Get-Weather-Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get weather data\n",
    "In this notebook, we will gather data according to the tutorial presented in https://stackabuse.com/using-machine-learning-to-predict-the-weather-part-1/, but we will use openweather insted of the weather underground's data. We can also check on the docs folder in the main tree of this file to see further references in the topic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data\n",
    "Notice the above apis just provide free access to daily weather reports. For a historic context it has been very difficult to find. Darksky went Apple this year, and OpenWeather is charging 10 bucks for historical data for a particular location. Luckily, the website of the NOAA does provide api access as well as the posibility of downloading data for a particular location in a variety of formats like csv or even json files.\n",
    "I have downloaded data for Limon from 1990 until 04/2020 in a csv file. The data downloaded is simple precipitation and air temperature data, on monthly basis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "datafile = 'data/2154121.csv';\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['STATION', 'NAME', 'DATE', 'CDSD', 'EMNT', 'EMXP', 'EMXT', 'HDSD', 'PRCP', 'TAVG', 'TMAX', 'TMIN']\n",
      "1990-01 28.6\n",
      "1990-02 28.7\n",
      "1990-03 28.8\n",
      "1990-04 29.9\n",
      "1990-05 29.3\n",
      "1990-06 29.4\n",
      "1990-07 29.1\n",
      "1990-08 29.3\n",
      "1990-09 30.3\n",
      "1990-10 30.6\n",
      "1990-11 29.7\n",
      "1990-12 28.3\n",
      "1991-01 28.6\n",
      "1991-02 28.5\n",
      "1991-03 29.4\n",
      "1991-04 29.6\n",
      "1991-05 29.5\n",
      "1991-06 30.1\n",
      "1991-07 29.1\n",
      "1991-08 29.0\n",
      "1991-09 29.5\n",
      "1991-10 30.1\n",
      "1991-11 28.2\n",
      "1991-12 28.4\n",
      "1992-01 28.4\n",
      "1992-02 29.0\n",
      "1992-03 29.1\n",
      "1992-04 29.5\n",
      "1992-05 29.8\n",
      "1992-06 31.1\n",
      "1992-07 29.4\n",
      "1992-08 29.1\n",
      "1992-09 30.2\n",
      "1992-10 29.6\n",
      "1992-11 30.1\n",
      "1992-12 29.3\n",
      "1993-01 28.7\n",
      "1993-02 29.3\n",
      "1993-03 29.2\n",
      "1993-04 30.8\n",
      "1993-05 31.3\n",
      "1993-06 30.3\n",
      "1993-07 29.2\n",
      "1993-08 29.7\n",
      "1993-09 29.8\n",
      "1993-10 30.1\n",
      "1993-11 29.6\n",
      "1993-12 28.9\n",
      "1994-01 29.0\n",
      "1994-02 28.5\n",
      "1994-03 29.9\n",
      "1994-04 29.6\n",
      "1994-05 30.3\n",
      "1994-06 29.0\n",
      "1994-07 28.2\n",
      "1994-08 29.6\n",
      "1994-09 29.5\n",
      "1994-10 30.0\n",
      "1994-11 29.4\n",
      "1994-12 28.3\n",
      "1995-01 28.9\n",
      "1995-02 28.6\n",
      "1995-03 29.8\n",
      "1995-04 29.9\n",
      "1995-05 30.6\n",
      "1995-06 30.6\n",
      "1995-07 30.2\n",
      "1995-08 30.8\n",
      "1995-09 30.7\n",
      "1995-10 30.6\n",
      "1995-11 28.9\n",
      "1995-12 29.3\n",
      "1996-01 28.2\n",
      "1996-02 28.2\n",
      "1996-03 28.7\n",
      "1996-04 29.6\n",
      "1996-05 29.8\n",
      "1996-06 29.2\n",
      "1996-07 29.8\n",
      "1996-08 29.4\n",
      "1996-09 30.4\n",
      "1996-10 31.0\n",
      "1996-11 29.6\n",
      "1996-12 27.7\n",
      "1997-01 28.7\n",
      "1997-02 27.4\n",
      "1997-03 28.9\n",
      "1997-04 29.9\n",
      "1997-05 29.1\n",
      "1997-06 30.3\n",
      "1997-07 29.7\n",
      "1997-08 30.0\n",
      "1997-09 30.6\n",
      "1997-10 30.4\n",
      "1997-11 29.7\n",
      "1997-12 29.1\n",
      "1998-01 29.7\n",
      "1998-02 29.8\n",
      "1998-03 29.9\n",
      "1998-04 29.9\n",
      "1998-05 30.5\n",
      "1998-06 30.4\n",
      "1998-07 30.2\n",
      "1998-08 30.1\n",
      "1998-09 31.9\n",
      "1998-10 30.5\n",
      "1998-11 30.1\n",
      "1998-12 27.9\n",
      "1999-01 29.1\n",
      "1999-02 28.9\n",
      "1999-03 29.3\n",
      "1999-04 29.8\n",
      "1999-05 30.8\n",
      "1999-06 29.9\n",
      "1999-07 29.4\n",
      "1999-08 29.8\n",
      "1999-09 30.4\n",
      "1999-10 29.8\n",
      "1999-11 28.6\n",
      "1999-12 27.1\n",
      "2000-01 27.9\n",
      "2000-02 27.9\n",
      "2000-03 28.9\n",
      "2000-04 29.5\n",
      "2000-05 29.4\n",
      "2000-06 29.9\n",
      "2000-07 29.5\n",
      "2000-08 29.5\n",
      "2000-09 30.8\n",
      "2000-10 30.0\n",
      "2000-11 29.6\n",
      "2000-12 29.1\n",
      "2001-01 28.2\n",
      "2001-02 28.6\n",
      "2001-03 28.7\n",
      "2001-04 28.9\n",
      "2001-05 30.3\n",
      "2001-06 29.4\n",
      "2001-07 29.4\n",
      "2001-08 29.6\n",
      "2001-09 29.8\n",
      "2001-10 30.6\n",
      "2001-11 28.6\n",
      "2001-12 29.0\n",
      "2002-01 28.7\n",
      "2002-02 28.8\n",
      "2002-03 28.9\n",
      "2002-04 29.4\n",
      "2002-05 29.0\n",
      "2002-06 30.4\n",
      "2002-07 29.0\n",
      "2002-08 29.6\n",
      "2002-09 31.1\n",
      "2002-10 30.3\n",
      "2002-11 29.0\n",
      "2002-12 29.3\n",
      "2003-01 28.8\n",
      "2003-02 29.2\n",
      "2003-03 30.0\n",
      "2003-04 30.4\n",
      "2003-05 30.1\n",
      "2003-06 30.1\n",
      "2003-07 29.6\n",
      "2003-08 29.6\n",
      "2003-09 30.7\n",
      "2003-10 30.8\n",
      "2003-11 29.9\n",
      "2003-12 28.5\n",
      "2004-01 28.5\n",
      "2004-02 29.2\n",
      "2004-03 29.4\n",
      "2004-04 29.8\n",
      "2004-05 28.5\n",
      "2004-06 29.1\n",
      "2004-07 29.0\n",
      "2004-09 30.0\n",
      "2004-11 28.7\n",
      "2004-12 28.6\n",
      "2005-01 \n",
      "2005-02 \n",
      "2005-04 29.4\n",
      "2005-05 29.9\n",
      "2005-06 30.2\n",
      "2005-07 \n",
      "2005-09 29.7\n",
      "2005-10 29.7\n",
      "2005-12 28.8\n",
      "2006-01 \n",
      "2006-03 28.9\n",
      "2006-06 30.7\n",
      "2006-07 \n",
      "2006-08 30.3\n",
      "2006-09 31.0\n",
      "2006-10 31.0\n",
      "2006-11 29.9\n",
      "2006-12 30.0\n",
      "2007-03 30.1\n",
      "2007-05 30.8\n",
      "2007-08 31.2\n",
      "2007-09 31.1\n",
      "2007-10 31.0\n",
      "2007-11 \n",
      "2008-02 29.4\n",
      "2008-03 29.9\n",
      "2008-04 30.2\n",
      "2008-05 30.5\n",
      "2008-06 30.8\n",
      "2011-11 \n",
      "2011-12 \n",
      "2012-04 30.0\n",
      "2012-07 \n",
      "2012-08 30.6\n",
      "2012-09 30.8\n",
      "2013-02 29.6\n",
      "2013-06 31.1\n",
      "2013-12 30.4\n",
      "2014-03 30.0\n",
      "2014-04 \n",
      "2014-07 \n",
      "2014-10 30.9\n",
      "2014-11 \n",
      "2014-12 29.5\n",
      "2016-12 29.9\n",
      "2017-06 31.2\n",
      "2017-09 31.7\n",
      "2017-11 30.5\n",
      "2017-12 29.5\n",
      "2018-05 30.5\n",
      "2018-12 29.6\n",
      "2019-03 \n",
      "2019-10 30.5\n",
      "2019-11 30.4\n",
      "2020-02 29.4\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "with open(datafile) as csvfile:\n",
    "    records = csv.DictReader(csvfile)\n",
    "    print(records.fieldnames)\n",
    "    for row in records:\n",
    "        print(row['DATE'], row['TMAX'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The field names of the csv file include the station identifier, its name, and the date (month) of the measurement, and it also includes:\n",
    "\n",
    "|Field|Description\n",
    "|--- | ---\n",
    "|EMXP | Extreme maximum precipitation for the period.\n",
    "|EMXT | Extreme maximum temperature for the period.\n",
    "|TMAX | Maximum temperature\n",
    "|TAVG | Average Temperature.\n",
    "|TMIN | Minimum temperature\n",
    "|PRCP | Precipitation\n",
    "|HDSD | Heating Degree Days Season to Date\n",
    "|CDSD | Cooling Degree Days Season to Date\n",
    "|EMNT | Extreme minimum temperature for the period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to put everything in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      DATE  TAVG   PRCP\n",
      "0  1990-01  25.1  501.3\n",
      "1  1990-02  24.8  109.1\n",
      "2  1990-03  25.2  293.0\n",
      "3  1990-04  26.0  148.6\n",
      "4  1990-05  26.3  749.6\n",
      "5  1990-06  26.4  572.4\n",
      "6  1990-07  26.1  279.5\n",
      "7  1990-08  26.2  690.7\n",
      "8  1990-09  26.7  181.1\n",
      "9  1990-10  26.6  152.8\n"
     ]
    }
   ],
   "source": [
    "import pandas\n",
    "#dataframe = pandas.DataFrame([records], index=None)\n",
    "#fieldnames=['DATE','TAVG','PRCP'])\n",
    "#To do: from read, and DictRead to Pandas.\n",
    "#Take into account that DictRead does create a dictionary object (keys-hash)\n",
    "\n",
    "dataraw = pandas.read_csv(datafile)\n",
    "dataframe = pandas.DataFrame(dataraw, columns=['DATE','TAVG','PRCP'])\n",
    "\n",
    "print(dataframe[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data distributions\n",
    "Now that we have the data, lets plot some values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(dataframe.TAVG, color='tab:blue', ec='skyblue');\n",
    "plt.title('Temperature');\n",
    "plt.xlabel('Temp in °C');\n",
    "plt.show();\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(dataframe.PRCP, color='tab:orange', ec='bisque');\n",
    "plt.title('Precipitation');\n",
    "plt.xlabel('Prec in mm');\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now with the magic of pandas, we can check the temperature and precipitation change per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          DATE  TAVG   PRCP\n",
      "0   1990-01-01  25.1  501.3\n",
      "1   1990-02-01  24.8  109.1\n",
      "2   1990-03-01  25.2  293.0\n",
      "3   1990-04-01  26.0  148.6\n",
      "4   1990-05-01  26.3  749.6\n",
      "..         ...   ...    ...\n",
      "228 2018-12-01  24.9    NaN\n",
      "229 2019-03-01   NaN  274.2\n",
      "230 2019-10-01  26.0    NaN\n",
      "231 2019-11-01  26.0    NaN\n",
      "232 2020-02-01  25.0    NaN\n",
      "\n",
      "[233 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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gprp6Ka4XNywDMhuHONEboaGyhML81L6SDZUlSIBlvzfUBUe073SKayIGItiBal8NhIgU4vzA36uq97ubW4H71eEpIArUJnisMQ9N7Y6BOJkhA+EFqD37XlMWQiS4HsRJN0D9vAZHZjuTBqK5ZyTlADVAcWE+KyqKA2wggiPa50maxJPZ8JjRYwroxZKHn1lMAtwNNKnqp2N2/Qi43n3OJiAEdCd4rDEHqjpjIFr7M/MjEZviClCQn+dWUwfzS+GluF6yyvEgMlkLcbwnsigDAQS6FmK9m8kUhDhES2+EytJCyosL53xO7RIR7PPTg7gGuB24QUT2uLdbgHuA9SLyDPA94G2qqiKySkQeWOBYYw5O9o8yODZFnmTGgxiITNI1ND6TweThFMsF04No7x8lT+CiVZ4HkZkv90BkkoHRSdZWp5bB5BFk2W9PtC8Iqa4tfaPzBqjB8aYh+HIbvqW5quqjwFwdT94S5/ltwC0JHGvEoandySjatraa3S19RKN6ViOTdHKk68wMJo/lFcWBNRAn+8eoLy9mWUkhofy8jHkQJ3qdZZU1i/Qg1lSX0jE4ztjkdFJqsLlAkET7WnsjXLCifN7nFBfmU74E5DasknqJ0NQ+iAjcuLmeyWn1fZnnyKwMJo8gy220D4yyqrIYEaG6LJQxwT6vBmKxS0yeaF9rAGW/wQlU57rsdzSqtPaNzpvB5FG3BHpTm4FYIhxoG2RtdSmbljs/2Cd9jkMc7himqCCPhlmudn15ET0j40wFsJq6rX+UlW6aaXVZKGNB6hNuDcSaFGU2PAKf6lpbxnPdIzkt2tc1PM7EdDSuSN9sasuLAt+C1wzEEqHp1CAXraqYWRv1O9X1SNcwG+rC5M9axqqvKEY1O2J3i0FVaRsYo8E1EDXhUOaWmHoi1JcXURpa3IpvkPtCgBOoHp+KzlSz5yJeEsDqhD0IMxBGlhken+JET4TNKypmruj9NhCHO4bPij9AbDV1sOIQPSMTTExFWbnMqeXIqAfRu/gMJnCqd0sK82nuzd0f2PnwUl2P5XAcYibFdZ4aCI/acMjSXI3sc+iUk966eWUFpaECqstCvhqIyMQUJ/tHz8pggphiuYDFITyRvlVZWGJq7omwZpEZTOBIVgQ5k2l9AET7Wlzju1AWEzjFcoMBl9swA7EE8CqoN7vpmaurSnx10492Old4cT0It6q6I2Cy397/a5UrMlhTFmJ4fIrxKX+/3GOT05waHEuLBwHBroWoDYeoKC7I6VqIlt4IdeVFCWWJebUQQVtujcUMxBLgQPsQy0oKWeUujzRUlviayeKluG5cfraBqA0XudXUwfIgvCK5VZXeEpPz5fbbi/Cu9tNlIIIs+y0irK8L57QmU0tfJKEANcTIbQR4mckMxBKgqX2QzSvLZyQvVleVcLJv1LcficMdwxTkCWvjSFMX5udRUxYKXG/q9oFRigryZjqEeX/9Fuw7neK6+CUmcBoHjU5OBza9cn1dWY57EImluEKM3EaAA9VmIALOdFQ5eGqQzSsrZrY1VJYwPhX17UfiSOcw62rL5hSWqysvDqAHMcaqypLTulJu43m/PQgvxTXVTnKz8YrtghqH2FAXpmNwPCdF+6amo5waHEsoQA3OkhmYgTCyyPGeEcYmo1wUYyBWux9gv+IQRzqHOb/u7OUlj+UVRYGLQbS5RXIengeRiSWm8uICKkvn1vVJBi/VNahxiJlMphz0ItoHxpiO6rwifbF4S0xBltswAxFwPIG+MzyImVTX9P9IjE9Nc6I3Ejf+4FFfXhRAD2L0jC54npaO3wFGT6Rvrt7GyeJdHATZgwByMg4x0wciQQ+iuDCf8qKCwC73gRmIwHOgbZCCPDnjB9szEH6I9h3vjjAd1bgZTB7LK4rpHh5nOocrYmOZnI7SOTR+RrOeiuJC8vPEd8E+R+Y7PfEHcH6UllcUBdZArMlh0T6vBmJ1ggYCgl9NbQYi4DS1D7KhLkxRwem0u4riQiqKC3yphfA0mOYzEPXlRUQVegLyxTg1MIYqM1lgAHl5QlWpv7UQU9NRWvtG0xZ/8AhyLURRQT5rqks5mpMehKP2u7Iy8Z7fdeEiy2IyskdT+9CMPHUsq6tKfYlBHO4cQuT0UkA86gPWWc7rQz273WdNWcjXLKb2gTGmopq2FFePINdCgFMwl4uZTC19EVYuS67rX215yILURnboHZng1OAYm1eeLT3cUOVPLcSRzmEaq0rnLRQKmtzG7BoID7+rqY/PiPSlb4nJOV8ppwbHAlvBm6uifS29kYQD1B61AVd0NQMRYOIFqD38qoU40hlfgymWoHkQnqcVG6QGqA77ayDSJfM9mzXVpaj6l8XmNxvqc1O0r7VvNOEAtUdtuIiB0UnfK/L9wgxEgJnPQDRUljAyMU1/ZDJtrzc1HeVY90hcDaZY6sLB8iDaB0apLC2krOhMNdWaMn8VXZt7I4QK8lhRkfiadiKsWQKy35Bbon1jk9N0Do0nXCTn4aW6+l1w6Rd+9qRuFJHtInJARPaLyB0x+94vIgfd7Z+Y4/iXi8ghETkiIh/2a5xB5kD7IPXlRTMfwli8TIt0Bqpb+kaZmIqyYQEDEXIrkoPiQbT1j53lPYCzxDQwOsmkT70tTvSMsKa6NO2d/wJfC+F+vnKpeZD3PUp+iSnYxXJ+ehBTwF2qehFwNfBeEblIRK4HbgO2qOrFwCdnHygi+cAXgZuBi4A3ichFPo41K+w80ccn/vdgystAB9oG43oPcFptMp2Ng053kZvfQECwaiHa+kdpiJOZ4tVC9EX8ufo70RNJewYTOBIPRQV5ge0LUVPmiPYd684dA5FKiisEX27DNwOhqu2qusu9PwQ0AQ3AXwD/rKrj7r7OOIdfBRxR1WOqOgF8D8eoLCm+91Qz//7IUQ6eGkr62ImpKEe7hhc0EOn0IA53OuNcyIMAJw4RFD2m2UVyHn4K9qkqzb2RRfehjkfQZb9FhA314RnV4FygNckiOY/Tgn3Jf4a2H+pk+8F4P4+ZIyMxCBFZB2wFngQ2AdeKyJMi8msRuTLOIQ1AS8zjVndbvHO/S0R2iMiOrq6u9A7cZw51OD+4P93XlvSxRzqHmZzWuCmuAMtKCgkXpbcW4kjnMCsqiqkoXlgWYnlAPIjh8SkGx6bOSnGFGLkNH9aPu4bHiUxMsy6NRXKxBNlAAKyvDeeYBzFKqCBvJkMvUTwPIpViuX/86QE+++CzSR+XTnw3ECISBu4D7lTVQaAAqMZZdvog8ANZhM6Aqn5FVbep6ra6urq0jDkTTEeVQ67n8JO97UkvMx1wA9QXxUlxBecqzJH9Tq+BWCiDyaO+wqkgzfVq6vY5UlzhtGCfH4Fqb/nHDw8CTtdCBFH2GxxV147BcYbG0pdksRhaeiOsrixJOl5UXJhPuKggaT2m/sgER7tG6PVpeTNRfDUQIlKIYxzuVdX73c2twP3q8BQQBWpnHXoSaIx5vNrdtmQ40TPC+FSUq86rprk3wtMnB5I6vql9kKKCvHmvQNPZOEhVkzMQ5cVMRzVjXdlSpW2OIjnwV7BvJsXVhxgEOB7EyMR0zv//58IrxHwuRzKZWvtGE+pDHY/acPLFcnta+gHoH8mugfQzi0mAu4EmVf10zK4fAde7z9kEhIDuWYf/HtgoIueJSAh4I/Bjv8aaDby4wx03bqQwX/jJ3uSWmZraB7lwRTkF81R1prNYrm1gjMjEdMIGYnlFMFJdTxfJnW0gqkpDiPjjQZzoGSFPkg96JkrQU11Pq7rmhoFIplHQbJxiueQMxO5mx0AMjU8xMeVPFl0i+OlBXAPcDtwgInvc2y3APcB6EXkGJ/j8NlVVEVklIg8AqOoU8D7gFzjB7R+o6n4fx5pxDp4aIk/girVVvHhTHT/d155w5aiquk2C4scfPFZXlTA0NsXA6OKvQpLJYAKnJwTkvtRxW7+jr7M8ztpyfp5QWVLoi2DfiV5HtiFU4M9XMOh9IdbUlJKfJzkhuTE0Nkl/ZDJlY15Xnnw19a7mvpn7/VlcZipY+CmpoaqPAnMt2L0lzvPbgFtiHj8APODP6LLPwfZB1tWWUVyYzysvXcWDTZ3sau5j27rqBY89NThGX2RyQQPRUOn2hegbZVnJ4voNHHYD6sl6ELmeydTWP8byiuI5PTG/5DZO9ERYV+uP9wCns22CWgtRVJBPY1VJTngQLb2p1UB41IaLePxoT8LPj0aVPS39M5+93sjEjDpBppnz8kVErhSRm+Nsv0VErvB3WEufQx1DbF7h/MDfdNFyigryEl5mmq+COpbTtRCLj0Mc7RqmuixETZyivHjUzegx5b4HsXLZ3F++mrIiX6pgm3sjaddgiqUklE9deXBlv8GJQ+SCB+HVQCSb4urhyW0kulR0rHuYobEprrvASbrpy2IcYj7/9l+AA3G27wf+1Z/hnBuMjE9xoifCBSucDKRwUQE3bq7nZ0+fSijrp6nduZq/cI4MJo90Ng5aqIvcbIoK8qkqLcx5D6J9YDRu/MHDDw9icGyS3pGJtGswzSbwqa51uSHaN9MoKNUgdbmXDZfYxdIuN/5w44XLAf8KNRNhPgNRrqonZm90t83OOjKS4Fl3uebCFad/4F956Sq6h8d58tjCruiBtkEaq0sWrEeoKQtRXJi36MZBqsrhzmHOn6eLXDzqy4tz2oNQVdoGxuY3ED4I9jX7nMHksaa6dGZ5JIisr8sN0b7WvlHKQs4FTyokWyy3u7mPiuICrlhbBfjf9nY+5jMQVfPs8/eTvcTxMpguXHF6iej6C+opC+XzkwSK5praB2eWp+YjXbUQPSMT9Ecmk/IgwKmFyGU9pp6RCSamomc0CppNTVmIvshEWq9iT6u4+rfEBM4Vb9vAaFazYBaDl+qa7WWm1r4IjdWpt4VNVm5jd3M/l62poqrMMUjZDFLPZyAeFJGPxxaxicM/AA/7P7Sly6FTQ5SF8mdiBOCsGd900XJ+/sypecXhIhNTPNczMmcF9WzS0TjocIebwZSCB9GZw2muXorrygWWmKIK/WnIBPM40ev2gcjAElOQZb/X50iqa0vv6KLSkT1140SqqYfHpzjUMcTlayopKnCK7HpzNAZxF7AeOCIi94nIfcBhHKmMD2RicEuVpvZBNq0oP6sq89ZLV9EfmeTRI7PLQk5z8NQQqgsHqD3SUQtxpGvhNqPxqK8oomtoPOtryHPR1u8Yr4YFDASQ1lTXE90RasMhwkW+JRECcOW6Kv7htoupKPb3dfyipizEspLCrHoQqkpLX+SMi7lk8ZaYEkn53tvSjypsXeMs4FSWFmY1BjHnJ0dVR3BUVNcDF7ub96vqsYyMbImiqhzqGOLmS1aete/aTbWUFxfw073tXH9Bfdzjm2YkNhL1IEroi0wyMj51Vr+DRDnSMUS4qCDpvgXLy4uYiip9kYmEs58yyXxFch41Zaf1/M+P/5YkzYnekZlCNj9ZW1PGW1/g7zKWn4gI6+vKkvIgekcm+Om+Nian03NRMj41TWRiOuUANTirA2Wh/ISWmHa79Q+Xra4EnAuUnDQQACJSCnSYUUgfHYPj9EcmzwhQexQV5PPyi1fwv8+cYmzykrhtPZvaBykvKkj4isZzjU/2j7Jp+fxZT3NxpGuYDfXhpNdgvdztjsHxnDUQRQV58wYf/ZDbaO6J8Pz1NWk731JmQ12Y3zybmAjn+NQ0b//G79nrylSkizyBS1cvW9Q5ahMsltvd3M/59WGWuZ/JqtIQfVkMUs9pIETkL4EXACoiT6nqZzM3rMyyv22AiuLCRV0lJMrBU44HcEEcAwFw65ZV/NfOVn79bBcvu3jFWfub2ofYvLIi4R9rb/nkZF/qBuJwxzAv2pS8EGJssdxFJObxZJL2gTEaKkvm/V+mW7BvfGqa9sEx31Nclwrr68r44c5WhsYmKV8ga+/jP2tib0s/n3/T1pQ+r3NRmC+Uhha3TFcbLqJ7gSUmVWV3Sz83XnjaVa0uC2VV1Xa+Wb8N2IZTDb0DWJIGYnBsktd96XFeeekqPvmGLb6/3ukMpvg/1n+woYbqshA/3dd+loGIRh2JjTdcsTrh12tcZC3EwOgknUPjSccfwAlSAzkr+32yf5SVcVRcY6kqTQkAuQ4AACAASURBVK8H0dI7imr6+1AvVdbXnhbtu9RddonH/+w5ybd+d4I/u/Y8bt2yKlPDS5i6cNGCsZQTPRF6RyZm4g/gxiByNEj9CeB+HDXWJWkcACqKC3nTVWv4790nMyJLcOjUECsqiql0f3hmU5Cfx82XrODBAx1EJqbO2NfcGyEyMZ1wgBqcK5dQfl7Kqa7JajDF4qX35WqxXPvAKKviNAqKJVSQR3lxQdoMxIkeN4PJxyrqpcT59c7/ab4f12c7hvjwfU9z5boqPvTyCzM1tKSoLQ8tmMW0u8WJP1y+9rQhrC4NMZxFwb45DYSqfl9VX+PevpXJQWWaP3/RBvJF+NKvj/r+Wk3tgwtWQL/y0lWMTk7z8KxuUjMB6gRTXAHy8sTJZEox1dHrC5yKB1FcmM+yksKcLJabmIrSOTQ+b4qrR01ZKG1LTKdrIMyDSIQ11WXk58mcgerh8Sne/Z2dlBUV8IU3X07hPOrG2aQ2XER/ZP7+5rub+ykL5bOx/vTvQ5UbA8tWLURu/jczzIplxbxh22p+uKOV9gH/csYnp502oXPFHzyuOq+a+vKis7SZDrQPkickHUtYTLHc4c4hQgV5KeeB15cX5aQH0TE4hipxe1HPxpHbSI+Ra+6NEC4qmOl3bcxPqCCPNdWlcT0IVeX/3LeP490jfP5NW1meJUG7RPBSXefT9drV3MeWxkryY9LfZ5Y4zUBkl3e/eANRVb78a/8Sto51jTA5rQtWQefnCbc8byXbD3Wd0VGrqX2Q9XXhuNlN87G6qiRluY0jncNsqAuf8aFNhuUVxTlZTZ1IiqtHdRoF+070OCmui2iieM6xvjZ+qus3Hj/Oz/a189cvu4AXbMjtrLAZuY05lplGJ6Zpah/i8jVnClh41dTZikOYgXBprC7lNVsb+O5Tzb5d8S6UwRTLrVtWMTEV5VcHOma2eRlMydJQWUL38Dhjk9NJH3s4iS5y8ajP0d7U7W4nuZULxCDAWWJKXwwiYstLSbKhPsxz3SNnCFnuPNHHx3/WxE2b63n3izZkcXSJsVBv6qdPDjAdVbauOTMQ76VZZ6sWwgxEDO+5/nwmp6Pc/dvnfDn/wVNDFOTJjMbMfFy+ppKGypKZZab+yAQn+0cTLpCLZXV1arLfrX0RWvtG2bKIHPD6imI6h8ZyrjfyyXl6Uc+mOuwUKy12DtNRpyrXb4mNpcb62jLGp6IzXl/P8Djv+89drKws5lNvuCzpPtHZoG5GsC++gfAaBF3WOMtApDmLLlkWNBAi8loROSwiAyIyKCJDIjKYicFlmvNqy7h1yyq+/cQJX96QQ6eG2FAXTqiLmIjwyktX8tvD3fRHJmYkvjcvEOCOh9c4KNk4xCOHnAKl6+ao6k6E+vIiJqeVvkhuNJ/3aOsfpbK0MKH89pqyEJPTyuDY1ILPnY/2gVEmp5W1lsGUFOtjRPumo8od39tDz8gEX/rjK2YKynIdT/J7Lg9id3Mfa2tKzyoo9bIds1Usl4gH8QngVaq6TFUrVLVcVRe8jBWRRhHZLiIHRGS/iNzhbv+oiJyc1YY03vF/5R73jIh8V0QyEoF67/XnE5mY5uuPpd+LOJhABlMst25ZxVRU+d9nTiUtsRHLTOOgFAxEY3XJTH/gVKjP0c5y7QNjC6a4eqSrmtqT+V5nHkRSeJ+/o10jfO7BZ3n0SDf/8KqLuaRhcdXNmaQ0VEBpKD+u5Leqsqu5/6z4AzhB+nBRQdYusBIxEB2q2pTCuaeAu1T1IuBq4L0icpG77zOqepl7O6utqIg0AH8JbFPVS4B84I0pjCFpNi0v5+ZLVvCNx46npZezx8DoJG0DYwnFHzwuXlXBuppSfrKvjab2QWrDoZm1zGRYXlFMQZ4kVSw3PjXNY0e6uf6C+kUFVJfHyG3kEm39owktL0H6BPuOuwbClpiSo9oV7fuvHS3828NHeP0Vq/mjKxuzPaykqQ0XxQ1Stw2M0TU0flb8waOqLHuCfYkYiB0i8n0ReZO73PRaEXntQgeparuq7nLvDwFNQEMSYysASkSkAKf/RGL9ONPAe68/n6HxKb71+PG0ndNrEpRIHwcPEeHWLav43dEeHj/ak5TERiz5ecLKyuKkYhBPPdfL6OT0nKKBiVLvFcvlmOy3YyAS8yBiBfsWw4neEQrzJaHAuHEaEWFDXRkHTw1x4YpyPnbbJYHMAqsNh+IaiF0nnPjD1sb4LXiqSv3pi54IiRiICiACvBS41b29MpkXEZF1wFbgSXfT+0Rkn4jcIyJn/VdU9STwSaAZaAcGVPWXc5z7XSKyQ0R2dHUlJuq1EJc0LOPGC+u5+7HnGBlf3Lqzx8H2xDOYYrl1yyqirqZ/KhlMHqsrS5OKQWw/2EVRQR5XL1JUbkZuI4dSXYfHpxgcm0r4h7o6nL4lpsaq0pRThs9lLlpVQXlRAf/xlisoCSWX5p0r1JXH9yB2N/dTXJg35/JzVWkodwvlVPXtcW7vSPQFRCSMI9dxp6oOAl8CNgCX4fz4fyrOMVXAbcB5wCqgTETeMsf4vqKq21R1W11d+gS63nfD+fRHJvnOE2d1XU2Jg6eGqCguYOU83cvisWl5OZvcRj2pxB88GpKshXjkUCdXr69Z9JexJJRPeXFBTnkQ7UlkMAEzRW2Lraa2FNfU+cjNm3norhezrja4Af7acFHcnhC7W/q4tKFyzirw6rJQ7hXKiciH3L+fF5F/m31L5OQiUohjHO5V1fsBVLVDVadVNQp8FbgqzqE3Ac+papeqTuJoQv1BclNbHFvXVHHtxlq++ttjKdUPzMZxj1NbIrr1Ukd87OIkJDZms7qqhI6hsYQ0XY53j3Cse4TrL0iPwc21YjlvqW2+RkGxFBfmUxrKX5QHoao090Z8bzO6VCkrKpiRjw8qteEi+mbJbYxPTbP/5OCc8QfIrmDffB6EF5jeAeyMc5sXt1Xp3UCTqn46Zntsp5zXAM/EObwZuFpESt3z3BgznozxvuvPp3t4gu8+1byo86gqh04NJZXBFMufXruer9x+BRtTlOsG58dQlYSkRB455GhALSa9NZb68iI6csmD8IrkEjQQ4MltpG4gekYmGB6fykijICM3qXXjcbGfo/1tg0xMR89QcJ1NNgX75uso9xP37zdTPPc1wO3A0yKyx932Nzhd6i4DFDgO/DmAiKwCvqaqt6jqkyLyQ2AXTjbUbuArKY4jZZ6/voarzqvmy78+xpufv4aigtSWW1r7Rhken0o6/uBREsrnpXF6QySDp6XU2je64FXs9kNdrK8tS5s7v7yimN8f703LudJBW/8oeeJ0vEuUxQr2mUifUefGsrqGxmey+3Y3O82N5vMgYgX7Mu1F+dasVlUfxeklMZuz0lrd57cBt8Q8/r/A//VndInz/hvO5/a7n+KHO1v54+evTekch2Z6QGSvaU6itRCjE9P87lgPb0lxrvHw5DZUNSeyT072jzqpv0kof1aXLSzXPB/NvY6WkBmIc5d4eky7mvtoqCyZV2hwJs06CwbCpDYW4IXn13JZYyVfeuTovFK985GMBpNfrFhWTJ4s3Djod8e6mZiKcv2F6Qv415UXMTEdTWtdyWJo7x9LOMXVo7qsiN5FpLme6IkgQsqquEbwmdFjionH7Wnun9d7ACcGAdmR2zADsQAiwvtvOJ/WvlF+tPtkSuc4eGqIxuoSwkW+OWwLUpifx4qK4gVTXbcf7KKkMJ+rzqtO22t7V0e5EqhuGxhNOpusJuwsMaWqx3SiJ8LKiuKklXiNpcNpD8L5oe8YHONk/+i88Qc47UH0Z6GaOiUDISJJ1UEEnRsurOeilRX8+yNHz1CUTJSDp4a4YHn2ezKvriqdt3GQqrL9UCfXnF+bcrwlHl6xXC4EqqNRnelFnQzVZSHGp6JEJlLLaDvRM2IV1Oc4ZUUFlBTmzywxJRJ/gOwK9qXqQVyZ1lHkOJ4X8Vz3CD/dl1xB99jkNM91j6QkspduFqqFONo1TGvfaFqXlyDGg8gBuY2ekQkmpqJJexCL1WNq7o2YSJ9BbXkoxkD0EcrPWzB9PZuCfSkZCDeAfE7xsotXsGl5mM8/fCQpL+JIp6NAmc34g8fqqhJODY4xNUcsJR3qrfHwBPs6khDsm5yO+tIj3EvzTTYGsZhiueHxKbqHJ1hbax7EuU6sHtPu5n4ubqhY0FvPpmBfInLfxSLyARG5X0Tuc1VWg12xkgJ5ecKdN23iSOcwP96beCzidAZT9g1EQ2UJ0+4SSzy2H+pk0/Jw0ssvC1EaKqC8qCApD+Iff3qA6z75CI8e7k7rWJLpJBfLYgT7TvS4GUzmQZzz1IWL6B6aYHI6yr6T/XPqL80mW4J9iXgQ3wIuBj4PfAG4CPi2n4PKVV5+8Qo2r6zgcw8envMqfDYHTw0SKshjXQ5U0HoZNPFE+4bHp3jqud5Fi/PNRV1F4r2p+yMT/GBHK1FV3nPvzrj9iFOlrd8ZQ/IeROqCfc1WA2G41JYX0TU8zsH2IcYmowvGHzyqsyTYl4iBuERV36mq293bn+EYjHOOvDzhrpds4nhPhPt3JeZFHDw1xMb6cFI5937h1ULEy2R67Eg3k9Oa9uUlj2Raj373qRZGJ6f52lu3ESrI453f+H3a1l/b+kcpLsyjKslGM4sR7DvRazLfhoMjtzExUzh6+drEPIjK0lDOehC7RORq74GIPB9HfuOc5MbN9WxprORzDx1OqPTd02DKBVa64nTxAtWPHOokXFTAtnWJfWCTJVE9psnpKN98/DjXnF/DjZuX8+Xbt9E2MMa7v7MzLVIDbQOjrFpWknTBXlkon1BBXmoGoidCVWkhFcXB6H5m+EddOIQqPHSwg/ryIlYlmCxRXZa7BuIK4HEROS4ix4HfAVeKyNMiss/X0eUgIsIHXrKJk/2jfH9Hy7zP7Rkep2toPCfiDwBFBfksryg6q1hOVdl+sItrN9bOqSi5WDw9poXqCH7+zClODY7xjmvOA+CKtVV84nWX8uRzvfz9j55ZdF/othSK5MB531OV23BSXLO/xGhkH68W4sljvWxdU5nwhUpVaSgrgn2JVG693PdRBIwXbazlynVVfOHhw7zhitVzFj/NBKhzIMXVo6Gy5KwYxMFTQ5waHPMt/gCOBzE+FWVwbIplJfGvpFWVux99jvW1ZWeM5dVbGzjaNcznHz7C+fVh/uxF61MeR1v/KC/elFoabyqCfarK/rZBbr5kcVpaxtLAE+ybimrcFqNzUVVaOCPYl0hP+3SRSD+IE8AgsAyo8W6qesLdd87heBEX0DE4zr1Pzq30etA1ELmQ4uqxuursxkHbXfXWF6dJ3jsedQl0ltvV3Mfeln7efs068mY11fmrmzZxy/NW8P9+3sSDBzpSGsPEVJSu4fGUPAhwDESyHsSJnggDo5NsaUwsGGksberCpwUiF6qgjiVWsC+TJJLm+jFgH/BvOM19PoXT7e2c5gUbarjm/Bq+9MgRIhPxu84dPDVITVnojA9FtmmoKqF9YPSMWo5HDnZx8aqKeQXDFksichv3PHqciuICXnv56rP25eUJn3rDZTyvYRl3fG83TW6HvmRwlrgS7wMxm5qyUNJprntbnWrZLavNQBinPYiCPOF5DcsSPi5WsC+TJOKr/CGwQVWvU9Xr3dsNfg8sCHzgJRfQPTzBNx+P70gdOjXEBSvKc0LB1GN1VQmT0zqTcjoQmWRnc5+vy0uwsNxGa1+Enz/Tzpuev4ayOTSrSkL5fPWt2ygvLuRPv7kjbneu+fBqIFYm2EluNqkI9u1u7qekMH+mK6BxblMWyqe4MI/NKyuS6taYLcG+RAzEM4Bd/sThirVVXHdBHV/+zVGGxs4MIE1HlUMduZPB5OFdPXuZTL890sV0VLnOx+UlYEameC4P4puPH0dEeNsL1s17nuUVxXztbdvoHZngXd/ekVS3v7YUq6g9asIhRiamk3rNva39PK9hWU6kORvZR0R4wfoabn5ecjEpz4PIdKA6kU/tPwG7ReQXIvJj7+b3wILCXS+5gP7IJF9/7PgZ25t7I4xNRnMmg8kjtnEQOOqty0oKucznNfJwUQFlofy4tRDD41N87/ct3HzJioR+vC9pWMZn/mgLu5v7+dAP9yWc2TRTJLcs9RgEJH4VNzEVZX/bIFsaE19KMJY+X3/7VbznuvOTOsYT7Mt0qmsiWUzfBP4FeBrIfM+7HOd5q5fx0ouW89XfHuNtL1jHMtcVPOiukedSBhPEeBD9o0Sjyq+f7eJFm+oycoVbX1EcV4/phztaGBqb4p0vPC/hc738kpV88GUX8K+/OMT6ujLuuHHjgkt5bf2jVJUWJuXaxxJrIBIxZIdODTExFbUAtbFosiXYl8ivQkRV/82tov61d/N9ZAHiAy/dxPD4FF/97bGZbQdPDSECG+tzy0CUhPKpDYdo7Yuwv22Q7uFxrvd5ecmjvryIrlkeRDSqfP3x42xdU5lUVgfAe67bwGsvb+CzDx7m1V98jB/tPjlvMV1b/ygrU/QeIHnBvj0tfYAFqI3F4wn25WKQ+rci8k8i8gIRudy7LXSQiDSKyHYROSAi+0XkDnf7R0XkpIjscW+3zHF8pYj8UEQOikiTiLwgyblljAtXVPCK563knseeo8dVajx4apDzaspSvlr1k4bKElr7Rtl+qBMReFGKdQHJEs+DeOhgJyd6Ikl5Dx4iwr+87lL+4baLGRqb4s7v7+Gaf3mYzz14+Iy2jh7tA6kVyXkkK9i3p2WA2nBoRuLEMBZDVVlhxpsGJbLEtNX9e3XMNgUWymSaAu5S1V0iUg7sFJFfufs+o6oLpcp+DvhfVX29iISAnBayufOmTTzwdDtf/s0x/uaWzRw6NcTmlbkVoPZYXVVKU/sgw+OdXLq6cqa602+Wx+lNffejx1i1rJiXX5xaIVlhfh5vfcE63vL8tfz6cBffeOw4n3nwWb64/Qi3blnF269ZxyVuOuHJ/tFFdcpLVrBvb2s/W1YnXi1rGPORDcG+BQ2Eql6fyolVtR1od+8PiUgT0JDIsSKyDHgR8Cfu8RNA5oVIkuD8+jCv3trAt353nDdftYYTvRFes/XsfP5coKGqhF8eOMVzPcodN27M2OvWVxQxOjnN0PgUFcWF7G8b4IljvXzk5gsXHQPJyxOuv6Ce6y+o50jnMN98/Dj37Wrlvl2tXLWumjde1cjQ2NSiPIiKkgIK8iShL+ng2CRHu4Z51ZZVKb+eYcSSDcG+RArllovI3SLyc/fxRSLyzmReRETW4XgiT7qb3ici+0TkHhGJt/B8HtAFfF1EdovI10QkrpiNiLxLRHaIyI6urq5khpV27rhxI5PTyl3/tRfV3KqgjsWrhVDF9/qHWOrLz+wsd8+jxykN5fPGq9ak9XXOrw/zsVdfwu8+ciN/94rNtA2M8oEf7AVST3EFZ0mrKkG5jadbB1DFAtRG2khF6mWxJHLZ9g3gF4B3KfQscGeiLyAiYeA+4E5VHQS+BGwALsPxMD4V57AC4HLgS6q6FRgBPhzv/Kr6FVXdpqrb6uoys5Y+F2trynjDFavZecIJTuZCm9F4eGviNWWhpKo5F4vXWa5zcIzOoTF+sreNN1yxek5tpsWyrKSQP712Pb/+4PV8+fYreNNVjbxoY+2izpmoYN+eFq+C2lJcjfRQVRrKeAxiTgMhIt7yU62q/gA3xVVVp4CEKoVEpBDHONyrqve7x3eo6rSqRoGvAlfFObQVaFVVz+P4IY7ByHnef+NGQvl5lIbyaazKzbBJQ6UzrhdfUHeW5pGfzHgQQ+N854lmJqNR/uSa5IPTyZKfJ7zs4hX802svnUkXTJVEr+L2tvRzXm3Zol/PMDyqyxzBvvGpxAs1F8t8HsRT7t8REanBCUzj9oYYWOjE4kTm7gaaVPXTMdtXxjztNTiV2megqqeAFhG5wN10I3BgodfMBRoqS7jjpo287vLVGf3xTYZ1taVcvb6aN16Z3qWdhVjuehAtvRHufeIEN15Yz3m1wZLBTthAtPab92CkFe9iI5NexHxBau/X7QPAj4ENIvIYUAe8PoFzXwPcDjwtInvcbX8DvElELsMxOMeBPwcQkVXA11TVS3t9P3Cvm8F0DHh7opPKNu+9PrkqyUxTVJDP996V+azhcFEBJYX5fPuJE/SMTMz0fAgSNWWhmVTmuWgfGKVjcNziD0ZamZHbiEz4KqwZy3wGok5EPuDe/2/gARyjMQ7chKPwOieq+iinjUwsD8zx/DbglpjHe4Bt872GESxEhOUVRRzviXDhinJesKEm20NKmuqyIgbHppicjs7ZXGmvG3/wW77EOLeoKk297W2qzLfElA+EgXKgDMeY5OPUI+Rm9NXIebw4xDtfeF4g6wO83tTzSR7saRmgMF9ytg7GCCZVZU4yRyYF++bzINpV9R8yNhLjnGBNTSnP9Yxwa0DrA2LlNurncPP3tvSzeWXFnJ0GDSMVPMG+TMptzOdBBO/yzsh5/v4VF/Hj910T2B/PhRRdp6PKPreC2jDSyUyQOoNLTPN5EDdmbBTGOcOy0kKW4U/dQyZYSLDvaNcwIxPTFn8w0k42BPvm9CBUtTdjozCMgDDjQcyRyTRTIGcGwvCBqrLCjEp+W5srw0iCytIQInMvMe1t6ae8uID1AavvMIJBdWmIvgzWQZiBMIwkyM8TqkrnltvY0+LEH3K1SNIINlVlmRXsMwNhGEkyVzX12OQ0B08NWYtRwzeqMiz5bQbCMJKkeg7Bvv1tA0xH1TKYDN/ItGCfGQjDSJKaOTyIPS2ORJllMBl+kWnBPjMQhpEkcy0x7WnpZ9Wy4jkL6AxjsWRasM8MhGEkSY0bKJyO6hnb97b0W3qr4SsLFWqmGzMQhpEk1WUhVKE/Jpukd2SC5t6IGQjDVzzBvkxlMpmBMIwkqQ47fS1ir+L2tnod5MxAGP4xI/mdIcE+MxCGkSTx5Db2NPeTJ3CpNQkyfKSq1JGpyZTchhkIw0iSeOvAe1v72VhfTlnRfPJmhrE4Mi3YZwbCMJJktgehqm6A2rwHw19CBXmUZ1CwzzcDISKNIrJdRA6IyH4RucPd/lEROSkie9zbLfOcI19EdovIT/0ap2EkS9WMYJ/zJW3pHaUvMmkBaiMjVGZQsM9Pf3gKuEtVd4lIObBTRH7l7vuMqn4ygXPcATQB1prLyBkK8/OoKC6gd8RRdN3d0gdYgZyRGapLQ/QGvQ5CVdtVdZd7fwjnh74h0eNFZDXwCuBr/ozQMFKnNlw0s8S0t2WA4sI8Ni23TryG/1SVhc5IsfaTjMQgRGQdsBV40t30PhHZJyL3iEjVHId9FvgQEF3g3O8SkR0isqOrqytdQzaMeYmtpt7b2s8lq5ZRmG8hPcN/qjMo2Of7J1pEwsB9wJ2qOgh8CdgAXAa0A5+Kc8wrgU5V3bnQ+VX1K6q6TVW31dXVpXfwhjEHnoGYnI7yzMkBiz8YGaOyNJSxGISvBkJECnGMw72qej+Aqnao6rSqRoGvAlfFOfQa4FUichz4HnCDiHzHz7EaRjLUhB1F10Onhhifilr8wcgY1WWFjExMZ0Swz88sJgHuBppU9dMx21fGPO01wDOzj1XVj6jqalVdB7wReFhV3+LXWA0jWarLnKu43W6LUTMQRqbwsugyIdjnpwdxDXA7ztV/bErrJ0TkaRHZB1wP/BWAiKwSkQd8HI9hpI3qsiKmospvn+2iuizE6qqSbA/JOEfw9JgyEYfwLc1VVR8F4vVdjGsEVLUNOKsmQlUfAR5J59gMY7F4xXKPHunm+edV4zjMhuE/M4J9GTAQlnZhGCngyW1EJqa5rHGuRDzDSD8zgn0BX2IyjCWL9yUFTGLDyCiZFOwzA2EYKVATjjEQJvFtZJBKW2IyjNzG8yDW1pTOZJUYRibwBPsy0TTIDIRhpEBRQT7lxQWW3mpkhaqyzBTLmXi9YaTIF998OetqyrI9DOMcpKq0MCOCfWYgDCNFXrTJpF2M7FBVlhk9JltiMgzDCBiZEuwzA2EYhhEwMiXYZwbCMAwjYGRKsM8MhGEYRsDIlGCfGQjDMIyAUZ0hwT4zEIZhGAEjU9XUZiAMwzACRqYE+8xAGIZhBIyqsswI9pmBMAzDCBiVJbbEZBiGYcTBE+wLbJBaRBpFZLuIHBCR/SJyh7v9oyJyclYb0oSONQzDMByqykL0+7zE5KcW0xRwl6ruEpFyYKeI/Mrd9xlV/WSyx6rqAR/HaxiGERgyIdjnZ0/qdqDdvT8kIk1AwyKPNQNhGIaB40H0DAd0iSkWEVkHbAWedDe9T0T2icg9IjJvQ984xxqGYZzzVJeGfG8a5LuBEJEwcB9wp6oOAl8CNgCX4XgJn0ri2HjPeZeI7BCRHV1dXWkfv2EYRi6SiaZBvhoIESnE+YG/V1XvB1DVDlWdVtUo8FXgqkSPjYeqfkVVt6nqtro60+c3DOPcoKrUEewbm/RPsM/PLCYB7gaaVPXTMdtXxjztNcAziR5rGIZhOGRCsM9PD+Ia4HbghlkprZ8QkadFZB9wPfBXACKySkQeWOBYwzAMg9OCfX7GIfzMYnoUkDi7HoizDVVtA25Z4FjDMAyDzAj2WSW1YRhGAPEE+/zUYzIDYRiGEUA8wT4/FV3NQBiGYQSQKltiMgzDMOJRmO+/YJ8ZCMMwjIBSVeZvNbUZCMMwjIDiGAiLQRiGYRizqCottBiEYRiGcTbVpSGLQRiGYRhn43fTIDMQhmEYAcVvwT4zEIZhGAHFb8E+MxCGYRgBxW/BPjMQhmEYAcXzIPzKZDIDYRiGEVA8uQ2/BPvMQBiGYQSUGcE+8yAMwzCMWGYE+yxIbRiGYcTit2CfGQjDMIwA46dgn28GQkQaRWS7iBwQkf0icoe7/aMicnKhXtMi8nIROSQiR0Tkw36N0zAMI8j4KdjnW09qYAq4S1V3iUg5sFNEfuXu+4yqfnKuA0UkH/gi8BKgFfi9A6kf9QAACD9JREFUiPxYVQ/4OF7DMIzAUV1aSPdwwDwIVW1X1V3u/SGgCWhI8PCrgCOqekxVJ4DvAbf5M1LDMIzgUuWjYF9GYhAisg7YCjzpbnqfiOwTkXtEpCrOIQ1AS8zjVuYwLiLyLhHZISI7urq60jhqwzCM3Gfbumqu3Vjry7l9NxAiEgbuA+5U1UHgS8AG4DKgHfjUYs6vql9R1W2quq2urm7R4zUMwwgSb37+Gv75dZf6cm5fDYSIFOIYh3tV9X4AVe1Q1WlVjQJfxVlOms1JoDHm8Wp3m2EYhpEh/MxiEuBuoElVPx2zfWXM014DPBPn8N8DG0XkPBEJAW8EfuzXWA3DMIyz8TOL6RrgduBpEdnjbvsb4E0ichmgwHHgzwFEZBXwNVW9RVWnROR9wC+AfOAeVd3v41gNwzCMWfhmIFT1UUDi7Hpgjue3AbfEPH5grucahmEY/mOV1IZhGEZczEAYhmEYcTEDYRiGYcTFDIRhGIYRF1HVbI8hbYhIF3AixcNrge40DiebLJW5LJV5gM0lF1kq84DFzWWtqsatMl5SBmIxiMgOVd2W7XGkg6Uyl6UyD7C55CJLZR7g31xsickwDMOIixkIwzAMIy5mIE7zlWwPII0slbkslXmAzSUXWSrzAJ/mYjEIwzAMIy7mQRiGYRhxMQNhGIZhxGXJGgi3W12niDwTs22LiPxORJ4WkZ+ISIW7PSQiX3e37xWR62KOucLdfkRE/s2VMQ/qXB4RkUMisse91Wd4Ho0isl1EDojIfhG5w91eLSK/EpHD7t8qd7u4//MjbgfCy2PO9Tb3+YdF5G2ZnIcPc5mOeU8yLmufwlwudD974yLy17PO9XL3M3ZERD4c4Hkcd79De0RkRybnkeJc/tj9XD0tIo+LyJaYc6X+nqjqkrwBLwIuB56J2fZ74MXu/XcAH3Pvvxf4unu/HtgJ5LmPnwKuxlGm/Tlwc4Dn8giwLYvvyUrgcvd+OfAscBHwCeDD7vYPA//i3r/F/Z+L+x486W6vBo65f6vc+1VBnIu7bzhb70mKc6kHrgQ+Dvx1zHnygaPAeiAE7AUuCto83H3HgdoAvSd/4H0HgJtjviuLek+WrAehqr8Bemdt3gT8xr3/K+B17v2LgIfd4zqBfmCbOM2NKlT1CXX+298CXu332GeTjrlkYJgLoqrtqrrLvT8ENOH0Gr8N+Kb7tG9y+n98G/AtdXgCqHTfk5cBv1LVXlXtw5n/yzM4lXTOJeskOxdV7VTV3wOTs051FXBEVY+p6gTwPfccGSGN88g6Kczlcfe7APAEThdOWOR7smQNxBzs5/Q/5w2cbmu6F3iViBSIyHnAFe6+BqA15vhWd1sukOxcPL7uus1/n43lMg8RWQdsBZ4Elqtqu7vrFLDcvd8AtMQc5v3/59qeFRY5F4BiEdkhIk+ISMYvQGJJcC5zkTPvyyLnAU5Ds1+KyE4ReZcvg0yQFObyThxvFRb5npxrBuIdwHtEZCeO2zbhbr8H5x+3A/gs8DgwnZURJk4qc/ljVX0ecK17uz2jI3YRkTBOr/I7VXUwdp/rqQUm9zpNc1mrjkzCm4HPisiG9I90YZbK+5KmebxQVS/HWa55r4i8KP0jXZhk5yIi1+MYiP+Tjtc/pwyEqh5U1Zeq6hXAd3HW5lDVKVX9K1W9TFVvAypx1vxOctpVw71/MtPjjkcKc0FVT7p/h4D/xHE/M4qIFOJ84O9V1fvdzR3ecov7t9PdfpIzvR/v/z/X9oySprnEvi/HcOJEW30f/CySnMtcZP19SdM8Yt+TTuC/yf3vCiJyKfA14DZV7XE3L+o9OacMhLhZOyKSB/wd8B/u41IRKXPvvwSYUtUDris3KCJXu8sxbwX+JzujP5Nk5+IuOdW62wuBVwLPxD25f2MW4G6gSVU/HbPrx4CXifQ2Tv+Pfwy8VRyuBgbc9+QXwEtFpMrN4nipuy1jpGsu7hyK3HPW4vRyP5CRSbikMJe5+D2wUUTOE5EQ8Eb3HBkhXfMQkTIRKffu43y+cvq7IiJrgPuB21X12ZjnL+49STSaHbQbzlV1O04AqhXH7boD52r6WeCfOV1Jvg44hBMIehDH5ffOsw3nw3EU+IJ3TNDmApThZDTtw4lffA7Iz/A8XojjEu8D9ri3W4Aa4CHgsDvmavf5AnzR/d8/TUwGFs4S2xH39vYsvCdpmQtO9snTOLGjp4F3BmAuK9zP4SBOEkQrTjIH7nHPuvP82yDOAyfjZ69725/peaQ4l68BfTHP3RFzrpTfE5PaMAzDMOJyTi0xGYZhGIljBsIwDMOIixkIwzAMIy5mIAzDMIy4mIEwDMMw4mIGwjBSxK1peFREbo7Z9gYR+d9sjssw0oWluRrGIhCRS4D/wql+LgB2Ay9X1aMpnKtAVafSPETDSBkzEIaxSETkE8AITjHiCLAWuAQoBD6qqv/jCq59230OwPtU9XFx+nV8DKfI6UJV3ZTZ0RvG3JiBMIxF4sox7MIRTPwpsF9VvyMilTj9RLbiVMVGVXVMRDYC31XVba6B+Blwiao+l50ZGEZ8CrI9AMMIOqo6IiLfB4aBPwRuldMdyoqBNUAb8AURuQxHXTfWU3jKjIORi5iBMIz0EHVvArxOVQ/F7hSRjwIdwBac5JCxmN0jGRqjYSSFZTEZRnr5BfB+rxmTiHjS3cuAdlWN4vThyM/S+AwjYcxAGEZ6+RhOcHqfiOx3HwP8O/A2EdkLXIh5DUYAsCC1YRiGERfzIAzDMIy4mIEwDMMw4mIGwjAMw4iLGQjDMAwjLmYgDMMwjLiYgTAMwzDiYgbCMAzDiMv/D36Pu8PHTgRpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=dataframe;\n",
    "df['DATE']=pandas.to_datetime(df.DATE);\n",
    "plt.figure(); plt.title('Temperature'); plt.xlabel('Year');  plt.ylabel('Temp. in °C');\n",
    "plt.plot(df.groupby(df.DATE.dt.year)['TAVG'].mean());\n",
    "plt.figure(); plt.title('Precipitation'); plt.xlabel('Year'); plt.ylabel('Prec. in mm/month');\n",
    "plt.plot(df.groupby(df.DATE.dt.year)['PRCP'].mean());\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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