Trips_1v4a.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import urllib2\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2016-01.csv\n",
      "https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2016-02.csv\n",
      "https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2016-03.csv\n",
      "https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2016-04.csv\n",
      "https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2016-05.csv\n"
     ]
    }
   ],
   "source": [
    "# Programmatically downloading the trip data for September 2015\n",
    "\n",
    "link=\"https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2015-09.csv\"\n",
    "\n",
    "response = urllib2.urlopen(link)\n",
    "csvreader = response.read()\n",
    "\n",
    "f=open(\"green_tripdata_2015-09.csv\", 'wb')\n",
    "f.write(csvreader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Programmatically downloading from web and loading the trip data for September 2015\n",
    "\n",
    "\n",
    "#link=\"https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2015-09.csv\"\n",
    "link=\"green_tripdata_2015-09.csv\"\n",
    "df=pd.read_csv(link)\n",
    "\n",
    "#print list(df)\n",
    "#df.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows:  1494926\n",
      "Number of columns:  21\n"
     ]
    }
   ],
   "source": [
    "a,b=df.shape\n",
    "print \"Number of rows: \", a\n",
    "print \"Number of columns: \",b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "#%matplotlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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JS9g1zXAnwr7o7r7dzNaZWexG5YuTlCVSLne/hGBMgaSZu48nmElWRKK3s+4MLSaYhToT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\nKiBFRERERESkSVRAioiIiIiISJP8H13KIxgj/k/9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa420c72910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of the number of the trip distance (\"Trip Distance\")\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "# evaluate the histogram\n",
    "values, base = np.histogram(df['Trip_distance'],bins=2000)\n",
    "#evaluate the cumulative\n",
    "cumulative = np.cumsum(values).astype(float)\n",
    "cumulative /= cumulative[-1]\n",
    "# plot the cumulative function\n",
    "\n",
    "# plot the histogram\n",
    "plt.subplot(121)\n",
    "plt.plot(base[:-1], values, c='blue')\n",
    "plt.xlabel('Trip Distance (Mile)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of the trip distance')\n",
    "plt.xlim([0,20])\n",
    "plt.grid()\n",
    "\n",
    "#plot the cumulative histogram\n",
    "plt.subplot(122)\n",
    "plt.plot(base[:-1], cumulative, c='green')\n",
    "\n",
    "plt.xlabel('Trip Distance (Mile)')\n",
    "plt.ylabel('CDF')\n",
    "\n",
    "plt.title('Cumulative distribution')\n",
    "plt.xlim([0,20])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram of the trip distance shows that the majority of trips are around one mile, and then the number of trips with higher trip distances decay drastically. \n",
    "\n",
    "The histogram of the trip distance shows two different patterns:\n",
    "    - Number of trips with distance less than 0.9-1 mile which increase rapidly until around one mile \n",
    "    - Number of trips with distance higher than 1-1.1 mile which decay in a light-tailed distribution\n",
    "\n",
    "Emperical cumulative probability distribution shows that %2 of trips are zero-disctance trips, %25 of trips are less than one mile, %52 of trips are less than 2 miles, %78 of trips are less than 4 miles, and %95 of trips are less than 9 miles.\n",
    "\n",
    "Due to the continuous positive-valued nature of the trip distance, the Rayleigh distribution ($f(x;\\sigma )={\\frac {x}{\\sigma ^{2}}}e^{-x^{2}/(2\\sigma ^{2})},~ x\\geq 0$) could be a potential generative probability distribution model. \n",
    "However, simulation shows that the decay rate is less than $e^{-x^{2}}$.\n",
    "\n",
    "\n",
    "Noticing here that the distances are measured in New York city which has a grid layout structure, it is reasonable to think about Manhattan distance rather than Euclidean distance. \n",
    "Therefore, the longtitute and latitute could be generated from independent Poisson variables, $X$ and $Y$ with parameters $\\lambda_x$ and $\\lambda_y$.\n",
    "\n",
    "Then the trip distance is $R= \\sqrt{L_x X^2+L_y Y^2}$, where $L_x$ and $L_y$ are the average horizental and vertical distance of the grids.\n",
    "\n",
    "To test this hypothesis, I can find the maximum likelihood estimation of the latent parameters $L_x$ and $L_y$ and Poisson distributions, $\\lambda_x$ and $\\lambda_y$ using Expectation-Maximization method and check how well the Poisson distribution hypothesis could fit the underlying distribution for the distance random variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eXPBzOEPNwNNPQ1QUXLlidiTuyxnaWdiXtLH7kzYuHEm8RKF9+y00awa1a5sd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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa420c5c890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reporting the mean and median trip distance grouped by hour of day\n",
    "\n",
    "import datetime\n",
    "\n",
    "# Changing the format to datetime\n",
    "df['lpep_pickup_datetime']=pd.to_datetime(df['lpep_pickup_datetime']) \n",
    "\n",
    "# grouping the data by the hour of day\n",
    "df['hour']=df['lpep_pickup_datetime'].apply(lambda x: x.hour)\n",
    "\n",
    "# Calculating the mean and median trip distance grouped by hour of day\n",
    "a=df.groupby('hour')['Trip_distance'].mean()\n",
    "b=df.groupby('hour')['Trip_distance'].median()\n",
    "\n",
    "#Ploting the mean and median trip distance grouped by hour of day \n",
    "\n",
    "#%matplotlib inline\n",
    "#%matplotlib \n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "plt.plot(range(len(a)),a,'-*',range(len(b)),b,'-o')\n",
    "plt.xlabel('Hour of day')\n",
    "plt.ylabel('Mile')\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "plt.legend(['Average trip distance','Median of trip distance'])\n",
    "plt.xlim([0,23])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trips originating or terminating at one of the NYC area airports\n",
    "\n",
    "To study the behavior of trips originating or terminating at one of the NYC area airports, I have used two appraches:\n",
    "    - Exatracting trips that originate or terminate at one of the NYC area airports from geo-location data for JFK, EWR and LGA airports\n",
    "    - Extracting trips that originate or terminate at one of the NYC area airports from 'RateCodeID' data\n",
    "    \n",
    "### Airports from Geo-Location Data:\n",
    "\n",
    "I retrieved the latitude and longtitude iformation for JFK, EWR and LGA airports using google map, and then, checked if the pick-up or drop-off locations are within a given distance of those airports as it is shown below.\n",
    "\n",
    "<img src=\"NYC_airports.png\" style='width: 900px' />\n",
    "\n",
    "The geo-location information are showon at the following table\n",
    "\n",
    "|Originate or destication| Count|% Correct RateCodeID|\n",
    "|---|---|--|\n",
    "|EWR_destination|666|% 63|\n",
    "|EWR_origin|40|% 1|\n",
    "|JFK_destination|12651|% 18|\n",
    "|JFK_origin|283|% 7|\n",
    "|LGA_destination|17919|% 0|\n",
    "|LGA_origin|362|% 0|\n",
    "|Town|1463005|-|\n",
    "\n",
    "As it can be seen from the table, the majority of trips that happen in an airport area don't have the airport RateCodeID.\n",
    "Therfore, the fare and accordingly, the tip are not calculated based on airport trips. \n",
    "Thus, the geo-location information doesn't identify the trips that originate or terminate at one of the NYC area airports\n",
    "\n",
    "### Airports from RateCodeID Data\n",
    "\n",
    "In this scenario, I use 'RateCodeID' data to identify if a trip is originated or terminated at JFK or EWR airports . \n",
    "\n",
    "|Airport|Count| Paid by credit Card| non-zero distance trip|Average distance| Average Fare|\n",
    "|--|--|--|--| --|--|--|\n",
    "|JFK|4435|1588|1563|10.24|49.0|\n",
    "|EWR|1117|395|385|10.91|48.8|\n",
    "\n",
    "#### Here are some interesting charcteristic of airport trips\n",
    "\n",
    "- The data on the tip amount is available just for the credit card payment method. \n",
    "- Though the averag trip distance and average fare value for EWR and JFK are close to each other, but the trip distance and the fare value for EWR and JFK have totally different distributions (histograms).\n",
    "\n",
    "The trip distance histogram shows that the majority of trips to or from JFK airport are populated around 16-20 miles. \n",
    "This observation makes sense since, using google map information, the distances from lower Manhattan, midtwon Manhattan and upper Manhattan to JFK are around 16-18 miles. \n",
    "\n",
    "However, trips to or from EWR are populated more uniformly around 0-30 miles, with higher density around 16 and 26 miles.\n",
    "This observation also tallies data from google map information, where the distances from lower Manhattan, midtwon Manhattan and upper Manhattan to EWR airport are around 15, 17 and 25 miles. \n",
    "\n",
    "<img src=\"Trip_histogram_airport.png\" style='width: 900px' />\n",
    "\n",
    "On the other hand, the fare distributions show that trips to or from JFK airport have flat rate of \\$52 and trips to EWR airport have a minimum charge of \\$20.\n",
    "\n",
    "It seems that the fixed rate of \\$52 makes the trip fare from (to) JFK airport happens to (from) Manhattan reasonable but not other origin-destination pairs.\n",
    "\n",
    "However, the variable rate to EWR airport makes the fare reasonable for both passengers located in manhattan and off-manhattan.\n",
    "\n",
    "<img src=\"Fare_histogram_airport.png\" style='width: 900px' />\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Airports from Geo-Location Data\n",
    "\n",
    "from geopy.distance import vincenty\n",
    "\n",
    "def loc_code(df):\n",
    "    \n",
    "    JFK=(40.6468217,-73.7846603)\n",
    "    LGA=(40.7738209,-73.8695469)\n",
    "    EWR=(40.6907392,-74.1772156)\n",
    "    \n",
    "    JFK_radius=1000\n",
    "    LGA_radius=700\n",
    "    EWR_radius=1000\n",
    "    try:\n",
    "        pickup=(df['Pickup_latitude'], df['Pickup_longitude'])\n",
    "        dropoff=(df['Dropoff_latitude'], df['Dropoff_longitude'])\n",
    "\n",
    "        \n",
    "        if vincenty(JFK, pickup).meters < JFK_radius:\n",
    "            return 'JFK_origin'\n",
    "\n",
    "        elif vincenty(JFK, dropoff).meters < JFK_radius:\n",
    "            return 'JFK_destination'\n",
    "\n",
    "\n",
    "        elif vincenty(EWR, pickup).meters < EWR_radius:\n",
    "            return 'EWR_origin'\n",
    "\n",
    "        elif vincenty(EWR, dropoff).meters < EWR_radius:\n",
    "            return 'EWR_destination'\n",
    "\n",
    "\n",
    "        elif vincenty(LGA, pickup).meters < LGA_radius:\n",
    "            return 'LGA_origin'\n",
    "\n",
    "        elif vincenty(LGA, dropoff).meters < LGA_radius:\n",
    "            return 'LGA_destination'\n",
    "\n",
    "        else:\n",
    "            return 'Town'\n",
    "    \n",
    "    except:\n",
    "        \n",
    "        return 'None'\n",
    "    \n",
    "df['loc_code']=df.apply(loc_code,axis=1)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Location code\n",
      "loc_code\n",
      "EWR_destination        666\n",
      "EWR_origin              40\n",
      "JFK_destination      12651\n",
      "JFK_origin             283\n",
      "LGA_destination      17919\n",
      "LGA_origin             362\n",
      "Town               1463005\n",
      "Name: VendorID, dtype: int64\n",
      "\n",
      "JFK_origin\n",
      "RateCodeID\n",
      "1     93\n",
      "2     19\n",
      "3      1\n",
      "4      1\n",
      "5    169\n",
      "Name: VendorID, dtype: int64\n",
      "\n",
      "JFK_destination\n",
      "RateCodeID\n",
      "1    9632\n",
      "2    2330\n",
      "3       5\n",
      "4       1\n",
      "5     683\n",
      "Name: VendorID, dtype: int64\n",
      "\n",
      "EWR_destination\n",
      "RateCodeID\n",
      "1    155\n",
      "2      3\n",
      "3    419\n",
      "4     22\n",
      "5     67\n",
      "Name: VendorID, dtype: int64\n",
      "\n",
      "EWR_origin\n",
      "RateCodeID\n",
      "1    13\n",
      "3     3\n",
      "5    24\n",
      "Name: VendorID, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print 'Location code'\n",
    "print df.groupby('loc_code')['VendorID'].count()\n",
    "\n",
    "print \"\\nJFK_origin\\n\", df.groupby('loc_code').get_group('JFK_origin').groupby('RateCodeID')['VendorID'].count()\n",
    "print '\\nJFK_destination\\n',df.groupby('loc_code').get_group('JFK_destination').groupby('RateCodeID')['VendorID'].count()\n",
    "print '\\nEWR_destination\\n',df.groupby('loc_code').get_group('EWR_destination').groupby('RateCodeID')['VendorID'].count()\n",
    "print '\\nEWR_origin\\n',df.groupby('loc_code').get_group('EWR_origin').groupby('RateCodeID')['VendorID'].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Airports from RateCodeID Data\n",
    "# Trips that originate or terminate at one of the NYC area airports\n",
    "\n",
    "grouped=df.groupby(['RateCodeID'])\n",
    "\n",
    "JFK_group=grouped.get_group(2)\n",
    "EWR_group=grouped.get_group(3)\n",
    "\n",
    "JFK_group['Tip_amount']\n",
    "\n",
    "a=grouped['Tip_amount'].mean()[1:3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JFK airport:  4435\n",
      "JFK paid by credit:  1588\n",
      "JFK paid by credit, non-zero trip distance:  1563\n",
      "JFK Average fair:  49.0218714769\n",
      "JFK Average fair:  52.0\n",
      "JFK airport:  10.2447846674\n",
      "\n",
      "EWR airport:  1117\n",
      "EWR paid by credit:  395\n",
      "EWR paid by credit, non-zero trip distance:  385\n",
      "EWR Average fair:  48.7985675918\n",
      "EWR Average fair:  44.5\n",
      "EWR Average fair:  20.0\n",
      "EWR airport:  10.9078961504\n"
     ]
    }
   ],
   "source": [
    "print 'JFK airport: ', JFK_group['Tip_amount'].count()\n",
    "print 'JFK paid by credit: ', JFK_group['Tip_amount'][JFK_group['Tip_amount']>0].count()\n",
    "print 'JFK paid by credit, non-zero trip distance: ',\\\n",
    "JFK_group['Tip_amount'][JFK_group['Tip_amount']>0][ JFK_group['Trip_distance']>0].count()\n",
    "print 'JFK Average fair: ', JFK_group['Fare_amount'].mean()\n",
    "print 'JFK Average fair: ', JFK_group['Fare_amount'].median()\n",
    "print 'JFK airport: ', JFK_group['Trip_distance'].mean()\n",
    "\n",
    "\n",
    "\n",
    "print '\\nEWR airport: ', EWR_group['Tip_amount'].count()\n",
    "print 'EWR paid by credit: ', EWR_group['Tip_amount'][EWR_group['Tip_amount']>0].count()\n",
    "print 'EWR paid by credit, non-zero trip distance: ', \\\n",
    "EWR_group['Tip_amount'][EWR_group['Tip_amount']>0][EWR_group['Trip_distance']>0].count()\n",
    "print 'EWR Average fair: ', EWR_group['Fare_amount'].mean()\n",
    "print 'EWR Average fair: ', EWR_group['Fare_amount'].median()\n",
    "print 'EWR Average fair: ', EWR_group['Fare_amount'][EWR_group['Fare_amount']>0].min()\n",
    "print 'EWR airport: ', EWR_group['Trip_distance'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-fdc2d163e796>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m121\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mJFK_group\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Trip_distance'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m40\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Trip distance (mile)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mhist\u001b[1;34m(x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, hold, **kwargs)\u001b[0m\n\u001b[0;32m   2894\u001b[0m                       \u001b[0mhisttype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malign\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malign\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2895\u001b[0m                       \u001b[0mrwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2896\u001b[1;33m                       stacked=stacked, **kwargs)\n\u001b[0m\u001b[0;32m   2897\u001b[0m         \u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2898\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mhist\u001b[1;34m(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[0;32m   5576\u001b[0m         \u001b[1;31m# Massage 'x' for processing.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5577\u001b[0m         \u001b[1;31m# NOTE: Be sure any changes here is also done below to 'weights'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5578\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0miterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5579\u001b[0m             \u001b[1;31m# TODO: support masked arrays;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5580\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    500\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    501\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 502\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    503\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    504\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   1398\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1399\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1400\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1401\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1402\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'integer'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'boolean'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3079)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:2810)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3670)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:7208)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:7146)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    },
    {
     "data": {
      "image/png": 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qqtck+fsk7+zu00cwx0mWrld3/0R3/3h3/3i2X0f6wzWNUbLa7+M/JPmlqjpWVa/M9ovP\nXzzkeU6xynqdSvL2JFm8HvK6JF851Fm+fOz7uf5Az5DaG2n3ZZX1SvLBJD+S5O7Fv/pf6O4bjmrO\nR2nF9WJhxd/HU1X1UJInk7yY5N7uXssgrfjz9eEkH6+qJ7L9D/r3dfd/Htmkj1BV/W2StyW5uqrO\nJLkz25eAL/q53htjARjB/8IcgBEECYARBAmAEQQJgBEECYARBAmAEQQJgBEECYAR/hfo4sq6xofP\ndAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa42026e850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib \n",
    "\n",
    "plt.figure(figsize=[15,5])\n",
    "plt.subplot(121)\n",
    "plt.hist(JFK_group['Trip_distance'],bins=100)\n",
    "plt.xlim([0,40])\n",
    "plt.xlabel('Trip distance (mile)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('JFK airport')\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.hist(EWR_group['Trip_distance'],bins=100)\n",
    "plt.xlim([0,40])\n",
    "plt.xlabel('Trip distance (mile)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('EWR airport')\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-8c3548958b55>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m121\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mJFK_group\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Fare_amount'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m150\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Fare amount\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mhist\u001b[1;34m(x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, hold, **kwargs)\u001b[0m\n\u001b[0;32m   2894\u001b[0m                       \u001b[0mhisttype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malign\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malign\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2895\u001b[0m                       \u001b[0mrwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2896\u001b[1;33m                       stacked=stacked, **kwargs)\n\u001b[0m\u001b[0;32m   2897\u001b[0m         \u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2898\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mhist\u001b[1;34m(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[0;32m   5576\u001b[0m         \u001b[1;31m# Massage 'x' for processing.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5577\u001b[0m         \u001b[1;31m# NOTE: Be sure any changes here is also done below to 'weights'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5578\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0miterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5579\u001b[0m             \u001b[1;31m# TODO: support masked arrays;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5580\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    500\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    501\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 502\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    503\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    504\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   1398\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1399\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1400\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1401\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1402\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'integer'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'boolean'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3079)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:2810)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3670)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:7208)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:7146)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    },
    {
     "data": {
      "image/png": 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qqtck+fsk7+zu00cwx0mWrld3/0R3/3h3/3i2X0f6wzWNUbLa7+M/JPmlqjpWVa/M9ovP\nXzzkeU6xynqdSvL2JFm8HvK6JF851Fm+fOz7uf5Az5DaG2n3ZZX1SvLBJD+S5O7Fv/pf6O4bjmrO\nR2nF9WJhxd/HU1X1UJInk7yY5N7uXssgrfjz9eEkH6+qJ7L9D/r3dfd/Htmkj1BV/W2StyW5uqrO\nJLkz25eAL/q53htjARjB/8IcgBEECYARBAmAEQQJgBEECYARBAmAEQQJgBEECYAR/hfo4sq6xofP\ndAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa420396090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,5])\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.hist(JFK_group['Fare_amount'], bins=100)\n",
    "plt.xlim([0,150])\n",
    "plt.xlabel(\"Fare amount\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"JFK\")\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.hist(EWR_group['Fare_amount'], bins=50)\n",
    "plt.xlim([0,150])\n",
    "plt.xlabel(\"Fare amount\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"EWR\")\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JFK airport:  942\n",
      "EWR airport:  242\n"
     ]
    }
   ],
   "source": [
    "print 'JFK airport: ', JFK_group['Trip_distance'][JFK_group['Trip_distance']==0].count()\n",
    "print 'EWR airport: ', EWR_group['Trip_distance'][EWR_group['Trip_distance']==0].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tip percentage\n",
    "\n",
    "The tip information is just available for credit card transations whihc are %46.91 of all data records. \n",
    "The histogram for credit card trnsactions is plotted here:\n",
    "\n",
    "<img src=\"Tip_percent_histogram.png\" style='width: 900px' />\n",
    "\n",
    "The histogram shows that the tip percentage is populated around %0, %16, %20 and %23.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Tip_percent']=df['Tip_amount'].div(df['Total_amount'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of credit card transactions: 46.91\n"
     ]
    }
   ],
   "source": [
    "print \"Percent of credit card transactions:\", \\\n",
    "(\"%.2f\" % (df.groupby('Payment_type').get_group(1)['VendorID'].count()/float(df['VendorID'].count())*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rn5ZMIJMcv5wdJzkqyY1JvpnkG0nOa/oPTnJ9kruSXJdk/dA2706yPcmdSU4e6t+U5I7m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\nkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa426e37790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,5])\n",
    "\n",
    "plt.hist(df.groupby('Payment_type').get_group(1)['Tip_percent'].dropna(),bins=200)\n",
    "plt.xlabel('Tip percentage')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlim([0,30])\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XX66q2V3r0qU9c+ceYOXKdWf8tdMXab4Y/L1Z1DWFgvNWayqffPI3Jk4cg6a1BlJxu01M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ZAAAg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kjz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fUd7dlev/zyOJs2be/Q9uJ6y3mG6i21NN9+REX9FbiWqKi/0r9/P+lNlfffP+A/r84iWkZD\nQ6sUS2371NWkpCWGUlcBIiOjSEkRqGoNqakQGSm3IVNYuAuXSxdj3weAyxVJYWHHzja0Z12NpLl5\nHR9/HCnNuvrznz+GVnYVjcZeGo2ihPmPd9Tut/wkQaDPX716daew8C2pdl8OsBzAIKJHD41hCoqB\ngWgOoH7cwuUIq6asa6G9w97QsDAkHXYLFtrj9deNSyIEyuRZVHSKW25poLFxBLfe2mQoGpaXt5Xv\nvjuDVn+4BAjjxIkz0pIImjTBMSCXF14oMlQ3BFoWxxtvnDOUxdEZ87+ZuslAoSgKJ0+W+2sPz6Ao\nZf7fctd50aI5LFnyQ7zewWRnT2Lx4n989E/HoUPHGTz4G2AkgwcfNUS0oY/1hg3GxtrjSadnz2g/\niYmCzaYSE9PdYL3lcaDEUL2lnmLs9XZDVb/D6+1mKMVYS8Uc5P+1GoDExMHSLLUjRyaSn5/CoUOr\nyM9PlU5d1Z2wjz6qp7XVx8GDDdJOmM1mIyGhD9o88BRgo3//3thsNqlzmyG+mT//fmJjo4FwtLV4\nOL16RTN//v1S7b7ppmv97Z4J2Ljppuuk2305wHIAg4hTp06j3XSJwARgGKD4j4cGQq026Z+hpiyU\nrtnFC7ZbQ9ZhD6VrZsEcMjPfMrTJUVR0is2bJ1FZ+QGbN082pBv21FNr2LtXAdazZw+GdOk8nnSm\nTbsdt7sOWIXbXcf06bdLPZuaNEE4Xq/G/Of1huFyhUulNOqbQitW7Keh4UYef3yf9HiZdZjN1k0G\nivT0h/nqqyI/Gch2FAUOHSqSulZtUc8LNDTs5M035bX4zGLMmKn8/vcfcOLEQCCXEycGsnfv+1L6\ncmbGOiPjESoqqvB6NbZXr9dOefk5aXKQ2bOX4vNFA2/i83WTrrfMzS1g4cKf07NnX3y+9fTs2ZcF\nC/5deqy1VMwYoDeqOhuIJTa2h3TEdMWKTO6+exI33TSdu++eZGBzQpO+KCs7D9goK6uSlr7IzS3g\n22/PEBHxLXA7ERHHOXZMznmEvyW+ucsw8U15eTXaOlzb1Cgvr5bWe9y7t5DIyOPAdiIjT7BnT2GX\n2ly2HMAgwmYLQ8vPrkaL/tUAXv/xro9A9J26AswI21q4tOgsrTILFroKzp/38s03x9m3r2MyFdAW\nfa+++gZnz44iP/9N6UVfm84agEJrK4ZIEDSq/dtobu5FUtJSQ+yl//+URlU6pdGMthyYm/+DlUGi\nk4to5DMqQvRh8eJ0aU28p55aSGVlPaBQWVl/yTbRHnhgJlFRTtrrtEVFhUsR0LSlYnrRpC+8hjQE\nk5OvRUuljAPKpEW+zRC5eDzprF37JGFhEYBCWFgE69b9zNBYR0ZGkZoqiIiIIzVVkU7F1PHYY89y\n8GAvfvKT56Rt9u8/QHFxGdrzmASoFBeXSbGAejzpbN36PJGRfYBSIiPj2LbteQPMujrxTSFaPd5B\naeIbjyede+9NxW4vAmKw24+TlnandLru1q3PExGRBNiIiEgy1O7LAZYDGER4va1olyAWuBPoBaj+\n410fr7++lxdf/CjkiFL0Oox9+/YZJhDoCgi1ejJ9wbZ2bWrIOuyhds0smEN4uJ2JEzsmUxkzZiqK\nMpzdu33ABnbt8qIow6UiLHPmLOKFF15FCAWdsGL16leYM2eRdDsDdab0lEa7vYKYmC+x2yukUxp1\nbbnKygjgTiorw6W15cAcgUywNqTMkIto4/UMlZVNwAwqKxtZsuQZ6fEyg3nz7mPYsIG0r11MTBwo\nRUCjj2tFxQlU9QQVFSekx1oX+daczhMA0iLf2li/iRAq8EOEUHjllTek9QfNRphPny7h6697UVcX\nz9dfu6VTMc2kVU+YMJawMBvtCZ3CwmxShE5mn8eXXtpOQ0MjcAOaBMVoGhoaeemljus1x45NYfv2\n3bS0XAGspqVlENu27ZKSOLn4uVhySZ+LzoLlAAYRTqeTthC58jfHuy6suisLlxPMLNgsWOh6OEtL\ni5BaNBYWvsUdd4ynvZ5eSsoEKSKDCRPGYLM5gG+AAuAoNptDKg1Th5lnc/bsqSxaNJqWlqtZvPgG\nZs/u2GmFNm25mJgoQCEmJkpaW64zEIwMEp1cxOU6CRTgcp2UJhfRxmsMMTG1wEJiYuqYOHGMofHy\n+XwkJ0/D5zMmcZWXt5XPPy+iPanJZ58VSafoBZpOaYYdFwR1dY1oWV0/BUr9v+UyoTojwlxZeRL4\nK5WVJ6Wjnm1p1TZgJV6vTTqtOitrDoMHx6O5FAqgcsUV/aQj8maex8LCXfTtG0P7OSw+3i1FIlNY\n+JZfvsKHLsGWmDhIav67+LlYFdBzEXQIIbrMR2tu6CAhYZSAKwVkCVgiwCPgKpGQMCrYTTMFn88n\ndux4W/Tv/7gAIfr3f1zs3PmO8Pl8wW6aBQsWuhD8c37Q3z1d5QMIeFAMHHiL9HybkpLpf/fcICBT\npKZ6pOx8Pp9ISXmo3fsrS6SmZhqe530+n1i+/DlDdjk5+SIpaaq48srlAu4SV165XCQlTRU5OflS\n9jt3viOioxcLt3uOiI5eJF57bY+hNndFaH1+VCQlLRHR0YsN9dmMrRBCZGc/LWCeWLbsGUN2Pp9P\n3HvvUuF2ZwjwCbc7Q6SlLZO6V/R1iNu9WIAQbvdi6XWImTWMz+cT2dm/EIqSJUAIRckSS5c+bej+\nDuSZEEJ7LhISfiBgrgCfgLkiIeEHUs9F2/N8l4B5Au6Sfp5zcvKFw3G1fx55VECmcDhGGHoeIyIW\nCpgmIiIWGr6/hg2b5J+HZgjIEomJU6TscnLyRVTUdf523ywgU7hc1xlqt8u1WLjdk4TLdennEbPv\nRysCGERUVFSh7ZiUou0OaTnU2vGuC6vuyoIFCxaChXOMH3+D9Hx75MhJXK4DwPW4XAc5csRoGuY5\nhg/3GUrDbI/XXtvD6tXf8frre6Vt9EjHt98eAOL49tsDhuv4AtXyCzaECKy23kxkKVBbPa1wzZrv\ngPX86lcnDLG16nWiTU1u3O4pNDXFSNeJ6il65841Aks4d04+Ra9tDdNAePgMzp+vN8TkaUZOAQJ7\nJkC7N2pqalBVTe9RVZ3U1FyQulfy8rby7rsfAT3QpC968PvfH5QaL48nnenT2xM61TNjhhyhU25u\nAQsW/ByH4ziQgMNxgvnzf24oY8zrVf1MsT0ZPPgbvF4518bjSSc6OgpV/RwYgap+icvlMjSPZGX1\nobk5iXnzutY8AlYKaFDR2FhLW+rnH9BTBLTjXRtW3VXXRSj2Swjjml9dCaF4zSwEih5s27aLQYM6\nrr8ZNGgsx459R23t9UAutbXXU1R0XMoWtDTM7dsf4quvVrN9e6Z0Gia0lQosXryX5uZ1LFq0R7pU\nwOUawsyZS4Ar0RarV3LPPY/icg2ROm+blt+PA9LyCyZef30v69eXGK6tD5zhMfC6d50oSFuQK3i9\nqiGiINCd9d7U1EBWVpz0WkIIQXV1PZrM1iqgmJqaOul3QFHRKX7wg3oaG3txyy2NhtYwupzC2rV3\nGpZTuPrqFBYtMv5MAGRlZfDQQzPQMm2z8fkgM3MGWVkdi7lrrLy3+bXtFNzu7kyfPkla+mLatNuo\nr++JoqRTX99D2lEHgdNp58KFfsBdXLgQj9MZhmzKbHr6w5w8Wczp00OBXE6fHspf/3paapNBY3s9\nj893NbAeny+JiooqKbbX9vPIhQuBaYIGG5YDGFQ0obGAngEGAyVouchNwWxUp8Cqu7JwOSEQzS8L\nFromHIwaNYxjxz7s8C+Tk0ehvXPaWBZB+I93DDPzvLZAr6G8vAlQKC9vktbvqqo6TESEnfZ1P5GR\nDqqqDndo21W1XDtDQuKxx57j4ME+LF/+/D+wpW1QVRVFUf21f7Px+byoqiItS6AvsrdtO0JLSxJb\nt34tvch+//0D1NXVAfFoBDJ9qa2tkxJFv1jiZINhiZMVKzKZNm0yGzf+mmnTJks/F/ozUVbWiKZp\n1yj9TIDm0KxenY+WVVYCnGXVqlelHBrNiZtEc7NCUlI2zc0wbdokKSdOj+J1716NEAV0714jHcUT\nAurr6/D5tE0CTcahHtm9Wn2Tob297CZDfv6LjB07Am0O0T7JySMNseN2tXmkPSwHMKiIRBORHAm8\nCYxAe6FFBrNRnYpQ1icL1b6FUr/MaH51JYTSNbNgFgqffnqY++9/tMO/vOWWm9DW4m0si6qqHf9H\nIysrg1mzbsfrBcjG6xXMmvUjqWiFw+HA5YpAa/c9gEpUVAQOh6ND287Qcg2U1MQMzMgamE3F1GF0\nntHJVHr0OA9so0ePagNkKqDry1VUhAOrqagIl9aXy89fw5AhA9GcoVVAGUOHyomit0UuFTRCFMVw\n5PL11/eye3ek4U3H+voGfwTvPnw+QX19g7TthAljCA93ogUWrgV8hIfLEblA4Km+Hk8669Y9SVhY\nPPD/CAuLZ/36J6XuzaysDIYPvwrNAXsTgKSkq6TmAWjbZPB6Bao6Hq/XJ73JsHHjNr7++jjaOnwJ\nYOPw4eOXhLH1coDlAAYR1103AtClIBT/t9d/3IIFC2ZhVvPLgoWuh89wOqOkFqtZWXNQVQdt2Scl\nqKpDir1PR6DOkKIonD1bhdNZidP5V5zOSs6ela+VqqpqIDz8A6Ab4eEfUFUlv1AuKjrF5s2TiY4+\nwebNUwyXKFzqSBqYkzUwEyXREUjtoceTzssv/4Lo6MGASnT0YF555Wnp+dfjyWDmzMkXpZDOmjUF\nj6dj50BVVXr16oEmin4z0JteveRE0dsil2XAcXy+UmmnwmykVtO0+wY4BRz1/5aFgqr6aOOWsPl/\nyz1TeuRyxYqVhiKXZu5NRVEYODDBPw+8h9NZxcCB/Qw5UocOHWfqVC8QTkqKjy+/PC5lp7HjxuNy\nVQGrcLnO079//CXRBL0cYDmAQcRnn33l/5cNuBX9crQd7/oI5dqkUO1bKPXLrMZQV0EoXTMLZnE9\njzwyW2qxqigKSUlXYbOFAaXYbGFcffVVhhZfS5c+w8GDbh577FnDLR0xYii33dadpqZYJk3qyYgR\ncrVS6ekPo6oKXu94YBNe73hUFUMpegcOfMbBg14++uhz6YVuZ0XSAnGadVmDHj3i8PkG0aNHnLSs\nQVuUBByONH+0RD4VEwLT9dWdgPLyWmA85eUXDEVJzBKqREVFk5Liw2aLIiVFEBUlJ4puRgbi4tTA\nfYZSAxUFf98igeGAJo0g+zgqCjQ1+dA0pVcBbpqbvdL2ADt2vM1zz21j5853pG3MSG5A+3kAJk3q\nIT0P6Oc+fbqEDz+swecbyQcf1EhrHyqKws9+Ng9F6Ut4+D0oSpz/t7zD3JVLnSwHMKjwooWeP0bb\n7fkEzRn0BrNRFiyEDIKt+WXBwqXGsmWDpHfAc3MLKCkpJzY2CthKbKyL4uJyg8LR3wB5rF59xJAz\nZKbOykxE62InbokhJ64zImkQWATR40ln7donaWoqB35DU1MF69b9THouO3ToOMuWDaShocDwPdKW\nRh9jKI1erw0LCysCzhEW9q1hhkedUOXQoVWGCVVOny7h448baWlJ4uOPG6UdAz1yGRXVDVhJVFQ3\n6chlewbRbt2eNsQg2oZYYB3Qy5Dz5vFkMHbsSL0lACQnXyMVMdWv8wMPrAZimDt3tfR11u9NRXEA\nySiKQ/re1OeBd94pB67k7bfLDdVb7t9fyNGj33H+fDSwmvPnozl69Dv27y+Usi8qOsXEifU0Np7j\nllsaulwUzwwsBzCo0J/sCGAAbbV/XSeHuCOEcm1SqPYtlPp1cWH7W4YK27sSQumaWTCHlSsfZ8+e\nPKm/NVO7c/TotzQ3NyJEOKAgRDjNzY0cPfqt1LnNOFNtES0fMBKvV55c5OLz3hrgeQOLpJmJIOrZ\nDFVVxcBYqqqKDWUz7N27kZUrH0dVVcP3SFsafRRVVfLi4jrDY21tFXALtbVVhhgewQx7qVY/WFam\n1Q+WlTml6wd1h6209DhwnNLSbw05cbrMiM+XZEhmZN++AzQ0NNCelKm+voF9+zomrtHbPWBAPBER\nEbjd9xEREU7//nLplJs3b+Pw4WM0NEQA19DQEMHhw8fYvFm+Hu7s2feB6zl79gPp8dIE6CPw+XoB\nO/H53LhckdJ1i/n5a5gyJZn2pVS33z5OqtZTdz7ffrsUsLN7d5kh5xMCl2W5HGA5gJcFegDX+L8t\nWLDQmejqefoWLBiBkYVIW4reCeAY5eXHpRduBw7sZtCgeLRF12QABg/ux4EDu6XO3Z68AWYadqYO\nHTrO6NFngZsZPbpcOqJl1okLNJIG5pze/fsLKS4uo0364gqKi8ukIx2BQnc8z52LAFZx7px8Gv3m\nzds5ffosGtHdBmAEp0+fZfPm7YbaEEjE1Ay7ZHr6w8yevRSfLxrYgM/XjVmzsqUcg4tlRozJAxQU\nrGHYsIG0J2VKTBxMQUHHzoyOkSMTWbAgnqamGBYuTJCOmD7wwGycTgfQB1gN9CY83MEDD8zu0HbM\nmKncc8+j+HxJaOM1nBkzFjNmTMeyMFlZc7jppmtoz+g7fvw10nXIGzdu47//+yP/rzRA8F//dVCK\nyEV3PoXwAUkI4TPkfELgsiyXAywHMKhoRduN6gP8GOjt/90azEZ1KkK5NilU+xZq/QpUw6orIdSu\nmYXAYUQ8Wq/diYiIAXKJjHRL1+7YbDZqaxuBo8BAoIgLF5qw2WzS587J2Un37qeA7XTvfor163dK\np5++995BvvgiDljPF1/05o9/PCC9c687ce+885BhJy7QSBqYcz7z89fwox+NQysb0RbKd9xxs1Sk\nwwx0aQKtiftQVYXqajlx8cLCXfTtG0P7xX18vJvCwl1S5zYTMb2YXXIJIM8umZ//IosWpQFOv72T\nxYszApAHMFYDqBHXxAJl2O0lQBmxsb0My2YE4nwqikJLSxNt10qlpaVZajOosPAt7rjjZtruzTBS\nUiZSWPhWh7YZGY/wu9/tR1v7zgB87Nq1T0q6QoPws6aWAgVAmf93x/fn++8fpKqqBm3t3ReIpaqq\nmvffP9ihbRvZzwkoa0cAACAASURBVH4/2U/XYxi3HMAgIj6+P9BM+wcOWvzHLViwYMGCBWN45JFt\n0gsRIQR2u43aWgCFCxfA4bBJLe5zcwuorq4jLKw/kEdYWH+qq2ulF0AeTzqvvPILunXTdv+7dbuG\nLVt+IbVQNluLZ8aJM4tDh46zdOkAFi++hqVLBxqKXJ4/X4u2VpgJaL+NELkEkq6WlZVBZuYMhAgD\n1iGEDY9HTlzcZrPRrZvL3+abAIVu3aKlNwnMCMnr7JJ2+zngLHb7OWl2yfaOOtxjyFFvLw8wcOA6\nw/IAUVGRLFs2hsbGrSxbNpbISHlZsDbn04umTScvFQIghILmOC1Bc6zk2myz2fxjo9+bWn9lrnN+\n/ouMHz8KOAvEAKXcfPMN0s+yx5PBwoWzgTi0NXQcDz88W6ruMT9/DTEx0UCF//zncLujpTZV9LGu\nrKwHFCor67scw3hQHEBFUVYoivKVoihfKoqyTVEUp6IoMYqivKsoylFFUX6vKErI50MWF58F7Gj0\n2zn+b7v/eGgglGuTQrVvVr+6HkK5b/+MMPOONKKVpmvxtbYCZNPaKq/Ft29fIT6fl9ZWJ7CS1lYn\nPl8r+/bJpSSa0dHqDFZLIQR79nx0yWt39u7dSHLy9eTklHLTTaOknc/c3AL+8pejF0VMP/30G0MR\nh0DS1drLdQwfnoDTWSUt15GbW8C335Zgs/0ZGIvN9meOHZMjYgFzQvK5uQW8++6fcLsjgK243ZH8\n/veF0ufWUoxLgBhGjy41FCXWyw5OnNhhuOxg796NPP/8cp544v/x/PPLDW1OtMkx1ALTqaiolX6m\nsrIyGDduFFCO5gxVMH78KGk9viNHTpKaKmht3U5qquDIEbk+a1p836IFQrTP4cPHpFI4QUtR3rjx\ndTTnc4b/2OtSKcrJyamcO1eDRrqjsaZWVNSQnJwqdd7s7GeorGwCllBZ2ciSJc90LYZxIcQl/QCD\ngOOA0//7N8D9wPPAT/zHlgPP/g+2IpQAvQUME3CHAK//O1FA72A3zYIFCxaCDv+cf8nfU8H8mH1H\nKkqWWLr0aeHz+Toc35ycfNGt22gBdwtYLOAu0a3bDSInJ79D2w0btoioqGsE3CVgnoC7RFTUNWLD\nhi0d2up4+uk88dpre4TP5xOvvbZHPPPMRmnbyZMfEsuWPSO8Xq9YtuwZMWVKprStEELs3PmOiI5+\nVLz22h5DdmaQk5MvkpKmiiFDnhDgE0OGPCGSkqZKjbfP5xM7drwt+vd/XIAQ/fs/LnbufEf6Ogd6\nXiECv05paQtEWNjVAv5FgE/Av4iwsKtFWtoCKfucnHwRFXW96NFjpgCv6NFjpoiKuv4fPl5paQuF\nwzFS2O0eAT5ht3uEwzFSpKUtlGq3WQR6b+bk5IvY2HGie/c7BCwS3bvfIWJjx0uNlzYX3CAgVcAz\nAlKl5wIzyMl5VTid1wiY679H5gqn8xqRk/OqlL3P5xP33rtUOJ3jBWQJp/NmkZa2TOo6t7a2Crd7\njP/czwmYK3r1GiNaW1ulz+t2ZwjwCbc7Q/q8nQWz78dgRABrgBYgUlGUMDTqy2LgTmCL/2+2AHcF\noW2XGLrkQ19gvv+71X88NBDKtUmh2jerX10Pody3f0KYekca0UrzeNKZOvUW3O5IYDVudxQpKbdI\n0923tPiAbmg+azdaWoQhht0VKzK5667b6Ncvmbvuus1Qfe7evRt57rmfMG7cDJ577ieGImltQt13\nGhbqNoOL68MUgxpxWiSnqqoBt3syVVXy8gJmzguB11G3MTS21QDKMjTq7daE5K9AE5K/IgA5BuMR\n5rbUUwVYiderGJb78Pl8JCVNMKz1aEZEvo11NR6Ip7Y2Xpp1VZsLJuJyRQCluFwRpKT8wFBKowgg\nxVgIiIpyoNUPTgTCiIpyIvtfZGQ8ws6de2hqGgKU0tR0FTt2vCNVQ7hp03YaG5uBSqAIqKSxsZlN\nmzomKdIYxm+judmN230/zc0xTJt2W5diGL/kDqAQohL4JXAS7aV2XgjxLtBHCFHq/7NSNGaUkMbM\nmXeiPZhlQIn/Wz9uwYIFCxb+2WD2HWmE+U9fxJw/7wLu5fz5KOlFjMeTQVxcL6AKLWWsir593VK1\nN+1x113zKSm5nrvvXmDIDgLX0zPjDJmBGacEtNTCrKw+NDcnMW+evLyA2fMGClVVqa6uB7RUXRBU\nV8vXLXbGeAXCAN2WeloGHMfnKzWcYvzYY8/x9dc9A7o3Gxq8wEoaGozV8Hk8GcycORmvtxz4K15v\nBbNmTZF6JvPytvL++59QV9cNWEVdXTf27//YUEpjICnGbWQ9x4CewDFpsh7QnPWxY0cA36HVAX5H\ncvJISWdd+B1AF5AHRNPQ0IysTIku99HUFGNI7uNywSV3ABVFuRJ4FG3LMB5wKYpy0ZXWQ5v/k/3c\nuXN56qmneOqpp3jhhRcu2vl+7733utTvHTt2oJHAxABv+v/dxI4db1wW7euM3+1xObSnM3//bR+D\n3Z7O+q3Xk10u7ems3/qxy6U9nfn7lltuuazaE+jvF1544fv5fe7cufwzwuw7Mjd3G7/85fOkpEzr\n8B2ZmJjMPfd48HqLgW14vV8yY0bm9/Ttf++a5eVtpaKiGC2LZRXQm/LyYpYuffJ/PV/732PGTEVR\nBrB79xlgA7t2eVGUASQmJndo3yZC/ydg5vfskLfddneH99i+ffsuIumoqCj63qm4FPf43r1/+N4p\nWbo0nr17/yBln5tbQG7uS/z6119x4YLG8JiTs5ns7J9K2RcVnWLp0r5cc00tL710O0VFpwy3Xz9m\npL+NjdUsWzaYhoYCZs6ExsaaSzJeADfdNAS324miKEyfPoXk5Kuk2q+N9U5crlZgFj17xrFhww6y\ns3/aof1tt93djrl0CatXf/g9c2lH7d23bx+HD39JRcUJVPUEpaUH+eqrL753eDuyX7r0SXJytqA5\nUuuABjZseOV7J+7v2Xs86Qwd2pvo6CpAISYmimHD+jB0aD+p8br66hQefbTgoqi6zHhNmjSNwsLP\ngcFo5DPh/OlPn5GR8YjU/TF58nQKC78ErkAjoIngT3/6gjlzFnVov2zZk3i91UAUWoT6PF5vNStW\nPNPheLV/HnXGVSPPYyC/X3jhhYt8INMwkz8ayAeYBWxq93sO2p36NRDnP9YXOPI/2HZC1uzlg+HD\nbxYwWECm0ALhmQKuEMOH3xzsplnoAD6fTyxf/twlzfe2YOGfDfxz1gCaekf2779cutaptbVV3HHH\nvwh4yF8H+JBISck0UAOTLdzuxQKEcLsXi7S0pdJzYtu5Pf73n0f63F6vVyxZ8h9CUbIECKEoWSI7\n+2nh9Xqlzm2m9jBYMFPTpiMYdY9dEWbGWr83bbZ5AoSw2eZJ35s5Ofmid+9xom/fxQJ8om/fxaJ3\n73HSdXgbNrwqunW7Xqiq9kypqkd06zZKbNggV0+3c+c7IiJioYBZIiJiofR9oo9XQsJyAc+JhAT5\nOWj9+i0iIuLqi+aBiIgRYv16uVpir9crBgyYKBTF458LPGLgwB9IjXdzc7OIjLxaQJaAJQI8IjJy\nhGhubpbus5nn0SzMvh+DUQN4BEhWFCVC0bY1bgMOA7vQCt3xf78RhLZdUkRGRqFt4irAcP9R4T8e\nGmi/exFKeP31vbz44kddUvyzI4TqNQvVfkFo9+2fEKbekaWl1dJpcm307cfQtLCOSdO3a+mjk2hq\nArd7Mk1NgmnTJkmn57WdWwAjACF97o0bt7Flyxv+OqE0hIBXXvmtNHPgihWZTJs2mfT0eUybNtmw\nNqjP5yM5eZqh+q72EMJ4rZSZdEg9QrNixT4uXIjj8cffM1z3KIQgLS3LUJs7C4GMl47W1lZcrqtp\nbZXXVzZTb9meoVZVJxpiqPV40lm79kmamwEUmpth3bqfSaeAZmVl8NBDM/z35Xh8Pi+ZmXKSHbm5\nBSxY8HMcjjJgGw5HOfPn/1xaQ1BjH9UilxUVJ6THa968Ofzwh5pECPwQULjttpuYN09OCF5VVWbM\nuB1NwmImQijcc8/tUuNtt9txOLSaR/gWKMPpDMdut3doG6y06s5EMGoAPwdeBT4GvvAfzgOeBSYp\ninIU+D/+3yGNTz75HE1otBAYAhwAnP7jFi5HtC/SbmhYeEkJBCxYsBD6MPuObG4+wqxZS6XEstPT\nH2b37vfR0q+0z65d+6UF1QOtSdNx5MhJBg8uAiYweHCRNHW8x5NOQkI/XK6TQAEu10n69+9nqI7v\n9df38sYb5wLaxAuk9vBvz220VgoCr2nTa8tOn/4GOMvp00WG6x7NjJdZBDpeAGPHTqeubgLJyTMM\n2Zm5tw8dOk5qqheHI5bUVCEtIWFWWqBXrxGsWvUqGqHJjcAxfvnLV+nVa0SHtkIIHA4bNTXRgEpN\njQunU5VyunNzC1i48Of07NkXn289PXv2ZcGCf5d2Hk+dqsBuP07//j2x249z6lS5tCOVm1tATs5O\nIiOPA9uJjDzO+vU7pddkTU2txMYWY7fHExtb7NdQlENR0SleemkKKSl9vk+r7lIwEz681B9CLAUU\n+vhlIB70098+6P/dJ9hNs/C/4HII+1uw8M8C/glTQM18AAH/ImJixkrJMaxfv0X07HmDgB8LeFTA\nXSIm5gap9Cuz0gJmqfb1dMakpCUiOnqxdLqamXabbbN+7quuWiHgWXHVVSsMjVmguPHGO/xrizY5\nBhgmbrzxDuk2B3qdhQi8ZMLMeA0cOOZ/XF8NHDhG+ryB9Nms1IcZaYF1614Wqjr8ouusqsPFunUv\nd2iblrZAqGqiPx3yUQEeoaqJUpId+rqofTq4kXWRmZTsDRteFf36TRBhYVrKbVjYPJGQMEEq7VW/\nVu1Tbo3e28FMqzb7fgz6C8tQY0PMAYyIGCzgCn/9xXP+7ytFRMTgYDfNwt9BoAsPCxYsGIPlABp/\nR4JHpKZmSurDvSpcrusvWjC6XNdLaXC1bYYt92+Gydf9CGGuVkoIbdG4c+c7YvnyZ8XOne9ILxqD\nVd/V/txud7qAR4XbnXFJNhBbW1vFsGGT/NdZCPgXkZg4RbrWM1i1h2bGa926l4XTmXQRx4LTebWU\nM2Smz2afCzPrC5/PJ2Jjb7yoz7GxN0rf28OGTRGaDuAYAakiMfFH0rWLCQkT/PfXFQIeFAkJEw05\nUl6vV4wde7f0s6TD5/OJ7OxfCEWZJ2CJIR1U3Xls/zwbdR4v9WZOe5h9PwajBtCCH16vD63+oQL4\nwP8t/MdDA6FYm6Sn4axdm2ooDaerIBSvGYRuvyC0+2bBGOx2hZMny6RSqISAxsZGNCZPBfDS2NiE\nkCi10utdSktrgNmGag/h4lophyPNUK0UaHV8QghWrfovAOk6vva1OwMH3mOodsdsm7UUv+eorIwA\nVlFZGc6SJc8aotoPBDabjSFDBqFprc0EwhgyZKB0rac2Xg106zaZ8+fl6+HM6tqZGa/58+8nPj4W\nrdIpDVCIj49l/vz7O7A0V9/VVg9XB/yQiopaw9IVL700hdRU42mFeXlbKS+v9v/SZDfKy6ulxktV\nVXr16oEmSRYFnKVXrx6S97agpqYBrZxpEnCQmpp6ZOUUAB577FkOHmzmJz95TtoGtPE+e7aKsLBy\n4BhhYRWcPSung5qVlcGsWbf719zj8Xq9zJr1I6maST2tuqrqJHCWqqqTl0xOprNgOYBBRHNzI+AA\notHob6MBu/+4hcsVuiiuTi1tlEDAggULFv5RuOaaUmbPTpH6W0UBuz0MrRZ9CeDAbg9DZq2anv4w\ns2cvpaXlDLCNlpZi6dpDHYcOHWfZsoE0NBSwbNkg6Vop3bFYtGg7LS1JPPLINkOOhb6J9/LLCwxv\n4gXaZtAWjRMmjCYmRqOdj4mJYuLEGy7JovHIkZOkpgpaW7eTmiqk6y1BG6/MzN7U1VXi8cQZrj0M\nVHNRHy+Xyw4ouFwO6fFSFIXy8gsoShHDhsWiKEX+38YcMaP1XTqZSvfu1cBP6d69RppMBdo2Nlav\n/isgv7EBsH9/IYpiQ9PlLABKURQb+/cXdmg7ZsxUPvzwMzR50TCgDx988On3kjB/D0888Qw1NReA\nZGADMJaamgs88cQzHdq2SbocA5bywgtF38tmyCA3t4B33/0TdnspEI/dXsbvf18oNd55eVvZtOm3\nwDdoNZNFbNz4n1IOs745ce6ctjlx7tyl2czpTFgOYFDhBXxoTuCt/m+f/3hoQNeUC0WEat+sfnU9\nhHLfLBjDJ5+4WbHil1ILN48ng1GjRqCx4K0Cyhg9eoSUcPTNN4+hW7dIhIgDVISIo3v3CG6+eYx0\nW/fu3cjKlY+jqiorVz7Onj15kpaCmppaKirCgdVUVIRTU1OLbMRB38S79dZbDW/iBd7mNubU5maF\npKRsmpsxxJxqBkVFe3jrrVxsNhtvvZXL0aNvS9nl5hZQUPAm27Ydweu9ma1bvyY//w1D7JCBMiVq\n4uR/prZWAEuorfWyf/+fpRfZTzwxj507f8LXX7/Azp3L+elP50nZgXaPAGzYcBZFUaTvEY8nnXXr\nnsRu7wf8H+z2fqxf/6SU06pvbCxevJfm5nUsWrTH0MbGhAljiYy0ozH6qkBvIiMdTJgwtkPbBx6Y\nhcsV6bdLQnO4I3nggVkd2v7bvz0GtAA2tEwCG9DqP95Rm8fgcoXj9YYBt+L1huFyhTNhgtw8sn9/\nIVVV1dTXDwM2UF8/lKqq81JOrzZfCFR1CLAaVb2q3fEOLIWguroGLUCqoKoK1dUX9FT8LgHLAQwi\nNPrZFrTdlmz0h0Y7bsGCBQsWLBiFjZSUCRQWvtXhXyqKwoABfXE4+qIo6TgccQwYEC9N3z5+/HVo\nCz4txW78+Oul6dvNwOPJYObMyXi9KqDg9arMmjVFynENNgJl8tQhROCSCIHBnLNthilRiwCOISam\nFlhFTEwdEyeOkY4gBpqtYyZ11YyEhO5UlJU1AQplZU3U1NRIX2tFUWhu9qKtKZcANpqbWyXTmxW/\nVEZvYDXQm9ZWL6rasW1YWBjaPKAC9/j/rfiP/31kZc1h3Lhr/bbZaPPIdWRlyc0j+flrmDIlmfbO\n5+23jyM/f02Hth5PBg89NA0hNFshbGRmTpOaR7KyMsjMnIEQ2vpdCBsej5zkxuUCywEMIn75y6fQ\non4lwEn/t8N/PDQQyrVJodo3q19dD6HcNwtGoUjr6eXmFvCHPxwgJqYWIQqIianj3Xfl0qfa07cn\nJvY2TN9uBoqiUFJyjvBwO0lJSwgPD6OkRK7upz2C8dyYLSEwI4kQiPN4sbO9z7CzHWgkDfSI6W00\nN/ciKWkpzc1upk277R9+j7Wlrmq1sY2NXkOpq7qERH19N0MSEm01aQJIw+v1Sdekae3OoG/f3rSP\n6MfH95G6Vh5PBg5HGJpboDlzTqdd2hmy2SLQuCzCgA+x2cKl2q0oCidPlmOzlQOfYbOVc/Kk/Dyi\nqirnz9cCCg6HthF1/nytVO1i+3nE7b7P0DzSVntYAZQYqj28XGA5gEFEdva/tft1+n85bsGCBQsW\nLMghNdVnSE9v3bonUdU44CZUNU46XQ1g1qw7KCh4lOrqQgoKHmXWrI7TTv8WgYqqjxyZyJYtdxAd\n/R1btkxl5MhhhuyFEOTlbe8yKVtmCVUgMOex/SJ54MC1hhbJndFmsxHTQNCeyEVR0gwRuegps2+9\nVUtDw0LefPOCdMpsW01aMXANUCJdk6bbV1VVo0XxpgCxVFbKkcAoisLUqbf6I36zUVWYOvVWaWdo\n1qzbCQvrC5QSFtaX2bNvlx6vkpJy3O4IoBm3O5Li4nJD90hUVDTZ2QO47roGsrMHEBXlkrYdOTKR\n+fPjqa4uYcGCftLziF572LNnONBIz54R0rWHlw3MUIhe6g8hJgMRFpbg16kZKuBW/3eiCAtLCHbT\nLFiwYCHowJKBMPyONEq1v3PnO0JRxgq4VShKsmH7lJRMAVkiNdVjyE5HdvbTAuaJZcueuaS2wdTv\nCgRmpAnMavkFqtPWWbq5geoIBoqcnHwRGztOxMXNEeAVcXFzRGzseGktPzMSEvfemy1crpkCHhUu\n10yRlrZUut+6jqDL9SMBi4XL9SNpHcE2rchUAV7/d6K0VmTv3uNEjx4zBSwWPXrMFL17jzM0Xk7n\nbQLmCadzUkD3SHb2LwRkiqVLn5a20Z+LuLgMAYtFXNwcQ5qNO3a8LVyu2wXMEy7Xjy65JrTZ96MV\nAQwiWltb/P8aghY2HwqIdsctWLBgwYIFeTz++D7pKMuYMVO5555HESIWuAYhYpkxY7EUgcyYMVNR\nlOHs3u0DNrBrlxdFGS5lC23sf2vWfAes51e/OiHN/mfGtjOiUkKYq8Pzer3Ex4/F65UnfGurLasn\nPHwCVVV10lEpsymNgaattpeQcLvvMyQh0R47d77N888X8dpr7xiyA2htbcXlutpf3yYHPTJus8UB\nM7DZ5CPjZmoANdKbT6ir6wasoq6uG/v3f2woAvj++x9TWxsP9KW2Nl6aNKew8C3uuONm2hPIyNYS\ng8DhsFNb2x1YTW1tdxwOOzJ1omPHpjBz5hKamgYA62lq6s899zzK2LFyTMYXs4jmGmQR1epby8q0\n+tayMqd0fWtGxiOkpT3mH+v11Nb25d57l5GR8YhUuy8HWA5gEKGqYWg3mgp0Q8u7Fv7joYFQrk0K\n1b5Z/ep6COW+WTCGqqpT0ov7Nua/PujED7LMf9qCcTztyRfkF4yQn/8iCxfOwufTao58PpWHH76X\n/PwX/6G2F0sT7DMsTQDm6vAA7rprHiUl13P33fMN2RUVneKWWxpobBzBrbc2SadDmklp1CGEIC0t\ny7DTW1R0Co+nD01NMWRlydfDQZuz/uCDv0WIPB544D8NO+tjx06nrm4Cycn3SNvoY1NS8inQm5KS\nTw1r+QVSA6jLXoSFCTQSFWFIJkS3V9Uy4CyqWiZtf999i3n77ffRnuc0QGX37n3cd99iifMGTspU\nWPgWw4YN8J93H2AjMXGQ9DxyMYuoYohFVAior6+7aB6pr69H5hbPz3+RsWNHoBPegEJy8kipOehy\ngeUABhE+n74j1RtY6P9uf9yCBQsWLFiQR2WlU1qPat68OQwd2p/2TtywYQOkmDxtNpufaEFBZ/6T\nJZ+B9qLqAlUdj9frkxZVb2+rKOMM2V4sBL/OkDSB2eihmahpevrDPPXUGvbuVYD17NkD//qvL0pH\nPXVtOiEKDGvTgeb0vvHGOUNOb/t6uNra1Ybq4QA2bdrG4cNHqavT7rO6OpXDh4vYtGlbh7aDBo1F\nURL59NNewAY++SQGRUlk0KCOJRH0yLjPNwAYjM83QDoybqYGcOzYFLZv301Li8bi2dJiY9u2XdLR\nsMGDk9m+/Xf4fL2BVfh8vdm2bTeDByd3aDthwhi/hmAJmobgWVTVJuVImSFl2rRpO6dPl6K5I/8X\nUDh16iybNm3v0Bbas4jqbMRIs4hmZWUwfPhVftu7AEhKukqKvGbjxm18/fVxtLlzMqBy+PBxNm7s\n+N68XGA5gEHElVcOQtP8U9F0AFXA6z8eGghlfbJQ7ZvVr66HUO6bBWNQVZ8hsewzZyqw2RTc7vuw\n2eDMmXPSkY4jR04yalQJYWHnGTXqrCFxcdBE1VNTfURF3UhqKoZE1Q8dOs7o0SUIcQ2jR5cZstWl\nCWbPvsGQNIFZYfO5c2f6ddraHO6oKDtz587s0NZs1LNNm04NSJvuiSfep6FhpyGn1+x4HTiwi+uv\nT6S9RMDo0cM5cGBXh7bHjn3I9dcPpX2EZtSoYRw79mGHtm3pkJVooupVpKRMlIpKXdznWw31ubDw\nLYYOHUx7Fs/ExCulo2HHjn1IXJwbTU9aAXzEx8dK9Tkra44/ot8Xbbz7MnXqzdJyDCNHJvLqq1NJ\nSYnj1VdTpMlUPJ50oqO7o6p/AZyo6mdER3eTvkfy8rby7rsH0a6T5rj+/vcHpYlvBg5MAA4CNcBH\nDBzYTzqtOiEhHqfzOHAVTucJ+vePN5RJEGxYDmAQ8e2337X7dRNaOqjyN8ctWLBgwYIFOXi9drZt\ne0tq1x9g8eK5PProQJqaYliyZDCLF98vZZebW4DDEUZxcXdaW6+muLgbdrsqHd3JzS3g9OkSjhzp\nxYULqzlyxM2pU8VS9unpD/Peewf54os4YANffNGbP/7xgGTdT5s0wfr1xqQJzNR3Acyffx+JiYPR\nll4zAZXExCuYP/++Dm3bop7gcKT5I6fGo55GBdnNOHFmheA3bdrOoUMn0BwiH1DGl19+KxUd2rz5\n1xw+fJz2mnhffXWczZt/LXXe/fs/BXqhOWJu3nvvE6nzmumzzWZj6NABaCnZs4A+DBnSXzqqfv/9\nj3L2bAWavNgSwE5xcRn33/+oVLtPnSrDZlNQlHux2eDkyTLpaxWo3EdGxiNUVFT6o5YKPl9vKioq\npWvpPJ50pk2bhNutPVdu9xVMnz5J6v4cM2Yq27btAhKARCCBrVt3SUV68/K2Ull5nqamBGAdTU0J\nnDtXJV2veTnAcgCDCgVtcioEmtB2IXRBzdBAKNcmhWrfrH51PYRy3ywYRSnx8X2kdv3NpeiZEwjX\nHYvKynpAobKyXtqxMBMNuziN807DaZx6fVdzc5Kh+i7QFtnFxVWo6hcMG9YXVf2CM2fktcMOHTrO\nsmUDaWgoYNmyQYajnoHIKVycMnuPYSfOjIyDx5OO292d8HAbsJrwcBsxMT2k7hGPJx273YHdfhZY\nhd1eit3ulLaNioogPFy7v8LDVVyuSEM6gC+/fDtr16Ya7vORIydJTRW0tm4nNVUYiqrn57+I0+lE\nk5FYBZTgdIZLPxclJRXExtYjxFZiYxsoLq6Qei7MpEYLIdqRIWlcGF6vV7rWVNOKnERTE7jdk2lq\nEkybNknq/myrgY4BBgIx0jXQ+/YVcubMWcCOtma3c+bMWfbtK5Rq9+WA0GEb6YLo168vZ87Uod14\nmpYIFNGv2YUEEQAAIABJREFUX9/gNsyCBQsWLHRR9KFvX4WwsI5f7x5POjExbpYu3Y8e3Xn66YeZ\nPn2KhG0GR4+eYvXqk2iLNsWQQHhe3lb+4z/yqKy8GlhCZWUjS5Y8w7lzFR3W4PxtNKy5uZt0NMxM\nn3WHuaXlWi5cWM2bbz6J3f4BPXvKiV4DLFo0hyFD+vPxx59zww3/l2PHTnds5MfevRu///fKlY9L\n20FbhAaQ6mt76A5NTIyDyspmQw6NmfMqisKaNU/y4IN7ueKKpZw6lcDatZnS2nQvv/w0Dz64lyFD\nlnLq1CBefnm+tO3atT/znzebU6ciWbv2SUPRMCEE6enz2Lo1xxDZTlHRnu///dZbudJ2oD0XCxfe\ny6pVJ3E40mlujueRR8YZfC72oaUJx7NmTZrkXBD4MzVx4lh++9v3aWzsA6QDW3E6v2PixI5rNXXo\nRENr1zbzyCNxhjY3WlqagfNoKaTVtLS0SF2vgoI11NR4+N3vVOA+IJKpUydSULBGut3BhhUBDCLi\n4vqg7Rz0AXb6vxX/8dBAKNcmhWrfQrFfQgj27PkoYNr2yx2heM0sBAab7c/85S9FUrvvZtLVdOIH\nu10FpmO3q9LED6AzFo4hJqYWWEVMTB0TJ46RjrIEGg27uM9vXbJ0SB26c7Bq1X8BSKfK6TAjQRGo\n7YoVmUybNpm9e//MtGmTL2mb9XrNlJQ+huo1O8N28+bJREefYPPmKYYF6HfufIff/OYYr722p+M/\n7kQcOnTcL4peT3b2QMPPRXHxeWA2xcVVhuaCQOeRrKw5uFzhtOfCcLnCpWsP9U2ZbdtKaW5ex7Zt\npdJZDIoCLS0+IBYtYtqLlhYvMlOYqqqcP98AHAUigG+orq6XcrYvF3SdloYgPvnkL0ArbWmfNqDV\nf9yCBQudBbO07RYsdBUoyg3Ex/dCNhUz0BS93NwC/vCHA0RE/BVIICLiJO++WyidSqmlbt1Gc3Mv\nkpKW0tzsZtq026QdyL17N7Jy5eOoqsrKlY+zZ0+elB10TjpkIDVteqrcokXbaWlJ4pFHthmWNTAz\nl3VF20Bry3RbIQSrVxt3tlesyOTAgc84eDCOjz76XNpWv8ZZWVvw+Ubi8bxi+BqbgR4l/uijvqiq\nIv1c6MynXm8xsA2vt4QZMx6V1vUM9JnKy9vKhQtaGrhWtwgXLjRI19IJIaiurqGsrA5QKC2to6am\nRmqzQQhwOvX6Su0ZdjrDpGQgcnML+Oyzr+nZcxiQQ8+eiXz66eFLdp07A5YDGFS0v8tm+L8VZF/c\nXQGhXJsUqn0LpX6ZrffpKgila2bBHFpbMZSKuWJFJj/+8Q+Jjh7Bj3/8QwOLZIHTaae6Og44QHV1\nHE6nrm0rB7NRlkAE1aEtopWePs9wRMtMTZuZukkzdVZttvv9tvsCsDU+h5qVzdDtV6zYz4ULcTz+\nuPF2B+Js6+Lia9Z8B6znV786IS0uvn9/IUePfsf589HAjzl/PpqjR79j/3752rBAI6Zm2n3llQPQ\niHbi0FyDOMDnP94xVqzIZPr0KSiKwvTpU6SfKY3E5XZ69qwF3qFnzzqmT79dOqquKNDQ0IjPZwPu\n82v5NUlF8bKyMhg1agRaEGYGYGP06BFS6dweTzovv/wLoqK6ASuJiurGK688bbGAWpDD1VcPR3P4\nSoFz/m/Ff9yCBQtm0RkpWxYsdCWo6nFDqZgQmFi2Lv4MXwLXA4cMOZ4QeJRFx113zfcLqi8wZAeB\n6dpB4AtdaBuz1lZNlqC1Vb5u0sxcZoZwpzPO29jo9dt6Dc2/un1V1UngLFVVpwzYB+5s6yRD7a+T\nLMlQfv4apkxJRlteK4DK7bePIz9fvjYs0IipGXKkiRNvwmZT/O2ejiZErx3/R0LPBKiuPg4Mpbr6\nhKFMACGgoaEJOIxWx/c1DQ1NUlG8NhmIz9DW4J9Ly0Dokf+SkiLgOCUlRYayAS4HWA5gEPHVV0fQ\n0z7hv9A0AW3+46GBUK5NCtW+hVK/zNT7dCWE0jWzYBaD+M1v3pba9Tcjlu1yDWHVqleBkcAGYAS/\n/OUWXK4hUq00E60wI6huRtfOLPLytrJx4+u0T3fLy3tdWrMsUAmKvLytZGc/Q2VlE+0Jd4yf92lD\n59X/rqKiDkVJo6Ki1tD8q7X7OSorI4BVVFaGs2TJs1Lt1p1tr1dzhrxeVdrZ3rhxG1u2vOF3ImYi\nhOCVV34rJfKdnJzK736nkaHASwDs3r2P5OTUDm3NRkzbyJEEijIOr9cnTY40b94cnM5I4FOgCvgL\nDkcU8+bJ1eJBYBF5PfXU5xsOjMfnS2TGjMXSqafPPrvG7wD2AJKAHjQ0NPLssx073G0yELHAtUBv\naRmItpTZbsAGvN5uhtp9OcByAIMKr//TJrzZdsyCBQudAXMpWxYsdC0IUcnixRlSu/5tYtltwuSy\nYtlVVYeJiAi7yDYy0kFV1WGpdpqJVmhC3eP9vzRnIiVlQgBC3Zc2K8DjSScl5VZcripgFS7XeVJT\nbzUkLxCIBIVZwp1Az5ubW8CCBT+ne/dqhCige/ca5s//ubRDo7V7NDExUYBCTEwUEyfeIK1BWFJy\njvBwO0lJSwgPDzMQGRfU1TUD3wDbgaPU1TUhEz2cO3cmihIGHAGigKOoqo25c2d2aNsZ9+ahQ8cZ\nNaoEIUYwenSpNAlMXt5WvF6dEOUaoDder9eQrl0gEfm5c2cSGelAi8CdBSqJinJIjRfA8uUL/Q5u\nLLAaiEVVVZYvX9ihbZsMRB+/bW9pGQhtDroZcKLNQU5SUiZKzUGXCywHMKhQ0SYUG3BPu9+hc1lC\nuTYpVPsWav3SU7b27dtnOGWrqyDUrpmFwCFEDFu2vCkVrQgLC/MviFX0d5CiqFISEg6Hw794arON\niorA4XBItdOMsPmmTdvZt+8T2ot8v/fenw0LdQeia2cGeXlbef/9j6iriwGyqavryf79H0ktsttr\nNmoSFPKajWYIdy4+748NndfjSWfduiex2/uhSQv0Y/36Jw3Ud2kab83NCklJ2TQ3I63xBjByZCL5\n+SkcOrSK/PxURo4cJmXn8WSwcOEsFGUYYENRhvHww7Olooc2m+qvhR0MzAQG4XDYsdk6vq/Nkgyl\npz/Me+8d5PPP3UAun30Wwx//eEAqqq7X9KpqPLAaVe2L0+lAxuk1E5F/5ZXfUF/fjObA3Qn0oq6u\nmVde+Y1Em0FRVIS4mExRCC+KIhf1HDq0P+03sYYNGyAV9bTZbP65SkG7ztp1stlsHVhePggdT6ML\nIjY2Fu3mOQucQK8B1I5bsGChsyCEIC9ve8jKQFiwoCM62klCQh/pRfZ335UxenQpLS3bGT26lO++\nK5U+V1VVA7GxfyE7+zpiY/9CVVWDobYGKuXg8aQTHR1FeHgFsIrw8HO4XC7DQt0vv7zgkmYFmInE\nmY0OBZoJYea8bemj9YSHT6Cqqs6ws20mgyPQek0z0UMhoKmpmfYC4U1NzVI1aWBOumLChDG4XOF4\nvZoz5PWG4XKFM2HCmA5tPZ4MHnpomj8ivxKfTyEzc5qU09sWkddsQZWOyBcW7sLhsKFlvimAF4cj\njMLCXR3aAuzff6Dde11LqxZCsH//gQ5tFUXhzJkKbDaF4cOXYLPBmTPnpO/PI0dOkpoqaG3dTmqq\n4MiRrpVdZAnBBxHFxZ9htw8HqoHRaHoiguLiz4LbsE5EKNcmhWrfQrFfr722h9deO8G0aXuZMeP2\nYDen0xGK18xCoLDzs58tkF7EnDtXiBCCFStW8uc//6ehxXlLy5e89toeHnxwLy+//O+Ghb4DFTY3\nIxAOF4uTBwJ9vJ555jFD46VH4nbv3oPbfT/NzTHSkbi/jQ6dOuUz5EwFKsjedt4GFGUV588PNnTe\noqJT3HJLPbt3Dyc1tdGws21GSB7A5/MxbtwM/vSn1wxptI0cmci0af35+OPPueGGazl27LSUXVZW\nBgUFv+PDD23A08Bgxo+/TopZErT+7tjxO1auPMZvfjPSUMZKVtYcfve799m9WwWyAdV/7o4jWrrT\na7OV4/Uq2GwVlJR0l7rObdGwcjQnrhxF6S0VDbPZbAwe3I9vvnECvwJ6c8UVCdKRtIKCNXz66R18\n800ZMAgoJzHxKmlB9sWL5zJkSAIrV+bx6197OHbsRik7gKKiPd/PBW++mdPluAWsCGAQERl5JVro\nuQ9whf/b5j9uwYIFs+gM3S0LFroSAolovfbaHlav/o7XX98rbWOWsMIsglnba0bXrqjoFB5PH5qa\nYsjKkq+n022D0eeiolP06vU5QvSlV68vpM+bnv4wTz21hnfe8QK5vP12K//6ry9KpiR2Dh577DkO\nHuzD8uXPG7LTHc/1643pD2rMkv1R1WPAFajqtwwcmCCdbnv11Sk8+OBvESKPBx74T0PPlKIonDxZ\njt1+juHDfdjtFZw8WS517vT0h9mxYw8amcoGoIc0mVR6+sPs3v0+itINyEFRurFr137p6+z1qowa\nVQKcZ9Sos37iHjmoqkqvXm5AwW4/C0BsbIy0s79iRSZ/+tOnfPTReQoL/2K4RKRLawwLIbrMR2tu\n6AB6CxgiIMX/SfX/7h3spnUa/vjHPwa7Cf8whGrfQqlfOTmvioSEH4iwsEwB/y3CwjJFQsIPRE7O\nq8FuWqcilK5Ze/jn/KC/e7rKx+g7MicnXyQlTRVxcYsE+ERc3GKRlDRV5OTkd2jr8/nEjh1vi/79\nHxcgRP/+j4udO98RPp/PUBvMwOfzieXLnwv4nEafG328hgx5QoBPDBnyhPR4dYa9WQQyXgMHjhEw\nTMCDAv7b/z1MDBw4pkPbDRu2iJiYGwVkCi05MlPExNwoNmzY8g9vd1raQuFwjBR2u0eAT9jtHuFw\njBRpaQs7tNWv01VXrRDwrLjqqhXS1yktbaGw2ZKEzfaAgP8WNtsDwmZLkjrvDTf8yL8GfEjAc/7v\noeKGG34k1WchhHj66TyxdeubAgaJrVvfFM88s1HKzuv1iiVL/kMoikeAEIriEdnZTwuv1yttq7U3\nScBD0rb6WEdG3i1guoiMvNvwMzF58kNi6dKnxU9+8qxYuvRpMWVKppSdfo/AVAHzBUw1fI8E61kW\nwvz70YoABhGKohee9kQL1/dAKyTtOkWkFixczjBDBW7BQqhDCEF1dQ2lpbWAQmnpBWpqanRn8u/C\nLGFFZ+BS776brcMLti5pIONlhik2K2sO48Zdi5ZsZiwl0Wy7dZbZ9nO/LMusGf3B/PwXWbQozS9M\nruDz2aRZeQ8c2MX11ycCZcBxoJTRo4dz4IBcPRxoEa15834G/H/snXl8VNX5/99nliwkAZIJS9ip\nrGFRQQkIQf1WRSVYBdkS8CtVJhFUZLGI1X796leo0oJV2UWhYbGCbV2qoLaWoKLWhValskiQNSEh\nZA9Z5j6/P+6MCf5a59wZIMl0Pq/XvCZzXzycc+49997znOd5Pp9RZGX9QjuiVS99IcAkRAxt6Qsf\noRN8DKQCf9MmdFq7dhN79hygsjIBmEllZQJ79hxg7Vr/7fqwffsahg69lBUr8hg2bBDbtq3WtBTq\n6uqARGAZkOj97f/519j38rlA2AFsRIh4MCdaFHC191u8x0MDoVybFKpjC6VxnV3M/4pFKvDmg1C6\nZmFcOOzc+SEnThQg4gRmI+Lg+PECdu70T6AAjZeSeC7ST0WEbds+1nJ2fQjW6W0spzmY83U2U+xy\nzE1qPabYhimJCQkFllISg+23zykxDAOYhGF4tJ2SYPQHTUfqFUTswCuI2Fi37o9ajlTbtgP5/PN9\nmEGBFUACn376NW3bDvRrCxAR0RWl+lBWdhmwgrKywSjVh4iIrn5tc3J2UVRUDpwALgXyKCoqIydn\nl19bl6sfS5asBy7z9nswv/rVOlyufn5td+16rcEGw9WAjUGDemuTwAQzR1JTU3A6bUAE5uZGBE6n\njdRU/zqoTWEDLFiEHcBGhQeopX53TGGKwoeOAxhGGI2NQKnAwwgj1DFiRIqXbCEPWALkY7fbGTHC\n/wIIzGjD2LHXsWDBYsaOve6CSayci933rVu3sWTJO5bqHiF4p7cxnOZgzteqVRvYvfsAERH7gM1E\nROzn88/3azvbkyaN5t57B1FT42LWrMuYNElfKDvYfq9atYVWrU4Dl9CqVTErVrykLV8RqP6g251B\np04dztJ67Ny5g5ZtXt5ubDahoUNitwt5eXrEgGVl+4iNjaBhtDYuLpKysn1+bbOzn2H06OFe2zzA\nQVpaKtnZ/slUHn10HuZ+QH27Dod53B+ee24zX37pY/9NB+CLL77RknSB4OZIZuZU4uJ8UjbmGjwu\nLkY7Qt3cNYbDDmAjIiqqJSYRaz7wKWbY3+49HhoIZX2yUB1bqI0rrAMYRhj/GllZU/nRjzoA7YEM\noD0XXdRRSwfLh8YgQQhm9/1sYqiWlomhgnV6A5UmCAbBnC+3O4MXX/wV7dqlADtp1y6F3/3uV9pO\nmE9DsLzcmnbhuej3Cy88jsMRAeTjcESybt1CbfmKQPUHlVI8/HAWSnWga9cJKJXk/e3f1ul00rVr\nEg21Nbt06YDT6fRrC7Bu3RbKyysb2CvKyipZt26LX9s1azbxl798gpkOuQRw8ec/f6wVubTbHd5I\na327hmFgt/uPErvdGbhcCURG5gJ3Ehl5CJcrwZJWZKBzZPXqjZSVncFcgxcAJykrq9KK9ELj3Mvn\nEmEHsBGRnOwLe/8dOOb9dniPhxFGGGGEEcb5g1KKqKgYnM6D9OnTFqfzIFFRLSw5UsGygIoIDzzw\npKVUTAhm910oLS2nsDAKmElhYRSlpeXo1P340ByZ/wI9Xw0X2F27LrPshAUbqfXp4o0ZY00Xz5fG\neepUFNCeU6citdM4fe0GGt0JRmcyL6+E7t33UlOzie7d95KXV6Jt63ZnoFQEkANsBHailFM7ctmz\nZxeiosyayagoG716ddWMpE1BqUjgQyAJ+AilIrWkL0xJlwdxOpNp2fJnOJ19efbZn1vWinz++euI\njc3l+edHWdK4HDv2OmJjzwAuYmOrGTduVLOq4wsKwTDIXOgPIcYCetll1wv0ELhcIFNgiEAPueyy\n6xu7a2GEEUYYjQ7CLKDn/R25cOFq2bLlTZk//5eyZcub2qyB9Syg870soPMDYgHdsuVNiYu7T7Zu\n3Wa574HAMAyZM+dxUSrzO7bDuXMXavW7KTD/NQYCnSMi9dc3OXm2xMXNCug6BzJHDMOQyZPnSGzs\nBIH7JDZ2gqSnz7U0P4NlmW0MBHq+V67Mlk6dUkWpLIH7RKlM6dRppPbcTk+fJ1FRE6Rv3/skKmqC\nZGTM0+7zwoWrZe7chRIXN0vmzVtkaX75MGfO4wLTZe7chdo25pivFLhD4JcCd3hZwvXv58acI8G+\nH8MRwEbEV1/txYwA9gO6A8mA3Xs8jDDCCCOMMKzBXBfoY8GC6YgYLF58EBBLemdKKfLzS4FJ5OeX\nWCJBaCwdQR8xlNNpQ6l0nE6bNjHUuWL+Ewks6gmmsPnQoWO9KXfW4PF46NAhBY/HGs9AoHMEzOjM\n2rXXcvr0+6xde52laJhvjixYkENZ2RIeeGCH9hxZvXojO3d+Snl5LHCI8vJYcnI+0Y4AArz44ms8\n8cRqfve717VtfAj0XENw8yPQyKVZ9ziE+PgyoD3x8eWMHDlEe27379+L7Oxp3HRTe7Kzp9G/v16t\nfX2acBllZUt55ZVSS2nCGRl3Exk5kN/85gCwiqee2k9k5EAtDUJfrafTWQDk4XQWatd6+tAcswG+\nQzDe44X+EGIRQFMH8CKvBuAl3u+LwjqAzQShOrbwuJofQnVshCOAlt+RVqIkvohWy5ZugZulVSu3\nZb0zm+1mAY/YbDeL3d5PS0NLpPF0BFeuzJY2ba6Q9u2nCvxZ2refKm3aDNfe9T8XEa2XXnpDnM5r\nZcuWNy3bBhLp8GH06DsFrpa0ND2dNJH6OdK6dabAX6R16yzLUc9A2hWpnyOmpp6I3T5Ne474IoB2\n+1UCd4rdfpV2BNA3Zhjozc662PKY09KmC4yRMWPc2jY+XOioeMN2o6ImiM2WJVFREyy3/7vf/Uls\ntv+Sl156Q9vGd41drlkC74rLNcvSc6Bea/KnXu3En2prTV5++Y1ejcs7BAzvd2+5/PIb/do2hWyA\nYN+P4Qhgo8JkSzJZPy8CDOpZlMIII4wwwgjDGqxE0nbs2MXevQcpLd0LtKekZB979x5kxw7/1O8+\nvTORdoANkXbMmpWhpXcGjUej7nZnsGzZQzgcHYDf4XB0YPnyh7R3/YOpDTubgCbZEgFNMJGOIUNG\no1Rf/vSnfGAgr7+eh1J9GTLEPyOnb44UF5tRz+Jij/YcCaZdgMTE/kyYMAuPJwKYjcfjZPz4e0lM\n7O/XNiUljc2b/4TH0xlYjcfTmU2bXiclJc2v7Zw5/8OePQdoKGuwZ88B5sz5H7+2vjG//roBzOa1\n1zzaY26sqLiv7ZkzHyM+PgnDWE58fBIzZjyq1bav3273OgyjN9Onv2ApUjtnziJOnToDLOTUqTPM\nnr1IO1L7/PMvUlRUDBQBuUARRUXFPP/8i35tDcNDPeu+77nj8R7/YdRnA3gwswE8YR3AMKxAMCdd\nB2ALZvGswkoxelNHKOuTherYwuNqfgjlsYVhDVbSEpUCM5PwIkyNtx9hGOZxf6gXjgZIRwRt4Wgf\nfAQfaWnWCD6Cgc/JLCzMxWazUViYa8nxXLBgOrfcci3Dho3jlluutcj815CAZqklAprU1CHExkbh\n8Tgwhc0dxMZGkZo6xK/ttGkTiY2NBtoCS4G2xMZGM23aRI12U4iMdGAuF1/FJAhxammlBdMumLII\nrVu3pKFMSXx8Ky1ZBBEDc1M9CnNdFQUY3uM/jCeeeAjTMaiXNQCP9/gPY9euV7nxRp+cwtWAnbS0\nVHbtetWvbWOmGLvdGTz77EM4HOb5cjiiWLbsYa22c3J2sW/fIUpKWgDLKClpwb59h7Q0BEWEkpJK\nwFf6tJfS0grtvvfo0Q1zbrbCfIa1Amze4z+MO+6YjM2mMNn40wE7Npvijjsm+7Wtf46UA+MoLCwP\n6wCGoQ+HwwnUUP+QsQE13uNhhBFGGGGEYQ1WImnZ2c/Qvn08DRe6SUkJWtpfpt5ZR2JjDwMbiI09\nTOfOHS0tVhcsmA7AihV5KKUuCI16MJEOH+6//wk++qgd8+c/aaltt3sKEyZcR12dAhR1dYqJE0fh\ndvtnS8zMnMoVV1xMvWaZjeHDL9HSLMvKmkqvXp1peJ179+6iJfeRlTWVzp07YFLlHwFO0rmznlTI\n/9+uTbtdMGUR4uNjMWVKJgDtad06VksW4cMPXycxsTUNNd7atInnww/91/PNnHk7ERE+p3cCoIiM\ndDJz5u1+be12u1dsXn1nq5Ty6m3+MM5VVDyQurR6h6YCpdItOTT1mwT1+oWRkXqbBEqB0+kA4jB5\nMOJwOBxam1AAI0cORSkDiPyubaWEkSOH+rW12WwYhg3T+TSAfRiG7/r9MFat2sCMGY8REZELdCIi\nIpe77nrsgkRrzxXCDmAjoq6ujvpLMML7bfceDw2Esj5ZqI4tPK7mh1AeWxjWYCUt0WazeRfTNiIi\n0gHzt84CqF7vrDfJyfNQqpe23hk0XrpbfaQjGtiBwxGtHenwpWE+88whYDm/+U2udhommOlua9a8\njLlQne099rJWuptSisOHC3A6T9G3r4HTWcjhwwXa2nTHjhVityv69p2N3Q7Hjp3S1ko7dOgEUAxk\nAcXk5h7T6vPq1RvZvXsf5uJ6OGDw+ef7LBGxBCqLYLfbqaysoaHjWlFRo+2IeTw24HN69WoP7Kau\nzqY9t7/++jBjxgjvvJPJmDHC119bl5AIJsU4kHvK59C0alWCyAZatSq14NAoamqqaehsm7/1zldV\n1RmgDfAToA1nzpzRsgNzYyQ6Osr7y7ynoqOjtDZG3O4ptGrl0902Mxdat26ptSEDQmSkk4qKjsBT\nVFR09DrBzSeDz79KYxjnDePGpfHyy+9haqd0BT4G7Iwb5z9HPYwwwggjjDC+j3HjRln693369GT8\n+K7YbArDEL78Up9d0rdYHTv2On7/+7csLVbd7gwSElzMnZuDL91t4cK7LfffKr6va1dU1NFCxPRp\n2rVbxNNPH8UUu7Yxa9ZkFi+er9W2253Bjh27eeutfE6dWoHLdRujRl2tHTWdNGk0vXp1Ceh8z5p1\n+/dsL7fQ58/Zvr2OoqKXSEgYyPXX62nLgRAbG0tp6T7gcuBzYmNjsLJIrqz8HBFhwYLFfPPNXyxF\nwzp0aE/fvm35wx+Wc8stM/j662pt28ceuy/gc71//zbA3Jh79dVV2nZQHxUH6/dyMPeUz3bOnB3A\nr3A4OvDMM+latpmZU1i06AUOH87HTLWtokOHJC0dQLd7CtnZf+L9983UZnAwaFCyphNmbjKYRKsF\nQBegAI9HWL16o9/2lVLceGMqf/xjDd27zyU3tx833hityQg8hX37jrBkybfAYurq0I7mNxWEI4CN\niMOHj2M+CC8FXgEuaXA8NBDKtUmhOrbwuJofQnlsYZxfbN++hqFDL2XlynyGDRvEtm2rtW0XLJjO\nuHGjUEoxbtwoSymcjUUCA/WOa27uS5YjpkrZ8HggIiIdj0ew2ZRWxBTMMY8dew01NYkkJ8+lpsbF\n2LHXWKo/DPR8B2pr9vlaKivzsNlsVFbmMXbstVp9zsn5kPLyCqAnZg1gT8rLK8jJ+VC73xA41f7+\n/dt49dVV2O12Xn11Ffv2vaFtG8y59iHQ53IgNXxQf0+dPl2Fy3Udp09Xat9Tvn9XUJALHKCg4KC2\n7erVGykuLsOM9G4EhNOnS7Uj2126dCQiwo5Sq4mIsNGlS0fte8In5u5yxQBLcbliLYm5DxjQh/Xr\nbyQu7hDr149mwAA9+QqfnIxSJ4GDKFWgLSfTVBB2ABsRn3zyD0wH0Im58+EEDO/x5o9g9IrCCCOM\nMMJJRcFLAAAgAElEQVQ4v6hPGcvxpozp66ydCwST7hYMglncf/nlQebN60pV1QbmzevGF18ctNR2\nY405UARTM5md/Qw9e3aiYe1hr16dtWpMfW0HmyYcqDMVLIJpNxhtuf37j+B2t6OsrAeZme2155fv\nOkdHxwOriI5O0L7ObncGo0ePJCGhG/ArEhK6kZZ2pZYTtmrVBt5550Pi48sQGUh8fDlvv71L+xqf\ni02VXbs+46OPivnww8+1nwVDhoxm8+Y/IdIKWIFIKzZtek2b4bZJIBgNiQv9IcR0ACMju3h1/9wC\ngwWmC1wkkZFdGrtr5wRz5iwUGCPz5i1q7K6cF4Sq9lp4XM0PoTo2wjqA5/UdebYGl1jW4GruCNX7\n5t/BMAyZP/8JS9f3bL3Gdy3rNY4YMVnALRERkwWmS2rqZMttd+r0M4EnpFOnn1men42pqRcdPS4g\nXc6ePRd4teUWWNKW89m3bXu3wM3Stu3d2vYrVvxWOnZM9WrhicAd0qlTqqxY8VuttrdseVPs9lsE\nLha7/RbtcfuucUzMeIHBEhMz3vI1XrhwtWzduk0Mw5CtW7fJokVrtOzS02dKRMQAUSpNYJYolSYR\nEQO0tEyXLVsnLVoMOOt8xcQMkGXL1mn3O1gE+34MRwAbEdXVZzB3xfYCdcA+wOY93nxxdqH8bMuF\n8mGEEUYYYQQGKxkXPg2uoqJq4DaKiqxpcIFJZhYb2y9g8jKRxovQrF69+YK3GyyCOV/BsEP6aiat\npuq2aBHDnDnduOSSSubM6U6LFrGW287PPwgcJD//G+22Gyt62LDdqqqZltr1yUAcOVIIKI4cKbQk\nAyEilJSUcvLkF0B7Tp78ktLSUq3+P//8Zo4dy8OkBpkN2Dl6NI/nn9/s1zYj424mTZqLx1MIDMPj\nOcXEiXO01nxTptxDevr9VFScAbpRUXGGyZPnMWXKPX5tfViwYDo33fRfRET04qab/stCRF+oq6tD\nJBHYhUii9znm/3zZ7TY8Hp9293WAjbo6wW5vPm5V8+lpSMKnL9MCuAqIwbwkzSeH+F8hO/tpZs6c\n6KXXvRrDsHH33ZO1BYKbC0K17io8ruaHUB5bGNbws589of1v3e4MUlOHEBNzCEggJuYwI0cOsSTl\nkJIyjoqKVIYOHW+9swSX7hYMXn55O6+/3iKgdhvLaQXYsuVNFi9+ka1bt2nbBOsM+fQaJ068zLJe\n4/btawD4+OMkbDZlqcbU51gYRhywAsNoqe1Y+JypqioPsJiqKutC3Vu3bmPp0kO8/PJ2bZuztfyu\ntqTll5KSxoQJ93HmjA2YzZkzivHjZ2mJ1wPMmfMIx46dpKGu59GjJ5kz5xG/trffPtHLkJqPT3PR\nbndw++3+NRv37z+Ix+PBrPVcDvTA4/Gwf7//9OjU1CFeGYhETKKgRJxOh5a+ZUP07n0tdXU/pnfv\n67RtRoxIwVQU+RJz/f0VTqdixAj/8hVu9xSio6OBf2LKV+wlOjoqTAIThlUkYRZIt2/sjpwTBFso\nH0YYYYQRRmBYuvRj7YyL1as3snPnJ5SXdwSSKC/vQE7O37QigN26paBUHz77LBFYwaefJqBUH7p1\n8794gsaTgTgX7TaG0+rr9/Tpz2AYrbnzzmcsR5YCFRgPVK8xWNmM7OynuffedAzD1JczjAhmzZqi\ntZlcr2uXi82WS2FhruXo4axZ26mpWca9927TPtfBkBvt2vUqvXp1p94JO0mfPhdpicgDFBf/E6fT\nRsOay4gIG8XF//Rre9ddt9G2bQKmHMNwoA3t2rm4667bNPr9Gt26tTur3e7d27Nr12t+bXNyPqKq\nqhI4BeQBRVRVVZGT85FfWwCXqx9K9SE39yJgBbm5P0KpPrhc/fzavvDCi1RXG5hr74FAe6qrPbzw\nwot+bVNS0rzEN73wERwVF5dpO+tNAeEVeaOiGqjFvGl2eL9rvcebN3yF8m++eWdAhfLNAaGqvRYe\nV/NDKI8tDGswDBexsS20dtDNCOBgYmPLgDxiY8sYOfIyLcfgwIH3ufTSXjRc9A0a1JsDB97X6mew\nTgkEFok7OzKUZSky1FhOK0BOzi727TtEaakTGEhpqYN9+w6Rk7PLr20wTsnZY77J0pjPzgZSlrOB\n1qzZxPr1f8S8vOmIwLp1f2DNmk1a/Q6UvMaXSllQYGrZFRRUa6dSQj3Rz7PPjrFE9PPcc5s5duw4\n0A6YCLTjyJFjPPec/zRMgBdeeInaWp++dDqmHp+HF154ya+tUgoRO/AJSg0EPsUw9OaI3W6nvLzK\n2+4EQFFWVqWluXjgQK7XLhGoAFyA8h73j7y83f/S+czL2+3Xdtq0Sd7xtcN04tqilGLatEl+bXft\nepXevbtgMp8qwKBPn27aznpTQNgBbFREUj9pn/Uec3iPN29s376GxYsfwGazsXjxA5bSPsIII4ww\nwggUdoYPH6glhGxGAD+loqIlsISKipbk5HyiFQFcu/ZF9uz5hoaLvq+++oa1a/3vnsO5kYEIpqbN\njAidsBQZOhdOa6BITU0hKioCaItvsRoVFUFqql7E1ZfGmZbWzlIaZzBjDjYbyO3OoFOnjsTEfAsM\nJCbmWzp37qjVttudwbPPPoTDEQ0oHI5oli17WMs2M3MKEyde79WXm4PHI0yceIOWrh0EzjLrdmcQ\nF9eayMivgM1ERn5FXFwrSzWA5v24F9iAj1dCx3FdtWoDhuEhKWkkIitIShqJYXi0HOYhQ0ZTWFiK\nmQ5ZDXxNYWGpFiPmrl2v0bp1DKaOXxFQQHx8rFb0EMDpdHrvXRswHl8ZldPM7fxBmPe9h/p1uA2l\nDK1nwXPPbebo0QIgArNm0snRo/naznpTQNgBbER06NABs9g0DzP3OR8Q7/HQQCjXJoXq2MLjan4I\n5bGFYQ1Op+Lw4ZPaDk1q6mDi41sAi4mPb6EdAXS7M3A6o3E4/gFsxuH4AqczypIzFKgkQjCRuIaR\nIZFXLEWGGlO7MDNzKh07JtIw0tGpU1stRx8CT+OsH3MVLtfzFBfra8tBcLIZSikefjgLj6clNtu3\neDwtefjhLEu6doFq4uXlnSYysoi+fYXIyNPk5VnXeLP6XFZK8cwzD+J0DsblugGncxDPPvtz7XYz\nM6cwZ85tmGmJlwE9mTv3Nk1B9sAd5l27XuXGG0dguhQdABtpaala0bDnnttMWVkV5sbGFqAtpaVV\nlhypvLwSunffy9y5l9C9+17y8kq07DIzpxAX1wpzHT4VEOLiWmqdLxHhzJlq4DhmCukJqqqqmxWp\nVNgBbESYu2AG5mU46P02wrVyYYQRRhhhBIR77+3KpEl6dSg+ke/i4iPAAYqLD2uLfCuleOGF/8Pp\nHIxSU3E6B7Fu3eOWFsnBREoCjUoFs9CFxtPxU0oRGRlNRISdvn1nExFhIzIy6oIwYvq05aqrE8jM\nTLJMAhNoNlAwaZy+fmdmtqOmJpmsLGv97t+/Fxs2/JSvvlrChg0/pX9/PYHwYBFMn32Oq832D2AI\nNtuX2o7r2Y7+bZYc/dtum8Wbb34AdAe6Ad3505/e47bbZvm1dbszSEyMx243gHHY7QZt2iRY2kiq\nrPycX/7yfpYufZknnvgZlZWfadkppRg9+irs9oPAP7Dbc0lLu0przJmZU5g1awrmhsxBwM59903V\njhI3CQSjIXGhP4SYDiC0F+gtMEXgL97v3gLtG7tr5wyhrLMUqmMLj6v5IVTHRlgH0PI7Milplrb2\n1+WX3+h950wVMLzfveXyy2/0a7tyZba0aXOFtG8/VcAj7dtPlTZthmtrlvkQiDadSL2+W3LybImL\nm2VJb81n27XrrZZtGxOB6p3V6+nN9+rpzdfWWqvXpntQ4C/Ss+eDlrTpgsG567cRUL8DnZs+WH0u\nB6sDmJ4+U+z2ZLHb7xQwxG6/U+z2ZC1dOxFzfs2du1BiY2fJvHmLtOeXx+ORli0vFbhJ4D6Bn0ir\nVoPF4/H4tV25Mls6dUoVuFpgjMBV0qnTSMvah61a3SCQJa1a3ah9zlauzJa2ba+QFi2uEciSFi2u\nlbZtr9CyTU+fKTZb37OenTZbX+1zfS4Q7PsxHGpqVNQBHsDciTS/Pd7jYYQRxrmCSPPU/AojDKuw\nQlgxbdoEYmKigCjMd1AUMTHRTJs2wa+t253BsmUP4XR2BGw4nR1Zvvwhy/VwgVDtQ3CROJ/tCy/M\nuKBRvGARaMQ0GEbMxqx7bOx+Bzo3A4Wvz0VFlYCiqKjSUp99rKkml4QCHNqsqatWbWDDhld49dVy\nysuX8sorZWRn/1Er2jp16r2Ul5/BZBBdAiRSVlbJ1Kn3+rVdu3YTR4+exIwezsaUrshn7Vr/RD9Q\nT45UUtIBWE5JSZI2OVJOzi6KikqorOwKLKeysgtFRSVatiIGhuGh4bPTMDyI6OuwNjqC8R4v9IcQ\niwDa7Z0EuglkencR3ALdxW7v1NhdCyOMkIJvx7+57PSHYYJwBNDyOxKmy9y5C7WiFoZhyKBBN3nf\nQbMF3DJ48E+0Ix5btrwpLVrcI3Z7b2nR4m5L95dv5759+1kChqXIZRjW4It0JCXVn2vdSIdIcNHW\nYNBY/fbNzYbtXoi5WR8NyxK4T5TKtBQNExGZM2ehQJZEREwWyJR58xZp2fmirZ07PyAg0rnzA9rR\nVo/HI126XCUwTUAEpknXrldpRQCXL18nkZHJAtO9ttMlMrKfLF++TqvfHo9HRo++Q+BOgScE7pS0\ntOlabdfb1rdtxXbQoDHedftkgekyePBPtGzPFYJ9P4YjgI0Ic6cgAvgaOOL9dtKsdhDCCKMJozFp\n28MIo3HwN0t1P8ePF2ESkB0D8jl2TJ/sYv/+I/Tp8y0ez1X07XvEUiRNxKTaP3nyDKDIzz9jiWo/\nDH00dt2jiHW5job9rqkBU9KAC9Lvs+fmYk6evDBz02y3EpMYcAki+ZSWVlhq98svDzJ7dmdcrm+Y\nPbuLNulOMKQ5a9Zsory8goaMmGVllVpyHe+99zHV1TU0ZBOurq7mvfc+1uq3zWajuLgcOIlZi5dP\ncXG5FpdGva0CTOkHXdupU+/ls8/2AScwGVfz+PTTvVpRz6aCsAPYiDDDx2bo2NQ+aYGpleNp1H6d\nS4SyPlmoji2UxnV2GtCOC5q+dCERStcsjGDRmY0bX9OiYM/IuJuCgiLM9c5mbDZFQUGRllD3kCGj\nefDBJQ2E4F0sWPBrrXZ9qKyswjAA5mAY5u8Lif+U+yZY9lJf6umOHTsspZ76EIhcB5gyJXPmLKKo\nqBqYTVHRGWbPXqQlU9Kw31ZTZutlIE4CuXg8+ZZkIHwIZH45HAqTETMDaOP9rY/t29ewf/8hTpy4\nlAMHvrVEuuMj+ykr60FmZntLDnNFRRUNBewrKqq0HNf9+00CFTMAkokpYeHwHvcPl6sf77+/G2iN\nmUYaz3vvfaYlBJ+RcTcffPAPzM2vTcBx3n9/t9bzzwzUGJjXyub9NppVAEfLAVRKdVNKXeP9u4VS\nquX57dZ/GjoBM4GOjd2RMMIIKTRc+HTtuuyC0raH8Z+DpvWONMWMe/To6vdf+mqGlGqPqYHVnlmz\nMrRqhsz6wQgayhLExERo1Q+CuchOTu6JuWj8EsgjObmXpUV2bW0tTmdPamtrtW18EAm8LtgwDIYO\nHYthNJ/FXmOwl/oyMBYs2EFZWXseeOCvljIwTJmSIcTHlwHtiY8vZ+TIIZY28AK5VqtXb+S55/4A\ntAKWA6157rnfazuegUIpOHOmDlNa4GJMaYE6dF9XQ4aMRqm+vP66Aazgtdc8KNVXa1PGVwO4aVMe\nNTXL2LgxX7sGEKBFiyhMQfW5QDtiYvT0rHftep3WrVsAPYBHgB7Ex7dg167XtewffXQeDgeYGoKb\ngSIcDvO4P5hzKxZIAn4FJBEfH0tq6hC/ths2PMugQX0wn39DABuDB/dlw4Zn/Vg2Hfh1AJVSbkxx\njlXeQ52AP5zPTv2nwAwzV2NOoKsxL0dNSMlAhLI+WSiOTUTYtu3jkErD8i18cnNfalaED1YQinOx\nuaDpvSNtiNSgcws3FOq228dZEurOyrqN3r270TB9qk+f7mRl3abVS6UUXbt2xOksx2a7CKezkq5d\nO1janOnd+1rq6n5M797Xadv4sHXrNrZuzQ2I4OP++5/go4/aMX/+k5ZtGwuBRsMawupzxpeBkZd3\nEMgjL++gpQwMU6bkGiora7HZDlFZWcvYsddYmiOBXCu3O4PRo0cSFeUEFFFRTkaPvtJy5oj18zXF\n61QozDRQxWWX9cXt1tsUMfX4htNwU0ZXj8+X9pqfX4GZkl2unfZ69mbOCaxs5tjtdhwOJ7AfcAIH\nsNud2O12v7YADocDERtQAgwDShGx4zC9wh9ETs5HFBWdxhSh3wYUUFR0mpycj/za2mw2CgvLgR3A\nIGAnhYVlzWr9rtPTmcAIoBRARPZhxjoDhlKqtVJqq1Lqn0qpPUqpFKVUglLqbaXUPqXUW0qp1sG0\n0Rxg7kg5MEUoJ3m/7c1qVzGM0EKgqTpNGedi4RNGGD+AJvaONIiIaMHGjXo70V9+eZC0NA9KlTJm\njKFdMxQb25PPPttLw/SpTz/dS2xsTy37Vas28M47H+ByJWMY3XC5+vL22x9oRRxcrn4o1Yfc3IuA\nFeTm/gil+milffmiUvfeu5na2mTuuWeTdlQqI+NuIiMH8swzh4Dl/OY3uURGDtRKGWvuCKSOb8qU\ne0hPv5/y8jhgCeXlcUyePI8pU+7Rsg9GBzCYazVlyj289NKbnDljA2Zz5ozid797Q7vfENj5Wr16\nI198sR/z8bEEaMvf/75fO/Jot9sbOCDmmlIppeVM7dz5ISdOFCDiAGYjYuf48QJ27vzQr+2UKffw\nwQe7MVnsLwYM3n//c63z1a1bCoWFJUBLYCAQR2FhMd26pfi1BZg9+xE8HgPoiRmt7YHH42H27Ef8\n2pr7CHZM5v2B3m+7VsTV5erH4cPHgeHACuAKvv32mNYzqKlAxwGsFpFq3w+llM9jCQa/Ad4Qkb6Y\nZ/1r4AHgbRHpBfzZ+zvEYWBOvqPezzFMhzB0HMBQrrEIpbGdTZZyU0iSpYTS9fo+QnlszQBN7B2Z\nh2GgRcCwatUGjh49wSefnKGurh9/+9sZjhw5rnXfP/nkzzGHaaaPmt+G97h/+Ag+amtPAXnU1hZp\nE3w8+uj93rSv+kiHw6F49NH7NVoWSkvLKSyMAn5CYWEUpaXl6Fyy7OynmTlzIoZhw6zXt3H33ZO1\nUmbPFYJJP62rqyM2th91ddalpl566U888cRqtmx5Q9smO/tpUlL6e38tBmDo0IHa58s3R2y2CGAc\nNluE9hzxXSuPRwEKj0dpX6vU1CFERERgpmIeAk4QERGhlRrow5Ytb/Lkk2vYunWbto3Jzmhgs5l9\nttkUIoa2E7lq1Qa2b99FixYFwCZatChk27ZdWveziGAYgo+ABvIxDLTazs5+miuuGIi5dn0LEIYP\nv0TrXM+fP4PIyAjM9NGfAO2Iiopg/vwZfm0Bxoy5Bqih4bMAar3HfxipqSk4HHZv20uBdjgcdlJT\n/TufeXm76dat3Vntdu/enry83Vr9bgrQcQB3KKV+DrRQSl2LmeryWqANKqVaAaki8jyAiNSJSAlw\nE7De+8/WAzcH2kZzgVJOzIlbCSQAVUCN93gYYVw4NKbWUxhhNHM0qXdkXFxX+vX7kea929AZWmrJ\nGZox43bi4qIwNy3nAHbi4qKYMeN2jXZ9BB9PUFQUDSyhqCiK2bN/qRXtmDHjv4mPb4m5hEkHFPHx\nLZkx47/92rrdU5gw4To8HtOJ83hsTJw4SivNrj5lVoAJllJmzxWCST9NSRlHRUUqQ4eO17bxbQ7e\ndtsSIJmpU3+tvTlos9lISbkYsz7rG+AUQ4cO1D5fvnrtY8f+BrTl2LG/addw+66V6ShPwjA82tcq\nM3MqP/7x5UAZpnNQxjXXDCEzc6pfW9/5crufRaQv06c/q32+MjOnMH36rRiGHUjHMGy43bdq18W6\n3Rls3Pgk0dFdABvR0V3YtOlJrWfBhg3PkpTkwtzIMev4OnRwadW0rVmziX/+8xBmBNABePjnPw9q\nbUJlZd1GUlIC9Y6UjaQkl3YquRndNO18LKKgF/XMzJxKq1YxNNRNbN06Rus6O51O7zysT4EHhdPZ\nfNbvOnfhA5gJsl9gUvS8ATwURJvdgQKl1AtKqc+UUmuUUjFAOxHJ9/6bfMy7LqQh4sGkzf0RZhi5\nO6YMROiwgIZybVIoje1slrhXQ5IsJZSu1/cRymNrBmhi70gnDz88Q1MsO3BnSClFRUUtkEefPgaQ\nR0VFnfYzwyT4GExCQgygSEiIYeTIy7QWq0opSkqqgL3Ex9uAfZSUnNGWvjhx4hRRUU6Sk18hKsrB\niRN60herVm1g5cottGp1BNhMq1ZHWL58ywXJlAgmpbFbtxSU6tOAsTUBpfpopdktWvQ0e/YcoKam\nGzCcmppu7NlzgEWL9MTFV63aQkxMArCSmBgXK1a8pH2+YmJ6MH78fZhrpBXAj7j11lnExPTwaztk\nyGiWLFmPGdHaBOTzq1+t1yJE6d59KK+/vvOsdl97LYfu3Yf6tT1bmPwVS8Lk9eQz9SQwa9bok8/4\nWFNPnaoCJnHqVKU2a+qaNZsoKCjGfLQcBU5y8uRpLSdu7dpNFBUVY9bwJQMOTp0q1hJzV0pRUFCC\nUoJSq1BKvL/1niOmg+oEPsckc9kNOLUcV6UUHTsmYbcrlJqM3Q4dO+rVIa9atYHc3JM4HGYKvMNx\nnNzc/GaVNeXXARQRj4isFpFbvZ81EhxDhAOzYnK5iAwCKvheKotP4DCINpoJDMycYzMFxtwl8xBK\nKaBhNB/s33+E558fxZgx7Xj++etDkiwljDDONZraO3Lt2lHa9+7ZztBsS84QwP/932y2br2LPXuW\nsnXrDB5/fLaWna/tsWOvpaYGXK7bqKkRxo69VrvtRx65l7lzr6O2NpF580bxv/+rX581YEAfsrPT\n+PLLJWRnj2HAgN5adm53BuvWPU7LlgMBOy1bDmT9+sctZ0oEUh8WTPrpgQPvc+mlvWiYrjZoUG8O\nHHhfw/YDb6pb/Tqle/f2HDjwgV9bEaF161iqq00ylepqJ/HxsdrjPn16D4mJcWf1u02blpw+vcev\nrUmIMgLTKbABSdqEKAcOvP+9qJSdDh1cWucrO/sZRo0a6m3TjExdf/0VZGc/49fWRz4TGxsN5BEb\nG01amj75TE7OLo4dK6RhXe6xYwVazqfbnUFyck+cznxgP05nPv369dJqe9q0Sd40zraYqZRtiYqK\nYNq0SX4sTUeqRYsWtGt3BpGNtGtXTXR0C21Has2aTd40ziTgIyAJh8OunQJ/4kQhiYkViFSRmFjJ\n8eOFWm273Rm89NKvadeuH3Ar7dr1Y8uWJc0qa8ovTY5S6gvMF03Dp3IJ8Dfg/0TklMU2jwJHReRv\n3t9bgQVAnlKqvYjkKaWSMFUd/z/cfvvtdOvWDYDWrVtzySWXfLf77auDaS6/bTbBMKqBRMx3fA1w\nBrs9skn071z83r17N/fdd1+T6c+5/P3UU0816/n3/d/DhvVkx46Pefrpv5GdfQlDh/bgr3/9a5Pp\nX/h6/fvfvr+bSn8C/b17926Ki4sBOHToEM0BTe0d+cgjP2f8+Jt45JFHtN6RMTGK7Ow0xo69jkcf\nXcyxYwXf/V86zwzvOWDcuFH89a9/tfTM2L79HW64oYI33kggK6s927e/g8sV6dd+796jbNjwCiUl\nrSkvv4NXXnkHp/M9Tp7M5aabrvVrv2DBdKD+mTBu3J1a/d2xYwd79nzxnVh2QUEcX30Vz623Xq9l\n7/tdWHiG5ctPEB29mCuvHKJ1vmw2G0ePHsLjycdmG47H05+jR3PJycnRsjcd6zzgSqAvStl47733\n/Pb31Vff5ujRQkySjS7APo4cKWDt2hfp3bvTD9r37t2JK67oxdatAHOoqzvGFVf0/i6l0d/5+uCD\nD6itLaM+va+Kmpoyb33eD9vb7XaKivK8tuMBF6dOHWfnzp1+z9eaNVs5caLAe75+DPTg+PGTXH/9\neB56aJZfe1NcvDVwCdCZ4uI4bDab3/HOnfsQb7/9Z8rLL8MkzUnjrbfeYfXqjWRmTvFr365dSyIj\nazhzxqdN5yEysua7mrYfsl+9eiPHjx/CVFQZRm3tPzl2LJe5cx9iyZLHf9A+K2sqjz22ihMn8jBZ\nMW24XC3p06ez3+dBr14dWbbsIebOzQGexuOpY/nyh757nvzQeH32yck9+cc/DmHOzy/p128AbneG\nX/tevToyY8ZN/PrXfwW6UVLyGQ88MO47J87f/bRnzxecOPEO0Ir8/E/56isXiYlR5+0d+dRTT7F7\n9+7vfKCgYRad/vsPZuXuImAAZjH6QuApzB3J1/zZ/5v/Mwfo5f37EeBJ72e+99gDwC//hZ2EEqCN\nQA+B0QLjvN89BNo0dtfOCQzDkMmT3WIYRmN35bzg3XffbewunDOsXJktycmjpWfPBwX+Ij17PijJ\nyaNl5crsxu7aOUF4LjZPeJ/5lt8xF/LT1N6Rdvs0iYgYIOnpM8/NRfADwzBk/vwnLN9bZz9zDEvP\nHMMw5KWX3pDOnR8QEOnc+QHZsuVNy30I5L5ZuHC1zJnzuDid18rcuQtl0aI12rbBjFlE5Lrr7pQx\nY6ZLXNwsGTPGLaNGTdduOyFhqAwefLPU1tbK4ME3i8s1TMvOMAyJiblE7PZpAu+K3T5NYmMv1T7X\n6enzJDJyvERGDpfIyPGSkTFPu8/p6TMFegtcIbDI+91Ha26vXJktDkd/iYoaLnCzREUNF4djgNa5\nXrFivUAfgRECQ7zffWTFivVafVaqr8BIgXcERopSfbX6vGLFbyUu7lJR6qcCIkr9VOLiBsmKFb/1\na2u2PUOgl0CmwH0CboFekp4+w6/t5Zff4D3XPxUwvN+95fLLb9CwvVGgp7e9+wSmC/SSyy+/UQz/\nKxAAACAASURBVKvfW7a8KXFx90nXrrdKXNws2bp1m5adiEhCQvK/7HdCQrJf2/T0meJw9DvL1uHo\np3Wt0tNnit2eLDBGYJbAGLHbky/Yc1ck+PejTcNHvEZEFojIFyLyDxF5ELhSRH4JdLPmbn6He4CN\nSqm/e1+YjwO/BK5VSu0D/sv7O8ThYwFtj8kb0N77OzRSQF9+eTuvv94ipCQFGsK3KxMKOJsE5uqQ\nI4EJz8UwziOa1DvS43EQGxtlibEwGAQqHRMM8dTZNctzAqpZlgA0T+vFsr+mtjaZjRv/aUksO5gx\n+xhbv/66DWVlS/n660RtxlaAU6d28cknf8DhcPDJJ3+gsPADLbvVqzcSHx+HxxMBrMbjcdK6dZx2\nXVr//r249trWVFcP4Lrr4unfXy/dFsy019mzpwIuzNo0F3Pm3KaV9up2Z7Bp05PY7XFAO+z2ODZv\n1iNEycycSlraCEyd5kuBGsaMSdUiB6kXF+8BfIopbK4nLq4UOJ0ORJyYUgwOnE6HthD8iBEpXgbR\neiZPm83GiBH+az137XrNmyZsw9zTsjFoUG927fLPZzVt2gSczghMDcAlQB5Op5Np0yZo9TsYrd68\nvN1eIfn6dN34+BgtNk4fS61JvKhQysnQoQO05pd8t+lWn/Zaf6x5QMcBtCulvps9SqkhDeyscwkD\nIvJ3EblcRC4WkbEiUiIiRSJyjYj0EpHrRKQ4kP+7ecGOWfMH9dlDHu/x5ouzJQWWhKSkQKjhXCyo\nmiLCczGMC4Am9o5UDB9+idZitUF7lmvSgr23fM8XXzrl6dOVlp45wdYsB+a4Bs6aCsGNud559GA6\njx7Lm3SBXGdzzFXYbHnABmy2fEpLK9EZc0bG3TzyyDNs366A5WzbBv/zP09r6yYOHTqGpUuzaaiL\nt2TJeoYOHePX1qdBWFHRAVhBRUUHbQ3CxMT+XhKYAZgkMP157bUcEhP7+7GEnTs/oqioBJPTIRco\noqiohJ07/YuLu91T6Nu3B6azuwQ4SXJyD20h+KysqfTs2QUzmJABtKdXry5kZfl/FtjtdqqqajAz\nyzcBJ6mqqtZi07TZbNhseNsdDLSzxI67YMF0xo69jgULFjN27HWWtHqdTmcDRuCbAEXr1i212Dh9\nLLXm/dAfEYOhQy/W6vfIkSk4nT72UfPb6bQxcqSefmFTgM7VuQNYq5Q6pJQ6BKwFpntZyRadz86F\nPswcbfM7FXMS+X43X5y9y7kj5KJJPvjys0MFvl24Z58dY3kXrqkiPBfDuABoUu9Iu72Aw4cLLG3e\nbN26jaVLD/Hyy9u1bc6FQ7J//xHc7naUlfUgM7O9pWfOggXTERGWLPkzgPaiMRjNUx9ral2dAhZT\nV6e0WVN92L//CJmZ7aipSSYrK8kSYY9SisLCcmAShYXlljfptm7dxpIl71i8zlO4885bEGkP5CDS\nnunTx2qNOVjdxF27XsXlauX9ZY4zMbG1FpHLiBFDMH0AO2ZEy47TqRgxwn8krj6qVB8N040qpaam\nYLc7gFaYdYutsNsdWtpySim6dEkiOroTLtd/Ex3dkS5d9FgpffZRUTHY7d8QGflP7PYDREW10Ga1\n/OabY5iO6zDgNAcO6EWY3e4pREVFYzJxpgB/Jzo6ytJ98fLL23n66Y8DytLJyyvBZvsLkITN9i55\neSXatl9+eZCuXb8G2tCt2z6++OKglt3zz79IdXUt5kbIWMCgurqW559/0XL/Gwt+PQ0R+ZuI9Mes\nZr1YRAaIyMciUiEiL53/LoYyBFMGYi/mDtc+IJLmToDaMJrUteuykIkmhToWLJjOuHGjviN0sLIL\n11QRnothnG80tXdkmzaxHD9eoLVw8zlDs2Ztp6ZmGffeu03bGTrbIRln2SHxpVNu3pxPTc0yNm3K\n106n9PX73ns3UVubzD33bLLgxAWXenrixCmcziJstlyczlOWWFN9Y3711XLKypbyyitllsY8Y8Zj\nREaaIt+RkQXcdddjFs/XZsvnqyFTbNeuz1piiq3XTYSIiHTLuol2u51hwy7G5CucADgYNuxirahU\nVtZUfvzjYZgKLQeBAq65ZphWNKw+qlSIqV9YqB1VMgXcBXMtp4AIzO7qzZEBA/pw111JlJbmMWNG\nR22GWh8mTryRG27oTnX1UG688SImTvQvewGm3EdtbS0m2U83oAe1tTVach/duw+lpKQMM1t9OTCA\n4uIyLdmMhhsyVVUzLWcSuFz9qKqqwjBSgZUYRipVVZW4XP382g4ZMpq33nqPb7+NAy7m0KFYtm/f\nqSUVctFF3TBdqH1AZ2A/YPMebx7QuguVUmmAG5illPqFUuoX57db/ynwMXn3xCR664lZ/9e8HUAI\nLqe7uSBU665CbVzhuRjG+UZTekc6nR1ZvvwhLYdGRCgpKaWgoBpQFBRUU1paqpUi6HNIIiJygU5E\nRORqOyTBtu1LxTx50kzFPHkyUjsVMxjN01WrNvDnP39IYmJHDGM5iYmdeOedDy5IDaCIEBFhp7w8\nAbBRXp5AZKTN0vkKNHXVJ5uRm7vFkmwGmBGWefO6UlW1gXnzumlHWHz4+uvDjBkj1NVtZswY4euv\n9Z7fq1dv5O23PwZaYqZxtuSttz7Sql1ctWoDhw7l43DEAStxOOI4dEhP4y0zcwpJST6JzlcASEpq\nqyXmHmyNaTApt8OGDQZqOVuWrM57/IdRV1fnta2vw4Na7/EfRrD8A9dck4qZad+wbY/3+A9DxLfe\nbodZx9cOEO/xH4ZZawnmuv0p77c0ON70oSMDsQqIxiw6X4PJp+s/mTkMv2jbti0nT5bTMIcYamnb\ntm3jduwcwJees2DBYhYtuj8ccQmj0eCjfAcYN25UI/YkjFBEU3tHFhaWajs0mZlT2L//CEuXHgHm\n4PEIEyfeoLVYBSEy0klhYXvgQ0pL+9OxYwW6ToWv7SVLzLbr6vTbFoHKyoqzUgsrKyvRLW3zbQqN\nHXsdv//9W9qbQm53BgkJLi9lvcLhiObppx/Wfq7UO59VREXdSnFxR8vXasmSw8B11NV11z5fbvcU\n9u07wtKlhwGFx2OzlLq6YMF0ampqsNsv4syZr7+TYdDB9u1rqKuro2XLARQX/x2Hw++y8yzs37/t\nu7XEK6+s1F5LuN0Z7NjxOW+95eHUKYXLFc+oUddqORb113kHR44okpI6sGTJEq3rrJRixIjL+P3v\nD9O9ewtyc0sYMeIyzX77HPUOmI56Jg6HvqOenf007dot4qmnDgGKujqD2bMns3jxfL+2V145jN/9\n7q+ItMGsP5yOUk6uvHKYX9tDhz4iNvZiqqt9khsJREVFc+iQ/8dgw3sC0iguvshSJsHmzct5441L\nKS9XwHCgH3FxMWzevNyv7Ycfvk5S0jAKChTmGlzRpk1LPvzwdb+22dnPsHVrf2pq6tfvERF2Lb3H\npgKdCOAVInIbUCQi/wsMBazFpMP4l+jcuSOmDy6YawcDcHiPN38Ek9PdHBCqdVfhcTU/hPLYmgGa\n1DsyNvYTS3VleXmncTgKgRM4HIXk5eml9/nq4US+AC5G5EtLTsXq1Rt57rnfYxJeGEA+a9b8XitC\nk5npI8sAmA0Iyck9NB3X+nT3HTt2WEp3b7hYdbluo7jYGnENmM7nlVdWcuZMIldddcbytXI6v8Fm\nuwinM1f7WjVM40xOnm0pjdOHTp1GINKfTp38R1a+j5SUcVRUpDJ06HjLthAYYY9SirFjr6Wysg6Y\nRGVlHWPHXqt9vpRSHDt2GpjEsWNFlq5z//692LDhpyxbdhMbNvxUm/n07BpTZbnGdM2aTaxf/woi\ndkwWURvr1v1RSxQ9M3MqdrsNM5qmgDrsdpsWmZTD4cAM9n2IuZ79iNpatJ198x5419vuDktZOjab\njaoqD2b9YX/g71RW1mmlGT/33GYKCkow1+GzATsFBSU899xmrXbbtzcjhjAJMEhKaqud3twUoNPT\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6fXs1Hs8GSkurI1IQtfB575vPw3TAhcnlG4ZBY2MbKl33BHCWxsYWrb4FBfP4xje+Zv5S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\nChkxkwcBh1DIZcmSP1nte+7cF7Xh5Kk0JhIJ5s59MbCtGC5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GFtDP/IN+1/w3//5b78gD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A/wC+hu/Z+2sz2x94oJ/fcZZzrqWf/38GOKGf/++rcdhze7h+v3PuU/38PhERkVLoaz+Ur5Nk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\ndkIJoIiIiIiISDuhBFBERERERKSd+H/JHk/JSnDbLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa42592a4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,20])\n",
    "\n",
    "M=3\n",
    "N=2\n",
    "i=1\n",
    "\n",
    "plt.subplot(M,N,i)\n",
    "\n",
    "plt.plot(df.groupby('Payment_type').get_group(1)['Trip_distance'],df.groupby('Payment_type').get_group(1)['Tip_percent'],'*')\n",
    "plt.xlabel(\"Trip distance\")\n",
    "plt.ylabel(\"Fare percentage\")\n",
    "plt.xlim([0,100])\n",
    "plt.grid()\n",
    "\n",
    "i+=1\n",
    "plt.subplot(M,N,i)\n",
    "plt.plot(df.groupby('Payment_type').get_group(1)['Fare_amount'],df.groupby('Payment_type').get_group(1)['Tip_percent'],'*')\n",
    "plt.xlabel(\"Fare amount\")\n",
    "plt.ylabel(\"Fare percentage\")\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "i+=1\n",
    "plt.subplot(M,N,i)\n",
    "plt.plot(df.groupby('Payment_type').get_group(1)['RateCodeID'],df.groupby('Payment_type').get_group(1)['Tip_percent'],'*')\n",
    "plt.xlabel(\"Rate Code\")\n",
    "plt.ylabel(\"Fare percentage\")\n",
    "plt.xlim([0,7])\n",
    "plt.grid()\n",
    "\n",
    "i+=1\n",
    "plt.subplot(M,N,i)\n",
    "\n",
    "plt.plot(df.groupby('Payment_type').get_group(1)['hour'],df.groupby('Payment_type').get_group(1)['Tip_percent'],'*')\n",
    "plt.xlabel(\"Hour of day\")\n",
    "plt.ylabel(\"Fare percentage\")\n",
    "plt.grid()\n",
    "\n",
    "i+=1\n",
    "plt.subplot(M,N,i)\n",
    "\n",
    "plt.plot(df.groupby('Payment_type').get_group(1)['Tolls_amount'],df.groupby('Payment_type').get_group(1)['Tip_percent'],'*')\n",
    "plt.xlabel(\"Toll amount\")\n",
    "plt.ylabel(\"Fare percentage\")\n",
    "plt.xlim([0,40])\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=[10,10])\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.hist(JFK_group['Tip_amount'][JFK_group['Tip_amount']>0], bins=100)\n",
    "plt.xlim([0,150])\n",
    "plt.xlabel(\"Tip amount\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "d=JFK_group['Tip_amount'][JFK_group['Tip_amount']>0].shape\n",
    "plt.title(\"JFK, Non-zero data size: %d\"% d)\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.hist(EWR_group['Tip_amount'][EWR_group['Tip_amount']>0], bins=50)\n",
    "plt.xlim([0,150])\n",
    "plt.xlabel(\"Tip amount\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "d=EWR_group['Tip_amount'][EWR_group['Tip_amount']>0].shape\n",
    "plt.title(\"EWR, Non-zero data size: %d\"% d)\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.hist(JFK_group['Fare_amount'][JFK_group['Fare_amount']>0], bins=100)\n",
    "plt.xlim([0,150])\n",
    "plt.xlabel(\"Fare amount\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "d=JFK_group['Fare_amount'][JFK_group['Fare_amount']>0].shape\n",
    "plt.title(\"JFK, Non-zero data size: %d\"% d)\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.hist(EWR_group['Fare_amount'][EWR_group['Fare_amount']>0], bins=50)\n",
    "plt.xlim([0,150])\n",
    "plt.xlabel(\"Fare amount\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "d=EWR_group['Fare_amount'][EWR_group['Fare_amount']>0].shape\n",
    "plt.title(\"EWR, Non-zero data size: %d\"% d)\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['VendorID', 'lpep_pickup_datetime', 'Lpep_dropoff_datetime', 'Store_and_fwd_flag', 'RateCodeID', 'Pickup_longitude', 'Pickup_latitude', 'Dropoff_longitude', 'Dropoff_latitude', 'Passenger_count', 'Trip_distance', 'Fare_amount', 'Extra', 'MTA_tax', 'Tip_amount', 'Tolls_amount', 'Ehail_fee', 'improvement_surcharge', 'Total_amount', 'Payment_type', 'Trip_type ', 'hour', 'Tip_percent']\n"
     ]
    }
   ],
   "source": [
    "print list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(701287, 15)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "VendorID                 0\n",
       "RateCodeID               0\n",
       "hour                     0\n",
       "Pickup_longitude         0\n",
       "Pickup_latitude          0\n",
       "Dropoff_longitude        0\n",
       "Dropoff_latitude         0\n",
       "Passenger_count          0\n",
       "Trip_distance            0\n",
       "Fare_amount              0\n",
       "Extra                    0\n",
       "MTA_tax                  0\n",
       "Tolls_amount             0\n",
       "improvement_surcharge    0\n",
       "Trip_type                3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictives=['VendorID',  'RateCodeID', 'hour',\\\n",
    "             'Pickup_longitude', 'Pickup_latitude', 'Dropoff_longitude', 'Dropoff_latitude', 'Passenger_count', \\\n",
    "             'Trip_distance', 'Fare_amount', 'Extra', 'MTA_tax', 'Tolls_amount', 'improvement_surcharge', \\\n",
    "              'Trip_type ']\n",
    "target=['Tip_percent']\n",
    "\n",
    "X=df.groupby('Payment_type').get_group(1)[predictives]\n",
    "y=df.groupby('Payment_type').get_group(1)[target]\n",
    "\n",
    "print X.shape\n",
    "X.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VendorID                 0\n",
       "RateCodeID               0\n",
       "hour                     0\n",
       "Pickup_longitude         0\n",
       "Pickup_latitude          0\n",
       "Dropoff_longitude        0\n",
       "Dropoff_latitude         0\n",
       "Passenger_count          0\n",
       "Trip_distance            0\n",
       "Fare_amount              0\n",
       "Extra                    0\n",
       "MTA_tax                  0\n",
       "Tolls_amount             0\n",
       "improvement_surcharge    0\n",
       "Trip_type                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['Trip_type '].fillna(3, inplace=True)\n",
    "\n",
    "X.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tip_percent    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y['Tip_percent'].fillna(0, inplace=True)\n",
    "y.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/amir/.local/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "X1, X_test, y1, y_test = train_test_split(X, y,test_size=.2,random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Extensions' object has no attribute 'get_extension_for_class'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-80c6c708d8f1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     12\u001b[0m     parameters=[\n\u001b[0;32m     13\u001b[0m         \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'max_depth'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'int'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m         \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'n_estimators'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'int'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m     ],\n\u001b[0;32m     16\u001b[0m )\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/sigopt/endpoint.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m     20\u001b[0m       \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_delete\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m     \u001b[0mraw_response\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_endpoint\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_response_cls\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/sigopt/interface.pyc\u001b[0m in \u001b[0;36m_post\u001b[1;34m(self, url, params)\u001b[0m\n\u001b[0;32m    118\u001b[0m     return self.requestor.post(\n\u001b[0;32m    119\u001b[0m       \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 120\u001b[1;33m       \u001b[0mjson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest_params\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    121\u001b[0m     )\n\u001b[0;32m    122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/sigopt/requestor.pyc\u001b[0m in \u001b[0;36mpost\u001b[1;34m(self, url, params, json, headers)\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mpost\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'post'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/sigopt/requestor.pyc\u001b[0m in \u001b[0;36m_request\u001b[1;34m(self, method, url, params, json, headers)\u001b[0m\n\u001b[0;32m     34\u001b[0m         \u001b[0mjson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m         \u001b[0mauth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauth\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m         \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     37\u001b[0m       )\n\u001b[0;32m     38\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRequestException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/requests/api.pyc\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     54\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/requests/sessions.pyc\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    486\u001b[0m         }\n\u001b[0;32m    487\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 488\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    490\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/requests/sessions.pyc\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    607\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    608\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 609\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    610\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    611\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/requests/adapters.pyc\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    421\u001b[0m                     \u001b[0mdecode_content\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    422\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 423\u001b[1;33m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    424\u001b[0m                 )\n\u001b[0;32m    425\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/requests/packages/urllib3/connectionpool.pyc\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, **response_kw)\u001b[0m\n\u001b[0;32m    592\u001b[0m                                                   \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout_obj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    593\u001b[0m                                                   \u001b[0mbody\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 594\u001b[1;33m                                                   chunked=chunked)\n\u001b[0m\u001b[0;32m    595\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    596\u001b[0m             \u001b[1;31m# If we're going to release the connection in ``finally:``, then\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/requests/packages/urllib3/connectionpool.pyc\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    348\u001b[0m         \u001b[1;31m# Trigger any extra validation we need to do.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    349\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 350\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    351\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    352\u001b[0m             \u001b[1;31m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/requests/packages/urllib3/connectionpool.pyc\u001b[0m in \u001b[0;36m_validate_conn\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m    833\u001b[0m         \u001b[1;31m# Force connect early to allow us to validate the connection.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    834\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sock'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# AppEngine might not have  `.sock`\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 835\u001b[1;33m             \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    836\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    837\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_verified\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/requests/packages/urllib3/connection.pyc\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    328\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverify_mode\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mssl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCERT_NONE\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    329\u001b[0m                 \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massert_hostname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 330\u001b[1;33m             \u001b[0mcert\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetpeercert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    331\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcert\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'subjectAltName'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    332\u001b[0m                 warnings.warn((\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/requests/packages/urllib3/contrib/pyopenssl.pyc\u001b[0m in \u001b[0;36mgetpeercert\u001b[1;34m(self, binary_form)\u001b[0m\n\u001b[0;32m    322\u001b[0m                 \u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'commonName'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx509\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_subject\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    323\u001b[0m             ),\n\u001b[1;32m--> 324\u001b[1;33m             \u001b[1;34m'subjectAltName'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mget_subj_alt_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx509\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    325\u001b[0m         }\n\u001b[0;32m    326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/amir/.local/lib/python2.7/site-packages/requests/packages/urllib3/contrib/pyopenssl.pyc\u001b[0m in \u001b[0;36mget_subj_alt_name\u001b[1;34m(peer_cert)\u001b[0m\n\u001b[0;32m    169\u001b[0m     \u001b[1;31m# faster than looping in Python)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    170\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 171\u001b[1;33m         ext = cert.extensions.get_extension_for_class(\n\u001b[0m\u001b[0;32m    172\u001b[0m             \u001b[0mx509\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSubjectAlternativeName\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m         ).value\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Extensions' object has no attribute 'get_extension_for_class'"
     ]
    }
   ],
   "source": [
    "from sigopt import Connection\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# SigOpt optimization loop\n",
    "\n",
    "conn = Connection(client_token=\"TCJITYTTBAVUNHZAFDQLWZAPFTCNAXNZTTTLLBLOWEASLINN\")\n",
    "\n",
    "experiment = conn.experiments().create(\n",
    "    name='SGD Optimization (Python)',\n",
    "    parameters=[\n",
    "        dict(name='max_depth', type='int', bounds=dict(min=10, max=100)),\n",
    "        dict(name='n_estimators', type='int', bounds=dict(min=1, max=15)),\n",
    "        dict(name='criterion', type='categorical', categorical_values=[{\"name\":'mse'}, {\"name\":'mae'}]),\n",
    "    ],\n",
    ")\n",
    "print(\"Created experiment: https://sigopt.com/experiment/\" + experiment.id)\n",
    "\n",
    "# Evaluate your model with the suggested parameter assignments\n",
    "def evaluate_model(assignments):\n",
    "    regr_rf = RandomForestRegressor(max_depth=assignments['max_depth'], random_state=2,\\\n",
    "                                    n_estimators=assignments['n_estimators'],\\\n",
    "                                   criterion =assignments['criterion'])\n",
    "    score=[]\n",
    "    \n",
    "    cv=3\n",
    "    for _ in range(cv):\n",
    "        X_train, X_cv, y_train, y_cv = train_test_split(X1, y1,test_size=.5)\n",
    "\n",
    "        regr_rf.fit(X_train, y_train)\n",
    "    \n",
    "        score.append(regr_rf.score(X_cv, y_cv))\n",
    "        print score\n",
    "    \n",
    "    return np.mean(score)\n",
    "\n",
    "best_value=0\n",
    "\n",
    "\n",
    "# Run the Optimization Loop between 10x - 20x the number of parameters\n",
    "for _ in range(30):\n",
    "    suggestion = conn.experiments(experiment.id).suggestions().create()\n",
    "    \n",
    "    value = evaluate_model(suggestion.assignments)\n",
    "    \n",
    "    print 'max_depth= ',suggestion.assignments['max_depth'], \\\n",
    "    ', n_estimators= ',suggestion.assignments['n_estimators'],\\\n",
    "    ', criterion= ' ,suggestion.assignments['criterion'],', Value= ', value\n",
    "    \n",
    "    if value > best_value:\n",
    "        best_value=value\n",
    "        best_suggestion=suggestion\n",
    "        \n",
    "    conn.experiments(experiment.id).observations().create(\n",
    "        suggestion=suggestion.id,\n",
    "        value=value,\n",
    "    )\n",
    "\n",
    "print '\\nBEST VALUE:'    \n",
    "print 'max_depth= ',best_suggestion.assignments['max_depth'],\\\n",
    "', n_estimators= ',best_suggestion.assignments['n_estimators'],\\\n",
    "', criterion= ', suggestion.assignments['criterion'], ', Value= ', best_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Amir\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=13,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=11, n_jobs=1, oob_score=False, random_state=2,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X_train, X_cv, y_train, y_cv = train_test_split(X1, y1,test_size=.2,random_state=4)\n",
    "\n",
    "max_depth = best_suggestion.assignments['max_depth']\n",
    "n_estimators= best_suggestion.assignments['n_estimators']\n",
    "\n",
    "regr_rf = RandomForestRegressor(max_depth=max_depth, random_state=2,n_estimators=n_estimators)\n",
    "regr_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24786243821231602"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr_rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16733212876984727"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr_rf.score(X_cv, y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15751045441448974"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15191488370827644"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([0.15191488370827644, 0.15191488370827644])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "a=regr_rf.feature_importances_\n",
    "\n",
    "index=np.arange(len(a))\n",
    "label=list(X)\n",
    "plt.barh(index,a,color='y')\n",
    "plt.yticks(index+0.5, label, rotation='horizontal')\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "447514     52.0\n",
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       "93485      52.0\n",
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       "1137192    52.0\n",
       "           ... \n",
       "57991      52.0\n",
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       "640779     52.0\n",
       "361811     52.0\n",
       "140415     52.0\n",
       "1035539    52.0\n",
       "Name: Fare_amount, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_JFK_train['Fare_amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_JFK_pred=regr_rf.predict(X_JFK_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 11.287       11.36387987   5.77597802   7.34436508  12.429        6.809\n",
      "  12.664       34.2         10.4382       9.36097802   8.67736508\n",
      "   5.13236508  14.851        8.60897802   9.42969231   2.508125    10.69\n",
      "  11.17657576   7.725        6.55        11.169       11.76772603  10.794\n",
      "  12.43490909   9.562        5.89072603  11.75877802  11.37681508  12.831\n",
      "  12.021       10.519       14.706        3.2         10.66597802\n",
      "  11.17388711   6.80772603  12.749       13.623        2.514        9.101\n",
      "  13.831       12.65757895   9.918       11.39        79.          10.95131893\n",
      "   8.514        8.70772603  11.503        9.14436508   9.89338095\n",
      "   7.79538095  16.917       10.113       10.461       12.252       10.334\n",
      "   4.487       12.75490909  13.707       11.559       11.11324561   3.8\n",
      "   8.468        8.257        3.          11.501        9.85435897   6.284\n",
      "   6.76336508  10.706        9.27697802   9.71897802  11.83597802   1.72\n",
      "   9.757       14.556       11.88325      8.09456508  14.846        3.458\n",
      "  11.95997802  12.543       13.72        13.414        1.7          4.70733333\n",
      "   6.467       12.774        2.8          9.19236508  13.39697802   9.825\n",
      "  10.918        9.04678409  10.302        9.01236508  11.207       10.227     ]\n"
     ]
    },
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tip_amount</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>3.00</td>\n",
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       "      <th>1265674</th>\n",
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       "    <tr>\n",
       "      <th>80040</th>\n",
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       "    <tr>\n",
       "      <th>1458945</th>\n",
       "      <td>14.58</td>\n",
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       "    <tr>\n",
       "      <th>172121</th>\n",
       "      <td>11.67</td>\n",
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       "    <tr>\n",
       "      <th>1138278</th>\n",
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       "      <td>11.67</td>\n",
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       "    <tr>\n",
       "      <th>1100114</th>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>1368689</th>\n",
       "      <td>11.67</td>\n",
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       "    <tr>\n",
       "      <th>1260885</th>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082582</th>\n",
       "      <td>14.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403846</th>\n",
       "      <td>11.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408768</th>\n",
       "      <td>19.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813350</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817379</th>\n",
       "      <td>11.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1473751</th>\n",
       "      <td>11.67</td>\n",
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       "    <tr>\n",
       "      <th>528135</th>\n",
       "      <td>14.58</td>\n",
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       "    <tr>\n",
       "      <th>847256</th>\n",
       "      <td>14.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274903</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457259</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61599</th>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438636</th>\n",
       "      <td>14.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1164507</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1152532</th>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460481</th>\n",
       "      <td>14.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193090</th>\n",
       "      <td>11.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478396</th>\n",
       "      <td>11.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221575</th>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858091</th>\n",
       "      <td>11.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155315</th>\n",
       "      <td>11.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595587</th>\n",
       "      <td>11.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456556</th>\n",
       "      <td>11.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Tip_amount\n",
       "1166051       11.65\n",
       "93485         11.67\n",
       "586080         4.00\n",
       "933572         5.00\n",
       "1028327       14.58\n",
       "1013604        3.50\n",
       "494555        14.58\n",
       "1137982       40.00\n",
       "1211779       11.67\n",
       "1472370       11.65\n",
       "1131790        7.50\n",
       "222447         0.00\n",
       "1114099       18.00\n",
       "451435         8.00\n",
       "940292        11.67\n",
       "263629         0.00\n",
       "1165518       11.67\n",
       "211245        11.67\n",
       "556770         8.00\n",
       "183187         8.00\n",
       "773605        14.58\n",
       "1459628       11.67\n",
       "627367        11.67\n",
       "65759         13.20\n",
       "1270597        7.66\n",
       "1112308        3.00\n",
       "1265674       11.67\n",
       "80040         11.65\n",
       "1137192       14.58\n",
       "870073        10.55\n",
       "...             ...\n",
       "103212         6.00\n",
       "1458945       14.58\n",
       "172121        11.67\n",
       "1138278       11.65\n",
       "1389116       11.67\n",
       "1100114        0.00\n",
       "1368689       11.67\n",
       "1260885       20.00\n",
       "1082582       14.58\n",
       "403846        11.67\n",
       "408768        19.16\n",
       "813350         0.00\n",
       "817379        11.67\n",
       "1473751       11.67\n",
       "528135        14.58\n",
       "847256        14.58\n",
       "1274903        0.00\n",
       "457259         0.00\n",
       "61599          5.00\n",
       "438636        14.55\n",
       "1164507        0.00\n",
       "1152532        8.00\n",
       "460481        14.55\n",
       "1193090       11.65\n",
       "1478396       11.67\n",
       "1221575        9.00\n",
       "858091        11.67\n",
       "155315        11.67\n",
       "595587        11.65\n",
       "1456556       11.67\n",
       "\n",
       "[99 rows x 1 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print y_JFK_pred[1:100]\n",
    "y_JFK_train[1:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IsbjHwy4HxgKo6kci8jjwERADrjE3wDhUsJCRcVBjye2MtoqFjAzDMIwWwwyCYRiGAZhB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\nGRkJ3FzCOFycPVB6HXEjUiW5a7cGF7b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7dGwGVaWUrFq1nvvv/5hDh7pjtT5Nmzb9OXZs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\nmnwGwUc+8pGPfATA/wOXmCr//aXQrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14cca748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(121)\n",
    "plt.plot(y_JFK_pred, '*')\n",
    "plt.subplot(122)\n",
    "plt.plot(y_JFK_test,'r*')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}