option_pricing_dissertation_simulation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb86a192",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import norm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from numpy import log\n",
    "import time\n",
    "\n",
    "from keras.models import Sequential\n",
    "\n",
    "from tensorflow.keras.layers import Input, LSTM, Dense, Dropout\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from keras.layers import Dense, LeakyReLU\n",
    "from keras.activations import elu\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.layers import LeakyReLU, ELU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b17c01b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class EuropeanOptionBS:\n",
    "    def __init__(self, S, K, T, r, q, sigma, Type):\n",
    "        self.S = S\n",
    "        self.K = K\n",
    "        self.T = T\n",
    "        self.r = r\n",
    "        self.q = q\n",
    "        self.sigma = sigma\n",
    "        self.Type = Type\n",
    "        self.d1 = self.d1()\n",
    "        self.d2 = self.d2()\n",
    "        self.price = self.price()\n",
    "\n",
    "    def d1(self):\n",
    "        d1 = (math.log(self.S / self.K) +\n",
    "              (self.r - self.q + 0.5 * (self.sigma ** 2)) * self.T) / \\\n",
    "             (self.sigma * math.sqrt(self.T))\n",
    "        return d1\n",
    "\n",
    "    def d2(self):\n",
    "        d2 = self.d1 - self.sigma * math.sqrt(self.T)\n",
    "        return d2\n",
    "\n",
    "    def price(self):\n",
    "        if self.Type == \"Call\":\n",
    "            price = self.S * math.exp(-self.q * self.T) * norm.cdf(self.d1) - \\\n",
    "                    self.K * math.exp(-self.r * self.T) * norm.cdf(self.d2)\n",
    "        if self.Type == \"Put\":\n",
    "            price = self.K * math.exp(-self.r * self.T) * norm.cdf(-self.d2) - \\\n",
    "                    self.S * math.exp(-self.q * self.T) * norm.cdf(-self.d1)\n",
    "        return price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b61a3ef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset\n",
    "r = np.arange(0.0, 0.1, 0.01)  # interest rates\n",
    "Strike = np.arange(50, 155, 5)  # strike price\n",
    "T = np.arange(0.1, 2.1, 0.1)  # time to maturity\n",
    "sigma = np.arange(0.1, 2.1, 0.1)  # volatility\n",
    "\n",
    "\n",
    "data = []\n",
    "for r_ in r:\n",
    "    for Strike_ in Strike:\n",
    "        for T_ in T:\n",
    "            for sigma_ in sigma:\n",
    "                \n",
    "                price = EuropeanOptionBS(100, Strike_, T_, r_, 0, sigma_, \"Call\").price\n",
    "                \n",
    "                data.append([r_, Strike_, T_, sigma_, price])\n",
    "\n",
    "\n",
    "data = np.asarray(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0d6cb403",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset\n",
    "r = np.arange(0.0, 0.1, 0.01)  # interest rates\n",
    "Strike = np.arange(50, 155, 5)  # strike price\n",
    "T = np.arange(0.1, 2.1, 0.1)  # time to maturity\n",
    "sigma = np.arange(0.1, 2.1, 0.1)  # volatility\n",
    "stock_price = 100\n",
    "\n",
    "data = []\n",
    "for r_ in r:\n",
    "    for Strike_ in Strike:\n",
    "        for T_ in T:\n",
    "            for sigma_ in sigma:\n",
    "                price = EuropeanOptionBS(100, Strike_, T_, r_, 0, sigma_, \"Call\").price\n",
    "                \n",
    "                \n",
    "                option_stock_ratio = price / stock_price\n",
    "                strike_stock_ratio = Strike_ / stock_price\n",
    "                data.append([r_, strike_stock_ratio, T_, sigma_, option_stock_ratio])\n",
    "\n",
    "\n",
    "data = np.asarray(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f8e6d21",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Test and train datasets\n",
    "X = data[:, :4]  # params r, strike, T, sigma\n",
    "y = data[:, 4]  # call price\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9bf1bfac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\spin 3\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "model_ML = Sequential()\n",
    "\n",
    "model_ML.add(Dense(30, input_dim=4))\n",
    "model_ML.add(LeakyReLU())\n",
    "model_ML.add(Dense(60))\n",
    "model_ML.add(ELU())\n",
    "model_ML.add(Dense(90))\n",
    "model_ML.add(LeakyReLU())\n",
    "model_ML.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b3551d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((67200, 4), (16800, 4), (67200,), (16800,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test and train datasets\n",
    "X = data[:, :4]  # params r, strike, T, sigma\n",
    "y = data[:, 4]  # call price\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "# Display the shapes of the resulting datasets\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "495a58ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5519d22d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Initialize the scaler\n",
    "#scaler = StandardScaler()\n",
    "\n",
    "# Fit the scaler on the training data and transform both training and testing data\n",
    "#X_train = scaler.fit_transform(X_train)\n",
    "#X_test = scaler.transform(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3076ba81",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ML.compile(loss='mean_squared_error', optimizer='adam')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da5948ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "history_model_ML = model_ML.fit(X_train, y_train, epochs=100, batch_size=15, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9ed035f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Last log loss: 0.0000048437\n"
     ]
    }
   ],
   "source": [
    "final_loss = history_model_ML.history['loss'][-1]\n",
    "print(f\"   Last log loss: {final_loss:.10f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed58d689",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 503us/step\n",
      "   MSA: 0.0018000903\n",
      "   MAE: 0.0013187795\n",
      "   MSE: 0.0000032403\n",
      "r-squared = 1.000\n"
     ]
    }
   ],
   "source": [
    "y_pred_ML = model_ML.predict(X_test)\n",
    "rmse_ML = np.sqrt(mean_squared_error(y_test,y_pred_ML))\n",
    "mae_ML = mean_absolute_error(y_test, y_pred_ML)\n",
    "mse_ML = mean_squared_error(y_test,y_pred_ML)\n",
    "print(f\"   MSA: {rmse_ML:.10f}\")\n",
    "print(f\"   MAE: {mae_ML :.10f}\")\n",
    "print(f\"   MSE: {mse_ML :.10f}\") \n",
    "print(\"r-squared = {:.3f}\".format(r2_score(y_test, y_pred_ML)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "524ca786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fa7a7c70d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "x_x = history_model_ML.history[\"loss\"]\n",
    "ax.plot(history_model_ML.history[\"loss\"], label=\"train loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.title('Loss function ML(C/S)')\n",
    "#ax.plot(history.history[\"accuracy\"], label=\"train accuracy\")\n",
    "#ax.plot(history.history[\"val_loss\"], label=\"validation loss\")\n",
    "#ax.plot(history.history[\"val_accuracy\"], label=\"validation accuracy\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "22735986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Comparison of real values and predictions on the test dataset\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.scatter(y_test, y_pred_ML)\n",
    "plt.xlabel(\"Real Value\")\n",
    "plt.ylabel(\"ANN Value\")\n",
    "plt.annotate(\"r-squared = {:.3f}\".format(r2_score(y_test, y_pred_ML)), (20, 1), size=15)\n",
    "plt.title('Real Value VS ANN Value ML(C/S)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fc452219",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fractions statistics\n",
      "Min: -3.693691596359393e+35\n",
      "Max: 370579093.19818705\n",
      "Mean: -2.19862595030144e+31\n",
      "Std: 2.8496601201258943e+33\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#y_test = y_test.reset_index(drop=True)\n",
    "newper_y_pred_ML = pd.Series(y_pred_ML.flatten(), name='Predictions')\n",
    "# Combine them into a DataFrame\n",
    "frac__ML = pd.DataFrame({'Actual': y_test, 'Predicted': newper_y_pred_ML})\n",
    "\n",
    "y_testf = frac__ML['Actual'].values\n",
    "y_predf = frac__ML['Predicted'].values\n",
    "\n",
    "# Calculate the fractions\n",
    "fractionsf = y_predf / y_testf-1\n",
    "\n",
    "# Print statistics for fractions\n",
    "print(\"fractions statistics\")\n",
    "print(\"Min:\", np.min(fractionsf))\n",
    "print(\"Max:\", np.max(fractionsf))\n",
    "print(\"Mean:\", np.mean(fractionsf))\n",
    "print(\"Std:\", np.std(fractionsf))\n",
    "\n",
    "# Plot the fractions\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(X_test[:, 2]*254,fractionsf, 'o', alpha=0.3)\n",
    "plt.xlabel('Days to Maturity')\n",
    "plt.ylabel('Predicted/Actual Price-1 Fraction')\n",
    "plt.title('Fraction of Predicted to Actual')\n",
    "plt.ylim(0, 2)  # Limit the y-axis for better visualization if needed\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b403803",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f3e0d18",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Test and train datasets\n",
    "X = data[:, :4]  # params r, strike, T, sigma\n",
    "y = log(data[:, 4])  # call price\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "58a1e97b",
   "metadata": {},
   "outputs": [],
   "source": [
    "history_model_ML = model_ML.fit(X_train, y_train, epochs=100, batch_size=15, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "52a8e007",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Last log loss: 0.0240514614\n"
     ]
    }
   ],
   "source": [
    "final_loss = history_model_ML.history['loss'][-1]\n",
    "print(f\"   Last log loss: {final_loss:.10f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "50483bff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 685us/step\n",
      "   MSA: 0.1316607460\n",
      "   MAE: 0.0407155033\n",
      "   MSE: 0.0173345520\n",
      "r-squared = 0.997\n"
     ]
    }
   ],
   "source": [
    "y_pred_ML = model_ML.predict(X_test)\n",
    "rmse_ML = np.sqrt(mean_squared_error(y_test,y_pred_ML))\n",
    "mae_ML = mean_absolute_error(y_test, y_pred_ML)\n",
    "mse_ML = mean_squared_error(y_test,y_pred_ML)\n",
    "print(f\"   MSA: {rmse_ML:.10f}\")\n",
    "print(f\"   MAE: {mae_ML :.10f}\")\n",
    "print(f\"   MSE: {mse_ML :.10f}\") \n",
    "print(\"r-squared = {:.3f}\".format(r2_score(y_test, y_pred_ML)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "11e90023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Comparison of real values and predictions on the test dataset\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.scatter(y_test, y_pred_ML)\n",
    "plt.xlabel(\"Real Value\")\n",
    "plt.ylabel(\"ANN Value\")\n",
    "plt.annotate(\"r-squared = {:.3f}\".format(r2_score(y_test, y_pred_ML)), (20, 1), size=15)\n",
    "plt.title('Real Value VS ANN Value ML(C/S)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "588b548e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.column_stack((y_test, y_pred_ML))\n",
    "np.savetxt('y_test_y_pred.csv', data, delimiter=',', header='y_test,y_pred', comments='')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1580137e",
   "metadata": {},
   "outputs": [],
   "source": [
    "datafrac = pd.read_csv('y_test_y_pred.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c0742b97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fractions statistics\n",
      "Min: -0.8699502675487625\n",
      "Max: 0.27995522533929207\n",
      "Mean: -0.034287281066399\n",
      "Std: 0.059653366421663315\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_testf = datafrac['y_test'].values\n",
    "y_predf = datafrac['y_pred'].values\n",
    "\n",
    "# Calculate the fractions\n",
    "fractionsf = y_predf / y_testf-1\n",
    "\n",
    "# Print statistics for fractions\n",
    "print(\"fractions statistics\")\n",
    "print(\"Min:\", np.min(fractionsf))\n",
    "print(\"Max:\", np.max(fractionsf))\n",
    "print(\"Mean:\", np.mean(fractionsf))\n",
    "print(\"Std:\", np.std(fractionsf))\n",
    "\n",
    "# Plot the fractions\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(fractionsf, 'o', alpha=0.3)\n",
    "plt.xlabel('Test Sample Index')\n",
    "plt.ylabel('Predicted/Actual Price Fraction')\n",
    "plt.title('Fraction of Predicted to Actual Prices')\n",
    "plt.ylim(0, 2)  # Limit the y-axis for better visualization if needed\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4caf7b7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fractions statistics\n",
      "Min: -0.8699502675487625\n",
      "Max: 0.27995522533929185\n",
      "Mean: -0.034287281066399\n",
      "Std: 0.05965336642166331\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#y_test = y_test.reset_index(drop=True)\n",
    "newper_y_pred_ML = pd.Series(y_pred_ML.flatten(), name='Predictions')\n",
    "# Combine them into a DataFrame\n",
    "frac__ML = pd.DataFrame({'Actual': y_test, 'Predicted': newper_y_pred_ML})\n",
    "\n",
    "y_testf = frac__ML['Actual'].values\n",
    "y_predf = frac__ML['Predicted'].values\n",
    "\n",
    "# Calculate the fractions\n",
    "fractionsf = y_predf / y_testf-1\n",
    "\n",
    "# Print statistics for fractions\n",
    "print(\"fractions statistics\")\n",
    "print(\"Min:\", np.min(fractionsf))\n",
    "print(\"Max:\", np.max(fractionsf))\n",
    "print(\"Mean:\", np.mean(fractionsf))\n",
    "print(\"Std:\", np.std(fractionsf))\n",
    "\n",
    "# Plot the fractions\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(X_test[:, 2]*254,fractionsf, 'o', alpha=0.3)\n",
    "plt.xlabel('Days to Maturity')\n",
    "plt.ylabel('Predicted/Actual Price-1 Fraction')\n",
    "plt.title('Fraction of Predicted to Actual')\n",
    "plt.ylim(0, 2)  # Limit the y-axis for better visualization if needed\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "37cb9db8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset\n",
    "r = np.arange(0.0, 0.1, 0.01)  # interest rates\n",
    "Strike = np.arange(50, 155, 5)  # strike price\n",
    "T = np.arange(0.1, 1.1, 0.1)  # time to maturity\n",
    "sigma = np.arange(0.1, 2.1, 0.1)  # volatility\n",
    "stock_price = 100\n",
    "\n",
    "data = []\n",
    "for r_ in r:\n",
    "    for Strike_ in Strike:\n",
    "        for T_ in T:\n",
    "            for sigma_ in sigma:\n",
    "                price = EuropeanOptionBS(100, Strike_, T_, r_, 0, sigma_, \"Call\").price\n",
    "                \n",
    "                \n",
    "                option_stock_ratio = price / stock_price\n",
    "                strike_stock_ratio = Strike_ / stock_price\n",
    "                data.append([r_, strike_stock_ratio, T_, sigma_, option_stock_ratio])\n",
    "\n",
    "\n",
    "data = np.asarray(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "090a5ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 5)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "74dcef7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((33600, 4), (8400, 4), (33600,), (8400,))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test and train datasets\n",
    "X = data[:, :4]  # params r, strike, T, sigma\n",
    "y = log(data[:, 4])  # call price\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "# Display the shapes of the resulting datasets\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10de92c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ML = Sequential()\n",
    "\n",
    "model_ML.add(Dense(30, input_dim=4))\n",
    "model_ML.add(LeakyReLU())\n",
    "model_ML.add(Dense(60))\n",
    "model_ML.add(ELU())\n",
    "model_ML.add(Dense(90))\n",
    "model_ML.add(LeakyReLU())\n",
    "model_ML.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "739de411",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ML.compile(loss='mean_squared_error', optimizer='adam')\n",
    "history_model_ML = model_ML.fit(X_train, y_train, epochs=100, batch_size=15, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c8eac79e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Last log loss: 0.0176888388\n"
     ]
    }
   ],
   "source": [
    "final_loss = history_model_ML.history['loss'][-1]\n",
    "print(f\"   Last log loss: {final_loss:.10f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5b8c354a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m263/263\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 721us/step\n",
      "   MSA: 0.0408183593\n",
      "   MAE: 0.0218298431\n",
      "   MSE: 0.0016661385\n",
      "r-squared = 1.000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "y_pred_ML = model_ML.predict(X_test)\n",
    "rmse_ML = np.sqrt(mean_squared_error(y_test,y_pred_ML))\n",
    "mae_ML = mean_absolute_error(y_test, y_pred_ML)\n",
    "mse_ML = mean_squared_error(y_test,y_pred_ML)\n",
    "print(f\"   MSA: {rmse_ML:.10f}\")\n",
    "print(f\"   MAE: {mae_ML :.10f}\")\n",
    "print(f\"   MSE: {mse_ML :.10f}\") \n",
    "print(\"r-squared = {:.3f}\".format(r2_score(y_test, y_pred_ML)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "900069e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fractions statistics\n",
      "Min: -0.8699502675487625\n",
      "Max: 0.27995522533929207\n",
      "Mean: -0.034287281066399\n",
      "Std: 0.059653366421663315\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_testf = datafrac['y_test'].values\n",
    "y_predf = datafrac['y_pred'].values\n",
    "\n",
    "# Calculate the fractions\n",
    "fractionsf = y_predf / y_testf-1\n",
    "\n",
    "# Print statistics for fractions\n",
    "print(\"fractions statistics\")\n",
    "print(\"Min:\", np.min(fractionsf))\n",
    "print(\"Max:\", np.max(fractionsf))\n",
    "print(\"Mean:\", np.mean(fractionsf))\n",
    "print(\"Std:\", np.std(fractionsf))\n",
    "\n",
    "# Plot the fractions\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(fractionsf, 'o', alpha=0.3)\n",
    "plt.xlabel('Test Sample Index')\n",
    "plt.ylabel('Predicted/Actual Price Fraction')\n",
    "plt.title('Fraction of Predicted to Actual Prices')\n",
    "plt.ylim(0, 2)  # Limit the y-axis for better visualization if needed\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ce4413ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fractions statistics\n",
      "Min: -0.23765303340938737\n",
      "Max: 0.09101469812710627\n",
      "Mean: -0.013065514862112793\n",
      "Std: 0.023301989367105612\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#y_test = y_test.reset_index(drop=True)\n",
    "newper_y_pred_ML = pd.Series(y_pred_ML.flatten(), name='Predictions')\n",
    "# Combine them into a DataFrame\n",
    "frac__ML = pd.DataFrame({'Actual': y_test, 'Predicted': newper_y_pred_ML})\n",
    "\n",
    "y_testf = frac__ML['Actual'].values\n",
    "y_predf = frac__ML['Predicted'].values\n",
    "\n",
    "# Calculate the fractions\n",
    "fractionsf = y_predf / y_testf-1\n",
    "\n",
    "# Print statistics for fractions\n",
    "print(\"fractions statistics\")\n",
    "print(\"Min:\", np.min(fractionsf))\n",
    "print(\"Max:\", np.max(fractionsf))\n",
    "print(\"Mean:\", np.mean(fractionsf))\n",
    "print(\"Std:\", np.std(fractionsf))\n",
    "\n",
    "# Plot the fractions\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(X_test[:, 2]*254,fractionsf, 'o', alpha=0.3)\n",
    "plt.xlabel('Days to Maturity')\n",
    "plt.ylabel('Predicted/Actual Price-1 Fraction')\n",
    "plt.title('Fraction of Predicted to Actual')\n",
    "plt.ylim(0, 2)  # Limit the y-axis for better visualization if needed\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "68e901a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "r = np.arange(0.0, 0.1, 0.01)  # interest rates\n",
    "Strike = np.arange(50, 155, 5)  # strike price\n",
    "T = np.arange(0.1, 0.6, 0.1)  # time to maturity\n",
    "sigma = np.arange(0.1, 2.1, 0.1)  # volatility\n",
    "stock_price = 100\n",
    "\n",
    "data = []\n",
    "for r_ in r:\n",
    "    for Strike_ in Strike:\n",
    "        for T_ in T:\n",
    "            for sigma_ in sigma:\n",
    "                price = EuropeanOptionBS(100, Strike_, T_, r_, 0, sigma_, \"Call\").price\n",
    "                \n",
    "                \n",
    "                option_stock_ratio = price / stock_price\n",
    "                strike_stock_ratio = Strike_ / stock_price\n",
    "                data.append([r_, strike_stock_ratio, T_, sigma_, option_stock_ratio])\n",
    "\n",
    "\n",
    "data = np.asarray(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d441eca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21000, 5)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "28544942",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16800, 4), (4200, 4), (16800,), (4200,))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test and train datasets\n",
    "X = data[:, :4]  # params r, strike, T, sigma\n",
    "y = log(data[:, 4])  # call price\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "# Display the shapes of the resulting datasets\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bbf1b4d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\spin 3\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "model_ML = Sequential()\n",
    "\n",
    "model_ML.add(Dense(30, input_dim=4))\n",
    "model_ML.add(LeakyReLU())\n",
    "model_ML.add(Dense(60))\n",
    "model_ML.add(ELU())\n",
    "model_ML.add(Dense(90))\n",
    "model_ML.add(LeakyReLU())\n",
    "model_ML.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9793562a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ML.compile(loss='mean_squared_error', optimizer='adam')\n",
    "history_model_ML = model_ML.fit(X_train, y_train, epochs=100, batch_size=15, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "02f8e8ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Last log loss: 0.1062733382\n",
      "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 725us/step\n",
      "   MSA: 0.1004519743\n",
      "   MAE: 0.0441224460\n",
      "   MSE: 0.0100905991\n",
      "r-squared = 1.000\n"
     ]
    }
   ],
   "source": [
    "final_loss = history_model_ML.history['loss'][-1]\n",
    "print(f\"   Last log loss: {final_loss:.10f}\")\n",
    "y_pred_ML = model_ML.predict(X_test)\n",
    "rmse_ML = np.sqrt(mean_squared_error(y_test,y_pred_ML))\n",
    "mae_ML = mean_absolute_error(y_test, y_pred_ML)\n",
    "mse_ML = mean_squared_error(y_test,y_pred_ML)\n",
    "print(f\"   MSA: {rmse_ML:.10f}\")\n",
    "print(f\"   MAE: {mae_ML :.10f}\")\n",
    "print(f\"   MSE: {mse_ML :.10f}\") \n",
    "print(\"r-squared = {:.3f}\".format(r2_score(y_test, y_pred_ML)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5e2d025f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.column_stack((y_test, y_pred_ML))\n",
    "np.savetxt('y_test_y_pred.csv', data, delimiter=',', header='y_test,y_pred', comments='')\n",
    "datafrac = pd.read_csv('y_test_y_pred.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b6321d3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fractions statistics\n",
      "Min: -0.6262813891184509\n",
      "Max: 0.12845785740027638\n",
      "Mean: -0.00933624378643713\n",
      "Std: 0.036820585963933984\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_testf = datafrac['y_test'].values\n",
    "y_predf = datafrac['y_pred'].values\n",
    "\n",
    "# Calculate the fractions\n",
    "fractionsf = y_predf / y_testf-1\n",
    "\n",
    "# Print statistics for fractions\n",
    "print(\"fractions statistics\")\n",
    "print(\"Min:\", np.min(fractionsf))\n",
    "print(\"Max:\", np.max(fractionsf))\n",
    "print(\"Mean:\", np.mean(fractionsf))\n",
    "print(\"Std:\", np.std(fractionsf))\n",
    "\n",
    "# Plot the fractions\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(fractionsf, 'o', alpha=0.3)\n",
    "plt.xlabel('Test Sample Index')\n",
    "plt.ylabel('Predicted/Actual Price Fraction')\n",
    "plt.title('Fraction of Predicted to Actual Prices')\n",
    "plt.ylim(0, 2)  # Limit the y-axis for better visualization if needed\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1a407f9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fractions statistics\n",
      "Min: -0.6262813891184508\n",
      "Max: 0.12845785740027615\n",
      "Mean: -0.009336243786437128\n",
      "Std: 0.036820585963933984\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#y_test = y_test.reset_index(drop=True)\n",
    "newper_y_pred_ML = pd.Series(y_pred_ML.flatten(), name='Predictions')\n",
    "# Combine them into a DataFrame\n",
    "frac__ML = pd.DataFrame({'Actual': y_test, 'Predicted': newper_y_pred_ML})\n",
    "\n",
    "y_testf = frac__ML['Actual'].values\n",
    "y_predf = frac__ML['Predicted'].values\n",
    "\n",
    "# Calculate the fractions\n",
    "fractionsf = y_predf / y_testf-1\n",
    "\n",
    "# Print statistics for fractions\n",
    "print(\"fractions statistics\")\n",
    "print(\"Min:\", np.min(fractionsf))\n",
    "print(\"Max:\", np.max(fractionsf))\n",
    "print(\"Mean:\", np.mean(fractionsf))\n",
    "print(\"Std:\", np.std(fractionsf))\n",
    "\n",
    "# Plot the fractions\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(X_test[:, 2]*254,fractionsf, 'o', alpha=0.3)\n",
    "plt.xlabel('Days to Maturity')\n",
    "plt.ylabel('Predicted/Actual Price-1 Fraction')\n",
    "plt.title('Fraction of Predicted to Actual')\n",
    "plt.ylim(0, 2)  # Limit the y-axis for better visualization if needed\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cf3212e",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "eaf712a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zZHCeIyMc8xzo9aIa7DQ1NeHAgQPK16WlpdiyZQsyMjKQmZmJv/zlL7jkkkuQn5+PmpoaLFq0CCdOnMCVV14JABg+fDimT5+Om2++Ga+88grsdjtuv/12zJkzhzuxiCimrT1UC6dLxIYjde2CnWqzFZ8cUmFwpRkj+2ZEaYRE8SOqu7E2bNiAcePGYdy4cQCA+fPnY9y4cXjwwQehVquxZ88eXH755Rg6dChmzZqF2tpa/Pjjjxg5cqRyjffeew/FxcW44IILMHPmTEyaNAmvvvpqtH4kIqKAVJlbAQB1Flu7+77cVo4fK1V4+YfSSA+LKC5FNbNz7rnnQhTFDu//7LPPurxGRkYG3n///VAOi4goLPZUmJCXakBaog7VZmk5vtZPsCMHQIdqLBEdH1G86jF9doiIerJD1U2YvvBHzHt3I5wuETVNUkBT22Rt91hTqwMAcLi2udMPhEQUGAY7REQRsLvcDADYeaIRdRYbnC4piPG3jGVukYKdZptTyQARUfcx2CEiioDyxhYAgMXmxIEqz2HF/oIdU6unURqXsohOHoMdIqIIKGtoVf68+Vi98uc6iw0ul+9SlbyMBQClDHaIThqDHSKiCJAzOwCw5WiD8meXCDS0+La8N3l9fZjBDtFJY7BDRBQBZY2ezM6WYw0+99VZfOtyvDM7sbSMtbvchBe/3w+rwxntoRAFhcEOEVEElDd4MjtVbYqOa5s8dTuiKPrU7MRSZufxxbvxzNJ9WLy9PNpDIQoKgx0iojCzOVyo9rPFXOZdpGx1uGB3emp4jtQ2Kzu3ok2uH9pVFth5RESxgsEOEVGYVZpa4a9djkqQ/l/jFezI9ToCRGjVAmxOF8q8skLR4nC6UO5eittX2dTFo4liC4MdIqIwk4MEQfC9fUBWEgCgzmsZS17CStAA/TMSAcTGjqwKU6uSYdpXaY7yaIiCw2CHiCjM5J1Yw3JTfG4fnpcKwLdAudHdUDBBDQzIjJ1g53i9J7tU3tjqU1dEFOsY7BARhZncY2d4fiqSdGrl9mF5UvBT62cZK0ED9HcHO0frmiM11A55BzsAsJ/ZHepBGOwQEYWZnNnJNxpQkJYAAEjQqtHPvUzlXaCsLGOpRRSmS4+NjWDHdwys26GehMEOEVGYyZmd/LQE9HEHMDmpemQm6wD4bj33zuzIwdDR2lgIdqSATauWCo/2VjCzQz0Hgx0iojCTMzsFRgP6uDM7OSl6ZCS5gx2fzI5Us5OoAQozPJmdaJ9+fsydXSopygQA7K9isEM9B4MdIqIwk3dj5RsT0DddytbkpBqQmaQHANQ3e87HUjI7aqDAmACVALTYnZ326YkEObNzXnEOAGBvBZexqOdgsENEFEY2h0upyck3GvDrU/vgV+P64HeTipCepAUAOF2ersmerecidBoV8o1SdudYm7qdOosNz3y7F0dqw79Ty+F0ocIkBWznDcsGANQ0Wf2e2E4UixjsEBGFkcXqOecqxaBBbqoBf7tqLMb1S4deo0aKXgMAqHHX7Zi8tp4DXnU7bYKdf/9yDC+uOIBXfjgY7h8B5Y1Sjx2dWoUBmUnISzUAaB+AEcUqBjtERGFksUnBi16jgkbd/lduVoq0lFXhXupq9CpQBjzbz4+0KVI+0SB93XZLeEdEUcQ7aw/jsa93KUtmgZK/R5/0BKhUAvKMUrAjZ3uIYh2DHSKiMGq2SSeEJ7kzOG2N6mMEAGw4UgfAt4MyABR2kNmpMll9/t8Zp0vE//vPDjz4xU78c3UpdpUHd7aVvO28r3snWb4c7DQy2KGegcEOEVEYyctYiV7NBL2dXpQBAFh3yB3stHj67AAdbz+XT06vNHcdcPx9+X58sP6o8nVlkBkZuYOzHOzkupexyhnsUA/BYIeIKIyUzI7Of2bnjIFSsLPpaD1sDpey9bztMlbbzE61O9hpaLaj1e7sdAw/Hajx+9xALd9dBQA4tV86AE9mp9LUila7E88u3YutxxqCuiZRJDHYISIKIyWzo/ef2RmUnYzMJB2sDhe2HW9QMjuJbQqUq8xWtLgDJ1EUfQKWroIX+dT0ITnJAKSdVIE6UGXG3koztGoB00bkAYBSs1Pe2IIlOyrwwvcH8Pji3QFfkyjSGOwQEYVRV5kdQRCUpayVe6vhcBcPy5kdY4IWKQbpi8XbywFIRcw2p0u5RmfLUt7bxkf3TQPg2fkViG+2VQAAJg3OgjFR2iov78aqaGzFHncn5UMxcFgpUUcY7BARhZG8G6ujmh0AKHEHO8t2VQIANCoBOvdvZ0EQMPOUfADA/368FS9+v1+p15FVdlKkXGW2wiVK1xyeLx08Wm22ot5iw+x/rMWHXrU8/sgB1sxR+cptcu+fClMrDlQ1Kdf03mZPFEsY7BARhVGztfPdWABwuvsIhr3uk8RTDBoIguf+v/zqFNx8dhEA4Lll+3Co2rd7cWeZHXkJK89oUAqLq5usWLmvCutL6/DmmsMdPtffEhYgnesFAK12FzYfrVdub7s9nihWMNghIgqjQDI7xXkpmDQ4S/k61aD1uV+jVuHPF41AbqoeLhFYc6DW5/7OdmSVuXdMFRgTkJUsBSk1TVYcq2tx399xn57NRxsAAOP7pytLWABg0Kr9nusViW7ORN3BYIeIKIy66rMDACqVgLdvPB0LrxqLMYVpmD2hj9/HDc2VlqFWt9ld1VmvHTmzU5BmQHaKFKBUm63K7i5zqwNNHSw/yae1989IanefXLfj7TAzOxSjGOwQEYVRV312ZGqVgMvG9cEXt52Fee4lq7aGuYMdue9Njrv7cmfLWOXuYCc/LQHZyVKAYm514KDXUlhFB9kduUtzH3d/HW/yjixvR+uY2aHYxGCHiCiMutqNFQw5syOTuy+3LVj2dsKdnSlIS0BqggY695EVO094uijLGZy2yrye25Z3sJOglQK5wzXM7FBsYrBDRBRGcmYnoYvMTiCG5vkGO6e4g51OMzvurE2B0QBBEJCVLC1leW9dL+8ws+M+E8tPsJPvtYw1aYhUb8SaHYpVDHaIiMKoxS7X7Jx8sCM3BZTJmR1zqwPNto7qbuSaHSlgkQ8e9X1M+2DJ5RI7DXZyvTI7U4bnSNdpbO2ymzNRNDDYISIKI0/NzskvYyXpNcr5VABQlJ2k1AL5K1JusTlR3yx1ZFaCneT2wY6/Az1rLTbYHC4Igv/6nHyv204vykSKuwD7WB2Xsij2MNghIuqGKlMr1h2q7fJxoazZATxFyoBUoCz3zvG3lCVvK0/SqZHq7sKc7SfY8bf9XM7q5KYYoNO0f6uQgyedRoXC9AT0z5KOtWCvHYpFDHaIqFd6fvl+PPzlToii2K3n3/LeJlz16s/YcaKx08cpfXZCsIwFeOp2ErRqJOs1nh1ZfoqUy70KjAV3l8Is9/ZzABjsXhbzd3q595Z1fwZmJeG28wbh0UtHQqNWoX+mtD39MOt2KAaF5qMGEVEPUmVuxXPL9gEAfnNGPwzOSeniGb6qzVZsPCJ1Dt5TYVYKhf1ROiiHKLMzNFcKUHJS9RAEQcnsVPnL7HhtO5d5L2OdXpSBA1VNKG9ogSiKSkAEACfq3fU66Yl+xyEIAu66sFj5usgd7PCMLIpFzOwQUa+zxqsp36Hq4N+cVx+oVv7c2U4oILAOysE4Z0g2BmUn4VfjpMaDuakd99qRDwD13jmV7VWgLJ/JZbE5YWr1LXDurDjZnyHuIOxAZVMXjySKPGZ2iKjXWb3fU2tTWmOByyXii60nMHFglt9iXADYU2HCb/65Dr8/ZxB2lXt61HQW7DhdIlrt0hbvzjooByMzWY/l/3uu8rWnZqf9MlZji1ScnOZ11IN3ZmdITgrSErVoaLajvLEFxgTP4zzBjv/5aEteEttXZW6XJSKKNmZ2iKhXEUXRJzNTWmPB51tO4M5/b8UjX+/s8Hlfby1HTZMNT3+7B8t3Vyq3+9vJJPPeDh6qzE5b2Z10UTa3SsFOisETaHkHO4UZCcoJ5m3rdjzLWIFldgZlJ0MlAA3NdtQ02bp+AlEEMbNDRL3KweomnyzIoRqLcsL41mMdFxvvLJPusztF2J2eIMZfYbBM3omlVgnQ+9nRFApKzY6fcZjdS1MpXgeL9s9MRHFeCnJTDUgxaFFgNGB3uUkpZpbJO7T6pPmv2WnLoFWjX0YiDtc2Y3+l2We5jCjaGOwQUa/y436pXiczSYdaiw2lNRalF86JhhY0WR1I9rPktLPM5PN1TooeVWYrKjvJ7HifixWuZR3vredtl488wY7n59GqVfjvn85WHpfvXqby7qJssTrQoPTnCWwZCwAG56RIwU5VE870OsWdKNq4jEVEvcqaA1K9zlWnFQKQdlbtqTAr9++vNLd7TrXZiiqzFYIAXFvSDwBw0yTpsM7qJiucLv/b10PdY8cfeet5s83Z7vRyzzKW1ud274AoN0UKZqq9MkPrD9cp12773M7IO8X2+ZlDomhisENEvcou93LUecU5yjlR3sGKvzdqeQmrKCsJj112Cn68+zzcNKkIKkF6bm2T/6UsJbMToh47/iTpNUr34rZFyv4yO22lJUlzUN/sqbP5amsZAGDGKXlBjUXekbW/ijuyKLYw2CGiXsPcakeZe9lpaE4KirKS2j1mn5+t0/IS1sgCIwRBQGFGIjRqlVKXUtHBjqxIZHYAqecO0L7XjimAYCfdvVNLPlai1e7E0p1SAfasMQVBjWOIu1/R/kpzt5s1EoUDgx0i6jUOuDMOOSl6GBO1PsFOgXvLub/Mzi4l2En1uT3PXS/T0Y6sUPfY6YhSt2P2HYe8jJXayVJUeqKU2WlwZ3ZW7q1Gk9WBAqMBp/ZLD2ocg7KTIQhS4FRr4Y4sih0Mdoio15CXV+TllqIszynivz61LwBgb0XHy1htg50cJcjwv4yldE8OUY+djvjrtWNzuGB1SD1+Ol3GapPZ+WqbtIR18ZgCqFTBFVUn6NQodHdcZt0OxRIGO0TUa8jFx/Jyi3dm59enSh2Jq8xWJcsBSNmRw+7DLUcW+B4LIWd2OtqRFanMTo6fLspyVgeA391lMu/MjiiK+MndXXp6kPU6shH5UkC4+WhDt55PFA4Mdoio12ib2TmlTyq0agFDcpIxMDtZORph4Xf7sWjFAThdopLpyUs1ICNJ53M9udtyVzU7YV/GSpHPx/JkduTi5ESdGhp1x7/q5WDH7hRR32xXMjwD/dQzBWLioEwAwNqDXZ8I35uVN7Zgd7kJrg528lFosc8OEfUa+93Fx0NzpcxO3/REfH7bWcob/tDcZJxoaMFbPx0GIB2BUOeuPZFPG/fm3ePGn2YlsxOpZSzvzE7XxcmAtPSk16hgdbiUpSedWuVzdEQw5GDnl8N1sDqc0GvCG+j1RC02J6Yv/BGNLXZkJulwz4xizJ5QGO1hxTVmdoioV7BYHcp5T0NyPLU6IwuMKHBndKaOkJZu5G7HO080KgGA93NknR3CKX1PuWYn3AXK7nGY2y9jBdInRw725GW+7BR9t5sgDslJRlayDlaHC1u4lOVXlblVObes1mLDP388FOURxT8GO0TUK8g7sbJT9EhL1Pl9zNWnF2Lnggtx74xiAMCucrPyPLlhnreudmNFPrNjVbZ8B7LtXCYXKe91BztZJ3HUgyAIOGOglN35iUtZfrVt/niivoVb9cOMwQ4R9QqdZWhkgiAgSa/BcHeR7Z4Kk/K8wTl+lrHcNTumVgda3PU53ixKn53wZnbkfj82hwumFumNtDuZnX0Vnq35J+PMQdJRET/sq8Z3uypxuMZyUteLN3LGL9/9+rHYnEqmh8KDwQ4R9QoHqt3FyZ0EO7LheVKwc7y+RdnOPcRPZidFr4HOveRVa2m//bxZ6aAc3syOQatWsjPyAZ6B1uwAQHqS9Nx9VZ5lrJMh1+1sOdaA372zAVe9uhYOp+ukrhlP5M7amck6pYv38fqWzp5CJ4nBDhH1CnVNUqGxnI3pjDFRq+zMAqTlKn+N+QRBQIY7K1Jvaf/J3BKhDsqAp+h645F6AJ5gJzWgZSx5+7n0M5xsZmdAZiLG9JW26atVAipNVqw9xCUtmbyMlaTTKK8zuZ6MwoPBDhH1CqYAugl7K/bafeUvqyNLd29Hr2tu3zFYqdkJc4EyAJztPmV8tftU92CWsTLa1DCdbGZHEAR8esuZ2PrQNOXA1cXby0/qmvFEzuwk6zXok+4OdpjZCSsGO0TUK8i1LKkBbqmW63YATxNCfzLcS0D1bY5HEEVReQPLSjq54CEQZw2Rgp2fDtbA6RI9y1gBLKHJS2CynJSus19d0bi3r180Kh8AsGRHBexcygLgldnRM7MTKQx2iKhX8GR2AltS8g52/O3EksnFvXVtgp1qsxX1zXaohM4zQ6Eyuo8RKQYNTK0ObD/RCLNVzuwEULMT4syOt5KiDGQk6VDfbMfPXMoC4N2SwBPsHK9vjuaQ4h6DHSLqFZRgJ+DMTmDLWHJX5fo2y1h73J2XB2QlwaAN/zKWRq3Cme7C4NX7q70KlAPYjZXk+5hQBjsatUo5eoJLWRL5GJFkvRp93GeJMbMTXlENdlatWoVZs2ahoKAAgiDg888/V+6z2+245557MGrUKCQlJaGgoAC//e1vUVZW5nONuro6XHvttUhNTUVaWhpuuukmNDU1RfgnIaJYpyxjBViz0z8zCYUZCchM0mFYXmqHj+sos7OnQjopfXgnzw21SUOyAQA/7q+BqSXwzE7bvkPyDqFQOcu9FX1fJX83Ax0sY7FmJ6yiGuxYLBaMGTMGixYtandfc3MzNm3ahAceeACbNm3CZ599hr179+KSSy7xedy1116LnTt3YtmyZfj666+xatUqzJs3L1I/AhH1AC6XqBTspiYEtoylVgn4z61nYfGfzu70IM0OMzvlUmZnmJ9jJsJFLlLedLQe1e6T2IPpswNI9TuhPuJBzhTVNvk/HT7WOMN8XpW/AuX6ZrtS0E6hF9WzsWbMmIEZM2b4vc9oNGLZsmU+t7344os4/fTTcfToUfTr1w+7d+/GkiVL8Msvv2DChAkAgBdeeAEzZ87EM888g4KCgrD/DEQU+yw2B+T3r0AzOwCQldz1co6yG6tdZkcKdoojGOz0z0xEbqoelSYrytxdnQOr2fHMycluO/cn050pqrW037EWa6rMrZix8EdceEoeHv/VqLB8D4tXZseYoEWKXgOz1YET9S0Ykhu510tv0qMOAm1sbIQgCEhLSwMArF27FmlpaUqgAwBTpkyBSqXCunXr8Ktf/crvdaxWK6xWzycMk0lKN9vtdtjtoetiKV8rlNck/zjXkRHr87zteCNW7K3GLZMHKs3+AKDOLC0R6DQqqOGC3R66XUGpeun71DXZPPPjdGG/u0Hf4OyEoOfrZOZ5Qr90fLOjQvk6QdP1dRLUgEoAXCKQlaQL+d9vqk6aI3OrA00tVuXssWjzN88/7q1CrcWGFXuqgp6HNQdrsbfCjBvO7N/p2WJyljFBI8But6MgzYC9lU04UmPGgIyT3wkXa8L5eyPQa/aYYKe1tRX33HMPrr76aqSmSmvgFRUVyMnJ8XmcRqNBRkYGKioq/F0GAPDEE09gwYIF7W5funQpEhMTQztwoF2GisKHcx0ZsTrPL+5UYb9JBfOJ/Tg1y7MUccICABroBScWL14c0u953H3tinqzcu2KZsDu1ECvErH1p5XY3r0zNbs1zwkWAYBnGWrdjyuxI4BkVoJaDYtDgM1UE/I5comASlDDJQr49KslSAv/TvygeM/z4iMqACrUmluCnoeHN6pRbxPQeHQ3hhk7Xgorq1IDELBr22YIx0RobNL3/Hb1BlgOxO8ZWeH4vdHcHNguth4R7NjtdsyePRuiKOLll18+6evdd999mD9/vvK1yWRCYWEhpk2bpgRSoWC327Fs2TJMnToVWm3gqXMKHuc6MmJ9np/buxpAMxLyB2Pm1CHK7esP1wHbNiDbmISZMyeF9HuWN7bir9tWodmpwowZ0yAIAr7eVg5s3Y4RfdJw8UUlQV/zZOa5qNyMT15aq3x92cXToVV3nUlZuG81SmubMWZYEWZOHxb0mLvyxM4fUGW2YkzJJIwsiFzRdmf8zfNn724CUAObS8AF06YHnIWyWB2oX/s9AMCV5fv6a2vhvtWApRnnnnUGThuQjg2u3di57hjS+w7CzGlDT/rnijXh/L0hr8x0JeaDHTnQOXLkCL7//nufYCQvLw9VVVU+j3c4HKirq0NeXl6H19Tr9dDr23+00Gq1YfkFHq7rUnuc68iI1XmW62YOVFt8xuc+BQGpCbqQjzvHKL0ZOlwiWl0CUg1a7KmSDr4szjee1PfrzjyP7JuOFIMG5lYHErRqJBoCS6OkJ+lQWtuMPGNiWP5uM5P1qDJb0dDqjLnXjvc87/faMWaxi0gOsFXB8SpPhmHd4fpOf0b5GBFjkh5arRYj+6QBOIY1B+vw/2JsbkIpHL83Ar1ebCycdkAOdPbv34/vvvsOmZmZPvdPnDgRDQ0N2Lhxo3Lb999/D5fLhZKS4D9NEVHP1Wp3wuwu/Gy7xVnehh1oj51gGLRqJLpPNa+32FBpasX7644CACb0Tw/59+uKWiUo3zfQnWeA52yt4vzwFMjK29lrm2K3SLmxxa4UdgOes8ICcbjWc7L7tuONyvZyf5q8dmMBwLSRedCoBOwsM+FgNbfnh0NUg52mpiZs2bIFW7ZsAQCUlpZiy5YtOHr0KOx2O6644gps2LAB7733HpxOJyoqKlBRUQGbTfrHMnz4cEyfPh0333wz1q9fjzVr1uD222/HnDlzuBOLqJfx3ulztK7ZZxtvsN2TgyVv3a612PDn/+yAudWBMX2NuHRsdH4PnVaUASCwbeeyhy8ZiW/+OAmT3NvXQy0zSZ6j2N1+vte9g07Wtp1AZw7XeIIdp0vEL6V1fh/ncolotnk6KANS+4JJ7uM+vtxS5vd5dHKiGuxs2LAB48aNw7hx4wAA8+fPx7hx4/Dggw/ixIkT+PLLL3H8+HGMHTsW+fn5yn8//fSTco333nsPxcXFuOCCCzBz5kxMmjQJr776arR+JCKKkrY9XA5UeT4hKyeAhyGzA3h67fxn0wl8t7sSWrWAp68YA00AtTLhMGV4LtQqAacEURtj0KoxssDY6S6ik5HhPh8slref763wrf8IJrNTWiMtY6lV0vx1dMq7xSsI9+7fdMkYKTD+alsZRFEqUv5xfzU2H60PeAzUsajW7Jx77rnKX6o/nd0ny8jIwPvvvx/KYRFRD1TTJtjZW2HG6L5pALyWsYLIdARD7rXzn80nAABzTusX0WaCbQ3NTcEvf54CY5iCu+7I7AHLWHvaZHYagsjslNZIwfX5xTlYtqsSaw92EOy4z8VSqwSf4udpI/Og12zHoWoLdpaZkJGkw9w31sOYoMWmB6aGLQjtLWK6ZoeIKFA1bd5E93tldkxBdk8OVoa7KZ9ci3FecXZYvk8wMpJ0SpYhFnhqdmJ/GSvBfZZZfVA1O1JmZ85phQCAHWWNaLU72z1OOSpCp/YJYJL1Gpw7THrdrNhThXWltXCJ0hha/FyHgsNgh4h6nFa7E3PfWI9Hv96l3CZnDDTuN3jv+otgz8UKlpzZAaRP7KcNyAjL9+nJMmN8GUsUReU1M2GAVODd0BLYWBub7cpOwJKBmUjUqSGKUluCtixtipO9ne0+2+yng7VYX+pZvmrbnZuCx2CHiHqclXur8cO+arz902G43OdAyMtYYwrTAAD7K72CndbAD8Xsjgyvs6VG9zUGVRjcW8T6MtaJhhaYrQ5o1QLG9XMHO5bAMjul7p1YOSl6JOs16Os+7+pYXfuGd0qw4+e1KJ9av/FoPdYcqFFurw9wHNQxBjtE1OMs310JQOptU+Pe3SMvj8hvGGWNrTjqXlrwLGOFP7Mjf3/yJWd2apqsAdVjRpqc1RmUnawcXBrobix5J9aArCQAQN90qRP/cT8nmXufeN5WUVYS8lINsDlcOOoVKNUFUTtE/jHYIaIexeUSsWKvp5lohXupQF4eKcpKUvrM3PzOBphb7WFfxsrwCnbOGhSerds9nZzZsTpcytbrWOJ9cKt8MGpDS4CZHXewU5QpBTuF7szO8Xo/mR1bx8tYgiD4DZaDKZQm/xjsEFGPsuV4g08xslwXUW2WMjuZyXq8cM045KTosbfSjLs/2aZkdoxhKlCW++zoNCqcGoVGgj1Bok4Ng1Z6y4nFpSw52BmWl4q0BOnvM9AgQ84KBZbZcffY0fl/LU70E+ywZufkMdghoh5FXsKStc3sZCXrkG9MwGu/nQAAWLKzAo1h3no+ptCIcf3ScPPZRTBo1V0/oRcSBMGzlBWDjQXlHjvFeSlIkzM7AezGarI6sHKflGmUszJ9O8vsdLKMBfgGO3LrgHoGOyeNwQ4R9SjLd0tvLFnJ0htnhakVLpeofPqVbx9TmIbivBSIIiCXiISrZidRp8F/bj0Ld11YHJbrx4tYPTLC6nDhULW0FDUsL0WpwWpotndZX7R0ZwVa7S4UZSVhdF8jAE9m55ifzI5nN5b/oLhveiIm9E9HqkGDmaPyAbBmJxQY7BBRj2FzuLDPvctq1hjpjaCisRUNLXY43buyvOtnzhnq6XejU6sCPsGawiPTHYjGWq+dQ9UWOFwiUg0a5BsNSHMHxTZn1/VFciPJy8b2UfrmyJmdarO1Xa+dzgqUZe/eVIIf7z4fQ3OTAQTX74f84798Iuoxyhtb4BIBvUaFMe7uyOWNLcqbZ1qiFlqvIxrOGeIJdlITNOxCG2UZSZ4zxGKJHEAX56VCEAQk6tTQuV9HnRUpV5lblS3i3uegpSVqlQLkEw2+2Z2ulrEAIEGnhjFRq8wXl7FOHoMdIuox5ILPvukJyDcaAEiZnWp3sJPpldUBpOZwclFsuOp1KHAFaVLGw/vQzFiwt1Lqti0f8SEIglK301mg8e3OSrhEYFy/NKU4WX6+p26nbbAjZXr87cZqSy58Z4HyyWOwQ0Q9htykrW96IvLkYMfUquzOkpdJZAatGmcMlAo+U2LonKjeakS+dDDp7jYHbkbbvjbBDuAJNBo7yewccGeETi9q3zG7oyJlcwCZHZmS2WHNzkljsENEPYb8KbkwIwG5qVKw02p34VC19GaV3SbYAYDJ7rqdtlkfiryR7lPY91U0we50RXk0HofcmaYhOcnKbUY5s9NJoCEXIBe6C5K9KUXKdf6XsQLK7CjBTteF0tS5qJ56TkQUjGP1nsyOQatGRpIOdRYbth9vBOBpXOdtzmn9UGW24iL3zhaKnr7pCUjRa2C2OnCwugnFeanRHhJEEah092iSl9kAKI0FOysOljONhRn+gh3/mZ2ggh33GGzuRoyBZIPIP2Z2iKjHON7mk7Sc3flhXzUAYGhuSrvnJOjUuGd6MU7pY4zQKKkjgiBguDu7s6ssNpayLA4pmAA8ryfAaxmrg8yOKIper8eEdvfLmZ3SNvVJ5lZ5GavrfkwJWrWyg7Bt3c6WYw1YurOiy2uQhMEOEfUYnpod6c1FLlJ2uEToNCrMGl3Q4XMpNsh1O7ES7DS4Y4isZB10Xq0J5GWsXeUmHKhqave8miYbWuxOCALQx0+wM65fmvJ8ubt3WUMLTjS0QCUA/TOT2j2nLUEQ/Nbt1FlsuOa1nzHv3Y04WN1+bNQegx0i6hFa7U5Uud805GUDuUgZAKaPzFPeoCh2jZAzO+WxEew02qR2BN6vJcBT/7V4ewWmPPcDlu3y7dwtL6nmphig17TP0uSmGjCqjxGiCKx0n+Umd/8e3z/dpx9UZ+QMk/dy2hurS5X+P5uO1Ad0nd6OwQ4R9Qhyv5JEnVqpZcjzWnaYPaEwKuOi4CiZnXJTTBTdypkd79cSAFwypgCXjS1AgTsI+vlQrc/93sXyHTm/OAeAp+v3Mvf/pwzPDXh86Um+W+AbW+x4+6fDyv3bTzQGfK3ejMEOEfUI3vU6cnNAuaC0T1qC39OiKfYMyU2GRiWgodmuHOIaTY1W/5mdnFQDFs4Zhz9cMAQAsL/NUpZSnOxnJ5ZMDmp+3F+NOosNaw9KDQinjAgi2GnTa+eN1aUwWx3QqKRxbzvOYCcQDHaIqEfw7HzxfJKeOSoPc04rxF+vGA2Vit2RewK9Ro3B7i3e8mnhkeJwunDfZ9vw8YZjym0dZXZk8nZ0uaeOTN5l1dfPTizZyIJU5KToYbE5seCrnbA7RQzMSsKg7OQOn9NWRpLnBPadZY14eeVBAMCdU4cCkDJksbSNP1Yx2CGiHsHTPdnz5pKo0+DJy0fjzMFZ0RoWdUN2ilQPE+lmeRuO1OOD9cfw4Bc70eKueWmUgx2j/+WoITnSDr+yxlaYWz11M3L/HH87sWQqlaAsZX2xpQxAcFkdwJPZKWtsxR0fboHN6cKU4bm4ZfIgpBg0sDlcEQ8aeyIGO0TUI3h67HT85kI9Q4pB6hcjH4oZKXKBe4vdiRXuouEGuUC5g8yOMVGLHHdwdrDas41cfj3667Hj7XdnF6GkKAODc5IxsiAV15b0C2rMcmbnk43Hsb+qCdkpejx1+SioVIJyyjrrdrrGDkVE1CMcdNdMBLJll2Kb3FBP7jkTKTVmz2nr32wrx9TiLK/Mjv9gB5DqjKrMVuyrNGNvhQktNifKGuQC5c6DncE5Kfj37yd2e8xpXjsM0xO1eOHqccqxKKP6pGHNgVpsO96Iq0/v9rfoFRjsEFHMszqcSq8Teesy9VzJeukNPNKZnZomT7CzfE8lapqGosXpv0DZ25CcFKw5UIsvt5RhtfuUcwDQqoUOM0KhMnFgJkYWpGJUHyPunl7ss2Xdk9lpCOsY4gGDHSKKefsqmuBwiUhL1CpbgannUpaxIp3Z8Qp2Wu0u/HvDCQBSpqmz4xvkgmrvQAeQdgGqw1wYn5NqwDd/PNvvfXLjwp1lJuwuN2F4Pj8IdIQ1O0QU83aWSTUJIwtSlW3n1HNFq2antklas5ILpN9eewQAkJva/gBZb94HhALAc7PH4MrxfXH39OIwjDJw+cYEXDQqH6IIPPPt3qiOJdYx2CGimLfTfbTAKQU83yoeRK1mx53ZuWXyIGjVgtKVuKulqCFeZ64NzU3Gr8b1wV+vHIOZMXC47PxpQ6FWCVi+pwobDtdFezgxi8EOEcU8ObPDep34kGyQg52OTxQPhxp3ZmdcvzRcfbpnV1RXmZ2MJB0y3bUy15b0j6ns4qDsZMye0BcA8OqqQ1EeTexisENEMcnmcGHhd/uw8UgddpdLfURGMrMTF+TMTiSXsURRRLU7s5OVrMft5w9GglZ6CwykyPieGcW4/NS+MXksyYxTpAzTUXfjTWqPwQ4RxaTvdldi4Xf7MefVn9FidyJBq0ZRFredx4No1OyYrQ7YHFKn4ewUPXJSDLhzyhBoVSLOHtL1USOzJxTi2dljkKBrf+hntGW5t6LLmStqj7uxiCgmldZIDdzsTumwyOH5KWHf+UKRoWw9j2DNjtxjJ1mvgUErBSw3nNkf2fU7MaF/esTGEQ5ZyfL5WVa4XCKPTvGDmR0iikny2UOy0X3TojMQCjmlZieCmR056yEHBrJ4iAvS3fVELjHyR3D0FMzsEFFMks8e+t+pQ2F3unBNSf8oj4hCRa7ZsTlcsDqc0GvCvzRU41WvE2+0ahXSErVoaLaj1mJTOiyTB4MdIopJcmbn9KIMlAzsuqaCeg7vBn4WK4OdUMhM0qGh2Y6aJiuGem2VJwmXsYgo5jhdIk64zx7q28XZQ9TzqFUCEt2FvpGq25FrdrJSdF08smeSszm1LFL2i8EOEcWcSlMr7E4RGlX4zx6i6FAaC1oj02unWqnZic/MjlyLVOt1JAZ5MNghophzvF7K6hRE4Owhio5In48V/8tY7syOhZkdfxjsEFHMOeZujlaYkRDlkVC4JBuk7eeROjIi7oMdd2aHvXb8Y7BDRDFHzuz0TWO9TrxKiXAXZTnYyY77mh0uY/nDYIeIYs6xemZ24p2nZidSBcrxXbOTLdfsBLiM9c22cvzu7V/Q2BzZ88mihcEOEcUcedt533RmduJVcgRrdlpsTrTYnQCkQz3jUaZyZERgmZ3XfjyE73ZXYdX+6nAOK2Yw2CGimCM3FGRmJ355DgMNf2ZB3vElCL49fuKJfCp7oFvPay1SUGSK8Mnz0cJgh4hiisPpQoWpFQAzO/FM3o1VZbJi1gurcf/n28P2veQi6GS9BoIQn7v75MxOk9WBVncWqzNyUGRqidyRHdHEYIeIYkpZQyucLhE6jQrZcVpfQZ4My4q91dh+ohH/2XQibN9LDnZS3TvA4lGqQQOtWgrkuqrbabU70WyTAiIzMztERJF3qKYJADAgM5GnN8cxuWZHrjGx2JwBZSS6Q64LkrNJ8UgQBE+vnS7qdryDod6yjBXw3/yNN94Y0OPeeOONbg+GiOhQtQUAMDArOcojoXDyVzvT0GxHnjH052TJ2Yt4rdeRZSbrUGFq7bJup87r/t6yjBXw3/xbb72F/v37Y9y4cRBFMZxjIqJeTM7sDMxOivJIKJz8ZVnqLDbkGUN/PIi5F2R2gMB3ZMnFyUDvWcYK+G/+lltuwQcffIDS0lLccMMN+M1vfoOMjIxwjo2IeiEls5PNzE48S/FTP1PfHJ7uv/JSjb/vGU+ykgLrtVPns4zVOzI7AdfsLFq0COXl5bj77rvx1VdfobCwELNnz8a3337LTA8RhYwn2GFmJ575W1KqC9O5TnKX5uQ4z+xkp0iZHblPVUe857m3ZHaCKlDW6/W4+uqrsWzZMuzatQsjR47ErbfeigEDBqCpqSlcYySiXsJidSjbzgexZieu+Qt2wpXZ6S3LWGML0wAA60vrOn2cT4Eya3Y6p1KpIAgCRFGE0xmeCnoi6h0aW+w4WN0EnVr6/JWZpIMxMb6XHHq7jmp2wkHOXsTz1nMAOGNgJgBgX2UTqs1WJdPTlneBMjM7flitVnzwwQeYOnUqhg4diu3bt+PFF1/E0aNHkZzMT2FE1D2Pfr0Lv37pJzyzdC8ALmH1BklemZ3ivBQAQH24l7HifDdWepIOI/JTAQBrD9V2+DjvzI7F5oTD6Qr72KIt4GDn1ltvRX5+Pp588klcfPHFOHbsGD7++GPMnDkTKhXb9RBR9x2okpbBV+6VzunhtvP4p1WrkJdqgE6twrnDcgAAdWE6lLK3LGMBwJmDpOzO2oM1HT6mzuK7W8v75PmyhhZYHfG3WhPw3/wrr7yCfv36YeDAgfjhhx/www8/+H3cZ599FrLBUWx7c00pRBG4cVJRtIdCPVzbxmZFzOz0Cu/dXAKL1aEEu+HK7JiUYCe+l7EA4MzBmfjn6lL8dDCwzA4g1e2kJerwy+E6XPnKWsyd2B8LLj0l3EONqICDnd/+9rdxe6YIBe9wjQULvtoFALhkbAGy2NafToKpxTfYGZjFYKc3GORuLyDX6oRtN1YvaSoIAKcNyIBaJeBIbTOO1zf7PV+urk3TQfnDxg/uzOp3u6uw4NLwjzWSgmoqSCRbsrNC+XNpjYXBDnWbKIpodAc7eo0KDpeIEQWpUR4VRVKGuz8Md2OdvBSDFqP7GrH5aAN+PlSHK8b7BjtWhxNm97JVVrIeNU1WJdjZXW4CAJxoaEGdxab8vcQDFttQtyzZ4Ql2DtdYojgS6ula7E7YnVKvro//ZyLe/10JTzvvZdITpTfVOostLH3besNBoN5OL5Ia/v5SWocmqwPTF67CHz/YDACot0iBjVoloG96AgDP/OypMCvX2H6iMZJDDruggp0VK1bg2WefxZo1awAA//jHP9CvXz9kZ2fj5ptvRktLS1gGSbGlvLEFW441KF8frmWwQ90nZ3U0KgGj+hhR4t4+S72HnEGwOlxoCfFhoA6n55rx3lRQdlp/d7BzpA4r91ZhT4UZ32wvh8PpUo6KSE/UwpggBX+mFjsam+040eB5D9/RW4Od1157DVOnTsUrr7yCCy64AE888QT+93//FxdddBFmz56Njz76CAsWLAjqm69atQqzZs1CQUEBBEHA559/7nP/Z599hmnTpiEzMxOCIGDLli3trtHa2orbbrsNmZmZSE5OxuWXX47KysqgxkHB+dYrqwMAh2s679ZJ1Bk52ElN0LIusJdK1Kmh00hvR6Gu2/HeadQblrEAYMKAdABSN/KPNxwHADhdImqabMr8ZibpkeoOdsytDuypMPlcY/vxXhrs/P3vf8ff/vY37N+/H59//jkefPBBLFq0CC+//DIWLVqEf/7zn/jkk0+C+uYWiwVjxozBokWLOrx/0qRJeOqppzq8xp133omvvvoKH3/8MX744QeUlZXh17/+dVDjoMA5nC58skn6x3PGQOnTQymXsegkyB1c5U+Z1PsIgoAM91KWvMwSKvISjUGrglbdOyo30hJ1GJorFX//sK9aub2ssUUJdjKSdErwZ2q1K/U66e5mnvG2jBVwmHvo0CFccsklAIDp06dDEAScfvrpyv0lJSU4duxYUN98xowZmDFjRof3X3fddQCAw4cP+72/sbERr7/+Ot5//32cf/75AIA333wTw4cPx88//4wzzjgjqPFQ155dtg87TpiQrNfgj+cPwc+H1uFIrQWiKPJTOXWLd2aHeq/0JB0qTK2oC3GRshzsJOt71+vrtAEZ2Ffpe4xTRWMrat07sTKSdUoNk7nVgYpG6ZiWS8f2wVs/HY67IuWAg53W1lYkJCQoX+v1euj1ep+vHY7InrGxceNG2O12TJkyRbmtuLgY/fr1w9q1azsMdqxWK6xWT1Mlk0mKaO12O+z20H2qkK8VymtG05qDtXh55UEAwOOXjcDoPilQCVIHzvJ6S4etyZ0uEX9dug+nDUjHBcU5YRlbvM11rArHPNc1SXUCqXo1//7ceuPrOT1BejuqNrWE9Oeut0ivrxQ/r694nudxhUa8t873tuN1FmXHW3qCBkla6QNqQ7MV+92B0amFqViZmYjDtc3YcqQWZw/JOumxhHOeA71mwMGOIAgwm80wGAzKp/impiYlUJD/H0kVFRXQ6XRIS0vzuT03NxcVFRX+nwTgiSee8FtftHTpUiQmhn4XyLJly0J+zWh4d78KgApn5LggHt2E5UeBNJ0adVYBH36zHIM62C28t1HA67vU+ODnw3hsghPhzCTHy1zHulDO88/lAgA1LPXVWLx4cciuGw960+u5pVH6/bJmwxZoT2wO2XV31EmvL2erpcPXVzzOs9kKyG/xqVoRJruAn7bshtkOACo0lB+GtQoA1NhXehx7GgUAAir3bkIGVDgMFT7+/heY94dud1w45rm5ObCa0YCDHVEUMXToUJ+vx40b5/N1T1nGuO+++zB//nzla5PJhMLCQkybNg2pqaHr72G327Fs2TJMnToVWm3PT6H++60NQE0dLj97NGaOLQAAfFS1EWsO1iJvyBjMHN/H7/OaN50Adu1Es1OAcdjpOCcEnxTaire5jlXhmOf9yw8Ahw9h2MB+mDlzREiu2dP1xtfzL67d2Fx7DPn9h2DmlMEhu659Sxmwdwf65mZi5swJvvfF+Tx/VbMex+qaceX4vnjph0NIzMxHVW0zADMumjQezTYnPi7djkpHAuwuKwxaFa771QwkbjqBTV/swlFXGmbOnHjS4wjnPAeaaAk42FmxYkW3BxMueXl5sNlsaGho8MnuVFZWIi8vr8PntV2Ck2m12rC84MN13UircxcO5hgTlZ9nYHYy1hysxfGG1g5/xpomT5rxvzurcMGI/LCNMV7mOtaFcp6bbNIhhOlJev7dtdGbXs+ZyQYAQGOrI6Q/c4tDykykJug6vG68zvOH8ybC6RKxYm8VAKDcZEWpe/fssHwjjtRKf66S0kA4bUAGDHodZozugwe/2o2dZWaUm+zolxmaFY9wzHOg1ws42Jk8eXK3BxMu48ePh1arxfLly3H55ZcDAPbu3YujR49i4sSTj0bJV62yZdFTsNbf/Y+gs147FaZW5c/f7qzA478apWwzJZKPiuBurN5NLoQN9dZzUy/qntyW/Hs2zygFkjvLTLA5XNCqBfTLSGw31+e5D2TNSNLhjIEZWHOgFv/dUY7fTx4U2YGHQbf/9nfu3Amn09P8Sa1WY+TIkUFdo6mpCQcOHFC+Li0txZYtW5CRkYF+/fqhrq4OR48eRVlZGQApkAGkjE5eXh6MRiNuuukmzJ8/HxkZGUhNTcUf/vAHTJw4kTuxQszlEpV/GN5HQxS5zzAq7aTXTqVXsGNudWD1gWqcX5wbppFST3CioQVXvvwT5pzeT9mNxWCnd5M3OHj/vggFZTdWLwx2ZAVGaXORzSFlUQdkJkGjVrXbAXm+1waSGafkY82BWizeUREXwU7AH69//PFHnHbaacrXZ5xxBsaNG4exY8di7NixGD16NL777rugvvmGDRswbtw4pfZn/vz5GDduHB588EEAwJdffolx48bhoosuAgDMmTMH48aNwyuvvKJc429/+xsuvvhiXH755TjnnHOQl5fHk9fDwNRqh9MlpYO9tyLmpkqfGGqbrH6fB3gyO3IW6NNNJ8I1TOohVu+vRlljKz7ffILBDgEACtKkN+TyRv/BTmOLHXanK+jrNlml11dvOPG8I9kpeqhVnprawTlSDx7vbFdRVhIGeB3Ae+HIPAgCsPVYg3IqfU8WcLDz0ksvKX1vZCtWrEBpaSkOHTqEP/3pT3j55ZeD+ubnnnsuRFFs95986Oj111/v9/6HH35YuYbBYMCiRYtQV1cHi8WCzz77rNN6HeqeGndvhlSDxmcJSmk33trx9r+KRikQuu08qehwyY4KlDfyaJHe7LC7VuBoXbOyPMpgp3crSJM+OFWaWtsFNbVNVkx8Yjmuf3N90Nf1nIvVezM7apWAHK/WIHKw431W2LnDsn2ek52ix6TB0maS615fh0PVnQc8rXYnLNbItp8JRsDBzoYNG5TGfbK+ffuif//+GDBgAK677jqsXbs25AOk2CBnbtqebi6nQVvtLlgd7c+0sXudxXJ+cQ5KijLgdIl4Z+2RMI+YYtlRd7DjcIlKvRebCvZuWUl66NQquMT2S1n7KpvQbHNiw+H6oA8K9TQV7L3BDuCp2wE8wU6iTq1kfOR6HW9/vWIMBmUnobyxFXNe/RnmDj7UiqKIi19YjXOfWYnWEJ9tFioBBzvHjx+H0WhUvn777bd9MigZGRmora0N7egoZijFycm+3TRT9BrIHQfk5Qhv1WYrRBHQqqV28DdOKgIAfLD+KFpssfmPgsLPu6Bdfu9iZqd3U6kE5LuzO2UNvsGOXC9odbiUguNANSkFyr379SXX7QDAoGwp2BEEATeeNQDTRuTiDD8H8OYZDfj37yeiX0YiqsxWfLLxuN9r1zfbcaCqCdVmT3PCWBNwsJOSkoKDBw8qX//617/2acBXWloa0h41FFvkzE5mkm9mR6USkOL+xGTyE+zI9To5KQaoVAKmDM9FYUYCGprtWLKzPMyjplgkiqKy5dUbMzskvyGXNfguc9dZPDWBVUEWMDe0uJfgE5jZAQBB8AQ7APDni0bg1d9O6HCHbFayHvPOGQgAeHPNYaV205v339eBanMohx0yAQc7JSUleOeddzq8/6233kJJSUlIBkWxp8brPJW2jO6D4xpb2n/iqnQXG8r/0NQqQUmXxuonAAqvWovN5yRqQPoFnNLLlxnIU6R8ok2wI//+ATw9YQLhcnkC68L00HfH70ny3b+D+6QlIEGnDuq5l5/aF8YELY7WNWP57sp29/sEOzFazBxwsDN//ny8/fbbuOuuu1BVVaXcXlVVhf/93//Fv/71L5+uxBRf5LqbLD+HwilFyp1kdvJSPevFfdOlX2jH61mk3Bv5zeoYtFCpekYHdgqfPsoyVtvMjifYCWZr+omGFlgdLujUKuX3Tm81Il9aeTm1X3rQz03QqXFNST8AwBtrStvd772DLlaDnYA/Sp133nl44YUXcOedd+K5555DamoqBEFAY2MjNBoNFi5c2K6AmeKHfFJuZnL7ztNyRb+/mh1lGSvV87y+7k9YbT+9Ue9wRC5INmiU+ovevsRAEjmz03mwE3hm51CN9Frrn5kITTgP5esBJg7KxJe3n4WBXktYwfjtxP54ddUh/HyoDjvLGjGywFPD2xMyO0H9hrn11lsxa9YsfPLJJ9i/fz8AYMiQIbjiiitQWFgYlgFSbPAEOx1ndvwFO8oylt/MTmAHuFF8kbednz00G99sk+q2WJxMgHew45u9qfWu2TEHntk56H7jHdTNN/h4IggCRvdN6/bz840JmDkqH19tLcObaw7jmSvHKPeVeWV2jtQ2w+50QRtjwWXQH6cKCwtx5513hmMsFMNqLP4LlIHOgx1lGctr22Mf9y+0SpMVVocTek1w68fUsx11Z3ZOKTBi3aE61DRZGewQgMAyO1VBZXakYGdgdlIXj6RA3HjWAHy1tQxfbinDPdOLla7X3n9fDpeII7UWDM5JidYw/Yqt0ItCrs5i81s9Hyw5s5PVSWbHX82OnHLO9crsZCTpkKCVApy2n+Ao/smZnQGZicqbEIMdAjyNBc1Wh0+jUp9gJ6jMjhRYd3fphnyN65eOcf3SYHO68P66o8rt5e5gR97RFYtLWQx24tiP+6sx4bFleHrJnpO6js3hUrI2fmt2OsjsiKKoFBN6L2MJgsClrF5Mrtnpn5mEgVkMdsgjUadBunt3p5wt8D6XDwi2ZkdexmJmJ1SuHC+VrGw4UgcAcDhdSgb/9AEZABjsUAQcrW3G3gqpz8Grqw7BJQKr9tec1DXrm6VfNCoBSPPzptRRsGO2OtDsbhzovYwFeOp2TnBHVq/S2GJHfbP0OumfmYhpI3ORotdg0uDsLp5JvYW8lPXJhuN4d+1h1Dfb4J2crjS1BtRFucnqUAIjZnZCR278KAegVWYrXO7GsWcMjN1gh1sg4ogoirjq1bWoMlvxt6vG4kd3kFNa0wSXS+z21t4ad0PBjCS932t0VLMjFycbE7QwaH3rcvpw+3mvJL+WUgwaJOk1OL84F1sfmsZt56QoSEvAzjIT/rla2uIsf2BK0KrRYncqXZS7ygbKZzllJeuYOQyhjESplKHeHezIGbjcVAOG5Ep1Oge6OEcrGpjZiSNNVgfKG1vhdIn404ebldtb7S6UBXDwZrPNfxt2OYL3V68DeB8G6vt8ObWZm9p+6Uvefs5lrN6lwZ3VSUv0vPkw0CFvQ3J8szAr91YDkJriyb9rKk2taGy2d5rhOVTNep1wyHD3WqtzZ/zlnVgFaQkYkCktF8bih9iAMjvp6ekQhMB+IdXV1Z3UgKj7ar26jMq/A1QC4BKlf/h9O+kg+u7aw3joy5149boJmDIi1+91/W07BzouUK5olIMdQ7vnsLFg79Tobt2fluD/tUR023mDcUofI/aUm/D89wew8Wg9AOlNVq0S0Nhix2Pf7MaqfdVIS9TirMFZ+OsVo5Go8307O1jNep1wSHcHO612F1psTiWzU2A0KB+IG5rtMbf9PKBgZ+HChcqfa2tr8dhjj+HCCy/ExIkTAQBr167Ft99+iwceeCAsg6TAyL0odGoVbE4XclP1GFlgxPd7qnCougnnDO24LmLDkXq4ROCXw3Xtgh3vZSx/Ug3Sy6jdMpaf4mSZJ7PDYKc38ZfZIfKWpNdg5qh86UPU9wdgc7gASMGOQavG/qomrNonZXsamu34Zls5Lh1TgGkj83yuIwc7A7OY2QmlJJ1aeY+pa7YpO7EK0hKQnigFpE6XiNomW7tazWgKKNiZO3eu8ufLL78cjzzyCG6//Xbltj/+8Y948cUX8d1337EHTxRVm6VPzSP7pOLe6cXISTXgow3H8P2eKhystnT6XPlk4Oqm9jsdlBPP/RwVAXgyO01WBxxOl9Kp1F+PHZnSa8fcCpvD1eEhdBRf5GCHNRTUleI83z4tmck6JLsDHwAYmJWEwoxE/LCvul1fHgDY5z57b0gug51QEgQB6UlaVJqsqLfYcMLdPiQ/LQEqlYCMJB2qzVbUNFljKtgJ+h3m22+/xfTp09vdPn36dHz33XchGRR1T61X47+SgZkoyvJs7ZW3YHbE7A52vA/cU67rDoA6qtnxPq3au27HX48dWVayDnqNCqIIlAdQT0TxoaGFmR0KTGayXmlaB0i/17x/l8w7Z6DSp8n7bCZAapdx2H1UxNDc2GpuFw/S3UXKdRabEmjK55pluduT+PvgHE1BBzuZmZn44osv2t3+xRdfIDMzMySDou7x1/hvkLvYT26u1RG5gVeNnxOFOzsXCwC0ahWS3KfoetftdLaMJQiCkt3hGVm9R2Mza3YocN7ZnYwkHXLdwU9Oih6/OrUPCozujsttgp3DtRY4XCKS9RrltG8KHbl+s77ZpnxYzXf/XcjvP/7eS6Ip6K3nCxYswO9+9zusXLkSJSUlAIB169ZhyZIleO2110I+QAqcnIHxLiQe5F6vrjC1osnqQLLe/1+5uZNlrJoulrEAKbtjsTl96nbkAuWOUpnZKXocqrH4zSZRfJJfH1zGokAU56UoLTQyk3U4a3AWVu2vwXVn9Ideo1Z6vpS3+cC0r1LqNTY4JzngzTUUODmzc6KhRembJfdHynZ/KI613+tBBzvXX389hg8fjueffx6fffYZAGD48OFYvXq1EvxQdNQoW8Q9GRhjohZZyTrUNNlQWm3BqL5Gv881uzM7dRZbu548niDKf2YHkN68yhtblTczh9OlFDbn+Nl6DkBJUVfH2CcACh95GcvIZSwKQHFeqvLnjCQdspL1eOP605Tb5GxC22Ws/e56naGs1wkLefv5zjITAKloWd6okpUiBzux9Xu9W00FS0pK8N5774V6LHSS5LRh26BkYFYyaprqcKimyW+wI4oimqxSZsfpElHfbPO5RmfnYsnadlGubpK6ampUArI62MXFYKf3UXZjMbNDARjWZhmrLfksrQqT1F9M7f6Qtr9KyuwMibHDKOOFnNnZeaIRgJTVkTNoyjJWjAU73doCc/DgQdx///245pprUFVVBQD473//i507d4Z0cBQceddUVptfCkXuIuUjtf4b+FlsTp927N7px2abAy12qYNpV5kdwFP7Iy9h5aT477oMMNjpjRqVAmXW7FDXBuckKzs1/W10yEkxKFudq81WmFql/i7ciRVecuApH+qb717CAjwrCz0+2Pnhhx8watQorFu3Dp9++imamqQX1datW/HQQw+FfIAUuI6Wm+SdL/5OJQc8285l3sGHnNXRazxFyP60PTJCLk7O7aQ4MDtGq/YpfBrkAmUuY1EADFo1Fl1zKp65cozP8rxMrRKUouXNR+txxuPLcdmiNdyJFWbpbT5QyzuxAK9gxxxbNTtBBzv33nsvHnvsMSxbtgw6necHPv/88/Hzzz+HdHAUOIfTpRSKtV1ukouSLR0cByHX68i8I/IaZdu5vtNCv1SDb7CjFCf7+TQmY2and3G5RE9mh8tYFKCpI3Jxxfi+Hd4vZxXeX38UzTYndpaZuBMrzDLaZGbl2inAN7PjconKB99oCzrY2b59O371q1+1uz0nJwc1NSd3ujZ1n3x+lUpov0SQ5A52zK3+g522Z1p5BztdHRUha3tkRKW54x47MgY7vYvZ6lCWS1MZ7FCIyAHNmgO+7z/ciRU+6Um+/34LvJexUjxnZ738w0GUPL4c/91REdHx+RN0sJOWloby8vJ2t2/evBl9+vQJyaAoeHKdjXx+jLdkd5W8xRpYZsd7WcnTqLDzYEf+hbP5aANEUVROPO+sg6Yc7NRZrHC6Oj7Qj+KDHAgbtCoYtB0viRIFQ36jlX+FXH16IXQaFaa2OfaGQqdtsXiB1+/5jEQdBEE6n/HdtUcAAEt2VkZ0fP4EHezMmTMH99xzDyoqKiAIAlwuF9asWYP/+7//w29/+9twjJEC4N09uS15Gaupw2Cn45odTxDVcXEyAEwbmQudRoU9FWZsO97Y6YnnsswkvXJQqTx+il+enVgsTqbQ8V6q0qoFPHjxSGx/eBpuO29wFEcV39LbrB54Z3Y0apWyzCW/D2w80oBODqiPiKCDnccffxzFxcUoLCxEU1MTRowYgXPOOQdnnnkm7r///nCMkQKgbA9Paf9G4gl2nH6f2zbY8d6NVWfpets5IC2dzTxFOojvb9/tw7bj0pbEPmkdn7SuVglKEMWlrPjX0MLiZAo973qR0X3TkKBTQ69h5jCcDFo1Er02rLTN4LctJq80W1EX5V/xQQc7Op0Or732Gg4dOoSvv/4a//rXv7Bnzx68++67UKv5AosWuc7GX2YnSQl2/O/Gkpex5NRkjc9urPZdmTty1Wn9AAAr91ajyerAqf3ScGq/tE6fw7qd3oOHgFI4FHjtBCopyojiSHoXObuTlaxrtyzt70P3IXN066eCDnYeeeQRNDc3o7CwEDNnzsTs2bMxZMgQtLS04JFHHgnHGCkANZ0UEqcoNTv+Mzvy8pbcj8enQFk5KqLzZSwAOGNgBgZkSpmcrGQ9Xv7NeOUE9I5kK902Y2ubIoUeDwGlcPDO7JQM5PmMkSJ/OPaef5l3Zkc+A/GQqYcFOwsWLFB663hrbm7GggULQjIoCl6t1xbxtpTMTge7seRlLDnYqXUfGQF0HkS1JQgC7plejFP6pOIf143vdCeWTOm1w8xO3OMhoBQOmUk6DM5JRr7RgAn906M9nF5DDna8M2uybK/3of+ZPBBA9DM7QR8XIYqi3+18W7duRUYGU4jRUttJbU2yTvprtjldsDqc7daz5a7HclbG6RJRbmpFeqK20yDKnxmj8jFjVH7A4/a3jFXW0IK8VEOHnZepZ1IKlJnZoRBSqQR8eftZcLhE5YMdhV+nmR337/UBmYmYOSofD3yxExUtAuqbbcgxRufff8CZnfT0dGRkZEAQBAwdOhQZGRnKf0ajEVOnTsXs2bPDOVbqgMPpwg73GSX+XnhJek9w428pS87spCfpkO5+Izr3rysw+a8rPctYAWR2ukMJdtxB1TfbynHmk9/jxRUHwvL9KHrkZSz22KFQS9RplMamFBlnDMyARiXgrMFZ7e4b3z8dGpWAS8b2QWayHgPdqwabjjZEeJQeAYfBCxcuhCiKuPHGG7FgwQIYjZ4DJXU6HQYMGICJEyeGZZDUuR/2VaPKbEVGkg5n+Fmz1qhVMGhVaLW7YLE62vVIkAuUUwxa5KYaUN9sh90p+mRb/B3CFwqezI60RXFnmRS0fb2tDH+8YEhYvidFRyNrdojixlWn9cNl4/r43fl22oAMbHt4GhLdqwpXTeiDjdv3YEBmUqSHqQg42Jk7dy4AoKioCGeeeSa0Wv7CihUfbTgGAPjVuD7KoXltJeu1aLVblSxOi82J615fh9OKMpTbUgwa3Dl1KJbtqsTkodn483+2w9TqQIpBE7atnG1rduQ3xH2VTagytSIngLof6hka2WeHKK509r4gBzoAcONZA5DXuAuDsntAsCObPHmy8ufW1lbYbL67aFJTU09+VBSwarMVy3dLJ89fdVphh49L1qtR0+Q5H2vDkTpsOFKPHWWNSj1Oil6DCcMycOFIqV+OQavGvHc3oDgvfIfpta3ZafA6rHT1gRr8+tSOz8ShnoV9dogoWoLejdXc3Izbb78dOTk5SEpKQnp6us9/FFlfbDkBh0vE2MK0Tk/4lY+MkLeZH6qWTgVutbtwvL4FgLSM5W3qiFx8N38yXvvthHAMHYAns2NqdaDV7lQ+/QNSsEPxo559dogoSoIOdu666y58//33ePnll6HX6/HPf/4TCxYsQEFBAd55551wjJE68e1O6YC1X5/a+blkSTrf7eeHqtu3D5D78XgblJ3c7mDRUEpN0ChneTU025VlLABYvb8GYrR7jFNIuFyi0o07XMXuREQdCTrY+eqrr/DSSy/h8ssvh0ajwdlnn437778fjz/+ON57771wjJE60NBsw8Yj9QCAC4Z3fuhdSpvDQA+6Mzv+HhNJgiAonThrLVZlqQMAqsxWHKhqH5RRz9PQYlcOew2kQSURUSgFHezU1dVh4ECpSVBqairq6uoAAJMmTcKqVatCOzrq1A/7quESgeK8FKVLZUeS9G2XsXyDCEHwZH8iTT5Rvd5iV3qxyD/Pyr3VURkThZZck5WWqO2wiJ6IKFyC/q0zcOBAlJaWAgCKi4vx0UcfAZAyPmlpaSEdHHXu+z1SYfJ5xTldPtb75PNmmwNlja3t7o9WE7/0JKmGo6bJs1vscvey3NJdFVEZE4VWTZDNKYmIQinoYOeGG27A1q1bAQD33nsvFi1aBIPBgDvvvBN33XVXyAdI/jmcLiXrcUEwwU6rA6U1Fp/bAES1IZe8rCGPCwCuGC/tLNtwpJ5HScQBT7DDeh0iiryg1y3uvPNO5c9TpkzBnj17sHHjRgwePBijR48O6eCoY9/vqUJjix1piVqM69f1Ljg5sLHYHEq9TnFeCqrMVhyta/YJfCJNzux4B2H9MhMxuq8R24434rvdlbj69H5RGx+dPDlgzU5h3yQiiryTfofr378/+vfvH4qxUIDWHKjBHz/cDAC4eHS+spupM3LNjrnVodTrDMxOQmayDkfrmqNSnCzLaJPZkbcmXzgyD9uON2LpzgoGOz1cNTM7RBRFAb3DPf/88wFf8I9//GO3B0Ndq7fYcPM7G9Bqd+HcYdm4/6IRAT0v2Ws3ltxjZ2B2MlrtTny7szK6wU6ib2bHE+zk4q/f7sWaA7Uwt9rb9QGinqPGLB9Uy5odIoq8gN7h/va3vwV0MUEQGOyE2ZZjDWi2OdEvIxH/uG58wMc4eBcoV7mXFAZmJWFgdhK+2FKGi0cXhG3MXclwvwHKO8XkDruDspPRJy0BJxpasKvMhBI/535RzyDX7GQz2CGiKAgo2JF3X1H07a4wAQDGFqYFdV5Vstcy1tG6ZgDAoJxkDMpOxor/Ozfk4wxGRpumhXJmRxAEFGZIwU6FqdXfU6mHUIKdFAY7RBR50Vu7oG7ZW2EGAAwL8rwquWbnQFUTHC4RBq0K/TMSQz6+7mh7orr32Un5RqnfTnkjg52eTC5Q5jIWEUVD0MHOjTfe2On9b7zxRrcHQ12Tg51gD+eUa3Ic7i62IwuM0Khjo7lb22DH6HUqdr5R2r1TwWCnx3K5RNS6j4rISmGBMhFFXtDBTn19vc/XdrsdO3bsQENDA84///yQDYzasztdOOjeSdXdzI5sVB9jyMZ1suSt5zLvgyLlYKe8sSWiY6LQ4VERRBRtQQc7//nPf9rd5nK5cMstt2DQoEEhGRT5d6jaArtTRIpe0+XxEG217aMzpjB2gh29Ro1kvaZdgTIA5LmXsSoaW2FuteMPH2zGRaPyceWEwqiMlYIn1+vwqAgiipaQ/OZRqVSYP39+wLu2qHv2uIuTh+alQBCCO9ohSedbzDyqT1qohhUS3tmdND+ZnbLGVizfXYWVe6vxzx9ZMN+TsF6HiKItZB+zDh48CIfDEarLkR/dLU4GAI1ahQStFPAk6zUYmJUU0rGdrAyv5Q3vZaw8d7BT02TFjhONAKTT0ann4FERRBRtQS9jzZ8/3+drURRRXl6Ob775BnPnzg3ZwKi97hYny5L0GrTYnTilT2rUDv3sSIbX0pXR68+ZSTro1CrYnC78uL8GAFBnscHpEgPqHE3Rx8wOEUVb0MHO5s2bfb5WqVTIzs7Gs88+2+VOLTo5e+TMTm73gp0UgwY1TVaM6ZsWwlGFhndmJ82r744gCMgzGnC0rhl7K6Wf3yUCDc02ZPLNs0eoaZJ2YrHHDhFFS9DBzooVK8IxDuqCw+lCmXtHUlF295ag5MLfMYVpoRpWyGR41ex4L2MBUIIdb7UWBjuRJIoi/vjhFiRqVTgzyFM7mNkhomhjU8EeotZigygCapXQ7e27904vxg/7qjF1RG6IR3fy5MyORiW0K6aWi5S91ZitGNrNDBcFr8psxVdbywAAp54e3HPlGivW7BBRtARdoFxbW4vbbrsNI0aMQFZWFjIyMnz+C8aqVaswa9YsFBQUQBAEfP755z73i6KIBx98EPn5+UhISMCUKVOwf/9+n8fU1dXh2muvRWpqKtLS0nDTTTehqakp2B8r5smfjjOTdN2uVSkZmIm7pxdDGyPNBL3JmZ20RG27nWZ5/oIdd5M6iow6r/luDHLq65vtAID0RAY7RBQdQWd2rrvuOhw4cAA33XQTcnNzg94C7c1isWDMmDG48cYb8etf/7rd/U8//TSef/55vP322ygqKsIDDzyACy+8ELt27YLBIL0BXnvttSgvL8eyZctgt9txww03YN68eXj//fe7Pa5YJAc78Vr3IGd2UhPar5EUGNv3FKpt4o6sSKpv9g52gvs33+h+bhqDHSKKkqCDnR9//BGrV6/GmDFjTvqbz5gxAzNmzPB7nyiKWLhwIe6//35ceumlAIB33nkHubm5+PzzzzFnzhzs3r0bS5YswS+//IIJEyYAAF544QXMnDkTzzzzDAoKoneSd6jJwU5OnAY7pw/IwCl9Uv2evu6d2Uk1aGBqdaC2iZmdSGpwZ2eAk8nsBFnsQ0QUIkGvZxQXF6OlJfyt+0tLS1FRUYEpU6YotxmNRpSUlGDt2rUAgLVr1yItLU0JdABgypQpUKlUWLduXdjHGElVZulsqHjN7BgTtfj6D2fjfya378LtXbNzxsBMAOy1E2newU5DEMGO0yXC1Co918hgh4iiJOjMzksvvYR7770XDz74IE455RRotb6/wFJTU0MysIqKCgBAbq5vMW1ubq5yX0VFBXJycnzu12g0yMjIUB7jj9VqhdXqebM0maTOxHa7HXa7vaOnBU2+ViiuWeneiZWZqA3pGHuC3GQtBEEqXp7QPw1Ld1Wi2tTqMw+hnGtqr9bs+YBjsgkBz3N9s1RYDwBJmsCf19vx9RwZnOfICOc8B3rNoIOdtLQ0mEymdod+iqIIQRDgdDqDvWTEPfHEE1iwYEG725cuXYrExMSQf79ly5ad9DW271cBUKHq6AEsXry/y8fHm2sHCTCogYqDuwCoceB4FRYvXtzucaGYa2pv02Hp9QdImZ1A57mqBQA00KtFfLd0SdjGF6/4eo4MznNkhGOem5ubu34QuhHsXHvttdBqtXj//fdPukC5M3l5eQCAyspK5OfnK7dXVlZi7NixymOqqqp8nudwOFBXV6c835/77rvPpxO0yWRCYWEhpk2bFrLMFCBFnMuWLcPUqVPbZcCC9W7ZeqCuAZNLxmHGKR3/bPFqpvv/m4424I196+HSJWLmzLOV+0M519TeD5/tAMqlreeNNiHged58rAHYsh5ZKQmYOfOcMI8yfvD1HBmc58gI5zzLKzNdCTrY2bFjBzZv3oxhw4YFPahgFBUVIS8vD8uXL1eCG5PJhHXr1uGWW24BAEycOBENDQ3YuHEjxo8fDwD4/vvv4XK5UFJS0uG19Xo99Pr2tS9arTYsL/hQXLfaXZCbn57Uq/9R5hqlzFttk83vPITr77C3M7V6zr1rtAU+zxabtIaVnqTj30s38PUcGZznyAjHPAd6vaCDnQkTJuDYsWMhCXaamppw4MAB5evS0lJs2bIFGRkZ6NevH+644w489thjGDJkiLL1vKCgAJdddhkAYPjw4Zg+fTpuvvlmvPLKK7Db7bj99tsxZ86cuNqJBXhtPe/lXWgz3Y3pLDYnWmxOJLRpQEjhUe9VoGyyAy6X6PdxDc02CBCUYmR5y3paAredE1H0BB3s/OEPf8Cf/vQn3HXXXRg1alS7qGr06NEBX2vDhg0477zzlK/lpaW5c+firbfewt133w2LxYJ58+ahoaEBkyZNwpIlS5QeOwDw3nvv4fbbb8cFF1wAlUqFyy+/HM8//3ywP1ZMs1gdaLZJtVDxuhsrUMl6DXQaFWwOF2otVvTVhb7Gitrz7rPjEgXUWmwo0PsGMC02J6Y8twpJejWW3TkZOo1K2cWVxp1YRBRFQQc7V111FQD4HPopCEK3CpTPPfdciKL/T4jydR955BE88sgjHT4mIyMj7hoItiVndZJ0aiTpe/cJH4IgICtJh7LGVtQ22dA3ncFOJHhvPQek4yMKMpJ9bitvbEFNkxU1TcCP+6txwfBcNCgNBRnsEFH0BP3OWVpaGo5xUCeq4rx7crAyk/VSsMNeOxHhcolK0JKbokel2YoKU2u7x3kvdX25tUwKdlp4VAQRRV/QwU7//v3DMQ7qRLwfFREsuW6nhl2UI8JsdUAu0Rmam4xKsxWVpvaBZmOL5+9j2a5KtNicSgDU9iR7IqJICijY+fLLLzFjxgxotVp8+eWXnT72kksuCcnAyKPa3T05J6X9gZi9kXzqO4+MiAw5q5OoU6NvunROmZxt9FZv8WR2mm1OfLe7UnkuMztEFE0BBTuXXXaZ0q1Y3gnlT09pKtjTcBnLV5Y7s3O0LrBmUnRyvE8tz02VAm5/mR15yUr21dYyFigTUUwI6Gwsl8ulHMvgcrk6/I+BTnhwGcvXqf3TAQAfbziGHScaozya+FfvVWScmyq9Biv91OzIWZxRfYwAgI1H6r2ey8wOEUVP0AeBUmS5XCL2VzUBYLAjmzYiF9NH5sHhEnHHv7eg1c4gO5y8l6I8wY6fZSz34yYOyoRaJW1PL2uQztRiZoeIoingYGft2rX4+uuvfW575513UFRUhJycHMybN8/ncE06eaIo4pGvd2HLsQZo1QJO7Zce7SHFBEEQ8PivRyEnRY8DVU34amtZtIcU1+RaHGOiFnnuZaxyv5kd6XF5qQYMyk4CAKWwmTU7RBRNAQc7jzzyCHbu3Kl8vX37dtx0002YMmUK7r33Xnz11Vd44oknwjLI3uqTjcfx1k+HAQDPXDkGg3OSO39CL5KRpMPFo6Uu2XsqzFEeTXzzZHa0yDdKwY651YEmq3SEhM3hcj/OU58zssDoc41UQ+/uD0VE0RVwsLNlyxZccMEFytcffvghSkpK8Nprr2H+/Pl4/vnn8dFHH4VlkL3Ve+uOAgD+eMEQXDq2T5RHE3vk4O+Ae5mPwsO7V06yXoMEtZSuKW9owaIVB3DKQ99i09F6NLR4lrtGFngO1E01aKBRc8WciKIn4N9A9fX1yM3NVb7+4YcfMGPGDOXr0047DceOHQvt6OLYsbpm/Pk/23G4xgIAWLKjHK/8cBAOp0u5f8uxBggC8Jsz+kVzqDGLwU5k1CsZG2kpKs29IlXW2IrvdlfC5nRh7cFan+WuEV7BDouTiSjaAg52cnNzle7JNpsNmzZtwhlnnKHcbzabeWpsEP617gjeW3cUb64phcsl4n8/2oon/7sHf/pwC+xOF/67oxwAUFKUwf46HZCDnRMNLWi2Obp4NHWX9zIWAKTpPZkdOVg/Xt+CRq8MkPcyVjqLk4koygJeSJ85cybuvfdePPXUU/j888+RmJiIs88+W7l/27ZtGDRoUFgGGY/k7eQHqy0oN7XC4j7o85vt5bDYHKholApALxodX6e3h1JGkg6ZSTrUWmw4VN2MmlagvLEV/bL45hpK9W0aA6a7EzW7y01K1udwjUWp4UlP1MKYoEVhRgKO1bXAyMwOEUVZwJmdRx99FBqNBpMnT8Zrr72G1157DTqd55fYG2+8gWnTpoVlkPFILuYsrbGgtFr6dJxq0ECnVmHl3mrsqTBDJQDTR+ZFc5gxb5A7u7O2tBZPb1XjylfXKQWzdPLsTheO1Unbx+VjOuTMztpDtcrjdpZJ/Y4EAUgxSMHmyHwpu8PMDhFFW8CZnaysLKxatQqNjY1ITk6GWq32uf/jjz9GcjJ3CwWqziJ9Wj7R0ILd5SYAwOlFGfjfacNwz6fbsO14I84dlsPeOl0YnJOM9aV1eO3Hw7C6BFSarNh4pB4TB2VGe2hx4aeDtWhssSMzSYcR+akQXU4ls7Ov0lMrZWqVsjrGBC3UKgEAUDIwA0t2VqAoKyni4yYi8hb0flCj0ej39oyMjJMeTG8iLw0AwIq9VQCAoqwkDM9PxWe3nIlfDtf7FHmSf4OzpQDb+8TtlfuqGOyEyJdbpB5GM0flQ6NWwe5yIq2T+Nu7n85vzuiP4rxUjOuXFuZREhF1jvtBo6Te4gl21pfWAQCKsqQ3bo1ahYmDMnlSdAD89R76YW91FEYSf1rtTizdWQEAuGSsp3YsTSd2+Bzv16zW/To2aNUdPp6IKBIY7ESB3elS0v4A4HC3mR2YzXR/sLyDnaIUESpBajJY3tgSxVHFh5V7q2G2OpBvNGC8V/futE7qjVmfQ0SxiMFOFDQ02/3ePpC1DUHLNxqQrJdWYyfnuTC6r7TMupLZnZNSZWrFM0v3AgBmjSmAyl2HAwA6tW9Q491AkMdCEFEsYrATBQ1e9TqyJJ2axcjdIAgCHrh4OH5TUohRGSImD8kCwKWsk1HbZMVVr/6MA1VNyEs14PozB7R7jHxshCAAZ3rVRxmZ2SGiGMRgJwrknVheH5ZRlJ0EQRA6eAZ15qrT+uGhi4dDowIm9JeWW/ZW8rys7vp003GU1ljQJy0BH/1+IgrSEto9Rg52CowJGJjtWUpkZoeIYhGDnSiQd2INzU1RbpOLk+nk9E2X3phP1LfA5eq4kJY6VtEoNby8eHQ++mUm+n2MHOwUZSUpcw5Ih4ASEcUaBjtRIG+T7puegNxUaemKvUhCIzdVD5UA2JwuVDdZoz2cHqnOIs1bRlLHWZrheVKgPqbQiD5p3sEOMztEFHuC7rNDJ09exkpP1GFYXioqTdUozkvp4lkUCK1ahXxjAk40tOB4fQtyU9ufK9Zqd+KlFQdwbnEOTvXaZUSSWvfrs7Ng5/JT+6C4wOhzBhbA3VhEFJuY2YkCucdOepIOD88agUcvHYlpI3K7eBYFqo97WeV4fbPf+19fXYrnvz+A+/+zI5LD6jHkZVb5eAh/1CoB4/tnwKBVw6BVK9kdf8ElEVG0MbMTBfIyVnqiDgOzk30KPOnk9U1PwPpS6STutlrtTry5phQAsLvCBFOrHakGZiO81TXJmZ3Adwc+N3sM9lc1+dShERHFCmZ2okD+5JyRxDfZcOibLhXV+gt2Pt10HDXuN3NRBDYdqY/o2GKdKIrKMlZmJ8tYbZUMzMRvzugfrmEREZ0UBjtRINfssJgzPPqm+V/GOljdhFd+OAgASHE3ItzIYMdHs80Jq/vU+M5qdoiIehIGO1HQ0Nx1ASh1n7L9vMGT2Xlp5QFM+9sqHKtrQVayDndMHQoA+OVwXVTGGKvkQFynUSFRxzOtiCg+MNiJAu/dWBR68jLWifoWiKKIeosNzy7dB6dLxJThufj37ydi8lCp0/KWYw2wuTMZ5HltZibp2OSSiOIGC5QjzOF1CCgzO+GRZzRAJQBWh9Rr5/vdVXC6RIzIT8U/504AINWmpCdqUd9sx86yRozjFnQAnmCHr00iiifM7ERYQ4u0E0sQAGMCC5TDQadRIc+9Bfp4fQu+2V4OALhodL7yGEGQtk4DwPpSLmXJAumxQ0TU0zDYiTC5x44xQQu1issE4SL32tlxohE/HawFAMwcle/zmEmDpQMs/7P5BESRR0sAnu7JwezEIiKKdQx2IkxZJmC9TljJdTuvrjoEp0vE8PzUdkdy/GpcXxi0KuypMOOXw9yVBXhndgLvsUNEFOsY7ESY3GOHByaG16BsKbCRe+1cNCqv3WOMiVpcNrYPAOCdtYcjNrZYVm9hDygiij8sUI6wRnfNDnvshNfcMwdAq1bhcK0FAPDbMwf4fdx1E/vjw1+OYcmOClSZWpHTy487qGNmh4jiEIOdCJODHRYnh1eKQYvfTx7U5eNGFhgxrl8aNh9twLe7KnFdL+8CzAJlIopHXMaKMAY7sefsIdkAgM3spuzps9PJIaBERD0Ng50Ik4OdVAY7MePUfmkAgI1HGex4DgFlsENE8YPBToQ1NDOzE2vGFUoNBY/UNqOmyRrl0USPzeGC2So1vOTWcyKKJwx2IozLWLHHmKjFkJxkAL37FHR5p6BaJSDVwNcnEcUPBjsRZmKwE5NOdR8XseloQ3QHEiUnGlrw+eYTAID0RC1UbHhJRHGEu7EijJmd2DS+fzr+veEYNvXCup0WmxMzFq5SzmzLNyZEeURERKHFYCfCGOzEplP7pwEAth1vgN3pglbde5KeO8oaYWp1IFmvwawx+ZhzWr9oD4mIKKR6z2/0GCCKovLpmcFObBmYlYyMJB1a7S6s2lcd7eFE1NZjDQCAiYMy8cSvR2NMYVpUx0NEFGoMdiKoyeqA0yUdOMlgJ7aoVAIuP1U6OuKtnw5HdzARtvV4IwBgTF9jlEdCRBQeDHYiSF7C0qlVMGg59bHmtxMHQCUAP+6vwYEqc7SHEzHbjjcAADM6RBS3+I4bQd4NBQWBu11iTWFGIqYMzwXQe7I7Dc02HKltBgCM7pMW3cEQEYUJg50I8hQnsy48Vl3vPjD0i81lcLmXHOOZvIQ1IDMRxkQurRJRfGKwE0HssRP7Ti/KgF6jgtnqwJG65mgPJ+y2uYuTuYRFRPGMwU4Ecdt57NOoVSjOSwEA7CozRXk04Sdndkb3TYvuQIiIwojBTgQx2OkZRhRIu5J2lTdGeSThZbE68POhWgDAOPdhqERE8YjBTgQx2OkZRhSkAgB2xnlm5/MtJ9BkdWBgVhLGMrNDRHGMwU4EMdjpGUbkS8FOPC9jiaKIf/18FABwTUk/noVFRHGNwU4ENbZI3ZNTGezEtOH5KRAEoMpsRbXZGu3hhMWmow3YXW6CXqPCFeP7Rns4RERhxWAngpjZ6RkSdRoUZSUBAHaVx2d255ONxwEAl4wpQFqiLsqjISIKLwY7EdTYbAMAvrn0APG+lLXbHcSdOywnyiMhIgo/BjsRxMxOzzHSvSNrZ1n87cgSRREHq5sAAINzkqM8GiKi8GOwE0EMdnqOQdnSMtaxOGwsWNNkg7nVAZUA9M9MjPZwiIjCLuaDHbPZjDvuuAP9+/dHQkICzjzzTPzyyy/K/aIo4sEHH0R+fj4SEhIwZcoU7N+/P4oj9k8URZhapQJlBjuxLztFD0AKDOKNnNXpm54Ig1Yd5dEQEYVfzAc7v/vd77Bs2TK8++672L59O6ZNm4YpU6bgxIkTAICnn34azz//PF555RWsW7cOSUlJuPDCC9Ha2hrlkftqsjrgdJ+1xGAn9mUlS8FOdZMVohhfZ2TJwY6cvSIiincxHey0tLTg008/xdNPP41zzjkHgwcPxsMPP4zBgwfj5ZdfhiiKWLhwIe6//35ceumlGD16NN555x2UlZXh888/j/bwfTQ0S0tYOrUKBm1MTzvBk9mxOVwwWx1RHk1oHaq2AAAGZbNeh4h6h5g+ftvhcMDpdMJgMPjcnpCQgNWrV6O0tBQVFRWYMmWKcp/RaERJSQnWrl2LOXPm+L2u1WqF1erpn2IySTtT7HY77HZ7yMYvX8tut+NApfQ9+qYnwOGIrzfPWOA916GgBpCkV8NidaKi3oKErPjJghyoNAMABmQmBD1foZ5n8o/zHBmc58gI5zwHes2YDnZSUlIwceJEPProoxg+fDhyc3PxwQcfYO3atRg8eDAqKioAALm5uT7Py83NVe7z54knnsCCBQva3b506VIkJoa+YHPZsmVYVS4AUCPJacbixYtD/j1IsmzZspBdK0FQwwIBX333AwanhuyyUbfjqBqAgMoD27C4alu3rhHKeaaOcZ4jg/McGeGY5+bmwDaRxHSwAwDvvvsubrzxRvTp0wdqtRqnnnoqrr76amzcuLHb17zvvvswf/585WuTyYTCwkJMmzYNqamhe1ez2+1YtmwZpk6divVLDgCHj+HMUwZh5rQhIfseJPGea602NDVR75xYj5qjDRhyyqmYcUpeSK4Zba12J+74eTkA4NqLL0CmuzYpUOGYZ2qP8xwZnOfICOc8yyszXYn5YGfQoEH44YcfYLFYYDKZkJ+fj6uuugoDBw5EXp70BlRZWYn8/HzlOZWVlRg7dmyH19Tr9dDr2/+S12q1YXnBa7ValNZK0eeQvFT+owqjUP4dZqdIy6f1Lc64+Ts7WNsCUZSK5HPTkiAI3TsTK1z/VsgX5zkyOM+REY55DvR6PaZSNikpCfn5+aivr8e3336LSy+9FEVFRcjLy8Py5cuVx5lMJqxbtw4TJ06M4mjb4w6YnicrRep0XdMUP+djHaiSXocDs7sf6BAR9TQxn9n59ttvIYoihg0bhgMHDuCuu+5CcXExbrjhBgiCgDvuuAOPPfYYhgwZgqKiIjzwwAMoKCjAZZddFu2hK8ytDlSapDfMgdwB02NkJ0uZnXgJduxOF15bdQiA5zgMIqLeIOaDncbGRtx33304fvw4MjIycPnll+Mvf/mLkrq6++67YbFYMG/ePDQ0NGDSpElYsmRJux1c0VRaI231zU7Rs8dODyJndqrN8dFY8O/f7cfW440wJmhx+/mDoz0cIqKIiflgZ/bs2Zg9e3aH9wuCgEceeQSPPPJIBEcVnEM1cl8TLmH1JHJjwXjI7BypteCllQcAAI//ahTyjQlRHhERUeT0mJqdnoxN3HqmeAp2tp9ohEsExvQ14qLR+V0/gYgojjDYiYCDNQx2eqJsr2Cnpx8Zcby+BQBQFEfNEYmIAsVgJwIOypmdHAY7PYlcs9Nqd8Fic0Z5NCdHPr29MIOnnBNR78NgJ8xqWqVgRxCA4Xkp0R4OBSFRp0GiTjoVvMbcs5eyjrkzO33TWatDRL0Pg50wW18tTfGkwVnISY2dHWIUGLlu50RDi9Kjpic6Xu/O7KQzs0NEvQ+DnTByuUSsr5Iat10xvm+UR0PdkZUsLWX9z7sbMeW5H7BqX3WURxQ8l0tUana4jEVEvRGDnTBaW1qHepuAVIMGF46Mj7OVehs5s2O2SifVv7mmNJrD6dDmo/WoMrX6va+6yQqbwwWVAOQZmV0kot6HwU4YfbrpBADg4tF5MGjVUR4NdUd2ihTsaFRShm7lvmplSShWHKhqwq9e+gnT//6j36U2ebz5xgRo1fwnT0S9D3/zhYkoihBFQCWIuOLUPtEeDnXTtJF5KDAa8PQVo3HW4EyIIvDh+mMBP9/hdOGrrWWo7CDrEgq7yqVTf+ssNvzmn+uU4GZPhQlfbDmBY3UsTiai3o3BTpgIgoC/zR6NR8c7cUoBzyHqqSYPzcZP912AX5/aF9eW9AcA/HvDMdidroCe/+3OSvzhg8145OtdYRvj0VqL8ucKUysWfrcfAHDnv7fiTx9uwRvupTfW6xBRb8VgJ8ySteDp0nFi6ohcJOs1qDZblfPOurKnQsq67Kswh21cR909dE7pk6p8T7vThf2V0vfcdrwRAHdiEVHvxWCHKEBatQr57gLfKlNgfXeO1EqByLH65rB1YZaDnfOG5QAASqstOFbXDIfL9/txGYuIeisGO0RByEmVCparzIHV4BxxLzG12l2oDtMZW3JNzlmDs6BWCbDYnPj5UF27x3EZi4h6KwY7REHITXFndgLsqHykzrNz61hd6HdxWR1OlDVKwc6g7GQUurM3y3dXAgAyknTKYwszmNkhot5JE+0BEPUk2e7MTiC7qxqb7WhotitfH6trwfj+oR3PifoWiCKQqFMjK1mHgdnJOFzbjNUHagAAV51WiB0nGqFWCUqgRkTU2zDYIQpCMJmdI3W+RcxHw5DZka/ZLyMRgiAop5pbHdJusSE5ybhnenHIvy8RUU/CZSyiICg1OwFkdg7X+gY34VjGanua+cDsJJ/75eCHiKg3Y7BDFIScIDI7cv8b+eT0o3XNMLXale3ooSDv9urnDnbaBjcDs5JD9r2IiHoqBjtEQchVMjvWLreSy5mdkqIMAMDx+hbc+q9NmL7wR2w8Uh+S8XgvYwFSkbIsM0kHY6I2JN+HiKgnY7BDFAQ5s9NidyqHg3bkqDvYmTQkGwBwoqFFKRxesacqJONpG+zkpOiR5M4kcQmLiEjCYIcoCAk6NVL0Ul1/V40FD7uXscb3T4dB6/tPbf3h9n1wgiWKolKz0y9TCnYEQUCRu26HwQ4RkYTBDlGQAilSbrY5lLqeosykdkc1bDnWAKvDeVLjMLU6YLFJ1ygwenroDM1Jkf6fm3JS1yciihcMdoiCJC9lbThSj+kLV+H9dUd97rc6nPh/n20HAGQlS3UzhV7LTFnJOtgcLuXMqu6qdXdkTtZrkOBeugKAO6cOxV0XDsNVpxee1PWJiOIFgx2iIMlFyq/8cBB7Ksx4Z+1hn/v/8P5mfL6lDBqVgAcuHgEAOKWPEQBw9en9cNoAqWB5fenJLWXVWmwAgMxknc/thRmJuO28wUg1sDiZiAhgsEMUtJxUKbPT7F5COljdBJu7iV9tkxVLd0lHNbxx/Wm4dGwfAMAtkwfhrRtOwx8vGILTi0IU7LgzO5lJui4eSUTUuzHYIQpSTore52u7U8SBqiYAwOajDQCAwTnJOGdotvKYBJ0a5w7LgVolKJmdTUfq4XR1/yT0miY5s6Pv4pFERL0bgx2iIMmZHW9yo8DNx6T+Oaf2S+vw+cPzU5GkU8NsdeBgdVO3x1HrDnaykpnZISLqDIMdoiB5Z3YGubd57y53BzvuzM64fukdPl+tEjCiIBUAsLOs+0XKtRZ5GYuZHSKizjDYIQrSwKwk6NQq5BsNuP6sIgDAngoznC4RW481AADGdZLZAYCRBVLB8s4T3T86orbJf4EyERH54qnnREHKSTXgs1vPhDFBq+yI2l1uwr5KMyw2J5L1GgzJ6bzHzYh8ObPT/WCnRi5QZs0OEVGnGOwQdYO8lTwzWQdBkIqFl7l3YY0pNEKtEjp9vryMtavcBFEUIQidP94fOdDK4m4sIqJOcRmL6CQk6jQoypTqdt5bdwQAMK6w43od2dDcFGjVAhpb7DjR0NKt713LzA4RUUAY7BCdpOHuJalK91lZEwdldvkcnUalLHV1ZynL4XShvtkOgDU7RERdYbBDdJIuH98HWck6TB2Ri7duOA1nDc4K6HkjC7pft1PXLC1hCQKQnshgh4ioM6zZITpJ5xfnYsP9U4N+3oiCVGAjsKsb28/lnVgZibou64OIiHo7ZnaIokQuct56vBGiGFwnZW47JyIKHIMdoigZ1ceIBK0a1WYrdpUHt5TFhoJERIFjsEMUJQatWqnvWbGnKqjn1jCzQ0QUMAY7RFF0XrF0WOj3QQY78rbzLG47JyLqEoMdoig6b1gOAGDzsQbUuZsEBkKp2WFDQSKiLjHYIYqigrQEFOelQBSBH/Z1nt3ZW2FGq90JwKtmh5kdIqIuMdghirLzi6Xszldbyzt8zFdby3DhwlV45tu9AFizQ0QUDAY7RFH261P7Qq0S8P2eKvx8qNbvY95fdxQAsPV4AwCgytQKAMhOYWaHiKgrDHaIomxwTjLmnFYIAHj06114YvFu3PvpNlgd0pJVRWMrfi6VgqCyhlY4XSIqzdIyVoExITqDJiLqQdhBmSgG3Dl1KL7YUoadZSbl+IjJQ7MxY1Q+vt5WBrnnYKWpFRUmKeDRqARmdoiIAsDMDlEMyErW476ZxdCpVUhL1AIADtVYAABfbClTHudwidh+XDpeIjfVwKMiiIgCwGCHKEZcW9If+/4yAzedVQQAKK2x4GhtM7afaIRaJSDVICViNx+tBwDkGw1RGysRUU/CYIcoxgzISgIAHK6xYIu7IHl0XyMG5SQDADbJwU4a63WIiALBYIcoxhS5g53SGgt2uet3RhakKpmcbe5lrAJmdoiIAsJghyjGyJmdWotN2Yo+PD8VealSJsfqcAEA8hjsEBEFhMEOUYxJ1muQ495lteVYAwBgRH4qCtJ8g5t8bjsnIgoIgx2iGCRndwBAJQDFeantMjltgx8iIvKPwQ5RDBroFewMyEpCgk7dLpPDzA4RUWAY7BDFoCKvYGdEfioA363mOrWKJ54TEQWIwQ5RDPJexhpRIAU7OSl6yD0E84wGqNhQkIgoIAx2iGKQ9zLWcHdmR6NWISdFyu6woSARUeAY7BDFoH6ZidBpVBAEqceOLN9dlFzAhoJERAHjQaBEMUivUeOV35yKZptTyeYAUkZnM9hjh4goGDGd2XE6nXjggQdQVFSEhIQEDBo0CI8++ihE+QhoAKIo4sEHH0R+fj4SEhIwZcoU7N+/P4qjJgqN84tzcfHoAp/bLh5dgMKMBEwbkRulURER9Twxndl56qmn8PLLL+Ptt9/GyJEjsWHDBtxwww0wGo344x//CAB4+umn8fzzz+Ptt99GUVERHnjgAVx44YXYtWsXDAZ++qX4MnNUPmaOyo/2MIiIepSYDnZ++uknXHrppbjooosAAAMGDMAHH3yA9evXA5CyOgsXLsT999+PSy+9FADwzjvvIDc3F59//jnmzJkTtbETERFRbIjpYOfMM8/Eq6++in379mHo0KHYunUrVq9ejeeeew4AUFpaioqKCkyZMkV5jtFoRElJCdauXdthsGO1WmG1WpWvTSbpsEW73Q673R6y8cvXCuU1yT/OdWRwniOD8xwZnOfICOc8B3rNmA527r33XphMJhQXF0OtVsPpdOIvf/kLrr32WgBARUUFACA317d+ITc3V7nPnyeeeAILFixod/vSpUuRmJgYwp9AsmzZspBfk/zjXEcG5zkyOM+RwXmOjHDMc3Nzc0CPi+lg56OPPsJ7772H999/HyNHjsSWLVtwxx13oKCgAHPnzu32de+77z7Mnz9f+dpkMqGwsBDTpk1DampqJ88Mjt1ux7JlyzB16lRotdqQXZfa41xHBuc5MjjPkcF5joxwzrO8MtOVmA527rrrLtx7773KctSoUaNw5MgRPPHEE5g7dy7y8vIAAJWVlcjP9xRtVlZWYuzYsR1eV6/XQ6/Xt7tdq9WG5QUfrutSe5zryOA8RwbnOTI4z5ERjnkO9HoxvfW8ubkZKpXvENVqNVwuFwCgqKgIeXl5WL58uXK/yWTCunXrMHHixIiOlYiIiGJTTGd2Zs2ahb/85S/o168fRo4cic2bN+O5557DjTfeCAAQBAF33HEHHnvsMQwZMkTZel5QUIDLLrssuoMnIiKimBDTwc4LL7yABx54ALfeeiuqqqpQUFCA3//+93jwwQeVx9x9992wWCyYN28eGhoaMGnSJCxZsoQ9doiIiAhAjAc7KSkpWLhwIRYuXNjhYwRBwCOPPIJHHnkkcgMjIiKiHiOma3aIiIiIThaDHSIiIoprDHaIiIgorjHYISIiorjGYIeIiIjiWkzvxooUURQBBN52OlB2ux3Nzc0wmUzszhlmnOvI4DxHBuc5MjjPkRHOeZbft+X38Y4w2AFgNpsBAIWFhVEeCREREQXLbDbDaDR2eL8gdhUO9QIulwtlZWVISUmBIAghu658wOixY8dCesAotce5jgzOc2RwniOD8xwZ4ZxnURRhNptRUFDQ7ngpb8zsAFCpVOjbt2/Yrp+amsp/SBHCuY4MznNkcJ4jg/McGeGa584yOjIWKBMREVFcY7BDREREcY3BThjp9Xo89NBD0Ov10R5K3ONcRwbnOTI4z5HBeY6MWJhnFigTERFRXGNmh4iIiOIagx0iIiKKawx2iIiIKK4x2CEiIqK4xmDnJC1atAgDBgyAwWBASUkJ1q9f3+njP/74YxQXF8NgMGDUqFFYvHhxhEbaswUzz6+99hrOPvtspKenIz09HVOmTOny74U8gn1Nyz788EMIgoDLLrssvAOME8HOc0NDA2677Tbk5+dDr9dj6NCh/P0RgGDneeHChRg2bBgSEhJQWFiIO++8E62trREabc+0atUqzJo1CwUFBRAEAZ9//nmXz1m5ciVOPfVU6PV6DB48GG+99VZ4BylSt3344YeiTqcT33jjDXHnzp3izTffLKalpYmVlZV+H79mzRpRrVaLTz/9tLhr1y7x/vvvF7Varbh9+/YIj7xnCXaer7nmGnHRokXi5s2bxd27d4vXX3+9aDQaxePHj0d45D1PsHMtKy0tFfv06SOeffbZ4qWXXhqZwfZgwc6z1WoVJ0yYIM6cOVNcvXq1WFpaKq5cuVLcsmVLhEfeswQ7z++9956o1+vF9957TywtLRW//fZbMT8/X7zzzjsjPPKeZfHixeKf//xn8bPPPhMBiP/5z386ffyhQ4fExMREcf78+eKuXbvEF154QVSr1eKSJUvCNkYGOyfh9NNPF2+77Tbla6fTKRYUFIhPPPGE38fPnj1bvOiii3xuKykpEX//+9+HdZw9XbDz3JbD4RBTUlLEt99+O1xDjBvdmWuHwyGeeeaZ4j//+U9x7ty5DHYCEOw8v/zyy+LAgQNFm80WqSHGhWDn+bbbbhPPP/98n9vmz58vnnXWWWEdZzwJJNi5++67xZEjR/rcdtVVV4kXXnhh2MbFZaxustls2LhxI6ZMmaLcplKpMGXKFKxdu9bvc9auXevzeAC48MILO3w8dW+e22pubobdbkdGRka4hhkXujvXjzzyCHJycnDTTTdFYpg9Xnfm+csvv8TEiRNx2223ITc3F6eccgoef/xxOJ3OSA27x+nOPJ955pnYuHGjstR16NAhLF68GDNnzozImHuLaLwX8iDQbqqpqYHT6URubq7P7bm5udizZ4/f51RUVPh9fEVFRdjG2dN1Z57buueee1BQUNDuHxf56s5cr169Gq+//jq2bNkSgRHGh+7M86FDh/D999/j2muvxeLFi3HgwAHceuutsNvteOihhyIx7B6nO/N8zTXXoKamBpMmTYIoinA4HPif//kf/L//9/8iMeReo6P3QpPJhJaWFiQkJIT8ezKzQ3HtySefxIcffoj//Oc/MBgM0R5OXDGbzbjuuuvw2muvISsrK9rDiWsulws5OTl49dVXMX78eFx11VX485//jFdeeSXaQ4srK1euxOOPP46XXnoJmzZtwmeffYZvvvkGjz76aLSHRieJmZ1uysrKglqtRmVlpc/tlZWVyMvL8/ucvLy8oB5P3Ztn2TPPPIMnn3wS3333HUaPHh3OYcaFYOf64MGDOHz4MGbNmqXc5nK5AAAajQZ79+7FoEGDwjvoHqg7r+n8/HxotVqo1WrltuHDh6OiogI2mw06nS6sY+6JujPPDzzwAK677jr87ne/AwCMGjUKFosF8+bNw5///GeoVMwPhEJH74WpqalhyeoAzOx0m06nw/jx47F8+XLlNpfLheXLl2PixIl+nzNx4kSfxwPAsmXLOnw8dW+eAeDpp5/Go48+iiVLlmDChAmRGGqPF+xcFxcXY/v27diyZYvy3yWXXILzzjsPW7ZsQWFhYSSH32N05zV91lln4cCBA0owCQD79u1Dfn4+A50OdGeem5ub2wU0coAp8hjJkInKe2HYSp97gQ8//FDU6/XiW2+9Je7atUucN2+emJaWJlZUVIiiKIrXXXedeO+99yqPX7NmjajRaMRnnnlG3L17t/jQQw9x63kAgp3nJ598UtTpdOInn3wilpeXK/+ZzeZo/Qg9RrBz3RZ3YwUm2Hk+evSomJKSIt5+++3i3r17xa+//lrMyckRH3vssWj9CD1CsPP80EMPiSkpKeIHH3wgHjp0SFy6dKk4aNAgcfbs2dH6EXoEs9ksbt68Wdy8ebMIQHzuuefEzZs3i0eOHBFFURTvvfde8brrrlMeL289v+uuu8Tdu3eLixYt4tbzWPfCCy+I/fr1E3U6nXj66aeLP//8s3Lf5MmTxblz5/o8/qOPPhKHDh0q6nQ6ceTIkeI333wT4RH3TMHMc//+/UUA7f576KGHIj/wHijY17Q3BjuBC3aef/rpJ7GkpETU6/XiwIEDxb/85S+iw+GI8Kh7nmDm2W63iw8//LA4aNAg0WAwiIWFheKtt94q1tfXR37gPciKFSv8/s6V53bu3Lni5MmT2z1n7Nixok6nEwcOHCi++eabYR2jIIrMzREREVH8Ys0OERERxTUGO0RERBTXGOwQERFRXGOwQ0RERHGNwQ4RERHFNQY7REREFNcY7BAREVFcY7BDRD3e9ddfj8suuyzawyCiGMWDQIkopgmC0On9Dz30EP7+97/z7CIi6hCDHSKKaeXl5cqf//3vf+PBBx/E3r17lduSk5ORnJwcjaERUQ/BZSwiiml5eXnKf0ajEYIg+NyWnJzcbhnr3HPPxR/+8AfccccdSE9PR25uLl577TVYLBbccMMNSElJweDBg/Hf//7X53vt2LEDM2bMQHJyMnJzc3HdddehpqYmwj8xEYUagx0iiktvv/02srKysH79evzhD3/ALbfcgiuvvBJnnnkmNm3ahGnTpuG6665Dc3MzAKChoQHnn38+xo0bhw0bNmDJkiWorKzE7Nmzo/yTENHJYrBDRHFpzJgxuP/++zFkyBDcd999MBgMyMrKws0334whQ4bgwQcfRG1tLbZt2wYAePHFFzFu3Dg8/vjjKC4uxrhx4/DGG29gxYoV2LdvX5R/GiI6GazZIaK4NHr0aOXParUamZmZGDVqlHJbbm4uAKCqqgoAsHXrVqxYscJv/c/BgwcxdOjQMI+YiMKFwQ4RxSWtVuvztSAIPrfJu7xcLhcAoKmpCbNmzcJTTz3V7lr5+flhHCkRhRuDHSIiAKeeeio+/fRTDBgwABoNfzUSxRPW7BARAbjttttQV1eHq6++Gr/88gsOHjyIb7/9FjfccAOcTme0h0dEJ4HBDhERgIKCAqxZswZOpxPTpk3DqFGjcMcddyAtLQ0qFX9VEvVkgsi2o0RERBTH+HGFiIiI4hqDHSIiIoprDHaIiIgorjHYISIiorjGYIeIiIjiGoMdIiIiimsMdoiIiCiuMdghIiKiuMZgh4iIiOIagx0iIiKKawx2iIiIKK4x2CEiIqK49v8BPuhFtNHo84IAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Parameters\n",
    "S0 = 85  # Initial value\n",
    "mu = 0.1  # Drift coefficient\n",
    "sigma = 0.3  # Volatility coefficient\n",
    "T = 1  # Time horizon (1 year)\n",
    "N = 252\n",
    "dt = T / N  # Time step\n",
    "\n",
    "# Generate the Brownian motion\n",
    "np.random.seed(0)  # For reproducibility\n",
    "W = np.random.standard_normal(size=N)\n",
    "W = np.cumsum(W) * np.sqrt(dt)  # Standard Brownian motion\n",
    "\n",
    "# Simulate GBM\n",
    "t = np.linspace(0, T, N)\n",
    "S = S0 * np.exp((mu - 0.5 * sigma**2) * t + sigma * W)\n",
    "\n",
    "# Apply boundary conditions\n",
    "#S = np.clip(S, 30, 170)\n",
    "\n",
    "# Plot the result\n",
    "plt.plot(t, S)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Simulated GBM')\n",
    "plt.title('GBM Simulation ')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "65b3c32b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n"
     ]
    }
   ],
   "source": [
    "# Parameters for Black-Scholes and ANN comparison\n",
    "K = 120  # strike price\n",
    "r = 0.05  # risk-free interest rate\n",
    "sigma = 0.3  # implied volatility\n",
    "T = 0.5  # time to maturity\n",
    "\n",
    "\n",
    "# Black-Scholes prices\n",
    "PriceBS = [EuropeanOptionBS(S_, K, T, r, 0, sigma, \"Call\").price for S_ in S]\n",
    "\n",
    "# ANN predicted prices\n",
    "PriceANN = [S_/100*\\\n",
    "            model_ML.predict(np.array([[r, K / S_*100, T, sigma]]))[0][0] for S_ in S]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "aef06232",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparison of Black-Scholes and ANN prices\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.plot(S, PriceBS, label=\"Black-Scholes price\")\n",
    "plt.plot(S, PriceANN, label=\"ANN price\")\n",
    "plt.xlabel(\"Asset Price\")\n",
    "plt.ylabel(\"Call Price\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "83687fda",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#frac_ML = pd.DataFrame({'Actual':y_test, 'Predicted': y_pred_ML})\n",
    "# Combine them into a DataFrame\n",
    "frac_ML = pd.DataFrame({'Actual':(PriceBS), 'Predicted': (PriceANN)})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b41f88ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fractions statistics\n",
      "Min: -0.0019009058232483111\n",
      "Max: 0.16642417522764097\n",
      "Mean: 0.031964319994694734\n",
      "Std: 0.04315973798946512\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "y_testf = frac_ML['Actual'].values\n",
    "y_predf = frac_ML['Predicted'].values\n",
    "\n",
    "# Calculate the fractions\n",
    "fractionsf = y_predf / y_testf-1\n",
    "\n",
    "# Print statistics for fractions\n",
    "print(\"fractions statistics\")\n",
    "print(\"Min:\", np.min(fractionsf))\n",
    "print(\"Max:\", np.max(fractionsf))\n",
    "print(\"Mean:\", np.mean(fractionsf))\n",
    "print(\"Std:\", np.std(fractionsf))\n",
    "\n",
    "# Plot the fractions\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(fractionsf, 'o', alpha=0.3)\n",
    "plt.xlabel('Test Sample Index')\n",
    "plt.ylabel('Predicted/Actual Price-1 Fraction')\n",
    "plt.title('Fraction of Predicted to Actual Prices')\n",
    "plt.ylim(0, 2)  # Limit the y-axis for better visualization if needed\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "979978bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8+PH64x//qP/5n//JZekAdgHMuQLwlWZZVrfen0cffVTr1q3r0nbiiSequblZt956a7d9bLx9Xl6eJHX7krk5xx57rCTppptu6tJ+ww03SJKOO+64bap/W+7Htu1udd94440yDENTp07Nyf30hGVZOvHEE/XYY49t9gv9xmXcpUz969ev19/+9rdsWzQa3eJQvU2deOKJcl13s70zm/7O8/Pzu/3OdlaNG+9f6v662Z7XyJo1a7Rw4UJ973vf00knndTtMmvWLC1btkyvv/66TNPU9OnT9cQTT2SXqN/UxudqS3VKmfCwcOHCLm133nlnt56rbX2/9aXp06fLNE39+te/7tabtrH2KVOmqKioSNdee61SqVS3fWx8fUSjUcXj8S7XDR8+XIWFhT1aTh9A/0HPFYCvtG9/+9v69a9/rVmzZunQQw/V+++/rwceeEDDhg3rst2MGTN03333afbs2XrjjTd0xBFHKBKJ6IUXXtD/+3//T8cff7yCwaDGjBmjhx9+WHvttZfKysq07777bnYuy7hx4zRz5kzdeeedamtr08SJE/XGG29o7ty5mj59uiZNmpSTxzdt2jRNmjRJv/zlL7Vy5UqNGzdOzz33nP7xj3/owgsvzB6B39l+97vfacGCBZowYYLOPvtsjRkzRq2trXr77bf1wgsvqLW1VZJ09tln69Zbb9WMGTP01ltvqaamRvfff382yG7NpEmT9IMf/EC33HKLli5dqmOOOUaO4+hf//qXJk2apPPOO0+SdMABB+iFF17QDTfcoNraWg0dOlQTJkzYKTVuvH9J+uUvf6lTTz1VXq9X06ZN267XyF//+tfsMvybc+yxx8rj8eiBBx7QhAkTdO211+q5557TxIkT9aMf/Uh777236urq9Oijj+qVV15RSUmJxo8fL8uydN1116m9vV1+v19HHXWUKisrddZZZ+nHP/6xTjzxRH3zm9/Uu+++q2effbZbb9S2vt/60ogRI/TLX/5S11xzjY444gidcMIJ8vv9+ve//63a2lrNmTNHRUVFuv322/WDH/xAX/va13TqqaeqoqJCq1ev1pNPPqnDDjtMt956qz799FMdffTR+t73vqcxY8bI4/Ho73//uxoaGnTqqaf29UMFsCPs/AUKASC3Ni7FvrllpF338+WaH3300W7XxeNx96KLLnJramrcYDDoHnbYYe6iRYvciRMnuhMnTuyybTQadX/5y1+6Q4cOdb1er1tdXe2edNJJ7vLly7PbvPrqq+4BBxzg+ny+LstTf3G5Z9d13VQq5V599dXZ/Q0aNMi97LLL3Hg83mW7wYMHu8cdd1y32jdX4+Z0dHS4P/3pT93a2lrX6/W6I0eOdH//+993WY584/56shT7tmwryT333HM3e11DQ4N77rnnuoMGDco+n0cffbR75513dtlu1apV7ne+8x03Ly/PLS8vdy+44AL3mWee+dKl2F3XddPptPv73//eHT16tOvz+dyKigp36tSp7ltvvZXd5uOPP3a/8Y1vuMFg0JXUZenwXNe4Jddcc407cOBA1zTNLsuyb+tr5IvGjh3r7rnnnlvd5sgjj3QrKyvdVCqVfQwzZsxwKyoqXL/f7w4bNsw999xz3UQikb3NXXfd5Q4bNsy1LKvLY7Nt2/35z3/ulpeXu3l5ee6UKVPcZcuWbXYp9m15v/V0KfbNvbc3tfH919TUtMXrvuiee+5x999/f9fv97ulpaXuxIkT3eeff77b/U+ZMsUtLi52A4GAO3z4cPeMM85w33zzTdd1Xbe5udk999xz3dGjR7v5+flucXGxO2HChC7L9gPYvRiu24ezoQEAAABgN8GcKwAAAADIAcIVAAAAAOQA4QoAAAAAcqBPw9WcOXN00EEHqbCwUJWVlZo+ffo2nRfk0Ucf1ejRoxUIBDR27Fg99dRTXa53XVdXXHGFampqFAwGNXny5OzZ1AEAAABgR+jTcPXyyy/r3HPP1Wuvvabnn39eqVRK3/rWtxSJRLZ4m1dffVWnnXaazjzzTL3zzjuaPn26pk+f3uU8JNdff71uueUW3XHHHXr99deVn5+vKVOmdDvXBAAAAADkyi61WmBTU5MqKyv18ssv6xvf+MZmtznllFMUiUT0z3/+M9v29a9/XePHj9cdd9wh13VVW1uriy66SBdffLEkqb29XVVVVbr33ns5rwQAAACAHWKXOolwe3u7JKmsrGyL2yxatEizZ8/u0jZlyhTNmzdPkrRixQrV19dr8uTJ2euLi4s1YcIELVq0aLPhKpFIdDlTuuM4am1t1YABA2QYxvY8JAAAAAD9mOu66ujoUG1trUxz6wP/dplw5TiOLrzwQh122GHad999t7hdfX29qqqqurRVVVWpvr4+e/3Gti1t80Vz5szR1VdfvT3lAwAAANiNrVmzRnvsscdWt9llwtW5556rJUuW6JVXXtnp933ZZZd16Q1rb2/XnnvuqTVr1qioqGin1wMAAABg1xAKhTRo0CAVFhZ+6ba7RLg677zz9M9//lMLFy780jRYXV2thoaGLm0NDQ2qrq7OXr+xraampss248eP3+w+/X6//H5/t/aioiLCFQAAAIBtmi7Up6sFuq6r8847T3//+9/14osvaujQoV96m0MOOUTz58/v0vb888/rkEMOkSQNHTpU1dXVXbYJhUJ6/fXXs9sAAAAAQK71ac/Vueeeq7/+9a/6xz/+ocLCwuycqOLiYgWDQUnSjBkzNHDgQM2ZM0eSdMEFF2jixIn64x//qOOOO04PPfSQ3nzzTd15552SMonywgsv1G9+8xuNHDlSQ4cO1eWXX67a2lpNnz69Tx4nAAAAgN1fn4ar22+/XZJ05JFHdmn/y1/+ojPOOEOStHr16i6rchx66KH661//ql/96lf6xS9+oZEjR2revHldFsH42c9+pkgkoh/96Edqa2vT4YcfrmeeeUaBQGCHPyYAAAAAX0271HmudhWhUEjFxcVqb29nzhUAAADwFdaTbNCnc64AAAAAYHdBuAIAAACAHCBcAQAAAEAOEK4AAAAAIAcIVwAAAACQA4QrAAAAAMgBwhUAAAAA5ADhCgAAAABygHAFAAAAADlAuAIAAACAHCBcAQAAAEAOEK4AAAAAIAcIVwAAAACQA4QrAAAAAMgBwhUAAAAA5ADhCgAAAABygHAFAAAAADlAuAIAAACAHCBcAQAAAEAOEK4AAAAAIAcIVwAAAACQA4QrAAAAAMgBwhUAAAAA5ADhCgAAAABygHAFAAAAADlAuAIAAACAHCBcAQAAAEAOEK4AAAAAIAcIVwAAAACQA4QrAAAAAMgBwhUAAAAA5ADhCgAAAABygHAFAAAAADlAuAIAAACAHCBcAQAAAEAOEK4AAAAAIAcIVwAAAACQA4QrAAAAAMgBwhUAAAAA5ADhCgAAAABygHAFAAAAADlAuAIAAACAHCBcAQAAAEAOEK4AAAAAIAcIVwAAAACQA4QrAAAAAMgBwhUAAAAA5ECfhquFCxdq2rRpqq2tlWEYmjdv3la3P+OMM2QYRrfLPvvsk93mqquu6nb96NGjd/AjAQAAAPBV16fhKhKJaNy4cbrtttu2afubb75ZdXV12cuaNWtUVlamk08+uct2++yzT5ftXnnllR1RPgAAAABkefryzqdOnaqpU6du8/bFxcUqLi7O/jxv3jxt2LBBs2bN6rKdx+NRdXX1Nu83kUgokUhkfw6FQtt8WwAAAACQ+vmcq7vvvluTJ0/W4MGDu7QvXbpUtbW1GjZsmL7//e9r9erVW93PnDlzssGtuLhYgwYN2pFlAwAAANgN9dtwtX79ej399NM666yzurRPmDBB9957r5555hndfvvtWrFihY444gh1dHRscV+XXXaZ2tvbs5c1a9bs6PIBAAAA7Gb6dFjg9pg7d65KSko0ffr0Lu2bDjPcb7/9NGHCBA0ePFiPPPKIzjzzzM3uy+/3y+/378hyAQAAAOzm+mXPleu6uueee/SDH/xAPp9vq9uWlJRor7320rJly3ZSdQAAAAC+ivpluHr55Ze1bNmyLfZEbSocDmv58uWqqanZCZUBAAAA+Krq03AVDoe1ePFiLV68WJK0YsUKLV68OLsAxWWXXaYZM2Z0u93dd9+tCRMmaN999+123cUXX6yXX35ZK1eu1Kuvvqrvfve7sixLp5122g59LAAAAAC+2vp0ztWbb76pSZMmZX+ePXu2JGnmzJm69957VVdX122lv/b2dj322GO6+eabN7vPtWvX6rTTTlNLS4sqKip0+OGH67XXXlNFRcWOeyAAAAAAvvIM13Xdvi5iVxMKhVRcXKz29nYVFRX1dTkAAAAA+khPskG/nHMFAAAAALsawhUAAAAA5ADhCgAAAABygHAFAAAAADlAuAIAAACAHCBcAQAAAEAOEK4AAAAAIAcIVwAAAACQA4QrAAAAAMgBwhUAAAAA5ADhCgAAAABygHAFAAAAADlAuAIAAACAHCBcAQAAAEAOEK4AAAAAIAcIVwAAAACQA4QrAAAAAMgBwhUAAAAA5ADhCgAAAABygHAFAAAAADlAuAIAAACAHCBcAQAAAEAOEK4AAAAAIAcIVwAAAACQA4QrAAAAAMgBwhUAAAAA5ADhCgAAAABygHAFAAAAADlAuAIAAACAHCBcAQAAAEAOEK4AAAAAIAcIVwAAAACQA4QrAAAAAMgBwhUAAAAA5ADhCgAAAABygHAFAAAAADlAuAIAAACAHCBcAQAAAEAOEK4AAAAAIAcIVwAAAACQA4QrAAAAAMgBwhUAAAAA5ADhCgAAAABygHAFAAAAADlAuAIAAACAHCBcAQAAAEAO9Gm4WrhwoaZNm6ba2loZhqF58+ZtdfuXXnpJhmF0u9TX13fZ7rbbbtOQIUMUCAQ0YcIEvfHGGzvwUQAAAABAH4erSCSicePG6bbbbuvR7T755BPV1dVlL5WVldnrHn74Yc2ePVtXXnml3n77bY0bN05TpkxRY2NjrssHAAAAgCxPX9751KlTNXXq1B7frrKyUiUlJZu97oYbbtDZZ5+tWbNmSZLuuOMOPfnkk7rnnnt06aWXbk+5AAAAALBF/XLO1fjx41VTU6NvfvOb+r//+79sezKZ1FtvvaXJkydn20zT1OTJk7Vo0aIt7i+RSCgUCnW5AAAAAEBP9KtwVVNTozvuuEOPPfaYHnvsMQ0aNEhHHnmk3n77bUlSc3OzbNtWVVVVl9tVVVV1m5e1qTlz5qi4uDh7GTRo0A59HAAAAAB2P306LLCnRo0apVGjRmV/PvTQQ7V8+XLdeOONuv/++3u938suu0yzZ8/O/hwKhQhYAAAAAHqkX4WrzTn44IP1yiuvSJLKy8tlWZYaGhq6bNPQ0KDq6uot7sPv98vv9+/QOgEAAADs3vrVsMDNWbx4sWpqaiRJPp9PBxxwgObPn5+93nEczZ8/X4ccckhflQgAAADgK6BPe67C4bCWLVuW/XnFihVavHixysrKtOeee+qyyy7TunXrdN9990mSbrrpJg0dOlT77LOP4vG4/vznP+vFF1/Uc889l93H7NmzNXPmTB144IE6+OCDddNNNykSiWRXDwQAAACAHaHX4Wrp0qVasGCBGhsb5ThOl+uuuOKKbdrHm2++qUmTJmV/3jjvaebMmbr33ntVV1en1atXZ69PJpO66KKLtG7dOuXl5Wm//fbTCy+80GUfp5xyipqamnTFFVeovr5e48eP1zPPPNNtkQsAAAAAyCXDdV23pze66667dM4556i8vFzV1dUyDOPzHRpGdvW+/ioUCqm4uFjt7e0qKirq63IAAAAA9JGeZINe9Vz95je/0W9/+1v9/Oc/71WBAAAAALC76dWCFhs2bNDJJ5+c61oAAAAAoN/qVbg6+eSTuywiAQAAAABfdb0aFjhixAhdfvnleu211zR27Fh5vd4u159//vk5KQ4AAAAA+oteLWgxdOjQLe/QMPTZZ59tV1F9jQUtAAAAAEg7YUGLFStW9KowAAAAANhd9WrO1aZc11UvOr8AAAAAYLfS63B13333aezYsQoGgwoGg9pvv/10//3357I2AAAAAOg3ejUs8IYbbtDll1+u8847T4cddpgk6ZVXXtGPf/xjNTc366c//WlOiwQAAACAXV2vF7S4+uqrNWPGjC7tc+fO1VVXXdXv52SxoAUAAAAAqWfZoFfDAuvq6nTooYd2az/00ENVV1fXm10CAAAAQL/Wq3A1YsQIPfLII93aH374YY0cOXK7iwIAAACA/qZXc66uvvpqnXLKKVq4cGF2ztX//d//af78+ZsNXQAAAACwu+tVz9WJJ56o119/XeXl5Zo3b57mzZun8vJyvfHGG/rud7+b6xoBAAAAYJfXqwUtdncsaAEAAABA6lk22OZhgaFQKLuzUCi01W0JJAAAAAC+arY5XJWWlqqurk6VlZUqKSmRYRjdtnFdV4ZhyLbtnBYJAAAAALu6bQ5XL774osrKyiRJCxYs2GEFAQAAAEB/tM3hauLEidn/Dx06VIMGDerWe+W6rtasWZO76gAAAACgn+jVaoFDhw5VU1NTt/bW1lYNHTp0u4sCAAAAgP6mV+Fq49yqLwqHwwoEAttdFAAAAAD0Nz06ifDs2bMlSYZh6PLLL1deXl72Otu29frrr2v8+PE5LRAAAAAA+oMehat33nlHUqbn6v3335fP58te5/P5NG7cOF188cW5rRAAAAAA+oEehauNqwTOmjVLN998M+ezAgAAAIBOvZpzddNNNymdTndrb21t/dITDAMAAADA7qhX4erUU0/VQw891K39kUce0amnnrrdRQEAAABAf9OrcPX6669r0qRJ3dqPPPJIvf7669tdFAAAAAD0N70KV4lEYrPDAlOplGKx2HYXBQAAAAD9Ta/C1cEHH6w777yzW/sdd9yhAw44YLuLAgAAAID+pkerBW70m9/8RpMnT9a7776ro48+WpI0f/58/fvf/9Zzzz2X0wIBAAAAoD/oVc/VYYcdpkWLFmnQoEF65JFH9MQTT2jEiBF67733dMQRR+S6RgAAAADY5Rmu67p9XcSuJhQKqbi4WO3t7ZzLCwAAAPgK60k26NWwwE3F43Elk8kubQQSAAAAAF81vRoWGI1Gdd5556myslL5+fkqLS3tcgEAAACAr5pehatLLrlEL774om6//Xb5/X79+c9/1tVXX63a2lrdd999ua4RAAAAAHZ5vRoW+MQTT+i+++7TkUceqVmzZumII47QiBEjNHjwYD3wwAP6/ve/n+s6AQAAAGCX1queq9bWVg0bNkxSZn5Va2urJOnwww/XwoULc1cdAAAAAPQTvQpXw4YN04oVKyRJo0eP1iOPPCIp06NVUlKSs+IAAAAAoL/oVbiaNWuW3n33XUnSpZdeqttuu02BQEA//elPdckll+S0QAAAAADoD3JynqtVq1bprbfe0ogRI7Tffvvloq4+xXmuAAAAAEg9ywY97rlKpVI6+uijtXTp0mzb4MGDdcIJJ+wWwQoAAAAAeqPH4crr9eq9997bEbUAAAAAQL/VqzlXp59+uu6+++5c1wIAAAAA/VavznOVTqd1zz336IUXXtABBxyg/Pz8LtffcMMNOSkOAAAAAPqLXoWrJUuW6Gtf+5ok6dNPP+1ynWEY218VAAAAAPQzPQpXn332mYYOHaoFCxbsqHoAAAAAoF/q0ZyrkSNHqqmpKfvzKaecooaGhpwXBQAAAAD9TY/C1RdPifXUU08pEon0+s4XLlyoadOmqba2VoZhaN68eVvd/vHHH9c3v/lNVVRUqKioSIcccoieffbZLttcddVVMgyjy2X06NG9rhEAAAAAtkWvVgvMlUgkonHjxum2227bpu0XLlyob37zm3rqqaf01ltvadKkSZo2bZreeeedLtvts88+qqury15eeeWVHVE+AAAAAGT1aM7Vxp6gL7b11tSpUzV16tRt3v6mm27q8vO1116rf/zjH3riiSe0//77Z9s9Ho+qq6t7XRcAAAAA9FSPwpXrujrjjDPk9/slSfF4XD/+8Y+7LcX++OOP567CrXAcRx0dHSorK+vSvnTpUtXW1ioQCOiQQw7RnDlztOeee25xP4lEQolEIvtzKBTaYTUDAAAA2D31KFzNnDmzy8+nn356TovpqT/84Q8Kh8P63ve+l22bMGGC7r33Xo0aNUp1dXW6+uqrdcQRR2jJkiUqLCzc7H7mzJmjq6++emeVDQAAAGA3ZLhfXKWijxiGob///e+aPn36Nm3/17/+VWeffbb+8Y9/aPLkyVvcrq2tTYMHD9YNN9ygM888c7PbbK7natCgQWpvb1dRUVGPHgcAAACA3UcoFFJxcfE2ZYNenUS4rz300EM666yz9Oijj241WElSSUmJ9tprLy1btmyL2/j9/uxQRwAAAADojT5dLbA3HnzwQc2aNUsPPvigjjvuuC/dPhwOa/ny5aqpqdkJ1QEAAAD4qurTnqtwONylR2nFihVavHixysrKtOeee+qyyy7TunXrdN9990nKDAWcOXOmbr75Zk2YMEH19fWSpGAwqOLiYknSxRdfrGnTpmnw4MFav369rrzySlmWpdNOO23nP0AAAAAAXxl92nP15ptvav/9988uoz579mztv//+uuKKKyRJdXV1Wr16dXb7O++8U+l0Wueee65qamqylwsuuCC7zdq1a3Xaaadp1KhR+t73vqcBAwbotddeU0VFxc59cAAAAAC+UnaZBS12JT2ZtAYAAABg99WTbJDTnqsNGzZkh/ABAAAAwFdJTsPV6tWrNWvWrFzuEgAAAAD6hR4taBEKhbZ6fUdHx3YVAwAAAAD9VY/CVUlJiQzD2OL1rutu9XoAAAAA2F31KFwVFhbql7/8pSZMmLDZ65cuXar//M//zElhAAAAANCf9Chcfe1rX5MkTZw4cbPXl5SUiMUHAQAAAHwV9WhBi//4j/9QIBDY4vXV1dW68sort7soAAAAAOhvOM/VZnCeKwAAAADSTj7P1dq1a+U4zvbuBgAAAAD6te0OV2PGjNHKlStzUAoAAAAA9F/bHa4YVQgAAAAAOQhXAAAAAIAchKtf/OIXKisry0UtAAAAANBvbVe4SiaTOuGEE1RQUJCregAAAACgX+pVuIpGozrzzDOVl5enffbZR6tXr5Yk/eQnP9Hvfve7nBYIAAAAAP1Br8LVZZddpnfffVcvvfRSl5MKT548WQ8//HDOigMAAACA/sLTmxvNmzdPDz/8sL7+9a/LMIxs+z777KPly5fnrDgAAAAA6C961XPV1NSkysrKbu2RSKRL2AIAAACAr4pehasDDzxQTz75ZPbnjYHqz3/+sw455JDcVAYAAAAA/UivhgVee+21mjp1qj788EOl02ndfPPN+vDDD/Xqq6/q5ZdfznWNAAAAALDL61XP1eGHH67FixcrnU5r7Nixeu6551RZWalFixbpgAMOyHWNAAAAALDLM1zXdfu6iF1NKBRScXGx2tvbVVRU1NflAAAAAOgjPckGveq5euqpp/Tss892a3/22Wf19NNP92aXAAAAANCv9SpcXXrppbJtu1u767q69NJLt7soAAAAAOhvehWuli5dqjFjxnRrHz16tJYtW7bdRQEAAABAf9OrcFVcXKzPPvusW/uyZcuUn5+/3UUBAAAAQH/Tq3B1/PHH68ILL9Ty5cuzbcuWLdNFF12k73znOzkrDgAAAAD6i16Fq+uvv175+fkaPXq0hg4dqqFDh2rvvffWgAED9Ic//CHXNQIAAADALq9XJxEuLi7Wq6++queff17vvvuugsGg9ttvP33jG9/IdX0AAAAA0C9wnqvN4DxXAAAAAKSeZYNt7rm65ZZb9KMf/UiBQEC33HLLVrc9//zzt3W3AAAAALBb2Oaeq6FDh+rNN9/UgAEDNHTo0C3v0DA2u5Jgf0LPFQAAAABpB/VcrVixYrP/BwAAAAD0YrXAVCql4cOH66OPPtoR9QAAAABAv9TjcOX1ehWPx3dELQAAAADQb/XqPFfnnnuurrvuOqXT6VzXAwAAAAD9Uq/Oc/Xvf/9b8+fP13PPPaexY8cqPz+/y/WPP/54TooDAAAAgP6iV+GqpKREJ554Yq5rAQAAAIB+q1fh6i9/+Uuu6wAAAACAfq1Hc64cx9F1112nww47TAcddJAuvfRSxWKxHVUbAAAAAPQbPQpXv/3tb/WLX/xCBQUFGjhwoG6++Wade+65O6o2AAAAAOg3ehSu7rvvPv3pT3/Ss88+q3nz5umJJ57QAw88IMdxdlR9AAAAANAv9ChcrV69Wscee2z258mTJ8swDK1fvz7nhQEAAABAf9KjcJVOpxUIBLq0eb1epVKpnBYFAAAAAP1Nj1YLdF1XZ5xxhvx+f7YtHo/rxz/+cZdzXXGeKwAAAABfNT0KVzNnzuzWdvrpp+esGAAAAADor3oUrji/FQAAAABsXo/mXOXawoULNW3aNNXW1sowDM2bN+9Lb/PSSy/pa1/7mvx+v0aMGKF777232za33XabhgwZokAgoAkTJuiNN97IffEAAAAAsIk+DVeRSETjxo3Tbbfdtk3br1ixQscdd5wmTZqkxYsX68ILL9RZZ52lZ599NrvNww8/rNmzZ+vKK6/U22+/rXHjxmnKlClqbGzcUQ8DAAAAAGS4ruv2dRGSZBiG/v73v2v69Olb3ObnP/+5nnzySS1ZsiTbduqpp6qtrU3PPPOMJGnChAk66KCDdOutt0qSHMfRoEGD9JOf/ESXXnrpNtUSCoVUXFys9vZ2FRUV9f5BAQAAAOjXepIN+rTnqqcWLVqkyZMnd2mbMmWKFi1aJElKJpN66623umxjmqYmT56c3WZzEomEQqFQlwsAAAAA9ES/Clf19fWqqqrq0lZVVaVQKKRYLKbm5mbZtr3Zberr67e43zlz5qi4uDh7GTRo0A6pHwAAAMDua5tXC/zf//3fbd7pd77znV4V01cuu+wyzZ49O/tzKBQiYAEAAADokW0OV1ubC7UpwzBk23Zv69mq6upqNTQ0dGlraGhQUVGRgsGgLMuSZVmb3aa6unqL+/X7/V1OjAwAAAAAPbXNwwIdx9mmy44KVpJ0yCGHaP78+V3ann/+eR1yyCGSJJ/PpwMOOKDLNo7jaP78+dltAAAAAGBH6NM5V+FwWIsXL9bixYslZZZaX7x4sVavXi0pM1xvxowZ2e1//OMf67PPPtPPfvYzffzxx/rTn/6kRx55RD/96U+z28yePVt33XWX5s6dq48++kjnnHOOIpGIZs2atVMfGwAAAICvlm0eFvhFkUhEL7/8slavXq1kMtnluvPPP3+b9vHmm29q0qRJ2Z83znuaOXOm7r33XtXV1WWDliQNHTpUTz75pH7605/q5ptv1h577KE///nPmjJlSnabU045RU1NTbriiitUX1+v8ePH65lnnum2yAUAAAAA5FKvznP1zjvv6Nhjj1U0GlUkElFZWZmam5uVl5enyspKffbZZzui1p2G81wBAAAAkHbCea5++tOfatq0adqwYYOCwaBee+01rVq1SgcccID+8Ic/9KpoAAAAAOjPehWuFi9erIsuukimacqyLCUSCQ0aNEjXX3+9fvGLX+S6RgAAAADY5fUqXHm9Xplm5qaVlZXZeVHFxcVas2ZN7qoDAAAAgH6iVwta7L///vr3v/+tkSNHauLEibriiivU3Nys+++/X/vuu2+uawQAAACAXV6veq6uvfZa1dTUSJJ++9vfqrS0VOecc46ampp055135rRAAAAAAOgPerVa4O6O1QIBAAAASDthtUAAAAAAQFe9mnM1dOhQGYaxxev7+3muAAAAAKCnehWuLrzwwi4/p1IpvfPOO3rmmWd0ySWX5KIuAAAAAOhXehWuLrjggs2233bbbXrzzTe3qyAAAAAA6I9yOudq6tSpeuyxx3K5SwAAAADoF3Iarv72t7+prKwsl7sEAAAAgH6h1ycR3nRBC9d1VV9fr6amJv3pT3/KWXEAAAAA0F/0Klwdf/zxXcKVaZqqqKjQkUceqdGjR+esOAAAAADoLziJ8GZwEmEAAAAA0k44ibBlWWpsbOzW3tLSIsuyerNLAAAAAOjXehWuttTZlUgk5PP5tqsgAAAAAOiPejTn6pZbbpEkGYahP//5zyooKMheZ9u2Fi5cyJwrAAAAAF9JPQpXN954o6RMz9Udd9zRZQigz+fTkCFDdMcdd+S2QgAAAADoB3oUrlasWCFJmjRpkh5//HGVlpbukKIAAAAAoL/p1VLsCxYsyHUdAAAAANCv9WpBixNPPFHXXXddt/brr79eJ5988nYXBQAAAAD9Ta/C1cKFC3Xsscd2a586daoWLly43UUBAAAAQH/Tq3AVDoc3u+S61+tVKBTa7qIAAAAAoL/pVbgaO3asHn744W7tDz30kMaMGbPdRQEAAABAf9OrBS0uv/xynXDCCVq+fLmOOuooSdL8+fP14IMP6tFHH81pgQAAAADQH/QqXE2bNk3z5s3Ttddeq7/97W8KBoPab7/99MILL2jixIm5rhEAAAAAdnmG67puLne4ZMkS7bvvvrnc5U4XCoVUXFys9vZ2FRUV9XU5AAAAAPpIT7JBr+ZcfVFHR4fuvPNOHXzwwRo3blwudgkAAAAA/cp2hauFCxdqxowZqqmp0R/+8AcdddRReu2113JVGwAAAAD0Gz2ec1VfX697771Xd999t0KhkL73ve8pkUho3rx5rBQIAAAA4CurRz1X06ZN06hRo/Tee+/ppptu0vr16/Vf//VfO6o2AAAAAOg3etRz9fTTT+v888/XOeeco5EjR+6omgAAAACg3+lRz9Urr7yijo4OHXDAAZowYYJuvfVWNTc376jaAAAAAKDf6FG4+vrXv6677rpLdXV1+s///E899NBDqq2tleM4ev7559XR0bGj6gQAAACAXdp2n+fqk08+0d133637779fbW1t+uY3v6n//d//zVV9fYLzXAEAAACQdvJ5rkaNGqXrr79ea9eu1YMPPri9uwMAAACAfqlH4WrPPffUeeedp+eee07pdLrLdZZlafr06f2+1woAAAAAeqNH4er++++X3+/Xueeeq/Lycp1yyil64IEH1NbWtoPKAwAAAID+oddzrj744AP97//+r/7xj39o8eLFOvTQQ/Wd73xH3/nOdzRs2LBc17lTMecKAAAAgLST5lzts88+uuyyy/Taa69p5cqVOu200zR//nztu+++2nffffXkk0/2dtcAAAAA0O9s92qBXxSJRPTcc8+psLBQkydPzuWudxp6rgAAAABIPcsGnp7sdFt997vf3eZtAQAAAGB3sM3hqqSkRIZhbNO2tm33uiAAAAAA6I+2OVwtWLAg+/+VK1fq0ksv1RlnnKFDDjlEkrRo0SLNnTtXc+bMyX2VAAAAALCL69Wcq6OPPlpnnXWWTjvttC7tf/3rX3XnnXfqpZdeylV9fYI5VwAAAACknbBa4KJFi3TggQd2az/wwAP1xhtv9GaXAAAAANCv9SpcDRo0SHfddVe39j//+c8aNGhQj/d32223aciQIQoEApowYcJWA9qRRx4pwzC6XY477rjsNmeccUa364855pge1wUAAAAA22qb51xt6sYbb9SJJ56op59+WhMmTJAkvfHGG1q6dKkee+yxHu3r4Ycf1uzZs3XHHXdowoQJuummmzRlyhR98sknqqys7Lb9448/rmQymf25paVF48aN08knn9xlu2OOOUZ/+ctfsj/7/f4e1QUAAAAAPdGrnqtjjz1Wn376qaZNm6bW1la1trZq2rRp+vTTT3Xsscf2aF833HCDzj77bM2aNUtjxozRHXfcoby8PN1zzz2b3b6srEzV1dXZy/PPP6+8vLxu4crv93fZrrS0dIs1JBIJhUKhLhcAAAAA6Ile9VxJmaGB11577XbdeTKZ1FtvvaXLLrss22aapiZPnqxFixZt0z7uvvtunXrqqcrPz+/S/tJLL6myslKlpaU66qij9Jvf/EYDBgzY7D7mzJmjq6++uvcPBAAAAMBXXq96riTpX//6l04//XQdeuihWrdunSTp/vvv1yuvvLLN+2hubpZt26qqqurSXlVVpfr6+i+9/RtvvKElS5borLPO6tJ+zDHH6L777tP8+fN13XXX6eWXX9bUqVO3eP6tyy67TO3t7dnLmjVrtvkxAAAAAIDUy56rxx57TD/4wQ/0/e9/X2+//bYSiYQkqb29Xddee62eeuqpnBa5JXfffbfGjh2rgw8+uEv7qaeemv3/2LFjtd9++2n48OF66aWXdPTRR3fbj9/vZ04WAAAAgO3Sq56r3/zmN7rjjjt01113yev1ZtsPO+wwvf3229u8n/LyclmWpYaGhi7tDQ0Nqq6u3uptI5GIHnroIZ155plfej/Dhg1TeXm5li1bts21AQAAAEBP9CpcffLJJ/rGN77Rrb24uFhtbW3bvB+fz6cDDjhA8+fPz7Y5jqP58+frkEMO2eptH330USUSCZ1++ulfej9r165VS0uLampqtrk2AAAAAOiJXoWr6urqzfYCvfLKKxo2bFiP9jV79mzdddddmjt3rj766COdc845ikQimjVrliRpxowZXRa82Ojuu+/W9OnTuy1SEQ6Hdckll+i1117TypUrNX/+fB1//PEaMWKEpkyZ0qPaAAAAAGBb9WrO1dlnn60LLrhA99xzjwzD0Pr167Vo0SJdfPHFuvzyy3u0r1NOOUVNTU264oorVF9fr/Hjx+uZZ57JLnKxevVqmWbXDPjJJ5/olVde0XPPPddtf5Zl6b333tPcuXPV1tam2tpafetb39I111zDvCoAAAAAO4zhuq7b0xu5rqtrr71Wc+bMUTQalZRZFOLiiy/WNddck/Mid7ZQKKTi4mK1t7erqKior8sBAAAA0Ed6kg16Fa42SiaTWrZsmcLhsMaMGaOCgoLe7mqXQrgCAAAAIPUsG/RqztUPf/hDdXR0yOfzacyYMTr44INVUFCgSCSiH/7wh70qGgAAAAD6s16Fq7lz5yoWi3Vrj8Viuu+++7a7KAAAAADob3q0oEUoFJLrunJdVx0dHQoEAtnrbNvWU089pcrKypwXCQAAAAC7uh6Fq5KSEhmGIcMwtNdee3W73jAMXX311TkrDgAAAAD6ix6FqwULFsh1XR111FF67LHHVFZWlr3O5/Np8ODBqq2tzXmRAAAAALCr61G4mjhxoiRpxYoV2nPPPWUYxg4pCgAAAAD6m14taPHiiy/qb3/7W7f2Rx99VHPnzt3uogAAAACgv+lVuJozZ47Ky8u7tVdWVuraa6/d7qIAAAAAoL/pVbhavXq1hg4d2q198ODBWr169XYXBQAAAAD9Ta/CVWVlpd57771u7e+++64GDBiw3UUBAAAAQH/Tq3B12mmn6fzzz9eCBQtk27Zs29aLL76oCy64QKeeemquawQAAACAXV6PVgvc6JprrtHKlSt19NFHy+PJ7MJxHM2YMYM5VwAAAAC+kgzXdd3e3vjTTz/Vu+++q2AwqLFjx2rw4MG5rK3PhEIhFRcXq729XUVFRX1dDgAAAIA+0pNs0Kueq4322msv7bXXXtuzCwAAAADYLWxzuJo9e7auueYa5efna/bs2Vvd9oYbbtjuwgAAAACgP9nmcPXOO+8olUpl/78lhmFsf1UAAAAA0M9s15yr3RVzrgAAAABIPcsGvVqKHQAAAADQ1TYPCzzhhBO2eaePP/54r4oBAAAAgP5qm3uuiouLs5eioiLNnz9fb775Zvb6t956S/Pnz1dxcfEOKRQAAAAAdmXb3HP1l7/8Jfv/n//85/re976nO+64Q5ZlSZJs29b/+3//jzlKAAAAAL6SerWgRUVFhV555RWNGjWqS/snn3yiQw89VC0tLTkrsC+woAUAAAAAaScsaJFOp/Xxxx93a//444/lOE5vdgkAAAAA/do2Dwvc1KxZs3TmmWdq+fLlOvjggyVJr7/+un73u99p1qxZOS0QAAAAAPqDXoWrP/zhD6qurtYf//hH1dXVSZJqamp0ySWX6KKLLsppgQAAAADQH2z3SYRDoZAk7VZzk5hzBQAAAEDaSScRTqfTeuGFF/Tggw/KMAxJ0vr16xUOh3u7SwAAAADot3o1LHDVqlU65phjtHr1aiUSCX3zm99UYWGhrrvuOiUSCd1xxx25rhMAAAAAdmm96rm64IILdOCBB2rDhg0KBoPZ9u9+97uaP39+zooDAAAAgP6iVz1X//rXv/Tqq6/K5/N1aR8yZIjWrVuXk8IAAAAAoD/pVc+V4ziybbtb+9q1a1VYWLjdRQEAAABAf9OrcPWtb31LN910U/ZnwzAUDod15ZVX6thjj81VbQAAAADQb/RqKfY1a9bomGOOkeu6Wrp0qQ488EAtXbpU5eXlWrhwoSorK3dErTsNS7EDAAAAkHqWDXp9nqt0Oq2HH35Y7777rsLhsL72ta/p+9//fpcFLvorwhUAAAAAaQeHq1QqpdGjR+uf//yn9t577+0qdFdFuAIAAAAg7eCTCHu9XsXj8V4XBwAAAAC7o14taHHuuefquuuuUzqdznU9AAAAANAv9eo8V//+9781f/58Pffccxo7dqzy8/O7XP/444/npDgAAAAA6C96Fa5KSkp04okn5roWAAAAAOi3ehWu/vKXv+S6DgAAAADo13o058pxHF133XU67LDDdNBBB+nSSy9VLBbbUbUBAAAAQL/Ro3D129/+Vr/4xS9UUFCggQMH6uabb9a55567o2oDAAAAgH6jR+Hqvvvu05/+9Cc9++yzmjdvnp544gk98MADchxnR9UHAAAAAP1Cj8LV6tWrdeyxx2Z/njx5sgzD0Pr163NeGAAAAAD0Jz0KV+l0WoFAoEub1+tVKpXKaVEAAAAA0N/0KFy5rqszzjhDJ5xwQvYSj8f14x//uEtbT912220aMmSIAoGAJkyYoDfeeGOL2957770yDKPL5YuBz3VdXXHFFaqpqVEwGNTkyZO1dOnSHtcFAAAAANuqR+Fq5syZqqysVHFxcfZy+umnq7a2tktbTzz88MOaPXu2rrzySr399tsaN26cpkyZosbGxi3epqioSHV1ddnLqlWrulx//fXX65ZbbtEdd9yh119/Xfn5+ZoyZYri8XiPagMAAACAbWW4ruv2ZQETJkzQQQcdpFtvvVVSZrn3QYMG6Sc/+YkuvfTSbtvfe++9uvDCC9XW1rbZ/bmuq9raWl100UW6+OKLJUnt7e2qqqrSvffeq1NPPfVLawqFQiouLlZ7e7uKiop6/+AAAAAA9Gs9yQY96rnKtWQyqbfeekuTJ0/OtpmmqcmTJ2vRokVbvF04HNbgwYM1aNAgHX/88frggw+y161YsUL19fVd9llcXKwJEyZscZ+JREKhUKjLBQAAAAB6ok/DVXNzs2zbVlVVVZf2qqoq1dfXb/Y2o0aN0j333KN//OMf+p//+R85jqNDDz1Ua9eulaTs7Xqyzzlz5nQZ1jho0KDtfWgAAAAAvmL6NFz1xiGHHKIZM2Zo/Pjxmjhxoh5//HFVVFTov//7v3u9z8suu0zt7e3Zy5o1a3JYMQAAAICvgj4NV+Xl5bIsSw0NDV3aGxoaVF1dvU378Hq92n///bVs2TJJyt6uJ/v0+/0qKirqcgEAAACAnujTcOXz+XTAAQdo/vz52TbHcTR//nwdcsgh27QP27b1/vvvq6amRpI0dOhQVVdXd9lnKBTS66+/vs37BAAAAICe8vR1AbNnz9bMmTN14IEH6uCDD9ZNN92kSCSiWbNmSZJmzJihgQMHas6cOZKkX//61/r617+uESNGqK2tTb///e+1atUqnXXWWZIkwzB04YUX6je/+Y1GjhypoUOH6vLLL1dtba2mT5/eVw8TAAAAwG6uz8PVKaecoqamJl1xxRWqr6/X+PHj9cwzz2QXpFi9erVM8/MOtg0bNujss89WfX29SktLdcABB+jVV1/VmDFjstv87Gc/UyQS0Y9+9CO1tbXp8MMP1zPPPNPtZMMAAAAAkCt9fp6rXRHnuQIAAAAg9aPzXAEAAADA7oJwBQAAAAA5QLgCAAAAgBwgXAEAAABADhCuAAAAACAHCFcAAAAAkAOEKwAAAADIAcIVAAAAAOQA4QoAAAAAcoBwBQAAAAA5QLgCAAAAgBwgXAEAAABADhCuAAAAACAHCFcAAAAAkAOEKwAAAADIAcIVAAAAAOQA4QoAAAAAcoBwBQAAAAA5QLgCAAAAgBwgXAEAAABADhCuAAAAACAHCFcAAAAAkAOEKwAAAADIAcIVAAAAAOQA4QoAAAAAcoBwBQAAAAA5QLgCAAAAgBwgXAEAAABADhCuAAAAACAHCFcAAAAAkAOEKwAAAADIAcIVAAAAAOQA4QoAAAAAcoBwBQAAAAA5QLgCAAAAgBwgXAEAAABADhCuAAAAACAHCFcAAAAAkAOEKwAAAADIAcIVAAAAAOQA4QoAAAAAcoBwBQAAAAA5QLgCAAAAgBwgXAEAAABADhCuAAAAACAHCFcAAAAAkAOEKwAAAADIgV0iXN12220aMmSIAoGAJkyYoDfeeGOL295111064ogjVFpaqtLSUk2ePLnb9meccYYMw+hyOeaYY3b0wwAAAADwFdbn4erhhx/W7NmzdeWVV+rtt9/WuHHjNGXKFDU2Nm52+5deekmnnXaaFixYoEWLFmnQoEH61re+pXXr1nXZ7phjjlFdXV328uCDD+6MhwMAAADgK8pwXdftywImTJiggw46SLfeeqskyXEcDRo0SD/5yU906aWXfuntbdtWaWmpbr31Vs2YMUNSpueqra1N8+bN61VNoVBIxcXFam9vV1FRUa/2AQAAAKD/60k26NOeq2QyqbfeekuTJ0/OtpmmqcmTJ2vRokXbtI9oNKpUKqWysrIu7S+99JIqKys1atQonXPOOWppadniPhKJhEKhUJcLAAAAAPREn4ar5uZm2batqqqqLu1VVVWqr6/fpn38/Oc/V21tbZeAdswxx+i+++7T/Pnzdd111+nll1/W1KlTZdv2ZvcxZ84cFRcXZy+DBg3q/YMCAAAA8JXk6esCtsfvfvc7PfTQQ3rppZcUCASy7aeeemr2/2PHjtV+++2n4cOH66WXXtLRRx/dbT+XXXaZZs+enf05FAoRsAAAAAD0SJ/2XJWXl8uyLDU0NHRpb2hoUHV19VZv+4c//EG/+93v9Nxzz2m//fbb6rbDhg1TeXm5li1bttnr/X6/ioqKulwAAAAAoCf6NFz5fD4dcMABmj9/frbNcRzNnz9fhxxyyBZvd/311+uaa67RM888owMPPPBL72ft2rVqaWlRTU1NTuoGAAAAgC/q86XYZ8+erbvuuktz587VRx99pHPOOUeRSESzZs2SJM2YMUOXXXZZdvvrrrtOl19+ue655x4NGTJE9fX1qq+vVzgcliSFw2Fdcskleu2117Ry5UrNnz9fxx9/vEaMGKEpU6b0yWMEAAAAsPvr8zlXp5xyipqamnTFFVeovr5e48eP1zPPPJNd5GL16tUyzc8z4O23365kMqmTTjqpy36uvPJKXXXVVbIsS++9957mzp2rtrY21dbW6lvf+pauueYa+f3+nfrYAAAAAHx19Pl5rnZFnOcKAAAAgNSPznMFAAAAALsLwhUAAAAA5ADhCgAAAABygHAFAAAAADlAuAIAAACAHCBcAQAAAEAOEK4AAAAAIAf6/CTC2LnS6bT+b3mLPlofkiNX4/co1qiaYpXl+2UYRl+XBwAAAPRbhKuvCNd19fC/V+ueV1ZozYaokunMuaO9plRTEtQx+9Zo+viB2qu6kJAFAAAA9ALh6iugMRTX3a98pgffWK1wwpZcyezMTwlbWtES019eWaFXljbrxK/toWP3q1FlUaBviwYAAAD6GcLVbsx1XS1tCOvFj+r1z3fXK5qwZUiyLMl2JNeVNvZRJW1Xa1rDmvfOWrVEEpo2bqBGVhXQiwUAAABsI8LVbqoxFNf7a9u14JNGfbC+TU3hpGRIlmnIcJVJVpJMU3IdyZGUsB3VdcT14scNqm+Pa8KwAdpvjxJCFgAAALANCFe7ocZQXC9+1KCP6ztU1xaTxzAluXIcScqEKhmZXqvOn+Qq05tlylAi6ejj+g41diT0f8ua9Y29KnTYiHKGCgIAAABbQbjazbiuqyffW69nP6hXKJZSayQpGYacziGArvt5oPriz6YhpR1XCcdRoWVo8IA8tcdSWvRZq5K2o0mjKglYAAAAwBZwnqvdzNNL6nTfa6u0rDGiDdGU0o4rj1wZkmxJtpsdESipa7iyDENe01Ce11LQa8nnsVRZGJDHkFY2R/Svpc1qDSfkbroDAAAAAJLoudqt1LdFddfLn3UOBZTihinXdRWVoYDHkJ105SgTpr6Yj/ymlOf3KuCzZFqmyvL9CnhNheNprWmLqT2W0sqWiJrDcY2sLNK+A4voxQIAAAA2Qc/VbsJ1Xd258DN9XN+htO3KNQwZhmSYhtKuo6QtFQQseYzPVwjcKOgxVVMSkM9jypQ0IN+nyiK/oklbnzVHFE6kVZLnVYHPq4DH0vKmDr30SZMaQ/G+eKgAAADALomeq93Ep3UhvfBhg9KOozyvKccwZHbOtfJZptJpR7btqCjg0bCKAlUW+RRP2XIcR60RW9FUWl7L0MDSoIZVFCjPZ2llc1Rt0ZT2LMtT0GtJhqOioFeS9FlzWK8tt/TtcdUyTTI6AAAAQLjaDbiuqwWfNqojmZLfY8k0DbmuZDuOLNOU7Uoey1DKdlQR8OrcSSN05OhKSdKGSFIf13fojRUtWtEckQxDfo+p9lhK69tjKsnzqrzAp/ZEWoV+j5Y3hdUaSaotmtKHdR2KJFM6anSlqoqDffwsAAAAAH2LcLUbaIum9FlzVI6TmUgVSzmyTMkwDNmdK1akbVeSob1rizRxVEX2vFVlBX4dOsKvEZUFemVps/61tElLG8Pyeyzl+UwNLAkqkrJluK7C8bQaOxJKpm1Fk7baY0k9+0GDPm0I6+QD99CY2uI+fBYAAACAvsV4rt3AuraYGtvj8liWvJYpj2XIlZFZ1c9xZZmZRSyCPkvf3b92s8P4KosC+u7XBuqcI0do6r41GllZoAK/V2nbUXVhQAUBr6IpW5FEWpGELa9pqjTPpz3KAlrVEtH9i1bp/bVt2hBJspogAAAAvpLouernXNfVZ01h+bymKgp8agglFPCaSjuuUraUdCSjsyfrkGHlmjymeov7MgxDe1UXamRVgTZEklrwcZPWtkU1qDSoN1dtUDJtK5l2VBzwqD2eUmm+X17TlMc0tHhtm+IpW/vvWaKSPJ+GVRRoYElQJXnebC8ZAAAAsDsjXPVzbdGU1m6IylCmt8owDSXSjjyS8r2m0o6UTNsaUZmv844avk2LTxiGobICv47Yq1wvfdKkFS0RtUVTiiZt+SxT7fGUAl6PCvyWVrVEFU/ZyvdZiibTWtYYUWNHizyWqTE1hRpdXcyy7QAAAPhKYFhgP7euLaoP6zpkWYaqigLaoyRPA/J9MkxDKUfyWqZqS/J05hEjtM/Akh7tu7IooCNHVWhkRZEStqP2WFK266g03689y4LqiNtKpB3l+zyyTFONHUm1RhPaoySogMdUWzSlpY0hPflenZasa2fIIAAAAHZr9Fz1Y67r6rPGqFK2o0ElQdmFUlM4oVDMq3AirUgipcrCgCYMLdMRIyt6dR+VRQF9e1y1IsmUnv2gQXuUBVQS9CmWchSOp5TvsxRJppVMZ05PXFUUUMDrUYlhqDmckONKa1ojem/dBg0vL9CeZQWaMKyU1QUBAACw2yFc9WNt0ZTaYgkNLsvThlhKlQV+DSnLUzztyHZcpdK2msIJ7TkgXyV53l7fj2maOmp0pT5tCGt1a1QBy1LKcZVIO0rZtizTVMq2NbAkT0GPJUlKO47WtMYUiqVkO44a2hNqj6b11uo2vb26Vd87aBCrCwIAAGC3wrDAfiyRdpRyXI2oKFS+16PGcELxtCOfZcoyjcz/PZaGVRRs96ISVcVBnXzgHtqzLE9r22JqDieUdhwFvB4FvB4FfR7VFAclw5Dkan1bXLF0WqF4WnXtCbXFkuqIp5RI2XpnTZvmvrpSDe2x3DwRAAAAwC6Anqt+zO8x5bNM+b2m9h1YpFWtUbVGMiHGY5kqy/epJM+rgSW5GYI3prZYP57o0+srWrW6NSq/ZSqWtLVHWVCFEUteKxPgYklbjaG4DFdqDSdkmIYCPktFQZ8s05DtuFqyvl3PflCvb+9Xq6Ttyu8xWVkQAAAA/Rrhqh/LBKc8LW/q0JAB+dpvYLEiCVspx5HHNNTUEdeIyqLtGhL4RVXFQU0bV6u2aErr2mL694pWpRxHpmGqJRJXaTCzHLzkKJF2ZZiGLNNQYcCrgMeUDENl+T61xVJ6+v16RZNpWaah5nBSQa+l/fYo0cS9ymVZVs5qBgAAAHYGwlU/ZhiG9h1YpKaOuD6sC6kk6FVBwCuvaagpnFBJvl/7DizKeW+QYRgqzfepNN+nykK/lqwLKW2HtL49pjVtMVUV+hVJBNQaDXf2rlkqCng7hwxK0ZSttO2oKZzQZ80RrWiKqK49rnjalu/NNdqrqlAnfG0PHTGygt4sAAAA9BuEq92Az2OqqSOhj+tCkiENyPfra3uW6vCR5Tv8/FKVRQFNKvRr/z1LtK4tqs8ao2qLJtTYkVA8Zas0z6fyAr8C3kxPlOs6auxIyDIlx5UWLWtR2nVVkudVketRcySpt1dv0NKGDr29aoO+sVcl58kCAABAv0C46mdc11VLR1zvrW3XuraYVjSHJVeqLQloZGW+XBkKxVJKOc5Oq2nTnqx9aovVFk1peGW+mjsSiqZsmUam7rTtZs6VZTvyeTxqCMVlyNUepXlKO1IolpJlmCrwG0rZjt5du0HFeR69v65NBw0p0+jqQpXm++jJAgAAwC6JcNWPNLTHNPfVlZr/UaOaI0lFE2nZrqOA11RlYUB7lOZpeEWh9q4p0IZoSkvWhTSp0L9Tw8jGoHXoiAotbYzoyffq1BxOyO+15LNMBXyWrLihtO3Idl1VFmbCUiyZUspxFfSaStqu8v2mVrXEtPDTJsWSjhZ+2qTR1YU6cEiZDhux43vkAAAAgJ4iXPUTH65v143Pf6LXV7QqZbuS6yqZdmUYUizlqiGUkGWaCifS6oinNHaPIq1ri6otmlJpvm+n12sYho4dW6O07WjJ+pBStivLlFxX6oil5fMY2hBLKuD1yHYyy8r7LEOuJMd1FUs6iiTTiiYdVRcHlEw7ao0k9cJHjWqNJDVtXC0BCwAAALsUwlU/0NAe0z2vLNcbK1qVTDsyDSntSK4heS1DpqRk2lEomlRpwNK69qgqCvwqK/Apkd55wwO/qLIooO+MH6ihFQVa1hhWNGkr6DU1tDxf9aGY1rXFlEjb8pqWHGVejAnbkeu6iqbSsgxDA/J9yvN5ZDspVRUF1BFP6t21bfKYhiYMG6DKQj9DBQEAALBLIFzt4lzX1XMfNOrtVe1K2a4CXlNp25VMV6YjpW1XXsuUZbrqSNhyDFOWI61ui6oozyu/p2/PE11ZFNBRhZkFNhJpR36PqWTa1oKPG7RkfUgt4YQq832S4ypuO7JMU2nXUSotFQU9KsnzKu24sgxDCdtRSzip1Rvi+qQupBc+rldRwK8xNQX65t7VGlldSMgCAABAnyFc7eI2RJJ6Z03mXFKGIVmGoZRcmTJkGJItV7bjyDINOXJlO648pqENkaTKC/w5PcdVb22ch7Wpo/auVlMkqUf/vVbrQ4lMD5xhyDKlcDQtj2VozwF5kgxFEikFvJZWN0dU15FQPJVW2jS0vi2u5YmI3lzZovkfNek742t17NgahgsCAACgTxCudnFNHUm1x1MqCXjUFknJkTp7Z1yZhiHXldKuK8N15TVNOa6rcCKtPUrzNHZg8S7bk1NZFNB5k0ZqREWBHn9nnVY0RRSKJZW2XRUHvCrJ8yrf51V7LCV/52StlmhKruMolXYVl6NSy1JFoV/RpK3GjrgeeXO11m2I6qQDBmmvbejFcl1XGyJJNXUk5ErympnhleGEo3yfoYTtyjRNFQU8GjwgT6ZpZm/XFk1le+I4FxcAAAAkwlU/kAlNFUUBrQ9lzh3lMQ3ZklxJpim5acl2pQKvIdt2VJzn09GjKzWyqqCvi98qwzA0dWytvjWmSu+vC2lFc1jNHQmtaI7ow7oOtceTqi0Kqijo1cqWiOS6iqccJR1bBf5MAEvZmbZwIq22aEJNHUm9tbpN3xpTqenjB6q6JE+O4+izxrDeXdumlC2NqMyT7bhatKJVnzVF1BFPqTmcVDieluO6cpUJWZZpqCToVW1JUGNqizRln2qVF2ROmryuLaqk7chnmRpYktflXFyELwAAgK8mwtUurqLQr/ICvxo7EhpUEtSypoiStivDcJVKu7JdyTSkAr+lwWX5GlDg18FDy3TEXhX95gu9ZVkav2epxu9ZKtd1tbQhrH++t14f14VkWUbn6oG2QvGUEmlbfstSSTATrFojSYXjSaVsV44rRRIpfVrfoXWtEf3fshZN2bdab61s1WuftSqcSGUWAnEdGYbx+dLwkmzXVSLtynFc2W5mjpfHMmUZ0oaIqbdWbdDyxohGVOYr3+9RZWFAAa+leMrW8qYONYcTOnJUhSR1C1+1JUENKs1TUTAzB64oYGlVS1Tr22JKph3VFAdVVRxgYQ4AAIB+znBd1+3rInY1oVBIxcXFam9vV1FRUZ/W4rqu5r2zTi981CjJVX17XKtaIookbKUcR64r5flM7VVVqL1rivW1PUt1+Mj+fx6oxlBcryxt1turN6iuPaa1rVHFUpmVD/0eU4VBr0LRlDbEUkqlbcXTtgwZ8ntNBb2WAh5T4aSjlO3Ith1ZliG/ZaojnlLSVmZ1QkMqzfMqmrTluK4KfJZiaUcp21Vx0COfx5IrV0UBn4YMCGp9W1yFQa9+8PU9ZZlWtlbXdbWyJaLyAn9m1cZ4Khu+Gjtien9dh1JpW4PL85RMO1reGNbaDTG1xVKyHUcFPq9GVhfqm3tXZX939H4BAADsGnqSDei52sUZhqHDRpSrNZLUJw1hDS23VFPs04ZoWmnH0bAB+Z2LOOSpotC32/R+VBYF9N2vDdSRoyrUGEro9RUten1Fi1a3RJR2MsP2YmlbjmMrYTtyXCnPa8hjmvJYlgoClkLxqMJxR5YpDSoMakM8JcMw5bXcbE9XKJ5S0GsplpZinfvxmFLalQKmIceR0raj5khSlmkonLTVHE6pqujzcGUYhioK/Xpz5QZVFPo1pqZIhmGoPZbU8saoHMeWI6luQ1yfNHZoTWtUrisVBSwVBXwKJ9Jasq5d4XhSrZGkDh0xQPXtiS0OPdwRwctxHK1qiaojkVaBz1Jx0Kt4ytbShpCWNUUlSfsPKtG4QcUKxW01dSQkGaoo9Kkkz6v2WLpLPa7ramVzROvb4/JZpoZX5qs0z6f2WFrxlK1YMq1k2pFhmN1etxsfXzxlK56y5feYSqQdBbyWAl5LBT5Dry5v0Uf1YQW9lg4dVqbhVYXZOXEAAAB9hXDVD1QWBTRtXK2GrGvPni8qz2dpRGWBxg4s7ve9VFtiGIbKCvydF59StqPGjkTmhMlGWum0rWRachxXPsuUaWRWUAx6TTmuFE93zkszpFjayc6jcpzMyZddN3O+sLTjZgKVnenEtczMQiGZr/pG5xwsV17TkO04iqXsbrXajtQSTmhkZYEMw5ArV6taYookU6ouCiqaTOuNlRvUHk/J7zGVsl15TEt5Po+CXkutkaSaI0ktXt2qdRtiqikNqGozQw/3HVjUJXh5DMlrWSoKelVVGNCwijyVFfi7ha3NBbKNAWjRZy1avGqD2mJpxdNpxdOuEilbjaGYNsRSsh3JlOTzmqoq8GtwRUFmkRFDyvNZKs3zqSzfL7/XlM8yZTuuPq3v0NKmsDriKZlGZu7a4AH5Ks33aV1bVOvb4nIcV0VBjwaW5GV7XKXMsMqP60Na3RpVczihRMpWwGtpQIFfkURa767ZoKZwUrbtSIahfJ+l8XsW68T9ByngzTy3ZXlelRX41BpOakMsrdI8r0ZUFagsv/tzAwDYdhsPxoViKbXHUyoKeFUc9GrPsqBCcXuHj7jY9PPMZ2U+b5s7ktmFqVJO5vO7ovM8mJI2e0Byc5+LkjoXukpKcrP7+OL2PivzuJK2K7/HVHHQkz14+MWDghtPibNx21w8L7k4yLqjR8hs6/63tF1/HsFDuNrFbXxxOW6m52D/QSU5fYP2FxtPSOyxTP3vu+u1pjWqeNKRK0emJI9pyDBMBb0e5fk8iqfScl1XhjIhze4c/WqahkzHUObPcSZguVI2bMnINBpGpl3K7MPnMWTbrgwzM+zwi8KJlGRIBYHMH+dIwlZLJKGSoE8yDMVTjlojiWydeT4paTuyHcljmSoIehWJp7WuPaZo2tF+g4qV78+8PfP9Hg3x5WvJ+pA+qgupptivqqKgmkIJLVjeqOVNUaXTtgqCXg0sDujgoeX6xqhyDSzJU0meV00diW7zwGzH1Sf1HXpvbZtWb4jJcVwVBCwVei2lJa3dEFPakSxD8lqSXCmWdPRZS0z1HQkdMbJcpUG/PqoPKZHq0KiqQh06YoCaw3E98W692qIJlRcGNLAkqGjC1srmiJY1RlSa55Hfa8lrGrKsTBBqCif04scNWtUSUVHQq1A8rcZQXKF4UpFEWuFEWgWOq+ZwQh/XhZSwXVmGlO+3Onsf03rpkxb937IW+T2mjMwvUa4hmTLktSwFfZYGlQb1rX2qN7tc/xd7yzZ+ILquq+ZwSl/8kN1RvuzDpNuXCtdVczgpyVB5Qea1t2m9JXletUVTXXoaN/cY+vOHGIDPbfpe9hiu1nQOQS/N82nswCJZltVt289HE2Q++zYXKDb+bVzdGtGry1r1YX2b1rXGFUvZCng9qiz0qao4oD1KgxpQ4N/sYk+50BiKZz/PWiIJrWmNqSWSUCrtqCOeVjRlK6/zYNzAkqBGVBaoKOBVJJnuMhKkutjfbYRIgd+j9mhKy5rCaokkJFcaUODXAYNLNaq6MLt9a+cqw5JUXuiTz8oEqcw88IRawsnM/HBP5rPHY5rK82VWNy7L923387Lpc7ClhbV2xj5ysf8tbbe538+OeD3tKISrXdjWXpxfPG/UV0FlUUAzDx2iQ4cN0HMfNGjBpw3ZoWyWaagkz6vCgFdey1A44Xb2ZGXCkbXJkDNzk++MhiFZMmQ7rlw3c46wtCPlG5keMVeuPJap8nxf55wrT/ZL7Eau66o9mtKAfL86D2YpZTtKO468ns6wlUzLdVyZlilHrizTUtJ25HQmukyvmKtIwlFloam084WpkIYUS9pa3x7TfnsUqyWc0LMf1amuLTPszh/wyDIMLW+J6NOmsBZ91qxDhperuiigDdGkHNfNzgNb3hTSE+/WZ9odR15Tyg9kjrqFopkv5k5mepssQwp4PUqkHGUqlxIpR0vrwxpVYyjPZynfa6kpkpkLuLI5qmgyLZ/HlL/zEoqnVRT0qCWcVF0orpqigPaoKJAMQ+2xlDyGIb/H0NurN6i6OKCqooBSti3DzZz7bI+SoNpiSa1sjiiRzvz+/F5Lrpv5HWWilJS0JbmZ3ry4nWnzmFJx0JRhZHrpHv73GrVFU/qPCXtm/0BvfJ9t7C0LJ9KyDCllu4om0zIMKeCxVJbv06iqAlUVB5Xv9yjfZ6my0K9EZ6+oKymesrOhpLLQr6DP0+Uo3IZIUo2huNpjKYViKaVcVwPyfBpRVaC07eqD9R1b/DDZ9O9BaySp1a1RtYQT2QMIG3tUA15TAU+mV9TXefQ0lkxLhjQg399tXmZvP2Q3F0gDXqtbMNt0yGmhv+tpDYCvgk3f+6F45sBfcZ5PlYV+FQc9Wt0a+3xIdp5XybS71ffUlmz6Xv64vl3vrgkpFE9lv9wPq8jX9w4cpMNHVnT5u/dJfUjr2ztHEwQ8Glia1yVQbPzbuH5DVKs3xDKfb3ZmJEhh0KNIPK3lTUmtb4+roS2mb+1TraJ8b5fFnnL1hf2lT5rUHkvK77G0rjWm5U2Z0TzJlC3bkVy5SqcdBb2W1m6I6b21baoo9GviXpXaoyRP8ZStxWs2qO79mGpL8zSsPD87P/rJ9+rU2BFXTUlQA0uCMiQ1hxN68r06zf+4UcPK81VR6FNLOKmOeEoyDCVStlK2q+ZIQsm0kw1y4XhaPo+tZMiRZZqqKvTJNAyVbefzsulzsKWFtb5sn7nYRy72v6Xt3l3bpqffj6qmJKjhFQU5r29nIFztohpDcS34uFH1obhKgl6VBH2yDPWrF9eOYBiG9qopyiwAsU+VHn1rjZ5bUq9o2lG+z5QpqSOR+UJcmudRY0dStusq6DEV95iZkND5GeVK8ltS0GeoLZb5UuwxM1/E4ylHaSezCmO+z1RHwtagAfkaUZGv1a2xLn8IGjviqi4OaI/SPDWFE8r3e+S1THlMU6m0k/mCazsK+j2SK7mOZDuZHjez8wMz1RmmDLnK91vyWl2/fEYStjoSKeV5LSXTtt5e06bmjqTyfaYKAz7FUrY2xFLK85pKuI6awnG1dMS1snPxk0mjypXv98hxHX24PqxYMi3TkBK2q8KAV5Zpyms5Stu2ounMfXqMzMIfaceV09kT5DUzy/43RRIqavOovDAow5Ai8ZSWNYa1ri3WGawsxVK2oklb0WRaHo8p0zTkpFylHFcpJ9MbmO/3KJxIK8/nVziRViiWluvGFfR61NQRVb7fI9M0lUi76khkaraszO85kXLkdvZAbuS4UlqfDweVmwk8xUGvgkFDsWRKb65q1diBRTpq7yo1dST00idNWtcWU2MorpRtK+A19Ul9h9ojSQX8nswXoICp99a26dkP6yXXyJz02swskhL0ZXrQYsm0Uk4m1Of7LFUW+bXvwGIdOHiAqov9+qS+Q/9a2qyP6trVEIpnXoumoYKARwOLg6otDWrIgPzNfpjsO7BIS9aF1B5LKuA1tXZDVMsbw0o5jvJ9lpJpV43huOQa2qMkoKISrz6qD2lDJKmyfJ/226NYQZ9HLZGkXvy4Qa2RpL4zvlaSevUh+/kXs/bOQGqrwO/RnmV5Gl1dlA1mH65v17Mf1Gt5UyR7JHd4Rb6m7FOtMbXFufmjoJ71vG3LEJTPewUzBxvKC30yZCiRdrJDfuIpO7Nyq6QBBV6FYml1JGw5jqOAx5RhmPJ5DAW8Vpc5g12HHmV6FMsLMjVsDMLJdGbIq88yFPR5NnO7rkOWJG016H7ZcKYNkaQ+a4oqaduqKQ5oSHm+TNPc4nO1pcCcHS4WT8l1MwcYAp09/Rufu54Ghp1ta68l1830TCxrCKs1mlRZnk/DK/NlGuYWR5Q0huL6v2XNemVps5Y2htQWTcuVq9I8nyoKfLIsU64rJdK2YilHHstQkd8jr8fa7HtqSzb9otoaSWrR8lZFkraCHlMBr6E8n6mP6kK66flPtSGaVCzpaF1bTKuaI1q3ISpXmb9pKcdVU0dc/+wMFNVFAUUSaSXTabXFUoqlbKXTjtKOq+I8rwIeS47HVTRpy28aiqYcLV7bru+My9OQAfla2RLRknUhTSrcviHZrutm/wYOLsvT++tDqgvFFfRkVvVdE03Ka5mq6jz3ZahzwSjXleIpR00dCdUUB5TnyxyYa42mVF3sKs9vdT5/ScVSmQ8/j2Eo6LVkGIb2KDW1eE2bmsIJDSkLZrcbVJon13W1eF27XNdVScCjtbGUHNeV3zI1qDSoFa1Rua6rQr9HHtNUNJlSU0dK+w4s1KqWaI+fl02fgyED8rO32zi6ZVue61zsIxc1Hlng2+x2eX5LjuN2/n6CyvNlfg+5qm9nIVztglzXza6UZ5mm1rfF5LFMleX7tGdpntpiyX7x4tqRDMPQqJoi/egbw1Uc9Orv76xTQ0dSAY+pwoBHFUUBRZOOUk4mmDRHk/JbpuKuo1jmIL6CPkOlQZ/iaUdBn0d5vkyo2fQ8V4UBr0rz/Zs9z1VzJJFZrKGiUPsOzKwc89InTVrZEsl86Qn6tGZDRD6Pqcp8v+wKV0sbw3JdR9Fk5mTJlpk5sh+OpTIrHfosDcj3K9/fdehhys58MQl6PQrF0lq7ISqPZSjoy3zhiqczRxLz8n0K+KRoPK21bTEVBLzymNLq1riK83xq6khqbVtUPsuUZWZ6lUwj85qTYWSGjKQ755RtDKGbLChqKDN+MmU7StiZeWhuputEkWRmrL3Z+aUwlnYzwcyVvIYhU5LjZpa7d9zP57fZnecWcxwp7ThKpB3l+T2ZJfE7k3DadrLz4AxlQpTdOYxz006+jf+3TEmuZHTuP5q0NaDAJ9eR2mMpvb8upP33LNWSdSG1xZJyHFdpx1F1UUCrWjJHZk3TUKHfo3jS1pL2kNo7v3AZcmUalmJpVy1JW17TlGm6cmXIYxiSkenxa+xI6K0VrWqLphSKpdUeS6qxI6Gm9pjitpOd05dI2VrWFNbqzg/iPcvyMj1jnR8mK5rDevaD+szPA/L0/rqQ6toTyvNZKg76taIlqnA8rSJf5s95RzItZ0NMruPKaxnZoZNl+X7tURJUY0dcSxs79N6aNhmm0eMP2Y1f4tZuiKqpI6F05xy3SDKtNa1RJdKOmsMJ1ZYE9I931qk1mtLAkoDyfJm5h0vWhbR+Q0yzDh+ak4C1tZ63ikJ/lzAiSWs3RLWsKdJl7mpNcSA7BKUlktDqlphaI4nMidqVORqe5838fYhuPC1EKvM3wjQy7z/HcZVyHMVTmQMnAZ+VHV5aFPBqQIFPe5blqbooqFDnwYiWcELxtN256qtHtuOqNZrM9IAq00NbWxzQqOrCrrfbZMjSiMoCFQe9qmuPZ3teC/xW55fy4i7Da1oiCTV3JCVlhn55LVNrWqNa2RJReywlx3FVGPRq/B4lmjiqQsm02+159XkM/Xtla7fAPKKyQMsaw/pgfUjN4czrosDvUVm+T0GfqbSdOXXI5gLDF8Pa5ubuSN2D5eYW05G6z6/Z3G03Nzx2a68lSXrq/To9+2G91rZGOz8jMuF0aHmB9qou0IB8f7fe5ifeXa9Fy1u0vj2WOdBkGpJhqCmc0OqWiDydp+vI93uUSNlauyEpnyWNrS1Ryra7vKe2dLBj0y+0g0oCeu7DBsWStmqLA7IMIzNyQoZGlOdrWXNE97+2SkeNqpBtO2qOJOT3WioOZkJ6eywlj2moI55QU4cj13WV57MU9HkUS9kq9luqS6Tl91iyHVcd8bRsx1W+36NIKq0BRX7Vh+Jq7EioqiioysKA1rVF1RZNbdeIm7ZoSuvaoqoszHy217XFMkHGa2lDNJX9rHDczAGNSDKt1mhKtSVBOa6ruvaYhldkzv3ZGk1qYElArdGkIonM511de0xypbL8zIG+eGfvVyKdmTvg95ha3Zo5eFialxnun0hnnp9U2tGGWFqFQY8aQwkNLAkq5XR+drqSz2MpnEirLD9PLZGEosn8Xj0vmz4HX3ztGoaxTfvMxT5yUeOqluhmt4sk7G6/n4KAJ2f17Sy7RLi67bbb9Pvf/1719fUaN26c/uu//ksHH3zwFrd/9NFHdfnll2vlypUaOXKkrrvuOh177LHZ613X1ZVXXqm77rpLbW1tOuyww3T77bdr5MiRO+PhbLelDWH9a2mTHEkVBV75LK+StqP6UFwdsbSGVeT3ixfXzlBZFNB5R43UgYPL9M9367S2LSoZrgr9Xh04pEAjKgv04kcN2fNcyTDl87jK83k0qDSoisKAKov8OnTYAI2uLlQy7SiccJTvM5SwXZmmqaJA16FMkwr9WzyyeeSoiuyHs9eTGdZmGoZGVBVqz7J8NYcTWt2a6lxMw1Y0aSicSEsytEehXwPL8pTX2cOlTf4ueUxDsaStigK/ZGwMf5kQkXYyvTNey5RpmLIMV46kSMqWz5MZutYSSSiSsBVL2Up2BiCPJzM7KdW5IIghdRky2Zm3Oh/bxhlomYTjtUz5rcxRTqNznlq+35OdxJu0M8P3PJ1fPjOz4zI9daZpZHvsbCdzTjFDmRNie8zMUEK3s912XJlW5pxjG8voMmByCyeSyFbsZh6H47oyOu/bth11JDLzkNa1RVXg92hVS1QlQZ8S6cyRacPNzJ+Lp2zJddXckZBlGQp6zUyQdSXDzTy+pG3LsKWgz5I6hxCmXVf5XkvRlK2l9R1K2LZSduZk12lXCvg88nYeJXbdTNi0XUfLGsMaPCBf44KZ15RhGCrwe/XeupAOHT6gy5eKAr9XaSfzHKZsRx6PTx7LVCSRUmMiIb9lqrjzRNsbIklVFwcV7PwS1R5N6v317crzeVRdtO0fshu/xLVFE5nXsOuqqvP2RQGvGjoyQ4taI3G9sqxJkYSt0VUFMjrfO0VBnwr9Hn3ckAmMo6u3b5XHrQ0/WdEclmUY2fkT8aSt9nhaXstURYFfQb8p1zX0wfqQHMfVsIp8lRf4tbY1ps+aI0rZtvI7j15viKUUS6ZV4M8MO26JJOUxDfk9ljZEE7KdzBBV05T8HkvxtJ1dvMYyTFUXufJZlj5tCOvV5c0KxdKqKQ6qLN+rNRvSauxIKBxLZZ7HzpU6XddVkTLzHxNpR4sSzWqP2aot8au2JE+GMkHxvbXtKs7zaECeX5YllXWeXmJNa0ytkZTao0nVlARVXuBXaziljkS684TstlrCCX1c3yHDNDKh3mepLZLUS5826vUVrTpoSKn226Mk+7y+urxZi1dvUMBnaVh5fjYwL1reon+8s06l+X4FvKaCHku25Wh1a1SrWyIqDHhVEPBoaHl+t8BQWxLoEtYyQdPqMndnc3Nh8nyZ4Faa75Xfa2W3c111mV+ztXk0h43oOjx2S6+lz5rCagjF9MqyZrXF0gp6TQV9HrWGE1rXllJ7NCXTkMqG+rO9vhP3KteS9SF90tChWDLzNz/gtZTv82SGksdSStmO8joXNEqkbRX6vSoOeBRL2moIJ3TwkFI1diTkOO5WD6pu+oV2zYa4GkJxleT5ZHW+t/wej6LJtNJBnwbk+bSqJar2aFpN4YRcx1We39vl4EprNCXbzhz8Wt8W174Di5RIZXqrfGZm4Sgpc/ArkbYlZYZ2J+3MZ0gi7WRPnRLwWmqOJJRIO71+n0vq/FzJ9ABnznnpZD+fbNfNfqY4buagkutmDkp6LEOptJuZE9U53j1tOyrM82lDLKmU7WT3Lxmdp3HJBEZJnf8a8liuYik7M4Kjc4EKe+OwdDczwiPPYyntZD5rnM4531Lm+Uo7rgzTUDqd+SwoCnh7/Lxs+hxszrY817nYRy5q7EikN7vdxikVhUGfNkST2d9ZrurbWfo8XD388MOaPXu27rjjDk2YMEE33XSTpkyZok8++USVlZXdtn/11Vd12mmnac6cOfr2t7+tv/71r5o+fbrefvtt7bvvvpKk66+/Xrfccovmzp2roUOH6vLLL9eUKVP04YcfKhDYtYfSua6r99e1qyOR1sjKguwfx4CZOQLaGE6oMZRQSb5nl39x7SyGYeiQEeWaMKxss0NVjhtbo88aw3p3bZtStjSiMk8lQa+iabdXc0AMw9hiqK0sCnQJX6FYSmtao1rfHpNhSYcMH6DKQn/2PFfxaLLLea5G1xRqybqQVrZEunzAN4UTqikOKuC1FPRa8nlMRRKZBTEcN9NrVeDP9IQlbFempPyNizIo0yOUsp3sbaPpdGauk9dSonOYk9cyMnOalBkOaLuSrzMg2XbmwyLlZD48K/L9qikJqi2WkuFKMqURlQXymKaWrG9XMm2rojAzBCPP51FHZ++QaWbml3nNzGs9kkirJOiV4zoq8HtUFPSoqiig+vaYCvwetcVSKgp45PdkepHaY2nJduQxrUyPm7rmK4/5hbbMQeJMD0Rnr5nXZ6nQ75VkKGk7yrc82flxsaTdOUQz0/sWTjhKpDNtQZ+llJ35EE+lMytM+qzOoaadPUSWmQmCtiuFU7ZKAl6ta4upKOhROJ75wmFZmefANDLPQzLtynXVOWzE1uqWqEZUFGaP1pmdX2BMw8j0GKYdGYYrj5U5cup09vxtDMcbv1T4LFNey1LKzgxX3PhlwWeZkgyF45lQ35MP2Y1f4gr8Xq1qjak4sMkQKMNQSdCn1mhSXsuvz5oiGlNblA1W2V+JaWpgSUDLmyJa1RLV0M6jyT21teEnpbZXTy9pUCieVG1xnqqLAlreGFZ9W0zqDPOjCwrlsQx9VNeh1khCFQU+2Y6julBc+V5TRUU+rWzNHM0u9FtKpNIKxZMyZKjAZ8mV1BFPKt05HCxq27JtQ36PFPRYCifTStmOivO9iqZtOa6tplDmaLrV+b4KxTNzMQt8ltqiSbmOo5RtyW8ZmQNBlimfZWhVSyQz/9PM9DhvHNrk6Ry619SRCbvj9iiWYZgqDGSGida3xxVNplVVFFBTR0KxtK09SoKSXC1enVnMZuPQQ9t25fdYqioOKNIUUXNH5vYThmaG9AZ9purb42qPp1RVFFBRwCsZRibQOG7n3B5Ded6AivO9agonVRiwtCGSUjiZVlm+T7GUkxlaFc4Ehk/rQ/rH4nUKeE0NLAnKdb1a1hTWqpZIdu6OLLfbXJho0tYnde36oM7V3tUFOmRYhRJ2Wi981ChDrg4eOkB7lOSpsSOmf75Xp6aOuGqKgxpY+vk8mhc+alRrJKlp42pVUejf4mtpsDdPz3/UoCXr2xVNpFUcyMy3DCXS8nf+jU2mHS1vCmtIeb72G1isVa1Rvf7ZBq1vjyqetDO9GHIV8Hg6h3/anX9zLNmussPHw4nMsEmvx1BbNKn2WDr7nqop2fIR+02/0HYk00rbrgKez993mc+FTPDw+cxMD2vazgYK7yZH1SzTUNrJ/E2xzMxcX8Mw5POY8nSupGsaRnZesNs5KiHVudCQ42YOvgW9mfuPp+zMvGBP7w+iSJmeI5+VGY7r3XgQzs38HbA6w8zGg5l250E/r5XpMXU7b+/t/FvksczOXkQzOwQ/U5+reNqRZRjZnjCrc3x52lbm89PKDPf3e63MysLKfMZk5ms7nc9R5sBs9pCkm5n37Tpu9j5787xs+hxsXPBqU9uyz1zsIxc1Fvo9m91u45SKaDItj/X57yxX9e0sfR6ubrjhBp199tmaNWuWJOmOO+7Qk08+qXvuuUeXXnppt+1vvvlmHXPMMbrkkkskSddcc42ef/553Xrrrbrjjjvkuq5uuukm/epXv9Lxxx8vSbrvvvtUVVWlefPm6dRTT915D64X2qIpNYcTKsnzKW272nTqjWEYKg541RCKqTBYsMu/uHY20zQ3+0XNNE2NqC7SiOqdc0LoTcNXdXFAI6sKuvR0FQUsrWqJan1bTMm0o5rioKqKA9lhKpsbejiiolCHjyjXknUhbYjEVVXo1yeRlMJOUoZpymOZCngzX7TCsZSCfksjKgpkWqZWt0aV3znksTjo0R4leVqyrk2JtK2KQp9C0cyKfB4z88Ho82TCg61MwIqnMmvaO+qcp+Y1NbK6QMVBn9a3Z+YOjaou1OAB+cr3W/qsOaK2aOZLeWbFKkOhWFqGYaqmyCe/11JDKC7LMuXzmEq7rhJp6WuDS1UUyKwW6LUsRY3MyZ3XtmWC1pDy/Oxqgcm0LY+R6bXbyGtKhUGvwp0nijYNZXrArMyX0ZTtKGnbqgwGNHZgkSo6V3lyXDc7P84yN37RcDPzaTqPOGaP0tqb3OEmc/c2/mtkL5kwY5qZoON2Dnl0HEemuXFFw02W++8MSLaTOTq66dE6x8l86XVcVwFr4yqGhtL2518mNvbUOZ29jd7OnsiUbXc+N59/WUjamd9kQSAzFLYnH7Ibv8Tl+zxK2458ga6Lu3g9ptKJlJKdz7VvC3+j8nwe1YXimV6UXtrS8JONp0EIJ1JK21J5Qea92BJJqcDvkWFK7fGUmiNJVRX5ZZmG8nwefdLYobI8XybsBDK9gnJcpV1XkYSrPL9HzZ1zpEqCXtmOq3DSUX5nb6ZpmHIMKZqyVejzdPa8Zv7+uI6r5khStu0qnrY1sCSgDbFkdshQcySZ+YIoqSOeVkWhTx7TVCzlyO/N9ES5rrRnWSAzZCmVWUAlnMgsFlPXHlcy7SiRdpXJPIYCHksrm6MaOiBP9aG4JKkkL/M3Jp7KnFIikrRVVeST37IUTWYm5kuSOuecrGuPqakjqarigJo6kmroiKuqyK9wMjNsKuC11B7PzMUpzc+cx66qWErZUjSZ6SW0LENp25FhSuF4SnE7oJKgT83hTA9LUzihw0cMUNDr0cqWaKanvzxf69oTemdtu4aUBbvOhfGYauxIKM/vUcDrqimc1JoNEUmGgt7MaqxN4YSqi/1qDCUVT6Uyt7UM5XktyTA0qNRSfSimTxrCGrKuXfsPKtniUKZoMrMKXVsklZ3/ZrtG5/y1zFKqjtTlwEimBymiUDydGfbcOaR54+d551swc1Cm88u37TiyTUOWacmUpXDCVtJ2VBT0Kp3InNIiZtubPai66RfaQp9HHstQPO0o3zKz92d2HmRKJh15zcyiNxsDRaqzRyqzrdt5QG3j3FgrswBH0KOSPJ8aQ3H5rMzcKjmuPMoEkPZYSgPyfIombA0pz1dloV+u66qxI67hFYXZ4Zm9VZLn1cCSPC1v6tDgsjzVlARVH4orkbKV77fUGk3KtAyZRmYYuMcwVJyX6bUsCnpVUxzMDrcvy/Ppw7qQxtQUZdtqioOqa88MBx5UmpcNp35PZqh3Iu1oz7KgDMNUfSim/9/encdFVe5/AP+cmWEWRhYBWcYFEBHTRNOUsGtakoB7uYB1FctcCrM9s/tLrNt2y9JuqVkhuLzK8FVSmmlIroRLCmrhJfQiboCiLLPPnHOe3x8jcx2ZAbERkL7v1+u8lHO+55lnzjPPOXx5znkmSKaEQmq7u8BDJrE9c1Vjgo/KA1beNiJZ/7UsFl5AR085jFYeGl81POUSlF02NPu4XHsMwuRqx/PeDR5rd5ThjjqG+nuitKphnFohddo+7qpfS2nV5MpiseDw4cNYuHChfZ1EIkFcXBzy8/Od7pOfn4/nn3/eYV18fDyys7MBAKWlpaioqEBcXJx9u4+PD2JiYpCfn+80uTKbzTCbzfafa2trAdi+jbmlVdWaYDXq4CvhUXlZi8AOSuCaD6coiqi8okN0oAIS3oi6qxdN0rZJAXhyAARArwcClEBA8DWjqKIZ2qtTuyoBDNQoEOFr+4uo/JpbDxUhChSVW1CpBv7LTKius6CDXApPmQTaOiOuiLa/unXz80GwJ4MoCijW1gEKDxh0HhA9ZAj3AYrPmlFjsEAlUSBQYZspqcZohcBg+86SDjJU682oMfGwWGy/GMg9JAhUy9E1QAWrUY+LJj26qKXw9VSgoydw8Uo15FIJxvX2xclKHU5V6VFWoQXHceis9kBXP0/4espxodaAiloTrCKDh0oGb5UK/br4IjaiIwCgqFwL0SzAZLDAQzTDEyLkIo9gbwUCFR1w/HwNqvS2X+CFq7fnSaUcPGQceKMZsIpgPAMP20iWXCKD0WAFzzP4qOXoG9ARYd4SSAUTfGUC/lulhScTUHnZiE4dFOjAWXHRrIfWIsBb6QHeaIbEKoA3WWG9mgxIJRKIPANEBmYWIEg5iAIHXiKBVZRBYIACUpj1VnBWI3iLFbAIYFYePLPNGlk/0YAoMnBXE0yZwgNSqwesBj30otk2KnFFh86eDJeuVKObnyc6egg4azKg2iDCWyUFsxjBWXmYdLZnaTgph45XbwO6rOOhVnhArQZgNsBoBi7pTJBwHLp7q8FJGE5XXEI3v4YXwTNX9AgP8HI4z5gNFggmPXRmDoLZgDrRDKXsfxc/My9AsAoQZQI4qwnaujooxYZ3C2hNFnAWK2AxoK7u5v5IVFVrQm1dLdScJ/TWa+/Z53G6vAqCyQQJYzDoZTBZBWi1dbbkSuRgFgRUXrJAIahhNhqgknKoqjaDmRUQBAaBk8HCi+BNJliZCB6AXCaB1WAb+eLlAnhBBG80QmRSWMzC1RlJGXjGwSrKIFp526iEXAAHDnpeYnte0WyFqGIwWHmAceAUUliMRohm4eqzfwy8hxISqQQWXoAFHjAbbF/kLVoAK2PQ62zv1WTQQSLhYDHqYZHw0OukwNVn76xmKww6LZgXB63RlmB4Sz1hstiewTHq9eCNeogKFUSpbaIc09XZbASzERAY9DoJrtTWoINUjSs1ehh0Wqi95DAaRVsd5DJotSaYDDoo5RJYjGZYjRxMvBQWo8mWMJrN4HkGq4GDIOFg0NlGAC5d0aG82gSVVAKjXg+zhMOVah0UHhLwZgm8pDxOXzBAr5PDauTRQSbBlZpaeHIWXK4x2m9l1ht4lJy12H7B9ZSDMaD8khHeUitOl9fAajDZ9+3oIdg/ryoIqNEbcLxUhK+Ud/pZAoBagwV6nRYWoxZyJofI2W61tRhtz/ECgMgLMAOoreNQW6eEl9IDer0WgkWEYDSAN5lhNVthstoSH5HnIZgNEMFdnUEWYJwMTJDCJJgh2P4aA2ZWQiuaIVgF6HS2B/3NBh3qOItDHSWMwVcmoLSiBp19lPDzEHD+ihZSb9szVwar7TwgmgRUXjEg2FMOOTPBVyrgrNmAWhODt8r2y2qdkYePSooaiwUms4guHVWora2FQlCii5qhutqEWqMZEEVcsQDqq5838CL0sMBbpkBURy9UXanFJZ0J3io5Qr28oNVqb6qfXyvUCzhTYcZ/zmrRQSaFn4zHqRo9jFYBCkEAbwUumPSQSWy3NcuZFHqTBRIPBTpI1NBqtTBbBZgNeqg5MyxGPaou257b8pKaIeVNsBrNMCgEVNfy4GD7/kopLyBYKYXFaECAlwcuWk04eb4O4Dj4yzhYBYbLNQZIeRGeMhkMRgH/reEhl0pgEUTU8mYomAc6SFXoIJHiP2cMN31c6o/BiTNadOqgtN990pxj7Y4y/mz5Op3OZZzFaIAaZpgNOlRdYW6v382qzwmufQ7dJdaKzp8/zwCwX375xWH9Sy+9xAYPHux0Hw8PD/bll186rFu+fDkLDAxkjDGWl5fHALALFy44xEyePJlNmTLFaZlpaWn1dxLRQgsttNBCCy200EILLbQ0WM6ePdtkftPqtwW2BQsXLnQYDRNFEVeuXIG/v79bZ+Orq6tD165dcfbsWXh7t8wtaqRx1CZtD7VJ20Lt0fZQm7Q91CZtC7VH23O7twljDFqtFhqNpsnYVk2uAgICIJVKUVlZ6bC+srISwcHBTvcJDg5uNL7+38rKSoSEhDjE9O/f32mZCoUCCoXCYZ2vr29z3kqzeHt735YfrPaM2qTtoTZpW6g92h5qk7aH2qRtofZoe27nNvHx8bmhuFadEUEul2PgwIHIzc21rxNFEbm5uYiNjXW6T2xsrEM8AOTk5Njjw8PDERwc7BBTV1eHAwcOuCyTEEIIIYQQQv6sVr8t8Pnnn0dKSgruvvtuDB48GMuWLYNer7fPHjh9+nR07twZ77zzDgDgmWeewbBhw/DBBx9g9OjR2LBhA3799Vd89tlnAGyzJD377LN48803ERkZaZ+KXaPRYMKECa31NgkhhBBCCCHtXKsnV0lJSbh06RIWLVqEiooK9O/fH9u2bUNQUBAA4MyZMw7fQTRkyBB8+eWX+L//+z+8+uqriIyMRHZ2tv07rgDg5Zdfhl6vx+zZs1FTU4O//e1v2LZtW6t/x5VCoUBaWlqDWxBJ66E2aXuoTdoWao+2h9qk7aE2aVuoPdqev1KbcIzdyJyChBBCCCGEEEIaQ99CSwghhBBCCCFuQMkVIYQQQgghhLgBJVeEEEIIIYQQ4gaUXBFCCCGEEEKIG1By5WbLly9HWFgYlEolYmJicPDgwUbjN27ciF69ekGpVKJv377YunVrC9W0/XvnnXcwaNAgeHl5ITAwEBMmTEBxcXGj+2RmZoLjOIeltWeZbE8WL17c4Pj26tWr0X2oj9w6YWFhDdqD4zikpqY6jaf+4X579uzB2LFjodFowHEcsrOzHbYzxrBo0SKEhIRApVIhLi4OJSUlTZbb3GsR+Z/G2sRqtWLBggXo27cv1Go1NBoNpk+fjgsXLjRa5s2c+4hNU31kxowZDY5tQkJCk+VSH7l5TbWJs+sKx3F4//33XZbZnvoIJVdu9PXXX+P5559HWloajhw5gn79+iE+Ph4XL150Gv/LL79g6tSpmDlzJgoKCjBhwgRMmDABv/32WwvXvH3avXs3UlNTsX//fuTk5MBqtWLkyJHQ6/WN7uft7Y3y8nL7UlZW1kI1/mvo06ePw/Hdt2+fy1jqI7fWoUOHHNoiJycHADB58mSX+1D/cC+9Xo9+/fph+fLlTre/9957+Pe//41PP/0UBw4cgFqtRnx8PEwmk8sym3stIo4aaxODwYAjR47gtddew5EjR/Dtt9+iuLgY48aNa7Lc5pz7yP801UcAICEhweHYfvXVV42WSX3kz2mqTa5ti/LycqxevRocx2HixImNlttu+ggjbjN48GCWmppq/1kQBKbRaNg777zjNH7KlCls9OjRDutiYmLYnDlzbmk9/6ouXrzIALDdu3e7jMnIyGA+Pj4tV6m/mLS0NNavX78bjqc+0rKeeeYZFhERwURRdLqd+setBYBt2rTJ/rMoiiw4OJi9//779nU1NTVMoVCwr776ymU5zb0WEdeubxNnDh48yACwsrIylzHNPfcR55y1R0pKChs/fnyzyqE+4j430kfGjx/PHnjggUZj2lMfoZErN7FYLDh8+DDi4uLs6yQSCeLi4pCfn+90n/z8fId4AIiPj3cZT/6c2tpaAICfn1+jcTqdDqGhoejatSvGjx+P33//vSWq95dRUlICjUaD7t2749FHH8WZM2dcxlIfaTkWiwXr16/H448/Do7jXMZR/2g5paWlqKiocOgDPj4+iImJcdkHbuZaRP6c2tpacBwHX1/fRuOac+4jzbNr1y4EBgYiKioKTz75JC5fvuwylvpIy6qsrMQPP/yAmTNnNhnbXvoIJVduUlVVBUEQEBQU5LA+KCgIFRUVTvepqKhoVjy5eaIo4tlnn8W9996LO++802VcVFQUVq9eje+++w7r16+HKIoYMmQIzp0714K1bb9iYmKQmZmJbdu2YeXKlSgtLcXQoUOh1WqdxlMfaTnZ2dmoqanBjBkzXMZQ/2hZ9Z/z5vSBm7kWkZtnMpmwYMECTJ06Fd7e3i7jmnvuIzcuISEBa9euRW5uLv71r39h9+7dSExMhCAITuOpj7SsNWvWwMvLCw8//HCjce2pj8hauwKEtITU1FT89ttvTd6/Gxsbi9jYWPvPQ4YMwR133IFVq1bhn//8562uZruXmJho/390dDRiYmIQGhqKrKysG/qrFrl10tPTkZiYCI1G4zKG+gch/2O1WjFlyhQwxrBy5cpGY+ncd+skJyfb/9+3b19ER0cjIiICu3btwogRI1qxZgQAVq9ejUcffbTJyY/aUx+hkSs3CQgIgFQqRWVlpcP6yspKBAcHO90nODi4WfHk5sybNw9btmzBzp070aVLl2bt6+HhgbvuugsnT568RbX7a/P19UXPnj1dHl/qIy2jrKwMO3bswBNPPNGs/ah/3Fr1n/Pm9IGbuRaR5qtPrMrKypCTk9PoqJUzTZ37yM3r3r07AgICXB5b6iMtZ+/evSguLm72tQW4vfsIJVduIpfLMXDgQOTm5trXiaKI3Nxch7/0Xis2NtYhHgBycnJcxpPmYYxh3rx52LRpE37++WeEh4c3uwxBEHD8+HGEhITcghoSnU6HU6dOuTy+1EdaRkZGBgIDAzF69Ohm7Uf949YKDw9HcHCwQx+oq6vDgQMHXPaBm7kWkeapT6xKSkqwY8cO+Pv7N7uMps595OadO3cOly9fdnlsqY+0nPT0dAwcOBD9+vVr9r63dR9p7Rk12pMNGzYwhULBMjMzWVFREZs9ezbz9fVlFRUVjDHGpk2bxl555RV7fF5eHpPJZGzJkiXsxIkTLC0tjXl4eLDjx4+31ltoV5588knm4+PDdu3axcrLy+2LwWCwx1zfJq+//jrbvn07O3XqFDt8+DBLTk5mSqWS/f77763xFtqdF154ge3atYuVlpayvLw8FhcXxwICAtjFixcZY9RHWoMgCKxbt25swYIFDbZR/7j1tFotKygoYAUFBQwA+/DDD1lBQYF95rl3332X+fr6su+++44dO3aMjR8/noWHhzOj0Wgv44EHHmAff/yx/eemrkWkcY21icViYePGjWNdunRhhYWFDtcWs9lsL+P6Nmnq3Edca6w9tFote/HFF1l+fj4rLS1lO3bsYAMGDGCRkZHMZDLZy6A+4l5NnbcYY6y2tpZ5enqylStXOi2jPfcRSq7c7OOPP2bdunVjcrmcDR48mO3fv9++bdiwYSwlJcUhPisri/Xs2ZPJ5XLWp08f9sMPP7RwjdsvAE6XjIwMe8z1bfLss8/a2y8oKIiNGjWKHTlypOUr304lJSWxkJAQJpfLWefOnVlSUhI7efKkfTv1kZa3fft2BoAVFxc32Eb949bbuXOn0/NU/XEXRZG99tprLCgoiCkUCjZixIgGbRUaGsrS0tIc1jV2LSKNa6xNSktLXV5bdu7caS/j+jZp6txHXGusPQwGAxs5ciTr1KkT8/DwYKGhoWzWrFkNkiTqI+7V1HmLMcZWrVrFVCoVq6mpcVpGe+4jHGOM3dKhMUIIIYQQQgj5C6BnrgghhBBCCCHEDSi5IoQQQgghhBA3oOSKEEIIIYQQQtyAkitCCCGEEEIIcQNKrgghhBBCCCHEDSi5IoQQQgghhBA3oOSKEEIIIYQQQtyAkitCCCGEEEIIcQNKrgghhLgdx3HIzs7+U2WcPn0aHMehsLDQLXUC3FOv5poxYwYmTJjwp8rIzMyEr6+vW+rjbmFhYVi2bFlrV4MQQtoESq4IIeQ2lp+fD6lUitGjRzd739b8pXjGjBngOM6++Pv7IyEhAceOHWuV+jTm6NGjGDduHAIDA6FUKhEWFoakpCRcvHixtavWJhw6dAizZ89u7WoQQkibQMkVIYTcxtLT0/H0009jz549uHDhQmtXp1kSEhJQXl6O8vJy5ObmQiaTYcyYMa1dLQeXLl3CiBEj4Ofnh+3bt+PEiRPIyMiARqOBXq9v7eq1KovFAgDo1KkTPD09W7k2hBDSNlByRQghtymdToevv/4aTz75JEaPHo3MzMwGMZs3b8agQYOgVCoREBCAhx56CAAwfPhwlJWV4bnnnrOPHgHA4sWL0b9/f4cyli1bhrCwMPvPhw4dwoMPPoiAgAD4+Phg2LBhOHLkSLPrr1AoEBwcjODgYPTv3x+vvPIKzp49i0uXLjmNFwQBM2fORHh4OFQqFaKiovDRRx81iFu9ejX69OkDhUKBkJAQzJs3z2Ud0tLSEBIS4nLELC8vD7W1tfjiiy9w1113ITw8HPfffz+WLl2K8PBwe9zvv/+OMWPGwNvbG15eXhg6dChOnTrlUNaSJUsQEhICf39/pKamwmq12rdVV1dj+vTp6NixIzw9PZGYmIiSkpJGj993332HAQMGQKlUonv37nj99dfB8zwAgDGGxYsXo1u3blAoFNBoNJg/f77LsurbfdWqVejatSs8PT0xZcoU1NbW2mPqb2986623oNFoEBUVBaDhCGhNTQ3mzJmDoKAgKJVK3HnnndiyZYt9+759+zB06FCoVCp07doV8+fP/8snqoSQ9oOSK0IIuU1lZWWhV69eiIqKwt///nesXr0ajDH79h9++AEPPfQQRo0ahYKCAuTm5mLw4MEAgG+//RZdunTBG2+8YR89ulFarRYpKSnYt28f9u/fj8jISIwaNQparfam34tOp8P69evRo0cP+Pv7O40RRRFdunTBxo0bUVRUhEWLFuHVV19FVlaWPWblypVITU3F7Nmzcfz4cXz//ffo0aNHg7IYY3j66aexdu1a7N27F9HR0U5fMzg4GDzPY9OmTQ7H9lrnz5/HfffdB4VCgZ9//hmHDx/G448/bk90AGDnzp04deoUdu7ciTVr1iAzM9MhGZ4xYwZ+/fVXfP/998jPzwdjDKNGjXJIwK61d+9eTJ8+Hc888wyKioqwatUqZGZm4q233gIAfPPNN1i6dClWrVqFkpISZGdno2/fvk7Lqnfy5ElkZWVh8+bN2LZtGwoKCvDUU085xOTm5qK4uBg5OTkOCVM9URSRmJiIvLw8rF+/HkVFRXj33XchlUoBAKdOnUJCQgImTpyIY8eO4euvv8a+ffsaTYAJIeS2wgghhNyWhgwZwpYtW8YYY8xqtbKAgAC2c+dO+/bY2Fj26KOPutw/NDSULV261GFdWloa69evn8O6pUuXstDQUJflCILAvLy82ObNm+3rALBNmza53CclJYVJpVKmVquZWq1mAFhISAg7fPiwPaa0tJQBYAUFBS7LSU1NZRMnTrT/rNFo2D/+8Q+X8QDYxo0b2SOPPMLuuOMOdu7cOZex9V599VUmk8mYn58fS0hIYO+99x6rqKiwb1+4cCELDw9nFovF5XsNDQ1lPM/b102ePJklJSUxxhj7448/GACWl5dn315VVcVUKhXLyspijDGWkZHBfHx87NtHjBjB3n77bYfXWbduHQsJCWGMMfbBBx+wnj17uqzT9dLS0phUKnU4Hj/++COTSCSsvLzc/j6CgoKY2Wx22Pfaz9H27duZRCJhxcXFTl9n5syZbPbs2Q7r9u7dyyQSCTMajTdUV0IIacto5IoQQm5DxcXFOHjwIKZOnQoAkMlkSEpKQnp6uj2msLAQI0aMcPtrV1ZWYtasWYiMjISPjw+8vb2h0+lw5syZZpVz//33o7CwEIWFhTh48CDi4+ORmJiIsrIyl/ssX74cAwcORKdOndChQwd89tln9te9ePEiLly40OR7fu6553DgwAHs2bMHnTt3tq9/++230aFDB/tSX+5bb72FiooKfPrpp+jTpw8+/fRT9OrVC8ePHwdgO85Dhw6Fh4eHy9fs06ePffQGAEJCQuwTYpw4cQIymQwxMTH27f7+/oiKisKJEyeclnf06FG88cYbDvWdNWsWysvLYTAYMHnyZBiNRnTv3h2zZs3Cpk2bHEbSnOnWrZvD8YiNjYUoiiguLrav69u3L+RyucsyCgsL0aVLF/Ts2dNlvTMzMx3qHR8fD1EUUVpa2mj9CCHkdkDJFSGE3IbS09PB8zw0Gg1kMhlkMhlWrlyJb775xv6cjEqlana5Eomkwe1v19+alpKSgsLCQnz00Uf45ZdfUFhYCH9/f/sEBzdKrVajR48e6NGjBwYNGoQvvvgCer0en3/+udP4DRs24MUXX8TMmTPx008/obCwEI899pj9dW/0/T744IM4f/48tm/f7rB+7ty59mSvsLAQGo3Gvs3f3x+TJ0/GkiVLcOLECWg0GixZsuSGX/f6xIvjOIiieEP1dUan0+H11193qO/x48dRUlICpVKJrl27ori4GCtWrIBKpcJTTz2F++67z+VthjdKrVY3ur2pY6HT6TBnzhyHeh89ehQlJSWIiIj4U3UjhJC2QNbaFSCEENI8PM9j7dq1+OCDDzBy5EiHbRMmTMBXX32FuXPnIjo6Grm5uXjsscecliOXyyEIgsO6Tp06oaKiAowx+yQX13/PVF5eHlasWIFRo0YBAM6ePYuqqqo//b44joNEIoHRaHS6PS8vD0OGDHF4DujaSSO8vLwQFhaG3Nxc3H///S5fZ9y4cRg7diweeeQRSKVSJCcnAwD8/Pzg5+fXZD3lcjkiIiLskzBER0djzZo1sFqtjY5euXLHHXeA53kcOHAAQ4YMAQBcvnwZxcXF6N27t9N9BgwYgOLiYqfPk9VTqVQYO3Ysxo4di9TUVPto24ABA5zGnzlzBhcuXLAnlfv374dEIrFPXHEjoqOjce7cOfzxxx9OR68GDBiAoqKiRutNCCG3M0quCCHkNrNlyxZUV1dj5syZ8PHxcdg2ceJEpKenY+7cuUhLS8OIESMQERGB5ORk8DyPrVu3YsGCBQBss7zt2bMHycnJUCgUCAgIwPDhw3Hp0iW89957mDRpErZt24Yff/wR3t7e9teIjIzEunXrcPfdd6Ourg4vvfTSTY2Smc1mVFRUALDNlvfJJ59Ap9Nh7NixTuMjIyOxdu1abN++HeHh4Vi3bh0OHTrkMGvf4sWLMXfuXAQGBiIxMRFarRZ5eXl4+umnHcp66KGHsG7dOkybNg0ymQyTJk1yeaw3bNiA5ORk9OzZE4wxbN68GVu3bkVGRgYAYN68efj444+RnJyMhQsXwsfHB/v378fgwYNvKDGJjIzE+PHjMWvWLKxatQpeXl545ZVX0LlzZ4wfP97pPosWLcKYMWPQrVs3TJo0CRKJBEePHsVvv/2GN998E5mZmRAEATExMfD09MT69euhUqkQGhrqsh5KpRIpKSlYsmQJ6urqMH/+fEyZMgXBwcFNvod6w4YNw3333YeJEyfiww8/RI8ePfCf//wHHMchISEBCxYswD333IN58+bhiSeegFqtRlFREXJycvDJJ5/c8OsQQkhbRbcFEkLIbSY9PR1xcXENEivAllz9+uuvOHbsGIYPH46NGzfi+++/R//+/fHAAw/g4MGD9tg33ngDp0+fRkREBDp16gTANoqyYsUKLF++HP369cPBgwfx4osvNnj96upqDBgwANOmTcP8+fMRGBjY7Pexbds2hISEICQkBDExMTh06BA2btyI4cOHO42fM2cOHn74YSQlJSEmJgaXL19uMJtdSkoKli1bhhUrVqBPnz4YM2aMyynNJ02ahDVr1mDatGn49ttvncb07t0bnp6eeOGFF9C/f3/cc889yMrKwhdffIFp06YBsN0y+PPPP0On02HYsGEYOHAgPv/882aNYmVkZGDgwIEYM2YMYmNjwRjD1q1bXZYRHx+PLVu24KeffsKgQYNwzz33YOnSpfbkydfXF59//jnuvfdeREdHY8eOHdi8ebPLmRgBoEePHnj44YcxatQojBw5EtHR0VixYsUNv4d633zzDQYNGoSpU6eid+/eePnll+0jpNHR0di9ezf++OMPDB06FHfddRcWLVrkcAsmIYTczjh2/c31hBBCCPlLWbx4MbKzsxvcAkoIIaR5aOSKEEIIIYQQQtyAkitCCCGEEEIIcQO6LZAQQgghhBBC3IBGrgghhBBCCCHEDSi5IoQQQgghhBA3oOSKEEIIIYQQQtyAkitCCCGEEEIIcQNKrgghhBBCCCHEDSi5IoQQQgghhBA3oOSKEEIIIYQQQtyAkitCCCGEEEIIcYP/B4PpxEtPVDSXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "y_testf = frac_ML['Actual'].values\n",
    "y_predf = frac_ML['Predicted'].values\n",
    "\n",
    "# Calculate the fractions\n",
    "fractionsf = y_predf / y_testf-1\n",
    "\n",
    "\n",
    "\n",
    "# Plot the fractions\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(frac_ML['Actual'],fractionsf, 'o', alpha=0.3)\n",
    "plt.xlabel('Actual Black-Scholes price')\n",
    "plt.ylabel('Predicted/Actual Price-1 Fraction')\n",
    "plt.title('Fraction of Predicted to Actual Prices')\n",
    "plt.ylim(0, 2)  # Limit the y-axis for better visualization if needed\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b6a4774",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6ec47d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a 3D plot\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Scatter plot of call option prices\n",
    "ax.scatter(data1[:, 2], data1[:, 3], data1[:, 4], c=data1[:, 4], cmap='viridis')\n",
    "\n",
    "ax.set_xlabel('Time to Maturity')\n",
    "ax.set_ylabel('Volatility')\n",
    "ax.set_zlabel('Call Option Price')\n",
    "ax.set_title('Call Option Prices with Black-Scholes Model')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}