 Clustering_win_duration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clustering Win_duration Pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xinyuyangren/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Spring</th>\n",
       "      <th>Summer</th>\n",
       "      <th>Autumn</th>\n",
       "      <th>Winter</th>\n",
       "      <th>Sum</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Std</th>\n",
       "      <th>CV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24941</td>\n",
       "      <td>36550</td>\n",
       "      <td>68088</td>\n",
       "      <td>22426</td>\n",
       "      <td>152005</td>\n",
       "      <td>38001.25</td>\n",
       "      <td>18169.185416</td>\n",
       "      <td>0.478121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5590</td>\n",
       "      <td>27239</td>\n",
       "      <td>15661</td>\n",
       "      <td>9224</td>\n",
       "      <td>57714</td>\n",
       "      <td>14428.50</td>\n",
       "      <td>8228.515495</td>\n",
       "      <td>0.570296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62287</td>\n",
       "      <td>71443</td>\n",
       "      <td>62007</td>\n",
       "      <td>22619</td>\n",
       "      <td>218356</td>\n",
       "      <td>54589.00</td>\n",
       "      <td>18844.257375</td>\n",
       "      <td>0.345202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46252</td>\n",
       "      <td>53647</td>\n",
       "      <td>69648</td>\n",
       "      <td>41878</td>\n",
       "      <td>211425</td>\n",
       "      <td>52856.25</td>\n",
       "      <td>10567.949715</td>\n",
       "      <td>0.199938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64243</td>\n",
       "      <td>85297</td>\n",
       "      <td>58520</td>\n",
       "      <td>50794</td>\n",
       "      <td>258854</td>\n",
       "      <td>64713.50</td>\n",
       "      <td>12806.382832</td>\n",
       "      <td>0.197894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>73</td>\n",
       "      <td>130</td>\n",
       "      <td>32.50</td>\n",
       "      <td>31.721444</td>\n",
       "      <td>0.976044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26000</td>\n",
       "      <td>32721</td>\n",
       "      <td>34583</td>\n",
       "      <td>24466</td>\n",
       "      <td>117770</td>\n",
       "      <td>29442.50</td>\n",
       "      <td>4295.045430</td>\n",
       "      <td>0.145879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>319</td>\n",
       "      <td>2934</td>\n",
       "      <td>367</td>\n",
       "      <td>0</td>\n",
       "      <td>3620</td>\n",
       "      <td>905.00</td>\n",
       "      <td>1179.905293</td>\n",
       "      <td>1.303763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>52577</td>\n",
       "      <td>66105</td>\n",
       "      <td>55458</td>\n",
       "      <td>38365</td>\n",
       "      <td>212505</td>\n",
       "      <td>53126.25</td>\n",
       "      <td>9900.520021</td>\n",
       "      <td>0.186358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22702</td>\n",
       "      <td>44446</td>\n",
       "      <td>26916</td>\n",
       "      <td>11196</td>\n",
       "      <td>105260</td>\n",
       "      <td>26315.00</td>\n",
       "      <td>11945.002009</td>\n",
       "      <td>0.453924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Spring  Summer  Autumn  Winter     Sum      Mean           Std        CV\n",
       "1    24941   36550   68088   22426  152005  38001.25  18169.185416  0.478121\n",
       "2     5590   27239   15661    9224   57714  14428.50   8228.515495  0.570296\n",
       "3    62287   71443   62007   22619  218356  54589.00  18844.257375  0.345202\n",
       "4    46252   53647   69648   41878  211425  52856.25  10567.949715  0.199938\n",
       "5    64243   85297   58520   50794  258854  64713.50  12806.382832  0.197894\n",
       "6        1      54       2      73     130     32.50     31.721444  0.976044\n",
       "7    26000   32721   34583   24466  117770  29442.50   4295.045430  0.145879\n",
       "8      319    2934     367       0    3620    905.00   1179.905293  1.303763\n",
       "9    52577   66105   55458   38365  212505  53126.25   9900.520021  0.186358\n",
       "10   22702   44446   26916   11196  105260  26315.00  11945.002009  0.453924"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('/Users/xinyuyangren/Desktop/Paper/winbyseasons.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.625, 11.125)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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vYbFYWLNmDTt37mThwoUYhsG4ceOIj49n2rRpbNmyheuuu46VK1eyYcMG3G43SUlJJCQk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\nRETE4lToRERERCzuvyjr5CwGMas2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c088790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Stacked bar plot\n",
    "# Create the general blog and the \"subplots\" i.e. the bars\n",
    "f, ax1 = plt.subplots(1, figsize=(10,5))\n",
    "\n",
    "# Set the bar width\n",
    "bar_width = 0.75\n",
    "\n",
    "# positions of the left bar-boundaries\n",
    "bar_l = [i+1 for i in range(len(df['Spring']))]\n",
    "\n",
    "# positions of the x-axis ticks (center of the bars as bar labels)\n",
    "tick_pos = [i+(bar_width/2) for i in bar_l]\n",
    "\n",
    "# Create a bar plot, in position bar_1\n",
    "ax1.bar(bar_l,\n",
    "        # using the pre_score data\n",
    "        df['Spring'],\n",
    "        # set the width\n",
    "        width=bar_width,\n",
    "        # with the label pre score\n",
    "        label='Spring',\n",
    "        # with alpha 0.5\n",
    "        alpha=0.5,\n",
    "        # with color\n",
    "        color='#F4561D')\n",
    "\n",
    "# Create a bar plot, in position bar_1\n",
    "ax1.bar(bar_l,\n",
    "        # using the mid_score data\n",
    "        df['Summer'],\n",
    "        # set the width\n",
    "        width=bar_width,\n",
    "        # with pre_score on the bottom\n",
    "        bottom=df['Spring'],\n",
    "        # with the label mid score\n",
    "        label='Summer',\n",
    "        # with alpha 0.5\n",
    "        alpha=0.5,\n",
    "        # with color\n",
    "        color='#F1911E')\n",
    "\n",
    "# Create a bar plot, in position bar_1\n",
    "ax1.bar(bar_l,\n",
    "        # using the post_score data\n",
    "        df['Autumn'],\n",
    "        # set the width\n",
    "        width=bar_width,\n",
    "        # with pre_score and mid_score on the bottom\n",
    "        bottom=[i+j for i,j in zip(df['Spring'],df['Summer'])],\n",
    "        # with the label post score\n",
    "        label='Autumn',\n",
    "        # with alpha 0.5\n",
    "        alpha=0.5,\n",
    "        # with color\n",
    "        color='#F1BD1A')\n",
    "\n",
    "# Create a bar plot, in position bar_1\n",
    "ax1.bar(bar_l,\n",
    "        # using the post_score data\n",
    "        df['Winter'],\n",
    "        # set the width\n",
    "        width=bar_width,\n",
    "        # with pre_score and mid_score on the bottom\n",
    "        bottom=[i+j+k for i,j,k in zip(df['Spring'],df['Summer'],df['Autumn'])],\n",
    "        # with the label post score\n",
    "        label='Winter',\n",
    "        # with alpha 0.5\n",
    "        alpha=0.5,\n",
    "        # with color\n",
    "        color='k')\n",
    "# set the x ticks with names\n",
    "plt.xticks(tick_pos, df.index)\n",
    "\n",
    "# Set the label and legends\n",
    "ax1.set_ylabel(\"Open records count\")\n",
    "ax1.set_xlabel(\"House no\")\n",
    "plt.legend(loc='upper left')\n",
    "plt.title('Window open duration: season breakdown')\n",
    "\n",
    "# Set a buffer around the edge\n",
    "plt.xlim([min(tick_pos)-bar_width, max(tick_pos)+bar_width])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = df[['Mean','CV']]\n",
    "data = data.drop(data.index[[5,7]])#drop the rows with too small means\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "data = scale(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=4, n_init=10,\n",
       "    n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,\n",
       "    verbose=0)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator = KMeans(n_clusters=4)\n",
    "estimator.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 3 0 0 0 2 0 1]\n"
     ]
    }
   ],
   "source": [
    "labels = estimator.labels_\n",
    "print estimator.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xMZESJZxwdHQE9HONFtID40/u7h4AlChRgjNnThEW9g+2tvZkZmYW2u/IyAje\ne+9tAEqVcsPBwZ6EhHgAfHyqAlC6dBkyMtK5du0q5ctXMLahKNUAcHV1LXDKOi4uxpjc1a1bz3iK\nu3z5Ctjb65OpkiULbpdb1aqKcf85p3VzYg8Pv0h09HWGDBmATqfj9u1krl6NyjMOuX30UR9WrVrG\nkCEDcHMrTfXqNbl48SLHjx/l9OmT6HQ6tFotSUm3cHFxYdSoUWg0Fly+HEmtWnVo0qQZUVFRjB49\nDAsLS3r06ENk5CVq1qxlnKqtTp0XiIi4aBg7fexlypQhMzMjTyyOjo74+CgcO3aULVs2M2jQUKys\nrEhIiMfffzw2NrbcuXPHeG2fqf64uLgwdepEbGxsjDGa8rAxPm5SaRNCiCIk5xq2unXrmbzG7VGU\nK1eetLQ7rF37A+3adcj1fgXKlCnLrFnzmTdvEV27vkvNmrWpXLmy8Uv+9OlTxvVz4rGysjJezH7u\n3NlC9no39vz9cHZ2JiUlhfj4OADCwo5QqZI7Op2O8+dVdDodaWlpXLoUjru7O+7unowf78/cuSEM\nGDCYZs1aAPkrZnn34eLiwu3bycYJ4s+ePW2I3ZrY2BhAXxlMSkoybpPzxfzbb5twdCzBhAmTee+9\nD4yJkUajQavV5tmPp2dlDh06BEBMzE2Sk5MpUcLJRHywb98eXnqpufG16aRc34/SpcsSGXkJgFOn\nTphYr+C45h8HU+2bmZmh1Wpxd/fAy8ubuXNDmDdvEe3bd8Lb2yfPOOS2bdtvdOjQiblzQ/D09GLT\npg14enrSunU75s4NYcaMubz2WmvMzc1ZujSUWbNmMXr0BGOFMyzsMK6upQgKCqZHjz4sXrwAD4/K\nnD59Cq1Wi06n4+jRMGOCdb8/WDp16sJPP31LenoG7u4eHDiwj5s3o/Hzm0L//gNJT083joVGk7c/\nKSm3Wbo0FH//gDwx5h8/0F8T+rAxPk5SaRNCiCIi/00H+a9xexxfDq1atWHr1i1UrFiJq1evAPrk\nqVu39xk0qC/Z2VrKlStPy5b6a8P8/Sewc+d2ypUrb9x/zv87duxCYOAktm3bQqVK7rn2Yvq0o6n4\nR40az9ixvpiZmeHo6Mi4cRO5ePECWVlZDB/+OUlJt+jV6xNKlHBi+PDRTJ78JdnZ2ZiZmTF69ARi\nYm7mazHvPszNzRk6dCRDhw7CycnJeHF+tWrVcXR0pH//3nh4eOapfOVo0KAx/v7jOXnyOJaWllSq\n5EFsbCzPOe9SAAAgAElEQVTe3lX45pvlVK1azdinDz/sTVBQAJs3/0Z6ejqjRo3D3NzcZP+PHg2j\nS5e3Cz1GufsxfPgoAgL8sbOzw9LSklKl3AqMpenPxb0/K7Vq1WHqVD+CguZTv34jBgz4mMzMTGrU\nqGnchynVq9fkq68mY2Nji7m5GSNHjsPVtRTTpk1h0KB+pKam8tZbb2Nv70CdOnV599130enA0dGJ\n2NgYmjVrgZ/fWDZsWItWq6V37754eXnTsmVrPv20Dzqdjrp169GixaucP3/uPmMEL7xQn+nTA+jZ\n82MAatSoycqVSxk0qB+gr0jGxsaYHKOcGPv164WFhbkxRgBPTy8mT/6Shg0bA/pq7muvtXqoGB8n\nmXv0P0zmfDNNxqUgGRPTnua4nDx5AkdHR5PXsCUkxHPmzGmaNm1ecMNn4EmPS1jYYTZuXM/EiVOf\n2D6ehCcxLuvX/0SrVm1wcnJm8eKFWFpa0qvXJ491H0+S/G4x7WHnHpVKmxBCFAG1atUudJmLS8ki\nk7CJp6tkyZIMHToQW1s7HBwcGDfu3z1XT/w3SdImhBCiSKlXrwH16jV41mEUCa++2opXX231rMMQ\nRYQkbUVQVlYWkZH70GiS0Gi099+gEPHxtty6deexxKTTWWFr60G5ctUfS3tCCCGEeDCStBUxWVlZ\nnD//DS+8cBVz80e7udfe3pqUlPTHFBnExBwiMrItHh4vPrY2hRBCCPHvyCM/iphLl3Y+loTtSXBz\nM8fefgvJybeedShCCCHEc6foZQbPOXPzxCKZsOUoU8aMhIQrzzoMIf7zVPXisw5BCFHEFN3s4Dll\nZvbw17A9DRYWZmRnP9knPgshYNCghdy583iuSQ0LO0yLFo3YsWN7nvd79nyPgIB/dzfi5cuXGDy4\nPwDDhw83PmW+uAkJCWbLls2P1MaNG9Hs3bsHgHnzgrh588YDt6HVahkzZgTLloWyceP6R4oHYPXq\nFcYHB5sSHn6BY8fCHnk/xdXEiePu+ZndvXsXcXGxxMfHERQ07SlG9mAkaSviDh+OYcyY/+V5b968\nk2zeHPlE9peVpWXChH/4+OO/6NHjT/766/oT2Y8QonDHjqmoal82bDjw2Nr08PA0ToEE+i/xe019\nZErOA0pnzpxpfEjt8+jIkX+MM0UMHjyM0qXLPHAbJ04co3btuo/tafofftiLatVqFLp8166dRESE\nP5Z9FUcTJ06952f2p5++IyUlhZIlXY1zjhZFz++/umLkKc6QwW+/ReHsbMXkyQ1JSsrgvfd28Mor\n5Z5eAEI8pyIiIpk4cRPW1tW4fj2ZtLQeLF++nj///IO0tCj6969Ns2YNH7p9b28foqIuk5qagp2d\nPVu3bqFt2/bcuBENwM6df/Djj99ibm5OnTov0L//QOLiYpk0aQKgf1ZcjpYtW7J69VqmTw+gdet2\nNG7chIMH97NjxzbGjvWjW7cu1KnzAlFRl6lfvyEpKbc5ffoU7u4eTJgwKU9chw4dYPHiEKytrXFy\ncmLMmC85d05l1aplaDRmJCTE0anT//HWW+8QHn6B2bNnAFCihBNjx36Jqp5lzZqVWFpacu3aNVq1\n0s/kkNuuXTtYtWoZzs4lycrKxMPDk7Cww2zYsA5//wAAOndux8aNWwkI8OfWrUSSkpKYNm0WCxfO\n5ebNm8TFxdK8+cv06dOP1atXkJ6eTq1adfjhhzX4+o6lZElXJkzwJSHhFtnZ2fTtO4D69RvSs2d3\n6tWrz4UL5zEzM+Orr2ZiZ2fP3r17eOONN/Mk0sHBszl+/Khhnth2tG3bniFDBrB8+becPHkCX98h\nbNmyk5iYmwQGTiYoaJ5x24AAf1q3bkdcXCz79+8lLS2Na9eu8sEHPWjU6EW2bNmMpaUl1apVJy0t\njdDQBZibm1OhQkVGjBjD9u2/8+uvv6DT6fj44/7Ur6//rCUmJuLnNwadTkdGRgYjRoyhShUf1q37\nge3bt6LRaGjdui1du3YjPPwiwcGz0Gq13LqVyPDhY3jttaYEBPhz7dpV0tPTeOed7rRt277Q436v\nY3nx4gXmzJnB3LkhAIwcOZS+fQdw9WoU69f/RHZ2NhqNhoCA6Vy8eIGFC+dhZWVFp05dWLIkhG+/\nXUdU1OUCMSYn3+L8+XNMmeLHhAmTmDLFj0WLlj9UjE+aJG3FQGGTVsyadYKwsFg0Gg3t21fivfe8\n8fP7h9dfr8RLL5Vhz57rbNwYjr9/Q/z8/uHq1VTS07Pp3t2bDh3cOXw4hvnzT2NhoaFiRXvGjatP\nmzYVaNNGP52LVqvDwkKKsUI8DZUre9CzZ3P8/W9w5sxHABw9+hbx8dsYPrzKIyVsOV59tSV//fUn\n7dt35MyZU3z4YS9u3IgmKSmJZctCWbr0G6ytrZk8+UsOHTrI33//RZs27ejYsQs7dmw3Thh/v+pQ\ndPR1goNDcXEpSYcOrViyZCVDh3ryzjudSUm5bZzoHODrrwMJCVmKq2sp1q79nhUrltK0aXNiY2NY\nvvxbsrOz6dmzO6+91ppp06YydqwfHh6ebN68kdWrV9Ko0YvcuBHNqlU/kJ6eTpcur+f5Es3KyiI4\neDbLl3+Lo6Mjvr5DjMvy9uPuzw0aNObdd7sTHX2dmjVrM2pUZzIyMnjrrQ588smnfPhhLy5fjqR5\n85f58cdvAVi5cinNmjXj9de7EBsbw4ABn/DTTxtJTU2hTZv2fPGFL5MmTWD//n20atWGy5cv5Zn9\nYt++v4mOvkZo6AqysrIYOLAv9es3xMnJmZiYmxw8uI+yZcty9uxpzpw5zSuvvFbo+KekpDBz5lyu\nXIli1KihtG/fkfbtO+LqWopq1WrQvftbLFy4DGdnZ5YsCWHLls1YWFjg6FiCwMAZedo6c+YkTk7O\njB/vT0REOGlpd7h0KYIdO7azcOFSdDodQ4cOpHHjl4iICGfQoKF4eXmzffvv/PbbLzRuXJfjx4+y\naNFyAA4dOnjP436vY+ntXYXMzAxu3IjGwsKCpKRb+PhU5cCBvUyfPgdra2umTw/g4MH9lCrlRmZm\nBqGhKwBYsmQRgMkYR44ch49PVUaOHIelpaXxc/EwMT5pkrQ9ZTnn1B/k1MKhQzH067fH8ErH1aup\n9Ojhw7VrKaxa9RpZWVo+/ng3DRuWKrCtRqMhNTWLo0fjWLnyVQAOHtTP1TdlShjLlr2Ci4s1Cxee\nZtOmSLp08QQgJSWTkSP/x8CBNR+6r0KIB9Oy5Qs4Oh6jS5czZGbWAFIZMCCe7t3feOS29dWb15k+\nPZBy5cpTt24940TjV69GkZiYgK/vEHQ6HXfu3OHatatERUXx5ptvAVCnTl1j0mZK7ikRnZ2dcXMr\nDYCtrS3u7p4AODo6kJGRgb29fr3ExEQcHOxxddX/7qpbtx6hoQto1qwFtWrVxcLCAgsLCypX9uLq\n1StERkYwc+ZXgP53acWKlQD9vJAajQYbGxusrW3yxJWYmEiJEk44OjoC+jk3C+mB8aecicBLlCjB\nmTOnCAv7B1tbezIzMwvtd2RkBO+9p59PtFQpNxwc7ElIiAfAx6cqAKVLlyEjI51r164WmOv00qUI\n6tSpB+i/H2rUqMWlS5d4+eXX2Lfvb06ePM6HH/bif/87wMmTJxgz5stC+pF/f3ljTkhIIC4uji+/\nHG2snjVq9CIVKlQ09ju3Jk2aERUVxejRw7CwsKRHjz6Eh18kOvo6Q4YMQKfTcft2MleuXMbNzY0V\nK5ZgY2NjTM7t7e0ZPHgY06ZNJTU1hXbtOhR63Js2bX7PYwnwxhud2bJlM1ZWVnTo0AkAFxcXpk6d\niI2NDVFRkcZjbKo/pmLMfyxB/7mxt3+4GJ8kSdqesk1zN6DRaOgytOu/3qZxYzcCAhobX8+bd5KM\nDC316uk/TBYWZtSq5UJ4eN753XI+gHZ2FgwfXofJk8NITc2iQ4dKJCSkExubxqhR+uvl0tOzadJE\n/0s2OjqVESMO0K2bN+3aVXyk/gohHsy2bVewtXWiU6el7N2rcOzY47s+oly58qSl3WHt2h/49NNB\nxgnjy5WrQJkyZZk1az7m5uZs2bIZHx+Fy5cvceLEMby9q3D69CljOzm/W6ysrIiLiwXg3Lmzhez1\n7hdh/rmunZ2dSUlJIT4+jpIlXQkLO0KlSu7odDrOn1fR6XSkp6dz6VI47u7uuLt7Mn68P6VLl+HE\niWPEx8cB+Stmeffh4uLC7dvJ3LqViJOTM2fPnqZ06TJYWVkbJwePjr5OUlKScRszM/0Zht9+24Sj\nYwl8fcdy5UoUmzb9bNyfVpv3pjFPz8ocOnSI11+vQEzMTZKTkylRwslEfLBv3x5eeinvtGSVK1fm\n119/4d13u5OVlcXJk8fo0KEjtWvXwd9/PM7OLrz4YlOGDv0MBwdHXFxcChlv0+NhZmaGTqfF2dmZ\n0qXLGE/T/v33buzs7LhxI9rY79zCwg7j6lqKoKBgTp48weLFC/j88+F4eXkzY8ZcAH788Tu8vKow\nZswIJk6cgru7J0uXLuLGjWhiYmJQ1TMEBEwnIyODrl070rZte5PHvaCCp5latWrLkCEDMDc3Iyho\nPikpt1m6NJT16381Vv3ujkPB/syePaNAjHfHJ+8fHqmpDxfjkyRJ21OUlpbGrQ36v7zSP0vH2tr6\noduysjLj8OFY3n+/CpmZWo4fj+fNNz345x9zYmP1FxefPp0AQGxsGmfOJDJzZhMyMrLp0OF32rev\nRJkytsya1QR7e0v++us69vYWxMenMXDgXkaPfoFGjdwevdNCiAdy6tRhFiwoR9u273L06Hn8/P4i\nK6vtY7vwv1WrNmzduoWKFSsZkzZnZ2e6dXufQYP6kp2tpVy58rRsqb9Wx99/Ajt3bqdcufLGZCDn\n/x07diEwcBLbtm3J94Vm+rSjqdOqo0aNZ+xYX8zMzHB0dGTcuIlcvHiBrKwshg//nKSkW/Tq9Qkl\nSjgxfPhoJk/+kuzsbMzMzBg9egIxMTfztZh3H+bm5gwdOpKhQwfh5ORkHMdq1arj6OhI//698fDw\nLFD5Av1pUn//8Zw8eRxLS0sqVfIgNjYWb+8qfPPNcqpWrWbs04cf9iYoKIDNm38jPT2dUaPGYW5u\nbrL/R4+G0aXL23n29dJLzTly5DCfftqHrKwsWrZsg4+PAkBmZiYNGzbGwcEBCwsLmjZtUSDWwun3\nqSjVWLBgLh4elRkyZDgjRgxBp9Nib+/A+PGTjMlLflWq+ODnN5YNG9ai1Wrp3bsv3t5VqF+/EQMG\nfExmZiY1atSkdOkytGvXnvHjR1GihBNubqW5dSsRNzc34uPjGDCgD+bmFnTv/hHm5uaMHDnO5HEv\n7JR1DltbW3x8qpKdnY2trS2grwL369cLCwtzHB2diI2NoWxZ09dim4oR9BXYKVO+xNd3rHHdh43x\nSdLk/8unmNLFxCTff61n7OegtXh+pf/FEDnmmslqW2Tk91SvfvcOn8OHY1i3LqJApa1yZUcuXEji\n6NE4srK0tG1bkR49qnL6dAL+/kdwdbXG29uJ5OQMJk5sQEBAGOfP38Lc3IyXXy5Ljx5VOXDgBqGh\nZ9HpdDg4WDJpUkOWLDnL9u1X8fR0RP8XhIbg4KZYWZkDkJ2t5ezZN6hcud4THasnyc3NkeLweXma\nZExMexbjkpGRgZWVlfF1ZmYmFhYWj+0uw8fhSY9LWNhhNm5cz8SJU5/YPp4E+XdUkIyJaW5ujg/1\nD1oqbU9JTpXNisoAJP4cZ7LaptPlPY4NGrjRoEHeitfgwbUK3U+NGi788IN+cuHc01iNHVswyWrS\npAxNmuS9Vd3Xty6+vnULbV+nM11yFkI8HrkTNgBLS8tnFIkQoqiRb9+nZMuCzdQ9W9v4uu7Z2mxZ\nUPABj9nZ9k8zrAeWkJCOo2P5Zx2GEOI/rF69BsWuyibE0yBJ21Nwt8p29y9oK6z01bb0vBO6lynT\njFOn7J52iP9KWlo2ly+/QMmSBe9SFUIIIcSTJadHn4L8VbYcOdW23Ne2OTi4AD05fnw7lpbJwMNP\na+XoaENy8oM98bwwOp0l2dkVqFatdZG6tkYIIYR4XkjS9oSlp6dz7ofT2BYy1Or3p0j/rGOea9sc\nHFyoUuXdR963XAAqhBBC/HdI0vaEmZub8/a33Qtd7kNNw23hQghx15ljp6let/C5JIUQzx+5pu0J\ns7CwwNvb557/Pc8TLwshCkpKusX3/b7hwslzj63N1atX8MUXnzFoUD+GDBmAquofhjt4cH8uX458\nqDbXrfvxscX3sA4e3E9AgP8jtZGRkcHmzRsA2LJlM3v37rnPFqYNHTqU/fv34uc3tsCyefOCuHnz\nRqHbvvPOmyZnXMixevUKzp49Xejy8PALHDsW9mAB/4dMnDjOOOOQKbt37yIuLpb4+DiCgqY9xcge\nL0nahBCiiNkyazNdI/6P/cF/P5b2Ll2KYO/e3cyevYDg4FAGDx7GV19Nuv+G97Fq1dLHEN2zFxcX\ny6ZNGwFo374jzZo9yMNr9TIyMtDpdNja2pq87nfw4GGULl3GxJY57n2t8Icf9qJatcIrr7t27SQi\nIrzQ5f91EydOvWcB5KefviMlJYWSJV0ZNmzUU4zs8ZISjxBCFCFJSbfI3HwHc8wpsdOBCyfPUaVW\n1Udq08HBgRs3brB580aaNGlKlSo+LF68yrh82bJQEhLiSUtLY+LEqZQrV57g4NkcP37UMGdpO95+\n+z0CAvy5dSuRO3dSaNiwCUlJSQQFTcvzJRgdfZ3AwElkZ2ej0Wj44gtfvL2r8M47nalVqzZXrkTh\n7V2F0aMnkJJym8DAySQn66eQGjJkBF5e3rz33lvUqVOXy5cjKVnSlalTv86TCEVGXiIwcBK2trbY\n2Njg6FgCgM6d27Fx41YA/PzG8n//9zbXr1/j119/QafT8fHH/YmICGf37j9JS0vDycmZgIDprFq1\nnMjICFasWIJWq8XVtRSdO79V6BhYWlpy/fp14uPjGDfODx8fhcOHD/Hiiy+i0+mIirqMr+8QEhIS\naNasBb1792Xw4P74+o7FyckJf//xZGZmUqmSO0eO/MP33/8M6JgxI5Br166i0WgICJiBg8PdeTED\nAvxp3bodcXGx7N+/l7S0NK5du8oHH/SgUaMX2bJlM5aWllSrVp20tDRCQxdgbm5OhQoVGTFiDNu3\n/55nHOrXbwjo59j08xtjnId0xIgxVKniw7p1P7B9+1Y0Gg2tW7ela9duhIdfJDh4Flqtllu3Ehk+\nfAy1atUmIMCfa9eukp6exjvvdKdt2/YcOnSAxYtDcHCww87OgTFjvuTcOZU1a1ZiaWnJtWvXaNWq\nTZ7J1i9evMCcOTOYOzcEgJEjh9K37wCuXo1i/fqfjJ+pgIDpXLx4gYUL52FlZUWnTl1YsiSEb79d\nR1TU5QIxJiff4vz5c0yZ4seECZOYMsWPRYuWG2O0trbGycnpX8X4rEnSJoQQRciWWZupE6mf8LpK\nojf7g/+mSsijJW2lSrkxbVoQa9f+wPLli7G1taVv38945ZXXAGjWrAVt2rzOsmWh/PnnDjw9KxMd\nfY3Q0BVkZWUxcGBf45d8gwaNGTiwHzExyaxb92OBqkVw8Gzeffd9mjVrwfnz5wgMnMSSJauIjb1J\n374DKF++An5+Y/jrrz85ffokDRs2pkuXrly5EkVAgD8LFizh+vWrBAcvolQpNwYM+JgzZ05Ro8bd\nh4rPnz+Hvn0H0KBBI9asWUlk5CXDEtPVKkfHEgQGzkCn03H8+FHmzFkIwLBhgzl79jQ9e/YhIuIi\nvXp9wrJloQDs2/d3oWNQtmx5fH3HsmnTBjZu/JkRI0azf//fDBkyiOPHz5KZmUFg4Eyys7Po2rUT\nvXv3NcayatUyXn75Vbp0eZtDhw5y6ND/jMs6depCrVp1CAjw59ChA7z2WmuT/UlJSWHmzLlcuRLF\nqFFDad++I+3bd8TVtRTVqtWge/e3WLhwGc7OzixZEsKWLZuxsLAwjkNuZ86cxMnJmfHj/YmICCct\n7Q6XLkWwY8d2Fi5capzPs3Hjl4iICGfQoKF4eXmzffvv/PbbL3h5eXP8+FEWLVoOwKFDBwH4+utA\nQkKWUq1aZRYuXMyKFUtp2rQ5N25Es2rVD6Snp9Oly+t5EiJv7ypkZmZw40Y0FhYWJCXdwsenKgcO\n7GX69DlYW1szfXoABw/up1QpNzIzMwgNXQHAkiWLAEzGOHLkOHx8qjJy5DgsLS2NfwDkxOjqWoq1\na7//VzE+a5K0CSFEEZG7ypbjcVTbrl69gp2dPWPGfAnA2bNnGDHic2MSoijVAChZ0pWEhHguXYqg\nTh39LCoWFhbUqFGLiIgIANzdPe65r8jICOrW1W/r41OVmBj9dVxlypQ1zu9Zs2YdLl+OJDz8AkeO\n/MPOndvR6XTGipuTkzOlSulngildugwZGRl59hEVFUn16vpThbVr182VtOWelvHuzzkxazQazM0t\n8PMbi62tLbGxNwu9DupeY1C1qmKM7cSJYwDcvHmDsmXLcvz4WSpX9sbCwgILC4sCN5pdunSJ9u07\nARjHKUfVqnePQ1pa4Y9r8vGpmmts8l4Hl5CQQFxcHF9+OdpYPWvU6EUqVKho8tg1adKMqKgoRo8e\nhoWFJT169CE8/CLR0dcZMmQAOp2O27eTuXLlMm5ubqxYsQQbGxtSUm5jb++AnZ0dgwcPY9q0qaSm\nptCuXQcSExNxcLDH1bWUsZ+hoQto2rQ5Xl5V0Gg02NjYYG1tUyCeN97ozJYtm7GysqJDB/04ubi4\nMHXqRGxsbIiKiqRWLf0fNab6YyrGHLmn7UxMTMTe/uFifJbkmjYhhCgiclfZcuRU2x7FhQvnCQr6\n2pigVKpUCUdHR8zNc74C8laoKleuzPHj+ovas7KyOHnyGO7u+gnhzcxyf20UnLva09OLo0ePAHD+\nvErJkq4AxMTcJCEhHoATJ47h5eWNh0dlunV7n7lzQ5g06Svatu2gj+Y+j4KsXNmbEyeOA+S5OD87\nO5u0tDQyMzPzXN+VE/PFixfYs2cX/v4BDB3qi1arRafTodFo0Gq1+fZR+Bjkv2bt4sULeHlVMb6+\n17Msvb29OXlSn+idPHk8z7J/+wzMvOvpjH3U6bQ4OztTunQZvvpqJvPmLeKjj3obk/O8x04vLOww\nrq6lCAoKpkePPixevAAPD0+8vLyZOzeEefMW0b59J7y8qjB79gw++aQ/Y8f6GfsbFxeLqp4hIGA6\nX389mwUL5uLo6EhKSgrx8XGGfRyhUiV3Ez0p+Plp1aot+/b9zZ49u2jT5nVSUm6zdGko/v4BjB49\nASuru4/HMjWloqkY747P3f05OzuTmvpwMT5LUmkTQogiwFSVLcejVtteeeU1Ll++xCef9MDOzg6d\nTsvAgV9gZ2dvMlF46aXmHDlymE8/7UNWVhYtW7bBx0cpsJ6npxeTJ3/JhAl3b2oYOHAI06ZN4fvv\nV5OdnWWs7llaWhEU9DU3bkRTq1ZtmjVrQe3adQgMnMzGjetJTU2lT59+hlbuxmQqvoEDhzB16kS+\n++4bnJ1djPO1vv32e/Tv34vy5StQtmzB6fYqVqyIra0dn332CTqdDldXN2JjY6hZszZZWZmEhAQb\nn5n5b8cA9KdSmza9980LOf344IOeTJ78JX/+uQNX11JYWOQc73v3+R4tA/pq6YIFc/HwqMyQIcMZ\nMWIIOp0We3sHxo+fxI0b0Sa3rlLFBz+/sWzYsBatVkvv3n3x9q5C/fqNGDDgYzIzM6lRoyalS5eh\nXbv2jB8/ihIlnHBzK82tW4m4upYiPj6OAQP6YG5uQffuH2Fubs7IkeMYO9YXa2tLbGzsGDduIhcv\nXsjXt4L9tLW1xcenKtnZ2dja2gJQp05d+vXrhYWFOY6OTsTGxlC2bDmT/TEVI0CtWnWYMuVLfH3v\n3tmbE6OZmRmOjo7/OsZnSZM78yzGdPIQ2YLk4bqmybgUJGNi2tMclx/nfUvq+tuYF3ICJLVJOv0D\nBz6VWO7nYcYl900C/1X/Zlz279+Li0tJqlWrzj///I9vvlnBnDkLnlKET5/8bjHNzc3xobJBqbQJ\nIUQR8O7g92Hws47iSSpaFYtnpXz5CgQGTsLc3BytVssXX/g+65BEMfLMkjZFUV4EvlJV9bV8738B\nfALcNLzVX1XV8087PiGEEI/Pxo2/P+sQigQPD09CQpY96zBEMfVMkjZFUXyBj4DbJhY3AD5SVfX5\nfbSzEEIIIUQ+z+ru0QvA/xWyrAEwRlGUPYqijH6KMQkhhBBCFFnP7EYERVE8gO9UVW2a7/0JwHwg\nCdgALFBV9bf7NPefuJtCCCGEEM+F/8yNCHNUVU0CUBTlV6AecL+kTe5OMUHu2jFNxqUgGRPTZFxM\nk3ExTcalIBkT09zcHB9qu2edtOXJNBVFKQGcVPSP574DtAT+GzMSCyGEEEI8gmedtOkAFEXpDtir\nqrpEUZQxwC4gDdihqqrcciSEEEKI594zS9pUVY0Emhp+/i7X+2uANc8qLiGEEEKIokjmHhVCCCGE\nKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYk\naRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNC\nCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGE\nKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYk\naRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRNCCCGEKAYkaRP/z969x0dVnfsf/0wSAuQCgkSU\nOyI+VrkKIlaP19Yq1R6sSsUqilisp/WcttqfRU9F8FLbHqSWWqtVFGtrvdaWUrU377YKEQQEHrAo\nWjW1TswAACAASURBVIKScIcQcpvfH3sCk2SSDCFzC9/368Urmb1nZj+zsiFf1t5rLREREckACm0i\nIiIiGUChTURERCQDKLSJiIiIZACFNhEREZEMoNAmIiIikgEU2kREREQygEKbiIiISAZQaBMRERHJ\nAAptIiIiIhlAoU1EREQkAyi0iYiIiGQAhTYRERGRDJCT6gJERFpr586tlJQ8TefOW4HqNn//LVs6\nsnPnnv18VQ4VFV04/PCLKCzs3uY1icjBS6FNRDLSrl072LjxEUaMKE/YMfLza9m1q3Y/X1UJlLFi\nxTxCoasoKOiaiNJE5CCky6MikpE++WQpxx23K9VlNOnYY3fz6adLU12GiLQjCm0ikpFCoXJCoVCq\ny2hWKJS4XkAROfgotIlIu1BcXMq0aW/X2zZnznL++Md1CT3usmWbmTr11YQeQ0QEdE+biLQjye54\nmzdvNQsWfERenv4pFZHEU0+biLQb4XDs7bNnL2PSpJe44oqX+e1v/wXA9OmL+Mc/PgXgzTc/Zfr0\nRXu3X331q1x++Uv84Q8fAkEv3lVXvcLUqa8yc2YxNTXBgfr2zWfWrLGJ/VAiIhH676GIpI2amhqy\ns7P3e1+dhQtLmTr1tcijMOvXlzNp0mBKSnbx6KNnUF1dy5QprzJ6dI9Grw2FQpSXV7NkySbmzTsd\ngCVLtgBw++2LmTv3NLp168h9961g/vx1jB8/gDPP7E1Jie5bE5HkUGgTkbRQU1PDk08+zoUXTiA3\nN7fevsrKSp555kkmTJjYbHAbM6aIO+8cs/fxnDnLqaysZeTIIKTl5GQxZEg31q7dUe914UgXXV5e\nDtdfP4zbbltMeXk1F1wwkC1b9lBWVsGNNwb3y+3ZU8PYsYe1yWcWEdkfujwqImkhOzubCy+cwDPP\nPEllZeXe7XWB7cILJ7TY0xZLbm4WS5ZsAqCqqpalSzfTv38BHTtmU1ZWAcCqVVsBKCurYOXKrcya\nNZZ77jmJH/94CYWFHejZszOzZ4/lgQf+g6uuMk44oajeMZq6LCsi0pZa7Gkzs0OArwLdgb23+br7\nzATWJSIHodzc3L3B7cILJwDs/b5h71u88vNz6NUrjyuvfJnq6lrOPrsPZocwfvwAZsx4h+ef/5h+\n/QoA6NGjE5s2VTB58stkZ2cxZcpnyMnJ4oYbhnHddW8SDocpKOjAzJmj6x0jzWceEZF2IhRu4b+I\nZvYXYBuwHNj7ZHefkdjS9ku4tHRHy886yBQVFaJ2aUzt0li6tUllZSWPP/4YABMnXhYzsK1d+xeG\nDFmY0Dry8zuya9f+LmO1z/LlozjyyC+0YUXpId3Ol3ShdmlMbRJbUVFhq/6rF889bYe7++db8+bN\nMbMTgbvc/YwG288Hvg9UAQ+7+4NtfWwRyXzhcIdUl9CicFi3DYtI24nnnrbFZjasLQ9qZt8Ffgl0\nbLA9B7gb+BxwOjDVzIoavYGItFt197BNnHgZEyde1ugetzr5+f3YuHF/1wVNnrKyWvLy+qS6DBFp\nR+IJbUMIgluJma01sw/MbO0BHvd94IIY2z8DrHH37e5eBbwOnHqAxxKRDBE96CA3N7fePW4Ng9vh\nhx/J5s1nU1JSRW1t+owEqK0Ns2FDFRs3nsURR1iqyxGRdiSevvtY4eqAuPvvzKx/jF1dCO6fq7MD\n6NrWxxeR9FNTUxNz0EF0cGs45Ue/fmPYvHkAK1Z8CFS3eU3duuWxZcv+zsOWQ9eu/Rgw4PA2r0dE\nDm7xhLaPgK8DZ0We/3fgZwmqZztBcKtTCGyN54VFRYUJKSjTqV1iU7s0lg5t8o1vTG1yWo+m9gV1\nD0pwZdJQOpwv6Ujt0pjapO3EE9p+BAwG5hJM+TEZGAh8uw2O33D0xErgqMg0I+UEl0Z/HM8baXRK\nYxq1E5vapTG1SWxql9jULrGpXRpTm8TW2iAbT2g7Gxjp7rUAZrYAWEbbhLZw5D0nAvnu/qCZfQf4\nM0Gge9DdN7TBcUREREQyWjyhLSfypzLqcc2BHtjd1wGfjXz/eNT2BcCCA31/ERERkfYkntD2a+Bl\nM6sLVhOBx5t5vkjG27lzG59++hZZWTvTerb7cBhqawvo1esUOnfOS3U5IiKSQC2GNne/08wWA2cS\nTBFyR6Q3TKRd2rlzGxs3PszQofs7ajA1wuEw7767mr59r1JwExFpx5qcp83Mjo98PRXYBcwHfg/s\niGwTaZc++WQBQ4ZkRmADCIVCDB++jX//+0+pLkVERBKouZ62a4GvAbHWGA0T9LyJtDsdOmTeSKdQ\nKERububVLSIi8WsytLn71yLfXufuy6P3mdnYhFYlklL1l0YqLi7l6ac/4Ac/GLN325w5yxk4sJDz\nzos1R3TbWLZsM3PmLOeBB4KO7Y8/3sn06cVkZYUYNKgL06aNaLZuERFpX5oMbWZ2MpANPGhmU9g3\np1oO8Avg6MSXJ5Iekj0YYd681SxY8BF5efv+it599zK++c3jOP74Htx552JefrmE00/vldzCREQk\nZZq7PPp54DTgCGBm1PZq4P5EFiWSbsJNLG05e/YyFi8uIxQKce65fbnkkkFMn76Ic87py0kn9eTN\nNz/lxRc/ZsaM0Uyfvoj168vZs6eGiRMHMW5cP4qLS7n33hXk5ITo0yefm28+nuzsEH375jNr1li+\n//1Fe4+1cuUWjj++BwAnn9yTf/5zo0KbiMhBpLnLo7cCmNnl7v6rpFUkkoYWLixl6tTXIo/CrF9f\nzqRJgykp2cWjj55BdXUtU6a8yujRPRq9NhQKUV5ezZIlm5g373QA3nprIwC3376YuXNPo1u3jtx3\n3wrmz1/H+PEDOPPM3pSUND0YIi+vAzt3tv1amyIikr7imaftbTO7BygguESaDQx0d40glYxXU1PT\n5FqX4XCYUOS66JgxRdx5Z/172ioraxk5MghpOTlZDBnSjbVrdzR6D4C8vByuv34Yt922mPLyasaN\n68uWLXsoK6vgxhvfBmDPnhrGjj2syVqzsvZdoy0vr6KwsEMrPrGIiGSqJqf8iPIEwaLtI4ElwGHA\n8mZfIZIBampqePLJx6msrIy57733lu0NXbHk5maxZMkmAKqqalm6dDP9+xfQsWM2ZWUVAKxatRWA\nsrIKVq7cyqxZY7nnnpO4557lFBZ2oGfPzsyePZYHHvgPrrrKOOGEonrHiD682SEUF5cB8MYbnzJy\n5KEH9PlFRCSzxNPTluXu082sA/AOwf1sbya2LJHEy87O5sILJ/DMM09y4YUTyM3NBaC6upqVK9/j\nM585bm9PWyz5+Tn06pXHlVe+THV1LWef3QezQxg/fgAzZrzD889/TL9+BQD06NGJTZsqmDz5ZbKz\ns5g0aTA5OVnccMMwrrvuTcLhMAUFHZg5c3S9Y0Qf/tvfHsptt71DdXUtAwcW8rnP9W77RhERkbQV\naq4nAcDM/kkwIGEi0NXd7zGz99z9uGQUGKdwaanmqGqoqKgQtUtjDdulsrJyb3ADWLDg61xyycAm\nL5umq5Ure9K//5RWvVbnSmxql9jULrGpXRpTm8RWVFTYqjkJ4ulpe4xgNYSvAv8ws3OA9a05mEg6\nys3N5cILJ/D4448BcMopo8jO3pbiqlojjRdJFRGRA9biPW3u/jPgQncvBU4HHgDGJ7gukZSpqclP\ndQmtUl1dkOoSREQkgZqbXHequz9gZrdEHkfvHkr9udtEMlbd5dGJEy8D4LHH7qdTpxDHHNP8rQPp\nZOXKjvTooZXlRETas+Yuj4YafBVpd6LvZ6sbiHDZZdfw2GMPsGdPDzp02ENWVvqGt9raEDU1+fTo\ncTpduzaeI05ERNqP5ibXrVv1YBvwuLt/mpySRJKjpqamUWCD4B63yy6byjPPPMmECZdl3IAEERFp\nn+IZiNAb+KeZOcGghGfdvemp2kUyRHZ2NhMmTIwZynJzc5vcJyIikgrxDET4rrsPBO4AxgJLzEzL\nWkm70FwoU2ATEZF0Es+KCJhZCOgA5AK1wJ5EFiUiIiIi9bV4edTM5hBM8bGY4PLof7t7RaILExER\nEZF94rmnbTVwfGSeNhERERFJgXhC2wPA9RZM1HYd8C3gLndvvMq2iIiIiCREPPe0/QwoAEYB1cBR\nwEOJLEpERERE6osntI1y95uAqshUH1cAIxNbloiIiIhEiye0hc0sF6ibFr5H1PciIiIikgTxhLaf\nAH8FDjeznwCLgNkJrUpERERE6mlxIIK7/8rMioEzgGzgfHdfmvDKRERERGSvJkObmU1qsGlH5OsI\nMxvh7o8mriwRERERidZcT9sZka+DCEaMLgBqgHOA9wCFNhEREZEkaTK0uftkADN7CRjm7mWRx92A\n55JTnoiIiIhAfAMRegGbox7vAo5ITDkiIiIiEks8KyIsAP5iZs8ShLyLgScSWpWIiIiI1NNiT5u7\nfwf4OXAMMBj4P3f/fqILExEREZF94ulpw92fAZ5JcC0iIiIi0oR47mkTERERkRRTaBMRERHJAM1N\nrntqcy9091fbvhwRERERiaW5e9pmRL4eSjC57hsEk+t+FlgGnJzY0kRERESkTnOT654BYGZ/Ar7s\n7u9HHvcH7k9OeSIiIiIC8d3T1r8usEV8BPRPUD0iIiIiEkM8U368Y2bzgCcJQt6lwGsJrUpERERE\n6okntE0BrgO+DoSBvxJMtisiIiIiSRJPaPuju58NzEp0MSIiIiISWzz3tHU2s74Jr0REREREmhRP\nT1sP4EMz2wjsBkJA2N2PTGhlIiIiIrJXPKHtnIRXISIiIiLNiie0fQKMAwoIetmygYHALQmsS0RE\nRESixBPangXyCFZFeA04FfhHIosSERERkfriGYhgwJnA74AfAWOA3oksSkRERETqiye0feruYWAV\nMMzdS4COiS1LRERERKLFc3n0PTObA9wH/NrMegEdEluWiIiIiESLp6ftWuBJd19BMPjgCIKlrERE\nREQkSZrsaTOzU2M83gY8A3RPcF0iIiIiEqW5y6MzIl8PBQYBbwI1wGeBZcDJiS1NREREROo0Gdrc\n/QwAM/sT8GV3fz/yuD9wf3LKExERERGIbyBC/7rAFvER0L+1BzSzEPBzYDhQAVzt7muj9n8LuBrY\nGNl0jbuvae3xRERERNqDeEJbsZnNA54kGLhwKcEku601Hujo7p81sxOBuyPb6owCLnf3xQdwDBER\nEZF2JZ7Ro1cDS4GvA18jWA3hvw7gmKcALwC4+1vA6Ab7RwHTzOw1M/veARxHREREpN1oMbS5eyXw\nS4Kg9k3gOaDXARyzC8Eo1DrVZhZdx+MEAfEM4BQzG3cAxxIRERFpF1q8PGpmNwHfAzYBYYJF48PA\nka085nagMOpxlrvXRj2+x923R469ABgJ/KmlNy0qKmzpKQcltUtsapfG1CaxqV1iU7vEpnZpTG3S\nduK5p20KMMjdS9vomG8A5wFPm9lYgulDADCzLsByMzsG2E2w5ulD8bxpaemONiqv/SgqKlS7xKB2\naUxtEpvaJTa1S2xql8bUJrG1NsjGE9o+Aja36t1j+x3weTN7I/J4splNBPLd/UEzmwa8TDCy9G/u\n/kIbHltEREQkI8UT2tYAr5vZSwRBCgB3n9maA0YWn7+2webVUft/Dfy6Ne8tIiIi0l7FE9rWR/5A\ncD+biIiIiCRZi6HN3WdEP45MjjswYRWJiIiISCPxjB79JnAnkB+1+QPgqEQVJSIiIiL1xTO57vUE\nS049QbBw/BTgrUQWJSIiIiL1xRPaNrr7BwSrIgx190cAS2hVIiIiIlJPPKFtl5mdQRDazjezw4Fu\niS1LRERERKLFE9quA75EsF7oocAqYE4iixIRERGR+uKZ8qOXu3878v2FAGb25cSVJCIiIiINNRna\nzOwrQEdgppnd0uA1NwHPJrg2EREREYlorqetC/BZgsXdz4jaXg3cnMiiRERERKS+JkObu/8S+KWZ\nneXuf6vbbmZd3H17UqoTERERESC+gQh5ZvZDMysws5XAWjP7RqILExEREZF94glttwAPA5cAbwMD\ngMkJrElEREREGogntOHuq4AvAn9w951AbkKrEhEREZF64gltn5rZHGA08IKZzQI+SmxZIiIiIhIt\nntA2EVgInOHuu4C1kW0iIiIikiQtTq7r7juAR6Me35vQikRERESkkbjuaRMRERGR1FJoExEREckA\n8aw9ipldChwH3AFc5O6PtvASEREREWlDLfa0mdldwDjgywQhb3JkBKmIiIiIJEk8l0e/AFwOVESW\nr/o8cG5CqxIRERGReuIJbbWRr+HI145R20REREQkCeIJbU8CTwDdzexbwKvAbxJalYiIiIjUE888\nbT80sy8A64B+wHR3/2PCKxMRERGRvVoMbWb2HPAYcLO7Vya+JBERERFpKJ7Lo78ExgP/MrMHzez0\nxJYkIiIiIg3Fc3l0AbDAzDoDXwRmmVkPd++f8OpEREQSqLKykpqamjZ5r927c9i9e3erX5+dnU1u\nbm6b1CLtU7yT6x4LXAJcDHwM/CSRRYmIiCTSpk3r2bLlKQoKtpOT01YTIuSya1fr7yKqrs5i585C\nunWbwKGH9m6jmqQ9ieeetmVANcF9bWe6+4aEVyUiIpIg27aVUVHxG4YNqyLOvou45Od3ZNeuA32X\n3bz//m/Ytm0yXbv2aIuypB2J52y91N2XJbwSERGRJCgrW8nQoVWpLqNJRx1VxbJlK+ja9dRUlyJp\npsnQZmYPuPtU4KdmFm64393PTGhlIiIiCRAKVaS6hBZlQo2SfM31tN0f+XprEuoQERFJkvr9EMXF\npTz99Af84Adj9m6bM2c5AwcWct55bT/mrrY2zG23vcO6dTsJheDmm0dy5JFdmq1RBJoJbe5eHPn2\nIne/Lnqfmc0DXklkYSIiIskSCiXvWK++uoFQCObOPY3i4lJ+9rP3uPvuk5JXgGSs5i6PPggcCYw2\ns+MavOaQRBcmIiKSLOEmOrZmz17G4sVlhEIhzj23L5dcMojp0xdxzjl9Oemknrz55qe8+OLHzJgx\nmmnT/smHH+5gz54aJk4cxLhx/SguLuXee1eQkxOiT598br75eE4/vRennnoEACUl5RQWdkjiJ5VM\n1tzl0duBAcA9wIyo7dXAygTWJCIi0irhcJiSkvUA9OrVm1CcXWgLF5Yydeprde/C+vXlTJo0mJKS\nXTz66BlUV9cyZcqrjB7deERnKBSivLya4uJSHn74NADeemsjALffvpi5c0+jW7eO3HffCubPX8f4\n8QPIygoxffoiXnppAz/+8YkH/sHloNDc5dEPgQ+B4WbWHcgHQkA2MAL4exLqExERicvCp55g2yMP\ncvSydwH429DhdL3yak64+CstvnbMmCLuvLP+PW2VlbWMHBmEtJycLIYM6cbatTvqvS4c6aLLy8th\n2rTjue22xZSXVzNuXF+2bNlDWVkFN974NgB79tQwduxhe187Y8Zo/ud/Krj88pd55pnP06lT9oE1\ngLR78czTdifwDaADsAnoBSwC9F8DERFJCwufeoIBN36HY3fuC1WjFr7FipUrWAhxBbeGcnOzKC4u\n49JLj6KqqpalSzfzpS/1Z9GibMrKgtGdq1ZtBaCsrIL33tvMrFljqaysYdy4Fzj33L707NmZ2bPH\nkp/fgVde2UB+fg4LFnzExo27mTzZyM3NJjs7RFY8i0rKQS+eedomAn0JLpPeDvQDrk9kUSIiIvEK\nh8Nse+TBeoGtzrE7d/DuvIcIXzQh7kuldfLzc+jVK48rr3yZ6upazj67D2aHMH78AGbMeIfnn/+Y\nfv0KAOjRoxOlpRVMnvwy2dlZTJo0mJycLG64YRjXXfcm4XCYgoIOzJw5muOO68attxZz9dWvUlNT\nyw03DCM3V71s0rJ4QtsGd99uZsuB4e7+rJn9KNGFiYiIxKOkZP3eS6KxHL10CSUl6+ndu0/M/aNG\nFTFqVFG9bdddN6TJ9zv22G488cRZjbbPmHECu3btqbdt7NiejB3bs9Fzf/hDXayS/RdPaNtmZpcD\nxcB1ZlYCdEtsWSIiIokRDqf/aM1wWAvHS2PxXEWfAhzm7i8TDEy4H/jfBNYkIiISt169erN66PAm\n968eNoJevfYtwN6ly1GsX5++k9euXx+mS5dBqS5D0lCLPW3uXgLMinyve9lERCSthEIhul55NStW\nrmh0X9uKgkK6XjGl3v1sRUV92bDhAtyf5YgjqujQoW1GAWRlZbF7d+vXNK2qqmXDhhyys79Mr179\n2qQmaV+am1y3lvrraIQij0NA2N1116SIiKSFEy7+CguBd+c9xNFLlwBBD1vXK6bEHDl6xBHHUlFx\nJJs2baSqqm3W+Tz00AI2bdrZ6tfn5HSkqKgnnTp1apN6pP1pbp42DUAWEZGMccLFXyF80YS9k+ue\n1cLkup06daJTp7br0SoqKqRz58YjWEXaSjzztOUCNwAGXAd8C7jL3SsTXJuIiMh+CYVCTY4SFcl0\n8fSm3QsUAKMIlrA6CngokUWJiIiISH3xhLZR7n4TUOXu5cAVwMjEliUiIiIi0eIJbeHIJdK6QQk9\nqD9AQUREREQSLJ7Q9hPgr8DhZvYTgnVHZye0KhERERGpJ54VEZ4nWA3hDCAbON/dlya0KhERERGp\nJ57Q9pq7fwZYkehiRERERCS2eELbu5G1R98GdtdtdPePElaViIiIiNQTT2g7MfInWhg4su3LERER\nEZFY4ll7dGAyChERERGRpmmpKhEREZEMEM/l0TZlZiHg58BwoAK42t3XRu0/H/g+UAU87O4PJrtG\nERERkXTTZE+bmf0w8vWcNj7meKCju38WmAbcHXXMnMjjzwGnA1PNrKiNjy8iIiKScZrrafuKmf0F\n+KmZTQFC0Tvd/dVWHvMU4IXIe7xlZqOj9n0GWOPu2wHM7HXgVOCZVh5LREREpF1oLrTdQdATdgQw\ns8G+MHBmK4/ZBdgW9bjazLLcvTbGvh1A13jetKiosJXltG9ql9jULo2pTWJTu8SmdolN7dKY2qTt\nNBna3P2XwC/N7PvuflsbHnM7EP0TrAtsdfu6RO0rBLbG86alpTvaprp2pKioUO0Sg9qlMbVJbGqX\n2NQusaldGlObxNbaIBvPQIS7I/e3nRV5/t+B77v7rlYdEd4AzgOeNrOxwLKofSuBo8zsEKCc4NLo\nj1t5HBEREZF2I57QNocgQF1FcF/b14BfAJe38pi/Az5vZm9EHk82s4lAvrs/aGbfAf4cOdaD7r6h\nlccRERERaTfiCW2j3H141ONvmlmr1yF19zBwbYPNq6P2LwAWtPb9RURERNqjeCbXzYpcrgQg8n11\n4koSERERkYbiuqcNeNvM5kcefwn4QeJKEhEREZGGWuxpc/eHgS8Da4EPgS+7+9wE1yUiIiIiUeJa\nxsrdlwPLE1yLiIiIiDRBC8aLiIiIZACFNhEREZEM0GJoM7O7Igu51z0+PGpQgoiIiIgkQTw9bd0J\nRo8ea2aXAW8DLyW2LBERERGJ1uJABHefamaXAO8CZcBn3f2DhFcmIiIiInvFc3l0MsH6nzcDLwBP\nmdmIRBcmIiIiIvvEM+XH14HPu/sqADP7IvAcMCCBdYmIiIhIlHjuaTvJ3VeZWTfYuzbo8BZeIyIi\nIiJtKJ7QNtTMVgHvmllvM3sfGJTgukREREQkSjyhbQ5wAbDJ3dcD1wK/SGhVIiIiIlJPPKEtz91X\n1j1w978AHRNXkoiIiIg0FE9o22xmw4EwgJl9Fdic0KpEREREpJ54Ro9eC8wDjjOzrcAa4LKEViUi\nIiIi9cQzue6/gFPMLB/IdvftiS9LRERERKI1GdrM7CUil0QbbAfA3c9MXFkiIiIiEq25nrZbI1+/\nBuwmuERaDUwEOie2LBERERGJ1mRoc/dXAMzs/9z9hKhd/zSzRQmvTERERET2imf0aGczO7rugZkN\nBTokriQRERERaSie0aPfAV42s/VANlAEXJrQqkRERESknnhGj/7ZzAYAQwkGJix19+pEFyYiIiIi\n+7QY2sysP/BNoDsQimzD3a9KcG0iIiIiEhHP5dEngdcifxpNASIiIiIiiRdPaOvg7jckvBIRERER\naVI8o0dfN7PzzSw34dWIiIiISEzx9LRdRHBP297VEICwu2cnqigRERERqS+e0aO9klGIiIiIiDSt\nubVHp7r7A2Z2S6z97j4zcWWJiIiISLTmetpCDb6KiIiISIo0t/bo/ZGvM5JXjoiIiIjEEs/oURER\nERFJMYU2ERERkQyg0CYiIiKSAZobPVpL/WWrqoBaoCOw3d27Jbg2EREREYlosqfN3bMiE+g+AFwB\ndHb3PGAC8HSS6hMRERER4rs8eqK7P+buYQB3fwY4IbFliYiIiEi0eJax2mVmk4EnCULe5cCmhFYl\nIiIiIvXE09N2GfBl4BPg38BZBMFNRERERJIknrVH1wHnm1l3d9+chJpEREREpIEWQ5uZjQB+C+SZ\n2VjgVWCCu7+T6OJEREREJBDP5dGfAhcAm9y9BLgW+EVCqxIRERGReuIJbXnuvrLugbv/hWCuNhER\nERFJknhC22YzG05kol0z+yqge9tEREREkiieKT+uBeYBx5nZVmANwYhSEREREUmSeEaP/gs4xczy\ngWx33574skREREQkWnNrj75E/bVH67YD4O5nJq4sEREREYnWXE/brckqQkRERESa12Roc/dX6r43\ns5FAARACsoGBwCtNvFRERERE2lg8k+vOAz4LdAdWAiOAN4C5iS1NREREROrEM+XHqcCxwFPAVOBE\nIDeRRYmIiIhIffGEthJ3ryLoZRvm7u8BhYktS0RERESixTNP23ozmwb8FfhRZPRoQUKrEhEREZF6\n4ulpmwJ84O4LgWeBSwgm3BURERGRJImnpy0MHBr5/lngMOCt1h7QzDoBj0XeZztwhbtvavCcnwAn\nAzsim/7T3XcgIiIicpCKp6ftN8ARke93EEz78asDOOa1wFJ3PzXyPt+P8ZxRwBfc/czIHwU2hUc2\nOQAAHvFJREFUEREROajFE9r6u/v/Arj79sj3gw7gmKcAL0S+fx74XPROMwsBg4EHzOx1M5t8AMcS\nERERaRfiujxqZkPdfRmAmR0DVMXz5mZ2FfBt9i2HFQI+AbZFHu8AujR4WT7wU+DuSH0vmdlCd1/e\n3LGKijSgNRa1S2xql8bUJrGpXWJTu8SmdmlMbdJ24gltNwB/MbN/E4SuHsBl8by5u8+lwSS8ZvYM\n+6YMKQS2NnhZOfBTd6+IPP/vwHCg2dBWWqorqA0VFRWqXWJQuzSmNolN7RKb2iU2tUtjapPYWhtk\nWwxt7v5XM+sHDCXoYXN339OqowXeAMYBiyJfX2uw/2jgCTMbEanvFOCRAzieiIiISMZr9p42MzvP\nzI5090qgL3AHcJOZxdND15T7gCFm9hpwNTAjcqxvm9l57r4KeJRghOpLwDx3X3kAxxMRERHJeKFw\nOBxzh5ndAHwFuIKgx+sfwP8QLGmV5e7fSlaRcQir+7UxdUvHpnZpTG0Sm9olNrVLbGqXxtQmsRUV\nFYZa87rmetouB05z9xXApcAf3P1B4HrgC605mIiIiIi0TnOhLezu5ZHvzyAyTYe7x+6aExEREZGE\nae7etGozO4RgndGRwJ8BzKw/UJ2E2kREREQkormetruAJcA/gQfdfYOZTQD+BvwoGcWJiIiISKDJ\nnjZ3f9rM3gR6uPvSyOadwNXu/nIyihMRERGRQLNTd7h7CVAS9fhPCa9IRERERBqJZ+1REREREUkx\nhTYRERGRDKDQJiIiIpIBFNpEREREMoBCm4iIiEgGUGgTERERyQAKbSIiIiIZQKFNREREJAMotImI\niIhkAIU2ERERkQyg0CYiIiKSARTaRERERDKAQpuIiIhIBlBoExEREckACm0iIiIiGUChTURERCQD\n5KS6ABERkdaorq5m7dr5dOy4CahOdTls3tyRHTuq2LOnC337nk/nzvmpLknaGYU2ERHJONXV1axe\nPY+RIzeQnZ0eF43y8zuya9cewuFS3n33Yfr2nXxQB7fq6mrKy8spLy9PdSmEQiE6depEKBRKdSkH\nRKFNREQyzr//vZxhw0rIzs5OdSmNhEIhhg/fxvLlbzNo0BmpLifpKisrWbv2Ubp0KaGmpgMVFXtS\nXRK1tVBa2ona2tMYMOCkVJfTagptIiKScWpqtpObm36BrU4oFCIU2pXqMpIuHA7z/vsPM2pUGaFQ\nDvn5uezaFU51WRG1lJb+lY8/zqVv31GpLqZV0qNPWUREZL80DgLFxaVMm/Z2vW1z5iznj39cl9BK\nNm+uYNy45/ngg+31todC6RJWkmfnzh307l2Stpchi4qyqa5eleoyWk2hTURE2o1kZ4Xq6lruuGMJ\nnTqlb69fMpWX76Bz5/QOq1lZFakuodV0eVRERNqNcBN5YfbsZSxeXEYoFOLcc/tyySWDmD59Eeec\n05eTTurJm29+yosvfsyMGaOZPn0R69eXs2dPDRMnDmLcuH4UF5dy770ryMkJ0adPPjfffDzZ2SFm\nz17GxRcPZO7c1cn9oGmsYXAuLi7l6ac/4Ac/GLN325w5yxk4sJDzzuufkBouvfTvFBR0AKB37zym\nT8/My6ENKbSJiEhaCYfDlJSsB6BXr977dalt4cJSpk59re6dWL++nEmTBlNSsotHHz2D6upapkx5\nldGjezR6bSgUory8miVLNjFv3ukAvPXWRgBuv30xc+eeRrduHbnvvhX84Q8fkpOTRffuHRk7tidz\n5/oBfeb2Lpk9oJWVNQA88MB/JO+gSaLQJiIiaWPhU0+w7ZEHOXrZuwD8behwul55NSdc/JW4Xj9m\nTBF33lm/R6eyspaRI4OQlpOTxZAh3Vi7dke914UjXXR5eTlcf/0wbrttMeXl1Ywb15ctW/ZQVlbB\njTe+DYTZs6eWsWMP4513ygiFgmDnvo0bb/wns2adSPfundqgJdqXZPaArl69jYqKav7rv16ntjbM\nN75xHEOHdk/uB04QhTYREUkLC596ggE3fodjd+4LVKMWvsWKlStYCHEHt4Zyc7MoLi7j0kuPoqqq\nlqVLN/OlL/Vn0aJsysqC+5tWrdoKQFlZBStXbmXWrLFUVtYwbtwLnHtuX3r27Mzs2WPJz+/AK69s\nID8/h2uvPXbvMaZOfZXbbz+R7t07tr4BMkBre0GT2QM6bNihTJp0NOPHD+Cjj3byzW++wXPPnU1W\nVnoOjtgfCm0iIpJy4XCYbY88WC+w1Tl25w7enfcQ4YsmtGpUYn5+Dr165XHllS9TXV3L2Wf3wewQ\nxo8fwIwZ7/D88x/Tr18BAD16dGLTpgomT36Z7OwsJk0aTE5OFjfcMIzrrnuTcDhMQUEHZs4c3eAo\nmR8IWnIgvaCJ7wGFPXtqGDv2MM47r4A+fYJJjfv1K+CQQ3IpK6vgsMM6H3gjpJhCm4iIpFxJyfq9\nYSCWo5cuoaRkPb1794m5v7y8kkMOqWby5J6sWbNvio9zzikEwKwLX/xil73b16xZR4cOcPvtR9V7\nnzVr1nHxxd2B7vW2HXooTJs2YO+2srJPKCvb97rvfrcflZWbWbOmMrIlxIYNXejTp4oOHTq08OnT\nXyJ6QRPVA/r733/ImjXbmTZtBKWlu9m1q5oePdrHJWuFNhERyUD7ptjYtm03Gzcu4eijq8jJSV2P\nV25uDpWV+9ZALSws5/33O3LUUVdmdHBLVC9oonpAhw8/lFtvLeaqq14hKyvE9Omj2sWlUYBQuKm7\nAzNLuLS08cl0sCsqKkTt0pjapTG1SWxql9gS0S7hcJi/nXc2Exe+FXP/42PGctb8F/cGg08++YCu\nXR/l0EM78N57bzNiROqXSooObdXVtWzefBRdu/Zi6dKjOPro1t2Plw7Wr/83NScdz6iK2PObFXfq\nRPY/3qF37z58+ul6une/n65dg56tuvVY08nKlT3p339KSmsoKipsVYrU5LoiIpJyoVCIrldezYqC\nwkb7VhQU0vWKKfV6cg4/fCAlJadSWlpJQcHuZJbaourqWkpLD6Nbt95kZYXo2HFTqktKmk6d8qio\nSO9erdra3FSX0Gq6PCoiImnhhIu/wkLg3XkPcfTSJQCsHjaCrldMiXnP1KBBZ/D++10Ihz9g48bU\n90F06tSBiooaQqHO9OjRa+/2UKgqhVUduF69evO3ocMZ1UQv6OphIzirV28ACgu7snp1N3r2LE9m\niXHbubMGSMyEvsmg0CYiImnjhIu/QviiCXunlTirhWklevc+ll27BtKt277ek2TPwF9VVcuttxaz\nYUM5nTtn873vjeCQQ9r8MCmztxd05YpG97U17AXNysqiT58rWbLkEfr23UwolEV5eepDa21tmC1b\nwmzePJbBg09NdTmtptAmIiJpJRQKNTlKNP73aKNi4vDssx+Ql5fDE0+czYoVZdx11xLuvfeU5BWQ\nBPvTC1pQcAiDBl3H1q2b2by5hk2bdqWg4vpCoWwKC3sweHBBqks5IAptIiLS7iRrBv6bbhrJ2rU7\nOPnkngD071/IBx+0z8Er+9MLmp2dzaGHFlFUVEjHju2zPVJBoU1ERNJWus/AP3/+Oo45piuvvfYJ\nX/ziQJYu3UxZWQXhcLhVEwGnu7boBZXWU2gTEZG0lCkz8F9zzWdYu3YHX/3qXxk6tBvHHHNIuwxs\nknoKbSIiknYyaQb+5cu3MGZMEbfccgILF37Chg3pOXJSMp9Cm4iIpJVMm4E/FIJp01bwyCNryM/P\n5pZbRrVJO4g0pBUR2jHN5h6b2qUxtUlsapfYEt0u+zMD/+7du9m580f075/6ZaKamv1/1aoC+vX7\n7xRUlHr6OxSbVkQQEZGDTqdOnaioSH1ga044nJ/qEqSdUGgTEZG00qtXb1YPHd7k/tXDRtArMgN/\nKBRi9+5hlJfXJKu8/bJxYy2hUNOfRWR/6J42ERFJK/szAz/A4MFfZM2abHJz15CVFfuSajLk5+ew\na1ddeAxRU5NHKDSG/v1Hp6wmaV8U2kREJO3szwz8oVCIwYPPBc5NfqFRdP+WJJpCm4iIpKX9XYdU\npL1TaBMRkbSlGfhF9tFABBEREZEMoNAmIiIikgEU2kREREQyQMruaTOzC4CL3P2rMfZ9DZgKVAF3\nuPuCZNcnIiIikk5S0tNmZj8B7gAaDQMys57AdcBJwDnAD8wsvae7FhEREUmwVF0efQO4tol9Y4DX\n3b3a3bcDa4BhSatMREREJA0l9PKomV0FfBsIE/SqhYHJ7v6UmZ3WxMu6ANuiHu8EuiayThEREZF0\nl9DQ5u5zgbn7+bLtBMGtTiGwtaUXFRUV7udhDg5ql9jULo2pTWJTu8SmdolN7dKY2qTtpOPkum8D\nt5tZLtAZOAZY3tKLtHRIY1pSJTa1S2Nqk9jULrGpXWJTuzSmNomttUE2bUKbmX0bWOPufzSznwKv\nE1xSvcndK1NbnYiIiEhqpSy0ufsrwCtRj2dHff8Q8FAq6hIRERFJR5pcV0RERCQDKLSJiIiIZACF\nNhEREZEMoNAmIiIikgHSZvSoiNS3e/duysrWUVWV+OHyW7bks2XLrhaelU2XLr059NDDCIUarUAn\nIiIJptAmkoY2by5h585fMWDAHjp1Svxf0/z8juzatafZ59TWhtm8uZLVq0dz9NHnKbiJiCSZLo+K\npJmqqiq2bn2MY46pSUpgi1dWVogePTpitoQPPng11eWIiBx0FNpE0syWLaX07r0z1WU0KS8vm6ys\nklSXISJy0FFoE0kzVVW76NgxO9VltECLlIiIJFv6XHsRkSYVF5fy9NMf8IMfjNm7bc6c5QwcWMh5\n5/Vv8+PNn7+O+fPXASH27KlhzZpt/PnP4ygo6NDmxxIRkfgotIlkiGTe93/++f05//wgDN511xIu\nuGCAApuISIoptIlkiHA49vbZs5exeHEZoVCIc8/tyyWXDGL69EWcc05fTjqpJ2+++SkvvvgxM2aM\nZvr0RaxfX86ePTVMnDiIceP6UVxcyi9+sYpQKEyfPvncfPPxZGcHCXHFii2sXbuD731vRBI/qYiI\nxKLQJpIC1dXVAOTkxP9XcOHCUqZOfS3yKMz69eVMmjSYkpJdPProGVRX1zJlyquMHt2j0WtDoRDl\n5dUsWbKJefNOB+CttzYCcPvti/ntb8+mY0e4774VzJ+/jvHjBwAwd65zzTXHtPpziohI21FoE0mB\n3/zmFUKhEJdffmbcrxkzpog776x/T1tlZS0jRwYhLScniyFDurF2bf3JeMORLrq8vByuv34Yt922\nmPLyasaN68uWLXsoK6vgW996nZqaMHv21DB27GEA7NhRxUcf7WTUqKID/bgiItIGFNpEUuD11/cQ\nCsHllx/Y++TmZlFcXMallx5FVVUtS5du5ktf6s+iRdmUlVUAsGrVVgDKyipYuXIrs2aNpbKyhnHj\nXuDcc/vSs2dnfv7zUwmFannllQ3k5wf/LLzzThknnKDAJiKSLhTaRJLkoYfms2tXLuFwmOLirgDc\nc88LhEIh8vMrmTLl/P1+z/z8HHr1yuPKK1+murqWs8/ug9khjB8/gBkz3uH55z+mX78CAHr06MSm\nTRVMnvwy2dlZTJo0mJycLG64YRhTp75CdXUNBQUdmDlzNADr1u2gT5/8tmsAERE5IKFwU3c3Z5Zw\naWni12fMNEVFhahdGktVu7zxxnvccss6li27CKibh62GYcOeYsaMAZx88nEArF//Pr16PUZ+fm7S\naotnGatoK1b0YcCASQmsKD3o71BsapfY1C6NqU1iKyoqbNV8AJpcVyRJTj75OJ577j8YPfpXe7eN\nHv0rnnvutL2BDSAvrxu7dqX3f6bC4c6pLkFE5KCjy6MiSdSpUyc2b+5G794vAbB58yF07Nix3nO6\ndu2Ge1969PiUrKz0W5S9pATy8oamugwRkYOOQptIEv3+96/TvfsHzJoVjBq9/vol/OEP2Vx44el7\nn5OVlcVRR13BkiW/JT+/hGQsGVVQUMPOnTUtPCtETU0Bubmfo1cvTQMiIpJsCm0iSTRkSD9+//tT\n6NAhWF3g97+/gn/966NGz+vQoQPHHHOAQ0v3g+47ERFJfwptIkl0zDGD6j0OwtmgJp4tIiKyjwYi\niIiIiGQAhTYRERGRDKDQJiIiIpIBFNpEREREMoBCm4iIiEgGUGgTERERyQAKbSIiIiIZQKFNRERE\nJAMotImIiIhkAIU2ERERkQyg0CYiIiKSARTaRERERDKAQpuIiIhIBlBoExEREckACm0iIiIiGUCh\nTURERCQDKLSJiIiIZACFNhEREZEMoNAmIiIikgEU2kREREQygEKbiIiISAZQaBMRERHJAAptIiIi\nIhlAoU1EREQkAyi0iYiIiGQAhTYRERGRDKDQJiIiIpIBFNpEREREMoBCm4iIiEgGUGgTERERyQAK\nbSIiIiIZQKFNREREJAMotImIiIhkAIU2ERERkQyQk6oDm9kFwEXu/tUY+34CnAzsiGz6T3ff0fB5\nIiIiIgeLlIS2SCg7G1jSxFNGAV9w983Jq0pEREQkfaXq8ugbwLWxdphZCBgMPGBmr5vZ5KRWJiIi\nIpKGEtrTZmZXAd8GwkAo8nWyuz9lZqc18bJ84KfA3ZH6XjKzhe6+PJG1ioiIiKSzUDgcTsmBI6Ht\nGne/tMH2LCDP3XdGHv8QWOruv05BmSIiIiJpIR1Hjx4NvGFmITPrAJwCvJPimkRERERSKmWjRxsy\ns28Da9z9j2b2KPAWUAnMc/eVqa1OREREJLVSdnlUREREROKXjpdHRURERKQBhTYRERGRDKDQJiIi\nIpIBFNpEREREMkDajB7dH2bWBXgM6AJ0AK539382eM7XgKlAFXCHuy9IeqEpoDVdY2uhXQ66c8XM\nOhH8HToM2A5c4e6bGjznoDhfIquw/BwYDlQAV7v72qj95wPfJzg/Hnb3B1NSaJLF0S7fAq4GNkY2\nXePua5JeaIqY2YnAXe5+RoPtB+X5As22yUF5rphZDjAXGADkEvx+mR+1f7/PlYwMbcB3gL+6+0/N\n7GjgcYL1SgEws57AdcDxQB7wupn92d2rUlJtkmhN19iaa5eD9VwhWEZuqbvPNLOvEPzD8a0GzzlY\nzpfxQEd3/2zkl87dkW11/+jeTdAWuwnmkPy9u5emrNrkabJdIkYBl7v74pRUl0Jm9l3gcmBng+0H\n7fnSVJtEHKznymVAmbtPMrNuBL+D5kPrz5VMvTx6N3B/5PsOBB842hjgdXevdvftwBpgWBLrSxWt\n6Rpbk+3CwXuunAK8EPn+eeBz0TsPsvNlb1u4+1vA6Kh9nyGYP3J7JMi/Dpya/BJTorl2geCXzTQz\ne83Mvpfs4lLsfeCCGNsP5vOlqTaBg/dceZLgP8QQ5K3ozoBWnStp39PWzPqlxWZ2OPAr4L8bvKwL\nsC3q8U6gaxLKTQqt6RpbK9ulXZ8r0KhdIGibT9j3uXcQtEO0dn++RGl4DlSbWZa718bYt4N2dn40\no7l2geAKx70El9efM7Nx7v6nZBeZCu7+OzPrH2PXQXu+NNMmcJCeK+5eDmBmhcBTwM1Ru1t1rqR9\naHP3uQTXhOsxs6HAbwjuZ3u9we7t1P8lVAhsTViRSdZUm7SgHPipu1cAmNnfCe5VaTe/hFvZLu36\nXIHY7WJmzxB8Voj9mdv9+RJlO/vaAiA6mLT786MZzbULwD2R3mnMbAEwEmj3v4hbcDCfL805aM8V\nM+sLPAv8zN2fiNrVqnMl7UNbLGZ2LEG34wR3XxbjKW8Dt5tZLtAZOIb2+ctmfxwNPGFmIwh+7qcA\nj6S0ovRwsJ4rbwDjgEWRr6812H8wnS9vAOcBT5vZWCD635SVwFFmdghBkD0V+HHyS0yJJtslMhhs\nuZkdQ3B7ypnAQympMrVCDR4fzOdLnXptcjCfK5F7pl8EvuHuLzXY3apzJSNDG3An0BG4J3LvzVZ3\nv6DB+qU/JbhGHAJucvfKFNabMlrTNTadK9wHzDOz14A9wKVw0J4vvwM+b2ZvRB5PNrOJQL67P2hm\n3wH+THB+POjuG1JVaJK11C7TgJcJRpb+zd1faOJ92rMwgM6XemK1ycF6rkwDDgG+b2a3ELTNLzmA\nc0Vrj4qIiIhkgEwdPSoiIiJyUFFoExEREckACm0iIiIiGUChTURERCQDKLSJiIiIZACFNhEREZEM\noNAmkuHM7CIzW2RmS8zsXTO7oZXvc5qZNZwAsqXX1MbYdo2ZTU3kcfeHmX3NzD40sx8m6hiZwsz6\nm9kHbfRe55nZtyLf79fPXERaJ1Mn1xURwMx6Af8HjHD3rWaWB7xiZqvc/Y+teMv9nbix0fPd/f4k\nHHd/XAJc7e5/TeAxMkXdmrxtYVTde7XyZy4i+0mhTSSz9SD4e1xAsDJIuZldQTDzOGb2OYJQFwLW\nEax8ECJYRqY30At41d2viH5TMxtEsGpCd4IlVv7b3ZdEFoR+jGBB+bdiFWRm04Gwu880sxLgaYJl\nsKoIlp5bZ2ZnEyxGvxvwZo57HcHySf8AfuHuD5vZA8Bmd/9eg+NOBr4D1ALFkdd+BxgD/NzM/jt6\nJvZIj9MTBEs1VREs5nw9cBTBmsZPm9lhwP1An8j73uTuf4uE5YcIFng+Anjc3W+KtP05kfqPBP7s\n7t+I0UY3AV8FqglmRP9/QD9gPvAvYDDwIXBZJIx/AZhJ8LP+APiau2+JfIZfAV8A8oBJ7r64wbFG\nAg8SBKylUdsfBl5y90cjj2vdPSvy8xsL9AV+BqwA7iBY5q1bpNYVwNeBsJmtAwaw72d+HnAbwXm2\nFrjG3UvjqVVEmqfLoyIZzN2XAn8A1prZW2Z2F5Dj7msj66k+Blzu7sMJfmFfAXwRWOzuJxOsMfrZ\nyC/2aPOA77r7aOAa4LeR7T8D5rr78QRrU7bkcOAvkee/BnwzUtcjwJfd/QSC4NbUcZ+ILFJ+BTDT\nzC4BRgP/G30QMxtCsGTMf0Q+azlwi7vfRrC+6pQmls75t7sPARYDNwKfBy6PvBfAPcBDkTr/E7jf\nzPKBicBv3P2zwHDgG2bWPfKak4ALgGHA+WZ2XINazyUIiiMjfwYTBCCAIcDdkZpWAbeaWQ/gLuBs\ndx9FEPJ+FPWWpe5+IkG4vCnGZ5wH3BBp07Ux9teJ7oHr6O5D3P0XwDcJ2m80cDVBu64EfkEQpOdF\nfbaiyPYvufsI4E2CcybeWkWkGQptIhnO3f8L6A/8PPL1H2Y2HhhKEEqWRZ73v+5+r7v/Fvirmf0P\nMIegV6ig7v0ioeQE4GEzWwz8BsiLhJLTgScjT/01QQ9VS16MfF0eOdZQYL27r45sn9fCcbu5+6pI\nrb8i6H2qbnCM04D57r418vgB4Kyo/Q0X9q5TF+TWAa9EAuI6gh4lgM8RhMXFwPNANjDI3WcBH5vZ\n9QTBrgNB7yPAm+5e7u67CUJSXZircyZBz1xl5Hhzo2p1d38tql3OAk4k6IV7KVLHN4BBUe/XsH33\nMrNDgSOiFqt+pIl2aCi6F/VyYKiZ/S9BT2RB7JcAQa/mW+7+ceRxw59Dk7WKSMt0eVQkg5nZOKDA\n3Z8k+CU/z8yuBqYQXO4LRT23C1AIfDny537gLwS9O9GhJhvYHekd+//t3U2ITWEcx/HvjPcVivJS\nykI/FMnGy0LyEqWklBIWhJJY2nmZhIWUjSRLI2UhC6yMxtvCa8aI/lnIRpFGKfIyuhbPc5rrzJk7\nQ4pbv8/mnnPuec7zP/d0p//9P8/TFG2nRERPXnjQChARtaqFCGUR8S1v1uibUzWs7pQiAavqd2pE\nfMi7s4D3wHzS8Fy98g/QFob29+1b3XY5ESxiWlYkg5ImA28lnSANCZ4HLpMSk+Iz/FLXvkb/hLFR\nrD9K533Pr7cjYl2OYSTpORaK/qr6Kh/rrXpP0ohSu/rq5x2gg/QPvztI9zyQ1lJ/rfz6HBrFamaD\ncKXNrLl9Bo7muWZIagFmA49Jc8UmSJqZz91HGoZbDpzJFbcWYB51SVREfAReStqUr7kSuJXfvk6q\nvCBpPTDqD2J+CkyUNCfvb2zQ7828vYZUoVuc73dS6ZqdwFpJ4/L+DuDGH8RW1kGqbCFpNtBFmtu1\nAjgeEZdIVbCp/JqINnID2ChptKThwNa6WCVpbt7eClwD7gOLJM3Ixw8Cx4fSUUT0AK/zkCykeXSF\n90AxdLuuqr2k8aQ5fgfy8PIq+u6zl/6J8T1ggaRpeX8nf+c5mBlO2syaWkR0Am3AFUkvSBWoVuBw\nRHwFNgPnJD0hVaqOkYbzDkl6SJpvdBeYXrr0ZmC7pC7SJPQN+fgeYH2+3mrg4yAhVq0u7SUtiGjP\nMYxp1K+kscAp0grQV8BJ4Gzpmt353m5Jek5aILB/oBgGOV5vL7Awx3OBNDT7KffVLukBacjwIf0/\nw8o+IuIqcCW36SYtLCjmffUAbZKeAROBIxHxFtgGXMxxzCMtsBjqPWwhPe9HpRhPA0vzs1wEvKmI\n9QNpEcPz3H4Cach6DCmR3yRpN32rSN+RErXLkrqBJcCu34jVzBpoqdX8PTIz+9dytbQzIqqSPzMz\nV9rMzP4j/hVtZgNypc3MzMysCbjSZmZmZtYEnLSZmZmZNQEnbWZmZmZNwEmbmZmZWRNw0mZmZmbW\nBH4CFxBv5Him5fQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f331650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = data[:,0]\n",
    "ys = data[:,1]\n",
    "plt.figure(figsize=(10,8))\n",
    "for i in range(0, len(labels)):\n",
    "    if labels[i] == 0:\n",
    "        c1 = plt.scatter(xs[i],ys[i],c='r',marker='o',s = 8*8)\n",
    "    elif labels[i] == 1:\n",
    "        c2 = plt.scatter(xs[i],ys[i],c='k',marker='x',s = 8*8)\n",
    "    elif labels[i] == 2:\n",
    "        c3 = plt.scatter(xs[i],ys[i],c='b',marker='*',s = 8*8)\n",
    "    elif labels[i] == 3:\n",
    "        c4 = plt.scatter(xs[i],ys[i],c='m',marker='^',s = 8*8)\n",
    "plt.legend([c1, c2, c3, c4], ['Long open duration/low inter season variation', 'Medium open duration/high inter season variation','Medium open duration/low inter season variation','Short open duration/high inter season variation'])\n",
    "plt.xlabel('Scaled index of mean open duration')\n",
    "plt.ylabel('Scaled index of relative standard deviation')\n",
    "plt.title('Duration patterns of window operation')\n",
    "\n",
    "housenum = ['House{0}'.format(i) for i in [1,2,3,4,5,7,9,10]]\n",
    "for housenum, x, y in zip(housenum, xs, ys):\n",
    "    plt.annotate(\n",
    "        housenum, \n",
    "        xy = (x, y), xytext = (45, 0),\n",
    "        textcoords = 'offset points', ha = 'right', va = 'bottom',\n",
    "        bbox = dict(boxstyle = 'round,pad=0.3', fc = 'yellow', alpha = 0.5))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}