equipment_failure_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Equipment Failure Prediction\n",
    "\n",
    "*by Marina Villaschi*\n",
    "\n",
    "This report aims to tackle equipment failure problems on operation of an FPSO vessel.\n",
    "\n",
    "I'll be answering the following questions, as well as drawing insights and explaining the analysis throughout the nootebook.\n",
    "\n",
    "1. Calculate how many times the equipment has failed.\n",
    "\n",
    "2. Categorize equipment failures by setups configurations (preset 1 and preset 2).\n",
    "\n",
    "3. Categorize equipment failures by their nature/root cause according to parameter readings (temperature, pressure, and others).\n",
    "\n",
    "4. Create a model using the technique you think is most appropriate and measure its performance.\n",
    "\n",
    "5. Analyze variable importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install scipy\n",
    "# !pip install scikit-learn\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_validate, GridSearchCV\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score, RocCurveDisplay\n",
    "\n",
    "from sklearn.inspection import permutation_importance\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# colors for plots\n",
    "fail_colors = [\"#555555\", \"#FB5072\"]\n",
    "\n",
    "random_state = 55\n",
    "\n",
    "df = pd.read_excel(\"data\\O_G_Equipment_Data.xlsx\")\n",
    "\n",
    "dict_fail = {\n",
    "    True: \"fail\",\n",
    "    False: \"success\",\n",
    "}\n",
    "\n",
    "df = df.replace(dict_fail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Cycle</th>\n",
       "      <th>Preset_1</th>\n",
       "      <th>Preset_2</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>VibrationX</th>\n",
       "      <th>VibrationY</th>\n",
       "      <th>VibrationZ</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Fail</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>44.235186</td>\n",
       "      <td>47.657254</td>\n",
       "      <td>46.441769</td>\n",
       "      <td>64.820327</td>\n",
       "      <td>66.454520</td>\n",
       "      <td>44.483250</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>60.807234</td>\n",
       "      <td>63.172076</td>\n",
       "      <td>62.005951</td>\n",
       "      <td>80.714431</td>\n",
       "      <td>81.246405</td>\n",
       "      <td>60.228715</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>79.027536</td>\n",
       "      <td>83.032190</td>\n",
       "      <td>82.642110</td>\n",
       "      <td>98.254386</td>\n",
       "      <td>98.785196</td>\n",
       "      <td>80.993479</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>79.716242</td>\n",
       "      <td>100.508634</td>\n",
       "      <td>122.362321</td>\n",
       "      <td>121.363429</td>\n",
       "      <td>118.652538</td>\n",
       "      <td>80.315567</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>39.989054</td>\n",
       "      <td>51.764833</td>\n",
       "      <td>42.514302</td>\n",
       "      <td>61.037910</td>\n",
       "      <td>50.716469</td>\n",
       "      <td>64.245166</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cycle  Preset_1  Preset_2  Temperature    Pressure  VibrationX  VibrationY  \\\n",
       "0      1         3         6    44.235186   47.657254   46.441769   64.820327   \n",
       "1      2         2         4    60.807234   63.172076   62.005951   80.714431   \n",
       "2      3         2         1    79.027536   83.032190   82.642110   98.254386   \n",
       "3      4         2         3    79.716242  100.508634  122.362321  121.363429   \n",
       "4      5         2         5    39.989054   51.764833   42.514302   61.037910   \n",
       "\n",
       "   VibrationZ  Frequency     Fail  \n",
       "0   66.454520  44.483250  success  \n",
       "1   81.246405  60.228715  success  \n",
       "2   98.785196  80.993479  success  \n",
       "3  118.652538  80.315567  success  \n",
       "4   50.716469  64.245166  success  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"Cycle\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (640, 9)\n",
      "Test set:  (160, 9)\n"
     ]
    }
   ],
   "source": [
    "df_train, df_test = train_test_split(df, test_size = 0.2, random_state=random_state)\n",
    "\n",
    "print(f\"Train set: {df_train.shape}\")\n",
    "print(f\"Test set:  {df_test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = \"Fail\"\n",
    "\n",
    "categorical_cols = [\n",
    "    \"Preset_1\", \"Preset_2\"\n",
    "]\n",
    "\n",
    "numerical_cols = [\n",
    "    \"Temperature\", \"Pressure\", \"VibrationX\",\n",
    "    \"VibrationY\", \"VibrationZ\", \"Frequency\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for missing values:\n",
    "\n",
    "- No missing values in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Preset_1       0\n",
       "Preset_2       0\n",
       "Temperature    0\n",
       "Pressure       0\n",
       "VibrationX     0\n",
       "VibrationY     0\n",
       "VibrationZ     0\n",
       "Frequency      0\n",
       "Fail           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the distribution of categorical variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_plot(\n",
    "    figsize, title, data, x, y, xlabel=None, ylabel=None,\n",
    "    palette=None, subtitle=None, hue=None, hue_title=None,\n",
    "    legend_loc='best', padding=0, ax=None\n",
    "):\n",
    "    \"\"\"\n",
    "    Plots a bar plot using seaborn.\n",
    "\n",
    "    Parâmetros:\n",
    "        figsize (tuple): figure size (width, height)\n",
    "        title (str): main title for plot\n",
    "        data (DataFrame): dataframe with data to plot\n",
    "        x (str): x axis column\n",
    "        y (str): y axis column\n",
    "        xlabel (str, optional): x axis label (default: None)\n",
    "        ylabel (str, optional): y axis label (default: None)\n",
    "        palette (str ou lista, optional): color palette for plot elements (default: None)\n",
    "        subtitle (str, optional): subtitle for plot (default: None)\n",
    "        hue (str, optional): column to be grouped (default: None)\n",
    "        hue_title (str, optional): title for group legend (default: None)\n",
    "        legend_loc (str ou tupla, optional): group legend location (default: 'best')\n",
    "        padding (float, optional): spacing between label and bar label (default: 0)\n",
    "        ax (Axes, optional): subplot instance to plot (default: None)\n",
    "    \"\"\"\n",
    "\n",
    "    if ax is None:\n",
    "        # Cria a figura para o gráfico\n",
    "        fig = plt.figure(figsize=figsize)\n",
    "        ax = fig.add_subplot()\n",
    "    else:\n",
    "        # Usa a subplot fornecida\n",
    "        fig = ax.get_figure()\n",
    "\n",
    "    # Título e subtítulo alinhados à direita com ajuste de espaçamento\n",
    "    fig.suptitle(title, fontsize=14, y=1.03)\n",
    "\n",
    "    ax.set_title(subtitle, fontsize=10, fontstyle='italic')\n",
    "\n",
    "    # Gráfico de barras\n",
    "    ax = sns.barplot(data=data, x=x, y=y, errorbar=None, palette=palette, hue=hue, ax=ax)\n",
    "\n",
    "    # Rótulos do eixo\n",
    "    ax.set_xlabel(xlabel)\n",
    "    ax.set_ylabel(ylabel)\n",
    "\n",
    "    # Adiciona rótulos nas barras\n",
    "    for c in ax.containers:\n",
    "        ax.bar_label(\n",
    "            c, fmt=lambda x: f\"{x:0.0f}%\" if x > 0 else \"\",\n",
    "            padding=padding, color=\"White\", fontweight=\"bold\"\n",
    "        )\n",
    "\n",
    "    # Adiciona títulos na legenda (hue)\n",
    "    if hue:\n",
    "        handles, labels = ax.get_legend_handles_labels()\n",
    "        ax.legend(handles, labels, title=hue_title, loc=legend_loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_freq_fail = df_train['Fail'].value_counts(normalize=True).mul(100).round(2).rename('percent').reset_index()\n",
    "\n",
    "bar_plot(\n",
    "    (6, 5),\n",
    "    'Relative frequency of equipment failure',\n",
    "    df_freq_fail,\n",
    "    x = 'Fail',\n",
    "    y = 'percent',\n",
    "    ylabel='Relative frequency (% of failures)',\n",
    "    subtitle='The dataset is unbalanced',\n",
    "    palette = fail_colors,\n",
    "    padding=-18\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_freq_preset_1 = df_train[\"Preset_1\"].value_counts(normalize=True).mul(100).round(2).rename(\"percent\").reset_index()\n",
    "\n",
    "df_by_preset1 = df_train.groupby(\"Fail\")[\"Preset_1\"].value_counts(normalize=True).mul(100).round(2).rename(\"percent\").reset_index()\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(13, 5), width_ratios=[1, 1.5])\n",
    "\n",
    "bar_plot(\n",
    "    (6, 5),\n",
    "    \"\",\n",
    "    df_freq_preset_1,\n",
    "    x = \"Preset_1\",\n",
    "    y = \"percent\",\n",
    "    ylabel=\"Relative frequency (% of presets)\",\n",
    "    subtitle=\"Preset 1 is well balanced between\\nthe three configuration categories\",\n",
    "    palette = \"deep\",\n",
    "    padding=-18,\n",
    "    ax=axes[0]\n",
    ")\n",
    "\n",
    "\n",
    "bar_plot(\n",
    "    (8, 5),\n",
    "    \"Equipment failures by Preset 1\",\n",
    "    df_by_preset1,\n",
    "    y=\"Fail\",\n",
    "    x=\"percent\",\n",
    "    xlabel=\"Relative frequency (% of failure)\",\n",
    "    palette = \"deep\",\n",
    "    subtitle = \"When the equipment fails, the configuration categories for Preset 1\\nare not as well balanced as when the equipment doesn't fail\",\n",
    "    hue=\"Preset_1\",\n",
    "    hue_title=\"Preset 1\",\n",
    "    legend_loc=\"lower right\",\n",
    "    padding=-30,\n",
    "    ax=axes[1]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_freq_preset_2 = df_train[\"Preset_2\"].value_counts(normalize=True).mul(100).round(2).rename(\"percent\").reset_index()\n",
    "\n",
    "df_by_preset2 = df_train.groupby(\"Fail\")[\"Preset_2\"].value_counts(normalize=True).mul(100).round(2).rename(\"percent\").reset_index()\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(13, 5), width_ratios=[1, 1.5])\n",
    "\n",
    "bar_plot(\n",
    "    (6, 5),\n",
    "    \"\",\n",
    "    df_freq_preset_2,\n",
    "    x = \"Preset_2\",\n",
    "    y = \"percent\",\n",
    "    ylabel=\"Relative frequency (% of presets)\",\n",
    "    subtitle=\"Preset 2 is well balanced between\\nthe eight configuration categories\",\n",
    "    palette = \"deep\",\n",
    "    padding=-18,\n",
    "    ax=axes[0]\n",
    ")\n",
    "\n",
    "\n",
    "bar_plot(\n",
    "    (8, 5),\n",
    "    \"Equipment failures by Preset 2\",\n",
    "    df_by_preset2,\n",
    "    y=\"Fail\",\n",
    "    x=\"percent\",\n",
    "    xlabel=\"Relative frequency (% of failure)\",\n",
    "    palette = \"deep\",\n",
    "    subtitle = \"When the equipment fails, the configuration categories for Preset 2\\nare not as well balanced as when the equipment doesn't fail\",\n",
    "    hue=\"Preset_2\",\n",
    "    hue_title=\"Preset 2\",\n",
    "    legend_loc=\"lower right\",\n",
    "    padding=-30,\n",
    "    ax=axes[1]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Association between target `'Fail'` and independent qualitative features:\n",
    "\n",
    "- The null hypothesis (H0): *\"The features are independent from the target feature\"*.\n",
    "\n",
    "    Using a significance level of 5%, we would **not reject** the null hypothesis, so the features are indeed independent from the target feature.\n",
    "\n",
    "\n",
    "- The Tschuprow's coefficient measure the degree to which two variables are related. It varies from 0 to 1 where 0 is no association and 1 is perfect association. As we can see, the coefficient is close to 0, meaning no association between the variables.\n",
    "\n",
    "    These features can be of low importance to the model, we'll dive deeper into this later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Feature Preset_1 with target 'Fail'\n",
      "chi-squared = 3.109341141262695\n",
      "p value = 0.2112589660867467\n",
      "degrees of freedom = 2\n",
      "Tschuprow coefficient = 0.058612021564601643\n",
      "\n",
      "\n",
      "-> Feature Preset_2 with target 'Fail'\n",
      "chi-squared = 11.505055390355801\n",
      "p value = 0.11805572749016921\n",
      "degrees of freedom = 7\n",
      "Tschuprow coefficient = 0.08242896750344542\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def chi_sq_test(cross_tabs):\n",
    "    \"\"\"\n",
    "    Prints the Chi-Squared Statistic, p-value, degress of freedom from a Chi-Squared test,\n",
    "    and Tschuprow coefficient\n",
    "    \n",
    "    Args:\n",
    "        cross_tabs: A crosstab dataframe.\n",
    "    \"\"\"\n",
    "    chi2, p, dof, con_table = stats.chi2_contingency(cross_tabs)\n",
    "    print(f\"chi-squared = {chi2}\")\n",
    "    print(f\"p value = {p}\")\n",
    "    print(f\"degrees of freedom = {dof}\")\n",
    "    print(f\"Tschuprow coefficient = {stats.contingency.association(cross_tabs, method='tschuprow')}\\n\\n\")\n",
    "\n",
    "# qualitative associations\n",
    "for var in categorical_cols:\n",
    "    print(f\"-> Feature {var} with target 'Fail'\")\n",
    "    tabs1 = pd.crosstab(df_train[\"Fail\"], df_train[var])\n",
    "    chi_sq_test(tabs1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the distribution of continuous variables:\n",
    "\n",
    "- All continuous variables show a gaussian-like distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train[numerical_cols].hist(bins=40, figsize=(8,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Equipment failures by quantitative features:\n",
    "\n",
    "- There's a noticeable diference in the distribution of all the continuous features (that represent the parameter readings) when the equipment fails.\n",
    "    \n",
    "- These features can be of hight importance to the model, we'll dive deeper into this later on.\n",
    "\n",
    "- There's some ouliers that could get in the way of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAHHCAYAAABZbpmkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA3RElEQVR4nO3dfVhUdeL//9cBg0EDFBWBFQVvMlPxrkI/qWmaiqufvNndNF2FXKst02TdjP2WirWLN6trmjdbKdQnW9PV3GpXy3uolN1UsltT84YSTDNByCGB8/vDdX5OoOlwc4bD83Fd57o473Pm8Bq9xnn5PmfmGKZpmgIAALApH6sDAAAAVCXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgCPpaWlyTCMcpcnnnjC6ngAIEmqY3UAADXfrFmzFB0d7TbWvn17i9IAgDvKDoAKi4uL06233vqT+zmdTvn5+cnHh0llANWHf3EAVIkdO3bIMAytXr1aTz75pH72s5+pbt26ys/PlyRlZmZq4MCBCg4OVt26dXXnnXfqvffeK3Ocd999V7fddpscDodatmypv/71r5o5c6YMw3Dtc/ToURmGobS0tDKPNwxDM2fOdBv7+uuvdf/996tJkyby9/dXu3bttHLlynLzr1mzRn/84x/VtGlTORwO9e3bV4cOHSrzezIzMzVo0CA1aNBA9erVU0xMjJ599llJUmpqqgzD0L59+8o87k9/+pN8fX319ddf/+SfKQDPMLMDoMLy8vJ0+vTpcrc9/fTT8vPz09SpU1VUVCQ/Pz9t27ZNcXFx6tq1q2bMmCEfHx+lpqbqrrvuUkZGhm6//XZJ0kcffaT+/furcePGmjlzpoqLizVjxgw1adLE46wnT55Ut27dZBiGJk6cqMaNG2vjxo0aP3688vPz9dhjj7ntP3v2bPn4+Gjq1KnKy8vT3LlzNXr0aGVmZrr22bx5swYPHqzw8HBNnjxZYWFh+uyzz/TWW29p8uTJ+sUvfqFHHnlEq1atUufOnd2Ov2rVKvXu3Vs/+9nPPH5OAH6CCQAeSk1NNSWVu2zfvt2UZLZo0cL8/vvvXY8pLS01W7dubQ4YMMAsLS11jX///fdmdHS0effdd7vGhg4dajocDvPYsWOusU8//dT09fU1L//n68iRI6YkMzU1tUxGSeaMGTNc6+PHjzfDw8PN06dPu+03cuRIMzg42JX1Uv62bduaRUVFrv2effZZU5L50UcfmaZpmsXFxWZ0dLTZvHlz87vvvnM75uXPb9SoUWZERIRZUlLiGtu7d+8VcwOoPJzGAlBhS5Ys0ebNm92WS8aNG6eAgADXelZWlg4ePKj77rtP3377rU6fPq3Tp0+rsLBQffv2VXp6ukpLS1VSUqK3335bQ4cOVbNmzVyPb9u2rQYMGOBRTtM0tW7dOg0ZMkSmabp+9+nTpzVgwADl5eVp7969bo9JSEiQn5+fa71nz56SpC+//FKStG/fPh05ckSPPfaY6tev7/bYy0+1jR07VidOnND27dtdY6tWrVJAQIBGjBjh0fMBcG04jQWgwm6//fYyFyjv2LFDksp8SuvgwYOSLpagK8nLy1NRUZHOnz+v1q1bl9nepk0b/etf/7runKdOndLZs2f1/PPP6/nnny93n2+++cZt/fKiJUkNGjSQJH333XeSpMOHD0v66U+f3X333QoPD9eqVavUt29flZaW6m9/+5vuueceBQYGXvdzAXDtKDsAqtTlszqSVFpaKkmaN2+eOnXqVO5jbrzxRhUVFV3z77h8BuVyJSUl5f7uMWPGXLFsxcTEuK37+vqWu59pmtec79Jx7rvvPr3wwgtaunSp3nvvPZ04cUJjxoy5ruMAuH6UHQDVqmXLlpKkoKAg9evX74r7NW7cWAEBAa6ZoMsdOHDAbf3SbMvZs2fdxo8dO1bmmIGBgSopKbnq774el57Pxx9//JPHHDt2rObPn68333xTGzduVOPGjT0+JQfg2nHNDoBq1bVrV7Vs2VJ//vOfVVBQUGb7qVOnJF2cCRkwYIA2bNig48ePu7Z/9tlnevvtt90eExQUpEaNGik9Pd1tfOnSpW7rvr6+GjFihNatW6ePP/74ir/7enTp0kXR0dFauHBhmbL149mfmJgYxcTE6MUXX9S6des0cuRI1anD/zmBqsarDEC18vHx0Ysvvqi4uDi1a9dOCQkJ+tnPfqavv/5a27dvV1BQkN58801JUnJysjZt2qSePXvq4YcfVnFxsRYvXqx27dpp//79bsf9zW9+o9mzZ+s3v/mNbr31VqWnp+uLL74o8/tnz56t7du3KzY2VhMmTNAtt9yiM2fOaO/evdqyZYvOnDlz3c9n2bJlGjJkiDp16qSEhASFh4fr888/1yeffFKmmI0dO1ZTp06VJE5hAdWEsgOg2vXu3Vu7du3S008/reeee04FBQUKCwtTbGysHnzwQdd+MTExevvtt5WYmKjp06eradOmSk5OVk5OTpmyM336dJ06dUp///vftWbNGsXFxWnjxo0KDQ11269Jkyb697//rVmzZmn9+vVaunSpGjZsqHbt2mnOnDkePZ8BAwZo+/btSk5O1vz581VaWqqWLVtqwoQJZfYdPXq0pk2bppYtW7q+TwhA1TLM673KDgAsNnPmTCUnJ1/3RcLe4PTp0woPD9f06dP11FNPWR0HqBW4ZgcAqlFaWppKSkr061//2uooQK3BaSwAqAbbtm3Tp59+qj/+8Y8aOnSooqKirI4E1BqUHQCoBrNmzdL777+vO+64Q4sXL7Y6DlCrcM0OAACwNa7ZAQAAtkbZAQAAtsY1O7p4v5wTJ04oMDDwivfYAQAA3sU0TZ07d04RERHy8bny/A1lR9KJEycUGRlpdQwAAOCB7OxsNW3a9IrbKTuSAgMDJV38wwoKCrI4DQAAuBb5+fmKjIx0vY9fCWVHcp26CgoKouwAAFDD/NQlKFygDAAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwCwjfT0dA0ePFjp6elWR4EXoewAAGzB6XQqJSVFubm5mj17tpxOp9WR4CUoOwAAW0hNTdXp06clSadOnVJaWpq1geA1KDsAgBovOztbaWlpMk1TkmSaptLS0pSdnW1xMngDyg4AoEYzTVNz5sy54vilAoTai7IDAKjRjh49qt27d6ukpMRtvKSkRLt379bRo0etCQavQdkBANRoUVFR6tatm3x9fd3GfX191b17d0VFRVkTDF7D0rKTnp6uIUOGKCIiQoZhaMOGDW7bDcMod5k3b55rn6ioqDLbZ8+eXc3PBABgFcMwNG3atCuOG4ZhQSp4E0vLTmFhoTp27KglS5aUuz0nJ8dtWblypQzD0IgRI9z2mzVrltt+jz76aHXEBwB4icjISMXHx7uKjWEYio+PV9OmTS1OBm9Qx8pfHhcXp7i4uCtuDwsLc1v/xz/+oT59+qhFixZu44GBgWX2BQDULgkJCXrjjTd06tQpNW7cWPHx8VZHgpeoMdfsnDx5Uv/85z81fvz4Mttmz56thg0bqnPnzpo3b56Ki4uveqyioiLl5+e7LQCAms3hcCgpKUlhYWF64okn5HA4rI4EL2HpzM71eOmllxQYGKjhw4e7jU+aNEldunRRSEiI3n//fSUlJSknJ0cLFiy44rFSUlKUnJxc1ZEBANWsV69e6tWrl9Ux4GUM00u+gMAwDL3++usaOnRoudtvvvlm3X333Vq8ePFVj7Ny5Uo9+OCDKigokL+/f7n7FBUVqaioyLWen5+vyMhI5eXlKSgoyOPnAAAAqk9+fr6Cg4N/8v27RszsZGRk6MCBA3rttdd+ct/Y2FgVFxfr6NGjatOmTbn7+Pv7X7EIAQAAe6kR1+ysWLFCXbt2VceOHX9y36ysLPn4+Cg0NLQakgEAAG9n6cxOQUGBDh065Fo/cuSIsrKyFBISombNmkm6OEW1du1azZ8/v8zjd+3apczMTPXp00eBgYHatWuXpkyZojFjxqhBgwbV9jwAAID3srTsfPDBB+rTp49rPTExUZI0btw4191qV69eLdM0NWrUqDKP9/f31+rVqzVz5kwVFRUpOjpaU6ZMcR0HAADAay5QttK1XuAEAAC8x7W+f9eIa3YAAAA8RdkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtlBrZOenq7BgwcrPT3d6igAgGpA2UGt4nQ6lZKSotzcXM2ePVtOp9PqSACAKkbZQa2Smpqq06dPS5JOnTqltLQ0awMBAKocZQe1RnZ2ttLS0mSapiTJNE2lpaUpOzvb4mQAgKpE2UGtYJqm5syZc8XxSwUIAGA/lB3UCkePHtXu3btVUlLiNl5SUqLdu3fr6NGj1gQDAFQ5yg5qhaioKHXr1k2+vr5u476+vurevbuioqKsCQYAqHKUHdQKhmFo2rRpVxw3DMOCVACA6kDZQa0RGRmp+Ph4V7ExDEPx8fFq2rSpxckAAFWJsoNaJSEhQY0aNZIkNW7cWPHx8dYGAgBUOcoOahWHw6GkpCSFhYXpiSeekMPhsDoSAKCKGSafuVV+fr6Cg4OVl5enoKAgq+MAAIBrcK3v38zsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsoNZJT0/X4MGDlZ6ebnUUAJWM1zfKw6exxKexahOn06lhw4bp1KlTCg0N1fr16/n4OWATvL5rnxrxaaz09HQNGTJEERERMgxDGzZscNt+6dtuL18GDhzots+ZM2c0evRoBQUFqX79+ho/frwKCgqq8VmgJklNTdXp06clSadOnVJaWpq1gQBUGl7fuBJLy05hYaE6duyoJUuWXHGfgQMHKicnx7X87W9/c9s+evRoffLJJ9q8ebPeeustpaen64EHHqjq6KiBsrOzlZaWpkuTmaZpKi0tTdnZ2RYnA1BRvL5xNZaWnbi4OD3zzDMaNmzYFffx9/dXWFiYa2nQoIFr22effaZNmzbpxRdfVGxsrHr06KHFixdr9erVOnHiRHU8BdQQpmlqzpw5VxznbC5Qc116HZeWlrqNl5SU8PqGpBpwgfKOHTsUGhqqNm3a6Le//a2+/fZb17Zdu3apfv36uvXWW11j/fr1k4+PjzIzM694zKKiIuXn57stsLejR49q9+7dKikpcRsvKSnR7t27dfToUWuCAaiwS6/vH5ca0zR5fUOSl5edgQMH6uWXX9bWrVs1Z84c7dy5U3Fxca43rNzcXIWGhro9pk6dOgoJCVFubu4Vj5uSkqLg4GDXEhkZWaXPA9aLiopSt27d5Ovr6zbu6+ur7t27KyoqyppgACqsefPmCg4OLndbcHCwmjdvXs2J4G3qWB3gakaOHOn6uUOHDoqJiVHLli21Y8cO9e3b1+PjJiUlKTEx0bWen59P4fkJpmnK6XRaHaNCJk+erDFjxriNGYahyZMn19jn5nA4ZBiG1TEASx07dkx5eXnlbsvLy9OxY8cUHR1dzangTby67PxYixYt1KhRIx06dEh9+/ZVWFiYvvnmG7d9iouLdebMGYWFhV3xOP7+/vL396/quLbidDrVs2dPq2NUuuLiYrdSXdNkZGQoICDA6hiApS7N3GZmZrqdyjIMQ926dWPmFt59GuvHvvrqK3377bcKDw+XJHXv3l1nz57Vnj17XPts27ZNpaWlio2NtSomAKAaGYahadOmlZnl9PHxKXcctY+lMzsFBQU6dOiQa/3IkSPKyspSSEiIQkJClJycrBEjRigsLEyHDx/W448/rlatWmnAgAGSpLZt22rgwIGaMGGCli9frgsXLmjixIkaOXKkIiIirHpatuRwOJSRkWF1jAo7f/68+vfvL0lKTk7WXXfdZXGiiuEL04CLIiMjlZCQoBUrVrjGEhIS1LRpUwtTwVtY+g3KO3bsUJ8+fcqMjxs3TsuWLdPQoUO1b98+nT17VhEREerfv7+efvppNWnSxLXvmTNnNHHiRL355pvy8fHRiBEjtGjRIt14443XnINvUK49zp8/7zodxykgwF74BuXa51rfvy2d2endu/dVv//g7bff/sljhISE6NVXX63MWACAGsjhcCgpKUlz587V448/TtGBS426QBkAgKvp1auXevXqZXUMeJkadYEyAADA9aLsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW+N7dgCgljNNU06n0+oYFXb583A4HDX+nlh2eA7egrIDALWc0+l03UYF3oNb2lQeTmMBAABbY2YHAGo5h8OhjIwMq2NU2Pnz59W/f39J0jvvvFPjZ0W4t1floewAQC1nGEaNLwY/FhAQYLvnBM9xGgsAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANiapWUnPT1dQ4YMUUREhAzD0IYNG1zbLly4oGnTpqlDhw6qV6+eIiIiNHbsWJ04ccLtGFFRUTIMw22ZPXt2NT8TAADgrSwtO4WFherYsaOWLFlSZtv333+vvXv36qmnntLevXu1fv16HThwQP/7v/9bZt9Zs2YpJyfHtTz66KPVER8AANQAdaz85XFxcYqLiyt3W3BwsDZv3uw29txzz+n222/X8ePH1axZM9d4YGCgwsLCqjQrAAComWrUNTt5eXkyDEP169d3G589e7YaNmyozp07a968eSouLrYmIAAA8DqWzuxcD6fTqWnTpmnUqFEKCgpyjU+aNEldunRRSEiI3n//fSUlJSknJ0cLFiy44rGKiopUVFTkWs/Pz6/S7AAAwDo1ouxcuHBBv/rVr2SappYtW+a2LTEx0fVzTEyM/Pz89OCDDyolJUX+/v7lHi8lJUXJyclVmhkAAHgHrz+NdanoHDt2TJs3b3ab1SlPbGysiouLdfTo0Svuk5SUpLy8PNeSnZ1dyakBAIC38OqZnUtF5+DBg9q+fbsaNmz4k4/JysqSj4+PQkNDr7iPv7//FWd9AACAvVhadgoKCnTo0CHX+pEjR5SVlaWQkBCFh4frF7/4hfbu3au33npLJSUlys3NlSSFhITIz89Pu3btUmZmpvr06aPAwEDt2rVLU6ZM0ZgxY9SgQQOrnhYAAPAilpadDz74QH369HGtX7r+Zty4cZo5c6beeOMNSVKnTp3cHrd9+3b17t1b/v7+Wr16tWbOnKmioiJFR0drypQpbtfxAACA2s3SstO7d2+ZpnnF7VfbJkldunTR7t27KzsWAACwEa+/QBkAAKAiKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDWKDsAAMDW6lgdAABqKtM05XQ6rY6B/zp//ny5P8NaDodDhmFYmoGyAwAecjqd6tmzp9UxUI7+/ftbHQH/lZGRoYCAAEszcBoLAADYGjM7AFAJ/q/D3XL4+Fodo1YzTVNFpSWSJH8fX8tPndRmztIS/fqjzVbHcKHsAEAlcPj4yuHLP6lWC9ANVkeAF+I0FgAAsDXKDgAAsDWPys6XX35Z2TkAAACqhEdlp1WrVurTp49eeeUVvmMCAAB4NY/Kzt69exUTE6PExESFhYXpwQcf1L///e/KzgYAAFBhHpWdTp066dlnn9WJEye0cuVK5eTkqEePHmrfvr0WLFigU6dOVXZOAAAAj1ToAuU6depo+PDhWrt2rebMmaNDhw5p6tSpioyM1NixY5WTk1NZOQEAADxSobLzwQcf6OGHH1Z4eLgWLFigqVOn6vDhw9q8ebNOnDihe+65p7JyAgAAeMSjb8BasGCBUlNTdeDAAQ0aNEgvv/yyBg0aJB+fi90pOjpaaWlpioqKqsysAAAA182jsrNs2TLdf//9io+PV3h4eLn7hIaGasWKFRUKBwAAUFEelZ2DBw/+5D5+fn4aN26cJ4cHAACoNB5ds5Oamqq1a9eWGV+7dq1eeumlCocCAACoLB6VnZSUFDVq1KjMeGhoqP70pz9VOBQAAEBl8ajsHD9+XNHR0WXGmzdvruPHj1c4FAAAQGXxqOyEhoZq//79ZcY//PBDNWzY8JqPk56eriFDhigiIkKGYWjDhg1u203T1PTp0xUeHq6AgAD169evzPVCZ86c0ejRoxUUFKT69etr/PjxKigo8ORpAQAAG/Ko7IwaNUqTJk3S9u3bVVJSopKSEm3btk2TJ0/WyJEjr/k4hYWF6tixo5YsWVLu9rlz52rRokVavny5MjMzVa9ePQ0YMMDtflyjR4/WJ598os2bN+utt95Senq6HnjgAU+eFgAAsCGPPo319NNP6+jRo+rbt6/q1Ll4iNLSUo0dO/a6rtmJi4tTXFxcudtM09TChQv15JNPur6c8OWXX1aTJk20YcMGjRw5Up999pk2bdqk//znP7r11lslSYsXL9agQYP05z//WREREZ48PQAAYCMezez4+fnptdde0+eff65Vq1Zp/fr1Onz4sFauXCk/P79KCXbkyBHl5uaqX79+rrHg4GDFxsZq165dkqRdu3apfv36rqIjSf369ZOPj48yMzOveOyioiLl5+e7LQAAwJ48mtm55KabbtJNN91UWVnc5ObmSpKaNGniNt6kSRPXttzcXIWGhrptr1OnjkJCQlz7lCclJUXJycmVnBhAbWOaputnZ0mxhUkA73L56+Hy14lVPCo7JSUlSktL09atW/XNN9+otLTUbfu2bdsqJVxVSUpKUmJioms9Pz9fkZGRFiYCUBNdfv3grz/eYmESwHs5nU7VrVvX0gwelZ3JkycrLS1NP//5z9W+fXsZhlHZuRQWFiZJOnnypNstKU6ePKlOnTq59vnmm2/cHldcXKwzZ864Hl8ef39/+fv7V3pmAADgfTwqO6tXr9aaNWs0aNCgys7jEh0drbCwMG3dutVVbvLz85WZmanf/va3kqTu3bvr7Nmz2rNnj7p27Srp4qxSaWmpYmNjqywbAEiSw+Fw/fx/7fvJ4VuhKwMA23CWFLtmOy9/nVjFo1emn5+fWrVqVeFfXlBQoEOHDrnWjxw5oqysLIWEhKhZs2Z67LHH9Mwzz6h169aKjo7WU089pYiICA0dOlSS1LZtWw0cOFATJkzQ8uXLdeHCBU2cOFEjR47kk1gAqtzls9oO3zqUHaAcVXH253p59Gms3/3ud3r22WcrfNHRBx98oM6dO6tz586SpMTERHXu3FnTp0+XJD3++ON69NFH9cADD+i2225TQUGBNm3a5NYSV61apZtvvll9+/bVoEGD1KNHDz3//PMVygUAAOzDo/+GvPvuu9q+fbs2btyodu3a6YYbbnDbvn79+ms6Tu/eva9amAzD0KxZszRr1qwr7hMSEqJXX3312oIDAIBax6OyU79+fQ0bNqyyswAAAFQ6j8pOampqZecAAACoEh5dsyNd/Ij3li1b9Ne//lXnzp2TJJ04cYKbcAIAAK/i0czOsWPHNHDgQB0/flxFRUW6++67FRgYqDlz5qioqEjLly+v7JwAAAAe8WhmZ/Lkybr11lv13XffKSAgwDU+bNgwbd26tdLCAQAAVJRHMzsZGRl6//33y9z0MyoqSl9//XWlBAMAAKgMHs3slJaWqqSkpMz4V199pcDAwAqHAgAAqCwelZ3+/ftr4cKFrnXDMFRQUKAZM2ZU6S0kAAAArpdHp7Hmz5+vAQMG6JZbbpHT6dR9992ngwcPqlGjRvrb3/5W2RkBAAA85lHZadq0qT788EOtXr1a+/fvV0FBgcaPH6/Ro0e7XbAMAABgNY/vWlenTh2NGTOmMrMAAABUOo/Kzssvv3zV7WPHjvUoDADUVM7Ssh/aQPUyTVNF//178Pfx9Yq7bddW3vZ68KjsTJ482W39woUL+v777+Xn56e6detSdgDUOr/+aLPVEQBcgUefxvruu+/cloKCAh04cEA9evTgAmUAAOBVPL5m58dat26t2bNna8yYMfr8888r67AA4LUcDocyMjKsjoH/On/+vPr37y9Jeuedd/jAjJdwOBxWR6i8siNdvGj5xIkTlXlIAPBahmHwhuqlAgIC+LuBi0dl54033nBbN01TOTk5eu6553THHXdUSjAAAIDK4FHZGTp0qNu6YRhq3Lix7rrrLs2fP78yctmGaZpyOp1Wx8B/nT9/vtyfYS2Hw8EnZwBUGY/KTmlpaWXnsC2n06mePXtaHQPluHRuH9bLyMjglAOAKuPRp7EAAABqCo9mdhITE6953wULFnjyK2ypefPmTNVbzDRNmaYp6eLpV/4+rGOapo4dO2Z1DAC1gEdlZ9++fdq3b58uXLigNm3aSJK++OIL+fr6qkuXLq79eCNxZxiGfHyYTAMkTocDqD4elZ0hQ4YoMDBQL730kho0aCDp4hcNJiQkqGfPnvrd735XqSEBAAA85dE0w/z585WSkuIqOpLUoEEDPfPMM3waCwAAeBWPyk5+fr5OnTpVZvzUqVM6d+5chUMBAABUFo/KzrBhw5SQkKD169frq6++0ldffaV169Zp/PjxGj58eGVnBAAA8JhH1+wsX75cU6dO1X333acLFy5cPFCdOho/frzmzZtXqQEBAAAqwqOyU7duXS1dulTz5s3T4cOHJUktW7ZUvXr1KjUcAABARVXoc9A5OTnKyclR69atVa9ePdf3lwAAAHgLj8rOt99+q759++qmm27SoEGDlJOTI0kaP348HzsHAABexaOyM2XKFN1www06fvy46tat6xq/9957tWnTpkoLBwAAUFEeXbPzzjvv6O2331bTpk3dxlu3bs3XvwMAAK/i0cxOYWGh24zOJWfOnJG/v3+FQwEAAFQWj8pOz5499fLLL7vWDcNQaWmp5s6dqz59+lRaOAAAgIry6DTW3Llz1bdvX33wwQf64Ycf9Pjjj+uTTz7RmTNn9N5771V2RgAAAI95NLPTvn17ffHFF+rRo4fuueceFRYWavjw4dq3b59atmxZ2RkBAAA8dt0zOxcuXNDAgQO1fPly/b//9/+qIhMAAEClue6ZnRtuuEH79++viizlioqKkmEYZZZHHnlEktS7d+8y2x566KFqywcAALybR6exxowZoxUrVlR2lnL95z//cX1Tc05OjjZv3ixJ+uUvf+naZ8KECW77zJ07t1qyAQAA7+fRBcrFxcVauXKltmzZoq5du5a5J9aCBQsqJZwkNW7c2G199uzZatmype68807XWN26dRUWFlZpvxMAANjHdZWdL7/8UlFRUfr444/VpUsXSdIXX3zhto9hGJWX7kd++OEHvfLKK0pMTHT7PatWrdIrr7yisLAwDRkyRE899VS53wN0SVFRkYqKilzr+fn5VZYZAABY67rKTuvWrZWTk6Pt27dLunh7iEWLFqlJkyZVEu7HNmzYoLNnzyo+Pt41dt9996l58+aKiIjQ/v37NW3aNB04cEDr16+/4nFSUlKUnJxcDYkBAIDVrqvs/Piu5hs3blRhYWGlBrqaFStWKC4uThEREa6xBx54wPVzhw4dFB4err59++rw4cNX/Bh8UlKSEhMTXev5+fmKjIysuuAAAMAyHl2zc8mPy09VOnbsmLZs2XLVGRtJio2NlSQdOnToimXH39+f21oAAFBLXNensS59tPvHY9UhNTVVoaGh+vnPf37V/bKysiRJ4eHh1ZAKAAB4u+s+jRUfH++aFXE6nXrooYfKfBrrp2ZfrldpaalSU1M1btw41anz/0c+fPiwXn31VQ0aNEgNGzbU/v37NWXKFPXq1UsxMTGVmgEAANRM11V2xo0b57Y+ZsyYSg1zJVu2bNHx48d1//33u437+flpy5YtWrhwoQoLCxUZGakRI0boySefrJZcAADA+11X2UlNTa2qHFfVv3//cq8PioyM1M6dOy1IBAD2YZqmnE6n1TEq7Pz58+X+XFM5HI5qu1TE7ip0gTIAoOZzOp3q2bOn1TEqVf/+/a2OUGEZGRkKCAiwOoYteHS7CAAAgJqCmR0AqOUcDocyMjKsjlFhl5+Os8MpIIfDYXUE26DsAEAtZxiGbU6XXO1WQai9OI0FAABsjbIDAABsjbIDAABsjbIDAABsjbIDAABsjbIDAABsjbIDAABsje/ZqWKX39OrtLTUwiSAd7n89VDeve8AoLJQdqrY5TfXO378uIVJAO/ldDr5MjgAVYbTWAAAwNaY2alil9/bpFmzZvLxoV8C0sXTWJdmO7kHEICqRNmpYpffiM7Hx4eyA5Sjpt+wEYB3450XAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAADYGmUHAGAb6enpGjx4sNLT062OAi/i1WVn5syZMgzDbbn55ptd251Opx555BE1bNhQN954o0aMGKGTJ09amBgAYBWn06mZM2cqNzdXycnJcjqdVkeCl/DqsiNJ7dq1U05Ojmt59913XdumTJmiN998U2vXrtXOnTt14sQJDR8+3MK0AACrvPDCC8rPz5ck5eXl6cUXX7Q4EbxFHasD/JQ6deooLCyszHheXp5WrFihV199VXfddZckKTU1VW3bttXu3bvVrVu36o4KALBIdna2Xn75Zbexl156Sffcc48iIyMtSgVv4fUzOwcPHlRERIRatGih0aNH6/jx45KkPXv26MKFC+rXr59r35tvvlnNmjXTrl27rIoLAKhmpmkqOTlZpmle0zhqH68uO7GxsUpLS9OmTZu0bNkyHTlyRD179tS5c+eUm5srPz8/1a9f3+0xTZo0UW5u7lWPW1RUpPz8fLcFAFAzHTlyRFlZWeVuy8rK0pEjR6o3ELyOV5/GiouLc/0cExOj2NhYNW/eXGvWrFFAQIDHx01JSVFycnJlRAQAAF7Oq2d2fqx+/fq66aabdOjQIYWFhemHH37Q2bNn3fY5efJkudf4XC4pKUl5eXmuJTs7uwpTAwCqUlRUlAIDA8vdFhgYqKioqOoNBK9To8pOQUGBDh8+rPDwcHXt2lU33HCDtm7d6tp+4MABHT9+XN27d7/qcfz9/RUUFOS2AABqpmPHjuncuXPlbjt37pyOHTtWzYngbby67EydOlU7d+7U0aNH9f7772vYsGHy9fXVqFGjFBwcrPHjxysxMVHbt2/Xnj17lJCQoO7du/NJLACoRaKiotStWzcZhuE2bhiGunfvzswOvPuana+++kqjRo3St99+q8aNG6tHjx7avXu3GjduLEn6y1/+Ih8fH40YMUJFRUUaMGCAli5danFqAEB1MgxD06ZN04gRI9w+eeXj46Np06aVKUGofby67Kxevfqq2x0Oh5YsWaIlS5ZUUyIAgDeKjIxUQkKCVqxY4RpLSEhQ06ZNLUwFb+HVp7EAALhWCQkJrpn/0NBQxcfHWxsIXoOyAwCwBYfDoaSkJIWFhemJJ56Qw+GwOhK8hFefxrIb0zRVWlpqdYxazTRN1zn9SzeXhTX4VltUhV69eqlXr15Wx4CXoexUIz7+CABA9eM0FgAAsDVmdqqYw+FQRkaG1THwX+fPn1f//v0lSe+8806FbjuCysO1FQCqEmWnihmGwRuqlwoICODvBgBqAU5jAQAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW/PqspOSkqLbbrtNgYGBCg0N1dChQ3XgwAG3fXr37i3DMNyWhx56yKLEAADA23h12dm5c6ceeeQR7d69W5s3b9aFCxfUv39/FRYWuu03YcIE5eTkuJa5c+dalBgAAHibOlYHuJpNmza5raelpSk0NFR79uxRr169XON169ZVWFhYdccDAAA1gFfP7PxYXl6eJCkkJMRtfNWqVWrUqJHat2+vpKQkff/991c9TlFRkfLz890WAABgT149s3O50tJSPfbYY7rjjjvUvn171/h9992n5s2bKyIiQvv379e0adN04MABrV+//orHSklJUXJycnXEBgAAFjNM0zStDnEtfvvb32rjxo1699131bRp0yvut23bNvXt21eHDh1Sy5Yty92nqKhIRUVFrvX8/HxFRkYqLy9PQUFBlZ4d3uP8+fPq2bOnJCkjI0MBAQEWJwIAeCo/P1/BwcE/+f5dI2Z2Jk6cqLfeekvp6elXLTqSFBsbK0lXLTv+/v7y9/ev9JwAAMD7eHXZMU1Tjz76qF5//XXt2LFD0dHRP/mYrKwsSVJ4eHgVpwMAADWBV5edRx55RK+++qr+8Y9/KDAwULm5uZKk4OBgBQQE6PDhw3r11Vc1aNAgNWzYUPv379eUKVPUq1cvxcTEWJweAAB4A68uO8uWLZN08YsDL5eamqr4+Hj5+flpy5YtWrhwoQoLCxUZGakRI0boySeftCAtAADwRl5ddn7q2unIyEjt3LmzmtIAAICaqEZ9zw4AAMD1ouwAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbo+wAAABbq2N1ANQMpmnK6XRaHaPCzp8/X+7PNZXD4ZBhGFbHAACvRtnBNXE6nerZs6fVMSpV//79rY5QYRkZGQoICLA6BgB4NU5jAQAAW2NmB9fE4XAoIyPD6hgVdvnpODucAnI4HFZHAACvR9nBNTEMwzanS+rWrWt1BABANeI0FgAAsDXblJ0lS5YoKipKDodDsbGx+ve//211JAAA4AVsUXZee+01JSYmasaMGdq7d686duyoAQMG6JtvvrE6GgAAsJgtys6CBQs0YcIEJSQk6JZbbtHy5ctVt25drVy50upoAADAYjW+7Pzwww/as2eP+vXr5xrz8fFRv379tGvXrnIfU1RUpPz8fLcFAADYU40vO6dPn1ZJSYmaNGniNt6kSRPl5uaW+5iUlBQFBwe7lsjIyOqICgAALFDjy44nkpKSlJeX51qys7OtjgQAAKpIjf+enUaNGsnX11cnT550Gz958qTCwsLKfYy/v7/8/f2rIx4AALBYjZ/Z8fPzU9euXbV161bXWGlpqbZu3aru3btbmAwAAHiDGj+zI0mJiYkaN26cbr31Vt1+++1auHChCgsLlZCQYHU0AABgMVuUnXvvvVenTp3S9OnTlZubq06dOmnTpk1lLloGAAC1j2Gapml1CKvl5+crODhYeXl5CgoKsjoOAAC4Btf6/l3jr9kBAAC4GlucxqqoS5NbfLkgAAA1x6X37Z86SUXZkXTu3DlJ4ssFAQCogc6dO6fg4OArbueaHV38qPqJEycUGBgowzCsjoMqlp+fr8jISGVnZ3ONFmAzvL5rF9M0de7cOUVERMjH58pX5jCzo4v30mratKnVMVDNgoKC+McQsCle37XH1WZ0LuECZQAAYGuUHQAAYGuUHdQ6/v7+mjFjBvdHA2yI1zfKwwXKAADA1pjZAQAAtkbZAQAAtkbZAQAAtkbZAQDUCKZp6oEHHlBISIgMw1BWVtZV9z969Kjbfjt27JBhGDp79myVZ4V34UsFAQA1wqZNm5SWlqYdO3aoRYsWatSo0VX3j4yMVE5Ozk/uB/uj7AAAaoTDhw8rPDxc//M//3NN+/v6+iosLKyKU6Em4DQWvMrf//53dejQQQEBAWrYsKH69eunwsJC9e7dW4899pjbvkOHDlV8fLxrvaioSNOmTVNkZKT8/f3VqlUrrVixwrX9k08+0eDBgxUUFKTAwED17NlThw8fdm1/8cUX1bZtWzkcDt18881aunSpa9sPP/ygiRMnKjw8XA6HQ82bN1dKSoqki1PrM2fOVLNmzeTv76+IiAhNmjSpav6AgFoqPj5ejz76qI4fPy7DMBQVFaVNmzapR48eql+/vho2bKjBgwe7vaZ/fBoLtRczO/AaOTk5GjVqlObOnathw4bp3LlzysjI0LV+FdTYsWO1a9cuLVq0SB07dtSRI0d0+vRpSdLXX3+tXr16qXfv3tq2bZuCgoL03nvvqbi4WJK0atUqTZ8+Xc8995w6d+6sffv2acKECapXr57GjRunRYsW6Y033tCaNWvUrFkzZWdnKzs7W5K0bt06/eUvf9Hq1avVrl075ebm6sMPP6yaPySglnr22WfVsmVLPf/88/rPf/4jX19fpaenKzExUTExMSooKND06dM1bNgwZWVlXfWmkKh9KDvwGjk5OSouLtbw4cPVvHlzSVKHDh2u6bFffPGF1qxZo82bN6tfv36SpBYtWri2L1myRMHBwVq9erVuuOEGSdJNN93k2j5jxgzNnz9fw4cPlyRFR0fr008/1V//+leNGzdOx48fV+vWrdWjRw8ZhuHKJ0nHjx9XWFiY+vXrpxtuuEHNmjXT7bffXrE/DABugoODFRgY6HZqasSIEW77rFy5Uo0bN9ann36q9u3bWxETXorqC6/RsWNH9e3bVx06dNAvf/lLvfDCC/ruu++u6bFZWVny9fXVnXfeecXtPXv2dBWdyxUWFurw4cMaP368brzxRtfyzDPPuKbE4+PjlZWVpTZt2mjSpEl65513XI//5S9/qfPnz6tFixaaMGGCXn/9ddeMEYCqc/DgQY0aNUotWrRQUFCQoqKiJF38DwhwOcoOvIavr682b96sjRs36pZbbtHixYvVpk0bHTlyRD4+PmVOZ124cMH1c0BAwFWPfbXtBQUFkqQXXnhBWVlZruXjjz/W7t27JUldunTRkSNH9PTTT+v8+fP61a9+pV/84heSLn7i48CBA1q6dKkCAgL08MMPq1evXm75AFS+IUOG6MyZM3rhhReUmZmpzMxMSRevsQMuR9mBVzEMQ3fccYeSk5O1b98++fn56fXXX1fjxo2Vk5Pj2q+kpEQff/yxa71Dhw4qLS3Vzp07yz1uTEyMMjIyyi0gTZo0UUREhL788ku1atXKbYmOjnbtFxQUpHvvvVcvvPCCXnvtNa1bt05nzpyRdLFMDRkyRIsWLdKOHTu0a9cuffTRR5X1xwLgR7799lsdOHBATz75pPr27au2bdte80wwah+u2YHXyMzM1NatW9W/f3+FhoYqMzNTp06dUtu2bVWvXj0lJibqn//8p1q2bKkFCxa4fTFYVFSUxo0bp/vvv991gfKxY8f0zTff6Fe/+pUmTpyoxYsXa+TIkUpKSlJwcLB2796t22+/XW3atFFycrImTZqk4OBgDRw4UEVFRfrggw/03XffKTExUQsWLFB4eLg6d+4sHx8frV27VmFhYapfv77S0tJUUlKi2NhY1a1bV6+88ooCAgLcrusBULkaNGighg0b6vnnn1d4eLiOHz+uJ554wupY8FKUHXiNoKAgpaena+HChcrPz1fz5s01f/58xcXF6cKFC/rwww81duxY1alTR1OmTFGfPn3cHr9s2TL94Q9/0MMPP6xvv/1WzZo10x/+8AdJUsOGDbVt2zb9/ve/15133ilfX1916tRJd9xxhyTpN7/5jerWrat58+bp97//verVq6cOHTq4Pu4eGBiouXPn6uDBg/L19dVtt92mf/3rX/Lx8VH9+vU1e/ZsJSYmqqSkRB06dNCbb76phg0bVuufH1Cb+Pj4aPXq1Zo0aZLat2+vNm3aaNGiRerdu7fV0eCFDPNaP9cLAABQA3HNDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDoBaJS0tTfXr13etz5w5U506dbIsD4CqR9kBUCPFx8fLMIwyy6FDh676uHvvvVdffPFFNaUE4A24XQSAGmvgwIFKTU11G2vcuPFVHxMQEKCAgICqjAXAyzCzA6DG8vf3V1hYmNvy7LPPqkOHDqpXr54iIyP18MMPq6CgwPWYH5/GAmB/lB0AtuLj46NFixbpk08+0UsvvaRt27bp8ccftzoWAAtxGgtAjfXWW2/pxhtvdK3HxcVp7dq1rvWoqCg988wzeuihh7R06VIrIgLwApQdADVWnz59tGzZMtd6vXr1tGXLFqWkpOjzzz9Xfn6+iouL5XQ69f3336tu3boWpgVgFU5jAaix6tWrp1atWrmWoqIiDR48WDExMVq3bp327NmjJUuWSJJ++OEHi9MCsAozOwBsY8+ePSotLdX8+fPl43Px/3Jr1qyxOBUAqzGzA8A2WrVqpQsXLmjx4sX68ssv9X//939avny51bEAWIyyA8A2OnbsqAULFmjOnDlq3769Vq1apZSUFKtjAbCYYZqmaXUIAACAqsLMDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsDXKDgAAsLX/D1Ttl6723YEcAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for var in numerical_cols:\n",
    "    fig, ax = plt.subplots()\n",
    "    ax1 = sns.boxplot(df_train, x=\"Fail\", y=var, palette=fail_colors)\n",
    "    ax1.set_title(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temperature\n",
      "               min         max       mean         var        std\n",
      "Fail                                                            \n",
      "fail     48.091962  150.880308  91.488798  532.946807  23.085641\n",
      "success   9.322154  255.607829  66.782200  560.541031  23.675748\n",
      "--------------------------------------------------------------------\n",
      "\n",
      "Pressure\n",
      "               min         max        mean          var        std\n",
      "Fail                                                              \n",
      "fail     50.822002  189.995681  118.015668  1037.291161  32.207005\n",
      "success   3.729680  170.876875   76.348121   946.063913  30.758152\n",
      "--------------------------------------------------------------------\n",
      "\n",
      "VibrationX\n",
      "               min         max        mean         var        std\n",
      "Fail                                                             \n",
      "fail     20.120467  161.188743  101.183409  743.527662  27.267704\n",
      "success   3.846343  230.861142   71.715461  915.761878  30.261558\n",
      "--------------------------------------------------------------------\n",
      "\n",
      "VibrationY\n",
      "               min         max        mean         var        std\n",
      "Fail                                                             \n",
      "fail     71.189361  193.569947  124.684308  924.393350  30.403838\n",
      "success  10.057744  176.197917   68.973889  861.738749  29.355387\n",
      "--------------------------------------------------------------------\n",
      "\n",
      "VibrationZ\n",
      "               min         max        mean        var        std\n",
      "Fail                                                            \n",
      "fail     20.522642  200.242674  106.393047  924.77285  30.410078\n",
      "success  18.824634  230.951134   69.360797  653.62462  25.566083\n",
      "--------------------------------------------------------------------\n",
      "\n",
      "Frequency\n",
      "               min         max        mean         var        std\n",
      "Fail                                                             \n",
      "fail     48.923777  178.090303  100.236164  576.541367  24.011276\n",
      "success   4.428284  158.811673   66.750269  810.181968  28.463696\n",
      "--------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Summary measures for quantitative independent features and label to search for relationships\n",
    "for var in numerical_cols:\n",
    "    print(var)\n",
    "    print(df_train.groupby(\"Fail\")[var].agg([\"min\", \"max\", \"mean\", \"var\", \"std\"]))\n",
    "    print(\"--------------------------------------------------------------------\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the multicolinearity between continuous variables:\n",
    "\n",
    " - There's some colinearity between them (Pressure with Temperature, Vibration Z with Vibration X and Y ).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pearson correlation\n",
    "pearson_corr = df_train[numerical_cols].corr()\n",
    "# spearman correlation\n",
    "spearman_corr = df_train[numerical_cols].corr(method='spearman')\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(14,6))\n",
    "\n",
    "sns.heatmap(pearson_corr, fmt='.0%', annot=True, square=True, ax=ax1)\n",
    "ax1.set_title(\"Pearson correlation (linear)\")\n",
    "\n",
    "sns.heatmap(spearman_corr, fmt='.0%', annot=True, square=True, ax=ax2)\n",
    "ax2.set_title(\"Spearman correlation\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "\n",
    "After testing some feature engineering approaches to get the model to generalize and not overfit, I propose two approaches:\n",
    "\n",
    "1. Drop variables `Preset_1` and `Preset_2`\n",
    "\n",
    "2. Drop variables `Preset_1` and `Preset_2`, create feature `VibrationXYZ` (product of `VibrationX`, `VibrationY`, `VibrationZ`) and create feature `Temp_Pressure` (product of `Temperature`, `Pressure`)\n",
    "\n",
    "Both of them have shown pretty great results on the model, let's take a look."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create the new features and plot their distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating datasets for the proposed approaches\n",
    "df_train2 = df_train.copy()\n",
    "df_test2 = df_test.copy()\n",
    "\n",
    "numerical_cols_2 = numerical_cols.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VibrationXYZ\n",
      "                  min           max          mean           var            std\n",
      "Fail                                                                          \n",
      "fail     74489.450928  2.554348e+06  1.331187e+06  2.936653e+11  541908.934366\n",
      "success   3738.141433  1.762027e+06  3.971142e+05  1.097564e+11  331295.055738\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_col_vibration = \"VibrationXYZ\"\n",
    "old_cols_vibration = [\"VibrationX\", \"VibrationY\", \"VibrationZ\"]\n",
    "\n",
    "df_train2[new_col_vibration] = df_train2[old_cols_vibration[0]] * df_train2[old_cols_vibration[1]] * df_train2[old_cols_vibration[2]]\n",
    "df_test2[new_col_vibration] = df_test2[old_cols_vibration[0]] * df_test2[old_cols_vibration[1]] * df_test2[old_cols_vibration[2]]\n",
    "\n",
    "df_train2.drop(columns=old_cols_vibration, inplace=True)\n",
    "df_test2.drop(columns=old_cols_vibration, inplace=True)\n",
    "\n",
    "numerical_cols_2 += [new_col_vibration]\n",
    "\n",
    "for col in old_cols_vibration:\n",
    "    numerical_cols_2.remove(col)\n",
    "\n",
    "\n",
    "print(new_col_vibration)\n",
    "print(df_train2.groupby(\"Fail\")[new_col_vibration].agg([\"min\", \"max\", \"mean\", \"var\", \"std\"]))\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.boxplot(df_train2, x=\"Fail\", y=new_col_vibration, palette=fail_colors)\n",
    "ax.set_title(new_col_vibration)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temp_Pressure\n",
      "                 min           max          mean           var          std\n",
      "Fail                                                                       \n",
      "fail     3368.306670  20708.904420  10915.903912  1.890168e+07  4347.605825\n",
      "success   214.377694  23976.505641   5410.484479  1.130779e+07  3362.705136\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_col_temp_press = \"Temp_Pressure\"\n",
    "old_cols_temp_press = [\"Temperature\", \"Pressure\"]\n",
    "\n",
    "df_train2[new_col_temp_press] = df_train2[old_cols_temp_press[0]] * df_train2[old_cols_temp_press[1]]\n",
    "df_test2[new_col_temp_press] = df_test2[old_cols_temp_press[0]] * df_test2[old_cols_temp_press[1]]\n",
    "\n",
    "df_train2.drop(columns=old_cols_temp_press, inplace=True)\n",
    "df_test2.drop(columns=old_cols_temp_press, inplace=True)\n",
    "\n",
    "numerical_cols_2 += [new_col_temp_press]\n",
    "\n",
    "for col in old_cols_temp_press:\n",
    "    numerical_cols_2.remove(col)\n",
    "\n",
    "\n",
    "print(new_col_temp_press)\n",
    "print(df_train2.groupby(\"Fail\")[new_col_temp_press].agg([\"min\", \"max\", \"mean\", \"var\", \"std\"]))\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.boxplot(df_train2, x=\"Fail\", y=new_col_temp_press, palette=fail_colors)\n",
    "ax.set_title(new_col_temp_press)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From now on, we'll go on with two dataframes for the two proposed approaches:\n",
    "\n",
    "1. *df_train* and *df_test* (removed features `Preset_1` and `Preset_2`)\n",
    "\n",
    "2. *df_train2* and *df_test2* (removed features `Preset_1` and `Preset_2`, created features `VibrationXYZ` and `Temp_Pressure`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply feature scaling preprocessing on the numerical variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing the target variable to feed the model \n",
    "dict_fail_model = {\n",
    "    \"fail\": 1,\n",
    "    \"success\": 0,\n",
    "}\n",
    "\n",
    "df_train = df_train.replace(dict_fail_model)\n",
    "df_test = df_test.replace(dict_fail_model)\n",
    "\n",
    "df_train2 = df_train2.replace(dict_fail_model)\n",
    "df_test2 = df_test2.replace(dict_fail_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating the feature matrix and target vector\n",
    "X_train = df_train.drop(columns=\"Fail\")\n",
    "y_train = df_train[\"Fail\"]\n",
    "\n",
    "X_train2 = df_train2.drop(columns=\"Fail\")\n",
    "y_train2 = df_train2[\"Fail\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = ColumnTransformer(\n",
    "    [\n",
    "        (\"cat\", \"drop\", categorical_cols),\n",
    "        (\"num\", StandardScaler(), numerical_cols),\n",
    "    ],\n",
    "    remainder=\"passthrough\"\n",
    ")\n",
    "\n",
    "preprocessor2 = ColumnTransformer(\n",
    "    [\n",
    "        (\"cat\", \"drop\", categorical_cols),\n",
    "        (\"num\", StandardScaler(), numerical_cols_2), # numerical columns updated with feature engineering\n",
    "    ],\n",
    "    remainder=\"passthrough\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model\n",
    "\n",
    "It's considered best practice in machine learning to make decisions about the model using only the training set and cross-validation set, and to not look at the test set at all.\n",
    "\n",
    "This ensures that the test set is a fair and not overly optimistic estimate of how well the model will generalize to new data. It should be used only to what it was made for: testing the model on unseen data.\n",
    "\n",
    "### Evaluation Metric\n",
    "\n",
    "The evaluation metric should be picked based on the business knowledge.\n",
    "\n",
    "Accuracy is inappropriate for imbalanced classification problems as we can get a pretty great accuracy score with a simple Dummy Classifier that always predicts the larger class, or that the equipment isn't failing. That wouldn't be very helpful so we need a better metric.\n",
    "\n",
    "The goal of the model I'm building is to predict whether the FPSO vessel equipment is on the verge of failing based on the sensors measurements over time. With that in mind, the model evaluation metric I'll be using is `recall`. \n",
    "\n",
    "Having a higher recall means when in doubt, the model would predict the equipment is failing so we don’t risk missing fixing the equipment that actually is failing. This way, we’re more likely to correctly identify more failures.\n",
    "\n",
    "It means we can come across some false positives, that is, predict that the equipment is about to fail when it's actually not. In this scenario, it's better to have some false positives than false negatives (when the equipment fails but the model didn't catch it).\n",
    "\n",
    "We'll be looking at the f1 score as well which is basically the harmonic mean between recall and precision. This way we don't loose track of how much we're hurting precision in order to improve recall.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_classifier_models(models_list, X, y, cv, scoring):\n",
    "    \"\"\"\n",
    "    Trains classification models (passed on a list of models) applying cross validation\n",
    "    with the amount of folds passed on cv and returns a dataframe with all\n",
    "    the models' mean and standard deviations of the selected scoring method for\n",
    "    both training and validation sets.\n",
    "    \n",
    "    :param models_list: (list) \n",
    "    List of instantiated models to train.\n",
    "    \n",
    "    :param X: (dataframe or numpy array) \n",
    "    Independent variables dataframe.\n",
    "    \n",
    "    :param y: (series or numpy array)\n",
    "    Target vector.\n",
    "    \n",
    "    :param cv: (int)\n",
    "    Amount of folds for the cross validation technique.\n",
    "    \n",
    "    :param scoring: (str)\n",
    "    Score metric to measure model performance on.\n",
    "    \"\"\"\n",
    "\n",
    "    df_models = pd.DataFrame()\n",
    "\n",
    "    # train and evaluate each one of the models\n",
    "    for model in models_list:\n",
    "        pipe = Pipeline(\n",
    "            [\n",
    "                ('preprocessor', preprocessor),\n",
    "                ('classifier', model)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "        scores = cross_validate(\n",
    "            pipe, X, y, cv=cv, scoring=scoring, return_train_score=True\n",
    "        )\n",
    "\n",
    "        mean_train_score = scores['train_score'].mean()\n",
    "        std_train_score = scores['train_score'].std()\n",
    "\n",
    "        mean_val_score = scores['test_score'].mean()\n",
    "        std_val_score = scores['test_score'].std()\n",
    "\n",
    "        model_dict = {\n",
    "            'model': type(model).__name__,\n",
    "            'scoring': scoring,\n",
    "            'mean_train_score': mean_train_score,\n",
    "            'mean_val_score': mean_val_score,\n",
    "            'std_train_score': std_train_score,\n",
    "            'std_val_score': std_val_score,\n",
    "        }\n",
    "\n",
    "        df_temp = pd.DataFrame({k:[v] for k,v in model_dict.items()})\n",
    "        df_models = pd.concat([df_models, df_temp], ignore_index=True)\n",
    "        \n",
    "    return df_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = DummyClassifier(strategy=\"constant\", constant=0)\n",
    "lr = LogisticRegression(max_iter=400, random_state=random_state)\n",
    "dt = DecisionTreeClassifier(random_state=random_state)\n",
    "rf = RandomForestClassifier(random_state=random_state)\n",
    "sgd = SGDClassifier(loss='log_loss', penalty='elasticnet', random_state=random_state)\n",
    "gnb = GaussianNB()\n",
    "ada = AdaBoostClassifier(random_state=random_state)\n",
    "\n",
    "models_list = [dummy, lr, rf, dt, sgd, gnb, ada]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>model</th>\n",
       "      <th>scoring</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>mean_val_score</th>\n",
       "      <th>std_train_score</th>\n",
       "      <th>std_val_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DummyClassifier</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.918750</td>\n",
       "      <td>0.918750</td>\n",
       "      <td>0.000957</td>\n",
       "      <td>0.003827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.941797</td>\n",
       "      <td>0.935937</td>\n",
       "      <td>0.002278</td>\n",
       "      <td>0.020010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.939063</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.934375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.935937</td>\n",
       "      <td>0.939063</td>\n",
       "      <td>0.011614</td>\n",
       "      <td>0.018087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.937109</td>\n",
       "      <td>0.932813</td>\n",
       "      <td>0.007350</td>\n",
       "      <td>0.029064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.999609</td>\n",
       "      <td>0.942187</td>\n",
       "      <td>0.000781</td>\n",
       "      <td>0.018222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    model   scoring  mean_train_score  mean_val_score  \\\n",
       "0         DummyClassifier  accuracy          0.918750        0.918750   \n",
       "1      LogisticRegression  accuracy          0.941797        0.935937   \n",
       "2  RandomForestClassifier  accuracy          1.000000        0.939063   \n",
       "3  DecisionTreeClassifier  accuracy          1.000000        0.934375   \n",
       "4           SGDClassifier  accuracy          0.935937        0.939063   \n",
       "5              GaussianNB  accuracy          0.937109        0.932813   \n",
       "6      AdaBoostClassifier  accuracy          0.999609        0.942187   \n",
       "\n",
       "   std_train_score  std_val_score  \n",
       "0         0.000957       0.003827  \n",
       "1         0.002278       0.020010  \n",
       "2         0.000000       0.015934  \n",
       "3         0.000000       0.020131  \n",
       "4         0.011614       0.018087  \n",
       "5         0.007350       0.029064  \n",
       "6         0.000781       0.018222  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_acc = run_classifier_models(models_list, X_train, y_train, cv=5, scoring='accuracy')\n",
    "df_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we stated before, the Dummy classifier does a great job accuracy wise even when it always predicts that the equipment is all good.\n",
    "\n",
    "On the other hand, it didn't do well even on the train set as far as recall is concerned because as it always predicts the negative class (success), when calculating the recall, the positive class (fail) is the one being counted:\n",
    "\n",
    "> Recall = TruePositives / (TruePositives + FalseNegatives)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>model</th>\n",
       "      <th>scoring</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>mean_val_score</th>\n",
       "      <th>std_train_score</th>\n",
       "      <th>std_val_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DummyClassifier</td>\n",
       "      <td>recall</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>recall</td>\n",
       "      <td>0.495122</td>\n",
       "      <td>0.478182</td>\n",
       "      <td>0.016200</td>\n",
       "      <td>0.132565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>recall</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.461818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.112581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>recall</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.634545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.092959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>recall</td>\n",
       "      <td>0.640650</td>\n",
       "      <td>0.610909</td>\n",
       "      <td>0.199739</td>\n",
       "      <td>0.219345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>recall</td>\n",
       "      <td>0.841231</td>\n",
       "      <td>0.810909</td>\n",
       "      <td>0.056246</td>\n",
       "      <td>0.164984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>recall</td>\n",
       "      <td>0.995122</td>\n",
       "      <td>0.554545</td>\n",
       "      <td>0.009756</td>\n",
       "      <td>0.154171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    model scoring  mean_train_score  mean_val_score  \\\n",
       "0         DummyClassifier  recall          0.000000        0.000000   \n",
       "1      LogisticRegression  recall          0.495122        0.478182   \n",
       "2  RandomForestClassifier  recall          1.000000        0.461818   \n",
       "3  DecisionTreeClassifier  recall          1.000000        0.634545   \n",
       "4           SGDClassifier  recall          0.640650        0.610909   \n",
       "5              GaussianNB  recall          0.841231        0.810909   \n",
       "6      AdaBoostClassifier  recall          0.995122        0.554545   \n",
       "\n",
       "   std_train_score  std_val_score  \n",
       "0         0.000000       0.000000  \n",
       "1         0.016200       0.132565  \n",
       "2         0.000000       0.112581  \n",
       "3         0.000000       0.092959  \n",
       "4         0.199739       0.219345  \n",
       "5         0.056246       0.164984  \n",
       "6         0.009756       0.154171  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_recall = run_classifier_models(models_list, X_train, y_train, cv=5, scoring= 'recall')\n",
    "df_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>model</th>\n",
       "      <th>scoring</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>mean_val_score</th>\n",
       "      <th>std_train_score</th>\n",
       "      <th>std_val_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DummyClassifier</td>\n",
       "      <td>f1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>f1</td>\n",
       "      <td>0.580170</td>\n",
       "      <td>0.546697</td>\n",
       "      <td>0.018585</td>\n",
       "      <td>0.100105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>f1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.550453</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.095055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>f1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.615787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.084788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>f1</td>\n",
       "      <td>0.611916</td>\n",
       "      <td>0.610376</td>\n",
       "      <td>0.034364</td>\n",
       "      <td>0.117101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>f1</td>\n",
       "      <td>0.684556</td>\n",
       "      <td>0.670334</td>\n",
       "      <td>0.039058</td>\n",
       "      <td>0.111749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>f1</td>\n",
       "      <td>0.997531</td>\n",
       "      <td>0.604706</td>\n",
       "      <td>0.004938</td>\n",
       "      <td>0.137159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    model scoring  mean_train_score  mean_val_score  \\\n",
       "0         DummyClassifier      f1          0.000000        0.000000   \n",
       "1      LogisticRegression      f1          0.580170        0.546697   \n",
       "2  RandomForestClassifier      f1          1.000000        0.550453   \n",
       "3  DecisionTreeClassifier      f1          1.000000        0.615787   \n",
       "4           SGDClassifier      f1          0.611916        0.610376   \n",
       "5              GaussianNB      f1          0.684556        0.670334   \n",
       "6      AdaBoostClassifier      f1          0.997531        0.604706   \n",
       "\n",
       "   std_train_score  std_val_score  \n",
       "0         0.000000       0.000000  \n",
       "1         0.018585       0.100105  \n",
       "2         0.000000       0.095055  \n",
       "3         0.000000       0.084788  \n",
       "4         0.034364       0.117101  \n",
       "5         0.039058       0.111749  \n",
       "6         0.004938       0.137159  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f1 = run_classifier_models(models_list, X_train, y_train, cv=5, scoring='f1')\n",
    "df_f1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this first round of models, I picked two of them to move to the next step:\n",
    "\n",
    "- Decision Tree:\n",
    "\n",
    "    Both decision tree and random forest show some overfitting as the performance on the train set is perfect and on the validation set isn't nearly as great. We'll move forward with decision tree over random forest as it's a simpler model and has greater explainability. We can work on tunning it to prevent overfitting.\n",
    "\n",
    "- SGD Classifier:\n",
    "\n",
    "    The model didn't do so well on the train set, but it shows similar performance on the validation set indicating it's not overfitting and we can work on improving the model. This model is somewhat similar to the classic logistic regression with stochastic gradient descent learning and the power of regularization (adding penalty to the loss function to avoid overfitting).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tunning chosen models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_pipeline(model, preprocessor):\n",
    "    final_pipe = Pipeline(\n",
    "        [\n",
    "            ('preprocessor', preprocessor), \n",
    "            ('classifier', model)\n",
    "        ]\n",
    "    )\n",
    "    return final_pipe\n",
    "\n",
    "\n",
    "def train_grid_search(pipeline, X, y, paramgrid, cv, scoring):\n",
    "\n",
    "    print(type(pipeline[-1]).__name__)\n",
    "    \n",
    "    grid_search = GridSearchCV(\n",
    "        pipeline, paramgrid, cv=cv, scoring=scoring, return_train_score=True, verbose=1, n_jobs=2\n",
    "    )\n",
    "\n",
    "    grid_result = grid_search.fit(X, y)\n",
    "\n",
    "    print(f\"\\nBest score: {grid_result.best_score_} using parameters:\\n{grid_result.best_params_}\\n\")\n",
    "\n",
    "    return grid_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First round - with approach 1:\n",
    "- DecisionTree (df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier\n",
      "Fitting 5 folds for each of 28 candidates, totalling 140 fits\n",
      "\n",
      "Best score: 0.9418181818181818 using parameters:\n",
      "{'classifier__class_weight': {0: 1, 1: 100}, 'classifier__max_depth': 3}\n",
      "\n",
      "CPU times: total: 109 ms\n",
      "Wall time: 2.08 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "final_pipe_dt = build_pipeline(dt, preprocessor)\n",
    "\n",
    "# hyperparameter interval to be tested\n",
    "paramgrid_dt = {\n",
    "    'classifier__max_depth': [None, 3, 5, 10, 15, 20, 50],\n",
    "    'classifier__class_weight': [{0:1, 1:1}, {0:1, 1:10}, {0:1, 1:100}, {0:1, 1:300}]\n",
    "}\n",
    "\n",
    "cv_dt = train_grid_search(final_pipe_dt, X_train, y_train, paramgrid_dt, cv=5, scoring='recall')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- SGDClassifier (df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGDClassifier\n",
      "Fitting 5 folds for each of 1152 candidates, totalling 5760 fits\n",
      "\n",
      "Best score: 1.0 using parameters:\n",
      "{'classifier__class_weight': {0: 1, 1: 10}, 'classifier__eta0': 0.05, 'classifier__learning_rate': 'invscaling', 'classifier__max_iter': 1000, 'classifier__penalty': 'l1'}\n",
      "\n",
      "CPU times: total: 1.06 s\n",
      "Wall time: 47.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "final_pipe_sgd = build_pipeline(sgd, preprocessor)\n",
    "\n",
    "# hyperparameter interval to be tested\n",
    "paramgrid_sgd = {\n",
    "    'classifier__penalty': ['l1', 'l2', 'elasticnet', None],\n",
    "    'classifier__learning_rate': ['constant', 'optimal', 'invscaling', 'adaptive'],\n",
    "    'classifier__eta0': [0.05, 0.1, 0.5, 1, 5, 10],\n",
    "    'classifier__class_weight': [{0:1, 1:1}, {0:1, 1:10}, {0:1, 1:100}, {0:1, 1:300}],\n",
    "    'classifier__max_iter': [1000, 3000, 5000]\n",
    "}\n",
    "\n",
    "cv_sgd = train_grid_search(final_pipe_sgd, X_train, y_train, paramgrid_sgd, cv=5, scoring='recall')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second round - with approach 2 (with feature engineering):\n",
    "\n",
    "- DecisionTree (df_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier\n",
      "Fitting 5 folds for each of 28 candidates, totalling 140 fits\n",
      "\n",
      "Best score: 0.9418181818181818 using parameters:\n",
      "{'classifier__class_weight': {0: 1, 1: 300}, 'classifier__max_depth': 3}\n",
      "\n",
      "CPU times: total: 46.9 ms\n",
      "Wall time: 1.04 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "final_pipe_dt2 = build_pipeline(dt, preprocessor2)\n",
    "\n",
    "# hyperparameter interval to be tested\n",
    "paramgrid_dt = {\n",
    "    'classifier__max_depth': [None, 3, 5, 10, 15, 20, 50],\n",
    "    'classifier__class_weight': [{0:1, 1:1}, {0:1, 1:10}, {0:1, 1:100}, {0:1, 1:300}]\n",
    "}\n",
    "\n",
    "cv_dt2 = train_grid_search(final_pipe_dt2, X_train2, y_train2, paramgrid_dt, cv=5, scoring='recall')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- SGDClassifier (df_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGDClassifier\n",
      "Fitting 5 folds for each of 1152 candidates, totalling 5760 fits\n",
      "\n",
      "Best score: 1.0 using parameters:\n",
      "{'classifier__class_weight': {0: 1, 1: 10}, 'classifier__eta0': 10, 'classifier__learning_rate': 'constant', 'classifier__max_iter': 1000, 'classifier__penalty': 'l1'}\n",
      "\n",
      "CPU times: total: 391 ms\n",
      "Wall time: 39.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "final_pipe_sgd2 = build_pipeline(sgd, preprocessor2)\n",
    "\n",
    "# hyperparameter interval to be tested\n",
    "paramgrid_sgd = {\n",
    "    'classifier__penalty': ['l1', 'l2', 'elasticnet', None],\n",
    "    'classifier__learning_rate': ['constant', 'optimal', 'invscaling', 'adaptive'],\n",
    "    'classifier__eta0': [0.05, 0.1, 0.5, 1, 5, 10],\n",
    "    'classifier__class_weight': [{0:1, 1:1}, {0:1, 1:10}, {0:1, 1:100}, {0:1, 1:300}],\n",
    "    'classifier__max_iter': [1000, 3000, 5000]\n",
    "}\n",
    "\n",
    "cv_sgd2 = train_grid_search(final_pipe_sgd2, X_train2, y_train2, paramgrid_sgd, cv=5, scoring='recall')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = df_test.drop(columns=\"Fail\")\n",
    "y_test = df_test[\"Fail\"]\n",
    "\n",
    "X_test2 = df_test2.drop(columns=\"Fail\")\n",
    "y_test2 = df_test2[\"Fail\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First round - with approach 1:\n",
    "- DecisionTree (df_test)\n",
    "\n",
    "    The model gerenalizes well, there's only one datapoint (0.071 * 14 = ~1) of the fail class (Fail=True) that the model can't correctly classify. It does a pretty good job regarding false positives, missclassifying only about 20 datapoints (0.14 * 146 = ~20) of the not fail class (Fail=False)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---> DecisionTreeClassifier\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.86      0.92       146\n",
      "           1       0.38      0.93      0.54        14\n",
      "\n",
      "    accuracy                           0.86       160\n",
      "   macro avg       0.69      0.89      0.73       160\n",
      "weighted avg       0.94      0.86      0.89       160\n",
      "\n",
      "AUC: 0.8924\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_model_dt = cv_dt.best_estimator_\n",
    "final_predictions_dt = final_model_dt.predict(X_test)\n",
    "\n",
    "# print classification report\n",
    "print(f\"---> {type(final_model_dt[-1]).__name__}\")\n",
    "print(classification_report(y_test, final_predictions_dt))\n",
    "\n",
    "# print roc_auc\n",
    "print(\"AUC: {:.4f}\\n\".format(roc_auc_score(y_test, final_predictions_dt)))\n",
    "\n",
    "# plot confusion matrix and roc_auc\n",
    "fig , (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "sns.heatmap(confusion_matrix(y_test, final_predictions_dt, normalize='true'), annot=True, ax=ax1)\n",
    "ax1.set_title(f\"Confusion matrix - {type(final_model_dt[-1]).__name__}\")\n",
    "ax1.set_ylabel(\"Real\")\n",
    "ax1.set_xlabel(\"Predicted\")\n",
    "\n",
    "ax2 = plt.gca()\n",
    "RocCurveDisplay.from_estimator(final_model_dt, X_test, y_test, ax=ax2, alpha=0.8)\n",
    "ax2.set_title(f\"ROC curve - {type(final_model_dt[-1]).__name__}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- SGDClassifier (df_test)\n",
    "\n",
    "    The model does an even better job than the one above. It gerenalizes well, correcly classifies all datapoints of the fail class (Fail=True) and it has less false positives, only about 15 (0.1 * 146 = ~15). With that, the f1 score is more balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---> SGDClassifier\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.90      0.95       146\n",
      "           1       0.48      1.00      0.65        14\n",
      "\n",
      "    accuracy                           0.91       160\n",
      "   macro avg       0.74      0.95      0.80       160\n",
      "weighted avg       0.95      0.91      0.92       160\n",
      "\n",
      "AUC: 0.9486\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_model_sgd = cv_sgd.best_estimator_\n",
    "final_predictions_sgd = final_model_sgd.predict(X_test)\n",
    "\n",
    "# print classification report\n",
    "print(f\"---> {type(final_model_sgd[-1]).__name__}\")\n",
    "print(classification_report(y_test, final_predictions_sgd))\n",
    "\n",
    "# print roc_auc\n",
    "print(\"AUC: {:.4f}\\n\".format(roc_auc_score(y_test, final_predictions_sgd)))\n",
    "\n",
    "# plot confusion matrix and roc_auc\n",
    "fig , (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "sns.heatmap(confusion_matrix(y_test, final_predictions_sgd, normalize='true'), annot=True, ax=ax1)\n",
    "ax1.set_title(f\"Confusion matrix - {type(final_model_sgd[-1]).__name__}\")\n",
    "ax1.set_ylabel(\"Real\")\n",
    "ax1.set_xlabel(\"Predicted\")\n",
    "\n",
    "ax2 = plt.gca()\n",
    "RocCurveDisplay.from_estimator(final_model_sgd, X_test, y_test, ax=ax2, alpha=0.8)\n",
    "ax2.set_title(f\"ROC curve - {type(final_model_sgd[-1]).__name__}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second round - with approach 2 (with feature engineering):\n",
    "\n",
    "- DecisionTree (df_test2)\n",
    "\n",
    "    With the feature engineerings performed, the tree model did really well on the fail class (Fail=True) not missing a single datapoint. However, in order to do that, it made a lot of mistakes regarding false positives. It missclassified about 66 datapoints (0.45 * 146 = ~66) of the not fail class (Fail=False)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---> DecisionTreeClassifier with feature engineering\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.55      0.71       146\n",
      "           1       0.18      1.00      0.30        14\n",
      "\n",
      "    accuracy                           0.59       160\n",
      "   macro avg       0.59      0.78      0.51       160\n",
      "weighted avg       0.93      0.59      0.68       160\n",
      "\n",
      "AUC: 0.7774\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_model_dt2 = cv_dt2.best_estimator_\n",
    "final_predictions_dt2 = final_model_dt2.predict(X_test2)\n",
    "\n",
    "# print classification report\n",
    "print(f\"---> {type(final_model_dt2[-1]).__name__} with feature engineering\")\n",
    "print(classification_report(y_test2, final_predictions_dt2))\n",
    "\n",
    "# print roc_auc\n",
    "print(\"AUC: {:.4f}\\n\".format(roc_auc_score(y_test2, final_predictions_dt2)))\n",
    "\n",
    "# plot confusion matrix and roc_auc\n",
    "fig , (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "sns.heatmap(confusion_matrix(y_test2, final_predictions_dt2, normalize='true'), annot=True, ax=ax1)\n",
    "ax1.set_title(f\"Confusion matrix - {type(final_model_dt2[-1]).__name__}\")\n",
    "ax1.set_ylabel(\"Real\")\n",
    "ax1.set_xlabel(\"Predicted\")\n",
    "\n",
    "ax2 = plt.gca()\n",
    "RocCurveDisplay.from_estimator(final_model_dt2, X_test2, y_test2, ax=ax2, alpha=0.8)\n",
    "ax2.set_title(f\"ROC curve - {type(final_model_dt2[-1]).__name__}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- SGDClassifier (df_test2)\n",
    "\n",
    "    On the other hand, the feature engineering performed really worked for the SGD Classifier. It didn't do a perfect job (which is totally normal) on the fail class (Fail=True) missclassifying one datapoint (0.071 * 14 = ~1) but it did much better regarding false positives. It missclassified only 9 datapoints (0.055 * 146 = ~9) making the f1 score much more balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---> SGDClassifier with feature engineering\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.95      0.97       146\n",
      "           1       0.62      0.93      0.74        14\n",
      "\n",
      "    accuracy                           0.94       160\n",
      "   macro avg       0.81      0.94      0.86       160\n",
      "weighted avg       0.96      0.94      0.95       160\n",
      "\n",
      "AUC: 0.9369\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_model_sgd2 = cv_sgd2.best_estimator_\n",
    "final_predictions_sgd2 = final_model_sgd2.predict(X_test2)\n",
    "\n",
    "# print classification report\n",
    "print(f\"---> {type(final_model_sgd2[-1]).__name__} with feature engineering\")\n",
    "print(classification_report(y_test2, final_predictions_sgd2))\n",
    "\n",
    "# print roc_auc\n",
    "print(\"AUC: {:.4f}\\n\".format(roc_auc_score(y_test2, final_predictions_sgd2)))\n",
    "\n",
    "# plot confusion matrix and roc_auc\n",
    "fig , (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "sns.heatmap(confusion_matrix(y_test2, final_predictions_sgd2, normalize='true'), annot=True, ax=ax1)\n",
    "ax1.set_title(f\"Confusion matrix - {type(final_model_sgd2[-1]).__name__}\")\n",
    "ax1.set_ylabel(\"Real\")\n",
    "ax1.set_xlabel(\"Predicted\")\n",
    "\n",
    "ax2 = plt.gca()\n",
    "RocCurveDisplay.from_estimator(final_model_sgd2, X_test2, y_test2, ax=ax2, alpha=0.8)\n",
    "ax2.set_title(f\"ROC curve - {type(final_model_sgd[-1]).__name__}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final models\n",
    "\n",
    "As explained above, we have:\n",
    "\n",
    "1. DECISION TREE\n",
    "\n",
    "    - False negatives = 7%\n",
    "    \n",
    "    - False positives = 14%\n",
    "    \n",
    "\n",
    "2. SGDCLASSIFIER\n",
    "\n",
    "    - False negatives = 0\n",
    "\n",
    "    - False positives = 10%\n",
    "    \n",
    "3. DECISION TREE with feature engineering\n",
    "\n",
    "    - False negatives = 0\n",
    "\n",
    "    - False positives = 45%\n",
    "\n",
    "\n",
    "4. SGDCLASSIFIER with feature engineering\n",
    "\n",
    "    - False negatives = 7%\n",
    "    \n",
    "    - False positives = 5.5%\n",
    "\n",
    "\n",
    "After evaluating its performance on the test set, I would move forward with models 2 and 4, both SGDClassifiers, with and without feature engineering.\n",
    "\n",
    "Both models 2 and 4 show a good tradeoff between bias and variance, generalizing well and well balanced between recall and precision, that is, false negatives and false positives.\n",
    "\n",
    "I would gather some more information about the business and bring it to the team as a way of deciding between the models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model interpretation\n",
    "\n",
    "Regarding models 2 and 4, that are both SGDClassifiers, it's interesting to consider the math behind it as it will help us interpret the model.\n",
    "\n",
    "When we calculate the exponential of each coeficient, we get the odds ratio.\n",
    "\n",
    "The odds ratio is the probability of success over probability of not success, in this case, probability of the equipment failing over probability of equipment not failing.\n",
    "\n",
    "The odds ratio from will indicate how likely the event (fail of the equipment) is to occur when each independent feature changes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 2: SDGClassifier\n",
    "\n",
    "When we have the variables as they are (without feature engineering), it's mudh easier to interpret the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As the Temperature increases one unit, the equipment is 1.48 times more likely to fail.\n",
      "As the Pressure increases one unit, the equipment is 1.61 times more likely to fail.\n",
      "As the VibrationX increases one unit, the equipment is 1.85 times more likely to fail.\n",
      "As the VibrationY increases one unit, the equipment is 2.62 times more likely to fail.\n",
      "As the VibrationZ increases one unit, the equipment is 1.97 times more likely to fail.\n",
      "As the Frequency increases one unit, the equipment is 2.08 times more likely to fail.\n"
     ]
    }
   ],
   "source": [
    "features_names = final_model_sgd[:-1].get_feature_names_out()\n",
    "coefs = final_model_sgd[-1].coef_\n",
    "exp_coefs = np.exp(coefs)[0]\n",
    "\n",
    "for variable, exp_coef in zip(features_names, exp_coefs):\n",
    "    print(f\"As the {variable.split('__')[-1]} increases one unit, the equipment is {round(exp_coef,2)} times more likely to fail.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model4: SGDClassifier with feature engineering\n",
    "\n",
    "With the feature engineered variables it becomes harder to understand the effect each variable has on the predictions. We exchange feature treaceability for predictive power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As the Frequency increases one unit, the equipment is 1.0195636073873138e+122 times more likely to fail.\n",
      "As the VibrationXYZ increases one unit, the equipment is 2.0500905086382017e+69 times more likely to fail.\n",
      "As the Temp_Pressure increases one unit, the equipment is 7.643393822844594e+65 times more likely to fail.\n"
     ]
    }
   ],
   "source": [
    "features_names = final_model_sgd2[:-1].get_feature_names_out()\n",
    "coefs = final_model_sgd2[-1].coef_\n",
    "exp_coefs = np.exp(coefs)[0]\n",
    "\n",
    "for variable, exp_coef in zip(features_names, exp_coefs):\n",
    "    print(f\"As the {variable.split('__')[-1]} increases one unit, the equipment is {round(exp_coef,2)} times more likely to fail.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Error analysis\n",
    "\n",
    "Let's take a look at the datapoints that the final models missclassified:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sgd model\n",
    "df_test_pred = df_test.copy()\n",
    "df_test_pred['final_predictions_sgd'] = final_predictions_sgd\n",
    "\n",
    "# sgd model with feature engineering\n",
    "df_test_pred2 = df_test2.copy()\n",
    "df_test_pred2['final_predictions_sgd2'] = final_predictions_sgd2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SDGClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15, 10)\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>Preset_1</th>\n",
       "      <th>Preset_2</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>VibrationX</th>\n",
       "      <th>VibrationY</th>\n",
       "      <th>VibrationZ</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Fail</th>\n",
       "      <th>final_predictions_sgd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>80.124221</td>\n",
       "      <td>185.406401</td>\n",
       "      <td>115.948396</td>\n",
       "      <td>40.246855</td>\n",
       "      <td>105.401973</td>\n",
       "      <td>74.996160</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>75.720346</td>\n",
       "      <td>63.913643</td>\n",
       "      <td>66.923618</td>\n",
       "      <td>114.544472</td>\n",
       "      <td>154.690775</td>\n",
       "      <td>74.755959</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>96.466297</td>\n",
       "      <td>75.068984</td>\n",
       "      <td>179.928948</td>\n",
       "      <td>129.620299</td>\n",
       "      <td>80.726200</td>\n",
       "      <td>30.222561</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>65.561130</td>\n",
       "      <td>136.592852</td>\n",
       "      <td>92.927337</td>\n",
       "      <td>53.344402</td>\n",
       "      <td>96.085591</td>\n",
       "      <td>134.384686</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>65.694839</td>\n",
       "      <td>63.346869</td>\n",
       "      <td>59.361535</td>\n",
       "      <td>129.522521</td>\n",
       "      <td>95.159646</td>\n",
       "      <td>109.273036</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>75.221692</td>\n",
       "      <td>95.162525</td>\n",
       "      <td>93.948089</td>\n",
       "      <td>94.734066</td>\n",
       "      <td>95.160753</td>\n",
       "      <td>86.253084</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>89.756210</td>\n",
       "      <td>97.287972</td>\n",
       "      <td>103.399601</td>\n",
       "      <td>164.432623</td>\n",
       "      <td>110.061636</td>\n",
       "      <td>46.113601</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>84.081769</td>\n",
       "      <td>61.200783</td>\n",
       "      <td>66.474525</td>\n",
       "      <td>134.787706</td>\n",
       "      <td>105.306100</td>\n",
       "      <td>98.092270</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>101.425593</td>\n",
       "      <td>133.489833</td>\n",
       "      <td>111.238549</td>\n",
       "      <td>95.735918</td>\n",
       "      <td>97.597540</td>\n",
       "      <td>61.513865</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>70.269678</td>\n",
       "      <td>93.989705</td>\n",
       "      <td>85.005101</td>\n",
       "      <td>110.009197</td>\n",
       "      <td>94.544989</td>\n",
       "      <td>78.383397</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>77.937777</td>\n",
       "      <td>83.146657</td>\n",
       "      <td>77.800590</td>\n",
       "      <td>109.679048</td>\n",
       "      <td>86.230728</td>\n",
       "      <td>99.681179</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>75.878205</td>\n",
       "      <td>111.438864</td>\n",
       "      <td>109.276309</td>\n",
       "      <td>110.652647</td>\n",
       "      <td>105.116292</td>\n",
       "      <td>79.271093</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>99.492158</td>\n",
       "      <td>163.840134</td>\n",
       "      <td>151.046505</td>\n",
       "      <td>97.169884</td>\n",
       "      <td>121.180832</td>\n",
       "      <td>70.681879</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>96.238401</td>\n",
       "      <td>146.731223</td>\n",
       "      <td>129.988750</td>\n",
       "      <td>120.029869</td>\n",
       "      <td>89.754545</td>\n",
       "      <td>121.598046</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>70.281039</td>\n",
       "      <td>119.845044</td>\n",
       "      <td>94.326347</td>\n",
       "      <td>120.724162</td>\n",
       "      <td>94.898435</td>\n",
       "      <td>99.382769</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Preset_1  Preset_2  Temperature    Pressure  VibrationX  VibrationY  \\\n",
       "232         2         1    80.124221  185.406401  115.948396   40.246855   \n",
       "739         1         2    75.720346   63.913643   66.923618  114.544472   \n",
       "87          3         1    96.466297   75.068984  179.928948  129.620299   \n",
       "476         2         8    65.561130  136.592852   92.927337   53.344402   \n",
       "211         1         7    65.694839   63.346869   59.361535  129.522521   \n",
       "249         1         6    75.221692   95.162525   93.948089   94.734066   \n",
       "196         2         8    89.756210   97.287972  103.399601  164.432623   \n",
       "698         2         5    84.081769   61.200783   66.474525  134.787706   \n",
       "267         3         3   101.425593  133.489833  111.238549   95.735918   \n",
       "645         1         2    70.269678   93.989705   85.005101  110.009197   \n",
       "6           1         3    77.937777   83.146657   77.800590  109.679048   \n",
       "366         2         5    75.878205  111.438864  109.276309  110.652647   \n",
       "268         1         3    99.492158  163.840134  151.046505   97.169884   \n",
       "409         3         4    96.238401  146.731223  129.988750  120.029869   \n",
       "717         3         4    70.281039  119.845044   94.326347  120.724162   \n",
       "\n",
       "     VibrationZ   Frequency  Fail  final_predictions_sgd  \n",
       "232  105.401973   74.996160     0                      1  \n",
       "739  154.690775   74.755959     0                      1  \n",
       "87    80.726200   30.222561     0                      1  \n",
       "476   96.085591  134.384686     0                      1  \n",
       "211   95.159646  109.273036     0                      1  \n",
       "249   95.160753   86.253084     0                      1  \n",
       "196  110.061636   46.113601     0                      1  \n",
       "698  105.306100   98.092270     0                      1  \n",
       "267   97.597540   61.513865     0                      1  \n",
       "645   94.544989   78.383397     0                      1  \n",
       "6     86.230728   99.681179     0                      1  \n",
       "366  105.116292   79.271093     0                      1  \n",
       "268  121.180832   70.681879     0                      1  \n",
       "409   89.754545  121.598046     0                      1  \n",
       "717   94.898435   99.382769     0                      1  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_errors_dt = df_test_pred[df_test_pred['Fail'] != df_test_pred['final_predictions_sgd']]\n",
    "print(df_errors_dt.shape)\n",
    "df_errors_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SGD Classifier with feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9, 7)\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>Preset_1</th>\n",
       "      <th>Preset_2</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Fail</th>\n",
       "      <th>VibrationXYZ</th>\n",
       "      <th>Temp_Pressure</th>\n",
       "      <th>final_predictions_sgd2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>129.210474</td>\n",
       "      <td>0</td>\n",
       "      <td>4.481464e+05</td>\n",
       "      <td>7466.924067</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>50.456673</td>\n",
       "      <td>1</td>\n",
       "      <td>1.013429e+06</td>\n",
       "      <td>8000.916312</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>134.384686</td>\n",
       "      <td>0</td>\n",
       "      <td>4.763110e+05</td>\n",
       "      <td>8955.181689</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>114.326316</td>\n",
       "      <td>0</td>\n",
       "      <td>2.234632e+05</td>\n",
       "      <td>9046.820117</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>61.513865</td>\n",
       "      <td>0</td>\n",
       "      <td>1.039367e+06</td>\n",
       "      <td>13539.285488</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>79.271093</td>\n",
       "      <td>0</td>\n",
       "      <td>1.271036e+06</td>\n",
       "      <td>8455.780895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>70.681879</td>\n",
       "      <td>0</td>\n",
       "      <td>1.778592e+06</td>\n",
       "      <td>16300.808559</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>121.598046</td>\n",
       "      <td>0</td>\n",
       "      <td>1.400398e+06</td>\n",
       "      <td>14121.178258</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>99.382769</td>\n",
       "      <td>0</td>\n",
       "      <td>1.080653e+06</td>\n",
       "      <td>8422.834178</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Preset_1  Preset_2   Frequency  Fail  VibrationXYZ  Temp_Pressure  \\\n",
       "183         2         4  129.210474     0  4.481464e+05    7466.924067   \n",
       "414         3         3   50.456673     1  1.013429e+06    8000.916312   \n",
       "476         2         8  134.384686     0  4.763110e+05    8955.181689   \n",
       "466         2         7  114.326316     0  2.234632e+05    9046.820117   \n",
       "267         3         3   61.513865     0  1.039367e+06   13539.285488   \n",
       "366         2         5   79.271093     0  1.271036e+06    8455.780895   \n",
       "268         1         3   70.681879     0  1.778592e+06   16300.808559   \n",
       "409         3         4  121.598046     0  1.400398e+06   14121.178258   \n",
       "717         3         4   99.382769     0  1.080653e+06    8422.834178   \n",
       "\n",
       "     final_predictions_sgd2  \n",
       "183                       1  \n",
       "414                       0  \n",
       "476                       1  \n",
       "466                       1  \n",
       "267                       1  \n",
       "366                       1  \n",
       "268                       1  \n",
       "409                       1  \n",
       "717                       1  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_errors_sgd2 = df_test_pred2[df_test_pred2['Fail'] != df_test_pred2['final_predictions_sgd2']]\n",
    "print(df_errors_sgd2.shape)\n",
    "df_errors_sgd2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature importance by permutation:\n",
    "\n",
    "This technique basically shuffles a feature and sees how the model changes its prediction. Thus, the change in prediction will be correspondant to the feature importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def permutation_importances(final_model, X, y, scoring, train_or_test):\n",
    "    result = permutation_importance(\n",
    "        final_model, X, y, n_repeats=10, random_state=random_state, n_jobs=2, scoring=scoring\n",
    "    )\n",
    "\n",
    "    sorted_importances_idx = result.importances_mean.argsort()\n",
    "    importances_ = pd.DataFrame(\n",
    "        result.importances[sorted_importances_idx].T,\n",
    "        columns=X.columns[sorted_importances_idx],\n",
    "    )\n",
    "\n",
    "    ax = importances_.plot.box(vert=False, whis=10)\n",
    "    ax.set_title(f\"{type(final_model[-1]).__name__} - Permutation Importances ({train_or_test} set)\")\n",
    "    ax.axvline(x=0, color=\"k\", linestyle=\"--\")\n",
    "    ax.set_xlabel(\"Decrease in recall score\")\n",
    "    ax.figure.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SGDClassifier\n",
    "\n",
    "- VibrationY is the most impactful for this model.\n",
    "\n",
    "- Comparing the feature importances between train and test shows us no significant sign of overfitting as the importance of features were similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAHWCAYAAAD6oMSKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABzo0lEQVR4nO3deVxUVf8H8M+wzAAzLCKDiiIgoCIiKppbiguKpQYuQemjkLjvlVm0KGiGmVuZ2uIjqGmS5aOGmhKJklpaLrmQCeIappaCww5zfn/44+YIIvvA+Hm/XvNi5t5zz/3eM3dmvpx777kyIYQAEREREdV7RvoOgIiIiIiqBxM7IiIiIgPBxI6IiIjIQDCxIyIiIjIQTOyIiIiIDAQTOyIiIiIDwcSOiIiIyEAwsSMiIiIyEEzsiIiIiAwEEzuiMoSGhsLZ2Vlv64+JiYFMJsOlS5d0pn/wwQdo0aIFjI2N0b59ewCAs7MzQkNDaz1Gql4RERGQyWT6DoPqEY1GA3t7e2zatKnW1imTyRAREVFr66sO586dg4mJCc6cOaPvUGoUEzuqcadPn8aIESPg5OQEMzMzNG3aFP3798fKlStLlNVqtdiwYQP69+8POzs7mJqawt7eHgMGDMBnn32GvLw8nfIymUx6mJiYwNbWFj4+Ppg5cybOnTv3yJgyMzMRGRkJb29vqFQqmJubo23btnj99dfx559/VnsbVKd9+/Zhzpw56NGjB6Kjo/Hee+/pO6Qy9e7dW+d9srW1RefOnbFu3TpotVp9h1cpmzdvxooVKyq9fHZ2NiIiIpCYmFhtMVUHmUyGadOm6TuMSjt8+DAiIiJw9+5dfYdSqz788ENYWlrihRdekKbt3r273iVe1eVRn882bdpg0KBBmDt3bu0HVZsEUQ06dOiQkMvlws3NTSxYsEB8/vnnYu7cuWLAgAHC1dVVp2x2drbw9/cXAET37t1FVFSUWLdunViyZIkYMmSIMDY2FmPHjtVZBoDo37+/2Lhxo9iwYYNYuXKlGDdunLC2thYmJiZi6dKlJWJKTU0VLi4uwtjYWLzwwgvi448/Fp999pmYNm2aaNiwoXB3d5fKhoSECCcnpxppm/IoLCwUOTk5QqvVStNef/11YWRkJPLy8nTK5ubmivz8/NoO8bF8fX1Fs2bNxMaNG8XGjRvFsmXLRPv27QUA8frrr+s7vEoZNGhQlfaLW7duCQBi3rx5JeYVFBSInJycygdXBQDE1KlT9bLu6vDBBx8IACItLU3fodSa/Px8oVarxXvvvaczferUqaImf+JzcnJEQUFBjdVfFWV9Pnfv3i0AiJSUlNoNqhaZ6C+lpCfBwoULYW1tjWPHjsHGxkZn3s2bN3Vev/zyy9i7dy9WrFiBmTNn6sx79dVXceHCBcTHx5dYR8uWLfGf//xHZ9qiRYswZMgQvPrqq2jdujWeffZZAEBhYSGGDRuGv/76C4mJiXj66adLxPv+++9XdnOrnbGxMYyNjXWm3bx5E+bm5pDL5TrTFQpFta23sLAQWq22xDoqy9raWuc9mjhxIlq1aoWPP/4YCxYsgKmpaaXrru5Y9c3ExAQmJvxqroisrCwolUp9h6EXcXFxuHXrFoKCgipdR2U+Q2ZmZpVenz75+fmhQYMGWL9+PebPn6/vcGqGvjNLMmytWrUSvXv3fmy5K1euCGNjYzFw4MAK1Y8yehguX74sTExMRPfu3aVpW7ZsEQDEwoULy1V/aT12H3zwgejWrZuwtbUVZmZmomPHjmLr1q0llt23b5/o0aOHsLa2FkqlUrRs2VKEh4frlPnoo49EmzZthLm5ubCxsRE+Pj5i06ZN0vzo6GidHggAJR7R0dFCCCGcnJxESEiITv137twRM2fOFM2aNRNyuVy4urqKRYsWiaKiIqlMWlqaACA++OADsXz5ctGiRQthZGQkTpw4Ua42ehxfX1/h6elZYvqIESMEAHH9+vVqiXXevHkCgDh//rwYNWqUsLKyEnZ2duLtt98WWq1WXLlyRTz33HPC0tJSNGrUSCxZskQnnofbutj+/fsFALF//35pex5+D4r3kby8PPHOO++Ijh07CisrK2FhYSGefvpp8cMPP5TYhocfxb13xdvxoIKCAjF//nzRokULIZfLhZOTkwgPDxe5ubk65ZycnMSgQYNEUlKS6Ny5s1AoFMLFxUWsX7++XO/Vw5+n4m2PjY0VERERwsHBQahUKjF8+HBx9+5dkZubK2bOnCnUarVQKpUiNDS0REzFdX7xxReiZcuWQqFQiI4dO4oDBw6UWP/x48fFwIEDhaWlpVAqlaJv377iyJEjOmWK36fExEQxefJkoVarhY2NjdRuDz+K389169aJPn36CLVaLeRyufDw8BCrV68uEUNF2vDOnTti1qxZwsnJScjlctG0aVMxevRocevWLalMbm6umDt3rnB1dRVyuVw0a9ZMvPbaayXaqTzfF6UZM2aMcHZ21pkWEhJSalsIUfZnqDz7b7EH91kh/t1vL1y4IEJCQoS1tbWwsrISoaGhIisr67Hb8ccff4hhw4aJRo0aCYVCIZo2bSqCg4PF3bt3dcpt3LhRdOzYUZiZmYkGDRqI4OBgceXKFWl+WZ/PYkOHDhXt2rV7bEz1Ff8tpBrl5OSEI0eO4MyZM2jbtu0jy+3ZswdFRUUlet6qonnz5vD19cX+/fuRmZkJKysr7Ny5EwAwevToStf74Ycf4rnnnsOoUaOQn5+PLVu24Pnnn0dcXBwGDRoEADh79iwGDx6Mdu3aYf78+VAoFEhJScGhQ4ekej7//HPMmDEDI0aMwMyZM5Gbm4vffvsNP//8M0aOHFnqujdu3IjPPvsMR48exdq1awEA3bt3L7VsdnY2fH19cf36dUycOBHNmzfH4cOHER4ejvT09BLnoERHRyM3NxcTJkyAQqGAra1tpduoPC5evAhjY2PY2NhUa6zBwcHw8PDAokWLsGvXLrz77ruwtbXFp59+ir59++L999/Hpk2bMHv2bHTu3Bm9evWqUNxvvfUWMjIycO3aNSxfvhwAoFKpANw/d3Pt2rV48cUXMX78eNy7dw///e9/4e/vj6NHj6J9+/ZQq9VYs2YNJk+ejKFDh2LYsGEAgHbt2j1ynePGjcP69esxYsQIvPrqq/j5558RFRWF5ORk/O9//9Mpm5KSghEjRiAsLAwhISFYt24dQkND4ePjA09Pzwpta7GoqCiYm5vjjTfeQEpKClauXAlTU1MYGRnhzp07iIiIwE8//YSYmBi4uLiUOIfpwIEDiI2NxYwZM6BQKLB69WoMHDgQR48elb4Xzp49i549e8LKygpz5syBqakpPv30U/Tu3RsHDhxAly5ddOqcMmUK1Go15s6di6ysLDzzzDP4448/8OWXX2L58uWws7MDAKjVagDAmjVr4Onpieeeew4mJib49ttvMWXKFGi1WkydOrXCbajRaNCzZ08kJydj7Nix6NixI27fvo2dO3fi2rVrsLOzg1arxXPPPYcff/wREyZMgIeHB06fPo3ly5fjjz/+wPbt26Vtf9z3xaMcPnwYHTt21Jk2ceJE/Pnnn4iPj8fGjRtLXa60z1B59t/HCQoKgouLC6KionD8+HGsXbsW9vb2ZR4Jyc/Ph7+/P/Ly8jB9+nQ0btwY169fR1xcHO7evQtra2sA94+ovPPOOwgKCsK4ceNw69YtrFy5Er169cKJEydgY2NT5uezmI+PD3bs2CH9LhgcfWeWZNj27dsnjI2NhbGxsejWrZuYM2eO2Lt3b4lzwV5++WUBQJw8eVJnel5enrh165b0uH37ts58POacoJkzZwoA4tSpU0IIITp06CCsra3LHX9pPXbZ2dk6r/Pz80Xbtm1F3759pWnLly8XAHT+c39YQEBAqT1ZDyqtFykkJEQolcoSZR/usVuwYIFQKpXijz/+0Cn3xhtvCGNjY+m/3OL/4K2srMTNmzfLjKcyfH19RevWraX3MDk5WcyYMUMAEEOGDKm2WIt7DCZMmCBNKywsFM2aNRMymUwsWrRImn7nzh1hbm6u017l7bET4tHn8BQWFpY49/HOnTuiUaNGOueHlnWO3cM9didPnhQAxLhx43TKzZ49WwDQ6U1xcnISAMTBgwelaTdv3hQKhUK8+uqrJdb1sIc/T8Xb3rZtW53P7IsvvihkMpl45plndJbv1q1biXbB//ea/PLLL9K0y5cvCzMzMzF06FBpWmBgoJDL5SI1NVWa9ueffwpLS0vRq1cvaVrx+/T000+LwsJCnXWVdY7dw59bIYTw9/cXLVq00JlW3jacO3euACC2bdtWot7ic2I3btwojIyMRFJSks78Tz75RAAQhw4dEkKU7/uiNAUFBUImk5X63j7qHLuyPkPl3X+FeHSP3cPlhg4dKho2bFjmdpw4cUIAKPXIR7FLly4JY2PjEkdbTp8+LUxMTHSmP+4c2M2bNwsA4ueffy4zrvqKV8VSjerfvz+OHDmC5557DqdOncLixYvh7++Ppk2bSr1nwP2eDqDkf1a7d++GWq2WHk5OThVaf3F99+7dk9ZjaWlZlU2Cubm59PzOnTvIyMhAz549cfz4cWl68fmEO3bseOSVnzY2Nrh27RqOHTtWpXgeZevWrejZsycaNGiA27dvSw8/Pz8UFRXh4MGDOuWHDx8u9W5Ut99//116Dz08PLBy5UoMGjQI69atq/ZYx40bJz03NjZGp06dIIRAWFiYNN3GxgatWrXCxYsXq3U7jY2NpfOUtFot/vnnHxQWFqJTp046+0dF7N69GwDwyiuv6Ex/9dVXAQC7du3Smd6mTRv07NlTeq1Wq6u8rWPGjNE5D7JLly4QQmDs2LE65bp06YKrV6+isLBQZ3q3bt3g4+MjvW7evDkCAgKwd+9eFBUVoaioCPv27UNgYCBatGghlWvSpAlGjhyJH3/8UfqOKDZ+/PgS55+W5cHPbUZGBm7fvg1fX19cvHgRGRkZOmXL04bffPMNvL29MXTo0BLrKh6uZuvWrfDw8EDr1q119uu+ffsCAPbv3w+gfN8Xpfnnn38ghECDBg3KvUyx0j5D1bH/Tpo0Sed1z5498ffff5d4/x5U3CO3d+9eZGdnl1pm27Zt0Gq1CAoK0mnLxo0bw93dXWrL8ihur9u3b5d7mfqEiR3VuM6dO2Pbtm24c+cOjh49ivDwcNy7dw8jRoyQhiQpTrY0Go3Osj169EB8fDzi4+MxYMCACq+7uL7i+q2srKQkr7Li4uLQtWtXmJmZwdbWVjq09uCPQ3BwMHr06IFx48ahUaNGeOGFF/DVV1/pfGm//vrrUKlUeOqpp+Du7o6pU6eW69BLeV24cAHfffedTmKsVqvh5+cHoOTFKy4uLuWq959//sGNGzekx8M/iqVxdnZGfHw8vv/+e/z444+4ceMG4uLipMNl1Rlr8+bNdV5bW1vDzMxMWteD0+/cuVOuba6I9evXo127djAzM0PDhg2hVquxa9eucrVTaS5fvgwjIyO4ubnpTG/cuDFsbGxw+fJlnekPbz9w/4esKttaWpsCgKOjY4npWq22xLa6u7uXqLNly5bIzs7GrVu3cOvWLWRnZ6NVq1Ylynl4eECr1eLq1as608u7vxY7dOgQ/Pz8oFQqYWNjA7VajTfffBMASsRbnjZMTU0t8/QS4P5+ffbs2RL7dcuWLQH8u1+X5/uiLEKIcpV70KPar6r778NtV5xElbX/ubi44JVXXsHatWthZ2cHf39/rFq1SmedFy5cgBAC7u7uJdozOTm5xHdEWYrby1DHi+Q5dlRr5HI5OnfujM6dO6Nly5Z46aWXsHXrVsybNw+tW7cGAJw5cwbe3t7SMg/+uH/xxRcVXueZM2dgbGwsfYm1bt0aJ06cwNWrV0v8KJVHUlISnnvuOfTq1QurV69GkyZNYGpqiujoaGzevFkqZ25ujoMHD2L//v3YtWsXvvvuO8TGxqJv377Yt28fjI2N4eHhgfPnzyMuLg7fffcdvvnmG6xevRpz585FZGRkhWN7mFarRf/+/TFnzpxS5xf/uDwYc3kMGzYMBw4ckF6HhIQgJiamzGWUSqX0PtZ0rKX14jyqZ+fBH8RHfckXFRU9cl0P++KLLxAaGorAwEC89tprsLe3h7GxMaKiopCamlruekpT3h+h8mxrRT2qzppYV3mVd38F7idh/fr1Q+vWrbFs2TI4OjpCLpdj9+7dWL58eYkEqrq2S6vVwsvLC8uWLSt1fvF3UHm+L0pja2sLmUxWqaS9tParjv23sm23dOlShIaGYseOHdi3bx9mzJiBqKgo/PTTT2jWrBm0Wi1kMhn27NlT6joePtpTluL2evifPUPBxI70olOnTgCA9PR0AMAzzzwDY2NjbNq0CaNGjaqWdVy5cgUHDhxAt27dpB67IUOG4Msvv8QXX3yB8PDwCtf5zTffwMzMDHv37tUZXiQ6OrpEWSMjI/Tr1w/9+vXDsmXL8N577+Gtt97C/v37pSRHqVQiODgYwcHByM/Px7Bhw7Bw4UKEh4dXeTgBV1dXaDSaMhOqyli6dKnOD4mDg0OV66ypWCuiuGfh4cFtH+4RAx6dZH399ddo0aIFtm3bplNm3rx55Vq+NE5OTtBqtbhw4QI8PDyk6X/99Rfu3r1b4dMT9OHChQslpv3xxx+wsLCQDgdaWFjg/PnzJcr9/vvvMDIyKtc/Yo9q12+//RZ5eXnYuXOnTo9SRQ7fPczV1fWxdzBwdXXFqVOn0K9fv8e+5+X5vniYiYkJXF1dkZaWVmJeZXqjyrv/1hQvLy94eXnh7bffxuHDh9GjRw988sknePfdd+Hq6gohBFxcXEr8o/ewx217WloajIyMHltPfcVDsVSj9u/fX+p/asXnDRUfemnevDnGjh2LPXv24OOPPy61ror8t/zPP//gxRdfRFFREd566y1p+ogRI+Dl5YWFCxfiyJEjJZa7d++eTvmHGRsbQyaT6fTiXLp0Sbq67cH1P6z4irLiu2f8/fffOvPlcjnatGkDIQQKCgoeu42PExQUhCNHjmDv3r0l5t29e7fEeVDl5ePjAz8/P+nRpk2bqoZaY7FWhKurKwDonM9XVFSEzz77rERZpVJZ6qGp4p6EB/fVn3/+ucS+ZmFhAaBkElma4jEYH74yuLgXqPhK7LrsyJEjOudoXb16FTt27MCAAQOksRoHDBiAHTt26Nw+76+//sLmzZvx9NNPl+vqxeKx7B5u19Lel4yMjFL/ISuv4cOH49SpUyWuSn5wPUFBQbh+/To+//zzEmVycnKQlZUFoHzfF4/SrVs3/PLLLyWmP6otylLe/be6ZWZmlviMe3l5wcjISNr+YcOGwdjYGJGRkSV+C4QQOt+nj/p8Fvv111/h6ekpnVJgaNhjRzVq+vTpyM7OxtChQ9G6dWvk5+fj8OHDiI2NhbOzM1566SWp7IoVK5CWlobp06djy5YtGDJkCOzt7XH79m0cOnQI3377bann4Pzxxx/44osvIIRAZmYmTp06ha1bt0Kj0WDZsmUYOHCgVNbU1BTbtm2Dn58fevXqhaCgIPTo0QOmpqY4e/YsNm/ejAYNGmDhwoWlbs+gQYOkOkeOHImbN29i1apVcHNzw2+//SaVmz9/Pg4ePIhBgwbByckJN2/exOrVq9GsWTNpUOQBAwagcePG6NGjBxo1aoTk5GR8/PHHGDRoUJUv8ACA1157DTt37sTgwYOloRqysrJw+vRpfP3117h06VKdORRRF2L19PRE165dER4ejn/++Qe2trbYsmVLqUmlj48PYmNj8corr6Bz585QqVQYMmQIBg8ejG3btmHo0KEYNGgQ0tLS8Mknn6BNmzY654+am5ujTZs2iI2NRcuWLWFra4u2bduWes6Wt7c3QkJC8Nlnn+Hu3bvw9fXF0aNHsX79egQGBqJPnz412i7VoW3btvD399cZ7gSAzikH7777LuLj4/H0009jypQpMDExwaeffoq8vDwsXry4XOspvkDjrbfewgsvvABTU1MMGTIEAwYMgFwux5AhQzBx4kRoNBp8/vnnsLe3l44aVNRrr72Gr7/+Gs8//zzGjh0LHx8f/PPPP9i5cyc++eQTeHt7Y/To0fjqq68wadIk7N+/Hz169EBRURF+//13fPXVV9i7dy86depUru+LRwkICMDGjRvxxx9/6PRAFbfFjBkz4O/vD2NjY51bjpWmvPtvdfvhhx8wbdo0PP/882jZsiUKCwuxceNGGBsbY/jw4QDu/+P17rvvIjw8HJcuXUJgYCAsLS2RlpaG//3vf5gwYQJmz54tbXtpn08AKCgowIEDBzBlypQa2x69q81LcOnJs2fPHjF27FjRunVroVKppNuLTZ8+Xfz1118lyhcWForo6GjRt29fYWtrK0xMTISdnZ3o16+f+OSTT0rcagkPDEJpZGQkbGxsRIcOHcTMmTPF2bNnHxnXnTt3xNy5c4WXl5ewsLAQZmZmom3btiI8PFykp6dL5Uob7uS///2vcHd3FwqFQrRu3VpER0eXGKIiISFBBAQECAcHByGXy4WDg4N48cUXdYbz+PTTT0WvXr1Ew4YNhUKhEK6uruK1114TGRkZUpmqDHcihBD37t0T4eHhws3NTcjlcmFnZye6d+8ulixZIg1f8eCApTXhUQMUP6yqsRa/Bw8PGfGo9iotrtTUVOHn5ycUCoVo1KiRePPNN0V8fHyJ4U40Go0YOXKksLGx0RkAVavVivfee084OTkJhUIhOnToIOLi4krdjw4fPix8fHyEXC4v1wDFkZGRwsXFRZiamgpHR8cyBygubVt9fX1LTH8YHjHcycPDUBTvl8eOHdOZXtp7UFznF198IX1uOnTooNOexY4fPy78/f2FSqUSFhYWok+fPuLw4cPlWnexBQsWiKZNmwojIyOdz87OnTtFu3bthJmZmXB2dhbvv/++WLduXYnPV0Xa8O+//xbTpk0TTZs2lQYfDgkJ0RmWKT8/X7z//vvC09NTKBQK0aBBA+Hj4yMiIyOlz3p5vi8eJS8vT9jZ2YkFCxboTC8sLBTTp08XarVayGSyUgcoflhF9t8H91khHv35e9QwQg+6ePGiGDt2rHB1dRVmZmbC1tZW9OnTR3z//fclyn7zzTfi6aefFkqlUiiVStG6dWsxdepUcf78eanMoz6fQtz/TcL/D6RsqGRC1MJZrkRE9ESSyWSYOnXqI0+xoKpbsGABoqOjceHChQoNAfMkCgwMhEwmK/UQuqHgOXZERET12MsvvwyNRoMtW7boO5Q6LTk5GXFxcViwYIG+Q6lRPMeOiIioHlOpVBUax+1J5eHhUSsXYukbe+yIiIiIDAR77IiIqMbwNG6i2sUeOyIiIiIDwcSOiIiIyEDwUGw9otVq8eeff8LS0tJgb15MREREuoQQuHfvHhwcHGBkVHafHBO7euTPP/+s1I3riYiIqP67evUqmjVrVmYZJnb1SPFtpq5evVqu+yaWl1arxdWrVwEAjo6Oj/1vgIiIiGpPZmYmHB0dy3W7SSZ29Ujx4VcrK6tqTeyysrLQrl07AIBGo5FuHk1ERER1R3lOw2LXDBEREZGBYGJHREREZCCY2BEREREZCCZ2RERERAaCiR0RERGRgWBiR0RERGQgONwJwcTEBFOmTJGeExERUf3EX3GCQqHAqlWr9B0GERERVRETOyIioipKu52FrLxCfYdRQl5RLv7MugwHpRMUxmb6DqcEpcIELnYcFL86MbEjCCFw+/ZtAICdnV25RrYmIqL70m5noc+SRH2HUSojs+tQuqxEVtp0aHOb6jucUu2f3ZvJXTViYkfIzs6Gvb09AN5SjIiooop76lYEt4ebvUrP0ehKyzyPN48CHwa3h4tVK32HoyPlpgazYk/WyZ7O+oyJHRERUTVws1ehbVNrfYehw8jsfqLpaq9Cm4Z1KzaqGRzuhIiIiMhAMLEjIiIiMhBM7IiIqNbl5BfhzPUM5OQX6TsUompRV/ZpJnZERFTrUm9pMHjlj0i9pdF3KETVoq7s03UusYuIiED79u2l16GhoQgMDKzVGJydnbFixYpaXScRERFRVdVqYjdkyBAMHDiw1HlJSUmQyWQYNmwYEhISaiWemJgY2NjYlJh+7NgxTJgwoVrWIYSAn58f/P39S8xbvXo1bGxscO3atWpZV2WZmJggJCQEISEhvKUYERFRPVariV1YWBji4+NLTWSio6PRqVMntGvXDg0bNqzSevLz86u0vFqthoWFRZXqKCaTyRAdHY2ff/4Zn376qTQ9LS0Nc+bMwcqVK9GsWbNqWVdlKRQKxMTEICYmBgqFQq+xEBERUeXVamI3ePBgqNVqxMTE6EzXaDTYunUrwsLCShyKLRYZGQm1Wg0rKytMmjRJJ3nr3bs3pk2bhlmzZsHOzk7qHVu2bBm8vLygVCrh6OiIKVOmQKO5f+w7MTERL730EjIyMiCTySCTyRAREQGg5KHYK1euICAgACqVClZWVggKCsJff/0lzS+OeePGjXB2doa1tTVeeOEF3Lt3DwDg6OiIDz/8ELNnz0ZaWhqEEAgLC8OAAQMwevToamhZIiIioloeoNjExARjxoxBTEwM3nrrLenWVVu3bkVRURFefPFFLF++vMRyCQkJMDMzQ2JiIi5duoSXXnoJDRs2xMKFC6Uy69evx+TJk3Ho0CFpmpGRET766CO4uLjg4sWLmDJlCubMmYPVq1eje/fuWLFiBebOnYvz588DAFSqkiOGa7VaKak7cOAACgsLMXXqVAQHByMxMVEql5qaiu3btyMuLg537txBUFAQFi1aJMUYEhKC//3vfxg7diyGDRuGM2fO4OzZs2W2V15eHvLy8qTXmZmZ5WjlihNCIDs7GwBgYWHBW4oRUY3LLbh/5WDKzfp/8UTxNhRvE5WPIe0DQN3ZD2r9hKqxY8figw8+wIEDB9C7d28A9w/DDh8+HNbWpY+KLZfLsW7dOlhYWMDT0xPz58/Ha6+9hgULFsDI6H6no7u7OxYvXqyz3KxZs6Tnzs7OePfddzFp0iSsXr0acrkc1tbWkMlkaNy48SPjTUhIwOnTp5GWlgZHR0cAwIYNG+Dp6Yljx46hc+fOAO4ngDExMbC0tAQAjB49GgkJCTrJ52effQZPT08cPHgQ33zzDdRqdZltFRUVhcjIyDLLVIfs7GwpqeUtxYioNly7kwMAmBV7Ur+BVKNrd3LQyVnfUdQfhrgPAPrfD2o9sWvdujW6d++OdevWoXfv3khJSUFSUhLmz5//yGW8vb11znnr1q0bNBoNrl69CicnJwCAj49PieW+//57REVF4ffff0dmZiYKCwuRm5uL7Ozscp9Dl5ycDEdHRympA4A2bdrAxsYGycnJUmLn7OwsJXUA0KRJE9y8eVOnLnt7e0ycOBHbt28v15W+4eHheOWVV6TXmZmZOnEQEdVXzRqYA6ib91etqOJ7nhZvE5WPIe0DQN3ZD/RyCWRYWBimT5+OVatWITo6Gq6urvD19a1SnQ/3Ml26dAmDBw/G5MmTsXDhQtja2uLHH39EWFgY8vPzq+3iiGKmpqY6r2UyGbRabYlyJiYm5b7yVKFQ8GIGIjJIZqbGAOrm/VUrq3ibqHwMcR8A9L8f6GUcu6CgIBgZGWHz5s3YsGEDxo4dW+Z5XadOnUJOTo70+qeffoJKpSqz9+rXX3+FVqvF0qVL0bVrV7Rs2RJ//vmnThm5XI6iorKPhXt4eODq1au4evWqNO3cuXO4e/cu2rRp87hNJSIiIqo1eknsVCoVgoODER4ejvT0dISGhpZZPj8/H2FhYTh37hx2796NefPmYdq0adL5daVxc3NDQUEBVq5ciYsXL2Ljxo345JNPdMo4OztDo9EgISEBt2/fli4geJCfnx+8vLwwatQoHD9+HEePHsWYMWPg6+uLTp06VWr7iYiIiGqC3u48ERYWhjt37sDf3x8ODg5llu3Xrx/c3d3Rq1cvBAcH47nnnpOGJnkUb29vLFu2DO+//z7atm2LTZs2ISoqSqdM9+7dMWnSJAQHB0OtVpe4+AK4f0h1x44daNCgAXr16gU/Pz+0aNECsbGxFd5mIiIiopokE0IIfQdB5ZOZmQlra2tkZGTAysqq2urNysriVbFEVKvOXM/A4JU/Im760/X+/Kq6vC3n/j6H4LhgxA6ORZuGdev0obrcbpVRk9tTkd9/3j+KYGxsjBEjRkjPiYhqmqtahbjpT8NVXf+vhiQC6s4+zcSOYGZmhq1bt+o7DCJ6gpjLjQ2il4aoWF3Zp/V2jh0RERERVS8mdkREREQGgodiiRdPEBFVQc7/3xv0zPUMPUdSUlrm/fuXpt7UQJtbt+IzlHvE1jVM7IiIiKog9f8TlDe2ndZzJCUZmV2H0gWYGXsS2txb+g6nVEoFU5HqxNYkIiKqggGejQEArvYqmNex24rlFeXiz6z2cOjjBIWxmb7DKUGpMIGLHY8SVScmdkRERFVgq5Tjhaea6zuMR7CGDxrpOwiqRbx4goiIiMhAMLEjIiIiMhBM7IiIiIgMBM+xIxgbG+PZZ5+VnhMREVH9xMSOYGZmhl27duk7DCIiIqoiHoolIiIiMhBM7IiIiIgMBBM7QlZWFpRKJZRKJbKysvQdDhEREVUSz7EjAEB2dra+QyAiIqIqYo8dERERkYFgYkdERERkIJjYERERERkIJnZEREREBoKJHREREZGB4FWxBCMjI/j6+krPiYiIqH5iYkcwNzdHYmKivsMgIiKiKmL3DBEREZGBYGJHREREZCCY2BGysrKgVquhVqt5SzEiIqJ6jOfYEQDg9u3b+g6BiIiIqoiJHRHVKWm3s5CVV1jlevKKcvFn1mU4KJ2gMDarhsjqJ6XCBC52Sn2HQUS1hIkdEdUZabez0GdJYrXUZWR2HUqXlchKmw5tbtNqqbO+2j+7N5M7oicEEzsiqjOKe+pWBLeHm72qSnWlZZ7Hm0eBD4Pbw8WqVXWEV++k3NRgVuzJaukBJaL6gYkdEdU5bvYqtG1qXaU6jMzuJ4au9iq0aVi1uoiI6gteFUtERERkINhjRzAyMkKnTp2k50RERFQ/MbEjmJub49ixY/oOg8qQk1+E1FsauKpVMJcb6zscoirjPk1UM9g9Q1QPpN7SYPDKH5F6S6PvUIiqBfdpoprBxI6IiIjIQNR6YhcaGgqZTFbikZKSUtuh0P/Lzs6Gs7MznJ2dkZ2dre9wiIiIqJL0co7dwIEDER0drTNNrVbrvM7Pz4dcLq/NsJ5YQghcvnxZek5ERET1k14OxSoUCjRu3Fjn0a9fP0ybNg2zZs2CnZ0d/P39AQBnzpzBM888A5VKhUaNGmH06NE69zXNysrCmDFjoFKp0KRJEyxduhS9e/fGrFmzpDIymQzbt2/XicHGxgYxMTHS66tXryIoKAg2NjawtbVFQEAALl26JM0PDQ1FYGAglixZgiZNmqBhw4aYOnUqCgoKpDJ5eXl4/fXX4ejoCIVCATc3N/z3v/+FEAJubm5YsmSJTgwnT55kbyURERFVmzp1Vez69esxefJkHDp0CABw9+5d9O3bF+PGjcPy5cuRk5OD119/HUFBQfjhhx8AAK+99hoOHDiAHTt2wN7eHm+++SaOHz+O9u3bl3u9BQUF8Pf3R7du3ZCUlAQTExO8++67GDhwIH777Tep53D//v1o0qQJ9u/fj5SUFAQHB6N9+/YYP348AGDMmDE4cuQIPvroI3h7eyMtLQ23b9+GTCbD2LFjER0djdmzZ0vrjY6ORq9eveDm5lZqXHl5ecjLy5NeZ2ZmVqg9yXDkFhQBuH8nAUNWvH3F20tVU5f3G77XRDVDL4ldXFwcVKp/bxf0zDPPAADc3d2xePFiafq7776LDh064L333pOmrVu3Do6Ojvjjjz/g4OCA//73v/jiiy/Qr18/APeTw2bNmlUontjYWGi1WqxduxYymQzA/aTLxsYGiYmJGDBgAACgQYMG+Pjjj2FsbIzWrVtj0KBBSEhIwPjx4/HHH3/gq6++Qnx8PPz8/AAALVq0kNYRGhqKuXPn4ujRo3jqqadQUFCAzZs3l+jFe1BUVBQiIyMrtC1kmK7dyQEAzIo9qd9Aasm1Ozno5KzvKOq/+rDf8L0mql56Sez69OmDNWvWSK+VSiVefPFF+Pj46JQ7deoU9u/fr5MEFktNTUVOTg7y8/PRpUsXabqtrS1atarYfSFPnTqFlJQUWFpa6kzPzc1Famqq9NrT0xPGxv+Ot9SkSROcPn0awP3DqsbGxvD19S11HQ4ODhg0aBDWrVuHp556Ct9++y3y8vLw/PPPPzKu8PBwvPLKK9LrzMxMODo6VmjbyDA0a2AOoHruoVqXFd/btHh7qWrq8n7D95qoZuglsVMqlaUeflQqlTqvNRoNhgwZgvfff79E2SZNmpT73DSZTFbiooAHz43TaDTw8fHBpk2bSiz74EUdpqamJerVarUA7g/y+zjjxo3D6NGjsXz5ckRHRyM4OBgWFhaPLK9QKKBQKB5bLxk+M9P7/1BUxz1U64Pi7aWqqQ/7Dd9roupVp86xe1jHjh3xzTffwNnZGSYmJUN1dXWFqakpfv75ZzRv3hwAcOfOHfzxxx86PWdqtRrp6enS6wsXLugM69GxY0fExsbC3t4eVlZWlYrVy8sLWq0WBw4ckA7FPuzZZ5+FUqnEmjVr8N133+HgwYOVWld1k8lkaNOmjfSciIiI6qc6PUDx1KlT8c8//+DFF1/EsWPHkJqair179+Kll15CUVERVCoVwsLC8Nprr+GHH37AmTNnEBoaWuJ+p3379sXHH3+MEydO4JdffsGkSZN0et9GjRoFOzs7BAQEICkpCWlpaUhMTMSMGTNw7dq1csXq7OyMkJAQjB07Ftu3b5fq+Oqrr6QyxsbGCA0NRXh4ONzd3dGtW7fqaagqsrCwwNmzZ3H27NkyexCJiIiobqvTiZ2DgwMOHTqEoqIiDBgwAF5eXpg1axZsbGyk5O2DDz5Az549MWTIEPj5+eHpp58uca7e0qVL4ejoiJ49e2LkyJGYPXu2TgJjYWGBgwcPonnz5hg2bBg8PDwQFhaG3NzcCvXgrVmzBiNGjMCUKVPQunVrjB8/HllZWTplwsLCkJ+fj5deeqkKLUNERERUUq0fin1w7LgHJSYmljrd3d0d27Zte2R9KpUKGzduxMaNG6Vpu3bt0inj4OCAvXv36ky7e/euzuvGjRtj/fr1FYp7xYoVOq/NzMywbNkyLFu27JH1XL9+HaamphgzZswjyxARERFVRp3usTMkeXl5uHbtGiIiIvD888+jUaNG+g5Jkp2dDU9PT3h6evKWYnWUq1qFuOlPw1Vdt65sJKos7tNENYOJXS358ssv4eTkhLt37+qM1VcXCCFw7tw5nDt3jrcUq6PM5cZo29Qa5nJeQUiGgfs0Uc2o01fFVtajDuvqU2hoKEJDQ/UdBhERERkw9tgRERERGQiD7LEjovop5//vG3rmekaV60rLvH8v0tSbGmhzq15ffVQX7xFLRDWLiR0R1Rmp/5+IvLHtdJXrMjK7DqULMDP2JLS5t6pcX32mVPCrnuhJwU87EdUZAzwbAwBc7VUwr+KtpvKKcvFnVns49HGCwtisOsKrl5QKE7jYKR9fkIgMAhM7gkwmg5OTk/ScSF9slXK88FTzaqrNGj6oO8MKERHVBiZ2BAsLC1y6dEnfYRAREVEV8apYIiIiIgPBxI6IiIjIQDCxI+Tk5KBz587o3LkzcnJy9B0OERERVRLPsSNotVr88ssv0nMiIiKqn9hjR0RERGQgmNgRERERGQgmdkREREQGgokdERERkYFgYkdERERkIHhVLAEA7Ozs9B0CERERVRETO4JSqcStW7f0HQYRERFVEQ/FEhERERkIJnZEREREBoKJHSEnJwe9e/dG7969eUsxIiKieozn2BG0Wi0OHDggPSciIqL6iT12RERERAaCiR0RERGRgWBiR0RERGQgmNgRERERGQhePEFEkrTbWcjKK9R3GCXkFeXiz6zLcFA6QWFspu9w9EapMIGLnVLfYRBRHcbEjgAAFhYW+g6B9Cztdhb6LEnUdxilMjK7DqXLSmSlTYc2t6m+w9Gr/bN7M7kjokdiYkdQKpXIysrSdxikZ8U9dSuC28PNXqXnaHSlZZ7Hm0eBD4Pbw8Wqlb7D0YuUmxrMij1ZJ3tUiajuYGJHRDrc7FVo29Ra32HoMDK7n2i62qvQpmHdio2IqC7hxRNEREREBoKJHSE3NxeDBg3CoEGDkJubq+9wiIiIqJJ4KJZQVFSE3bt3S8/pyZRbUKTzl+oWvj9EVB7ssSMiAMC1Ozk6f6lu4ftDROVRpxK7iIgItG/fXnodGhqKwMDAWo3B2dkZK1asqNV1EhEREVWHWkvshgwZgoEDB5Y6LykpCTKZDMOGDUNCQkKtxBMTEwMbG5sS048dO4YJEyZU23oSExMhk8ke+ejTp0+1rYuIiIiebLV2jl1YWBiGDx+Oa9euoVmzZjrzoqOj0alTJ7Rr167K68nPz4dcLq/08mq1usoxPKh79+5IT08vMX3nzp2YNGkSpkyZUq3rIyIioidXrfXYDR48GGq1GjExMTrTNRoNtm7dirCwsBKHYotFRkZCrVbDysoKkyZNQn5+vjSvd+/emDZtGmbNmgU7Ozv4+/sDAJYtWwYvLy8olUo4OjpiypQp0Gg0AO73or300kvIyMiQes4iIiIAlDwUe+XKFQQEBEClUsHKygpBQUH466+/pPnFMW/cuBHOzs6wtrbGCy+8gHv37gEA5HI5GjdurPO4c+cOZs+ejTfffBPPP/98NbQuERERUS0mdiYmJhgzZgxiYmIghJCmb926FUVFRXjxxRdLXS4hIQHJyclITEzEl19+iW3btiEyMlKnzPr16yGXy3Ho0CF88sknAAAjIyN89NFHOHv2LNavX48ffvgBc+bMAXC/F23FihWwsrJCeno60tPTMXv27BLr1mq1CAgIwD///IMDBw4gPj4eFy9eRHBwsE651NRUbN++HXFxcYiLi8OBAwewaNGiUrfn7t27CAgIQO/evbFgwYIy2ywvLw+ZmZk6DyIiIqJHqdWLJ8aOHYvU1FQcOHBAmhYdHY3hw4fD2rr00eTlcjnWrVsHT09PDBo0CPPnz8dHH30ErVYrlXF3d8fixYvRqlUrtGp1/3ZDs2bNQp8+feDs7Iy+ffvi3XffxVdffSXVaW1tDZlMJvWiqVQlb6GUkJCA06dPY/PmzfDx8UGXLl2wYcMGHDhwAMeOHZPKabVaxMTEoG3btujZsydGjx5d6rmCWq0WI0eOhImJCTZt2gSZTFZme0VFRcHa2lp6ODo6llm+spRKJYQQEEJAqeQ9KImIiOqrWk3sWrduje7du2PdunUAgJSUFCQlJSEsLOyRy3h7e+vcoL5bt27QaDS4evWqNM3Hx6fEct9//z369euHpk2bwtLSEqNHj8bff/+N7OzscsebnJwMR0dHnYSqTZs2sLGxQXJysjTN2dkZlpaW0usmTZrg5s2bJep78803ceTIEezYsUOn/KOEh4cjIyNDejy4zUREREQPq/XhTsLCwvDNN9/g3r17iI6OhqurK3x9fatU58O9TJcuXcLgwYPRrl07fPPNN/j111+xatUqANA5P6+6mJqa6ryWyWQ6PYoAsGXLFixZsgRbtmyBu7t7uepVKBSwsrLSeRARERE9Sq0ndkFBQTAyMsLmzZuxYcMGjB07tsxDkqdOnUJOzr8Dcv70009QqVRlHpb89ddfodVqsXTpUnTt2hUtW7bEn3/+qVNGLpc/9i4LHh4euHr1qk5P2blz53D37l20adPmcZsqOXnyJMLCwrBo0SLp4o66JDc3F88//zyef/553lKMiIioHqv1xE6lUiE4OBjh4eFIT09HaGhomeXz8/MRFhaGc+fOYffu3Zg3bx6mTZsGI6NHh+7m5oaCggKsXLkSFy9exMaNG6WLKoo5OztDo9EgISEBt2/fLvUQrZ+fH7y8vDBq1CgcP34cR48exZgxY+Dr64tOnTqVa3tv376NwMBA9O7dG//5z39w48YNncetW7fKVU9NKioqwtdff42vv/6atxQjIiKqx/Ry54mwsDDcuXMH/v7+cHBwKLNsv3794O7ujl69eiE4OBjPPfecNDTJo3h7e2PZsmV4//330bZtW2zatAlRUVE6Zbp3745JkyYhODgYarUaixcvLlGPTCbDjh070KBBA/Tq1Qt+fn5o0aIFYmNjy72tu3btwuXLl7F79240adKkxKNz587lrouIiIioLDLx4NgjVKdlZmbC2toaGRkZ1Xq+XVZWlnRVsEaj4ZWxT6jtJ65jVuxJrAhuj8AOTfUdjo5zf59DcFwwYgfHok3D8p8GYUjq8vtDRDWrIr//depesUSkP80amOv8pbqF7w8RlQcTOyICAJiZGuv8pbqF7w8RlQcTOyIiIiIDwcSOiIiIyECY6DsA0j8LCwtoNBrpOT2ZcgruD3Vz5nqGniMpKS3z/v6ZelMDbW7di682pNzU6DsEIqoHmNgRZDIZr4QlpP5/4vDGttN6jqQkI7PrULoAM2NPQpur/7Ef9Ump4Nc2ET0avyGICAAwwLMxAMDVXgXzOnaCfl5RLv7Mag+HPk5QGJvpOxy9USpM4GLHf8KI6NGY2BHy8vIwceJEAMCnn34KhUKh54hIH2yVcrzwVHN9h/EI1vBBI30HQURU53GA4nqEAxQTERE9eThAMREREdETiIkdERERkYFgYkdERERkIJjYERERERkIJnZEREREBoKJHREREZGB4Dh2BAsLC9y8eVN6TkRERPUTEzuCTCaDWq3WdxhERERURTwUS0RERGQgmNgR8vLyMHXqVEydOhV5eXn6DoeIiIgqibcUq0d4SzEiIqInD28pRkRERPQEYmJHREREZCCY2BEREREZCCZ2RERERAaCiR0RERGRgWBiR0RERGQgeOcJgrm5OdLS0qTnREREVD8xsSMYGRnB2dlZ32EQERFRFTGxIyJJ2u0sZOUVlrt8XlEu/sy6DAelExTGZuVeTqkwgYsdB8ImIqpuTOwI+fn5eOuttwAACxcuhFwu13NEpA9pt7PQZ0lihZYxMrsOpctKZKVNhza3aYWW3T+7N5M7IqJqxsSOUFBQgCVLlgAAIiIimNg9oYp76lYEt4ebvapcy6RlnsebR4EPg9vDxapVuZZJuanBrNiTFeoZJCKi8mFiR0Q63OxVaNvUulxljczuJ4Cu9iq0aVi+ZYiIqOZwuBMiIiIiA8HEjoiIiMhAMLEjqgdy8otw5noGcvKL9B1KleUWFOn8JSKi6sPEjqgeSL2lweCVPyL1lkbfoVTZtTs5On+JiKj61KnELiIiAu3bt5deh4aGIjAwsFZjcHZ2xooVK2p1nURERETVodYSuyFDhmDgwIGlzktKSoJMJsOwYcOQkJBQK/HExMTAxsamxPRjx45hwoQJ1baeoqIidO/eHcOGDdOZnpGRAUdHR2n8OH0yNzfHmTNncObMGd5SjIiIqB6rtcQuLCwM8fHxuHbtWol50dHR6NSpE9q1a4eGDRtWaT35+flVWl6tVsPCwqJKdTzI2NgYMTEx+O6777Bp0yZp+vTp02Fra4t58+ZV27oqy8jICJ6envD09ISRUZ3qxCUiIqIKqLVf8cGDB0OtViMmJkZnukajwdatWxEWFlbiUGyxyMhIqNVqWFlZYdKkSTrJW+/evTFt2jTMmjULdnZ28Pf3BwAsW7YMXl5eUCqVcHR0xJQpU6DR3D8/KTExES+99BIyMjIgk8kgk8kQEREBoOSh2CtXriAgIAAqlQpWVlYICgrCX3/9Jc0vjnnjxo1wdnaGtbU1XnjhBdy7d08q07JlSyxatAjTp09Heno6duzYgS1btmDDhg0cDJiIiIiqTa0ldiYmJhgzZgxiYmIghJCmb926FUVFRXjxxRdLXS4hIQHJyclITEzEl19+iW3btiEyMlKnzPr16yGXy3Ho0CF88sknAO73Qn300Uc4e/Ys1q9fjx9++AFz5swBAHTv3h0rVqyAlZUV0tPTkZ6ejtmzZ5dYt1arRUBAAP755x8cOHAA8fHxuHjxIoKDg3XKpaamYvv27YiLi0NcXBwOHDiARYsW6ZSZPn06vL29MXr0aEyYMAFz586Ft7d3xRuyBuTn5yMiIgIRERFV7vEkIiIi/anVO0+MHTsWH3zwAQ4cOIDevXsDuH8Ydvjw4bC2Ln3UerlcjnXr1sHCwgKenp6YP38+XnvtNSxYsEA6bOju7o7FixfrLDdr1izpubOzM959911MmjQJq1evhlwuh7W1NWQyGRo3bvzIeBMSEnD69GmkpaXB0dERALBhwwZ4enri2LFj6Ny5M4D7CWBMTAwsLS0BAKNHj0ZCQgIWLlwo1SWTybBmzRp4eHjAy8sLb7zxxmPbKy8vD3l5edLrzMzMxy5TGQUFBVKy/Nprr7EXsQ4qHhok5WbNXRVbXDeHISEiqr9qNbFr3bo1unfvjnXr1qF3795ISUlBUlIS5s+f/8hlvL29dc5569atGzQaDa5evQonJycAgI+PT4nlvv/+e0RFReH3339HZmYmCgsLkZubi+zs7HKfQ5ecnAxHR0cpqQOANm3awMbGBsnJyVJi5+zsLCV1ANCkSRPcvHmzRH3FCWpaWhquXbsGZ2fnMtcfFRVVoneSnkzFQ4PMij1ZK+vq5FzjqyEiohpQ6/eKDQsLw/Tp07Fq1SpER0fD1dUVvr6+VapTqVTqvL506RIGDx6MyZMnY+HChbC1tcWPP/6IsLAw5OfnV+vFEQBgamqq81omk0Gr1epMO3z4MJYvX459+/bh3XffRVhYGL7//nvIZLJH1hseHo5XXnlFep2ZmamTZNKTo1mD+1crrwhuDzd7VY2sI+WmBrNiT0rrIiKi+qfWE7ugoCDMnDkTmzdvxoYNGzB58uQyk5tTp04hJydHGobjp59+gkqlKjPB+fXXX6HVarF06VLpcO1XX32lU0Yul6OoqOxDTh4eHrh69SquXr0qre/cuXO4e/cu2rRpU67tBYDs7GyEhoZi8uTJ6NOnD1xcXODl5YVPPvkEkydPfuRyCoUCCoWi3Oshw2VmagwAcLNXoW3T0k9bqO51ERFR/VPrY1uoVCoEBwcjPDwc6enpCA0NLbN8fn4+wsLCcO7cOezevRvz5s3DtGnTyhyWw83NDQUFBVi5ciUuXryIjRs3ShdVFHN2doZGo0FCQgJu376N7OzsEvX4+fnBy8sLo0aNwvHjx3H06FGMGTMGvr6+6NSpU7m3OTw8HEII6YIKZ2dnLFmyBHPmzMGlS5fKXQ8RERFRWfQyaFlYWBju3LkDf39/ODg4lFm2X79+cHd3R69evRAcHIznnntOGprkUby9vbFs2TK8//77aNu2LTZt2oSoqCidMt27d8ekSZMQHBwMtVpd4uIL4P4h1R07dqBBgwbo1asX/Pz80KJFC8TGxpZ7Ww8cOCAddn7wEPDEiRPRvXt3hIWF6VwlTERERFRZMsGsot7IzMyEtbU1MjIyYGVlVW31ZmVlQaW6f96WRqMpcc4i6d+Z6xkYvPJHxE1/usYOxVZmHef+PofguGDEDo5Fm4blOz1h+4nrmBV7EiuC2yOwQ9OqhExE9ESoyO9/rZ9jR3WPmZkZjh49Kj0nIiKi+omJHcHY2FgauoXqJle1CnHTn4arumauiK1NxVfd8upbIqLqx8SOqB4wlxvX+NWwtaX4qltefUtEVP2Y2BHy8/Px4YcfAgBmzpzJO08QERHVU0zsCAUFBdJ9dKdMmcLEjoiIqJ5iYkdEAICc/79H7JnrGeVeJi3z/v1lU29qoM0t33I1eb9bIqInHRM7IgJwPzkDgDe2nS73MkZm16F0AWbGnoQ291aF1qdU8OuHiKi68ZuViAAAAzwbAwBc7VUwL+eFDXlFufgzqz0c+jhBYVz+oXKUChO42HG8RCKi6sbEjogAALZKOV54qnkFl7KGDxrVSDxERFRxermlGBERERFVPyZ2RERERAaCh2IJZmZm2L9/v/SciIiI6icmdgRjY2P07t1b32EQERFRFfFQLBEREZGBYI8doaCgAJ999hkAYMKECTA1NdVzRERERFQZMiGE0HcQVD6ZmZmwtrZGRkYGrKysqq3erKwsqFQqAIBGo4FSyfHFiIiI6oqK/P7zUCwRERGRgWBiR0RERGQgmNgRERERGQgmdkREREQGgokdERERkYFgYkdERERkIDiOHUGhUCAuLk56TkRERPUTEzuCiYkJBg0apO8wiIiIqIp4KJaIiIjIQLDHjlBQUIBNmzYBAEaNGsVbihEREdVTvKVYPcJbihERET15eEsxIiIioicQEzsiIiIiA8Fz7Mhgpd3OQlZeYbnL5xXl4s+sy3BQOkFhbFbu5ZQKE7jY8fA1ERHpHxM7Mkhpt7PQZ0lihZYxMrsOpctKZKVNhza3aYWW3T+7N5M7IiLSOyZ2ZJCKe+pWBLeHm72qXMukZZ7Hm0eBD4Pbw8WqVbmWSbmpwazYkxXqGSQiIqopTOzIoLnZq9C2qXW5yhqZ3U8AXe1VaNOwfMsQERHVJUzsCAqFAl999ZX0nIiIiOonJnYEExMTPP/88/oOg4iIiKqIw50QACAnvwhnrmcgJ79I36HUK7kFRTp/iYiI9ImJHaGwsBCro79An6mLcD79rr7DqVeu3cnR+UtERKRPdT6xCw0NhUwmg0wmg1wuh5ubG+bPn4/CQl6FWF3y8vIwe1Iobu9YhPz8PH2HQ0RERJVU5xM7ABg4cCDS09Nx4cIFvPrqq4iIiMAHH3xQolx+fr4eoitbXYyJiIiIDFO9SOwUCgUaN24MJycnTJ48GX5+fti5cydCQ0MRGBiIhQsXwsHBAa1a3R977OrVqwgKCoKNjQ1sbW0REBCAS5cuSfUlJibiqaeeglKphI2NDXr06IHLly8DAE6dOoU+ffrA0tISVlZW8PHxwS+//AIAiIiIQPv27XViW7FiBZydnaXXlY2JiIiIqKrqRWL3MHNzc6knLCEhAefPn0d8fDzi4uJQUFAAf39/WFpaIikpCYcOHYJKpcLAgQORn5+PwsJCBAYGwtfXF7/99huOHDmCCRMmQCaTAQBGjRqFZs2a4dixY/j111/xxhtvwNTUtELxVTQmIiIioupQr4Y7EUIgISEBe/fuxfTp03Hr1i0olUqsXbsWcrkcAPDFF19Aq9Vi7dq1UrIWHR0NGxsbJCYmolOnTsjIyMDgwYPh6uoKAPDw8JDWceXKFbz22mto3bo1AMDd3b3CcVY0pgEDBpRaT15eHvLy/j3nLTMzs8KxVNTFW1mwsMio8fXUtJSbGgC8WpWIiJ4s9SKxi4uLg0qlQkFBAbRaLUaOHImIiAhMnToVXl5eUgIF3D+UmpKSAktLS506cnNzkZqaigEDBiA0NBT+/v7o378//Pz8EBQUhCZNmgAAXnnlFYwbNw4bN26En58fnn/+eSkBLK+KxvQoUVFRiIyMrNC6q2rO17/BSG5Wq+usSdfu5KCTs76jICIiqh31IrHr06cP1qxZA7lcDgcHB5iY/Bu2Uql743WNRgMfHx9s2rSpRD1qtRrA/d6yGTNm4LvvvkNsbCzefvttxMfHo2vXroiIiMDIkSOxa9cu7NmzB/PmzcOWLVswdOhQGBkZQQihU2dBQUGJ9VQmptKEh4fjlVdekV5nZmbC0dHxkeWrw+IR7dDWyb5G11Ebiu/h2qyBub5DISIiqjX1IrFTKpVwc3MrV9mOHTsiNjYW9vb2sLKyemS5Dh06oEOHDggPD0e3bt2wefNmdO3aFQDQsmVLtGzZEi+//DJefPFFREdHY+jQoVCr1bhx4waEENIh1ZMnT1ZbTA9TKBS1cosvuVyOBctWYcX3F9DKoUG5761aH5iZGus7BCIiolpTLy+eKMuoUaNgZ2eHgIAAJCUlIS0tDYmJiZgxYwauXbuGtLQ0hIeH48iRI7h8+TL27duHCxcuwMPDAzk5OZg2bRoSExNx+fJlHDp0CMeOHZPOwevduzdu3bqFxYsXIzU1FatWrcKePXuqHJO+mZqaIjBoFFRefhW+UISIiIjqDoNL7CwsLHDw4EE0b94cw4YNg4eHB8LCwpCbmwsrKytYWFjg999/x/Dhw9GyZUtMmDABU6dOxcSJE2FsbIy///4bY8aMQcuWLREUFIRnnnlGOs/Nw8MDq1evxqpVq+Dt7Y2jR49i9uzZVY6JiIiIqDrIxMMnjVGdlZmZCWtra2RkZFRrQlhYWIjPNm9DxLfnsG/JdLR3alhtdevLmesZGLzyR8RNf7rch5bP/X0OwXHBiB0cizYN25Rrme0nrmNW7EmsCG6PwA5NqxIyERFRqSry+18vzrGjmpWXl4epIcEAgPz3Jug5GiIiIqosgzsUS1XTwk6l7xDqleKrbnn1LRER1QVM7EiHuZxXkVZE8VW3vPqWiIjqAiZ2RERERAaCiR0RERGRgeDFE2SQcv7/HrFnrpf/vrdpmffvL5t6UwNtbvmWK74nLRERUV3AxI4MUur/J1xvbDtd7mWMzK5D6QLMjD0Jbe6tCq1PqeBHiYiI9I+/RgS5XI6PP/5Yem4IBng2BgC42qtgXs4LG/KKcvFnVns49HGCwtis3OtSKkzgYqd8fEEiIqIaxgGK65GaGqCYiIiI6q6K/P7z4gkiIiIiA8FDsYSioiIkJSUBAHr27AljY47JRkREVB8xsSPk5uaiT58+AACNRgOlkueLERER1Uc8FEtERERkIJjYERERERkIJnZEREREBoKJHREREZGBYGJHREREZCCY2BEREREZCA53QjA1NcXixYul50RERFQ/8ZZi9QhvKUZERPTk4S3FiIiIiJ5APBRLKCoqwvHjxwEAHTt25C3FiIiI6ikmdoTc3Fw89dRTAHhLMSIiovqMh2KJiIiIDAQTOyIiIiIDwcSOiIiIyEAwsSMiIiIyEEzsiIiIiAwEEzsiIiIiA8HhTgimpqaYN2+e9Lwm5RTm4GDaOfRyaQNzE/MaXRcREdGThokdQS6XIyIiolbW9b/TJxD120S8mfUpXmzfvVbWSURE9KTgoViqVX9l5gIAbmTk6jkSIiIiw8MeO4JWq0VycjIAwMPDA0ZGzPeJiIjqIyZ2hJycHLRt2xYAbylGRERUn7FrhoiIiMhAMLGjWpWTX6Tzl4iIiKoPEzuqVVf+ydL5S0RERNWn3ImdTCYr81Fbw2XUJmdnZ6xYsULfYVAFFRUVITExEV9++SUSExNRVMTeQSIiejKU++KJ9PR06XlsbCzmzp2L8+fPS9NUKlX1RlZDhBAoKiqCiUntXTeSn58PuVxea+t7km3btg2vvvoqLl26JE1zdnbG0qVLMWzYMP0FRkREVAvK3WPXuHFj6WFtbQ2ZTKYzbcuWLfDw8ICZmRlat26N1atXS8teunQJMpkMX331FXr27Alzc3N07twZf/zxB44dO4ZOnTpBpVLhmWeewa1bt6TlQkNDERgYiMjISKjValhZWWHSpEnIz8+Xymi1WkRFRcHFxQXm5ubw9vbG119/Lc1PTEyETCbDnj174OPjA4VCgR9//BGpqakICAhAo0aNoFKp0LlzZ3z//ffScr1798bly5fx8ssvS72SABAREYH27dvrtM2KFSvg7OxcIu6FCxfCwcEBrVq1AgBcvXoVQUFBsLGxga2tLQICAnQSEKqabdu2YcSIEfDy8sKRI0dw7949HDlyBF5eXhgxYgS2bdum7xCJiIhqVLV0W23atAlz587Fxx9/jA4dOuDEiRMYP348lEolQkJCpHLz5s3DihUr0Lx5c4wdOxYjR46EpaUlPvzwQ1hYWCAoKAhz587FmjVrpGUSEhJgZmaGxMREXLp0CS+99BIaNmyIhQsXAgCioqLwxRdf4JNPPoG7uzsOHjyI//znP1Cr1fD19ZXqeeONN7BkyRK0aNECDRo0wNWrV/Hss89i4cKFUCgU2LBhA4YMGYLz58+jefPm2LZtG7y9vTFhwgSMHz++wm2SkJAAKysrxMfHAwAKCgrg7++Pbt26ISkpCSYmJnj33XcxcOBA/Pbbb3rt0TM1NcXs2bOl5/VRUVERXn31VQwePBjbt2+XxuLr2rUrtm/fjsDAQMyePRsBAQEwNjbWc7REREQ1RFRCdHS0sLa2ll67urqKzZs365RZsGCB6NatmxBCiLS0NAFArF27Vpr/5ZdfCgAiISFBmhYVFSVatWolvQ4JCRG2trYiKytLmrZmzRqhUqlEUVGRyM3NFRYWFuLw4cM66w4LCxMvvviiEEKI/fv3CwBi+/btj90uT09PsXLlSum1k5OTWL58uU6ZefPmCW9vb51py5cvF05OTjpxN2rUSOTl5UnTNm7cKFq1aiW0Wq00LS8vT5ibm4u9e/eWGk9ubq7IyMiQHlevXhUAREZGxmO3pa6auOV/om1MWzFxy/+qtd7i9/nIkSOlzj98+LAAIPbv31+t6yUiIqppGRkZ5f79r3KPXVZWFlJTUxEWFqbTs1VYWAhra2udsu3atZOeN2rUCADg5eWlM+3mzZs6y3h7e8PCwkJ63a1bN2g0Gly9ehUajQbZ2dno37+/zjL5+fno0KGDzrROnTrpvNZoNIiIiMCuXbuQnp6OwsJC5OTk4MqVKxXZ/Efy8vLS6YU7deoUUlJSYGlpqVMuNzcXqamppdYRFRWFyMjIaonH0BWfA1o80PLDiqc/eK4oERGRoalyYqfRaAAAn3/+Obp06aIz7+FDXg8e5is+Z+3haVqttsLr3rVrF5o2baozT6FQ6Lx++G4Ks2fPRnx8PJYsWQI3NzeYm5tjxIgROufvlcbIyAhCCJ1pBQUFJco9vD6NRgMfHx9s2rSpRFm1Wl3qusLDw/HKK69IrzMzM+Ho6FhmfJWh1WqlhLZ58+b18pZiTZo0AQCcOXMGXbt2LTH/zJkzOuWIiIgMUZUTu0aNGsHBwQEXL17EqFGjqiMmHadOnUJOTg7Mzc0BAD/99BNUKhUcHR1ha2sLhUKBK1eu6JxPVx6HDh1CaGgohg4dCuB+4vXwhQxyubzEUBlqtRo3btyAEEJKTk+ePPnY9XXs2BGxsbGwt7eHlZVVuWJUKBQlEtSakJOTAxcXFwD195ZiPXv2hLOzM9577z2dc+wA3QtsevbsqccoiYiIala1dM1ERkYiKioKH330Ef744w+cPn0a0dHRWLZsWZXrzs/PR1hYGM6dO4fdu3dj3rx5mDZtGoyMjGBpaYnZs2fj5Zdfxvr165Gamorjx49j5cqVWL9+fZn1uru7Y9u2bTh58iROnTqFkSNHlugtdHZ2xsGDB3H9+nXcvn0bwP2rZW/duoXFixcjNTUVq1atwp49ex67HaNGjYKdnR0CAgKQlJSEtLQ0JCYmYsaMGbh27VrlG4gA3O8dXrp0KeLi4hAYGKhzVWxgYCDi4uKwZMkSXjhBREQGrVoSu3HjxmHt2rWIjo6Gl5cXfH19ERMTI/UCVUW/fv3g7u6OXr16ITg4GM8995zOYMgLFizAO++8g6ioKHh4eGDgwIHYtWvXY9e9bNkyNGjQAN27d8eQIUPg7++Pjh076pSZP38+Ll26BFdXV+lwqYeHB1avXo1Vq1bB29sbR48ela4oLYuFhQUOHjyI5s2bY9iwYfDw8EBYWBhyc3PL3YNHZRs2bBi+/vprnD59Gt27d4eVlRW6d++OM2fO4Ouvv+Y4dkREZPBk4uETxuqQ0NBQ3L17F9u3b9d3KHVCZmYmrK2tkZGRUa3JYFZWljTAdE0fip0Uux2Hct9BD7MF+CQ4sEbWUVRUhKSkJKSnp6NJkybo2bMne+qIiKjeqsjvf+3dfoGolhgbG6N37976DoOIiKjW1b/LH6lea26r1PlLRERE1adO99jFxMToOwSqZuZyY52/REREVH3qdGJHtcPExARTpkyRnhMREVH9xF9xgkKhwKpVq/QdBhEREVURz7EjIiIiMhDssSMIIaQBmO3s7KQ7ahAREVH9wsSOkJ2dDXt7ewA1P45dcHsfAB/+/18iIiKqTkzsqFY5WFvjZd+++g6DiIjIIPEcOyIiIiIDwcSOiIiIyEAwsSMiIiIyEEzsiIiIiAwEEzsiIiIiA8GrYgkmJiYICQmRnhMREVH9xF9xgkKhQExMjL7DICIioirioVgiIiIiA8EeO4IQAtnZ2QAACwsL3lKMiIionmKPHSE7OxsqlQoqlUpK8IiIiKj+YWJHREREZCCY2BEREREZCCZ2RERERAaCiR0RERGRgWBiR0RERGQgmNgRERERGQiOY0cwNjbGiBEjpOdERERUPzGxI5iZmWHr1q36DoOIiIiqiIdiiYiIiAwEEzsiIiIiA8HEjpCVlQUjhRHMnc3xd8bf+g6HiIiIKomJHQEAFE0UcIt0w87kk/oOhYiIiCqJiR3puJmZp+8QiIiIqJKY2BEREREZCCZ2RERERAaCiR0RERGRgWBiRzpy8ov0HQIRERFVEhM7grGxMZq2ag8AuJ6Rq99giIiIqNLqfGIXGhoKmUwGmUwGuVwONzc3zJ8/H4WFhXqLSSaTYfv27eUuv23bNvTv3x9qtRpWVlbo1q0b9u7dW3MBVpCZmRn8xr4OADA2MdVzNERERFRZdT6xA4CBAwciPT0dFy5cwKuvvoqIiAh88MEHJcrl5+frIbrHO3jwIPr374/du3fj119/RZ8+fTBkyBCcOHFC36ERERGRAakXiZ1CoUDjxo3h5OSEyZMnw8/PDzt37kRoaCgCAwOxcOFCODg4oFWrVgCAq1evIigoCDY2NrC1tUVAQAAuXbok1ZeYmIinnnoKSqUSNjY26NGjBy5fvizN37FjBzp27AgzMzO0aNECkZGRUg+hs7MzAGDo0KGQyWTS67KsWLECc+bMQefOneHu7o733nsP7u7u+Pbbb6utjYiIiIjqRWL3MHNzc6l3LiEhAefPn0d8fDzi4uJQUFAAf39/WFpaIikpCYcOHYJKpcLAgQORn5+PwsJCBAYGwtfXF7/99huOHDmCCRMmQCaTAQCSkpIwZswYzJw5E+fOncOnn36KmJgYLFy4EABw7NgxAEB0dDTS09Ol1xWh1Wpx79492NraVlOLVE1WVhY2vR0KACjI5wDFRERE9ZWJvgOoCCEEEhISsHfvXkyfPh23bt2CUqnE2rVrIZfLAQBffPEFtFot1q5dKyVr0dHRsLGxQWJiIjp16oSMjAwMHjwYrq6uAAAPDw9pHZGRkXjjjTcQEhICAGjRogUWLFiAOXPmYN68eVCr1QAAGxsbNG7cuFLbsWTJEmg0GgQFBZVZLi8vD3l5/yZamZmZlVpfeRQWMKEjIiKq7+pFj11cXBxUKhXMzMzwzDPPIDg4GBEREQAALy8vKakDgFOnTiElJQWWlpZQqVRQqVSwtbVFbm4uUlNTYWtri9DQUPj7+2PIkCH48MMPkZ6errP8/PnzpWVVKhXGjx+P9PR0ZGdnV3lbNm/ejMjISHz11Vewt7cvs2xUVBSsra2lh6OjY5XXT0RERIarXvTY9enTB2vWrIFcLoeDgwNMTP4NW6lU6pTVaDTw8fHBpk2bStRT3NsWHR2NGTNm4LvvvkNsbCzefvttxMfHo2vXrtBoNIiMjMSwYcNKLG9mZlal7diyZQvGjRuHrVu3ws/P77Hlw8PD8corr0ivMzMzmdwRERHRI9WLxE6pVMLNza1cZTt27IjY2FjY29vDysrqkeU6dOiADh06IDw8HN26dcPmzZvRtWtXdOzYEefPny9zfaampigqqthAvl9++SXGjh2LLVu2YNCgQeVaRqFQQKFQVGg9RERE9OSqF4diK2LUqFGws7NDQEAAkpKSkJaWhsTERMyYMQPXrl1DWloawsPDceTIEVy+fBn79u3DhQsXpPPs5s6diw0bNiAyMhJnz55FcnIytmzZgrfffltah7OzMxISEnDjxg3cuXPnsTFt3rwZY8aMwdKlS9GlSxfcuHEDN27cQEZGRo21AxERET15DC6xs7CwwMGDB9G8eXMMGzYMHh4eCAsLQ25uLqysrGBhYYHff/8dw4cPR8uWLTFhwgRMnToVEydOBAD4+/sjLi4O+/btQ+fOndG1a1csX74cTk5O0jqWLl2K+Ph4ODo6okOHDo+N6bPPPkNhYSGmTp2KJk2aSI+ZM2fWWDsQERHRk0cmhBD6DoLKJzMzE9bW1sjIyCjzMHNF5eTkwGNQD1iGFKCL8TtY+5+yr9YlIiKi2lOR33+D67GjijM3N8fAiXMBACam8seUJiIiorqKiV018PT01Bke5cFHaVfn1kXNbZU6f4mIiKj+qRdXxdZ1u3fvRkFBQanzGjVqVMvRVI653FjnLxEREdU/TOyqwYMXVtRHWVlZWDhtJJrMViM/L1ff4RAREVEl8VAsAQCy7nHoFSIiovqOiR0RERGRgWBiR0RERGQgmNgRACAvPQ8p81IwzOvxAy4TERFR3cTEjgAAIl8g93IuHKpx4GMiIiKqXUzsiIiIiAwEhzshGBkZoVOnTtJzIiIiqp+Y2BHMzc1x7NgxfYdBREREVcTuGSIiIiIDwcSOiIiIyEAwsSNkZ2fD2dkZzs7OyM7O1nc4REREVEk8x44ghMDly5el50RERFQ/sceOiIiIyEAwsSMiIiIyEEzsiIiIiAwEEzsiIiIiA8HEjoiIiMhA8KpYgkwmQ5s2baTnREREVD8xsSNYWFjg7Nmz+g6DiIiIqoiHYomIiIgMBBM7IiIiIgPBxI6QnZ0NT09PeHp68pZiRERE9RjPsSMIIXDu3DnpOREREdVP7LEjIiIiMhBM7IiIiIgMBBM7AgDI5DKYOZkhtzBX36EQERFRJTGxIwCAookCbpFu2Jl8Ut+hEBERUSUxsSMdNzPz9B0CERERVRITO4JMJoONXSPpOREREdVPTOwIFhYWmLN0HQDAVK7QczRERERUWUzsiIiIiAwEEzsCAOTkF+n8JSIiovqHiR0hJycHqyNnAQAu/nVHv8EQERFRpdX5xC40NBQymQwymQxyuRxubm6YP38+CgsL9RaTTCbD9u3by10+PT0dI0eORMuWLWFkZIRZs2bVWGyVodVq8fe1iwB4SzEiIqL6rM4ndgAwcOBApKen48KFC3j11VcRERGBDz74oES5/Px8PUT3eHl5eVCr1Xj77bfh7e2t73CIiIjIQNWLxE6hUKBx48ZwcnLC5MmT4efnh507dyI0NBSBgYFYuHAhHBwc0KpVKwDA1atXERQUBBsbG9ja2iIgIACXLl2S6ktMTMRTTz0FpVIJGxsb9OjRA5cvX5bm79ixAx07doSZmRlatGiByMhIqYfQ2dkZADB06FDIZDLpdVmcnZ3x4YcfYsyYMbC2tq62diEiIiJ6UL1I7B5mbm4u9c4lJCTg/PnziI+PR1xcHAoKCuDv7w9LS0skJSXh0KFDUKlUGDhwIPLz81FYWIjAwED4+vrit99+w5EjRzBhwgRp/LakpCSMGTMGM2fOxLlz5/Dpp58iJiYGCxcuBAAcO3YMABAdHY309HTpNREREZG+meg7gIoQQiAhIQF79+7F9OnTcevWLSiVSqxduxZyuRwA8MUXX0Cr1WLt2rVSshYdHQ0bGxskJiaiU6dOyMjIwODBg+Hq6goA8PDwkNYRGRmJN954AyEhIQCAFi1aYMGCBZgzZw7mzZsHtVoNALCxsUHjxo1rdHvz8vKQl/fvnSAyMzNrdH1ERERUv9WLHru4uDioVCqYmZnhmWeeQXBwMCIiIgAAXl5eUlIHAKdOnUJKSgosLS2hUqmgUqlga2uL3NxcpKamwtbWFqGhofD398eQIUPw4YcfIj09XWf5+fPnS8uqVCqMHz8e6enpyM7OrtXtjoqKgrW1tfRwdHSs1fUTERFR/VIveuz69OmDNWvWQC6Xw8HBASYm/4atVCp1ymo0Gvj4+GDTpk0l6inubYuOjsaMGTPw3XffITY2Fm+//Tbi4+PRtWtXaDQaREZGYtiwYSWWNzMzq+YtK1t4eDheeeUV6XVmZmaNJXcKC6saqZeIiIhqT71I7JRKJdzc3MpVtmPHjoiNjYW9vT2srB6drHTo0AEdOnRAeHg4unXrhs2bN6Nr167o2LEjzp8/X+b6TE1NUVRU8wP5KhQKKBQ1f4svpVKJF+Z9ikO57/CWYkRERPVYvTgUWxGjRo2CnZ0dAgICkJSUhLS0NCQmJmLGjBm4du0a0tLSEB4ejiNHjuDy5cvYt28fLly4IJ1nN3fuXGzYsAGRkZE4e/YskpOTsWXLFrz99tvSOpydnZGQkIAbN27gzp3yDeh78uRJnDx5EhqNBrdu3cLJkydx7ty5GmkDIiIiejIZXGJnYWGBgwcPonnz5hg2bBg8PDwQFhaG3NxcWFlZwcLCAr///juGDx+Oli1bYsKECZg6dSomTpwIAPD390dcXBz27duHzp07o2vXrli+fDmcnJykdSxduhTx8fFwdHREhw4dyhVXcQ/hr7/+is2bN6NDhw549tlna6QNiIiI6MkkE7zVQL2RmZkJa2trZGRklHmYuaJycnLgMagHLEMK0MX4Haz9T1C11U1ERERVU5Hff4PrsaOK02q1+OtiMgDeUoyIiKg+Y2JXDTw9PXWGR3nwUdrVuXWZYwMLfYdARERElVQvroqt63bv3o2CgoJS5zVq1KiWo6kac7mxvkMgIiKiSmJiVw0evLCCiIiISF94KJaIiIjIQDCxIyIiIjIQTOwIACDnHSeIiIjqPZ5jR1AqlUg+dhmxJ39FcHsffYdDRERElcTEjgAADtbWeNm3r77DICIioirgoVgiIiIiA8HEjpCbm4tBgwZh0KBByM3N1Xc4REREVEk8FEsoKirC7t27pedERERUP7HHjoiIiMhAMLEjIiIiMhBM7IiIiIgMBBM7IiIiIgPBxI6IiIjIQPCq2HpECAEAyMzMrNZ6s7KypOeZmZm8MpaIiKgOKf7dL84DysLErh65d+8eAMDR0bHG1uHg4FBjdRMREVHl3bt3D9bW1mWWkYnypH9UJ2i1Wvz555+wtLSETCar1rozMzPh6OiIq1evwsrKqlrrrm/YFrrYHv9iW/yLbfEvtoUutse/qqsthBC4d+8eHBwcYGRU9ll07LGrR4yMjNCsWbMaXYeVldUT/0EsxrbQxfb4F9viX2yLf7EtdLE9/lUdbfG4nrpivHiCiIiIyEAwsSMiIiIyEEzsCACgUCgwb948KBQKfYeid2wLXWyPf7Et/sW2+BfbQhfb41/6aAtePEFERERkINhjR0RERGQgmNgRERERGQgmdkREREQGgomdAVu1ahWcnZ1hZmaGLl264OjRo2WW37p1K1q3bg0zMzN4eXlh9+7dOvOFEJg7dy6aNGkCc3Nz+Pn54cKFCzW5CdWmutsiNDQUMplM5zFw4MCa3IRqU5G2OHv2LIYPHw5nZ2fIZDKsWLGiynXWJdXdFhERESX2i9atW9fgFlSvirTH559/jp49e6JBgwZo0KAB/Pz8SpR/Ur4zytMWT8p3xrZt29CpUyfY2NhAqVSiffv22Lhxo06ZJ2W/KE9b1Mh+IcggbdmyRcjlcrFu3Tpx9uxZMX78eGFjYyP++uuvUssfOnRIGBsbi8WLF4tz586Jt99+W5iamorTp09LZRYtWiSsra3F9u3bxalTp8Rzzz0nXFxcRE5OTm1tVqXURFuEhISIgQMHivT0dOnxzz//1NYmVVpF2+Lo0aNi9uzZ4ssvvxSNGzcWy5cvr3KddUVNtMW8efOEp6enzn5x69atGt6S6lHR9hg5cqRYtWqVOHHihEhOThahoaHC2tpaXLt2TSrzpHxnlKctnpTvjP3794tt27aJc+fOiZSUFLFixQphbGwsvvvuO6nMk7JflKctamK/YGJnoJ566ikxdepU6XVRUZFwcHAQUVFRpZYPCgoSgwYN0pnWpUsXMXHiRCGEEFqtVjRu3Fh88MEH0vy7d+8KhUIhvvzyyxrYgupT3W0hxP0PY0BAQI3EW5Mq2hYPcnJyKjWZqUqd+lQTbTFv3jzh7e1djVHWnqq+j4WFhcLS0lKsX79eCPFkfWc87OG2EOLJ/M4o1qFDB/H2228LIZ7s/UII3bYQomb2Cx6KNUD5+fn49ddf4efnJ00zMjKCn58fjhw5UuoyR44c0SkPAP7+/lL5tLQ03LhxQ6eMtbU1unTp8sg664KaaItiiYmJsLe3R6tWrTB58mT8/fff1b8B1agybaGPOmtDTcZ94cIFODg4oEWLFhg1ahSuXLlS1XBrXHW0R3Z2NgoKCmBrawvgyfrOeNjDbVHsSfvOEEIgISEB58+fR69evQA8uftFaW1RrLr3CyZ2Buj27dsoKipCo0aNdKY3atQIN27cKHWZGzdulFm++G9F6qwLaqItAGDgwIHYsGEDEhIS8P777+PAgQN45plnUFRUVP0bUU0q0xb6qLM21FTcXbp0QUxMDL777jusWbMGaWlp6NmzJ+7du1fVkGtUdbTH66+/DgcHB+mH70n6znjYw20BPFnfGRkZGVCpVJDL5Rg0aBBWrlyJ/v37A3jy9ouy2gKomf3CpNJLEj3BXnjhBem5l5cX2rVrB1dXVyQmJqJfv356jIz06ZlnnpGet2vXDl26dIGTkxO++uorhIWF6TGymrVo0SJs2bIFiYmJMDMz03c4evWotniSvjMsLS1x8uRJaDQaJCQk4JVXXkGLFi3Qu3dvfYdW6x7XFjWxX7DHzgDZ2dnB2NgYf/31l870v/76C40bNy51mcaNG5dZvvhvReqsC2qiLUrTokUL2NnZISUlpepB15DKtIU+6qwNtRW3jY0NWrZsWaf3C6Bq7bFkyRIsWrQI+/btQ7t27aTpT9J3RrFHtUVpDPk7w8jICG5ubmjfvj1effVVjBgxAlFRUQCevP2irLYoTXXsF0zsDJBcLoePjw8SEhKkaVqtFgkJCejWrVupy3Tr1k2nPADEx8dL5V1cXNC4cWOdMpmZmfj5558fWWddUBNtUZpr167h77//RpMmTaon8BpQmbbQR521obbi1mg0SE1NrdP7BVD59li8eDEWLFiA7777Dp06ddKZ9yR9ZwBlt0VpnqTvDK1Wi7y8PABP3n7xsAfbojTVsl9U66UYVGds2bJFKBQKERMTI86dOycmTJggbGxsxI0bN4QQQowePVq88cYbUvlDhw4JExMTsWTJEpGcnCzmzZtX6nAnNjY2YseOHeK3334TAQEB9eYS9epsi3v37onZs2eLI0eOiLS0NPH999+Ljh07Cnd3d5Gbm6uXbSyvirZFXl6eOHHihDhx4oRo0qSJmD17tjhx4oS4cOFCueusq2qiLV599VWRmJgo0tLSxKFDh4Sfn5+ws7MTN2/erPXtq6iKtseiRYuEXC4XX3/9tc5QDffu3dMp8yR8ZzyuLZ6k74z33ntP7Nu3T6Smpopz586JJUuWCBMTE/H5559LZZ6U/eJxbVFT+wUTOwO2cuVK0bx5cyGXy8VTTz0lfvrpJ2mer6+vCAkJ0Sn/1VdfiZYtWwq5XC48PT3Frl27dOZrtVrxzjvviEaNGgmFQiH69esnzp8/XxubUmXV2RbZ2dliwIABQq1WC1NTU+Hk5CTGjx9f5xOZYhVpi7S0NAGgxMPX17fcddZl1d0WwcHBokmTJkIul4umTZuK4OBgkZKSUotbVDUVaQ8nJ6dS22PevHlSmSflO+NxbfEkfWe89dZbws3NTZiZmYkGDRqIbt26iS1btujU96TsF49ri5raL2RCCFH5/j4iIiIiqit4jh0RERGRgWBiR0RERGQgmNgRERERGQgmdkREREQGgokdERERkYFgYkdERERkIJjYERERERkIJnZEREREBoKJHRGRHiQmJkImk+Hu3bv6DqVSZDIZtm/fDgC4dOkSZDIZTp48qdeYiIiJHRHVgtDQUMhkMshkMpiamqJRo0bo378/1q1bB61Wq+/w9KJ79+5IT0+HtbW1vkMhIgPCxI6IasXAgQORnp6OS5cuYc+ePejTpw9mzpyJwYMHo7CwsMbWm5+fX2N1V4VcLkfjxo0hk8mqrU4hRI22ZX1RV99zotrAxI6IaoVCoUDjxo3RtGlTdOzYEW+++SZ27NiBPXv2ICYmRip39+5djBs3Dmq1GlZWVujbty9OnTqlU9e3336Lzp07w8zMDHZ2dhg6dKg0z9nZGQsWLMCYMWNgZWWFCRMmAAB+/PFH9OzZE+bm5nB0dMSMGTOQlZUlLbdx40Z06tQJlpaWaNy4MUaOHImbN29K8+/cuYNRo0ZBrVbD3Nwc7u7uiI6OluZfvXoVQUFBsLGxga2tLQICAnDp0qVHtsfDh2JjYmJgY2ODvXv3wsPDAyqVSkqGH1fHnj174OPjA4VCgR9//BFarRZRUVFwcXGBubk5vL298fXXX+sse/bsWQwePBhWVlawtLREz549kZqaCgA4duwY+vfvDzs7O1hbW8PX1xfHjx9/ZBzlsXr1ari7u8PMzAyNGjXCiBEjpHlarRaLFy+Gm5sbFAoFmjdvjoULF0rzT58+jb59+8Lc3BwNGzbEhAkToNFopPmhoaEIDAzEwoUL4eDggFatWgGo+HtCZAiY2BGR3vTt2xfe3t7Ytm2bNO3555/HzZs3sWfPHvz666/o2LEj+vXrh3/++QcAsGvXLgwdOhTPPvssTpw4gYSEBDz11FM69S5ZsgTe3t44ceIE3nnnHaSmpmLgwIEYPnw4fvvtN8TGxuLHH3/EtGnTpGUKCgqwYMECnDp1Ctu3b8elS5cQGhoqzX/nnXdw7tw57NmzB8nJyVizZg3s7OykZf39/WFpaYmkpCQcOnRISswq0nuUnZ2NJUuWYOPGjTh48CCuXLmC2bNnP3a5N954A4sWLUJycjLatWuHqKgobNiwAZ988gnOnj2Ll19+Gf/5z39w4MABAMD169fRq1cvKBQK/PDDD/j1118xduxYqbfv3r17CAkJwY8//oiffvoJ7u7uePbZZ3Hv3r1yb8uDfvnlF8yYMQPz58/H+fPn8d1336FXr17S/PDwcCxatEhq482bN6NRo0YAgKysLPj7+6NBgwY4duwYtm7diu+//17nvQOAhIQEnD9/HvHx8YiLi6u294So3hFERDUsJCREBAQElDovODhYeHh4CCGESEpKElZWViI3N1enjKurq/j000+FEEJ069ZNjBo16pHrcnJyEoGBgTrTwsLCxIQJE3SmJSUlCSMjI5GTk1NqPceOHRMAxL1794QQQgwZMkS89NJLpZbduHGjaNWqldBqtdK0vLw8YW5uLvbu3VvqMvv37xcAxJ07d4QQQkRHRwsAIiUlRSqzatUq0ahRo0dua3Ed27dvl6bl5uYKCwsLcfjwYZ2yYWFh4sUXXxRCCBEeHi5cXFxEfn7+I+t+UFFRkbC0tBTffvutNA2A+N///ieEECItLU0AECdOnCh1+W+++UZYWVmJzMzMEvMyMzOFQqEQn3/+eanLfvbZZ6JBgwZCo9FI03bt2iWMjIzEjRs3hBD3969GjRqJvLw8qUxl3hMiQ2Ciz6SSiEgIIZ1ndurUKWg0GjRs2FCnTE5OjnSY8OTJkxg/fnyZdXbq1Enn9alTp/Dbb79h06ZNOuvVarVIS0uDh4cHfv31V0RERODUqVO4c+eOdFHHlStX0KZNG0yePBnDhw/H8ePHMWDAAAQGBqJ79+5S/SkpKbC0tNRZb25urhR3eVhYWMDV1VV63aRJE53DweXZ3pSUFGRnZ6N///46ZfLz89GhQwcA99uwZ8+eMDU1LbW+v/76C2+//TYSExNx8+ZNFBUVITs7G1euXCn3tjyof//+cHJyQosWLTBw4EAMHDgQQ4cOhYWFBZKTk5GXl4d+/fqVumxycjK8vb2hVCqlaT169IBWq8X58+elnj0vLy/I5XKpTHW9J0T1DRM7ItKr5ORkuLi4AAA0Gg2aNGmCxMTEEuVsbGwAAObm5o+t88EkoLjeiRMnYsaMGSXKNm/eXDrc5+/vj02bNkGtVuPKlSvw9/eXDts988wzuHz5Mnbv3o34+Hj069cPU6dOxZIlS6DRaODj46OTOBZTq9WPjbfYw4mWTCaDEKJC21t87tmuXbvQtGlTnXIKhQLA49swJCQEf//9Nz788EM4OTlBoVCgW7dulT6EaWlpiePHjyMxMRH79u3D3LlzERERgWPHjpXr/SyP0t7z6nhPiOobJnZEpDc//PADTp8+jZdffhkA0LFjR9y4cQMmJiZwdnYudZl27dohISEBL730UrnX07FjR5w7dw5ubm6lzj99+jT+/vtvLFq0CI6OjgDunxf2MLVajZCQEISEhKBnz5547bXXsGTJEnTs2BGxsbGwt7eHlZVVueOqCW3atIFCocCVK1fg6+tbapl27dph/fr1KCgoKLXX7tChQ1i9ejWeffZZAPcvQrh9+3aV4jIxMYGfnx/8/Pwwb9482NjY4IcffsCzzz4Lc3NzJCQkYNy4cSWW8/DwQExMDLKysqTk7dChQzAyMpIukihNXXpPiGoTL54golqRl5eHGzdu4Pr16zh+/Djee+89BAQEYPDgwRgzZgwAwM/PD926dUNgYCD27duHS5cu4fDhw3jrrbekRGvevHn48ssvMW/ePCQnJ+P06dN4//33y1z366+/jsOHD2PatGk4efIkLly4gB07dkgn4Ddv3hxyuRwrV67ExYsXsXPnTixYsECnjrlz52LHjh1ISUnB2bNnERcXBw8PDwDAqFGjYGdnh4CAACQlJSEtLQ2JiYmYMWMGrl27Vt1NWSZLS0vMnj0bL7/8MtavX4/U1FQcP34cK1euxPr16wEA06ZNQ2ZmJl544QX88ssvuHDhAjZu3Ijz588DANzd3bFx40YkJyfj559/xqhRo6rUsxYXF4ePPvoIJ0+exOXLl7FhwwZotVq0atUKZmZmeP311zFnzhxs2LABqamp+Omnn/Df//4XwP22NTMzQ0hICM6cOYP9+/dj+vTpGD16tHQYtjR16T0hqlV6PsePiJ4AISEhAoAAIExMTIRarRZ+fn5i3bp1oqioSKdsZmammD59unBwcBCmpqbC0dFRjBo1Sly5ckUq880334j27dsLuVwu7OzsxLBhw6R5Tk5OYvny5SViOHr0qOjfv79QqVRCqVSKdu3aiYULF0rzN2/eLJydnYVCoRDdunUTO3fu1LkgYMGCBcLDw0OYm5sLW1tbERAQIC5evCgtn56eLsaMGSPs7OyEQqEQLVq0EOPHjxcZGRmltklpF09YW1vrlPnf//4nyvqafriOYlqtVqxYsUK0atVKmJqaCrVaLfz9/cWBAwekMqdOnRIDBgwQFhYWwtLSUvTs2VOkpqYKIYQ4fvy46NSpkzAzMxPu7u5i69atJdoVFbh4IikpSfj6+ooGDRoIc3Nz0a5dOxEbGyvNLyoqEu+++65wcnISpqamonnz5uK9996T5v/222+iT58+wszMTNja2orx48dLF7UI8eiLcyr6nhAZApkQ5TiBg4iIiIjqPB6KJSIiIjIQTOyIiIiIDAQTOyIiIiIDwcSOiIiIyEAwsSMiIiIyEEzsiIiIiAwEEzsiIiIiA8HEjoiIiMhAMLEjIiIiMhBM7IiIiIgMBBM7IiIiIgPBxI6IiIjIQPwfIat49oK0n+YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAHWCAYAAAD6oMSKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAB0+klEQVR4nO3deXxMV/8H8M9kmUkyk0VkghAJiSWIIKilJAjRoomlSfEjIWrfqmjThYRqFEWrdKESlEqp0oYWTYVY+tBaakmViLWxtZGY7Mmc3x+e3MdIkH0y4/N+veZl7r3n3vudM5OZr3PPPUcmhBAgIiIiIoNnou8AiIiIiKhyMLEjIiIiMhJM7IiIiIiMBBM7IiIiIiPBxI6IiIjISDCxIyIiIjISTOyIiIiIjAQTOyIiIiIjwcSOiIiIyEgwsSMqpdDQULi6uurt/DExMZDJZLh8+bLO+sWLF6Nx48YwNTVFmzZtAACurq4IDQ2t9hipckVEREAmk+k7DDIgGo0Gjo6O2Lhxo75DqRE6deqE2bNn6zuMasXEjqrV6dOnMWTIELi4uMDCwgL169dH7969sWLFimJltVot1q9fj969e8PBwQHm5uZwdHREnz598MUXXyA3N1envEwmkx5mZmawt7eHt7c3pk2bhnPnzj02poyMDERGRsLLywsqlQqWlpZo1aoV3njjDfz999+VXgeVac+ePZg9eza6du2K6OhovP/++/oO6Yl8fX113id7e3t06NABa9euhVar1Xd45bJp0yYsX7683PtnZWUhIiICCQkJlRZTZZDJZJg8ebK+wyi3w4cPIyIiAvfu3dN3KNXqo48+grW1NV555RVp3a5duxAREVGl59Xn5/jcuXOIiIgo9p9eAHjjjTewcuVK3Lx5s9rj0htBVE0OHTok5HK5cHd3F/PnzxerV68Wc+bMEX369BFubm46ZbOysoS/v78AILp06SKioqLE2rVrxZIlS8SAAQOEqampGD16tM4+AETv3r3Fhg0bxPr168WKFSvEmDFjhK2trTAzMxMffvhhsZiSk5NFo0aNhKmpqXjllVfEJ598Ir744gsxefJkUbt2bdGkSROpbEhIiHBxcamSuimNgoICkZ2dLbRarbTujTfeECYmJiI3N1enbE5OjsjLy6vuEJ/Kx8dHNGjQQGzYsEFs2LBBLF26VLRp00YAEG+88Ya+wyuXfv36VehzcefOHQFAzJ07t9i2/Px8kZ2dXf7gKgCAmDRpkl7OXRkWL14sAIiUlBR9h1Jt8vLyhFqtFu+//77O+kmTJomq/rl/0ue4qm3ZskUAEPv27Su2rbCwUNStW1e8++671R6XvpjpLaOkZ86CBQtga2uLY8eOwc7OTmfb7du3dZZfe+017N69G8uXL8e0adN0tr3++uu4cOEC9u7dW+wcTZs2xf/93//prFu4cCEGDBiA119/Hc2bN8eLL74IACgoKMCgQYNw69YtJCQk4Pnnny8W7wcffFDel1vpTE1NYWpqqrPu9u3bsLS0hFwu11mvUCgq7bwFBQXQarXFzlFetra2Ou/RuHHj0KxZM3zyySeYP38+zM3Ny33syo5V38zMzGBmxq/pssjMzIRSqdR3GHoRFxeHO3fuICgoSN+h1BgmJiYYMmQI1q9fj8jIyGeja4O+M0t6djRr1kz4+vo+tdzVq1eFqamp6Nu3b5mOjye0MFy5ckWYmZmJLl26SOs2b94sAIgFCxaU6vgltdgtXrxYdO7cWdjb2wsLCwvRrl07sWXLlmL77tmzR3Tt2lXY2toKpVIpmjZtKsLDw3XKfPzxx6JFixbC0tJS2NnZCW9vb7Fx40Zpe3R0tE4LBIBij+joaCGEEC4uLiIkJETn+GlpaWLatGmiQYMGQi6XCzc3N7Fw4UJRWFgolUlJSREAxOLFi8WyZctE48aNhYmJiThx4kSp6uhpfHx8RMuWLYutHzJkiAAgbty4USmxzp07VwAQ58+fF8OHDxc2NjbCwcFBvPPOO0Kr1YqrV6+Kl156SVhbW4s6deqIJUuW6MTzaF0X2bdvn07LgI+PT7H3oOgzkpubK959913Rrl07YWNjI6ysrMTzzz8vfvnll2Kv4dFHUatH0et4WH5+vpg3b55o3LixkMvlwsXFRYSHh4ucnBydci4uLqJfv34iMTFRdOjQQSgUCtGoUSOxbt26Ur1Xj/49Fb322NhYERERIZycnIRKpRKDBw8W9+7dEzk5OWLatGlCrVYLpVIpQkNDi8VUdMyvvvpKNG3aVCgUCtGuXTuxf//+Yuc/fvy46Nu3r7C2thZKpVL07NlTHDlyRKdM0fuUkJAgJkyYINRqtbCzs5Pq7dFH0fu5du1a0aNHD6FWq4VcLhceHh5i1apVxWIoSx2mpaWJ6dOnCxcXFyGXy0X9+vXFiBEjxJ07d6QyOTk5Ys6cOcLNzU3I5XLRoEEDMWvWrGL1VJrvi5KMHDlSuLq66qwLCQkpsS6KFBYWimXLlokWLVoIhUIhHB0dxdixY8W///6rc5xjx46JPn36iNq1awsLCwvh6uoqRo0aJYR4+ue4JHl5eSIiIkK4u7sLhUIh7O3tRdeuXcWePXt0yiUlJYnBgweLWrVqCYVCIby9vcWOHTuk7UWfgUcfD7fe7dixQwAQx48ff2odGgP+V5CqjYuLC44cOYIzZ86gVatWjy33448/orCwsFjLW0U0bNgQPj4+2LdvHzIyMmBjY4Pvv/8eADBixIhyH/ejjz7CSy+9hOHDhyMvLw+bN2/Gyy+/jLi4OPTr1w8AcPbsWfTv3x+tW7fGvHnzoFAocPHiRRw6dEg6zurVqzF16lQMGTIE06ZNQ05ODv744w/85z//wbBhw0o894YNG/DFF1/g6NGjWLNmDQCgS5cuJZbNysqCj48Pbty4gXHjxqFhw4Y4fPgwwsPDkZqaWqyPWHR0NHJycjB27FgoFArY29uXu45K49KlSzA1NYWdnV2lxhocHAwPDw8sXLgQO3fuxHvvvQd7e3t8/vnn6NmzJz744ANs3LgRM2fORIcOHdC9e/cyxf32228jPT0d169fx7JlywAAKpUKwIO+m2vWrMHQoUPx6quv4v79+/jyyy/h7++Po0ePok2bNlCr1fj0008xYcIEDBw4EIMGDQIAtG7d+rHnHDNmDNatW4chQ4bg9ddfx3/+8x9ERUUhKSkJ3333nU7ZixcvYsiQIQgLC0NISAjWrl2L0NBQeHt7o2XLlmV6rUWioqJgaWmJN998ExcvXsSKFStgbm4OExMTpKWlISIiAr/++itiYmLQqFEjzJkzR2f//fv3IzY2FlOnToVCocCqVavQt29fHD16VPpeOHv2LLp16wYbGxvMnj0b5ubm+Pzzz+Hr64v9+/fjueee0znmxIkToVarMWfOHGRmZuKFF17AX3/9ha+//hrLli2Dg4MDAECtVgMAPv30U7Rs2RIvvfQSzMzM8MMPP2DixInQarWYNGlSmetQo9GgW7duSEpKwujRo9GuXTvcvXsX33//Pa5fvw4HBwdotVq89NJLOHjwIMaOHQsPDw+cPn0ay5Ytw19//YXt27dLr/1p3xePc/jwYbRr105n3bhx4/D3339j79692LBhQ7F9xo0bh5iYGIwaNQpTp05FSkoKPvnkE5w4cQKHDh2Cubk5bt++jT59+kCtVuPNN9+EnZ0dLl++jG3btkn1WtbPcUREBKKiojBmzBh07NgRGRkZ+O2333D8+HH07t1bqouuXbuifv36ePPNN6FUKvHNN98gMDAQ3377LQYOHIju3btj6tSp+Pjjj/HWW2/Bw8MDAKR/AcDb2xsAcOjQIbRt2/ap9Wjw9J1Z0rNjz549wtTUVJiamorOnTuL2bNni927dxfrC/baa68JAOLkyZM663Nzc8WdO3ekx927d3W24yl9gqZNmyYAiFOnTgkhhGjbtq2wtbUtdfwltdhlZWXpLOfl5YlWrVqJnj17SuuWLVsmAOj8z/1RAQEBJbZkPaykVqSQkBChVCqLlX20xW7+/PlCqVSKv/76S6fcm2++KUxNTcXVq1eFEP/7n7eNjY24ffv2E+MpDx8fH9G8eXPpPUxKShJTp04VAMSAAQMqLdaiFpuxY8dK6woKCkSDBg2ETCYTCxculNanpaUJS0tLnfoqbYudEI/vY1dQUFCs72NaWpqoU6eOTv/QJ/VNerTF7uTJkwKAGDNmjE65mTNnCgA6rYEuLi4CgDhw4IC07vbt20KhUIjXX3+92Lke9ejfU9Frb9Wqlc7f7NChQ4VMJhMvvPCCzv6dO3cuVi/4b2vKb7/9Jq27cuWKsLCwEAMHDpTWBQYGCrlcLpKTk6V1f//9t7C2thbdu3eX1hW9T88//7woKCjQOdeT+tg9+ncrhBD+/v6icePGOutKW4dz5swRAMS2bduKHbeoT+yGDRuEiYmJSExM1Nn+2WefCQDi0KFDQojSfV+UJD8/X8hkshLf28f1sUtMTBQAdK4MCCHETz/9pLP+u+++EwDEsWPHHnv+svax8/LyEv369XtimV69eglPT0+dFk2tViu6dOmi0//5SX3sisjlcjFhwoRSxWboeFcsVZvevXvjyJEjeOmll3Dq1CksWrQI/v7+qF+/vtR6Bjxo6QD+1/JRZNeuXVCr1dLDxcWlTOcvOt79+/el81hbW1fkJcHS0lJ6npaWhvT0dHTr1g3Hjx+X1hf1J9yxY8dj7/y0s7PD9evXcezYsQrF8zhbtmxBt27dUKtWLdy9e1d6+Pn5obCwEAcOHNApP3jwYKl1o7L9+eef0nvo4eGBFStWoF+/fli7dm2lxzpmzBjpuampKdq3bw8hBMLCwqT1dnZ2aNasGS5dulSpr9PU1FTq66fVavHvv/+ioKAA7du31/l8lMWuXbsAADNmzNBZ//rrrwMAdu7cqbO+RYsW6Natm7SsVqsr/FpHjhyp0w/yueeegxACo0eP1in33HPP4dq1aygoKNBZ37lzZ6kFBXjQmh4QEIDdu3ejsLAQhYWF2LNnDwIDA9G4cWOpXL169TBs2DAcPHhQ+o4o8uqrrxbrf/okD//dpqen4+7du/Dx8cGlS5eQnp6uU7Y0dfjtt9/Cy8sLAwcOLHauoj5dW7ZsgYeHB5o3b67zue7ZsycAYN++fQBK931Rkn///RdCCNSqVavU+2zZsgW2trbo3bu3Tkze3t5QqVTFYoqLi0N+fn6pj/8kdnZ2OHv2LC5cuFDi9n///Re//PILgoKCcP/+fSm2f/75B/7+/rhw4QJu3LhR6vMVfZ88C5jYUbXq0KEDtm3bhrS0NBw9ehTh4eG4f/8+hgwZIg1JUpRsaTQanX27du2KvXv3Yu/evejTp0+Zz110vKLj29jYSEleecXFxaFTp06wsLCAvb29dEni4R+H4OBgdO3aFWPGjEGdOnXwyiuv4JtvvtH50n7jjTegUqnQsWNHNGnSBJMmTSrVpZfSunDhAn766SedxFitVsPPzw9A8ZtXGjVqVKrj/vvvv7h586b0ePRHsSSurq7Yu3cvfv75Zxw8eBA3b95EXFycdLmsMmNt2LChzrKtrS0sLCykcz28Pi0trVSvuSzWrVuH1q1bw8LCArVr14ZarcbOnTtLVU8luXLlCkxMTODu7q6zvm7durCzs8OVK1d01j/6+oEHP3AVea0l1SkAODs7F1uv1WqLvdYmTZoUO2bTpk2RlZWFO3fu4M6dO8jKykKzZs2KlfPw8IBWq8W1a9d01pf281rk0KFD8PPzg1KphJ2dHdRqNd566y0AKBZvaeowOTn5id1LgAef67Nnzxb7XDdt2hTA/z7Xpfm+eBIhRKnKFcWUnp4OR0fHYnFpNBopJh8fHwwePBiRkZFwcHBAQEAAoqOjiw05VRbz5s3DvXv30LRpU3h6emLWrFn4448/pO0XL16EEALvvvtusdjmzp0LoPh3wZMIIZ6NGycAsI8d6YVcLkeHDh3QoUMHNG3aFKNGjcKWLVswd+5cNG/eHABw5swZeHl5Sfs8/OP+1VdflfmcZ86cgampqfQj0Lx5c5w4cQLXrl0r9qNUGomJiXjppZfQvXt3rFq1CvXq1YO5uTmio6OxadMmqZylpSUOHDiAffv2YefOnfjpp58QGxuLnj17Ys+ePTA1NYWHhwfOnz+PuLg4/PTTT/j222+xatUqzJkzB5GRkWWO7VFarRa9e/d+7ECdRT8uD8dcGoMGDcL+/ful5ZCQEMTExDxxH6VSKb2PVR1rSa04j2vZefgH8XE/AIWFhY8916O++uorhIaGIjAwELNmzYKjoyNMTU0RFRWF5OTkUh+nJKX9gSrNay2rxx2zKs5VWqX9vAIPkrBevXqhefPmWLp0KZydnSGXy7Fr1y4sW7asWAJVWa9Lq9XC09MTS5cuLXF70XdQab4vSmJvbw+ZTFampF2r1T5xMOOilnCZTIatW7fi119/xQ8//IDdu3dj9OjR+PDDD/Hrr78Wu7pSGt27d0dycjJ27NiBPXv2YM2aNVi2bBk+++wzjBkzRnofZs6cCX9//xKP8eh/cJ7k3r17xf5DZ6yY2JHetW/fHgCQmpoKAHjhhRdgamqKjRs3Yvjw4ZVyjqtXr2L//v3o3Lmz1GI3YMAAfP311/jqq68QHh5e5mN+++23sLCwwO7du3WGF4mOji5W1sTEBL169UKvXr2wdOlSvP/++3j77bexb98+KclRKpUIDg5GcHAw8vLyMGjQICxYsADh4eGwsLAo5yt/wM3NDRqN5okJVXl8+OGHOj8kTk5OFT5mVcVaFkWXsx4d3PbRFjHg8UnW1q1b0bhxY2zbtk2nTFFrw9P2L4mLiwu0Wi0uXLig0zn81q1buHfvXpm7J+hDSZfe/vrrL1hZWUmJhJWVFc6fP1+s3J9//gkTE5NS/UfscfX6ww8/IDc3F99//71Oa1zRZcfycHNzw5kzZ55a5tSpU+jVq9dT3/PSfF88yszMDG5ubkhJSSm27XHnc3Nzw88//4yuXbuWKjnu1KkTOnXqhAULFmDTpk0YPnw4Nm/ejDFjxpSrNcze3h6jRo3CqFGjoNFo0L17d0RERGDMmDHSZXhzc/Onfhc87dw3btxAXl6ezt+MMeOlWKo2+/btK/F/uUX9hoouvTRs2BCjR4/Gjz/+iE8++aTEY5Xlf8v//vsvhg4disLCQrz99tvS+iFDhsDT0xMLFizAkSNHiu13//59nfKPMjU1hUwm02nFuXz5snR328Pnf1TR1F9FlzL++ecfne1yuRwtWrSAEKJS+rQEBQXhyJEj2L17d7Ft9+7dK9YPqrS8vb3h5+cnPVq0aFHRUKss1rJwc3MDAJ3+fIWFhfjiiy+KlVUqlSVeWi1qWXn4s/qf//yn2GfNysoKQPEksiRFYzA+emdwUStQ0Z3YNdmRI0d0+hheu3YNO3bsQJ8+faSxGvv06YMdO3bozCRw69YtbNq0Cc8//zxsbGyeep6isewerdeS3pf09PQS/0NWWoMHD8apU6eK3ZX88HmCgoJw48YNrF69uliZ7OxsZGZmAijd98XjdO7cGb/99lux9Y+ri6CgIBQWFmL+/PnF9ikoKJDKp6WlFfvOfTSmsnyOgeLfeSqVCu7u7tLxHB0d4evri88//1z6T//D7ty589TXV+T3338H8PhRA4wNW+yo2kyZMgVZWVkYOHAgmjdvjry8PBw+fBixsbFwdXXFqFGjpLLLly9HSkoKpkyZgs2bN2PAgAFwdHTE3bt3cejQIfzwww8l9sH566+/8NVXX0EIgYyMDJw6dQpbtmyBRqPB0qVL0bdvX6msubk5tm3bBj8/P3Tv3h1BQUHo2rUrzM3NcfbsWWzatAm1atXCggULSnw9/fr1k445bNgw3L59GytXroS7u7tOX5F58+bhwIED6NevH1xcXHD79m2sWrUKDRo0kAZF7tOnD+rWrYuuXbuiTp06SEpKwieffIJ+/fpV+AYPAJg1axa+//579O/fXxqqITMzE6dPn8bWrVtx+fLlGnOZoibE2rJlS3Tq1Anh4eH4999/YW9vj82bN5eYVHp7eyM2NhYzZsxAhw4doFKpMGDAAPTv3x/btm3DwIED0a9fP6SkpOCzzz5DixYtdPqPWlpaokWLFoiNjUXTpk1hb2+PVq1aldhny8vLCyEhIfjiiy9w7949+Pj44OjRo1i3bh0CAwPRo0ePKq2XytCqVSv4+/vrDHcCQKfLwXvvvYe9e/fi+eefx8SJE2FmZobPP/8cubm5WLRoUanOU3SDxttvv41XXnkF5ubmGDBgAPr06QO5XI4BAwZg3Lhx0Gg0WL16NRwdHUtMIEpj1qxZ2Lp1K15++WWMHj0a3t7e+Pfff/H999/js88+g5eXF0aMGIFvvvkG48ePx759+9C1a1cUFhbizz//xDfffIPdu3ejffv2pfq+eJyAgABs2LABf/31l06XhaK6mDp1Kvz9/WFqaopXXnkFPj4+GDduHKKionDy5En06dMH5ubmuHDhArZs2YKPPvoIQ4YMwbp167Bq1SoMHDgQbm5uuH//PlavXg0bGxvpPxtl+RwDD25K8fX1hbe3N+zt7fHbb79h69atOtPYrVy5Es8//zw8PT3x6quvonHjxrh16xaOHDmC69ev49SpUwAeJJmmpqb44IMPkJ6eDoVCgZ49e8LR0REAsHfvXjRs2PDZGOoE4HAnVH1+/PFHMXr0aNG8eXOhUqmk6cWmTJkibt26Vax8QUGBiI6OFj179hT29vbCzMxMODg4iF69eonPPvus2FRLeGhwShMTE2FnZyfatm0rpk2bJs6ePfvYuNLS0sScOXOEp6ensLKyEhYWFqJVq1YiPDxcpKamSuVKGu7kyy+/FE2aNBEKhUI0b95cREdHFxuiIj4+XgQEBAgnJychl8uFk5OTGDp0qM5wHp9//rno3r27qF27tlAoFMLNzU3MmjVLpKenS2UqMtyJEELcv39fhIeHC3d3dyGXy4WDg4Po0qWLWLJkiTR8xcOD/laFxw1Q/KiKxlr0Hjw6ZMTj6qukuJKTk4Wfn59QKBSiTp064q233hJ79+4tNqyCRqMRw4YNE3Z2djoDFGu1WvH+++8LFxcXoVAoRNu2bUVcXFyJn6PDhw8Lb29vIZfLSzVAcWRkpGjUqJEwNzcXzs7OTxyguKTX6uPjU2z9o/CY4U4eHYC76HP56FAYJb0HRcf86quvpL+btm3bljhMxfHjx4W/v79QqVTCyspK9OjRQxw+fLhU5y4yf/58Ub9+fWFiYqLzt/P999+L1q1bSwPtfvDBB2Lt2rXF/r7KUof//POPmDx5sqhfv740+HBISIjOsEx5eXnigw8+EC1bthQKhULUqlVLeHt7i8jISOlvvTTfF4+Tm5srHBwcxPz583XWFxQUiClTpgi1Wi1kMlmxz9QXX3whvL29haWlpbC2thaenp5i9uzZ4u+//xZCPHgvhg4dKho2bCgNYty/f3+dYWuEePznuCTvvfee6Nixo7CzsxOWlpaiefPmYsGCBcWGv0pOThYjR44UdevWFebm5qJ+/fqif//+YuvWrTrlVq9eLRo3bixMTU11/kYLCwtFvXr1xDvvvPPU+jMWMiGqoWcrERE982QyGSZNmvTYLhZUcfPnz0d0dDQuXLhQpiFgjNX27dsxbNgwJCcno169evoOp1qwjx0REZGReO2116DRaLB582Z9h1IjfPDBB5g8efIzk9QB7GNHRERkNFQqVZnGdzN2Jd0YZ+zYYkdERERkJNhiR0RE1YJduomqHlvsiIiIiIwEEzsiIiIiI8FLsQZEq9Xi77//hrW19TMzmTEREdGzTgiB+/fvw8nJCSYmT26TY2JnQP7+++9yTVZPREREhu/atWto0KDBE8swsTMgRVNLXbt2rVRzJZaWVqvFtWvXAADOzs5P/d8AERERVZ+MjAw4OzuXaopJJnYGpOjyq42NTaUmdpmZmWjdujUAQKPRSBMqExERUc1Rmm5YbJohIiIiMhJM7IiIiIiMBBM7IiIiIiPBxI6IiIjISDCxIyIiIjISTOyIiIiIjASHOyGYmZlh4sSJ0nMiIiIyTPwVJygUCqxcuVLfYRAREVEFMbEjIqoGKXczkZlboO8wqJLkFubg78wrcFK6QGFqoe9w9EqpMEMjBw5sX1MwsSMIIXD37l0AgIODQ6lGtiai0ku5m4keSxL0HQZVIhOLG1A2WoHMlCnQ5tTXdzh6t2+mL5O7GoKJHSErKwuOjo4AOKUYUVUoaqlbHtwG7o4qPUdDlSEl4zzeOgp8FNwGjWya6Tscvbl4W4PpsSfZGl2DMLEjIqom7o4qtKpvq+8wqBKYWDxI0N0cVWhRm+8p1Rwc7oSIiIjISDCxIyIiIjISTOyIqETZeYU4cyMd2XmF+g6FiGqonPxCnX9J/5jYEVGJku9o0H/FQSTf0eg7FCKqoa6nZev8S/pX4xK7iIgItGnTRloODQ1FYGBgtcbg6uqK5cuXV+s5iYiIiCqqWhO7AQMGoG/fviVuS0xMhEwmw6BBgxAfH18t8cTExMDOzq7Y+mPHjmHs2LGVcg4hBPz8/ODv719s26pVq2BnZ4fr169XyrnKy8zMDCEhIQgJCeGUYkRERAasWhO7sLAw7N27t8REJjo6Gu3bt0fr1q1Ru3btCp0nLy+vQvur1WpYWVlV6BhFZDIZoqOj8Z///Aeff/65tD4lJQWzZ8/GihUr0KBBg0o5V3kpFArExMQgJiYGCoVCr7EQERFR+VVrYte/f3+o1WrExMTorNdoNNiyZQvCwsKKXYotEhkZCbVaDRsbG4wfP14nefP19cXkyZMxffp0ODg4SK1jS5cuhaenJ5RKJZydnTFx4kRoNA/6CyUkJGDUqFFIT0+HTCaDTCZDREQEgOKXYq9evYqAgACoVCrY2NggKCgIt27dkrYXxbxhwwa4urrC1tYWr7zyCu7fvw8AcHZ2xkcffYSZM2ciJSUFQgiEhYWhT58+GDFiRCXULBEREVE1D1BsZmaGkSNHIiYmBm+//bY0ddWWLVtQWFiIoUOHYtmyZcX2i4+Ph4WFBRISEnD58mWMGjUKtWvXxoIFC6Qy69atw4QJE3Do0CFpnYmJCT7++GM0atQIly5dwsSJEzF79mysWrUKXbp0wfLlyzFnzhycP38eAKBSFR8RXqvVSknd/v37UVBQgEmTJiE4OBgJCQlSueTkZGzfvh1xcXFIS0tDUFAQFi5cKMUYEhKC7777DqNHj8agQYNw5swZnD179on1lZubi9zcXGk5IyOjFLVcdkIIZGVlAQCsrKw4pRgB+N9dbhdv8+aJiiqqQ945SERVrdo7VI0ePRqLFy/G/v374evrC+DBZdjBgwfD1rbk0bvlcjnWrl0LKysrtGzZEvPmzcOsWbMwf/58mJg8aHRs0qQJFi1apLPf9OnTpeeurq547733MH78eKxatQpyuRy2traQyWSoW7fuY+ONj4/H6dOnkZKSAmdnZwDA+vXr0bJlSxw7dgwdOnQA8CABjImJgbW1NQBgxIgRiI+P10k+v/jiC7Rs2RIHDhzAt99+C7Va/cS6ioqKQmRk5BPLVIasrCwpqeWUYlSk6C636bEn9RuIEbmelo32rvqOgoiMWbUnds2bN0eXLl2wdu1a+Pr64uLFi0hMTMS8efMeu4+Xl5dOn7fOnTtDo9Hg2rVrcHFxAQB4e3sX2+/nn39GVFQU/vzzT2RkZKCgoAA5OTnIysoqdR+6pKQkODs7S0kdALRo0QJ2dnZISkqSEjtXV1cpqQOAevXq4fbt2zrHcnR0xLhx47B9+/ZS3ekbHh6OGTNmSMsZGRk6cRBVpQa1LAFwftPKUDSfZlGdEhFVFb3cAhkWFoYpU6Zg5cqViI6OhpubG3x8fCp0zEdbmS5fvoz+/ftjwoQJWLBgAezt7XHw4EGEhYUhLy+v0m6OKGJubq6zLJPJoNVqi5UzMzMr9Z2nCoWCNzOQ3liYmwLg/KaVqahOiYiqil7GsQsKCoKJiQk2bdqE9evXY/To0U/s13Xq1ClkZ/9v8MNff/0VKpXqia1Xv//+O7RaLT788EN06tQJTZs2xd9//61TRi6Xo7DwyX1ePDw8cO3aNVy7dk1ad+7cOdy7dw8tWrR42kslIiIiqjZ6SexUKhWCg4MRHh6O1NRUhIaGPrF8Xl4ewsLCcO7cOezatQtz587F5MmTpf51JXF3d0d+fj5WrFiBS5cuYcOGDfjss890yri6ukKj0SA+Ph53796VbiB4mJ+fHzw9PTF8+HAcP34cR48exciRI+Hj44P27duX6/UTERERVQW9zTwRFhaGtLQ0+Pv7w8nJ6Ylle/XqhSZNmqB79+4IDg7GSy+9JA1N8jheXl5YunQpPvjgA7Rq1QobN25EVFSUTpkuXbpg/PjxCA4OhlqtLnbzBfDgkuqOHTtQq1YtdO/eHX5+fmjcuDFiY2PL/JqJiIiIqpJMCCH0HQSVTkZGBmxtbZGeng4bG5tKO25mZibviqViztxIR/8VBxE35Xn2sasg1qXxOffPOQTHBSO2fyxa1H52u+VsP3ED02NPYnlwGwS2ra/vcIxWWX7/OX8UwdTUFEOGDJGeEwGAm1qFuCnPw03NO2KJqGRFd3rzju+ag4kdwcLCAlu2bNF3GFTDWMpN2bpERE9UdKc37/iuOfTWx46IiIiIKhcTOyIiIiIjwUuxxJsniKpY9n/niD1zI13PkVBlScl4MP9v8m0NtDnP7vvKuaRrHiZ2RERVLPm/P35vbjut50iosphY3ICyETAt9iS0OXf0HY7eKRVMJ2oKvhNERFWsT8u6AAA3RxUs2cncKOQW5uDvzDZw6uEChamFvsPRK6XCDI0ceKWnpmBiR0RUxeyVcrzSsaG+w6BKZQtv1NF3EETF8OYJIiIiIiPBxI6IiIjISDCxIyIiIjIS7GNHMDU1xYsvvig9JyIiIsPExI5gYWGBnTt36jsMIiIiqiBeiiUiIiIyEkzsiIiIiIwEEztCZmYmlEollEolMjMz9R0OERERlRP72BEAICsrS98hEBERUQWxxY6IiIjISDCxIyIiIjISTOyIiIiIjAQTOyIiIiIjwcSOiIiIyEjwrliCiYkJfHx8pOdERERkmJjYESwtLZGQkKDvMIiIiKiC2DxDREREZCSY2BEREREZCSZ2hMzMTKjVaqjVak4pRkREZMDYx44AAHfv3tV3CERERFRBTOyIqEql3M1EZm6BvsMweLmFOfg78wqclC5QmFroOxyDp1SYoZGDUt9hEFU6JnZEVGVS7maix5IEfYdhFEwsbkDZaAUyU6ZAm1Nf3+EYhX0zfZnckdFhYkdEVaaopW55cBu4O6r0HI1hS8k4j7eOAh8Ft0Ejm2b6DsegXbytwfTYk2xJJqPExI6Iqpy7owqt6tvqOwyDZmLxIDF2c1ShRW3WJRGVjHfFEhERERkJttgRTExM0L59e+k5ERERGSYmdgRLS0scO3ZM32FQDZOdV4jkOxq4qVWwlJvqOxwiohqtpnxnsnmGiEqUfEeD/isOIvmORt+hEBHVeDXlO5OJHREREZGRqPbELjQ0FDKZrNjj4sWL1R0K/VdWVhZcXV3h6uqKrKwsfYdDRERE5aSXPnZ9+/ZFdHS0zjq1Wq2znJeXB7lcXp1hPbOEELhy5Yr0nIiIiAyTXi7FKhQK1K1bV+fRq1cvTJ48GdOnT4eDgwP8/f0BAGfOnMELL7wAlUqFOnXqYMSIETrzmmZmZmLkyJFQqVSoV68ePvzwQ/j6+mL69OlSGZlMhu3bt+vEYGdnh5iYGGn52rVrCAoKgp2dHezt7REQEIDLly9L20NDQxEYGIglS5agXr16qF27NiZNmoT8/HypTG5uLt544w04OztDoVDA3d0dX375JYQQcHd3x5IlS3RiOHnyJFsriYiIqNLUqLti161bhwkTJuDQoUMAgHv37qFnz54YM2YMli1bhuzsbLzxxhsICgrCL7/8AgCYNWsW9u/fjx07dsDR0RFvvfUWjh8/jjZt2pT6vPn5+fD390fnzp2RmJgIMzMzvPfee+jbty/++OMPqeVw3759qFevHvbt24eLFy8iODgYbdq0wauvvgoAGDlyJI4cOYKPP/4YXl5eSElJwd27dyGTyTB69GhER0dj5syZ0nmjo6PRvXt3uLu7lxhXbm4ucnNzpeWMjIwy1SdRReTkFwJ4MEp/eRXtW3QsopqgMj7bRI+qKd93ekns4uLioFL9b3qhF154AQDQpEkTLFq0SFr/3nvvoW3btnj//feldWvXroWzszP++usvODk54csvv8RXX32FXr16AXiQHDZo0KBM8cTGxkKr1WLNmjWQyWQAHiRddnZ2SEhIQJ8+fQAAtWrVwieffAJTU1M0b94c/fr1Q3x8PF599VX89ddf+Oabb7B37174+fkBABo3biydIzQ0FHPmzMHRo0fRsWNH5OfnY9OmTcVa8R4WFRWFyMjIMr0WospyPS0bADA99mSlHKu9a4UPQ1QpKvOzTfQofX/f6SWx69GjBz799FNpWalUYujQofD29tYpd+rUKezbt08nCSySnJyM7Oxs5OXl4bnnnpPW29vbo1mzss2jeOrUKVy8eBHW1tY663NycpCcnCwtt2zZEqam/xubpl69ejh9+jSAB5dVTU1N4ePjU+I5nJyc0K9fP6xduxYdO3bEDz/8gNzcXLz88suPjSs8PBwzZsyQljMyMuDs7Fym10ZUXg1qWQKo2DyvRXNyFh2LqCaojM820aNqyvedXhI7pVJZ4uVHpVKps6zRaDBgwAB88MEHxcrWq1ev1H3TZDJZsZsCHu4bp9Fo4O3tjY0bNxbb9+GbOszNzYsdV6vVAngwyO/TjBkzBiNGjMCyZcsQHR2N4OBgWFlZPba8QqGAQqF46nGJqoKF+YP/xFTGPK9FxyKqCSrzs030KH1/39WoPnaPateuHb799lu4urrCzKx4qG5ubjA3N8d//vMfNGzYEACQlpaGv/76S6flTK1WIzU1VVq+cOGCzrAe7dq1Q2xsLBwdHWFjY1OuWD09PaHVarF//37pUuyjXnzxRSiVSnz66af46aefcODAgXKdq7LJZDK0aNFCek5ERESGqUYPUDxp0iT8+++/GDp0KI4dO4bk5GTs3r0bo0aNQmFhIVQqFcLCwjBr1iz88ssvOHPmDEJDQ4vNd9qzZ0988sknOHHiBH777TeMHz9ep/Vt+PDhcHBwQEBAABITE5GSkoKEhARMnToV169fL1Wsrq6uCAkJwejRo7F9+3bpGN98841UxtTUFKGhoQgPD0eTJk3QuXPnyqmoCrKyssLZs2dx9uzZJ7YgEhERUc1WoxM7JycnHDp0CIWFhejTpw88PT0xffp02NnZScnb4sWL0a1bNwwYMAB+fn54/vnni/XV+/DDD+Hs7Ixu3bph2LBhmDlzpk4CY2VlhQMHDqBhw4YYNGgQPDw8EBYWhpycnDK14H366acYMmQIJk6ciObNm+PVV19FZmamTpmwsDDk5eVh1KhRFagZIiIiouKq/VLsw2PHPSwhIaHE9U2aNMG2bdseezyVSoUNGzZgw4YN0rqdO3fqlHFycsLu3bt11t27d09nuW7duli3bl2Z4l6+fLnOsoWFBZYuXYqlS5c+9jg3btyAubk5Ro4c+dgyREREROVRo1vsjElubi6uX7+OiIgIvPzyy6hTp46+Q5JkZWWhZcuWaNmyJacUI4mbWoW4Kc/DTc27BomInqamfGcysasmX3/9NVxcXHDv3j2dsfpqAiEEzp07h3PnznFKMZJYyk3Rqr4tLOW8o5WI6Glqyndmjb4rtrwed1lXn0JDQxEaGqrvMIiIiMiIscWOiIiIyEgYZYsdEdUM2f+dM/HMjXQ9R2L4UjIezEOZfFsDbQ7rsyI4RywZMyZ2RFRlkv/7A/rmttN6jsTwmVjcgLIRMC32JLQ5d/QdjlFQKvgTSMaHn2oiqjJ9WtYFALg5qmDJacUqJLcwB39ntoFTDxcoTC30HY7BUyrM0MhB+fSCRAaGiR1BJpPBxcVFek5UWeyVcrzSsaG+wzAStvBGzRkmiYhqJiZ2BCsrK1y+fFnfYRAREVEF8a5YIiIiIiPBxI6IiIjISDCxI2RnZ6NDhw7o0KEDsrOz9R0OERERlRP72BG0Wi1+++036TkREREZJrbYERERERkJJnZERERERoKJHREREZGRYGJHREREZCSY2BEREREZCd4VSwAABwcHfYdAREREFcTEjqBUKnHnzh19h0FEREQVxEuxREREREaCiR0RERGRkWBiR8jOzoavry98fX05pRgREZEBYx87glarxf79+6XnREREZJjYYkdERERkJJjYERERERkJJnZERERERoKJHREREZGR4M0TRESlkHI3E5m5BfoOQ69yC3Pwd+YVOCldoDC10Hc4FaJUmKGRg1LfYRBVOiZ2BACwsrLSdwhENVbK3Uz0WJKg7zD0zsTiBpSNViAzZQq0OfX1HU6F7Zvpy+SOjA4TO4JSqURmZqa+wyCqsYpa6pYHt4G7o0rP0ehPSsZ5vHUU+Ci4DRrZNNN3OOV28bYG02NPPvMtsGScmNgREZWSu6MKrerb6jsMvTGxeJDUujmq0KL2s1sPRDUZb54gIiIiMhJM7Ag5OTno168f+vXrh5ycHH2HQ0REROXES7GEwsJC7Nq1S3r+rMvOK0TyHQ3c1CpYyk31HQ4R1UD8nqCaii12RI9IvqNB/xUHkXxHo+9QiKiG4vcE1VQ1KrGLiIhAmzZtpOXQ0FAEBgZWawyurq5Yvnx5tZ6TiIiIqDJUW2I3YMAA9O3bt8RtiYmJkMlkGDRoEOLj46slnpiYGNjZ2RVbf+zYMYwdO7bSzpOQkACZTPbYR48ePSrtXERERPRsq7Y+dmFhYRg8eDCuX7+OBg0a6GyLjo5G+/bt0bp16wqfJy8vD3K5vNz7q9XqCsfwsC5duiA1NbXY+u+//x7jx4/HxIkTK/V8RERE9Oyqtha7/v37Q61WIyYmRme9RqPBli1bEBYWVuxSbJHIyEio1WrY2Nhg/PjxyMvLk7b5+vpi8uTJmD59OhwcHODv7w8AWLp0KTw9PaFUKuHs7IyJEydCo3nQFyIhIQGjRo1Cenq61HIWEREBoPil2KtXryIgIAAqlQo2NjYICgrCrVu3pO1FMW/YsAGurq6wtbXFK6+8gvv37wMA5HI56tatq/NIS0vDzJkz8dZbb+Hll1+uhNolIiIiqsYWOzMzM4wcORIxMTF4++23IZPJAABbtmxBYWEhhg4dimXLlhXbLz4+HhYWFkhISMDly5cxatQo1K5dGwsWLJDKrFu3DhMmTMChQ4ekdSYmJvj444/RqFEjXLp0CRMnTsTs2bOxatUqdOnSBcuXL8ecOXNw/vx5AIBKVXw0ea1WKyV1+/fvR0FBASZNmoTg4GAkJCRI5ZKTk7F9+3bExcUhLS0NQUFBWLhwoU6MRe7du4eAgAD4+vpi/vz5T6yz3Nxc5ObmSssZGRlPLE+VIyf/wZ3BF2+zUzQ9UPRZKPpskGGrjL9xfiaopqrW4U5Gjx6NxYsXY//+/fD19QXw4DLs4MGDYWtb8ijmcrkca9euhZWVFVq2bIl58+Zh1qxZmD9/PkxMHjQ4NmnSBIsWLdLZb/r06dJzV1dXvPfeexg/fjxWrVoFuVwOW1tbyGQy1K1b97HxxsfH4/Tp00hJSYGzszMAYP369WjZsiWOHTuGDh06AHiQAMbExMDa2hoAMGLECMTHxxdL7LRaLYYNGwYzMzNs3LhRSm4fJyoqCpGRkU8sUxmUSiWEEFV+HkNxPS0bADA99qR+A6Ea53paNtq76jsKqqjK/BvnZ4JqmmpN7Jo3b44uXbpg7dq18PX1xcWLF5GYmIh58+Y9dh8vLy+dCeo7d+4MjUaDa9euwcXFBQDg7e1dbL+ff/4ZUVFR+PPPP5GRkYGCggLk5OQgKyur1BPeJyUlwdnZWUrqAKBFixaws7NDUlKSlNi5urpKSR0A1KtXD7dv3y52vLfeegtHjhzB0aNHdco/Tnh4OGbMmCEtZ2Rk6MRCVaNBLUsAnBeU/qdobtGizwYZtsr4G+dngmqqah+gOCwsDFOmTMHKlSsRHR0NNzc3+Pj4VOiYSqVSZ/ny5cvo378/JkyYgAULFsDe3h4HDx5EWFgY8vLySp3YlZa5ubnOskwmg1ar1Vm3efNmLFmyBDt37kSTJk1KdVyFQgGFQlFpcVLpWJg/GGz0WZ8XlIor+myQYavMv3F+JqimqfZx7IKCgmBiYoJNmzZh/fr1GD169BMvSZ46dQrZ2dnS8q+//gqVSvXElqvff/8dWq0WH374ITp16oSmTZvi77//1ikjl8ufOsuCh4cHrl27hmvXrknrzp07h3v37qFFixZPe6mSkydPIiwsDAsXLpRu7qhJcnJy8PLLL+Pll1/mlGJEREQGrNoTO5VKheDgYISHhyM1NRWhoaFPLJ+Xl4ewsDCcO3cOu3btwty5czF58mSpf11J3N3dkZ+fjxUrVuDSpUvYsGEDPvvsM50yrq6u0Gg0iI+Px927d5GVlVXsOH5+fvD09MTw4cNx/PhxHD16FCNHjoSPjw/at29fqtd79+5dBAYGwtfXF//3f/+Hmzdv6jzu3LlTquNUpcLCQmzduhVbt27llGJEREQGTC8zT4SFhSEtLQ3+/v5wcnJ6YtlevXqhSZMm6N69O4KDg/HSSy9JQ5M8jpeXF5YuXYoPPvgArVq1wsaNGxEVFaVTpkuXLhg/fjyCg4OhVquL3XwBPLikumPHDtSqVQvdu3eHn58fGjdujNjY2FK/1p07d+LKlSvYtWsX6tWrV+xR1E+PiIiIqKJkgrdDGoyMjAzY2toiPT0dNjY2lXbczMxMabgXjUZTrM/is+bMjXT0X3EQcVOeZx87AsDPRJFz/5xDcFwwYvvHokXt0ndHqWkq4/3kZ4KqU1l+/2vUXLFENYGbWoW4Kc/DTc07YomoZPyeoJqq2u+KJarpLOWm/B84ET0RvyeopmKLHREREZGRYGJHREREZCR4KZZgZWUFjUYjPSciXdn/nQ/0zI10PUeiXykZD74nkm9roM0x3LrgPNBkzJjYEWQy2TN/JyzRkyT/NxF4c9tpPUeiXyYWN6BsBEyLPQltjv7H4KwopYI/gWR8+KkmInqKPi3rAgDcHFWwfIankMotzMHfmW3g1MMFClMLfYdTIUqFGRo58D+0ZHyY2BFyc3Mxbtw4AMDnn3/O+WmJHmGvlOOVjg31HUYNYAtv1NF3EET0BByg2IBwgGIiIqJnDwcoJiIiInoGMbEjIiIiMhJM7IiIiIiMBBM7IiIiIiPBxI6IiIjISDCxIyIiIjISHMeOYGVlhdu3b0vPiYiIyDAxsSPIZDKo1Wp9h0FEREQVxEuxREREREaCiR0hNzcXkyZNwqRJk5Cbm6vvcIiIiKicOKWYAeGUYkRERM8eTilGRERE9AxiYkdERERkJJjYERERERkJJnZERERERoKJHREREZGRYGJHREREZCQ48wTB0tISKSkp0nMiIiIyTEzsCCYmJnB1ddV3GERERFRBTOyIqkDK3Uxk5hboO4wKyS3MQVr+dXR1aQFLM7bkEhEZAiZ2hLy8PLz99tsAgAULFkAul+s5IsOWcjcTPZYk6DuMCjOxuAFloxVYhhj4uXnrOxwiIioFJnaE/Px8LFmyBAAQERHBxK6Cilrqlge3gbujSs/RlN+Byyfw6UUgO69Q36EQEVEpMbEjqiLujiq0qm+r7zDKLSXDSt8hEBFRGXG4EyIiIiIjwcSOiIiIyEgwsSN6RHZeIc7cSGffMpLwM0FEhoKJHdEjku9o0H/FQSTf0eg7FKoh+JkgIkNRoxK7iIgItGnTRloODQ1FYGBgtcbg6uqK5cuXV+s5iYiIiCpDtSV2AwYMQN++fUvclpiYCJlMhkGDBiE+Pr5a4omJiYGdnV2x9ceOHcPYsWMr7TyFhYXo0qULBg0apLM+PT0dzs7O0vhx+mRpaYkzZ87gzJkznFKMiIjIgFVbYhcWFoa9e/fi+vXrxbZFR0ejffv2aN26NWrXrl2h8+Tl5VVof7VaDSuryhvmwdTUFDExMfjpp5+wceNGaf2UKVNgb2+PuXPnVtq5ysvExAQtW7ZEy5YtYWJSoxpxiYiIqAyq7Ve8f//+UKvViImJ0Vmv0WiwZcsWhIWFFbsUWyQyMhJqtRo2NjYYP368TvLm6+uLyZMnY/r06XBwcIC/vz8AYOnSpfD09IRSqYSzszMmTpwIjeZB/5iEhASMGjUK6enpkMlkkMlkiIiIAFD8UuzVq1cREBAAlUoFGxsbBAUF4datW9L2opg3bNgAV1dX2Nra4pVXXsH9+/elMk2bNsXChQsxZcoUpKamYseOHdi8eTPWr1/PwYCJiIio0lRbYmdmZoaRI0ciJiYGQghp/ZYtW1BYWIihQ4eWuF98fDySkpKQkJCAr7/+Gtu2bUNkZKROmXXr1kEul+PQoUP47LPPADxohfr4449x9uxZrFu3Dr/88gtmz54NAOjSpQuWL18OGxsbpKamIjU1FTNnzix2bq1Wi4CAAPz777/Yv38/9u7di0uXLiE4OFinXHJyMrZv3464uDjExcVh//79WLhwoU6ZKVOmwMvLCyNGjMDYsWMxZ84ceHl5lb0iq0BeXh4iIiIQERFR4RZPIiIi0p9qnXli9OjRWLx4Mfbv3w9fX18ADy7DDh48GLa2JY/QL5fLsXbtWlhZWaFly5aYN28eZs2ahfnz50uXDZs0aYJFixbp7Dd9+nTpuaurK9577z2MHz8eq1atglwuh62tLWQyGerWrfvYeOPj43H69GmkpKTA2dkZALB+/Xq0bNkSx44dQ4cOHQA8SABjYmJgbW0NABgxYgTi4+OxYMEC6VgymQyffvopPDw84OnpiTfffPOp9ZWbm4vc3FxpOSMj46n7lEd+fr6ULM+aNeuZb0XMyX8wpMXF2+W7A7Jov6LjGKrcAi0A4Nq/WThzI13P0eiXsbynRGT8qjWxa968Obp06YK1a9fC19cXFy9eRGJiIubNm/fYfby8vHT6vHXu3BkajQbXrl2Di4sLAMDbu/gE5T///DOioqLw559/IiMjAwUFBcjJyUFWVlap+9AlJSXB2dlZSuoAoEWLFrCzs0NSUpKU2Lm6ukpJHQDUq1cPt2/fLna8ogQ1JSUF169fh6ur6xPPHxUVVax1kqre9bRsAMD02JMVPk5714rHoy+3M3IAAEv2/oVFOZl6jqZmMPT3lIiMX7XPFRsWFoYpU6Zg5cqViI6OhpubG3x8fCp0TKVSqbN8+fJl9O/fHxMmTMCCBQtgb2+PgwcPIiwsDHl5eZV6cwQAmJub6yzLZDJotVqddYcPH8ayZcuwZ88evPfeewgLC8PPP/8MmUz22OOGh4djxowZ0nJGRoZOkklVo0GtB3cGLw9uA3dHVZn3v3hbg+mxJ6XjGCpHGwsAwMzeTdHdta2eo9EvY3lPicj4VXtiFxQUhGnTpmHTpk1Yv349JkyY8MTk5tSpU8jOzpaG4fj111+hUqmemOD8/vvv0Gq1+PDDD6XLtd98841OGblcjsLCJ19W8fDwwLVr13Dt2jXpfOfOncO9e/fQokWLUr1eAMjKykJoaCgmTJiAHj16oFGjRvD09MRnn32GCRMmPHY/hUIBhUJR6vNQ5bAwNwUAuDuq0Kp+yV0EynIcQ6Uwe/C342xvVaF6MCaG/p4SkfGr9rEtVCoVgoODER4ejtTUVISGhj6xfF5eHsLCwnDu3Dns2rULc+fOxeTJk584LIe7uzvy8/OxYsUKXLp0CRs2bJBuqiji6uoKjUaD+Ph43L17F1lZWcWO4+fnB09PTwwfPhzHjx/H0aNHMXLkSPj4+KB9+/alfs3h4eEQQkg3VLi6umLJkiWYPXs2Ll++XOrjEBERET2JXgYtCwsLQ1paGvz9/eHk5PTEsr169UKTJk3QvXt3BAcH46WXXpKGJnkcLy8vLF26FB988AFatWqFjRs3IioqSqdMly5dMH78eAQHB0OtVhe7+QJ4cEl1x44dqFWrFrp37w4/Pz80btwYsbGxpX6t+/fvly47P3wJeNy4cejSpQvCwsJ07hImIiIiKi+ZYFZhMDIyMmBra4v09HTY2NhU2nEzMzOhUj3oS6bRaIr1WXzWnLmRjv4rDiJuyvPlugRZ0f1rih+SjuKto2F4v+OXGODRUd/h6JWxvKdEZJjK8vtf7X3sqOaxsLDA0aNHpedERERkmJjYEUxNTaWhWwhwU6sQN+V5uKnLfkcsGSd+JojIUDCxI3qEpdyUl9tIBz8TRGQomNgR8vLy8NFHHwEApk2b9szPPEFERGSomNgR8vPzpXl0J06cyMSOiIjIQDGxI6pk2f+dT9TQ51e99m/xsR2JiKhmY2JHVMmS/zth/JvbTus5kooxsbgBZaMH/cuIiMgwMLEjqmR9WtYFALg5qmBpwFNQ5RbmIC3fG11dSj99HhER6RcTO6JKZq+U45WODfUdRiWwBVBH30EQEVEZ6GVKMSIiIiKqfEzsiIiIiIwEL8USLCwssG/fPuk5ERERGSYmdgRTU1P4+vrqOwwiIiKqIF6KJSIiIjISbLEj5Ofn44svvgAAjB07Fubm5nqOiIiIiMpDJoQQ+g6CSicjIwO2trZIT0+HjY1NpR03MzMTKpUKAKDRaKBUKivt2ERERFQxZfn956VYIiIiIiPBxI6IiIjISDCxIyIiIjISTOyIiIiIjAQTOyIiIiIjwcSOiIiIyEhwHDuCQqFAXFyc9JyIiIgMExM7gpmZGfr166fvMIiIiKiCeCmWiIiIyEiwxY6Qn5+PjRs3AgCGDx/OKcWIiIgMFKcUMyCcUoyIiOjZwynFiIiIiJ5BTOyIiIiIjAT72BGVIOVuJjJzC/R2/tzCHPydeQVOShcoTC30FodSYYZGDrw0T0RkKJjYET0i5W4meixJ0GsMJhY3oGy0ApkpU6DNqa/XWPbN9GVyR0RkIJjYET2iqKVueXAbuDuq9BJDSsZ5vHUU+Ci4DRrZNNNLDBdvazA99qReWy6JiKhsmNgRPYa7owqt6tvq5dwmFg8SSjdHFVrU1k8MRERkeJjYERQKBb755hvpORERERkmJnYEMzMzvPzyy/oOg4iIiCqIw50QACA7rxBnbqQjO69Q36EQ1Tj8+yAiQ8HEjlBQUIBV0V+hx6SFOJ96T9/hENU4yXc06L/iIJLvaPQdChHRE9X4xC40NBQymQwymQxyuRzu7u6YN28eCgp4p15lyc3Nxczxobi7YyHy8nL1HQ4RERGVU41P7ACgb9++SE1NxYULF/D6668jIiICixcvLlYuLy9PD9E9WU2MiYiIiIyTQSR2CoUCdevWhYuLCyZMmAA/Pz98//33CA0NRWBgIBYsWAAnJyc0a/ZgvK9r164hKCgIdnZ2sLe3R0BAAC5fviwdLyEhAR07doRSqYSdnR26du2KK1euAABOnTqFHj16wNraGjY2NvD29sZvv/0GAIiIiECbNm10Ylu+fDlcXV2l5fLGRERERFRRBpHYPcrS0lJqCYuPj8f58+exd+9exMXFIT8/H/7+/rC2tkZiYiIOHToElUqFvn37Ii8vDwUFBQgMDISPjw/++OMPHDlyBGPHjoVMJgMADB8+HA0aNMCxY8fw+++/480334S5uXmZ4itrTERERESVwaCGOxFCID4+Hrt378aUKVNw584dKJVKrFmzBnK5HADw1VdfQavVYs2aNVKyFh0dDTs7OyQkJKB9+/ZIT09H//794ebmBgDw8PCQznH16lXMmjULzZs3BwA0adKkzHGWNaY+ffqUeJzc3Fzk5v6vz1tGRkaZYymrS3cyYWWVXuXnqcku3n7QQT4n/9m+A7Lo9RfVx7OMnwkiMhQGkdjFxcVBpVIhPz8fWq0Ww4YNQ0REBCZNmgRPT08pgQIeXEq9ePEirK2tdY6Rk5OD5ORk9OnTB6GhofD390fv3r3h5+eHoKAg1KtXDwAwY8YMjBkzBhs2bICfnx9efvllKQEsrbLG9DhRUVGIjIws07kravbWP2Ai19+k8zXJ9bRstHfVdxT6cz0tGwAwPfakfgOpQZ71zwQR1XwGkdj16NEDn376KeRyOZycnGBm9r+wlUrdyck1Gg28vb2xcePGYsdRq9UAHrSWTZ06FT/99BNiY2PxzjvvYO/evejUqRMiIiIwbNgw7Ny5Ez/++CPmzp2LzZs3Y+DAgTAxMYEQQueY+fn5xc5TnphKEh4ejhkzZkjLGRkZcHZ2fmz5yrBoSGu0cnGs0nPUdEVzpDaoZanvUPSq6PXrc87cmoKfCSIyFAaR2CmVSri7u5eqbLt27RAbGwtHR0fY2Ng8tlzbtm3Rtm1bhIeHo3Pnzti0aRM6deoEAGjatCmaNm2K1157DUOHDkV0dDQGDhwItVqNmzdvQgghXVI9efJkpcX0KIVCUS1TfMnlcsxfuhLLf76AZk619DY/ak1jYW6q7xD0quj163PO3JrmWf9MEFHNZ5A3TzzJ8OHD4eDggICAACQmJiIlJQUJCQmYOnUqrl+/jpSUFISHh+PIkSO4cuUK9uzZgwsXLsDDwwPZ2dmYPHkyEhIScOXKFRw6dAjHjh2T+uD5+vrizp07WLRoEZKTk7Fy5Ur8+OOPFY5J38zNzREYNBwqT78y3yhCRERENYfRJXZWVlY4cOAAGjZsiEGDBsHDwwNhYWHIycmBjY0NrKys8Oeff2Lw4MFo2rQpxo4di0mTJmHcuHEwNTXFP//8g5EjR6Jp06YICgrCCy+8IPVz8/DwwKpVq7By5Up4eXnh6NGjmDlzZoVjIiIiIqoMNf5SbExMTJm31a1bF+vWrStxm42NDb777rsSt8nlcnz99ddPjGf8+PEYP368zrq33nqrQjHpW0FBAQ7E70ZW8jkUFHTSdzhERERUTjU+saOql5ubi0khwQCAvPfH6jkaIiIiKi+juxRLFdPY4dm++5GoJG5qFeKmPA83Nf8+iKhmY4sd6bCU864/okdZyk15ZzARGQS22BEREREZCSZ2REREREaCl2KJHpH93/lAz9zQ35y5KRkP5iZNvq2BNkc/cXCOWCIiw8PEjugRyf9NaN7cdlpvMZhY3ICyETAt9iS0OXf0FgcAKBX8miAiMhT8xibI5XJ88skn0vNnXZ+WdQEAbo4qWOppCqncwhz8ndkGTj1coDC10EsMwIOkrpGD8ukFiYioRpCJR2e1pxorIyMDtra2SE9P54wVREREz4iy/P7z5gkiIiIiI8FLsYTCwkIkJiYCALp16wZTU45lR0REZIiY2BFycnLQo0cPAIBGo4FSyT5VREREhoiXYomIiIiMBBM7IiIiIiPBxI6IiIjISDCxIyIiIjISTOyIiIiIjAQTOyIiIiIjweFOCObm5li0aJH0nIiIiAwTpxQzIJxSjIiI6NnDKcWIiIiInkG8FEsoLCzE8ePHAQDt2rXjlGJEREQGiokdIScnBx07dgTAKcWIiIgMGS/FEhERERkJJnZERERERoKJHREREZGRYGJHREREZCSY2BEREREZCSZ2REREREaCw50QzM3NMXfuXOn5sy67IBsHUs6he6MWsDSz1Hc4REREpcbEjiCXyxEREaHvMGqM706fQNQf4/BW5ucY2qaLvsMhIiIqNV6KJXrErYwcAMDN9Bw9R0JERFQ2bLEjaLVaJCUlAQA8PDxgYsJ8n4iIyBAxsSNkZ2ejVatWADilGBERkSFj0wwRERGRkWBiR/SI7LxCnX+JiIgMBRM7okdc/TdT518iIiJDUerETiaTPfFhjMNluLq6Yvny5foOo8rl5eVJzz/55BOdZXo2FRYWIiEhAV9//TUSEhJQWMjWSyIiQ1DqxC41NVV6LF++HDY2NjrrZs6cWZVxVhohBAoKCqr1nDU5UZo9ezbUarW0/Oabb0KpVGL27Nl6jIr0adu2bXB3d0ePHj0wbNgw9OjRA+7u7ti2bZu+QyMioqcodWJXt25d6WFrawuZTKazbvPmzfDw8ICFhQWaN2+OVatWSftevnwZMpkM33zzDbp16wZLS0t06NABf/31F44dO4b27dtDpVLhhRdewJ07d6T9QkNDERgYiMjISKjVatjY2GD8+PE6iZJWq0VUVBQaNWoES0tLeHl5YevWrdL2hIQEyGQy/Pjjj/D29oZCocDBgweRnJyMgIAA1KlTByqVCh06dMDPP/8s7efr64srV67gtddek1olASAiIgJt2rTRqZvly5fD1dW1WNwLFiyAk5MTmjVrBgC4du0agoKCYGdnB3t7ewQEBODy5culfQsq3ezZs7F48WLUrl1bWvfJJ5+gdu3aWLx4MZO7Z9C2bdswZMgQeHp64siRI7h//z6OHDkCT09PDBkyhMkdEVFNJ8ohOjpa2NraSstfffWVqFevnvj222/FpUuXxLfffivs7e1FTEyMEEKIlJQUAUA0b95c/PTTT+LcuXOiU6dOwtvbW/j6+oqDBw+K48ePC3d3dzF+/HjpuCEhIUKlUong4GBx5swZERcXJ9RqtXjrrbekMu+995503OTkZBEdHS0UCoVISEgQQgixb98+AUC0bt1a7NmzR1y8eFH8888/4uTJk+Kzzz4Tp0+fFn/99Zd45513hIWFhbhy5YoQQoh//vlHNGjQQMybN0+kpqaK1NRUIYQQc+fOFV5eXjr1sWzZMuHi4lIs7hEjRogzZ86IM2fOiLy8POHh4SFGjx4t/vjjD3Hu3DkxbNgw0axZM5Gbm1uqek9PTxcARHp6eqnfq8fJzc0VZmZmok6dOiIzM1PMnDlTzJw5U+Tm5or8/HxRp04dYWZmVurYjMm4zd+JVjGtxLjN3+k7lGpVUFAgXF1dxYABA0RhYaHOtsLCQjFgwADRqFEjUVBQoKcIiYieTWX5/a+UxM7NzU1s2rRJp8z8+fNF586dhRD/S+zWrFkjbf/6668FABEfHy+ti4qKEs2aNZOWQ0JChL29vcjMzJTWffrpp0KlUonCwkKRk5MjrKysxOHDh3XOHRYWJoYOHSqE+F9it3379qe+rpYtW4oVK1ZIyy4uLmLZsmU6ZUqb2NWpU0cnKdqwYYNo1qyZ0Gq10rrc3FxhaWkpdu/eXWI8OTk5Ij09XXpcu3at0hK7ZcuWCQBi9erVJW7//PPPBYBir/9Z8KwmdkV/K0eOHClx++HDhwUAsW/fvuoNjIjoGVeWxK7CAxRnZmYiOTkZYWFhePXVV6X1BQUFsLW11SnbunVr6XmdOnUAAJ6enjrrbt++rbOPl5cXrKyspOXOnTtDo9Hg2rVr0Gg0yMrKQu/evXX2ycvLQ9u2bXXWtW/fXmdZo9EgIiICO3fuRGpqKgoKCpCdnY2rV6+W5eU/lqenJ+RyubR86tQpXLx4EdbW1jrlcnJykJycXOIxoqKiEBkZWSnxPKronP379y9xe9H6x8VGxic1NRUApMGqH1W0vqgcERHVPBVO7DQaDQBg9erVeO6553S2mZqa6iybm5tLz4v6rD26TqvVlvncO3fuRP369XW2KRQKneVHZ1OYOXMm9u7diyVLlsDd3R2WlpYYMmTIU290MDExgRBCZ11+fn6xco+eT6PRwNvbGxs3bixW9uGbFx4WHh6OGTNmSMsZGRlwdnZ+Ynyl5ebmBgCIi4vD6NGjpYS2YcOGMDExQVxcnE45Mn716tUDAJw5cwadOnUqtv3MmTM65YiIqOapcGJXp04dODk54dKlSxg+fHhlxKTj1KlTyM7OhqWlJQDg119/hUqlgrOzM+zt7aFQKHD16lX4+PiU6biHDh1CaGgoBg4cCOBB4vXojQxyubzYMA9qtRo3b96EEEJKTk+ePPnU87Vr1w6xsbFwdHSEjY1NqWJUKBTFEtTKMnHiRMyaNQvvvPMOXn75ZTRq1AjAg3pQKBSYM2cOzMzMMHHixCo5P9U83bp1g6urK95//31s375dZ87gh29S6tatmx6jJCKiJ6mUAYojIyMRFRWFjz/+GH/99RdOnz6N6OhoLF26tMLHzsvLQ1hYGM6dO4ddu3Zh7ty5mDx5MkxMTGBtbY2ZM2fitddew7p165CcnIzjx49jxYoVWLdu3ROP26RJE2zbtg0nT57EqVOnMGzYsGKtha6urjhw4ABu3LiBu3fvAnhwt+ydO3ewaNEiJCcnY+XKlfjxxx+f+jqGDx8OBwcHBAQEIDExESkpKUhISMDUqVNx/fr18ldQOcnlcrz22mu4desWmjZtKq1fu3YtGjRogFu3buG1117TuZxMxs3U1BQffvgh4uLiEBgYqHNXbGBgIOLi4rBkyZJiLfFERFRzVEpiN2bMGKxZswbR0dHw9PSEj48PYmJipFagiujVqxeaNGmC7t27Izg4GC+99JLOYMjz58/Hu+++i6ioKHh4eKBv377YuXPnU8+9dOlS1KpVC126dMGAAQPg7++Pdu3a6ZSZN28eLl++DDc3N+lyqYeHB1atWoWVK1fCy8sLR48eLdUYflZWVjhw4AAaNmyIQYMGwcPDA2FhYcjJySl1C15lW7RoEWbNmoV//vlHWjd16lT8888/mDVrFhYtWqSXuEh/Bg0ahK1bt+L06dPo0qULbGxs0KVLF5w5cwZbt27FoEGD9B0iERE9gUw82mGsBgkNDcW9e/ewfft2fYdSI2RkZMDW1hbp6emVmgympaXB3t4eALBw4cJnvqVufOx2HMp5F10t5uOz4EB9h6MXhYWFSExMRGpqKurVq4du3bqxpY6ISE/K8vtf4T52ZPgeTuImT578TCd19ICpqSl8fX31HQYREZVRpVyKJTImDe2VOv8SEREZihrdYhcTE6PvEOgZZCk31fmXiIjIUNToxI6qx8PDmpiZ8SNBRERkqPgrTlAoFFi5cqW+wyAiIqIKYh87IiIiIiPBFjuCEEIagNnBwUGaUYOIiIgMCxM7QlZWFhwdHQE8mFLs0XlunzXBbbwBfPTff4mIiAwHEzuiRzjZ2uI1n576DoOIiKjM2MeOiIiIyEgwsSMiIiIyEkzsiIiIiIwEEzsiIiIiI8HEjoiIiMhI8K5YgpmZGUJCQqTnREREZJj4K05QKBSIiYnRdxhERERUQbwUS0RERGQk2GJHEEIgKysLAGBlZcUpxYiIiAwUW+wIWVlZUKlUUKlUUoJHREREhoeJHREREZGRYGJHREREZCSY2BEREREZCSZ2REREREaCiR0RERGRkWBiR0RERGQkOI4dwdTUFEOGDJGeExERkWFiYkewsLDAli1b9B0GERERVRAvxRIREREZCSZ2REREREaCiR0hMzMTJgoTWLpa4p/0f/QdDhEREZUTEzsCACjqKeAe6Y7vk07qOxQiIiIqJyZ2pON2Rq6+QyAiIqJyYmJHREREZCSY2BEREREZCSZ2REREREaCiR3pyM4r1HcIREREVE5M7Aimpqao36wNAOBGeo5+gyEiIqJyq/GJXWhoKGQyGWQyGeRyOdzd3TFv3jwUFBToLSaZTIbt27eXuvy2bdvQu3dvqNVq2NjYoHPnzti9e3fVBVhGFhYW8Bv9BgDA1Mxcz9EQERFRedX4xA4A+vbti9TUVFy4cAGvv/46IiIisHjx4mLl8vLy9BDd0x04cAC9e/fGrl278Pvvv6NHjx4YMGAATpw4oe/QiIiIyIgYRGKnUChQt25duLi4YMKECfDz88P333+P0NBQBAYGYsGCBXByckKzZs0AANeuXUNQUBDs7Oxgb2+PgIAAXL58WTpeQkICOnbsCKVSCTs7O3Tt2hVXrlyRtu/YsQPt2rWDhYUFGjdujMjISKmF0NXVFQAwcOBAyGQyaflJli9fjtmzZ6NDhw5o0qQJ3n//fTRp0gQ//PBDpdURERERkUEkdo+ytLSUWufi4+Nx/vx57N27F3FxccjPz4e/vz+sra2RmJiIQ4cOQaVSoW/fvsjLy0NBQQECAwPh4+ODP/74A0eOHMHYsWMhk8kAAImJiRg5ciSmTZuGc+fO4fPPP0dMTAwWLFgAADh27BgAIDo6GqmpqdJyWWi1Wty/fx/29vaVVCMVk5mZiY3vhAIA8vM4QDEREZGhMtN3AGUhhEB8fDx2796NKVOm4M6dO1AqlVizZg3kcjkA4KuvvoJWq8WaNWukZC06Ohp2dnZISEhA+/btkZ6ejv79+8PNzQ0A4OHhIZ0jMjISb775JkJCQgAAjRs3xvz58zF79mzMnTsXarUaAGBnZ4e6deuW63UsWbIEGo0GQUFBTyyXm5uL3Nz/JVoZGRnlOl9pFOQzoSMiIjJ0BtFiFxcXB5VKBQsLC7zwwgsIDg5GREQEAMDT01NK6gDg1KlTuHjxIqytraFSqaBSqWBvb4+cnBwkJyfD3t4eoaGh8Pf3x4ABA/DRRx8hNTVVZ/958+ZJ+6pUKrz66qtITU1FVlZWhV/Lpk2bEBkZiW+++QaOjo5PLBsVFQVbW1vp4ezsXOHzExERkfEyiBa7Hj164NNPP4VcLoeTkxPMzP4XtlKp1Cmr0Wjg7e2NjRs3FjtOUWtbdHQ0pk6dip9++gmxsbF45513sHfvXnTq1AkajQaRkZEYNGhQsf0tLCwq9Do2b96MMWPGYMuWLfDz83tq+fDwcMyYMUNazsjIYHJHREREj2UQiZ1SqYS7u3upyrZr1w6xsbFwdHSEjY3NY8u1bdsWbdu2RXh4ODp37oxNmzahU6dOaNeuHc6fP//E85mbm6OwsGwD+X799dcYPXo0Nm/ejH79+pVqH4VCAYVCUabzEBER0bPLIC7FlsXw4cPh4OCAgIAAJCYmIiUlBQkJCZg6dSquX7+OlJQUhIeH48iRI7hy5Qr27NmDCxcuSP3s5syZg/Xr1yMyMhJnz55FUlISNm/ejHfeeUc6h6urK+Lj43Hz5k2kpaU9NaZNmzZh5MiR+PDDD/Hcc8/h5s2buHnzJtLT06usHoiIiOjZY3SJnZWVFQ4cOICGDRti0KBB8PDwQFhYGHJycmBjYwMrKyv8+eefGDx4MJo2bYqxY8di0qRJGDduHADA398fcXFx2LNnDzp06IBOnTph2bJlcHFxkc7x4YcfYu/evXB2dkbbtm2fGtMXX3yBgoICTJo0CfXq1ZMe06ZNq7J6ICIiomePTAgh9B0ElU5GRgZsbW2Rnp7+xMvMZZWdnQ2Pfl1hHZKP50zfxZr/e/LdukRERFR9yvL7b3QtdlR2lpaW6DtuDgDAzFz+lNJERERUUzGxqwQtW7bUGR7l4UdJd+fWRA3tlTr/EhERkeExiLtia7pdu3YhPz+/xG116tSp5mjKx1JuqvMvERERGR4mdpXg4RsrDFFmZiYWTB6GejPVyMvN0Xc4REREVE68FEsAgMz7HHqFiIjI0DGxIyIiIjISTOyIiIiIjAQTOwIA5Kbm4uLcixjk+fQBl4mIiKhmYmJHAACRJ5BzJQdOlTjwMREREVUvJnZERERERoLDnRBMTEzQvn176TkREREZJiZ2BEtLSxw7dkzfYRAREVEFsXmGiIiIyEgwsSMiIiIyEkzsCFlZWXB1dYWrqyuysrL0HQ4RERGVE/vYEYQQuHLlivSciIiIDBNb7IiIiIiMBBM7IiIiIiPBxI6IiIjISDCxIyIiIjISTOyIiIiIjATviiXIZDK0aNFCek5ERESGiYkdwcrKCmfPntV3GERERFRBvBRLREREZCSY2BEREREZCSZ2hKysLLRs2RItW7bklGJEREQGjH3sCEIInDt3TnpOREREhoktdkRERERGgokdERERkZFgYkcAAJlcBgsXC+QU5Og7FCIiIionJnYEAFDUU8A90h3fJ53UdyhERERUTkzsSMftjFx9h0BERETlxMSOIJPJYOdQR3pOREREhomJHcHKygqzP1wLADCXK/QcDREREZUXEzsiIiIiI8HEjgAA2XmFOv8SERGR4WFiR8jOzsaqyOkAgEu30vQbDBEREZVbjU/sQkNDIZPJIJPJIJfL4e7ujnnz5qGgoEBvMclkMmzfvr3U5VNTUzFs2DA0bdoUJiYmmD59epXFVh5arRb/XL8EgFOKERERGbIan9gBQN++fZGamooLFy7g9ddfR0REBBYvXlysXF5enh6ie7rc3Fyo1Wq888478PLy0nc4REREZKQMIrFTKBSoW7cuXFxcMGHCBPj5+eH7779HaGgoAgMDsWDBAjg5OaFZs2YAgGvXriEoKAh2dnawt7dHQEAALl++LB0vISEBHTt2hFKphJ2dHbp27YorV65I23fs2IF27drBwsICjRs3RmRkpNRC6OrqCgAYOHAgZDKZtPwkrq6u+OijjzBy5EjY2tpWWr0QERERPcwgErtHWVpaSq1z8fHxOH/+PPbu3Yu4uDjk5+fD398f1tbWSExMxKFDh6BSqdC3b1/k5eWhoKAAgYGB8PHxwR9//IEjR45g7Nix0vhtiYmJGDlyJKZNm4Zz587h888/R0xMDBYsWAAAOHbsGAAgOjoaqamp0jIRERGRvpnpO4CyEEIgPj4eu3fvxpQpU3Dnzh0olUqsWbMGcrkcAPDVV19Bq9VizZo1UrIWHR0NOzs7JCQkoH379khPT0f//v3h5uYGAPDw8JDOERkZiTfffBMhISEAgMaNG2P+/PmYPXs25s6dC7VaDQCws7ND3bp1q/T15ubmIjf3fzNBZGRkVOn5iIiIyLAZRItdXFwcVCoVLCws8MILLyA4OBgREREAAE9PTympA4BTp07h4sWLsLa2hkqlgkqlgr29PXJycpCcnAx7e3uEhobC398fAwYMwEcffYTU1FSd/efNmyftq1Kp8OqrryI1NRVZWVnV+rqjoqJga2srPZydnav1/ERERGRYDKLFrkePHvj0008hl8vh5OQEM7P/ha1UKnXKajQaeHt7Y+PGjcWOU9TaFh0djalTp+Knn35CbGws3nnnHezduxedOnWCRqNBZGQkBg0aVGx/CwuLSn5lTxYeHo4ZM2ZIyxkZGVWW3CmsbKrkuERERFR9DCKxUyqVcHd3L1XZdu3aITY2Fo6OjrCxeXyy0rZtW7Rt2xbh4eHo3LkzNm3ahE6dOqFdu3Y4f/78E89nbm6OwsKqH8hXoVBAoaj6Kb6USiVemfs5DuW8yynFiIiIDJhBXIoti+HDh8PBwQEBAQFITExESkoKEhISMHXqVFy/fh0pKSkIDw/HkSNHcOXKFezZswcXLlyQ+tnNmTMH69evR2RkJM6ePYukpCRs3rwZ77zzjnQOV1dXxMfH4+bNm0hLK92AvidPnsTJkyeh0Whw584dnDx5EufOnauSOiAiIqJnk9EldlZWVjhw4AAaNmyIQYMGwcPDA2FhYcjJyYGNjQ2srKzw559/YvDgwWjatCnGjh2LSZMmYdy4cQAAf39/xMXFYc+ePejQoQM6deqEZcuWwcXFRTrHhx9+iL1798LZ2Rlt27YtVVxFLYS///47Nm3ahLZt2+LFF1+skjogIiKiZ5NMcKoBg5GRkQFbW1ukp6c/8TJzWWVnZ8OjX1dYh+TjOdN3seb/girt2ERERFQxZfn9N7oWOyo7rVaLW5eSAHBKMSIiIkPGxK4StGzZUmd4lIcfJd2dW5M517LSdwhERERUTgZxV2xNt2vXLuTn55e4rU6dOtUcTcVYyk31HQIRERGVExO7SvDwjRVERERE+sJLsURERERGgokdERERkZFgYkcAADlnnCAiIjJ47GNHUCqVSDp2BbEnf0dwG299h0NERETlxMSOAABOtrZ4zaenvsMgIiKiCuClWCIiIiIjwcSOkJOTg379+qFfv37IycnRdzhERERUTrwUSygsLMSuXbuk50RERGSY2GJHREREZCSY2BEREREZCSZ2REREREaCiR0RERGRkWBiR0RERGQkeFesARFCAAAyMjIq9biZmZnS84yMDN4ZS0REVIMU/e4X5QFPwsTOgNy/fx8A4OzsXGXncHJyqrJjExERUfndv38ftra2TywjE6VJ/6hG0Gq1+Pvvv2FtbQ2ZTFapx87IyICzszOuXbsGGxubSj02lYx1Xv1Y59WPda4frPfqV5V1LoTA/fv34eTkBBOTJ/eiY4udATExMUGDBg2q9Bw2Njb8EqhmrPPqxzqvfqxz/WC9V7+qqvOntdQV4c0TREREREaCiR0RERGRkWBiRwAAhUKBuXPnQqFQ6DuUZwbrvPqxzqsf61w/WO/Vr6bUOW+eICIiIjISbLEjIiIiMhJM7IiIiIiMBBM7IiIiIiPBxO4ZsnLlSri6usLCwgLPPfccjh49+sTyW7ZsQfPmzWFhYQFPT0/s2rWrmiI1HmWp87Nnz2Lw4MFwdXWFTCbD8uXLqy9QI1KWOl+9ejW6deuGWrVqoVatWvDz83vq3wUVV5Y637ZtG9q3bw87OzsolUq0adMGGzZsqMZojUdZv9OLbN68GTKZDIGBgVUboBEqS53HxMRAJpPpPCwsLKo8RiZ2z4jY2FjMmDEDc+fOxfHjx+Hl5QV/f3/cvn27xPKHDx/G0KFDERYWhhMnTiAwMBCBgYE4c+ZMNUduuMpa51lZWWjcuDEWLlyIunXrVnO0xqGsdZ6QkIChQ4di3759OHLkCJydndGnTx/cuHGjmiM3XGWtc3t7e7z99ts4cuQI/vjjD4waNQqjRo3C7t27qzlyw1bWei9y+fJlzJw5E926daumSI1HeercxsYGqamp0uPKlStVH6igZ0LHjh3FpEmTpOXCwkLh5OQkoqKiSiwfFBQk+vXrp7PuueeeE+PGjavSOI1JWev8YS4uLmLZsmVVGJ1xqkidCyFEQUGBsLa2FuvWrauqEI1ORetcCCHatm0r3nnnnaoIz2iVp94LCgpEly5dxJo1a0RISIgICAiohkiNR1nrPDo6Wtja2lZTdP/DFrtnQF5eHn7//Xf4+flJ60xMTODn54cjR46UuM+RI0d0ygOAv7//Y8uTrvLUOVVMZdR5VlYW8vPzYW9vX1VhGpWK1rkQAvHx8Th//jy6d+9elaEalfLW+7x58+Do6IiwsLDqCNOolLfONRoNXFxc4OzsjICAAJw9e7bKY2Vi9wy4e/cuCgsLUadOHZ31derUwc2bN0vc5+bNm2UqT7rKU+dUMZVR52+88QacnJyK/aeGSlbeOk9PT4dKpYJcLke/fv2wYsUK9O7du6rDNRrlqfeDBw/iyy+/xOrVq6sjRKNTnjpv1qwZ1q5dix07duCrr76CVqtFly5dcP369SqN1axKj05EZCAWLlyIzZs3IyEhoVo6OD/LrK2tcfLkSWg0GsTHx2PGjBlo3LgxfH199R2aUbp//z5GjBiB1atXw8HBQd/hPDM6d+6Mzp07S8tdunSBh4cHPv/8c8yfP7/KzsvE7hng4OAAU1NT3Lp1S2f9rVu3HttJv27dumUqT7rKU+dUMRWp8yVLlmDhwoX4+eef0bp166oM06iUt85NTEzg7u4OAGjTpg2SkpIQFRXFxK6UylrvycnJuHz5MgYMGCCt02q1AAAzMzOcP38ebm5uVRu0gauM73Rzc3O0bdsWFy9erIoQJbwU+wyQy+Xw9vZGfHy8tE6r1SI+Pl7nfxMP69y5s055ANi7d+9jy5Ou8tQ5VUx563zRokWYP38+fvrpJ7Rv3746QjUalfU512q1yM3NrYoQjVJZ67158+Y4ffo0Tp48KT1eeukl9OjRAydPnoSzs3N1hm+QKuOzXlhYiNOnT6NevXpVFeYD1X67BunF5s2bhUKhEDExMeLcuXNi7Nixws7OTty8eVMIIcSIESPEm2++KZU/dOiQMDMzE0uWLBFJSUli7ty5wtzcXJw+fVpfL8HglLXOc3NzxYkTJ8SJEydEvXr1xMyZM8WJEyfEhQsX9PUSDE5Z63zhwoVCLpeLrVu3itTUVOlx//59fb0Eg1PWOn///ffFnj17RHJysjh37pxYsmSJMDMzE6tXr9bXSzBIZa33R/Gu2LIra51HRkaK3bt3i+TkZPH777+LV155RVhYWIizZ89WaZxM7J4hK1asEA0bNhRyuVx07NhR/Prrr9I2Hx8fERISolP+m2++EU2bNhVyuVy0bNlS7Ny5s5ojNnxlqfOUlBQBoNjDx8en+gM3YGWpcxcXlxLrfO7cudUfuAErS52//fbbwt3dXVhYWIhatWqJzp07i82bN+shasNX1u/0hzGxK5+y1Pn06dOlsnXq1BEvvviiOH78eJXHKBNCiKptEyQiIiKi6sA+dkRERERGgokdERERkZFgYkdERERkJJjYERERERkJJnZERERERoKJHREREZGRYGJHREREZCSY2BEREREZCSZ2RER6kJCQAJlMhnv37uk7lHKRyWTYvn07AODy5cuQyWQ4efKkXmMiIiZ2RFQNQkNDIZPJIJPJYG5ujjp16qB3795Yu3YttFqtvsPTiy5duiA1NRW2trb6DoWIjAgTOyKqFn379kVqaiouX76MH3/8ET169MC0adPQv39/FBQUVNl58/LyquzYFSGXy1G3bl3IZLJKO6YQokrr0lDU1PecqDowsSOiaqFQKFC3bl3Ur18f7dq1w1tvvYUdO3bgxx9/RExMjFTu3r17GDNmDNRqNWxsbNCzZ0+cOnVK51g//PADOnToAAsLCzg4OGDgwIHSNldXV8yfPx8jR46EjY0Nxo4dCwA4ePAgunXrBktLSzg7O2Pq1KnIzMyU9tuwYQPat28Pa2tr1K1bF8OGDcPt27el7WlpaRg+fDjUajUsLS3RpEkTREdHS9uvXbuGoKAg2NnZwd7eHgEBAbh8+fJj6+PRS7ExMTGws7PD7t274eHhAZVKJSXDTzvGjz/+CG9vbygUChw8eBBarRZRUVFo1KgRLC0t4eXlha1bt+rse/bsWfTv3x82NjawtrZGt27dkJycDAA4duwYevfuDQcHB9ja2sLHxwfHjx9/bBylsWrVKjRp0gQWFhaoU6cOhgwZIm3TarVYtGgR3N3doVAo0LBhQyxYsEDafvr0afTs2ROWlpaoXbs2xo4dC41GI20PDQ1FYGAgFixYACcnJzRr1gxA2d8TImPAxI6I9KZnz57w8vLCtm3bpHUvv/wybt++jR9//BG///472rVrh169euHff/8FAOzcuRMDBw7Eiy++iBMnTiA+Ph4dO3bUOe6SJUvg5eWFEydO4N1330VycjL69u2LwYMH448//kBsbCwOHjyIyZMnS/vk5+dj/vz5OHXqFLZv347Lly8jNDRU2v7uu+/i3Llz+PHHH5GUlIRPP/0UDg4O0r7+/v6wtrZGYmIiDh06JCVmZWk9ysrKwpIlS7BhwwYcOHAAV69excyZM5+635tvvomFCxciKSkJrVu3RlRUFNavX4/PPvsMZ8+exWuvvYb/+7//w/79+wEAN27cQPfu3aFQKPDLL7/g999/x+jRo6XWvvv37yMkJAQHDx7Er7/+iiZNmuDFF1/E/fv3S/1aHvbbb79h6tSpmDdvHs6fP4+ffvoJ3bt3l7aHh4dj4cKFUh1v2rQJderUAQBkZmbC398ftWrVwrFjx7Blyxb8/PPPOu8dAMTHx+P8+fPYu3cv4uLiKu09ITI4goioioWEhIiAgIAStwUHBwsPDw8hhBCJiYnCxsZG5OTk6JRxc3MTn3/+uRBCiM6dO4vhw4c/9lwuLi4iMDBQZ11YWJgYO3aszrrExERhYmIisrOzSzzOsWPHBABx//59IYQQAwYMEKNGjSqx7IYNG0SzZs2EVquV1uXm5gpLS0uxe/fuEvfZt2+fACDS0tKEEEJER0cLAOLixYtSmZUrV4o6deo89rUWHWP79u3SupycHGFlZSUOHz6sUzYsLEwMHTpUCCFEeHi4aNSokcjLy3vssR9WWFgorK2txQ8//CCtAyC+++47IYQQKSkpAoA4ceJEift/++23wsbGRmRkZBTblpGRIRQKhVi9enWJ+37xxReiVq1aQqPRSOt27twpTExMxM2bN4UQDz5fderUEbm5uVKZ8rwnRMbATJ9JJRGREELqZ3bq1CloNBrUrl1bp0x2drZ0mfDkyZN49dVXn3jM9u3b6yyfOnUKf/zxBzZu3KhzXq1Wi5SUFHh4eOD3339HREQETp06hbS0NOmmjqtXr6JFixaYMGECBg8ejOPHj6NPnz4IDAxEly5dpONfvHgR1tbWOufNycmR4i4NKysruLm5Scv16tXTuRxcmtd78eJFZGVloXfv3jpl8vLy0LZtWwAP6rBbt24wNzcv8Xi3bt3CO++8g4SEBNy+fRuFhYXIysrC1atXS/1aHta7d2+4uLigcePG6Nu3L/r27YuBAwfCysoKSUlJyM3NRa9evUrcNykpCV5eXlAqldK6rl27QqvV4vz581LLnqenJ+RyuVSmst4TIkPDxI6I9CopKQmNGjUCAGg0GtSrVw8JCQnFytnZ2QEALC0tn3rMh5OAouOOGzcOU6dOLVa2YcOG0uU+f39/bNy4EWq1GlevXoW/v7902e6FF17AlStXsGvXLuzduxe9evXCpEmTsGTJEmg0Gnh7e+skjkXUavVT4y3yaKIlk8kghCjT6y3qe7Zz507Ur19fp5xCoQDw9DoMCQnBP//8g48++gguLi5QKBTo3LlzuS9hWltb4/jx40hISMCePXswZ84cRERE4NixY6V6P0ujpPe8Mt4TIkPDxI6I9OaXX37B6dOn8dprrwEA2rVrh5s3b8LMzAyurq4l7tO6dWvEx8dj1KhRpT5Pu3btcO7cObi7u5e4/fTp0/jnn3+wcOFCODs7A3jQL+xRarUaISEhCAkJQbdu3TBr1iwsWbIE7dq1Q2xsLBwdHWFjY1PquKpCixYtoFAocPXqVfj4+JRYpnXr1li3bh3y8/NLbLU7dOgQVq1ahRdffBHAg5sQ7t69W6G4zMzM4OfnBz8/P8ydOxd2dnb45Zdf8OKLL8LS0hLx8fEYM2ZMsf08PDwQExODzMxMKXk7dOgQTExMpJskSlKT3hOi6sSbJ4ioWuTm5uLmzZu4ceMGjh8/jvfffx8BAQHo378/Ro4cCQDw8/ND586dERgYiD179uDy5cs4fPgw3n77bSnRmjt3Lr7++mvMnTsXSUlJOH36ND744IMnnvuNN97A4cOHMXnyZJw8eRIXLlzAjh07pA74DRs2hFwux4oVK3Dp0iV8//33mD9/vs4x5syZgx07duDixYs4e/Ys4uLi4OHhAQAYPnw4HBwcEBAQgMTERKSkpCAhIQFTp07F9evXK7sqn8ja2hozZ87Ea6+9hnXr1iE5ORnHjx/HihUrsG7dOgDA5MmTkZGRgVdeeQW//fYbLly4gA0bNuD8+fMAgCZNmmDDhg1ISkrCf/7zHwwfPrxCLWtxcXH4+OOPcfLkSVy5cgXr16+HVqtFs2bNYGFhgTfeeAOzZ8/G+vXrkZycjF9//RVffvklgAd1a2FhgZCQEJw5cwb79u3DlClTMGLECOkybElq0ntCVK303MePiJ4BISEhAoAAIMzMzIRarRZ+fn5i7dq1orCwUKdsRkaGmDJlinBychLm5ubC2dlZDB8+XFy9elUq8+2334o2bdoIuVwuHBwcxKBBg6RtLi4uYtmyZcViOHr0qOjdu7dQqVRCqVSK1q1biwULFkjbN23aJFxdXYVCoRCdO3cW33//vc4NAfPnzxceHh7C0tJS2Nvbi4CAAHHp0iVp/9TUVDFy5Ejh4OAgFAqFaNy4sXj11VdFenp6iXVS0s0Ttra2OmW+++478aSv6UePUUSr1Yrly5eLZs2aCXNzc6FWq4W/v7/Yv3+/VObUqVOiT58+wsrKSlhbW4tu3bqJ5ORkIYQQx48fF+3btxcWFhaiSZMmYsuWLcXqFWW4eSIxMVH4+PiIWrVqCUtLS9G6dWsRGxsrbS8sLBTvvfeecHFxEebm5qJhw4bi/fffl7b/8ccfokePHsLCwkLY29uLV199VbqpRYjH35xT1veEyBjIhChFBw4iIiIiqvF4KZaIiIjISDCxIyIiIjISTOyIiIiIjAQTOyIiIiIjwcSOiIiIyEgwsSMiIiIyEkzsiIiIiIwEEzsiIiIiI8HEjoiIiMhIMLEjIiIiMhJM7IiIiIiMBBM7IiIiIiPx/zYUPRvJXw9UAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "permutation_importances(final_model_sgd, X_train, y_train, scoring='recall', train_or_test='train')\n",
    "permutation_importances(final_model_sgd, X_test, y_test, scoring='recall', train_or_test='test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SGDClassifier with feature engineering\n",
    " - The three features are very close to each other on importance.\n",
    "\n",
    " - There's no significant sign of overfitting when comparing the importance of features on train and test sets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "permutation_importances(final_model_sgd2, X_train2, y_train2, scoring='recall', train_or_test='train')\n",
    "permutation_importances(final_model_sgd2, X_test2, y_test2, scoring='recall', train_or_test='test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next steps\n",
    "\n",
    "Now it's time to put this model to production. This can be done a few different ways.\n",
    "\n",
    "Here's a couple of ways I understand can be good for this problem:\n",
    "\n",
    "- Embedded:\n",
    "\n",
    "    The trained model can be embedded as a dependency in an application.\n",
    "\n",
    "    Example: The application that is responsible for measuring the datapoints by the sensors will have the model dependency installed on it and it will call the model module to make the predictions.\n",
    "\n",
    "    This is a simple approach but once the model needs to be retrained/updated, the entire application would need to be redeployed.\n",
    "\n",
    "    Latency for predictions: low\n",
    "\n",
    "- Model API:\n",
    "\n",
    "    The trained model would be a separate API service that can be requested.\n",
    "\n",
    "    Example: The application server would make an API request to the dedicated model microservice that returns the prediction.\n",
    "\n",
    "    This can be done via REST, GRPC or some message broker like Kafka.\n",
    "\n",
    "    This approach is more complex to implement but it's more flexible when retraining and redeploying is needed because it's a separate service.\n",
    "\n",
    "    Latency for predictions: moderate\n",
    "\n",
    "Either way, the machine learning pipeline needs to be reproducible, scalable and make reliable and efficient predictions.\n",
    "\n",
    "In order to monitor the model in production, we need to collect log data. Monitoring will allow us to understand when data is shifting and the model is becoming less accurate. When that happens, the model can be retrained and updated.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.10.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}