{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Identifying Similar Stations\n",
    "\n",
    "##Introduction\n",
    "\n",
    "Consider the scenario where the MBTA is looking to conduct a survey of commuter preferences. The MBTA could hire a market research team to survey patrons at each station, but that could be expensive since there are over 60 stations. Instead, the MBTA could survey riders at a few stations and extend the conclusions to similar stations. One natural existing grouping of stations is by line color. MBTA stations along the same route are colored Red, Orange, Green, or Blue. This grouping is helpful when looking at a map and planning a trip, but this relationship has nothing to do with the ridership patterns of the stations. \n",
    "\n",
    "The motivation of this notebook is to apply Machine Learning techniques to find stations with similar *ridership patterns*, regardless of line color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Libraries.\n",
    "import matplotlib, matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"paper\")\n",
    "\n",
    "# Setup.\n",
    "% matplotlib inline\n",
    "\n",
    "# Paths.\n",
    "path_data = '../../../data/gatecount_%d/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Load in Processed Dataset\n",
    "\n",
    "Running the next cell will load in a processed data set that has the necessary columns for the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>locationid</th>\n",
       "      <th>entries</th>\n",
       "      <th>servicedate</th>\n",
       "      <th>servicetime_fraction</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 0</td>\n",
       "      <td> 1002</td>\n",
       "      <td> 0</td>\n",
       "      <td> 2013-01-01 00:00:00</td>\n",
       "      <td> 3.00</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1002</td>\n",
       "      <td> 1</td>\n",
       "      <td> 2013-01-01 00:00:00</td>\n",
       "      <td> 5.00</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2</td>\n",
       "      <td> 1002</td>\n",
       "      <td> 2</td>\n",
       "      <td> 2013-01-01 00:00:00</td>\n",
       "      <td> 5.25</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 3</td>\n",
       "      <td> 1002</td>\n",
       "      <td> 3</td>\n",
       "      <td> 2013-01-01 00:00:00</td>\n",
       "      <td> 5.50</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 4</td>\n",
       "      <td> 1002</td>\n",
       "      <td> 6</td>\n",
       "      <td> 2013-01-01 00:00:00</td>\n",
       "      <td> 5.75</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  locationid  entries          servicedate  servicetime_fraction  \\\n",
       "0           0        1002        0  2013-01-01 00:00:00                  3.00   \n",
       "1           1        1002        1  2013-01-01 00:00:00                  5.00   \n",
       "2           2        1002        2  2013-01-01 00:00:00                  5.25   \n",
       "3           3        1002        3  2013-01-01 00:00:00                  5.50   \n",
       "4           4        1002        6  2013-01-01 00:00:00                  5.75   \n",
       "\n",
       "   weekday  month  \n",
       "0        1      1  \n",
       "1        1      1  \n",
       "2        1      1  \n",
       "3        1      1  \n",
       "4        1      1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This dataset is now being using\n",
    "\n",
    "gatecount_1315=pd.read_csv('../../../data/gatecounts_edit_1315.csv')\n",
    "gatecount_1315.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "##Station Data\n",
    "\n",
    "Aside from the gate fare data, I also load in some general information about each station. Once the groupings are created, I will append a column containing each station's group so that this information can be used in subsequent analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gate_stations=gatecount_1315.locationid.unique()\n",
    "\n",
    "# function to preprocess the station id numbers. Only want to keep the stations that appear in gate_count\n",
    "# For example, silver line stations are included in Stations.csv, but do not appear in gate fare records\n",
    "def is_in_gate_count(id):\n",
    "    #print id\n",
    "    if(len(np.where(gate_stations==id)[0])==0):\n",
    "        return False\n",
    "    else :\n",
    "        return True\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stationid</th>\n",
       "      <th>nameshort</th>\n",
       "      <th>namelong</th>\n",
       "      <th>name</th>\n",
       "      <th>line_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td> 1002</td>\n",
       "      <td> Andrew Square</td>\n",
       "      <td> 1002</td>\n",
       "      <td> Andrew Square</td>\n",
       "      <td> Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td> 1004</td>\n",
       "      <td>    JFK/U Mass</td>\n",
       "      <td> 1004</td>\n",
       "      <td>    JFK/U Mass</td>\n",
       "      <td> Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td> 1005</td>\n",
       "      <td>  North Quincy</td>\n",
       "      <td> 1005</td>\n",
       "      <td>  North Quincy</td>\n",
       "      <td> Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td> 1006</td>\n",
       "      <td>     Wollaston</td>\n",
       "      <td> 1006</td>\n",
       "      <td>     Wollaston</td>\n",
       "      <td> Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td> 1007</td>\n",
       "      <td> Quincy Center</td>\n",
       "      <td> 1007</td>\n",
       "      <td> Quincy Center</td>\n",
       "      <td> Red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    stationid      nameshort namelong           name line_temp\n",
       "20       1002  Andrew Square     1002  Andrew Square       Red\n",
       "21       1004     JFK/U Mass     1004     JFK/U Mass       Red\n",
       "22       1005   North Quincy     1005   North Quincy       Red\n",
       "23       1006      Wollaston     1006      Wollaston       Red\n",
       "24       1007  Quincy Center     1007  Quincy Center       Red"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_info=pd.read_csv('../../../data/Stations.csv')\n",
    "# remove rows where line==Nan\n",
    "station_info=station_info[~station_info['line_temp'].isnull()]\n",
    "# Take out bus stations\n",
    "station_info=station_info[station_info['line_temp']!='Bus']\n",
    "# Take out ids that aren't found in gate_count\n",
    "station_info=station_info[map(is_in_gate_count,station_info['stationid'].values)]\n",
    "\n",
    "# Remove some columns\n",
    "station_info=station_info[['stationid','nameshort','namelong','name','line_temp']]\n",
    "station_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# save cleaned info for later\n",
    "station_info.to_csv('../../../data/Stations_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n",
      "12\n",
      "14\n",
      "19\n"
     ]
    }
   ],
   "source": [
    "red_stations=station_info[station_info['line_temp']=='Red']\n",
    "red_stations_ids=list(red_stations.stationid.values)\n",
    "# Park is only listed on Green in stations.csv. Want to add it to red because its a junction\n",
    "red_stations_ids.append(1052)\n",
    "# Downtown Xing is only listed on Orange in stations.csv. Want to add it to red because its a junction\n",
    "red_stations_ids.append(1039)\n",
    "print len(red_stations_ids)\n",
    "\n",
    "\n",
    "blue_stations=station_info[station_info['line_temp']=='Blue']\n",
    "blue_stations_ids=list(blue_stations.stationid.values)\n",
    "# Add govt center junction\n",
    "blue_stations_ids.append(1051)\n",
    "# add state st\n",
    "blue_stations_ids.append(1077)\n",
    "print len(blue_stations_ids)\n",
    "\n",
    "green_stations=station_info[station_info['line_temp']=='Green']\n",
    "green_stations_ids=list(green_stations.stationid.values)\n",
    "#add haymarket\n",
    "green_stations_ids.append(1076)\n",
    "#add north station\n",
    "green_stations_ids.append(1075)\n",
    "print len(green_stations_ids)\n",
    "\n",
    "orange_stations=station_info[station_info['line_temp']=='Orange']\n",
    "orange_stations_ids=list(orange_stations.stationid.values)\n",
    "print len(orange_stations_ids)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Comparing Stations\n",
    "\n",
    "The idea of similar stations needs some clarification. Some stations are just as busy in the morning as they are in the evening, while others are busiest during the morning rush hour with no traffic in the evening. One could consider two stations similar if they are busiest at the same time.\n",
    "For each station, we computed its mean entries at each fifteen minute interval. For simplicity, we only considered weekdays in our dataset as those patterns are much more stable than weekend ridership patterns. One issue that arises when comparing stations is that some have ridership figures that are more than an order magnitude larger than others. We can remove magnitude from each time series so that they have a mean of zero with unit variance. Once scaled, we can look at the time series for two stations in one plot and start to identify similar stations based on their ridership patterns.\n",
    "\n",
    "Once all series are on an level playing field, the zero-lag correlation of pair-wise comparisons of stations can tell us how similar two stations are. You may be familiar with the use of correlation in the context of two univariate samples, X and Y. [Correlation](http://en.wikipedia.org/wiki/Correlation_and_dependence) is defined by $$\\rho_{corr}(X,Y)= \\frac{E[(X-\\mu_X)(Y-\\mu_Y)]}{\\sigma_X \\sigma_Y} $$ In the case of two univariate samples, correlation coefficients can be interpreted as...\n",
    "- 1 if the two samples are essentially the same\n",
    "- -1 if the stations are opposites of one another\n",
    "- 0 if the stations have no relationship with each other\n",
    "\n",
    "The two-sample univariate case can be extended to this time series set up where a correlation of 1 denotes that two stations have very similar ridership patterns, and so forth.\n",
    "\n",
    "Below is a function that computes the pair-wise correlation between a reference station and each station in a list of comparison stations. Note that we are only considering the weekday ridership patterns when computing the correlation\n",
    "\n",
    "The sample output below shows an example of two time series with of correlation < 0.45 look like when plotted together. This function only considers the morning rush hour but all other analysis in the notebook will consider from 5 a.m. until midnight.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ErCsYXux+LD9ZFzM6d4FfNHrpG+BezBfIy1KlsWJ5AjOJvTamjeX/5StHaC8V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\nnMjb3Im8zvaBVwGjgH7Ag5afLFsuQ1V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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ee58a70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Want to compare portions of time series across various stations. \n",
    "\n",
    "def compare_series(station_id,comparison_station=False,begin_time=4.5,end_time=9.5, print_plot=False):\n",
    "    \n",
    "    if(not comparison_station):\n",
    "        comparison_station=[station_id]\n",
    "    \n",
    "    station_records=gatecount_1315[gatecount_1315['weekday']<5]\n",
    "    station_records=station_records[(station_records['servicetime_fraction']<=end_time) & (station_records['servicetime_fraction']>=begin_time) ]\n",
    "    #get subset of station \n",
    "    base_records=station_records[(station_records['locationid']==station_id)]\n",
    "    base_grouping=base_records[['servicetime_fraction','servicedate','entries']].groupby('servicetime_fraction')\n",
    "    base_entries=base_grouping.agg(np.mean)['entries']\n",
    "    \n",
    "    # Standardize the entries so we can indentify stations with similar behavior without worrying about scale\n",
    "    base_entries_scaled=preprocessing.scale(base_entries)\n",
    "    \n",
    "    correlations=[]\n",
    "    count=0\n",
    "    \n",
    "    for station in comparison_station:\n",
    "        \n",
    "        comp_records=station_records[(station_records['locationid']==station)]\n",
    "        comp_grouping=comp_records[['servicetime_fraction','servicedate','entries']].groupby('servicetime_fraction')\n",
    "        comp_entries=comp_grouping.agg(np.mean)['entries']\n",
    "        \n",
    "        comp_entries_scaled=preprocessing.scale(comp_entries)\n",
    "        \n",
    "        # np.correlate for two series of the same length returns a correlation which is <= length of the two series\n",
    "        # since each pairwise comparison of the terms can yield an individual correlation of 1. Want to think of correlation\n",
    "        # of series from [-1,1] so I divided by the length of the series\n",
    "        \n",
    "        correlations.append((np.correlate(base_entries_scaled,comp_entries_scaled)*1.0/len(base_entries_scaled))[0])\n",
    "        \n",
    "        #plt.figure(figsize=(12,8))\n",
    "\n",
    "        # Print dissimilar stations\n",
    "        if(print_plot& (correlations[count]<0.45)):\n",
    "            plt.plot(base_entries_scaled,label='Base',lw=2)\n",
    "            plt.plot(comp_entries_scaled,label='Comp',lw=2)\n",
    "            plt.title('Comparison of Station '+str(station_id)+' and Station '+str(station))\n",
    "            plt.legend()\n",
    "            plt.xticks(range(13), list(comp_entries.index))\n",
    "            plt.show()\n",
    "        count+=1\n",
    "        \n",
    "    return correlations\n",
    "\n",
    "# sample output\n",
    "t=compare_series(1005,comparison_station=red_stations_ids,begin_time=6.5,end_time=9.5,print_plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "source": [
    "##How to Represent Correlation\n",
    "The function above outputs a vector of correlation values for each pairwise comparison of the reference stations with each of the other stations. Calling this function for each station on the red line as the reference station against all other stations on the red line, we have n vectors, where $n =$ the number of stations on the red line, each of length $n$.  What we need is a good way to represent the pairwise comparisons not just for one station, but for *all* stations. [Similarity matrices](http://en.wikipedia.org/wiki/Similarity_matrix) are an intuitive and condensed way to represent comparisons between many stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_ids=red_stations_ids\n",
    "\n",
    "temp_ids.sort()\n",
    "\n",
    "correlation_vectors=[]\n",
    "\n",
    "for station in temp_ids:\n",
    "    print station\n",
    "    output=compare_series(station,comparison_station=temp_ids,begin_time=5.,end_time=19.5)\n",
    "    \n",
    "    correlations_df=pd.DataFrame(zip(temp_ids,output))\n",
    "    #correlations_df=correlations_df.sort([0])\n",
    "\n",
    "    correlation_vectors.append(list(correlations_df[1].values))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iORMmFInh4VGtf7/edv31BVoCVx54YJGcc7feukiOChncCkx3P9Abuu6IiNHA\nt4GftLLUihUYSZLa12BWYJYDUzs9Hl/v+z8RsRvwfeCazPxoKy9mB0aSpHY1uBWYa4BTImIX4Amq\nlfmP2XgwIramuu3QNzPzM602wA6MJEnta9AqMJm5IiJOBJZQTaO+MDOXRsQi4JPAs4GXAltFxOH1\n027NzPf15/XswEiS1K4G+W7UmXkpcGmXfYfUm7cBxS6SsgMjSVK7ciVeSZLUQMP7XkgRMQlYmJmT\nI2IOMBEYDcwGdgJOAx4Clmbmgoi4iKpM1AF8MTNvGZDWS5Kkng3nCkxEjANmAmsjYhQwNTNnRMQ0\nqquLf0C1GM0K4HJgAfDXwC1U87/vHJimS5KkXgzfCkxmrgTmRsRiYGdgVX1oOTA+M2+LiPFUU6OW\n1MfmZOb1EXEI8CHgjPJNlyRJmzScKzBdrALG1tsTgBURsQ9wX2YeHBGXR8SzgRcC1wMPA9sUa60k\nSdocw7cC00lHZq6PiCURMR8YA8yiWgH9gohYBtyTmb+NiL0i4gvAM4A55ZstSZJ6NcjTqAdTn99Z\nZk6vv87rcuhWqrtNdj73w603TZIktcQhJEmS1EAOIUmSpIaxAiNJkhrICowkSWoYKzCSJKmB2rYC\nM2KoXvgG+F6JnNElQoDtCuX81UEHlQm67rqWI77f0dF6O4CD//CHIjk3jhpVJKfUrUzXF8pZXijn\nVwUyPlogA2DH5zynSM7v7r23SM7YY44pksPq1WVy1q0rk3PNNS1HrF+zpkBDoOv00v6aUShnEryp\nUNSwtXTp0o6XPf74yoHIvm2HHcZNmTJlyPoQYAVGkqT25RCSJElqoLYdQrIDI0lSu7ICI0mSGsgK\njCRJapiBuhdSoUkirbADI0lSuxqoIaQ//rEZQ0gRMQlYmJmTI2IOMJFqBvNsYCfgNOAhYGlmLqjP\n2RV4FnBqZt49IK2XJEmbMnyHkCJiHDATWBsRo4CpmTkjIqYBxwA/AE4AVgCXR8RiYH/gf4D/Be4b\nmKZLkqRNGs4VmMxcCcytOyY7A6vqQ8uB8Zl5W0SMB64ElgB71s/7SEQcBbwL+OoAtF2SJG3a8K3A\ndLEKGFtvTwBWRMQ+wH2ZeXBEXA78K/Bofc5DVMNIkiRpsDmNGoCOzFwfEUsiYj4wBpgF7AVcEBHL\ngHsy87cRsSwizgN2BI4t32xJktQHVmAyc3r9testM24FDuty7omtN02SJLVkoKZRbwHa951JkjTc\nOYQkSZLdLmzUAAAgAElEQVQayCEkSZLUMFZgJElSA1mBkSRJDWMFprwDPv3pIjk3nFhmwlPH9WVu\nTDXzE0ViePGs1jP2+eKI1kOAo2dtWyTnS38o8xlfd12RGMaMKZPz+1eU+Zw/WiDjrAIZACPvvbdI\nztM+X+Z7/vOfF4nhV8vK5Oy6a5mcfda0/rMz+pwyn/FHPlzm5/jMIikqyAqMJElqGKdRS5KkxnEI\nSZIkNZBDSJIkqWEGuQITEe8ETgS2Bc7OzPO7HH8pcCHwDOA/gVmZub4/DbADI0lS+xq0CkxE7A58\nCpgMPAX8OCKWZOYvO512MXB0Zt4SEf8CHAN8qT+v16cOTERMAhZm5uSImANMBEYDs4GdgNOo7jy9\nFPghsHGK0cuoemBf7U/jJElSCwa3AvNa4NrMXA0QEZdT3Svx9PrxRGC7zLylPv9rwKkMVAcmIsYB\nM4G1ETEKmJqZMyJiGlXP6QfACcAK4PLMXAAcVTf043ZeJEkaMoN5DcxuwIOdHj8A7Nvp8fh630YP\nAhP6+2Jb9XZCZq7MzLnA48DOwKr60HJgfGbeBvwRuBK4qdNTPwF8sr8NkyRJrekYOXJA/vSgu8WE\nNmzG8c2yudfArALG1tsTgBURsQ9wX2YeHBGXR8Ro4GlAR2au7G/DJElSazrYajCHkJYDUzs9Hl/v\n63y88zKQu1GN3vTL5nRgOjJzfUQsiYj5wBhgFrAXcEFELAPuycxHI+Ig4I7+NkqSJLWuo2NQh5Cu\nAU6JiF2AJ4BDqS41ASAz74uI30fEfpn5Y+A9wFX9fbE+d2Ayc3r9dV6XQ7dSXaTT+dxv97dBkiSp\njPXrGbQKTGauiIgTgSVU06gvzMylEbEIOCkzbweOAC6MiB2B24GufYo+cxq1JEltapArMGTmpcCl\nXfYd0mn7p8ArSryWHRhJktrUYFZgBpsdGEmS2tRgV2AGkx0YSZLa1Lp1Q92CgWMHRpKkNuUQkiRJ\nahyHkAbCAQcUidm2SAosuqZMzo03lslZvbr1jINajwDg7rvL5Fx9dZmcUp/xmDFlcl5QJoYdn/Oc\nljNG3ntvgZZAqarzddeVyVm2rEzOHYVWp5rQ78XP/9zeBTKuKfRv14de9aoiOdvddFPvJ2nQWIGR\nJEmNYwVGkiQ1jhUYSZLUOFZgJElS4ziNWpIkNc6wH0KKiEnAwsycHBFzgInAaGA2sBNwGvAQsDQz\nF0TEecAGYATwycx8ZEBaL0mSejSsh5AiYhwwE1gbEaOAqZk5IyKmUd0m+wfACcAK4PKI+A6wW2a+\nNSKmUHVyPjlQb0CSJHVvWFdgMnMlMDciFgM7A6vqQ8uB8Zl5W0SMB64ElmTm6oi4LiK+DCwDxg1Q\n2yVJ0iYM6wpMF6uAsfX2BGBFROwD3JeZB0fE5RExBliTme+PiL+l3JpYkiRpMwzrCkwnHZm5PiKW\nRMR8YAwwC9gLuCAilgH31BWYiRHxJarrY2aVb7YkSeqNFRggM6fXX+d1OXQrcFiXc09tvWmSJKkV\nTqOWJEmN4xCSJElqHIeQJElS4wxUBWbrra3ASJKkAWIFRpIkNc5AVWBGjrQCI0mSBogVmAFwxYEH\nFsl56znnFMm58cMjiuScXSQFfv+z1jMOv+SS1kOAfY8o89l894YiMexeJobtCuW8ZcGCIjm/e+97\nW8542uc7CrQErruuSAwv+16Zn523FkmBNYVytr6/TM5N7279+3XgN8p8xj8pkgL731bmZ5CXlXlf\nw53TqCVJUuM4jVqSJDWOQ0iSJKlxrMBIkqTGsQIjSZIaZ9hXYCJiErAwMydHxBxgIjAamJ2ZD9Xn\nXAJ8NzO/1dM5kiRp8LRzBWar3k6IiHHATGBtRIwCpmbmccBFwDH1OR+lmqHY0dM5kiRpcK1bNzB/\ntgS9VmAycyUwNyIWAzsDq+pDy4DxEfEm4BHgJmBEl3OWA+NLN1qSJPVu2A8hdbIKGFtv7wGsAI6g\n6sA8H/gjsKTTOROoOjGSJGmQtfMQ0uZ0YDoyc31ELImI+cAYYFZmrgGIiPcCT2bmqq7nlG+2JEnq\njRUYIDOn11/n9XB8Qaftbs+RJEmDxwqMJElqHCswkiSpcbaECkxEPBu4GHgmcBdwRGY+3uWc3YCv\nAuOADcDxmblkU7m9TqOWJEnNtIVMoz4fOC8zXwAsBU7q5pzPUq0ltw/wDuBfI2KTtyS3AiNJUpsa\n6iGkiNgGmArMqHd9DbgeOKHLqf9GNYsZ4B5gO+DpVGvMdcsOjCRJbWoLGELaBXgsMzfUjx+kWmLl\nz2Tmv3d6eDxw+8ZZzj2xAyNJUpsazApMRBwOnNVld3bz3A3d7NuY8RGqFfwP7K0BQ9aB2blU0J57\nFokptVzwY4Vyti8RcsABJVK4vUgKPKdQztjeT+mTbQvl8Pa3F4kZe+ONLWf8/OcFGgIsW1Ym561l\nYvifQjljCuU8WSjn7rtbz3hN6xEA7DNik5cb9Nm81n+MVdBgVmAy8zLgss77ImIk8LuIGJGZHcBu\nVIvg/oWI+CzwBuBvMrPbczrzIl5JktrUUF/Em5nrgBuAjf/Tew9wVdfz6srLNOCAvnRewCEkSZLa\n1lBfxFs7FlgQEZ8A7qOaZUREvB8Yn5knA58EHgWui4iNz3tDZj7YU6gdGEmS2tQWcBEvmflb4KBu\n9n+50/ZmX1liB0aSpDa1hVRgBoQdGEmS2tSWUIEZKH3uwETEJGBhZk6OiDnARGA0MDszH6rPuYRq\nJb1vRcTWwD8C6zPzswPQdkmStAnDvgITEeOAmcDaiBgFTM3MGRExjWq+9hkR8VGqFfM66qcdAzwN\neLybSEmSNMDauQLTp2nUmbkyM+dSdUZ2BlbVh5YB4yPiTcAjwE3AiPo5XwKuLd5iSZLUJ0M9jXog\n9ecamFX8aS2xPagWpDmCqgPzfGBdRPwwMx8u00RJktQfw34IqZOOzFwfEUsiYj7VwpazNt6vICLe\nCzxp50WSpKHXzkNIm9WByczp9dd5PRxf0OXx9VR3nZQkSYPMCowkSWocKzCSJKlxrMBIkqTGsQIj\nSZIaZ0uZ8jwQ7MBIktSmHEIaAJt928kePPHaGUVyHiuSAqXmjxf5xowZUyKFDUVSqoWCSvhDoZxR\nhXJ+s2zbIjnPXb265YxfLSvQEOCOO8rkrCkTQ5mfZGj9E66U+Y6X+ZxX9X5Kn3R0dPR+Uh+U+tlR\nGQ4hSZKkxrECI0mSGscKjCRJahwrMJIkqXGswEiSpMZxGrUkSWqcYT+EFBGTgIWZOTki5gATgdHA\n7Mx8qD7nEuC7mfmtiDgXeAqYAJyQmfcOTPMlSVJPhvUQUkSMA2YCayNiFDA1M2dExDTgGOCMiPgo\n1ZIPHRGxA7A4M6+KiEOB1wEXDNg7kCRJ3RrWFZjMXAnMjYjFVOvPbVw3aRkwPiLeRLVG2U3AiMx8\nHLiqrtq8DTh6QFouSZI2aVhXYLpYBYytt/cAVgBHUHVgng+si4gfAn8DvBo4MjOfLNRWSZK0GYZ1\nBaaTjsxcHxFLImI+1ereszJzDUBEvBd4EtiJasjoauCCiLgsM79buuGSJGnTrMAAmTm9/jqvh+ML\nOj18VovtkiRJLXIatSRJahyHkCRJUuM4hCRJkhrHCowkSWocKzCSJKlxrMAMgKcK5Wz/4G+K5JT6\nIErlbFUiZPXqEimUuoh9RKGcbQvljCqUM2FCoaAC0wV23bVAOyj3nra+v0xOqcWkSv3slPr3a889\nW88Y+cvWM6Dceyr290FFWIGRJEmN4zRqSZLUOA4hSZKkxnEISZIkNY4VGEmS1DhbQgUmIp4NXAw8\nE7gLOCIzH+/h3B2BO4CjM/P6TeXagZEkqU1tIRWY84HzMnNhRHwCOAk4oYdzz6O6WXRHb6F96sBE\nxCRgYWZOjog5wERgNDA7Mx+qz7kE+G5mfisiTgee3umcR/vyOpIkqZyhrsBExDbAVGBGvetrwPV0\n04GJiLcBjwE/pQ8rb/S63EhEjANmAmsjYhQwNTOPAy4CjqnP+Siwhj/1mO7IzNnAL4DJvb2GJEkq\nb926gfmzGXYBHsvMDfXjB4G/WC2oHmb6EHB8vav1CkxmrgTmRsRiYGdgVX1oGTA+It4EPALcRN1j\nyswrImIW8HaqcS9JkjTIBnMIKSIOB87qsju7ee6Gzg8iYiuqoshxmfmHiIA+VGA29xqYVcDYensP\nYAVwBFUH5vnAuohYArwgM78UET8CPgbM3szXkSRJLRrMIaTMvAy4rPO+iBgJ/C4iRmRmB7AbVd+h\ns72o+hBfqTsvk4B/iYi/29SFvJvTgenIzPURsSQi5lNdZDMrM9fUjXwv8GRmroqIT0bEYcAzgLM3\n4zUkSVIhQ30Rb2aui4gbqEZkLgXeA1zV5ZxfAM/e+LguhJycmf+5qew+d2Ayc3r9dV4Pxxd02j6u\nr7mSJGlgDPVFvLVjgQX1DKT7gHcARMT7gfGZeXJ/Qp1GLUlSmxrqCgxAZv4WOKib/V/u4fy/OLc7\ndmAkSWpTW0gFZkDYgZEkqU15N2pJktQ4W8IQ0kCxAyNJUptyCGkAvHzRoiI5Nz7veUVyHv18r4v+\n9cnppxSJ4aUvbT1j/MRe1wHqk3NfVeazOfPMIjHceGOZnDFjyuQ8PKrM5/y2HXdsOWOfNWXasneR\nFLjp3WV+du6+u0gMd9xRJmfPPcvkvO2XrX+/flLoMx51cxTJGXt6mZ9BlWEFRpIkNY4VGEmS1DhW\nYCRJUuNYgZEkSY3jNGpJktQ4DiFJkqTGGfZDSBExCViYmZMjYg4wERgNzAZ2AL4D/AR4IDM/HhHn\nAk8BE4ATMvPeAWm9JEnq0bCuwETEOGAmsDYiRgFTM3NGREwDjgHuBx4AOoAfR8QOwOLMvCoiDgVe\nB1wwUG9AkiR1b1hXYDJzJTA3IhYDOwOr6kPLgPHAFcAP6/3X8KfOyyTgbcDRA9FwSZK0acO6AtPF\nKmBsvb0HsALYB7gpMzsiYg2wdUS8CXg1cGRmPlmstZIkqc/auQKz1Wac25GZ64ElETEf+DvgPODX\nwOfq616+R9WxuQAYA1wQETMKt1mSJPXBunUD82dL0OcKTGZOr7/O63Lodqqhos6e1WK7JElSixxC\nkiRJjdPR0dG2Q0h2YCRJalsdVmAkSVLTWIGRJEmNs36oGzBg7MBIktS2NjiEVNq1hxxSJOc1P/pR\nkZxb9x9RJOfEIimw7obWM95x0UWthwB7zCzz2fzX/kViGF0mhm0K5bzt+uuL5Kw/8MCWM0af01Gg\nJXDNNUViOPAbZX52XlMk5U+rcLZq5C/L5Pzk3a1/v55X6DMu9JZ4wffL/AxycJn3JYeQJElS41iB\nkSRJjWMFRpIkNY7TqCVJUuNYgZEkSY0zzKdRR8QkYGFmTo6IKcBnM/PV9bHPAttSTQ55PzAF+Hvg\n98BlmfmDAWm5JEnqxTC+iDcixgEzgccj4kXA66m7dBHxXOAZmTkrIo4E3gqsBD5INUv1TMAOjCRJ\nQ2IYDyFl5kpgbkQszsw7gTsjYmp9eBywrN5eBuydmZdGxF8DXwbOG4hGS5KkvhjGFZhe3A/sXm/v\nAayIiAOAWzJzv4j4D+BfW3wNSZLUL8O4ArMpmbksIh6OiC8AO1JdA3Mw8PWIWAVcVaCNkiSpX6zA\nkJlv6GG76+r5i+o/kiRpSFmBkSRJjbNhqBswYOzASJLUtoZ+CCking1cDDwTuAs4IjMf73LOtsDn\ngQOo+iYfycxrN5VrB0aSpLa1RQwhnQ+cl5kLI+ITwEnACV3O+Udgp8zcJyJeSLUEy4RNhdqBkSSp\nbQ1tBSYitgGmAjPqXV8DrucvOzD/P/BOgMz8RUS8NiK2yswex8DswEiS1LaGvAKzC/BYp47Ig3Rf\nWZkETIuIrwJ/BD6emb/aVLAdGEmS2tbgVWAi4nDgrC67s5vndldVGQnsnpkvrxfD/X5E7JWZj/XU\ngCHrwKwqlPPf++9fJGdKkRTYt1DOigIZV86cWSAF3jh3bpGclWecUSRnTJGUctfmX3nggUVyfl0g\n4yMfHlEgBT70qlcVyflJkRTYZ0SZ99XR0VEk56kiKTDq5mg545cF2gGwsFDOsQeX+V6plMGrwGTm\nZcBlnfdFxEjgdxExIjM7gN3o/lfcg8A365yfRcT9QABLe3q9rUo1XJIkbWnWD9CfvsnMdcANwNvr\nXe+h+0Vuv7fxnPo+i8+mmrHUI4eQJElqWx1DPo0aOBZYUM9Aug94B0BEvB8Yn5knU13Ue15E/Lx+\nzszMXLOpUDswkiS1rSG/iJfM/C1wUDf7v9xpew3w3s3JtQMjSVLbGvqF7AaKHRhJktrW0FdgBkqf\nOzARMQlYmJmTI2IK8NnMfHV97G+pxqsOj4hdgC9QXVG8C3BMZv5xANouSZI2aZhXYCJiHDATeDwi\nXgS8HlhXH5sGPBd4en36s4B/ysyfR8Q84Dl0Pw9ckiQNqGFegcnMlcDciFicmXcCd0bE1PrYdcB1\nEfGm+vEvACJiOrA+M+28SJI0JPo+5blpBuQamIg4CXg0M2cPRL4kSeqLgRpC2qoZQ0h91AEQEe8B\n3gXcEhHfAE7LzBKLjEqSpM0yzIeQNsrMN3S3XT+eXn/9OvD1Iq2TJEkt2CIWshsQTqOWJKltWYGR\nJEmNM8ynUUuSpCayAiNJkhrHadSSJKlxHEKSJEmN4xBSce+YOrVIzhU33FAk53WPdhTJ+eDxRWJ4\n5Stbz9hm5ojWQ4D3PfSZIjkX3HVkkZwnJkSRnO2321Ak59ytty6SM6NAxpkFMgC2u+mmIjn731bm\n79W8G4vEcMcdZXImTCiTM/b01v+OvuD7ZT7jYw8u8+/F+UVSVI4VGEmS1DhWYCRJUuO4kJ0kSWoc\nKzCSJKlxnEYtSZIaZ5hexBsR+wHvB9YAK4FPAx8D1mfmZyNid6qJDw8Dd2bm+RExB9gVeBZwambe\nPZBvQJIk9WT4DiGNAY7NzMcj4vvA+4CnAY/Xx98PnJOZN0fEooi4HNgf+B/gf4H7BqbZkiSpd8O0\nApOZV0XEiIj4OHBxZn4jIg4ENq5SMg64v95+BNizft5HIuIo4F3AVwek5ZIkqRfDtAITETsCXwAu\nycz/6OaU3wJ7AMuBnYEHgUfrYw9RDSNJkqQhMXynUX8BmAQcFRHvycwjuxz/F+CsiDgSuCIzfxsR\nyyLiPGBH4NjSDZYkSX01TCswmTmzm33XA9fX2yuBI7ocP7FkAyVJUn85jVqSJDXOML2IV5IkNdkw\nHUKSJElNZgVGkiQ1jhUYSZLUOFZgJElS47RvBWbEUDdAkiSVt3Tp0o4pU36+cmCyXzxuypQpQ9qH\nsAIjSVLbcghJkiQ1TvsOIdmBkSSpbVmBkSRJjTP0FZiIeDZwMfBM4C7giMx8vMs5o4AFwIuAdcDx\nmXntpnLtwEiS1La2iArM+cB5mbkwIj4BnASc0OWcI4GOzPzriHgxsBjYY1OhdmAkSWpbQ1uBiYht\ngKnAjHrX16huCN21A7MG2CEitgKeDjzRW7YdGEmS2taQ3416F+CxzNxQP34QmNDNef8GHAesAMYA\nb+8t2A6MJEltq2PQhpAi4nDgrC67s5vnbuhm37nAjzJzv4j4K+DaiLg9M3/bUwPswEiS1LYGbwgp\nMy8DLuu8LyJGAr+LiBGZ2QHsRlVl6Wo/4PA659cRcTOwL2AHRpKk4WdoL+LNzHURcQPVkNClwHuA\nq7o59VbgLcAvIuKZwBRg7qay7cBIktS2hn4aNXAssKCegXQf8A6AiHg/MD4zTwaOBy6IiJ9TXbgz\nNzPv2VSoHRhJktrW0E+jrq9jOaib/V/utP0QcOjmNMAOjCRJbWuLqMAMCDswkiS1rSGfRj1g7MBI\nktS2hn4IaaDYgZEkqW05hCRJkhrHCowkSWocKzCSJKlxulu1vz3YgZEkqW05hCRJkhrHISRJktQ4\nVmAkSVLjWIGRJEmNYwVGkiQ1jhUYSZLUOE6jliRJjeMQkiRJahyHkCRJUuNYgZEkSY1jBUaSJDWO\nFRhJktQ4VmAkSVLjrB/qBgwYOzCSJLWtDoeQJElS0ziEJEmSGseLeCVJUuNYgZEkSY1jBUaSJDWO\nFRhJktQ4TqOWJEmN4xCSJElqHIeQJElS41iBkSRJjWMFRpIkNY63EpAkSY1jBUaSJDWO06glSVLj\neBGvJElqnC1nCCkiTgPWZ+ap3RzbFrgIeBnwJPDOzLxrU3l2YCRJaltDX4GJiNHAWcDbgX/u4bQP\nAWsy84URMRVYALxyU7l2YCRJaltbRAVmBpDA54ERPZwzHTgJIDNviIhdImKPzLy/p1A7MJIkta2h\nn0admd8AiIiTN3HaeOCBTo8fAHYH7MBIkjTcLF168kBdbPtI1x0RcTjVUFFnv8zM1/chr7vKzIZN\nPcEOjCRJbWjKlCk9DdcMiMy8DLisn0//f+2de7BfVXXHP0HMDW+blcdNSIgBsnjII0CBElBQRDDQ\nUtEarUhx7FSnUuwMjBQFxU5HZ4oUKbUd7cO2M0WCaaSQGAihTIDgIKmIBKqLkJhAEzSsQDBAYhLS\nP9a+4SYTM3fv38YcM/szk8kPZs43a62zzt7r7Nf5P2AcsCz99zhg1a4u2KvwH2o0Go1Go9GoxXeB\nSwBU9QzgVTN7dlcXtAKm0Wg0Go3Gr4utAz9U9ROqOrCl+magT1WXAF8FPro7jGs0Go1Go9Fo7Cmo\n6q91PrKx59Byp1FKjdxp+ddoDBFV3UdVT+lRYy9VHdaVB09V909/T1DVA3vQmVbPqu5Q457XouXO\nbwaqepCqzlDVaap6hKq+pQetzuQfdM8eAFV9UwWNtmyhUY2eE7I2qnowcDUwUkS2iMgaETnP3Z/K\n0XH3re6Ou6Oqfe5e9EUrVZ3g7i+p6hEiMszdXynQ6AeuEpF9gXOASSKyxN13uUVsJzpvBb4hIs+4\n+9O5duxEb7qIHCgiG9391R50zhaR0SKyoUSn1j1PWuPd/ReqOsbdX869HlruDFFvt+eOiEwBLgZG\nAocC7xSRMe7+RKYN1fIv6fWUg7XsUdW9RWSaiJwuIj8TkVHuvq7AnqkicjlwpIgsFZE3pzi/lKmz\nv5ltVNUJIoK7b8y1pdEYTOcKGBE5ARDgLuB84JfAee5+z1A1VPUQEfm6iPSlRm66uy/KtUVVxwJX\niMgBwLnAMSLyqLtvytERkcOJI5TXArcACpzv7gszdU4CJgIviMhbROSlwoJhrIh8EugH3gscIiI/\nzPVLVceJyFXAgcCJRAO3OLfDr3TPx4rIlcCUdM9niMjT7r42x5aWO7umS7nj7j8XkTHACmA+8Dzw\nVO49r5F/UC8Ha9kjIqcB04jtqO8BLhWRF3MLIRE5B9hAbPrYF3gbcKK7D/mAtFqFeKMxmC4O500C\nRpjZEuAR4K+AxZkavyAeuHOIBuDP0kekSmw5DpgAfBlYClxRoHMQsfJ6NLCZaJBWFugcDdxNNNbv\nA65V1ZMKddYBtxIxXkU6wjmTo4BfmtmNZvY5ogO5pkDnEHq/50cSHwB7HjgVmAn8UYEtA7nzburm\nztP0njub0p9ecuce6uTOt+hG7swC5pnZs2a2yMyWFmhMAob3mH9QLwcnAX3JnsU92HMykSsPEff+\n88CYAp3jgGVm9ndEEfwh4IeZGv1E4TMZ+Ffi0LLPFtjSaGyjiwXMbOCbqjrMzO4G+oA35wiY2QvA\nF4DbiAf/SjP7fIEtk4hGaYuZvQyMAH5aoLOIeFhfTn9PBf6nQGceMMvMnjGzywn/st7uEqOBtxN7\n7l8EnDK/ACaq6h+o6lFEcVaiczvRqAEsoOCeA6OIRnEhcD3h47JdXrETBuXOLCJ3rugxd15LudNH\nWWweInJmE9EBHUdZ7twF3GZmK3vMnVFE7lxK77lzsKq+X1WPpiB30pqeccBYVT1dVfcrtGM2MDut\n8biXeM5z8w8iNluB++ghB4l7MxvAzO6i7HmAyN9XzGw+cf9/l+2Pah8q/07kH8Q9Oh74WaZGrZe4\nRmMbXTyJ91TgHcAqVX0M+CDxRpOFma1Q1fHE28LfQqykN7Otu75yO75LfIdhs6qeTjy4JacMnkUM\n5a4m3vDuM7OSdRWHAR9T1ZXAj4FFZra5QOcOYE36vRk4jRjVyWUh0QmeBnyauE/fLNA5BjhXVW8n\nGuuLiVMZc5gHHGRmq9OXT08i3jqzSbnTD3yE+PhYSe7MIxroDap6FuW5czJwNtGxbgUWm1nJ2oF+\n4BJVnQ0ckHSy1+QAc4jc2UK0HydTljsPELl3AnAm8BLwL0O9WFVHAFcCPyGKhE8CNxGdbS5HEh3q\nIcR6mhnkd9AQ7cVEM3s2LSj+baJwyOUjwP2qegnxAbwZvP68Dhkze1BVp6fFwPsBDwLfK9B5fNDv\n+ar65wUjXYuItuICoiB/jddfWhqNIjpVwKjqPsRXKxcQQ41/Ajyahi5zdM4jGsXvA/eT3oIyOyCI\nYupEovBYDnwmVyP5dD7RAU0mCqqjiEN7cnXOTTqHEof8HAtkxSYxjSg6lhOF0PUFsYGYijgKuN3M\n/kFV35RbmCW/LgIeJkY+VgObzezTmbYcD7xTVecC+wNfKunoVfUdxHqBrxNv0qugKHeOIWJ8J7HO\n41oz25Bpyz7AhUTReznRic1Iv3N1zk861xI+vZ8oOnM5nvDr24RfNxUWVFOJInV50vlxTu6Y2QZV\nvZGIT1/S2eWx4zsjFULXAE8Sowt/CtxoZiWF0NuA9ao6GRgLfLHgeegjXuAmEiOKpwM/MrOvZeqM\nIEbsViW71gIzzWx9ps5hRHyWAM8RcSp5MTiLOi9xjcY2ujaFNBkYY2ZzzOxm4C+AI3IEUmP9HqLi\nn0S8if9hriGDiikjhoY/DlyWq0P4NHaQT1cTCzFLdPrNbO4gnazYwHZ+PUbMh19GgV9J50wiPhem\n4fv/yNUhRpVGAXOBhWb2KeLNPNeW9wFPEUXQh4kh/CySznRifv9mYr3HVYU6FxGdx/VE0fHXuTpE\nbGxbpPkAAAY1SURBVEYTsXkoxSZrsewgnTG8HuPLyIwxbOfXUuArxDOR7VfqXD8L/DFwHpE3F+fq\nmNkDwD8R0xMLzGxBgcYG4uNzfenPMgqmWZJPnyOmZd8N/D0FPgFTiOez38yuI+J0WIHOJOIF9WvA\nPwMbgS8V6GwkCueRRBv0ReAzOQKDCugfENNzHyIKxUajJzo1AkM0jHeo6qeIt7NJwC6/hbATBjr6\nOQCqOoayRYYDxdQdSWc08UaTSw2faupsKxKhJ78OBYaZ2UxV/SjwCQqm+ghfbiPWitykqh8Acqc2\nBoqgO4HxSeeGAlsGOvo5wAQz+2oPOqOA/yJysdSegdhsSbaUxGawTi8xhkpxTqMnf0OMnowgFjjn\nThmSFiGfABwOPJ7+X+5UH2b2gKquIUal7jGzrN0+SWOwTwMjQtk+peuuBtamqdDfI9Ya5TKSuFfT\niaLsRcrWhD2rqncCK8ysdM3Kjm3OKOJFo9HoiU5to3b3LSIyEfh9Yl76O2Z2a46GiKwD9hKRM0Xk\nYOAM4JXcrbA70Xl7iU4Nn2rq1PJLRPYGhovIk8RozheAn7r7vTk67r4h+XW2iIwnGv27c85MEZFN\nxM6YZWb2UOqgxd3vy7FlB51FlXSK7akRm5o6tfxKNq0UkZVEAbLEzG7LuV7j+yljibU4Pydyb6W7\n55oyUAidSkxrPeLuP1HVYblavfqUNDalrfdHE9OzTwO3u3vW9KOIrCKmjY4m1uIcCdzr7s8U2LSy\n5PyYQbasA/YVkQtFZDIxlbS+5HiCRmMwnSpgUqO0N9EhvgA87+4rcjS6VjDU8KmmTsX4rE8F0AXE\nTpTngNXu/mSOTvJrP2K+/63AKjP730xbOtXR19KpEZuaOrX8Sjb1VDSIyIeBa8xssYgsAW4QkXty\nz7bpWiGU7NmfGCE7kBj5sFxb0mGMK9z9YXdf6O7zS4oXVZ0tIqeIyLHp/KCt7v5Cpi1b0ovOa4RP\nD5jZd3JtaTR2pGtTSP3EwrfX0rzpt1R1iZkNuQVIDcBzxNbcUcDwEkNq6VDBp5o6FeNzHdHgv0Is\nKJ5jZg8XSPUD15nZ1rSAcaaqPlFwz9cQDeRxwDozy9610TUdKsSmpk7l+LxI5M4PiIJ8yAulU/6v\nB25IOxVXA8sL4gLbx2Zf4BZVfbIwNsU+7WDPwHM+Ari10J7ZxC645aSdWrmFkManNGYR52ktJbZh\n/xaxxixH54PEDrplwH8T62oajZ7pTAEzqFH6So+NUmcKhlo+VYwN1IvPOOAvk85wYJaqfqwwPr12\nRJ3q6Gvo1IrNG9jZ14pPdtFgZq8CV6jq7xAjSlOJgjyLrhVCFduLwYXHJmIdTXbhkYqvW1T1wR7W\nv0CM9I4kDnUcDhygql+2OB+p0SimM1NI7r7Z3een+dIDiB029+ZMk6QG4AzgXBHpJy1kyx2urKVT\nw6eaOm9gfEYBsjvisxNbhDo+7Vad34DcqRWf7BxU1Y+LyM3ElNjLxGF/j+d+zqBrz1XFe467Py4i\nj5rZAnef6+7/maMB2+L8DWC8iBwuwfMF63F+ROwU+zdi48ETZpa95b3R2JHOFDA1GqUONvpVGtqu\nNdhdik/XfOpSbGrqdCk+ImLEeTRriKMgLg1pzzpcrWuxqXzPaxQeRiwkfobYdn8RsfZuyHFW1YnE\ncRjHEKNka4EN3j7k2KhAZwqYGo1S1xr9Wg1t1xrsLsWnaz51KTY1dboUH4+dOmZmd7r79919Zq4/\n0L3YVL7nPRUesC3OT5nZHHd/xN2/XWDLe4lF/suIM42OBS5w9/tzdBqNnTFsdxswGFWdYmZFn7BP\n1+9HHBy2nPio2ruAfzSzebtDJ2n15FNNnT0xPl3zqUuxqanTxfgkvWHEeURFXzXuUmxq2VNTZ5Be\nUZxV9Vrge2a2QOMMq1OIb3LNrWVbo9EpVHWYqhadEqyqUyrZUEVnkF6xTzV19sT4dM2nLsWmpk5X\n41ODrsSmlj21dXr496dqnBeEqr5LVR/T+Hhno9H4VewpDcAbxZ4Yn6751KXY1KTF51ezJ/rUKxpf\n+R74/QGN7eGNRqPRaDQajUaj0Wg0Go1Go9FoNBqNRqPRaDQajUaj0Wg0Go1Go9FoNBqNRqPRaLzB\n/D8MSmEx9XsDPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe3fc7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots()\n",
    "heatmap = ax.pcolor(pd.DataFrame(correlation_vectors),cmap=plt.get_cmap('seismic'),alpha=0.7,vmin=-1,vmax=1)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,8)\n",
    "\n",
    "ax.invert_yaxis()\n",
    "ax.grid(False)\n",
    "ax.set_frame_on(False)\n",
    "\n",
    "ax.set_yticks(np.arange(len(temp_ids)) + 0.5, minor=False)\n",
    "ax.set_xticks(np.arange(len(temp_ids))+0.5, minor=False) \n",
    "\n",
    "ax.set_xticklabels(temp_ids, minor=False)\n",
    "ax.set_yticklabels(temp_ids, minor=False)\n",
    "\n",
    "plt.xticks(rotation=80)\n",
    "plt.rc('xtick', labelsize=11)\n",
    "plt.rc('ytick', labelsize=11)\n",
    "plt.title('Correlations for Stations on Red Line')\n",
    "colorbar=plt.colorbar(heatmap)\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comment:\n",
    "\n",
    "The purpose of the notebook was to identify similar stations. By looking at this similarity matrix, deeper shades of red denote that two stations are very similar while blue denotes dissimilarity. While it's easy to compare any two stations by identifying the correct row and column, it's difficult to name *groups* of similar stations at first sight. It would be nice if the rows and columns were ordered such that similar stations were consecutively ordered on the axes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##PCA and K-means Clustering\n",
    "\n",
    "One way to organize the similarity matrix above is to identify similar stations through another method and order the rows according to this other measure of similarity.\n",
    "\n",
    "As mentioned above, we computed average entries at 15 minute intervals, resulting in 59 measurements for each station. These 59 measurements can be considered a point in $\\mathbf{R}^{59}$, the 59-dimensional span of real numbers. People are generally better at picturing things in 2-dimensions than in 59-dimensions. What would really be useful is a way to map our stations down from a 59-dimensional space to a lower-dimensional space while preserving as much of the original information as possible. [Principal Component Analysis](http://en.wikipedia.org/wiki/Principal_component_analysis) is a machine learning technique used to transform data from a high-dimensional space to a lower-dimensional space. The new dimensions are a linear combination of the original dimensions that contain the greatest amount of variation. In simpler terms, PCA is only going to keep the parts of the original dataset that help differentiate the stations from one another. \n",
    "\n",
    "From there, we should be able to identify similar stations by how close they are to each other in this lower-dimensional space. [K-means clustering](http://en.wikipedia.org/wiki/K-means_clustering) is a popular machine learning technique used to identify groupings from data points that aren't currently labeled. In our case, the label we seek is a grouping where similar stations belong to the same group. Below is a diagram from Wikipedia explaining the k-means algorithm.\n",
    "\n",
    "![k_means](https://github.com/azampagl/harvard-capstone/blob/master/src/exploration/k_means.PNG?raw=true)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Red Line Stations\n",
    "\n",
    "The analysis will be performed line by line to illustrate clustering within a station, but the final grouping will be derived on the collection of all stations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "# helper function to return station name corresponding to station_id\n",
    "def get_name(station_id):\n",
    "    return(station_info[station_info['stationid']==station_id]['name'].values[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Returns a vector of series where each series is the scaled time series for each station in station_ids\n",
    "\n",
    "# station_ids is a list\n",
    "def get_scaled_entries(station_ids,begin_time=5,end_time=19.5):\n",
    "\n",
    "    vector_entries_scaled=[]\n",
    "    \n",
    "    for station in station_ids:\n",
    "        station_records=gatecount_1315[gatecount_1315['weekday']<5]\n",
    "        station_records=station_records[(station_records['servicetime_fraction']<=end_time) & (station_records['servicetime_fraction']>=begin_time) ]\n",
    "\n",
    "        #get subset of station \n",
    "        base_records=station_records[(station_records['locationid']==station)]\n",
    "        base_grouping=base_records[['servicetime_fraction','servicedate','entries']].groupby('servicetime_fraction')\n",
    "        base_entries=base_grouping.agg(np.mean)['entries']\n",
    "\n",
    "        entries_scaled=preprocessing.scale(base_entries)\n",
    "        \n",
    "        vector_entries_scaled.append(list(entries_scaled))\n",
    "        \n",
    "    return vector_entries_scaled\n",
    "    \n",
    "    ## now for each station, have a vector of scaled entries over the window of interest.\n",
    "    \n",
    "c=get_scaled_entries(red_stations_ids)    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stations: 22\n",
      "Number of time intervals: 59\n",
      "Number of PCA components: 10\n",
      "[ 0.77756367  0.12088187  0.04385288  0.01986184  0.00946769  0.00579901\n",
      "  0.00506189  0.00342932  0.00266491  0.00194105]\n"
     ]
    }
   ],
   "source": [
    "print \"Number of stations: \"+str(len(c))\n",
    "print \"Number of time intervals: \"+str(len(c[0]))\n",
    "\n",
    "pca = PCA(n_components=10)\n",
    "pca.fit(c)\n",
    "\n",
    "print \"Number of PCA components: \"+str(len(pca.components_))\n",
    "print(pca.explained_variance_ratio_) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comments:\n",
    "It seems that we can capture almost 90% of the variation using only 2 principal components. I will perform PCA with 4 components for now to capture approximately 95% of the variation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=4)\n",
    "pca.fit(c)\n",
    "\n",
    "c_transformed=pca.transform(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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sJ4avBfYiFIw9zOzimqcSEZG6yHI4aDl337T9iZlpT0BEpIfIUgTeM7NTgMeB1YCZZrYT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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe71e3b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the plot of first two principal components\n",
    "components_transposed=c_transformed.transpose()\n",
    "\n",
    "plt.scatter(components_transposed[0],components_transposed[1],color='blue',label='Stations')\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second principal component')\n",
    "plt.title('Projection of Red Stations onto first two principal components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "###Comments:\n",
    "Above is the plot of the first two components comprising of the majority of the variation. As promised, it's much easier to visualize the stations in a two-dimensional space than a 59-dimensional space. This is an unsupervised clustering task and I'm not sure how many groups to cluster the stations into. Based on the plot above, it seems like we can use k=3 clusters since you can roughly identify 3 groups of points with the naked eye. I'm not too concerned whether k=2,3, or 4 is the best choice because it's all in an effort to group similar stations closer together on the similarity matrix. Thus, the visual differences between k=2,3,4 won't be too stark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4.09453351  0.23384375 -0.19992251 -0.18436462]\n",
      " [ 6.64787203  0.51453127  0.08592574 -0.14697023]\n",
      " [ 0.86643261 -2.13594796  0.59919662  1.08038903]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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DzKwncDZwDmqTE5F0LN/GvEqbaX4BPNf6wd2HAycC3xAO7tsQOo096O5/BlZw9xeBN939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\nERERERERERERERERERERERGRuvD/DIrzVQDApvcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16a710d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_fn=cluster.KMeans(n_clusters=3)\n",
    "cluster_fn.fit(c_transformed)\n",
    "\n",
    "print cluster_fn.cluster_centers_\n",
    "\n",
    "clusters=cluster_fn.cluster_centers_.transpose()\n",
    "components_transposed=c_transformed.transpose()\n",
    "\n",
    "#get the groupings\n",
    "group0=cluster_fn.labels_==0\n",
    "group1=cluster_fn.labels_==1\n",
    "group2=cluster_fn.labels_==2\n",
    "\n",
    "#plt.figure(figsize=(12,8))\n",
    "plt.scatter(components_transposed[0][group0],components_transposed[1][group0],s=50,color='blue',label='Group 0')\n",
    "plt.scatter(components_transposed[0][group1],components_transposed[1][group1],s=50,color='green',label='Group 1')\n",
    "plt.scatter(components_transposed[0][group2],components_transposed[1][group2],s=50,color='gold',label='Group 2') \n",
    "plt.scatter(clusters[0],clusters[1],color='red',label='Centroids',s=50)\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second principal component')\n",
    "plt.title('Projection of Red Stations With Centroids')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comments:\n",
    "\n",
    "In the plot above, the red dots represent the center of the groupings identified by the k-means algorithm. The centroids look pretty reasonable given our data. I'm going to continue with k=3 and see what the similarity matrix ends up looking like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First grouping: [1005 1006 1007 1020 1032 1033 1034 1040 1041 1042 1043 1103]\n",
      "Second grouping: [1004 1009 1035 1037 1039 1052 2106]\n",
      "Third grouping: [1002 1036 1112]\n",
      "Re-ordered ID list: [1005, 1006, 1007, 1020, 1032, 1033, 1034, 1040, 1041, 1042, 1043, 1103, 1002, 1036, 1112, 1004, 1009, 1035, 1037, 1039, 1052, 2106]\n"
     ]
    }
   ],
   "source": [
    "group_0_ids=np.array(red_stations_ids)[group0]\n",
    "print \"First grouping: \"+str(group_0_ids)\n",
    "group_1_ids=np.array(red_stations_ids)[group1]\n",
    "print \"Second grouping: \"+str(group_1_ids)\n",
    "group_2_ids=np.array(red_stations_ids)[group2]\n",
    "print \"Third grouping: \"+str(group_2_ids)\n",
    "\n",
    "#reorder the IDS so that the similarity matrix has like-grouped ids near each other\n",
    "reordered_ids=list(group_0_ids)+list(group_2_ids)+list(group_1_ids)\n",
    "print \"Re-ordered ID list: \"+str(reordered_ids)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##New Similarity Matrix\n",
    "\n",
    "Now let's build the similarity matrix, this time ordering the axes according to our reordered ID list. Note that the reordered ID list has stations of the same group listed consecutively. This will be the order that the rows are arranged in the similarity matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "temp_ids=reordered_ids\n",
    "\n",
    "correlation_vectors=[]\n",
    "\n",
    "for station in temp_ids:\n",
    "    print station\n",
    "    output=compare_series(station,comparison_station=temp_ids,begin_time=5.,end_time=19.5)\n",
    "    \n",
    "    correlations_df=pd.DataFrame(zip(temp_ids,output))\n",
    "\n",
    "    correlation_vectors.append(list(correlations_df[1].values))    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TVAAjIiIiBp9UAKN/maPmeLPXHA/glZrjLbDkkrXGm+uhh6bdaAa9XHO8uv+A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\nRVnaItB2HvBDVmCQes2K6HDcE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v0Sf0Bu6V8P/JQcW9YapdfWNm250D9FRoB+Jqmn9Pzb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\nZmZmZmZmZmZm1kj/BZXFqSckCKDhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d908d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "station_names=map(get_name,temp_ids)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "heatmap = ax.pcolor(pd.DataFrame(correlation_vectors),cmap=plt.get_cmap('seismic'),alpha=0.7,vmin=-1,vmax=1)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,8)\n",
    "\n",
    "# Clip the axes to remove white border\n",
    "plt.ylim(0, len(temp_ids))\n",
    "plt.xlim(0, len(temp_ids))\n",
    "\n",
    "#invert so we orient the diagonal properly\n",
    "ax.invert_yaxis()\n",
    "ax.grid(False)\n",
    "ax.set_frame_on(False)\n",
    "\n",
    "# reorganize the ticks\n",
    "ax.set_yticks(np.arange(len(temp_ids)) + 0.5, minor=False)\n",
    "ax.set_xticks(np.arange(len(temp_ids))+0.5, minor=False) \n",
    "#put labels on the ticks\n",
    "ax.set_xticklabels(station_names, minor=False)\n",
    "ax.set_yticklabels(station_names, minor=False)\n",
    "\n",
    "plt.xticks(rotation=80)\n",
    "plt.rc('xtick', labelsize=11)\n",
    "plt.rc('ytick', labelsize=11)\n",
    "plt.title('Correlations for Stations on Red Line')\n",
    "colorbar=plt.colorbar(heatmap)\n",
    "\n",
    "# plot lines for the groups\n",
    "plt.axhline(y=len(group_0_ids),xmin=0,xmax=(len(temp_ids)-15),color='black',linewidth=4)\n",
    "plt.axhline(y=len(group_0_ids)+len(group_2_ids),xmin=0,xmax=(len(temp_ids)-15),color='black',linewidth=4)\n",
    "\n",
    "plt.axvline(x=len(group_0_ids),ymin=0,ymax=(len(temp_ids)-15),color='black',linewidth=4)\n",
    "plt.axvline(x=len(group_0_ids)+len(group_2_ids),ymin=0,ymax=(len(temp_ids)-15),color='black',linewidth=4)\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comments:\n",
    "\n",
    "This is the organized similarity matrix we desire! Notice the large concentration of red squares along the diagonal and how any dissimilar stations, identified with blue squares, are further away from the diagonal. This is evidence that we were able to identify similar stations with PCA and k-means clustering and organize the rows properly.\n",
    "\n",
    "The next few sections show the same analysis performed on the other lines followed by the similarity matrix for all stations. Feel free to skip over this portion to the analysis on all stations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Green Stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stations: 14\n",
      "Number of time intervals: 59\n",
      "Number of PCA components: 10\n",
      "[ 0.70666128  0.15291648  0.05047327  0.03086441  0.02673166  0.01110493\n",
      "  0.00873607  0.00620418  0.00259329  0.00130377]\n"
     ]
    }
   ],
   "source": [
    "## green scaled entries    \n",
    "c=get_scaled_entries(green_stations_ids)    \n",
    "        \n",
    "print \"Number of stations: \"+str(len(c))\n",
    "print \"Number of time intervals: \"+str(len(c[0]))\n",
    "\n",
    "pca = PCA(n_components=10)\n",
    "pca.fit(c)\n",
    "\n",
    "print \"Number of PCA components: \"+str(len(pca.components_))\n",
    "print(pca.explained_variance_ratio_) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comments: \n",
    "3 principal components account for 90% of the variation in the dataset. Like with the red lines, going to use PCA with 4 components just to be safe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BibSEWvxSVCvwZdKH0AhaJqdYRFpKiV+K6hFgQtXzD4D7copFpKXU1SMFVfoA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\niIiIiIiIiIiIiIiIyNf8H9szC1DioQbQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1891db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pca = PCA(n_components=4)\n",
    "pca.fit(c)\n",
    "\n",
    "c_transformed=pca.transform(c)\n",
    "\n",
    "#Visualize the plot of first two principal components\n",
    "components_transposed=c_transformed.transpose()\n",
    "\n",
    "plt.scatter(components_transposed[0],components_transposed[1],color='blue',label='Stations')\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second principal component')\n",
    "plt.title('Projection of Green Stations onto first two principal components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comments:\n",
    "\n",
    "Seems like 3 clusters may be sufficient again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.86167307 -1.17921854  0.93484901  0.15354039]\n",
      " [-2.14871859  0.11435034  0.15370392 -0.00732814]\n",
      " [ 2.53837372  0.44309467 -1.08434443 -0.08037585]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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SJ0vPZ9szJnGc1YHzxl5k5jHAxynmSN9CEZZbwCmZ+UXgBZl5AXB5Zv4GWILiXtELA5dm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\nSZIkSZL+/4YiAAB6Kxdk2fdubgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xed7aa70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_fn=cluster.KMeans(n_clusters=3)\n",
    "cluster_fn.fit(c_transformed)\n",
    "\n",
    "print cluster_fn.cluster_centers_\n",
    "\n",
    "clusters=cluster_fn.cluster_centers_.transpose()\n",
    "components_transposed=c_transformed.transpose()\n",
    "\n",
    "#get the groupings\n",
    "group0=cluster_fn.labels_==0\n",
    "group1=cluster_fn.labels_==1\n",
    "group2=cluster_fn.labels_==2\n",
    "#group3=cluster_fn.labels_==3\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.scatter(components_transposed[0][group0],components_transposed[1][group0],s=50,color='blue',label='Group 0')\n",
    "plt.scatter(components_transposed[0][group1],components_transposed[1][group1],s=50,color='green',label='Group 1')\n",
    "plt.scatter(components_transposed[0][group2],components_transposed[1][group2],s=50,color='gold',label='Group 2') \n",
    "#plt.scatter(components_transposed[0][group3],components_transposed[1][group3],s=50,color='brown',label='Group 3') \n",
    "plt.scatter(clusters[0],clusters[1],color='red',label='Centroids',s=50)\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second principal component')\n",
    "plt.title('Projection of Green Stations With Centroids')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First grouping: [1101 1075]\n",
      "Second grouping: [1051 1052 1053 1054 1055 1057 1058 1059 1060]\n",
      "Third grouping: [1056 1061 1076]\n",
      "Re-ordered ID list: [1051, 1052, 1053, 1054, 1055, 1057, 1058, 1059, 1060, 1056, 1061, 1076, 1101, 1075]\n"
     ]
    }
   ],
   "source": [
    "group_0_ids=np.array(green_stations_ids)[group0]\n",
    "print \"First grouping: \"+str(group_0_ids)\n",
    "group_1_ids=np.array(green_stations_ids)[group1]\n",
    "print \"Second grouping: \"+str(group_1_ids)\n",
    "group_2_ids=np.array(green_stations_ids)[group2]\n",
    "print \"Third grouping: \"+str(group_2_ids)\n",
    "\n",
    "#reorder the IDS so that the similarity matrix has like-grouped ids near each other\n",
    "reordered_ids=list(group_1_ids)+list(group_2_ids)+list(group_0_ids)\n",
    "print \"Re-ordered ID list: \"+str(reordered_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "temp_ids=reordered_ids\n",
    "\n",
    "correlation_vectors=[]\n",
    "\n",
    "for station in temp_ids:\n",
    "    print station\n",
    "    output=compare_series(station,comparison_station=temp_ids,begin_time=5.,end_time=19.5)\n",
    "    \n",
    "    correlations_df=pd.DataFrame(zip(temp_ids,output))\n",
    "\n",
    "    correlation_vectors.append(list(correlations_df[1].values))    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i8t0Vtsf2aSdeHuekMvbd/TFeRERELJg2LwGvBExv+HwfsEFTnwnABZKOAZ4E3tnXCQyJ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\nimN5ILB9coSPl7e/+iT3xKvk3A0ws1sk7QcMNbMHJe1JKhC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fpdSbHfCeeyeY2YNpEvgJ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\n7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe310c30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "station_names=map(get_name,temp_ids)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "heatmap = ax.pcolor(pd.DataFrame(correlation_vectors),cmap=plt.get_cmap('seismic'),alpha=0.7,vmin=-1,vmax=1)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,8)\n",
    "\n",
    "# Clip the axes to remove white border\n",
    "plt.ylim(0, len(temp_ids))\n",
    "plt.xlim(0, len(temp_ids))\n",
    "\n",
    "#invert so we orient the diagonal properly\n",
    "ax.invert_yaxis()\n",
    "ax.grid(False)\n",
    "ax.set_frame_on(False)\n",
    "\n",
    "# reorganize the ticks\n",
    "ax.set_yticks(np.arange(len(temp_ids)) + 0.5, minor=False)\n",
    "ax.set_xticks(np.arange(len(temp_ids))+0.5, minor=False) \n",
    "#put labels on the ticks\n",
    "ax.set_xticklabels(station_names, minor=False)\n",
    "ax.set_yticklabels(station_names, minor=False)\n",
    "\n",
    "plt.xticks(rotation=80)\n",
    "plt.rc('xtick', labelsize=11)\n",
    "plt.rc('ytick', labelsize=11)\n",
    "plt.title('Correlations for Stations on Green Line')\n",
    "colorbar=plt.colorbar(heatmap)\n",
    "\n",
    "# plot lines for the groups\n",
    "plt.axhline(y=len(group_1_ids),xmin=0,xmax=(len(temp_ids)),color='black',linewidth=4)\n",
    "plt.axhline(y=len(group_1_ids)+len(group_2_ids),xmin=0,xmax=(len(temp_ids)),color='black',linewidth=4)\n",
    "\n",
    "plt.axvline(x=len(group_1_ids),ymin=0,ymax=(len(temp_ids)),color='black',linewidth=4)\n",
    "plt.axvline(x=len(group_1_ids)+len(group_2_ids),ymin=0,ymax=(len(temp_ids)),color='black',linewidth=4)\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comments:\n",
    "It's interesting to note that the stations on the green line are all pretty similar to one another. While some are 'more similar' than others, the similarity matrix shows positive correlations for all green line station comparisons.\n",
    "\n",
    "\n",
    "##Blue Lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stations: 12\n",
      "Number of time intervals: 59\n",
      "Number of PCA components: 10\n",
      "[ 0.90825734  0.03835394  0.01730337  0.00929305  0.00771629  0.00595549\n",
      "  0.00415006  0.00357851  0.00301575  0.00195743]\n"
     ]
    }
   ],
   "source": [
    "## green scaled entries    \n",
    "c=get_scaled_entries(blue_stations_ids)    \n",
    "        \n",
    "print \"Number of stations: \"+str(len(c))\n",
    "print \"Number of time intervals: \"+str(len(c[0]))\n",
    "\n",
    "pca = PCA(n_components=10)\n",
    "pca.fit(c)\n",
    "\n",
    "print \"Number of PCA components: \"+str(len(pca.components_))\n",
    "print(pca.explained_variance_ratio_) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comments:\n",
    "90% of the variation is captured with one principal component! I'll reduce my parameter space to two dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Wk7RluamakzQB2Jm08eo4kj4AnAxsDqwErC5p03JTzS5/lf14YF3SuvnxvFw7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\nWS4EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe40c570>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(c)\n",
    "\n",
    "c_transformed=pca.transform(c)\n",
    "\n",
    "#Visualize the plot of first two principal components\n",
    "components_transposed=c_transformed.transpose()\n",
    "\n",
    "plt.scatter(components_transposed[0],components_transposed[1],color='blue',label='Stations')\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second principal component')\n",
    "plt.title('Projection of Blue Stations onto first two principal components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comments:\n",
    "Two clusters is definitely enough."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4.07482045 -0.06235657]\n",
      " [ 8.1496409   0.12471314]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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E6P9k5pHAbZn5XuB5Pa5JkiRJGlidBOh/RcQrgYcj4h3Ayj2uSZIkSRpYnQTo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\nTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe3fcc30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_fn=cluster.KMeans(n_clusters=2)\n",
    "cluster_fn.fit(c_transformed)\n",
    "\n",
    "print cluster_fn.cluster_centers_\n",
    "\n",
    "clusters=cluster_fn.cluster_centers_.transpose()\n",
    "components_transposed=c_transformed.transpose()\n",
    "\n",
    "#get the groupings\n",
    "group0=cluster_fn.labels_==0\n",
    "group1=cluster_fn.labels_==1\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.scatter(components_transposed[0][group0],components_transposed[1][group0],s=50,color='blue',label='Group 0')\n",
    "plt.scatter(components_transposed[0][group1],components_transposed[1][group1],s=50,color='green',label='Group 1')\n",
    "plt.scatter(clusters[0],clusters[1],color='red',label='Centroids',s=50)\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second principal component')\n",
    "plt.title('Projection of Blue Stations With Centroids')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First grouping: [1010 1011 1013 1014 1015 1016 1017 1018]\n",
      "Second grouping: [1012 1019 1051 1077]\n",
      "Re-ordered ID list: [1012, 1019, 1051, 1077, 1010, 1011, 1013, 1014, 1015, 1016, 1017, 1018]\n",
      "1012\n",
      "1019\n",
      "1051\n",
      "1077\n",
      "1010\n",
      "1011\n",
      "1013\n",
      "1014\n",
      "1015\n",
      "1016\n",
      "1017\n",
      "1018\n"
     ]
    }
   ],
   "source": [
    "group_0_ids=np.array(blue_stations_ids)[group0]\n",
    "print \"First grouping: \"+str(group_0_ids)\n",
    "group_1_ids=np.array(blue_stations_ids)[group1]\n",
    "print \"Second grouping: \"+str(group_1_ids)\n",
    "\n",
    "\n",
    "#reorder the IDS so that the similarity matrix has like-grouped ids near each other\n",
    "reordered_ids=list(group_1_ids)+list(group_0_ids)\n",
    "print \"Re-ordered ID list: \"+str(reordered_ids)\n",
    "temp_ids=reordered_ids\n",
    "\n",
    "correlation_vectors=[]\n",
    "\n",
    "for station in temp_ids:\n",
    "    print station\n",
    "    output=compare_series(station,comparison_station=temp_ids,begin_time=5.,end_time=19.5)\n",
    "    \n",
    "    correlations_df=pd.DataFrame(zip(temp_ids,output))\n",
    "\n",
    "    correlation_vectors.append(list(correlations_df[1].values))    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ASpIkNYoVQEmSpMaxAihJktQoPgZGkiSpYZwCliRJahyngCVJkhrFCqAkSVLj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\nsSLONwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xed71ff0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "station_names=map(get_name,temp_ids)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "heatmap = ax.pcolor(pd.DataFrame(correlation_vectors),cmap=plt.get_cmap('seismic'),alpha=0.7,vmin=-1,vmax=1)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,8)\n",
    "\n",
    "# Clip the axes to remove white border\n",
    "plt.ylim(0, len(temp_ids))\n",
    "plt.xlim(0, len(temp_ids))\n",
    "\n",
    "#invert so we orient the diagonal properly\n",
    "ax.invert_yaxis()\n",
    "ax.grid(False)\n",
    "ax.set_frame_on(False)\n",
    "\n",
    "# reorganize the ticks\n",
    "ax.set_yticks(np.arange(len(temp_ids)) + 0.5, minor=False)\n",
    "ax.set_xticks(np.arange(len(temp_ids))+0.5, minor=False) \n",
    "#put labels on the ticks\n",
    "ax.set_xticklabels(station_names, minor=False)\n",
    "ax.set_yticklabels(station_names, minor=False)\n",
    "\n",
    "plt.xticks(rotation=80)\n",
    "plt.rc('xtick', labelsize=11)\n",
    "plt.rc('ytick', labelsize=11)\n",
    "plt.title('Correlations for Stations on Blue Line')\n",
    "colorbar=plt.colorbar(heatmap)\n",
    "\n",
    "# plot lines for the groups\n",
    "plt.axhline(y=len(group_1_ids),xmin=0,xmax=(len(temp_ids)),color='black',linewidth=4)\n",
    "\n",
    "plt.axvline(x=len(group_1_ids),ymin=0,ymax=(len(temp_ids)),color='black',linewidth=4)\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Quick comment:\n",
    "\n",
    "The blue line stations are much more separable than the other lines. Note that the two groupings seem to follow those stations in Boston versus East Boston."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Orange Stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stations: 19\n",
      "Number of time intervals: 59\n",
      "Number of PCA components: 10\n",
      "[ 0.78397383  0.06836642  0.05341448  0.03464654  0.01826433  0.01317482\n",
      "  0.00887342  0.00673241  0.00315521  0.00264334]\n"
     ]
    }
   ],
   "source": [
    "## green scaled entries    \n",
    "c=get_scaled_entries(orange_stations_ids)    \n",
    "        \n",
    "print \"Number of stations: \"+str(len(c))\n",
    "print \"Number of time intervals: \"+str(len(c[0]))\n",
    "\n",
    "pca = PCA(n_components=10)\n",
    "pca.fit(c)\n",
    "\n",
    "print \"Number of PCA components: \"+str(len(pca.components_))\n",
    "print(pca.explained_variance_ratio_) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J5VL43f1adx8NzM1j+SJN6CMW3BkJ8HoeQaT5ZdraNrO9CV06paa5+/bx76cD\nVOrjLxaLCTAly3w1sg4wLe8QKShn7eSS8a67ll1qzJgvrvLRR4v2Wm+9mTNGj37hxcUWS9rbIdkM\n6xKUs9YGDR48uHF7Uszs9LbiXy4W/jpIekEyLFwQovPDBBaLzXE5NuWsnfwzpttO88+ZjnLWVpra\n2eLH8Se9gVuBCcB9wJiuFH+R+mqdww4lG41Q+PPciA8Ddoq3F433h+QXR0Qke7kex98Ax+/3Kbu/\nKGHcDhGRHqsRWvx5ug74T8n98YRuHxGRHqvFh2wovAnJjsB+wGzg8ji2vohIj9XihR+g8DrhRDIR\nkZbQ6l09IiItR4VfRKTFqKtHuij5OmHsmEeg8FjeaUTqI9kE2AeYBZwLhY9yDtQlKvzSBcluwOXA\nCsB7kBwNhRtyDiWSqSlTllwC+CvwxfjQEEh2gMKcHGN1ibp6FpIsB8mVkNwFya/j2b2yoKMIRR+g\nH3BkjllE6uLuu5dblvlFH2AbYO2c4nSLWvwL+y2wb7y9DWEE0ePzi9OQyoe10DAX0uP17Tu3vGX/\nEU16uUa1+Bf29Q7uC1wBvBdvT4/3RXq0Qw55423gFuBT4F3g9HAuUPNRi39hzwHrl9x/Pq8gjatw\nEyTPEXbuToXCQ3knEsla795JAuwFrAjMgsL0nCN1mQr/wo4hdO+sATwOnJhvnEZVmEJzXC9BpIYK\nCfBG3im6S4V/IYVXCJ/qIiI9kvr4RURajFr8PUpyELA58ArwCw04JyKVqPD3GMkI4CJgsfjAGsDw\n/PKISKNSV0/PMYz5RR9gy7yCiEhjU+HvOd4tu/9OLilEpOGpq6fnOB1YExhE6OM/Kd84ItKoVPh7\njMIbwLaQ9AVmxuONRUQWosLf4xRm5J1ARBqb+vhFRFpMXVv8ZjYEuJBw9Mm7wMHu/lI9M4iItLp6\nt/j/SCj2GwHXAb+u8/JFRFpe3Qq/mfUGTnH3x+ND/wVWq9fyRUQkqFtXj7vPBq4HMLNFgDMIlzET\nEZE6yqTwm9newAVlD09z9+1jy/8awreNs7NYvoiIVFfXS+aZWV/gVsLlyr7v7lUHESsWi/cAW9Up\nmohITzFp8ODBW+cd4nNm9jczuyzvHCIiraxuLX4z24hwxaYngLaLFr/q7t+qVwYRERERERERERER\nkR6krkf1dIaZrUk47HNJ4APgwEYc3sHMVgauAL4IfEQ4WqnhcraJ+1r+5e6L552lkkYf1sPM9gNO\nAXoDF7psCfifAAAHWElEQVT7mJwjVWRmpwN7x7tj3b1hh+k2s3OBAe7ekFeMM7PdgNOAJYB/uPvI\nnCNVZGYHAScCCSHnidWmbeRB2s4Cro3DO/wF+HnOeaq5Fvh7yTAU5+WcpyozWwK4mAWv1NVoGnZY\nDzP7EmG7HAJsAPzAzNbJN9XCzGxbYDtgw/gzyMx2zzdVZWY2DDiQUKwajpl9BRgD7AoMBDY2s53y\nTbUwM1uScO7UNwnb5hZx3VbUyIX/I2CZeLsvMCvHLBWZ2QBgoLv/Lj50FTAqx0gdOZ+wcTTkN70m\nGNZjW+Aud//A3WcBNwN75ZypkteAH7n7HHefAzwFrJpzpoWYWT/CB+nZNOg2CewB/MndX4/rcl/g\noZwzVZIAs4GlCN9GF6OdmtnI4/GfB9xrZj8k/BOb55ynkjWBl8zsQmAo8BJwdL6RKotfVxd397+Y\nWd5xKmqCYT1WBt4ouf86sElOWapy9yfbbpvZ14B9gM3yS1TVpcBPaKwP93JrArPN7A5gJeA2dz81\n50wLcfdZZnYO4UP+Y2Ciu/+r2vS5F/4qwzs4oc/8MHe/zcz2JBSAgfXO16ZKzmeAjYDT3P14MzuE\nsF9iaL3ztamS8ylgaWBbM2uIllWTDutRad3Nq3uKlMxsXeB2YKS7P5d3nlJmdijwsrtPjH3TjWox\nYEvCKAIzgb+b2YHufk2+sRZkZlsAhxE+RKcDfzSzE9y9YtdzQxSBcma2PPCkuy9f8thbwDruXn5R\n8dzE/r+p7r5svL8E8La7L5lvsgXFD6RRhO4zCH2AjwJbuntDXbGrM8N61JuZHQBs4e6HxfunAom7\nn5VvsoXFneQ3A8e6+0155ylnZuMJ36DmAP0I3blXN9qOUzM7E1ja3Y+L90cA67n7UfkmW5CZnQis\n0LZD18x2AUZUO0E29xZ/Fe8AM8xsiLs/EDfi6Y1U9AHc/Xkze9XMdnT3Owg7gIp55yrn7lcCV7bd\nN7N57v6NHCO154/AM+5+eN5BKpgAnBH37cwC9iS0shqKma0K/A3Y293vyTlORe6+fdttMzsQ2LrR\nin50O3CNmS0DzAB2Bm7JN1JFDwMXxsbnx4RaNLnaxA1Z+N09MbP/BS6Oe6unA/+bc6xq9gAui4ek\nfUg4QqHRNeoRFBsBuwFPmNkj8eGGGdbD3V8zs1OAiYQdaJe7e8N90AMnEA83Ldmf89uSgxAaUUNu\nk+4+2cx+CdxP6PYZ7+5X5RxrIe5+j5ldS2h4ziHsgD4n31QiIiIiIiIiIiIiIiIiIiIiIiIiIiLS\nCMxsazN72cwmxp9b4+OXpnjuBmbWqWE70sy3ZNqTzOzLnZj+4c5kaQRdWYfS8zTkCVzSoyXADe7+\n49IH3f2IFM/dg3CG4n/SLizlfNumHZ122ibW6XUoPY8Kv+RhoTGizOxhd9/YzJ4ijIA5Gvgh4eIX\nrxGGRjgI2MPM7nH3mfF59xAGoRtMGOvlYjN7kDAu0Y2E8Uo2NrNJwIuEcYrOdffrzOwkwrALAPsT\nxjM6j3AxiwJgwCR3P9nMvgX8KOb5Z6URGuOZx2MIZ83+njBM942EoXLfi8vYm3Da/7KEoUleBHYA\nrnH3X5vZf+NjXwKOAR4kDFi3OvBJXAdrASMJZ5L2I5ztPD1ONwB4NU73U2ANwpDMH8TlL7QOpfU0\n8nj80jMVgH1Lunq+V/b3/sAw4GVCsRoG3BSfdxUwqqxgJYQLtmwCHGBmixKGz93X3X9fMt0qhIK+\nA3BEHAt+J3ffFDgEWL9snhPcfTNgYLwAy2rAjoSLsOxW5X87jTCw3CBCAT4cuN3dtwbGx/sJ8FYc\nq6Y/YfyfzYHvxHl8CfgeYayVU4DdCcNWbEW4MtmoOI9F3H1HwkWKdiF8MI5396GE0/b3jdP91923\nAb4Q10uldSgtRoVf6i0BbnT3ofHnurK/v+Tuc+PFWO4FxhEKW9vwx5VGlP23u88DniW0jN939w/K\npnnf3d9y9zeBxQnjrD8G4O6Pu/tfyucZfz9KaGF/QLja2hhCq7+SlduGP3b3nwFfI3SrEH9/Ld5+\nKv6eDjzr7p+W/F9Puft0d3+V8MFQOo9iyTymxd9vxP9nLcIVwSYSiv4Xq0wHDToqr9SPCr80mnkA\nZrYeMCu2jF8nXOMgoXLRGhhb+l+N01YaI798ELCXgK/HZW1gZqeVzzP+3hB4HjjT3fcltOr7Vsn+\ntpl92cwKZnYT4XoNG8e/bUrowqmUpdSaZrZ4HGFzOuHDrG0emwAvVJnHc4QurKEx4wNV5l9tHUoL\nUeGXPFQqfJUK2XfM7D5CEZ5EaKGfZWb9y6Y9mdAXPsbdP06zrNjy/4eZ3Q/8itCaLzU87iu4391f\nBO6OR/GcBzwTR40t9xNCP/sDwB3A5cBOZnYvocvqovYyxd9zCOPo/xk4iTC88hfjPI4n9NuXzyMB\nLiN0oU0idGl5lWVVW4ciIs0h7ifoV+N5XmVmuVyvIO7cFcmUWvwijaUhx6UXERERERERERERERER\nERERERERERGRmvk/KtVOoqeA99MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16621fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pca = PCA(n_components=4)\n",
    "pca.fit(c)\n",
    "\n",
    "c_transformed=pca.transform(c)\n",
    "\n",
    "#Visualize the plot of first two principal components\n",
    "components_transposed=c_transformed.transpose()\n",
    "\n",
    "plt.scatter(components_transposed[0],components_transposed[1],color='blue',label='Stations')\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second principal component')\n",
    "plt.title('Projection of Orange Stations onto first two principal components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comments:\n",
    "Consider using k=3 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-5.64043167 -0.00608484 -0.58744744 -0.13036626]\n",
      " [ 5.34574341 -0.12105565 -0.39327983  0.01162935]\n",
      " [-0.8207314   0.25275978  1.81459269  0.20488226]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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8OkBEvDUidqLosV6eogd7VWArYC7wVIrAHBHxGmBviqEh36BYdvsBigVP9qKY65mIOJpi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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d87450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_fn=cluster.KMeans(n_clusters=3)\n",
    "cluster_fn.fit(c_transformed)\n",
    "\n",
    "print cluster_fn.cluster_centers_\n",
    "\n",
    "clusters=cluster_fn.cluster_centers_.transpose()\n",
    "components_transposed=c_transformed.transpose()\n",
    "\n",
    "#get the groupings\n",
    "group0=cluster_fn.labels_==0\n",
    "group1=cluster_fn.labels_==1\n",
    "group2=cluster_fn.labels_==2\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.scatter(components_transposed[0][group0],components_transposed[1][group0],s=50,color='blue',label='Group 0')\n",
    "plt.scatter(components_transposed[0][group1],components_transposed[1][group1],s=50,color='green',label='Group 1')\n",
    "plt.scatter(components_transposed[0][group2],components_transposed[1][group2],s=50,color='gold',label='Group 2')\n",
    "plt.scatter(clusters[0],clusters[1],color='red',label='Centroids',s=50)\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second principal component')\n",
    "plt.title('Projection of Orange Stations With Centroids')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First grouping: [1039 1074 1077 1078 1079 1080 1082]\n",
      "Second grouping: [1070 1071 1072 1073 1084 1085 1086 1087]\n",
      "Third grouping: [1075 1076 1081 1083]\n",
      "Re-ordered ID list: [1039, 1074, 1077, 1078, 1079, 1080, 1082, 1075, 1076, 1081, 1083, 1070, 1071, 1072, 1073, 1084, 1085, 1086, 1087]\n",
      "1039\n",
      "1074\n",
      "1077\n",
      "1078\n",
      "1079\n",
      "1080\n",
      "1082\n",
      "1075\n",
      "1076\n",
      "1081\n",
      "1083\n",
      "1070\n",
      "1071\n",
      "1072\n",
      "1073\n",
      "1084\n",
      "1085\n",
      "1086\n",
      "1087\n"
     ]
    }
   ],
   "source": [
    "group_0_ids=np.array(orange_stations_ids)[group0]\n",
    "print \"First grouping: \"+str(group_0_ids)\n",
    "group_1_ids=np.array(orange_stations_ids)[group1]\n",
    "print \"Second grouping: \"+str(group_1_ids)\n",
    "group_2_ids=np.array(orange_stations_ids)[group2]\n",
    "print \"Third grouping: \"+str(group_2_ids)\n",
    "\n",
    "\n",
    "#reorder the IDS so that the similarity matrix has like-grouped ids near each other\n",
    "reordered_ids=list(group_0_ids)+list(group_2_ids)+list(group_1_ids)\n",
    "print \"Re-ordered ID list: \"+str(reordered_ids)\n",
    "temp_ids=reordered_ids\n",
    "\n",
    "correlation_vectors=[]\n",
    "\n",
    "for station in temp_ids:\n",
    "    print station\n",
    "    output=compare_series(station,comparison_station=temp_ids,begin_time=5.,end_time=19.5)\n",
    "    \n",
    "    correlations_df=pd.DataFrame(zip(temp_ids,output))\n",
    "\n",
    "    correlation_vectors.append(list(correlations_df[1].values))    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b1Rq+R6kQRkREREQqhBERERHtLBXCiIiIiEiFMCIiIqKddQ72AGqShDAiIiKijzJlHBER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\nfwFEfLltfdUOgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17257c30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "station_names=map(get_name,temp_ids)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "heatmap = ax.pcolor(pd.DataFrame(correlation_vectors),cmap=plt.get_cmap('seismic'),alpha=0.7,vmin=-1,vmax=1)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,8)\n",
    "\n",
    "# Clip the axes to remove white border\n",
    "plt.ylim(0, len(temp_ids))\n",
    "plt.xlim(0, len(temp_ids))\n",
    "\n",
    "#invert so we orient the diagonal properly\n",
    "ax.invert_yaxis()\n",
    "ax.grid(False)\n",
    "ax.set_frame_on(False)\n",
    "\n",
    "# reorganize the ticks\n",
    "ax.set_yticks(np.arange(len(temp_ids)) + 0.5, minor=False)\n",
    "ax.set_xticks(np.arange(len(temp_ids))+0.5, minor=False) \n",
    "#put labels on the ticks\n",
    "ax.set_xticklabels(station_names, minor=False)\n",
    "ax.set_yticklabels(station_names, minor=False)\n",
    "\n",
    "plt.xticks(rotation=80)\n",
    "plt.rc('xtick', labelsize=11)\n",
    "plt.rc('ytick', labelsize=11)\n",
    "plt.title('Correlations for Stations on Orange Line')\n",
    "colorbar=plt.colorbar(heatmap)\n",
    "\n",
    "# plot lines for the groups\n",
    "plt.axhline(y=len(group_0_ids),xmin=0,xmax=(len(temp_ids)),color='black',linewidth=4)\n",
    "plt.axhline(y=len(group_0_ids)+len(group_2_ids),xmin=0,xmax=(len(temp_ids)),color='black',linewidth=4)\n",
    "\n",
    "plt.axvline(x=len(group_0_ids),ymin=0,ymax=(len(temp_ids)),color='black',linewidth=4)\n",
    "plt.axvline(x=len(group_0_ids)+len(group_2_ids),ymin=0,ymax=(len(temp_ids)),color='black',linewidth=4)\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#All Stations\n",
    "\n",
    "Try to run similarity matrix across all stations, see if we can cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_ids=orange_stations_ids+blue_stations_ids+red_stations_ids+green_stations_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stations: 67\n",
      "Number of time intervals: 59\n",
      "Number of PCA components: 10\n",
      "[ 0.79430153  0.07933529  0.03933715  0.02921026  0.01094845  0.00869538\n",
      "  0.00625548  0.00486287  0.00464133  0.00350084]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "c=get_scaled_entries(all_ids)    \n",
    "        \n",
    "print \"Number of stations: \"+str(len(c))\n",
    "print \"Number of time intervals: \"+str(len(c[0]))\n",
    "\n",
    "pca = PCA(n_components=10)\n",
    "pca.fit(c)\n",
    "\n",
    "print \"Number of PCA components: \"+str(len(pca.components_))\n",
    "print(pca.explained_variance_ratio_) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comments:\n",
    "90% of variation is captured in only two principal components. I'm going to keep 4 components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ilJq6jzgZe8lDxDbDpSqOccQL7uJpeTCwexyNsBaym4GDl1lmwgRiKX8XyOrR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe2153b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pca = PCA(n_components=4)\n",
    "pca.fit(c)\n",
    "\n",
    "c_transformed=pca.transform(c)\n",
    "\n",
    "#Visualize the plot of first two principal components\n",
    "components_transposed=c_transformed.transpose()\n",
    "\n",
    "plt.scatter(components_transposed[0],components_transposed[1],color='blue',label='Stations')\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second principal component')\n",
    "plt.title('Projection of All Stations onto first two principal components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.49045892  0.40392596  0.02161905 -0.05701207]\n",
      " [-5.62888594  0.37809589  0.15954156 -0.2028939 ]\n",
      " [-0.16145022 -2.23005376 -0.50941159  0.73343793]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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nDEuQSsGoWTnvIhWUr15LtmHZLknSGFHMC8W6UCw+fV/2JGQbQbZo+fHx0bP5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\nSZKkQfT/zujldhyt9UwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16652050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_fn=cluster.KMeans(n_clusters=3)\n",
    "cluster_fn.fit(c_transformed)\n",
    "\n",
    "print cluster_fn.cluster_centers_\n",
    "\n",
    "clusters=cluster_fn.cluster_centers_.transpose()\n",
    "components_transposed=c_transformed.transpose()\n",
    "\n",
    "#get the groupings\n",
    "group0=cluster_fn.labels_==0\n",
    "group1=cluster_fn.labels_==1\n",
    "group2=cluster_fn.labels_==2\n",
    "#group3=cluster_fn.labels_==3\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.scatter(components_transposed[0][group0],components_transposed[1][group0],s=50,color='blue',label='Group 0')\n",
    "plt.scatter(components_transposed[0][group1],components_transposed[1][group1],s=50,color='green',label='Group 1')\n",
    "plt.scatter(components_transposed[0][group2],components_transposed[1][group2],s=50,color='gold',label='Group 2')\n",
    "#plt.scatter(components_transposed[0][group3],components_transposed[1][group3],s=50,color='purple',label='Group 3')\n",
    "plt.scatter(clusters[0],clusters[1],color='red',label='Centroids',s=50)\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second principal component')\n",
    "plt.title('Projection of Orange Stations With Centroids')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First grouping: [1039 1074 1076 1077 1078 1079 1080 1082 1012 1019 1051 1077 1004 1009 1035\n",
      " 1037 1039 1052 2106 1051 1052 1053 1054 1055 1057 1058 1059 1060 1076]\n",
      "Second grouping: [1070 1071 1072 1073 1084 1085 1086 1087 1010 1011 1013 1014 1015 1016 1017\n",
      " 1018 1005 1006 1007 1020 1032 1033 1034 1040 1041 1042 1043 1103]\n",
      "Third grouping: [1075 1081 1083 1002 1036 1112 1056 1061 1101 1075]\n",
      "Re-ordered ID list: [1070, 1071, 1072, 1073, 1084, 1085, 1086, 1087, 1010, 1011, 1013, 1014, 1015, 1016, 1017, 1018, 1005, 1006, 1007, 1020, 1032, 1033, 1034, 1040, 1041, 1042, 1043, 1103, 1075, 1081, 1083, 1002, 1036, 1112, 1056, 1061, 1101, 1075, 1039, 1074, 1076, 1077, 1078, 1079, 1080, 1082, 1012, 1019, 1051, 1077, 1004, 1009, 1035, 1037, 1039, 1052, 2106, 1051, 1052, 1053, 1054, 1055, 1057, 1058, 1059, 1060, 1076]\n",
      "1070\n",
      "1085\n",
      "1013\n",
      "1018\n",
      "1032\n",
      "1042\n",
      "1083\n",
      "1061\n",
      "1076\n",
      "1082\n",
      "1004\n",
      "1052\n",
      "1054\n",
      "1060\n"
     ]
    }
   ],
   "source": [
    "group_0_ids=np.array(all_ids)[group0]\n",
    "print \"First grouping: \"+str(group_0_ids)\n",
    "group_1_ids=np.array(all_ids)[group1]\n",
    "print \"Second grouping: \"+str(group_1_ids)\n",
    "group_2_ids=np.array(all_ids)[group2]\n",
    "print \"Third grouping: \"+str(group_2_ids)\n",
    "\n",
    "\n",
    "\n",
    "#reorder the IDS so that the similarity matrix has like-grouped ids near each other\n",
    "reordered_ids=list(group_1_ids)+list(group_2_ids)+list(group_0_ids)\n",
    "print \"Re-ordered ID list: \"+str(reordered_ids)\n",
    "temp_ids=reordered_ids\n",
    "\n",
    "correlation_vectors=[]\n",
    "count=0\n",
    "for station in temp_ids:\n",
    "    if(count%5==0):\n",
    "        print station\n",
    "        \n",
    "    count+=1\n",
    "    output=compare_series(station,comparison_station=temp_ids,begin_time=5.,end_time=19.5)\n",
    "    \n",
    "    correlations_df=pd.DataFrame(zip(temp_ids,output))\n",
    "\n",
    "    correlation_vectors.append(list(correlations_df[1].values))    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2K3HV6190APaNiPtyPXoBr2FmZmZmZg15SGf79vANIb0A55KGVP5X0tKkIY2b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vSjoKGAiszazRw9dz/o1aez8tafv8fUlgEGmE0BhjjDHGGNMqdulcGA2+UaQM87Mh6RzgdpIx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mCkcJ4O5dqOwsFrHQjo/BCw80sczPS+VUiQLsjefbbVpoA8Tghnd38xDJtnkCBPtM9bkWG8\nFkKRW6B8ixPd/aWkTFwVMu9AldsuQUbKGyUo31LhmNRKrLaaeYzzUDU2zGxz5BQ9g6KIayCFt7Yp\nCtMxjOBfkSH8vYuq0hBFzVIu2ihT0YrVEIo3Pea9JVHYJ+79c8x/tdIPp2olFE3oicryOwIJhqA8\nmhO8JMKAjIgnUJuNQykAhKmmnNEp7r5mXH81ZJStjwyDW8xsqruvWHLNO1Gxmj08CmAgyl57pJw+\njWe9BimzWa7y/9eiHIfl0b59FdjbVTigFbCbu99uRfS3dKwOHB5G7WHI6dwj9tVr8fxrUUR4d0LG\nTaqu14hCrn6KoqITPSsOZLXpYM8jEGawFzkdae/8jqJ6pKFIWAN0RhM17DTUAmAYov8QRnGaq9Xj\n79J+jsT8nYZQ//NdkYg5FDTs+eG0pb5f6yMGwQVxbldC0dzk8J+BHK+rXZGHN1AuYGkkK3cOOiDn\n/QRkULREINJgZPw1i7VtgRyrN12Rw1WRU/8Mkl3vB7CU8ilPdPcHTBTzveJ6jyBU/wjUZgLkLKaI\nQzrDzdG56Yxofc+g8zkbRUbauihbS6G93Bad2VQRdUDmXIxCxu+VcZ1Hy5wh4l6LNM/ZHCZ5shvK\ncV7HVPGxFQICUmGic5A8f4Gi/UlXBHr9K57rM3c/0xQZXxoBFK3L3LOB16RO57K2IZJbxyFg5FPk\nwBwPrGhmN7v7sdQeTwP9zOxod785fjbPlJe2PaIFb+juQ+PnLVAu7+zsPHVHwMEsE1g5Fzk2iSXS\nAOmUlYE9Tc2mJyIAqxuwp6s3Y3IMexCOZnz/OjPbD+nIfmXeAeT0f25mR3m0O8iM911QhKau+cPd\njzcVz+pAUfF1U8Ru2M5FrW2AjPI7Q953RODU9Pjdqmi9T0GRv9SfbgJa97uQ01w6eqJiXuPMrAeS\nC/3d/W+mCFx/BHym4h6LtP+8YPlUee2CI+n9e7l7D1Pl3S9RZPdRytupC53ncvfN1nZtRPN+L95h\niCkP+gIEPFUhm2FZJJe6hM4YjxxEJ/L3St6nD4okvmRiXG0KHBZ2x+0IMHik3LqXG+7+oInOvRGy\nmTaNNXi7zMfbo/5635vZlciZPNhVxfRVpC9HxvudhOTYYSaa/KMIEEhO/yLJgvqxZI16h+9/aJQI\n4DwKVCpYrkFo73GIrtIWKdyzkeCejigIL2eG3PJIUSTHogo5Wquiql0zTDlouyBDPlFGm2fOyGQU\naWiHUMZ+HiV+kVL72JT78pmJmvhPF7Wz0vsuoIRSGUI5UZfS6IRoXu+bqBRbIUrkAityOn6HoiWn\nI35+V2TUvxf3qr6PC3UeiAyF0gIcbVCT2fFIaX2GlM9n7j4ud8Lj3k3CyF8G9b+7ouRdQOf0OWSY\nNo93nmlm31E+MnEfomDMJfLAkKNWg/cfijDJgLmhuBJtsRWixvRFxvhGSAG2i3W9192vjfmYYypE\ncE+898torVN/sv7ZukxDCOmXCPEfYyoyUjoeRmv2jSnp/3MEGvQB7nKht8NQPspViOL5KtGfL5Re\n3/j9R2Y2INYhOV7lAITWBJ049sm+aD+kzy6HIkvfAY3DcDwcUW6SYd0LAQkg43crFLGbiKJc47zo\nPZXGwPjcs6bo2DCEXp+NDIxEo3oNna+tkeN/ODqHyUlI1Kjj0X7+c1y3FSoPXqPtBUAAGP9B0Y2L\nTFS/FmEkJNnxBfCemY1DoMFD7v5ROEXbxbwmOtPqyGEbb2Ybokjq56X3jZGQ8R7Auu7+d1RoJn+X\njSn6na2L0O1UNj/1FwSdg1TB8gBqVvfrTkEFfBXJnelmdi4FrTPlzySH5QQE6oxEDtIs5DA8gCqX\nTjJFvc9AxuRpISM3cfdqemjM8WhTMast4h3+ZGaPl5mXRZrnCmM9BFI9iiotl1LNt0Y9OJNR18BF\nNzsdyevRiPJ9DIqc/YKAilrnJPTMUigVoLQ9T/p8NxRdfCH/pSlqVmu4+wQzuw0Bkeej9foR7d1X\n472eMbMpyEn7mgII6WVmR6IIxKVIhnVCsm1rClpyMsxXQE7kzmj/zEDnKTENGsZnOxIFUrIxj9r9\nQvP3+MHMBqOozCbo7FahdW+GKs/WNX/pOj8heZvyUzGzXRA4c2Bc4wYEQLQHDoo5PASBGQ+7+xlI\nRhDO7ZUINJqC9P911C7gtDqFTtsJ7alUFGktirOYWg+kUXb/WVGZOTlalfZTynO9FDmZe8b116Ak\nzzW+s7B5vj/uX+m+sxB1t5O7j4mfdaU4Y1WuNjf3mipVDkIO05rIKd4WyYJfSmyslbLnPQzt3SRb\nOhKymppRwRrDCiBsBeTkOWJsTESU3Vp7JnRXAxcjCUTRPz0D2tpS6L4qxJx4Denim9CaJ8ZLaxZR\nFtSPJWvUO3xL+CgxjCZ79GLzjAde6vS5aCGnhfJtihRAkzDy+iIj/0TUrHs2MkTeRpSCoXGNJOxn\nolYMDyKnZayr8EIjZOCkRtgNUOXAJ5FBdWugYKsjI24qyvlZEAjhyciBeApFCKqVbCYQOyLjbiqi\n8X2YKZqEQG6NDPeVkGE5GiGhvRBXf76JevirK+9vNDLK7zZVwkp9bGqMcNIeyf6f5ncGUrLrIarf\n+shoXzEM4qHufkq8w/yYPxBdKBnOLVDEKiWuf2ZFefKVUNS1HTJw2qKy4Xm0ZHVXBKSRi/LZH0Uj\ndyx5hwUUQj7NbQIMproiNKk4wj1IWfVBlKZR2XcahNLc1BRd2RgpkunIGfsUKe99kfOYGo8fbWYD\nXMV78ghpVbz74aZoal+k+BsDl7n7c7Gfbon5/ZiiOuYcj3yHcKyONFXgOwFFOcbFvv+V2hHYBqhn\n23nIgZjn7qk/0iZxzVlmdhNa+wtQZO96MzNklLemiCg8gBz13ZDC7gKsYWbruno5kV3zLLRv+qFo\nSPN4v2u9KMH+g5md7KKEjUN02dFEn8aQA23QmTgCRbXOQo7Q9QhkqTFi7WaZojyfIorPeDPby90f\nit/PMbNLUDR4AfC4iTJ0Bzp7F8TlzkK5WG2QYz8z3vcWd7+B2iMZjRuhNgqXIXrTu5lRMw34wRRZ\nORT42t2TQ70JBQD1NEKrt0V07b1i77dARuCjYWRvhPZEF2SIPe3uj2fPlPbEWqilwSBToakOSH78\ngHqhLUC97DpTFL/oDpxpokI/bDUphD8gEKZnrM+FZrZmBnixCPN8Xpk5THKnGbCRiUL2qalC4bSY\ny+nACq5cnSpkGM6LM/QicpI6I7myP9qvE1GE/h5XTliSLwnkWAe42FT05QfPqOrpdYC+8fsJKAdt\nipeJ9EL1mX8GFTXqixzG5VDO0KMmwOh+BAx2BL7wou/oVRS09LWRcb4CAjE6o3OUj4ko6v9ryP7O\niIUwJn6fnOVHUU7a6+gsz0A67Q0qjFjz+015u/sjPTMrvnsr6ml5XqX5K9XV8bNECW2K5KYmWPS8\n69F6zoq1aY7om49mztYqMf8DWfh4CzUivwtVZrwq9GKj+H+qcLmg5O9y+68ZkmVbeJGGsDwCXP6T\n3zT21jXISToenesvURXi0r21sHl+HNkv91a6r6uX5O+BwXGNpVAqwx0l91kB5Z0+hs6gk+X7pmdP\nz4Qc9A1DH/QBTnb35CR3IgqTee0oZ63rURQ4a4/25NLAPmb2itdOg2gIDDGzf6N9MivTXT3iurNN\nqTRHomqqN5mAkhPc/UgrIuVrUIcsqPTc9eP//qh3+Jbw4UXE6CBkYJ1qis6sAZxhZo941pAZijyr\nUL5JAc8MZXMFgbLHZ1dAuSvrIgVaQ9CEEfp3ZAT8QigaZPz1oqBwVrkqR3VFCvazQKXORkp6fy8q\n2n3vaqC+DRLc3czsend3q0mHuw2hwB2Q0blMKKbOFO0MNkfGwAsIfR9q6lt0sqlqXiNEyXvfVOTi\nDYocmN9yPma7UPkNkPC/GqGi68S7jI55W2DqDfitqzR3e8T7x8vQJsLx/JmapbsbIcpJnkPYGdF6\nmnpBD5xGmQT9MKIOiOs+7+6zsv2U5rk3yv1L0cHnzOxQsnybcJqTofIEMMgLGmZyIheYKhAORHmc\nc8KwO9NUBOK97Ho5OPGamb2RKe4Ukd0cOMvdd4ifn4QKsyRnIN/nX4cz8Q5Cjv+F9ueYkjn+MRTm\nRQgN3SHO0gnI2L89PjrL3bcPB6LKFfFN1eeODge9TXz2UoLiF3M5M3f2snuPRWj10nH9lMdXHakx\nVTLc2xR9fAMp5O9c0ao0/90QYDHCzK5CoMMQyvRvi/vOj79/RTkmg1FhpVvNrKe7/zXWr7p1RzzL\nPBRBnEhBFd0EGd8boz2XaFHPV7h3ere7UOSkN4qqLRfnb2dXC5DOSPa8iyoRt4n32oIiypOiOOsC\n+8a+2QGdueFo7XeMd3sCOQE7ozzDYR65UZkh9h5yLHC1e/kgvpf283xTC4ohaT3DiHwIAVgPewEm\nDUNgyTAka14E/po7e9mcVJxnL4nQlzzvQwiYWy3msRkyYo+IufzCzLZy91dirtO5bYCchBEoKjAB\ngVU9kHOcHIxEfb8WUWM3i99dBXxrZqe48n2TI9kTgT4roT0y0cwmA//x8kUfqsKIn+fuLwIvWgHa\n7YOocO3QuXwH0e1/QftreXc/wURjfh1F71ZG4MGT1I4Q/RXYxkSFnITWZVQ4BV8BTUwA6mh0pk9F\n+6YNkgWPUGF4QRv8CDELlkOyd1L87sG65g+BZk2R/pqCztEcZPCvR81KxsR30p4cT+iQGOl8dafo\n07t0/HxOuXVw97dNlSX7IpbF47HPz0HOVHr3JDfq2n/NUc/CK1AOZ19kpwyiTDsD5NB8CnzlArW6\nk1W0LnnOsvPsinK2QOe61n3N7BskO8Yhau4YBCTNRUXRUnGUpIM6AdNMkdPnkXNetj5CPNPfEDi6\nADjGRfvug5y/D11tNRY6Qp6PMLOzkeOXWtz0pWZF7XTvySYa57ExZ3+L65yCzkPSXbuhCGyq0n4/\nsHmcsYfiuSdQhyywmkBW/ViCRr3Dt4SPEOAzkVIaYEKIm6JIwYPI0ak1Sg+sZeXI45qpGMWn7n6d\nmb3qym+oKv2eKz/qMhQRmWcqrDANOMejFLsXeWtTicRyU5GJ47xmuexuKG9tF4J2gZKg+5rZJe5+\nV3y3DbCiu/cteZ6ern5i6XodkcG7HUq+BxldPyDjf5f4+SdIibVGCvJUSpC8RRwparElymVMNIqh\npuhhrmTPRKhoo/je70zFA94BhmXIYHq3RIvEla84lywvL8bbyKB40lSCexKav7fyD8U9/4aS+/sB\na5rZMyjHbVRmDNyIqnXdhyK8VZTpxZictPh7Wsx/A1feY7pWe+RYzonPvhj7tWJeQGb0pQI4VTG/\nrZCh1M7dJ7oKMDxV5pnSc01GTZeHxnu3qnCvT1F+T4qOd0NO9QMeiCnKA2rg0XA3zsQAd785Ox9d\nUIPf6abS71NiLp+kAEHye1fFc07L/l0dnTeBI48i9Ho+coxSFbbe2aWmoSI9o5HzOxwZqWOpY8Te\nqop3utaE8Ce6ZHXklaKK7th0zeystYw1XQFRhO+PPVWRCmQqyPGNu/cv+flaaH2rEEjwOHKEZ5jZ\nH5Dhc7m7vxZzNQ8VdXnWFe1oiiJE91JUe90JFUAZEPv/XYTo9wNuzvZaYxThPcUUQRmNnIIhiC6c\n9vMg4Px4xjeRvNoE5eisEvM3ETkr36GoecUy7tm7l53nCp9tjRD7uz3Q93j+pVDF1B/jZ3ejHLQb\nkHwZHfc4FTl7f0Ay8hYX6Pc+mVGevfOaLvbAvqhE/x5mNhwV55oPLB3PvLcJLOmGaHDdgFUrOHuV\n9FGjcHivREV4jkXO2x+Rc/AwRSEjkHHeDeU43YFa51RfN/v3VHSWno3rHB3vPxE5iuOQc9kOGe89\n0PouQL1pKxq7uR6Ne1bTrWNdK84fAk4OQ7p3BnJmxyGnaf141gvK3K+qdF7NbDsk2x9GIEhi5pRW\nQy59/ipXteAhmXzriKiJtwcwlEe1yu2/bsgmWRHttbuRc/UpAmkmZPdLDKUj0Vr+GYGAS6H9cvyi\nzrOZrRFzPAHl+pbe94d4jwcQGP0Cco5eQoDshmb2RejWtMbN0bk+ARVYG21mX8f7VoOL2dyNBjY2\ns7YuMLwdAuaWQvTMSmk2pddJeisVFhoBvGRKoallt8d3xprYLHNCBq6HQIq/UfSMXJkMAHGxupKu\nTu+8MFlQ7+wtoaPe4VuCRyj2U5AxMR4Zd1cg9HpdL8/1zvnhSyEa6K9h6DSk6FvXDxnF082sF6rC\nd7SXoMxeRAhmZ9evLh5R4bkbxHdSE3UohPdVSDA/iRClr9z9jUDwXjSzUe4+JJ5tWKBnDvzo7tMD\n8cuF0gUoj2s/4Me4TjeUsL49yttbCdECU8GNQQjxK1dBrc7hRdTiThSxaYm48lORYf637LNbxnw0\nRUZRH5SUvT+wmilKl/fsK1Xq5RL+Z4UTtRcS2i0Ruvd8yXe6x599gCdcSfq3UVSxTOOBmJMNsrk6\n3utuWJ7mf368/4EIGR4APBH3+TaerQFlcjTiWXMlVEXN4j0bIcNvbVPVsc+RwTbIa/bBK32usfHO\nZZ85d7bi788QvTj/XI0c0fj/7BIj5EvgJ1NV1++QYf0RRQ5H6b1Lr1e6voYid2eaokZN0Rlonn/H\nFV0+Bin6kchQ/gtCbMsOq1lEohEyIPO2AtVrUGEPpnl7NhyLf6K9PxIZ4TXyXON7jdE+OwH4o6n8\nfkOUnzfUFeXJRzW12iO3psw7zE8yxRXdTuBQkjFziMh1rO/ckHml+6UX0NfdG5gKJqWWNDu7aFDp\nXk/He2yMzuy6CHR4Cp3jRui8XI9kwHdm9hUyQD/zyr2sKs1zKdVvrZivGQjEwsSgGICMtTuyM/QU\ncmB2QHltK6FozKOobP44U2GYk8zsYXReX/AiJyjR4saGM7GUK9e1HUoH+N7UZ6+zmb2HHI0XUWT0\nXVTUq+zeN0VymqCo1o+ZwT3b1L7lO1fxnfVQfvAQYEt3H2+i5aUKjp2RLLs6rtvYBNzMzu61MlrH\nbtl5HYucxGMRRfhdpbYAACAASURBVPwid1/XFNl6CUWGpyM2xJUIJCw70lkJ2T3TCiBl7sLmLy7x\nEDoTTRHzZYNYp7eAB0vnsA7j21BU6xR0Bn+OvfwFAjdfKmcfELll2Tla4KIz1r5B5f33KgKjfkXA\nYHcCLEFrWSvNBOmr45FT+zkCYZ82s43c/Z3Se1eY5wuQs1L2vkjHznP3v5uo0kPcvZ0pAt4HFUi6\no+Q+g5GzSDjdpyHHbRRywMr2DPSI5LnSCg4vuWaduXCZ/E821KfoHH2BIsP/rPSd5NDHuS9nh92A\n2E3LoLPUEeVtvp9d69g4dydXkgX1Y8kc9Q7fkj1ao3D/qkhQzqXg2J8UzlF1g9PM2TsWGTW/Im72\nZKTQ7kPGTz9kUK2FaBLvhnLcBVVVrBXSt6JiZvr3/FKjJQ2vibouKPl7hwrfmWyKziTj0ZAAPwsJ\n9cmmhP7hXvTmaoAMr/+g/LgdEKJ3vKuB9nBTqfOTEAX2KWSUd0CCNVEif8t4FRXH2RwpxQ1Qrk+1\nIRvG9fwwThNtrEYj8GzNDFX7OjObkwVm1s1FIUyfWx8p+o+RofkFopmmd0nJ4j1RQZnuFInqjpLR\n87ECQhhfQoU/RlBmhMJth9ZlFnLaf0FRluOQ0/cGUkArIPrlmsBfyilAEy34S1Pxnhs8cn/CKN0M\nIe8pN+9wVJykOXICy9IXF2WUOlth0Fch1LS6GmwYqRNcNNDkRFTTTl0VVE9AMnYZCkP7pnL3DaOj\nu6sCXyt3/yXutzVC6hsBY6woMjCVrOpmgAbLI8NrUxTVbYUMe6gQRa00zxXmZr4pcjUb5TzNQlGY\npkiWXIGMU4/nOx94tYKBsymKfPVFdMV52uJMQrTNvUv3WjovFEZpNZU4m/uGiB6YnLy52f2vRRUG\n10XO8LoxX6U5We0o+ruNjM8OjOuXyr4ZCGh4D+U7jYnPfYbO/CoxLytSVE29BEUeyuZUlZvnCnO4\nJzpnZ4QR3cjdvzAViDnczIZ7AYDNBm4yFTZaE63PF2gPzQlnZDKSlVci2XWzmf3bg3oaTt1VSI41\nM0VldiXApHCAG1JEzdojp2UXYD0zu9zdLy/zHrsho/NnCn00AYFCrVHhppVRxKsxilZdjc7/5sBB\nZvYFcsyqC9t4QXfMnYtlUKXMbh70vdi/Hdz9u9hjyUheCbE03onP3Y3o4GWHqSXRikiXzEF9FhOQ\n0hTJ3YrzZ6Ii9qaIut7i7lfWcb9GKC96G6Q7RqC9Osndr0MR3dTvbU0UqeyOooh7kxWEyeas0jma\nV8a5rLT/Dkcy+RGkQ1dDsm8Q6lm5oRcU/nTN1mjN10DsllkxH+Xy9yrN8wFx763RHqxxX5QDm+7b\nhWLfzjazn4hesPHuHRDI0wXpqz7x9zOoWvWDJc+f5m9Bdo0koxabAhlrexxau7WQvjwE9V38pI7v\n5Tm6SS/lYOLg2BOHIcf1C+CkkHPpvq2RHfgfKsiC+rFkjnqHbwkegbj8GappmEsjA2NtRFEojf4k\nA/Z0VEBlHjLI2yDhmErLz0GCen0KBTeXou9MuWepVTGz3AiBsmV8dgIylpJx2wY4FxWO+AUhkUNR\n1GYwKrOe8tWGIsR1FYSwr4qU2mQz+yCe53eoxP62FHTO/Fmq3P0p4Ckz2wIpzl4o/+auhb3LQkZT\npGRmIMNuT6iF7lUbrfH/lC9T5QXtMX2+MbCuKaL5CTImz0cO6uHZ2t6BUOKLUT7KGqgy6kquCGhS\nPIMQIn41opu2Rk7Ze1bQGQ9DBkXqW7Z7IIP3ezSwtaLn4/5oX32PjK5f0Ro/6+6/CyW9OzL010fG\nwFleJl8x1uVXM+uN9sLNpsbNTyFl2wkp/LuQEfMiihRe8N9AIXMD0Wvna6S1uhkBBT/maxqG8x/M\n7B1Xk+rl0Hm8xoN+WOZ+y8R7vWNqgHtzOLYHx3v+Be2N1ijH5zW0ByZRRDTvoug5+BZy5g9CDvB9\nXiaaVNc8e0EtTcbDpsiIb4DyXiaZ2TRkBPVETvYP6Dw3QGj70plhVzrWBj52RZZSxH8eyh1sjByW\nU2K+26KzUmttvciFHevun1YCmUI+tkdOwb5xza+Bv7lyn6CQl7NQ5cdXESXtu3i3lwg6milCcCIy\nxj4lKuGi/DAQELUFYhMc7e7/CNBmWyRnatG1FzLPw1y5hPnojirdzrXoPxp/P2nqebo+ym9KIEWV\ni7L8adwz/XwjZFxuiJysp5B87YRas/zFVba+Bzrff0UVHHshpyTlNqbI5C+miO2XiIr7K3KsSytC\npvESMjzbIme7CzrXIBbKKARCjkQ5R9XFwxCb5TMk65uhar1fUgBOpybgIPbZSKIlTziJ89BZujiu\ntwWicIN06JDsOXtSRBPLjdbIwTgEOZW/Q0DLSFRIyIHHKs1fyMKzQx70RvJ2S3SeP/eMQh4yZ2Pk\nHL4V1z8s5m84Apjwmv3enqnj2Ym9N8Pdh1c6RyWj7P5D+2cv4Fx3t9hn1Y5+pu/S+W2Iolb/RDlj\nrU1R0MaxX0tHpXn+EM3rhu5u8dn8vtcDO5py+A+iKPgENaNcjeJaw9EZHIFso8dRL7tqIKqcI2dB\n54x/16CeLmxkzuGmFDnqFXNGS0fsi3lWkv8d125EYStdAnzpNSOC85HMqEsWHFMKxNWPJWfUO3xL\n8MgM89VQ4vFyCMEajiiRP2efTUqiPfCKu9+S/a4FsJwXUaArERq+MUr2PwIp7FShqTQKkhoP74iE\n5uuoJ1Z1kZX43Kqo0MosJExSlcjvEAq+IxI4XZHzthkyjtZCeWl5/ssUhCIucPcrTJz/+cgxSlGs\nZVEbgVMRAjoOoZ81msfHe7xm6jl3NnCHmQ1099tZjJEZUOshKs08ZITsA/TxknyEMPDSGjaJzzfI\nnQwza+buM9z9E1O1zb/EOy6L1undbG27IoO0bGPu7F0XIOdgdMzJbgjxvRI5DklBbY+U3ONo37SO\nv3OFl561b9zvERQ56YjWLTnon7j7h6Y+enshg2ZVM7smM7jTNZOzNcpU0W5XZMz0RlGwHlbQrfoh\nQ3J/1KR6pzqWqOLI9nI7YH8z+xFVfjwN7Z2HvGY+4jeo8MM0otk22stXo7l/Nd71CWT4zjazCe6e\nF93Je0KtiJTrschYHQCc7e4HmirlOlqrbdEe3QadyYPM7HOo2HPwLqvQc7COee5uZte5IjrJoDky\n5vlNtAe6IOfyJ7SeqRx5okWnyNHcMo4KCGxIc9HYzOYCjcKoXR7JrpSzuS3KlXsBMQ/eKjG08lzY\nn5ABn8qOD4/91Svm8/emPLalvHLLl1YoGrAMReGRzeK6k2JOdkUo+KPIOWmE9sMoFy2sHzKSOqN2\nF6shGvIKcZ138xtm+6rSPE+mdqPsVGG5mlLvBX1xBSrk2SbnMpvDlkhmn4j21LRMXlyJ5v4NdLbS\nGlzvak2xuxXRsoaIJjsAOWCptcJkFD0p1/cNV2+9odl5SBGxTsDanjX6jr28PEVxpo/y/WVFRKsr\n0l+/xM/z/m/Xmtqf/B7JjX5IdoDkZy8zexqBat+EbB6OwJO8omvpeAc5CO2QkTwTrf++KGKUIspl\n588UxZzs6iX5LJIDuyIQdJiZ3eeKsDRHDk43RLWrZn2EHkxR/cUdB6AKtlUosvYZNc/RWKsZLa21\n/yiYEKsiGulWyIaYgvTQ7GwfNqUoHnO3FaD1C0jHHFHhOSvNc+/42eS4b2rlMzue7zE0p9sgcKGT\nmb2I5OouCKQBgS2XoT30MlrzligndIQVIGetEfvvvZBV13hRBGZRR2oJ0gJRwFfyklz+MvfM2Sg7\nI7voPjN7Eq3R9zHH+yNH92dkL7Y1tZa51dXCAxYuC45C1Nv6sQSOeodvyR5J8N6EBF8vCppjFxRR\nSQoqOUFdgT6mfjePoqIs082slZm9i5DqJxBavScyXr8FrkyGeSbwU8+qk+N+dyKH7S8ot+o6dz8x\nDLG5KBdmDkKQDAnn9hSRwzYoujAe5SS+TJScL7kvSGl+DpxlZo8g4/yYcNwSgtgUKY6ecR8QleYB\npMBXRw5Jd1OUoFl8vhHKBVgsh48i+rM9cixPASF+qHT/n5KjbapQORYZU+d4URJ8Xo4Qoj5AGyCK\n5icoL28gsLsXeZMJRTQUtWhAmcbcJc96pbtvTVHlsDpal83fF6is+7dkaKgVRTowsz/G55qhKMs0\npDBej9/vbypQsLopkjQXGbtT0R57Ea119bXDaV4JOUI7IoX+A1qTxqaKmD8hutXb8b351EG3WoSR\nqhAejPbKi0jpbYeQ/h9Rf8Uq5Ah0RHt+G7SO38Y7dXX3DU0o/QXIMDsRncWzESpdOnoi53tTZBQe\nGe/ewFQspzU6041R9OdlNI8XelHOfXF7DtY1z0ciR/JcFAmegM76pe7+dXaNZmjPP4CinZ2Q0/ox\nMnDTeS83nkRn9yUvyusnp2c9VM0xFeu5GwFJmyMZ18rM3nbRvKu8yIVthuTbRshB+xtqC9ECrWkD\nM9sd7c1yPQnTPPVCzd3/HdddGTl+n6bPmfKhHkLy9lNXdcDBQAdTq5mv4/lnIWPyGUSZf9jLVOoz\nsxVd+dF1zXPp+Adwm5mdjBzEmWitV0Io/ojk6MQ6HwZc5wU9OhnvQ+K73RD4NtPMDO3J++PaHeL3\n3yAwq7GZ/YCAor1iXuaG0b5JvHNHZIi3R61iahUMyfbh6sgx3gfJ9Skouudm9iaSqZsD77n79dnz\nl1Lq8ojWs9mvOpmiZZ3QWm4dz/UkNaN4/0Byf9n4fad4j35IXwylwnABpmNNVM1m2Tyvg5yRi1FU\nrOz8IV33AAXVcGi6nwm0bB2f2yvmqx0CNLdDzv3EkAdjShyzRR3Hu/tx4VTn5+h8pM97hsOTrl12\n/8Ve64TkVX+kH0ahdi/DvIgQ7YqayX+B1utNRHUe7bUjT3XOc+b09EHrV+6+L5nZy/FczZHT1x7t\n7WkUBe5mUoClx6K9/ANFrnlZJlM8w4zQ1wejKumvofzV7xdlTUL/NkDyeDdgLVPe7/CYo5e8do56\n0l1HISB3KQS4zEE6Zwvk5G2J5M/N8bypJclMW3RZUGfRn/rxf3vUO3xL9kiCcFkkoOYgZdESoZvV\nFLJMaI5Dhso6SKg0N9H5zkeOxPrIcXgW0Z3qElDpmu2RIVEjd8qKhtrpGi2A59z9G4q2CQm1BQnS\nHUxo/4fIyJ6GjKc5mVDvgIyaPwDbupp3nxjv0CeLXDwfAje903REcZyEBPmFKBIxAKHPCxAaWKNi\n4G8YayPFTTzHD6Ycgdxo640iat2BDcxsFEJUX0WGz85h9B1NQVfthiJo2wKfx/xuGvMJRWPuPjFv\nh5E15o5nWRBGWRtT5HYoqpL4o2c00rj2scCJpmqfg5ER/GGGwjeIe+2LjJEnTHQZj3cZifIIJyCH\n82wUhW4FPO7uh9Yxh7ciZ/wlpMCeRevWLebkMxaPbrWwkZRmd3QOugAdXcUbzkBRnBdQBHYKsJkJ\n+V+TAmjZGPXZaoCiAxsCf3cVl0htFKpHdiafQ/vzCASaNEQUy5HA7zMHsj8CMf6BjIEbEWKbigMs\nTs/BNGrNs7sfFEbbA6hf549oDw40s4HIoXNX0YlytOimLIQW7e4vm1D4V0x9zt5FTvMBaJ1fTXMU\nDtvseIftEB3pO1PBjm9ivhtR5DO/imisee/OGciQuTSuM0Z2DBd6VhQirpXatpwDXOGKMNWIQqPz\nsB/a//1NTebXQcyIHhTG4Trxbge58pIaWdHrMh/3mSpSlp3nCtP4BgI5jkX7YUx8vx+i0+WGYWMk\np082Ra8+DJnaEDnprZBj8wECNIah3Kz3Caqbiab5Pqr01wEZi0+j6NPK8fPmKBqUch8XNpJM3AfJ\n4r0ROLJNPEczxDqYi6iLu4QTfXupI1BpmCIfpRS9/yDAs79nOVEBok2OPymvqSE6G0uT5c2W3CPp\npuVQdOiAAIdGozl9C9Esy85fXOZLdN5eDacGU/GM9dEap0rLc9F+/xmt9+FIxk439Ye8MdMJizRM\nubw3mvInv0fpAB+jIjilhWKSPq9z/yG5tiICsbojvdWCgiHyFnJ+UxupvZF+aGpiKPT3mj0y65xn\nM0vzvEGl+8aemRV/fqJY4zu9qCSemEf3o5zfo1DeZXMz2xGBYHkrpBrzEoDOVWa2J2IK9Dblr35R\n1xrEc/RFgFtXpJtvizldDzl0deWob4zkzxSUanCfqS/urijVI1G52yCdMM0jxzcbC5MF9WMJHvUO\n3xI8QvC1RMbQHKSkVnc1Db/M3S/KP29Fb5cLkHE0ARnq3VB0ZmQI/u2RcLnCzB4DBroXVLRs5BUJ\n9w/FOB4J0qleUETT59YDjjWh7IMR/fRjd/82DK2Wca0NkbCeg4zwc6mpaLvG53pSRDFqFVgxVRe9\nOj77LaL/XBy/2xoZhk0oDLd3gU9MBRMGeZn8srpGZsTdhprqroaE9gzkENyRffavZpbyFq6K9+2B\ncjI/cvWOa4FQxrHIgHwTCeo5MVcdcsUejuVJroIhP8e1v0KOcD7aIuW8D1qTX83sV5Qnck9ca66J\nmroGUiS7ov0w3d1XjM/MBw4Oo2Q1FKFNqH4/tHZHo9zAPeO5B3gdpemTEefuO4XT+yRAgBK90N7o\nROxZW3S6VZ3DC4rOd/Gs+6A8stXjXc4Lw2JBGCN7orU6Axltj6IIxE7IaPkjOgOpwt2myEmtHqYC\nO5PQ3hyACviMMEXWnkNGRcf4+Abx/le4qFWDyaqN2uL3HKw4zyEnvgsQ4kFkYC6L5MK2CGhpZmY/\nufvO6f6++LTo/sho7IccuXbIuL/Oi3ylVLhhJIqQvRjzOAXJmpRjmND4BUSk0kQ1H4uckPfjHvvH\n2sxFhmcqm5/yCOejaNwzsf93D0f2MWBwBoo8EA7aCghBPwjJj9eQU9kh3rEn0eMyvlcul7IBokmt\nvLB5zkc4bDfFO26JnMv3gB28NpXsGwQUHIIiz0+b2S3x/K2R/BmAmBPbAyuHHEkGdjN07v6EWr98\ngfbozzH/66J1n4GiEq8jgMTjs594BRpcjPbI0f4GyawxaO9NRmyEduG4bY0cgVvruFbpqETR+9lF\nla9I0YNquf4rlXMQoWDQbIWi/C1Cj2yMdEsv5KCVnb+4z2UxjxcFyHRSvO/a8fyJBnxfzPm+aE07\nxp+V0B6qBBDUNRqjs7g02hNbIUB4gYk94V5ScKeu/Yf2weHoLH6G5r1/ZhPg6n05ztQC5mJUQXJ5\ntDadyCpHZmNh87wFclIq3rfcKAPAVKfMuPu/zOwOJDcuQbLnDcsidlazmFcftG5fxpz2RuyQf7n7\npXU9ByqQcgMC8w5D5/AfC/lOAj6aIoBkc8QqAcmTZCPthNZ2PST7xpjZRMTiSSyA1u6+jqkfbQ1Z\nsJBnqB9LwKh3+Jb8MRUhUeehKMh5gfLVogCF4t4TCeduyBDv7e73QXWlwPYI2dkPIYsnoXLrh7sK\np5RerwlSpG2Rcf8joj9MMrMbvWZPm6vjGTsTFRpR9baNXNW9/oaMs+Q8zEaC/Kd0v7jOG0S/K5S0\nvXncu5puEw7CDUhAz0JI3wWh3O9292tRLkcLZGh2Q4p1E0RlKVvFbBHHYKTAeiC6Th9kZNWgA4WD\ndgRC1MYBd3pRlAakwPsgQ6Nh/P19/PmRIgcqvfNyiCbX0t3/ZGZ7eNEHMB/jkXG8PDJIW6E5b1ry\nudlI8X4F3OWKpFbLDFMJ7jWJCoTufm72uxVRH6X3gAdMvZn+ANxuZh8iJ2lkBQO4NXKOJ6B+gqsj\ng/gSRNfpgPZHJxaRbrWwYWbtwwDpj/b+k+7+nIn6PAwZMsm5uByh31ORAt0F7ZsT0Xp9jhTq6aY8\nlf1RjmOpQ3oDMrAmIyPibVMBgofRnt8QFcBYnoU4kL6YPQfjnSvNc3+0v85FzuhawLLufnCsfxs0\n38uYIlu/iRYda/+8idZZqaLv9ABnUoGmISiCMSPeIe3HbVB0dAFF5dltYp4GoPV5PZ71ZXTW33D3\nL+P7DQLk6IL2dHMEtvRFIEBzhPA/ifKGF6D1/DCe63uUIzjDRFHbzFQBeDXUnmNNtC/KlcRfmiLq\nUW6eK44wZB+hjsIOVlAfP4+9uykqsHQqYoQkp3hsPN8nFMUuEm1/b7SeE1AEeTpyEp+IeUrMklRF\ntWO819Zxr38QbTJKRtINPyFnZTxaq/eQofwuBcVxjqnZesopKhcpLTdHs8xssSl6v3E0I3qeBZD5\nmLs/bKK+H0Tl+Uvjz8jx3QjtqTfcvTovOaIzJ4Rc2QLpjR9Q8a55KJJcq7LlwoYrinxb/jNTKsKq\n8ZxlKZal+y8DCIYhXX8wmucuQBcTLbS0TUtblLs4hcgJNLOPFrK2pfP8SMzzMETjXJT7LmxO5mb/\nnmmKuv9AmTYzFKD2qUhP34eiwsPcPRXWe9PM3nf358vdz8wMAQA3xf8no9zSfwbYUa5Sam4XnYLs\njq2AncOBq6JgvhyNIp0bIQBiRwSGpMroXZET2IIysqB07evHkjfqHb4lfzRy9+q+LKYmz0cio63G\nCGVxIqps1hehYEebiktsgQ75d0jhdkNI5McImaxGwksE/mwzOxAJjtUpKqyt5FnZ9Pj4WGSMNURK\n/CKkbH+OZzsSOYGfIMH/VDl0K4yqm5Bg3wfRGG4C7smerzuiNSQj+zlTIvwAsrw1VxQv5Xw8XddE\nL8ZIhQM6IIPwTM+ihVZU5NsZVWxbBhl8E0zUz2fcfUgYo3uaCom0QY7yusgw6o2iCW/FNVshmtIr\nwD7xnXtNVbU+jM+ktWiIoj9rIFrpFGQIN8yebT80t5Pi80eb2QB3/3d2nWXQPuoX1++BHIX34jkn\nhNKa6ypKcCNKFj8EGT/nkDVLz9ZuE6Chux9lRQGBxjGPp5BRVG0R6FYLG7H/dzblvp2IjKiZplyM\nYYh+mnIrQE7yX1EUZqS7DzWzPwHdXDmrlyDnbIaZbYvoTo8hYKZ6uPvGpqbG/0GRjVYIhNkBOSc7\nsngOZI3hFXoOLsI8n4uMh57IYeuMHM9lkUH+GgKGDkBFAn4zLTo32ssZ8AHQDA7n9VhEXf/ZzC51\n92coHIblkKHcETlkt5vZJjEPfw9H7i0v+m5tg+TPs/GZuTEHhyID/L14p7+i/b8KMnwauPuj6TsI\nxf/SlB+2efz8ZlTxdFFL4idEvtI8L0tN+nI+P1UUharmlQIomQH+IIqwz0aRmFGIstsDnZ3e8buj\n0T5oEpdIwGEPZNR/hs7Ec2ivDorf74Qiyf8GbgoDeYd4t1OpQLfOdMNFiGJ/a8zDpshpb0RNA7tG\nz7BFHa4I3mJR9H7jWBrYPgz0oWg9pyB5WHH+Yn+2cvdhpvSES1FkqpRyNw85d4egPK19kdz4BYFP\nbdEcLtawonhYbySXV0B77h0ELI6E2lUpy+2/0OUL4h2WR+eoNzCz1OkyUYG3BQaZqMavoDzNir1U\nY5TO809W5J4v9L6/ZWTObfr/gtJ/u/vhAdA18ehVm+nLVygBaUtGd7KCaGgtk06rla+aXzucu10Q\niOhIdwwD9vOalPBdUIQ4OZWtMpk7DMmzjalbFtSPJXTUO3xL8AhjcndTovIUZLz82UuS461mNcDv\nXJX7Dnfl4JyCIm8LkKHzCHCCu48OwV2VC1/P8rfCoeuAjJj1KRzEh6lZ8jgZ1f2RYfYxSgbfiMIx\n3QspxdUQ2rwNsJ+Zubu/UnKtpZDDUOXuu1pUsozfJaHUChVAWdUL2uNmlFSu+2+NbD42RlHR6cj5\n2R05a5da7aTtg5ByfgLN35poTt6LayZKyUQz2wmhrV8Bj4YD1Tpb254IWXwUUbommmgolxIOGQUd\n5hainQPqZ3chakJ7gRX5lAegXLbHwtnpC5xpZh97UWr/fXd/L5TNy0jRrIWQwtbIODkHFRZYN37/\nEkI/d0YOY7nRnihbH/M10VT1bK98XuL3i0K3WtiYheiyKyG6S3cU7fwlrtuKmjlDPyOAo52rsAJI\nKX4ZzzQpu/brCKWv8XzZO3RHOarHZL/rhypszkA5lIvkQP6GUec8I8rzhyhyNhoBGFuhKMQa8fv/\nL1p05uw1IMszzPb1gWZ2ejznywgg2B3JCdA6zEfr93fkOOxpZh8hGmzqsbcaNftu/UhRwbEhkg0n\nof3ePc7x4Sgf7UYUHfsRMRLe96yoSoy1KHFqfNFL4r9M3fN8Th3zt4Ca1YtrOM6ZvHkSOa4fuPtw\nE2XrIORUbogMy0dRAa651C6AtAo6u3OA5V30/+WRPAfJkL29Zp7SNBThG+Hur1Z6h1j7dsAD4She\njxyZ0XHfzax8NcXFGrYYFL3FHZlsmIH2WXsUEU0A21LIyas0f9sBfzWzqQiI7QJcYqqM+HWAN7ii\nYI+amts3QRGnNVEOcBW/nZWS3vsqFI09DMnDC+Jd+iDWTingW73/Mt1rKC98ugmAnoIoik8C15Xc\ndyI6e+uiqql7ogJfQ91949KHrGOe5yMn9ftFvO9/fVjR3+44YGSAgH9EgPRsVIypLj21JdDRzJYN\nm6sLka/nldMgUu75ZsA6roJG7xBtRpI+N7VYMmQLNDWz4cD27n5oyIKuyG5LoH9dsqB+LKGj3uFb\nQkcc4OOQIXECEvqboKpZp3nN3K4kJOciJOwGCodsbUSnORE5WRsBR5jZEx4VECuMZGj9FaFAM1GU\ncBeiBDpCxBshJdcPKZWD4rvdkOI+0t1vNrOeqOriFFOe2F1mthYyfF6xIvLUCBlGH6EE93sRZfAY\nd/84Q9qeNUWcHjezEcj5WpnF6GmzmCPNx14Ixb3cRSVaH7jQ1Eg6NWtNynVOvPMEFF0sjTAm5/pG\nij5WqyMq6mkRxUgOWmeUlG0UVL8pZK05sn3Q01WYYxeUS7IzMMTMrveigmB7lHuUcpZeDMdjena9\n9B6NgQddZcbfQujmT6bee63RXvsOGRDNgXEeuZT5yJ7vWeCPpgIwTyFjKbVhyOfvvzLCQZtkogZf\n5+5vmMqKUNpgcgAAIABJREFU/w6dj0lQw5C+FCnVFc3sGoQmf+glxQ3i2jNKf1byDisBy4fTPDHm\noBMZzWxRHMjFfN9FneeZLrrUNBQJHocM1GWQszTZiwbJi0yLzsCRVxGqfEWcgXLP+BUy6Kah8zvM\nszL0EVFohPJNXkdnYBxy3kAFE3Zl4X23LkP01udNxWCeRUbTJmb2PXKwn0K5rJuEk/IjMpBeQYbe\nw7Vne5HGrIXNc6UvmqjSuyCw4QN3H5U50W2RXmgLmLufayoaU4UicbsjedECOerzkRwbSUSJvMht\n+weKip0OrGNKDeiFCn0YahD/Re4QuPvrJsr6bWh9Sp89fbZXvPfb8RzvxjP/AxmzlaopLha9zBeP\novebhrv/x8SumIP21VIU0crTKDN/8dUnEOW1LdqbPRBgcWl8fj9XY/sEFJ2FQL6XkC4cjMDc30RP\nzaI88xEgt3I806/Ivvg4Plca4aux/xCg+imKbB6NmAsnkeXLlt7XzEbH7/6dAbctFvK85eZ5a9Qa\nZaH3/W+ObB9vhdb6TGSbzUHA6WhUxGphMvsL5Li9ZmJyrIKApp9QxP+FMvok6ZFlUPX1y9E6jESy\nKVVA3w4VUGuKgKlHgaMCXLwF5epthsBEKC8LFqU3Y/34PzzqHb4ld5yIEJrUo+U5M3sYHc49TC0R\navTLC0N2BSSMxpvZ/ciwuCwQ6wHAAFO+wSUm6tMdiFo5oeT+SdCsiig7GyIDaBSKFKboQ1IQayJj\nbUr8f4iZvRPfByXptzezpl4kWPekQJfS/bohA2UA0NdVAfEO5Fz2M7ODgUdceQzXILpHH+RoXOCL\n3xdnUUd6vjVRddNUpOF9UwGV6rNW4oA/bsoLcmTcjvUoW519rjewgReFLFYCHjKzR72Ivj6IDKQH\nkdG6HzLoBucPacqtm2TKFcNVmGE55LTNNrNjkVM8AFXdvA0Zyi2RAvi85HotkIPSErWimE44hR5U\n0mx8mX2vYj6Aqxn3bogmtmLM6QMU9M//ah5B9iyGKi6+hxrsPk9EhTLAYQWkmHujfdUVUZD/vhj3\nq8qU531xjZtQ4/VWyOkqbaMB1OlALvZYlHkOZ2pp9+qiTbWq//li0qKzdT8SGUQXmirBvuBq+J7v\njVEoarcAFZLaxMyGuKJ06XPbACe7+3YAZnY2irj9G1EKF9Z3qysqmHNLrO+fgNvc/RIz2wxR6x40\ns1tRRDyV7E8FinZFcvTFhb17hflYsCjznEbmMPdEhZnGI1rcyrF/hrnyvpaPOd4aVeVtgQCg9xGj\noI2772FmQ5Ch2g7J3B1QxccqZEjPp2hiPibk9vmIaTDCFHX+IXu+KgT6zEJRp0qGbg6SvejuF5so\nel+i6MZe/6+9846Tsrre+JcqqCA6ICJV0SMqIHZUovGnJrFEYyyJNUaNSdSYojFRE40xibFEsCRY\nYkvsiUks2At2bBFE0BxRqgrCKCrShd8fz313XobZ3ZndmZ3d5T6fDx92Z2fmvXPnvvee8pznuOix\ndakpNhheB0WvFFiOVtcDZYXPRIGO4xE1+nS0TgrOX3ibi1HwM7mPHkm9/waEGrqUQ3cdCqpsjah3\no4CMme3kKdXREj9Hcp2t0Pe9n7uPMrOvuvuZqefVtf7WRw7F18nV4x+F9pc/p94j2U8vQM5tT0SN\nXoTur1HksvPJa+qa51+ieb61rutWCElmcxC5voDT3P0jU+3oIcDdVqAZehq+OhV8cxRAG46CaNPI\nC6Cl9sn3ydUN7oS+j15o73weOfAT0ByNDPvnCmCeS6X4BYCwt+4S7ISavaChExPRvBAdvhYIk1jG\nYpeSXjqiOtPMTkC0pCtSL/mamX2ONoVx5ArG30EOQWcTZbAnMgjeQpHH45DDdTowKu9aicHaDlHi\nFgCbu/s9ZnYNQZEy9bwnUUZuMaIsroc2saQB/FXImJpl6k80B2VRVtn00abqYaxTw2MzyGVEfhve\nB+BIVxPksfXNaWOR+px/By4NxuEbKDM3gLymySGCdz/ajNdHBs6OKCp4Yup5vVCUuwO5OpPPgS7h\nQNkYHQjvoiDANEQjGYEEOFbJaLr6hY1G9VYbhQjft1BUPQkGHI3m/b/hsZ0IIjsFjKIN0D4y2cym\nQU1Lhse8luL0MI5anTZTPcue6OCegyLN8zzXXLrcGb6k1jQJeByH1GSnIgdmAoqeDkHO0dsuUZw7\nXTTLzSlA6avjeivNbGekXLgA9WvaH2XIFyMDblo5P2Mh1DXPKeOqH7CrSfL8MbR/jPec2EmD4e5u\nZhehOf0+sJOZXZwEMYLzdT1yQs9EdTsXoP3oZXIOQ1cUwNrE3ae6Mq1Phfd4gvr7bg0lN9+7Igfw\nlNRQkyz6svDeJUn21wXL0QgbMs/DkWF5XHivpN9gItIz3SUAcwQ5Ovf2aH0tAm40ickkY5gd/j0S\n3q83qhXtg4Ib+5tZz+AIX4HmDZRh+sjMTnT3RPgj2asOpvb2DMl9nIwtySjOMWVV80WkmmuWIaHV\nJYGToxDFeQW6p28Ojxecv2Bcb+2iInZFwYaa2lsvUM/m7q+R2m9C4GY7imuFURDhPLkSZYOeAEaH\nYODH4Rr5Nbb56+/raG0NRg7pGe4+3sy+5qtTEpPv/kDkFJ+E7I6haO0UKr2obZ63QA7mAWjtnl7H\ndSuBxOH7GN0rp6CAbEfkMCX1t0WdW74qFbzWMzTvNQ+bBGt2Q3vRe8hGSgK0f0DtuPYAbjHVaq6H\nzu3O4RwblHq/VfaCiNaB6PC1TAxA2TRgtYhqhtUjqsegm7stotiNQVHUxSgSdiy5AuEt0cY0jlwT\n9qRmq1DR7mUoWnkecug+Q5HjVdQhgyPYARkQQ1B0/BngzeC07O7uh4To3VbIiBsbMnVpB+Fh5Cg+\nALwaostHh583Q0bOMlPN4IlIHr5WhatywNQj7VhUg3Knie6V0NqGApd5XmbRRSl6ADl6HdEGvUH4\nmXDwL0F0wr+iuo1/o4NwV3LF3yPReuiJHN0hyFlbQa7vUDo6Ogg5oueiA/NsVKt3S8jUbG2iYh4c\nrrM9chrP8rx6rPCeM0201e4oozAIRRf3RpnGopT0UuPbBK25N5AjvxZyXrPI6K8IwrU3RJHtR1Bk\n9bxw7f3R/dYbzfNAU/uBj0104Z1Rhu+1Yj6vqQZ1DNDNzJYiCtSTyEmZ5O5T63p9Y1DsPKec6tno\n82+O1tmxSDXuSXc/qhHXX4tc4/r2KCByImqF8SeXBPy26L54GmV7PjKzv6O63/3IZXq3RqyCXcxs\nBprPaUicYDb19N0K1z7IVC+1G5IpT+jsu6GMLhRWPm5UDWkj5/kj1PexB6LVL3T3dAb+5ybKcffw\n+zOorrKGImwSm0nmJHGwVgRDuRP6jg4O73E7sMTM3kYOajvUm3O2mT2O2gkcigIkbRA18TNqqQFK\nzf/1KNAyEFEHt0F7SUurHRqKjPtN0fpLeuXtjRyVgvOHztyEVjoEZWYI59by/ABXcOw3R+f1QnJZ\n2zPd/dxSB22qz+4LPOOpvncmyutA5MTB6g5L/vq7EGXTLgZ+CJxqag9VE0BKXhiCbO0RnflxU3P6\n54Iz/Ah1t5bIn+dLkfP7KRJM+1Ft160EPFdPfqOZ/QTZL99Dmb0PybEeKmV/tEM20E/QPb6cUGaB\n9vWkxGU5ykAeheyr43xVRdeeBBZOgb0gohUgOnwtE68hWt7JLkEByEVUDyU02Ezg7kea2QBEcXoA\nHRJ7oBqbZ8JrOiN1vZqbO53RsxQNzVZVhprl7seExx8L73V++vXBIe2GDIEJiGL0CDKkTkUb5FAz\n+zCM7TVkXP+YXPFxsnFvixzR98j1J7oeURD3JecIDSbnFCeRwUqhJzrgjjXRM+9C9K51UcF9Wowi\nobJ8CdFSDkOZ1I0RpSvJTp6ONt+nUAR9Gfp8iaH+fVOm11zNwZPaq/1R9uJodPAeGN4viaIfEK75\nZ3dPZxLTxuwb7j4hOM2HIvpiHzMb6e7vJ88Pa6ALMmi+Ej7vo8DxBTLB9SEZ3xDgJVcx+SbI8Osb\nPn/ZpaFTn6Mvyg7vhByDe5EznLRiSARqssjA6IIi6juibFPSLqHesYXATPdw/c3Q+t8dCSRsaWaf\neeh1WAGUNM/uvjA4tz3ROrwJOScdC715EUhk/n+BnLbHUPbtbWT4gtgEJ6I9zdF9njSnXoGykcln\nAbERfo+M4G2RCuURKPAxu9CaSa9LFy3xZnQfPokcrX4omNIBCVlAmWtH88bTkHnemxyt+GVkfM9D\nlM6FwFOumugOyHDfAQk2GMpEXIUCOjPDGGp6lYX74h3U5uch9Nk/RnM8BO1NaSn+W001mYciZ6YD\n+u6u8gJNwIORurVLhXIWUkfdH51Dk5BScYOoiU2N1Fp6FTEivoEa379jYrRcgdQ4C84f2kOSkokh\nhH57ntcbMHUG7oKEQKaE132AnOtVhNJKwAB0XpwTzoG5yAH9N2LZTAvjyd/b0utvGjpnJ6J7NYuE\nrs6vY8/uitbsz8Prv22qV+vmBerdapnna8IY2ofxTi7iumVDcFpHor3mJRcF9mq0Z630nKhX2Zkp\nqc+3Pbp/TkL36TBkj2wKTAhByZ+jIP4kVGs82VV//Us0j/9Gwa3a9oKKOs0RTYPo8LVAuMQxxqA6\nu4OQgdAB1SDNYFWufMIbH4yKui9I/e124KSwka6i7BmuU9NWoZYbfhfgGjO7B4mU1KYmlxSf/x0d\nCF8N45yOolDHIcPmxyjLtRQZc68mn4Gcw3a5uw+jAH3O1CC5X/h1W3RYJQZ2xeCiXQ021TQcihyC\nt9EmOpfCHPhfoxqiDsgAXoacuBeDobU+OjQOQ87x9cDfPFXDFZzGxBDuhQRgxoW/3UIqQp46+K5F\nVNj/C0GAf7n7G8HpORLN82bhvZcj4+MzVLj/KKIFQ85xOA+tu4dRhPVQoKOphUMph21Cz+tBEEkJ\nma6ppPrrlfsAT63rTujQ/ilySpYm0c+UU7ge6nOV9HZ8hDzKSzEHoykDnARQPkJR2D+6+xnh75Xc\nl4ua59RYj0eR6pnoO/8Gqvktir5aAMn3tzZSrbs3da1EGn56eN7dqJbnfOA5U73PYESXaosi+YsR\nzXNYcBAmIcrSDoQ9opg14+73s2qLkC+jzOKTwSmpCJ0wFRQrep5Tn+dO5OzujIy+fRDV7Wso2zc2\nPO8KtM/0QYby8PCaDdH38FUzm4Lq8JJavTdMohhHoczPyyEr2w+4zVNZ6GTNB8duVHDmVtYz75sA\nXzGzWcBF6Hubi6imU0nVBLYUuITGLkGBi4dMTa2nIQXk52ubPzQX/U2UyEOBiSaaZwfUly1huawM\ne9FjZrYnCg5shxyux5EaZUPGfR1wnZn9Du1FzyCm0KloneyO6osTW6DQ+jsE1dZfgdZfD3IB1/as\nriTbxpWx/zU6695HVMjzWbU1QaHxpuf5xvDeV4Y5GFrXdSuArui+Ogedmx2RbTMOOVvreJ7KeBmR\nBLyGIgfu5fD7K2b2ASrpuArtCTNQQK8ncpB7mdQ5H0T3nFHHXlCh8Uc0MaLD1wIRNsu7zOwZVH+1\nCTocxqGIaprzn2zOnZA8/nAUeZ2PnKLF1IFCBmw4eNq6+7/M7EkUXbrAVPx/D1JhXEFuQ9obbcYv\noMj+/5BjcZy7/9zUTHeGu39mOXWqXuSoVF+Ea64DzDWJMjyGIpsLgU+CU1uo4fHm4fFCDY8bjbzo\n1yR3fz1kTI5BheSTQ2YsEddJfx9T0ME42aVwdwHKhIIO771RNrQjofmtmc1GG/FtKPO2QXj+MFbt\n1TWUAk6xSzTnbjN7GRlaz4SN/0x0OMxGgiFno0N/PdSH7rt575N8jgOBQcFoXRtRTi9GGbK0umSd\n8Fwxew/gJ2b2XXTwTwzzdIu7f1zb6xuKVJQ0izINB6HDcY6ZrcuqxmdPlFk/A9W4zEPrb0E6IloE\n2oSs4VEour8PsJ5J3Kenu/+5UlHVEuY5ufbJyNmfgvaY45F8/Alef6+sQkjedzdC9r7A2C5D+9I3\n0Tq8B83Tpigz/VhYa8tRJq8tWseJszAL+Kq7b9KA8SVjGUsT1P7SwHkOwYczUI3O34PRjpl1cwkx\nJY5kD0J9JKoXfgO1B7ghuZ8sJxKxBXIGEzrdpcgYfyJkwMcQ9mQzO9vdC9aLFeMYhyDZpeG9kszm\ntijY0gUxGy6r732aE0xsh4sQU2AdFHhdC5hXz/zdhloS7IuCawPQvTEf9QMd7VJxTQJPnVA2J+nb\n9u+Usd/QsbdD99vQ5D40CRydFa6xii2Qv/7CWFYCPwwB6QOpo6VA6r3moaxue3RurEB7a11jTc/z\npchZGRUcyGJaGZQN4d48ODW2hAY9AjnBuyCV8ToFWxqI5AxeCPQ2s21dLVfWQXbD8672TFk0R472\nzG3QnvlaCC7XCMHUthfELF/rQHT4WiBSEdUPqCeimnrsbhRd/AkS1RiAjL7VGpsXOYYVZtYxGA2X\nmdkpiDawP/Ad5DgkG0RvlAH6dhjnWWZ2HjJy70eqUZNM/QQPASZ6aAyahwyK2CXqZB+jQ3EyipqW\n2vC40aglM7YMZUs+REZc4pwCtAnZiWcRz38jRDs7EDnIbwRH4wp33zJkMG5Exu5myLnfGlFY5yCh\nizEoizorRBhfQzVA98AqGapuiOqxPXJqJiNDq1+49g8QteeQ8BluqOvQNKm6zUVO5zwXDed5M+vr\nq7YSqBdmtnkwgP6KRA42RofUYBT1fpjQXLfMB08SlLgJGR6G5m8jZIz80t3nmqhOXdF62weJBMxH\nmfH/oEx1qTgSUREHICflc1QH+4K7/7euFzYUxc5zeG43oLO7P596iwtMNUgNcr49V7+3PsoaPIjm\n+00khvOZh9YgZnauu99CgZYlYa1dZWY3AHu7+70mUZ3t0Ho+MbxHxWldjUHqvix1nj9DFODdUC/W\nQcBDibNHbl0fj/bLZ9F+eCDaD69DNX5tfVWRiAehRhhsC3ffKRjYv0EZ/B+j7OM5iJnRIJjZWShz\ndF+45vRUwG8TKkifLSdSjrWhoNlAckJmQxGT4x3qnr/PUDuY5LvbCO33g5Bxns8QuR3dLy+Gaxxr\nZmems+UNQA+0321GTuijHbCXF+6lucr6Q076hsD9Vk9LATPr4KqzPx+xfWYieyHJbk8KP9egjnnu\ngvawiSHgU0wrg7LBcqyEDVFZw6HIRvmnu38leV4FnL104P02UxuhkSbhtI7o/rklPPVGlMW710Xj\nPBa1wFhNGKfQXpBcq9zjj2h6RIevFaCuiKqZ7Yeok9PQjf82ygY9jyKDjaHOHGtm/4cMxkeR4TsY\nbS5pZ3M0kkteBHzLRF/aHR0yV6EmpSMQper3SK1uHto0V3pOtvw9FIXsjjJhA1EELVGlS+aj2IbH\n5UJtmbH73P34vLElNZG/QnSY9ZB61gKUvVhiUsebb1Jy+xLKNMxD4hLzU+8xElE9E5n4AUgMZ390\ncL8UrpluGdELOSfrAq+7+wUpJ+ou1NNwS5Q5ud7MJiBj++0Ch9bnKJP3D5Mya1f0fSQKiUUZ2yEi\nebiZ/QHRUGYhB/ltlLUcmazTch88qXvH3H2QSbhjHFqb+5Cri2nrajh/YHBYMmgd7kmon7UiBWrI\nRWZ7uqhenVAT+7nB4GtME/laUeo8Byf3ZVNrjmvQ59wamN3I76Edyhxsgozbr6B61qyZHY6Mvl5I\nefMyRJv9AN0j89z9k9Rcd0TtI04Ln+e/KMAyN3yOZuvspVD0PNuqtLqXzWwlCmT9CvXEO8vVWDxR\nFt0UsT4eznufLvWMaRg5FeQdUIbwUlcbnOcQ1bMxeBQ5QEchx2GhmS1DQah3KKHNSZWRONbfRGv5\nCLQHfxut4ZXUMX+mDOxv0OdfjoKEU5FDd6fnVE/TwYH+7p7OKm2PzpIGO3wu4Z2rUKb8PRQQXEiu\nTjOpPa9t/Q1Hjte56MyttaWA52oTD0GZbdB51Q+d6YWco9rmuR+yOeagzHW9rQzKjOT+/CNyPu9C\n+9uhJlruaM+rxSwnghPc1t0vNrM7UMBrReL8m9pkJP2MV4Rg8rso0P+1So0ronkiOnytGCHb81ty\nNMHEIJqAbvoulFgrYbn6og0QbeAmRIdrh5pPF6qXW46izBu4WklsjDKOR7h6X3VHG/9fXb2Yanpf\nhWuui7KGXZCAwuDwfm+g4viqSXWbBBGKyoyFyNrHqOYy4+4/CK/vhyity8Lm/a6pH95v0Hd3IJrv\nxcBKM3vW3W8JB0lRMvHhoB4XIp+9Wf0gXxc5Ocvc/U1Ts/fvoij0MSgifX94TSLosQS4yMzeR2th\nGfpO/hkuW6xTsAIZCR1RHcEO4XOvQGvnf+TR/8oJk/T47DAHc1A/wX+Z2WGuBvKJcMuuKLr8JXTf\nPAOc7Ll2EUWtwyQyC9xkynAPAwaZFBPbk6MylxslzXP47OejWp7jUFauM0GUqVSkAgDd0X0wOVz3\nC3RvJ8EdwmNTUAb6eLT2lyFVwxtSb3slCnZMQsbeV9G9cjEySJs9FanEeW4HLDez21CW+S7kDE9E\n399bec+fAxxvYj28G547L2TT2tThEC9CzbN7ILbAZ+6eCBPtihySBiMET14xqTOOQtmETigQsA+q\nBWsJSOZvEnLQZ5taSvREtM57EP2wtvn7PPy9K8qQ9UTreXtgPzO7xEXLS9bxAOQop5GmYDcYrjKR\nR9F+tDkS73o+/C3dhqmu9TcxnF/T84MMUEMZ3Da8Zpq7Px3+9HT+c/NQcJ5dPeT+kdpbim5lUCYk\n4zKkJDw72EgvowzbgyigXZF9yERtP8fUSmgK2h/Hm9lW7j4ZBXwWuvv88JJlJmG548o9lojmj+jw\ntWIEQ3QHgJDy35RcU84hiHqwQ4lvm9SNjUWZoo9QNHAB0N7MVvqqfbwGokjlMHK0xqcQtXBa+H0E\ntfS+Cg7MH5GTMjo4RT9D3PIj0OF4eYmfoWwITlexmbG30Li/hnocJQ7bK8AHZnaVu08Mh9drZvYb\n9B29jeanH4r81+qkewGZeMvRYU5A3/deyEEdhpz/F5HDbajOc1vE938MNXP9OjLIE4w0s8EoE/Y6\nEtcZi4RO5qSvWeQcLkItDfoiKuvM4BD1RIX5SauKStHz2qAD+gT0uX9lEjTqmve8y5FDdBSKRJ8a\nnntBsVHcYPC0czUYvxbdN3ORU22oSL4iDkoJ83xE+Pk1RK26BGWHF6OWBY0NsByM5no6ymh8goyV\nB011jQNRtuLksJesgwzdYay+9vsAhyOjt3/4LAPIydw3W5jZt1Fwpuh5Tu0llyJnYR2U+f+dq9VL\nm/C85LWdw79D0J7wObDIzH5bS3AuwaNon3gLBXF+HjLRRyKH7J46XlsUQqBvb3f/XnhogZmdC2zs\n7tMa+/5NhES8qjvwA1NLm6RFQDfk2NU6fy5qck3tddgf1kPO7wXoTEy3WpiAAhnjw3t8hO6XFxsy\n+NTZMBydCf1RhvEVYH0z6+qq+yaMt9T1l48BSNVyASqBSD5HEqx4ywsodFLLPJuYQJ+gQHbFqJu1\nwXM1lUvQHjo7zNFbZrbYQ51muff01Fm4JzmV7J3J1UbPR7beJER3nYgCsfOQHViRkoGI5o3o8LVi\npLI3HZEh1AcZ8Td4wwQXQIfBBLS5nY027pXImfsEUTef81yd4TtmdruZ/QdF3g5DogRTUcP3S1DE\ns67eV/u6+8DUGPqiOoiEt/43r4CgRzEoJTPmQaLZzAa4e9vw8w6IfnJAeB+Qs7sCZTUHIsfKkSHx\nZC0HYl1IC0Mcjeb3Q7QmzgmZx4eR0fAe+i57oSzQTHfPz67diJz0YYgauCE6bGeGw+/EUgw2y9Hz\nDkMOVJK1fBkZTP+G8tPzUpnKeSaaYzdUm3IQCmwkdaRtAj2ng+f6VL0ejNPHvbTeVyOAz0IWYASa\n74eR4fZSKhJbdhQ7z+je3pdc787ZqAbsTSQO8IA3oC4mRT+63KQiuwVycA5CTuC4cL3B6N5ZN4xx\nOsqmTiRkAsK+1gntOQNcNaNTyEW5k2s25+zeVjRwnl01nt8LTuPPUO3nHYhevjz1vJ9DTcBvIAoq\nZOpx9pLgwI/N7PcoO7XI1PP0G4i6f2fDP3YN2iL11RORAvFS5AxtVYb3bhKk9qRz0Nm3LhJgWQvt\n4zsB79c1f+nsT5j3pNbtC3JKugkrAKSM/F9kuA9H1P2kPVOpSO6PG1E/1qfR+boz2gfeBD7Nz1AV\nu/4KYCoKPnRH5/dWKKO9L1JIfgjVPa6Ceua5E2IBNLnDF9AdUfzvN7OX0F6+AcqEbugl1rMXieS7\nWAu433MKxTXsh7BelqIWT0m7lETZ+89ErHEo1Eg7opXAcgXF5yEaxQbIQfsC1c+MdveSbnwzSxy2\n55Eh1g5x9wchA/Zhd38uROoHIAdzOjKe2yBnM+GXfx05NW8hCk8nUr2v3P2pkDX7m7vvGF6zNsqC\nXGC52qetGzRBZYCZnUnIjKE5TjJjbVFmbJS73x6emzjgDuzjec3pC7z3HsgoHoAO4cEo8/JVL9Db\nqp736oa+m53N7CV33yk87kicoVbDuL7MWvhORiIRmnHIQa+rcW5d4+yM1tNm6BDfBam5PlGJDJ+Z\nHYAc4AWoyP+58P+6rqbdyfMMUa/Od/fx4bEdkdjC8GLHZqpVnY4MwROQQfcZclzao2bh48v4EWsb\nR1HzbJLm74coknuEl/fx0I+xgdfu6HmUZzP7F3Cq5/o8/gllPh9HAYWTUWBo1xBYaYMMxjHoHpmK\n7j0HnnD3pqjdLRtKmWeTQMS1aL98DmUEj0JqxtunsjaHIvbBU8ipfwrtl7Pz37PIMXZGCrNlqzE1\ns13Q3rEZOpeeQmfEQ+W6RqURvo9R7n5k+D1Zm309JcRT7PyFveYk5KCf4+6TTdTaI4AX3f3FsJ9/\nA7V8aBQFPJyjd7r7QcFJ6EAoC2jI+ivietuiDNTs4AgPQM5aF2CRuxfsJ1jsPDclLMdkWhs5fUnN\n3JfBsqNSAAAgAElEQVSRTbANMNbdjyvzdZN7fAuUbV2CgmIz0Pk1CznWyXrJoMDa8+6eT/uOWEMQ\nM3ytG4kRvwuqfRuODNs2KJrcEK771sjA+i46FD5GUfUJqD4o6eW1FjLQvok28r7h2pNMDdv/6e73\nkeodZAV6X6FN9MOUkbiUXOZlK3J94aqFojNjwdnbANXVnGbi0r+P5iXNs0+e/5SpwL8jmsODgd6l\nOHupyGx7pKB5W3i8D6KCzKkvC1KbI5Nylr6DajJ3R1mqBslhh0z0Epd62Dtm9gSi8xbdT63E652B\nar4WoYxqF+BCFGU+JTwnESlwM7sLRXEd0eQ+IJUFLPKyPYDx7n63mT2AKFRGzqGvWIF/gmLn2SRO\nMxAp+05F9N0rG+nsdUWU5/eRszsdrf8hKWevK3CAu2+Ret2rqKa1phWAi5K6M4qw90c09Z0RzekB\nK15Ep6powDx3RvfbVLQ/vuPul6T+nqj3/TMwCHZEe/9ooI+ZnRcCZiUFUBqS1a0NqX1pBqLkLkD0\nwE88RSFszkjN32DU1/QS4Dp3d0TRnZme4xLmbwai0ntqLi5DzsRTJkr2vSj7tp+ZneO1tMgocvy9\ngM/N7HvA7e6+ADkQtX0Pda6//Gxg3jUPQ7oC16FsICj4lYgLrdYPuNR5biqknL0u5GiVXZDI1A/D\neb8egSpfIdyJ6LczkfM7GJW/LEPB+GS93IMC619r6HqJaPmIDl/rRrLpdnX3R0OEbLm73xmMzZLo\nTiH6d1P4tTM6gHqhbOFOiCr2TaQSdQNwQ3hNH3QwDENZhWNQzVc2j84yltV7XyX1LecDZ7n48fNM\n6lOHU1kFrnrh7vnXn5L8UMsh1BZRKvqgGo3FyNh5FvhT6hBZFxlAH4aI8EIze4zc/Bc7vmRu55nZ\nKESb6YqyVV0QPahomArS90WH28bIaN8JyIZobbu6Xl/H+3YltAwxs5mo1qA7cKS7n96Q96zneh1Q\nhu1bqeACJoXUUcAxZnZN+C72QdmXB5AM+nYoWz7dJRpQSlPuK9H9AnC4u9/M6kIbFUMx8xyyTTsj\ngZq1kQz8k+7+SoiqNwbtkUG0DqIy7o4y2EkfubaIWjvNQl+p8Lo+wAgXYyFtaO2MaIC3oBqV68Ie\nUZFG6eVEqfOc2itnouzKSeHnNmY2LTGWw3OS+/6/aL+5JrzH7xEDoWpIZSe+gwJGJ6I95JvI0Xm9\nrtc3F6T29g/Q2hsOnBQyZh2AX+Q54sW+72JkxAPU1iLjc9QG6SAa2CIjNf7+aE/bHtUaJm0SbnP3\nmnYoJa6/2vAr4KDgrCWYFT7L+4hWWts4yzrPZUBSV3geCto9jJzkw4C1zOwGbyDTpT6k5mQh6rPZ\nDtlj66MAz2nuvmNqvSykkeslouUjOnytGOFQbYPEEG5FtMlzTf2dBlC6sMEgFJ0bjzJaM5CD8xqi\n46z0lHhFMP4PRobt4Wb2IfANdx+RGmN92aXPzexm5DwejgRC2iIH8hWasZpbLRHHLqjeYkZwkDZF\nRm/S66g9is4dhIzgiWY2C1HV+pLKcBSD4BjvgjKx41Dbiy1R7daH7j63jpcXwoWoJgBkqN6EnL3E\nwPzCGqZI1hl9vqGIwvdFGGNiqJY7grsFUit83SQKsgRRkt4ztWa41N2vNrPfhTGtjQ7JU929QQaz\nmW0GvOsSHuqLDN2bgxO9gqBS2fiPVifqm+djEEWoHXCC52oWgcbXxLmaI98QxrAF2kNqhI3CdzzD\nzK4DHjGzBSgjPgv1DiSMbTnaizoiFds70bp8DznVzRpmdjRiXZQyz23R9zUS7RMT0Tz+AWUI/5R3\njUNQQGkeotBNRUIsd4SnVKW+MXUf/wQ5edug/W0O8FMz+2Fwepo9wl73JhJwSh7rhD7TZ2W6TG0t\nMt6zRrbIMJV9jEVnO2bWH7F49kJnbE1pCCWuvwLXMlQP6nmZz+eD8/9XCjh84bVNMc8lIbWODwQG\nBXtrbdRW5GKUVSt7/V4q4NUjXOsXKFiURTT4bcjZdmVdLxEtG9Hha6UIGYxOKLJzEaJMONoczwOe\naoDx9hHKRGWQDH9vZLB8FN73XmSsJZv59khm+9jw+o7Al8xsqhdZYxM2txeALU01f1ujzMBjwE0t\nyDDojJytrdHh2NvM3gJ+6u43hee0Cc5AB3e/1cweQrTVzdChP46csVYsLkJR9C2Bk9z9r2a2ENHe\nfmtmF7ok0ovFbUhddSNEh/w+Umedg+rQTnf3N0ocI2htXAS8l3KE13H3iVCRfmpDUV0THhoLp7Iq\nHYDF4R46BPXU+hBlBH9lZkekM00lXHMIugdB1JtEOKkdyoo3hQFe5zyjjP1M5AxcZma/DL+/ikQB\nHi+BmlYDy9Wv7ooy22ujQNESFNiYnPeSu8n18+oLTHb3d2EVpcB9Xb0Tt0MZiXuBS83sjgYEMpoa\nG1P6PCf3wHDU0uZdqFG7HGNm//HQTNlE2z0UGenLUeahC6LE/w+qK2hjqtta4O5Tw3nxqUkZ98QW\ntKcnhvePUKZ2IdoD30fr8T91vb4EVKRFRhj/cjMbCnwL2A8FGj9PZfvbpO63otdfLRhASmU37Lcd\nXQJCbalFdKUJ57lkhIDqXMT4mBfYOM+bWV+vjFhL+r7tg9giA5CDPg85fZ9QwZYqES0X0eFrvTgU\niYY8gzaCOWhT/Q0qxi7FyAfUnBUYZapD+8LVALkrciZ3Jsf5T1Qmh6DefJPMbO1wuN+DRCKKqrHx\nVZXB7jOz+6tpqDQCo8L/VyCneQNERRttZie7+5OpqN23zexlV9+8F5ED8pi7z2zAdb+KDoVNgN+Z\n2SC06XdHRvaUOl67GgLFLqHZJY5sL5StGY4OmzrrONIIUdoLkCO8GdDfzKYgAZgPTPLbk738zWtH\nAEeFrPN0lC2eiozj/ogqvAWqcZwQxnot8FADnT1QJrdf+HlbgpS616OYWA6UMM8XAxeHrGfSrmEw\nCjycgOhKJdOoU/f55YjW9Taai6OBU03iRwtDlHx/5GT3Rhm78ahdyHwP6sJhD5pmqkXt6Kp3e9/M\nNm4Bzh4NmeewP3RG+2s29fi88D6zUo8tDdnaDshhNrT/P+ENrLEtM5YB48zsIpR9BAURs7W/pHkh\nfB8dkErlhSj7lUGZ8y0JvU7LgIq0yEjtX1ei+s5voO/lSFPt58h0oK2U9VcLXgc+MrMTPddQPtn7\nDqYW9koTznNDsAjN/z/M7BoUjB5OKE2pADOF1Pu+Boww0eA3Rvf4Luh72I4KtlSJaJmIDl/rxQDk\n9PVCN3778G8uar653EtUA0w5aOeiTN1HiFf/MjKWnwlPTSKCWaCLmQ1OZX12QwZcg9ASnb1wWO0M\nbJfa/D8J1LZ2wKFm9lwwxJLi/KcD7W8MygqtNLOzvYRiaxOFcGYwopegAMAYRMO8290bbVyFLMS7\n4d+DqceL/Z4SR/jPaP10RzWCF6CD/W+ItlJuh280inQOQofgeajuYgZao6egLOBrqdcMCWMhPHdp\niYf5/4DdTD2RNgU+NrPNw+OTkFNfKWnxkuY5ZD2T73VM/puViuCYLUB7wyNhfbwLjDVRzNfynGDD\nX1AvqenIKdwUfSf3pd5yMTJcbkf72ZGIWvVUuF6Tizg0BKXOc8jKXg28ZFI2nY6EgKa6+xLL1cf1\nR2JKu6D99qVwjWUNDFaUFYFe9k/EetjQzKYj47SUFidVQ2oO+yIVxmtTf1uHIlpfFAuvYIsMM9sY\n3Xt3mNkv3P0DE536OndfjaJZxPqrdW25mpI/DlxgUpCdgOrgNkMMoWsKjK/J5rkhcPUeHIUC6nui\nfWkSUseFCtCmU/f4rsiJ609OOO8pJJx2cyXWS0TLRnT4Winc/cJgWP4EOX/3I859P2QElGxAp6L0\nl6B+L0mT46ORKufLSFZ5Zdio/xOcjrFmljQQH0+OgtHinLcGYivUlHxFiJAuBlHTzOxupOi11AoX\n538KnIY261KLrYeQEwQZhr6fY8PB3L6YDGslERzhXYBtU8b5p8CfzewklJG4swLZPQKFMaExpjOV\ngxAVeQzqM3mUqWfeq4jy9GZ4/eLwuqKNZ3e/Grg671pD0Po4ngZmz+pDNec5XL836q01C7ENrjOz\nK9F9sCUyFrPhud1QH8/rAuWrHaKhdk4HKNx9oZndiAzFPVFAZTyiHEMr21tStLauiNY9F33mfVEd\n5DHhqW0Ru+JcdL7fjmp6TkT1PH9095HVuvdN7XrWRlS8KSib2Q3Rm+dVMOBRbiSCHYOAvczsAhSA\neDM48Z+XO+iQRxF8BnjWG9giI7VvrYfqxL+CgregrNXy8LzEuSh2/dV3zVvN7ClyfeE6oIDmVV5Y\nfbrJ57lYmNlvUbDu38HBeh341HO06koFVpL3vJ5c78R+6P7+LjpL3gHmpq7fqPUS0ToQHb5WirDZ\n3I9k5HdHssGdUGbnlsa8t6tPznvhOu2QI3Gcu89JPSdRmrwaFXP3Abp7TnWvEnVZzRXrAnPNrGcy\nR5arFxtArpZrWwoX579vDSu2TlMIh6I+fEtglTqoamJLFCCocYTDuumJ6D8bV8oJyUdepvIBADNL\nZwH3QrWXa5vahyxB9Zcl1ysWulaFUe15Xoqcsa4o6NML+BEy9gYDd1muqXR/oJuZ/Qz4O/BRMFJq\nDJWUofUvd7/FzN5A9WBTwt+rnsGqAHYPjvsPUX3fX8xsTAga9UYBN1C2FmQoX+5S6QRyNVPh12rt\nvRcjB+9WxADpgIKPc4EFZnanV0jZsJxInV3voIzJHigo1yk4RWe7+/UVvH6jHOPU/fE2UnH9O/Ce\nqXl6d+Dm8PfknCpq/dV13yV/C47dqGA7rKzLDqj2PNeGkNXriWyfZO/cBvi+mf3A3SdUag8Ke3cn\n/egXhvs60Wyo6Z2Yvn4LCqREVBDR4WulSG72YPw8beoddjZSBfxbqZtkKtJnSJL/RWCau881NfXc\nK+95XVErhQOQwTcDGG9m/1eIKtJaEeb/OTObBPzVzC5Ecv7vmerpTkE1GqCMRzmLrQtRCAehrF+l\nKYTFoDMwx1R3lfRga+PucwJd6NTwWFWyEXVkARtUr1hFVHWeXTV1o0O2/5PwbxASERmE1GJXBAOw\ne3j8KOQUTg10v9tcrWXShlYStBhGExhaVUZPlKXbBdjVpPI5zczGhcd/jTIfSUbkViQwdD8KIn2A\nIv5ZqCo1flfEWNgBidRMRM5nT7QWbq79pc0HqXvlNOBCd59l6qfYHWXsp9b5BlWGqR/eE2E9jDaz\np9Ha6gy84hJKS7N6ilp/pWTbitlrmuM8B8d3T2B43vn5DxSwOtLMJlUiqGp1905cSu29EyMiosPX\nGmESNNgMZSG2MrN90Ub+BvrOj0B0gIZgI0TD+QHKdnyOePxpyfQVqOZme3QYnAo8iaKDk8IYW0RT\n5MYiZViNRg73b4H5ptqtdZGIRSJF/TA6SMpSbF0tCmExCA7Hi2Y2GTnCFwFTgiO8GbA/oqpA8U3N\nKwpvfL1ik6M5zLPlZN3PRXvDd13tMLoiZy2hyX4BPB7+YWbbA2egWrR3zGwsVTK0qg13vwtlQn+E\nRCr6IqdpB/T5x4anrjT1W70GZW+3R9S7lYj++vOmHfmqcPfpZvYH4Mvou58L3OPuWTPr7lWsxyoF\nqbOrNxLMmBXGXsN+aeY4BdWKXxuciElmNjlkj04wKWnPTp5cyvor5yCb6TwPQtTNRWbWyQO939VC\n6nrgXnf/RSUunHKmi+qdGBGRRrMwpCLKCzP7FXIs5qJau/vQRrzU3V9txPtmPFVHY+o5swmiE0xy\ntRRI5Nd/Hh6/CUn3X4aoGItdjd+be1ak7Ai0tcEoQ7QQRSff9FzNY5KV3ZBVi61PRXWPt3kLkSwv\nFiZ59rORwth8tJ66Ikf4JnefvyaulXKj2vOcyvwnvakWIoNzAFIKXYroWgOR+t5e4f8HkPz6NYiO\n+Bd3/1La0Arv3wcZWttVYvzVhuXqGTdCwbT5KPDwAdrXl1quzmoP1B7lQFNd8IbIQE9qqKp2P+Vf\n28zOAA5HQYdRXriOq1nCzNZDdYhvogDQS4gi+WFz36fNbDfUT+84d59s6gW6D2LlrAV82d0/Tj2/\n3vVXwbE2q3kOc3c6cLy7zw+PJffeDigTuU+lgtqpAFrye7p34gR3/1v+cyIiIGb4Wis+Q2pNHZG8\n+V6IAjUp8O0f99B7rFiY6gB/amZ/RZvunqhp7uvuvpq6FjoQ+qP2A+u4Wjj0I1c7kjRxXWMQonOv\nh3/5f0vz7ctWnN/c4e7TzOz76MAahAQDpiFHeEV09sqDZjDPbcxsOMrwnwqsA3zP3W8IFKksUkSd\nhrLbN6CM9u881EkGQ2uumXXLN7SQIZoIv7Qq9kDKWd4ZqaxOQaqAHcO/Sag2LtlTOyC63ZauZtX/\nS79fFZ299q6arwHAl1DA+XWUvfkxCgRcXI2xNRCLkYhWb1QvfRjKQo0Hvla9YdWPUGZwH+rF+mek\n1rs+Yuo8lOfsFbX+KriHNJt5tlyJxsGoPOZiJDr1vplthJhPL4anlz2hYqX3ToyIqEF0+Foh3P1y\n4HJbtcfTNoh/vzMlUvmCQXYhqq8Yj+ow7keS2kl7hn+6+8rE0HKp7F2MaAbdTdLrWaQaCq1MRa9S\n8DWg2DoYCW+Ef4X+FlEGVGOeU0bgIOCfiNo8BDW038nM/osi9xeizPcTyNHrCnzs7m+E/Wd5NQ2t\nZoKdUEuLM81sCJLD3wRlQCG3p+6FaLD7meofJyNH+jZ3/4Dq4Soz2wlR12ciavkyRAe8GZ0VLQYu\nteNx6Ew9Lhji7cgJZTVruPsFZnYvcAuiW9/j7vPqeEl96y+pHy33OJvNPKf2yasRY+I3qLfgABR0\nuo5cuUzZhZFS1y+qd2JERBqt9WCMKCPMbDAwOlCpuqPm4Vl3/1GIup/v7nuHTfg8ZFw4it4uQpHn\n/ZBYw9Mx+hQRsWbBcj2zZqQeuwZJ8Z9jZh1R8+WTERV9HtDT3Q/OoztvhgytvkjddgApQ8vVR6xF\n9OArFima/FnAfHcfXcdza2T0URZkG1Trsw/wM3d/tVrzY2pvkgm/jkHZpAlIxKrFBHZS38cJqFb+\nGOCXqN3EQpdybLNdgyahj70QXbo/EmE7w91frOX5Ra+/Mo+zWc9zsHe2RjT5xUiY7q1K0lvDdTdG\nCsXDzew1d982OHvXufu2lbx2RMtGzPBFFINhyIkDGIEi8aek/t4u/L8+EofZBhXlr4U2wlmIJvIM\nERERawxSztoKYLtgNG6AhB9+j4SLQBm8O4E7A/X0F0gUaj8k4pK0E5kSDNZaDa3mamg3FCl6aj/g\n92b2E9QXMsmOPuKhrUZw9vZAVLcFiM75H+AKd/80eU4TfwTCdXsEY3V75HD8Gtgc6GJmn7p7t2qM\nqwFInNMjUG/UbVFN2TrAeWb2krt7tQZXBN5BIl67IedpU+DWkEVbCFzsocUJlLb+yoxmPc9hXgqW\naFQCqb20qN6JTTGmiJaF6PBFFIPxwEGBRrUb8LK7jwt/241cjch84I+IjpVBlI8N0IFyspmt6+4P\nEhERsaYgoXn9Dhn3DyPp8GOADh56ggZHpb27L3f3a8zsZtSa5Pfh+c8mBk9TG1rNCFeinqaDkNO0\nI6ptes7MPgmiEVug5vNXoVq+LyP67JLw3KrC1RbkfSQkBtRkSrao2qBKRMqY7uVSwF0bGB+oh5eg\nGvpmC3d/wqR42wG1QFkPZfr6Ibr1EijYbqbW9YfO/nKPs0XPc7mR+i6K7Z0YEbEK4sKIKApmdgDa\n3N9ClM5OSOWrA/And3+qkFhCipbxB6Cju5/R2kQVIiIi6oapD+ggzzV/H4aMx2/4qiJF6dd0QlTw\ncaFWb40V8AliW08itdK/58+Z5VQC9wQOdfdTTG0v1kGBt/bu/t9qz6Hl1B4T22NFchZUe2ylINSV\n/gw5PrsghdlNUA3V0GqOrTGo7Wyub/1VcDytcp4bAlu1dyJmtjUFeidGRNSG6PBFNAhm9mVE3XzS\n3V/P+1sbJAG+IkTleiN1rzvd/fro8EVErDkw9QW9DzgoLQphZtPdvX/1RtYykKrL6wWchKhtE4G7\nEJV1Weo5Q4HvAi8ghd9PUb1Ti3CkWhoCRXlnoBvKlJ3q7s9Vd1TlRTHrrwnG0OrnuT6ErOxt7n5t\n6jtJAj0nAGM81TsxIiIfkdIZ0SC4+1hyzVbz/7aSHP++P3AWig7eGx6L/PKIiDUHnyOH7x9BqKUL\nooI/A7HmpC4Eg26FmfVEolejUaud76P+dachmmwSvD0KiXB8GVFi5wILzOzydF1WNWFmA939HTPr\nggKDn1Z7TMXCVu0zuyeqLbsV1UvOb23ruIT1V+7rrlHzXCTOAUaa2bOe6p1oZknvxH9Vd3gRzR1t\nqz2AiNYNVx+oE939QnefGx6L0eaIiDUE7r7E3f8I3AT8H6JnvUJs0VIMkjP6Z0j8KpF8vyn8nh+0\n3QvN8R7ARcDzqFfaMqhhX1QNZnY1cIiZ9UD1mXeY2bBqjqkUpJgpN6Fetz8AvopKHFrjOi5q/YU6\nzLJhDZznehEymknvxD1RwP1S4Hbg257qnRgRUQiR0hkRERERUXYEqfDvI4fjgqCw2dndFwXq4fte\nd9+viAAzOxRl7DYHngau9NCQPvWcTqiH4a/d/aWmH2VhpGhnfYE7gKOBA4Gvo5qw49x9/2qOsSEI\nrUS+hGrbOwKXu/vzVR1UhVDM+qvgtdeYeS4Gpt6J21Nc78SIiBrEDF9ERERERCUwEsmFb4YaA3cG\ndjazu5FjMgCqn3VqCXD3f7r7gUik4RngDDM7Le9p3ZHc/jNm9paZPWBml5lZtZ2p5PsdihqudwQO\nRa19JtECS0uCmMgI1A/yCVSn/qyZrVvnC1soilx/ZceaNs+1wcy+Z2Z3mNmxwMvAu8Ab0dmLKAUt\nbqONiIiIiGjeCIZaD3c/LdC9nkV0w97Ag6iedyJEindtSGXGeqPsxibIaVqO6iAHAleYWVtgpbvP\nMrPh5FriDCJXzzemWmJZqXqrV4F9UR3Y/cA8VN89ranH1BCkvo8tEc3wTmApakM0CjjM3RdUc4zl\nRCnrr5w1dWvaPBeJknonRkQUQnT4IiIiIiLKjS2ARLp9U2Ar4HzUK+qj6OTVj9Qc9UAZjtuA36JW\nCwvJze/KYCB3AfZGTt66wKPA8YkxXi1lZDPrEeq3B6HPsDi0iEgyRGdVY1ylIsxxG3d/08x2DA8v\naa3KiMWuv3ILqKxp81wMGtE7MSKiBmUttI2IiIiIiMhkMnsD7bLZ7EOZTGYHoJO7n5nNZhdlMpkO\nmUymfTabja1Z6oCZtclms2Sz2dmZTGYRcBiwD2o4/aK7LwXIZDJts9nsykwm8wfk8L0ATEa0yXUy\nmcz4bDZbNSMwk8kckclkvg78AnDgg0wms6G735vJZPoAU7LZ7OfVGl+xCJmslWa2IXA6ynoNyWQy\n22YymSnZbLZVZZ2KXX8VuO4aNc/FIpvNrsxms19ks9mF2Wz242w2OzWbzY7PZDKPu/v88JxqDzOi\nGSNm+CIiIiIiyo0RwDFm9j/UPH1G8oekb1eMRteLtsAXZjYS1UE+iGTpjwa6mtkN7v5FKsNyILnm\n9msjGtjFiD7bJI2y8xGk4xehVjx9UC+13YBlZrYU+Abq59YSkNQiXgWMR4IzXVBvun5m9lN3X1it\nwVUARa2/Clx3TZvnRiH2NI4oFtHhi4iIiIgoN0YDbwJbA52Br5jZFFS39QlwelOp/LVUpAy5/ck5\ncp2Bt5Ejdw/BkTOz9VHPvQ2AecEgft7M+rp7VZy9gB2B45HTvwLVYBmiCG4A3NKC6rESx3og8B13\nXxR+v8XMXkX1qW9XZWQVQCnrr8xYo+Y5IqKpEB2+iIiIiIiywt0nEkRZAIKh2AvV9g1HTl/M8tWD\nAo7cIuAFM+uX58gtQgZ40tx+PTTPY8P7VKu5/bqodOQG9P0/iyiBLwCfufuSKoypQQg0w3WA+ajX\n4RiAQD1sD7Q60YwS1l/ZsCbOc0REUyDKYUdERERERDRDBGrmKcDXgOuQUuEuQFt3/07akQv9yo4A\ndgUWoxYId7n7jGo51kGhdQRwSXjoeaAbEv1oD9zh7k809bgaAzMbgZrazwX+h0RNFrv7yVUdWAVQ\nyvqrwLXXmHmOiGgKRNGWiIiIiIiIZgQzaxtEGpZlMpnXwsMjgJ6IznZlNpv9JJPJkMlkfpvJZDYG\n3N1fyWQy7wFPu/uYbDb7STWzqJlMBneflslkHPgL8AASbpmH+vGNz2azc6oxtlJhZm0zmUxHd5+a\nyWReQVnq9YGx7v6nKg+vrChl/ZVbKGRNmueIiKZEpHRGREREREQ0I4R6qQOQwMnnwHTgauD1pLYq\nNKwfiYzwl4Fl4eXbAN83sx+4+4RmQpn9KaKdvoGarU8AHnH3xVUdVZEIjb6/A6wbREy6o36Hz6FG\n5K0Kxa6/cq+tNW2eIyKaEjHDFxERERER0YxgZmcAR6HMRi9Uj3caMCyTyTyZzWYXZzKZDsCFwNfd\nfVI2m10BkMlk3gY2BLbLZDJjk8ergWw2S2gMPx/4GDmnu6PP8u1sNntDtcZWCjKZzGXAMOA+d/dM\nJnM1ohgeCyzLZrMvVXWAZUYx668SLQDWtHmOiGhKxBq+iIiIiIiIZgIz6wC8DnzL3V9PPd4HuAx4\nHLgWGAz8xd2/ZGad0tmy8Nx73X27ph39qkhngUKLhrWQAMjRwEJ3v7ya4ysWZvaOuw9M/T4Z2B61\nmhgJHOPuH1drfOVEseuvEpnjNWmeIyKaGm2rPYCIiIiIiIiIGmyBFBFfN7N1zKx9cJxmIfGTI4Ox\n3RWYa2bdEmcv0DwBNgKy4bGqMXmC4mIPM+vh7svd/XN3n4l6BO5QrXGVAjPbAvgo9fvawO1BsXIm\nsEkrc0KKXX9lxRo4zxERTYro8EVERERERDQfDEU1eQQHaXnqbx2AxcGxGwe8C9xsZruZ2cbBwa85\nXrgAAAJZSURBVNoI+AHwYnhNVZg8ZtYr/Hg6MMXMXjCz28zsd8B3UR1fS0AP4MOgggqwFPWZBNgK\neL8qo6oc6l1/UJFAwpo2zxERTYoo2hIREREREdF8MAI4ysw+RGIZrwBTgeVAf2BCcOxAQhpnA78B\nPjKzASi7dx1wfXi/atXwnW5ml7v7L4MAx1aInrcVcCNwf5XGVSpeQxmm84GzggM0L/SoO5yW47gW\ni3rXX4Wuu6bNc0REkyLW8EVERERERDQTmNkQ4MvAoPCvN6p9m4FUE09x92vMrJ27fxEyLVsDhrIv\nM4C33H1pNcafwMwmufvW1RxDuWBmu6Dm8R2BVxE7KoOcoSvdfUYVh1dWlLr+ynztNWaeIyKaGtHh\ni4iIiIiIaKYws85IKXEQypDd6O6zqtlfrz6Y2WbAo8hxmNNS2i8UQp7wzNeRc70eyn7d1JI/WzFo\nqvW3ps9zRESlER2+iIiIiIiIiLLBzL4J3Ib67nVHzbPfBcYDE4EX3P2D6o2w4WjOjnZrQpzniIjy\nIvbhi4iIiIiIiCgbMpnMUcCj7n5sJpO5F4mALAIGAscA62ez2cerOcaGohL95yJWR5zniIjyIoq2\nRERERERERJQTvYE7ws/T3P1d4LHkj6HXW0RERERERERERERERERLg5ltEmq/IiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKoX/B2W+0vln7d2AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16a71d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "station_names=map(get_name,temp_ids)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "heatmap = ax.pcolor(pd.DataFrame(correlation_vectors),cmap=plt.get_cmap('seismic'),alpha=0.7,vmin=-1,vmax=1)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15,12)\n",
    "\n",
    "# Clip the axes to remove white border\n",
    "plt.ylim(0, len(temp_ids))\n",
    "plt.xlim(0, len(temp_ids))\n",
    "\n",
    "#invert so we orient the diagonal properly\n",
    "ax.invert_yaxis()\n",
    "ax.grid(False)\n",
    "ax.set_frame_on(False)\n",
    "\n",
    "# reorganize the ticks\n",
    "ax.set_yticks(np.arange(len(temp_ids)) + 0.5, minor=False)\n",
    "ax.set_xticks(np.arange(len(temp_ids))+0.5, minor=False) \n",
    "#put labels on the ticks\n",
    "ax.set_xticklabels(station_names, minor=False)\n",
    "ax.set_yticklabels(station_names, minor=False)\n",
    "\n",
    "plt.xticks(rotation=80)\n",
    "plt.rc('xtick', labelsize=11)\n",
    "plt.rc('ytick', labelsize=11)\n",
    "plt.title('Correlations for All Stations')\n",
    "colorbar=plt.colorbar(heatmap)\n",
    "\n",
    "# plot lines for the groups\n",
    "plt.axhline(y=len(group_1_ids),xmin=0,xmax=(len(temp_ids)),color='black',linewidth=4)\n",
    "plt.axhline(y=len(group_1_ids)+len(group_2_ids),xmin=0,xmax=(len(temp_ids)),color='black',linewidth=4)\n",
    "\n",
    "plt.axvline(x=len(group_1_ids),ymin=0,ymax=(len(temp_ids)),color='black',linewidth=4)\n",
    "plt.axvline(x=len(group_1_ids)+len(group_2_ids),ymin=0,ymax=(len(temp_ids)),color='black',linewidth=4)\n",
    "\n",
    "plt.savefig('all_stations.png',format='png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comments:\n",
    "\n",
    "Victory! Remember that we set out to find ways to identify similar stations that was based upon ridership patterns rather than the line to which a station belongs. \n",
    "\n",
    "It's good to see that the analysis we ran on each line scaled well when considered comparisons between all stations. \n",
    "\n",
    "##Note:\n",
    "When looking at box plots below, the top group of stations in the similarity matrix is group1, the middle is group 2, and the bottom is group 0.\n",
    "\n",
    "###Save groupings for later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##Save Groupings!\n",
    "global_group_0=group_0_ids\n",
    "global_group_1=group_1_ids\n",
    "global_group_2=group_2_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Store the groupings into t he stations data frame\n",
    "\n",
    "grouping_dict={}\n",
    "\n",
    "for station in global_group_0:\n",
    "    grouping_dict[station]=0\n",
    "    \n",
    "for station in global_group_1:\n",
    "    grouping_dict[station]=1\n",
    "    \n",
    "for station in global_group_2:\n",
    "    grouping_dict[station]=2\n",
    "   \n",
    "#want to map station id to column for grouping\n",
    "def group_col(station_id):\n",
    "    return grouping_dict[station_id]\n",
    "\n",
    "station_info['grouping']=map(group_col,station_info['stationid'].values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comments on the Groupings\n",
    "\n",
    "When I first looked at the names of stations within Group 1, such as Oak Grove, Sullivan Square, and Savin Hill, I noticed that I hardly ever used those stations as they are on the far reaches of the orange, blue and red lines. However, I'm very familiar with a lot of the stations in Group 0, like Government Center, Downtown Crossing, and Park Street, because I often use them to go downtown. Using Boston City Hall as an unofficial city center, we can visualize the box plots of distances from City Hall within each of our three groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>locationid</th>\n",
       "      <th>name</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>dist_to_center</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 0</td>\n",
       "      <td> 1002</td>\n",
       "      <td> Andrew Square</td>\n",
       "      <td> 42.329550</td>\n",
       "      <td>-71.056960</td>\n",
       "      <td>  3.404767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1004</td>\n",
       "      <td>    JFK/U Mass</td>\n",
       "      <td> 42.321438</td>\n",
       "      <td>-71.052393</td>\n",
       "      <td>  4.328881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2</td>\n",
       "      <td> 1005</td>\n",
       "      <td>  North Quincy</td>\n",
       "      <td> 42.274816</td>\n",
       "      <td>-71.029176</td>\n",
       "      <td>  9.777437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 3</td>\n",
       "      <td> 1006</td>\n",
       "      <td>     Wollaston</td>\n",
       "      <td> 42.265615</td>\n",
       "      <td>-71.019402</td>\n",
       "      <td> 10.976943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 4</td>\n",
       "      <td> 1007</td>\n",
       "      <td> Quincy Center</td>\n",
       "      <td> 42.250879</td>\n",
       "      <td>-71.004798</td>\n",
       "      <td> 12.909591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  locationid           name        lat        lon  dist_to_center\n",
       "0           0        1002  Andrew Square  42.329550 -71.056960        3.404767\n",
       "1           1        1004     JFK/U Mass  42.321438 -71.052393        4.328881\n",
       "2           2        1005   North Quincy  42.274816 -71.029176        9.777437\n",
       "3           3        1006      Wollaston  42.265615 -71.019402       10.976943\n",
       "4           4        1007  Quincy Center  42.250879 -71.004798       12.909591"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##read in a stations csv with lat/long values\n",
    "stations_latlong=pd.read_csv('../../../data/stations_with_dist.csv')\n",
    "stations_latlong.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "latlong=stations_latlong[['locationid','dist_to_center']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>stationid</th>\n",
       "      <th>nameshort</th>\n",
       "      <th>namelong</th>\n",
       "      <th>name</th>\n",
       "      <th>line_temp</th>\n",
       "      <th>grouping</th>\n",
       "      <th>locationid</th>\n",
       "      <th>dist_to_center</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 20</td>\n",
       "      <td> 1002</td>\n",
       "      <td> Andrew Square</td>\n",
       "      <td> 1002</td>\n",
       "      <td> Andrew Square</td>\n",
       "      <td> Red</td>\n",
       "      <td> 2</td>\n",
       "      <td> 1002</td>\n",
       "      <td>  3.404767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 21</td>\n",
       "      <td> 1004</td>\n",
       "      <td>    JFK/U Mass</td>\n",
       "      <td> 1004</td>\n",
       "      <td>    JFK/U Mass</td>\n",
       "      <td> Red</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1004</td>\n",
       "      <td>  4.328881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 22</td>\n",
       "      <td> 1005</td>\n",
       "      <td>  North Quincy</td>\n",
       "      <td> 1005</td>\n",
       "      <td>  North Quincy</td>\n",
       "      <td> Red</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1005</td>\n",
       "      <td>  9.777437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 23</td>\n",
       "      <td> 1006</td>\n",
       "      <td>     Wollaston</td>\n",
       "      <td> 1006</td>\n",
       "      <td>     Wollaston</td>\n",
       "      <td> Red</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1006</td>\n",
       "      <td> 10.976943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 24</td>\n",
       "      <td> 1007</td>\n",
       "      <td> Quincy Center</td>\n",
       "      <td> 1007</td>\n",
       "      <td> Quincy Center</td>\n",
       "      <td> Red</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1007</td>\n",
       "      <td> 12.909591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  stationid      nameshort  namelong           name line_temp  \\\n",
       "0          20       1002  Andrew Square      1002  Andrew Square       Red   \n",
       "1          21       1004     JFK/U Mass      1004     JFK/U Mass       Red   \n",
       "2          22       1005   North Quincy      1005   North Quincy       Red   \n",
       "3          23       1006      Wollaston      1006      Wollaston       Red   \n",
       "4          24       1007  Quincy Center      1007  Quincy Center       Red   \n",
       "\n",
       "   grouping  locationid  dist_to_center  \n",
       "0         2        1002        3.404767  \n",
       "1         0        1004        4.328881  \n",
       "2         1        1005        9.777437  \n",
       "3         1        1006       10.976943  \n",
       "4         1        1007       12.909591  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_info=station_info.merge(latlong,left_on='stationid',right_on='locationid')\n",
    "\n",
    "#station_info=station_info[['stationid','nameshort','namelong','name','line_temp','grouping','dist_to_center']]\n",
    "station_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# more saving, just to be safe\n",
    "station_info=station_info[['stationid','name','line_temp','grouping','dist_to_center']]\n",
    "station_info.head()\n",
    "\n",
    "station_info.to_csv('../../../data/Stations_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16622290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "sns.boxplot(station_info['dist_to_center'],groupby=station_info['grouping'],color=['green','red','yellow'])\n",
    "plt.xlabel('Group Number')\n",
    "plt.ylabel('Distance from City Hall')\n",
    "plt.title('Distance from City Hall by Grouping')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group 0 stations: Downtown Crossing, Community College, Haymarket, State Street, Chinatown, Tufts Medical Center, Back Bay, Ruggles, Aquarium, Bowdoin, Government Center, State Street, JFK/U Mass, South Station, Harvard, Kendall Square, Downtown Crossing, Park Street, Charles MGH, Government Center, Park Street, Boylston, Arlington, Copley Square, Hynes, Prudential, Kenmore Square, Science Park, Haymarket\n",
      "\n",
      "Group 1 stations: Oak Grove, Malden Center , Wellington , Sullivan Square, Jackson Square, Stony Brook, Green Street, Forest Hills, Maverick, Airport, Wood Island, Orient Heights, Suffolk Downs, Beachmont, Revere Beach, Wonderland, North Quincy, Wollaston, Quincy Center, Braintree, Alewife, Davis Square, Porter Square, Savin Hill, Fields Corner, Shawmut, Ashmont, Quincy Adams\n",
      "\n",
      "Group 2 stations: North Station, Mass Ave, Roxbury Crossing, Andrew Square, Central Square, Broadway, Symphony, Lechmere, Riverside, North Station\n"
     ]
    }
   ],
   "source": [
    "print \"Group 0 stations: \"+ ', '.join(map(get_name,group_0_ids))\n",
    "print\n",
    "print \"Group 1 stations: \"+ ', '.join(map(get_name,group_1_ids))\n",
    "print\n",
    "print \"Group 2 stations: \"+ ', '.join(map(get_name,group_2_ids))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comments on Groupings:\n",
    "The box plots above affirm what I saw just by looking at station names within our grouping, namely that stations within groupings not only share similar ridership patterns, but also they are similarly distanced from Boston City Hall. Group 0 stations, including most notably Government Center, Copley, Kenmore, and South Station, are much closer to downtown than the other two groupings. Group 1 stations are much further from downtown, as in the case of Alewife, Braintree, and Forest Hills. Group 2 comprises of those stations that may not be outside of the city limits, but are not as close to downtown as Group 0 stations.\n",
    "\n",
    "\n",
    "Below you can see our groupings overlayed onto the standard MBTA map. Note how the Group 0 stations are all centered around Government Center and how Group 1 stations are all towards the outskirts of the city and extend into the neighborhing suburbs. The really interesting cases are the intermediary Group 2 stations. Note that there are Group 0 stations that are geographically further from the city center than Group 2 stations (ie Central/Harvard and JFK/Broadway). Our methods were able to detect significant differences in ridership patterns at those stations that does not follow a simple geographic relation. \n",
    "\n",
    "Our contact at the MBTA explained that stations at Central, Broadway, and Andrew are considered *peak load points*. In the morning, inbound trains leaving Alewife, Braintree, and Ashmont all pick up large amounts of commuters entering the city. It's only upon reaching Central, Broadway, and Andrew that a lot of people start to exit the trains. In the afternoon, the reverse phenomenon occurs where outbound red line trains accumulate a lot of passengers from South Station to Kendall, and then the passengers disembark en masse at Central/JFK/ Broadway and beyond. \n",
    "\n",
    "Other fine details include...\n",
    "- North Station belonging to Group 2. Its ridership patterns are largely affected by the Commuter Rail and Amtrak trains flowing into the station.\n",
    "- Harvard belonging to Group 0. This station has an interesting history as it was once the terminal station on the red line so many bus routes run through Harvard Square. It is still a morning destination for many commuters as students and professionals disembark here in the morning.\n",
    "- JFK/UMass is classified as a Group 0 station while it is further away from City Hall than Broadway and Andrew. JFK/UMass is less residential than neighboring stations and the nearby destinations like UMass Boston may serve as a terminal morning destination for many commuters. This likens its ridership more to downtown Group 0 stations than the Group 1 stations further from the city."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![map](Similar Stations/mbta_edit.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "##Plotting Dist from center vs. AM/PM Ratio \n",
    "\n",
    "Here's an example of the clarity our grouping can provide. One statistic of interest when looking at stations is their ratio of morning rush hour entries compared to evening rush hour entries. When plotting a station's distance from downtown against this ratio and coloring the plot by line color, there is no clear pattern under this grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
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       "      <th>entries</th>\n",
       "      <th>exits</th>\n",
       "      <th>servicedate</th>\n",
       "      <th>servicetime_fraction</th>\n",
       "      <th>weekday</th>\n",
       "      <th>am_rush</th>\n",
       "      <th>pm_rush</th>\n",
       "      <th>rush_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td>  7</td>\n",
       "      <td> 1002</td>\n",
       "      <td> 19</td>\n",
       "      <td> 4</td>\n",
       "      <td> 2013-01-01 00:00:00</td>\n",
       "      <td> 6.50</td>\n",
       "      <td> 1</td>\n",
       "      <td> True</td>\n",
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       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td>  8</td>\n",
       "      <td> 1002</td>\n",
       "      <td>  9</td>\n",
       "      <td> 5</td>\n",
       "      <td> 2013-01-01 00:00:00</td>\n",
       "      <td> 6.75</td>\n",
       "      <td> 1</td>\n",
       "      <td> True</td>\n",
       "      <td> False</td>\n",
       "      <td> True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td>  9</td>\n",
       "      <td> 1002</td>\n",
       "      <td> 14</td>\n",
       "      <td> 3</td>\n",
       "      <td> 2013-01-01 00:00:00</td>\n",
       "      <td> 7.00</td>\n",
       "      <td> 1</td>\n",
       "      <td> True</td>\n",
       "      <td> False</td>\n",
       "      <td> True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td> 10</td>\n",
       "      <td> 1002</td>\n",
       "      <td> 23</td>\n",
       "      <td> 6</td>\n",
       "      <td> 2013-01-01 00:00:00</td>\n",
       "      <td> 7.25</td>\n",
       "      <td> 1</td>\n",
       "      <td> True</td>\n",
       "      <td> False</td>\n",
       "      <td> True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td> 11</td>\n",
       "      <td> 1002</td>\n",
       "      <td> 11</td>\n",
       "      <td> 3</td>\n",
       "      <td> 2013-01-01 00:00:00</td>\n",
       "      <td> 7.50</td>\n",
       "      <td> 1</td>\n",
       "      <td> True</td>\n",
       "      <td> False</td>\n",
       "      <td> True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  locationid  entries  exits          servicedate  \\\n",
       "7            7        1002       19      4  2013-01-01 00:00:00   \n",
       "8            8        1002        9      5  2013-01-01 00:00:00   \n",
       "9            9        1002       14      3  2013-01-01 00:00:00   \n",
       "10          10        1002       23      6  2013-01-01 00:00:00   \n",
       "11          11        1002       11      3  2013-01-01 00:00:00   \n",
       "\n",
       "    servicetime_fraction  weekday am_rush pm_rush rush_hour  \n",
       "7                   6.50        1    True   False      True  \n",
       "8                   6.75        1    True   False      True  \n",
       "9                   7.00        1    True   False      True  \n",
       "10                  7.25        1    True   False      True  \n",
       "11                  7.50        1    True   False      True  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "subset=gatecount_1315[gatecount_1315['weekday']<5]\n",
    "\n",
    "am_rush_bool=((subset['servicetime_fraction']>=6.5) & (subset['servicetime_fraction']<=9.5))\n",
    "pm_rush_bool=((subset['servicetime_fraction']>=16.5) & (subset['servicetime_fraction']<=19.5))\n",
    "\n",
    "subset['am_rush']=am_rush_bool\n",
    "subset['pm_rush']=pm_rush_bool\n",
    "\n",
    "subset['rush_hour']= (am_rush_bool | pm_rush_bool)\n",
    "\n",
    "subset=subset[subset['rush_hour']==True]\n",
    "subset.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>locationid</th>\n",
       "      <th>entries</th>\n",
       "      <th>pm_entries</th>\n",
       "      <th>am_pm_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1002</td>\n",
       "      <td> 1743.633880</td>\n",
       "      <td> 1137.439781</td>\n",
       "      <td> 1.532946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1004</td>\n",
       "      <td> 1682.384755</td>\n",
       "      <td> 2074.090744</td>\n",
       "      <td> 0.811143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 1005</td>\n",
       "      <td> 3001.119782</td>\n",
       "      <td>  942.152727</td>\n",
       "      <td> 3.185386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 1006</td>\n",
       "      <td> 2260.174545</td>\n",
       "      <td>  339.927405</td>\n",
       "      <td> 6.648992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 1007</td>\n",
       "      <td> 3708.607273</td>\n",
       "      <td>  871.707273</td>\n",
       "      <td> 4.254418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   locationid      entries   pm_entries  am_pm_ratio\n",
       "0        1002  1743.633880  1137.439781     1.532946\n",
       "1        1004  1682.384755  2074.090744     0.811143\n",
       "2        1005  3001.119782   942.152727     3.185386\n",
       "3        1006  2260.174545   339.927405     6.648992\n",
       "4        1007  3708.607273   871.707273     4.254418"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_2=subset[['locationid','entries','servicedate','am_rush']].groupby(['locationid','servicedate','am_rush']).agg(np.sum)\n",
    "subset_2=subset_2.reset_index()\n",
    "\n",
    "subset_3=subset_2[['locationid','am_rush','entries']].groupby(['am_rush','locationid']).agg(np.mean)\n",
    "\n",
    "pm_rush_mean=subset_3.loc[False]['entries']\n",
    "\n",
    "ratio_subset=subset_3.loc[True]\n",
    "ratio_subset['pm_entries']=pm_rush_mean\n",
    "\n",
    "# in ratio subset, 'entries' is the mean am rush hour entries for that station\n",
    "ratio_subset['am_pm_ratio']=ratio_subset['entries']/ratio_subset['pm_entries']\n",
    "\n",
    "ratio_subset=ratio_subset.reset_index()\n",
    "ratio_subset.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>locationid</th>\n",
       "      <th>entries</th>\n",
       "      <th>pm_entries</th>\n",
       "      <th>am_pm_ratio</th>\n",
       "      <th>stationid</th>\n",
       "      <th>name</th>\n",
       "      <th>line_temp</th>\n",
       "      <th>grouping</th>\n",
       "      <th>dist_to_center</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1002</td>\n",
       "      <td> 1743.633880</td>\n",
       "      <td> 1137.439781</td>\n",
       "      <td> 1.532946</td>\n",
       "      <td> 1002</td>\n",
       "      <td> Andrew Square</td>\n",
       "      <td> Red</td>\n",
       "      <td> 2</td>\n",
       "      <td>  3.404767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1004</td>\n",
       "      <td> 1682.384755</td>\n",
       "      <td> 2074.090744</td>\n",
       "      <td> 0.811143</td>\n",
       "      <td> 1004</td>\n",
       "      <td>    JFK/U Mass</td>\n",
       "      <td> Red</td>\n",
       "      <td> 0</td>\n",
       "      <td>  4.328881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 1005</td>\n",
       "      <td> 3001.119782</td>\n",
       "      <td>  942.152727</td>\n",
       "      <td> 3.185386</td>\n",
       "      <td> 1005</td>\n",
       "      <td>  North Quincy</td>\n",
       "      <td> Red</td>\n",
       "      <td> 1</td>\n",
       "      <td>  9.777437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 1006</td>\n",
       "      <td> 2260.174545</td>\n",
       "      <td>  339.927405</td>\n",
       "      <td> 6.648992</td>\n",
       "      <td> 1006</td>\n",
       "      <td>     Wollaston</td>\n",
       "      <td> Red</td>\n",
       "      <td> 1</td>\n",
       "      <td> 10.976943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 1007</td>\n",
       "      <td> 3708.607273</td>\n",
       "      <td>  871.707273</td>\n",
       "      <td> 4.254418</td>\n",
       "      <td> 1007</td>\n",
       "      <td> Quincy Center</td>\n",
       "      <td> Red</td>\n",
       "      <td> 1</td>\n",
       "      <td> 12.909591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   locationid      entries   pm_entries  am_pm_ratio  stationid  \\\n",
       "0        1002  1743.633880  1137.439781     1.532946       1002   \n",
       "1        1004  1682.384755  2074.090744     0.811143       1004   \n",
       "2        1005  3001.119782   942.152727     3.185386       1005   \n",
       "3        1006  2260.174545   339.927405     6.648992       1006   \n",
       "4        1007  3708.607273   871.707273     4.254418       1007   \n",
       "\n",
       "            name line_temp  grouping  dist_to_center  \n",
       "0  Andrew Square       Red         2        3.404767  \n",
       "1     JFK/U Mass       Red         0        4.328881  \n",
       "2   North Quincy       Red         1        9.777437  \n",
       "3      Wollaston       Red         1       10.976943  \n",
       "4  Quincy Center       Red         1       12.909591  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio_subset=ratio_subset.merge(station_info, left_on='locationid',right_on='stationid')\n",
    "ratio_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PAucAvwHeWD3/oZTSseRltbcif7LVA3yUvLLhDsCG5IVT5tL8j/dm+lNKnyevMrgG8IHq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\nJEmSJEmSJEmSJEmSJEmSpAb4/wRr/gy72zuWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1af94630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "for i,group in ratio_subset.groupby('line_temp'):\n",
    "    group_line=group['line_temp'].iloc[0].lower()\n",
    "    plt.scatter(group['dist_to_center'],group['am_pm_ratio'], s=45,alpha=0.75,color=group_line,label=group_line+' Line')\n",
    "\n",
    "plt.xlabel('Distance to Boston City Hall')\n",
    "plt.ylabel('Ratio of AM Rush hour entries/PM Rush hour entries')\n",
    "plt.legend()\n",
    "plt.title('Station Distance vs AM/PM Rush Hour Ratio')\n",
    "plt.annotate('Riverside',xy=(16,1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Color by our grouping\n",
    "\n",
    "As seen below, we can color the stations by the grouping derived earlier and the picture is much prettier. Note that Riverside is an outlier among Group 2 stations because it is the only above-ground rail line that we have gate fare data for.\n",
    "\n",
    "\n",
    "The fact that the ratio of AM/PM rush hour entries is so cleanly separated by our grouping lends some insight as to why two principal components were sufficient to account for 90% of the variation in the data set. It seems that stations can be almost completely characterized by their rush hour traffic with most of the mid-day activity having little part in differentiating stations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xjvcV4P8Bm4zz9ZIkSRqTsgD2BXYH1gSuAU6A4pq+hjVNjTj7GxGrAnsAe1Ml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\nJEmSJEmSpD74fz8rdPeIZ1FVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2242f610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "color_dict={0:'red',1:'blue',2:'green'}\n",
    "\n",
    "for i,group in ratio_subset.groupby('grouping'):\n",
    "    group_num=group['grouping'].iloc[0]\n",
    "    plt.scatter(group['dist_to_center'],group['am_pm_ratio'], s=45,alpha=0.75,color=color_dict[group_num],label='Group '+str(group_num))\n",
    "\n",
    "plt.xlabel('Distance to Boston City Hall')\n",
    "plt.ylabel('Ratio of AM Rush hour entries/PM Rush hour entries')\n",
    "plt.legend()\n",
    "plt.title('Station Distance vs AM/PM Rush Hour Ratio')\n",
    "plt.annotate('Riverside',xy=(16,1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Conclusion:\n",
    "\n",
    "We set out to discover a more natural way of identifying similar stations based upon their ridership patterns as opposed to arbitrary groupings like line color. Our methods relied **solely upon average entries** and did not incorporate any information regarding station location or which stations were connected to one another. This makes it all the more exciting that our groupings reflect similar ridership patterns possibly brought about by a station's proximity to the city and the general area surrounding the station.\n"
   ]
  }
 ],
 "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": "2.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}