{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  # Steigende Temperaturen in Berlin Tegel\n",
    "\n",
    "  Die in diesem Notebook genutzten Daten stammen vom [Deutschen Wetterdienst (DWD)](https://www.dwd.de/)\n",
    "  und werden über das [Climate Data Center (CDC)](https://www.dwd.de/DE/klimaumwelt/cdc/cdc_node.html) zur Verfügung gestellt.\n",
    "  Konkret werden die\n",
    "  [historischen](https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/kl/historical/tageswerte_KL_00430_19630101_20181231_hist.zip)\n",
    "  und\n",
    "  [aktuellen](https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/kl/recent/tageswerte_KL_00430_akt.zip)\n",
    "  täglichen Wetterdaten der DWD Messstation 430 in Berlin Tegel seit 1.1.1963 verwendet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython import get_ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ms-python.python added\n",
    "import os\n",
    "try:\n",
    "    os.chdir(os.path.join(os.getcwd(), 'notebooks'))\n",
    "    print(os.getcwd())\n",
    "except:\n",
    "    pass\n",
    "\n",
    "from IPython import get_ipython\n",
    "get_ipython().run_line_magic('matplotlib', 'inline')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import shapiro\n",
    "import seaborn as sns\n",
    "from statsmodels.tsa.stattools import adfuller, kpss\n",
    "\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,10)})\n",
    "\n",
    "data_files = [\n",
    "    '../data/historisch/produkt_klima_tag_19630101_20181231_00430.txt',\n",
    "    # '../data/aktuell/produkt_klima_tag_20180215_20190818_00430.txt'\n",
    "]\n",
    "df = pd.concat((pd.read_csv(f, \n",
    "                            sep=';',\n",
    "                            header=None,\n",
    "                            skiprows=1,\n",
    "                            names=['STATIONS_ID', 'MESS_DATUM', 'QN_3', 'FX', 'FM', 'QN_4', 'RSK', 'RSKF', 'SDK', 'SHK_TAG', 'NM', 'VPM', 'PM', 'TMK', 'UPM', 'TXK', 'TNK', 'TGK', 'eor'],\n",
    "                            usecols=['MESS_DATUM', 'RSK', 'TMK'],\n",
    "                            na_values=['-999'],\n",
    "                            parse_dates=['MESS_DATUM'],\n",
    "                            ) for f in data_files), ignore_index=True)\n",
    "df.index = df['MESS_DATUM']\n",
    "df = df.drop(columns=['MESS_DATUM'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_source(_ax):\n",
    "    \"\"\"Hilfsfunktion um Quellenangabe einem Plot hinzuzufügen\"\"\"\n",
    "    _ax.text(1, 1, 'Quelle: Deutscher Wetterdienst (DWD)', ha='right', va='top', transform=_ax.transAxes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tägliche Regenmenge (RSK) und Durchschnittstemperatur (TMK)\n",
    "\n",
    "Die Datensammlung vom DWD umfasst eine Vielzahl von Messwerten wie Sonnenscheindauer,\n",
    "relative Luftfeuchte, max. Windgeschwindigkeit, etc.\n",
    "\n",
    "Ich picke mir lediglich zwei Werte raus:\n",
    "\n",
    "- tägliche Regenmenge (RSK) - nehmen die Niederschläge ab und wird es immer trockener?\n",
    "- tägliche Durchschnittstemperatur (TMK) - steigt die Temperatur?\n",
    "\n",
    "Beide Werte stehen für alle Tage seit dem 1.1.1963 zur Verfügung."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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>RSK</th>\n",
       "      <th>TMK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESS_DATUM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-04</th>\n",
       "      <td>1.3</td>\n",
       "      <td>-2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RSK   TMK\n",
       "MESS_DATUM           \n",
       "1963-01-01  0.0 -12.4\n",
       "1963-01-02  0.0 -10.2\n",
       "1963-01-03  0.0  -7.2\n",
       "1963-01-04  1.3  -2.6\n",
       "1963-01-05  0.0   1.7"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 20454 entries, 1963-01-01 to 2018-12-31\n",
      "Data columns (total 2 columns):\n",
      "RSK    20453 non-null float64\n",
      "TMK    20454 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 479.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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>RSK</th>\n",
       "      <th>TMK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20453.000000</td>\n",
       "      <td>20454.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.503432</td>\n",
       "      <td>9.851545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.975919</td>\n",
       "      <td>7.914333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-18.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.300000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>196.900000</td>\n",
       "      <td>30.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                RSK           TMK\n",
       "count  20453.000000  20454.000000\n",
       "mean       1.503432      9.851545\n",
       "std        3.975919      7.914333\n",
       "min        0.000000    -18.100000\n",
       "25%        0.000000      3.900000\n",
       "50%        0.000000     10.000000\n",
       "75%        1.300000     16.000000\n",
       "max      196.900000     30.800000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Man erkennt an der Standardabweichung im Vergleich zu den Mittelwerten,\n",
    "dass die Tageswerte für Temperatur und Niederschlag wenig hilfreich sind,\n",
    "da die Messwerte stark schwanken (sie sind saisonal - Jahreszeiten).\n",
    "\n",
    "Dies lässt sich für am Beispiel der Temperaturwerte in einem Boxplot visualisieren."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_source(sns.boxplot(x=df['TMK']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse Tagesmittel der Temperatur\n",
    "\n",
    "Im Fall der Tagestemperaturwerte (TMK) ist es daher sinnvoller, einen jährlichen Durchschnitt zu bilden. Dadurch spielen Jahreszeiten keine Rolle mehr."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "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>TMK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESS_DATUM</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963-12-31</th>\n",
       "      <td>8.266575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-12-31</th>\n",
       "      <td>8.927322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-12-31</th>\n",
       "      <td>8.337260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966-12-31</th>\n",
       "      <td>9.263562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-12-31</th>\n",
       "      <td>10.159452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  TMK\n",
       "MESS_DATUM           \n",
       "1963-12-31   8.266575\n",
       "1964-12-31   8.927322\n",
       "1965-12-31   8.337260\n",
       "1966-12-31   9.263562\n",
       "1967-12-31  10.159452"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = df[['TMK']].resample('Y').mean()\n",
    "temp_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "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>TMK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.851656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.919631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.096448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.098838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.023410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.480392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.595890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TMK\n",
       "count  56.000000\n",
       "mean    9.851656\n",
       "std     0.919631\n",
       "min     8.096448\n",
       "25%     9.098838\n",
       "50%    10.023410\n",
       "75%    10.480392\n",
       "max    11.595890"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 56 entries, 1963-12-31 to 2018-12-31\n",
      "Freq: A-DEC\n",
      "Data columns (total 1 columns):\n",
      "TMK    56 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 896.0 bytes\n"
     ]
    }
   ],
   "source": [
    "temp_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nnvlHDBs2NI45pl+MHHn1et8Ur/aNb1wVdXUr45RTToiBA4+Mb3zjoqitXfXT2nPnzo3Kyj7rPZnw6l8oqqo6Ii655IJo375D3Hzzz4oni917733joou+HmPGfDsGDjwyTjrp2Ea/xvSVr3wtpk9/NKqqjozq6gejT5++xet23fUj0a9f/zjhhKFRVXVE1NTMi9///ndx6qknRGVln7j++v+KK664Nlq3bh3dunWL7373+rjrrtvj6KOPijPOOLnRr0utS0XFNjFmzPdj6tQpceyxVTFkyID44Q9viJUr13+y4zX17HlQLFgwv9EnMg44oGcsWDA/Djjg7fOjbOi1OvLIfhERcfTRR8WZZ66KXcOGfTZ++9upUVV1ZIwd+51NGvOIERfF8uXLo3fv3nHttVfE0UcPXue+//7v58ZOO+0cX/ziGdG/f984//xziueg2ZDJkx+Iz3xmcPTv3zfGj783Lrts1U9er+21XNNee+0d7dq1i5kz/9Zo++c//9morOwTJ554XEyYMD7OPfeC+MIXzm60T1Of/9UuvviCqKw8PI4//pi47bZb4sQTT45LL/1m8fqampp48cUX4vDDj9jgY25VKBQK69uhtnZpNDSsd5dN0qXLtjFv3pL3/LhsPcwhmsscornMIZrLHKK5zCGaauLE/41bbrkpbrzx5kYn8jWHaK7sc+gPf5gR9913T1x33X+16DhuuGFM7LjjTnH88cOipKRVbL/9us9V4xw1AAAAH3CDBg2NsrKy+NvfntzoX1yC97NDDjk0Djnk0JYeRpx77ogm7yvUAAAAbAWqqo5p6SEATeAcNQAAAABJCDUAAAAASQg1AAAAAEkINQAAAABJCDUAAAAASQg1AAAAAEkINQAAAABJCDUAAAAASQg1AAAAAEkINQAAAABJCDUAAAAASQg1AAAAAEkINQAAAABJCDUAAAAASQg1AAAAAEkINQAAAABJCDUAAAAASQg1AAAAAEkINQAAAABJCDUAAAAASQg1AAAAAEkINQAAAABJCDUAAAAASQg1AAAAAEkINQAAAABJCDUAAAAASQg1AAAAAEkINQAAAABJCDUAAAAASQg1AAAAAEkINQAAAABJCDUAAAAASQg1AAAAAEkINQAAAABJCDUAAAAASQg1AAAAAEkINQAAAABJCDUAAAAASQg1AAAAAEmUtfQAALYWd9xxW8ye/VJLD4P3UHl5aaxcWd/Sw+B9bF1zaNGihRER0aFDxy09JJLaeedd43OfO62lhwHAFiDUAGwhs2e/FH9/5tkobeONF7B+9StWhZp5i+taeCRksHo+ALB1EGoAtqDSNh2jYtejWnoYQHJvvjQ1IsJ6QUS8PR8A2Do4Rw0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEkINAAAAQBJCDQAAAEASQg0AAABAEltFqJk+/ZGYPv2Rlh4GAAAAsAm2pvf1ZS09gC1h2rSHIyKid+/DW3gkAAAAwMbamt7XbxWfqAEAAAB4PxBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIQagAAAACSEGoAAAAAkhBqAAAAAJIoa+kBbAmLFi2MRYsWxahR32rpofAeKi8vjZUr61t6GLyPbek59PLLL0VDfekWuz8APhga6lbEyy+/tNa/Zf09RHOZQzTXlppDL7/8UnTo0GGz308GPlEDAAAAkMRW8YmaDh06RocOHePiiy9r6aHwHurSZduYN29JSw+D97EtPYdGjfpWPDu7ZovdHwAfDCVlbWKXnTuv9W9Zfw/RXOYQzbWl5tDW9A0Zn6gBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASEKoAQAAAEhCqAEAAABIQqgBAAAASKKspQewJRx2WN+WHgIAAACwibam9/VbRajp3fvwlh4CAAAAsIm2pvf1vvoEAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkERZSw8AYGtSv2JhvPnS1JYeBpBc/YqFERHWCyJi9Xzo3NLDAGALEWoAtpCdd961pYfAe6y8vDRWrqxv6WHwPrauObRo0ao/0Tp06Lilh0RKnf0/BGArItQAbCGf+9xpLT0E3mNdumwb8+Ytaelh8D5mDgEAa3KOGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgL6AnN8AAAZ5SURBVCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCSEGgAAAIAkhBoAAACAJIQaAAAAgCTKNrRDSUmrzXbnm/PYbB3MIZrLHKK5zCGayxyiucwhmsscornMoY2zoeerVaFQKGyhsQAAAACwHr76BAAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkIRQAwAAAJCEUAMAAACQhFADAAAAkMRmDTW//e1v49hjj42hQ4fG4MGDY8qUKe/ap76+Pq688sro169fVFZWxj333LM5h8T7TFPm0A033BCf+MQnYujQoTF06NC48sorW2CkZPXQQw/FcccdF4MHD45TTjklZs+e/a59rEOsT1PmkHWIdxo1alR86lOfir322iv+8Y9/FLe/8MILceKJJ8aAAQPixBNPjBdffHGtt7cm0dw5ZE1iXXNoXdvXZB2iuXPIOtRMhc2koaGhcPDBBxf+/ve/FwqFQmHWrFmFnj17Furr6xvtd9999xXOPPPMQn19faG2trbQp0+fwuzZszfXsHgfaeoc+t73vlcYOXJkSwyR5BYuXFg45JBDCs8//3yhUCgUxo8fXzjzzDPftZ91iHVp6hyyDvFOf/zjHwuvvfZa4cgjjyz+P6xQKBROPfXUwvjx4wuFwqq5dOqpp6719tYkmjuHrEmsaw6ta/uarEM0dw5Zh5pns36ipqSkJJYsWRIREUuWLIkddtghSkoa3+UDDzwQw4YNi5KSkujUqVP069cvJk+evDmHxftIU+YQrMtLL70UnTt3jt122y0iIvr27RvTpk2L+fPnN9rPOsS6NHUOwTsdfPDB0b1790bbamtr4+mnn45BgwZFRMSgQYPi6aefXutcsibR3DkEa5tD69u+JusQzZ1DNE/Z5jpwq1atYuzYsXHOOedERUVFLFu2LMaNG/eu/ebMmRMf/vCHi5e7d+8ec+fO3VzD4n2kqXMoImLSpEkxbdq06NKlS5x77rlx4IEHbuHRktFuu+0WNTU18dRTT0WPHj1iwoQJEbFq3enUqVNxP+sQ69LUORRhHWL95syZE127do3S0tKIiCgtLY0ddthhrXPJmsTabMwcirAm0TzWId4L1qFNt9lCTV1dXYwbNy5uvPHG6NWrV/z5z3+OESNGxKRJk2KbbbbZXHfLB0hT59BJJ50UZ599dpSXl8f06dPjnHPOiQceeCC22267Fhw9GWy77bYxZsyYuO666+Kf//xnHH744dG+ffsoK9tsSx8fME2dQ9YhIBNrEtDSrEPNs9m+QzJr1qx44403olevXhER0atXr2jbtm0899xzjfbr3r17vPbaa8XLc+bMiW7dum2uYfE+0tQ51KVLlygvL4+IiN69e0f37t3jmWee2eLjJadPfvKTceedd8a9994bp5xySqxYsSJ23nnnRvtYh1ifpswh6xAb0r1793j99dejvr4+IladqPONN95Y68fHrUmszcbMIWsSzWUdormsQ82z2UJNt27dYu7cufH8889HRMRzzz0XNTU1scsuuzTar6qqKu65555oaGiI+fPnx69//esYMGDA5hoW7yNNnUOvv/568d+zZs2KV199tXg+CZg3b15ERDQ0NMTo0aPjpJNOioqKikb7WIdYn6bMIesQG7L99tvHPvvsExMnToyIiIkTJ8Y+++yz1q+sWJNYm42ZQ9Ykmss6RHNZh5qnVaFQKGyug99///3x3//939GqVauIiDjvvPOiX79+MXz48DjvvPPi4x//eNTX18dVV10V06dPj4iI4cOHx4knnri5hsT7TFPm0MUXXxwzZ86MkpKSKC8vj/POOy/69u3bwiMni69//evx+OOPx8qVK6N3795x6aWXRuvWra1DNFlT5pB1iHe6+uqrY8qUKVFTUxPbbbdddOzYMSZNmhTPPfdcXHLJJbF48eJo3759jBo1KnbfffeICGsSjTR3DlmTWNccWtf2COsQjTV3DlmHmmezhhoAAAAAms7vHAMAAAAkIdQAAAAAJCHUAAAAACQh1AAAAAAkIdQAAAAAJCHUAAAAACQh1AAAAAAkIdQAAAAAJPH/AZC9wigK9ciWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_source(sns.boxplot(x=temp_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dieses Histogramm sieht einer Käsekurve (Normalverteilung) ziemlich ähnlich."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_source(sns.distplot(temp_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mit dem Shapiro Test kann man prüfen, ob eine Wertemenge normalverteilt ist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro Statistik: 0.970\n",
      "Shapiro p-value: 0.173\n",
      "Die jährliche Durchschnittstemperatur ist wahrscheinlich normalverteilt.\n"
     ]
    }
   ],
   "source": [
    "stat, p = shapiro(temp_df['TMK'])\n",
    "alpha = 0.05\n",
    "\n",
    "print('Shapiro Statistik: {:.3f}'.format(stat))\n",
    "print('Shapiro p-value: {:.3f}'.format(p))\n",
    "\n",
    "if p > alpha:\n",
    "    print('Die jährliche Durchschnittstemperatur ist wahrscheinlich normalverteilt.')\n",
    "else:\n",
    "    print('Die jährliche Durchschnittstemperatur ist nicht normalverteilt.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auch wenn die durchschnittliche Jahrestemperatur stark schwankt, scheint es zumindest optisch einen klaren steigenden Trend zu geben."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5606747390>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = sns.lineplot(data=temp_df, legend=False)\n",
    "temp.set_xlabel('Jahr', fontsize=20)\n",
    "temp.set_ylabel('durchschn. Jahrestemperatur', fontsize=20)\n",
    "add_source(temp)\n",
    "\n",
    "x = np.arange(temp_df['TMK'].size)\n",
    "fit = np.polyfit(x, temp_df['TMK'], 1)\n",
    "fit_fn = np.poly1d(fit)\n",
    "\n",
    "temp.plot(temp_df.index, fit_fn(x), 'k-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bei steigenden Temperaturen über die Jahre würde man erwarten, dass die Durschnittstemperatur der letzten Jahre über der Durschnittstemperatur der Anfangsjahre liegt.\n",
    "\n",
    "Deshalb unterteile ich die gesamte Messreihe in zwei gleichgroße Blöcke X1 und X2 und ermittel für jeden Block Durschnitt und Varianz.\n",
    "\n",
    "Achtung: Ab hier nutze ich logarithmisch vereinheitlichte Messwerte."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean1=2.2328857443616745, var1=0.006938846110188799\n",
      "mean2=2.3336491381848457, var2=0.005676896339597946\n"
     ]
    }
   ],
   "source": [
    "tmk_log = np.log(temp_df['TMK'].values)\n",
    "tmk_split = int(len(tmk_log) / 2)\n",
    "\n",
    "X1 = tmk_log[:tmk_split]\n",
    "X2 = tmk_log[tmk_split:]\n",
    "\n",
    "mean1 = X1.mean()\n",
    "mean2 = X2.mean()\n",
    "\n",
    "var1 = X1.var()\n",
    "var2 = X2.var()\n",
    "\n",
    "print('mean1={}, var1={}'.format(mean1, var1))\n",
    "print('mean2={}, var2={}'.format(mean2, var2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Werte sehen sehr ähnlich aus, auch wenn die zweite Hälfte der Werte eine leicht höhere Durchschnittstemperatur hat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es gibt verschiedene statistische Tests, um zu prüfen, ob eine Zeitreihe stationär ist oder ob sie einem Trend folgt.\n",
    "\n",
    "Zunächst führe ich den Augmented Dickey-Fuller Test durch. Die Null-Hypothese dieses Tests ist, dass die Zeitreihe nicht stationär ist, sie also einem Trend folgt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H0 abgelehnt: Zeitreihe anscheinend (alpha=0.05) stationär.\n",
      "ADF Statistik: -4.314\n",
      "p-value: 0.000421\n",
      "Critical Values:\n",
      "\t1%: -3.555\n",
      "\t5%: -2.916\n",
      "\t10%: -2.596\n"
     ]
    }
   ],
   "source": [
    "adfuller_alpha = 0.05\n",
    "\n",
    "adf_result = adfuller(tmk_log)\n",
    "adf_statistic = adf_result[0]\n",
    "adf_p_value = adf_result[1]\n",
    "\n",
    "if adf_p_value < adfuller_alpha:\n",
    "    print('H0 abgelehnt: Zeitreihe anscheinend (alpha={}) stationär.'.format(adfuller_alpha))\n",
    "else:\n",
    "    print('H0 bestätigt: Zeitreihe anscheinend (alpha={}) nicht stationär (folgt einem Trend).'.format(adfuller_alpha))\n",
    "\n",
    "print('ADF Statistik: {:.3f}'.format(adf_statistic))\n",
    "print('p-value: {:3f}'.format(adf_p_value))\n",
    "print('Critical Values:')\n",
    "for key, value in adf_result[4].items():\n",
    "    print('\\t{}: {:.3f}'.format(key, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Der Augmented Dickey-Fuller Test führt zu einer klaren Ablehnung der These, dass die Zeitreihe einem Trend folgt.\n",
    "\n",
    "Der KPSS Test ist ebenfalls anwendbar. Im Gegensatz zum vorherigen Test prüft er, ob eine Zeitreihe stationär ist, sie also keinem Trend folgt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H0 abgelehnt: Zeitreihe anscheinend (alpha=0.05) nicht stationär (folgt einem Trend)\n",
      "KPSS Statistik: 0.593\n",
      "p-value: 0.023313\n",
      "\t10%: 0.347\n",
      "\t5%: 0.463\n",
      "\t2.5%: 0.574\n",
      "\t1%: 0.739\n"
     ]
    }
   ],
   "source": [
    "kpss_alpha = 0.05\n",
    "\n",
    "kpss_result = kpss(tmk_log, lags='legacy')\n",
    "\n",
    "kpss_statistic = kpss_result[0]\n",
    "kpss_p_value = kpss_result[1]\n",
    "kpss_critical_values = kpss_result[3]\n",
    "\n",
    "if kpss_p_value < kpss_alpha:\n",
    "    print('H0 abgelehnt: Zeitreihe anscheinend (alpha={}) nicht stationär (folgt einem Trend)'.format(kpss_alpha))\n",
    "else:\n",
    "    print('H0 bestätigt: Zeitreihe anscheinend (alpha={}) stationär.'.format(kpss_alpha))\n",
    "\n",
    "print('KPSS Statistik: {:.3f}'.format(kpss_statistic))\n",
    "print('p-value: {:3f}'.format(kpss_p_value))\n",
    "for key, value in kpss_critical_values.items():\n",
    "    print('\\t{}: {:.3f}'.format(key, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Der KPSS Test lehnt bei einem Konfidenzniveau von 95% (alpha=0.05) ab, dass die Zeitreihe keinem Trend folgt, sie also stationär ist. Allerdings ist es nur eine schwache Ablehnung. Schon bei Anhebung des Konfidenzniveaus auf 97,5% kommt der Test zum entgegengesetzen Ergebnis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretation Temperatur Zeitreihe\n",
    "\n",
    "Und nun? Der KPSS Test bestätigt (gerade so), dass es wohl einen Trend in den Temperaturwerten gibt, während der Augmented Dickey-Fuller Test dies stark verneint. Das Diagramm weiter oben hingegen zeigt einen klaren steigenden Trend bei den durchschnittlichen Jahrestemperaturen.\n",
    "\n",
    "So einfach ist die Sache also nicht. Wenn ich im Netz nach ähnlichen Analysen suche, dann spielen die Tagestemperaturen keine Rolle. Genutzt wird stattdessen die Temperatur der Böden, denn diese sind letztendlich entscheidend, ob die Eismassen und Permafrostböden schmelzen. Solche Messwerte liegen mir aber nicht vor.\n",
    "\n",
    "Die Ergebnisse der beiden Zeitreihen-Tests (Augmented Dickey-Fuller Test und KPSS) sind besonders kritisch zu sehen, da unklar ist, welchen Trend (oder eben gerade nicht) die Tests erkennen? Hinzu kommt, dass ich mit gerade mal gut 50 Messwerten über wenig Futter für die Tests verfüge."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zeitreihe jährliche Regenmenge (RSK)\n",
    "\n",
    "Bei den Regenwerten (RSK) ist die jährliche Regenmenge interessanter als die täglichen Einzelwerte."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "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>RSK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESS_DATUM</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963-12-31</th>\n",
       "      <td>464.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-12-31</th>\n",
       "      <td>482.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-12-31</th>\n",
       "      <td>630.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966-12-31</th>\n",
       "      <td>752.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-12-31</th>\n",
       "      <td>634.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              RSK\n",
       "MESS_DATUM       \n",
       "1963-12-31  464.1\n",
       "1964-12-31  482.2\n",
       "1965-12-31  630.6\n",
       "1966-12-31  752.3\n",
       "1967-12-31  634.3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regen_df = df[['RSK']].resample('Y').sum()\n",
    "regen_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "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>RSK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>549.101786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>104.560831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>351.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>464.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>545.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>617.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>858.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              RSK\n",
       "count   56.000000\n",
       "mean   549.101786\n",
       "std    104.560831\n",
       "min    351.200000\n",
       "25%    464.075000\n",
       "50%    545.650000\n",
       "75%    617.450000\n",
       "max    858.200000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regen_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 56 entries, 1963-12-31 to 2018-12-31\n",
      "Freq: A-DEC\n",
      "Data columns (total 1 columns):\n",
      "RSK    56 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 896.0 bytes\n"
     ]
    }
   ],
   "source": [
    "regen_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  Das folgende Histogramm deutet daraufhin, dass die jährliche Regenmenge normalverteilt ist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pX88wjUiO1KwJydq0u0DrtuXqr//arYSoIE0dk6RLhsSxlhMAAMA5QtEEAEA3U1HTqMxsm7Lzq+V0uRQR6qcxKTHqGx8qP1/Pvy3OXQL8rJpyUZK+P7qntu0r1pqtuXph9X69/fFRpV2UpImpPRXoz1shAAAAd+LdFQAA3URReZ0yj9p0oqRWFrNJA5LC1D8xTJGh/kZHM5TVYtbYYXG6ZGisMrNtWrM1Vys+PKI1W3L1g4nJGp+a0C1ndwEAABiBogkAgC7M5XLpeHGNMrNtKqlokJ+PRan9IzWoV7j8ffk2/20mk0nDkiM1LDlS2QVVWr7xkF5ae0Af78rXDWmDlJwQanREAACALo93oAAAdEEOp0vZ+VXKzLapsrZJwQE+GpMSo349w1h/qAP6xofqgRtGa0tWkd744LB+v+wLjU+N1w8m9lNIoK/R8QAAALosiiYAALoQl8ulnMJq7ThYqpr6ZvUI8dO4EfHqExcis5nbvzrDZDJp7NA4jewfpfc2Z2vDFyf05YESzZrYTxNTE7ieAAAAZ4CiCQCALqKgrFbbD5SorKpRPUL89P3RPdUzOkgm1hc6KwF+Vs35/gCNG5Ggf647oJf/fUAf78zX3KkD1S8hzOh4AAAAXQpFEwAAHs5W1aDtB0uUX1qnIH+rLh0ep74JoSxg7WY9o4J0/3WjtG1/sZZ/cFgPv7xdV0/oqysu6c21BgAA6CCKJgAAPFRNfbN2HirV0fwq+fqYdcGgaA3uFS6LhTWYzhWTyaQxKbEanhypl9bu15v/OaoDuRW6Y9oQhQaxdhMAAEB7KJoAAPAwdodTe46UKTO7XCaTNLRvhIYlR8jPx2J0NK8R4GfVj2YM1eDePfTq+kNa9MLnumvGUA3q1cPoaAAAAB6NogkAAA+SV1KrrVlFqqlvVnJCqEYNiFJQgI/RsbySyWTSZSN7ql9CmJa+s1ePvrZDGeP6atrYPiwUDgAAcBoUTQAAeIC6Bru27S/WscJqhQX5aspFSYqLDDQ6FiQlxQRr0S0X6uV/H9A7m7J1ILdCd04forBgP6OjAQAAeByKJgAADOR0uXQwt0I7DpXK4XRp5IAoDe3bQxYz6zB5En9fq+6YNkSDexRup88AACAASURBVPXQP9cf1KIXtulH04copU+E0dEAAAA8Cu9iAQAwSEl5ndZ8lqvP9xUrKsxfMy7toxH9IimZPJTJZNL41AT9+uYLFeRv1WNv7NLmPQVGxwIAAPAozGgCAOA8czid2nmoTFnZNvn5WjQ+NV594kJkMrHuT1eQGB2sX990of721h499/4+Vdc1K/3iXkbHAgAA8AgUTQAAnEfl1Y36ZHeByqsbNaRvhEYkR8iXvybX5QT4WXXv7FT9Y1WW3vjwsCpqGnXN9/vLTFkIAAC8HEUTAADngdPlUlaOTdsPlsrXatblo3tqSHKUqmsajI6GM+RjNetHGUMVGuirdduOq6quSbddmSKrhVsfAQCA96JoAgDgHLNVNejZlVk6eLxCidFBGjssTgF+fAvuDswmk65PG6CwYF+99fFR1dQ16+6rh8nfl/EFAADeiXdBAACcQ1uyCvXKvw/K7nRq7NBY9U8MYy2mbsZkMmna9/ooNMhXL63drz++tkM/nZ2q0EBfo6MBAACcd8ztBgDgHKhraNYz72Xq2feyFB8ZqAVzL9CApHBKpm5sQmqCfjxruE6U1Orhl79UaUW90ZEAAADOO4omAADcLLeoWr998Qtt21esq8f31YK5oxUdHmB0LJwHowZE6xfXjlRNfbMe/ud2lVZSNgEAAO9C0QQAgBt9urdAf3j5SzXZHVpww2hNv7SvLGa+3XqTAYnhuv+6UWpscujPr+9UVW2T0ZEAAADOG975AgDgBnaHUy+vO6B/rNqnvvGhWnTLReqfGGZ0LBikV2yI7p2dqvLqRj22fKfqGuxGRwIAADgvKJoAADhLtqoGLfnndn24PU/pY3rpF9eNVFiwn9GxYLD+iWH68azhyiut1V//tUuNzQ6jIwEAAJxz/NU5AADOwr5j5fr7u3vVZHfq7pnDdOHgGKMjeT2T2aTaRs+YQdS3Z5huSh+sF1fv05Nv7dGd04fIZatTnYfkcwc/H6us/OoSAAB8jaIJAIAz4HK5tPbzXP3royOKiwjU/KuHKyEqyOhYkNTY7NCugyVGx2jl4qGx2pJZpMdX7NKV3+urmtpGoyO5zUUpsbL68ZYSAAB8hXcFAAB0UrPdqRdW79OWrCJdOChat16ZogB+0EYbBiaFq7HZoR0HS/XxzjyN6h8pk8lkdCwAAAC3410xAACdUFPfrL+9tUcHj1do1oRkXTW2N4UBOmRY3wg1NTu090iZTJJGDYgyOhIAAIDbUTQBANBBxRX1evyNXSqtrNedM4bokiFxRkdCF2IymTR6YLScLpP2HCmTn49ZQ/pEGB0LAADArSiaAADogCP5lfrrv3bL6XTpF9eO0sCkcKMjoQsymUy67IJE1dY36Yv9JQoL8lPPaNb2AgAA3Qd/IwQAgHZ8eaBEf3x1h/x9LfrVjRdQMuGsmE0mXTo8Xj1C/LRpV76q65qMjgQAAOA2FE0AALRh3bbjWvr2HiXGBOt/b7xQ8ZHMPsHZ87GaddmoBMkkfbg9T812p9GRAAAA3IKiCQCAU3A6XXp1/UG9vvGQRg2M1v3XjVJokK/RsdCNhAT6akJqgiprmvTZ3kK5XC6jIwEAAJw11mgCAOA77A6n/m9llrbtL9aUi5J0zeX9ZTbzl+XgfglRQRo1MErbD5YqMsxfQ/uyODgAAOjaKJoAAPiWZrtTf393r3YcKtXsy/vpiot7Gx0J3dzQvhEqq2zQ9gMl6hHip4Qobs8EAABdF7fOAQDwtaZmh558a7d2HCrVDWkDKZlwXphMJn1veLzCgn21aVeBauqajY4EAABwxiiaAACQ1NBk1+MrdinzqE23XDFYky5INDoSvMhXi4P3lMvl0oc78mR3sDg4AADomiiaAABer67Brsfe2KUDxyt0x7QhmpCaYHQkeKHQIF+NS41XeXUji4MDAIAui6IJAODVauqb9eflO5SdX6X/yRimscPijI4EL5YYHayRA6KUXVCt/ccqjI4DAADQaRRNAACvVVXXpD++tkPHi2s0/+rhunBwjNGRAA1PjlBSTLC+PFAsW1WD0XEAAAA6haIJAOCVKmsa9eirO1Rkq9M9PxyhkQOijI4ESPpqcfCxw+Lk52vRJ7sL5GC9JgAA0IVQNAEAvE5NfbP+tHynyiobdO/sVA3rG2l0JKAVf1+LvjcsThU1TdpxqNToOAAAAB1G0QQA8Crf/HW5IludfvKD4Rrcu4fRkYBT6hkdrIFJYcrKKVehrc7oOAAAAB1C0QQA8BrNdqeefHOPcgqqdVfGMA3pE2F0JKBNFwyKUUigjzbvLlBTs8PoOAAAAO2iaAIAeAWH06ln38vUvmPluvXKwRo9MNroSEC7fKxmjRsRr7oGu7btKzY6DgAAQLsomgAA3Z7T5dJLaw7oy4Mlum7SAF06PN7oSECHRYcHaHi/SB3Jr9Kxwmqj4wAAALSpQ0VTdna25syZo6lTp2rOnDnKyck56RiHw6EHH3xQkydPVlpamlasWNGhfW+++aamT5+ujIwMTZ8+XcuWLevQ8wAA6AiXy6U3PjisT/YUaMalfZR2UZLRkYBOG9EvUpGhftqSWaT6RrvRcQAAAE7L2pGDFi1apOuvv14ZGRl69913tXDhwlaFkCStXLlSubm5WrdunSoqKjRz5kyNHTtWiYmJbe6bOnWqZs2aJZPJpJqaGk2fPl1jxozR4MGD23weAAAdsfLTHK3bdlyTL0hUxri+RscBzojZbNKlI+L1/qfH9OneQn1/dE+ZTCajYwEAAJyk3RlNZWVlysrK0rRp0yRJ06ZNU1ZWlmw2W6vjVq9erdmzZ8tsNisiIkKTJ0/W2rVr290XHBzc8kapoaFBzc3NLY/beh4AAO3Z8MVxvbMpW5cOi9O1kwfwgzm6tPBgP40eGK28klodOlFpdBwAAIBTardoKigoUGxsrCwWiyTJYrEoJiZGBQUFJx2XkJDQ8jg+Pl6FhYXt7pOkjRs36qqrrtLll1+uO+64Q4MGDerQ8wAAOJ2tWUV6dcMhjRoQpVuuHCwzJRO6gcG9wxUXGagv9herqrbJ6DgAAAAn6dCtc+fapEmTNGnSJOXn52v+/PmaMGGCkpOT3fLakZHBbnkdI0RHhxgdAQZh7L0T4+4++4/Z9PzqfRqaHKlf336JfH0sRkeSy1ankGD/k7afahvOjo+P1eOv69nkm3pxb72+/qC2ZhXp6sv6Gz5TLzDQT9ERgYZm6Cr4d957MfbeiXH3Tox7B4qm+Ph4FRUVyeFwyGKxyOFwqLi4WPHx8Scdl5+frxEjRkhqPRuprX3flpCQoOHDh+ujjz5ScnJyh5/XlrKyGjmdrk49xxNER4eopIS/LOONGHvvxLi7T2llvX730hfqEeynO6elqLKizuhIkqS6RruqaxpabQsJ9j9pG85ec/PJ19qTuGPcLxgUrU/3FmrHgSINSAx3U7IzU1fXqBKHw9AMXQH/znsvxt47Me7eyVvG3Ww2tTmpp91b5yIjI5WSkqJVq1ZJklatWqWUlBRFRES0Oi49PV0rVqyQ0+mUzWbThg0bNHXq1Hb3HTlypOU1bDabtm7dqoEDB7b7PAAAvqu+0a4n/rVbzQ6Xfjp7hEICfY2OBJwT/XqGKqZHgL48UKKGJv4KHQAA8BwdunVu8eLFWrBggZYuXarQ0FAtWbJEkjRv3jzdc889Gj58uDIyMrRr1y5NmTJFkjR//nwlJX31J6Tb2rd8+XJt3rxZVqtVLpdLc+fO1bhx49p9HgAA3+Z0uvTMe5kqKK3TfdekKj4yyOhIwDljMpl08ZBYrfo0R9sPlup7w+KMjgQAACBJMrlcrq53X1kncOscuhrG3jsx7mfv9Y2HtG7bcd04dZAuH9XT6DgnqW20a9u+olbbuHXu3EgdGK1dB0uMjnFa7hz3Lw8UKzO7XOkXJymmhzHrJF2UEqsgP49Y9tOj8e+892LsvRPj7p28ZdzP+tY5AAA83Uc78rRu23FNviDRI0sm4FwZ0S9Kgf5Wbcks6pK/WAMAAN0PRRMAoEvLzLHplXUHNTw5UnMm9Tc6DnBe+VjNGpMSo4qaJu07Vm50HAAAAIomAEDXVVBWq6ff3qv4yEDdlTFUFjPf1uB9kmKC1TM6SLsOl6q2vtnoOAAAwMvxjhwA0CXV1DfriX/tlsVi0k9/OEIBrBEDL2UymTQmJUYul7Rtf7HRcQAAgJejaAIAdDlOl0vPvpcpW1WDfjxruKLCA4yOBBgqJNBXI/pFKreoRnklNUbHAQAAXoyiCQDQ5bz/aY72Ztt03eSBGpAYbnQcwCMM6RuhsCBfbc0qlt3hNDoOAADwUhRNAIAuZV+OTe98kq1LhsTqspEJRscBPIbFbNLFQ2JVU9+sPUdtRscBAABeiqIJANBllFc36pn3MhUXEaib0gfJZDIZHQnwKHGRgeobH6LMo2WqrGkyOg4AAPBCFE0AgC7B4XTqmfcy1dDs0N1XD5e/L4t/A6dy4eAYWSxmfb6vSC6Xy+g4AADAy1A0AQC6hLc+PqqDxyt089TB6hkVZHQcwGMF+Fk1sn+UCsrqlF9aZ3QcAADgZSiaAAAeb+fhUq3ZkqvLRiZo7LA4o+MAHm9gr3CFBProywPFcjKrCQAAnEcUTQAAj1ZSUa9/rMxS79gQXTd5gNFxgC7BYjZp1MBoVdQ06UheldFxAACAF6FoAgB4rGa7U0+/s1cuSf9z9TD5WC1GRwK6jN6xwYoK89fOQ6WyO5xGxwEAAF6CogkA4LFe/+CQcgqrdftVKYoJDzA6DtClmEwmXTA4WvWNdmXllBsdBwAAeAmKJgCAR9q2v1gfbs9T+pheGj0w2ug4QJcU2yNQSTHByjxqU32j3eg4AADAC1A0AQA8Tnl1o5at3a++8aGaNTHZ6DhAlzZ6YLTsTqd2HykzOgoAAPACFE0AAI/icrn0wup9arY7NW/6EFktfKsCzkZYsK8GJIbr4PEKVdY0GR0HAAB0c7x7BwB4lA935Glvtk1zvt9fcRGBRscBuoXU/pGymE3acajE6CgAAKCbo2gCAHiMQlud3vjgsIYlR+iyUT2NjgN0GwF+Vg1LjlRuUY2KyuuMjgMAALoxiiYAgEdwOJ36v5VZ8rGadesVKTKZTEZHArqVIX16KMDPqi/3l8jlchkdBwAAdFMUTQAAj/D+p8eUXVClm9IHq0eIn9FxgG7HajFr5IBIlVY26FhRjdFxAABAN0XRBAAwXHZBld7bnKNLhsbqosExRscBuq1+PcMUHuyrHQdL5HAyqwkAALgfRRMAwFCNzQ7938oshQX7am7aQKPjAN2a2WTSBYOiVV3XrIO5FUbHAQAA3RBFEwDAUP/68IgKbXW6/aoUBfr7GB0H6PYSooIUFxmoPUfL1Gx3Gh0HAAB0MxRNAADDZGbbtHH7CU2+MFFD+kQYHQfwCiaTSSP7R6mhyaEDx5nVBAAA3IuiCQBgiNqGZj2/ep/iIwP1w4n9jI4DeJWYHgGKjwxU5lEbs5oAAIBbUTQBAAyxfONhVdY0ad70IfL1sRgdB/A6I/tHqbGZWU0AAMC9KJoAAOfdvhybPtlToPSLe6lPXKjRcQCvFN0jQAlRzGoCAADuRdEEADivmpodeunfBxQTHqAZl/YxOg7g1VL7fT2rKbfc6CgAAKCboGgCAJxXKz/NUXF5vW5KH8Qtc4DBWmY1ZZczqwkAALiF1egAAADj2J1SY7P9vH28/NJardmaqzFDYtU7PlS1jefvYxvN6TI6AXBqqf2jtGZLrg7klmtYcqTRcQAAQBdH0QQAXqyx2a5t+4rOy8dyulxauyVXPhaz+sQFn7eP6ylSB0YbHQE4pejwACVEBSkzu1yDevWQj5UJ7wAA4MzxTgIAcF4czK1QaWWDLkqJlr8vv+cAPElq/0jWagIAAG5B0QQAOOdqG5q142Cp4iMD1TeevzIHeJpvz2pirSYAAHA2KJoAAOeUy+XS1qxiOV0uXTI0ViaTyehIAE7hm1lN+5nVBAAAzgJFEwDgnMotqtGJ4hqlDohSSKCv0XEAnEZ0eIB6RgUpi1lNAADgLFA0AQDOmaZmhz7fV6QeIX4a0ruH0XEAtINZTQAA4GxRNAEAzpntB0vV0OjQ2GFxMpu5ZQ7wdFHhAeoZHaTMbBuzmgAAwBmhaAIAnBMl5fU6eLxCg3v3UFSYv9FxAHRQar9INTU7tf8Ys5oAAEDnUTQBANzO6XJp674iBfpZNXJAlNFxAHRC1DdrNeWUy+5gVhMAAOgciiYAgNsdPlEpW1WjLhgcLR8r32qArmZYcoQamx06fKLS6CgAAKCL4d0/AMCtmpod2nGwVDE9AtQnLsToOADOQEyPAEWH+ysrp1xOp8voOAAAoAuhaAIAuNWuw2VqanZoTEqMTCYWAAe6IpPJpKF9I1RT36xjhdVGxwEAAF0IRRMAwG0qahq1P7dcA5LCFBHKAuBAV5YUE6ywIF/tzbbJ5WJWEwAA6BiKJgCAW7hcLm3bVywfi5kFwIFuwGQyaUjfCJVXNyq/tM7oOAAAoIugaAIAuMXx4hoVlNUpdUCU/H2tRscB4AbJCSEK8LMqM9tmdBQAANBFUDQBAM6aw+HUF/tLFB7sq0FJ4UbHAeAmFrNZQ/r0UKGtTqWV9UbHAQAAXQBFEwDgrGXmlKumvlkXpcTIbGYBcKA7GZAUJh+rWZlHmdUEAADaR9EEADgrtQ3N2nu0TL1igxUfGWR0HABu5mu1aFCvcB0rqlFVbZPRcQAAgIejaAIAnJUvD5TI5ZIuHBRjdBQA50hK7x4ym02s1QQAANpF0QQAOGNF5XXKKajW0L4RCg70MToOgHMkwM+q/j1DdSS/SvWNdqPjAAAAD0bRBAA4I06XS59nFSvQ36phyRFGxwFwjg3pEyGX06V9OeVGRwEAAB6MogkAcEaO5FWqvLpRFw6KltXCtxOguwsN8lWvuBAdOF6hJrvD6DgAAMBDdegng+zsbM2ZM0dTp07VnDlzlJOTc9IxDodDDz74oCZPnqy0tDStWLGiQ/ueeuopXXXVVZoxY4ZmzZqlTZs2texbsGCBJkyYoIyMDGVkZOjpp58+i1MFALhLs92pnYfKFB3ur95xIUbHAXCeDO0boWa7U4eOVxodBQAAeChrRw5atGiRrr/+emVkZOjdd9/VwoULtWzZslbHrFy5Urm5uVq3bp0qKio0c+ZMjR07VomJiW3uGzFihG677TYFBARo//79mjt3rj755BP5+/tLku68807NnTvX/WcOADhj+46Vq77Rrokj42UymYyOA+A8iQrzV1xEoLJyyjW4d7gsZmYzAgCA1tp9d1BWVqasrCxNmzZNkjRt2jRlZWXJZmv9V0dWr16t2bNny2w2KyIiQpMnT9batWvb3Td+/HgFBARIkgYNGiSXy6WKigq3niQAwH0amuzKPGpTUkywYnoEGh0HwHk2LDlC9Y12Hc2vNjoKAADwQO3OaCooKFBsbKwsFoskyWKxKCYmRgUFBYqIiGh1XEJCQsvj+Ph4FRYWtrvv29555x316tVLcXFxLdteeOEFLV++XElJSfr5z3+ufv36deoEIyODO3W8J4mO5nYUb8XYeycjxt1lq1NIsH+nnrNzZ57sDqfGjezZ6ed6Mx8f6ymvF9fQ/U53rT2Jp+dry8AgP+08XKb9x8o1alCMAgP9FB1B6dwRfH/3Xoy9d2LcvRPj3sFb586Hzz//XE888YSef/75lm333XefoqOjZTab9c477+iOO+7Qhg0bWkqvjigrq5HT6ToXkc+p6OgQlZTwm0JvxNh7J6PGva7Rruqahg4fX13XpL1HStU/MUw+ZnXqud6uufnkax0S7M81PAdOda09SXcY90FJYdq8p1AHj9mU0itcJQ4WB28P39+9F2PvnRh37+Qt4242m9qc1NPurXPx8fEqKiqS4+s3EA6HQ8XFxYqPjz/puPz8/JbHBQUFLTOT2tonSTt27ND999+vp556SsnJyS3bY2NjZf763v+ZM2eqrq7ulDOhAADnx46DpTKZTErtH2V0FAAG6hMfogA/i/bllBsdBQAAeJh2i6bIyEilpKRo1apVkqRVq1YpJSWl1W1zkpSenq4VK1bI6XTKZrNpw4YNmjp1arv7du/erfvuu09//etfNXTo0FavWVRU1PL/mzZtktlsVmxs7NmdMQDgjJRWNiinsFpD+vRQoL/HTIgFYACL2axBSeHKK61Voa3O6DgAAMCDdOgnhcWLF2vBggVaunSpQkNDtWTJEknSvHnzdM8992j48OHKyMjQrl27NGXKFEnS/PnzlZSUJElt7nvwwQfV0NCghQsXtny8Rx99VIMGDdIDDzygsrIymUwmBQcH6+mnn5bVyg83AHC+uVwubT9QIj8fi4YmR7T/BADd3oCkcO0+atN/duSpX3yo0XEAAICHMLlcrq63gFEnsEYTuhrG3jsZNe61jXZt21fU7nF5JbXa+OUJXZQSo5TePc5Dsu4ndWC0dh0sabWtO6zV44lOda09SXca90/3FCq3qFp/mn+pggN8jI7j0fj+7r0Ye+/EuHsnbxn3s16jCQDg3Zwul7YfLFFIoI8GJoUbHQeAB0npE64mu1ObduW3fzAAAPAKFE0AgDZl51epvLpRIwdEyWI2GR0HgAfpEeKvgUnh2vDlCdkdTqPjAAAAD0DRBAA4LYfDqR2HShUZ6q8+cSFGxwHggS4b1VPl1Y3a7sG3KwIAgPOHogkAcFr7citU12DXBYOiZTIxmwnAyYYmRygmPEDrvzhudBQAAOABKJoAAKfU2OTQ3iNl6hkVpLjIQKPjAPBQZpNJky9M1JG8Kh3JrzQ6DgAAMBhFEwDglDKzbWqyOzV6UJTRUQB4uEuHxyvAz6INX5wwOgoAADAYRRMA4CT1jXbtzy1Xn7gQ9QjxNzoOAA8X4GfV+BEJ+mJ/sWxVDUbHAQAABqJoAgCcJDPbJofDpdT+zGYC0DGTLkiU0+XShzvyjI4CAAAMRNEEAGilrsGuA7kV6psQqrBgX6PjAOgiosMDNHpAtD7akafGZofRcQAAgEEomgAArew9Wiany6UR/SKNjgKgi5l8YaJqG+zakllodBQAAGAQiiYAQIvahmYdPF6pfj3DFBrEbCYAnTMwKVy9YoO1/osTcrlcRscBAAAGoGgCALTYc6RMkksjkpnNBKDzTCaT0i5MUn5prbJyyo2OAwAADEDRBACQJNXUNevwiUr1TwxTcKCP0XEAdFFjUmIVGuSr9V8cNzoKAAAwAEUTAECStPtomWQy6f+zd+fBUd0Huvef04u6tUutfUXsCMQiDNh4ibHN5oRETCYOHmfWpPxOEr/jO8m8eZN665ZtUskfTs1NzSRj1504ydzrydxJhskkDovBJl7xBphNIEAs2neptS+t3t4/IMQEgwRI+vXy/VRRIJ3Tp58+R7S6n/6d31nK3EwAboPTYdO6FYWqvtCjzt4R03EAAMAMo2gCAGhgeFwXWvq1oCRdyW5GMwG4PfevKJLNZun1oy2mowAAgBlG0QQA0IkLPbJZlipmM5oJwO3LTHWpckGODpxok88fNB0HAADMIIomAIhz/UM+1bUOaGFphpLcDtNxAMSIh1YWaXgsoIM1HaajAACAGUTRBABx7vj5HtntlirmeExHARBDFpRkqCg7Wa8daVE4HDYdBwAAzBCKJgCIY63dw6pvH9SiWZlyJzCaCcDUsSxLD64sUkPHoC62DpiOAwAAZghFEwDEsT3vNchpt2lxGaOZAEy9u5bky51g12tHmk1HAQAAM4SiCQDiVGPHoI6f71Z5WabcCXbTcQDEoESXQ/dUFOjQmU4NDI+bjgMAAGYARRMAxKmd79TLnWDX4rJM01EAxLAH7yhSIBjWW8dbTUcBAAAzgKIJAOJQS9eQPqzt0v2VRUpwMpoJwPQpyEpW+axMvXGsRcFQyHQcAAAwzSiaACAO7X6vQS6nXesqi0xHARAHHlxZLO+AT8fP95iOAgAAphlFEwDEmQ7viD443aEHVhYpJdFpOg6AOLBifpY8aS4mBQcAIA5QNAFAnNn9XoMcdps2rS4xHQVAnLDbbLp/RZFq6nvV1jNsOg4AAJhGFE0AEEe6+0b13ql2fWJ5odJTXKbjAIgjn1heKLvN0utHWkxHAQAA04iiCQDiyMsfNEqSHr6z1HASAPEmPTlBqxfl6p2TbRobD5iOAwAApglFEwDEid5Bn94+0ap7lxXIk+Y2HQdAHHpwZbFGfUG9d6rDdBQAADBNKJoAIE7s/aBRoZD08F2zTEcBEKfmFqWpNDdFrx1pVjgcNh0HAABMA4omAIgDA8PjevNYi9YuyVNuRqLpOADilGVZevCOYrV0Dau2qc90HAAAMA0omgAgDuw71Ch/IKRPrmU0EwCz7lycp2S3Q68xKTgAADGJogkAYtzQqF+vHWnR6vJcFWQlm44DIM65nHbds7RAR2q71D/kMx0HAABMMYomAIhx+w83yTce1Ja1ZaajAIAk6f4VhQqGwnr7RJvpKAAAYIpRNAFADBv1BbT/cLMq52erODfFdBwAkCQVZCWrfFam3jzWolCIScEBAIglFE0AEMNeO9KsEV9An76nzHQUALjKA5VF6hnwqfpij+koAABgClE0AUCM8o0Hte9gk5bOyVJZfprpOABwlRXzs5WenKDXjzIpOAAAsYSiCQBi1JvHWzU06teWu7nSHIDI47DbdN/yQlVf6FF3/6jpOAAAYIpQNAFADAoEQ3rlUKMWlGRofnGG6TgA8LHuX14oWdKbx1pNRwEAAFOEogkAYtAHNR3yDvj0ybtKTUcBgOvKSndr+dxsvX2iTYFgyHQcAAAwBSiaACDGhMJhvfxBo4pzkrV0TpbpOABwQ+sqCzUwPK4jtV2mowAAgClA0QQAMeb4+W61dg/r4btmybIs03EA4IYqZmcpO92tN5gUHACAmEDRBAAxJBwOa8/7DcpO75zxDQAAIABJREFUd2tNea7pOAAwIZvN0v0rCnWmsU9tPcOm4wAAgNtE0QQAMeRcc78utAxo05pS2W08xQOIDvcuK5TdZumNo0wKDgBAtONdCADEkD3vNygl0al7lxWYjgIAk5aenKA7Fuboneo2+fxB03EAAMBtoGgCgBjR1DmkExd6tGFVsVxOu+k4AHBTHqgs0ogvoEOnO01HAQAAt4GiCQBixMsfNMjltOuBlcWmowDATVtQkqGCrCS9zqTgAABENYomAIgB3X2jOljTqftXFCol0Wk6DgDcNMuytK6ySHVtA2poHzQdBwAA3CKKJgCIAfsONsmypI2rS0xHAYBbdk9FvhIcNkY1AQAQxSiaACDKDYyM6+0TrVq7JF+eNLfpOABwy5LcTq1ZnKcPajo0MhYwHQcAANwCiiYAiHK/PdwsfyCkzXeWmo4CALftgcoi+fxBvXeq3XQUAABwCyiaACCKjY0H9NqRZq2Yn63C7GTTcQDgts0uSNOs/FS9caxF4XDYdBwAAHCTKJoAIIq9daxVw2MBffKuWaajAMCUeaCySC1dwzrX3G86CgAAuEmTKprq6uq0bds2bdq0Sdu2bVN9ff016wSDQW3fvl3r16/Xhg0btGPHjkkte+655/SpT31Kn/nMZ/TZz35Wb7/99pVlo6Oj+tu//Vtt2LBBmzdv1uuvv34bDxUAYksgGNK+Q01aWJKhuUXppuMAwJS5szxPiS6H3jjGpOAAAEQbx2RWevrpp/XYY4+pqqpKL730kp566im9+OKLV62zc+dONTY26pVXXlFfX5+2bt2qtWvXqri4+IbLli1bpi9+8YtKTEzUmTNn9Kd/+qc6cOCA3G63fvKTnyg5OVmvvvqq6uvr9YUvfEGvvPKKkpM5PQQAPqjpUO+gT3+xeZHpKAAwpVwJdt1dka83j7Xo0YfmKy0pwXQkAAAwSROOaOrp6VFNTY22bNkiSdqyZYtqamrk9XqvWm/Pnj165JFHZLPZ5PF4tH79eu3du3fCZffdd58SExMlSQsXLlQ4HFZfX58k6eWXX9ajjz4qSSorK1NFRYXeeuutKXroABC9wuGw9h5sVHFOspbO8ZiOAwBTbt2KQgWCYb1T3WY6CgAAuAkTjmhqa2tTXl6e7Ha7JMlutys3N1dtbW3yeDxXrVdYWHjl64KCArW3t0+47KN+/etfq7S0VPn5+ZKk1tZWFRUVTXi7G8nKSrmp9SNJTk6q6QgwhGMfn27muB8506mWrmH97aOVys1Nu+X7DHtHlJrivuXbY/KcTsfH7mv2/9S73r6OJJGe72YkJbmU40ma8u3m5KRqyZwsHTjRrj/95BLZbNaU38dM4/d7/OLYxyeOe3ziuE/y1LmZcPDgQf3jP/6jfvrTn07pdnt6hhQKRd8VS3JyUtXVNWg6Bgzg2Menmz3u//HqGWWkJGhxSfpt/byM+AIaHBq75dtj8vz+a/d1aoqb/T8NPm5fR5JYO+4jIz51BYPTsu17KvL0o9/U6M3DDaqYnTUt9zFT+P0evzj28YnjHp/i5bjbbNYNB/VMeOpcQUGBOjo6FLz8AiIYDKqzs1MFBQXXrNfa2nrl67a2tisjk260TJKOHj2qb3zjG3ruuec0Z86cK98vLCxUS0vLdW8HAPGosWNQp+p79dAdxXLYuXgogNh1x4JcpSY59foRJgUHACBaTPgOJSsrS+Xl5dq1a5ckadeuXSovL7/qtDlJ2rx5s3bs2KFQKCSv16v9+/dr06ZNEy47ceKEvva1r+kHP/iBlixZcs02f/GLX0iS6uvrVV1drfvuu+/2HzUARLF9B5vkctq1rrJo4pUBIIo5HTbdu6xAx8/3yDsQO6PAAACIZZP6KPyZZ57Rz372M23atEk/+9nPtH37dknS448/rurqaklSVVWViouLtXHjRn3+85/XE088oZKSkgmXbd++XWNjY3rqqadUVVWlqqoqnT17VpL0pS99SQMDA9qwYYP++q//Wt/+9reVkhK9cy4BwO3yDozp4OkO3be8QMlup+k4ADDt7l9RpHA4rLeOt068MgAAMG5SczTNnTtXO3bsuOb7L7zwwpV/2+32KwXUH7rRsl/+8pfXvd+kpCT94Ac/mExEAIgLv/2wWaFwWBtWlZiOAgAzIjcjUUvmePTW8VZ9+p4y2W2cMgwAQCTjNzUARIlRX0BvHGvVqoW5yslINB0HAGbMA5VF6hsa17FzPaajAACACVA0AUCUePtEm0Z9AW2+s9R0FACYUcvmZikz1aU3jjEpOAAAkY6iCQCiQDAU0quHGrWgOF2zC9JMxwGAGWW32XT/ikKdqvOqo3fEdBwAAHADFE0AEAUOn+lSz4BPmxjNBCBO3besUDbL0ptHmRQcAIBIRtEEABEuHA5r78FG5XmStHxetuk4AGBEZqpLlQuydaC6Tf5A0HQcAABwHRRNABDhapv61NA+qE1rSmSzLNNxAMCYByqLNDTq1+EzXaajAACA66BoAoAIt/eDRqUmOXX3knzTUQDAqEWzMpWXmajXmRQcAICIRdEEABGstXtYxy/06MGVxUpw2k3HAQCjbJaldZVFOt/cr+bOIdNxAADAx6BoAoAI9sqhJjkdNj2wssh0FACICPcsLZDDbmNUEwAAEYqiCQAiVP/wuN492a57KvKVlpRgOg4ARISURKfWlOfqvZPtGhsPmI4DAAD+AEUTAESo1z5sVjAY0obVJaajAEBEWVdZpLHxoN6v6TAdBQAA/AGKJgCIQD5/UK8fbdGK+dkqyEo2HQcAIsrcwjSV5KbojSMtCofDpuMAAICPoGgCgAj0bnWbhkb92rSm1HQUAIg41uVJwRs7h3SxbcB0HAAA8BEUTQAQYUKhsPYdatLsgjTNL043HQcAItJdi/PkSrDrjSNMCg4AQCShaAKACHPsfLc6e0e1+c5SWZZlOg4ARKREl0Nrl+Tr4JlODY36TccBAACXUTQBQITZe7BR2elurVyQbToKAES0dSsK5Q+E9G51m+koAADgMoomAIgg51v6db65XxtWl8hu4ykaAG6kNC9Vc4vS9PqxViYFBwAgQvAuBgAiyL6DjUpyOXTfsgLTUQAgKjxQWaQO74jONPSajgIAAETRBAARo7N3REdqu/TAyiK5Exym4wBAVFi9KFfJbodeP9ZqOgoAABBFEwBEjFcPNctmWXpwZbHpKAAQNZwOu+5dVqCjtV3qG/KZjgMAQNyjaAKACDA4Mq63q1t115I8Zaa6TMcBgKiybkWRgqGw3j7BpOAAAJhG0QQAEeDld+s17g9p05pS01EAIOrkeZK0uCxTbx1rUSjEpOAAAJhE0QQAhvkDIe08cFEVsz0qzkkxHQcAotIDlUXqGfDpxMUe01EAAIhrFE0AYNj7p9rVN+jTpjsZzQQAt2r5vGylpyTojaMtpqMAABDXKJoAwKBwOKx9h5o0uzBNi2dlmo4DAFHLYbfp/uWFqr7Qo+6+UdNxAACIWxRNAGBQ9UWvWruHtfX+ebIsy3QcAIhqn1heKFnSm8dbTUcBACBuUTQBgEH7DjYqM9Wl+1YUmY4CAFHPk+bWinnZevt4qwLBkOk4AADEJYomADCkoX1Qpxt6tX5VsZwOno4BYCqsqyzSwIhfR2q7TEcBACAu8c4GAAzZd6hRrgS77l9eaDoKAMSMJbM9yk53Myk4AACGUDQBgAHegTEdrOnU/csLleR2mo4DADHDZllaV1mkM419au0eNh0HAIC4Q9EEAAbsP9wsSVq/qthwEgCIPfcuK5DdZumNY4xqAgBgplE0AcAMG/UF9ObxFq1alKPs9ETTcQAg5qQlJWjVoly9W90unz9oOg4AAHGFogkAZthbx1s16gtq05pS01EAIGY9UFmkEV9AB093mI4CAEBcoWgCgBkUCIa0/3CTFpRkaHZBmuk4ABCz5henqyg7mUnBAQCYYRRNADCDDp/tVM+AT5sZzQQA08q6PCl4Xdug6tsHTMcBACBuUDQBwAwJh8Pad7BJ+Z4kLZuXZToOAMS8tUvyleC0MaoJAIAZRNEEADOktqlPDe2D2rimRDbLMh0HAGJektuhuxbn6f2aDo2MBUzHAQAgLlA0AcAM2ftBo1KTnLp7Sb7pKAAQN9ZVFmncH9J7p9pNRwEAIC5QNAHADGjrGdbxCz16oLJICU676TgAEDfK8tM0uyBVbxxtUTgcNh0HAICYR9EEADNg38EmOR02Pbiy2HQUAIg76yqL1NI9rHPN/aajAAAQ8yiaAGCaDQyP692T7bq7Il9pyQmm4wBA3FlTnqckl0OvMyk4AADTjqIJAKbZa0eaFQiGtHF1iekoABCXXE677l6ar8NnOjUwPG46DgAAMY2iCQCm0bg/qNeOtGjFvGwVZCWbjgMAcWvdiiIFQ2EdqG4zHQUAgJhG0QQA0+jdk+0aGvVr0xpGMwGASYXZyVpUmqE3jrYoxKTgAABMG4omAJgmoXBY+w41qSw/VQtKMkzHAYC4t66ySN39YzpV5zUdBQCAmEXRBADT5Pj5bnV4R7RpTaksyzIdBwDi3soFOUpLcuoNJgUHAGDaUDQBwDTZd7BJWWkurVqUYzoKAECSw27TfcsLdex8t7wDY6bjAAAQkyiaAGAa1LUNqLapTxtWlchu46kWACLF/csLpbD05rFW01EAAIhJvPsBgGmw72CjEl123be80HQUAMBHZGckauncLL15vFX+QMh0HAAAYg5FEwBMse6+UR0606n7VxQp0eUwHQcA8Ac2rCrRwPC4Dp3pMB0FAICYQ9EEAFPs1cPNslmW1t9RbDoKAOBjLC7LVGF2sl493KxwOGw6DgAAMYWiCQCm0MiYX2+daNWa8lx50tym4wAAPoZ1+cOAhvZBnW/pNx0HAICYQtEEAFPozWOt8o0HtWlNqekoAIAbWFuRr2S3Q68eajIdBQCAmDKpoqmurk7btm3Tpk2btG3bNtXX11+zTjAY1Pbt27V+/Xpt2LBBO3bsmNSyAwcO6LOf/awqKir07LPPXrXNH/7wh1q7dq2qqqpUVVWl7du33+LDBIDpFwiGtP/DZpXPylRpXqrpOACAG3A57frE8kIdqe1WT/+Y6TgAAMSMSc1S+/TTT+uxxx5TVVWVXnrpJT311FN68cUXr1pn586damxs1CuvvKK+vj5t3bpVa9euVXFx8Q2XlZSU6Dvf+Y727dun8fHxa+5769at+uY3vzk1jxYAptGh053qHfTpLzYvMh0FADAJD64s1r6DTXrtSLMeeWCe6TgAAMSECUc09fT0qKamRlu2bJEkbdmyRTU1NfJ6vVett2fPHj3yyCOy2WzyeDxav3699u7dO+GyWbNmafHixXI4uDITgOgVDoe192CjCrOTtXSOx3QcAMAkZKW7tXJBtt46fum0ZwAAcPsmbHfa2tqUl5cnu90uSbLb7crNzVVbW5s8Hs9V6xUWFl75uqCgQO3t7RMum8ju3bt14MAB5eTk6G/+5m9UWVk5uUd2WVZWyk2tH0lycjj1Jl5x7KPP8douNXUO6cnPr1BubtotbcPEcQ97R5SawqTlM8HpdHzsvmb/T73r7etIEun5bkZSkks5niTTMW7Z59Yv1LeeO6Dqxj49vLZsWu+L3+/xi2Mfnzju8YnjPslT50x59NFH9eUvf1lOp1PvvPOOvvrVr2rPnj3KzMyc9DZ6eoYUCkXfZWtzclLV1TVoOgYM4NhHp5+/ekZpyQlaUppxS8fP1HEf8QU0OMTcJDPB7792X6emuNn/0+Dj9nUkibXjPjLiU1cwekcD5aQ4NSsvVb9+47zumOuRZVnTcz/8fo9bHPv4xHGPT/Fy3G0264aDeiY8da6goEAdHR0KXn4BEQwG1dnZqYKCgmvWa21tvfJ1W1ub8vPzJ1x2Izk5OXI6nZKke+65RwUFBTp37tyEtwOAmdTSNaSTF716aGWRnA4u5gkA0cSyLK1fVazW7mHV1PeajgMAQNSbcERTVlaWysvLtWvXLlVVVWnXrl0qLy+/6rQ5Sdq8ebN27NihjRs3qq+vT/v379e//du/TbjsRjo6OpSXlydJOn36tFpaWjR79uxbeZwAokQgJPn8AdMxbsru9xvkdNh055J8DftuLXvYO6KRW7zt7YjCAZ8AMOXWlOdpxxsX9OrhJi2ZzTx7AADcjkmdOvfMM8/oW9/6lp5//nmlpaXp2WeflSQ9/vjjevLJJ7V06VJVVVXp+PHj2rhxoyTpiSeeUElJiSTdcNnhw4f19a9/XUNDQwqHw9q9e7e++93v6r777tP3v/99nTp1SjabTU6nU9/73veUk5Mz5TsBQOTw+QM6dLrDdIxJG/UFdLCmU/NL0lVT7534Btdh6lSa5Qt4TgUAp8OmdSsK9Zt36tXhHVFeFM85BQCAaVY4HI7pz7OZownRJt6P/bAvuoqmo7Vdqr7o1db7ZistOeGWt2OyaDpe2zXj9xuPPm5fx9pcPZEi0n+uY+24ry7PU7Iroqf9nJT+IZ/+n+ff1brKIn1hw4Ip3368/36PZxz7+MRxj0/xctxve44mAMDH8wdCOtvUp9K8lNsqmQAA5qWnuLSmPE8Hqts0MhZdp3ADABBJKJoA4Badb+nXuD+kxWWTvxImACBybVhdLN94UAeq20xHAQAgalE0AcAtCIXCqqnzKifDrdxM5vIAgFhQlp+mecXp+u2HTVE59QIAAJGAogkAbkF9+6CGxwKqmJNlOgoAYAptWFWirr4xHb/QbToKAABRiaIJAG5SOBzWqTqv0lMSVJyTbDoOAGAKrVyQLU+aS68eajIdBQCAqETRBAA3qbV7RL2DPi0p88iyLNNxAABTyG6zaf0dJTrT2Ke6tgHTcQAAiDoUTQBwk07W9SjJ7dDswjTTUQAA0+D+FYVKdDn08geNpqMAABB1KJoA4CZ0942qwzuqxbMyZbcxmgkAYlGiy6EHKov04dlOdfaOmI4DAEBUoWgCgJtwss6rBIdN80syTEcBAEyj9auKZbdZ2sdcTQAA3BSKJgCYpIHhcTV2DGlhaYacDp4+ASCWZaS4tHZJvg6caNPAyLjpOAAARA3eKQHAJJ2q88pms7RoVqbpKACAGbBpTan8gZBe+7DZdBQAAKIGRRMATMLIWEAXWgY0ryhNiS6H6TgAgBlQmJ2sFfOy9dqRFvnGg6bjAAAQFSiaAGASTjf0KhwOa8lsj+koAIAZ9PBdpRoa9etAdZvpKAAARAWKJgCYwLg/qNqmPpXmpyo1KcF0HADADJpfnKG5RWnad7BRwVDIdBwAACIeRRMATKC2uV/+QEgVjGYCgLj08J2z1N0/pg/PdpmOAgBAxKNoAoAbCIZCOl3vVX5WkrLS3abjAAAMWDE/W3meJL38fqPC4bDpOAAARDSKJgC4gYutAxr1BRnNBABxzGZZ2rymRA0dgzrT0Gs6DgAAEY2iCQCuIxwO61RdrzxpLhVkJZmOAwAw6O6KfKUlJ+jlDxpNRwEAIKJRNAHAdTR2DGlgeFxLZntkWZbpOAAAg5wOuzasKtbJOq8aOwZNxwEAIGJRNAHAxwiHw6q+2KPUJKdm5aeajgMAiADrKovkctq17yCjmgAAuB6KJgD4GK3dI/IO+FQxxyMbo5kAAJKS3U7dv6JQH9R0qrt/1HQcAAAiEkUTAHyM6os9SnI7NKcw3XQUAEAE2bCqRJL0yqEmw0kAAIhMFE0A8Ac6vCPq7B1VxWyP7DZGMwEAfi8r3a21S/L01rFW9Q+Pm44DAEDEoWgCgD9w4kKP3Al2zStmNBMA4Fpb7i6TPxjSPq5ABwDANSiaAOAjuvtH1dYzosVlmXLYeYoEAFwrz5Okuxbn6bWjzRpgVBMAAFfhXRQAfET1Ba8SnDYtLM00HQUAEMG23F0mvz+kfYcY1QQAwEdRNAHAZb2DPjV1DmlRaaacDp4eAQDXV5CVrDWL8/Tahy0aHGFUEwAAv+MwHQAAIkX1xR457JbKZzGaCQAmy7JZGvYFTMcw4qFVxTpY06Fd7zXoM/fOvuG6Ye+IRm5zP7mcDvE5CAAg0lE0AYCkgeFxNbQNavFsj1wJdtNxACBq+PxBHa/tMh3DmNL8VL1+pFmeVNcNf3+kprg1ODR2W/e1ujxPDhcv3wEAkY3PRABA0sk6r2w2S4vLGM0EAJi8ZXOzFAiGVdPQazoKAAARgaIJQNwbHvXrYku/5hWnK5FPigEANyEz1aVZeSk609Arnz9oOg4AAMZRNAGIe6fqvApLWjLbYzoKACAKLZuXJX8gpNP1jGoCAICiCUBcG/UFdK65X3ML05WS6DQdBwAQhTJT3SrNS9Hphl6NM6oJABDnKJoAxLWa+l6FQmFVzGE0EwDg1i2be3lUE3M1AQDiHEUTgLjlGw/qbGOvZuWnKi05wXQcAEAU86S5VZybotP1vRoPMKoJABC/KJoAxK2ahl4FgmEtnZtlOgoAIAYsn5ul8UBIZxr6TEcBAMAYiiYAcck3HtSZ+l7NyktRZqrLdBwAQAzISnerOCdZNfVe+QMh03EAADCCoglAXKqp98ofDGnZvGzTUQAAMWTZvCyN+0M6w1xNAIA4RdEEIO6MjQd0uuHS3EyMZgIATKXs9EQV5STrVJ1XvnHmagIAxB+KJgBxp6bu0txMy+cxNxMAYOqtXJCj8UBI1Rd7TEcBAGDGUTQBiCtj4wGdaexVWX6qMlIYzQQAmHqZqS7NLUzTmcY+DY36TccBAGBGUTQBiCunLo9mWsZoJgDANFo+/9IcgMfPdRtOAgDAzKJoAhA3Rn0BnW3s1ewCRjMBAKZXSqJTi0ozdKF1QL2DPtNxAACYMRRNAOLGqTqvgsGwls3lSnMAgOm3dE6WEhw2HantMh0FAIAZQ9EEIC5cGs3Up9mFaUpPSTAdBwAQB1wJdlXM8aila1gtnUOm4wAAMCMomgDEhVN1XoVCYS2by9xMAICZs2hWppLcDr1b3apwOGw6DgAA046iCUDM++hoprRkRjMBAGaOw27TinnZ6uwdVUMHo5oAALGPoglAzDt50atQmNFMAAAz5hSlyZPm1tHaLoVCjGoCAMQ2iiYAMW1kLKDapj7NYTQTAMAQm2VpbUWBBkf8qm3uMx0HAIBpRdEEIKadrOthNBMAwLhZBanKy0zUifM98gdCpuMAADBtKJoAxKyRMb9qm/o1tzBdqUmMZgIAmGNZllYuzNHYeFA19V7TcQAAmDYUTQBi1vHzPVI4rKVzPaajAACgnIxEleal6FSdV6O+gOk4AABMi0kVTXV1ddq2bZs2bdqkbdu2qb6+/pp1gsGgtm/frvXr12vDhg3asWPHpJYdOHBAn/3sZ1VRUaFnn3120tsEgBvpHxrX+ZZ+LSjJYDQTACBiVM7PUTAU1vHz3aajAAAwLRyTWenpp5/WY489pqqqKr300kt66qmn9OKLL161zs6dO9XY2KhXXnlFfX192rp1q9auXavi4uIbLispKdF3vvMd7du3T+Pj45PeJgDcyLFzXbLbLC1lbiYAQARJT0nQwpIMnWns0/ziDGWlu01HAgBgSk04oqmnp0c1NTXasmWLJGnLli2qqamR13v1ueV79uzRI488IpvNJo/Ho/Xr12vv3r0TLps1a5YWL14sh+PazutGtwOA6+nuH1VDx5AWl3mU6JpUnw4AwIxZMT9b7gS7PqjpUDgcNh0HAIApNWHR1NbWpry8PNntdkmS3W5Xbm6u2trarlmvsLDwytcFBQVqb2+fcNlE930rtwMQ347UdsvltGvx7EzTUQAAuEaC0647Fuaou39M51sGTMcBAGBKxfxH/VlZKaYj3LKcnFTTEWBIPB/7sHdEqSm3fhpBU8eg2ntGdO/yQmVlJE9hsul3O4/7VjmdDiP3G4+ut6/Z/1MvGn6uIz3fzYiG/R0pPrqfli9w6WLrgI7WdmnxnCy5EyZ+WZ6U5FKOJ2k6I2KaxPNru3jGcY9PHPdJFE0FBQXq6OhQMBiU3W5XMBhUZ2enCgoKrlmvtbVVy5Ytk3T1aKQbLZvovm/ldh/V0zOkUCj6hiTn5KSqq2vQdAwYEO/HfsQX0ODQ2C3dNhwO68DxFiW7HZqVl3zL2zEhNcVtJK/ff+v7Gzfn4/a1qeMe6yL95zrWjnuk7+9I8XHH/Y6FOdr9boPePtqiu5bkTbiNkRGfuoLB6YqIaRLvr+3iFcc9PsXLcbfZrBsO6pnw1LmsrCyVl5dr165dkqRdu3apvLxcHs/VlwvfvHmzduzYoVAoJK/Xq/3792vTpk0TLruRW70dgPjU0D4o74BPK+Zny26b1EU1AQAwxpPm1sJZGapt6lNPP2UdACA2TOrUuWeeeUbf+ta39PzzzystLU3PPvusJOnxxx/Xk08+qaVLl6qqqkrHjx/Xxo0bJUlPPPGESkpKJOmGyw4fPqyvf/3rGhoaUjgc1u7du/Xd735X99133w1vBwAfFQqFdfRctzJSEjS7MM10HAAAJmXFvGzVtw3qg5oOPXxXqSzLMh0JAIDbMqmiae7cudqxY8c133/hhReu/Ntut2v79u0fe/sbLVu1apXeeuutm74dAHzUueZ+DY749eDKItl4kQ4AiBK/mxj8nep2nW/p1/ziDNORAAC4LZxbAiDq+QMhnbjQrdzMRBXlRNcE4AAAzClMU25moo6c7ZZvnDmYAADRjaIJQNQ709CrUV9QKxdkc8oBACDqWJalOxfnajwQ1NFzXabjAABwWyiaAEQ133hQJ+u8Ks5JVm4ml3wGAESnzFS3FpVmqrapX91MDA4AiGIUTQCiWvXFHvkDIVUuyDEdBQCA27J8XpbcCXYdrOlQOBw2HQcAgFtC0QQgag2P+nWmsU9zC9OUmeoyHQcAgNuS4LRr1aIcdfeP6Vxzv+k4AADcEoomAFHrSO2leSyWz882nAQAgKkxuyBNeZ5EfXi2S8OjftNxAAC4aRRNAKJSV++o6toGtaQsUymJTtNxAADMlZmyAAAgAElEQVSYEpZl6e6KfIXDYb17sp1T6AAAUYeiCUDUCYfDOnSmU4kuuyrmZJmOAwDAlEpNStAdC3PU1jOi2iZOoQMARBeKJgBRp65tQN39Y1q5IEdOB09jAIDYs6AkQwVZSfrwbKcGR8ZNxwEAYNJ4hwYgqvgDIR05262sNLfmFKaZjgMAwLSwLEtrK/JlWZbereYUOgBA9KBoAhBVTtV5NeILaHV5jizLMh0HAIBpk5Lo1OpFueroHdXphl7TcQAAmBSKJgBRY2jUr1N1XpXlpyo3M8l0HAAApt3cojQV5yTraG23OrwjpuMAADAhiiYAUeNIbZckaeXCHMNJAACYGZZl6a4l+bLbLf3slbMKhkKmIwEAcEMUTQCiQmfvqOrbBrVktkcpiU7TcQAAmDFJbofuLM9Tfdug9n7QaDoOAAA3RNEEIOKFw2EdOtOpRJdDS2Z7TMcBAGDGlRWkasW8bL10oE7NXUOm4wAAcF0UTQAi3sXWAfX0j2nlgmw5HTxtAQDij2VZ+vxD85TocujHu2oUCHIKHQAgMvGODUBE8wdCOlLbrex0t+YUppmOAwCAMalJCfrzTQvV2DGk37xTbzoOAAAfi6IJQEQ7WefVqC+g1YtyZVmW6TgAABh1x8Jc3bM0X7vfrVf1xR7TcQAAuAZFE4CINTTiV02dV2UFqcrJTDQdBwCAiPCnGxeqKCdZP/rNKXX3j5qOAwDAVSiaAESkcDisg6c7ZFnSHQtyTMcBACBiuJx2PfFHSxUKh/X8r07KH2C+JgBA5KBoAhCRmjqH1Nw1rOXzspWc6DQdBwCAiJLnSdIXP7lY9e2D+vlvz5mOAwDAFRRNACKOPxDSwZpOZaa6VD4r03QcAAAi0h0Lc7T5zlK9frRF755sMx0HAABJFE0AItCxc90a8QV015I82WxMAA4AwPX88f1ztKAkQy/uPavmziHTcQAAoGgCEFmaOod0pqFXC0rSlZPBBOAAANyI3WbTV6qWKNHl0HO/qtbIWMB0JABAnKNoAhAxQqGwfr7/nFwJdlUyATgAAJOSnuLSV7ZWqKtvTP+y57TC4bDpSACAOEbRBCBivHGsRY0dg1q1KFcup910HAAAosaCkgx9bt1cfVjbpX0Hm0zHAQDEMYomABGhf8inX755QQtLMzS7INV0HAAAos6mNSW6Y0GO/vONCzrb2Gs6DgAgTlE0AYgI//7bc/IHwvr8g/NlWUwADgDAzbIsS1/8VLlyMhP1T/9VrbaeYdORAABxiKIJgHEn63p08HSntqydpdxMJgAHAOBWJboc+trnl8tus/T9XxxT76DPdCQAQJyhaAJg1Lg/qJ/tq1WeJ0kP3zXLdBwAAKJebkai/vbzyzU0GtA/7DjOlegAADOKogmAUbvea1Bn36j+fOMCOR08JQEAMBXK8tP0xB9VqLV7WM/9qlqBYMh0JABAnOBdHQBjWrqH9fL7DVq7JE/lZR7TcQAAiCkVc7L0lw8v0umGXv1092mFwmHTkQAAccBhOgCA+BQIhvSTXTVKdDm07cH5puMAABCT7llaoL4hn3755kVlpLj0+QfnmY4EAIhxFE0AjNjzfoPq2wf11a0VSktOMB0HAICY9cm7Zql30Ke9BxuVkerSxtUlpiMBAGIYRROAGdfYMaid79TrzsV5WrUo13QcAABimmVZemz9AvUPjesXvz2njJQErSnPMx0LABCjmKMJwIzyB0L68a4apSQ69YUNC0zHAQAgLthslh7/9GLNK07Xj3fV6HS913QkAECMomgCMKN+806dmruG9RcPL1JKotN0HAAA4kaC064nP7dMeZlJ+sf/PKGTF3tMRwIAxCCKJgAz5kJrv/a836B7lxZoxbxs03EAAIg7yW6nvvFYpfI9SfrBL0/oaG2X6UgAgBhD0QRgRoz7g/rJrtPKTHXp0Ye4yhwAAKakJSXoG49VqjQvVc/96qQOnu4wHQkAEEMomgDMiP9666LavSP6q0+WK8nNdQgAADAp2e3U321boXnF6frn35zS2ydaTUcCAMQIiiYA0662qU+vHmrSA5VFWlLmMR0HAABISnQ59LXPL9fiMo/+Zc8Z/fbDZtORAAAxgKIJwLQaGw/oJ7trlJ3h1iMPzDUdBwAAfITLadeTf7xMK+Zl699erdXLHzSYjgQAiHIUTQCm1Y7XL6i7b0xf+tRiuRM4ZQ4AgEjjdNj01T+q0JryXO14/YJeOlCncDhsOhYAIErxrg/AtDlxoUevH23RxtUlWlCSYToOAAC4Dofdpv/r00uU4LDrpQN1Ghr169GH5slu43NpAMDNoWgCMC28A2P68a4aFeek6LOfmGM6DgAAmIDNZukvP7lISW6HXjnUpNbuYX1la4VSEp2mowEAoggfUQCYcsFQSP/8m1PyB0L6ytYlSnDaTUcCAACTYLMsPfrQfH3pU+U619yvb/+vQ2ruHDIdCwAQRSiaAEy5X79dp3PN/frzzQtVkJVsOg4AALhJ9ywt0De/UCl/MKTv/uuHOnym03QkAECUoGgCMKVOXuzR7vca9InlBVq7JN90HAAAcIvmFqbrqb9YraKcZD3/65P61VsXFWKScADABCiaAEyZ3kGffrSzRkU5yfqT9QtMxwEAALcpM9Wlbz5WqXuXFmjnu/X6p19Wa9QXMB0LABDBKJoATInfzcs0HgjqK1UVcjEvEwAAMcHpsOuvPrlIj62frxMXevTdf/1Q7d4R07EAABGKognAlHjpQL1qm/r0ZxsXqjCbeZkAAIgllmVp/aoS/d225eof8umZnx7Ubz9s5lQ6AMA1KJoA3LZTdV7tfrde9y4t0D1LC0zHAQAA06S8zKNvf+lOLSjN0L+9Wqv/8fNj6ukfMx0LABBBKJoA3Ja+IZ9+tPOUCrKT9YUNzMsEAECsy0x16WuPLNdfbF6oi20DeuqnH+jtE60KM7oJAKBJFk11dXXatm2bNm3apG3btqm+vv6adYLBoLZv367169drw4YN2rFjx20v++EPf6i1a9eqqqpKVVVV2r59+208VABTLRQK60e/OSXfeFBfqVoiVwLzMgEAEA8sy9L9K4r07S+uUWluqv5lzxn98JfV6h/ymY4GADDMMZmVnn76aT322GOqqqrSSy+9pKeeekovvvjiVevs3LlTjY2NeuWVV9TX16etW7dq7dq1Ki4uvuVlkrR161Z985vfnPpHDuC2/cfr53WmsU9f/GS5inJSTMcBAAAzLCcjUd94rFL7Dzfrl29e0H//8Qf6s00LtaY8z3Q0AIAhE45o6unpUU1NjbZs2SJJ2rJli2pqauT1eq9ab8+ePXrkkUdks9nk8Xi0fv167d2797aWAYhcbx1v1SuHmvTQHcW6dxnzMgEAEK9slqWNq0v0zF+tVm5mkv7nS6f0g/88oQ6uTAcAcWnCoqmtrU15eXmy2y+dEmO325Wbm6u2trZr1issLLzydUFBgdrb229rmSTt3r1bn/70p/XFL35RR48evZXHCGCKnW3s1b/uO6slsz169KF5puMAAIAIUJCVrP/vz1bqkXVzdbqxV//9xx/o3/ef0/CY33Q0AMAMmtSpc6Y8+uij+vKXvyyn06l33nlHX/3qV7Vnzx5lZmZOehtZWdF7Ok9OTqrpCDAkko99e8+wnv/1KeVnJeu/f+kupSQ6p3T7Ye+IUlPcU7rNaGHicTudjrjd3zPtevua/T/1ouHnOtLz3Yxo2N+R4nb3U4LLqbA9sq/lY7PbtfmeOVq7vEi/evOC9h9u0run2lV13xzdv7JYjgjP/1E2mxQK3f52Or0jkp15LG8k0e1QalKC6RhTLpJf02P6cNwnUTQVFBSoo6NDwWBQdrtdwWBQnZ2dKigouGa91tZWLVu2TNLVI5VudVlOTs6V7d9zzz0qKCjQuXPntGbNmkk/wJ6eIYVC0XcFjJycVHV1DZqOAQMi+diP+gL67r9+qFAopP/7jyo0OjSm0aGpvaTxiC+gwSneZjRITXEbedx+f3zubxM+bl+bOu6xLtJ/rmPtuEf6/o4UU3Hch0Z8Ol7bNUWJpt/s/BSl3z1Lh8926f+8cla736nTHQtzVJSTLMuyTMeb0PIFOVOyv2Pt//x0WF2ep7Hh2JpIPpJf02P6xMtxt9msGw7qmfAjhaysLJWXl2vXrl2SpF27dqm8vFwej+eq9TZv3qwdO3YoFArJ6/Vq//792rRp020t6+jouLL906dPq6WlRbNnz77JXQBgKoRCYf3Pl06pwzuir26tUJ4nyXQkAAAQ4Txpbm1YVawHVhYpHA7rtSMt2n+4WT39FC8AEKsmdercM888o29961t6/vnnlZaWpmeffVaS9Pjjj+vJJ5/U0qVLVVVVpePHj2vjxo2SpCeeeEIlJSWSdMvLvv/97+vUqVOy2WxyOp363ve+d9UoJwAz5z9eP6/qiz36800LVV7mmfgGAAAAkizLUkluigqzk1Xb2KfjF7q1+70G5WclqWK2RwVZSVExwgkAMDmTKprmzp2rHTt2XPP9F1544cq/7Xa7tm/f/rG3v9Vlvyu0AJj10SvMrassMh0HAABEIbvNUnlZpuYWpam2qU+nG3q1/3CzPGkuLZnt0az8VNkonAAg6kX0ZOAAzOMKcwAAYColOO2qmJOl8rJMXWwZ0Kk6r94+3qajtd1aMjtTc4vSo2rScADA1SiaAFxXu3dEz/3qpHIyEvWVqiWy23jRBwAApobdZtP8kgzNLU5XU8eQTtV59UFNp46f79H84nTNLUpXWnLsXYkMAGIdRROAj9XTP6a///lRWZb03z63TElup+lIAAAgBtksS7PyU1Wal6IO76hO1Xt18qJX1Re9ys1M1LyidM3KT5XTwQdeABANKJoAXKN/eFx//4tjGvUF9f/+SSVXmAMAANPOsizlZyUpPytJI2N+XWgZ0PmWfr17sl0HT3doVn6q5hWlKzczkcnDASCCUTQBuMrImF/f/8Ux9Q6M6e8eXaFZ+ammIwEAgDiT5HZq6dwsVczxqKtvVOdbBlTfNqALLQNKTXKqLD9VJbkpykp3UzoBQIShaAJwhW88qH/YcUKt3cP6b59bpvnFGaYjAQCAOGZZlnIzk5SbmaTVi3LV2DGoCy0DOll36dS6RJddRTkpKslNUUFWEpOIA0AEoGgCIEnyB0L6p19V60Jrv75SVaGKOVmmIwEAAFzhdNg0t+jSJOG+8aBauofV1DmkhrZBnW/ul91mqSA7WSW5ySrISlZKIvNLAoAJFE0AFAyF9KOdp3Sqzqu/eniRVi3KNR0JAADgulwJds0pTNOcwjQFQ2F1eEfU1Dmk5st/JCnZ7VCeJ0l5mYnK8yQpNcnJaXYAMAMomoA4FwqH9b9fPqsPz3bp0Qfn6b7lhaYjAQAATJrdZqkwO1mF2claU56rviGf2ntG1dE7otbuYV1sHZAkuRPsyvMkKTczUdnpbmWmujjVDgCmAUUTEMfC4bD+47XzOlDdps/cU6aNa0pNRwIAALhllmUpM9WtzFS3yssyFQ6HNTA8ro7eUXV4R9TRO6qG9sFL60pKS0mQJ9UlT9ql4smT5pY7wW72QQBAlKNoAuJUKBzWz/ef0/4Pm7X+jmJV3TvbdCQAAIApZVmW0lNcSk9xaUHJpYucDI365R0Yk3fAJ+/AmDp6R1XXNnjlNkluh9KTE3ShdUDj40GlJScoLdmpZLdTNhun3gHARCiagDgUDIX0v14+o3eq27VhVYm2PTSPOQsAAEBcSEl0KiXRqdK81CvfGxsPyDvgU+/gpfJpYHhcH57p1Nh48Mo6NktKSUpQWtKl2yclOpXsdlz+41Siy0ERBQCiaALijj9waeLvD892qere2frMPWWUTAAAIK65ExwqzHaoMDv5yveWzc/WByfbNTAyroFhvwaHxy//+9KpeP5A6KptWJISLxdPiS6H3Al2uRIcykh1SeGw3Al2uRMuf99pp5QCELMomoA44hsP6p9+Va1TdV49+tB8bVxdYjoSAABARLIsS4muS6VRXua1y8cDQY2MBjQ85tfwWEDDYwGNjF7698DwuDp7g/KNBxW+zvZdTvvl8unyH5fj0vdcdrmddrkuF1K/K6bsTFwOIEpQNAFxYmQsoH/4z+O60NKvv3p4EVeXAwAAuA0JDrsSUu2XRixdRygcVkKCU929wxrzBTXmD2rMF9DYePDyn4B840H1DY1r1DuicX/outty2C25PlJAuRKuLqQ+Wkz9buQUo6YAmEDRBMSBgeFxff8/jqmla1hfrqrQ6kW5piMBAADEPNvlUVEZKS4pZeL1Q6GwfP5LI6HGLv/t+8jfYx/5emjUL994UOOB65dTvxst9buRWYkuuxITHEp0O5Tidiop8dL3bUyjAGAKUTQBMc47MKa///kxeQfG9OTnlmnpnCzTkQAAAPAxbLbfn643WVfKqd8VVJdHSo36fv/3qC+ggeERjY4HFQpdfTKfZUlJLoeS3JcnN090KjXRqdRkp1KTEpTsdjCfJ4CbQtEExLCWriH9w44TGvH59fVtK65c1hcAAACx4WbKqXA4LH8gdHlOKf8fzDHlV8/AmBo7hhQK/76MstmsS8VT0qXiKTXZqYxklzJSE+RO4O0kgGvxzADEqGPnuvXPO0/J7bTrG39SqbL8NNORAAAAYJBlWUpw2pXgtCvzOnNLhcNhjYwFNDji18DIuAZH/Bq8fLW9tp4RBT8yIsqdcGmOqswUlzJSEpSR6lJGiktOBxOXA/GMogmIMeFwWHveb9B/vXlRs/JT9Td/vOy6LyQAAACAj7IsS8mJTiUnOpWflXTVsnA4rBFfQP1D4+ob9Kl3yKe+oXGda+5TIPj7Aio1yansdLey0t3KSnPLk+amfALiCEUTEEN8/qD+Zc9pHTzdqbsW5+kvH16kBKfddCwAAADEAMuylOx2KtntVGF28pXvh8NhDY361Tc0rt5Bn7wDY+roHVVd2+CVddJTEpSV5lZ2uls5GYnKTHVxVTwgRlE0ATHCOzCmH/5XtRrbB/W5dXP18J2lTNwIAACAaWdZ1qX5m5ISVJL7+8vrjfoC6ukfU8/AmHr6x9TaPayLrQOSJIfdUk5GovIyE5We4tLiWZly8QEpEBMomoAYcL6lX//0X9Ua9wf1N59bphXzsk1HAgAAQJxLdDlUnJui4svlUzgc1vBYQF19o+rsvfTn2PkeHTvfI7vNUmleqhaUpGtRaaYWlGTc1NX3AEQO/ucCUe6d6jb9771n5El16xt/UqmijwxjBgAAACKFZVlKSXQqJdGp2QWXLlTj8weVmepWU8egapv69NsPm7XvYJPsNktzCtNUPitTi8s8mlOYJoedeZ6AaEDRBESpUV9A/+fVWr1zsl3lszL1la0VSkl0mo4FAAAATJrLadeS2R6tWZQrSRr3B3W+pV819b2qqfdq5zv1+s079XIl2LWwJEOLZ2WqYk6WCrKSmCYCiFAUTUAUOt/Srxd2nlJ3/5g+fXeZPnNvmew2PuEBAABAdEtw2rW4zKPFZR5JczU06tfZxl7VNPSqpr5XJy70SK+dV3a6W0vnZmnZnCwtYn4nIKJQNAFRJBgKade7Ddr5Tr08aS596wsrNb84w3QsAAAAYFqkJDp1x8Jc3bHw0oin7v5Rnbzo1YkLPXq3ul2vH2mRw27TotKMS8XT3CzlZSYZTg3EN4omIEp09o3qhZ2ndKFlQGuX5OsLGxYoyc1/YQAAAMSP7PRErass0rrKIvkDIdU296n6Qo9OXOjRv+8/p3/ff07/f3t3HxxVee8B/Hv2fbPJZt+y2U2yJCQkGGJ4C6JIL2JEopaQaIs6DN7O9YV7h1ZbO86USlvagtOmnam2I7ZO63SmHa/e8QKK6LVoAxap8k4gEJOQAAlkk83uZvO+m3157h8LK1gNLybZZPf7mdnZPed5Fp49v3OenP3tc55jN6dgflEG5hVmIM+eBhkvsSOaUPyWSjTJCSHwz/pOvPp+EyRJwtqVs3DbLFu8m0VEREREFFdKhQwleSaU5Jnw8F2FcPmGcfy0G0eb3fi/T9rwzsfnYEhVYW5hBuYXWnBTrpETihNNACaaiCaxvsER/PcHTTjQ4EKRw4DHVxTDkq6Nd7OIiIiIiCYdq0GLZQscWLbAgUF/EMdPe3C0uRsf13diz9EL0KrlKM03Y35RBkrzzdCq+XWYaDzwyCKahCIRgT3HLmDbh60IBMN4YEk+7rstFzIZh/0SEREREV2NTqPEopttWHSzDcFQGKfO9uBoczeONbtxoMEFuUxCca4R84oyMHeGBcY0dbybTJQwmGgimmSa2nrwu/85inOd/SjONWLN8iLYzbp4N4uIiIiIaEpSKuSYM8OCOTMsiFQItHT04mizG0eauvHXvzXir39rRH6WHvMKLZhXmIEsC8+9ib4KJpqIJomB4SC2/aMVHx67AL1Ohf9cWYKFxVZInLyQiIiIiGhMyGQSCnMMKMwxYNXSAnR4hnC0qRtHm7ux9cNWbP2wFTZTCspmZmB+UQbybGk8Hye6Tkw0EcVZRAjsO+HEG7tbMOgPovLf8lFRlsNrxomIiIiIxpEkSci26JBt0WHF7Xno6Q/gaHM3jjR1xyYTN+nVmF+YgbKZGSjMMXAqC6JrwG+yRHHU1O7D/+5pwekLvSjI1uOR5XNRdnMWurv74900IiIiIqKkYkxTo3x+Dsrn52BgOIi609HL6z6s68AHh88jVavEvEIL5hdlYFaeCUoF72BH9EWYaCKKgzPOPmzf24r6Vi/0OhX+496bsHi2HTIOyyUiIiIiirtUrRKLS+1YXGqHfySE+lYvjjR141CjC3uPO6FRyTG7gHewI/oiPBqIJtB51wC2723F0WY3dBoFVi0tQPn8HKhV8ng3jYiIiIiIvoBGpcCCm6xYcJMVwVAEn7b14HBjdF6nAw0uKOQylOQZMX9m9A52aSmqeDeZKK6YaCKaAE7PIN766AwONrigUctR/bXpuPsWB3/5ICIiIiKaQpQKGUrzzSjNN+PfK2bi9IVeHG6MzutU1+KBTJJQmJOOr83LRqE9DVZjSrybTDTh+C2XaByddw3gbwfa8M+TnVAqZLhvUS4qFk5DqlYZ76YREREREdFXIJNJKHIYUOQw4OG7ZqCtawCHm7pxrNmNV3acBABkWXSYV2jB3BkWTM/Sc6oMSgpMNBGNsUhE4NhpNz441I5P23xQKmS4e4ED992WC72Ow2iJiIiIiBKNJEnItaUh15aGB5bkIyyToXb/ORw77Y7dwU6vU2HuDDNmF1gwK88IjYpfxykxcc8mGiND/iD2Hnfi74fPw93rh0mvxjeXFmDJnCyOYCIiIiIiSiI2sw533+LA3bc4MOgP4kSLB0eb3TjQ4MI/6pxQyKOjoWbnm1FaYIbNlAKJo50oQTDRRPQVOT2D+Pvh89h3ohOBYBiFOel48M4ZmFdkgVzGW54SERERESUznUaJ20psuK3EhlA4gubzvTjR4sHxVg9erz2N12tPw2rQorTAjNkFZhQ5DFArebMgmrqYaCK6Af1DIzjQ4MInJzvR0tEHhVzCwuJMLFuQgzybPt7NIyIiIiKiSUghl6E414jiXCMeLJ+Bbt8wTrR6cLzFg711Hfj74fNQyCUU5hgwK8+IWXkm5GamQSbjaCeaOphoIrpGI8Ewjp1245OTXTjR6kE4IpCdocM3lxZgcakd6Zx/iYiIiIiIrkOGQYvy+Tkon5+DkWAYje0+nDrrxckzPdj6YSu2ftgKnUaB4jwTSvKMKMkzwWLQxrvZRKNiooloFKFwBI3tPuw/2YVDjS74R8IwpKpw9y0OLCqxwWFNjXcTiYiIiIgoAaiUcpTmm1GabwYA9A4EcOpcD06d8eLkWS8OfeoCAJj1GhQ5DJg5zYCZDgOsRi3nd6JJhYkmos/pGxrBiRYP6lo8OHnGg+FAGBqVHAtmWrGoJBMzpxk5dJWIiIiIiMZVeqoai0psWFRigxACTs8QTp31orHdh/ozHnx8svNiPRVmOqJJpyKHAXaLDjImniiOmGiipCeEQLtrAHWn3Tje4kFrRx8Eoh32LTdZMbvAgpunm6DihHxERERERBQHkiQhy6JDlkWHZQscEEKg0zuExjYfmtp9aGz34UBDdMSTVi1Hvl2P6VnpKMjSIz9Lj7QUTvNBE4eJJko6ESHQ4R5Ec7sPTed70djWA9/ACABguj0NVV+bjjkzLHBkpvKXACIiIiIimnQkSYLdrIPdrMPSedkQQqC714/mdh9aOvrQeqEX73x8FkJE61sNWuRn65Fv1yPXlgaHNRUaFdMBND64Z1HCC4UjaOsaQFN7NNvffN6HQX8IAGBIVaHIYUDJdBNm55uRnqqOc2uJiIiIiIiujyRJsBq0sBq0WFxqBwD4R0I419mP1o4+tHT0oeFsDz452RWtD8BqSkFuZiqmZaZhmjX6rOcNjmgMMNFECSUYiuCCewBtXQM419mPtq5+tLsGMBKKAACsRi3mFWagyGFAkSMdGQZOnEdERERERIlHo1Jg5jQjZk4zAohOGdLTH8C5rn60dw3gXFc/Wi70xS65A6I/xGdbdLBbdMgy62KX66VqlfH6GDQFMdFEU1JECHh7/ej0DsHpGUKbqx9tXQPocA8iHImOD9Wq5XBY07BkbhZmZKejMMcAYxpHLBERERERUfKRJAkmvQYmvQbzCjNi6wf9QbR1DcR+pO9wD2JvnROBYDhWR5+iRJYleqme1RgdOZVh1CLDoIWac9nS5zDRRJOWEAJ9Q0F4ev3o8g7B6R1Cp3cInZ4hdPUMIXhxlBIQ7fim2dIwu8AcHfqZmYoMg5ZzLBEREREREY1Cp1GiONeI4lxjbF1ECHj7/HB6htDhHow+PIPYf6oLQ4HQFe83pKpiiSerQRtLZpn0apjS1FAqmIhKNkw0UVwIITDoD6F3cAR9AwF4+wPw9Prh6bv46PXD0xdAKPxZMkkmSVpBNCMAAAwVSURBVLAYNLCbUlAy3QibKSX6MOugT1HyEjgiIiIiIqIxIJMkWNK1sKRrUZpvvqJsYDgIV88wXL4hdPcMw+UbhqtnGPVnvOi9eJOly6WlKGFKu5R40iA9VQW9ToV0nQrpqSqk69RIS1FCIZdN1MejcXZNiaYzZ85g/fr18Pl8MBgMqKmpQV5e3hV1wuEwNm/ejL1790KSJKxduxarVq0atzKaXIKhMAaGQxj0BzE4HMSgP/TZsz+IvsGRaFLpsudLl7hdLl2ngjldA0dmGuYVZsCcroFZr4kOzzRq2fkQERERERHFUapWiVStEvlZ+n8pGwmG0dMfgLfPHx1M0OeHty8Ab78frp5hfNrmw/DnRkRd/u/qdSqkahTQXfw/UrXK2GudRolUrQIalQIalfziQwGVUsZBB5PMNSWaNm7ciNWrV6OqqgpvvfUWfvKTn+Avf/nLFXXefvtttLW1YdeuXfD5fKiursaiRYuQk5MzLmX05SJCIBIREEIgEokuB8MRhEIRBC89wpe9ji2Hr1wOflbPPxKGfyQUfQ5En4cvLY+Er7iM7fNkkoQ0nRLpumjmOjtDdzGDrY6t47BKIiIiIiKiqU2llCPTlIJMU8qX1gmGwuj9/ECEgRH0DkWXB4eD6PYN44yzDwPDoSuucvkikoRY0kmtlF+RhNKoLz5fXK9WyaFUyKCQy6CQS1DIZVDKZVBcXBd9fdn6i2XKi3UVchkkCUxsXcVVE00ejwenTp3Cn//8ZwDAihUrsGnTJni9XphMpli9d999F6tWrYJMJoPJZMKyZcvw3nvv4fHHHx+Xsmslk03dHeBS29/YfRqnL/RCiM+SSBEBiIiILiOaUMKlBNMYtkEukyCXy6BWyaFWRh/mdA3UKsUV61LUcmjVSqRoFNCq5UhRK6PrNEqomWG+blN5v/2qFHIZUjTJd1cLrVqBcGjiP3eybu94+KJtHa+4J7rJvl8nWtwn+/aeLMYi7tzWE2ustneiHfPjQSGXJeT5byJ+prGgVilgVSlgNX55MuoSIQSCIYEhf/RqmaFAECPBCALBMAIjYQSCYfiD4cuWIwgEQ9E6I2H4BgIIBIcRCIYRGmVwxPWSpOiACkgSZBcTTzIJkMmiV+DE1skkSBfrSxJg1mvwRGXJmLUjXq62b1810eR0OpGZmQm5PDrSRC6Xw2q1wul0XpFocjqdyMrKii3b7XZ0dnaOW9m1Mhp111V/MjGbUwEA//XNuXFuCU20S7FPVjn29Hg3Iank5xivXonGBLf1xOG2nljc3hOH23picXvTV5Hs5/SUvDjhDRERERERERERjYmrJprsdju6uroQDocBRCfodrlcsNvt/1Kvo6Mjtux0OmGz2catjIiIiIiIiIiIJperJprMZjOKi4uxc+dOAMDOnTtRXFx8xWVzAHDPPffgjTfeQCQSgdfrxQcffICKiopxKyMiIiIiIiIiosnlmu4699Of/hTr16/HSy+9BL1ej5qaGgDAE088gaeeegqlpaWoqqpCXV0dli9fDgD49re/DYfDAQDjUkZERERERERERJOLJIQYy5uUERERERERERFRkuJk4ERERERERERENCaYaCIiIiIiIiIiojHBRBMREREREREREY0JJpqIiIiIiIiIiGhMMNEUBz09PXjiiSdQUVGByspKfOc734HX6wUAHDt2DCtXrkRFRQUeffRReDye2PtGK6OpYd26dVi5ciWqq6uxevVqNDQ0AADOnDmDhx56CBUVFXjooYdw9uzZ2HtGK6Op5cUXX8TMmTPR1NQEgMd7oisvL8c999yDqqoqVFVVYe/evQAY90QXCASwceNGLF++HJWVlfjxj38MgP18ojt//nzsWK+qqkJ5eTkWLlwIgLFPdLt370Z1dTWqqqpQWVmJXbt2AWDcE92ePXtw//33o7KyEmvWrEF7ezsAxj3R1NTUoLy8/Irzd+DG45xU+4CgCdfT0yM++eST2PIvf/lL8cMf/lBEIhGxbNkycfDgQSGEEFu2bBHr168XQohRy2jq6Ovri71+//33RXV1tRBCiEceeUS8+eabQggh3nzzTfHII4/E6o1WRlNHfX29eOyxx8TSpUtFY2Mjj/ckcOedd4rGxsYr1jHuiW/Tpk3iueeeE5FIRAghRHd3txCC/Xyy2bx5s/jZz34mhGDsE1kkEhELFiyI9fUNDQ1i7ty5IhwOM+4JzOfziYULF4rW1lYhRDSGjz76qBCCx3uiOXjwoOjo6PiXc7objXMy7QNMNE0C7733nvjWt74l6urqxNe//vXYeo/HI+bOnSuEEKOW0dS0fft2cf/99wu32y3KyspEKBQSQggRCoVEWVmZ8Hg8o5bR1BEIBMSDDz4o2traYn+oeLwnvi9KNDHuiW1gYECUlZWJgYGBK9azn08ugUBA3HrrraK+vp6xT3CRSEQsXLhQHDp0SAghxIEDB8Ty5csZ9wRXV1cn7rvvvthyT0+PKCoqYtwT2OXndDca52TbBxTxHlGV7CKRCF577TWUl5fD6XQiKysrVmYymRCJRODz+UYtMxgM8Wg63aANGzZg3759EELgT3/6E5xOJzIzMyGXywEAcrkcVqsVTqcTQogvLTOZTPH8GHQdfvvb32LlypVwOByxdTzek8MzzzwDIQTKysrw/e9/n3FPcO3t7TAYDHjxxRexf/9+6HQ6fPe734VGo2E/n0Rqa2uRmZmJkpIS1NfXM/YJTJIkvPDCC1i3bh1SUlIwODiIl19+med2CW769Olwu904fvw4Zs+ejbfffhsAGPckcaNxTrZ9gHM0xdmmTZuQkpKCNWvWxLspNEGee+457NmzB08//TR+9atfxbs5NM6OHj2KEydOYPXq1fFuCk2wV199FTt27MDWrVshhMDPf/7zeDeJxlkoFEJ7eztmzZqFbdu24ZlnnsGTTz6JoaGheDeNJtDWrVvxjW98I97NoAkQCoXw8ssv46WXXsLu3bvx+9//Hk8//TSP+QSXlpaG559/Hr/4xS/wwAMPwOPxQK/XM+5El2GiKY5qampw7tw5vPDCC5DJZLDb7ejo6IiVe71eSJIEg8EwahlNTdXV1di/fz9sNhu6uroQDocBAOFwGC6XC3a7HXa7/UvLaGo4ePAgWltbcdddd6G8vBydnZ147LHHcO7cOR7vCe7ScapSqbB69WocOXKE/XyCy8rKgkKhwIoVKwAAc+bMgdFohEajYT+fJLq6unDw4EFUVlYCwKjxZeynvoaGBrhcLpSVlQEAysrKoNVqoVarGfcEd/vtt+O1117Dtm3bsGbNGvj9fmRnZzPuSeBG+/Vk2weYaIqT559/HvX19diyZQtUKhUA4Oabb4bf78ehQ4cAAK+//jruvffeq5bR1DA4OAin0xlbrq2tRXp6OsxmM4qLi7Fz504AwM6dO1FcXAyTyTRqGU0Na9euxUcffYTa2lrU1tbCZrPhlVdeweOPP87jPYENDQ2hv78fACCEwLvvvovi4mL28wnOZDLh1ltvxb59+wBE7y7j8XiQl5fHfj5JbN++HXfccQeMRiMA8G98grPZbOjs7ERraysAoKWlBW63G7m5uYx7guvu7gYQnQblN7/5DR5++GFkZ2cz7kngRvv1ZNsHJCGEiHcjkk1zczNWrFiBvLw8aDQaAEBOTg62bNmCI0eOYOPGjQgEAsjOzsavf/1rWCwWABi1jCY/t9uNdevWYXh4GDKZDOnp6fjBD36AkpIStLS0YP369ejr64Ner0dNTQ3y8/MBYNQymnrKy8vxhz/8AUVFRTzeE1h7ezuefPJJhMNhRCIRFBQU4Ec/+hGsVivjnuDa29vx7LPPwufzQaFQ4Hvf+x7uuOMO9vNJoqKiAhs2bMCSJUti6xj7xLZjxw788Y9/hCRJAICnnnoKy5YtY9wT3IYNG3DkyBEEg0EsXrwYzz77LNRqNeOeYDZv3oxdu3bB7XbDaDTCYDDgnXfeueE4J9M+wEQTERERERERERGNCV46R0REREREREREY4KJJiIiIiIiIiIiGhNMNBERERERERER0ZhgoomIiIiIiIiIiMYEE01ERERERERERDQmmGgiIiIiIiIiIqIxwUQTERERERERERGNCSaaiIiIiIiIiIhoTPw/gF2jvqMS5EkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_source(sns.distplot(regen_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ich nutze den Shapiro-Wilk Test, um die jährliche Regenmenge auf Normalverteilung zu testen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Die jährliche Regenmenge ist wahrscheinlich normalverteilt.\n"
     ]
    }
   ],
   "source": [
    "stat, p = shapiro(regen_df['RSK'])\n",
    "alpha = 0.05\n",
    "if p > alpha:\n",
    "    print('Die jährliche Regenmenge ist wahrscheinlich normalverteilt.')\n",
    "else:\n",
    "    print('Die jährliche Regenmenge ist nicht normalverteilt.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auch in folgendem Diagramm kann ich keine steigende oder sinkende Regenmenge ablesen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f56069fb5d0>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regen = sns.lineplot(data=regen_df, legend=False)\n",
    "regen.set_xlabel('Jahr')\n",
    "regen.set_ylabel('jährliche Regenmenge in mm')\n",
    "add_source(regen)\n",
    "\n",
    "x = np.arange(regen_df['RSK'].size)\n",
    "fit = np.polyfit(x, regen_df['RSK'], 1)\n",
    "fit_fn = np.poly1d(fit)\n",
    "regen.plot(regen_df.index, fit_fn(x), 'k-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fazit\n",
    "\n",
    "Wie bereits weiter oben diskutiert, kann ich mit den vorliegenden Messwerten dem Klimawandel nicht auf die Schliche kommen, auch wenn ich visuell einen klaren Trend zu steigenden Temperaturen sehe.\n",
    "\n",
    "Ich kann aber sagen, dass sich mit Jupyter Notebooks und den verschiedenen SciPy Bestandteilen sehr effizient eine explorative Datenanalyse durchführen lässt!"
   ]
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 2
}