[MODELS] Cleanup Jupyter Notebooks.

[thoth.git] / models / failure_prediction / jnotebooks / LSTM_Attention.ipynb

diff --git a/models/failure_prediction/jnotebooks/LSTM_Attention.ipynb b/models/failure_prediction/jnotebooks/LSTM_Attention.ipynb
index 9959bd7..ea2d34f 100644 (file)
--- a/models/failure_prediction/jnotebooks/LSTM_Attention.ipynb
+++ b/models/failure_prediction/jnotebooks/LSTM_Attention.ipynb
@@ -1,1658 +1,885 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oQvr9zfcPRi-"
+   },
+   "source": [
+    "Contributors: Rohit Singh Rathaur, Girish L.\n",
+    "\n",
+    "Copyright 2021 [Rohit Singh Rathaur, BIT Mesra and Girish L., CIT GUBBI, Karnataka]\n",
+    "\n",
+    "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
+    "\n",
+    "http://www.apache.org/licenses/LICENSE-2.0\n",
+    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
     "colab": {
-      "name": "LSTM_Attention.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "YQ6lT1e2hrx4",
+    "outputId": "cb071d50-8fcb-426d-8fa9-6f7033f783d1"
+   },
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
   },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "oQvr9zfcPRi-"
-      },
-      "source": [
-        "Contributors: Rohit Singh Rathaur, Girish L.\n",
-        "\n",
-        "Copyright 2021 [Rohit Singh Rathaur, BIT Mesra and Girish L., CIT GUBBI, Karnataka]\n",
-        "\n",
-        "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
-        "\n",
-        "http://www.apache.org/licenses/LICENSE-2.0\n",
-        "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "YQ6lT1e2hrx4",
-        "outputId": "cb071d50-8fcb-426d-8fa9-6f7033f783d1"
-      },
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Mounted at /content/drive\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "tLhroy5BnMnC"
-      },
-      "source": [
-        "# Importing libraries\n",
-        "import tensorflow as tf\n",
-        "import matplotlib.pyplot as plt\n",
-        "import matplotlib as mpl\n",
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "import os"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 521
-        },
-        "id": "2-UpMVsSnfCI",
-        "outputId": "120b11e9-5c7d-4332-d133-9636a8d002aa"
-      },
-      "source": [
-        "df_Ellis  = pd.read_csv(\"/content/drive/MyDrive/LFN Anuket/Analysis/data/Final/Ellis_FinalTwoConditionwithOR.csv\")\n",
-        "df_Ellis"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Timestamp</th>\n",
-              "      <th>ellis-cpu.system_perc</th>\n",
-              "      <th>ellis-cpu.wait_perc</th>\n",
-              "      <th>ellis-load.avg_1_min</th>\n",
-              "      <th>ellis-mem.free_mb</th>\n",
-              "      <th>ellis-net.in_bytes_sec</th>\n",
-              "      <th>ellis-net.out_packets_sec</th>\n",
-              "      <th>Label</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>14-09-2016 0:00</td>\n",
-              "      <td>0.5</td>\n",
-              "      <td>12.9</td>\n",
-              "      <td>1.730</td>\n",
-              "      <td>3949</td>\n",
-              "      <td>5413.200</td>\n",
-              "      <td>62.067</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>14-09-2016 0:00</td>\n",
-              "      <td>0.4</td>\n",
-              "      <td>10.3</td>\n",
-              "      <td>1.790</td>\n",
-              "      <td>3950</td>\n",
-              "      <td>5201.667</td>\n",
-              "      <td>59.567</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>14-09-2016 0:01</td>\n",
-              "      <td>0.4</td>\n",
-              "      <td>11.8</td>\n",
-              "      <td>1.520</td>\n",
-              "      <td>3950</td>\n",
-              "      <td>5370.733</td>\n",
-              "      <td>61.200</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>14-09-2016 0:01</td>\n",
-              "      <td>0.4</td>\n",
-              "      <td>12.9</td>\n",
-              "      <td>1.430</td>\n",
-              "      <td>3949</td>\n",
-              "      <td>5292.467</td>\n",
-              "      <td>60.400</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>14-09-2016 0:02</td>\n",
-              "      <td>0.5</td>\n",
-              "      <td>12.1</td>\n",
-              "      <td>1.440</td>\n",
-              "      <td>3950</td>\n",
-              "      <td>5318.167</td>\n",
-              "      <td>61.700</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>176995</th>\n",
-              "      <td>13-12-2016 21:20</td>\n",
-              "      <td>0.4</td>\n",
-              "      <td>0.3</td>\n",
-              "      <td>0.030</td>\n",
-              "      <td>3484</td>\n",
-              "      <td>230.967</td>\n",
-              "      <td>2.167</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>176996</th>\n",
-              "      <td>13-12-2016 21:20</td>\n",
-              "      <td>0.2</td>\n",
-              "      <td>0.3</td>\n",
-              "      <td>0.018</td>\n",
-              "      <td>3484</td>\n",
-              "      <td>218.433</td>\n",
-              "      <td>0.767</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>176997</th>\n",
-              "      <td>13-12-2016 21:21</td>\n",
-              "      <td>0.6</td>\n",
-              "      <td>0.3</td>\n",
-              "      <td>0.010</td>\n",
-              "      <td>3483</td>\n",
-              "      <td>160.967</td>\n",
-              "      <td>1.867</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>176998</th>\n",
-              "      <td>13-12-2016 21:21</td>\n",
-              "      <td>0.6</td>\n",
-              "      <td>0.3</td>\n",
-              "      <td>0.007</td>\n",
-              "      <td>3484</td>\n",
-              "      <td>188.367</td>\n",
-              "      <td>2.100</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>176999</th>\n",
-              "      <td>13-12-2016 21:22</td>\n",
-              "      <td>0.4</td>\n",
-              "      <td>0.1</td>\n",
-              "      <td>0.040</td>\n",
-              "      <td>3484</td>\n",
-              "      <td>229.833</td>\n",
-              "      <td>2.400</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>177000 rows × 8 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "               Timestamp  ...  Label\n",
-              "0        14-09-2016 0:00  ...      1\n",
-              "1        14-09-2016 0:00  ...      1\n",
-              "2        14-09-2016 0:01  ...      1\n",
-              "3        14-09-2016 0:01  ...      1\n",
-              "4        14-09-2016 0:02  ...      1\n",
-              "...                  ...  ...    ...\n",
-              "176995  13-12-2016 21:20  ...      0\n",
-              "176996  13-12-2016 21:20  ...      0\n",
-              "176997  13-12-2016 21:21  ...      0\n",
-              "176998  13-12-2016 21:21  ...      0\n",
-              "176999  13-12-2016 21:22  ...      0\n",
-              "\n",
-              "[177000 rows x 8 columns]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "92xBt43BnjAo",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 293
-        },
-        "outputId": "2104e011-6066-4e86-e6f8-515dfb9ea715"
-      },
-      "source": [
-        "df_Ellis.plot()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd54812ab50>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 5
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "RSo-aa-SIoBR",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 879
-        },
-        "outputId": "ea97c36d-cb27-4fb3-d9bd-e8f1776b098d"
-      },
-      "source": [
-        "# we show here the hist\n",
-        "df_Ellis.hist(bins=100,figsize=(20,15))\n",
-        "#save_fig(\"attribute_histogram_plots\")\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 1440x1080 with 9 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "gggaMJ_2LtFs",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 634
-        },
-        "outputId": "9e21fcc0-a838-4449-ef31-8e7d1df4d060"
-      },
-      "source": [
-        "cpu_system_perc = df_Ellis[['ellis-cpu.system_perc']] \n",
-        "cpu_system_perc.rolling(12).mean().plot(figsize=(20,10), linewidth=5, fontsize=20) \n",
-        "plt.xlabel('Timestamp', fontsize=30);"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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BUzC1/MBrclWu/rq0XgvrQx/0eZvCdOfJgYdTsw/9zG87ofXMhPaa3ezfMTU+OLMaAnH496okVfZI7hb/ZuEownIfgh/41hUX+eHizLElbtNkvju1XMmxXf+C35Vq8ltXGnRFD4VjBuW3+anYvQuGeJx+1VZPgoRopy6d9m3oER/t0rxhvXTJ1ApV57fdG8PldOjuU+r0m1Pr9JPja/To2SPVIzVOAwvT9N2pFTp5WC+vFxxOh9Hs2nxdNr2vRvqo7vClOCtBtxxbrdtOGKC/nlnv1wVbSZZ7iFJXlO5WDTK4KE1HeAiQ7p5f12rbpEr3Bskt75eVFOPXdCpJimujN88JQwJvaJ6XEqePbpquh5cM0w+O6a8nzhutQb28n6stQ7iUuCgNK87wsrdnRRnxWtTGp+GHXz6n98/Tuptn6KXLJqg4K7Dw45jafKXERWlBfW9dNKVcffO8/3wrcr+tOCvPSfLa/2hGs+kDQ3una3r/wC/A7l0wRKP7eD+f5w4t1BPnjdLUQ1VXaQnR+tOZI/TzEwcqP63zPxhoXgUy0M8eXbMH9vQZ5nVXCTEulWYnqocfF+lDe6e7nwMdfE0/cUihVn4/ONOho10OXX9UP7/3L0iP7/Bj9kyN09JxpVo6rtQtRGrLw6cPb1rcYEF9kV/3yWr2em2M0ZvfmxTQOG+fO7DVQheH/eKkWj194Vjdt7D9U0enVOXq8ul9Na1/ntt7vzmDChRo9rBkdLFumt2/zQv18yaVtXeoYeey6X01okX1jq/+dFLjIiPN37cEk6dzZUBBqq46olLXeXkO3nPqYL/GXZ2f0u5gb93NM3SZh+852uXQBB+9ke6eX+fWazE7KUZLD4WYwWo7IUm3zqnRg6cN1S3HVuvxczt/Sn9LU/u5/2wvnNynzRWQAYnl3IGAFKTH66kLx2jFhu0qSI+zZVVTS5dMrdBJd7/qti1Yn1ROrcrV4+9+0Wp7808ai7MStHbTrlb7eAtZzhhborc+3qaX125RQrRTNxzdX2P6ZOn9L3aoX8/kpjfq/mpeBRfjcmp8hf8rQ3nSkRj1z2eOUHXPFLnamEJ5xfS+uuGf7zV9HR/t1Kwa908YnQ6jnxw/QGeNL9XufQdV1SPFY8g2sTJHd8+v06L7l0lq7Ady1cxKt32umN5Xq7/cofe/2KG0+CjddsIAv8PixBiX6nqladn6r5q2HV5567i6Av3wiVXad/Dbxnv+XAwaYzS0OEND/QhwLp5Sofe/2KEVG7YrOdal207wvwS/+eNdObNS84cXafStz7S6PdvHtDpfavJTPFZfjSvP0jMfbHLbFu1yaM6gbwMzY4y+P6tKF/9xRav7337iQJ07oUx79zeoqkdyu6acxkY5de2sKi2493V9tLn1c/SEwQUqzXaf+pgcG6Ujanqotlea6m9+2u22/LQ4bfhqj9+Pf/URlSrPTdJbH2/TrU980Or2W4+t1hljSrRr7wFV9UjWDf98T/e8uM5tnyum99W84b3cXtPXbNrp9xi6Un1phi6b1lczf/5fr/sY0/jc/s5v39TWXfs87vOXM0eoJj/V7d/cn6mdtxxbrUsOnUsF6XG6d8EQbfx6ryrzkpUS4Cfe50/so7ueX6Nd+w56vL1vnu8m8+0R43Jo74FvX1MCnQ552BE1PdyqqxbW99Z9L61TQ7Oi6aQYl3bsPdD0dVl2oopaVDKmJ0RrzY3TtWLDNqXERSku2qnRtzyj/QctpcRFqTQ7USs2bNPBBktzBhVoWr88vfXxV/Jkar9cGWM0pk+Wz+dSbWGq3vx4m9/fb0p8lG45tkYX/WG5132WjivRHc+skdQYul4ypbzpd9X7103VP1Z8rgbLUpTTKCHapVFlWW2G/5HA1zSTw31nF40q1ozqPH2ydY+q81NkWdKKDduUkRijiT9+rt2P39aHePOG9dKkvjk69Z7X9P4XO+R0GC0YUaSx5Vn66fEDtOSBN7zeNy8lVjfPrlZynEtrNu3U6o07lZEQrSn9cvXQqx+3OaYLD031NMbo4SXDdNLdr+pAg6Xc5Fj96cwRPltITKzM0cTKHH359Tdat3mXagpSg75AypMXjJbTYTSyrHOn4d01b5DHn3OWhxVYE2Jc+v3iYXp21UYtvHdZp47LTm57arVmD+ypKVW5EdF6g+AHCFBslDOiUvX60kxdMb2vfvXCWkU5HTp7fKmqerRvRaaWlo4r9Rj8NA+W5g4p1PWPved2e1uN7JJjo/S7JcO0eedepcRFNb1JaL5qmjcjSjL00potbttm9G/fhYE3De3sJDeuPEu1hb6nti2oL9LHW3frb29/qtyUWF0zq8rjm2tjTKuLck8mVuZo7Y3TtWnnXmUnxbQKdXJTYvX4eaO18etvlJEYE3BwctuJA3XBw2/rnU+3a0jvdN00u3H6SWp8tH45b5Cuf2ylNu/cpzmD8jV3SHCn4GQlxejvZ41s99ibK8yI12kje+vX//2oaduw4vQOrX4nST8/sdbj9luOrdF5D7+lFz9sPF+TYly6/aTaVhfg3kI4Y4z6eFjB7fTRxfrl82ubvo6Pdmq3l4tzSSrKTNDTF45R78v+6fN7aa5napzumFurmx9/T59s3aNzxpcqNT5a1/5jpd/HmD0wXynxUerfM8Vj8NN4jn9bXXXJlApt3rlPT7/3pdISonX73NqmVbCav6YH2icnUDOr87R8wzZt/Hqv6ksz9fT7G33e56gBPXTzMdV+XcAMLc7QsismavPOvYp2OXThI8v19AcbVZqVqF+fMliFGa0rZNq6/sxLidWP5tRoRGmmjqnN15ade5sWHihpUb0WF+XUnv3ez5fDvjO2RFU9kptC5ZZqC9PUOzPBY6DYXjUFqfrZCQN04SPLtfLzrzWiJFPfn+U9TJ7YN0dPvvelh+3Zuv5I9/sVpMfrZycO1K1PfKAd3xzQyUMLtXh0sS7+wwo9v3qT+uQk6UfH1Xh8HKfDaGCz1/bVN0x3e03atfeADjRYPqsVmj/XGxo8n8M5yTE6Y0yJTh7WS2VX/MvttvnD2+5Fd+ygfF3/2Ept272/1W0L63vr4ikVWjquVPsPWK1eh2KjnG4VePBP8/cLeSnuHzb68+GGL74+pDn8+/3rb/bLYUxT2Dm5KlePnzdKU3/6gsf7vXTp+KZj//v80dq4Y6+yEmP0t+Wfeg1+kmJcmt4/z22xk6HFGVp1/TRt3rlXWc3ef7T8XXtY876NOcmxyumkBVI8vXe68ej+uvwv73jc/9QRRSpMj9evXlirz7d/49djPHbOSFX1SNHcoYWtfmanjijyeB+Hw2h8RY4Sop1eQ/XuIjbKoW/2h35Frf+s/FL/WfmlTh1RpGtm+V4kJNwR/ADwafHo4qZeKcGc+tc/P0W/nDdIpx/6RCM2yqG/Lq13u7g5pjZfdz63Vpt37pXU+ImtP6u2tKdnxTkTyvTqR1t18NCb5oL0OJ9lxYHy1Mh3YGGqLppcrl88u0b//XBzq9tdDtNqSXRvXE6Hrjuqn649sipo/1YOh/H5Bqq9K9D1TI3Tw6cPl2VZrcY7riJb4yqyPd4WTMFaPe/SaRVyOYxeWL1ZFXlJunJGpe87HXLxlPJW4cUtx1R7vEiXGkOr3y4a1hRSBOvnc9GUchlj9MLqTSrPSdLlM/rqn+98rqv+9q7bft9rVvnl7bF95SczqvM0ozqv6d/3Nx7eyEuNlWYfbmxdhRNolUlctFM/P3Ggz/PJyzWzV6PKMrV20y59us2/aqWSrETdPre2aRzlV/7LrQqlpUUje+vKmf6fS1Ljc/bwef3rUwf7/J5PHtZLf3v7M7dtxw7K163HVrvdz9nsuJ6cObZEP/rPqjZvP9w0eGJl6+rJIUWNAZwxRvcvHKKb//W+1m/dpalVuaotTNPcFtWnx9d5nhJ67oQy3fbUardt9546WGkJ0frjd0b49Zpy5rgSPbdqo/YfbDwheqTE6t8XjPHaR2lmdQ/NrO7hduw75w1q1+tX859xy55plR6m0ha3mM7rLSB85bIJTWNZftVkXffYSq387GsNL8nQJVM9TyFrLjHG5TH4OTwVKT7aJUX7PIybM8aU6M7n1gR2pwgxpo2ptP54aPFQDS/O0IsfbtHJv3Z/7pw30f9pdp5aGlTkJrf6oOCw5ud744ISjedzWxVOK66Z7PF54vDwmjOyNNNj8HNDkBcQGFuepWdbVNZ6C0jnDi3UgYYG/emNDZKkvQca5HQYTajI1rkT+zRWTNUXac/+g7r5X+9r2bqvNKAwVW9/vE0rP/+66Tg/nFPjFpIurO+tv771adMHMGnxUZrjI0R9/cqJqrzqiYC+1wkV2br7lDqNufVZfbx1d0D3bY8VV09Rnyv/5XvHLnLvS+t08ZRyW6/SKBH8APBTZ114T6nKbbORZlpCtB45fZjue2mddu87qBOGFLTZ46QjhhVn6L4FQ/TnNzcoPSFaC0b2DnqJcHpCtI4e2FN/eetTSY0XUkvHlqq+NFP1pZmyLEt/evNTPfDKei3/ZJum98/VycN6tdmrxpNw683V1njD5XuJcjp02fS+uqwd9z2mNl/3v7xOX37dGHAWZcRrih/9EXz9bPzpxdBclNOhS6dVuPWUmNE/T3c9v7Zp6kjP1Lim5eh9Hcsfvr6HO0+u1cQfP++2rXmD60DPD1/7B9JIdUBBqh44baj+/e4XrUryb57dXys+3d7q09rDiwQcHkduSqzWb/H+Rnv+8CK/x+ONr++5tjBNg3ql6Y1D0y7jo506dURRwD/bxaOLvQY/a2+c3qqU/uXLxmv4TY1T/ib2zXZb4rogPV53nPRtxdv+gw2qKUjV8k8apyglxrh0qpfeOudNLFNmUoxeWbNFVT2TtWhksaJd356P/nxftYVpun/hUP3hjU+UGhetBfVFfjXPbnnsYL9+xUe7dMrwXrrv5fVN284Z734Rf/n0vq2qqW48ur/bWFLio/TDOZ6rkLy55ogqj8dt/rMN1CVTypWbHKPX132lx975vN3HCUdtVRceUdMjKB9KGNM4JWnV9dN093/X6t1Pv9awkgydHIQm9hdM7qP7X17vVuXXVlg1oa/nqfLTDk1T9Je392W9A1wUoi23HFOtOXX5+t+nX+uI2xun184f3kvXHum9SnD+8KI2X6+NMYqPdrkd4+tv9uuu59Zq3ZZdmlKV26pvYml2on63eJgeevVjRbmM5g8v8nlexEe79NzFY3X0/73UNOW3PCdJEyuzm6ZjtnTj7MbXh/MmlumCR7xP6fRXUoxL9y4covteWqe/L3f/UGH+8F6Kdjl0TG2+/vTmhg4/VrC88+n2gPs8hhuCHwDdXnFWor7fxi/bYBpZltnp87ZvObZao8oytW7zLo2tyHabwmWM0bGD8imLjzC5KbH685n1enT5Z4pyOjSrpkfAFS2ejK/IbtXX5JjawM6tjMQY/ek7I/To8s/UYFmaVdOzVY+Blj2HMhOj3RpN+8NT3wKpsaz+0bNGavH9y3TQsvSr+XWdFv5KjRcPOckxTSGcN/cv/La59eg+WW79qipykzStX54G9UrT4//7ounN94lDCpXbovny1UdUeu3JcExtvteqr2ByOozuWzhEf3/7M23asVfT+ud6nAroi7cLMoeRx/4JeSlxfq2gJTUGiQ+eNkR/e/szfbVrn6b1z3ObytecMUbzhvXSvGFtT1/yZXhJhl/ThLva1UdUqa4oXau+3KH60sxWFysjyzI1uChNr6/79nxsTwP3lkaWZWpIUbpeW7dVUuPFZEeP63AYnVrfW6fW99aQl9bp6r+/6/tONlHVI6XpZ9ncj+bUaFY7+095E+1yuK0EFQwxLqfOGl/aVK0aG+XQGWO8L7CRGOPSmWNL9H/PuocPi72s4OXN4KK0Vj20OnIepsRFafuexkq23pkJeuT04U2/j/rnp/j9GtUeybFRushLw/bDagpSVVPg3wIFh/XKSNBfz6zXoys+U2KMS7Nqeig1PkoDCtK0/JNt2t/QoN+9+rHSEqL161PqmqqyZtfmyxjp/Ic9hz83ze6vowf2VMX3Hvd4+6TKHFX3TGl6fR7UK00XTu6jK/7yP63YsE2XTuurEw8t3rFwZFGbwY+nXoaHZSREa0Z1nu5vFoDDN4IfAOhiUU6HZgd48Q3765ka1+ab5vaIjXLq6iOqdMVf35FlNfZrOWdC4G/+c5Jj21zt6pKpFVq+Ybu27tonl8Po+7P6BdwocXJVTqseMYdXTOufn6JXLp/g8X5RzuBWVBhjdO2R/ZqmoHoSF+V0W9EsNsqpBxcN1UtrNmvfAUujyjKVEONSSnyUnjhvtF5as1l5KXGq69W6T9eosixN75+rf77T2O9sQEGqThpaqIL0eA0ual3pV5ge3yml+IkxLs0NQhXAqLJMvbDafcpqR6esHJYUG6WTOxjm2IHDYTyuxHhYbJRTD5z27fk4sizTr2olX2KjnLr/tCF6ec0W7T3QELTjfnt8ey42HOclEL1wch8df9crbtt+fUqd18qY7mjpuFKNLsvSqi93aGhxuvLT2g6qL55SromVOfrbW58qJyVWRw/sGfBCKS6nQ9ceVaULH1muBqtxav8Fh5pC+9K84lpqXPXrhe+O0ytrt2rX3gMaWZZpm9V6CzPiWy3GMqkyp2l11sum9fV4v6MH5uujzbv1s2bTZW87YYCm98/zWcn7Kw+rwfbKSNCDi4a22t5Wv9B+PZN1xYxKPfNB6+blt50wQPWlmfps2x6CnwAR/AAAYGNzhxZqSlWOPt66W1U9Ujo0LcObvnnJeunS8Vr5+dcqyUxsV7VSjKuxB89Ff1yubbv3a3hxhs6e4LsPRYzLqfrSjKZG15Ka3ti215SqXC2/erJ+9tRqj70krj2ydRPI2CjPq/5lJcXoyAHeG9JHOR26Y26tPtq8SwcbLJVmJ7Y57WFyZY7u9tIPqTu4YFKfVsHPOX78OyK4vJ2PwTjuuIrg9r6zO29P5yG903XWuFLd9cJaWZalecOKND7An+2S0cW6y0Ofna7UPz9F/fP9W/TDGKPawjS/Fqtoy9ED8zWuPFtrN+9SVY9kxbj8m9Z82fQKrd20U8s3bFdmYoxun1urGJczaOG0XVwwqY/mDinUph17VdkjudXiF8b47uPnj0fPGtk0le6wy6dXaNHIYm3a6bnq9vDv04yEaBWkx+mTrf6vBhrpCH7gppMXMwEAhEBGYozHZeGDKTbK2eE38xMrc/TGlZO045v9So33v0vsT44boIv+uELLP9mmul5p+sEx1R0ah9RY/v+9mZW6fHpf/WPFZ/rFs2u0Zdc+HV9XEPSpmMYYFWd5nrbU0tR+ua2Cn/J2TMvqLAML03TT7P6687k1arAsnTm21G3VKsAbTxVudmaM0UVTyrV0XKksWY3NsQN0waQ+2rprn/74RuspM+2ZrhlOUuOjVVsYWDfx7KRY/e2skdq6a5/S4qPCpodgKOSmxLaamnyYkRSMS8b++Sm67YQBuu4fK+V0GC0eVdxUXezrn8YYo9+cMliX/vmdpv50aBvBD3ziJREA0FWcDhNQ6CM1roJ0/8IhnTaeIwf0bLNqpyvVFqapX89k/e/TxpVgjJFfKzJ1pROHFDY1sQb85W/4GW58rcoZF2AT/uZio5z64ZwaVeenuK2+OK1fbrtWN40U6QkBLj+HTuPt92tGQoyiXQ7ta9ajsGeq+7TAspwk/ek7I7Rs3VYde+fLXh9jXHmW1m/drbWbdgVv4GGI4CcCbdm5V/HRrg79ogEAAF3P4TB6YOFQ3fPSOn2+bY9mVOdpbDlTb4DuamRp5y4YITWuKJUSF6XnPtikspwkLfCy4h0QDCZYc73a4HQYnT2u1G2lyJY9iw4bWJjm1tC+pQZmtEgi+Iko3+w/qHN//5aeePdLRbscumRKeZvNOgEAQPeTlhDtdzNTAKHlb/+ZjupOlYmwt5r8FL358Ta3bVU9gr/a5lnjS9WvZ4pWbNiuwb3TNKLEc4jqdBjdu2CIHnvnc13yxxWtbi/OStCGr4K/KEK4sWf7fHj08Ouf6Il3v5Qk7TvQoOsfe0/rt0R2yRsAAAAAwD8XT6lote3Saa23dZQxRuMqsnXuxDKvoc9hCTEuHVdXoBuP7t/qtoX1vX0/VrtHGT4IfiLI1X9/t9W2u1/oviuDAAAAAAC6j2HF6TpnQpniopyKjXJo6biSLpnS6I/j6vI1Z1C+opxGSbEu3XB0PxWkx4d6WN0CU70iHGVvAAAA6A7qeqVpWYsVem6a3V+X/fmdEI0IQEvGGF0wqY/OOrQiXVdNZ/SHy+nQrXNqdO2R/RTlNHI5qXM5jJ8EAAAAgJA7Z0KZHM3mXPTvmaLj6wr0xzOGh25QADyKdjm6VejTXFy0k9CnBSp+AAAAAITc6D5ZevC0ofrHO58rLzlW84b3ksNhNKAgVUN6p+u1j7aGeogBs8SSQkCoGRMJXXzaRvADAAAAoFsYUZqpES36hbicDt27YLAqr3oiRKMCYGeREM9S/wSfCEgBAAAQSvHRLv3j7JFKT4gO9VAAIOxQ8QMAAACg2+vXM0WvXj5Bazft0qovd+js370V6iEBQFig4gcAAABAWIhyOlSem6RoV3hcxhSylDTQ7UXCBJfweMUEAAAAgDATH80ECwChR/ADAAAAAABsKRIqenwh+AEAAAAAALApgh8AAAAAEeu+hUNCPQQA6FQEP3BjWVaohwAAAAB0mTF9skI9BADoVAQ/8Ik5kQAAAAAAhCeCnwhHfQ8AAAAAIFIZY/9SB4IfAAAAAGHF/pdpAIIlAnIdn/wKfowxxxpjfm6MecEY87UxxjLGPNjZg0Pn4zkAAAAAAIB9ufzc70pJNZJ2StogqaLTRgQAAAAAANAFImGBI3+nep0vqY+kZEnf6bzhAAAAAAAAIFj8Cn4sy3rGsqzVViREYQAAAAAiyrxhvUI9BADoNOH1xZEAACAASURBVDR3BgAAABBWYqOcQT3e/OEEPwDsi+AnwrUs4aKkCwAAAN3dsOIMxUcHL/wpy0kK2rEAdC+GJY0IfuCbYf07AAAAdCPRLoeuPqKyWy/TXF+aEeohAPBDJFzvdnrwY4xZYoxZZoxZtmnTps5+OATI/qc4AAAA7Oj4wYV67fKJbe6TFOPS/51U2+HHGlCQ6td+ucmxkqSijHhdf1T/Dj8uAASDv8u5t5tlWXdJukuS6urqIm4mkWVZ+uMbG/TC6s0qz03SaSN7B31OMgAAABCJspJiPG6//qh+mtovV6lxUXI5O/5Zd25yrFZdP02nP7BMr6/7Sjv3HvC438uXjdemHXuVlRQTEVUEAMJDpwc/ke7X//1I1z/2XuMXy6WVn32tO4LwqQMAAAAA7zITvw2FFo/qrV+98FGHjhftcuieBUNkWZYqvve49h5ocLs9xuWQMUbZh6p+AKC7oMdPJ2sKfQ557J3PtW33vhCNBgAAAIg8l07rq/Mn9mm1fVZNj4CPZYzRLcdWt9ruaRuA0KP4juAnJD7avCvUQ2gScXPvAAAAYHst3+M6HUbnTizT6WOKm7Ylxbq0eNS3X8cF0I5hQt8c1eSnNH1dk5+iCX1z2j1eAOhMfk31MsYcJemoQ1/mHvpzuDHm3kN/32xZ1kVBHhsAAAAABM2lUys0tSpX67fs1oiSDLdpWTcc3U8XPLLcr+Mkxrj0+yXD9dKazZKkESWZigvi8vIAEEz+9vgZIOmUFtuKD/0nSeslEfzYgEUJEAAAAGzKGKOBhWkaWJjW6rYjB/TUq2u36uFln/h1rLhoJ1U+AMKCX1O9LMu6xrIs08Z/RZ08ToQQcyIBAADQXRWkx7XaNrw4PeDjOB1GP6BPDxBxIuF6lx4/ES4CznEAAADY2CVTKty+HlacrtLspHYf75ThvVptWziyd7uPB6B7i4RZLyznHgJ79h8M9RAAAAAAWziipodio5x64t0vVJgerwX1RR063snDeukvb32qr785IEkaXJSm2sLUIIwUAEKD4CcE5v7qVV0xva8Wjy72vTM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hc5LGqnpMjXqxcAPpmI/Jt6Q2U6bWcW1HzFnf+GSeT4mIVyuWu+2xKp3UtjimVi31roUAdlIaR+rLko6oM/ZFK8f1XcDawE35Ovs1+Tp8ev5aHIfI1+r9oxPOTa3Em/WNo/P8h3XyuO1p0Zr9XYEBYts8f6DG8geBfUnPAs6okccGAElrAkfmr1UnpvflqVhmFjAxIh4ppA0D3kga62V+xXoezPNtCmlbkp6pfzgiqi60qso0E5vlMtZ7NgV+UkqbI+moiJhdSKt5nCLiFUlzgLeRnmm+r4ky8yUtAraQtE5ELG5jzFkvk7Q2cDjwKnBujWxue6xKJ7UtjqlVhKQ3A+8h/Yf7hhrZPlf6/qqkc4ETImJJIb2V49pXZXyt3vs64dzktmcVIGl30t3PD0TE9XWyuu1pke/4ac7wPH+hxvKu9A3aUBfrW6cD2wNXR8T0Qvpi4OvAzqRxEDYkPQN/Pel2xhn5pNallZhpVxnrHeeRLow3BYaRTlbnkG5/vkbSjoW8fXlsh5fmjp+B52OkfXxtRDxaWua2x+rppLbFMbUKyHeH/RQYApwSEc+VsswhPY6xLenctzmpDZsLHAP8uJTf8dOZOunc5NhZNXTd+fyjGsvd9vSQO37MMknHk9408FfSiPKviYinI+JrEXFHRDyfpxtIPbi3AFsB/9b2Slu/iYhTI2JmRDwVEYsj4p6IOJY0kNvawCn9W0MbQLouds4pL3DbY2btkh9V/gnpDTgXA98q54mI2RFxVkQ8kM998yPiUtLjDs8Bh5Z++LAO5HOT9SZJw0mdOMuAqVV53Pb0nDt+mlP+9ausK/35NtTF+kB+te13Sa80HRsRC5spl2897Xo0492FRa3ETLvKWN86O8/bFQ8vlOaOnwFE0ttIYxg8BlzdbDm3PZZ1UtvimOpHudPnQtJrji8BDo88Omkz8t2KXW2Y26TV1AA9Nzl2+t/hpHFwfhkRz3anoNue5rnjpzn353mtZzu3zvNaz/bZKkzSCcCZwD2kTp8nu7mKZ/L8tVtaI2IR8DiwrqTNKspUxczfSGN8jM5jDTVTxrG56lkpHqhznPKxHkUaQPPhJstsltf/WEQshrbGnPWuRoM61+O2xzqpbXFM9RNJawEXAYcAPwM+XmNMlUa6df7LeisWHD+rjoF2bnLs9L+uQZ1XuvO5SW57muCOn+Z0DTC1r6QV9pmk9Ui3xC4G/tDuilnPSDoJ+DZwJ6nT5+kWVtM1SvvDpfSZef6BijIfLOUhD0h2M6nHe69mypBOio8A20ga1WQZ61tV8VAvFt5NOuY3R8TSJsvUOq7tiDnrJfmNJUeQLmyntLAKtz3WSW2LY6ofSBoMXEq60+cC4IgWOqG77JbnxTapleP6B+AfwB75OrtY3zVIjxPB8uvz4mdfq/e/gXZuaiXerJdI2g3YkTSo86wWV+O2pxk9eRf86jSRXt8WwHGl9DNy+tn9XUdP3T6mX83H7nZgowZ53wGsUZH+HmBJXs+Y0rIxOf0hYMNC+khgQS43slTm0FzmJmBoIX1XYCnwNLB+qcxXcplLi3UE9s/p91bV3VOPYmc7YFhF+kjS6P0BnFxIX5/0a8RSYJdC+lDSRU0Ah5TWNSrHyIJinJAGUHwol9m9P2LOU6/F0RF530+rk8dtz2o8kQZJDeDCGss7qm1xTLU9foYAV+U85zazb4txVkhbo3DsnumN40r65T+AyaX043P6tRX18LV6+2Kno85NrcSbp9bjp5R3Ss77xQb53Pb0cFKulDUgaUvSRdQmwBWk16LuRhpQ6gFS47ag/2po3SFpImnwsFdJj3lVjcQ+NyKm5vyzSLfq3UwaiwNgB2Bc/vzViPjviu1MBr6Qy1wGDAYOBjYmNQ5nlfKL9Gz9QaRBpqflvAeTLuQ/EhFXlMoMIfVWjyF1Ys0A3kT69W4ZMC4ibmm4U6xpkk4hDQR+AzAPeJH02tEPkY7T1cABEbGsUGYCKQaWAD8HFgLjSW8nuAz4WJQaZEnHAd8jXRBdTDqeBwFbkE5IJ1bUrc9jznqHpBuBPYHxETGtRp5ZuO1ZreS2YkL+uinwftKvmDfmtGeL//Y7qW1xTPVcd+JH0nnAJOBZ4P9I/0EpmxWFX+ElBenR+LtIj+4MJ/2ivT3pV+0DIuK6Up26fVwlbUxq97bJZW8l/eiyP+k/+2Mi4m+lMr5W74Fuxs4sOujc1Eq82Yq6e+7KZdYHngDWBLaIOuP7uO3pBf3d8zSQJmAE6RXO80nBMg/4DoVea08DYyK9cSkaTLMK+T8B/Jr0ysCXSL8yPEK6YN6rwbYmAbcBi0gdBLOB/erkXxP4PPBn0u2Gz5E6EsbUKbMOcBrpbpOlpF7vS4G39ve+7sSJ9MrSi0gXIc8DL+d9/hvgSEid6hXl9sjH8rl8bP+cj/WgOtv6cI6ZF3MM3QZM7O+Y89TjGNoutzOPNjj+bntWs6mJ89PcijId07Y4ptoXP8CsBnmD9Er34vq/mY/7E6TOxsWkc+FZwOjePK7ARqQXb8wjXXfPJ72yeYs6ZXyt3p7Y6bhzUyvx5qm1+CmU+VRedlET63fb08PJd/yYmZmZmZmZmXUoD+5sZmZmZmZmZtah3PFjZmZmZmZmZtah3PFjZmZmZmZmZtah3PFjZmZmZmZmZtah3PFjZmZmZmZmZtah3PFjZmZmZmZmZtah3PFjZmZmZmZmZtah3PFjZmZmlSTNkhSSor/rYmZmZmatWbO/K2BmZma9Q9JIYE4vre6oXlpPx5J0Sv44NyKm9mNVzMzMzGpyx4+ZmZlZa/4rz2cDU/uxHmZmZmY1uePHzMysczwNHFBn+TjguPz5euB7dfLe4btYzMzMzAY+d/yYmZl1iIhYDFxea7mkDQpfH4mImnnNzMzMrDN4cGczMzMzMzMzsw7ljh8zMzOr1OitXpImdS2XNCmn7SLpfElzJP1D0jxJF0navlR2kKSPS5opab6kJZIekHS6pPWbrN9gSZ+QdKWkR/M6npd0t6TJebDrRuvYXNJpkn4vaaGklyU9J+lBSTdKOkPSXqUy5X2yd2E/FKd9SuXWl3SYpCmS/pTr+nLe7h25zls2Uee5ef1z8/ehkk6QdIukBZL+LumPkj4raXCp7GhJ35V0n6RFedvTJb23wTanFv6ukTntMEm/LRy/uZLOlfTWRn+DmZmZtY8i/IZWMzOz1UHunDkvfz0/IiY1yD8L2BsgItRgfUcB6wLfpvpR8qXA/hExXdJ6wMXAB2ts+q/AuyPimTp12wW4BBhV509YBhwfEefUWMeHgJ/netfzQkS89phcN15vPzYiZuUyg4G/A0MalHkFOCEivl8rQ+7weTMwD9gduAp4e43s00n7famk/YGfAsNq5D0uIs6qsc2pwMT8dVvgf4EJNdazDPh0REyp9TeYmZlZ+3iMHzMzM+sN+wEHAs8A5wL3AGvntA+ROjwuljQKOJ/U6XMTcCkwn9SR8Zk8fwupA+nwqg1J2h34LbAOEKTOjeuAx/M2dweOyMvPlrS0PFC1pDeyYqfPVcBvgCdId0RvAuwIvA8YXqpC1wDav8rze4H/rKjqPYXPa+R98ETezt3AU8A/gRHAGGA86drsLElPRMSvqG8t4BekTp+rgWnAQtL+Ow54HfB+4GRJVwOXAf8AvgvcntfxQeBQQMAZkmZExH0NttvV6TMP+DFwP7BRTtsXGAz8SNIzEXFlg3WZmZlZH/MdP2ZmZquJPr7jB+BW4AMR8Vwp3w+Bo/PXPwI7A1+JiNNL+TYB7gQ2A14FRkTE/FKe9UgdLSOA54EJETG7om5bATOANwGLgJER8Wxh+YnAN/PXkyLiGzX2gYA9I+LGimVdF1GzI2KfqvKFvINInUjTo8bFl6QdSJ1YmwIPA1tHxD8r8s0ldZBB6vg6MiIuLOXZmrQv1wFeIHXIAYyLiEdLeb8KnJa//iAiPl2xzaksv+MH0lvhxkfES6V8nwXOzF+fzH/DCnnMzMysvTzGj5mZmfWGZcDHyp0+2amkDgpInT7XlDt9ACLiaaDrUaNBQNW4M0eTOn0gdXis1OmT1/UQ6fEzSI82fbKUZavC5x9VrSOvJ6o6fborIl6NiGtrdfrkPHcDJ+evo0l3ATXyw3KnT17Xg0BX+nDS33tYudMn+ybwYv78/ia2+QJwSFWHTn5U7OL8dVPgsCbWZ2ZmZn3IHT9mZmbWG6ZFxLyqBRHxODC3kFRz/Brgd4XPVYMEH5HnD0TEtHoVioiZpEerID2CVLS48Plt9dbTZjcXPu/WRP7KMXmymwqfb4+IW6syRcQSlj/6NUrS0AbbvDB30tUyufD5gJq5zMzMrC08xo+ZmZn1hlsaLH+K5QMxV3ZAFPJ12bC4QNJwYIeufJJqDS5c1HVXynal9N8An8+ffynpf4BLI+KxJtbZsvxGrInAPqSxeDYAanW0bNFgdYtIj73VUtyX9fZ5Ma9ynZ6sk3dGg3XdThrIen1g1wZ5zczMrI+548fMzMx6w4IGy5c2mbeYr9whMoLldyvvladmrdCJFBHXSPoZ8HHg9cAZpMGNHyTddXMD8OsGd7Z0i6QTgNNp/GavLo1ea7+w3qNjNL/Py3kb3fHzUL2FERGSHgZ2AjaSNCQiltYrY2ZmZn3HHT9mZmbWG1YahLiWqgGLm1R+u1Z3rFWRdjgwk3TnT9fjXlvnaSLwqqRLgC+WB5nuLkmHkd5U1uVGYDbpEbgXSWMkQXqbWNfr5wc1WG139mOr+7zK4sZZWFT4vC4rdiyZmZlZG7njx8zMzAaK4mDCF0TExJo5m5DvlpkCTJE0GtiTNKDyOFLnzyDSq873lLRrRDxVc2WNfT3PXyG9DeuaqkySVqXxhmpZp4k8wwqf/VYvMzOzrFdlXgAAA2tJREFUfuTBnc3MzGygeLzwudH4N90SEQ9HxAURcWxEbEN6+9if8uIRwJdaXXfuVOoa3+jyWp0+2ZvrLFtVbFVvoSSR3koG6XE03+1jZmbWj9zxY2ZmZgNCRDwL/CV/fZekRmPg9GRbd7D8DWKQ7gZaKVueq8Hq3lD4/LcGeZt5nXp/G9dg+c4sH5/otj6ui5mZmTXgjh8zMzMbSM7P83WAL/fxtuYWPlc9Ht/1CNOwimVFxTFxtqyVSdII4Kimata/Dpf0+jrLv1D4/Mu+royZmZnV544fMzMzG0i+D8zLn78s6UuSal7PSBou6XhJ7y2lf03S++qVBT5d+HxXxfI5ef4WSWvXWc99LB/seH9J76yo5xuAy4H16qxnVbEBcJGklTq8JB1LGhcJ0ivhf9bOipmZmdnKPLizmZmZDRgRsUjSBNIbsdYHvgEcI+kXpMfAXsrpo4F3AvsAg1nxsS1IjyudCjwpaTpwJ6mjYg1gc2A8y18Xv5T0uveyGcAOpDt+pkk6H3iW5Y+A3RoRCyNimaRzSHfCrAXcIOnHpMegXgbeQbrTZwPgAuDIlnZO+1wOTADukTQFeJBU9wNY/qhaAMdEhAd2NjMz62fu+DEzM7MBJSLuzHfNXAS8nfT41L/XKbKU1CGzwmryfFPSq9trvSHsWeCwiLi3Ytlk0ivhXw+8J09FY4FZ+fN/5LqOBYYAn8pT0TmkjqxVvePnJNL+O4DlbysrWgZ8JiKubGutzMzMrJI7fszMzGzAiYj7Je0MfBg4ENid1IkzDHiR9DjYXcBM4MqIeK60ivHAe4G9SYMRbwVsTOrQWAjcC1wDTImI52vU4XFJ7wBOJHX6jMzbX2mw54hYImlf4GjS3Ufbk+5EehK4JW/nOkkju7832m5ZRBwo6TDSnUrbAxuS/pbfApMj4i/1VmBmZmbto4honMvMzMzMVluSprL8rqhRETG3/2pjZmZm3eHBnc3MzMzMzMzMOpQ7fszMzMzMzMzMOpQ7fszMzMzMzMzMOpQ7fszMzMzMzMzMOpQ7fszMzMzMzMzMOpTf6mVmZmZmZmZm1qF8x4+ZmZmZmZmZWYdyx4+ZmZmZmZmZWYdyx4+ZmZmZmZmZWYdyx4+ZmZmZmZmZWYdyx4+ZmZmZmZmZWYdyx4+ZmZmZmZmZWYf6f6OMJMBlK1bnAAAAAElFTkSuQmCC\n",
-            "text/plain": [
-              "<Figure size 1440x720 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "R_ctvXcQL1Xf",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 634
-        },
-        "outputId": "1a35f11e-eb94-4404-bd86-6f6fc2765ba2"
-      },
-      "source": [
-        "load_avg_1_min = df_Ellis[['ellis-load.avg_1_min']] \n",
-        "load_avg_1_min.rolling(12).mean().plot(figsize=(20,10), linewidth=5, fontsize=20) \n",
-        "plt.xlabel('Timestamp', fontsize=30);"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 1440x720 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Gkd5ecCmL6Bw",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 634
-        },
-        "outputId": "2396e496-512e-45de-b804-acfb32e79b41"
-      },
-      "source": [
-        "cpu_wait_perc = df_Ellis[['ellis-cpu.wait_perc']] \n",
-        "cpu_wait_perc.rolling(12).mean().plot(figsize=(20,10), linewidth=5, fontsize=20) \n",
-        "plt.xlabel('Year', fontsize=30);"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 1440x720 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "EycZrQU0MBSX",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 624
-        },
-        "outputId": "dcf3d389-1ac0-4005-ae48-53e0f96548b1"
-      },
-      "source": [
-        "df_dg = pd.concat([cpu_system_perc.rolling(12).mean(), load_avg_1_min.rolling(12).mean(),cpu_wait_perc.rolling(12).mean()], axis=1) \n",
-        "df_dg.plot(figsize=(20,10), linewidth=5, fontsize=20) \n",
-        "plt.xlabel('Year', fontsize=20); "
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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+lOTEJCesu7goikFZEy6tTnJ4WZafKMvyoiSjkjyS5KSiKD7SgZ8DAAAAqKKxY8dm2rRpefnll5Mkr7zySp555plN3jd//vzssssuOe+883L88cdn5syZOeeccyqh07Bhw9a7/qCDDsrtt69ZY3Lrrbfm0EMPTZK89tpreec735kk+fd///fK9WPGjMltt615Wve9996bV199tVkN64KhbbfdNkuXLl2v+fa73/3u9OzZM9/4xjcyfvz4JMluu+2W+fPnV1b5rFt5VC9a1eC6LMv7N3D8xaIork1yeZLDs2a1UZKclGRokpvKsmz4q+uXF0VxcZLpSc6OFUZbrDdWvJGt+2xd6zIAAAC2LBtpPl1te+yxRyZNmpSjjjoqTU1N6d27d66++upN3nfHHXfk5ptvTu/evbP99tvny1/+8kav/973vpcJEybkyiuvrDS4TpJLL700J598cgYPHpwjjjii0mfokksuyamnnpqRI0fmoIMOyk477VQZ6+ijj84PfvCDDBs2LGeddVb23HPPbL/99tl///3Xm3P8+PG56KKLKmP2798/11xzTcaNG5cBAwY0u/7tXnzxxYwePTqvv/56evToke985zt54oknMmjQoE1+f7qior1Ll4qiuCjJt5N8pyzLz609dkuSjyY5rSzL//W263sleS1rtqQNLMvyLxsbf/To0WVDQ8PGLqEK9vr3vdp1//eO+F4O3/HwjikGAACgzs2dOze77757rcuoK0uXLs3AgQNTlmXOOeec7Lrrrvnc5z5X67I2S0s/P0VRzCjLcnRL17fraWhrg59/Wvv2Z391are1r398+z1lWa5K8nTWrGrapT3zAwAAAFTDDTfckFGjRmXkyJF57bXX8ulPf7rWJXWaVm1D24hvZU2T63vKsvz5Xx3fZu3rhtbHrTv+Ny2dLIriU0k+lWS95WMAAAAAneFzn/tcs5VEN954Y7773e+ud+zggw9ucTvekiVLMnbs2GbHp0+fniFDhnRssR1ss8OioijOS/L5JE8m+ViHVZSkLMvrk1yfrNmG1pFjAwAAAGyOCRMmZMKECa26dsiQIWlsbKxyRdWxWdvQiqI4N8l3kzyR5O/KsnzlbZesWzm0TVq27vifN2d+qqvaj+ADAAAAuq42h0VFUVyQ5HtJZmdNUPRiC5fNW/v6nhbu75Vk5ySrksxv6/xU39KVS2tdAgAAAFAjbQqLiqL4YpL/maQxa4Kilzdw6f1rX8e1cG5Mkq2S/HZTT0KjNspYWQQAAAD1qtVhUVEUX82ahtYzkowty3LxRi6flmRxko8URVF5DFtRFP2STFr79l/bXi4AAAAA1dSqsKgoijOSfD3J6iQPJjmvKIpL3/bn4+uuL8vy9SRnJemZ5FdFUfygKIpvZ82KpAOzJkya2rEfhY7SET2LihQdUAkAAABd3YgRI7J48Zr1JAMHDkySPP/88znppJNqWVa7fPKTn8wTTzyRJLniiitqXE3na+3T0HZe+9ozyQUbuObXSaase1OW5V1FURyW5CtJTkzSL8lTSS5MMrnURRkAAAC2SMOGDcu0adNqXcZm+8EPflD5+oorrsiXv/zlqs21atWq9Oq12Q+rr4pWVVOW5aVJLm3r4GVZPpzk6LbeBwAAADS317/vVfU5Zp0xa4PnbrnllkyePDkrVqzIAQcckGuuuabF6xYsWJBjjz02s2fPzpw5czJhwoSsWLEiTU1NufPOO7Prrruud/3SpUszceLENDQ0pCiKXHLJJTnxxBMzcODAnHXWWfnFL36R7bffPrfffnuGDh2aww8/PFdddVVGjx6dxYsXZ/To0VmwYMF6Y1555ZXp27dvzjvvvHzuc5/L448/nvvvvz/3339/fvjDH+bWW2/N2WefnUcffTTLli3LSSedlMsuuyxJKuNPmzYty5Yty6hRozJy5MjceuutLX7WcePGZb/99stjjz2WkSNH5qabbspWW22VGTNm5MILL8zSpUuz7bbbZsqUKdlhhx1y+OGHZ9SoUXnooYdy6qmnZsyYMTn//PPz5ptvpm/fvpk+fXq23nrrNv7NdZw2Pw2NLd+yVctqXQIAAABdzNy5czN16tQ8/PDDaWxsTM+ePVsMT97u2muvzfnnn5/GxsY0NDRk+PDhza75xje+kW222SazZs3KzJkzc8QRRyRJ3nzzzYwePTpz5szJYYcdVglzWuPQQw/Ngw8+mCRpaGjI0qVLs3Llyjz44IMZM2ZMkuTyyy9PQ0NDZs6cmV//+teZOXPmemN861vfSv/+/dPY2LjRzzpv3rx89rOfzdy5czNo0KBcc801WblyZSZOnJhp06ZlxowZOfPMM/OVr3ylcs+KFSvS0NCQiRMnZvz48fnud7+bxx9/PPfdd1/69+/f6s9ZDV1rnRNdwi+f+WWtSwAAAKCLmT59embMmJH9998/SbJs2bJst912m7zvwAMPzOWXX56FCxfmhBNOaLaqKEnuu+++3H777ZX3gwcPTpL06NEj48ePT5KcfvrpOeGEE1pd73777ZcZM2bk9ddfT9++ffO+970vDQ0NefDBBzN58uQkyR133JHrr78+q1atygsvvJAnnngie++9d6vnWGfHHXfMwQcfXKlz8uTJGTduXGbPnp0jjzwySbJ69erssMMOlXvWfa558+Zlhx12qHxfBw0a1Ob5O5qwiGaWrlxa6xIAAADoYsqyzBlnnJFvfvOb6x2fMmXKRu877bTTcsABB+Tuu+/O0Ucfneuuuy5z587NDTfckCS55557Wl1DUax5mFKvXr3S1NSUJFm+fHmL1/bu3Ts777xzpkyZkoMOOih77713HnjggTz11FPZfffd8/TTT+eqq67Ko48+msGDB+fjH//4BsdqbV1//b4sy4wcOTKPPPJIi/cMGDBgs+bqDLah0YwnmQEAAPB2Y8eOzbRp0/Lyyy8nSV555ZU888wzm7xv/vz52WWXXXLeeefl+OOPz8yZM3POOeeksbExjY2NGTZsWI488shcffXVlXteffXVJElTU1OlUfZtt92WQw45JMmaJ7DNmDEjSTbaSPvQQw/NVVddlTFjxuTQQw/Ntddem3333TdFUeT111/PgAEDss022+Sll17Kvffe2+IYvXv3zsqVKzf6GZ999tlKKLSuzt122y2LFi2qHF+5cmXmzJnT7N7ddtstL7zwQh599NEkyRtvvJFVq1ZtdL5qs7KIZoRFAAAAXdPGmk9X2x577JFJkyblqKOOSlNTU3r37r1ewLMhd9xxR26++eb07t0722+/fYtPFrv44otzzjnnZM8990zPnj1zySWX5IQTTsiAAQPy+9//PpMmTcp2222XqVOnJkm+8IUv5JRTTsn111+fY445pjLO888/n09+8pOV1UqHHnpoLr/88hx44IEZMGBA+vXrl0MPPTRJss8++2TffffNe9/73vW2kb3dpz71qey999553/vet8G+RbvttluuvvrqnHnmmdljjz1y9tlnp0+fPpk2bVrOO++8vPbaa1m1alUuuOCCjBw5cr17+/Tpk6lTp2bixIlZtmxZ+vfvn/vuuy8DBw7c5Pe2Woqu/gT70aNHlw0NDbUuo65c9/h1+X7j99s1xveP+H4O2/GwDqoIAACgvs2dOze77757rcvodAMHDszSpV27VcpfP/mtq2rp56coihllWY5u6Xrb0AAAAACosA2NZnoUMkQAAABqryutKlqyZEnGjh3b7Pj06dO79KqizSEsopm3d3EHAACAejdkyJA0NjbWuoxOYQkJAAAAdANdvecwXdPm/NwIiwAAAKCL69evX5YsWSIwok3KssySJUvSr1+/Nt1nGxpVMf+1+Z6GBgAA0EGGDx+ehQsXZtGiRbUuhW6mX79+GT58eJvuERZRFb985peZsOeEWpcBAACwRejdu3d23nnnWpdBnbANjWaKtL/BtSeqAQAAQPfkN3qaKdP+PbA9i54dUAkAAADQ2YRFVIWVRQAAANA9+Y2eqtim7za1LgEAAADYDMIimumInkWH73h4+wsBAAAAOp2wiGaKov1hUd+efTugEgAAAKCzCYtopizb3+AaAAAA6J6ERVSFBtcAAADQPfmNnqroiL5HAAAAQOcTFtHMroN3bfcYHdH3CAAAAOh8wiKaGdh7YLvH6OFHCwAAALolv9FTHRYWAQAAQLckLKIq9CwCAACA7klYRFUIiwAAAKB7EhZRFT0KP1oAAADQHfmNnmbKlO0ew9PQAAAAoHsSFgEAAABQISyiKvQsAgAAgO5JWERV6FkEAAAA3ZPf6GmmLDugZ5GVRQAAANAtCYuoDlkRAAAAdEvCIgAAAAAqhEUAAAAAVAiLaLN/GPEPtS4BAAAAqBJhEc2U2XiD67NHnb3pp521v0c2AAAAUAPCIjbLBe+7oNYlAAAAAFUgLGKzHPfu4zK47+BalwEAAAB0MGERbbZN320ypP+QTBk3JeN3G1/rcgAAAIAOJCyizd7R7x1Jkl3+Zpdc/IGLc+AOBza7ZlN9jwAAAICuSVhEuxVFUesSAAAAgA4iLKJNNLYGAACALZuwiDY5bffTal0CAAAAUEXCItqkf6/+tS4BAAAAqCJhEc2UZcvNqU/c9cTWj6HBNQAAAHRLwiLarYgG1wAAALClEBbRap56BgAAAFs+YREAAAAAFcIimtlQv6Hj331868fYQN8jAAAAoGsTFtFqew/du+UTdqcBAADAFkNYRKuMHDIyPQo/LgAAALCl89s/rTKg94BalwAAAAB0AmERAAAAABXCIprZUINrAAAAYMsnLKJVCl2sAQAAoC4IiwAAAACoEBYBAAAAUCEsopmybH/PIn2PAAAAoHsSFtE6G2lZpJ8RAAAAbDmERQAAAABUCIsAAAAAqBAWAQAAAFAhLKIZzakBAACgfgmLaBVNrAEAAKA+CIsAAAAAqBAWAQAAAFAhLKI5LYsAAACgbgmLaBU9iwAAAKA+CItoN0ESAAAAbDmERQAAAABUCIsAAAAAqBAW0UypwzUAAADULWERrVIU+hIBAABAPRAWAQAAAFAhLAIAAACgQlhEM3oWAQAAQP0SFtEqRdrWs6gsBU4AAADQHQmLaDfNrwEAAGDLISwCAAAAoEJYBAAAAECFsIhm9BsCAACA+iUsonW0JQIAAIC6ICwCAAAAoEJYBAAAAECFsIhmyuhZBAAAAPVKWESrFJoWAQAAQF0QFlEVVicBAABA9yQsot2sOgIAAIAth7AIAAAAgAphEQAAAAAVwiKaKcvm/Yb0IAIAAID6ICyimccXPd7s2MPPPVyDSgAAAIDOJiyimRtm3VDrEgAAAIAaERaxnpa2oAEAAAD1Q1jEeua/Nr/WJQAAAAA1JCxiPf82+99qXQIAAABQQ8Ii1nPfM/d1yDi2swEAAED3JCxiPW+teqvN9xQpqlAJAAAAUAvCIgAAAAAqhEUAAAAAVAiLAAAAAKgQFgEAAABQ0avWBdAxVjatzPce+15++/xv857B78lF+1+Uwf0G17osAAAAoJsRFm0hvjPjO7npiZuSJPNenZcX3nwhN467scZVAQAAAN1Nq7ehFUVxUlEU3yuK4sGiKF4viqIsiuKWDVw7Yu35Df25veM+AkkqQdE6DS81ZNFbi2pUDQAAANBdtWVl0cVJ9kmyNMnCJO9txT2PJ7mrheOz2zAvm2nRskUZutXQmsxdpqzJvAAAAED7tCUs+lzWhERPJTksyQOtuKexLMtLN6MuupOi1gUAAAAAHaXVYVFZlpVwqCikAwAAAABbomo3uB5WFMWnkwxJsiTJI2VZzqzynF1WU9mUHkWr20S1W2HJDwAAANBG1Q6Ljlz7p6Ioil8lOaMsy2erPHeXsbJpZU7+8cn5r9f+K0nynb/7TsbuNLbGVQEAAAA0V61lLm8l+UaS/ZIMXvtnXZ+jw5NML4piwIZuLoriU0VRNBRF0bBoUfd/ote508+tBEVJcsEDF+StlW9VfV7bBQEAAIC2qkpYVJbly2VZfq0sy8fKsvzz2j+/SXJUkv9M8t+SfHIj919fluXosixHDx1am6d5daTfPv/bZse+3/j9qs9rGxoAAADQVp3XQCdJWZarkvxg7dsxnTl3V3PzEzfXugQAAACAZqrdswLfWKoAACAASURBVKgl6/aVbXAbGtX3zOvP5O75d6dvz7457t3HZehW3X8FFwAAANB+tQiLPrD2dX4N5q4rG+pZ9Kc3/pTT7zk9f/7Ln5MkP/o/P8rtx96erftsnZ5Fz6wuV3dmmQAAAEAXUpVtaEVRvK8omj8jviiKsUk+t/btLdWYm0278493VoKiJHn2jWdz3zP3JUl6NP9r2yxlyg4ZBwAAAOhcrV5ZVBTFh5J8aO3b7de+HlgUxZS1Xy8uy/ILa7/+lyS7FkXx2yQL1x7bO8kRa7/+almWzbs+16lXl7+aix++OH946Q/Za+hemXTwpA7ZFrahBtc/nP3DZseun3l9Przrh9OrR6+sbFrZIfMAAAAA3U9btqGNSnLG247tsvZPkjyTZF1YdHOSDyfZP8k/JOmd5KUkdyT5flmWD25uwVuSyY9Nzk6DdsrPF/w8Dz33UJI1T077ykNfyfVHXd+qMV5686VcN/O6DqvpnQPfmaf+/FSHjQcAAAB0L60Oi8qyvDTJpa289odJmi9fYT03zLqhxeOPvPBIVjWtSq8evVKWZea9Oi8vLH0h+22/Xwb1GVS5rqlsyt9P+/sNjr85K372GbqPsAgAAADqWC0aXNMKq8vV6ZVeuarhqtz0xE1JkqH9h+bGcTfmXYPelSS566m7NjrGhhpcd/Q9AAAAwJajKg2uab+yLDNn8ZxKUJQki5Ytys1P3Fx5f8lvL+mw+YREAAAAQCIs6rI+eOcH85G7P9Ls+NR5U1s9xof+94fypQe/lMXLFufh5x7OvFfmpSxbfkrZuuMbOg8AAADUB9vQuqhXlr+ywXO/Wfib7DN0n1aNc/f8u3P3/Lsr78fvNr7dtQEAAABbLmFRN3TO9HM2+94NrUyyDQ0AAABIbEOjWuxmAwAAgG5JWES7FbEqCQAAALYUwiIAAAAAKoRFtMrROx9d6xIAAACATiAsolXOGHlGrUsAAAAAOoGwqMr+svovtS6h3U5+z8nZY8getS4DAAAA6ATCoiq78tEra11Cu+0+ZPdalwAAAAB0EmFRlU2dN7XWJbTKxp5o5mlnAAAAUD+ERQAAAABUCItIkixdubTWJQAAAABdgLCIJMkry1/p0PHKlB06HgAAANA5hEW0W1HoaQQAAABbCmERAAAAABXCIgAAAAAqhEUAAAAAVAiLWI/G1AAAAFDfhEVsUhENrAEAAKBeCIsAAAAAqBAWAQAAAFAhLKIq9D4CAACA7klYRLvpaQQAAABbDmERAAAAABXCIgAAAAAqhEUAAAAAVAiLWE9ZakwNAAAA9UxYxCYVhQbWAAAAUC+ERQAAAABUCIsAAAAAqBAWAQAAAFAhLKIqNMoGAACA7klYRLtpgA0AAABbDmERAAAAABXCItZTxvYxAAAAqGfCIjapiG1mAAAAUC+ERQAAAABUCIsAAAAAqBAWAQAAAFAhLAIAAACgQlhEVXiqGgAAAHRPwiIAAAAAKoRFAAAAAFQIi1hPWdo+BgAAAPVMWAQAAABAhbAIAAAAgAphEQAAAAAVwiIAAAAAKoRFAAAAAFQIiwAAAACoEBZRFWXKWpcAAAAAbAZhEe1WpKh1CQAAAEAHERaxHiuCAAAAoL4Ji9ikorByCAAAAOqFsAgAAACACmERAAAAABXCIgAAAAAqhEUAAAAAVAiLAAAAAKgQFlEdZa0LAAAAADaHsAgAAACACmER7VYURa1LAAAAADqIsIhNKiIMAgAAgHohLAIAAACgQlgEAAAAQIWwCAAAAIAKYREAAAAAFcIiAAAAACqERQAAAABUCIuoijJlrUsAAAAANoOwiCTJkH5DkiRl2faQp0jR0eUAAAAANSIsIkly9C5Hb/BcUQiDAAAAoF4Ii0iS9O/Vv9YlAAAAAF2AsAgAAACACmERSTavVxEAAACw5REWAQAAAFAhLAIAAACgQlgEAAAAQIWwiCRJGT2LAAAAAGFR1Z38npNrXUKbdFRopGE2AAAAdE/Coir7yHs/kkF9BtW6jE3aWLhTpNjovZs6DwAAAHQfwqIqe8/g9+RHx/0oXz/o67UuBQAAAGCThEWd4G8H/G0+vOuHc/Y+Z9e6lA3SswgAAABIhEWdaocBO9S6BAAAAICNEhZ1ov69+9e6hA2ysggAAABIhEWd6vDhh6dPjz61LgMAAABgg4RFnahfr3754vu/WOsyWmZhEQAAABBhUac7ZbdT8qtTflXrMgAAAABaJCyqgSH9h+TIdx3ZIWN9/aCvd8g463oWdVTvIj2QAAAAoHsSFtXIJ/b8RK1L6DhFrQsAAAAAOoqwqEaGbz28Q8bZYeAOHTJOWVoJBAAAAAiLaqYoOmY5zsDeAztkHAAAAIBEWFQzg/oM6pBxihSZuO/Edo+jxxAAAACQCItq6srDrmz3GFv13ip9evTpgGoAAAAAhEU1NW7EuMz8p5m5+8N3b/YYIwaNyLv/5t3trsXKIgAAACARFtVcURTZadBOm3XvFYdckaIoctCwgzq4KgAAAKBe9ap1AbTezf9wc97R7x2ZvXh2DtvxsAzoPSBJ0rNHz3aPve5paJ6KBgAAAPXNyqIu4txR5270/Na9t86e2+6ZnQbtlKN3OboSFHWGjnpyGwAAAND1CYu6iE/t/akcs8sxGzx/wX4XpFePDS8EO2LHI6pR1mbTAwkAAAC6J9vQuoiiKHLFIVfkxTdfzIyXZlSOnzPqnBy7y7EZvvXwjd6/uX2P1mlPuFPEyiMAAADYUgiLupAeRY9cM/aa/GzBz7J42eL8/U5/n13+ZpdOmftnT/8sX3r/lzplLgAAAKDrEhZ1MVv13ion7HpCm+87YIcDMmXOlM2ed8nyJZt9LwAAALDlaFXPoqIoTiqK4ntFUTxYFMXrRVGURVHcsol7DiqK4p6iKF4pimJZURQzi6K4oCiK9j+6i2Y+sMMHsuPWO7Z7nLdWvdUB1QAAAADdVWsbXF+c5Nwko5I8t6mLi6I4PslvkoxJ8h9Jvp+kT5L/meT2zaqUjerVo1d+eNQPc/J7Ts6Y4WPyzUO/uVnj/OpPv+rYwgAAAIBupbXb0D6XZGGSp5IcluSBDV1YFMWgJDckWZ3k8LIsG9Ye/2qS+5OcVBTFR8qyFBp1sB0G7pCvHfi1yvsbZt6Q+a/Nb/X9r/3ltWqUBQAAAHQjrVpZVJblA2VZ/p+yLFvzyKyTkgxNcvu6oGjtGMuzZoVSkpzd5kpps4+P/Hibrn/xzRerUwgAAADQbbR2G1pbHLH29WctnPtNkreSHFQURd8qzM1fGdRnUIeMU6TokHEAAACArq8aYdFua1//+PYTZVmuSvJ01mx/65xnwlMTrVuEBgAAAHQ11QiLtln7uqEGOOuO/00V5uavddKCoKKw8ggAAAC2FNUIi9qtKIpPFUXRUBRFw6JFi2pdTrf17m3e3abrhT4AAABANcKidSuHttnA+XXH/7yhAcqyvL4sy9FlWY4eOnRohxZXT0ZsMyJ7D917vWPnv+/8GlUDAAAAdAfVCIvmrX19z9tPFEXRK8nOSVYlaf0z3dls3zviezlh1xOy73b75vz3nZ8z9zyz1iUBAAAAXVivKox5f5KPJhmX5H+97dyYJFsl+U1Zln+pwty8zTv6vSOXHXRZq6711DMAAACgGiuLpiVZnOQjRVGMXnewKIp+SSatffuvVZiXdnrouYdqXQIAAABQY61aWVQUxYeSfGjt2+3Xvh5YFMWUtV8vLsvyC0lSluXrRVGclTWh0a+Korg9yStJjkuy29rjUzumfDrSv8z4l1qXAAAAANRYa7ehjUpyxtuO7bL2T5I8k+QL606UZXlXURSHJflKkhOT9EvyVJILk0wuy7JsT9F0LtvTAAAAoH60Kiwqy/LSJJe2ZeCyLB9OcnTbSwIAAACgVqrRswgAAACAbkpYRLvZpgYAAABbDmERAAAAABXCIgAAAAAqhEUAAAAAVAiL6lD/Xv1rXQIAAADQRQmL6tCEPSfUugQAAACgixIW1aGdB+3cpuuLwtPOAAAAoF4IiwAAAACoEBbVoVeWv1L1OcqUVZ8DAAAA6HjCojp079P3tun61/7y2kbPF7FNDQAAALYUwqI61JSmNl1/11N3VakSAAAAoKsRFrFJm1pZBAAAAGw5hEV1qK3bxpatWlalSgAAAICuRljEJi1ZvqTWJQAAAACdRFgEAAAAQIWwqA55ehkAAACwIcKiOrSiaUWtSwAAAAC6KGFRHXpiyRO1LgEAAADoooRFVEVZlrUuAQAAANgMwiLarSj0QAIAAIAthbAIAAAAgAphEQAAAAAVwiIAAAAAKoRFAAAAAFQIiwAAAACoEBYBAAAAUCEsqkN7DNmj1iUAAAAAXZSwqA4VKao+R5my6nMAAAAAHU9YVIc6IywCAAAAuidhUR2y6gcAAADYEGFRHbKyCAAAANgQYVEdKgphEQAAANAyYVEdKkvb0AAAAICWCYvqUFtXFg0fOLxKlQAAAABdjbCoDrU1LOpR+DEBAACAeiEFqEdt3IXWp2ef6tQBAAAAdDnCojr00lsvten6PYbsUaVKAAAAgK5GWFSHtttquzZd369nvzbPoYk2AAAAdE/CojrU1h5Em9qGVqRtPZAAAACArktYxCaVbW1yBAAAAHRbwqI61NaVQLaUAQAAQP0QFrFJVhYBAABA/RAW1aG2hj9WFgEAAED9EBbVoTZvQ7OyCAAAAOqGsIhNsrIIAAAA6oewqA61eRualUUAAABQN4RFbFJT2VTrEgAAAIBOIiyqQ23tWQQAAADUD2ERm7SpbWhFIXwCAACALYWwiE3S4BoAAADqh7CITdLgGgAAAOqHsIhNGjN8TK1LAAAAADqJsIhNOmz4YbUuAQAAAOgkwiI2qU/PPrUuAQAAAOgkwqI6tOvgXWtdAgAAANBFCYvq0AdHfLDWJQAAAABdlLCoDvXr1a/qc3iCGgAAAHRPwqI6VKTo0uMBAAAAtSMsqkNW/QAAAAAbIiwCAAAAoEJYVIdsGwMAAAA2RFgEAAAAQIWwqA6tLlfXugQAAACgixIW1aHZi2e3+tpLD7y0eoUAAAAAXY6wqA717tG71deOGT6mipUAAAAAXY2wqA7tNXSvVl+7Ve+tqlgJAAAA0NUIi+pQr6JXq6/d3CenlWW5WfcBAAAAtSUsqkPbbbVdh45XFJsXKAEAAABdj7CoDg3uN7jWJQAAAABdlLCIjbJqCAAAAOqLsIiN2tyeRQAAAED3JCxio6wsAgAAgPoiLGKjrCwCAACA+iIsYqOERQAAAFBfhEV16oRdT6h1CQAAAEAXJCyqUxfud2HrLtzMhUVlys27EQAAAKgpYVGd2qbvNvnJh37SIWPZqgYAAABbDmFRHRuxzYj84KgfZLv+223wml5Fr06sCAAAAKg1YVGdO2CHA3Lfyfe1eO74dx+forBqCAAAAOqJsIgURZHP7POZZse/cfA3alANAAAAUEv2GJEk+fTen85bK9/Krxf+Ou8a9K586f1fsqoIAAAA6pCwiCRJrx69ctH+F+Wi/S+qdSkAAABADdmGBgAAAECFsAgAAACACmERAAAAABXCIqqiTFnrEgAAAIDNICwCAAAAoEJYBAAAAECFsAgAAACACmERAAAAABXCIgAAAAAqhEUAAAAAVAiLAAAAAKgQFgEAAABQISyiKsqyrHUJAAAAwGYQFtFuRVHUugQAAACggwiLAAAAAKgQFgEAAABQISwCAAAAoEJYBAAAAECFsAgAAACACmERAAAAABXCIgAAAAAqhEUAAAAAVAiLAAAAYAvz57dW5Hfzl2TpX1bVuhS6oV7VHLwoigVJ3rWB0y+VZbl9NeencxQpal0CAAAAa/1izos557bHsnJ1mQF9euamT7w/+73rHbUui26kqmHRWq8l+U4Lx5d2wtwAAABQN8qyzGU/eSIrV5dJkjdXrM4/3zsvd3zmwBpXRnfSGWHRn8uyvLQT5gEAAIC6tuiNv+S5Py9b79jvF7xSo2rorvQsAgAAgC3ES6//pdYlsAXojJVFfYuiOD3JTkneTDIzyW/KslzdCXMDQLv8/ulX8uSLr+eAnYdkt+23rnU5AAAb9aM/LKx1CWwBOiMs2j7JzW879nRRFBPKsvx1J8wPAJvl6geeypU/n5ck6dmjyLWn75cj9/jbGlcFALBhNz68oNYlsAWo9ja0G5OMzZrAaECSvZJcl2REknuLotinpZuKovhUURQNRVE0LFq0qMolAkBzK1c35V9/9V+V96ubylz36//ayB1dV1mWeerlpfnTK2/VuhQAALqBqq4sKsvysrcdmp3kM0VRLE3y+SSXJvlwC/ddn+T6JBk9enRZzRoBoCVPvvBGlv5l1XrHGp55tUbVbL7lK1fn7Ftm5IF5a/7nywnve2euPGmf9OxR1LgyAAC6qlo1uL527euYGs1PlZWljA+gK/jZ7BcrQVGS/Oix5/Kf85fUsCIAALq6WoVF6/7VOqBG89OBivi/07ClWrGqqdYl0E7//Uezmh375r1P1qASAAC6i85ocN2SD6x9nV+j+YG3aWoq8/sFr2Thq8tyyH/bNttv06/WJVFDd/3huVwwtbHy/sH/7++y4zu2qmFFbK5lK5s/fPSF15bXoBLoesqyTFMZ2zIB4G2qFhYVRbF7kmfLsnzzbcdHJPn+2re3VGt+6CxlWeaHDz2de2a9kJ3esVUuGvfevPNv+te6rDb72o9n55bfPZsk2bpfr/yvsz6QPd+5TY2rohbKslwvKEqSQ7/9QBZ865gaVUTHs1UYfjb7xUy6+4n8+a2VOWm/4fnqsXsIjUhTU5kefg4AqroNbXySF4uiuLsoimuKovjnoiimJZmb5L8luSfJVVWcHzrFzb97JpPunpvHnv1z7mp8Ph/7wX92u55NL7+xvBIUJckby1d55GY7NTV1r5+Bv3bf3JdrXQJV1o1/PKFDLF76l5x722NZ+OqyLP3Lqkz57YJMffRPtS6LGnr4qcU54qpfZc9Lf54vTpuZlau77zbs7vxvEKDrqGZY9ECSnyZ5d5LTklyY5LAkDyU5I8mxZVmuqOL81InfPrU4l/1kTm56ZEGWt7Ddotq+9r/nrPd+/uI3u90Tk+76w3PNjt352MIaVNL9rW4qc9lP5mSfy36RMd9+IPc98VKtS2qz3j39H9Ut3Stv+s8v9e3W3z2bVW/7hfrL/9G8v1d38Lv5S/L1nzyRGx9+OstWdP6/g7YEy1aszif/vSHzF7+Zt1b8X/buO76psosD+C/p3hPKKFD2EChbtqioCO6FyqviFl8njhfFgaLiBgVBhsoQBGTPMgsFSindg+6998oe975/hKZNM5qdND3fz8eP5Obemydpcsd5znMeOfbEleCPywW2bpbBGIbFymM3EP6l4hrkVHqlrZtE7ExX69AmtmWxYWgsy14EcNFS+ycEACLSKrBkZwJaj3uXc2qx6dlJtm0UgPSyJkwOC7R1M/TWhTvP7M7u68XKrKwWsQxLdsbj2sdzEejlatuGGcDDxcnWTSCEEIvKqmq2dRPM4lxGFV7eHqfMFryQVYNtL0yxbaO6oKPJ5Wr13b49mYnXbhtsoxYZZ2+7IFeLSIb/7kxAzMd3ItjbzcYtI/YiubQJ4/r527oZpIuw1WxohJjFmrM5aB8gP32jCmWNQts1qItiqX6J2Sw/mKbyWCpn8U9ssZa17ROHY77MopJ6AZqEUrPtjxBCzMFROtd/PZejMqz0YnYN8mt4tmtQF5VX6xif2bIOs1/KGBb/XOta1yDEstaczbZ1E0gXQsEi0qVlVraoLbuaV2eDlhCinabvqT0zR4qyQCLDoi0xmPV9JMZ/eRprz+WYoWXGYVnW5mnX1c0iGvpFCDG75NImtWUXsmps0BJirzIqHSOLjphHRSPNhkr0Z7FhaIQQQrqvXdeKcSVXEbhlWOCnM9m4P7wPwoK9rNqOjRfz8MflAri5cPHOncPw6MRQq76+VM7gvb3JOJJcDgB4btoArHjgFrNmbxnDz8PFpq9PCCHEOjiw/vkmv4aHdedzUcMT47GJoXhwXF+rt4FoRqMJiCEos4hYBB2ICOm6zBHI+Op4htoyaxcLvZBVjVUnM1HdIkZJvRDv70tGdpV1s7xOpFYoA0UAsO1qEWLy663aBk1sHKsixOYcZRgaIZ2y8vFeJJXjyU0xOJBYhks5tXh7dxLOdMHJPhwVHfuIIShYRExm6x5yYjo6cZD2LDVkq7LZuqnPq05kqjxmWWBzVL7e25sj6P327iS1ZU9tjjF5v6aiozYhhLSxRfaNtVj7nZ1Kr0R1i1hl2fcRmVrWJtZGl/zEEDQMjRAL6AoBtITiBjQLpbh1YJBB29W0iDH567MAFFOsn39vDvoFelqiiXqTyRlsuJCHmII6jO7rh3fuHAYPV/PO6JVd1YKXtsWhrFGIHx8fi4fH6z+cyf6/DdZh7c8hS0MW0bUC22f1EEKIJXWBSxDiwC5mq9fMyqnuegXEZXIGh5PKkVfDw5zhPTFlYNeZ5VgXhnqIiQEoWESIkViWRbNQpvU5e7b8YCp23pwdY1APL0wfrH/AaN6aKOW/pXIWs76PxLn3bsPgHt5mb6e+vj2ZiS03hzhdya1DXGED9i+Z3ul2fLEM1wrqEODpivBQf3C5mq+wm4RS3L267X2/uycZTlwuHgjvo1f7On4bWl/X/+brOml5XUdjDzcwxfUCvL07EaseGQNPV92nQEfuaSbGkcoZ/BaZi/iiBozv54//3jEEbs7mDUwTQqyPyieQjpYdSMW++FIAwIaLeVj/9ATcO6a3jVtlOju/RSF2hoJFhBihRSTFoxuikV3VNXpKWkRSLNufiui8Wvi4u6C4XqB8Lr+Gj9IGoV77kTMs6jTM6HTnTxdR+O0Cs7XXUFs61MKJL2oAXyyDl5v2Q1xVswgLN15FYZ3is3h4fF/8/ES4xqywr47dUFv21j+JegeL2qtuFmHhphgU1PIBAA+E98GaheO0Bqo0EcvkOJxUjtJ6AeaOCsHYUH+D22GMsGXH4e/pgoWT+uF/80YY1GbAssEXvliGX8/nIKOiBTOHBOGlmYO0rns4qRw+7s746qExKsslMgYf7kvGoaRyvHbbYMwf08ti7bW1rpD9aC0iqRyn0ivh7+mKGYOD4OykfYT+50fSsetmoP1STi0K6gRY+9R4azXVodwob8aWS/kQSuVYdOsAzBwabOsmEeKQ6HhvuEaBRBkoAhQBlg/2pSiDRUV1fGRUtGBCf3/09HW3WrtiC+qx+3oxZgwOxkPj+xrV2WjvHdrEvlCwiDgcaxwEvz2ZqTNQZKsTM8uyqGgSoZevu8qN/OdH0nE8tQIA0CCQqm0nkTF67T+/Rvt7ji2ox9hQP7i72Ecv+6n0SjwyQTFULLmkEX9dKYCMYfHstDBMGRiIf2KLlYEiADiYWIZXZg/CyN6+avv6t90FgzHafxt2Xy9RBooA4EhyOV69bRBu6eOn9/7e+icRp9IVxSI3XMzDX4unGHyjVccTw9PV2eDheo0CKTZG5aNvgAeenRZm0LaW/Fk8uiEamZWKYWdR2TXIqtQdyP07plgtWDTsk5PKf/9+MQ+/X8wzf0O7KYZhkVrWhAaBBFMGBnaa1WUt7YfVtsr9+l6tAaPWQFGro8nlWLNwXLfJDjSXmhYxntocgyah4nx0NqMK+5dMt1rgGzBPJolExmDF0XScTq9CWJAnvn54DIb38jFD64i1OXImqa53JpLKwRPLEOztZrX2dAXXCxvUlvHEitEEe64X43/7UwEA7i5c/LV4CqYZkKFvrD8vF+DLm52XBxLK8NPpLER/dKfB+xHrec1PCEAFrgkxys4ONwwd6RuwYhgWKaWNuJhdA6FEblKbcqt5mPV9JKZ/ex6DPj6BdedzlM8dSCgzad+tNl/SXhz4iY1XMfaL0/izkxmvKpqEOJ1eicomyxY73nBBcaNf2iDAoi3XcCipHMdSKvDMH9eQU9WCNWdztG5jST+fyVZbtt6A1y2q4ysDRYBiKKCuv0t7cobFldxahC07jolfncXIzyLwW2Su3q/d3meH0w3exlLBoga+RBkoarU/wbAAX7WVi2/bWvs/RXWzCGduVKG8Ub8MQ0MxDIule5Pw4G9XsPiv67hv7WXUdCh+aisvbruutmzb1SKD9sETaR6OTLTbG1eiDBQBiuPY+kjjjr8yOYP4ogZE59VCLDPtPGqoX8/lYNe1YtTyxIgrasDiv2LBMNRrT+wLh6M4/x9OKsPPZ7KRUKwIhKw8dgMjPo3ApK/OImzZcZOvQx1JUR1f43KGYfHVsbbZXkVSBmvOql/XWcKXHbLcy5tEaNLQAdyZiiYR9lzXfR9DSCsKFhGiQS1PrDXbRiTV/2QqkTF4fWc8wpYdR9iy4zif2XaTz7Isnt4SgwfWXcFzf8Zi5GcRJt1AfXIoVWU42Y+ns7EpyvTgR3mjEGduVKG6RYTjKRU615XIGHx57AZK2g1za+9UeiVmfReJV3bEY9b35y06larw5t/p75hiZW8QoOhR0TaFu0CiftNnjpuPI8nlaBFpP6G3XqAxDItVJzIwbdU5PLz+CpJKGtXWPZVeqbZMUzHJjmRyBjO+PY9FW66pLP/hVBaK6zT/vczNUhl31wtNL1p9PrPaDC1RpelvZW8is6ox87tIvLw9DrO+j8SJVN2/cWPcqGjGoaRy5eP8Gj72xpVoXb9JKMX5zCqdmYyGquOJsfVKATZH5aOiqe04mVLapLbuSg3DTgHtnQANAvWhuZ1hWRbv7E5E2LLjmPndeZ3HB0d0QEMwNyK90uCbVYmMwSs74vHohmg8vfkanvj9Kpqt+Fmu6xBsr2gSqR2PGgUSg64bzOFchvmPZ0RxjXAhqxo3ypu71FAeDoCPDqTg7d1J+PVcDh5ZH41NUXlq10JLdsab5fXaZ093VXItQd+COj5axKrXiq0TZ4hlctRrKNXQWSRcBwAAIABJREFUSiJjkF7eBJZlcSylHK9sj8Pnh9NQZUJn1UYjr/OXHUjV2VZCWtlHHjghdoBlWUxddQ5VzW0Bm9ULw9Vmvfr6eEbHTdWU38ya+XBfMk6ktt0wvrA1DsfenImRvX1xLqMKMfmqF5XLD6Zi07OTdO6bL5bhQGIZRBI5HpsYigAvVwBQ2xcAfHMiUzkUyxjHUsrx9u4kyBkWrs5cvYerbY0uxKf3jVJbvnRPEmQ3T8BSOYslf8cj95v5RrcPgNZe3LGhimFdO2PUswR2X9d8o6rpxHkhS3cghmFYpJc3o0UkxZAQ7UW+X9+ZgB0v3qpzX1ujC7Hx5tTuFU0iPPTbFWSunKcytE8q1/x+n/njGlY9MgahAaoz08nkDDZfKsB3Oqat3RiVh68fHqP1eX1EZlbjpzNZaBHJ8MzUARrXMVeoKLagHtF5tRjV2xd3jQqBq7Nx/R4sy+Kf2BKcz6zG2Qz9A5c8sQzeOuphAYogzKs7zHPhbUk/RGRBIlf8ruUMi+8iMjHfzAU8NWWv/XAqC/+9fYja8szKZjy1KUY5XNbNmYsRvX0xf3QvvDRrkFHDvZpFUjz++1Xk37yB+eNyAQ6/MQMhBtaZ0JYwYkybHlofjeSbweDSBiHGrDht07pvlsayLGp4YvTwdgOHw9E6AOy9f5OwftFEvfcbk1+nEuhNLm3C8ZQKPDWlv4ktNl5JgxC3QlEM/f1/k3EkuRwuXC7euWsoXp+j/p03RS1PcwfT5dxas76OJYllchxPqcD5zGqwAO4c0RMLxva2u8Lx5Y1CLNx0FSX1imDzM1MHYOVDo5XPNwokSCxuxOAe3ugfZPgMsQ18CbzcnI0+n+nct0Cq1qn0zQn1a4LOrnf0Vd2sX8dnXg0PEWmV6OHthvvCe9vN8GQAWksD5NdoDoTtuFqIr09kQCRlMHdkCNY9PV7l2u37iEytWeSXc2tx+t3bjDqX3KhoNngbQFGDac/1EiyZM9io7bu6juckop39/CqJQ+kqs0ocSS7H+3uTlTdLHb27Jxl3jAiBn4eLctkODcGHjjZF5eOZqQNUetNb3bf2stbtTmvJtOHf7MVw4nJwy+enlMu/PpGB+E/mKrNoNJn01Vmtz3XmjV2Jyn/rGygCgLKbGU4yOYMTaZUoruNj9rAe4HfoNZYxrPLGWyZnEHuzR3ZKWKDOIrPtaRt7fSK1Etfy6/RuMwAkFKtn8ujqeWEYFu/sScKRZPW/c0eXcmq1DvM5n1mNK7m1ainGAPDY79E49uYs5WNtvV2Xcmrx6o54HH+rbV2WZfHsn7GIztP9ObS/iJQzrN69TdlVLRja0xuVzSI8v7VtOM9XWgKqGUZe1Px5uQA7Yorg7eaM6haRSkD39uE9EGnkBe7Aj04Ytd3u2GK8NEt7AW0AWHfeuOF9xpAziqGIcYX1GNfPH6/eNhgunfx+Wq+NOl5oFtUJ9AqGGUJbpkeLSAof97Zjq0Aiw7w1l1TWEcsYJJc0IrmkEWvP5+K3RRPQL8ADA4K8kFbWBLGMwbh+/jpvsE6nVykDRQBQ2SzC6zsTMGdYD43rD9Byo6ftt2dooXcAykBRe3k1PAzu4Y3o3FpcL2xAeD8/zBne0+B925vcah5e2HodxfUC9PX3wB+LtXeInEitRLNICt923wtdnv0zVm3ZRwdS9QoWCaWWqdvR+m04kFCKwzevASRyBt9HZGHuyBAMCzFPTSORVG7S+d1evLkrUeXa53hKBU6kVmDLc5Mt9poMw0IsY+DsxFEeKzu7X9x2tVAZKAIU14IvzxqE/kGeSC1twtNbYtByc0jqlw/egmenhUHOsDiZVoGCGj4G9vDC7tgS5FbzcPuInljxwCi4OTtBKJHjxW3Xlefpbx8ZgyfNHOy8YuXgIVePy7cb5c14YuNVZeb3voRS7H55qlHHU01YlsWmqHzsii2Gn4cLPrhnOGYN1XzM10Rb4OZ4iubrvU/bDc0/m1GFHVeL8PJsxXWCVM7oLDeQV8PHtfw6TB9ieJH/+CL12kr6SitvAsOwZvvMu4qsyha8uO06ShuE6BfogT+fm4yhZjouOyIKFhGLOJZcgek9+BgQ5GXrpmh1OKkMb+9O6nS98C+M6/E1tGaKJkKJHCM/i9C5zkQ7vFhsvVB4YVscom4GIn48rXlM9+jPT2HmkGCtPaHj+/ujskmE6YODsfKhW1R6nkRSuVpx2vYWbooxuO2lDQJldg7Lstioo8jx4xuvGnSi/vhgqtbnOg4Pa5VWpkh3b+35kGoJbAJAenmzsv0sy2LemkvIqmrRun6r0gYhiusEyK/l4Z09SWjUcwz83auj9FqvVV4NHyKpHO4uTqjlifH7hTyUNQpx39g+WDC2N0rqBfjkUJoiCBXig/BQP6ztJOhibKDIFF8dz+g0WGTKBZyhvjp+A39dKQQAnM2oRkJxI2YOCYabCxcLjMgSMvfwiiu5moOVY1acxpOT++Hrh8fAicvBqM9OaVyvFU8sw3MaggMAkLLibq0Bhvf/TVZbFl/UoPVv5KWld1tbsMjQy2xtQ1vjCusRnVurctPx4bzhZs9GsbaPD6QqZ+AsaxSqdEJoUt0s0itYdM6AbMD2WJbFz2eylecmc2sNOqw8ph40X3s+1yyz59XyxCYHivJreChrFGJC/wA4cTk4mVaBiiYR7h4VgiE9rXPjVFTH19hJdjajGvk1PAzqoT1b11gl9QK8sSsByaVN6OvvgV+eHIdJYYGdTie+8aJ6bcCt0YX47P5R+OVcjjJQBADfnczEU1P6Y/nBVOyNU78W/Ce2GN5uTli+YBS+OZGh0qGz7EAq7hwZgh4+5is4LbNyHS2uHpkaf18rUikREFtQj6TSRkzoH2CWNpxMq8Sqk23ZUy9ui8OV/92h9+eq7S3o+0l+fSJDGSzSls3e3lUjg0XTBikKa1/Lr8O1gnqMCfXDnGE99MqWOZ5SgeMpFdj76jRMGRho8Gt3VcsOpCjLdpTUC/HRgVTsWzLdxq2yXxQsIhYRW1iPhRtjcO6923ROX24sqZzRmU2jj9UaCg2bk6YCyobqLFBkr06kViJs2XG919eVMp94M9tnf0IpmoRSbHmurVc6KrtG5WLDHGZ+F6kMDn57MlNlxrSODA0IGJvizbCA083zfmcFqc9nViOzskVt1qbOzP4h0qi2Gerl7XH48fFw3PrNOeWyk2mV+O8u1fUqmkQWu5kzh+uF9ZgcZtrFFcOwuFHRDKFUjmEhPtgclX+z17kHnpjUT+1ir0kghbe7s1qPZ2ugqNX5zGrl0Jzt0doyIbVfSFrzvmL39RJMHxKM+aN7mbSfsVYYxiXXcjepz41Re+0L1LfHAUclUAQAv5zNwWuzB1u15ze/hoc/rxSAJ5Jh4eT+arP8NIuk+P1CHvJr+Jg7KgSPTdQ91Dm2Qw2f3Goe+gV6mNxOY2crjMmv7zQIbYrWzFpN56ajyeVmCRZ9uC9Fr/VEUjkYllUb3rP6TDZ+Oae4RgnxdYOXq7My+27d+Vz88/JUhPez/Mx0R3Vk5R5KKsfSu4aZ/TV/PpON5Ju1ysoahXjs96soWDXfqCB5a9Z3x2HMfIkcp9OrNAaKWm2+VIDlC0ZpzFb/+Uw2Vj1i2tBwW9J2TAxbdhxv3jEE7909XOM1yvboQqOCRXGF9dh1rRhuLk54fkYYhoX4qJWMkMgY7LpWjLfnDjV4/+2dN6IeWGVT55NHNAmluJBVjTF9/RBkwMx0UwYGYndsMZYdaOuMfO+uYXjzTv3f5xMbryL7q3stMgTS3jAMq7yvaBVnxc69roiCRcRiKptFiEirxKOdXEi2J5DIwOVwdE6/viOmCKtOZJhcMFJXEKCj6avO4eibMw06gBPz63hB9oqFasLkVvPQ09dNWT/I1mQMAy6Hi6c2x3R6I2/MLGXWdCmnViVQ1FWtPZ+L7S9MMXr713bEI0JLAeyI9Eo0CaV4edYgcDgc/HouR2UWvV+fGo8Hwvvo9TraMsvkjO4hOHk1PGy9Ugi+WIYnp/RH/0BPfHwwFSmlTfjywVvMWtfok4OpCDFjL7qptMV+tA5DMzCO89Y/mjNrPjmUprZMLGMgkMrNOixQl0aBBAs3xSgnWziRWol9S6bhlj5+uJpXBy83J6w6kakMAEWkV2J3bDG2vTDFoI6h9sN5OtI3WKlpamt9PLXZ8IxTQ2Tqkc1pKn0K8rfvsFk8PQyf3TcKXC4HDXwJfm03W6piWG/b0F6BRI5t0YX4eeE4s7aZZVlczK5Benkzpg4KwsQBATqzeUwNj5bUC9DLz11tSO7BRPXZYXOqeUad7xlWMaOtJrv1mG0qvVy9wD4AnLlRpTVYVFwnwOkblQj0csW9o3vDw1VxvVzeKMT0b8/r2XLTMAyLFpEMfp4uKGsUoqCGj3H9/ZXHKUbHH3bt+VytwdpDSeWYPDAQD4T3AYfDQVJxIwb28EJffw+cSK3A8dQKhPp74NXbBiPwZr3OjIpmPL3lmrJUQkRaBU6/exvKNAz772wyjKpmUafXJx2LW+tDpqXWZHvbrxZh+9UiuDpz8f2jYxHo5YrRff2U71MbD1cnte/u+gt5eF1DTUBd/rxSgNduc7z6RdUtImRVtmBsX3/4ebp0kSIp9oWCRcRk2mrGAMDqs9kag0WJxQ2Iya/HqD6+mD1UkXbZvn7IoB5eWL9oArZfLYJUxmDR1AEY188fpQ0CfKrhgroz6eVNePOfROTX8DFxQAB+ejzcoO3LmxQ1Lva8Os3g1ybmJZExFu/9yKpswUsaptS2lTqexGoXgUQ/Udk1uFHejBG9fMDlclDZJEJEWgU83ZwxT48sGW2BolbfnMjENycyMX9ML5Ui+YAi2ODuzMWwEB/08TcuQ6NBx1DDqOwafH4kXVm76kCHm6vXdyZg1SNj8PjEUKSUNcHd2Un5OURmVmPNuRyIJHI8Nz0MT9/aee2NZpEMz2gZXmaIWp4YwWYI6KeXN0MmZ1TqpklkDN7UEuQxpDjm5RztWZTaaufpIpDIkFTciAHBihsqfbXW89gTV4IgL1d8cM8IDA/xwdHkcpVZOSVyBg+su6JzX3FFDbh/7WWcene22o25MbMinblRpVbXp5YnxonUCni7OeOB8D4o0jLjZmesMUtTYS3f6rOfdWZrdCFmDAnGXaNCcCiprNMhVwcSy8weLFq6N1klULN6YTiO65h9MTKrGu9qySyqbhEhu5KHMaF+KjUlAeBCVjUW/9V2/g4P9cPhN2YC0F4/La1Mc9AGADZcyMPsYZqHBx1OKse/8Zqzhy7p+K23WvCr5hqWs4aqvx7LsogvasCL2+LQJFS8j6V7k5H46V0I8HI124QKkVnVuL1DrTQ5w4IvkcHTxQl3/HRROay0oyE9vZFbbdoslssPpmH5wTR4uzkrs/MmhwWoBIdbgyOZK+dhc1S+Sk3NBoEUh5PUA4KAIotd13nCUh1ZhgQiJTIG7+xpK5Hx6uxBeGnWIK3D57gcjtpxTSiVG1zk/tuTmQ4XLDqaXI63dyeCYQFPVydse2GK2YY5dicULCIm65jO115pgxDP/hmLDYsmKHsdDyWWqRwI3507DJmVqkVW82v4KoVOj6VU4NhbM7FKw+wNHX2wLwV5NXy8MnsQAr1cUVIvUDkhxxc1YM6PF/R9e0qtU2MS25IxDFxh2WDRucwqgzLPLI0CRfZp/q+X4ObMxZl3b8PjG6OVhbf1HSKij46BolbmyKp75g/NdbK0BUXa++hAKvbHl+pM3/74YCqCvXX3irYypIC+NtNXncfZpbfh3/gS5Nfw0dvPHS/MHGjUvg4kluGJSf2Uj3dfL9Y6LLKySaR3HYyfz2QZ3BaRlsyi3GoentwUo5wR69P7RuFFPd/vkeRyZT2P/Bo+nth41eB2tZdfy8eUr89iTKg/Xpk1CDOGBKFFLMO26EKD99VxpryjyeUq38nlBw3vMGp10IBagjUtYpxKr4RIqph51N9Tv+9yenkzRnxquSHk2jLcOvPlsXTcNSrE5CH8xkgra1LL6Hl3j3otsfZSSptQUi9ATnULbh0YBBnDIq2sCZ8eSlMpWL9/yXRMHKC4AWzgS1QCRYBihrywZccxJSxQbUhkq6V7tbflu4hMfKflz2lMgFcfrUONZXIGf14pwF9XClHRpHl69fErz+Dz+0chVUfAyxDP/3Ud15fPVR7TjqWUY/nBNDSLpJ0GGU0NFLXXfhintixCbb+znTqG4U/66iwKVs1XC/KXNljmmq/EyMB2q41R+dh8KR+/PKl5+OpHBzTXwtRW38+SpHIGf1wuQEppI6YOCsJ/bh1gs+LZLMti5bEbykxVgUSOH09lYdfLU7WuT7OiacYxdyFLc5s0aRIbFxdn62YQHYavfhmugapp3aLKByFtaMvC4XKA/FULkFrahPvXaZ8NTJf/TO2Pv2P0r8NySx9fHHtzJt7/N8UsxaYBoPDbBQbV4jFU6+fUypKv1VX9+Hi4skYGfT6EEEsp/HYBGvgSPPtnbKc3YmeX3oYATxfsTyjFNycycevAQDw3PQzv7klSZt8eeWNGp1k6mqy4fxQWz2gLAskZFucyqjQGDPsHeiLE1w1L7xquVmuoPUc/di4Y2xsCsQyers5YvmAkMiubUdMixv/2a59koNUjE/rig3uG486fLkLQbgZPTcM/jfkcjamtxTAsIrOqcSCxDMdTtGfj6PPa6y/k4vuIzoOWxtYAk8gUN4yn0iuRpGHWP0tYs3Ac/rpSoKxF5AiSPrtLUUumynwBGH1NGRiIva9OQ7NIiglfnrF6gWxTubtwIdIx2+GB16djQv8AXMmtxeoz2eCJZejh46ZXNpghLH3PYG6m1v37374U7IlTL+Y9opcPnp2mX6axuZTUCzDre/VanLlf34shy0+qLdcUQOxOOBxOPMuyGqcqpcwiYhUMC2y9UoAVR9WnBteXIYEiQNG7l1DcaLZAkTXoM3ylu/vyaDoendC3Wx/UCSGWx7Isxq88o9e6c3++qPL4WkG9WjaqMYEiAKhqNywMAD46kKK1cG5xvQDF9QI8tTkG6xdN0Fhbyt6GSFlC+4CKrqFOmhxIKMOBBPVhLG/9k4h7bgmBm7P2mor6YBgWO2KKEFfUgHH9/LF4epha0XqWvVn8XiLH+P4BWHnsBrYakaXVUUEtHxyTqwG14Yllallv/9uforEukCW1z1Z3FOO+1O/YYwmxBfWo50twMLGsywWKAOgMFAFAdG4t+vh54IWt15XB/MxKy9cac2RimVxjoAhQfLYfH0zFb5G5mDuyJ56ZFoYhPc0702F1swifH0lHZmULZg4JxqKpmgNT2iaqYNm2eoVyhoVYJlebGKC7ok+BWI0pgSJj6aoPYQx7z8TrDppFMpTUC9E/yBOerk4qPb+EEGIuP1t4xkx9idvd+NTxxDpnWGrv9Z0JeGX2IHw8f6TK8lOd1Msi2g3/pG3Yy7PTBhi1j+8iMpU1TI4ml6OkXoAVD9yifJ5hWAz6+IS2zU3y+s4EvYvj65JV2YIlf8cjv5aPW/r4YuMzExEa4Am+WGb1QBGxjGv5dRYbmmVpwd5uyuG5msgYFv/EFuusudpdxRfV48dT2WgQSPD0rf3x7LQwjeuVNQqxLboQjQIJHpvYDwODvTrdd1mjENuuFuFYSgUi3pmtHOrYJJDi8yNpSCltwuSwQHx2/yiDZ9J+7e94JNwsi1JQy8ee65oDV+2P4Zr8G1eCr45ngC+W4YHwPvjusbFqtfi6GwoWEYembZYKY1k6VkSxKP1Ib87k5GyjsdCEEMdnyenVDfHnlQI8fWt/DOnpjdM3qjrfoJ1NUfk4mlyOiiYR3rh9CN6/Z7jWGhfEMNuvqk95ro+OxW63Rhfi0/tGKbOL1kVa7nuXUdGMjIrmzlcEsHRPEgYEeeHFWQPVsoeW7k1S1g1KL2/Gf3clYvrgIETqMUsb6Rre2ZOEJyf363xFO6QrUAQAa87mYNIAKnTcUaNAgqc2t80s99nhdLAs8Nz0MJX1BBIZFm68itIGxYxzBxPLsPGZiXq/Th1fgn/jS/D6HEVtuqV7k3Du5rEjv5YPiZzBagMK7Fc2iZSBolaG1hPbl1CKAwmliMlvywg+kFiG0kYhxDIGLlwOlswZjDtHhhi0X0fQvUNlxILsI+pxzswXLpZ+VxQs0k9rhhd9XISQ7mDdzanO+UZM29xaGHddZC7+s+UaZWPaofYF3u0lo+1AYhlWn81Wmxm0gS9BerlqwCm5pBEbLuTRUB4HIpYxRs862BV092wRTXZfL1GbbOLzI+kqj1mWxdHkcmWgCACkchbrI/MMeq2tVwoBKIZFd7xXMzQ7saZFd3BQHx/uS1EJFLWKLahHckkj4ooa8OqOeLMWce8q6JdCzICyO4h1UVCNENKdHEoqR1pZE/ZpmaZbX4ZOp0ysg7l5UqvUMuOVLcXk16tMLa9rBkTiWC5kaZ4B0hGUNjpuIMxY0Xl1Gpfn1ygCJFHZNZi26rzGiQKMPS4IzdB5Ya0SpjKGxeYOmaHdAQ1DI8QAlq5ZxIKFRMbAiWvOEpSOh2JFhJDu5r61xs0kSuxfSmkTBgZ7Yeqqc7Zuikb3rb2MuSND4O3mhENJ5bZuDiEmK6kXdr5SN8NoKWb++8U8rHxoNJb8HQ++mTJT2Q7/N4U157s5mVaB7x4ba70XtAMULCLEAIYc1Ib29EaOgemKp9KrMOwT9SkdiSqGUosIIYQ4iKc2x9i6CZ06m2FYvSxCujupgXVzbE3GaG7v3rhSzBzaw2yBIqBthIA5OuG5NDuyRdEwNGIyH/fuE3M05JhmaKCI6K/jmGpCCCGEEELshTE15mxJriWzCABOm30WTfPVHm0QSMywF6INBYuIyfw9XWzdBKthaQCUXXhg3RUs2hKDFlHXOhETQgghhBDHx+liGS+6gkUiqXk7aVs737WNFPjvzgSU6FFgfee1Ijy9+Zo5m0Y66D4pIcRiPFycAKmtW2EdNPrJflzJ1VyIjxBCCCGEEFvidq1Ykdr08+2Zu2YrrzXrSstuj6dWIKOiGefeu00ZdEsqacShxDL4e7pg0a0D4O3mjC+O3DBru4g6ChYRQgghhBBCCOkWQnzdUNVs+pTruiSVaA++dDXmrhUqljE4mlyOKQMDta6TX8tHQnEDJg4IRHJJIx7bEA3Zzeyno8nleHX2YEi6WF2oroiGoRGH19ffw2z7oswiQgghhBBCui5LB4oA4Jk/Yi3+GtYit8D9z5v/JOJSTq3OdTIrWyCWyfHgb1eUgSIAyKvh42gKzcxoDRQsIg4vLNjT1k0ghBBCCCGEkC4no6LZIvt9/99knc+zLPD7hXyNz3UWaCLmQcEiYjLNwWb7ScExZzYQFbgmhBBCCCGEdBc1LZbPxNJm9dlsm702oWARMQMO7LuCm1mDRRQrIoQQQgghhBCL6mITyjkkChYRh2fObCCKFRFCCCGEEEKIZVEnve1RsIg4PPNmFtFRixBCCCGEEEIsKb+Gb+smqGgWyWzdBKujYBExmb2HT8zZPnt/r4QQQgghhBDS1f15pcDWTej2KFhEHB/VLCKEEEIIIYQQQvRGwSLi8GgGM0IIIYQQQgghRH8ULCKms/NYDEPj0AghhBBCCCGEEL1RsIg4PHMWpaYsJUIIIYQQQgghjo6CRcQM7DuAYtbEIvt+q4QQQgghhBBCiMkoWETMgGPrBuhkzgAPxYoIIYQQQgghhDg6ChYRk9l7AMW8mUX2/m4JIYQQQgghhBDTULCIOD6z1iwihBBCCCGEEEIcGwWLiMOjmkWEEEIIIYQQQoj+KFhEHB4FeAghhBBCCCGEEP1RsIiYzs6DMeac7t6c+yKEEEIIIYQQQuwRBYuIwzNrZhHFigghhBBCCCGEODgKFhGT2Xu2jTmDRfb9TgkhhBBCCCGEENNRsIg4PMacs6FRtIgQQgghhBBCiIOjYBGxEMeMqth7FhUhhBBCCCGEEGIqChYRM+DYugE6mXUYGsWKCCGEEEIIIYQ4OAoWEYdn3tnQCCGEEEIIIYQQx0bBIuLwKBuIEEIIIYQQQgjRHwWLiMnsPRZjzvaxFHkihBBCCCGEEOLgKFhEHJ45AzwUKyKEEEIIIYQQ4ugoWEQcHsV3CCGEEEIIIYQQ/VGwiDg+mg2NEEIIIYQQQgjRGwWLiOnsPIBi1ppF9v5mCSGEEEIIIYQQEznbugGEWBpDNYuIo+EK4BoYDY5zE2QtYyHnD7V1i4hJWHDdKsEybmClgbZuDCGEEEIIIRQsIqaz9/iJOQM85gw8EWIcFp79/4STRykAwMU/DsKSxZDzh9u2WcQ4HDE8+m2Fs1cBAEDScCvElQ+CEn8JIaRrcPZNgmtANFjGDZLa2yEXDrJ1kwghxCzoapQ4PHMOHWMoVkRsjOteogwUAQCHw8KtxxkbtoiYwsU/QRkoAgDXgGvgupfbsEWEEEL05eSZB4++u+HkWQxn7xx49P8LHOemTraSw/67WgkhhIJFpBugzCLiSJx9MtSWtQ8eka7FvddhtWVuweds0BJCCCGGcu1xWuUxhyuFS0CMlrVlcO+9B97DP4fXkO/g5J1p+QYSQogJKFhkKywLFF4GYjcDNVm2bo3xyhIQ3nLJ1q3QyZzxHTmlFhFCLIzjzLN1EwghhOjB2bNIfZm3eqcOALgGXYaLfyI4XBm4Lo3w6Ps3wBFbuomEEBNwPYrg4h8LjkutrZtiE1SzyFYurAIufqf4N9cFeGo3MHSubdukS2Ox4v/+/duW8euAzbcjMNAfgK9NmqWPymaR2fa18WKe2fZFCCGEEEK6B7eeESqPOVwZXPwSIW2caqMWEUJ0cQ2KhFvPUwAAlnGCsPQ5AAts2ygro2CRLUhFwJVf2h4zUiD6F/sKFrEscGgJkPyP+nNP7wXc/YA/77F+u4xgzmygQ0lUS4TYGsfWDSCEEEIciAxOXnngOAkh4w8F5F5We2WJTLapAAAgAElEQVSua73VXosQYggZXIMjlY84XDlcgy4AeNdmLbIFChZZi0wC5F8A6nIAuRSQdch2KYjSbx/FVwGZGAibCXCdFEPZpAJg0BzAzcd87f3CX/tzu57odHN/Dg815msNIYQQQgghZiaHR78dcPZuKwnBSH0ha7kF4up5AOtmw7YRQmyF61EGDleisszZK99GrbEdChZZw7WNwMkPDduGZYHabIBXDYROBjgcRZAm/4Liea6LIiOpVfAw4PmTgFew6e1tMr1YbjCniYJFhFgClc0ihBBCzILrUaoSKAIArkszXAOvguPcAlHZf9o9w8DJsxAc5ybI+UPAyvXrpOW6lYHrXmHGVhNCLI1DF9wAKFhkeXV5+geKNs0B+k4EJj4PJGwDYjcplgcNAYbNawsUAaqBIkARWEr9F5i6xPQ2F2ubxUF/bpB2vhIhhBBCCCGWxJHCrccZOHkUQi7sB3HNPQDrCgBwCz6rdTMX3zSIyqUA66JYt/cBuPrHAQAYmReERa92+tIuAZfh3uuYGd4EIcQeSBkpXLgutm6G1VCwyNKu/qb/uuWJiv+ub1FdXpcLXF3X+fYRy4CeIxVD0kzh6m3a9oQQC6KaRYQQYg1c9xK4Bl0AhyuBtGEqZLxbbN0kYgS3XoeVQR4nz2JwnPkQlT+peJIj170xVwIwLNx7HYaLf3zbYmc+XAKiNW/iVgNwpADrBLce5zppHWUvENKV3Ki7gfAe4bZuhtVwbd0Ah1ebbd3X2/4gcPEH0/bBoa8FIYQQQrovjlMLPPtvgYtvOpy9c+Ae+je4HurTpBP71xooauXilwRAESTqrAaJi28ifEZ8phIoUu43UHMmPocjh/ewL+AaFAmOk9C4RhNC7JJELul8JQdCUQFLYxnrv2bkV4CYZ9y2cimw63GTm8CFDd43IYQQQogZuATEguMkVj7mcFidQ5ZIF8Nh4OSZ0+lqxg4h43BlcOt5xqhtCSH261rFNVs3waooWGRpRVds87oZR4zbLv2QWV5eTl8tQgghhHRRzj4p6su8Ow8ukK6B4ySEW8/Ttm4GIaTL6V7lIOiO3lEdMrLQ9bF3zPLyQriaZT+EkI6610mKEEIIMTeP0B1w8iixdTMIIV3M8IDhtm6CVVGwyJGxRhTNkxg5fI0QQgghhJAugAJFhBBjxFbG2roJVkWzoTmyqjQg6yRQEgv0mwL49gESdwIe/sDs9wFnd8C3r+KxmVHuAyGEGImlIyghhBBCiL3ZnfUPlk/92NbNsBoKFjmy32e2/Tu3Q5G9rBNt/75vDTDpebO+9OyhwciqM+suCSGEEEII0YnjUgv33vvh5FYFGX+YrZtDCCFdFg1DI4o6RcbOnqZrt2/O7HwlG0hZcbfG5SN6+eDax3ci48t5ePOOIRZvx/t3D0Owt5vFX4c4Gso6IYQQQrTx6Lcdzl4F4DgL4OKXZOvmEEIczItbr6OOJ+58RQdAmUVEIeMIMO5p8+2PZTG6rx9yvr4Xu6+X4LPDacoSSh/PH4GXZg7CxwdTsft652PGM1fOg6sTF2/tTsSxlAq150f19sVPT4Rjwa+XwLDAHSN6Yv6Y3th1rQgJxY0q647v7w9fdxcce3MmHlkfDYmcgb+nC75/dCzuGhUCDkdxI/7e3cPx3t2KAmbHUyrwzYkMBHi54LEJoRgQ7IVpg4Lg7uKEQ4llWH02GxwA7941DGND/XH7jxc6fU8Jn96FQC9XvHHHUFzMrsHxlHLsjSuFn4cLmoRS5Xperk7gS+Sd7k+T5M/uRnWLCHetjlJZ/smCkXhp1iAIJXK8sycRaWXNeGbaALw6exBkDIvovDpkVjTjsYmhEErl6OXrjpIGIdaez8GBhDIAgIeLE1JW3A0Xp7Z4cwNfgq3RheCJZfjjckGn7dv18q14enPb9JP9Az1x8PXp2BiVj01R+QCAiQMCsHh6GN78J9Goz8Ccpg8OQnSe5dPlBgZ7oaCWr30FI0qR2YPvHx2LD/erzy5ECCGEmAvHtQZObtW2bobeXIMugetWDa57KeSCMIgqHgUYT1s3i8C0a3Di2M5lVuODfSn4c/FkWzfF4jisMUWQrWjSpElsXFycrZthvBV+tm6Bfnz7AnOWAUfeNHjT7wP9scPPV2XZk353YflDP3e6bWWTCFNXnVM+9nF3xtE3ZuJYSjn8PFywcHJ/uDorAhJyhkVSSQMa+FJMCgtAZmULhFI5pg4Mgoerk8b988UyTPzqDERSBq/PGYwP541QPidnWJQ1CNE3wANOXPNma5TUC/DT6SzU8iRIKmkETyxTPnf78B746/kpBu0vrawJz/xxDQ0CRSBpSE9v/PrkePQN8ADLsvD3dEWjQILd10vAssBTU/rB39N2M9JtisrDNycyVZZ9dO8I3DooCEN6esPbTRGnZlkWNTwxfN1d4O6i+W/YqpYnxncnM/FvfKlJbQvxdUNVs2G9AYFeroj/ZC7EMgYjPo3Qe7v37hqGXbHFqGgSaV3nxZkDsXz+SJQ1CtHHX/FdPJZSjjd2aQ6QuQadg1vPM2rLWzK+1atNPm7OuLzsDnx8IBXHU9WDr61il9+JTRfzEV/cgMQOQddWlz68HSwL/BaZi7waHvJr+ajnS9TWe3xiKH54PBzRebWIL2zAT2ey1db597VpePz3qwCAWUODseE/E5XfkyaBFLV8Me786aLGdozp64fUsiaNz90+vAe83Jxx7+jemDe6FzZcyMWPpxWvv+L+Ufj6RAakctXz4G9PT0AdXww3Zy7G9QvAucwqfB+RpeWTUnhqSn9MGRiAZqEMnx9J17mut5uz8pjgM3KZ2vNyQX8Iil5XW75gTG8sXzASXA5H5bhJiLE+WTASI3v74tUd8Srnqa5m+fyR+PpEhkHb/Gdqf4T4uKOOL0F4Pz+8uydZ5XnPgavh5F6lth0v81u8ecdQ/Houx+j2duwYchSPTwxFiK871kXm2qwNTp758BywyWavbypp8xiIyhapLff3dMHsoT1wJLm8030cfH06Hl4fbYnm4aFxfXAoqfM2OILCbxcgbNlxWzfDbP5aPBm3j+iJe1ZHIauqxdbNsXtOHoXwDPtd43Ot19yF3y6wZpMshsPhxLMsO0njcxQssrCuEizS00rPj/DJonvA2TxHucyUYBGgCBiUNgjR09cNbs66AwZdEcOw+D0qD5dzajGyty/emTsUPu4utm6WRQkkMry6Ix6XcmrhxOXgg3uG47XbBptl3xVNQkxbdV5t+YZFE9DLzx1hQV6o5YnVMqpemDEQH80foZIN1YovlmHZgVRcyKrGsBAffHrfKOyPL0VsQT2emTYAi27tr8w6k8kZJBQ3okUkxdRBQYgtrMdL2+IgZxTH0jtH9MQf7XoayhuFOJRUBpYFHhrfF339PVDVLML1wnoMC/HBsBAfje+TZVmklTWjtEGASWGBeGDdZVQ0ieAadB5uPU+rra8tWOTr7oxvHhmD/Bo+3Jy5eHh8X/T0dVdZp6xRiJnfnVdm/215dhLmjgpR29flnFrsiCmEM5eLl2YNxPj+AWrr1PMleH7rdSSXKAJMT07uhy8fHK0M+gKqf8M+fu44/MZM9PDRf0jmqfRKvLM7CUKpHEN7emPHi7eil587qppFWLTlGnKrFcNqHwjvg5+eCNf4N28lkspxKacWBxNLMTzEFw+O64OwYC+19Rr4EnA4QEm9ED+dyUJmRQvCgj2x9K7hmBwWoPx+aNp/TYsYoQEeKusklTRiy6V8XJQ+p7bN2B5jsXP+ThTU8vG/fSmYN7oXnpsephbUjsysxvNbr6ttv+qRMXhqSn9IZAyuF9ZDKmcwpKc3Eoob8VaHLL31iyZg/pjeKssuZFVj8V+q+33/7mGYNjgIm6Ly4ershLfvHIoBQZ5oFkpxLqNaJWtMW2/sy7MG4u25w9AokOD9f5MRk1+vts5LMwdi3uhemBQWqFyWVdmCI8ll4IvlOJtRhdIGofK5lQ/egk8PqwbnFk8Pw9boQrV93ze2N24b1gMf7NOc4Ta0pzd+fDwca85mo1EoxTcPjwEAPP77VZVAyq9PjUdRLV8Z9NywaAJuH9ETY784DYmM0bhvTX57egL+uytBZdkH9wxHYnEDzmZYJzOi9UI3sbgBnx5OQ0m9UGMQY3APL6xeOA5jQ9smxIgvasCjG7TfkI7p64eiOj6aRW2fXbC3K2p5ioByaIAHNj87Ceczq/HDKd3BWEAR+I3MqlFb/uG84Xh9zhAU1wkw+4dI5XJfd2dseW4ynth4VWX95fNHYvGMMLXjwueH07DtapHysbZg0flH4tDDxw01LWJEpFVgRG9fjO/nD7GMwfcRmSr7aG/JnMF4ZdYgBHgpOnJkcgZDlp9UPn/HiJ5YMmewMmgOAAden44J/QPQJJRCLJOjp487qltEmPK1fsHihZP6YeVDo7HzWhG+OHpD7fm/Fk/GyN6+OJxUhrMZVbhe2KBxP+GhfvD1cMGlnFqV5cffmglXJy6yq3iYOCAAvfxUzy3HUyrUvuOtIt6Zhdf/TkD+zUzakb19cceIHpAxLEb19sVHB1Ih6HAc+Xj+CDwyIRRfHL2Bo1qCJl09WAQozucTBwRgyW2DMXVwkLLjBAC2Xy3EZ4c1d0hMGRiIva9OAwDk1fDwW2Qu6vkSPDIhFPk1PMQW1GNMqB82Xsw3ql33je2NdU9PUFueW83DhaxqcDkc/BaZi7p2nUaf3z8Kfh4uSCxuxMHEMq1B6fB+/tjx4hQIJXLc+o1hnSE9fNzw2MRQPD89DJmVLXj2z7bZquI/mYsmoRRL9yYjqUS94yvQyxX1fAkmDgiAE5eDBr4ES+YMxiMTQpXrGBIwat1fRze+vAcccPDr+RxIZQxenDUQF7Jq8NGBVIPea2d6+rihukW9Q7T1WNJePV+CTw6l4kRqpVnb4Ci0BYtYxhm8rK8AULDILlCwyH6MFm0BD57Yv2Q6JvbzBb4fBIgaNQaLnvKdi48fXm2jlhJNmsRNSK5JxkC/gejn088qr1neKISvh4vKhY45NIukOJZcgdIGAe4d3RtjQtV/ZyX1AhxKLIOTEwePjA9Vu4jVhGVZrTf9usQX1ePMjWr0C/TAoxNCO82SMkZFkxA/nMpCOv8AyrkH1Z7/KjwC353MRHmTCO/dNQwPje+LfoH6p7KzLIviegF6+3moBHZM0dnnaeznDQBimRxVTWL0C/RQ20ejQAKJnEFPn87/5rY2ZtsYtWXhPcLx9/y/9dr+2M0hrP4eLnhofB/MGdYTXB2ZkhIZg/JGITgcIMTXXet3dculfHx7MhMyhsWtAwPxx+LJOn/HhbV8pJQ1YVyoP/oHeYIvluHfuBJcyK6Br7sLlswZjJG9Vc8TicUNeGHrdTQIpAj2dsO2Fybjlj6dnzPreGI4cTnw93QFw7B4c3cijt8cohwa4IG9r05DH38PrDiSrgwavXH7ELx39zCV78rSPUk4kFimfPzrU+PxQHgftdcTy+RoFsrg4+6s87d9KLEM7/+bDBmj+7rq4/kj8PKsQQCA9RfysC26EJ6uTnj3rmF4cFxf8MUy/G9/Ci5m1WBIiDd+eGwshvT0UQk8imUMHlx3RaV32MfNGYN6emPF/aPQw8cNoQFtv/+8Gp5aZt5bdw7F0rtUi/+2/iZlcgalDUL0D/TU+X1KKmnE5kv5EEvluGNECB4a3wc1LWL08nNX6fzp7Le+/GAqdl4rVlm26Nb+GBjshemDgzGqT9t3p7JJhIWbrqKoToAfHw/HYxPbburkDIvE4gbU8iSYNjgIfh4uiC9qwB+X8yGRsVg8PQwzhwZrbAPLsjiSXI5jKRUI8XVDEvMJilrUb6pTn9N9c5dY3IDTN6rQx88dD4T3RZNQqvE41aq17kXQzRqGcoZFSb0A/QI9Dcp6bv8Za/q8K5qEWH0mG+WNIiwY2xtPTemvtv226EJsisqHsxMXH907AhMGBMDdxQl+HooOLqmcweXcWgglckwZGKhX3UWWZbHhYh62XilEdYsYd48KwapHxijfrzHnAYZhcTi5DBkVLRjd1w9v/ZMIjmsNXAMvwdnnBrjO5q/DaU0tGd8i8dO7lIHF9ur5Eizacg0ZFc3KZXeO6IkhId54d+4wva4/tlzKx1fH1TPxZg0NxrqnJ0AokcPXwxlR2TU4mVaJYG83zBvdC5PbBfE7o+3vyhPLsPZcDjZG5cPbzRkPje+DFfffAucOwdv220dl16gEgJ6fEYa37hgKGcMi2NtV4+toev1fzuZg9dm2zObz792GQT28O30vf14uwJfH2oKtn903ChwOsCkqH1I5AzdnJ/i4O+M/UwfgP1MH4EZ5MxZtiUGDQIrNz07CXRo631pVNYuwL74Us4f2gJRh0CiQYFJYIHzcnMHhcFDVLIK7ixMqm0TIqmrBrQMD4eHqhAPxpajlSTB1kOI4N6yXt8oxN6OiGcdTKhDk7YpHJoQqf8O6xOTX4ZezOajjizE21B9v3TEUEjmDRzdEq3Ui/PPyVEwOC0BRvQClDUL8FpmL2AL1DqBWI3v7qnxnVbHoPygGfNcrCHAPwIzAZ7HtfFt7pw8Owk9PhCPA0xUSOYOxK9Q7TFvdN7a3xpIlnq5OmDooCPNG98KHWjqMOtIWLJI0ToK44jEAFCyyCxQssh9hol0AFMOJXr1tMMCrASK/xvcFBylYZOdu1N3AS6dfQotEcWPx0ZSP8PRIM9aoIlazKWUT1iauVVve2Q2MvWoUNcLb1RvO3O5ZQk9TsKg1s8jWeGIZGvgSg4KOhpLJGRTXCzAgyMvo4cCt2X71fAmmDwmCr56Zm1I5gwMJpcit5uH24T0xfYjmIIIhhBI5yhoFyK2rQFWDC6qaxXC9mdE3IEg9Yw3QflOlz010RZMQni7O8PN06XT9v2OK8MmhNADA/DG98NvTE4wO1lpCTlULjiSXw9fdBY9M6KsMJtjKw4cfRm6j+nCqrnqstTVTOgc6E/bxfngN/glcZ8cYWtOStQKF3zyq9XmxTI7o3Dq4uXAxbVCQUZ/r6fRKHE+twOGkcni4OGHigAD8/ES4WuaxJRn6nWi9ZzXle8QXy1DPl6hl+3amqI6PpJJGjOnrpwwwWfI73VU18Fs769zUPpuY/Do8tTlGmcXu5+GCC+/PQXTVGSy71DYk383JDWcfOwt/d39oE1tQj5XHbiAs2Auvzh6EXn7uyuB1RFolPtyXjGaRDGND/bB+0QT09W/7e/PEMuyLK0FyaRMOtusw6khbsEhccwcktYrJkgpWzXeI74CuYFH3vDon5uHdA7h/DWrXXwUgUH2u6/9uHMrvyb8rA0UAsCZhDR4f9jhcnBx7OJwj4jjIj0sgFWDphaW4Un4FXi5e+HTqp1gwyDF6aByFt5uz2bMCO3J24urVs9vZPqYM1L/Hu5WLExcLJ/fvfEUDlPEL8MalN1DGK0M/n35Yd8c6DPIfpHMbbRea+lyA9vbz0Hv91l5ve73BGRrio5xYgjgeS37n3rifh23ZjhEoAgDXoEgA2oNFbs5OuH1ET5Ne4+5beuHuW3rhlyfH2+yYYOhrmqONXm7O8DLivDYgyEst4G+Px1Fb05QN12rqoCBsWDQBu2JL4OPujCW3DUaAlyu+jVUtoyCWi7E/Zz9eHPOi1n1NGRiIo1pm3Z43uhdmDAmCUCJHgJer2pBjbzdnLJ4xEACweuE41PHEkDEszmZU4VhyBUIDPPD23KGolmTiuQgNNYu62Z+dgkXEYPadi0Y0iSyJVHkslAkRVxWHaX2m2ahFxFiOcnHyV/pfuFJ+BQDAl/LxyeVPMDt0NnxcNddw6k4cJSDYHX0e/TnKeIqeypKWEqy4ugLb791u41apcpRjCCGtbjRrnvygq3ILtu776YrHBIlcgtNFp1EtqMacfnMwyE93UJ7Yh3mje2PeaNU6iY1i9VpSKTWmzZ7r4+6id31Y5RBg74uo9P8LcvceKBUthVAq1LJF97oTNk9hCkI66l6/oy5JyjjeTCyk6/g9WbW3RsbKcDj3sI1a07WxLIvchlzEVcZBIlcvrKkPqVyK+Kp45DTkwN6Hp9srOSNHSq3qBW5iteZZDYn9o99B10EB9u6FZVksvbAUH136CKvjV+PJY08itYaGhxLjRRRE4Ie4H1ArrEVGfQZePv0yThac1LJ227mhO5wmKLOImEzT74RO24QQQ1XyaUYOQ7Esi5UxK/Fv9r8AgCH+Q7Dl7i0I8gjSex+Noka8dPolZDUoZqR6aMhD+GL6F+ByqD/JEAz0nwmNEEKIcQqaC3CxtC37SigT4u+Mv/Fdj+9s2CpiTqyVsw4+iPpAbdnJQm3Bou6FrgSJGWgIDVG0iBBiIJFcZOsmdDl5jXnKQBEA5DbmYn/OfoP2cSD3gDJQBACHcg8hq77zqcwJIcQexFbGdr4ScRiHcg+pLTtRcMIGLSFdkUAqwPqk9Xj/4vs4knfEiD10g3SidiiziBhMrzhQd8jL6+Ioxb5r4knsY0pglmWR3ZANGSPDiMARcOJ2Pl1vZ66UXTFDy6yPZVnwpXx4u5pWqNkYW9O3qi1bm7gWr4x9Re99rI5Xn7lyc+pm/DznZ1OaZjCGZZDdkA2WZTE8cLjBmU1yRo4LpRdQ2lKKmX1nYrD/YAu1VAsth1R7LShNCOl6hDIhzhefR6O4Ebf3ux19vPvYukmEdCnvX3wfl8ouAQBOFZ5CSUuJQdtzOO2GoZm1ZfaJgkVEL5fko5X/7vjD0HgJTNfFds/aKZ6kjUQuQVptGnp59TL4Qu9g7kGztSG1NhV9vPqgt3fvzjdoR8pI8dzJ55Baq6gR4O3ijfNPnIeHs0cnW+rm7my9KXvNJb4qHh9GfYhqQTUm9JyANbevQYB7gNVev/0sh+bULG62yH61kcglWHphqXJowYw+M/DLHb/AzUn/6dNXXF2h7HFem7gWG+ZuwOReky3SXkOwYLtkTRWpXAoWLFydtM9uQwixHjkjxxvn3lBmUv2e/Du23but2xV37orHU22uV17H4dzD8Hfzx39G/Qe9vHrZukkWw7Istt/YjkO5hxDoHog3x7+JcT3HWbUNVfwqZaCoVccamp3rXkPOaRga0ctv8ocM3IICEfaOMotso4JXgQcPPYjnIp7DPfvvMfgkVS+qN7kN5bxy3Lv/XiyOWIy799+NzSmbDdo+qjRKGSgCAJ6Uhw3JG0xuV3iPcIPWZ1kWUaVR2J6+HbkNuSa/vqEYlsF7F95DtaAaAJBQnYA1CWtM3q89XAhbO5h8ueyySg2KK+VXcLFE/xmB6oR1KkMTxHIxdtzYYdY2dkbbZ9bVjrUsy2Jj8kbM2D0DM/6ZgTXxa7rceyDEEaXXpasMuWsUN2J/tmHDjh2BPZwjzSGpOgmvnHkFh/MOY9uNbVgcsRhCmbYZuOxXUnUSXjz1Ih498ih2ZezSul5EYQR+jPsRuY25iK2MxWtnX0OzxLodU+2H3BP9ULCI6CWGGaX1OY0Frum6khCNtt3YhlJeqfLx+qT1ZgkAGeLra1+jWlitfPxr4q9oFKlPXarNO5HvqC37K+0vk9tl6LCj765/h/+e+y9+iPsBjx97HNHl0QZtz7IsjuQdwbJLy/BH6h8GzySWWpuKOlGdyrIDOQcM2ocux/OPY9mlZdicshkimXXrOVk7WPRt7Ldqy1bGrNR7+7NFZ9WWRZZEGtyOvVl78cTRJ/DiqReRUJVg0LZag0VdrPMkqSYJ65LWQSgTQiQX4Y+0P3C57LLB+6kT1iGhKsHq311HJGNkRs90aC+SqpOwPX272Wet4kv5Zt2fPdN0nt1+Y7sNWkLMYXPqZsgYmfJxGa8MF0ouWL0dOzN24rEjj+Hl0y8b/PtsEjfh1TOvIrYyFtkN2VgVuwrnis5pXLfjOZ0v5SOiIELjupElkdiXvc+gtlhP1zqnm4qGoREzcIwIvyUJpAJk1mdiSMAQ+Lr62ro5NsGyLPbn7MefaX9i7oC5eGPcGwYPbxBIBYgqjQJPysOcfnMQ7BFsodZqx5PwsCNjB0pbSjG3/1zc3v92vbet5FdiZ8ZOlWUsWBzNO4rnbnnO3E3VKqo0Sm3Z7qzdeC38Nau1wVQ8CQ97MvcoH8sYGXbc2IHhAcOxN2svAt0D8fDQh3V+x3bc2IEf4n5QPs6sz8QPt/2gdf2OdA3VkjJS/BL/C84Wn8WooFH43+T/IcQrRK/9MmCwJ3MPvrr2lXJZam0qfr3jV7V1LVUL53rldYzfMR4z+sxAiGcI3JzdsHD4QgzwHaB1m3JeORpEDUbVsKoR1KgtaxTrH8A0x+dwseSiysVs6tlURDwagUD3QL2215Z909WCRWsT16ot+zn+Z8wKnaX3Pg7mHMTn0Z+DBYsg9yD8cc8f1q8h5SC2pG7BLwm/AABeGP0C3pnwjkHf99Zzb3xVPEYHj8bC4QvhzLXu5f+ujF1YFbtK+fjL6V/i4aEPm2XfpwtPm2U/XQHVPlNwlM9B07XYsfxjuHfgvWZ/LZZlkdOYA2eOMwb6DVR+hhEFESqdNWm1aTjz2BllDUYZI8Plssso55VjVt9Z6OPdB3JWrry2Opx7GAKZQOW1for/CXcOuFOtDZqGzSfXJGtt8xdXv0CIZ4hB557ObE//f3v3HSZVdf9x/H1mti9L7yCiFAXFig0rYo/YDfxsGI1GTSzRJCSoiTUxsSuxK9jFAkGwIoqCCEpvSq+7tAW2zu7U8/vjzg5bZnZntsLyeT3PPLN77z13zsw9c+6d7z2lHoKr5ccsspbm/jtYwSKpFWstY2eu4+OFOXTzBSGz0gZqWhSxYNsCbvrqJor9xSS7knnytCc5db9Tmzpbjf4D5r4f7ou0uhizZAxjloxh8Yj472D4g35u/upm5m1z7vY/v/B53jz3zUYf3PHWr29lztY5AHy8+mP+c8p/4jqxP7fguZhdtSo3w8335rPVs5VOGZ1oldoKgNyS3IS65TG4sYgAACAASURBVMSyNHdp1OUTVk5gS/EW+rTpw+V9L68QZAnZEIFQgEmrJ1WYeauykkAJj/30GHO3zuWQ9ocw8tiRVYKj3qA3ZpAlZHf3A8/35vP2z2+zuXgzZ/c8m5O6nVRh25k5MwnYQIVlM7JncNr7p0X+f2j2Q3TK6ESH9A5c0e8KhvYaWmH7yl3GPl/3Ofccf0/kM69J0AajLr944sWsytvdLS67KJsp66fw4dAPmbt1Lj1a9mBQ10ExW1It2r6oymxk32z8hkmrJ9Elswv92vUjM9mpdKNdbCbC4/fEXBcIBSp0Dft0zad8dMFHtEtvV2E7ay1Pznsyctf7oDYH8fJZL1cZu2ld/jo+WPEBvqCPy/pexkFtD6oxf8MmD+PJ055kTf4abp16KwEbYNhBw7j+0OsrjLVVHz8enphbcUDvkkAJ41eO57cDfhtX+ph1aoJVrbWWtQVrCYVC9Grdq9F/GC3JXVJlWfnyXBN/0M+/fvxX5PPYUbqD5xc+z2OnPlZvedxXLNi2IBIoAnhtyWv0a9ePc3qeE/c+nlv4XKS78+Q1k1mbv5Z7jr+n3vMai7WWV5e8WmHZq0terbdgUX2N47eniTYwfnPpflVX6wvWN3UWGkyiXX43Fm7k/eXvUxIo4eLeF3NI+0OqbFPgK+DEd0+M/H/m/mfy71P+TbIrmUd/qniDrMhfxOQ1kxl+8HAA7v3+XiavmQzAv3ACvqnuVC7ufTGjjhvF7C2zo+YpXsmu5GrXPzP/mXoNFkXLb+L2rd+4ChZJrXw0L5v7Jy0DoFu0sdj2kO9R2d3973O+p0+bPvzlmL80emuUJ+Y+EWkm7Q/5eeTHR/bJYFG07jk7S3fGfdd+3rZ5kUARwDbPNiatnsTvDv9dwnnxBX0ku5IT/hG2Om91JFBU5tn5zzKkxxDmbJ2DL+hjm2cbC7cvpF/bfgw7eBjv/PwOj82p/kfSS4teYsQhI2iZ0pKp66dyx7Td3byeG/IcPVv15Lzx51W7j2PfPpZOGZ0YfvBwLu97OU/Pe5oF2xfQu3VvhvQYwrcbv+XrjV+TW5IbNX1OcU5kyvVHfnyEMWeP4bAOh3Hb17fxfU58s5Qd+/axkb9X56/m49Ufc/tRt1f4sVOdj1Z+xNX9r+aAVgdw0nu7g0OVp8lNT0qPu1//Vs9Wtnq2MmrGKL7Z+A2X9rmU0fNHk1uaiz/kr7L9XdPu4viux1fI86jjRtE+vT13TrszrteM9cP6skmXVfi/e4vuMffhDXqrLBs1Y1Rcrx/LT1t+4uFZD7M6f3Wt0u8o3cEZH5zBhAsn0LNVz8jyjYUbK3SPWL5rOeOWj6vQUi23JJcRn4+IdLmcvGYy484fR4+WPZwNDFHPG8t2LOPsj86usGzc8nGMW767Vdkp3U+pc9AMYE3+mirLZuXMij9YVA8ti/whPyO/G8mU9VMA5709edqTe9Ug0z9u+bHK9/OLdV8kHCzaWLCRR356hJANceuRt9K/Xezu8Hs6b9Cb0GDtZa7/4voqy/787Z8TChZVHhdv3PJxjDxmJMnu6n+k1ZeSQElkXLcy9fljv7mOp7Vsx7IKP/yttXy5vu6tqPwhP8/Me4YZ2TPo07oPfzn2L2SlZJHiSon7mshay4bCDfiCPnq37l2rgPb0TdOZsn4KXVt05cp+V5KVklVjmmAoyIuLXozUj/ua7zZ9x9PznubwDodzx9F3YK3l2s+ujQwr8PHqj3nnvHfo3aZ3hXTlA0UAU9ZP4ZLNl3BSt5MqDElQZt7WeQw/eDhbirdEAkXleYNe3lv+Hp+s/YTDOhxWp/dUU734y85f4tqPN+hl+qbp5HnzOKX7KXTM6FinfFVrH2sQoWCRJGzF1kIe+Sy+L29Te3bes7y+7HXA+QGXU5TDW+e91ah5mL9tfoX/NxVtIrckt0m6UJXXmLMdxWq98Pnaz7mi3xVx7SNa14jRC0YnFCwqDZRy94y7+Xrj17RNa8s/TvgHp3Q/Je70P235qcqyjYUbOfqto6ss/3j1x/z7p3/Hve+T3j2Je0+4lwd+eKDC8lum3hJX+pJACesK1vHIj49UaFK8aPuiWo2j85svfpNwmmjiDRSVuWhizYPp13YAyCnrp9R4kTl7y+wqd57+OfuftXq9mpQfu6qhbfNs47ovrqvzfgI2wND/DeWbX38TqcPe+rlqnfrfBf/lpsNvImRDvLToJf674L8V1hf5i/hgxQfcNfCuOuepPgJFDSmRYNEPOT9UKKPfbfqOaRuncVbPsxoia1HV9cd3fYxRtKlwE+dN2B0gn5E9g4kXTuTA1vHP+uTxe3h49sNMXjOZC3tdyN+O+1udZ2xM1PqC9dw17S5W7FrBoe0P5YnTnkhotiNfqGHGKSr0F9LWHd+NGmkaawvWVggWlW/lWdknaz7h3V/eJS0pjRsH3MixXY6Nue2z855l7NKxgHNd/Nm6zyLrXjv7NY7pfAwbCzby7vJ3SXIlcU3/aypcrwZDQe75/p5IEGFQ10E8PfjphGYzrXxTbGbOTF4/5/Uag06PzXks6vmmqRT7i3nn53fYVLSJIT2GxHU96Q16efvnt1mXv67GbT1+D+/88g4Lti2ocPxX7FrBBys+4K/H/rVCsKckUMK7v7zLVf2v4uHZD9MypSW/OuBXUfd981c3M//q+VHXlZm4amK16wt9hXyfHd/NxFje+SX2gNjxCoaC3Pb1bZFxK9ultePNc99kv5b71Xnf0dkofzVfChY1Y98HD6G/az1tTFG97nf8vOwK/8e6l1pfthRvId+bT582fRIeAHfM0oqDAS7cvpDNRZsTniq8vgVD0buvVCcQCuAyroQ/g7rwBr1MXT8Vt8tNmjuNN5a9QSAU4JpDrmFIj4r9kQt8BXy1/ivWFaxjm2cbn6z5pMb992nTp9r132z4hmmbplHkK6q2X3O8xiwZE7kzt82zjTun3cn04dPj/gHRkE3ALbZKoEikLt5c9ib/+ek/DbLvCSsncMNhNwBOl8Hq8lA5UFRm7NKx9RIs2tMlEnyJdrwenPVgwsGivNI8souy6du2b43N/OtdPVSTl358aZVlt31zG5MvrnqXO5bzxp8XGYB+wqoJzN82n0kXT6p75uJQ6CvkvV/e45n5u8cZW5y7mPt+uI8Xzkh0mub6V5tzmT/kJ8kkNdl4MTtLd/LI7EfI8+Yx6rhRkdaNzWX8msr+Nv1vDOw0MBJcfGpu7Jk2/zr9r5G/F25byMcXfRzzOrfydXF5131xHZ9c/Am/mrA7wDBmyRhmXzGbjOQMAOZsnVOhtcnMnJlMWT+lSjfv6oxeMLrC//O3zWfFrhXVdk0OhoJ1DhT5g342Fm4kKyWLDhkd6rQvgD9M/UOktfn4leN54rQnOHP/M6tNc+e0O6u9wVH+5sLQCUOjtvwpE21iiPdXvM/7K96P/F/dDbJYAcjP1n1Ggb+A/m33jtacS3csrTDByY7SHYxfNZ7bj7q9gV5xXwgR7aZgUTN2pf9uXIR4NPkFLnUnPpNJ/KKcqOvh3G2t5bE5j0Vmeji8w+E8f8bzcTVVrc5Wz9YmDxYlwlrLcwuf461lb+F2ubnpsJu4qv9VMbcPhoJ8u+lbiv3FDOkxJHKCr86W4i0U+4s5sNWBkQuv3JJcBr8fffDmBdsX8O6v3qV/u/7ke/MrdBlKhNvEHgR38prJ/G3632q131ieW/hchf+9QS9frvuSC3tfCDT9xbBIfVmxa0WDBYrAGUegLFhUXQC7pi6YGwo20KNljwozwuxREqgKYrUgKvQVxn3XfWvx1irLEhnoG+DDFR/ywA8PYLF0a9GNV856he5Zsbs8VlbXLsr1EVSvPGAqJNZ1KRgKVpmpcF3BuoTyUNvPwVrLxRMvZqun6rGs6134phCyIR796VE+WvkR6Unp3HHUHfU23lC88krzOHXc7u77Q/83lHHnj6N/u/5kF2ZXk3LvduaHZ0bGdoy3+3BpsJSPVn7EH478Q61es3ygqMzI6SO5pv81vLDwBX7c8mOV9f+Y+Y+EgkXRumrPzJlZbbDo641fx73/aLYUb+HWr2+NdGu657h7GHbwsFrtK7som192/FJlWILnFjxXbbAopyinxpawZfXODzk/VBsoqg/RZrYt833293Wqr5btWEa3Ft3wh/zkluTSt03fWu8rGn/IH7kRcuWnV1ZZ/8riV5iZM5Nze57LiENG1O91vQnVvE0z0nhNFPZR7wVOa9LXD+Hi2cDFbLFtat64HgWCdf8irc5bXWFK0IXbF9bLtNSNfpc1ikQuQqdnT+eFhS9Q5C8i35vPv3/6N0t3RB+kOGRD3DL1Fm7/5nZGzRjFce8cF3OMmjL/XfBfzvzwTC6aeBFD/zeUIp/TEu2MD86ImSZkQ4xdMhZrba0DReDclbn606urjEkD1DpQlFuSy8RVE/l247cs37mcnKIcgJg/RnOKcij2FzPg9QEc9eZRHPbGYXy8+uOo2yqIJHuLqz+9utFeqy6tHc+fcH7U7p17o1gtiH7Y/EPcXSfrEqgJhAKUBkr51+zdg0tnF2Xz8uKXE9pPcxgDJtbn3Rjv7ZXFr0QNFO1JEgnofbr2U976+S1KAiXsLN3J32f+nY0F8Q9gG8vM7Jk1bxQWrXXiH7/5I+B0qWvOos0gVZMXF71Yr3mYtnEa131xXdRAERB1/L9EVXceufHLG+MeMzCW0fNHVxj/5qHZD+Hxe2q8UeHxe1iauzQyjuAZH5zBOR+dU6EbXZmaJgKYtnFajfn8Pvt7lu9cXqG12N5o2ORhnPTeSQx+fzCXT7qcw984vNb7Kv+bZ7tnO8MnD+eoN49iwOsDqq1Hlu1YxuNzH+ewNw7jz9/+OTJmYl25knfvp8/dn/HEl8ur2Xrvp5ZFDeyZwCUMT5rW6K+7LrR7iuZ1tgunep9kgFnDetuJn9J+3+CvX1jiJ2RDvLbkNaaun8p+LffjzqPvTKiv/mtLXquy7LE5j9V5ivFEp4wt65Ob5EpiUNdBcbXUqU+VZ2wCeGHBCzw7pOoYPou2L6rQFBOI2ToInHEhyg9+ub5gPSe8ewKLRyyOOdNTmc/WfcZ5B1Y/6HJNCv2FLNi+gAXbF3Dv9/dyUreTuGHADRzQ6oBa7W9j4UZGfDaC7SVVp+GOZd62eVVaHN09425O7X5qlVmxFCySvUW0lhn17S/f/qXCeBe1YbE8MeeJmjespQGvD+Cqflcx8tiRFZbne/N5bM5j/G/V/0hPSueGATdETb++YD3bPNviGiwzVqDn7hl3c/eMu5ly2ZSEzoGxePwe3lz2JkX+Iob2GkrbtLb85bu/xAy6jV85ngHtB7A2fy0DOw1kcI/Y54Tq3sfeJNYPT0/AQ2ZyJsX+Yi79+FKyi7Lp1qIb9w26j+O7HB/Xvp+Y+wRXHnwlnTI7VVlnra3Q9WxPVf5ctqt0F9tLttO7dW+enPskY5eOpVuLbjx+2uMc0u6QKrMlAbyx7A3uPv7uuF4rVnl6bclrDOo2KPJ/yIaw1uJ27W5xnO/NJ8+bx3vL36uSPqfYuRHU3GcIW1+wnkPbH1qnfZQGShk9fzTLdi6rp1zVbPzK8UxYOYE2aW34/RG/Z/+W+7Mmfw29W/euOXE5IRvih80/1Dk/E1dXHX/nuHeOi/xdNl5TeX///u+R2fYykjK4+fCb4w4ElwRKGDphaGT7D4d+GPXGaDQjPh8RmRhHYPjk4XRI78C9x9/Lbd/cVmHd776Kb9zSz9d9ntAsbdVJylxDWpcPKN18CeDmhW/XcOdZNc/uurdSsKiB5RB7EOM8m0lr0zCVwaOBik0rvaQwxx4MwJ/9N/Jo8ksN8rplFrqW8+ayNyOD2y7ZsYRfdv7CsIOG8fKilzm47cE8dNJD1Q7yXFOLmNpKJFiUV5rHtZ9fG2n+e1iHw3jlrFfqPEhmInc3V+5aWWXZtE3T8Pg9VQJX5WckisekNdHHb1ixa0Vc6W/9+taEXq8mM7JnMHfr3IQG/84uysZt3ARCAcavHJ9QoAhg1uZZUZd/tPIjRvQfUeHC1aXGmCIRNQWKBrw+IK79LNlRdar2+vTWz2/Rr10/Luh1AV+u+5KR340kYHffTS4JlMT8gb+leAtDPhhCtxbdmHzxZFbuWsmvJ/8agD8N/BNX9rsSi2Vt/toaB3Y+80One8IFvS7gvkH3kexKxlrLpsJNpCWl0SGjQ7U/fN9f/j4PznqwwrKygWprcv8P9wPOj/zjOh/H6CGj+XL9lzy/4Hk2FW3i5G4n88CJD9A+vX1CwaI5W+ZEBsMf0mMI9xx/T8z3sHzncrpndSczOTPu/Vc24PUB9Gvbj3Hnj6s2eB/rHHb9F9cz9pyxHP/O7sBQdlE2N3x5A8d3OZ5L+1zK6T1OJ8WdEvN9jFkyhjFLxvD8Gc8zqOugCgGOtQVra3wPT819it8O+C0tUlrE3Gbq+qm8+8u7Ne6rzPRN03l49sNc2OtCbjr8pshnE+ta46T3TuLVs17l/h/uZ0Phhirrs4uyGT55ON8O+zbq3fhlOyoGHfwhP1uKtpCenF7l/B2y0Vuaz94ym7FLxjI9e3rUFivJruQaW6xsKNjQLIKb1Zm1eVatbqCd9eFZuIyL7KLG7ab33abv+P3Uijelv9n4TY3pDIbNRZuZtXkW3bO6M7DTQIwxMa/Rolmau5ThnwyP/D/xwomsylsVV+vX6764jgcGPUCnzE78bkrVAIQn4OHxuY/XuJ+n5z1Nj6we/H3m3yssv2zSZaS54+uOrEBRVdtLtlcJFCUqVq+M2khuPZdgaTf8uwbhq4feNHsys6c3Nx44cKCdM2dOzRvuoXr+9RNeTn6cM91zKyyfFjyc5wMXMC71wRgpa+93vjv4IhR7JoSu5DIzLf4v3PjgSdzpjz0j03mdH2B6m9rdxV48YjEvLXqpwkxXV/W7ipTSAl5bG70r0IQLJjBpzSRaJLfgyn5XVtvSJ9qPladOe4oh+1ccnDkQCvDiohd55+d3SEtKY9Sxoxiy/5CoF+ePnfoYSSYJjDOtsQsXk9ZMYk3+Go7scGTkrm3Ihlidt5pLPr6kSh6+uPQLurboCjhjKzw460FmbXamaB7aayjTN03Hbdyc0v0UjnjziKjvLdmVzH9O+Q9n7L+7u1i8P84A7jvhPuZunRszYCQiIlLms0s+IzM5kzZpbbDWcsvUW5iRXXU8xDP3PzPmoKpJriQeOfkRzu55NgW+An7I+QFf0MeBrQ4kyZVEr9a9CNlQ1BkmK/vvkP/Su3VvurboygsLX2Bz8WZCNhT33fv61L1Fd47oeATfbvw27m5REy+cyNilY5mwagJdMrsw6rhR7CjZwRfrvqhzS4oHBj1AyIbwBDwNNnbZFQdfEXMmo1aprRh5zEjcxs1Li16Ke7wd2Xud0OWEemkBJLK38az6G6sfjG9m5z2VMWautXZg1HUKFjWsiQuyef2L2YwvqTgN9Xnef7LM9uQC1/c8k7K7L/ZrXMSsA2/lm2XZBHDxB/f/uCv5w8j6KcGj8ZLM2a6f2EkWYwPnUEoyJ7mW8Ivdj9GBiyih5sj1urT4C/VhpS9TQOw7gXUJFtW37i2686+T/8UHKz5gyvopcY0TcXqHo2mfvYD3U3Z3u3Jby9ctjuHU4sTL3lX7n0uH9gfz5Nwnq93ulsNvJrsoJ2rT2NpIT0pPaErxbkHIjj2+tIiIiIiIiFSjbCD6vZWCRXuKou2way10HgDJ5boxbVlM6YqvSe5yKO7ep0P5ptXWEpj3FmbdDFzdjsAccwNB4+bjhdnkFfton5XGAe0zObhzFkluF0c+8CW7PBWb7d4+pA+DD+7ID6t30KtDJvklfi6fHF//58AFz+M64v9YsCmP575ZxVc/b6NVejK/O/VALjqiG1lpSTw0djCfpseeOllERERERESkuVGwqAk1q2BRIwsEQ1gg2R2lr+59raoui+a+moNAj749hDcCDTu9o4iIiIiIiEhDyQqGGJezmd927kROcnzj3DbnYFGDjtZqjOlujHnNGJNjjPEaY9YZY54yxjTuPO77qCS3K3qgCGBoHLN1HB/frGlTA/UzFaHIviQj1LwHxGvOBnni6+7ZNlj9jILpoRAXFhbVKg/tAsG48xHLwJJSTvaU8Mi2XO7J3cmQYg/vZG/h8oI9Zyrqvl4fn27M5ve78mgbDHJWkQb+FBERkfqzv9/PJxtzWLx2AzM3bGK/QJDPNuXElXbkMSNr3mgv1mCzoRljegEzgY7AROAX4FjgduAcY8yJ1todDfX6UoOjR0DJTvjqvtjbdOof166yCdS8kcg+4N7cnbzWqiXZMe5EzF63kVJjaBMKYYDZaanc1qkDHpeLDoEAYzZv46vMdJ5q68TTOwcCnFdUzJGlProGAlzavUujvI8nt27njHKBiBDwTUY6q5OT6RAM8lC7tvhc1U9XfEV+IXfu2sX81FQ6BIMc6A9UmFvIBxS7XEzLSGd5Sgpvt8qqkP5UTwmtgkGuKigkhCFgYIDXF7nDYYHVyclcXMNn8uuCQk4oKWVSi0x+SUlhYGkpVxcU0tfnZ2pGOluTkjjI5+Ngrw+Py8XrrbKYmZ7G6pSUCvtpFwhySVER1+YX0DJkKTGGF1u35NXWTivNP+3YxVUFhdRmGLD7c3fyaYsM8l1uzij20DkYpMQYXmnVkpUpyRzm9TErPY3Z6WlkBUP8fcdOzil2xolblJrClV1rNx37w7k76BrYHdAaFg5cDdjho5ffz9NtWnNSSSl+YFpm7IkEGsp1efn8cZfTuvWmvAJuyisAwJO7k3+2a8Ps9DT6+vzctiuPg3x+BhzQo9HzKCIiInuv97K3cIjPV2V59Ve5u13V/6r6zdAepsG6oRljvgDOAm6z1j5bbvkTwB+BF621N9W0H3VDayTjb4RF43b/b9wwch2ktawxaSIzcMm+YfFaZyreABAwBoNlYM/m+UOuTTDIdxtiT03rCY9BllEPde0Wt5sxrVqyPcnNeUXFnFRSyqrkZG7s3JHCcCvC9oEguUm7QxY378qn0GXYlJREf58PvzG0CjrBqlJjOLa0lG6BADPS09nldnFGcQk9AtUHgAuNYW5aGl9lppPvcnGU18s1+YVMy0hnQWoqJ5aUcHypN+H3l53kZk5aGqd6SmgdR8srCxwWJUAwIr+Afl4f5xZ76tR81mvAWEipedNGkedykRkKkVxpeW2DJGXf05qEgMObIBAzd+2GhD77q7t0YkFaaoPlR0RERJqX6q6Fbu3YvtqbZRMvmsiBrQ5siGw1qkYfsyjcqmgVsA7oZa0NlVuXBWzGCdh1tNZW26ZcwaJGsnUpjD3faW0EcOpfYfDf4kr6+aNd+HPH9g2YOdmb/HtbLucVV50db0FqClfXsgUEwP825XBR9651yVqD+GjTZvr6/TVv2Ih2ulwsTk3hIJ+fzjV0hdrbRQuUxBsEaS5y3S4G9+ieUJpEAzHLU5K5rFvjtGwDeGbrdgYn2M0uBAzr2plfUveU8J6IiIjsqWas30irUOxYSJ7Lxf3t27IwNYX9/QE6BoN82iKT84uKuWvHLtqHQnDG/XDSHY2Y6/rXFMGi3wIvAy9Za38XZX1Zq6MzrLVTq9uXgkWNyLMT1s+EtgdAx/4VZ2WrRvCJQziiXYMOfyV7kR/XbSQ9Rr1Sl24ii9duoMBlOHH//Wq9j/p2b+5Ofl3LMWekfniM4aT9u+M3hv5eL09vzW32AbJofMDoNq3ZnOSmayCAC5iZnsbRpV6uyy+g0OXCbamx1Vh1PMbwXssWLEtJ4aKiYg7x+siwIVKt0wqr1LiwwOg2rRjX0ulW+PtdeZzqKeHaLp3wuFz08vkYn72FALAtyY0FWoQs93Zox7cZziyhj2zL5VdRAs61YYHtbjeZoRDTM9KZnZbKMaVe+nt9GKB7+LPyGEOytex0u5mamc7KlBSSrOWjrBYEyp0L08Mt3vp7fVyfX0D3QIA1ycns7w+Qk+QmxVqO8PrYkJTE/LRUHmrfFoAefj/vZW8hJymJBWmpnOIpwYXTlbJlKES3QIAdbjf/ateGIPBg7g52ud20CIVwW2gfDJJhLTlJblYnJzvvyVr6+nw82K4tc9LTInm8Pi+fIcUldAwGublzB1ampHCw18c1BQUc6vWxODWV/f1+DvX62O52EzLQLhjk+3SnpeDS1JTI8YtlP7+fjcmV27hVlBUMRVo8VtYiFCIl/HnHY0CpFxewMM6WYxcWFpHrdvN9uEwd4vWyNNVJ67aWjJCN5K2Pz8fKct1Ozy0q5rMWmTH3/eWGbB5o35YZGekxt4mmQyDAxYXFvNQm9gQjnQMB/pG7k3FZLeLq+nl+UTGrkpNZnZKMy1q8Luc9Her1siQ1/lZ2nQMBbtuZR7tQiA+zWjAlgW6nGaEQvX1+FtVwbI4rKWV2uXJak9bBIKd4Svg4q0WF5amhEF6Xi5M8JSxPSabA5eKYUm/M45EVDJFqLaUuQ5HLRZdAgFc3b2V+Whq7XC4Ge0poFQrxcuuWLE9JZoDXx/CCIlKtZVVyMgf4/XhchimZGXyVkUGnYJD9/H5+Tk2hn9dHVsjydNvWABxVWsroLdt5q1UWz7Vxlh1dUsri1FR8LsOZxR728/u5Jr+QJCxLUlN5rnWryGd3fEkJQQxnFHu4qKiYVSnJrE9Ook0wxHOtW7Elyc1tu/KZl5bKj2lpdAsEOKGklB1uF0tSU6u0rOzmD+BxGdJDlouKihhUUsrHLTL5IKsFtoZr/PRQiBKXi+NKSvlH7g5KjYuXW7eMfDc6BQL8aWde5GZxkrW4reVXxR5O85SwJjmJnv4AXmMYGd6mRSjE9XkFdAoGcFmnK/qD4ToymsNKvZzu8dDH52djchKvtWrJtqQk/rpjJ8+0aY3HVbV+eWrrdhalpvBa69jfsx5+Pxuqqb8GF3u4Lr+ARampPNqu5mF2D/F6ubCwmDahEE+1aR0ZimBg2dEFpQAAGwtJREFUSSkel2FVckqk6/6vCwq5pLCINqEQPgyz0tPIc7vITkqij8/PYE8JHmPwuJwW3N0CAc4o9uBxGUqMixUpybzTMitStwGMzdnKV5npvNVqd2+QrGCIIR4P/8tqwQWFRTyQuxM3EAR2uV0EMIzPakGStZxT7CErFOLn1BRSQ5YjvV5CwJrw+anA5eLvHdqyNDWVfl4f6TZE6/D3alVKMucVeejt8/NzajLHlng5wuvFDRS4DEnWaVm/KcnNxqQkDvL5aRUKUWoMqdZGxsMJAHlu5xqibTAU6dLvMYadbhddAkGmZGawJjmZE0tKONxbtdsYgB/nPJFkLYd4fSQBxcZQ6nJa1fuMIcXaKi206yyOCaH2ZE0RLHoU+BPwJ2vt41HWjwZ+D9xirX2+un0pWLQX8BXDP7uyy+Vih9tNh2AQrzF8nplBwMBZxR7aBEO4gHRrscBnmRlMyMokM2Q5tqSUrFCIIZ6SSFedIDCmVcvICbhMb5+P3+YVMD0jnXbBIOcUebiim9NapUsgQFd/gLnhi5HDS71ck19A+2CIgIEf09LY4XaR53ZT4HLhNzA/reYLl35eH3/YlcfvO3essm5YQSEH+vz8K8rJruz17+rUocK+bsjL50ivly8zMiqkO9jro2UoxBPbtuNnd9elEJBpLQbIdxn+0qE9i1NTuSc8bsm65CTWJieT63ZHuhP19PtpFwzyXJtWrApfBHcIBLioqJj9/AEebtcGr8vFFfmFXFFQSOtQkE1JyWxMTqKn38+BPj/LUlP4T9s2bEtyszXJqc7v2OmMy/JlRgajOrZnf7+f3j4/UzMzcFnLZ5tyKoyBUtnSlBSu6NqJUJyBSIDntmzj5JLSCssWhPO2uBZdTjqGL9jXJydxgD/A8+EL97dytnC414fHGFakJFPocnFUqZfMcJksNYZiY5iblkqGtQwqKW3YGQJE9iEW54IuCUjbw2dpFZHqhYAFqalY4EivN+a50mMMBmLeYBIR2SsoWJTwC74E3ADcYK19Jcr6h4FRwChr7b+irL8RuBGgR48eR69fv77e8yj1bOtS+Gwk7FgNhfGNHl8nncLjJLmTIGd+zdunt4UjroAfRlddl9kRznwAeg+BOa/BL5NhSz1NgXjuo84+t/9cdV2/C6Bdb5jxxO5l5z8Jk/+Y2GtkdYHCzVWXn/JnOO5mmDsGvnsMehwPWZ2hIBtO/hO8cUFir1OeOwWClaL6A6+H5HTn89u1LmZSP1ASvtNVJbKfkgW++p+JyY8T7EkvdxcjYtBtkL8Rlk5IbKdnPQTeIgh6YcaT8aU5437o2A9Wfw0bZsFB58G0fzrrkjPAH25N0fEQ2LY09n5+953TEvDNi6Kvb9cbepwASWlQsgvWf1+xjPQ5C7Yvh7xE6lYDB//KOb4A+x0P7mRYNz365ultoUVHOPEO2LEKpj/mLE9tBW17Qt7G3d1eAdr2gp2rK+6jyxFw6avgyXW+uz9Piv5avU6HTofCzCizPB50Hhx1DaycAnNeLZdmCKyu1LD18Ctg4G/g1TOjv06rHs57rpxPgGFvw8ZZkLMADjgVDjwVtv8CU/7uHIPyBt0GnQ+Dn15x0riS4cDTnFadP74U/bXB2ebQywALLTrBO7+OvW1lPU+Grkc6dVtyBiz/pOL666c45aY0D4J+mP2CU3eVd+ilsOSj3f8PvN5JM/OZ6HVQfTn5Lphe5b6TU4f+/PHu/zM7QPH2qtsdcSUseLvh8nfsjTD/bfA3g5naep8J2XOd72ZqK/DW4gI4vU3VMr83c6c69fw+xeCEcPdyKVlw3I3R64+6Ss6EE37vnFc8jThfT6cBsLWaa9TMjnDQuTDv9cTTVufE251rnvLn0eau7YGwc030dQMuh8UfNG5+quTh17D4/fi2daeCyw0pmdD3bJj/VsPmbV/iToV7tzV1LupkrwsWlaeWRc1AKAgYKN9c1F8CAS+ktqy4fE+Uu9L5Ud2xP7Ssw5g51joX0O5kSK2+iX+jCIWgaKtz4igbyLxwq/PDvE1PZ/meytq4u0nuE/I2OGWrfV8nYFefgn7n807aA8aB8RVDab4TiAr5wZVU/+8XIOCDTT85QdHWPZzva0odZwML+gHjBLj3NmXXCXX5zoVCYIPOMTPGCVaunOIEsLse6Vx0J6fDYcMhqxMUbQNvIbTrVXE/AS/YUMMc91h2rYeF7znl4OhrAVv7+jFW3RXtPFmet8jpJt6mJ3ToW3F/NuT8CKhOwOvczElvXbfzWEMpyXMClS06Q3KcXZX8pTD7eeeHev+LofvRDZvHWAI+J2hpQ9BvaHxl0+dx6rC02F1lAKfsBX1OMD1a2bDWuQGUlAaZe8jYkSV5zjHJ6rznXEdY6+TJ5XaCmeXlbXSuSdv1rvgZ71rvBE079IuvTOZtcF6jXR9IbVF1fSjkBOlLC6DvOZDZLlwvhr+/TXFNU5LnXAdmda65LJaxFgq3OJ9J5c+yqSR6TejzOPVNWmvnmjwUaPhzir/EOeclpTpBeZfLuZ7x7ICMds71hjul5rq8slDQuf5LTq/9923Nt7BqCvQ9F3qe6OzTW+DkOWc+LHzXKR+D74GWMcYu9BbCvDdh7XdOgK1DXzj7n841VHUKcpxJlbI6VVwe8IVvWmQ5+96y2LnpVf67uHmRcxy7DXSu0XzFzk3F7HnQ/wLY/8TY5SIUqlqnbpoLu9Y61yTgvJfJcYxFNCpnz6nraknd0ERERERERERE4lGwGV4Z4gTlo7lnmxMA3MtVFyxqqFucy8PPfWOs7xN+XtFAry8iIiIiIiIikriWXeDOZU2diybVUP1/vgk/n2WMqfAaxpgs4ETAA8xqoNcXEREREREREZFaaJBgkbV2NfAl0BOnu1l59wOZwJvW2mYwGqSIiIiIiIiISPPRkCNt3gLMBJ4xxgwBfgaOAwbjdD+7uwFfW0REREREREREaqHBpqEKty4aCIzFCRLdBfQCngaOt9Y24jyTIiIiIiIiIiISjwadw9dauxH4TUO+hoiIiIiIiIiI1J8Ga1kkIiIiIiIiIiJ7HwWLREREREREREQkQsEiERERERERERGJULBIREREREREREQiFCwSEREREREREZEIBYtERERERERERCRCwSIREREREREREYlQsEhERERERERERCIULBIRERERERERkQgFi0REREREREREJELBIhERERERERERiVCwSEREREREREREIhQsEhERERERERGRCAWLREREREREREQkQsEiERERERERERGJULBIREREREREREQijLW2qfNQLWPMdmB9U+ejHrQHcps6E7LXUvmR2lLZkdpS2ZG6UPmR2lLZkdpS2ZG62FfLz/7W2g7RVuzxwaLmwhgzx1o7sKnzIXsnlR+pLZUdqS2VHakLlR+pLZUdqS2VHakLlZ+q1A1NREREREREREQiFCwSEREREREREZEIBYsaz0tNnQHZq6n8SG2p7EhtqexIXaj8SG2p7EhtqexIXaj8VKIxi0REREREREREJEIti0REREREREREJELBIhERERERERERiVCwqAEZY7obY14zxuQYY7zGmHXGmKeMMW2aOm+SOGNMO2PMb40xE4wxq4wxJcaYfGPMDGPM9cYYV6XtexpjbDWP96p5rRHGmB+NMUXh15hmjDm/mu3dxpg/GmMWhfO10xjzqTFmUDVp0o0x9xtjlhtjSo0x24wx7xtj+tXuE5LqhL//scrClhhpBoWP487wcV1kjLnDGOOu5nXOD5eX/HD5mW2MGVFD3hq8vEntGWOuraEuscaYYLntVffsY4wxlxljnjXGTDfGFISP81s1pGk29YvKVN0kUn6MMX2MMSONMV8bYzYaY3zGmK3GmInGmMEx0tRUh90UI13Cx9UY09Y419rrjHPtnWOca/Hu1aTR9XotJVh2mt25qTblTRwJlp2xNZQda4yZWimN6p16oDGLGogxphcwE+gITAR+AY4FBgPLgROttTuaLoeSqHCl8jywGfgG2AB0Ai4BWgEfAZfb8JfKGNMTWAssBP4XZZdLrLUfRnmdx4C7gE3Ah0AKMBxoC9xqrR1daXsDvA9chlO2JoW3HQakAZdaaydWSpMKTAVOBOYAXwP7AZcDPuB0a+3seD8bqZkxZh3QGngqyuoia+1jlba/EKdMlQLjgJ3AUOAg4ENr7eVRXuMPwLPAjnAaH0656A48bq39U5Q0DV7epG6MMUcAF8VYfTJwOvCJtfb88PY9Ud2zTzHGLAAOB4pwjt/BwNvW2qtibN9s6heVqbpLpPyEf9APA5YBM3DKzkHABYAbuN1a+0ylNNcCY3CuhxdEycJka+2cSmkSPq7GmHY41959w9v/FH4vFwLbgBOstWsqpdH1eh0kWHZ60ozOTbUpb7JbgmXnIuCIGLu6GjgQ+HP5a2nVO/XEWqtHAzyALwCLU4mVX/5EePkLTZ1HPRI+pqfjXEy7Ki3vjBM4sjgnoLLlPcPLxibwGoPCaVYBbSrtawfOhX3PSmn+L5zmeyCt3PJjAC9ORZVVKc3fwmk+KP9+cCo2Cyyt/D71qHP5WQesi3PbluHj5gUGlluehnNyscDwSml6hsvHjvJlBGgTLk8W54TV6OVNjwYtVz+Ej8cFlY6f6p596IFzgdkHMMBp4c/yrRjbNqv6RWWq0cvPtcCRUZafivNjygt0iZLGAtcmkKeEjyvwYnjd45WW3xZe/nmU19H1euOVnZ40o3NTbcqbHrUrO9XsozXgCR/X9pXWqd6pj+PU1Blojg+gV/hAr41SoLJwIqjFQGZT51WPejvmo8LH/Nlyy2pzUnwjnOY3UdY9EF53f6Xl34WXD45nf+FKeX14+QFR0sTcnx51KiPriD9YdF34GLweZd3p4XXfxlM+qttfY5Q3PRq0TA0If96bAHe55ap79uEHNf9gazb1i8pU45efGtJ+SaUbZ+Hl15LAj7baHFegBc6PxiKqBgJcOOdgCxxYbrmu1xux7DSnc1NtypsetS871aS7NZzu3SjrVO/Uw0NjFjWMsj7bX1prQ+VXWGsLcSLdGcDxjZ0xaTD+8HMgyrquxpjfGWNGhZ8Pq2Y/p4efP4+y7rNK22CMScO56+IBpseTBqeS6gGssNaujTON1I9UY8xV4bJwuzFmsIk+Pkh15eA7nOM9KNxcNp40sY5pY5Q3aTg3hp9ftdYGo6xX3SPRNKf6RWVqz1LdtRDAEcYZF+uvxpirqxnPozbH9XggHfg+fK0dEb4W/yL8b/lxlXS93jSaw7mpNuVN6t8N4eeXqtlG9U4dJDV1Bpqpg8LPK2KsXwmchdO3cWqMbWQvYYxJAq4J/xvtZHZm+FE+zTRghLV2Q7llmUA3nPFrNkfZz8rwc99yy3rhjBGwxlob7eIsWpp4ymflNFI/OgNvVlq21hjzG2vtt+WWxTxG1tqAMWYtcAhOH+2f40iz2RhTDHQ3xmRYaz2NWN6kARhj0oGrgCDwSozNVPdINM2pflGZ2kMYY/YHhuD8SP8uxma3V/o/aIx5BbjDWltabnltjmtDpdH1ev1rDucm1T1NzBhzAk4L6xXW2m+q2VT1Th2oZVHDaBV+zo+xvmx560bIizS8R4BDgU+ttV+UW+4BHgSOxhnXoQ1On/5vcJpbTg2fCMvUptw0VhqpuzE4F9KdgUycE9yLOM2yPzPGHF5u24Y8rq0qPavs7J1+jfM5f26t3VhpneoeqU5zql9UpvYA4VZobwOpwH3W2l2VNlmL013kIJzzX1ecOmwd8DvgtUrbq/w0T83p3KSy0/TKWle/HGO96p16oGCRSB0YY27DmaHhF5zR+COstdustX+31s6z1uaFH9/hRItnA72B3zZ6pqVJWGvvt9Z+ba3daq31WGuXWGtvwhnMLh24r2lzKHuZsoukFyuvUN0jIo0l3JX6TZzZg8YBj1Xexlr7rbV2tLV2Rfj8t9la+wFOd4xdwP9VumEizZDOTVJfjDGtcAI/PmBstG1U79QPBYsaRuU7bJWVLc9rhLxIAwlPI/w0zvSxg621O+NJF24WW9Zt5JRyq2pTbhorjTScF8LPjVUW8is9q+zsZYwxh+CMybAJ+DTedKp7JKw51S8qU00oHCh6C2da6feBq2x4lNZ4hFtFltVhqpP2UXvpuUllp2ldhTOuz3hrbW4iCVXvJEbBooaxPPwcq59qn/BzrL6KsoczxtwBPAsswQkUbUlwF9vDz5HmttbaYiAbaGGM6RIlTbRysxpnzJIDw2MnxZNG5XPPUqUsUM0xCh/nA3AGEF0TZ5ou4f1vstZ6oFHLm9S/mga2ro7qHmlO9YvKVBMxxiQD7wLDgXeAK2KMEVOThM6BYfVVFlR+9hx727lJZadplQ1sXaV1dZxU78RJwaKGUTbI1lnGmAqfsTEmC6eprgeY1dgZk7ozxowEngQW4ASKttViN2Uj3K+ptPzr8PM5UdKcW2kbwgOzzcSJrp8cTxqcE+kGoK8x5oA400jDiVYWqisHp+Ac75nWWm+caWId08Yob1KPwjO9XI1zMfxqLXahukeaU/2iMtUEjDEpwAc4LYreAK6uReC6zHHh5/J1Um2O6yygBDgxfK1dPr8unK5OsPsavfzful5venvbuak25U3qgTHmOOBwnIGtp9VyN6p34mWt1aMBHjhT5Vng1krLnwgvf6Gp86hHrY7rveHjNwdoW8O2RwGuKMuHAKXh/QyqtG5QePkqoE255T2BHeF0PSul+b9wmu+BtHLLjwG8wDagZaU0fwun+aB8HoELw8uXRsu7HrUuN/2AzCjLe+LMemCBUeWWt8S56+EFBpZbnoZzEWSB4ZX2dUC4fOwoX0ZwBpBcFU5zQlOUNz3qtSxdHf78J1WzjeqeffiBM1CsBd6Ksb5Z1S8qU41eflKBT8LbvBLPZ1u+nJVb5ip37LbXx3HFaWVggccrLb8tvPzzKPnQ9XrjlZ1mdW6qTXnTo3Zlp9K2r4a3vauG7VTv1MPDhDMm9cwY0wvnoqsjMBFn+tnjcAbVWoFTGe5ouhxKoowxI3AGUQvidEGLNor9Omvt2PD203CaEs7EGVsE4DDg9PDf91prH4ryOo8Dd4bTfAikAMOAdjiVyuhK2xucsQIuwxloe1J422E4F/+XWmsnVkqTihMZH4QT+JoK9MC5S+gDTrfWzq7xQ5G4GGPuwxkI/TtgPVCIM73rr3CO0afAxdZaX7k0F+Ec/1LgPWAncAHOrA4fAr+2lSpwY8ytwDM4F1DjcI7lZUB3nJPYn6LkrcHLm9QfY8x04CTgAmvtpBjbTEN1zz4lXF9cFP63M3A2zh3T6eFlueW//82pflGZqrtEyo8xZgxwLZALPIfzw6ayabbcHX9jjMXptr8Qp1tRK5y754fi3EG/2Fr7ZaU8JXxcjTHtcOq9vuG0P+LcrLkQJ0AwyFq7ulIaXa/XQYJlZxrN6NxUm/ImuyV63gqnaQnkAElAd1vNeEWqd+pJU0ermvMD2A9nuuzNOAVsPfAU5SLjeuw9D5zZqmwNj2nltr8emIwzRWMRzt2MDTgX2SfX8FrXAj8BxTiBhW+B86vZPgn4I7AYpznkLpwAxKBq0mQAD+C0bPHiRNg/APo39Wfd3B44U8O+i3PRkgf4w5/3FOAacAL3UdKdGD6Ou8LHdXH4OLurea2h4fJSGC4/PwEjmrq86VEv5ahfuJ7ZWEMZUN2zjz3iOD+ti5Km2dQvKlONV36AaTVsa4H7Ku3/0fBxz8EJUHpwzoejgQPr87gCbXEmH1mPc+29GWeK7O7VpNH1euOUnWZ3bqpNedMj8bJTLs3N4XXvxrF/1Tv18FDLIhERERERERERidAA1yIiIiIiIiIiEqFgkYiIiIiIiIiIRChYJCIiIiIiIiIiEQoWiYiIiIiIiIhIhIJFIiIiIiIiIiISoWCRiIiIiIiIiIhEKFgkIiIiIiIiIiIRChaJiIiIiIiIiEiEgkUiIiKyTzLGjDfGWGPMndVsc4wxxm+MWWuMadmY+RMRERFpKsZa29R5EBEREWl0xph2wGKgLXCMtXZxpfUZwHygN3CqtXZG4+dSREREpPGpZZGIiIjsk6y1O4DfACnA28aY1EqbPA70BR5RoEhERET2JQoWiYiIyD7LWvsFMBoYAPyrbLkx5jzgJmAucJ8xJskYc4sxZpYxpsAY4zHGzDfG/MEYU+V6yhhzrTHmI2PMGmNMSTjN98aYq6LlwxgzLdwlLsUY83djzHJjjNcYM7ZB3riIiIhINdQNTURERPZpxpg0nKBQP+BMYCGwBMgCjgLWAJOAs4HlwDSgFBgMHAa8Za29utI+S4Cl4f1sBtoB5wHdgIestfdW2n4acCowGTgG+AzYBmyz1j5ez29ZREREpFpJTZ0BERERkaZkrS01xlwJzAZeBxYBnYBbrLXLjTH34QSKRgN3WGuDAMYYN/AScJ0x5kNr7cRyuz3UWru6/OsYY1JwgkB/Nca8YK3NjpKd/cNpc+v3XYqIiIjET93QREREZJ9nrV0A3IvT8udc4BNr7fPhLma3AluAP5YFisJpgsBdgAWurLS/CoGi8DIf8F+cm3VDYmTlXgWKREREpKmpZZGIiIiI4zHgj0Bn4M/hZX1xZktbCdxjjImWrgSnC1uEMaYHMBInKNQDSK+UpluMPPxYm4yLiIiI1CcFi0REREQAa23IGOMN/1sSfm4Xfu4D/KOa5C3K/jDGHIgT9GkDTAe+BPKBINATGAFUnnmtzJba5F1ERESkPilYJCIiIhJbfvh5grX2kjjT3IkTZPqNtXZs+RXGmP/DCRZFZTXziIiIiOwBNGaRiIiISGy/AHnA8caY5DjT9A4/fxRl3an1kisRERGRBqRgkYiIiEgM1toA8CzQBXjGGFN57CGMMV2MMf3LLVoXfj6t0nZnA79tmJyKiIiI1B91QxMRERGp3oPA4cBNwFBjzNdANtARZyyjE4G7gWXh7Z8DfgN8YIz5EMgBDgXOAd4HhjVq7kVEREQSpGCRiIiISDWstX5jzEXAVcC1wPk4A1pvB9YC9wJvl9t+kTFmMPAQ8Cuc662FwCU4XdoULBIREZE9mtE4iiIiIiIiIiIiUkZjFomIiIiIiIiISISCRSIiIiIiIiIiEqFgkYiIiIiIiIiIRChYJCIiIiIiIiIiEQoWiYiIiIiIiIhIhIJFIiIiIiIiIiISoWCRiIiIiIiIiIhEKFgkIiIiIiIiIiIRChaJiIiIiIiIiEiEgkUiIiIiIiIiIhLx/1iYlwY4LNZoAAAAAElFTkSuQmCC\n",
-            "text/plain": [
-              "<Figure size 1440x720 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "YoQA_MIBMknS"
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Pi8UMMitMa3Q",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 710
-        },
-        "outputId": "57b8d44c-ad3d-45e0-82da-1787d6d47d55"
-      },
-      "source": [
-        "# we establish the corrmartrice\n",
-        "import seaborn as sns\n",
-        "color = sns.color_palette()\n",
-        "sns.set_style('darkgrid')\n",
-        "\n",
-        "correaltionMatrice = df_Ellis.corr()\n",
-        "f, ax = plt.subplots(figsize=(20, 10))\n",
-        "sns.heatmap(correaltionMatrice, cbar=True, vmin=0, vmax=1, square=True, annot=True);\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 1440x720 with 2 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "rkYwyKtXMvpy",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "f5386858-2a38-4b20-9be9-f841ee5af0d6"
-      },
-      "source": [
-        "df_Ellis.corrwith(df_Ellis['ellis-load.avg_1_min'])"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "ellis-cpu.system_perc       -0.316956\n",
-              "ellis-cpu.wait_perc          0.886739\n",
-              "ellis-load.avg_1_min         1.000000\n",
-              "ellis-mem.free_mb           -0.335300\n",
-              "ellis-net.in_bytes_sec      -0.681849\n",
-              "ellis-net.out_packets_sec   -0.116851\n",
-              "Label                        0.279330\n",
-              "dtype: float64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 12
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "5oQK-ddinvCM",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 235
-        },
-        "outputId": "a611220f-df65-4d99-9c8f-dc0dd715f93f"
-      },
-      "source": [
-        "## ## using multivariate feature \n",
-        "\n",
-        "features_3 = ['ellis-cpu.wait_perc', 'ellis-load.avg_1_min', 'ellis-net.in_bytes_sec', 'Label']\n",
-        "\n",
-        "features = df_Ellis[features_3]\n",
-        "features.index = df_Ellis['Timestamp']\n",
-        "features.head()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>ellis-cpu.wait_perc</th>\n",
-              "      <th>ellis-load.avg_1_min</th>\n",
-              "      <th>ellis-net.in_bytes_sec</th>\n",
-              "      <th>Label</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Timestamp</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>14-09-2016 0:00</th>\n",
-              "      <td>12.9</td>\n",
-              "      <td>1.73</td>\n",
-              "      <td>5413.200</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>14-09-2016 0:00</th>\n",
-              "      <td>10.3</td>\n",
-              "      <td>1.79</td>\n",
-              "      <td>5201.667</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>14-09-2016 0:01</th>\n",
-              "      <td>11.8</td>\n",
-              "      <td>1.52</td>\n",
-              "      <td>5370.733</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>14-09-2016 0:01</th>\n",
-              "      <td>12.9</td>\n",
-              "      <td>1.43</td>\n",
-              "      <td>5292.467</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>14-09-2016 0:02</th>\n",
-              "      <td>12.1</td>\n",
-              "      <td>1.44</td>\n",
-              "      <td>5318.167</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                 ellis-cpu.wait_perc  ...  Label\n",
-              "Timestamp                             ...       \n",
-              "14-09-2016 0:00                 12.9  ...      1\n",
-              "14-09-2016 0:00                 10.3  ...      1\n",
-              "14-09-2016 0:01                 11.8  ...      1\n",
-              "14-09-2016 0:01                 12.9  ...      1\n",
-              "14-09-2016 0:02                 12.1  ...      1\n",
-              "\n",
-              "[5 rows x 4 columns]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 13
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "qbqn755fo81g",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 386
-        },
-        "outputId": "8750fe62-a1e7-40ca-a3c1-4285546f97ff"
-      },
-      "source": [
-        "features.plot(subplots=True)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fd5476b2bd0>,\n",
-              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fd54764c5d0>,\n",
-              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fd5471169d0>,\n",
-              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fd54747cd10>],\n",
-              "      dtype=object)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 14
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 4 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "jJQD1x9psWCH"
-      },
-      "source": [
-        "features = features.values"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "xf8WCiykpUzN",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "b43752ab-2ff0-4e21-a4bc-633cd0aff7dd"
-      },
-      "source": [
-        "### standardize data\n",
-        "train_split = 141600\n",
-        "tf.random.set_seed(13)\n",
-        "\n",
-        "### standardize data\n",
-        "features_mean = features[:train_split].mean()\n",
-        "features_std = features[:train_split].std()\n",
-        "features  = (features - features_mean)/ features_std\n",
-        "\n",
-        "print(type(features))\n",
-        "print(features.shape)\n"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "<class 'numpy.ndarray'>\n",
-            "(177000, 4)\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "1a0hNDmppnLB"
-      },
-      "source": [
-        "### create mutlivariate data\n",
-        "\n",
-        "def mutlivariate_data(features , target , start_idx , end_idx , history_size , target_size,\n",
-        "                      step ,  single_step = False):\n",
-        "  data = []\n",
-        "  labels = []\n",
-        "  start_idx = start_idx + history_size\n",
-        "  if end_idx is None:\n",
-        "    end_idx = len(features)- target_size\n",
-        "  for i in range(start_idx , end_idx ):\n",
-        "    idxs = range(i-history_size, i, step) ### using step\n",
-        "    data.append(features[idxs])\n",
-        "    if single_step:\n",
-        "      labels.append(target[i+target_size])\n",
-        "    else:\n",
-        "      labels.append(target[i:i+target_size])\n",
-        "\n",
-        "  return np.array(data) , np.array(labels)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Z0CivgkitfgE",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "614149cc-6746-4d02-a608-2957cef79338"
-      },
-      "source": [
-        "### generate multivariate data\n",
-        "\n",
-        "history = 720\n",
-        "future_target = 72\n",
-        "STEP = 6\n",
-        "\n",
-        "x_train_ss , y_train_ss = mutlivariate_data(features , features[:, 1], 0, train_split, history,\n",
-        "                                            future_target, STEP , single_step = True)\n",
-        "\n",
-        "x_val_ss , y_val_ss = mutlivariate_data(features , features[:,1] , train_split , None , history ,\n",
-        "                                        future_target, STEP, single_step = True)\n",
-        "\n",
-        "print(x_train_ss.shape , y_train_ss.shape)\n",
-        "print(x_val_ss.shape , y_val_ss.shape)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "(140880, 120, 4) (140880,)\n",
-            "(34608, 120, 4) (34608,)\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "VBdr2epGu3aq",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "f9dc4c4f-ccfa-4379-add5-cd732dc8a310"
-      },
-      "source": [
-        "## tensorflow dataset\n",
-        "batch_size = 256\n",
-        "buffer_size = 10000\n",
-        "\n",
-        "train_ss = tf.data.Dataset.from_tensor_slices((x_train_ss, y_train_ss))\n",
-        "train_ss = train_ss.cache().shuffle(buffer_size).batch(batch_size).repeat()\n",
-        "\n",
-        "val_ss = tf.data.Dataset.from_tensor_slices((x_val_ss, y_val_ss))\n",
-        "val_ss = val_ss.cache().shuffle(buffer_size).batch(batch_size).repeat()\n",
-        "\n",
-        "print(train_ss)\n",
-        "print(val_ss)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "<RepeatDataset shapes: ((None, 120, 4), (None,)), types: (tf.float64, tf.float64)>\n",
-            "<RepeatDataset shapes: ((None, 120, 4), (None,)), types: (tf.float64, tf.float64)>\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "9eQpwUyGglu_"
-      },
-      "source": [
-        "def root_mean_squared_error(y_true, y_pred):\n",
-        "        return K.sqrt(K.mean(K.square(y_pred - y_true))) "
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "81S0BbNa7Rce"
-      },
-      "source": [
-        "import tensorflow as tf\n",
-        "from keras.layers import Activation, Dense, Dropout,Permute,Reshape,Lambda,dot,concatenate\n",
-        "\n",
-        "INPUT_DIM = 100\n",
-        "TIME_STEPS = 20\n",
-        "# if True, the attention vector is shared across the input_dimensions where the attention is applied.\n",
-        "SINGLE_ATTENTION_VECTOR = True\n",
-        "APPLY_ATTENTION_BEFORE_LSTM = False\n",
-        "\n",
-        "ATTENTION_SIZE = 128\n",
-        "\n",
-        "def attention_3d_block(hidden_states):\n",
-        "    # hidden_states.shape = (batch_size, time_steps, hidden_size)\n",
-        "    hidden_size = int(hidden_states.shape[2])\n",
-        "    # _t stands for transpose\n",
-        "    hidden_states_t =  Permute((2, 1), name='attention_input_t')(hidden_states)\n",
-        "    # hidden_states_t.shape = (batch_size, hidden_size, time_steps)\n",
-        "    # this line is not useful. It's just to know which dimension is what.\n",
-        "    hidden_states_t = Reshape((hidden_size, TIME_STEPS), name='attention_input_reshape')(hidden_states_t)\n",
-        "    # Inside dense layer\n",
-        "    # a (batch_size, hidden_size, time_steps) dot W (time_steps, time_steps) => (batch_size, hidden_size, time_steps)\n",
-        "    # W is the trainable weight matrix of attention\n",
-        "    # Luong's multiplicative style score\n",
-        "    score_first_part = Dense(TIME_STEPS, use_bias=False, name='attention_score_vec')(hidden_states_t)\n",
-        "    score_first_part_t = Permute((2, 1), name='attention_score_vec_t')(score_first_part)\n",
-        "    #            score_first_part_t         dot        last_hidden_state     => attention_weights\n",
-        "    # (batch_size, time_steps, hidden_size) dot (batch_size, hidden_size, 1) => (batch_size, time_steps, 1)\n",
-        "    h_t = Lambda(lambda x: x[:, :, -1], output_shape=(hidden_size, 1), name='last_hidden_state')(hidden_states_t)\n",
-        "    score = dot([score_first_part_t, h_t], [2, 1], name='attention_score')\n",
-        "    attention_weights = Activation('softmax', name='attention_weight')(score)\n",
-        "    # if SINGLE_ATTENTION_VECTOR:\n",
-        "    #     a = Lambda(lambda x: K.mean(x, axis=1), name='dim_reduction')(a)\n",
-        "    #     a = RepeatVector(hidden_size)(a)\n",
-        "    # (batch_size, hidden_size, time_steps) dot (batch_size, time_steps, 1) => (batch_size, hidden_size, 1)\n",
-        "    context_vector = dot([hidden_states_t, attention_weights], [2, 1], name='context_vector')\n",
-        "    context_vector = Reshape((hidden_size,))(context_vector)\n",
-        "    h_t = Reshape((hidden_size,))(h_t)\n",
-        "    pre_activation = concatenate([context_vector, h_t], name='attention_output')\n",
-        "    attention_vector = Dense(ATTENTION_SIZE, use_bias=False, activation='tanh', name='attention_vector')(pre_activation)\n",
-        "    return attention_vector"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "1cKtTAzqyiyL",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "e1863cec-a19f-4419-fe8c-527e549bccdf"
-      },
-      "source": [
-        "from keras.layers import Activation, Dense, Dropout\n",
-        "from keras.layers import Input, LSTM, merge ,Conv1D,Dropout,Bidirectional,Multiply,Flatten\n",
-        "from keras.models import Model\n",
-        "from keras.utils.vis_utils import plot_model\n",
-        "### Modelling using LSTM\n",
-        "steps = 50\n",
-        "TIME_STEPS=120\n",
-        "INPUT_DIMS=4\n",
-        "\n",
-        "EPOCHS =20\n",
-        "\n",
-        "single_step_model = tf.keras.models.Sequential()\n",
-        "\n",
-        "inputs = Input(shape=(120, 4))\n",
-        "lstm_units = 32\n",
-        "lstm_out = LSTM(lstm_units, return_sequences=True)(inputs)\n",
-        "lstm_out=tf.keras.layers.Dropout(0.6)(lstm_out)\n",
-        "attention_mul = attention_3d_block(lstm_out)\n",
-        "#attention_mul = Flatten()(attention_mul)\n",
-        "attention_mul=tf.keras.layers.Dropout(0.6)(attention_mul)\n",
-        "output = Dense(1, activation='sigmoid')(attention_mul)\n",
-        "\n",
-        "single_step_model = Model(inputs, output)\n",
-        "\n",
-        "  \n",
-        "single_step_model.summary()\n",
-        "\n",
-        "plot_model(single_step_model, to_file='/content/drive/MyDrive/LFN Anuket/Analysis/data/Final/LSTM-Attention-B.png', show_shapes=True, show_layer_names=True)\n",
-        "\n",
-        "single_step_model.compile(optimizer = tf.keras.optimizers.Adam(), loss = 'mae',metrics=[tf.keras.metrics.RootMeanSquaredError(name='rmse')])\n",
-        "#single_step_model.compile(loss='mse', optimizer='rmsprop')\n",
-        "\n",
-        " "
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Model: \"model_1\"\n",
-            "__________________________________________________________________________________________________\n",
-            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
-            "==================================================================================================\n",
-            "input_2 (InputLayer)            [(None, 120, 4)]     0                                            \n",
-            "__________________________________________________________________________________________________\n",
-            "lstm_1 (LSTM)                   (None, 120, 32)      4736        input_2[0][0]                    \n",
-            "__________________________________________________________________________________________________\n",
-            "dropout_2 (Dropout)             (None, 120, 32)      0           lstm_1[0][0]                     \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_input_t (Permute)     (None, 32, 120)      0           dropout_2[0][0]                  \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_input_reshape (Reshap (None, 32, 120)      0           attention_input_t[0][0]          \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_score_vec (Dense)     (None, 32, 120)      14400       attention_input_reshape[0][0]    \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_score_vec_t (Permute) (None, 120, 32)      0           attention_score_vec[0][0]        \n",
-            "__________________________________________________________________________________________________\n",
-            "last_hidden_state (Lambda)      (None, 32)           0           attention_input_reshape[0][0]    \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_score (Dot)           (None, 120)          0           attention_score_vec_t[0][0]      \n",
-            "                                                                 last_hidden_state[0][0]          \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_weight (Activation)   (None, 120)          0           attention_score[0][0]            \n",
-            "__________________________________________________________________________________________________\n",
-            "context_vector (Dot)            (None, 32)           0           attention_input_reshape[0][0]    \n",
-            "                                                                 attention_weight[0][0]           \n",
-            "__________________________________________________________________________________________________\n",
-            "reshape_2 (Reshape)             (None, 32)           0           context_vector[0][0]             \n",
-            "__________________________________________________________________________________________________\n",
-            "reshape_3 (Reshape)             (None, 32)           0           last_hidden_state[0][0]          \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_output (Concatenate)  (None, 64)           0           reshape_2[0][0]                  \n",
-            "                                                                 reshape_3[0][0]                  \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_vector (Dense)        (None, 128)          8192        attention_output[0][0]           \n",
-            "__________________________________________________________________________________________________\n",
-            "dropout_3 (Dropout)             (None, 128)          0           attention_vector[0][0]           \n",
-            "__________________________________________________________________________________________________\n",
-            "dense_1 (Dense)                 (None, 1)            129         dropout_3[0][0]                  \n",
-            "==================================================================================================\n",
-            "Total params: 27,457\n",
-            "Trainable params: 27,457\n",
-            "Non-trainable params: 0\n",
-            "__________________________________________________________________________________________________\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "94000u5xIjWD",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "c3f7f857-fbb7-4fc5-b2bf-d860a828d0a9"
-      },
-      "source": [
-        "single_step_model_history = single_step_model.fit(train_ss, epochs = EPOCHS , \n",
-        "                                                  steps_per_epoch =steps, validation_data = val_ss,\n",
-        "                                                  validation_steps = 50)\n"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Epoch 1/20\n",
-            "50/50 [==============================] - 10s 50ms/step - loss: 0.6677 - rmse: 0.6899 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 2/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5433 - rmse: 0.5433 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 3/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5432 - rmse: 0.5432 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 4/20\n",
-            "50/50 [==============================] - 2s 41ms/step - loss: 0.5432 - rmse: 0.5432 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 5/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5432 - rmse: 0.5432 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 6/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 7/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 8/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 9/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 10/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 11/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 12/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 13/20\n",
-            "50/50 [==============================] - 2s 41ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 14/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 15/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 16/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 17/20\n",
-            "50/50 [==============================] - 2s 41ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 18/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 19/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 20/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Pgev0dgzzBVx",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 281
-        },
-        "outputId": "a97e1688-da30-4042-c349-fa8971512907"
-      },
-      "source": [
-        "## plot train test loss \n",
-        "\n",
-        "def plot_loss(history , title):\n",
-        "  loss = history.history['loss']\n",
-        "  val_loss = history.history['val_loss']\n",
-        "\n",
-        "  epochs = range(len(loss))\n",
-        "  plt.figure()\n",
-        "  plt.plot(epochs, loss , 'b' , label = 'Train Loss')\n",
-        "  plt.plot(epochs, val_loss , 'r' , label = 'Validation Loss')\n",
-        "  plt.title(title)\n",
-        "  plt.legend()\n",
-        "  plt.grid()\n",
-        "  plt.show()\n",
-        "\n",
-        "plot_loss(single_step_model_history , 'Single Step Training and validation loss')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "EnYf6j4okEoC",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 281
-        },
-        "outputId": "be978504-7501-42fe-c192-4c6b023e992a"
-      },
-      "source": [
-        "## plot train test loss \n",
-        "\n",
-        "def plot_loss(history , title):\n",
-        "  loss = history.history['rmse']\n",
-        "  val_loss = history.history['val_rmse']\n",
-        "\n",
-        "  epochs = range(len(loss))\n",
-        "  plt.figure()\n",
-        "  plt.plot(epochs, loss , 'b' , label = 'Train RMSE')\n",
-        "  plt.plot(epochs, val_loss , 'r' , label = 'Validation RMSE')\n",
-        "  plt.title(title)\n",
-        "  plt.legend()\n",
-        "  plt.grid()\n",
-        "  plt.show()\n",
-        "\n",
-        "plot_loss(single_step_model_history , 'Single Step Training and validation loss')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "WMegV8mNAwe_"
-      },
-      "source": [
-        "### fucntion to create time steps\n",
-        "def create_time_steps(length):\n",
-        "  return list(range(-length,0))\n",
-        "\n",
-        "### function to plot time series data\n",
-        "\n",
-        "def plot_time_series(plot_data, delta , title):\n",
-        "  labels = [\"History\" , 'True Future' , 'Model Predcited']\n",
-        "  marker = ['.-' , 'rx' , 'go']\n",
-        "  time_steps = create_time_steps(plot_data[0].shape[0])\n",
-        "\n",
-        "  if delta:\n",
-        "    future = delta\n",
-        "  else:\n",
-        "    future = 0\n",
-        "  plt.title(title)\n",
-        "  for i , x in enumerate(plot_data):\n",
-        "    if i :\n",
-        "      plt.plot(future , plot_data[i] , marker[i], markersize = 10 , label = labels[i])\n",
-        "    else:\n",
-        "      plt.plot(time_steps, plot_data[i].flatten(), marker[i], label = labels[i])\n",
-        "  plt.legend()\n",
-        "  plt.xlim([time_steps[0], (future+5) *2])\n",
-        "\n",
-        "  plt.xlabel('Time_Step')\n",
-        "  return plt"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "q99i2c-9XKF3"
-      },
-      "source": [
-        "### Moving window average\n",
-        "\n",
-        "def MWA(history):\n",
-        "  return np.mean(history)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "xFJT1rZDAUVL",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "outputId": "56ae677f-dadd-434f-f31b-d7cc9ee0eb89"
-      },
-      "source": [
-        "# plot time series and predicted values\n",
-        "\n",
-        "for x, y in val_ss.take(5):\n",
-        "  plot = plot_time_series([x[0][:, 1].numpy(), y[0].numpy(),\n",
-        "                    single_step_model.predict(x)[0]], 12,\n",
-        "                   'Single Step Prediction')\n",
-        "  plot.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_KXWQVmyCSix"
-      },
-      "source": [
-        "# **MultiStep Forcasting**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Lu7m2Rr4AbMK",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "9436a6eb-214e-47a3-d241-3a5bd056c31c"
-      },
-      "source": [
-        "future_target = 72 # 72 future values\n",
-        "x_train_multi, y_train_multi = mutlivariate_data(features, features[:, 1], 0,\n",
-        "                                                 train_split, history,\n",
-        "                                                 future_target, STEP)\n",
-        "x_val_multi, y_val_multi = mutlivariate_data(features, features[:, 1],\n",
-        "                                             train_split, None, history,\n",
-        "                                             future_target, STEP)\n",
-        "\n",
-        "print(x_train_multi.shape)\n",
-        "print(y_train_multi.shape)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "(140880, 120, 4)\n",
-            "(140880, 72)\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "GLRv5D16CrHj"
-      },
-      "source": [
-        "#  TF DATASET\n",
-        "\n",
-        "train_data_multi = tf.data.Dataset.from_tensor_slices((x_train_multi, y_train_multi))\n",
-        "train_data_multi = train_data_multi.cache().shuffle(buffer_size).batch(batch_size).repeat()\n",
-        "\n",
-        "val_data_multi = tf.data.Dataset.from_tensor_slices((x_val_multi, y_val_multi))\n",
-        "val_data_multi = val_data_multi.batch(batch_size).repeat()"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "fjXexah9C8yg",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "185bb543-b7ad-4f25-fd89-75be524af04e"
-      },
-      "source": [
-        "print(train_data_multi)\n",
-        "print(val_data_multi)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "<RepeatDataset shapes: ((None, 120, 4), (None, 72)), types: (tf.float64, tf.float64)>\n",
-            "<RepeatDataset shapes: ((None, 120, 4), (None, 72)), types: (tf.float64, tf.float64)>\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "7mtLZ6S-DPU-",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 385
-        },
-        "outputId": "553066ce-28bf-4e95-95c1-f7f34f907647"
-      },
-      "source": [
-        "#plotting function\n",
-        "def multi_step_plot(history, true_future, prediction):\n",
-        "  plt.figure(figsize=(12, 6))\n",
-        "  num_in = create_time_steps(len(history))\n",
-        "  num_out = len(true_future)\n",
-        "  plt.grid()\n",
-        "  plt.plot(num_in, np.array(history[:, 1]), label='History')\n",
-        "  plt.plot(np.arange(num_out)/STEP, np.array(true_future), 'bo',\n",
-        "           label='True Future')\n",
-        "  if prediction.any():\n",
-        "    plt.plot(np.arange(num_out)/STEP, np.array(prediction), 'ro',\n",
-        "             label='Predicted Future')\n",
-        "  plt.legend(loc='upper left')\n",
-        "  plt.show()\n",
-        "  \n",
-        "\n",
-        "\n",
-        "for x, y in train_data_multi.take(1):\n",
-        "  multi_step_plot(x[0], y[0], np.array([0]))"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 864x432 with 1 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "snN_Flr5DWQN",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "8c9fb49d-7743-4966-845b-f56a06bbe9e6"
-      },
-      "source": [
-        "multi_step_model = tf.keras.models.Sequential()\n",
-        "\n",
-        "inputs = Input(shape=(120, 4))\n",
-        "lstm_units = 32\n",
-        "lstm_out = LSTM(lstm_units, return_sequences=True)(inputs)\n",
-        "lstm_out=tf.keras.layers.Dropout(0.6)(lstm_out)\n",
-        "attention_mul = attention_3d_block(lstm_out)\n",
-        "#attention_mul = Flatten()(attention_mul)\n",
-        "attention_mul=tf.keras.layers.Dropout(0.6)(attention_mul)\n",
-        "output = Dense(1, activation='sigmoid')(attention_mul)\n",
-        "\n",
-        "multi_step_model = Model(inputs, output)\n",
-        "multi_step_model.compile(optimizer=tf.keras.optimizers.RMSprop(clipvalue=1.0), loss='mae',metrics=[tf.keras.metrics.RootMeanSquaredError(name='rmse')])\n",
-        "\n",
-        "multi_step_history = multi_step_model.fit(train_data_multi, epochs=EPOCHS,\n",
-        "                                          steps_per_epoch=steps,\n",
-        "                                          validation_data=val_data_multi,\n",
-        "                                          validation_steps=50)\n",
-        "multi_step_model.summary()\n",
-        "\n"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Epoch 1/20\n",
-            "50/50 [==============================] - 5s 50ms/step - loss: 0.5863 - rmse: 0.5965 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 2/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5432 - rmse: 0.5432 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 3/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 4/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 5/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 6/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 7/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 8/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 9/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 10/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 11/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 12/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 13/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 14/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 15/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 16/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 17/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 18/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 19/20\n",
-            "50/50 [==============================] - 2s 39ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Epoch 20/20\n",
-            "50/50 [==============================] - 2s 40ms/step - loss: 0.5431 - rmse: 0.5431 - val_loss: 0.5431 - val_rmse: 0.5431\n",
-            "Model: \"model_2\"\n",
-            "__________________________________________________________________________________________________\n",
-            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
-            "==================================================================================================\n",
-            "input_3 (InputLayer)            [(None, 120, 4)]     0                                            \n",
-            "__________________________________________________________________________________________________\n",
-            "lstm_2 (LSTM)                   (None, 120, 32)      4736        input_3[0][0]                    \n",
-            "__________________________________________________________________________________________________\n",
-            "dropout_4 (Dropout)             (None, 120, 32)      0           lstm_2[0][0]                     \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_input_t (Permute)     (None, 32, 120)      0           dropout_4[0][0]                  \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_input_reshape (Reshap (None, 32, 120)      0           attention_input_t[0][0]          \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_score_vec (Dense)     (None, 32, 120)      14400       attention_input_reshape[0][0]    \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_score_vec_t (Permute) (None, 120, 32)      0           attention_score_vec[0][0]        \n",
-            "__________________________________________________________________________________________________\n",
-            "last_hidden_state (Lambda)      (None, 32)           0           attention_input_reshape[0][0]    \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_score (Dot)           (None, 120)          0           attention_score_vec_t[0][0]      \n",
-            "                                                                 last_hidden_state[0][0]          \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_weight (Activation)   (None, 120)          0           attention_score[0][0]            \n",
-            "__________________________________________________________________________________________________\n",
-            "context_vector (Dot)            (None, 32)           0           attention_input_reshape[0][0]    \n",
-            "                                                                 attention_weight[0][0]           \n",
-            "__________________________________________________________________________________________________\n",
-            "reshape_4 (Reshape)             (None, 32)           0           context_vector[0][0]             \n",
-            "__________________________________________________________________________________________________\n",
-            "reshape_5 (Reshape)             (None, 32)           0           last_hidden_state[0][0]          \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_output (Concatenate)  (None, 64)           0           reshape_4[0][0]                  \n",
-            "                                                                 reshape_5[0][0]                  \n",
-            "__________________________________________________________________________________________________\n",
-            "attention_vector (Dense)        (None, 128)          8192        attention_output[0][0]           \n",
-            "__________________________________________________________________________________________________\n",
-            "dropout_5 (Dropout)             (None, 128)          0           attention_vector[0][0]           \n",
-            "__________________________________________________________________________________________________\n",
-            "dense_2 (Dense)                 (None, 1)            129         dropout_5[0][0]                  \n",
-            "==================================================================================================\n",
-            "Total params: 27,457\n",
-            "Trainable params: 27,457\n",
-            "Non-trainable params: 0\n",
-            "__________________________________________________________________________________________________\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Ay5m27doDsTt",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "e9e4b8ea-77d6-438f-9698-91b95180e6f2"
-      },
-      "source": [
-        "plot_loss(multi_step_history, 'Multi-Step Training and validation loss')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "6ZFP49W4D2wp",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "41c0548f-3fd9-41eb-b2c8-045ec24f59c3"
-      },
-      "source": [
-        "for x, y in val_data_multi.take(5):\n",
-        "  multi_step_plot(x[0], y[0], multi_step_model.predict(x)[0])"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "error",
-          "ename": "ValueError",
-          "evalue": "ignored",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-41-ff89c8e0e80a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval_data_multi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mmulti_step_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_step_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-            "\u001b[0;32m<ipython-input-38-27efd3ecf8d8>\u001b[0m in \u001b[0;36mmulti_step_plot\u001b[0;34m(history, true_future, prediction)\u001b[0m\n\u001b[1;32m     10\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     plt.plot(np.arange(num_out)/STEP, np.array(prediction), 'ro',\n\u001b[0;32m---> 12\u001b[0;31m              label='Predicted Future')\n\u001b[0m\u001b[1;32m     13\u001b[0m   \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'upper left'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m   \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2761\u001b[0m     return gca().plot(\n\u001b[1;32m   2762\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[0;32m-> 2763\u001b[0;31m         is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2764\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1645\u001b[0m         \"\"\"\n\u001b[1;32m   1646\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1647\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1648\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1649\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    214\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m             raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[1;32m    343\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[1;32m    344\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (72,) and (1,)"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 864x432 with 1 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "DNKMjVoAVqZP"
-      },
-      "source": [
-        "scores = multi_step_model.evaluate(x_train_multi, y_train_multi, verbose=1, batch_size=200)\n",
-        "print('MAE: {}'.format(scores[1]))"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "YXcsNZ8yu9Ay"
-      },
-      "source": [
-        "scores_test = multi_step_model.evaluate(x_val_multi, y_val_multi, verbose=1, batch_size=200)\n",
-        "print('MAE: {}'.format(scores[1]))\n"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "uCFgbZEOvZ9A"
-      },
-      "source": [
-        "y_pred_test = multi_step_model.predict(x_val_multi, verbose=0)\n",
-        "\n",
-        "plt.figure(figsize=(10,5))\n",
-        "plt.plot(y_pred_test)\n",
-        "plt.plot(y_val_multi)\n",
-        "plt.ylabel(\"RUL\")\n",
-        "plt.xlabel(\"Unit Number\")\n",
-        "plt.legend(loc='upper left')\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "1Pdu1pZyZExX"
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tLhroy5BnMnC"
+   },
+   "outputs": [],
+   "source": [
+    "# Importing libraries\n",
+    "import tensorflow as tf\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 521
+    },
+    "id": "2-UpMVsSnfCI",
+    "outputId": "120b11e9-5c7d-4332-d133-9636a8d002aa"
+   },
+   "outputs": [],
+   "source": [
+    "df_Ellis  = pd.read_csv(\"/content/drive/MyDrive/LFN Anuket/Analysis/data/Final/Ellis_FinalTwoConditionwithOR.csv\")\n",
+    "df_Ellis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 293
+    },
+    "id": "92xBt43BnjAo",
+    "outputId": "2104e011-6066-4e86-e6f8-515dfb9ea715"
+   },
+   "outputs": [],
+   "source": [
+    "df_Ellis.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 879
+    },
+    "id": "RSo-aa-SIoBR",
+    "outputId": "ea97c36d-cb27-4fb3-d9bd-e8f1776b098d"
+   },
+   "outputs": [],
+   "source": [
+    "# we show here the hist\n",
+    "df_Ellis.hist(bins=100,figsize=(20,15))\n",
+    "#save_fig(\"attribute_histogram_plots\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 634
+    },
+    "id": "gggaMJ_2LtFs",
+    "outputId": "9e21fcc0-a838-4449-ef31-8e7d1df4d060"
+   },
+   "outputs": [],
+   "source": [
+    "cpu_system_perc = df_Ellis[['ellis-cpu.system_perc']] \n",
+    "cpu_system_perc.rolling(12).mean().plot(figsize=(20,10), linewidth=5, fontsize=20) \n",
+    "plt.xlabel('Timestamp', fontsize=30);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 634
+    },
+    "id": "R_ctvXcQL1Xf",
+    "outputId": "1a35f11e-eb94-4404-bd86-6f6fc2765ba2"
+   },
+   "outputs": [],
+   "source": [
+    "load_avg_1_min = df_Ellis[['ellis-load.avg_1_min']] \n",
+    "load_avg_1_min.rolling(12).mean().plot(figsize=(20,10), linewidth=5, fontsize=20) \n",
+    "plt.xlabel('Timestamp', fontsize=30);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 634
+    },
+    "id": "Gkd5ecCmL6Bw",
+    "outputId": "2396e496-512e-45de-b804-acfb32e79b41"
+   },
+   "outputs": [],
+   "source": [
+    "cpu_wait_perc = df_Ellis[['ellis-cpu.wait_perc']] \n",
+    "cpu_wait_perc.rolling(12).mean().plot(figsize=(20,10), linewidth=5, fontsize=20) \n",
+    "plt.xlabel('Year', fontsize=30);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 624
+    },
+    "id": "EycZrQU0MBSX",
+    "outputId": "dcf3d389-1ac0-4005-ae48-53e0f96548b1"
+   },
+   "outputs": [],
+   "source": [
+    "df_dg = pd.concat([cpu_system_perc.rolling(12).mean(), load_avg_1_min.rolling(12).mean(),cpu_wait_perc.rolling(12).mean()], axis=1) \n",
+    "df_dg.plot(figsize=(20,10), linewidth=5, fontsize=20) \n",
+    "plt.xlabel('Year', fontsize=20); "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "YoQA_MIBMknS"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 710
+    },
+    "id": "Pi8UMMitMa3Q",
+    "outputId": "57b8d44c-ad3d-45e0-82da-1787d6d47d55"
+   },
+   "outputs": [],
+   "source": [
+    "# we establish the corrmartrice\n",
+    "import seaborn as sns\n",
+    "color = sns.color_palette()\n",
+    "sns.set_style('darkgrid')\n",
+    "\n",
+    "correaltionMatrice = df_Ellis.corr()\n",
+    "f, ax = plt.subplots(figsize=(20, 10))\n",
+    "sns.heatmap(correaltionMatrice, cbar=True, vmin=0, vmax=1, square=True, annot=True);\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "rkYwyKtXMvpy",
+    "outputId": "f5386858-2a38-4b20-9be9-f841ee5af0d6"
+   },
+   "outputs": [],
+   "source": [
+    "df_Ellis.corrwith(df_Ellis['ellis-load.avg_1_min'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 235
+    },
+    "id": "5oQK-ddinvCM",
+    "outputId": "a611220f-df65-4d99-9c8f-dc0dd715f93f"
+   },
+   "outputs": [],
+   "source": [
+    "## ## using multivariate feature \n",
+    "\n",
+    "features_3 = ['ellis-cpu.wait_perc', 'ellis-load.avg_1_min', 'ellis-net.in_bytes_sec', 'Label']\n",
+    "\n",
+    "features = df_Ellis[features_3]\n",
+    "features.index = df_Ellis['Timestamp']\n",
+    "features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 386
+    },
+    "id": "qbqn755fo81g",
+    "outputId": "8750fe62-a1e7-40ca-a3c1-4285546f97ff"
+   },
+   "outputs": [],
+   "source": [
+    "features.plot(subplots=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "jJQD1x9psWCH"
+   },
+   "outputs": [],
+   "source": [
+    "features = features.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xf8WCiykpUzN",
+    "outputId": "b43752ab-2ff0-4e21-a4bc-633cd0aff7dd"
+   },
+   "outputs": [],
+   "source": [
+    "### standardize data\n",
+    "train_split = 141600\n",
+    "tf.random.set_seed(13)\n",
+    "\n",
+    "### standardize data\n",
+    "features_mean = features[:train_split].mean()\n",
+    "features_std = features[:train_split].std()\n",
+    "features  = (features - features_mean)/ features_std\n",
+    "\n",
+    "print(type(features))\n",
+    "print(features.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "1a0hNDmppnLB"
+   },
+   "outputs": [],
+   "source": [
+    "### create mutlivariate data\n",
+    "\n",
+    "def mutlivariate_data(features , target , start_idx , end_idx , history_size , target_size,\n",
+    "                      step ,  single_step = False):\n",
+    "  data = []\n",
+    "  labels = []\n",
+    "  start_idx = start_idx + history_size\n",
+    "  if end_idx is None:\n",
+    "    end_idx = len(features)- target_size\n",
+    "  for i in range(start_idx , end_idx ):\n",
+    "    idxs = range(i-history_size, i, step) ### using step\n",
+    "    data.append(features[idxs])\n",
+    "    if single_step:\n",
+    "      labels.append(target[i+target_size])\n",
+    "    else:\n",
+    "      labels.append(target[i:i+target_size])\n",
+    "\n",
+    "  return np.array(data) , np.array(labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Z0CivgkitfgE",
+    "outputId": "614149cc-6746-4d02-a608-2957cef79338"
+   },
+   "outputs": [],
+   "source": [
+    "### generate multivariate data\n",
+    "\n",
+    "history = 720\n",
+    "future_target = 72\n",
+    "STEP = 6\n",
+    "\n",
+    "x_train_ss , y_train_ss = mutlivariate_data(features , features[:, 1], 0, train_split, history,\n",
+    "                                            future_target, STEP , single_step = True)\n",
+    "\n",
+    "x_val_ss , y_val_ss = mutlivariate_data(features , features[:,1] , train_split , None , history ,\n",
+    "                                        future_target, STEP, single_step = True)\n",
+    "\n",
+    "print(x_train_ss.shape , y_train_ss.shape)\n",
+    "print(x_val_ss.shape , y_val_ss.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "VBdr2epGu3aq",
+    "outputId": "f9dc4c4f-ccfa-4379-add5-cd732dc8a310"
+   },
+   "outputs": [],
+   "source": [
+    "## tensorflow dataset\n",
+    "batch_size = 256\n",
+    "buffer_size = 10000\n",
+    "\n",
+    "train_ss = tf.data.Dataset.from_tensor_slices((x_train_ss, y_train_ss))\n",
+    "train_ss = train_ss.cache().shuffle(buffer_size).batch(batch_size).repeat()\n",
+    "\n",
+    "val_ss = tf.data.Dataset.from_tensor_slices((x_val_ss, y_val_ss))\n",
+    "val_ss = val_ss.cache().shuffle(buffer_size).batch(batch_size).repeat()\n",
+    "\n",
+    "print(train_ss)\n",
+    "print(val_ss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9eQpwUyGglu_"
+   },
+   "outputs": [],
+   "source": [
+    "def root_mean_squared_error(y_true, y_pred):\n",
+    "        return K.sqrt(K.mean(K.square(y_pred - y_true))) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "81S0BbNa7Rce"
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from keras.layers import Activation, Dense, Dropout,Permute,Reshape,Lambda,dot,concatenate\n",
+    "\n",
+    "INPUT_DIM = 100\n",
+    "TIME_STEPS = 20\n",
+    "# if True, the attention vector is shared across the input_dimensions where the attention is applied.\n",
+    "SINGLE_ATTENTION_VECTOR = True\n",
+    "APPLY_ATTENTION_BEFORE_LSTM = False\n",
+    "\n",
+    "ATTENTION_SIZE = 128\n",
+    "\n",
+    "def attention_3d_block(hidden_states):\n",
+    "    # hidden_states.shape = (batch_size, time_steps, hidden_size)\n",
+    "    hidden_size = int(hidden_states.shape[2])\n",
+    "    # _t stands for transpose\n",
+    "    hidden_states_t =  Permute((2, 1), name='attention_input_t')(hidden_states)\n",
+    "    # hidden_states_t.shape = (batch_size, hidden_size, time_steps)\n",
+    "    # this line is not useful. It's just to know which dimension is what.\n",
+    "    hidden_states_t = Reshape((hidden_size, TIME_STEPS), name='attention_input_reshape')(hidden_states_t)\n",
+    "    # Inside dense layer\n",
+    "    # a (batch_size, hidden_size, time_steps) dot W (time_steps, time_steps) => (batch_size, hidden_size, time_steps)\n",
+    "    # W is the trainable weight matrix of attention\n",
+    "    # Luong's multiplicative style score\n",
+    "    score_first_part = Dense(TIME_STEPS, use_bias=False, name='attention_score_vec')(hidden_states_t)\n",
+    "    score_first_part_t = Permute((2, 1), name='attention_score_vec_t')(score_first_part)\n",
+    "    #            score_first_part_t         dot        last_hidden_state     => attention_weights\n",
+    "    # (batch_size, time_steps, hidden_size) dot (batch_size, hidden_size, 1) => (batch_size, time_steps, 1)\n",
+    "    h_t = Lambda(lambda x: x[:, :, -1], output_shape=(hidden_size, 1), name='last_hidden_state')(hidden_states_t)\n",
+    "    score = dot([score_first_part_t, h_t], [2, 1], name='attention_score')\n",
+    "    attention_weights = Activation('softmax', name='attention_weight')(score)\n",
+    "    # if SINGLE_ATTENTION_VECTOR:\n",
+    "    #     a = Lambda(lambda x: K.mean(x, axis=1), name='dim_reduction')(a)\n",
+    "    #     a = RepeatVector(hidden_size)(a)\n",
+    "    # (batch_size, hidden_size, time_steps) dot (batch_size, time_steps, 1) => (batch_size, hidden_size, 1)\n",
+    "    context_vector = dot([hidden_states_t, attention_weights], [2, 1], name='context_vector')\n",
+    "    context_vector = Reshape((hidden_size,))(context_vector)\n",
+    "    h_t = Reshape((hidden_size,))(h_t)\n",
+    "    pre_activation = concatenate([context_vector, h_t], name='attention_output')\n",
+    "    attention_vector = Dense(ATTENTION_SIZE, use_bias=False, activation='tanh', name='attention_vector')(pre_activation)\n",
+    "    return attention_vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1cKtTAzqyiyL",
+    "outputId": "e1863cec-a19f-4419-fe8c-527e549bccdf"
+   },
+   "outputs": [],
+   "source": [
+    "from keras.layers import Activation, Dense, Dropout\n",
+    "from keras.layers import Input, LSTM, merge ,Conv1D,Dropout,Bidirectional,Multiply,Flatten\n",
+    "from keras.models import Model\n",
+    "from keras.utils.vis_utils import plot_model\n",
+    "### Modelling using LSTM\n",
+    "steps = 50\n",
+    "TIME_STEPS=120\n",
+    "INPUT_DIMS=4\n",
+    "\n",
+    "EPOCHS =20\n",
+    "\n",
+    "single_step_model = tf.keras.models.Sequential()\n",
+    "\n",
+    "inputs = Input(shape=(120, 4))\n",
+    "lstm_units = 32\n",
+    "lstm_out = LSTM(lstm_units, return_sequences=True)(inputs)\n",
+    "lstm_out=tf.keras.layers.Dropout(0.6)(lstm_out)\n",
+    "attention_mul = attention_3d_block(lstm_out)\n",
+    "#attention_mul = Flatten()(attention_mul)\n",
+    "attention_mul=tf.keras.layers.Dropout(0.6)(attention_mul)\n",
+    "output = Dense(1, activation='sigmoid')(attention_mul)\n",
+    "\n",
+    "single_step_model = Model(inputs, output)\n",
+    "\n",
+    "  \n",
+    "single_step_model.summary()\n",
+    "\n",
+    "plot_model(single_step_model, to_file='/content/drive/MyDrive/LFN Anuket/Analysis/data/Final/LSTM-Attention-B.png', show_shapes=True, show_layer_names=True)\n",
+    "\n",
+    "single_step_model.compile(optimizer = tf.keras.optimizers.Adam(), loss = 'mae',metrics=[tf.keras.metrics.RootMeanSquaredError(name='rmse')])\n",
+    "#single_step_model.compile(loss='mse', optimizer='rmsprop')\n",
+    "\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "94000u5xIjWD",
+    "outputId": "c3f7f857-fbb7-4fc5-b2bf-d860a828d0a9"
+   },
+   "outputs": [],
+   "source": [
+    "single_step_model_history = single_step_model.fit(train_ss, epochs = EPOCHS , \n",
+    "                                                  steps_per_epoch =steps, validation_data = val_ss,\n",
+    "                                                  validation_steps = 50)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 281
+    },
+    "id": "Pgev0dgzzBVx",
+    "outputId": "a97e1688-da30-4042-c349-fa8971512907"
+   },
+   "outputs": [],
+   "source": [
+    "## plot train test loss \n",
+    "\n",
+    "def plot_loss(history , title):\n",
+    "  loss = history.history['loss']\n",
+    "  val_loss = history.history['val_loss']\n",
+    "\n",
+    "  epochs = range(len(loss))\n",
+    "  plt.figure()\n",
+    "  plt.plot(epochs, loss , 'b' , label = 'Train Loss')\n",
+    "  plt.plot(epochs, val_loss , 'r' , label = 'Validation Loss')\n",
+    "  plt.title(title)\n",
+    "  plt.legend()\n",
+    "  plt.grid()\n",
+    "  plt.show()\n",
+    "\n",
+    "plot_loss(single_step_model_history , 'Single Step Training and validation loss')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 281
+    },
+    "id": "EnYf6j4okEoC",
+    "outputId": "be978504-7501-42fe-c192-4c6b023e992a"
+   },
+   "outputs": [],
+   "source": [
+    "## plot train test loss \n",
+    "\n",
+    "def plot_loss(history , title):\n",
+    "  loss = history.history['rmse']\n",
+    "  val_loss = history.history['val_rmse']\n",
+    "\n",
+    "  epochs = range(len(loss))\n",
+    "  plt.figure()\n",
+    "  plt.plot(epochs, loss , 'b' , label = 'Train RMSE')\n",
+    "  plt.plot(epochs, val_loss , 'r' , label = 'Validation RMSE')\n",
+    "  plt.title(title)\n",
+    "  plt.legend()\n",
+    "  plt.grid()\n",
+    "  plt.show()\n",
+    "\n",
+    "plot_loss(single_step_model_history , 'Single Step Training and validation loss')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "WMegV8mNAwe_"
+   },
+   "outputs": [],
+   "source": [
+    "### fucntion to create time steps\n",
+    "def create_time_steps(length):\n",
+    "  return list(range(-length,0))\n",
+    "\n",
+    "### function to plot time series data\n",
+    "\n",
+    "def plot_time_series(plot_data, delta , title):\n",
+    "  labels = [\"History\" , 'True Future' , 'Model Predcited']\n",
+    "  marker = ['.-' , 'rx' , 'go']\n",
+    "  time_steps = create_time_steps(plot_data[0].shape[0])\n",
+    "\n",
+    "  if delta:\n",
+    "    future = delta\n",
+    "  else:\n",
+    "    future = 0\n",
+    "  plt.title(title)\n",
+    "  for i , x in enumerate(plot_data):\n",
+    "    if i :\n",
+    "      plt.plot(future , plot_data[i] , marker[i], markersize = 10 , label = labels[i])\n",
+    "    else:\n",
+    "      plt.plot(time_steps, plot_data[i].flatten(), marker[i], label = labels[i])\n",
+    "  plt.legend()\n",
+    "  plt.xlim([time_steps[0], (future+5) *2])\n",
+    "\n",
+    "  plt.xlabel('Time_Step')\n",
+    "  return plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "q99i2c-9XKF3"
+   },
+   "outputs": [],
+   "source": [
+    "### Moving window average\n",
+    "\n",
+    "def MWA(history):\n",
+    "  return np.mean(history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "xFJT1rZDAUVL",
+    "outputId": "56ae677f-dadd-434f-f31b-d7cc9ee0eb89"
+   },
+   "outputs": [],
+   "source": [
+    "# plot time series and predicted values\n",
+    "\n",
+    "for x, y in val_ss.take(5):\n",
+    "  plot = plot_time_series([x[0][:, 1].numpy(), y[0].numpy(),\n",
+    "                    single_step_model.predict(x)[0]], 12,\n",
+    "                   'Single Step Prediction')\n",
+    "  plot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_KXWQVmyCSix"
+   },
+   "source": [
+    "# **MultiStep Forcasting**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Lu7m2Rr4AbMK",
+    "outputId": "9436a6eb-214e-47a3-d241-3a5bd056c31c"
+   },
+   "outputs": [],
+   "source": [
+    "future_target = 72 # 72 future values\n",
+    "x_train_multi, y_train_multi = mutlivariate_data(features, features[:, 1], 0,\n",
+    "                                                 train_split, history,\n",
+    "                                                 future_target, STEP)\n",
+    "x_val_multi, y_val_multi = mutlivariate_data(features, features[:, 1],\n",
+    "                                             train_split, None, history,\n",
+    "                                             future_target, STEP)\n",
+    "\n",
+    "print(x_train_multi.shape)\n",
+    "print(y_train_multi.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "GLRv5D16CrHj"
+   },
+   "outputs": [],
+   "source": [
+    "#  TF DATASET\n",
+    "\n",
+    "train_data_multi = tf.data.Dataset.from_tensor_slices((x_train_multi, y_train_multi))\n",
+    "train_data_multi = train_data_multi.cache().shuffle(buffer_size).batch(batch_size).repeat()\n",
+    "\n",
+    "val_data_multi = tf.data.Dataset.from_tensor_slices((x_val_multi, y_val_multi))\n",
+    "val_data_multi = val_data_multi.batch(batch_size).repeat()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "fjXexah9C8yg",
+    "outputId": "185bb543-b7ad-4f25-fd89-75be524af04e"
+   },
+   "outputs": [],
+   "source": [
+    "print(train_data_multi)\n",
+    "print(val_data_multi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 385
+    },
+    "id": "7mtLZ6S-DPU-",
+    "outputId": "553066ce-28bf-4e95-95c1-f7f34f907647"
+   },
+   "outputs": [],
+   "source": [
+    "#plotting function\n",
+    "def multi_step_plot(history, true_future, prediction):\n",
+    "  plt.figure(figsize=(12, 6))\n",
+    "  num_in = create_time_steps(len(history))\n",
+    "  num_out = len(true_future)\n",
+    "  plt.grid()\n",
+    "  plt.plot(num_in, np.array(history[:, 1]), label='History')\n",
+    "  plt.plot(np.arange(num_out)/STEP, np.array(true_future), 'bo',\n",
+    "           label='True Future')\n",
+    "  if prediction.any():\n",
+    "    plt.plot(np.arange(num_out)/STEP, np.array(prediction), 'ro',\n",
+    "             label='Predicted Future')\n",
+    "  plt.legend(loc='upper left')\n",
+    "  plt.show()\n",
+    "  \n",
+    "\n",
+    "\n",
+    "for x, y in train_data_multi.take(1):\n",
+    "  multi_step_plot(x[0], y[0], np.array([0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "snN_Flr5DWQN",
+    "outputId": "8c9fb49d-7743-4966-845b-f56a06bbe9e6"
+   },
+   "outputs": [],
+   "source": [
+    "multi_step_model = tf.keras.models.Sequential()\n",
+    "\n",
+    "inputs = Input(shape=(120, 4))\n",
+    "lstm_units = 32\n",
+    "lstm_out = LSTM(lstm_units, return_sequences=True)(inputs)\n",
+    "lstm_out=tf.keras.layers.Dropout(0.6)(lstm_out)\n",
+    "attention_mul = attention_3d_block(lstm_out)\n",
+    "#attention_mul = Flatten()(attention_mul)\n",
+    "attention_mul=tf.keras.layers.Dropout(0.6)(attention_mul)\n",
+    "output = Dense(1, activation='sigmoid')(attention_mul)\n",
+    "\n",
+    "multi_step_model = Model(inputs, output)\n",
+    "multi_step_model.compile(optimizer=tf.keras.optimizers.RMSprop(clipvalue=1.0), loss='mae',metrics=[tf.keras.metrics.RootMeanSquaredError(name='rmse')])\n",
+    "\n",
+    "multi_step_history = multi_step_model.fit(train_data_multi, epochs=EPOCHS,\n",
+    "                                          steps_per_epoch=steps,\n",
+    "                                          validation_data=val_data_multi,\n",
+    "                                          validation_steps=50)\n",
+    "multi_step_model.summary()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Ay5m27doDsTt",
+    "outputId": "e9e4b8ea-77d6-438f-9698-91b95180e6f2"
+   },
+   "outputs": [],
+   "source": [
+    "plot_loss(multi_step_history, 'Multi-Step Training and validation loss')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6ZFP49W4D2wp",
+    "outputId": "41c0548f-3fd9-41eb-b2c8-045ec24f59c3"
+   },
+   "outputs": [],
+   "source": [
+    "for x, y in val_data_multi.take(5):\n",
+    "  multi_step_plot(x[0], y[0], multi_step_model.predict(x)[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "DNKMjVoAVqZP"
+   },
+   "outputs": [],
+   "source": [
+    "scores = multi_step_model.evaluate(x_train_multi, y_train_multi, verbose=1, batch_size=200)\n",
+    "print('MAE: {}'.format(scores[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "YXcsNZ8yu9Ay"
+   },
+   "outputs": [],
+   "source": [
+    "scores_test = multi_step_model.evaluate(x_val_multi, y_val_multi, verbose=1, batch_size=200)\n",
+    "print('MAE: {}'.format(scores[1]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "uCFgbZEOvZ9A"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_test = multi_step_model.predict(x_val_multi, verbose=0)\n",
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "plt.plot(y_pred_test)\n",
+    "plt.plot(y_val_multi)\n",
+    "plt.ylabel(\"RUL\")\n",
+    "plt.xlabel(\"Unit Number\")\n",
+    "plt.legend(loc='upper left')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "1Pdu1pZyZExX"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "LSTM_Attention.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}