Anomaly_Detection_using_LSTM.ipynb

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      "source": [
        "from tensorflow import keras\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import plotly.graph_objects as go\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Dropout, RepeatVector, TimeDistributed"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"GOOG (1).csv\")"
      ],
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        "id": "71R4d8VSW9YU"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "df.head()"
      ],
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              "         Date      Open      High       Low     Close  Adj Close     Volume\n",
              "0  2004-08-19  2.490664  2.591785  2.390042  2.499133   2.499133  897427216\n",
              "1  2004-08-20  2.515820  2.716817  2.503118  2.697639   2.697639  458857488\n",
              "2  2004-08-23  2.758411  2.826406  2.716070  2.724787   2.724787  366857939\n",
              "3  2004-08-24  2.770615  2.779581  2.579581  2.611960   2.611960  306396159\n",
              "4  2004-08-25  2.614201  2.689918  2.587302  2.640104   2.640104  184645512"
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              "      <th>Date</th>\n",
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              "      <td>2004-08-19</td>\n",
              "      <td>2.490664</td>\n",
              "      <td>2.591785</td>\n",
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              "      <th>1</th>\n",
              "      <td>2004-08-20</td>\n",
              "      <td>2.515820</td>\n",
              "      <td>2.716817</td>\n",
              "      <td>2.503118</td>\n",
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              "      <td>2004-08-23</td>\n",
              "      <td>2.758411</td>\n",
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              "      <td>2004-08-24</td>\n",
              "      <td>2.770615</td>\n",
              "      <td>2.779581</td>\n",
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              "      <th>4</th>\n",
              "      <td>2004-08-25</td>\n",
              "      <td>2.614201</td>\n",
              "      <td>2.689918</td>\n",
              "      <td>2.587302</td>\n",
              "      <td>2.640104</td>\n",
              "      <td>2.640104</td>\n",
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        "df = df[[\"Date\", \"Close\"]]"
      ],
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4698 entries, 0 to 4697\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Date    4698 non-null   object \n",
            " 1   Close   4698 non-null   float64\n",
            "dtypes: float64(1), object(1)\n",
            "memory usage: 73.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Date\"].min(), df[\"Date\"].max()"
      ],
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      "execution_count": 6,
      "outputs": [
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          "data": {
            "text/plain": [
              "('2004-08-19', '2023-04-18')"
            ]
          },
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          "execution_count": 6
        }
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      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(x=df['Date'], y=df['Close'], name='Close price'))\n",
        "fig.update_layout(showlegend=True, title='Apple Inc. Stock Price 2004-2023')\n",
        "fig.show()"
      ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = df.loc[df['Date'] <= '2020-12-24']\n",
        "test = df.loc[df['Date'] > '2020-12-24']\n",
        "train.shape, test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vlHN7PzBeUqR",
        "outputId": "2bcbb22b-ce31-4db2-defc-fb412d1f6b4b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4118, 2), (580, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "scaler = scaler.fit(np.array(train['Close']).reshape(-1,1))\n",
        "\n",
        "train['Close'] = scaler.transform(np.array(train['Close']).reshape(-1,1))\n",
        "test['Close'] = scaler.transform(np.array(test['Close']).reshape(-1,1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y-GwhHCHrTbw",
        "outputId": "9d502a08-a4e9-47f2-c5af-5caa23c271be"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-3e103d77f8ae>:4: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-9-3e103d77f8ae>:5: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(train['Close'], label = 'scaled')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "HU7EWe0PreGI",
        "outputId": "db6b9a79-de81-4ff7-aea7-a0727172efe8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TIME_STEPS=30\n",
        "\n",
        "def create_sequences(X, y, time_steps=TIME_STEPS):\n",
        "    X_out, y_out = [], []\n",
        "    for i in range(len(X)-time_steps):\n",
        "        X_out.append(X.iloc[i:(i+time_steps)].values)\n",
        "        y_out.append(y.iloc[i+time_steps])\n",
        "    \n",
        "    return np.array(X_out), np.array(y_out)\n",
        "\n",
        "X_train, y_train = create_sequences(train[['Close']], train['Close'])\n",
        "X_test, y_test = create_sequences(test[['Close']], test['Close'])\n",
        "print(\"Training input shape: \", X_train.shape)\n",
        "print(\"Testing input shape: \", X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uj8YYHokrqZY",
        "outputId": "8612dfff-4785-44ce-ed76-974e255a292f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training input shape:  (4088, 30, 1)\n",
            "Testing input shape:  (550, 30, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(21)\n",
        "tf.random.set_seed(21)"
      ],
      "metadata": {
        "id": "ncL3rjM6r968"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(128, activation = 'tanh', input_shape=(X_train.shape[1], X_train.shape[2])))\n",
        "model.add(Dropout(rate=0.2))\n",
        "model.add(RepeatVector(X_train.shape[1]))\n",
        "model.add(LSTM(128, activation = 'tanh', return_sequences=True))\n",
        "model.add(Dropout(rate=0.2))\n",
        "model.add(TimeDistributed(Dense(X_train.shape[2])))\n",
        "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss=\"mse\")\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Qh0mR0jsFpr",
        "outputId": "47215a6e-2d10-47d5-a09a-313be525425d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 128)               66560     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " repeat_vector (RepeatVector  (None, 30, 128)          0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 30, 128)           131584    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 30, 128)           0         \n",
            "                                                                 \n",
            " time_distributed (TimeDistr  (None, 30, 1)            129       \n",
            " ibuted)                                                         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 198,273\n",
            "Trainable params: 198,273\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train,\n",
        "                    y_train,\n",
        "                    epochs=100,\n",
        "                    batch_size=32,\n",
        "                    validation_split=0.1,\n",
        "                    callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, mode='min')],\n",
        "                    shuffle=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-HX3GZUsToW",
        "outputId": "4246fed7-310b-46ce-9439-1053ade17da3"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "115/115 [==============================] - 12s 15ms/step - loss: 0.0526 - val_loss: 0.1147\n",
            "Epoch 2/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0210 - val_loss: 0.0909\n",
            "Epoch 3/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0126 - val_loss: 0.0649\n",
            "Epoch 4/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0127 - val_loss: 0.0538\n",
            "Epoch 5/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0148 - val_loss: 0.0629\n",
            "Epoch 6/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0125 - val_loss: 0.0502\n",
            "Epoch 7/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0096 - val_loss: 0.0520\n",
            "Epoch 8/100\n",
            "115/115 [==============================] - 1s 9ms/step - loss: 0.0122 - val_loss: 0.0515\n",
            "Epoch 9/100\n",
            "115/115 [==============================] - 1s 12ms/step - loss: 0.0103 - val_loss: 0.0693\n",
            "Epoch 10/100\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.0096 - val_loss: 0.0490\n",
            "Epoch 11/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0071 - val_loss: 0.0515\n",
            "Epoch 12/100\n",
            "115/115 [==============================] - 1s 9ms/step - loss: 0.0080 - val_loss: 0.0494\n",
            "Epoch 13/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0070 - val_loss: 0.0508\n",
            "Epoch 14/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0078 - val_loss: 0.0531\n",
            "Epoch 15/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0075 - val_loss: 0.0487\n",
            "Epoch 16/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0080 - val_loss: 0.0503\n",
            "Epoch 17/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0088 - val_loss: 0.0501\n",
            "Epoch 18/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0084 - val_loss: 0.0538\n",
            "Epoch 19/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0091 - val_loss: 0.0559\n",
            "Epoch 20/100\n",
            "115/115 [==============================] - 1s 8ms/step - loss: 0.0093 - val_loss: 0.0721\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'], label='Training loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "nBKUF64OsY0i",
        "outputId": "c7eccdec-53fb-46a3-efdd-2d58e5be2907"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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vL3bu3ImzZ89i/PjxCAsLw+OPP97g+QDA+++/j3fffRcff/wx+vfvj9WrV2P06NH466+/EBgYiA8++ACbNm3Cd999h86dOyM9PR3p6ekAgB9++AHvvfcevv32W/Tp0wcZGRk4evRoo163uRiATEXwKBGAkn8GYt9s1C8pEZHZqSgG3vST57VfugzYOzdq18ceewyLFy/G7t27cfvttwMQ3V8PPPAA1Go11Go15syZo99/5syZ2L59O7777rtGBaD4+HicOnUK27dvh5+f+Pd48803bxq388orr+i3AwICMGfOHHz77bf497//DUdHR7i4uMDW1hY+Pj51vtbXX3+N0tJSfP7553B2Fue/fPlyjBo1Cm+//Ta8vb0BAO3atcPy5cthY2ODoKAgjBw5EgkJCY0OQEuWLMHcuXPx8MMPAwDefvtt7Ny5E8uWLcOKFSuQlpaGwMBA3HrrrVAoFOjSpYv+uWlpafDx8UFMTAzs7OzQuXPnRv07tgS7wExF92Hir5O8dODKEbmrISKyakFBQRg0aBBWr14NADh79ix+//13TJs2DQCg0WjwxhtvICQkBO3bt4eLiwu2b9+OtLS0Rh0/OTkZ/v7++vADANHR0Tftt27dOgwePBg+Pj5wcXHBK6+80ujXqPlaoaGh+vADAIMHD4ZWq0VKSor+vj59+sDGxkb/va+vL7Kyshr1Gvn5+bh8+TIGDzbswRg8eDCSk5MBiG62I0eOoFevXnj22Wfx66+/6vd76KGHUFJSgm7duuHxxx/Hhg0bUFlZ2aTzbCq2AJkKO0cg8C7g5E+iFcivv9wVEREZn52TaImR67WbYNq0aZg5cyZWrFiBNWvWoHv37hg6dCgAYPHixXj//fexbNkyhISEwNnZGbNmzUJ5ufHWc0tMTMTEiRPx2muvITY2Fmq1Gt9++y3effddo71GTXZ2dgbfKxQKaLVaox1/wIABOH/+PH755RfEx8dj3LhxiImJwffffw9/f3+kpKQgPj4ecXFxeOaZZ/QtcDfWZSxsATIlukURk3lxVCKyUAqF6IaS49bEoQXjxo2DUqnE119/jc8//xyPPfaYfjzQnj17cN999+Ef//gHQkND0a1bN5w+fbrRxw4ODkZ6ejquXLmiv2/fPsNJMHv37kWXLl3w8ssvY+DAgQgMDERqaqrBPvb29tBo6p89HBwcjKNHj6KoqPqak3v27IFSqUSvXr0aXXN93Nzc4Ofnhz179hjcv2fPHvTu3dtgv/Hjx2PVqlVYt24dfvjhB+Tk5AAAHB0dMWrUKHzwwQfYtWsXEhMTcfz4caPUVxu2AJmSnncDSjsgOwW4mgJ0MM4PJhERNZ2LiwvGjx+PefPmIT8/H1OmTNE/FhgYiO+//x579+5Fu3btsHTpUmRmZhp82NcnJiYGPXv2xOTJk7F48WLk5+fj5ZdfNtgnMDAQaWlp+PbbbxEREYEtW7Zgw4YNBvsEBATg/PnzOHLkCDp16gRXV9ebpr9PnDgRCxYswOTJk7Fw4UJcvXoVM2fOxKOPPqof/2MML7zwAhYsWIDu3bsjLCwMa9aswZEjR/DVV18BAJYuXQpfX1/0798fSqUS69evh4+PD9zd3bF27VpoNBpERUXByckJX375JRwdHQ3GCRkbW4BMiYMa6Ha72Oa1wYiIZDdt2jRcv34dsbGxBuN1XnnlFQwYMACxsbG4/fbb4ePjgzFjxjT6uEqlEhs2bEBJSQkiIyPxz3/+E//5z38M9hk9ejT+9a9/YcaMGQgLC8PevXsxf/58g30eeOABDB8+HHfccQc6dOhQ61R8JycnbN++HTk5OYiIiMCDDz6IO++8E8uXL2/aP0YDnn32WcyePRvPP/88QkJCsG3bNmzatAmBgYEAxIy2d955BwMHDkRERAQuXLiArVu3QqlUwt3dHatWrcLgwYPRr18/xMfH4+eff4aHh4dRa6xJIUmS1GpHN1P5+flQq9XIy8uDm5tb2774wc+An58FfMOAJ3e37WsTERlRaWkpzp8/j65du8LBwUHucshC1Pdz1ZTPb7YAmZpe9wAKpZgJltu0kf5ERETUOAxApsalA9C5airkqS3y1kJERGShGIBMke7aYBwHRERE1CoYgEyR7urwqXuBwsYtQkVERESNxwBkitz9qxZClICUrXJXQ0TUIpxrQ8ZkrJ8nBiBTxW4wIjJzuhV8i4tluvgpWSTdz1NLV4jmQoimKmgUkPA68PduoDRPrBFERGRGbGxs4O7urr+elJOTk34lZaKmkiQJxcXFyMrKgru7u8F1y5qDAchUdegJePYSq0Kf/hXo95DcFRERNZnuKuWNvagmUUPc3d31P1ctwQBkyoJHAb+nAMmbGICIyCwpFAr4+vrCy8sLFRUVcpdDZs7Ozq7FLT86DECmLHgU8PsS4Gw8UFEirhhPRGSGbGxsjPbBRWQMHARtynxDAXVnoKIYOLdD7mqIiIgsBgOQKVMogOCqNYE4G4yIiMhoZA9AK1asQEBAABwcHBAVFYWkpKQ69/3rr7/wwAMPICAgAAqFAsuWLWvxMU2ebjp8ylZAw/5zIiIiY5A1AK1btw6zZ8/GggULcOjQIYSGhiI2NrbO2QLFxcXo1q0b3nrrrTpHgDf1mCbPPwpw7iCmwl/4Xe5qiIiILIKsAWjp0qV4/PHHMXXqVPTu3RsrV66Ek5MTVq9eXev+ERERWLx4MR5++GGoVCqjHNPkKW2AoJFim91gRERERiFbACovL8fBgwcRExNTXYxSiZiYGCQmJprMMU1CUFU32KktgFYrby1EREQWQLYAlJ2dDY1GA29vb4P7vb29kZGR0abHLCsrQ35+vsHNpHQdAqjcgMJM4OJ+uashIiIye7IPgjYFixYtglqt1t/8/f3lLsmQrT3Qc7jYTt4kby1EREQWQLYA5OnpCRsbG2RmZhrcn5mZ2ewlrpt7zHnz5iEvL09/S09Pb9brt6qaF0fllZWJiIhaRLYAZG9vj/DwcCQkJOjv02q1SEhIQHR0dJseU6VSwc3NzeBmcnrcCdg6ALmpQOYJuashIiIya7JeCmP27NmYPHkyBg4ciMjISCxbtgxFRUWYOnUqAGDSpEno2LEjFi1aBEAMcj558qR++9KlSzhy5AhcXFzQo0ePRh3TbNk7Az1igFObRSuQT4jcFREREZktWQPQ+PHjcfXqVbz66qvIyMhAWFgYtm3bph/EnJaWBqWyupHq8uXL6N+/v/77JUuWYMmSJRg6dCh27drVqGOateBR1QHojpfkroaIiMhsKSSJA0pulJ+fD7Vajby8PNPqDiu5DizuAWgrgRkHAc8ecldERERkMpry+c1ZYObEsZ2YEg8Ap7goIhERUXMxAJmbIN3FUTfLWwcREZEZYwAyN0EjASiASweAvEtyV0NERGSWGIDMjauPuEAqIC6NQURERE3GAGSO9IsiclVoIiKi5mAAMkfBVeOAUvcARdfkrYWIiMgMMQCZo3YBYiFESQuc/kXuaoiIiMwOA5C5Ch4tviZzOjwREVFTMQCZK904oHM7gLICeWshIiIyMwxA5qpDEODRA9CUA2d+lbsaIiIis8IAZK4UCi6KSERE1EwMQOZMNw7ozK9ARam8tRAREZkRBiBz5tcfcOsIlBcCf++SuxoiIiKzwQBkzpTKGt1gnA1GRETUWAxA5k43GyxlK6CplLcWIiIiM8EAZO46RwOO7YGSHCBtr9zVEBERmQUGIHNnYwsE3SO22Q1GRETUKAxAlkC/KvRmQKuVtxYiIiIzwABkCboOBexdgYLLwOVDcldDRERk8hiALIGdAxB4l9hmNxgREVGDGIAshW42WPImQJLkrYWIiMjEMQBZisC7ABsVkPM3kHVS7mqIiIhMGgOQpVC5At3vENspv8hbCxERkYljALIkgXeLr7w6PBERUb0YgCyJLgBd3A8U58hbCxERkQljALIk7v6AVx9A0gJnE+SuhoiIyGQxAFmanrpusO3y1kFERGTCGIAsTWCs+Ho2HtBq5K2FiIjIRDEAWZpOEYCDO1ByXYwFIiIiopswAFkaG1ugx51i+zS7wYiIiGrDAGSJdN1gZ+LkrYOIiMhEMQBZoh4xABRA5nEg75Lc1RAREZkcBiBL5OwhxgIBXBSRiIioFgxAloqrQhMREdWJAchS6dYD+nsXUFEqaylERESmhgHIUvn0A1x9gYpiIHWP3NUQERGZFAYgS6VQAIF3iW12gxERERlgALJkuunwp7cDkiRvLURERCaEAciSdRsKKO2A6+eBa2flroaIiMhkMABZMpUrEDBYbHNVaCIiIj0GIEunXxWaAYiIiEiHAcjS9awKQKmJQGm+vLUQERGZCAYgS+fRHWjfHdBWiDWBiIiIiAHIKuhXhWY3GBEREcAAZB10q0KfiQO0WnlrISIiMgEMQNagy2DAzhkozAQyjspdDRERkewYgKyBrQrofofYPhMnby1EREQmgAHIWujGAXE9ICIiIgYgq6ELQJcOAkXZ8tZCREQkMwYga+HmC/iEAJDYDUZERFaPAciacFVoIiIiAAxA1kW3KvTZHYCmUt5aiIiIZCR7AFqxYgUCAgLg4OCAqKgoJCUl1bv/+vXrERQUBAcHB4SEhGDr1q0GjxcWFmLGjBno1KkTHB0d0bt3b6xcubI1T8F8dAwHnDyAsjwg/U+5qyEiIpKNrAFo3bp1mD17NhYsWIBDhw4hNDQUsbGxyMrKqnX/vXv3YsKECZg2bRoOHz6MMWPGYMyYMThx4oR+n9mzZ2Pbtm348ssvkZycjFmzZmHGjBnYtGlTW52W6VLaAD1ixDa7wYiIyIopJEmS5HrxqKgoREREYPny5QAArVYLf39/zJw5Ey+++OJN+48fPx5FRUXYvHmz/r5bbrkFYWFh+laevn37Yvz48Zg/f75+n/DwcIwYMQL/93//16i68vPzoVarkZeXBzc3t5acouk5/j3wwzSgQzAwfZ/c1RARERlNUz6/ZWsBKi8vx8GDBxETE1NdjFKJmJgYJCYm1vqcxMREg/0BIDY21mD/QYMGYdOmTbh06RIkScLOnTtx+vRp3H333XXWUlZWhvz8fIObxeo+DFAogavJQG6a3NUQERHJQrYAlJ2dDY1GA29vb4P7vb29kZGRUetzMjIyGtz/ww8/RO/evdGpUyfY29tj+PDhWLFiBYYMGVJnLYsWLYJardbf/P39W3BmJs6pPeAfJba5KCIREVkp2QdBG9uHH36Iffv2YdOmTTh48CDeffddTJ8+HfHx8XU+Z968ecjLy9Pf0tPT27BiGQTWuDgqERGRFbKV64U9PT1hY2ODzMxMg/szMzPh4+NT63N8fHzq3b+kpAQvvfQSNmzYgJEjRwIA+vXrhyNHjmDJkiU3dZ/pqFQqqFSqlp6S+egZCyS8Bpz/DagoAewc5a6IiIioTcnWAmRvb4/w8HAkJCTo79NqtUhISEB0dHStz4mOjjbYHwDi4uL0+1dUVKCiogJKpeFp2djYQKvVGvkMzJhXb8CtI1BZApz/Xe5qiIiI2pysXWCzZ8/GqlWr8NlnnyE5ORlPP/00ioqKMHXqVADApEmTMG/ePP3+zz33HLZt24Z3330Xp06dwsKFC3HgwAHMmDEDAODm5oahQ4fihRdewK5du3D+/HmsXbsWn3/+OcaOHSvLOZokhaJGNxjHARERkfWRrQsMENPar169ildffRUZGRkICwvDtm3b9AOd09LSDFpzBg0ahK+//hqvvPIKXnrpJQQGBmLjxo3o27evfp9vv/0W8+bNw8SJE5GTk4MuXbrgP//5D5566qk2Pz+T1jMWOLgGOP0rcI8kQhEREZGVkHUdIFNl0esA6ZQXAW93BTRlwDN/Al5BcldERETUImaxDhDJzN4Z6Hqb2GY3GBERWRkGIGumGwd0+ld56yAiImpjDEDWTBeA0hKBklxZSyEiImpLDEDWrH1XwLMnIGmAczvkroaIiKjNMABZO64KTUREVogByNr1jBVfz8YBXCySiIisBAOQtescDdi7AkVXgcuH5a6GiIioTTAAWTsbO6D7HWKb0+GJiMhKMABRdTfYaQYgIiKyDgxABPS4S3y9cgQoyJS1FCIiorbAAESAqzfg119sn+VsMCIisnwMQCQEshuMiIisBwMQCbr1gM7tBCrL5a2FiIiolTEAkeDXH3DuAJQXiEtjEBERWTAGIBKUyurB0Gd4cVQiIrJsDEBUrafushgMQEREZNkYgKha92GAwgbIPg3knJe7GiIiskTXLwB7PwTKCmQtgwGIqjmoxaUxALYCERFR69i7HPj1FWDj07KWwQBEhnTdYJwOT0RExlZ4FTj8hdiOfELWUhiAyJBuPaALfwDlRfLWQkREluXPlUBlKdAxHAi4TdZSGIDIUIdegHtnQFMGnP9N7mqIiMhSlOYD+1eJ7Vv/BSgUspbDAESGFAquCk1ERMZ3cC1Qmgd4BAK9RspdDQMQ1SKwxnR4SZK3FiIiMn+VZcC+/4rtwc+JtedkJn8FZHq63gbYOgL5l4DMv+SuhoiIzN2xdUDBFcDVD+g3Tu5qADAAUW3sHIGuQ8T2GXaDERFRC2g1wJ73xXb0dMBWJW89VRiAqHb6VaHj5K2DzF9Bplj0jLMKiazTqc3AtbOAgzsQPlnuavQYgKh2uoHQ6X8CxTny1kLmbdNMsejZrrfkroSI2pokAX+8J7YjnwBUrvLWUwMDENXO3R/w6g1IWuDcDrmrIXN17Vx1N+qxdYCmUt56iKhtnd8NXD4sxpVGPSl3NQYYgKhugVVXh+d0eGqupFXV24WZDNNE1uaPZeLrgEcBZ09ZS7kRAxDVTdcNdjZeDGIjaoqyQuDIV2Lbp5/4qvueiCzf5cPA3zvFRbajZ8hdzU0YgKhu/lHiAqklOcDFA3JXQ+bm6DdAWT7Qvjtw33JxX8pWjikjsha61p+QB4F2XWQtpTYMQFQ3G1ug+51im1eHp6aQJCDpE7Ed9STgGwp4hwCacuDED/LWRkSt79o54ORPYnvwc/LWUgcGIKpfz6puMK4HRE3x904g+zRg7wKEThD3hT0ivh75Wr66iKht7HkfgAT0HA5495G7mloxAFH9esQAUAAZx4H8y3JXQ+biz4/F17CJgIOb2A55CFDaApcPAVmn5KuNiFpXQYboAgeAwbNkLaU+DEBUP2dPoGO42GY3GDVGzt/VMwcjn6i+36VD9cD6o2wFIrJY+/4rurv9bwG6RMtdTZ0YgKhhum6w0wxA1AhJnwKQROuhZw/Dx3TdYEe5JhCRRSrJBfavFtu3/kvWUhrCAEQN010d/u9d4oq+RHUpKwQOfym2I2tZ9CzwbsDJAyjMEOOEiMiyHPgfUF4gFtLVfXaYKAYgaphvKODiA1QUAal75K6GTNmxb4GyPKB9t6rxYzewtRdjgQCuCURkaSpKgH0fie3BswClaUcM066OTINCAQRWfZixG4zqIknAn1VT3yOfqPs/P1032KktQMn1tqmNiFrfka+AoquA2h/oe7/c1TSIAYgaJ7DGdHhJkrcWMk1/7wKyU8TUd13IqY1PP8C7L9cEIrIkmkpgzwdie9BMwMZO3noagQGIGqf7HYCNSszwOb5e7mrIFOkWPgydIFYQr4tCUWNNoG9avy4ian0nNwK5qWKMX/9H5a6mUZoVgNLT03Hx4kX990lJSZg1axY++eQToxVGJkblCgyZI7a3zgHyLslbD5mW6xeAlF/Eds2p73UJGSfWBLp0ALia0qqlEVErk6Tqy15EPQXYO8laTmM1KwA98sgj2LlTzODIyMjAXXfdhaSkJLz88st4/fXXjVogmZBbZwN+A4DSPGDTDHaFUbWkVQAkoPswoEPPhvd36VA9Q4QrQxOZt7MJQOZxwM4ZiPin3NU0WrMC0IkTJxAZGQkA+O6779C3b1/s3bsXX331FdauXWvM+siU2NgCYz8GbB2AczvEdEei8iLg8Bdiu7ap73XRdYMdWwdoNcavi4jaxh/via/hUwCn9rKW0hTNCkAVFRVQqVQAgPj4eIwePRoAEBQUhCtXrhivOjI9HXoCMQvF9q/zxQXvyLodWydaBdt1bdq6H4GxgGN7oOAKcI5rAhGZpfQkIPUPQGkHRE+Xu5omaVYA6tOnD1auXInff/8dcXFxGD58OADg8uXL8PDwMGqBZIIinwQCbgMqioGNT5vHX+8FmcBvS4DCq3JXYlkMpr4/3rR1P7gmEJH504396TceUHeUtZSmalYAevvtt/Hxxx/j9ttvx4QJExAaGgoA2LRpk75rjCyYUgmM+S9g7wqk/wns/UDuiupXVgh8eT+w4w1g41Mcu2RM538DriaLvv+wiU1/PtcEIjJfV1OAlC0AFMDgZ+WupsmaFYBuv/12ZGdnIzs7G6tXr9bf/8QTT2DlypVGK45MmHtnYMRbYnvnm0DmX/LWUxetFvjxCSDzhPj+bLz4sCXj0E99fxhwdG/6831DAa8+gKYM+GuDUUsjola2533xNWgk0KGXvLU0Q7MCUElJCcrKytCuXTsAQGpqKpYtW4aUlBR4eXkZtUAyYWETgZ4jxIJ2Pz4JVJbLXdHNdrwh/kKxsQeCR4n7ts0DyovlrcsSXE8FUraK7cZMfa+NwZpAnA1GZDbyLorxf4DJX/S0Ls0KQPfddx8+//xzAEBubi6ioqLw7rvvYsyYMfjoo4+MWiCZMIUCGP2BWPgq8ziw+y25KzJ07Dvgj6Vie/SHYgabWycgLw3Ys0zW0izC/k8BSQt0ux3wCmr+cfqNAxQ2wMX9wNXTRiuPiFpR4gpAWynGg3YaKHc1zdKsAHTo0CHcdtttAIDvv/8e3t7eSE1Nxeeff44PPjDx8SBkXC5ewL1VUyD/eA9I3y9vPToXDwA/zRDbg2eJLhp7Z2D4m+K+P5aJVa2pecqLgUPijyBEPdWyY7l4Vc8eO8pWICKTV5wDHFwrtm+dJWclLdKsAFRcXAxXV1cAwK+//or7778fSqUSt9xyC1JTU5t0rBUrViAgIAAODg6IiopCUlJSvfuvX78eQUFBcHBwQEhICLZu3XrTPsnJyRg9ejTUajWcnZ0RERGBtLS0JtVFTdD7PrGyr6QFNjwpf/dS3kXgmwliXEmve4A7F1Q/Fjwa6HaHeGzbPPlqNHfHvwNKcwH3Lk2b+l6XsAni69FvzWNWIZE1S/pEzAL2CQG63yl3Nc3WrADUo0cPbNy4Eenp6di+fTvuvlv8B5iVlQU3N7dGH2fdunWYPXs2FixYgEOHDiE0NBSxsbHIysqqdf+9e/diwoQJmDZtGg4fPowxY8ZgzJgxOHHihH6fc+fO4dZbb0VQUBB27dqFY8eOYf78+XBwcGjOqVJj3fMO4OoH5JwD4hc0vH9rKS8S4acoSwyuvf8Tw6nZCgUw4h2xZsXpbUDKNvlqNVc3XfXdpuXH7DkccGwn1gT6m2sCEZms8iLgz4/F9q3/Ev+nmimFJDV9TvD333+PRx55BBqNBsOGDUNcXBwAYNGiRfjtt9/wyy+/NOo4UVFRiIiIwPLlywEAWq0W/v7+mDlzJl588cWb9h8/fjyKioqwefNm/X233HILwsLC9LPPHn74YdjZ2eGLL75o6mnp5efnQ61WIy8vr0mBzuqdTRDTzQFg0k9ibEhb0mqB9ZOB5E2Akyfw+A6gXZfa9417VcxgaBcAPPMnYMeA3Gjnfwc+uxewcwJmJzdv9ldttr4g/rLs+wDw4OqG9yeitrdvJbBtrlj4dMYBcYUAE9KUz+9mtQA9+OCDSEtLw4EDB7B9+3b9/XfeeSfee++9Rh2jvLwcBw8eRExMTHUxSiViYmKQmJhY63MSExMN9geA2NhY/f5arRZbtmxBz549ERsbCy8vL0RFRWHjxo1NPENqlh53AgOnie2NzwAluW37+rvfEuFHaQeM/7Lu8AMAQ/4NuPqKi3ia+jpGpiap6q+/fuONF36AG9YEyjXecYnIODQVwN4PxfagmSYXfpqqWQEIAHx8fNC/f39cvnxZf2X4yMhIBAU1bjZIdnY2NBoNvL29De739vZGRkZGrc/JyMiod/+srCwUFhbirbfewvDhw/Hrr79i7NixuP/++7F79+46aykrK0N+fr7BjZrp7jfEXwb5l4BtN7fitZoTPwC73xbbo94HukTXv7/KBbj7/8T27++KKd3UsNy06nWUoppw3a/G8A0DvHoDlaVcE4jIFB3/Hsi/CDh7NW/hUxPTrACk1Wrx+uuvQ61Wo0uXLujSpQvc3d3xxhtvQKvVGrvGJtUFiGn6//rXvxAWFoYXX3wR9957b70LNC5atAhqtVp/8/f3b6uSLY+9s5hurlACR78Bkjc3/JyWunRQtDgBQPQMoH8jfzH7PiCmcFaWAttfar36LMn+/4nB7l2HAF7Bxj021wQiMl1abfXyIbc8bRHDBpoVgF5++WUsX74cb731Fg4fPozDhw/jzTffxIcffoj58+c36hienp6wsbFBZmamwf2ZmZnw8fGp9Tk+Pj717u/p6QlbW1v07t3bYJ/g4OB6Z4HNmzcPeXl5+lt6enqjzoHq0DkKGFS1LPrPz7Xu9bfyLwPfPCJCTGAscNfrjX+uQgHcs1isQXNqs1glmupWUQIc+kxst3Tqe11CdGsCJQHZZ1rnNYio6c5sB66eAlRuQMQ0uasximYFoM8++wyffvopnn76afTr1w/9+vXDM888g1WrVmHt2rWNOoa9vT3Cw8ORkJCgv0+r1SIhIQHR0bV3X0RHRxvsDwBxcXH6/e3t7REREYGUlBSDfU6fPo0uXeoeD6JSqeDm5mZwoxa64yUxC6s4G9g8q3Wuv1VeDHz7CFCYAXQIAh74tOkzkryCqz/Mt/4bqCwzfp2W4vh6cb0u985i1lZrcPUGelSN82MrEJFpkCTg96pFZQc+Bjio5a3HSJoVgHJycmod6xMUFIScnJxGH2f27NlYtWoVPvvsMyQnJ+Ppp59GUVERpk6dCgCYNGkS5s2rXqvlueeew7Zt2/Duu+/i1KlTWLhwIQ4cOIAZM2bo93nhhRewbt06rFq1CmfPnsXy5cvx888/45lnnmnOqVJz2aqA+z8WA5JPbRbruxiTJAE/TQcuHwYc2wMTvgUcmhlcb38RcPEWU/gTVxi3TkshSdVTXyMeN87U97rousG4JhCRaUhLFK2yNvai+8tCNCsAhYaG6qeu17R8+XL069ev0ccZP348lixZgldffRVhYWE4cuQItm3bph/onJaWhitXruj3HzRoEL7++mt88sknCA0Nxffff4+NGzeib9+++n3Gjh2LlStX4p133kFISAg+/fRT/PDDD7j11lubc6rUEj4hIlwAwC//FgsUGstvi4G/fgSUtsD4L4D2XZt/LAc34K43qo9rzDotRepecUFZW0eg/z9a97V6jQAc3IGCy8Dfu1r3tYioYX9Uze4OewRwrX2Iijlq1jpAu3fvxsiRI9G5c2d991NiYiLS09OxdetW/WUyzBXXATIiTSWwZri4zlPXocCjGw0XJmyOkz8B300S26PeB8KntLRK0cKxZoT4S6fPWOChtS0/piVZ96hYYiB8ivg3b21b5gD7VwEhD4muTSKSR8YJYOVgMbFlxgHAo7vcFdWr1dcBGjp0KE6fPo2xY8ciNzcXubm5uP/++/HXX3+1aAFCskA2tmJWmK0jcH63uIBmS1w5Kq48DwBRTxsn/AA1BkQrxRRstjxUy7tYPfW9uVd9bypdN1jyz0BpXtu8JhHdTDfzq/d9Jh9+mqpZLUB1OXr0KAYMGACNxrz77dkC1AqSVgFb54gg9NQfgGePph+jIANYNUysMdT9TuCR74y/ENfWf4uF/jx7iTpt7Y17fHMU/xrwx1KxZMCUNljWABAtcv+NBq4mG6+Vj4ia5voF4IMBgKQBntgN+IXJXVGDWr0FiKjJBk4Tl8aoLAE2PiW6xpqiohT4dqIIPx6B4lIJrbEK6R0victoZKdUr3hszSpKqq/6bOyFD+ujUFRfIJWzwYjksXe5CD/d7jCL8NNUDEDUNpRK4L4VgEotxgPpmlUbQ5KATTOBSwfE4NhH1hn3Egw1OboDd70mtne9BeRfqXd3i3fiB6AkB1D7Az1HtO1r9xsvuiTT/wSyz7btaxNZu8KrwOGqIS23/kveWloJAxC1HXUncdV4QISLjOONe94fS4Hj34kF8sZ93vr90KGPAJ0igPJCIK5xC3taJEkC/qxaQT3in21/3R9Xn+o1gY6yFYioTf25Uiww6zdArPxugZr0P9r9999f7+O5ubktqYWsQb/xYmDrqc1iMPMTO8WaQXU5tQVIqFrd+Z53gG5DW79GpVIMiP7kDrH4X/hUIGBw67+uqUnbJ0KqrQMwYJI8NYQ9Apz5VawJdMfLrbv+EBEBxTliEsj+VeL7W/8luqQtUJMCkFpd/+qParUakybJ9B8lmQeFQgxqTdsHZP0F7HyzusvpRhnHgR8eF9sRj4tWiLbi1x8YOBU4sBrY+gLw5G9mf+XjJtO1/vQbBzi1l6eGnlVrAuVfErMIuw+Tpw4iS6XVAlcOA2fixeWALh0Q1/sDxGSQoJHy1teKmvQ/+po1a1qrDrImzp7A6A/EZSz2fgD0ukdcP6ymwizgmwlARZEYPD38rbavc9h84K+NIqjtX2VRK6A2KO+SaKkDgMg2HPx8IzsHIORBsXzCkW8YgIiMoTALOLdDBJ5zO4Dia4aPdwgS3c+3PGPRra5W9ictmYygkWKszdGvgQ1PiinnKhfxWGUZsO4fQF460L67WJRQjtYXp/bAna+Ka5ntfFNcPd7Fq+3rkMOB/4nZH11uBXz6Nrx/awp7RAQg3ZpAFnIdIqI2o6kUl7I4W9XKc+Wo4eMqNzG8oEeMWGLE3V+eOtsYAxDJZ8RbwPnfgOvngbhXgXuXioG3P88SM39U6qoZX+3kq3HAJHEF9MuHgbgFwNiP5KulrVSU1pj63kYLH9bHb4Bois9OES1y4ZPlrojI9OVdBM4miMDz926g7IYFRX1DReDpESMmfdjYyVOnjBiASD4OamDMCuDz+0SLQ9BIIPMv0SqksAEeWgN4Bspbo9IGuOdd4NNhoq7wKTd311maEz+IJnG3TkAvE+j/VyhEK1D8ArEmEAMQ0c0qy8Q1+87Gi+BzNdnwccf2QI87q1p5hllPa3Y9GIBIXt1uF2NMkj4GfvgnUHJd3D98kfhlNQWdwoH+j4o1MbY+L1ZEtdR+cUmqXgAyYprpDPzuNx5IeA1I3wdcO2dxS/ITNUvO39WtPOd/AyqKqx9TKIGOA6tbefzCLPf/rWYykf/dyKrFLATOJQDXqha7C5/adtecaqyYheJioBnHxcywyMflrqh1pCeJ8QG2DsAAE2ppcfMVYxPOxolWoDuteH0msm5ajbhe4R/LgMwb1lJzqVo7q8ed4o9LuWZvmgkGIJKfvRMw9hPgqwcA/1uqLkpqYutOOHuKWWFb5wA73hBXjHf2lLsq49NNfQ95EHD2kLeWG4U9IgKQfk0gruNKVkRTIdYl+/3d6j8WlXZA51uqW3m8+5je/50mjAGITEOncOD5lPoXRZTbwMfEgOiM40D8QuC+5XJXZFz5l0UrFyDv1Pe69LpHjBvLvwhc+E38hUttr7IMOP87kLIFSNkmukmHvgiETmAobQ2VZaLV84/3gNxUcZ+DOxA9XbREyzlJxMwxALWh0goNjqbnor2zPQK9XeUux/SYcvgBqgZELwFWx4rxQOFTRXCzFAdWA9pKoPMgwLef3NXczM4B6PugGDB/5GsGoLZUkguciROh50w8UF5g+PhPz4j3ZcRiy/qdkFNFCXDoC3HdxPxL4j4nT2DQDLEorIqfIS3FuN6GFm1NxvhP9uGrP9PkLoWaq/Mt4i9dQAyI1mrkrcdYKsuAA1ULnZrC1Pe6hD0ivp7cBJTmy1uLpcu7CPz5iZilubg78OM/xdiT8gIx1iR8KjDxe+Cu1wF7F+DSQTFbcuN0sdAeNU9ZIbD3Q+D9UOCXF0T4cfEBYhcBs46LS1Mw/BgFW4DaUETX9vgsMRVJ53PkLoVa4q7XxTXKLh8GDn0uLpnRmoquiXWRSvMABzfxn5/KrWq76mZr37LXOPEjUJwNuHUEgu41Tt2toWM44NkTyD4NnNwo3zXKLJEkiWUoUraKa/XduFhehyDRDRl0r7hUjK67K/AuMUsvfiFw9BvgyJeiK3XoXDGZoaU/m9aiNB9I+gRIXAGUVH1GqP2BW2cBYf8QLaBkVAxAbSgyQIzIT87IR15JBdSO1rfwlEVw8QLueAnY9qKYmt37PuPNtpAkIDcNSEsUa3qkJYoP+4bYOtwQjHTb6jrurxGeVK7VU98HPmbaC6Lp1wRaKLrBGIBaRlMpfsZ0oSe3Zuu0QrR49rpHrNFV39IDrj7A2JXi52frC8CVI8CvL4sxc8PfMp0lLUxRcY6YfPDnSvFHDgC06wrc9rwIlgyQrUYhSZIkdxGmJj8/H2q1Gnl5eXBzczPqsW9fvBMXrhVj9ZSBGBbkbdRjUxvSVAIf3wZknRT/6d/7XvOOo9UCV08BaXuB1ETxYaTr76+pQxDg6guUFQBl+eJrab64Vpqx2KiA2SdNf3Zb/mXgvT7igo0zD7XemkC56eLyG6e3iYDZ/Q6xgJxnT/OeaVNeJNaOSdkqzk239hZQdZ7DROjpORxw6dD042u1ohUo/jXRqgiIBTVj/wO072qcc7AEhVeBxOXiMi/lheI+z17AkDlAn/tNZw0uM9OUz2/+C7exyK7tceFaMZLOX2cAMmc2tmJA9Np7xNiZAZNEt0BDKsvFX8e61p20fUBpruE+SlvANwzoEg10jhZLA9Q1JV1TKcZklOZXhyP9dl51UKoZmsrya+xX9VXSALc8ZfrhBwDc/MSH9Nl40eUy7BXjHfvaOeDkT6IL5/Jhw8fObK96/Y7VYajbHeax1kphFpDyiwg9f+8CKkurH3NsL8JO0EhxXvbOLXstpVL8PgSPBna/I1o2UraI92vQTOC22S1/DXOWf0VcBPrAGqCyRNznHSKCT/BozqRrQ2wBqkVrtgCtP5COF74/hgGd3fHjM4ONemySwQ//FGtzdIoAHvv15v+8ygrE4oJp+0TguXig+j89HTtnwD9ChJ3O0UCngW37ASFJYo0Rc2pqP/ED8P1jYozEc8ea/6EhSaIV7+QmEXqyTtZ4UAF0GQQEjxL/PucSRCudpsxwH7/+Igx1HyZ+DuT+d9RUANlngMwT4pa2T/wMosZ/9e0CRKtM0EjAP6p1WxuyTgHb5orgBYgAedfr4uLC5tyS1lS5aWLxwsNfAJpycZ9ff2DIv4FeI6zr36IVNeXzmwGoFq0ZgNKuFWPI4p2ws1Hg2IJYONpzaXKzln8FWD5QNGHftwIIjK1q2akaw5NxXLSu1OTkUR12ukQDPv1Me9yNKaooBZb0FK1ckzaJK1k3liQBlw9VhZ6fgZxz1Y8pbYGuQ8Rf4kEjb75eUnmx6K48txM4t+OGwAQxGyrgtupA5NG9dT/YinPEz1jmCTGAOeO46FLVfcDW5Ne/OvR4BbftB64kiYkD2+dVjzPqPAi45x3AJ6Tt6qhJq6laV0chuv5sVdVfjXnJiGvngD+WigU8tZXiPv9bgKEviNXNGXyMigGohVozAEmShOhFO5CRX4qvH4/CoO5m0OVA9dvzARA3X3x46v6Dq8m9s/jPXtelZe5jSEzFz7OAg2uAfg8D939c/75ajZhJl/yzuOWlVz9moxKDdINHia6gpnRp5V8WLRvndohb8TXDx907V4ehrkOav2idplIENYOwcwIouFz7/vauYlVgn74iYPeIAdQdm/faxlRRAuxdLlYzriwR16sKnyq6MVuzK1GSxNi6SwdFK+ylQ6KLs64xdEq7m0NRza92DvU/rvt65ahoIZa04rhdh4gWn4Bb+X9AK2EAaqHWDEAAMPObw/j56GXMignErJieRj8+tTFNBbDytuqrL3v1rmrdGSS+msIHjyVK3w/8LwawdQTmnBYz22rSVAAX/hBdW8mbgaIaa9PYOYvp271HA4F3G2ddFa0WyDhWHYbS9gHaiurHFUoxjV8XiDoOrL3rqeR6dcDJPC62s5INx+3U1C4A8O4rWlK8+4rQo+5s2mNJ8i4Cv84H/vpRfO/gLkJQ+FTjdMeV5IqAc+lg9a0w8+b9bB3EHy6VpbX/8WIsgXcDt80BOke13msQAAagFmvtAPTFvlTM33gCg3t44Kt/3mL045MMirLFX+a+oVyavq1IErA8Arh2Bhi9HBjwqFjQ8dxOEXpSthrOcFKpxViL3qNFALFzbN36youAC3uqA1F2iuHjKjfRIhBwa/XPT+Zfhq1TNdk5A969q0OOd18Rtm8Mfubkwh/AL3PFuQPinEa8Lf5NGquyTDz/0qHqFp5rZ27eT2EjWsU6DRRBVLemlK67S1MpglBlWdXXmtuN/XrDfXaOYp2wxkyQIKNgAGqh1g5AKRkFiF32GxztbHBs4d2wszHhv9SITNnvS8VaTF69xbiW078aXqbByVOMeek9GggYIu8A5byL1WHo712G4exG6s5VIadPdetOu66m3arTXJpK0ZW54/+qZ0T2uR+4+w1A3clwX60WyPnbsGUn41jtY57aBVQFnarA49uv9UMvyY4BqIVaOwBptRIG/F8ccosrsOGZQejfmS0GRM1Sc00gHVc/MZ4neJTohjTmgFZj0WrEcgjndoiuPBev6i4s7z6Ao7vcFba94hxg53/ENekkrejavO15EQRrBh7dYoE1ObavbtXpNBDwG1D30hFk0bgOkIlTKhWICGiPuJOZSDqfwwBE1FxufsCgZ8WFOnsMA4LvEx+Cpt5SorSp/sAmwak9MPJdIHyK6BZL3QPs/L+b97N1EF3Nun+/juGitYeDiqmJ2AJUi9ZuAQKAVb/9jf9sTcadQV7435SIVnkNIiKzJEligPRvS0RrUMeBQMcBIux49+GyEVQntgCZgciuYsrn/gs50GolKJX864WICIBozen7gLgRtRITbye2XH383OBkb4P80kqkZBY0/AQiIiIyGgYgmdjaKBHeRYz9STqfI3M1RERE1oUBSEaRAaIbLOkCAxAREVFbYgCSUUTVOKCk8zngWHQiIqK2wwAkozB/d9jbKHG1oAyp14rlLoeIiMhqMADJyMHOBqH+agAcB0RERNSWGIBkFlE1DuhPBiAiIqI2wwAks5rrAREREVHbYACSWXiXdlAqgLScYmTklcpdDhERkVVgAJKZq4MdevuJ5bo5HZ6IiKhtMACZgMgAcdXipPPXZK6EiIjIOjAAmYDIGusBERERUetjADIBEQHikhinMwtxvahc5mqIiIgsHwOQCfBwUaGHlwsAzgYjIiJqCwxAJkK3HhC7wYiIiFofA5CJiOJ6QERERG2GAchE6AZCn7icj6KySpmrISIismwMQCbCz90RHd0dodFKOJR2Xe5yiIiILBoDkAmJ4nR4IiKiNsEAZEJ03WC8MCoREVHrYgAyIRFVAehIei7KKjUyV0NERGS5TCIArVixAgEBAXBwcEBUVBSSkpLq3X/9+vUICgqCg4MDQkJCsHXr1jr3feqpp6BQKLBs2TIjV2183Tyd4elij/JKLY5dzJO7HCIiIoslewBat24dZs+ejQULFuDQoUMIDQ1FbGwssrKyat1/7969mDBhAqZNm4bDhw9jzJgxGDNmDE6cOHHTvhs2bMC+ffvg5+fX2qdhFAqFgusBERERtQHZA9DSpUvx+OOPY+rUqejduzdWrlwJJycnrF69utb933//fQwfPhwvvPACgoOD8cYbb2DAgAFYvny5wX6XLl3CzJkz8dVXX8HOzq4tTsUoeF0wIiKi1idrACovL8fBgwcRExOjv0+pVCImJgaJiYm1PicxMdFgfwCIjY012F+r1eLRRx/FCy+8gD59+jRYR1lZGfLz8w1uctEFoIOp16HRSrLVQUREZMlkDUDZ2dnQaDTw9vY2uN/b2xsZGRm1PicjI6PB/d9++23Y2tri2WefbVQdixYtglqt1t/8/f2beCbGE+TjBleVLQrLKpF8Rb4gRkREZMlk7wIztoMHD+L999/H2rVroVAoGvWcefPmIS8vT39LT09v5SrrZqNUYGDV1eE5HZ6IiKh1yBqAPD09YWNjg8zMTIP7MzMz4ePjU+tzfHx86t3/999/R1ZWFjp37gxbW1vY2toiNTUVzz//PAICAmo9pkqlgpubm8FNTpFdPQAA+xmAiIiIWoWsAcje3h7h4eFISEjQ36fVapGQkIDo6OhanxMdHW2wPwDExcXp93/00Udx7NgxHDlyRH/z8/PDCy+8gO3bt7feyRhRZFfRApR0IQeSxHFARERExmYrdwGzZ8/G5MmTMXDgQERGRmLZsmUoKirC1KlTAQCTJk1Cx44dsWjRIgDAc889h6FDh+Ldd9/FyJEj8e233+LAgQP45JNPAAAeHh7w8PAweA07Ozv4+PigV69ebXtyzRTS0R0qWyVyispx7moheni5yl0SERGRRZE9AI0fPx5Xr17Fq6++ioyMDISFhWHbtm36gc5paWlQKqsbqgYNGoSvv/4ar7zyCl566SUEBgZi48aN6Nu3r1ynYHT2tkr07+yOfX/nIOn8dQYgIiIiI1NI7GO5SX5+PtRqNfLy8mQbD7Q07jQ+SDiDMWF+WPZwf1lqICIiMidN+fy2uFlgloJXhiciImo9DEAmqn9nd9gqFbicV4qL14vlLoeIiMiiMACZKCd7W/TtqAbAViAiIiJjYwAyYbpusP0XGICIiIiMiQHIhOmuDM8VoYmIiIyLAciERQS0h0IB/H21CFcLyuQuh4iIyGIwAJkwtZMdenmLNYAOsBuMiIjIaBiATFxkV3aDERERGRsDkImL5EBoIiIio2MAMnGRVQOhT17JR35phczVEBERWQYGIBPn5eaAAA8nSBJw8MJ1ucshIiKyCAxAZkDXDZbEbjAiIiKjYAAyA7r1gLgiNBERkXEwAJmBqK4eAIBjF3NRWqGRuRoiIiLzxwBkBvzbO8LbTYUKjYTDablyl0NERGT2GIDMgEKhQGRVKxC7wYiIiFqOAchMcD0gIiIi42EAMhO69YAOpl5HhUYrczVERETmjQHITAR6ucDdyQ4lFRqcuJQndzlERERmjQHITCiVCv10eHaDERERtQwDkBmJ5HpARERERsEAZEaqB0Jfh1YryVwNERGR+WIAMiN9/NzgZG+DvJIKnM4qkLscIiIis8UAZEZsbZQI79IOALvBiIiIWoIByMxwHBAREVHLMQCZmYiu1QFIkjgOiIiIqDkYgMxMmL877G2UyCooQ+q1YrnLISIiMksMQGbGwc4Gof5qAEAS1wMiIiJqFgYgMxTBcUBEREQtwgBkhnhhVCIiopZhADJD4V3aQakAUq8VIyOvVO5yiIiIzA4DkBlydbBDbz83ABwHRERE1BwMQGYqMsADALCf44CIiIiajAHITEV25YrQREREzcUAZKZ0M8FSMguQW1wuczVERETmhQHITHm4qNDDywWAuDo8ERERNR4DkBmrXg/omsyVEBERmRcGIDMWpbsuGFuAiIiImoQByIzpLox64lIeisoqZa6GiIjIfDAAmbGO7o7o6O4IjVbCoTS2AhERETUWA5CZ03WDcT0gIiKixmMAMnO6brA/GYCIiIgajQHIzOkujHokPRdllRqZqyEiIjIPDEBmrpunMzxd7FFWqcXxi3lyl0NERGQWGIDMnEKh0K8HxG4wIiKixmEAsgC6brD9vDI8ERFRozAAWQBdC9DBC9eh0UoyV0NERGT6GIAsQLCvG1xVtigoq0TylXy5yyEiIjJ5DEAWwEapwMCAdgCAJI4DIiIiahADkIXQrQfEAERERNQwBiALEVVjILQkcRwQERFRfUwiAK1YsQIBAQFwcHBAVFQUkpKS6t1//fr1CAoKgoODA0JCQrB161b9YxUVFZg7dy5CQkLg7OwMPz8/TJo0CZcvX27t05BVSEd3qGyVuFZUjnNXC+Uuh4iIyKTJHoDWrVuH2bNnY8GCBTh06BBCQ0MRGxuLrKysWvffu3cvJkyYgGnTpuHw4cMYM2YMxowZgxMnTgAAiouLcejQIcyfPx+HDh3Cjz/+iJSUFIwePbotT6vN2dsq9dPh5/14HKUVXBWaiIioLgpJ5v6SqKgoREREYPny5QAArVYLf39/zJw5Ey+++OJN+48fPx5FRUXYvHmz/r5bbrkFYWFhWLlyZa2vsX//fkRGRiI1NRWdO3dusKb8/Hyo1Wrk5eXBzc2tmWfW9lIyCvDQyr3IL61ETLA3Vv5jAGxtZM+4REREbaIpn9+yfjqWl5fj4MGDiImJ0d+nVCoRExODxMTEWp+TmJhosD8AxMbG1rk/AOTl5UGhUMDd3b3Wx8vKypCfn29wM0e9fFzx6eQIqGyViE/OxCsbT3A8EBERUS1kDUDZ2dnQaDTw9vY2uN/b2xsZGRm1PicjI6NJ+5eWlmLu3LmYMGFCnWlw0aJFUKvV+pu/v38zzsY0RHZtjw8n9IdSAXy7Px1L407LXRIREZHJsej+kYqKCowbNw6SJOGjjz6qc7958+YhLy9Pf0tPT2/DKo3v7j4+eHNsCADgwx1n8dneC/IWREREZGJs5XxxT09P2NjYIDMz0+D+zMxM+Pj41PocHx+fRu2vCz+pqanYsWNHvX2BKpUKKpWqmWdhmh6O7IyrBWV4N+40Fv78Fzxc7HFvPz+5yyIiIjIJsrYA2dvbIzw8HAkJCfr7tFotEhISEB0dXetzoqOjDfYHgLi4OIP9deHnzJkziI+Ph4eHR+ucgImbMawHJkV3gSQB/1p3BHvOZstdEhERkUmQvQts9uzZWLVqFT777DMkJyfj6aefRlFREaZOnQoAmDRpEubNm6ff/7nnnsO2bdvw7rvv4tSpU1i4cCEOHDiAGTNmABDh58EHH8SBAwfw1VdfQaPRICMjAxkZGSgvL5flHOWiUCiwYFQfjAzxRYVGwpNfHMSJS3lyl0VERCQ7WbvAADGt/erVq3j11VeRkZGBsLAwbNu2TT/QOS0tDUpldU4bNGgQvv76a7zyyit46aWXEBgYiI0bN6Jv374AgEuXLmHTpk0AgLCwMIPX2rlzJ26//fY2OS9TYaNUYOn4UFwvLsfec9cwZU0Sfnh6ELp4OMtdGhERkWxkXwfIFJnrOkD1KSitwPiP9+HklXx0bu+EH54ehA6uljXuiYiIrJvZrANEbcfVwQ5rH4tA5/ZOSMspxpQ1SSgorZC7LCIiIlkwAFkRL1cHfP5YJDxd7PHX5Xw8+cVBlFXykhlERGR9GICsTICnM9ZOjYSzvQ32nruG2d8dhVbLXlAiIrIuDEBWqG9HNT5+dCDsbBTYcuwKXvv5L14yg4iIrAoDkJW6NdATS8eFQaEAPktMxX93nZO7JCIiojbDAGTFRoX6YcG9vQEAi7en4NukNJkrIiIiahsMQFZuyuCumH5HdwDASxuOI+5kZgPPICIiMn8MQIQ5d/fCuIGdoJWAGV8fwv4LOXKXRERE1KoYgAgKhQJvjg1BTLAXyiq1mLZ2P1IyCuQuy2gqNFr8+fc1rN1zHgdTr0PDWW9ERFaPK0HXwhJXgm6MknINHv3fnziQeh3ebir88PQgdGrnJHdZzZKeU4zdp6/it9NXsffcNRSWVeof83Sxx7AgL8QEe+O2wA5wtLeRsVIiIjKWpnx+MwDVwloDEADkFpfjoZWJOJNViG4dnPH9U4PQ3tle7rIaVFKuwb6/r+lDz9/ZRQaPt3e2Rx8/NxxJy0VBjTCkslXitkBPxAR7485gb14ehIjIjDEAtZA1ByAAuJJXggf+uxeX80oR5u+Orx+PgpO97NfNNSBJEk5nFmL36Sz8djobSRdyUF6p1T9uo1RgQGd3DO3ZAUN6dkBfPzWUSgXKK7XYfyEHcSczEXcyE5dyS/TPUSiAMH93xAR7467e3gj0coFCoZDj9IiIqBkYgFrI2gMQAJzNKsCDKxORW1yB23t1wKpJA2FnI++QsdzicvxxNhu/nb6K305nIyO/1ODxju6OGNKzA4b29MSgHp5wc7Cr93iSJOFURgHiT2YiPjkTRy/mGTzexcMJMcHeiAn2RkRAO9jKfP5ERFQ/BqAWYgASDqZex8RP96G0Qov7+3fEkodCoVS2XYuIRivh6MVc/Hb6Knafvoqj6bmoOX5ZZavELd089K083Ts4t6jFJjO/FPHJmYg/mYk9564ZtCipHe1wR68OiOntjaE9O8C1gXBFRERtjwGohRiAqu04lYnHPz8IjVbCk0O6Yd49wa36epn5pdhdFXj+OJONvBLDK9YHernoA09k1/ZwsGudAcxFZZX4/Uw24k5mYsepTFwvrq7DzkaBW7p54K7eYtxQR3fHVqmBiIiahgGohRiADH1/8CLmrD8KAPjHLZ3h5+6ISo2ESq2ESo0WGm31tvha9b1Wa7BPhUaq2ldruE/VdmmFBhevlxi8tquDLW4L9MSQQBF6/GQIGxqthENp1xFfNW7oxgHWvX3dcFdvb9zdxxu9fd04boiISCYMQC3EAHSzlbvP4a1fTrX66ygUQL9O7hga6ImhvTogtJO7yY29OXe1UD9u6GDqdYNuuSAfVzwY3glj+3eEh4t5zCgrKqtE0oUceLs6IMjHtU27OYmIjIkBqIUYgG4mSRLWH7yIfeeuwdZGARulEnY2CtgoFbBVKmBroxRflcqqx6vur/lY1VcbpaLquWLfms/r3sHFLKbd61wrLMOOU1mIO5mJXaev6scN2SoVGBbkhYcG+uP2Xh1kH0B+o9IKDXalZOHno1eQcCoTpRWi7vbO9oju5oHo7h4Y3MMTAR5ObNEiIrPBANRCDEDUHHnFFdh07DK+P5BuMKPM00WFsf398NBAf/T0dpWtvvJKLf44exU/H72CuJOZBotDdnR3xPXichSXawye46t2wKDunhjU3QODenjAV83xTkRkuhiAWogBiFoqJaMA3x9Mx4bDl5BdWK6/P7STGg8N9MeoUD+oHVt/JlmlRot9f+fg56OXse2vDINB5R3dHXFvP1+MCvVDHz83VGolHE3Pxd5z17DnbDYOp+WiXKM1OF43T2d969At3TzMqrWOiCwfA1ALMQCRsVRotNh5KgvrD17EzlNZqKwaMKSyVSK2jw8eGtgJg7t7GnXcjVYr4UDqdWw+dhlbj18xCGAdXFUYGSJCT39/93pft6Rcg4Op17HnXDb2nruG4xcNlyEAgGBfNwyuah2K7OoBF5VpLZhJRNaFAaiFGICoNWQXlmHj4UtYf+AiUjKrLzbrp3bAA+Gd8GB4J3TxcG7WsSVJwtGLedh89DI2H7tisEhkOyc7jAjxxah+fojs2h42zQxbeSUVSDqfg73nsrH37DWDcwDE6tuhndSiy6yHBwZ0btdqyxQQEdWGAaiFGICoNUmShOOX8rD+wEX8dOQS8kurx+JEdW2Phwb6454QnwYvPyJJEpKvFGDzscv4+dhlpOdULyHgqrJFbF8f3NvPF4N7eLbKIOyrBWVI/PsaEs9lY8/Za0jLKTZ43N5WiYFd2mFwD09EdW2PTu2c4OFib3IDwonIcjAAtRADELWV0goNfj2ZifUH0vHH2Wzofhud7W0wsp8vHhroj4Fd2hnMxDqbVShCz9HLOHe1ek0iRzsbxPT2xqh+vhjSs0Obt76k5xQj8e9r2HtWdJllFZTVul87Jzt0cFWJm4tKv+1ZY7uDiwrtnOw5JZ+ImoQBqIUYgEgOl3NL8OOhi/j+4EVcuFbdmtLV0xkPhneCQgH8fPQKkq/k6x+zt1Xijl4dMCrUD8OCvEzmorWSJOHc1ULsPXcNe89ew5H0XGQXlunHQDWGjVIBTxf76nBUMyDVCE+eriq4qmw5XZ+IGIBaigGI5CRJEvZfuI71B9Kx5fiVm6am2yoVuC3QE/f288NdfbwbvOirqdBqJeSWVOBqQZm4FZbqt7MLy2vcX4acovKGD1hDe2d73N3bG/eE+CK6uwe72YhkJFb+16Jco0VFpRYVmhrfa7SoqJRQrtHC202FTu2cjPraDEAtxABEpqKorBJbjl/Bz0cvAwDuCfHF8D4+aGfh088rNFpcKyxHdmGZQTCquZ1dtV1QYz0jAHB3stOHodYa/0RkCSRJQm5xhf53K6vA8I+SknKNPrSUV1aFl1rCjO573T6NbeidcUcPzIntZdRzYgBqIQYgIvNRUq7BobTr2HL8CrafyMC1Gq1HakfDMGRvyzBExidJEvJLK5GZX4qMvFJkVH0tKK2Ao70tnO1t4KSyhYvKBk72tnC2t4WzygbOKls42dvARWULJ3tbo/18llZoqgKN7o+G0pv+iMgqKEN2YRkqNK0fAexsFLCzUepv9jYK2NkqMTGqM54Y0t2or8UA1EIMQETmqVKjRdKFHGw9fgXbTmQiu7B6ILabgy3u6u2Dkf18MLiHJ1S2nKJPDavUaJFdWI4reSU1Ak7ZTWGnpELT8MEaYGejgLNKBCQnexGQnFU2VYHJMCw5q2xQrtFWt4rWuN3YKtoQdyc7/Zg6rxqTEpxVtrC3UcLOVlEjvOiCjAgx9jZK2NtW36d/3LZqH6WyTSczMAC1EAMQkfnTaCXsrwpDv5zIwNUas9JcHWxxV7BoGbqtJ8OQNZIkCUXlGmTmlyJTF2R0oSavVAScfNFy0tguHbWjHXzVDvB2c4CPmwPcnexQUqFBYVkliss0KCqvRFFZJYrLq+4r16CorBJlldqGD95E9rZKfZjx0k8ccDCYSODlqoKHi71F/fwzALUQAxCRZdFoJRyoEYZqTtF3Vdkiprc3RvT1aZXlA0orNLiUW4JL10tq/ZqZXwp3Jzt0bOeETu6O6NjOER3dq27txM2UBrqXlGv0g9iz8stQUqGBytYGKlslVHZKg217GyVUdlXf24rH7GwULZ6xJ0kSSio0KCitREFpBfJLK/XbBaWVyC+pMPy+xnZBme6xSmgamWxslQp4uargrRbBxqfGV13Y8XZzgKN98352KjRafRgqLq9EUZnYLirXfRUBqrDq8cKqx21tFPBydTCYGenlJratdWYkA1ALMQARWS6tVsLBtOvYcuwKfjlxBZn51WHIRWWLO4O9cE+IL4Y2MgzllVTUCDXF4muNgFPzUiTN5epgi07tnNDR3RGd2tUIR1VfPZztW/Rhp9FKyCkq148TycovrTEwtmpQbNV2YRO7V26kUEAEI9ubw9GNocneVonySq0IOSXV4aWwtLJJSyrUx1Vlqw823m4O8FGr9Nu+akd4q1XwdFZxTSozwQDUQgxARNZBq5VwOP06thzLwC8nruBKXvUlRJztbTAs2BsjQ3zg7eZQZytOY8ZbONvbGASWju5O+u991A64XlSuP97F6yW4lFus//56cUWDx3ewU8KvqtWoUzsng5DUzske14tFuKkt2FwtKMO1ovJGt4YA4lp2Xm6ixcFZZYuySq24VWhQrtuu1KCsQouyqtlBrUGpAFwd7ODqYKv/6la17VbjvuqvYlvtWH2fqaydRcbBANRCDEBE1keEoVz8UtVNdim3pOEnVWnvbG/YbVXja6d2jlA72jW7haaorBKXc0UwumgQvkRIyioogzH+F1coAA9ne/2K3AZdKzXHkTSje0WrFeu+GASjSm1VWNIYBCjddnmlFqUVGtjbKuHmaBhudEHG2d7GKrt5qG4MQC3EAERk3SRJwpH0XGw9fgVxJzNRVqmtM9z4uTvK2opQVqlBRl6paDkyCEkiIOUWVaC9i/1Ns3yqg40IOu2deZ02Mn8MQC3EAERERGR+mvL5zbhPREREVocBiIiIiKwOAxARERFZHQYgIiIisjoMQERERGR1GICIiIjI6jAAERERkdVhACIiIiKrwwBEREREVocBiIiIiKwOAxARERFZHQYgIiIisjoMQERERGR1GICIiIjI6tjKXYApkiQJAJCfny9zJURERNRYus9t3ed4fRiAalFQUAAA8Pf3l7kSIiIiaqqCggKo1ep691FIjYlJVkar1eLy5ctwdXWFQqEw6rHz8/Ph7++P9PR0uLm5GfXYpobnarms6Xx5rpbLms7XWs5VkiQUFBTAz88PSmX9o3zYAlQLpVKJTp06tepruLm5WfQPYU08V8tlTefLc7Vc1nS+1nCuDbX86HAQNBEREVkdBiAiIiKyOgxAbUylUmHBggVQqVRyl9LqeK6Wy5rOl+dquazpfK3pXBuLg6CJiIjI6rAFiIiIiKwOAxARERFZHQYgIiIisjoMQERERGR1GIBawYoVKxAQEAAHBwdERUUhKSmp3v3Xr1+PoKAgODg4ICQkBFu3bm2jSptv0aJFiIiIgKurK7y8vDBmzBikpKTU+5y1a9dCoVAY3BwcHNqo4uZbuHDhTXUHBQXV+xxzfE91AgICbjpfhUKB6dOn17q/Ob2vv/32G0aNGgU/Pz8oFAps3LjR4HFJkvDqq6/C19cXjo6OiImJwZkzZxo8blN/59tCfedaUVGBuXPnIiQkBM7OzvDz88OkSZNw+fLleo/ZnN+FttLQeztlypSbah8+fHiDxzW39xZArb+/CoUCixcvrvOYpvzethYGICNbt24dZs+ejQULFuDQoUMIDQ1FbGwssrKyat1/7969mDBhAqZNm4bDhw9jzJgxGDNmDE6cONHGlTfN7t27MX36dOzbtw9xcXGoqKjA3XffjaKionqf5+bmhitXruhvqampbVRxy/Tp08eg7j/++KPOfc31PdXZv3+/wbnGxcUBAB566KE6n2Mu72tRURFCQ0OxYsWKWh9/55138MEHH2DlypX4888/4ezsjNjYWJSWltZ5zKb+zreV+s61uLgYhw4dwvz583Ho0CH8+OOPSElJwejRoxs8blN+F9pSQ+8tAAwfPtyg9m+++abeY5rjewvA4ByvXLmC1atXQ6FQ4IEHHqj3uKb63rYaiYwqMjJSmj59uv57jUYj+fn5SYsWLap1/3HjxkkjR440uC8qKkp68sknW7VOY8vKypIASLt3765znzVr1khqtbrtijKSBQsWSKGhoY3e31LeU53nnntO6t69u6TVamt93FzfVwDShg0b9N9rtVrJx8dHWrx4sf6+3NxcSaVSSd98802dx2nq77wcbjzX2iQlJUkApNTU1Dr3aervglxqO9/JkydL9913X5OOYynv7X333ScNGzas3n3M5b01JrYAGVF5eTkOHjyImJgY/X1KpRIxMTFITEys9TmJiYkG+wNAbGxsnfubqry8PABA+/bt692vsLAQXbp0gb+/P+677z789ddfbVFei505cwZ+fn7o1q0bJk6ciLS0tDr3tZT3FBA/019++SUee+yxei8MbK7va03nz59HRkaGwXunVqsRFRVV53vXnN95U5WXlweFQgF3d/d692vK74Kp2bVrF7y8vNCrVy88/fTTuHbtWp37Wsp7m5mZiS1btmDatGkN7mvO721zMAAZUXZ2NjQaDby9vQ3u9/b2RkZGRq3PycjIaNL+pkir1WLWrFkYPHgw+vbtW+d+vXr1wurVq/HTTz/hyy+/hFarxaBBg3Dx4sU2rLbpoqKisHbtWmzbtg0fffQRzp8/j9tuuw0FBQW17m8J76nOxo0bkZubiylTptS5j7m+rzfSvT9Nee+a8ztvikpLSzF37lxMmDCh3gtlNvV3wZQMHz4cn3/+ORISEvD2229j9+7dGDFiBDQaTa37W8p7+9lnn8HV1RX3339/vfuZ83vbXLwaPLXY9OnTceLEiQb7i6OjoxEdHa3/ftCgQQgODsbHH3+MN954o7XLbLYRI0bot/v164eoqCh06dIF3333XaP+qjJn//vf/zBixAj4+fnVuY+5vq8kVFRUYNy4cZAkCR999FG9+5rz78LDDz+s3w4JCUG/fv3QvXt37Nq1C3feeaeMlbWu1atXY+LEiQ1OTDDn97a52AJkRJ6enrCxsUFmZqbB/ZmZmfDx8an1OT4+Pk3a39TMmDEDmzdvxs6dO9GpU6cmPdfOzg79+/fH2bNnW6m61uHu7o6ePXvWWbe5v6c6qampiI+Pxz//+c8mPc9c31fd+9OU9645v/OmRBd+UlNTERcXV2/rT20a+l0wZd26dYOnp2edtZv7ewsAv//+O1JSUpr8OwyY93vbWAxARmRvb4/w8HAkJCTo79NqtUhISDD4C7mm6Ohog/0BIC4urs79TYUkSZgxYwY2bNiAHTt2oGvXrk0+hkajwfHjx+Hr69sKFbaewsJCnDt3rs66zfU9vdGaNWvg5eWFkSNHNul55vq+du3aFT4+PgbvXX5+Pv78888637vm/M6bCl34OXPmDOLj4+Hh4dHkYzT0u2DKLl68iGvXrtVZuzm/tzr/+9//EB4ejtDQ0CY/15zf20aTexS2pfn2228llUolrV27Vjp58qT0xBNPSO7u7lJGRoYkSZL06KOPSi+++KJ+/z179ki2trbSkiVLpOTkZGnBggWSnZ2ddPz4cblOoVGefvppSa1WS7t27ZKuXLmivxUXF+v3ufFcX3vtNWn79u3SuXPnpIMHD0oPP/yw5ODgIP31119ynEKjPf/889KuXbuk8+fPS3v27JFiYmIkT09PKSsrS5Iky3lPa9JoNFLnzp2luXPn3vSYOb+vBQUF0uHDh6XDhw9LAKSlS5dKhw8f1s98euuttyR3d3fpp59+ko4dOybdd999UteuXaWSkhL9MYYNGyZ9+OGH+u8b+p2XS33nWl5eLo0ePVrq1KmTdOTIEYPf4bKyMv0xbjzXhn4X5FTf+RYUFEhz5syREhMTpfPnz0vx8fHSgAEDpMDAQKm0tFR/DEt4b3Xy8vIkJycn6aOPPqr1GOb03rYWBqBW8OGHH0qdO3eW7O3tpcjISGnfvn36x4YOHSpNnjzZYP/vvvtO6tmzp2Rvby/16dNH2rJlSxtX3HQAar2tWbNGv8+N5zpr1iz9v4u3t7d0zz33SIcOHWr74pto/Pjxkq+vr2Rvby917NhRGj9+vHT27Fn945bynta0fft2CYCUkpJy02Pm/L7u3Lmz1p9b3flotVpp/vz5kre3t6RSqaQ777zzpn+DLl26SAsWLDC4r77febnUd67nz5+v83d4586d+mPceK4N/S7Iqb7zLS4ulu6++26pQ4cOkp2dndSlSxfp8ccfvynIWMJ7q/Pxxx9Ljo6OUm5ubq3HMKf3trUoJEmSWrWJiYiIiMjEcAwQERERWR0GICIiIrI6DEBERERkdRiAiIiIyOowABEREZHVYQAiIiIiq8MARERERFaHAYiIqBEUCgU2btwodxlEZCQMQERk8qZMmQKFQnHTbfjw4XKXRkRmylbuAoiIGmP48OFYs2aNwX0qlUqmaojI3LEFiIjMgkqlgo+Pj8GtXbt2AET31EcffYQRI0bA0dER3bp1w/fff2/w/OPHj2PYsGFwdHSEh4cHnnjiCRQWFhrss3r1avTp0wcqlQq+vr6YMWOGwePZ2dkYO3YsnJycEBgYiE2bNrXuSRNRq2EAIiKLMH/+fDzwwAM4evQoJk6ciIcffhjJyckAgKKiIsTGxqJdu3bYv38/1q9fj/j4eIOA89FHH2H69Ol44okncPz4cWzatAk9evQweI3XXnsN48aNw7Fjx3DPPfdg4sSJyMnJadPzJCIjkftqrEREDZk8ebJkY2MjOTs7G9z+85//SJIkSQCkp556yuA5UVFR0tNPPy1JkiR98sknUrt27aTCwkL941u2bJGUSqX+iuB+fn7Syy+/XGcNAKRXXnlF/31hYaEEQPrll1+Mdp5E1HY4BoiIzMIdd9yBjz76yOC+9u3b67ejo6MNHouOjsaRI0cAAMnJyQgNDYWzs7P+8cGDB0Or1SIlJQUKhQKXL1/GnXfeWW8N/fr10287OzvDzc0NWVlZzT0lIpIRAxARmQVnZ+ebuqSMxdHRsVH72dnZGXyvUCig1WpboyQiamUcA0REFmHfvn03fR8cHAwACA4OxtGjR1FUVKR/fM+ePVAqlejVqxdcXV0REBCAhISENq2ZiOTDFiAiMgtlZWXIyMgwuM/W1haenp4AgPXr12PgwIG49dZb8dVXXyEpKQn/+9//AAATJ07EggULMHnyZCxcuBBXr17FzJkz8eijj8Lb2xsAsHDhQjz11FPw8vLCiBEjUFBQgD179mDmzJlte6JE1CYYgIjILGzbtg2+vr4G9/Xq1QunTp0CIGZoffvtt3jmmWfg6+uLb775Br179wYAODk5Yfv27XjuuecQEREBJycnPPDAA1i6dKn+WJMnT0ZpaSnee+89zJkzB56ennjwwQfb7gSJqE0pJEmS5C6CiKglFAoFNmzYgDFjxshdChGZCY4BIiIiIqvDAERERERWh2OAiMjssSefiJqKLUBERERkdRiAiIiIyOowABEREZHVYQAiIiIiq8MARERERFaHAYiIiIisDgMQERERWR0GICIiIrI6DEBERERkdf4f63fpGxBqAjsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_pred = model.predict(X_train)\n",
        "train_mae_loss = np.mean(np.abs(X_train_pred - X_train), axis=1)\n",
        "\n",
        "plt.hist(train_mae_loss, bins=50)\n",
        "plt.xlabel('Train MAE loss')\n",
        "plt.ylabel('Number of Samples');\n",
        "\n",
        "# Set reconstruction error threshold\n",
        "threshold = np.max(train_mae_loss)\n",
        "\n",
        "print('Reconstruction error threshold:',threshold)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "ziXTLXWfsoXG",
        "outputId": "b52b6f65-9599-4826-93a9-8bdf4b20abca"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "128/128 [==============================] - 2s 5ms/step\n",
            "Reconstruction error threshold: 0.5572234220388699\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_pred = model.predict(X_test, verbose=1)\n",
        "test_mae_loss = np.mean(np.abs(X_test_pred-X_test), axis=1)\n",
        "\n",
        "plt.hist(test_mae_loss, bins=50)\n",
        "plt.xlabel('Test MAE loss')\n",
        "plt.ylabel('Number of samples')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "0zBgfTWQs0f4",
        "outputId": "078dc180-243e-43ee-c096-f14e2e9110d3"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/18 [==============================] - 0s 4ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of samples')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "anomaly_df = pd.DataFrame(test[TIME_STEPS:])\n",
        "anomaly_df['loss'] = test_mae_loss\n",
        "anomaly_df['threshold'] = threshold\n",
        "anomaly_df['anomaly'] = anomaly_df['loss'] > anomaly_df['threshold']"
      ],
      "metadata": {
        "id": "yM1wdMXps4tO"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "anomaly_df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CKtlLtp9uIfB",
        "outputId": "9249b6a7-b406-4064-f779-ac5e0bc6c61c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Date     Close      loss  threshold  anomaly\n",
              "4148  2021-02-10  3.806055  0.371511   0.557223    False\n",
              "4149  2021-02-11  3.807311  0.394533   0.557223    False\n",
              "4150  2021-02-12  3.827547  0.416520   0.557223    False\n",
              "4151  2021-02-16  3.871345  0.444111   0.557223    False\n",
              "4152  2021-02-17  3.887125  0.463349   0.557223    False"
            ],
            "text/html": [
              "\n",
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          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(x=anomaly_df['Date'], y=anomaly_df['loss'], name='Test loss'))\n",
        "fig.add_trace(go.Scatter(x=anomaly_df['Date'], y=anomaly_df['threshold'], name='Threshold'))\n",
        "fig.update_layout(showlegend=True, title='Test loss vs. Threshold')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "fNwwm_BTuKwf",
        "outputId": "2bb757a8-c4c7-404c-8c3b-8f8714a5c9ac"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
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        "anomalies = anomaly_df.loc[anomaly_df['anomaly'] == True]\n",
        "anomalies.head()"
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              "            Date     Close      loss  threshold  anomaly\n",
              "4156  2021-02-23  3.745689  0.587040   0.557223     True\n",
              "4157  2021-02-24  3.805538  0.618225   0.557223     True\n",
              "4158  2021-02-25  3.648445  0.639306   0.557223     True\n",
              "4159  2021-02-26  3.661985  0.684974   0.557223     True\n",
              "4160  2021-03-01  3.771909  0.718012   0.557223     True"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(519, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(x=anomaly_df[['Date']], y=scaler.inverse_transform(anomaly_df[['Close']]), name='Close price'))\n",
        "fig.add_trace(go.Scatter(x=anomalies[['Date']], y=scaler.inverse_transform(anomalies[['Close']]), mode='markers', name='Anomaly'))\n",
        "fig.update_layout(showlegend=True, title='Detected anomalies')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Wvk2_SzSubqy",
        "outputId": "e32fa3a9-e0cc-4602-c690-ec67bf4c9fe3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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