EnthalpyPrediction_Deep_Learning (1).ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "k9k12xUx6I0K"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "from pylab import rcParams\n",
        "\n",
        "# Ignore harmless warnings\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Activation\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error,explained_variance_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rcParams['figure.figsize'] = 14,8\n",
        "rcParams['axes.titlesize'] = 15\n",
        "rcParams['font.size']=10\n",
        "sns.set_style('whitegrid')\n",
        "\n",
        "#show all columns in Jupyter Notebook\n",
        "pd.set_option(\"display.max_columns\", None)"
      ],
      "metadata": {
        "id": "VRDlSYXB7wel"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = pd.read_csv('inputs.csv',index_col=0)\n",
        "df_Enthalpy = pd.read_csv('df_Enthalpy.csv',index_col=0)"
      ],
      "metadata": {
        "id": "dbcdiu8S79JS"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(inputs.values,df_Enthalpy.values,test_size=0.2,random_state=101)"
      ],
      "metadata": {
        "id": "Bkk5uVoP9Ig7"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "metadata": {
        "id": "I7YuebMl9Q4K"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = scaler.fit_transform(X_train)"
      ],
      "metadata": {
        "id": "jBrKI4RD9UFW"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "q3y2_wwa9cJy"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7N8hILfu9hV3",
        "outputId": "0c3a7ceb-e5f6-43bb-9fd1-fc4f9088577b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(36388, 37)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIx4kYGX9m60",
        "outputId": "6b030e81-a28b-4fe4-9a0a-7c42bcdba6b8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9097, 37)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(37,activation='relu'))\n",
        "model.add(Dense(37,activation='relu'))\n",
        "model.add(Dense(37,activation='relu'))\n",
        "model.add(Dense(37,activation='relu'))\n",
        "model.add(Dense(20))\n",
        "\n",
        "\n",
        "model.compile(optimizer='adam',loss='mse')"
      ],
      "metadata": {
        "id": "F8J4Tp9T9uyD"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# - Hyperparameter Tunning:\n",
        "\n",
        "batchsize = 128\n",
        "epochs = 400"
      ],
      "metadata": {
        "id": "zeR0tBsY92ri"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=25)"
      ],
      "metadata": {
        "id": "v3cAs9fA99Ft"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x=X_train,\n",
        "        y=y_train,\n",
        "        validation_data=(X_test,y_test),\n",
        "        batch_size=batchsize,\n",
        "        epochs=epochs,\n",
        "        verbose=1,\n",
        "        callbacks=[early_stop])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hbQV365f-FUt",
        "outputId": "e9a07c22-bbeb-434f-afa6-c11ea5f540dd"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/400\n",
            "285/285 [==============================] - 2s 4ms/step - loss: 169919.0938 - val_loss: 40452.7891\n",
            "Epoch 2/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 33566.4453 - val_loss: 29349.7129\n",
            "Epoch 3/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 28160.1289 - val_loss: 27290.1777\n",
            "Epoch 4/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 26333.3047 - val_loss: 25336.2930\n",
            "Epoch 5/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 24092.2285 - val_loss: 23029.5684\n",
            "Epoch 6/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 21665.7324 - val_loss: 20873.2988\n",
            "Epoch 7/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 19983.9727 - val_loss: 19775.7969\n",
            "Epoch 8/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 18994.5859 - val_loss: 19311.3789\n",
            "Epoch 9/400\n",
            "285/285 [==============================] - 1s 4ms/step - loss: 18517.2148 - val_loss: 18849.0156\n",
            "Epoch 10/400\n",
            "285/285 [==============================] - 1s 4ms/step - loss: 18228.6504 - val_loss: 18341.8340\n",
            "Epoch 11/400\n",
            "285/285 [==============================] - 1s 4ms/step - loss: 17953.0469 - val_loss: 18459.5605\n",
            "Epoch 12/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 17667.1914 - val_loss: 18340.1289\n",
            "Epoch 13/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 17438.9355 - val_loss: 17712.1660\n",
            "Epoch 14/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 17072.4570 - val_loss: 16998.8613\n",
            "Epoch 15/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 16577.1113 - val_loss: 16648.1133\n",
            "Epoch 16/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 16002.2529 - val_loss: 16118.1396\n",
            "Epoch 17/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 15615.7412 - val_loss: 15616.3496\n",
            "Epoch 18/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 15069.1260 - val_loss: 15077.0234\n",
            "Epoch 19/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 14731.9102 - val_loss: 14973.1318\n",
            "Epoch 20/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 14383.6582 - val_loss: 14777.1650\n",
            "Epoch 21/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 14182.4092 - val_loss: 14291.6348\n",
            "Epoch 22/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 13965.9951 - val_loss: 14180.9258\n",
            "Epoch 23/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 13806.2266 - val_loss: 14042.5498\n",
            "Epoch 24/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 13669.8916 - val_loss: 14127.0898\n",
            "Epoch 25/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 13606.5088 - val_loss: 13730.7578\n",
            "Epoch 26/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 13426.0977 - val_loss: 13654.7666\n",
            "Epoch 27/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 13314.6123 - val_loss: 13625.5938\n",
            "Epoch 28/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 13171.4141 - val_loss: 13626.0020\n",
            "Epoch 29/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 13133.6953 - val_loss: 13452.1592\n",
            "Epoch 30/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 13054.7520 - val_loss: 13700.8496\n",
            "Epoch 31/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12939.3896 - val_loss: 13181.4814\n",
            "Epoch 32/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12821.8115 - val_loss: 13165.2109\n",
            "Epoch 33/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12723.8857 - val_loss: 13015.6973\n",
            "Epoch 34/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12633.4619 - val_loss: 12922.1094\n",
            "Epoch 35/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12614.0039 - val_loss: 12930.5400\n",
            "Epoch 36/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12578.5381 - val_loss: 13153.2686\n",
            "Epoch 37/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12462.0010 - val_loss: 12820.8711\n",
            "Epoch 38/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12382.9346 - val_loss: 12647.1338\n",
            "Epoch 39/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12273.7051 - val_loss: 12819.1514\n",
            "Epoch 40/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12258.1504 - val_loss: 12629.9785\n",
            "Epoch 41/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12197.4355 - val_loss: 12515.8984\n",
            "Epoch 42/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12176.9121 - val_loss: 12460.1699\n",
            "Epoch 43/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12151.1084 - val_loss: 12753.8184\n",
            "Epoch 44/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12081.8691 - val_loss: 12630.6475\n",
            "Epoch 45/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12022.8721 - val_loss: 12724.9287\n",
            "Epoch 46/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 12003.2559 - val_loss: 12327.3613\n",
            "Epoch 47/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11978.8564 - val_loss: 12197.1807\n",
            "Epoch 48/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11945.7773 - val_loss: 12324.9678\n",
            "Epoch 49/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11869.1934 - val_loss: 12478.2656\n",
            "Epoch 50/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11874.0381 - val_loss: 12233.0137\n",
            "Epoch 51/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11843.6162 - val_loss: 12282.7588\n",
            "Epoch 52/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11802.7090 - val_loss: 12203.2031\n",
            "Epoch 53/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11782.4688 - val_loss: 12162.7188\n",
            "Epoch 54/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11722.3652 - val_loss: 12220.0615\n",
            "Epoch 55/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11737.4170 - val_loss: 12388.4678\n",
            "Epoch 56/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11679.3975 - val_loss: 12079.3428\n",
            "Epoch 57/400\n",
            "285/285 [==============================] - 1s 4ms/step - loss: 11664.1992 - val_loss: 12278.0068\n",
            "Epoch 58/400\n",
            "285/285 [==============================] - 1s 5ms/step - loss: 11666.6660 - val_loss: 12153.9414\n",
            "Epoch 59/400\n",
            "285/285 [==============================] - 1s 4ms/step - loss: 11656.0234 - val_loss: 12213.2119\n",
            "Epoch 60/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11606.2764 - val_loss: 11989.2246\n",
            "Epoch 61/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11582.0742 - val_loss: 11983.8281\n",
            "Epoch 62/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11531.8613 - val_loss: 12377.7373\n",
            "Epoch 63/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11525.4902 - val_loss: 12403.4619\n",
            "Epoch 64/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11537.1123 - val_loss: 11941.3760\n",
            "Epoch 65/400\n",
            "285/285 [==============================] - 2s 5ms/step - loss: 11517.9541 - val_loss: 11924.4121\n",
            "Epoch 66/400\n",
            "285/285 [==============================] - 2s 5ms/step - loss: 11489.1455 - val_loss: 12055.8154\n",
            "Epoch 67/400\n",
            "285/285 [==============================] - 1s 5ms/step - loss: 11447.6250 - val_loss: 11857.0479\n",
            "Epoch 68/400\n",
            "285/285 [==============================] - 1s 4ms/step - loss: 11452.8643 - val_loss: 11817.9492\n",
            "Epoch 69/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11433.4922 - val_loss: 11824.2393\n",
            "Epoch 70/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11403.3223 - val_loss: 11992.0508\n",
            "Epoch 71/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11419.6953 - val_loss: 12294.4805\n",
            "Epoch 72/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11355.6484 - val_loss: 12247.5986\n",
            "Epoch 73/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11339.9219 - val_loss: 11872.6133\n",
            "Epoch 74/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11399.4102 - val_loss: 11793.3164\n",
            "Epoch 75/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11313.9893 - val_loss: 11771.9150\n",
            "Epoch 76/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11319.3936 - val_loss: 11821.0889\n",
            "Epoch 77/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11277.9697 - val_loss: 11892.1885\n",
            "Epoch 78/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11235.8691 - val_loss: 11767.1123\n",
            "Epoch 79/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11312.0615 - val_loss: 11876.3633\n",
            "Epoch 80/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11255.4102 - val_loss: 11785.8213\n",
            "Epoch 81/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11235.8359 - val_loss: 11789.5029\n",
            "Epoch 82/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11211.2520 - val_loss: 11631.8135\n",
            "Epoch 83/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11220.3564 - val_loss: 12026.9521\n",
            "Epoch 84/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11222.0576 - val_loss: 11708.7773\n",
            "Epoch 85/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11130.5225 - val_loss: 11733.3584\n",
            "Epoch 86/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11116.7471 - val_loss: 11736.1748\n",
            "Epoch 87/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11173.1035 - val_loss: 11684.8779\n",
            "Epoch 88/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11198.3018 - val_loss: 12068.1426\n",
            "Epoch 89/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11057.0830 - val_loss: 11723.8877\n",
            "Epoch 90/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11085.7715 - val_loss: 11641.5801\n",
            "Epoch 91/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11033.6133 - val_loss: 11658.9609\n",
            "Epoch 92/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11031.4805 - val_loss: 11870.1143\n",
            "Epoch 93/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11141.5264 - val_loss: 11710.8828\n",
            "Epoch 94/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10998.0352 - val_loss: 11998.8379\n",
            "Epoch 95/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11040.4941 - val_loss: 12050.3750\n",
            "Epoch 96/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10996.9707 - val_loss: 11622.2178\n",
            "Epoch 97/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11010.1104 - val_loss: 11592.0977\n",
            "Epoch 98/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10958.4473 - val_loss: 11539.7109\n",
            "Epoch 99/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10982.2324 - val_loss: 12198.3252\n",
            "Epoch 100/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10955.9854 - val_loss: 11646.0889\n",
            "Epoch 101/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 11029.9941 - val_loss: 11503.3594\n",
            "Epoch 102/400\n",
            "285/285 [==============================] - 1s 4ms/step - loss: 11019.2324 - val_loss: 11624.6777\n",
            "Epoch 103/400\n",
            "285/285 [==============================] - 1s 5ms/step - loss: 10888.9473 - val_loss: 11660.9961\n",
            "Epoch 104/400\n",
            "285/285 [==============================] - 2s 6ms/step - loss: 10996.7100 - val_loss: 11532.6279\n",
            "Epoch 105/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10897.8555 - val_loss: 11487.0859\n",
            "Epoch 106/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10948.1680 - val_loss: 11634.2627\n",
            "Epoch 107/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10851.7646 - val_loss: 11511.4648\n",
            "Epoch 108/400\n",
            "285/285 [==============================] - 1s 4ms/step - loss: 10815.9287 - val_loss: 11707.8906\n",
            "Epoch 109/400\n",
            "285/285 [==============================] - 1s 4ms/step - loss: 10882.9453 - val_loss: 12206.7559\n",
            "Epoch 110/400\n",
            "285/285 [==============================] - 1s 5ms/step - loss: 10913.8887 - val_loss: 11654.6523\n",
            "Epoch 111/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10812.3906 - val_loss: 11491.7568\n",
            "Epoch 112/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10870.3975 - val_loss: 11435.1318\n",
            "Epoch 113/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10825.6240 - val_loss: 11460.9570\n",
            "Epoch 114/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10806.7412 - val_loss: 11837.6904\n",
            "Epoch 115/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10833.0596 - val_loss: 11550.0732\n",
            "Epoch 116/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10803.8301 - val_loss: 11674.6807\n",
            "Epoch 117/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10781.1230 - val_loss: 11522.3125\n",
            "Epoch 118/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10802.0469 - val_loss: 11637.0293\n",
            "Epoch 119/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10766.2119 - val_loss: 11580.7344\n",
            "Epoch 120/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10731.9307 - val_loss: 11670.2021\n",
            "Epoch 121/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10753.6641 - val_loss: 11643.6045\n",
            "Epoch 122/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10701.2539 - val_loss: 11447.9814\n",
            "Epoch 123/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10731.5303 - val_loss: 11556.4648\n",
            "Epoch 124/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10749.7207 - val_loss: 11572.0029\n",
            "Epoch 125/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10721.6797 - val_loss: 11356.5078\n",
            "Epoch 126/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10705.5322 - val_loss: 11452.7393\n",
            "Epoch 127/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10724.3008 - val_loss: 11587.8252\n",
            "Epoch 128/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10710.6172 - val_loss: 11779.1562\n",
            "Epoch 129/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10683.4854 - val_loss: 11470.8926\n",
            "Epoch 130/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10663.4736 - val_loss: 11554.0352\n",
            "Epoch 131/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10636.1387 - val_loss: 11330.3818\n",
            "Epoch 132/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10629.8604 - val_loss: 11793.5859\n",
            "Epoch 133/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10694.4141 - val_loss: 11483.2139\n",
            "Epoch 134/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10656.4521 - val_loss: 11398.4707\n",
            "Epoch 135/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10629.2324 - val_loss: 11358.2812\n",
            "Epoch 136/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10669.0166 - val_loss: 11469.2568\n",
            "Epoch 137/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10640.3418 - val_loss: 11786.1553\n",
            "Epoch 138/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10558.1436 - val_loss: 11415.2119\n",
            "Epoch 139/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10679.6240 - val_loss: 11425.3535\n",
            "Epoch 140/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10563.9873 - val_loss: 11376.0225\n",
            "Epoch 141/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10590.4756 - val_loss: 11428.3301\n",
            "Epoch 142/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10593.6533 - val_loss: 11748.5430\n",
            "Epoch 143/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10584.1875 - val_loss: 11325.6650\n",
            "Epoch 144/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10511.2197 - val_loss: 11406.0244\n",
            "Epoch 145/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10578.6621 - val_loss: 11347.2910\n",
            "Epoch 146/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10580.8662 - val_loss: 11359.8525\n",
            "Epoch 147/400\n",
            "285/285 [==============================] - 1s 4ms/step - loss: 10532.7637 - val_loss: 11480.5166\n",
            "Epoch 148/400\n",
            "285/285 [==============================] - 1s 4ms/step - loss: 10542.1836 - val_loss: 11614.3691\n",
            "Epoch 149/400\n",
            "285/285 [==============================] - 1s 4ms/step - loss: 10551.8682 - val_loss: 11373.8945\n",
            "Epoch 150/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10517.8916 - val_loss: 11423.0586\n",
            "Epoch 151/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10544.6318 - val_loss: 11896.7256\n",
            "Epoch 152/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10504.7617 - val_loss: 11489.7725\n",
            "Epoch 153/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10523.0518 - val_loss: 11444.0000\n",
            "Epoch 154/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10471.6816 - val_loss: 11300.9785\n",
            "Epoch 155/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10461.8086 - val_loss: 11441.9053\n",
            "Epoch 156/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10420.0068 - val_loss: 11505.6357\n",
            "Epoch 157/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10484.3809 - val_loss: 11431.9727\n",
            "Epoch 158/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10469.0518 - val_loss: 11281.0010\n",
            "Epoch 159/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10516.1162 - val_loss: 11578.4395\n",
            "Epoch 160/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10408.9590 - val_loss: 11266.8447\n",
            "Epoch 161/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10446.2871 - val_loss: 11195.3877\n",
            "Epoch 162/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10499.6670 - val_loss: 11527.6396\n",
            "Epoch 163/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10408.5078 - val_loss: 11377.2686\n",
            "Epoch 164/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10422.9795 - val_loss: 11277.4707\n",
            "Epoch 165/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10452.8379 - val_loss: 11303.2969\n",
            "Epoch 166/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10400.3799 - val_loss: 11412.8965\n",
            "Epoch 167/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10372.6338 - val_loss: 11429.6260\n",
            "Epoch 168/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10421.1670 - val_loss: 11428.6572\n",
            "Epoch 169/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10359.6768 - val_loss: 11655.7305\n",
            "Epoch 170/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10427.9307 - val_loss: 11463.0850\n",
            "Epoch 171/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10355.2041 - val_loss: 11204.4434\n",
            "Epoch 172/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10330.0283 - val_loss: 11475.9316\n",
            "Epoch 173/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10350.3486 - val_loss: 11219.0996\n",
            "Epoch 174/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10366.8223 - val_loss: 11328.7568\n",
            "Epoch 175/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10342.6455 - val_loss: 11359.0566\n",
            "Epoch 176/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10357.5654 - val_loss: 11378.1035\n",
            "Epoch 177/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10336.5234 - val_loss: 11363.7188\n",
            "Epoch 178/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10300.0977 - val_loss: 11280.7158\n",
            "Epoch 179/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10281.7139 - val_loss: 11313.3213\n",
            "Epoch 180/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10305.6172 - val_loss: 11303.7754\n",
            "Epoch 181/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10275.7881 - val_loss: 11738.3242\n",
            "Epoch 182/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10271.5195 - val_loss: 11474.7754\n",
            "Epoch 183/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10314.7910 - val_loss: 11212.7549\n",
            "Epoch 184/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10268.8555 - val_loss: 11302.0439\n",
            "Epoch 185/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10307.8857 - val_loss: 11374.6240\n",
            "Epoch 186/400\n",
            "285/285 [==============================] - 1s 3ms/step - loss: 10294.2793 - val_loss: 11211.3281\n",
            "Epoch 186: early stopping\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f37eef26950>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "losses = pd.DataFrame(model.history.history)"
      ],
      "metadata": {
        "id": "sh7-0f1RBbsN"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "losses.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "rb3o4-IIB2HW",
        "outputId": "977332d0-323b-4ef2-ac07-23989c2edb77"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f37eee8f310>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_metrics_timeseries(real_values,pred,name='Variable'):\n",
        "    print(f\"\"\"\n",
        "        ------- {name} ------\n",
        "        \n",
        "MAE: {mean_absolute_error(real_values,pred):.3f},\n",
        "MSE: {mean_squared_error(real_values,pred):.3f},\n",
        "RMSE: {np.sqrt(mean_squared_error(real_values,pred)):.3f},\n",
        "Explained Variance Score: {explained_variance_score(real_values,pred)*100:.3f}%,\n",
        "\n",
        "Compare Mean values per Year: Predictions VS Real:\n",
        "\n",
        "{pd.concat([pd.DataFrame(real_values).mean(),pd.DataFrame(pred).mean()],axis=1,keys=['Avg real',' Avg Pred'],names='Year')}\n",
        "    \"\"\")"
      ],
      "metadata": {
        "id": "YQMaeOoTCHqp"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_enthalpy = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ehf0K-EqCKzc",
        "outputId": "c6c661c4-d5e8-4ecf-cc69-91fba39af782"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "285/285 [==============================] - 1s 1ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eval_metrics_timeseries(y_test,predictions_enthalpy,name='Power Enthalpy (Kj/Kg)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJP0M8D3CWIH",
        "outputId": "e23d9f0e-663e-4374-ffdb-dd00258c3dae"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "        ------- Power Enthalpy (Kj/Kg) ------\n",
            "        \n",
            "MAE: 68.364,\n",
            "MSE: 11211.327,\n",
            "RMSE: 105.884,\n",
            "Explained Variance Score: 76.378%,\n",
            "\n",
            "Compare Mean values per Year: Predictions VS Real:\n",
            "\n",
            "Year    Avg real    Avg Pred\n",
            "0     720.673024  719.532593\n",
            "1     685.503591  686.211670\n",
            "2     669.999661  671.599915\n",
            "3     659.121084  661.051697\n",
            "4     650.643491  652.674866\n",
            "5     643.678181  645.790710\n",
            "6     637.751328  639.945740\n",
            "7     632.605190  634.865234\n",
            "8     628.050429  630.393250\n",
            "9     623.965584  626.258667\n",
            "10    620.241453  622.611206\n",
            "11    616.822240  619.285095\n",
            "12    613.660062  616.241821\n",
            "13    610.725212  613.382385\n",
            "14    608.000128  610.756104\n",
            "15    605.442206  608.410156\n",
            "16    603.023039  605.962219\n",
            "17    600.744027  603.853882\n",
            "18    598.577764  601.731384\n",
            "19    596.526776  599.653198\n",
            "    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(predictions_enthalpy,columns=df_Enthalpy.columns).mean().plot(legend=True,label='Avg Enthalpy PRED',\n",
        "                                                                           title='Avg Power Enthalpy: Predicted Vs Real values')\n",
        "pd.DataFrame(y_test,columns=df_Enthalpy.columns).mean().plot(legend=True,label='Avg Enthalpy REAL')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "epRwzGMlCgzx",
        "outputId": "eca02029-8ff7-4f81-ef0c-aa5ca80b3015"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f37ee8c7e90>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ],
            "image/png": 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S8ePHs2HDBtLS0nK0g4ODAwsXLmT27Nm88sorGI1G2rZty+TJk3N048rN8OHDCQgI4MqVKyxevJhFixbh7e1Nx44dgRtts3v37hxB46Y2bdoAD/e+u11gYCDLli3j6NGj1K1b1zTm7+aXJqdOneL555+nSZMmvPfeezg6OmJlZcWoUaMeeir4/P5teNC2FpGioVAjUsrc/OZ3/Pjxdzy3Zs0a3nzzTSwtLQkMDGT06NGcOnWKyMhIKleuTOvWrU3rVqtWjXPnzuXYPj09ndTU1HzVUbVq1buGgWrVqpGSkpJjWVZWFhcuXDBdsTAYDKZxNRUqVDB1dfHy8iI2Npb09HSaN29u+tDs4OBAmTJl+O6773KdXe3mWImb+y5J1q9fT6VKlfjnP/9pqv3m4Pu83N7ON5fdOiV1rVq18Pb2ZtWqVcTHx5Odnf3AUwo3btzYNPtZbm5v+5uv95w5c3INKXXr1sXW1hZLS8s7ziW3c7tVpUqVAO57YHv58uUxGAyMHTvWFAJu5ejoCOT+Pr527Vq+f0d69OjBjz/+yL59+4iMjKRx48bUr18/xzoeHh58/fXXpKWlsXXrVj766CMmTpzIsmXL7rnvGjVqmH7/vLy86NmzJzNmzKBDhw4YDAYcHBzo0qVLrrOh3Xz9HuZ9d7uWLVtStWpVIiMjCQ4OZvfu3aauaAC//PILaWlpfPHFF6ZQmJmZafqS426sra3vmMzi9qss+f3b8KBtLSJFQ6FGpBRJTU1l06ZN9OjRg/79++d4Li4ujunTp7N9+3batm1L27ZtqVChAlFRUURFReHn55ejO5ibmxuhoaE5xnVs2LChQOps2rQp69ev59VXXzUdc926daYuPje1aNGCefPmUa5cOVNIa9GiBTNmzCA9Pd00iBxuTFublZXF5cuXc3QFKy6srKweeJKFtLQ0rKyscnwg+/HHH/O1bUpKCjt37jR1QTt16hT79++/o0tW3759efPNNzl8+DBdu3a94+pRYWnWrBm2trYkJyfn6Ip0u8aNG7NhwwYGDhxoWvbTTz/dc99169bFycmJsLCwHON6bpXb62Jvb4+HhwdHjx41jfPJzc3fkWvXrpm6oOVV061ujjX57rvv+PXXX3P9IuImW1tbunTpwqFDh5gzZ06+jwE3znH8+PG88sorbNy4ER8fH9q0aUNUVBQNGjTI0cX0Vg/zvrudpaUl/v7+REVFYWNjYxrzd+uxLCwsckzAERUVlecNQ52dnUlISCA9PR0bGxvgxr1+bnW/fxsepq1FpPAo1IiUIhs2bODatWsMHjyYpk2b5niuefPmfPnll4SHh9O2bVusrKzw9fVl4cKFnDlz5o47mA8dOpT//Oc/jB49mqFDh3LmzBnmzZuHnZ3dQ1/pePHFF3nqqad46aWXGDhwIImJifz973+nXbt2NGvWzLSep6cn06dP5+zZs6YrNZ6enpw4cQIgx8xn9erVY8CAAbz66qsMHz4cd3d30tPTOXToEMeOHWPatGkPVOvN8RW3q127do6rP3mpW7cuGzZsYP369Tg5OeHo6JjvG1K2bduWxYsXM23aNLp06cLOnTv54Ycf8rVtpUqVeO2113jllVewtbXl008/pXLlyneEmq5du/Lee++xb98+Xn311RzP+fr60qJFi3yPq7kfFSpUYOzYsUybNo2EhARatGhBdna2aVayzz//HIDRo0czduxY3nnnHXx9fdmxYwe//PLLPfdtYWHBa6+9xqRJk5g4cSI9evTAYDCwfft2unfvjru7O/Xq1ePs2bOEhobSoEEDKlWqRK1atZg0aRJDhw7FwsICPz8/ypYty+nTp/n555+ZMGECdevWNf2OjBo1imHDhpGcnMycOXPuGhJuZ2VlhZ+fn+lKwO1d8H7++WdWrlyJj48PNWrUICkpiaVLl+a4oppffn5+1KtXj6+//hofHx+GDh3KDz/8wJAhQ3juuedwcnLi7Nmz7NixA09PT3r06PFQ77vcBAQE8O2337Jo0aIcY/7g/4PH3/72N/r27cuhQ4dYsGBBnuH65ux0b731Fr1792b//v2sXLkyxzr5+dtQkG0tIoVDoUakFImIiKBOnTp3BBq48QEqICCA8PBw3nvvPaytrenevTsrVqzA0dHxjpv9OTk5MWfOHKZNm8bYsWOpX78+H374IcOGDcsxPuZBNGjQgHnz5vGPf/yDsWPHUq5cObp3737HrFFPPvkk9vb2ODo6msZFVK5cmXr16pGQkGCaJeqmd955hzp16rB8+XI+/fRTypUrx+OPP07fvn0fuNYjR47w9NNP37F8xowZBAUF5Xs/zzzzDHFxcbz55ptcvHiRsWPH8vLLL+dr244dOzJp0iS+/fZbli9fjoeHB3PmzMHPzy/PbWvUqMHo0aOZNWuWqc1mzZpl+lb7Jmtra9q3b09MTAze3t45nsvKyiI7Ozvf53q/Ro4ciaOjI4sXL2bhwoXY2NhQp04dAgMDTev4+voyefJk5s6dS1hYGC1btmTatGkMHz78nvvu2bMnNjY2fPXVV4wbNw57e3uaNm1qCqQBAQFER0czc+ZMzp07x1NPPcVHH32El5cXS5Ys4dNPP+X1118nOzubGjVq0L59e9O4ICcnJ+bOncvUqVN5+eWXqV+/PjNnzryvG1x2796dpUuX0rx5c2rUqJHjudq1a2MwGPjkk09ISUmhcuXKdOrU6Y7QmR8WFhaMGjWKN954g927d+Ph4cHSpUv55z//yfTp07l06RKOjo40b97cNE7oYd53ufH09KR69eqcPn06x2sLN+4vNH36dD777DN++uknGjVqxL/+9a88Z1t84okn+PDDD/niiy/46aefaN26NdOnT89xRQ/y/ttQkG0tIoXDYLw5nY2IyEOKiYnh2WefZfHixfoGswQICQnh4MGDhIaG5rluZmYmnTt3pk+fPrzyyitFUJ2IiEj+6UqNiDywmTNn8uSTT1K1alWOHj3KF198QcOGDWnZsqW5S5MCcv36dQ4cOEB4eDgXLlxgwIAB5i5JRETkDgo1IvLArl+/zowZM0hJSaFs2bK0bduWkJCQfN+IUYq/5ORk+vXrR5UqVXj//fdN016LiIgUJ+p+JiIiIiIiJZq+ThURERERkRJNoUZEREREREq0YjGmZvfu3XdMH2pOt96kS4qG2tw81O5FT21e9NTm5qF2L3pqc/NQuxed9PR0PDw8cn2uWIQaGxsbGjdubO4yTOLi4opVPaWB2tw81O5FT21e9NTm5qF2L3pqc/NQuxeduLi4uz6n7mciIiIiIlKiKdSIiIiIiEiJplAjIiIiIiIlWrEYUyMiIiIikpGRQXx8PGlpaeYuJd8yMjLuOdZD7p+trS21atXCysoq39so1IiIiIhIsRAfH0/58uWpU6cOBoPB3OXky7Vr17CzszN3GY8Mo9FISkoK8fHx1K1bN9/bqfuZiIiIiBQLaWlpVKlSpcQEGil4BoOBKlWq3PfVOoUaERERESk2FGjkQd4DCjUiIiIiIrdYv349DRs25K+//iqQ/YWGhtK6dWuCgoJMP4cPH77nNosWLeLatWumx82aNbvvY77//vsPVO+toqOj8fT0JCgoiICAAD777LM7lvv7+/Pxxx/nOHZu5xsfH0+TJk0IDg4mICCAvn37Ehoa+tA1gsbUiIiIiIjkEB4ejqenJxEREYwbN65A9hkYGMiUKVPyvf4333xDr169isV4HS8vL+bMmUNqairBwcF07tw5x/K0tDSCg4Pp2rUrnp6eQO7nGx8fT+3atQkLCwPg5MmTjB07FqPRSJ8+fR6qRl2pERERERH5n6tXrxIbG8u0adOIiIgAYMuWLTnCTXR0NKNGjQJg1apV+Pn50bdvX95+++37ujoSHR3NoEGDGDduHP7+/kycOBGj0cg333xDcnIyQ4YMYdCgQab1P/nkE3r16kX//v05e/YsABs3bqRfv34EBwczdOhQ0/JbhYSEMGXKFHr37o2fnx+bNm0C4Nlnn80xc9vAgQM5cODAXeu1t7fH1dWV48eP51hua2tL48aNSUpKyve5A7i4uBASEsK///3v+9ouN7pSIyIiIiLFzsrYeJbFnCzQffb3cqGPZ617rrNhwwbat29P3bp1qVSpEnv37sXb25spU6aQmpqKvb09kZGRBAYGkpSUxNy5cwkLC6Ns2bIMGTKERo0a5brfyMhIYmNjTY+XLl0KwP79+4mIiMDR0ZGBAwcSGxvL4MGDWbRoEYsXL6Zy5coApKam0rRpUyZMmMCMGTNYtmwZY8aMwdPTk2XLlmEwGFi+fDnz588nJCTkjuMnJCSwYsUKTpw4weDBg/H29jZ1/3rrrbc4evQo6enpd60f4Pz58/zxxx+MGTOGc+fOmZZfvHiR48eP06JFizzP93aurq4cOXLkrsfML12pERERERH5n4iICLp37w7c6EIVERFBmTJlaN++PZs2bSIzM5PNmzfj4+PDn3/+iaenJxUrVsTKygp/f/+77jcwMJDVq1ebfmxtbQFo0qQJzs7OWFhY0KhRIxISEnLd3srKytTty83NzbReYmIiw4cPp2fPnsyfP59Dhw7lun1AQAAWFhbUqVMHFxcXjhw5gr+/Pz///DMZGRmsXLmS3r1757ptTEwMwcHBDB8+nJEjR9KgQQPT8l69etGhQwfatWtHtWrV8jzf2xmNxru22f3QlRoRERERKXb6eNbK86pKQbtw4QLbt2/n4MGDGAwGsrKyMBgMvP766wQGBrJkyRIcHBxwc3OjXLlyBXJMa2tr078tLS3JysrKdT0rKyvTrGAWFham9aZOncrQoUPx8fEhOjraNJD/drfPKGYwGLCzs8Pb25sNGzYQFRV110H7N8fO3G35yZMnefrppwkICKBx48Z5n/Qt9u/fT/369e9rm9zoSo2IiIiICLB27VqCgoLYtGkTGzduZPPmzdSqVYuYmBhatmzJ/v37WbZsGYGBgQC4u7sTGxvLxYsXyczMZN26dQVWS9myZbl69Wqe612+fBknJycA0wD83KxZs4bs7GxOnDjByZMnTTe27NevH1OnTsXd3R0HB4cHqtXFxYUXXniBefPm3dd28fHxzJgxg+eee+6BjnsrXakREREREeHGrGcjR47Msaxbt26Eh4fTokULOnXqxKpVq0zTFzs5OTF8+HD69euHg4MD9erVo3z58rnu+/YxJu+88849a+nfvz8jRozA0dHxngPpx44dy/jx43FwcKBVq1bEx8fnul716tXp27cvV69e5b333sPGxgbAdNXpbl3P8mvAgAF8/fXXpuPndr6Ojo6cOHGC4OBg0tPTKVu2LIMGDXroYwMYjAXVke0hxMXF3felqsJU3OopDdTm5qF2L3pq86KnNjcPtXvRexTavCSeQ0pKClWqVCEzM5OxY8fSp08ffH19zV1WDiEhIXTq1CnXMT9JSUkMHjyYqKgoLCyKTyeu3N4L93p/5Hml5siRI0yYMMH0+OTJk4wbN46kpCQ2bdqElZUVtWvXZvr06VSoUAGAOXPmsGLFCiwsLHj77bdp3779w5xT0crONncFIiIiIlJCfPXVV/z++++kp6fTrl07unbtau6S8i0sLIxPPvmEkJCQYhVoHkSeoaZevXqsXr0agKysLDp06ICvry9Hjx5l4sSJlClThpkzZzJnzhxee+01Dh8+TEREBBERESQlJTFs2DDWrl2LpaVloZ/MQzMaYXYzKjwxBErYtwQiIiIiUvReffXVYnGDzHv56KOPcl0eHBxMcHBwEVdTOO4rkm3btg0XFxdq1qxJu3btKFPmRiby8PAgMTERuDG3d/fu3bG2tsbFxYXHHnuMPXv2FHzlhcFgAOtyVDqc+8wPIiIiIiJS/NxXqImIiKBHjx53LF+5ciUdOnQAbvTLc3Z2Nj3n5OR033cXNSvXYOxT/oSLuQ+yEhERERGR4iXfs59dv36djRs3MnHixBzLv/zySywtLenVq9cDF5Genk5cXNwDb1+QDl9sQE8gcdM8zjccYO5ySo20tLRi8x4oTdTuRU9tXvTU5uahdi96j0KbZ2RkcO3aNXOXcV+MRmOJq7kkyMjIuK/3c75DzZYtW3B1daVq1aqmZaGhofz8888sWrTIdAi0YfAAACAASURBVEMfJycnU1c0uHHl5ubc2XdjY2NTbGa66L/0BM2s61Lr7Facg98zdzmlRkmc7eRRoHYvemrzoqc2Nw+1e9F7FNo8Li6u2I9Pud21a9dKXM0lgZWVVa6zn91NvrufRURE0L17d9PjLVu2MH/+fL788sscL2SXLl2IiIjg+vXrnDx5kmPHjtGkSZP7OQezavd4VcKut4T439UFTURERKQUWr9+PQ0bNuSvv/4qkP2FhobSunVrgoKCTD+HDx++5zaLFi3KcQWoWbNm933M999//4HqvVV0dDSenp4EBQXh7+9vukfPzWPc67wWLVqEu7s7ly9fzrG/UaNGPXRdt8tXqElNTWXr1q1069bNtOyDDz7g6tWrDBs2jKCgIKZMmQJAgwYNCAgIIDAwkBEjRjBlypSSMfPZ/wR51GBlessbD/avNm8xIiIiIlLkwsPD8fT0JCIiosD2GRgYyOrVq00/jz/++D3X/+abb4pNtzYvLy9Wr15NWFgYmzZtynFTzXudV0REBO7u7qxbt67Qa8xXqLG3tyc6OjrHHVJ/+uknNm/ebDqBW5Pgiy++yPr161m7di0dO3Ys+KoLUaeGjiSVqUGCzeOwL8zc5YiIiIhIEbp69SqxsbFMmzbNFGq2bNnCuHHjTOvcerVh1apV+Pn50bdvX95+++37ujoSHR3NoEGDGDduHP7+/kycOBGj0cg333xDcnIyQ4YMYdCgQab1P/nkE3r16kX//v05e/YsABs3bqRfv34EBwczdOhQ0/JbhYSEMGXKFHr37o2fnx+bNm0C4Nlnn83RpWvgwIEcOHDgrvXa2trSuHHjfE0CduLECVJTU3nllVcKNBzeTb7H1JQWtlaWtH2sLCtPejEu/vsbXdAcapm7LBEREZHSZfd3sOvbgt1ns+fAY+A9V9mwYQPt27enbt26VKpUib179+Lt7c2UKVNITU3F3t6eyMhIAgMDSUpKYu7cuYSFhVG2bFmGDBlCo0aNct1vZGRkjiscS5cuBWD//v1ERETg6OjIwIEDiY2NZfDgwSxatIjFixdTuXJl4EbPqaZNmzJhwgRmzJjBsmXLGDNmDJ6enixbtgyDwcDy5cuZP38+ISEhdxw/ISGBFStWcOLECQYPHoy3tzd9+/YlNDSUt956i6NHj5Kenn7X+gEuXrzI8ePHadGixT3Py9bWloiICAIDA/Hy8uLo0aOcPXs2x9j8glaybx1aSDrXLceq6/97sdQFTURERKTUuHUceWBgIBEREZQpU4b27duzadMmMjMz2bx5Mz4+Pvz55594enpSsWJFrKys8Pf3v+t+b++mZWtrC0CTJk1wdnbGwsKCRo0akZCQkOv2VlZWdO7cGQA3NzfTeomJiQwfPpyePXsyf/58Dh06lOv2AQEBWFhYUKdOHVxcXDhy5Aj+/v78/PPPZGRksHLlSnr37p3rtjExMfTq1YsOHTrQrl07qlWrlud53WxHCwsLunXrxpo1a+7V7A9NV2py0cTZjivl6nDSUB+XfWHQ5iVzlyQiIiJSungMzPOqSkG7cOEC27dv5+DBgxgMBrKysjAYDLz++usEBgayZMkSHBwccHNzo1y5cgVyTGtra9O/LS0tycrKynU9Kysr02zDFhYWpvWmTp3K0KFD8fHxITo6ms8++yzX7W9ue+tjOzs7vL292bBhA1FRUYSG5n4Dei8vL+bMmcPJkyd5+umnCQgIuOdMe//97385duwYzz//PHDj1jC1atXiueeeu+s2D0tXanJhaWGgZ5MaLL/mpVnQREREREqJtWvXEhQUxKZNm9i4cSObN2+mVq1axMTE0LJlS/bv38+yZcsIDAwEwN3dndjYWC5evEhmZmaBDogvW7YsV69ezXO9y5cvm26fEhZ29/Hga9asITs7mxMnTnDy5Enq1q0LQL9+/Zg6dSru7u44ODjc81guLi688MILzJs3757rRURE8PLLL7Nx40Y2btzIr7/+SnJy8l2vQhUEhZq7CPKowQ+ZmgVNREREpLQIDw+na9euOZZ169aN8PBwLC0t6dSpE7/88oupG5iTkxPDhw+nX79+DBw4kJo1a+aYWOtWkZGROaY+3rlz5z1r6d+/PyNGjMgxUUBuxo4dy/jx4+nduzcVK1a863rVq1enb9++jBw5kvfeew8bGxsA01Wnu3U9u92AAQPYsWMH8fHxdz2viIiIO9rR19fXNGHAtm3b6NChg+ln165d+Tr2vRiMRqPxoffykIrbzaLi4uJo1KgRnf/+M4uuT6SOU2UYsd7cZT3Sitt7oLRQuxc9tXnRU5ubh9q96D0KbV4SzyElJYUqVaqQmZnJ2LFj6dOnD76+vuYuK4eQkBA6deqU65ifpKQkBg8eTFRUFBYWxed6R27vhXu9P4pP5cWMwWCgl0dNll/zhPgdcOGkuUsSERERkWLmq6++IigoiB49elCrVq07rlAUZ2FhYfTv359XXnmlWAWaB6GJAu4hyKMGwze24rUyy250QfMea+6SRERERKQYefXVV7GzszN3Gff00Ucf5bo8ODiY4ODgIq6mcJTsSFbI6lcrR/kajThiWQ/260acIiIiIiLFkUJNHoI8arAizUtd0ERERESKQDEY7i1m9iDvAYWaPPRoUoMoY6sbDzQLmoiIiEihsbW1JSUlRcGmFDMajaSkpJhu4plfGlOTB2cHW5zruHEosS6P7w/DoHE1IiIiIoWiVq1axMfHc+bMGXOXkm8ZGRlYWVmZu4xHiq2tLbVq1bqvbRRq8iHIowarVrfk9filN7qgVXQxd0kiIiIijxwrKyvTTSFLipI4DfWjSN3P8iHArTrraHPjgbqgiYiIiIgUKwo1+eBgb0W9hu4coC7GfavMXY6IiIiIiNxCoSafgjxq8kNGSwwJMZoFTURERESkGFGoySefxo5ssvS+8UBd0EREREREig2FmnyytbKksZsH+6lL9t5Qc5cjIiIiIiL/o1BzH4I8ahKe0RKLU7Fw4YS5yxERERERERRq7kvb+lXYatvuxgN1QRMRERERKRYUau5DGUsLPJp6ss9Yh6y9mgVNRERERKQ4UKi5T708ahCe2RpLdUETERERESkWFGruUzOXiuws3+HGA3VBExERERExO4Wa+2QwGGjRzIu92XXI+FOzoImIiIiImJtCzQMI8qhBRFZrrE7vVBc0EREREREzU6h5AA2cyvPfKj43HqgLmoiIiIiIWSnUPKBWnp78mV2H9D9WmrsUEREREZFSTaHmAfVsWoPIrNbYJO2C88fNXY6IiIiISKmlUPOAalS0I75GNwCM6oImIiIiImI2CjUPobWXF39m1+HabnVBExERERExF4WahxDoVp2o7DbYn9mtLmgiIiIiImaiUPMQKpW15lydAACy96kLmoiIiIiIOSjUPCTvFi34M7sOqbtXmLsUEREREZFSSaHmIXVt7Mg6vCl39g91QRMRERERMQOFmodkb12G1Md7AJC5d5WZqxERERERKX0UagpAu5Yt2JNdl6u71AVNRERERKSoKdQUgHYNqrLJ0huHc3+qC5qIiIiISBFTqCkAVpYWZDUKAiD9T3VBExEREREpSgo1BaRDq/91Qdu53NyliIiIiIiUKgo1BaR57Ur8Yt2eyhf2qguaiIiIiEgRUqgpIBYWBizdngLQhAEiIiIiIkVIoaYAdWrtxZ7sulzbvdLcpYiIiIiIlBoKNQWokXMFfrfvSNVL++D8MXOXIyIiIiJSKijUFDCbpr0BuBCjLmgiIiIiIkVBoaaAdWrVgj+y65G+J9TcpYiIiIiIlAoKNQXMpbI9eyp0wunyPoznjpq7HBERERGRR55CTSEo16wPAMnRumeNiIiIiEhhU6gpBDduxFmP7L3qgiYiIiIiUtgUagpBlXI2xFX2ofrVOLJT1AVNRERERKQwKdQUkkpe/QBI2PqdmSsREREREXm0KdQUkrYtPPnTWA/D/tXmLkVERERE5JGmUFNIytqU4S/HbtS6doDrZ9QFTURERESksCjUFKKqLW50QTvx6xIzVyIiIiIi8uhSqClELZs3Zy/1sf7vD+YuRURERETkkaVQU4isy1hwsroftdP+y7Wkw+YuR0RERETkkZRnqDly5AhBQUGmn+bNm7No0SIuXLjAsGHD6NatG8OGDePixYsAGI1Gpk6diq+vLz179mTfvn2FfhLFmXObAQAc2fIfM1ciIiIiIvJoyjPU1KtXj9WrV7N69WpCQ0Oxs7PD19eXuXPn0qZNG9atW0ebNm2YO3cuAFu2bOHYsWOsW7eODz74gHfffbewz6FYa+rWhP2Gxyl76EdzlyIiIiIi8ki6r+5n27Ztw8XFhZo1a7JhwwaCg4MBCA4OZv369QCm5QaDAQ8PDy5dukRycnLBV15CWFgYSKzlT53rB7mQcNDc5YiIiIiIPHLuK9RERETQo0cPAFJSUnB0dASgWrVqpKSkAJCUlISzs7NpG2dnZ5KSkgqq3hKpVruBABzZrFnQREREREQKWpn8rnj9+nU2btzIxIkT73jOYDBgMBgeuIj09HTi4uIeePuClpaWVqD1GI0WxBnqU/7wj8TFDSiw/T5KCrrNJX/U7kVPbV701ObmoXYvempz81C7Fw/5DjVbtmzB1dWVqlWrAlClShWSk5NxdHQkOTmZypUrA+Dk5ERiYqJpu8TERJycnO65bxsbGxo3bvwg9ReKuLi4Aq/ntzo9aHv0XyTaG3B+rFGB7vtRUBhtLnlTuxc9tXnRU5ubh9q96KnNzUPtXnTuFR7z3f0sIiKC7t27mx536dKFsLAwAMLCwvDx8cmx3Gg0snv3bsqXL2/qplaa1enwLADH1AVNRERERKRA5SvUpKamsnXrVrp162Za9sILL/Dbb7/RrVs3tm7dygsvvABAx44dcXFxwdfXl8mTJ/POO+8UTuUlTM26DTlY5gkqH480dykiIiIiIo+UfHU/s7e3Jzo6OseySpUqsXjx4jvWNRgMCjJ3cbFud1oc+oQjB/dS7wk3c5cjIiIiIvJIuK/Zz+Th1Ov0HAAnf9WNOEVERERECopCTRGqUvNx/rJuhNPJNRiNRnOXIyIiIiLySFCoKWJXH+9BI+Nf7N23x9yliIiIiIg8EhRqili9jjdmQUvc+p2ZKxEREREReTQo1BSxck71OGbTiFqn1pKRlW3uckRERERESjyFGjNIaxhEY44QuyvW3KWIiIiIiJR4CjVmULfDMwCcjV5m5kpEREREREo+hRozsKlahxN2T1I3+SeuXc8ydzkiIiIiIiWaQo2ZZD8ZhKvhKNtidpi7FBERERGREk2hxkxc2g4E4HzMcjNXIiIiIiJSsinUmIll5ceIL+tKw5QNXEi9bu5yRERERERKLIUaM7J0fQo3w1F+iVYXNBERERGRB6VQY0bObfoDcHmnuqCJiIiIiDwohRozMlR6jNPl3WhycROnL14zdzkiIiIiIiWSQo2ZWTfpg5vFMTZv+93cpYiIiIiIlEgKNWZWpUU/ANL2rDRzJSIiIiIiJZNCjblVdCHZoQleVzZzOPmKuasRERERESlxFGqKATuPG13Qtmzbbu5SRERERERKHIWaYqB8sz4AZO5dhdFoNHM1IiIiIiIli0JNcVDRhbMVm+Kd/it/xF80dzUiIiIiIiWKQk0xUa75/7qgbY82dykiIiIiIiWKQk0xYdukNwAWcavJylYXNBERERGR/FKoKS4qunC+clM6Zf7G1r/OmrsaEREREZESQ6GmGCnXvB9uFsf4NVo34hQRERERyS+FmmLEyv0pAOwO/UhaRpaZqxERERERKRkUaooTh1pcqtoMX+NWNh1INnc1IiIiIiIlgkJNMVOueV9cLY6z7Xd1QRMRERERyQ+FmmLGwjUYgIrHIrh4LcPM1YiIiIiIFH8KNcWNQy2uOjbHz7CdtXsTzV2NiIiIiEixp1BTDNl79MHV4jjbY9QFTUREREQkLwo1xZDhf13QqiesIflSmpmrEREREREp3hRqiiOHWlxz8iTQIpof95w2dzUiIiIiIsWaQk0xZedxYxa0HbHqgiYiIiIici8KNcXVk0EA1Etez9GzV81cjIiIiIhI8aVQU1w51OR69Rb0sIxm9e4Ec1cjIiIiIlJsKdQUY9ZNevOkxXFid8ZgNBrNXY6IiIiISLGkUFOc/a8LmvvFTexNuGTmYkREREREiieFmuLMoSaZNVvSs4y6oImIiIiI3I1CTTFXxu0pGhuOs/X37RxOvmzuckREREREih2FmuLuf13QepT5nZHfxHLxWoaZCxIRERERKV4Uaoo7h5rg0orny/5GyrlzTFi6m+xsTRogIiIiInKTQk1J0GUytlfiWf3YcjYeSOKT9QfNXZGIiIiISLGhUFMS1G0Pnd+k7ulI/lF/N7M3Hibqz9PmrkpEREREpFhQqCkp2k2E+j48lfgpvaunMHH5H/w3URMHiIiIiIgo1JQUFhbQey4G+yrMMP4DR+t0Xvh3DBdTNXGAiIiIiJRuCjUlSdmq0G8hZS6eYFWt7zl1IZWXv99FliYOEBEREZFSTKGmpKndGrq+S6VjkXzXdA9bDp5h5tr/mrsqERERERGzUagpibxfhicC8Dowizfcr/DV5r8I33PK3FWJiIiIiJiFQk1JZDDAU19C+eqMTv6ATi5leG35HvafumTuykREREREipxCTUllVwn6LcJwOZE55efjYFuGUd/GcP7qdXNXJiIiIiJSpBRqSrJanuA3DZsj61jZLJaki+m8/N0uMrOyzV2ZiIiIiEiRUagp6Vq+AE8GUXPHx3zZ8Tq/Hj7Lx2sOmLsqEREREZEio1BT0hkM0Gs2VHoMnz9DeNGrAvN+Ocrq3QnmrkxEREREpEgo1DwKbB2g32JITeG11H/Qqk5FXl+xh70JF81dmYiIiIhIoctXqLl06RLjxo3D39+fgIAAdu3aRVxcHP379ycoKIjevXuzZ88eAIxGI1OnTsXX15eePXuyb9++Qj0B+Z/qTSDgYyyObGRB/S1UKWvNqH/HknIl3dyViYiIiIgUqnyFmmnTptG+fXvWrFnD6tWrqV+/PjNnzuSll15i9erVjB8/npkzZwKwZcsWjh07xrp16/jggw949913C7N+uZXnUHDvT9mtM/jW5zpnr6Qz9j+7yNDEASIiIiLyCMsz1Fy+fJkdO3bQt29fAKytralQoQIGg4GrV6+a1nF0dARgw4YNBAcHYzAY8PDw4NKlSyQnJxfiKYiJwQA9PoEqj1Nv8zj+EejMtiMpfBgZZ+7KREREREQKTZ6hJj4+nsqVK/O3v/2N4OBg3nrrLVJTU3nzzTeZMWMGHTt25OOPP+bVV18FICkpCWdnZ9P2zs7OJCUlFd4ZSE425aD/N5B+me4HJzPcuzYLfzvGyth4c1cmIiIiIlIoyuS1QmZmJvv372fy5Mk0bdqUqVOnMnfuXK5cucLf/vY3/Pz8iIyM5K233mLRokUPVER6ejpxccXnakJaWlqxqudBODSfRI3fP2CU3XxinHsRsnIPZVLP8kRVG3OXlqtHoc1LIrV70VObFz21uXmo3Yue2tw81O7FQ56hxtnZGWdnZ5o2bQqAv78/c+fOJTY2lrfeeguAgIAA3n77bQCcnJxITEw0bZ+YmIiTk9M9j2FjY0Pjxo0f+CQKWlxcXLGq54E0bgwZR3HctYhv+/rjH27LR7+m8MPYdlQrX/yCzSPR5iWQ2r3oqc2LntrcPNTuRU9tbh5q96Jzr/CYZ/ezatWq4ezszJEjRwDYtm0b9evXx9HRkd9//x2A7du3U6dOHQC6dOlCWFgYRqOR3bt3U758edN4GyliATPB0ZXyEWNY0Ls651Ov89KSnVzP1MQBIiIiIvLoyPNKDcDkyZOZNGkSGRkZuLi4MH36dHx8fPjwww/JzMzExsaG999/H4COHTuyefNmfH19sbOz48MPPyzUE5B7sLaH/othbica/jKeGU/NZdyyfUyN2M/7QW7mrk5EREREpEDkK9Q0btyY0NDQHMu8vLzuWAZgMBh45513CqY6eXhVG0CvT2HF8/Ry+Zq9HZ5h7pYjuNVwoH8LF3NXJyIiIiLy0PJ1nxop4dz6gNdw2Popb9T5i/YNqvJ22F52nThv7spERERERB6aQk1p4fchVG+K5Q9j+DywCs4Otoz+NpbkS2nmrkxERERE5KEo1JQWVrbQbzEYocKPI5n3rBuXrmXyoiYOEBEREZESTqGmNKlcF4I/h1M7afjHTP7erymxx8/z7o/7zF2ZiIiIiMgDU6gpbRr3hNYvwe9z6G65nTGd6vOf6BMsiT5u7spERERERB6IQk1p1PVdqOkFq19mopcVnRpW490f9hFz7Jy5KxMRERERuW8KNaVRGWvotwgsy2C5Ygj/6t2ImhXteHHJThIvauIAERERESlZFGpKq4ou8NQcSPwThy2TmTvYi9T0TEZ9G0taRpa5qxMRERERyTeFmtLsCT9oNwFiF/FEYiSz+jflj5MXmLJ6L0aj0dzViYiIiIjki0JNadf5bajtDeGv4O90iXFdHmdZTDzfbtfEASIiIiJSMijUlHaWZaDvArCyh2VDeKVDTXwaOfLej/uJPpJi7upERERERPKkUCNQoTr0mQdnDmARNYlPnm5K7Sr2jFmyk1MXrpm7OhERERGRe1KokRvqd4GOb8Af31EhbilzB3mRnpnNqH9r4gARERERKd4UauT/dXwd6naEyEk8nn2MT5724M+Ei7y56k9NHCAiIiIixZZCjfw/C0voMx9sK8KywfjWs2NC1ycI3ZnAoq3HzF2diIiIiEiuFGokp3KO0PdrOH8UfhzPy53r0+1JJ6ZGxLH1r7Pmrk5ERERE5A4KNXKnOu2gy9uwLxSLnQuY1b8pdauWZex/dhF/PtXc1YmIiIiI5KBQI7lrOwEe94U1f6P8ub3MHeRJRtaNiQOuXdfEASIiIiJSfCjUSO4sLKD3XCjrCMuGUK9cJp8OaMb+05cICd2jiQNEREREpNhQqJG7s68M/RbCpQRY/RKdG1ZjUreGrN59ivm/HDV3dSIiIiIigEKN5MWlJfi+DwfCYfsXjOlUn0B3Z6ZHxfHrIU0cICIiIiLmp1AjeWs9Bhr1gJ+mYIiPYWbfpjRwLM/Y73YSd/qSuasTERERkVJOoUbyZjBA0GdQoSYsH0rZrEvMHeyJbRlL+n+1jW1/pZi7QhEREREpxRRqJH/sKkG/RXA1GVaN4rFKdoSO8cbJwZYhC34n8s/T5q5QREREREophRrJv5rNwe9DOLQOfvsnNSrasWJ0G5rUcuCl/+xk8dZj5q5QREREREohhRq5Py1GgGtv2PgBHPuVivbWfDuiFV0bO/HOD/uYufaApnsWERERkSKlUCP3x2CAnv+CSnVhxXC4cAJbK0u+fLY5A1vW5vNNf/Haij1kZGWbu1IRERERKSUUauT+2VaA/t9A5jX42g+SD1DG0oIPn3JjQtcnWBEbz8hvYki9nmnuSkVERESkFFCokQfj7AZDI8GYBQsDICEWg8HA+K4N+PApd7YcPMPAedGkXEk3d6UiIiIi8ohTqJEH5+wGz68Bm/KwuBcc2QzAM61q89Vznhw4fYm+X23j5LlUMxcqIiIiIo8yhRp5OJXrwfNroWJtWNIX4sIB6ObqzJIRrTh39Tq9v9zKvlMXzVyoiIiIiDyqFGrk4VWoDkMjoHpTWDYIdn0LgFedyqwY3QYrCwNPz9nO1sNnzVyoiIiIiDyKFGqkYNhXhsGroV4nWP0SbJ0NQAOn8qwc403NinYMWfg7P/5xyqxlioiIiMijR6FGCo51WRj4PTwZDOvehg3vg9FIdQc7lo1qQzOXSrz83S4W/HrU3JWKiIiIyCNEoUYKVhkb6LsAPIfCL7MgfAJkZ+Fgb8U3w1vi7+rM++H7mR4VR3a2btIpIiIiIg9PoUYKnoUl9PgntHsVYhfCyhGQeR1bK0s+f7Y5z7WuzZzNR5i0/A/dpFNEREREHloZcxcgjyiDAbq+A3aV4KfJkHYRnv43ltZl+SDIDecKtvx93UHOXr3Ol882N3e1IiIiIlKC6UqNFK6246DXbDiyCb4JhmvnMRgMjO3SgI/7uPPb4bMMnLedC9eyzF2piIiIiJRQCjVS+JoPhn6L4fRuWNgdLicC8HSL2swd5MnBpMu8GpXA8ZSrZi5UREREREoihRopGk/2gmeWwfljsMAPzt2YAc2nsRP/GdmaK9ez6fPlVvYm6CadIiIiInJ/FGqk6NTvDEN+vDG+ZoEfJO0DoHntSswKqIFNGUuenrONXw6dMXOhIiIiIlKSKNRI0arlCcPWgMESFgbAiWgAXBysCR3jjUtle4Yt3EHYrgQzFyoiIiIiJYVCjRQ9x0bw/BqwrwL/DobD6wFwqmDL0lFt8HysEq8s3c28LUfMXKiIiIiIlAQKNWIelR6D59dClfrwnwGUP3Ej2DjYWbH4+ZYEujszLTKOqeH7dZNOEREREbknhRoxn3KOMCQcanlRc9tkiFkAgK2VJbMHNmdIm8eY/+tRXlm6m+uZukmniIiIiOROoUbMy64iPBfKlereED4BfpkFRiOWFgbe7eXK6/4N+eGPUzy/aAeX0zLMXa2IiIiIFEMKNWJ+1vbEt/sY3PvBhvdh3dtgNGIwGBjT6XFm9m3CtiMpDJi7nTOX081drYiIiIgUMwo1UjxYlIGn5kLLF2DbZ/DDWMjKBKCflwvzh3hx5MxV+ny5laNndZPO/2PvvsOjqvI/jr9nJjPpvU16I50Ueq/Suwj2hq4Fd9eK+rOguy6Ca1kVG6CLBdcCiqCAiJRQQwm9hF4TSOgdEkLy++OGsCxITYN8Xs9zHnTm3Dvn3ifPhA/n3O8RERERkbMUaqT6MJuh85vQ6nlY+jWMuQ9OnQSgTXwA3z7cmKMFRfT5ZB7Ldxys4sGKiIiISHWhUCPVi8kEbV6ETm/AKHp6JAAAIABJREFU2gnwTV8oOAJAepgXPzzaBGebhTs+nU/Gut1VPFgRERERqQ4UaqR6atwfbh4OW+fClz3g2D4Aov3dGNu/KZG+rvzpyyzGLsmp4oGKiIiISFVTqJHqK+12uP0/kL8aPu8Mh3IBCPBw4vtHGtMwyoenRy9n2MxNlJRoLxsRERGRmkqhRqq3+M5wz1g4vBNGdoS9GwFwd7Lyeb8GdE8L5o1f1/KaNukUERERqbEUaqT6i2wO90+AUyeMYLNrOQCODhbevy2dB5pF8fncrfz1u6UUFJ2u4sGKiIiISGVTqJHrQ3A6PDAZHJzgi27GszaA2WxiYLdEXuicwMQVu7h/5CIOa5NOERERkRrlskLN4cOHefzxx+nUqROdO3dm6dKlAIwaNYpOnTrRtWtX3nzzzbL+w4cPp3379nTs2JHZs2dXzMil5vGLhQd/A3c7fN0b1k0GwGQy8UirGP51axqLtu6nxwdzWKaSzyIiIiI1hsPldHr99ddp0aIFQ4cOpbCwkJMnTzJ//nymTZvGzz//jM1mY98+ozrVxo0bmThxIhMnTiQ/P59+/frx22+/YbFYKvRCpIbwDIV+v8J/+sB3d8LNwyD1VgB61w0lxMuZp75fRp9P5vFU+zgebRWDxWyq4kGLiIiISEW65EzNkSNHWLRoEX369AHAZrPh4eHBt99+y8MPP4zNZgPA19cXgGnTptG1a1dsNhthYWFERESwYsWKCrwEqXFc/eC+XyCiKYx9CBYML3urUbQvvz7Rko617bz12zru/HQ+Ow+eqMLBioiIiEhFu2SoycnJwcfHhxdeeIFevXrx0ksvcfz4cbZu3UpWVhZ9+/bl7rvvLgsu+fn52O32suMDAwPJz8+vuCuQmsnRHe76AeK7wq/PQcYbUFrW2dPFyod31OHtvmmsyj1Ep/dmMWHFzioesIiIiIhUlEsuPysqKmLNmjUMHDiQtLQ0Bg0axIgRIzh9+jSHDh1i9OjRrFy5kieffJJp06Zd1SAKCgrIzs6+qmMrwsmTJ6vVeGqCq77nqS8QVABeGUPYn7uR/DpPgcnI6skuMLRrMG/O3s1fvlnK+AUbeLSRHy5W1cc4Qz/rlU/3vPLpnlcN3ffKp3teNXTfq4dLhhq73Y7dbictLQ2ATp06MWLECAIDA2nfvj0mk4nU1FTMZjMHDhwgMDCQvLy8suPz8/MJDAy86Gc4OjqSmJh4jZdSfrKzs6vVeGqCa7rniV/DlJfxmf8RPs5m6PkRWKzGW0DL+sW8P3UDH2VsZP3BYt6/vQ7pYV7lN/jrmH7WK5/ueeXTPa8auu+VT/e8aui+V56LhcdL/pO1v78/drudzZs3A5CZmUlMTAzt2rVjwYIFAGzZsoVTp07h7e1N27ZtmThxIoWFhezYsYOtW7eSmppaTpcicgFmM3R8Hdq+DCu+h1E3G5t1lrJazAzoGM93DzWm6HQJt3wyjw+nb+C0NusUERERuSFcVvWzgQMHMmDAAE6dOkVYWBhDhgzB2dmZF198kW7dumG1WnnjjTcwmUzExsbSuXNnunTpgsVi4ZVXXlHlM6l4JhO0fBY8QmHiM/BJU+jxISR2K+vSKNqXSU+04KWfVvL2lPXMWr+Xd29PJ8TLuQoHLiIiIiLX6rJCTWJiImPHjj3v9bfffvuC/fv370///v2vbWQiVyP9DghtAD8+AN/fBfUfgI6DwWoEF09nKx/cUYc28QG8Mn4Vnd6bxeCbU+ieFlzFAxcRERGRq6UnpuXG41cLHvwdmvwFskbCiNaQv7rsbZPJxC31Qpn0RAti/N3467dLeWb0co4WFFXdmEVERETkqinUyI3JwdF4zubuH+H4fhjRBhaMKCv7DBDh68qYR5vweNta/LQ0hy7vz2bp9gNVOGgRERERuRoKNXJjq9UO+s+DqJbw67Pw7R1wbF/Z21aLmac7xPPdw004XVxCn2GZfDBNRQREREREricKNXLjc/OHO0dDxyGwaZpRRGBzxjldGkb5MOmJFnRJCeKd39dzx4j55Bw4XjXjFREREZErolAjNYPZDE0egz9NA0d3+KoX/P4qnD5V1sXT2crQ29P5161prNl1mM7vz+bn5TsvclIRERERqQ4UaqRmCUqFR2ZC3Xth7nvw7w6wb1PZ2yaTid51Q5n0eAtqBbjx+LdLeXr0Mo6cPHWRk4qIiIhIVVKokZrH5go9hkLfL2H/JhjeEpZ/d06XcF8XxjzShMdvimXc0ly6Dp3DEhUREBEREamWFGqk5kruBY/OBXsq/PQI/PgQnDxc9raDxczT7eP4/hGjiEDfYZkMVREBERERkWpHoUZqNq8wuH8CtH4RVv0Aw5rDjkXndGkQaRQR6JoSxL9+X8/tIzLZsV9FBERERESqC4UaEbMFWj8P/X419rEZ2RFmvQ3Fp8u6eDpbGXpHHd69LY3sXUfo8v5sxi/LrcJBi4iIiMgZCjUiZ4Q3hkdnQ1JPmP4P+KonHD63+tnNdYwiArGBbjzx3TKe/l5FBERERESqmkKNyH9z9oI+I6HnR5C7xNjTZu3Ec7qE+7ow+pEmPHFTLOOW5dJl6GwWb1MRAREREZGqolAj8r9MJqhzNzwyC7zC4bs7YcLTcOpEWRcHi5mn2scx+pEmlJTArcMzeX/qBopOF1fhwEVERERqJoUakT/iVwse/B2a/AWy/g0j2kD+6nO61C8tItA9NYh3p67n9hHzVURAREREpJIp1IhcjIMjdHwd7v4Rju8zgs3CT42CAqU8nKy8d3sd3rstnbV5KiIgIiIiUtkUakQuR6120H8eRLWESQOMJWnH9p3TpVedEH59ogVxdnee+G4ZT6mIgIiIiEilUKgRuVxu/nDnaOg4BDZOhWHNYPPMc7qE+bjw/cONebJdLOPLigjsr6IBi4iIiNQMCjUiV8JshiaPwZ+mgc3NKPs89W9w+uyMjIPFzJPt4hjz6JkiAvP51+/rOXnq9B+fV0RERESumkKNyNUISoVHZkLde2HOu/DvDrB/8zld6kUYRQR6pAUzdNoGOrw7i9/X5FPyX8/jiIiIiMi1U6gRuVo2V+gxFPp+Cfs3wbAWsPy7c7p4OFl597Z0vn6wEY4OZh76Kov7Pl/Ext1Hq2jQIiIiIjcehRqRa5XcCx6dC/ZU+OkR+PEhOHn4nC7NY/2Y9EQLXumWxNLtB+j03ixen7hGhQREREREyoFCjUh58AqD+ydA6xdh1Q8wvAXkZJ3TxWox80DzKGYMaM0tdUP5bM4W2rw9kx8W51BcrCVpIiIiIldLoUakvJgt0Pp56PcrFBfDyI4w+x0oPrdAgJ+bI//sk8q4x5oR5uPMgDHL6f3JPJbvOFhFAxcRERG5vinUiJS38Mbw6GxI7AHTXjMqpB3eeV63tDAvfny0Ke/0TSPnwAl6fjSX535Yzp4jBVUwaBEREZHrl0KNSEVw9oI+I6HnR5C7BD5uDAs/hdNF53Qzm03cUi+UGQNa8UjLaH5amkvbtzP4bPZmTp0urqLBi4iIiFxfFGpEKorJBHXuhkdmQVA6TBoAI1rDtszzuro7WXmhSyKTn2xJnQhvBk3MpvP7s5m9YU/lj1tERETkOqNQI1LR/GrBveON0s8nDsDnnWDsw3Ak77yuMf5ufNmvAZ/dW5/ComLu+fdCHhmVxY79x6tg4CIiIiLXB4UakcpgMhmln/+yEFoMgNU/wQf1YN4HcPrU/3Q10S4pkClPteTZjvHMWr+Xm/41k39NWceJwtN/8AEiIiIiNZdCjUhlsrnCTQPhsfkQ0QymvAyfNINNM87r6mS18Oc2tZg+oBWda9sZOn0jN72TwYQVOykpUQloERERkTMUakSqgm8M3DUa7vgeThfAqF4w+l44uOO8rkGezrx/ex1GP9IETxcbf/lmKXd8Op+1eYcvcGIRERGRmkehRqQqxXeCxxZAm5dh/RT4sAHMegtOnTyva8MoHyb8tTmDetVmbd4Rurw/m1fHr+Lg8cIqGLiIiIhI9aFQI1LVrE7Q6ln4yyKI6wDTB8HHjWDd5PO6Wswm7m4cQcaA1tzdOIJR87fR5u0M/rNgG6eLtSRNREREaiaFGpHqwisMbv0K7hkHFht8exv851bYt+n8ri42XutZm4mPtyAu0J2XflpFjw/nkLV1fxUMXERERKRqKdSIVDcxbeDRudBhEGyba2zcOe0fUHjsvK6JQR5893BjPrijDvuPFdJnWCZPfreUvEPnL18TERERuVEp1IhURw42aPpX+OtiSL4ZZr8NHzaE1ePgfyqfmUwmuqcFM+2ZVvy1bS0mrcqj7TsZfJyxkYIilYAWERGRG59CjUh15m6H3iOg36/g7A1j7oOvesLuted1dbE58EyHeKY+1Yrmtfx4c/I6Orw7i2nZ+VUwcBEREZHKo1Ajcj2IaAoPZ0CXt2HXMhjWDH57CU6eX9Y53NeFEffW56sHGmIxm3jwyyzu/3whm/ccrfRhi4iIiFQGhRqR64XFARo+BH9dAul3QeZH8GF9WP7deUvSAFrG+TP5iZa83DWRrK0H6PjeLIb8ms3RgqIqGLyIiIhIxVGoEbneuPpBj6Hw0DTwDIWfHoGRnWDXivO62hzM/KlFNNMHtKJXegjDZ26mzdsZjF2SQ7FKQIuIiMgNQqFG5HoVUg8enAo9PoR9G2FEK5j4DBw/v6xzgLsTb/VN46fHmhLs5czTo5fTZ9g8VuadqIKBi4iIiJQvhRqR65nZDHXvgb9mQYOHIGskfFAPFn8BxedXPqsT7s1P/ZvyVp9Udhw4wXO/7eKOEfOZv3lf5Y9dREREpJwo1IjcCJy9ocub8Mhs8E+AX56Az26CnKzzuprNJvrWD2PWs214uIEvG/cc5fYR87l9RCaZmxRuRERE5PqjUCNyI7HXhn6ToPdncCTPCDbj/gxH95zX1dlm4eYkT2Y/14ZXuiWxac8x7vh0PrcNV7gRERGR64tCjciNxmSC1L7wl0XQ7AlY8Z2xJG3+MDh9fuUzJ6uFB5pHMfu5NrzaPYkte41wc+vwTOZt2kvJBSqriYiIiFQnCjUiNypHd2j/GvTPhJC6MPl5GN4Sts65YHcnq4V+zaKY9Vwb/tY9ia17j3Hnpwu4bfh85m1UuBEREZHqS6FG5EbnHwf3/AS3fQ0FR+CLrvDDg3B45wW7O1kt3F8abv7eI5lt+49x52cLuHV4JnMVbkRERKQaUqgRqQlMJkjsDn9eAK2eh+xf4IP6+K8cDicOXPAQJ6uF+5pGMvPZNrzWM5kd+09w12cL6DsskzkbFG5ERESk+lCoEalJbC7Q5kUj3MS2x2/N5/BeKswYAicOXvAQJ6uFe5tEkvFsa/7RM5ncgye4+98L6DMsk9kb9ijciIiISJVTqBGpiXyi4NYv2dxxFES1hJlvwPupkPFPOHnogoc4WS3ccybc9KrNroMnuOffC7nlk3nMWq9wIyIiIlVHoUakBivwioXb/2PsbxPRHDIGGzM3s94ynr+5AEcHC/c0jmDGs60Z1Ks2eYdOcu/IhfT+ZB4zFW5ERESkCijUiAgEpcId38DDGRDeGKYPgvdSYPY7UHD0goc4Oli4uzTcvH5zbXYfLuC+kQu5+eN5ZKzbrXAjIiIilUahRkTOCq4Dd34PD02H0AYw7TVjWdqc96Dw2AUPcXSwcFejCGYMaM3gm1PYc6SA+z9fRK+P5zFD4UZEREQqgUKNiJwvpB7cNQb+NA2C0mHqq8aytHkfQOHxCx5iczBzZ6NwZgxozZDeKew9UkC/zxfR66O5zFircCMiIiIVR6FGRP5YaH24Zyw8+DvYU2DKy/B+GmR+BKdOXPAQm4OZOxoa4eaN3insO1ZIvy8W0fOjuUxfm69wIyIiIuVOoUZELi2sIdw7DvpNhoAE+O1FI9zMHwanTl7wEJuDmdtLw80/b0nhwPFCHvgii54fzWVatsKNiIiIlJ/LCjWHDx/m8ccfp1OnTnTu3JmlS5eWvTdy5Eji4+PZv38/ACUlJQwaNIj27dvTvXt3Vq9eXTEjF5HKF9EE7vsF7p8EfnEw+XkYmg4LRvxhuLFazNzWIJzpz7TmzVtSOXj8FA9+mUWPD+cydY3CjYiIiFy7ywo1r7/+Oi1atGDy5MmMHz+emJgYAHbt2sXcuXMJDg4u6ztr1iy2bt3KlClT+Mc//sHf/va3Chm4iFShyGZw/wQj4HhHwa/PwtA6sOgzKCq44CFWi5lbG4Qx7ZlWvNknlUMnTvGnr7Lo/uEcfle4ERERkWtwyVBz5MgRFi1aRJ8+fQCw2Wx4eHgAMGTIEJ599llMJlNZ/2nTptGrVy9MJhPp6ekcPnyY3bt3V9DwRaRKRbWEfpPg3vHgFQ4Tn4GhdSFrJBQVXvAQq8XMrfWNcPNWn1SOnCzioa+y6PbBHKaszlO4ERERkSt2yVCTk5ODj48PL7zwAr169eKll17i+PHjTJ06lYCAABISEs7pn5+fj91uL/t/u91Ofn5++Y9cRKoHkwmiW8MDk+HuseARBBOegg/qweIv4fSpCx5mtZjpWz+MaU+34u2+aRwrKOLhUYvp9N5svl24nROFpyv1MkREROT65XCpDkVFRaxZs4aBAweSlpbGoEGD+OCDD8jKymLkyJHlMoiCggKys7PL5Vzl4eTJk9VqPDWB7nnVKP/7HgxNh+KaNx//VZ/i/MvjFE5/g71JD3AoshOYL/yVk+wCH3YJJGPLUcauOcQLY1cyeOJqOsd60C3BA3/XS35VXTf0s175dM+rhu575dM9rxq679XDJf+mYLfbsdvtpKWlAdCpUyc++OADcnJy6NmzJwB5eXn07t2bMWPGEBgYSF5eXtnxeXl5BAYGXvQzHB0dSUxMvJbrKFfZ2dnVajw1ge551aiw+56UBG36wYYp2GYMJnjRIII3/gdaPQcpt4Llwl89tZPhz11LWLhlP5/P3coPq/P4cc0hOiXb6dcsknoR3ucsd70e6We98umeVw3d98qne141dN8rz8XC4yWXn/n7+2O329m8eTMAmZmZJCUlkZmZyfTp05k+fTp2u52xY8fi7+9P27ZtGTduHCUlJSxbtgx3d3cCAgLK72pE5PpgMkFcR3g4A+74DhzdYVx/+KghLP8eii+8vMxkMtEo2pdh99Rj5rNt+FPzKGZv2EOfYZn0+HAuY5fkUFCkpWkiIiJy1mWt6Rg4cCADBgzg1KlThIWFMWTIkD/s26pVK2bOnEn79u1xdnZm8ODB5TZYEbkOmUwQ3xniOsHaiZDxBvz0MMx6C1o9D7V7g9lywUPDfFx4oUsiT7SLZeySXL6Yt5WnRy9n8KS13NUonLsahxPg7lTJFyQiIiLVzWWFmsTERMaOHfuH70+fPr3sv00mE6+++uq1j0xEbiwmEyR2g/gusHaCEW7G/skIN62fh6SbwXzhyWMXmwN3N47grkbhzNm4l8/nbuX9aRv4OGMj3VOD6dcsipRQz0q+IBEREakubpynb0Xk+mA2Q1IPSOgG2eMh45/wwwPgXxpuEnv+YbgxmUy0iPWnRaw/W/Ye48t5WxmTtYOxS3OpH+HN/c0i6ZRsx8FyWVtwiYiIyA1Cv/lFpGqYzZB8M/SfB31GQkkxjLkfPm4MWZ9D4fGLHh7l58rfeiST+eJNvNItiT1HC/jLN0tp8eYMPs7YyIFjF94nR0RERG48CjUiUrXMZqh9CzyWCbf8G6xOMOFJeDcJpv4dDu+86OEeTlYeaB7F9Gda89m99Yn2d+XNyeto8sY0Xhi7gnV5RyrpQkRERKSqaPmZiFQPZguk9DECzvZMmP8xzH0P5g01ZnQa94eQen94uMVsol1SIO2SAlmXd4Qv5m3lp6U5fLtwB01jfOnXLIq2CQFYzNd3SWgRERE5n0KNiFQvJhNENDXaga2wYAQs+QpWjoGwRka4Sej+h3vdAMTb3RnSO4XnOsbz3aIdjMrcykNfZRHu48J9TSPpWz8UDydrpV2SiIiIVCwtPxOR6ss7EjoNhqfXQKd/wtF847mboekwdyicOHjxw11t9G8dw6zn2vDxXXUJ9HDkHxPW0GTwNF4dv4rNe45WymWIiIhIxdJMjYhUf04e0PhRaPgQrJ8M8z+B3wcaZaHT74RGj4JfrT883MFipktKEF1SgliZc4jP523h24U7+DJzG23i/enXLIoWsX6YTFqaJiIicj3STI2IXD/MFkjoCvdPgEdmQ3IvWPIlfFgPvrkNNmdASclFT5ES6sm/bk1n7v+15al2cazaeZh7Ry6k/buzGDV/G8cLiyrnWkRERKTcKNSIyPUpKBV6fQxPrYZW/we5i+GrnvBJU+MZnFMnLnq4v7sjT7SLZe7zbXn3tjRcbBYGjltF48HTGDwpm5wDFy8pLSIiItWHQo2IXN/cAqDNC/DkKuj5MZjM8PNf4d1kmP46HMm76OE2BzM31wll/J+b8WP/JrSM8+ffc7bQ8s0ZPDpqMfM376PkErM/IiIiUrX0TI2I3BisTlDnLuMZm61zjJLQs96COe8aZaIb94fg9D883GQyUS/Ch3oRPuw6dIJRmdv4duF2Jq/OIzHIg9vqh9IzPQRvV1slXpSIiIhcDoUaEbmxmEwQ1cJo+zbBwhGw9GtY8R1ENDPCTXwX4/mcPxDk6cxznRJ4/KZYxi/LZdT8bfztlzUMnrSW9smB9K0XSotYf+15IyIiUk0o1IjIjcs3Bjr/E9q8CEtGwYLh8P3d4BVuVEyrczc4ef7h4U5WC7c1COe2BuFk7zrMmKwcflqaw8QVu7B7OHFLvRD61gsj0s+1Ei9KRERE/peeqRGRG5+TJzT9Czy+FG4dBR6h8NuL8K9k+PX/YP/mS54iMciDV7onseDFdnxyV12Sgj34JGMTrd/O4NZhmYzJ2sGxAlVOExERqQqaqRGRmsPiAEk9jLZzqbHfzaLPYMEwY0la4/4Q2dxYwvYHbA5mOqcE0TkliPzDJxm7JJcxWTt49ocVvPrzarqlBtG3fhj1I7y1742IiEglUagRkZopuA70HgHt/g5Z/4askbBuIthToPFjRnEBB8eLniLQw4n+rWN4tFU0S7YfYPSiHCas2MnorByi/FzpUy+UW+qGYvd0qqSLEhERqZm0/ExEajaPIGj7srHfTfehcLoIxvU3SkJnvAFH91zyFGcqp/2zTyqLXm7H233T8Hd35K3f1tH0jWnc//lCJq3cRUHR6Uq4IBERkZpHMzUiIgBWZ6h3H9S9FzZnGEvTMobA7HcgpS/Uux9CG1x0aRqAi82BPvVC6VMvlK17j/HD4hx+XJLDY/9ZgpeLlVYRzjzseYjk4D8uUCAiIiJXRqFGROS/mUwQ08ZoezcYz9ss+xaW/Qf84o3Qk3Y7uPpd8lSRfq4M6BjPU+3jmLNxL6OzdjBx1S7GZ88hOdiDvvW0942IiEh50PIzEZE/4hcLXd+BAeugxwdGFbUpL8E7CfD9PbDhdyi+9JIyi9lEqzh/PrqzLt/cGsHfeyRjMsHffllDo8HT+PM3S8hYt5vTxSWVcFEiIiI3Hs3UiIhciqO7MUNT917YvRaWjoLl30L2z+ARAul3GXveeEdc8lTujhbuS4/kvqaRrN55iDFZOYxflqu9b0RERK6BQo2IyJUISICOr8NNr8K6SUbAmfWW0aJbQZ17IKEbWC9d8Sw52JPkHp680CWBadm7GZO1g08yNvHRjE00jPShb/1QuqQE4eqor2oREZGL0W9KEZGr4WCD5F5GO7gDln0DS7+GHx8EZ29Ivc0IOPbalzyVo4OFLilBdEkJIu/QScYuzWFMVo72vhEREblMCjUiItfKKwxaPw8tn4UtGbBklLHvzYJhEFwX6t4DtfuAk8clT2X3dOKx1rXo3yqGxdsOMDprBxNW7NLeNyIiIhehUCMiUl7MZohpa7Tj+2HF90bAmfAUTH4Rkm/G2bclJCRcsjS0yWSifqQP9SN9eLV7MpNW7mJMVg5v/baOd6aso2WcP73rhtI+MRBnm6WSLlBERKR6UqgREakILj7QuD80ehR2LoElX8HKH4ks/AaWv20sTUu7A9wDL3kqV0cH+tYPo2/9MLbsPcYPi3fw4+JcHv92KS42Cx2T7fRID6Z5LT+sFhW1FBGRmkehRkSkIplMEFLPaB0Hs3PqJwTnTYWpr8K01yC+sxFwarUDy6W/kqP8XHm2YwJPt49n4Zb9/LzcqJz209JcfFxtdE0JoledYOqG6/kbERGpORRqREQqi82VQ1FdCe4yAPasP1saeu0EcA8yZm7q3A2+MZc8lcVsokmML01ifPlbj2Rmrd/LuGW5jM7awaj52wj1dqZHWjA900OIt7tXwsWJiIhUHYUaEZGq4B8HHf4BN70C638zAs7c92DOvyCyhbEnTmJ3sDpf8lSODhbaJwXSPimQowVFTFmdx/hlOxk+azMfZ2wiwe5Oj/RgeqQFE+rtUgkXJyIiUrkUakREqpLFCondjHZ4Z2lp6FEw9iFw8oSUvkbACUq7rNO5OTrQu24oveuGsvdoARNX7GL8slzenLyONyevo0GkNz3SQ+iaEoSPq62CL05ERKRyKNSIiFQXHsHQcgA0fxq2zTEqpy0ZBYs+A3uqEW5S+hj74FwGPzdH7msayX1NI9m+7zi/rNjJuKW5DBy3ir//vJoWsX70qhNCu8RAbfApIiLXNf0WExGpbsxmiGpptC5vwsofjOppkwbAlJchsYex901Ec6PvZQj3deHPbWrxWOsY1uYdYdyyXH5ZtpMnvluGs9VYvtYzPZgWsf7YHFRBTUREri8KNSIi1ZmzNzR8yGg7lxlL01aMgZWjwTMMavc2Nva0p1xy7xsw9r9JDPIgMciD5zsmkLXtAOOX5TJx5S5+Xr4TLxcrXVKC6JUeQv0Ib8xmVVATEZHqT6FGROR6EZxutA6DIPsXWDkGMj+Cue+DX5wRblL6XFb1NACz2UTDKB8aRhkbfM7ZuIfxy3aNYyA/AAAgAElEQVTy05JcvlmwnWBPJ7qnB9MzLYTEIHeViBYRkWpLoUZE5HpjdYbUW412bB9kj4eVP0LGEMgYDEHpRrhJ7g2eIZd1SpuDmbYJgbRNCORYQRFTs/MZv2wn/569heEzNxMb4EavOiH0SAsmzEcV1EREpHpRqBERuZ65+kL9B4x2eCesGgurfjCevZkyECKaQu1bIKmX0fdyTunoQM/0EHqmh7D/WCGTVu7i52U7eeu3dbz12zrqhnvRMz2ErqlB+Lk5VvAFioiIXJpCjYjIjcIjGJr+xWj7NsGqH40lahOfhl+fg5i2xhK1hC7geHkbcvq42ri7cQR3N44g58BxfllulIh+9efVvDZhDc1r+dEzPZgOyXbcVEFNRESqiH4DiYjciHxjoNVz0PJZyFtpzN6sGgs/PQwOThDXyViiVqs9WJ0u65Sh3i70bx1D/9YxrMs7ws/Lcxm/bCdPj16Oo8NK2iUF0j01mJZxfrjY9OtFREQqj37riIjcyEwmCEo12k1/g5yFRono1T/BmnHg6AGJ3Y0lalGtwHJ5vxbi7e48a09gQId4lmw/wPhlO5mwYhcTV+zC0cFMi1g/OiTZuSkxAF8tURMRkQqmUCMiUlOYzRDe2Gid3oAtM40latm/wLL/gKu/8exNSh8IbXhZe+CYTCbqRfhQL8KHV7olsXDrfqaszuf3NflMzd6N2QT1IrzpkGSnfVIgkX6ulXChIiJS0yjUiIjURBYHqHWT0br+Czb+bszgLB0Fiz4t3QPnFiPgBNa+rD1wHCxmmsb40TTGj1e7J7Fm1+GygPP6pGxen5RNXKAb7ZMC6ZBkJyXEU/vgiIhIuVCoERGp6axOxhK0xO5QcATWTjQCTuaHMPc98Is3wk3tWy57DxyTyURysCfJwZ481T6OHfuPMzU7nymr8xk2czMfzdhEoIcj7ZMCaZ9kp0m0LzaHS88MiYiIXIhCjYiInOXoDmm3G+3YPuO5m1U/wozXjRZcx6igVru3UW3tMoX5uNCvWRT9mkVx8Hgh09fu5vc1+YxdksvX87fj7uhAq3h/OiTbaR3vj4eTtQIvUkREbjQKNSIicmGuvtDgQaMdyoXVY40ZnCkvGfvgRDSDlNI9cFx8Lvu0Xi42etcNpXfdUE6eOs3cjXtLn8HJZ8KKXVgtJhpH+9Ih2U77xEDsnpdXnU1ERGouhRoREbk0zxBo+lej7d1ozN6s+gEmPAWTnoWYm4wlavFdwNHtsk/rZLVwU2IgNyUGcrq4hGU7DjBldT5T1uQzcNwqBo5bRVqop/EcTrKd2AA3TJfxfI+IiNQsCjUiInJl/GpB6+eNfXDO7IGz8kfY8BtYHI1NPhO7QVxnY7bnMlnMZyup/V/nBDbtOcqUNcZzOG9PWc/bU9YT4etCh9LncOpFeGNRoQEREUGhRkRErtb/7oGzYwGsGQ9rJ8D6X8FkgYimRgGChK7gGXoFpzZRK8CdWgHuPNa6FvmHT5YVGvhy3jY+nb0FX1cbbRMC6JBsp0WsH05WS8Vdq4iIVGsKNSIicu3MZohoYrROQ2DXcmP/m7UT4NfnjBZcpzTgdAf/uCs6faCHE3c1iuCuRhEcOXmKmev38PuafCavzmPM4hycrGZaxhqFBtomBODjaqugCxURkepIoUZERMqXyQTB6Ua7aaDxDM7aX4yQM+01o/nFQUI3Y5lacN3L2gfnDHcnK91Sg+mWGkxhUTELt+xnypo8fl9jPItjNkGDSJ+y/XBEROTGp1AjIiIVy68WNH/KaIdyYd0kI+DMfR/m/As8Qo3laYndILypsTHoZbI5mGke60fzWD/+3iOZVbmHywLOoInZDJqYTaS3jU5bTbSO96dehDdWi/bDERG50SjUiIhI5fEMgYYPGe34flg/GbInwJIvYeFwcPYxKqgldoPoNsbGoJfJZDKREupJSqgnz3SIZ/u+40xZk8fPi7fw2ezNDJu5CTdHB5rV8qVVXACt4/0J9nKuwIsVEZHKolAjIiJVw8UH0u80WuEx2DjNmMHJ/gWWfQ1WV4htB4k9ILY9OHle0enDfV34U4tomvkVEBpVi3mb9pGxbg8z1+3mt9X5AMQGuNE63p/W8QHUj/TG0UHFBkRErkcKNSIiUvVsrpDUw2hFhbB1tlFkYO1Eo6Ka2QrRrYzncBK6glvAFZ3e3clKx2Q7HZPtlJSUsHH3UWau30PGuj1l1dRcbBaaxvjSKs4IOWE+LhV0sSIiUt4UakREpHpxsEGtm4zW5R3IWVRaaGACTHjS2PAzvPHZQgPekVd0epPJRGygO7GB7vypRTTHC4vILJ3FyVi/m6nZu4HVRPu50qp0FqdRlI9KRouIVGMKNSIiUn2ZzRDeyGjt/wH5q40ZnOwJMOUlo9lTjDLRid0gIOmKKqkBuNgcuCkxkJsSAykpKWHL3mNlszjfLNjO53O34mQ10zj67CxOlJ9rBV2wiIhcjcsKNYcPH+bll19m/fr1mEwmBg8ezJQpU5gxYwZWq5Xw8HCGDBmCh4cHAMOHD+eHH37AbDbz8ssv06JFiwq9CBERqQFMJrDXNlrr/4P9W84GnIwhkDEYvKOMcJPYA0LqG6Hoij7CRLS/G9H+bvRrFsXJU6eZv3kfM9fvYea6Pfz9lzX8/Zc1RPi6lAYcfxpH++Ji078RiohUpcv6Fn799ddp0aIFQ4cOpbCwkJMnT9KsWTOeeeYZHBwceOuttxg+fDjPPvssGzduZOLEiUycOJH8/Hz69evHb7/9hsWiaXsRESlHPlHQ9K9GO5J/tlT0/GEw7wNws0NCF1xd0yA2Ghwcr/gjnKwWWscH0Do+ALrD9n3Hmbl+Nxnr9jAmK4evMrdhczDTKMqnLOTE+LthusLZIhERuTaXDDVHjhxh0aJFvPHGGwDYbDZsNhvNmzcv65Oens7kyZMBmDZtGl27dsVmsxEWFkZERAQrVqygTp06FXQJIiJS47kHQv1+Rjt5CNZPMZ7DWf494adGQuZLEN0aYjsYldQ8gq/qY8J9XbinSST3NImkoOg0i7YcKAs5Z/bFCfFyNp7FifOnaS0/3Bw1iyMiUtEu+U2bk5ODj48PL7zwAmvXriU5OZmXXnoJF5ezVWF+/PFHOnfuDEB+fj5paWll7wUGBpKfn3/RzygoKCA7O/tqr6HcnTx5slqNpybQPa8auu+VT/e8klhrQ0ptTIlPYc2Zh8/eRbhtn4d17QQATnrFcTSoCUeDm3HCJxnMV7eawBfoHW2id3QAu4/6kLXzOItzj/PTkh18s2A7DmZIDnCiXogL9UNciPSy1phZHP2sVz7d86qh+149XDLUFBUVsWbNGgYOHEhaWhqDBg1ixIgRPPnkkwB88sknWCwWevTocdWDcHR0JDEx8aqPL2/Z2dnVajw1ge551dB9r3y655Uv28EJ785/hpIS2J0NG6bgtGEKTmu/xi/7S3D2hlrtILajUXHNxeeqPicRaFX634VFxSzedqC04MBuRi7ez8jF+7F7ONEqzp9W8f40i/HD08VabtdZ3ehnvfLpnlcN3ffKc7HweMlQY7fbsdvtZbMvnTp1YsSIEQCMHTuWjIwMvvjii7J/eQoMDCQvL6/s+Pz8fAIDA6/pAkRERK6ZyQSBSUZr/iScOACbZsCGKbDhd1g5BkxmCG1gLFGL7WhUVruKmRWbg5kmMb40ifHl/zonkH/4JDPX7WHm+j1MWrWL77N2YDZB7RBPmsT40izGj/qR3io4ICJylS757env74/dbmfz5s1ER0eTmZlJTEwMs2bN4rPPPuPrr7/G2dm5rH/btm155pln6NevH/n5+WzdupXU1NQKvQgREZEr5uwNtXsbrbgYdi6FDb8ZIWf6IKO5B5UGnA7GMzmO7lf1UYEeTtzaIIxbG4RRdLqYZTsOMmfjXuZt2sfIOVsYPnMzVouJOmHeNK3lS9MYP9LDvLA5XFn1NhGRmuqy/klo4MCBDBgwgFOnThEWFsaQIUPo06cPhYWF9OvXD4C0tDRee+01YmNj6dy5M126dMFisfDKK6+o8pmIiFRvZjOE1jNamxeNamobpxohZ/U4WPIVmK0Q2ay02EBH8Kt1VR/lYDFTP9KH+pE+PNkOjhcWkbX1APM27WPepr28P20D703dgLPVQoMoH5qWzuQkBXtgMdeM53FERK7UZYWaxMRExo4de85rv//++x/279+/P/3797+2kYmIiFQV90Coc5fRTp+C7fNLZ3F+h99eNJpPdGnA6QARzcDqdFUf5WJzoGWcPy3j/AE4dPwUC7bsKws5b/y6FgAPJwcaR/vSrJYfTWN8qRWg0tEiImdo8a6IiMjFWKwQ1cJoHQbBga1GuNkwBRZ/AQuGgdWltGR06VI1z9Cr/jhPFysdku10SLYDsPvISTI37SNz0z7mbtrLlDVGRVF/d0eaxviWNj/CfFwudloRkRuaQo2IiMiV8I6Ehg8Z7dQJ2DLbmMVZP8XYABQgIBniSpephTYAy9X/ug1wd6Jnegg900MA2LH/OPM27S2dydnH+GU7AQjzcaZptB9NaxkFCgLcr27mSETkeqRQIyIicrWszkZ4iesAXUpgz7rSampTYN4HMOddcPIySkXHdjBKR7v6XdNHhvm4cJtPOLc1CKekpISNu4+WLVX7tbSyGkBsgBvNavnRJMaXxlG+N3T5aBERhRoREZHyYDJBQILRmj0OJw+dWzJ61Y+ACULqQVzpnjhB6Ve98afxkSZiA92JDXTnvqaRnC4uYc3Ow8zbtJe5m/bx/aIdfDFvq8pHi8gNT99oIiIiFcHJE5J7Ga24GHYtK30W5zeYMRhmvG7M4kS3gug2ENPGWNp2DSxmEymhnqSEevJIqxgKi4zy0WeWq6l8tIjcqBRqREREKprZDCF1jdb6eTi6BzZnwOYZxmzOmvFGP+8oI9xEt4GoluDsdU0fa3Mw0zDKh4ZR55aPnrtpL5mb9p1XPrpJtC+Non1ICfHEalHIEZHrh0KNiIhIZXPzh9S+Rispgb3rjXCzeQasGA1ZI8FkhuC6Z0NOaANwsF3Tx16ofPT8LUZltXmb9vLPyUb5aGerhXoR3jSM8qFRlA9pYV44WbXnnIhUXwo1IiIiVclkAv94ozV+1NgXJ2fR2ZAz+x2Y9RZYXY3NP88sVfNPMI69Bp4uVjom2+lYWj5679ECFm3Zz4LS9u7U9ZSUgM1iJj3Mi0bRxqxP3XBvXB31VwgRqT70jSQiIlKdWKwQ0dRobV+CEwdh6+yzIWfDFKOfe5CxN050G+NP98Br/mg/N0c6pwTROSUIMGZyFm3dz8Kt+1mweR8fZ2zig+kbcTCbqB3iSaMoHxpF+1AvwgdPZ1VXE5Gqo1AjIiJSnTl7QWJ3owEc2GaEm80ZsH4yLP/WeD0g+exStYimYLv2zTg9Xay0SwqkXZIRmI4WFLFk2wEWbNnHwi37+XzuVobP2ozJBIl2DxpFG8vVGkT6XPNni4hcCYUaERGR64l3BNS732jFxZC3/OwszsIRkPkhWGwQ1uhsyAlKu6bS0We4OZ77TM7JU6dZuv0gC7fsZ8GWfXy7cDufz90KQLinlRZri2gY5UPjaF8CPbQZqIhUHIUaERGR65XZDMF1jNbiaSg8DtvnlYacDJj2mtGcvSGq1dmQ4x1RLh/vZLXQJMaXJjG+QCyFRcWszD3Egi37mL5yO+OX7eQ/C7YDEOHrQsNIHxpF+9IoyodQb2dM1/hMkIjIGQo1IiIiNwqbC9RqZzSAo7uNcHNmJmfNOON1n+izBQciW1xz6eiyj3cwUy/Cm3oR3rQJPEVsXDzZu46wYMs+FmzZz+/Z+YxZnANAsKdTablpo4x0tJ+rQo6IXDWFGhERkRuVWwCk3mq0khLYs+7s3jjLv4Osfxulo0PqnS04EFofHBzL5eMdLOayzUD/1CKa4uISNuw+WhZy5mzcx7hlOwHwc7OVlpD2pWGUD/GB7pjNCjkicnkUakRERGoCkwkCEozWuD8UFRqlo8+EnNlvw6w3wcEJwhoaMziRzY3AU04hx2w2EW93J97uzr1NIikpKWHL3mOlz+QYFdYmrcwDwNPZSoNIo/BAvUhvkoM9cHTQXjkicmEKNSIiIjWRg83Y9yayGbR9GU4cgK1zYdtco4T0jMFASYWGHJPJRLS/G9H+btzeMByAnAPHWbB5Pwu3GKWkp2bnA8ZeObVDPKgX4U3dcG/qRnir+ICIlFGoEREREaOYQGI3owEc3w/bM2HrnEoLOQCh3i6E1nPhlnqhAOw+fJIl2w+wZPtBlmw7wJeZ2/h09hYAQrycqRvhTd1wL+qGe5MU7IHVYi63sYjI9UOhRkRERM7n4gMJXY0GVRZyAjyc6FQ7iE61jQ1BC4pOs2bn4bKQk7V1P78sN57LcbKaSQ3xok6EEXLqhnvj715+YxGR6kuhRkRERC7tCkOOn2sCuNxc7iHH0cFCnXBv6oR782DzKAB2HjxhzOZsO8iS7QcYOWcLw09vBiDcx4W64V7UizCOSbC746DZHJEbjkKNiIiIXLlLhBy/LZ/CqhEVPpMDEOzlTLCXM91SgwFjU9BVuYfKgs7cTWerrLnYLKSGepY9m1Mn3BsfV1u5jkdEKp9CjYiIiFy7/wk565fNJ95p3wVmcpz/J+TULfeQ42S1UD/Sh/qRPgCUlJSQc8CYzVm6/SCLtx1g2MzNnC4uASDKz7W0+ICxbC0u0B2LykmLXFcUakRERKTcFTt6QkLjiyxXe53KCjkmk4kwHxfCfFzomR4CwInC06zIOcji0tmcjHW7+XGJsTGom6MD6WFe1A33ok6EN3XDvPF0sZbrmESkfCnUiIiISMW75DM5lRdyAJxtFhpF+9Io2hcwZnO27z/O4m0HypatfThjI6WTOdQKcCurslYvwpsYfzdtDipSjSjUiIiISOW7kpATUg/CGxsttAE4e5X7cEwmExG+rkT4utK7rlFO+mhBESt2HCwrKT1lTT6js87O5iQHe5AS4klKqCcpIZ5E+roq6IhUEYUaERERqXoXCznbM2HOu1ByGjBBQBKEN4LwJhDWCLzCwVT+YcLN0YGmtfxoWssPMGZzNu89xpJtB1iec5CVuYf5av42CouKy/or6IhUDYUaERERqX7+N+QUHoPcxbB9vtFWjIGskcZ77sHnhpzA2mAp/7/imEwmYvzdiPF3o2/9MABOnS5mQ/5RVuUeYmXuIVbkHlLQEakCCjUiIiJS/dlcIaql0QCKT8PuNWdDzo4FsPqn0r5uEFofws4sWasPju4VMiyrxUxSsAdJwR7c2uDCQWflJYJO7RBPohR0RK6JQo2IiIhcf8wWsKcYreFDxmuHcv4r5MyHWW9CSTGYzEa/MyEnvDF4BFfY0BR0RCqfQo2IiIjcGDxDIaWP0QBOHoacRcYszvZMWDoKFg4v7RteGnBKl635J4LZXGFDu9ygM2r+NgoUdESumEKNiIiI3JicPKDWTUYDOH0K8laeDTlbZsHK0cZ7jp4Q1sAIOmGNjYprNpcKHd4fBZ2Nu4+yMkdBR+RKKNSIiIhIzWCxGvvehNSFxv2hpAQObju7ZG37fJg+yOhrdoCgtLPFB8Ibg1tAhQ/RajGTGORBYtAFgk7uobKwc6GgE+J8mtaFO0kP9SLMxxlTBVSEE6muFGpERESkZjKZwDvSaGm3G68d328sWTtTfGDRZ5D5ofGeT/TZ53LCGoFfXIUuWTvjnKBT//ygsyr3ECtyDjFh3WHGrlkKgLeLlZRQL9JDPUkN9SI1zJMAd6cKH6tIVVGoERERETnDxQfiOhoNoKgQdi03lqvtWAAbpsDyb4z3HD2MZWqhDYwKayH1wdW3UoZ5oaCzcvUaTF4hrMg5xIqcgyzbcZAPZ+yhuMQ4JtjTidRQL9LCvEgL9aR2qCceTtZKGa9IRVOoEREREfkjDjbjWZuwBsb/l5TAvk2QsxBysoxZndnvlG4MijGbE9rACDih9Y09cxxslTNUs4nEEOM5mzsbhQNwvLCI1TsPs3zHQVbkHGJ5zkEmr84DjImqaD9X0kqDTmqoJ4lBHjhZLZUyXpHypFAjIiIicrlMJvCrZbT0O43XCo/BzmVGwMnNgs0zYcX3xnsOThCUbgSc0PpG4PEIMc5TCVxsDjSI9KFBpE/ZaweOFbIi9xArdhxkec4hZm/cy9iluQBYLSYS7B6khnqWhZ1aAW5YVIhAqjmFGhEREZFrYXOFyGZGA2M253CuEXJysoy28NOzz+a4B/3XsrUGEJxunKOSeLvaaBXnT6s4/9LhlpB3+CTLS0POipyD/LxsJ/9ZsB0AF5uF2iGepJU+n5Me5kWotwoRSPWiUCMiIiJSnkwmY88cz1BIvtl4ragQ8ledXbKWmwVrJ5T2t0Bg0tmQE9oAfGIqpQiBMVwTQZ7OBHk606l2EADFxSVs2XfsnGVrX2Zuo7BoCwA+rjZSS0POmbDj7+5YKeMVuRCFGhEREZGK5mA7W0660cPGa8f2Qu7i0hmdRbDyB8gaabzn5HXubE5IXaOIQSUxm03E+LsR4+9G77qhABQWFbM+/wjLcw6WhZ1Z6zeUFSII8XI2lq2VPp+TEuKJuwoRSCVRqBERERGpCq5+51ZaKy6GvevPhpycLJj1JpQY+9HgW+tspbXQBhCQDJbK+6uczcFM7dJCBHc1igDgWIFRiOBMtbUVOYf4ddXZQgQx/m4kB3uQYPcgIcidRLsHgR6OWrom5U6hRkRERKQ6MJshIMFode8xXis4AjuXng05G6fC8m+N96wu/1WEoAEOJzyBxEodsqujAw2jfGgYdXYWaf+xQlbklC5b23GQrK0HGL9sZ9n7Xi5WEuzuJNg9SAwy/owLdMfZpqprcvUUakRERESqK0d3iGppNDCKEBzcdrYAQc4iWDAM5g0lFmCG3ViqFlwXgutU+rI1MJ63aR0fQOv4gLLXDh0/xbr8I6zNO0z2LuPP0Vk7OF5olMI2mSDK15WE0pCTYHcnMciDEC9nzKq8JpdBoUZERETkemEygXek0VL6GK8VFUDeSvIWT8B+eifkLoF1k84e4xVRGnTqlIaddCMsVSJPF+t5MzrFxSXsOHC8LOSs3XWENTsP8+uqPEpKn9Nxc3Qg3u5uzOwEeZBodyfe7q5ndeQ8CjUiIiIi1zMHRwitz4EjrtgTS5efnTxk7J2zcynsXAI5i2H1T6UHmMAv7tygY08Bq1OlDttsNhHh60qEryudatvLXj9WUMT6/COszTvC2l2Hyc47wi/Lz5aYBgj1dj5n+VpCkDuRvq7aT6cGU6gRERERudE4eUJ0K6OdcXRPacgpDTobp519PsfsAAFJ5wadgESwVP6MiKujA3XCvakT7l32WklJCbsOnfyv5WtG4JmxbjenS8uvOTqYiQs8d1YnIcgDH1dbpV+DVD6FGhEREZGawM0f4joYDc5uEpq75GzQWf0TLP7CeN/BCeypZ5/NCa5rVGCrpP1z/pvJZCLYy5lgL2faJgSWvX7y1Gk27j5aFnLW5h1h+v+3d7dBUdaNHsd/y8Lu8rCgIOyCgSESEgrabc1xzp3O0duxUg90XnReFC+aSd/ak02aTxWiL5zG6tQpzvQ01Zw5Y9PBvOlkE3dpc47dqSmIoFKKije7FGKAtzws7Hlx7QOk5hO7y8L3M3PNxv+6dvf6X/OfdX79n463a+eh1sA1GXbrsJBj9OzkpSfJEhv+eiB0CDUAAAAT0fBNQu/+Z6PM65UunDJCzvkfjKBz+EPp+7eN8xa7MSdneNCZlGN8VgTY4syBZaaH+7m7LzBPp8n3+t5PHeofNJbHjo0xaUZGkgozk0f07KTbWW46WhFqAAAAYDCZpLQ84/AvRDDoMfbP+dsPwaDz3b9LQwPG+YS04JA1f9CxO679HWGQbrcq3Z6u+/PTA2UDg0M6/cslNfl6dI63dem7Ux3678PnA9ekJVoCvTn+wDMjI0m2OJabHusINQAAALg2c6zkuNs45j5mlHn6JPcxX9DxDV376S/BjULtWcPm58wx9tNJnBK5OkiKMxtzbu5y2FU6rLzzUr8RclxdgcDz0Xdn1Ocx6mKOMSkvPTG4gWhmMpuIjkGEGgAAANycWKsRWqbeI93rK+u/JLXVD+vROSwd/3PwPclTjTk6mSW+o9goi3AwmJxo0fy8NM3PSwuUDQ551dLh69XxLTl96EynPqu7chNRh9Wjf+w5p5mZRmCiVycyCDUAAAC4fZZEadp84/C73Cm5jkptdUbgaauTTn4hybcRTUJaMOT4A8/k3IgsRjCc0TuTpLz0JC0vDpb/enlAJ4b16jS1deuLs93a1VQvSYoxSblTEgNzdAozkzUzM1lZKTZ6dUKMUAMAAIDQiJ8s5S4wDr/+S8bQtbY6qe2I8fp//xaco2NNNvbNCfTolEhp+cYwuAhLib9yE9FjjY1KyJgW2FPneFuX6lsvqqa+LXCN3RarQv++Or65OgVOuxIska/TeMGTBAAAQPhYEqXs+4zDz9MntTcZAcfl69E5+J7kuWycj7VJjlkjh65l3G0Mg4uwGJNJuVMSlTslUQ/OzgyUd/cO6KS7W41tweWmPznUqkv9g5KMUXd3piUaq68N20j0jsnximET0ZtGqAEAAEBkxVp9S0XPCZYNeqSO5uCwtbY66ehO6eA7xvmYWGODUOewHh3nLCM0jQF2W5z+MC1Vf5gW7NUZGvKqtfNyYJlp/zC2L4655PWNyEu0mHWX09hEtMAR7NmZlMAmor+HUAMAAICxx+wLLRmFUsm/GmVDQ9LFlivn6Bz5yPcmkzQl/zfzdIqNYXBjQEyMSTlpCcpJS9DSImeg/FKfRyfc3Trh6g7M2fmfBpf+8/tzgWucyTYV+MLOzEy7ChzJystIlDWWhQmkGww1XV1dWr9+vU6ePCmTyaTKykrl5ubqqaee0vnz5zV16lTt2LFDKSkp8nq92rJli/bu3SubzWUO28UAAAvmSURBVKZt27apqKgo1PUAAADAeBcTI6VON46ih40yr1fqbgv25rTVS2f2G706fpOmBYetZc4xwk6E99IZLtEaq3tyJuuenGD48nq9au/uC+ypY4Sdbu3/zSai09MTVeD0bSLqm6szdVL8hFuY4IZCzZYtW3T//ffrtddeU39/v3p7e/XWW29p/vz5WrVqlaqqqlRVVaU1a9Zo3759amlp0Zdffqm6ujpt3rxZO3fuvP6XAAAAADfLZJKSs4yj4MFg+aVfRs7RaauTmj4Lnk9MN+bpOGcbh2OW0ctjjgt/Ha7CZDLJkWyTI9mmhXeN3ES05ZdLanJ164TLCDs/nOnU7mHLTdutsSrwBRyjZydZdznsSokfG3ULheuGmu7ubh04cEDbtm2TJFksFlksFtXW1urDDz+UJJWVlam8vFxr1qxRbW2tysrKZDKZNGfOHHV1dam9vV0ZGRmhrQkAAADglzhFmrHYOPx6fzWWmHYdlVwNkvuo9Ne3pMF+47zZYgx3c8w25uc4ZhmvY2T4mmRsIprvsCvfYZdKsgLl/oUJmtqCw9g+q/ubPv6rJ3BNVopNMzOTg2HHmazp6YmKM0d2Ce3RcN1Q09raqtTUVK1du1bHjx9XUVGRXnjhBXV0dASCSnp6ujo6OiRJbrdbTmdwjKDT6ZTb7SbUAAAAILJsKdKdfzQOv8EB6Zdmyd1ghB13g9S8Z9g8HUkp2cGA4+/dGQP76Qx3tYUJvF6v2n7t1QlXt5p8vTonXN3ad/JneYaMlQnizMaePMbQtWTNzDQCjzM5uvbWuW6o8Xg8amxs1IYNG1RSUqKKigpVVVWNuMZkMt1Wpfv6+tTU1HTL7x9tvb29Y+p+JgKeeWTw3MOPZx5+PPPI4LmHH8/8dpikuNlS9mwp2ygxX+6Q7WKzrBebZbvYLJvruCzNX8rkNZZkHoxNUF9KnqbYc9X2Y6F6J+Wrb1KevLHxEazH1TklOR3SPzlsUolNA4NT1No1oJbOfp3u7FNLZ7/+t9mt6iPBIWxJlhjdOcmix+ZMVknm2KvTb1031DidTjmdTpWUlEiSHnjgAVVVVSktLS0wrKy9vV2pqUYqdDgccrlcgfe7XC45HL8/EctqtaqwsPB26jGqmpqaxtT9TAQ888jguYcfzzz8eOaRwXMPP555KPxx5J8DvdLPTZLrqMyuBiW4G2Q9/xeZW/xzdUzGIgZOX2+Ofxhb8lRj7s8YUnyVsl//PuBbhc3YSPTH9h4lp2eqsDDzKleH3++F9uuGmvT0dDmdTp06dUrTp0/X/v37lZeXp7y8PFVXV2vVqlWqrq7W4sXGeMVFixbpo48+0rJly1RXVye73c7QMwAAAES/OJuUNdc4fE42NqowM9E3fM03T6etTmrcFXxf/OTgsDX/MLb0mWNi89DhUhLidF9uqu7LTb3+xWPMDa1+tmHDBj377LMaGBhQdna2tm7dqqGhIT355JP65JNPlJWVpR07dkiSFi5cqL1792rJkiWKj49XZWVlSCsAAAAARIzJJE2eZhwzlwXLe7uk9sbgwgTuBunge5LnsnE+JlaaUjByQYKMIikpY8z16kSDGwo1hYWF+vTTT68o/+CDD64oM5lM2rRp0+3fGQAAABCtbMlSzj8Yh9/QoHTh1Migc/pbqf6/gtckpEkZd0uOIt/mo0VSxkzJag9/HaLIDYUaAAAAALcpxmzshTMlX5r1L8HySx1GwGlvlNzHjNcfPpQGLgWvmTTNF3buDoaetBljZl+dSCPUAAAAAJGUmCZNX2gcfkND0sUzvqDTaLy2N0rNX0q+FdgUEydNuSsYdPyhJyV7wg1hI9QAAAAAY01MjJSaaxzD5+p4+qRfTkrtTcFenTP7paM7g9dYk31D13zD1/yhJyH6FgC4UYQaAAAAIFrEWo1V1JyzR5b3/joy6LQ3SceqpUPvB69Jcl7Zq5M+U4ob+/vQXA+hBgAAAIh2tpQrFybweqXutpHD19zHpO//QxrsM64xxRh764zo1SkyeohizJGpyy0g1AAAAADjkckkJWcZR/6fguWDHqnzdLBXx33MCD5Nf5bkNa6JtUnpBdLijdKMP13148cSQg0AAAAwkZhjg6uwFZUFy/v/Lv18PDh87ecTkqc/cvd5Ewg1AAAAACRLgjT1HuOIMjGRvgEAAAAAuB2EGgAAAABRjVADAAAAIKoRagAAAABENUINAAAAgKhGqAEAAAAQ1Qg1AAAAAKIaoQYAAABAVCPUAAAAAIhqhBoAAAAAUY1QAwAAACCqEWoAAAAARDVCDQAAAICoRqgBAAAAENUINQAAAACiGqEGAAAAQFQj1AAAAACIaoQaAAAAAFHN5PV6vZG+iSNHjshqtUb6NgAAAACMUX19fZozZ85Vz42JUAMAAAAAt4rhZwAAAACiGqEGAAAAQFQj1AAAAACIaoQaAAAAAFGNUAMAAAAgqo3bUNPa2qrly5eH/HvefvttLVmyREuXLtW3334b8u8DhgtHO29tbVVxcbFKS0tVWlqqjRs3hvT7AL9wtO/Ozk6Vl5dr7ty5eumll0aca2ho0IoVK7RkyRJVVFSIxUIRKpFu6+Xl5Vq6dGngd76joyOk9wKEQmykb2As8ng8io29/qP58ccfVVNTo5qaGrndbj3++OPas2ePzGZzGO4SuD032s4lKScnR7t27QrxHQGj50bbt9Vq1erVq9Xc3Kzm5uYR5zZv3qyXX35ZJSUlWrlypfbt26eFCxeG6paBWzIabV2Stm/frtmzZ4fiFoGwGNehZnBwUOvXr9fhw4flcDj05ptvauXKlSooKNCBAwc0ODioyspKFRcX6/XXX9fZs2d17tw5ZWVl6emnn9a6devU2dmp1NRUbd26VVlZWSM+v7a2VsuWLZPFYlF2dramTZum+vp6zZ07N0I1xkQU6nYORFKo23dCQoLmzZuns2fPjihvb29XT09PYJO3srIy1dbWEmoQMpFq68B4MW6Hn0nSmTNn9Oijj6qmpkZ2u1179uyRJPX29mrXrl3atGmT1q1bF7j+p59+0vvvv69XXnlFFRUVevjhh7V7926tWLFCFRUVkowg8+qrr0qS3G63nE5n4P0Oh0NutzuMNQRC384lY2hEWVmZHnvsMR08eDC8FcSEFo72fTW//X13Op38viOkItXW/datW6fS0lK98cYbDLVEVBrXoeaOO+5QYWGhJKmoqEjnz5+XJC1btkySdO+996qnp0ddXV2SpEWLFslms0mSDh8+HBjfWlpaqkOHDkmSFi9erNWrV4e1HsDvCXU7z8jI0Ndff63q6mo9//zzeuaZZ9TT0xO+CmJC43ccE0Uk2/r27du1e/duffzxxzp06BDDjRGVxnWosVgsgf82m80aHByUJJlMphHX+f+Oj4+/qc93OBxyuVyBv91utxwOx63eLnBLQt3OLRaLJk+eLEmaNWuWcnJydPr06du5ZeCGhbp9X8tvf99dLhe/7wipSLV1SYG2nZSUpOXLl6u+vn7UPhsIl3Edaq7l888/lyQdPHhQdrtddrv9imvmzp2rmpoaSdLu3bs1b968K65ZtGiRampq1N/fr3PnzqmlpUXFxcWhvXngBo1WO79w4ULgH1d/O8/Ozg7hnQPXN1rt+1oyMjKUlJSkI0eOyOv1qrq6WosXLx6dmwduQqjbusfj0YULFyRJAwMD+uabb5Sfnz8Kdw6E17heKOBarFarysrK5PF4VFlZedVrNmzYoLVr1+qdd94JTLqTjPGpDQ0NWr16tfLz8/Xggw/qoYcektls1saNG1n5DGPGaLXzAwcO6LXXXlNsbKxiYmL04osvatKkSeGsCnCF0WrfkvE/qHp6ejQwMKCvvvpK7777rmbMmKFNmzZp7dq16u3t1YIFC7RgwYKw1Q/wC3Vbz8rK0hNPPKGBgQENDQ1p/vz5euSRR8JWP2C0mLwTbDZYeXm5nnvuOZYtxLhGO8d4RvvGREFbB27chBx+BgAAAGD8mHA9NQAAAADGF3pqAAAAAEQ1Qg0AAACAqEaoAQAAABDVCDUAAAAAohqhBgAAAEBUI9QAAAAAiGr/DztJMlpnYIfeAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##References:\n",
        "###Utah FORGE Reports\n",
        "###https://openei.org/wikiR%26D_Reports#Utah_FORGE_Well_58-32"
      ],
      "metadata": {
        "id": "dYrg7NvhDPcU"
      }
    }
  ]
}