GraemeMcMillan/BEST.ipynb

## BEST.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Berkley Earth Surface Temperature (BEST Project Data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ## [Data Source]( http://berkeleyearth.lbl.gov/auto/Global/Complete_TMAX_daily.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data input and cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I edited the data file and removed the data description lines at the top and saved a copy. I also edited the column names. Lets make a start. NB The data is not a CSV file but rather white space delimited. Hence the regex '\\s+' separator which looks for whitespace of one or more characters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Tmax1',sep=\"\\s+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_Number</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Day_of_Year</th>\n",
       "      <th>Anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880.001</td>\n",
       "      <td>1880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1880.004</td>\n",
       "      <td>1880</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1880.007</td>\n",
       "      <td>1880</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1880.010</td>\n",
       "      <td>1880</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1880.012</td>\n",
       "      <td>1880</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date_Number  Year  Month  Day  Day_of_Year  Anomaly\n",
       "0     1880.001  1880      1    1            1   -0.293\n",
       "1     1880.004  1880      1    2            2   -0.117\n",
       "2     1880.007  1880      1    3            3   -0.209\n",
       "3     1880.010  1880      1    4            4   -0.144\n",
       "4     1880.012  1880      1    5            5   -0.164"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data description says that the 'anomaly' column is the variance to the __'Estimated Jan 1951-Dec 1980 land-average temperature (C): 14.41 +/- 0.11'__. \n",
    "\n",
    "That gives a range of 14.30 to 14.52. I don't know what confidence interval this represents, what the STD is or how the mean was calculated.\n",
    "\n",
    "Anyhow. Lets add 14.41 to the anomaly to get a Tmax measurements in Celsius."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TmaxC'] = df.Anomaly + 14.41"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_Number</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Day_of_Year</th>\n",
       "      <th>Anomaly</th>\n",
       "      <th>TmaxC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50945</th>\n",
       "      <td>2019.484</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>177</td>\n",
       "      <td>1.337</td>\n",
       "      <td>15.747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50946</th>\n",
       "      <td>2019.486</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>178</td>\n",
       "      <td>1.443</td>\n",
       "      <td>15.853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50947</th>\n",
       "      <td>2019.489</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>179</td>\n",
       "      <td>1.629</td>\n",
       "      <td>16.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50948</th>\n",
       "      <td>2019.492</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>180</td>\n",
       "      <td>1.662</td>\n",
       "      <td>16.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50949</th>\n",
       "      <td>2019.495</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>181</td>\n",
       "      <td>1.566</td>\n",
       "      <td>15.976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date_Number  Year  Month  Day  Day_of_Year  Anomaly   TmaxC\n",
       "50945     2019.484  2019      6   26          177    1.337  15.747\n",
       "50946     2019.486  2019      6   27          178    1.443  15.853\n",
       "50947     2019.489  2019      6   28          179    1.629  16.039\n",
       "50948     2019.492  2019      6   29          180    1.662  16.072\n",
       "50949     2019.495  2019      6   30          181    1.566  15.976"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date_Number    float64\n",
       "Year             int64\n",
       "Month            int64\n",
       "Day              int64\n",
       "Day_of_Year      int64\n",
       "Anomaly        float64\n",
       "TmaxC          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data exploration \n",
    "\n",
    "Ok. That's given something to work with. Now I want a table with Columns as Month, Rows as Year and data the mean of each Month. Round the mean down to 2 decimal places to make it readable and reduce the entropy of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:last-of-type th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"12\" halign=\"left\">TmaxC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>15.64</td>\n",
       "      <td>16.02</td>\n",
       "      <td>16.01</td>\n",
       "      <td>15.54</td>\n",
       "      <td>15.63</td>\n",
       "      <td>15.61</td>\n",
       "      <td>15.26</td>\n",
       "      <td>15.39</td>\n",
       "      <td>15.32</td>\n",
       "      <td>15.76</td>\n",
       "      <td>15.40</td>\n",
       "      <td>15.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>15.79</td>\n",
       "      <td>16.68</td>\n",
       "      <td>16.58</td>\n",
       "      <td>16.28</td>\n",
       "      <td>15.66</td>\n",
       "      <td>15.39</td>\n",
       "      <td>15.33</td>\n",
       "      <td>15.88</td>\n",
       "      <td>15.42</td>\n",
       "      <td>15.12</td>\n",
       "      <td>15.32</td>\n",
       "      <td>15.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>15.79</td>\n",
       "      <td>16.08</td>\n",
       "      <td>16.37</td>\n",
       "      <td>15.75</td>\n",
       "      <td>15.82</td>\n",
       "      <td>15.33</td>\n",
       "      <td>15.53</td>\n",
       "      <td>15.48</td>\n",
       "      <td>15.33</td>\n",
       "      <td>15.47</td>\n",
       "      <td>15.17</td>\n",
       "      <td>15.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>15.41</td>\n",
       "      <td>15.53</td>\n",
       "      <td>16.09</td>\n",
       "      <td>16.06</td>\n",
       "      <td>15.72</td>\n",
       "      <td>15.56</td>\n",
       "      <td>15.58</td>\n",
       "      <td>15.42</td>\n",
       "      <td>15.10</td>\n",
       "      <td>15.46</td>\n",
       "      <td>15.01</td>\n",
       "      <td>15.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>15.69</td>\n",
       "      <td>15.50</td>\n",
       "      <td>16.32</td>\n",
       "      <td>16.02</td>\n",
       "      <td>15.51</td>\n",
       "      <td>15.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TmaxC                                                                 \\\n",
       "Month     1      2      3      4      5      6      7      8      9      10   \n",
       "Year                                                                          \n",
       "2015   15.64  16.02  16.01  15.54  15.63  15.61  15.26  15.39  15.32  15.76   \n",
       "2016   15.79  16.68  16.58  16.28  15.66  15.39  15.33  15.88  15.42  15.12   \n",
       "2017   15.79  16.08  16.37  15.75  15.82  15.33  15.53  15.48  15.33  15.47   \n",
       "2018   15.41  15.53  16.09  16.06  15.72  15.56  15.58  15.42  15.10  15.46   \n",
       "2019   15.69  15.50  16.32  16.02  15.51  15.58    NaN    NaN    NaN    NaN   \n",
       "\n",
       "                     \n",
       "Month     11     12  \n",
       "Year                 \n",
       "2015   15.40  15.90  \n",
       "2016   15.32  15.38  \n",
       "2017   15.17  15.63  \n",
       "2018   15.01  15.21  \n",
       "2019     NaN    NaN  "
      ]
     },
     "execution_count": 582,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_View1 = df[['Year','Month','TmaxC']]\n",
    "df_group1 = df_View1.groupby(['Year','Month'],as_index=False).mean().round(2)\n",
    "df_pivot_group1 = df_group1.pivot(index='Year',columns='Month')\n",
    "df_pivot_group1.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A heat Map will represent this table and present graphically, what is going on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "plt.ylabel('Year')\n",
    "plt.ylabel('Month')\n",
    "sns.heatmap(data=df_pivot_group1,cmap='Reds');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean teperatures for each month have risen over the data period, but oddly, more so in the last 20 years especially in February - April. I can choose any month and plot a scatter plot to have a better look at the data over the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [],
   "source": [
    "MONTH = 3 #USE THIS NUMBER TO CHOOSE THE MONTH TO ANALYSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11.7,8))\n",
    "plt.title('Scatter Plot of Month %s Mean Temperature - Celsius' %MONTH,\n",
    "          fontsize=20);\n",
    "#plt.style.use('fivethirtyeight')\n",
    "plt.style.use('ggplot')\n",
    "plt.ylabel('Mean Temperature Celsius')\n",
    "plt.xticks(rotation=90)\n",
    "sns.scatterplot(x=df_pivot_group1.index,y=df_pivot_group1['TmaxC'][MONTH]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From many of the montly graphs, it looks at first glance, as if there are several clusters of measurements. Let's use a KMeans clustering analysis from 'sklearn' to see."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Many thanks to [sentdex](https://pythonprogramming.net) for his tutorials on machine learning!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QUJBte1BQkG2egCuXgfv6669VqlQpvfTSS3ZzE+rUqaMHH3xQFy9ezDbKGxUVZdd/N1K1alW7keysv3NgYKD69+9v216tWjVVrFhRJ0+eLFB8TZo0UcOGDW23b7rpJjVs2FCpqak6deqUUzEPGTJEpUuXtt2OioqSj4+PraTIWQ0bNtQjjzyihg0baufOnRo6dKjOnz/v9P19fX3VoUMH7d692/ZLlCTbRL22bdtmu096erq++eYb3XHHHRo4cKDdvg4dOuiuu+7SDz/8oKSkJElSz549NW3aNLVs2dKubb169RQQEOCwnKZz5852k5mrVaumGjVq6NSpU/mar5Gf12ZBzqss3bp1s6uvvu222zRw4EBdvHjRNm/HVe8b+elnV7h+lahvv/1WaWlpGj16tN38loCAAI0ePVpWq1Vff/21S2O4dOmSJNm9ppyxZ88eHTx4UH369LGbIyJJTz/9tPz8/HKtf8/6/Lm2rNHPz08LFixQdHS0brrppjzFk9NjGIahuLg4u/eXunXrat26dbZfhuHdWD0GRULbtm3Vtm1bXb58WTt27NDPP/+sDRs26NixY/rggw9ktVo1btw4SZl156GhoapcubLdMSwWi8aMGWO7nfVz8uXLl7Vnzx4dO3ZMR48e1d69e3Xs2LEb1s3mZN++fZIyf1bP+kDOEh8fL0nZJglmlZTkxfX1zIGBgapSpYoGDx6sYcOGKSQkJMf7HjhwQD4+Pg7rPrO25bYEYF4kJSXpzz//1F133aUyZco4fLyFCxdme7y89sn1K9VklXOEhITI19fXbl/JkiVta9bnN77bb789W9usD2dnamAdHcPPz0+lS5fO84oeWee+JL3xxhv66KOPNHv2bL366qtOH6Nz585atmyZ1q1bZ0vCs0pjHCVBR44cUXJysjIyMhyu3Z6amqqMjAzFxMSoUaNGaty4sRo3bqzz58/r999/1/Hjx3XkyBHt3r3b1vZ6ufVxWlpanlf+yM9rM7/n1bWySnWuVa9ePUmZr7OePXu67H0jP/1cUH5+fqpUqZLdtt9++01SZk37oUOH7PZlrULkqveYLFnlgDeay3O9rL4/fvy4w3O5dOnSiomJkWEYslgs2fb37dtX69at09///nfNnTtXrVu3Vps2bdS8eXO7gaOCKFu2rLp166bvv/9enTp1UsOGDdWmTRu1a9dONWvWdMljwPxI2lGklC5dWlFRUYqKitLf//53ffXVV3rllVe0ePFijRo1SqVKldLFixdVsWLFGx4rNTVVb7/9tr744gvb5MHKlSurSZMmqlChghISEvIVY9ZozwcffJBjm+s/VPKz7FhMTEye75MlKSlJJUuWdPiBctNNN6lUqVK2PimorAQ0p9GmrA/7lJQUu+157ZOcRuVv9KGZ3/gcHTfrA90wjNyD/a/CWG7umWee0dKlS7V+/fo8Je0tWrRQ2bJlbUn70aNHdfDgQT3++OMO22clp7GxsQ4nxGbJOtcvXLig6dOn61//+peuXr0qi8WiqlWrqnnz5rZlQq/nij6+Vn5em/k9r651/QCCJNt7VNYvEa5638hPPxdUQEBAtm1Zz+fzzz/P8X65JdcnTpxwOBI/dOhQlS1b1uF9qlWrJkkOJ1Rf7/Dhw6pRo4ak/53Lmzdv1ubNm3O8z+XLlx1+sY+KitKiRYv00Ucf6aefftKnn36qTz/9VOXLl9eoUaNynLyfVzNmzFCdOnW0YsUKbd++Xdu3b9fMmTNVp04dTZ06Nd9zElB0kLTDtJKSknTfffepevXqDieeWSwW9e3bV6tXr9aWLVsUHx+v6tWrKzAwMMeRyuTkZNtI2euvv66lS5fq7rvv1sCBAxUREWEbqenatWu+k/bAwED5+vpqz5498vPzy9cxClvp0qV15coVXbp0KVuympqaqpSUFJct2Zg1SpvTRW6yPjBzmzRbmMwenyPnz5/Xzp07FRoamm1tdX9/fwUHB9tGZ53l5+endu3a6fvvv9f58+e1Zs0alSxZUu3atXPYPqvfevXqpTfeeOOGxx83bpw2bdqk/v37q1evXgoPD7clQN99912eYs0vT702r//CJ/0vqc16nbkqNlf1s6MvRXn5Ip/1Prtu3bobTiJ35OTJkw6/DPbu3TvHpL1169aaN2+etmzZkuuVmH/99Vf17dtXjRo10tKlS22xTps2TX369MlzrFLmrylNmzZVcnKyduzYof/7v//T119/ralTp+rWW2/NcfUlyXFfOzpn/Pz89Mgjj+iRRx5RXFyctm7davv8e+KJJ7R+/XrTfubANahph2mVKVNGly5d0k8//aSzZ8/m2tbHx8dW2xkeHq64uDiHSfe9995rq73817/+pZtvvlmzZ89Ws2bNbElZSkqK4uLiJN14NM/RT6URERHKyMhwuE72rl27NHPmTO3YsSPX4xa2rETPURz/+c9/ZBiGy35yLVOmjKpVq6YjR444nCfwyy+/SJLHfuI1e3yOHD58WCNGjNA//vGPbPsuXbqkuLg43XrrrXk+bufOnZWenq7/+7//05o1axQZGelwZFHKXHHE399f+/btc/g6+eSTT/T+++/rr7/+0sWLF7Vp0ybVqVNHkyZNsitFOnHihFJTU/M1cp4bM702r182NOvxpP+Vybgitrz2s6M+kjKTQ0cX0spp2VVHslYvcvTcjx49qhkzZmjDhg053r9Zs2aKiYnJ9i9rNN2Rhg0b6vbbb9f27dtzXTZ30aJFkmSr+8+KNauk51pXr17V66+/rk8//TTH4/3zn//UO++8Iynzy0qbNm00YcIE2y9ducXi5+enlJSUbOf/9b8W/Pnnn3r77bdtS4WGhoaqb9+++uijj9S8eXOdPn06z3NhUPSQtMPUBg4caJvM5GgkdP369frpp5/UqVMn24dTz549ZRiGZs6caVe/uWrVKh07dkwtWrSQlPnTcmpqql0NakZGhqZNm2Yb5bi2NtnPzy9brXLWpMVr1+LNWhLxtddes/30LWX+cjBx4kQtWLCgUOpK8+K+++6TlHnV0msT1cTERNuo6bXrNGeN3jhbq3293r17KyUlRa+99prdOtb79u3T4sWLVbZsWY9evKcw4/Pz88u2JnxBNWjQQKGhoVq/fr1dIpeenq5JkyYpPT1d999/f56P27p1awUGBmrp0qXat2+funTpkmPbkiVLqlu3bvrjjz9s65ZniY6O1htvvKHly5erXLly8vPzk4+Pjy5evGj3WklJSdGUKVMk5f/cyomZXptffvmlDh8+bLt95MgRffrpp6pcubJteUJXxJbXfs7qo+v7PiwsTCdOnLCrRT958qS++eYbp59zz5495evrq3feecduACU9PV1TpkzRwoUL8zRZ2hm+vr566aWXJEmjR4/W7t277fZnZGRo7ty5+u6771SlShUNHTpUUuak8mrVqumrr76yfZnK8sEHH+jjjz+21b07smXLFs2bNy/b42VNSg4NDc3xvmFhYUpPT7ctVyll/pJ2/dKhAQEBWrBggWbPnm33t01LS1NCQoLtFzZ4N8pjYGojRozQwYMHtWbNGnXu3FmRkZG6/fbblZ6erj179mjnzp0KCwuzWwu3T58+Wrt2rb755hvFxMSoWbNmOn36tNauXauqVavaJqP26NFDCxcu1P3336+OHTsqPT1dW7Zs0ZEjRxQUFKTExESdP3/eVtNcqVIlxcbG6tVXX1VUVJTat29vq1WdO3eufv/9d40aNUrNmzfX4MGD9emnn+qee+5RVFSU/P39tW7dOp06dUr9+/fPdlEPd2vSpIkefvhh2wWoskogNm7cqISEBD322GNq0qSJrX3W83zxxRfVqlUr2/rOznrssce0ZcsWfffdd4qJiVHz5s117tw5rVu3ToZhaNasWTmO6LpDYcZXqVIlHT16VGPHjlVkZGS21Yryw9fXV9OmTdPjjz+uhx56SF27dlWFChX0008/6dChQ2rbtm2e/0ZSZiIeFRWlVatWyd/f/4ZfVP7+979r165dmjFjhtavX6969erZXmslSpTQa6+9Jh8fH5UqVUqdOnXSmjVr1LdvX7Vq1UrJycnauHGjzp49q3LlyunSpUuyWq3y8XHNWJKZXpsWi0UPPPCAunTpIsMwtHbtWqWkpOiNN96w1aW7Ira89nOFChXk7++v6OhoTZ8+XZ06dVLjxo31wAMPaMqUKRo8eLC6d++utLQ0rVq1SuHh4U7/EnH77bdr3Lhxev3119W9e3e1b99e5cqV048//qjDhw+rXbt26tmzZ4H79npt2rTR1KlT9eqrr6pfv35q2rSp/va3vyk5OVm//PKLYmNjFRwcrHnz5tlKA319fTVjxgw99thjGjRokDp06KBbbrlFv/32m7Zt26Zq1arp2WefzfExn3rqKUVHR2vIkCHq0qWLKleurD/++EMbN25UjRo1cn2eDzzwgDZs2KBnnnlGPXr0kL+/v1avXq3bbrtNR44csbULDg7W0KFD9fHHH6t79+62laY2b96sw4cPa+TIkR59D4V7MNIOU/P19dW7775ru5rg3r17tWjRIi1btkypqal67rnn9PXXX9stW+jr66u5c+fqmWeeUUpKipYsWaJt27apR48e+uyzz1SuXDlJmRe2eeqpp+Tj46OlS5dq3bp1qlq1qj766CMNHz5ckrRp0ybbcSdMmKBq1app+fLltovfdOvWTV27dtWff/6ppUuX2kZWXn75Zb3xxhuqUqWKvv32W3399deqWLGiXnvttTxNDixM48eP15tvvqmqVavqu+++06pVq1S9enW99957Gjt2rF3b4cOHq379+tq6davDi8fcSMmSJfXJJ59o9OjRunr1qj777DNt27ZN7dq10xdffJHtokPuVpjxjRs3TnfccYdWr17t8AI5+dWyZUt9/vnnioyM1MaNG/XZZ5/JYrHohRde0Pvvv2+3dGVeZF1oqVWrVjdMAoKCgvTll1/qkUce0enTp/Xpp59qx44dat++vb788ku7JPO1117T0KFDdenSJS1evFibN29W3bp19dlnn+nee+9VSkqKSy8QY6bX5vDhwzV48GBt3LhRa9asUf369bV48eJsdc6uiC0v/ezv768JEyaoXLlyWrp0qe3Kq4MGDdKLL76ocuXK6fPPP9fPP/+sJ554Qi+++GKenvfDDz+sDz74QLVq1dLatWv1xRdfqESJEho/frzefffdfJ+jN9K3b1+tXLlS/fv3119//aVly5bpm2++ka+vr4YPH67vv/8+21yQxo0ba9myZerSpYt27NihRYsWKS4uToMHD9YXX3yRbXWca9WrV0+LFy9Wq1attG3bNn388ceKiYnRkCFDtGTJEocXJsvSrl07zZw5U7feequ+/vpr/fDDD+rdu7et3OZa48aN08SJE1WmTBl9/fXX+vLLL1W6dGm9/vrrevrpp/PfYSgyLIarCwkBAIBWrFihF154QS+88IIeeughT4cDoIhjpB0AAAAwOZJ2AAAAwORI2gEAAACTo6YdAAAAMDlG2gEAAACTI2kHAAAATM5rL66UdRl6TwgNDfXo4xcX9LP70NfuQ1+7D33tHvSz+9DX7lNYfZ3bFXQZaQcAAABMjqQdAAAAMDmSdgAAAMDkSNoBAAAAkyNpBwAAAEyOpB0AAAAwOZJ2AAAAwORI2gEAAACTI2kHAAAATI6kHQAAADA5knYAAADA5EjaAQAAAJMjaQcAAABMjqQdAAAAMDmSdgAAAMDkSNoBAAAAkyNpBwAAAEyuhKcDAAAAAAqTNSFeWrlExvlEWcoHSb0Gyic4xNNh5QlJOwAAALyWNSFexqwJUkK8JMmQpNgYWcdMLlKJO+UxAAAA8F4rl9gSdpv/jrwXJSTtAAAA8FrG+cQ8bTcrknYAAAB4LUv5oDxtNyuSdgAAAHivXgOl62vXg0MytxchTEQFAACA1/IJDpF1zGRWjwEAAADMzCc4RBr2nKfDKBDKYwAAAACTI2kHAAAATI6kHQAAADA5j9e0Hzp0SEuWLNHEiRN14cIFzZ8/X5cvX5bVatWTTz6pkBD7SQLPP/+8AgMDJUmVKlXSyJEjPRE2AAAA4DYeTdpXrlypH3/8UQEBAZKkxYsXKzIyUi1bttRvv/2muOqRj6EAACAASURBVLg4u6Q9LS1NkjRx4kRPhAsAAAB4hEfLYypXrqyxY8fabsfExCgxMVFTpkzRli1bdOedd9q1P3bsmFJTUzV16lRNmjRJBw8edHfIAAAAgNtZDMMwPBnAmTNnNHv2bE2bNk0DBgzQ448/rnbt2umrr75SRkaG+vXrZ2t7/PhxHTx4UB06dNCpU6c0ffp0vfPOO/L19fXgMwAAAAAKl8dr2q9VpkwZNW7cWJLUqFEjff7553b7q1SpopCQEFksFoWGhqpMmTL666+/VLFixWzHiouLc0vMjoSGhnr08YsL+tl96Gv3oa/dh752D/rZfehr9ymsvg4NDc1xn6lWj6lVq5Z27dolSfr9999VrVo1u/0bN27UokWLJEmJiYm6cuWKKlSo4PY4AQAAAHcyVdI+ZMgQbdq0SS+//LJ2796t3r17S5LmzJmjs2fPqn379rp8+bJeeeUVvfPOOxoxYgSlMQAAAPB6Hq9pLyyUx3g/+tl96Gv3oa/dh752D/rZfehr9yn25TEAAAAAsiNpBwAAAEyOpB0AAAAwOZJ2AAAAwORI2gEAAACTI2kHAAAATI6kHQAAADA5knYAAADA5EjaAQAAAJMr4ekAAAAAAHeyJsRLK5fIOJ8oS/kgqddA+QSHeDqsXJG0AwAAoNiwJsTLmDVBSoiXJBmSFBsj65jJpk7cKY8BAABA8bFyiS1ht/nvyLuZkbQDAACg2DDOJ+Zpu1mQtAMAAKDYsJQPytN2syBpBwAAQPHRa6B0fe16cEjmdhNjIioAAACKDZ/gEFnHTGb1GAAAABRvZl9S0Sc4RBr2nKfDyBOSdgAAALhMUV1S0eyoaQcAAIDrFNElFc2OpB0AAAAuU1SXVDQ7knYAAAC4TFFdUtHsSNoBAADgOkV0SUWzYyIqAAAAXKaoLqlodiTtAAAAcKmiuKSi2VEeAwAAAJgcSTsAAABgciTtAAAAgMmRtAMAAAAmR9IOAAAAmBxJOwAAAGByJO0AAACAyZG0AwAAACZH0g4AAACYHEk7AAAAYHIk7QAAAIDJkbQDAAAAJlfC0wEAAACgcFkT4qWVS2ScT5SlfJDUa6B8gkM8HRbygKQdAADAi1kT4mXMmiAlxEuSDEmKjZF1zGQS9yKE8hgAAABvtnKJLWG3+e/IO4oOknYAAAAvZpxPzNN2mBNJOwAAgBezlA/K03aYE0k7AACAN+s1ULq+dj04JHM7igwmogIAAHgxn+AQWcdMZvWYIo6kHQAAwMv5BIdIw57zdBgoAMpjAAAAAJMjaQcAAABMjqQdAAAAMDlq2gEAAFDsWf97wSmzTtYlaQcAAECxZk2IlzFrgu3KsYYkxcbIOmayaRJ3ymMAAABQvK1cYkvYbf478m4WjLQDAADA7cxUjmKcT8zTdk8gaQcAAIBbma0cxVI+KDMGB9vNgvIYAACA/7ImxMv64VvKmPmSrB++lTkaDNczWzlKr4HS9V8WgkMyt5sEI+0AAAAy3+ivNzNbOYpPcIisYyabplzHEZJ2AAAAKffR32HPeSYmL2XGchSf4BBT/50pjwEAAJD5Rn+9WhEoRzEbRtoBAABkztFfb1UUylHMhqQdAABAyhzljY2xL5Fh9LfQmL0cxWxI2gEAAMTor7cw0/rvruTxpP3QoUNasmSJJk6cqAsXLmj+/Pm6fPmyrFarnnzySYWE/K+TrVarPvzwQx07dkx+fn4aPny43X4AAICCYPS3aPPmFYA8OhF15cqVmjdvnq5evSpJWrx4sSIjIzVp0iT169dPcXFxdu1/+eUXXb16VdOmTdODDz6oRYsWeSJsAAAAmJHZ1n93IY8m7ZUrV9bYsWNtt2NiYpSYmKgpU6Zoy5YtuvPOO+3aHzhwQA0aNJAkhYeH6/Dhw26NFwAAAOblzSsAebQ8pnnz5jpz5oztdkJCgkqXLq1XXnlFX331lVauXKl+/frZ9l+5ckWBgYG22z4+PsrIyJCvr2+2Y4eGhhZu8Dfg6ccvLuhn96Gv3Ye+dh/62j3oZ/cp7n19rkpVJcfszbY9sEpV3ezivnF3X3u8pv1aZcqUUePGjSVJjRo10ueff263v1SpUrpy5YrttmEYDhN2SdlKa9wpNDTUo49fXNDP7kNfuw997T70tXvQz+5DX0vWzvdJ+3ZnWwHoSuf7XNo3hdXXuX0RMNXFlWrVqqVdu3ZJkn7//XdVq1bNbn9ERIRt/8GDB3Xrrbe6PUYAAACYk09wiCxjJsvSLEqKqCtLsyhZvGASqmSykfYhQ4Zo3rx5Wrt2rQIDAzV69GhJ0pw5c9S/f381bdpUv/76q15++WUZhqGRI0d6OGIAAACYibeuAGQxDMPRxb+KPMpjvB/97D70tfvQ1+5DX7sH/ew+9LX7FPvyGAAAAADZkbQDAAAAJkfSDgAAAJgcSTsAAABgciTtAAAAgMmRtAMAAAAmR9IOAAAAmBxJOwAAAGByJO0AAACAyZG0AwAAACZH0g4AAACYHEk7AAAAYHIk7QAAAIDJkbQDAAAAJkfSDgAAAJhcCU8HAAAAgKLLmhAvrVwi43yiLOWDpF4D5RMc4umwvA5JOwAAAPLFmhAvY9YEKSFekmRIUmyMrGMmk7i7GOUxAAAAyJ+VS2wJu81/R97hWiTtAAAAyBfjfGKetiP/SNoBAACQL5byQXnajvyjph0AAAAO3XCSaa+BUmyMfYlMcEjmdrgUSTsAAACycWaSqU9wiKxjJrN6jBuQtAMAACC73CaZDnvOtsknOMTuNgoHNe0AAADIhkmm5kLSDgAAgGyYZGouJO0AAADIrtfAzEml12KSqcdQ0w4AAIBsmGRqLiTtAAAAcIhJpuZBeQwAAABgciTtAAAAgMmRtAMAAAAml6ea9oSEBG3cuFF9+/aVxWJRYmKi3nvvPR04cEAVK1ZU//791apVq8KKFQAAACiWnB5pP378uMaOHavly5fr3LlzkqT58+dr//79CgoKUlJSkt599139+uuvhRYsAAAAUBw5PdL+1VdfKT09XaNGjVJQUJDOnj2r3bt3q0aNGpo6daqSkpL0/PPP69tvv1W9evUKM2YAAABAkmRNiC8Wy1I6nbT//vvvatGihVq3bi1J+s9//iNJat26tXx8fFS2bFk1bdpUmzdvLpxIAQAAgGtYE+JlzJogJcRLkgxJio2Rdcxkr0vcnU7ak5OTdfPNN9tu7969W5JUv3592zY/Pz9ZrVYXhgcAAABkun5U3Ui5YkvYbf7bxtvWl3c6aQ8ODtaJEyckZSbwv/32m4KDgxUaGmprs3//fgUHB7s+SgAAABRrDkfVS/g5bGucT3RfYG7idNJer149rV27VgsWLNCff/6ptLQ0RUZGSpJiY2P19ddfKzY2Vv369Su0YAEAAFBMrVySfVQ9/arDppbyQW4IyL2cTtr79++vEydOaN26dZKksLAw3XvvvZKkLVu2aPv27WrUqJG6d+9eOJECAACg2Mpx9NzPT7p6TfIeHCL1GuieoNzI6aQ9MDBQEyZMUGxsrKxWq2rUqCGLxSJJioyMVJMmTVS7du1CCxQAAADFl6V8UGZJzPXubChLQClWj7leWFiYU9sAAAAAl+k1UIqNsS+RCQ6Rpd8wr0zSr+d00p5VFuOMjh075isYAAAAwBGf4BBZx0wuFmuyO+J00r5gwQKnD0rSDgAA4H08fSEjn+AQr1vK0VlOJ+3Dhg1zuD01NVXx8fHaunWrbrvtNj344IMuCw4AAADmUJwuZGRGTiftnTp1ynV/9+7dNX78eB0+fFjh4eEFDgwAAAAm4mjJRS+9kJEZ+bjqQCEhIWrRooXWrFnjqkMCAADAJHJactEbL2RkRi5L2iUpICBACQkJrjwkAAAATCCnCxZ544WMzCjPSz7mJD4+Xlu2bFHFihVddUgAAAC4yQ0nmeaw5KI3XsjIjJxO2l9++WWH2w3DUEpKiuLi4mS1WtWrVy+XBQcAAIDC58wk0+K+5KKnOZ20Hzp0KNf9VapUUYcOHdS9e/cCBwUAAAA3cnKSaXFectHTnE7aFy9e7HC7xWKRr6+vLBaLy4ICAACA+zDJ1PycTtr9/PwKMw4AAAB4iKV8UGZJjIPtMIcck/Z169YpLCxMYWFhttvO4oqoAAAARQiTTE0vx6R9wYIF6tu3ry1pX7BggdMHJWkHAAAoOphkan45Ju3Dhg1TjRo17G4DAADAOzHJ1NxyTNo7deqU620AAAAA7lHgiyvFx8fr4MGDCg4OVu3atV0REwAAAIBr5Clp//bbb7VmzRrNmjVL/v7+2r59u2bNmiWr1SpJatCggcaNG6cSJVx2oVUAAADk0Q2vbooix8fZhhs2bNCSJUuUmpqqpKQkGYahf/7zn7JYLOrdu7datWql3bt369tvvy3MeAEAAJCLrKubGtGbpJi9MqI3yZg1ITORR5HldNK+fv16VapUSbNnz1ZQUJBiYmJ09uxZtWrVSv3799fo0aN15513asuWLYUZLwAAAHKT29VNUWQ5Xcfy559/qmPHjipdurQkadeuXZKkJk2a2NrUrFlTq1atylMAhw4d0pIlSzRx4kTFxsZqxowZqlKliiSpc+fOatmypa2tYRgaPny4bX94eLgefPDBPD0eAACAN+Pqpt7J6aTd19fX7vbu3bvl4+Ojv/3tb7Ztly9fVmBgoNMPvnLlSv34448KCAiQJB05ckTdu3dXjx49HLY/ffq0qlevrvHjxzv9GAAAAMUJVzf1Tk6Xx1StWlV79uzR1atXdfDgQR09elQRERG2kfezZ88qOjpat9xyi9MPXrlyZY0dO9Z2OzY2Vjt37tSrr76quXPn6sqVK3btY2Nj9ddff2nSpEmaPn264uLinH4sAACAYqHXwMyrmV6Lq5sWeRbDMBx9Gctm06ZNev/991WmTBlduXJFGRkZGjNmjJo3b67Vq1friy++UHJysl544QU1aNDA6QDOnDmj2bNna9q0adq4caNuu+02hYWFacWKFUpKStKQIUNsbffv368LFy6oRYsWOnDggP75z39q+vTpeX/WAAAAXuxq/Eld/HSuMhIT5BsUrLKDR8gvpKqnw0IBOF0eExUVJcMw9N1336lcuXLq2LGjmjdvLklKTk5WmTJl9MQTT+QpYb9e06ZNbSP3TZs21cKFC+3216hRw1amU6tWLSUmJsowDFkslmzH8uQofGhoKL8CuAH97D70tfvQ1+5DX7sH/ew+9n1tkQaOlCSlS0qwSuLv4DKFdV6HhobmuC9PC6q3bdtWbdu2zba9e/fuuu+++/Ic2PWmTZumRx55RDVr1tTevXsVFhZmt3/ZsmW66aab1KtXLx09elQVK1Z0mLADAAAA3sQlV0Hy9/d3xWE0bNgwLVy4UCVKlFD58uX1+OOPS5KmTp2q8ePH695779V7772nnTt3ytfXVyNHjnTJ4wIAAKDwcLGngsuxpv3ll1/O3wEtFk2ZMqVAQbkC5THej352H/rafehr96Gv3YN+dh+z9nXWxZ7s1o4PDpFlzOQim7ibqjzm0KFDLg8EAAAAxUxuF3sa9pxnYiqCckzaFy9e7M44AAAA4IW42JNr5Ji0+/n5uTMOAAAA5ENWvfjpK0mylipjunpxLvbkGnmeiLpv3z5t3LhRx44dU1JSkubOnauffvpJCQkJ6tq1q8smpQIAACB319aLp2VtjI2R1Uz14r0GSrEx2WraudhT3uQpaf/kk0+0atWqbNsPHTqkf//739q5c6defPFFlSxZ0mUBAgAAIAdFoF7cJzhE1jGTWT2mgJxO2jds2KBVq1apUaNGGjBggH7++WctX75cktS7d29duHBBW7du1ffff++SNdsBAIB3Ydk/1ysq9eI+wSGm+RJRVPk423Dt2rWqVq2axo4dq1tuucXuokZly5bV6NGjVb16dW3durVQAgUAAEVXVhmHEb1JitkrI3qTjFkTMhN55FtOdeHUi3sfp5P2EydOqGHDhvLxyfkuderU0ZkzZ1wSGAAA8CK5lXEg/3oNzKwPvxb14l7J6fIYf39/JSUl5drm/PnzTEQFAADZFJUyjqLm2npx/ytJSjPh6jFwDaeT9vDwcEVHR6tfv36qUKFCtv1nzpzRL7/8otq1a7s0QAAAUPSx7F/hyaoXr2TSK6LCNZwuj+nTp49SU1P14osvas2aNTp9+rSkzJVjVq9erZdffllpaWm69957Cy1YAABQRFHGARSI0yPtNWvW1DPPPKO5c+dq4cKFtu0vv/yypMzymZEjR6pWrVqujxIAABRpLPsHFEye1mlv2rSp6tWrp23btik2NlbJyckKCAjQrbfeqhYtWuimm24qrDgBAEARx7J/QP7l+YqoAQEBatu2rdq2bVsI4QAAAMAdWDe/aHGqpn3//v1KTHQ8u/uDDz7Qtm3bXBoUAAAACg/r5hc9uSbtycnJmjRpkiZNmqRdu3Zl23/27FmtX79es2bN0iuvvKLz588XWqAAAABwEdbNL3JyTNrT0tI0ceJE7d+/X2FhYapSpUq2NmXLltWTTz6pmjVr6uDBg5o2bZrS09MLNWAAAAAUjBnXzbcmxMv64VvKmPmSrB++xaj/dXKsaV+9erWOHTume+65R0OGDHHYxt/fX23atFFkZKQ++OADbdy4UWvXrlW3bt0KLWAAAAAUjNnWzc8q18ka/TckKTZG1jGTqbP/rxxH2n/66SdVqVJFgwcPvvFBfHz0yCOPKCgoSFu3bnVpgAAAAHAxs62bT7nODeU40h4XF6c2bdrIYrE4dSB/f3/VrVtX0dHRLgsOAAAArme2dfNzLNc5c0rWD98yRYyeluuSjyVLlszTwcqWLSvDcPRjCwAAAMzETOvm51Suo7jjMo4clETJTI7lMcHBwYqLi8vTwU6ePKmbb765wEEBAACgGHFUrlMyQEpNsd9WjEtmckza69atq927d+vMmTNOHej06dPavXu3qlev7rLgAAAA4P18gkNkGTNZlmZRUkTdzP+G3uawrSdXuPGkHJP2zp07y2KxaMaMGfrrr79yPUhiYqLeeOMNWa1WdenSxeVBAgAAwLv5BIfIZ9hz8h07TT7DnpOlkuMSGE+tcONpOda0h4aGatCgQfrnP/+p0aNHq127dqpXr55CQ0MVEBCgy5cvKz4+Xnv27NGmTZuUlpamXr16KTw83J3xAwAAwBv1GijFxtivKuPJFW48LNeJqN26dVPJkiW1aNEirVmzRmvWrHHYLjAwUAMGDGB9dgAAALiE2Va48bRck3ZJ6tChg5o0aaKffvpJu3fv1rlz55ScnKwyZcqocuXKatCggZo2baoyZcq4I14AAAAUE2Za4cbTbpi0S5lLOXbp0oV6dQAAAMADcpyICgAAAMAcSNoBAAAAkyNpBwAAAEyOpB0AAAAwOZJ2AAAAwOScWj0GAAAUDmtCPOtQA7ihAo20r1ixQoMHD3ZVLAAAFCvWhHgZsybIiN4kxeyVEb1JxqwJmYk8AFyjQEl7RkaG0tLSXBULAADFy8ol9pdolzJvr1zimXgAmBY17QAAeIhxPjFP2wEUX9S0AwDgIZbyQTJy2A5IzHnA/5C0AwDgKb0GSrEx9iUywSGZ21HsZc15yDo/DEmKjZF1zGQS92KoQEl7o0aNVL58eVfFAgBAseITHCLrmMmMpMLm2pF1nT0tnTtj3yBrzsOw5zwTIDymQEl7WFiYwsLCXBULAADFjk9wCAkYJGUfWc8Jcx6KJyaiAgAAmIGj1YQcYM5D8URNOwAAgBvcaFKpUyPozHkotkjaAQAACpkzk0pzWk1IN1eSKlZmzkMxR9IOAABQ2HK7kFbWnIYcVhOysFoMRNIOAABQ6Jy5kBarCSE3eU7ak5OTFR0draNHj+ry5csaNWqUDh48KB8fH9WsWbMwYgQAACjSnL2QFqsJISd5Stq3bdum+fPnKzk52bZt1KhR2rFjh1auXKl77rlHQ4YMcXmQAAAABeXRq4tyIS0UkNNJ+4EDBzR79mwFBQWpX79+io2N1aZNmyRJd911l3bs2KHvv/9eYWFhioyMLLSAAQAA8srTVxel9AUF5fQ67cuXL1eZMmU0ffp0denSRcHBwbZ9tWrV0tSpUxUUFKTVq1cXSqAAAAD5lttEUDfxCQ6Rz7Dn5Dt2mnyGPUfCjjxxOmk/dOiQWrRoobJlyzrcHxgYqKZNm+rkyZMuCw4AAMAVnJkICpiZ0+UxGRkZslgsN2yTkZFR4KAAAO7l0VrfQuStzwt55+xEUMCsnE7ab731Vu3evVvp6ekqUSL73VJSUrRr1y7deuutLg0QAFC4PF3rW1i89XkhnwpxIihfDuEOTpfHdO3aVfHx8XrzzTd14sQJWa1W277jx4/rjTfe0NmzZ9WxY8dCCRQAUEhMUOtbKLz1eSFffP57kSJLsygpoq4szaJcctGirC+HRvQmKWavjOhNMmZNyEzkARdyeqQ9MjJSR48e1Xfffafdu3fbtg8ePFhpaWmSpA4dOqht27YuDxIAUHi8tdbXW58X8q9Q1kB35kqngAvkaZ32QYMGqVGjRtqwYYOOHDmiy5cvKyAgQLfddpvatm2rBg0aFFacAIBC4q21vt76vGAuOX45PHNK1g/fomQGLuN00r527VqFh4erdu3aql27dmHGBABwJ2+96Iu3Pi+YSk5fDhV3XMaRg5KYTwHXcLqm/bPPPtOKFSsKMxYAgAcUVq2vp3nr84LJ9BqY+WXwWiUDpNQU+23Mp0ABOT3SbhiGbr755sKMBQDgIYVS62sC3vq8YB6OrnRqnImXjsRka8t8ChSE00n7Pffco1WrVqlRo0aqU6eOywI4dOiQlixZookTJyo2NlYzZsxQlSpVJEmdO3dWy5YtbW3T0tL07rvv6uLFiypVqpSefPLJHC/2BAAA4A7Xfzm0fviWDAdJO/MpUBBOJ+3nz59XiRIlNGXKFAUGBio4OFj+/v7Z2lksFk2ZMsWpY65cuVI//vijAgICJElHjhxR9+7d1aNHD4ft165dq1tvvVUPPPCAtm7dquXLl+vhhx929ikAAADYFNr66synQCFwOmlft26d7f+Tk5N17NixAj945cqVNXbsWM2ZM0eSFBsbq7i4OO3YsUMhISF66KGHVKpUKVv7AwcOqGfPnpKkhg0bavny5QWOAQAAFD+FefEtRyUzrB6DgnI6aV+8eLHLH7x58+Y6c+aM7XbNmjXVoUMHhYWFacWKFVq2bJmGDBli23/lyhUFBgZKkgICApScnJzjsUNDQ10eb154+vGLC/rZfehr9/H2vr4af1IXP52r9MQElQgKVtnBI+QXUtUjsXh7X5uFO/o5r+fVuSXvK9nB+uql1q7QzeOmFjyg0FCp/l0FP06eH5Zz2l3c3ddOJ+1+fn6FGYckqWnTpipdurTt/xcuXGi3v1SpUkpJyZyNnZKSYmvrSFxcXOEFegOhoaEeffzign52H/rafby9r68f3UyTlLxvt0dWdfH2vpYKsfwjD9zRz/k5rzJOnXS4PfnUSaUW0fOiOJzTZlFYfZ3bFwGnk/Y9e/Y4/YD169d3uu21pk2bpkceeUQ1a9bU3r17FRYWZrc/IiJCO3fuVM2aNbVr1y7VqlUrX48DwHzMkFzADbh6pNu4svzD9K/PfJxXXHwLRY3TSftrr73m9EG/+OKLfAUzbNgwLVy4UCVKlFD58uX1+OOPS5KmTp2q8ePHq3PnzvrHP/6hV155RSVKlNDTTz+dr8cBYC6FWVsKc8nx6pEshed6LvqCVBRen/k6r5gsiiLG6aS9Z8+eslgs2banpqYqPj5ev/76qyIiItS5c+c8BVCpUiVNmzZNkhQWFqapU7PXkb388suZwZYooWeffTZPxwdQBDD6Wmwwuuk+LvuCVARen/k5r5gsiqLG6aR94MDcv3keOnRIkyZNUseOHQscFIDihdHXYoTRTbdx1RekIvH6zOd5ld+Lb5m+XAheyemk/UbuuOMONW/eXCtXrlRkZKSrDgugGPD20Vc+4P+H0U03ctEXpKLw+nTneVUUyoXgnVyWtEtS+fLl9fPPP7vykACKAy8efeUDPrv8jm4ib1yWyBaR16fbzqsiUC4E7+SypD0pKUnR0dEqX768qw4JoJjw6tFXPuDhQa5IZL369ZkPRaJcCF7J6aR99uzZDrcbhqGUlBQdOnRISUlJ6t27t8uCA1B8eOvoKx/w8Abe+vrMj6JQLgTv5HTS/tNPP+W6PyAgQHfffbf69OlT4KAAwFvwAQ94mSJSLgTv43TSPmvWrJwPUqKEgoKCVKKES0vkAaDo4wO+SGLyMHJCuRA8xeksOzk5WUFBQQoKynl06NSpU4qNjVWrVq1cEhwAFHV8wBc9TB7GjVAuBE/wcbbhSy+9pA0bNuTaZsOGDZo3b16BgwIAb+ITHCKfYc/Jd+w0+Qx7jsTP7HKbPAwAHpLjSPv27dv1xx9/2G3bu3ev0tLSHLZPT0/X5s2b5e/v79oIAQBwIyYPAzCjHJP2qlWr6p133lFGRoZt24EDB3TgwIFcD3j//fe7LjoAANyMycOexXwCwLFck/bJkycrKSlJkjR9+nS1bt3a4dVOLRaLfH19FRQUpNDQ0MKLFgCAwsbkYY9hPgGQs1wnotasWdP2/7169VK9evVUp06dQg8KAABPYfKwB3ExMiBHTq8e8+CDDzrVLjExMdcVZgAAMDtWB/EM5hMAOcvTwup79+7V1q1bdeHCBVmtVtt2wzCUkZGhS5cu6fjx4/r8889dHigAAPBuzCcAcuZ00r5jxw7NnDlThuHo5ZTJ399f9evXd0lgAACgmGE+AZAjp5P27777ThaLRSNHjtSdd96pGTNmqEaNGurTp49OnDihJUuWKCEhQY8++mhhxgsAALwU8wmAnDmdtB87dkxNmjRRmzZtJEm1a9fWvn37VLFiRVWsWFFhYWEaM2aMli9frhEjRhRawAAAwHsxnwBwD6kLWwAAIABJREFUzOmkPS0tTSEh//umW7VqVf3www9KT09XiRIlVLZsWTVu3FgHDx4slEABADfGGtcA4J2cTtrLlStnW7NdkipXriyr1aoTJ07o9ttvt7U5e/asy4OEa/BhDng31riGt+LzC5B8nG1Yq1YtRUdH68yZM5Kk2267TVLmBNUshw4dUunSpV0cIlwh68PciN4kxeyVEb1JxqwJmW+EALxDbmtcA0UUn19AJqeT9l69eiklJUXPPvustm/frgoVKqhhw4ZasWKF5syZo2nTpmn//v1cfMms+DAHvB5rXMMr8fkFSMpD0n777bfrlVde0R133CF/f39J0iOPPKLg4GBt3rxZv/76q26//XYNHMiyTGbEhzng/XJay5o1rpEba0K8rB++pYyZL8n64VumG8Hm8wvI5HRNe2JiomrVqqVXX33Vtq1SpUqaNWuWYmNj5efnp1tuuUU+Pk5/D4AbccEKFBfFuvaVNa6RR0VhHoSnP7+K9XsKTMXppH3ChAkKDw/X6NGj7bb7+PioZs2aLg8MLsaHOYqBopCAFCbWuEae5VZ6YpZlFz34+VXc31NgLk4n7X/99ZeCghiVLar4MEexUBQSkELGGtfIi6JQeuLRzy/eU2AiTiftERER2r9/v21ddhQ9fJjD2xWFBAQwE0+XnjjLU59fvKfATJzOvrt27aoFCxbo2Wef1V133aVKlSrZJqRer2PHji4LEACcVVQSEMA08ll6UlzqvHlPgZk4nbTPnDlTknThwgWtWrUq17Yk7QA8grkbQJ7kp/SkWNV5m/Q9pbh8aYI9p5P2Rx99VBaLpTBjAYACYe4GkHd5Lj0pRnXeZnxPye1Lk0JDPRYXCp/TSXvnzp0LMw4AcAnmbgCFq7jVeZvuPSW3L0317/JMTHCLPM8oNQxD+/bt09GjR3X58mX169dPJ06cUGBgIKvLAADg5XKq81ZAKVk/fMs0I9Leqrh9acL/5ClpP3DggP7xj3/ozJkztm39+vXT1q1btXLlSg0ePFhdu3Z1eZAAAMAkHNV5V6go/XlERmKCJEo2ChOTY4svp5P248ePa9q0afL19VX37t0VHx+vHTt2SJKqV6+uMmXK6JNPPlGVKlXUoEGDQgsYcIRJOQDyi/ePvHFU522kXJH2bLdvSMlG4TDp5FgUPqeT9i+//FK+vr56/fXXFRISomXLltmS9qZNmyosLEzPP/+8vv32W5J2uFWxWskAgEvx/pE/19d5Z8x8yWE7SjZcz4yTY+EeTift+/fvV4sWLRQS4vikqFixolq0aKHo6GiXBQc4pRitZAAgd3keNef9wyUo2cif/P7KY7rJsXALp5P2tLQ0lSpVKtc2fn5+Sk1NLXBQQF4wKQeAlL9Rc94/XISSjTzjVx7klY+zDUNDQ/Xbb7/JMBzOGVdGRoZ+/fVXValSxWXBAc7IaSSHER6gmMlt1DwHvH+4hk9wiCxjJsvSLEqKqCtLsyhZSD5zl4/zFcWb0yPt7dq10yeffKL58+dr6NChdvuSkpK0cOFCnTx5UkOGDHF5kMC1sn5OPH0lSdZSZWREdmaEB0D+Rs0LeYS4OE1ypWQjb/iVB3nldNLepUsXHThwQBs3btSmTZvk7+8vSXrmmWd05swZZWRkqEGDBiz5iEJ17c+JaVkbY2OkIU/JsmVtsfhgBArKWxPJ/NRVF+akPsofkBvmASCvnE7aLRaLxowZox9//FEbNmzQkSNHJElnz55V9erVFRUVpY4dO8rHx+mKG+D/27vz6Kjq84/jn5ksJJAiYkhCxIoVScQttZbFU9m16nGpCwpSlFPREuSAKIUi6MkPxO0IogdUNBWwjWs9Ci5VesCiuOGCAmoQQeCwBALKEghkmfv7I2bMMgl3kpm7vl//aCaXyXeeuTP3ud/7fJ8bvSYuJwZWLlWQGR7gmDydSLZw1jxuM8QsckVzWAeAKEV9R9S+ffuqb9++kqRQKESSDktxORFoJQ8nkk5rhcf3FZrjtP0Vzhd10i5JP/zwg7Zs2aLDhw+rffv26tatW5OtIIFY4nIivKrhWo14Hby9nkg6qa6a7ysci5P2VzhfVEn7N998o8LCQm3fvr3R73Jzc3XLLbeoS5cuMRsc0AiXE+FBTa3ViEfJComkhfi+AhBDppP277//Xvfdd5+qqqrUq1cvdevWTR06dNDhw4e1fv16ffzxxyooKNC9997LrDvipu7lxOTyMlXEcUYSsIyVJSskkpah/AFALJlO2l988UVJUkFBgXJzc+v97uKLL9bgwYM1c+ZMvfTSSxo3blxsRwnUUXs5MSM7Wzt27LB7OECrWVmyQiJpLcofAMSK6aR9w4YN6t27d6OEvdYZZ5yhXr166auvvorZ4ADAD6wuWSGRBAD3Md36JSEhQWlpac1u86tf/UqhUKjVgwIAX7lyeE2JSl2UrAAA6jCdtPfp00fvv/++9u7dG/H3Bw4c0KpVq9SnT5+YDQ4A/KDuLeDbnP07bgEPAGjEdHnMpZdequ+//16TJk3SZZddptNPP10dO3ZURUWFNm7cqNdee01VVVXKy8trVCJzzjnnxHzgAOAlrNUAADTHdNI+YcKE8P+/8MILTW43a9asRo/VLmIFAMDPavvxswgYQLRMJ+1XXHGFAoFAPMcCAGgGCZ+zHev9qduPX1LN4uM49eMHnxd4j+mkffhwFkQBgF1I+JzN1PtjZT9+n+PzAi8yvRAVAGCj5hI+2M/E+2NlP36vC5WWKFQ4S9UPT1WocFbNrHpdfF7gQaZn2iVpzZo1WrlypUpLS1VZWRlxm0AgoBkzZsRkcACAGiR8zmbm/bG6H79XmZlF5/MCLzKdtC9btkxPPfVUPMcCAI5mZ40sCZ+zmXp/rhwubVpffwaYfvzRM1FmxOcFXmQ6aX/jjTeUlpamcePG6bTTTlNSUlI8xwUAjmJ7jSwJn7OZeH+CnbIUmjCdxZGtZGoWnc8LPMh00l5aWqoLL7yQnusA/MnmRYStSfjoohF/Zt+f2n78flC73+0qL1MoNS1m+52ZWXROkOBFppP27OxslZWVxXMsAOBYTqiRbUnCZ/sVAh/xU0J+LHX3u4raB2O135mcRef9gNeY7h5z9dVX68MPP9S6detiOoANGzaooKCg3mMrV67U1KlTI24/adIkFRQUqKCgQI8//nhMxwIATWmqFtbxNbJ00YAd4rjfBTtlKTBhugK9+kk5ZynQq58CnITCB0zPtPfu3VsbNmzQjBkz1LVrV3Xq1CliXXsgENC4ceNMPefixYv13nvvKSUlJfzY5s2btXz58ojbV1TUnK83TPIBIO5cWiPrhCsE8J9473fMosOPTCftH330kd58801JNYn15s2bm9zWbNKemZmpiRMnau7cuZKkgwcPqqioSCNHjtT8+fMbbb9lyxYdPXpU9957r6qrqzVs2DB1797d7EsAgBZza40sXTRgB/Y7IPZMJ+0vv/yyEhMTNXz4cOXk5NSbHW+p3r17a/fu3ZKkUCikJ554QjfddJOSk5Mjbt+mTRtdfvnlGjRokHbu3Kn7779fc+bMUUJCQqvHAqBlzCxy9MpCSFfO7rn0CgHqc91niP0OiDnTSfvu3bvVv39/XXLJJXEZyKZNm1RSUqLCwkJVVlZq27ZtWrhwoUaOHBnepnPnzsrKylIgEFB2drbS0tL0008/KT09vdHzZWdnx2WcZtn99/2COFsnUqwrS7ar9LHpqi7ZJqlmkWPClo3qNHOekrJONL0N6ovpfp2drcoH5uvAP59Q9Y+lSujYSe1H5BP7n7nhO8SVnyH2O9u4YZ/2CqtjbTppT09Pl2FEutgVG926ddPs2bMl1ZwgPProo/USdkl69913tXXrVo0aNUo//vijysvLdfzxx0d8vh07dsRtrMeSnZ1t69/3C+JsnaZiHSqcLePnRKJWdck27XpqtoI/z0ib2Qa/iGa/Nj/7GpCGj5EkVUkqDUnis+Oa7xD3foZq9rvaOLPfxZ9b9mkviFesmzsRMN095uKLL9aHH36obdu2HXvjGJs7d6727NmjgQMH6tChQ7r77rs1Z84c5efnUxoD2MjMYjMWQsZHbUs945MV0vq1Mj5ZIeORe2oSeXgKnyEAUhQz7R06dFBGRoYmT56sM888U1lZWWrTpk2j7QKBgIYNG2Z6ABkZGZo5c2azj40dOzb8/+PHjzf93ADiy8xiMxakxYnNN3uCdfgMAZCiSNofeeSR8P9/+eWXzW4bTdIOwMXMLDZjQVpcMPvqI3yGACiKpH3KlCnxHAcAFzLTBtGtrRLtdqx6dSfMvrquo4lL8RkCIEWRtOfl5cVzHABcykwbRFe2SrRR3VvASzXdQhrdAt7m2VdTY0TM8BkCYHohai3DMLRu3Tq98cYbeuGFFyRJ27Zt048/ckkWAGLCxC3gbb+VexxvUw8AaMz0TLskFRcXa968eeEbIknS0KFD9cEHH2jx4sUaMWJE3Pq4A4BfmK1Xt3P2lZp6ALCW6Zn2rVu3aubMmTp48KAuu+wynXfeeeHfnXLKKUpLS9PChQuPuUgVANC8purSndQtxA1jBAAvMZ20v/TSS0pISNADDzygESNGqGvXruHf9ezZU/fdd5/atWunJUuWxGOcAOAfVw6vqU+vy2ndQmI4xlBpiUKFs1T98FSFCmfRax4AIjBdHvPNN9+oT58+ysqKXC+Znp6uPn366JNPPonZ4AB4l5WdR9zW5cQN3UJiNUYWtAKAOaaT9oqKCqWmpja7TVJSko4ePdrqQQHwNisTNbcmhW7oFhKTMXKTKAAwxXR5THZ2ttatWyfDiNQZWKqurtaaNWvUuXPnmA0OgEdZ2XmELieO5pYFrZTwALCb6Zn2AQMGaOHChZo/f75uuummer8rKyvTM888o+3bt+vGG2+M+SCBaLmtHMJvrEzU3JIUekW0nz0n3CTqWKy+WsP3F4BITCftF198sYqLi/Xuu+9qxYoVSk5OliTdfvvt2r17t6qrq5WXl0fLR9jOreUQfmJlouaGpNArWvTZs/kmUaZYWMLD9xeAppgujwkEApowYYJuu+025eTkhB/fs2ePTjnlFN18882aPHmygsGo79cExBblEM5nZXcUN3Ri8YoWfPZsv0mUCZZereH7C0ATorq5kiT17dtXffv2lSSFQiGSdEhy1uVcyiGcz8ruKG7oxOIVLf3sOX3RrZVXa1rz/eWk72EAsddk0j527FhdeumluvTSS5v8xyTskJx3OZdyCHewMlFzelLoFZ797FlYwtPSGJr9HiaxB9yryaS9tLRUhw8ftnIscCuntWxzQ40s4EUe/exZerUmihjWTcC1Z5e0d3f9DRp8DzttggVAdKIujwEaclo5CuUQgD28/Nmz6mqN2Rg2TMCbUu972GkTLACiQtIeR365DOnES+KUQwD24LPXeqZiGCkBj6Du97DTJlgARKfZpP3QoUPas2dP1E+anp7e4gF5ha8uQ3r0kjgAOJWpRLvB97ATJ1gAmNds0v7WW2/prbfeiuoJA4GAXnjhhVYNyhN8dBnSy5fEAcCJmkrAdUKGlJ4Z+XuYCRbA1ZpN2tPT05WRkWHVWDzFb5chuSRuH7+UYQGoo4kEvLke90ywAO7WbNI+YMAAXXvttVaNxVO4DAkr+KoMC0BYSxNwJlgA92IharxwGRJWaKIMy3ixUKGUVGbTAA8jAQf8haQ9TrgMCSs0WW71zWoZlZU120jMvgMA4HIk7XHELAjiodENVSL5OWEP8+giaCdgTYF9iD0AP2kyab/22mvVo0cPK8cC4Bgi3lAlmCCFqn/5OTFJqqps9G+9ugjaTqwpsA+xB+A3TSbtQ4YMsXIcQNx5YlYuUg17qLpemzfjSLn01apG/9Sri6BtfV991NrVcYg9AJ+hPAa+4JVZuSZny9MzlTBxpqSfX+uOrb5YBG33++q31q5OQuwB+E3Q7gEAlmhuVs5Fmpotr/t48OdezYFe/aScsxTo1a/Z3s2uZvP7aub98LtQaYlChbNU/fBUhQpn1VwZiQFiD8BvmGmHL7RmVs5RZTUmW4n6ZRG07bOttHZtVlyvhBB7AD5D0g5faOnNruwuv2iIVqL12X0TM96PY4hj3bkTYu+oE3oAnkfSDn9o6aycAxe7+WUW3RQHzLbyfjQt3ldC7Iy9007oAXgfSTt8oaWzcraXX6BZTphtbQm3ztBGO267r4TElQNP6AF4G0k7fKMls3KeTjpayGkJp9tmut06Q9uicTvgSki8cEIPwGok7UBzPJx0tIRbE85IbDv5cOsMbQvG7dYrIWZwQg/AaiTtQDO8nHS0iFsTzgbsPPlw6wxtS8fttishpnFCD8BiJO1oEaeVSMSTZ5MOE2rf513lZQqlpsnYHbnHttMTzkZsPPlw6wytW8cdL5zQA7AaSTui5qUSCTSt7vtcUftgm5SI27otcbN1ttutM7RuHXcc+fmEHoD1SNoRPY+USOAYIr3PR4/UJO5Hj/zymAsTNztnjd06Q+vWcQOAV5C0I2purclFdJp8P7N/rUBGZ3cnbjbPGrt1htat4wYALyBpR9SobfWHJt/njM4KujxxY9YYAOA2JO2IHrWt/uDx95lZYwCAm5C0I2rMUvpD3fc5ubxMFalpvM8AANiEpB0twiylP9S+zxnZ2dqxY4fdwwEAwLeCdg8AAAAAQPOYaUfc+OkGTLFCzAAAQCQk7YgLbsAUPWIGAACaQnkM4qO5GzD5VKi0RKHCWap+eKpChbNqZtXrImYAAKAJzLQjLrgBU31mZtGJGdA0SscA+B1JO+KCGzA10Nws+s9deLwUs3glWFYnbiSKzkDpGACQtCNePH5jnmiZmkX3SMzilWBZnbiRKDqIiZNeAPA6atoRF8FOWQpMmK5Ar35SzlkK9OqngI+TnaZmy+s+7pmYxas23+qaf5+vMTjmGgwLUToGAMy0e4YTL+NzA6Y6TM6ieyFm8UqwrE7c/JwoOu0qg5dKxwCgpUjaPcBpB1g0FuyUpdCE6Y47sWooFid/8UqwrE7cfJ0oOq0cxSOlYwDQGiTtXuC0AywicvosenMnf8rONv9E8UqwrE7cfJwoOu0qg1tOegEgnkjaPcBpB1i4VHMnf+eca/pp4pVgWZ24tfTvObFULVpOvMrg9JNeAIg3knYPcOIBFu4Ty5O/eCVYVidu0f49z5Sq+fgqAwA4Fd1jvODK4TUH1Lo4wCJKZjrc4Bg80nHGM52MAMBDmGn3AOo9ERPMrraal0rVKEcBAGchafcIDrDO4oa65khjDHDy1yqUqgEA4sX2pH3Dhg0qKipSQUFB+LGVK1fqP//5j2bOnFlv21AopMLCQm3ZskVJSUkaPXq0srJIKOAsbqhrbmqMgQnTFeTkr+W4WgEAiBNba9oXL16sJ598UpWVleHHNm/erOXLl0fc/tNPP1VlZaVmzpypG264Qc8++6xVQwXMc0NdsxvG6ELUggMA4sXWpD0zM1MTJ04M/3zw4EEVFRVp5MiREbcvLi5WXl6eJKl79+7auHGjFcMEouKGumY3jNGtgp2yFBx1pxImzlRw1J0k7ACAmLC1PKZ3797avXu3pJrSlyeeeEI33XSTkpOTI25fXl6utm3bhn8OBoOqrq5WQkJCo22zo7kZTBw09fcrS7brwD+fUNWPpUrs2EntR+QrKetEi0cX3XicNua67H6fI9nb+UQdXr+20eNtO5+oExwy3paM0YmxjiUn7edej7WTEGtrEGfrEGvrWB1r22vaa23atEklJSUqLCxUZWWltm3bpoULF9abdU9NTVV5eXn4Z8MwIibskrRjx454D7lJ2dnZEf9+wzriCkmHv/7StsvnZsbjtDHX1VSc7Ra66Grp6y8b1TWXX3S1Y8Yb7RidGutYcdJ+7vVYOwmxtsax4uyGhftuwT5tnXjFurkTAcf0ae/WrZtmz56tgoICjR8/Xl26dGlUJpOTk6PVq1dLkr777jv9+te/tmGkreC0OmIz43HamF3ADXXNbhijpdjPAVvUnjAbn6yQ1q+V8ckKGY/cU5PIA6jHMTPtzZk7d66GDh2qnj17as2aNZo2bZoMw9CYMWPsHlpUnFZHbGY8ThuzW7ihBacbxmgV9nPAJs2dMPP9BNRje9KekZHRqLVjw8fGjh0b/v9bb73VsrHFmtN6OJsZj9PGDMQD+zlgD06YAfMcUx7jBaHSEoUKZ2nXlL8qVDir8eW9K4fX9Gyuy84ezmbG47QxA/HAfg7YoqkTY06YgcZsn2n3iroL2SpqH2xwQ51gpyyFWnDHyXgt0jEznpaOGXAT9nPAJtyQDDAtYBhGpKvCrmf16ulQ4ayahTQNBHr1a9UdJht2tZAk/byI0O8JBavkrUOsrUOsrUOsrUH3GOuwT1vHju4xzLTHSNzq8likAwDwMBbFA+aQtMdIvBaysUgHscBMFgAA7kbSHitxqsujqwVaq2GJlSE1Wm8BAACcje4xMVL3ZjVtzv5d7G5WQ1cLtBY3DgIAwPWYaY+h2rq8jBguTqCrBVqLEisAANyPpN0FrFykQ+2z91BiBQCA+5G0I4zaZ4+iDzIAAK5HTTt+Qe2zJ9Vdb6Gcs2K33gIAAFiGmXaXikcZi1NrnynZaT36IAMA4G4k7S4UrzIWJ9Y+N/da1cxdw7yCExYAACBRHuNO8SpjcWJ7SR+X7NSesBifrJDWr5XxyQoZj9xTk8gDAABfYabdheJVxuLE9pJOLNmxbPa7uRMWSl0AAPAVknYXimcZi9Nqn51WsmNlhx0nnrAAAAB7kLS7kZ9a+MXxtbZoxjyGs9/H+vtOO2EBAAD2IWl3ISeWscRLvF5rS2fMYzX7berv++nkDAAANIuk3aWcVsYST3F5rS2cMY/Z7LeJv++nk7Om0D0HAIAaJO3wpRbPmMdo9tvs3/fTyVlD3KEXAIBf0PIRvtTUzPixZsxjdXfRlv59X/Fxu08AABpiph3+1IoZ85jMflOvfkx0zwEA4Bck7fAlu+vF7f77bkD3HAAAfkHSDt+yu17c7r/veFyNAAAgjKTdgeiYAXA1AgCAukjaHYaOGcAvuBoBAEANusc4DR0zAAAA0ABJu8PQMQMAAAANkbQ7DP27AQAA0BBJu9NcObymQ0ZddMwAAADwNRaiOgwdMwAAANAQSbsDxapjBq0jo0fMAACAE5G0exStI6NHzAAAgFNR0+5VtI6MHjEDAAAORdLuUbSOjB4xAwAATkXS7lG0joweMQMAAE5F0u5VtI6MHjEDAAAOxUJUj3Ji60ind2ZxYswAv3L69wUAWI2k3cNi1ToyFtzSmcVJMQP8yi3fFwBgJcpjYA06swAwi+8LAGiEpB2WoDMLALP4vgCAxkjaYQk6swAwi+8LAGiMmnZY48rh0qb19S95e6gzC4vmgBjy+PcFALQESTss4eXOLCyaA2LLy98XbsAkBOBMJO2wjGc7szS3aM6LrxewgGe/LxyOSQjAuUjabeb3GQ0vvH4WzQHwDCYhAMciabeR32c0vPL6Ax061ow9wuMA4CZMQgDORfcYO/m9F7FXXv+Vw2sWydXFojkALkTnHsC5mGm3kd9nNLzy+lk0B8Az6NwDOBZJu438XlbhpdfPojkAXsAkBOBcJO128vuMht9fPwA4EJMQgDORtNvI7zMafn/9AAAAZpG028zvMxp+f/0AAABm0D0GAAAAcDiSdgAAAMDhSNoBAAAAhyNpBwAAAByOpB0AAABwOJJ2AAAAwOFsb/m4YcMGFRUVqaCgQNu2bdP8+fMlSSeffLL+8pe/KBisf14xadIktW3bVpKUkZGhMWPGWD5mxE+otIS+7QAAAA3YmrQvXrxY7733nlJSUiRJzz33nIYNG6YePXpo3rx5+uyzz9SzZ8/w9hUVFZKkgoICO4aLOAuVlsh45J7wHVINSdq0XqEJ00ncAQCAr9laHpOZmamJEyeGf544caJ69Oihqqoq7du3T8cdd1y97bds2aKjR4/q3nvv1f/93//pu+++s3rIiKfFReGEPeznmXcAAAA/s3WmvXfv3tq9e3f452AwqNLSUs2YMUNt27ZVdnZ2ve3btGmjyy+/XIMGDdLOnTt1//33a86cOUpISGj03A3/rdXs/vtutKu8TBURHk8uL1NGE/EkztYh1tYh1tYh1tYgztYh1taxOta217Q31KlTJz322GNatmyZFi1apLFjx4Z/17lzZ2VlZSkQCCg7O1tpaWn66aeflJ6e3uh5duzYYeWw68nOzrb177tVKDUt4uMVqWkR40mcrUOsrUOsrUOsrUGcrUOsrROvWDd3IuCo7jEPPvigdu7cKUlKTU1ttAj13Xff1bPPPitJ+vHHH1VeXq7jjz/e8nEiTq4cLjWsXe+UVfM4AACAjzlqpv1Pf/qTHn/8cSUmJio5OVmjR4+WJM2dO1dDhw7VwIEDNW/ePN19990KBALKz8+PWBoDdwp2ylJownS6xwAAADQQMAzDsHsQ8UB5jPcRZ+sQa+sQ6/irbS2bVF6mytQ0JgfijH3aOsTaOnaUxzhqph0AgHiq21o2vPCd1rIAXMBRNe0AAMQVrWUBuBRJOwDAN4x9P0b1OAA4BUk7AMA3Ah06RvU4ADgFSTsAwD9oLQvApViICgDwjbqtZZPLy1RB9xgALkHSDgDwlWCnLGnUncqgPR4AF6E8BgAAAHA4knYAAADA4UjaAQAAAIcjaQcAAAAcjqQdAAAAcDiSdgAAAMDhSNoBAAAAhyNpBwAAAByOpB0AAABwOJJ2AAAAwOFI2gEAAACHI2kHAAAAHI6kHQAAAHA4knYAAADA4UjaAQAAAIcjaQcAAAAcjqQdAAAAcLiAYRiG3YMAAAAA0DRm2gEAAACHI2kHAAAAHI6kHQAAAHC4RLsH4CYbNmxQUVGRCgoKtHnzZj399NMKBoPq3LmzRo8erWAwqCVLluiDDz5QMBjUVVddpZ49e6qiokLt9x74AAAMJklEQVSPPfaYDhw4oNTUVN12221q37693S/H0erGetOmTXr66aeVlJSkrl27auTIkQoGg3r55Zf1xRdfKCEhQSNHjlS3bt1UUlKiefPmKRAI6KSTTtLNN9+sYJBz0+aYifU///lPFRcXKxQKadCgQRo8eLAOHDigxx57TBUVFTr++OM1ZswYtWnTxu6X42hmYi1JR48e1bRp0zR8+HDl5eUR6xYwE+v//e9/Wrp0qUKhkM477zxde+21xDpKZuK8aNEiFRcXKxgMasSIEcrNzSXOUaiqqtITTzyh0tJSVVZW6pprrlGXLl0iHus4LrZONLG25bhowJTXXnvNuOOOO4y77rrLMAzDeOihh4zPP//cMAzDePTRR41PP/3UKCsrM0aPHm1UVlYaBw8eNPLz8w3DMIzXX3/dePHFFw3DMIyVK1cazzzzjD0vwiUaxnry5MlGcXGxYRiG8fzzzxsrVqwwNm7caBQUFBihUMgoLS01/v73vxuGYRgPPPCAsW7dOsMwDGP+/PnGJ598Ys+LcAkzsV67dq3x0EMPGYZhGBUVFcbYsWONgwcPGv/4xz+Md9991zAMw3j11VeN119/3ZbX4BZmYl1r3rx5xt/+9jdj9erVhmEYxDpKZmK9c+dOY8qUKcbRo0eN6upq4/nnnzcqKyuJdRTMxPmHH34w7rrrLiMUChk7duwwJk2aZBgG+3Q0li9fbixYsMAwDMM4cOCAMXr06IjHOo6LrWc21nYdFznVMikzM1MTJ04M/3zKKaeorKxMhmGovLxciYmJatOmjTp16qQjR47o6NGjCgQCkqTi4mLl5eVJkn77299q7dq1trwGt2gY67179yonJ0eSlJOTo+LiYhUXF+ucc85RIBBQenq6qqurdeDAAW3atEk9evSQVBPrNWvW2PIa3MJMrLt37678/HxJUiAQUCgUUmJiotavXx/er/Py8tivj8FMrCVpyZIl6t69u04++eTwtsQ6OmZivXbtWv3mN7/R3LlzVVBQoNzcXPbrKJmJc8eOHZWcnKzKysrwsVJin45Gnz59dP3114d/TkhIiHis47jYemZjbddxkaTdpN69eyshISH8c1ZWlhYsWKAJEyZo//794Tf0hBNO0B133KHJkyfrkksukSSVl5erbdu2kqSUlBQdPnzY+hfgIg1jnZmZqW+++UaS9Pnnn+vo0aP1YipJqamp4bjWnizVfQyRmYl1cnKy0tLSVFVVpblz52rw4MHh/bj2PSDWx2Ym1mvXrlVJSYkGDx5c798S6+iYifXBgwf17bffKj8/X3feeaeeeeYZHTp0iFhHwUycExISFAwGNWHCBM2YMUOXX365JPbpaKSkpCg1NVXl5eWaPXu2hg4dKqnxsY7jYuuZjbVdx0Vq2lto4cKFmj59uk466SS9/fbbevbZZ5WXl6d9+/Zp7ty5kqSZM2cqNzdXqampOnLkiCTpyJEjateunZ1Dd50xY8ZowYIFWrx4sU499VQlJiaGP1S1ar+saj9YtY8R6+hEirUklZWVafbs2erRo4euuuoqSVLbtm1VXl6u5ORkYt0CkWK9fPly7dmzRwUFBdq+fbt++OEHdejQgVi3UqRYp6Wl6YwzzlBqaqpSU1PVpUsX7dy5k1i3QqQ4r1ixQscdd5ymTp2q8vJy3XPPPerevTtxjtKePXv08MMP66KLLtIf/vAH/etf/wr/rjZ+HBdjw0ysJXuOi8y0t1BaWppSU1MlSR07dtShQ4fUrl07JScnKykpScnJyWrXrp0OHTqknJwcffHFF5Kk1atXKzc3186hu84XX3yh/Px8TZkyRWVlZTr77LOVm5urr776SqFQSHv27JFhGGrfvr26du2qr7/+WlJNrE8//XSbR+8ukWJdUVGhGTNmaMCAAbr22mvD2+bk5Gj16tWSpC+//JL9OkqRYj1+/HjNmDFDBQUFysvL05///Gd17dqVWLdSU98hX3/9tSoqKnTkyBFt27ZNWVlZxLoVIsU5LS1NKSkpCgaDSk1NVWJioo4cOUKco7Bv3z7NnDlTw4cP18CBAyUp4rGO42LrmY21XcdFZtpbaPTo0Xr00UcVDAaVmJiov/71r8rIyNDatWs1depUBQIB5ebmhg8O8+bN0913363ExESNHz/e7uG7SlZWlu6//361adNGZ5xxhs4991xJUm5urqZNmybDMHTzzTdLkm688UbNnz9fVVVVOvHEE9W7d287h+46kWL9xhtvaNeuXVq2bJmWLVsmqWZG7eqrr9a8efO0bNky/epXv9K4ceNsHr27NLVfR0KsW6epWA8cOFB33323JOmaa65RWloasW6FSHEOhUIqLi7WtGnTFAqFdMEFFyg7O5s4R+HVV19VWVmZXnnlFb3yyiuSpJEjR2rBggX1jnXBYJDjYiuZjfVbb71ly3ExYBiGEdNnBAAAABBTlMcAAAAADkfSDgAAADgcSTsAAADgcCTtAAAAgMORtAMAAAAOR9IOAB72zDPP6LrrrtN9993X7HarVq3Sddddp/Hjx6uiosKi0QEAzCJpBwAPu+GGG3TCCSfoyy+/1AcffBBxm/Lyci1YsECBQED5+flKTk62eJQAgGMhaQcAD0tJSdGoUaMkSYsWLdKhQ4cabfP8889r7969uuSSS7gzJQA4FEk7AHjc7373O51//vnat2+fioqK6v3u+++/1zvvvKPMzEwNGzbMphECAI4l0e4BAADib+TIkVqzZo2WLVum/v37q3v37gqFQnr66aclSfn5+WrTpk29f/Phhx/qzTff1NatWxUMBtWtWzddc8016tGjR73tqqqq9M477+iDDz7Q9u3bVVlZqQ4dOujcc8/Vddddp/bt20uSSkpKNG7cOA0ZMkT79+/XihUrlJycrFtvvVW9evWyJhAA4FLMtAOAD3To0EEjRoyQYRhatGiRDMPQ0qVL9cMPP+iPf/xjo0T8ueee05w5c7R//34NGDBAffv21datWzV9+nStXLmy3razZ8/WokWLlJSUpMGDB2vQoEFKSkrS0qVL9eCDDzYay9KlS/Xpp5/qwgsv1GmnnabTTjstrq8dALyAmXYA8IkBAwbo/fff17p16/T222/rpZdeUkZGhm644YZ623333Xd67bXXdNZZZ2ny5MnhhalDhgzR1KlT9dRTTykvL09paWkqLi7WZ599pn79+um2224LP0dVVZUmTZqkDRs2aNeuXcrMzAz/7sCBA3r44Yd10kknWfPCAcADmGkHAB+59dZblZycrAULFujw4cMaPXq0UlJS6m2zfPlySdKIESPqdZJp3769Lr/8ch05ckQfffSRJCk9PV1jxozRkCFD6j1HYmKicnJyJEn79++v97sTTzyRhB0AosRMOwD4SFZWlq644gr9+9//1gUXXKAzzzyz0TabNm2SJH300UdatWpVvd/t2bNHkrR582ZJNUl7//79VVVVpU2bNmnHjh0qKSnR5s2btXbtWklSKBSq9xydOnWK9csCAM8jaQcAn8nIyKj334Zq20K++uqrTT5HWVlZ+P+XLl2qV155RT/99JMkqV27durevbuys7O1cePGRv+WPvAAED2SdgBAPSkpKUpISFBRUZGCwearKFeuXKnCwkJ17dpVt9xyi7p27ar09HRJ0pNPPhkxaQcARI+adgBAPSeffLKqq6vDJTB1FRcXq6ioSMXFxZIU7iRz++2367zzzgsn7JK0fft2SZJhGPEfNAB4HEk7AKCe/v37S6q5g2p5eXn48cOHD6uwsFCLFy8OP1Zb6rJv3756z7F8+XKtX79eklRdXR3nEQOA91EeAwCo5+yzz9ZFF12kpUuX6o477tC5556rxMRErVq1Snv37tXFF1+s3NxcSdIFF1ygjz/+WA899JDOP/98tW3bVhs2bNC3336r4447Tvv379fBgwdtfkUA4H4k7QCARkaNGqVu3brpv//9r9577z0lJCQoOztb119/vfr16xfe7ve//73GjRunJUuW6P3331dKSooyMzM1atQonXrqqZoyZYpWr16tPn362PhqAMD9AgbFhgAAAICjUdMOAAAAOBxJOwAAAOBwJO0AAACAw5G0AwAAAA5H0g4AAAA4HEk7AAAA4HAk7QAAAIDDkbQDAAAADkfSDgAAADgcSTsAAADgcP8PEFqQL0SRKn0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#This grabs the data for the month we want to look at and\n",
    "#converts it into a numpy array for further analysis\n",
    "\n",
    "MonthData = pd.DataFrame( df_pivot_group1['TmaxC'][MONTH])\n",
    "MonthData.reset_index(inplace=True)\n",
    "X =np.array(MonthData)\n",
    "#Pandas assumes NaN for July - Dec 2019.\n",
    "#Delete 2019 data for the month\n",
    "X = np.delete(X,-1,0)\n",
    "\n",
    "#Plot the data to check the code works and compare with the last plot.\n",
    "\n",
    "plt.figure(figsize=(11,8))\n",
    "plt.xlabel('Year',fontsize=20)\n",
    "plt.ylabel('Temperature - Celsius',fontsize=20)\n",
    "plt.title('Scatter Plot for month %s Mean temperature - Celsius'%MONTH,\n",
    "          fontsize=20)\n",
    "plt.scatter(X[:,0],X[:,1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use sklearn Kmeans to analyse the data\n",
    "\n",
    "clf = KMeans(n_clusters=5,n_init=30,max_iter=900) \n",
    "#THIS DEFINES THE NUMBER OF CLUSTERS TO LOOK FOR ETC.\n",
    "\n",
    "clf.fit(X)\n",
    "centroids = clf.cluster_centers_\n",
    "labels = clf.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Choose some colours for the final plot\n",
    "\n",
    "colors=['g.','r.','y.','b.','c.']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data for the number of clusters used in the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.xlabel('Year',fontsize=20)\n",
    "plt.ylabel('Temperature - Celsius',fontsize=20)\n",
    "plt.title('KMeans Scatter Plot for month %s Temperature - Celsius'%MONTH,\n",
    "          fontsize=20)\n",
    "for i in range(len(X)):\n",
    "    plt.plot(X[i][0],\n",
    "            X[i][1],\n",
    "            colors[labels[i]],\n",
    "            markersize=15)\n",
    "plt.scatter(centroids[:,0],\n",
    "           centroids[:,1],\n",
    "            marker='x',\n",
    "            s=150,\n",
    "            linewidth=5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial thoughts\n",
    "\n",
    "The data set raises a few important questions:\n",
    "1. What is the Berkley Earth averaging method?\n",
    "2. How were the 'anomalies' calculated as far back as 1880?\n",
    "3. Why has a mean from 1951 to 1980 been used at all for calculations?\n",
    "4. What changed in 2000 to cause such massive jumps?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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