gistfile1.txt

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Code to download and plot data for bicycle crossings of the Fremont Bridge"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Import packages and initialize some things"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "from datetime import datetime\n",
      "import time\n",
      "import calendar\n",
      "import matplotlib.dates as dates\n",
      "import matplotlib.ticker as ticker\n",
      "import matplotlib.pyplot as plt\n",
      "import seaborn as sns\n",
      "colors = sns.hls_palette(8, l=.3, s=.8)\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import warnings\n",
      "warnings.filterwarnings('ignore')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Download data and put it into a Pandas Dataframe"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get Data, put it into a Pandas dataframe (nb = northbound crossings, sb = southbound crossings)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fileUrl = 'https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD'\n",
      "df = pd.read_csv(fileUrl)\n",
      "df.columns = ['timestamp','nb', 'sb'] #rename columns\n",
      "df['tot'] = df['nb']+df['sb'] #add a total column"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Parse the timestamp data to make later analysis easier"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Parse the timestamp column"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['timestamp'] = df['timestamp'].apply(lambda x: pd.datetools.parse(x).strftime(\"%m/%d/%Y %I:%M:%S %p\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Add columns for weekday, hour, month, day of week and day of year"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Note: This is slow. There must be a better way\n",
      "df['year'] = df['timestamp'].apply(lambda x: int(pd.datetools.parse(x).strftime('%y')))\n",
      "df['weekdayname'] = df['timestamp'].apply(lambda x: pd.datetools.parse(x).strftime('%A'))\n",
      "df['weekday'] = df['timestamp'].apply(lambda x: int(pd.datetools.parse(x).strftime('%w')))\n",
      "df['hour'] = df['timestamp'].apply(lambda x: int(pd.datetools.parse(x).strftime('%H')))\n",
      "df['month'] = df['timestamp'].apply(lambda x: int(pd.datetools.parse(x).strftime('%m')))\n",
      "df['day'] = df['timestamp'].apply(lambda x: int(pd.datetools.parse(x).strftime('%d')))\n",
      "df['year'] = df['timestamp'].apply(lambda x: int(pd.datetools.parse(x).strftime('%y')))\n",
      "df['dayofyear'] = df['timestamp'].apply(lambda x: int(pd.datetools.parse(x).strftime('%j')))\n",
      "df['dayofyear_float'] = [df.dayofyear[i]+df.hour[i]/24.0 for i in range(0,len(df))]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "What's it look like?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Take a look at the first 20 columns"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>timestamp</th>\n",
        "      <th>nb</th>\n",
        "      <th>sb</th>\n",
        "      <th>tot</th>\n",
        "      <th>year</th>\n",
        "      <th>weekdayname</th>\n",
        "      <th>weekday</th>\n",
        "      <th>hour</th>\n",
        "      <th>month</th>\n",
        "      <th>day</th>\n",
        "      <th>dayofyear</th>\n",
        "      <th>dayofyear_float</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 10/02/2012 12:00:00 AM</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td>  0</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 10/02/2012 01:00:00 AM</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td>  1</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.041667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 10/02/2012 02:00:00 AM</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td>  2</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.083333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 10/02/2012 03:00:00 AM</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td>  3</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.125000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 10/02/2012 04:00:00 AM</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td>  4</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.166667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 10/02/2012 05:00:00 AM</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td>  5</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.208333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 10/02/2012 06:00:00 AM</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td>  6</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 10/02/2012 07:00:00 AM</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td>  7</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.291667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 10/02/2012 08:00:00 AM</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td>  8</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 10/02/2012 09:00:00 AM</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td>  9</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.375000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 10/02/2012 10:00:00 AM</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td> 10</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.416667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 10/02/2012 11:00:00 AM</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td> 11</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.458333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 10/02/2012 12:00:00 PM</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td> 12</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 10/02/2012 01:00:00 PM</td>\n",
        "      <td>   7</td>\n",
        "      <td>  48</td>\n",
        "      <td>  55</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td> 13</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.541667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 10/02/2012 02:00:00 PM</td>\n",
        "      <td>  55</td>\n",
        "      <td>  75</td>\n",
        "      <td> 130</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td> 14</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.583333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 10/02/2012 03:00:00 PM</td>\n",
        "      <td>  81</td>\n",
        "      <td>  71</td>\n",
        "      <td> 152</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td> 15</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.625000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 10/02/2012 04:00:00 PM</td>\n",
        "      <td> 167</td>\n",
        "      <td> 111</td>\n",
        "      <td> 278</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td> 16</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 10/02/2012 05:00:00 PM</td>\n",
        "      <td> 393</td>\n",
        "      <td> 170</td>\n",
        "      <td> 563</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td> 17</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.708333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 10/02/2012 06:00:00 PM</td>\n",
        "      <td> 236</td>\n",
        "      <td> 145</td>\n",
        "      <td> 381</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td> 18</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.750000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> 10/02/2012 07:00:00 PM</td>\n",
        "      <td> 104</td>\n",
        "      <td>  71</td>\n",
        "      <td> 175</td>\n",
        "      <td> 12</td>\n",
        "      <td> Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td> 19</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2</td>\n",
        "      <td> 276</td>\n",
        "      <td> 276.791667</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "                 timestamp   nb   sb  tot  year weekdayname  weekday  hour  \\\n",
        "0   10/02/2012 12:00:00 AM    0    0    0    12     Tuesday        2     0   \n",
        "1   10/02/2012 01:00:00 AM    0    0    0    12     Tuesday        2     1   \n",
        "2   10/02/2012 02:00:00 AM    0    0    0    12     Tuesday        2     2   \n",
        "3   10/02/2012 03:00:00 AM    0    0    0    12     Tuesday        2     3   \n",
        "4   10/02/2012 04:00:00 AM    0    0    0    12     Tuesday        2     4   \n",
        "5   10/02/2012 05:00:00 AM    0    0    0    12     Tuesday        2     5   \n",
        "6   10/02/2012 06:00:00 AM    0    0    0    12     Tuesday        2     6   \n",
        "7   10/02/2012 07:00:00 AM    0    0    0    12     Tuesday        2     7   \n",
        "8   10/02/2012 08:00:00 AM    0    0    0    12     Tuesday        2     8   \n",
        "9   10/02/2012 09:00:00 AM    0    0    0    12     Tuesday        2     9   \n",
        "10  10/02/2012 10:00:00 AM    0    0    0    12     Tuesday        2    10   \n",
        "11  10/02/2012 11:00:00 AM    0    0    0    12     Tuesday        2    11   \n",
        "12  10/02/2012 12:00:00 PM    0    0    0    12     Tuesday        2    12   \n",
        "13  10/02/2012 01:00:00 PM    7   48   55    12     Tuesday        2    13   \n",
        "14  10/02/2012 02:00:00 PM   55   75  130    12     Tuesday        2    14   \n",
        "15  10/02/2012 03:00:00 PM   81   71  152    12     Tuesday        2    15   \n",
        "16  10/02/2012 04:00:00 PM  167  111  278    12     Tuesday        2    16   \n",
        "17  10/02/2012 05:00:00 PM  393  170  563    12     Tuesday        2    17   \n",
        "18  10/02/2012 06:00:00 PM  236  145  381    12     Tuesday        2    18   \n",
        "19  10/02/2012 07:00:00 PM  104   71  175    12     Tuesday        2    19   \n",
        "\n",
        "    month  day  dayofyear  dayofyear_float  \n",
        "0      10    2        276       276.000000  \n",
        "1      10    2        276       276.041667  \n",
        "2      10    2        276       276.083333  \n",
        "3      10    2        276       276.125000  \n",
        "4      10    2        276       276.166667  \n",
        "5      10    2        276       276.208333  \n",
        "6      10    2        276       276.250000  \n",
        "7      10    2        276       276.291667  \n",
        "8      10    2        276       276.333333  \n",
        "9      10    2        276       276.375000  \n",
        "10     10    2        276       276.416667  \n",
        "11     10    2        276       276.458333  \n",
        "12     10    2        276       276.500000  \n",
        "13     10    2        276       276.541667  \n",
        "14     10    2        276       276.583333  \n",
        "15     10    2        276       276.625000  \n",
        "16     10    2        276       276.666667  \n",
        "17     10    2        276       276.708333  \n",
        "18     10    2        276       276.750000  \n",
        "19     10    2        276       276.791667  "
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Take a look at the last 20 columns"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.tail(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>timestamp</th>\n",
        "      <th>nb</th>\n",
        "      <th>sb</th>\n",
        "      <th>tot</th>\n",
        "      <th>year</th>\n",
        "      <th>weekdayname</th>\n",
        "      <th>weekday</th>\n",
        "      <th>hour</th>\n",
        "      <th>month</th>\n",
        "      <th>day</th>\n",
        "      <th>dayofyear</th>\n",
        "      <th>dayofyear_float</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>18220</th>\n",
        "      <td> 10/31/2014 04:00:00 AM</td>\n",
        "      <td>   1</td>\n",
        "      <td>   3</td>\n",
        "      <td>   4</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td>  4</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.166667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18221</th>\n",
        "      <td> 10/31/2014 05:00:00 AM</td>\n",
        "      <td>   7</td>\n",
        "      <td>  22</td>\n",
        "      <td>  29</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td>  5</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.208333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18222</th>\n",
        "      <td> 10/31/2014 06:00:00 AM</td>\n",
        "      <td>  18</td>\n",
        "      <td>  57</td>\n",
        "      <td>  75</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td>  6</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18223</th>\n",
        "      <td> 10/31/2014 07:00:00 AM</td>\n",
        "      <td>  52</td>\n",
        "      <td> 128</td>\n",
        "      <td> 180</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td>  7</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.291667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18224</th>\n",
        "      <td> 10/31/2014 08:00:00 AM</td>\n",
        "      <td>  66</td>\n",
        "      <td> 203</td>\n",
        "      <td> 269</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td>  8</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18225</th>\n",
        "      <td> 10/31/2014 09:00:00 AM</td>\n",
        "      <td>  42</td>\n",
        "      <td>  94</td>\n",
        "      <td> 136</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td>  9</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.375000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18226</th>\n",
        "      <td> 10/31/2014 10:00:00 AM</td>\n",
        "      <td>  18</td>\n",
        "      <td>  34</td>\n",
        "      <td>  52</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 10</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.416667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18227</th>\n",
        "      <td> 10/31/2014 11:00:00 AM</td>\n",
        "      <td>  14</td>\n",
        "      <td>  22</td>\n",
        "      <td>  36</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 11</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.458333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18228</th>\n",
        "      <td> 10/31/2014 12:00:00 PM</td>\n",
        "      <td>  17</td>\n",
        "      <td>  16</td>\n",
        "      <td>  33</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 12</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18229</th>\n",
        "      <td> 10/31/2014 01:00:00 PM</td>\n",
        "      <td>  16</td>\n",
        "      <td>  25</td>\n",
        "      <td>  41</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 13</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.541667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18230</th>\n",
        "      <td> 10/31/2014 02:00:00 PM</td>\n",
        "      <td>  25</td>\n",
        "      <td>  27</td>\n",
        "      <td>  52</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 14</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.583333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18231</th>\n",
        "      <td> 10/31/2014 03:00:00 PM</td>\n",
        "      <td>  76</td>\n",
        "      <td>  44</td>\n",
        "      <td> 120</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 15</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.625000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18232</th>\n",
        "      <td> 10/31/2014 04:00:00 PM</td>\n",
        "      <td> 134</td>\n",
        "      <td>  81</td>\n",
        "      <td> 215</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 16</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18233</th>\n",
        "      <td> 10/31/2014 05:00:00 PM</td>\n",
        "      <td> 228</td>\n",
        "      <td> 100</td>\n",
        "      <td> 328</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 17</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.708333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18234</th>\n",
        "      <td> 10/31/2014 06:00:00 PM</td>\n",
        "      <td> 106</td>\n",
        "      <td>  75</td>\n",
        "      <td> 181</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 18</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.750000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18235</th>\n",
        "      <td> 10/31/2014 07:00:00 PM</td>\n",
        "      <td>  44</td>\n",
        "      <td>  21</td>\n",
        "      <td>  65</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 19</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.791667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18236</th>\n",
        "      <td> 10/31/2014 08:00:00 PM</td>\n",
        "      <td>  22</td>\n",
        "      <td>   9</td>\n",
        "      <td>  31</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 20</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.833333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18237</th>\n",
        "      <td> 10/31/2014 09:00:00 PM</td>\n",
        "      <td>  20</td>\n",
        "      <td>  12</td>\n",
        "      <td>  32</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 21</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.875000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18238</th>\n",
        "      <td> 10/31/2014 10:00:00 PM</td>\n",
        "      <td>  13</td>\n",
        "      <td>  11</td>\n",
        "      <td>  24</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 22</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.916667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18239</th>\n",
        "      <td> 10/31/2014 11:00:00 PM</td>\n",
        "      <td>   6</td>\n",
        "      <td>  12</td>\n",
        "      <td>  18</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 23</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.958333</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "                    timestamp   nb   sb  tot  year weekdayname  weekday  hour  \\\n",
        "18220  10/31/2014 04:00:00 AM    1    3    4    14      Friday        5     4   \n",
        "18221  10/31/2014 05:00:00 AM    7   22   29    14      Friday        5     5   \n",
        "18222  10/31/2014 06:00:00 AM   18   57   75    14      Friday        5     6   \n",
        "18223  10/31/2014 07:00:00 AM   52  128  180    14      Friday        5     7   \n",
        "18224  10/31/2014 08:00:00 AM   66  203  269    14      Friday        5     8   \n",
        "18225  10/31/2014 09:00:00 AM   42   94  136    14      Friday        5     9   \n",
        "18226  10/31/2014 10:00:00 AM   18   34   52    14      Friday        5    10   \n",
        "18227  10/31/2014 11:00:00 AM   14   22   36    14      Friday        5    11   \n",
        "18228  10/31/2014 12:00:00 PM   17   16   33    14      Friday        5    12   \n",
        "18229  10/31/2014 01:00:00 PM   16   25   41    14      Friday        5    13   \n",
        "18230  10/31/2014 02:00:00 PM   25   27   52    14      Friday        5    14   \n",
        "18231  10/31/2014 03:00:00 PM   76   44  120    14      Friday        5    15   \n",
        "18232  10/31/2014 04:00:00 PM  134   81  215    14      Friday        5    16   \n",
        "18233  10/31/2014 05:00:00 PM  228  100  328    14      Friday        5    17   \n",
        "18234  10/31/2014 06:00:00 PM  106   75  181    14      Friday        5    18   \n",
        "18235  10/31/2014 07:00:00 PM   44   21   65    14      Friday        5    19   \n",
        "18236  10/31/2014 08:00:00 PM   22    9   31    14      Friday        5    20   \n",
        "18237  10/31/2014 09:00:00 PM   20   12   32    14      Friday        5    21   \n",
        "18238  10/31/2014 10:00:00 PM   13   11   24    14      Friday        5    22   \n",
        "18239  10/31/2014 11:00:00 PM    6   12   18    14      Friday        5    23   \n",
        "\n",
        "       month  day  dayofyear  dayofyear_float  \n",
        "18220     10   31        304       304.166667  \n",
        "18221     10   31        304       304.208333  \n",
        "18222     10   31        304       304.250000  \n",
        "18223     10   31        304       304.291667  \n",
        "18224     10   31        304       304.333333  \n",
        "18225     10   31        304       304.375000  \n",
        "18226     10   31        304       304.416667  \n",
        "18227     10   31        304       304.458333  \n",
        "18228     10   31        304       304.500000  \n",
        "18229     10   31        304       304.541667  \n",
        "18230     10   31        304       304.583333  \n",
        "18231     10   31        304       304.625000  \n",
        "18232     10   31        304       304.666667  \n",
        "18233     10   31        304       304.708333  \n",
        "18234     10   31        304       304.750000  \n",
        "18235     10   31        304       304.791667  \n",
        "18236     10   31        304       304.833333  \n",
        "18237     10   31        304       304.875000  \n",
        "18238     10   31        304       304.916667  \n",
        "18239     10   31        304       304.958333  "
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "View the raw data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "fig.set_size_inches(20,5)\n",
      "ax = fig.add_subplot(111)\n",
      "df.plot(ax=ax, x='timestamp',y='tot')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10c3631d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ntBwDQdZiOvtbLsMiLhkOXWi4UMLDqvKsoKbmVzOZaSfsGvH2w2az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\nlrxqAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b72db50>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The big spikes in early 2014 were reported by SDOT as faulty sensor data, and were supposed to have been removed from the raw data, but apparently weren't. See here: http://www.seattlebikeblog.com/2014/04/29/monday-appears-to-smash-fremont-bridge-bike-counter-record/"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Clean up the data a little bit - get rid of the points that are obviously wrong"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Show the spurious data points:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df.nb>1000]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>timestamp</th>\n",
        "      <th>nb</th>\n",
        "      <th>sb</th>\n",
        "      <th>tot</th>\n",
        "      <th>year</th>\n",
        "      <th>weekdayname</th>\n",
        "      <th>weekday</th>\n",
        "      <th>hour</th>\n",
        "      <th>month</th>\n",
        "      <th>day</th>\n",
        "      <th>dayofyear</th>\n",
        "      <th>dayofyear_float</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>13641</th>\n",
        "      <td> 04/23/2014 09:00:00 AM</td>\n",
        "      <td> 1217</td>\n",
        "      <td> 178</td>\n",
        "      <td> 1395</td>\n",
        "      <td> 14</td>\n",
        "      <td> Wednesday</td>\n",
        "      <td> 3</td>\n",
        "      <td>  9</td>\n",
        "      <td> 4</td>\n",
        "      <td> 23</td>\n",
        "      <td> 113</td>\n",
        "      <td> 113.375000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13689</th>\n",
        "      <td> 04/25/2014 09:00:00 AM</td>\n",
        "      <td> 1186</td>\n",
        "      <td> 167</td>\n",
        "      <td> 1353</td>\n",
        "      <td> 14</td>\n",
        "      <td>    Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td>  9</td>\n",
        "      <td> 4</td>\n",
        "      <td> 25</td>\n",
        "      <td> 115</td>\n",
        "      <td> 115.375000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13762</th>\n",
        "      <td> 04/28/2014 10:00:00 AM</td>\n",
        "      <td> 2621</td>\n",
        "      <td>  58</td>\n",
        "      <td> 2679</td>\n",
        "      <td> 14</td>\n",
        "      <td>    Monday</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10</td>\n",
        "      <td> 4</td>\n",
        "      <td> 28</td>\n",
        "      <td> 118</td>\n",
        "      <td> 118.416667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13785</th>\n",
        "      <td> 04/29/2014 09:00:00 AM</td>\n",
        "      <td> 1795</td>\n",
        "      <td> 222</td>\n",
        "      <td> 2017</td>\n",
        "      <td> 14</td>\n",
        "      <td>   Tuesday</td>\n",
        "      <td> 2</td>\n",
        "      <td>  9</td>\n",
        "      <td> 4</td>\n",
        "      <td> 29</td>\n",
        "      <td> 119</td>\n",
        "      <td> 119.375000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "                    timestamp    nb   sb   tot  year weekdayname  weekday  \\\n",
        "13641  04/23/2014 09:00:00 AM  1217  178  1395    14   Wednesday        3   \n",
        "13689  04/25/2014 09:00:00 AM  1186  167  1353    14      Friday        5   \n",
        "13762  04/28/2014 10:00:00 AM  2621   58  2679    14      Monday        1   \n",
        "13785  04/29/2014 09:00:00 AM  1795  222  2017    14     Tuesday        2   \n",
        "\n",
        "       hour  month  day  dayofyear  dayofyear_float  \n",
        "13641     9      4   23        113       113.375000  \n",
        "13689     9      4   25        115       115.375000  \n",
        "13762    10      4   28        118       118.416667  \n",
        "13785     9      4   29        119       119.375000  "
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Replace the spurious points with the average of the points before and after each"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in df[df.nb>1000].index:\n",
      "    df.nb[i] = (df.nb[i-1]+df.nb[i+1])/2\n",
      "#recalculate the total column\n",
      "df['tot'] = df['nb']+df['sb']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the raw data again to confirm that the big spikes in data are gone"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "fig.set_size_inches(20,5)\n",
      "ax = fig.add_subplot(111)\n",
      "df.plot(ax=ax, x='timestamp',y='tot')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10b72de50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8uZwTEdmQSKZx1/rSIrq43CpXjq1de2fZvxvt5557Zitut24lb+vWrRVOQyRt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\n8LZ9dzmQ9Yd2NSKV9mZV3nmH6zy9oX+yffOl+ts/2Dg/Vop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bqKKhErSezOgh\nrWo8kLsMxKfyiu5v1q9tsCq5Uu8kYWbb/hbTVQguHvYJ65YDvRpPKAXU66ck85Luwcu8fBWi8l41\nzw0aA5sXAPjzU6vU0+v0gPIaa0yhbJ2n0XolLGs3VlgqagidffyVRTv1ZUZICrYHTPehASpg6BT7\n6QT0bO0Y6kkSb53XrSTqoLtXvU7PvLMVryza4V9lQoD6KpzdfzSGgNKu04VlFc70iqYqjAFFch1V\nD141Y6z+b0FrIG6PmPyyf/6P97Bhp8YT4XxygKqN8bCOvbWrpy+t8pXwmJdinG1fMFG0p+ZRPQXP\na74qeYZk7PSyFTcfCclrJEk470OHmK6CEX770DKt+WUqI2mACiOqBgSfqxEUbnt66OpcGJ7bw4Kd\ndlS3UA3GgNKoZuhWwMKiB3iZVCo9k28xoNwz3lWT3aD5hOgkV7Y8+DIJ8jLxVPLWVq9kfUu3cloV\nVA3lXu1981buO/gPp4y12+vxnT/Px5vLdh/8See46fxXtapajZhe8zK52mLQ6GcoS18Ii6GMkHQo\nLgrDLFEv/ugLmWVKA1TA0NlH9ta1Y8WmSMb5pOPxEPTh696X16GhxZ/4S0rN5aQxslqo+nKcyumM\n+aO9X4QkOKvu9+zXYy/dUOua5p0Ve30qnZBwYXac82OLkHqeKgaWp+duRV2z3lOjgkS0BZasqwEA\nzF4aY4Ay2DkKNRqBTE7VlIN2e6ikalJVI6sXVA07PR68+v1APQh50DV9QhIQli0JxsjOd00DVICw\nLAsvKxzL66Vr3PHcGuW0HV2JAzn3p7Ni7DUGlM6jfxSyem9dDR57c9Pgv99cthtSW9wEhQCOXrPU\nKC+VV4adQu97ZYNaxgp11B0s1K9YSLrx5AGl2EaPzJbpVierbNsXrH3nJL9RlhiGRItu74P99R1Y\ntFbvSatb9zYrp33w9Y1ay1bFt7lxjCiPlhFdo/F9XqOYv0kHKBN4qqKqqmlZ2vUL1T75wGuKOpdP\n0AMqNbniRZuvhECkGcVT/MQMRAUNUGniR5yGLXubsXDNAe35qo4lybwfYmMAhSH+i+qb6XJOiuns\n7sMTb23GHx9fkXHZXgfuffWKJ/ZpbPbbn12tlC7axdeonoCjUMcn5mxWy0uV4HdHAPpjclgWMHd5\nODyRfvfwMtQ15a4XBAkXyoGNTZ8I4la0ZY9dKigvIijyt+fWYMf+VqW0HV1qddSN1t1YMX2mIIGG\nEe0rBQmMUyYwGUdMJ14NQCrpw+DRpb9gb8nDoOebZNFa/fM0kkVyQzx6xMM37VP8xHhogAoQ3b1q\nR+d6feeZ6iKxg3oujUtRo0C/zi1mlrf3E+uFlS1U3bu99BvVFaHmth71TBXgYS3hoLFVb3wXQtJF\nVazVNJgxmiob/AFc95f5Wsv2Mr4faFRbPDEmor2eoJYqTczfQ41M1pAEuWL4CQzKW/DU2101bU/v\ngA+HpoRDYVmzrcE1je0hRkj4oJROzeL1NVkphwaoAJFoZS0IRMfM1o4evLFU7chQo0OTooLhh7Jo\nwVKMAeV1j6Ji+RoVHC/98bu3zdcu1VWyM6rQedpWp5Zuf327ao7KZQeBcNWW5DI65b4f/frZeduM\nle0JT3762ScbQ8OfnlyJAw0dgx4jtY2dWLE5GndTfwVUx2SdQ7HOeFKeMVj0bx5839NWUxWMf7OK\ntHUmDsdBSC4Q1Lm2n/i1OJKJTKMBKk18eZWeoiOqk+kkPXr/A69uwCuL1AxQfqFzAC90XKB0GjFU\ns/JDEUkWwytdvMgrP4JmBt67yYcZzn/duwSRJn+C45skJAu/JA8oVNwPe8KRY32uSfDwFoRcNZ2Z\nj1/5rA2F7pDMCFPT0IFHZ8sh8u2OZ9d4Kl83T7yld5u76jZPL+heOIq+Ht3jjI6wDLGExQNKlRx7\nHJIv5J/9KZCPTANUgFC3P3ncF5/hIBE1BOxT9s4wS7J2nLdyb8J0emNFaMzMI9+9bQGa2/VtcVuw\nej+WbvDgiqn52VW2beWiAhRRiJcUvucOXYVJjjKuslQpnVHPDxXC8knlQDB3N5tlfFlb9jZrPT12\nEIUu+eay3dhbp09XW7VVfUuoKtGW6e1LvXDl1eNL5ZXnYwyoIt1BKAHMijnxkZAwEfCR3Rd82fGT\noUCjASpLtLQrTKZ9KjvTQS+dThbEnVHzVu6LSxf1gNJZuqXUXn61z55Im9b87nlxndb8dBOWKARe\nahn0eS8hYSbwnpWKLFi9zz2RRzyNS8rB3MNPKuW9sKAAy2RkyLU/PPIBFqzWe/KgFwJ/SpfTKR6Z\npeskVw/e7AbHV1MeULtq9eqFgG3orFWMA0dIoMhDHTuI8woaoLLA7c+sxrodjfoytICdB9ROoLGT\nZ7oFb+h/w0r8Bxj9t86VStNtZHRvs4GiTbe3Mh4qukPh277rhbWZ1IaQvEV5EqgWhC6jumTCis11\nxsoGgMY2tYMFTDWR1nJj+sIwRT5JP9m0u0ljBbxhcq6h9Nk4OukHmyIuKdWob+nCBzISeH0g6PUj\nhOQmvo0JGQi1Yo3VIElYuUVNUVTtIG99sAervbhFG/CA8gsVK65qdf2KAWWytfxwtVblrWV7jJWd\nSyzJ0gkU2SRAIoTkOTxiPDleFqvqm1Vj1elr787uPl9iErkxdDumuTFWteSiomCvLUc/QTd9xcuq\n/Z3Pr1FqH5OLdLkme4oKg93PCCEOvmzBYxByMxhcYvJkfELm6t9AgDyg1FbXEtMdFyg76lav4gHV\n3tWLlQorzqoKvF9bx1QD7BJCSD6i1QEqo5oEDy9j/Hvrsm8ov+HuRfjxXYtgKewwU53se921NdwB\nKnFPMakvhUUN0L24qZJbELeihJVcM6gRkqv4MSYon/qeBBqg0sSPMSyoovygkmC+hko1SPBFPDZ7\nE/bFBeaMfpAqg+hfn16N7ftb1IoOYPwrop93Vu5DXbN7wHAgCF+OWfL9+UlwUN++nofC1IcJpY4c\nLcvCPS+uRXuX7f3UP5DdGEfpBHA1GZ8w6PJWNayDH4FzTepIuRJ/LkqQdkcQokwedtuNu8xtCU8G\nt+AFCCugo1N0jKlvUYv54CsKTZQoyZzlw7eHFRQU4JHZEnOX701wx1C27G1WqJz5LXgkuzzzzlbT\nVQgH/ChIQNB5Qliuzb/8eBwdbdTd24+lG2oH/63itazs6aZgkEiVJoiLPi0aT8P1B8v5/+x/QO/H\n9KNsk2sGm4BOWQgh2cDKTKbRAypA9AdUmvs9aK7YrB6IUs0DSi2vwgIoGZ+8oVi4BWzcqR6YXlXH\n9ZJnLmD6qHTVAPbB/LKzR1hOKyQkSj722bDMj1XErur7W7+j0dUrLnaYCaLBKZ6XFu4wXYWURPtZ\nX3/2O9wqjyEsdJJ7Bqjceh6SH7DXBgMaoAKEztPYdJKOs7uXcemOZ9d4yNc9Y9VB0Q/3bgtQengL\nwM1PrPCWrwJaT1sMAaYVINXit+xR86DLWYIp2ghJitq3zY7tRvzW93SIfxdKi3UeXs2js6W3Cqng\nQ9cIuvGrvatXKZ0FW9/t7UutXZp+3EPGl2vNL9fsNUHdtUEI8R8Lmc3BaIBKG/1Do/HJdJLrT7+9\nxXjd/MAP7xnVLXi5thKWr+Tid+EH1FNJ2Oju6VdIZXqKrJegjkvxclbnFjwA2LrPPb6jV0YU61ev\nA/p6BvnebQvUElpwNT7lIkF/f16h/kNI/mIBmPNB+qef0wAVIIK6Be+DTRFs3h2MAGYq453O2A9e\n8UuBz61pTu5A/UuNoE5sCUnGyi3up57y1NHssDUuBqOKrqRzC+Xc5XsHY4epek77YYAKqpe8Vxau\n3W+6CkqUlRRpzS/XtvV2dPeZrgIh3smtz9Aos5buTvteGqAy4Ks3va01P5MGqLkr9qY0cnT3qqwG\nH8TrQGtigurHFrxHZm9SMkp0dHHgJvkDV0pJLpJr9qegfqaL19cM+beKPNH5LFv3teCX9y0BoL4Y\n5IeHdX9QX5BHHp29CV0edUoTTDtsjNb8cuT1DfL8/G2mq0CIZwZogdJDhgKNBqgAYXJ165FZMuUn\nGZSFNxXDlupk14/V62Uba/GjOxe6pnt9yS5P+Qak+QlJi1xZuSckllzzgArqV7oh7nCN/v7gbN9K\nFuPKjwWuXJKjQXqHydC9MJprCzGRpi7TVSDEM/oPn8pPfvr3xRndTwNUmjS1dWvPM6hb8IDgbKHx\nUg3LstDQ0pW07kEP6ElIrhBg0UZI2pg+hVM3QRnn42lu6xnyb6UteFl6lrrmxJPw5ZvUT/dVJcg6\noldUjWlrtpk7tU63wSignxchhGSdYtMVCCvxK3I6MK1cpFKl/R44dWYfreu8lfvw8CyJa64QCdOF\nYfJQAKC5rdv1mGhiBpU4MSS3Vu4JiZJrHlBhQUXu+qazKL5yr2ELVOgJwbY1VfoUx4S/PLXK55ok\nR/uwRQsUIYQAoAdUoDDtkpxyC57XkdgCWjt63NN5yVJx8I5u01vixI1YGhc/IkoI7E9AAfCTuxeZ\nrgUhGbF6q7lVbEL8IgyLGF4Iy/T4A+nuXXTb06vQmUNBkl99bwea2vTqVCbpU9B3TW9Z071wwnUY\nQgixoQdUgAiyl4DXmi3fHMFbHo5nVAoqCsUFJGvIf5IamirKRqhUzSgFKEBff3D7BSEqmJ5IEOIH\nhTm2hLdlT7N7opDQ0zeAt5enf0R0MkyZHJ+dl1sBn5duSLwwGMuumrYs1CQ5unXyoG5xJYSQbJNj\n6lO4eXHhDqPlp96C523gbGjxFiNLaaC31IKQD1bVpc5hUAZybIGdEEJyhlzzgMo12v04bZbvPDWK\nzfPKop2uaV5b7J7GTxgDihBC/IEGqABh2l081djo98CpYoCybAuUK39/aR1aOnpiPKCBmKq/AAAZ\nYElEQVQSa0RhUAb8OEmHEEJI5tAAFWze8HjarAp84y6EQK9SRbcBqiOHtoQSQkgm0ABFlFDxPMoE\nJQcoNfsT6lu6cMsTK1zT+v1MOuD8hhBCCCFhIAye5aoEOSwGIYSEGRqgiBo+j8NTqkdprceeSPtg\nPItkRhwVF3DT9PaZDUxPCCEkMRaAlZt5Ema+8OSczVwUcsH0ac46yaFHIYSQQEEDFBnk8Tc3Jf0t\nCItaj8ySaXktUV8khBCiG8uycPuzq01Xg2SJ2e/vRlkJz+5JRVtnr+kqaGPZxlrTVSCEkJyEBigy\nSKqVK7+3q/UPuHv6LFi9PxCGMEIIIaSmsdN0FUiWoQdUaiaPLzddBUIIIQGHBiiihN+Gn3dW7lNK\nd9Njyz3n3dtPqxUhhBC9LFnvfpQ8yS0enZ3cU5wQQggh7tAARZQI875+xlEihBBCCPEXBu4mhBDi\nBg1QRIkHX99ougpp09vXb7oKhBBCCCE5TT89zgkhhLhAAxTJeaZMVDxhjxBCCCGEpEWYveUJIYRk\nBxqgSM5TVMhuTgghhBDiJzRAEUIIcYMzc5Lz9CmcsEcIIYQQQtJn/iq1A2UIIYTkLzRAkZynjzEJ\nCCGEEEIIIYQQo9AARXKeprZu01UghBBCCCHEOKcfNxEVZcWmq0EIyVNyRvoIIQoB3AXgZADdAK6V\nUm41WysSBLbsaTZdBUJIljisugJ7I+2mq0EIIYQEkv4BCwUFBaarQQjJYS798OFJf8slD6hPAyiR\nUp4D4KcAbjVcH0IIIVnmyjOOQElxsIe2U44Zj0PGl5uuBiGEkDykr38AhbQ/EUJ8pK8/eQzmYGvp\n3jgXwBsAIKVcAuA0s9UhhBCSbQoLCnDxzMOU059/8iGuaUaNHJFJlTBxzMgh/y4qKsTUyZVK937m\nomNw5/UXuKa7xMMzm+LimYfhC5cdqz1fFWPeVWcdgdKSIu1lE0JI2NhXl7tewodXj1JKFz8up2J8\nVVladRlXVTr4t2q9vBL7HJXlmekqyZg8jgtmAHDxjODrWUEi1amoObMFD0AVgJaYf/cLIQqllCmP\nQDv5mPE4YepYVI0qwYYdjVi+KYL2rj589uJj0Ndvob2zF/XNXVi/swGd3f3476+cjm37W3CgvgPN\n7T0oAFAyohBdPf04TUzE+NFlqCwfgRvufg8TRpdhysRROOHIcXh/Yy027W4CAIytLMWnzz8Ky2UE\nq7bWD9Zl+pQxg2lSMbayFBPHjITc3YRph43G5PHl+Pg5R+IX9y4ZtDaefeJknHDkWGze05z1U0lK\nigvR06f35LkZx07Ais11rumOOqQSrR29qGvuSplubGUpGluzHxuquKgwpUV48rhy9PUPuNbfjeox\nZYg0pZ9HcVEhRpYWobWj1/O9E0aXobysGLtq2tIuPxXFRQWYOb0aFWUjsKu2FVv3tqRMf8j4csyc\nXo1Nu5uwOcF2zOOOGIONu1J/dycdNQ5769qV+8y0w0Zjy173rZ8zp1dj+aYIAODD06vxgfO3CvFb\nzX7/9TPxz9c2upZ7/NSx+OjZU1Hb0IFHZm8CAJw6bQJOnjYe67Y34AN5sA6fvegYPP3OVowaOQKf\nv3QaLAuYOrkSlgX89qFlSfvypTMPx4jiQlRVlOC1xTvR1jm0H511wiQsXl+DD4tqXHOFwA9ufxeT\nxpXjkHHlWLmlDud+aDLGV5XhpYU7MHVyJY6fOhZ1zV248owjUFxUgJcX7kBv/wAmjS3HnkgbNuxs\nHMz70AkVOPPESfjQ0eNxy5MrB6+LKWMg4+Trrdedi7GVpViwen/KNrv9++ejsbUbb32wG68v3jXk\nt6s/Mh0dXb2INHXh4+ceiY07G7HjQCumVFcMtu9N3zobX73pbQD2e7768ukoKS7CMYfa/aS2sRPb\n9x/sx+eeNBndfQM470OTcfIxEwAA0w4fjS17mvG1jx2PxtZu7KtrR1tnLy489TAUFRXg1GkT8Pby\nvcPqfuYJkzDj2AlYJiNYtrEWADCytAgzjq2GZVkYWVqMbftasONAK04T1YN9clxVGWa/vztpm3z5\nquNw6rQJKC4qRHtXL/ZE2nDHs2sGfz/uiDGYMb0am3Y14cJTD8XUyZWoLC8BYL//h2dJjK8qQ0VZ\nMSorStDS3oP12xuwaU8zPn/JNJSMKMKabfWYcWw1npizCZ3d/ThkfDk+c9ExmDqpEj++axEuOvVQ\nfPqCo1FVXoK/PbcGyzdF8LGzp6KprRtrtzegua0HgN1nP3vRNFx++hFYur4GT8zZnPCZbr3uXPzf\n25tx5vGT8PyC7dgTSSzDbv7W2Zj1/m7M+WBP0vYBgKqKEpx01Dh88bLp+O5t81OmBYAzjp+IspLi\nwTH7iImjMHVy5bD+ecox43HWiZNx3BFjcP3fFgIApkwchd21w+s7srQIv/3amfjxXYsSlnn56VMw\naVw5lstarNvRmDCNTmYcOwGTx5Uj0tSJstJirHB0rhnHTsCqLfUYsA4qrFd/ZDoee3NT0ryOmDgK\nu2Ke+dRpEzBzejUeeG1DyjpUlY/AsYePGSJvD5tQgYljRyrpGUGhoACwLFu2iSPG4IQjx+Gmx5YP\nSTNhdBkOrx6Frfua0dndN3goy9RJlbBgKY3TquOZG5nqJdkg1RbuosKCwQnVf1wp8NAbMmk+pSVF\nOE1U42NnH4l5K/di1tLksrS4qBBTJlagp3cAe10MQ4dXj8J1/3oSfvb3xQpPE3tfBfoHLOyv7wBg\nv/9xVWV4c9nBeqXSs3/95dMxcexIrNvegLteWDvs95OOHjc4B5K7mvDKoh3D0oyrKsW0w0ajqLAA\nZxw/CR86Zjzue2U9qspLsDfSNkT+/PbaM/HL+5YkrEthQcEQORHL0YdW4Rf/fhrWbKtHbWMn3lq2\nGzWNnYO///FbZ6OtsxcNLV34sJg4OG58eHo1Tpk2AX0DA1i9pR4VZcU4bupYTJ8yBtUxBp5n523F\nu2v2D44tR0wche/+24cAC7jhnvcAACceNQ5nHDcRZaXFOP24iXht8U4UFxbg8jOOQEdXL4ACdPX0\n4Zf3L0F/v4UTjxqHmdOrMXHsSNQ1d6F0RBGenbcV++s7cPePLsSjsyQWrj0AwNatPnX+Ufjnaxtw\n+elTcMj4ClRVlODGexbBsoCbv30ONuxsxF3Pr0F7Vx8unXk4qseOxPb9LViyvgZiyhgcfWgVXl+y\na7Cu67Y3ALDHq+lTxmDznqbB57vm8umYKSZidEUJWjt6sGR9DR5/azNOPHIsTjhyHMpKi7FjfwtK\nS4pQ39yFmdOrUVVRgq6efqzYHEFVeQkKCoBJ48px6Hi7D9Y0dOC4qWPx2uKdeHf1/mHv88tXHYdV\nW+rwmYuOAQDI3U0YWVKMqvIR6Ozpx9RJlbjnxbXYuq8FH55ejdGjSlBUWIiPnHY4Wjt7caChA+9v\nqMWVZx6BggLg9cW7cPpxE9HQ2oVn521L2G8Ae7w59vDRqG3sHNbHTz9uIq65QmDUyBGoHjMSFiyU\nlxYPkwHXXCEAAI/N3oRzTpqMT59/FJ6auwVLN9QOK+/ajx+P+16xx6ovXnYsHn/roF5y8czDUDai\nCHvr2rF6az2KiwrwqfOOGqz/jGMn4DMXHYP/unfoN3LRjMNQVT4CLy3cAQD4wmXHwhqwEGnuwpwP\n9gwaQQ80tOPT5x+N5Zsi2LYv9Rwqli9cdizGVZZi1dZ6vOuiNwPABaccmvS3AivJRxw2hBC3Algs\npXza+fduKeUUw9UihBBCCCGEEEIIyXtyaQveQgAfBQAhxFkAVputDiGEEEIIIYQQQggBcmsL3vMA\nPiKEWOj8+ysmK0MIIYQQQgghhBBCbHJmCx4hhBBCCCGEEEIICSa5tAWPEEIIIYQQQgghhAQQGqAI\nIYQQQgghhBBCiK/QAEUIIYQQQgghhBBCfIUGKEIIIYQQQgghhBDiK0qn4AkhzgRwk5TyYuff0wA8\nCGAAwFoA10kpLee3lwB8CsDdAE4G0A3gWinlViHEqQBuB9DvXP93KWVtzH3/CuCfAKYCKAXwOynl\nyy7lVQNYCOAkKWWPEGI0gEcBVAIoAfBDKeXiBM/k+T4hxNcBfANAn1O3V4UQI537qgG0AvgPKWWd\njvti2qVASvmJmGs7AGyQUl4Vc+2HAG6RUtKoSEgMQohCAHchTh7F/P5FAN+VUp4Tcy2VPDoOwH0A\nLACbnPwshfu8yLEKAI8DGAOgB7Z82Bf3XInkSjmAJ2Lu+5KUskbhPsoxQkJAvD7mXPsXAJ+RUl4d\nc+02AH8F8GkAn3Muvyal/J9U+o7LfUm/dyFEEYD/A3CvlHKWc+33AC6FLSt/KqWcF/csZwG4DbZM\nmS2l/J+Y38oBLAJwYzQ/t/uEEL8G8FHn+g+klO9ruu8dACMBtDuX+gD8B2z5vg3Az6SUf4xJ/xKA\nyth3RAhJro8JISYCuBe27lIAe364w7nnJSnlJ52/4+ejpwC4B/Y3uRnAt6SUPdH7oEEfi6n7cQAW\nA5gYez3md84rCVHEtVMJIW6ALRRKYy7/GcDPpZQXwBYUn3LSHgFgF2zFpdSZ0P0UwK3OfbfBnuhd\nDOA5ADfG3fclABEn3ysB/M2lvCsAzAYwMaZu1wN4U0p5EYAvA7gzwTN5vk8IMRnA9wCcA+AKAP8r\nhCgB8G0Aq5y6PQzgFzrui2mXCgBVQoij4n4+VAgxPubfHwXQEJ8HIQSfBlCSQB5BCDEDwFdjEyvI\no/+GPeCfD1sufkzxPi9y7FoA70spL4StUNwQV8dkcuXfYSsRF8KeDP5E8T7KMUICTiJ9TAjxVwB/\ngC1TYjna+e8XAZwtpTwLwOVCiA8htb6T6r6E37sQ4hgA8wGcBtvYFJWtZzj3fx62USueuwF8QUp5\nHoAznUXKKHfCnhwmOqp52H1CiJkALpBSnumUN0z3y+A+C8A1UspLpJSXwNZff+xc3wp7kgvnuccD\nmJak3oTkO8n0sZsBPOLoLr8CcBIwRK9KNh+9D8D1jj62F8B34u7ToY9BCFHl1LUr0UNxXkmIN1Ss\nmltgD66xys1MKeV85+/XAVzm/P1xAK8COM+5DinlEthKCQB8Xkq52vl7BIDOmPteAfA0bMETrVuv\nS3n9sFfXGmPq9hcA/0hQRizK9wkhrhdCfALA6QAWSil7pZQtTrucDOBcAG84970RrVu698XxVQAv\nwJ6AfifmugW7rT7rlHW8k29vfAaEkIPfWqw8cgba3wP4AYbKNzd51AlgvBCiAPbKVo/ifcpyTEoZ\nnVQC9spdrKwCgDOQWK50AogqEKOjdaMcIyQnSKSPLYQ98Ri8JoQ4AcAGALsBXBld2cdB3SaZvpPq\nvi4k/94rAHwNwNxoPaSUK2BP+ADgSMTJMGdCVyql3O5cmoWDcufHAN4FsCom/cVCiF8KISqT3Hcu\n7AkgpJS7ARQLISakeV/sJCxKbJuPh+1hAAB1AGod7wgA+H+w5Vq8QZAQkkQfg21MmSKEeBPA1QDe\ndq5H9Sogsfw7PMaraBGAC+Puy1gfc3S9vwP4GRLPKRPeB84rCUmKqwFKSvkcbBe/WGI//jbYEx0A\nuAi20KgC0BKTpl8IUSilPAAAQohzAFwH++McvE9K2S6lbHMUhWdw0HqbsDwp5VtSyiHWWSlls5Sy\ny7EQPwJbYMQ/k/J9Usq/SClfdp6pOeaWVqcesc8avZb2fVEcN9UvwBYS/wfgc0KIWKv/k7AVHcBe\nqXws/jkJIQASy6MSAPcD+CFsmRLLRRguj57GQXl0B+zV/PWwV7vmpbgvLTnmXB8QQsyBLStfiPu5\nEsPlShWA5wGcJ4RYB+BHAB5w8qIcIyTkJNLHpJRPJUj6MQAvSyn7pJT1QogCIcQtAJZLKbek0JM+\nnuK+zRgqB2LlxGop5cYE9e0X9ja8l2Fvg4klXi63AhgthLgUwDQp5f2wZWbUoDVXSvlbp8xh9yGx\njKpK874hcszhYSHEXEcmHwrgTzgo05+A7T0FAJ/EcHlNCLFJpI8VwTZSN0gpPwLbc+lG5/eL4Bij\nksxHtwkhLnD+/gSA8tj7NOljvwbwaowDxTDjMueVhHgj3X2dAzF/VwJocvaeDkgpu2F/AJWx5Ugp\nBwBACPE52G7QH3UUnHLnvuhK/RTYwuYhKeWTycpLVTnHVfwt2PvyF6g+lMt98c8UrUfs9UR1S/e+\nK5zrj8MWFAWwVwWi7AZQIIQ4HMC5Xp6TkDxjmDwCcArsbRJ3w548nCCE+HOMHIuXRw/HyKNHAZwv\npTwetlJxa4r70pZjACClvBTABQCedXmmqEHqFgB/llKeCFuGqNxHOUZIbnEObM8oCCHKYE8kKhCz\n4p1E3znb5b4W2JMcQF2G/Rdsg80NcVs+ksmUrwI4SQgxF7b8uFnYcV6S3VeF4bIoUf3SvS/KNVLK\ni6WUl0opr5NStsf89iKATwohpgI4AKAjwf2EkMTzw34A9QBecq69AuC0eL0qCV8B8DMhxFsAagDU\n+aCPXQ3ga45Mmgzbe1IJzisJSUy6BqgVQoiom+NVsPf+fwT2RwbYCsxHgcGgj6udv78EezX/IukE\nl4PtIviW8/sk2K7QN0gpH3QpLyGOC/nTsPf5exESbvctBXC+EKJU2IHljocduG7wWZPULd37rgXw\nNSnlVdIOCvc52G0Xy5Ow9zEvUn1OQvKQYfJISvm+lPIkacej+zyA9VLKHyJGjqWQR+U4uP1iP4Cx\nsOXYHJf7vMixnwkhrnH+2Y7hq37J5EoFDq6ARXBwsuh2H+UYITmAEGIsgBYppeVsHXkRwEop5bfl\nwSC7w/Qdlfvg/r3H1uMSIUQ03ko37K0cg5M+Z+tIjxDiaKe8KwDMl1JeLaU8z5HNbwD4iZRyVYr7\nLnfqsRDAFY7X1hGwJ7YNmd4XQ9ItdY4xSsKOY/NYqrSE5DkJ54ewt9x+zPn7Atj6xaBelYKPA7ha\nSnkZ7K2xs6GmxynrY1LKYx3j88WwDcyXuz8m55WEpELpFDyH2ICKPwJwr7ONZT3sVfa7APzG+f15\nAB8RQix0/v0Vx8XyrwB2AnhOCAHYW1cmx9z3c9gug78SQkT37F6VoLxnUtTtD7BPG7jdKaNJSvkv\nCs+U8D4hxPUAtkj71ITbASyAbbj7uZSyWwhxN4CHhBALYCtZXwTsvbrp3OfcOwn2Ht/PRq9JKRc5\nwuZsp96W0w63w/bmiH8eQojNMHkU93sBDn47H0VyeWQ5v18L4BkhRBfsb/cbsF2rdcqx+2HLh68C\nKIqvs5SyJolc+blTxnWw5fu1QPryiHKMkEAS/41YMdeuhBODE3bA3wsAjBBCRE83+hns4L+x+k4z\n7BXxVPf9FLbHaMLvPUHd3gHwGSHEu7Bl2N+klDvj0n4LtsGmCMAsGXf6XCxCiIsBnOdsp0t4n1Ov\n92DLqO9kcl+K50p2/THYp3F9HoBIkZ6QfCaZPvYjAPcJIb4N23PnagA34aBeFUvst7UJwFtCiG7Y\nhplHMHQ+qkMfU7me6HfOKwlJQoFlsW8RQgghhBBCCCGEEP9IdwseIYQQQgghhBBCCCFK0ABFCCGE\nEEIIIYQQQnyFBihCCCGEEEIIIYQQ4is0QBFCCCGEEEIIIYQQX6EBihBCCCGEEEIIIYT4Cg1QhBBC\nCCGEEEIIIcRXaIAihBBCCEkTIcRoIcTzQohDhBCv+ljOb4QQ5/mVPyGEEEKI3xSbrgAhhBBCSIgZ\nC+BUKeV+AB/zsZwLALztY/6EEEIIIb5SYFmW6ToQQgghhIQSIcRLAK4A8CqAGVLKo4QQDwJoA3Ae\ngDEAfgDgGgCnAHhBSvljIUQRgD8BuBBAEYAHpZS3CSEOB/AYgHIAAwD+E4AAcCeA/QD+FcB4AL9z\n0owFcIOU8hnFcr8M4JMAJgKYBOAlKeWPfGsgQgghhBAHbsEjhBBCCEmf7wHYB+D6uOuHSClPBfAr\nAP8E8E0ApwL4uhCiCsDXAVhSyg8DOBPAp5wtdl8F8LKU8nQANwA4V0r5MIBlAK6VUq4F8F0AX3Pu\nvdYpQ7VcADgdwKcAnAjgLCHEv+hrDkIIIYSQxHALHiGEEEJI+hQkuGYBeN35exeAtVLKOgAQQjTA\n9lq6DMApQohLnHQVAE4C8BaA54QQM2B7Vd2ZoKwvAfiEEOL/ATjLuVe1XAvAM1LKeuf6kwAuAfB8\nWk9PCCGEEKIIPaAIIYQQQvTTG/N3X4LfCwH8REo5Q0o5A8C5sLfhLQJwAoBZAD4H4OWYe6JxE94F\ncBpsr6jfY6g+51YuAPTH/F0Udw8hhBBCiC/QAEUIIYQQkj59sD3KYz2hEnlFxfM2gG8IIYqFEJUA\n5gM4UwjxvwCucbbdfQ/AjJhyRgghxgE4FsCvpZRvwI4/VeSh3ALY3lOjhBBlAD6Pg15ThBBCCCG+\nQQMUIYQQQkj6HIC93e0BHPRQspL8jZhr9wDYDGAFgKUAHpBSzoO95e7fhBArADwH4NvOPW849wgA\n9wFYJ4RYCDvoeKkQotylXCvmfxHYHlYrYQchfzOD5yeEEEIIUYKn4BFCCCGE5AnOKXhnSim/7ZaW\nEEIIIUQn9IAihBBCCMkfEnlkEUIIIYT4Dj2gCCGEEEIIIYQQQoiv0AOKEEIIIYQQQgghhPgKDVCE\nEEIIIYQQQgghxFdogCKEEEIIIYQQQgghvkIDFCGEEEIIIYQQQgjxFRqgCCGEEEIIIYQQQoiv/H96\nAFrPRtsilgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c949fd0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Make some plots to show crossings by day of week or month"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Make a new dataframe with average crossings for each day of the week"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.rcParams['figure.figsize'] = (10.0, 5)\n",
      "daydf = pd.DataFrame(columns=('day', 'year', 'm_tot','standev_tot','m_nb','standev_nb','m_sb','standev_sb'))\n",
      "week = ['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday']\n",
      "count = 0\n",
      "for day in np.sort(df.weekday.unique()):\n",
      "    for year in df.year.unique():\n",
      "        tot = []\n",
      "        nb = []\n",
      "        sb = []\n",
      "        for d in df[(df.year == year) & (df.weekday == day)].dayofyear.unique():\n",
      "            tot.append(int(np.sum(df[(df.year == year) & (df.dayofyear == d)].tot)))\n",
      "            nb.append(int(np.sum(df[(df.year == year) & (df.dayofyear == d)].nb)))\n",
      "            sb.append(int(np.sum(df[(df.year == year) & (df.dayofyear == d)].sb)))\n",
      "        daydf.loc[count] = [week[day],year,np.mean(tot),np.std(tot),np.mean(nb),np.std(nb),np.mean(sb),np.std(sb)]\n",
      "        count += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot average crossings by day of week for each year:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "fig.set_size_inches(10,5)\n",
      "ax = fig.add_subplot(111)\n",
      "bar_width = 0.85/len(df.year.unique())\n",
      "for i in range(len(df.year.unique())):\n",
      "    means = np.array(daydf[daydf.year == df.year.unique()[i]].m_tot)\n",
      "    standev = np.array(daydf[daydf.year == df.year.unique()[i]].standev_tot)\n",
      "    plt.bar(np.sort(df.weekday.unique())+i*bar_width,means,width=bar_width,yerr=standev,color=colors[i],error_kw=dict(ecolor='gray'))\n",
      "ax.set_xticklabels(['Sun','Mon','Tue','Wed','Thu','Fri','Sat'])\n",
      "ax.set_xticks(np.sort(df.weekday.unique())+0.85/2)\n",
      "ax.legend(['2012','2013','2014'],loc=2)\n",
      "ax.set_xlabel('Day of Week',fontsize =14)\n",
      "ax.set_ylabel('Count',fontsize=14)\n",
      "ax.set_title('Total crossings by day of week',fontsize=16)\n",
      "\n",
      "fig = plt.figure()\n",
      "fig.set_size_inches(10,5)\n",
      "ax = fig.add_subplot(111)\n",
      "bar_width = 0.85/len(df.year.unique())\n",
      "for i in range(len(df.year.unique())):\n",
      "    means_nb = np.array(daydf[daydf.year == df.year.unique()[i]].m_nb)\n",
      "    standev_nb = np.array(daydf[daydf.year == df.year.unique()[i]].standev_nb)\n",
      "    means_sb = np.array(daydf[daydf.year == df.year.unique()[i]].m_sb)\n",
      "    standev_sb = np.array(daydf[daydf.year == df.year.unique()[i]].standev_sb)\n",
      "    plt.bar(np.sort(df.weekday.unique())+i*bar_width,means_sb,width=bar_width,color=tuple(x/2 for x in colors[i]))\n",
      "    plt.bar(np.sort(df.weekday.unique())+i*bar_width,means_nb,bottom=means_sb,width=bar_width,color=colors[i])\n",
      "ax.set_xticklabels(['Sun','Mon','Tue','Wed','Thu','Fri','Sat'])\n",
      "ax.set_xticks(np.sort(df.weekday.unique())+0.85/2)\n",
      "ax.legend(['sb 2012','nb 2012','sb 2013','nb 2013','sb 2014','nb 2014'],loc=2)\n",
      "ax.set_xlabel('Day of Week',fontsize =14)\n",
      "ax.set_ylabel('Count',fontsize=14)\n",
      "ax.set_title('Total crossings by day of week, separated by northbound/southbound',fontsize=16)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.text.Text at 0x10cd51fd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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H0lDVX3t7B0C/z239NU9d/nY7zz+c6m8gdQej47NvoIbr925vSWazW9iuB66K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\nx38J3J2ZTizpRUScB7wKeB4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       "text": [
        "<matplotlib.figure.Figure at 0x10c841f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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R9O79/SYft+wd8vM3cMXsY2nbKbYF79sLKph58YoG954WEZHdUsKN0vdGmzdv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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c575690>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot by month"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.rcParams['figure.figsize'] = (10.0, 5)\n",
      "monthdf = pd.DataFrame(columns=('month', 'year', \n",
      "                                'm_tot','standev_tot',\n",
      "                                'm_nb','standev_nb',\n",
      "                                'm_sb','standev_sb',\n",
      "                                'sum_tot','sum_nb','sum_sb'))\n",
      "months = range(1,13)\n",
      "count = 0\n",
      "for month in months:\n",
      "    for year in df.year.unique():\n",
      "        tot = []\n",
      "        nb = []\n",
      "        sb = []\n",
      "        for d in df[(df.year == year) & (df.month == month)].dayofyear.unique():\n",
      "            tot.append(int(np.sum(df[(df.year == year) & (df.dayofyear == d)].tot)))\n",
      "            nb.append(int(np.sum(df[(df.year == year) & (df.dayofyear == d)].nb)))\n",
      "            sb.append(int(np.sum(df[(df.year == year) & (df.dayofyear == d)].sb)))\n",
      "        monthdf.loc[count] = [month,year,np.mean(tot),np.std(tot),np.mean(nb),np.std(nb),np.mean(sb),np.std(sb),\n",
      "                              np.sum(tot),np.sum(nb),np.sum(sb)]\n",
      "        count += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "fig.set_size_inches(20,8)\n",
      "ax = fig.add_subplot(111)\n",
      "bar_width = 0.85/len(df.year.unique())\n",
      "for i in range(len(df.year.unique())):\n",
      "    plt.bar(np.sort(df.month.unique())+i*bar_width,\n",
      "            monthdf[monthdf.year == df.year.unique()[i]].sum_tot,\n",
      "            width=bar_width,color=colors[i])\n",
      "month_names = [calendar.month_name[i] for i in range(1,13)]\n",
      "ax.set_xticklabels(range(1,13))\n",
      "ax.set_xticklabels(month_names,rotation=30)\n",
      "ax.set_xticks(np.sort(df.month.unique())+0.85/2)\n",
      "ax.legend(['2012','2013','2014'],loc=2,fontsize=14)\n",
      "ax.set_xlabel('Month',fontsize =14)\n",
      "ax.set_ylabel('Monthly Crossings',fontsize=14)\n",
      "ax.set_title('Total Crossings by Month',fontsize=16)\n",
      "ax.set_xlim([1,13])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "(1, 13)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9vwdgxoxpzJgxbXl7VVUV99zzR84++3wmT57IhAn7semmm3LWWVMYPnz4e65T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\nM8Bv86fsXQTMAvqQbXr+dr7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6ZUBKkiRJkiRJnTIgJUmSJEmSpE4ZkJIkSZIkSVKnDEhJ\nkiRJkiSpU5P73QBpPJg9ey63335bJ6+12mqrM2XKlE5eS5IkSZKkkWBAShoG99w/l3+/+C0sO3Vk\nkw4fnDmHk/aezpprrj2iryNJkiRJ0kgyICUNk2WnTmS5VSb1uxmSJEmSJI161pCSJEmSJElSpwxI\nSZIkSZIkqVMGpCRJkiRJktQpA1KSJEmSJEnqlEXNOzRr1ixmzLhtoR93113LM3Pm/Y/5/rffvvCv\nIUmSJEmS1BUDUh2aMeM2pn1qK6auNLKJabf+7hHYyF+tJEmSJEkanYxadGzqShNZZeVJI/oaM++Z\nwx9G9BUkSZIkSZIWnTWkJEmSJEmS1CkzpCRJkiRJkoZJV/WjAVZbbXWmTJmy0K81GhiQkiRJkiRJ\nGiZd1Y+eec8c9j50OmuuufaIvs5IMSAlSZIkSZI0jLqoHz3WWUNKkiRJkiRJnTIgJUmSJEmSpE4Z\nkJIkSZIkSVKnDEhJkiRJkiSpUwakJEmSJEmS1CkDUpIkSZIkSerU5H43ACAingT8BNgWmAOc2v7/\nOXBgZs6NiH2AdwKPAEdn5g8iYlngdGAV4D7gbZl5Z0RsBhzf7ntxZh7Z9c8kSZIkSZKkofU9Qyoi\nlgK+ADwATAA+AxyWmS9ut3eOiFWBdwObAy8HPhERU4D9gZ+1+54GfLg97SnAbpm5JbBpRGzU5c8k\nSZIkSZKk+et7QAr4FHAy8Md2e+PMvKJ9fQGwHbAJcGVmPpyZ9wK/BjYAtgAubPe9ENguIlYApmTm\nre34Re05JEmSJEmSNAr0dcleRLwduCMzL46ID1IZURN67nIfsBKwInDPfI7fu4BjA8fXWFA7Vl75\ncUyePGnRf5DH6K67lh/x1+ja1KnLs8oqK/S7GfNln3fPPu+efd49+7x79nn37PPu2efds8/LeOyH\nLtnn84z2v/mudPn7Hct93u8aUnsCcyNiO2Aj4GtUPagBKwJ3UwGm3h5eYYjjQx3rfY75uuuuvy36\nT7AQZs68v5PX6dLMmfdzxx339bsZ82Wfd88+75593j37vHv2effs8+7Z592zz+c9RovOPp9ntP/N\nd6XL3+9o7/MFBcv6umQvM7fOzG0y8yXADcAewIURsXW7y47AFcC1wFYRsXRErASsSxU8vxLYqfe+\nmXkfMCsi1oiICcD27TkkSZIkSZI0CvQ7Q2qwucAhwJda0fL/A77Vdtk7AZhOBdEOy8yHIuJk4GsR\nMR14CNi9Pc9+wNeBScBFmXld1z+IJEmSJEmShjZqAlItS2rANkN8fxowbdCxB4E3DnHfa4AXDXMT\nJUmSJEmSNAxGwy57kiRJkiRJWoIYkJIkSZIkSVKnDEhJkiRJkiSpUwakJEmSJEmS1CkDUpIkSZIk\nSeqUASlJkiRJkiR1anK/GyBJkiRJkjSSZs2axYwZt3XyWrff3s3rjHUGpCRJkiRJ0rg2Y8ZtTNtx\nK6ZOGvmFYrfOeoRnvcFwy6OxhyRJkiRJ0rg3ddJEVpk8acRfZ+bsOSP+GuOBNaQkSZIkSZLUKQNS\nkiRJkiRJ6pQBKUmSJEmSJHXKgJQkSZIkSZI6ZUBKkiRJkiRJnTIgJUmSJEmSpE4ZkJIkSZIkSVKn\nDEhJkiRJkiSpUwakJEmSJEmS1CkDUpIkSZIkSeqUASlJkiRJkiR1yoCUJEmSJEmSOmVASpIkSZIk\nSZ0yICVJkiRJkqROGZCSJEmSJElSpwxISZIkSZIkqVMGpCRJkiRJktQpA1KSJEmSJEnqlAEpSZIk\nSZIkdcqAlCRJkiRJkjplQEqSJEmSJEmdMiAlSZIkSZKkThmQkiRJkiRJUqcMSEmSJEmSJKlTBqQk\nSZIkSZLUKQNSkiRJkiRJ6pQBKUmSJEmSJHXKgJQkSZIkSZI6ZUBKkiRJkiRJnTIgJUmSJEmSpE4Z\nkJIkSZIkSVKnDEhJkiRJkiSpUwakJEmSJEmS1CkDUpIkSZIkSeqUASlJkiRJkiR1yoCUJEmSJEmS\nOmVASpIkSZIkSZ0yICVJkiRJkqROGZCSJEmSJElSpwxISZIkSZIkqVMGpCRJkiRJktQpA1KSJEmS\nJEnqlAEpSZIkSZIkdcqAlCRJkiRJkjplQEqSJEmSJEmdMiAlSZIkSZKkTk3u54tHxFLAV4DVgaWB\no4GbgVOBOcDPgQMzc25E7AO8E3gEODozfxARywKnA6sA9wFvy8w7I2Iz4Ph234sz88hufzJJkiRJ\nkiTNT78zpN4M3JGZLwZ2AE4EjgMOa8cmADtHxKrAu4HNgZcDn4iIKcD+wM/afU8DPtye9xRgt8zc\nEtg0Ijbq8oeSJEmSJEnS/PU7IHU2cET7eiLwMLBxZl7Rjl0AbAdsAlyZmQ9n5r3Ar4ENgC2AC9t9\nLwS2i4gVgCmZeWs7flF7DkmSJEmSJI0CfQ1IZeYDmXl/CyKdTWU49bbpPmAlYEXgnvkcv3cBx3qP\nS+4kudQAACAASURBVJIkSZIkaRToaw0pgIhYDfgOcGJmnhkRx/Z8e0XgbirAtELP8RWGOD7Usd7n\nmK+VV34ckydPWpwf4zG5667lR/w1ujZ16vKsssoKj37HPrHPu2efd88+75593j37vHv2effs8+7Z\n52U89kOX7PN5RvPfvH0++vS7qPmTgYuBAzLzsnb4+ojYOjN/BOwIXApcCxwTEUsDywDrUgXPrwR2\nAq5r970iM++LiFkRsQZwK7A98NEFteOuu/427D/bUGbOvL+T1+nSzJn3c8cd9/W7GfNln3fPPu+e\nfd49+7x79nn37PPu2efds8/nPUaLzj6fZzT/zdvn/bGgYFm/M6QOo5bTHRERA7WkDgZOaEXL/w/4\nVttl7wRgOrWk77DMfCgiTga+FhHTgYeA3dtz7Ad8HZgEXJSZ13X3I0mSJEmSJGlB+hqQysyDqQDU\nYNsMcd9pwLRBxx4E3jjEfa8BXjQ8rZQkSZIkSdJw6vcue5IkSZIkSVrCGJCSJEmSJElSpwxISZIk\nSZIkqVMGpCRJkiRJktQpA1KSJEmSJEnqlAEpSZIkSZIkdcqAlCRJkiRJkjplQEqSJEmSJEmdMiAl\nSZIkSZKkThmQkiRJkiRJUqcMSEmSJEmSJKlTBqQkSZIkSZLUKQNSkiRJkiRJ6pQBKUmSJEmSJHXK\ngJQkSZIkSZI6ZUBKkiRJkiRJnTIgJUmSJEmSpE4ZkJIkSZIkSVKnDEhJkiRJkiSpUwakJEmSJEmS\n1CkDUpIkSZIkSeqUASlJkiRJkiR1yoCUJEmSJEmSOmVASpIkSZIkSZ0yICVJkiRJkqROGZCSJEmS\nJElSpwxISZIkSZIkqVMGpCRJkiRJktQpA1KSJEmSJEnqlAEpSZIkSZIkdcqAlCRJkiRJkjplQEqS\nJEmSJEmdMiAlSZIkSZKkThmQkiRJkiRJUqcMSEmSJEmSJKlTBqQkSZIkSZLUKQNSkiRJkiRJ6pQB\nKUmSJEmSJHXKgJQkSZIkSZI6ZUBKkiRJkiRJnTIgJUmSJEmSpE4ZkJIkSZIkSVKnDEhJkiRJkiSp\nUwakJEmSJEmS1CkDUpIkSZIkSeqUASlJkiRJkiR1yoCUJEmSJEmSOmVASpIkSZIkSZ0yICVJkiRJ\nkqROGZCSJEmSJElSpwxISZIkSZIkqVMGpCRJkiRJktQpA1KSJEmSJEnqlAEpSZIkSZIkdcqAlCRJ\nkiRJkjplQEqSJEmSJEmdmtzvBoyUiJgInARsADwE7J2Zt/S3VZIkSZIkSRrPGVKvAaZk5ubAB4Dj\n+tweSZIkSZIkMb4DUlsAFwJk5jXAC/rbHEmSJEmSJME4XrIHrAjc23N7dkRMzMw5g+94yy2/6qRB\nt99+GzPv+ZeXH3b33DeHB2eO+Mvw4MxF/1ns80Vjn89jn89jn89jny8a+3we+3we+3we+3zR2Ofz\njIU+nzl75PsB4J7Zc+CeDl7HPv+Hrvp85j1zuP3220b+hYA111x7kR5nny+6Re3zBZkwd+7cYX/S\n0SAijgOuzsyz2+0Zmblan5slSZIkSZK0xBvPS/auBHYCiIjNgBv72xxJkiRJkiTB+F6y913gZRFx\nZbu9Zz8bI0mSJEmSpDJul+xJkiRJkiRpdBrPS/YkSZIkSZI0ChmQkiRJkiRJUqcMSEmSJEmSJKlT\nBqQkSf8QEU+KiBX73Q5JkiSNXRExod9t0OhnQGqciQh/pxp3ImKyH2ojKyKWjognAScCm/S7PUsK\nz9nds8+743l79IiI5SJi84h4XL/bonn8GxkZETHJvu2viJiUme6e1qGImNTvNiwKB2XjTGbOiYiJ\nEfGEfrdlSTDwYdfz//P626LxJSLWA8jMRzJzbkQs0+82jUcRsRxwKnAH8HNgi4h4Wl8btYTIzDkD\nXzt4HlkRMSEiJvb2+cDxfrVpvBu4GImI1frdliVVz/t7ZeBFwF4RsWYfmyTmXTj2XrB7Lho+mTm7\njRvXi4i1+92eJVFmzgaIiH0iYq1+t2dJ0NPnb4yIx/e7PY/VhLlzDVyOZQMfXj2DvhWAfYH/zcyL\n+tm28a5F/mcPOnY1cHBmXtOnZo0rEXElcH5mHhMRnwWeDByWmb/tb8vGh5YpMikzH46IpYDHA7OB\nE4CzgQsyc1Y/2zjeDHHOXg04JDPf09eGLUEiYn1gR+DGzLyw3+0ZTyJixcy8t309EZgLbN3+fRyY\nPTggqJHTO05pAZALgBcCu2Tmf/W1cQIgIrYF1snMk/vdlrFu0Pt9OeA4YCpwdGbe2NfGLQEGso8H\nzvER8Uzgg8DqwG7APZ7/h9cQfb4+8FHgz8DhwF1joc/NkBqj2kzvpMyc22YA1gfIzPuAjYGBjB1/\nx8OsZ1ZrdltKttVA/wOXUFkmWkQtzXpyu7k3cGBEnA38CvgL8N6IeGrfGjiOZOacFoxaC3g5cAuw\nPvAdYDvgmX1s3rjSG4hq5+yl2remAHPbchpnx4fZwPm65/+3AJ8HEjgiIg7o+V1oMUREANe3z8XJ\n1EX2XOqCcO3MfHgsDIzHk56L892AZwNHAV8Glvd8062BcXvv1xFxGnAYcEV/Wze29VyU904SPx7Y\nCTgzM28cq0uZxoqImNDGlHMi4skRsQowB3g6cHZmjonAyFgykPHd+nzldngS8FLg3zPzr31s3kIx\nWDFGtYua2a0A8f7A9yPimIh4NvAV6iSMf/zDp+eCcmCA9wbgXKqvj2+zMc8Gon3fv6+F1D7QZmfm\nIxHxvMy8GfgqsFZmnkRF/R8PvDAipvSzrWNV76CsDYrfDlwM3AzsDxwJnEd9qG1lgfPFFxFLDVqW\n8Vrg6IjYHJgFrJuZD1hrYfgMvkDpuVBZEzgeuBVYlqqX5rlkMbWBcQL/C/wbFdA+PSJ2Bn4ATHbZ\nTDd6A02tZtQ1wPOBbTJzOvA/1MTlul6kd6dn3L48NaaZDdwOLJ+ZNzlmXHQ92SFviIgfRsR7gecB\n76fGNQOTyAZhh1nvZFsrGXMslWH/GWAd4HJgTZcJD78WiJocEccA34mILwF3AmcCYyrr3pPfGDJ4\n4BARLwduBGYAWwLXU8Go5wE3DfUYLbzebLR2e6mI+CSwF3BUZn4QuIEahP8N2BAMBj5WvbOG7QPt\naRFxEXBcRHwCOBZ4akRsmpl3Az+mUn+f0r9Wjz2DL9BbVtQkYEUqpfeWzPw6tWRvd+B0apZlzKxB\nH20i4sURsXRmPtxu7xkRb6Uu2m+gAiNzgQci4kV9bOq40TM4HrhA2SwiLo6IoyPiucADwCeAA4FN\nqc/K5/arveNFGxivSmW17kuNR/YDXgv8BzVWuc8LwpHXPkfXaDffAZwBHE1dFL4WuJLKXDgLeLW/\nk5ET8+qLTmz/v4PKpH9HRHwzMz8MTI2I7QYuLvvY3DFlcF9FxOupscs+wH3Ah4FrgD9HxL7tbr7X\nh0lELA3/XAMNeBkwJTNfTK1oOAj4KVVuY+twQ4XFNkRG978BS2XmS4DHAZ+kxjg7R8S67bwy6t/3\n1pAaI1rmyEBAZDPqD/yJwO+Ap2Tmn9v33koNQB6XmZv2q73jUVsmtjU127sj8CrgyMz8ZTtBbEnV\nyLg8Mz8YQ9SY0vxFFSx/ErALQGYeFxGrZ+ZtEfEu4G2ZuUm777aZeWkfmztmRMQymfn3ntvrAZ8G\nlgGmA/8NbA9kZn6hBUa+D6wGrJCZf+lDs8e8iDgM+AB1Pp4OfA34PfA04NzMPCki9qMmEDYE9srM\nm/rV3vGmZaweAzwCXEtl7NxIBaReDFxFLUndFNgvM3/Vn5aOPRGxEvAm4CuZ+Ug7NpWqT7Q/1dcv\nyMw3tiDVccA2wO6Z+aPe8YyGX/ss/S7wJeAhYHNgLWAa9bt4B7Vk9fmZeXmfmjmuRcRymfnAoGOr\nUp+9/0aN3y8H3gwsDRyfmWsMfh79q4g4gKqTO70tU1o1M2+OiPcAd1GfsVsC1wGXAssB7wZe5Zh8\n8bXzyweB8zLzunZNuiq17HQj4N+pjPsHgN9SGffrUtnJJ/SOR/XYtKzKL2bm7u3286n6r9e2ceTa\nwCpUnz8IfIzKDpybmR/qU7MXihlSo1jLHOlNhdwwIs4EPkfNOEItrzll4DGZ+Z/AAcDNEbFO120e\nLwanTkfEgcD3qOV5F2fmN4C/A5tExPKtNsZlwCHUBc7gtezqMUS232uoi/ZVgQ2oAQQtGPUVKltn\nmbbECYNRj66l8X6cSuNdux37IPAh6qJkd+rDazcqyLp9RDw9M68Cts/MBzPzL2NhZmWUupxajvFq\nKmvkxMzcC/gNsGtERGaeQi1DXR7YAdxlaVH0ZCFMaP/e3C4GdwR+n5nfpOqiPRm4mwpUrda+t63B\nqIU2lwo8vSwitm3v5ZnUWOTQzDwWWDsidsjMPwH/DzgReA78y4y6FtEQn6NbRsQL2gXffwDvBH5E\njV1OpsYsfwQezsz7BoJRnnOGV0S8jspKIyLWiKpTtwZ1UX4PFZQ6nMrkWSszzwWui4hn+buYv56+\neRDYLyL2pILgn42It1FBviOAhzJzJ+B+qn7dhcArHZMvvoiY3M4vDwKvjYijqIn4LYBvAE+lEiX+\nmpkHUJM+O2bmtzPzWINRC68lN9wPTIyIT0ctR/0M8J428bkWFQi8MjP3pwLcz8/Mw8ZKMAoMSI1q\nA8Vv4R8Bkn2B06gLnEnA/pn5UWCjiNip56EPt+//ptsWjx89yz2Wi4inUH/sWwOHAs9tA44TgZ2p\nulEDngDkQCqr/lX7QBtYNrZURDyDCo7ckJnXUstOt4yIZ7Sg6qrAZOCFmfnffWv42LQO8CfgoIjY\nBfg/auvv6Zn5R+Ai6gL9L8AvgWcAZOZPB57Ai8dH14IgR0bETj3B7AnUQPga6vyxeptQ+CY1mDuo\npVP/ETiYqiVlfy+EiFgrIqZRA7ABy1Lb2r+OWi7wZoCs3fQmUrV0ZgBHZOaJHTd5zGuD43upoPaR\n1LK8Ldtn3jeAZaPKCRxFDZppWZZTqQCtFlM730zs+RxdNyI2pbKLt4yIlTLzfOp9/k4qM3OP9vX+\nmfmz3ufznDM8BpaQZeZ3gFUi4vPUBPLzqImgjYA1gJlUMOpZwPrtb+pNmXmrv4t/FVWXaGJP35xF\nZf7tk5mbUZ+fL6A+a28C5kTE96lz/TXtWsoSGosh5pV8eKQd+iyVkbMW8NrMPJQaT76OOu8/PiIu\nB+7MzOO6b/HYF/9aqH8/avOhZ2Tm1tSyvEfavwuoa9PLgJmZ+cM+NHmxGJAaZQbPjkTEKyLixHYy\nvYqqm/NJKvK/cUS8lJp97M2G2gH4dVZhaGdbHqOYt/vJxJZdcgB1ApgMnE8NLN5BpUJ+mZppvJoa\n7A2sp14VOC0zH+r+Jxi9IuIpEfG+iHhye1+u1TKfTqCCp+dQg4g1MvNHwH9RJ9tTqcySOzPzwb79\nAGNMG+AOLFW6l+rLd1MXhcsDL2l33Q6YnJn/CxxuwG+RrU7VJDoBeFc7dgs1IL6FmjHclCoe/3vq\nd3IN8JuopcCHU4FDLYTM/DWV6XRQW9a7RWb+jQqGHEDVavlTRLyvPeRUKu39IS/8FtkcgMw8nVr2\nNZVaLrBu6/tfUks2vktN2BARG1C16MxEGwYDF9gtEHUyleG6GbVk5olUwXKAX1BjmNnAQZn51sz8\nTRvjODYcJgOZmT0X6wAfoYKAe2fmPlQG+CRqLHk79ffxDOCwnsCi12RDyHm7iK0TVSfqccAXgeWi\nShIkVb91TeCN1N/BpzNz1zbho0U0cK7Ify4a/21qGfBXqSz7gZpGlwP/l5k/pjLVXpOZR/Sh2WPa\nEH2+e0R8lBq7f4p6n9PG7UsDP8nMT1MrSd6UmYf3p+WLxxpSo0SL/s/pub1OVm2iJ1CDuE2ptbhH\nUTMtm1NLbf46+M0XtaPTw501foxrGTsDdTBWAlbMzBkR8SbqgvJcagCxPzUrvAOwJ3BA+yDUo2hp\npUdTM1nXUim+J1NLIH8F/BDYlkq1Pq495omZeWd/Wjy2tQ+zuS1LZBXqwvxbVNbkK6gLyYFZ8uMG\nZsyjZ4lw960eu9qFxG7AW6gi8SdRff5KqrjqstSF4YpUltoxmXlDe+wKwMqZafbIQmhB19kRsQW1\nPOlWakD89cycGRH/Se0mdglVO2dLl2wMj4ExRtQS6pOo8/dEKjj1B+CitoRdIyRqU4ovU8u/Hgfs\nSmUmvJTK1F6Ouki/oGVLDTzO2pbDpH1e9maqbU6Nzy/JzM9GxPeoEg8nRS2bPxrYIzMfiohnDJzz\nB4//Ba2/ZmWVbViK6rsXUhuCrEEtD34q8PTMPKJlo92QmdP61uhxpvdcERGrUatEXkT9Dt5D1QU8\nlJpwuwF4H/DDzPxUXxo8Dgzq8ydR10W7UOP1V2fm8yPiPGpy7ar2/7SsMjJjmgGpUWCIYNRzqSjo\nxzLz6nYx/1IqEPJTKjgyBTg4M2+e3/No4bRss92ADTPzhS3j6YPUoO526mS8A/B54HM9QSwLtA6h\nNzDa3tPHAndQRSfPofr0aKquyMHU4GJzqm+dSR8GLaj6OeAy4KzMPCdqW9iXUwPlvdv9fA8vpoh4\nOlUrahVqhnbv9v+Z1AXjG6gLlSva/QfSsT1nL6aobaa3oIrYnpOZl0Xt0PkS4CVmVw6/nqDUqVQB\n2z8Az8zMo/rbsvFliPHhFlSA+3Yq22+bdvzzVOb86cB6VJbUkTmosLaGR+/vJSKeTGXJ7gb8HFif\n2nr9S1QNr7dSmTsrA+8F7s95O19N8DPgn7Wlj9swb0L4fqrW4rsy88GI2JWaIH47FQy/DfgJlW3m\n6oTFFP+8idZKVF/vRE04HJi1McUXqX4/lxpj/ooKRo35wEi/tUnKDwNbUXX/ds2q53o2Nel2LbWK\n5LvAf2XmGf1q63AyIDVKRMSy1I5M11OR5lcAa2fme9r3/9aO/QLYIDMv6nmsM14LYYhZrTWogcON\nVIT/x1RQ5IyI2I7aqWAatVvHlMz8Q3uc/T6EqJpb51CFVE9p2QpbU7syXUZtA34dtTPK+yPiDCpI\ndTaV7uuubsOknVfOowYRv2jHlqaWdXwPeOvAcS2edl7ZgRok708tkTyA2lXmmEH39dwxDAayW6Pq\n0J1LZUM9gbqQORX4Wmb+tY9NHJdafx9AZaW9CfjkoDGJk2OLKQbtjtqOLU2dVwY+Y19JLdf4RtS2\n9vtSE5XTex7j72IYDREg3Itarv0H4N7M3D0inkNNwO1LZZK8DvhMWrNuvoYYl7+TWgJ/AjVW+WJm\nPrd97+nUMvdDqB1Tf9mWb2sxDB6XtEmzq4GbMnPPiPg0sE5mvrpl7/yE2jhkReBqzzMLb4g+X53K\ndP0NFROYRtWEOiQiAriSKtHzAuDS8TSONCDVB0O8AV9LZUAtR/1hX0DNshxM7Qx0N5UG/M3MPHl+\nz6MFa7NYL83MM9vtpwF/pmazPk9tu3t2W+Z0DDXgfiNVbPj4zLy1PW4SMMeMkqFFxBOp9+3q1Law\nB7YPtvOoJQYbUfUufkbV03kh8NmsguYaRu3C8TNUnbk/tCVOAxfxOwNXZOZd/W3l+NFmtj4A/D0z\nj4qIlwE/GwiyenE4/AaW9kbE8dRS1GuAZdJ6aCOqZSlsC5yZPQVUfY8vnpYdciT1OXlwZv4qIj5M\n1QU9KyI2pAJRD1D16Q6hNqV4iMqcuiMz9zcDc3gN7s8WQDmYynx9HZU98hHgpMycHhEfoja82QOY\nOhAYd9y+YBHxLCpr/i/AXsCfM/MjEXExVYfr69Tfx71Zu7hpmEXEK6kl2BdRWX8fysy127npF9R5\n6QdR9byuyMw7+tjccaGt0PkztSJnP+DJmfn2iFgXOB74aGZeFbWb5A/GYzkTA1IdGpye225Ppf7A\nP9LWme9KpVtfShUrO4YadHxgICCiRdNmVc6jBgg7UDO7l1Drn38HvB7YNzMfiIiDqaDJVZl5Qp+a\nPGZFxPZUPZ1VqQDrDVSw9e/t/wOorcPPzyrGpxESEacB78vaft3leSOsLal5NT3LZbxIHxltUuF4\n6lzyVGpJxw39bdWSJ6w9N2zaRd9Z1MYHD1BLNCZRk2P7teDr/6OCHZ+hCpZv38aPh1IFhj/h72J4\nDDGBvCG15P0MKkthV+ALVLBkb2qCbW+qiP+TejKTncjs0fpjH6o27t2tPMnhVMZNUjt23kkFRM6h\nsjH3pmoY/SAzP9ePdo83g5bnrUBlpC0PfJ8Kdm9Evbe/mplfjohDgM0z8/X9avNY1psJ2L5ehaqn\nO4HKOHsSVe91D+A/M/OKqILmy2XtZDhuuaNDByLi8RGxN7BUWzf+jKjtv4+iakEdSZ2EobYJXwbY\nkloj+qps28FG2/q0Hz/DWBURk3oGy7+jduY4BViB2mHsfGqWdyJVk+HA9tDPA28ZCEa1D089dtdS\nhQ9/RaX8HksNLP5Efbh9AdjFYNTIy8w9BoJR7bYD4pF1VWa+vycYZY2QEZKZv6fq/H0f2NZgVPda\nsHWu55XFF0PvjnowlT3/RCoQAhWAXZHKQp4JrBoRP6ImMz/v72J4tAzjjXtuf4iqlwOVdfxMqobR\n1tRY/jKq/MAzM3NmZv6iZ/w5299LiapteTH1fl0X+EJEPI9airprZu6ZmRdSE5g3AodRy+A/RV0T\nGYxaTL2TCFG7iu9EZVj+iQoULkfVPNsZ+DfgxHb/4wxGLZp2fp/bs1JhLvX+vzQzBzYgCmoi/yrg\noPbQT4z3YBSYIdWJiFiRKmz4AWr26r1U4cnlga0z8/URcT2VBXVRq1tEZl7S8xym+S6EqG2m78rM\nGe32ptTA7Rbg28DZWTWilqaWQ/60PXR9ahA4u52oJwIOthdBRLyQGrQdRvXrMVS/H9nXhi2BzNDp\nnn0uaWHE/HdHvRXYngq8rg38Ejg8M29pj1uLmvC8eehn1qKIiJdQmyUkVVv0g5l5UFvJcAi1YUUC\nrwIua7W8zEBegIh4PLUr6ucy83/asddSQY8Nqdqi90fVdn0TVd/15VQdIyccRkBEbEkFnq6m6p49\njspQOw/YITNPjoh9qLqMsx3XLJ7Wl68BPkll029FBQKvpHYGnk0tWX0e8I0l5drfgNQIi3lbU7+D\n2jXi+8BfqXWiuwGbUsX5lqOioOv2q63jQdTWpIdTSyEPo3Z1O5aaSfw9VZvr99TJYK92v2nUUg93\ndhtGETGFivCv1woiPr1lqUmSpCHE0Lujnk5drPw0/3mnTifMhskQdaK2pFYtXEFdON7Y7noGdbG+\ncfv/ucANmTlz4Hm8aB9aROxJ7Xy6R0RMycxZ7fixVFDqMKp20b5UiZN9+9fa8aP3vd2yozYANsrM\nr0XExtQuhjsD36Te5/9F7fR2IxX8XiKCIsNpqDp+EXEEdd6YRpWKuZVaSbI/tWRvGjUJ8YUl7bw+\nud8NWALMAcjMr0TEDlSmyM1URPTT1DrRacAawE1gjZdFFbUDxAbA9zPzP9qxl1Nb7G4fER+jtt99\nHbVc71tUFPr83mCUg4nhkZmzIuIcKgCIwShJkh7VucA7gY/lvF1Q39NbyNas+eHVO+6LiKCWj91M\nBUimAs+gVjmcDHy1fb0m8L1sRf0Hxu6OHxfoZmCTiFgxM++NiOXa8vafUAHYmVRx+Ksy85P9bOh4\nEBHLAQ+1pcBExKpUfeI5wMtaMOpwql7X2tRKhudSu4ufkpnn9aXhY1xvqYaIeAKV9XQvVbB/v8z8\nU9Qu2OtQJWKeTe2g+pnMPLdPze4rM6Q6EBFLZebDbSne+4DrqUyd5wD3AdcOLM8zGLJo2h/2tdTW\nsAPBqFdR6/rfTdWIOpv6sPsLtXTvzdTuefe1+xsIlCRJfRND7I7a8z3HiMNoUCBqCtXnb6LGiBOo\nkg57ULscfrWtdlgH+FNmHt+nZo9ZbSnevsD03mBHRBwJXJ2Z50fEspn5YN8aOU5ExAuAXagd2n8S\nER+n6p3dTWX2Tac2eBo4dthAmRMtvFZreHPgtz3lYj4OvJg6nxxHBbj/kJnvjYinUuf5d2Tm3/rU\n7FHDgFRHeoJSJ1HLx24CHsnMw/rctHEjInakgk//Tm2buSzwFSor6rZ2/AvAlZn52Z7HmfYuSZJG\nhcG7o2p4DTXui4gDgb0yc+N2+wzge9Qk5j5UXa93AL/PeTuTmam2kKK2rn8F8AMqY+o91Hj9/2Xm\nb/vYtHElIp5CLYO8mUp+2Ckz3xYR61NB11+1JXtvoVbs7JmZF/SvxWNXRLyBCmbfRNVCOw5YDVgp\nM49o1/5Poq5DT6MSJF5C1Zc+Cnh4Sb8GNSDVgTbbdQC1venrqTfqJT0plM54DYO2Lvo/qYj/Lpl5\nWTu+BfXhtx7wncz8Ws9j7HtJkqRxLiKWycy/99zemtpp+SxqV7EDgJMz88etuPwumblbRKxHXTT+\nsj3uH7uUdf5DjANtV7dNqYv372TmaX1u0pjXAlBfpYrG/6gVh9+J2rX9EeA5mfmGdt/DqQLlH2+3\nV87Mu/rU9DErIp5MlX/5I3BkZv68lefZhEqGuJpaHvl3aoXOV6idODcD/jxQ2F8GpDrTduXYFjiz\nd705VbTPgMgwaZH/LwLbZ+Z9A0UTB6cAG4iSJEmjleOU4RMRq1OFmydTmVF7RMSnqOV351I7Wt1G\n7Zr3Smq35Y9QReRP6HkefyfDyFIZwycingicT02+fxe4nApQfZgKhjwHuLztBvl14Jre97YWXqvJ\ndSLw7bZz+wSqRMw2VJH4AH6cmUdFxH8A12fmV/rW4FHMgFQfOLMystouButn5i49xwa2Uza9WpIk\naQnQyjkcQl2kf4OaHL6AulA/nFqO9yZqZ7EfAO+lAlVfzsxT+tFmaVG0jZzeClxMBUTuBp4O/Ab4\nNZUtNZXKoDqiX+0cT1pG1J7AoZl5ezu2HvAxaofO51IF4/87Mz/Ut4aOchP73YAlTZtdsV7RyPoS\ncE9ErDQ4+GcwSpIkaYnxAuD0zDyx7VR4F/B86sL9UmAZ4O3AH6hdgc8CrhsIRkWEO5JrrLiGdD0k\nsAAABFlJREFUCj49ITNfT9WG+gtV+2xDKgC7s8GoYXUl8Etgr4EDmXkTtanWWcAngTcajFowM6Qk\nSZIkjTsR8WZqa/UvUtlRy1G7Mr+Vyog6jCr+vCLwFuAJwP7Ucr2z+9FmaVFFxCZUlt/xmXlNO7Yx\nMCkzr+tr48ap1ucHAUdQ9bqOBe6kCvU/0s+2jRUGpDRuuTxPkiRpyRYRL6MCUbMz8/vt2BXAN6kl\nTT/PzNPb8YlUhskd/WqvtKgiYgpwIPCCzHxzv9uzJGh9/i5qafDPgVMz88z+tmpsMSAlSZIkaVxq\nF4xPz8zftNvvo+pEHZCZs3ruN9mMBo11EbE2tSz1G5aI6UZErEFtiHBK7zlFj40BKUmSJEnjUkRM\npYoMP5XKiLoeODozf9e+7+55ktQnBqQkSZIkjVsR8XiqwPm9mXltO2YgSpL6zICUJEmSpCVC24F5\ngsEoSeo/tzKVJEmSNO5FxIRWV8cZeUkaBcyQkiRJkiRJUqcm9rsBkiRJkiRJWrIYkJIkSZIkSVKn\nDEhJkiRJkiSpUwakJEmSJEmS1CkDUpIkSR2IiDnt37OG+N5+7XtHDdNrrRERO7Wvn9mee43heG5J\nkqThYEBKkiSpO7OAVw1x/DXUVvTDtf3xl4HNhum5JEmShp0BKUmSpO5MB17deyAiVgReBFwPTBim\n15kwjM8lSZI07Cb3uwGSJElLkHOA4yJixcy8tx3biQpULdd7x4h4JXAk8Gzgt8ARmfmt9r3LgUuA\nLYEXA78HDsrMCyLi1HbsxRGxBbBne8qdI+JA4KnApcDbMnPmCP2ckiRJC2SGlCRJUndupoJLO/Yc\n2xn4Xvt6LkBEvBT4NnAqsAHwReCMiNik53EfBM4A1gN+CnwpIiYABwFXAZ8FXse8TKm3A7sC2wDP\na4+XJEnqCwNSkiRJ3TqHVkcqIpYCtm/Her0L+E5mnpCZv87M46kA1aE99zk/M0/LzFuBo6nMp6e1\nzKtZwAOZeXfP/d+fmf+TmdcC3wQ2HIkfTpIk6bEwICVJktSduVTwaceImAS8FPh5Zt7BP9d8ejZw\nzaDHXgWs23P7lp6v72v/L7WA1+69/73AMgvRbkmSpGFlQEqSJKlbVwGPUPWfdga+24737rD34BCP\nm8Q/j91mDXGfBRUyn70Q95UkSRpRBqQkSZI6lJlzgPOoYNQrmReQ6vULYLNBx14EZPt6Lgv2aN+X\nJEnqK3fZkyRJ6t45wOnArzPztnZsAvOylj4DXBURBwPnA68AXgvsMMR9h3I/sHZErDLcDZckSRoO\nZkhJkiR17xJqCd73eo7Nbf/IzJ8AuwP7Av9L7ZC3S2ZeOvi+gx4/4AtUsfQLFnBfs6gkSVLfTJg7\n17GIJEmSJEmSumOGlCRJkiRJkjplQEqSJEmSJEmdMiAlSZIkSZKkThmQkiRJkiRJUqcMSEmSJEmS\nJKlTBqQkSZIkSZLUKQNSkiRJkiRJ6pQBKUmSJEmSJHXq/wN9/dM6M5hq2QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c9aa610>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Look at yearly trends"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Calculate the cumulative sum for each calendar year and rolling 12 month period"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['yearCumulativeSum']=0\n",
      "df['yearCumulativeSum_PercentIncrease']=np.nan\n",
      "df['RollingYearlyTotal'] = np.nan\n",
      "df['RollingYearlyTotal_PercentIncrease']=np.nan\n",
      "for i in range(len(df)):\n",
      "    df.yearCumulativeSum[i] = np.sum(df[:i][df.year==df.year[i]].tot)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "idx = 0\n",
      "for i in range(len(df)):\n",
      "    lastyear = df[(df.year==(df.year[i]-1)) & (df.dayofyear_float==df.dayofyear_float[i])].index\n",
      "    if len(lastyear) == 0:\n",
      "        idx = idx #if a datapoint didn't exist for the same time last year, use last idx\n",
      "    else:\n",
      "        idx = lastyear[0] #the zero is necessary because there's at least one example of two datapoints for the same datetime\n",
      "    df.RollingYearlyTotal[i] = np.sum(df.tot[idx:i])\n",
      "    df.RollingYearlyTotal_PercentIncrease[i] = 100*(df.RollingYearlyTotal[i] - df.RollingYearlyTotal[idx])/(1.0*df.RollingYearlyTotal[idx])\n",
      "    if df.year[i]>=14: #Percent increase on the cumulative calendar year total only makes sense for 2014 and beyond\n",
      "        df.yearCumulativeSum_PercentIncrease[i] = 100*(df.yearCumulativeSum[i] - df.yearCumulativeSum[idx])/(1.0*df.yearCumulativeSum[idx])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>timestamp</th>\n",
        "      <th>nb</th>\n",
        "      <th>sb</th>\n",
        "      <th>tot</th>\n",
        "      <th>year</th>\n",
        "      <th>weekdayname</th>\n",
        "      <th>weekday</th>\n",
        "      <th>hour</th>\n",
        "      <th>month</th>\n",
        "      <th>day</th>\n",
        "      <th>dayofyear</th>\n",
        "      <th>dayofyear_float</th>\n",
        "      <th>yearCumulativeSum</th>\n",
        "      <th>yearCumulativeSum_PercentIncrease</th>\n",
        "      <th>RollingYearlyTotal</th>\n",
        "      <th>RollingYearlyTotal_PercentIncrease</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>18235</th>\n",
        "      <td> 10/31/2014 07:00:00 PM</td>\n",
        "      <td> 44</td>\n",
        "      <td> 21</td>\n",
        "      <td> 65</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 19</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.791667</td>\n",
        "      <td> 901985</td>\n",
        "      <td> 9.607194</td>\n",
        "      <td> 1005085.5</td>\n",
        "      <td> 10.231400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18236</th>\n",
        "      <td> 10/31/2014 08:00:00 PM</td>\n",
        "      <td> 22</td>\n",
        "      <td>  9</td>\n",
        "      <td> 31</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 20</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.833333</td>\n",
        "      <td> 902050</td>\n",
        "      <td> 9.599377</td>\n",
        "      <td> 1005032.5</td>\n",
        "      <td> 10.219181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18237</th>\n",
        "      <td> 10/31/2014 09:00:00 PM</td>\n",
        "      <td> 20</td>\n",
        "      <td> 12</td>\n",
        "      <td> 32</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 21</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.875000</td>\n",
        "      <td> 902081</td>\n",
        "      <td> 9.595553</td>\n",
        "      <td> 1005006.5</td>\n",
        "      <td> 10.213671</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18238</th>\n",
        "      <td> 10/31/2014 10:00:00 PM</td>\n",
        "      <td> 13</td>\n",
        "      <td> 11</td>\n",
        "      <td> 24</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 22</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.916667</td>\n",
        "      <td> 902113</td>\n",
        "      <td> 9.594648</td>\n",
        "      <td> 1005002.5</td>\n",
        "      <td> 10.210815</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18239</th>\n",
        "      <td> 10/31/2014 11:00:00 PM</td>\n",
        "      <td>  6</td>\n",
        "      <td> 12</td>\n",
        "      <td> 18</td>\n",
        "      <td> 14</td>\n",
        "      <td> Friday</td>\n",
        "      <td> 5</td>\n",
        "      <td> 23</td>\n",
        "      <td> 10</td>\n",
        "      <td> 31</td>\n",
        "      <td> 304</td>\n",
        "      <td> 304.958333</td>\n",
        "      <td> 902137</td>\n",
        "      <td> 9.592637</td>\n",
        "      <td> 1004989.5</td>\n",
        "      <td> 10.206851</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "                    timestamp  nb  sb  tot  year weekdayname  weekday  hour  \\\n",
        "18235  10/31/2014 07:00:00 PM  44  21   65    14      Friday        5    19   \n",
        "18236  10/31/2014 08:00:00 PM  22   9   31    14      Friday        5    20   \n",
        "18237  10/31/2014 09:00:00 PM  20  12   32    14      Friday        5    21   \n",
        "18238  10/31/2014 10:00:00 PM  13  11   24    14      Friday        5    22   \n",
        "18239  10/31/2014 11:00:00 PM   6  12   18    14      Friday        5    23   \n",
        "\n",
        "       month  day  dayofyear  dayofyear_float  yearCumulativeSum  \\\n",
        "18235     10   31        304       304.791667             901985   \n",
        "18236     10   31        304       304.833333             902050   \n",
        "18237     10   31        304       304.875000             902081   \n",
        "18238     10   31        304       304.916667             902113   \n",
        "18239     10   31        304       304.958333             902137   \n",
        "\n",
        "       yearCumulativeSum_PercentIncrease  RollingYearlyTotal  \\\n",
        "18235                           9.607194           1005085.5   \n",
        "18236                           9.599377           1005032.5   \n",
        "18237                           9.595553           1005006.5   \n",
        "18238                           9.594648           1005002.5   \n",
        "18239                           9.592637           1004989.5   \n",
        "\n",
        "       RollingYearlyTotal_PercentIncrease  \n",
        "18235                           10.231400  \n",
        "18236                           10.219181  \n",
        "18237                           10.213671  \n",
        "18238                           10.210815  \n",
        "18239                           10.206851  "
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot cumulative sums (the dashed line represents the 1 million crossing pace)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "fig.set_size_inches(20,8)\n",
      "ax = fig.add_subplot(111)\n",
      "for year in df.year.unique():\n",
      "    df[df.year==year].plot(x='dayofyear_float',y='yearCumulativeSum',color=colors[np.where(df.year.unique()==year)[0][0]],linewidth=3)\n",
      "\n",
      "ax.plot([0,365],[0,1E6],'--k')\n",
      "ax.legend(['20'+str(year) for year in df.year.unique()],loc=2,frameon=True,shadow=True,fontsize=14)\n",
      "\n",
      "ax.xaxis.set_major_locator(dates.MonthLocator())\n",
      "ax.xaxis.set_minor_locator(dates.MonthLocator(bymonthday=15))\n",
      "\n",
      "ax.xaxis.set_major_formatter(ticker.NullFormatter())\n",
      "ax.xaxis.set_minor_formatter(dates.DateFormatter('%b'))\n",
      "\n",
      "for tick in ax.xaxis.get_minor_ticks():\n",
      "    tick.tick1line.set_markersize(0)\n",
      "    tick.tick2line.set_markersize(0)\n",
      "    tick.label1.set_horizontalalignment('center')\n",
      "ax.set_xlabel('Month',fontsize=14)\n",
      "ax.set_ylabel('Cumulative total crossings',fontsize=14)\n",
      "ax.set_title('Cumulative crossings by calendar year',fontsize=16)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.text.Text at 0x10bad3510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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aDgwM5G9/e5S2bdsBcMkllzN9+idVen8iIiIiIiIiRQoKCpgzZzbWWu699wGP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9vJv//9DN98M4vKykr8/Py46qprGTv2cneXVqOSikK+2vgysze/7hC32Ty4yEjhnhH/ojCv2mXz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\nRERERERERgQlqkREREREREREZET4/3SEr8SpISLkAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e4d50d0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the year over year increase in the cumulative total (this only makes sense for 2014 and beyond):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "fig.set_size_inches(20,8)\n",
      "ax = fig.add_subplot(111)\n",
      "for year in df.year.unique()[df.year.unique()>=14]:\n",
      "    df[df.year==year].plot(x='dayofyear_float',y='yearCumulativeSum_PercentIncrease',color=colors[np.where(df.year.unique()==year)[0][0]],linewidth=3)\n",
      "\n",
      "ax.legend(['20'+str(year)+' increase over 20'+str(year-1) for year in df.year.unique()[df.year.unique()>=14]],loc=2,frameon=True,shadow=True,fontsize=14)\n",
      "\n",
      "ax.xaxis.set_major_locator(dates.MonthLocator())\n",
      "ax.xaxis.set_minor_locator(dates.MonthLocator(bymonthday=15))\n",
      "\n",
      "ax.xaxis.set_major_formatter(ticker.NullFormatter())\n",
      "ax.xaxis.set_minor_formatter(dates.DateFormatter('%b'))\n",
      "\n",
      "for tick in ax.xaxis.get_minor_ticks():\n",
      "    tick.tick1line.set_markersize(0)\n",
      "    tick.tick2line.set_markersize(0)\n",
      "    tick.label1.set_horizontalalignment('center')\n",
      "ax.set_xlabel('Month',fontsize=14)\n",
      "ax.set_ylabel('Cumulative total crossings',fontsize=14)\n",
      "ax.set_title('Percent increase inumulative crossings over previous calendar year',fontsize=16)\n",
      "ax.set_ylim([0,40])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(0, 40)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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M8XgTGTp0GEOGbNeg9hx//Clss81gtt12CPvvP4rZs2cB8PLLLzB48HaMG3cS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1AZUl5VgjWnFgMAvJ0DIQT0w5G0TdJEJbqmxT0OF3t3pOScQrRfb/QJm4DVJ/6DnXWG3LX3CQRs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\nTQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e4dc210>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot a rolling 12 month total"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "fig.set_size_inches(20,6)\n",
      "ax = fig.add_subplot(111)\n",
      "df.plot(x='timestamp',y='RollingYearlyTotal',ax=ax,linewidth=2,color='k')\n",
      "ax.set_xlabel('Date',fontsize=14)\n",
      "ax.set_ylabel('Crossings in past 12 months',fontsize=14)\n",
      "ax.set_title('Rolling 12 month total crossings',fontsize=16)\n",
      "ax.set_ylim([0,1050000])\n",
      "xticks = [0]\n",
      "month = df.month[0]\n",
      "for i in range(len(df)):\n",
      "    if df.month[i] > month or df.month[i] == (month - 11):\n",
      "        month = df.month[i]\n",
      "        xticks.append(i)\n",
      "xticks.append(len(df))\n",
      "ax.set_xticks(xticks)\n",
      "xtl = [str(df.month[xt])+'/'+str(df.year[xt]) for xt in xticks[:-1]]\n",
      "ax.set_xticklabels(xtl)\n",
      "\n",
      "ax.xaxis.set_major_formatter(ticker.NullFormatter())\n",
      "\n",
      "xtickminor = [(xticks[i]+xticks[i+1])/2 for i in range(len(xticks)-1)]\n",
      "ax.xaxis.set_minor_locator(ticker.FixedLocator(xtickminor))\n",
      "ax.xaxis.set_minor_formatter(ticker.FixedFormatter(xtl))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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55eeHHWP16pWsXr2Sjz/+gIYNGzJo0BDOOONMWrVqzUcfvc+UKZMAiI9vzNVX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\nVFTEs88+xf3338OHH45evHz33Vtz2GFH0Lbtfqy11loJFkqSJEmSVL4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       "text": [
        "<matplotlib.figure.Figure at 0x10c759a90>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'The millionth crossing in a one year period first occurred on',df.timestamp[df[df.RollingYearlyTotal>1E6].index[0]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The millionth crossing in a one year period first occurred on 09/29/2014 09:00:00 AM\n"
       ]
      }
     ],
     "prompt_number": 22
    }
   ],
   "metadata": {}
  }
 ]
}