afsis-soil-chem-MODEL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    "# AfSIS Soil Chemistry Dataset - Modelling¶\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sys import stdout\n",
    "#from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import  RandomForestRegressor, AdaBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "\n",
    "import re\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model.ridge import Ridge\n",
    "from sklearn.linear_model import LogisticRegression, Lasso\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import xgboost \n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn import svm\n",
    "import numpy as np\n",
    "import time\n",
    "import statistics \n",
    "import statsmodels as sm\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from IPython.display import Image\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from scipy.signal import savgol_filter\n",
    "\n",
    "from sklearn.cross_decomposition import PLSRegression\n",
    "from sklearn.cross_decomposition import PLSCanonical\n",
    "from sklearn.cross_decomposition import PLSSVD\n",
    "\n",
    "\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv1D,Conv2D, Reshape, MaxPooling1D\n",
    "from keras.constraints import maxnorm\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "from keras.layers.noise import GaussianNoise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SSN</th>\n",
       "      <th>M3 Ca</th>\n",
       "      <th>M3 K</th>\n",
       "      <th>M3 Al</th>\n",
       "      <th>M3 P</th>\n",
       "      <th>M3 S</th>\n",
       "      <th>PH</th>\n",
       "      <th>Psa asand</th>\n",
       "      <th>Psa asilt</th>\n",
       "      <th>Psa aclay</th>\n",
       "      <th>...</th>\n",
       "      <th>Zn</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Mn</th>\n",
       "      <th>Mo</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Country</th>\n",
       "      <th>Cultivated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>icr006475</td>\n",
       "      <td>207.1</td>\n",
       "      <td>306.30</td>\n",
       "      <td>1095.000</td>\n",
       "      <td>4.495</td>\n",
       "      <td>18.960</td>\n",
       "      <td>4.682</td>\n",
       "      <td>97.848667</td>\n",
       "      <td>1.845333</td>\n",
       "      <td>0.306000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12501.3</td>\n",
       "      <td>81.9</td>\n",
       "      <td>184.5</td>\n",
       "      <td>-6.088750</td>\n",
       "      <td>36.435982</td>\n",
       "      <td>2</td>\n",
       "      <td>sub</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>icr006586</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>1186.00</td>\n",
       "      <td>1165.000</td>\n",
       "      <td>12.510</td>\n",
       "      <td>13.600</td>\n",
       "      <td>7.062</td>\n",
       "      <td>89.520000</td>\n",
       "      <td>9.553667</td>\n",
       "      <td>0.926333</td>\n",
       "      <td>...</td>\n",
       "      <td>38.5</td>\n",
       "      <td>24094.6</td>\n",
       "      <td>422.4</td>\n",
       "      <td>184.5</td>\n",
       "      <td>-6.055750</td>\n",
       "      <td>36.457722</td>\n",
       "      <td>8</td>\n",
       "      <td>top</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>icr021104</td>\n",
       "      <td>258.7</td>\n",
       "      <td>35.25</td>\n",
       "      <td>441.400</td>\n",
       "      <td>4.424</td>\n",
       "      <td>3.608</td>\n",
       "      <td>5.522</td>\n",
       "      <td>89.950000</td>\n",
       "      <td>5.205000</td>\n",
       "      <td>4.845000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2213.4</td>\n",
       "      <td>13.2</td>\n",
       "      <td>184.5</td>\n",
       "      <td>-8.049305</td>\n",
       "      <td>37.333698</td>\n",
       "      <td>14</td>\n",
       "      <td>sub</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>icr033622</td>\n",
       "      <td>11858.3</td>\n",
       "      <td>1156.00</td>\n",
       "      <td>108.286</td>\n",
       "      <td>31.233</td>\n",
       "      <td>25.460</td>\n",
       "      <td>8.583</td>\n",
       "      <td>91.445000</td>\n",
       "      <td>6.310000</td>\n",
       "      <td>2.245000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.8</td>\n",
       "      <td>24135.0</td>\n",
       "      <td>460.4</td>\n",
       "      <td>184.5</td>\n",
       "      <td>4.178087</td>\n",
       "      <td>38.261890</td>\n",
       "      <td>2</td>\n",
       "      <td>sub</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>icr006570</td>\n",
       "      <td>896.2</td>\n",
       "      <td>607.30</td>\n",
       "      <td>1151.000</td>\n",
       "      <td>5.986</td>\n",
       "      <td>20.080</td>\n",
       "      <td>6.661</td>\n",
       "      <td>97.789667</td>\n",
       "      <td>1.885000</td>\n",
       "      <td>0.325667</td>\n",
       "      <td>...</td>\n",
       "      <td>13.4</td>\n",
       "      <td>9135.1</td>\n",
       "      <td>132.6</td>\n",
       "      <td>184.5</td>\n",
       "      <td>-6.069970</td>\n",
       "      <td>36.464588</td>\n",
       "      <td>7</td>\n",
       "      <td>top</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         SSN    M3 Ca     M3 K     M3 Al    M3 P    M3 S     PH  Psa asand  \\\n",
       "0  icr006475    207.1   306.30  1095.000   4.495  18.960  4.682  97.848667   \n",
       "1  icr006586   1665.0  1186.00  1165.000  12.510  13.600  7.062  89.520000   \n",
       "2  icr021104    258.7    35.25   441.400   4.424   3.608  5.522  89.950000   \n",
       "3  icr033622  11858.3  1156.00   108.286  31.233  25.460  8.583  91.445000   \n",
       "4  icr006570    896.2   607.30  1151.000   5.986  20.080  6.661  97.789667   \n",
       "\n",
       "   Psa asilt  Psa aclay  ...    Zn       Fe     Mn     Mo  Latitude  \\\n",
       "0   1.845333   0.306000  ...  22.0  12501.3   81.9  184.5 -6.088750   \n",
       "1   9.553667   0.926333  ...  38.5  24094.6  422.4  184.5 -6.055750   \n",
       "2   5.205000   4.845000  ...   2.3   2213.4   13.2  184.5 -8.049305   \n",
       "3   6.310000   2.245000  ...  29.8  24135.0  460.4  184.5  4.178087   \n",
       "4   1.885000   0.325667  ...  13.4   9135.1  132.6  184.5 -6.069970   \n",
       "\n",
       "   Longitude  Cluster  Depth   Country  Cultivated  \n",
       "0  36.435982        2    sub  Tanzania       False  \n",
       "1  36.457722        8    top  Tanzania       False  \n",
       "2  37.333698       14    sub  Tanzania       False  \n",
       "3  38.261890        2    sub  Ethiopia     unknown  \n",
       "4  36.464588        7    top  Tanzania       False  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_composition = pd.read_csv('elemental_analysis_dataset.csv')\n",
    "#df_composition = df_composition.rename(columns={\"P\": \"%P\", \"K\": \"%K\",\"Ca\": \"%Ca\", \"Cu\": \"%Cu\", \"Fe\": \"%Fe\", \"Mo\": \"%Mo\",\"Mg\": \"%Mg\",\"S\": \"%S\",\"Mn\": \"%Mn\",\"Zn\": \"%Zn\" , \"Cl\": \"%Cl\",})\n",
    "df_composition.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['SSN', 'M3 Ca', 'M3 K', 'M3 Al', 'M3 P', 'M3 S', 'PH', 'Psa asand',\n",
      "       'Psa asilt', 'Psa aclay', 'Volfr', 'Awc1', 'Lshrinkpct',\n",
      "       'Flash2000_N_ppm', 'Acidified carbon', 'pH', 'Leco_N_ppm', 'C % Org',\n",
      "       'ICP OES K mg/kg ', 'ICP OES P mg/kg ', 'P', 'K', 'S', 'Ca', 'Mg', 'Cu',\n",
      "       'Cl', 'Zn', 'Fe', 'Mn', 'Mo', 'Latitude', 'Longitude', 'Cluster',\n",
      "       'Depth', 'Country', 'Cultivated'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_composition.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Elemental analysis and soil fertility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Soil chemistry variables correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0dcfa828b0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the correlation matrix\n",
    "corr = df_composition.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.95, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figures show poor correlation. Surprising that this is true comparing the same element concentration estimated using ICP Optical Emission Spectrometry (ICP-OES-K) and X-rays fluorescence (K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>M3 K</th>\n",
       "      <th>ICP OES K mg/kg</th>\n",
       "      <th>K</th>\n",
       "      <th>M3 P</th>\n",
       "      <th>ICP OES P mg/kg</th>\n",
       "      <th>P</th>\n",
       "      <th>M3 S</th>\n",
       "      <th>S</th>\n",
       "      <th>PH</th>\n",
       "      <th>pH</th>\n",
       "      <th>Flash2000_N_ppm</th>\n",
       "      <th>Leco_N_ppm</th>\n",
       "      <th>Acidified carbon</th>\n",
       "      <th>C % Org</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>306.30</td>\n",
       "      <td>1487.768795</td>\n",
       "      <td>12991.3</td>\n",
       "      <td>4.495</td>\n",
       "      <td>83.555482</td>\n",
       "      <td>50.6</td>\n",
       "      <td>18.960</td>\n",
       "      <td>45.7</td>\n",
       "      <td>4.682</td>\n",
       "      <td>4.57</td>\n",
       "      <td>392.82719</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.429813</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1186.00</td>\n",
       "      <td>9379.867053</td>\n",
       "      <td>15173.5</td>\n",
       "      <td>12.510</td>\n",
       "      <td>230.593961</td>\n",
       "      <td>50.6</td>\n",
       "      <td>13.600</td>\n",
       "      <td>45.7</td>\n",
       "      <td>7.062</td>\n",
       "      <td>7.06</td>\n",
       "      <td>859.46908</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.912364</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.25</td>\n",
       "      <td>272.882131</td>\n",
       "      <td>6838.9</td>\n",
       "      <td>4.424</td>\n",
       "      <td>29.281256</td>\n",
       "      <td>50.6</td>\n",
       "      <td>3.608</td>\n",
       "      <td>45.7</td>\n",
       "      <td>5.522</td>\n",
       "      <td>5.27</td>\n",
       "      <td>186.29957</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.219614</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1156.00</td>\n",
       "      <td>5259.289698</td>\n",
       "      <td>15845.8</td>\n",
       "      <td>31.233</td>\n",
       "      <td>622.223963</td>\n",
       "      <td>431.6</td>\n",
       "      <td>25.460</td>\n",
       "      <td>45.7</td>\n",
       "      <td>8.583</td>\n",
       "      <td>8.50</td>\n",
       "      <td>1026.56581</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.083015</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>607.30</td>\n",
       "      <td>2434.413016</td>\n",
       "      <td>12201.6</td>\n",
       "      <td>5.986</td>\n",
       "      <td>115.206106</td>\n",
       "      <td>50.6</td>\n",
       "      <td>20.080</td>\n",
       "      <td>45.7</td>\n",
       "      <td>6.661</td>\n",
       "      <td>6.50</td>\n",
       "      <td>377.73812</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.527140</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      M3 K  ICP OES K mg/kg         K    M3 P  ICP OES P mg/kg       P  \\\n",
       "0   306.30       1487.768795  12991.3   4.495         83.555482   50.6   \n",
       "1  1186.00       9379.867053  15173.5  12.510        230.593961   50.6   \n",
       "2    35.25        272.882131   6838.9   4.424         29.281256   50.6   \n",
       "3  1156.00       5259.289698  15845.8  31.233        622.223963  431.6   \n",
       "4   607.30       2434.413016  12201.6   5.986        115.206106   50.6   \n",
       "\n",
       "     M3 S     S     PH    pH  Flash2000_N_ppm  Leco_N_ppm  Acidified carbon  \\\n",
       "0  18.960  45.7  4.682  4.57        392.82719       500.0          0.429813   \n",
       "1  13.600  45.7  7.062  7.06        859.46908      1100.0          0.912364   \n",
       "2   3.608  45.7  5.522  5.27        186.29957       200.0          0.219614   \n",
       "3  25.460  45.7  8.583  8.50       1026.56581       900.0          1.083015   \n",
       "4  20.080  45.7  6.661  6.50        377.73812       600.0          0.527140   \n",
       "\n",
       "   C % Org  \n",
       "0     0.47  \n",
       "1     1.09  \n",
       "2     0.25  \n",
       "3     1.01  \n",
       "4     0.62  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected = [ 'M3 K', 'ICP OES K mg/kg ', 'K','M3 P', 'ICP OES P mg/kg ', 'P', 'M3 S', 'S','PH','pH', 'Flash2000_N_ppm', 'Leco_N_ppm','Acidified carbon', 'C % Org']\n",
    "df_corr = df_composition[selected]\n",
    "df_corr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance of sample set is 1.2\n",
      "pH MSE 0.2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0cf312cc40>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#max_pH = 10\n",
    "ph_mse = mean_squared_error(df_corr['pH'],df_corr['PH'])\n",
    "ph_rmse = np.sqrt(mean_squared_error(df_corr['pH'],df_corr['PH']))\n",
    "print(\"Variance of sample set is % s\" %(round(statistics.variance(df_corr['pH']),2)) )\n",
    "print('pH MSE',np.round(ph_mse,2))\n",
    "# Compute the correlation matrix\n",
    "corr = df_corr.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Composition - fertility correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.1 Features selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AWC1 is a meaurement of water content\n",
      "Flash2000_N_ppm is a measurement of nitrogen concentration expressed in ppm\n",
      "acidified carbon is a measurement of % organic matter into the soil\n",
      "\n"
     ]
    },
    {
     "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>pH</th>\n",
       "      <th>Flash2000_N_ppm</th>\n",
       "      <th>Awc1</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>S</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Cu</th>\n",
       "      <th>Cl</th>\n",
       "      <th>Zn</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Cultivated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.57</td>\n",
       "      <td>392.82719</td>\n",
       "      <td>0.034962</td>\n",
       "      <td>50.6</td>\n",
       "      <td>12991.3</td>\n",
       "      <td>45.7</td>\n",
       "      <td>944.1</td>\n",
       "      <td>5575.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>210.2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12501.3</td>\n",
       "      <td>sub</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.06</td>\n",
       "      <td>859.46908</td>\n",
       "      <td>0.042649</td>\n",
       "      <td>50.6</td>\n",
       "      <td>15173.5</td>\n",
       "      <td>45.7</td>\n",
       "      <td>9301.0</td>\n",
       "      <td>5519.1</td>\n",
       "      <td>18.4</td>\n",
       "      <td>152.6</td>\n",
       "      <td>38.5</td>\n",
       "      <td>24094.6</td>\n",
       "      <td>top</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.27</td>\n",
       "      <td>186.29957</td>\n",
       "      <td>0.091913</td>\n",
       "      <td>50.6</td>\n",
       "      <td>6838.9</td>\n",
       "      <td>45.7</td>\n",
       "      <td>884.3</td>\n",
       "      <td>5575.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>229.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2213.4</td>\n",
       "      <td>sub</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.50</td>\n",
       "      <td>1026.56581</td>\n",
       "      <td>0.159708</td>\n",
       "      <td>431.6</td>\n",
       "      <td>15845.8</td>\n",
       "      <td>45.7</td>\n",
       "      <td>46529.1</td>\n",
       "      <td>33771.2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>58.2</td>\n",
       "      <td>29.8</td>\n",
       "      <td>24135.0</td>\n",
       "      <td>sub</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.50</td>\n",
       "      <td>377.73812</td>\n",
       "      <td>0.033089</td>\n",
       "      <td>50.6</td>\n",
       "      <td>12201.6</td>\n",
       "      <td>45.7</td>\n",
       "      <td>1790.1</td>\n",
       "      <td>5575.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>122.5</td>\n",
       "      <td>13.4</td>\n",
       "      <td>9135.1</td>\n",
       "      <td>top</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pH  Flash2000_N_ppm      Awc1      P        K     S       Ca       Mg  \\\n",
       "0  4.57        392.82719  0.034962   50.6  12991.3  45.7    944.1   5575.0   \n",
       "1  7.06        859.46908  0.042649   50.6  15173.5  45.7   9301.0   5519.1   \n",
       "2  5.27        186.29957  0.091913   50.6   6838.9  45.7    884.3   5575.0   \n",
       "3  8.50       1026.56581  0.159708  431.6  15845.8  45.7  46529.1  33771.2   \n",
       "4  6.50        377.73812  0.033089   50.6  12201.6  45.7   1790.1   5575.0   \n",
       "\n",
       "     Cu     Cl    Zn       Fe Depth Cultivated  \n",
       "0  13.0  210.2  22.0  12501.3   sub      False  \n",
       "1  18.4  152.6  38.5  24094.6   top      False  \n",
       "2   2.8  229.5   2.3   2213.4   sub      False  \n",
       "3  13.0   58.2  29.8  24135.0   sub    unknown  \n",
       "4   7.7  122.5  13.4   9135.1   top      False  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elemental_variables = ['pH','Flash2000_N_ppm','Awc1','P', 'K', 'S', 'Ca', 'Mg', 'Cu',\n",
    "       'Cl', 'Zn', 'Fe', 'Depth', 'Cultivated']\n",
    "# major and oligoelements are taken into account for the soil composition\n",
    "\n",
    "df_composition_reduced = df_composition[elemental_variables]\n",
    "print(\"AWC1 is a meaurement of water content\")\n",
    "print(\"Flash2000_N_ppm is a measurement of nitrogen concentration expressed in ppm\")\n",
    "print(\"acidified carbon is a measurement of % organic matter into the soil\")\n",
    "print('')\n",
    "df_composition_reduced.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.2 Categorical features encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating instance of one-hot-encoder\n",
    "enc = OneHotEncoder(sparse=False)# label encoded values of depth\n",
    "enc_df = pd.DataFrame(enc.fit_transform(df_composition_reduced[['Depth']]), columns = ['depth_sub','depth_top'])\n",
    "\n",
    "df_composition_reduced = pd.merge(df_composition_reduced, enc_df, left_index=True, right_index=True)\n",
    "df_composition_reduced = df_composition_reduced.reset_index(drop = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['False' 'True']\n"
     ]
    }
   ],
   "source": [
    "df_composition_cultivated  = df_composition_reduced[df_composition_reduced['Cultivated'] != \"unknown\"]  # eliminate unknown values\n",
    "print(df_composition_cultivated['Cultivated'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating instance of labelencoder for the column \"Cultivated\"\n",
    "labelencoder = LabelEncoder()# Assigning numerical values and storing in another column\n",
    "df_composition_cultivated['Cultivated_n'] = labelencoder.fit_transform(df_composition_cultivated['Cultivated'])\n",
    "\n",
    "print(df_composition_cultivated.shape)\n",
    "df_composition_cultivated = df_composition_cultivated.drop(['Depth', 'Cultivated'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% fertile land samples 22.3\n"
     ]
    }
   ],
   "source": [
    "pc_pos  = (df_composition_cultivated['Cultivated_n'].sum()/148)*100\n",
    "print('% fertile land samples',np.round(pc_pos,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pH</th>\n",
       "      <th>Flash2000_N_ppm</th>\n",
       "      <th>Awc1</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>S</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Cu</th>\n",
       "      <th>Cl</th>\n",
       "      <th>Zn</th>\n",
       "      <th>Fe</th>\n",
       "      <th>depth_sub</th>\n",
       "      <th>depth_top</th>\n",
       "      <th>Cultivated_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.57</td>\n",
       "      <td>392.82719</td>\n",
       "      <td>0.034962</td>\n",
       "      <td>50.6</td>\n",
       "      <td>12991.3</td>\n",
       "      <td>45.7</td>\n",
       "      <td>944.1</td>\n",
       "      <td>5575.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>210.2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12501.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.06</td>\n",
       "      <td>859.46908</td>\n",
       "      <td>0.042649</td>\n",
       "      <td>50.6</td>\n",
       "      <td>15173.5</td>\n",
       "      <td>45.7</td>\n",
       "      <td>9301.0</td>\n",
       "      <td>5519.1</td>\n",
       "      <td>18.4</td>\n",
       "      <td>152.6</td>\n",
       "      <td>38.5</td>\n",
       "      <td>24094.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.27</td>\n",
       "      <td>186.29957</td>\n",
       "      <td>0.091913</td>\n",
       "      <td>50.6</td>\n",
       "      <td>6838.9</td>\n",
       "      <td>45.7</td>\n",
       "      <td>884.3</td>\n",
       "      <td>5575.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>229.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2213.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.50</td>\n",
       "      <td>377.73812</td>\n",
       "      <td>0.033089</td>\n",
       "      <td>50.6</td>\n",
       "      <td>12201.6</td>\n",
       "      <td>45.7</td>\n",
       "      <td>1790.1</td>\n",
       "      <td>5575.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>122.5</td>\n",
       "      <td>13.4</td>\n",
       "      <td>9135.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.41</td>\n",
       "      <td>2520.21961</td>\n",
       "      <td>0.112162</td>\n",
       "      <td>50.6</td>\n",
       "      <td>4731.1</td>\n",
       "      <td>45.7</td>\n",
       "      <td>4924.8</td>\n",
       "      <td>5575.0</td>\n",
       "      <td>56.1</td>\n",
       "      <td>145.1</td>\n",
       "      <td>40.6</td>\n",
       "      <td>67075.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pH  Flash2000_N_ppm      Awc1     P        K     S      Ca      Mg    Cu  \\\n",
       "0  4.57        392.82719  0.034962  50.6  12991.3  45.7   944.1  5575.0  13.0   \n",
       "1  7.06        859.46908  0.042649  50.6  15173.5  45.7  9301.0  5519.1  18.4   \n",
       "2  5.27        186.29957  0.091913  50.6   6838.9  45.7   884.3  5575.0   2.8   \n",
       "4  6.50        377.73812  0.033089  50.6  12201.6  45.7  1790.1  5575.0   7.7   \n",
       "5  5.41       2520.21961  0.112162  50.6   4731.1  45.7  4924.8  5575.0  56.1   \n",
       "\n",
       "      Cl    Zn       Fe  depth_sub  depth_top  Cultivated_n  \n",
       "0  210.2  22.0  12501.3        1.0        0.0             0  \n",
       "1  152.6  38.5  24094.6        0.0        1.0             0  \n",
       "2  229.5   2.3   2213.4        1.0        0.0             0  \n",
       "4  122.5  13.4   9135.1        0.0        1.0             0  \n",
       "5  145.1  40.6  67075.9        0.0        1.0             0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_composition_cultivated.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.3 Data modeling - logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_cultivated = df_composition_cultivated.drop(['Cultivated_n'], axis = 1).values[:, 1:]\n",
    "\n",
    "X_cult_scaled = StandardScaler().fit_transform(X_cultivated) # scale those features\n",
    "y_cultivated = df_composition_cultivated['Cultivated_n'].values\n",
    "\n",
    "X1_train, X1_test, y1_train, y1_test = train_test_split(X_cult_scaled, y_cultivated, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score of the logistic regression model for soil composition and fertility =  0.83\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marco/anaconda3/lib/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n"
     ]
    }
   ],
   "source": [
    "lr_cultivated = LogisticRegression(solver='liblinear',dual=True, random_state=42)\n",
    "\n",
    "lr_cultivated.fit(X1_train, y1_train)\n",
    "y1_pred = lr_cultivated.predict(X1_test) # estimation of cultivated value based on fit\n",
    "accuracy_lr = lr_cultivated.score(X1_test, y1_test)\n",
    "print('Score of the logistic regression model for soil composition and fertility = ',np.round(accuracy_lr,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.93      0.91        27\n",
      "           1       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.83        30\n",
      "   macro avg       0.45      0.46      0.45        30\n",
      "weighted avg       0.80      0.83      0.82        30\n",
      "\n"
     ]
    },
    {
     "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>predicted negative</th>\n",
       "      <th>predicted positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>actual negative</th>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual positive</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 predicted negative  predicted positive\n",
       "actual negative                  25                   2\n",
       "actual positive                   3                   0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "\n",
    "print(classification_report(y1_test, y1_pred))\n",
    "\n",
    "df_confusionmatrix1 = pd.DataFrame(data=cm1, index=[\"actual negative\",\"actual positive\"], columns=[\"predicted negative\",\"predicted positive\"])\n",
    "df_confusionmatrix1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marco/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_split.py:670: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "/home/marco/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_split.py:670: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.83333333, 0.5       , 0.83333333, 0.83333333, 0.83333333])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validation\n",
    "y_cv = cross_val_predict(lr_cultivated, X1_test, y1_test, cv=5)\n",
    "scores = cross_val_score(lr_cultivated, X1_test, y1_test, cv=5)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression: 1-dimensional representation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Logistic Regression: 1-dimensional representation\")\n",
    "Image(filename='img/logisticregression.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the new component contains [30.6097796  18.49971864 15.10061963 10.03959827  7.97674046] % of the information from the 5 variables for a total of 82.22645659713287 %\n"
     ]
    }
   ],
   "source": [
    "n_components = 5\n",
    "\n",
    "pca = PCA(n_components= n_components)\n",
    "principalComponents = pca.fit_transform(X_cult_scaled)\n",
    "\n",
    "df_PCA_composition = pd.DataFrame(data = principalComponents , columns = ['compositional_component1','compositional_component2','compositional_component3','compositional_component4','compositional_component5'])\n",
    "\n",
    "\n",
    "print('the new component contains',pca.explained_variance_ratio_ * 100, '% of the information from the', n_components ,'variables', 'for a total of',sum(pca.explained_variance_ratio_ * 100),'%' )\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all features Index(['pH', 'Flash2000_N_ppm', 'Awc1', 'P', 'K', 'S', 'Ca', 'Mg', 'Cu', 'Cl',\n",
      "       'Zn', 'Fe', 'depth_sub', 'depth_top'],\n",
      "      dtype='object') 14\n",
      "[0.73846109 0.51768887 0.4817683  0.43768256 0.2221693  0.18772211\n",
      " 0.12030957 0.11968293 0.11968293 0.11810195 0.07462063 0.07322147\n",
      " 0.04785942]\n",
      "Index(['Cl', 'Awc1', 'Ca', 'Cu', 'S'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "importance = lr_cultivated.coef_[0]\n",
    "cult_features = df_composition_cultivated.drop(['Cultivated_n'], axis = 1)\n",
    "print('all features',cult_features.columns, len(cult_features.columns) )\n",
    "names_of_variables =cult_features.columns\n",
    "\n",
    "#create random weights and get the magnitude\n",
    "weights = np.random.rand(4)\n",
    "abs_weights = np.abs(importance)\n",
    "\n",
    "#get the sorting indices\n",
    "sorted_index = np.argsort(abs_weights)[::-1]\n",
    "\n",
    "#check if the sorting indices are correct\n",
    "print(abs_weights[sorted_index])\n",
    "\n",
    "#get the index of the top-2 features\n",
    "top_5 = sorted_index[:5]\n",
    "\n",
    "#get the names of the top 2 most important features\n",
    "print(names_of_variables[top_5])\n",
    "ls_top5 = names_of_variables[top_5].tolist()\n",
    "ls_top5[1] = 'water content'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot feature importance\n",
    "plt.bar(ls_top5, abs_weights[sorted_index[:5]])\n",
    "plt.ylabel('Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.4 Data modeling - major elements correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The %N cannot be detected by XRF. It is interesting then to predict it by a correlation with major elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U5aramIkBYGk5G0vLKivKSjfmpmGhiw9qNmcCYDm7e8kccqxmUyYACLPyN2woNVeuT7vaOCoqKyt2b16bye+Zc0aTgncO1ZtzMjMy1/ffBqnqzTmZmZmZ+aU1FEW5qkvXZgl73Hf8tJyrU8bdqpO5flNX9A8A8DNyr86Qd02Dr88Uhk45ri+trt6U2TP11C8FuKo2BeNTgJ9bVn5t8M+NzgZlTu2WGRNm5m6qbLiRut+AAijKXrk5t/v1sLTczVvWpvYu3FCxMSeNDyEPzO8Jn6IoV/WW/MyQOsRSs9aW1lx/OU9NWZBFeoqv7wnN6btM6MaFB00BwdiH/AxhSIhPRu6mni86EAVQDVeDlSBjUw1Vs3t9aPPBhJn5W+74SqbhoQDaO++8U15evnPnzrFo4pCE1WolSFwglvTy/pM6peDJPbLNDcYCAWG1WMm+Bbq9yRaL7drLB3qqzUYALrjOIPTGZ6/KDAKRWDwUsw2E1WIlQCSRXPdmwZcE/DplSJvVYiMAF4jF4psZVhNWS7DKb6b4oArfZOUHxb3RG9+gvdhsJC66+m26m88QfYs7jnffffdaZWfCWAYuEEukAxaRXrcILpLcQmQ4LhKJbvXsTck8qGHywDVw45fEReJB1cGgxB/ad70VcfvUvUg8fJ8CuQMREBAQBYSLeQAAGI6j748w7sEcnwyg1JMU+vgICGgggICAKAABAQFRAAJCF8ir4YskSaLaQBSAMN5g1cplxRYAsJUUasyoPhAFjPEejyAIAvV1vSEi9cV6AtUDooBxAHORLDpaqjahdhAKgbJApNNaEDMiChgPrR3n81F4wDW1Ii1UWjQGZAiMCzDH9dtL1WZCjRrBNcBFygKBIs8kU6C6QBQQRuhOpN+VCR568ujbzHqdzmghQCxVKIP55q/ihnsBmHQ6MylVqrpT0t0gr77NWKIzgyxPFZK/zmbU6sy4TKWSQd9tBVR5SokACLNeZzCarYCLJTKZQikfJamncFmhSqK0IAUZBxg9i4WvJtLP78oEDwAA/KzNVRVX08335IZ33eReABvSQhb8DpBXv3J9KkBq77y3PXln+24rkLq20l6xIaMrk39qd7L8zdUUAgLaU/CW0bj9NYNEU2WnSNJetSETa9tfKFOUCIoqGlwkSbqqt+QI3ae1RTobABCGQtXbRyF7c5WdtNlsNoKo2Z2fBhe3q/ub7LJq8xRvHMRzNlfaSZvNRtqrtuQKDr6mzCux3rR0+98uIfOC2wpo1QJtwWtHBTlbagjCZrURRE1pbmpjeaFah8bYCMgdeOvAsjUlBVJB0GmlkgG4cYVWVygX4QCAS1RqpRDcFpMVAGw2XJqVU6wpuLrpFC5WFhVkAFit12g1aSwu2u8W5ut0BV1ZugVSVUnJWmFbeZHm5mcMUgtKSgoUMrlSpRBbzFYAkfyq6Y+L8zQlmzdpVBLU6hCQL+DWIZH37CInEAmw3ke6UtDagAS42b0AAOBm8urfFD+Fbisglcuw7ftfkslMwUT8MolIXlAoR20OAVHA7aCf5PL4jfbVHHgvgGDBAfPq35xwIcllxAU6vSVPpd2//Y2D2994CTBhpiKvoKhIJUX76CIgChgJ3OReAEEeARgwr37Qmug9fuhtTvQhI5FCY7QVW03BPPwG48E9bxw0GMxGs0YW1vMCJEniKFoCUcCox6D2Ahg4r35Q03svnSH67pQTqkeExWwmBDKZRKYskCkLioG0aBSyl/aXaE0aWViNB2z6woISG+BSdYlahtt0qkJCoysgdQVqA8jVWhWh0ZAFajkihTGJMRwdOKi9AAbMq9/lZWg0m6w953Vao/t6j7dq8xYvlxeGbFyAi6USfAh3IBoqECaLpFivK1FaSoykUV0QFJk0m8XqIqnVZDYYcSXSf0QBow9ShYwPbTp1QYnRbLGYjbriPLmyxIYBQD9LgyQFmrVp2NHX5DKVRm80GQ0laoW8cL+bn11cpMABAMTKvEwMDqrlikKNVqtR50mlaptYeL3HS1TqbL67vECep9EZTSaTQVecpyppxNJUBfLwqiiBUl0gAdJsJKUSXF6sL1YIAACXSm3aYotYYLKIlWgWYwxjlIUG9cqDH9xxunca/eqNGQDCtRU3txdAr9CggfLqU1RN2fru5Pf8tJxNldWluUJh5sbq/rcVsFduzg25H/DTsjeUheduFDVb8nM3db1r9ca1PVVatWnjltIN+fn5GyvsKLZmLIYGjaqtRG6NORqqq6qqqmv6a8B9KCD0gqrqhuvtHOGyNzTYXYMUoKq6IWxVyF62PndDz64bIRTgKtu4qbJi48YKV9mGjZVIh8YiBYz9ZUI3SpNPEEQ/+3UPtHkALhCJhkiAsIBVq9aZBWalQqAs0hfKevkJzSKlWmrRFxbqJEoNspnRjMDYAWHS6Uwmnb4R+DLZOB/pitVmW+h6SYla21Ujojy1CgDExSVoySCigLEF0qgpeH57G2CpOZpiBfJ2IyAKGGfAlTqbXUOAQCRA+o+AKGB8ssDgRvQICGMTKIMwAgKyAhDGBC5cuNDU1AQADAYDAPx+f/B4908Gg9F9MHi8z0E6nU6j0fx+P51ODwQCPR0Fne7z+dLT069dpIWAKABh2OHxeJqammg0GkVRfr/fZrM1NjY21dU5m5oiOByHy9Vit58/dUocGSkWCEifj8tmA4DT4wle3v2Ty2YHDwZvFfwZyWZ3Xi3JZjDYDEanx8NmMDwhZMFmMOg02vsNDbOXLvVePY6xWCwWy+Px4AkJwpSUhIQEkUhEUVTw5ikpKejDIQpAuF1QFLWrtPTrXbs4JEmn0QIUFRMZyQFICASmREZGsFgMGs1PUTEczpQlS2g02vBKk5p6vL7e5fN1mQY0WlDb7adPNzocp9hsv9/f4nBEYJjD7W6i0e594olV998fFxeHviOiAIQbwW63WyyWoAL7fD5TWVnjlSvBU2Rz8z0REX+fMydMRJ0tFN6s5eL3f1Fe/scdO+KmT2ez2anp6dPS030+HwBERkbOmjULfXdEAeMRwbF3c3PzqVOnak6ebG9uZjAY1ZWVUqGQ9PkAgE9Rj8fFsaKiguUTRCImfVS6bNkMxoPTpz8IcLm9ne7zHSkv/37fPl8gAAA0JtOYnBxgMiN5vAmzZqWnpycnJwcCATodOacRBYxRtb9w4cLFixdNZWUN588TDkc8h5PO4WTweDQajcNkvrBo0Rh+/RQeDwCSrpJaECebmhxeL72+/kRl5V6S7KTTaQA8oTBz5Uomh5OSkjJ58mQej4caD6KAUQyXy5W9YsWciRPbWlunRERMw7BH4+JmzJ2LagYAZiYkBP9YkJQEAAGKotNoFwmies8eL0Xt9fnOd3aKJkxY+uijM2bMYDAYiYmJqNIQBYwytLS0+NrazIcPr54+/WmJRIAycV0fdBoNAFIFglSBAAAeCrpIXK49JSVfEgQvOtrS1JSUmjp9zpyn161D1YUoYNQgjs+fj2Ff19Toz5x5JiMjf/ZsVCc3j2gORzV9evDvwNSp9R0dtjNn1Lm5ta2t09LSVj/7bEZGBqolRAGjAPOEQgD4yGJ5+8gR9dKlD0+diurkFsyEFB4vBWC2QIAxmQ0dHf9+9dUKiSRr9eq5aGx1m3WLqmBksCA+Pjs1VVtV9cjHH1fW1qIKuTVgTCYAJEZFyfl8yxdfrHn0UbvdjqoFWQGjBpnx8XaX6+X//ndmbOy6efMkKGxmkKjv6NhrtX5w/LjX70/AsAlCocvlio6ORjWDKGBo0NLS8sF7H3D53Kefe3qYgu2iOZzlHI7Vbv/pl18umzDh+YwMYWQkqvkB8fXFix+dOXO8qYnyeDKTk4NRz9+TJKoZRAG3hfb29srKSofDUVNdc9B40HvF6zV7mRHMD976YN1v1j2S+8gwPVccHS0GOF5X9+TFi4/OmPHM7NlBExehDy7Y7Z/X1JQeO8ag0VIjI+XJyahOxg4FdG9aQxIkPoKpO7744ovj3x1vuNTg9XpPfX+KfYHtdXhxwKMhOhqiAQAcIDwm3Pri1g/++cEv//zLxYsXD5MksxMSAOAzi2XHyZMvLFjwhAQl6+6Cx++vqKnZfvp0jd3ODgRWTpyIYgfHHgWY1CqLWqfCDQV5hjydRj4Cy1AvX7780pqXOg90spwsHvAAYDJMvl5h0RWRe5/71cdfTV2Ruv6366dfnaAacsgSEwHg3SNH3q2qelkmWz5p0nhukaeamz+/cGFXdTWXTp/E58vRisMxPRAgSYu2sESq0Q27/tfU1Bw5eGTbn7bxT/FjIOYmr8IAS65Lbv1367qv1y1+bPGLG14cvnVvS5OSnB7PqybTOydO/HrhQuk4y2nU7nYbrdZtp041d3ZG0ekKsRgp5zigAMJQqLRK9OZhMn+PHz9e+m5pY2MjzU2rO1VHnaFSIfUW7sMDHs/KO/q/R5/8z5Orn1n9/EvPD5OnkMtm352Y2NDR8ev9++cIheuk0tRxkKLjSEPDnvPnvzh3DqfR0uPiZk2YgNRy3PgCbKSiOM9QrCdKlEPY0uvq6nZ9sOvTnZ9iDZigTkADGg1oE2Hibd42ARISqhP2/n7vzq07h9VTmBgVlRgVZblyRVVW9tDUqU/Pns0fi8HFTQ7Hl1ar7uRJgiTj2GzU7Y9HCsAleUplntSoVBsV2iHat/L1P73+7a5v6UfpYhDThyHwKcGRkHAs4Z8/+edwewolcXESgH3nzu2qri6YN+9HY2iB/YHa2o/Onj1cV8emqDkJCbyrS4YQxqEvAABwWVGBNq/ILC+W3sZdzpw5U7G3omJ3hecrjxCEwy10qjPVvc/9yoOvTH1gauH/Kxw+T+HCxEQAKD1+XHv4sHrp0odGc3Dxpba2vTU1H5w44fH7J0ZE3IMM/nFPATKNTgYAIFCWGJS3fBev11v5deUffvIH/BKeCIkMYIyM9Bhgk1on2bfZ130z7J7CzMTEQCDwj+++23Ls2K8XLZKNKic5RVEVVqvu9OnTzc3MQGBxcjIbRUAgChgq7Czdue3Nbbx63tRLd6Z77OMpLCgsGKbpazqdviQxsdXp/JXRmCEUrps7Ny3sg4vPtLSUWa3bjx1j0+mTeLww7Pa///77jIwMFouFKGCU4cKFC3a7/b3i96yfWyc473zD6vYUfrT1oxd/8+LweQpjuNyVXO4Fu33tF1/cIxYXZGQkRESE29dxer3B6b2mzk42RWWHX5hDS0vLZ4avtu3+4go2yXO5eNUS6colGXPmzGGxWElJSYgCwh36nfptf9zmqHfEtcZNgDDqWPp4CicMW6c3OTp6cnS0+dKlJy5cyJ016+nZs9kMRjjUwLHGxs8vXPjszBk6RUliYiThF8xrMpk+3LP3y+/PX8RmO2OfApwPEkX12TPvmk+KmP9N5DF+9ZR80gRRamoqn89HFDAC6A4Rvllsfn3zl//4Mu5SXByEqRnc7SlMWp7U6XIBhg3TgzJEIgAoq64uPXbsZ5mZj6Wl3alXbnW5vrRadadONTkcsSzWqtTUcPsoly9f/rx8378+Kr9MT63jZsDk+3udjp3ujJ1+AeACwLeb9k7GWiK9jdlL57z03A/GQ8KykaUAS4lKbSABlxeXFEhIi1ZZLNKXKC1aVbFJoCzSKMzFJWJ14bVTAx6P5zeFv+m0dzZ82iByhHvYXNBT2L6r3Y75/5vQMG9iHHfYRpvzExMBYPN3371z5MjLixfLR3Z2/VBd3Z5z5/ZfvIhR1Kz4+IzY2HD7FkajcctH5Ucu2E9Q6ZD0U2AO1OFMWXUhaNGYT3z+k1cWzpiQs2z2PffcM4ZzmY4oBVhNNoVGlwcaldacJyspMpC4CsBmtsqKCwmt2UJaRHn9zg2Ubis9ufWk0CkUwagJm+UBb5F7cVNt0xctJ1MT2dKUuOHb6mNZSkqnx/OHb7/dcvLkz+fPzxjm4OKGjo69Vuv7x497/f44DFs1cWK4Vf65c+c++nTvh3v21nPntPAy4RYGZQmzTsKskw2Xd/zzdEaJ4Yv3/zdyjK7pHlEKEKvUYgCbzgxSlUCp0QkKCwgAkVRsLtbiMoXBJlWJ+rXidry5I8WZQh+FOY4SICHBmXC55uJOW/WcVIEkIWaYHhTJZsuTk+va23/+1VfzkpPXzZ07cRhGs19fvLjr7NljNhvl9S5MTo5ks8Oqtl0ul9FofGe74VQzdZo+C9J+e9tEnuLkpVR2zvzRut/+6RfPjskdUEbaF0CaNYUGRXFJaECwtECrBdJQrMVFRSoVKSvUFEiv2mvl5eV/ffGvKedGpf53IyWQmuJIPW85c7reOm9S9IRhczUl83jJPF51c/OPPv10tUTy9OzZUUPhjAhdtD8xMjIMV+8dP35812dffrT3YA17tjNOASlDGnEYKdI7FCd/v2MS6x/PP6l44IEHxtI84shSgFVXUIwXleRdO2C16c0ihcSiL9SIdSVWkErA5XI98+gzrQdbU1tT6WMix+GUwPQUIvX08dOW2Nq5qbFxXO4wPWhGfPwMgL2nT39cXf3c3Lk/vNW+y+v3V1itOovlgt3ODgTumTgx3LY28vh8u/b855OK7y44eBfYs2Har4brSREJZyNyzrrs32i/u6vUsOn362bOnIkoYNAwadRGi5pvdicAACAASURBVNiq0ItUGp0qdH2gRW+V5OVJDbaiQqu0QAkAsEa5hvqCuv3lPWEFDLB035z2xvZDbad48W1zJ8RGDJstvSg5GQDeP3p08+HDv7nrrgemTLn5a081N5fV1Ow8eTKCwRDz+cvDr9u/3N5+vCNwMsD75lPCFZ8L0SOypJIT7Uq9d6/X9dCv3pstpL/5p8IxsIEy7Z133ikvL9+5c2dYiXXw4MGiB4omtowp/e+DJmiyck+lJrKG1VMIAL5AoLK+ns/h/DozM/OGTbbD7a64GtUTRacvDL+J/Xa3+2ybs8pBb0uY3zHxHoidfsdEcXdOav70/z2anpubGxF+AVr94t13371W2cMxNCgQCLz289fGtv7DCHoKmXT63SkpV5xOtdE4SyhcN2/e9Gtm76oaGvZcuFB+9iwGEJ6L9q0EYe6k1THimiflQtKCgaf3ht2ii6xJeXLdzr3aTzf84iercx9agQYCQ4ZtW7Y5DzhhfGDEPIVxXK6cy71gtz/z+ecrp0x5PiMjPiIiuGh/+6lTrS5XfFgu2m91uU51eE62e4mJK1zTl4EgvOKOnBNXfQewbuPfJRPjRunuRmFHAR6P56udX4VV5O8IYMQ8hcHg4qqLFx85d04+der+8+exQGCWUDgvPj6sKsQXCFgJ4nAno4k7oWXSfZC0AIZzrHSbaJ7ylObtf29566+IAoYAWo22s7yTAxwYZxhJT6E0MbGuvV135MgPw2+iu8nhONXuOenGHBMUzlnLIHI0BINx40rqZzHVr//6aeW0adMQBdwWTh89jQEG4xU84ElJWVNt096WUxMTWXOH01MYExUVPi9O+nw1dvv3TqyFn0ZI7gXhaNuCVThnu61+/7q/bXrp8Qey70EUcIu4ePHimUNnbpDYe5xgxDyF4YC69vbjndS5AK9tYp43ZTHgozVjageW1JH6TOHm3ZMmiNLT0xEF3ApeKXwl8VwiIADACHoK74zCuN1n253HOqmW+IUds+6BuLSx8V7nhKuf/X//+PbjtxAF3NI48HLTeHMEDohuT2F1bO284fQUjpytRxBVDnodPaZZ/AQkLQDWWPP7mJ0p//nPfx544AFEAYMGm8lGOn8tGMAQ+6a2NEbvbbGweZ5H50wbjW9hd7lOdnhPdXjtKctd05eBQDxWv5czfsG/tu9GFHBL9Gk2Z0P2OFTyAAQ84HGDu/ufk97pojlcFNkJnRQFFI1iAI0WoNuIjrr29uTRs3zdHwjUEMQRB7MRT746vTfWdwfkxn58SvDOlvefXfNDRAGDgNvt9lP+MdwwQpXcAx4Xzemgt5OUxwGdHspLowGNBgyK5abcdIrJCrBYwMIAiwcRE5hAdd2Ew24eLe97dXqP7Zhwr3OWfHRM7w0V0h/b+tm/1vwojxn26ZLDSD4Mw1gslpfuZblG60pMH/g84CGB7NZ2gkl4A24XzeWknAxg+Gk+JjB9lI8K0JgUi+1ns4DFhxg2sLuVfLSD9PmsBPGdk93Cm06krQJRBoxLnGqPOnfunCTsd4sOL4riCDn7a/evhJWjyGL3cXwupssZcDrAEfAH6Cw6g8ag/BTpJcEDLB+LBSwORPAhemwseb4B6js6jnUEzgai2ic87k1ZDJwYGMewT378xy+/tW/b/4R5uqEwowCc42A4WqH15jf/HQGL3cPyeLgep9fpoDlIP8lgMhgMBj1Ad/vdPpeP6WIGLXY+8JnABHLctfVOj+dMm+NYJ9USv6Bj1j0QJwEEAABo7Ax0dHQgChgEGHQGzU/zw7B7BIIWe2hn7o/2O71OB+VwBBwMYAAbGBTDR/m8Li/NS2O3sVnA4gCHD2Nncv72ow4vtbVVddIu06OJiQ97U5YAi4vUPhReYFNUuA/wws5X4fP4Orgd8c7bXbXSr8VOskin19lJ7wwEAgwWg06j0yia2+v2u/wse5f7LQ7i6EAHV1h/No/f3+Zyxd9e93I7bZP0+d6/1NmSfLdrWtYYnt67TTRESl8qemP7P19DFDAICEFopVuj8ehoMvpWLPYIj9PjdNAc7oCbyWDSmXR6gO6hPD6nj+6is1whFnt4KzkFlA98fvB3/89LjqVYAIzI/U5n0/nzcTExP7xzFqah0Xl50csQMwXp+Y3g91QfO4SsgEEjojPiWOSxLMjq32KP8jsppxOcnf5OBo1BZ9MZFCMAAa/b6yf9bGI0WeyhSu4HPx4byeLhHp+bGx8V8PtjhClxSYmChPjUOZIogaCzvf3Uoe++r9gXERGxNDPz/Dff9NPkAgHG8Kf3a3Y4rNxpSP8HBKd+/8z0qYgCbkEmJr2TbuQYvXRvgArQWXQGnUEP0N0Bt7fDy+robbGHd26RoG6HduYxUxI9Pjebz/VR3ghetGjSxIgoXkr6tPiUpCiBgBcTExUdzYuO5l7t4TsI4rDRuOstLdHaIoiPk69eHTzehwLsLlfFpYtTBIIM4bBPvx8gAoT0MaThA4JBo/l9XkQBtwIucM/7z6e6UpkhEkZBVBiK2sdi94M/KjmGYtECjAArCqPTGSnTJ0dERiWIU5KmTeZFRwc1PPj/jRcCnzh06MvtO47s3x/B502ZPXtG9IJ+u30rQVxhBpp8Hk8k203vumGAot79rmp2YvzioU4B1uxw1HCnQvQkpOED4IqFaq5OXK5AFHCLoNPozLARr1u9g3/QOcwIkcDrc3MSeH6fNzohOS45MSKSN2XRHP7VbpwXExMlELAHn8bfVltbWWYoe/99AIifkHLXQw/2W8zt8VS1NLsiWIqnsg4dsbCdrnhRbH3lmaCW7jl5LhCg869u2PjByerlE5KThiKsuLItQMx5BCn4gBBc+oyAUZBWlIk+VVe3CYE+nXn0FJHH56ZzWDQWcHnRQvHESB4vecbU+JSk7m6cFxPDHSKfnN/vP2w0fvrelsa6yxiXI112N7O//SrcJNlgtXqIJvnKec88mxMVFfHC8xvTJGJJ+qTLlxsB4HSzvcbWidFZIqxLsI/PN9h9jCHZU+SK03kBn4y8ADdhLJ3y2WsBj0MUEI4We7eeR6XEUkygGBQrCqPRaEkTU/hxscJJExKnTgpV8gEt9tvEhVOn9u/W7//kUy4vcuK0aQvSlvdbrKmurr2xjkP3Prn67gfuezo+PvqDbZ//6709K1ZldpdpvOKIYgsaybpZ/NQGlx0AjhGkixUdNUTrcQ8QAWLOo0jBBwS/YV8bbXSEkIw1Cgh1vPnBz+CwuCK+1+fmJvB9Xk+0MDkuKTEiijdl0ZweJY+O5sXEsLGRzlYW9PPteec9R2dHjDBhyf39DxodHR1NtRc99qali9Mf/+kPpNKu1Bo/+9nr1vN1y5bPD/4kiI7vy44/ELPg3xeNUkHXQL3JTeFJS6CjkoMNQdBOi9N5ARdDzFSk4QPA2eprOAlxc6GzEVHAMFrs3Xoe9LHTOUxg0ziRgtRJqRFRUSnp0+KSE7vH5LyYmIiwSZV34uDBvdt3HDtwgBcbM3nmTM51NqKot1pdLbZ4Afb8k3ffuyozIqKrK79w4fJPnv5z6kTh/EVd2akuWGptBxueSVj+1gXDlIiutEsOnztp2mO25prE5IwrtQeHwARoCxCzkAkwoN/IzT30mgMbNcmvRgEFtEEbN0UATAgwKBYPB4CgxZ4gnpA4VRzszPlX9ZxOD9+lOEE/n+HDD5ksVqI4dfF9/Xf7ba2tLXW1ZKvtoQcWKx96aPr0Xpnz9+zZv+kNXdYyafebfl9xIqaO/WjMou31Bzg0dhQLBwCnz82ffld6xiOWnS+lJc68Unu7wre6XOfYqRA7Den4jRFl+mOHHwOBCFHA0KAJmgSylJ+9/pfgmDwqOhrD8dHVJnw+35H9+z99b0vj5dqo2JiMu5b26+fzeb31Vqu3rWlSSuzaF7Ll8vnXOiB+9/u3jxyxLM+a29XPO1zffnLkbpg6mS+saD3Z6CQmRwgBwE8FmvmxLzyne/edJxJTpEPlBWibiSYCBgDr7B53px3ipaNI5nCnAC5wKY/fesKiLHhm1DWIC6dOGfX6r/V7+PFxE6ZNm5DWfxfa0tjY3lgHZPujyrseuO+plBThtWUaG1t+8vSf4+L4ixbOCB6pvWA79/WFx/jzI5j48c7aI60X0iKSgqcuslnPvfhpTc0hgmgQJQ7BBrh2l+ssK2XMZPgcJuCeJtxWSYwq/R8dAwFOROQO7ZsRAt6qvCdGRZ222+2VZYZynY50OeOTkpY8cH+/xUins95qDXRemTt70uP5j2RmXndXj6+/PvJK0T8Xy2ZhWFduxaMHLOzz/qdilwBAvau1zFaVHtG1BWgtk/Hksx/ieNSXe/+alDxnSN7ooN3ThrwAN26l5reYLdWEYPaok3x0uAPlq5Wfbdn62b+35r7w4pIH7gtbOU8cPPjl9p0nvjsoiI9PlaRdz8/XePmyq8UWyQ48/cjd965aFBNzo9mj11/ftq/ie3nWvOBPj8f77WdH5rpSZgiSAcDhI7fW7p8d1bUFawODturx1+Pjp1it3xFEXWLSEGSzt7tcNUwRygJwXXjJiO9ed7TUQdK80Sj+qJkRSFs0r625+cNNmoSJKVNnhxfX2i5dqjSUf6HT0ZmMlKlTF63sP+tRZ1tbS12tm2havmzO6p/9cPbsAWbXOjoczz77KoPJkGV2GfMNDVeOGk4qefOjeV1hP29av+i2/6/QqHn3/mLq9GUA8NXev6VMGBqLtMrubJz5ItL063z7o1Gnt3XQBKNU/0cTBTCZzNjExNjExP+X+9SGt9+aNmdO5J3eWsPn9R7ev/8/JVubbQ3RCQmzly7pN1ckRVFBP58ohrNelbV8+QIcHzgG4fvvT/3il28sXDAjKqprSv/U4XPeU441cVndZbZcNgqZPDadCQD2gHfCkh/MX/QkAFit39mJOmHiEPTbBElaGEkQPwMp+zWdvzPK9GfK3dkRPWtUv8foiwu4J/exf/3hzy5H56s7twtTUu6IDOdPnvx6zyfffPZprEgknDgxZVr//Tlx5UpbY52baFr98F0P3Ld6ypSblVar3bV7z/57rob9AMC3/zk8rS02Q9CTivOzK1VON5nKjQeADq+Lm758Rfavg6f2falJThkaL8CRVkdT+vNI3/vCeSXyQFFH9BwY/XmSRmV04Oy7lwDAn1Q/Wb/pr9NGcFDQ1tpaWWbYu2OHz+uJTUyUZfe/5YHX46m3WqnOK9MnJ7zw4n3Llg3CRAwEAs899z+dDtcSWVff0tJCfLvnSE7UPFFUz2Z7prZzZ9rqp3NFAOD2e13J05986h/BU1br9632yxLR9CF4X5K00BMhYSZS+d4jOlvUwVc7oueMjbcZxQHCc+5e+j8/efbhp5+ekjErfcGCfifbhwrHTaZ9H+06dfj7OJEoVZKGX2dXrysNDa5WG8PryFt9d/aqHycmDm6VSHV1zTPPvrpgftqECQnBI2eOW4mqK2vje+1Ue9Zp+7r5VM8UAIdT+Pzu7rP7vtKkTBgiE8DubJpRgFS+G5HVW73n9kXEJreOQs//GKQAAFj84H1f7tyx76NdLU021csv3/tk7tDev+Hixcry8i937GDjuHDixPlyeb/FXA5HS12tt61p4bypyqcfWbjwVrrN9z8o27r1P/euWtR95OCXR5MaI7J6pwlo9XTuqjPNiuzKAnCRzfrJC3u6z168eJgg6hKEfQMQ/IHAoIRx+3yW1tbDkAwJs5DmQ2cT+JyRx//Z6XBB0lL32Hq5Ub9MKGPZXcE/dG/8/dSh7zKWLV1+Na/OLcPn9R42Gsu2vd/S1CiIj5+9eDHjOnvC2GprfW2NUThtzSN3r1q5iM+/xYXDL730t8t1jXct7eq929s7v/3kyEpW+kReLzvCTwXeqvlCyusKGa5nMZU/fofHS+guUPGVJim5nw6KhuPVJDkDYMAlww0dHS1MZkAggOQpnaR8vCu/j+Sc0+MtR72u9s6oGcAdg3tejp2VgrL77j1/4oTl/47s/L+3lM88vfh+RZRg0DvVnztx4ptPPjlgMMQnJcWIRElTJvdbrIMg2hrryNbG7JXzHrw/Oz198i2LfemSbc2aP0yfPnHWzK5F+DVn6y4fqP0BfzHG6Du02Xxx7xROl8I30qmlD7+SnDwr1ARotV+entCPbzIuPn7979aVvvfe5ZqaqTjOYjD6FHB4PJddrjYud8HKlc8+/nhSUtKqn7wCE2ePU81vq424/KXf+l8WJ6qDDLiEswAfs+86phYLT5nVpQ8fv/32v//yl8WK+14ofvWmvnhr64Gysn27dvn9fkF8/MIVK/otFvD7661WynElJYG3Zo1cLp/PYt1WBX7yydeaN0qXLJ7dfZ/D35zkXWQ8ESO7tvAOm4kdoEdhHABoAZ8k6/mZs3pFSe376o3klOsq7axZs7Zs3/7V3r1vFBfz2tq6nRm1bW0EjuNC4aNPPbXy3nv5fD4A/OE1zUm2bBzqftTZHZ7zFWwmo8PPBlEmCTCGNo4YBxTQjXn3yAHg0tmzP12x8vEXfsqPjZ2XldVvyeMm01cf7Tpz1BwrFE6YNu16fj57c7OzxeZtv7L64aXZ9z42aVLy7Qv5u99pT548n7Wsa82Py+X+9tPDi/2Tp/L7WWRW0Xqy0WGfxE0AgHbKGz/34aXL1va2JqoI4nJ8wgD5fFasWrVi1SrdBx9oiovbXK4DbveKRx65X6mcNavHmrDZbDsqTkLquHME4of+2lF/EpIXuOnjKJXOWH7VidOmxSUmvv+3v3Ew7ldpu6ZJ50ybmzFz4UII+vkMhq927eJGRMQmJkrvuqvfO3jc7qCfb2ZasjLvvrvuGpp4u5YWQrXmj8IEQUZGl9/u8kWbpeJ8Dm8ej9NPfp/QVUAOnxumLLw/5099yuy7jhegX+T94AerH3ts375999xzD3aNd+CdbTtOYePGBPB0gu0ox1mLua94KB9MGHe2zxhnO25kZOaqVQBgPX368w/ep5VsC9Apcbqkub4+Kjp6Vmbm9fx8zfX1vvYmVoDMy7lr5cofJyQM2R6HFRXfF/3hHVnmTC63a3x5/NAZ2mn3j2KX9ls+dBWQL+An4lLWqv51jUOhym6vjYsfhEsCw7D77utntUVTU1Ppl0dB/NOx3Cxaz7PsFq7bxsQinGe+9FF0F+AuXAD8FBh/GC8GjzgtTZyWBgDHTKbTR47e9VD/q/ecnZ2Erc5LNC6VzXjw/sfmzh3itTF/+cu2b781d4f9+Xz+b/9zZJZTOEvQfySPw+cOXQVUy+Gsff7jfmhl3xtDtSjwnW07qrHFY+3zu1qBsALZxmIE8EsVQNo73NDGwoEKQMIiGN8YdxmEWWw2J6LvgD8QCDTW1oLjSkwk87kn7l6+fEFk5BBHfrpc5NPP/BnHmPPmda26b2xs/f7zY8qo+XGR181o9qa1vHsV0GU2+6lnS9nsviOF2lqznaibGjcEif2vXLnygeEwTB79i4J8biAJoNHAfiGq7ssAcZkCmpP0eJlsb0waRE8EhHFLAf3CdunShUPfLF46JyVZuGBB+pDr/9GjZ9ate33BghndgQPV5vOu4x3PxMlvcFXoKqAGJkPx5BuxsanXFttvfCsxMX1I5Hzn3zuq8dFtAkQ27A+c/pwi2+h0AJ8HWJEd3IkQNw81ckQBNwLpcKSkxLU0t7bZ23OUv1y3Lvfhh+6+5TifPtC+vevTT75esWJh95EDhiOTWqPvEdzIuRi6CqiZDvOzfykWL7y2WG3t0ZYW65SpS29fzpaWln+XHYLJ60bZx/N7wdEIJAFARdgOeBqrPbgQ4tFOB6OTAmL4Mc3Q3PWDNrKPjuFLJGIAmDVryoel5e++t2eGROxwkJmZM594fCWTSb9xYo/rYW3BRpfTtWhRV8iw3d7+308OP8iRJkXdaN/k0FVAdvCLF/943oLH+y253/imKHFoVvK+s22nZbSYAM5WoHzQep7XsM/bcolO82N8oc/Z7o9J88SkI60exRTg8/t6flB3TAzZonQAqK9vjuBin+wxvv9BGQDwoiIWLZqZtUyalTWfzR54SdK5c7WqNX+QZkxLurpG+JzlUvOhxmeis+i0G6U5Dl0F1BHwRc1auXzlS/2WHEITwG63b/2sEqb8LJxaAwkd9dDZCD4SAIDOAKBHEcf8zadZbMzb2UJjRbRzUyBhPgA4AIANEEAaPcopYIl8yfbK7ZEQGQ7CJCXFA0BiYmzwZ2tr24njZ789cHTja//uaHeIJyX/7MXHWSxmZCQnKSm+j43w4YeGbds+X7liYXcW4EMVxxIaOI9EL7zxQ0NXAZF+r2dC+pOP/+/1Cn+9/62hMgH++e8dI2oCeJ3gagV3O1AUQABodCAuRbZbaJ0N3s4WVmSst7OFiUUA5fP4KI/HAwBAowHQOzjREDkJ2FyIQDuajEUK+On6n361+yuXxUUb4WHAzY0UQvXc6SSL/vgunUb3B/wBf4DNZt1zz4If/uA+Fov5l9f/XV/XJLu64L+z0/nfTw8vp6WJoxIGGNWGrAKiKMrGi37x2dLrFb58+diVlktTpgxBKEtLS8umf37AZcTSGr6hvE4AoLG4lNfJ4Ao6klcC52pMBI0G1A3MM1rPKeeVqLp9lN8T8HTt/c7g8P2uNgY3mvK6/fYaoPz0gBfjCYHyB/y+AEB7W3tnRDxEiSFyKgkAkUjDxyUF8Hg80XSRxWLBw35ZBpeLL7srI5QRvjt0Ys+erzs6OqdNTene9uv8mcuHyo8u5U1n4IwB7xm6CugSh7OmvxCAUBNAKByajT1cLhc94HVSDPB5ABgA0PVHezun4W/0QNfojIFFAFB+t5PGxChfz5LZ4E8GHuknO4NHAnRWR4QYgNZ1NwDo6ARgQEc7UACRU4DN7bLeuxGBlBFRAACHw3n4kYfNn5jx0bYyi8vF58yZNmcOfLRrX3x8j6vvv19XBUjvfvcpBosRzYpYJpAw6Aw2nZnM6RtuGLoKqI7FejT/3YiI64Yk1tUdv9JycfLkIYtmZbJxYMX2ww7cwWQ94SOFQhRw25gydUrcxDjfJd/wPYI2nHuO9dkCKJoXFevFcAYbANo8zrIWM41GY9GZCRiPQevqIdkMRrOzo9PjnMCJA4B6OmSt/pNIJLmhCaBNSEB7e4U9aDQmk4UoYHBYunTpytyVH7/+8fA9Ar9DW5Lx2Vz+1XSTtk67t9t/TdFq3Vfm8cQA0OLusLFZllNfuch2BoPt93twnBcTMzE6ekJ3XGBd3YkrV2omTc5EKhbuoALJonhEAYOG+vfqff/Z13h+uDZmpoXB1qMiTq+4AJvXHvyDTqNF84QNTZYz579hMJh+vw8CgaAbLkk0w+tzA4DDRcTEDi7ElU6nIX0ceUzm+x95+D5EAYNGZGSk9kPtyrtXcqdzfW4fjTW6mu8QSDuhv11A2tub2EwWAJw7+82NhwkI4WIEAFAUhSjgVjAnY05VdZXT6Tx//vwb//rXOGOA/tGdI5BGZyDtCn9wvc35jy6fNGkSooBbRHJyMgC0tLRcu8f2mOwuhq+410tyuaykpCSkliMHn3vlDMErL/8i/CWlo4815tHefmH9+rWoHkYUzitLFmaMCkkRBYxxeL1uHs+rUNyLqmIkMT2RuyJrKaIAhHAwAc6vW/ccqocRhbtjuXTSggULEAUg3HkTICrKc//9ClQVI4l4X+2zP35itEiLKGCMewF+9jPkBRhRzBO07tT+ef78+aNF4HDPGiQWizlsNmpYtwCfzx0V5UYmwEgiGer+79WiJZkLRpHM4W4FiESigvz82upq1LwGbwLUIC/AiKKjPv/BJaNL/0fHQGDVypWL58yx1daiNjYYE8ATEUE+8MB9qCpGBtFkzXP3z3616DejTvLR4Qv43W9+E8tkWs1mP0mGuagBvz9MvADr1j2LNHNEPrlP5L2g+fUP3970PyFHLboCpUKhkCuLjQTyBQwF3ty06fjx49s+/NDu9UZERYWtnBz8zmc68Pk8ERGuBx+8H6nnsIMkVkyma/5nU+imjABgKykyKEoMSgFpKlYbjgHxoc4MsgK1SioItzcYNTMCHA5n0aJF/6fRRHo8DRcvdhAEan43NAGQF2CY4fck+mrW3CX68pPSPvoPAGYjKOQCAMBlao1kn0FQqCmWm4p1NjQQuH1o//737MxMzOFwNTejdtivCcDlOpEJMLzwOu+b7D1ctvVf2jcGtBNIsUJqLioo1JqJcBzHhjMFkCRB9vwdUnkvrF275Z//XJCWVn/+PGqNfWC3n0MmwPDC03n/NPh81/uJiYnXKyKVg8FIAABpVD+85udGeXFJiVomCMe3CWMKsGrlYpWeBAAwq6UyjaXP+Z+tWzdn8uSLp093trWNryZ4/SXoPp+Xw3E89NADSE+HA1xXXYqn+uGZ2H8+2nbjkqK8Irk+T6FQKDSi3/50mU1bWFCgswEZhsPXsHYHisU2owmUcqvJioulQFr1Go1JIJNKZHlyMQDAq0VF/3jzTeulS6eOHo0UiaKFwnHREq+fAshuP/frX6OJgOEY+XsnY82/Vz+5asXy6OjogcvjElWJQXX1V9aq8H2zsKYAXCYlTWaQmK0imQDqtGqjVKMGtaJE0EUBAPDiCy8AQGNj4z+02qPnz9PYbNGECaOugQUCg0gBgOGc/lup34vjHatX5yCFHcqe390wIw6SEmLeKP57+Of/GGsUAGK5xGY0m0iRXGI7Xm0WKApFIsiT6a8pKBQK//TKK3s+/fTk6dMHDh1iCwTxycmj6DMMKqPp9RIBtrae+9WvkAkwJJTs57obYpmOxISY55569Ok1+WP4XcM8LkAsFxmKDZLCQjBbeALSRgCQNhsp6r90zkMP5Tz0UF1dXen27XsrKpg8Xsr06QM+o4MgCJstLplzB9/z9ndP8vu9GNb+yCNKpL+3gwh/azLWKcCpF5/NX7E8i8vlCgSCsf3KYU4BuEQOlhKpFDcDLCqQJfjD8gAAC11JREFUFeUpDQLCiqtvdE1ycvIvf/7zgueeO2Ay/e8//hERH89is6MT+m7m5fN6r9TV+Ts6khMSVt11V03N4VH9IVtbz/3yl8gEuL3W1nb6t0/fn/vEY5MnT77NW5EkdBl2PX8hChi0BVBYogYArVkGAPISNZj1eRq9EteqtKKBr46MjLx35cqU5GTdzp2dTmf1iRMUmx0rEgGAo6Oj8cyZgMuV8+CDK1esSE1N3blz56imAL/fh2Htjz66GqnxrcB5hUM5xHHY+hfXrlU9efv3IwwqpaHAqJEBYSxQmQr0hWRxgcYiUmmKpfpig1ytEiMKuDVOEFnVKhUpySuW3uwl6TNm/PH3vweAkydPHqmqOnL06JmWFgGH89vCwoULF46ZNmy3n/vFL5AJcNNwd4C7DQJeoDGm88kHHlq6MmuJRCK5/c4fAADMJToLLgIAs6ZAawUpgNVEKLUqi9ZsttokeeLwqozRRAECWaH2VjfRmzlz5syZM3/0wx+eOXMmISFhLA3w/H4fi0UgE2Ag07wN/G4I+GclBCQS0X0rFfyoCAaDsWLFiqghXXJiLSkBlUpsAABpoU4jKCgBEMtEGrVOLJcbbPLCcGt5zPHWEqb35yAM//0ebmwC/PznyATo+0XB6wCggacjKdLPp3XOnj9dJBRNnpj87E9+xOVyh+u5hEFjlKgKCBNBEABXtR2XFWplYNMV68VEoUqLy9UalQRRQDhhyZIlO3dura6uodFocXGCmBgenT46Vk8EAn4Wi3jssUfQR+zS/LZLYgFEMckFCzNcLme6JGP1Q/elpaWxWCOyvSeJiwWWkhKLxWw12RSKUKeVVW8Vy0RmKFKD1kyARIAoIIyQkpLy+98XnzlzhsPh7N37eVXVmYkTBaF7hN8yfL7hTR/Q2nr2pZeeGdcfz+vieK8IIwJ+D5kYG/lz9bMZs2dKJHeokxXJCzVyIEoKiiW99B9Ik56UFUgFhLaoGJerw2g0EMYUYNGq1EYScHlxIRQXmwTKIo3CXFwiVhdKh+Fps2fPnj17NgDcf//9brf7N7/59aVLjW43iWG0iRMTbu2ely+3+APDuPlXIOBnMu2PP/7oOFV+l30K3z05CVc9pVp5j9zn8yUkJDCZYdCkBSptcRclqLTqq2OBQgCAAm1JAfIF3CQD6I2SYp0aNKptZSJZcSGhNVtIiyhvBGJfMAz761/fOHDgAJPJNJuP7N//ldPpBPCzWMDhMCdPvqm4Q4ulYfny+x0t9CsHLIMzZm96v7DW1nOFhWPaBPB0AtkGAQ8AAI0OQEvi0Rl+1wShoIOwL8qS/mLdczNmzEBm7NikALFCalApjABybbFAW6zFZQqDTaoSjZgAS5YsAYBFixatWfN0IBC4fPlyfX39uXNnyss/aWtr43KxlJQYJpPBZrNwvFeO485O5/ffX/zd7/6waFGm0bBvcB07RQHjptwQgYCfyWx54onHRmvTC/jBF7IC3N3GAVIkYPtIBwuP8JKO+Oio1MS4B+59XMCLBAAmk0mn0+Pj45OSklJSUkaLswZRwK3DrDMrtLoCUlugI3VaLZCGYi0uKlKpSFmhpkA6ggFXQU/SlClTpkyZcvfdd+fkrCZJ0uVyffjhtrq6uvp6orOzg8tlczi0SZNEDAa9pSWwa9fuyMjIW3iWI4YpiL6pgaLdfm79+meH6gX5HDrGaKTTmYGADwCCf9g7vS0QC6zBh057nbFUSyyfG7wbANCZ7IDPQ2diEPC7XR18DOiUf/bsWUwa5XI5+XyBeML8RfMzpk+fTqfT6XR6IBCIjIzk8XhIRcctBZA2QiCVCAQgFZTYSABCbxYpJBZ9oUasK7GC9M7NqcTExAT/+N3virr01uGor68/ceL4xx9vj4qK/de/Srr13+3xtHmdLr+XSaNzGBjzhn0XwaNeUv/6vfc+vwkTIECjXRkqE0AoFO5+/+3W1tag7gFA8A86nf7H/33TeuF0sFgkPxpo0EnYcW4E6XR0Xx78yYuObbe3BI9MSp/yyq/+HAgEgncDAAaD4ff7GQyGz+cTCARCoXDMx94jCrhN4AqVRJWXVwIgU5fgYNFbJXl5UoOtqNAqLQiztTARERHTpk2bNm3a6tV9J+d++rMX9k4rp9PpTbbGs2fOMrxUe0d7NJ3DcfYuR4N2zL/hT68kpiQDDEwBLS3nfvGLp4fwFaZOndrv8U90W5CSIAq4QxwgK9T1rAqWFKglAJCnKckb3seaCqUqs0AMJCkp0mkVt+t6WHHvqhX39uSLaGpq8vl8x8xH/7B+g6+1iwboQOsknXf96MnVjz1qMpkGvCdFUYFA0w9/mIuaL8JYpoA7BqlaV6ISkXqVykgo8obWWk1ISACApKSkmbNnHTp0KHiQzWYLhcJFixYBQEpKCoPhqK8/yeHwaTQ6RQUYDBaGRWBYj2fB6Wxfu/bH6EMhIAoYTleE1WrDJcPnc5wwYcKE/rIbpaSkfPzxtvLyL48dq+ZwOC6XiyDaamsvNTQ0RkbGu1ydADB16rTnnnsafSMERAHDA3NxnlyHi6QFxYV3ZJ13dHR0Xt7jedcMeIKDCADgcDg3lb4OAQFRwK0PBMJProSEBPRxEIYcKL5i+GDTFyjkCrlcoTairY8QEAWMEsiKtXmiIWIAPV5kNBj1hWAwkahmERAFjA7gQ5XpTSSXWdV5hRqjSF2suMV7kiTiDgREAaMVkgK9QaMSW4uVBYab12TSrFUpFAqFIk9rPlhUGI4bUSIgCkC4CVU2FOSVECKpslAtI603rcgWTZFFpTcYDDqVVfe9D9UjAqKA0TqgkBcqDEqZTC5Tk0ql+CavIkxmgUKOA4BAUVy8FE3YIAw3UBsbPg6QqHQm1aCvCnUEoEpEQFbAiMGqVUhkcrlCbbCZilV5KrWBAFtJcYl1RKUQSGWkwUgCAKEvUH3iQN8FAVHASFGARVJsNBoNxQrCRCi1KoHZbNbbJDdtwg8RJAWFYq1SoVAodTJ1dgT6LghoIDAysFmtRp1KoQGlRntHs77jskKdofDqL20m+jQIyAoYCYiUWqNeZ9DJjVqzrFCr1arAKhCbC1WqwhILqh4ERAFjfhxg0BkJABK6NoG06q1iGWGVFqklFjOK70VAFDDWIZbiugKlstCsLJR1ZX1XSqVic1GxRSId1GiAMKrzlHlKpdpAWHUFeaoCrQVIk6bYiBz8CMgXEMaQFOj0BSED8lvN+k6azaJCXbHUWFDwySGRWF0k0BnNBhuuVOOokhGQFTD2gcsLC2U4+f/bu0NYtYEAAMO3ZCSTSHCVqAW1IJHIyspKJMFQiaMSWYlEIiuRleCKQ1YRZB1PLMuWl+w9wbb3Hvs+1yY1J/5c7tK7et/0vn4bNkVeR92qjuLXzjttdrM4TuIkr9r2DXclkQDu1+xmy5BlwyhZF8V6dL72wjpN0/yFn4avVT3Id9tNXG/257fclUQCuE9bZbN9XOSj7ysIbVmGcXTupkVyrX6/t9CNs+kgtId9OxxEo16ZbUPUlO144qhtJOBjzQC2y82hypPxJCvbEJrdoRePhsPrZrbtvnL7wXkzLQbLNPpiV5J/xnLgn9ZLyyb95THJ0hBClG8mL393LWdZPS3yH2eWvte7qDEL4G84F9n2UGXxJF5X4b5dSTAL+Hii7NBkPx/f713UmAUAEgBIACABgAQAEgBIACABgAQAEgBIACABgAQAEgBIACABgAQAEgBIACABgAQAEgBIACABgAQAEgBIACABgAQAEgBIACABgAQAEgBIACABgAQAEgBIACABgAQAEgASAEgAIAGABAASAEgAIAGABAAP6nMI4XQ6LRYLYwGP7Xg8djqdZy8/3W631Wp1uVwMEDy8+Xze7/efJ8C4gLUA4H/0BGMQH5A9utwpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='img/majorelements.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(467, 8)\n"
     ]
    },
    {
     "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>Flash2000_N_ppm</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>S</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Cl</th>\n",
       "      <th>Fe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>392.82719</td>\n",
       "      <td>50.6</td>\n",
       "      <td>12991.3</td>\n",
       "      <td>45.7</td>\n",
       "      <td>944.1</td>\n",
       "      <td>5575.0</td>\n",
       "      <td>210.2</td>\n",
       "      <td>12501.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>859.46908</td>\n",
       "      <td>50.6</td>\n",
       "      <td>15173.5</td>\n",
       "      <td>45.7</td>\n",
       "      <td>9301.0</td>\n",
       "      <td>5519.1</td>\n",
       "      <td>152.6</td>\n",
       "      <td>24094.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>186.29957</td>\n",
       "      <td>50.6</td>\n",
       "      <td>6838.9</td>\n",
       "      <td>45.7</td>\n",
       "      <td>884.3</td>\n",
       "      <td>5575.0</td>\n",
       "      <td>229.5</td>\n",
       "      <td>2213.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1026.56581</td>\n",
       "      <td>431.6</td>\n",
       "      <td>15845.8</td>\n",
       "      <td>45.7</td>\n",
       "      <td>46529.1</td>\n",
       "      <td>33771.2</td>\n",
       "      <td>58.2</td>\n",
       "      <td>24135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>377.73812</td>\n",
       "      <td>50.6</td>\n",
       "      <td>12201.6</td>\n",
       "      <td>45.7</td>\n",
       "      <td>1790.1</td>\n",
       "      <td>5575.0</td>\n",
       "      <td>122.5</td>\n",
       "      <td>9135.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Flash2000_N_ppm      P        K     S       Ca       Mg     Cl       Fe\n",
       "0        392.82719   50.6  12991.3  45.7    944.1   5575.0  210.2  12501.3\n",
       "1        859.46908   50.6  15173.5  45.7   9301.0   5519.1  152.6  24094.6\n",
       "2        186.29957   50.6   6838.9  45.7    884.3   5575.0  229.5   2213.4\n",
       "3       1026.56581  431.6  15845.8  45.7  46529.1  33771.2   58.2  24135.0\n",
       "4        377.73812   50.6  12201.6  45.7   1790.1   5575.0  122.5   9135.1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "major_elements = ['Flash2000_N_ppm','P', 'K', 'S', 'Ca', 'Mg', 'Cl','Fe']\n",
    "df_composition_major = df_composition[major_elements]\n",
    "print(df_composition_major.shape)\n",
    "df_composition_major.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Variables selection\n",
    "X_major = df_composition_major.drop(['Flash2000_N_ppm'], axis = 1).values[:, 1:]\n",
    "\n",
    "X_maj_scaled = StandardScaler().fit_transform(X_major) # scale those features\n",
    "y_maj = df_composition_major['Flash2000_N_ppm'].values\n",
    "\n",
    "X0_train, X0_test, y0_train, y0_test = train_test_split(X_maj_scaled, y_maj, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multilinear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: -1.187\n",
      "RMSE: 1125.092\n",
      "scaled RMSE 0.43\n"
     ]
    }
   ],
   "source": [
    "reg = LinearRegression()\n",
    "reg.fit(X0_train, y0_train)\n",
    "\n",
    "y0_pred = reg.predict(X0_test)\n",
    "\n",
    "multilinear_r2 = r2_score(y0_test,y0_pred)\n",
    "\n",
    "\n",
    "# Calculate mean squared error for the regression\n",
    "multilinear_mse = mean_squared_error(y0_test, y0_pred)\n",
    "multilinear_rmse = np.sqrt(multilinear_mse)\n",
    "print('R2: %5.3f'  % multilinear_r2)\n",
    "\n",
    "print('RMSE: %5.3f' % multilinear_rmse)\n",
    "\n",
    "y0_pred_scaled = y0_pred/max(y0_pred)\n",
    "y0_test_scaled = y0_test/max(y0_test)\n",
    "scaled_RMSE = np.sqrt(mean_squared_error(y0_test_scaled, y0_pred_scaled))\n",
    "print('scaled RMSE' ,np.round(scaled_RMSE,2) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.366\n",
      "RMSE: 605.714\n",
      "scaled RMSE 0.16\n"
     ]
    }
   ],
   "source": [
    "#kfold = KFold(shuffle=True, random_state=0, n_splits=4)\n",
    "xgb = XGBRegressor(n_estimators=500, min_child_weight=20, n_jobs=-1, objective='reg:squarederror')\n",
    "\n",
    "xgb.fit(X0_train, y0_train)\n",
    "\n",
    "y_pred_xgb = xgb.predict(X0_test)#.reshape(1, 1))\n",
    "\n",
    "# Calculate scores for the regression\n",
    "xgb_score = r2_score(y0_test, y_pred_xgb)\n",
    "\n",
    "# Calculate mean squared error for the regression\n",
    "xgb_rmse = np.sqrt(mean_squared_error(y0_test, y_pred_xgb))\n",
    "\n",
    "print('R2: %5.3f'  % xgb_score)\n",
    "\n",
    "print('RMSE: %5.3f' % xgb_rmse)\n",
    "y0_xgb_scaled = y_pred_xgb/max(y_pred_xgb)\n",
    "y0_test_scaled = y0_test/max(y0_test)\n",
    "scaled_RMSE = np.sqrt(mean_squared_error(y0_test_scaled, y0_xgb_scaled))\n",
    "print('scaled RMSE' ,np.round(scaled_RMSE,2) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.539\n",
      "RMSE: 516.346\n",
      "scaled RMSE 0.17\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor(max_depth=20, random_state=42)\n",
    "rf.fit(X0_train, y0_train)\n",
    "\n",
    "y_pred_rf = rf.predict(X0_test)#.reshape(1, 1))\n",
    "\n",
    "# Calculate scores for the regression\n",
    "rf_score = r2_score(y0_test, y_pred_rf)\n",
    "\n",
    "# Calculate mean squared error for the regression\n",
    "rf_rmse = np.sqrt(mean_squared_error(y0_test, y_pred_rf))\n",
    "\n",
    "print('R2: %5.3f'  % rf_score)\n",
    "\n",
    "print('RMSE: %5.3f' % rf_rmse)\n",
    "y0_rf_scaled = y_pred_xgb/max(y_pred_rf)\n",
    "y0_test_scaled = y0_test/max(y0_test)\n",
    "scaled_RMSE = np.sqrt(mean_squared_error(y0_test_scaled, y0_rf_scaled))\n",
    "print('scaled RMSE' ,np.round(scaled_RMSE,2) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all features Index(['P', 'K', 'S', 'Ca', 'Mg', 'Cl', 'Fe'], dtype='object') 7\n",
      "[0.33644053 0.28435927 0.15141579 0.11729529 0.08574149 0.02474762]\n",
      "Index(['Cl', 'K', 'P', 'Mg', 'S'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Cl', 'K', 'P', 'Mg', 'S']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_importance = rf.feature_importances_\n",
    "comp_features = df_composition_major.drop(['Flash2000_N_ppm'], axis = 1)\n",
    "print('all features',comp_features.columns, len(comp_features.columns) )\n",
    "names_of_variables =comp_features.columns\n",
    "\n",
    "#create random weights and get the magnitude\n",
    "weights = np.random.rand(4)\n",
    "abs_weights = np.abs(rf_importance)\n",
    "\n",
    "#get the sorting indices\n",
    "sorted_index = np.argsort(abs_weights)[::-1]\n",
    "\n",
    "#check if the sorting indices are correct\n",
    "print(abs_weights[sorted_index])\n",
    "\n",
    "#get the index of the top-2 features\n",
    "top_5 = sorted_index[:5]\n",
    "\n",
    "#get the names of the top 2 most important features\n",
    "print(names_of_variables[top_5])\n",
    "ls_top5 = names_of_variables[top_5].tolist()\n",
    "ls_top5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot feature importance\n",
    "plt.bar(ls_top5, abs_weights[sorted_index[:5]], color = 'r')\n",
    "plt.ylabel('Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Poor correlation between elements concentration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Elemental analysis - Infrared spectroscopy correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Loading mid-infrared spectra and target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_FTIR_raw = pd.read_csv('middle_infrared_spectra_dataset.csv')\n",
    "todrop = ['SSN'] #irrelevant\n",
    "df_FTIR = df_FTIR_raw.drop(todrop, axis = 1)\n",
    "X = df_FTIR.values #spectra are already scaled\n",
    "X_scaled = StandardScaler().fit_transform(X) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Targets selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shapes verification (467,) (467, 3)\n"
     ]
    }
   ],
   "source": [
    "df_composition['%N'] = df_composition['Flash2000_N_ppm']/1000\n",
    "df_composition['%sand'] = df_composition['Psa asand']/100\n",
    "# single target regression\n",
    "target_feature = '%N' #['C % Org', '%P', '%K']#, '%Ca', '%Cu', '%Fe', '%Mo','pH'\n",
    "# multiple targets regression\n",
    "target_features = ['%N', 'pH',  '%sand']#, '%Ca', '%Cu', '%Fe', '%Mo','pH', ,  Psa asilt', 'Psa aclay', 'Volfr', 'Awc1', 'Lshrinkpct',       'Flash2000_N_ppm', 'Acidified carbon', 'pH', 'Leco_N_ppm', 'C % Org',]\n",
    "\n",
    "y2 = df_composition[target_feature].values #PLS1 - single feature regression\n",
    "Y2 = df_composition[target_features].values #PLS2 - multiple features regression\n",
    "Y2_scaled = StandardScaler().fit_transform(Y2) \n",
    "print('shapes verification',y2.shape, Y2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test train split\n",
    "X2_train, X2_test, y2_train, y2_test = train_test_split(X_scaled, y2, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Multivariate linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear regression R² -4.398603753886251\n",
      "linear regression RMSE 1.76763619689215\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "\n",
    "lr_model = lr.fit(X2_train,y2_train)\n",
    "\n",
    "y_lr = lr.predict(X2_test)  \n",
    "r2_lr = r2_score(y2_test, y_lr)\n",
    "rmse_lr = np.sqrt(mean_squared_error(y2_test, y_lr))\n",
    "print('linear regression R²',r2_lr )\n",
    "print('linear regression RMSE',rmse_lr )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Multivariate linear regression')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y2_test, y_lr, label = target_feature)\n",
    "plt.legend()\n",
    "plt.xlabel('actual values')\n",
    "plt.ylabel('predicted values')\n",
    "plt.title('Multivariate linear regression')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Multivariate partial least squares (PLS) regression\n",
    "#### 3.3.1 PLS1: univariate target "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NIR spectra should be correlated to COH elements, i.e. organic carbon and we investigate the correlation between these data with multivariate partial least squares regression. The predicting variables are the FTIR wavelenghts. The target is % organic C. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# what is the optimal number of latent predictive features?\n",
    "n_comp = 30\n",
    "mse = []\n",
    "r2 = []\n",
    "component = np.arange(1, n_comp,2)\n",
    "    \n",
    "for i in component:\n",
    "    pls = PLSRegression(n_components=i)\n",
    "    model = pls.fit(X2_train, y2_train)\n",
    "    \n",
    "    y_c = model.predict(X2_test)\n",
    "\n",
    "    mse.append(mean_squared_error(y2_test, y_c))\n",
    "    r2.append(r2_score(y2_test, y_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of PLS components')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (4,8))\n",
    "plt.subplot(211)\n",
    "plt.scatter(component, mse, label = 'mse', marker = '+', c='r')\n",
    "plt.title('PLS')\n",
    "plt.ylabel('MSE')\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.scatter(component, r2, label = 'R²', alpha=0.5, marker=r'$\\clubsuit$', c='g')\n",
    "plt.ylabel('R²')\n",
    "plt.xlabel('Number of PLS components')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's run this function by setting the optimized mumber of components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PLS R²: -0.597\n",
      "PLS RMSE: 0.961\n"
     ]
    }
   ],
   "source": [
    "pls = PLSRegression(n_components=20)\n",
    "model_pls = pls.fit(X2_train, y2_train)\n",
    "\n",
    "y_c = model_pls.predict(X2_test)\n",
    "\n",
    "\n",
    "# Calculate score\n",
    "score_c = r2_score(y2_test, y_c)\n",
    "\n",
    "\n",
    "\n",
    "# Calculate root mean squared error \n",
    "rmse_c = np.sqrt(mean_squared_error(y2_test, y_c))\n",
    "\n",
    "print('PLS R²: %5.3f'  % score_c)\n",
    "\n",
    "print('PLS RMSE: %5.3f' % rmse_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PLS regression')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(y2_test, y_c, lowess=True, color=\"r\")\n",
    "plt.legend()\n",
    "plt.xlabel('actual values')\n",
    "plt.ylabel('predicted values')\n",
    "plt.title('PLS regression')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3.2 Multivariate target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- PLS canonical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2_train, X2_test, Y2_train, Y2_test = train_test_split(X, Y2, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n targets:  3\n"
     ]
    }
   ],
   "source": [
    "print('n targets: ',len(target_features))\n",
    "\n",
    "plsca = PLSCanonical(n_components= len(target_features))\n",
    "model_pls_canonical = plsca.fit(X2_train, Y2_train)\n",
    "X_c, Y_c = plsca.transform(X2_test, Y2_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² score %N :  -0.18514679707524984\n",
      "R² score pH :  -33.07396764367303\n",
      "R² score %sand :  -48.144788339965494\n",
      "\n",
      "R² PLS Canonical average: -27.135\n",
      "RMSE score %N :  0.83\n",
      "RMSE PLS Canonical average: 3.610\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Calculate scores \n",
    "plsca_score_c = r2_score(Y2_test, Y_c)\n",
    "\n",
    "# Calculate mean squared error \n",
    "plsca_mse_c = mean_squared_error(Y2_test, Y_c)\n",
    "\n",
    "\n",
    "print('R² score',target_features[0],': ',r2_score(Y2_test[:,0], Y_c[:,0]))\n",
    "print('R² score',target_features[1],': ',r2_score(Y2_test[:,1], Y_c[:,1]))\n",
    "print('R² score',target_features[2],': ',r2_score(Y2_test[:,2], Y_c[:,2]))\n",
    "#print('R² score',target_features[3],': ',r2_score(Y2_test[:,3], Y_c[:,3]))\n",
    "print('')\n",
    "print('R² PLS Canonical average: %5.3f'  % plsca_score_c)\n",
    "print('RMSE score',target_features[0],': ',np.round(np.sqrt(mean_squared_error(Y2_test[:,0], Y_c[:,0])),2))\n",
    "print('RMSE PLS Canonical average: %5.3f' % np.round(np.sqrt(plsca_mse_c),2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y2_test[:,0], Y_c[:,0], label = target_features[0], color = 'r')\n",
    "plt.scatter(Y2_test[:,1], Y_c[:,1], label = target_features[1], color = 'b')\n",
    "plt.scatter(Y2_test[:,2], Y_c[:,2], label = target_features[2], color = 'k')\n",
    "#plt.scatter(Y2_test[:,3], Y_c[:,3], label = target_features[3], color = 'g')\n",
    "plt.legend()\n",
    "plt.xlabel('actual values')\n",
    "plt.ylabel('predicted values')\n",
    "plt.title('PLS Canonical - 3 variables prediction')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Random forest regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4.1 One target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random forest regression R²: -0.180\n",
      "random forest regression RMSE: 0.830\n"
     ]
    }
   ],
   "source": [
    "X2, y2 = make_regression(n_features=len(df_FTIR.columns), n_informative=150,random_state=0, shuffle=False)\n",
    "\n",
    "rf = RandomForestRegressor(max_depth=20, random_state=42)\n",
    "rf.fit(X2_train, y2_train)\n",
    "y_pred_rf = rf.predict(X2_test)#.reshape(1, 1))\n",
    "\n",
    "# Calculate score R2\n",
    "rf_score_c = r2_score(y2_test, y_pred_rf)\n",
    "\n",
    "# Calculate root mean squared error\n",
    "rf_rmse_c = np.sqrt(mean_squared_error(y2_test, y_pred_rf))\n",
    "\n",
    "print('random forest regression R²: %5.3f'  % rf_score_c)\n",
    "\n",
    "print('random forest regression RMSE: %5.3f' % np.round(rf_rmse_c,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Random forest regression')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(y2_test, y_pred_rf, lowess=True, color=\"r\")\n",
    "plt.legend()\n",
    "plt.xlabel('actual values')\n",
    "plt.ylabel('predicted values')\n",
    "plt.title('Random forest regression')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4.2 Multivariate target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random forest regression R²: -0.070\n",
      "random forest regression RMSE: 0.780\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#X2, y2 = make_regression(n_features=len(df_FTIR.columns), n_informative=150,random_state=0, shuffle=False)\n",
    "\n",
    "rf = RandomForestRegressor(max_depth=20, random_state=42)\n",
    "rf.fit(X2_train, Y2_train)\n",
    "Y_pred_rf = rf.predict(X2_test)#.reshape(1, 1))\n",
    "\n",
    "# Calculate score R2\n",
    "rf_score_c = r2_score(Y2_test, Y_pred_rf)\n",
    "\n",
    "# Calculate root mean squared error\n",
    "rf_rmse_c = np.sqrt(mean_squared_error(Y2_test, Y_pred_rf))\n",
    "\n",
    "print('random forest regression R²: %5.3f'  % rf_score_c)\n",
    "\n",
    "print('random forest regression RMSE: %5.3f' % np.round(rf_rmse_c,2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² score %N :  -0.14604758535058426\n",
      "R² score pH :  -0.050488510389144814\n",
      "R² score %sand :  -0.01440851542187449\n",
      "\n",
      "R² RF average: -0.070\n",
      "RMSE RF score %N :  0.81\n",
      "RMSE Random Forest average: 0.780\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Calculate scores \n",
    "RF_score = r2_score(Y2_test, Y_pred_rf)\n",
    "\n",
    "# Calculate mean squared error \n",
    "RF_MSE = mean_squared_error(Y2_test, Y_pred_rf)\n",
    "\n",
    "\n",
    "print('R² score',target_features[0],': ',r2_score(Y2_test[:,0], Y_pred_rf[:,0]))\n",
    "print('R² score',target_features[1],': ',r2_score(Y2_test[:,1], Y_pred_rf[:,1]))\n",
    "print('R² score',target_features[2],': ',r2_score(Y2_test[:,2], Y_pred_rf[:,2]))\n",
    "#print('R² score',target_features[3],': ',r2_score(Y2_test[:,3], Y_c[:,3]))\n",
    "print('')\n",
    "print('R² RF average: %5.3f'  % RF_score)\n",
    "print('RMSE RF score',target_features[0],': ',np.round(np.sqrt(mean_squared_error(Y2_test[:,0], Y_pred_rf[:,0])),2))\n",
    "print('RMSE Random Forest average: %5.3f' % np.round(np.sqrt(RF_MSE),2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y2_test[:,0], Y_pred_rf[:,0], label = target_features[0], color = 'r')\n",
    "plt.scatter(Y2_test[:,1], Y_pred_rf[:,1], label = target_features[1], color = 'b')\n",
    "plt.scatter(Y2_test[:,2], Y_pred_rf[:,2], label = target_features[2], color = 'k')\n",
    "#plt.scatter(Y2_test[:,3], Y_c[:,3], label = target_features[3], color = 'g')\n",
    "plt.legend()\n",
    "plt.xlabel('actual values')\n",
    "plt.ylabel('predicted values')\n",
    "plt.title('Random Forest Regressor - 3 variables prediction')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Convolutional neural network for chemiometrics\n",
    "\n",
    "Results can be improved as described in https://www.sciencedirect.com/science/article/abs/pii/S0016706119302368\n",
    "\n",
    "- algorithm can be modified\n",
    "- FTIR data can be further processed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Published convolutional neural network (CNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X2_train, X2_test, Y2_train, Y2_test = train_test_split(X, Y2, test_size = 0.2, random_state = 42)\n",
    "#Y2_pc = Y2/10000 # % instead of PPM\n",
    "\n",
    "X2_train, X2_test, Y2_train, Y2_test = train_test_split(X, Y2, test_size=0.2, random_state=42)\n",
    "X2_train, X2_val, Y2_train, Y2_val = train_test_split(X2_train, Y2_train, test_size=0.1, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1.1 CNN used for chemiometrics in the literature\n",
    "https://github.com/EBjerrum/Deep-Chemometrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hyperparameters for the network\n",
    "DENSE = 128\n",
    "DROPOUT = 0.5\n",
    "C1_K  = 8 #Number of kernels/feature extractors for first layer\n",
    "C1_S  = 16 #Width of the convolutional mini networks\n",
    "C2_K  = 32\n",
    "C2_S  = 16\n",
    "\n",
    "activation='relu'\n",
    "\n",
    "input_dim = X2_train.shape[1]\n",
    "\n",
    "#The model\n",
    "def make_model1():\n",
    "    model = Sequential()\n",
    "    #Adding a bit of GaussianNoise also works as regularization\n",
    "    model.add(GaussianNoise(0.05, input_shape=(input_dim,)))\n",
    "    #First two is number of filter + kernel size\n",
    "    model.add(Reshape((input_dim, 1)))\n",
    "    model.add(Conv1D(C1_K, (C1_S), activation=activation, padding=\"same\"))\n",
    "    model.add(Conv1D(C2_K, (C2_S), padding=\"same\", activation=activation))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dropout(DROPOUT))\n",
    "    model.add(Dense(DENSE, activation=activation))\n",
    "    model.add(Dense(Y2_train.shape[1], activation='linear'))\n",
    "\n",
    "    model.compile(loss='mse', metrics=['accuracy'], optimizer=keras.optimizers.Adadelta(lr=0.01))#, metrics=['mean_absolute_error'])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gaussian_noise (GaussianNois (None, 2542)              0         \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 2542, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, 2542, 8)           136       \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 2542, 32)          4128      \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 81344)             0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 81344)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               10412160  \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 3)                 387       \n",
      "=================================================================\n",
      "Total params: 10,416,811\n",
      "Trainable params: 10,416,811\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "original_model = make_model1()\n",
    "print(original_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "84/84 [==============================] - 4s 52ms/step - loss: 1.2384 - accuracy: 1.0000 - val_loss: 0.9298 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 2/100\n",
      "84/84 [==============================] - 5s 54ms/step - loss: 0.9185 - accuracy: 1.0000 - val_loss: 0.9777 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 3/100\n",
      "84/84 [==============================] - 4s 53ms/step - loss: 0.9426 - accuracy: 1.0000 - val_loss: 0.8706 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 4/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.9111 - accuracy: 1.0000 - val_loss: 1.0194 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 5/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.7976 - accuracy: 1.0000 - val_loss: 0.9191 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 6/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.8195 - accuracy: 1.0000 - val_loss: 0.8399 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 7/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 0.8252 - accuracy: 1.0000 - val_loss: 0.8817 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 8/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.8017 - accuracy: 1.0000 - val_loss: 0.8499 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 9/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.7868 - accuracy: 1.0000 - val_loss: 0.8281 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 10/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.7955 - accuracy: 1.0000 - val_loss: 0.8709 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 11/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.7574 - accuracy: 1.0000 - val_loss: 0.9615 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 12/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.7848 - accuracy: 1.0000 - val_loss: 0.9742 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 13/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 0.7569 - accuracy: 1.0000 - val_loss: 0.8325 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 14/100\n",
      "84/84 [==============================] - ETA: 0s - loss: 0.7740 - accuracy: 1.0000\n",
      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.004999999888241291.\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 0.7740 - accuracy: 1.0000 - val_loss: 0.8388 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 15/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 0.7578 - accuracy: 1.0000 - val_loss: 0.8246 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 16/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 0.7303 - accuracy: 1.0000 - val_loss: 0.8106 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 17/100\n",
      "84/84 [==============================] - 4s 48ms/step - loss: 0.7325 - accuracy: 1.0000 - val_loss: 0.8074 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 18/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.7241 - accuracy: 1.0000 - val_loss: 0.8642 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 19/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.7156 - accuracy: 1.0000 - val_loss: 0.8303 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 20/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.7211 - accuracy: 1.0000 - val_loss: 0.8032 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 21/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 0.6956 - accuracy: 1.0000 - val_loss: 0.8179 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 22/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.7344 - accuracy: 1.0000 - val_loss: 0.8075 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 23/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.7148 - accuracy: 1.0000 - val_loss: 0.7983 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 24/100\n",
      "84/84 [==============================] - 5s 56ms/step - loss: 0.7338 - accuracy: 1.0000 - val_loss: 0.8117 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 25/100\n",
      "84/84 [==============================] - 5s 57ms/step - loss: 0.7177 - accuracy: 1.0000 - val_loss: 0.8026 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 26/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.7203 - accuracy: 1.0000 - val_loss: 0.8028 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 27/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.7030 - accuracy: 1.0000 - val_loss: 0.8085 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 28/100\n",
      "84/84 [==============================] - ETA: 0s - loss: 0.6938 - accuracy: 1.0000\n",
      "Epoch 00028: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455.\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.6938 - accuracy: 1.0000 - val_loss: 0.8125 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 29/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.7278 - accuracy: 1.0000 - val_loss: 0.7920 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 30/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 0.6898 - accuracy: 1.0000 - val_loss: 0.8081 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 31/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 0.6976 - accuracy: 1.0000 - val_loss: 0.7949 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 32/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.7353 - accuracy: 1.0000 - val_loss: 0.7970 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 33/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 0.7048 - accuracy: 1.0000 - val_loss: 0.7945 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 34/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 0.7019 - accuracy: 1.0000 - val_loss: 0.7913 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 35/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 0.6839 - accuracy: 1.0000 - val_loss: 0.8164 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 36/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.6931 - accuracy: 1.0000 - val_loss: 0.7943 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 37/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6830 - accuracy: 1.0000 - val_loss: 0.7902 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 38/100\n",
      "84/84 [==============================] - 4s 52ms/step - loss: 0.7008 - accuracy: 1.0000 - val_loss: 0.7945 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 39/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.6920 - accuracy: 1.0000 - val_loss: 0.7944 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 40/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6968 - accuracy: 1.0000 - val_loss: 0.7933 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 41/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.7135 - accuracy: 1.0000 - val_loss: 0.7894 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 42/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6897 - accuracy: 1.0000 - val_loss: 0.7923 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 43/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.6976 - accuracy: 1.0000 - val_loss: 0.7872 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 44/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6680 - accuracy: 1.0000 - val_loss: 0.7894 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 45/100\n",
      "84/84 [==============================] - 4s 53ms/step - loss: 0.6770 - accuracy: 1.0000 - val_loss: 0.8249 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 46/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.6962 - accuracy: 1.0000 - val_loss: 0.7966 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 47/100\n",
      "84/84 [==============================] - 4s 52ms/step - loss: 0.6789 - accuracy: 1.0000 - val_loss: 0.7886 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 48/100\n",
      "84/84 [==============================] - 4s 53ms/step - loss: 0.7161 - accuracy: 1.0000 - val_loss: 0.7861 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 49/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6846 - accuracy: 1.0000 - val_loss: 0.7930 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 50/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.7016 - accuracy: 1.0000 - val_loss: 0.7888 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 51/100\n",
      "84/84 [==============================] - 5s 55ms/step - loss: 0.6952 - accuracy: 1.0000 - val_loss: 0.7871 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 52/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6929 - accuracy: 1.0000 - val_loss: 0.7871 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 53/100\n",
      "83/84 [============================>.] - ETA: 0s - loss: 0.6962 - accuracy: 1.0000\n",
      "Epoch 00053: ReduceLROnPlateau reducing learning rate to 0.0012499999720603228.\n",
      "84/84 [==============================] - 4s 52ms/step - loss: 0.6950 - accuracy: 1.0000 - val_loss: 0.7893 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 54/100\n",
      "84/84 [==============================] - 5s 54ms/step - loss: 0.6854 - accuracy: 1.0000 - val_loss: 0.7889 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 55/100\n",
      "84/84 [==============================] - 5s 55ms/step - loss: 0.6707 - accuracy: 1.0000 - val_loss: 0.7845 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 56/100\n",
      "84/84 [==============================] - 4s 52ms/step - loss: 0.6819 - accuracy: 1.0000 - val_loss: 0.7918 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 57/100\n",
      "84/84 [==============================] - 4s 53ms/step - loss: 0.6987 - accuracy: 1.0000 - val_loss: 0.7937 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 58/100\n",
      "84/84 [==============================] - 4s 53ms/step - loss: 0.6891 - accuracy: 1.0000 - val_loss: 0.7952 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 59/100\n",
      "84/84 [==============================] - 4s 53ms/step - loss: 0.7202 - accuracy: 1.0000 - val_loss: 0.7889 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 60/100\n",
      "84/84 [==============================] - ETA: 0s - loss: 0.6884 - accuracy: 1.0000\n",
      "Epoch 00060: ReduceLROnPlateau reducing learning rate to 0.0006249999860301614.\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6884 - accuracy: 1.0000 - val_loss: 0.7998 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 61/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6836 - accuracy: 1.0000 - val_loss: 0.7887 - val_accuracy: 1.0000 - lr: 6.2500e-04\n",
      "Epoch 62/100\n",
      "84/84 [==============================] - 4s 52ms/step - loss: 0.6721 - accuracy: 1.0000 - val_loss: 0.7903 - val_accuracy: 1.0000 - lr: 6.2500e-04\n",
      "Epoch 63/100\n",
      "84/84 [==============================] - 4s 53ms/step - loss: 0.6784 - accuracy: 1.0000 - val_loss: 0.7894 - val_accuracy: 1.0000 - lr: 6.2500e-04\n",
      "Epoch 64/100\n",
      "84/84 [==============================] - 4s 52ms/step - loss: 0.6844 - accuracy: 1.0000 - val_loss: 0.7926 - val_accuracy: 1.0000 - lr: 6.2500e-04\n",
      "Epoch 65/100\n",
      "84/84 [==============================] - ETA: 0s - loss: 0.6901 - accuracy: 1.0000\n",
      "Epoch 00065: ReduceLROnPlateau reducing learning rate to 0.0003124999930150807.\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6901 - accuracy: 1.0000 - val_loss: 0.7874 - val_accuracy: 1.0000 - lr: 6.2500e-04\n",
      "Epoch 66/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.6913 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 3.1250e-04\n",
      "Epoch 67/100\n",
      "84/84 [==============================] - 4s 52ms/step - loss: 0.6936 - accuracy: 1.0000 - val_loss: 0.7881 - val_accuracy: 1.0000 - lr: 3.1250e-04\n",
      "Epoch 68/100\n",
      "84/84 [==============================] - 4s 52ms/step - loss: 0.6725 - accuracy: 1.0000 - val_loss: 0.7876 - val_accuracy: 1.0000 - lr: 3.1250e-04\n",
      "Epoch 69/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6848 - accuracy: 1.0000 - val_loss: 0.7877 - val_accuracy: 1.0000 - lr: 3.1250e-04\n",
      "Epoch 70/100\n",
      "84/84 [==============================] - ETA: 0s - loss: 0.6540 - accuracy: 1.0000\n",
      "Epoch 00070: ReduceLROnPlateau reducing learning rate to 0.00015624999650754035.\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.6540 - accuracy: 1.0000 - val_loss: 0.7874 - val_accuracy: 1.0000 - lr: 3.1250e-04\n",
      "Epoch 71/100\n",
      "84/84 [==============================] - 5s 55ms/step - loss: 0.6793 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 1.5625e-04\n",
      "Epoch 72/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.6809 - accuracy: 1.0000 - val_loss: 0.7894 - val_accuracy: 1.0000 - lr: 1.5625e-04\n",
      "Epoch 73/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6790 - accuracy: 1.0000 - val_loss: 0.7899 - val_accuracy: 1.0000 - lr: 1.5625e-04\n",
      "Epoch 74/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.6937 - accuracy: 1.0000 - val_loss: 0.7890 - val_accuracy: 1.0000 - lr: 1.5625e-04\n",
      "Epoch 75/100\n",
      "84/84 [==============================] - ETA: 0s - loss: 0.6762 - accuracy: 1.0000\n",
      "Epoch 00075: ReduceLROnPlateau reducing learning rate to 7.812499825377017e-05.\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.6762 - accuracy: 1.0000 - val_loss: 0.7886 - val_accuracy: 1.0000 - lr: 1.5625e-04\n",
      "Epoch 76/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6855 - accuracy: 1.0000 - val_loss: 0.7884 - val_accuracy: 1.0000 - lr: 7.8125e-05\n",
      "Epoch 77/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6929 - accuracy: 1.0000 - val_loss: 0.7884 - val_accuracy: 1.0000 - lr: 7.8125e-05\n",
      "Epoch 78/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.6940 - accuracy: 1.0000 - val_loss: 0.7884 - val_accuracy: 1.0000 - lr: 7.8125e-05\n",
      "Epoch 79/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.6935 - accuracy: 1.0000 - val_loss: 0.7886 - val_accuracy: 1.0000 - lr: 7.8125e-05\n",
      "Epoch 80/100\n",
      "83/84 [============================>.] - ETA: 0s - loss: 0.6754 - accuracy: 1.0000\n",
      "Epoch 00080: ReduceLROnPlateau reducing learning rate to 3.9062499126885086e-05.\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 0.6722 - accuracy: 1.0000 - val_loss: 0.7887 - val_accuracy: 1.0000 - lr: 7.8125e-05\n",
      "Epoch 81/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 0.6764 - accuracy: 1.0000 - val_loss: 0.7888 - val_accuracy: 1.0000 - lr: 3.9062e-05\n",
      "Epoch 82/100\n",
      "84/84 [==============================] - 4s 48ms/step - loss: 0.6873 - accuracy: 1.0000 - val_loss: 0.7888 - val_accuracy: 1.0000 - lr: 3.9062e-05\n",
      "Epoch 83/100\n",
      "84/84 [==============================] - 5s 54ms/step - loss: 0.6412 - accuracy: 1.0000 - val_loss: 0.7887 - val_accuracy: 1.0000 - lr: 3.9062e-05\n",
      "Epoch 84/100\n",
      "84/84 [==============================] - 5s 54ms/step - loss: 0.6828 - accuracy: 1.0000 - val_loss: 0.7885 - val_accuracy: 1.0000 - lr: 3.9062e-05\n",
      "Epoch 85/100\n",
      "84/84 [==============================] - ETA: 0s - loss: 0.6852 - accuracy: 1.0000\n",
      "Epoch 00085: ReduceLROnPlateau reducing learning rate to 1.9531249563442543e-05.\n",
      "84/84 [==============================] - 4s 52ms/step - loss: 0.6852 - accuracy: 1.0000 - val_loss: 0.7885 - val_accuracy: 1.0000 - lr: 3.9062e-05\n",
      "Epoch 86/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6614 - accuracy: 1.0000 - val_loss: 0.7884 - val_accuracy: 1.0000 - lr: 1.9531e-05\n",
      "Epoch 87/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6870 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 1.9531e-05\n",
      "Epoch 88/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6802 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 1.9531e-05\n",
      "Epoch 89/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6421 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 1.9531e-05\n",
      "Epoch 90/100\n",
      "84/84 [==============================] - ETA: 0s - loss: 0.7088 - accuracy: 1.0000\n",
      "Epoch 00090: ReduceLROnPlateau reducing learning rate to 9.765624781721272e-06.\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.7088 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 1.9531e-05\n",
      "Epoch 91/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6707 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 9.7656e-06\n",
      "Epoch 92/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 0.6760 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 9.7656e-06\n",
      "Epoch 93/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6674 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 9.7656e-06\n",
      "Epoch 94/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6771 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 9.7656e-06\n",
      "Epoch 95/100\n",
      "84/84 [==============================] - ETA: 0s - loss: 0.6797 - accuracy: 1.0000\n",
      "Epoch 00095: ReduceLROnPlateau reducing learning rate to 4.882812390860636e-06.\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6797 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 9.7656e-06\n",
      "Epoch 96/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6893 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 4.8828e-06\n",
      "Epoch 97/100\n",
      "84/84 [==============================] - 4s 52ms/step - loss: 0.6845 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 4.8828e-06\n",
      "Epoch 98/100\n",
      "84/84 [==============================] - 4s 52ms/step - loss: 0.6815 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 4.8828e-06\n",
      "Epoch 99/100\n",
      "84/84 [==============================] - 4s 51ms/step - loss: 0.6782 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 4.8828e-06\n",
      "Epoch 100/100\n",
      "83/84 [============================>.] - ETA: 0s - loss: 0.6787 - accuracy: 1.0000\n",
      "Epoch 00100: ReduceLROnPlateau reducing learning rate to 2.441406195430318e-06.\n",
      "84/84 [==============================] - 4s 53ms/step - loss: 0.6740 - accuracy: 1.0000 - val_loss: 0.7883 - val_accuracy: 1.0000 - lr: 4.8828e-06\n"
     ]
    }
   ],
   "source": [
    "rdlr = ReduceLROnPlateau(patience=5, factor=0.5, min_lr=1e-6, monitor='val_loss', verbose=1)\n",
    "\n",
    "h1 = original_model.fit(X2_train, Y2_train, epochs=100, batch_size=4, validation_data=(X2_val, Y2_val), callbacks=[rdlr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig, ax = plt.subplots(1, 2, figsize=(10, 3))\n",
    "plt.figure(figsize= (6,12))\n",
    "plt.subplot(211)\n",
    "plt.plot(h1.history['loss'], label='loss')\n",
    "plt.plot(h1.history['val_loss'], label='val_loss')\n",
    "plt.ylabel('MSE Loss')\n",
    "#plt.xticks(epochs,\" \")\n",
    "_ = plt.legend()\n",
    "ax2 = plt.gca().twinx()\n",
    "ax2.plot(h1.history['lr'], color='r', linestyle='dashed')\n",
    "ax2.set_ylabel('lr',color='r')\n",
    "\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(h1.history['accuracy'], label='accuracy')\n",
    "plt.plot(h1.history['val_accuracy'], label='val_accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1.2 CNN with additional layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modified model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hyperparameters for the network\n",
    "DENSE = 100\n",
    "DROPOUT = 0.3\n",
    "C1_K  = 32 #Number of kernels/feature extractors for first layer\n",
    "C1_S  = 20 #Width of the convolutional mini networks\n",
    "C2_K  = 64\n",
    "C2_S  = 20\n",
    "C3_K  = 128\n",
    "C3_S  = 20\n",
    "C4_K  = 256\n",
    "C4_S  = 20\n",
    "\n",
    "activation='relu'\n",
    "\n",
    "input_dim = X2_train.shape[1]\n",
    "\n",
    "#The model\n",
    "def make_model():\n",
    "    model = Sequential()\n",
    "    #Adding a bit of GaussianNoise also works as regularization\n",
    "    model.add(GaussianNoise(0.02, input_shape=(input_dim,)))\n",
    "    #First two is number of filter + kernel size\n",
    "    model.add(Reshape((input_dim, 1)))\n",
    "    model.add(Conv1D(C1_K, (C1_S), activation=activation, padding=\"same\"))\n",
    "    model.add(MaxPooling1D(pool_size=2, padding='valid'))\n",
    "    model.add(Conv1D(C2_K, (C2_S), padding=\"same\", activation=activation))\n",
    "    model.add(MaxPooling1D(pool_size=5, padding='valid'))\n",
    "    model.add(Conv1D(C3_K, (C3_S), padding=\"same\", activation=activation))\n",
    "    model.add(MaxPooling1D(pool_size=5, padding='valid'))\n",
    "    model.add(Conv1D(C4_K, (C4_S), padding=\"same\", activation=activation))\n",
    "    model.add(MaxPooling1D(pool_size=5, padding='valid'))\n",
    "    \n",
    "    model.add(Flatten())\n",
    "    \n",
    "    model.add(Dense(1028, activation='tanh', kernel_constraint=maxnorm(3)))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dense(512, activation='tanh', kernel_constraint=maxnorm(3)))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(DROPOUT))\n",
    "    model.add(Dense(256, activation='tanh', kernel_constraint=maxnorm(3)))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(DROPOUT))\n",
    "    model.add(Dense(128, activation='tanh', kernel_constraint=maxnorm(3)))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(DROPOUT))\n",
    "    model.add(Dense(DENSE, activation='swish'))\n",
    "    model.add(Dense(Y2_train.shape[1], activation='linear'))\n",
    "\n",
    "    #model.compile(loss='mse', optimizer=keras.optimizers.Adadelta(lr=0.005))#, metrics=['mean_absolute_error'])\n",
    "    model.compile(loss='mse', metrics=['accuracy'], optimizer=keras.optimizers.Adadelta(lr=0.005))#, metrics=['mean_absolute_error'])\n",
    "     \n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gaussian_noise_1 (GaussianNo (None, 2542)              0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 2542, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 2542, 32)          672       \n",
      "_________________________________________________________________\n",
      "max_pooling1d (MaxPooling1D) (None, 1271, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 1271, 64)          41024     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 254, 64)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 254, 128)          163968    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 50, 128)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_5 (Conv1D)            (None, 50, 256)           655616    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 10, 256)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2560)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1028)              2632708   \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 1028)              4112      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 512)               526848    \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 100)               12900     \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 3)                 303       \n",
      "=================================================================\n",
      "Total params: 4,205,959\n",
      "Trainable params: 4,202,111\n",
      "Non-trainable params: 3,848\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = make_model()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "84/84 [==============================] - 4s 48ms/step - loss: 11.8991 - accuracy: 0.3463 - val_loss: 12.8959 - val_accuracy: 1.0000 - lr: 0.0050\n",
      "Epoch 2/100\n",
      "84/84 [==============================] - 4s 47ms/step - loss: 10.9021 - accuracy: 0.3582 - val_loss: 12.6345 - val_accuracy: 0.3421 - lr: 0.0050\n",
      "Epoch 3/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 9.8980 - accuracy: 0.4448 - val_loss: 12.3687 - val_accuracy: 0.2368 - lr: 0.0050\n",
      "Epoch 4/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 9.3189 - accuracy: 0.5015 - val_loss: 12.6331 - val_accuracy: 0.9737 - lr: 0.0050\n",
      "Epoch 5/100\n",
      "84/84 [==============================] - 4s 50ms/step - loss: 8.6981 - accuracy: 0.5642 - val_loss: 9.2094 - val_accuracy: 0.8947 - lr: 0.0050\n",
      "Epoch 6/100\n",
      "84/84 [==============================] - 4s 48ms/step - loss: 8.2660 - accuracy: 0.6000 - val_loss: 6.1647 - val_accuracy: 0.8421 - lr: 0.0050\n",
      "Epoch 7/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 7.7586 - accuracy: 0.6896 - val_loss: 7.5883 - val_accuracy: 0.6053 - lr: 0.0050\n",
      "Epoch 8/100\n",
      "83/84 [============================>.] - ETA: 0s - loss: 7.4749 - accuracy: 0.6657\n",
      "Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455.\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 7.4537 - accuracy: 0.6687 - val_loss: 6.9500 - val_accuracy: 0.6579 - lr: 0.0050\n",
      "Epoch 9/100\n",
      "84/84 [==============================] - 4s 47ms/step - loss: 6.9258 - accuracy: 0.7045 - val_loss: 5.9294 - val_accuracy: 0.7368 - lr: 0.0025\n",
      "Epoch 10/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 7.2436 - accuracy: 0.6985 - val_loss: 4.2696 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 11/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 6.8430 - accuracy: 0.7373 - val_loss: 4.6672 - val_accuracy: 0.9474 - lr: 0.0025\n",
      "Epoch 12/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 6.7821 - accuracy: 0.7284 - val_loss: 4.0348 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 13/100\n",
      "84/84 [==============================] - 4s 47ms/step - loss: 6.5233 - accuracy: 0.7672 - val_loss: 4.0219 - val_accuracy: 0.8684 - lr: 0.0025\n",
      "Epoch 14/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 6.5596 - accuracy: 0.7493 - val_loss: 3.3360 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 15/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 6.1297 - accuracy: 0.8030 - val_loss: 3.5180 - val_accuracy: 0.9474 - lr: 0.0025\n",
      "Epoch 16/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 6.0388 - accuracy: 0.8328 - val_loss: 2.9722 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 17/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 5.8419 - accuracy: 0.8299 - val_loss: 2.6821 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 18/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 5.5993 - accuracy: 0.8478 - val_loss: 2.3733 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 19/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 5.5509 - accuracy: 0.8448 - val_loss: 3.0680 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 20/100\n",
      "83/84 [============================>.] - ETA: 0s - loss: 5.3023 - accuracy: 0.8494\n",
      "Epoch 00020: ReduceLROnPlateau reducing learning rate to 0.0012499999720603228.\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 5.3127 - accuracy: 0.8478 - val_loss: 3.1169 - val_accuracy: 1.0000 - lr: 0.0025\n",
      "Epoch 21/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 5.5998 - accuracy: 0.8567 - val_loss: 2.5884 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 22/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 5.4766 - accuracy: 0.8567 - val_loss: 2.3349 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 23/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 5.2349 - accuracy: 0.8746 - val_loss: 2.3784 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 24/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 5.2777 - accuracy: 0.8537 - val_loss: 2.0987 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 25/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.9423 - accuracy: 0.8657 - val_loss: 1.8508 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 26/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.9905 - accuracy: 0.8806 - val_loss: 1.8718 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 27/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.8258 - accuracy: 0.9045 - val_loss: 1.8182 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 28/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.8221 - accuracy: 0.8896 - val_loss: 1.9035 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 29/100\n",
      "84/84 [==============================] - ETA: 0s - loss: 4.9292 - accuracy: 0.8896\n",
      "Epoch 00029: ReduceLROnPlateau reducing learning rate to 0.0006249999860301614.\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.9292 - accuracy: 0.8896 - val_loss: 1.9837 - val_accuracy: 1.0000 - lr: 0.0012\n",
      "Epoch 30/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.9393 - accuracy: 0.8716 - val_loss: 2.0587 - val_accuracy: 1.0000 - lr: 6.2500e-04\n",
      "Epoch 31/100\n",
      "84/84 [==============================] - ETA: 0s - loss: 4.9047 - accuracy: 0.8985\n",
      "Epoch 00031: ReduceLROnPlateau reducing learning rate to 0.0003124999930150807.\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.9047 - accuracy: 0.8985 - val_loss: 1.8545 - val_accuracy: 1.0000 - lr: 6.2500e-04\n",
      "Epoch 32/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.9404 - accuracy: 0.8657 - val_loss: 1.8802 - val_accuracy: 1.0000 - lr: 3.1250e-04\n",
      "Epoch 33/100\n",
      "83/84 [============================>.] - ETA: 0s - loss: 4.8483 - accuracy: 0.8976\n",
      "Epoch 00033: ReduceLROnPlateau reducing learning rate to 0.00015624999650754035.\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.8485 - accuracy: 0.8985 - val_loss: 1.8282 - val_accuracy: 1.0000 - lr: 3.1250e-04\n",
      "Epoch 34/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.6833 - accuracy: 0.9313 - val_loss: 1.8479 - val_accuracy: 1.0000 - lr: 1.5625e-04\n",
      "Epoch 35/100\n",
      "83/84 [============================>.] - ETA: 0s - loss: 5.0303 - accuracy: 0.8946\n",
      "Epoch 00035: ReduceLROnPlateau reducing learning rate to 7.812499825377017e-05.\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 5.0263 - accuracy: 0.8955 - val_loss: 1.8320 - val_accuracy: 1.0000 - lr: 1.5625e-04\n",
      "Epoch 36/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.7571 - accuracy: 0.9224 - val_loss: 1.8316 - val_accuracy: 1.0000 - lr: 7.8125e-05\n",
      "Epoch 37/100\n",
      "83/84 [============================>.] - ETA: 0s - loss: 4.7607 - accuracy: 0.8886\n",
      "Epoch 00037: ReduceLROnPlateau reducing learning rate to 3.9062499126885086e-05.\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.7528 - accuracy: 0.8896 - val_loss: 1.8192 - val_accuracy: 1.0000 - lr: 7.8125e-05\n",
      "Epoch 38/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.8317 - accuracy: 0.9045 - val_loss: 1.8180 - val_accuracy: 1.0000 - lr: 3.9062e-05\n",
      "Epoch 39/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.7589 - accuracy: 0.9194 - val_loss: 1.8181 - val_accuracy: 1.0000 - lr: 3.9062e-05\n",
      "Epoch 40/100\n",
      "83/84 [============================>.] - ETA: 0s - loss: 4.8287 - accuracy: 0.9096\n",
      "Epoch 00040: ReduceLROnPlateau reducing learning rate to 1.9531249563442543e-05.\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.8405 - accuracy: 0.9075 - val_loss: 1.8261 - val_accuracy: 1.0000 - lr: 3.9062e-05\n",
      "Epoch 41/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.7469 - accuracy: 0.8896 - val_loss: 1.8121 - val_accuracy: 1.0000 - lr: 1.9531e-05\n",
      "Epoch 42/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.7431 - accuracy: 0.9254 - val_loss: 1.8084 - val_accuracy: 1.0000 - lr: 1.9531e-05\n",
      "Epoch 43/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.5747 - accuracy: 0.9164 - val_loss: 1.8116 - val_accuracy: 1.0000 - lr: 1.9531e-05\n",
      "Epoch 44/100\n",
      "84/84 [==============================] - ETA: 0s - loss: 4.6537 - accuracy: 0.9224\n",
      "Epoch 00044: ReduceLROnPlateau reducing learning rate to 9.765624781721272e-06.\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.6537 - accuracy: 0.9224 - val_loss: 1.8176 - val_accuracy: 1.0000 - lr: 1.9531e-05\n",
      "Epoch 45/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.6542 - accuracy: 0.9194 - val_loss: 1.8336 - val_accuracy: 1.0000 - lr: 9.7656e-06\n",
      "Epoch 46/100\n",
      "83/84 [============================>.] - ETA: 0s - loss: 4.8134 - accuracy: 0.9006\n",
      "Epoch 00046: ReduceLROnPlateau reducing learning rate to 4.882812390860636e-06.\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.8067 - accuracy: 0.8985 - val_loss: 1.8217 - val_accuracy: 1.0000 - lr: 9.7656e-06\n",
      "Epoch 47/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.6239 - accuracy: 0.9194 - val_loss: 1.8233 - val_accuracy: 1.0000 - lr: 4.8828e-06\n",
      "Epoch 48/100\n",
      "83/84 [============================>.] - ETA: 0s - loss: 4.6952 - accuracy: 0.9127\n",
      "Epoch 00048: ReduceLROnPlateau reducing learning rate to 2.441406195430318e-06.\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.6791 - accuracy: 0.9134 - val_loss: 1.8144 - val_accuracy: 1.0000 - lr: 4.8828e-06\n",
      "Epoch 49/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.6613 - accuracy: 0.8896 - val_loss: 1.8075 - val_accuracy: 1.0000 - lr: 2.4414e-06\n",
      "Epoch 50/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.7457 - accuracy: 0.9284 - val_loss: 1.8326 - val_accuracy: 1.0000 - lr: 2.4414e-06\n",
      "Epoch 51/100\n",
      "83/84 [============================>.] - ETA: 0s - loss: 4.9333 - accuracy: 0.9066\n",
      "Epoch 00051: ReduceLROnPlateau reducing learning rate to 1.220703097715159e-06.\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.9243 - accuracy: 0.9075 - val_loss: 1.8280 - val_accuracy: 1.0000 - lr: 2.4414e-06\n",
      "Epoch 52/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.6056 - accuracy: 0.9164 - val_loss: 1.8099 - val_accuracy: 1.0000 - lr: 1.2207e-06\n",
      "Epoch 53/100\n",
      "83/84 [============================>.] - ETA: 0s - loss: 4.9668 - accuracy: 0.9006\n",
      "Epoch 00053: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.9513 - accuracy: 0.9015 - val_loss: 1.8223 - val_accuracy: 1.0000 - lr: 1.2207e-06\n",
      "Epoch 54/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.8828 - accuracy: 0.9045 - val_loss: 1.8343 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 55/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.7692 - accuracy: 0.9015 - val_loss: 1.7862 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 56/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.6181 - accuracy: 0.9134 - val_loss: 1.8022 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 57/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.7030 - accuracy: 0.9045 - val_loss: 1.8113 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 58/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.6430 - accuracy: 0.8925 - val_loss: 1.7891 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 59/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.8389 - accuracy: 0.8806 - val_loss: 1.7787 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 60/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.9218 - accuracy: 0.8955 - val_loss: 1.8016 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 61/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.6690 - accuracy: 0.8985 - val_loss: 1.7776 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 62/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.8209 - accuracy: 0.8836 - val_loss: 1.7803 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 63/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.7482 - accuracy: 0.8985 - val_loss: 1.7902 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 64/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.6084 - accuracy: 0.9134 - val_loss: 1.7983 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 65/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.3614 - accuracy: 0.9433 - val_loss: 1.8086 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 66/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.9018 - accuracy: 0.8776 - val_loss: 1.7978 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 67/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.7583 - accuracy: 0.8896 - val_loss: 1.7926 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 68/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.6420 - accuracy: 0.9224 - val_loss: 1.7987 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 69/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.6888 - accuracy: 0.8955 - val_loss: 1.7893 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 70/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.8689 - accuracy: 0.8896 - val_loss: 1.7724 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 71/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.6942 - accuracy: 0.9134 - val_loss: 1.7953 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 72/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.7629 - accuracy: 0.9045 - val_loss: 1.8191 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 73/100\n",
      "84/84 [==============================] - 4s 49ms/step - loss: 4.7790 - accuracy: 0.9075 - val_loss: 1.7945 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 74/100\n",
      "84/84 [==============================] - 4s 47ms/step - loss: 4.3482 - accuracy: 0.9194 - val_loss: 1.7908 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 75/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 4.8660 - accuracy: 0.8746 - val_loss: 1.7940 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 76/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 4.9478 - accuracy: 0.8925 - val_loss: 1.7895 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 77/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 4.9423 - accuracy: 0.8806 - val_loss: 1.7750 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 78/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 4.7219 - accuracy: 0.9134 - val_loss: 1.7810 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 79/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.8983 - accuracy: 0.9015 - val_loss: 1.8147 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 80/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 4.6049 - accuracy: 0.9194 - val_loss: 1.7949 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 81/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 4.8220 - accuracy: 0.9045 - val_loss: 1.8128 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 82/100\n",
      "84/84 [==============================] - 4s 47ms/step - loss: 4.8348 - accuracy: 0.9075 - val_loss: 1.7989 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 83/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 4.7895 - accuracy: 0.8896 - val_loss: 1.7814 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 84/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 5.1641 - accuracy: 0.8567 - val_loss: 1.7926 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 85/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.8465 - accuracy: 0.9015 - val_loss: 1.7844 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 86/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 4.5505 - accuracy: 0.8985 - val_loss: 1.7965 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 87/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 4.8251 - accuracy: 0.8836 - val_loss: 1.7950 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 88/100\n",
      "84/84 [==============================] - 4s 48ms/step - loss: 4.7971 - accuracy: 0.8985 - val_loss: 1.7824 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 89/100\n",
      "84/84 [==============================] - 4s 47ms/step - loss: 4.4707 - accuracy: 0.9164 - val_loss: 1.8150 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 90/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 5.0633 - accuracy: 0.8716 - val_loss: 1.7922 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 91/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.6663 - accuracy: 0.8776 - val_loss: 1.7977 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 92/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.7009 - accuracy: 0.9224 - val_loss: 1.7857 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 93/100\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 4.6961 - accuracy: 0.9284 - val_loss: 1.8206 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 94/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.5027 - accuracy: 0.9254 - val_loss: 1.8073 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 95/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.8113 - accuracy: 0.9015 - val_loss: 1.8000 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 96/100\n",
      "84/84 [==============================] - 4s 46ms/step - loss: 4.7969 - accuracy: 0.8955 - val_loss: 1.8080 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 97/100\n",
      "84/84 [==============================] - 4s 47ms/step - loss: 4.7290 - accuracy: 0.9015 - val_loss: 1.8353 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 98/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.5470 - accuracy: 0.9164 - val_loss: 1.8269 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 99/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.9319 - accuracy: 0.8716 - val_loss: 1.8079 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
      "Epoch 100/100\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 4.8764 - accuracy: 0.8925 - val_loss: 1.7333 - val_accuracy: 1.0000 - lr: 1.0000e-06\n"
     ]
    }
   ],
   "source": [
    "rdlr = ReduceLROnPlateau(patience=2, factor=0.5, min_lr=1e-6, monitor='val_loss', verbose=1)\n",
    "\n",
    "h = model.fit(X2_train, Y2_train, epochs=100, batch_size=4, validation_data=(X2_val, Y2_val), callbacks=[rdlr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'lr')"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W/5T3ziPGdZJThvn/fzgmysbl03N4f9dx6lr8T+cCPL/e+HedHBfFrlIpGOkpS4OZUmqxUqpAKVWolPqJn+dTlVKvKaW2KaXWK6WsbUhoizKZmdkhZbiRmfXmN2Qza2aeacWaYuPzYgb1/HMGWH9lC8dqWygsa+DM/AwAhqfGk50Sx7qiKt7baXxhXjxlKAvyMzhW18L+8kbva6sb23jk/b0syE/noslDAbh5wSjOGZfJ/W/v5mhtx2/x64uqmDlisKmSfKUUXz1jBFsP17CtpIZn1xaTmhDNpdOyvedcNWM4Dpfmza3GLzmbio3fnmfmGcEsP2sQC/LTaWx18MfrZnDR5KF886zR5KbFc9+bOzutg/h+4be0O7nj+c0kxUXx7p1nc/XM4fx+5V7+8tH+nv1gg2huc/LrFbuZmJ3MDXPzvMeT46JJTYj2G8xWF1YwYWgS+VmDmDc63e9Uo8ul+ffmIywan8kd5+bz4ueH+Z83dvDyhsN87an13P7CZqbnDuauC8cz2f1LxU4TX9QHKho5WtvCWWMz2FBczaP/LeRvq4pocTi5dVE+NpvivssmU17fyq/e3s2fPtjHhb//mKn3vce2HkxlBrNi+1EOVDTyrbNGA0am6s+O0loGxUYxwv2LgT9XzhxOq8PFO9uP+n2+urGNh/+zl/lj0rl2Ti67j9VLz8wesiyYKaXswOPAxcAk4Hql1KQup/0M2KK1ngbcCPzRqvEYg7Kb65rvyczamzrWtHrCzJqZZ1qxurjz9i8nqRXbjzL9F/+huLIx9MkheEq9F7iDmVKKuaPSWFdUxTs7jjF+SBKjMwd5g51vafjD7xfQ0Org3ssme7MLm03xwBVTcLk0f/34AGCsWe0+Vsfckemmx3XVrOEkxNh5+D97+c+u43xpTi5x0R3XjY0fmsTknGTv1NamQ9XERduYlNOxVvKHa2fwxu1ncok7CMZF2/l/X5jE3uMNPPrfQv768X4u/dOnjPv5O9z09Hpe3VTCL97cScHxeh7+0mkMSY7joS9O4/LpOfzm3T28srF/quD++sl+jtQ0c99lk7DbOv9by/NTnt/S7mRDcbX372DxlKEUVTRS0KV6dO2BSo7WtnDlzOH84IJx3HL2aP6x9hA/+tc29pc3cMvZo3nyxtlE220MT40nJT6aHUdCd3zx/J0/cMUUrp45nD/9dx9Prz7IF6Zmk59l/OI3PXcw18wazoufH+bh9/cyOD6GhBh7v2S1Lpfmsf8Wkp81iDvPH4vdptgZKJgdqWVSdjI2W+D/h6cPT2F0RmLAqcZH3t/r/Xc9ZVgKbQ4XhWV9vzQlkliZmc0FCrXWB7TWbcCLwJIu50wCPgDQWu8BRiqlhlg2IluUcYFyIL6ZWXKOcb83U42OFvOZWfXBsCj+eHZNMXUtDh4wuf4TzOrCCtITY5gwNMl7bO6oNMrrW1lfVMXiKUbGlZeeQG5avHe6a1dpHc+vO8RXzxjBuCFJnd4zNy2BK2cM44X1hyirb2HDwSq0Nt7XrOS4aK6YMYyP95bj0pqvnD6i2zlXzhjGtpJaCssa2FhczbRhg4n2udg7Mym223TTRZOHsCA/nUc/2Mf/vrOHKJuN6+fmsfd4Az94aSsvrD/MLWeP5pxxRlGG3aZ45EvTmTsqjXte39Hn6+2O1DSz7OP9XDotm9NHdw/uuWkJ3dbMNhysps3hYsFYI5hdOHkIShmFIL5e3XyEpNgoLpw0BKUUP714An+6fgb/vm0Bn/54EXcvnkBmknGZilKKKcOSTWVmq/ZVMDw1nry0BH65ZDKj0hNpbndy+6L8Tufdc9kkfvvFaaz56bm8tHQe3zhzNCt3B26R1tjq4OyHPuRLy9bw7JqDAS+7eH/3cQqO13P7onwSYqIYk5nIDj/v6XRpdh8NXPzhoZTiqpnDWFdUxbNrDnozXJdL87dVRTy3rpivnJ7n/oXJk8FKm7eesDKYDQMO+zwucR/ztRW4CkApNRcYAXS7VF4pdYtSaoNSakN5eeiKoIBsIQpAPNWMNjsku4fa22AWas3Mk5k1V530xR+lNc2sLaokLy2B93cdN1WVVdvczsV//NRbDeehtWZVYQXzxqR3+k329FEdX7IXTx3qvX9mfgZr91ficLq4782dpMRH8/3zx/n9zFsX5dPudPG3T4tYX1RFjN3GjLye/WxvnGcEsEXjs7zrSb4uPy0Hm4IX1x9iZ2ktM93rZcEopfjdNdP5xeWT+fTHi/j3bQu4/4oprLp7Ea98Zz6/XDKZuy4c3+k1UXYbf7zuNGKibNzxwma/18B5FByr53fvFXD+Ix/zyH8Kuj3/6xXGLyA/+8JEv6/PTUugpLrZ23gYjPWyaLti7kjjl4GspDjmjEjrFMya25y8s/0oF08d6s1glVJcNj2H03IH+12bnJyTwp6jwafQHE4Xaw5UcmZ+BkopEmOj+L+b5/LEjbOZmN05aCTHRXPN7FyyU4yCnq8vGElSbBSPf+g/O9t+pJZDVU0crGzkntd3MvdXK3lpw+FO57hcmkc/2MeI9ATvNPOUnBS/QbioooHmdmfA4g9fN84fyVljM7jn9Z3c+NR6NhZXccOTa7n/rV0sHJ/FXRcZ/wZGZSSSEGMPOK0p/LMymPnLubsuujwIpCqltgB3AJuBbtFGa71caz1baz07MzOzDyMKMc3YqZrRHcx6c+F0TzKzrvdPQv/ecgSt4amvz2FEegK/eHNXyPn8lbuOs/toHa9v7vzLQGFZA2X1rZzl/o3fY0xmIumJMYzKSGS8T9a1ID+D+lYHD76zh/VFVdx10XhSEvy3CRuVkchl03N4dm0xH+wpY3puSqdpQjMmDE3moaun8fNL/H/xZyXFcfa4TJ5ZU0y7UzPTZLDMTonna/NHdgqQSilmjUjlxnkj/a7rZafE89DV09hZWsdD73YPUu1OF9cvX8tFf/iEP39USHObk8c/2k+BT4HFmv2VvL3tKN85J79bBadHXloCDpfutN64urCCGXmpJMZ2dLxbPGUoe47V85r7AuD/7DpGY5uTq2aab9U0OSeZNmfwKbTtR2qpb3F4p6HBCLgXTAo9aZMSH82N80ewYsdRvxdWewLE2989i3fvPItZI1J54K1d3qIeMC4U31laxw8uGOdtsTZ5WArH61opq2/p8n7Biz98JcdF88zNc7n/iilsOFjN1X9Zw/aSWn5z9VT+9rXZJMUZ/67tNsXE7O4ZrNWXIYQ7K4NZCZDr83g40OmbTWtdp7W+SWt9GsaaWSZQZNmIQhWA+K6ZDRpiXG9W3YvhtJsIZr7Z2EmcmWmteXXTEeaMTCU/axA/v2QShWUNPLOmOOjrPJVvq7pUI3qmDOeP6RzMlFL86sop3L9kSqff6D3nPbmqiEnZyVw3J49gbl+UT1Ob0319WS/6agJfmpPL6MzABTlXzhhGmzuYm8nM+uLCyUO5cd4I/raqiP/uOd7puWfWFLPmQCU/vGAc6352Pm/dcSaDYqP45Vs70Vobpd5v7mTY4Hi+fc7ogJ+R16WisbqxjR2ltd71Mo+rZw1nRt5gvv/Prdz2/CaeW3eIYYPjvdmbGZ4vfd+s47H/7uOR9/d6H6/2/hsxv97p6xtnjiYuys7jH3YvoNlVWseQ5Fgyk2KZMDSZX185lcY2Jw+7P7+upZ3fvlfA7BGpXD49p2Pc7nXRrlN/O47UEhtlY0xmoqmxeQqNVnzvLL59zmjevfNsrp2T1y2LnZKTzK7SOm8Ac7k0l/xpFU9+esD8DyLCWBnMPgfGKqVGKaVigOuAN3xPUEoNdj8H8E3gE621dRPFZjuAKDvYoyB9LJR3/404JDMFINHxYHe3vDqJM7MdR+ooLGvgyhnGb9/nT8zi7HGZ/O69Aq54fDVXPL6a65evpdTnWqCGVgef7CsnLTGGIzXNnYoLVrtbWPmbwls8JZszu2RsaYkxTHZ/kdx3+eRuxQtdjR2SxMXuNbe5o3r3ZRjKhZOGGtVr6QlkDArRtqwf/OwLE5kwNIm7Xt7GcXdHlIqGVv6wci9nj8vk9nPzyUyKJTUxhh9eOI7VhZW8t/MYL3x+mD3HjEsYgmWoeV2uNftobxla0ykzAiPrefnb84xr7nYaZf5XzMgJWvjQ1aj0RBJj7N6gcLCikUfe38ujH+zz9rZcXVjJpOxk0nv5s01LjOErZ+Tx+pYj3dYCd5TWdpoSHDskia/NG8kL6w+x40gtj67cR2VjG/ddPrlTgPEU+XRdi9tRWsuE7OQeN8kelZHITy+e6Pf/AzAywcY2JwfdBVefFlaw+2idd/1RdGdZMNNaO4DbgfeA3cBLWuudSqmlSqml7tMmAjuVUnswqh6/Z9V4ACOYBe0A4pOZAWSOh/JetN9xNIdeM4OOjOwkzsxe3VxCTJSNS6YaawdKKX51xRQWTcgkOT6a5PhoNhZX85jPGsVHBWW0OVz82L0G4MnGHE4Xaw9UdfuSDOWOc/P58eLxpjOtuxdP4Pq5uZzey8wslPgYOz+/ZCLfPXesJe/fVVy0ncdumEFTm4Pv/3MLTpfmd+8V0Nzm5H8undTpS/eGuXlMGJrE/W/t5uH/FDBvdLq3oCaQ7JQ47DZjKxij1H0PY7MGMX1496kzzzV3r992Jl+aPZyvzRvZoz+LzT2F5snM/vxRIdF2G9OGp/D/XttBwbF6NhZXd/ulpqdunDcSlzb2y/Nodmfsk3M6r7t97/yxpCXEcNfLW/n7Zwe5bk5ut2nDpLhoRqYndMootdbsLK3zZm39yRNwPUUnz645SMagmJB/l5HM0uvMtNYrtNbjtNZjtNa/ch9bprVe5r6/Rms9Vms9QWt9ldba2mZrZhsNezbyzJxglM639aC/nssFzrbQmRl0ZGQnaWbW7nTx5tZSzp+Y1WmdKjctgT9/eRbP3DyXZ26eyzWzh/OvDSXeNZd3dhwjY1AM18zOJSclzjtttLWkloZWR7fpq1AWT8nm1oX5oU90G5mRyP9eNa3H62U9cd0Ab+uRn5XEfZdN5rP9lfzo5a38c8Nhvj5/pLdM3SPKbuN/LpvEkZpm6prbuffySX4LMbq+ZtjgeA5WNvHDl7dS39LOYzfMDJptTMpJ5qEvTicr2cS/8y6mDEth19E6Dlc18eqmI1w/N48/f3kmNgU3PLGWNqerx7/wdJWblsCojMROl3XsPlaHSxtZj6+U+Gjuumg8e47VEx9j71aM4zF5WAo7fNaxDlc1U9/iMLVe1lNjhwwixm5jp3uLng/2lHHdnDxio07+LYZOFOkA4kt3ycyyJgAaKvYGfEk3DvcCsZlgdpJnZp/sLaeioc07xRjI0nPG4NLGNV4t7U4+3FPGhZOHYrcpFuRn8Nn+SpwuzerCCpSCeX7Kw0Vo187J5ZJp2by6+QjpiTF893z/meH8MRncvijfPT1pLmvIS0vgvR3H+GRvOT+/dBLjhyaFflEvTc5JpqnNyU9f3Y5ScMvZoxmemsBvrp5GZWMb0XbFnJF9X4ucPyaddUVV3mIlz9Smv+Dzpdm5fGn2cB68alrA6c0pOSkcrmr2NmX2BLaumV5/iLbbGD80iZ2ldTy//hAKuOH04OvFkS5yNucE8wUgyh3jMycYt+UFkHOauc/oSTA7iTMzp0vz+5V7yU6J817/FIjvNV7jhiTR1Ob0rludOTaDlzeWsKu0jlWFFUzJSSE1MSbo+wn/lFL8+sqp1Lc4+PLpeSTHBd781VPmbVZuWgKrCiu4aPIQvmLxl6YnmKwqrOD6ubneKsuLp2Zz26IxNLQ4SIjp+1fTmfkZPLfuENtKapg1Io2dR2pJTYgmJ6X7/5t2m+KhL04P+n6eoLXzaC3zx2SwvqiKKJvqds1jf5kyLJkV24+x62gd508cErAaVRgiK5iZKc1Xto5uHGmjwRYN5T24UNgbzEws1J7AzGzzoWqa25zMDzCd8/KGw+w4Uue91imUWxfl88qmEu+1YJ5+i/PcFWnv7zrG5kPV3HzmqP77Q0SglHijvLu/nT02g11H6/jN1dNCTkv2VX7WIGKibDhdmu+c03n6+EcXTei3z5k3Jh2lYNW+SmaNSGNHaS2Tc1J6/efzBLO1+yt5ddMR/rWxhPMnZlk2nT05J4UX1hvXwN3Yw7XJSO6a4NUAACAASURBVBSB04whCkCUzz9MezSk5/esotETzKJN/BZ1gjKzVoeTW5/bxLf/sZGmtu6Zam2zUZ48Z2Tn8uRgPNd4tTlcXDBpiLcrRlZSHOOHJPH06oO0O3WP18vEwLh4ajav37aAwQnWZ83RdhsXTBrCjfNGkJceuJ9hXw1OiGHqsBRWF1aE3KbFjPRBseSkxPHofwt5dVMJd5ybz5+/PKsfR9yZJ4MdnZnIgnyZmg8l8oJZqEbDti6/ZfW0orG9B5mZp1P+AHfMf3XTEY7WtlDf4uCNLd07nPxx5T6qmto69T80445z80mKi+KLXQojPBc+x0TZmNODa5LEqevxG2Zy72WTLf+cBfkZbDpUzdaSmqDbtJh13sQhjBsyiH99Zz4/vHC8qVmL3powNInslDiWnjPG8mz5VCDTjL5crs6ZGUDWRNj1utEJ30y25Z1mNHFu+hiITYbEPnQ16SGH08WfPypk+vAUWh0unllTzLVzOnbHLSyr55k1B7luTl6Pq7Tys5LYdu+F3f7HO3NsOk+tLmL2iFRLKwyF6OrM/Az+8tF+nlplND/oa+Xh/VdYu7GHr7hoO5/95FwJZCZFWGZmYgsYf5kZGir2mfuMnqyZTf0S3LkNYqybaunq9S2lHK5q5o5zx/KVM0aw62gdm9wbAbY7Xdz18jYSYuzcdaH//oeh+Psfb+6odJLjoky1IxKiP80akUpMlI13dx4LuU3LyUgCmXkRFsxMbM6puvxIvBWNJqcae7JmZrNBvLXtkHw5XZrHPypkYnYy503M4soZw0iKjeLZNQcBYxuKLYdr+N8g5cm9MSg2itU/ObfHF9gK0Vdx0XbmjExFa0Ju0yLCW4RNM9qCdwDxl5mljTEyOrPBrCdrZgOgrqWdkirjYubPD1ZxoLyRx2+Y6e1GfvWs4Ty/7hCLJhxh2cf7uX5urncvrv6UFKSMXAgrLcjPYHVhZZ+KP8TJL7KCmZnrzLqumUXFGAHNbEVjT9bMLLb3eD3XL19LZWOb99iYzMROLXG+csYI/v7ZQb734hbyswbxP5davygvxEA6e2wmD71bwGm5J9/1nKL/RFgwM9EBxObnR5I5Ho7vNPcZDvdWEic4MztQ3sANT6zDZlM8ev0MYtyl8lOHp3Rq1pufNYgF+el8frCax26YQXyMFGiIU8uUYSm8efuZnXYEF6eeCAtmUaGrGbtOM4JR0bjnLWMKMVQDYYe7e7yZNTOLFFc2csMT69Ba88K3zmBsiA4Ff7p+JhUNrZZ1MhDiRJvqp2myOLVEVjAz2wGkq8zxxsXWlYUwNERp7gBlZhUNrVzy6KcMTYnnsmnZLJ4ylIMVTby5tZQVO45itylTgQyMLTPSpMWUECKMRVYws9lCVzP6y8x8KxpDBbN2d2Zm8ZrZ31YVUVbfSnpiLA+8vZsH3jZabg2KjeLCSUNYunCMZFpCiIgRYcHMxHVmXQtAwGhppWzmikAGIDOraWrjmc8OcsnUbB67YSYHyhv4YHcZuWnxLBxvXa84IYQ4WUVWMAvZASRAZhYVC7FJ0GpiE2xHs9Ex38KLHZ9efZDGNie3n2s0aR2dOYjRmYNCvEoIIU5dctG0L+2nnZVHVDy0m9ik09FqaVZW39LO06uLuHDSENN7VQkhxKkusjKzkNWMTmNdzZ/o+I4LooNpbza3l5lJNU1t/L9/72DqsBQunZbN61tKqWtxcMe5/jdmFEKISBRZwUzZTFQzBsjMonuSmfVfMFux/RhvbzvK29uO8uA7e4i2KxaOz5RSYyGE8BFZwcxMBxB/a2ZgBCiHiczM0b+Z2YcFZQwbHM+Lt5zBm9tKWV1YwY/7cQNDIYQ4FURYMDPRASRgZpZgbprR0Rr6wmqTWh1OVhdWcNXMYeSmJXDrwnxuXZgf+oVCCBFhIqsApLfVjGAEKDPTjP24ZrbuQBVNbU7OnZDVL+8nhBCnqsgKZrYoQAfunB+smjE63uQ0Y/+tmX1YUEZslI15ozP65f2EEOJUFWHBzP3HDTTVGKya0XRpfv9lZh/uKWPemHRp/iuEECFEVjDzZF2BikCCrpnFDeiaWVFFIwcrm2SKUQghTIisYObZ3iXQulnQNbOEjo74wfTTmtl/95QBsGi8BDMhhAjF0mCmlFqslCpQShUqpX7i5/kUpdSbSqmtSqmdSqmbrByPN1AFmmYMlplFxXU0EQ6mn9bMPtxTRn7WIHLTEvr8XkIIcaqzLJgppezA48DFwCTgeqXUpC6n3Qbs0lpPBxYCDyulrNuLJGRmFmA/MzAyM2db8GpIMIpE+hjMGlsdrCuqlClGIYQwycrMbC5QqLU+oLVuA14ElnQ5RwNJSikFDAKqgCBXNfeRZ6+yQAEp0H5m0LEOFio7c5jYwDOE9QeraHdqzhmX2af3EUKISGFlMBsGHPZ5XOI+5usxYCJQCmwHvqe1DlA33w9CTTMG7QDi3p8sWHm+1v2Sme09Vg/AlBxpWSWEEGZYGcz87YGiuzy+CNgC5ACnAY8ppbq1gldK3aKU2qCU2lBeXt77EXmnGXtTzegOZsEyM+9eZn0LZvvKGshMiiUlIbpP7yOEEJHCymBWAuT6PB6OkYH5ugl4VRsKgSKgW+NBrfVyrfVsrfXszMw+TL15S/N7U81oJpi5s7Y+BrPCsgbGZsn+ZEIIYZaVwexzYKxSapS7qOM64I0u5xwCzgNQSg0BxgMHLBtRf2RmwcrzvcGs9/uZaa0pLGsgX4KZEEKYZlmjYa21Qyl1O/AeYAee0lrvVEotdT+/DLgf+LtSajvGtOTdWusKq8bUsWYWYFkuWDVjlIkCEE8w8wS+Xjhe10pDq0MyMyGE6AFLu+ZrrVcAK7ocW+ZzvxS40MoxdNKnakYT04ztfc/M9pUZxR9jJJgJIYRpEdoBJMA0Y7+tmfU+MyssawBgbFZSr99DCCEiTYQFs750ABmYNbN9ZQ2kxEeTMci6a8eFEOJUE1nBLFSjYVOZWZDrzPphzcxTyWhcRy6EEMKMyApm3mnGXu5nBsG3gemHNTOpZBRCiJ6LsGBmZj+zENWMwTqA9HHNrLKhlarGNglmQgjRQxEWzEIVgDj6Vs3YwzWzo7XNPLu2GK2Nxiie4g8JZkII0TORFcxCdQDRQTIze7QRDPvxOrP/+6yYe/69g48KjBZdheUSzIQQojciK5jZTBSABFozA/cGncGmGXvWm3HToWoAHv3vPm/nj4QYOzkpvS8gEUKISBRhwcw9zeivA4jWgA6cmYF7g85gBSDNHeeF0O50sa2khiHJsWw+VMNn+yspLGtgTOYgbDapZBRCiJ6IrGAWrAOI51jQzCwuRGm++cxs99E6Wtpd/PiiCQxJjuXRD/ZJg2EhhOilyApmwQpAPBWOtiA/kuiE4JmZoxnsMcHfw21TsTHFOG9MOt8+ewzriqo4WtsibayEEKIXIiyYBekAYiYzi4oLvWZmcr1s46EahibHkTM4nuvn5nk7fkhmJoQQPRdZwSxYNaM3MwtRABK00XCz6bL8TcXVzBqRCkB8jJ1bzh6NUjAxu9vepEIIIUKwtGv+Scc7zdiHNbOWusDPO1pNXTB9vK6FIzXN3LRgpPfYN88czaLxWeSmJYR8vRBCiM4iKzML1gHEU+EYtJoxPsQ0o7nMzLNe5snMjKEpxg6RTvlCCNEbkRXMgjUa9mRmtiDJanR8iAKQViN7C2HToWpiomxMzkkJea4QQojQIiuYBZtm9GRrgdpZQejS/PZmvwUgT60q4pJHP+VorbHetrG4mqnDUoiJiqwfvxBCWCWyvk3NVDOGKgAJtp9ZUwUkpHc7/MbWUnaW1nHDE+soqW5ix5G6TlOMQggh+ibCgpmZzCxUB5AgwayuFJJzOh1qaXeys7SWs8ZmcLyuhSseX02b08XMvME9HLwQQohAIiuYmekAEjQzcxeA+NsPrb0Fmiq7BbMdR2ppd2q+csYInvr6HBpajfW6mXmSmQkhRH+JsNL8IAUgnmrGoKX57rJ7RwvEdCmhrzti3CYP63TY00x4Zl4qmUmx/OMbp7OtpJasZHMXVwshhAgtwoKZp9FwLzOzqGDBrNS47ZKZbSquIS8tgcwko2R/9sg0Zo9M6+nIhRBCBBFh04wmOoAErWYMskGnN5gN73hLrdl4qFqKPYQQwmKRFczMdAAJtWYGAYKZZ5ox23uopLqZ8vpWKfYQQgiLRVgwC1Kab7aaEfyX59eVQtxgiEn0HvKsl82QYg8hhLBUZAUzpQAVogNIiOvMIHBm1rX4o7iahBg7E4ZKmyohhLBSZAUzMKYa/a6ZmalmdGdmAYNZ5+KPjYeqmT58MFH2yPsxCyHEQLL0W1YptVgpVaCUKlRK/cTP8z9SSm1x/7dDKeVUSllb6mezh6hm7H0BSF1MFjuO1ALQ1OZg99F6Kf4QQogBYFkwU0rZgceBi4FJwPVKqUm+52itf6u1Pk1rfRrwU+BjrXWVVWMyBmbvQwcQT2l+l2DmaIXGct48qLjssVU88NYuPj9YjdOlmTlCij+EEMJqVl5nNhco1FofAFBKvQgsAXYFOP964AULx2MINM1oas3MM83Ypdmwuyx/a20Co9ITeXJVEc+sLQZgRq5kZkIIYTUrpxmHAYd9Hpe4j3WjlEoAFgOvBHj+FqXUBqXUhvLy8r6NymbrfTWjtwCkyzYw7mBWqtN54IopPHPzXNISYpgyLJnUxJi+jVcIIURIVmZmys8xHeDcy4DVgaYYtdbLgeUAs2fPDvQe5tiiel/N6C3N95+Zlat0ZuSlEh9j56MfLaTd6aeHoxBCiH5nZTArAXJ9Hg8HSgOcex0DMcUIQdbMzFQzGplZe0sjyunqqFJ0XzCdkTOK+Bjj9XHRduKig7yXEEKIfmPlNOPnwFil1CilVAxGwHqj60lKqRTgHOB1C8fSwRYgmJmpZrRHg7LxyrpC7nl9p/ewo7qEOp3AtDHDA79WCCGEZSwLZlprB3A78B6wG3hJa71TKbVUKbXU59Qrgf9orRutGksngUrzzayZKYWOiqexsZ5XNpZQ1dgGQG3ZQY7qNOaOkgbCQghxIljaNV9rvQJY0eXYsi6P/w783cpxdBJomtHMmhmgo+OJbW6lzenin58f5jsLx9BeXcIx0uSaMiGEOEEirzVFoAIQM5kZ4LTFEqfaibHbeG5dMU6XJrbpGC3xQ0mOi7ZgwEIIIUKJwGAWqgNI8GDWbosjjlZuOD2PkupmVu44RIqzhti0PAsGK4QQwoyQwUwpdalSwTb5CjN9qWYEWokhjja+csYIspJiWfbWZ9iUJmPYyP4fqxBCCFPMBKnrgH1KqYeUUhOtHpDl+lLNCLS4g1nO4Diun5uHvd642mDEyLH9PVIhhBAmhQxmWuuvADOA/cDTSqk17o4c4bmvSV+qGYEmHcMgWzsJMVHccHoew+zGnmVJWSP6e6RCCCFMMjV9qLWuw2g19SKQjVFOv0kpdYeFY7OGsve+AwjQ6Iom0d4OwJDkOG4Y7z4/2W+nLiGEEAPAzJrZZUqp14D/AtHAXK31xcB04C6Lx9f/Au5nZi4zq3dGk6javI9Pz2iBmCSIS+7PUQohhOgBM9eZXQP8Xmv9ie9BrXWTUupma4ZloZBrZsGDWZ3DTpxq9znQfVNOIYQQA8vMNOO9wHrPA6VUvFJqJIDW+gNrhmWhgGtm5qoZa9rtxNKRmVErwUwIIU40M8HsZcC3/bvTfSw8hewAEvhH0tDqoN4ZQ4zL3TVfa6gshPQxFgxUCCGEWWaCWZTW2puKuO+H7yZdtgAFICbWzMrqWmghmihXqxHI6o9Cax1kTrBosEIIIcwwE8zKlVKXex4opZYAFdYNyWK2qAAdQNwBLsiaWXl9Ky06FoUGRyuU7TaekGAmhBAnlJkCkKXAc0qpxzA23DwM3GjpqKyk7ODys2mmy0RmVt9KiycpdTRDeYFxX4KZEEKcUCGDmdZ6P3CGUmoQoLTW9dYPy0IBpxldHc8H0CmYtTdD+R6IT4PEDAsGKoQQwixTW8AopS4BJgNxSikAtNa/tHBc1gnVaDhoZtZCu4o1HniCWdZEcP9MhBBCnBhmLppeBlwL3IExzXgNEL69mwJ1ANGhqxnL61qJiU80HniCWeZ4CwYphBCiJ8wUgMzXWt8IVGutfwHMA3KtHZaFAnUAcTlDXmNWVt9KXPwg40H1QWiplfUyIYQ4CZgJZu6LqmhSSuUA7cAo64ZkMZu9Y33Ml3aG7P5RVt9CQqI7MyvdbNxKMBNCiBPOzJrZm0qpwcBvgU2ABp6wdFRWUrbAjYZNZGaJWe7MTIKZEEKcNIIGM/emnB9orWuAV5RSbwFxWuvaARmdFQI2GnYZzwXQ6nBS09ROUlKKceDoFogbDIOyLBqoEEIIs4JOM2qtXcDDPo9bwzqQQeDSfJczaPFHRYPRBCUlyb2NW1OlVDIKIcRJwsya2X+UUlcrdYp8awfqAKKDTzOW1RlLh4NTfLZ6kUpGIYQ4KZhZM/sBkAg4lFItGOX5Wmsdnht4BesAEuKCaYD0lMEdB2W9TAghTgpmOoAkDcRABowtQAFIqMzMHczSBvtmZhLMhBDiZBAymCmlzvZ3vOtmnWEjYKNhV/Amw3UtKAXpKRLMhBDiZGNmmvFHPvfjgLnARuBcS0ZktWAdQEJkZumJsURF2SEqHuwxkDTUwoEKIYQwK2QBiNb6Mp//LgCmAMfNvLlSarFSqkApVaiU+kmAcxYqpbYopXYqpT7u2fB7wRZllOFr3fl4iGrGsvpWspLcfRmj4yFrglQyCiHEScJUo+EuSjACWlBKKTvwOHCB+zWfK6Xe0Frv8jlnMPBnYLHW+pBSyvqLtjxTidrVORMLmZm1kJXsDmapIyFvnnVjFEII0SNm1sz+hNH1A4xM7jRgq4n3ngsUaq0PuN/nRWAJsMvnnBuAV7XWhwC01mXmh95Lyp19uRyd18hCVTPWtTIp271edvN7He8jhBDihDOTmW3wue8AXtBarzbxumEYG3l6lACndzlnHBCtlPoISAL+qLV+pusbKaVuAW4ByMvLM/HRQXi6fHTtAhIkM6traae8oZXhqQnGgaiYvo1BCCFEvzITzP4FtGhtlAAqpexKqQStdVOI1/lbUOqyUEUUMAs4D4gH1iil1mqt93Z6kdbLgeUAs2fP7voePeOdZuwSzIJUM244WIXWMHtkap8+WgghhDXMzJV9gBFoPOKBlSZeV0LnrWKGA6V+znlXa92ota4APgGmm3jv3vNkX10rGrUz4NThuqIqou2KGbkSzIQQ4mRkJpjFaa0bPA/c9xNMvO5zYKxSapRSKga4DnijyzmvA2cppaKUUgkY05C7zQ29l7zTjF26gARZM1tfVMW04YOJjwneVV8IIcSJYSaYNSqlZnoeKKVmAc2hXqS1dgC3A+9hBKiXtNY7lVJLlVJL3efsBt4FtgHrgSe11jt6/sfoAZtPAUinAftfM2tqc7C9pJa5o9IsHZYQQojeM7NmdifwslLKM0WYDVxr5s211iuAFV2OLevy+LcYe6UNDE9m1m3NzH9mtvlQDQ6X5nQJZkIIcdIy05vxc6XUBGA8RlHHHq11u+Ujs4p3zaxrNaPLb2a2rqgKm4JZI2S9TAghTlYhpxmVUrcBiVrrHVrr7cAgpdSt1g/NIrYABSABMrP1RZVMzkkhKS56AAYnhBCiN8ysmX3LvdM0AFrrauBb1g3JYt5pxi4FIH6qGVsdTjYfqpH1MiGEOMmZCWY234053W2qwveqYRWgAMRPZratpJZWh0uCmRBCnOTMFIC8B7yklFqGcdHzUuAdS0dlpR50AFlfVAXA3JESzIQQ4mRmJpjdjdFK6jsYBSCbMSoaw1PADiDdM7N1RVWMH5JEamL4JqJCCBEJzGwB4wLWAgeA2Ritp6y9sNlKATuAdK5mdLo0Gw9WyRSjEEKEgYCZmVJqHEbXjuuBSuCfAFrrRQMzNIsE7QDSEdtLa5ppbHMyOScZIYQQJ7dg04x7gE+By7TWhQBKqe8PyKisZLIDyMHKRgBGpCcO1MiEEEL0UrBpxquBY8CHSqknlFLn4b8TfnhR5tbMiiuNTQFGZphpQymEEOJEChjMtNavaa2vBSYAHwHfB4Yopf6ilLpwgMbX/0xWMxZXNhITZWNIUtwADk4IIURvmCkAadRaP6e1vhRjG5ctwE8sH5lVAnYAcXXLzEakJWCzhX8yKoQQpzozF017aa2rtNZ/1Vqfa9WALBeo0XC3zKxJ1suEECJM9CiYnRK8pfmBqxm11hRXNTIiXdbLhBAiHEReMDNRzVhW30pLu4uREsyEECIsRGAwC72fmaeSMU+mGYUQIixEXjAL2AGkIzPzXGMmmZkQQoSHyAtmgUrzO2VmjdhtipzB8QM8OCGEEL0RgcHMc9G0nwIQ9/YwxZVNDE+NJ9oeeT8eIYQIR5H3bR1oPzPdec0sL02mGIUQIlxEXjALNs2o7GitOVjZyEgp/hBCiLARgcEsSAGIzU5NUzv1LQ65xkwIIcJI5AUzf42GPRdQKzvFVUZZvnT/EEKI8BF5wczffmaewGazUyxl+UIIEXYiMJj5KQBx+QYzIzPLlQIQIYQIGxEYzPx0APHcV3YOVjaSnRJHXLS9+2uFEEKclCwNZkqpxUqpAqVUoVKq27YxSqmFSqlapdQW93//Y+V4jA/1UwDik5kdkrJ8IYQIO1FWvbFSyg48DlwAlACfK6Xe0Frv6nLqp+690gaGt5oxUGbWxHkTsgZsOEIIIfrOysxsLlCotT6gtW4DXgSWWPh55ninGX0KQNzFIK0uqGhoJU+KP4QQIqxYGcyGAYd9Hpe4j3U1Tym1VSn1jlJqsoXjMfjrAOLOzCqbjNtRGVKWL4QQ4cSyaUZA+TmmuzzeBIzQWjcopb4A/BsY2+2NlLoFuAUgLy+vj6NSxrqZ7zSj+35ZQzsAozMlmAkhRDixMjMrAXJ9Hg8HSn1P0FrXaa0b3PdXANFKqYyub6S1Xq61nq21np2Zmdn3kdnsfqsZyxscKIW0shJCiDBjZTD7HBirlBqllIoBrgPe8D1BKTVUKaXc9+e6x1Np4ZjcH2z3W814vNHB8NR4KcsXQogwY9k0o9baoZS6HXgPsANPaa13KqWWup9fBnwR+I5SygE0A9dprbtORfY/W5TfDiDH69sYnTHI8o8XQgjRv6xcM/NMHa7ocmyZz/3HgMesHINfNluXzMwIbMfq2xk9QaYYhRAi3EReBxAwphn9rJm1OGBMpmRmQggRbiIzmNmi/FYzOrFJJaMQQoShCA1mdr/XmTmxSWYmhBBhKEKDWVSXDiBGMIuJjiYrKfYEDUoIIURvRWYwUza/mVlmSgLuKwWEEEKEkcgMZrauHUCMLG1IsvRkFEKIcBShwSyqUzVjS1sbAEMGS/GHEEKEo8gMZl06gBytaQRgqAQzIYQIS5EZzLp0ADlabQSz7FQJZkIIEY4iNJh1LgA5JpmZEEKEtcgMZl06gHiCWWx09IkakRBCiD6IzGDWpQNIWW2j+7h0yxdCiHAUocGsowBEa015bbNxXEXmj0MIIcJdZH57K7u3A8jxulbaHcYO05KZCSFEeIrMYOZz0XRJdRM23FuoKQlmQggRjiI4mBnTjMfrWrHj6jguhBAi7ERoMOvoAHK8rqUjmElmJoQQYSkyg5lPB5Cy+lZibO5pRsnMhBAiLEVmMLPZvR1AyupaGBzv/jFINaMQQoSlyPz2tnVcNH28voXBcfaO40IIIcJOZAYz1bkAZHCcreO4EEKIsBOZwcynA8jxuhZSPMFMMjMhhAhLERrMjOvMmtuc1Lc4SI51BzHJzIQQIixFZjBzNxouq28BICVWMjMhhAhnkRnM3JnZ8bpWAJJj3celmlEIIcJSZH57uzuAHK8zMrNBMZKZCSFEOIvMYOadZjQys6QY1XFcCCFE2LE0mCmlFiulCpRShUqpnwQ5b45SyqmU+qKV4/FyVzOW1bUQE2XDc5mZZGZCCBGeLAtmSik78DhwMTAJuF4pNSnAeb8B3rNqLN1418xaGJIci9LSm1EIIcKZlZnZXKBQa31Aa90GvAgs8XPeHcArQJmFY+nM3QHkeF0rWUlx3m4gkpkJIUR4sjKYDQMO+zwucR/zUkoNA64ElgV7I6XULUqpDUqpDeXl5X0fmbsDSFm9kZnhchqVjEr1/b2FEEIMOCuDmb/IoLs8/gNwt9ae1Mg/rfVyrfVsrfXszMzMvo/MPc1Y5puZyRSjEEKErSgL37sEyPV5PBwo7XLObOBFZWREGcAXlFIOrfW/LRyXUQCCpqG1jSHJcdDmlClGIYQIY1YGs8+BsUqpUcAR4DrgBt8TtNajPPeVUn8H3rI8kIE3C7PjIispFipckpkJIUQYsyyYaa0dSqnbMaoU7cBTWuudSqml7ueDrpNZymbMrtpxGZlZmWRmQggRzqzMzNBarwBWdDnmN4hprb9u5Vg6sRl/bCOYxbrXzCLz+nEhhDgVROY3uO80Y3KcUc0omZkQQoStyAxm7swsPgqS46KkmlEIIcJchAYzI3ANTYpCKSWZmRBChLnIDGbu9bEhg6KNx1qqGYUQIpxFZjBzTzNmDXLXv7ic3gpHIYQQ4Scyv8HdU4qZie5gJmtmQggR1iIymLU4jU5bnTMzCWZCCBGuIjKY1bYaW75kJrgDmGRmQggR1iIymB2obAYgM9FdACKZmRBChLWIC2aHq5p4aeNRACZnJxgHpZpRCCHCWkQFs3ani+++uBmX+48drdw70kg1oxBChLWI+gb/w8q9bD5Uw5fnjTYOuNzbqMmamRBChLWICWafFVbw54/2c+3sXOaOyTIOeoKZrJkJIURYi5hglpUcy8VThnLv5ZM6OuRrycyEEOJUYOkWMCeT/Kwk/vzlWcaDqFjj1tFi3LpckpkJIUQYi5jMrJOEdOO2qdK4lf3MhBAirEXmN3hCfHMc8gAAIABJREFUhnHbWGHcypqZEEKEtQgNZmmA6ghmsmYmhBBhLTKDmc1uBLQmycyEEOJUEJnBDIypRsnMhBDilBC5wSwxo6MARKoZhRAirEVuMEtI75KZRe6PQgghwl3kfoMnZkBjuXFf1syEECKsRXAwy4TmaiOQyZqZEEKEtcgNZgkZgIamKsnMhBAizEVuMEv0dAGpkP3MhBAizFnam1EptRj4I2AHntRaP9jl+SXA/YALcAB3aq1XWTkmL98uILKfmRC91t7eTklJCS0tLSd6KAKIi4tj+PDhREdHn+ihDCjLgplSyg48DlwAlACfK6Xe0Frv8jntA+ANrbVWSk0DXgImWDWmThLdwaypQtbMhOiDkpISkpKSGDlyJEqpEz2ciKa1prKykpKSEkaNGnWihzOgrExH5gKFWusDWus24EVgie8JWusGrbV7u2cSAc1A6ZaZSTATojdaWlpIT0+XQHYSUEqRnp4ekVmylcFsGHDY53GJ+1gnSqkrlVJ7gLeBm/29kVLqFqXUBqXUhvLy8v4ZnadzfmMFuBySmQnRBxLITh6R+ndhZTDz9xPtlnlprV/TWk8ArsBYP+v+Iq2Xa61na61nZ2Zm9s/o7FEQn9pRACKZmRBChC0rg1kJkOvzeDhQGuhkrfUnwBilVIaFY+rM05/RJWtmQggRzqwMZp8DY5VSo5RSMcB1wBu+Jyil8pU7J1ZKzQRigEoLx9SZpz+jlmpGIURoDofjRA9BBGDZN7jW2gHcDrwH7AZe0lrvVEotVUotdZ92NbBDKbUFo/LxWp+CEOt5+jO6nGCz9CoFIYTFrrjiCmbNmsXkyZNZvnw5AO+++y4zZ85k+vTpnHfeeQA0NDRw0003MXXqVKZNm8Yrr7wCwKBBg7zv9a9//Yuvf/3rAHz961/nBz/4AYsWLeLuu+9m/fr1zJ8/nxkzZjB//nwKCgoAcDqd3HXXXd73/dOf/sQHH3zAlVde6X3f999/n6uuumogfhwRx9JvcK31CmBFl2PLfO7/BviNlWMIKjEDDq+T0nwh+skv3tzJrtK6fn3PSTnJ3HvZ5JDnPfXUU6SlpdHc3MycOXNYsmQJ3/rWt/jkk08YNWoUVVVVANx///2kpKSwfft2AKqrq0O+9969e1m5ciV2u526ujo++eQToqKiWLlyJT/72c945ZVXWL58OUVFRWzevJmoqCiqqqpITU3ltttuo7y8nMzMTJ5++mluuummvv1AhF+RnY4kZrqnGaUARIhw9+ijj/Laa68BcPjwYZYvX87ZZ5/tvd4qLS0NgJUrV/Liiy96X5eamhryva+55hrsduM7ora2lq997Wvs27cPpRTt7e3e9126dClRUVGdPu+rX/0q//jHP7jppptYs2YNzzzzTD/9iYWvyA5mCRlGIAPJzIToB2YyKCt89NFHrFy5kjVr1pCQkMDChQuZPn26dwrQl9bab/m677Gu12klJiZ6799zzz0sWrSI1157jYMHD7Jw4cKg73vTTTdx2WWXERcXxzXXXOMNdqJ/RXbVQ6JP4aQUgAgRtmpra0lNTSUhIYE9e/awdu1aWltb+fjjjykqKgLwTjNeeOGFPPbYY97XeqYZhwwZwu7du3G5XN4ML9BnDRtmXDL797//3Xv8wgsvZNmyZd4iEc/n5eTkkJOTwwMPPOBdhxP9L7K/wT0XToNkZkKEscWLF+NwOJg2bRr33HMPZ5xxBpmZmSxfvpyrrrqK6dOnc+211wLw85//nOrqaqZMmcL06dP58MMPAXjwwQe59NJLOffcc8nOzg74WT/+8Y/56U9/yoIFC3A6nd7j3/zmN8nLy2PatGlMnz6d559/3vvcl7/8ZXJzc5k0aZJFPwGhBrJ4sD/Mnj1bb9iwoX/e7Nh2WHamcf+CX8KC7/XP+woRQXbv3s3EiRNP9DBOarfffjszZszgG9/4xoB8nr+/E6XURq317AEZwAkQ2ZO3iT7dRCQzE0JYYNasWSQmJvLwww+f6KGc0iI7mPlOM0o1oxDCAhs3bjzRQ4gIkb1mZo+GuBTjvmRmQggRtiI7mEHHVjBSzSiEEGFLvsE95fmSmQkhRNiSYObNzCSYCSFEuJJgJpmZEEKEPQlmiZKZCRFJfLvji1OHBLMEycyEEANP9kbrX5F9nRn4ZGYS14Xos3d+YnTW6U9Dp8LFDwZ8+u6772bEiBHceuutANx3330opfjkk0+orq6mvb2dBx54gCVLloT8qIaGBpYsWeL3dc888wy/+93vUEoxbdo0nn32WY4fP87SpUs5cOAAAH/5y1/Iycnh0ksvZceOHQD87ne/o6Ghgfvuu4+FCxcyf/58Vq9ezeWXX864ceN44IEHaGtrIz09neeee44hQ4bQ0NDAHXfcwYYNG1BKce+991JTU8OOHTv4/e9/D8ATTzzB7t27eeSRR/r04z1VSDDzXDgtmZkQYem6667jzjvv9Aazl156iXfffZfvf//7JCcnU1FRwRlnnMHll1/ut6u9r7i4OF577bVur9u1axe/+tWvWL16NRkZGd4mwt/97nc555xzeO2113A6nTQ0NITcH62mpoaPP/4YMJocr127FqUUTz75JA899BAPP/yw3z3XYmJimDZtGg899BDR0dE8/fTT/PWvf+3rj++UIcEsdSSgICHtRI9EiPAXJIOyyowZMygrK6O0tJTy8nJSU1PJzs7m/7N33+FRllnjx78nM5MOKYRAQhIINfTQm2BhRUEWLKjYu4uuumtvWyyvrq+u+uquK6ura0PFxYaI7acougrSQlNAehIChIQESEi/f388k5CESTJDZjJJ5nyua66ZedqcJ4Q5ufutt97K0qVLCQoKIjs7m3379tG1a9dGr2WM4b777jvuvK+++opZs2YRF2fV5FSvVfbVV1/VrE9ms9mIiopqMplVT3gMkJWVxYUXXkhOTg5lZWU1a681tObaaaedxqJFi+jfvz/l5eUMHjzYw59W+6XJrFMvuGU1xKT6OxKl1AmaNWsWCxYsYO/evcyePZt58+aRm5vLqlWrcDgc9OjR47g1ylxp6LyG1ipzxW63U1VVVfO+sbXRbr75Zm677TZmzJjB119/zQMPPAA0vDbatddey6OPPkpaWpquWF2PNhQBxPYEN39RlVKtz+zZs3n77bdZsGABs2bNorCwkPj4eBwOB0uWLGHXrl1uXaeh8yZPnsw777xDXl4ecGytssmTJ/P8888DUFlZyaFDh+jSpQv79+8nLy+P0tJSFi1a1OjnVa+N9uqrr9Zsb2jNtTFjxpCZmcmbb77JRRdd5O6PJyBoMlNKtXkDBw7k8OHDdOvWjYSEBC655BJWrlzJyJEjmTdvHmlpaW5dp6HzBg4cyP3338/JJ5/M0KFDue222wB45plnWLJkCYMHD2bEiBFs3LgRh8PBn/70J8aMGcP06dMb/ewHHniA888/n4kTJ9ZUYULDa64BXHDBBUyYMKGm6lFZAns9M6VUs+l6Zi1r+vTp3HrrrUyePLnBYwJxPTMtmSmlVBtQUFBA3759CQsLazSRBSrtAKKUCjjr16/nsssuq7MtJCSE5cuX+ymipkVHR7NlyxZ/h9FqaTJTSjWbJ739WoPBgweTkZHh7zB8oq01HXmLVjMqpZolNDSUvLy8gP0SbU2MMeTl5REaGurvUFqclsyUUs2SlJREVlYWubm5/g5FYf1xkZSU5O8wWpxPk5mInAk8A9iAfxljHqu3/xLgbufbI8ANxpi1voxJKeVdDoejZuYKpfzFZ9WMImIDngOmAgOAi0RkQL3DdgAnG2OGAA8DL/gqHqWUUu2XL9vMRgNbjTHbjTFlwNtAnWmrjTHfG2OqJzJbBgRe2VgppVSz+TKZdQMya73Pcm5ryDXAJz6MRymlVDvlyzYzV/10XXZ3EpFTsZLZSQ3svx643vn2iIhsPsGY4oADJ3huWxaI9x2I9wyBed+BeM/g+X1391UgrYEvk1kWkFzrfRKwp/5BIjIE+Bcw1RiT5+pCxpgX8EJ7moisbM/TuTQkEO87EO8ZAvO+A/GeIXDvuyG+rGZcAfQRkVQRCQZmAwtrHyAiKcB7wGXGGB3arpRS6oT4rGRmjKkQkZuAz7C65r9sjNkoInOc++cCfwI6Af9wzh5QoX9pKKWU8pRPx5kZYxYDi+ttm1vr9bXAtb6MoZ5A7fofiPcdiPcMgXnfgXjPELj37VKbWwJGKaWUqk/nZlRKKdXmaTJTSinV5gVMMhORM0Vks4hsFZF7/B2PL4hIsogsEZGfRWSjiPzOuT1WRL4QkV+cz+1uvXURsYnIGhFZ5HwfCPccLSILRGST8998XIDc963O3+8NIvKWiIS2t/sWkZdFZL+IbKi1rcF7FJF7nd9tm0XkDP9E7V8BkczcnCeyPagAbjfG9AfGAr913uc9wJfGmD7Al8737c3vgJ9rvQ+Ee34G+NQYkwYMxbr/dn3fItINuAUYaYwZhNVTejbt775fAc6st83lPTr/j88GBjrP+YfzOy+gBEQyw415ItsDY0yOMWa18/VhrC+3blj3+qrzsFeBs/0ToW+ISBJwFtbg+2rt/Z47ApOAlwCMMWXGmALa+X072YEwEbED4ViTMbSr+zbGLAXy621u6B5nAm8bY0qNMTuArVjfeQElUJKZp/NEtnki0gMYBiwHuhhjcsBKeEC8/yLzif8D7gKqam1r7/fcE8gF/u2sXv2XiETQzu/bGJMN/BXYDeQAhcaYz2nn9+3U0D0G3PebK4GSzNyeJ7I9EJFI4F3g98aYQ/6Ox5dEZDqw3xizyt+xtDA7MBx43hgzDCii7VetNcnZTjQTSAUSgQgRudS/UfldQH2/NSRQkplb80S2ByLiwEpk84wx7zk37xORBOf+BGC/v+LzgQnADBHZiVV9fJqIvEH7vmewfqezjDHLne8XYCW39n7fvwJ2GGNyjTHlWNPhjaf93zc0fI8B8/3WmEBJZk3OE9keiDUn2EvAz8aYp2rtWghc4Xx9BfBhS8fmK8aYe40xScaYHlj/rl8ZYy6lHd8zgDFmL5ApIv2cmyYDP9HO7xurenGsiIQ7f98nY7UNt/f7hobvcSEwW0RCRCQV6AP86If4/CpgZgARkWlYbSvV80Q+4ueQvE5ETgK+BdZzrP3oPqx2s3eAFKwvg/ONMfUbl9s8ETkFuMMYM11EOtHO71lE0rE6vQQD24GrsP5Abe/3/SBwIVbv3TVYU+JF0o7uW0TeAk7BWuZlH/Bn4AMauEcRuR+4Gutn8ntjTMCtDRkwyUwppVT7FSjVjEoppdoxTWZKKaXaPE1mSiml2jxNZkoppdo8TWZKKaXaPE1mSiml2jxNZkoppdo8TWZKKaXaPE1mSiml2jxNZkoppdo8TWZKKaXaPE1mSiml2jxNZkoppdo8TWZKKaXaPE1mSiml2jxNZkoppdo8TWZKKaXaPE1mSiml2jxNZkoppdo8TWZKKaXaPE1mSiml2jxNZkoppdo8TWZKKaXaPE1mSiml2jxNZkoppdo8TWZKKaXaPE1mSiml2jxNZkoppdo8TWZKKaXaPE1mSiml2jxNZkoppdo8TWZKKaXaPE1mSiml2jxNZkoppdo8TWZKKaXaPE1mSiml2jxNZkoppdo8TWZKKaXaPE1mSiml2jxNZkoppdo8TWZKKaXaPE1mSiml2jy7vwPwVFBQkAkLC/N3GEop1aYUFxcbY0y7LcC0uWQWFhZGUVGRv8NQSqk2RUSO+jsGX2q3WVoppVTg0GSmlFKqzdNkppRSqs1rc21mSqnAVF5eTlZWFiUlJf4OpVULDQ0lKSkJh8Ph71BalCYzpVSbkJWVRYcOHejRowci4u9wWiVjDHl5eWRlZZGamurvcFqUVjMqpdqEkpISOnXqpImsESJCp06dArL0qslMKdVmaCJrWqD+jDSZKaWUavM0mSmllJsiIyP9HYJPiciZIrJZRLaKyD0u9ouIPOvcv05Ehjd1rog8ICLZIpLhfEzzReyazJRSSiEiNuA5YCowALhIRAbUO2wq0Mf5uB543s1znzbGpDsfi30Rf2Als+J8f0eglGoHjDHceeedDBo0iMGDBzN//nwAcnJymDRpEunp6QwaNIhvv/2WyspKrrzyyppjn376aT9H36DRwFZjzHZjTBnwNjCz3jEzgdeMZRkQLSIJbp7rU4HTNX/9ArjuMlhVUXd7SAgcOWK9vuoqeOONuvvj4iAnx3o9axZ8+GHd/ampsGWLb2JWSrn04Ecb+WnPIa9ec0BiR/7864FuHfvee++RkZHB2rVrOXDgAKNGjWLSpEm8+eabnHHGGdx///1UVlZSXFxMRkYG2dnZbNiwAYCCggKvxu1F3YDMWu+zgDFuHNPNjXNvEpHLgZXA7caYg94KulrgJLMeJ0GvYEgeCH3POLbdXutHMG0aJCbWPS88/Njrc86Bfv3q7o+J8X6sSqlW7bvvvuOiiy7CZrPRpUsXTj75ZFasWMGoUaO4+uqrKS8v5+yzzyY9PZ2ePXuyfft2br75Zs466yymTJnir7DtIrKy1vsXjDEv1Hrvqhukqfe+oWMaO/d54GHn+4eBJ4Gr3YrYA4GTzDp0hYuvg1X/hlt+A9Epxx9z/vnWoyGXXOK7+JRSbnO3BOUrxtT/jrdMmjSJpUuX8vHHH3PZZZdx5513cvnll7N27Vo+++wznnvuOd555x1efvnlFo4YgApjzMhG9mcBybXeJwF73DwmuKFzjTH7qjeKyIvAIo8jd0NgtZmd9HtA4L/PeO+as2bB2Wd773pKqVZv0qRJzJ8/n8rKSnJzc1m6dCmjR49m165dxMfHc91113HNNdewevVqDhw4QFVVFeeddx4PP/wwq1ev9nf4DVkB9BGRVBEJBmYDC+sdsxC43NmrcSxQaIzJaexcZ5tatXOADb4IPnBKZgBRSZB+Max+HSbeAR0Tmj6nKYWFoOurKRVQzjnnHH744QeGDh2KiPD444/TtWtXXn31VZ544gkcDgeRkZG89tprZGdnc9VVV1FVVQXAX/7yFz9H75oxpkJEbgI+A2zAy8aYjSIyx7l/LrAYmAZsBYqBqxo713npx0UkHauacSfwG1/ELw0Vl1uriIgI06zFOfN3wN9GwJjfwJle+KU66yzYtw9Wrmz6WKXUCfv555/p37+/v8NoE1z9rESk2BgT4aeQfC6wqhkBYlNhyAWw8t9wJLfuvspyWPIXKMx2/3rBwVBe7t0YlVJKeSTwkhnAxNuhogSW/aPu9nXz4ZvHYMun7l8rOBjKyrwbn1JKKY8EZjKL6wMDZsKKf8FR55iPqkr49inrdYUHM05PnGh16VdKKeU3gZnMwCqdlR6CH1+03m98H/K3Wa/Li92/zk03wZNPej8+pZRSbgvcZJYwBPqcYVU1lh62SmVx/UCCoDzw1gJSSqm2LHCTGVils6P58M7lsH8jTLwNHOFQftT9a9x11/GzhiillGpRgZ3MUsZAj4mw7SuI7g6DZoE9FCo8SGZVVXDIu3PEKaWU8kxgJzOASXdYzxNvB5vd85KZ9mZUSrnQ2NpnO3fuZNCgQS0YTfsXWDOAuNLzFLhpJXTqbb13hHqezMrLwRgI0OXKlVLK3zSZgdVVv5ojzPNkBlZCq36tlPKtT+6Bveu9e82ug2HqYw3uvvvuu+nevTs33ngjAA888AAiwtKlSzl48CDl5eX8z//8DzNneraMV0lJCTfccAMrV67Ebrfz1FNPceqpp7Jx40auuuoqysrKqKqq4t133yUxMZELLriArKwsKisr+eMf/8iFF17YrNtuLzSZ1ecI96zNbMQImDPHKpkppdqt2bNn8/vf/74mmb3zzjt8+umn3HrrrXTs2JEDBw4wduxYZsyYgXhQS/Pcc88BsH79ejZt2sSUKVPYsmULc+fO5Xe/+x2XXHIJZWVlVFZWsnjxYhITE/n4448BKCws9P6NtlGazOqzh0LZEfePP+MM66GUajmNlKB8ZdiwYezfv589e/aQm5tLTEwMCQkJ3HrrrSxdupSgoCCys7PZt28fXbt2dfu63333HTfffDMAaWlpdO/enS1btjBu3DgeeeQRsrKyOPfcc+nTpw+DBw/mjjvu4O6772b69OlMnDjRV7fb5mgHkPoc4Z6PM6us1JKZUgFg1qxZLFiwgPnz5zN79mzmzZtHbm4uq1atIiMjgy5dulBS4tn3R0OTvV988cUsXLiQsLAwzjjjDL766iv69u3LqlWrGDx4MPfeey8PPfSQN26rXWixZCYiL4vIfhHZUGvbEyKySUTWicj7IhLdUvE0yBHq2QwgL79srVadmdn0sUqpNm327Nm8/fbbLFiwgFmzZlFYWEh8fDwOh4MlS5awa9cuj685adIk5s2bB8CWLVvYvXs3/fr1Y/v27fTs2ZNbbrmFGTNmsG7dOvbs2UN4eDiXXnopd9xxR2teG63FtWTJ7BXgzHrbvgAGGWOGAFuAe1swHtccYZ7NzehwWM86c75S7d7AgQM5fPgw3bp1IyEhgUsuuYSVK1cycuRI5s2bR1pamsfXvPHGG6msrGTw4MFceOGFvPLKK4SEhDB//nwGDRpEeno6mzZt4vLLL2f9+vWMHj2a9PR0HnnkEf7whz/44C7bphZdz0xEegCLjDHHDbAQkXOAWcaYSxq7RrPXM2vKx3fAhgVw9073jp8/H2bPhp9+Al1rSSmf0fXM3KfrmfnX1cAnrnaIyPUislJEVlZUVPg2CkeYZ21m1d3xdeC0Ukr5TavozSgi9wMVwDxX+40xLwAvgFUy82kwjjCra35VFQS5keurqxk1mSml6lm/fj2XXXZZnW0hISEsX77cTxG1X35PZiJyBTAdmGx8WOf52ca93LVgHYtuPonk2PCGD3SEWc8VJRDcyHHVeveGO++ELl28E6hSqkHGGI/GcPnb4MGDycjIaNHPbMmmo9bEr9WMInImcDcwwxjjQRdCz3UIsVN4tJzM/CY+xl4rmbkjLQ0efxxSUpoXoFKqUaGhoeTl5QXsl7U7jDHk5eURGhrq71BaXIuVzETkLeAUIE5EsoA/Y/VeDAG+cP61tcwYM8cXn19dGss82EQyqy6ZlRcDsU1fuLISDh+G8HCdzkopH0pKSiIrK4vc3Fx/h9KqhYaGkpSU5O8wWlyLJTNjzEUuNr/UUp/fNSqUIIGsg01MVVWTzNwsma1ZA6NGwUcfwfTpzQtSKdUgh8NBamqqv8NQrVRr6s3oUw5bEAlRYU1XM9YpmblBezMqpZTfBUwyA0iODSOzqZKZp21mtWfNV0op5ReBlcxiwrVkppRS7VBAJbOkmHD2Hy6lpLyy4YMczl5A7raZ6TgzpZTyu4BKZsmxVqkru6CRqkaHc2yZuyWz6Gh48EEYNqyZ0SmllDpRfh803ZJquufnF9Orc6Trg+zOkpm7bWYdOsCf/uSF6JRSSp2ogCqZJcVYJbNGu+d7WjIzBrKyQFd8VUopvwmoZNalQyjBtqDGB0572mZWUQHJyfD3vzc/QKWUUickoJJZUJDQLSaMrHx3SmZNdOGvZnfW1GoHEKWU8puASmZgVTU2WjKzOUBs1sz57hCxejRqMlNKKb8JwGQW7saUVuHul8zAGmumyUwppfwm4JJZcmwY+UVlFJU2ssinI9TzZKYzgCillN8EVNd8sGYBAWv2/LSuHV0f5AjzLJk9+ij06+eF6JRSSp2IgEtmNd3z8482nMzsYe63mQHM8cmqNUoppdwUgNWMbqxr5mnJbNs2yMxsZmRKKaVOVMAls04RwYQ5bGQ22j3fw2Q2fTrcfnvzg1NKKXVCAi6ZiYhzKRgvlsy0A4hSSvlVwCUzcKN7vj3M/bkZQbvmK6WUnwVkMkuOCSMrvxhjjOsDHGHuz80IOmhaKaX8LDCTWWw4h0srKDzaQNWgI8z9uRlBS2ZKKeVnAdc1H6xqRoDM/KNEhwcff4CnJbO77vJSZEoppU5EgCaz6qVgihmcFHX8AQ4P28ymTfNSZEoppU5EwFYzQiPrmlV3AKmqcu+CW7dCRoaXolNKKeWpgExmUWEOOoTaG+6e77BKbm7PAvKHP8Ds2d4JTiml/EREzhSRzSKyVUTucbFfRORZ5/51IjLcg3PvEBEjInG+iD0gkxlYczQ2WDKrTmbudgLRDiBKqTZORGzAc8BUYABwkYgMqHfYVKCP83E98Lw754pIMnA6sNtX8QdsMkuKCSMzv4mSmbudQDSZKaXavtHAVmPMdmNMGfA2MLPeMTOB14xlGRAtIglunPs0cBfQwHio5gvYZJYca5XMXI41s1dXM7pZMtNxZkqptq8bUHuS2SznNneOafBcEZkBZBtj1no74NoCsjcjWCWzo+WV5BWVERcZUnenlsyUUu2PXURW1nr/gjHmhVrvxcU59f/ab+gYl9tFJBy4H5jiUaQnIICT2bEejccns1Dr2d02syuvhFNP9V5wSinlfRXGmJGN7M8Ckmu9TwL2uHlMcAPbewGpwFoRqd6+WkRGG2P2nshNNCSAqxmPjTU7jsNKdG6XzIYNg7PP9lJkSinlFyuAPiKSKiLBwGxgYb1jFgKXO3s1jgUKjTE5DZ1rjFlvjIk3xvQwxvTASobDvZ3IQEtmrpeCsTtLZu62me3cCVu2wOmng7gqbSulVOtmjKkQkZuAzwAb8LIxZqOIzHHunwssBqYBW4Fi4KrGzm3J+AM2mUWG2IkJd3inZPbWW3DffVBSAiEhTR+vlFKtkDFmMVbCqr1tbq3XBvitu+e6OKZH86N0LWCrGcEqnWW6GmvmaZuZw2E9aycQpZTyi4BOZsmxYd4pmQU7JyvWZKaUUn4R0MmsepHOqqp6vU89bTOrTma62rRSSvlFQCez5JgwyiqqOHCktO6OmnFmbs7NqCUzpZTyq4BOZjU9GutXNdocEGR3P5lNmQKffgpxPpk/UymlVBMCtjcj1B5rdpQR3evtdITCePvtAAAgAElEQVS7n8ySkqyHUkopvwjoklm36OqxZi46ethD3V8CZu9eeO89OHjQi9EppZRyV4slMxF5WUT2i8iGWttiReQLEfnF+RzTUvEAhAXbiIsMdr0UjCPM/ZLZqlVw3nnWIp1KKaVaXEuWzF4Bzqy37R7gS2NMH+BL5/sWZY01c9U934Nkph1AlFLKr1osmRljlgL59TbPBF51vn4VaPEJDpNiwppfMtNkppRSfuXvNrMuzkkqcT7Ht3QAybHh7Ck4SuVxY83CPB9npslMKaX8wt/JzC0icr2IrBSRlRUVFV69dlJMGOWVhn2H6iUuR5jOAKKUUm2Ev5PZPueS2zif97s6yBjzgjFmpDFmpN3u3dEEybXWNavDEeb+3Iz9+sF//wsTJng1NqWUUu7xdzJbCFzhfH0F8GFLB5AaFwHA5r2H6u7wpGQWGQnjx0NsrJejU0op5Y6W7Jr/FvAD0E9EskTkGuAx4HQR+QU43fm+RSXFhBEXGcKazIK6O+yh7reZHTkCr75qrWmmlFKqxbXYDCDGmIsa2DW5pWJwRUQYlhJNxu56ycwR7n7JrKAArrwSXngB+vb1eoxKKaUa5+9qxlZhWEo02w8UcbCoVgcOR6h2zVdKqTZCkxkwLNmaeCSjdlWjIxwqy6CqsukL6BIwSinlV5rMgCFJUQQJrNlda25FT5aB0ZKZUkr5lSYzICLETlrXjnU7gdidycydTiAOh/WsyUwppfwioJeAqW1YSjQLM/ZQVWUICpJaJTM3OoHY7ZCRAQkJvg1SKaWUS1oycxqWEsPh0gq25h6xNtQkMzdKZiIwdCjEt/hsXEoppdBkVmNYSjRQq93Mk5IZwIsvwjff+CAypZRSTdFk5tQzLoKoMAdrqseb2UOtZ3cHTt9zDyxY4JvglFJKNUqTmVP14OmaZOaw5mx0u2TmcGgHEKWU8hNNZrUMS45hy/7DHCoptwZNg/uTDQcHazJTSik/0WRWy7CUaIyBtZkFnpfMgoN10LRSSvmJJrNahiZbnUDWZRV63mamJTOllPIbHWdWS1SYg9S4CKtkNqq7tdHd+Rk//xxCQ30XnFJKqQZpMqtnSFIUy7fn12ozc7OaMSnJd0EppZRqlFYz1jO4WxR7D5Ww/6jzR+NuyeyNN6yHUkqpFqfJrJ6adrM9R8AW4n7J7KWXrIHTSimlWpwms3oGJnYkSGBddiEEh0OZB70ZtQOIUkr5hSazesKD7fSJ78C6rAJwRHjWNV+TmVJK+YUmMxeGJEWxPqsQ4wiDsiL3TtJkppRSfqPJzIUhSVHkFZVRbgt1vwOIJjOllPIb7ZrvwpAkqxPIkaoQYt2tZvznP8EYH0allFKqIZrMXEhL6IDDJhSU24l1t5qxY0ffBqWUUqpBWs3oQojdRlrXjhwos7lfzfjRR/Dgg74NTCmllEuazBowOCmKvUdtmHI3S2ZffglPPeXboJRSSrmkyawBQ5OiOFzhoKrUg96MOmu+Ukr5hSazBgzuFk0xIRgdNK2UUq2eJrMG9O0SSaUtDHvlUfd6KQYHQ2Wl9VBKKdWiNJk1wG4LoktcLADGne75Dof1rFWNSinV4jSZNaJHQmcANu3e1/TBd95pJbKQEB9HpZRSqj5NZo3o3S0egGWbs5o+2G63HiI+jkoppXxDRM4Ukc0islVE7nGxX0TkWef+dSIyvKlzReRh57EZIvK5iCT6InZNZo3o0MEaCL1qa3bTB3//Pdx4IxQU+DgqpZTyPhGxAc8BU4EBwEUiMqDeYVOBPs7H9cDzbpz7hDFmiDEmHVgE/MkX8Wsya4wjHIDMfbnkHi5t/NjNm+H556GwsAUCU0oprxsNbDXGbDfGlAFvAzPrHTMTeM1YlgHRIpLQ2LnGmEO1zo8AfDLvnyazxjiTWRilfL15f+PHBgdbz9o9XynVNnUDMmu9z3Juc+eYRs8VkUdEJBO4BC2Z+UFwBAAJ4VUs0WSmlGrb7CKystbj+nr7XTX41y9FNXRMo+caY+43xiQD84CbPAnaXTrRcGOcJbORiaE8tuUAZRVVBNsbyP+azJRSrVuFMWZkI/uzgORa75OAPW4eE+zGuQBvAh8Df3YzZrdpyawxjjAAhnRxcLi0gpU78xs+NiTEelRUtFBwSinlVSuAPiKSKiLBwGxgYb1jFgKXO3s1jgUKjTE5jZ0rIn1qnT8D2OSL4LVk1hhnNWPfGBvBtiCWbN7P+N5xro8980woKWnB4JRSynuMMRUichPwGWADXjbGbBSROc79c4HFwDRgK1AMXNXYuc5LPyYi/YAqYBcwxxfxazJrjLOaMcSUkJ4SzbLtjZTMlFKqjTPGLMZKWLW3za312gC/dfdc5/bzvBymSx5VM4rIBBGJcL6+VESeEpHuvgmtFXBWM1JWzLiendi4p5DCow1MV7V9O1x+OWRktFx8SimlAM/bzJ4HikVkKHAXVpHxNa9H1VoE2cAeCuXFjO3ZiSpDw+1mBQXw+uuwe3fLxqiUUsrjZFbhLGbOBJ4xxjwDdGhOACJyq4hsFJENIvKWiIQ253pe5wiH8mKGpUQTbA9i2fY818dpb0allPIbT5PZYRG5F7gU+Ng5hYnjRD9cRLoBtwAjjTGDsBoOZ5/o9XzCEQ5lxYQ6bAxLjuaHhpJZ9az5msyUUqrFeZrMLgRKgWuMMXuxRng/0cwY7ECYiNiBcFyPTfCf4HAot1abHtuzExv3HHLdbqYlM6WU8huPS2ZY1YvfikhfIB1460Q/3BiTDfwV2A3kYI1Z+Lz+cSJyffWo9YqWHsflCIfyowCM69UJY2DFDhftZiEhEBdnzZyvlFKqRXmazJYCIc7qwS+xxhi8cqIfLiIxWO1vqUAiECEil9Y/zhjzgjFmpDFmpL2lk0VwBJRZi3OmJzfSbpaYCLm5cOlx4SullPIxT5OZGGOKgXOBvxljzgEGNuPzfwXsMMbkGmPKgfeA8c24nvc5wmqqGUMdNoanRLNsRwPtZkoppfzC42QmIuOwZj7+2LnN1ozP3w2MFZFwERFgMvBzM67nfbWqGaFWu1lxvXazsjI45xxYsKCFA1RKKeVpMvs9cC/wvnOak57AkhP9cGPMcmABsBpY74znhRO9nk/UqmYEGNfTajf7sf54s6Ag+OAD+Ll15WKllAoEHjVAGWO+Ab4RkQ4iEmmM2Y7Vtf6EGWP+jA9mUPaaWtWMAEOTowmxB/HfrQc4fUCXY8fZbCAC5Q3MEKKUUspnPJ3OarCIrAE2AD+JyCoRaU6bWevnHGdWLdRhY3L/eD7IyKakvPLYcSJW93ztmq+UUi3O02rGfwK3GWO6G2NSgNuBF70fVisSHAEVR6GqqmbTpWO7U1BczsK19YbEORyazJRSyg88TWYRxpiaNjJjzNdAhFcjam2qJxuuONYJZFzPTvSJj+T1H3Zhze7l1Ls3REe3cIBKKaU8HbS1XUT+CLzufH8psMO7IbUyDmeuLiuuWd9M/t8D/LFHApf/mExGZgHDUmKsY9as8VOQSikV2DwtmV0NdMYaD/YeEAdc6eWYWpdga00zyo+1m7HyZcYXfEREsI3Xl+3yT1xKKaVqeJTMjDEHjTG3GGOGOx+/x2pHa78c9ZJZeQmUHsK+bz3nDuvGonU55Bc528muvBIeftgvYSqlVCDztGTmyjgvXKP1qk5m1T0ai/Zbz6WFXD3QUFZRxfwVmda2FStg3bqWj1EppQKcN5JZ+1ZTzegca3Ykt2ZXatkvDEuJ5tMNOdYG7c2olFJ+4VYHEBEZ3tAumrGeWZtQ3QGkekqr6pIZwJ4Mxve6hLnfbKeotIIIHWemlFJ+4W5vxicb2bfJG4G0WtVd88uqS2bOZBYRDzkZjB53C88t2caa3QWcFBwMR45Y+wsKIDPz+Ov16gXh4ZCfD9nZx+/v29daTiY3F/buPX5/WppVAty3D/bvP37/gAHWbCRKKRVA3EpmxphTfR1Iq1W/N2N1yaz3r2Dzx4xIiSZI4McdeZwUFQXTp1v7P/kELr74+OutWAEjR8K778L11x+/f9Mm6NcPXn8dbr/9+P1ZWdCtG8ydCw88cPz+wkLo2NHj21RKKb8TCQLWYcwgT0/VlSSbclw14wEI6QgpY2Dtm0QWZzKoWxTLd+RbCSYkBIBf+gyl6u//pl/XDnWv16uX9fyrX7meYT8x0Xr+9a+he/fj98fGWs/nnw+DXPx7h4XBjBkweDA88oiHN6uUUn5kTBUiaxFJwZjdnpyqyawp1SWz2tWMEZ0hId16v2cNo3r05/VluyhNGE2I3YYxhpu/O8CeggRWzTkdh81FP5vUVOvRkD59rEdDBgywHq5s3VqTVJVSqo1JADYi8iNwbJZ3Y2Y0dpIms6bYnW1mNdWMuRAZD/EDwBYMezIYnTqBl77bwfqsQkb2iGX17gI27T0MwPLt+ZzUJ65lY46MPNZ2p5RSbcuDJ3KSW13zReTSWq8n1Nt304l8cJsRFGQltOpkVl0yswdbCS0ng1E9rKq/5TusNc7e+nE3EcE2whw2PtvoohOHr2kyU0q1VcZ84/LRBHfHmd1W6/Xf6u272u0g26rg8LqDpiPjrdeJ6ZCzlthwB327RPLjjnwKj5azaN0eZqR345R+nfls416qqkzD1/aFiAhNZkqptkXkMCKHXDys7U1wN5lJA69dvW9/HOFWyayyHI4etLrlg9VuVlIIB3cwqkcsq3Yd5N1VWZSUV3Hx6BTOGNiV/YdLycgqaNl4R4yA4Q0NDVRKqVbImA4Y09HFw9reBHeTmWngtav37Y8j3OoAUuSc/SPC2QaWWN0JJIPRqbEcKa3gmS9/YXC3KAYnRXFqWjz2IOGzDS1c1fjAA/DSSy37mUop5UfuJrM0EVknIutrva5+38+H8bUOweFW1/zqAdPV1YzxAyDIYQ2eTrXazQqPlnPR6BQAosIcjO8dx2cb99Zd90wppZRXuZvM+gO/BqbXel39voH+4e1IdTVjTcnMmczsIRCfBvt+IiEqjJTYcCKCbcxIT6w59YyBXdiZV8yWfS3YhvXss1a3f02gSqkA4VYyM8bsqv0AjgDDgTjn+/atfjVjZOdj+2J6QIE1tu/OM/rx4MxBRIYcG/Fw+oAuiMCnLVnVWFwMO3dCSUnLfaZSSvmRu13zF4nIIOfrBGADVi/G10Xk9z6Mr3WoX81YXTIDiO5uJTNj+PXQRGaNSKpzanyHUIanxPDOyky+33qgZaobIyOt56Kixo9TSql2wt1qxlRjzAbn66uAL4wxvwbGEAhd8x0Rx6oZHeEQEnlsX3QKVBy1prlqwG2n96W8soqL/7Wcc/7xPd/90vCxXhHhnIJLu+crpQKEu8msvNbrycBiAGPMYaDK20G1Oo4wq5qxesB0bdFWZ4/qqkZXJvSOY+ldp/LIOYPIKyrlin//yK48H5aaqktmmsyUUgHC3WSWKSI3i8g5WG1lnwKISBjtfT0zcFYzFtcdMF2tJpk13nQY6rBxyZjuvDtnPLYg4R9LtvkoWKBHDzj3XGvSYaWUCgDuJrNrgIHAlcCFxpjqUcBjgX/7IK7WxREBFSVweF/d9jKAqGTruZGSWW3xHUO5eHQK767OIjO/2MuBOo0aZS0xUz1Dv1JKtXPu9mbcb4yZY4yZaYz5vNb2JcaYv/ouvFaieoHOgt11ezIChHaEsBi3kxnAb07uSZAIz3/jw9KZUkoFEHd7My5s7OHrIP0uuHpNs6LjS2ZgVTUWulhVugEJUWFcMCqJ/6zMZE/BUS8FWcuOHRAXB/Pne//aSinVCrm7BMw4IBN4C1hOIMzHWJsj/Njr+m1mYCWzA794dMkbTunN/BWZPPbJJtISOvD15lxyCo/ywY0T6BTZzLXIQkIgL89adVoppQKAu21mXYH7gEHAM8DpwAFjzDfGjan52zxHrY4UES7WJotKqRlr5q5u0WHMGpHEwrV7ePzTzeQXlZGZf5Qvf97f/Hiru+brODOlVIBwt82s0hjzqTHmCqxOH1uBr0XkZp9G11pUVzNCw9WM5cVQnOfRZe+d1p/nLh7Oj/dN5otbJ9G1YyhfbfJiMtOu+UqpAOH2StMiEgKcBVwE9ACeBd7zTVitjDvVjGB1z3dVcmtAx1AHZw1JqHl/Wv94PlyTTWlFJSF224lGC3Y7hIZqMlNKBQx3O4C8CnyPNcbsQWPMKGPMw8aYbJ9G11oE10pm9QdNg1sDp90xOS2eorJKfnSuWN0sV14Jw4Y1/zpKKdUGuFsyuwwoAvoCt4jU9P8QwBg3Fk5r06pLZrZgCI06fn+0Z2PNGjK+Vxwh9iC+/Hk/E/u4SJqeeP755p2vlFJtiLttZkHGmA7OR8dajw7tPpHBsWQWEQ/ioiNnaBSERjc7mYUF25jQO46vNu33zoTEugSMUsoDInKmiGwWka0ico+L/SIizzr3rxOR4U2dKyJPiMgm5/Hvi0i0L2J3tzdjYKvuAFJ/wHRt0SnNTmYAp6XFszu/mG25zeyJOGkSTJvW7HiUUoFBRGzAc8BUrHUqLxKR+utVTgX6OB/XA8+7ce4XwCBjzBBgC3CvL+LXZOaO6q75rnoyVvNSMjs1zfqMrzbta96FHA7tAKKU8sRoYKsxZrsxpgx4G5hZ75iZwGvGsgyIdi4L1uC5xpjPjTEVzvOXAUn4gCYzd9idyazRkll3j8eaudItOoy0rh2aP94sMlKTmVLKE92wJseoluXc5s4x7pwL1pJhnzQ7Uhc0mbkjKAjiB0JCesPHnOBYM1cm949n5a6DZDdnqquICE1mSqna7CKystbj+nr7Xc3sVP+v84aOafJcEbkfqADmuRuwJ1pFMhORaBFZ4Gwk/FlExvk7puPc+D2Mvq7h/V7qng9wdno3HDZh+rPf8vnGvSd2ES2ZKaXqqjDGjKz1eKHe/iwgudb7JGCPm8c0eq6IXAFMBy4xXunddrxWkcywpsj61BiTBgwFfvZzPJ7zYjLr06UDi26eSLeYMK5/fRX3vb+e0opKzy5y+ulwzTXNjkUpFTBWAH1EJFVEgoHZQP2J5BcClzt7NY4FCo0xOY2dKyJnAncDM4wxPlr3yoMZQHxFRDoCk7DWSsPZeFjmz5hOiJfGmlXrHR/JezdM4MnPN/PPpduJDnNw15lp7l/g/POth1JKucEYUyEiNwGfATbgZWPMRhGZ49w/F1gMTMOa0rAYuKqxc52X/jsQAnzhHKO8zBgzx9vxi49KfO4HIJIOvAD8hFUqWwX8zhhTVOuY67G6gRIcHDyitLTUH6E27bEUGHwBnOXdJd7u+M9a3l+TzcKbJjAw0cWgbVeqqqyJhiMirDY/pVRAE5FiY0xE00e2Ta3hW86ONU3W88aYYVgzjdQZrGeMeaG6ntdu93thsmHRKdb8jF72h7P6ExMezN3vrqOissq9k/7xD+jY0VoKRiml2rnWkMyygCxjzHLn+wVYya3t6ZAIR5o5PsyF6PBgHp45kA3Zh/jXdzvcOyky0nrWTiBKqQDg92RmjNkLZIpIP+emyVhVjm1PeCwUe2GSYBemDk7gjIFdePqLLew84MbsIJrMlFIBxO/JzOlmYJ6IrAPSgUf9HM+JCe/ks2QG8PDMQdiDhCc+39z0wbqmmVIqgLSKZGaMyXC2iQ0xxpxtjDno75hOSHgslBdBeTMGOzcivmMo15yUysfrctiQXdj4wdUlM11tWikVAFpFMms3wmKtZx+Wzq6d1JOoMAd/bap0lpoKf/wjdO/us1iUUqq10GTmTeGdrOejvktmHUMd3HBKL77enNv4Ip5JSfDQQ9Cnj89iUUqp1kKTmTdVJzMvzM/YmCvG9SC+QwhPfLap4XXPqqpg3z44dMinsSilVGugycybwqurGX2bzMKCbdw8uQ8rdh7koUU/sT3XRSePI0ega1e+vuMRXli6zTuLfSqlVCulycybakpmvqtmrHbhyGTOGpLAq9/v5LQnv2HW89/z055apTBnb8Y1P2Xx6OJNPLr4Z01oSql2S5OZN4XFWM8tkMyC7UE8d/Fwlt07mXumprHjQBF3v7uOqipnwrLZKA8OoUNlKReMTOLFb3fw54Ubj+1XSql2pBXPDdUG2RwQEuXzasba4juGMufkXsRFhnDHf9ayeEMO04ckknekFLGHMCjKxjXnDSEqzMGL3+4g1GHjvmn9Wyw+pZRqCVoy87bwWJ/2ZmzIOcO60a9LB/762WbKK6uYt3w3RY4wBkTZEBHum9afC0cm89J3O1y3sSmlVBumyczbwmNbtGRWzRYk3HlGP3bmFfP6D7t47YddfHHOtXS88jIARIQ7zuhHsC2Ip77Y0uLxKaWUL2ky87bwTn5JZgCT+8czsnsMjyz+mQNHSul73+9h6tSa/Z07hHD1ST1Y5M4MIkop1YZoMvO28E5Q7J/ZuESEe6amUVllSOvagQnhpbBtW51jrp/Uy70ZRJRSqg3RDiDeFuafasZqI3vE8sfpAxiSFIXceBXs2gUZGTX7o8IczDm5F//76Sb+szKTXXnFfLw+h9iIYBbMGYdzJVillGpTtGTmbTWTDZf4LYRrTkplVI9Ya7JhFxMNXznemkHkzgXr+MfXW7EFCat2HWT17gI/RKuUUs2nJTNvqz0/oyPRv7FERrpcAiYs2Mbcy0awZe9hfjWgC6EOG6P+5/+xYFUmI7rH+CFQpXxry77D9O4cSVCQ1jy0V1oy87YWmtLKLRERDa5nNjwlhtmjU4iLDCEyxM60wQl8tDaHo2WVLRykUr61PquQKU8v5a0Vu/0divIhTWbe1oJTWjWpuprRjWmszh+ZxJHSCj7dmNMCgSnVcj7MyAbgjWW7dUq3dkyTmbe10Mz5bjn7bHjpJWsG/SaMSY0lJTac/6zMaoHAlGoZVVWGj9fnEB5s4+ecQ6zNat6QlJe+26HjNFspTWbeFtaKqhmHD4errgKbrclDRYTzRyTx/bY8MvOLATDGUFbRdCL0ltveyeCRj39qsc9T7d/q3QfJKSzh3mn9CXPYeHP5rmZd77UfdjL3m20cKa3wToDKazSZeVt1m9lR/4w1qyM/H374AYqL3Tr8vBFJiFh/fT7/9TZ+9dQ3jHrk/7GtBaa/yswv5r3V2bz83501ybQ9McZQpF+ALhljKCmv9EkV4KJ1OYTYgzhnWDdmDE3ko7U5HCopP6Fr5ReVsSuvmLKKKr7evN/Lkarm0t6M3uaHyYYb9NVXcP75sG4dDB7c5OGJ0WGc1DuOV77fCcDI7jEcLC7nhjdW8cFvJxAefPyvy75DJfxjyVZyCkvILyrjSGkFAxOjmNQ3jlE9Yvll/xG+2ZzLql35/Kp/F248tTc2Fz3K3l9jtWsECbz47XYemjmoeffuJzsPFLH3UAlje3aqs/3f/93Jo4t/5qLRKdw8uTfxHUKb/VnGGL7Zkkv/hI506dj86/nD8u153Pv+erbnFhEkEOawccagrjx5/tBmj3msdFYxntovnsgQOxePSWH+ykw+XJPNZeN6eHy9tZnW0BUR+HTDXqYP8W1v5aoqw1NfbKFbTBjnDOtGqKPpGpZApsnMF8JjWkcyi4y0nhvo0ejKfdP68+XP+5g2OIGenSP59pdcLn/5R+5/fwNPXVD3C2ZDdiHXvrqS/OIyesZFEBMeTEJUKF9u2se7q4+1vQXbg+jVOZInv9jCt78c4OnZ6XSLDqvZb4zhvdVZjO0ZS/fYCOavyOTm0/rQuUNI838GLaiguIyLXlxGXlEZP943mejwYMC6v3nLdxEd7uCtH3fz7uosrpvYk5tP643ddmKVI9kFR7n3vfUs3ZLLhN6dmHft2GbF/tHaPbywdDv/umKk24mxuKzC5R847jhUUs5jn2zizeW7SY4N4/bT+1JWWcX23CLeW53NlAFdOHNQQs3xH2Zk89Wm/Tx9Qbrb3euX78gj93Ap04da1xmSFMXAxI7MW76bS8d29zhZrsksIEhg+pBEvvx5HyXllT5NMGsyC/j7kq0APP7pJi4ek8JlY3vQNapt/uHia5rMfCG8U+vpzQgeJbP+CR3pn9Cx5v3EPp257Vd9efKLLQzuFsU5w7oRbA/i219yuXX+WmLCHXxw4wQGJB47p7LKsCG7kFW7DtKzcwRjUjsR6gjivdXZ/OnDDUz9v6U8f+kIJvSOA2DVroPszCvmt6f2ZkT3GN5Zlcm//7uDu85MOy6+otIKikoriK/3hZt1sJiVOw8yfUjCCSeI5jDGcN/769l/uJTKKsN7q7O5+qRUANZmFbItt4jHzh3M2J6deOLzzTzz5S+szy7k7xcP8zghvPXjbv5n0U8YYHJaPF9u2s+PO/IZnRp7wvF/tHYP67MLueLlH/nPnHF0CHU0evyXP+9jzhureHjmIGaPTvH48657dSUrduZz7Ump3Dalb83PoKKyim25R/ifj3/mlH7xhDqsjht3LlhHWUUVM4YmMrl/lzrXKimvZN+hEvYfLqWotILRqbGEB9tZtC6HMIeN09LiAatd+KLRKfzhgw2sySxgeErdMZVlFVXYgsRlzQFARmYBfbt04Nzh3Vi4dg//3XrguFi8afH6HIJtQfzzshG8vWI3//h6G0WllTwwY6DPPrMt02TmC2GxUJTr7yhOKJm58ttTe7N690EeWvQTDy061kFjWEo0/7xsxHFVZrYgYWhyNEOTo+tsP29EEiO6x3D96yu5cd5qFt18Esmx4by7Ooswh42pgxOsMW+DEnj9h13MOaUXHZ1fqsYYFq7dw8OLfiKvqIzZo5K57fR+xEYE88r3O/nrZ5s5Wl7Jm8t383+z00msVfLzRGWV4fmvt3Jy33gGJ0W5fd5/VmWxeP1e7j4zjc827uXNH3dz1YQeiAjvrc4ixB7EtCEJdAx18NzFwxnfaxd//GADF724nJevGEmnSPdKoV9v3s+9763npN5x/OXcwcRFhjDx8SU8/cUW3rreKp2VlFdyzasrKCmv4r5p/ZscCG+MYeWug/RP6PXFXBkAACAASURBVMgv+w4z541V/PvK0QTbXf9RkHu4lLsWrKOiyvDnhRsZ3j2Gvl06uP2zKiwu58ed+dx8am9um9Kvzj67LYg/TR/Axf9azkvf7eDqCanc8tYaosIcBIlVXVs7gSxen8Pv3l5DeeWx9rbwYBtTBnThmy25TO4fX+ePhZnpifxl8c+88cOuOsmspLySyU9+Q0FxGUOToxmWEs0lY7rX/B4ZY1ibWcC0wV0Z3yuODiF2Pt2w16Nktj33CA8t+onoMAcJ0WF0jw1n+tBEIkOO/xo2xvDJ+hwm9Y3j1LR4Tk2LZ3deMSEO7ebQEE1mvhDeCXJbwUS+ERHWs4sprTwRFCT8/eLhLF6fQ1FpBaUVVYQF27hgZLLH1Sw94iJ48fKRTP/bd9w4bzVvXDuGRWtzmDqoa81/6htO6cXH63P484cbGdUjlhB7EB9kZPPtLwcYkhTFtMEJvLl8Nx+tzSE5Npyfcw5xar/OnJYWz2OfbGLas9/y4IyBpHXtSIg9iOhwR02VX1P+99NNvLB0O/OW7+bT300iKrzxEgrAjgNFPLBwI+N6duI3k3rSKSKYu95dx8pdBxmaFM3CtXuYMrBrTWIGuGRMdzpHhnDzW2s47/nveec3444rbdZ3pLSC+9/fQK/OEbx05UhC7Laan9fDi35i2fY8xqTGcve76/jv1jziIoM57/nvmZmeyD1T00iIcp3gtx8oIr+ojLvO6IfdFsQd/1nLnQvW8tQF6ceVUowx3LVgLUdKK5h37RhueWsNN725moU3nUSow8b+wyW8+v1ONu89TNbBo1ZPwqlpdUpvK3bmYwyMd5bM6xvfO44zB3bluSVb+SnnEL/sP8Lr14xmXVYhT3y2mc17D9OvaweOlFbw54Ub6R3fgasn9CC+YygCfLIhh4/X5XCopIKz07vVuXaHUAfnj0xm3vJd3DMtreYPsQWrssguOMqMoYlsP3CEud9sZ8WOg7wzZ1zNv3Hh0XLSk6MJtgcxuX88X/y8j4rKKrdrAuZ+s43vt+bRuUMI+w7lUFFlePyzzdx0am8uGZtS8+8JVilwT2EJd5xxLNmndAp363MClSYzXwjv5JcFOo+TmAjz58OYMc2+VESInfNHJnshKOjeKYKnLkjnutdWcv7c7zlcWsF5I5Jq9g/qFsWUAV14f012TceQyBA7D84YyKVju2MLEq4Y34NHP/6ZddmFPDM7nRlDExERTurTmZvfWs3v3j42ubLDJvzjkhGcPqDxv6LfWZnJC0u386v+Xfh6837++OEGnr1oWKPnLNlklZQctiCeunAoQUHC9KEJPLzoJ95avpv8ojIKiss5d3i3486dMrAr864dw+Uv/8h1r63k7evHERZ87AutpLySEHtQTdvOE59uYk/hURbMGV/ni++SMSn885ttPP3FFk7qHceHGXu4Y0pfrpqQytxvtvHC0u18vy2P924YT3Ls8V+IK3ZYv6ujUmPp1TmSfYdKeOKzzVRUGp66cGidz3p92S6WbM7lwRkDGd8rjicvSOeKl3/kzx9upEtUKP/6djulFVX0iY8kKSacwqPl/GdVVp1ktnxHHsH2INLrldxru29af756ej8fr8vh+kk9mdinM4MSo3j2y1945fsd/OXcIfzty1/IPVzKi5ePrHOtSX0788CMgWzdf4QBtarMq10+rjuvfL+TN5fv5ve/6ktlleHFb7czNCmKZ2anIyI8//U2/vfTTWzLPUKvzpFkODt/pCdbpbkzByXwQcYeftyRz/jecVRVGQqOlhPqCCLUbjuuXa+wuJyFa/dw3ogk/nLuYCqrDGuzCvjrZ5t5aNFPvPzfHbxy1Sh6x1sl3MXrc3DYxKfVmO2NJjNfCI+BsiPWZMOOUCg9bHXVj/a8baFZIiLgggta9jPddPqALsw5uRdzv9lGYlQo4+r1/pt76QgOlZRTUl5FSXklsZHBdUo2vTpH8tKVozDG1GnIT42L4N0bxvP9tjyKSysprajkpe92cPs7GXx8y0SXX+YAP+7I5/73req7uZcO5/mvt/HkF1uY3D+emendKCwu5701WRwuqaB7p3CSYsJ4c3km767Ool+XDvzripE1JZ/wYDtnD+vG/JWZ7Ck8SucOIUxsoBQyskcs/3dhOr95YxW3/yeDv180nJKKSh7/dDOvfL+TocnR3HByL2LCHbz6wy6umtDjuGrDUIeNG07pxYMf/cTyHfmcnZ7Ib0/tjYhw+5R+TB+SyAX//IHLXlrOghvGE1evSnPFzoPERgTTM84qyf/21N44bMKjizdReLScuZeN4GBRGe+vyea5JVs5pV9nLh/XHYCT+3bm+kk9eWHpdgDOGpLAHVP6keq81tNfbOFvX/1CflEZsRFW6Xj5jnzSk6MbLdWndArnvqlpfL8tjzucVZExEcGcM6wb763OZtaIZF7+7w7OH5HkMimG2G0MTHRdTdyzcySn9OvMG8t2c+Mpvfnip33syivmnkuG1/wunTeiG3/9fDPvrMzk3qn9ycgsICLYRu/4yJr7DnUE8fclW5n3426+33qAg8XHuvz37RLJO78ZV1MjsGB1FiXlVVw61voOsAUJw1NimHftGL7beoDfv53BLW9l8P5vxxNsC2Lx+r1M7NOZqLCmawaURZOZL9SfbPiTe2DrF3D7Zqtfb0v68ktISoJ+/Zo+toXdMaUvhUfLGJ0ae9xfskFB4lbVoKseaSF2G6f2i695P7J7LGf97VtuenM1/5kzHnuQ8M7KTP7v//1C4VHrC6i0opIecRE8d/Fw7LYgbjilF19t3s8fP9jA8h35vL86m6PldeettAUJN5/Wm5tO612n9AJw0egUXl+2i2Xb87luYmqjVVFTBnbl3qlpPLp4E3c61rFiZz6784s5Oz2RNZkFzHljFUECSTFhNV/s9V00OoWXvttBl46hPHbekDo/l35dO/DylaO45F/LuOrfK3jr+rF12mlW7spnZPeYOudcP6kXsREh3P3uOk5+fAl5RWUAnNQ7jidm1e3VeseUfkSHO5jQK+64dtLT0uJ55stf+GbLfs4ZlsThknI2ZBdy06m9G/x5VLtyQipXTkits+2qCam8vSKTy19aTqjd5rKTkDuuHN+DK/+9go/X7+Hl73aSGhfBlIFda/bHdwhlclo8767K4o4p/cjILGBIUnRNtWtYsI3J/bvw8bocunQMYXL/LvRP6Eh5ZRVHSiqY+802HvroJ566MN3qzbpsF8NToo9LsCLCxD6d+d/zhnDtayt56ostTBuUQHbBUW49ve8J3Vug0mTmC7XnZwyPg00fQUkhHNwJsamNnup1v/41/Pa38MQTLfu5brDbgvjLuUN8/jkpncJ5YtZQ5ryxirsWrCXr4FFW7jrIqB4x/Do5ARHBYbN6ulW3kdltQTx9QTrTnv2WBauymDk0kSsn9KBX50h25xezK6+Y1Ljwmmqh+gYkdmRocjRrMws4d3iSy2Nqu25iT7btL2L+ykxSYsOZf/1YxvTsREVlFZ9s2Ms7KzO5ZXIfIlx0FoD/396dx0dVng0f/12Z7BtJSEiAsBt2CAiy+ljFWkBr1bqgb12qta1tRW19bdW3z6fLp31q3y62faq1Vmm17au1rVYed6UuqEAFUQQRRGRRSEhYQhbIer1/3CfNkMxkg8nJzFzfz2c+k3Puc+Zc98zkXHOfc5/7uNbZMzedRlpSIGRvvBkjcvnN52Zw7YNrueGh9Sz7/CkA7Dt8lJ3767h89ogO61w0o5i8jCTufvEDrjl1FOdNG0JxbseWbXJiAl89PXRymjJ0APmZKazY7JLZ2p0HaVGY3a4l3l3jirKYf9JAXtu2n2+fM6HXl2+cVlLA6PwMfvjkZiprGvivC6Z0eN8unTWM594t56l39rJ572Gu/Y/Rx5Tf8dkp3HzWWEblZ3T4UZWQIPxqxft8unQwKYkBtlfWcueS0rDxfHJiIZfNGsa9r2xnw+4qkgLCWXaIsUcsmUVC8JBWO1a6RAawZ33fJ7Mw9zSLN4smF3HN/FEse+1DctOT+MlFU7loRnGn1xqNzM/g2ZtOIz05cExvw7GFWd3qvXfb4vGsfL/imEsdwhERfnDBZM4YP4jTxub/uwdeYiCBc0uHcG5p1xfohuoVF+yM8YO4ddF4fvjUZl7fVsm8k/J5Y4cbqeaUMN36F4wvZMH43u9UExKEBeMLeGZjGY3NLazZfoCkgHToFt8T31w4nr8M3M1V80YeV1xXzRvJd5ZvIj8zJeQ5zdNKCijKTuWOp9+jsVk7HM7MSk0KewnD9WecxLMby7jt0XcYW5hFbnoSi4Oumwvl2+dMZNUH+1m1fT+njyvoVucj08b6eUZC8GDDm/8HkjIgkAx73+p8vUjIyIC//hXmzoV3vW71jz/upts/duxw5Q89FLq8wrvcYNmyY+efdRbs6//D+9y6eDw/v6SUFTefzsUzh3Xrotlheend7jbf3pzRA7llYfcPgyUFElg0uajXFyJ3xxVzRzB4QCo/fW4LqsobOw6QmpTApCFdJ9zeWjB+EIePNrFu50HWfLifqcU5x3R06anSYTn81wVTSDrO6wkvnFHM4AGpXH/GmJDn7xIDCVw0o5i9Ve5Gu9M76bDSXnJiAj+5eCqVNQ2sfL+SS07puudvRkoidy6ZRnJiQrda8+ZYlswioTWZ1VbCe0/C2E/BoImuZdbXvvY1N+BwdnbbgMPJyW66/aOr8tadf3C5CLzwAqxZ0/d166HWnURrR4R4lJoUYOmCEt7cdYiXtlTwxo4DTB+We9yJoTOnlhSQFBCe2LCHdz6qOq6Lu0+kzJREVt12ZofzcsEu8XrwDhmQ2uWlE+1NLc7huk+MJikgfG5Wx8O4oUwfnsv6/zyLc6d23oozHUm03d8nIyNDa/v7YbOmBvhBAYxZAB/8Ey5aBh+uhI2Pwq07+74TSCRVVrrzcZdf3q3xH43/GptbOPNnL5OalMC2fTVcv6CEb0S4s8EV969h9fb9NDYrf7j6FE4P6qDT333jkbcoyEzhtrMn9HhdVWVfdX2/GDtTROpUNcPvOCLFWmaRkJgMKdkukQWSoeRTMGQa1FfBge1+R3di5efDj39siSyKJAUSuPHMEraW19CicMrI3p+/6q4zxg2isVkJJAgzR/aPlll3/fySab1KZODOhfaHRBYPLJlFSuutYMYsgJQsGOJdfOvHebNIO3y47XyaiQrnTx/KmIIMEsQd2oq0Mye4ltjkIdlddlQxpjcsmUVKa4/GCee654IJrpXmx3mzSJs1y52bM1EjkCD87JJp/OD8KX2SXEYMzGDhpEIuOkGjyBjTnv1EipT0gSABGHe2m05MhsLJsCcGW2ZFRVBe7ncUpoemDcvpdEipE+23V8zss22Z+GMts0iZdD7Mv6HtcCO482Z734aWFv/iioTCQigr8zsKY0wcs5ZZpEy/vOO8IdNh7TLXCSS/6+F8okZhobXMjDG+spZZXxo8zT3HWieQoiKoqoKjR/2OxBgTp/pFy0xEAsBa4GNV/bTf8UTMoAkQSHGdQKZc5Hc0J86nPgVZWRBl1ywaY2JHf2mZ3Qhs9juIiAskQVEMdgKZOROWLoW03t3d2RjTP4jIIhHZIiLbROTWEOUiIr/yyjeIyMldrSsiF4vIJhFpEZGI9QLyPZmJSDFwDnCf37H0iSHTYedr8PNJ8Lsz4YmvR3+HkMZGN+5jZaXfkRhjesk7QnYXsBiYCFwmIhPbLbYYKPEeXwJ+0411NwKfBV6JZPy+JzPgF8A3gSjfo3fTvBvg1K/DqP+A5gavQ8gHfkd1fMrKYNIkeOwxvyMxxvTeLGCbqm5X1QbgYeC8dsucBzyozmogR0QGd7auqm5W1S2RDt7XZCYinwb2qeq6Lpb7koisFZG1TU1NfRRdhOSOgE9+By64B867y80r2+BvTMdrkDfOnvVoNKY/S2zdj3qPL7UrHwrsDpr+yJvXnWW6s25E+d0BZD7wGRE5G0gFskXkT6p6TL92Vb0XuBfcQMN9H2aEFIyDhEQo2wiTL/Q7mt5LSYHcXLvWzJj+rUlVOztnFWoE9Pb723DLdGfdiPK1Zaaqt6lqsaqOBC4F/tk+kcW0xBQoGA9l7/gdyfGza82MiXYfAcHjjRUDe7q5THfWjaj+cM4svhVNgfKNfkdx/GxIK2Oi3RtAiYiMEpFkXANjebtllgNXer0a5wBVqrq3m+tGlN+HGf9NVV8CXvI5jL5XOBnefghqKiCzwO9oeu/22/2OwBhzHFS1SUSuB54FAsAyVd0kItd55fcATwFnA9uAOuDqztYFEJELgP8GCoAnReQtVV14ouO3m3P6bfvL8OBn4IrH3O1ijDEmAuzmnCayirybWpZF+aHG8nJ47jloaPA7EmNMHLJk5rf0PMgeGv2dQJ58EhYuhD19es7XGGMAS2b9Q+Hk6O8EUljonq0TiDHGB5bM+oOiKVCxBRqjeNT5oiL3bNeaGWN8YMmsPyiaDNoMFe+56aOHYc1vobnR37h6wlpmxhgfWTLrD4qmuufW82ZP3ARPfxO2rfAvpp6yIa2MMT6yZNYf5I6CpAx33mzDX2Hj39383av9jasnkpNdJ5DL42cAF2NM/2HXmfUX950FR6ugugwGjYemekhKg2ue8TsyY0wMsOvMTN8omgyVW9y5swt+CyNPhY/fdEktWqxZ41pnxhjTxyyZ9ReDp7nnRXdA3igYPgea66PrrtS/+AXcdJPfURhj4lC/GZsx7k1dArkjYdRpbnrYHPe8axUMn+1bWD1iI+cbY3xiLbP+IikVRn8CxLstUGYB5I2B3Wv8jasnioqguhrq6vyOxBgTZyyZ9WfD58Ku1dDS4nck3WPXmhljfGLJrD8bPgeOHID97/sdSfcEJ7O6Oqiq8jceY0zcsGTWnw2f6553rere8vXV8OYf4cHz4Z2/RS6ucObPh3XrYOpU+NGPICcHHnus7+MwxsQdS2b92cAxkJ4Pu7px3mzF9+Gn42D59bD9JVj3h0hH19GAAXDyyZCeDuecA2lp8PLLfR+HMSbuWDLrz0TcocauWmY1FbDyZzBiLnzheZh9HXz0BjT5eG+xOXNgyhR4+23/YjDGxA1LZv3d8Dlw8EOo7qRTRevtY+YthWGzYMQ8aDoKe9b3TYzhlJbChg0QZaPMGGOijyWz/m7EfPe85anwy7Qms0LvrtUj5rnnna9FLq7uKC2F2lqoqPA3DmNMzLNk1t8Nme7ud7bmnvAtnLKNkDUYMga66Yx8yB/b/Y4jkXLNNVBT0zaivjHGRIgls/5OBOZ8zd3rbPuLoZcp3+juVh1sxDzvGrXmyMcYTloaJNogM8aYyLNkFg0mfxYyBsGquzuWNTW4u1QXtU9m86H+MJRv6psYw/ne99zDGGMiyJJZNEhMgVOuhW3PQ8XWY8sqt0BLY8eWWes1ajtf75sYw3nzTXj4YX9jMMbEPEtm0WLmNRBIcefOgpV5nT+Kphw7P2cYDBjePzqBbN0KR474G4cxJqZZMosWmQUw9WJ4+yGoO9A2v3yjS3J5YzquM2Ke6wTiZ9f40lI3tuQmnw93GmNimiWzaDLnq9BYB+v/2DavfCMMmgCBEB0tRsyF2grYv63vYmxv6lT3bBdPG2MiyJJZNCmcBMPnwdrfu9aOqjvM2L7zR6vWa9T8PG82ZgyMHw9NTf7FYIyJeZbMos0pX3Ajgmz/J9SUQ11l28XS7Q08CTIK4P3n+jbGYAkJsHkzfPnL/sVgjIl5lsyizYTPuAT1xv1BnT/CtMxEYPoV8N6TsG9z38VojDF9zJJZtElMhpOvhK3PwNan3bzCSeGXn7cUkjPg5R/3TXyhPPOMO9y4e7d/MRhjYpols2g04/PufNnaZTBgGKTlhl82PQ9mfxk2/QPK3+2zEI+RnQ3bt8Pf/w6rVsHq1dZV3xhzQlkyi0Y5w2HsItCWjhdLhzL3ekjO9K91NmUKJCfD178O8+bBzTdbV31jzAllA+dFq1O+4A4zdnaIsVVr62zlT+Hdx2Hv266lll8C/+svkY81K8vdgfrjj910cTFM6kbcxhjTTaJRdq+pjIwMra2t9TsM/7W0wGt3wuQLIXdk18vXHYBfTIWGapAE19Oxcqu7meewWREPt4M9e9yI+mPH9v22jYlDIlKnqhl+xxEplsziybYX4NAuGH8uJKXBnZNg1H/Akj/1bRwtLTB8OMyaBY8+2rfbNiZOxXoys3Nm8eSkT7oxHjMLICXTHarc/ATs/6Bv40hIgEsugSeegP37+3bbxpiYZMksns36MgSSYNVdfb/tK6+ExkZ45JG+37YxJuZYMotnWYUwdQm89WeorezbbZeWwuTJ8OCDfbtdY0xMsmQW7+Ythaaj8MZ9fbtdEbjiCvjXv9p6ORpjTC/5nsxEZJiIvCgim0Vkk4jc6HdMcaVgHIxd7O6TVl99fK+lCnvech08uuPaa2HXLhg6FG69FWbMgDvvPL4YjDFxyfdkBjQBN6vqBGAO8DURmehzTPHlE7fAkYOw+p6ulw1nz1tw/6fg3k/Aqz/r3jp5eS6RAeTmutbaN74BTz/d+ziMMXGp33XNF5HHgV+r6vOhyq1rfoQ8dBnseA1uervz4bHaqzsAK74H6x6AjHzIGQFl78BXV8HAEDcM7cyRIzB7NpSVufufDR7cs/WNMWFZ1/w+JCIjgenAmnbzvyQia0VkbZPdFysyzrgd6qu637NRFTY8Ar8+Bd78o7tx6NJ17pq1QDI8eXPP73CdlgYPP+wupr7llp7XwRgTt/pNy0xEMoGXgR+qatgraa1lFkGPXOUurL5xA2QMDL/cwZ3wxE3wwT9h6Aw495dQFHRPtTX3wtO3wGfvg6kX9zyOZ59158/y8+G734V9+2DiRLjuOki0EdiM6Y1Yb5n1i2QmIknAE8Czqvrzzpa1ZBZBFVvg7jkw6QLIHwcV77mejqWXwbizISEA634Pz/2nW/7M77gLrxMCx75OSzPc90mo2g2ll7rzaeWbYMh0d9H22EUQSITmRjciSUIiZA9189qbMwe2bXMXV8+dCw88ACUlkX8vjIkxlswiHYCIAA8AB1T1pq6Wt2QWYY9dB28/BAjkjnAJ5/DHLtkMKIbda2D06fCZX0POsPCvs3cD/G6B69RRNMUlx+0vQfUeyBoMKVlwYDu0eIeNJeC2kTrAm8aN9J9Z6B6v74RfLoemZvjpF2HqaFi7FTbtgMajLummCHxiBoyb7NZJSnOHPAPJ0NLo6tJUDw210FDjem8e/tgl1KqPICnd216Bt04zaLObn5rjziWmDnCxp2RBbQXsfcsN3Fy7H5JSITHVLZc32p0zTMuFmn3uruDVZW1/tzS6G62WXuruggCuF2hdpTsPeeSAiy9nuBtHM5AU+n1uPAKBFDeqSnc1HnXbqa1070l6nrvha0qW+7zaUw09v/0y1Xvd+dKK9yCzCAZPhYEloX+k9ERzI3y8zn1GeaPdANnJme69PLAdave5zy17iNtuYvLxbS/YkUOw83X3PS2cBLmjOn+va/a573nl+5A5qC2m1GxIyXbfkfoaOFrlvgd71ru6lW90n2Oa9z3LGeE+9/yTYMR8SEw57qp0J5mJyCLgl0AAuE9V72hXLl752UAd8HlVfbOzdUUkD/gLMBLYAVyiqgePu0LtY+8HyexUYCXwDtDap/t2VX0q1PKWzCKs8Ygb3ipvNCSnux361mfhX/e61tUZt8GMq7veuYHbwadkte1cmpvcTUXf+n9u/fwSt7PTZpdQDu50SUYVUPdPX1MG1eVugOTDLbCyHhamQqLAs0dhdcOx20wCbsmCJIFNjVDT7vudKTDJSwzvNEJWJowdASPHQNMRqKnwkk2Ta3FKgntPGmpC1zGQDIMmup1W4xGXVGsrXF1aGoMWFNdBJrPI7eQaj8Cu1938IdNc4jq0G5rrQ29jYInbRka+29lV7Xafx4EPXbItmux+NKTlup1/c6N7z+oOQN1+11v1aJXbOTeFuZdcQiIkZbT9CGisdYm/qd4l8bRcr5PPcDe4dWYRHPzQ7YjLN7nttJeY5pJlINntkFWhucHFJwneD4AU99rZxe4HU3IGNNa5be/fBjte7fj+J6a697oDca+RN8q9Xv1h93nUHXDbSs1xSaX+sPuca/a573hCgvtBlZbjfmxlDnLvbdkGd6ulVkkZkD7Qe2/q3Hckc5BLpvU1UP5O6Pe2M3ljXOJvaYajh1ysB3e01Tl3FCy6A8Yu7N7/XRhdJTMRCQBbgbOAj4A3gMtU9d2gZc4GluKS2Wzgl6o6u7N1ReT/4hord4jIrUCuqn6r1xUJF7/fyaynLJnFKVW3U2l9ADQ3g0rbL//du2H9elh8httRLV4Cb7bbucwshWcfdb/sZ58O725287OyIDMTFi+G++938y680O080tJcq6mlAWZNgyVnw9HDcPfDkDbw2MOsc+a412iohx99162Tmu120AkB11tz3jyoq4P//on7Vb5/m4snPQ9mzYAZJ0NTEix/xSWI6r2uPvXVMAzIqQPJh/dTIavI7fgP74HDe2FEExSmQW0CvNfitpuc7hJeYhrMnwKjR0EVsPp9l8BaW6qNtTBrJOQkwu5K2FDmEo0EXAJsrIOpWcA+2LoDtje4FmNmoUsA2UPgys9DySxY9SI8/z8ursYj7sdBSxOcfpJ7nz84AFvLXWJraXIxHD0Ek+shoLCrCSoSIWWAa+Hml0B6AZw/Gw5sg1VvQnkTpOe7cUbra6CxGhaOc0no1bWwY4+rc0qmV3+FuQPdZ7elHg4HvNZoAGiBtESYN8S1oF99D46mutZR3hj3HQvUwvQs96Ng9W6oaXLfi4ZqrxWdBksugzEL4IW3ofxjt2x9tUu8BRkwd5zb5tNroTEA2YPdZwRw0knuuwNwzz1Qvd/9yNv6tPuRNe0UuPl+9170QjeS2Vzgu6q60Ju+DUBVfxS0zG+Bl1T14Ia2vgAACQVJREFUIW96C3A6rtUVct3WZVR1r4gM9tYf16tKdEZVo+qRnp6uxnTLoUOqlZXHPg4daiuvqFBduVL1N79RXbpU9YtfVL377rbys85SnTBBdfRo9xg1SvVb32orDwRUvTT778eNN7qympqOZaD67W+78vLy0OV33OHKt20LXX7XXa58/frQ5Q8+6MpfeSV0+WOPufKnngpd/sILrvwvfwldvnq1K7/3t6HLN21y5XfeGbp81y5X/v3vhy6v3Kd6tFr1lltClzc1ufW/8pWOZampbZ/N5Zd3LC8oaCs/77yO5aNHt5UvWNCxvLS0rXzWrI7lp57aVj5hQsfyRYvayouLO5ZffHFb+YABHctnpKs+c7v2FlAPrA16fEmD9q3ARbjDg63TV+Aukwpe5gng1KDpFcDMztYFDrV7jYPB0yfqYS0zYyJBFWprXesxeF5KSltL72CI0wbp6a68uRkOHepYnpEBqanQ1ARVVR3LMzPdNhob4fDhjuVZWe6u3w0NUB1ixJfsbEhKgvp6d4lEuPKjR1392hswwPU4PXLEtT7by8mBQMCVHQlxuDM31x3yq61122gvL8+1lmtqXIztDfR64VZXuzoGE3Hrg3tvGhuPLU9IcNsH9962vwwoEHDxhytPTHT1B/fZBX/24N637Gz398GDHUfKSU52nw/AgQMdL21pPAw5A11Lvxe60TK7GFioqtd601cAs1R1adAyTwI/UtVXvekVwDeB0eHWFZFDqpoT9BoHVbUHF7N2j/VzNiYSRFxiCSchoW3HG0og0Hl5YmLn5UlJnZcnJ3denpLiHuGkprpHOGlp7hFOerp7hJOR4R7hZGZ2/v62JoVwsrtICK1JqbflOTmdl+d2sS9vTbrH6OTzOjE+wh3IblUM7OnmMsmdrFsuIoO17TDjvhMatadfXTRtjDHGN28AJSIySkSSgUuB5e2WWQ5cKc4coEpV93ax7nLgKu/vq4DHIxG8tcyMMcagqk0icj3wLK57/TJV3SQi13nl9wBP4XoybsN1zb+6s3W9l74DeEREvgDsAnoxkkLX7JyZMcbEgVi/aNoOMxpjjIl6lsyMMcZEPUtmxhhjop4lM2OMMVHPkpkxxpioZ8nMGGNM1LNkZowxJupZMjPGGBP1ou6iaRFpAcLckKlLiUBTl0vFnnisdzzWGeKz3vFYZ+h5vdNUNWYbMFGXzI6HiKxV1Zl+x9HX4rHe8VhniM96x2OdIX7rHU7MZmljjDHxw5KZMcaYqBdvyexevwPwSTzWOx7rDPFZ73isM8RvvUOKq3NmxhhjYlO8tcyMMcbEIEtmxhhjol7cJDMRWSQiW0Rkm4jc6nc8kSAiw0TkRRHZLCKbRORGb36eiDwvIu97z7l+x3qiiUhARNaLyBPedDzUOUdE/iYi73mf+dw4qffXve/3RhF5SERSY63eIrJMRPaJyMageWHrKCK3efu2LSKy0J+o/RUXyUxEAsBdwGJgInCZiEz0N6qIaAJuVtUJwBzga149bwVWqGoJsMKbjjU3ApuDpuOhzr8EnlHV8UAprv4xXW8RGQrcAMxU1clAALiU2Kv3H4BF7eaFrKP3P34pMMlb525vnxdX4iKZAbOAbaq6XVUbgIeB83yO6YRT1b2q+qb3dzVu5zYUV9cHvMUeAM73J8LIEJFi4BzgvqDZsV7nbOA04H4AVW1Q1UPEeL09iUCaiCQC6cAeYqzeqvoKcKDd7HB1PA94WFXrVfVDYBtunxdX4iWZDQV2B01/5M2LWSIyEpgOrAEKVXUvuIQHDPIvsoj4BfBNoCVoXqzXeTRQAfzeO7x6n4hkEOP1VtWPgZ8Cu4C9QJWqPkeM19sTro5xt38LJV6SmYSYF7PXJIhIJvB34CZVPex3PJEkIp8G9qnqOr9j6WOJwMnAb1R1OlBL9B9a65J3nug8YBQwBMgQkcv9jcp3cbV/CydektlHwLCg6WLcoYmYIyJJuET2Z1V91JtdLiKDvfLBwD6/4ouA+cBnRGQH7vDxAhH5E7FdZ3Df6Y9UdY03/Tdccov1en8S+FBVK1S1EXgUmEfs1xvC1zFu9m+diZdk9gZQIiKjRCQZd7J0uc8xnXAiIrhzKJtV9edBRcuBq7y/rwIe7+vYIkVVb1PVYlUdiftc/6mqlxPDdQZQ1TJgt4iM82adCbxLjNcbd3hxjoike9/3M3HnhmO93hC+jsuBS0UkRURGASXAv3yIz1dxMwKIiJyNO7cSAJap6g99DumEE5FTgZXAO7SdP7odd97sEWA4bmdwsaq2P7kc9UTkdOB/q+qnRWQgMV5nEZmG6/SSDGwHrsb9QI31en8PWILrvbseuBbIJIbqLSIPAacD+UA58B3gH4Spo4j8H+Aa3Htyk6o+7UPYvoqbZGaMMSZ2xcthRmOMMTHMkpkxxpioZ8nMGGNM1LNkZowxJupZMjPGGBP1LJkZ4xGRZhF5K+hxwkbUEJGRwSOgG2NOrES/AzCmHzmiqtP8DsIY03PWMjOmCyKyQ0R+LCL/8h4nefNHiMgKEdngPQ/35heKyGMi8rb3mOe9VEBEfufdi+s5EUnzlr9BRN71Xudhn6ppTFSzZGZMm7R2hxmXBJUdVtVZwK9xI8ng/f2gqk4F/gz8ypv/K+BlVS3FjZe4yZtfAtylqpOAQ8CF3vxbgene61wXqcoZE8tsBBBjPCJSo6qZIebvABao6nZvIOcyVR0oIpXAYFVt9ObvVdV8EakAilW1Pug1RgLPezdWRES+BSSp6g9E5BmgBjdc0T9UtSbCVTUm5ljLzJju0TB/h1smlPqgv5tpO2d9Du5O6DOAdd5NJ40xPWDJzJjuWRL0vMr7+3XcSP0AnwNe9f5eAXwFQEQC3l2hQxKRBGCYqr6Iu8FoDm7QXGNMD9gvQGPapInIW0HTz6hqa/f8FBFZg/sBeJk37wZgmYjcgrvr89Xe/BuBe0XkC7gW2Fdwd0UOJQD8SUQG4G6yeKeqHjphNTImTtg5M2O64J0zm6mqlX7HYowJzQ4zGmOMiXrWMjPGGBP1rGVmjDEm6lkyM8YYE/UsmRljjIl6lsyMMcZEPUtmxhhjot7/B4jbp52cm6kiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig, ax = plt.subplots(1, 2, figsize=(10, 3))\n",
    "plt.figure(figsize= (6,12))\n",
    "plt.subplot(211)\n",
    "plt.plot(h.history['accuracy'], label='accuracy')\n",
    "plt.plot(h.history['val_accuracy'], label='val_accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "\n",
    "#plt.xticks(epochs,\" \")\n",
    "_ = plt.legend()\n",
    "\n",
    "\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(h.history['loss'], label='loss')\n",
    "plt.plot(h.history['val_loss'], label='val_loss')\n",
    "plt.ylabel('MSE Loss')\n",
    "plt.xlabel('Epochs')\n",
    "\n",
    "_ = plt.legend()\n",
    "ax2 = plt.gca().twinx()\n",
    "ax2.plot(h.history['lr'], color='r', linestyle='dashed')\n",
    "ax2.set_ylabel('lr',color='r')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(94, 3) (94, 3)\n"
     ]
    }
   ],
   "source": [
    "Y2_pred = model.predict(X2_test)\n",
    "print(Y2_pred.shape, Y2_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² score %N :  -1.613130923301938\n",
      "R² score pH :  -2.108750706582395\n",
      "R² score %sand :  -12.153247920532396\n",
      "\n",
      "R² CNN: nan\n",
      "RMSE score %N :  1.23\n",
      "RMSE CNN average: 1.330\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-56-2342acd03527>:1: RuntimeWarning: invalid value encountered in sqrt\n",
      "  model_R2 = np.sqrt(r2_score(Y2_test, Y2_pred))\n"
     ]
    }
   ],
   "source": [
    "model_R2 = np.sqrt(r2_score(Y2_test, Y2_pred))\n",
    "\n",
    "# Calculate mean squared error \n",
    "model_RMSE = np.sqrt(mean_squared_error(Y2_test, Y2_pred))\n",
    "\n",
    "\n",
    "print('R² score',target_features[0],': ',r2_score(Y2_test[:,0], Y2_pred[:,0]))\n",
    "print('R² score',target_features[1],': ',r2_score(Y2_test[:,1], Y2_pred[:,1]))\n",
    "print('R² score',target_features[2],': ',r2_score(Y2_test[:,2], Y2_pred[:,2]))\n",
    "#print('R² score',target_features[3],': ',r2_score(Y2_test[:,3], Y2_pred[:,3]))\n",
    "print('')\n",
    "print('R² CNN:', model_R2)\n",
    "print('RMSE score',target_features[0],': ',np.round(np.sqrt(mean_squared_error(Y2_test[:,0], Y2_pred[:,0])),2))\n",
    "print('RMSE CNN average: %5.3f' % np.round(model_RMSE,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(Y2_test[:,0], Y2_pred[:,0], label = target_features[0], color = 'r')\n",
    "plt.scatter(Y2_test[:,1], Y2_pred[:,1], label = target_features[1], color = 'b')\n",
    "plt.scatter(Y2_test[:,2], Y2_pred[:,2], label = target_features[2], color = 'k')\n",
    "#plt.scatter(Y2_test[:,3], Y2_pred[:,3], label = target_features[3], color = 'g')\n",
    "plt.legend()\n",
    "plt.xlabel('actual values')\n",
    "plt.ylabel('predicted values')\n",
    "plt.title('CNN - 3 variables prediction')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(94, 3) (94, 3)\n"
     ]
    }
   ],
   "source": [
    "Y2_origpred = original_model.predict(X2_test)\n",
    "print(Y2_origpred.shape, Y2_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² score %N :  -0.04661099525158896\n",
      "R² score pH :  -0.08472233755908531\n",
      "R² score %sand :  -0.04207622349009843\n",
      "\n",
      "R² CNN: nan\n",
      "RMSE score %N :  0.78\n",
      "RMSE CNN average: 0.780\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-59-749d2fe7346d>:1: RuntimeWarning: invalid value encountered in sqrt\n",
      "  origmodel_R2 = np.sqrt(r2_score(Y2_test, Y2_origpred))\n"
     ]
    }
   ],
   "source": [
    "origmodel_R2 = np.sqrt(r2_score(Y2_test, Y2_origpred))\n",
    "\n",
    "# Calculate mean squared error \n",
    "origmodel_RMSE = np.sqrt(mean_squared_error(Y2_test, Y2_origpred))\n",
    "\n",
    "\n",
    "print('R² score',target_features[0],': ',r2_score(Y2_test[:,0], Y2_origpred[:,0]))\n",
    "print('R² score',target_features[1],': ',r2_score(Y2_test[:,1], Y2_origpred[:,1]))\n",
    "print('R² score',target_features[2],': ',r2_score(Y2_test[:,2], Y2_origpred[:,2]))\n",
    "#print('R² score',target_features[3],': ',r2_score(Y2_test[:,3], Y2_origpred[:,3]))\n",
    "print('')\n",
    "print('R² CNN:', origmodel_R2)\n",
    "print('RMSE score',target_features[0],': ',np.round(np.sqrt(mean_squared_error(Y2_test[:,0], Y2_origpred[:,0])),2))\n",
    "print('RMSE CNN average: %5.3f' % np.round(origmodel_RMSE,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(Y2_test[:,0], Y2_origpred[:,0], label = target_features[0], color = 'r')\n",
    "plt.scatter(Y2_test[:,1], Y2_origpred[:,1], label = target_features[1], color = 'b')\n",
    "plt.scatter(Y2_test[:,2], Y2_origpred[:,2], label = target_features[2], color = 'k')\n",
    "#plt.scatter(Y2_test[:,3], Y2_origpred[:,3], label = target_features[3], color = 'g')\n",
    "plt.legend()\n",
    "plt.xlabel('actual values')\n",
    "plt.ylabel('predicted values by published CNN')\n",
    "plt.title('CNN - 3 variables prediction')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Spectral reduction and baseline correction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 FTIR spectra baseline correction by differentiation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2542\n",
      "(467, 2542)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Get wavelengths\n",
    "wavelenghts_list = df_FTIR.columns.tolist() \n",
    "wavelenghts = np.array(wavelenghts_list)\n",
    "print(len(wavelenghts))\n",
    "\n",
    "\n",
    "# Calculate second derivative. That should average baselines\n",
    "X2 = savgol_filter(X, 17, polyorder = 2,deriv=2)\n",
    "print(X2.shape)\n",
    "# Plot second derivative\n",
    "plt.figure(figsize=(8,4.5))\n",
    "with plt.style.context(('ggplot')):\n",
    "    plt.plot(wavelenghts, X2.T)\n",
    "    plt.xlabel('Wavelength (nm)')\n",
    "    plt.ylabel('D2 Absorbance')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PLS R² after spectra differentiation: -1.180 vs. -0.5954643262899628 for primitive wave functions\n",
      "PLS RMSE after spectra differentiation: 1.123 vs. 0.9609387553175943 for primitive wave functions\n"
     ]
    }
   ],
   "source": [
    "y2 = df_composition[target_feature].values\n",
    "X3_train, X3_test, y3_train, y3_test = train_test_split(X2, y2, test_size = 0.2, random_state = 42)\n",
    "\n",
    "pls = PLSRegression(n_components=10)\n",
    "model_pls = pls.fit(X3_train, y3_train)\n",
    "\n",
    "y3_c = model_pls.predict(X3_test)\n",
    "\n",
    "\n",
    "# Calculate scores for calibration and cross-validation\n",
    "score3_c = r2_score(y3_test, y3_c)\n",
    "\n",
    "\n",
    "\n",
    "# Calculate mean squared error for calibration and cross validation\n",
    "rmse3_c = np.sqrt(mean_squared_error(y3_test, y3_c))\n",
    "\n",
    "print('PLS R² after spectra differentiation: %5.3f'  % score3_c,'vs.',score_c,'for primitive wave functions')\n",
    "\n",
    "print('PLS RMSE after spectra differentiation: %5.3f' % rmse3_c,'vs.',rmse_c,'for primitive wave functions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PLS regression for FTIR derivative function')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y3_test, y3_c, label = target_feature)\n",
    "plt.legend()\n",
    "plt.xlabel('actual values')\n",
    "plt.ylabel('predicted values')\n",
    "plt.title('PLS regression for FTIR derivative function')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 FTIR spectra reduction\n",
    "\n",
    "I select only the regions containing the peaks corresponding to C=O and C=C bonds and filter away the rest of the spectrum containing either a flat or a noisy curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "FTIR_spectra = pd.read_csv('MIR_spectra_transposed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2542\n"
     ]
    }
   ],
   "source": [
    "print(len(FTIR_spectra.labda))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FTIR spectrum example\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Wavelengths (cm-1)')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wavelenght_lower_limit = 500\n",
    "wavelenght_upper_limit = 2000\n",
    "print('FTIR spectrum example')\n",
    "plt.figure(figsize= (6,6))\n",
    "\n",
    " \n",
    "plt.plot(FTIR_spectra['labda'],FTIR_spectra['icr033603'])\n",
    "plt.plot(FTIR_spectra['labda'],FTIR_spectra['icr048642'])\n",
    "plt.axvline(x= wavelenght_upper_limit , ymin=0, ymax=1, color='r', linewidth = 1)\n",
    "plt.axvline(x= wavelenght_lower_limit , ymin=0, ymax=1, color='r', linewidth = 1)\n",
    "plt.text(3000, 2.25, 'range selected \\n for wavelenghts between \\n red lines')\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlim(4000,400)\n",
    "plt.ylabel('Absorbance (A.U.)', fontsize=14)\n",
    "plt.xlabel('Wavelengths (cm-1)', fontsize = 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Wavelengths (cm-1)')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_MIR_FTIRspectra =  FTIR_spectra[FTIR_spectra['labda'] > wavelenght_lower_limit]  \n",
    "df_MIR_FTIRspectra = df_MIR_FTIRspectra[df_MIR_FTIRspectra['labda'] < wavelenght_upper_limit] \n",
    "\n",
    "plt.plot(df_MIR_FTIRspectra['labda'],df_MIR_FTIRspectra['icr042897']) \n",
    "plt.xlim( wavelenght_upper_limit+100,wavelenght_lower_limit-100)\n",
    "plt.title('Reduced MIR spectrum', fontsize=16)\n",
    "plt.ylabel('Absorbance (A.U.)', fontsize=14)\n",
    "plt.xlabel('Wavelengths (cm-1)', fontsize = 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_FTIR_reindexed = df_MIR_FTIRspectra.set_index('labda')\n",
    "mid_infrared_df = df_FTIR_reindexed.T.reset_index()\n",
    "mid_infrared_df = mid_infrared_df.rename(columns={'index': 'SSN'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before selection (1888, 1059)\n",
      "after selection (467, 1059)\n"
     ]
    }
   ],
   "source": [
    "sampleslist = df_composition['SSN'].tolist()\n",
    "print('before selection',mid_infrared_df.shape)\n",
    "mid_infrared_df = mid_infrared_df[mid_infrared_df['SSN'].isin(sampleslist)]\n",
    "print('after selection',mid_infrared_df.shape)\n",
    "mid_infrared_df = mid_infrared_df.drop(['SSN'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(467, 1058) (467,)\n"
     ]
    }
   ],
   "source": [
    "X4 = mid_infrared_df.values\n",
    "y4 = df_composition[target_feature].values\n",
    "\n",
    "print(X4.shape, y4.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PLS R² after spectra reduction: -0.127 vs. -0.5967031493414543 for full spectra\n",
      "PLS RMSE after spectra reduction: 0.808 vs. 0.961311750836408 for full spectra\n"
     ]
    }
   ],
   "source": [
    "X4_train, X4_test, y4_train, y4_test = train_test_split(X4, y4, test_size = 0.2, random_state = 42)\n",
    "\n",
    "pls = PLSRegression(n_components=10)\n",
    "model_pls = pls.fit(X4_train, y4_train)\n",
    "\n",
    "y4_c = model_pls.predict(X4_test)\n",
    "\n",
    "\n",
    "# Calculate scores for calibration and cross-validation\n",
    "score4_c = r2_score(y4_test, y4_c)\n",
    "\n",
    "\n",
    "\n",
    "# Calculate mean squared error for calibration and cross validation\n",
    "mse4_c = mean_squared_error(y4_test, y4_c)\n",
    "\n",
    "print('PLS R² after spectra reduction: %5.3f'  % score4_c,'vs.',score_c,'for full spectra')\n",
    "\n",
    "print('PLS RMSE after spectra reduction: %5.3f' % np.sqrt(mse4_c),'vs.',rmse_c,'for full spectra')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Verify linear correlation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Law of Lambert Beer suggests linear correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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ln03geBBO6KBkmc8s1X30J/TXpeNUN2AQKv8xrYGabFYcnCj48jZymH02yJas36ZfqArfKSV6c8HGlK+ar2Gdo0ly9lNgwq7Hj4wIwy4/1iQraV20RNHQJ6sjanfILY5oZHyiUg2kjfwhR03FHDWeZD8Zd0QFfbuhsjF41M/IXSUpa5ZWK+B31y/SA6pKJ8CPTV7eosEpaofyTfxEpXZG7HxDpUQwGxEjofa/UA265HSsHwvnP1k+utZw2KOGKBEpZwMqwN4bxG5C52DmvcXjbJkidQcT9P0lb7CSzvteU3GivlO+J1scSBx1naGaIU9heWv5gOqHvIar+JagCXbNM641Z5td5ufbEZXhOR21qQGbGPzQJymRD+iiq8yByXOFr6vQtWx409lE7VEKk3+I2ZRZdmbzML58p2qY+gB45bQlhRJNVLDFLe1l8VJTWJ1fPL7e8LXGuDvFLY9zyxjhf5CS6V1882MSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Law of Lambert Beer suggests linear correlation')\n",
    "Image(filename='img/linear.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-192-0054089c12cb>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_PH['diff'] = df_PH['pH'] - df_PH['PH']\n"
     ]
    },
    {
     "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>SSN</th>\n",
       "      <th>pH</th>\n",
       "      <th>PH</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>icr068677</td>\n",
       "      <td>2.84</td>\n",
       "      <td>8.320</td>\n",
       "      <td>-5.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>icr068101</td>\n",
       "      <td>3.89</td>\n",
       "      <td>4.180</td>\n",
       "      <td>-0.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>icr068322</td>\n",
       "      <td>4.12</td>\n",
       "      <td>4.220</td>\n",
       "      <td>-0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>icr062195</td>\n",
       "      <td>4.13</td>\n",
       "      <td>4.190</td>\n",
       "      <td>-0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>icr068321</td>\n",
       "      <td>4.17</td>\n",
       "      <td>4.220</td>\n",
       "      <td>-0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>icr033393</td>\n",
       "      <td>8.59</td>\n",
       "      <td>9.205</td>\n",
       "      <td>-0.615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>icr075756</td>\n",
       "      <td>8.73</td>\n",
       "      <td>7.120</td>\n",
       "      <td>1.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>icr033467</td>\n",
       "      <td>9.05</td>\n",
       "      <td>9.214</td>\n",
       "      <td>-0.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>icr075757</td>\n",
       "      <td>9.38</td>\n",
       "      <td>9.720</td>\n",
       "      <td>-0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>icr072857</td>\n",
       "      <td>9.92</td>\n",
       "      <td>9.860</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>467 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           SSN    pH     PH   diff\n",
       "311  icr068677  2.84  8.320 -5.480\n",
       "276  icr068101  3.89  4.180 -0.290\n",
       "290  icr068322  4.12  4.220 -0.100\n",
       "265  icr062195  4.13  4.190 -0.060\n",
       "289  icr068321  4.17  4.220 -0.050\n",
       "..         ...   ...    ...    ...\n",
       "166  icr033393  8.59  9.205 -0.615\n",
       "158  icr075756  8.73  7.120  1.610\n",
       "170  icr033467  9.05  9.214 -0.164\n",
       "159  icr075757  9.38  9.720 -0.340\n",
       "365  icr072857  9.92  9.860  0.060\n",
       "\n",
       "[467 rows x 4 columns]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_composition = df_composition.sort_values(by=['pH'])\n",
    "df_PH = df_composition[['SSN','pH','PH']]\n",
    "df_PH['diff'] = df_PH['pH'] - df_PH['PH']\n",
    "df_PH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'difference between the two labs measurement')"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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toMKTwyOF37Y/PDTMRYvn5lbDtUPSUQKo29GjlqQ9KaRqJStDrtW7sLoK8vhDald/5l3hlz24MqgXUBkkHQ6ca2YL48dnA5jZ+VmvabUXUF7vgbzuZ1lqZeaVvihDqvV9SJ+ondDbIku6ITyvITLpZZT+0uT1fmpk32mVPlHpVyHVDUnPkOovfDMN7PUyjLy5gap7oFT6xA7bb8fQxpHCz4lKn0rJoKdPqbDmY0cx7+PXBVXDFa26o0SjnyE03uhb7/xuVJ+omVf0S2wxo6+gxuqsRuCQFcHeImmNpMclPSXpaUlPBR8520zgwdTjh+Jt1cc/VdIqSas2bNjQ1IHOWbGOMy5fm5n5i+iKq9EVn0a22DZf0pEttTN/iHoSJQ27EGWea5cexbK3HxS0uEi7JAOdag33T6u1ClRe1UX6Fvu8792Rue8+RZmLiK5eEXUz/35p9NY6ryqg+hb9nBXR1daShXOo9Id9Buk2iDxZVYD90jbvfWSL8cTG6Cp9aHik4cw/7/wp6+o8yZcaqYYrykClf5teco1m/vU6ctRSdNVc1kez2QyjdrVtkVNvh7QBLAdOAXY1s53MbEcz26mAY9c6Y7d5t2Z2sZnNN7P5M2bMaPggK9YMclnG4K/0QS+7+QHOuHwtk7frY0qlnOUOhoZHas5vsmjeTLaM051YLTsPVIJGJ8O2X7q8QnT7OF1XrBnMvWLcafvoyvKixXN5+tmwK+HNZqNtFGYEZebJ555U5W0OzHRD62Cz5uRpZGBViJnTBrjwHQdlLtRSz0Clv6n68yfjxW762nzxUmuZzWRkfJ5JqXNi+pQKy95+UN1CvLp+/oiXzWh4WdAipC9wihyrEDIO4Cbg9WZWaHN/u6qAmrktbKdpAxXWLj2qlDinTurnmU2NNWwOVPrpE8Gvq1UNlNd3PrSh/eTDZnHF6sGmG2bzquJqUcZYj8znE9Z/u9YiO420W4SYPqXC0Mbm6viTKohpTVQ71RsRXXYvoXQ1XOj3Z8E+u3DZew8P2n/W6N3jD5nJTXdtaHu+srzqe9aIVgaCvYJoRtCfAM8l283sc01FsnW/2wF3A68HBoFfAe80szuyXtNMAVB0nV0Zli+eC+TPC1/PvrtPZeOmLaPtCo2MG1CcKyd937Omy8iSVR/ZSqHW6ICfdmukDvacFesKa9Ct1t8nNo9TA2xe/fWJr9wztwBPMu8iBrtNz5jzKsuCfXbhvseGxxTKtdp0ss7fvKk2ytTKSOWm2wCATwEbge2BHVM/LTGz54G/Bq4F7gS+lZf5N6sTutNNn1LJvcU+96robU/ervmqp4eeeJYlC+dw0eK5PPPc8+GZP9GVb9Ioes1tjzR87GQWyuqubK30VOjkzB9g9q5h51VSBVnG+5EYt8wf8uuvr7ntkcyMPd2GUt01cvniuZx82KyG2sSSEcKhVt7z+GjGntyJDQ4Nc8bla5n38etYsWYwt4faZjO+fvMDbc38odjun4mQO4BVtUqO8dDMHcCKNYMs+c6t49LLJn2VmDcRHYRNDxFyPGi8Mcw15+TDZo12g80aBFRWD5myeva0SzK9w9DGkTGjrkOn3OhVzU4d0codwA2Sjmr4iJ2k6myq9Impk8ptyKluqb/prvweTEUUUA8PDXfFCNKJ4pu/iDqx5Q0OKqt75BbomEFYzUgGKibp9fWbHxi9cGnlm5DcCUwb6N60yVN0jUZIAXAa8ENJwwV3A22LZdeu3+ZKaWSLUenvC2rNb7Z/Q1/qhY0MeGrFHtMGOqLKq1dsNstdHSspIEo59hZrqNG6VxipjhX77DKusRTdOSrprl6kugVA3O2zz8wGCu4G2hZZV8RPDo9w/nEHlNb//plNm1nynVs5Z8U6PtjCnEChRHQl9fgzz9V9ritOVl9tcrYX5cnhkbbeBTT6XRmvoS1DcffUy957eMPtCUWo9Ivli+dy0TvmNn0BWYsBV6weDJpKJFQ5Hd47SN56oYvmzazbf7qVr/DIZuOymx+gHdNlJXEOT7DJucZLcgfXOcPztrXHtIHRkeVlGqj0s3zxXC58x0E175qnVPq2GXeRdC4Yr/RLGks/uegA7jn/zYXOyzVz2kBuFfIOk6MZdhbNm1l4W0bRDcETvgCot0B2GR9SWhH7zvsSdXIGVbYplbBqvGYktYadWsuSjGJdNG9m5l3AtIFKzbrwRgZ+pQceZfXa+fUn3sSytx80msmmG3HHK/2q7/yLahtLOnZ86m0HZJ57T2wcGe1RVMYdWpHtfBO+AKh10lb3pW3XrJ2hpg1UxsT7qn12qZnRT59S6dgMqmyVfvH3xx045rOdNlAJns6hDMlozSJV+sXJh80ak5FXj2Jdesx+NS9yzj12P9YuPYrli+duc/7Xek21JLNLf1cWzZvJyrNex70XHD3mf8n2mdMGap6TWYURRAXGgn12GRPjyYfNCpq9N0v1nX9WTUC9qUOqJZlvOl/J8sTGEf747POFn5NFtvPVnQ1U0j7AQ2b2nKTXAgcC/25mQ4VFUbLk6iXLkoVzmpq7v+gRnYknh0dYuzTqeJX0MKkeTXtS3AWxU0c6J5ObhfaC6ZfYaSD7+dMGKkiMdhtMD9rJGoVcpuquiulBOvuc/f3CzouRzcZNd20YPR9qSa/ZkAxuSlcV5J3/WSO2m51vJq/N7d4Ljq55vhpw32PD2wysm7/XLrlLamatjVxrjp9ag85ENJ7jjofDV7dNZ75JuuYNNh3ZYqMFXxHjBoqcBwjCpoO+Apgv6aXAJcBVwDeANxcWxThKMoxGJUPCyxjkkz7Jak0/a2ztVtrsaMpkNGORYxOqR1VC+PS8W8xYesx+LS+cnXwpQ9aAblZ6OoBaM4vWS9dGhdzyJ8duZPGQdMFQ1KpT9Va+amR643oXbhAVEulZftNraFfva9X9j4/5vho0PI16rV44O9dYwzhtaHikparKZKqPMhauDxkIdouZHSxpCfCsmX1B0hozm1dYFIHmS9bcZNDOOde7BE2vCTwi6USiGUGPibeNzyiLQw6BJtcDqKVe9Ul6jvaQNW0bWdwka86d9OjSvBhrLV5f6zY5uRLPurpbsWZwmwU9Kn1i2QnbzpSYNTnW+cdF8TY7r0src5zkyUo7CS56R/bEWiFXw3lpUeu5yf6y5nfP0kjajPfC44m89AtZB6KV86HW5HvJHWkRd4S10jKvCqioUc0tf0cyusKG3AG8HHg/8HMz+6akvYHFZnZBc5E0r5UFYWrJ++BqzY5Y70PYZjH5GpJ9ZBUY1ZOMNZrpNnOiNLLGKdQuTBppi5g2UGHq5O0aWlO1GY1k0o2qVzA3ElM6tryqpbJiard6izNBczHXS9vtK30NtUnVKqirF6GB+heSRRUCrXyOTc8GGr94EvAyovex3sw2NRVFi4ouAPK+MFB7Tp28D6HW1XRaun6ykau1rIyxzC98o5ln6KyreVfJZWTWRRcqiVautvOuUju1wCtaSKaZlY6Nfh8S9aavhrA72iRDT7d11buraXZa9Op9NHsnl1UAhPQCOhr4EnBPHMPekt5nZj9oKpIOUqsBNWllPzOjV1Beg1yt3hhZX+56jWXV+631BS5zvdC8xahrxZL1frKu9ls9XqiQhsRmNPL5tSum9PlXdIFXtHrnaFY65q2hW2+fTw6PcNJhs7bpuFGdoafTK1l7Oq16rd/zjzsg964+eU16OdIjXjZjm+my69UOlDHNS0gbwIXAEWb2WxjtFnoN0PUFQN4Xpt6HkHUVEvrlzit8QrWSCdUTWrjkLf6S9EUPSY+yF79uRt7dQxGfXxnKKlyKlnXuQn465l0o5O0zOeZNd23Y5so7yZyr75oXzZuZeSFYfexkTEQjd+XV3VzT51e7zq2QAuDRJPOP/Q54tPBIxknWFybvC553FdJId0Vo7WqtzEwopHCpTgcj/2qq1eO1U73PuJuutjtRVvflrG6cibwLhYsWz81tAwi9s2+00T792ka+k1l5TzvPrcwCQNJx8Z93SPo+8C2i7/YJRKt3TWh5H8KCC24spLqi1au1Mk+UkBM5a4xCM20QnXZFHVIl1S1X252o2XM370IhpAo25M4+fR6G9NiqHhzWzPuq1q5zK+8O4JjU378HXhP/vQGYXlpEHWQ86t4bNZ71yUWmQ6ddUXfSZzxRNXPu1rtQCBn1n/f6WgU/ZDfa1rpI6aYLg8wCwMze3c5AukmnVVeUpd6JXHQ6dNIXp1c+427T6oVCvddnFfBZ9wHHH9I552wz6k4GJ2l7SadJ+qKkryQ/7QiuU9WbYbRXTOR0mMjvrdstmhdNPHfR4rlANN1Isg51I6+vntAOGi/gr7618TW0O0nIbKBfA14ILAR+ArwYCJ89qQZJJ0i6Q9IWSR2x3nAjFs2rP8NoL5jI6TCR39tEkLUMZ6uLpdQq+PMki890q5CRwGvMbJ6k28zsQEkV4Foza3qkkaQ/IVrW9MvAh8zCpvgpeiCYc647lT0IMqkiqjfRW1HHLFvTA8GA5N0PSdof+B9gdivBmNmdcVCt7MY516PKbKRPt0UtuODGugVAN3cMCCkALpY0Hfgo0VTQOwAfKzWqFEmnAqcCzJo1q12Hdc51sDIb6dN3ACHTNXRzx4CQReH/1cyeMLOfmNlLzGx3M/tSvddJukHS7TV+3tpIgGZ2sZnNN7P5M2ZsOxe3c673lNVIX922UE+3dwzIGwj2wbwXmtnn6vz/yGaDcs65PGWNG8kaB5DW7Gj3TpRXBbRj26JwzrkGlTFuJK8+XzDuAxSLljcQ7LyyDirpbcAXgBnANZLWmtnCso7nnHMhstoWuqGnTzNCxgEUzsy+a2YvNrPJZvYCz/ydc52g1wYAhvQCcs65ntBpc1KVzQsA55xL6aQ5qcoWMhfQzpIukrQq/rlQ0s7tCM4551x5QtoAvgI8Bbwj/nkK+GqZQTnnnCtfSBXQPmZ2fOrxeZLy10lzzjnX8ULuAIYlvTp5IGkB0L2TXzjnnAPC7gD+Erg0rvcX8DhwSqlROddl8haQd65T1S0AzGwtcJCkneLHT5UelXNdpN4C8s51qpBeQPdIugx4J9FiMM65lLwF5J3rZCFtAC8nWrhlV+Czkn4n6bvlhuVc9/AF5F23CikANhMtCrOZaBWv3wOPlhmUc90kaz74bp4n3vWGkALgKWA5cC9wipkdbmbvKzcs57pHr80f4yaOkF5AJwKvBv4K+AtJ/w381Mx+VGpkznWJXps/xk0cdReFH32i9DLgTcAZwO5m1vb7W18U3jnnGpe1KHxIL6ArJN0DfB6YCvxfYHrxITrnnGunkCqgzwMrzWy0n5ukyeWF5Jxzrh1CGoGXpzP/2M/LCMY551z75C0K/0JgJjAgaR7RNBAAOwFT2hCbc865EuVVAS0E3kU0+vdCthYATwEfLjcs55xzZctbFP5SokngjjezK4o8qKRlwDHAJuAe4N1mNlTkMZxzzuWr2wZQdOYfux7Y38wOBO4Gzi7hGM4553KENAIXzsyuM7Pn44c345PMOedc241LAVDlz4EfZP1T0qnJesQbNmxoY1jOOTexhQwEmyLpo5L+JX68r6S3BLzuBkm31/h5a+o5HwGeBy7L2o+ZXWxm881s/owZM8LelXPOubpCBoJ9FVgNHB4/fgj4NnB13ovM7Mi8/0s6BXgL8HoLnY/COedcYUKqgPYxs88QTQmNmQ2ztUtoUyS9Efh/wLFmtrGVfTnnnGtOSAGwSdIAYACS9gGea/G4/wjsCFwvaa2kL7W4P+eccw0KqQJaCvwQ2DNeGnIB0QCxppnZS1t5vXPOudaFLAp/vaRbgMOIqn5ON7M/lB6Zc865UoXcAQBsDzwRP//lkjCzn5YXlnPOubLVLQAkfRpYDNxBtCYwRO0BXgA451wXC7kDWATMMbNWG36dc851kJBeQL8DKmUH4pxzrr3y1gP4AlFVz0ZgraQfker+aWYfKD8855xzZcmrAkpWX18NXFX1Px+565xzXa7eegBIOt3MPp/+n6TTyw7MOedcuULaAE6pse1dBcfhnHOuzfLaAE4E3gnsLSldBbQj8FjZgTnnnCtXXhvAfwOPALsRrQmceBq4rcygnHPOlS+vDeB+4H62TgPtnHNuAumEFcGcc3h0PxIAAA8xSURBVM6NAy8AnHOuR3kB4JxzPSpkMrgFwLnAXvHzBZiZvaTc0JxzzpUpZDK4S4AziUYEby43HOecc+0SUgA8aWY/KD0S55xzbRVSANwkaRlwJWMng7ultKicc86VLqQAeGX8e35qmwGva/agkj4BvJVogZlHgXeZ2cPN7s8551zjQtYEPqKE4y4zs48CSPoA8DHg/SUcxznnXIa63UAlvUDSJZJ+ED9+uaT3tHJQM3sq9XAqPr20c861Xcg4gH8DrgX2iB/fDZzR6oElfUrSg8BJRHcAWc87VdIqSas2bNjQ6mGdc87FQgqA3czsW8QLwpvZ8wR0B5V0g6Tba/y8Nd7PR8xsT+Ay4K+z9mNmF5vZfDObP2PGjKA35Zxzrr6QRuBnJO1KXE0j6TDgyXovMrMjA2P4BnANsDTw+c455woQUgB8kGhJyH0krQRmAG9v5aCS9jWz38QPjwXuamV/zjnnGhfSC+gWSa8B5hBNA7HezEZaPO4FkuYQVSvdj/cAcs65tguZC2gK0V3AXmb2Xkn7SppjZlc3e1AzO77Z1zrnnCtGSCPwV4FNbF0Y5iHgk6VF5Jxzri1CCoB9zOwzwAiAmQ0TVQU555zrYiEFwCZJA2ztBbQPqTmBnHPOdaeQXkDnAj8E9pR0GbAAeFeJMTnnnGuDkF5A10laDRxGVPVzupn9ofTInHPOlSqkF9DXgJ8CPzMz76/vnHMTRGgvoBcBX5B0j6QrJJ1eclzOOedKFlIFdKOknwCvAI4gGrS1H/D5kmNzzjlXopAqoB8RTdn8c+BnwCvM7NGyA3POOVeukCqg24gGgu0PHAjsH3cLdc4518VCqoDOBJC0A/BuojaBFwKTyw3NOedcmUKqgP4GeDVwCNHEbV8hqgpyzjnXxUIGgm0PfA5YHS8G45xzbgIIaQM40Mx+kc7847EBzjnnulhIAbBf+oGk7Yiqg5xzznWxzAJA0tmSngYOlPSUpKfjx78H/rNtETrnnCtFZgFgZueb2Y7AMjPbycx2jH92NbOz2xijc865EoRUAX1E0smSPgogaU9Jh5Ycl3POuZKFFAD/RLQa2Dvjx3+MtznnnOtiIQXAK83sNOBZADN7AphUxMElfUiSSdqtiP0555wLF1IAjEjqZ+uKYDOALa0eWNKewBuAB1rdl3POucaFFAD/AHwXeIGkTwH/Bfx9Ace+CPg74oLFOedce4XMBXRZvCLY6+NNi8zszlYOKulYYNDMbpXy15eXdCpwKsCsWbNaOaxzzrmUkKkgAKYASTVQ0Eygkm4gmjSu2keADwNHhezHzC4GLgaYP3++3y0451xBQiaD+xhwAnAF0ZrAX5X0bTP7ZN7rzOzIjP0dAOwNJFf/LwZukXSomf1Pg/E755xrUsgdwInAPDN7FkDSBcAtQG4BkMXM1gG7J48l3QfM94XmnXOuvUIage8jmhE0MRm4p5RonHPOtU3mHYCkLxDV+T8H3CHp+vjxG4h6AhXCzGYXtS/nnHPh8qqAVsW/VxN1A038uLRonHPOtU1mAWBml7YzEOecc+0V0gbgnHNuAvICwDnnelRwASBpapmBOOeca6+6BYCkV0n6NXBn/PggSV8sPTLnnHOlCrkDuAhYCDwGYGa3An9aZlDOOefKF1QFZGYPVm3aXEIszjnn2ihkKogHJb0KMEmTgA8QVwc555zrXiF3AO8HTgNmAg8Bc+PHzjnnuljIegB/AE5qQyzOOefaKKQX0KWSpqUeT5f0lXLDcs45V7aQKqADzWwoeRAvCj+vvJCcc861Q0gB0CdpevJA0i6EryTmnHOuQ4Vk5BcC/y3pO/HjE4BPlReSc865dghpBP73eFH4I4iWhDzOzH5demTOOedKFVqVcxfwRPJ8SbPM7IHSonLOOVe6kEXh/wZYCvyeaASwiFYGO7Dc0JxzzpUp5A7gdGCOmT1W1EElnQu8F9gQb/qwmX2/qP0755yrL2gqCODJEo59kZl9toT9OuecCxBSAPwO+LGka4gWiAfAzD5XWlTOOedKFzIO4AHgemASsGPqp1V/Lek2SV9JjzNwzjnXHjKzsCdKU83smeAdSzcAL6zxr48ANwN/IGpM/gTwIjP784z9nAqcCjBr1qxD7r///tAQnHPOAZJWm9n8bbbXKwAkHQ5cAuxgZrMkHQS8z8z+qqDAZgNXm9n+9Z47f/58W7VqVRGHdc65npFVAIRUAS2n4BXBJL0o9fBtwO2t7M8551zjggaCmdmDktKbWl0R7DOS5hJVAd0HvK/F/TnnnGvQuKwIZmb/p5XXO+eca52vCOaccz0q9w5AUj+w3Mx8RTDnnJtgcu8AzGwzMCOu+nHOOTeBhLQB3AeslHQVMDoOwEcCO+dcdwspAB6Of/ooZgSwc865DhCyIMx50PhI4E6xYs0gy65dz8NDw+wxbYAlC+ewaN7M8Q7LOefGXd1eQJIOl/Rr4q6fkg6S9MXSIyvAijWDnH3lOgaHhjFgcGiYs69cx4o1g+MdmnPOjbtxGQncLsuuXc/wyNgxa8Mjm1l27fpxisg55zpHSAGAmT1YtanVkcBt8fDQcEPbnXOul4QUAGNGAkv6EC2OBG6XPaYNNLTdOed6yYQeCbxk4RwGKv1jtg1U+lmycM44ReScc50jsxeQpE+b2f8DjujWkcBJbx/vBeScc9vKXA9A0jrgYOAXZnZwW6PK4OsBOOdc47LWA8gbB/BDolW7pkp6ChDR9M0CzMx2KiVS55xzbZHXBnCOme0MXGNmO5nZjunf7QrQOedcOfIKgJ/Hv59qRyDOOefaK68KaJKkU4BXSTqu+p9mdmV5YTnnnCtbXgHwfuAkYBpwTNX/DPACwDnnulhmL6DRJ0jvMbNL2hRPLkkbgPubfPluRI3avczTIOLp4GkAvZUGe5nZjOqNed1AX2dmN9aq/oHuqwKStKpWN6he4mkQ8XTwNABPA8ivAnoNcCPbVv+AVwE551zXyywAzGxp/Pvd7QvHOedcu+RNBfHBvBd24ZKQF493AB3A0yDi6eBpAJ4GuW0AS+M/5wCvAK6KHx8D/NTM/qL88JxzzpUlpBfQdcDxZvZ0/HhH4Ntm9sY2xOecc64kIdNBzwI2pR5vAmaXEo1zzrm2CSkAvgb8UtK5cbXQL4BLyw2rWJLeKGm9pN9KOmu84ymLpK9IelTS7altu0i6XtJv4t/TU/87O06T9ZIWjk/UxZK0p6SbJN0p6Q5Jp8fbeyYdJG0v6ZeSbo3T4Lx4e8+kQUJSv6Q1kq6OH/dcGuQys7o/RNNCnx7/zAt5Taf8AP3APcBLgEnArcDLxzuukt7rn8af1e2pbZ8Bzor/Pgv4dPz3y+O0mAzsHadR/3i/hwLS4EXAwfHfOwJ3x++1Z9KBaMbeHeK/K0QXbYf1Uhqk0uKDwDeAq+PHPZcGeT+hawLfYmafj3/WhLymgxwK/NbMfmdmm4D/AN46zjGVwsx+CjxetfmtbL1juxRYlNr+H2b2nJndC/yWKK26mpk9Yma3xH8/TbR86Ux6KB0s8sf4YSX+MXooDQAkvRg4GvjX1OaeSoN6ggqALjcTSC9q/1C8rVe8wMwegShzBHaPt0/4dJE0G5hHdAXcU+kQV32sBR4FrjeznksDYDnwd8CW1LZeS4NcvVAAqMa2/K5PvWFCp4ukHYArgDPMLG9K8wmZDma22czmAi8GDpW0f87TJ1waSHoL8KiZrQ59SY1tXZ0GIXqhAHgI2DP1+MXAw+MUy3j4vaQXAcS/H423T9h0kVQhyvwvs61zVvVcOgCY2RDwY+CN9FYaLACOlXQfUbXv6yR9nd5Kg7p6oQD4FbCvpL0lTQL+jK2D2nrBVcAp8d+nAP+Z2v5nkiZL2hvYF/jlOMRXKEkCLgHutLGj1XsmHSTNkDQt/nsAOBK4ix5KAzM728xebGazib7zN5rZyfRQGgQZ71bodvwAbybqDXIP8JHxjqfE9/lN4BFghOiK5j3ArsCPgN/Ev3dJPf8jcZqsB9403vEXlAavJrp1vw1YG/+8uZfSATgQWBOnwe3Ax+LtPZMGVenxWrb2AurJNMj6qTsS2Dnn3MTUC1VAzjnnavACwDnnepQXAM4516O8AHDOuR7lBYBzzvUoLwCcyyDpx5LGfdFwSe+S9I/jHYebeLwAcG6Ck9Q/3jG4zuQFgOsakqZKuiae5/52SYvj7R+T9Kt428XxaODkCv4iST+N1wd4haQr47ngPxk/Z7akuyRdKuk2Sd+RNKXGsY+S9HNJt0j6djzXUPVzfizp0/Fc/HdL+t/x9jFX8JKulvTa+O8/xq9ZLekGSYfG+/mdpGNTu99T0g/jueqXpvZ1cny8tZK+nGT28X4/LukXwOEFJL+bgLwAcN3kjcDDZnaQme0P/DDe/o9m9op42wDwltRrNpnZnwJfIhr2fxqwP/AuSbvGz5kDXGxmBwJPAX+VPqik3YBzgCPN7GBgFdE887VsZ2aHAmcASzOekzYV+LGZHQI8DXwSeAPwNuDjqecdCpwEzAVOkDRf0p8Ai4EFFk38tjl+TrLf283slWb2XwFxuB603XgH4FwD1gGflfRpoqH9P4u3HyHp74ApwC7AHcD34v9dlXrtHRZPBSzpd0STfw0BD5rZyvh5Xwc+AHw2ddzDiBYMWRnfXEwCfp4RYzL53GrClk7dxNaCbB3wnJmNSFpX9frrzeyxOPYriaa8eB44BPhVHNcAWyc320w0IZ5zmbwAcF3DzO6WdAjR3D7nS7qOaIWnLwLzzexBSecC26de9lz8e0vq7+Rxcv5Xz4dS/VhEGfCJAWEmx9ic2v/zjL3bTsc3YlvnYxmN0cy2SEp/P2vFKOBSMzu7RhzPmtnmgHhdD/MqINc1JO0BbDSzrxNdoR/M1sz0D3G9/Nub2PUsSUk9+YlAdZXJzcACSS+N45gi6X81sP/7gLmS+iTtSXMrTb1B0Xq2A0SrWK0kmszs7ZJ2j+PaRdJeTezb9Si/A3Dd5ABgmaQtRDOe/qWZDUn6F6Lqk/uIpv9u1J3AKZK+TDRL5D+n/2lmGyS9C/impMnx5nOIZpgNsRK4N47xduCWJmL8L+BrwEuBb5jZKgBJ5wDXSeojSpPTgPub2L/rQT4bqOtpipaNvDpuQHaup3gVkHPO9Si/A3DOuR7ldwDOOdejvABwzrke5QWAc871KC8AnHOuR3kB4JxzPer/Ax6xDVe9fFjvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xph = np.arange(len(df_PH.index))\n",
    "plt.scatter(xph,df_PH['diff'])\n",
    "\n",
    "plt.axhline(y=.5, xmin=0, xmax=500,color='r', linewidth = 1)\n",
    "plt.axhline(y=-0.5, xmin=0, xmax=500,color='r', linewidth = 1)\n",
    "plt.xlabel('sample number')\n",
    "plt.ylabel('difference between the two labs measurement')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(403, 4)\n"
     ]
    }
   ],
   "source": [
    "df_PH = df_PH[df_PH['diff'] < 0.5]  \n",
    "df_PH = df_PH[df_PH['diff'] > -0.5]  \n",
    "print(df_PH.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(403, 2546)\n"
     ]
    },
    {
     "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>SSN</th>\n",
       "      <th>pH</th>\n",
       "      <th>PH</th>\n",
       "      <th>diff</th>\n",
       "      <th>3998</th>\n",
       "      <th>3997</th>\n",
       "      <th>3996</th>\n",
       "      <th>3994</th>\n",
       "      <th>3993</th>\n",
       "      <th>3991</th>\n",
       "      <th>...</th>\n",
       "      <th>412</th>\n",
       "      <th>411</th>\n",
       "      <th>409</th>\n",
       "      <th>408</th>\n",
       "      <th>406</th>\n",
       "      <th>405</th>\n",
       "      <th>404</th>\n",
       "      <th>402</th>\n",
       "      <th>401</th>\n",
       "      <th>399</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>icr062175</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.787581</td>\n",
       "      <td>0.788137</td>\n",
       "      <td>0.788412</td>\n",
       "      <td>0.788415</td>\n",
       "      <td>0.788219</td>\n",
       "      <td>0.787929</td>\n",
       "      <td>...</td>\n",
       "      <td>2.043272</td>\n",
       "      <td>2.042429</td>\n",
       "      <td>2.042811</td>\n",
       "      <td>2.044874</td>\n",
       "      <td>2.049155</td>\n",
       "      <td>2.055884</td>\n",
       "      <td>2.064780</td>\n",
       "      <td>2.075154</td>\n",
       "      <td>2.086159</td>\n",
       "      <td>2.096880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>icr068381</td>\n",
       "      <td>4.22</td>\n",
       "      <td>4.01</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.823309</td>\n",
       "      <td>0.823787</td>\n",
       "      <td>0.824382</td>\n",
       "      <td>0.824995</td>\n",
       "      <td>0.825560</td>\n",
       "      <td>0.826082</td>\n",
       "      <td>...</td>\n",
       "      <td>1.989507</td>\n",
       "      <td>2.004840</td>\n",
       "      <td>2.022895</td>\n",
       "      <td>2.040807</td>\n",
       "      <td>2.053985</td>\n",
       "      <td>2.058005</td>\n",
       "      <td>2.050506</td>\n",
       "      <td>2.031779</td>\n",
       "      <td>2.004429</td>\n",
       "      <td>1.973044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>icr068382</td>\n",
       "      <td>4.29</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.834674</td>\n",
       "      <td>0.835663</td>\n",
       "      <td>0.836557</td>\n",
       "      <td>0.837386</td>\n",
       "      <td>0.838197</td>\n",
       "      <td>0.839024</td>\n",
       "      <td>...</td>\n",
       "      <td>2.075219</td>\n",
       "      <td>2.072237</td>\n",
       "      <td>2.070053</td>\n",
       "      <td>2.068897</td>\n",
       "      <td>2.069716</td>\n",
       "      <td>2.073739</td>\n",
       "      <td>2.081663</td>\n",
       "      <td>2.092782</td>\n",
       "      <td>2.104237</td>\n",
       "      <td>2.110837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>icr068101</td>\n",
       "      <td>3.89</td>\n",
       "      <td>4.18</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>0.885259</td>\n",
       "      <td>0.885662</td>\n",
       "      <td>0.886088</td>\n",
       "      <td>0.886574</td>\n",
       "      <td>0.887100</td>\n",
       "      <td>0.887618</td>\n",
       "      <td>...</td>\n",
       "      <td>2.202463</td>\n",
       "      <td>2.206593</td>\n",
       "      <td>2.216625</td>\n",
       "      <td>2.232258</td>\n",
       "      <td>2.250889</td>\n",
       "      <td>2.267877</td>\n",
       "      <td>2.277374</td>\n",
       "      <td>2.274044</td>\n",
       "      <td>2.255781</td>\n",
       "      <td>2.226002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>icr062195</td>\n",
       "      <td>4.13</td>\n",
       "      <td>4.19</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.769183</td>\n",
       "      <td>0.769078</td>\n",
       "      <td>0.769096</td>\n",
       "      <td>0.769320</td>\n",
       "      <td>0.769739</td>\n",
       "      <td>0.770266</td>\n",
       "      <td>...</td>\n",
       "      <td>2.252506</td>\n",
       "      <td>2.260601</td>\n",
       "      <td>2.265513</td>\n",
       "      <td>2.267597</td>\n",
       "      <td>2.265792</td>\n",
       "      <td>2.259664</td>\n",
       "      <td>2.248696</td>\n",
       "      <td>2.230576</td>\n",
       "      <td>2.202916</td>\n",
       "      <td>2.166067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2546 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         SSN    pH    PH  diff      3998      3997      3996      3994  \\\n",
       "4  icr062175  4.20  4.00  0.20  0.787581  0.788137  0.788412  0.788415   \n",
       "5  icr068381  4.22  4.01  0.21  0.823309  0.823787  0.824382  0.824995   \n",
       "7  icr068382  4.29  4.06  0.23  0.834674  0.835663  0.836557  0.837386   \n",
       "0  icr068101  3.89  4.18 -0.29  0.885259  0.885662  0.886088  0.886574   \n",
       "2  icr062195  4.13  4.19 -0.06  0.769183  0.769078  0.769096  0.769320   \n",
       "\n",
       "       3993      3991  ...       412       411       409       408       406  \\\n",
       "4  0.788219  0.787929  ...  2.043272  2.042429  2.042811  2.044874  2.049155   \n",
       "5  0.825560  0.826082  ...  1.989507  2.004840  2.022895  2.040807  2.053985   \n",
       "7  0.838197  0.839024  ...  2.075219  2.072237  2.070053  2.068897  2.069716   \n",
       "0  0.887100  0.887618  ...  2.202463  2.206593  2.216625  2.232258  2.250889   \n",
       "2  0.769739  0.770266  ...  2.252506  2.260601  2.265513  2.267597  2.265792   \n",
       "\n",
       "        405       404       402       401       399  \n",
       "4  2.055884  2.064780  2.075154  2.086159  2.096880  \n",
       "5  2.058005  2.050506  2.031779  2.004429  1.973044  \n",
       "7  2.073739  2.081663  2.092782  2.104237  2.110837  \n",
       "0  2.267877  2.277374  2.274044  2.255781  2.226002  \n",
       "2  2.259664  2.248696  2.230576  2.202916  2.166067  \n",
       "\n",
       "[5 rows x 2546 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_PH_FTIR = pd.merge(left=df_PH, right=df_FTIR_raw, left_on='SSN', right_on='SSN').sort_values(by=['PH'])\n",
    "print(df_PH_FTIR.shape)\n",
    "df_PH_FTIR.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    icr068101\n",
      "2    icr062195\n",
      "1    icr068322\n",
      "3    icr068321\n",
      "Name: SSN, dtype: object\n"
     ]
    }
   ],
   "source": [
    "lowest_pH = df_PH_FTIR.loc[:,'pH'] < 4.2\n",
    "lowest_pH_samples = df_PH_FTIR.SSN[lowest_pH]\n",
    "print(lowest_pH_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400    icr033467\n",
      "401    icr075757\n",
      "402    icr072857\n",
      "Name: SSN, dtype: object\n"
     ]
    }
   ],
   "source": [
    "highest_pH = df_PH_FTIR.loc[:,'pH'] > 8.5\n",
    "highest_pH_samples = df_PH_FTIR.SSN[highest_pH]\n",
    "print(highest_pH_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Wavelengths (cm-1)')"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(FTIR_spectra['labda'],FTIR_spectra['icr033467'], label = 'high pH', color = 'r')\n",
    "plt.plot(FTIR_spectra['labda'],FTIR_spectra['icr072857'], label = 'high pH', color = 'r')\n",
    "plt.plot(FTIR_spectra['labda'],FTIR_spectra['icr062195'], label = 'low pH', color = 'b')\n",
    "plt.plot(FTIR_spectra['labda'],FTIR_spectra['icr068322'], label = 'low pH', color = 'b')\n",
    "\n",
    "plt.axvline(x= 2700 , ymin=0, ymax=1, color='g', linewidth = 1)\n",
    "plt.text(3200, 2.25, '2700 cm-1')\n",
    "\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlim(4000,400)\n",
    "plt.ylabel('Absorbance (A.U.)', fontsize=14)\n",
    "plt.xlabel('Wavelengths (cm-1)', fontsize = 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Absorbance')"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fb2uciC3GOWgwGBaNWREskUDTPz2lhYAAjgeCYAl89pEjQHM0+f2HhhidLuK4CgFKrsepiRyTuZJxDhoMhkVjVgRLJND0vSAvTwAFjucRtYXpoo4oakaY390HBmlviZKKR8oVRSOW0NcaX5SN2JQmMBgMYATBkgnyCCyhQhjoPgCQiulVQDNqDwXnEhHaEjp3QSnFZK604HHXK01w48kJHh0cM8LBYNhAGEGwAGpp0IGm3xqzmSq4hCt2eAp+ZG8fN91zkBPjWdrikfLEvZjaQ81MHqrlsxjJ5Pn0/hfZ2tVi6tYYDBsI4yNokHpRP9fu6mYyVyJb8pDQ5wV4xY5Onjw+Wd6n6HpkSx4ff8ul3HfzNQueXJuZs1DLZzGZLeF6al2UyzAYDI1jVgQNUi/q59HBMfpa42TyDiIQsy362uLYlvDsmTR9bfElRQpVr0JuvHILjw6OLbmiaa3VRcH1SERMaKqhNsantH7ZUIJgKQ/yXDWFFLC7vxWRmTWBUorposv2JUQK1bLjP/DUqaZkjtbyWUQsi7ZE5SNh6tYYwJS7Xu9sGNPQUhO65qopVG9bKmYvqQ5RMyqb1qtLVKs0wa/su4hYxDZ1awyzWE9Vdg2z2TArgqUmdM0X9VNr2y+86kIeeOrUoiOFllrZdD4trl5pgs8+coTpohZkv/CqC43GZ1h3VXYNlWwYQbDUB3m+mkL1tl22tXPRXcrmixKaz9S1UOG3/9AQDzx1ir62ONt9wfXAU6e4bGunEQYbHFPuen2zYQRBMx7kuYp71du2lIJgc61CGrHZLlT4mYJ2hnqshh7chuVjw/gIVrJu+2L7B8xVYrgRm+1CeyWYgnYbh4U+k6bc9fpmw6wIVqqZ/FKjLeqtKGpp+47r8dTxcV71iYfZ1pXk2l3dC/JRmOX/xmCxz6Qpd71+2TCCAJrzIFfb5a/d1V2zJEPwuaeOjyPAQEeirLk3w9xSPWmn8yVOTeSJVIWaLiTvwCz/NwbGBGioZkMJgqVSrUkdHc3w2NEx+lpjxCMW3z4xznvufYIL2uMUHI/2liie0pVCT0/k2dwJbYloU8wt1ZP22ck8AJvaKgXOo4Nj3HfzNQ0dM7xqOjyUpuh4FeYmM0msD0wEkKGaZRMEIvI54E3AkFLq0hrbfw64zX+ZAd6rlHq62eNoZjZktSY1lXOwBMazJTylUApcT3FyQk/Klggx28JxFQgMpwu0JaJNMbdUm7oUsKUzQXvoB76YH3dwb+548Fk6WoSWqG2Sh9YY8z3zxgRoqGY5ncWfB26YY/sR4DVKqcuAjwP3NHsAze7vW+1MLboelkDR8cpCIFxvaChdIBWz8VAopSg4blOd1EFntG/cdj1Xbu8iYld+nc0oSLcWkocW64xfjzTyzJuG94Zqlk0QKKUOAGNzbP+mUmrcf3kQ2NrsMTRrQgsmmuF0gReGMgxN5RkczuC4ioKjUGghUI0CRqaLeAocf/tyRVssd0G61Wo6aLawX+s08sybCCBDNavFR/Ae4KF6G0XkZuBmgO3btzd80GbYQsN+gYH2OCfHc5xLF4hYYFu6GxnoST9YDdgCri8XPAUxCzyErmR0WSOVUjGbwZFpAC7sSfLhN76saQXpVqvpwDg+K2n0mTcRQIYwK55HICKvRQuC2+p9Ril1j1LqKqXUVX19fQ0fe6Fx9LUITzTtLTEiliCA60E8YtPfGiPq30UFRCwhYs3cVgGitsXWrhbaW6JlzayZ5oxAWBVdjz39rWztaiFb8hZ9vLVkOlhLq5fzQTOeecPGY0UFgYhcBnwWeItSarTZx2/GhFY90XhALCJEbGFXXyubOlp4yUA7bXGLeMTSwsDW/YotIB6xKLoew+kCjutxcjw7pzljMQKi2Tb9tWQ6MBNfJWtJiBtWDytmGhKR7cA/AG9XSn1/Oc7RjCSyajNJzNYTeyzkmM2VXC7d0sUt1+0qn2skUyg7kW0RHFdxaiLP7r5UXXPGnQ89T7bkLTjRZznCAdeK6cDkPlSykomTplfB2mU5w0fvA/YBvSJyEvgIEAVQSn0GuAPoAf7Er+PvKKWuavY4ljqhVU80bYkIw5ki7S267WR44gnOc/eBQc5M5nEVWCjsoE+BAhGpO3EfHsqwtatlXnt39Y+uLR4hV3LXhE2/2azUxLeaOd9C3PQqWPssmyBQSt00z/ZfAH5huc7fLKonmgt7W/nZV3TXzNYN/yBEtNPYcXV+QTxiMdAeJ1Nw6jpjgXnt3fsPDfHBB54mnXdwPI+RdIGYLcT9/ZaqFTeaOb1aMJroymMc9muf1RI1tKqppWHdyswkdPuXnmHbgSQT2WL5BxGzLRx0IlnE0v6EbNGhvy1R15yxqzc1r2b/ia8cYjxbwraEiG2hFGRLOou5vy2xJK24WrM7MqIzp/vbYvSk4qtO0zOa6OrAZCqvfYwgWCS1JqEjI9NELcFDO4q1aQgKjlfhtKtnzoDaDW7Cmv3gyLR2RPvmJhFQohjOFHnkQz+ypGuq1uzSeZ05PZVz6G1NrDpNz2iiq4O1FG5sqI0RBIukehJyPW0CKriKRFRr6srzQATbssorgWCCqmfHPd/27k997fvljmSeUvSmouVrKmdOuzOhqKtJ0zOa6Oqg3gr32l3d3HTPQWO2WwMYQbBIqieh4XQBy08kU3r+x7IEEeHun395wz+A+Rx9F/YkeWF4GvGUXg0onbS2u3fh2tenvvZ97nr4BSyBiAUFB4YzJYQcmzpaKiKk0vkSw+kCecclFYuw/9DQiv+ojSa6Oqi1wg1KoBuz3drACIJFUj0JFV1P2+3ReQTBBNoStZr64H/oDS/lAw88Tabg4HoK2xI641E+9IaX1t2nnkP1s48cAaVwFTjanVEui9HfnihHSMUjFqfGc+CbpJIxu/yjBlbMWWs00fPLXI75agXmpnsOGrPdGmLFM4vXKtWJO7YInoKBjhZ29bWyd6CdgY4Eeza1N/W8+/b288kbL+eKbV0MtCe4YlsXn7zx8ro/rrmS1zIFR69gqsokeQomcyUu7G3lfdfvxvF0PaWYbbG5o4W+tgRRW7jzoedXtM5PrcS3G6/cwgNPneLISIbx6SKPHx3jli88yae+tiypKhuGhdZ0MhnfawuzIlgks8NKUwxnCkRsmZVfEKYZ4Y7V2leQjVzrmHM5VCWwLYVLpiqwLeEbt11ffutvnzzJ9u4kIjMfXEjeQz2W417cdM9Bio7L6HQRCyFiCa5SfHr/i1y2tdNoo4tkoY55Y7ZbWxhBsARqTchzOXqXI9xxrmMCPHV8HE8pYrZFb2uc9paZxjjJqEW64M5aESSjlQvFpeQ9LGbcS5msT4xndbQTgmVpwWULlFzPmCWWwEId8ybje21hBEEDNKq5zufoDWtVU7kSI5kCBcfj1vu/zafedsWitOhw7gLMLlchaIXfcRWnJ3OA9mFs7UrSGo9w6GyasBwQmKW1LSTvYXS6wHTBLfdNrnevliv0c1tXkrOTeSLWzOpFKYjbljFLLJDwszaVK+G4Hn1tifL2uTR8k/G9tljXgqAZpodGNddGzhVoVVO5Eqcnc1gItgXTRachbbjWWI6OZtnamaj4XNhsM9CR4PREHgREwbl0vhzKeudDz2Nbgi1SjkBylW6iE2a+vIfhdJ503iFfcnEVdLZE5tXylyv085brdvHU8XFcpbD9a1IKOlJRY5ZYANXPmut5DKWLAPS2xhvS8NdKvSrDOhYEzTI9NKK5NnquwMQykimUTReegkTEouS63Hr/t2lvidYVJLXGYgscH8sRsfPEbIu+tji2rw23RG1EhM2dOry14HiIEj725ksAODKaxVNK91UWIRGxGGiNM12srOYJ9c1gY9N5pgs638BT2gwzXXRJ5x3aW6J1tfzlsCEHY4rZQqbg4XmKeMSiIxUlatvGLLEAqp+13latbEwXXCZzpWXV8MNKVVtc1/TKFF0TAbaMrNuooWaVZm4k+iF8rkzB4exknlMTWW69/9s1WwQWHA9Elfsct8YjDE0VmMo7nJvK8+0T43zggadnRWRUj2UqVypH9AjaDn5yPMdUrlQ22wC0JaLs6mtlR0+KK7d3AVqTF3T5i6hlYYnW9CK2Ne9kHI4gUUqX5LYtC8sSohELC2EkU6h5r6rvRbPKJYfHtKMnxaZ2fS3JeISdPa2rtoz2aqXWc9+TitPREuUbt13PfTdfs2xCIPgebYHDQxleGJ7GFjZ897nlZN0KgmaFr1XXu5/Klfj+uTQnx3O85PaHuOEPvs7hoTQtUZt0vsTpiTyOp4hYQrboVjy4QbhjMmaXP7O5U4c++v3tsUVQHkxkS9z50PNzjmUkU8C2hLgtRO2ZXgg9qRi33bC37kQbCK6BjgTaZqTPfS6db2gyDgu+ouuFTEtasEkoG7melt/sngfVgr+vLcHWrhb29Lct26S1ngk/a+l8icHhDM+fnWIyV1rWiTj8PY5kimXT5UimuOp7Z69l1q0gaFbDkrDmOpUrcmI8S9HVWnjR8Tg8lGEiW2R0usBwuqAzinUBIOIRq2a/2E+97Qq2dCYZ6EjQGo+Q9/tdRvxMZMvSjW2OjGbrjkUpf2Wh4ILOmdyF3X2tTBfdOSfaQEi2JaJs7kwQsaS8OmlkMg4L2Zhf+C647sDPELOtebX8fXv7ue/ma5qiYZq49dkspQte8KyNZPKcGs/55UZmkgmXSxiEv8ei6yFSqVhs9O90uWjIRyAiO4A9SqmviUgLEFFKpZd3aEujWeFrYUfpt46MEu5Rr9AlJSIijE2XcDxPT6oeeCh6WxN1+8WGna/idzOL2HPL5er9kjGbVFxP6PVKQMwX492WiNKW0Lb8qKWF1v/8u+9QchWxiMWe/rbyPQtHkLieR29rgt7WuI5G8iBmCx3JKMNp3ZTn5HiOXb2pBd3vxWLi1itZqo9s395+bjw5wV0Pv4DrKSyBnlSMvrbEsmYIh7/HmG3heAoU5UZQG/k7XU7mFQQi8ovoxvHdwEXAVuAzwNJKXS4z1ZNmKmYTs61yyeiF1NkP3n90sHY3TddTdLZEKLoW2aJLPCL0tibKztJ6ppHguG/4wwMcHsr4TtuZ+kF7+mZPouH9gh/7SCbPSLpYswRErWuqJSQncyWKJZfBkWmKriqHnR61M3zggacRoL0lSmdLFMediSDpScXpcWKMZ0sk4xE6ElEcV5XzFXIl97zUmDFx65UsNTx3/6EhHnjqFKCIR/QKdyJX8pWHyLxa+WIj9sLfY29rjFMTeQAGWuOm7eYy0ohp6FeAHwamAJRSh4E1YXANTA8ff8ulZEseRdejsyXK0dEMdz38AkdGMg2XRpjPLrlnUzufetsVbO7UIZttiUjDD+5tN+ylKxn14/117H9XMsptN+yd9/o+9uZLmC64NUtA1BtzLbNRImKRLXmUXL3kUej+zGOZIpmCQzrvVNjf+1pj5QiSC3tbufvnX84Tt/8oXSmdtNas/smN0gyfw1JMKauNpZrKAkGSiNjgR7gFQQDzaeULLUcRJvw9egr29Leyuy+Fp1jVvbPXOo2YhgpKqWJQXkBEIoCae5fVRbV2NJXTdfbTeYe+tsbq7J8YzxK3hYI7+9Jtizn7DMz34O7b28/v3nj5opJv9u3tp70lWrMExFw/+mqz0Utuf0hXT8WvOOGvTIquIgLl3IIgES6wGX/8LZdWHKfR/IDl6CxWyxTW6HnWW5ObpZrKgu+xry3O6Yk8HgpEUXDUvMrNUlcjJv/g/NOIIPi6iPwG0CIiPwr8MvBPyzus+rieIlNwSESseW3qAdWT02Lq7G/rSuJ6HsPpou494L8vwK3X75m3z0BAvYlpKQ9/9Y9+KlfiXDqPUrr2zkIm2cA0Fca2tGkgnAgXmI7Ck+X+Q0NM5UqcmcyRiNj0tcUBODuZRzEzFvz9lnvSXcjkvt6a3CzVVBY8UzqgAN//5JGKRebVyk2fiLVHIzPph4Bh4LvALcCXgduXc1Bz4SrF0FSe42NZjo9mOTeVZyJbJF9y8bzaC5XqCKKYbeGFHFAwv7Z0y3W7iNo2fW0xWqIWtqX7EL//dXu49XUXNzT2pSyZ56I6sunURA7HVQy0xxs+x65evfwOOp8Fki5qC61xbRc+l86XVwugw08Ds/lr+34AACAASURBVE9wbcmYjSVCruRydDTL0dEsBcejPWGXx3LnQ883JcdjPhaSS7Leoo6WaioLP1Ot8QgDHQm2dCYbKoXSrIg9w/mjkRVBC/A5pdSfAYiI7b+34r8Qx/NwCh7ThZn3YhGLeMQmHrWIRyxitjVLO2pviTCULtKWiMxZKTRMhdnHWlztlOXSOsNje+r4eLmV5elJnW3c3hKZ9xy33bCXDz7wNOm8Dk3VQkELiKDXwS1feBJPKeJ+BnNbIopSipPj2fK1dbQkcD3FSKZYPrZtCRM5h2QsStQWjoxm2dPfWnH+5Zh0F6KZrseoo6WsMpdSK8g47tcejQiCfwNeB2T81y3AV4FXLteglkLR8Sg6HmkdbICIcPFAGx/4sYv5wsHjnJ7IsrOnlZuu1lFDC7XlLzXWfbmWzMHYrvrf/8pktoQSPQE7nmIkXaTk1o/2DcxVntIho62JSDlsNHy9V27vqjtZhq8tW3SJ2RYF3/EdtS08TzGSKXChH05aXayu3qS7FF/CQib3jTR5LaaIYrDP7V96pqEoO1Nwbm0h1QXGZn1A5DtKqR+c770a+30OeBMwpJS6tMb2vcBfAFcC/0sp9clGBnzZFVeqf/zqgUY+WpeIZZGIVq4cwo7W5eKmew7OmpiyRYeYbdGZjDXFcXrZR/+FXMklYs2YvRzPoyVq858fff2sz4ft6OEJsJYZodZnp3IlelIxjoxmEWCgI8GpiRy2iF9KAxIRG6UUrlJs704Ssy2mi+6851zI2Gqx0P3nKyO+HljMPV3q92BYHYjIk0qpq2pta2RFMC0iVyqlnvIP9nIg18B+nwf+GLi3zvYx4FbgJxo4VlNxPI9MwSNTcAC9atAmJf33rcFR/vyRo01vdVitdY5OFxjJFHFcXRxtU8imv1jHadQWciXwQj2NQSd71WIh5qpqTa81HkEBJU/7I05N5Dk5nsP2m8EExe88v6KpbQklV/HhN+4tn3uuSbcZ0Se1eulWa7bBuYLvuzoSaj2xmHu63hzphtk0Igh+Dfg7ETntv74AeOt8OymlDojIzjm2DwFDIvLGBsZQJqj1s707SaLKubdYlFIUSi6Fkstjg2Pc9fBhoraQitmcmczxgb/7Dj2pGNMlb0mCITwxHR5Kk847oBRRW1DAmckCmzsTDVUircfFm9o5MpIhnXfKfZPbElEu7G2t+fla5irH9Xjq+HjNngJhc8FN9xyk6HqhFY5wLp3HcT1sy6K7NUrMtjg3VcBRil3d2t8QPtZcNMOUVisBLxxFFCTLRWxhMqsjnp46Ps6v7Luo4SCAtcRi7uli9lmO8GDD8jGvIFBKPe6bcV6Cjhc5pJQqLfvIQojIzejsZmIDu/mlLzxVNkPs6EmysyfFjp6k/utO0RJbvIC4//ETRCzxzRm6oud4tsRUwWFnd4rTk1lu/8dnuONNL+NHL9m0YJNSMDEFZqLjY1ks0TWGPBRnJ/Pl8NTt3ckFrxCCVcdAR6QhW3e1HT2dL3FqIk+kgZDL6gmivSVKWyLCZK7Ex99yaVkTv2J716ImgmY4cKubq6TiNh0tuqRyMhbh1ETOjzbThfOitoXrrd/Wlou5pwvdZ73lZGwEGu1HcDWw0//8FSKCUqqeyafpKKXuAe4BSG6+WAG+Bp3nzGSeg4NjFZ/f1B5nR0+KHd1JdvYk2dmbYnt3klR8/ss9M5WjPTHzubHpoq6174emxm2bnOfyJ/tfZM9AW4VJKR6xiUVmbPNzaUXBJBqzLRxXlYtrFUoeEVuI21Y55HGp5pC5JuFqc9XZSe1l39SWmPf8c00QzUgKWooDd/+hIe586HkOD2eIWtrsli265Iou8YiuzwQ6L6Xk6jIKQeisLeB4al2aPhZzTxe6jzElrT0aqTX0l+gaQ99BJ56CnofPmyAIs3tTK59/77U6Rn1kmmNjWY6NZjk2Os14Vi9Uzk0VODdV4LEjlQKirzU+s3LoSWkh0ZOiNTTxX9Dewuh0oRxTXgpFvgQkohZnp3IVJqUAS4R41OKJI2N84l++R6yOVhRMouGibYqZRLXe1nj5mOFleCNL7oVMwtWCQwFbOnWdpFrnD9PIBLEUE8Fio08qa9rPmN2CRvbD6UJZEAR+i/DCTimdI7KWcggWEgm00Hs63z7V5z48lGagfXbXvLV0PzcajUQNPQ+8TM33wdr77gT+X62oodBnPgpkmhE1NJktcWxsuiwkjvtCYnS6WPPzAT2pGDt94aCU4sDhEeIRi1Tc5thoFtdTbGrXJaNBa709qTi//9bL6x7zf/zN0zMCxY/rz5dc+tsS/PUv/hCPHB4pL58d1+NcuuBrphbtiQh9bYmKvsbJmM0vvOpCHnjq1LJGb9SLbOpvS3DfzdfM+vxckTbBhFx0dMeyguNhW7Ls9vew2c0OzG5+z0oP7bx+6UB7udjeZE4rELZfRttDkYrZeIpF+WnONysZ1VPr3CfHc3SnouWuZjD3M2Q4Pyw1augZYAA4s8CT3gfsA3pF5CTwESAKoJT6jIgMAE8A7YAnIr+GFjhTcx13cCjD//ibp3nb1dt4xa7uim0dySiXJTu5bGtnxftTuRLHx7J+put0eQURJD2NThcZnS7y5PGJiv0s0Qlq4mfKBlE4Cnjb1dvmvP4KE5PSk0/UFo6PaQG1q6+V979uD3/1reOcmczxg1s7+aXXXATM9AEOBJgAqbjNp/e/SHcqWmHjXuiSez7NcaFmgLlWH3cfGKTouIxOF7GQska+3Pb3emY3V2mhny3OtFv88Btfxn+enODT+1/E8VtbRm2LdMGlvy224jbuRjT9lTTF1Dp3VzLK2LSuVLreczLWC40Igl7gORF5DCjn8Cql3jzXTkqpm+bZfhZd0npBWJYwOl3grocP8z72zBIGtWhviXLplg4u3dJR8X6m4HCsLBhmhMRQWl+mpyBf0vWICo5XNj2lYjb3PX6cbw6Olk1NO3tSuoKob2OoNjGBPtZAewugQ1gv39bJ5dtmhFbUtohFLP6/17+EX/+H/8T1FC1Ru5zFO5wuMpktVWhaC1lyN+LEa2Yy0InxLOm8U+7PDNr+XnK8RUdFNXKNU7kSZyd1q0NHQcQ3u9kixCI2d/7kZRXn27e3n8u2dpavWZdkiJXv80rZuBt1uq5kbZ9a5+5tjeO4Hv1tiXWdk7GeaEQQfHS5B7EQhJkl6P2Pn2hIENSjNR7hks0dXLK5UkBMF5yyWSkQDkdHpzk3pQXEdNHl6ZOTPH1ysmK/9kSk7HvY1t3C8fFpXE+RjFkUHIXjqTlXEiXXo+R6vGxzO0m/voslFiJacMRsKXczCwhHb8ynPTaqOQb/Do4V1OZpNIkrHKWTL7nlSBzH88qNfTIFZ1FRUfOd+44HnyUVt8kVXTz89pn+auCivhS33bB33izaV33i4VVRNK3R72u5y2ME3+nhoTRFxyNqCxdvaueW63bVPfeeTe3GDLSGaCR89OvnYyALJXDYLgepeISXXtDOSy9or3g/V3I57puVjoZWEUF1zam8w3dPTfLdU2EBUUKAZMzmks3tHB/PIkdhZ0+K3tZY3fDTmRWFlJPCkjGbkqeYzBVJRm3yjofrKW5+9YXsPzRUrhXkeB4j6QIffOBpfvfGy2dFKoWpVyJ6MeF/1fs5rsdU3kG53qyKpkrpMuBB855gcqsWZgtpIBSudxSP2LpipnJJRO2GiqUFrETdoVpCvNHvaznLYwTfacl1mcyWQCBXgiMjGe548FluvHILDzx1akOU5ljPNBI1dA3wR8BLgRhgA9NKqfY5d1wmRCAa0f1wt3Un6UzGdPE5V/lap2I+v/Zjg2Pc//gJzkzlSEVtEGG66HBBe0tN30NAS9TmJQNtvGSgreL9fMnlxFiWY2N+JNOo/vfpiRx+pz2miy6PHR3nsaPj5f2SMbuc+7CzN0mh6PGtI2OMTOdpjUfLmc+JqEW+5BGN2Pz8Kzbz7ROTnJ3KMeCPd2dfK7/4fx9nbNpv9u33IB7PlvjEVw6VJ8BGJ7j5NNF6K4/wflO5kk6Yg/IqICzybIGRTKHcyezkeHaWIDkykuGxo2P0t8XoSc1kXd94coIvf/dMuafzrl6t6YcnzqAFp1KKyVxpzvIJ1ddSb2K9dlc3N91zsOlJUvUEb2vMZiRTqEoOjMxKDqw251V341vKOIPvdDTj+L20Bc9TpPMOAx0RHh0c42NvvmTdl+ZY7zQSNfQE8Dbg74CrgHeg+xf/xvIPbzad2/eql7/vM3NGRTiuh+MLBcfVXbcC08Q3D4/wB/92WDsuPa9s7tnUHse2dI/U913fmO9hPoqOxwnfSX1sbJqjI3o1ccoXEPUQgahvU2+NR/CU4oKOFt5+zQ6uuagHqBRmF7S38MypCd372LJwXQ9XzXQP+vRNV/AjL9vEoy+M8JF/em7e6JLANBJerQQT6sffcmndCJXbv/QMnS1R0nmn3LcgaGYiaP+OUrr/bcSycJVi70B7OaIEqBBUg8OZ8gS4q09PfiOZPMPpAiD4twhP6Y5uPakYJU8tKOKp3rXA7NIUyxWxVS9Sq1ByOTNV8KvJ6uv0FLzv+t11o66aHUEUPAvfO5fGtgRBynWjXrKpjclciW/cdv2ir91w/lhq1BBKqRdExFZKucBfiMg3mzrCBeB6iv62xJxaR8S2iNRJLv7id07TErVIxiIMDmewRUBgIltie0+KbNHhb55Ymu8hIBaxuKi/lYuqSi4XHY+vfPcsX/zOKUYyBSK2VW5rCTOdwQDGfAf1RC7Nb/6/59jenaQ1HuGFoUw5xPXEWAZH4feXrPQhANz+pWdI5x1+6KIebr1+N/c9doKzkzm2dCX5pddU3sdazWXaEtHyymGu1UKw4hjJFMoOYl3OWtcZivnlq09P5HGVIha67luu21UWJOX7VKOB0GS2hOvp2kmBA1qU1lB7W+OUXK8piU/33XxNxX256Z6DyxaZU6/Mx6lJ3f/BVaBEdGhxi9bCb61zrGZHEAXfadBIPoici9nWmi/TbZihEUGQFZEY8B0R+R10GOnsrurniZcMtC3JCRX86ESEkucXRhNdOC1qW7QnouVyyXpFoVcSQQaq/r9+7S08tQKA7xyf4G+e1KUsNncmyJc80vkSF7THiEUiupS261FwXLJFtxyyWnA8Dg9lysdJF2Bkev7zTeVK3HPgRV6xq5srd3Rx5Y6uynsyliUesXjsyBh3fuUQLVGLXEkouh6nxnP0trlEbbvmZA0ztYnaEhHSeYeS4xGNaCGgFPSmYoznSuQdl9Z4hJ5WHV7YErUqhPq2A5Wmq5htlVcEAQVfKIRdKyJ6DMPpPH1tCQb9m3Jhjw4PrTcBLiTaJvhsuFVnzLaYzM6do9II9cp8KAWxiO4FpxT0tcVpjc/dOL7ZEUSBmay9JcJIuogn+plvS0SNL2Ad0YggeDu6k9mvAu8HtgE/tZyDWk7CP7ogzhxmupUFWo6IELUFHf1Ze3kRmJvCQqJslvIFSC2CekZBaGlL1CZiC6PTJfraLCZyRUquB35zmIGOBLYlZAouBUcLCBGh5HgNNY/2FBwdy3Lg8DA7u1Ns7kxUtPkMopU++40jCNDREsO2LMazRYqORybv8Ac/cylKqZqtKIPaRAPtCaJ2gbOTBYqOjpYKVhSWJeX4/Z09rfz2f529oqu2zbclIgxnirS3zDQQilgWShRKzQgDpbT/IV1w6Uh67OlvJVdyyZZq3/+AbV1Jjo5mmMrN2ODbWyLs7JldoG9bV5IjIxmG0oWyYHZcl4Ljsv/Q0JJWBfXKfMQjln4GLF2HajhdwLakqXWB5iPsfyi5OmooZgsX9rYaX8A6opGooWP+imAn8A/A95RSS1eDVojwj663NcapiTwoGGiPV5gpGkE7ZusXuFNKVQgFx1WUPI+z6Rzt8arY61SMM5MFzk7msfzlt+ube4bSBfra4vSkYuRKLmPTiu5UjETEYnBkWidLedQVCgp9vI8++BwAEUvY1p1kR/dMuY0dPUlOT2bp8LXJ1njELzOtzS4jmSJ3PXyYeMQiW4SC43JyLOtPxlKuTRTE3g+nC7hK6d4EVp7WeGTeyJ1qp+eFva387CsqGwi95fLN3HvwGBPZEsp3tHhKlxLvTkXnNYmEncMoxbkpPbkGJqihdJGbrp5tFtTRS6M17+udDz2/pAmxXpkPkHL5EUSRd7ym1wVqdHwrPeGbaqbLSyNRQ28EPgO8iFa8LhSRW5RSDy334JaD6h/d7r4UIkKm4Mzre1go4VXF/kOj5Qc5W3BBzdQT0iYAm3hE18UpuarCmVx0Facn8mzuTJCM6cJ2jqcnhljEouTqlYHtx8vPGgd+hjSQd/SK5cjINEdq2JXGpkskorrFZzxi4aEL0AWrmLZ4jKitVwv5kg4LtUQxlNZVU9sSEd8RD7b4EVxKKiKG5qLWpFNtD79sayd3PvR8OWpoT1+K4UyBnlS84nPVJpHq6JwXhjL+qktHWQVROdU2+P2HhnjgqVOzxhrxBUgwjqVQXd47rNXrMiOqocbxzUwIPF/MN8mbaqbLTyOmod8DXquUegFARC4C/hlYk4IAzr+GU/0gu57WPEELg7zj4iloTUQZaE/wvXNpvKoZXQHnpvJs7mxhR0+Kt121jfsfP0EmX8LxFBYKywK82cKgMxnlttfv5aoLuxiaKpST5J44Os7zZ6bIldzyasLxFJmCy0x9Qa3di4ju6uZnP7fGIxSdIq7yq3W6irNTOTyVYGS6QNQWdvSmEL+8c77k8un9L3Dt7h5i9tI6wtX6/mpF3lSbRKodqa5SRGxd+mJXnw4JDnowhwn2E/97EP8fnlLliqXNJKzVtyUiRGxZUOTPatDgw8w10TcyyZtqpstPI4JgKBACPoPA0DKNZ11S/SAH5pPpwkzNm1uu28XdBwYZSufL0UPV6KRi4dd+ZA+vvriPn7hyCyXX4+vfG+ZD//CfZIsu8YhVNgkUHY9E1Oa21+8tR0EdH83yt4+f5OhohumiS2dLhM2dcaaLLvmSx+6+Vl4cniZdKPkOcT8PQCmmCy7TIQERHpeIDhEdyuRxXIhaOvwzalt0p2IkYzYnxrKcGs+VO8JFbR1JFPX/wiW8F8q1u7r59P4Xcf16QW2JCLGIXWESqXakxmy9mgpHJdWyp5drF0UsCs5McpxS2k908abaTX8Wy1rU6usx30TfyCS/kiU0Ngp1BYGI/KT/z2dF5MvA36IVop8GHj8PY1v1NGq3DD/I4agTS4SffvlWHh0c4/YvPUNbPFKuhFmPsFZoWzaJqM2bLt9MazxSET+eLToUXcWv37CXq3d1U3IV//H9Ye56WOdQ6AlNMZF1iEVs2uJRIpa2Jz/w3msBrR2PZIocHZ3m7584yWPHxmuOKbCVA+WFROCndTyPUxN5UjGbXX4i1Ez57srjBKa0WESbpiL2jLD4+veG62Yct8UjDGcKdKeiTGZ1dJKTVfzKvu1zlmLoa4tzcjxHxJayM7qWPT3YryMRYShT6R5TwBsuHZjzO1sMq02rXyzzTfSNTPIrkem90ZhrRfBfQv8+B7zG//cw0DX74xuLhdgtgwfZcbXzNOhABoo//LfD9LfF6W2NMzpdmFMQzGWEaESLDHIoWmIRzkzmyrX6x6aLtMajs8p2iAh9bXH62uLcvf9FfMtTBZZAWzyiVyB1VjKgM6u/e3qSm/7sYLke08z/kyRjOjKo6CiKVfWUHhsc41Oh9qEvDqX51pHRcsbxi8PTOJ5iS2cLF/Xr1Va26Myy9Vc7Um1Lyolo4ZVZvWimqbyD7Sd2KXRUT1cyOmdc/1piORyy8030bX5OTJBX0tcWnxUZtZwlNAyauoJAKfVuEbGBW5VSf3Aex7QmWIjdMniQz07mdFIOM5O6p3QyWyJqM5rRQiDq24Sr6UpG57SLzqdFhnMo4hEbx9V+BcdTxCIW0wWXHT0pBjoSlNyZrOyS63FiIodtQdTS5hvPU5R801F/e5xjo9magqKaek2D+tt006B4xObY6DSZgsPWziQ/90Pbuf/xE9iWHrNSkC44WAKTOYf2hC4xIgRZyba+PtvixFilMzwQlnc+9Hw5HyMoT9GIA/aWLzyJZQkJ26K3NU57S7SmT6EWqz3qZbkcsnNp8/sPDTGcKWgfl+gw5pPjObqSUT78xpeVP7+eTGWrlTl9BEopV0TeDBhBUEU9c8/J8dysuPLgQX7357VFTfwSCyXfNp13PI6PZXUYJPg1XWZq9Fiiu6tpU8bi7aLhH2W4M1rUErJFB8eD977mooofbTCBBYIpiirXM3I93eglnXdQwEBHnPFsiVxV/H7UFiwUqXiUd7xyZ0VV1zG/58JQulAu/x0wnp3kN/7xu1gitMZtCo5H3LYoOh6WRfn+RW3LF1o6pwO0mae3NcHRkWmivj8ialnkii6ZosuWzgTJmF7JNDLh7dvbz5XbuxZlolgLUS/NdMiGhV5rzGbKX+VWa/N3HxikoyWKoHuCeH7eTNyWWedcL6ay1UojzuJvisgfA38DlFUspdRTyzaqNUDY3BPU1Qk0/Vo/8n17+4lFLJRS2L5W7XgztvVACOgqEar8WQXsHdD1/bJFZ0l20epolB4nxni2RDIeqRk6G57A4r6jtOQFjV50iYeX9Lfy/259dTlqp7fV4sR4VVVYBe3JKFs6U7z58s0Vm2aaBk1z76PH/Oqpyp/Qg/o6iomcAzjl/VxXTxpD6YJ24pY8bFv3HciXvHLJb6/KH3H3gUEELYiLrkfEsnBchz/+9xe4YkcXMdvCtrSvojqyabEmirUQ9dIsh2y10Asi0mK2Ncv8dvuXnsEWmMiViFq63LqnFGfShSUn6RkWRiOC4JX+/z8Wek8BG7rSVDApDKV1PRg9i0s5C7jWj/zCniQvDE8j/mQaxK8HBP8S0G0VlY5Vn8uRuRBqJWzdOU9Z55LrMppxyto36LwGQdHeEuG2G/ZiW8Iv77uIOx58lljEoq81yrBv5opZ0NUaJWLZvPPaHURti/84PFJRMO9tV2/jTZdt5q++dZzt3S0IerURlNmYzGkB4Li62FmAp/QkUn7twovD07REbX5gSwenJ3N8+/g4O3tTZZNYdec4hRa6J8ezjGYqVyS2Jdph7a+ArtjRxf96w14+982jnBrPsq071ZCJYi1EvTTLIVtL6AF0JmM89GvXzTrnt0+MVzQuEiAqtX8/huWjkczi156Pgaw1wnZjTyni9kw5hcBuXG0X/vEfuIB7Dx4jU3BwPR3D7tQoQ+qh7fZdqRh9rfE5HZmLGTc01nTm8FCayWwJy9Kx9mFncDyqQz6r74duT+mxd1O8nKgXHvv+Q0N8ev8LRG2hOxljIlfkU/9+mPdbF1d0dbMtocXP2n7pQJK3Xa3zJk5PZulIxNjUHud75zJM5ksopcqmK09px/TBI2McDPkhgqZBjqsYThdpjdtl7T/vzHSOC+N6CtdzCYuHPQNt/PZP/gCgVxW2LQxN5f1ChzrCKeILkIC1EPXSLIdstdALzKZHR7PcdM/Bimf4lut28Z57n8CWmcgzpbSJcTUJyY1AI2WoO9D9hgNx/nXgY0qpyfp7LR9XXXWVeuKJJ1bi1DWpV0I4ZltMF91Z5YBvvHJLRcmEp09OUPJNFOEuXrYl/Pk7rmq6VrSQMsWXffRfyvV9Cs5M8TtL4JLNHYtqSF7vfvW3Jbj51Rdyx4PPErG0KSqIRLrhZZv49onJihVEdXXYb3x/mE89/ELZnJYruZScyhVELSzR9/qKbZ1ctbO73HZ0rqZBjSCihWfUtnhscJTf+ZfvEY3oe54vuTge56W5/EIIFJelOGTD3+9UrqT9UGg/1AWdLbOetTf84QGOjEzPihoyje6bz1LLUH8O3cD+Z/zXbwf+AvjJuntsIOppUlFL1bQLPzo4VvGAv/zjX6Xkenh+5ETUsvBQdCQiTWndWB2pshB7ddQWciXtswgvXDw10yuglnN8LuYyk7z2pZv4uG8WCPcB+LsnTxKxhK6WKOPZAp96+DC3VvWr/uK3T5OM2RU9onMll86WGL9y/UV+RzntoP7e2TSjvpPaU+C5albToFTc1g2DembqMe3sSdLXFm9IQASrlJLrcenWDn71tbu5//ET5YZCP/uKbewZaGNoKj9jgrK1CSpqWWVTSbNoJGqpGQ7Z8O9hxDe1CUJ/e6Lms3bbDXtrKiYmNPT80ogguEgpFa42+psi8p3lGtBao15oW61yzbXswhdvauf5M5NM5Z1y1ER7IsKeTUtrAFcvUiVbdBhoT8w7rmBsR0Yyfocs/V7g0HZcNadzvB7zmUmqJ6Ob7jlILGKVNcyRTJGC4/FbX36OT954OT90UQ9Fx+PsVI62ROXjnIhaDGfyXLypjYs3VXaVK5RcToznytFLQSRT0FVuuuDy3JkpnjszNete7fCFw06/WN/OnhT97fE5y028Ylf3rFVMoVRpdgpjifgCQohY2txk+5FP9ZzZ9TifUUvh38PR0SxxWwuBtoT+LVQ/ayY0dHXQiCDIicirlFKPAIjIDwPzNgsWkc8Bb0KXqLi0xnYB7gJ+HMgC71pNkUgLifuupUlV19aH2nbha3d189jRMR2SqRQlDyZyDidGM0uKnKin+Rcdj1zJbcheHWh3Ax0R3ShlQheWsy0aco7XIjjmSCbPZLZEwTeLvaUqmigg3AcgiM6yLciWXH7roUNlM8OFva2cm8rREtO5Bp5SZIsuAx2zbf8A8ajN7v5WdtdoGnRyfEYwHBvLcmwky0k/ETBXcjl0Ns2hs+mK/RIRi+0h4RAIiIGOxILrEVV3n6vXPrXamR0p+ycqVxXLGbVU73eyb29/Q/WfwISGrgYaEQTvBf6v7ysQYAx4VwP7fR74Y+DeOtvfAOzx/34I+FP//yvOQjSoej+ERp1vjw7qnrzj0yXyro4msgVGsqUlaW31TDAxP1mtEadgrUqtLw5Po1BELJnlHG+EfXv7wxUzswAAIABJREFUufHkRLkuUCKi+w488NQpLtvaWTMrO+gDEHQ7swQSEZuoPSOAgvudL7k4rm5BWvI8WiIWLw5luOaiHr+Xg0fRL69Ri1hEt8UMWmMGlFyPUxO5crvRwNR0cjxXrgT7/XMZvn8uU7FfPGKxvVzue6Y/9QUdLbopUhWPDY6Vy4C0JyKMThe46+HDvI/Z7VNrObPDBMLh6Og0HYkIrp/MiGjBtVSH7Hy/E5MRvHZoJGroO8DlItLuv56aZ5dgvwMisnOOj7wFuFfpX+RBEekUkQuUUmcaOf5y0qgGNd8PYb4l7/5DQzx1fBzXdxAHzkWldBx9eKJbKPVMMHs2tZd9BY0sxcPa2v5DQ9x6/7fJ+naiYC6tpeXNtaJ6dHCMrV0tsxzGta41WDEFPoqgCF637w8IJrPgfn/iK4c4OpolagtbO1twFHz8n5+vcFAqpcpCoeC4FPy8g7kCJ6K2xc6eFDt7UkBf+X3HFxDHxrL8x+ERHj86Xo4KC3eVC3eW08fTPSGqVxD3PXZ8VtOiXMnl/scX3j41EBSb2hJ+NNbMNp1wF+fURK5iRRENmaLm81PM9zsxZp+1QyP9CN6Hdg6ngT8TkSuBDymlvrrEc28BToRen/TfW3FB0GjcdyM/hHoPfSBEgnyCYCKy/CqeFnB2Ml8z7K4R5tLGFrMUD8abitvkii5F1+P0ZI6C486q8rn/0BAffOBpPznMYyRd4IMPPM3v3nh5w4XGAoIV07mpQtmHYokwXXRnCaCgmuVOv3ZRQLWQ0SW1dcE+0OP49+fP8RlfcG3uaOGmq7dx5c7uOYUD6P7YO3pSnJss8MzpKdoSEfraYuSKupvc6y8ZIB61ODqiTU0nxnMU/QYzg8PTDA7P7gkRs8XvT6GL70UjwpnJxWvvb7t6G3c9fJhcySURtcoJd2+9atucfopapqdAUERtaeh7NGaftUEjpqH/ppS6S0ReD/QD70YLhqUKglrqRs1fnYjcDNwMsH379iWedn4ajfteSqJQIEQ2tSU4PZkrO2FLrlfO3PVcj7gti3Lu1dPGQDtgF1rzJhhvR0uCeMRmOF0g7/dU/tlXbOfuA4Pc/qVn2NaV5OR4lvFsqTx5KAXj2RKf+Moh9u3tX1Bc/ffPTZEveYgIopQf7qk17VpmhsV8J/sPDfGRf3qOkusymS1xbirPd09N8suv2cUvvXa3XjmU9OqhuiBeQHX70WQsgojLM6em+P23Xl7+nOspX8D7TuqxLEdHpjk+lqXgH7voKoquQ1V+G+/+/ON65eCbl3b0pNjS2TJv+e5X7OrmfeypiFqq53cIM5/pqc8vlJiMRcjkHcamCxQcj2TM5mvPnuX6l25qevSTYXloRBAE3+SPA3+hlHpalhJgPcNJdP/jgK3A6VofVErdA9wDOo+gCeeek0Ztm0tJFAomrIznYDEjAYN0fE+pOcPuGqFaG1tK9Eh4gm1LRGlLRJnKFTk9keeuh1/whVqcoXSeE+M5bAFL9AQlAkpUual80DvA8XTtoI5klKhtz7q/+w8NkSm4eEoRtQSHYHLSndBqxeEv9DsJzF2ZgoNSaEerrXM6/uTrg1y+rUufww+0KpuVSh75slnJq8xY9qmu5gpay97S1cKWrhZ+ePfM+55SnJvK86/PnuPvv32qfJ3FUF/qILrpACPl/SyBLZ0t7OxNVfggtnUlKwRErailpfLWq/RKYySdL2d4i3/dH37wWUYyRa65qGfO6KdGTFCG5acRQfCkiHwVuBD4dRFpY/4ik43wIPCrInI/2kk8uRr8A9B4SNtSnGFB4/TRTMk3Vehm9B56VZCIWHOG3S2mmuVSokeqJ9ipXKkcRRS1BeXB6ck8m/0onRoJ0+VxP/DUKbqSUdJ5h4LjMTZdmtU7IBhvdyrKaKaEQk/SgSmtXv/jhXwngWDMFt2yv0OHxQq2b66rvjdhs1KHb1YquR47ulOcS+dJ+DWiAPKl2hnLtbBEuKCjhXe8cid7B9rL2vumtgQ3XDpAVypW4aQ+Nppluqg7250Yz3FiPMc3DoePh+5m150MCYkk27uTxKP1+2wvhGCl8fF/fg5Pacd4dypGyi/mF/g1PFdRcoEaTY2Caw+EhRYOM0Ii+H84U9vQfBoRBO8BfhAYVEplRaQHbR6aExG5D9gH9IrISXR2chRAKfUZ4MvoVcYL6PDReY95PmnEtrkUZ9i1u7o5ODiqWx+iNUXLEvpSMbJFl762eF2tdrGa/VJMWdUT7Ll0HkLjFgQ83V83ZkHR04loItqp7LiKRNTili88iYjug9zXV793QDDenlS8bIoquh5RS0jG6yfbhb+Tw+emKLq6llCtUhqBYIxHdBZ4oJc6nkfU0j2bG7k3Udsq11pyPC3EsyUtXN71yp1EbauiVtN81NPeX3HhzHvhpkFBqOvRkSzHxqaZLmgBcXI8x8nxHP/x4mh5PwEu6Eywo9vPgejViXLbupMVyXgLoeh6s+y8tVZD9fCUCgmL2oho4axXE7PDZQPhYVgcjUQNeX70z8+LiAIeUUp9sYH9bppnuwJ+pcFxrloW63gNmqEHE6XrKXpbY/S2xjk7mZszxHOxmv1CzCbVK45rd3WTjFrlRu2ep9jSmWAkU9Q9FkRfS9H16G2L6z7Hlr4ulC6t2pGMMJwuYImUSw+0t0TrCqNgvIEpCmbKUcxFcA/uePBZOvyM1WphGURseUoR6Jpl85wCD0VbItpwPaD5lALXU+UIpSBaKaiw2mjeQJhw06Crd1YKiLHpYsXK4ai/kgjKhZ+eyHN6Is+jg6MVxxxoT8xKlttR5XgPE4S66lWaro81NFWgv11r+Y2uhhpBKYWjFI5HXZ9FUNajGUl4G41Goob+BNgN3Oe/dYuIvE4pteYn8ZViZiK3/aYcgufpBKjqEM/DQ2mKjlcOJYXGNPtapqNGzSbVK44jIxkeO6qjd/b0t5IruZwcz1F0Pfra4pyeyOOh/BLbQtS2ufX6PXz5u2c4Mpql6Gkbfzxi64Y4nkKUXj20+6WK50poW4zpbS5hCVpIzDQIEgRVFgSWQE8qNisaaj7mUgpsS48lGZt5r+R6fO25c/zRv79AxIL2RHTOvIFGEBF6WuP0tMZ5+Y6ZRoJKKcazpZB5aSYfIuiKd3Yqz9mpPN+q0TSosqNciu09ybKDvDelfUOi4P9v79yDI7ur/P45/dL7Lc2M5+UZmYExGGO8xtiLyzGPXTC7C1ni1GJ2symSXXspXiEFwaTY1BZUJXYttVmoUGs7hiUBApsMcSDEmF3ieI3ZMdgee/xgxjBoPDPSjEeakUZqqdWve0/+uPe2bre6W93qe9Xq1u9DmZG6W61f31b/zu+8vgeUmWSGkd4O3v+mPWwkK7IeUC0M5fcgPGOxEobamp5FLaJzLwJXuSd4RCQCPK+qr9uA9a1is4nOrYeb7nnESRRn8py9lMY5pCiW7cR1vSRoJYG4nkSUrGWXFW771h03VBWWg7VDWV5HqGU7H+pU1tGU74xFOOBKNcwk08ylcuwe6ipq4Dow1stdt14JUFjD6dlUYcMd6o4zl8qBu/HuHe4uEuMrzXmsVwjNu8b+E6CqFpRcvdfnXX/vcyAi9HXGOLCtb0Nq3ku7b21VljJ5Rno6+I+/dw15O4h0XGV+NjHL1584xdn5ZXoSMfYMd7Gcszl1ccl9nyoTcRvTErEoirKcdf7OFBjsirFvpLcm72az4XkWxXmKSCGH4d3eat5Fo6JzLwF7gVPu93uA5wJa25bEH/LYOYhbimnTkyiuhKl0qvVOPqls3tmEkxlylqPe6G2clU7D37rjhpoqhKIC5+YzqO+knM7bTC+k2dbfyWhvB3nLZltfJ5NzKd64d6ho47z9/icKa0hEI+QtJzy0mMmzc7CTV+bTKLCtr5Mbx4c5dGSqas6jFtnscte4XBjMP7LTu/6ZvE00EuG+P/i1Da17L/XunGlsMaaTafaOdJOzbNI5i/Qa5avrwd/FPNqbIJ2zefliio+/zfFG5pdznL7o5B38HdV+wb5UziaVW72mTN7mxEySf/+DY9z+pr286/U7GCjxYjcrxZ5FZaIRKXgY5ZLc8WjreBcVDYGI/G+csOkAcExEfube9Sbg8AasrW3xhzx6O2JEI1JWCrpSCGh+Ocfn33sVd//gGC9fXCYeibB7sJOsZfPvvvciS5kcl5Vo7NSSFPaMyEwyU9hwSv3F6WSGroQzL+DA9v6yUsH++HsiGqE7EeXScg5R3A3XKYv1Xq/faIATT59eSHPnN55m/0g3F5ey9HfF60qMe9d4JpkuVCdFI+JoGk1QMBJe/sHzqDa6+WmtvE086sx98NIitq0FmYxM3nL7KdZnHEp7H0q7mAe64rx+9wCv3z1Q9HPJdI5TF1M8+tIMP3zxFVc+XYtma/jHld734wnu+/EEQ91xLi8I9a3kIQb98bIWwrIVi7WT3F7ewlOW3YyhqGoewRfK3CbATUDVRLBhbXoS0UJd/f6R7sKwbn+zV28iWlEgrtBFa+uqEFHO0sLPeYNBvEafSkJ2/nDSjv4OTs2Wr/hQnI7nbf2dVUsyvfh73lIuLecY7IqTzOQRlVVjMf0GL5nOcfZSGi909PJsiryl9HTEEJEiI3FtiRfix69p5O9XOHRkituu3cWhI1ObQgOn3jxIJCJ0JaJ0JVa6oqsloqtRa+9DpWT2VbsGuH7fcKHU9cJihsGuONGIM1c6YzkGyzMQc6kcc6lLPHvmUtHzD3TFVyWo9430MNQdb7nwSym1eBfllGYLX0c3JgxV0RCo6t97X4vINcAHcGYSnATuDXVVbYx/w/USrxeXsnz2wec4l8wQj0TY3u8k3xaWc4UTeblNoqKwXCxCzlJmkumCGy84GvuVTtOl4SRhuXybN44xqDRUxXueHQOdzoYuIArJjHPiXqsJbCaZcXIm6lR5ZC1HIO70bMrptnbVT0XW7riupGl0eGKWz73ndZtCAycIPZ5KiehM3gsrlQ8p+SfCeZT2Pqwlgucvdf3Xf3N01fN5MyE+9NZxp4LpgqvoejHFuXmnBHl+Ocdzk/M8N1k868qZKlecpL58pJuRnsaGBm02istny1uMmM+TiJXkLIIIQVULDb0aeD/O6f8izvB6MaMrG6N0w7Vsp5rDVkfRU3Fi8zsHO+nvipOIRhjsTpTdJCoKy7mJzo99+xlsdRQ+PaXQSmWmq2LVESmcKj35C49s3uaeh48DqzfgSvF3UeG2a3cVSVGUU2rN5C0i4lyH0d5OXllIk3MTuRF3FXkbOmOyZtmsl+vwhugkohFGe51ruZk0cMJYixdS6u1wk9C2FryFdM4xEJU0iPzVPmuFj/xUer7ff/NeDu7o5+CO4hkbyzmLHzz3Ct87epYLS5mC6OLsUhYFFtJ5np9a4PmpYp3L0qFB+0Z7uHy49qFBrUjetmsunfWHoOKRCD/55QwPPH6S+Ni+11d6/mqhoePAj4HfUdUT7i/7xPpfigFWb7hOXb2zuUXEcQFttwRv/2gP88u5VUO/PdYSluvvirN3uLvow7FWzb5nVDrdUZG2rs4TKHDslSR//PWn+OhbX8XH3vHqss/jj7/HI1I1IbxyKl52xPeAs24/hYc/2uFV+eQtmyOn57jpnkdWVRvNLmVJZS1EIObWuU9dSnOgZAZBUKyn23ujKA4pOewc7KK/K8ZXHn+Zs5dSbC/Tw1Br+Ajq1zR6/sw833nGmT63a7CzYDg+957Xsa2/o+A5eF7EWkODuhPRgszG5T4jsa2v+tCgdqBSCMrv0aF2vtLPVzME/wTHI/h/IvIw8G3KC8UZ6qB0w/W6MiNuY5m/MWst3aK1wgr1NJCVGpW+zhjpvM1wV4y5VK5smChnKV9+9FdFcwQqGaeEq1y5llLrl370C774yAn3eqz8Vr9XEnWamDk/v8z0ohP6ml3MYNlOsvy2yUscOjJF3k2iqkJOIaaO3Pepi6lVhqNRNnIKWFAkYhHeffVO3n21MxTIsrUQSkq7Celawkd+6tE0quRtfOfIFH/xe28olCp7ZPM2Z+ZShQ5qz0hMuQYilbU4di7JsXMlQ4PikZVOai/UNNrN9v76hwa1GqXXuBLVcgQPAg+KSA/wj4FPANtF5K+ABwOQod6SlG6UnqbNSE/CEe6ynTRpVKSmBGa1sEI9ichSo7J/tJcPXD/M4YlZZx6AexxXwPvsqDouqz80U8k41Tq605OdXljOs5RdOd4ojjS3DVjq/OHOuEYgERUshYuLOUZ64YHHTzLW1+EK+DnX11bIq6tcalmBb9ZhTgFrlFo9lWhE6OmI0eOGk1SVD90yzue+f6wgN57OWavCR+ulHm8DHMN1xVgvV5QZGjQ5t1zwHDzZjcm5Zde42bx0PslL54sNhH9okH/sqDd1rx0od43LUYvExBLwTeCbIjIM/FPgLhqXod6SrN5we5hxO2y7E1HOJzPkLbhirIdbr9rB3T84xp3feBqA8dEePv2ugw1LUZcqklbbJD6GU8n0zJk50v56cactgI6oo8fz6PFp7v7BsYIExfhoD59/71Ur3kmV0Z3+NcwkM+zo72C0t4Ol2WIjYbPiGXghK0d3JuLer+74S2VvPOr0L9hKhysEl8nbqO08zwtnF4gIDHbFA9msG9FxCpNGPBUR4Z1XXUZHLFr4G9o52MUf3nA5bxofJpNbf+kq1JasroV4NML+0R72j/YU3V4YGnQx5dNkSjE5lyJnacWhQYlYhL1DvqlybrJ652D5qXK1sh4pkUYpd43LsWZn8WajHTqLSynXPQvwyUNHuZTK4f3t2QpD3fHCgJcgfm+lDuRSY/HJQ0e5uJgtiOQpuOMqEwx2JZhZzFRcK8Bn/9fzTF5yqkQ6osJwb4J4NFoo5fTWcGJ6kbztSFWU62WICLx6Wy+LWYu5pSx5SwsyxpZtk3XHfXZEIwx0OV3MXudw1ioONXnP3d8Z47k/e2dD17LcfF5/t3ezCHtdpaWrTrlobcbBH7/2J5e9hrawcDrKiw3EKbdxzp+TKiUeFfYMrfYgdg52rqmO2qzX6v+9P/3CB1PZmVM95R5XS2exIWTKhXduv/8JFjN5orKi1y6qJNPBhRtqDWfccnAbX7jtDXz2weeYnM+gFG/mqlp2rfPLOT70zafJ5J3YvLf5ZixHOfOjb30Vhydmi9awY6CTybllMnmbeAQ8JyQRdTd7hbtuvZL7Hpsgb9lcWMySzdlFxmK0J85cKs/FpSzD3XEW0nny/nyD/1CnFIWg1stmnc8btqdSrnQ1b3n9DCvd0OX6GtY7MCeINe8ZdhRXbzowWrjdspVz88sFw+CJ9Z1xhwblLGemhtP/M1P4uVhE2D3UVdQsd/lID7uHuoi7BqKe6qsg8V9jJFJxvzeGYANYTzXJmbnUyonYojDUXiGwD3E9m8QtB7fx+GfeUdZ7+ex3X8CynbyGh3cCF2ulBBUcN14AiVDQFvKvoa8zzq5B5fTcMjZO9RI4yXOAqDjiezeOD3NiOlnIXfjpTsTpTsQ5n0wzn84XGs8++LUnUVZmLXuUe4562azzeRsZnrReYtEIsWiEno6V2/yd0J7noKqhDMxZL9GIsHuom91D3auGBnlT5U4Xpso5HoR3sn/ZDTv5iQjsdj2IX04n6euMERHnMxARqUuquxG8a/zdj778fKXHGEMQMuuN0fYmoiXlkk64JR6hpg9xLcZnPZtEOe9lz2PdXFjMoPbKadvvYhcSzG5yOeFOAJt01+YXuMtaNlERdg10EnFn4zqxXiestHOwk+lkmkNHpuiICom481x5S4lFnKloFxYzjI/10tcZY345VwiB9HXGWMzkC9fTW2tnLFpxfGc9Rnwz9SZ4bBZPJRGL8A8nLhSu5e7BLv7FW/bz5itGnLnJDUhlhE1EhJ2DXewc7OLXr1i53VZlOpnh5QuO5+DpMp26mCLlDg06PZvitJvr8nuecbc5rL8zzo+OnWffSA97hroCGxpULyZHEDLrjdHe+peP8YvzSSwtjmfHo8J//mfXVd1w6on91/K4tfByCP4cQdZyOoBjrtyAn0Q0gkTgjXuck/qnDh1lriS/MNgd5w9vuJzDE7McOT2H4ISN/HMJJueWObCtFxFhYsbJLdhu443XWLNvuJuHP/GPAIpKUyPi/B7LVnoTUcb6O8sqtQZxfYKgkR6F9Sq4Bkktf2te+arXEZ1xvYZWQ9U51HjzqE9dTPHC2QXOzKYqduvDytCgFakNJ0m9d7ibzgAMxBXb+hpSHzU0wHpjtMlMnt1DXZxfyBSGmndEhb6ueGAjKb1wxt0/OFaonBgfLZtLqoqXQ/BXDXXHI/R3xeiIRZm6tFzQmxHAUmWwI17YkEZ6EiQzeSxb3e7fDmJR4fDELN+644ayktJerNXTVBrr6+DMbKpgOD2do4tL2YK+ktf49sDjJ1nKWvQkovR3xuiIRyvOLdgMJaGN9iisd3hSkM1xtfxNrpSvOj/jnw/tdUSHLctdjnqrfUQcUcVt/Z1FQ4N++quLfP2J05xbWKY7HmPXUCeprMWp2dVDg/6hZKrcjoHOwrhR/3xqf4NgIxhDEDLrjdF6P+dvqqllOhfUb3xSOZvdQ12Fk1q1TabSBlG62XibVyzqdI2em0+TtRwZjSvGerjr1isLj1/MWrxqrHfV7ABvvZWu4fhoD0tZq0jF1XL18HOWUzYacwf6eL/rY+94dVEn9E33PLKqtM67Vo6ufvNLQms17EFt3mE0x63nQOSfD+0J7OUtm3TeJuNregvTa1hLa6ke3nzFCG++YmTV7d7QIH8FkzN6dIkF10Ccm09zbj7NExPFQ4O293c4SephV9F1tIe9w92FXpBaMYYgZNYbo20ktluP8amnEarWDcLbkJYyOaerOBbh1y4frrgxrbXeStfiT3/rYOE1TM6lUHUS6rFopDAC9EIyS85KFq3Lv1H6f3epUuvOgc6K6q8bSa0T6YLavMNojgsqaR2LRuj1aSj5vYa0W8YapNewEdU+IsJwT4LhngTX7h0quu9SamXsqH+ynDc06PxChvMLGX5WMlVurLeDfaPdRZIb1TCGIGTWW03SSBVKPUaknpNaLRuEf0O6bKBrlf5ROW4cH+bLj/4Ky23+6uuMrRoT6c1Ltm0lHo3QlXAG0t9583gh13L1n/2Q5ZxVkA0QAVuUbN6uuFF6fQzllFovuuJn3jVpVqK1lk00yM07jJLTsJLWfq9hwPUa/MqrmQa9hnq7n4NmsDvBNd0JrtkzWHT7fCrnDAzyGYdTvqFBM4sZZhYzPPnyXE2/xxiCDWC91SSN/FytRmTPUDcvX1xkYTlfUOjsd8cMllLLBlHvhvTo8WkOHZkiFoFMXslnLZayFjfuHyqIx/nnJExdSjuzkjsSq0698aiwnHPKQT2PAJwehErr8iSpKym1VlN/9dYfttBcLZtokJt3GCWnG1leW6q86nkN/klvtcxrgOC6n4NmoDvO1d2DXL272EAsLOc4PZtyy1mXCkbigivHUgljCNqUWo3IjePD/Ozl2UIlTdaymU5muf1Nq93eWjaIejek+x6bYGE5y1K22J0/fHKOL/3oF0UNZxMzi06Lv8KFxSzjY71FRubV2/s5eWGRZHrFqPV1xtk/2lt1XdWUWqupv26U0Fwtm2iQm3dYp/dmldcW5xocvL4GT5K7UulqLVLdm4n+rjhX7Rrgql3FU+UW03necE/ln6veF90gIvIuEXlJRE6IyF1l7h8SkQdF5DkR+ZmIXBXmerYijx6f5vb7n+Cmex7h9vuf4NHj00X3H56YZaw3QSIawVantHOsN8HhkqQUOBuENytZVQvT0PwbxJ4hp6dgYmaR468sMDGzyIXFTMUN6cxcioW0U9vvKa96e/EDj5/kzFyqcBrLWnaROisUG5k7bx4nEYuyY6CT12zvY8dAZyHEtGeom+USjV7/RrnW/eXwexkizr9x1/sImlsObuNbd9zAjz/9trJzp8u9N/PLOS6lshXf+2q/63PveR3b+jqZX85VHCjUyiRiziFhrK+DPcMrYnND3Qm6EtFCePH68WE+/rYDjPR0kEznGenpCF0WIgx61xCeC80jEJEo8GXgN4BJ4EkR+Z6q/tz3sH8LPKuqvysiB93Hvz2sNW01ajmxnplLMdrbwZivGslfseOnlpNpOQ9jZjHLB64v/8HZM9TN5FxJvNUtAV3KWrxu50Ch4cyyHc11wfkgQ/Fmvdb6qp1y13MK3kxCc6WvvScRRXCu/0aVnLYykTJSGdm8k4B+22u38ZYDo5u24S0IwgwNXQ+cUNUJABH5NvBewG8IXgv8BwBVPS4i+0Rku6qeD3FdbUm5WPU9Dx9neiGN5Q6RH+vrKJxY1zOzANbeIPwy0v6cw+GJWT5W5vF33jzOEycvolos/RARZ67znTeP8/G/eYb55ZWZGk55qM1MMr0qqVxpfWsZifXEsOu9dmHnE/yv/fb7nyDnm2e9mWSxW4VELEIiFqG/c/VsaK9CyW7BhrdyhGkIdgFnfN9PAm8uecxR4H3A4yJyPXA5sBsoMgQicgdwB8DevXvDWm/LUu7k/8lDR5ldyhKLOB22eVs5eynNZQMdRSfWoOPBZ+ZSjPR0MNq7tocBzuZ1w74hDp8srm6wFd5+cAyApUy+oHjq/zeVtbj7fVcXksr3PTbBL6eTZPM28ajw6u39RZvtc5OXePHsPEtZy52Te2lVg109m2Q9126jB9dsJm+lXSgnsOd5DZk1cg2bnTANQTnh7lLzeTfwRRF5FngeeAZYNU5NVe8H7gdHYiLgdbY85Spipi454Rbx/ieOXv/5hQxv9NUq33JwG7dNXirqtv2jm/ZvbL24RBjsirGQzmOr4w10xSP83+MzPPTCefK2M3fAk/q1VYkKDLhd1t4mm7Ms5lM5EFjOwckLi4XN9rvPTvLgs+fcawJL2TxffOQEQFGDWT3U40Vs9OCaZojNbUWOZGkEAAAQIElEQVQ8rwH33NOqMhlhGoJJwJ9a3w2c9T9AVReADwKIU65x0v3PUAflTn+WO7nFdv6voMmf1+ITq1e+OdbXwV73VHvoyFTR+Ml6WG+sfffQSsVOMp1jam4ZxZk3LEDOVkScOQUikLHswqbmbbIXF/NEIkJEBNt2JLt3DMS45+HjHHenU3nid7YNkYjywOMn120IoHYvYqNP6JtFbG6rsVllMtYiTEPwJHBARPYDUzjzjz/gf4CIDAIpVc0CfwQ85hoHQx2UO/1FI0JUhO39nVxYdFU9I8L4cE/RxhX0STWIWPtMMgPucBkAtS3y6uQFIhLBUiUWiRQ2NW+T9V5j3rKxVMlY8Mp8mkzeKpae9hmDIGYReFTLAWz0CX2zymJvNTaDTEYthGYIVDUvIh8BfghEga+q6osi8ifu/fcCVwL/VUQsnCTyvwxrPe1MudNfb0cMwVHh3D/aUzgR3nXrlUU/G8ZJtdFYezrvdAeP9jrHqrPzy0S9ucPu9LIP33LFqk02EY2QyVt4Yqde1UzeXsktFHAbznoCEu1aKwfQjBP6Vqv8aRU2WiajpjWF+eSq+hDwUMlt9/q+PgwcCHMNW4Fyp78//a3XAmufCDdDLHl16WOM7kSUfp+BOp9Mo0phyIz/dXibbH9XjFfmnRO+4HhFgpCIOqEyq6QyCeCPbtofyGtYy7MyJ3RDJcKWyagF01ncJlQrm6zGZokl+9fvna69NcWiUrWpyb/Jnl/IgDpGoCPmlMyqKmfmlld5Bd3xyKoW/fVSi2dlTuiGWglSJqMWjCEw0JOIunNYYf+I4000c8Naz+nZ22QrDQLqjEexbS3qqYhGJLCqnT1D3WXkLWLsH12t2WQw1Es5mYycteIxNOo1GEPQxqzVwOSPax/Y1styziKVa25FQ+maP//eqwKp7e+KR7hsoKvizINGqbej2mBoFM9r8CaWNJJrMIagRalnk6/UwLTRte21vKZqa66lM9fzJu55+Hhh6tr+kW56EuHOFqi3o9pgCJpyuQZ/hVI1jCFoQYLa5Ddb92m1NQN1deYuZa2iqWvzy9lCh2MYuZB6O6oNho2gtEKpEqGqjxrCoRbVS79qp0fpJr8exc0wqbbmepQ+yz12oCvOSE8iNEXNzXYtDYZ6MIagBQlqk69FVnojqbbmWl6zR6XHLmWtqlLOjbDZrqXBUA/GELQgQW3yterOrzXTICiqrbmeE3czTueb7VoaDPUgzW5trpfrrrtOn3rqqWYvo6n4cwT+eHfpxuMlVxtpYKr1dwVFpTXXs46NXnOtbNZ1GbYGIvK0ql5X9j5jCFqTIDb5WqhUl7+tr7MwNL4R6tHo/9KPfrFKJbWSYNxGXZ96CPtabhQbMafZEDzVDIGpGmpRNqpLNczKono0+utVSfVXT51xk82Ued6NZLNVaa2HjZ6rYNgYTI7AUJVq8fZG492NVgJVmw/sbVjTyXTRhrXemHwQsf12qCzayDnNho3DGAJDVSolcG8cH254ow2iEqjSaTrIDSsoo9IOlUX1vg+G1sAYAkNVylXD3HbtLh54/CRTl1K8Mp9mMZNf10YbZiWQf8NaWM4xMbPI6dkUR07Pheq5VKPWyqLNTDt4NYbVmByBYU3KK4NaxHyzkHcOQm9HrK6TYT3Kp/WqpHry2nlLOTu/TARBcOSpP3XoKCM9CRazViHZCVRMgAYZ2291BdLNolZrCBZjCLYYjVZ8eKfjjliEvKVEIoKNMpPMEI1IXSfDaiqj5db5ufe8ruZKIG/Dmk6mHWkJAVQY6IpzcSlLMpPnVWO9TCfTfPLQUQTo74qXTYBuhpkNmwUzV6E9MeWjbUilzT6IOvab7nmEwa44yXS+cNJGlLyt7BrsDiTUEVS9/aPHp7nzG09j2TYdsShjfR3MJDPkLBsFDu7oB+CX00lQOLC9r/Cz/rJOU/9vaAeqlY+aHEGbUS2xGUSs24sR93fF2TnQRSwqWDb0JGKBbYxBxuSv3TvE5SM9jI/10tfpzDUGSERX/vQtW1fJ9fpDP+0Q2zcYqmFCQ21GNQXPIGLd/hhxX2eMWFQCPx0HGZMvjWlHxclrePOQwZlmhkrRz5WGflo9tm8wVMMYgjaj2iYaRKx7I2LEQcbkS9c71tfBufllJi+l6EhGGOiO09sRQ2BDE6CmO9ewmTA5ghBpxoe9moyBdzre7LHusGLy3vNm8xbJdJ5M3iYaET58yxVcvXuwyLjdOD7M4YnZUN47k3MwNAOTI2gCQXe21kq1pqVWiXWHtU4vbDbW18n4WC9XXtbP7qEuDk/McsvBbQWJ6jtvHufQkanQ3jvTnWvYbIQaGhKRdwFfBKLAA6p6d8n9A8A3gL3uWr6gqn8d5po2imaNgVwrdLNRse5GvSFvnd7zfPa7L7DnserPs9bvrDX3EPZ71w6aQ4b2IjRDICJR4MvAbwCTwJMi8j1V/bnvYR8Gfq6qvyMiY8BLIvJNVc2Gta6Nopkf9mYnNoMSJqtXlG6tx9aaewj7vTN9CYbNRpihoeuBE6o64W7s3wbeW/IYBfpERIBeYBbIh7imDWMrt+IHFfoIWpSuVq2fsN+7dtAcMrQXYRqCXcAZ3/eT7m1+/hNwJXAWeB74uKraJY9BRO4QkadE5KmZmZmw1hsorfRhD3pqVlDCZEGL0tWaewj7vWuVXI1h6xBmjkDK3FZaovRO4FngbcAVwN+JyI9VdaHoh1TvB+4Hp2oohLUGTqu04oehLx9U6KOe56n1sbWEzTbivWt2+M5g8BOmIZgE9vi+341z8vfzQeBudWpYT4jISeAg8LMQ17VhtMKHPYjEaGmS9sbxYQ4dmWq4Lj9MUbq1aIX3zmAIijANwZPAARHZD0wB7wc+UPKY08DbgR+LyHbgNYCpoauDRqtzGk2MlvMoDh2Z4rZrd3F4YrahE3U9J/NW8cAMhs1IaIZAVfMi8hHghzjlo19V1RdF5E/c++8FPg98TUSexwklfVpVL4S1pnYjiLBOo2GcSh7F4YnZQObw1nMyN6d4g2F9hNpHoKoPAQ+V3Hav7+uzwG+GuYZ2JoiwTqMhlbBnGt/z8HEmLiwBsH+km7tuvdJs9gZDwJjO4hYmiOqcRitY6im1rKc66dHj03zq0FF+Ob2IqqKqnJhZ4pOHjobenW0wbDWM6FwLE1R1TiMhlVo9inrDWPc9NkEynScaESLiFKCJrSxmwu/ONhi2GsYjaGE2Q69CrR5FvU1mZ+ZS5G0b8RUhizizA4wUg8EQLMYjaGE2S6VMLR5FvbmEPUPdXEhmUKVgDFSpexymwWBYG2MIWpxWqZSpN4x1583jfOrQUeZSOVScHkJbYbAjvim7sw2GVsaEhgwbQqUw1o3jw2UTyLcc3Maf3/YGDmzrRUQQEV411sMXbntDSxg+g6GVMINpDBuG1/zmH/5y6MiUGdBiMGwA1QbTmNCQYcMoDWPdfv8TTZnZYDAYijGhIUPTCEql1GAwNIYxBIamsZVnNhgMmwljCAxNYzP0QRgMBmMIDE3EDGgxGDYHJllsaCqt0gdhMLQzxiMwGAyGLY7xCAxbnkaH+xgMrY7xCAxbGk8VdTqZLlJFNVLXhq2EMQSGLU29qqgGQztiDIFhS2Oa2gwGYwgMWxzT1GYwGENg2OKYpjaDwRgCwxbHNLUZDKZ81GAwTW2GLU+oHoGIvEtEXhKREyJyV5n7PyUiz7r/vSAilogMh7kmg8FgMBQTmiEQkSjwZeBW4LXA7SLyWv9jVPXPVfUaVb0G+Azw96o6G9aaDAaDwbCaMD2C64ETqjqhqlng28B7qzz+duBbIa7HYDAYDGUI0xDsAs74vp90b1uFiHQD7wK+U+H+O0TkKRF5amZmJvCFGgwGw1YmTEMgZW6rNCD5d4CfVAoLqer9qnqdql43NjYW2AINBoPBEG7V0CSwx/f9buBshce+nxrDQk8//fQFETlV5q5R4EJdK2wt2vn1tfNrg/Z+fe382qC9Xt/lle4Q1UqH9MYQkRjwC+DtwBTwJPABVX2x5HEDwElgj6ouNfD7nlLV6xpY8qamnV9fO782aO/X186vDdr/9XmE5hGoal5EPgL8EIgCX1XVF0XkT9z773Uf+rvA3zZiBAwGg8GwfkJtKFPVh4CHSm67t+T7rwFfC3MdBoPBYKhMO0lM3N/sBYRMO7++dn5t0N6vr51fG7T/6wNCzBEYDAaDoTVoJ4/AYDAYDOvAGAKDwWDY4rSNIRCRqIg8IyLfb/ZagkREXhaR511hvqeavZ6gEZFBETkkIsdF5JiI3NjsNQWBiLzGJ6j4rIgsiMi/ava6gkREPiEiL7qCkd8Skc5mrykoROTj7ut6sd3et3K0kwz1x4FjQH+zFxICb1XVdmlqKeWLwMOqepuIJIC2GA2mqi8B10BBgHEKeLCpiwoQEdkFfAx4raoui8h/x2kM/VpTFxYAInIV8Mc4emlZ4GER+T+q+svmriw82sIjEJHdwG8BDzR7LYbaEZF+4GbgKwCqmlXVS81dVSi8HfiVqpbriG9lYkCX2zzaTWXlgFbjSuAJVU2pah74e5x+p7alLQwB8JfAvwHsZi8kBBT4WxF5WkTuaPZiAmYcmAH+2g3rPSAiPc1eVAjULKHSKqjqFPAF4DRwDphX1b9t7qoC4wXgZhEZcQUx302xXE7b0fKGQER+G5hW1aebvZaQeIuqXosz1+HDInJzsxcUIDHgWuCvVPWNwBKwaoBRK+OGu94D/I9mryVIRGQIR1Z+P7AT6BGRP2juqoJBVY8B9wB/BzwMHAXyTV1UyLS8IQDeArxHRF7GmXnwNhH5RnOXFByqetb9dxonxnx9c1cUKJPApKr+1P3+EI5haCduBY6o6vlmLyRg3gGcVNUZVc0B/xP49SavKTBU9Suqeq2q3gzMAm2bH4A2MASq+hlV3a2q+3Bc8EdUtS1OJiLSIyJ93tfAb+K4rW2Bqr4CnBGR17g3vR34eROXFAbtOnDpNHCDiHSLiOC8d8eavKbAEJFt7r97gffRnu9hgXaqGmpHtgMPOp8zYsB/U9WHm7ukwPko8E03hDIBfLDJ6wkMN778G8CdzV5L0KjqT0XkEHAEJ2zyDO0lx/AdERkBcsCHVXWu2QsKEyMxYTAYDFuclg8NGQwGg6ExjCEwGAyGLY4xBAaDwbDFMYbAYDAYtjjGEBgMBsMWxxgCgyFARORREbnO9/0+EWmb3g9De2IMgcFgMGxxjCEwGNaBe9I/LiL/RUSec2cqtIWEtmHrYRrKDIZ1ICL7gJPATar6ExH5Ko48xm8DlwHL7kMTgK2qVzVjnQZDLRiPwGBYP2dU9Sfu198AbnK//n1VvUZVr8GRMDYYNjXGEBgM66fUnTbutaElMYbAYFg/e30zlm8HHm/mYgyG9WIMgcGwfo4B/1xEngOGgb9q8noMhnVhksUGwzpwk8XfN0lgQztgPAKDwWDY4hiPwGAwGLY4xiMwGAyGLY4xBAaDwbDFMYbAYDAYtjjGEBgMBsMWxxgCg8Fg2OL8f11LiZmQ2XB6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.regplot(x=\"pH\", y=\"2700\", data=df_PH_FTIR)\n",
    "plt.title('Absorbance at 2700 cm-1 vs. pH')\n",
    "plt.ylabel('Absorbance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.075\n",
      "RMSE: 1.085\n"
     ]
    }
   ],
   "source": [
    "x_PH = df_PH_FTIR['2700'].values\n",
    "y_PH = df_PH_FTIR['pH'].values\n",
    "\n",
    "Xph_train, Xph_test, yph_train, yph_test = train_test_split(x_PH, y_PH, test_size = 0.2, random_state = 42)\n",
    "\n",
    "Xph_train = Xph_train.reshape(-1,1) \n",
    "Xph_test = Xph_test.reshape(-1,1)\n",
    "\n",
    "\n",
    "reg = LinearRegression()\n",
    "\n",
    "reg.fit(Xph_train, yph_train)\n",
    "\n",
    "yph_pred = reg.predict(Xph_test)\n",
    "\n",
    "linear_r2 = r2_score(yph_test,yph_pred)\n",
    "\n",
    "\n",
    "# Calculate mean squared error for the regression\n",
    "linear_mse = mean_squared_error(yph_test, yph_pred)\n",
    "linear_rmse = np.sqrt(linear_mse)\n",
    "print('R2: %5.3f'  % linear_r2)\n",
    "\n",
    "print('RMSE: %5.3f' % linear_rmse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}