{"cells":[{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport seaborn as sns\nsns.set()\n\nfrom IPython.display import HTML\n\nfrom os import listdir\nprint(listdir(\"../input\"))\n\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\n# Any results you write to the current directory are saved as output.","execution_count":2,"outputs":[{"output_type":"stream","text":"['test', 'sample_submission.csv', 'train.csv']\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.read_csv(\"../input/train.csv\", nrows=10000000,\n                    dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64})\ntrain.head(5)","execution_count":3,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"text/plain":"   acoustic_data  time_to_failure\n0             12           1.4691\n1              6           1.4691\n2              8           1.4691\n3              5           1.4691\n4              8           1.4691","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>acoustic_data</th>\n      <th>time_to_failure</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>12</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8</td>\n      <td>1.4691</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.rename({\"acoustic_data\": \"signal\", \"time_to_failure\": \"quaketime\"}, axis=\"columns\", inplace=True)\ntrain.head(5)","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"   signal  quaketime\n0      12     1.4691\n1       6     1.4691\n2       8     1.4691\n3       5     1.4691\n4       8     1.4691","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>signal</th>\n      <th>quaketime</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>12</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8</td>\n      <td>1.4691</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"for n in range(5):\n    print(train.quaketime.values[n])","execution_count":5,"outputs":[{"output_type":"stream","text":"1.4690999832\n1.4690999821000001\n1.4690999809999998\n1.4690999799000002\n1.4690999787999999\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, ax = plt.subplots(2,1, figsize=(20,12))\nax[0].plot(train.index.values, train.quaketime.values, c=\"darkred\")\nax[0].set_title(\"Quaketime of 10 Mio rows\")\nax[0].set_xlabel(\"Index\")\nax[0].set_ylabel(\"Quaketime in ms\");\nax[1].plot(train.index.values, train.signal.values, c=\"mediumseagreen\")\nax[1].set_title(\"Signal of 10 Mio rows\")\nax[1].set_xlabel(\"Index\")\nax[1].set_ylabel(\"Acoustic Signal\");","execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x864 with 2 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AJKSkuDp6QmtVgsAmDdvHnr37i1u69NPP8XGjRvFmAYOHIiePXvC19cXe/bsqTEGIiIiqh+YwCIiIqJ6oU2bNpDJZJg9ezZOnDghJmWqk5OTgxkzZuDjjz9GUFAQ2rRpg7CwML1lLl26hDZt2uD8+fOYOnUq5s6dC0EQAACOjo5Ys2YNQkND8fXXX+Prr79GVFTUXWPMy8vD22+/jYkTJyIoKAiTJk3C22+/jdzcXCxbtgwvv/wypkyZgrCwMDz99NP39PpjY2PRokUL2NjYiNM6deqE2NjYGtexsLDAwIEDsW/fPgDArl27MGLEiFrFX5N9+/Zh7dq1CAkJgSAIeOedd9CnTx+cPXsW8+bNwyeffIL4+Hgx9ujoaABAcHAwrK2tERcXJ/7t7e2NkpISLFmyBL/88gvCwsKwdetWeHh43NN7RURERHUPE1hERERUL9jY2OCPP/6ARCLB/Pnz0bt3b7zzzjvIysqqsuzJkyfRoUMHPP/88zAzM8Mbb7yh1+sIANzc3DBmzBjIZDKMHDkSmZmZ4rYGDBiAli1bQiKRwNvbG3369EFISMhdYzx+/DhatWqFESNGwMzMDEOHDkXbtm1x7Nix+379xcXFsLW11Ztma2uL4uLiO643fPhw7N69GwUFBQgODsagQYMeaPwTJ06Eq6srrKysEBERgZKSEkyfPh0WFhbo3bs3nn32WTGB5uXlheDgYGRmZgIABg8ejAsXLiApKQlFRUVibzqpVIqYmBiUlZXB2dkZHTp0MOg9IiIiorqLCSwiIiKqN9q1a4dly5bh5MmT2Lt3LzIyMrB06dIqy2VkZKBp06bi3xKJRO9vAHoJrQYNGgAASkpKAAAnTpzAmDFj4O3tDU9PT5w8efKOvZAq79fNzU1vmpubG9LT0w1/kTVo2LAhioqK9KYVFRWhYcOGd1zP09MTOTk5+PnnnzFgwABYWVnVuGxt4nd1ddVbv2nTppBKb52CVl7f29sbQUFBCA4OhpeXF3x8fBAcHIzg4GB4enpCKpXC2toa33//PbZu3Yq+ffti+vTpYi8tIiIiqr+YwCIiIqJ6qV27dhg1ahRiYmKqzHNyctJLugiCgLS0NIO2q1QqMWPGDEyePBlnzpxBSEgI+vXrJw4vvBNnZ2ekpqbqTZPL5XBxcTFo33fSvn17saeSztWrV9G+ffu7rjts2DBs2LDhjsMHgdrFX7kgvbOzM9LS0sQ6V7ev7+XlhYsXL+LChQvw8vJCr169EBoaKia0dJ555hls2LABp0+fRtu2bTF//vy7vkYiIiKq25jAIiIionohLi4O69evFxNRcrkcgYGB6N69e5Vl+/fvj2vXruHw4cNQq9XYsmVLtUMNq6NUKqFUKuHg4AAzMzOcOHECZ86cMWjd/v3748aNG9i7dy/UajX+/fdfxMbGYsCAAQatr9VqoVAooFKpIAgCFAoFlEolgPIaYB4eHli1ahUUCgUOHTqEa9euYfDgwXfd7sSJE7Fhwwa9JNHDiL9bt26wsrLCunXroFKpEBQUhKNHj+LFF18EUP5UREtLS+zZswfe3t6wsbGBo6MjDhw4IMaWlZWFw4cPo6SkBBYWFrC2ttbr0UVERET1E3/tiYiIqF6wsbFBREQEXn31VfTo0QNjxoxBx44dMWfOnCrLOjg44IcffsA333wDHx8fxMbGomvXrjA3NzdoP/PmzcNHH30ELy8vBAYGwtfX16AYGzdujICAAGzYsAE+Pj5Yt24dAgIC4ODgYND6wcHB6NatG6ZPn47U1FR069YNU6ZMEed/9913iIyMhJeXF1asWIEff/zRoG3b29ujd+/eer2lHkb8FhYWCAgIwMmTJ/HUU09h0aJFWL58Odq1aycu4+3tDXt7e3Hoobe3NwRBQJcuXQCUJ/E2btyIZ555Bt7e3ggODsbChQsN2j8RERHVXRLBkP7uRERERPWYVqtFv379sGLFCjz11FPGDoeIiIiIbsMeWERERPRYOnXqFAoKCqBUKhEQEAAA6NGjh5GjIiIiIqLqmBk7ACIiIiJjCA8PxyeffAKlUon27dtj1apVd3wCHxEREREZD4cQEhERERERERGRSeMQQiIiIiIiIiIiMmlMYBERERERERERkUljAouIiIiIiIiIiEwai7jfRW5uMbTaul8mzNHRBtnZRcYOg8jksa0Q3R3bCZFh2FaIDMO2QmSY+tJWpFIJGjdueM/rMYF1F1qtUC8SWADqzesgetjYVojuju2EyDBsK0SGYVshMszj3FY4hJCIiIiIiIiIiEwaE1hERERERERERGTSmMAiIiIiIiIiIiKTxgQWERERERERERGZNCawiIiIiIiIiIjIpDGBRUREREREREREJo0JLCIiIiIiIiIiMmlMYBERERERERERkUljAouIiIiIiIiIiEwaE1hERERUryUUy5FQLDd2GERERER0H5jAIiIionrt48gAfBwZYOwwiIiIiOg+MIFFREREREREREQmjQksIiIiIiIiIiIyaUxgERERERERERGRSWMCi4iIiIiIiIiITJqZsQOo7L333kNycjKkUimsra0xf/58eHh4ICEhAXPmzEFeXh7s7e3h7++P1q1bA0Ct5xERERERERERUd1gUj2w/P39sWfPHuzatQuTJ0/GF198AQDw8/PD+PHjceDAAYwfPx4LFiwQ16ntPCIiIiIiIiIiqhtMKoFla2sr/r+oqAgSiQTZ2dmIjo7G0KFDAQBDhw5FdHQ0cnJyaj2PiIiIiIiIiIjqDpMaQggAc+fOxZkzZyAIAtatWwe5XA4XFxfIZDIAgEwmg7OzM+RyOQRBqNU8BwcHo70+IiIiIiIiIiK6NyaXwPrqq68AALt27cLy5csxc+ZMo8bj6Ghj1P0/SE5OtndfiIjYVogMUBfbSV2Mmeo+fu+IDMO2QmSYx7mtmFwCS2fEiBFYsGABmjZtivT0dGg0GshkMmg0GmRkZMDV1RWCINRq3r3Izi6CVis8pFf56Dg52SIzs9DYYRCZPLYVorurq+2kLsZMdVtdbStEjxrbCpFh6ktbkUolteosZDI1sIqLiyGXy8W/jx49ikaNGsHR0REeHh4IDAwEAAQGBsLDwwMODg61nkdERERERERERHWHyfTAKi0txcyZM1FaWgqpVIpGjRohICAAEokECxcuxJw5c7B69WrY2dnB399fXK+284iIiIiIiIiIqG6QCIJQ98fHPUQcQkj0eGFbIbq7utZORgX5AQB2+CwyciT0uKlrbYXIWNhWiAxTX9pKnR9CSEREREREREREVB0msIiIiIiIiIiIyKQxgUVERERERERERCaNCSwiIiIiIiIiIjJpTGAREREREREREZFJYwKLiIiIiIiIiIhMGhNYRERERERERERk0pjAIiIiIiIiIiIik8YEFhERERERERERmTQmsIiIiIiIiIiIyKQxgUVERERERERERCaNCSwiIiIiIiIiIjJpTGAREREREREREZFJYwKLiIiIiIiIiIhMGhNYRERERERERERk0pjAIiIiIiIiIiIik8YEFhERERERERERmTQmsIiIiIiIiIiIyKQxgUVERERERERERCaNCSwiIiIiIiIiIjJpTGAREREREREREZFJYwKLiIiIiIiIiIhMGhNYRERERERERERk0pjAIiIiIiIiIiIik8YEFhERERERERERmTSTSWDl5uZi2rRpGDx4MF5++WV88MEHyMnJAQCEh4dj2LBhGDx4MCZPnozs7GxxvdrOIyIiIiIiIiKiusFkElgSiQRTp07FgQMHsHfvXrRo0QIrVqyAVqvFp59+igULFuDAgQPw9PTEihUrAKDW84iIiIiIiIiIqO4wmQSWvb09fHx8xL979OiB1NRUREZGwtLSEp6engCAcePGYf/+/QBQ63lERERERERERFR3mEwCqzKtVos///wTvr6+kMvlcHNzE+c5ODhAq9UiLy+v1vOIiIiIiIiIiKjuMDN2ANVZvHgxrK2tMWHCBBw6dMiosTg62hh1/w+Sk5OtsUMgqhPYVojuri62k7oYM9V9/N4RGYZthcgwj3NbMbkElr+/P27evImAgABIpVK4uroiNTVVnJ+TkwOpVAp7e/taz7sX2dlF0GqF+39hRubkZIvMzEJjh0Fk8thWiO6urraTuhgz1W11ta0QPWpsK0SGqS9tRSqV1KqzkEkNIfzuu+8QGRmJVatWwcLCAgDQtWtXlJWVISQkBACwdetWDBky5L7mERERERERERFR3WEyPbBiYmKwZs0atG7dGuPGjQMANG/eHKtWrcLy5cvh5+cHhUKBZs2a4ZtvvgEASKXSWs0jIiIiIiIiIqK6QyIIQt0fH/cQcQgh0eOFbYXo7upaOxkV5AcA2OGzyMiR0OOmrrUVImNhWyEyTH1pK/ViCCEREREREREREdHtmMAiIiIiIiIiIiKTxgQWERERERERERGZNCawiIiIiIiIiIjIpDGBRUREREREREREJo0JLCIiIiIiIiIiMmlMYBERERERERERkUljAouIiIiIiIiIiEwaE1hERERERERERGTSmMAiIiIiIiIiIiKTxgQWERERERERERGZNCawiIiIiIiIiIjIpDGBRUREREREREREJo0JLCIiIiIiIiIiMmlMYBERERERERERkUljAouIiIiIiIiIiEwaE1hERERERERERGTSmMAiIiIiIiIiIiKTxgQWERERERERERGZNCawiIiIiIiIiIjIpDGBRUREREREREREJo0JLCIiIiIiIiIiMmlMYBERERERERERkUljAouIiIiIiIiIiEwaE1hERERERERERGTSmMAiIiIiIiIiIiKTZlIJLH9/f/j6+sLd3R3Xr18XpyckJGDs2LEYPHgwxo4dixs3btz3PCIiIiIiIiIiqhtMKoE1cOBAbNmyBc2aNdOb7ufnh/Hjx+PAgQMYP348FixYcN/ziIiIiIiIiIiobjCpBJanpydcXV31pmVnZyM6OhpDhw4FAAwdOhTR0dHIycmp9TwiIiIiIiIiIqo7zIwdwN3I5XK4uLhAJpMBAGQyGZydnSGXyyEIQq3mOTg4GLx/R0ebB/+ijMTJydbYIRDVCWwrRHdXF9tJXYyZ6j5+74gMw7ZCZJjHua2YfALL2LKzi6DVCsYO4745OdkiM7PQ2GEQmTy2FaK7q6vtpC7GTHVbXW0rRI8a2wqRYepLW5FKJbXqLGTyCSxXV1ekp6dDo9FAJpNBo9EgIyMDrq6uEAShVvOIiIiIiIiIiKjuMKkaWNVxdHSEh4cHAgMDAQCBgYHw8PCAg4NDrecREREREREREVHdIREEwWTGxy1ZsgQHDx5EVlYWGjduDHt7e+zbtw9xcXGYM2cOCgoKYGdnB39/f7Rt2xYAaj3PUBxCSPR4YVshuru61k5GBfkBAHb4LDJyJPS4qWtthchY2FaIDFNf2kpthxCaVALLFDGBRfR4YVshuru61k6YwCJjqWtthchY2FaIDFNf2kptE1gmP4SQiIiIiIiIiIgeb0xgERERERERERGRSWMCi4iIiIiIiIiITBoTWEREREREREREZNKYwCIiIiIiIiIiIpPGBBYREREREREREZk0JrCIiIiIiIiIiMikMYFFREREREREREQmzaymGf/8849BG3jllVceWDBERERERERERES3qzGBtXv37ruuLJFImMAiIiIiIiIiIqKHqsYE1qZNmx5lHERERERERERERNWqMYFVE0EQIAiC+LdUyjJaRERERERERET08BiUwEpPT8eXX36JkJAQFBQU6M27cuXKQwmMiIiIiIiIiIgIMPAphH5+fjA3N8fGjRthbW2NnTt3wtfXF4sWLXrY8RHRY6JUo4BaqzF2GERERERERGSCDEpghYWFYenSpfDw8IBEIkGnTp3w1VdfYf369Q87PiJ6TLweshQLrmw0dhhERERERERkggxKYEmlUpiZlY82tLOzQ05ODqytrZGenv5QgyOix8vVokRjh0BE9ZhCqzJ2CERERERUSwbVwOrevTtOnDiB5557Dn379sVHH30EKysrdO3a9WHHR0RERPRAZCvy4dagibHDICIiIqJaMKgH1vLly+Hl5QUA+OKLL+Dj44MOHTrg22+/fajBEVH9V6wuw0eXVhk7DCKDFapLMPPSKiSXZho7FLpHwt0XISIiIiITZVAPLDs7O/H/VlZWeP/99x9aQET0eInIj0NiaYaxwyAyWHDuNSSVZmBn6ml82G6kscOhe1CmVRo7BCIiIiKqJYMSWEqlEjt37sSVK1dQUlKiN2/58uUPJTAiqj9Ccq+jWQNHuFo5VpknlUiMEBFR7QkC+/HUVZfz49GuoZuxwyAiIiKiWjBoCOGcOXPw22+/oWHDhmjZsqXePyKiu1l6fQvej/ix2nkSPPwE1unsy/j15n8PfT9UN6yM24lPI9fUev0bpeUPMDmWFf6gQqJH5FGmHjWCBr8nHkShuuTuCxMRET1i1wqTIC/LNnYYRPfEoB5Yp06dwpEjR/SGEtLjI19VjFKNAk2tHIwdCgBgj/wsogpu4HP38cYOhR4AqcSgPPp9+S72HwDAlFYvPPR9kem738RTRllutdMFQUBYfix6NGr3SL7X9UlYXizC82MxqdWQh7ofoVIKSyNoUKJRwNbM+qHsKyjnKnbJzyBPVYQZ7UY9lH0QERHV1ufR6wAAO3wWGTmSR29+9Ab0b9INg5x7GTsUukcGnWG7urpCqWTdiMfVlNAVeC/iB/FvQRBwvSjZaPFsTDyA4Lxr97UNQRDq/TCgzYmH8GPcjlqtq9CqkFAsf8ARVe9h98Aq1Sge6vZNVX36jmsELVRada3WzVDkQaFVPdB4JDUMez2VfRlLrm3GwYyQB7q/x8Hia5uwN+3cQ9/P5qTDCMq5AgBYkxCINy/6o1Bdgj3ys9AK2ge6L03F9tSC5oFulx6ua4VJ9ebYSXQvNIIW4y4sxqGMi8YOheihiyq8gdUJe4wdBtWCQQmsESNG4L333kNgYCDOnTun94/qPy30T+r3p1/AnKhfcDHv+gPf14H0YEwP++6Bb7cyjaDB6AsLMePST1XmXS9KRkw1ybmAhL2YGrrigew/W1lQ64vx2x3NDMMnlwOgFbT4I+kI8lRF4rwd8tM4nhVR7XrHMyNQrC6tcbs/xe3Cx5EBKFQ9/KEv1dXAkpdl41xOtEHrRxXcwKggPxTV8Hq2Jh8zOJbw/Di8emTxA0941NbNknQkltSuwP0fyUcw+sLCB/Zdux8ZilyDLgjjilNxPPPWd/bP5KP4PGodFl/dhLHBiwGUJ+ZWxu1EVMENAOXJ1vjbkq2FqhLkq4oBAO+Ef4/l17fWKu6bJelVjgdRBTf02lllmYo8AECWIr9W+zMVCo0SRepSbE0+BrX24SZfBEGose0+LP4xW5FamoXT2ZEAyoeUbkw8gNC8GADlvY4LKr4/d1OsLqsx8SXcNmDxWGY4cpSF9xE51YYgCMg18H0/n3MFn0evw+HM0Ae2/6Op4ThfkTQ1FZuTDuPbmL+NHcZja3/6BexMPW3sMKpQa9VQCmqWXLhNXHFqnf9dN0XXi5Kh4Q0e0ZXCm1CayPWHqTMogbV582ZkZWXhu+++w9y5c8V/8+bNe9jx0QNwLDMcZ9KjHtj2kioeHV/dMJorhTcxKsgPORVJmnM50fd0J3PNjUBkKfOx7sa/992Ic5QF1V4s5CrLLz5TyrKqzJsT9QtmR/1SZfrBjBDkqG5ta2fqaSRXvA81uZQfjx9u6wGl0qoxLexb/BS/q9p10spykKMswPmcKwjOrbmXmUbQYEfqKfwUvwvxJXJcLkjAP6knEZCwt8qyN0vSUaa51YMyoTgNP8bvwKr43TVuX9fD7lHcmZDe1gNrcug3+DBiJb6J2aY3fWvyMSy99keV9edf2QAA+DFuB26WlNcmUmhV4mdf+bXnKguRUJxWYyy/3NiHtNJcfB/7D3KVhdgrP/dA78RrBC2OZYbftadHUkkGClUlmHV5NT66vMqgbd8sScf5Skm/MU3NAAAAIABJREFUfWlBAIDcapItSq0K21NO4npRMhSau/euvZh3HRdyriKq4IbehaBG0KJQVVLlPRIEAedyoqHSqhFTlIJ3wv+Hxdc2I6MiwVOTTyPX4Mf4HeJn9HfKCVwrSsKlgnhxGS20OJYVDr8rGwGUJx8+iQzQSzi8GeqPSaG3HjASlh97x/2WaZQYFeSHI5lhetNnXV5d5Xgw/8qGGnugigmL25Ky/te3ir1+HpXU0ix8FrlWL1EdV5yKUUF+YvJPp0RdhtRKNTBeC/kKb1xchr9SjuNwZtU78TnKQuxLugCFRomkWiZYdQ5mhOCNi8vuaxt3U10b/uDSSphJZACAkIqbMYczQxFVcAOTQpfjrdDleC14CULucBwu1Sgw8eLX+C3xoN70InUpVsXvhqLiiYcSSJCnKsLK+J1YfG3Tg3pZZKBDmRcxJWwFEorl+CvlONJrGAIMAGmKHACo8fc9T1Uk9uoNyb12x98THb/Q37E8pnZJ9IdlR+opnMmJNHYYJu1aYdId2//9WHtjHzYlHXqg28xU5GFr8jHxeFesLhV7gRpKN/T9QfVGzVLkY3TQQsQVpz6Q7d1JWF7sQ7vp+mnkGkwPf7g31x83ccWpmBP1C/5MOqo3XStosTP1NIrVZQ90f/mqYiy88pt4c9PUpJZlY270evxy4987LqfQqnCl8OYjisp0GZTAOnr0aLX/jhw58rDju28JCQkYO3YsBg8ejLFjx+LGjRvGDumRWxm/E3OCf33o+1FrNZgbvR4A8OvN/7A56TC+idmGywUJ4jIKrQoH0oOh0qohL8tGnqpIvMNf+aDyb3oQ3rzoj18rGvLmxEM4mx1VYzIhrjgV08O+w6ggP3Ho29SwbzE17FavKZVWjdXxu5GtLBCn/ZF0BGMvLIYgCAbfDVNp1diUdAifR60TpwmCIPZ0UWpVuFmSjoVXf8OJ23pA6YaS6O7865zKuoyE4jS8F/EDpoZ9i+UxW/H19arJGp2jmWHYnHRYLyYA1faWmHV5NcaHfIUFVzZCXpaNMm35yXfebQdxQRAQkR+HE1kRyFSWJxqCcssvutPLcjEqyA+nsy/f4Z2pnduHY+WpiqCtSASE58fhh7gdyFUW4q+U4wi5w9DRkLzrmHV5NQDgy6u/i5995eE7My+vwseRP+O3xAN662oFLTIUeWIhywu5VzElbAU2JO7HjZK7X6AA5Umnsrskg169sAgr43eKd/czFLliUufjywEYFeSH2KIUzLy8Cm+G+hu0X51Zl1djeaWkny4B/E749zibHaV393CP/Cy2JB/BnKhf8E3sX9X20qo87atrW7As5k/Mv7IBU8JWYFSQH5JLM/FF1Dq8GeqPf9OD9NaNKIjHNzHb8GfyUfFCMDw/Fh9f/hnLr2/Dp5FrkKMsRHrZrZ5ZlYd6fhz5c7WvccGVjbh1CCj/3pzNiap4vVVfQ3h+XJVpKaW3Etejgvyg0qqRW5Gc/iflRLX7vROVVo3L+eUJNl1oVwsToRW02JZ8DDFFKQjKvQL/ai5gP49ah+0pJwGUJ4V035/IggR8F/t3leNdZEGC2EsIAPalnceU0BVQaFUYFeSnN3Rx/pWNiC1OwS75GZRqFBgV5CcWrtdtI6kkA3HFqfg8eh0+iPix2uOHrv0kl2Zi2fU/cT7nCqaGrcCyiK1YePV3zLy8Su+zu1mSjpTSLL22kKHI1etZB5Qn2FRaNTbc1hY1ggbRBTdr7AVZpC7F2WzdZ17+updc3QyNoMVX17bgy6u3EkSJJRmIKrhRpSeUuC2Nfs+vC7lXxYQ4UP579b+47RVxaXEiKwLjg78S24YuxtuHPv6dcgJHMkNxKONWL57Jod8AMLx3XpG61Gh3psPyYsXf40MZF+t8gd898rMAyn83tyYfw7LrfwIo7w19+4X6xdzyZOZ/6Req3dbk0G8w81L5TYWl1/+o8VhVH2kELdYmBGLdjX+r9Hq9V5fy42t8kMa93jRKL8s1uMfk7RRaVY2/259Hr8PSO5yHPSg5ysIHUupg2fU/8VfKcaSWZSO9LBcTLy7Dhpv7a7Ut3XH/SmGiwUmwy/nx+L6i3qhOaH4MBAgITDtfqzgAILYoRfydrEmpRoHF1zbhq+tb7rq9lNKsakda3K/T2ZerHN/P5UQjtijlnraTWpaN3xIP6CXR96Wdr3LjyVRoBM0db4SOCvITz80r0/3GxFc6xw7KuYILudewKekQfr15K5GToywQ22mOsgDjg7+q8l27m52pp3GpIB7/3Xa+aiy3f1d0PdETK27EawUtvo35GwWqYii0KvEYsTp+N+ZGr0d6ac03Yh4HBhVxr8v8/Pwwfvx4DB8+HLt378aCBQvw+++/Gzsso7hamIgidRnii1NhKTPH2ewoxBSn4N02L2PtjX0Y3vRpjG3+LNSCBg1klgD0TyTC8mL17h7/cvNf9GrcEU4W9gi4sVdvzHxYXizKKu4+L7z6GwDA3aYFrhUlASjvaVXZYo9J+Pu2i0eFVoV96UHYV+lgU7kId5YiHzZmDWAls8AXUb9CJZRfVMy/sgEllU4GcpSFSCnNwk75aYTnx4q9dADgn9TyH8WLeTF6d8P+S78AmURapUfTuZxo8WS4WFOG/9IvoH3DZjiccRGHKnoq9HPshpPZl8R1RgX54X9PvA9784Z4q6JXiAAB08O+w9MOXbAn7SwMcSwzXBy2qbto19GdZIXmxyChWA55WU6V9SMLEvSeBHi1KBHzotcjuvAm3m87Aqtq6BVWqCrBudzynj3fxf6DXvYdUaguxTvh3wMAfuz2AQrVpTiWGQalVo2EEjmSSjMhhQSbPD9HcqWEgUqrxt8pJ8T3zt2mxR1rYH15tbytVk4Ebk48hEJ1KXwcPKodFpKhyMOVwkQAwIyIn5BcduskQPcDsVt+Fk87dIWblQPiiuXid7Q6lb9Ly69vxcW8GKzqPgMNzazwe+IhHMgIxp+eczGzoqfU6u4zUapRYGX8LnSza4v4EjlmthuFuKJbdyADEvbiSEYoYorLT2w87d2RUFJ+QfBNzF9VYng3/H8Y1rQ3kkoz8WarwTCTSPHqhS8BAL89ORvWZpbisgqtCmllOWISEABWxN7a5tRWLyI499bw39C8GIwNXoznnT3hYdsS/Zt0x6ggPwDln21NPQErD8H99eZ/6N+kO8wlMlwvShEveLOVBXC1chSXK9aU4XzFd0mXYHSzckRqWTZktxU9r66XRGRBgji0QQutGCdQ3padLe1hLbv1Xui+P0B5LyNrMyt8eGml3jZ1QxMBIF2Ri5/id+FsdhS8GruL049lhmNl/E6Ma/5slZjW3fgXhzIvws6sIQrU5Sdk0YU3sTxmGy7kXsW2lOPisrnKQtiYNUBoXgwu5l3HtaIkXCtKgqXMHOsrLjT6N+kuft/fbzMcr4V8BQDobtcOEQXlCbkXXLzR0aaF+F7o2m5Awl487+yJiPw4MSm3PfVUlRpMheoSlGmU4ndWZ9bl1RjbfIDetPU39+NC7jVEVtyIuJB7VZynO57/kXQEk1u9AIlEUuVE1cnCXkyIezV2h7XMEkWaUnxw2+ego/teA8Csdq+gg00zhObFoL1NM8yp1BtuQF53DHb2AlB+3Hv1wq0CtFmKfMjLsuFX0a63ec2vdl+GKNEosPjqJr1efF9d24LOdq3Q0aaFOG2P/Cw2Jh7Ah21Hij30Yivad+V1izXlQw6vFyWjTKNED/v2CMq5goMZIfi4/atIKctCA5klZlz6CU819sBnHcfpxaPSqnE+5wr6OnYVk/8aQYMStQK25tZQalUQAFhKzfXWWxHzF7KVBfi6y9QaX6sgCNBCi8XXNqFFA2cotSqkK8rb4Q6fRUgtzYJbgyYV+9TidHYkLKVmOJ0diU86jLn1nlX06Gtv06za/ZRqFDifcwU+jTvB2syqxnh0tIIWEkj0bnaklmXjk8sBKNMqsbr7TGQrC+Bh2xISSCBA0HuIgq53oe5cQiWokassxLSwbzHStS8mtnxOXFZ3Q+H2NpNQnIYmluUPMspS5lebHK9sU+IhXC5IwPKu08VpgiDUWD+vsmxlAVJLs/FEozbiNIVWhbPZUVgZvxNfdHwdno07Vrvu2ewomElk6GLXGtYySyi0KhRryuBoYYdsZQEEQUATy0bi8lpBq/de6db3auyuF6tu6Lbu3Obf9CAs9pgEWzNrfB61Dgs93kSHis87T1UEc4kZ/ku/AJWghiAIGN9iIIDy3qhPOXTGDxWJ4ZmXVuGHbu8jpigZN0rS0MWuDT6I+BGfdRiLpxw6Aygf7pihyMX/tX+1yuvVClq8G/E/WEkt8OuTn8BCagZZRc9KXSzHM8MxxMUbKq0a0YU30cGmGRws7KDQqvBa8BIA5ceIk1mX8KxTDwAwKGmz7Pqf8LTviIFOTxr0uUYWJMDFsjGcLO31pk8NW4HmVk74sfsH0ApaBKadx3POvaAWNFBoVHqfl45Co4QWgniuDgAJFYmAUo0Cn0WtBVCeVJna+sW7xqZTuce27px/QJPusJRa4FxONKa3fglPO3apdl3d8fbDtiNhJi3/DHQJ4RNZEWhr3RRDm/ZGnqoIMokUqWXZcLSwwz8pJ/FWq8E4kBGCSY7PIakkA4mlGejj2BUaQSu+ltHN+mF25C+IKU7GU407o4d9Ozzv7Am1VoPCinO7tGrOfW9vd7pzgNFuzyA8Pw7fdH1bb9njWRFo29AVsy6vhrnEDGt7/p84X6VVw0wiq/bz1j0sqHIxdN1IguoKpJ/JjoRa0KB/k+560z+oOE/fLT8rrqf7ra+8nVKNAhZSc8gkUmQrC5ClyEcraxdYySz0thdfLEeRuhTdGrXFscxwxJfIq32gkUqrxv70YPR17IrGFrYoUBXDXGqGYnVZle/gXvk5ZCnz0a1RW3x1bYtebJmKPPE7viP1FIDyG1sqrRrm0ltph5yKzgThFb+PQTn6N/qyFPkQBAFpihy8H/EjOjRsBv+u07El6QjKtEqcyr6MCS0GoUSjwMGMEPR26IyudreOmZVf18msS+K11vGsCLS2boon7TtUWbayQlUJ3gz1x9edp6JNw6b4K+UEXnHrV+X9LVSXYNLFb6CFFtu9F0ItaPReZ+XlrGWWkElkOJAejDU3AjHQ6Um833Y4kkszxWto3Xdrb9o5nMmJxJmcSDiY2yFHVYDt3gvF354yjRLW9T+NUyOJYMCtjv79+1fbWC0sLODi4oLnn38er732GszMTOuNzM7OxuDBgxEUFASZTAaNRgMfHx8cPHgQDg6GPVEvO7sIWm3dLuZZ+SKPiIiIiIiIiOqm+vDkSKlUAkdHm3tez6CM08SJE7Fnzx5MnDgRrq6ukMvl2LJlC4YMGYJGjRphw4YNkMvl+Oyzz+45gIdJLpfDxcUFMln5nQCZTAZnZ2fI5XKDE1h1nSH1bYiIiIiIiIiITJlBCaydO3di/fr1cHFxEaf169cPkydPxr59++Dj44NJkyaZXALrQahNVtCU8FHQRERERERERPWDk5OtsUMwGoMSWJmZmWjYsKHetAYNGiAjo/zpQ23atEFBQUF1qxqVq6sr0tPTodFoxCGEGRkZcHV1NXgb9WEIoc42r/lQCxpkKvKRWpaFnalnEFOcjIAeH+FgxkU0NrfBCy7eevUQBEHA6AsLAZTXqVoVvxvmUpn4JMK/vRdAJpHhv7Qg/FKp4F5jc1uxBgtQXv+qgcyixroRf3rNw9qEQBzLCq8yr3Ltl7+8FmBM8JcV+/aDtKIuxoLoDYgsvAEAcLFsjCxlvljD4EuPt9CqgQuOZ4VjQ+IBeNi2FGsk6Wzq9TkmXvxa/Psfbz+UaBR466K/Xi2hVd1n4JPLa1BaUQh9XLNnMab5AGxPOYktyeUPNfj+iffwV8pxnKv0RLjNvT6HRCLBNzHbxPfgRRcfvODiLY7Ht5E1qFJUeIfPImgEDaSQ4khmKMyl5kgty6pSL2xcs2exNeUY2jV0w/TWL1X7JEWdyjVpmlk1QUpZFr5/4r1qiyzqYvg3LQjrKj7f7d4LoYUg1pzZ4bMIeaoiHM0MQyOzhujt0Bk7Uk+hkbkNBjk/iTKNElMq6h3t8FmEq4WJkJflYGX8TjSxaARXKwe9Qv93E9DjIxxMD4GtuTWec+qFCZU+NwBY/+SnYsHkynXXbre6+0w4WthBC0Gsg1Edv05voHujdgCA45kRyFDkYpjr0yjRKBBfLEdI3jVMbfWi+L38y2sB8lRFOJl9CR62raARNPCwbYlcZZH4FBsLiRn8PN4QH3owwrUPdsnPACive7QqQf8Jke0auqGLbWs0b9AEg5x7QRAE/JVyHCpBgxdcvGFnZi3Wctrhs0iv3QLl9XBmtXsF6Ypc9GrcEUczQrFDrv/Qgmeb9MBrzX3RxLKROOw4oMcs/JsWZFCdtn+8/VCqUSKi4vu9IvYvPGnfAX0cumJl/M4a13vJxQf70oPgad9RfBocAPzWazbevFi1kL1/l+mYXVETo7KnHbrA3aY5nC0bV1swXdfVelzwkipPONXVzdHFk6nMxyftx4if6dedp+KvlOMY32JglcLDP3WfgQ8ifkR/x25ILcsW65rJJNIqdVSWeExGS2tnXClMxH/pQeKxYEKLQeJDGZ5t0kM8Dm7x/AKvhyzFQKcn4WHbUnx66YdtR6KltbMYy0CnJ3Gk4sEAf3v74UZJmjjPQmoO3yY9sD8jWIzjOademN5mqF7dKJ2udm3EelcA4GBuB3fb5nrHs9st9piELnatAVQdsu5gbis+wVVXi0oqkeCLqF/hZuWIE5XqBQLl9b3+S78AO7OG+PaJd2Ats0SxugzmUjO9J0t+3XkqGpk3xHsRP1SJZ7v3Qii0KoyvqB+2zWu+Xq2ze/WkfQdkKvKRVFp+zjOhxSB0sW2NIk2pWP/jGccncCr7MsY1fxYxRSlivUIAaCC1FH8zdPFFFMRDK2jRvEETSCBBuiIXXe3aQCtooRI0eC14CXo7dManHcbqxaLQqnAq65LBdXd0QvNioNCq0LuirlBN1FoNxgR/CU97d72HZuzwWYQcZSEcLPRPmG+WpONi3nWMcntGnFaiLkNKWRY62DSvdh+CIGB32ln0dehabX2f26WV5cDRwk6vrkiWIh/+MVsRV5yKgB4foVitQEtr5yq19IDqvpN2WNNzFgIS9mKEax80q6jrBQDTwr4VH/RSeXhGeF4smjdwEo/hM9uNEp8yXN0wjg039yMo9woCeswS93+/wz2K1KV44+IyrOkxq0odJZ2oghswk8jgZGmPRuYNUaZRolBdgqZW+qMOaopJEIQqNcSA8joyi69tFo8N7Rq64Zuub0OlVeP3pEN41a0f7MxvXSdoBA2uFCaigcwSRepS8Td03IXFGO7WRzyHedK+A+a5T0BoXgwiCxIwscVz2J56CkObPiXWmtE9SdndtgVup9CqsDYhEK2sXfCUQ2fYmVnr1ai5VpiEgBt7sazzVFjKLJBelgt7CxtYSs2h0KowIWQpetl3xGcdxiKq4AaeaNT2ru+RzrHMcPS0bw97c8NudKeV5cDWzBoNzaz0tl35/2qtBluTj2K4Wx/YmlnfcXuVaztpBS1eqTieL+z0Jr6L/QcF6mJYSS3wh9dcg+IDymvA6eqb/trzEyy6+jveajkYZVoVkksz0a6hG3rat692Xd3r2O69UIxrydXNCM0vf2jItFYvYoiLN0o0ZTCTyGAps0BsUQp+TtiDJZ0nQyNo0cbVGSE3Y5FYkoFnmjyht93K75WvU0+E5cXil57/B6lEigJVsVhj9m7tTLeNpx264FJ+PH73nKM3P7EkA1KJBNYyK0gANLawFdf5tuu7aGntpFdnDdC/Vqq8/1FBfmggtcQWry+qxJFQLIcWAto1dNObvvDKb7hUEA8Xy8b4ucdHVd6D6lwrTEKuqhBP2LVBQ7MGevN0RcCdLO0RV5yKTEWeWF/u9m0rtCpYSMzEz08QBBRrymBz2zYjCxKQVJIBewtblGgUOJIRiqVdplSJ65+UE/gjufwpg5W/FwBwOOMiVifsgbXMEps9v8Dl/Hixjhpw69igq0Wli/GPpCNi/eK/vRdAKwi4WpiIFtbO1bbFQnUJNiceFusUP2nfAZ93HA+ZRHrH91UjaLE99SRecPGGhcQMm5IO4/UWA/XqzgHldfb+F7sd5lIzzHV/vcp2dHQdSiQSCfbKz2JD4gGMa/4sxjQbgGJ1GcLyY/Fd7N9oZtUEK7t/iH1p58XaZz0atUd0wQ386TUPn0auQXyJHOv6zkJmZtU6wHXNQx1C+Oyzz+Ldd9/Fu+++CxcXF6Snp2PNmjV49tnygrZhYWFo3rz6ExVjcnR0hIeHBwIDAzF8+HAEBgbCw8PjsRk+eDtzqRnMYYaW1s5oae2MnvYdUKAqhpOlPSa0GFTtOpUPNl3sWmN1j5n4PfEgkkozMaHFIPEg/kJTH7zQ1Ec8GLhaOeBFF29sST6CrzpPgYdtSwDlDTgk7zq62LbCpYJ4NG/gBCdLe1hKzfF2m6F6CazPOozDUw4eAIDg3GtoZuUoFoYEoHei+mnHsfgr5QT2pZ3Hx+1fRXubZmIsuqJ+LzZ9CgXqEvRv0l0sQL3EYzIu5F5FQzMrvR9HqUQKG7MG+MdnIQBUel2O2Oz5OX6K34XujdqhX5Nu5fPdnsEzTbrB3rwhLKTm+LTDWFwvSsa5nCi86tZfLFQ7130CXr2wCBNaDBJP+Hf4LEJqWTZsZFZ4K3Q5zCQyvQKyuvd4kHMv8T0EoJfE0r2/VlILdLBprvdadJ5z6oV32w5DQnEaPo78GT0atceCThOh0KpgKTXHj90+QK6yCBZSM3wevU5v3acdu2BfehA+7TAGEokEMkiwtsf/IUtZ/hQNe3MbvQuYCZWK4t5+sO9k2xKdbFuKxVKPZYZXSWCNbTYA21KOY5vXfOxNO4fnnT1xMS8G6WU5cLZsrLf929mb2+D9NsNxODMUSztPwdob+3Cg4uL9S4+3sCnxEEa49dU7od/hswjXCpP0XveHbUcipTQTT1QqCjnA6VaxTSuZBRwsbMViuiu7fYgmlo1gJpWVJ4EqvR8A9C7URrj1gYdtK2z3XiheLLzR8nnkqYpgb26DZ5164EphovhEtMpFRoHydjm2moLilefbmVmjQF2Crrat8bn7eL3PoX+T7mICy9HCDr/0/Fhv/e3eC6EU1LCUmuOtVoOrJLBGuvbF+BYDkVSagf+7/DM+6zAWUokUDc2s8LRjFyg0Sng37oTJrYaIBa0BYKBTTzzv7CVeULlVFHif0vpFFKvLxCTy5FZDqj15f8Wtn1goGNA/oZ3QYpD4mS7s9CZUghotGjiLJ+M6v/T8P73E2FedJ6O5lRPeDPXH0w5dMKWagrfuti0wv9PEat9rNytH8eRnr/wsYopT4G7TAks7T0FQ7lV0tGkOeVk2mjVoIp5ceTV2h1djd6SWZaNAVYxOti3xoosPzCQymEllyFcXIzQvBhZSc70TK12CQ/c6//Sah7PZUejX5AkMaNIdbRu6QiaRol1DN/Sy74iLedfxfpvheKbJE3jWqSfmRv8KtaARL/RXd58JCSQISNiLiII4bPOaD3Opmd6xY6RbH7zU9CmUqMuwI/UUWlq74L/0C7hWlAQppNBCi9bWTcXlt3h+AQkkeheRaWU5uFKYqJeA8K8obn21KEksFA4A01q/hO527dDZrpV40qz77t5+kqnWatDcyglvtHwODhZ2cLCwha1ZA0gk5ft/0cUHEflx1RZUBVDlWFtdwnt665cwxMUbQPnTbgPl5zDCtQ+kEikyKwp+A8CMdqMwtOlT6GDTHCmlWbiUH49JrQZj7Y19cLdtDpVWg6jCG2hk1hASiQQ9Ki7odXQJCalECkuJFOt6fgLb2y4agPJktO634F7crWCtjplUhp+6fQhHCztIJVLkqgrRqOJ7e3vyCgBaWbuglbWL3jRrM6sak1dA+fFphGsfg2O/PfkClB9P9Y6LllUWEemSooOdPXEgIwQNZBaQSaR4v+3wKsv+f3v3HR1Vnf9//HUnDUjBBBIyCUVEiMFQhCCLQGApEtYQitIERWSxIoosZQHBoCuGlfWnCCrfBbGL7kJYEGmColI0FmRVZEUpkgSSACJNIJnfHyFjem7q3Emej3M4J5nPnZn3hHnP3Pu6n3tvfU9fZV44VejiIu0LbLT3aNhOz+xfme8iFXmNbRarsc1iS3hVZefnWbfUjfPcIDmXr2cd+Zo4UX4uwzCKvLCKl81TCdeM0b9StinSv6lzo9vL5lnkCaE9DI8iT6j81vU5IfaPZ1L1+cl9mhUxWlLO+zP3PXpLeEy++xR8f+XlY/PSAy0GFzse4d9ET7e5z/l7ozqB+e77zvW/f9blDa8kqW3AVdp/JkXFyV2HMauo97GUs66R+xnqafMocf0mr7zr5zbDpiZ1g3X4XLqu8PLTkusmaXTyvDKdwF1Svs+cQG9//b+29+cZjSzxvonXji+0o+Ou5nG656unNaPVrYq+fGGUvAHL1X7hWtDm3nz3KfiZknen82sd/yoPwyafAifSzv399ial/+3+HnV3vlC1oKb1Qoq9b3Pf0CJvNwxDt4TFqHNQ/r/Rik6PyFbMjobmvkVPpng0ckyho2cSrx2v+l6+RS4vqchwN1feYLmFb1ihwCyvghf/MAyjUHgl5ayL5O3v3sHXFfl4t4T3UB0Pb7XwDSu0wyX3cdsE5PRdm/pX6YnW4xTkHaB7vnpaA0K7SJL8veppVsRo5/ri0PAezgDLw/CQh1G4d/Py96yne6+KV1zoH/TgnkW6o2m/fNuPbQOKvq+HYdOw8J7O34vrpSu8/PRo5Jhinz9X3tcfF9pFrfyaOP/ffD3rKLxOzo4Uz8vbfN0atNHSg+/p0WvGqG2e15elnB2jBXcy1DamAqy5c+dq4cKFmj17to4dO6bg4GD1798kli+/AAAgAElEQVRf99+f88HWpEkTvfhi0ZfDdbVHH31U06dP1+LFixUQEKDExLJdmr4muP+qQWpcRGjnY/Mqdi9eSXJnFRS1p7NfSCdtOPaZ4kL/oOsDr1Hb+i3ybXAahuG8utcfCuwJ9rZ5aVyz/lp68D0tajcx34ph3iuCFcXfs57GNeufb0UqN2HP5WHYNKpJH528fJWVht711TqgmVoHNDP70p2voeAKk2EYCinwt2zl11itCqzEexi2IldAcz+Yl3WYIi/DQ79cPFPs3nXDMDSycS91CWqth/fkXMK7pP+T2RG36YczKboptLMk6cp6jTT+ypvUNSjnSjK5X1iN6warcd1gSb/PzHoscqyknA/oRe0m5nvchj71Te09L03BleU3O82Sj83LGdDkBkE9LoeFxWnl19j5ZdM7pIN6h3QotExUQHPnhnNBEf5NnDMH+4V0Us+G7co0wyHvXvzi3Nq4t974+X352HJWtgpuLOQGHDbDpsAiNhZLUvB9lTtzcErL4YVCxCb1QvRSh6ka+8V82X0KfzYYhiEf4/cVmcXtHtTGY8m6sl6o2gQ0d9Z2Zb3QIt/PPh7emt5qpCQp/fKlgoeF99Sw8B7Ffun6etbR45F3qqFP/UK9lCv3SlaSFOCZs1KWO/PF1+P3jbS8X/YDQrvomssBr6R8wVje2v/R5t5CG6N5Z2YVpbV//s+OOpf/zh2vaCXDMJwBfFEb/lJO3+f2ft7AZ0rL4cq8cKpQPxf8u/jYvJwbUQU3XMc0vVHHL5xSh8sb3i39wrWi0yP68pcfnOFJ7gbVjIhbdSH7ojPoudo3XIFefmpaL0Q3hkRLygklcjesbgi6VvLPVkr6CX2cuSfflR8Lvtdyn6e4jbeHWtysuXtfzTdD6fqga4pctiBPm4eebTeh2PGSNt7GNeuvY7+d1Jq0Hc7b/tSoc74AK+GaMflWjFv4hunBq292/h5w+b009vIKcW5oE163oVZc/4iSL1+ByyabEiJHa/FP/3FeObE0xb1nqkNYns+yEJ/AEpZ0D+Oa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ax = plt.subplots(3,1,figsize=(20,18))\nax[0].plot(train.index.values[0:50000], train.quaketime.values[0:50000], c=\"Red\")\nax[0].set_xlabel(\"Index\")\nax[0].set_ylabel(\"Time to quake\")\nax[0].set_title(\"How does the second quaketime pattern look like?\")\nax[1].plot(train.index.values[0:49999], np.diff(train.quaketime.values[0:50000]))\nax[1].set_xlabel(\"Index\")\nax[1].set_ylabel(\"Difference between quaketimes\")\nax[1].set_title(\"Are the jumps always the same?\")\nax[2].plot(train.index.values[0:4000], train.quaketime.values[0:4000])\nax[2].set_xlabel(\"Index from 0 to 4000\")\nax[2].set_ylabel(\"Quaketime\")\nax[2].set_title(\"How does the quaketime changes within the first block?\");","execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x1296 with 3 Axes>","image/png":"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2bBhg7p27WrdunixGj/++GN9/vnnmjlzphISEjR69OgLnnfKlCl65JFH9MQTTyghIUF9+/bVnj179O6776pDhw7q2rWrVq1aZT338ePHNW7cOHXq1EmdO3fWn//854ve1udwODR9+nR1795dCQkJGjhwoDIyMqz21atX6+abb1ZiYqKef/55GWMkSfv379ddd92l5ORkJScn6/HHH9exY8eK9dvMmTPVt29ftWvX7oJzacaMGerUqZM6deqkOXPmFLu1sKCgQJMnT9aNN96oG264QePHj9fp06clSTk5OXrggQeUmJiopKQkDRs2zDrPz/XWW2/pT3/6kySpsLBQ1157rSZPnixJOn36tNq0aaOjR4/qwIEDatmypYqKivTnP/9ZGzZs0MSJE5WQkKCJEydesh/On6nYsmVLffjhhyVue66VK1fq3Xff1aJFi5SQkKB+/fpZbQcPHtTQoUOVkJCgESNGKCcnx2rbtGmThg4dqsTERPXr1++it8beddddWrt2rXUse/bsKTbD8Oy5/pe//EUdO3bUH//4x4v27ZNPPqn09HSNHj1aCQkJmjFjRonPKUnTp09XcnKyUlJSNH/+/ItuV9rryM8//2xdKzfccIOmT59+wf6FhYV67LHH9PDDD6ugoECjR49W8+bN5enpqeuuu07Z2dkXfW4AAMrEAACAKtetWzfz3XffFfvev/71LzN06FBjjDEFBQWme/fuZtq0aSY/P9+sXr3aXHvttWb37t3GGGO6du1qtm7daowx5uabbzYpKSlm165dVtsPP/xwwXPm5+ebG2+80bz//vumoKDALFq0yFx99dXmjTfeMMYYs3r1apOUlGS2bdtm8vPzzcSJE82wYcOMMcbk5eWZLl26mH/+85+msLDQ/PDDDyYpKcn8/PPPxhhjOnbsaNavX2+MMebo0aNm27ZtJR73ucd41lNPPWWSkpLM5s2bTWFhoXnsscfMo48+WqbnPd/gwYPNiy++aPLz8826devMtddeax5//HFjjDFr1qwxnTt3vug4bN261WzcuNEUFhaatLQ006tXL/P+++9b21511VVm7969ZsWKFaZLly5m8+bNZarxqaeesvq4pOd96623THx8vFm5cqUpLCw0Tz75pOnWrZt55513TEFBgfn4449Nt27drH0ffPBB8+yzz5q8vDxz5MgRc9ttt5kPP/ywxP6YMWOG6dOnj9m9e7dxOp1mx44dJicnxzqeUaNGmV9//dUcPHjQJCcnmxUrVhhjjNm7d69ZtWqVyc/PN9nZ2WbYsGHmhRdeKFb/bbfdZjIzM01ubq7p1auX+eCDD4wxxqxYscLccMMN5qeffjInT540jz/+uNV3xhgzadIk88ADD5jc3Fxz/Phx88ADD5jXXnvNGGPMa6+9Zp599llTUFBgCgoKzPr1643T6bzguFavXm369OljjDHmP//5j7npppvM7bffbrX17dvXGGNMWlqaueqqq0xhYaExxpg777zTfPLJJ8Ueq7R+OP98LW3b87311lvWuXfWnXfeaW666Sbzyy+/mFOnTpk777zTvPrqq8YYYzIzM01SUpJZvny5cTgcZtWqVSYpKclkZ2eX+PjnH8u559maNWtMXFyceeWVV0x+fr45depUqX1b0uvRuc4+3tlra+3ateaaa66xXo/Ofe7SXkeOHz9uOnbsaGbOnGlOnz5tjh8/bjZt2lSsv06dOmVGjhxpnnrqKVNUVFSsjiNHjpju3bub2bNnX7RWAADKgplfLjRz5kz17NlTrVq10rJlyy65fU5Ojm644QY98sgjxb6/cOFC9e3bV3369FHfvn115MgRSdLhw4f1+9//Xn379lXv3r01b948ax+Hw6Hnn39e3bt3V48ePYqth1HafgCAinvooYeUmJho/Xv++eetts2bN+vkyZMaNWqUvL291aFDB3Xr1k1ffPGFJKl9+/Zav369Dh8+LEnq2bOn1q1bp7S0NJ04cUKtWrW64Pk2b96swsJC3X333fLy8lKvXr3Upk0bq/3zzz/XbbfdptatW8vb21uPPfaYNm3apAMHDmj58uVq2LChbrvtNnl6eurqq69Wz549tXjxYkmSp6endu3apRMnTqhevXpq3bp1ufqie/fuatu2rTw9PdWvXz/t2LFDki75vOdKT0/X1q1bNWbMGHl7e6t9+/ZKSUkpcw3x8fG69tpr5enpqdjYWA0ZMkTr168vts3ixYs1YcIEzZgxQ23bti13jReTmJiozp07y9PTU7169VJubq5GjRolLy8v3XLLLTp48KCOHTumI0eOaMWKFRo3bpz8/f0VHh6ue+65xzovzjdnzhyNGTNGV1xxhWw2m1q1aqXQ0FCrfeTIkQoODlZMTIySk5O1c+dOSVKTJk3UsWNHeXt7KywsTPfee+8FfTF8+HA1aNBAISEh6tatmzVmixYt0sCBA3XllVfKz89PDz/8sLWPMUaffPKJxo0bp5CQEAUGBuqBBx6w6vf09NThw4eVnp4uLy8vJSYmymazXXBcCQkJ2rt3r3Jzc7VhwwbdfvvtysrKUl5entavX6+kpKQy931p/XC525Zk4MCBatasmXx9fdWrVy+r3+bNm6cuXbqoa9eustvt6tixo+Lj47VixYpyPf5ZdrtdjzzyiLy9veXr61vmvi3N2WsrKSlJXbt21aJFiy7Y5lKvI/Xr19eIESPk4+OjwMBAXXPNNda+J06c0P3336/GjRvrpZdestZqk87MGLzvvvvUtWtXDR8+vEJ9AgDAWZ6uLqCuSElJ0dKlS4t9r3379urRo4eefvrpMj3Gc889p65duyovL8/63tatW/X222/rb3/7myIiInT8+HF5e3tLkl5++WXFx8dr2rRpysnJ0cCBA5WUlKTo6Gh9/vnn2r9/v5YsWaKjR4+qf//+6tChg2JjY0vdDwBQcVOnTtUNN9xgff3pp59af3w4dOiQoqKiZLf/9+9SMTEx1u1DSUlJ+uabb9SgQQO1b99eycnJmjdvnnx8fJSYmFhsv7MOHTqkBg0aFPuFNyYmplj7uaFVQECAQkJClJWVpYMHD2rLli3FblV0OBzW7VxvvfWWpk2bptdff10tW7bU448/roSEhDL3Rf369a3/+/r66uTJk5J0yec9//iCg4Pl7+9f7PjOvc2vNHv27NHLL7+sbdu26dSpU3I4HBeEeH/729+Umpqqq666yvpeeWq8mPDwcOv/vr6+Cg0NtX7x9/X1lSSdPHlShw4dUlFRkTp16mRt73Q6L/qenJmZqcaNG1/0eSMiIqz/+/n5WT9THDlyRJMmTdKGDRuUl5cnY4yCg4NL3ffQoUOSzoxDfHy81XZubTk5OTp16pQGDhxofc8YY93aeN999+ntt9+2PmFxyJAhGjVq1AV1+/r6Kj4+XuvXr9f69es1evRo7dixQ99//73Wr1+vO++886LHXJ5+uNxty7L/2XM9PT1dixcvLvYH0KKiogp/MEJoaKh8fHysr8vatxdT0rV1dszPVdrrSEZGRqnn4+bNm1VUVKTXX3/9gmBu3bp1ysvL07hx48pcMwAAF0P45UJn/4JcFvPnz1f9+vUVHx+v5cuXW9+fNWuWRowYYf1gFRQUZLXt3LlTd999tyQpLCxMrVq10qJFizRixAgtXLhQgwYNkt1uV1hYmLp3767Fixfr/vvvL3U/AEDViIyMVGZmppxOpxVkZWRkqGnTppLO/MHklVdeUVRUlNq3b6927dppwoQJ8vHxUfv27Ut8zIiICGVlZckYY/1imZ6erkaNGlnPefDgQWv7kydP6ujRo2rQoIGio6PVvn17vf/++yU+dtu2bTVt2jQVFhbqH//4hx599NESZ6yUd6bJpZ73/OM7duyYTp48af2Snp6ebj2nn5+ftbaUdCagOne9peeee05XX321Xn/9dQUGBmrWrFn68ssviz3Hm2++qaefflpRUVHWe+OlaizvMZcmKipK3t7eWrNmjTw9L/1jW1RUlPbv318srCuLN954QzabTZ9//rlCQkL09ddfF1sjqzSRkZHF1ng6N3wMDQ2Vr6+vvvjiCzVo0OCCfQMDAzV27FiNHTtWP/30k+6++261adOm2Jp2ZyUlJWnNmjXasWOH2rRpo6SkJK1atUpbtmy56DVQnSpyrqempuqFF16okucvT9+W5PxrKyMjo8QF9y/1OlLamn0dO3ZUy5Ytdc899+j//u//ioXihw8fVv369UsM9gEAKC/eTWqBrKwszZo1S48//vgFbbt371ZaWpp+97vfacCAAXrnnXeshVhbt26thQsXyhijtLQ0bdy4Uenp6ZLO/ABz7l//o6OjlZmZecn9AABVo23btvL19dV7772nwsJCrV27VkuXLtUtt9wiSWratKl8fHw0f/58JSUlKTAwUOHh4fryyy8v+ov/2Vv6Zs+ercLCQi1ZskRbt2612vv06aNPP/1UO3bsUEFBgd544w21bdtWsbGxuvHGG7V371599tlnKiwsVGFhobZs2aLdu3eroKBA8+fP1/Hjx+Xl5aWAgICL/oIaHh6urKysiy4kf77Snvd8DRs2VHx8vKZMmaKCggJt2LCh2CyaZs2aKT8/X8uXL1dhYaGmTZtWrI68vDwFBAQoICBAu3fv1ocffnjBc0RGRmrWrFmaPXu2PvjggzLVGB4ergMHDpTpeC8lMjJSHTt21Msvv6wTJ07I6XRq//79WrduXYnbDxo0SG+++ab27t0rY4x27typ3NzcSz5PXl6e/P39FRQUpKysLL333ntlrrFXr1769NNPtXv3bp06dUrvvPOO1Wa32zVo0CC9+OKL1qLlWVlZ+vbbbyVJy5Yt0759+2SMUVBQkDw8PC4aIrVv316fffaZmjdvbt2KN2fOHMXGxiosLKzEfUHh0OoAACAASURBVOrXr6+0tLQyH8vlCA8P18GDB0tcsL8k/fr107Jly/Ttt9/K4XAoPz9fa9eutX4eu1yl9W1Z++Xca2v58uXq1avXBdtc6nXk8OHDmjVrlgoKCnTixAlt3ry52P4jR45Unz59dM899xQLp3v37q1p06ZdZi8AAHAG4VcVGj16tFJTU5WamqpDhw5Z/z936n9ZPPvss3ryyScVEBBwQZvD4dCPP/6o999/X//3f/+nlStXWmt0jR07VkeOHFFqaqomTZqkDh06FFtL4WIquh8AoOK8vb01ffp0rVy5Utdff72ef/55vfLKK2revLm1TVJSkkJCQqzbypKSkmSMueh6W97e3poyZYrmzp2rpKQkLVy4UD169LDab7jhBo0ZM0YPP/ywOnXqpLS0NOvT4wIDAzVz5kwtXLhQnTt3VqdOnfTaa69Z4dG8efOUkpKi6667Th999JFeffXVEmu4/vrr1aJFC3Xq1KlMt3Nd6nnP9/rrr2vz5s1KTk7W1KlT1b9/f6stKChIEyZM0DPPPKMuXbrIz89PUVFRVvtTTz2lBQsW6LrrrtOzzz5rBY3ni4mJ0axZszRjxgzNmTPnkjXefvvt2rVrlxITE/Xggw9e8pgv5ZVXXlFhYaFuueUWtW/fXo888oi19tv57r33XvXu3VsjRozQddddp6effvqCT/gsyR/+8Adt375diYmJGjVqlG6++eYy13d2Taa77rpLPXr0sNZ0OrsMw5NPPqkmTZpo8ODBuu6663TPPfdoz549kqR9+/bp3nvvVUJCgoYMGaI77rhD119/fYnPk5CQoPz8fCvsbdGihXXb78XcddddVkBcWTOsLuZsMJScnKwBAwZccvvo6Gi98847xT7lc+bMmWUOzy6ltL4dNWqUpk2bpsTExBI/qVU6E5AFBwerc+fOeuKJJ/Tcc88Vez0661KvI3/961+1bNkydezYUT179izxEy0feugh3XTTTbr33nt19OhRSdKSJUv02GOPVUpfAABgM6aEz2tGpStpza+zhg8frhEjRqhbt24ltp/9C7905i+zZ3/wmzFjhh544AH17t3b+mF/xowZysjI0Pjx4y94nJEjR+rmm2/WoEGDNGrUKA0cOND6QW3ixImKiYnR/fffX+p+AADUZFOmTNG+ffv02muvubqUOmv37t3q06ePtm7dWqZbNQEAAKoaM79qgXXr1mnp0qVaunSpnnrqKXXp0kUzZsyQdGaq+apVq2SMUWFhodasWWN94ldubq6KiookSf/+97/1008/qU+fPpLO/HVyzpw5cjqdysnJ0ddff62ePXtecj8AAIDzffXVVyooKNCvv/6qV199Vd26dSP4AgAANQY/lbjQe++9p9mzZysnJ0djx46Vj4+PFi5cqMDAQD399NNKSUnRTTfdVOpj3Hrrrdq2bZtuueUW2e12derUSbfffrskacuWLZo0aZLsdrtCQ0M1ffp0+fn5SZJSU1O1efNm67aGhx56yFoAubT9AAAAzvfRRx9p7Nix8vDwUPv27TVhwgRXlwQAAGDhtkcAAAAAAAC4LW57BAAAAAAAgNsi/AIAAAAAAIDbIvwCAAAAAACA22LB+yqUm5snp7P2L6kWHh6o7OwTri4DLsDY112Mfd3EuNddjH3dxdjXXYx93cXY113uMPZ2u02hoQHl3o/wqwo5ncYtwi9JbnMcKD/Gvu5i7Osmxr3uYuzrLsa+7mLs6y7Gvu6qq2PPbY8AAAAAAABwW4RfAAAAAAAAcFuEXwAAAAAAAHBbhF8AAAAAAABwW4RfAAAAAAAAcFvVFn7t2bNHQ4YMUc+ePTVkyBDt3bv3gm0cDoeef/55de/eXT169NCcOXOqtG3VqlUaOHCg4uPjNXny5DLXAgAAAAAAgNrBs7qeaMKECRo2bJhSU1M1b948jR8/XrNnzy62zeeff679+/dryZIlOnr0qPr3768OHTooNja2StoaNWqkSZMmafHixSooKChzLQAAAAAAAKgdqmXmV3Z2trZv364+ffpIkvr06aPt27crJyen2HYLFy7UoEGDZLfbFRYWpu7du2vx4sVV1takSRPFxcXJ0/PCDLC0/QAAAAAAAFA7VEv4lZGRoQYNGsjDw0OS5OHhocjISGVkZFywXUxMjPV1dHS0MjMzq6ztUjVXZD93U1DoUH6hw9VlAAAAAAAAVEi13fZYF4WHB7q6hMv2yOvLdODQCbVqEqa2V9ZX2xb1dVXjUHl68FkJdUVERJCrS4CLMPZ1E+NedzH2dRdjX3cx9nUXY1931dWxr5bwKzo6WllZWXI4HPLw8JDD4dChQ4cUHR19wXbp6elq27atpOKzr6qi7VI1V2S/c2Vnn5DTacq1T01z180ttXlPjr7fmaUPFu/UPyT5eHnoykb1FNckVHFNQtU4Mkh2u83VpaIKREQE6fDh464uAy7A2NdNjHvdxdjXXYx93cXY112Mfd3lDmNvt9sqNNGoWqbvhIeHKy4uTgsWLJAkLViwQHFxcQoLCyu2Xa9evTRnzhw5nU7l5OTo66+/Vs+ePausrTQV3c/dNIkK0n394vXcvUl6c0xnPTQgXh3bRCn719Oas2y3Js7aoEfe/FZT/rVFX29I08HDJ2RM7Q78AAAAAACA+6i22x6fe+45jR07Vu+8846Cg4M1efJkSdLIkSP1yCOPqE2bNkpNTdXmzZt18803S5IeeughNWrUSJKqpG3Dhg167LHHdOLEmcDmiy++0KRJk9S5c+dS96urAv281K5lpNq1jJQkHT2Rr537crXjt38bfz4iSQoO8LZmhbVqEqqIer6y2ZgZBgAAAAAAqp/NME2nyrjDbY9S2adGHj566kwYtj9XO/bm6te8AklSeLBvsTAsNMinqktGJXGHabGoGMa+bmLc6y7Gvu5i7Osuxr7uYuzrLncY+4re9siC96g0ESF+igjxU+drYmSMUWbOyXNmhR3Wqq1nPt0zKszfCsNaNg5RkL+3iysHAAAAAADuivALVcJmsyk6PEDR4QFKuS5WTmN04NAJKwxb/UOmlm08KElqFBlozQpr2ShEfj6clgAAAAAAoHKQMqBa2G02NW4QpMYNgtQzqbGKHE7tyzxuhWHLNh7UkvVpsttsahodZIVhLRrWk4+Xh6vLBwAAAAAAtRThF1zC08Ou5g3rqXnDeupzQ1MVFjm06+Ax7diXq537crV47X598e998vSwqXlMPSsMuyImWJ4e1fIhpQAAAAAAwA0QfqFG8PL0sNYBk6RT+UX6+cCv1qdJzlu1R5+t2iNvL7uuig2xwrAmDYJkt/NJkgAAAAAAoGSEX6iR/Hw81bZ5uNo2D5cknThVqB/3H7U+TXLO8t3Wdq0ah6jVb8FZw/oBstkIwwAAAAAAwBmEX6gVAv281K5lhNq1jJAk/XoiXzv251ozwzb+fESSFOzvpVa/zQqLaxKqyBA/wjAAAAAAAOowwi/USvUCfXT91VG6/uooSdKRo6eKhWHrdhySJIUF+yiu8X/DsLBgX1eWDQAAAAAAqhnhF9xC/RA/dQ7xU+e2MTLGKDPnpBWEbd6dre+2ZUqSGoT6WeuFtWocquAAbxdXDgAAAAAAqhLhF9yOzWZTdHiAosMD1O26WDmN0YFDJ6wwbM32LC3flC5Jio0IsGaFtWwUIn9fLxdXDwAAAAAAKhPhF9ye3WZT4wZBatwgSDcnNZbD6dTezONWGLZiU7q+3nBANpvUNCrICsOubBgiH28PV5cPAAAAAAAuA+EX6hwPu13NY+qpeUw93dqhqQqLnPol/Vft+C0MW7IuTYvW7JeH3abmMcFWGHZFTD15edpdXT4AAAAAACgHwi/UeV6edrVsHKqWjUPVv7N0uqBIuw78Nwz7/Lu9mv/dXnl72nVlbL3fwrAwNYkKlIedMAwAAAAAgJqM8As4j6+3p+KvCFf8FeGSpLzThfpp/9EzYdj+XP1rxS+SfpGfj4daNjozKyyuSahiIgJkt9lcWzwAAAAAACiG8Au4hABfLyVcFaGEqyIkSb/mFejH/bnWzLBNu45IkoL8vdSq8X/DsMhQP9kIwwAAAAAAcCnCL6Cc6gV4KymugZLiGkiSsn89rZ3nhGHrdx6SJIUG+VhBWKvGoQqv5+vKsgEAAAAAqJMIv4DLFF7PVx3bRKtjm2gZY3Qo95QVhG3Zna3V2zIlSZGhfsXCsOAAbxdXDgAAAACA+yP8AiqRzWZTgzB/NQjz140JDeU0RumH86wwbN2OLK3YlC5JahgRoLjfbpNs2ThE/r5eLq4eAAAAAAD3Q/gFVCG7zabYyEDFRgaqR/tGcjid2p91Qtv35mjnvlyt3Jyur/9zQDab1KRBkDUz7MrYEPl4e7i6fAAAAAAAaj3CL6AaedjtahYdrGbRwbq1Q1MVFjn1S/qv2rEvVzv35WrJ+jQtWrtfHnabrogJtsKwK2LqycvT7uryAQAAAACodQi/ABfy8rSrZeNQtWwcKnWW8gsc+vngUSsM+3z1Xs3/bq+8PO26MrbemfXCmoSqaVSQPOyEYQAAAAAAXArhF1CD+Hh7KL5ZuOKbhUuSTp4u1I9p/w3D/rXiF0mSr7eHWjYKscKw2MhA2W02V5YOAAAAAECNRPgF1GD+vl5KuDJCCVdGSJKO5RVo5/4zQdiOfbnavDtbkhTo56VWjf8bhkWF+ctGGAYAAAAAAOEXUJsEB3grKa6BkuIaSJJyjp22ZoVt35erDT8eliSFBHpbQVhck1DVr+fnyrIBAAAAAHAZwi+gFgsL9lXHNtHq2CZaxhgdOnrKCsO27cnRv3/IkiRFhPj+NwxrHKp6gT4urhwAAAAAgOpB+AW4CZvNpgah/moQ6q8br20oY4wOHsmzwrD1Ow9r5eYMSVJM/QDFNT4ThrVsHKJAPy8XVw8AAAAAQNUg/ALclM1mU2xEoGIjAtUjsZGcTqN9Wcet9cK+3Zqub74/IJukxg2CrJlhVzWqJ19vXhoAAAAAAO6B33CBOsJut6lZdLCaRQer9/VNVORw6pf0Y1YY9vV/0rR43X55/LZdqyah6nBNjOoHeMnL08PV5QMAAAAAUCGEX0Ad5elh11WNQnRVoxD169RM+YUO7Tr4qxWGffHvvVqweq88Pey6MraetXh+s+ggedjtri4fAAAAAIAyIfwCIEny8fJQ66Zhat00TJJ08nSRso7na+2WdO3Yl6u5K3/RXEm+3h66qlGI4n4Lw2IjA2W32VxbPAAAAAAAF0H4BaBE/r6eSmoUqmYRAZKkYycL9NP+o9rx28ywLbuzJUkBvp7WrLC4JqGKCvOXjTAMAAAAAFBDEH4BKJNgf28ltopUYqtISVLu8XzrFskd+3L0nx8PS5LqBXqfCcIanwnD6of4ubJsAAAAAEAdR/gFoEJCg3zUIT5KHeKjZIzR4V9PW2HY9r25WvNDliSpfj1fa1ZYqyahCgn0cXHlAAAAAIC6hPALwGWz2WyKDPFTZIifulwTI2OM0rNPaue+XG3fe2ZW2LdbMiRJ0eH+VhjWsnGoAv28XFw9AAAAAMCdEX4BqHQ2m00N6weoYf0A3dQuVk6n0f5Dx631wr7bmqml3x+UTVKjBoFWGHZlbIj8fHhZAgAAAABUHn7LBFDl7HabmkYFq2lUsHonN1GRw6k9Gce0Y1+udu7L1Tf/OaAv16XJbrOpWUyQtWZY84b15O3l4eryAQAAAAC1GOEXgGrn6WHXlbEhujI2RP06NlNBoUO7Dv5qhWEL/71fC1bvk6eHXS0aBv82MyxMTaOD5Olhd3X5AAAAAIBahPALgMt5e3no6qZhurppmCTpVH6Rfko7aoVhc7/do7nf7pGPl4euahRi3SbZKDJQdrvNxdUDAAAAAGoywi8ANY6fj6euaVFf17SoL0k6frJAP+4/qh37z4RhnyzLliQF+HqqZeP/fpJkTLi/bDbCMAAAAADAfxF+Aajxgvy9ldgqUomtIiVJucfztXP/mcXzd+zN1fc/HZYkBQd4W7PCWjUJVUQ9X8IwAAAAAKjjCL8A1DqhQT7q0DpKHVpHSZIOHz1l3SK5Y1+u1m7PkiSFB/sWC8NCg3xcWTYAAAAAwAUIvwDUehEhfooI8VOXa2JkjFFG9kkrDNv482Gt2pohSYoK87fCsJaNQxTk7+3iygEAAAAAVY3wC4BbsdlsiqkfoJj6AbqpXaycxigt68SZMGx/rlb/kKllGw9KkhpFBlqzwlo2CpGfDy+JAAAAAOBu+E0PgFuz22xqEhWkJlFB6pXcWEUOp/ZmHrdmhi39/qCWrE+T3WZT0+ggKwxr0bCefLw8XF0+AAAAAOAyEX4BqFM8Pexq0bCeWjSsp743NFVhkUO7Dh6zwrBFa/bri3/vk6eHTc1j6llh2BUxwfL0sLu6fAAAAABAORF+AajTvDw9rHXAJOlUfpF+PvCrtXj+vFV79NmqPfL2suuq2BArDGvSIEh2O58kCQAAAAA1HeEXAJzDz8dTbZuHq23zcEnSiVOF+nH/0TNh2P5czVm+W5Lk7+Oplo1D1Oq34Kxh/QDZbIRhAAAAAFDTEH4BQCkC/bzUrmWE2rWMkCQdPZGvnftzrZlhG38+IkkK9veygrC4JqGKCPEjDAMAAACAGoDwCwDKISTQR9dfHaXrr46SJB05eko7fgvDtu/L1bodhyRJ4cE+VhjWqnGowoJ9XVk2AAAAANRZhF8AcBnqh/ipc4ifOreNkTFGmTknrVlhm3dl67utmZKkBmH+1qywlo1DFOzv7eLKAQAAAKBuqLbwa8+ePRo7dqyOHj2qkJAQTZ48WU2bNi22jcPh0AsvvKBvv/1WNptNo0aN0qBBg1zSdvjwYY0fP14HDhxQUVGRRo8erdTU1GrqLQC1kc1mU3R4gKLDA9Ttulg5jdGBQyesMGzND5lavvGgJCk2ItAKw65qFCJ/X/4WAQAAAABVodp+25owYYKGDRum1NRUzZs3T+PHj9fs2bOLbfP5559r//79WrJkiY4ePar+/furQ4cOio2Nrfa2l19+WfHx8Zo2bZpycnI0cOBAJSUlKTo6urq6DEAtZ7fZ1LhBkBo3CNLNSY3lcDq1N/O4FYYt33RQX21Ik80mNY0KtsKwFrH15OPl4eryAQAAAMAt2KvjSbKzs7V9+3b16dNHktSnTx9t375dOTk5xbZbuHChBg0aJLvdrrCwMHXv3l2LFy92SdvOnTvVuXNnSVJYWJhatWqlRYsWVX1nAXBbHna7msfU060dmuqJoQl6+9HO+n93JKhPh6by8LDpy3X79frHm/SHP6/Uy//4XvNW7dFPaUdV5HC6unQAAAAAqLWqZeZXRkaGGjRoIA+PMzMZPDw8FBkZqYyMDIWFhRXbLiYmxvo6OjpamZmZLmlr3bq1Fi5cqDZt2ujAgQPauHGjYmNjK6dDAECSl6eHWjUJVasmoRog6XRBkX4+8Kt2/DYzbP6qPZq3ao+8vey6MjbEmhnWpEGQ7HY+SRIAAAAAyoJFZi5i7NixevHFF5WamqqYmBh16NDBCu/KKjw8sIqqq34REUGuLgEuwthXr0YNQ5WS3FSSdOJkgbbuztaWXYe1ZdcR/XP5bklSgK+n4pvXV9sW9dX2ygg1rqIwjLGvmxj3uouxr7sY+7qLsa+7GPu6q66OfbWEX9HR0crKypLD4ZCHh4ccDocOHTp0wfpZ0dHRSk9PV9u2bSUVn5lV3W1hYWF67bXXrNpGjhypFi1alOu4s7NPyOk05dqnJoqICNLhw8ddXQZcgLF3vRZRgWoRFaiBnZrp17wCa72wnftytfaHMzNVg/y91KpxqDUzLDLUTzbb5YVhjH3dxLjXXYx93cXY112Mfd3F2Ndd7jD2drutQhONqmXNr/DwcMXFxWnBggWSpAULFiguLq7YLY+S1KtXL82ZM0dOp1M5OTn6+uuv1bNnT5e05ebmqqioSJL073//Wz/99JO1ZhmA/8/evUbHWd53v//d95w05xlJM9JIMyPJ1hEfwWDj2CYkAQLExNDEWeshWU1hY5KGQHaS3cZtE2CtkKRuVpv0eVpKs/YKCSvpbksoJwccxxCwjW0MxiewZHyQJVlnSyOfD9LM7BdSb+MSQAZLI2m+nzfBnjH+M9dyLH99XdeNXAh6nVpwWYn+7KZ6/e1XF+rv/nyh7ry5QTOrirTv8IAe+91e/dXPtuj/eXiT/t/Ve/TK7k71HzuT67EBAAAAIKfG7djjgw8+qJUrV+rhhx9WIBDQqlWrJA3vqLrvvvs0a9YsLVu2TDt37tQNN9wgSbrnnnuUSCQkadxf27Vrl37wgx/INE2Fw2E98sgjcrvdY/45AcBoFQfdWjzbrcWzY8pms+pOnbbuC9t1oE+b3hzeGRYNu61dYfXJsAJeZ44nBwAAAIDxY2Sz2cl/Lm+C4tgjJjvWfvLKZLNq7z1pHZHc25bS6bNpSVJ5xKuGkWOSdcmQPAWOd/141j4/se75i7XPX6x9/mLt8xdrn7+mwtp/2GOPXHgPAFOQaRhKRH1KRH264aqE0pmMWrpOqLGlX00tKa3f2aF12w7LMKSKEr+1M6wmHpLLeXEP9wAAAACAiYz4BQB5wGaamlYW0LSygD6zsFKDQxkd7Dhq7Qxb+1qbnn+1VTbT0LSygOY1lKoi4tG0sqAc9nG5HhIAAAAAxgTxCwDykMNuqi4ZVl0yLC2Rzp5La1/7gBXD/nPdXmWyktNuqjoeHL4vrCKsylK/bCYxDAAAAMDkQfwCAMjltGlmVZFmVhVJkty+Am3a3mbFsCdePihJKnDaVJcIDR+TrCxUecQr0zByOToAAAAAvC/iFwDgXXxuhy6viejymogk6djJc2pqHQ5hjS0p7TzQZ72vfuS+sIaKsErCbhnEMAAAAAATCPELAPCBAl6n5jeUaH5DiSSp/9gZa1fYnpaUXm/qkSSF/S7VjzxJsr4ipOKgO5djAwAAAADxCwBw8QoDBVo0K6ZFs2LKZrPqGThtxbA3m/u0+a0uSVI05LZ2htVXhBX0OnM8OQAAAIB8Q/wCAHwkhmGoJOxRSdija+eWK5vNqv3ISSuGvdbUo/U7OyRJ5cVeK4bVJUPyFjhyPD0AAACAqY74BQC4pAzDUDziUzzi0/VXJpTJZNXSfVyNI/eFbdjZoRe2HZYhKVnqt+4Lq4kHVeDktyUAAAAAlxZ/ygAAjCnTNFQVC6gqFtDNV1doKJ3RwY5jVgz7/WttWvNqq2ymoaqygBpG7gybXh6Qw27L9fgAAAAAJjniFwBgXNltpmoTIdUmQlq2uEpnB9Paf/ioFcNWbz6kZzcdksNuqro8aO0Mq4z5ZTPNXI8PAAAAYJIhfgEAcsrlsGlGVaFmVBVKkk6dGdLbbQNWDPuv9QclSQVOm2oTISuGxaM+mYaRy9EBAAAATALELwDAhOIpsGtuTbHm1hRLko6dOqe9redj2K4DfZIkb4Hdujy/oSKs0kKPDGIYAAAAgP+B+AUAmNACHqeuqo/qqvqoJKn/2Bk1taasGLZtb68kKehzDoewkTvDikPuXI4NAAAAYIIgfgEAJpXCQIE+NjOmj82MKZvNqnfgtBXC9jT3a8tb3ZKk4mCBtSusviKskM+V48kBAAAA5ALxCwAwaRmGoWjYo2jYo4/PLVc2m1XHkZMX7ArbsKtTkhQr8lgxrC4Zls/tyPH0AAAAAMYD8QsAMGUYhqHyiE/lEZ+uuzKhTCar1p7jVgzbuLtTL77RLkNSosRnxbCaeEhuF78lAgAAAFMRX+kDAKYs0zRUWRpQZWlANy2o0FA6o+bOY2psSampJaUXth3W77a2yTQMVZX5rTvDppcH5XTYcj0+AAAAgEuA+AUAyBt2m6maeEg18ZA+u6hK5wbT2t9+1Iphz21u1epNLbLbTFWXB0Z2hhWqMuaX3WbmenwAAAAAHwLxCwCQt5wOmy6rLNRllYWSpNNnh/R224AVw57c0KwnNzTL5bSpLhFS/ciTJBMlPpmGkePpAQAAAIwG8QsAgBFul11zqos1p7pYknT81DntbR1QY+twDNt1oE+S5C2wqz45/BTJhoqwYkUeGcQwAAAAYEIifgEA8B78HqeurI/qyvqoJCl1/KyaWocvz288lNK2t3slSUGvUw0V52NYJOTO5dgAAAAA3oH4BQDAKIX9Li2cUaqFM0olSb0Dp60jko0tKW3Z0y1JKg4WWCGsPhlW2O/K5dgAAABAXiN+AQDwIUVCbkVCbl0zp0zZbFadfaesGLb97V5t3NUpSYoVeYZj2MhRSZ/bkePJAQAAgPxB/AIA4BIwDENlxV6VFXv1qXlxZTJZtfWcGD4i2ZLSpt1d+sMb7ZKkZNRn7QyrTYTkdvHbMQAAADBW+GobAIAxYJqGKkr9qij168YFSQ2lMzrUeVyNLf1qbEnpxTfatfa1NpmGoaqY34ph1eVBOR22XI8PAAAATBnELwAAxoHdZqo6HlR1PKhbFlVpcCit/e3HrGOSz29p1W83t8huM1RdHrRiWFUsILvNzPX4AAAAwKRF/AIAIAccdpsaRgKXJJ0+O6R9hwesY5JPb2jWUxua5XLYVJMIWu9NRv0yTSPH0wMAAACTB/ELAIAJwO2ya/b0Ys2eXixJOnF6UHtbU1YMe/wPByRJHpdddcmQFcPKir0yDGIYAAAA8F6IXwAATEA+t0Pz6qKaVxeVJA2cOKumlvMxbPu+I5KkgMdhHZFsqAgrEnITwwAAAIB3IH4BADAJhHwuXT2jVFfPKJUk9Q6cHo5hI7vDtjb2SJKKAq53xLBChf2uXI4NAAAA5BzxCwCASSgScisScmvJnDJls1l19Z+ydoXt2HdEr+zukiSVFHqsXWF1yZACHmeOJwcAAADGF/ELAIBJzjAMxYq8ihV59ckr4spkszrcc8KKYZvf6tJL29slSfGIz4phtYmQPAV8KQAAAICpja94AQCYYkzDULLEr2SJX5+eW/JgKgAAIABJREFUn9RQOqOWruNWDHtpR7t+/3qbDEOqLA1YMezqoDvXowMAAACXHPELAIApzm4zNb08qOnlQS39WKUGh9I60H5sOIa1pvS7ra16bkuL7L/ZpWll52PYtLKA7DYz1+MDAAAAHwnxCwCAPOOw21RfEVZ9RVi3STpzbkj7Dh9VS89JbWvq1jMbm/X0xmY5HaZq4iErhlWU+GWaPEkSAAAAkwvxCwCAPFfgtGvWtCJ9ckGlll6d1Mkzg9rbOqDGlpSaWlL6zUsHJElul111iZAaKodjWHmxV4ZBDAMAAMDEdlHxK5VK6eWXX1Zvb69WrFih7u5uZbNZlZaWjtV8AABgnHkLHLqiNqIraiOSpKMnzqrpHTFsx/4jkiS/x6GGkR1kDRVhRUNuYhgAAAAmnFHHr61bt+ree+/VzJkz9cYbb2jFihVqaWnRz3/+cz3yyCNjOSMAAMihoM+lBZeVaMFlJZKkI0dPq6llYOQC/X5tbeyRJBUGXGpIno9hhYGCXI4NAAAASLqI+PXDH/5QP/3pT7Vw4UJdddVVkqQ5c+Zo165dYzYcAACYeIqDbi2e7dbi2TFls1l1p05bT5LceaBPr7zZJUkqCbutnWH1ybACXmeOJwcAAEA+GnX8am9v18KFCyXJOtLgcDiUTqfHZjIAADDhGYah0kKPSgs9+sTl5cpks2rvPWkdkXy1sVsv7eiQJMUjXmtXWF0iJE+BI8fTAwAAIB+MOn5Nnz5dGzZs0JIlS6zv27Rpk2pra8dkMAAAMPmYhqFE1KdE1KcbrkooncmopeuEGlv61dSS0vodHVr3+mEZhlRZ6rdiWE15SC6nLdfjAwAAYAoadfxauXKlvvKVr+jaa6/VmTNndP/99+vFF1/Uww8/PJbzAQCAScxmmppWFtC0soA+s7BSg0MZHew4ah2TXLu1Tc9vaZXNNDS9LGDFsGllQTnsZq7HBwAAwBQw6vg1d+5cPfPMM3rmmWf0uc99TrFYTL/5zW940iMAABg1h91UXTKsumRYty6Rzp5La9/hASuGPbvpkJ555ZCcdlM18eBIDCtURalPNpMYBgAAgIs36vglSSUlJVqxYsVYzQIAAPKMy2nTzGlFmjmtSJJ08syg3m4diWGtKT3x8kFJB+V22VSXOP8kyfKIV+bIHaQAAADA+xl1/Dp+/Lgee+wxNTY26tSpUxe89vOf//ySDwYAAPKPt8Chy2sjurw2Ikk6evKc9ramrJ1hO/YfkST53A4rhDVUhFUSdlsP5AEAAADeadTx6xvf+IbS6bSuv/56uVyusZwJAABAkhT0OjW/oUTzG0okSX1Hz6jpHTHs9aYeSVLY71J98nwMKwoW5HJsAAAATCCjjl87duzQli1b5HQ6x3IeAACA91QULNCiWTEtmhVTNptVT+q0FcJ2H+zT5re6JEnRkNvaGVZfEVbQy9cvAAAA+WrU8WvevHk6ePCg6uvrx3IeAACAUTEMQyWFHpUUenTt5eXKZLPq6D1pxbDXmrq1fmeHJKm82GvFsLpkSN4CR46nBwAAwHgZdfz627/9W61YsUJz5sxRUVHRBa99/etfv+SDAQAAXAzTMBSP+hSP+nT9VQmlMxm1dp+wYtiGnR16YdthGZKSpX7riGRNPKgC50U9AwgAAACTyKi/0vvJT36irq4uxeNxnThxwvp+LpcFAAATkc00VRULqCoW0M1XV2hwKKPmzmNWDPv9a21a82qrbKahqrKAGkbuDJteHpDDbsv1+AAAALhERh2/fvvb3+p3v/udotHoh/qJmpubtXLlSg0MDCgUCmnVqlWqrKy84D3pdFoPPfSQNmzYIMMwdPfdd2v58uU5ea2vr09/9Vd/pc7OTg0NDWnBggX67ne/K7udvxkGAGAycthN1SZCqk2EtGxxlc4OprX/8FErhq3efEjPbjokh91UdXlweGdYZViVpX7ZTDPX4wMAAOBDGnXJSSQSHyn8PPDAA7r99tu1bNkyPf3007r//vv12GOPXfCeZ599Vq2trVq7dq0GBgZ06623auHChYrH4+P+2iOPPKLp06frZz/7mQYHB3X77bdr7dq1uvnmmz/0ZwAAACYOl8OmGVWFmlFVKEk6dWZIb7cNWDHsv9YflNZLBU6b6hIh6/L8eNQnk53vAAAAk8aoa9ayZcv0ta99TV/60pfedefXwoUL3/fH9vX1ac+ePXr00UclSUuXLtX3v/999ff3q7Cw0Hrfc889p+XLl8s0TRUWFuq6667TmjVrdNddd437a4Zh6OTJk8pkMjp37pwGBwdVUlJyMZ8tAACYRDwFds2tKdbcmmJJ0rFT57S39XwM23mgT5LkcztUnzwfw0oLPVwDAQAAMIGNOn79+te/liT9wz/8wwXfbxiGXnjhhff9sZ2dnSopKZHNNnx/hs1mUzQaVWdn5wXxq7OzU2VlZda3Y7GYurq6cvLa1772Nd17771avHixTp8+rS9+8YuaN2/e+/53AgCAqSPgceqq+qiuqh++8qH/2Bk1taasGPb63l5JUsjntEJYQ0VYxUF3LscGAADA/zDq+PXiiy+O5RwTzpo1a1RXV6df/vKXOnnypFasWKE1a9boxhtvHPW/o6jIN4YTjq9IxJ/rEZAjrH3+Yu3zE+v+3iIRv+qmR7RMUjabVVffKe3a36td+45o1/4j2vxWtySptMij2dURza4u1uzqYoUDBbkdfJRY+/zF2ucv1j5/sfb5K1/Xflxub4/FYuru7lY6nZbNZlM6nVZPT49isdi73tfR0aHZs2dLunBn1ni/9qtf/Uo//OEPZZqm/H6/PvnJT+rVV1+9qPjV13dCmUz24j+wCSYS8au393iux0AOsPb5i7XPT6z7xbFLumJ6ka6YXqRstlYdR05au8I27mjX2ldbJEllxV41JId3htUlQ/K5Hbkd/I9g7fMXa5+/WPv8xdrnr6mw9qZpfKiNRu8bv2666SY9//zzkqSPf/zj73mfxUsvvfS+P0lRUZEaGhq0evVqLVu2TKtXr1ZDQ8MFRx4l6cYbb9Tjjz+uG264QQMDA1q3bp113HK8X4vH41q/fr1mz56tc+fOafPmzbr++uvf978TAADkJ8MwVB7xqTzi03VXJpTJZNXac3w4hh1KacPuDr3wxmEZkpIlfuuYZE08KLeLJ0kDAACMpff9auv73/++9c8//vGPP9JP9OCDD2rlypV6+OGHFQgEtGrVKknSihUrdN9992nWrFlatmyZdu7cqRtuuEGSdM899yiRSEjSuL/213/913rggQd0yy23KJ1Oa8GCBfrCF77wkT4DAACQH0zTUGVpQJWlAd20oEJD6YwOdhxT08jOsHXb2rRma6tspqGqWMC6L6y6PCCH3Zbr8QEAAKYUI5vNjupc3vPPP6+bbrrpXd9/sfdg5ROOPWKyY+3zF2ufn1j38XN2MK397UetGNbceUzZrGS3maqJB60YVlnql91mjvk8rH3+Yu3zF2ufv1j7/DUV1n5Mjj2+09/8zd/80fh1//33E78AAAAugsth04zKQs2oHL4C4tSZIb19eMCKYU+uP6gnJbmcNtUlQqpPDsewRIlP5ntcQwEAAIA/7gPjV1tbm6Thpxr99z+/8zWn0zk2kwEAAOQJT4Fdc6uLNbe6WJJ0/NQ57W0dsC7Q33WgT5LkLbCrfuTy/IaKsGJFnve8kxUAAADDPjB+XX/99TIMQ9ls9l0XvhcXF+vee+8ds+EAAADykd/j1JX1UV1ZH5UkpY6ftXaFNbb0a9vbvZKkoNdpXZ7fUBFWJOTO5dgAAAAT0gfGr6amJknSl770Jf3qV78a84EAAABwobDfpYUzS7VwZqmy2ax6j56xYtielpS27OmWJBUHC6wQVp8MK+x35XhyAACA3Bv1nV//Hb46OzvV3d2tuXPnjtlQAAAA+OMMw1A05FY05NY1c8qUzWbV0XfKimFv7O3Vxl2dkqRYkWc4ho0clfS5HTmeHgAAYPyNOn51dnbqm9/8ppqammQYhrZv3641a9Zow4YN+sEPfjCWMwIAAOA9GIah8mKvyou9+tS8uDKZrNp6Tlj3hW3a3aU/vNEuQ1Ii6rN2htUmQnK7Rv2lIAAAwKQ16q94vve97+naa6/Vv/3bv2nBggWSpEWLFmnVqlVjNhwAAAAujmkaqij1q6LUrxsXJDWUzuhQ53E1tvSrsSWlF99o19rX2mQahqpiftVXhLVwdrmKfQ45HbZcjw8AAHDJjTp+7d69Wz/72c9kmqb1VCG/36/jx4+P2XAAAAD4aOw2U9XxoKrjQd2yqErnBtM60H5Uja3DO8Oe39Kq325uGX5fecDaGVYVC8huM3M9PgAAwEc26vhVVFSklpYWVVVVWd+3f/9+xWKxMRkMAAAAl57TYVNDZaEaKgslSafPDqnn+Dlt2dWuxpaUnt7QrKc2NMvlsKkmEVTDSAxLRv0yTSPH0wMAAFy8UcevO++8U1/96ld19913a2hoSKtXr9a//uu/asWKFWM5HwAAAMaQ22XXlfGwKoo9kqQTpwe1d2RXWGNLSo//4YAkyeOyqy4ZsmJYWbHXOg0AAAAwkY06fn3+859XKBTSf/zHfygWi+mpp57SN77xDV133XVjOR8AAADGkc/t0Ly6qObVRSVJAyfOWk+SbGxJafu+I5KkgNdphbD6irAiwQJiGAAAmJBGHb9OnDih66677l2xq6OjQ2VlZZd8MAAAAOReyOfS1TNKdfWMUklS78Dp4Rg2sjvs1T3dkqSiQMEFMSzsd+VybAAAAMuo49dXvvIVPfroo3I6ndb3tbW16ctf/rJefPHFMRkOAAAAE0sk5FYk5NaSOWXKZrPq6j/1jl1hvdq4u1OSVFrosWJYXTIkv8f5Af9mAACAsTHq+DV79mx97Wtf0yOPPCK73a7m5mbdcccduueee8ZyPgAAAExQhmEoVuRVrMirT14RVyab1eGeE1YM2/RWl/6wvV2SlIj6rF1hdYmQ3K5RfxkKAADwkRjZbDY72jd/97vf1bFjx/T1r39dd911l771rW/p1ltvHcv5JrW+vhPKZEb98U5YkYhfvb3Hcz0GcoC1z1+sfX5i3fPXWK39UDqjlq7jVgzb335Ug0MZmYahypjfimHV5UG5HLZL/vPjg/HrPn+x9vmLtc9fU2HtTdNQUZHvon/cRf2V2/e//31961vf0vLly/WjH/1IN99880X/hAAAAMgPdpup6eVBTS8PaunHKjU4lNaB9mPDMaw1pTWvtuq3m1tktxmaXha0Yti0soDsNjPX4wMAgCnifePX7bff/q6n9gwODqqgoEC//vWv9etf/1qSrP8FAAAA3ovDblP9SOC6TdLps0Pad/io9TTJpzc266mNzXI6TNXGQ1YMqyjxyzR5kiQAAPhw3jd+LV++fLzmAAAAQJ5xu+yaPb1Is6cXSZJOnB7U3tYB62mSj790wHpffTKk+pEL9MuLve/6C1oAAID38r7x67bbbhuvOQAAAJDnfG6H5tVFNK8uIkk6euKsGltT1s6w7fuOSJICHoe1g6yhIqxoyE0MAwAA7+mi7vw6cuSIdu3apVQqpXfek//5z3/+kg8GAACA/Bb0uXT1ZaW6+rJSSdKRgdMXxLCtjT2SpMKASw3J8zGsMFCQy7EBAMAEM+r4tW7dOv3FX/yFKioqtH//flVXV2vfvn264ooriF8AAAAYc8Uht5aE3Foyu0zZbFZd/aesELbzQJ9eebNLklQSdlv3hdUnwwp4nTmeHAAA5NKo49dPf/pT/fCHP9RNN92kq666Sk899ZSeeOIJ7d+/fyznAwAAAN7FMAzFiryKFXn1iSviymSzOtxzwophW/Z066UdHZKkeMRr7QqrS4TkKXDkeHoAADCeRh2/Ojo6dNNNN13wfbfddpsWLVqk73znO5d8MAAAAGC0TMNQssSvZIlfN8xPKp3J6FDXcSuGvbyjQ+tePyzDkCpL/VYMqykPyeW05Xp8AAAwhkYdv4qKinTkyBEVFxervLxc27dvVzgcViaTGcv5AAAAgItmM01NLwtqellQn1lYqcGhjA52HFXjSAxbu7VNz29plc00NL0sYMWwaWVBOexmrscHAACX0Kjj1/Lly7Vt2zZ9+tOf1p/92Z/pT//0T2Wapu64446xnA8AAAD4yBx2U3XJsOqSYd26RDpzbkj7D5+PYc++ckjPvHJITrupmnhwJIYVqqLUJ5tJDAMAYDIbdfy6++67rX++9dZbNX/+fJ0+fVrTp08fk8EAAACAsVLgtGvmtCLNnFYkSTp5ZlBvtw4Mx7DWlJ54+aCkg3K7bKpLDO8Ka6gIqyzilWkYuR0eAABclFHHr/+prKzsUs4BAAAA5Iy3wKHLayO6vDYiSTp68pz2tqasnWE79h+RJPk9DtUnz8ewaNgtgxgGAMCENur49fGPf/w9f2N/6aWXLtU8AAAAQM4FvU7NbyjR/IYSSVLf0TNqekcMe62pR5IU9rusEFafDKsoWJDLsQEAwB8x6vj14x//+IJv9/b26rHHHtPNN998yYcCAAAAJpKiYIEWzYpp0ayYstmselKnrRC260CfNr3ZJUmKht1WDKtLhhX0OnM8OQAAGHX8mj9//h/9vrvuuktf/vKXL+lQAAAAwERlGIZKCj0qKfTo2svLlclm1dF70ophWxu79fKODklSecSrhuR/x7CQPAWOHE8PAED++dB3fkmS0+nU4cOHL9UsAAAAwKRjGobiUZ/iUZ+uvyqhdCaj1u4TVgxbv7ND67YdlmFIFSV+a2dYTTwkl9OW6/EBAJjyRh2//vEf//GCb585c0Yvv/yyrrnmmks+FAAAADBZ2UxTVbGAqmIB3Xx1hQaHMmruPKY9h/rV1JLS2tfa9PyrrbKZhqaVBawYNq0sKIfdzPX4AABMOaOOX11dXRd82+1264477tCyZcsu+VAAAADAVOGwm6pNhFSbCElLpLPn0trXPqDGlpSaWlJ6dtMhPfPKITnspmriweHL8yvCqiz1y2YSwwAA+KhGFb/S6bQWLFigjRs3KpVKKRwO64orrtAtt9wip5NLPAEAAIDRcjltmllVpJlVRZKkU2cGtbftfAx74uWDkqQCp011iZAVw+JRn8z3ePo6AAB4bx8Yv06cOKE777xThw8f1jXXXKPLLrtMvb29+slPfqLHHntMP//5z5VOp/XGG2/w5EcAAADgInkKHLq8JqLLayKSpGMnz6mpdTiENbaktPNAnyTJ53aoPnk+hpUWemQQwwAA+EAfGL/+/u//XqFQSL/4xS/k8Xis7z958qS++c1v6tvf/rba2tr0zW9+c0wHBQAAAPJBwOvU/IYSzW8okST1Hztj7Qrb05LS63t7JUkhn9MKYQ0VYRUH3bkcGwCACesD49fvf/97/ed//ucF4UuSvF6v7r//fl133XX60Y9+pM9+9rNjNiQAAACQrwoDBVo0K6ZFs2LKZrPqGThtxbA3m/u1+a1uSVIkVHA+hiXDCvpcOZ4cAICJYVTHHktKSv7oa6WlpXI6nbrtttsu+WAAAAAALmQYhkrCHpWEPbp2brmy2azaj5y0YthrTb1av7NTklRW7FVDcjiG1SVD8rkdOZ4eAIDc+MD4lUgktGXLFi1atOhdr23evFkVFRVjMhgAAACA92cYhuIRn+IRn66/MqFMJquW7uPWfWEbdnfohTcOy5CULPFbO8NqE0EVOEf94HcAACa1D/wd74477tB3vvMdfe9739P1118v0zSVyWS0du1aPfTQQ9z1BQAAAEwQpmmoKhZQVSygm66u0FA6o4Mdx6wYtm5bm9ZsbZVt5H3zGkqUjHhVXR6Qw27L9fgAAIyJD4xff/Inf6KBgQGtXLlS3/72txUKhTQwMCCHw6F77rlHn/vc58ZjTgAAAAAXyW4zVZsIqTYR0mcXV+nsYFr7249aMezxF95WJjv8vpp40Lo8vyrml800cz0+AACXxKj2Ot955536whe+oO3btyuVSikcDuvyyy+Xz+cb6/kAAAAAXCIuh00zKgs1o7JQkuTxFWjTjsNWDHty/UE9KanAaVNtIqSGkRgWj/pkGkZuhwcA4EMa9UF/n8+nJUuWjOUsAAAAAMaR1+3Q3Opiza0uliQdP3VOe1sH1DgSw3Yd6Bt+X4Hd2hXWUBFWaaFHBjEMADBJcMslAAAAAEmS3+PUlfVRXVkflSSljp+1doU1tvRr295eSVLQ5xwOYcnhGFYccudybAAA3hfxCwAAAMAfFfa7tHBmqRbOLFU2m1Xv0TNWDNtzKKUtb3VLkoqDBdausPqKsEI+V44nBwDgPOIXAAAAgA9kGIaiIbeiIbeumVOmbDarjr5TVgzbtrdXG3Z1SpJiRR4rhtUlw/K5HTmeHgCQz4hfAAAAAC6aYRgqL/aqvNirT82LK5PJqq3nxPCusJZ+vbK7Sy++0S5DUqLEZ8WwmnhIbhd/DAEAjB9+1wEAAADwkZmmoYpSvypK/bpxQVJD6YyaO4+psSWlppaUXth2WL/b2ibTMFRV5rfuDJteHpTTYcv1+ACAKYz4BQAAAOCSs9tM1cRDqomH9NlFVTo3mNb+9qNWDHtuc6tWb2qR3WaqujwwsjOsUJUxv+w2M9fjAwCmEOIXAAAAgDHndNh0WWWhLqsslCSdPjukt9sGrBj25IZmPbmhWS6HTbWJkHVMMhH1yTSNHE8PAJjMxi1+NTc3a+XKlRoYGFAoFNKqVatUWVl5wXvS6bQeeughbdiwQYZh6O6779by5ctz8tpf/uVfau/evdZse/fu1T//8z/rU5/61Fh/VAAAAMCU53bZNae6WHOqiyVJx0+d097WATW2Dsew//xDnyTJW2BXXfL8kyTLijwyDGIYAGD0xi1+PfDAA7r99tu1bNkyPf3007r//vv12GOPXfCeZ599Vq2trVq7dq0GBgZ06623auHChYrH4+P+2t/93d9ZczU1NenLX/6ylixZMl4fFwAAAJBX/B6nrqyP6sr6qCQpdfysmlqHnyTZeCilN97ulSQFvE5rV1h9RViRYAExDADwvsblMH1fX5/27NmjpUuXSpKWLl2qPXv2qL+//4L3Pffcc1q+fLlM01RhYaGuu+46rVmzJievvdNvfvMb3XLLLXI6nWP2GQEAAAA4L+x3aeGMUt15c4N+/LWPadVXF+rPbqrXZRVhNbWk9Ivnm7Tykc36y3/ZrJ//tlGb3+xS6vjZXI8NAJiAxmXnV2dnp0pKSmSzDT/FxWazKRqNqrOzU4WFhRe8r6yszPp2LBZTV1dXTl77b+fOndOzzz6rX/ziFx/pMwAAAADw4UVCbkVCbl0zp0zZbFadfaes+8K27+vVxt2dkqTSQo+1M6wuGZLfw19gA0C+48L7D7Bu3TqVlZWpoaHhon9sUZFvDCbKjUjEn+sRkCOsff5i7fMT656/WPv8NVnXPhoNaE5DqSQpk8mqueOodu0/ol37j2jLni79YXu7JKmqLKDZ1RHNrinWzGlF8hQ4cjn2hDJZ1x4fHWufv/J17cclfsViMXV3dyudTstmsymdTqunp0exWOxd7+vo6NDs2bMlXbgza7xf+29PPPGEPve5z32o/+6+vhPKZLIf6sdOJJGIX729x3M9BnKAtc9frH1+Yt3zF2ufv6bS2gdcNi2eUaLFM0o0lM7oUNdxa2fYb19p1tPrD8g0DFXG/NZ9YdXlQbkctlyPnhNTae1xcVj7/DUV1t40jQ+10Whc7vwqKipSQ0ODVq9eLUlavXq1GhoaLjjyKEk33nijHn/8cWUyGfX392vdunX69Kc/nZPXJKmrq0vbtm3TLbfcMuafEQAAAIBLw24zVV0e1C0fq9Rf/K/L9c/fXKK/+F+X6+aFFTINQ2tebdXf//sO3fvT9Vr16zf0zMZmvd02oKF0JtejAwDGwLgde3zwwQe1cuVKPfzwwwoEAlq1apUkacWKFbrvvvs0a9YsLVu2TDt37tQNN9wgSbrnnnuUSCQkadxfk6Qnn3xSn/jEJxQMBsf0swEAAAAwdhx2m3UPmCSdPjukfYePqqll+GmST29s1lMbm+V0mKqNh6ydYRUlfpkmT5IEgMnOyGazk/9c3gTFsUdMdqx9/mLt8xPrnr9Y+/zF2g87cXpQe1sHhmNYa0odR05Kkjwuu+qSIdWPhLPyYq8MY2rEMNY+f7H2+WsqrP2HPfbIhfcAAAAA8prP7dC8uojm1UUkSQMnzqqpNWXtDNu+74gkKeBxWCGsoSKsSMg9ZWIYAExlxC8AAAAAeIeQz6WrLyvV1ZcNP03yyMBpNY7EsD0tKW1t7JEkFQVcVgyrT4ZVGCjI5dgAgPdA/AIAAACA91EccmtJyK0ls8uUzWbV1X/K2hW2c3+fXtndJUkqKfRYu8LqkiEFPM4cTw4AkIhfAAAAADBqhmEoVuRVrMirT1wRVyab1eGeE1YM2/JWl17a3i5Jikd8VgyrTYTkKeCPXwCQC/y/LwAAAAB8SKZhKFniV7LErxvmJ5XOZHSo67gVw17a0a7fv94mw5AqSwNWDKuOB+Vy2HI9PgDkBeIXAAAAAFwiNtPU9LKgppcF9ZmFlRocSutA+zE1jjxJ8ndbW/XclhbZTEPTy4NWDJtWFpDdZuZ6fACYkohfAAAAADBGHHab6ivCqq8I6zZJZ84Nad/ho8MxrCWlZzY26+mNzXI6TNXEQ1YMqyjxyzR5kiQAXArELwAAAAAYJwVOu2ZNK9KsaUWSpJNnBrW3dUCNLcNPk/zNSwckSW6XXXWJ8zGsLOKVaRDDAODDIH4BAAAAQI54Cxy6ojaiK2ojkqSjJ89Z94U1taS0Y/8RSZLf41B9MmzFsGjYLYMYBgCjQvwCAAAAgAki6HVqwWUlWnBZiSTpyNHTamoZGDkm2a/XmnokSWG/ywphDRVhFQYKcjk2AExoxC8AAAAAmKCKg24tnu3W4tkxZbNZdadOW/eF7TrQp02PBhDLAAAgAElEQVRvdkmSomG3FcLqk2EFvM4cTw4AEwfxCwAAAAAmAcMwVFroUWmhR5+4vFyZbFbtvSetI5JbG7v18o4OSVJ5xKuGkWOSdcmQPAWOHE8PALlD/AIAAACAScg0DCWiPiWiPt1wVULpTEYtXSfU2NKvppaU1u/s0Lpth2UYUkWJ39oZVhMP5Xp0ABhXxC8AAAAAmAJspqlpZQFNKwvoMwsrNTiU0cGOo9bOsLWvten5V1tlMw3VVYRVXRZQQ0VY08qCctjNXI8PAGOG+AUAAAAAU5DDbqouGVZdMiwtkc6eS2tf+/Dl+fvbj+nZTYf0zCuH5LSbqo4Hh+8LqwirstQvm0kMAzB1EL8AAAAAIA+4nDbNrCrSzKoiRSJ+tbT1a2/bgLUz7ImXD0qSCpw21SVCw8ckKwtVHvHKNIwcTw8AHx7xCwAAAADykKfAoctrIrq8JiJJOnbynJpah0NYY0tKOw/0SZJ8bofqR+4La6gIqyTslkEMAzCJEL8AAAAAAAp4nZrfUKL5DSWSpP5jZ6xdYXtaUnq9qUeSFPa7VD/yJMn6ipCKg+5cjg0AH4j4BQAAAAB4l8JAgRbNimnRrJiy2ax6Bk5bMezN5j5tfqtLkhQNua2dYfUVYQW9zhxPDgAXIn4BAAAAAN6XYRgqCXtUEvbo2rnlymazaj9y0ophrzX1aP3ODklSebHXimF1yZC8BY4cTw8g3xG/AAAAAAAXxTAMxSM+xSM+XX9lQplMVi3dx9U4cl/Yhp0demHbYRmSkqV+676wmnhQBU7+GApgfPH/OgAAAACAj8Q0DVXFAqqKBXTz1RUaSmd0sOOYFcN+/1qb1rzaKptpqKosoIaRO8OmlwfksNtyPT6AKY74BQAAAAC4pOw2U7WJkGoTIS1bXKWzg2ntP3zUimGrNx/Ss5sOyWE3VV0etHaGVcb8splmrscHMMUQvwAAAAAAY8rlsGlGVaFmVBVKkk6dGdLbbQNWDPuv9QclSQVOm2oTISuGxaM+mYaRy9EBTAHELwAAAADAuPIU2DW3plhza4olScdOndPe1vMxbNeBPkmSt8BuXZ7fUBFWaaFHBjEMwEUifgEAAAAAcirgceqq+qiuqo9KkvqPnVFTa8qKYdv29kqSgj7ncAgbuTOsOOTO5dgAJgniFwAAAABgQikMFOhjM2P62MyYstmsegdOWyFsT3O/trzVLUkqDhZYu8LqK8IK+Vw5nhzARET8AgAAAABMWIZhKBr2KBr26ONzy5XNZtVx5OQFu8I27OqUJMWKPFYMq0uG5XM7cjw9gImA+AUAAAAAmDQMw1B5xKfyiE/XXZlQJpNVa89xK4Zt3N2pF99olyEpUeKzYlhNPCS3iz8CA/mIX/kAAAAAgEnLNA1VlgZUWRrQTQsqNJTOqLnzmBpbUmpqSemFbYf1u61tMg1DVWV+686w6eVBOR22XI8PYBwQvwAAAAAAU4bdZqomHlJNPKTPLqrSucG09rcftWLYc5tbtXpTi+w2U9XlgZGdYYWqjPllt5m5Hh/AGCB+AQAAAACmLKfDpssqC3VZZaEk6fTZIb3dNmDFsCc3NOvJDc1yOW2qS4RUP/IkyUSJT6Zh5Hh6AJcC8QsAAAAAkDfcLrvmVBdrTnWxJOn4qXPa2zqgxtbhGLbrQJ8kyVtgV31y+CmSDRVhxYo8MohhwKRE/AIAAAAA5C2/x6kr66O6sj4qSUodP6um1uHL8xsPpbTt7V5JUtDrVEPF+RgWCblzOTaAi0D8AgAAAABgRNjv0sIZpVo4o1SS1Dtw2joi2diS0pY93ZKk4mCBFcLqk2GF/a5cjg3gfRC/AAAAAAB4D5GQW5GQW9fMKVM2m1Vn3ykrhm1/u1cbd3VKkmJFnuEYNnJU0ud25HhyAP+N+AUAAAAAwCgYhqGyYq/Kir361Ly4Mpms2npODB+RbElp0+4u/eGNdklSMuqzdobVJkJyu/jjN5Ar/OoDAAAAAOBDME1DFaV+VZT6deOCpIbSGR3qPK7Gln41tqT04hvtWvtam0zDUFXMb8Ww6vKgnA5brscH8gbxCwAAAACAS8BuM1UdD6o6HtQti6o0OJTW/vZj1jHJ57e06rebW2S3GaouD1oxrCoWkN1m5np8YMoifgEAAAAAMAYcdpsaRgKXJJ0+O6R9hwesY5JPb2jWUxua5XLYVJMIWu9NRv0yTSPH0wNTB/ELAAAAAIBx4HbZNXt6sWZPL5YknTg9qL2tKSuGPf6HA5Ikj8uuumTIimFlxV4ZBjEM+LCIXwAAAAAA5IDP7dC8uqjm1UUlSQMnzqqp5XwM277viCQp4HFYRyQbKsKKhNzEMOAiEL8AAAAAAJgAQj6Xrp5RqqtnlEqSegdOD8ewkd1hWxt7JElFAdc7Ylihwn5XLscGJjziFwAAAAAAE1Ak5FYk5NaSOWXKZrPq6j9l7Qrbse+IXtndJUkqKfRYu8LqkiEFPM4cTw5MLMQvAAAAAAAmOMMwFCvyKlbk1SeviCuTzepwzwkrhm1+q0svbW+XJMUjPiuG1SZC8hTwR3/kN34FAAAAAAAwyZiGoWSJX8kSvz49P6mhdEYtXcetGPbSjnb9/vU2GYZUWRqwYtjVQXeuRwfGHfELAAAAAIBJzm4zNb08qOnlQS39WKUGh9I60H5sOIa1pvS7ra16bkuL7L/ZpWll52PYtLKA7DYz1+MDY4r4BQAAAADAFOOw21RfEVZ9RVi3STpzbkj7Dh9VS89JbWvq1jMbm/X0xmY5HaZq4iErhlWU+GWaPEkSUwvxCwAAAACAKa7AadesaUX65IJKLb06qZNnBrW3dUCNLSk1taT0m5cOSJLcLrvqEudjWFnEK9MghmFyI34BAAAAAJBnvAUOXVEb0RW1EUnS0RNn1fSOGLZj/xFJkt/jUH0yrIbK4RgWDbllEMMwyRC/AAAAAADIc0GfSwsuK9GCy0okSUeOnlZTy8DIBfr9eq2pR5JUGHCpITl8nLKhIqzCQEEuxwZGZdziV3Nzs1auXKmBgQGFQiGtWrVKlZWVF7wnnU7roYce0oYNG2QYhu6++24tX748J69J0nPPPad/+Zd/UTablWEYevTRR1VcXDwOnxYAAAAAALlTHHRr8Wy3Fs+OKZvNqjt12nqS5M4DfXrlzS5JUknYrYaRu8Xqk2EFvM4cTw6827jFrwceeEC33367li1bpqefflr333+/HnvssQve8+yzz6q1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= \"../input/test/\"","execution_count":8,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_files = listdir(\"../input/test\")\nprint(test_files[0:5])","execution_count":9,"outputs":[{"output_type":"stream","text":"['seg_d5df41.csv', 'seg_abda04.csv', 'seg_421a96.csv', 'seg_d31583.csv', 'seg_14c054.csv']\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(test_files)","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"2624"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_submission = pd.read_csv(\"../input/sample_submission.csv\")\nsample_submission.head(2)","execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"text/plain":"       seg_id  time_to_failure\n0  seg_00030f                0\n1  seg_0012b5                0","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>seg_id</th>\n      <th>time_to_failure</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>seg_00030f</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>seg_0012b5</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(sample_submission.seg_id.values)","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"2624"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, ax = plt.subplots(4,1, figsize=(20,25))\n\nfor n in range(4):\n    seg = pd.read_csv(test_path  + test_files[n])\n    ax[n].plot(seg.acoustic_data.values, c=\"mediumseagreen\")\n    ax[n].set_xlabel(\"Index\")\n    ax[n].set_ylabel(\"Signal\")\n    ax[n].set_ylim([-300, 300])\n    ax[n].set_title(\"Test {}\".format(test_files[n]));","execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x1800 with 4 Axes>","image/png":"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            signal     quaketime\ncount  1.000000e+07  1.000000e+07\nmean   4.502072e+00  5.183598e+00\nstd    1.780707e+01  5.091286e+00\nmin   -4.621000e+03  7.954798e-04\n25%    2.000000e+00  6.498971e-01\n50%    4.000000e+00  1.298899e+00\n75%    7.000000e+00  1.089170e+01\nmax    3.252000e+03  1.154080e+01","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>signal</th>\n      <th>quaketime</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1.000000e+07</td>\n      <td>1.000000e+07</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>4.502072e+00</td>\n      <td>5.183598e+00</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.780707e+01</td>\n      <td>5.091286e+00</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-4.621000e+03</td>\n      <td>7.954798e-04</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2.000000e+00</td>\n      <td>6.498971e-01</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>4.000000e+00</td>\n      <td>1.298899e+00</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>7.000000e+00</td>\n      <td>1.089170e+01</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>3.252000e+03</td>\n      <td>1.154080e+01</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, ax = plt.subplots(1,2, figsize=(20,5))\nsns.distplot(train.signal.values, ax=ax[0], color=\"Red\", bins=100, kde=False)\nax[0].set_xlabel(\"Signal\")\nax[0].set_ylabel(\"Density\")\nax[0].set_title(\"Signal distribution\")\n\nlow = train.signal.mean() - 3 * train.signal.std()\nhigh = train.signal.mean() + 3 * train.signal.std() \nsns.distplot(train.loc[(train.signal >= low) & (train.signal <= high), \"signal\"].values,\n             ax=ax[1],\n             color=\"Orange\",\n             bins=150, kde=False)\nax[1].set_xlabel(\"Signal\")\nax[1].set_ylabel(\"Density\")\nax[1].set_title(\"Signal distribution without peaks\");","execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x360 with 2 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= np.diff(train.quaketime)\ntrain = train.drop(train.index[len(train)-1])\ntrain[\"stepsize\"] = stepsize\ntrain.head(5)","execution_count":16,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"   signal  quaketime      stepsize\n0      12     1.4691 -1.100000e-09\n1       6     1.4691 -1.100000e-09\n2       8     1.4691 -1.100000e-09\n3       5     1.4691 -1.100000e-09\n4       8     1.4691 -1.100000e-09","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>signal</th>\n      <th>quaketime</th>\n      <th>stepsize</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>12</td>\n      <td>1.4691</td>\n      <td>-1.100000e-09</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6</td>\n      <td>1.4691</td>\n      <td>-1.100000e-09</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>1.4691</td>\n      <td>-1.100000e-09</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5</td>\n      <td>1.4691</td>\n      <td>-1.100000e-09</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8</td>\n      <td>1.4691</td>\n      <td>-1.100000e-09</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.stepsize = train.stepsize.apply(lambda l: np.round(l, 10))","execution_count":17,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"stepsize_counts = train.stepsize.value_counts()\nstepsize_counts","execution_count":18,"outputs":[{"output_type":"execute_result","execution_count":18,"data":{"text/plain":"-1.100000e-09    5655193\n-1.000000e-09    3907598\n-2.000000e-09     434767\n-1.095496e-03        882\n-1.095495e-03        679\n-9.954955e-04        497\n-9.954950e-04        381\n-1.095497e-03          1\n 1.154000e+01          1\nName: stepsize, dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import TimeSeriesSplit\n\ncv = TimeSeriesSplit(n_splits=5)","execution_count":19,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"window_sizes = [10, 50, 100, 1000]\nfor window in window_sizes:\n    train[\"rolling_mean_\" + str(window)] = train.signal.rolling(window=window).mean()\n    train[\"rolling_std_\" + str(window)] = train.signal.rolling(window=window).std()","execution_count":20,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train[\"rolling_q25\"] = train.signal.rolling(window=50).quantile(0.25)\ntrain[\"rolling_q75\"] = train.signal.rolling(window=50).quantile(0.75)\ntrain[\"rolling_q50\"] = train.signal.rolling(window=50).quantile(0.5)\ntrain[\"rolling_iqr\"] = train.rolling_q75 - train.rolling_q25\ntrain[\"rolling_min\"] = train.signal.rolling(window=50).min()\ntrain[\"rolling_max\"] = train.signal.rolling(window=50).max()\ntrain[\"rolling_skew\"] = train.signal.rolling(window=50).skew()\ntrain[\"rolling_kurt\"] = train.signal.rolling(window=50).kurt()","execution_count":21,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}