ischurov/lecture 13.ipynb Secret

## lecture 13.ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import sys\n!\"{sys.executable}\" -m pip install --upgrade pandas",
      "execution_count": 23,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "\u001b[33mRetrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x10ebfd780>, 'Connection to pypi.org timed out. (connect timeout=15)')': /simple/pandas/\u001b[0m\n\u001b[33mRetrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x10ebfd828>: Failed to establish a new connection: [Errno 51] Network is unreachable')': /simple/pandas/\u001b[0m\n\u001b[33mRetrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x10ebfd898>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known')': /simple/pandas/\u001b[0m\n\u001b[33mRetrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x10ebfd550>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known')': /simple/pandas/\u001b[0m\n\u001b[33mRetrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x10ebfd470>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known')': /simple/pandas/\u001b[0m\nRequirement already up-to-date: pandas in /usr/local/lib/python3.7/site-packages (0.25.3)\nRequirement already satisfied, skipping upgrade: numpy>=1.13.3 in /usr/local/lib/python3.7/site-packages (from pandas) (1.14.5)\nRequirement already satisfied, skipping upgrade: pytz>=2017.2 in /usr/local/lib/python3.7/site-packages (from pandas) (2018.5)\nRequirement already satisfied, skipping upgrade: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/site-packages (from pandas) (2.7.3)\nRequirement already satisfied, skipping upgrade: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas) (1.11.0)\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "tables = pd.read_html(\"https://bit.ly/2TpCEWe\")",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "type(tables)",
      "execution_count": 4,
      "outputs": [
        {
          "data": {
            "text/plain": "list"
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "len(tables)",
      "execution_count": 5,
      "outputs": [
        {
          "data": {
            "text/plain": "1"
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = (tables[0]\n      .drop(['STATION_ID', 'STATION_NM',\n                     'Q', 'Q.1', 'Q.2', 'Q.3', 'D'], axis=1)\n      .dropna())",
      "execution_count": 62,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = df[~df['DATE_OBS'].str.startswith(\"1948\")].copy()",
      "execution_count": 63,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline",
      "execution_count": 64,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "df[:365].plot()",
      "execution_count": 65,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x114338a90>"
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df[:3650].plot()\ndf[:3650].rolling(window=30).mean().plot()",
      "execution_count": 66,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1140a5c50>"
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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fv/cFfPmNp2FZcH8HFD+dQtR//fV53Pxu2SFc1syO0mAnivMbhFQkgGa2N0f52D5J7Tt5WXLGewPxIEbNaquYmmPsLjlhcM5tBuLtj1Qsy6Kkm6h++V0j/ghrU2vRazoH8jJtlLANQrt18Gs1i90TRdYOzkwXgTSSqUigbbRTzahR0Ws8Uvoab/r5m8jWdgGd84Sd+/2mLQdd2rNjhKdHCE6jYK5N9+ihhtqVQ/Q43mZ5OSmjxdAzatkgWJZ1yLKsR+yf88AzwFLg1cAN9mY3AH/c6r6mY3rubTYkQn7+6MRhAqrCg7vbPylr31SJNf1RzlrVy6alSV53xjKeOJBtuyRAI9IFmTJKl9P80/3/1ER3hM7kKJ3u8QvWzUxNODTQdi7GVaOGWbOIBOYm0g3Egu4QoXYeh2WBzyfv1ag/ylBUUn/Hy3KmcKeKyvmqgW5aDMwTFfVE/G3zTJ18/JSxG4C0Jg3CRIcWvkYF0fM+dSeX/POv+eAPtwAze3actNZcWlitYroy8/tesh7FlvE/plNGjRBCrAJOAx4AhizLOmS/NQoMebkvaGDTzFI8bURAVTry8IPUtl/WwOBYaqes7tneuXkEEwWNvliQLz/6ZW587kZu3908cCPVATZJyX74GxfkO/bcwU+3/dSNXtqpdOk8/LMxjBwMxIPkq0ZbNY2cvy0UaZTjgTiDYRm1jJccg9CZlJHj7SbmabjqiQTa5ig4KduwKiOU7TlJEf/bHz3ekQh6+nXeMV4kbdcvpkcIjkFoV7SULlSbMhkvPG7A/bknEiCoKosy+tYzgyCEiAE/Bt5nWVaTLoEl4/dZk7lCiCuFEJuFEJvHx8d/r33O1oA1F/pjgY4M0Nk3VWJ5T71QefHxc6cs2oGSZlDSTPpiATdH/eDog03b9HSATeI8/NFg/UF7/6/fz0fv/SjDNuXzO/ftmfWzXsDxRiPB5gf9t/t/y8GC1I3pxLD7kp2msHzy4U4EEvRHZM+MEyGE/T4CPoXJYnvvT8fbna8DNxXxt20RdAxe3pD6VjuyW933/uz6B/nAjVtcTR+vYVkWZd3knS9ay7+/7awZ70eDCgWtTrToiQTw+wSH23RvpAuSgedoix0/LJvSdmZ28szkU6zujy6KnpEnBkEI4Ucag+9ZlvUT++XDQogR+/0RYFZhEMuyvmFZ1pmWZZ05MDAw2yZzYtKWX5iNXjkdUyWdu7el2d7GYSR7J0pkSjrLeuoRwsnLUvzVRWuAznQvO2Jh/dEgOU3a5W1Tzf0QTs9Gu8JhmN9DN3yyS/eZ0fbpGdX3r/L0xNPcc+Aenpp4iqt+dRWX/vhSoN6c1s5IpWzLPdeEfLgTgQRxf5yQL8SXH/0y53zvHIp6keW94bbLt7s6QvM0v7U/QqiR1aQhPFQ85CrQPrh7kp88eoAP/LA9NGDNlCnEaMDHuoHYjPdv2f0TzvvBeUyUZSSvKIJ4yN+2GsJYvspAPMSNf3UuX3vT6YTt5+TVN72aN9zyBgYTIcYXYVaEFywjAXwTeMayrH9peOu/gbfYP78FuKnVfU3HVFFDEbA9+zhX33216xHPBken5MaH9nl9GAD8Zus4F31Oqmcu722mMjqNWe1cgB04+di+WIDDRXvhnXyGD9/9YXebdudHQXroQVVB9c28xe47dB9vu2AVE4X2NSU5uvtj2lauuPkK/vqOv+b1N7/efV83dbc5rV3Kq5Zl8Qm7W97EllgOJBBCoNd0KmaFklHi/kP3M5QItX02QD1lNHddJRWVEUI7rkuhqoOiYWHRG+olW81y07vP4qfvOt/dpl3T/Jx0UTigzupAfurBTwLyWXEQC6pt6UOo1SwOZSosSYVY2Rfl8pNGZmwzEPN3pHlzOryIEC4A/gy4WAixxf73MuDTwEuFENuAl9i/e4qpkkYqEuBzD3+OW3bewu7s7jm3/fqbzyDkV9r20DkCZgDLe5q7QFPhztHIJuy0WF8syGhx1H395p03UzbK6DW9IwahUDVctdU33vJGPvDrD7jvbRnbwpJkmJJmNskIeAlnMtd4dees7+/L73Ob09o1enX/VJm7npPecN4Yxyd8DERkFGxa9WgxXU4zlAi1fXqY05DlXH/N1KgYFd54yxv55hPfBGRkqZtWW65LvmIgFPkd16ek0F9WH+O0FT189U2nA7RN08mJGCMB3yxigvXv2hhNR4Mqhar3UX26UEUzayyb1gNj1OrHkYhpjOfbT82eDi9YRr+zLEtYlnWyZVmn2v9+blnWhGVZl1iWtd6yrJdYluU5xWeqpNET8bsnsrHZZzqiQZWTl6ba4g0+uncKVal3w07XxnFy9p3Qfnf2oaolpqpTLI8vd9+77MeXcfp3TudweR9+n2grzbGkmUSCPkp6iSfST/DLPb9037v/0P0MxKWRbFf+3okQNGt21cj9hf2kIn5URbSN5dK4wFdreZLBJKoijeTJAye77+3J7WEwEeRwrtrW3pBG6WmA19/yes763lk8kX6CLzzyBUB2kQNtkXopVA2ET/5d5/tvz0gJj5edNMJHX3EiZs1ix7j3TZMO5TQS8DV1rr/h7OW8//J6qrrRIMSCvrZECM4aNJxsNghfevRL7s+RUAmjZnW8F+GY7lSeLGr0RgMkArIgszu3e97th5Mhz73BfZMlXvPVe7n2f54G4LKNwzNC0hNGEqiK4FfPtF9f/WC2jBDwt/f+BQBv3fhWrjr1KgAmK9Imv+uOdxEP+dvaGVuoGkQDKoeKh5peP3fkXDLVDIGgXKjbNZOgZBsEfQ6DcKBwoO28+9Fs/buVazmSwXpPxkfP/Shxf5zeUC/bprYxFA+hGbW2N4UJATFbRmN6balm1dwiZzscp3zFAEWekxP6TgDgYLFeRD5+RPLwt7ahi95JGYX8zdHBVS9ex5nrpBEO+UJsyzRHCI5j4SVm6314bvI5vvXkt9zffQFZX+t02uiYNghSttaPVpMP9HwRAkhWyYTHC9DWhlSREPC1N58+Y5ueaICXnDDEf23e1/YQcLKokQz72VeQtZLh6DBvOuFNTdscKh4iHvI1abp4iScPZPnl04cRQswwCK9e92oApgyZymlXhOB8t9v2/QyB4Jpzrml6/5MPfJK79t4lefdtmg3RGCGUjDypYF1xd0PvBu594728cNkLeWD0AZb1SM/cGfjUDuTKOomQn5yW5dzvnzvj/fHSOMPJ9qXR8hUdnyr/7mBkkHggzqFC/f44xVYk3tWGUbiNKaNGJMN+N0q5dNWl7MzsdDMOMmXk/TMyfYAVwBU3XwHAentmRs7cDXQNwu+FsiZFwzIVKdi1MysXmXQ5za27b52xfW80QFEzPWX7vOOGze7PlsWccrUXHz9IvmqwtY0sJ6g/9APhAQYjg7xg6QuIB+LEA9L7WplYiWmZxEJm2yIEh076zKEs77zjnU3vnb9EFhA1MY4Q7Xn4wamPmBT1AqcMnMIVx1/B11/ydd5/xvs5oVd6p++56z30RNs3tctJN7xowwCPpTfzxPgTM7bxKXKBeijzIwB3pm87IGcRqDyRnnkcUK9lQHuknwsVg3DQluvwxxmODjNaqte5okGVgXiwLUbRiRinG4RYUGXr1FYGwgOcPXI2Wk1jb34v2WpWRlJtMAjFWSjZ/WFJRf7xK3/MysRKDlakkeqkSjMc4wahqJlEAj6mqlIraFdmF5Zl8bH7PsaHfvMhtoxtadre0Qjp1DzhRpy4RKa1LvvC3W3OExvEw/KcvGLNK1wD9aEzP8TZw2dzxQbpiYRDlbY1QjkLrJp43H3tCy/6At+69FukgilURSWjTbC8J8Jzh9tjILNlnUhU3hd/uuFPAbhg6QW8fdPbueHyG9ztEmGrbSmjombi9wm++uZNABjWzPP9lyf9JQBBv7wn0m2qM334J4/zsy0HGYqHuH1PvVHx8tWX8+LlL5b7LqcJ+X2kIv62pYyCQfl3E8EEA+EBl+bpYFWbhsM4TmDYLxfh9XZTmBCCrVNb2dC7gQ09GwC46o6ruOjGi0hzH4U2PCN1g1A3Tj7h45VrXokQghN7T2RPXvZodCOE3wNlzSTot8hreeKBOHk9z8nfPplf7/s1IBuhvvfM99wFuDdqF3c9bABaPxjjklmaz27bfVtTOLzRNgjQ3q7lXFknFtYxagYD4Xqx7DXrX8M3L/2mW2QOBittKSpXdJOgrbIaCMvv/5cn/SWXrLyEs4bPQhEKg+FBxkpjrO6Pzphv6xVyZZ1oxJ63HVvS9F5YDfOZF3wGgGAo4/azeI1SVUaw+/Iyfff5F35+xjZLYkuIqBFMqrLA3aaayg8elMewec8U2arUmTpn+Bw+ceEn+Ptz/h6oT3EbTnhfawPprAT88u8mg0n6Qn2ky+mmbVb0RtnThvGRJZd2KhfhH7/rfO7+W2kIJ8uTDIQHWJdaB0jCQc2qkbGepaiZnqd5HeZSPFjvB5msTNIbkqrNS2JLGC+PEw/5OqIK3Ihj1iBYlkVJMygLmZ74i5P+YsY26XKaTz/4aXblpGZKb1Tmab3MGY8XqixJhbn68uP54V+dB8De3F7+5jd/w9V31/X8hBA8fM1LAHjzNx9oWy0hW9YJBuQD5YShjXDy2P5A2fMIYd9kieM/cis3Py4NwaUnJ+kN9fKe09/TtN1gRBqEvliAJw5k22IUHt4zRU2RhbnZzoNjJKLRPOlCtS3ps6JmEg342Dolvb0V8RWzblcySty6+xf0RgNtYaI1Sjas7o9yuHSYC5ZcwPWXXo9f8dMf7ifqj/Lx+z/Ot578FiPJUJP4mlfIV3R8/hJxf9zd70R5oiliXtYTZixf9bwfYXoNIRHyu2zAnCYL/j7Fx0XLLnI/U7VkhFnyuKG0WDVQBIT8iv33S1TMCr1haRB6Qj3oNZ3+hOXOBe8UjlmDUDVq1CwwFentnL/kfD54xgfd9z9x4Sfcn53UkRMheJUzrugmmZLOcDLEX79wLWevlhd0b14O6X5k7JGm7RvZR7vaVDzMlnX8NoOnLzxTWM5hughfqUnsywu84LN3uT+//qzlmCI36zEMRAYYK42xw77Z//Hmpzw9jrF8hZ3pIjldsqpmMwjD0WEAqj7pULSjP6WsmUSCKo+OPYoqVFYnVsH3Xw+3fHDGtulymr5YsC2sq8aI+Kb/ewEHCweboiZVUXnlmlcCcN3D1zGSCrfJIBgoaolUSDolfeE+tJpGQa8vekt7wlgWrhqpV3BTRtNqCFWzSsWsuEzFL138Ja455xpOGTiFnCEdG6/rCIWqQTSouulch/3nRAiO0xaLaG2h4M6HY9YgOF6PKWQOuifYw1s3vZUPnfkhvvjiL/Kqta9y87PX3nstZaPs+YjAg7bUgEPVc3DrrnpB+5Rvn+KGxUIIV0fFGbPpNXIVHZ9f3kTzRQj4Sm0VdRtOhpioTLg3eSOGIkOMlcb46Cs3utt6ia/cKQtyqXiZsBomos4cF+mk08qWpAK3o3hX1AyiAR/ZapZl8WWEnvkf2PoLeOh6qNRlO9626W0A9MVU0m3oiXBUgb/xZ2fg82lkqhmWxpY2bfP+M95PRI2gCpXheJDJoua51Eq6UEX4iu496NwbjXUEp1nL6xGvJc1ECY7y2c3/iGbWz3GuKq+D4ygpQuGK46/g5IGTKZjyuLxmGhUbmjahTpd3rklPSI6O2bTMx2iu0hHJGwfHrEFw+MEGtkGwT+Kfb/xzXrxC5gadVIWFxd/99u9Ihv0I4Z3OuEM5XdkwM7hqVrlpR12lo2bV+OIjX3R/d6h1XrNJvn3fbs7+xB1U9Br45k6VJAIJBIIaRTSzhm56F5qvajgPq/ujTJRnNwiDkUFKRokNI376Y8GmCXNeYMg2MCeszDMQHpiV+eVTfJwxdAYZXbJc2iF8WKqahAM+ikaRqD8Kh5+sv7m/Loc+FJFCwImI1pYawn57cV3aE2ZXVqZPl8abDULEH+Fdp74LwzLoTch7wss6glmz5OB4pW4QnOixsY7gqAPv91jXqaSZhAZ+xU07ftbEsnLqKU6E4KAv1Ideq4Koel5YLmpGE8Po8w/J2tLxvce7+waIRYtYVt3x7ASOWYPgeLealSfmjxHwzS5wd+1518rtjTKqTyEeVD1jlThh9ZoGsax7DtyLUyWoAAAgAElEQVTj/vy5iz4HwE+3/5QfPvdDQHYtx4Iq+zw2CB+96SmXkWCKHEFfkJh/poiXT/GRCCaYNCVF18u0kW5aXL5pmI+9aiOXbRpmsjLZVNh2MBiRRfix0hjJsOq5hMbBTJlUxM+j4w8TVucekbkkuoSJijQI7WCeyQhBdUdnUs6APwJCgX0PuNv1BKUzEwy2h/n1y6dlsXhNf4zbd9+OqqicMzxzvKyzQAcCMsL00khOFjVqFuhWwTUIjsMyUalHCCPJMEJ4HyGUNQOfXzpwjaqmjgBkIjjNINjnQqiFNqSMTKJBle8+/V0eGn2IHdkdAC41fHVyNQJBriaJAJ3sVj5mDYKzkFVqOTc6mA2vPe61XLj0QtcTiIf8numTjOclMyTVICfsaNzf8Sd3cNnqy3jHpncA8PH7Pw7ItNHy3oinEcJ0GqtGlv5w/5w9EacOnMqkJnPnXqaNsmWdkWSYt5y/iopZoGyU3cW/Ec5rB4sHSYb9bTAIFZbYsgAbejfMud1IbIR0ZRww2zKdqmTXEIp6kYg/ApUsJJbC0MYmg+Dk1BW15PLlvUK6UOWnj8rJbOGAjycnnmRj38ZZnxnHeOtCPiteFtp32rlwzSq46RnHE/785s+793BAVViaCnueOy/rJoqQ9YPGiMRZFxq7yKGeWhW+UltSRpGgxWce+gxvv+3tAHzk3I+470f8EVYkVjBe3Q10RvLGwTFvEMpmdl6DAJLdsSe3B8uyiAVVqbroAaSEbdCddASSuqcIxfV+XrHmFe57zk2/ojfMbo+KyoZZ4/xP39n0WrmWmbWY62Bj30apSS90zxYgw6xRqBqukubenCysN2opOXBee2j0oTYZhDLDKXkcq5Or59xuJDpCzaqRiJXa0otQrMoaQlG3U0aVDIRTMHIKHK4X0p0IAaWIblqeMmxufkxKQ3z81bJesye7h1WJVbNu6xiEqiUbPb3sZL/z2TGEqFExS64n7Cy6o8VRvvrYV91tl/WEPW+MK2kmPtsgPDb+GDdtv4mTbjiJ99wl08rTU0ZOdC2USlsMQs2/u+m1FYlmBtr61HpGy9Jp6+QozWPWIJR1AzA5UNrBstiyebddkVhBySiRLqeJhbxrRx/LVxmcNrt4rDRGX6jP7UBd17POZT8VdWkE1g3G2Dle5OmDrc8DeGDX5AxGSNGYoj80s37gYFl8GRYWwp/xLGXknFNHa9/htE/vAQDJ8FkWW8a+/D5SkYDnBuHAVJmBhPxeM9Jmmb1QlnTCJVF5bIl4gck2pIxKmknESRmpURkhhJIweCIUx6Ego0nHoakpRftz3i1Ah7IVFAFvOmclJb3EWHmMVclVs27rDO4pmfL8eBkhHMxWWN4nHSdn8fUpPlYmVgKyb8eoGViWRV806LngYFkzEYq8J366/adcc0+zlMn0CMExWr7wPs9TRvmKgaE2y/AvjTbXdAYjg0xVZSTTLr2v2XDMGoRi1UQJpMnrWS5ceuG8257cL5UV7zt0n4wQPMrTjttDLhrxePpxV7jLgSN5PFaWjJbjhuTN5gXd8oGdM5vc8vrUrAVlBy71VCl7ZhAcb9JR0sxrMl/rPFgA3PhncG0StBLrUuvYld1FIqSS9XAxPpQtk68aDNjPdywwzSB84ST4wikA7mzjUKjg+TjRWs2yi4eNEUIWQikYtO+Pcam971wPhzFX9DCNt2O8yNqBGIoi3AltThF7OpzBPQVDGgQvJbDT+So9cbshq+Ge+MHLf8BVp17FruwuTvvOaVz38HWkIn5P7wmQxlkoc1/jWO4w/ORKqMpUlUNLFmqRvNcRgmZgKlNNrzn7czAQGaCgFxhKKDzlgeP4fHHMGgQppSsLT04uci5s7N+IqqjszOwkHlI9u8Dj+Yo7hhFAr+nsy+3juJ7jmrZzcubpkrT4rzpFeqbOnIRWMJavkgip3PxuxyiaZLX5DYLjoQlf2TNvtD6NS6ZqHG55zB+Dzd+ShuCZ/5Ybb/0FKxMr2ZvbSzggKGqmZ3IeTrTUZ0cIcX+DQTLt71rNQmnSPUeBYMHzwl2+YmBZEAsJtJomawjlTD1CALjhlXBtknB+jLAaRrfkOSt7GCHkK7o7czxTlamg6d6wAyEE/eF+pqppAj7Fs052y7K4b+cEqi1s12gQ4oE4F6+42P3935/6dxJhP7mK7qnES1k3scTcnrZy/SXw+I3wlBz4mAwmiagRFISnEYJlWeQrBrqYZE1yDT94+Q/4mzP/Br/P37iRe2+eulrhwV2TbZW7acQxaxByZR1sbfUZXuA0NMolxEPeRAiGWWOiqDWljA4VDmFYxoyOVCc3++UtXwbkg3fx8YPs8aCwXKga9MeDbFqa5Pb3X8S3/1IuNm4NoZJ1UxPsvgc+u4bEftkwJw2CN97oo/vkYpOyez0cgxD1R+Hm9zdvfPtHWZFYgVbTUPx5zJpF1aO8+ZjdYBYO2p2pfpsKO/YM7L23vuHN7yNx12dQFRXhK3ruhTkGJmIfR1S1i8rhFMSmeehP30RPsIdKTRY4ix4OZclXDOI2xdEpoDaqrk7HQGSAdDlNIqx6xnj6+m8koy03W9QIHNdznLxPbMSCCrppSQq1R8hVdCwhG9DeePwbZ25QkeeGqbpiciqYQg2UPaWdFqoGZs2iYqUZiY2wqX8Tb9n4lvoG//1u+Mo5rkFYPSTXmX2TnaGeHrsGoaLje54GASSTI1PN2EXl1i9wtqxjWc3dx/cclJTT6YXUZXFZ43h07FHMmnzYBz2S4m6cTHbcUJy+lPyb/eF+KE7Ap1fA56VGCz/8cyhNkPjlxwBvDULGTrk4In4FrUBYDbsDYZqQ20+f3Z8gfEX3e3gBhyoZ8Evv1l1ovnqu9MgdPH0T4v6vkAokyetyMXjWwxnPGbsuErLVPaNCBcuUEcJ09pdWYDg6TFaXhttLDf5C1XB19+di1DRiKDLEntweYiHVs3nCTg78yhfaNRt/HB74Bmz5vrsQ3/eG+7j6bCn1ovtkh7CXWluTRY0aVV697tXufgBes+41vPukK+sbZve7P8YDcVS16unUNIfenDfHGYnOHJ3JI9+G9HP0CbmuJGJyjWunCm4jjlmDkK8Y7sPWlBaYA8lAUkraBv2UNLPlZignv+qwah4+/DCffEDOZXUMgANVUfnkhfI9R3s9HvLGAytUmrsena7P/nA//Paz9Q2vTYKdsorb3ZlCqXiWnshVdEJ+xT2Wgl6Q18WYZvROkItyjyGvXU0U3O/hBcbzVYQAVbU9dDUsaxdzIOULsW5YLtDPjXqnvOpECI5hclv2bIopb/pRfePRJxmODpPRZI2p5GmEoBO3C/0O5z4ZmNsgnDZ4GodLh4mGi55FCMWqIdl4qvRyE9vvhF98CH72TrhdFneFEJw6cCoAGvI+9YpdY5g1MqUqJlWi/mgTHfuDZ36QK5dfWt84vdX9MR6Io/gqnrESAX793BjCV6Bs5liTXNP8plnfT09JRtz4pCHwUpBzPhyzBiFX1gkF5A3zvCKEYIqsliXm5LhbvNkdBoajWHiwUJ/8NBv3fmOfpP39y8P/Ij8X8lPWzZY7hQvT2uAdjnV/uB/E9NmxEsFajaASQPjKnhUws+X6rGaQReVYIAYHHpYvxEfguMvhNLk4D+x9EICMuc/9Hl5gx1iB3kiAiikXn2gpU69dOPjwfrjoQwAkhYopigRVhYf3TE3/cwvG4WwFhMY1m98qj8O5ziF7MV7/Uvj7Q3DKG+DgIwxFh2xWSc2zCMGyLHl/TIsQ3JSNadTrKjYcVlgwVPDMQ08XqvTHgq5Bij9UnwzG7noj50hMesyGIp0ar/SMMmUdFNswT5MxiQfikJN9Gqx+IRx8REYu151EvDQFStnTFJ7qU1CCkoHnqKsCoBXhZ+9yf00efAyoKzGM5aqe6zvNhmPXIFQM/IEqqqISqhThd9fJk3rfV+ALJ0uPWK+fwEQwQaaacfOp+Rat/vQxeA6l9CuXfAVFzDyta1LSG9g+tb3pc60aprkMQl+4D0oT4AtAbBg2/h+5wRlSNyfuj4KHLKPpBqGoF2VBeUJ2YfJnP4U3/iesvQSAZbrJYHiQdHWX+z1aRa6ic8sTh5goau71iI7XPT6GTpIGKRiHF/wNIOip1chWMxw/kvBUX+oztz6LEqwPf4k7C2+4oWcmEIHkMsgfYqjmw7B0hK/kWdd01aihm5Z7r+W0HDF/zKVE8/n18K8XNX3GqXf5AwUPU0Ya/bEAOS2HT/iI9jWQLiJ1aZOeYA8hXwhTkWJv/3z71ul/akGYLGoIe3Snk0J0dIMUocDUbrnhef9X/v+zd0J2L/HDT2MpFU9TeMWq4aZJm4gf//pCeOKH7q/hO/8JgP987tsEVIVP/PwZzvvUnW2XsZglwXtsIFfWUYNV4v444u7PwQNfhzuubd7oi6fD2hfDq75EX7iPXDWH3w7hWw2H3QjBDscd7+vckZmjCR18+OwP86kHP8Xu7G73c/mK4bJAFoJitVkXJV1OEw/ECWYPwuP/CUtOhyttFdLX/Ts8ews8/O/E1TB+f9WzlNF0g1DQCjJCuE1q7ZO002g+FVIrEPlDLIsvI6NJb8mL9ISjnvqW81ZS1HchEITz8u/zd3tkQdeBPwSp5SS1CplaiQ2JILc9dRjdrOH3te4nTZV01Hi9JhHV7RxwbFr0uEJKpg+PPQeAEsh4xsF3nRb7/shVc5JhZurw6ZWgF6E8CbUaKPI7O2SESKTE/qkyZs3Cp8ze8f58kS5UWd0fJVvNkgwmEeUcrDgf+tbC1tvc7YQQLE8s53B5P3CSZ4X+TEkHIc+pI2Vy/R9dX1dZPfgo+IKw7pKmz8XxURPeikBmyzrC12ycAKja6cqTr5BsJ2SqOa/niYdUt1t572SJJam55VhaxTEcIegoPpkTpDbHYpI/CFu+B0/fxKa+TVhY5C3JeGh1EpETRsaCKrfsvIVduV2EfKG6ptJ/vAJ+94Wmzzj9Eh/4zQeIBhX3eywUlmWRq9S7g0EahP5wP1xv39zF5gEkjoeaUAIoqpcpI8M1CIcKh3g8/Tg1U4NqTso1BBvqPPElYBuEZzOPAd6M83RE3N54zkpXLkJsv0O+GZolb963jqFygcnKJEG/TOl8+hfPtnwcIOvGFx5fN/TRqh19RKbRgdddAmqYpZa8H+LRvGcCd41Oy6/3/ZoHRx+UKZLH/lMaAwf/2AP3yU5hxyBEwyWqRq3lcZaWZdkpowDZalYapNKEjAxSK6A4Bnq9sXJtci07Mju4fJPk5W/zYKJe4yLssM6WxZe5YnKMPg4rzgHFB6te4H4uLlRMKhQ07yjJ2bJeZ8A1pq8SS2DtxfB/vgEvl2nl950oZS0mnHoC7Rlt2ohj1yCUDYSvKm9wreGmXXkBXJuFN/xn/bVDWzhl8BQEgkNV2Qw02mI+zhmaka7u5+q7r+aWnbe4DWjoFdh9N9zxD02fccLUbVPbSOvb5PdoYSEs67I47kQbILnmPcGeejH35f/c/CG7qBkXfny+imddwrmy7jal/dfW/wLg/sP2vOlXfrF54+w+2H03o3bdxd97jycRwraxAoqAFb0RpqpTkl656zfyzdl0nXrXsLIwgYXFK86Qi7cXD5xm1LAsMIV8kE8fPJ0BrQqI5pSRg2CcJXaNIRLJeVZMbRzm/u473+12j7PjVzM3vu3DAPgVP4PhQQyfLHC3WleRM8xr9MWCZDUZIVBMQ6QPkjYbz8nhA2uSazhQOMCzo7KO8K17drW0f4BMSUOIWbxyB/lRSNpU8dd+U/4f6SNezgEWJc07hk+mpBMKTGPAAWT2SAMJ0oEC+mwK7D/8cZ3G3jUIcyBX0UFU5EnN13O1rie64XKZPwe45/8jUUizNrWWXfmnEUIKoLWCkv2wPTX1sPta1VmEJ7bXN/zaBZLu+ND1+BQfb98krf6ULouprSyEbndwqLmYmwgkZDg+cAIc90fNH3IiBOFD+MqeeaMV3XSnUZmWNJZ/krQ7coc2Nm+88TUAvCEkFwQlMO5JhLBnosjSnjDhgI/nJp8jrNpd5Ik5pE1617CylLWPIc2JIwlPaipuI5NSIRlMcsPlN+AvT8mUlW+WLG0wTmJqL3F/HDWY8UzMzLm3QoE6cWG8PC7TI+EeeM+j0w5cRpOnD53OzvyT9MeC3L9zsqVjcO6v/liQXDUnGU6lCYj219OImTr3f01qDRYW//BamVpTldaXqGxZB8VhnU2bjVEzoTAGcbtTOD4kHcqLryFus35KpndCe9myTtBvoCpqvRlNK8pzkpIyHvSvB6B383cAOHmlyu5Pv5xowMdotr1so2PSIOhmjZJmUhMVWbgsjMHwSZLS+Kov1Tf8uz3wiuvkz7+7jnWpdezJ7aY/FmxZ691ZOD67+ZPua440BV+/oL7h4Sdh12/llKyayXtPfy9BX5DxqhR/a8Ug1FMC9UUmV81JKd/J3bDyvJkfsvPocQsspezZUPeqUSOoSoOQrWZRhMI10RNAUesPmwOb4fOSUoXByCA+xZsawuFcheFEiN3Z3WzPbGd7xi5oX/i+2T/Qs5pB0470ymn6YgFPvHPHM69RlRpGYKdJ5uioV0Nw4BEGIgP4/HnPKIbOOXU6+gGue9F1sog6uBF6p9EeczJiO773eA4VD7Gk12I011oknXYNgiwqJ9Ww7MeI9MGAnbL5zmvgmZuBur6UULNsWppg31Tr3nm2rKP45jAIpQl5PNPv0cQy4jVpSLVa0bOZHZmyjurXp0UHe+192npG9nXpUaTByFRkpDkQD7ZlbkcjjkmD4NzohlUiGohCYRSWnQVXfLe5aBeIwJlvlzdeaZLByCDpcprhZJBDLYZeJc0g7PcRUGQU8vI1L+c/LvsPKDV4VKnmjmUKYyhCYVViFaMlxyAs3DN25SKm0T3jvrCUZ0jMFJbDLwtSib0PYFAiXfAmBK0apjsjdrQ0yvG9x+MrpSE6MDNdE07BktMg/RzJYJJAoOxJIXWioNEfC/KT7VJ+4PSk9LRcz2s6etfQY9YQSE3+3mjAEwkL5/6siWq9U7owPrdB2PjHoBdJ+WOglDxLGTn3Vt6QEfQnLvwEpw+dLg1Czyq50Vv+R6qvAhzYDKbO+h553sLRNJMtzh93Pt8XDcqisr3IEemDWMOsjBvfBNSZN+lympFkmEMtRvJg9yzNlqYByMsmuBkGIZR0DYJQKpQ9mlqWLWmoqtZsmP7Nlu5I2I1qQsBZf0GPsEf+VuWa0hcLMtnmfoRj1CDIi6tbZWK+sLTy0+UAGhEbgsJhBiODVMwKeq3Eb7eOt3QMJc0kHBDUqPG2TW/j0y/4NGcMnSF5zAAnvx6u/I3897ob7AOXHtia5Br2FXbb36X1lJETIZg1k7yeJ+H0H0Rn9kM4iNZqgEWmXGq5F8KsWeim5UYIh4uHGY4My07p6UVUBz2rILOPVDCFP1DxJDc6VdLoiQZcpdcvrpSpqRmG2T2GlahAjxIiXU7TE/EmQnBoiqZVkQahVoNDW+Y2THaKoEcJYooCUyUdw4NJdk6kcrAsiRRnD58NWkk6UI5BWH0R/Om35c83vx/+572u0Jrqz7VMPXU+Hw7IZsWEQ2x0jOM5f13fuDzVNEVtSTLkCc2yrJkuu9A10A4cFlpsukFIELMNAr6KmyJuFdmyjqJodcNkWeAw0FacX98wOkiP7Vw6EUJfNND22QieGAQhxLeEEGNCiCcbXusVQvxSCLHN/n/+oQW/B2Qbt0W1ViLqfIX5DEJ8BPKHXY51KCxzgq1MLStrJqFwDqM2TbvI0UK55COSSbHkVOi1NfntkHx1cjUHCgeIBs2WirrTBeWymt145AhhzXVOXv99onYILJRqy4ugo9/v91l8/L6Psz2zXS4qxXGZK54NyWWQ3U8ykEDxlVtuza/VLKZKOr2RABOVCfyKn4TzsKdmzmSQBxwGf4RexS/HfUYDUnis1WZB28hrVlmmjEpp0Aoyip0NcVvsEB+aJVk1Ux70IjjOxkOH7yXmj0mV08e+L99sFFNrXAy3fM/10oU/37JBcJw3R8k16dybTv/B5Z+Bt94if/7MKgIHt5AIJKRBSIXJV42WG+SKmoHfL89Fk2f+/dfD918nf55OB+7fQMJ5Rjxq4KwaJlMlHeGzIwTTgAO2A7nptc31pdgAQUtqYI2VZCq6Lxb0LMU7F7yKEP4DuGzaa1cDv7Isaz3wK/t3T3AwUwahY1oGcct+eOczCIkRyO5lwG7Z/z9nSr2dVqQKipqBL7wbwA2xAVkg8wXch1zu384N3vhmoD4MIzT4q5aakHJuc5yfr235Gi+88YVyd3ZevCkkb0RyuR0hAEqV8RYpuFVD7i9r7uaHW2VzzUh0RBbX5zIIiWVgVkn6QlhK0eW8LxT5ihQN64kGGC2OMhgZREzuklFSYBZmiYNQin58TJQn3H6QVtNGjmeu18rSI916q3yjb+3sH7DTFYlajUqtAFie1BHyFZ2Q3+J3B+9meXy5lGxwGHmnvbm+oT8kU6s2ErYUjOKrkLfF2BYK51z85qA8B5uEvSA3Pq9Lz6z//M2X0h/uZ6Iy4c4qd/pLFoqyZuLzafgVf72Qq1dg6y/qG0WnPSuKQtwxjErFE8XTw3ZBWCh2KvFza+B6O13kNMU5sM/PpvgqtoxvAWSEMFmsUvN4BnkjPDEIlmX9FphOR3g1YOdKuAH4Yy/2BXImqVMoS9iL0YwcYCP65II9/JikQ/oCMgR7rgWOc0kzMYM7SAVT7rwFQEYIqRVuow/QvCgWxrhkhewRUAOFliZ15d0Iwc/XHvua+3pCs9Mvc6WMwj3ELMf7qbS8ADpKpTeN1gvsa0N9ckLYbOJ24J6TpPCjWQXMWq2l45i0P9sb9bO/sF/qSY0/A4PHz//BcIr+Guwv7KfXVmqdajFv7iyCVbMsUwO77pZvLD199g/Y927S0DEtHYTOpAeeYKFqEOmRfRWvWW+nz3IHIRCfaahfcZ1LwFCz+4j741iKjNpaqXPlKwZBVeFgUYrGnahpNtGgQdjNH4IX/z/31/5QH+lymo1LpAP3mq/eSyso2QahKV1UmtafE5iWSgKidlrLi2cE4KBNdTcoEVeCdYVVmFngt++JkyPDbJvaRsWo0BcLULPqwontQDtrCEOWZdkVG0aBeVz43w+FioFQ5KKXcJpaZiugOjj5CgBG7HAxZ6TpjwXcRqaFoKyZ1HxpViVWNc8unto9e674VVL6mrGnCakhTuw7EZ9aauni5soGfp8gqIomuYze3GH50M91TkJJN0IQSrVl+YyqLVNcMCR3/H2nv4/zVDslsOHy2T9k01+T+DAtA4TW0mQop9jWEwlwIH9ATtGb3AV96+b/YCiFMDUmK5OgZuy/1WKEYJ/PslmSqYFKVlKAZ+tBAFCD4I+QNOusIC/YJLmKgRqWC/Fr179WvpjdD8mls3/ASWnt30wimKCGjCZaS2tKtdWJ8gQbejagZvfL+1KZprN10Yfgss8A0Cf8pMtplvXUO3JbGSta0k0Un0ZUCcD3r5AT86Y3bM4Cf6SPsCUQStmT2pLDbNRqRbdg7WJ646RtME8UEUzLZEdmB30xKbXvFVV8NnSkqGzJ6Q6zxjlCiCuFEJuFEJvHx59fobeoGQQCtkHQSiCUmSFfI3wqJJejliaJ++OyY9IewrFQFDUTQ5l0BblcZPZAzywGYfk58v/bpLrjSHQEU5lsyfNwlCwPlw673H+AofyYrFtMf+gcBGJuDQGl2jLl00kZDQSX89KVL+UdJ70D1VZUnbOgay+OqbJchIWv3FLqymGzxEIWE5UJmbKq5mfvUG5EKMllVXkuDlWfBlpPGWXLOj5FUDZKMkIoT84fwQIEYiT3y54W1V/mO/ftmX/754FCxUDxZ1keX17voB97BnrnSF0NnAD+COx/iGQwiY5M1bQyW9m5R8fL47I2kdk3u8MkBKySdO1+yyJdTiOE4JqXy16WVuQjypoh0zR6RabvvvlH9V6hCz9QJ31MRyglI2ml6kkx14kQymaRuOPIrr0EPrRzJhPPju6H9stekXQ5Tb+d0mxnHaGdBuGwEGIEwP5/bLaNLMv6hmVZZ1qWdebAwDyLegMKVYNwSJ6URCkn821zLX4OYoNQOFwXuQv5WyqYlTQNjanm0XeVnPQ+HAZHI/rXgxqGiW3uRCRD5FoaFZivGCRCKn/04+bms+Hs6OxGyYGiuCwHoVRbniDnpIzKZr4+fMWh387lFdtdqsm9D8nj8BVbihCca1lFRilLIoNQ0+FISrixAc7PThBWwzw+eS8IvWUPLFvWiYfkBL2IP1KXapgP/etJIO/hpb0Wz47mW84V5ys6li9TH5lZzsDkDlh25uwf8KmS8fLgN+gJpiibWff7LPwYDGIhwVMTT9Eb6pWc++QcRX7bqeu3FMpGmZJecoUbWxGYK1ZNUDQiDvsuvRV+/A7586lvkrTf2RDuIVarofgqTBY1nhvNtzQHfTRbIR4SlI0ysW2/lC++9nqIzkJH9qkQ7qFnSjawTlYm3QjBK1rybGinQfhvwBkF9BbgJq/+cKFiEAzKhzZemmjOR86FUAoqWVJBOSgnEVJbmhlbMnJYGJJe6cDpuJzLA7r4GjAq8LEUPcKPZhXJlKsLHo+Xq+iEw3XZjh+/6sfc9OqbJLc6MUdawEbMLhx6kTKq6CZgUWo0CPYg+zkNQmwAkitI+WQ3sfCVWooQnDx30ZQGYVi1O9YbNZRmQ88q1FKapdER7tp/O9HhW9mZbk2/J1PWSUblNW2apTwfVp5P0k5jvOiEKIWqwRv+7f6WUiWFqoGpZOty7A7nfj5nwb5vRtQYE1XJ0molks5XdAjLOsbGxGpJvXZYd9MR7gUE/Ua9WTDiGIQWnJaybsqeEHOWKGM+Qx3ukSq1SoVfPHmIS7/wW172xbsXfBwHMxWG7cchHuw58v4v+hC9WSnrMVGZcMf1Hsi0bxb1TfMAACAASURBVFiOV7TTHwD3ARuEEPuFEO8APg28VAixDXiJ/bsnKFYN/KpcPBKHnqh3PM6HUBIqWeKBOAWtQCLsb6lYVjalp9Abarigj35X/j9XmqQhn94zuQewqImFD/HOVwzUsHzI33v6ezmu5zjWhAekoNwRjGQkKJlWgYDW8gCQqlEDpUrNMuvTuMpTch6DvZ9ZsepCeg5usY+jta5pJ+1VMqUh6ndkK44UIdjRXFKRD5uSvL/l9EC2rBMJ2/dnICEHtx/JMKVWkDLkd+hJyOvxwK5JHtg1seDjyFcMTIp1I+3kzefqDQG3sLzMnyCvZ0BpTe8qXzEw1D0oQuFPh22e/fQCqgM1AD0r6S/K73y4dJiIX3r1rUiKlDSDGhUixizXdT5DHU4RrZn4lDI7PJBGH82ViSdkoiRm6jI6mQ+9awhbFktDA/zrY//KC390BvHEaMuyO/PBK5bRGyzLGrEsy29Z1jLLsr5pWdaEZVmXWJa13rKsl1iW1ZooSgMKVQPVb0cI5Swsn4Pf3YjpBiHkX3Bu1LIsKqa00k3MhbQUrGNo0+wfbHgQeu2JYcJXXHDaKF/R0dSt+BV/fU5szvYC5yuyA+FQEsWSE728qCE4Gu9NEUI4NbuonINILymb8x+LtkZ/LVQlmyVjd3X2Y+fMg0cyCNJbvWpYKtGqtf6WKYbZkkYkJB/alBoFs3rk40gup880EQhUf4ENQ9KAPHto4Uy4QkXDoCSlTECmrmDujmmwvXRY7Zef8YUOtpRazVcMLF+O3lAvfkfiea6oEWDwRJZPyjTJHXvuIBJszSCYNTmb2aRCRK/Cpj+Ba8brTtt8WknhHmI1i2Sk+fsvlIabzmsEQ9KRPCUz5t57cyLSjwCEVaNiyvspmNriCeNpLhyTncpFzcCnlon4QrLv8UiFQ2ebSpaoGpXdvGF1waFw1ahhiWnaKEa1riKpzjHfQAh4xx0QiJHKyoVbqIUFX+Cpko5QCwxGBuuGyW3Fnz9CEKEUUcDv11qvIeg1FFU+7E0G4UhpkvOuIlGT0hHhUKW1GkLFcIuXIV+ImD27+vlGCGeZPv70uD/FUgotD+vJlHVCQdsgKI5hmidSAkitwA/0qhHGy2Pc9v6L6IsG2L5ADn6tZlEw5DVJBByDYEcIc/WGgJvCOF2V106N7GspQpBpq5xsCnUjlHkM0uAJLE3LzurvP/t9wm6EsLBr4khOaLUCCa0oGVZqAP7iTnjv4/N/ONxLrFbDpzZ75AutMeUrOsJ+ToZMsy7uN+f+peHcX61HiWE16EnT4lw4Jg1CoWKAr0zCWYyfr0EwNWJqiKJeZDAeQjNq7Bz//R+4kmbOmMDkzmI9bg6apYPlZ8Fxl9JjdzQvdEKWZVlMFTVQyvWRiCC7g2F+1hVAOEWiZuFTSy13o1aNGkpYfh+XdVWePHIhNbEEdeAEelBRA7mWawixkODbT3+billBaPZ1PdJCHO6BYBKmdjMYGcQURfLV1uQSsmUdn1/uv1fYfRhHShnZufslSsgdx7puMMa2sYVFCAXNAMXu1XENgh2kz7cg2w1Rqb0PkAgkCAQzC46YzJoc4alZGckwKtid4/MxrgZPRKkZROza0gPjsqFtoRFCSZOUZs0qMaDrdSclNjB/LQUgtZx4rUbZzgY4Udvh3O9/nxpmjaJmYokCEV+IsGUd+fmw3//Zqje4Lyn+1nqXjoRj0yBUTbkQ+mTel+DzMAi2VxSz5HjHl508SMCn8O0F0PtKmlGX03U9c1uC+wUfOPIfGDyR3ozkhwtfcUG9CLmKgVGzqIlS/YGHelpgPi8QIJRira6jq3s8aEwzET7pRa1P2V3bpUk3/TAvEiOsqoHhO9wi7VQjFq0PEjkiy8mBEK6MhlN8zesLz9vXahbZss5E7THCvhDD19sD3I9kEPwhiA0xbAkOFWWUt6I3suB8cb5i1Js3nfsjd1AuiI2yFdPhpLYe+wFLAinUQFY+bwuAjLRqZM0DrEqukqrEiPlrGIOSZvrOYTmoZm9RpmEXSjstayZClWmaQdM88nVoRKSPpFmjYul8+Y2b+IdXngjA6AJ0t9zudZGj11HAPdLzEUoCgrU1ePTPHmVj30aEL99NGU1HsWpQo0TCCcefT4Rg5+9jmnxIYqEam5Ym2LqAbuXGCMFNGbme+REWYoC+tW7uXKgLs/gO9cyg2GwQiuOyL+NIC2E4xWmlElUxRrrY2qjCqlFD+Ir0BHvrTXqjjx/5GADiI6yvVinU9pFtgXF1KFshFpcF5e9c/h0ZocCRvTCQaYTsfpeeWTQXXu7KVwwsy+Kg9hgvHzwLlwz9fBai2CBDZq1Ju2aiuLBzkq/orpFOBBPwwz+Hh/995myK2bBS1lOGa4A6teAIIV/RQalgWpqUtS6MyudjtpkQDvrWg6LyFpIsjS0lp8lrsVDaaUkzUWyDMGAYR44YGxHpI2k3kJ27PsraQWksFyLE6NQrNStHn89uuDvSvan4AAv23oeqqJKqruTItNhJP+8u2/aX2wTNqMlJYaJUV/UMPY+LbHesJiuy+JmpZBhKhBZ0cUuaOTNCeL6pGoCVF+AHYkJdcMrI8RKqtWK9aAgyTxvuPXJfRijFEpvVMlkdW/BCDLKGIHylev1g9An7WJ5Ho2FqBWuLWXSrjCkKVPTfn2ZpWRYHM2Uq/qcAWJtaa0cI4vk5C5UcjD7O4H4pNFaxFj4lLFuW0hOGVWWZ0eDVzqe15SA6QEqrUDJK6KZOfyyAbloLokfnKwaiMWX0tM36Hj7pyB9+683gCzCEj5ovu+CaioxSZLolGUzKCOFI50ENQN86xNgzrEut40BRysS3kjJy8vYDZu35rRUO/BGStknPVDL02Y1hC4lknXpluZahz5EAfz4OE8h5KkhpcM3KkK+2LsA4F445g+C09BtWibg9h/Z5WX37Rhzc+wAgKW1DiRBjC8gHluzOR4CQneukMCYbz45UxATZJJdYRhIffv/CdFJKVZv7b+TlFCr3jfTzi1JCSXptXrZJoSXKp0wZleuGaVwOjOe8q4784Wi/a5iEf2pBVOBsWadq1FBVHb/ilzWV8qRkOR3JMIIr9DZ0q+wiN8Tkgpkkl37ht65nHm+YFTxnM1YjogP0VOUCOlmZpMfVVvr9r02urLspo7jSQHKYL13jQAiIDjBk1qiJErnqwnjvMw3C4ZmqorOhbx1M7WZ1cjXbM9tQI7sWnDIqaSZClfWc/t83ZSQEA375PI+VxlB9CtGAb0EG0qnTFY0Mffh43s7KWlv8rpKlP9xPpZYDai2JYs6HY84gpG3rXK0VSFiA4neHvswLISDcy7BdMBwrjREJ+MhXDcZ+zyihVDURikZQCeFzFpziHMNg5kJ8mJQlZQoWQjt16hiGpdETavA0nOM4EsL/P3nvHS9HXfXxv2dme9/b701PSA8gvQUIVZq0oIIUH0WQbgEeBQV5BB9FFMUCgoCijyhIRxEpSpESaoBAQnpyk5vcvr1M/f3xnZ3dza17s7x+8Pudf3KzOzM7Ozvz/ZzzOed8ToxYSc/IlWPbTsyYLuomklwkVFIVTYr8yIhyz1Xn0UC77UnL7sSE8iklMDNIi8HpW96A1+8sN8eNZTYghC2LuGEgefon7BXnNQNKBQdaXniB31w5dtkpQLCZprygN3rzvYRsWfOJnEu6Uu9rfUUz1S5HjO8AwSZaNXFdU9rEZoeI4fY1Rghgy9V3MTMqaF7/tNvJTrBXpgQIMhIR06yNMgI6PCLq7cqKRH/I55pQI6eo1NJJawmaTUuAwXiclX3OE//2raXJ34SFoGc/qsTyJw4QBHduUDTz4gf2Rca/CO9+Oi0pUelwzwf30B4TQPLimrGFriotZz/0PlcFEGV7R5abHs4i7cR0DcWdn9AimFOHqf2H8gDzscwXK+cxlNxOTSwr6qYQDysly5Kd4oYfT3geaKC9IkL41oNjlAIOY6UyQNXKiIVn3b9rO0DF/RMzTKF/vxOlp3tME8UO4WJezBoYoyfEsWATrbY3vim1yfHwJ3IuqYIGSh5FUvA/akdqn/vjyIqrQ86lmTabXi11f9dqWwdzzneIeWqIECLtUEiyd2O5nyetTqwxLK8aSHKBkOwVi10tEQLQYCd+BwoD9Of78ftyooKrRkvmNSS3APs23RxfbgvK1yvXX55T4Up/ZKWnn0hAcKonDH18YVfJYlMJ2TfWB/0fMHuSLUe7o/LgGJYr2mJZleP4sj3j88xLFm4XSq1ydkLaJFlVdwChKkIYL2VUGSEo2Z3qzi1qhh0h2F5wcuv4KBIAfwNhyyKk+JDdiQl5X6Xr52gplbzxi5aN/yCfFtLdIdPEJWcntAiXRP7mtAuOWEQIY/RiVFqwmcl2w+K3X/w2ly87EcndN/EIQRElyQ7czf9MTecSyYmqrcIEh8xvGsjhtfsxorlBMNTxUar2LJHJpsSMqGjeSqsTO4ecAwg2b1/LegF4A82ELQEIS+5fQn/jdyYcIchuEbG2acXxVeBBOc+QH6wAhNRHVmn0iQOEwZzqlJHVXDVgh6vnzToVgM6s6B2otVM3YwNCyB0UI/BevU0kUseSWq60cDsxtYgpZSeUpBLldDsAgqEJmmQ8wOSL4bMsfHZie2cE3YR0RaFccTVeTxAcT2mKO0I8liTqH6UkcgTrswEhq6cEIOT6RaVV0+wx9qywAy6GpXcRskw8SnZCi3DWLs9UXOJ8QoXs+BOHAIEmojvkLlyhVRPjrAsaiqtIpBS1zTl2/JE0QHQKEbvJsWBkJzTSs3MgRzSoIiER7t8gXmzbbfSdwOlTkDLb+epuXwUgo00sj5FTdVCKBEv9IOMBpEoLNFLEYtm2snMxkd8jmddQvCLSmlbIjf++KG23+RVm/OYw3JILV2jNTvcOjWSfOEDoz6q47KafFlWtrWrA9pwvbF+ChOQMuq+1imMwpyIpKhFPCHpXwZP2MLhJe43/IOF2YqaJTo7uVLbmKp9ssUwZNXgboH8drHxcvDkeysj2XGOyB8W9c4PdC5oBUsWc2GxvFVecffll1h1/ApY6zGcEGgGJE1/M86Pb358QdSWGyejkdFu3p2u5iFBG4GjVzk703l6K69ZVvxHpIGRaKHJhgl65/ZDa1T3hYnrsbu1KswHs6YZD2SUmnAslsGmCSUwdt6tAyJ6xwK6n1XaA9t2IlEBAmRit2ZMu4vMWCXvCKP32tZ5xyNg7lii21DbnnkoVdi5CCCODyzeyisBIFmhAlSTWJtY6L/Vna6evEnkNv0/cF025xPgpo1JE8+bviJomC4oFZO+2nRLmHM0+cYAwmFUJBcSiEVVzQyKEjWeexeoDDxp+gbU9Z3diM3FfnMHiACGvq+bKlv6MistVJOgOwK37l9+oKUJoI2rLKxhSvmb5iJyq43YLrynui8P/LYUHviTeHA9l5PKCO0hccuF257n/jc6aPr/S8noOJIuQOyQkPNLbqqQzNn/5XNR16+j+8U1Dd3b7sULt7PXMIK0Jgy3diZorfFKFsphczBsTWvcjSDyb+TzrjjqaNQcfwvrjT2D79Tew8fQzyLzwAkQmicFBcnFClFEp0tSxZTxyg7VRRo2zwBelzZJ4+KSHObD9YGRP34QqbNIFDdlVLM/XnrPjhNsxrGUBActCQkJSJjYgJpHTkF12OXJqi6hwGmYy2RAr3Tv/vNoBhGRx4oAgK0WCUHNCGYBAI+ckq/t0+rW1I2w8siXzGl5PAb/LjyfTO356WVaqxDLbfI1IrvROCXOO+nEfyVE/QhvMqfh94uGPFjJDOMH8m29iDAywav4Csq++Wr1z0xyhwNm/lgZfAwOFAcI+V82UUX9WFUnUHacuNc0Z/0EiHUQdDyxXc7NJVtXxeHO4Zbd4aEqd0iDmFe9gqX8+xZqDD8HMVYTebj9Rw8TrLTCY0yYsspfXhefjU7xwQwuYOrSXqQH3FJFPUNevH3Z/3VWW6n7s8at46uk3a/r8bFEn4CsBQhRSW0fUiUk9+c+q/w/+6U/kly+n86KLnQjBkNUJleE63ahk8Ck+AsVMbRECCMEzW/J4enQasqef/ASSmKUqo5ChQ3Tq+KqcdjgPWfEQwoUk5yeUY0rmNSw5JxL9yS1ja/eUrBT1Z3sIqOJ+LRgTq4LLq7p4Vk2r5oQyAIFG9stXVyHmrT5b8n38linoKO4ccU8U9HxtuYwKUcyYaSK7sjs1tGg0+8QBQiKnlael5ZNVqG/uQEls/q8vVe8sK+LiDm7cARBqWwgHsiqSXCC4+TXxgjcCX18h5AfGa+G2iqRuruYkUbZo4HLniPviSFpeKGqWbBjveNu116L39qJtrwAOxUMs3U3YjrhWdCWH7DceKxpif2/ljNiZhwFgpNNonSL6KLz//rD75/wHV/2/sOyVmj4/U9TxldRFTUskL0dIavf+4hfDH0TXQXETdAXQFJPNA7V7pM6QHjNNrNQ9XksOAcSimRKA0BFqQ5JVUhNIqKbsLuGQroqqnVpNliEyiYgdIdR6f5qmRaqgCWkVb8SelDbOQgMQ1VmA/w0xzaxoTCyHkFVFibjfNGqjl0vmjzNTE7/r4kmig3siVXl51QA5S7SUy3B5x7/zsT+2/5CIq3kkOU8i/9FIYH/iACGZ13C584TcIVxquupH3nLBBWMfINwG2V4afY02ILhrjxAyRSypQNCyhI7SVZ1YoXbWHH44m798btW2lmWhDw6iDwxUA5Y3QkwSfKakZJ0h8eM1QQlkxTyGnpVgmWJQ+fnPD0keFjdswEyKxdqs5D93/zwx0ySvieqHiVYuFG2deXep3PPcZxyqZNt3r3G2M5LJ6giltH93EVMu00S5vtqkI7JFHY/HLm+0pUlGAgR927ZhXy9Z2J6jsHGg9m7lkipowUwRL+VTJgIIdoRQ6oKfSMlluqBjSnnCan7s8Z2jnEvENJGUQs0LoJDwAM3KEHNH7UlpI8wJGc6OEPdNaYaBak5sAcyrIr/lN/QJRwiTdYM75p7LTw79CRIykivr9EON13KajiXnyuq3s4XGlWUYrD3qaAbuvXfknZvnwnVJ2OdcYvk0SBaD+cTI2++EfeIAYTCnIit5oiWFz4oIIfvyUM/SSO7g9doy2A3+BgbyE6WM8hhogm9eeicA2vbt6F3byL78ctW2uWXLWHPAgaw58CA+3G13rFKJqyQRLZWRKfmab7B0QQclS9wbh3tOEC/uehp0fKr6XO+6m/XHHuf838xUeJvzTqBVN0gbWSQlM+G5CEU7OvGmumD6wVXzKdL/rKZotK1bh+xf6OxDi5n87CRxO6qDtd3sYj6GDQj50iznGrxR23Kvv07I7jzvSk8cEDJaglhJr6aWHAII1VM1DYWkyMkAmQlFCEUMCoSLWcfbrtkCjUSLaTFkvkbKKOlESxmR69PzMHU/533LNNn4+dNZOW8+1nCTzPY4C6YdRMCe76FTmFD3eE7VsSQVv65VrRX6wAAbPv951M2bRz+AXaBxgKeBoDuI3xVEkgs155hyqoEhZYhZtrNmR20bPvtZtM5Our9/fbWzNpyF2ogXxTbbs3UbL1NlnyhA2NSfpTtVxOMtVkhfDw0DG754jvP31m9ejlmsWGztUZoNvgbSWpqA16qJMirqBmmb1wyaltOMZgyWF5DKSEDdsKFqfzNX5kJj9oMqKVmufKC2hqycLaUb98bEHFyA2PQh26WfemqHz6/w0EOtLLSvjeztnnCiqkQZefLJId6ob6EQU2s8/3zx+fmhXLDe34cVdfPKAplEJASJ2hbjTNFAsRPssVduEy/uwFenn32WlfPmV73W8MVzmP2fF8ElwvhNZ59D0L6vJiL4l8prSBKktSQxewJb7RGCnU8Z2EDQrszJahOIEOyFI7QzEUK4jYhh4pvAzA6h3WNQMDPESsqz08vUYOqJf5B/5x1xrv/61/AHaZpNYMsbgBj1OpGZCFm1CJJBINNTHnELpJ58ksI777Lx9DNGB4VSxV6uH969n4glISkFCnptOYSCaqCTIWYY4A464KSuL68PRmaM3zncStwGz62p2pppx2ufKEB4db2o4/V6CkRLXcL2hS0twv699iJ+Rlk/PPvSS/TfeWf5IHaEUJI6Timv1VTWN5jVyrMQTNNJDvXfcYezTaUX3v2jG6v2N3PlHz0U7sBl4ZSP1mI5VUeX0jSUvKb2Tw07/cmyu4Abvvxl8X+t4rsGGmmzbzDFnZxwhKBVAkKwuv/AMgxCRxxBcPFBAMNSRmYiSTwkIr5kzMuMrWvYevkVYz8gtmWLQjPHr/jw9or5vTsm7RIPP+z87Vu0iHkfvE/rVVfhamoS+QPbQpa4rwbyqZqH3PdnVWJ+N4lCgniJK64BEExVZfsfn6OYdMEdhxJ8+vsA5PTa7g/NMCkYYp+waY5v5vhwtvAUwqaFpORrpoyElpKd68slxCCiilLL4qqVzt9bL71MnHdlfgtg0t64AK9p4pWyTp9HLZaxKUS/ZUHFdMPen/0cAGNggPXHnzDyATwBUa6a64eHziOe60OS8zWLMGY1Mb0urhVFdGDTusH9y1WKVn6MPEm4g7jNMOSM5IQ0rsayTxQgbE+KhbhgpJ0ZuKUIobQIR449Fs/06ezy7DPOfumnni4fxBcFNcORk5YA0Gu+XtNC2J8tlgHBssATwjJN0k+XP2/w3j8DIn9g2R54x00iMVQZFkrhNuKmwR7TRTNWLZOpsloegwLxD21K5pjhR1Zbuk7oiCOInXaa83/HPAHa7DyG15eeMCCopg0IWn6IfIeZyaCEgshBwakPBwhGMok/FmOeZjIQhdbcAKm//53kQw+O6/OzRZufdY1c0ij7yjIj8TNOR6oAz5Yrryy/d4+gtAzyNed1OgfztEXdpLU0sZTQvqmlyij/1lsMPvoM6//RQrbbQ9ASD3+pimu81pfIcd1Lf+arTxjE+pl4hOCLETFNdLnIYK42SlNIZ9hRWyE9pPJNHxxEiYlrEz1tKbm33mLtksNYOW8+6X/buahFSwEIWBYeJTehyW0lMPVbJux/kfO60lAGask/hhZaoBEyQs8pagptpFpLgfN2t3e0mKkCaEstX1ezMEaeZMbBVXIzH2zbOdn64ewTAwhaTw99g2lmkCOpJomVGvLtBigzJS6OEha8q6u9fNG1ykSi7TmGLJNDJx9Kl/o2RV1D1ceH+P0Z1REvC5gmeIIUP/ywapu+X/2K4rp1FFeL4R7R05aWF8RsxYLojxPXDSRZ1K13Doy/kiJnCW9qWrAdQ5Po/vPz6ANDeUVL15FcLiSXYv+/+qHy+xuJSS7c3tSER4qqpSojyxoiXmZmMsjBIHJALNZV3x+wVBUzm0WJhGjTNfqj5d/BLBSH5oB2/H6WRbqoY0oZYqWa+8uWD9nOM306AG3XXUds6dKq9xrOPsv5W96Wx61ZSEqhJml03TB5fcMAkzsEoDR22xP0xtuABGy//gbn78E1QYJiQKzj7Y/XNry5gn23reeIdyxaH4mMT1BuOPMLQDAlk1ShNlDqTReRS1pbg1soqC1sPOMLpJ8VY2a1zi24OtqRvF6UaJTMc887+2658CLhuHgC8Lk/EDAtXHJ+QoBQ0OwIwbSq6GXZU67ykX1jVAe2fwre/QsA0zQdj6enJsrIMC10SwBCLJ9yGu8G//xnsstec7Yr0cmWabJ6/wNIPPBA1XH0RBolewD7fmjitVJcfO9b4z6H8donAhAsVWXtoUs47uZvcOsj1zJ/+QCNpR/EbtooLYZKg+D8Kj1A2VtR4lWiEvKDbEoJTtHd8LJDG43VMSy0lAQghEwRhpZC3eavf83Zbv3xJ7DhpJPERy5YICQugJ6bbiov3L4ocdOkee0mzv7gSbK3/boavEaxopEhnrZosCCTn8fA7+9hzYEHDamzt3QNSVGQbJ68kh4BhLicpSB5t024HT6vi+vhtqwhkhVGNoscDCEHho8QCh98AIAcDtOsqWwIlgGg9+abWb3f/kO+U9VnawaqbqKRIq6p0LoIGkYeXh773GeHvCZ5PMz655PO/8N5kN19NUmj92dV8prBqzlBEbZZwN7njlsuwrIs1IrO6fQWP/5poqu3WGsN/h2/dv6ULAnLX925blkW6pYtYx/HjhAA0mpt3uiyDQMEA+K3bs6nyPd5yb/9Nttv+AHa1q3kXnsN/8JFoCgM3HV3FeUKkHvD7kXxhglYJrJcGBMQLNPkwz33ovOrF6Bu3AhARqugjGJiZGZx3TqKa9bQeN5XaPiv/8LIjJG0r0iGt+oGhqKSLtTgvKllGfBYuldQUMD2//k+GAZyWNClxbXCgUw9/jhGIsG2715D9rUyYKxZfDB9j6zniodMPrNq9Ucigf2JAAR9YAAsi9igmCQ1vduiTS0IgSi7lEzvEe+5mso3/+xXXiZywgnV2ftSkuiXe3L6TLFgS0qWTEGn5+c/Z+0hh44auvVlKigjxQ+ShN4tFFR9CxYMu48SDDrJ1dyyZaw58CAKH34I/jizVI2L7trCF1Y/Q+SBP5J8/G9jXo/EM89y/723c/uvDAIvbcNUyrTEwD33VG+s6Uhul5M4tYYAQiPH6C5U13r61DEqLoYxVTfRLXFjei0LmkXiVu3sRB8cBE1DDoWQg3aEsAMgpJ8RVFto7/k0Gwbbh6Hc8++NnHAXHbQmCW0LMzQNopNROztRN1WPRrVUFVyuKkeh0jzTpjHp54JXnpwxcLv7ndkb47Ed9aj2T/Zj+sYvdlgqSihFMgBuSXiS2g6AkHvzTYprh++Wzbz0EvEVIhH70IECjDKvvoNlWWz95uWsnDefVfMXsO7IoyjuUPAwxNw+IvaAmIxWGyCs7ckwpVncFy2GgWEKWkbfto2+O34LQOiIw7Eq7ofICScw41ExyKeUcMYTJmBaSHJxTEBYtWAhZi5H5vnnWXfMsaQGkg7d5jfLCqPaNuHAhZYsAcvCyuVIPPTwSIeFhL3lRgAAIABJREFUhlnOn812zm1QHV4BVt2ylfS//03qyX9i2f0L+UplYtWg54VB1M6yMoBiA8L2a79H/+9+T9e3vu28N/CHPwDVCed0APbvHcTvHod8do32iQAEYwcqxJRgTi7jRAeWadLz05sBcLWV+VJXPI53l10wc7lypVFFSeQXzAAe2YckaaSXv03/b25H7+0dsYEKhCeoKKXxmQLpEw8+JP6///5MuetOQkdUa85LgQDutjbc08p12BtOOhnT8rFwTfXxC+Mouey+5ho8tuyF/LyGYZRVV/Nvv10ubcUGAJcLyS3yFFVJZYBAI7v1ig7itDp6KVvikUfIvlJd2pst6iCJY3okBcLtGJks6445lq7LLxfnGAwi2zxt6sl/kHvjDTZ8/vP0/eZ2tK4u3NOm4pkyleMyOVZMG+pRZ196echrJRvMakhKFs0qMK2ogi/GuqOOZt2nj2H7968vXwdNc67BSKbEBRpNzpq43An6agCEvkzRuQ6XzjuH/EY/H17yBzIvvjjiPtllrzH417+K/X/zGwBarryCtuu/b385AeKSWT3mddOZZ7H+hM/Qf9fdVc5LfsX7dJ77FQBeWzCTvy4Wj3du+TuoGzaQeuKJquP03jJCk16Fhe28jJEfoPumm6rKhhMPPUzvL345ZB/TtFjflyUYyOGWFKKmycDfyr9h4r77kHw+wkuWVO0X2GcffHPnoDQ1oW62Ad0bsuVE1Jopo+4P1pQnG1qWU4CSsaua5FAI30LhxPXddlvVvpaqsnLefLZcehm5Xrt34PDvErSXzMwOvSFaTw/rTjiBdUceyZYLL2Lr17/Oh3uJBlEhVS+AL9Tlov9vb9D5lfOcfSfdcovzd8+N1UUoeq/IXaibNorLMbWJzZMsoqkcec2Y8OCgkewTAQglT+bW3U4GIJSH5lQ3xEUImF++3OmGdcWrXUylUXgFH+7+KQb++H9VST4p20Pc28yMxHbki7/ivD7w+9+PeC7dqQLhgPgRQnbVgrZlC8HFi5E9HkIHHYQkVy9qSlTQVMF99616ffCp12ntEj/Bnf91CQYSL7+zcdRroXZ2Yg5Wl2XqWR05EsHVYldO/a0cZYgcgtuhjIZECAMbnFK2rC7omv67f0fXVVeXj2FZ6H19bPv2VWz+0pdZOW8+6ubN6IODDNx+O/tsEdfeE2wBWaaw4j0wDKcvRA4FkRThzRTeeZdNZ51N4Z13GbzvPvTBQVwNjeD2M1XXcSkuvn7Kvly335eInHQSSixWTfntYP3ZoqMz31LMkl5b9qYH770XfWBAnH/39jEBwd0h8k4HvQOKO01fevxJ5f6M6gykaV21ja5XxX3YY+s3Dd5/P5u+9CXHa9S6u9n8xS+y/ZprUbdsYfAPfwQguHgxoUMPBSD18ho8poVklRefykqcnptuovOCC7Esi23XXMPG08oCdv/87H4YirgPB+6+m/XHHT/knNNPPlnlPAxnEbsX4rTl/2HgrrtZe8SRrJw3n61X/jfbrr6avltvdb5TyXozRVTdxEuauYMuJMBIpvDOm0fwUEGDle6HKXfcTvDQQwgfdRSRY4Xekm/ObIor7Woxb5iQYWFVAEJx7Vqyr71Gz09vZu1RR5P+17+dDvTggQc6Dln2/vuQbEDwuwIgKxRWrWLQbgKTgyGiJ56IEo/jnjTJ6YewLItVu+0urtHTT7Pp/K+R2useWHy50C9jKCCsPeRQ1LXVYomWqlJcv8EBhKakhf6+AKXSQt/y7W/h33URc5e/XbXv7FdexjNtGoV33mXb965zgLjj4s9iek0CtnTFQDrPgH3v1MM+EYCQf1MkT/41eS+Kfg+fftsintjiRAglxB+Or1WCZe+5+wc/ENssvUu8MLCOoCvC5FS1B6ZuGp46SeRUHnprKwGf+DGCdoWRkUzi27U8zKOy7BUgsKcYStJ2zTXMffMNJ1LoufN+Iu976QvDa9P/wrZQE03dm4dv1LGtuEZQBY8unMnf95FE1+LzqzFTKaY/ILzNwvsfONuXk8oCEHpuvBF1S0Vz2PE/JWyXV+aNDEYmS8+Pf0zy4YdZ9+ljSD7+N1KPPcaaxdXyEj03/4wNJ5+CesetXPvsS4RzFp5wG4mHHh4iGRI5ZqiwmuT1om/bRu6VVwVgTtkfCWhVAvj3CLKsfSGh711PYJ99MNLpIfuXTCjP2jLghQxb7n5dXHO7nE/v7qb/t3eSeuIfTuHBSOaeNAl3SwMzNki4SI87QrAsi9gdP2NRv0gkT/rbO857xTVrKK5dy/Zrv0fulVf5cN/9ULdsdSICgHVHHuX8LXu9uG1gz7y5Gp9lIlcAQu71N6o+O/fqq6yav4DEX8sJyGNP/gnBsIl7HFWz5ijXFiDijRDNWMztqy4JTT3+uPP3ql13I7+iHFWv3JZCtkzO+MNrXHtblp4VwimLfPpoYqecIj7XpnFDhxzC1NtvZ/Ivf4ESEYulZ8ZMCh98gNbdzfZbfstXfuVh7zV5UnmN3Ouvs/6Ez7D5nC/S/9vfonV2suWii+i7VXj4oSMOp/37/wOA96m/ORGC35YS2XDyKc55KhFB1QT23ovcq6/SdeV/YxaLfLjnUNXirVdehZ5MCmABCmoWI5FA27ZtSH9LqCLyST/9NHlNR3Jl+cE9BsVeAYQl6lT2i+OVABLAs8ssXPE44aOOBERElXlWrHFKSzuW3yKcNfDqRRLXfofu//3fIec7UftEAILe20uhtYOsx0/fDHFzubQCxKah9/XRf6dY4Ge//NKQff17DaN6uetppLLzSb26ivZwnN22iIVi1pP/IHrSiRRXr3aqIUAsqhvP+ALb9tydjkwvg/kMLgs8/kaxyJgmrlg58ggeeCDzV62k9eqraP/BD5zFWPJ4kINBdtmhe1cCEsUEAQXat66l56c3Y1kWqaefpvPiS6poAWNAcJePL5rC9rgEVhkE3S0tuNraMCoWvhJV4iSVgb5fVlAFLfMJ2QtH0cyx8XOfc95SN22i68orqzjNjh/fSOCA/cn+5z9O7gTgO38x8Pgb2f697wEQqIiGSlUc3gXlB6f1qvIxJbcbwq0Q7qDVktBlEQEN5FQktwt1/Xr6f/97LNMc4tEOZDXHMw9V1IY3XXQhABtOOZXemwWdWKp0GskkWabx9BNw6RJtg3l6x9kHYWYydDz/BD9+7l4Ofdck+F4XnrBG29XfBGDwz39xtrXyedYdeSSJiteGM+9soZx7548t9l3TTX7FCuf3AJj19FPDlkv23i6O6/NohC0LT1P5Owf22Yf2H/6QGY88TOMFYs7A6v32J1XRvJhd9hqbzj6HzosvYfX+ByC9rPLbXxos6O2D2XOZblNcO1rfbbehbd2K1tPDnS9u4Lo3/siMtYL+7F8hfn85FEbyjC0/7Z4sSlTXHrqEwXvvB+Cix1X2+NMtbDr7nNF2xTN9Oq7GRuJnicqx2x75G9O6LbJ3GlULtxyJOMlcV4uowko98QR9t96GZTdPNp4naB2lWSgKbLnwIgJ2ZLD788+zev8DWHvY4c4xZzz6CPNXrWTKb25j3krhlGVffplUQaclM0jcvp2888vn4VyPiufTtO+75m98w3ktaedWlNYpqDHhMO7bvRL52erG0521jyUg5N56m86LL6H7ppvIv/su/es2slr34ZIlNs63+w40ieQ7vY7n2nr11UPoIgB3awvzVn5A41fFA1BYuZLVBx7E1seTbH1oC/PW5Dn8fVFl8JoadMpVt1x8iXOM7dffQP5tEdLd9cyNXPbaSo5/0yTf7xaJU8r8c6U1nHMOsaWnDvsd/Z8qS0w02k7as0vFOQ7cfTdrDzmUrZdeRubZZ1l/3PEO1ZO38xvJoMnKmeWbaMYjIimmRCIOIFiGgZXPI4dCSG437f/7v3hmzCD19DNsv/4Gihs2oKdz+MIdyBbsuXmzo0iqfaVcs11p0RNPxL/b7k7fR2GfA+mMBpnZDcW1BSxNI3T44Uz93d20/+iHtFx5hbPv5Aq+OXpK2VMLHmJHHw0zadU0MroAvcGcStOFYmHv+dGNrN5vfzrPOx+tAogGsyqy3QAV6BNeVtv3rq1KzoLw2ua8We1dD2eeOSL5HyhaxFYvY8Npn2Xwvvud9ys5dCORYOvlV7B6nzL4nfmcACXLkokuFZHi4J/+BED7D35Q9VmNF3zVoYcAdnm+XHoZPPBA5+9zn9nOxtM+y+bzzndec0+axC7PPlO13Zw33mCVJCgexVUkZJpMu/RgJt96K9P+fC+TfnELsVNOxjdvHo1fKkdxJbkVddMmNn/xi+Ref53Ms89iJBJkXyonT611a/DvuogZDz2I5PPReOEFxD4rqrbyb7zB2iOOZO0hhxJ47UX22foeH86v1g7yzt7FaZ7077EHI1n8jNOHfX3OO+V8TCUYRk86URx/zhyHlg0dtgSAyck0N91tYGnlcMnd0cHc15Yh2YxC/MwvOO/13347AJN+/jNaLv8m81etZPYLL9gfKuGzJSN2e+u9qnNztbTgmzu3fH6SRMMXzyG3bBnS3x5hfo8Ax2lntjLz4Yec7Yxkwtm+ZKUIR1IUpv3xDw4gAcjxNpSQAISrX/8/TJeb1u9+d9jrNRFzjb3JzpkkSccAtwAKcKdlWcN3UNlmWRZdV1yB1iUaewbuuhsPkOwQcsqb5AEOAtb/owXLL8LWwN57Eztt6QhHFBfb1SIqPjacUr1Af/oXYhLSS+2L2L66l/3PPpvkgw+h9/aKlnZJInHffbja29nQOJUpK5ax+M1OFgMbWYH3BZE4HQ4QRrNJt/wcvaeHjZ8te+S5+S08N2VPlnS+5XCMkteL1tVF/p13COy1l+NZ6l6VQqSM5177ZlQikSFCdnJI0GaxU0+huG4tA3fdzeCf/uQsVC2HNRLaJ8k1TwmqY/Ktt7LrUzkO2fssrnrj/9gWaOCQ117AZfcyuCt6PAY+fRJ/8wxw4Uur6LpXUCbNl1yMpCjETj656juXekR8Cxcie734dtuNwrvvOlUWxKbQ3r2RhKIBJsmchnfebIIHH0z2xRcx02myL73E2kOXENhvP5RolMXL3+ftfabxbptF7iXhLPgWLcLd0sKkm3/K1m+K36f9h/9b9dCNZHKLyEt9988mGe+9FAo5tq9YQf+dd9L89a/RdXkZ4Hy770bhneoKqJjtBfrbXcjBasnp6Ckn42pqpPOrQoSx5etfB0Dr7gFdw91aLtltvuwyBu75Q9X+2ubNhI44gvYbrkeSZVwNDUy9+y7y772He/JklFCQd7YkmRTzUzSzhAwDV2Oc8OGHDfmeSjTKjMceZeNpn0XdtInEgw+hbR1aiqqEPNx2tMbZj0kMfOtHLEBU0819/TUnJ+OdN5fuih6Kb7wgIvZ/7+cnWMgweYOFe/JkAnvuSXHDRgDip39++B+AoX0Bm441mfYP+153uZi/4j2K69ax+dyvMP3P9+Jub6fjxht5c9MA0aJJo1tEQ2tm7c7sdWX6Tg4EaP/hDwkffVTV8b0zZ9L41a86YADVNKckSURPOYX0s8/inXII9/9wFZAiuvRUYqecgm/+/GHzU00XX8zAPX+g9bc/o+TreztEleO8lR+Q/sc/CB95pLN97LOnYSRThA4pDxEK7LMPc158kewrr6A0NoK/AV+8nLPp3f8wFp51JlT00uyMfaQRgiRJCvBr4FhgAXCGJEnD12aWzDBo+Mq5NJz75aqXl7UtYPfpBqu8wiPX8wrGQALfokVM+78/jkkHRI47zkm6Aky+4DBa9yzXvG+fvycrupK4Ghvp+OlPANh6+RWsO+posf2vfslTn/8GX//cD1G95ctWXCla8F2N45hSVmHu1lb8u+5K22KT/BdEWVsokOc3i05EniJyDM1fu4xdnhFd1pvOPIvNdgVJ5qDDQckTkmTiu/sIHXaYs9jJ0ShGKkVh5Uo6LxJevlyRR2mqUISNnrYUV0c7qVUZjn3DHu5y0KGsmCbA94XJn+LU42/gy0dfzfaKskr/HiK6abroQvrnfIqUv8x/upqbRy6/jUaZcuedTPuDKI2devfdNH/jG+WHItJBRzaBYen2IHHB/07++c+cY0ROEDIDuWXLSD/1FNGerXz37y9z268NjKyLhjNOw7/rruJ6Hnkk0dOWMvPxx4aNHocz9yRBVygWRAs53msUWvRaZ2cVGAAOGMx46EG+dc5Pee6AReS84Gr30nqUoCHmr1rJjEceZpfnn0OSZUKHHopv0SKHjgARxbonTao6thwMMu+1f3HjpQYXX1IuX204++wh38W/667Oa29vGmTPaXEyalrIVrhHfi58c+YQOGB/cq+8yrbvfIe+W29D8vmY+85yZj7xd1qv+S6zf/w5tk/XOfPCuXTPKc+4qFwAoyeUpR+a7yvnMt7uUHnzOJUZVxzGLs88jeTx4Js7h3kr3iNq9+iMZHPffIOOG3/E3OVvI832ceYVCsunz2X6X4RD5J01i9nP/dtxToq6wdLbXuHYW0QUIXs83HXcpdx+ajkfMOfVV4h8+uhhHYPG885zaKbG874y5P3ICcdjplLkHxPJ7g9mtNJ+3XUE9t4bORgclgpTIhEix5WFJfvjIEcFtSxJEpHjjqvar/3665n8i1uGHAcgeMAB+ObMAW+YyRW9Uu8dNTKwTsQ+aspoX2CtZVnrLctSgb8Ao94JkstFwxe+QOuVVzJvxXvM++B9zj7rlzw7dW/OOzzOqsmQuGQRgTYRNnmmjk/V0hWPM+uZp5ly+29ovPACQseeSnx2lrdPy/Hr42V6Fh/G2p4sG/uyBPfdl+CBB1B4T4SFsTNOx79wIZmCjhWPc/U1k/ntpSotnxdUR+N5X6niBWux+OTthBQxyOet5F9JBXVyd/2FXf79L5ouvBBXc7PDt2dfeglXSwtbzzgfSc4TNS3ajp/GlNtudY7nbm2luHo1G045lfwbb+JbsKCqukkJh3HZ1TQdN9yAf+EiCtuKLP0XZHwyPed9g5fXCeGshqCHvD3joXOgXL3jmzuXucvfpvmyy8hqBq/MipG1C4Gav/nNUb9vaPFBDkApoSBNXz2/nN+YtBdtNjUmuRMM2o03cjDI1D/cw+z/vMikn9zETLt8ctItt/Dup5YAZdqt6Wvl5kDZ46Hjhhvwzh7/bGVXYyOe3cV3/ufsmXx78QXcddFP8cyahdLYyJTf/pb5q1Yy9Xd3A4Kz9i1YQA9enjxqNudd7mXW54O4msrdwb5583C3lv8/44G/0nL56NcJQPIEcCsmpstA+e7/4Jk1C39F8cKOVtQNupIF5rSE6M310GQY4B5dlqHSG516zz3MfORhZK8X78yZNJx5JpI/TtDuFB5pKIsSjdJ02aVM/+tfKU6byRWLL2LrWReQsLKEDXWIg1CZzxrJ5GCQ6EknIft8xFx+NLfET444Bv+ihcNuX2oi7EkXnY77TEFj9ewId50A0751wqj5CyUUpO2732H+qpW02OXSlRY66CB8i8S1v/LLCvecMG/MqjWA1u9+h3cXHMjtx7bx4GkgTWQmQ6VJEnOUILoMqgLvFmscCTqGfdSU0SSgcjbjFmC/EbYdYqUbJ6/qfGXxDCRXJ0gSTa1e2pZ4SQTPofnSS8d9MrLHQ+jQQwVva+gggRLXeX62zNKoRV+myJKfPMeq64+h7brryDz3PP699sRvN5VlijpBr8zGXA9H6hqNS4+i8X/uGONTx/yWNNg9BWvSy/FP7qMrcTh77FamZRq++EXMQpHgfvvi2203Xl22GUnJEzX1IeJpTRdfRHHdOnLLlhE+9hgm/+xn7GgzH3sc7M+MHH886adFFHLdFxr5L1eAX/5LeL4n7t7B71/eCAhZjQNmlaOgUlifUw2QDb59GTwz82w4rJomqslmHUGHXhLbGyRRoSVUCWremTOYb4ujPbwxxCtTtrFkxVo+s3c3SrS2SG04az95Hgcd/R65/vmYAzKFpjZm/b26YTB4wAHMffMNp7ItXdBokpM0BVqQezdD+8gc+bjNHcBnWpiKjnrYp1l01udG3bw05jIedLF9azcduj4mIDSceSbBAw5E9vuqqEDHgs2ETBOXKz+qtEmzHY2mt6d4v2kmieNnY717p+h0nsiAngqLukNAlqw+ckXU9gqZkXc6Exw8u5l0QcfvKrJiHgQWjN8pGMlmPPBXrPUvsPmFi5iWGb1TuS9T5N5lmzn/kJn8YLfPEZ71K/bM6BMb47mDxXwN/PdVCsvNKNG+2oUxR7OPPIcwlkmSdD5wPsDUqUMHaOiGSVY1CPuEiiRAXM3jbYvResGVQ7YftykuWHAygc0iS2941gMinEvkNNqmTqXhnLOrdkkXdQL+Irql02IYEKhR2ng4W3on8QfLQ3UkJUtXYqhUQdNXywnFrD04PKSrQwDB1djItHt+LzpzleE7GZVQmUKKHPNpQs8/ytceOJZBl17V/HPp4buwrjfDi2v62DyCzlK2qOOWVTyW5bTkT9jcPjrssr5AIDWu1vx00WDrwjBbdg9zcldu3FIRo1kgOhlX4T1wie+8PTl853op0jFMi6xqoFkJmv3NkH1z/DNzRzPFjc8CUzbIj2NkY2nMpeXqx7BM2scBCCAAdkQLNRM0TWRl7E5hKM+VVlzimoVNU8x42AmLucNgdGNIGQqagW+YDt1K3ak3Nw1y8OxmMkWdNqkghO08wSH7TMQkTwCfZWFYo+tc3fd6Jzc/vZqtg3lSBZ0GZSemtu1ogQbajQRvydkJ6TuNZh81ZbQVqOR0JtuvOWZZ1h2WZe1tWdbezc1DH6LSDRbxuxgsivxBtDh0lvKE7KRfUxrU9WR3uRRzJN31TEHD4xHVNc26Iaal7azFpqIA4ZKKoRli6zCAUGk5eyxgQCuMKK8seTxVtc2jmRxtJYiFqWj02LmCcxfPoDHk5Y/n7sekmH9YkAIRNXkUTQDCOBafsSwQtGdEhN4dlwZ/uqCDnCdsSXXxvgAItxMzDVyK8Ei3jQAIJSvpYBWsQVo2vy5mStcDEACfpGBIw3ekvrC6t+p3KV2vhCEaORcW1Z1/ToItYu6HrLJqHOqapZkakiLOK7Iz8tu2RXxx+5i5EfW2SqDdGPSwalsaw7RE9CoVCJj1uTcBcPlsQBi9R6UEWs+uEpI6llTEV9EtvVPmbyCqqWhWhmS+vhLYHzUgvA7MliRphiRJHuB04LFaDlAKUyM+N4ligrA7jLuQqg8geEMcOXkJALPju7BokvixcsM8fAXNsMd32oBgGBMbybej2aMe/9W5laMnH4riSY+4+JYsr+oosioEu2odnj6cecL2cHnNmTkxsznIw2se5on1TxANZ0aUgk7mNTyKjseiPg9dWHDthnvL+CKEgoYp5QmZRk3KoqNaqJW4YeJxiaKD7tTo07pKgJAzBmjW7Os099i6nIoXBV02h40Qzrn7NZbc9Jzz/xJlNKh1IiOxi6qVtbsmasFmQpaJLmms7R17clvJgbMkcQ/v1DwG2/z+BuFw2Iqnz33Yw/Rv/52eiqhge7KA362w17Q4q3vSzm9iUBTPSZ0ihBIgmKhoxshd3hn7OpSaG3WziM+sEyAEGmhS85joFK0M68bxu4zXPlJAsCxLBy4B/gmsBO63LGtkoSDbXl3fz0trRWKzHCG4GSgMEPOEoOcDKOnN76R5g02ETIv3+t7lsL2Efspw4/HmXfMkfRkVbK+xyTDrsxhH2mGPs/FZFjO8TZhSkq3J0X/gbFHDlHUh6TvtoJ0/B1kmLLtRZYN3twhaLiW9z7UvX8u3XvwWnaHvMJAd3kseyKq4FN2mjOoACKFWzrI/aiA3Oj9qWRaZoo5BnohWGDIlbWfOIW4YKEqamU1BdNMatWs5XdBAUimYWUEl7n3uqIqrtZhPdqFL1pAIoWjnWtSKRalEGW3Lr2eqtwEPQKCJnbJAA1HTwpQs0mpmzDGWKft5LVoimoh7ouDaucSn5I8RMwxkl6BIfvfSRgCueXQFIO6DO/+zgYJusLAjyoa+rBMxGBSFsN0o1VY1mdsnFnZZcxb94SxTrHZmCmZRUFf1oIyiU2jLCrZEdiVHpDQnYh95Y5plWU9YljXHsqxZlmX9YKztc6rO6Xe8ypd/LyQISiFi2OcSk6hK9//8z9TnBP0NXN8vFsG3Bp63z6H64auUxDZkcaM31itCANhNlI61yV6QLLrSPaNuXp4CVb8bPSR7MSVAEtf71pXfqnq/Lz/8Ob3flcLrNkQ47N7JHAJAqJVpeQG6g4XRKYqibqIZFpqVJVTI7jRXXT6HFmKmSVPU4MwlBqCPmEMB4Q1KLnHOzYYBbSNXAtVqPsmFLltkd1hgKocZFezoYTCnIkuwOrGShR47MtjZqElWyoOHxsFZlyijQVVIXXT4dhKQwJHhdstpknmNKQ22UKJd9fTWZvH8uqLLCMc6sSz4+n1iJoZuFYdMS9spc/nFvS7ppAs6P39mNY8uHzonvHoKo4lqajZlVIc1IzbVkSWXlAJn3rls549p28euUzngcbH7lBhelzi1SsposDhIzAIkBQ68rE4f2MCRmTRLJi0mWRTNYNkdcghp58e16MptJCi5hHqipw4RAjh8c+tm0Umb0npHVTFMF8Xi5DMtMUSkDha2E8KlsYc7Wne+a8jAGM0wGciqyLIdIdTjoQu3EdGEN54qjj4cpzS3V7XyRHXVGTyy0xZsosEw2KoO8vP3v46n6V+j5hHSBR3ZJcCr2TDAUydHAfAr9jS9HfT3KwFhY7+IpPqzKrFQgZ5cDwtd9jnUgaJo8Ah6VnJlqiq/hrN0QcclS3TnumhAJlCPBTDUQswwcbsEIJSkxkv1A9uSecDE1/4wP3tflB2vtPMdqqXZlFEdIwTLxJJ1UgWNnz+zhq/9ZehAplK0NqXBzyOXiAo5f70oo9hUQcUByPWLDuBjCAgA+06PO6FwyQsIemVWDayiqXeNWHiUOhVI2T/QJN2iM7MB2dNDbocp8uzQAAAgAElEQVTZraXZpZ7Gf7PN+A8KdgJTrpMeuQ0IbetEhCK5knQlR84jJIulsYCWGNhdj1OwvcArj53GIfPE33u1lpt6JFeS5z6sjhJKnDWS7f3sbJURQKjN8X7yZpb/rBl5mHimUBo8YomIrV6A4IsRrdBMUvyd9I9GGRUrIgTdqA+VaFtAEQ0eiXw1jViZXD3m56IZazCrEooIp2YebgFMdXhOmmzFU9mVdnpDRrJ0QSPsc7E9u502U66P0xTpIGqaKIqIUNb3ivu/VAAhhlaVn5fj9ih/55xVIFRPysiOEExJr3KQXlzTW8UkdCXzLJnbzIv/fThTGsX5+C2rPpRRfBoRm7q76vhpO3+8CvtYAkIs4KGgmWSLupPMTBn2aELDAHV0hcaazF7UO1b+HROT4KybSRWrUbf0EMyfJaaZpSwNgnUIhUtmVwqVm7KSoyaWk4UKQKhbhCDC8MVzQpxykAjBz5hyFA8vEdO3oiGV5z7srdqn5KmZ6HWrMiI6ibgzNzbLWXctG3GYeLqCqmkwjPpRRm4/Eavi0bDcPL+6d8TNBWVkS3AbRv0iR8CnCP59sJBje7LAmXe+ypruofOvDdOiP6vi94t7o03X61N4ATS7hZcvudLjihDCPjfduW7a6pVnCzQSM01MpUBfpugkUUvn0pdRkVxlwExKtpS0pFK0dHE/1QsQZBmfJWHKRlU14Nl3vcY37itHCl2JAh0x8TyUhvT4LKs+0WNkEiHJlrP//0OEMLVB/Hgb+rL86B+iVby/KBbjJbk8zD66fh+2QDRSdbTs7rw0WKimKkrlfK0BUS1xMy11KysEHMGvkGURMC1kd4KtgyMDQloV9EGgjp5PyCUijYyWIWWPSzzwgYuZ+dDFyJJMR4PJf9b0Vc2eLgGCTilCGHluwbitZQHx0vAf+yH/z9rhowQBCGIBbDB2vprFMUlyABJAUdQRQUmch4bsTuGRXCK6qVduCfArIupKFrM88GYnL63t59HlXSR2KDccyKr0Z4r0uYUqZkshB/7YkONNxOLuMLIFkpIeRw5BJ+xz0Z3tplXX67MA+mJEDBNdUXm/KwnubiLTf0+ykMWyLAayRSLB8sKYtkTZbWkoTdww6kcZAT5kDMlgyw7PaGnofV41GMiqTNoREEyzPuchSYRmipkPxRrnbY9lH0tA6IiJh6A0wlBS0jy9SXTTTtZ0OOzqEfet2ewQrmlzOTGTKlRf5JInYlh55jXM46hsfuerN4YxCWgzQR4jQsjYgOCX3XWjrUL2g5tRM6TVNBISQctCTm4m4okQD+ukizrn3vO6s0+Ju9bR8Zh1ooz8MSdC2G8XcbxV24dPLmeKGpIiQKPRMOoHCEC4gooLBfP0jwIImaLIITS7gkhQv/JXwG8DU7qYd7px0wXNkWq44WSRwO7PFulOFTHI4pJceAc3OvNCdtYUb5i4ZSG5MhT1kUstS+cW9EFaS9OgqfWJEPwxYqaBKVk8t7oLb9sjWP5VEHmVtT0Z+jMqoYC4Nu3BdjKmcB5L0WPcNOtTAWdbQJJRZZN1PeLeO2qBKJUO+0S+Z9OAeC7ao+L+LQFCoF60KuD2xwmZFgPFkSPXidjHEhAag8LT7M8IlG2fey+PrXsMt6SIH7deDUiOSexeVDnRKzjoHachDWbtKVdWjoArANne+lJGAKf/GVoX0aoV8fh62JoYPhQsaAZFU7znV+p3kzuAoGVY3rMcC8u5OWLpXkIBsSC+WMHpb+zLEvK6UE1DzFOu083utyyCpsmiqRKxgJtf/3sdq7uH0oSpgu5QBQ2yb6fLGyst7C57tiqJ0XMIBR2XJ0OzbGvbjNAsOBHz2dc0XcywPSnO4Z5XNvHYO114FJlZzWLB3dSfI1PUcclels4+FfrXQdOc+pyEJ0iTYSK7Uk5F00iWLuj4veI8Y1qhPvX/bj8xm8KTlByYYn1wR5Zz1M9eoHMwR9AGhD1b9yShdQEmikdIVU+2FCcKr4dFJRdFxWCl7ahcevguLN1zshPVb+wTDlvpt3HmOsueunTSi4PFmKfprBlcU9fZyh9LQIgHxIN9y7Or2ZrIOyFga8kDqxM36tjX3kECji2KRE2JkilZqZyvaOQJuoOQ668/IMw7DvY5F59pYrp72ZIcfoh3Kq8hSfYUqDp5gACh0rxZNcOy7dVlbDHTRDUS7DNdLHTfeuBdpn/779zzyiamNfkpWlpdAYH5n6FZN+jJbHea0+5dNnSKXbqgIykZXEiE68UR26ZUOB2alaVgJYbtTymdh+wqENUNCHfUlTIK2JFKVsvTny2D0vLOBNG2l9mmCgHGD7pSgIFqZohbgKlB89xhjjgB8wRp1jVkTy8FbfQIIZXX8HrF4hwz9LrlU6Ku0mSxHO0RcR+aulgHVm1LE/DlkSWZ3Zp2Q7OKSK40ircbBYnpUh2ozAqLSC4MCbqSAhCifjeTYj660wU0w3S6h5vC4nNzmh3RK3U8D1+MFk2lN9+L3/P/cUC44fWr8TY94yhsNnhESPY/DbYuXr0jhPg0WHgK/oyooikY1XTNQLZIaOo9fDDwAUHZDZZR3xxCySKTONie1jSgjTDGM6+VxwL66sMRAwRLgGCXes4plimSmGGQyPc5v8d9b5T1CjOFHBYQtMz65BAAZi6hwTQYzPdy6h4iUTwcVZEuaEiuLHHJhVSvTlTb5gRamaPDebsKmWrJlRixOS1T1JDkIgG9WDeapmRlQMjZpYzl61CIPML3XhPiju93JZGUPBYWcbtPhaY6AUKolUmahuzKOA1xI1mqoPNKUVC6kzS9buAYtWViJCXHtBZbBdcenaqbFmu1RzAtk+khcb94Gl8ATx+TZC+eOjsLUdljn4u4zrGAh0lxP5YF/3y/5MRYbMy8j27qZckdpU4OE4AvSrNh0Jfrxeeu3zL+sQMEzdB4cuOTeJqfAWDf6Q3IisHS2UvZV/ILLrCO1IBj4Xb8ORFiFncAhP5cFisg1DWDpUv2EeQQiHSwX14sOmlj+7CbJHJaeXB4HatZFE+YsGHSmxX86+GmRzTMnfEXYqbJYGGQI+a3DNlv3iTxWwTrqRfjixEzTBKFBDeeJjT4n3hv25DNMgUdt0slbNWpvLHCAv5GHuzs5NNvPwKA7B4ZELJFA+QCIbVQv9LX0nnYVF5eS9NX3ERo3ndQ/OtBKkcrLllECKXZ0g15O+fStPMKnwD4RWMYsjpqf4xhWnZDloi0F6lq/SKEoHAKJSXHptQm+++s/W+5wmjmM2K+sKfhJaY2IaimOgNCRPY65xKY/ktuWf5Dp6LoknvfZjCn4Q2v44Jnv8Q979/DptQmXEi01ZHixR+jWTcomipBX/0E7j52gOBW3Bw7/VgUXXjg7TEfaTVNxBOBYqo+dbzDWaiVgF3fvyMgDOTL/HWgVJ9eb8oIIDJJJEeBgjF8U1YipyLJ4gbwueu4CHoCTNZ1PhgQVV3xfEokaRtmMk3T6VGTfOPoKVx93Dxnl4NnN3H+EuGRhSwL5Dr1hviixEyTpJrErYhbdDjuOl3QxahIqJ9WTcn8ccBi8pa3ANGL0Jseofy1qGNSIFTM1B8Q7AjBNLNo7nVIksWn5m9w5kgDxCN5upIFp+Iqlu4V1FW9nhVvWAjESRY5beQyRyHlIH6ni2adJujdOvVkxEKiYOA7J06iN28nUm0PXfaIyP6X23tp2/QqfsXH5NBkGsKGED2sY4URQNimfiQ5j+Lfyl9X/5X2aHmx7xzIEfALmmj14Go2pzYzGTeuegKTLyaaIIFYeHTts1rs/3X56+Es5AlhyeJLtscUit1Fwp4wFJIfQULZtnA7IUss9qpVfYEH82mw84XGB6Ksr27eV6X54wRkDy5LomANr2eUyAvdHMmy8HnqeC3cQZoNgxcSawBo0FUIt4E/zhxVLISbM+s5d/EeNIe9HLOwHb9H4b1ewWGHJFf9Ema+GFHDJKmJssITdmtnxdahAJkuaiiKKkC67oAg6Lig3WzkaXzRqXobch6FAlZII2io9euFsC1g3+9uOU/JDzQtq6pDNRwepDfhJ+AT9218+wcwac/6nYQ3QtB2hLJaueDi/a4kAY+LGU3i2qcKGsjiGoUlm9euW4TQAknYkFoLQJMrwAAFwHKqiSbbfTxLCPCBrJBSU0ytp2yFbRGXH3S48PAO7hEjyIkFyk1pf39vG+1TNEpP8Ob0ZqZaSn3Pw18GhExsZ2eylO1jFyEAhD1hTEnc8E0Ry3mNwkcYIUQ6BO0BGEZ1mWOyUF6cE7bHWu8HHwBJQop0ELYUTHl4vjZpU0Y+y0KqY0csbj+JikqMZt0ANQPeCHNVsRQ9tOYhFFnilD0mO4msjCauTUAae3rUuM0XJWoaqJbODa/eQGPQM2zZp5C+FsPkP5oIQdgMIRPHQGb4CCGjimsQMk1HrbVe5vaEcVliYpnktrnogFIlMZIOPghAOCjOryG5pc6AEHaAMVcBCMf/4j8sve1l5/+pgubw6uHS0lInQPD4G/CYFg+vfRiAOal+TAxaohKyu6L0GAhoebJaVjALplF/QFDE8Tqayg7CYQ8eiOwt07z9OdHcaVkWnelOpprUj1IFkVS214ctmaGzsCdqH1tAQNJB0oiHzPJrhSTUMZFaZZP3wWdZKJaFZVYDQlotA4K/pPZYL294R4t00GBoSEpuWDXFwZyKWynUV58FwBMQJb22tRg6zD0e3D5a7cX+0XWPDtmt5DGG5TrmdfwihwBw/+r7cXsTpAt6VVMclAChQNDQ6w8IFffZsboAv4F8YthNS9cgYFoQqi8g4PbhNy1kuYDsFp//5sA/+eaJ5cqSgiS6+AP+UnWPCW27Dz3WRM0XJWL/HjlDeOMlmYaBCqBO5XWHygrZelR168nwRVHl8jO30I5aI7FOQoECLklx5EZChu7000T0+jalAUTsvE5Xplpx+dyjylx+wC/+XpdcR17PM0036wsI/hhTdJ3jInUqHLDtYwkIca89EMOVpilSAQhdb9X3olaa24d08m/wWBZWxfCL7lQB1Z6OdIirgW8ODsIX/zbSUXbeNr1EXC/iUYbKE4CQ0fC7VQFM9UykugN8r69c6iomwtmKmb4Yn7d7NDpTnVW7lSKEoFJHQPBFBeDZlkAopg/sECWkCxqWVCCoFcFfv2aw0jmUbGZGLMS9haGS64ZpkTcEXxwyTQgOTbzvlLkDBCwTRS46nbcAt7+34zB2A48nT1jxCnazXlLgAN4wMbt7vDTGcrj5DCJCsCellZRqo+ObeT6mVQD0Yncjc+yotTvwa1riBeJuu9yjY0+CuQQFo4BhGUR0tW56XyUL2QnurTt45llrM49fspio383iOQKEVg+uBqBZV+sbqfhiyMAlkQVjblqLfSwBYXJY3Mx3fGkmbrfNSZZom1V//+g+2O0XgGCWw/FTb30Zyc5n/LfSSqRxLsw4+KM7h33PJ2SauOXcDhK6wgazKl5Xsa7S1wC4/TQbJv+34CKu+H/aO/Mwuaoycb+n9rXXdEhIBxLClkBCBwKIAgaEHxAhyLAERsFIFDODowOICsMMyIigIy4oTgQcEQcTlhEGF4gBYWBcguCEENYkEMxClu70Vl173fP745xbdau70km6762qTu77PP30rXtr+erWvec733K+r+V4gpLSoBhq5BhDWQnzHptX9jJzdhyzM4PCF2SaUbo0l2/7AcLbP6STXH8mR54U0XzG1tXBQFm65AFJ5arpzuwc8rT+tEo5BZ1pFbM5HdkfJmJIhCddvA6tFNNivWmkJ0GT6bqz07UajKt1JkCmoO4Ns+iklUTaYiFkBtQgbldGoEVB3y7HFS0WgNamXprMlM5JxxLJlyYO8XzWduvRG1f9Mt7tWV+2/+3ut5nZ3sgrN/8/PL7y3+qYVNpeS8UXUJOFzPC9nfeWulQITUE1GxDeFH05NdOIF/QFeO7QpvG24Y+oIm0WCyGTL5T8oj2bbV10VJFjLiMi1QBQqal5dzKL15tW5W/tvNC1cjkm1MYnQ+3gDZb6G4QaOVWLMqVhStnLTP+5rRYCcIQ3xh+aTi4+DrQ+P0QhJDIpQK1qtl0htB4KR18Ec28sVl8dyA0N9HcmMsVAakx47Xdp+sI0GAbSmy7m3Zt4hIeDG3S1S0+KgkjQnNBWno2rpfGFCKJcVNmCGmz7K1ybA9k8mBZCJjH6bm1WLOe1cWBH8TcBeK//PZqEvv4mHacsNU1D1uaBGCDUyPh8gfV9G4q7fMLHtuS24mOzHlhR5mzK9lgGoUai2crJJyOlLhVCXK9K7M/206sLzTXk9QU43l4TqQx/WLWCpDTDiAZ9HDVJ3QzxrWucVwjxieqC9mR5ZWN5Zk1vKsfKd3ciPWnitruM9Aw/l9SxGstq8HATjaleZo2bhd/rJ50vWVADuQGCUuC3+2IPNxFPJ1h09CIApPSUFfzLFwxSBXUzNBiG/S4jXwAu+jHMupiYjhsl80MLiXUmssVZcTTUbH9syR+mtVCg4EshvGk+1dPHx3zKLdXiCdK0VbnTPnZcM9FwuhQHstO1KgQhQ91/WUP3qrAoBDOekMiULIR4qs9eJR2M8Q87e7hk3HGI3s1MzZU+vzfTS7Ou/smBs2myKoR8xv57NtigXKqaX8z/BYuPWUxPpqeoMLvT3WUv8WcH7Hd3R8YRHOjGK/bllcq5FPGHPwkoLbvsrWUAjDMX29g9E7Tij+CXEonlYkvlCHiSBAypfLM2L4AaQmw8UQkFb54/rC+v8vngSrUgJ1kYUIOgnTMfc0DPpaBzHcQsvvCGSdC7iYZgA2u713L989cXD3WmOmnFAzZbCIQaId3LF45VDU88gc6yxWkJSw+C1oIDFoJJ40HFGWeqokLIILzaZRSzr7heEX+E1kKBfHAAKVTfh8m6YU1Dqo/4C98F4OLjx9GX7VaZNh/8B9vFCGilmNGTAWu/a7OcxUAmj0dbCNFUj70WQiDGVb19/HPL8TCwg6jHz7LNpeuhbYtKf6b1sLJeFo2GYX+gP9SoKrman+2P0xZRrsIdqR3kCjne6X2Hy2dcDsD0liNVdQO7FUKsDZHcQcTGyVj9KQR/mGghhwelENb1qLxjz6/+UR23K0i1i88OSAlaIUgp6Uvl8HqSqjQDOLcOwsTjJeqLkBeSTT3l5qAZZPb688QLzriMyPTBzndgwszSseaDIbWTtTtVgOy5jc8VD21PbWecxL46RiahJkj3IPSM2xPo4tXNvcUFav3F5ji6vLGdLhIrXh9hKfFISb4wtMBeZ3+muCYgZmcg18QfpqVgkNMLvhoMo7hafmI+X3Sd9GZ72ZneqftC2C9H6NiFAKQLSvlZFYLZYXAgUyAQyBL2hfEnu+1VCOYsf/3v1P8JM8sGfjPlFK+P5mlnFvePyxccUAjlFkLsPy9iXFgtVN06sJX7X7sfgM39m1l9xWqWnr5EPdF2l1ETpHtVfTWbqD+FAHii44lJwUtbVUvJk1IW37EdfXt3RaSFABKPyBYbuBsSBCmVUgiqRrXDRPWF05suH4A27kwihEGajIoh2Jk94Q+pG2fnBl28zxIcbVJ+6vE63S5sKSXcmexkvIF9dYxMtIUAcGr7qURCatAxrYT+dL4YzI0Y0n6XkQVx2FlEDUnUGFo+ozORxaMthEjQgZRof7jYHwKUQogU1GB8UipdVAibE5vJy4LqC2HnQKwJ6mvSdIlY28yaHQYTmTw+X4aYPwapnfYqadMyX6dK2tA2nUZLYHlGNgsnXwPAuEDpd2gxjPLJjR0EG4tp0QC+ra/SsPFlABY+tbBoLXwqdBCikMOrz5ntFkIgAtkkUd8+rhAINRKXkr9sV2UD/hjWJ/Kq/3H2c4NxZSGIPJm8QZ+ekRsiWQpUJYdmmthN1GvWwFcrdbf1pXn+7R38avX7SJHFQGqXkc259/GJ0L0BCpnyQUUrhO8csoDpLdNJ5VPFCo7bU9tpK9hY2M4k3AQ9G6F7AwfFDwKPctdc+/ArgA5q6ppOEemgywjg1OuJSANJqqxNIiiXUTiYJ2oYeJyIL/nDxf4QoFwg8wpBvnLsNVzSnygqhHd7VUXglkIBIvZbSz5/BI+U5GQGw5CqfpOuWVSyEHQpEX9UxaLsDLAPLl89froql6I5PJuFQ88AoNHyuZ5Qo+3lRAg1FBfqnZtQ12XD775WPLyuW3k1Jj3zNXjmq+pcgP0WQiAG2YF930IgGC9LK1uWjsHhZ8OBHc5+rjeIXwKiQLZg0KvN4oJMlVxG+V3XxbcL00IoiDTJbIF533uBK/7jRQCOmKiCZ3G7YwigZnRd6mIuG2CblUI4YO0zLD5mMQDXPnctyVyS/my/WkLvhIUgC/C9Y2gLtpAxUsVsHjADmNp37wk6tz5FyxIxJB6RGZJ/35nIEPJnVKaTE/ElX6g8SFowCK5dzse9rYSlJCQlAUPyWqcKLk/K5x1xnwl/mKCU+ESGZK5AKpsndvithA5cRtJUCNkCHm+mmBTiWFWBI8+FSceWGetNBaNUPSAQY2FPHzcFDnbGcgw28NHEAN9omsPXdqisLut4tWVArVeJGRJ6NzmnEHxByPRyWNg+l1h9KoRAjAbtozt2/LEclU45n90D4PURkGCIApmcUcykyMlMyWXUbG9T60oUNb4nw8buZFnZhtlT1MDbYLfLCNRAktDL760Wgrn96iPMaFVZXr/f8nt+vObHALTlcipN1W5ZNG09agGQ2bc4XzC0y0idl2jIofiBSaiRiDQQnsyQ1eOdiSwhf1qtQbCzlIiJEDRa1ngU/eaPLS7uazAM3u1TFkJboeDMIOhXzeW9Qp2DgaxKx/Y3rtLWgrIQ8KSKxd8ci7dd+OMh1ocfSpZAIMp13T0sSOacsRz9IbzeIPNyHsz8Hms8Y8V7K/DhUW1lvQGVqAH2KwRdav2W539s21vWp0LwR4gXLHnOmf7qKATAh0AKg6wedABSRlLNyE++Fs76uuMyRPVMU3hTnP3dF8rl8+u0Pk8AvDbXJhywtOOzKgQh4NgrADjgtdIq7XtWq6Jabbms/UFlS9bS+F4141p4ihr4E5m8dhll8AIBpwLKJtpCkJ4s/ZnBCiGDz5vS9ZScyUBrsXRvazaDmbnSgqRGo4ChLdgWpzKufCGCUuL1ZElk8iQyJWstaXEZSU+KWLfu5eGUheAPDa02HGosWammYu75q3OxpVAD9G0uiTTocB5DWTDCU4yF2T6GTf2wve9HvSqEQLTYBmRTYpOzVU4H4ceDIQwyuUIxo6W3kFR+3OMXOeua0BwSnoBHSnyRd4YcO/kIZRU02Ngjtog1JjH4RjruUwCI31zH8guXlx1qy2Xs71HRPqf0/q/+AoC8UDdWfzqv2md6MkSkQDgZPwDwBQlLMDz5MgtBSqnSTj1p5VK0O6ajabJUtR1yludcyTjtrogIH2E8ztwr2mXk9WQYyOTpy5YSHpLZUlDZIEVsp0qPJjbBXhmOWwjT5+v3Hg/Hf4bHN23h4X5P+fVqKub+LY4E2AGlgCwKAeDuQqXrUMKATh+3u2S+A1lt9akQIi1kdLrhF2Z+VrUDdPqm1/iFh4KQZAuG6tLlSTEgs8oUt9vk2wUN4RamZ7Mcc8jQvPdUXi/G8jkwG/3wl0rbg2+kA2er/8EGDsTHgiMWFA+Nz6ZtbWJe/Lwb34eWacWqjjlUTaG+dI7eVA6fL0vEWmLDKYQgjIeCp1BWTmQgWyCdMzCE6TJyxoot64x34mKYfXnpccMkFUgGWoRfudqcKLzoCxI0JB6RYyCTJ2FRCGZr0XSuQJ6UsqbB3oqrAOd9Dxb8rPT46AuZlsszvfO98vHBaqk50bcElNLtK69tdWr3Nv730v8FYEFId87LDkBSKwS7lZM/DFc9Z+tb1qdCaGwvKoQ23Z3IybRCKz682kIwSOcKhCf/FIDZ6Yzzi9JMAjFaC8aQJjkfPrytuCS+wYnBZ9Jxpcbs4UEZIkLA5BPVOoX7P1osLwLaf+qE/zwQgfl3EZWSsPBR8Kjzsak7RU8yi9+fUzPzKliPEeEl7ynw8ftW8ucNKtOss1+5TXIyrYLKTk0YQo0syHq5bUeX6tRnjWM1T2GcVgjt+Ib+bnbhUzEEIXLKZZQvlWYY0BZCJpfFIKsUwllfd64isElxIjAo7dhqqTmlEEINYAyq5zTQSWOggVc/+So3BfR6qUy/Uhy+sDMTF5vL8I9KIQghLhZCvCaEMIQQcwYdu0EIsU4I8ZYQ4qy9euNQE/N1Ole7R/umq2whZPLKQvBFNgAwK2c407qzEsE4UcOgIJOccpi6oH/yqeP5ycLj6cv24ZEQtbM5jpVPPanSez0VlsNn9EK5zreRlFL+BDjmLuGgDyI8flIyz1MbHwbgra39ykLw5ogUClVR1GHhJyvUzPdff/U6QLHpfUZmlIVgd9ZX8cObuGnrZnVPhBqHpASbCqHVkM6Vh/erGILw5BjI5otVT8FiIUhd6NCJqq+VsMYodmUhONHqFsonIWfcAh/+ivJkmLEds9prNgG9G5USd0JB2uzCHm1Ucg3wN8CPrDuFEDOAS4GjgAOBp4UQh0sph+/QbRKMc35igA+fdhstpohVshACwkdeZMnmlYVQSB7MSQ39+AN9u3+xXWiFMJAb4NeLTiw71JfpI47Or3aC6Lhdz6pM01d4ufjwi3li/RPcPOPT8O5Vzg3KHk/ZjT+5Jchb2/rpSebw+DNEs/Y1ch+OqDdA1qM6dHX2Z9i4M8nW3gyQJynTavGY3VlfJvEJYC5uCjWUp/hGWjhAu9Ra83lHLQS1RidHIlMglS9ZrwOZAgVDktedBuPScE4OK9ZB2To+BKvgMrLefx2fgDd1skWqR02OzEBydgAS28sXetqJzVbpqCwEKeUbUsq3Khw6H1gmpcxIKd8F1gEn7PEbB1W/pRbDgB4doKqaheAlL1SV00yugPAmaRL2N3EfFt2haiA/tNxxX7ZPFbZzKp9Au+gAACAASURBVINjOMxZX6iRCdEJrLhoBSc3Hqr2OXl+DpzN1QmVdXbwAbmiheDxZFT7TCfcVYOIeoNIAYg8W3rTnPLNZ7nm4VUIn7KaVIzJoYSDuKVGUqixNAgc87cQbOCsgSTXjP8Qn0zmnYun+EMqjdJT4P2eFKl8KcspkVYTKI9fDYJt+UJ1kkCs15zXX3m/UwOx9TwHYyUFmO4BowDvqVgCmQRsXOnc7+Lx2pry7VQMYRJg7aSySe/bM8wfNJuAX6nl6I79sIM/2uNDCkjlcmTyBsKXpMnAOXdAJXRT87SRpWCUG1V92T61RqNKWVdlXLZUZY7k02CuEs3qwLeT52diBzMG1GDT0tjLe10DdCezIFJEpHSujpGFiE6rFZbFcdm8gS+gFMK4vHNZRsQt2TqhRjjkNPjot2HeNyEYxwdcGTuM8clu51xGPpVl5PEW2NSdImWZrPRlk6RzhWKxwfGFQnUmLNbVy0dY+nRYxwqnsoys958vVDrvj14JKy0Okz7dRMfu4o9WbLz3dusyEkI8DVTKH/snKeXQnop7iRDiKuAqgIMO0pH5QBQQKiATaVW1dapkIQQ8aqaRyKZJZfMIT4q4EXTuZq9EMK5mvqiOZI3B0uyiP9tHQyFXG4XQNBk+9AVYfoOudzRO/UZaZscIN9Oqy58/23sbucIdbOvL0DZJlxSJO1BldBARbwgMOHxigLcsjbJikSR5cDYLzeoHDzaoWNbxi0r7vEGV2pjqLlcedqJjCNJT4Jk3tpFryGDOSxPZATV50n1DGgxjtzPiXC7Hpk2bSKfTwz5vt5z1sBYiDm+8MXT/1iRse2Po60ZL44fhLF054c03Id9S+kxfqLRtEm0rl2+EhEIh2tvb8fstFpGNrsrdKgQp5RkjeN/NgLUsabveV+n97wHuAZgzZ46adgqhBpita9TAc9QFIxBhZIS8fpDQk0yRyqdBGMQKefBXwSdqEoipmS9w6x9v5c65dwKQzCV5ZcdqTquVywhU4xhQJS7KFIKD8kRayqpLKgwypNT6ELurWVYSwR+BHMyfPY5/21SKJ4XDA/Sjq23avTiv+CEWC6iS8gs1wg7tuXVKIeiFaVmZYyBbICBKC0cTuZRqJGVWfTXkbq+HTZs2EY/HmTJlSrGi7YjYohXKxBlqEdjg/QceNfL3Ho7sAHS+rdYCRNuU1bxdp9tGWlTNs2hbabHnuCNGPZOXUtLV1cWmTZuYOnVq6YCNk1WnXEZPAJcKIYJCiKnAYcCLe/UOwTi8/aTafnv58M+1kZA27XamksV+wXEnmrgPRzDOKUk12/rte78tFlT7+Zs/B2BlOFQbCwGUlQCqRgtURyGEW1TPgyIS4U0icbj0tQWznMjgngidKVXLptUTHFqAzS6sAdpKlrJVIdi9GMzEFyJkSPCoa9HjKZVTSeaSqieCJ40PQRDPbu+XdDpNa2vr6JSBFTG4+N0MNQg7RSAKE44puaesTWrMFp7W38I7eC3z3iOEoLW1dahVZeMkYLRppxcIITYBJwG/FkIsB5BSvgY8DLwOPAVcvccZRibWwFDz1F0/z2ZC+ofrTiaLLRNjuUyVYwgNTCwUuL5LdV0y1x6YFUbPHEhWrZTHEMy856JC0LNlh11GHuALB31UPRa5Ui8EJ6q+VsBcLTzrID//ev5RnDBVDcxefz/Nwm9/xzgr1rhApQE01FjyVcccSvfUK5XzQlU5DXhKQeWBbFK3ms0QwYsINexRiqUtyiA2vvJkxBd0/p61TgCsadpm2Z2y4/aUmal4zhb8py3vDaPPMnpMStkupQxKKQ+QUp5lOXablHKalPIIKeWTe/3m1syRKx4fjZh7hWkhbO7rLVkIOfsbdQ+L1wcHn0xTo1qA9Mf3/8iK91aQ1wthbu7cWTuXUahB+SxfU+UkSPeBx+94nwqAqA6wC08GzD7XTi4Is9CsB+X+TCeXnzSlWNZk5kEexgm/s4PP7s6t1V/vVJql109U6oLXIovPU5qldqcHSOcMXcLDodIZu6JhErRO2+uXdXV10dHRQUdHBxMmTGDSpEnFx0IIPvGJTxSfm8/naWtr49xzzwXg/vvvp62tjY6ODmbMmMG9994LwsP9Dz2BmHQsTz/7PCBAeHh8+fOIScfy6H/9V/H9Ojs78fv9LFmypLivv7+fadOmsXbtWkDFWGbOnMnKlSt3/2VsHAvqc6UylGac0z7i3KynAgGdwtWZSBZ76MYGOquW5VQk3ESjNj2v/5/rufa5a+lKdzEx0KwKaQUdLtcwHLkBeP8V6Fqvcqyd/n20SRzVGU1PXXsCP7pC+YZjhqxKfanmkBpo7/zLd+nN9NKXUrPAtOxhHB7nlZI3AMdcVvlYmUvJoawaIKb7FgtvGp8oKYRMIa1rOmWIgfOlRGygtbWVVatWsWrVKhYvXsw111xTfByNRlmzZg0p3ZhrxYoVTJpUniS5YMECVq1axXPPPceNN97Itm3bIBhn5vRDWfbEcsxeEUuf/D3HzJpV9tpHHnmED3zgAyxdurS4Lx6Pc/vtt/O5z30OgG9961t88IMf5MQTy9chOU39KgTTZVRFZQAQ1AqhN13KtY5IAw4+uapyEIjRmC33FS7fsJwD/Pq81MplBCW30T2nqQJiTmf5BOMQbiaaVO6pAmkMPSDFPEHnSyQAIT3r7c8luOUPt+DzqltnIN9NW8HBFcIm/7wDLlhS+Zg5APujjirHokLwZIgEcjQWTIsty4auAfCklYIeAwphd8ybN49f//rXACxdupTLLqusjMePH8+0adN47z1VT+mUE4/jxVVryOVyJBIJ1q1fT8fs2WWvWbp0KXfeeSebN29m06ZSytoll1wCwDe/+U2WLFnC7bff7tC32zU210+2EXPAq7pCUOb5QDaF8KsZelBKOOgDVZWDYIxYNgmUZp6ZQoakmf9dK5cRqCJjD14EmV7oex/aDnf+M1PdTH7zKWifyPqe9cVWjjGnMnsGY3EZbkps4gd/O5vn39rBkr920ZoXjnQp22NM69XhayKm3ak/+8wsfvinh+nrMej1epVC6BxAeLLKrRfeOzm++svXeH2LvZUAZhzYwM3njTzD6NJLL+XWW2/l3HPPZfXq1Vx55ZW88MILQ573zjvv8M4773DooYfy+uuvIyKtnHHyiSz//f/RK9cyf/583n333eLzN27cyPvvv88JJ5zAJZdcwkMPPcR1111XPP69732P6dOnc88999DSUp1Ueyv1ayGYC7KqkFJoJagHmFQhTVI3tgg1T6vKatgyAlHaUokhuz/deLTaqFWWEaiFUQCBuGqoU4V1AACH5nJEfVFW71hdjO9EvdVXCCFviCMnNLDgA+PIGTnaspmqZDrtEt0ohf6hPZ/tJKbX6AhPhqyRoUXfox6R4f3eNHhyhIzCPmEhzJo1iw0bNrB06VLmzZs35PhDDz1ER0cHl112GT/60Y9Kg7cQXPrpz7PsNy+wbNmyIZbFQw89VLQELr300jK3EcBTTz3FxIkTWbNmjTNfbDfUr4VgFomqRpEsC6ZC8Is0qXyGEBBwKlA3HIE4jfk0/3769/m73/1DcfeZ/laV4lYFv/ku8fpUbfrXHlM1W6rxG51+E57ffY2JkTa2JrfSrLukRaoV7A9E+fa2HVx7QKni7I6kyjEflxmoTu2eXTFZW6/jZzj6MVFPCEjQn+snUcgwWacCBzxJtvWlEbE8oXxury2V0czknWT+/Pl88Ytf5LnnnqOrq6vs2IIFC/jBD35Q8XUnnHACr776KpFIhMMPL7eely5dytatW3nwwQcB2LJlC2vXruWwww5jy5Yt3HXXXbz44oucdtppLFq0iFmD4g9OU78WgqkQquwaMV1GPpEBvfgmGKzBza4HusnBcrPRl00qa6UKfvNhCTaUCnjFqhBw16nHBwSa2J7cTiKXIIrAW7WS5FHOTKa4cPyJvNf3HrevvL3YO7c1M1BbC6F1Gpz9DfjELxz9mLieLCWyCbrySSbm83ilxOdJs71f3S/BfHafsBAArrzySm6++WZmzpy516+94447+PrXy7srvv322yQSCTZv3syGDRvYsGEDN9xwQ9FKuOaaa7jxxhtpb2/n29/+NldffXVxDVK1qF+FYNYmGVcF/7SFoJ55ez1ZhFBpnkGnA4YVBVED3cHBFn52jqUpSKa/tu4iE6uiroaFoAfc8f6oUgjZBFEpqta0yCwP0Ch8FGSBn7/5c65+5mq1r2A4H1QeDiHgA4uhwVnXXVw3QVrXs46UzNOWLxCWkrA/o6rPenIq3lYP16cNtLe38/nPf35Erz3nnHM47bTTyvYtXbqUCy4or7pw4YUXsnTpUlasWMFf//pXFi1SJUnOO+88mpubeeCBB0Ym/AipX5fRCZ+Boy+sWg0jk6BPDTBekQGPoVpZ1mL2Zw50O97kiCkfAmB8eDxk+6tbeXVXWAfAagT+tUvmgGQ/XakuejI9xCQQrJ7LCKBRDO0T0WQYtbUQqkTMH6U96+HPW/8MqHIdYcNA+vTKXKEVQi0THkbALbfcUvY4kRgau5s7dy5z584FYOHChSxcuHDIc3a1//777wfgoosuGnJs1qxZvKFrHJ155pllx5544ondC28z9WshQNWVAUBI3/heTxqfyKimILXwD5uzrEeuJOwL87NzfsZ9Z92nyulWO8BdCesssBprNHR+fdv655BINvZvJGYUqjczNxWCrKQQqlM+o+b4QmzyGLzVrcpktBWUheDzZgBVGjsoJSS21VZOlxFT3wqhBoR8EQKGxONNERJJdYFXOdMJgMP0bOHAYwDoGN/B1MapsPkvtV2DYGJt0lINC0FX/GzVue/retbRnMtVL5irFUJ0UDnysMdPUOK4u6YuGJTI0FYoEDEkXm8WhDovQSlh1oJKr3YZA7gKYRDCH6LRKIB3AK83pVojVstPXSaIUDdW1/rSvnXPqNz/9b+rvjyDyVma91Qj4ykYg3FHFNtFAjRXM4DpDYAvXPb5AI0eXefe5t62dYkvyC39pT7C4woFooaB9FgSMKrUn8LFGVyFMBhvkAPzBQx/H8KTISwN58oa7462I6Fvs2rLB7DdgbruI2XqKdX/zNmfKFcI1QzmCgGywJyXl/LY6UuYO3kuAE34VD+CfSSzZlh8YU5KlYraNYgAMSkpWBMwpHSulaiL49RvULlWeP3EDQOvL4eRF8pCqJVCMBccvfqIqqYodQnoqafWRh4rje3wpXdLndOqQTBWVgbbEFQ3/1+vjj70zeVEgspqbBd+JUOt04CrgT9ESyYFqPiRiLTSUMiQQS1KAwh6A86VAXdxHPeXG4wvSNQwGNfoYep4P1HDKPeXVxMzcPubL6pWonld2+hvH971a6pJpAWizhVTG0Igpvr6aj4ykKzNzLxzHSE9SZhsePYfF4k/SihfaiFKpJXJ+Ry9IlksRx6s1spxF0dwFcJgvEGiUpIqpMkYadW5rFYWwuD0vWSXqnJay1XKNUXNwu8+4HSunTKf2ZlsdfP/L/6p+p/uZW23KlM8OZ+v7RqEamJRvj4JRJo5KJdHAp5AJ1CqFlzv2F7+Wu8XQvD0008XX/v4448jhODRRx8FVPrqSy+9BMCUKVO48MILi8999NFHK6atVhNXIQzGFyBiGAwUMiQLGdXb2Mla/8MxeIHPyiW1k6UeOEx1cz311V/zqdbj1L5qWghHfQwOPwfSvVx8+MUAnNHfV52V2vWAds/999wfsDwdg0CcJj2ECJ9atR4aI5MVR8pfAzNnzmTZsmXF5y1dupRjjjlml3K8/PLLvP766w58w5HhKoTBeINEDUnSyJAopGsbQ6jUdGV/zvE2XTMDO0plM6q9RiTcBOleLjjsAlZ//C80d72rgv/7A9oSOsQXY3w2Df4wTTrLyuNX9Z0Cvhpk5DnAiMpfA6eccgovvvhiqfz1unV0dHTs8nOuu+46brvtNvu/wAhxg8qDCUSISgMJ9BfSqhdCrWIIzVPg+M/An+8t7TtuYW1kqReOugDe/E1JIVQ7hhBqhLTK+hLJHYCEhgOrK0OtMM91qkf9hZuIe5VFILxqdW9wJBbCk1+Bra/aJaViwkw4544Rv3zE5a+F4IwzzmD58uX09vYOKX89mEsuuYQf/vCHrFu3bsSy2olrIQwmEFNWgaamFgLAsZeXP573rdrIUS80TwFZUIOyx1/9NSKhRtVH2iiUyk071di+3jCtsVQ3pHaCP0xM1zcSvn4AgvtIyumIy1+jlMmyZcsqlr8ejNfr5frrr69JM5xKuBbCYAIxYkYptTFSy3UIMHTBk9dfGznqhWADGHno36oG52qne5oB5FtbYMb5atvBtpV1hfnd3/yV+p9JEPdHgCzCZ1oII1AIo5jJO4kT5a8rcfnll3P77bdz9NFH2yL3aHAthMF4fcRFSU9Gam0hRMfB/MoX3n6J6bbofq82KafWz3z9v/W+sVXMbcSYFsLOd9T/OVfi98cISkEsogKwwXoovGgTdpe/3hV+v59rrrmG73znO3v9OXbjKoQKxC251FFD1n5Wfsjc2n5+PWEOSj3v1WYgrqSE9odVylBKd37/FfW/4UAIRIgBqYJ2GQXqoM6WTdhd/no4Fi1aRD6f3/0THcZ1GVUgHmwCdBN34av9KtT4fuKj3hNMt0XfZmg7ogafvx8rhMGEW8AfIZ6DLn2LBOuh8OJeUq3y1wDPPfdccXvDhg3F7WAwyJYtW/ZCamdwFUIFYtE2YCMAE0Qd+Oy9frjkZ9AytdaS1B5rmmktVggPLskuvLUpflgPeDwQiBJLyKKvIRDYT9xn+yiuy6gCcX9pljNBBGooiYUZ81Uq3f5OqMYKYfwM9WcqgWC89hZkNbn05+WP/RFieVXjySclvjGyMM2lMq5CqEDYkikRqdUaBJfKWJVALRSCEPD3f4RDtH94H2kXucccfnb54/73iReU7zsgZe3W7LjYgqsQKiCsHcl87oynrrD6682y4LXAnAmPQZ/5qPAM6hg3/TxVABJU4UFXIYxpRqUQhBD/JoR4UwixWgjxmBCiyXLsBiHEOiHEW0KIs0YvahUJRHlpczcvcvD+GzCsV6wD0gmfqZ0cuoNaXbQzrSXjjiiu2wlIqXpDuIxZRhtUXgHcIKXMCyG+AdwAfFkIMQO4FDgKOBB4WghxuJSyMMx71Q/+CMFsAtJ9+88q1LHE39wHO96sTZaRiRlc3l9KX1v57Aul4H4gQlyv7PfXsjKwiy2MykKQUv5WSmkmz/4JaNfb5wPLpJQZKeW7wDrghNF8VlUJxgCpVsNWu3iay+6ZdTF85J9rK4O5Onl/DKJOnFVq3uSPFC2EvBDgq5MkjN1Qy/LXc+bMKR5/6aWXiums9YCdMYQrgSf19iTMvE3FJr1vbGCutkxsc11GLpVpPUz9F97hn7evE4ip8i5AAcaMy6iW5a+3b9/Ok08+ST2yW4UghHhaCLGmwt/5luf8E5AHHtxbAYQQVwkhXhJCvLRjx469fbkzWJff7y/NT1z2jsPOhDNugTO/WmtJaksgosq7mOwjQWUny19ff/31dVXy2spuYwhSyjOGOy6EWAicC3xEymJ/w83AZMvT2vW+Su9/D3APwJw5c6rYoHcYrIFC10JwqYTHCydfU2spao8vNGqF8I0Xv8GbO9+0USg4suVIvnzCl0f8eifLX5900kk89thjPPvss8Tj9ZWlNtoso7OBLwHzpZRJy6EngEuFEEEhxFTgMODF0XxWVbFaCG4MwcVl1whBRNf+krDPWAhOl7++6aab+NrXvuaY/CNltFlGPwCCwAqhVmv+SUq5WEr5mhDiYeB1lCvp6jGTYQSDXEauheDiMhxlizdHEEMYzUzeSZwsf3366adz00038ac//cl2uUfDqBSClPLQYY7dBtSno2x3uC4jF5c9JuILAwUkYp+xEECVv25qamLmzJllRen2hDvuuINQaPgU3JtuuonFixdzyCGHjEJKe3GL21XCaiFE95MG6i4uIyToiwCq/PW+pBBGW/56d8ybN4+2tvoaX0QpDlx75syZI1966aVai6FaBH5jitr+l51Dl+u7uLgUyfz4TM5lEzd2dXPatX/do7UIb7zxBtOnT6+CdPselc6dEOJlKeWcXbxkj3EthEpYLQRXGbi4DEswEGPFel3Lv9bNpFxGhVvcrhLuRe3isueYpcB9of2rFPg+iGsh7IrZn4AJs2othYtL/WNa1GNklbLLrnEVwq44/+5aS+DiMjYImBaCqxDGOq7LyMXFZXT4XYWwr+AqBBcXl9FhuoxchTDmcRWCi4vL6DBdRmMMr9dLR0cHRx99NBdffDHJZHLI/vPOO4+eHtWZb8OGDYTD4WKZ7I6ODh544AEAEokEn/3sZ5k2bRrHHXccc+fOZeXKlQDEYrEhr58xYwaLFy/G0KXD6wVXIbi4uIwO02VUR2ua9oRwOMyqVatYs2YNgUCAJUuWDNnf0tLC3XeX4onTpk0rlsletWoVV1xxBQCf/vSnaWlpYe3atbz88sv85Cc/obOzc8hnmq9fvXo1r7/+Oo8//nh1vuwe4gaVXVxcRofpMpL1NdvdG0455RRWr149ZP9JJ51Ucb+V9evXs3LlSh588EE8HjXHnjp1KlOnTt3la3w+Hx/84AdZt27d6AS3GVchuLi4jA7TZTRChbD1618n84a95a+D049kwo037tFz8/k8Tz75JGeffXbZ/kKhwDPPPMOiRYuK+9avX1/W3+D73/8+3d3ddHR04PXu+SLWZDLJM888w6233rrHr6kGrkJwcXEZHYGo3hhbLqNUKlUc3E855ZTiwG/u37x5M9OnT+fMM88svsZ0+Vh54okn9vgzTYUihOD888/fo5pH1cRVCC4uLqPDrxXCCGMIezqTtxszVrCr/clkkrPOOou777572CJ3Rx11FK+88gqFQmG3VkIlhVJPuEFlFxeX0TFKl1G9EolEuOuuu7jzzjvJ5/O7fN60adOYM2cON998M2ax0A0bNhRbcI4lXIXg4uIyOopB5bHlMtoTZs+ezaxZs1i6dClQcvmYf3fddRcA9913H9u2bePQQw/l6KOPZuHChYwfP76Woo8I12Xk4uIyOsxikGOs3Wwikdij/b/85S+L26lUquJrGhoauPfee4d9vylTprBmzZqRiFo1XIXg4uIyOhonw4e/DB0fr7UkLqPEVQguLi6jQwg4rTaBYRd7cWMILi4uLi6AqxBcXFxqRD217x0rOH3OXIXg4uJSdUKhEF1dXa5S2AuklHR1dREKhRz7DDeG4OLiUnXa29vZtGkTO3bsqLUoY4pQKER7e7tj7+8qBBcXl6rj9/uHLf7mUhtcl5GLi4uLC+AqBBcXFxcXjasQXFxcXFwAEPUU5RdC7ADe24uXjAOGtiWqf1y5q4srd3Vx5a4u44ColLJttG9UVwphbxFCvCSlnFNrOfYWV+7q4spdXVy5q4udcrsuIxcXFxcXwFUILi4uLi6asa4Q7qm1ACPElbu6uHJXF1fu6mKb3GM6huDi4uLiYh9j3UJwcXFxcbGJulcIQogNQohXhRCrhBAv6X0tQogVQoi1+n+z3i+EEHcJIdYJIVYLIY6topz/IYTYLoRYY9m313IKIT6pn79WCPHJGsl9ixBisz7nq4QQ8yzHbtByvyWEOMuy/2y9b50Q4isOyzxZCPGsEOJ1IcRrQogv6P11fb6Hkbvez3dICPGiEOIVLfdX9f6pQoiVWoaHhBABvT+oH6/Tx6fs7vtUWe77hRDvWs53h95fF9eJ5TO9Qoj/E0L8Sj92/nxLKev6D9gAjBu075vAV/T2V4Bv6O15wJOAAD4ArKyinKcCxwJrRion0AK8o/836+3mGsh9C/DFCs+dAbwCBIGpwHrAq//WA4cAAf2cGQ7KPBE4Vm/Hgbe1bHV9voeRu97PtwBietsPrNTn8WHgUr1/CfB3evvvgSV6+1LgoeG+Tw3kvh+4qMLz6+I6schzLfBz4Ff6sePnu+4thF1wPvBTvf1T4GOW/Q9IxZ+AJiHExGoIJKV8Htg5SjnPAlZIKXdKKbuBFcDZNZB7V5wPLJNSZqSU7wLrgBP03zop5TtSyiywTD/XEaSU70sp/6K3+4E3gEnU+fkeRu5dUS/nW0opzUbDfv0ngdOBR/X+wefb/B0eBT4ihBDDfJ9qy70r6uI6ARBCtAMfBe7TjwVVON9jQSFI4LdCiJeFEFfpfQdIKd/X21uBA/T2JGCj5bWbGP6Gc5q9lbOe5P+cNpv/w3S9UIdya/N4Nmr2N2bO9yC5oc7Pt3ZfrAK2owbE9UCPlDJfQYaifPp4L9BaD3JLKc3zfZs+398RQgQHyz1IvlpcJ98FvgQY+nErVTjfY0EhnCylPBY4B7haCHGq9aBUtlHdp0qNFTk1/w5MAzqA94E7aytOZYQQMeC/gH+UUvZZj9Xz+a4gd92fbyllQUrZAbSjZplH1likPWKw3EKIo4EbUPIfj3IDfbmGIg5BCHEusF1K+XK1P7vuFYKUcrP+vx14DHUxbjNdQfr/dv30zcBky8vb9b5asbdy1oX8Uspt+kYygHspmZl1I7cQwo8aVB+UUv5C7677811J7rFwvk2klD3As8BJKJeK2VPFKkNRPn28EeiiPuQ+W7vupJQyA/yE+jvfHwLmCyE2oNyBpwPfoxrn2+5AiJ1/QBSIW7b/gPLd/RvlwcNv6u2PUh4UerHK8k6hPDi7V3KiZivvogJXzXq7pQZyT7RsX4PyQwIcRXmQ6h1UgNOnt6dSCnIe5aC8AngA+O6g/XV9voeRu97PdxvQpLfDwAvAucAjlAc5/15vX015kPPh4b5PDeSeaPk9vgvcUU/XyaDvMJdSUNnx8+34FxrlyThEf6FXgNeAf9L7W4FngLXA0+aPo3/Iu1H+zVeBOVWUdSnK3M+hfHWLRiIncCUq+LMO+FSN5P6Zlms18ATlA9Y/abnfli21hwAAAKtJREFUAs6x7J+HyppZb/5ODsp8MsodtBpYpf/m1fv5Hkbuej/fs4D/0/KtAf5F7z8EeFGfu0eAoN4f0o/X6eOH7O77VFnu3+nzvQb4T0qZSHVxnQz6DnMpKQTHz7e7UtnFxcXFBRgDMQQXFxcXl+rgKgQXFxcXF8BVCC4uLi4uGlchuLi4uLgArkJwcXFxcdG4CsHFxcXFBXAVgouLi4uLxlUILi4uLi4A/H+XWJQKese5WgAAAABJRU5ErkJggg==\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df['TMPMN'].rolling(window=365, center=True).mean().plot()\ndf['TMPMN'].rolling(window=3650, center=True).mean().plot()",
      "execution_count": 67,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1146580b8>"
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# df['TMPMN'].rolling(window=365, center=True).mean().plot()\ndf['TMPMN'].rolling(window=3650, center=True).mean().plot()",
      "execution_count": 68,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1146c51d0>"
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df['DATE_OBS'] = pd.to_datetime(df['DATE_OBS'])",
      "execution_count": 69,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.set_index('DATE_OBS', inplace=True)",
      "execution_count": 74,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df",
      "execution_count": 75,
      "outputs": [
        {
          "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>TMPMAX</th>\n      <th>TMPMIN</th>\n      <th>TMPMN</th>\n      <th>PRECIP</th>\n    </tr>\n    <tr>\n      <th>DATE_OBS</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1949-01-01</th>\n      <td>-2.1</td>\n      <td>-6.7</td>\n      <td>-4.2</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-02</th>\n      <td>-0.5</td>\n      <td>-6.7</td>\n      <td>-1.2</td>\n      <td>4.2</td>\n    </tr>\n    <tr>\n      <th>1949-01-03</th>\n      <td>1.1</td>\n      <td>-2.1</td>\n      <td>-0.7</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-04</th>\n      <td>3.3</td>\n      <td>0.9</td>\n      <td>2.3</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-05</th>\n      <td>4.0</td>\n      <td>-0.9</td>\n      <td>1.1</td>\n      <td>0.8</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2006-10-27</th>\n      <td>8.7</td>\n      <td>2.7</td>\n      <td>5.6</td>\n      <td>0.3</td>\n    </tr>\n    <tr>\n      <th>2006-10-28</th>\n      <td>11.0</td>\n      <td>4.1</td>\n      <td>8.2</td>\n      <td>0.3</td>\n    </tr>\n    <tr>\n      <th>2006-10-29</th>\n      <td>4.6</td>\n      <td>1.4</td>\n      <td>2.4</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>2006-10-30</th>\n      <td>1.8</td>\n      <td>-1.9</td>\n      <td>-0.7</td>\n      <td>3.5</td>\n    </tr>\n    <tr>\n      <th>2006-10-31</th>\n      <td>1.3</td>\n      <td>-7.7</td>\n      <td>-3.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>21121 rows × 4 columns</p>\n</div>",
            "text/plain": "            TMPMAX  TMPMIN  TMPMN  PRECIP\nDATE_OBS                                 \n1949-01-01    -2.1    -6.7   -4.2     0.0\n1949-01-02    -0.5    -6.7   -1.2     4.2\n1949-01-03     1.1    -2.1   -0.7     0.0\n1949-01-04     3.3     0.9    2.3     0.0\n1949-01-05     4.0    -0.9    1.1     0.8\n...            ...     ...    ...     ...\n2006-10-27     8.7     2.7    5.6     0.3\n2006-10-28    11.0     4.1    8.2     0.3\n2006-10-29     4.6     1.4    2.4     0.5\n2006-10-30     1.8    -1.9   -0.7     3.5\n2006-10-31     1.3    -7.7   -3.0     0.0\n\n[21121 rows x 4 columns]"
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# df['TMPMN'].rolling(window=365, center=True).mean().plot()\ndf['TMPMN'].rolling(window=3650, center=True).mean().plot()",
      "execution_count": 76,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x11474a4e0>"
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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UHHxPkLah2FZa5fcgeOuXU1rfSSPqmHE3jDjj+L9+TGVtPT946HO/4z86eFDIz6zYuiesa7+zbCsbdlewtbRxQnX8PjkUzpzBOd9zVBt9C1cbnYcZd8OIMzwqimuLylFVdu6t9jvu4bObpjJugCOF+/N/Lwzr2v9b6chGrSsqb3Ju5g8nUDhzBiPystrcdyNymHE3jDhh6aYSbnppKbnpjTVFV+3Yy1XPLgLgq40lfu0H9czgv1c3hlR8V5mGwhNK759jC5S6OjahahhxwmkPft7k2LbSKr7e4oRcPB68L74TnzvLq+nbI7TRVlX+U+Dksz95UaACidHVMM/dMOKYraWV3iIY9551YNA2Z0waCMDk//tfsymRhT5CY4MDQjxG18OMu2HEMHO+3cHC9bubHP/NyU7VopteXsaTnxcCsG/v4AbZU1ga4OaXl4W8V70bthncy9QeYwEz7oYRo9TVN3DhE1/yw4fnsSsg5DJ1TKPqanWdU6EiOURVpLE+9UWLgoRuPHh0Y26bMa7NfTY6DjPuhhGjzHxnpXf7+L9+4ndueJ/wM1YSEoTPb54GNF9FyWPc21Puzug4zLgbRozy9rKt3u3dAeqOCQnCh63INx+Ym85xY/tRVNac5+4YfjPusYEZd8OIURITm642/fTGqbzppjcOz8viMbcY9BtXHdHi9XplJrNyW5mfbK8vnuPpZtxjAjPuhhGjjO6X7bd/71kTGdwrg/0H5niPHTeuH4UzZzBhUG6L1/uysNi5zvvfBT1f6Q3LmNmIBcL6lkSkUESWichiEWmq1N/Y7nsiUiciZ0aui4ZhBKO8uo4DBuZwz5kTWPV/J3HGpNDyAuFw04mOwmOoxUwWc48tWrOIaapbHzUoIpII/Bl4L1QbwzAiR1l1LX17pPGj/MERud4J4/uTlZpEXSjj7mbdpJrnHhNE8lu6GngZ2BHBaxqGEYI9lXVkp0VukbmI0Dc7NeSkalWNee6xRLjGXYH3RGShiFwWeFJEBgI/AB6OZOcMwwhNWVWtX1WkSNAzI4WSyuB1Vb1hmSQz7rFAuI/9Kaq6WUT6Au+LyEpV9U2svQ+4SVUbmqub6D4YLgMYMmRIW/tsGN0eVWVPVR3Z6ZGVh8pNT2ZraVXQcxW19SQlCClJFpaJBcL6llR1s/vvDuBVIFA1KB94XkQKgTOBh0Tk+0GuM0tV81U1Py8vL/C0YRhhUllbT32DRtxzz81IoaSiJqjGTGVNPRkp5rXHCi0adxHJFJEenm1gOrDct42qDlPVoao6FHgJuEJVX4tCfw3DAMqq6gDoEcGYu3PdWraUVjHslrebVHKqqKkjI8WEZGOFcDz3fsBnIrIEWAC8paqzReRyEbk8ut0zDCMYnrJ42RH23H1DMhN//55XLAygoqaejFTz3GOFFh/DqroWmBjk+CMh2l/Y/m4ZhtEcO/c6k56R9txH9s1i2eZS7/5f3vuWG08cA7jG3cIyMYPNjBhGDPLQnNUA7GhGC6Yt3H7qOI4c2ce7//KiTd7tD1fuYPWOvRG9nxE9zLgbRgxy+AjHAB8/tl9Er5ubkcK/LzmE1690tGim7OckPngmWD3iYUbXx4y7YcQgHmneSIdlPEwcnMuY/j0odXPePZrwk4f1isr9jMhjU9+GEYOU19SRmpRAUogCHJGgT1Yqu8pr2FRc4c2cOXXCgKjdz4gsZtwNIwapqK4nMzW6f769MlNYvqWUKX/+yHss2vc0IoeFZQwjBimvqYt65sqnq4ooqfDPdc8y4x4zmHE3jDB54H+rGHrzW1TXBS9m0ZFUVNeTGeUFRcGKctgiptjBjLthhMlf3CIWyzeXcssry0JWLIo217+4hNlfb4v6gqJ3rjmqybGGILIERtfEjLthhEmyW9buwQ9X89yCDcxevq1T+vHSQif3/KsNJVG9T05GMoUzZ1A4cwZH7NcbgH1y06N6TyNy2DuWYYSJZyW+p9zctS8s5uB9ezK4V0aH9aGmrnPyzJ++5BB2lFXTLzutU+5vtB7z3A0jTIIt5Dny7o9CNY8KZVWNE5zRynEPhoiYYY8xzLgbRph4PPetpZV+x8ur6zrk/lW19Rx81wfe/T/+4IAOua8Rm5hxN7o1T89fz4crt7fYbk1Ro6bK9j3VjOnfgwmDcgAYf8e7UeufL9f9Z4nffkIzhXEMw2LuRrfmt685pQl6ZiSz6LbjCVVJbOXWMr/97PRkjtyvD0s3lQZtH0lUlWtfWMzyLf73GtkvK+r3NmKXsDx3ESkUkWUislhECoKcP09Elrpt5opIE4lgw+jKFFfUMue7opDnA+PbC9bt5hfHjABg8tDo6q2s2rGX1xdvYf2uCiYP7cUlU4ZR8NvjGNWvR1Tva8Q2rfHcp6rqzhDn1gFHq2qxiJwEzAIOaXfvDCOKNDT452zvbEY+9/XFW5oc8+i6LCjcHdmOBbDXJ6Z/yPBeXDd9dFTvZ8QHEYm5q+pcVS12d+cDgyJxXcOIJuU1/hOhT81bH7Ktr645wH1nHwjA/gOzI9+xAOZ82/hGESgHYBihCNe4K/CeiCwUkctaaHsJ8E77umXEKx9/V8QHK1qewOwIPB6xJ+tkaJ/MFj8zpr8TCumdlQLAmh3lAHyxdlc0ugjAm0sa3xouO2p41O5jxBfhGvcpqjoJOAm4UkSarksGRGQqjnG/KcT5y0SkQEQKiopCxzeN+OWCxxfws6eaTNt0CltKnHqhPdKSGN2vB9Xu4qTa+gZueHGJt+qQqpKWnMBPDh3CuH2y3c84tUt/dfxIgJATsZHAd+J0UE9bIWqER1jGXVU3u//uAF4FJge2EZEJwGPA6aoa1I1R1Vmqmq+q+Xl5eW3vtRHzbNxd0dld4IcPzwVgW2kVuRnJ3pDHqu17eXHhJn71wmIAnvliA1W1DWSkJHHHqeO5+8wJTHTTIA8a0hMgqmJi63dVcOTIPiy5fXpUHyJGfNGicReRTBHp4dkGpgPLA9oMAV4Bfqqq30Wjo0bsU+oTL/YUf+gKpKck0jMjheKKGr/je6pqWbeznPfcMFJ2WhI56cmclT/Ya2Q9yokVNdEz7kVl1QzulUFORnLU7mHEH+F47v2Az0RkCbAAeEtVZ4vI5SJyudvmdqA38FCodEmje1NcXsPEP7zn3f/Dmys6sTdQ4TOZeubBg+iZmUyx+/DxaMes31XB1P83hx17nPDNpUHi3R5N9cooGfeGBqWkspZeGSlRub4Rv7SYCqmqa4Emeeuq+ojP9s+An0W2a0Y8cdCd7/vtL1gX3fTBlvBd7ZmWnEhuRgolFTWoahNDvXJbGRkpiaQmhdY3j5bnXlZVR32Dkmteu9FKTH7AiDraBTXAB+Q4E5NL7pgOQP/sNOoalKKyaj+vvrF9cNGs9BRPWCY6+jKeUFGvTPPcjdZhxt2IOm8t2+rdfuDcg7zbnVnR6PHP1wFOHB0a0yC/3rrHG5bxJZjXDo1hmWjJEOx2jXtPC8sYrcSMuxF1rnr2KwAeOz+fUyfuw5/OcPLKd+wJvSI02ozs66QXeiZGh/V2jPvyTaVBFwqt2Lon6HWS3VWqbyxpuoI1EpR4jLt57kYrMeNudBjHju0LQFKCY1AXb4xuJaHmqFflOLc/AAPd/PFvt5dxxxtfe4/Puf4YAObePC3ktQbkpDF9XL+o9HN3ufOg6Wkxd6OVmCqkEXXG75NN/+w0r5d8+H59ALyLhDqaxz5dy9qicr83h0T3gfPm0sYQ0vLfn0BWahKFM2c0e73+OWlBQzmRwDx3o62Y525EnZKKWr8c7X49UgG4/3+rInqfTcUV3D17JXfPXkltfehydHe99Q0AR47s0+z1MlPCK0CdlZrkJ+4VSXaX15CUIPRINT/MaB32G2NEndLKWnLSG417UmICCQIH79szYvdYt7Ocqf9vjne/cFc5D513cLOfefDHk0KeG92vR9irQb/bXsb2MOYP6uobmP31No4f1y/kBG0gxRW15Gak2MpUo9WY525Elbr6BvZW15Gb7h9WmDamH3sqI+ft+hp2gA++2RG0nUfm9yeHDvGGYjzcdso4AP7+40m8+6ug8klBCcewA8z6dC1XPfsV+T6l8lqiuLzG4u1GmzDjbkQVz6rPnpn+Bmqf3DS+3V4W7COtZujNbzU5Nm103yAtYWe5Y4j37dVUAfKSKcMonDmDGRMGtOr+vzpuFOA8yJrDszgqMyX8F+biihpLgzTahBl3I6os3eRkxKQk+v+qfVnoyP9/0kz1o3CoCjGRuXNvcG/6qw1Of+oaIrewKjvdMdYtxd0999zmyhmEwxfrdvvVbzWMcDHjbkQVT0ZM/4AVnldN3Q9ov4DYn2evDHp8Y3FT1cn/fLnRK17mmwbZXrJd+d+Wwky+Y23NitbA8JFhhIMZdyOqeDRRAut9HjLcqTu6K4SHHS4eZYN/XpDPG1cdAcBPD92X7Xuq+dKn/N1zCzZw48tLufHlpQD0zQ4uJ9AWst3J4j1VzT+ofFUxq2qbD+F4SElM4AeTBra9c0a3xYy7EVXKq52wSUZAWmHvzBRSEhPYWhp+iCKQipo6/lOwEYApI/swYVAuhTNnMMotbjHznUav/pZXlvl9NpKphR4Jgz3NvIVsKq7wk2GoqWveuDc0KGc9Mo+a+oZWxegNw4MZdyOqeBb3ZAQYKBGhf05a2MY9sJg1wMNz1njVGH1TC398yL4ALFxfTE1dA8s3N9V9SYhgqCMcz33Knz/y228pLPPdjjJv4e3AB6NhhENYxl1ECkVkWSitdnH4m4isFpGlIhI6gdjoVpRV1ZGcKKQkNf1VG5CTxtbSSu/+7974mqE3v9VkAdKrX21i+K1vsy3gQfDAh6uD3tM3Rv2zpwqaTHSODggRtRdPNsuu8pqg5zf5xP899572l4+bvWZRWWO4Kt2Mu9EGWvO+N1VVd4Y4dxIw0v05BHjY/dfo5ny7bQ+19cEzU/pkpfqFKp6cWwjAp6uKmDbG0Wopq6rlVy842uvfbN3jNzHbIzWJshYyVD75rojePkv3X7vyCK9IWKTwyPHu3hvcuN/7vlOcrGdGMlPH9A0rBXS3z4PCwjJGW4hUWOZ04Cl1mA/kikjrkoWNuGT+2maKcrgOdmB++MVPFlBRU0dNXQP/50oFAFz05Jd+7cYMCM8Df/Wrzd7tAwfnRrxcXUpSAj1Sk7zyvIG8ssi5/z9+ms+J+/cP65qPfLzWu22eu9EWwjXuCrwnIgtF5LIg5wcCG332N7nHjG5Or8wUJg/tFfScJ4VwVRABsXG3v8s5s+Y1ybLxpcZ9I3j6kqYviQtuPbYt3W0zPTNTKA4RlvEwYVAOBw7O9e4Hm0fw8I2PxLDF3I22EK5xn6Kqk3DCL1eKSPhrs30QkctEpEBECoqK2rd4xYgNKmvrGelmrwRy4OAcAL+4u69Y16INJfTOCr46s7a+gSUbS5gwKIcpQQTA+man8X6AhMB7rZAUaC19slJCLk6aPKwXk4f1Is0tpn27K3NQEiK75ruAsE1diLCWYTRHWMZdVTe7/+4AXgUmBzTZDAz22R/kHgu8zixVzVfV/Ly8vLb12IgZ6uob2F1eQ++s1KDnJw/rDUBxeS1z1zjTOdUBKYKeiUWP97+lpJLa+gaueGYR0HwFpJH9elA4cwaThjje8vA+kY21+zIgJ50dZcFz9ksrav30Yfq4qpihVtHe/4GjlulZaHXw0MgJrBndhxZnakQkE0hQ1TJ3ezrwh4BmbwBXicjzOBOppaq6FaPdfP/vn5OenMhzlx3a2V1pNZ4J0jUhdNv7ukbuqXmFLHGN9P4Dc/yKePzX1Vdf4soYPPLxGooranl/xXYAfn708Bb78cRFk1lbtJekxOhl/uZmJAet4ASOPsyB6Y3hmD7u28jOsuqgYaf93CpRs36aH9GUTaN7Ec5vez/gMxFZAiwA3lLV2SJyuYhc7rZ5G1gLrAYeBa6ISm+7IYs3ljBv7a7O7kabWLXdMeoe7ZVAMt2FREt8vO/DRvSmcOYMLjpiKAC7PUJfvTMAeGreeuataUzaOnxE85rsADnpyRw0JLreb8+MFEoqavzi6A0NSn2DUlJRS66PcFof903mla+avNwCTvpoVmqSGXajXbTouavqWmBikOOP+GwrcGVlRF4zAAAegklEQVRku2bEOiP6OmGQm08cG7JNSmICNT7ZMjeeMBqAW08ey5NzC9m424nHP3HRZI6Y+SEAO31SDrvKZGNuRjIN6hhmTzbO8FvfbjzvI3nseVCFkl7YU1VLlhXnMNqJrVDtwlTXRad0W0dRWllLYoKE9NwBTj9wH++2bym+5MQEcn0KfAzMTQ/6+a6SA+7J/CnaG3xSddGGYu92alIiw/pket9cAtlSUsmA3Mhp3xjdEzPuXRjfGqPNlY3rqpRU1JKbntxsFaG7frC/N0yRmervhYdTN7RHWtcw7oW7ygF4fsHGoOcvOGyo336/7NQmK249bC2tCvkwM4xwMePehZnxt8+82+2Vxu0MSgLK6wUjNSmRo0Y5cfOstOQm53yZfe2RTT4fyvvtaM53jbcn5LLeNfYeJg/zz/XvmZESNFtGVdlcUsk+ZtyNdmLGvYsSqIfyj4/X+O3f8+5KXl8cfEKuq7Cnsjas1aCekEZagP6Mqn9+95j+2UwclON3LNDb7yw8GTD/KdhEbX0DR98zx3tu0pDcJto67yzfRuGuCi54fAFLN5WwcbejP7OrvIaaugb2ybGwjNE+zLh3UW52dcc97Oujh9LQoPz9ozVc8/ziju5WSBoalBe+3OBXGSncEnFfufHoL9YFlyp44NyDvNuvXzWF1648wrsfbqHpaONJs1y2uZQT7vvE79yVbmGSYHz8XRGnPfg5R97tqEZuLXFCNQPMczfaiRn3LsqeKsdzP8Wt5+nrxRasLw76mc5kznc7uOnlZfzVFcnaU1XL8s17+HBl8ELVvkwd4yzWGdzL36A1uGMe3CvD77jvEv6uyNqixpDMBYfty7Fj+zVpE0weYcqfP+TZBRuA0BPIhhEuZty7KIN7On/cf/7hBACvbjnAM1+s75Q+NYenxNyLCzcBcPqDn4f92V9OG8mc64/hg18f7Xd8+jhHZGtIgHHvqvQJIpXgWYUbSN/sNF7+xeF+xzYVV/Kca9wHWFjGaCddYzbK8OPu2St55osN7Nc3i3RXj8TXuL++eAsAeT2CL+vvDEpcRUSPVO26nY73eu7kwSE/4yEhQRgaRBrgV8eP4sIjhnoldX159Pz8FoW6Opq7vn8Alz+90O9YeTOSxOMGZIc8F2zMhtEazLh3QR6a40yeDshJIyFBSE9O9FbuaWhQ8vftScH6Yr+CDnX1DazYuocJg6IfsnixYCPrd1Wwq7yaP53hvFls9+mLbwm5ATltDy8kJog3TTKQ48c1DXV0NmODSBCf5pPHH0hzUr7NpY8aRjiYce/CeF7NM1ISqaip55utezjp/k/92qzeUcawPllc8/xi3lq2lVtOGsPPjx4R1X7d8FLjZO/100fTOyuV7T6KiGuKGvPzf3FMdPvSlRjcszF8tPLOE70qkM3x3KWHcu6j8/2OvRCDOkJG18Ni7l0YT4pgRmoilTX1fLqqqUzywvXFjLj1bW9Foz+9s5IXC4IvpIkE9QEa5De5WT079jR67p45ARFnpWl3ISFBuPzoEZwyYUBYhh0cLZ1Hz8/37hfOnMEhw4PH6Q2jNZjn3sUo3NmYaZHr5ohnJCexYXeFn9BUSlICNXUN/OOTtU2uccNLS/lRfsux7rYQWNj5g2+cbJgdZVVMGpLLog0lrNjiFJp44sLvRaUPXZmbTxrT6s9MHZ3H3WdO4KiRJoNtRI7u41bFCHO+bUwdPHqUkyKYkZrol/54+ynj+OtZBwL+aXcdQWWNv97NIDerZ09lHSPyskhNSmDRBkeed/w+OU0+bzQlKTGBs/IH+9WHNYz2Ysa9i+F5nf/wuqM5wF2NmR7win/xlGGdlilTHmDcE9yJv7KqWnqkJfsV22hJesAwjOhhxr2L4cmAGdizMcvEV9b2jElOadrUpM756nzDMmnJCRRX1FDfoJTX1DcR8Qpccm8YRscR9l+fiCSKyFci8maQc0NE5CP3/FIROTmy3ew+FO2tJjcjOeiy+l9O24973XBManLjV5ftGtXx+zTmTW8PUc+zvXjy7c8/bF+unjaSsqo67wOpR1oSX912PEeO7MOnN06Nyv0NwwiP1kyoXgN8AwRbefFb4D+q+rCIjMOpzDS0/d3rfjw1r+nq00pXr2WQT6pdmo/xv+HEMQzplcH4fbK5/4NV/Hv++qhJBHuM++kHDmStm/L4xNx1AORmpNAzM4V/X3JIVO5tGEb4hOW5i8ggYAbwWIgmSqPRzwG2tL9r3ZcRef6rNatqHUPtq2/uG/LISU/m6FF59MlKJd8tphxYaDpSVLgrLn1DRf/4eK23H4ZhdA3C9dzvA24Emi7Bc/gd8J6IXA1kAse1v2vdD0/lpR8cNNDvuCe3PJTxzPaJdXti8dW10THuZa5xz0xJ4rgAQazcMOR9DcPoGFr03EXkFGCHqi5sptm5wJOqOgg4Gfi3iDS5tohcJiIFIlJQVNR0QU53x1OQIydAJneYq7vS08d4+gpL+S4s8sTqo1Wi74UvnQVSaSkJTSol5ZrnbhhdhnDCMkcAp4lIIfA8ME1Eng5ocwnwHwBVnQekAU3K0qvqLFXNV9X8vDxbsBFIaYVr3AOM5J3f359ZPz2Ykf0aX5xEhM9vnsaMAwZwxH6N/9WeidZohWU8eu15rubLmQcP8p7rKlWRDMMIw7ir6i2qOkhVhwLnAB+q6k8Cmm0AjgUQkbE4xj2uXfPvtpexpaTSux9YNagtlLiee6AHnJWaxPTx/Zu0H5ibzt/Pm+S31N3jufsWzYgkX7urTz3CVre4KzL7ZKXSP9sW4RhGV6HNrpaI/AEoUNU3gOuAR0XkVziTqxdqJKxdF2Pn3mpSkxJ47+vtXPfiEsDRArn55aXs3FvNYxc0XW5fULibhARh0pCeLV6/xPXc2xO79vy3f71lD8eM7us9XloRXsm7ljhmdJ6fGmXvrFRW3nkiqUkJpmRoGF2IVhl3VZ0DzHG3b/c5vgInfBPX5N/1AQDJif5G7PkvQwt1nfnIPMB5CLREqddzb7uWt8eLX7W9zHtsU3EFU/78Eb8/bTwXHD7Ue7ykoobSylq/En4tUVlT3yT8Eq5IlmEYHYctIWwDtfWNLyUNPpOZgbnlvufCeZHxFLxoT0rhkN5OLvz+Axt1XTbudsJHby3d6td2xt8+8yvk3BJVtfV8sW6337gMw+iamHEPE1/BrL4+ui6frd7p3R75m3f8PlNc0VgpaFOxY2BVlaE3v8Vx937c5B6eBUIZqW33hDNTHK+6rKpRJsA3WjL05rcYevNbVNXWs9mdM6gLc8HT7OXbgK5Zw9UwDH/MuIeJJ2QC/qmH5z++wK/dQh/DV1zR+Jkj7/6IbaVVbNhdAcDqHXsJ5IEPVwHt00BPTBAyUhL9yrvVuW8aCwp3e4/5yhMs21wa1rUV89gNI1Yw494MizYUs3ijI1+7t7rRUO8qr/EKeAXiG5p5ZdEmv3NXPLOQnXtD1/30Dfe0h8zUJMp9BL6CZc5sK2007p4Vpi3REJ3sSsMwooAlJjfDGQ/NBZxSaH98+xu/c1mpSSQmSJPKRL755Z5aqB4WbSjxFpAGJ0TjyTAJvE57yEpNYm91o0GvDGLcf/Pacu/27K+3hXVdz4PLN7fdMIyuiXnuYXDuo/ObhC6emrc+qEH2aK/4ZqvMvvZI7/alTxV4t/f6hE58wz7tJSs1ib1VjdcL5rkHhoUCi3AEwzMncNuMce3soWEY0caMexv5yaFD+MPp45sc9xSzmL+uMb49pn8wIU0ob8G7biuZqYl+167yeZu4fvqooJ/ZU9Xyw8Wj5Z6eYqmPhtHVMeMegpYkc39/2v786ODB9MlK4d+XTPYef2vpFsqqar0evIfbT2nq7ZZUNoZoIrmidOH6Yr/J02r32st+N52rpo0M+pmyMIx7eU09yYliRTgMIwawv9IQlFQ0b+wSE4T0lEQKfns8R47MY+WdJwLw0bdF3PDiUv70zkq/9hdPGdbkGife9yn/mluIqnpVHNtSYDmQwIlZz4PDs9jI471/dP0x3jZ7qvwfRsGoqK4jI8WmaQwjFjDjHoLSSv+slomDc7nw8KGs/ePJrPtT00JTvmXvQk1Q3nDCaDJSEunto6Z4xxtfM+yWt6lyVRxH9w+lqhw+N5wwGmg06pW19SQmiDfF8qppIymcOcOrNgn+efGh2Ftd76fjbhhG18WMewh8jd0Vx4zg9SuP4HenjSchQYJqqISjq3Ll1P1Y8YcTefbSQ5uc83rXQcrrtRaPYqNHA6aqtoG0EKGUFy8/DAgvLLOnqtYKchhGjGDGPQSezJB/XpDP9dNHt+kaV04dwd1nTmhyfHT/Hiz//QkcNCTXe2yNm72Sltz+ryTbNcDfbHUUHKtq60NOgg7MdQpxt+S5V9XWU1JRYwU5DCNGsABqCDwrPPtlp5GQ0Da1wxtOCB0/z0pN4tUrjuDNpVu46tmvuO31rwGCFsZuLZ5CHtvLqlFVnvliQ8i2ngdBc3MMu8trmHTn+wCcGER62DCMrod57iHweO7RLkAxpFeG334kPPfB7jVve22536KpYGS6Hv2fZ69k197qoG2emd9YtLuksvnrGYbRNQjbkohIooh8JSJvhjh/loisEJGvReTZyHUxsizdVMLyzaVU1NQ1m37orRXaChGvZy89xLsdbrrguAH+OfCRkM/1jYvf9PKyZtv6zhUc7EoaB/JCQaOk8fy1u4O2MQyja9EaN/Ea4JtgJ0RkJHALcISqjgeujUDfosJpD37OKQ98xrjb3+XUBz4L2W6Pu2I0Oy38GPPhI/rwq+OcNMPZ1xzZQmuHpMQEnrq4MU8+NQI55IkJwmi3JN8H32wHnFJ9oejZQhz96FGNJREPHJzbTEvDMLoKYVkSERkEzAAeC9HkUuDvqloMoKo7ItO9yPKfgKIaq4IoM3rYU1VLalJCqz3pq6ftR8Fvj2N4XlbYn+mR1hj6iVThi1euONxvf9qYviFawqtX+NdZqa1vYLmP3MLu8hqyUpM446CB/Ofnh0Wkf4ZhRJdw3cT7gBuBUMs2RwGjRORzEZkvIidGpHcR5saXlzY5VhpiIvEfH69tU5HphAShT1Zqyw19GOsTmomE5w5N5wp8NegDGdonk4muR/7VhmLuenMFpzzwGSvceqm79tYwfp9s7j37QFudahgxQot/qSJyCrBDVRc20ywJGAkcA5yLU0+1yfu7iFwmIgUiUlBU1LH1s0PJCazZGdp77yh8vfWkdmi5B3L6gft4t1vSiPeEXn7w0Fz+Nc+ZQN2wuxyAneXVrX5gGYbRuYRjSY4AThORQuB5YJqIPB3QZhPwhqrWquo64DscY++Hqs5S1XxVzc/Lyws8HVV+9q+CoMfvenNF0OMpiQl+HnW0efPqKdxxamTVFrNakelz7bHBNGecydZde2vok9X2uq6GYXQ8LRp3Vb1FVQep6lDgHOBDVf1JQLPXcLx2RKQPTpgmvAoQHcSXrpBW4OThog0lQdv3SEvyW2QUbfYfmMNFRzTVn2kPP8ofHHbbYLn8FTV11NQ1UFpZS2/z3A0jpmhzDEBE/iAip7m77wK7RGQF8BFwg6ruikQHI8VffjQRgPvPOQiA0yY2hizm+tRBXbaplNU79lJWVec30RmLjHF1asLVg7n2OH/vvaq2wZsn39s8d8OIKVplvVR1DjDH3b7d57gCv3Z/uiQnHTCAxbcfT25GCoUzZwDw36VbUIUfP/YFz116KIeN6M2pDzamR7YmDbIrkpacyG9OHsvh+/UOq/21x43i0OG9OWfWfMCRHNjpLmzqnWmeu2HEEt0q9SE3w9/7/N2pjcU2zn10fhPxrNbErLsqlx41nPH75ITd/tDhvb3pjtV1DexyPXeLuRtGbNGtjHsggQqHgRrssR6WaSv5+/YEHM/dI0lgMXfDiC26tXEP1HEZkJ3mt98jxsMybSUhQUhJTKCqrp5de81zN4xYpJsb98aJxlH9srxFLTzEQ1imraQmJ1Bd28DO8mpSkhK69f+FYcQi3fov1te4f7d9L5OG9CQ3PdkbZ+6uYRlwMmyenFtIUoLQt0dqWMVIDMPoOpjn7sMH32wnxycPPtazZdpDqSucVtegFm83jBikmxt3/+Hv3FvD2qJyr/Jhdy4pV1XbKNewuaSyE3tiGEZb6L5xByA9hALjPy/Ip2B9sZ8X351pqeCHYRhdj27uuTc17o9fmE/vrFRO6Obl5L649VhuOMGpHXuqz2pewzBig27tuQcroTeyb49O6EnXo192GldO3Y8LDx8aMRliwzA6ju5t3INorvTKtHxuX6JdQ9YwjOjQrf9yPel9g3ul88wlh/L5mp1mzAzDiAu6vSWbc/0x5GYkk5uRwpDeQzq7O4ZhGBGh2xv3oX0yO7sLhmEYESfsmTIRSRSRr0TkzWba/FBEVETyI9M9wzAMoy20Jg3iGuCbUCdFpIfb5ov2dsowDMNoH2EZdxEZBMwAHmum2Z3An4GqCPTLMAzDaAfheu73ATcCDcFOisgkYLCqvhWpjhmGYRhtp0XjLiKnADtUdWGI8wnAvcB1YVzrMhEpEJGCoqKiVnfWMAzDCI9wPPcjgNNEpBB4HpgmIk/7nO8B7A/McdscCrwRbFJVVWepar6q5ufl5bW784ZhGEZwxKltHWZjkWOA61X1lGbazHHbFLRwrSJgfdg3hz7Azla0j1VsnPFDdxgj2Dg7mn1VtUXvuM157iLyB6BAVd9oy+fD6VzA/QpUNe5TLG2c8UN3GCPYOLsqrTLuqjoHmONu3x6izTHt7ZRhGIbRPkzuzzAMIw6JJeM+q7M70EHYOOOH7jBGsHF2SVo1oWoYhmHEBrHkuRuGYRhhYsbdMAwjDjHjbhiG4YO76j7m6RKDEJEB7r9N697FETbO+EJE8tx/u8TfUTToRt/lJBH5MYCqBtXQijU69ZdSRLJE5N/AZhE5QFXr4/GXyMYZX4hItjvOxSIySlUb4s3Ad6PvUkTkTuB/wHUicoR7POa/z07NlhGRc4EDgDRgsqpO6bTORJFuNM6zgYnE/zivAg4GdgEDVfXcTu5SxBGRc4AJxPl3Cd6/z13AIOBoVb2gk7sUETrcuIvI8e593xORbCBVVYtEZANwo6o+LyJJqlrXoR2LMCJyJtBPVf8uIjlASpyO8wycP4hrRKQnkBSn45wEVKjqSvf7zAQqgXeBO1T1HRFJVNX6Tu1oOxCRYcA2Va10v8tkVd0Rh9/lWTiGfL6qznW9dAXGArcDr6vqc7H+faKqHfIDjMdRlfwIyPc57nnAnAls6Kj+RHGcWcDLwHzgXJ/xJcTZOMcBzwJfAfU4DzKAxDgb5zDgLWAeTpWxYwPOXwJ80tn9bOcYhwLv4IQmXgZGB5yPl+8yEcd4LwR+DSwDzvA5nw6cB7wG9Ozs/rb3J6pxJRER999ewCfAblWdqj6KkaqqIiKq+hKwSUR+734mLZp9iySecboMBrar6qGq+py6vzXqxGXjYpwichTwKI7ncxBwP3AYgDqx2bgYp8v1wGJVPQznj/6SgObPAOVuqAYRSemYXraPIGP8QlWPxXG+7hSR8Z6Tsfxd+qKOFz4auE5V7wXuAK4SkbHu+UpgLrAZOANAREZ0UnfbTZtVIcMkDahU1d0icg8wEkBELgS2AKtVdS3OE7UO+D7wrYgoMEBEblfV7VHuYyRIw3lFBydOOQhARK4A8nDE1r5QVU8JwlgdZzpQAawApqtquWvMRuIKyrmvuAnEwffpGsByoNY9ngN8IyKjVfVbAFWtEpFbgH+KSG8AEblPVUs7o+OtwDNGjw1YAaCqD4rI9cB57jh2uOdj8rsUkfNxpMWXqGoJsB3o6YaXXhGRacDZInKnqtar6joReR54XkT+CvwcWNN5I2g7UfHcReR4EXkfuMedmAHHu/ueiGwFTgNOBl4Tkf20MYaXB2QDxwAPdvVfHp9x3u1OygAsAraKyOM43mwpcAtwoU+2QV9id5znqOpO17CnqWoNzuvteeC8ocTB93mPiJzlvnV9BowUka+AE3EckadFZLqP99sXp2DNccBLXdmwBxljHbAbOEhEJorIRGA5MATo5fPRmPku3QyYASLyEXABzu/m30UkC0eP/QCc8CnAA8APcL5DXC/+UeBrYIqqPtfR/Y8YUYhr7YcTmzwdOAjntfVW99ypwAU+bf8J3OVuDwIeAc7u7FhVO8Z5Hc7b0F+AApwJKYCfAg/heH4DY3ycT/t8n57xHe0ez/P53D4xPs5ncYrOgPMq/4pP29uAv7rbI4DXgR919hjaMMbngCtwqqndBryJ8zDLd8d/lfu5mPnbpHHOZxTwtOcY8DDwOJALzAaOAjLc8y8A17jbA4BTOnscEfm/iNB/aAKNE4bnAQ/5nLsYKAH6+rZ3//2ha/Sks/8jIjDOS9xx5rq/OB8C57nnJgCven7xuvpPG77P41zDkNTZfY/COPvheK33A2Pdc1OAlzyf7co/Yf7O5rn7w33OXQn8rLP734pxJgJ/BP6M42ycCvwr4PwOnMSO8127c7Z77hngkM4eQ6R/2h2WEZGLgE3Ane6hZcA5bloVQDJOzOr/eT6jzuTiBTgTGu+q+z/clQljnEnAOuBuVf0Exxj8WkRuwskS+hzQgImsLkcbv88PcLy9wzuwq+0izHGudc+X4YQofiki1wD/AD6gi3+fYf7OrgH+6u6vcz93GY7hX9RxvW07InI0TgZMT2A1znhrgakiMhm8k6m/B+5R1aeA94Dz3XBbEs7/TXzRzqdlFk4GwTU4vwhj3OP34bzyfY7zun4ATjpZX6A3cA/OBNz3OvvpFoVxvg30d89/D2dC5rDOHkOUvk/POJOBy4ChnT2GKIzzHZyc9rHA1cC/gEM7ewxR+C49qazXAl/Gyt+m2+cjgZ/67D8E/AK4EFjoHksA+uO8cQ12j/XH520l3n4i8R87xP13JvCCu52I4+lMcfcHA0/iPCGTcAq8dvrgozjOtM7ubweM8wmcBWid3ucoj/NfOAvQOr3PURzjk57vEjcOHUs/QAaQSmO8/TzgT+72YuBqdzsfeK6z+9tRP+0Oy6jqBnfzPmCYiJygzitQqap+5p67HCeFDlWtU9X17b1vR9PKcdYGu0Ys0IpxVuKkO8YkrRhnOc4irZijlb+zde5nKjq+p+1DVStUtVobV5MeDxS52xcBY0XkTZw3lpgINUWECD9Bfw587LM/GSeTwBuqiIcfG6eNM9Z+uskYE3HCL+8A+7nH9sNJcpiCowPU6f3sqJ+IacuISII6E6UvAVuBapxJp1WqGpOLAIJh47RxxhrdYYzgXXWbAjyGk512MY4g2NWquqcz+9YZRGwRk/vLk4EzaXoujhbF7Hj65QEbp40z9ugOYwRHygQnf/88HO2YV1X1gu5o2CHy8gNX4MS0jlfV6ghfuyth44wvusM4u8MYwUn9/A1wb5yPs0UiKvnref2L2AW7KDbO+KI7jLM7jNHwp1OLdRiGYRjRIeZLSRmGYRhNMeNuGIYRh5hxNwzDiEPMuBuGYcQhZtwNwzDiEDPuRkwhIvUislhEvhaRJSJynVvaz7fNfSKy2XNcRC5yP7NYRGpEZJm7PVNELhSRIp/zi0VkXDP3Hy8iH4rItyKySkRu88j+BlzraxF5yV08hIiMFpE57rlvRGRWNP+fDMNSIY2YQkT2qmqWu90Xp2LQ56p6h3ssAUeXfCtwi6p+FPD5QiBfVXe6+xe6+1eFce90nBJ0v1DV91zD/TLwpqr+PfBaIvIs8L6qPiEi7+IUynjdPXeAqsafhrjRZTDP3YhZ1CnefBlOBXtP0YxjcOpfPoyz1D6S/BjnQfKee/8K4Crg5sCG4hSezgSK3UMDcFZPevpuht2IKmbcjZhGVdfiqAH2dQ+diyPt+iowQ0SSw7jM2QFhmfQQ7cbjVPzxvf8aIEtEsn2vBWzG0U3/r3v8r8CHIvKOiPxKRHLDHaNhtAUz7kbcICIpwMnAa65Y1BfACWF89AVVPdDnp7Id3XhBVQ/EqfKzDLgBQFWfwKnm9CLO28V8EUltx30Mo1nMuBsxjYgMxymmsQPHkOcCy9zY+hQiG5pZARwc5P57A5UHXYXC/+IUS/cc26Kqj6vq6TjFMfaPYN8Mww8z7kbMIiJ5wCPAg64xPRf4maoOVdWhwDDgeE/GSgR4BpgiIse5908H/gbcHaL9FJwC1IjIiZ4QkYj0x6klvDlC/TKMJkRa8tcwok26G9NOxvF+/w3c6xrwE3HKxgGgquUi8hlwKvBCM9c8W0Sm+OxfoapzAxupaqWInA48ICJ/x4n1/xt4MMi1EnAmUC90j08H7heRKnf/BlXdFu6gDaO1WCqkYRhGHGJhGcMwjDjEwjKGEYCIHIATbvGlWlUP6Yz+GEZbsLCMYRhGHGJhGcMwjDjEjLthGEYcYsbdMAwjDjHjbhiGEYeYcTcMw4hD/j8NF9lQA8WdPgAAAABJRU5ErkJggg==\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# df['TMPMN'].rolling(window=365, center=True).mean().plot()\ndf[:365]['TMPMN'].rolling(window=30, center=True).mean().plot()",
      "execution_count": 78,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1148bcda0>"
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df[\"1949\"]",
      "execution_count": 80,
      "outputs": [
        {
          "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>TMPMAX</th>\n      <th>TMPMIN</th>\n      <th>TMPMN</th>\n      <th>PRECIP</th>\n    </tr>\n    <tr>\n      <th>DATE_OBS</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1949-01-01</th>\n      <td>-2.1</td>\n      <td>-6.7</td>\n      <td>-4.2</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-02</th>\n      <td>-0.5</td>\n      <td>-6.7</td>\n      <td>-1.2</td>\n      <td>4.2</td>\n    </tr>\n    <tr>\n      <th>1949-01-03</th>\n      <td>1.1</td>\n      <td>-2.1</td>\n      <td>-0.7</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-04</th>\n      <td>3.3</td>\n      <td>0.9</td>\n      <td>2.3</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-05</th>\n      <td>4.0</td>\n      <td>-0.9</td>\n      <td>1.1</td>\n      <td>0.8</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1949-12-27</th>\n      <td>0.1</td>\n      <td>-2.8</td>\n      <td>-0.8</td>\n      <td>4.2</td>\n    </tr>\n    <tr>\n      <th>1949-12-28</th>\n      <td>-0.1</td>\n      <td>-9.8</td>\n      <td>-3.9</td>\n      <td>3.1</td>\n    </tr>\n    <tr>\n      <th>1949-12-29</th>\n      <td>-9.3</td>\n      <td>-27.0</td>\n      <td>-21.4</td>\n      <td>0.9</td>\n    </tr>\n    <tr>\n      <th>1949-12-30</th>\n      <td>-20.9</td>\n      <td>-27.5</td>\n      <td>-23.9</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>1949-12-31</th>\n      <td>-15.6</td>\n      <td>-21.7</td>\n      <td>-18.6</td>\n      <td>0.2</td>\n    </tr>\n  </tbody>\n</table>\n<p>365 rows × 4 columns</p>\n</div>",
            "text/plain": "            TMPMAX  TMPMIN  TMPMN  PRECIP\nDATE_OBS                                 \n1949-01-01    -2.1    -6.7   -4.2     0.0\n1949-01-02    -0.5    -6.7   -1.2     4.2\n1949-01-03     1.1    -2.1   -0.7     0.0\n1949-01-04     3.3     0.9    2.3     0.0\n1949-01-05     4.0    -0.9    1.1     0.8\n...            ...     ...    ...     ...\n1949-12-27     0.1    -2.8   -0.8     4.2\n1949-12-28    -0.1    -9.8   -3.9     3.1\n1949-12-29    -9.3   -27.0  -21.4     0.9\n1949-12-30   -20.9   -27.5  -23.9     0.2\n1949-12-31   -15.6   -21.7  -18.6     0.2\n\n[365 rows x 4 columns]"
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "df[\"1949-01\"]",
      "execution_count": 82,
      "outputs": [
        {
          "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>TMPMAX</th>\n      <th>TMPMIN</th>\n      <th>TMPMN</th>\n      <th>PRECIP</th>\n    </tr>\n    <tr>\n      <th>DATE_OBS</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1949-01-01</th>\n      <td>-2.1</td>\n      <td>-6.7</td>\n      <td>-4.2</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-02</th>\n      <td>-0.5</td>\n      <td>-6.7</td>\n      <td>-1.2</td>\n      <td>4.2</td>\n    </tr>\n    <tr>\n      <th>1949-01-03</th>\n      <td>1.1</td>\n      <td>-2.1</td>\n      <td>-0.7</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-04</th>\n      <td>3.3</td>\n      <td>0.9</td>\n      <td>2.3</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-05</th>\n      <td>4.0</td>\n      <td>-0.9</td>\n      <td>1.1</td>\n      <td>0.8</td>\n    </tr>\n    <tr>\n      <th>1949-01-06</th>\n      <td>-0.8</td>\n      <td>-3.2</td>\n      <td>-2.3</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-07</th>\n      <td>-0.6</td>\n      <td>-5.1</td>\n      <td>-3.4</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-08</th>\n      <td>0.5</td>\n      <td>-3.0</td>\n      <td>-0.6</td>\n      <td>1.7</td>\n    </tr>\n    <tr>\n      <th>1949-01-09</th>\n      <td>0.8</td>\n      <td>-1.6</td>\n      <td>-0.6</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-10</th>\n      <td>-1.1</td>\n      <td>-4.2</td>\n      <td>-2.5</td>\n      <td>1.6</td>\n    </tr>\n    <tr>\n      <th>1949-01-11</th>\n      <td>-3.0</td>\n      <td>-5.3</td>\n      <td>-4.3</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-12</th>\n      <td>-3.3</td>\n      <td>-8.4</td>\n      <td>-5.8</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-13</th>\n      <td>-4.8</td>\n      <td>-10.2</td>\n      <td>-6.8</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>1949-01-14</th>\n      <td>-1.0</td>\n      <td>-5.7</td>\n      <td>-2.5</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-15</th>\n      <td>-1.9</td>\n      <td>-3.2</td>\n      <td>-2.9</td>\n      <td>2.2</td>\n    </tr>\n    <tr>\n      <th>1949-01-16</th>\n      <td>-0.5</td>\n      <td>-4.9</td>\n      <td>-2.5</td>\n      <td>0.9</td>\n    </tr>\n    <tr>\n      <th>1949-01-17</th>\n      <td>-0.7</td>\n      <td>-4.0</td>\n      <td>-3.2</td>\n      <td>0.1</td>\n    </tr>\n    <tr>\n      <th>1949-01-18</th>\n      <td>-3.8</td>\n      <td>-10.3</td>\n      <td>-7.5</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-19</th>\n      <td>-6.9</td>\n      <td>-10.4</td>\n      <td>-8.8</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>1949-01-20</th>\n      <td>-2.4</td>\n      <td>-13.5</td>\n      <td>-8.6</td>\n      <td>2.5</td>\n    </tr>\n    <tr>\n      <th>1949-01-21</th>\n      <td>1.6</td>\n      <td>-2.6</td>\n      <td>0.0</td>\n      <td>3.6</td>\n    </tr>\n    <tr>\n      <th>1949-01-22</th>\n      <td>0.2</td>\n      <td>-11.7</td>\n      <td>-4.2</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-23</th>\n      <td>-11.4</td>\n      <td>-15.5</td>\n      <td>-13.4</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-24</th>\n      <td>-10.5</td>\n      <td>-14.4</td>\n      <td>-12.4</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-25</th>\n      <td>1.7</td>\n      <td>-11.5</td>\n      <td>-1.8</td>\n      <td>4.4</td>\n    </tr>\n    <tr>\n      <th>1949-01-26</th>\n      <td>-0.7</td>\n      <td>-6.8</td>\n      <td>-3.1</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-27</th>\n      <td>1.1</td>\n      <td>-9.1</td>\n      <td>-3.7</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-28</th>\n      <td>1.2</td>\n      <td>-2.6</td>\n      <td>-1.2</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-29</th>\n      <td>1.4</td>\n      <td>-2.6</td>\n      <td>-0.1</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-30</th>\n      <td>-0.5</td>\n      <td>-8.1</td>\n      <td>-6.5</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1949-01-31</th>\n      <td>-0.7</td>\n      <td>-7.9</td>\n      <td>-2.4</td>\n      <td>2.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "            TMPMAX  TMPMIN  TMPMN  PRECIP\nDATE_OBS                                 \n1949-01-01    -2.1    -6.7   -4.2     0.0\n1949-01-02    -0.5    -6.7   -1.2     4.2\n1949-01-03     1.1    -2.1   -0.7     0.0\n1949-01-04     3.3     0.9    2.3     0.0\n1949-01-05     4.0    -0.9    1.1     0.8\n1949-01-06    -0.8    -3.2   -2.3     0.0\n1949-01-07    -0.6    -5.1   -3.4     0.0\n1949-01-08     0.5    -3.0   -0.6     1.7\n1949-01-09     0.8    -1.6   -0.6     0.0\n1949-01-10    -1.1    -4.2   -2.5     1.6\n1949-01-11    -3.0    -5.3   -4.3     0.0\n1949-01-12    -3.3    -8.4   -5.8     0.0\n1949-01-13    -4.8   -10.2   -6.8     0.2\n1949-01-14    -1.0    -5.7   -2.5     0.0\n1949-01-15    -1.9    -3.2   -2.9     2.2\n1949-01-16    -0.5    -4.9   -2.5     0.9\n1949-01-17    -0.7    -4.0   -3.2     0.1\n1949-01-18    -3.8   -10.3   -7.5     0.0\n1949-01-19    -6.9   -10.4   -8.8     0.2\n1949-01-20    -2.4   -13.5   -8.6     2.5\n1949-01-21     1.6    -2.6    0.0     3.6\n1949-01-22     0.2   -11.7   -4.2     2.0\n1949-01-23   -11.4   -15.5  -13.4     0.0\n1949-01-24   -10.5   -14.4  -12.4     0.0\n1949-01-25     1.7   -11.5   -1.8     4.4\n1949-01-26    -0.7    -6.8   -3.1     0.0\n1949-01-27     1.1    -9.1   -3.7     0.0\n1949-01-28     1.2    -2.6   -1.2     0.0\n1949-01-29     1.4    -2.6   -0.1     0.0\n1949-01-30    -0.5    -8.1   -6.5     0.0\n1949-01-31    -0.7    -7.9   -2.4     2.1"
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "df[\"1949-01-01\"]",
      "execution_count": 83,
      "outputs": [
        {
          "ename": "KeyError",
          "evalue": "'1949-01-01'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2896\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2897\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: '1949-01-01'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-83-daf5924a8e56>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"1949-01-01\"\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/usr/local/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2993\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\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[0m\n\u001b[1;32m   2994\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2995\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2996\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\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   2997\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\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/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\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[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: '1949-01-01'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df['TMPMN']",
      "execution_count": 85,
      "outputs": [
        {
          "data": {
            "text/plain": "DATE_OBS\n1949-01-01   -4.2\n1949-01-02   -1.2\n1949-01-03   -0.7\n1949-01-04    2.3\n1949-01-05    1.1\n             ... \n2006-10-27    5.6\n2006-10-28    8.2\n2006-10-29    2.4\n2006-10-30   -0.7\n2006-10-31   -3.0\nName: TMPMN, Length: 21121, dtype: float64"
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df['mon'] = df.index.month",
      "execution_count": 90,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df['year'] = df.index.year",
      "execution_count": 91,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df['day'] = df.index.day",
      "execution_count": 92,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_pivoted = df.pivot_table(values='TMPMN', \n               index='year', \n               columns='mon')",
      "execution_count": 95,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.scatter(df_pivoted[3], df_pivoted[4])",
      "execution_count": 102,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.collections.PathCollection at 0x114c4aeb8>"
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_pivoted.corr()",
      "execution_count": 103,
      "outputs": [
        {
          "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>mon</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n    </tr>\n    <tr>\n      <th>mon</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1.000000</td>\n      <td>0.384755</td>\n      <td>0.257302</td>\n      <td>0.216484</td>\n      <td>0.047153</td>\n      <td>0.039691</td>\n      <td>0.206568</td>\n      <td>-0.120758</td>\n      <td>0.134955</td>\n      <td>0.117936</td>\n      <td>-0.142560</td>\n      <td>-0.059667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.384755</td>\n      <td>1.000000</td>\n      <td>0.379095</td>\n      <td>0.216300</td>\n      <td>0.017000</td>\n      <td>0.028284</td>\n      <td>0.252928</td>\n      <td>-0.097432</td>\n      <td>0.033740</td>\n      <td>0.071301</td>\n      <td>0.059700</td>\n      <td>-0.012276</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.257302</td>\n      <td>0.379095</td>\n      <td>1.000000</td>\n      <td>0.328143</td>\n      <td>0.071455</td>\n      <td>0.177538</td>\n      <td>0.091281</td>\n      <td>0.022202</td>\n      <td>-0.081379</td>\n      <td>-0.037189</td>\n      <td>-0.031521</td>\n      <td>-0.075481</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.216484</td>\n      <td>0.216300</td>\n      <td>0.328143</td>\n      <td>1.000000</td>\n      <td>0.041331</td>\n      <td>0.017182</td>\n      <td>0.159124</td>\n      <td>-0.117359</td>\n      <td>0.028134</td>\n      <td>0.037973</td>\n      <td>0.077483</td>\n      <td>0.000211</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.047153</td>\n      <td>0.017000</td>\n      <td>0.071455</td>\n      <td>0.041331</td>\n      <td>1.000000</td>\n      <td>-0.121644</td>\n      <td>0.028159</td>\n      <td>0.118944</td>\n      <td>0.038196</td>\n      <td>0.178622</td>\n      <td>0.175155</td>\n      <td>-0.156485</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.039691</td>\n      <td>0.028284</td>\n      <td>0.177538</td>\n      <td>0.017182</td>\n      <td>-0.121644</td>\n      <td>1.000000</td>\n      <td>0.306204</td>\n      <td>0.093280</td>\n      <td>-0.060803</td>\n      <td>0.111169</td>\n      <td>-0.370548</td>\n      <td>0.150160</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0.206568</td>\n      <td>0.252928</td>\n      <td>0.091281</td>\n      <td>0.159124</td>\n      <td>0.028159</td>\n      <td>0.306204</td>\n      <td>1.000000</td>\n      <td>0.411425</td>\n      <td>0.131770</td>\n      <td>0.132230</td>\n      <td>0.000195</td>\n      <td>0.053797</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>-0.120758</td>\n      <td>-0.097432</td>\n      <td>0.022202</td>\n      <td>-0.117359</td>\n      <td>0.118944</td>\n      <td>0.093280</td>\n      <td>0.411425</td>\n      <td>1.000000</td>\n      <td>0.222137</td>\n      <td>0.179547</td>\n      <td>0.123741</td>\n      <td>0.002483</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0.134955</td>\n      <td>0.033740</td>\n      <td>-0.081379</td>\n      <td>0.028134</td>\n      <td>0.038196</td>\n      <td>-0.060803</td>\n      <td>0.131770</td>\n      <td>0.222137</td>\n      <td>1.000000</td>\n      <td>0.277824</td>\n      <td>0.077645</td>\n      <td>0.054884</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0.117936</td>\n      <td>0.071301</td>\n      <td>-0.037189</td>\n      <td>0.037973</td>\n      <td>0.178622</td>\n      <td>0.111169</td>\n      <td>0.132230</td>\n      <td>0.179547</td>\n      <td>0.277824</td>\n      <td>1.000000</td>\n      <td>0.146520</td>\n      <td>-0.031469</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>-0.142560</td>\n      <td>0.059700</td>\n      <td>-0.031521</td>\n      <td>0.077483</td>\n      <td>0.175155</td>\n      <td>-0.370548</td>\n      <td>0.000195</td>\n      <td>0.123741</td>\n      <td>0.077645</td>\n      <td>0.146520</td>\n      <td>1.000000</td>\n      <td>-0.112312</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>-0.059667</td>\n      <td>-0.012276</td>\n      <td>-0.075481</td>\n      <td>0.000211</td>\n      <td>-0.156485</td>\n      <td>0.150160</td>\n      <td>0.053797</td>\n      <td>0.002483</td>\n      <td>0.054884</td>\n      <td>-0.031469</td>\n      <td>-0.112312</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "mon        1         2         3         4         5         6         7   \\\nmon                                                                         \n1    1.000000  0.384755  0.257302  0.216484  0.047153  0.039691  0.206568   \n2    0.384755  1.000000  0.379095  0.216300  0.017000  0.028284  0.252928   \n3    0.257302  0.379095  1.000000  0.328143  0.071455  0.177538  0.091281   \n4    0.216484  0.216300  0.328143  1.000000  0.041331  0.017182  0.159124   \n5    0.047153  0.017000  0.071455  0.041331  1.000000 -0.121644  0.028159   \n6    0.039691  0.028284  0.177538  0.017182 -0.121644  1.000000  0.306204   \n7    0.206568  0.252928  0.091281  0.159124  0.028159  0.306204  1.000000   \n8   -0.120758 -0.097432  0.022202 -0.117359  0.118944  0.093280  0.411425   \n9    0.134955  0.033740 -0.081379  0.028134  0.038196 -0.060803  0.131770   \n10   0.117936  0.071301 -0.037189  0.037973  0.178622  0.111169  0.132230   \n11  -0.142560  0.059700 -0.031521  0.077483  0.175155 -0.370548  0.000195   \n12  -0.059667 -0.012276 -0.075481  0.000211 -0.156485  0.150160  0.053797   \n\nmon        8         9         10        11        12  \nmon                                                    \n1   -0.120758  0.134955  0.117936 -0.142560 -0.059667  \n2   -0.097432  0.033740  0.071301  0.059700 -0.012276  \n3    0.022202 -0.081379 -0.037189 -0.031521 -0.075481  \n4   -0.117359  0.028134  0.037973  0.077483  0.000211  \n5    0.118944  0.038196  0.178622  0.175155 -0.156485  \n6    0.093280 -0.060803  0.111169 -0.370548  0.150160  \n7    0.411425  0.131770  0.132230  0.000195  0.053797  \n8    1.000000  0.222137  0.179547  0.123741  0.002483  \n9    0.222137  1.000000  0.277824  0.077645  0.054884  \n10   0.179547  0.277824  1.000000  0.146520 -0.031469  \n11   0.123741  0.077645  0.146520  1.000000 -0.112312  \n12   0.002483  0.054884 -0.031469 -0.112312  1.000000  "
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "corr = df_pivoted.corr()",
      "execution_count": 97,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt",
      "execution_count": 98,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np",
      "execution_count": 104,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.imshow(corr[np.abs(corr) > 0.3], vmin=-1, vmax=1, \n           cmap='seismic')\nplt.colorbar()",
      "execution_count": 109,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.colorbar.Colorbar at 0x114fc5e10>"
          },
          "execution_count": 109,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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VwA+ASwEkjQC/a/sTRdn5wNslXV6cd3nRI3ubpIWAgO3A77a6YBJeREzSq15a289TmwunsXwc+ESx/jXga1Ocv3K610zCi4hJ8mlZRFRGEl5EVEYGAI3qeuyxUqrZUEotsEEqpx5P+cpX5aWFFxGVkYQXEZWRhBcRlZGEFxGVkU6LiKiMtPCmIOmz1N6INrADuML2kP7bEFENw5zwOh48QNJi4NPAiO33AnOANWUFFhH90cPBA3qu21vaucDPSXodOAH4UfchRUQ/DXMLr+OEZ3uvpD8Ffgj8FPiO7e80HleMgLoOYOnSpZ1eLiJ6aFgTXje3tPOpjUm/DDgVOFHSxxuPsz1qe8T2yMKFCzuPNCJ6olezlvVDNwOAXgg8Y/uA7deBbwG/XE5YEdEveYbX3A+BD0g6gdot7QXAeClRRUTf5BleE7YflrQZ+B5wGHiU2kQaETHLJeE1Yfta4NqSYomIATDMLbxM4hMRk/TqGZ6kkyXdLenp4u/8KY57Q9L2YhmrK18m6WFJuyR9vZjw55iS8CJikh720q4H7rW9HLi32G7mp7ZXFMvFdeVfAq63/S7gBeDKVhdMwouIo/Sol3Y1cGuxfiu1qRbbUkzNuBKYmLqxrfMzeEDMKmWNVFzWyMkwfKMnT/MZ3gJJ9W9njNput/PylLrJtJ8DTpniuOOLaxwGNtr+a+DtwIu2JyLdAyxudcEkvIg4in2k3UMP2h6Zaqeke4B3NNl1zeTr2ZKm+pfj9OLLrncCWyXtAF5qN8B6SXgR0cDAG+XUZF841T5JP5a0yPY+SYuA/VPUsbf4u1vS/cDZwDeBkyTNLVp5S4C9reLJM7yIaGDgtTaXrowBa4v1tcC3Gw+QNF/SccX6AuA8YKdtA/cBlxzr/EZJeBHRxJE2l65sBC6S9DS1T1U3AkgakXRjccx7gHFJ/49agttoe2ex7/PA5yTtovZM76ZWF8wtbUQ0KO+W9phXsZ+n9klqY/k4tYGFsf1d4H1TnL8bOGc610zCi4gGvUl4/ZCEFxFNJOFFRCWkhRcRlWHg9X4HMSOS8CKiQVp4EVEpSXgRUQlp4UVEpXT9UvFASsKLiAZp4UVEZUx8Szt8kvAiokFaeBFRKXmGFxGVkBZezDIrV5ZTz9at5dQzaMoclr2s4eIHa6j4JLyIqIR0WkREZZg8w4uICsktbURUwvB2WrSc00LSJkn7JT1eV3aypLslPV38nT+zYUZE70wkvHaWzrWTRyT9J0nb65ZXJH202HeLpGfq9q1odc12JvG5BVjVULYeuNf2cuDeYjsihkZPJvFpmUds32d7he0VwErgZeA7dYf80cR+29tbXbBlwrP9AHCooXg1cGuxfivw0Vb1RMRs0bNpGqebRy4B/t72y51esNNpGk+xva9Yfw44pdMAImLQ9OaWlunnkTXA7Q1lX5T0mKTrJ+avPZauOy1sW9KUb0xKWgesA1i6dGm3l4uInmg7mS2QNF63PWp7dGJD0j3AO5qcd039Rht5ZBG16Rrvqiu+mlqinAeMUpun9rpjBdtpwvuxpEW29xWB7J/qwOLHjwKMjIwM0qvkEdHUtN7DO2h7ZMqa7Aun2iep7TwCXAr8le2fTbZR1zp8VdLNwB+2CrbTW9oxYG2xvhb4dof1RMTA6dkt7XTyyGU03M4WSRJJovb87/Em503SzmsptwMPAu+WtEfSlcBG4CJJTwMXFtsRMTR6kvCa5hFJI5JunDhI0hnAacD/aTj/Nkk7gB3AAuC/t7pgy1ta25dNseuCVudGxGzUm29pbT9Pkzxiexz4RN32vwKLmxw37SEy8qVFRDTIt7QRUSnD+WlZEl5ENBjeb2mT8CKiQRJezDLDOlLxICprpOIyRk5eBL/YfSQZADQiKiWdFhFRCbmljYhKScKLiEpICy8iKiXP8CKiEo6QXtqIqJDc0kZEJeQZXkRUSp7hRUQlpIUXEZWShBcRlZBe2oiolLTwIqIShnfE405nLYuIoTbzk/hI+g1JT0g6ImnKqR4lrZL0lKRdktbXlS+T9HBR/nVJ81pdMwkvIhr0bJrGx4H/Ajww1QGS5gA3AL8KnAVcJumsYveXgOttvwt4Abiy1QWT8CKigYHX21y6uIr9pO2nWhx2DrDL9m7brwF3AKuLuWhXApuL426lNjftMfX0Gd4jjzxyUNIPWhy2ADjYi3jalHhaG7SYqhzP6d1X8dJd8DcL2jz4eEnjddujtke7j+FnFgPP1m3vAc4F3g68aPtwXflRUzk26mnCs72w1TGSxm1PeT/fa4mntUGLKfF0x/aqsuqSdA/wjia7rrH97bKu06700kbEjLF9YZdV7AVOq9teUpQ9D5wkaW7RypsoP6Y8w4uIQbYNWF70yM4D1gBjtg3cB1xSHLcWaNliHMSEV+b9fxkST2uDFlPimQUk/WdJe4BfAv5O0l1F+amStgAUrbergLuAJ4E7bT9RVPF54HOSdlF7pndTy2u6pCnmIiIG3SC28CIiZkQSXkRUxsAkvKk+H+ljPKdJuk/SzuLzl8/0OyaovXku6VFJfzsAsZwkabOkf5b0pKRf6nM8ny3+Wz0u6XZJx/chhk2S9kt6vK7sZEl3S3q6+Du/13FFzUAkvBafj/TLYeAPbJ8FfAD45ADEBPAZag9vB8GfA/9g+0zgP9LHuCQtBj4NjNh+LzCHWo9er90CNL7Hth641/Zy4N5iO/pgIBIeU3w+0s+AbO+z/b1i/SfU/s/c8k3umSRpCfBrwI39jKOI5W3A+RQ9Y7Zfs/1if6NiLvBzkuYCJwA/6nUAth8ADjUUr6b26RO0+QlUzIxBSXjNPh/pa3KpJ+kM4Gzg4f5GwpeBP2Ywxu5ZBhwAbi5usW+UdGK/grG9F/hT4IfAPuAl29/pVzwNTrG9r1h/Djiln8FU2aAkvIEl6S3AN4Hft/1vfYzj14H9th/pVwwN5gLvB75i+2zg3+njrVrxXGw1tUR8KnCipI/3K56pFC/M5l2wPhmUhDfV5yN9JenN1JLdbba/1edwzgMulvSv1G75V0r6Wh/j2QPssT3R6t1MLQH2y4XAM7YP2H4d+Bbwy32Mp96PJS0CKP7u73M8lTUoCa/p5yP9DKgYfuYm4Enbf9bPWABsX217ie0zqP3vs9V231owtp8DnpX07qLoAmBnv+Khdiv7AUknFP/tLmBwOnfGqH36BG1+AhUzYyAGD7B9WNLE5yNzgE11n4/0y3nAbwI7JG0vyv6b7S19jGnQfAq4rfhHajdwRb8Csf2wpM3A96j1sD9KHz7pknQ78EFgQfHZ1LXARuBOSVcCPwAu7XVcUZNPyyKiMgblljYiYsYl4UVEZSThRURlJOFFRGUk4UVEZSThRURlJOFFRGX8f1s/Tz5+BSPgAAAAAElFTkSuQmCC\n",
            "text/plain": "<Figure size 432x288 with 2 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "months = ['jan', 'feb', 'mar', 'apr', 'may', \n          'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']",
      "execution_count": 111,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import calendar",
      "execution_count": 122,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(7, 7))\nplt.imshow(corr, vmin=-1, vmax=1, \n           cmap='seismic')\nplt.xticks(range(12), months, rotation=90)\nplt.yticks(range(12), months)\nplt.colorbar()",
      "execution_count": 121,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.colorbar.Colorbar at 0x119b5aac8>"
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 504x504 with 2 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.groupby(['mon', 'year']).mean()['TMPMN'].unstack().T",
      "execution_count": 135,
      "outputs": [
        {
          "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>mon</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n    </tr>\n    <tr>\n      <th>year</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1949</th>\n      <td>-3.670968</td>\n      <td>-7.339286</td>\n      <td>-2.780645</td>\n      <td>4.280000</td>\n      <td>15.225806</td>\n      <td>16.973333</td>\n      <td>17.425806</td>\n      <td>16.074194</td>\n      <td>11.506667</td>\n      <td>4.800000</td>\n      <td>-0.423333</td>\n      <td>-4.322581</td>\n    </tr>\n    <tr>\n      <th>1950</th>\n      <td>-18.022581</td>\n      <td>-6.764286</td>\n      <td>-2.225806</td>\n      <td>9.043333</td>\n      <td>11.738710</td>\n      <td>15.123333</td>\n      <td>16.177419</td>\n      <td>14.080645</td>\n      <td>11.990000</td>\n      <td>4.745161</td>\n      <td>-0.433333</td>\n      <td>-5.503226</td>\n    </tr>\n    <tr>\n      <th>1951</th>\n      <td>-12.138710</td>\n      <td>-12.264286</td>\n      <td>-3.996774</td>\n      <td>8.403333</td>\n      <td>9.738710</td>\n      <td>17.706667</td>\n      <td>18.590323</td>\n      <td>18.325806</td>\n      <td>11.990000</td>\n      <td>2.780645</td>\n      <td>-4.810000</td>\n      <td>-1.251613</td>\n    </tr>\n    <tr>\n      <th>1952</th>\n      <td>-4.135484</td>\n      <td>-7.096552</td>\n      <td>-9.106452</td>\n      <td>5.166667</td>\n      <td>10.358065</td>\n      <td>17.340000</td>\n      <td>17.870968</td>\n      <td>16.848387</td>\n      <td>12.136667</td>\n      <td>3.925806</td>\n      <td>-1.133333</td>\n      <td>-5.883871</td>\n    </tr>\n    <tr>\n      <th>1953</th>\n      <td>-10.403226</td>\n      <td>-15.614286</td>\n      <td>-2.606452</td>\n      <td>7.183333</td>\n      <td>11.645161</td>\n      <td>19.193333</td>\n      <td>19.025806</td>\n      <td>17.290323</td>\n      <td>10.046667</td>\n      <td>5.790323</td>\n      <td>-3.156667</td>\n      <td>-5.632258</td>\n    </tr>\n    <tr>\n      <th>1954</th>\n      <td>-14.306452</td>\n      <td>-14.000000</td>\n      <td>-3.425806</td>\n      <td>3.030000</td>\n      <td>12.883871</td>\n      <td>18.963333</td>\n      <td>21.025806</td>\n      <td>18.354839</td>\n      <td>12.286667</td>\n      <td>5.738710</td>\n      <td>-1.573333</td>\n      <td>-4.974194</td>\n    </tr>\n    <tr>\n      <th>1955</th>\n      <td>-6.312903</td>\n      <td>-6.792857</td>\n      <td>-4.664516</td>\n      <td>1.466667</td>\n      <td>10.506452</td>\n      <td>15.100000</td>\n      <td>17.906452</td>\n      <td>17.916129</td>\n      <td>13.856667</td>\n      <td>7.712903</td>\n      <td>-3.023333</td>\n      <td>-14.306452</td>\n    </tr>\n    <tr>\n      <th>1956</th>\n      <td>-10.916129</td>\n      <td>-18.503448</td>\n      <td>-3.600000</td>\n      <td>3.940000</td>\n      <td>10.664516</td>\n      <td>20.570000</td>\n      <td>15.190323</td>\n      <td>14.625806</td>\n      <td>8.423333</td>\n      <td>4.670968</td>\n      <td>-5.180000</td>\n      <td>-4.254839</td>\n    </tr>\n    <tr>\n      <th>1957</th>\n      <td>-6.038710</td>\n      <td>-1.760714</td>\n      <td>-6.319355</td>\n      <td>6.640000</td>\n      <td>14.435484</td>\n      <td>15.240000</td>\n      <td>18.554839</td>\n      <td>17.087097</td>\n      <td>12.313333</td>\n      <td>5.241935</td>\n      <td>-0.893333</td>\n      <td>-4.622581</td>\n    </tr>\n    <tr>\n      <th>1958</th>\n      <td>-6.932258</td>\n      <td>-7.710714</td>\n      <td>-6.080645</td>\n      <td>4.063333</td>\n      <td>13.290323</td>\n      <td>14.890000</td>\n      <td>18.367742</td>\n      <td>15.696774</td>\n      <td>9.080000</td>\n      <td>6.038710</td>\n      <td>-0.786667</td>\n      <td>-7.641935</td>\n    </tr>\n    <tr>\n      <th>1959</th>\n      <td>-4.335484</td>\n      <td>-5.492857</td>\n      <td>-1.425806</td>\n      <td>6.630000</td>\n      <td>11.490323</td>\n      <td>16.886667</td>\n      <td>20.538710</td>\n      <td>16.932258</td>\n      <td>8.253333</td>\n      <td>2.229032</td>\n      <td>-5.110000</td>\n      <td>-10.990323</td>\n    </tr>\n    <tr>\n      <th>1960</th>\n      <td>-9.351613</td>\n      <td>-7.651724</td>\n      <td>-5.480645</td>\n      <td>5.046667</td>\n      <td>11.625806</td>\n      <td>18.486667</td>\n      <td>21.009677</td>\n      <td>16.190323</td>\n      <td>9.800000</td>\n      <td>2.338710</td>\n      <td>-3.620000</td>\n      <td>0.167742</td>\n    </tr>\n    <tr>\n      <th>1961</th>\n      <td>-6.174194</td>\n      <td>-2.335714</td>\n      <td>0.245161</td>\n      <td>4.310000</td>\n      <td>12.058065</td>\n      <td>19.173333</td>\n      <td>19.374194</td>\n      <td>16.825806</td>\n      <td>9.686667</td>\n      <td>6.590323</td>\n      <td>-1.550000</td>\n      <td>-8.070968</td>\n    </tr>\n    <tr>\n      <th>1962</th>\n      <td>-4.248387</td>\n      <td>-6.057143</td>\n      <td>-5.012903</td>\n      <td>7.590000</td>\n      <td>13.225806</td>\n      <td>13.506667</td>\n      <td>16.361290</td>\n      <td>14.832258</td>\n      <td>10.790000</td>\n      <td>6.451613</td>\n      <td>1.343333</td>\n      <td>-7.351613</td>\n    </tr>\n    <tr>\n      <th>1963</th>\n      <td>-15.929032</td>\n      <td>-10.057143</td>\n      <td>-9.429032</td>\n      <td>3.886667</td>\n      <td>17.022581</td>\n      <td>13.540000</td>\n      <td>19.132258</td>\n      <td>17.796774</td>\n      <td>13.216667</td>\n      <td>5.651613</td>\n      <td>-0.216667</td>\n      <td>-8.751613</td>\n    </tr>\n    <tr>\n      <th>1964</th>\n      <td>-8.074194</td>\n      <td>-9.975862</td>\n      <td>-6.190323</td>\n      <td>4.206667</td>\n      <td>11.529032</td>\n      <td>18.983333</td>\n      <td>19.990323</td>\n      <td>16.006452</td>\n      <td>11.806667</td>\n      <td>6.893548</td>\n      <td>-2.306667</td>\n      <td>-2.858065</td>\n    </tr>\n    <tr>\n      <th>1965</th>\n      <td>-9.529032</td>\n      <td>-10.071429</td>\n      <td>-3.303226</td>\n      <td>2.523333</td>\n      <td>9.738710</td>\n      <td>16.016667</td>\n      <td>16.661290</td>\n      <td>15.735484</td>\n      <td>12.923333</td>\n      <td>3.806452</td>\n      <td>-5.786667</td>\n      <td>-1.493548</td>\n    </tr>\n    <tr>\n      <th>1966</th>\n      <td>-9.664516</td>\n      <td>-8.857143</td>\n      <td>0.009677</td>\n      <td>8.716667</td>\n      <td>15.422581</td>\n      <td>16.540000</td>\n      <td>19.212903</td>\n      <td>16.645161</td>\n      <td>9.543333</td>\n      <td>6.087097</td>\n      <td>-0.900000</td>\n      <td>-10.525806</td>\n    </tr>\n    <tr>\n      <th>1967</th>\n      <td>-13.922581</td>\n      <td>-10.435714</td>\n      <td>0.338710</td>\n      <td>6.566667</td>\n      <td>16.945161</td>\n      <td>16.453333</td>\n      <td>17.706452</td>\n      <td>18.361290</td>\n      <td>11.353333</td>\n      <td>9.003226</td>\n      <td>0.420000</td>\n      <td>-9.583871</td>\n    </tr>\n    <tr>\n      <th>1968</th>\n      <td>-15.548387</td>\n      <td>-8.265517</td>\n      <td>-1.000000</td>\n      <td>6.020000</td>\n      <td>12.383871</td>\n      <td>18.330000</td>\n      <td>15.725806</td>\n      <td>17.712903</td>\n      <td>10.790000</td>\n      <td>2.954839</td>\n      <td>-2.533333</td>\n      <td>-5.322581</td>\n    </tr>\n    <tr>\n      <th>1969</th>\n      <td>-16.170968</td>\n      <td>-13.435714</td>\n      <td>-6.912903</td>\n      <td>5.973333</td>\n      <td>11.125806</td>\n      <td>14.736667</td>\n      <td>17.803226</td>\n      <td>16.445161</td>\n      <td>10.220000</td>\n      <td>4.690323</td>\n      <td>1.786667</td>\n      <td>-9.158065</td>\n    </tr>\n    <tr>\n      <th>1970</th>\n      <td>-10.374194</td>\n      <td>-8.325000</td>\n      <td>-2.851613</td>\n      <td>5.833333</td>\n      <td>12.600000</td>\n      <td>15.820000</td>\n      <td>19.316129</td>\n      <td>16.258065</td>\n      <td>11.090000</td>\n      <td>5.393548</td>\n      <td>-1.823333</td>\n      <td>-5.887097</td>\n    </tr>\n    <tr>\n      <th>1971</th>\n      <td>-3.538710</td>\n      <td>-9.417857</td>\n      <td>-4.161290</td>\n      <td>3.826667</td>\n      <td>12.774194</td>\n      <td>16.380000</td>\n      <td>17.467742</td>\n      <td>16.729032</td>\n      <td>10.780000</td>\n      <td>3.222581</td>\n      <td>-0.630000</td>\n      <td>-5.651613</td>\n    </tr>\n    <tr>\n      <th>1972</th>\n      <td>-14.874194</td>\n      <td>-7.403448</td>\n      <td>-2.541935</td>\n      <td>5.880000</td>\n      <td>12.574194</td>\n      <td>18.983333</td>\n      <td>22.412903</td>\n      <td>20.632258</td>\n      <td>10.983333</td>\n      <td>5.167742</td>\n      <td>-0.186667</td>\n      <td>-0.906452</td>\n    </tr>\n    <tr>\n      <th>1973</th>\n      <td>-10.248387</td>\n      <td>-3.385714</td>\n      <td>-1.016129</td>\n      <td>7.880000</td>\n      <td>13.254839</td>\n      <td>18.240000</td>\n      <td>17.977419</td>\n      <td>15.909677</td>\n      <td>7.633333</td>\n      <td>3.845161</td>\n      <td>-2.060000</td>\n      <td>-5.861290</td>\n    </tr>\n    <tr>\n      <th>1974</th>\n      <td>-10.219355</td>\n      <td>-1.460714</td>\n      <td>-0.619355</td>\n      <td>3.403333</td>\n      <td>9.641935</td>\n      <td>16.446667</td>\n      <td>18.180645</td>\n      <td>15.874194</td>\n      <td>13.103333</td>\n      <td>8.680645</td>\n      <td>1.776667</td>\n      <td>-2.335484</td>\n    </tr>\n    <tr>\n      <th>1975</th>\n      <td>-3.670968</td>\n      <td>-6.385714</td>\n      <td>1.238710</td>\n      <td>10.066667</td>\n      <td>15.638710</td>\n      <td>17.776667</td>\n      <td>18.500000</td>\n      <td>15.019355</td>\n      <td>13.663333</td>\n      <td>4.093548</td>\n      <td>-3.296667</td>\n      <td>-4.032258</td>\n    </tr>\n    <tr>\n      <th>1976</th>\n      <td>-12.241935</td>\n      <td>-11.455172</td>\n      <td>-2.558065</td>\n      <td>5.723333</td>\n      <td>10.925806</td>\n      <td>13.713333</td>\n      <td>16.103226</td>\n      <td>14.500000</td>\n      <td>9.653333</td>\n      <td>-0.948387</td>\n      <td>-0.803333</td>\n      <td>-3.690323</td>\n    </tr>\n    <tr>\n      <th>1977</th>\n      <td>-11.254839</td>\n      <td>-6.332143</td>\n      <td>-0.851613</td>\n      <td>7.030000</td>\n      <td>14.229032</td>\n      <td>16.836667</td>\n      <td>18.764516</td>\n      <td>15.825806</td>\n      <td>9.506667</td>\n      <td>3.100000</td>\n      <td>1.726667</td>\n      <td>-8.174194</td>\n    </tr>\n    <tr>\n      <th>1978</th>\n      <td>-7.270968</td>\n      <td>-9.500000</td>\n      <td>0.390323</td>\n      <td>4.613333</td>\n      <td>10.493548</td>\n      <td>14.313333</td>\n      <td>16.345161</td>\n      <td>15.774194</td>\n      <td>9.723333</td>\n      <td>3.341935</td>\n      <td>2.003333</td>\n      <td>-14.509677</td>\n    </tr>\n    <tr>\n      <th>1979</th>\n      <td>-9.954839</td>\n      <td>-8.828571</td>\n      <td>-0.883871</td>\n      <td>3.310000</td>\n      <td>17.106452</td>\n      <td>17.153333</td>\n      <td>16.674194</td>\n      <td>16.938710</td>\n      <td>11.730000</td>\n      <td>3.838710</td>\n      <td>-0.913333</td>\n      <td>-5.658065</td>\n    </tr>\n    <tr>\n      <th>1980</th>\n      <td>-11.312903</td>\n      <td>-7.151724</td>\n      <td>-6.370968</td>\n      <td>5.846667</td>\n      <td>8.370968</td>\n      <td>17.870000</td>\n      <td>17.200000</td>\n      <td>14.709677</td>\n      <td>10.536667</td>\n      <td>5.190323</td>\n      <td>-2.040000</td>\n      <td>-4.248387</td>\n    </tr>\n    <tr>\n      <th>1981</th>\n      <td>-5.393548</td>\n      <td>-4.907143</td>\n      <td>-3.135484</td>\n      <td>3.340000</td>\n      <td>14.019355</td>\n      <td>19.813333</td>\n      <td>21.512903</td>\n      <td>17.396774</td>\n      <td>10.816667</td>\n      <td>7.806452</td>\n      <td>-0.583333</td>\n      <td>-3.509677</td>\n    </tr>\n    <tr>\n      <th>1982</th>\n      <td>-10.158065</td>\n      <td>-8.792857</td>\n      <td>-0.648387</td>\n      <td>5.336667</td>\n      <td>11.977419</td>\n      <td>13.856667</td>\n      <td>18.400000</td>\n      <td>16.645161</td>\n      <td>11.753333</td>\n      <td>4.064516</td>\n      <td>2.033333</td>\n      <td>-1.135484</td>\n    </tr>\n    <tr>\n      <th>1983</th>\n      <td>-3.967742</td>\n      <td>-6.878571</td>\n      <td>-1.403226</td>\n      <td>9.290000</td>\n      <td>15.612903</td>\n      <td>14.550000</td>\n      <td>17.938710</td>\n      <td>16.032258</td>\n      <td>12.413333</td>\n      <td>6.200000</td>\n      <td>-1.466667</td>\n      <td>-3.180645</td>\n    </tr>\n    <tr>\n      <th>1984</th>\n      <td>-4.409677</td>\n      <td>-10.268966</td>\n      <td>-2.354839</td>\n      <td>7.466667</td>\n      <td>15.970968</td>\n      <td>15.590000</td>\n      <td>17.567742</td>\n      <td>15.125806</td>\n      <td>12.410000</td>\n      <td>6.800000</td>\n      <td>-3.543333</td>\n      <td>-9.590323</td>\n    </tr>\n    <tr>\n      <th>1985</th>\n      <td>-10.012903</td>\n      <td>-14.014286</td>\n      <td>-3.067742</td>\n      <td>5.346667</td>\n      <td>12.974194</td>\n      <td>14.663333</td>\n      <td>16.438710</td>\n      <td>19.419355</td>\n      <td>10.123333</td>\n      <td>6.070968</td>\n      <td>-3.333333</td>\n      <td>-6.535484</td>\n    </tr>\n    <tr>\n      <th>1986</th>\n      <td>-6.725806</td>\n      <td>-13.539286</td>\n      <td>0.158065</td>\n      <td>6.680000</td>\n      <td>13.648387</td>\n      <td>18.633333</td>\n      <td>17.796774</td>\n      <td>16.516129</td>\n      <td>8.580000</td>\n      <td>4.174194</td>\n      <td>-0.093333</td>\n      <td>-7.454839</td>\n    </tr>\n    <tr>\n      <th>1987</th>\n      <td>-17.506452</td>\n      <td>-6.057143</td>\n      <td>-5.312903</td>\n      <td>2.810000</td>\n      <td>12.832258</td>\n      <td>17.736667</td>\n      <td>16.764516</td>\n      <td>15.087097</td>\n      <td>9.046667</td>\n      <td>3.590323</td>\n      <td>-3.646667</td>\n      <td>-6.983871</td>\n    </tr>\n    <tr>\n      <th>1988</th>\n      <td>-7.241935</td>\n      <td>-6.113793</td>\n      <td>-0.993548</td>\n      <td>5.346667</td>\n      <td>13.848387</td>\n      <td>19.530000</td>\n      <td>21.587097</td>\n      <td>16.474194</td>\n      <td>11.280000</td>\n      <td>4.870968</td>\n      <td>-4.440000</td>\n      <td>-6.935484</td>\n    </tr>\n    <tr>\n      <th>1989</th>\n      <td>-2.112903</td>\n      <td>-0.503571</td>\n      <td>1.990323</td>\n      <td>7.656667</td>\n      <td>13.396774</td>\n      <td>20.053333</td>\n      <td>19.161290</td>\n      <td>16.248387</td>\n      <td>12.176667</td>\n      <td>5.329032</td>\n      <td>-2.633333</td>\n      <td>-5.293548</td>\n    </tr>\n    <tr>\n      <th>1990</th>\n      <td>-5.667742</td>\n      <td>0.382143</td>\n      <td>1.980645</td>\n      <td>8.120000</td>\n      <td>10.803226</td>\n      <td>14.506667</td>\n      <td>17.535484</td>\n      <td>15.954839</td>\n      <td>9.320000</td>\n      <td>5.358065</td>\n      <td>0.250000</td>\n      <td>-3.393548</td>\n    </tr>\n    <tr>\n      <th>1991</th>\n      <td>-4.875862</td>\n      <td>-6.678571</td>\n      <td>-1.219355</td>\n      <td>7.030000</td>\n      <td>13.387097</td>\n      <td>18.783333</td>\n      <td>18.080645</td>\n      <td>16.090323</td>\n      <td>10.973333</td>\n      <td>6.538710</td>\n      <td>0.970000</td>\n      <td>-3.974194</td>\n    </tr>\n    <tr>\n      <th>1992</th>\n      <td>-5.270968</td>\n      <td>-4.313793</td>\n      <td>1.654839</td>\n      <td>5.140000</td>\n      <td>11.932258</td>\n      <td>16.693333</td>\n      <td>18.622581</td>\n      <td>18.041935</td>\n      <td>13.146667</td>\n      <td>2.154839</td>\n      <td>-2.533333</td>\n      <td>-4.396774</td>\n    </tr>\n    <tr>\n      <th>1993</th>\n      <td>-4.416129</td>\n      <td>-4.882143</td>\n      <td>-1.932258</td>\n      <td>5.730000</td>\n      <td>14.490323</td>\n      <td>14.023333</td>\n      <td>17.506452</td>\n      <td>15.425806</td>\n      <td>6.870000</td>\n      <td>4.645161</td>\n      <td>-8.003333</td>\n      <td>-3.612903</td>\n    </tr>\n    <tr>\n      <th>1994</th>\n      <td>-3.422581</td>\n      <td>-11.300000</td>\n      <td>-2.929032</td>\n      <td>7.226667</td>\n      <td>9.848387</td>\n      <td>14.546667</td>\n      <td>17.561290</td>\n      <td>15.870968</td>\n      <td>13.676667</td>\n      <td>4.958065</td>\n      <td>-2.526667</td>\n      <td>-7.948387</td>\n    </tr>\n    <tr>\n      <th>1995</th>\n      <td>-5.874194</td>\n      <td>-0.846429</td>\n      <td>0.587097</td>\n      <td>9.116667</td>\n      <td>14.470968</td>\n      <td>19.683333</td>\n      <td>17.548387</td>\n      <td>16.793548</td>\n      <td>12.823333</td>\n      <td>6.683871</td>\n      <td>-2.816667</td>\n      <td>-9.506452</td>\n    </tr>\n    <tr>\n      <th>1996</th>\n      <td>-10.019355</td>\n      <td>-9.689655</td>\n      <td>-3.045161</td>\n      <td>6.403333</td>\n      <td>15.703226</td>\n      <td>16.526667</td>\n      <td>18.900000</td>\n      <td>17.274194</td>\n      <td>9.880000</td>\n      <td>6.051613</td>\n      <td>3.863333</td>\n      <td>-7.019355</td>\n    </tr>\n    <tr>\n      <th>1997</th>\n      <td>-7.732258</td>\n      <td>-4.682143</td>\n      <td>-0.932258</td>\n      <td>4.650000</td>\n      <td>11.093548</td>\n      <td>17.830000</td>\n      <td>18.729032</td>\n      <td>17.122581</td>\n      <td>8.516667</td>\n      <td>3.677419</td>\n      <td>-0.843333</td>\n      <td>-7.538710</td>\n    </tr>\n    <tr>\n      <th>1998</th>\n      <td>-4.725806</td>\n      <td>-7.642857</td>\n      <td>-1.348387</td>\n      <td>3.900000</td>\n      <td>13.712903</td>\n      <td>19.976667</td>\n      <td>18.864516</td>\n      <td>15.487097</td>\n      <td>10.700000</td>\n      <td>5.658065</td>\n      <td>-8.030000</td>\n      <td>-5.938710</td>\n    </tr>\n    <tr>\n      <th>1999</th>\n      <td>-4.577419</td>\n      <td>-6.253571</td>\n      <td>-0.835484</td>\n      <td>9.683333</td>\n      <td>8.674194</td>\n      <td>21.423333</td>\n      <td>21.719355</td>\n      <td>16.406452</td>\n      <td>11.770000</td>\n      <td>7.374194</td>\n      <td>-4.933333</td>\n      <td>-1.706452</td>\n    </tr>\n    <tr>\n      <th>2000</th>\n      <td>-6.112903</td>\n      <td>-2.703448</td>\n      <td>-0.700000</td>\n      <td>11.110000</td>\n      <td>10.825806</td>\n      <td>16.236667</td>\n      <td>19.332258</td>\n      <td>16.754839</td>\n      <td>10.043333</td>\n      <td>7.180645</td>\n      <td>-0.050000</td>\n      <td>-2.625806</td>\n    </tr>\n    <tr>\n      <th>2001</th>\n      <td>-4.312903</td>\n      <td>-7.192857</td>\n      <td>-2.125806</td>\n      <td>10.966667</td>\n      <td>11.251613</td>\n      <td>16.266667</td>\n      <td>22.967742</td>\n      <td>16.964516</td>\n      <td>12.193333</td>\n      <td>4.806452</td>\n      <td>-0.526667</td>\n      <td>-10.580645</td>\n    </tr>\n    <tr>\n      <th>2002</th>\n      <td>-4.754839</td>\n      <td>-0.425000</td>\n      <td>2.229032</td>\n      <td>7.183333</td>\n      <td>12.732258</td>\n      <td>17.310000</td>\n      <td>22.638710</td>\n      <td>17.041935</td>\n      <td>12.030000</td>\n      <td>2.545161</td>\n      <td>-1.476667</td>\n      <td>-12.561290</td>\n    </tr>\n    <tr>\n      <th>2003</th>\n      <td>-7.351613</td>\n      <td>-8.653571</td>\n      <td>-2.729032</td>\n      <td>4.673333</td>\n      <td>15.535484</td>\n      <td>12.820000</td>\n      <td>20.641935</td>\n      <td>16.948387</td>\n      <td>11.333333</td>\n      <td>5.567742</td>\n      <td>1.146667</td>\n      <td>-2.061290</td>\n    </tr>\n    <tr>\n      <th>2004</th>\n      <td>-6.458065</td>\n      <td>-7.006897</td>\n      <td>1.345161</td>\n      <td>4.556667</td>\n      <td>11.412903</td>\n      <td>15.283333</td>\n      <td>19.022581</td>\n      <td>18.383871</td>\n      <td>12.133333</td>\n      <td>5.935484</td>\n      <td>-1.560000</td>\n      <td>-2.935484</td>\n    </tr>\n    <tr>\n      <th>2005</th>\n      <td>-3.000000</td>\n      <td>-8.903571</td>\n      <td>-6.029032</td>\n      <td>7.116667</td>\n      <td>14.832258</td>\n      <td>16.506667</td>\n      <td>19.309677</td>\n      <td>17.638710</td>\n      <td>13.123333</td>\n      <td>6.022581</td>\n      <td>1.370000</td>\n      <td>-4.148387</td>\n    </tr>\n    <tr>\n      <th>2006</th>\n      <td>-10.787097</td>\n      <td>-13.307143</td>\n      <td>-3.719355</td>\n      <td>6.020000</td>\n      <td>12.432258</td>\n      <td>18.203333</td>\n      <td>17.977419</td>\n      <td>17.535484</td>\n      <td>13.303333</td>\n      <td>7.003226</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "mon          1          2         3          4          5          6   \\\nyear                                                                    \n1949  -3.670968  -7.339286 -2.780645   4.280000  15.225806  16.973333   \n1950 -18.022581  -6.764286 -2.225806   9.043333  11.738710  15.123333   \n1951 -12.138710 -12.264286 -3.996774   8.403333   9.738710  17.706667   \n1952  -4.135484  -7.096552 -9.106452   5.166667  10.358065  17.340000   \n1953 -10.403226 -15.614286 -2.606452   7.183333  11.645161  19.193333   \n1954 -14.306452 -14.000000 -3.425806   3.030000  12.883871  18.963333   \n1955  -6.312903  -6.792857 -4.664516   1.466667  10.506452  15.100000   \n1956 -10.916129 -18.503448 -3.600000   3.940000  10.664516  20.570000   \n1957  -6.038710  -1.760714 -6.319355   6.640000  14.435484  15.240000   \n1958  -6.932258  -7.710714 -6.080645   4.063333  13.290323  14.890000   \n1959  -4.335484  -5.492857 -1.425806   6.630000  11.490323  16.886667   \n1960  -9.351613  -7.651724 -5.480645   5.046667  11.625806  18.486667   \n1961  -6.174194  -2.335714  0.245161   4.310000  12.058065  19.173333   \n1962  -4.248387  -6.057143 -5.012903   7.590000  13.225806  13.506667   \n1963 -15.929032 -10.057143 -9.429032   3.886667  17.022581  13.540000   \n1964  -8.074194  -9.975862 -6.190323   4.206667  11.529032  18.983333   \n1965  -9.529032 -10.071429 -3.303226   2.523333   9.738710  16.016667   \n1966  -9.664516  -8.857143  0.009677   8.716667  15.422581  16.540000   \n1967 -13.922581 -10.435714  0.338710   6.566667  16.945161  16.453333   \n1968 -15.548387  -8.265517 -1.000000   6.020000  12.383871  18.330000   \n1969 -16.170968 -13.435714 -6.912903   5.973333  11.125806  14.736667   \n1970 -10.374194  -8.325000 -2.851613   5.833333  12.600000  15.820000   \n1971  -3.538710  -9.417857 -4.161290   3.826667  12.774194  16.380000   \n1972 -14.874194  -7.403448 -2.541935   5.880000  12.574194  18.983333   \n1973 -10.248387  -3.385714 -1.016129   7.880000  13.254839  18.240000   \n1974 -10.219355  -1.460714 -0.619355   3.403333   9.641935  16.446667   \n1975  -3.670968  -6.385714  1.238710  10.066667  15.638710  17.776667   \n1976 -12.241935 -11.455172 -2.558065   5.723333  10.925806  13.713333   \n1977 -11.254839  -6.332143 -0.851613   7.030000  14.229032  16.836667   \n1978  -7.270968  -9.500000  0.390323   4.613333  10.493548  14.313333   \n1979  -9.954839  -8.828571 -0.883871   3.310000  17.106452  17.153333   \n1980 -11.312903  -7.151724 -6.370968   5.846667   8.370968  17.870000   \n1981  -5.393548  -4.907143 -3.135484   3.340000  14.019355  19.813333   \n1982 -10.158065  -8.792857 -0.648387   5.336667  11.977419  13.856667   \n1983  -3.967742  -6.878571 -1.403226   9.290000  15.612903  14.550000   \n1984  -4.409677 -10.268966 -2.354839   7.466667  15.970968  15.590000   \n1985 -10.012903 -14.014286 -3.067742   5.346667  12.974194  14.663333   \n1986  -6.725806 -13.539286  0.158065   6.680000  13.648387  18.633333   \n1987 -17.506452  -6.057143 -5.312903   2.810000  12.832258  17.736667   \n1988  -7.241935  -6.113793 -0.993548   5.346667  13.848387  19.530000   \n1989  -2.112903  -0.503571  1.990323   7.656667  13.396774  20.053333   \n1990  -5.667742   0.382143  1.980645   8.120000  10.803226  14.506667   \n1991  -4.875862  -6.678571 -1.219355   7.030000  13.387097  18.783333   \n1992  -5.270968  -4.313793  1.654839   5.140000  11.932258  16.693333   \n1993  -4.416129  -4.882143 -1.932258   5.730000  14.490323  14.023333   \n1994  -3.422581 -11.300000 -2.929032   7.226667   9.848387  14.546667   \n1995  -5.874194  -0.846429  0.587097   9.116667  14.470968  19.683333   \n1996 -10.019355  -9.689655 -3.045161   6.403333  15.703226  16.526667   \n1997  -7.732258  -4.682143 -0.932258   4.650000  11.093548  17.830000   \n1998  -4.725806  -7.642857 -1.348387   3.900000  13.712903  19.976667   \n1999  -4.577419  -6.253571 -0.835484   9.683333   8.674194  21.423333   \n2000  -6.112903  -2.703448 -0.700000  11.110000  10.825806  16.236667   \n2001  -4.312903  -7.192857 -2.125806  10.966667  11.251613  16.266667   \n2002  -4.754839  -0.425000  2.229032   7.183333  12.732258  17.310000   \n2003  -7.351613  -8.653571 -2.729032   4.673333  15.535484  12.820000   \n2004  -6.458065  -7.006897  1.345161   4.556667  11.412903  15.283333   \n2005  -3.000000  -8.903571 -6.029032   7.116667  14.832258  16.506667   \n2006 -10.787097 -13.307143 -3.719355   6.020000  12.432258  18.203333   \n\nmon          7          8          9         10        11         12  \nyear                                                                  \n1949  17.425806  16.074194  11.506667  4.800000 -0.423333  -4.322581  \n1950  16.177419  14.080645  11.990000  4.745161 -0.433333  -5.503226  \n1951  18.590323  18.325806  11.990000  2.780645 -4.810000  -1.251613  \n1952  17.870968  16.848387  12.136667  3.925806 -1.133333  -5.883871  \n1953  19.025806  17.290323  10.046667  5.790323 -3.156667  -5.632258  \n1954  21.025806  18.354839  12.286667  5.738710 -1.573333  -4.974194  \n1955  17.906452  17.916129  13.856667  7.712903 -3.023333 -14.306452  \n1956  15.190323  14.625806   8.423333  4.670968 -5.180000  -4.254839  \n1957  18.554839  17.087097  12.313333  5.241935 -0.893333  -4.622581  \n1958  18.367742  15.696774   9.080000  6.038710 -0.786667  -7.641935  \n1959  20.538710  16.932258   8.253333  2.229032 -5.110000 -10.990323  \n1960  21.009677  16.190323   9.800000  2.338710 -3.620000   0.167742  \n1961  19.374194  16.825806   9.686667  6.590323 -1.550000  -8.070968  \n1962  16.361290  14.832258  10.790000  6.451613  1.343333  -7.351613  \n1963  19.132258  17.796774  13.216667  5.651613 -0.216667  -8.751613  \n1964  19.990323  16.006452  11.806667  6.893548 -2.306667  -2.858065  \n1965  16.661290  15.735484  12.923333  3.806452 -5.786667  -1.493548  \n1966  19.212903  16.645161   9.543333  6.087097 -0.900000 -10.525806  \n1967  17.706452  18.361290  11.353333  9.003226  0.420000  -9.583871  \n1968  15.725806  17.712903  10.790000  2.954839 -2.533333  -5.322581  \n1969  17.803226  16.445161  10.220000  4.690323  1.786667  -9.158065  \n1970  19.316129  16.258065  11.090000  5.393548 -1.823333  -5.887097  \n1971  17.467742  16.729032  10.780000  3.222581 -0.630000  -5.651613  \n1972  22.412903  20.632258  10.983333  5.167742 -0.186667  -0.906452  \n1973  17.977419  15.909677   7.633333  3.845161 -2.060000  -5.861290  \n1974  18.180645  15.874194  13.103333  8.680645  1.776667  -2.335484  \n1975  18.500000  15.019355  13.663333  4.093548 -3.296667  -4.032258  \n1976  16.103226  14.500000   9.653333 -0.948387 -0.803333  -3.690323  \n1977  18.764516  15.825806   9.506667  3.100000  1.726667  -8.174194  \n1978  16.345161  15.774194   9.723333  3.341935  2.003333 -14.509677  \n1979  16.674194  16.938710  11.730000  3.838710 -0.913333  -5.658065  \n1980  17.200000  14.709677  10.536667  5.190323 -2.040000  -4.248387  \n1981  21.512903  17.396774  10.816667  7.806452 -0.583333  -3.509677  \n1982  18.400000  16.645161  11.753333  4.064516  2.033333  -1.135484  \n1983  17.938710  16.032258  12.413333  6.200000 -1.466667  -3.180645  \n1984  17.567742  15.125806  12.410000  6.800000 -3.543333  -9.590323  \n1985  16.438710  19.419355  10.123333  6.070968 -3.333333  -6.535484  \n1986  17.796774  16.516129   8.580000  4.174194 -0.093333  -7.454839  \n1987  16.764516  15.087097   9.046667  3.590323 -3.646667  -6.983871  \n1988  21.587097  16.474194  11.280000  4.870968 -4.440000  -6.935484  \n1989  19.161290  16.248387  12.176667  5.329032 -2.633333  -5.293548  \n1990  17.535484  15.954839   9.320000  5.358065  0.250000  -3.393548  \n1991  18.080645  16.090323  10.973333  6.538710  0.970000  -3.974194  \n1992  18.622581  18.041935  13.146667  2.154839 -2.533333  -4.396774  \n1993  17.506452  15.425806   6.870000  4.645161 -8.003333  -3.612903  \n1994  17.561290  15.870968  13.676667  4.958065 -2.526667  -7.948387  \n1995  17.548387  16.793548  12.823333  6.683871 -2.816667  -9.506452  \n1996  18.900000  17.274194   9.880000  6.051613  3.863333  -7.019355  \n1997  18.729032  17.122581   8.516667  3.677419 -0.843333  -7.538710  \n1998  18.864516  15.487097  10.700000  5.658065 -8.030000  -5.938710  \n1999  21.719355  16.406452  11.770000  7.374194 -4.933333  -1.706452  \n2000  19.332258  16.754839  10.043333  7.180645 -0.050000  -2.625806  \n2001  22.967742  16.964516  12.193333  4.806452 -0.526667 -10.580645  \n2002  22.638710  17.041935  12.030000  2.545161 -1.476667 -12.561290  \n2003  20.641935  16.948387  11.333333  5.567742  1.146667  -2.061290  \n2004  19.022581  18.383871  12.133333  5.935484 -1.560000  -2.935484  \n2005  19.309677  17.638710  13.123333  6.022581  1.370000  -4.148387  \n2006  17.977419  17.535484  13.303333  7.003226       NaN        NaN  "
          },
          "execution_count": 135,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "groupped = df.groupby(['mon', 'year']).mean()",
      "execution_count": 139,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "groupped.loc[[(1, 1949), (2, 1958)]]",
      "execution_count": 145,
      "outputs": [
        {
          "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></th>\n      <th>TMPMAX</th>\n      <th>TMPMIN</th>\n      <th>TMPMN</th>\n      <th>PRECIP</th>\n      <th>day</th>\n    </tr>\n    <tr>\n      <th>mon</th>\n      <th>year</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <th>1949</th>\n      <td>-1.300000</td>\n      <td>-6.493548</td>\n      <td>-3.670968</td>\n      <td>0.854839</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <th>1958</th>\n      <td>-3.907143</td>\n      <td>-11.442857</td>\n      <td>-7.710714</td>\n      <td>1.967857</td>\n      <td>14.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "            TMPMAX     TMPMIN     TMPMN    PRECIP   day\nmon year                                               \n1   1949 -1.300000  -6.493548 -3.670968  0.854839  16.0\n2   1958 -3.907143 -11.442857 -7.710714  1.967857  14.5"
          },
          "execution_count": 145,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "groupped.reset_index('year')",
      "execution_count": 150,
      "outputs": [
        {
          "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>year</th>\n      <th>TMPMAX</th>\n      <th>TMPMIN</th>\n      <th>TMPMN</th>\n      <th>PRECIP</th>\n      <th>day</th>\n    </tr>\n    <tr>\n      <th>mon</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1949</td>\n      <td>-1.300000</td>\n      <td>-6.493548</td>\n      <td>-3.670968</td>\n      <td>0.854839</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1950</td>\n      <td>-14.348387</td>\n      <td>-21.616129</td>\n      <td>-18.022581</td>\n      <td>0.261290</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1951</td>\n      <td>-8.709677</td>\n      <td>-15.745161</td>\n      <td>-12.138710</td>\n      <td>1.070968</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1952</td>\n      <td>-1.648387</td>\n      <td>-6.722581</td>\n      <td>-4.135484</td>\n      <td>1.606452</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1953</td>\n      <td>-7.300000</td>\n      <td>-14.067742</td>\n      <td>-10.403226</td>\n      <td>1.080645</td>\n      <td>16.0</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    </tr>\n    <tr>\n      <th>12</th>\n      <td>2001</td>\n      <td>-7.825806</td>\n      <td>-13.325806</td>\n      <td>-10.580645</td>\n      <td>1.738710</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2002</td>\n      <td>-9.509677</td>\n      <td>-15.980645</td>\n      <td>-12.561290</td>\n      <td>1.187097</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2003</td>\n      <td>-0.112903</td>\n      <td>-4.032258</td>\n      <td>-2.061290</td>\n      <td>1.367742</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2004</td>\n      <td>-0.725806</td>\n      <td>-5.309677</td>\n      <td>-2.935484</td>\n      <td>1.835484</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2005</td>\n      <td>-2.274194</td>\n      <td>-5.951613</td>\n      <td>-4.148387</td>\n      <td>2.538710</td>\n      <td>16.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>694 rows × 6 columns</p>\n</div>",
            "text/plain": "     year     TMPMAX     TMPMIN      TMPMN    PRECIP   day\nmon                                                       \n1    1949  -1.300000  -6.493548  -3.670968  0.854839  16.0\n1    1950 -14.348387 -21.616129 -18.022581  0.261290  16.0\n1    1951  -8.709677 -15.745161 -12.138710  1.070968  16.0\n1    1952  -1.648387  -6.722581  -4.135484  1.606452  16.0\n1    1953  -7.300000 -14.067742 -10.403226  1.080645  16.0\n..    ...        ...        ...        ...       ...   ...\n12   2001  -7.825806 -13.325806 -10.580645  1.738710  16.0\n12   2002  -9.509677 -15.980645 -12.561290  1.187097  16.0\n12   2003  -0.112903  -4.032258  -2.061290  1.367742  16.0\n12   2004  -0.725806  -5.309677  -2.935484  1.835484  16.0\n12   2005  -2.274194  -5.951613  -4.148387  2.538710  16.0\n\n[694 rows x 6 columns]"
          },
          "execution_count": 150,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
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    "language_info": {
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      "nbconvert_exporter": "python",
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    "gist": {
      "id": "",
      "data": {
        "description": "lecture 13.ipynb",
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  "nbformat": 4,
  "nbformat_minor": 2
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