>\n",
       "      <th>y_1</th>\n",
       "      <th>y_2</th>\n",
       "      <th>y_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:15:00+02:00</th>\n",
       "      <td>201706012015</td>\n",
       "      <td>81.069332</td>\n",
       "      <td>292.984123</td>\n",
       "      <td>52.657244</td>\n",
       "      <td>65.770239</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2893.344828</td>\n",
       "      <td>23.933333</td>\n",
       "      <td>73.860700</td>\n",
       "      <td>23.299014</td>\n",
       "      <td>4319.766667</td>\n",
       "      <td>24.083333</td>\n",
       "      <td>24.283333</td>\n",
       "      <td>24.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:20:00+02:00</th>\n",
       "      <td>201706012020</td>\n",
       "      <td>81.850261</td>\n",
       "      <td>293.840653</td>\n",
       "      <td>94.364403</td>\n",
       "      <td>62.829177</td>\n",
       "      <td>22.0</td>\n",
       "      <td>59.137931</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>76.042533</td>\n",
       "      <td>23.778789</td>\n",
       "      <td>2893.344828</td>\n",
       "      <td>23.933333</td>\n",
       "      <td>24.083333</td>\n",
       "      <td>24.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:25:00+02:00</th>\n",
       "      <td>201706012025</td>\n",
       "      <td>82.624676</td>\n",
       "      <td>294.700379</td>\n",
       "      <td>72.713713</td>\n",
       "      <td>55.785907</td>\n",
       "      <td>22.0</td>\n",
       "      <td>58.241379</td>\n",
       "      <td>23.650000</td>\n",
       "      <td>64.981967</td>\n",
       "      <td>23.767140</td>\n",
       "      <td>59.137931</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>23.933333</td>\n",
       "      <td>24.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:30:00+02:00</th>\n",
       "      <td>201706012030</td>\n",
       "      <td>83.392049</td>\n",
       "      <td>295.563581</td>\n",
       "      <td>24.601803</td>\n",
       "      <td>43.887954</td>\n",
       "      <td>22.0</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>23.633333</td>\n",
       "      <td>46.667567</td>\n",
       "      <td>23.760643</td>\n",
       "      <td>58.241379</td>\n",
       "      <td>23.650000</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>23.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:35:00+02:00</th>\n",
       "      <td>201706012035</td>\n",
       "      <td>84.151733</td>\n",
       "      <td>296.430536</td>\n",
       "      <td>11.237512</td>\n",
       "      <td>33.883988</td>\n",
       "      <td>22.0</td>\n",
       "      <td>61.103448</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>35.002967</td>\n",
       "      <td>23.751253</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>23.633333</td>\n",
       "      <td>23.650000</td>\n",
       "      <td>23.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:35:00+02:00</th>\n",
       "      <td>201706031635</td>\n",
       "      <td>43.923091</td>\n",
       "      <td>252.722275</td>\n",
       "      <td>64.970386</td>\n",
       "      <td>314.462614</td>\n",
       "      <td>24.0</td>\n",
       "      <td>62.137931</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>361.247267</td>\n",
       "      <td>20.947152</td>\n",
       "      <td>4348.068966</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>22.683333</td>\n",
       "      <td>22.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:40:00+02:00</th>\n",
       "      <td>201706031640</td>\n",
       "      <td>44.746130</td>\n",
       "      <td>253.882437</td>\n",
       "      <td>530.910153</td>\n",
       "      <td>219.485890</td>\n",
       "      <td>24.0</td>\n",
       "      <td>57.482759</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>596.456167</td>\n",
       "      <td>21.039538</td>\n",
       "      <td>62.137931</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>22.683333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:45:00+02:00</th>\n",
       "      <td>201706031645</td>\n",
       "      <td>45.573942</td>\n",
       "      <td>255.018953</td>\n",
       "      <td>428.243363</td>\n",
       "      <td>250.653973</td>\n",
       "      <td>24.0</td>\n",
       "      <td>56.233333</td>\n",
       "      <td>22.316667</td>\n",
       "      <td>550.335400</td>\n",
       "      <td>21.153586</td>\n",
       "      <td>57.482759</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>22.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:50:00+02:00</th>\n",
       "      <td>201706031650</td>\n",
       "      <td>46.406107</td>\n",
       "      <td>256.133161</td>\n",
       "      <td>667.400308</td>\n",
       "      <td>167.328816</td>\n",
       "      <td>24.0</td>\n",
       "      <td>53.379310</td>\n",
       "      <td>22.450000</td>\n",
       "      <td>627.393133</td>\n",
       "      <td>21.322202</td>\n",
       "      <td>56.233333</td>\n",
       "      <td>22.316667</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>22.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:55:00+02:00</th>\n",
       "      <td>201706031655</td>\n",
       "      <td>47.242228</td>\n",
       "      <td>257.226338</td>\n",
       "      <td>514.333795</td>\n",
       "      <td>215.275641</td>\n",
       "      <td>24.0</td>\n",
       "      <td>58.379310</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>564.347267</td>\n",
       "      <td>21.371962</td>\n",
       "      <td>53.379310</td>\n",
       "      <td>22.450000</td>\n",
       "      <td>22.316667</td>\n",
       "      <td>22.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>537 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           timestamp_int     zenith     azimuth         dni  \\\n",
       "timestamp                                                                     \n",
       "2017-06-01 20:15:00+02:00   201706012015  81.069332  292.984123   52.657244   \n",
       "2017-06-01 20:20:00+02:00   201706012020  81.850261  293.840653   94.364403   \n",
       "2017-06-01 20:25:00+02:00   201706012025  82.624676  294.700379   72.713713   \n",
       "2017-06-01 20:30:00+02:00   201706012030  83.392049  295.563581   24.601803   \n",
       "2017-06-01 20:35:00+02:00   201706012035  84.151733  296.430536   11.237512   \n",
       "...                                  ...        ...         ...         ...   \n",
       "2017-06-03 16:35:00+02:00   201706031635  43.923091  252.722275   64.970386   \n",
       "2017-06-03 16:40:00+02:00   201706031640  44.746130  253.882437  530.910153   \n",
       "2017-06-03 16:45:00+02:00   201706031645  45.573942  255.018953  428.243363   \n",
       "2017-06-03 16:50:00+02:00   201706031650  46.406107  256.133161  667.400308   \n",
       "2017-06-03 16:55:00+02:00   201706031655  47.242228  257.226338  514.333795   \n",
       "\n",
       "                                  dhi  OutsideTemp            u          y  \\\n",
       "timestamp                                                                    \n",
       "2017-06-01 20:15:00+02:00   65.770239         22.0  2893.344828  23.933333   \n",
       "2017-06-01 20:20:00+02:00   62.829177         22.0    59.137931  23.666667   \n",
       "2017-06-01 20:25:00+02:00   55.785907         22.0    58.241379  23.650000   \n",
       "2017-06-01 20:30:00+02:00   43.887954         22.0    59.000000  23.633333   \n",
       "2017-06-01 20:35:00+02:00   33.883988         22.0    61.103448  23.800000   \n",
       "...                               ...          ...          ...        ...   \n",
       "2017-06-03 16:35:00+02:00  314.462614         24.0    62.137931  22.300000   \n",
       "2017-06-03 16:40:00+02:00  219.485890         24.0    57.482759  22.300000   \n",
       "2017-06-03 16:45:00+02:00  250.653973         24.0    56.233333  22.316667   \n",
       "2017-06-03 16:50:00+02:00  167.328816         24.0    53.379310  22.450000   \n",
       "2017-06-03 16:55:00+02:00  215.275641         24.0    58.379310  22.700000   \n",
       "\n",
       "                               SolRad  SimulatedTemp          u_1        y_1  \\\n",
       "timestamp                                                                      \n",
       "2017-06-01 20:15:00+02:00   73.860700      23.299014  4319.766667  24.083333   \n",
       "2017-06-01 20:20:00+02:00   76.042533      23.778789  2893.344828  23.933333   \n",
       "2017-06-01 20:25:00+02:00   64.981967      23.767140    59.137931  23.666667   \n",
       "2017-06-01 20:30:00+02:00   46.667567      23.760643    58.241379  23.650000   \n",
       "2017-06-01 20:35:00+02:00   35.002967      23.751253    59.000000  23.633333   \n",
       "...                               ...            ...          ...        ...   \n",
       "2017-06-03 16:35:00+02:00  361.247267      20.947152  4348.068966  22.500000   \n",
       "2017-06-03 16:40:00+02:00  596.456167      21.039538    62.137931  22.300000   \n",
       "2017-06-03 16:45:00+02:00  550.335400      21.153586    57.482759  22.300000   \n",
       "2017-06-03 16:50:00+02:00  627.393133      21.322202    56.233333  22.316667   \n",
       "2017-06-03 16:55:00+02:00  564.347267      21.371962    53.379310  22.450000   \n",
       "\n",
       "                                 y_2        y_3  \n",
       "timestamp                                        \n",
       "2017-06-01 20:15:00+02:00  24.283333  24.300000  \n",
       "2017-06-01 20:20:00+02:00  24.083333  24.283333  \n",
       "2017-06-01 20:25:00+02:00  23.933333  24.083333  \n",
       "2017-06-01 20:30:00+02:00  23.666667  23.933333  \n",
       "2017-06-01 20:35:00+02:00  23.650000  23.666667  \n",
       "...                              ...        ...  \n",
       "2017-06-03 16:35:00+02:00  22.683333  22.900000  \n",
       "2017-06-03 16:40:00+02:00  22.500000  22.683333  \n",
       "2017-06-03 16:45:00+02:00  22.300000  22.500000  \n",
       "2017-06-03 16:50:00+02:00  22.300000  22.300000  \n",
       "2017-06-03 16:55:00+02:00  22.316667  22.300000  \n",
       "\n",
       "[537 rows x 14 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formalize everything into a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_autoregressive_df(exp_id, lu = 1, ly = 3):\n",
    "    \n",
    "    df_wdb = pd.read_pickle(f\"../Data/Experimental_python/Exp{exp_id}_WDB.pkl\")\n",
    "    \n",
    "    df_carnot = pd.read_pickle(f\"../Data/CARNOT_output/Exp{exp_id}_full.pkl\")\n",
    "    df_data = df_carnot.loc[:, ['Power', 'Heat', 'Setpoint', 'OutsideTemp', 'SupplyTemp', 'InsideTemp', 'SolRad']]\n",
    "    df_simulated = df_carnot.loc[:, 'SimulatedTemp']\n",
    "\n",
    "    df = pd.concat([df_wdb, df_data.reset_index(), df_simulated.reset_index()], axis = 1)\n",
    "\n",
    "    df = df.loc[:,~df.columns.duplicated()]\n",
    "    \n",
    "    # Select the potentially useful columns\n",
    "    #df = df.loc[:, ['timestamp', 'zenith', 'azimuth', 'dni', 'dhi', 'OutsideTemp', 'Power', 'InsideTemp', 'SolRad', 'Setpoint']]\n",
    "    df = df.loc[:, ['timestamp','SolRad', 'OutsideTemp', 'Heat', 'SimulatedTemp']]\n",
    "\n",
    "    df.drop(columns = ['timestamp'], inplace = True)\n",
    "    df.loc[:, 'timestamp'] = df_data.index\n",
    "    df.set_index('timestamp', drop = True, inplace = True)\n",
    "    \n",
    "    # Select the input/output and drop the columns that doesn't make to be used\n",
    "    dyn_in = 'Heat'\n",
    "    dyn_out = 'SimulatedTemp'    \n",
    "    df.rename(columns = {dyn_in: 'u', dyn_out: 'y'}, inplace = True)\n",
    "    \n",
    "    # Add the regressive inputs/outputs\n",
    "    for idx in range(1, lu + 1):\n",
    "        df[f\"u_{idx}\"] = df['u'].shift(idx)\n",
    "        \n",
    "    for idx in range(1, ly + 1):\n",
    "        df[f\"y_{idx}\"] = df['y'].shift(idx)\n",
    "        \n",
    "    # Since some lines now have holes, drop them\n",
    "    df.dropna(inplace = True)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>SolRad</th>\n",
       "      <th>OutsideTemp</th>\n",
       "      <th>u</th>\n",
       "      <th>y</th>\n",
       "      <th>u_1</th>\n",
       "      <th>u_2</th>\n",
       "      <th>y_1</th>\n",
       "      <th>y_2</th>\n",
       "      <th>y_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:15:00+02:00</th>\n",
       "      <td>73.860700</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-14466.724138</td>\n",
       "      <td>23.299014</td>\n",
       "      <td>-21598.833333</td>\n",
       "      <td>-21435.000000</td>\n",
       "      <td>22.696056</td>\n",
       "      <td>22.632962</td>\n",
       "      <td>23.324679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:20:00+02:00</th>\n",
       "      <td>76.042533</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-295.689655</td>\n",
       "      <td>23.778789</td>\n",
       "      <td>-14466.724138</td>\n",
       "      <td>-21598.833333</td>\n",
       "      <td>23.299014</td>\n",
       "      <td>22.696056</td>\n",
       "      <td>22.632962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:25:00+02:00</th>\n",
       "      <td>64.981967</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-291.206897</td>\n",
       "      <td>23.767140</td>\n",
       "      <td>-295.689655</td>\n",
       "      <td>-14466.724138</td>\n",
       "      <td>23.778789</td>\n",
       "      <td>23.299014</td>\n",
       "      <td>22.696056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:30:00+02:00</th>\n",
       "      <td>46.667567</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-295.000000</td>\n",
       "      <td>23.760643</td>\n",
       "      <td>-291.206897</td>\n",
       "      <td>-295.689655</td>\n",
       "      <td>23.767140</td>\n",
       "      <td>23.778789</td>\n",
       "      <td>23.299014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:35:00+02:00</th>\n",
       "      <td>35.002967</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-305.517241</td>\n",
       "      <td>23.751253</td>\n",
       "      <td>-295.000000</td>\n",
       "      <td>-291.206897</td>\n",
       "      <td>23.760643</td>\n",
       "      <td>23.767140</td>\n",
       "      <td>23.778789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20 05:35:00+02:00</th>\n",
       "      <td>3.260000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-28.551724</td>\n",
       "      <td>22.586479</td>\n",
       "      <td>-27.931034</td>\n",
       "      <td>-19.137931</td>\n",
       "      <td>22.586479</td>\n",
       "      <td>22.586479</td>\n",
       "      <td>22.586479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20 05:40:00+02:00</th>\n",
       "      <td>3.250000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>22.586479</td>\n",
       "      <td>-28.551724</td>\n",
       "      <td>-27.931034</td>\n",
       "      <td>22.586479</td>\n",
       "      <td>22.586479</td>\n",
       "      <td>22.586479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20 05:45:00+02:00</th>\n",
       "      <td>3.240000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-27.413793</td>\n",
       "      <td>22.518578</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>-28.551724</td>\n",
       "      <td>22.586479</td>\n",
       "      <td>22.586479</td>\n",
       "      <td>22.586479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20 05:50:00+02:00</th>\n",
       "      <td>3.340000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-12.620690</td>\n",
       "      <td>22.518578</td>\n",
       "      <td>-27.413793</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>22.518578</td>\n",
       "      <td>22.586479</td>\n",
       "      <td>22.586479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20 05:55:00+02:00</th>\n",
       "      <td>3.380000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-12.700000</td>\n",
       "      <td>22.497085</td>\n",
       "      <td>-12.620690</td>\n",
       "      <td>-27.413793</td>\n",
       "      <td>22.518578</td>\n",
       "      <td>22.518578</td>\n",
       "      <td>22.586479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4221 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              SolRad  OutsideTemp             u          y  \\\n",
       "timestamp                                                                    \n",
       "2017-06-01 20:15:00+02:00  73.860700         22.0 -14466.724138  23.299014   \n",
       "2017-06-01 20:20:00+02:00  76.042533         22.0   -295.689655  23.778789   \n",
       "2017-06-01 20:25:00+02:00  64.981967         22.0   -291.206897  23.767140   \n",
       "2017-06-01 20:30:00+02:00  46.667567         22.0   -295.000000  23.760643   \n",
       "2017-06-01 20:35:00+02:00  35.002967         22.0   -305.517241  23.751253   \n",
       "...                              ...          ...           ...        ...   \n",
       "2017-07-20 05:35:00+02:00   3.260000         22.0    -28.551724  22.586479   \n",
       "2017-07-20 05:40:00+02:00   3.250000         22.0    -17.000000  22.586479   \n",
       "2017-07-20 05:45:00+02:00   3.240000         22.0    -27.413793  22.518578   \n",
       "2017-07-20 05:50:00+02:00   3.340000         22.0    -12.620690  22.518578   \n",
       "2017-07-20 05:55:00+02:00   3.380000         22.0    -12.700000  22.497085   \n",
       "\n",
       "                                    u_1           u_2        y_1        y_2  \\\n",
       "timestamp                                                                     \n",
       "2017-06-01 20:15:00+02:00 -21598.833333 -21435.000000  22.696056  22.632962   \n",
       "2017-06-01 20:20:00+02:00 -14466.724138 -21598.833333  23.299014  22.696056   \n",
       "2017-06-01 20:25:00+02:00   -295.689655 -14466.724138  23.778789  23.299014   \n",
       "2017-06-01 20:30:00+02:00   -291.206897   -295.689655  23.767140  23.778789   \n",
       "2017-06-01 20:35:00+02:00   -295.000000   -291.206897  23.760643  23.767140   \n",
       "...                                 ...           ...        ...        ...   \n",
       "2017-07-20 05:35:00+02:00    -27.931034    -19.137931  22.586479  22.586479   \n",
       "2017-07-20 05:40:00+02:00    -28.551724    -27.931034  22.586479  22.586479   \n",
       "2017-07-20 05:45:00+02:00    -17.000000    -28.551724  22.586479  22.586479   \n",
       "2017-07-20 05:50:00+02:00    -27.413793    -17.000000  22.518578  22.586479   \n",
       "2017-07-20 05:55:00+02:00    -12.620690    -27.413793  22.518578  22.518578   \n",
       "\n",
       "                                 y_3  \n",
       "timestamp                             \n",
       "2017-06-01 20:15:00+02:00  23.324679  \n",
       "2017-06-01 20:20:00+02:00  22.632962  \n",
       "2017-06-01 20:25:00+02:00  22.696056  \n",
       "2017-06-01 20:30:00+02:00  23.299014  \n",
       "2017-06-01 20:35:00+02:00  23.778789  \n",
       "...                              ...  \n",
       "2017-07-20 05:35:00+02:00  22.586479  \n",
       "2017-07-20 05:40:00+02:00  22.586479  \n",
       "2017-07-20 05:45:00+02:00  22.586479  \n",
       "2017-07-20 05:50:00+02:00  22.586479  \n",
       "2017-07-20 05:55:00+02:00  22.586479  \n",
       "\n",
       "[4221 rows x 9 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lu, ly = 2, 3\n",
    "\n",
    "df_compiled = load_autoregressive_df(1, lu = lu, ly = ly)\n",
    "for idx in [2,4,6,7]:\n",
    "    df_compiled = df_compiled.append(load_autoregressive_df(idx, lu = lu, ly = ly))\n",
    "df_compiled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian Process fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_scaler = RobustScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample the training data from the whole dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sampled = df_compiled.sample(n = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the dataset since it's needed when loading the model from saved files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sampled.to_pickle(\"gp_trainset.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the output from the GP input dataset. Also drop the input at the current time (`u`),\n",
    "since input at time `t` only influences output at time `t+1`, not `t` directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_input = df_sampled.drop(columns = ['u', 'y'])\n",
    "df_output = df_sampled['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_input = df_input.to_numpy()\n",
    "np_output = df_output.to_numpy().reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_input_sc = x_scaler.fit_transform(np_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>name                       </th><th>class    </th><th>transform  </th><th>prior  </th><th>trainable  </th><th>shape  </th><th>dtype  </th><th>value         </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>Sum.kernels[0].variance    </td><td>Parameter</td><td>Softplus   </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>1.0           </td></tr>\n",
       "<tr><td>Sum.kernels[0].lengthscales</td><td>Parameter</td><td>Softplus   </td><td>       </td><td>True       </td><td>(7,)   </td><td>float64</td><td>[1., 1., 1....</td></tr>\n",
       "<tr><td>Sum.kernels[1].variance    </td><td>Parameter</td><td>Softplus   </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>1.0           </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = gpflow.kernels.SquaredExponential(lengthscales=([1] * np_input.shape[1])) + gpflow.kernels.Constant() + gpflow.kernels.White()\n",
    "k = gpflow.kernels.SquaredExponential(lengthscales=([1] * np_input.shape[1])) + gpflow.kernels.Constant()\n",
    "print_summary(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>name                              </th><th>class    </th><th>transform       </th><th>prior  </th><th>trainable  </th><th>shape  </th><th>dtype  </th><th>value         </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>GPR.kernel.kernels[0].variance    </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>1.0           </td></tr>\n",
       "<tr><td>GPR.kernel.kernels[0].lengthscales</td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>(7,)   </td><td>float64</td><td>[1., 1., 1....</td></tr>\n",
       "<tr><td>GPR.kernel.kernels[1].variance    </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>1.0           </td></tr>\n",
       "<tr><td>GPR.likelihood.variance           </td><td>Parameter</td><td>Softplus + Shift</td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>1.0           </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = gpflow.models.GPR(\n",
    "    data = (np_input_sc, np_output), \n",
    "    kernel = k, \n",
    "    mean_function = None\n",
    "    )\n",
    "print_summary(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = gpflow.optimizers.Scipy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished fitting in 0:00:03.674935\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>name                              </th><th>class    </th><th>transform       </th><th>prior  </th><th>trainable  </th><th>shape  </th><th>dtype  </th><th>value                                    </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>GPR.kernel.kernels[0].variance    </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>405.164581621276                         </td></tr>\n",
       "<tr><td>GPR.kernel.kernels[0].lengthscales</td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>(7,)   </td><td>float64</td><td>[246.9104482, 24.82707355, 20.11777904...</td></tr>\n",
       "<tr><td>GPR.kernel.kernels[1].variance    </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>254.598042387761                         </td></tr>\n",
       "<tr><td>GPR.likelihood.variance           </td><td>Parameter</td><td>Softplus + Shift</td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>0.028165256077095475                     </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_time = datetime.now()\n",
    "opt.minimize(m.training_loss, m.trainable_variables)\n",
    "print(f\"Finished fitting in {datetime.now() - start_time}\")\n",
    "print_summary(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_day = 5\n",
    "df_test = load_autoregressive_df(test_day, lu = lu, ly = ly)\n",
    "np_test_in = df_test.drop(columns = ['u', 'y']).to_numpy()\n",
    "np_test_in_sc = x_scaler.transform(np_test_in)\n",
    "np_test_out = df_test['y'].to_numpy().reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean, var = m.predict_f(np_test_in_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 5))\n",
    "plt.plot(df_test.index, np_test_out[:, :], label = 'Measured data')\n",
    "plt.plot(df_test.index, mean[:, :], label = 'Gaussian Process Prediction')\n",
    "plt.fill_between(\n",
    "    df_test.index, \n",
    "    mean[:, 0] - 1.96 * np.sqrt(var[:, 0]),\n",
    "    mean[:, 0] + 1.96 * np.sqrt(var[:, 0]),\n",
    "    alpha = 0.2\n",
    ")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## generate test points for prediction\n",
    "xx = x_scaler.transform(np_test_in)\n",
    "\n",
    "## predict mean and variance of latent GP at test points\n",
    "mean, var = m.predict_f(xx)\n",
    "\n",
    "## generate 10 samples from posterior\n",
    "tf.random.set_seed(1)  # for reproducibility\n",
    "samples = m.predict_f_samples(xx, 10)  # shape (10, 100, 1)\n",
    "\n",
    "## plot\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.title('Posterior sample functions')\n",
    "plt.plot(df_test.index, mean, \"C0\", lw=2)\n",
    "plt.fill_between(\n",
    "    df_test.index,\n",
    "    mean[:, 0] - 1.96 * np.sqrt(var[:, 0]),\n",
    "    mean[:, 0] + 1.96 * np.sqrt(var[:, 0]),\n",
    "    color=\"C0\",\n",
    "    alpha=0.2,\n",
    ")\n",
    "plt.plot(df_test.index, samples[:, :, 0].numpy().T, \"C0\", linewidth=0.5)\n",
    "#plt.plot(df_test.index, np_test_out[:, :], ':', color = 'darkorange', lw = 2)\n",
    "#_ = plt.ylim(21, 23.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "err = abs(np_test_out - mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0630612964229158"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(df_test.index, 1.96 * np.sqrt(var), label = '1.96 * std')\n",
    "plt.plot(df_test.index, err, '.', label = 'Error')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "305\n"
     ]
    }
   ],
   "source": [
    "big_err = (err > 1.96*np.sqrt(var)).numpy().sum()\n",
    "print(big_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5598845598845599"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - big_err/len(err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the fitted model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_params = gpflow.utilities.parameter_dict(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(m_params, open(Path(Path.cwd(), 'gp_params.gpf'), 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(x_scaler, open(Path(Path.cwd(), 'x_scaler.pkl'), 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-import the model and compare prediction with first model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_loaded = gpflow.kernels.SquaredExponential(lengthscales=([1] * np_input.shape[1])) + gpflow.kernels.Constant()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_loaded = gpflow.models.GPR(\n",
    "    data = (np_input_sc, np_output), \n",
    "    kernel = k_loaded,\n",
    "    mean_function = None\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_params_loaded = pickle.load(open(Path(Path.cwd(), 'gp_params.gpf'), 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpflow.utilities.multiple_assign(m_loaded, m_params_loaded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_loaded, var_loaded = m_loaded.predict_f(np_test_in_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LHTt2ZNmyZfTq1euae/qrCxcu8M4777B//34cHR1p27YtISEhN32t7jUp/AghhBD/z/xfR/J1xjFcFPxceAkPk6JFw+du2F4pxeztCUxZE0tGXhF1PRxo5edOkUmx50wqa45d4t2Vx3mtkz8DI2tgMGgAGIwWvPfQPPot68XTO97kC1M+wYH979dtCiGEKEVmUxHjlvbhnMHM16Ev0yh02A3b1nPxZ/Gli1xM2v+PFwt44Pw6DpIOl+45qzSALh/cdvPnnnuOrVu3YmVlxZ49eygsLGTUqFFER0djNBqJjY296fFOTk7Y2Njw1FNP0a1bt5KeJc2bN2fo0KH069ePPn36/O249PR0hgwZwsmTJ9E0jcLCwpJ97dq1w9nZGYCAgADOnDlz3cLPwIEDsbW1xdfXl2nTpgFgNBrp27cvAFu3bqV3797Y29sD0KdPH7Zs2YKmaTz88MO4ubkB4OrqCsDatWuvmZcmIyODzMxMmjdvzssvv8zAgQPp06cP3t7eNGrUiGHDhlFYWEivXr0IDQ297utzq4zr1q1j3759NGrUCIDc3Fw8PDzYtWsXrVu3xt3dHYD+/fv/7e8iMzOT8+fP07t3bwBsbGyum+Gvtm7dWlJga9u2LSkpKaSnpwPQrVs3rK2tsba2xsPDg0uXLuHt7V1y7J49e67JNHDgQDZv3kyvXr2uuae/2r17N61atSp5jR955JFb/pu616TwI4QQQvxFZsZ5PkvaTAuDAx/1Xcw7ywfQ1DsSC8vrv7HILzLx5tIjLNqXSJSfO6908CPEp1LJfqUU+85c5ZO1sUz4+SjR59L5T7//PfVxda3DrI7f8szvTzNs9zu8eGY9AztNx2CUX9FCCFHWKLOZY7HLsLV2pmaNNhQUZGJl5QjA1GWPskllMb5K65sWfQDCanWCSxvZfXwRvSpa4UcHgYGBJR/8AaZPn05ycjIREcWv/SeffIKnpycHDx7EbDaXFBMsLCww/2Uevry8vJLtu3fvZt26dSxYsIDPP/+c9evX8+WXX7Jr1y5WrlxJaGhoSa+SP02YMIE2bdqwdOlSEhISaN26dck+a2vrkq+NRiNFRUXXvZf58+eX5P6TjY0NRqMRKH7fcT1KKTRN+9t2s9nMjh07sLW1vWb7uHHj6NatG6tWraJJkyasXbuWqKgoNm/ezMqVKxk0aBBjxoy57rw7t5NxyJAhvP/++9e0WbZs2XUz/v/7uFPXO+bP69zqdb/Z9f56T/80470m7yqFEEJUeGZTERMXdgbAv1Jtcg0aoyJfw9HJi48e33TD465k5jN83j72nbnKC+3q8lK7uiW9ef6kaRoRvq7MezKSD1ef4MtN8XQPqUobf4+SNt7eTZjTYxFvrXqCj65s58D3rRkcOoKvD0xjVONxMgRMCCF0ZDYVceDIfLbFr2RD+gniDMWFAFuzItegUcdsoLLBil3k0deqKo92vPWEsn51uuK2dRzbLu6g1z3OX+bcQc+c0tK2bVvGjx/PF198wYgRIwCuGaaUnp6Ot7c3BoOB2bNnl8w3U6NGDY4dO0Z+fj55eXmsW7eOFi1akJWVRU5ODl27dqVJkybUqVM8FDw+Pp7IyEgiIyP55ZdfOHfu3DU50tPT8fLyAiiZK6i0RUVFMXToUMaNG4dSiqVLlzJ37lysrKzo3bs3o0ePpnLlyqSmpuLq6krHjh35/PPPSyZhjo6OJjQ0lPj4eBo0aECDBg3YsWMHMTEx2Nra4uXlxdNPP012djb79++/6YTLN9KuXTt69uzJ6NGj8fDwIDU1lczMTCIjI3nxxRdJSUnBycmJRYsW/W2IlJOTE97e3iXDrfLz8zGZTDg6OpKZmXnD12T+/PlMmDCBjRs34ubmhpOT021l/TNTcnIyLi4u/PDDDyVzQ91I48aNGT16NFevXsXR0ZHFixeXDHnTixR+hBBCVHgzVg7j58JLAFheTiJYWRFY7+9dtP8qKT2PAd/s5EJ6LtMHhNMtuOpN22uaxugOdVl7/BJvLj3C2pdbYWv1v6dEbm71+Pzxbcz59VkmJ+9kzf7iN8aV93/OO1L4EUKI++pqajy7jsxj+/ktbM1N4opRw6gUwZo1k6q1xqRMxF09SSUrJ9alHeOoOZdxVaJ4tMOntzVnj2Yw0NymChvykygqzLthr1JROjRNY9myZYwePZqPPvoId3d37O3t+fDDDwEYOXIkffv2ZdGiRbRp06ZkmJSPjw/9+vUjODiYunXrlsw3k5mZSc+ePcnLy0MpxSeffALAmDFjOHnyJEop2rVrR0hICJs2/e8B0muvvcaQIUOYMmUKbdu2vSf3Gh4eztChQ2ncuHgF0qeeeqok9xtvvEGrVq0wGo2EhYUxa9Yspk6dynPPPUdwcDBFRUVERUXx5Zdf8umnn7JhwwaMRiMBAQF06dKFBQsW8PHHH2NpaYmDg8NNl1e/mYCAAN599106duyI2WzG0tKS6dOn06RJEyZNmkTTpk2pWrUq4eHh110BbO7cuTz77LNMnDgRS0tLFi1aRHBwMBYWFoSEhDB06NBr5gaaNGkSTzzxBMHBwdjZ2TF79uzbzlq1alXef/992rRpg1KKrl270rNnz5se4+Xlxfjx44mMjKRatWoEBASUDOPTi3Y/uyFFRESom02SJYQQQtxvR2OWMGDnRDpZVMbSYGR54RU+qNGbbq3fvuExqdkF9P1iO5cz8pg9rDERvq63fb0d8Sk89s1OJnYPYFiLmtdts3rzv9h7cRdJ+Vc5bMpk/aD9150cVAghROlaunYMC86t4bhWhNI0HM2KJhYudKjejpbhz+Lg+Pciv/pjKNCdTtK8evPbjDm9iLkNXyc0aECp5C+rjh8/Tv369fWOIcR9k5WVhYODA0VFRfTu3Zthw4aVzEtUGq73f0rTtH1KqeuOHZUeP0IIISq0z3d9hKOCiT0XYGXlQNdDs2kaPvyG7QuKzIyYt4/zabl8/1TkHRV9AJrWrkxkTVe+2hzPgMjq2Fj+fWx456i36ExxAWjT6Z84eHQB4SF33pVaCCHE7cnLvcrknwewMD+R+hgZ6RJKszoPEejf+5aF99sp+GTmFfLD7rOsj7nMh32DqVHZnqbBgzGc+pHNsUvLfeFHiIpm0qRJrF27lry8PDp27EivXr10zSOFHyGEEBVW9JHv2Uo2L7k1KnmK27zRqJseM/n3E+w6ncpnj4becdHnTy+0q8vAb3fx495zDG7qe8N2LcKeweLUIjacXCqFHyGEuEeiD89n/J4POGeEJ+zr8GKvhaXay/LPocGnkrMxaPDR6hNMHxiOcyVfWhgcmZt+nA4nfqa+/82HjwghHhyTJ0/WO8I17qw/ohBCCFFOXLl8lLd2f4CrWfFYu49v65gDZ6/y7ZZTPNa4Oj1Dve762s1qV6ZJLVcm/3aCyxl5N2zn4FiVSM2eVRknyc5KuuvrCSGEuL7jJ35m+N73UcA3QaN4+eGlpVr0OZGUyaNf7+BSRh7fPxXJqDZ1WHn4IkfOFy8l/XbX2TgreGnbm1xNjS+16wohxF9J4UcIIUSFYjYVsW77hwxe8SgXNTOTw17Gzs7tlsel5RTwyqKDeDrZML5rvX+UQdM0/t27AflFZt5YduSmy34OD3+BKwb4+Bfp8SOEEKVFmc2s3vw2w7e/gaOCWd3m06Ths6V2frNZ8c3mUzw0bStZ+UXMeTKSZnXceCqqFpXsLPlkTSwAld38+LTJWyQbFGN+eZSiwhs/DBBCiLslhR8hhBAVyvs/9eClk/PQgG8avUmj0GG3PCYlK59BM3aTeDWXKf1CcbSx/Mc5ark78EpHP9Ycu8RXm0/dsF1og4EMdfRjccFFJv3QkcTEnf/42kIIUdF9sqQvY04vwh0jX7WZiqdncKmcVynFlpNXePTrnby36jit/N357aUoGtZwAcDJxpKhzXxZF3OZU1eyAAgKeIQJPt3YRR7frHyyVHIIIcRfSeFHCCFEhXHq9Hp+zD1LX6uqLB+0h5CgR2/aXinFFxvjifpoAzFJGXwxMJymtSuXWp6nWtSie3BVPlwdw8/R52/YblT32Txq48Py/As8suYp1m59n2XrxrLnwIxSyyKEEBVFzInlzM46SS9LTxYO2kOtmqWzrHZKVj6Dv9vNoBm7ib+SxUd9g/l6UEMqO1hf025gZA2sjAZmbU8o2dar3Ye0Mzgx9+pBsjIvlkoeIYT4kxR+hBBCVBifb52IjYIXOv0XC0ubW7b/78Z4PlwdQ7M6bvz6Ykva1fcs1TwGg8bkR0Jo7OvKiwui+WpT/HWHfVlZO/JG/1X80uE7qmFkdPz3TEhcxfCDn3DwyAIAcnKSOXhkAas3/4vFa15h+frxxJxYzrGYZcTHrynV3EII8aBSZjPvbp9EJQWvdvuu1Obz2XfmKt2mbmXX6VT+1SOQbePa0q+RD5qm/a2tu6M1D4VUY9HeRNJyCkq2P93oFTINGgs3ji+VTOJaly5dYsCAAdSqVYuGDRvStGlTli5des+vu3fvXl544YVSOVfr1q3x9/cnJCSE5s2bc+LEiVI5b2kqzYyzZs1i1KjiRTe+/PJL5syZc8O2CQkJfP/99yXfl+br7uvrS3Jy8j8+z1/v535fW1b1EkIIUSGcjFvNGnM6Iyo1wNW1zg3bJaXnMXzePg4lpmFW0Cu0GlP6hWIw/P3Ne2mwsTQye1hjXll0kPd/jWH/2as8FFKNOh4O1KvidE1bL6/GzHnkN37d8RE+lesxKXoaw/e8i+vu9zhvUJj+/weMc7+UfNljlwevdP7qpvcuhBDl3cFjCzhoKGRClXY4O1f/x+c7nZzNvJ1nmL09gWqVbFkyohlBXs63PO6ZqFosPZDIlDWxvN0zCIDAen1ouusD5l7Zw2NZl7Fz8PjH+UQxpRS9evViyJAhJcWBM2fOsHz58nt+7YiICCIiIkrtfPPnzyciIoKvv/6aMWPG/O0eTCYTRqOx1K53N+5FxuHDh990/5+FnwEDBgCl/7o/6KTHjxBCiAph/p4p2JgVA9u8f8M2Jy9l0mv6NuIuZ/FMVG0mdg/go4dD7lnR5082lkamPRrGm93qsz7mMqO+P8BD07aWrPryV/YOVXi4wxQiw59hepvPaGrhQoCVC086BzLVbwiLm3/Emo5z+DnqMz6u+TCf1hnIUw7+rCq4xEM/92L5+uInycpsprAw557elxBClAVmUxH/XtiNTTun8POR2diaFd2av37H58kpKGJT7BWmb4hj5Px9NP9gPW0mb2T29gR6hFbjl1EtbqvoA+BfxZHHm9Rg3s4zHL3wv5/1zzV8mVQDTFslc/2UpvXr12NlZXVN8aBGjRo8//zzQHHRoGXLloSHhxMeHs727dsB2LhxI927dy85ZtSoUcyaNQuAcePGERAQQHBwMK+++ioAixYtIigoiJCQEKKiov52jt27d9OsWTPCwsJo1qxZSW+YWbNm0adPHzp37kzdunV57bXXbnlPUVFRxMXFAeDg4MDEiROJjIxkx44dTJkyhaCgIIKCgvj0009LjpkzZw7BwcGEhIQwaNAgAK5cuULfvn1p1KgRjRo1Ytu2bQBs2rSJ0NBQQkNDCQsLIzMzk4sXLxIVFUVoaChBQUFs2bLlrjPOmzePxo0bExoayrPPPovJZAJg5syZ+Pn50apVq5IsAJMmTSpZIj0uLo727dsTEhJCeHg48fHxjBs3ji1bthAaGsonn3xyzeuemppKr169CA4OpkmTJhw6dKjknMOGDaN169bUqlWLqVOn3vJ1v9Fr26tXLxo2bEhgYCBff/11yfYb3c+NXveUlBQ6duxIWFgYzz777E0XALkjSqn79qdhw4ZKCCGEuN9SUk6q8JmB6l8/dPrbPrPZrPadSVUrD11Q4W//riLeXaOOnk/XIWWxK5l56nBimmr07hrV/j8bVW5BUamcNy7udzV4ZrgKmhWk/vNTb9XvuxDVcUaQSkjYXCrnF0KIsmrZurEqaFaQavxdoIr8LlCNn9/mto89fzVHzdmRoIZ8t0vVfWOVqjF2haoxdoWK+mi9Gjl/n5q59ZS6kJZzV7nSsgtU+Nu/q4embVEFRaaS7e8s6KIazAxUB48suKvzlkXHjh3T9fqfffaZeumll264Pzs7W+Xm5iqllIqNjVV/fm7dsGGD6tatW0m75557Ts2cOVOlpKQoPz8/ZTablVJKXb16VSmlVFBQkEpMTLxm21/PkZ6ergoLC5VSSq1Zs0b16dNHKaXUzJkzVc2aNVVaWprKzc1V1atXV2fPnv1bzlatWqk9e/YopZT66KOPVL9+/ZRSSgFq4cKFSiml9u7dq4KCglRWVpbKzMxUAQEBav/+/erIkSPKz89PXblyRSmlVEpKilJKqccee0xt2bJFKaXUmTNnVL169ZRSSnXv3l1t3bpVKaVUZmamKiwsVJMnT1bvvvuuUkqpoqIilZGRcVcZjx07prp3764KCgqUUkqNGDFCzZ49W124cEH5+Pioy5cvq/z8fNWsWTP13HPPKaWUeuutt9THH3+slFKqcePGasmSJUoppXJzc1V2dvbf/q7++v2oUaPUpEmTlFJKrVu3ToWEhJScs2nTpiovL09duXJFubq6lmT6qxo1aqgrV67c8LX96+uZk5OjAgMDVXJy8k3v50av+/PPP6/+9a9/KaWUWrFihQJK/s7+6nr/p4C96ga1GBnqJYQQolwrLMxh8q/PUKBpDGz0yt/2T1kTy7T1xU+jPJ2s+eHpJtRyd7jfMUu4OVjj5mDNx4+EMOS73Xy4Ooa3Hgr8x+etXbsDM6q35I0fOzMz6yTOKAwaDFk3gnkdv8Xbu0kppBdCiLIlJyeZqQkr8NOMXMREpkGjZ/0BNz3mbEoOP+1PZO2xSxy7mAFAjcp2PB5Zgzb13An2roSz7T9f3dHZzpJ3egUxcv5+pm+I46X2fgC81HUGmxZ14PVd77KwehQOjlX/8bXKkg93f0hMakypnrOeaz3GNh572+2fe+45tm7dipWVFXv27KGwsJBRo0YRHR2N0WgkNjb2psc7OTlhY2PDU089Rbdu3Up6ljRv3pyhQ4fSr18/+vTp87fj0tPTGTJkCCdPnkTTNAoLC0v2tWvXDmfn4h5jAQEBnDlzBh8fn7+dY+DAgdja2uLr68u0adMAMBqN9O3bF4CtW7fSu3dv7O3tAejTpw9btmxB0zQefvhh3NzcAHB1dQVg7dq1HDt2rOT8GRkZZGZm0rx5c15++WUGDhxInz598Pb2plGjRgwbNozCwkJ69epFaGjodV+fW2Vct24d+/bto1GjRgDk5ubi4eHBrl27aN26Ne7u7gD079//b38XmZmZnD9/nt69ewNgY3PrORu3bt3K4sWLAWjbti0pKSmkpxf3tOvWrRvW1tZYW1vj4eHBpUuX8Pb2vuF5rvfahoWFMXXq1JI5o86dO8fJkydJSkq64f3c6HXfvHkzS5YsKcnm4uJyy/u7HVL4EUIIUW4VFeYx4vtW7CKPZ5wCqF27wzX75+08w7T1cTzc0JsBkdWp7e5QKm/mS0MrP3eGNvNl5rYE2tbzoGVd9398TgtLG/7d/3dabHmbyIBHycq5xKMbX+TTja8x+fHNpZBaCCHKlkUbxnPZqDE5bAy5+emsP7WSiOChf2unlGLNsUvM3XmGLSeTMWjQsIYL47rUo319D2q7O1x3ouZ/qmuDqvQO82La+jg6BHgSWM0ZB8eqfNjwNYYd+Ii3f36UDwdsYO7q4exLOcpng7bd+qTibwIDA0s++ANMnz6d5OTkkjlgPvnkEzw9PTl48CBms7mkmGBhYYHZbC45Li8vr2T77t27WbduHQsWLODzzz9n/fr1fPnll+zatYuVK1cSGhpKdHT0NTkmTJhAmzZtWLp0KQkJCbRu3bpkn7X1/1Z/MxqNFBUVXfde/pw/569sbGxK5sxRNxgapJS67r9hs9nMjh07sLW1vWb7uHHj6NatG6tWraJJkyasXbuWqKgoNm/ezMqVKxk0aBBjxoxh8ODBd5VxyJAhvP/+tcPvly1bdsv/Zze6vzs95s/r3O7rfrNrb9y4kbVr17Jjxw7s7Oxo3bp1yb+VG93PjV73mx3zT0jhRwghRLn1/ZoX2EUeE6u255GOn1yzb0d8Cm8tP0rbeh580KcBFsayN+3duC712BqXzPM/HGDGkAga1nD9x+c0WljxUJt3AfAgiMHRQXyVcZQnYpYQWO/vTyeFEOJBpcxmll3eRTAWhAU/DkCzRs/9rd2VzHzGLT7EupjLVHW2YXR7P/o38qGK8617EpSGSQ8FsuXkFcYvOcySkc0xGjTCQwYz8swapl2NpurSR5iTeYIiTSPt6mkqudS8L7nulTvpmVNa2rZty/jx4/niiy8YMWIEADk5/5vnLj09HW9vbwwGA7Nnzy6Zb6ZGjRocO3aM/Px88vLyWLduHS1atCArK4ucnBy6du1KkyZNqFOneOGE+Ph4IiMjiYyM5JdffuHcuXPX5EhPT8fLywugZK6g0hYVFcXQoUMZN24cSimWLl3K3LlzsbKyonfv3owePZrKlSuTmpqKq6srHTt25PPPP2fMmDEAREdHExoaSnx8PA0aNKBBgwbs2LGDmJgYbG1t8fLy4umnnyY7O5v9+/dft/BzK+3ataNnz56MHj0aDw8PUlNTyczMJDIykhdffJGUlBScnJxYtGgRISEh1xzr5OSEt7c3y5Yto1evXuTn52MymXB0dCQzM/OGr8n8+fOZMGECGzduxM3NDScnp+u2vZvX9uzZs7i4uGBnZ0dMTAw7d+4EuOn93Oh1/zPrm2++ya+//srVq1fvOOf13PJdrqZpPpqmbdA07bimaUc1TXvxL/ue1zTtxB/bPyqVREIIIUQpSLp4gOmXttNSs+fh9v+5Zt+hxDRGzt9HTTd7Pns0tEwWfaB40ucZQyJwsbPisW92serwxVK/xtD2n+BiVny664NSP7cQQugp5uQvxBnM9KjW8oZtDiem033aFrbGJTOxewBbXmvDi+3r3reiDxQP+ZrQPYCDienM2p5Qsv3JrjNogi3fZcVi+mPbsbhV9y1XeaJpGsuWLWPTpk3UrFmTxo0bM2TIED788EMARo4cyezZs2nSpAmxsbElQ3l8fHzo168fwcHBDBw4kLCwMKB4uFH37t0JDg6mVatWfPJJ8cOlMWPG0KBBA4KCgoiKivpb0eK1117j9ddfp3nz5iXFpdIWHh7O0KFDady4MZGRkTz11FOEhYURGBjIG2+8QatWrQgJCeHll18GYOrUqezdu5fg4GACAgL48ssvAfj0009LJqq2tbWlS5cubNy4sWSy58WLF/Piiy/eLMoNBQQE8O6779KxY0eCg4Pp0KEDFy9epGrVqkyaNImmTZvSvn17wsPDr3v83LlzmTp1KsHBwTRr1oykpCSCg4OxsLAgJCSk5O/jT5MmTSq5x3HjxjF79uy7yn2j17Zz584UFRURHBzMhAkTaNKkePj8ze7nRq/7W2+9xebNmwkPD+f333+nevV/vvoggHarrlKaplUFqiql9mua5gjsA3oBnsAbQDelVL6maR5Kqcs3O1dERITau3dvqQQXQggh/j9lNnP4+CL2nFrNzOQ95ANL2n+Dj0/TkjYrDl3glR8P4uZgzfynIvF1s9cv8G1KzS7g6Tl72X/2Kk+1qEmvMC8Cq93eyjG3Y+6qZ/noyna+DnqOpg1vvlyqEEI8KD788SEW5pxmQ68VOFfy/dv+VYcv8vKP0VS2t+abwREEVLvzHgClRSnFU7P3siUumWUjm5dkSb5ynBdWDKCXVyveubiOF13CGdD2Y37bNZmjVw4zov1nVHbz0y337Tp+/Dj169fXO4YQ5cb1/k9pmrZPKXXdNexvWfj52wGa9jPwOfA08LVSau3tHiuFHyGEEKUtK/MiWdmXqFIllCmL+zAz6yQAjZU1r7f8N3VqdwSK31RPXRfHJ2tjiajhwpeDGuLmYH2zU5cpeYUmXl9ymGXR51EKujaowpvdAqhW6e9jw+9Ufl46D33fHBfNgh8G78VglJHgQogHmzKbaTsrmFDLSnwyaOu1+5Ti8/Vx/GdNLOHVK/HVoAjcHfX/fZCSlU+Xz7bgaGPBL8+3wM7q2p/F3b5rgJ+lMwrFOnPxpNPjPVvxWOfP9Yh7R6TwI0TputPCzx29s9M0zRcIA3YBHwMtNU17D8gDXlVK7bnOMc8AzwCl1k1JCCFExWY2FbH/8DymHPiMw4biSfhaYM9Wsult5cnINpOpUiX0mmN+3HuOT9bG0ifci/f7NMDawqhD8rtnY2nkk/6hvNGtPt/vOsvn6+NYdTgJP08HXO2tsLOywNbSiMGg4WBtQSU7Swwa+Hk60srPnUp2Vjc8t7WNM8/5dufNcyvZunc6UZF313VbCCHKitNnNpBs1GhZ9doVC01mxauLDrL0wHl6hxX/PrCxLBu/Dyo7WPNp/1AGztjFv5Yf48OHg6/ZH2DlwvbCVDI1eMKhLj9nnuRoKa+OJYQon2678KNpmgOwGHhJKZWhaZoF4AI0ARoBP2qaVkv9vy5ESqmvga+huMdPqSUXQghRIX26+GFmZ8ZQpGl4KsWoSmFkFmQyLzuO+sqCN3ovxtrm2mFQMUkZTPz5KC3quPHxwyEYDaW/WsL94uZgzQvt6tIr1IsVhy+wN+EqWXlFXM7MI7fAhFlBZl4haTmFmJXCrMDSqNG2ngfDW9UmrPr1lwXt2nIiU+atYEnsj1L4EUI8sJavH4+rQ1USU4uXTG7k3/ua/Z+tO8nSA+cZ3d6PF9rVuSer5/wTzeq4MbJ1baZviKdZncr0DPUq2RdYyY/VKbswKMWA5hM5+fsIjuZf0TGtEOJBcVuFH03TLCku+sxXSi35Y3MisOSPQs9uTdPMgBsgP32EEELcE8lXjjM7M4bGmh2dvKLo3GwcdnZuADyauBNnJ5+/FX2y84sYOX8/TraWfNI/9IEu+vxV9cp2jGxd56ZtTGbFocQ0Vh2+yJL95+l9dDvNalfGwdqCKD93eod5YW9d/FbA0tKOHo51mZd1kuTkGNzc6t2P2xBCiFKTlBTNW2eX42qGQAtnqpgU3l7/m+NtQ8xlpq47ycMNvctk0edPL7X3Y9epVF5fcph6VZzwr+IIQKBXU0jZRZTBiSpVwwh08mV7+hFycpJLfheWZTdaTlwIcWfuZkn721nVSwNmAMeVUlP+smsZ0PaPNn6AFZB8xwmEEEKI27RsxwcUaRqvt/qYPu0nX/NG19u7CY5OXte0N5kV45ceJiE5m88eDS0TczjcT0aDRlh1F97oFsCm19owsnVtMvIKOXEpkzeXHaHTp5u5lJFX0r53w+cp0jR+2fmxjqmFEOLuzNsyiSJN47JRY4PKoJG1B5qh+OPOudQcXloYTf2qTrzTM6hMFyAsjQamDwzH3tqC3v/dRvMP1rPm2CWC/HvTHDuejRgNQGCVCMyaxom41TonvjUbGxtSUlLu6gOrEOJ/lFKkpKRgY3NnKw/ezqpeLYAtwGHA/Mfm8cBa4DsgFCigeI6f9Tc7l0zuLIQQ4m6ZTUV0nR1GNYM13w299e+StJwCXlgQzebYK7zSwY/n29W9DykfDEopdsSn8PScvfi42vHj8KY42VgCMHhWQ66aC1k+NLrkA5MQQpR16eln6bikK62t3IgtuEqcwczb3l1o3/w95uw4w6K950jJLuCXUS0eiNUcAY5eSGfezjNsjk3G1srI7y9FYfhLr9XLl47QbvVjjPVozuNdvtQx6a0VFhaSmJhIXl7erRsLIW7KxsYGb29vLC0tr9n+jyZ3VkptBW5UEn/8jlMKIYQQd2HFpomcN8JLNR+6ZdtzqTkMmbmbxNRc/t27AY819rkPCR8cmqbRrI4bXw5qyBMz9/DaokN88Xg4mqbR26cdExN/5cDheYSHDNY7qhBC3Javfn+eXA2ejBxL/PldTDj1E00DHmPCsiMsi75AAy9n3uvd4IEp+gAEVnPm/T7B/HLwAs//cIDfjibRpUHVkv0enkF4mBRHU0/omPL2WFpaUrNmTb1jCFFhyaM8IYQQZV5ycgwfnVlOmLKiY/M3btjuQlou7/96nK5Tt5Ccmc/cJxszILJ6me7Sr6eWdd0Z27keq48mMXNbAgCdIl/FzqxYcnSOvuGEEOI2xcX/zvfZ8fS18cKvThe6tJrE1ke3kJhfg2XRFxjVpg6/PN+C5nXK/jw419O1QVVqudnz2k+HaDt5I20nb2TsT4cACLasxO78K5hNRXz7y1B+/P0lfcMKIcokKfwIIYQo8z5a/Sw5Gkxq9TEG4987q55LzWH0wmhafrSBbzafIqquO0tGNieyVmUd0j5YnmpZk7b1PJj8+wkuZ+Zh5+BBF5tq/J6fRGbGeb3jCSHELU3eOgF7BS90/G/JNivrSkz65ShVnW0Y2aa2jun+OaNB473eDWhTz4NAL2fcHa1ZuPccu06l0M6nTfGcRjsnMz1lLz+e36B3XCFEGXTby7kLIYQQetgbPYtfTakMd25ArZptgeI5atYcu8T0jfGcvpJFdoEJS6PGE818GdrcF28XO51TPzg0TWNC9wDaT9nEtHVxvNMriP7hz7F455t8u+YFRvddrHdEIYS4oWMxy9hGDi+5NcLF9X8Fnl8OXeDI+Qw+7R+KndWD/5Gnae3KNK1d/DAjr9BEy4828Nm6k3zz2HNYn17KpNh5FBk0EpQJU1EBRgsrnRMLIcoS6fEjhBCizCoqzOPDA59QxaQY1mk6AOk5hYyYt59n5u4jLaeAPuHejGhVmw2vtubN7gFS9LkLNd3seayxDz/sPsvp5Gzq+/ekh6U7czNPcO7cNr3jCSHEDX23dwoOZkW/Vu+WbCsoMvOf32OpX9WJHiHVdEx3b9hYGhneqjbb41M4ctmSlhaVSDNoGJUi36Bx4eI+vSMKIcoYKfwIIYQos75a8QQxBjOv+Q3A1s6VuMuZPPT5VtYev8TrXeqx7uVWTOoRyKud/KnqbKt33AfaC+3qYmVhYPJvxZOEvthuKhYK3lv/MspsvsXRQghx/507t401Ran0c/LH0cmrZPvcnWc4m5rDa538r1kFqzwZGFkdd0drPl17ks6+nQDoa+MNwOkLO/WMJoQog6TwI4QQokzafeBbvko/TE9LTzq0GE/c5Sx6/3c7OQVF/Di8Kc+2qo2FUX6NlRYPRxuealmLlYcvEn0uDQ/PIF6u2oZt5DD/t5EAnDi5kik/9cZsKtI5rRBCwKxt72AEHm/5dsm2y5l5fLomlig/d1r7u+sX7h77s9fPjlMpOFZ9hq8ChjOqY3HP2FNXjjLph47M/3WkzimFEGWFvGMWQghR5pw4uZKXoj/F12xgfI/vMZsVYxcfwqBpLB3ZnPDqLnpHLJeeblmTyvZWfPDrcZRS9O/4Ga01R6Zc2sqBQ/MYt3U8M7PjSDi7Se+oQogKLjk5hmV5ifSwroa7RyAX0nIZ9f1+Bnyzi7wiE5MeCij3Kzr+2etnwLd7efqX2lzMc8fVrNiWcojFBRdZlbRD74hCiDJCCj9CCCHKlOysJEZsGYudgq86z8TOwYPZOxLYd+YqE7sH4OMqc/jcK442ljzftg47T6WyKfYKmsHAOw/Nx8OsMWz/B8QZiod8HTr1u85JhRAV3febJ1AIDG36BgATfz7KmmOXsLe24N1eQdRyd9A34H1gY2nkvwPDeb5tHcwK5uxIoKZmw05yAYilEFNRgc4phRBlgRR+hBBClCm/7ZzMFaPGhw3HULVaQ46cT+f9VTG08XenT7jXrU8g/pEBkTWo7mrHB7/GYDIrKrnUZFrUx1gpiNIccDArjlw5RGFhDsnJMXrHFUJUQNlZSSxIP057YyV8fVuxIeYya49fYnQHP35+rjn9G1XXO+J908jXlVc6+tMjpBrLD17A17p45S9NKfIMGmcTt+ucUAhRFkjhRwghRJmy5Nx6apk0whsMIiOvkJHz9+Nqb8V/+oWW+277ZYGVhYExnfyJScpk1vYEAOrW6cyqh5bwaf+1BBpsOZx7kak/D6THLw+Tn5eub2AhRIXz06YJZBo0hjV8kfwiE//65Si13OwZ1rym3tF0MyCyOjkFJgpziws/3SyL5zc6IUNzhRBI4UcIIUQZEh+/hoOGQvp4RoKm8dqiQ1xIy2X6wDBc7a30jldhdA+uSrt6Hny0Ooa4y1kAVHbzw9LangYONYjVivgp8ySZBo1DMYt1TiuEqEgK8jOZc2kHjZU1QQGP8O2W0ySk5DCpRyBWFhX3o02wtzMhPpXYeiaY2nkazWu9gYVSxFw5rHc0IUQZUHF/OgohhChzFuz9BAul6B45htnbE1h9NImxnevRsIar3tEqFE3TeL9vA+ysjDz/wwFyC0wl+4KqNqJI08j6Y4nkvQnr9IophKiAFqx7hctGjSeDhnExPZfP18fRKdCTKL/yu4LX7dA0je+fimTGc89xMWUKPx53opYyciIrsaRNTk4yew7MICvzoo5JhRB6kMKPEEKIMiHp4gEW556lp3U1rpqr8f6vxfP6PNWy4nbd15OHow1T+ocSk5TB6IXRLNmfyOWMPILrdAcg0GzE32xgX3qszkmFEBVFeloCX13aTjNsaRYxkk/WxGIyK97sFqB3tDLB3tqCOh6OPB5Zg82xV6hl4cIJU3GvzS27p9Lyx9YMO/Qp3/7+vM5JhRD3mxR+hBBClAlfb3odBQxr8S6vLDqIrZWRD/sGy7w+Omrj78HL7f1YfTSJl388yEsLo3H3CGSofR1eCXuBRvbVOWjOpTA/W++oQogKYMa6l8nS4JXm/yL2UiY/7UtkUNMastrj//NYZHWsjAaMBVW5YtS4cGEv3x2bg7tZw9ekcSgzQe+IQoj7TAo/QgghdHf8xM8szUukr211lp904eC5NN7tFYSHk43e0Sq859vVZefr7Xilgx/b41PYEZ/CKw8vpVHoMCK8mpNn0DhyYqneMYUQ5Vx+XjpLMmJpZ3TBr04XPlodg721BaPa1NE7Wpnj5mDNwxHebDvbFKNSTF73Enu1fHpXDiXS3ptjKg+zqUjvmEKI+0gKP0IIIXRVkJ/J+O0TqWSGTqEf8dm6k3QPrkr34Gp6RxN/qOJsw9NRtfBwtObtFceYviGOSxl5NAzoj5VSvLr3A1ZunKh3TCFEObZm52TSDRr9Ah5n9+lU1h6/zIjWtXGRif+v67k2dbhUVJfIgkqsMRevvtgpbBSBlQPJNmgknN3ElMV9iI9fo3NSIcT9IIUfIYQQuvrviieIM5iZFPAkH2/MxNnWknd6BukdS/w/NpZGXunoR0xSBh//doIR8/bhXMmXGQ1fx0OzZHzCEk6dXq93TCFEOfVTwq/4mKBR6DDe//U4VZxseKKZzAF3I16VbHm0sQ8nzncDwC/XQK9Z6fh4Ngfg6+3vMjPrJAv3fqpjSiHE/SKFHyGEELo5eGQBMzNj6G3lCa7FT3Gfb1tXnuCWUf0bVSfuva582LcB+8+msfzgBUIbDOTzhxZireDzrW/pHVEIUc6YTUVMXdKPfVo+/TwiWXM8mQNn0xjdoS62Vka945Vpr3T0Z3CHRxlo0YKW7sNJzy1k7bmaWJsVK4uSAdiTk3iLswghygMp/AghhNDNv/a8j6cZxnSfw8e/xZQ8oRRll9Gg8UhDHxp4OfPuyuOcT8ulspsfQyo1YI05jU07p+gdUQhRjnz+8wC+yTxOX6uq9G//GR+tPkEdDwf6hnvrHa3Mc7a1ZFiLmowb+AUv9R1BtwZVmbvzIn5YAuBiVsQZzFxNjdc5qRDiXpPCjxBCCF2kpsZx0mBmgEcTtiZoHDmfwegOflhbyBPcss5g0PiwbzB5BSYGfbuL5Kx8hrSfQh2zgVEnZvLt8iF6RxRClANnzmxhZsYxulu48Vb/1SyOTuZUcjZjO9fDwigfY+7U823rkl1gwiLdEU0pXvR+CID9xxfpnEwIca/JT0whhBC6OHF6LQB+VRox+ffiJ7i9w7x0TiVuV0A1J2Y+0YgL6bm88MMBbO2r8H3/DbTU7Pk6ZR+FhTl6RxRCPOAmbxqLtYJXOv6X1JxCJv9+gsY1XWlf30PvaA8k/yqOzBgSQWP/N/FNbM/R7D7YmBV7z2/TO5oQ4h6z0DuAEEKIiunExb0AJOQGEX8ljS8fD8do0HROJe5EhK8rb/cM4rWfDvH5+jhebF+XHr5d2XJ6ETGxKzl1aR/5hbn06/SZ3lGFEA+YpKRoNqpMRrqE4OZen5d/jCY7v4j3egWhafK74m61q+9Ju/odOZ9bhTm7E2lZ14aV2afJ+L4dgyNG4+/XHYC90bPYfmol2UW5ZBfl0b5WF1o3eUXn9EKIuyWFHyGEELqISY/H06SYvi2fBl7OdAqsonckcRceaejNzvgUPlkbi6uDFZ3r9YXTi9h1aiVzk/diVNBP75BCiAfOnj+GH7UNeIw9Caks2X+e59rUpq6no87JyoeXO/jz65EkTCmNqea6nQ35l/h92zhGnFiCGTPTUoofztgryNPgTMx8KfwI8QCToV5CCCF0cSI/hRrKjvNpuYzp5C9PcB9QmqbxQd9g2tf3YMKyIxxKcaO6CeYm7yXVoHHFqHHp0iG9YwohHgA5WZcZODOchb+9wK6Lu6hkVtSu1Ym3fzlGFScbnmtTR++I5Ya7ozU/PtuU2IK+HD37Ie+Ff0ugZsMnqXv4LHUfUQYndjy8nu1PHKG7dRXOmQv0jiyE+Aek8COEEOK+y8u9ymmDGbJciKzpSsu6bnpHEv+AlYWB/w5sSI3Kdny+IY6Gtp6kGjQ0pQA4Ev8r587tIOniAZ2TCiHKsmm/PsUhQyGfX1jPzrwkGllU4qf9Fzl8Pp2xXfyxs5LBCqXJz9ORxSOb4e5ozfBFV2lRayEr233H/Ig3+XTARuwciudS8rGvRopRIyfrss6JhRB3S356CiGEuO/iTq/FpGmkZ/swultd6e1TDlhZGBjWvCZvLT9KN68GUHCJLpZu/FaYzJGLe/g4Zh7VDNZ8N3Sv3lGFEGXQkWOLmJ99inCs2W8o7l1Sxz6QicuPElnTlZ4hMvn/veBVyZafhjdj9MJo3l0Vw/sGDYPmCBQvwGBraeSNpjWAA5y7uAf/ut10zSuEuDtS+BFCCHHf7T31GwCWtg1pVruyzmlEaXkkwpspa2JZHhuCj8saugc9S/yhD/gpI4Y0o8YVlUdBfiZW1jJHhxDif5TZzEe7P6CyGaY/soJnf+rKIUMRv8X64elkzX8HhmOQyf/vGVd7K2Y90YhNsVfYfTq1ZLsCvt58ioS04jn4Ei8fxsJgxU/7p3Es+zwWaNgYLPG19eDVvkvRDDKYRIiySgo/Qggh7qv8vHTmXN6JX4GR3s27Sm+fcsTOyoLXOvszf6ctcac/ZrlzNYJsPDlRcBGAAk3j6ImfCQt+XOekQoiyZP3OjzmgFTDRuwMOjlV5rdFYZmz/llUZ/vw8qiGVHaz1jljuaZpGa38PWvt7XLP96IUM1p/JAk84kxrLv0/MI80AgVhTiOJ8URabc7IYdPkQVaqE6hNeCHFLUpYVQghxXy3eOJ4rRg3D1Q70CPPWO44oZQMja7DqxZYMiKzO0gPn8XX0B6C7RfE8TvvPrNMznhCiDJp+4ntqmjR6t3kfgFSLtiw/NZoRresSWM1Z53QVW/cGVTmRao+z2cy65GguGzUmVu/OnKH7mDN0H/8KfR6AMxd265xUCHEzUvgRQghx35iKCph1YRN+eQaq1XgMG0uj3pHEPfJsq1oYNI1f40LxLzDweKNJ1DRp7L8ao3c0IUQZkpCwiZMGM49Wa4mFpQ2ZeYWMX3qYuh4OjGorq3jprVNgFSyNGu6FRg4ZCgHYfqERL/8YzSs/HiTHUA+AM1eO6hlTCHELUvgRQghx3+zY/xUXjRqGlAi6BFfTO464h6o62/JSh7qkU5fo0+8z74gL4bZVOGDKxGwq4sixRYyY3YS83Kt6RxVC6GjT0fkANPQbyIytp3lpQTRJGXl8+HAw1hbycEBvznaWDGrii2ORLQDVCxTbzruw+3QqKw9f4L87wNasOJ1xWuekQoibkcKPEEKI+2bJiYVUMpmJye1OlJ+73nHEPTaydR1WvxTFoKY1WLjnHLWdQsg0aOw9NIupez5mK9kcP7lS75hCCB1turKfOmYDU7ZZ8c6KY6yLuczzbesSXt1F72jiDxMfCiCiSg0AWlWqzdaxbdk6ti0vtKvLnjOZVFdGzuTKUu9ClGVS+BFCCFGqCvIz+WRxX9LTEq7ZnpIcywZTGnWyPGnm54OdlawvUFG80LYuDtYWrD3fEU+TYuKBz9hBLgAnL+7ROZ0QQi8Z6efYTx5hljX57eglXu3oR8w7nXm5g5/e0cT/U925JgBNa7Qr2dY7zAtNg8pmO84UZV/TvrAwh7Sr0gtIiLJCCj9CCCFK1eGYJXyXFcsvOz64ZvuPWydRpGmcTu3IqDZ1dUon9OBib8UrHf3ZdKqA3s5dOG8sHhpgZ1bEpp3UO54QQicrd3yISdOIvRRCLXd7nomqLXO/lVEdIkczxr0pTcOeKdlW1dmW5rXdMOc4cd6g+HXTJEbNacqJ2BUMnNeM/kt7oMxmHVMLIf4khR8hhBClKjXzPAA7rxws2ZadlcS8q4cIyraiUWAXGnjLKi0VzeNNahDiU4mvDrejUZ49va0b4KdZcTL3Erv2f83IOU0oLMzRO6YQ4j65cvko0y5uJKTQkq1XIpjQPQArC/loUlbZO1RhcNevsbC0uWb7M1G1yMnzwKRpTDq1iE0qi4d3vM5xg4kLRog/vVanxEKIv5KfrkIIIUpVanYSAHtMmRQW5pCfl863v40iw6CRfbUbYzr765xQ6MFo0Jj8cDBBXpU4dOVdliU8SR1rd06qfGYf+Y4tKpv4U/IBQYiKIDPjPG/++gT5GqRcepzmddxpLfO+PZCi/Nx5pk0nAHIMBqIygwjLt2aEY2MA9pxczrlz2zgas0TPmEJUeDLBghBCiFKVmpsCQI5BY8aqZ5iVGk22QSMg25JWzQfj4WhzizOI8qqupyPfP92Edccv8eTsvTgafMg0XGCrOQs0jSNnN1LPv4feMYUQ91BSUjRDVw0iyaDoY4jku0x/Vgytj6ZpekcTdynULwqOfUi9QgNn1QgSLuRyKduaKs672HMlmkVrt5CpTKyp10fvqEJUWFL4EUIIUapS869iY1YUaDA97SC1lIHKqW05XdCBYc1r6h1PlAGt/T2o7mpHzGUvcAKlaRiV4kjyER7WO5wQ4p6as+UtLhkUX4aM5dnlnnQIcCXIS4b/PsicnavzkmsEzf16U88/itVHkhg+bx9dHSux3pSGyaABGpcvHcHDM0jvuEJUSDLUSwghRKlKLcykqjIQpCxxNCvqGl5m4+UO/PvhJjJppwCKh30NbebLjovFhcCqRYpGmh1H8y7pnEwIcS/lZF1mWVY8HSwqE53RjPTcQka1qaN3LFEKnnxoZkmPzU6BnrTxd+dqig8mTcPGrAA4HL+SnKzLZGcl6RlViApJCj9CCCFKVWpRLq4GK/7VZjotDS/zU4w7YzrVo2ntynpHE2XIkGa+zBvRi5ACSzxSa1PL2ouTmom83Kt6RxNC3CO/bHuXTINGV78n+O+GOFrWdSPEp5LesUQp0zSNrwZF8GS7p7BQiqCr/lgoxZGLe3j+p668tFiG9Apxv0nhRwghRKlKNRfgYrThtVWKH2M8ebWjH8Nb1dI7lihjjAaNEJ9KfD5wJ9FZI0nL9MWkacTE/6p3NCHEPfLzhS3UMxv49pA/hSbFOz1l2E95ZWVhoH1EOz5vNIsNl4fiW2RgVXoMu7V8os05FBXm6R1RiApFCj9CCCFKVapmJi/HggPn0pg+IJxRbevKpJ3ihirZWTG0mS8bzxYP9zhybrPOiYQQ90JG+jmOaoXUN9Ria1wqb3Srj6+bvd6xxD3WPDCC9vWr4pDtxAVj8XuBPIPGqTMbdE4mRMUihR8hhBClJjc/hzSDRna2FRO6BdC1QVW9I4kHwJMtapKl1aRGIaxI2okym/WOJIQoZXuP/oBZ0ziUWJdWfu4MjKyudyRxn0wfGEaHOsXLu9fML9529MwGJi/qxawVT+mYTIiK45aFH03TfDRN26Bp2nFN045qmvbi/9v/qqZpStM0t3sXUwghxIPgk5XrAfBz92JYC1nBS9weF3srBjerhXOqP0cNJg4fXwTAiZMrWb35bZ3TCSFKw67EzVibFYlFzfno4WDpCVqBWFsY6RA2EHeTojCpG/ZmM+vObWVudhwLL+/SO54QFcLt9PgpAl5RStUHmgDPaZoWAMVFIaADcPbeRRRCCPGgOJJwHIDGvrV1TiIeNE+1qElM9sPYm838EP0lAO9um8C4Uz+SfOW4zumEEP/Ujqyz1MqzZHjbBng62egdR9xnVas1ZN0Th7Fy7Y5XvgWbtEzMmkaiEZKvHMdsKiIu/neiD8/XO6oQ5dItCz9KqYtKqf1/fJ0JHAe8/tj9CfAaoO5ZQiGEEA+ErPwisnMSAXB19NY5jXjQVHawZnDLhvilV2Z14RV+3/Iu0VohJk1j5e4pescTQvwDly4d4rRRYZ/nzaONZYhXRaVpGnOGRRLiWPxvwNlUPKw3+sQynprbhN5bX2HQ/g+4eGGfnjGFKJfuaI4fTdN8gTBgl6ZpPYDzSqmDtzjmGU3T9mqatvfKlSt3n1QIIUSZsfC3F3hsZtg1q3IcTkzHxli8FLers7yxF3fu6aiaJOU+ia1Z8Vr8AiyVoo5JY9nl3TLvjxAPsMm/v4CFUvj7PIqDtYXecYSOnO0saeTdEADfFH+slOKrmAXs0fKJUo4AnDi7UceEQpRPt1340TTNAVgMvETx8K83gIm3Ok4p9bVSKkIpFeHu7n63OYUQQpQRhfnZfHV+PUcMRWzZ+3nJ9oOJaVhaZABQ2UWWbxd3zs7KgokPd6NhbggmTSMw257ublHEGcws/P1FsrOSuHL5KM/PacaYeVGYTUV6RxZC3MK67R+y2pxC8NVqPNGxr95xRBnQpvGLvFI5Evdqo6mRbyTG0oyDyUxS6kgATl6+ab8CIcRduK3Cj6ZplhQXfeYrpZYAtYGawEFN0xIAb2C/pmlV7lVQIYQQZcPq7f/milHDUimWnFxSsv1QYhq2NjlYKIWjDPUSd6lFXTc+fXYWTzs04GrmAOYcbkltk8Z7lzbSZHEHOq7qz1ZzBqtNV/l25RN6xxVCXEdhfja79n/NlJ9683LsXGrkg2fVt2VuHwGAja0LQ7t/yycDW9LCrXhOwNbG6uy77EJVk+Jkxhni4n9n+tLHpLenEKXkdlb10oAZwHGl1BQApdRhpZSHUspXKeULJALhSqmke5pWCCGE7uYmrKS2SWOQoz9bzBmcjFtN8pXjZFxcSKb1FVzMYDBKV35x94wWVrzQ93v+M3QYBmtPDp98hz70Z7B1ID0tq7Oo5ad0MboyPfUAm3d9xvY903lmdmOZBFqIMuLVHzvy1OFpzMyOo0WRC2cSXuPpNoF6xxJlUKs6PXE0K55s+ToudpZ4FtpwsuAq3+58ny8zjnD+wm69IwpRLtzOO/PmwCDgsKZp0X9sG6+UWnXPUgkhhCiTEhL3ctxgIiK1Dr9dbInmdYI+28YU7/xjNG97QyXd8onyxb+KI78834KPV8fww24rCkxhAOxLtWFch69I2PEorxz7BrMGBZrGjA2vMbbfLzqnFqJi275nOuvNGTzp4E9wrdE8syidzkFVqOvpqHc0UQY1DBnCtgaD0AwGHmscw5HjziS4XOJSwWUwGDh2ei3e3k30jinEA09T6v4tyBUREaH27t17364nhBCidKzcOJG63k35ec8a5hSsoSdPYazUDovsreTn7USpQmyt/enXshd1fYLQDHe0doAQt3Q5I49jFzO4mJ7HtHUnuZiRx7ORBrakvIadZsDb0pEtRWms7r4IN/f6escVokIyFRXQd04EBSg+7bKGATOO4GRjwbJRzXGysdQ7nijjcgqKmLH8Nb7OXlOybZiDH238+rDz1K8M7zlPx3RClH2apu1TSkVcd58UfoQQQtxMYWEOjec3JgI7CjNsOeqYwvbHd2Npaad3NFFBZecXMfHnoyzen4gledha2/BeR8WbJ8fRTHPg7S4zcPcoHlaizGZ+3vA6Z9NPUWguotBchEIR5B7CQ23e0/lOhChfduz7kmeOTGe850N8ur8thSbFj882oY6H9PYRtyc27lf6bnsNa7PC06xR1WgDGuwij11912Hn4KF3RCHKrJsVfmQSBiGEEDd19uw2ijSNPSoHD9tsApWtFH2EruytLfhPvxBeal+X1OwCxi05zIsrMnjcP5QVKpruK/vTy96XdnV78fuJxSzMT8SoFFaq+I1PAbAsO4Hu5nekd5oQpWjliZ9wMCu+PdiMIpPih6el6CPuTE2fKCyUIrDQFnOeFYcd08hDA03jzPkd1PfvqXdEIR5I8m5HCCHETcVd2AmASdO4aGUgvJIs1S7KBh9XO0J8KvHT8Kb0CfdmTsxjBKSPoIWxMj/mJPDkoc9YmJ/IEw51OTD4ELufOML2J47wUpUW5Bg0rqad0vsWhCg38nKvsjY/iaC8SpzLNPLNkAj8q0jRR9wZS2t7/l3zYUY1fYt6rvXJMRgwaxoACUkHdE4nxINLevwIIYS4oaz8InacPoCmFB5FikuWBkK9musdS4hr2FtbMPmREDoEeDJ+iRW7Lo+lTyD42kYDikpVB7JwbyIARoOGm0NNuLyN8xcP4OpaR9fsQpQXG/dMI9ugcSklgrd7BBJe3UXvSOIB1aXVJADsLYtYsGsXVQsVSRaQcDWW9LQE0jMSqV69hb4hhXjASOFHCCHEDX2+Po7YzLNUsVKEanVYb44npF5vvWMJcV2dAqvQsIYLn6+PY8Ges+QVhvyx5+g17UY2rAzA+ZRjNLjPGYUoj86f382HcT9S1QzKqQf9Inz0jiTKgbo1O+C6/U2qptdGVYrndNZ53l/5BHvyr7Bu2BG94wnxQJHCjxBCiOta9Pur7D1oINctHz9LZ9559Huev3wIZ+fqekcT4obcHKyZ1COQMZ38ycgr/Nv+0QujWX+mENwhMf00+XnprN/1CVvPbybPXNzezmDN2Ifm4uBY9X7HF+KBk56WwIjfniJfU6hzj/PmwAYYDJresUQ5YGltz+8DtvHCwlhccp7hmCmVNAsz6UYDaVdPU8mlpt4RhXhgyBw/QgghSiizGYBDx5by9sXfuOy+kgsWCj9HL6yt7fHxaapzQiFuj721BVWdbf/2p3twNWKSLalkUpzPvsi/lz7MawmL2Zp/mbiCdI4VpLGs8BL7jv6g9y0IUaYVFuZw8cI+XljWl0SDGZ/L3anm3YaWdd30jibKEWsbZ97oHoi3lRtnrCHdWPzxNSFxu87JhHiwSI8fIYQQJV6Z34ozBRmYihR2VmauWhS/wartWl/nZEKUjk6BVZjw8xE8zUYS868Sa86ho6ULHz++AYPRgqup8UT90ouzqSf0jipEmWU2FdFnbiQJRkCDAcYOfHU1imUD6qFp0ttHlK7qle1o5OXPmktJJdtOX4om0K8XZlWEtY2zjumEeDBI4UcIIQRQ3NtnZ9FVMi00sIBHtAjOm2PYruVQu2qE3vGEKBXujtY08nXFMs+aA8Yc8g0aVy9Xp+mHGwHoEuiJo1lxNvOcvkGFKMNOxq8mwQiP2vjQ1PdRnl/pQedAd0J9KukdTZRTvu5BcGkTvvlwwUpx8upJJi3uwbmCNOYM3ad3PCHKPBnqJYQQAoALl46SadBoeNWTVyq34fXHvuDNdp/ypIM/dWt31jueEKVmbOd6eFhXJv+PeUi8qnSljb8HVZ1tmb/7HN7KgnN5KTqnFKLs2hG7DIDHm7/HRzt8sbQw8GZ36Rkq7p1a3s0AqGHyoUoB7L1yhvX5SRwmn/y8dJ3TCVH2SY8fIYSo4OLif6ewKI+52/aDEZrX7MDQ7mMB8PFpyks+P+mcUIjS1bCGC6dq1GPtxUQ8TYq3Bz2GZjBwODGdhz7fihv2JJgy9I4pRJm1M+UwtUwaX+42cuJSJrOeaIy3i53esUQ55ukZzDT/oXhW7clnawez0yoDk1bchyE+YQMB9XrpG1CIMk4KP0IIUcG9u2U8l035uKT4gSf0bNJN70hC3HNernXh4loaWbujGYo/PAR5OVHLzR4tz4kLdukUFuZgaSkfZoX4q4L8TPaZs+lo4c383ecY3qo2rfzc9Y4lKoDWTV4BIMi1OtsyjpZsP5G4jZMXdmJWZnq3+0iveEKUaTLUSwghKrjTKo9zFmB2TMDBrHB3D9A7khD3XC3vZhiVolm15iXbNE2jZ6gXqZkumDSNixf365hQiLIp+thC8gwaZy7Vpn5VJ17u4Kd3JFHB1HQp/jdXN8+AndnMb3G7mXzmF/57ZpXOyYQou6TwI4QQFVh6+llS/5jn5KhtPrWwLOn9IER5VqVKKCvaf0v3Vm9fs/3hCG/yTNUBSJDCjxB/syV+JRZKcSi9GZMfCcbKQn5niPurZpVwAMLs/PEptGC/dTJpBgNJRo2kpGh9wwlRRslPaiGEqKCUUqzctfp/32satawr65hIiPvL27vJ3wqdXpVsebhZJwA2nZCVYoT4K2U2szYjjro5lvRrEkZgNVlGW9x/9f168LpnFC/0mEyYsxe5BgOaUgAcPPkLUxb3Ye6qZ3VOKUTZIoUfIYSogFZunMgHC/qzfP9mAOoWFPf6qeVUQ89YQpQJj7WMwtZs5lDOQaYteZzC/Gy9IwlRJsSd+p1EI9jm+PGSDPESOtEMBgZ0no6zc3XquRavJhecbYe1WTHvyHJmZ8YyI2kbZlORzkmFKDuk8COEEBXQjwkr+TH/GDZ25zAqxWM1OgBQyy1Q52RC6M9gNFJLWRFja+LrzIOs2DJJ70hClAlrD89DUwqvagNwsJY1YoT+wup0xagUnWv1o3aRJdF2eZg1jRSjxqHjP+odT4gyQwo/QghRAZ1W+RRpGrEOyXiZNXpETWScRwuahUnXaCEApnaby1D7l/DNh+/OrpYnx6LCS08/y4rUg9TON9CjSQu94wgBQK2a7djS5zce7/IqkS6+ANTJ1bBQig0nFusbTogyRAo/QghRQaSnn+Xs2a0cOHmQq39M6JxpNFDDwh5rG2cGdvkCS2t7nVMKUTZ4eAbxdPfBOKc1JMEIm/d8pnckIXSTnpbAsCUPcdGocMxsS2NfV70jCVHC0ckLgHDv4oJkiG1H6uRasC49Vs9YQpQpUvgRQogKYtrq4Qxc9ywTF8wGwNlkBsDX1lPPWEKUWU42lgQGjsa9yMwPMTJkQFRcCzZNINZgpnpiW0KCR2D44+GBEGVJq8ajmRX6KqN6v4NdTh3OGGHD3mVs3/cNg2c1JC/3qt4RhdCNFH6EEKKCiMu9TJrBQKVKOwDo5Vg8IaKvk6+OqYQo255s6Y9Ppge7ySYz47zecYS475TZzMqUgwQWWHA0tzODmsgiAKJs0gwGGoYMwc3Jlkb1n8agFDO3TOOLfV9yQCsg9tTvekcUQjdS+BFCiArirDkPgOMOmdiaFcPafUBTbGka8KjOyYQouzycbKjh2o0iTWP1zs/1jiPEfXcsdhmnjQrLNH96hHjh4WSjdyQhbum5Lh1pZLYjwTmJg1b5AJy8sFvnVELoRwo/QghRjpmKCsjJusz2mFiuGIu75ucbNGpigatrHb4eshsfn6Y6pxSibBvc6SncisysPrNe7yhC3HcrD8/GUikOpnXmqZY19Y4jxG3RNI0+tbtw1WhAaRqWSnHyqsz5IyouWYdRCCHKsXm/PcfMSzuwSewJNcCz0MwlSwO1rWRiTiFuVx3PSgQUebLL6jIXLp+jmoeP3pGEuC8yM87zc1Yc/jm2+Ec2on5VJ70jCXHb2jR6EftTi6lZaEmeMhHLRb0jCaEb6fEjhBDl2JGrJ0gxang4bQOgT+VwAGo5ygdXIe5Ez+AnyTdofLb8Vb2jCHHfzFn3KhkGAyq3N6919tc7jhB3xNbOlRlN/sXQsP/gkO/ASXMuymzWO5YQupDCjxBClGNnCjMAOOV0BYBHW71FF6MrbRsM0TOWEA+cjk0HElxgyW7tCDvjZJJnUf6lXT3N3LRDBGVZM6jjMOysZKCAePAE1u9Lx4g22ONFmtFAckqM3pGE0IUUfoQQopxSZjPnKAIg3WjA3aRwda3DR49volbNtjqnE+LBMyxwMMkWBmaseJX0nEK94whxT81cP4YcTcOicCBdgqroHUeIu6ZpGiHVGwEwf+NSDp1L4d2FXYk+8r3OyYS4f6TwI4QQ5cy5c9v4bsWT9Ju6kCyDhrVZAVDDICuxCPFPtG3yAqEmS/ZUOsw7c0eglNI7khD3RHJyDN9nxNAgy44B7R7FYND0jiTEP9KzaS8A9p7ZxZjvPmNh3jkWHZqhbygh7iMp/AghRDkzbdN4PknZjUX+zwA0U8UTOdewlgmdhfgnNIOBL/utIrjImt+tdzLlx9cAKCzMYcriPpxO2KhvQCFKgTKb+WT1KAo1cLJ4ks7S20eUA9U8/fAxgWXlC3g5Fy/rfiDvss6phLh/pPAjhBDlSHr6WdYVpQCQW+kYAEMaPomFUtSpVEvPaEKUC/YOVfhqwBr8C418n/MrE+d8wOQfX2Bm1kmW7puqdzwh7lpRYR7HYpbx9o9dWW66RGi6O+P7DULTpLePKB96u4Wx15DPGadEAM4ZIfnKcZ1TCXF/SOFHCCHKkZU7PqRA07A1m4m3URiVIrjewyxqOYVH2nyodzwhygVbO1emdF+EuwnWFs3l17wdABzKPKtzMiHuTmF+Nk/Pb0H/XRP4Kf88YVfd6Bg5Ex9XO72jCVFqejUZi4VSXDZqhORYA7D3+BKdUwlxf0jhRwghypGl57dSp1DDL8sRgKpmDUtre+rU7oi1jbPO6YQoP3yq+vFNp2+wROOqhQH/fANHVR5FhXl6RxPijr2/tC97tXweNQZT+fRAXKpM4bHIGnrHEqJUuXsE0tpYCYBe9V7E2qz4/eQmjpxP59DRH1m16S19AwpxD0nhRwghyonLaVeJNZpwzPDEmF8HgOpGW51TCVF++fg0ZWabz3jSthOGlHDyDBqx8av1jiXEHYmN+5VF+efpVOTNN0cG4OPThn/3biBDvES59GLL93jVrSl9Ww2kdqElcUUX6T5tMxN2vMuE04vJzUnVO6IQ94QUfoQQopxYsu03zJpGqzrBTHpkFADVbdx0TiVE+VarZjue6/sRJqumABw4vVbnRELcma0xPwGw6fRDPNbYh++GNsLe2kLnVELcG76+rRjS7Ws0g4E2nkGctoFHas3llKWiQNPYd2Q+6WkJLFs3lm+WD5ZCkCg3pPAjhBDlgFKKfSeL5xlpWKshNWs0YaRzML1CntI5mRDln6XRwKiuPXAtMrM1MZptccn8a0FnnpndWO9oQtyQ2VQEwMaLB/EuUDzRviP/7t0AS6N8PBAVw6B2H+NhUqy2Po692Yy1WfHNjp8YurAHExJXMfXqATbukUn7RfkgP9mFEKIciD6XRm5hAgA1qkUCMKLXfALr9dExlRAVR5v6VahZZE+sdpUXZy7m57xEdpDL+fO79Y4mxN+cPbuV9rNDmbJwKMcs8qirqjCydR0Z3iUqFHuHKoyt+ygAna1qUN9kzXGnFOJsFKFXfLFQipMpx3ROKUTpkMKPEEKUA8sOnMdonYqjWVGpUk294whRIQ0M7MtlSwPVa/6Xwj8+QK+N/lbnVEJcq6gwj9fXvcAVo8bMvH3kGzR6BHbSO5YQuujQfDz/qdWf0d2/pH3VcHINGp4mRXT6MLyKNOKyzusdUYhSIYUfIYR4QCUkbOLluS1JTT3DysMXKbLJpgaWaAb50S6EHto3G0OEsuakpRn/fAM18uH3S3v0jiXENb5c8QSHDIV0KQjAwWTGqBRNgh/XO5YQutAMBjq2fBNn5+q0DHgMg1I8Va0N3cPq4JxvQ2xhOuui9/Dugi5kZyXpHVeIuyafDu6QUorcAhMFRWaUUmSkn2P15n/x7fIhzFn1DGu2/puzZ7eizGaSkqLlB4QQ4p6Zsnk8a8xpzF33NslZBVyxKMDH0knvWEJUWJrBwJgmb2KhFANq96BKrg+HjEXEn9mvdzQhAFi/4zO+Tj9MSIY9P8YPpqtlb0a7N8XBsare0YTQXa2abVnV4Tv6d/yMJ1v4YlXgxgUjzN80gYX5iew8OEvviELcNZmy/w6lZefz6H/ew6PSRq7YXeWCJai/joe+sgPif8DObCbHYMDBbKap5k6COZUAGw9e7zEPe4cq+t2AEKJcWL19LhtUBgalWJ+2G2er7lwyQA37anpHE6JCC6jXiy1ekTg4VsXRcRV7j73Gu7+O5Ksnt2JlKW+7hH4OHV/Bm8e/pnqRgc5hX/NKTx9CfbrKvD5C/IWXV/Gk/HU8HHm8cSv2xs0nxvEcYODY5QO00zeeEHdN3oHcodTkA1zwXUayUoQU2RJu9sbJtgUmu2bkF2SSmnIIc+F+8g2J5Oe6YrJNZJNdMjUKDPxiSOLQjx35ttv3eHgG6X0rQogHVFZ+EXOiP8XZxkzNVF+i3c/Ss+Y6ftY0qleqrXc8ISq8P3tPdGjclb6xs/jR9jhvzx3JO098JR+yxX2Tnn6WHQdn4uroRVzSCb68uAorBQPq/psBLYL1jidEmVfHuznEzSfzj5XuDl09zdlL59h7+DN6t/1IhtaLB4oUfu5Qbd+mfJI4gMYNHsfJ2ec6LTqWfJWRV0h6TiEpmXn8evQS5gPfcrHaap5aMYDZvZfh4lrr/gUXQpQb32w6QbxtHu0svBj26H/ot/ZhNrMR0KjuLkVlIcqS8Y/O4/jMJqy22I7X8i95stuzZGaeZ8HGsZzJvkC+KsTdypnX+y7DaGGld1xRThw+uohXd73NBeP/trkreNH/HXq2eEi/YEI8QLy9IrE2K/INGjXz4KRFNh/+9BybHU7jFxNJUMAjekcU4rZJ4ecutG/x+m21c7KxxMnGEh9XO0JruBIXMYkpi2zZZbeMgUt68t8OX+Bbo8U9TiuEKC8mL+qFScGm2EhyfAw09W5MHe96TKreg8Vnf8PDXERd37Z6xxRC/IXRwoqpvRcy4OfeLEj+nFmTTNT1+J5DThl4FCk0TSPJnMHjiTvw9W2ld1xRDuw7OJvh+z+mkoJ6F9qRb9Ko4V6HV/o8jq97Jb3jCfHAMFpYURsLMk0mOrs35ovM3Ry3iQcMnDi/Uwo/4oGiKaVu3kDTfIA5QBXADHytlPpM07SPgYeAAiAeeEIplXazc0VERKi9e/eWRu4HltmsmLzoY5Znz0YDFndfiIeHPKEXQtzcsnVjmZC4CkulCEyuRbT7aVa2+YLq1aV4LMSD4MiJX3lq+6u4mDXOG6Gz2Yfj2W/gmLeSwx4r+CpwBM0iRuodUzzALl06xNKdHzEzNRqXIrh67kWaBzVicFNfQn0q6R1PiAfSsZhlKMzk5WcyNHpyyfYBtjV4vd8KHZMJ8Xeapu1TSkVcb9/tDEwsAl5RStUHmgDPaZoWAKwBgpRSwUAscHvdYCo4g0Hjtf6v0dNpDLmaxvjlT6LMZgBMRQU6pxNClEXJV47z3tmVeJugUNM46xpPJbPCx7uZ3tGEELcpyL8LkwOHc8EIrmaY+Mh3DG5ag9PpxfMBXbgap3NC8SDLzUll8MqBTE87SPU8C1xyxrD81UFM6RcqRR8h/oGAer0IrNeHerU7AaApRdVCxb6r55j600S6f9eA3JxUnVMKcWu3HOqllLoIXPzj60xN044DXkqp3//SbCfw8L2JWD692ncIl79ZyWrrGEZOfxZbxxbsN3/MSK929Ov0md7xhBBlyO7jK8gzaDhd6kYlt19INRqI0hxkUkEhHjAtGj/Pl0ZLnOw9cHCsSpt6+bxu8sFBKS5kJuodTzzAvl1dXFT0PdsBm2r9+WJYQxysZUYHIUqLvUMVaps0KmlWOJid2Gd5mZ1XVnLGDk6f3UxAvV56RxTipu7oN4Kmab5AGLDr/+0aBiy8wTHPAM8AVK9e/c4TllOapvGvwXM4Pa8Jh+23UzV3Dyl2Gv+5sJaopINUqRJCfl46BQVZODp56R1XCKGj7fHRYIBqns3wMu9iDckEO9fRO5YQ4i40bTi85Gs3B2tCqnuSboIzWZc4kphKTMzXbL/wK1mmPDQDWGsWvNh8ErVrd9AxtSjL4uLXMjPjGMFZ9rjXHcRHD4dgZSEPBoQobVPb/xdrayc2Rn/LpqQNHLXNBzTOXj4ohR9R5t32bwVN0xyAxcBLSqmMv2x/g+LhYPOvd5xS6mulVIRSKsLd3f2f5i1X7Gxs+bDNZHIMGjF2JprmuGJC48VlQ3ni04n0/L45A3/qIkPAhKjA0nMKOZd5FkeTmSmDuvNIcH8AInzb65xMCFEaOgZ44lxo5HROGu9//zT/Oj+f3UXJXCrI4VJ+LhtUBmuPzNE7piij0tPP8vzGl7A3K4KqTWBKv1Ap+ghxj1Sv3gJPz2D8q0UCYNY0AM7IUF3xALit3wyapllSXPSZr5Ra8pftQ4DuwEB1q1mixXXVrt2BcdXa0wJ7pg79hf42TYi1KWSvy1KSNThtVHzww2t8tLA3XywboHdcIcR9UliYw1s/dGD2mkXkWGbhgyWawUDThsNZ1fZLGoYM0TuiEKIUDGnmS3UbZzKsTNi6JVK9CMY3XEH7OkuIPvU+lU2KCzlJescUZZDZVMQrix8myQitjIMY17sbBoOmdywhyr26NdsB4GoyU7nIzIHLp9l2eDurN7+tczIhbuyWQ700TdOAGcBxpdSUv2zvDIwFWimlcu5dxPKvX8dP6ffH12Me+5ZHz23jp12fciG7C4cLprDcYg05ZgPkQfiBeUSGPa5rXiHEvRd3ag1LCpJokjyTNLtCwq3dSvb5+DTXMZkQojTZWBqp5VyV9emp5KhsOthWpWtITfIKTXy5KR53k5HzBWl6xxRl0NQlT7LLmEvLjAa8OXwMmiZFHyHuB3uHKoSYLfGz9uZw5lkuaRnM3fYG22yTaRoyFGdnmd5ElD23M8dPc2AQcFjTtOg/to0HpgLWwJo/ftHsVEoNv+4ZxB3x8WnO6D8+2C1de5yJ51cTnmvLWcts3tnzIYF711LX048mwY/i6ODGqfglxF/agpkCOoU8Q40aLXW+AyHEP3X28iEAkqyTuGSEGnZVdU4khLhXvBx9MGccI1ODUPdQoLgg1Nrfg4w0G84b5fmaKGY2FfHvRQ+xIvcc2QaN0Bwb3ho8AxtLo97RhKhQ5j2xH2U286+FnVmXe5480xVA4/TZLYQ2GKh3PCH+5nZW9doKXO8RwqrSjyP+vx6t38NlbxWahD7JovWTmXx5GWe0fXB1H59t+gFNKdRfnvBs2/ASswbvITPzPJeuHCU7L5XCwlxCAx7F0tpexzsRQtyJc2nxACRYA2hUd5HJnIUor6q61IHzvwEQUrtLyfaOgZ4s3uBAkn02H646TN6VzzlSuJNEYxE2SmNGm8+o9ceQA1ExTF7Sl4X5iYTk2GFn8mBs36l4OtvpHUuICkkzGKju4E1awUXS/qi9nrp0QAo/okySdR7LOKOFFa2bvALAoK7v0iX5cXLzs9kTs4ljSbspNOXialuTI2mtyE2bzX6PeIZ91oOjlU6T+5elnofGr+CVh5eSmhpH0uUjeFUNl26IQpRRlzLyOJp85ppZ2Gq4B+sXSAhxT1VzCwTA2azwrR5Vsr1NPQ9+2eBJkXaZ/QencqzaTqqbFX55ldjvmMHB+NVS+KlA9h+cw9ycU4SnuXIyZwI/Dm+Gt4sUfYTQUw1XP0jdU/L9hviDODktIz9nP91ay5w/ouyQws8Dxs2tHgA+Xg3p85ftJrNi1qbanD/9GHtdz+BdpNFYa8PpZANFNtuYp05y9NNO7K90HpOm4WpSvBM+DStTHHY2TgQH9tfnhoQQ11i79X1WxAdxLu8qNYxw3kpRpGlU947UO5oQ4h6pWiUUgBCjIwbj/96aOdlYMqxlC3YdPYyH11GOKXg2eDqjl6XiWG8C5zIS9AksdDFj31c4YqbQ6nWWDWuBh6ON3pGEqPCqe4ZC3HwczWZciwxcMacw78C/ibbOoX3eK1jbOOsdUQhACj/lhtGg8WSbYDy1Psw5/QufdvmWatUiyMgr5Ns1GzidPJo9LhcIybfGdDWQBPd9jNs/ikyjASul6L1vM7nGE4R5BPNwhym3vqAQotSdObOF0fHf0zDNnTSHQiKsPahUmM5ZVSA99IQox6ysHXnMpjrNa/y9946XewMAtpoz8DZrdIpogd2va/E0wbmcS/c7qtDJnuPb2GZIp0lONaY+01GWbBeijPDxKn4w18iiElaWRg7kp5Kt5WPSDMQlrCOwXp9bnEGI+0MKP+VM99bv0L31OyXfO9lY8vJDHQnZPpjDF/fw3IA5pOQY2LDrK2YkfkNotjdnLBNZaLUZimBd4m/4n47FkH8Edxc/PDyDdLwbISqWLcfmA3DR7jKXLQzUdPSip9cjXEo7rXMyIcS9Nr7/yutur1olFE0V9/wLt3HH2sJIh0BPElMtOatlkJCcTX5uCodjZpKZn4iLrSs927yPZpDCQHmRk5fN9I0vo2xhRNuJUvQRogyxsXXhJdcIGtbswI64Faw2XeXP6XG3ndhIrZqd0cz52Ni66BtUVHhS+Kkg2jUby5/PEas4w2MdX+QxXgQgJTmWGWsncuCMC0eqbOX9VQM4bp9HOwtXJj++Wb/QQlQwmy/vB+CCVfEbBh/nWjSLGKlnJCGEzqysHXE3w2UjhP2x4tdDwdX47jd7TlldZci0aRTUmEuW8X/FgPrxUfjX7aZTYlGaCgtzGD4vigN2BTxmEUBIPVm5VYiy5smHZgKQlBoH6YcxKoWFgq2nozk+7yHOGpJZ/MRBnVOKik4eGQgqu/nx2qML+GzEJ0SYrDnskE+RprGrMBWzqUjveEJUCMv2HGKvyqF+zv/q8dX/GOIhhKjYvA3WAITXKS7mtPZ3p4GnLxlGAy1rbyHLaKBzQQ98z3UF4EzSAd2yitI1+achHLAsoEt+BOMH/qh3HCHETdSq1giAYGVNHWVJjl06u7QkYg1mUpJjdU4nKjop/IgSHk42vNJkLA1N1oQle5Fm0Hjy8w8ZPrsJqza9pXc8IcotpRSrtn9Doabhb9sTN5MCwKdahM7JhBBlQU3rylQ2KWrWaAOApmmEexevBLbFdAZ/s4EhXV/jVE7x6n9nr57ULasoPYvXf8mP+cdpmGPPG49/rXccIcQt+Pq0oJJZ0bFKIwLsPDlhbSbzj96YR+OvP5xXiPtFCj/iGkEBjzBr2F66N34NgFSbhWwjmwWnV+icTIjy61BiOnnqIDZmxcTHxhBlWxU3k8LZuYbe0YQQZcCLnb5gTvsvr5m3x9u9uPCTatBoZF+dwGpOeFb2wrXIzNns8yXtEhN3cvT4YpKSou93bHGXlNnM2LltmXRuOpVMird6zMLZzlLvWEKIW7CyduS3/hsZ0PFz/F38ALAxKwxKEZ24S+d0oqKTOX7EdfVr2ZbvT2rE2xT3PDhIPi/M/Ak7y+k80mAoDUOG6JxQiAdfVuZFjsf/ypJTEWTaplEfayyt7RnTfQ5PpSXI5KxCCABcXGvj4lr7mm0+Xo1Lvo7wboGmafQK9WLjSQuOpF+h1VsfYO89j3N/zBlmZ1ZsfmybLC38APht2zRWma8QkeHC2J4zqOlVV+9IQojbZGfnBoB/tUhI2kATzZnTBRnsvxqnczJR0cmnCnFDTRyKext0LKqKWdM4n/8OK03JfLF/Ohl5heTmpOqcUIgH24KNrzPs4CfsObqWc1Zmghy8AXBwrIqPT1Od0wkhyjJ7hyq4mhWaUkQE9AdgSFNfvCydSbUqIrz6Wq5YQIuMekSmVyHHoHEmcbvOqcWtnE7OZtbR2bgWmRneZR71qkvRR4gHUb06XQk2WzIo5AmqmStx0pBLTl6h3rFEBSaFH3FD/Ru/wjAHPz4YvAw3kyLW1oyFUuzWchg+9TmiFkaxdO0YvWMK8cCKzzgDQFXnxeQbNALdQ3ROJIR4kNTQrKmnLHCu5AuAs50lwe6+pBgNHNWSiDA40LPNVBJSWwBw6sIeHdOKW1m89nXeX9CLo7aF9HIMIbJOdb0jCSHuko2tC/Of2E/jsKcI9wgmw2jgm9+W6h1LVGBS+BE3VNO3NaP7LsbS0o4o26oAPF+5MwBHPbaTZ9D47uxqlNmsZ0whHkhKKU4XpAFw1CkdgEDfdjomEkI8aP7V+j982ObTa7ZVdynuIXLRqNHELZh29T3JsQhBU4r9iYfZcOIye45tY/Xmt8nOStIhtbie37fPYNL5FexwTMLJbOaJDu/pHUkIUUraBncHYE/sTyQkZ+ucRlRUUvgRt+XZVu/zfo1eDHtoMpGaHWZNIzzLgQQjrN3xX73jCfFAMZsVD03bwmkKACjUNBzMiuo+zXVOJoR4kNT0bU1N39bXbKvh8b+eg038emJpNNCnUX08ixRHUhP49ufnGLZnOGNOL2LJZlmxUy+mogIOH11EUWEeX284ztQjn+JRaGZ87RlsGLCTSi419Y4ohCgldWt1oq7JQJrzcd5cdgillN6RRAUkhR9xW6pVi6B763cAGNfiPT6o0ZtXui3C2WRm9uFZZOUX6ZxQiAfHnoRULl0+So7BQESRDQABmg0Go8y3L4T4Z/6c9NnVrKhbqxMAL7SrSy2jA7l2+eRUPoFvPlQymYlNi9czaoX26bL+DNj7Nh3nRPDDqYc5Yw0v1RnAYy0aY2XtqHc8IUQp0gwGHq/egTPWkHZhMcsPXtA7kqiApPAj7ljt2h3o1vptgn296WYbyEHbfN6c+RaFJhnyJcTNbN71Gdv3TGfx/kSq2xWv7jA4oD/2ZkWYcx2d0wkhygN7hyp4maC5lUdJMdnKwoCfYzVOG8zEWSiqF9ahWqEVcfkpOqetWJKTY5j4Q3v+vfAhZmXH0SDbGo98a2qZHfioVj+6R72ud0QhxD3StfkbOJsVTp7reeeXQ/x71XE2xiSyaecUmTZD3BfyeFn8Iy/3/oYt81twzOJnXlvQicmPtcRo0PSOJUSZU1iYw9hj35Bl0AjP+IG67jWIBerXbM8y33a4uNTSO6IQopz4rvNMHOw9r9lWq1IdTNnFBecmfo/y+7HpnLBK4fFvtuFWNJerhv1kGPNwsLDgo47fULVaQz2il2v/+W0EKwuvoDQN33xwtv8PozqEEljNWe9oQoh7zMbWhWc9m/PRle00rjSZ77a+QlzMt+ypfJKZNq5EhA7VO6Io56THj/hHrG2ceSvseS5aapxPf5Een67kiZm7+Xb5cJavH693PCHKjG0HvifLoOGXY2S/Uzp7jQexNSs8PYKpUjUMaxt54y+EKB3VqkXg5OxzzbaaVcIA8DbBw1F98LTxJddgoFL+TNbZbeOcRTaWhUaitUJ2HPtBj9jl2v6Dc1hRlEzLHB9s419mTIulfDGklRR9hKhAHu/8BY/a+LDb6TKvRqzijNMJAI6e365zMlERSOFH/GOR4c/wtldnYuwKsXd4k9zLs/ns6jamJCzHVFSgdzwhdJeeW8gPu3/CqBStfacSVGTBJaOGLxZoBvkxLIS492r5tMSgFK0dfLG1tmRAk/YAnLPbjaVSfNFxJfvOTsLGrDh5NVbfsOWMMpv5eP8nuBeZWX9uMON7dyQqQIb3ClHRaAYD4/ouIxIb/puxkcuWBjSlOHAlhuSsfL3jiXJOhnqJUtG7/cfYbLJn/OmfKKqyGSuzIsWoMfrbDwn1SqKORx2iIkfrHVOI++6jRT1JysjlouVFAsyWPN8lijbHxvPYnrepaVVJ73hCiArCydmHrxo8T/3axRM+1/ZtDdEfc8qoaKk54O/jS5t6V7iSb+BY5gV+P5qEqTCblKQV2Fmk4l3Zh8ZhT+l7Ew+oHfu/5IihiAZXAnmiZzN6hXnpHUkIoROjhRXvd5nFwyv742ACh3xLYi1TeWPmv4izX86PPZZQ2c1P75iiHJLCjyg1XVpNwsbKgf8cn00n50f4LnMRl9QSPk0ppMqVTfza8DmMFlZ6xxTivlmz9d/MzTlV/JPWAp6rFAhAUMAjfHTlKLW9IvUNKISoUJo0fLbka2fn6ribFFeMGm2rNgNgWPOa/HeVPaftM3j/xxnk1fieLOMfvRLPw3KXOn9bPl7c2peHZ+BmNhMcMJZBTWroHUcIoTN3j0B+7Po9oDFrwyR+yDuBY+FqLhs19sf8RIcWMl2GKH0yxkCUqjZNX2XFsMM83/ctIjV7jtkVoQEXjRq/75iudzwh7puM9HO8F/s9dQo1wq66Fg/zqtevZH+XVpPwq9NFx4RCiIquttEOTSlahz4NQLM6brTwCeCqhYGwWv/X3n3HR1Hnfxx/fWc3vYcklAChhCa9ClIERMSCBcSGonIePz3Pcl7xVNS707vTu9OzoCJnP/EUFAVUBAtVKdJ7lRI6IZCQhLTd7++PrIhIFZLZLO/n45EHm5mdmffuB/JdPpn5zmSKHcNg76U03toVgFWbp7oZt0patPRtFjkl1Mtrwq/6tHI7jogEierVW1G9ekva1W6PzxhWxpQCsGTHty4nk1Clxo9UmD61yj8oDo3pRILPz9vL3uHS52byysejePr9qygrLXI5oUjFmb30bfZ6DGU7LqR2nef48rJxNG1yuduxREQOGVj/Mm6Na0JKarNDy1rX7QjAlzaH85w47rr6cTaUXozXWtZkL3crapX1woLnSPD56dzqQeIjw9yOIyJBpmlGr0OPY31+FuZtci+MhDQ1fqTCXNHjMf7d8AZ+fdVILopsyMrog9ThLd7e9SyvF6xnxvxRWGvdjilSIWauXwDAFd2u468DWul6bREJOv16PMJvBn7wo2WNMnoC4DeGPuk9iInwcu25jUkvMSzK2UxWTqELSaumFas/ZK5zkKYH6nNzj7ZuxxGRIFQ7/Vzi/JY0n6XlweqsdUopOFjgdiwJQZrjRypMWEQMfbo9AMAdff/B4onX8nXCHMKsIdrv59X5o/lg/mJyYxbzTP/RP/qNo0hVlltYyubCraRFWm7r1dHtOCIiJ61acmOS/JYDBnq1vwOAO3tlsvmtBFY7++jz9HQGn5tBalwEvRr4cUrW67LVY3hp9r+Iwc/5bR4lMszjdhwRCULGcbgtpRMpMTXYUwCzcyZy76sj8MX2Jqn4fZzIecRFRPDwNZ/qTrByWtT4kUqRktKU/w76nKc/vpkWaR2ZsWkWM2N3Y5jLQcfhtvHX8sagT0lMqO12VJHT9uqs79gfVkB9J9rtKCIip8Q4Dp3CkrFYEhLrARAfGUaX9HP4Ons2/eu+yRe7l5GxNoMP129hjxcmx9UhrXoLd4MHma3b5jPL5NIxvwbXdWvvdhwRCWJD+78GwI4dC3lmykTC7ELyc2uyIOETHAtlxYbb96zUz1k5LWobSqWJjk1j+HWTuLL3Xxjc/maKHIPBcEVZJzZ7/fzm3QEUl5S5HVPkZ7N+P5NmPM7YuSvZEWZpHFvT7UgiIqfsnzdM4183TP/RsiY1ypsXX4Qvp8gxLE7J4qBjKTOGcbOfciNmUBv5xZ8AuKj1/YR59HFbRE6sRvU21PRZCuLW06v+5/iNodnu8jvCbtg6y+V0UtVpJBJXtG0xmN5OPMPrXcnjv3iVK51WzI88yAOvDaXM53c7nsjPsnzVB/xh43tkxj9NsWNomJjpdiQRkVNmHOcnlxQ0rn8BAD5jGNXhAV5peRfDW/+HzCLDxD3fsnZHrhtRg9K+3F185dtI66IYBna70O04IlJFGMfhF+m9WWhKeGXvIjr4I9hQ0AeADbsXuxtOqjw1fsQVjsfLszd9Tf9ejwPw8OC3aFcWwYyIhTz+3uua9FmqpNXbvgFgecI+ABpU12SeIhIakpMzqeuDC50E2rQczLnthnFR+/O4uHo3toQbHnrrabbs1cTPAC9OeIADHofLm9yGMcbtOCJShQzo/QT1fHDQMQyo14/zmrYn3udndc4Gt6NJFafGjwQFx+PlX/3fJMpa5uc/w5i5q92OJHJSfGUlPP/htTw9/iM+Xl1+J6+ywAf9BnW7uRlNROSMGn3VBJ64ZtKPll3b64/E+i1b0t7n8beHkFdU6lK64JCTX8jMonk0KnEYcP4v3I4jIlVMWFg0D7X+NV2Ios+593Fv3ybUKPGyumC3roqQ06LGjwSN1LTmPNbsNjZHwGfz7iCnoMTtSCIntHbDJEblrWTpxhEURubRoNQQ6bek+H6YGFVEJBQkJtUnPCLuR8sSEury3gUjaUUMsxPX8Ne37sPvt2ftmbsvfPg3toUbBtS+RHfgEZGfpXP7/2PUzfOIik4mo1oMDSJT2eYt49nP17gdTaowjUgSVHp2+Q0DvXWYn7CXB197gAfGLWPjzm0U5u92O5rIUa3fPg+AnKhdbAsrpX1sLYYmtaZ/QmOXk4mIVI66dbvx4g1TaVrqMD3sK7r96Tmuf/ETHn/3EnbtWup2vEozd81aZhV9RFqZ5do+D7sdR0RCRJvqjcn3OLw7azqzN+x1O45UUWr8SNB5YOD/aFBmWBv9GRMWbuDB8f0ZNraf27FEfmJH7kFmb1kMwMZIOOA4NE5sxB1Xjua+gePcDSciUonCImJ46qKR+DE0qT2a8OLneK84iy8WvuR2tEqx70AeL3x1IzvD4E+t7iEsLNrtSCISIjKrtwGgRbUN3PW/hSzdut/VPFI1ed0OIHKkiMgEftviF9y5+hUGNH6Fj2z5fAFZWbOpU6eLy+lEfvDQh8vJL9xORJSl2Cmf16dRzQ4upxIRcUfdOl24rVpbnt+3mPCwQsCwZv86t2NVuOWrv+DBmb9hYzQMiWxN946/dDuSiISQhnW6w7Ln2ZwwiRbh3zD45d/RsEYaYf4ckjxPc8CTS7HxYYCIMIfLa3bjun7Pux1bgozO+JGg1K3DndTzGT6ym4nwl88T8Ob05/H7z845AyT4ZOUUMnXNbrIjSunpTSY28HezUb3eLicTEXHPkAufo4bP4jNQq8SyvGAPk1fspKy0yO1oFWLumhXcN+tecjx+7km6kt9fO9rtSCISYlJSm/G7lC60CktgQWwe59V5ivhIQ0z448yN3kOJ4yPSevD4HLbaEsbvmOV2ZAlCavxIUHI8Xm5M7wnA1TH1aVhimF+wnAYPfspto8bx6sTbKC7KdTeknNVGz91CgiebPV5Ds8SGdPEmUscH8Ql13I4mIuKayKgkRnR7gsfq3UDd4jS2eHz8+4MXOXd0B5avHOt2vDPq/XkrePrLm9jjhd81uIfbLn/M7UgiEqJuvnQUz9z0Nb9KaMk3Ebmsc4axILKQ+1K78MEvlzB62AI25fyTZqVJZNuz++6KcnS61EuC1hU9/syuSTu4ocdjxM98jJfylvJ/7fezfNczPJNTxIbRd0Li70mxM2iaup9O51xNSmozt2NLiFq3M4/nPhhKrskizBfNvF13cFHGZiYDjdLaMKDrcPILNAm5iEiTxpfRpPFl+P15zMn6hMTUj9lmDGMXvEVa7f6kxUe6HfFnKystYvHaaXw45xOW+qaxKQbuT+3NlT2HuR1NRM4Ct1/+NrWnP8y3O+eSEpHMkItfBsAYQ4d6SZTsjSI7Yj/W79edBeVHTGXebrNDhw52/vz5lXY8CR3bts3j6ilDCQdyHEOE35Lks9Q+mMr8+PLZ7S/xVuPJwdNczSmhaerq3bz56c0sqLaVGmWW3R5oWuqlWUwtPijN4vO+b1GjZlu3Y4qIBJXVayYwaM5Dh76vU2yJ3jeIktQPeLnf69Ss1d7FdKdm1rzneXvFaOaTf2hOtzi/n8eaDOWC837rcjoREXh11kamzr6PJWnrmdH/I5KSG7odSSqZMWaBtfaoE46qDShVQnp6J17p8hf8QEOf4fGGg9gZ5jA/fi89iqvRIj+Cr4v38OXKrW5HlRCRl5vFGx/fxjNjb+LVL/qzoNpWLnaqMeXWpTzZ4BrWhpXxQWkW1XyW6tVbux1XRCToNKjXC2/gF4xNfA5ZEYaipPfZ6LGMm/u0y+lOjvX7ef6DG7lj1ShW+w/QMr8aV9OVl1o8wtTrv1HTR0SCRsd6SZSUJQGwJ2ety2kk2OhSL6kymjcdwIS01jieMGJjatBsw4ckOxH85bqJ/OHV35AbO5dnxj3Fs7O/5MY6vbmm7zNuR5Yq7OPZT/DU3rkAJEb7uTIsneEDxmIch349HqFdkwHszF5BalKmTqUVETmK8Ig46lkPm/AxvNP93LTg72yOgHC/5ZO9S7i9zIfH63E75jFZv5+H376M8TaL1vnRdDvnP9zctTlR4cGbWUTOXs1qxlPiTwXgk0ULmLqtMb6C1eTsfR+/LaV6fCQNUhpwYbcHXU4qbtClXlJllRYX4A2LwjgOu/ds4MJPriDFZ9ntdcgsNdx53ud0rRdGZGSi/mMup+yB//ZhRukOYvY+zitDL6ButXi3I4mIVDnvfHYn2YV7uOvKd7n4jdYcNJYLve14z7+IVnuvw0now3WZMzknowP16/V0O+4h1lruf/0SJnm20qWgGsOvnaBxQESC3u9ef4PJzlM039EKr+cAK1O/o9SYHz1n2qUfUC2lsUsJpSLpUi8JSWERMYcaOmmpDWlhw9ntdYjy+1kfZnl+3F/p/V53xn5xn8tJparZc6CYNYV7qFUaxtv/d7E+7IuI/Ew39HuBuweMwTgOf+9wP892eIC7+j9OmLXERn9G7s7PeHDjGzzy1X1k5RS6HReA4jIff3zleiZ5ttKjKJVnf/G5xgERqRL+POhiAHqfE8bGlO9oSQRjuz7LZRH/ovWu8nnVduxe4mZEcYkaPxIyzq/WHGMtTza9DYAd6V+S7zhMzJrJ6p15LqeTYPfSR4P5z4QhAPxu7CK2hZfRLKZGlb77jIhIMGnb6kbatBxMQmJdro1pwDdR+8mrORa/MSz2lHL7iL9w42vtmPCVe5chfDrjSQa/1o5Pw1fQuTSG526bTFREmGt5RERORUxsdaL9ltW5G8hzDP1qdKFpZm+6N2/FrsIMAHbmrHc5pbhBjR8JGUMufI6x5z1Bry6/oa0N56DjkFLmZ5mnmHv+8xJXvtaK+YvfcDumBKn3c5bw6t6FrNy6nbXfzaPQcWiR1tTtWCIiIem3V46hh4llr8fwm+TuhFtLdp2JLPGU8vrGj5m+dg++spJKy+PzW0ZOHMXw7/5LvvFxrbcZzw+ehMejpo+IVC2p1jCnbB8ATWp2AuC8htXYW1YHgJ0HNruWTdxzwsaPMaaOMWaqMWaVMWaFMeaewPJkY8znxph1gT+TKj6uyLFFRiXRpPFlAAzJvJpuxPDwOXfhMwZfzbfY4LFMW/eRuyElKOXkrGe3x1DgGMZ8+Ry1Y9YA0LhmR5eTiYiEJm9YJE8N+pS32t7PrZeOoF94GoWOQ0YxrPda/j3mL5z/Vlvem3z3Ke13587F5O7fRO6BPdz2ekdufb0DIz+8nTUbF+L3H31eyy17C/m/l9/g/V3PkuizPNZjNMMHjyEySh9tRaTqSXEiOOCUz+vTqP4FACRGh1O7RhMi/JZtB3aQe7CUkjK/mzGlkp1wcmdjTE2gprV2oTEmDlgAXAncAuRYa58wxvwRSLLW3n+8fWlyZ6lsvrISev23HfsCP/za+MJ4csDX1EqMcjmZBIMVqz7gQGEOq3ft56k9bwHQ9kAsaRG1mBy+lm+u+oy4+HSXU4qIhL49u1fw1aKXOa/lL7hsymDCLBQ7hvQyy/ghC4kICz/u9tZaFq+exp1z7iLcWqoXRbAquoQapZYd4eW/58wsMlQrrkWYiSXMicVxwikqLaGwbBvrErdjDYzsOJw2La6vjJcsIlIhfvd2Dyb79lHLB5OHLju0/Kkpa5i8eQApxdEs334n7Wu9yrqYfQxveA39ejziYmI5U443ufMJb+durd0B7Ag8PmCMWQWkA1cAPQNPexOYBhy38SNS2TzecHpH1uKzou00KIhiTUwhN704lua1R/KH3k9Tu3ZntyOKi/4x929s9BeRkVMPUuGcgjDWRecRbnzU9Fk1fUREKklqWnOuveg5AM410cw2B2le7GVFRBm3P/8APTvfRUa1aBKjw2laI47IsB9uqb51XyH/+Ogzvit9FLwWrx9WxpRyjbcpPTq/wO6dM1ix5T2WeNYyN2HbT47tWEsbG8lfL3xBnwtEpMpLDU+Ag/to7I370fKhXeuzaEsEpZGlXNjgXb707sMA327/hn7uRJVKdMLGz+GMMfWAtsBcoHqgKYS1docxJu0Y2wwDhgHUrVv3tMKK/By/7/8Ww/K28vmi91iW/RmNY0cy1R6gzaJRDNUHvLOW9ftZa4vJ9zhEJG4mzQdDWw/j/nUjmGsO0tPoDi4iIm4Y1vpX1F39Hr8dNJp+7/YgN/ZzPpu5nYLSNBJjl7IiYQ+ZxWEkFjekjHwKvdmsjS7CHw53JN9A5+aXs33bOPp2fQjH44WmgxjEIABKSwspLNhNQWE2RcV5eD3hVE9rSURkgsuvWkTkzEiJSoGDm2gcV+dHy5NiwsmITmJu0S7CzW4aFnvAgc1mr0tJpTKddOPHGBMLfADca63NM8ac1HbW2lHAKCi/1OvnhBQ5HTGxNYiJrUH34jz+NeMzlsbnAYavdy+n07ZcWqTrw97ZaMfOheQHLgHcGWbobmK4qOvttKjXjmUbJnFOvQtcTigicnbq0OYWOrS5BYDba/Xk7zunYWssB8BYS6eyGNaH57Myqnw+tvQyy6Xh6fyi23AyMroD0Kphy6PuOywsmoTEeiQk1qvw1yEi4oaUmOqQA01SfvpzsEZUCntKdrHfltDDk87uolw2efJdSOm+5z+8lp6Nr6Zl80FuR6kUJ9X4McaEUd70GW2tHRdYvMsYUzNwtk9NYHdFhRQ5E+rV7UGs35LvGGJ9ftaYAh584xn21RrLa93/RaNMneQYygoLs/nFexfymzZ3ktl4CH/98G2IhZQyP9leh6ax5b8VSU/vRHp6J5fTiogIwPX9RnB1cQG79ixjR/ZKkuJqk9mwL6WlhRw4sJ3YmOqER8SdeEciImeJdo3602rzFNo3u+Yn62rE1MKft5JiA+2rd+CzVQvZFVdAYWE20dEpLqR1x47tCxiVt5L53z7Bm2dJ4+dk7uplgFeBVdbapw9bNQG4OfD4ZmD8mY8ncuY4Hi/nmEjCrWVYWhdyPQ5OygfsdwyfLH4D3zHu9iGhYdnqcSx3yvjvstd499st5BWtBeCiqHMBaHyU34qIiIj7wiJiqF27Mx3bDCWzYd/yZWHRJCdnqukjInKEOnW6MvrWhVRLafyTdTUS6x96fH7ry0mLaQjA9MVfVlq+YLBo3QQAFpoSHnzzBYrLfC4nqngnbPwAXYGbgN7GmMWBr0uAJ4ALjTHrgAsD34sEtV+3u5fH6g+kV4ubANgQWd7smbZ3JcNe+g/3/bc7eblZbkaUCrJs2zcAzPLnMWHuN3hj9lLbB/cNfJb707rSq9M9LicUEREREak4NZLLm0HJfkt6zY7c0K0PAGNmf8GBolI3o1WqRTu/JcpvifBbdhX876y4tf3J3NVrFnCsCX00CYZUKW1b3Uhbyif2TfZbchxDD188MyNy8fpfZJ7fR5e5TzOo77/djipngK+shHveOZ8Bja5nYfYaYvGT7zikOOPYE1ZI47B4wiPiuPHikW5HFRERERGpUDXSys9wb+WJxzgOzRp0gwXgYytPTVnLny5v7nLCilN0cB//GH89yWHtmJObRYbPS6wvliUx+7HFOyGyzol3UoWd0l29REKFcRz6Rtdlb0keN7f/NTMW/JU10eWn+E3MmsHZcaVn6Nu0eTrTbT7Zy19nl1NCg4Mx5IUfJCt5Hfs9hotjdadBERERETk7xMWn04UoLqpbfv5GdHQK1X2W6PgDvDl7E1e1Tad1nUR3Q1aA4qJc7h3Tj68pJK4wi4JwwyX+DK46916iTTbxCaHd9AE1fuQs9tC1nwLlZ4XEf/s4PqB1UQpzorJ5/tMp1K3VnB51i4mJSdUcAlXUyi3TAVgRXgY4DKjRkoy0rozZMIIcU0q7uue7G1BEREREpBKNunnej76v54lipdlPRuYfeOnDnoy483m8npOZEaZqKC0u4L5A0+cSXz2mOBvxG0P/VhfRqXkvt+NVmtCpqMjP5PGGc3/G5fyp4TXcfv4f8BvDnBUj+cvYT+k/vj/PTxzidkQ5SVu2zGLEh9fhKyvB77cs3bWEMGsxtnwup+5NenN5j6G8fetCZg+YQpf2t7ucWERERETEPU2ia7HPMRQ7sCZmGq9MW+R2pDPG+v3cP6YfM2w+rXY15b21t9P1YB2i/ZbWTQe4Ha9S6YwfEeDy3n879LjZ3AfZnLKWzNiNrHQcluZvcTGZnIoPv32GV/LX0G7xK8zb34eF+7dQ1zjEOF5WhJXQNPOSQ8+NjavpYlIREREREff96uKXuWbvOnbnrGXo0meYs+RhLm37PnWSo92Odtre/HQYn/v302ZPBqn1HuLFi6tzbr0P8fp2EhNbw+14lUpn/Igc4ak+L+EBVkaXEe33s84WY/2hP9N7KFiVX35HtvGrx/D6rPVsiyijcUR1/tDtr/y9/tVERiW5nFBEREREJHjExNYgI6M7Hdv+gvOJY1XSZv467mMKisvw+63b8X621Wsm8Gz2HFrmR1Ac+wBPDmzFJS1rUi0ukoTEem7Hq3Rq/IgcoU6dLozq9iTXhNejUXZDDjiGXbuWuB1LTsD6/azyFwDwVWk2acylwHHoXLs1rZtdzMXn/8ndgCIiIiIiQex35/+dEmMoKHyW5o9O5pej3uW+/3YnO3u129FO2ZvznyHcWrL23ceIG9oTFkLzFv0cZ/erFzmGJo0u5eHrJ5IS3xGApd995XIiOR5rLXe88i45jqFNfgxFjiEqfRwATWt3czmdiIiIiEjwq1fvfK6Nrs/S+FxubLaRkuKX+Ny/nxmLX3E72inZv28jU0p30zgvmT9ffQFp8ZFuR3KdGj8ixzG410AApq2Zw4GiUpfTyNHk5m7h9Y+Hk509FYBGCQPoXpLEdq8lwW/JrH+BywlFRERERKqGO/o+T5IflvpGsSo+B4Ale5a6nOrUjJ76Z0qMoXbiDfRuWt3tOEHBWFt51+116NDBzp8/v9KOJ3Im9Hm1BdUPRvP11kf5dZsFFPA1j1wzCeOob+q2vdlrGTZxEGsdP+kllu1hMOeaaURHp1BaXEBR8X7i4tPdjikiIiIiUmXMmvc8d6wahbGW9FJwLLSM/Tup4a9x56WvBfW8mWvWfcKtM/9ArRIvI2+cR0pshNuRKo0xZoG1tsPR1ul/riIn0MgbS35UEb2apDIz933eL97Oxs1T3Y511rN+P3eOv5bN+Mgohm3hhnp+h+joFADCImLU9BEREREROUXdOt3F71O7cEdiG9qHZbI1HLbt+SdvFKxn1oKRbsc7pl27lvJ/M+8nwkLfjL+cVU2fE9Ht3EVOoElsbeYcWM29TRdw95ryM+Se/ewlfM4q8IzloLeA+7s9RuPMi11OenZYt/4zLJZpq9azwlvGufua4/f2ZEf4C7SISHU7noiIiIhIlTfkklEAzJj7b8av3sCyhBzAMGfrTPrwgLvhjuGN6cPJdaDB7qHcfPNlbscJKmr8iJzAZW2G8faM+/j9qhfwGkjxwU5nAwXOGvZ4/BQah69WjFbjp5I8OPN+tlJGrWIvsRF+nh76LPHxNVi1phYpSZluxxMRERERCRktGvWH1a/hM4Z4n59vDmxh1Y48mtWMdzvaj+Tu38QHBd/RPD+WKy+4lgivx+1IQUWXeomcQGbDvjxQ+yKKHUMvTyL9EhqxMrKMzeHQcE8napda1uRtcjvmWaEgfydrjY98x2FtlJ/LoxsQH18DgGZNriA1rbnLCUVEREREQkdycia1fZDms1wR3ZqscMOwUf9j/OJtrNl5gDU7D7Bw7VKmzX+fJSveZ/eu5a7kfOOLBznoGOK8NzCgraZ7OJLO+BE5CQMu+CeRM2Jo33QAW3bM542lz5LpM9RreDfsHMYqZz+Tlu3AlGygOPdT6qW1onWL69yOHXJmLHofvzG0y6mOLyGHm7s/6nYkEREREZGQ9nDL2/F6I4iNSuG/c5fRoPqrjP5mJNt33kiDpE9ZlLz70HPj/JaZNy3E4w2vtHx+n4+Pc5fSrCSM4df/EscxlXbsqkKNH5GTYByHS3v+BYBqSY3otex1bmx7K0m1Mnns7VS2xWbx7LgRZNcdT7FjqLtpIp+o8XPGTJr+J7bu38OsDd9BNbig3WMMOb+L27FERERERELeeR3vBMDvKyNh9nAWRh0kzFrC6r/GIsehtz+JsoIWFLOauXF72LlrMenpnSot39jp77DTa7gw+lzSE6Mq7bhViS71EjlFYRExPDfkazq1vY1G1eO4pkNvAKrVmYTPQLvcJLZ4YMf2BS4nDQ3fzH+RP258nxf2TSc7Lov6PqOmj4iIiIhIJXM8Xp5ucy+jWtzJ++c/R7J16O9N5embvqBrxz+ya19bALbsXFhpmYrLfHyx4h0ca7mlz72VdtyqRmf8iJymTk37waY3WeoppauNZdP+vpDwHn8dN4LMGsXUik/jmr7PuB2zStq5YxH3L3uRDOuw3fjZEm64KjzN7VgiIiIiImelTm1vO/T4k7qLcDzlLYULz6nOsx83ACArexWV9WvaF6ZuYFtEFq384aSlNq2ko1Y9avyInKbU1HNI8lv2OYb+9fpi2t3C3+f/j+1hC5mR7yM1Dwb5/RhHJ9idCl9ZCfd/dhvFWPK3DKFF4iQWJO2mdUprt6OJiIiIiJz1vm/6AKTGRZCR3orv/JavN69iwXtz8Oa/zS7fArzhli6prbj50lE/+1h+Xxkjxt9AWnR1ure8ieioakxd9BGLVo8lK95wQ1rlXVpWFanxI3KajOPQxIlmib+QXp3uJjo6hS+WJPO5Zz9g2O2BFya+Tp24jdRNaUrbVje6HblKeGfK3Sx0Smi7pw2NO1xC6+q9aLLxUfp0vMvtaCIiIiIicoSbz8vkhbmQ7cshcvdDzIvfTTX84IfFu79hyGn8Mnz2wpH858AqOLAKdk07tDw61s/AsDoMPP9vZ+hVhCY1fkTOgLs7/J7svM1ER6cAcG5aWz7fOZVzi2OZG5HP7C2j+E9cAS02hzNajZ9jys3dws7dy2jS6FIm7JhNgzLDZT3+xjWdMgDo3/FjlxOKiIiIiMjRXNqqJp8tjmert4D9djfnEUVi3Ei2b36SxWkr2bZ9HrVrdz7p/ZUWF3Db/3rQI7kd3+xaSoLHT0zWAFKi1pAY7aNeShuu6n4Lmel1KvBVhQY1fkTOgJbNB/3o+ws73s3iScv47aUv8euJ17E0vhAwLDWlvDtjKjnZk9lcOJcIj4dHB31aqbc7DFbW7+e3Hw1kqf8go/zJrPX46FGSwaCOdd2OJiIiIiIiJ6FuVCozC/LwOYbr09rRplVDfr+qIbCSpRumEZPUjihvKdl7lp2wCTTt22dZaEpYnDMbEwY9imtz6dV30rl+NRKiwyrnBYUINX5EKkByciZ/HzwVgB6JzViRt5zWhZEsiS7ik8XDWRKfS5S1FPocrlo5Rpd/AR9Pf4S5FIFjeGbavfgjDVe0uRxjjNvRRERERETkJNSNz8BX+B0AXRoPpGF6ArHVumLsBN6dP43Rc79lQ+pqChyHl5vfwXkdfnXU/Szdup+3Vo4jxeMn3G/YHm74bb/hZGTUqMyXEzLU+BGpYAM6/4Fd0/7AXf1fY8hHl7A4IY8En6X0uzvwZo7kwyXvsj4nijmbRrDLl0sJfoyBCMfDI93/TmbDvm6/hAp3sDCHf236iFaEsceWsiCykAi/pUe7G9yOJiIiIiIiJ6lOchPYOZU0n6VB/QsAePLa87lzAoQl7mODyaJGiYfsMB//Xfo28dUHUrb/C1o3vx7jOOTu38RD428gN9/PkpgiuuTXoWeLu6kZuYqMjO4uv7qqS40fkQpWo2Zb/nz95wD0jmvIm4Ub+FWtnqyu1pPV215ldvhG5q99lL1hlvRSL16/AwYWRZcwbcXos6LxM3Hmn8lxDP9odTevzHqHHfE7aetEEx4R53Y0ERERERE5SXVqtIOV0Dky7dBEzg1SY2kanshXvv34jcPgpKuZs/lL5iTu5cBHl7MkvpABCyeyq7gvm83T7AoDb7TFwfDIVX8hPb0TcIm7L6yKU+NHpBLd2O1RYub+k0EX/BOvN4rXPu7KMznTARhe8xpatriX77Lz+fU7i2jS6H7W5m1yN3Al8PvKeHvbVDJ9hvu/yMBT0APix9A5ubnb0URERERE5BTUqtme/t5Urmk17EfLG8dl8EVuLol+yy8v+x3nrm7FLQsfZkl8IUllfj7yLCcybBke4PrIm+jb8UrinG2Bpo+cLjV+RCpRjZptuePKdw59f0XnO3nuk2l0MTFc02c4xnFoVjOOETXWk1IaxRqzD4DS0kKWrXyf1ORG1KnTxa34FeKbBS+x0WM5Z1cb0lOj6dzuOjompdG66aATbywiIiIiIkHD4w3nb4O/+snyxmmtIHcpfaNqExYWTfuWV9JhweOUWD9PXz6GX00cSIzj5Ym+L1OrVofAVk0rN3wIU+NHxEUpqc34T+t7aZTR69CpkMYYbuycwZRvktgUuZ1fvfwyK8KeI8fj0Nzv4d1bF7sb+gz7ZvPnRPgtOd4bmDz0XBzHAI3cjiUiIiIiImdIu6ZX03LNu1zb4Z5Dy0Ze9yVebxQebzhjb16A41F7oqLonRVxWae2t/1k2VVt01m4uAl+s4N8+yo5Hod2xREsCS+isDCb6OgUF5JWjHX5u6hRariu+zmBpo+IiIiIiISSpOSGvHProh8ti4hMOPRYTZ+K5bgdQER+KibCy939rgZgUdRBzinxkJ/dCZ8xrFg7weV0Z1aWr4CE0kgGtqvtdhQREREREZGQo8aPSJCqU7sLkX4LwEU1OpNVVD6x2ZKsmW7GOqP2HtjPTi/UjKhOVLjH7TgiIiIiIiIhR40fkSDl8YaTSRjGWvqfexdDenalVonlm50rsNa6He+MGP/1Z/iMoUV1TdwmIiIiIiJSEdT4EQlil9fsyg3R9UlNa86dvTJpQCLrnQJen/Wd29FOi/X7KczfzYL1swFo10C3aRQREREREakImkFJJIhd32/EocfGGM6v245ZO6cydvUVbN/SmCH93uBfn1xMu+Rm3HLZKy4mPTXvf/Fb/rntc5r4kgDIqN3Z5UQiIiIiIiKhSWf8iFQhPVsPpb0/Ao81jC1dy/C3rmSqzeO/u+fg95W5He+kWL+f0Vu/5KBjWJawjySfJSGhrtuxREREREREQpIaPyJVSI0abXjj1vk8c8k44qxlUfIeIv2W3R7D0lVj3I53UhYte5sNHkuSz4/PGOo7EW5HEhERERERCVlq/IhUQZm1G3F3rT54rKXezu6EWcvkle8CUFZaxPbt811OeGxjl79OjN+SsO1iADIiklxOJCIiIiIiEro0x49IFXXNRc/Sd99GPl3rYcKCeUzxfUf3dTv4YsFQxhdlMeWycVRLaexqRr+vjKUr36N18+sxjsO+vB18XrKHZgcSmFnQi/vj93N+s8tczSgiIiIiIhLKdMaPSBWWmFSf6zvVITOyB7u9huemXMb7RVmUGMOU+c+5HY+vZv+TmxY+wcfTHwHgPx8/SbFjaJF2BS/c0I47rnqFc5pe6W5IERERERGREKbGj0gVZ4zhL7c8zUBvXVbEllDNZ6lbBp/u/MbtaMzeOh2AFzaOZ2fOXhbtn0lqmZ/fD7qPS1vVdDmdiIiIiIhI6FPjRyQEOI7hkevGc09yezo7Q6i2vy6LTSnbts0Dyu+kNXPec0yb81SFZ9mxfQEDXm/NmnWfMK9wO2k+yzYP/GnsVayOLqZXdAMcj64yFRERERERqQz635dIiHA8Xm7r/waFJWUMG1kNeJYnJv+Wmzo+xL/nP8Byp4wwa5l+znXExadXWI4vFo9inePnb7MeYZPHMshpxdaiTXwduQ8wDGhzS4UdW0RERERERH5MZ/yIhJjocC9P33oj5+XVYZpnP79c8DuybAmX+OtSagzTFrzIli2zWLHqgwo5/qw9iwBY6JQAMHNDI6ZseIievkd4u8PDNG82sEKOKyIiIiIiIj9lrLWVdrAOHTrY+fOD9zbTIqHEWsuIcUNZuH85cdzNJ+vjqd/oIeqYSLbYIgoNTBk4mbj4dKzfj3FOrw98IG8bXm8U3cb0oG1ZDN96C4ixlo+vnofHE058lBdjzBl6dSIiIiIiIvI9Y8wCa22Ho6074aVexpjXgMuA3dbaFoFlbYCRQCRQBvzKWjvvjCUWkdNmjOGuga8DkF9cxrLnZ1ErL4X5SXsxFqwxvDP1jyzNXU+E8fDU4Bk/u/mzePk73Dr/bzT1hVHiNezf3Zke1VaRmRJPclzMmXxZIiIiIiIicgpOeMaPMaYHkA+8dVjjZwrwb2vtJGPMJcAfrLU9T3QwnfEj4p6duUVMmPkGz+99gbb7kimLPsCyiNJD6x+vcymfZH1FnYhkHr7us5Pa55Yts0hOasht71/MOlNGiTFE+C1t7QgevaoTtZOiK+rliIiIiIiISMBpnfFjrZ1hjKl35GIgPvA4Adh+WglFpMLVSIhk2GW3035pLGNWN2bDhjGYmp/RKa82u2O2MzzrEwBmF2+j+oSbmLx3KfXDEvjzlWNYsuoDaqQ0pUH9CyguyiU3N4vXpj/A6IObiPBbih3D5WXdydq3gy4Nk7njqp6uvlYREREREREpd1Jz/AQaPx8fdsZPM2AyYCifIPo8a+3mY2w7DBgGULdu3fabNx/1aSLigrmrFnHPhzmklE0iP30SrYt7sivya1Z4fCT4LXkGwi0UO+Vz82T4IMux+ANz9QwMT2dL3n6KS/18vfVhbu3agEf7N3fzJYmIiIiIiJx1jnfGz89t/DwHTLfWfmCMuQYYZq3tc6L96FIvkeCTX1zGpuwCFm/J4W+T1pDIGjKTPySv7GaaJS1nm3867RJ6Uli2mY1FW6gdVouDxfHsyq/D0gPnk1dUyv/1aEitxCgGtEsnMszj9ksSERERERE5q1RE4ycXSLTWWlN+m55ca2388fYBavyIBLvdeUVszC5g2/6DvPttFjtyD5J3sIzcg6U/el5chJdzGyQTGebhlvPq0aFeskuJRURERERE5LTm+DmG7cD5wDSgN7DuZ+5HRIJIWnwkafGRAAxoVxsovy38lpxCDhSVHXpeZlqszuwRERERERGpAk7mdu7/A3oCKcaYrcCjwC+BZ40xXqCIwBw+IhJ6jDFkVNMt2UVERERERKqik7mr1/XHWNX+DGcREREREREREZEzyHE7gIiIiIiIiIiIVAw1fkREREREREREQpQaPyIiIiIiIiIiIUqNHxERERERERGREKXGj4iIiIiIiIhIiFLjR0REREREREQkRKnxIyIiIiIiIiISotT4EREREREREREJUWr8iIiIiIiIiIiEKGOtrbyDGbMH2HyST08BsiswjpwZqlPwU42qBtUp+KlGVYPqFPxUo6pBdaoaVKfgpxpVDaFQpwxrberRVlRq4+dUGGPmW2s7uJ1Djk91Cn6qUdWgOgU/1ahqUJ2Cn2pUNahOVYPqFPxUo6oh1OukS71EREREREREREKUGj8iIiIiIiIiIiEqmBs/o9wOICdFdQp+qlHVoDoFP9WoalCdgp9qVDWoTlWD6hT8VKOqIaTrFLRz/IiIiIiIiIiIyOkJ5jN+RERERERERETkNJyRxo8xpp8xZo0xZr0x5o9HrLsrsG6FMeYfx9j+MWPMUmPMYmPMFGNMrcPWPRDY7xpjzEXH2P7XgedYY0zKYcsTjDETjTFLAse/9Uy83qroWDUyxrwXeN8XG2M2GWMWH2P7ZGPM58aYdYE/kwLLqxljphpj8o0xI45z/PrGmLmB7d8zxoQHlhtjzHOBXEuNMe3O8EuvUoK1ToF1PQPHX2GMmX4GX3aVEgQ1OtbPu8GBf0NLjTHfGGNan8GXXeUEcZ00LgVUYI0uNMYsMMYsC/zZ+xjba1w6CcFap8A6jUsBQVAnjU0nEMQ10rh0mAqsU6fDtl9ijLnqGNtrbDqBYK1RYF3wjkvW2tP6AjzABqABEA4sAc4JrOsFfAFEBL5PO8Y+4g97fDcwMvD4nMD+IoD6geN4jrJ9W6AesAlIOWz5g8CTgcepQA4Qfrqvuap9Ha9GRzzvKeCRY+zjH8AfA4//eNj7GgN0A24HRhwnwxjgusDjkcAdgceXAJMAA3QG5rr9fqlOR61TIrASqBv4/qj/lkP9K0hqdKyfd+cBSYHHF+vfUtDWSeNSxdeoLVAr8LgFsO0Y22tcqtp1SkTjUjDVSWNT1a2RxqXKqVM04A08rgns/v77I7bX2FR1a5RIEI9LZ+LN7wJMPuz7B4AHDntT+pzi/h4AXjpyX4HvJwNdjrPtkT/IHgBeDPwDqQ+sBxy33/RKL/JxanTYMgNkAY2OsY81QM3A45rAmiPW38Ix/hMU2Hf2Yf+QDuUBXgauP9pxzravIK/Tr4DH3X6P3P5yu0ZHPO9HP++OWJfEMT74nQ1fwVwnjUuVV6PD9rGXwC+gjliucalq10njUpDU6YjnaGyqYjXSuORKneoDuziiqaCxqcrXKKjHpTNxqVd64I393tbAMoDGQPfAqVDTjTEdj7UTY8xfjTFZwGDgkZPY98kYATQDtgPLgHustf5T2D5UnMz72B3YZa1dd4x9VLfW7gAI/Jl2CsevBuy31pYd5finW+NQEsx1agwkGWOmBU4jHnIK+w0lbtfoZP2C8t8Kna2CuU4al8pVVo0GAoustcVHLNe4dHKCuU4al37gdp1O1tk8NgVzjTQu/aBC62SMOdcYs4Ly9/n2w362fU9j04kFc42CelzynoF9mKMss4ftP4ny09E6AmOMMQ1soCX2ow2sfQh4yBjzAPBr4NET7PtkXAQsBnoDDYHPjTEzrbV5p7CPUHAy7+P1wP9cOP7p1jiUBHOdvEB74AIgCphtjJljrV1bQVmClds1OiFjTC/KP1x3cytDEAjmOmlcKlfhNTLGNAeeBPqe4vE1Lv0gmOukcekHbtfpZLY/28emYK6RxqUfVGidrLVzgebGmGbAm8aYSdbaopM8vsamcsFco6Ael87EGT9bgTqHfV+b8o7x9+vG2XLzAD+QYox5PTDp0adH2d87lHerT7Tvk3HrYcdfD2wEmp7C9qHiuO+jMcYLDADeO2zZkTXaZYypGVj3/TWPJysbSAwc58jjn26NQ0mw1+kza22BtTYbmAG0PoV9hwq3a3RcxphWwCvAFdbavWdqv1VQMNdJ41K5Cq2RMaY28CEwxFq74SjH17h0coK9ThqXyrldp+PS2AQEd400Lv2gUj4/WGtXAQWUz8l0OI1NJxbsNQracelMNH6+BRoFZrcOB64DJgTWfUR59xhjTGPKJ2DKttbeaq1tY629JLCu0WH7uxxYHXg8AbjOGBNhjKkPNALmnUK2LZR33DDGVAeaAN+d+kus8o5XI4A+wGpr7dbvFxxZo8Dzbw48vhkYf7IHD5zhNRW4+ijbTwCGmHKdgdzvT707CwVzncZTftmm1xgTDZwLrDqlVxcaXK3R8Rhj6gLjgJuC5TcLLgraOqFx6XsVViNjTCLwCeXX/H99tINrXDppwVwnjUs/cLVOx6Ox6ZCgrREalw5XkXWq/32zwBiTQfn7vOnwg2tsOinBXKPgHpfsmZlk6RJgLeUzbD902PJw4G1gObAQ6H2M7T8IPGcpMBFIP2zdQ4H9rgEuPsb2d1PeYSujvOP2SmB5LWAK5dfoLQduPBOvtyp+HatGgXVvUH4N4/G2rwZ8CawL/Jl82LpNlN8BID9Qh6PNrN6A8qbdemAsP9zpzQAvBHItAzq4/V6pTj+tU2Dd7ymfqX45cK/b79VZXKNj/bx7BdhH+enai4H5br9XqpPGJTdqBAyn/Ld0iw/7+smdNY718w6NS1WiToF1GpeCp04am6pujTQuVU6dbgJWBOqzELjyGNtrbKqiNQqsC9pxyQQCioiIiIiIiIhIiDkTl3qJiIiIiIiIiEgQUuNHRERERERERCREqfEjIiIiIiIiIhKi1PgREREREREREQlRavyIiIiIiIiIiIQoNX5EREREREREREKUGj8iIiIiIiIiIiFKjR8RERERERERkRD1/9pLgqYgkqSJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 5))\n",
    "plt.plot(df_test.index, np_test_out[:, :], label = 'Measured data')\n",
    "plt.plot(df_test.index, mean[:, :], label = 'Gaussian Process Prediction orig')\n",
    "plt.plot(df_test.index, mean_loaded[:, :], label = 'Gaussian Process Prediction loaded')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>SolRad</th>\n",
       "      <th>OutsideTemp</th>\n",
       "      <th>u</th>\n",
       "      <th>y</th>\n",
       "      <th>u_1</th>\n",
       "      <th>u_2</th>\n",
       "      <th>y_1</th>\n",
       "      <th>y_2</th>\n",
       "      <th>y_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-21 08:00:00+02:00</th>\n",
       "      <td>258.371200</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-12847.137931</td>\n",
       "      <td>17.174013</td>\n",
       "      <td>-12796.551724</td>\n",
       "      <td>-12868.500000</td>\n",
       "      <td>17.036159</td>\n",
       "      <td>17.091241</td>\n",
       "      <td>17.196910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-10 02:15:00+02:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-156.500000</td>\n",
       "      <td>18.854145</td>\n",
       "      <td>-145.000000</td>\n",
       "      <td>-152.241379</td>\n",
       "      <td>18.854145</td>\n",
       "      <td>18.854145</td>\n",
       "      <td>18.854145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-20 23:05:00+02:00</th>\n",
       "      <td>4.039867</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-75.620690</td>\n",
       "      <td>19.798158</td>\n",
       "      <td>-93.206897</td>\n",
       "      <td>-96.931034</td>\n",
       "      <td>19.841970</td>\n",
       "      <td>19.885761</td>\n",
       "      <td>19.696626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-15 22:45:00+02:00</th>\n",
       "      <td>3.390000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.100000</td>\n",
       "      <td>22.849435</td>\n",
       "      <td>29.275862</td>\n",
       "      <td>24.413793</td>\n",
       "      <td>22.985754</td>\n",
       "      <td>22.985754</td>\n",
       "      <td>22.985754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-18 07:00:00+02:00</th>\n",
       "      <td>80.510000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.517241</td>\n",
       "      <td>21.003325</td>\n",
       "      <td>-12.310345</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>21.030239</td>\n",
       "      <td>21.030239</td>\n",
       "      <td>21.030239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-18 00:05:00+02:00</th>\n",
       "      <td>3.390000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-23.689655</td>\n",
       "      <td>24.136657</td>\n",
       "      <td>-18.310345</td>\n",
       "      <td>5.566667</td>\n",
       "      <td>24.193921</td>\n",
       "      <td>24.231217</td>\n",
       "      <td>24.251695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-18 20:45:00+02:00</th>\n",
       "      <td>23.950000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-2823.300000</td>\n",
       "      <td>26.411437</td>\n",
       "      <td>-61.448276</td>\n",
       "      <td>-58.965517</td>\n",
       "      <td>26.832942</td>\n",
       "      <td>26.871086</td>\n",
       "      <td>26.801725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-16 23:30:00+02:00</th>\n",
       "      <td>3.440000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-6.103448</td>\n",
       "      <td>23.780358</td>\n",
       "      <td>-12.551724</td>\n",
       "      <td>-10.827586</td>\n",
       "      <td>23.826292</td>\n",
       "      <td>23.906094</td>\n",
       "      <td>23.747423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-18 06:30:00+02:00</th>\n",
       "      <td>28.720000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.896552</td>\n",
       "      <td>21.094534</td>\n",
       "      <td>7.241379</td>\n",
       "      <td>15.310345</td>\n",
       "      <td>21.094534</td>\n",
       "      <td>21.154323</td>\n",
       "      <td>21.154323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-11 12:35:00+02:00</th>\n",
       "      <td>886.534233</td>\n",
       "      <td>22.0</td>\n",
       "      <td>34.034483</td>\n",
       "      <td>23.341083</td>\n",
       "      <td>35.233333</td>\n",
       "      <td>34.586207</td>\n",
       "      <td>23.222502</td>\n",
       "      <td>23.089790</td>\n",
       "      <td>22.949199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               SolRad  OutsideTemp             u          y  \\\n",
       "timestamp                                                                     \n",
       "2017-06-21 08:00:00+02:00  258.371200         21.0 -12847.137931  17.174013   \n",
       "2017-07-10 02:15:00+02:00    0.000000         18.0   -156.500000  18.854145   \n",
       "2017-06-20 23:05:00+02:00    4.039867         26.0    -75.620690  19.798158   \n",
       "2017-07-15 22:45:00+02:00    3.390000         19.0     25.100000  22.849435   \n",
       "2017-07-18 07:00:00+02:00   80.510000         19.0      6.517241  21.003325   \n",
       "...                               ...          ...           ...        ...   \n",
       "2017-07-18 00:05:00+02:00    3.390000         23.0    -23.689655  24.136657   \n",
       "2017-07-18 20:45:00+02:00   23.950000         29.0  -2823.300000  26.411437   \n",
       "2017-07-16 23:30:00+02:00    3.440000         22.0     -6.103448  23.780358   \n",
       "2017-07-18 06:30:00+02:00   28.720000         19.0      2.896552  21.094534   \n",
       "2017-06-11 12:35:00+02:00  886.534233         22.0     34.034483  23.341083   \n",
       "\n",
       "                                    u_1           u_2        y_1        y_2  \\\n",
       "timestamp                                                                     \n",
       "2017-06-21 08:00:00+02:00 -12796.551724 -12868.500000  17.036159  17.091241   \n",
       "2017-07-10 02:15:00+02:00   -145.000000   -152.241379  18.854145  18.854145   \n",
       "2017-06-20 23:05:00+02:00    -93.206897    -96.931034  19.841970  19.885761   \n",
       "2017-07-15 22:45:00+02:00     29.275862     24.413793  22.985754  22.985754   \n",
       "2017-07-18 07:00:00+02:00    -12.310345      7.100000  21.030239  21.030239   \n",
       "...                                 ...           ...        ...        ...   \n",
       "2017-07-18 00:05:00+02:00    -18.310345      5.566667  24.193921  24.231217   \n",
       "2017-07-18 20:45:00+02:00    -61.448276    -58.965517  26.832942  26.871086   \n",
       "2017-07-16 23:30:00+02:00    -12.551724    -10.827586  23.826292  23.906094   \n",
       "2017-07-18 06:30:00+02:00      7.241379     15.310345  21.094534  21.154323   \n",
       "2017-06-11 12:35:00+02:00     35.233333     34.586207  23.222502  23.089790   \n",
       "\n",
       "                                 y_3  \n",
       "timestamp                             \n",
       "2017-06-21 08:00:00+02:00  17.196910  \n",
       "2017-07-10 02:15:00+02:00  18.854145  \n",
       "2017-06-20 23:05:00+02:00  19.696626  \n",
       "2017-07-15 22:45:00+02:00  22.985754  \n",
       "2017-07-18 07:00:00+02:00  21.030239  \n",
       "...                              ...  \n",
       "2017-07-18 00:05:00+02:00  24.251695  \n",
       "2017-07-18 20:45:00+02:00  26.801725  \n",
       "2017-07-16 23:30:00+02:00  23.747423  \n",
       "2017-07-18 06:30:00+02:00  21.154323  \n",
       "2017-06-11 12:35:00+02:00  22.949199  \n",
       "\n",
       "[500 rows x 9 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sampled"
   ]
  }
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