trajectory_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the best way to classify the un-labelled data from the mtl Trajet application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Initialization cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#%reset # Clears all variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline \n",
    "matplotlib.rcParams['figure.figsize'] = (10,10)\n",
    "\n",
    "# Load the data\n",
    "%run \"E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/load_mtlTrajet_data.py\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### A key for the modes of transit and purposes of displacements:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Mode of transport:\n",
    "    \"pedestrian\"\n",
    "    \"cyclist\"\n",
    "    \"publicTransit\"\n",
    "    \"otherCombo\"\n",
    "##### Purpose:\n",
    "    \"toHome\"\n",
    "    \"toWork\"        \n",
    "    \"leisure\" \n",
    "    \"errand\" \n",
    "    \"mealSnackCafe\" \n",
    "    \"foodDrink\"        \n",
    "    \"childDropoffPickup\" \n",
    "    \"school\"        \n",
    "    \"health\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Some notes on PCA vs LDA:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "    - Both are linear transformation models. \n",
    "    - PCA yields the directions (PCs) that maximize variance.\n",
    "    - LDA aims to find direction that maximizes discrimination between different classes, and is therefore usefull in pattern recognition\n",
    "    From the plotly documentation:\n",
    "        \"In other words, PCA projects the entire dataset onto a different feature (sub)space, and LDA tries to determine a suitable feature (sub)space in order to distinguish between patterns that belong to different classes.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Data processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "Create a matrix and associated vector of IDs for trajectories that don't have a label. Use the trajet_final data, since there is more metadata easily accessible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Test first that there are blanks being properly read with the \"\" string\n",
    "Should reflect that there are ~80% uncategorized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "218379\n",
      "276418\n",
      "0.7444823236627689\n",
      "0.7900317634886296\n"
     ]
    }
   ],
   "source": [
    "blankCount=0\n",
    "countNotNone=0\n",
    "cycCount=0\n",
    "pedCount=0\n",
    "pubCount=0\n",
    "carCount=0\n",
    "othCount=0\n",
    "\n",
    "for nTraj in range(0,numIds):\n",
    "    if trip_final[\"features\"][nTraj][\"geometry\"] is not None: \n",
    "        countNotNone=countNotNone+1 # Don't want to consider these for the analysis\n",
    "            \n",
    "        if mode[nTraj]==\"cyclist\":\n",
    "            cycCount=cycCount+1\n",
    "        if mode[nTraj]==\"pedestrian\":\n",
    "            pedCount=pedCount+1\n",
    "        if mode[nTraj]==\"publicTransit\":\n",
    "            pubCount=pubCount+1\n",
    "        if mode[nTraj]==\"Automobile\": # this capitalization will surely be confusing down the line..\n",
    "            carCount=carCount+1\n",
    "        if mode[nTraj]==\"otherCombo\":\n",
    "            othCount=othCount+1\n",
    "        if mode[nTraj] is None:     #==\"None\":\n",
    "            blankCount=blankCount+1       \n",
    "\n",
    "print(blankCount)\n",
    "print(countNotNone)\n",
    "print(blankCount/numIds)\n",
    "print(blankCount/countNotNone)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Also added in counts for the seperate modes of transit so I can preallocate the np.arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Extract the relevant information for each trajectory. \n",
    "Make necessary calculation on tempArray of coordinates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Create array to store info for each trajectory. 5 Dimensions corresponding to:\n",
    "# 0: trajectory ID - will probably be usefull.. \n",
    "# 1: average velocity\n",
    "# 2: euclidean distance\n",
    "# 3: cummulative distance\n",
    "# 4: \n",
    "extractData_all=np.empty((countNotNone,4)) \n",
    "extractData_nil=np.empty((blankCount,4)) \n",
    "extractData_cyc=np.empty((cycCount,4)) \n",
    "extractData_ped=np.empty((pedCount,4)) \n",
    "extractData_pub=np.empty((pubCount,4)) \n",
    "extractData_car=np.empty((carCount,4)) \n",
    "extractData_oth=np.empty((othCount,4)) \n",
    "\n",
    "# Restart the counts, to properly index the np.arrays\n",
    "blankCount=0\n",
    "countNotNone=0\n",
    "cycCount=0\n",
    "pedCount=0\n",
    "pubCount=0\n",
    "carCount=0\n",
    "othCount=0\n",
    "\n",
    "for nTraj in range(0,numIds):\n",
    "    if trip_final[\"features\"][nTraj][\"geometry\"] is not None: \n",
    "        \n",
    "        tempArray=[]\n",
    "        tempArray=np.asarray(trip_final[\"features\"][nTraj][\"geometry\"][\"coordinates\"][0][:]) # Load data as array instead of as a list.\n",
    "        \n",
    "        # Calculate the total length and euclidean distance for each set of coordinates.\n",
    "        \n",
    "        # Euclidean distance \"as the crow flies\" \n",
    "        ncoords=int(tempArray.size/2)\n",
    "        x1=tempArray[0,0]\n",
    "        y1=tempArray[0,1]\n",
    "        x2=tempArray[ncoords-1,0]\n",
    "        y2=tempArray[ncoords-1,1]\n",
    "        eucdist = np.sqrt(np.power((x2-x1),2) + np.power((y2-y1),2))  #Import math, need to use numpy?\n",
    "\n",
    "        # Cummulative length of segments\n",
    "        sumdist=0\n",
    "        for i in range(1,ncoords): # Start at 1 because will index zeroth in distance calc\n",
    "            x1=tempArray[i-1,0]\n",
    "            y1=tempArray[i-1,1]\n",
    "            x2=tempArray[i,0]\n",
    "            y2=tempArray[i,1]\n",
    "            currdist=np.sqrt(np.power((x2-x1),2) + np.power((y2-y1),2))\n",
    "            sumdist=sumdist+currdist\n",
    "            \n",
    "        # Populate the array of extracted data\n",
    "        # Warning, if index out of bounds, may be because counts re-zeroed\n",
    "        extractData_all[countNotNone,0]=ids[nTraj]\n",
    "        extractData_all[countNotNone,1]=avgSpeed[nTraj]\n",
    "        extractData_all[countNotNone,2]=eucdist\n",
    "        extractData_all[countNotNone,3]=sumdist\n",
    "        countNotNone=countNotNone+1 # Don't want to consider these for the analysis\n",
    "        \n",
    "        if mode[nTraj]==\"cyclist\":\n",
    "            extractData_cyc[cycCount,0]=ids[nTraj]\n",
    "            extractData_cyc[cycCount,1]=avgSpeed[nTraj]\n",
    "            extractData_cyc[cycCount,2]=eucdist\n",
    "            extractData_cyc[cycCount,3]=sumdist          \n",
    "            cycCount=cycCount+1\n",
    "        if mode[nTraj]==\"pedestrian\":\n",
    "            extractData_ped[pedCount,0]=ids[nTraj]\n",
    "            extractData_ped[pedCount,1]=avgSpeed[nTraj]\n",
    "            extractData_ped[pedCount,2]=eucdist\n",
    "            extractData_ped[pedCount,3]=sumdist              \n",
    "            pedCount=pedCount+1\n",
    "        if mode[nTraj]==\"publicTransit\":\n",
    "            extractData_pub[pubCount,0]=ids[nTraj]\n",
    "            extractData_pub[pubCount,1]=avgSpeed[nTraj]\n",
    "            extractData_pub[pubCount,2]=eucdist\n",
    "            extractData_pub[pubCount,3]=sumdist              \n",
    "            pubCount=pubCount+1\n",
    "        if mode[nTraj]==\"Automobile\":\n",
    "            extractData_car[carCount,0]=ids[nTraj]\n",
    "            extractData_car[carCount,1]=avgSpeed[nTraj]\n",
    "            extractData_car[carCount,2]=eucdist\n",
    "            extractData_car[carCount,3]=sumdist              \n",
    "            carCount=carCount+1\n",
    "        if mode[nTraj]==\"otherCombo\": \n",
    "            extractData_oth[othCount,0]=ids[nTraj]\n",
    "            extractData_oth[othCount,1]=avgSpeed[nTraj]\n",
    "            extractData_oth[othCount,2]=eucdist\n",
    "            extractData_oth[othCount,3]=sumdist             \n",
    "            othCount=othCount+1\n",
    "        if mode[nTraj] is None:   \n",
    "            extractData_nil[blankCount,0]=ids[nTraj]\n",
    "            extractData_nil[blankCount,1]=avgSpeed[nTraj]\n",
    "            extractData_nil[blankCount,2]=eucdist\n",
    "            extractData_nil[blankCount,3]=sumdist                \n",
    "            blankCount=blankCount+1            \n",
    "     \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Ideas for followup:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    " - Using trip duration, distance and euclidean distance, can we detect a different between modes of transport with respect to how direct they are?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Take a look at how the different groups behave considering only the speed and distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAOlCAYAAAACVbG1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMzOzKvQ3GZPnBqup7FMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0ae2bd940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams[\"figure.figsize\"] = (10,16)\n",
    "fig1, (ax1, ax2) = plt.subplots(nrows=2, ncols=1)\n",
    "\n",
    "ax1.scatter(extractData_car[:,3],extractData_car[:,1], c='blue',alpha=0.05) # plot cars first..\n",
    "ax1.scatter(extractData_pub[:,3],extractData_pub[:,1], c='magenta',alpha=0.05)\n",
    "ax1.scatter(extractData_cyc[:,3],extractData_cyc[:,1], c='green',alpha=0.05)\n",
    "ax1.scatter(extractData_ped[:,3],extractData_ped[:,1], c='yellow',alpha=0.05)\n",
    "\n",
    "plt.title('Speed versus commute distance ')\n",
    "plt.xlabel('Distance of commute')\n",
    "plt.ylabel('Average Speed during commute')\n",
    "\n",
    "ax2.scatter(extractData_car[:,3],extractData_car[:,1], c='blue',alpha=0.05) # plot cars first..\n",
    "ax2.scatter(extractData_pub[:,3],extractData_pub[:,1], c='magenta',alpha=0.2)\n",
    "ax2.scatter(extractData_cyc[:,3],extractData_cyc[:,1], c='green',alpha=0.5)\n",
    "ax2.scatter(extractData_ped[:,3],extractData_ped[:,1], c='yellow',alpha=1)\n",
    "\n",
    "ax2.set_yscale('log')\n",
    "ax2.set_xscale('log')\n",
    "\n",
    "plt.xlabel('Log distance of commute')\n",
    "plt.ylabel('Log aaverage speed during commute')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This graph is unsatisfactory, and smaller points won't solve everything. It will be usefull to try several other plotting options, including:\n",
    "    - Datashader, to better see the distribution of points.\n",
    "    - Contour/Relief plots or violin diagrams for a single factor. \n",
    "##### Of note:\n",
    "    - The variation in pedestrian speed is so small compared to the other modes of transit, that all pedestrians appear within the size of a single point on the log/log scatter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm4AAAOlCAYAAADU8GvQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIf5/Ilu3R/OzQbUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a027883d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#matplotlib.rcParams[\"figure.figsize\"] = (10,10)\n",
    "fig2, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(10,16))\n",
    "\n",
    "plt.xlabel('Distance of commute')\n",
    "plt.ylabel('Average Speed during commute')\n",
    "plt.title('Speed versus commute distance ')\n",
    "ax1.scatter(extractData_nil[:,3],extractData_nil[:,1], c='black',alpha=0.01) \n",
    "\n",
    "\n",
    "ax2.scatter(extractData_nil[:,3],extractData_nil[:,1], c='black',alpha=0.05)\n",
    "ax2.set_yscale('log')\n",
    "ax2.set_xscale('log')\n",
    "plt.xlabel('Log Distance of commute')\n",
    "plt.ylabel('Log Average Speed during commute')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Comparing the graphs in figures 1 and 2 introduces the categorization problem. We can see how easily pedestrians and cyclists can be seperated from cars (within what I expect would be tolerable error). However, there is so much overlap between public transit and cars that we'll need more advanced methods to categorize the unlabelled data.\n",
    "\n",
    "Of note: \n",
    "    - There don't seem to be any data points at a speed of 10, does this mean none of the unlabelled datapoints were pedestrians?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Alternative visualizations of the categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#####  few new imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Violin plot to see shape of distributions (Python plot gallery OR seaborn)\n",
    "http://seaborn.pydata.org/examples/elaborate_violinplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### 2D density plot (Python plot gallery)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### KDE plots with marginal histograms (seaborn)\n",
    "http://seaborn.pydata.org/examples/hexbin_marginals.html\n",
    "This would look nice for each individual mode of transit (4 subplots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x19e0212ed30>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAGkCAYAAAB+TFE1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsAKBBACwAoEEALACgQQAsML/B+kuZn9Iu/7UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e020e0588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAGkCAYAAAB+TFE1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e024c3e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAGkCAYAAAB+TFE1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e02580c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAGkCAYAAAB+TFE1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAHMZJREFUeJzt3X1wXAW9//HP2d08NQ9Ng2ALpUCg\nhVt5sCU3xQGC3twaYWQ6xUJKIcB4HX+gRIIo7Y+haUWuFRl79ZcMU5XfwExLUXF8ut656hC4ppAx\nlIIW00IRpJA+AH2g2WybZB/O/eMkafqQTbrdzfl29/2a2dmn7PI9Ju675+w5u47ruq4AAPBZwO8B\nAACQCBIAwAiCBAAwgSABAEwgSAAAEwgSAMAEggQAMIEgAQBMIEgAABMIEgDABIIEADCBIAEATCBI\nAAATbAcp/JrU/f/9ngIAMAFsB+mtVdLfvixFtvk9CQAgw2wHKbbfOz+wyd85AAAZZzxIvd55z6v+\nzgEAyDjjQQp75z2v+DsHACDjbAcpPrSG9IrEN60DQFazHaShNaTYAenQP/ydBQCQUbaDFA8fvtzD\njg0AkM3sBikRlRL9kpPvXT/Ajg0AkM3sBmloc13JP3nnrCEBQFazG6ShHRryT5cKpnm7frNjAwBk\nLbtBGlpDCkySJl0gRfdK/bv8nQkAkDF2gzS0Q0OwSCqY7l0++KZ/8wAAMspukIbWkIKTpMKzvMsR\nggQA2cp+kAJFh4N08O/+zQMAyCi7QRraqSFYJBWyyQ4Asp3dIA19sGpwkhSa4q0pRVhDAoBsZTdI\n8RGb7BzHW0s6+HfJTfg7FwAgI+wGaeRODZL3PlKiT+rr9m8mAEDGnAJBKvLO2fUbALKa3SAN7dQQ\nGLGGJLHrNwBkKbtBOnoNiV2/ASCr2Q1S/Kj3kIY22bGGBABZyW6QYr2SAoe/fiJUJgVLWUMCgCxl\nOEhhb+3IcbzrjuNttjv4lpSI+TsbACDt7AYp3nv4/aMhhWdLbpSvMweALGQ3SLGwd1DsSEXneOe9\nWyZ+HgBARtkNUjx8eIeGIYUzvPPI6xM/DwAgo2wGKRGVEv1J1pC2TvxMAICMshmko49BGlIwTXLy\nCBIAZCGbQYqP+KTvkZyg9yGrkdcl1534uQAAGWMzSMNfzjfp2PsKz/GCxYesAkBWsRmk+Cib7CSp\naHDHBva0A4CsYjNIo72HJLGnHQBkKdtBOt4mO/a0A4CsZDNIwzs1HG8NabqkgBQhSACQTWwGKTbK\nXnaSFMj3dv/u3cLXmQNAFrEZpKGdGo4+MHZI6SVSdJ+0/4WJmwkAkFE2g5RspwZJOu1fvfOdT03M\nPACAjDMepONsspOk0kulvI9Ju5+R4n0TNxcAIGOMBmm/d368vewk7xMbTvsXKXZA+vC/Jm4uAEDG\n2AxS71bvm2ILPj76zwxvtls3MTMBADLKaJC2eMcbOcHRf2bS+VLR+dIHv5MOvDJxswEAMsJmkBJ9\nUtF5Y//c2f9HUkLqulNy4xkfCwCQOTaDJEmTKsf+mcmXe5vuejZJ2x/L/EwAgIyxG6TxrCFJ0tl3\nSsFSadsyjksCgFOY3SCNZw1JkvKmSOc/ILlRadMCqeevmZ0LAJARNoMUKpdCU8b/85OrpXO/KcU+\nkjrmSi9cIr39PSkRy9yMAIC0shmkovMkxzmxx3xsvnT+cqnsn6WDb0vb/q/UeY0Ufi0zMwIA0spx\nXYPfBf7qTdKMr6T++FhY2v7/pH3PeddLLpGm3SRNvUkqviA9MwIA0spmkF5fKp1+7ck/z/4Oac9/\nSwc2eu8xSVL5ldI5d0sVNd7OEH3vSZE3vM/NK5gqFV8oBQq8n40flJw8KZAnxSLe12IkO1gXAJAy\nm0F69ydS8cz0PV+sV/roRWnvc1LPy8l/1sn3Pk184H2pr3vwtrzDQSu9TDpziZR/uqSAd/CuExi8\nPOLcCR6+nBiQBj70zgvO8N4jk7zQhcqlQKGkuHcs1RHHUzmDmy5HnI57XZLrShr6VY44d0fedtT9\nRzxm6L939LI4I37OHfGco1w/7n0anDMoBULeuXP01mJnlMsae/PtqH/CE/2nPcqco84/8vbj/U6O\n93s8+jlHnjuj3KfBr2pJHHt+vN+5HClxyPsHmJMvhUq8v914xPvHWrDYuxwLe583GSr1Pn0/us97\n7zdUJvW9K/W/LxWe7e141POK93zn3pv8gHfkNHNBisVi2v335zP3H+jbIe35gzTwgbcGlDfF+z+N\nG5cG9kgH35QO/kPKL5cKzpbkegfqBku8y+HNksvOEsAJc4JS9f9IhWf6PcmEmjp1qkKhkN9jnBLM\nBam7u1u1tbV+jwEAadHW1qbp06f7PcYpwVy2p06dKsn7JeaS2tranFtmKTeXm2XOHbW1tcOvaRib\nuSANrdrm4r8ocnGZpdxcbpY5d7C5bvxsHocEAMg5BAkAYAJBAgCYEFy5cuVKv4c4nnnz5vk9woTL\nxWWWcnO5WebckavLnQpzu30DAHLTuDbZ/fWvf1VDQ4Mkafv27br55pu1ZMkSrVixQolEQpLU2tqq\nRYsWafHixdq8eXPmJgYAZKUxg/STn/xEDz74oPr7+yVJq1atUlNTk9avXy/XddXW1qauri699NJL\neuaZZ7R69Wp961vfyvjgAIDsMmaQZsyYoZaWluHrXV1dqq6uliTV1NSoo6NDmzZt0lVXXSXHcXTm\nmWcqHo9r3759KQ0Ui8XU3d2tWIyP5wGQ/XjNO2zMI7bq6urU3d09fN11XTmDH95YXFyscDis3t5e\nlZeXD//M0O0VFRVJn7ulpUWtra3HvY+P2wCQbXjNS+6EDyEOBA6vVEUiEZWVlamkpESRSOSI20tL\nS8d8rsbGRjU2Nh5xG59lByBb8ZqX3AkfhzR79mx1dnZKktrb21VVVaW5c+fqhRdeUCKR0M6dO5VI\nJMZcOwIAYKQTXkNaunSpli9frtWrV6uyslJ1dXUKBoOqqqpSfX29EomEmpubMzErACCLmTsOaWj1\nle2pAHIBr3mH8dFBAAATCBIAwASCBAAwgSABAEwgSAAAEwgSAMAEggQAMIEgAQBMIEgAABMIEgDA\nBIIEADCBIAEATCBIAAATCBIAwASCBAAwgSABAEwgSAAAEwgSAMAEggQAMIEgAQBMIEgAABMIEgDA\nBIIEADCBIAEATCBIAAATCBIAwASCBAAwgSABAEwgSAAAEwgSAMAEggQAMIEgAQBMIEgAABMIEgDA\nBIIEADCBIAEATCBIAAATCBIAwASCBAAwgSABAEwgSAAAEwgSAMAEggQAMIEgAQBMIEgAABMIEgDA\nBIIEADCBIAEATCBIAAATCBIAwASCBAAwgSABAEwgSAAAEwgSAMCEUCoPikajWrZsmXbs2KFAIKBv\nf/vbCoVCWrZsmRzH0cyZM7VixQoFAvQOADA+KQXpT3/6k2KxmH7605/qxRdf1A9+8ANFo1E1NTVp\n3rx5am5uVltbm+bPn5/ueQEAWSqlVZjzzjtP8XhciURCvb29CoVC6urqUnV1tSSppqZGHR0daR0U\nAJDdUlpDmjRpknbs2KFrr71W+/fv15o1a7Rx40Y5jiNJKi4uVjgcHvN5Wlpa1NramsoIAHDK4TUv\nuZSC9OSTT+qqq67Sfffdp127dun2229XNBodvj8SiaisrGzM52lsbFRjY+MRt3V3d6u2tjaVsQDA\nNF7zkktpk11ZWZlKS0slSZMnT1YsFtPs2bPV2dkpSWpvb1dVVVX6pgQAZL2U1pDuuOMOPfDAA1qy\nZImi0ajuvfdeXXzxxVq+fLlWr16tyspK1dXVpXtWAEAWSylIxcXF+uEPf3jM7evWrTvpgQAAuYkD\nhQAAJhAkAIAJBAkAYAJBAgCYQJAAACYQJACACQQJAGACQQIAmECQAAAmECQAgAkECQBgAkECAJhA\nkAAAJhAkAIAJBAkAYAJBAgCYQJAAACYQJACACQQJAGACQQIAmECQAAAmECQAgAkECQBgAkECAJhA\nkAAAJhAkAIAJBAkAYAJBAgCYQJAAACYQJACACQQJAGACQQIAmECQAAAmECQAgAkECQBgAkECAJhA\nkAAAJhAkAIAJBAkAYAJBAgCYQJAAACYQJACACQQJAGACQQIAmECQAAAmECQAgAkECQBgAkECAJhA\nkAAAJhAkAIAJBAkAYAJBAgCYQJAAACYQJACACQQJAGBCKNUH/uhHP9Jzzz2naDSqm2++WdXV1Vq2\nbJkcx9HMmTO1YsUKBQL0DgAwPikVo7OzU6+++qqefvpprV27Vrt379aqVavU1NSk9evXy3VdtbW1\npXtWAEAWSylIL7zwgmbNmqWvfvWruvPOO/XpT39aXV1dqq6uliTV1NSoo6MjrYMCALJbSpvs9u/f\nr507d2rNmjXq7u7WXXfdJdd15TiOJKm4uFjhcHjM52lpaVFra2sqIwDAKYfXvORSClJ5ebkqKyuV\nn5+vyspKFRQUaPfu3cP3RyIRlZWVjfk8jY2NamxsPOK27u5u1dbWpjIWAJjGa15yKW2yu/zyy7Vh\nwwa5rqv3339fhw4d0qc+9Sl1dnZKktrb21VVVZXWQQEA2S2lNaTPfOYz2rhxoxYtWiTXddXc3Kzp\n06dr+fLlWr16tSorK1VXV5fuWQEAWSzl3b7vv//+Y25bt27dSQ0DAMhdHCgEADCBIAEATCBIAAAT\nCBIAwASCBAAwgSABAEwgSAAAEwgSAMAEggQAMIEgAQBMIEgAABMIEgDABIIEADCBIAEATCBIAAAT\nCBIAwASCBAAwgSABAEwgSAAAEwgSAMAEggQAMIEgAQBMIEgAABMIEgDABIIEADCBIAEATCBIAAAT\nCBIAwASCBAAwgSABAEwgSAAAEwgSAMAEggQAMIEgAQBMIEgAABMIEgDABIIEADCBIAEATCBIAAAT\nCBIAwASCBAAwgSABAEwgSAAAEwgSAMAEggQAMIEgAQBMIEgAABMIEgDABIIEADCBIAEATCBIAAAT\nCBIAwASCBAAwgSABAEw4qSDt3btX11xzjd566y1t375dN998s5YsWaIVK1YokUika0YAQA5IOUjR\naFTNzc0qLCyUJK1atUpNTU1av369XNdVW1tb2oYEAGS/lIP0yCOPaPHixTrjjDMkSV1dXaqurpYk\n1dTUqKOjIz0TAgByQiiVB/3yl79URUWFrr76av34xz+WJLmuK8dxJEnFxcUKh8NjPk9LS4taW1tT\nGQEATjm85iXnuK7rnuiDbrnlFjmOI8dxtHXrVp177rnasmWLtmzZIkl69tln1dHRoebm5hMeqLu7\nW7W1tWpra9P06dNP+PEAcCrhNe+wlNaQnnrqqeHLDQ0NWrlypR599FF1dnZq3rx5am9v1xVXXJG2\nIQEA2S9tu30vXbpULS0tqq+vVzQaVV1dXbqeGgCQA1JaQxpp7dq1w5fXrVt3sk8HAMhRHBgLADCB\nIAEATCBIAAATCBIAwASCBAAwgSABAEwgSAAAEwgSAMAEggQAMIEgAQBMIEgAABMIEgDABIIEADCB\nIAEATCBIAAATCBIAwASCBAAwgSABAEwgSAAAEwgSAMAEggQAMIEgAQBMIEgAABMIEgDABIIEADCB\nIAEATCBIAAATCBIAwASCBAAwgSABAEwgSAAAEwgSAMAEggQAMIEgAQBMIEgAABMIEgDABIIEADCB\nIAEATCBIAAATCBIAwASCBAAwgSABAEwgSAAAEwgSAMAEggQAMIEgAQBMIEgAABMIEgDABIIEADCB\nIAEATCBIAAATCBIAwASCBAAwgSABAEwgSAAAE0KpPCgajeqBBx7Qjh07NDAwoLvuuksXXHCBli1b\nJsdxNHPmTK1YsUKBAL0DAIxPSkH67W9/q/Lycj366KPav3+/Fi5cqIsuukhNTU2aN2+empub1dbW\npvnz56d7XgBAlkppFeZzn/uc7rnnnuHrwWBQXV1dqq6uliTV1NSoo6MjPRMCAHJCSmtIxcXFkqTe\n3l597WtfU1NTkx555BE5jjN8fzgcHvN5Wlpa1NramsoIAHDK4TUvuZTf5Nm1a5duu+02LViwQNdf\nf/0R7xdFIhGVlZWN+RyNjY164403jji1tbWlOhIAmMZrXnIpBWnPnj364he/qG9+85tatGiRJGn2\n7Nnq7OyUJLW3t6uqqip9UwIAsl5KQVqzZo16enr02GOPqaGhQQ0NDWpqalJLS4vq6+sVjUZVV1eX\n7lkBAFkspfeQHnzwQT344IPH3L5u3bqTHggAkJs4UAgAYAJBAgCYQJAAACYQJACACQQJAGACQQIA\nmECQAAAmECQAgAkECQBgAkECAJhAkAAAJhAkAIAJBAkAYAJBAgCYQJAAACYQJACACQQJAGACQQIA\nmECQAAAmECQAgAkECQBgAkECAJhAkAAAJhAkAIAJIb8HGNXAh1K/3fEApKBgqt8T2NX3nqTpfk/h\nK9aQAMCCN5dL0Y/8nsJXBAkALNi3Qer8tBTd7/ckviFIAGDBaf8q9b4mbf2635P4hiABgAVn3SE5\neVLvFr8n8Q1BAgALHEcKlXs7dOUoggQAVuR/TOrfKSVifk/iC4IEAFYUnSO5Ualnk9+T+IIgAYAF\n0QNSz6veZSfo7yw+4chTALBg3/9I0fel06+Vyi73expfsIYEABaUXCw5+dK+dimyze9pfEGQAMCC\n4vOlc+6W4hFpxxN+T+MLggQAVlR8RgpMkt75D6nrq9KBjZLr+j3VhLH/HtKBl4+8PrnKnzkAINOC\nk6SZ35b+8T3pvTXeqeAs6eMLpI/VSeVXeLuGZym7QQq/Jh3Y4V3ueVkqGwzRUKAIE4BsVPZJ6dK1\n0kd/lvZv8M7ffcw7SVLRuVJRpTTpXO9y4XQp77TBU4WUf5qUN+WU3FPPbpB6N0vvDb6x1/OypB97\nUZo899ifJU4AsokTlKZc6Z0SMe8z7sKbpd6t0qG3pH3PSfvGeI5Q+WCcBkM1HKtRruefJgVLvU+M\n8IndIIW7tGv77/XyiJ1Nqmb9p6ZNG7G2NHKtiSgByEaBkFQ2xzsNSfRL/R9IA7ukgT1SrGfwFPbO\n44PXox9Jh971DrYdDyc0GKvTBmNVcdT1KVL+GVLBNO+7rQqmSoGCtC2q2SB98Paz+s2L0it/l15+\n07vty9dKVTN3eWG6cMQPl1XxXhOA3BEokIrO9k5jcV0p0TciWoOnePjY24ZO/TukyOuSxrFDRV6F\nt9mwZLa3U8ZZt0mB/JQWy2yQXnvbi1HKWGsCAG8TXLDIOxV8fPyPcxNSvPfINa9YjxTbLw3sk6L7\nBi/v9Y6bCm+Wdv3U+yy+C5pTGtVskDa/450PrR2NNG3aNO/CgVe895RG7vQAADh5TkAKlXmno7mu\nFN0j9XV7X70e2Sbt+W/vvoprUv5Pmg3SaKpmjbjCDg4AkF6J2OE1n+heb01o6Hxg72CIdkiJQ0c+\nLv8Mac4z0pSrUv5Pmw3SpedK296TqmYevm3k5SMMrR0RIwAYXbxPin7ofefSMac9XnhiB5T0vaNA\nkTTpAql4llR8oXdecpFUOsfbAeMkmA3SJZVSRcmRt1XNGtxcN3L3b2IEAJ5Yr/ceTv9OqW+nNPDB\n4OlDb80m1jP6Y4PF3kG4pZd5e88Vnjlib7ppUsHg9VBZxnYNNxukM04/Q3P/ydWuXbuGb5s2bZp0\n9pe9K4QIQC6KHZAObfc2mw3HZ5d3Hg8f/zHBYqnwbGlytbdHXOHZUuFZg+eD1zMYmvEyGySVXiqV\n5WnayPfTJs8lRAByg5vwdhqIvCEdfFM69I53iu499medfGnSedKkSqnofGnS+d71onMHYzPZ99iM\nh+EgfUKafNQ2u7IqQgQge0UPSPuelz560QtRPHLk/YUzpPJq76sqimcejk/hWd5ecac4u0EquVQq\nq/AuEyEA2a53m7R7tbeHm+TtMDC5Sir7Z2/rUMnFUt5kf2fMMLtBKr1EmjzN7ykAYGK8/e/SaVHp\ngm9J0/9NKsy91z+7QQKAXBIskqr+UzrtX/yexDen/kZHAMgGZ96W0zGSCBIA2FBc6fcEvkvrJrtE\nIqGVK1fqjTfeUH5+vh5++GGdc8456fxPAEB2Cpb6PYHv0rqG9Oyzz2pgYEA/+9nPdN999+m73/1u\nOp8eALJXcJLfE/gurWtImzZt0tVXXy1J+uQnP6m//e1vJ/wc8XhckrR7X1zKj6VzPAC+6/Z7gAk3\ndepUhULjeKktmZ35YYxLa5B6e3tVUnL4YNZgMKhYLDbqL6OlpUWtra3Hve+WW25J52gA4Iu2tjZN\nnz5dUvLXPIWy+xij8XBc1x3HVwKOz6pVq3TZZZfpuuuukyTV1NSovb39hJ6jr69Pl112mf74xz8q\nGAymazTzamtr1dbW5vcYEy4Xl5tlzh21tbXq6upKuoYUi8W0e/fu8a9JZbG0Lv3cuXP1/PPP67rr\nrtNf/vIXzZo1a+wHHaWwsFCScnJniKF/ReWaXFxuljl3jBWZUCiUs//bHC2tQZo/f75efPFFLV68\nWK7r6jvf+U46nx4AkMXSGqRAIKCHHnoonU8JAMgRHBgLADAhuHLlypV+D3E88+bN83uECZeLyyzl\n5nKzzLkjV5c7FWndyw4AgFSxyQ4AYAJBAgCYQJAAACYQJACACQQJAGCCb0FKJBJqbm5WfX29Ghoa\ntH379iPu//nPf64bbrhBN910k55//nmfpky/sZZbkvbt26fPfvaz6u/v92HC9BtrmZ988kndeOON\nuvHGG0f/4MlTzFjL/NRTT+kLX/iCFi1alHN/34lEQl/60pf09NNP+zBh+o21zA8//LBuuOEGNTQ0\nqKGhQeFw2KdJTwGuT/7whz+4S5cudV3XdV999VX3zjvvHL7vgw8+cD//+c+7/f39bk9Pz/DlbJBs\nuV3Xddvb290FCxa4c+bMcfv6+vwYMe2SLfO7777rLly40I3FYm48Hnfr6+vdrVu3+jVq2iRb5r17\n97rXXXedOzAw4IbDYbempsZNJBJ+jZpWY/19u67rfv/733cXLVrkrl+/fqLHy4ixlnnx4sXu3r17\n/RjtlOPbGlKy707avHmz5syZo/z8fJWWlmrGjBl6/fXX/Ro1rcb6zqhAIKAnnnhC5eXlfoyXEcmW\neerUqXr88ccVDAYVCAQUi8VUUFDg16hpk2yZKyoq9Jvf/EZ5eXnas2ePysrK5DiOX6Om1Vh/37//\n/e/lOI5qamr8GC8jki1zIpHQ9u3b1dzcrMWLF+sXv/iFX2OeEnwL0mjfnTR0X2np4a/zLS4uVm9v\n74TPmAnJlluSrrzySk2ZMsWP0TIm2TLn5eWpoqJCruvqkUce0ezZs3Xeeef5NWrajPV7DoVCWrdu\nnerr61VXV+fHiBmRbLm3bdum3/3ud7rnnnv8Gi8jki3zwYMHdeutt+rRRx/V448/rvXr12fNP64z\nwbcglZSUKBKJDF9PJBLDH9N+9H2RSOSIQJ3Kki13thprmfv7+/WNb3xDkUhEK1as8GPEtBvP7/nW\nW2/Vhg0btHHjRv35z3+e6BEzItly//rXv9b777+v22+/Xb/61a/05JNPnvD3pVmUbJmLiop02223\nqaioSCUlJbriiisIUhK+BWnu3LnDf4xHf3fSpZdeqk2bNqm/v1/hcFhvvfVWSt+tZFGy5c5WyZbZ\ndV195Stf0YUXXqiHHnooa76UMdkyv/3227r77rvluq7y8vKUn5+vQCA7dnhNttz333+/nnnmGa1d\nu1YLFy7UHXfckRWb7pIt8zvvvKMlS5YoHo8rGo3qlVde0Sc+8Qm/RjXPt3+aH++7k5544gnNmDFD\ntbW1amho0JIlS+S6ru69996seF9BGnu5s1GyZU4kEnrppZc0MDCgDRs2SJK+/vWva86cOT5PfXLG\n+j1fdNFFqq+vl+M4uvrqq1VdXe33yGnB3/exy3z99dfrpptuUl5enhYsWKCZM2f6PbJZfLgqAMCE\n7NhOAAA45REkAIAJBAkAYAJBAgCYQJAAACYQJACACQQJAGACQQIAmPC/7Ppxl+Q2CJIAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e026276d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAGkCAYAAAB+TFE1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAIABJREFUeJzt3XtwVOd9PvDn7J5d7Wp1Q3ckIZCQ\nsBEGbCwDtkG+CEzqacZjlwTMhDjT9o+kKQ3pJfZ0HHDTTKknU087Ylq3zYzdnzFuYydpHbdN0shO\nwCbGWBcuElcBAglJoLu0kvZ2zu+P1a4kJK1Wu2f33J7PDJa0K539HvC8j77vvue8gizLMoiIiFRm\nUbsAIiIigIFEREQawUAiIiJNYCAREZEmMJCIiEgTGEhERKQJDCQiItIEBhIREWkCA4mIiDSBgURE\nRJrAQCIiIk1gIBERkSYwkIiISBMYSJO6urrQ2toC3vyciEgdotoFaEFnZwf++4MPEAgEkJKSgpUr\nK9QuiYjIdEzfId2+fRv/+z//A0mSYLFY8PHxj+H1etUui4jIdEwdSAP9/fjvD34Gn8+HB6urUVm5\nCmNjbpz67DO1SyMiMh3TBtLIyDB+9rP3MTExgfX334+iomJUVFbC5XLh7NkzuHPnjtolEhGZiikD\naWxsDD97/3243W5UVa3B8uUrAABWqxVr162HLMs49ptfQ5IkdQslIjIR0wWSx+PBBx/8DENDQ6io\nrERFZeWM5/Pz81FcXIzbt2/jfGurSlUSEZmPqQLJ5/Phf//nv9HX24vly1dg9eqqOb9vzX1rIYoi\nPv30txgbG0tylURE5mSaQAoEAvi/X/4CXV1dKCoqxrr16yEIwpzf63A4sHp1FbxeL3574pMkV0pE\nZE6mCCRZlvHRh/Vob29Hfn4+Njz44LxhFLKirAxZWVm4dOkSOjo6klQpEZF5GT6QZFnGx8eP4/Ll\ny1iSnY3qhzbCYln4tAVBwLr19wMAjv3mNwgEAokulYjI1AwfSKdOfYZz584iIyMDmzZthihGf3OK\nrKwslJWVYWhoEM1NTQmskoiIDB1Ip083o+Hzz+FyubD54Udgt9sXfYx7V1chJSUFDQ2fY2hoMAFV\nEhERYOBAunDhAk588gkcDgc2P/wIHA5HTMex2Wy4b+1aBAIBHD92nDdfJSJKEEMG0vXr1/Drjz6E\nzWbH5ocfgcvliut4RUXFyMvLw82bN3C1rU2hKomIaDrDBVJoEYMgCNi8eTMyMjLiPqYgCFi7bn3w\n5qsfH+fNV4mIEsBwgdTT3Y2RkREUFRVjSXa2YsdNS0tD5apVGBsbw2cnTyp2XCIiCjJcIF2+fBkA\nUFJSovixKyqCN189d+4sb75KRKQwQwWSJEm4cuUy7HY7cvPyFD++1WrFOt58lYgoIQwVSB0dHZiY\nmEBRUXFUF7/GIi8/H8XFJbh9+zZaW1sS8hpERGZkqEC6MjldV5yA6brp1tx3H0SbDSc//RRjY+6E\nvhYRkVkYJpD8fj+uXm2D0+lEtoKLGeYSvPnqani9XnzyySe8NomISAGGCaT29nb4fD4UF5cseONU\nJaxYEbz56pXLl/FhfT18Pl/CX5OIyMgME0iXL18CkPjpuhBBEFD90EZkZS3BpUsX8eP33kV/f19S\nXpuIyIgMEUgejwc32tuRnp6uyIWw0UpNTcWWrVtRXl6OgYEBvPfee7hw4XzSXp+IyEgMEUjXrl1F\nIBBI2nTddBaLBfetXYeHHtoIQRDw0Ycf4sMPOYVHRLRY0e/FoGHJWl0XydKiImRkZuLzU6dw8cIF\n3O65jR07dih6twgiIiPTfYc0NjaGjo4OZGUtifsmqvFyuVzYsnUrysrKMDDQj/feexeXLl5UtSYi\nIr3QfSC1tV2BLMsJuVVQLKxWK9auW4/q6ocAAPX1v8KvP/oIfr9f5cqIiLRN91N2ly8Fp+uKiotV\nrmSmouJiZGRmouHzUzh/vhU9Pd14ascXsGTJErVLIyLSJF13SMPDw+jp6UZubl7MG/AlUlpaGrZs\nrcGKFWXo7+/Hj997N7w8nYiIZtJ1IE0tZtBWdzSd1WrFuvXr8eCD1ZBlGb/6v//Drz/6CCMjI2qX\nRkSkKbqesrt8+TIsFguWLi1Su5QFFZeUIDMzE59PTuFduHAepaWlqFqzBqWlyxN2M1giIr3QbSD1\n9fWhv78PhYWFsNvtapcTlbT0dGyteQydHR24fv062tvb0d7eDpfLhdWrq7C6qgppaWlql0lEpApB\n1umdQU9++ikaGxvwYPVDKNbYgoZoDQ0N4vr16+js6IDf74cgCChdvhxrqtZgWWkpuyYiMhVdBpIs\ny3j7yBGMj4/hqR1fgCjqttEDELxTeahrGhoaBBBcELG6qgqrV1epfn0VEVEy6DKQuru78dOf/Bgl\nJSXY8GC12uUoanBwEO3Xr6Oj4yYCgQAEQcCKFSuwenUVioqLYbPZ1C6RiCghdBlIHx8/hrNnz2LT\n5odRUFCgdjkJ4ff50NHZgfbr1zE0NAQgeN+8vLx8FBUVYWnRUhQWLkVKSorKlRIRKUN3gSRJEv7f\nv72JQCCAp3Z8wfDvs8iyjMHBQdzq7ERfXy+GhobCGwIKgoCcnBwsLSpC0dIiLC0qgtPpVLliIqLY\n6O7Nl87OToyPj2PFijLDhxEQDJ0lS5aE7/Dg9/nQPzCAvr7eyZWG/ejt7cXZM2cAAEuWLMHSyXAq\nKiriqj0i0g3dBdKV8EZ8+lxZFy/RZkN+fj7y8/MBAIFAAIMDA+jr60NfXy/6+/sxMDCA1tYWAMEb\nvuYXFKCgoAAFBYXIy8vj+1BEpEm6mrLz+/34tzffgNVqxbbtTyV97yM9kCQJQ0NDwXDq68PAwAA8\nHk/4eUEQkJ2djYKCQhQUFCC/oABLlizh3yURqU5XgXT16lX84uf/i5UVFViz5j61y9EFWZYxPj6O\ngYEBDAz0Y3BgAIODg5AkKfw9drsdefn5wYDKL0BWZibS0tPZSRFRUulqyi50Y1KtbDWhB4IgIDU1\nFampqeELiCVJwvDwMAYGgtN7gwMD6OzoQGdHx4yfdTqdSE9PR3p6BtIzJj9ObhOfnp6u++u/iEhb\ndDOieL1etF+/jrS0NGRkZKpdjq5ZLBZkZWUhKysLZWXBx7xeLwYHg93T2NgYxsbGMD42hjt37uD2\n7dtzHsfpdCI9IwPpaelIdaUi1RkMPmeqE6nOVDhTU+F0OmG1WpN4dkSkV7oJpGtXryIQCKC4pITv\ndySA3W5Hfn5wym46WZYxMTERDqixsTGMjU8LrNu3cbunJ+KxU1JSgkE1K7Cc00KM4UVkdroJpMuh\nrSaKOV2XTIIgwOl0Bq9vysmZ9bwsy/BMTGDC44HHMwHPhAceT+jPRPhzt9uNgYGBBV9vZng5Z4WY\nw+mAw+FASooDKSkp/OWEyEB0EUhjY2Po6LiJrKwsXlejMYIgwOF0whHFBbmSJM0Mq3nCa3R0NKrw\nAoIB5nA4kOJwwBH6PGUytBwzv3akpCDF4YDdbmeQEWmQLgLpalsbZFlGMRcz6JrFYpnqthYwX3h5\nfV54vV74vF54fT74vF5MTExgeHgY0S4YFQRhKsjuCi9HSjDcbDYbRNEKq1WEaLXCKs7/0Wq1MuCI\nFKCLQApP1xWZ82JYM1pMeAHBqcNAIDAjrMKfe73w+maGmNfrxfj4+IxbMcXDarXCarVCnAyo0Eer\nVZwKttBj4uzvE63itJALfT31ccbPT/6xWCzhPwxEMgLNB9LIyDC6u7uQm5sb1bQQmZMgCBDF4KCN\n1NSof06WZfj9/mB4TQsxf8APKSAhIAUQCATCn099DCAw7XNJkhCYfMzv98Pj8UKa/NlkXOpnsVhm\nhFTo86nHrLBaZ34MPj/5udUC6+RHi2Xy88mgC/+xCBBw10dhrj8WCALu+jj7+yzBJ+f8ONfrQRBg\nmVVD5Ne4+48sy5AkCbIsQ5YlSJI88zFJgnTXc6G/p+Df5cy/P/4ioCxNBtI7R48iNy8Xubm5GJ68\n0zWn6ygRBEGAzWZL6EXAkiSFA2vqYzC8pEBgMvSkcIDdHX4zHwsgIEnBgVMKDaRS+DVCj0lSMBiD\ng+rU8zq6Dl4X5gr/0BSuIAjY/fwetUvUFc0Fkt/vx61bnWi/0R5+zCIIcDic6O/vV7EyIuUJFgtE\niwXJusY41A2EwkkKh5s04zlJkiBDhiwDkIOdQvDr4GNy8D8IxlvocXnyccz4OvQ9sx+/6/sx+Vrh\nx6e93vTvnX6sub53xmvOPMaMjgnCZHclTHVYk10fIMAiABAEyHeFeijkA5Nfe33+qecDAUih0Jdl\ndHR0oLCwkBeRR0lztw7q6OhAbW2t2mUQESmivr6ed5eJkuZiu7CwEEDwH9FMamtrTXfOgDnPm+ds\nHrW1teExjRamuUAKtbZm/I3CjOcMmPO8ec7mwem66Bl/hzsiItIFBhIREWkCA4mIiDTB+sorr7yi\ndhFz2bRpk9olJJ0Zzxkw53nznM3DrOcdC80t+yYiInOKasru9OnT2Lt3LwCgvb0dzz//PPbs2YOD\nBw+Gt8I+fPgwdu7cid27d+PMmTOJq5iIiAxpwUD613/9V7z88svweDwAgEOHDmH//v04evQoZFlG\nfX09Wlpa8Nlnn+Hdd9/Fa6+9hr/6q79KeOFERGQsCwZSaWkp6urqwl+3tLRg48aNAICamhqcOHEC\nDQ0N2LJlCwRBQFFREQKBQMy3+fH7/ejo6IDf74/p54mI9IRj3pQFr9jasWMHOjo6wl/Lshy+w63L\n5cLIyAhGR0eRlZUV/p7Q49nZ2RGPXVdXh8OHD8/53AcffICioqKoToKISItcLteMryONeUePHp3z\nrg6SJOGDn72PwqVL8eyzzyWkTq1Y9CXEFstUU+V2u5GRkYG0tDS43e4Zj6enpy94rH379mHfvn0z\nHuO97IjIqGIZ8ywWCxwOJ0ZHRxNdnuoWfR1SVVUVTp48CQA4duwYqqursWHDBnz88ceQJAm3bt2C\nJEkLdkdERBQdZ6oT7tHR8CIyo1p0h/Tiiy/iu9/9Ll577TWUl5djx44dsFqtqK6uxq5duyBJEg4c\nOJCIWomITCnV6cRAfz/GxsaQlpamdjkJE1UglZSU4Ec/+hEAoKysDEeOHJn1PXO1okREFD+nM7gL\n8ujIiKEDibcOIiLSOKfTCQAYMfj7SAwkIiKNc6YGA2l0dETlShKLgUREpHFTU3bskIiISEVTU3bs\nkIiISEU2mw2iKGJ0hIFEREQqEgQBTqfxL45lIBER6YDT6YTH44HX61W7lIRhIBER6UB4YYOBuyQG\nEhGRDoQWNhh56TcDiYhIB8KBZOCl3wwkIiIdmLo4loFEREQqCr2HZORrkRhIREQ64HA4AHDKjoiI\nVGa1WpGS4uCiBiIiUl9qavDiWFmW1S4lIRhIREQ64XQ6IUkSxsbG1C4lIRhIREQ6MXVxrDGn7RhI\nREQ6YfRrkRhIREQ6EboWyahLvxlIREQ6YfSN+hhIREQ6YfT72TGQiIh0wm63w2KxYMSgtw9iIBER\n6URwo75Uw+4cy0AiItIRZ6oTExMT8Pl8apeiOAYSEZGOpDqNe9dvBhIRkY6EVtq5GUhERKSm0Eo7\nI16LxEAiItIRZ6pxr0ViIBER6YiRr0ViIBER6cjUlB07JCIiUlFwo74UQ16LxEAiItIZp9OYG/Ux\nkIiIdMbpdCIQCGB8fFztUhTFQCIi0pmpjfqM9T4SA4mISGemNuoz1vtIDCQiIp0JbdQ3MDCgciXK\nYiAREelMTk4urFYrWltbEAgE1C5HMQwkIiKdSUlJwfLlyzE6OorLly6pXY5iGEhERDq0sqICgiCg\nqakRkiSpXY4iGEhERDrkdKaiZNkyDA4O4tq1a2qXowgGEhGRTlVWVAIAGhsaDHGRLAOJiEin0tLT\nsbSoCL29d9Bx86ba5cSNgUREpGOVlasAAI2NDSpXEj8GEhGRjmVlZSEvPx+3bt1Cd3e32uXEhYFE\nRKRzRumSGEhERDqXk5ODJdnZaL9+HX19fWqXEzMGEhGRzgmCEO6SmnTcJTGQiIgMoKCgAOkZGbhy\n5QqGh4bULicmDCQiIgMIdUmyLKOpqUntcmLCQCIiMoiioiKkpqbiwoXzcLvdapezaAwkIiKDsFgs\nqKhcBUmScOb0abXLWTQGEhGRgSxbtgwpDgdaWs7BMzGhdjmLwkAiIjIQq9WKlStXwufz4ey5s2qX\nsygMJCIig1mxfAVsNhvOnjkDn8+ndjlRE2P5IZ/Ph5deegmdnZ2wWCz467/+a4iiiJdeemlypUcl\nDh48CIuFeUdElGyizYay8nJcungRra2tWL9+vdolRSWmxPjNb34Dv9+Pf//3f8c3v/lN/P3f/z0O\nHTqE/fv34+jRo5BlGfX19UrXSkREUSovXwmr1YrTzU262eY8pkAqKytDIBCAJEkYHR2FKIpoaWnB\nxo0bAQA1NTU4ceKEooUSEVH07HY7lq9YAbfbjUsXL6pdTlRimrJLTU1FZ2cnfud3fgcDAwN4/fXX\ncerUKQiCAABwuVwYGRlZ8Dh1dXU4fPhwLCUQEelOsse8lSsrcO3qVTQ1NeKee+/V/NsoMQXSm2++\niS1btuDP/uzP0NXVhRdeeGHGG2dutxsZGRkLHmffvn3Yt2/fjMc6OjpQW1sbS1lERJqW7DHP6XRi\nWWkpbrS349rVq1hZUZGQ11FKTHGZkZGB9PR0AEBmZib8fj+qqqpw8uRJAMCxY8dQXV2tXJVERBST\nisltzhsatb/NeUyB9LWvfQ0tLS3Ys2cPXnjhBXz729/GgQMHUFdXh127dsHn82HHjh1K10pERIuU\nlpaGoqJi9PX24ubNG2qXE1FMU3Yulwv/8A//MOvxI0eOxF0QEREpq3JVJW7d6kRjQwNKS5erXc68\ntP0OFxERxS0zMwv5+QXo6upCV1eX2uXMi4FERGQClauC7yVpeQM/BhIRkQnk5OQiOzsH7e3t6O3t\nVbucOTGQiIhMYqpLalS5krkxkIiITCI/vwAZGRloa7uCoaFBtcuZhYFERGQSgiCgcpV2tzlnIBER\nmUhRUTFcLhcuXriguW3OGUhERCYiCAIqKishSRJONzerXc4MDCQiIpMpKVkGx+Q25xMa2uacgURE\nZDLBbc4r4Pf7cfbsGbXLCWMgERGZ0PIV07c596pdDgAGEhGRKYmiiPLylfB4PGhtaVW7HAAMJCIi\n0yorL4fVakVzc7MmtjlnIBERmZTdbseKFWUYG3Pj4sULapfDQCIiMrOVK1fCYrGgqakJkiSpWgsD\niYjIxBxOJ5YtK8Xw0BCutrWpWgsDiYjI5CoqgzddbVR5m3MGEhGRyblcLhQXF6Ovrw83brSrVgcD\niYiIUFG5CgDQ2KDe1hQMJCIiQmZmJgoKCtDd3YVbt26pUgMDiYiIAEx1SWptc85AIiIiAEBOTg5y\ncnJw48YN3LlzJ+mvz0AiIqKwynCXlPz3khhIREQUlpefj8zMTLS1XcHgYHK3OWcgERFRmCAIU11S\nU3K7JAYSERHNsLSoCC5XGi5dvIjR0dGkvS4DiYiIZgh2SZPbnJ9O3jbnDCQiIpqlZNkyOJxOtLa0\nYHx8PCmvyUAiIqJZLBYLVq5cmdRtzhlIREQ0p+XLV8But+PsmTPwehO/zTkDiYiI5iSKIsrKy+H1\netHa0pLw12MgERHRvMrKyiGKIk6fbobf70/oazGQiIhoXna7HctXrMDY2FjCtzlnIBERUUQrV1bA\nYrGgOcHbnDOQiIgoIofDgWWlpRgeHkZb25WEvQ4DiYiIFlRRMbnNeUPitjlnIBER0YJcLheKS0rQ\n39+P9vbEbHPOQCIioqhUVia2S2IgERFRVDIyMlFQWIienm50JWCbcwYSERFFLbQ1RWMCNvBjIBER\nUdSys7ORk5uLmzdv4M7t24oem4FERESLEu6SFN7Aj4FERESLkpeXh8zMLFxta8PAwIBix2UgERHR\nogiCgMpVwRV3zQp2SQwkIiJatKVLJ7c5v3RJsZuuMpCIiGjRBEFAZlYmJEmCx+NR5JgMJCIiioko\nigAAn8+nyPEYSEREFJNwICm0mywDiYiIYiJag4HkZYdERERqmpqyY4dEREQqCgWS18sOiYiIVMQO\niYiINGFqUQM7JCIiUlF4yo4dEhERqSkUSH6FVtmJsf7gP//zP+PDDz+Ez+fD888/j40bN+Kll14K\n3uOoshIHDx6ExcK8IyIyqqkOScUpu5MnT6KpqQnvvPMO3nrrLXR3d+PQoUPYv38/jh49ClmWUV9f\nr0iBRESkTZp4D+njjz/GqlWr8M1vfhNf//rX8fjjj6OlpQUbN24EANTU1ODEiROKFEhERNqk9Cq7\nmKbsBgYGcOvWLbz++uvo6OjAN77xDciyDEEQAAAulwsjIyMLHqeurg6HDx+OpQQiIt0x2phnVXjK\nLqZAysrKQnl5Oex2O8rLy5GSkoLu7u7w8263GxkZGQseZ9++fdi3b9+Mxzo6OlBbWxtLWUREmma0\nMU8QBIiiqO697B588EEcP34csiyjp6cH4+PjePjhh3Hy5EkAwLFjx1BdXa1IgUREpF1Wq6jY3b5j\n6pCeeOIJnDp1Cjt37oQsyzhw4ABKSkrw3e9+F6+99hrKy8uxY8cORQokIiLtEkURXoU6pJiXfX/n\nO9+Z9diRI0fiKoaIiPRFFEW43ROKHIsXChERUcxEmwi/3w9JkuI+FgOJiIhipuSusQwkIiKKGQOJ\niIg0IbRrrBJLvxlIREQUMyXvZ8dAIiKimCl5+yAGEhERxUzJG6wykIiIKGacsiMiIk3gKjsiItIE\nvodERESawPeQiIhIE6beQ2KHREREKrIq2CHFfLdvtfT09IQ/LygoULESIiLie0iTenp6ZgQUEREl\nF5d934XBRESkDqvVCsCkU3aRcDqPiCi5BEGAaLOZb8puMV0QuyYiouQQrSKn7KLBYCIiSixRFLn9\nxGIwmIiIEkMURUVuHWSo95CiwfeZiIiUJYoiAoEAAoFAeJFDLEzTIc2FXRMRUfyUusGqqQMphMFE\nRBQ7UZxc+h3nSjvdBFIoMHq6u9DT3ZWw12AwEREtjijaAADeOK9F0k0g3S1RoQQwmIiIFoNTdkhs\ntwQwmIiIojG1BYVJpuwiSWQoAQwmIqJITNkhRQqeRIcSwGAiIpqLUjdY1VUgLSQZoQQwmIiIplNq\nCwpdBNKi7mGXpFACFlcXEZFRKbWNuS4CabESvdhhxmsxlIjI5KwKbWNuyEAKYSgRESWeKRc1xIKh\nRESUWKabsgsFSywLChhKRESJI5p9yi6WUErW0nAiIjMxTYcUaYCPZfBnKBERKctqtUIQBHMs+1Ya\nQ4mISFmiGP825roKpLkG+VgHft7ZgYhIOUpsY66rQJqPlkMJYLdERMYnijZzLPuOJjgYSkRE6glN\n2cmyHPMxdBFI0WIoERGpQxStkCUJgUAg5mNoOpBiWkXHUCIiSjol7tag6UCaSyIHdIYSEVFslNik\nTzeBNH0QX2hAj2fAZygRES2eKTukkESGUrLooUYiomiIog1AfJv0aT6Q4ulYtP5+EsBQIiJjUGKT\nPs0HUiTRDOZ6CSUGExHpmRL3s9NsIPX29s56LOZw0UEoAeyWiEi/lNikT7OBFK1ED+IMJSKihRm6\nQ5pOiQUMelh5F349hhIR6YypV9nFgqFERJQYSmzSp+lAWkwIRDuAM5SIiJRnmim76bq7u+d9jqFE\nRKSO4aGh4CdC7MfQTSBNH5gjhVIyMJSIiKaMjY3hzNkzEEUR69atj/k4cQVSX18fHnvsMbS1taG9\nvR3PP/889uzZg4MHD0KSpHgOHbNkdEkAQ4mICABkWUZTYwP8Ph+2bN2KrKysmI8VcyD5fD4cOHAA\nDocDAHDo0CHs378fR48ehSzLqK+vj7mo6eYbiJXokvQ2yPMCWiLSmiuXL6Ovrw/l5Stx772r4zpW\nzIH06quvYvfu3cjPzwcAtLS0YOPGjQCAmpoanDhxIq7C4pGsQTvZXVL4dRlKRKQBAwMDuHDhPFwu\nFx57/HEIQhxvIAEQY/mhn/zkJ8jOzsbWrVvxL//yLwCCbVuoGJfLhZGRkQWPU1dXh8OHD8/53J07\nt5GXmzvjsbu7ou7ubhQWFs758z09PSgoKFiwhmi/T2v0WjeRmUUa8/TG7/ejseFzyLKMJ2u3hWfL\n4hFTIP34xz+GIAj47W9/i/Pnz+PFF19Ef39/+Hm3242MjIwFj7Nv3z7s27dvxmMdHR2ora2NpSxV\n9HR3oaBwqTqvzVAi0hUjjHkh586ehdvtxv33P4CSkhJFjhnTlN3bb7+NI0eO4K233sLq1avx6quv\noqamBidPngQAHDt2DNXV1YoUCESeolJ7Gbja9Fw7EenTrVuduHGjHbm5udi4aZNix1Vs2feLL76I\nuro67Nq1Cz6fDzt27FDq0HEx4rVJRERqGR8fx+nmZlhFEdu2bYfValXs2DFN2U331ltvhT8/cuRI\nvIebIdqQiPRekhlw6o6IkiG0xNvn86Gm5jEsyc5W9Pi6uTA2JJZOxgxdEqfuiCjR2tquoLe3FytW\nrEDVmjWKH183gTT9vaK5Bl+l7t7AgZ2IaLahwUGcb21FamoqHn/iybiXeM9FN4EUr2QEDbskIjIi\nv9+Phskl3k88WQun05mQ19F8IM03yMbSJZlh1R0RkdJaW1owOjqKdevWo7S0NGGvo/lAmk4PQcEu\niYiMpLurC9evX0N2djY2bd6c0NfSbCD13uld8HvYJc1Nz7UTkXZMTEygubkJVqsV27Y/Fd7zKFE0\nG0jTKb3dRKJDSe0uiYgoXsEl3o3wer14+OFHkJOTk/DX1EUgLZba+yVpAbskIorHtatXcefObSwr\nLcV9a9cm5TV1H0gxdzHskoiI5jQ0NITW1hY4HA48+WRtQpZ4z0XTgRTPb/nsktglEdHiBQIBNDY0\nQJIkPPlkLVJTU5P22poOpOlCg+tcm9SxS4pQA0OJiBahtbUFIyPDuO++tVi+YkVSX1s3gRQLJbsk\nDuxEZHQ9PT24dvUqlixZgocfeSTpr6/5QNL71Bu7JCLSA4/Hg+amRlgsFmzbvj3hS7znovlAmk+0\n03ZKXZe02O/VGj3XTkSJJcuKGJcKAAARe0lEQVQympsa4fF4sHnzw8jNzVOlDl0Fkl4HVS10SURE\n87l+/Rp6enpQUlKCdevXq1aHZgOpt/fOgt/DLmlx9Fw7ESXGyPAwWs6dQ0pKCp6s3Za0Jd5z0Wwg\nTae1gTSmPZnYJRGRxgQCATQ0fA5JkvD4E0/C5XKpWo8uAmkxktEl6ZlZzpOIFnbh/HkMDw9jdVUV\nysvL1S5H24EUzQo7tQZYPXdJDCUiunP7NtrariAzMwuPPrpF7XIAaDyQlMYuiYgI8Hq9aGxqhCAI\n2L59O2w2m9olAdBRIC3mTg3JChZ2SUSkN8El3k3wTExg06bNyMvPV7ukMN0EklLYJRGRmd1ob0d3\ndxeKioqx/v771S5nBs0HUqz3m2OXtDCGL5G5jI6M4Ny5s7Db7aitrYXFoq0I0FY1i5DIO4GbaaA2\n07kSmZkkSWhoaEAgEMDjjz+BtPR0tUuaRbOBdOfO7AtjFzt4anmw1UqXRETmcOHCeQwNDeKee+/F\nyooKtcuZk2YDKRrxTNPx7g1T9F4/EUXW23sHVy5fRkZGBrZs2ap2OfPSRSAZdcBkl0REieb1etHY\nGFzivW37dtjtdrVLmpcuAimSeJaAs0uaovf6iWg2WZZx5nQzJsbH8dBDG1FQUKh2SRFpOpDMMEhq\nqUsyw983kZncvHkDt27dQuHSpXhgwwa1y1mQpgNpLl3dMSyzjqNLIiLSo9HRUZw7exY2ux21tds0\nt8R7LtqvcNL0ULk7lBJ5DVIypu3YJRGRkiRJQlNjA/x+P2pqHkNGRobaJUVFN4GUSOySiMhILl28\niIGBAVRWrsKqVavULidqmg+k+X5jX2yXFM9v/uySiEgv+vr6cOnSRaSlp2NrTY3a5SyK5gMpWbTQ\nJWkplIhIf3w+HxobGoJLvLdtQ0pKitolLYpmA6m3t3fWY7EsaJhO612SlhjhHIjM5syZ0xgfH8OG\nBx/E0qVFapezaJoNpGgovbiBXdJMDCUi/ei4eROdHR3Izy/Agw9Wq11OTHQRSGqtmktkHURESnG7\n3Thz5jRsNhu2bd8Oq9Wqdkkx0UUgRaLVLimu4GOXRERRmr7Ee8vWGmRmZqpdUsw0HUhaHAy1WBMR\nmdfly5fQ39+PlSsrcM8996hdTlw0HUhziWlDPB1t3hf+WXZJRLSA/v5+XLp4ES6XC4899hgEQVC7\npLjoJpCmT83dPUAuNG23WFrZwI+hRETz8ft8aGz4HLIso3bbdqQ4HGqXFDfdBBIREU05e/YsxsbG\n8MADG1BcXKx2OYpgIMWgoKAgOa9TuDQprxONZJ0zES2ss7MTN2/eQF5eHh7auFHtchSjm0BaWhgc\nEAsKCmYNjqHnQu5+frGDaWGhMnuGGGUQN8p5EBnB+PgYzpxuhiiKqN2m3yXec9F0ICVqIIznuGbs\njohIG2RZRmNDA3w+Hx7dsgVLlixRuyRFaTqQomHE7khLYcTuiEg7rly+jL6+PpSVlWP16iq1y1Gc\nLgJJyUFRD92RVpjtfIm0bHBgABcunEdqqguPP/GE7pd4z0UXgTQfrXZH8dBKd8QwItIOv9+PhtAS\n79paOAywxHsumg2k3NzcWY/dHUCLlazuKNbX0UoYEZG2tJw7C7fbjfXr70fJsmVql5Mwmg2khcQb\nTnfTQnekFeyOiLSj69YttLe3IycnB5s2b1a7nITSfCBFOzguNF3H7ig6DCMi7ZgYH0dzcxOsViu2\nbX/KUEu856L5QEoGtbsjrYQREWmHLMtobGqEz+fDI48+iuzsbLVLSjhR7QKiVVBQgJ6enjmn6vTe\nHWmF3usnMpKrbW3ovXMHy5cvx5o196ldTlJoukNKxgDJ7iiIYUSkHUNDgzh/vhVOpxOPP/GkIZd4\nz0XTgRQNPXdHDCMiupvf70fD5w2QJAlP1tYiNTVV7ZKSJqYpO5/Ph7/8y79EZ2cnvF4vvvGNb6Ci\nogIvvfQSBEFAZWUlDh48CItFmbwLTdcpTe3uiIjobq0tLRgdHcHatetQWrpc7XKSKqZAev/995GV\nlYUf/OAHGBgYwLPPPot7770X+/fvx6ZNm3DgwAHU19dj+/btStc7g5Ld0ELHVup7wz/D7oiI7tLd\n3Y3r168hOzsbmx9+WO1yki6mFuYLX/gCvvWtb4W/tlqtaGlpwcbJ26DX1NTgxIkTcRWWl5c367HF\nDp6Rvp/dEcOISEs8Hg+amxrDS7xFUTdrzhQT0xm7XC4AwOjoKP7kT/4E+/fvx6uvvhp+483lcmFk\nZGTB49TV1eHw4cOxlBBXd7RQGCV6oNZCd8QwIkq+SGNea8u54F28H92CnJycJFemDTG/ydPV1YWv\nfvWreOaZZ/DFL35xxvtFbrcbGRkZCx5j3759uHjx4ow/9fX1M74n5gtNk7VNBAd2IopSpDGvt68P\ny5aVYu26dSpXqZ6YAqm3txe///u/j7/4i7/Azp07AQBVVVU4efIkAODYsWOorq5Wrsq7xBMC7I4Y\nokRalJKSgieeNM8S77nEFEivv/46hoeH8Y//+I/Yu3cv9u7di/3796Ourg67du2Cz+fDjh07FC00\nNIjONZgmcnFDNDXpiR5rJjKDDRs2hN8OMauY3kN6+eWX8fLLL896/MiRI3EXNF1hYSG6u7sVPV4k\nZuiOiEib7Da72iWozvAXxibrdfVAjzUTmYXH61G7BNXpIpCUGEiV7I70eN0Rw4hI2zwer9olqE4X\ngTQftbojveHfC5H2eTzskDQbSLm58V8YG2L27oiItI+BpOFAWki03ZGSd2TQYxixOyLSBy8DSfuB\nlOhb/Bh5wDbyuREZDTskHQRSyPTBNdqB1szdEcOISF+4yk5HgRQSzYWx8Rwrnu8jIooVp+w0HkiJ\nvEVQorE7IqLF8Hg8kGVZ7TJUpelAiga7o5n0Vi8RBUmyDL/fp3YZqtJFIC2221FqmXfMYcdl3kQU\ng4mJCbVLUJUuAmk+7AZm4t8Hkb5NTJj7fSTNBlJuXu6sx6IZcM3aHTGMiPTPww5Jn2Jahs33jYhI\nwyY8DCRNU3Jr8kTje0dEFA9O2enE9LCZK6TUnqpTi97qJaL5ccpOZ7Q6VadGd8QwIjIWTtlpmBq3\nCNILhhGR8XDZt8EZsTtiGBEZk4fvIWlfpAE4UndktDAqKChgGBEZlACBHZLaBSSK0QZuo50PEc1k\nt9v4HpLaBcwnLy9/1mN3d0NKvHekh+6IYURkfKIocpWd2gUsRKur6pJFDzUSUfxsNpvp7/it+UCa\nz3zdUbIG8GR0RwwjIvOw2e2QZRler1ftUlSjm0CaPjibYaqOYURkLjbRBsDcS791E0ghZlhVxzAi\nMh+bjYGk6UBazODPMCIiPRMnA8lj4pV2mg4kpTGMiEir7HZ2SLoIpIUG6mgGcoYREWlZaMrOzHdr\n0Gwg5ebO3qBvLgwjIjICkYsatBtIc0n2wJ3oMOKtgIgoxG63AzD3Hb81H0iRQiGR3VEywoiIKMRm\nEwGYe08kzQfSfBhGRGQkU1N2fA9J86YP4gwjIjIaURRhsVi47FtPGEZEZFR2u52LGrQqkTdWnfVz\nDCMiUpmNgWQcDCMi0jO7zQav1wtJktQuRRW6CKRoAoNhRER6F1r67fGYc2GDLgJpIQwjIjKC8LVI\nJp2201UgzTXAM4yIyChstskOiYGkTZEGdoYRERnJ1N0azDllJ6pdQKxiWoHH+9IRkYbZTH7Hb813\nSCHTw4RhRERGFF7UwEDSB4YRERmV2W+wqtspu2gkI4gAhhERKSO0qMGsU3aGDSR2RUSkN1NTdlzU\noFkFBQXo6emJ7nvZFRGRTnHKzkDYFRGRnlksFoiiyEUNehBxsz7u7kpEBmCz2fgekl7xIlciMhK7\n3Q632612GarQdSAlMowYRESkBpvdDv/QEAKBAKxWq9rlJJVupuzuDohEhRGn5ohITXYTL/3WXYeU\nyCAiIlLb9Dt+u1wulatJLt0FktIYRESkJfbJ+9mZcU8k0wYSg4iItMhm4j2RFA0kSZLwyiuv4OLF\ni7Db7fj+97+P5cuXK/kScWMQEZGW2U28J5KigfSrX/0KXq8X//Ef/4Hm5mb87d/+Lf7pn/5JyZeI\nCUOIiPTCZuK7NSgaSA0NDdi6dSsA4P7778e5c+cWfYxAIAAA894qqLe3N6rj5Obmhj+/devWousg\nIoqX0+lEYWEhRDH6odbM25grGkijo6NIS0sLf221WuH3++f9x6irq8Phw4fnfO4P/uAPlCyNiEgV\n9fX1KCkpARB5zCsoKEBxcTGKi4uxdu3aZJaoGYIsy7JSBzt06BDWr1+Pp59+GgBQU1ODY8eOLeoY\nExMTWL9+PX75y1+a6qKw2tpa1NfXq11G0pnxvHnO5lFbW4uWlpaIHZLf70d3d/eiOykjUvTsN2zY\ngI8++ghPP/00mpubsWrVqkUfw+FwAIDmFkMkQ+i3KLMx43nznM1joZARRdG0fzd3UzSQtm/fjk8+\n+QS7d++GLMv4m7/5GyUPT0REBqZoIFksFnzve99T8pBERGQSurmXHRERGZv1lVdeeUXtIuayadMm\ntUtIOjOeM2DO8+Y5m4dZzzsWiq6yIyIiihWn7IiISBMYSEREpAkMJCIi0gQGEhERaQIDiYiINEG1\nQJIkCQcOHMCuXbuwd+9etLe3z3j+Rz/6EZ577jl8+ctfxkcffaRSlcpb6LwBoL+/H0899ZRhdoxc\n6JzffPNNfOlLX8KXvvSleW88qTcLnfPbb7+N3/u938POnTtN9/+3JEn4wz/8Q7zzzjsqVKi8hc75\n+9//Pp577jns3bsXe/fuxcjIiEqV6oCskl/84hfyiy++KMuyLDc1Nclf//rXw8/dvn1b/t3f/V3Z\n4/HIw8PD4c+NINJ5y7IsHzt2TH7mmWfkBx54QJ6YmFCjRMVFOucbN27Izz77rOz3++VAICDv2rVL\nPn/+vFqlKibSOff19clPP/207PV65ZGREbmmpkaWJEmtUhW10P/fsizLf/d3fyfv3LlTPnr0aLLL\nS4iFznn37t1yX1+fGqXpjmodUqS9k86cOYMHHngAdrsd6enpKC0txYULF9QqVVEL7RllsVjwxhtv\nICsrS43yEiLSORcWFuKHP/whrFYrLBYL/H4/UlJS1CpVMZHOOTs7G//1X/8Fm82G3t5eZGRkQBAE\ntUpV1EL/f//85z+HIAioqalRo7yEiHTOkiShvb0dBw4cwO7du/Hee++pVaYuqBZI8+2dFHouPT09\n/JzL5cLo6GjSa0yESOcNAI8++iiWLFmiRmkJE+mcbTYbsrOzIcsyXn31VVRVVaGsrEytUhWz0L+z\nKIo4cuQIdu3ahR07dqhRYkJEOu9Lly7hgw8+wLe+9S21ykuISOc8NjaGr3zlK/jBD36AH/7whzh6\n9KhhfrlOBNUCKS0tDW63O/y1JEnh27Tf/Zzb7Z4RUHoW6byNaqFz9ng8+PM//3O43W4cPHhQjRIV\nF82/81e+8hUcP34cp06dwqeffprsEhMi0nn/53/+J3p6evDCCy/gpz/9Kd58881F75emRZHO2el0\n4qtf/SqcTifS0tKwefNmBlIEqgXShg0bwv8z3r130rp169DQ0ACPx4ORkRG0tbXFtLeSFkU6b6OK\ndM6yLOOP/uiPcM899+B73/ueYTZljHTOV69exR//8R9DlmXYbDbY7XZYLMZY8BrpvL/zne/g3Xff\nxVtvvYVnn30WX/va1wwxdRfpnK9fv449e/YgEAjA5/OhsbERa9asUatUzVPtV/O59k564403UFpa\nitraWuzduxd79uyBLMv49re/bYj3FYCFz9uIIp2zJEn47LPP4PV6cfz4cQDAn/7pn+KBBx5Quer4\nLPTvfO+992LXrl0QBAFbt27Fxo0b1S5ZEfz/e/Y5f/GLX8SXv/xl2Gw2PPPMM6isrFS7ZM3izVWJ\niEgTjDFPQEREusdAIiIiTWAgERGRJjCQiIhIExhIRESkCQwkIiLSBAYSERFpAgOJiIg04f8DNPmG\nzIRUeBIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e026b8048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\")\n",
    "\n",
    "sns.jointplot(extractData_car[:,3], extractData_car[:,1], kind=\"kde\",  \n",
    "                xlim=[0, 0.5], ylim=[0, 100], stat_func=None, color=\"#6b02ff\")\n",
    " \n",
    "sns.jointplot(extractData_cyc[:,3], extractData_cyc[:,1], kind=\"kde\",  \n",
    "                xlim=[0, 0.5], ylim=[0, 100], stat_func=None, color=\"#96ff02\")\n",
    "\n",
    "sns.jointplot(extractData_pub[:,3], extractData_pub[:,1], kind=\"kde\",  \n",
    "                xlim=[0, 0.5], ylim=[0, 100], stat_func=None, color=\"#ff029a\")\n",
    "\n",
    "sns.jointplot(extractData_ped[:,3], extractData_ped[:,1], kind=\"kde\",  \n",
    "                xlim=[0, 0.5], ylim=[0, 100], stat_func=None, color=\"#ffb702\")\n",
    "\n",
    "sns.jointplot(extractData_nil[:,3], extractData_nil[:,1], kind=\"kde\",  \n",
    "                xlim=[0, 0.5], ylim=[0, 100], stat_func=None, color=\"#969594\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Note: \n",
    "    \n",
    "    The distributions don't line up correctly with what's plotted. Have the values been changed in one place but not another? (mostly a problem with the final plot..)\n",
    "\n",
    "Unfortunately, the marginal distributions don't allow columns and rows of subplots, so each figure had to be made individually."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Do a quick test to see if it's possible to have them all on the same axis!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Multi-bivariate KDE plots (seaborn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 41)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAHdCAYAAADSEjovAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngloIIYQoYf8HqcJmIkMDoRwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e02171198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Set up the figure\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "#ax.set_aspect(\"equal\")\n",
    "\n",
    "# Draw the two density plots\n",
    "ax = sns.kdeplot(extractData_car[:,3], extractData_car[:,1],cmap=\"Blues\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax = sns.kdeplot(extractData_pub[:,3], extractData_pub[:,1],cmap=\"Purples\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax = sns.kdeplot(extractData_cyc[:,3], extractData_cyc[:,1],cmap=\"Greens\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax = sns.kdeplot(extractData_ped[:,3], extractData_ped[:,1],cmap=\"Oranges\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "\n",
    "# Add labels to the plot\n",
    "#red = sns.color_palette(\"Reds\")[-2]\n",
    "#blue = sns.color_palette(\"Blues\")[-2]\n",
    "#ax.text(3.8, 4.5, \"Cars\", size=16, color=red)\n",
    "#ax.text(2.5, 8.2, \"Cyclists\", size=16, color=blue)\n",
    "ax.set_xlim((0,0.21))\n",
    "ax.set_ylim((0,41))\n",
    "#plt.ylim((25,250))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Overlapping densities (seaborn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.526889    0.24993751  0.20392964  0.01924384]\n",
      "[[-1.14479527 -1.27876668 -0.16208252 -0.02745414]\n",
      " [-0.29519292 -1.30671609 -0.30438843  0.02841119]\n",
      " [-0.75106091 -1.01988099  0.02249351 -0.07979798]\n",
      " ..., \n",
      " [-1.03770661  1.56099406 -0.31094143 -0.04679554]\n",
      " [-0.70747485  1.04057928 -0.1869936  -0.03891433]\n",
      " [-0.16103846 -1.68648951  0.14908688  0.04280571]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([  2.56674000e+05,   1.60570000e+04,   3.23300000e+03,\n",
       "          3.82000000e+02,   5.50000000e+01,   6.00000000e+00,\n",
       "          5.00000000e+00,   2.00000000e+00,   0.00000000e+00,\n",
       "          4.00000000e+00]),\n",
       " array([ -1.23253486,   2.21272617,   5.65798721,   9.10324824,\n",
       "         12.54850927,  15.99377031,  19.43903134,  22.88429238,\n",
       "         26.32955341,  29.77481444,  33.22007548]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAHdCAYAAABrMMGWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e02101e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f2, (ax1) = plt.subplots( figsize=(8, 8))\n",
    "from matplotlib.mlab import PCA\n",
    "\n",
    "pca_results = PCA(extractData_all)\n",
    "print(pca_results.fracs)\n",
    "print(pca_results.Y)\n",
    "#type(pca_results.Y)\n",
    "ax1.hist(pca_results.Y[:,0])\n",
    "\n",
    "#ax.imshow(pca_results.Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAOQCAYAAADotH/2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e02e58550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "f2, ((ax1, ax2), (ax3, ax4), (ax5, ax6), (ax7, ax8)) = plt.subplots(nrows=4, ncols=2, figsize=(10, 16))\n",
    "\n",
    "X_std = StandardScaler().fit_transform(extractData_all[:,1:3])\n",
    "# ax1.hist(extractData_all[:,0], 40)\n",
    "\n",
    "# ax2.hist(X_std[:,0], 40)\n",
    "# ax3.hist(extractData_all[:,1], 40)\n",
    "# ax4.hist(X_std[:,1], 40)\n",
    "# ax4.set_xlim((-3,3))\n",
    "# ax5.hist(extractData_all[:,2], 40)\n",
    "# ax6.hist(X_std[:,2], 40)\n",
    "# ax6.set_xlim((-3,3))\n",
    "# ax7.hist(extractData_all[:,3], 40)\n",
    "# ax8.hist(X_std[:,3], 40)\n",
    "# ax8.set_xlim((-3,3))\n",
    "\n",
    "#print(extractData_all)\n",
    "#print(X_std)\n",
    "#sklearn_pca = sklearnPCA(n_components=2)\n",
    "#Y_sklearn = sklearn_pca.fit_transform(X_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Perform PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Test PCA(matplotlib) on entire data set (without categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.70228772  0.27204951  0.02566277]\n",
      "[[-1.11265886  0.22389862 -0.03224827]\n",
      " [-0.26276857  0.36724039  0.02358047]\n",
      " [-0.72532677  0.02696006 -0.08364855]\n",
      " ..., \n",
      " [-1.07670579  0.23531033 -0.04093138]\n",
      " [-0.73344666  0.13660367 -0.0350104 ]\n",
      " [-0.11869716 -0.06737285  0.03645783]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19e02f94780>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAOQCAYAAADGxyOVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAIABJREFUeJzs3Xl8VOXZN/DfLJklCZOFJCQICMGi\nMWGpIqYa1IqUmhqkVXxQXmqNlZZqFBW1WhUpKC7Fxza0KLQ8tRQXfGip6Yv1VVAECZsVTCJqFSME\nEpKQCdlmySzvH2GGmeTMzJmZM8uZ+X0/n35qZjlzDzNzrnPf93Vft8LpdDpBREREsqKMdQOIiIgo\neAzgREREMsQATkREJEMM4ERERDLEAE5ERCRDDOBEREQyxABOREQkQwzgREREMsQATkREJEMM4ERE\nRDLEAE5ERCRD6lg3YDCz2Yz6+nrk5uZCpVLFujlEREQRZ7fb0dbWhpKSEuh0OlHPibsAXl9fj/nz\n58e6GURERFG3ceNGTJ06VdRj4y6A5+bmAhh4E/n5+TFuDRERUeS1tLRg/vz57hgoRtwFcNeweX5+\nPkaNGhXj1hAREUVPMFPHTGIjIiKSIQZwIiIiGWIAJyIikiEGcCIiIhliACciIpIhBnAiohCZrTY0\nt/fCbLXFuimUhOJuGRkRUbyz2x1YX9OAPfXNaOs0ITdTj9KSAlRWFEOlYr+IooMBnIgoSOtrGvDm\nziPuv1uNJvffd8yZGKtmUZLhpSIRURDMVhv21DcL3renvpnD6RQ1DOBEREEwdlnQ1mkSvK+90wRj\nlyXKLaJkxQBOJAEmM3lL5H+PLIMWuZl6wftyMvXIMmij3CJKVpwDJwoDk5m8JcO/h06jRmlJgdcc\nuEtpSQF0Gp5WKTr4TaOEYbbaYOyyIMugjdpJVKpkpkBtD/a9xeLfAkie5K7KimIAA3Pe7Z0m5Hhc\nqBBFCwM4yV6sen2BkpkWlBcBgN9A6qvt82edj9O9/chIS8HGtz/3eX+qTo0+s809bNveaULNziM4\ncPgk2jpNGG7Q4lujs7HoRxORlSE87CsVMf8eidI7VamUuGPORCwoL4rJhRIRwABOMeCvdxhKz1HK\nXp/ZakPLqV4ACmQN07qDo6stnu3zl8zU1mnC6jcO4vDXHWg1mpBt0KK0pAAL50yESqXEsZPd2P/p\nSXzVZMQHB08Mafs7+47CbLVBp1HDZLENuf/tvd/AYrVDqQAcTiBFrYBKqYDZ6vBqR/tpC9pPN6O2\nvhljC4Zh1d1XQKNRR6SHLia5qyAnsU45Oo064d4TyQe/eRQ2MQE5VadGd5/Vq3fo2VM2W21Yu6Ue\ndV+1o/3MfVOLRqBieiGGpWrQZ7Z59TZdr9NnsuKdfd8ItmtPfTNmXnou8oenBgxSdrsDf3yzHtv2\nH4XJYve6LzMtBZMm5CJNl4KPPmt1t31q0QjkZOjQ1mkecjynE9jx7+Puvzu6LNi6uxGf/KcNze29\nsDv9/5u6grZn8PZksQ600XHmOP02J/rh/6CNzd24bfn/wxUXjcK+hhbJRytcyV2txqFBnMldRNJT\nOJ3OAKeS6GpqasKMGTOwbds2jBo1KtbNSWqDA7Ord2rttwNQQKUE3trdiAOHW9HRbfYKBgCwZvMh\n7K47ge4+/5nII3NT0WbsQ3+AhGUFACeA4QYdLp88EnOuLMSD1bvQfnpoAPWUbdBi4vgcVFYUo/20\nCad7+5GuU+Pjz09Cp0mBSgV8/EU7PvqsVfw/zhlpOjV6zfLPtJ49vVCSOep1W+oEk7ukOj5Rogol\n9rEHnqQ8e8au3u3Jjj60n+5DdroOb+9pxIHPWtHZY0VWugZ6fQo6uswwD+qdenIN73594jS+ONoB\nS7+4a8MTbX2iHuc62qkuM97ceUQwUAjp6LJgx8fHsePj44EfHKRECN6AdHPUTO4iih4GcJk73WPB\nF8eMUCkU6O6zos/cD4cTuKQoHwCw/3AztClqjMkfhhHZae5h7H0NzWg/La7ghLHHCmOPVXSb6r46\nFdJ7odiRao6ayV1E0cNfVpwxW2349MgpfHm8ExefPwJ2hxPNp7pxutuKkx190KiVMFltaDrZjS+O\ndsI0KGnJ5UXURbnlJGdSz1EzuYso8vgLixKz1YajLd3Yvu8b7PnsBM4/ZziyMrU40TpQrepUlwlG\nowWeo84btn4WuwZTUmEBEiL54S82RKd7LNj/aQs+/rwFh/7Tiq5eB9K0gM2pgNkaeO53t7ElCq0k\nOVOrFLAFSlcPQl6WHtOKB6ZW9jW0cI6aSOaSPoB7Zlq3GU1Y9/eD+Pg/HSEdq8cCIMBSHqJA8rIG\ngurN13wLC5/Zju6+fvd9CgUw85IxmH1lIbZ+2Ig99c3o8LN5xvAMHS4tzkfF9ELkZOrdvexbf3Ah\n56iJZC4pfrkV9/8j1k0gCmjWpWNww9UTvILqK8vL0drRh4+/aMM5uWk4b3Sm+75FN0zGLbMuwN2r\n3hMM4tkGLX5731XISB86t805aiL5S+hfMAM3xROlEkhRKWHpd0CvVQFQwGK1eQ1jCxVTyctOxazS\ncwWPmZGuRdnkcwSX1JVNPkcweBNRYpA0gDscDjzxxBP4/PPPodFosGLFCpx7rvCJhyjRpKiBi84f\ngZ9cdyHe2PYl6s9UlRueocOk83KxcE4JlCqle+ga8F8nXSyuvSZKTpIG8HfffRdWqxWvv/46Dh48\niKeffhpr1qyR8iVEY++boq3fBuxtOIkR2Wm49+aLfJaY9Ry6lmIYm2uviZKTpFs1ffTRR5g+fToA\nYMqUKaivr5fy8ERRk6ZXY+UvLkf5ZWMF7x+Tn46cDOHh6T31ze6NSApy0qIWTKP9ekQUW5L+0nt6\nepCenu7+W6VSwWazQa0Wfpnq6mqsXr1ayiYQSaLPZMPwDD0WzpkItUo5sJ2n0YSsM7uKVUwvxC+e\n3S74XH9VzWK1TzcRJR5JzyDp6eno7e11/+1wOHwGbwCoqqpCVVWV122ugu5EkabXqobsPOaSmzVQ\nmUylUmJBeRFmXjoGgMK9s5nZagtq561Y7VlORIlL0jPHRRddhA8++AAAcPDgQUyYMEHKwwelZtX1\nMXttkocZl4xB4UiD4H2lJQVIUSmxbksd7nx2O+5e9T6W/2kPNmw9DLvdAZ1GjdKSAp/PHdy7du1Z\n3mo0wek8u/HL+poGr8eZrTY0tw9U5yMi8kfSHvjMmTPx4YcfYt68eXA6nXjqqaekPDwlgZF5qchO\n16H+SGjFdMQYnqHD5ZNGurO0126pw576Zhi7LMjNOtszdgVdF1fQBYA75kwUnf1tttqwp75ZsC2u\nXcBSVEr20IkoKJIGcKVSiV//+tdSHjIsrl44M9Ll40RrH060itteNBQZ6ZohxU0W3TAZt1UUD9n7\nPFDQ1WnUuGPORNx0zQQ0NndhbIFBcN21scuCts6hQ+3A2fnyf+464vdigYhosKTIoqlZdT3sdgfm\nPFgT66ZQjF0+aaSoymRigm5elrhec5ZB63e+PFWnFnWxQETkKWnG5lQqJWpWXY+7b2JvJlkVjjRg\nocjerCvoCnElqYmd1/Y3Xz61aAQam7sEgztw9mKBiGiwpAngLjMvLUTNquuRnxHrllCkKRUD/59t\n0KL8srF4fvGVUKmUXolivpLGAiWpAfDbax58vMqKYsyeXoi8LD2UCiA3U4fCkQYcOHwSj720G0of\nv0Sp9+kmosSRtONy6x7n/Hiic5zZGK60pACLbpgMu92BdWcS1to6TWeGpZ0wWezuHcA8h7/9Jam1\nGk0Bh9g9h+QHV0vbsuNLbN3d6L7f6WMTO+7TTUS+JP2ZgYluie/A4ZMwW23YsPWwV6KYyXK2lyyU\nNNZvd+C6skLcdM0E9JltXsVXAs1r++o16zRqZBkG2iREqQScDnhlwxMRCUn6AO5Ss+p6HD9pxM+f\n/SDWTSEPOo0SZqsjrGO0d5rQcqrX55C3p12HjuOG756Hze99KZicdrZdA0PsQruABeo1+0uQgxNY\n/vPLcP65Wex5E5FfSTcH7s85I7IGEt3mTop1U5LerEvHYNU9VyBVlxL2sXIy9QAUvoOmh44uCxY+\nvU1Uctrgee28LD1mTy8M2GsOlCAXi+DNAjJE8sNLfAEzS8dhZuk4HPyiGY+9tC/WzUlKOz4+jn2f\ntsDYbQ37WKUlBcgfnupzyHswi1W4vOrgJV2+dgEzW21oPVM3XSgQh9N7lxpLvBLJFwO4H1MmFKBm\n1fWo+08rHnmxNtbNSSpmqx1mH4E0GHqtGvNnne83aIrla5MS1xrywUly/oJhvOzhHajaHBHFLwZw\nESZ+Kw81q67HzkNH8exfPo51cxJCiloBu93pzhSPFIvVhtO9/UjVa4YEzWBf219ymtlqw4ubP8G2\nA8fct/kLhvGwh7fYanNEFJ84RhaE6ZPHoGbV9bi+bEysmyJ7/bbIB2/AO+i6gubvH7wav/7ZZUEf\nS2h429Xr/sWz272CtyehdeEusdzDW0y1OSKKXwzgIfjpD7+NTSuujXUzZEvlqrASBUJBV6dR4/xz\ns5CXJZxI5qLXqgImp7mGoNv8zK3HazAUU22OiOIXx8dCpNdrULPqenx93Ii7n+fSM7GUABy+qpaE\ne2wloE1RwWK1B5xT9jcnrteqMXPaGNwy63x09fb7HN72NwTtKV6DYTwl0xFR8PgLDdO4cwaWnn11\n3IhHfv8h+izhJ14lMq1WhXR9Cto6zWEdR6dRDUlyczgAk8WO0Xnp+M3d05Gq1/g9xuA58eEZOkw6\nLxcL55S4n5vm5xh+13N7iOdgGC/JdEQUvPg8q8jQ+HOy8PpT16G9ow93PP0ObIzjgkwWO0pLCvDe\nR00hPV+pBL5fOhY2mwP/b99Rwccca+3By1sPY9ENk/0eK9xEMn/V2ICBeuffmTgy4sHQbLWFnAgX\nD8l0RBQazoFLLCc7Ff+7sgJXXTwy1k2JuFCnsnUaFcovG+tzAw+lEhibP0zwvu+XjsVtFcX46LNW\nv6+xt6FFdFESVyIZgKCKmfjb8OTqqaPxh4dm4I45EyO2ntqVQHfns9vxs6ffxZ3Pbse6LXWw24Ov\nXBfLZDoiCg1/rRGgUilx/y2XYNEPrXjqz3tw6EtjrJsUEZqUocPYYnz0WSt+/+DVsNsdeHvv0F70\n90vHYuGcie4CI0IbiXR0+x+CN3aZBddsD2a22tDeaULNziM4cPhk0MVM/A1BR7oQCtdwEyU3BvAI\nStVrsGLRFejuMaNq1Xac6uqPdZMkFWqhlVajCWs2f4K6L9sADPS4HQ4M2RFMaGjXbndgy44voVD4\n3sELAIZn6GDpH9guVKhX6VmBbPAQeDCBMFZD0FzDTUT8hUfBsHQd/ry0HB2nTVj557347OjpWDcp\n5rZ7rJl2nBnxnVo0YkjAdFU5c1lf0+C1DacvPSYb7l71vs/e9ODeq5BgAuHgdkaamDXc0WwPEUUf\n58CjKDtDj+fuuQp/fnSGz/nfZOba9tMXMcu2tCkD/7Ami83nRiRil38NXr8dTxt+cA03ETGMxMDw\nrHT847nr8exdpbFuSsSMyR+GzPTgdhJrO7Ptpy+Blm2lqBSw9AsncNXWnXAHXrHLv1yB0G53YM3m\nQ/jZynexcGV4yWJS8ZdAF8/L1ohIOgzgMVQ0bgRqVl2Pp38RfFnPeHe0pRvZBv+VzgZzOoFf/3EP\n1m2pQ5/JOqS366/XCQD9dt+T4m2dZqzZ/AnsdkfA47iUlhQgRaXEfS/swNbdjeg40xv3tb1otIW6\nnSkRJQZepseB4vG52PJsBV54/d94/6PjsW5O0JQKCNY17zb1CxZc8aet04w3dx7BO/uOwmy1ec1h\nh7uj2PYDx5CuT8Edcyb6PY5nMt3aLXU4cqJL8HGxThbjGm6i5MZfe5wYWHo2FXfeOAW/fe1j7Dp0\nItZNEs3XpiTtnSa/meLZBq27VzuYyTLQ8x6cEV5ZUYweU79XElwwXEFXaPnX1KIRqJheiJxMvXtf\n7731LT6P1WaMj2SxaCfQEVF84K8+zug0aiyZfzEMqSnYWvtNrJsTltxMPZxOp2DZVKUSKB6XjZ2H\nAieTAd693UU3TELdl20hlWM9m6GdFrD3auyy+F9vrgC27PgSCyNYrIWIyBeedeKQSqXEohun4PUV\n1+LS4rxYNydkmhQVphaNELzP4QB2HmqGXivuGtIzI1ynUeM7E0OrdDc4Q9tfBbJAc+VOJ7B1d2PM\n58KJKDkxgMexVL0Gj1Z+B68sm4W8bF2smxO0ptYevP/vJowtGAaFj7Kr1n5xS7IGB97KimJcVzYO\neq0qqDYFk6HtL9Pbk7/9vomIIoUBXAaGpevwp1/Nwl8e/x4mf2t4rJsTFJPFjsbmbp9z4WJXYg0O\nvCqVEkqFAiaB3d90GhWuKxuH68rGhZ2hfcus8wNeJMTrft9ElNg4By4jWRl6rPh5Gbp7zLj/dx+g\n+VTgtczxwlUuVQy9Vo10vRqnTpt9bm/prxiLIU2DW39wIXQaNW79wYVhZWh39fYHzKJn4RQiigUG\ncBkalq7D2ke+h7aOXvzyDx/63M4ynogN3gBgsdrwbFUZtClqn4FXbCnRcDO0A20ZCrBwChHFBofQ\nZSw3Ow1/evR7+Ouy72NacX6sm+NXMFuP5mTqkT88ze/2ltEqJepvHlyvVbFwChHFDLsNCSAjXYtH\nbr0E62saUFt3IqTlVZHma624EDE9Wn9FXUrG57j/22y1hV3kRGjNeMn4HCycU4I0vSakYxIRhYsB\nPEF4VuU62dGLZ/5yAMdO9sSkLRq1AiqVEmaLHQoRc996rRoWq83nfLcvlRXFcDid2Lb/mLvwCzBQ\nca3uyzYMS9Wgu8+K9tPmoPb4HowVz5KbFBeBRJHAb2OC0WnUODc/A394cAaMp014sHonWqI8R261\nOQGbHd+ZWIDaOt+FWrINWpRNPgfzZ52P0739QZ8gz2aiD13C1dZp9hqJCGaPb19Y8Sy5eO4Z39Zp\nCusikCgS+C1MYFkZerz48DWYVTomJtuXftXUiZwM4bnorGEa/Oq2aVhQXoRUvcbvfLcvYrcF9cQ1\n2ySWa8/4VqPJ59a0RLHEAJ7gVCol7pr7bbz+5A9w+eToJrq1d5qg1wpvKdrVa8WS3+0Ma2tOsduC\nDm6TnNZsx9Me5MnE38UhLwIpXnA8MEnoNGo8MP8SZA2rx7v7jga1Q1ioModpfZ7oXPE6nKFtMUu8\nBpPLmm0O38aW2GWKRLHEM0ESUamU+NkPJ2HDsu+jeslVuGJKaPXExdJr1aIz4kPp1YgtdepJLmu2\nOXwbW9FapkgUDgbwJKTTqDG2IAP33XIxZk8v9LthRziOt/WKfmyoQ9uVFcWYPb0QeVne70GhGKjI\nlpOhDauUaiyGsDl8G3v+Lg7lchFIiY/fwiTmuTyqua0bv3qxFt19/TFpS6i9Gtd7sNkd2Lq70X27\n0zkwz15+2VjMufK8oDPcYzmEzeHb+CC0/j+YZY5EkcazAEGnUWPcOVl4ZXk5Wjv6cPuT70S9DeH0\nasxWGw4cPil434HDJ3FbRXHQx3YNYbtIsQxNLH9z+xy+jR6u/6d4xyF08pKXnYq/Pj4zYsdXKIAr\nLzonpF3CfA1ni+mxBiPWQ9gcvo0v/vaMJ4olfiNpiIyMVGx5tgLPv/oR9jechEnCjHWnE9hb34Lv\nXjwKs68Yj5xMfcATY6Dh7FSdGtnDdDjVNTRhLsugQ6ouuK95PAxhc/iWiAJhACdBKpUSD/yfS2C2\n2vDi5k+w7cAxyY5tttrxVu03sPQ7sOiGST4ec7Z85YathwWHsx1OJ5QKBfbUNwsGbwA4ddqMxc+/\nh8smnSN6/joehrA5fEtEgfCMQH7pNGpU3TQFep0aWz/8OqhNSQJx1Sz/zsSR7uBqtzuwdksd9ta3\noKN7YD/wnj6r4PMH10D3pf20Jaj5a38bpUR7CJvlW4nIF86BU0Cu9eOvLL8WV3x7JBRBbA0aSFun\nGW/uPIK1W+rQZ7Lirt+8h627G3GqywynE2gzmmCyCA/hiwnenoKZv/ZcnhbOMjQiokgJ6dK+u7sb\nDzzwAHp6etDf349f/vKX+Pa3v42DBw/iySefhEqlQllZGe666y6p20sxlKbX4IH/cwl+cYMVf9h8\nCHsbWmCxBl8CVcjW3Y34V22jpD38wYKZv47GEDZ3uSKicIR01vif//kflJaW4ic/+QmOHDmC+++/\nH3//+9+xdOlSVFdXY/To0Vi4cCEaGhpQXMweS6JxBXKz1YYTbT145uX9OHGqL+zjRjJ4A6HNX0di\nCJtlUolICiGdLX7yk59g3rx5AAC73Q6tVouenh5YrVaMGTMGCoUCZWVlqK2tlbSxFF90GjUKz8nE\nHx6age9NG+3zcXqtKuzX0mqUyM3UuYezZ0z1/Xq+xMsSLJZJJSIpBDybvfHGG3j55Ze9bnvqqacw\nadIktLW14YEHHsAjjzyCnp4epKenux+TlpaGY8f8Zy5XV1dj9erVITad4oVKpUTVf10EtVrlVQ3N\n5dLifLz/7+NhvYYCClxyYT4qphci50zp17qv2kVtZJKToXVnoQOxHboOtMZ8QXlRXFxkEFH8C3im\nmDt3LubOnTvk9s8//xz33XcfHnzwQUybNg09PT3o7T1b+7q3txcGg8HvsauqqlBVVeV1W1NTE2bM\nmCG2/RRHFs6ZCLVKOWTt8tWXjA47gJutdmzd3Qj1mblpAD4zxV0UAJb//DKcf24WdBo17HYH1m2p\ni+nQdTysMSeixBDSmeLLL7/EPffcgxdeeAEXXHABACA9PR0pKSk4evQoRo8ejV27djGJLcn4Svwy\nW23QaVUw+8gmD4ZnL7Wyohg2uwP/2tMIh0AunRPArkPHUVI4HEBsy6O6xMMacyJKDCF1O1atWgWr\n1Yonn3wSCxYswKJFiwAAy5Ytw5IlS3DjjTfiwgsvxOTJkyVtLMnD4NKTOo0a11wyRpJje5ZGVamU\nWHTDZHy/dKzPx/+r9husr2mQrDxquLuTsUwqEUklpLPFmjVrBG+fMmUKNm3aFFaDKDH9dHYJAODd\nfUdhDqM0q1Av9dbyIrz30TGf68Vr605g5qVjwhq6ljJznGVSiUgKvNynqHAVg7n1Bxei5VQv7A4n\n/vnBV3j3QFNQxxk70oCjLd0Ykz/M3Vtt7TT5HZ5v7zQDUIQ1dC3l8DvLpBKRFHjWoKjSadQYW5AB\nALjn5ovx5fHTaGzuHvI4pQIYW2BAd58VbZ1mqJSA3QHsaziJfQ0noVQA3/vOuVArldhb3wx/S8hz\nMnXIH54acnnUSGWOs0wqEYWDZw+KqVV3X4EHqneisaULDgegVAIjc9LxzJ2Xw5Cug9lqw73/vQNN\nrT1ez3M4gX/t/kbUa3xn4kh30hsQ/NA1M8ejj1XqiALjL4NiSqNR47f3fxeneyxobO7C2AIDMtLP\nDmdbrHacaO/xcwTf9FoVZlwyxh2gQx26TsTM8XgNkKxSRyRe/PxyKallpGsx+Vu5Q25vbO4SXCIW\nyOO3X4qJ5+UAGJiv9gxUYoeuPYNcvOxOFq54D5DxsNSPSC7kc+ahpDS2wAAF4HeOe7CcDC2yM7T4\nn5oG7D98Eu1GE3Ky9PiOyEAlFOSmFefjurJx2NfQIuvM8XgOkKxSRxQc/hoormWkazE6Px1HW8QP\noxu7LVj8/Adet7WdCVT9Njt+ceMUv88XCnL/3PU1Zk8vxO8fvDouh57FiPcAyVwDouDEfsyMKID/\nvudKpKjFf1Xtfobc36r9Bms2H4Ldx4MCBTkAXkVq5ERMgIwlV66BELnmGhBFEgM4xT2NRo3Xll+L\nMfnpUCgGbnP9fyi27m70ufNXvAe5cMR7gGSVOqLg8BdBsqDRqPH7B2a4s9ULhqfh4T/sErUbmRBf\nQ8apOjWyh+lwqss85DmRCnLRygh3Bch4TsZjlToi8WL/iyUKgme2eqDdyPwZPKfqmbgmFLxdrydl\nkItFRni8B0hWqSMSj78Mkq3BwcgRRKr64N704MQ1T3lZ4Qc5oV52LDLC5RIgWaWOKDD+Qki2Bgej\nv7//H7xVK646m6s3bbba0HKqF7V1JwQfl23Q4vnFV3oVlwmGr172LbPOj2lGuE6jRpYBZ4I44jKI\nE5F//NWS7Ll6az/74SSkqFVewfKiC/JgsdrRcOSU15DxreVFWLelDnvqm/3Oo3d2W9BntoUcwH31\nsntM/TFbMhWvxVzitTocUbzir4QShr/h4cHBYd2WOlHz5+Ekrvlbklb/VTtyMnRo64xespxLvBVz\nidcLCqJ4x18HJZyBHrn3Wm3P2/wF1sHCSVxr7zT57N23d5ow6byhpWPDfc1AAq1zN1ttEXldf1wX\nFK1GE5zOsxcUvpb6EdEABnBKOsYuS8DlZ7mZOpRfNhbXXjY25KBW46eHn5Opx8I5JZg9vRB5WXoo\nFQPJcrOnF0qaEW622tDc3ut+D/G2zj0eLyiI5IJD6JR0sgxaZBu06PATrIoLh+PA4ZN4q7ZxyJCu\nmLlas9WGA4dP+jz+t8/PQ6peE7GMcH/Jc/G0sxrLpxKFjr8MSjqugiZbdzcK3q/XqvD+v4+7/3YN\n6TqcTigVClFztf4CEwDsb2jBupQ6VFYUR2TJlL957ngq5pKIW7USRQuH0CkpLZwzEYUjDT7uFa7T\num3/UcG52rVb6oY81l/ZUgDo6LZEbJ430LD0/FnnR3zoXiyWTyUKHX8dlJRUKiWeX3wl1m6pw96G\nFhi7zMjJ1GPi+Bxs/+iY4HNMFrvg7Vt3N8LpdOJnP5zkNcQ+tWiEz16+SyTWfAcalj7d2x9XxVzi\nvTocUbxiAKekpVIpseiGybitotgdyACg7qv2oGusv1X7DVQq5ZAh9sKRBnR2m9HRbRV8XqvRhPZO\nE0blDQv7/biIHZaOl2pncqkORxRvOIROSc9ziZm/IV29VuX3OO/u+2bIEPuRE1245MJ8DDfofD7P\nX7Z6KOQ6LC20/I+IfGMAJxqksqJYcI7YV1B0MVuF9xj/+Is2TC3K8/m8A4dPSr5cytd74LA0UeLg\npS7RIL6GdHtNVtTWN8PsYy7cl/ZOEy6fPBJv7z3q836pl0txWJoo8bEHTuTD4CHdNL0G35t2rs/H\n67XCATJzmBYpahVys4Sz0iNLWOAuAAAgAElEQVS5XIrD0kSJi79qoiBUVhTD4XRi2/5jMFkGhr31\nWhVmXDIGAPDPXV8PeU5HlwUP/+FD+Crr7Rqab27vZU+ZiETjmYIoCCqVEj/74STc+oML0XKqF4AC\n+cNTodOoYbc73FnoQhng9jNT5DqNCtZ+O3Iy9ZhWnA+H04k7n93OjTyIKCg8QxCFQKdRY2xBBsYW\nGNw9Zte88/OLr0S2nyFxa78dV3z7HPzu/qugVCjwz11fiyoOQ0TkiT1wIon1mW1+NwVxOIH3/30c\nqboUn/XS/7WnEcBAxTj2xIlICM8MRBLLMmiR4yNhzdPehhafBWMcjoEKb9xSk4h8YQAnkphOo8Z3\nAqwZBwBjl9nvUDvALTWJyDcGcKIIqKwoxnVl46DT+K7e5qr57U8s9ugm/wbvsU4UK5wDJ4oAz2z1\n379x0Gt7UhfPDTv+tacRDoFCbtxSM3742mOdKwYoVhjAiSJIp1Fj8byLYEjTCu625dpQBYDgzmVT\ni0acqaQ28LdQVTXX7mdcQx5Z/vZYv2POxFg1i5IYf+1EESamrOnCOROhVindQX54hg7DUjU4cPgk\n3qpthDZlYCjebLUjL2vgAuDW8iK8vPUwe4RREGiPdam3hCUSg984oijxt33n4CC/ZceXXj1ys/Vs\n/XVXz6/+q3YcOdE15HaAPUKpBdpjXepa9kRi8DKdKI7oNGpkGbQ+14d7amzpErydmevSc+2xLoR5\nChQrDOBEccZfb8+TUNIbwMz1SJDrHuuU2PitI4ozWQYtsofpcKrL7PdxSqVwEGePMDJcKwaEkhGJ\nYoEBnCjO6DRqXFqSL5iV7mlsvsFrDtyFPcLI4B7rFG84hE4UhxbOmYjCkQbB+/RaFWZPL8RzVdMx\ne3oh8rL0UCqAvCw9yi8bi2svG8s58AjiHusUL/gNJIpDKpUSzy++Emu31GFPfTOMXRbkZulRMj4H\nC+eUIE2vAQB3j7C904SanUfcy864pIwo8TGAE8UpV5GX2yqK/Q7Z6jRqvLW70WvInUvKiBIfL82J\n4lygIdtARUaEhtNZz5tI/tgDJ5K5YIqMsJ43UeII6xf71Vdf4eKLL4bFMrDm9ODBg5g7dy7mzZuH\n1atXS9JAIvIvmCIjrnrerUYTnM6zQ+3cd5xIfkIO4D09PXjmmWeg0Wjcty1duhSrVq3Cq6++ikOH\nDqGhgScFokgTW2QklKF2IopfIQVwp9OJxx57DPfddx/0+oEr/56eHlitVowZMwYKhQJlZWWora2V\ntLFEJKyyonjIkrLZ0wu9ioyIGWonIvkIOAf+xhtv4OWXX/a6beTIkSgvL8cFF1zgvq2npwfp6enu\nv9PS0nDs2DG/x66uruZQO5EExBQZcQ21txqHBnHPoXZuT0okDwF/nXPnzsXcuXO9bps5cyY2b96M\nzZs3o62tDZWVlXjppZfQ29vrfkxvby8MBuFCFC5VVVWoqqryuq2pqQkzZswI5j0Q0Rn+djxzDbV7\n7mntUlpSgBSVEuvOrDtnghtR/Avp8vqdd95x//fVV1+N9evXQ6vVIiUlBUePHsXo0aOxa9cu3HXX\nXZI1lIjC56+etyvBzYVryYnim6TjY8uWLcOSJUtgt9tRVlaGyZMnS3l4IgqTr6H2QAluC8qLOJxO\nFGfC/kVu377d/d9TpkzBpk2bwj0kEUXY4KH2YNaSE1F84MQWEQW1lpyI4gMDOBGJXktORPGDv0oi\nAuA/wY2I4g8DOBEBELeW3BeuHSeKPv7SiMiLv7Xkg3FzFKLYYQAnopBx7ThR7PASmYhCws1RiGKL\nAZyIQsLNUYhiiwGciELCteNEscUATkQh4dpxotjiL4yIQsa140SxwwBORCELZ+04EYWHvzQiClsw\na8eJSBqcAyeiuGG22tDc3sslaEQi8JKZiGKOFd2IgscATkQxx4puRMHjpS0RxRQruhGFhgGciGKK\nFd2IQsMATkQxxYpuRKFhACeimGJFN6LQ8JdBRDHHim5EwWMAJ6KYY0U3ouDxF0JEcYMV3YjE4xw4\nERGRDDGAExERyRADOBERkQwxgBMREckQAzgR0RncDY3khOmeRJT0uBsayREDOBElPe6GRnLES0si\nSmrcDY3kigGciJIad0MjuWIAJ6Kkxt3QSK4YwIkoqek0akwrzhe8b1pxPmuyU9xiACciIpIhBnAi\nSmpmqw37GloE79vX0MIkNopbDOBElNSYxEZyxQBOREmNSWwkVwzgRJTUdBo1SksKBO8rLSlgEhvF\nLX4ziSjpVVYUAxgo3NLeaUKORylVonjFAE5ESU+lUuKOOROxoLwIxi4Lsgxa9rwp7vEbSkR0hk6j\nRkFOZE+LZquNFwkkCX57iIgk5CtAc8czkhoDOBGRBAIFaO54RlLjZR8RkQRcAbrVaILTeTZAr69p\n4I5nFBEM4EREYQoUoFtO9bJYDEkupABut9uxYsUKzJs3Dz/60Y/w3nvvAQAOHjyIuXPnYt68eVi9\nerWkDSUiileBqrkBChaLSSBmqw3N7b0xHzkJaQ78H//4B2w2G1577TWcPHkSb731FgBg6dKlqK6u\nxujRo7Fw4UI0NDSguJjrKIkosbmqubUahwbxnEw98oenorSkwGsO3IXFYuQj3hIRQ3rFXbt2IT8/\nHwsXLsSjjz6Kq6++Gj09PbBarRgzZgwUCgXKyspQW1srdXuJiOKOmGpulRXFmD29EHlZeigVQF6W\nHrOnF7JYjIz4y3OIhYCXfW+88QZefvllr9uysrKg1Wrx0ksvYf/+/Xj44YexatUqpKenux+TlpaG\nY8eO+T12dXU1h9qJKCEEqubGYjHyFijPYUF5UdQ/z4CvNnfuXMydO9frtnvvvRdXXXUVFAoFpk2b\nhsbGRqSnp6O3t9f9mN7eXhgMBr/HrqqqQlVVlddtTU1NmDFjRjDvgYgo5sQG6GgUiyHpidm1Ltqf\na0hD6BdffDF27NgBAPjss89QUFCA9PR0pKSk4OjRo3A6ndi1axemTp0qaWOJiOLdQIBOY+86wcTj\nrnUhfcNuuukmLF26FDfddBOcTieWLVsGAFi2bBmWLFkCu92OsrIyTJ48WdLGEhERxYIrzyGeEhFD\nekWNRoOVK1cOuX3KlCnYtGlT2I0iIiKKN/G2ax3HeIiIiESIt0REBnAiIqIgxEsiIkupEhERyRAD\nOBERkQwxgBMREckQAzgREZEMMYATERHJEAM4ERGRDDGAExERyRADOBERkQwxgBMREckQAzgREZEM\nMYATERHJEAM4ERGRDDGAExERyRADOBERkQwxgBMREckQAzgREZEMMYATERHJEAM4ERGRDDGAExER\nyRADOBERkQwxgBMREckQAzgREZEMMYATERHJEAM4ERGRDDGAExERyRADOBERkQwxgBMREckQAzgR\nEZEMMYATERHJEAM4ERGRDDGAExERyRADOBERkQwxgBMREckQAzgREcUts9WG5vZemK22WDcl7qhj\n3QAiIqLB7HYH1tc0YE99M9o6TcjN1KO0pACVFcVQqdj3BBjAiYgoDq2vacCbO4+4/241mtx/3zFn\noujjmK02GLssyDJoodMkVshLrHdDRESyZ7basKe+WfC+PfXNWFBeFDAYJ0MPPjHeBRERJQxjlwVt\nnSbB+9o7TTB2WQIew9WDbzWa4HSe7cGvr2mQurkxwwBORERxJcugRW6mXvC+nEw9sgxav88P1INP\nlIQ4BnAiIoorOo0apSUFgveVlhQEHD6XogcvB5wDJyKiuFNZUQxgoMfc3mlCjsccdiCuHnyrcWgQ\nF9ODlwsGcCIiijsqlRJ3zJmIBeVFQWeR6zRqTCvOxz93fT3kvmnF+QmTjZ4Y74KIiBKSTqNGQQ5D\nlRDOgRMRUUIxW23Y19AieN++hpaESWIL6bKmu7sb9957L0wmE1JSUvDcc88hNzcXBw8exJNPPgmV\nSoWysjLcddddUreXiIjILzFJbInQqw+pB/63v/0NEyZMwMaNG1FeXo4//elPAIClS5di1apVePXV\nV3Ho0CE0NCTOejsiIpKHcJehyUVIAXzChAno7e0FAPT09ECtVqOnpwdWqxVjxoyBQqFAWVkZamtr\nJW0sERFRIOEuQ5OLgO/ijTfewMsvv+x12+OPP44PP/wQ5eXlOH36NDZu3Iienh6kp6e7H5OWloZj\nx45J32IiIqIAwlmGJhcBA/jcuXMxd+5cr9vuuusu/PSnP8W8efPw2WefoaqqCq+++qq7Vw4Avb29\nMBgMfo9dXV2N1atXh9h0IiIiYeEsQ5OLkIbQDQYDhg0bBgAYPnw4ent7kZ6ejpSUFBw9ehROpxO7\ndu3C1KlT/R6nqqoKn3/+udf/tm3bFkqTiIiIhhhYhpaWcMEbCDEL/Z577sGjjz6KV155BTabDcuX\nLwcALFu2DEuWLIHdbkdZWRkmT54saWOJiIhoQEgBfMSIEVi3bt2Q26dMmYJNmzaF3SgiIiLyj4Vc\niIiIZIgBnIiISIYYwImIiGQo7tLy7HY7AKClRbiOLRERUaJxxTxXDBQj7gJ4W1sbAGD+/PkxbgkR\nEVF0tbW14dxzzxX1WIXT6XRGuD1BMZvNqK+vR25uLlQqVaybgxkzZiTN2nS+18TE95qY+F4Ti91u\nR1tbG0pKSqDT6UQ9J+564DqdLmABmGgbNWpUrJsQNXyviYnvNTHxvSYWsT1vFyaxERERyRADOBER\nkQwxgBMREcmQ6oknnngi1o2Id5deemmsmxA1fK+Jie81MfG9Jre4y0InIiKiwDiETkREJEMM4ERE\nRDLEAE5ERCRDDOBEREQyxABOREQkQ3FXSjUeOBwOPPHEE/j888+h0WiwYsWKoEvcycmcOXMwbNgw\nAAPlCleuXBnjFknv0KFD+M1vfoMNGzbgm2++wS9/+UsoFAp861vfwtKlS6FUJs61rOd7bWhowM9/\n/nOMHTsWAHDzzTejvLw8tg2UQH9/Px555BEcP34cVqsVixYtwnnnnZeQn6vQe83Pz0/Iz9Vut+PR\nRx/F119/DZVKhZUrV8LpdCbk5yoFBnAB7777LqxWK15//XUcPHgQTz/9NNasWRPrZkWExWIBAGzY\nsCHGLYmcdevW4c0334RerwcArFy5EosXL8all16Kxx9/HNu2bcPMmTNj3EppDH6vn376KW677TZU\nVlbGuGXSevPNN5GZmYnnnnsORqMRP/zhD3HBBRck5Ocq9F7vvPPOhPxc33vvPQDAa6+9hr1797oD\neCJ+rlLgZYyAjz76CNOnTwcATJkyBfX19TFuUeR89tlnMJlMqKysxI9//GMcPHgw1k2S3JgxY1Bd\nXe3+u6GhAdOmTQMAXHHFFdi9e3esmia5we+1vr4e77//PubPn49HHnkEPT09MWyddL7//e/jnnvu\ncf+tUqkS9nMVeq+J+rlec801WL58OQDgxIkTyMnJSdjPVQoM4AJ6enqQnp7u/lulUsFms8WwRZGj\n0+lw++23409/+hOWLVuGJUuWJNx7nTVrFtTqs4NNTqcTCoUCAJCWlobu7u5YNU1yg9/rpEmT8OCD\nD2Ljxo0YPXo0fv/738ewddJJS0tDeno6enp6cPfdd2Px4sUJ+7kKvddE/VwBQK1W46GHHsLy5csx\na9ashP1cpcAALiA9PR29vb3uvx0Oh9dJMZGMGzcOs2fPhkKhwLhx45CZmYm2trZYNyuiPOfPent7\nYTAYYtiayJo5cyZKSkrc//3pp5/GuEXSaW5uxo9//GNcf/31qKioSOjPdfB7TeTPFQCeeeYZvP32\n23jsscfc03xA4n2u4WIAF3DRRRfhgw8+AAAcPHgQEyZMiHGLIud///d/8fTTTwMATp48iZ6eHuTm\n5sa4VZF14YUXYu/evQCADz74IO72n5fS7bffjk8++QQAUFtbi+Li4hi3SBrt7e2orKzEAw88gBtv\nvBFA4n6uQu81UT/XLVu24KWXXgIA6PV6KBQKlJSUJOTnKgXWQhfgykL/4osv4HQ68dRTT2H8+PGx\nblZEWK1WPPzwwzhx4gQUCgWWLFmCiy66KNbNklxTUxPuu+8+bNq0CV9//TUee+wx9Pf3o7CwECtW\nrIBKpYp1EyXj+V4bGhqwfPlypKSkICcnB8uXL/eaHpKrFStW4K233kJhYaH7tl/96ldYsWJFwn2u\nQu918eLFeO655xLuc+3r68PDDz+M9vZ22Gw23HHHHRg/fnxC/17DwQBOREQkQxxCJyIikiEGcCIi\nIhliACciIpIhBnAiIiIZYgAnIiKSIQZwIiIiGWIAJyIikiEGcCIiIhliACciIpIhBnAiIiIZYgAn\nIiKSIQZwIiIiGWIAJyIikiEGcCIiIhliACciIpIhBnAiIiIZYgAnIiKSIQZwIiIiGWIAJyIikiEG\ncCIiIhliACciIpIhBnAiIiIZYgAnIiKSIQZwIiIiGWIAJyIikiEGcCIiIhliACciIpIhBnAiIiIZ\nYgAnIiKSIXWsGzCY2WxGfX09cnNzoVKpYt0cIiKiiLPb7Whra0NJSQl0Op2o58RdAK+vr8f8+fNj\n3QwiIqKo27hxI6ZOnSrqsXEXwHNzcwEMvIn8/PwYt4aIiCjyWlpaMH/+fHcMFCPuArhr2Dw/Px+j\nRo2KcWuIiIiiJ5ipYyaxERERyRADOBERkQyFFcAPHTqEBQsWAAAOHz6MW265BQsWLMDtt9+O9vZ2\nSRpIREREQ4UcwNetW4dHH30UFosFAPDkk0/isccew4YNGzBz5kysW7dOskYSERGRt5AD+JgxY1Bd\nXe3++/nnn0dRURGAgfVsWq02/NYRERGRoJCz0GfNmoWmpib333l5eQCAf//73/jrX/+KjRs3BjxG\ndXU1Vq9eHWoTiIhiymy1wdhlQZZBC50m7hb1UIKT9Bu3detWrFmzBmvXrkV2dnbAx1dVVaGqqsrr\ntqamJsyYMUPKZhERScpud2B9TQP21DejrdOE3Ew9SksKUFlRDJWKucEUHZIF8H/84x94/fXXsWHD\nBmRmZkp1WCKiuLO+pgFv7jzi/rvVaHL/fcecibFqFiUZSS4V7XY7nnzySfT29qKqqgoLFizA7373\nOykOTUQUV8xWG/bUNwvet6e+GWarLcotomQVVg981KhR2LRpEwBg3759kjSIiCieGbssaOs0Cd7X\n3mmCscuCghzOh1PkcbKGiCgIWQYtcjP1gvflZOqRZeAKHIoOBnAioiDoNGqUlhQI3ldaUsBsdIoa\nftOIiIJUWVEMYGDOu73ThByPLHSiaGEAJyIKkkqlxB1zJmJBeRHXgVPM8BtHRBQinUbNhDWKGc6B\nExERyRADOBERkQwxgBMREckQAzgREZEMMYATERHJEAM4ERGRDDGAExERyRADOBERkQwxgBMREckQ\nAzgREZEMMYATERHJEAM4ERGRDDGAExERyRADOBERkQwxgBMREckQAzglBLPVhub2Xpittlg3hYgo\nKrgTPcma3e7A+poG7KlvRlunCbmZekwtGoGK6YXIydRDp+FXnIgSE89uJGvraxrw5s4j7r9bjSZs\n3d2IrbsbkZelR2lJASoriqFScbCJiBILz2okW2arDXvqm33e32o04c2dR7C+piGKrSIiig4GcJIt\nY5cFbZ2mgI/bU9/MuXEiSjhhBfBDhw5hwYIFAIBvvvkGN998M2655RYsXboUDodDkgYS+ZJl0CI3\nUx/wce2dJhi7LFFoERFR9IQcwNetW4dHH30UFsvAiXHlypVYvHgxXnnlFTidTmzbtk2yRhIJ0WnU\nKC0pCPi4nEw9sgzaKLSIiCh6Qg7gY8aMQXV1tfvvhoYGTJs2DQBwxRVXYPfu3eG3jiiAyopizJ5e\niLws3z3x0pICZqMTUcIJ+aw2a9YsNDU1uf92Op1QKBQAgLS0NHR3dwc8RnV1NVavXh1qE4igUilx\nx5yJWFBehPZOE2p2HsGBwyfR3mlCTubZLHQiokQjWbdEqTzbme/t7YXBYAj4nKqqKlRVVXnd1tTU\nhBkzZkjVLEoSOo0ao/KGYdENk2G22mDssiDLoGXPm4gSlmRZ6BdeeCH27t0LAPjggw8wdepUqQ5N\nFBSdRo2CnDQGbyJKaJIF8IceegjV1dX4r//6L/T392PWrFlSHZqIiIgGCauLMmrUKGzatAkAMG7c\nOPz1r3+VpFFERETkHwu5UELhpiZElCw4SUgJQWhTE9ZBJ6JExgBOCUFoUxPX33fMmRirZhERRQy7\nJiR7/jY1YR10IkpUDOAke/42NWEddCJKVAzgJHv+NjVhHXQiSlQM4CR7/jY1YR10IkpUPLNRQnDV\nO99T38w66ESUFBjAKSF4bmrCOuhElAx4hqOEMlAHnV9rIkp8nAOnmGHVNCKi0LGrQlHHqmlEROFj\nAKeoY9U0IqLwsbtDUcWqaURE0mAAp6hi1TQiImkwgFNUsWoaEZE0GMApqlg1jYhIGjxbUtSxahoR\nUfgYwCnqWDWNiCh8PGtSzLBqGhFR6DgHTlHF6mtERNJg94eigtXXiIikxQBOEWe22rBm8yfYfuCY\n+zZWXyMiCg8DOEWMq9ddW3cCbZ1mwcfsqW/GgvIiJrEREQWJY5cUMa6a576CN8Dqa0REoZK029Pf\n349f/vKXOH78OJRKJZYvX47x48dL+RIkE/5qnnvSatSsvkZEFAJJe+A7duyAzWbDa6+9hjvvvBMv\nvPCClIcnGfFX89ybM+JtISJKRJIG8HHjxsFut8PhcKCnpwdqNec1k5W/mueezBY7h9CJiEIgaYRN\nTU3F8ePHce2118JoNOLFF1/0+/jq6mqsXr1ayiZQnHDVPPfc91tIblb8bGBittpYGY6IZEPSs9Sf\n//xnlJWV4f7770dzczNuvfVW1NTUQKsVPkFXVVWhqqrK67ampibMmDFDymZRjHjWPG81Cg+nR2MD\nk0CBmWvUiUiOJD1zGgwGpKSkAAAyMjJgs9lgt9ulfAmSAc+A6ap53t5pQs3OIzhw+GTUNjARG5hd\n2fIuXKNORHIgaQD/yU9+gkceeQS33HIL+vv7ce+99yI1NVXKl6A45i9gjsobhkU3TBbsDUdq6FpM\nYPaXLc816kQUzyQ9M6WlpeG3v/2tlIckGRETMD03MAln6DpQ0BcbmP1ly7vWqHPDFSKKRzwzkSRC\n6cmGMnQtNuiLDcyubHmhOfqczPhJsCMiGowZOiQJMQHTU6CA72u3MlfQbzWa4HSeDfrraxq8Hudv\nGZtnYHZlywuJRoIdEVGoGMBJEv4CZuYwLVJ1Z+e7m9t70XKqL6iA73qu2KAfTGCurCjG7OmFyMvS\nQ6kA8rL0mD29MKIJdkRE4WL3goImNP+s06gxtWgEtu5uHPL4ji4L7nthB9L1Kejus6L9tBk5mXro\nNCqYLENXKfgaug52vtpzGZu/zHeVSunOluc6cCKSC56lSDRf88+3lhfh5a2HceDwSQCAUgk4HN7P\nbTWavOaZ23ysCwd8D10HO18dbGD2TLAjIop3PFuRaL6Szuq/aseRE13u2wcHb3/0WjXS9WqcOtMr\n97c23F91N3/z1QzMRJSIeFYjUfzNPzc2dwneLobFasOzVWXQpqhFDV2LHRYnIkp0DOAkir/5Z0cY\nG4ppNWrkZeqRqteIejznq4mIBjALnUQRu7tYsEwWGza+/XnQzxsYFk9j8CaipMUATqL4W5bli0qp\ncC/Nys3Sw1dxNX/rvomISBi7LySa5/xzW+dAIRV/nE4nHru9FNoUFSz9dty96j3Bx7FkqW/c4pSI\nfOEZgURTqZSYP+t8dPVYcOg/bTD2WP0+PidTj/zhqdBp1DBbbSxZGgRucUpEgTCAkyiugPLOvqMw\nWcQNd3su7Qp1CViy4hanRBQIL+VJFFdA8Re8FYqB/8/N1AmWImXJUnFCrRNPRMmF3R4KyF9A8VQ2\neSQWXHuhz/laLgETh1ucEpEY7IFTQP4CiqfPvzGKCspcAuaf2J3UiCi5MYBTQGJ7yr52EaPgcItT\nIhKDZwISKXC5NfYOpcOSsUQUCAM4BWTssghu+znY1KIR7B1KhPkCRBQIh9ApIENaCvRalc/7lWe+\nRfs/bcG6LXWw2x0wW21obu9lxnSYmC9ARL7wrEABvfL253574K7tQ9s6ze7tRXtM/SxAQkQUQQzg\n5JfZakNt3YmgnuO5NzgLkBARRQa7ROST3e7Ams2foK3THPaxWICEiEha7IETgLObZqTq1Ogz25Bl\n0GLD1sPYfuCYJMdnARIiImnxbJrkXDXOa+tOoK3TDKVyYE47N1OPHpP/zUpUSgXsjsDLywAuMSMi\nkprkAfyll17C9u3b0d/fj5tvvhlz586V+iVIQoM3zTibkOa/8pomRQFrv7jgDZwtQMLtMYmIpCHp\nGXTv3r34+OOP8eqrr8JkMmH9+vVSHp4kJrbGuRBrvxPDDTqc6vI/Pz48Q4fLJ43EreVFWLeljttj\nEhFJRNIAvmvXLkyYMAF33nknenp68OCDD0p5eJKYscsiuD+3GHlZekwtGoGtuxt9PibboMVv77sK\nGelarNtSx+0xiYgkJGnXx2g0or6+Hr/97W+xbNkyLFmyBE6n+GFWih673YEtO750bwHqi1op/IDS\nkgIsnDMRs6cX+izyUjb5HGSka7k9JhFRBEjaA8/MzERhYSE0Gg0KCwuh1WrR0dGB4cOHCz6+uroa\nq1evlrIJJNL6mga/vWeX7182Fg6HE3vqm2HssiA3y3vo+445E3HLrPOxdks96r9qR5vRhCyD1qtu\nN7fHJCKSnqRnzYsvvhh/+ctfcNttt6G1tRUmkwmZmZk+H19VVYWqqiqv25qamjBjxgwpm0WDiJ37\nLr9sLH46uwQqlRK3VRT7TD5L02tw901TsHZLHfbWt6Cj24wDh09CrVKisqLYvT2m0HA9s9OJiEIj\naQD/7ne/i/379+PGG2+E0+nE448/DpXKdw1tig2x+3tfe9lYd4LZQE1u31+XwT36wXPcpSUFXnPg\nLsxOJyIKjeRnSiauxaASZSUAACAASURBVL8sgxY5mXq0BUhgc4hc4x1ojntBeREqK4rhcDqxbf9R\nd111vVYNm8OBl/7+CfY1tDA7nYgoCDxDJqnx5/ie2nB5e883oo4lZo5bpVJCqVB4bYpistiw9cNG\n/HPX12g1muB0nu25r69pEPdGiIiSFAN4EnBt7dlrsmLdljrc+ex27G0IPAd+4PBJd4a4v+1BXXPc\nQlxz3MGuOWd2OhGRf5xsTGCDy6TqNCqYrb63BR2svdOE9k4T3trd6LcAi06jDjjH3dzeK2re3fO1\nmZ1OROQbz44J7I9v1uOfu752/x1M8AYGes81O48IJqfZ7A7MufI8d9KZa8nYnvpmtHeakOMR6AH4\nzUT39dqJnJ3e2tGH+iOnUFI4HHnZqbFuDhHJEAN4gjJbbdi2/2hYxygam40Dh08K3vevPY14q7bR\nq0d+x5yJWFBeJJhN7q+XLsTVc080JpMVP125DV29ZzeKMaRp8MeHZ0Cv18SwZUQkN5wDT1Atp/q8\nEsaCpdeq8MOrxvsc9nY4IJh0NrDcLE0w+FZWFGP29ELkZemhVAyUY72ubByuKxvnddvs6YXunnui\nGRy8AaCrdyCoS8lfzgIRJYbE6+LQGeGVsJ057VyckzdM9LC3a7mYv16zq3KbUC/91h9cmPDrwFs7\n+oYEb5euXitaO/rCHk535T1w0xiixMdfdAKy2x14S0SZVCFK5UAFtsqKYvewtxiupDMxhHrp/nru\niaL+yKmw7hfDtT0sl+URJT4G8AQkts65EKcDmHPleVCplDBbbbj2srEov2ws8rL0UGAgwAtJ9KQz\nKZQUCu8JIPb+QLhpDFFySdzuThIyW21oOdWH2hD3+AaA3Cw9DGkpQ/bunlo0AhXTC4dkpbskatKZ\nlPKyU2FI0wgOoxvSNGEPn3PTGKLkwl9zAvCc9wx1f2+X0pICvPL250P27t66uxFqlRIL50yEWqX0\nuVyM/PvjwzN8ZqGHi5vGECUXBvAE4Jr3DJdeq0a/zY6PPmsVvP/DT07gpmsm+F0uRv7p9Rps/PW1\nEVkHLqagDhElDv6iZS7YEqX+mCw2vFXru/75qdNm3L3qPZRNPgeVFcUoyEmT5HWTUV52Kq6OQAGX\nQAV1iChxMIDLnNitQYOhVA6s8xbS0WXx2iaU4ou/pXpElFiYhS5zWQYtsocFV8FLq/H/sfsK3p5i\nmdXMIiWBJcOyPKJkx1+3jNntDrz8fz9FR5dwcRAh5ZeNxS2zLsDdv3kPHd2+122PLRiG7r5+nDpt\nFrw/FlnNLFISPLPVxp44UYLiL1rG1tc0eG1W4o9KCfzg8oESpa1GE4w9/ouu9JltWP6zy/DwH3bh\ndM/QC4RYZDUPTtZzFSkBOJw/GC92iBIff8kyZbbaglrvnZGWggXlRVCplH7373ZpNZrw2Iu7BYM3\ncDarOVrD2SxSEhxWZCNKfOyBy5Sxy4L2INZ8d3T3o73ThFF5w0TvDHaqa+jweV7WQE/u1vKiIcVe\nItnDY5ES8QJd7ASqWU9E8sAeuExlpKVAkxLcx7fhrcPunmplRTGuKxuHYGLt8Awdnl98Je6YMxEv\nbz0c1R6ev1EDFinxJuZih4jkjwFcBgYPU9vtDjz0hw9h6ReRLu5h9yfNWLD0Lbz0908AAEqFAvYg\nDmHsMqPPbEOvyYp39gmvF4/UcLa/jVUmjs+R/PXkjBc7RMmB42hxzFciUq/ZisYTXSEd02x14J+7\nvobD4cSBwyeDeq5Wo4IhLQVrt9T73Gvcczhb6gxozyIlbUYTdFoVAAW2f3QMdV+1M0nrDFZkI0oO\n/CXHMX9Z1+HaU3cCHd3il58BgMlix1+2HkbdV+0+HzM8Qye4GYoUwdWzSMmazZ9g+4Fj7vuYke6N\nFdmIEh8DeJySskSqkI5uK5QKwOEM7nl7G1p8rg0HgEnn5QpuhiJ1cK33cRHBJK0BrMhGlPiSe6wx\njkWiROpgwQZvYGAePNvHHKpeq8at5UWSL/canAMgZZJWold1Y0U2osQVkV/1qVOn8KMf/Qjr16/H\n+PHjI/ESCS/LoEVOph5tYWwPOjI3DSfaeiVs1cA8eJ+5X/C+mdPGwNzvkGy5l68cgFtmnR/2tpks\ndEJEcif5maq/vx+PP/44dDqd1IdOKjqNOuzs6hNtvRg70gC99mzADFQHPSPdf111k8UOs9U7dV2v\nVWP29IEqb36LxCiALTu+hF1k6ruvYiSvvP25z4x0sUlaLHRCRHIneQB/5plnMG/ePOTl5Ul96KRz\na3lR2MfoM/Vj3SPXoHrJVVh1zxV4atHlyM0SDrB5WXo8f8+VGG4I7uIrXa92V3nzt9zL4QC27m4U\nFSQDFSOZP+t8zJ5eiLwsPZSKgba7LiLCPXaiDqcTUWKRdAj9b3/7G7KzszF9+nSsXbtWykMnpWDX\neQtp7zShu8+Kd/YedQ8X6zQqwceWlhQgLzsVl08eGVS2+6nTZq+h8cqKYtjsDvxrT6PgzmZiEs0C\nzXOf7u0POUkrElXduGkIEUWbpGeazZs3Q6FQoLa2FocPH8ZDDz2ENWvWIDc3V/Dx1dXVWL16tZRN\nSAiuYOBrrjkYwzN0qNl5BFt3N7pvc63h1mvVsFht7iVGt8w6H83tvbhl1vkAgNq6E2jr9J1x7uI5\n7+xq+7WXjfV6TU9igqRrKD7QPPdAklZwX2OxxxaDc+lEFCuSBvCNGze6/3vBggV44oknfAZvAKiq\nqkJVVZXXbU1NTZgxY4aUzZINu92BtWfWT3dIVO6yu6/fa720pzSdCs9WlSEvU4+Nb3+OqlXvo91o\nwvBMHQypGjhFZqmXlhQgRaX0Wvudk6GDXquGyTJ0OFpMkIxkMRIpj80d0ogoVjjWFyfsdgfue2EH\njoRYYc0Xs1W4YhoAtJ+2QJuixoZ/fea1LWl7pxntInre2QYtyiafg8qK4iGBzF/PXWyQjGQxEimO\nzU1DiCiWInZ22bBhQ6QOnZDWbqmTPHgHogDQZ+7Htv3CPXR/soZp8VzVFcjLTvUbyPRaFYalakIK\nkpEsRiLFsblDGhHFEs8ucSDSVdd8cQJY/N87QnqusduCh/+wC6UlBbj2srE+A5nFasezVaXQpqhC\nDsChzHNH49hSzqUTEQWLWTZxoL3TJNmcdzS55ntrdh7xu/tV/vDUhKwG5m/JHDcNIaJIYwCPAzVh\nbFByxRThABJNBw6fxNSiEYL3SRnI4rHsaWVFccjr0YmIwsEuQoyd7rGENXxef6RDwtaEpr3ThIrp\nhVCrlBFJOIvnpVrcNISIYoVnmhjpNVmxdks9Dn7RGtbweSSG3nMzdUjTp6CxuVvU4zOHaTEsVSMY\nyMxWG1qNprACmxyWakVynp6ISAjPOFHm6k2+s+8bd0GVeFQyPgc2uxNNrT0BH9vRZcF9L+xw94oL\nctJgtzu81oVnD9Ph0pJ8LJwzUVSv2VUQJlWnls1SLVZjI6Jo4lkmygb3JuNRW6cZ/9z1NWZOGy0q\ngANDe8WD3+epLjO27m7EZ40deH7xlT6DuGtkou6rdrR3mpA1TOtzlCESS7U8Lxz6zDZRwTieh/iJ\nKHExgEeR2WpDbQyWi4Vq/6cng37Onvpm3HTNBJ+95iMnurB2Sx0W3TDZ63ZfIxP+pgikXKrlGYRb\njSYolQObr2QN02DahfmYc9V5yMnUCwZzOQzxE1HiYfcgSux2B17c/ElY+3tHW2ePFYogn9PeaUJj\nc5fPdeGA945frszytVvq8ObOI0FNK0iZ4e65vSgA9yYsxm4r3t57FIue2Y47n92OdVvqvLZD5c5m\nRBQr7IFHyfqaBmzzUZM8noksh+6Wk6nH2AIDsofpcKpLuJxqR5cF7Z0mvLW70T3srBBxpZBt0KKz\n2yJphjsgvpCOUM+a1diI/j979x4eVXXvj/89l8wlCblAEhPKJQZFgYCKiOBPvBR9sLRQrH7bI/7w\ngtXq8QQpFms9aD312lp79ISKiqWnh+qp9thS8EtLJSpKuatAEjEehQAhCclA7pn7zPePYYaZyZ49\neyZ7Zvbe8349D48m2bNnTWayP3ut9VmfRZnCHngaOFwe7KxvzXQz0mLmlHIU5psxY7LwunAA0OuB\nDR98Gerx+v0Q3HZ06ON0uPbSsah98BrcLTEZTgqxICwkvGcdrMYmhNXYiCiVGMDTIBAg4m8Ooibx\nYueiqyfE/JnPB+w9lPj8uq3bgbp9x/H6lqaEHytGLAgLtyPQswZYjY2IMocBPA1yLUZJQ8RqUpgv\n3LPc09gOx5k9xkuLLILHjCyInVkuhdxzy2JBWEhJkRVOtzfUBlZjI6JMYPcgDQYdHsl7a6vByAIz\nuvqEA3BHlx0vv30QNd+9GLOnjo655/a+QycFNwGRIvw55BpGX7pgClo6B/DJ5/FHBvoGXVj2/PsR\ny8VYjY2I0o098DRwujOTiVyQZ5J0XL41sWAzq7pCdMi5bt9xrNvUGLNnes+iqQn1eMWeQy4Ggx4r\nbrlE9BiLyQAAsDu98PvPJrUF2xGoxqa9TVuISJl4pUmhvn4HVq7ejhOdAxl5/uoJI7HjYLvoMQYd\n0G/3wGI2AH7A6faKjhbMnTEWt8+fBKfLK5pV/4+DrfjudRNj9kyDw8vBLPRkRiiSqcQmVi2tMN+M\nyvJ8NLcPLV5TMcoKj9cPh2voMrfg2nephV+IiOTAK00KBIuCvPOPw5Kyq1PBajZIWsPtPRM4HRLX\nX+t0OtQ8/0Hc4e9TPQ4se/59XHnR10LlVcOFbwJyoqMPD/96Oxyuob8ss0kPp8D3gcSWaUmtlvb8\nA1djZe1HONzaG/pe1egCrLh1Omp++YHguTu67Hjg+Q9wus/BKmxnsKwsUerxLysFXtvYgHe2H8lo\nGwx6Hf4Rp/edjK17j0k+9nSvMzQHHmt+2GIy4r19LYLBGwDmzhiHvZ+1C2bxJ7JMS2q1NJPJiBcf\nvBY9/U40t/WisqIAhflmOFwelBZZY964BNe8Z3sVNpaVJUofBnCZOVwe1CUQ5FKl366cCmDv7jmK\nnWe2GY2+oIsVUbGajbh9/iQYDfqYyXBSenfxqqUJDcMX5ptx0fmloa9zDHrkW3MkJ94pbaOVdGFZ\nWaL04S2xzI609ip6l7FMsDu96DxTsCU68UusiIrT5UHPgDvuMq1gOdZYS8ukVEuLZ92mxohh9Xik\nnldLWFaWKL2yq3uQQl6vD69tbMDmf2R26DxVRuTmwGI2ylbLPdhDDRZREerZBofIw+fLw4fho7cs\njTVcK+U5ooXP4QbbKyS46YnU82oZy8oSpRf/mmSyblNjxue9U8Fk1OOqS8bgX/7PRbJuhWrrtqP9\n1ADMOUbMmHQONu9oHnJM9BB5YJnW2a9jDdd6vL6I3c6ChVqkDMMLzeFOnVASMzDFSlLMxipsydwo\nEVHysusKkyIDdhf+vrs5082Qncmoh8vjw8EvO7FuUyNunz8JHq8PuxvacarXEbP3KYXZZMBjr+xA\nV58LZcVWVI4uQLutP5TMZjUb4fP74fX6BJOfBu0uvLtHONfgb7uaAQD3hNVLD1+2Zuu2Y1ShBdPO\nK8Wt8y6IeKzQTUHdvuOwmo2wO6UNAVeNLpBchU1L2dqJ3CgR0fDxL0oGr25oiJlFrWYuT+A1BXu2\nnzZ1wOHy4FSvAyMLzJg+sRSTqkah9q0DCZ/b7vSGcgU6uuxAVK/N7vTgne1H4PP5h+wdDgR+57EC\nqs+HUI8++NjgMPzieRfg1Q0NqP/Khvc+Po76r2yhYXe31yeyK5n0her9djfcMW48grSarR19oyT3\nznFEdBYD+DD19Tuw7RP1bROajOMdZwucnO51Yuu+Fmzd15LS5xTqTTtcHhz8sjPuYzfvaEa7bQDL\n/+kSFBda4XB58OqGBrwXVoAmPEv6W1dWxRwqdzi9+PqMsWj4ygZbtx3FBRac6hHeoEbKfK9Ws7Vj\n5SsQkfz4lzVMP6r9CF7tdb4VI9ibNp4JDA6XB01HuyTv7vbJF5247Wd/x4jcHJiMOpzqdQkeF6ym\nFmsOt7TYivtumgYgkKyVazFixQvbkprvTWZZm9pE5ysQkfxk/Qtzu9145JFHcOLECbhcLtx3332Y\nO3eunE+hGF6vDy/+4RO02gYz3ZSssLO+FR6vD/sOnURntz3h+fe+Qbfoz23ddgw6PDHncHPNxtCQ\ncLCqXLLzvczWJiI5yHqV2LhxI4qKivDcc8+hq6sLN954oyYDuNfrw/IXtqE5gXXBNDyd3Y6ITHW5\nd3czm4wozMvB0gVT0PCVbcia7+b2Ptz38/dQVnx2TjfZ+V5maxORHGQN4DfccAPmzZsX+tpgMMh5\nesV4dUM9g3eaDSfjXQq704PXtzRhyfxJ6LfH7q1Hz1UnM9/LbG0ikoOsV4q8vMDQYn9/P5YtW4bl\ny5eLHl9bW4vVq1fL2YSU6+t3Cq5ZptRKx6YwuxracP3l42MOb0cfG5yrTma+l9naRDRcst/qt7W1\n4f7778fixYuxYMEC0WNrampQU1MT8b2WlhbFDrt7vT7c/fS7mW5GViktsiDPmoNjJ/tSHsRt3XYA\nftFNS8KPlTpXLbTWm9naRDRcsl4xbDYbli5disceewyzZ8+W89SKsOZPBzHAOudpowMwpaoEH3wi\nz1K1ojwjegY80ImUPy0flRdzeDv62Hhz1VLWeislW1tLBWWIsoWsf6kvv/wyent78dJLL+Gll14C\nAKxduxYWi0XOp8kIh8uDHQdPZLoZWaWkyILGw6dkOZfZpMfqh67DoMODDdu+FC3dGj68HasnLmWu\nWspa70wHTq0WlCHKBrJeMVatWoVVq1bJeUpF8Hp9+I83P0HfIHdTSqfJVaOw7RN5bpq+fulYmE0G\nDDo8oS1KY80/hw9v27rt2PTRYew7dDKhuep4a70Xz7sAb2xpynjg1GpBGaJswLEyCV7b2ICP9scq\nsUmpMLYsHw0Sqq1JUVkxAgaDHvf/4r2IYPkfD16D3gF3zN6vxWTEmLIRuO+mixLuKcdb6y1WES5d\ngTMbCsoQaRnHyOLo6hnU5C5jSmY1G3C8oz9m1TQhuhjfrywfgeoJJXhn+xF0RO1J/saWJlSU5Ele\n+iX1WODsWm8howotqP/KJvizdO6bLcc+6USUOQzgIrxeH27/GbPOU+GOb07CuPJ82c6n1wuH8AGH\nG7sb2wV/lspgGVzrLWTaeaVnMt6HSmfgFLvJYEEZIuVjABfxq9f3JrAHFSXiP//vIZw8NYjw6V5z\njh5XXfK10C5lifD6hN+pzm4HbDES0aQGS4fLgzbbAHr6nWizDUgO+ksXTMHCOVUoK7ZCrwPKiq1Y\nOKcK9yyqVkTgFLvJYEEZIuXjX2gMXT2D+PCAcM+N5OF0+4Z8/eGnJ2A0AB4ZV+vFugmLFyzDM7Q7\nus7WXy8tsmD21NFxE87E1norpRIbC8oQqRcDeAws2JI5cgZvMX2DLvx2UyMWzKlCSZF1SOCMztAO\nrh3v7HYklHAmtNZbKYGTBWWI1It/qVG8Xh+ee2MvnFwxpnl2pxebdzRj847miE1KDAa9aIZ20HAy\ntZUWOJVSUIaIpOMceJTXNjbgH/s5dK5U5pzUfGSDmenrNjUCEM/QDn9MrGQ0qRLNbiciCmIADzNg\nd3HJmIJZzYaYyV9yCWami2Voh9sUp+QqEVGqMICf4XJ5sPSJv2e6GSTC7vSipXMAxhhLxuQQzEwX\ny9AOt/ez9rSt2yYiCscAfsbyF7dhkBuVqII/hYv7wjPTly6YgvlXVKIoP3ameme3Y9jrtoPL1Hgj\nQESJ4MQbgM7TAzje3p/pZpBEXoGdxHQ6wO9HaKmX1WwE4IfD6UVxgRl2p0fS+vLgMq7gErJ9h06i\nuz92gNbrgVxLcn9G3EiEiIaDARzAD//9g0w3gYbJf6ZTHlzqde2lY3Dngino6nXC6fZg2fMfxHys\nToeI4AkMXUIWi88HDDo8KBTppcfCjUSIaDiyPoAfOtKJHu4ypjn7Dp3EnQumoKIkDw6XB6VFVsGt\nQUuLLHjs+7NQPupsJnhPvxP/ONAq6XnKipOrnCa2TO0fB1vx3esmJnVTQETZI6vH6Y61ncZDq3dk\nuhmUArZuO9pPDaDNNgAAmDmlXPC4frsb7+4+hhyDHl6vD2s31GPZ8+/jVK9D0vMkWzlNbJnaqR4H\nlj3/PtZuqIdXaL4gBs6lE2WXrO2Bu1we3P/LjzLdDEoRs8mIn722C7YeB0qLrMiLMU9td3ojhrGl\nDJsDgbnvG2ZVJl05LbhMTWhUAABO9zolD6dzLp0oO2XlX7fX68PNP/m/mW4GpZDd6UFntyO0feiR\ntj7R43c1tGFnvbRhcwC46uIxuO+mi5IOkFKXqQXXpYv1roNz6dHbpQaL0hCRNmVlD/zxV9/nLmMa\nZtALZ6qL6ey2hxLh4rGaDbj3O+K9YofLE7dEarD3/o+DrTjVIzxk39Flx4p/3waHyxMaTZBa8nU4\npV6JtELK36JaaevVSPD50U7s/5JLxrQs0eANBLLQ/X4/Orvjz31fP3M88qymiO8FLxIFeTl4Y0uT\npOHsYD307143Ecuefx+nY6wnP95x9vManakuNpceLErDGueUjbJhaimr/rI7T/Vi5X8waY2GCg5n\nC82BW80GOF1ewR3Doi8SFpMhYr252NKw8J7BlRd9TfL8O3C2dy02l57OvcW1Qsu9tWyTDcs0s+oT\nuvTp9zPdBFIgc44ePr8fd35zMgBgZ30rOrsdoaIw+dYcXDF1NO5ZVI3cqJ539EUiVrGY8OFsoZ7B\nzCnluObSMfjg4xZJbT7bu85TzN7iapYNvbVski1TS+p/BRI9VPvXTDeBFMrp9uGd7Ueg1+lw96Kp\n8Hh92LyjOWL/77p9x5FnzYm4cx+wu/DunqOSniN8OPvVDfXYvKM59LOOLjve2X4E86+oRGmRRdIw\nfnTJVyDze4urWTb01rJJtkwtqf8VSLD6zb041OzKdDNI4XbWt+Kma8/D+x8fF/x59J37qxsaJJVn\nBQIB1+50Y/Vbn+LdvccEj9l36CQum1weEdxjCe9di+0tziHh+LKlt6ZWyXyGs2VqSfOfyhMnu7Bl\nj/TlQZS9Orsd+PHq7TGDcvidu8PlQf1XNsnn7ht04YFfbRM9xtZtx4I5VTAa9KHe9MgCMxwuLwYd\nHvjO1HqvLC/A7fMnDXl8YG/xwJ90vCFhh8uD9lMDAHQoH5UbcWFM5oIp141CJm445O6t8aZJHsOZ\n1ggu09T61JI2XkUMXq8P9/7iw0w3gxRCB8RdPth+ejDmz0YWmOF0e9DT78QXx7vQGaMICwCYcvTw\neHwwn0lqk9JTLy6wYESuCXcvmorF8y7AqxsasKO+FY6wx/p8wOHWXvxu86HQ0G54wAACAWnDti+H\nDNNv/OgwfGfWytXtPQ67M7Cm3Go2YO5l43DnNyfjd5sPJXTBlGvuOJNz0MUFZpQUCk9djCq0SO6t\ncR5dXsOd1rh9/iQ0fGVDc3svfD7xm1+1kjWA+3w+PP7442hqaoLJZMKTTz6J8ePHy/kUCVn00KaM\nPTcpz3DX/vcOuFDzyw8kHety+2DK0UseYgcCJVR/+MI2zK6ugM/vx3v7hIfygcDQ7q3zLsDrZ5as\ndXTZYTUbAOjgcHmgi7FlenjgDrI7vXhn+xF8dvgUDrf2hr4v5YIp19xxJuegLSYjRuSaBAP4iFyT\n5N6a1NfAHnp8ckxr/G7zoYjPs9DNr9rJelu4detWuFwuvPnmm3jwwQfx7LPPynn6hLAeNMnN5Uns\nFsDlTnxBeueZi35djHnyoI4uO17+c32oAhuAMz19D/z+s7uyRYsO3uGa23oFvx+sBhct3kVW6t+g\nXOdJlsPlQb/dLfizfrtb0vNLeQ3BWvv3/+I9/ODZrbj/F+8lXO8+W0iZ1hCT6c9UusgawD/++GPM\nmTMHAHDxxRejoaFBztMn5P+wVCqpmJSe+8564QtUsnwx7k9iXTCHe5GV+zzJkuP5pZyDJW+lCyah\nCZGShJbpz1S6yBrA+/v7kZ+fH/raYDDA44l9p1NbW4sLLrgg4t/cuXPlbBKRZjlc0ofngyxmQ8yf\n6WMMu8e6YA73Iiv3eZIV7/lzLca4u7xJOUc29AjlIrZXgJQktEx/ptJF1gCen5+PgYGB0Nc+nw9G\nY+xfdE1NDZqamiL+1dXVydkkoqym1weS98qKrVg4pwrXXTYu5rGVFQWC3491wRzuRVbu8yRL7Pnz\nrTlY8cK2uEPe8V7DoMOTFT1COS1dMAUL51ShrNgKve7sZ1hKfYNMf6bSRdZXMX36dLz//vuYP38+\n9u/fj4kTJ8p5eqKsYTHpUT4qD80iu6hZzYa4Q+03zKrEoqvPCyVMBYNPvCx0qQVh5Coik+liNELP\nn2/NSSipT+w1uL2+rFiXLCex+gZSZPozlQ46v1/qHkzxBbPQv/jiC/j9fjz99NOYMGFCQudoaWnB\n3LlzUVdXhzFjxgyrPQse/MuwHk+UKd+68lx8f2E1Xt1Qj7p9x+EUGC7/1pXnQq/TBZYtddnPDI/r\n4HR5Ii5WQkuYuA5c/PlzLUaseGGbYMAtK7bi1w99PWb7Yr2GtRvqBdclL5xTpZmsaCXK9GdKqmRi\nn6yvRq/X42c/+5mcpyTKChZTcMMUC2ZPHR0KvPfddBFunz8Jr25owMEvO3GqxzEkOIf3UABEXKwc\nLg+Od/QhOlBbTEZUVhTGaIsx4TKTyTwmlecZ7vO32QaSLu4S6zVkQ49QiTL9mUolbb6qMzY9/20A\n7ImTcun1gWHu2+ZPQu+AW7CXkGs1Yfkt0wV7EkLfqygJDJW/8ueDgkPl319YzcIicaSiFOdwh4SJ\nomXFpycYyAHgwBcdWPXKzgy2hpSmMC8HPQPC64CTYdAH9iQ35ejgcovPUN0wqxL33XQRAAzZYzxa\nIqVS121qxDvbj0Q8PliwJbhpC8WWylKcWu4RZgulDMtn3afooollEQEdALZ9fAy/fOPTDLWIMsmg\n12HW1Aps2SVeIBVv4wAAIABJREFUOCURowqtmFw5Eh98eiLmMVazAdfPHD9k+FTqhUGs6teS+ZOw\nM8aSJSCwaQs36IiPQ94UTWnlcvkXDODqS8fh6ksjl9ccP9mHx1/Zho6exNfaknp4fX7sqG+H1WwU\nrVKWCFu3HQdjbHSi1wFXXfI13PudaRE97kQuDPGqTF1/+TjYROq027odmtlOMZU45E3RlLbtLD+N\nMYw9ZwR+89i3Ir7XcXoQf9r2Bd7deRRJ1NAgheobkHer2eIRFpzqFd7T2+cHrps5fkhQTuTC0H5q\nUHBuFsCZDVZ0KCm2xtxspaTIEipOwqAUH4e8CVDmtrP8VCagbGQu7r3xYtx748Wh7x34ogOvbzmE\nE+3diHHNpiwzIi8HBoNOMMjq9cCjr+yI6GG7vT5JFwav14fXNjZgq0iddIvZgPJRuZgdY/4WCGzQ\nseKFbYoYAgynlHlFIiFybzsrB/6VDNNFE8tw0cSyiO85XB4ca+/FVy1d+PzwKRzt7MeRE30xa02T\ntgzY3bhscnnEdp5BwU1GwnvY37qyStKFQSgxbahAPdSlC6bA5/cPyUIvH5mb8I5jqaa0ecV4eKOR\nnVKxMmG4+OlLAYvJiInjRmLiuJH4xhVnC9n09Dux97N2NDWfwlctPWjvGsDAoBfci0hbbN0OzKou\nBwDsO3QSHV126HXCm4XsamjDd6+bGPfC4HB5sLO+Ne5zO88El4qSPPzgxmm4/ZuTQwVbikeYseKF\nbYKPy9QQIBB7+mDA7sa9N01TTJBU240GySuVKxOSpYy/jCxRmG/GdTPH47qZkXukO1we7P+iA9s+\nPo7m1m602DgWr2Y6HfDTtbtQemYTi4JcI3oHhRPkbN12NLf1YsakcwR77MELQ6CwSPzPxahCS0RP\nILxgy3CKk6SK2Lxi3b7jOPiVDbMVEiSVlsBE6ae0lQkM4AoQuLMbjVnVo0PfO/i/J/HUb/diUMK2\nkqQswZ52rESzCDpg1cs7UFpkQdXoAvTb3YIXhsDwnSVuEB+Ra4rZE1DiEKDYvCJwdn90ILNBUokJ\nTJR+SluZwE+cQk07/xy8+fS30NPvxP4vOvB582kcaetFnsWAPZ91Zrp5JJPgnHhntwOd3Q5cPf1r\nuPnrE4fUJ7eYjJg9dXTMxLSgfrsbDpdHdPcwJQ0Bit1UhMt0kFRiAhNljlJWJmS+BSSqMN+Mq6eP\nxdXTx0Z8//jJPuysb8Poklx0dg/iv7c0we7ibLpa6AAI5TRu++QEPjtyWnDYOJiY9u6eo3DGeK/j\nBZNgj35nfSts3Y6I2uuZIHZTES7TQVKJoxdEDOAqNfacERh7zojQ1zdeMxE9/U40t/WisqIAHrcb\ndz1dB4Gti0kBxBYkxBo2Nhj0+MGN0/BP11+AZc+/j9MCe0ibTUYU5uXEb4BOF/nfDAq/qYg1RZDp\nIKnE0Qsifuo0pDDfjIvOLz3zlRkbnvt2oKfe0IaKURYY9EZ4fV6sfvMTDMpbu4QSYNDr4JWwpjDW\nsHFhvhlXXvQ1wWBid3rw+pammPPF0YlYSphjDp9XXPP2Qby37/iQY5QQJJWWwETEAK5x0T11AJhz\n8Vj09DvxefNpfPBxC/Z+flJwv2lKjRyjHl4Jv+/gsHFxAYYkzCyedwHe3XMUdoEkx1iBX+mJWBaT\nEcu+ezHyrTmKDJJKS2Ai4qcvSxXmm3F5dQUur66Aw+UJrRU26HX47MhpjCo042+7mrG74WSmm6op\nM6eUY09ju6RjS4qs2LDtS+z5rD00X33Fmfnq3gE3HDFuAmLNF6shEUsNQVIpCUxE/BRSxFphAKEe\n+4xJ5aGqcl19Lnz4yTFs39/GwjNJspgMONzSJfn4XIsxYm24rduBjR8dhsfnw53fmpJwUpWaErEY\nJIniY/kgEhWsKnf5lHKsXDITbz7zTTz7z1egpMiS6aYpwlUXj8YNs8fDajbEPdbh8sLWMzTxLJrV\nbMT8KypxrL1P8Odbdh4FEJgXFhL8fpttAA7X2QIywUSsWI9RWk+XiMTxL5YSYjEZMWVCKX776LyI\nrHezyRAahi8tsuCNLU2iWcVqV1YcWUbzroXVONbeiyd/sxtd/YllCOr1gfXgJYVmXHR+Ge5ZVI2W\nzn7BymxAYAvUY+29uH3+JDR8ZUNzey98vsB5xpUXwOPz4f5fvCdY7pOJWETawQBOSYvMekfEMHz4\nPGauxQhbjx1/ev9/sfezk6HEK4vJgGsuHYOuPodq5tr1OuBnP7gCF4wvHlJoZUSuOeHgDQDP1VyF\nEbmmiPnengG36GN6Btz43eZDERuT+HxAc2svmkU2K1HDHDMRScO/XEqZ8HnMwnwzVv7/l0UkzAWr\njQU3iQj22EeOMKEgz4zmGEPImVRSZB0SvIOkljsNp9cD54zMRWF+5PzzxLFFoo8bf84IvPz2AcnP\nE51lzjlmIvXjXzClVXTCHCCceZxj0Id2frJ122E2GUPbYsaj1wFlI60YXZqPI6296OlzoiDPjKnn\njYLFbMS7u2Pvpx2P2Fyx1HKn4Xw+YNDhGRLAC/PNqBxdENGbDqocXQCvzy9aQzyaUrLMiUg+/Gsm\nxYjuFYYH9cK8HLy+pQm7GtrQ0WVHrtmAghEmuFw+nO51YmSBGdMnluKq6WNQ9bWiUECM3rvZ6/XB\najKG3RgY4PP74XT5kGME3FH3CMFtQMPnvMWc3Yf7mOAa7WilxbGzv5+vmYOVtR+hua0XPn+gLZUV\nBXiuZg58gKQa4kFKyzInouFjACdFCw/qQnO30QFa7PGAcG8fQMT/H2vvQ8+AExPHFsNsMiQ0Vxws\ndxrYh3sQnd2DeOI3u+GPUXht6oSSmOc1mYx48cFrI5IFw3vqUmqIhx/LuW4ibeFfNKlKdEBOdi43\n+nHh/z9xXHHEscmev7KiAOWjcmP2lK1mA+5ZVB33XNHJgkFCGeUzp5QDAPY0tjPLnEjjZAvgfX19\nWLlyJfr7++F2u/Hwww/jkksukev0RKoktgnG9TPHI89qSvrcYhnlt39zMrPMiTROtr/s3/72t5g1\naxbuuOMOHD58GA8++CD+/Oc/y3V6ItVK9dproVEIZpkTaZ9sf+F33HEHTKZAb8Lr9cJsZsIMEaCO\n+t5EpD5JXUX++Mc/4ne/+13E955++mlMmzYNnZ2dWLlyJR555JG456mtrcXq1auTaQKR6qS6Vxwv\noY+ItEXn98fKj01cU1MTVqxYgYceeghXX311UudoaWnB3LlzUVdXhzFjxsjVNCLNChbC2dXQJlg+\nlYiUL5nYJ9tt+pdffokHHngAL7zwAi688EK5TktEcazb1BiRJBddPpWItEm2AP7888/D5XLhqaee\nAgDk5+djzZo1cp2eiAQ4XB7samgT/Fl0+VQi0hbZ/rIZrInSr6vXGbOkKsunEmkbJ8iIVCywgYpV\n8Gcsn0qkbQzgRCoWLBQjhOVTibSNf91EKpfqQjFEpEwM4EQqx0IxRNmJf+VEGsHyqUTZhXPgRERE\nKsQATkREpEIM4ERERCrEAE5ERKRCDOBEREQqpLiUVa/XCwBob2/PcEuIiIjSIxjzgjFQCsUF8M7O\nTgDArbfemuGWEBERpVdnZyfGjx8v6VhZ9wOXg8PhQENDA0pLS2EwGDLdnND+rNmAr1Wb+Fq1ia9V\nW7xeLzo7O1FdXQ2LxSLpMYrrgVssFsyYMSPTzYggdXN1LeBr1Sa+Vm3ia9UWqT3vICaxERERqRAD\nOBERkQoxgBMREamQ4fHHH388041QussvvzzTTUgbvlZt4mvVJr7W7Ka4LHQiIiKKj0PoREREKsQA\nTkREpEIM4ERERCrEAE5ERKRCDOBEREQqpLhSqkrg8/nw+OOPo6mpCSaTCU8++WTCJe7UZNGiRRgx\nYgSAQLnCZ555JsMtkt+BAwfwy1/+EuvXr8fRo0fx8MMPQ6fT4fzzz8dPf/pT6PXauZcNf62NjY24\n9957UVlZCQC45ZZbMH/+/Mw2UAZutxuPPPIITpw4AZfLhfvuuw/nnXeeJt9XoddaXl6uyffV6/Vi\n1apVOHLkCAwGA5555hn4/X5Nvq9yYAAXsHXrVrhcLrz55pvYv38/nn32WaxZsybTzUoJp9MJAFi/\nfn2GW5I6a9euxcaNG2G1WgEAzzzzDJYvX47LL78cjz32GOrq6nD99ddnuJXyiH6tn332Ge68804s\nXbo0wy2T18aNG1FUVITnnnsOXV1duPHGG3HhhRdq8n0Veq3333+/Jt/X999/HwDwhz/8Abt37w4F\ncC2+r3LgbYyAjz/+GHPmzAEAXHzxxWhoaMhwi1Ln888/h91ux9KlS3Hbbbdh//79mW6S7MaNG4fa\n2trQ142NjZg5cyYA4KqrrsKOHTsy1TTZRb/WhoYGfPDBB7j11lvxyCOPoL+/P4Otk88NN9yABx54\nIPS1wWDQ7Psq9Fq1+r5ed911eOKJJwAAra2tKCkp0ez7KgcGcAH9/f3Iz88PfW0wGODxeDLYotSx\nWCy466678Jvf/Ab/9m//hh/96Eeae63z5s2D0Xh2sMnv90On0wEA8vLy0NfXl6mmyS76tU6bNg0P\nPfQQXn/9dYwdOxa//vWvM9g6+eTl5SE/Px/9/f1YtmwZli9frtn3Vei1avV9BQCj0Ygf//jHeOKJ\nJzBv3jzNvq9yYAAXkJ+fj4GBgdDXPp8v4qKoJeeeey4WLlwInU6Hc889F0VFRejs7Mx0s1IqfP5s\nYGAABQUFGWxNal1//fWorq4O/f9nn32W4RbJp62tDbfddhu+/e1vY8GCBZp+X6Nfq5bfVwD4+c9/\nji1btuDRRx8NTfMB2ntfh4sBXMD06dPx4YcfAgD279+PiRMnZrhFqfM///M/ePbZZwEAJ0+eRH9/\nP0pLSzPcqtSaPHkydu/eDQD48MMPFbf/vJzuuusuHDx4EACwc+dOTJkyJcMtkofNZsPSpUuxcuVK\n3HzzzQC0+74KvVatvq8bNmzAK6+8AgCwWq3Q6XSorq7W5PsqB9ZCFxDMQv/iiy/g9/vx9NNPY8KE\nCZluVkq4XC785Cc/QWtrK3Q6HX70ox9h+vTpmW6W7FpaWrBixQq89dZbOHLkCB599FG43W5UVVXh\nySefhMFgyHQTZRP+WhsbG/HEE08gJycHJSUleOKJJyKmh9TqySefxF//+ldUVVWFvvev//qvePLJ\nJzX3vgq91uXLl+O5557T3Ps6ODiIn/zkJ7DZbPB4PLj77rsxYcIETf+9DgcDOBERkQpxCJ2IiEiF\nGMCJiIhUiAGciIhIhRjAiYiIVIgBnIiISIUYwImIiFSIAZyIiEiFGMCJiIhUiAGciIhIhRjAiYiI\nVIgBnIiISIUYwImIiFSIAZyIiEiFGMCJiIhUiAGciIhIhRjAiYiIVIgBnIiISIUYwImIiFSIAZyI\niEiFGMCJiIhUiAGciIhIhRjAiYiIVIgBnIiISIUYwImIiFSIAZyIiEiFGMCJiIhUiAGciIhIhRjA\niYiIVIgBnIiISIWMmW5ANIfDgYaGBpSWlsJgMGS6OURERCnn9XrR2dmJ6upqWCwWSY9RXABvaGjA\nrbfemulmEBERpd3rr7+OGTNmSDpWcQG8tLQUQOBFlJeXZ7g1REREqdfe3o5bb701FAOlUFwADw6b\nl5eXY8yYMRluDRERUfokMnXMJDYiIiIVGlYAP3DgAJYsWQIAOHToEBYvXowlS5bgrrvugs1mk6WB\nRERENFTSAXzt2rVYtWoVnE4nAOCpp57Co48+ivXr1+P666/H2rVrZWskERERRUo6gI8bNw61tbWh\nr3/1q19h0qRJAALp8GazefitIyIiIkFJB/B58+bBaDybA1dWVgYA+OSTT/D73/8ed9xxx7AbR0RE\nRMJkzULfvHkz1qxZg1dffRUjR46Me3xtbS1Wr14tZxOIiIhSyuHyoKvXieICMyymzC3mku2Z//KX\nv+DNN9/E+vXrUVRUJOkxNTU1qKmpifheS0sL5s6dK1eziIiIZOH1+rBuUyN2NbShs9uO0iIrZlVX\nYOmCKTAY0r+oS5YA7vV68dRTT6GioiIUkC+77DIsW7ZMjtMTERFl3LpNjdj40eHQ1x1d9tDXdy+a\nmvb2DCuAjxkzBm+99RYAYM+ePbI0iIiISGkcLg92NbQJ/mxXQxuWzJ+U9uF0FnIhIiKKo6vXic5u\nu+DPbN12dPU609wiBnAiIqK4igvMKC2yCv6spMiK4oL0L51mACciIorDYjJiVnWF4M9mVVdkJBtd\ncZuZEBERKdHSBVMABOa8bd12lIRloWcCAzgREZEEBoMedy+aiiXzJ2lrHTgREVE2sJiMqCjJfPjk\nHDgREZEKMYATERGpEAM4ERGRCjGAExERqRADOBERkQoxgBMREakQAzgREZEKMYATERGpEAM4ERGR\nCjGAExERqRADOBERkQoxgBMREakQAzgREZEKMYATERGpEAM4ERGRCjGAExGRZjlcHrTZBuBweTLd\nFNllfkdyIiIimXm9Pqzb1IhdDW3o7LajpNCCaeeV4p5F1ci1mjLdPFkwgBMRkeas29SIjR8dDn3d\n2e1A3b7j2FHfhutnjsPSBVNgMKh7EFrdrSciIoricHmwq6FN8Gd2pwcbPzqMdZsa09wq+TGAExGR\npnT1OtHZbRc9ZldDm+rnxYcVwA8cOIAlS5YAAI4ePYpbbrkFixcvxk9/+lP4fD5ZGkhERJSI4gIz\nSousosfYuu3o6nWmqUWpkXQAX7t2LVatWgWnM/ALeOaZZ7B8+XK88cYb8Pv9qKurk62RREREUllM\nRsyqrhA9pqTIiuICc5palBpJB/Bx48ahtrY29HVjYyNmzpwJALjqqquwY8eO4beOiIgoCUsXTMHC\nOVWwmg2CP59VXQGLSd153Em3ft68eWhpaQl97ff7odPpAAB5eXno6+uLe47a2lqsXr062SYQEREJ\nMhj0uHvRVCyedwFe3dCAhq9ssHXbUVJkxazqCixdMCXTTRw22W4/9PqznfmBgQEUFBTEfUxNTQ1q\namoivtfS0oK5c+fK1SwiIspieVYTfnjLdDhcHnT1OlFcYFZ9zztItiz0yZMnY/fu3QCADz/8EDNm\nzJDr1ERERMNiMRlRUZKnmeANyBjAf/zjH6O2thbf+9734Ha7MW/ePLlOTURERFGGdSsyZswYvPXW\nWwCAc889F7///e9laRQRERGJYyEXIiLKGlra3EQ7kwFEREQxRG9uUhqWja7WmugM4EREpHnRm5t0\ndNlDX9+9aGqmmjUs6rztICIikkhscxM110RnACciIk0T29xEzTXRGcCJiEjTxDY3UXNNdAZwIiLS\nNLHNTdRcE12drSYiIkpAsPb5roY2zdREZwAnIiLNC25usmT+JM3URFd364mIiBIQqImujdDHOXAi\nItI0LVVfC6eN2xAiIqIoWqy+Fo4BnIiINEmL1dfCqf8WhIiIKIpWq6+FYwAnIiLN0Wr1tXAM4ERE\npDlarb4WjgGciIg0R6vV18Kp/xUQEREJ0GL1tXAM4EREpElarL4WTjuvhIiISICWqq+F4xw4ERFp\nhlarrgnR3i0JERFlHa1XXRPCAE5ERKqn9aprQrR5W0JERFlDrOrau3uOYdDu0uTQOnvgRESkamJV\n1+xOD370Hx/B6fZqbmhd1gDudrvx8MMP48SJE9Dr9XjiiScwYcIEOZ+CiIgoQnGBGSVFVnR2CQfx\n4x39of/X0tC6rLcf27Ztg8fjwR/+8Afcf//9eOGFF+Q8PRER0RAWkxFTJ5Qk9BgtbGgiawA/99xz\n4fV64fP50N/fD6ORI/RERJR69yyqhtVskHy8FjY0kTXC5ubm4sSJE/jGN76Brq4uvPzyy6LH19bW\nYvXq1XI2gYiIslCe1YTrZ46PyEQXo4UNTWTtgf/nf/4nrrzySmzZsgV/+ctf8PDDD8PpjH2HU1NT\ng6ampoh/dXV1cjaJiIiyxNIFU/CtK8+N6Ikb9DrBY7WwoYmsrS8oKEBOTg4AoLCwEB6PB16vV86n\nICIiEuT2+tA34ILdeTbueH1+AIDVbIDT5dXUhiayBvA77rgDjzzyCBYvXgy3240f/vCHyM3NlfMp\niIiIIni9Pry2sQFb9xyFw+WLedyLD16L8lG5qu95B8n6KvLy8vDiiy/KeUoiIiJR6zY14p3tR0SP\nCfTK/ZoJ3gArsRERkYo5XB7srG+VdOz/1P0vvN7YPXS1YQAnIiLVClRhc0g6dtunJ7BuU2OKW5Q+\nDOBERKRaxQVmlBZZJB+vhQIuQQzgRESkWhaTEZdOOkfy8Voo4BKkndl8IiLKKsE9wLd90iL5MWaT\nUfUFXIIYwImISJWi9wCXxp+StmQCh9CJiEh1xPYAF2N3etF0tEsT8+DsgRMRkerYuu3oiLF9qBid\nDlj18g6UFat/X3B1tpqIiLLapoSHzgP8Z0bQg/uCq3lZGQM4ERGpisPlwb5DJ2U5l5qXlTGAExFl\nkMPlQZttQLVBJBMCxVviD5+PKrRArwOKR8TOOlfzsjLOgRMRZUBwCdSuhjZ0dttRWqT+Odl0CRRv\nsYrOgZcVW/Gr5Vdj0OFBrsWIFS9sEzxezfuC81NCRJQBwSVQHV12+P3amJNNF4vJiHxrjugx+dYc\n5FtzUFGSh8J8M2ZVVwgep+Z9wRnAiYjSTGwJlJrnZNNlwO5Cq21A9JjDrb0RN0NLF0zBwjlVKCu2\nQq8L9NAXzqlS9b7g6rztICJSMbE53OCcbEUJL8+xvLqhAQ6XN+5xuxrasGT+JACB3/mS+ZOwZP4k\ndPU6UVxgVm3PO0jdrSciUiGxOVw1z8mmg8PlQf1XNknH2rrtePntg6j/yqbJPAP1vwIiIpWxmIya\nnJNNh65eJ2wSMtCBQN3zun3HNZtnwABORJQBWpyTTYfg6IU0wnXPtZJnwNs8IqIMMBj0uHvRVE3N\nyaZDcPRCaBMTq9kAh8uL0iIrJlWOxLZPTwieQyt5BupuPRGRyllMRtUHknQLjlLsamiDrduOkiIr\n8q056B1wwu70onfAhd2NsTc60UqeAT81RESkKtGjFxu2fYnNO5pDP4+Xoa6VPAP1vwIiIso6DpcH\nXb1O5FqMkuuilxZZMHvqaM3kGTCAExGRIgSDslg+QHQJ2uIRZpyWWMv84dsvw8RxI+VsckYxgBMR\nUUYlUhc+WII2SGrwBgBTjkG2NisBAzgREWVUdFAOrtcGgLsXTQ19X6wErRTFIyzJN1KBZF8H/sor\nr+B73/sevvOd7+CPf/yj3KcnIiINSaQuvNRtRGMZdKh/7Xc4WQP47t278emnn+K///u/sX79erS3\nt8t5eiIi0hgpdeGDEiviEqm0yKKJpWPhZA3g27dvx8SJE3H//ffj3nvvxTXXXCPn6YmISGPEgnL0\nem2xErTxzJ46WhNLx8LJ+mq6urrQ2tqKl19+GS0tLbjvvvvwt7/9DTqdTvD42tparF69Ws4mEBGR\niohVVhNarx1cAvb33Ucl7UgGAFazEbfOu2D4jVUYWQN4UVERqqqqYDKZUFVVBbPZjNOnT2PUqFGC\nx9fU1KCmpibiey0tLZg7d66czSIiIgUTqqwWzEKP5vb68I0rKrH/iw4cO9kv6fxOlwc9A27kWk2y\ntjvTZA3gl156Kf7rv/4Ld955Jzo6OmC321FUVCTnUxARkcZIqQsfvtRMaBtWMVopnRpN1gB+7bXX\nYu/evbj55pvh9/vx2GOPwWDQ1ro7IiJKDbG68NFLzRIxY9I5Z24MoKl5cNlfyUMPPST3KYmIKIsl\nu/57VIEJhfkW7Dt0En/d2SxaIEaNtHMrQkREmtTV60x42BwARuSZcbi1N/R1rAIxaqX+WxAiItK0\nXIsR+iSiVXNbn+D3owvEqBUDOBERKdqgwwOfT77zRReIUSsGcCIiUrRAsRf56pgXF1hw8EsbOk4P\nynbOTOAcOBERKZrFZMTsqaOTzkKPdqrHgdV/3A8AKMgz4bWfzIVVhWvE2QMnIiLFW7pgChbOqUJZ\nsRU6IKk5cSG9Ay7c9fRWtNkGVDcvzh44EREpXnixF1u3HStrP0T/oDwBt2/QjR88sxWlxepaZqb8\nFhIREZ3h9frw5LrdsgXvID/OLjNbt6lR1nOnCgM4EREpntfrw9oN9bjjZ3/Hic6BhB6r1wPXTh8j\n+Xi1LDNjACciIsULllKVugNZuBtmVWLFrZeiIE9aoppalpkxgBMRkaIlW0rVbNJj4Zwq3HOm6tpr\nP5krKYirZfMTJrEREZGidfU60dmdeCnV2gevRUVJfuhrq9WE13/2DXScHkTD4VOo/9KGrXuPDXmc\n0D7kSqT8FhIRUVYrLjCjpNCCzm5HQo9zuoWH28tG5uLrI3Nx9SVfQ67FKGkfciViACciIkWzmIyY\ndl4p6vYdT+hxDqd4IpqUfciVjHPgRESkePcsqobVnFhwffSVHVi7oR5er3gh9cA+5HmqCt4AAzgR\nEalArtWEuZeNTegxDpcPGz86jNc2Ngj8zKPK6mvh1HW7QUREWcvn9yf1uLq9x3D7NyfDYjLC6/Vh\n3aZG7GpoQ2e3HaVF6qq+Fk5drSUiIlUabo/X4fLg/QTnwIPsTi/aTwV2HguuJ+/ossPvV1/1tXDs\ngRMRUcrI1eNtPzUIuzPxIi5n+UXXk+9qaMOS+ZNUNQ/OHjgREaWMfD3e5IbPAcBqNqJ8VJ7oenK1\nVF8LxwBOREQpEa/Hm8hwevmoPJhzkgtZcy8bC4vJiOICM0qLrILHqKX6WjgGcCIiSgk5e7wWkxGX\nTTon4TaYTYEwN2h3oavXiRkxzqGW6mvh1NVaIiJSjWCPt6NraBBPpsd7wxXnYvvBxGqiO10+vLP9\nCOr2HofD5UFJoQVVowvQb3ersvpaOAZwIiJKCYvJiFnVFdj40eEhP0umxztgdyXdFvuZqmyd3Q50\ndjsw/4pKLLr6PNVVXwvHIXQiIkqZpQumYOGcKpQVW6HXAWXFViycU5VUj7cgT7456n2HTqo6eAMp\n6oGfOnW4P70BAAAgAElEQVQK3/nOd7Bu3TpMmDAhFU9BREQqIEe98eBStJ31rbK1KzgHX1HCAB7i\ndrvx2GOPwWKxyH1qIiJSqUC98eRCTnApmpzMZ7LS1Uz2IfSf//zn+Kd/+ieUlZXJfWoiIsoyYkvR\nhif5deVKIWsA/9Of/oSRI0dizpw5cp6WiIiylK3bLpjFPlwOp1d1hVuiyTqE/vbbb0On02Hnzp04\ndOgQfvzjH2PNmjUoLS0VPL62tharV6+WswlERKQhm2QeOg+ymA0oyMtJybnTRdYA/vrrr4f+f8mS\nJXj88cdjBm8AqKmpQU1NTcT3WlpaMHfuXDmbRUREKuRwebD3s/aUnNvu9OKNLU24e9HUlJw/HbiM\njIiIFClQyc2RsvMnWs5VaVIWwNevX88lZEREWWS4W4ZGKy4wY+QIkyznEqLGDUzCqXcBHBERKYJc\nW4ZGyzHokZubg9N9yVdgE6PGDUzCMYATEdGwRK/TDm4ZCmBYc8zrNjWi5eTAsNsXixo3MAnHOXAi\nIkqanFuGSj1vsopHmIZdzlVJ1HvrQUREGSdly9BYFdgcLk/M8qqpWP89c0oFbrr2fNXXQA9S/ysg\nIqKMSWbLUClz5qlY//1pUwe+/+1qTQRvgEPoRESalWxWeCKPC24ZKiTWHHNwzryjyw6//+yc+bpN\njaHn33foZEJtlkLtWefRtHEbQkREIclmhSf7uOBc8q6GNti67SgJe1y0eHPmS+ZPSln51FEqzzqP\nxgBORKQxyWaFJ/s4g0GPJfMn4frLxwPwo3xUXsxh6nhz5k1Hu/DRpy0xn2s4TvfYsX7zoWEvb1MK\nBnAiIg2R0sMVCq7JPs7r9eHVDfXY1dCG071OlBWL99rF5syhA1a9vAN6XZwXmSSvD7Isb1MK9d+C\nEBFRiJSscLke5/X6sOKFbdi8oxmnz/w8ej47mticuc935r8p3ulz+4ET6OlX/1w4AzgRkYYEe7hC\nxCqPJfO4VzfU43Brr+BjYq0Bd7g8+MYVlZh/RSXKiq3Q6wB9miPR6V4nHnj+A6zdUA+v15feJ5cR\nAzgRkYYkkxWezOMcLg92N8TeKayzK7LX7vX6sHZDPe7/xXv451+8h32HTmLGpHPw07tnh3re6XSq\n1yE6UqAGnAMnItKYRLLCk31cV68Tp/ti7xRWXGCO6LULJcht3tEMIFAZLRVZ51KIze8rnfpaTERE\nogwGPe5eNBVL5k+KWelsuI8TTUZDZK9dLEFu36GTuPTCMvx151GJr05e8arFKRmH0ImINMpiMqKi\nJPaSruE8TmzIvWp0Ae4Jy/KOlyDncHkTap+c1LwjmfpuOYiISBGih9yLCyy4fEo57lk0NWIJWbxy\nqw1f2dLW5mhq3pFMna0mIqKMkzrkHuytbxSob149oQTv7TuejuZGGFVowf83bfSQ+X2xDVaURtmt\nIyJKAzVdtJUoMOQu/nsTSpCbOaUcHq8Pej3SmoluNhnw4oprUJh/dug82TKymcRPKhFlLTVetNVK\nqLe+fvMh/C0DyWs6+GE2GSK+l2wZ2UziJ5SIsla8XbFIfsEEOQAxM9NTzeHyRaxRj1dGNtHd3NKF\nAZyIspJaL9paIZaZnmolhZFr1JMtP5tpDOBElJXUetHWiuICM0oKLYI/S9VmJkEDDi/Wbz4UKqOa\nbPnZTGMAJ6KspNaLtlo4XB602QZCIxnRX1tMRozINQk+NtWbmdidnoipkmTLz2aaMltFRJRiYkub\nlHzRVjqhxMB8aw76Bl2w9ThCiYK3zrsA/XZ3RtsaXkY12fKzmcRPKBFlLTVetJVOKJs7vIBLMFFw\nwO7O2Bx4UHgZ1WTLz2aSsltHRJRCarxoK5lYYmC0A//bgZIiKzoztIkJECjmEj1VImVNu1LIOgfu\ndruxcuVKLF68GDfffDPq6urkPD0RUUokWzNcLaLnn1MlkcxyW48T5hxD/ANTqHfQFZHMpjayflo3\nbtyIoqIiPPfcc+jq6sKNN96IuXPnyvkUREQkUboL1cTboSxaS0c/KitG4NjJvozsCe50+RRfrEWM\nrO/gDTfcgAceeCD0tcGQ2bsrIqJslu5CNWLZ3LH0DrgyErzDqXXdv6w98Ly8QHWd/v5+LFu2DMuX\nLxc9vra2FqtXr5azCUREhPiFaoLZ13ILTwyU0hM/3evEyAIzTmdw3b1a9wSXfQylra0Nt912G779\n7W9jwYIFosfW1NSgqakp4h/nzYmyW7rma7VO7kI1Qu+L0PeCiYEzJp0j+dyDjsy+12pd9y/r7YbN\nZsPSpUvx2GOPYfbs2XKemog0jhuLyCveHtxSA5bQ+zJzSjkAYE9je+h7MyadgwVzqlBypjjOvkMn\nJbfV4fJKPjYV1LruX9YWv/zyy+jt7cVLL72El156CQCwdu1aWCzC5fKIiILUuBuUkslVqEbofXln\n+5GIYzq67Ni8oxmbdzSjrNiKqRNKMr7GW0xw+9LSIgtmTx26J7hayBrAV61ahVWrVsl5SiLKApma\nr9W64RaqSWRdd1BHlx11+47DajbC7hw6NF5cYM54nflg0txlk8tVfXPIvwgiyjgp87VqSzBSAimF\nahwuT8yftZ8aGEahFeGC5jMnl+PTpg7JS81Sad+hk3C4PKq9OVRnq4lIU+SaryVhQtXFxHIOgMDQ\n+c761hhhOD6H04uvzxiLhq9s6Oiyh4atP/n8JEbkmhQRwNV+c6jOVhORpnBjkfQTyzkAIPheJKK0\n2Ir7bpqG325qxOYdzaFh685uBzq7HagsH4Hm9r5hPcdwqf3mkH8VRKQI3FgkktjQ9nDPmWsxiuYc\n+P2x+91lxZFZ6LF60sGCLrGy0XsHXdAh1kB7eqj95lC9LSciTeHGIgGpWE4Xfc6RIyw41esQPLaz\nO1C1TYgOwKN3zUJlRQEA4PZvToat245NHx3GvkMnh9x4dXTZY+Y2ZKpwiw6B0QEt3Bxm318HESma\nmnaDSoVULKeLPmes4A0ApUVW+Hw+2HqGBtjSYivKR+WGvraYjBhTNgL33XSR4IhBorXR0+GyyeVY\nueRSTdwcsjoCEZFCxFtOl0x1ukSXguVbczDgEC6sIjbkLLSjm8VkTKgiWzrs+7wd7gwXjpELAzgR\nkULIXf403jkBYGSBGXpdYG67anQBDrf2Dlm/bTUbsHBOVUJDzoN2F17470+w97N2AIGhayXw+YB7\nnq1T7Rai4dQ/hkBEpBGpWE4nds6yYit+tfxqDDo8yLUYseKFbYLnGJFrwpL5kyTNwQfn2/+++2hE\nidRMJqtF67e7sebtA/iX716S6aYMC3vgREQKIbYdZ7IZ0/HOWZhvRnGBGc1tvbL0/l/b2ICNHx3O\neH3zeHY1tqt+wxz2wIlItVKx1CrTgsPUO+tbYet2oESGet1LF0yBz+9H3d7joeFxq9kAj8+HV/58\nMLQcTK+HYAZ6rN5/+O8fCFRu27rnaNLtTKeefpeqi7gADOBEpEJZsXOZThf532EwGPTQ63QRc9t2\npxeb/9EccZwvxrTwjEnnhG6QHC5PxNKxji47rGYDAB0cTo+ihsrF6HRArkXdIVDdrSeirKTlncui\nX1unDK8t0Ux0nS7QEw+WP93zWTv0+sCNhFDxFrtT2cPlQvz+wD7khfnqrcSmkVtVIkonh8uDNttA\nRuYQU7HUSilS9driZaJHCw6jB3vktm4H3tl+BO9sP6KoNd3DUZxvVHUZVYA9cCJKgBKGrpW6c5kc\n8/FyvLZYBVUsJuHtPbOV0+1Xfd6EultPRGmlhKFrpe1cJudNzXBeW7zdxZS1kCvz7E4vevqdHEIn\nIu1TytB1KpZaDUfwpqajK1BDPHhTs25TY8LnGs5rE2tHV69TlfPUqeQH8MXxLjS39aC5rVeVUy/s\ngZ+hxeUoRHJS0tC1UnYui3dTs2T+pISvJ4m+NofLg/ZTA9hZ3yr48531rfjudRNRVizcszeb9HC6\n1F+VLBk//91eON2B1241GzD3snH4/sJq1axkyPpIpYQ5PSI1UNLQtVJ2LhO7qenossPWbceYshEJ\nnVPqa4u4dnXZYw6Qd3Y7sG5TI2ZOKcc7248M+Xm2Bm8AoeANBIbU39l+BHqdTjUrGbI+Qsk5/EWZ\nkcmM6GyitKFrQHgDjaBUfC6izxm8qYllU1i+QKLEXhsQde2Kc6739h0HAMy/ohIjC8yBLTWLLLCa\ns74PN8TO+lbVXEuy+t1LxfAXpQ9HT9JPKUPXYtKxn3b4OWdMOgebdzQLPm7foZNwuDzDuo4ITe8l\nuq4bAOr2HkN+rgldfU6MLLBgctUofPjpiaTbpVW2bodqKrQpv4UppKQ5PUqcEjKis81wh67TkWuS\njv20w8+5YE5VzACe7HUkutpZ9E2Drdue8Hpsu9MLuzPwmFO9Dmz75AQMeh28QrVTs1hJkUU168Oz\nOjopaU6PEsPRk8wKDO9K//1K6RXLEdxT8bmId06xBLFEryPhv6fo8wVvGnoGnPB65Qm6Xh+Dd7TZ\nU0er5tqhjlYOU6wLQ3BOb6PAPFXfoAvrNx/icOwwyNXbEjpPukdP5AouyZ4j3mNT+buWg1gPdvG8\nC/DqhgbUf2WDLYHgPtzPRU+/E81tvaisKBBcCxw8v9PtEU1U6xt0xbyOXDKx9EwbA9eb4DlzLUYM\nOjxDfs/Rvych2z7hsHcqBLPQlTQdFI+mA7iUu/7gm/XunmNDCv1zODY5cs1Bip0nXaMncryW4Zwj\n3mPFfu72+iQH49M9dqz5Uz2+bOnCqR6H5DaGB6STpwfQM+DGxLFFEQFRrAf77p6j+PvuZjjCMqHD\ng/vSBVOw5k8HsbuhHT0DTpQWWTFzSjmAQE3u6NecazGiON+E032uIc9VUmSFQa/DvkPteO0vDWi1\nDYTqfVeWF+C5mjkwmYyh3+nO+lZ0djtQUmiGOccQc3vM17ccQs3NF8Pj9WFXfStO97mgQ2Cd8Zbd\nx7Bl9zEU5hlhtZgwYHejb9AdqjU+qsCMaeeX4rwxxcgx6vDhp8dF3yeS39XTv4abvz4R5aNyVdPz\nDtL5/fJNgPh8Pjz++ONoamqCyWTCk08+ifHjxyd0jpaWFsydOxd1dXUYM2bMsNqzdkO94N3swjlV\nEUHZ4fLgn39eh85ux5Bjy4qt+PVDX1fdG5tJsX7v86+oxH03XTTs8wTfP6nv73DI8RzDOUeyv4Oq\n0QXot7vj3jC4XB6srP0Ih1t7BZ8/VhvFhnoBoHJ0AZ4/ExDbbAP4wbNbBbepFFNWbIXd6UHfoFvS\n8ZWjC3Dy1EDMgiV6HSA2Ylw1ugAvPngtXvnzQcHlVmL0OsBo0MHl4ZC0muQYdHjtX69HrjUn43VA\nkol9so4Nb926FS6XC2+++SYefPBBPPvss3KePiGJVI3q6nXC1jM0eAOJbWRP4r/3v+1qxpq3D8Dr\njb/uVMr7t3TBFCycU4WyYiv0usAFf+GcKtmGwOSoPDacc8R7bE+/M+bPD7f2SloaKRa8xdoYvoRJ\nSHNrL1bWfgQASV8UA8PT0oJ38DnFqo3Fm+5tbu9Fx+lB1O09Jvk5w8/N4K0+bq8f9zy7Fbc9vgU/\neHYr7v/Fe1i7oV7SNUoJZL3V+PjjjzFnzhwAwMUXX4yGhgY5T5+QRObCmMwmH7Hfu88HbN7RDOOZ\nTOZkz3P2/ctLaTEPOebZh3OOeI9tbutNaIep6CSunn4nmttjB+9YbZS6hKm5rRc9/U6YTQb4VVCH\n2+cDPmk6yZKjWSZQyCYQsNW2kkXWHnh/fz/y8/NDXxsMBng8sXsYtbW1uOCCCyL+zZ07V5a2iBVY\niA7KSixQoVbxClsA0nqvib5/YgUvkpVIG1JxjniPrawoiPu7Dhc9mtTc1hvaLjIWoTZK3ZrS5w88\nR1evEw4VBEWdDvj0845MN4MUQC3b0soawPPz8zEwMBD62ufzwWiMfVGtqalBU1NTxL+6ujpZ2pJo\nUE71cGy2EPu9B0mZllDCTZUcbRjOOeI9tjDfHPd3HS46GFdWFECvE39M9YSSIW2UcpMGBOaFKysK\nAscXS7/RyJSvlebhky86M90MUgC1TJ3KehWcPn063n//fcyfPx/79+/HxIkT5Tx9whKpGqWU2spa\nsHTBFHi8PvxtV7NgD09q71UJVb/kaMNwzhHvsUI/z7fmCM5rR98wFOabMa58BJrb+gSf22o24J5F\n1UO+L7b8Mlz48qzZEo6PVpBnQu/A0GzyaGXFsV+zVCajHhPGFKGlg0u0SD1TpynJQv/iiy/g9/vx\n9NNPY8KECQmdQ84s9CDuNJYZa94+IFihKtEscSW8f2paB55j0IcyxKODvlAW+p1PvisYKMXep0Sy\n0IPHv7axAVv3HItYjmUxGVA20oqTp+1wnvl+cD3ubTdciIdf+geOtPXGzGAfVWjBiyuuQb41B69t\nbEDd3uMRy0HD5Rj1cHuG3lGOK8/Hs/ddiQf+/QPBlSiUfeRcySJVMrFP1gAuh1QEcMqM8It8vEBC\n8pN6w+D1+rDm7QPY1diOnn4Xyoqlv09S1oFHH99+agAutw+mHD3KR+WFCpy0nxoAoBuyHveLY134\n0YsfCqbB6XXAyw9fh4qSvKjzewHo4HJ7YHd5MXFsMQrzzaHiLSMLLDjd6wiNEkhd6laQa4THBzic\nnrhZ7UHnjLTg5GneGKhBaZEFs6eOzsg1KpnYx+4opQynJTJLarlTg0GPf/nuJfh+EiME4c8RK2hH\nH19ZUSj5+wAwrnwESiWWKhU7T7CNF51fCgAYe87ZbT7FVqIEfX3GWNx30zRYTMbQjUDFqDz81+bP\nsC3GpiDzLh+HvYdOxjwnKceYsnz8ctkc5FlNmW6KZOwG0bBI2bIxVVniJC+lvk/pSGgUew6r2YiF\nc6qw7LsXh54reCNQNjIXP7xlOr515bkRW3NazQZ868pzseia83BaJBnKnBP/Ejwid+jri5d8SIlr\n6ejH7//2eaabkRBl/aWSanArT0qndCQ0Rj/HqEILpp1XinsWVSNXpFdmMOjxgxun4fZvTh4yDeBw\neWJudAIEevU5RkPg76jLDovZAEAHp8sT8Rr77W58cawrlHgIIDRVYDDo8as3Psax9n7ZfhfZqm7v\ncdz+zcmKu4mNhXPglJR0lDElipaOhEa5n0Os3O2vll8Ng0Ef8ZwAEn5+r9eHVzfUY3djO7p6HSgp\nsmLGpHMwb9Z46PU6FOWacNfTW+ESSOKjSLU/ukZ0GiZVOAdOacGtPClTEt3GNCiRoJzsc8QS3rPv\n7LKjuCCwfv+eRVNDo1XRz5no8xsMetx300W4c8GUmK/zrae/idV/PIBtn7YIZuObc/RwuX0oLbYi\n15qDY229khP1tEU98xO8ylLC0r2VJ2Wv4faGlTDVk85kTrGbD4NBjx98ZyoO/G+H4HI5vV6HF1Zc\njdGlgWqa9/28DrYsW1ZnMRlQPio3082QjFdZShhrx2eHTK6/lyvwiu1Dnu6pHrl79ok4u7e5N+bG\nTXanFxs/PIzlt0xHS0df1gVvALhu5jhVjR6qp6WkGGKVuFg7Xv2U0GuVI/CKTfXsrG/Niqme6Pey\npMgKs8kQszb9wS874XB5sCnBqnlaYM7RY8kNF2a6GQlhujAlhbXjtSt8q9B425GmghzbuALiUz2d\n3Q6sefugaraNTFb0e9nZZRfdWOZUjwPtpwaxLwvXrrs8PvQMSN++Vgm0fftJKcMiLdqkhARFuXIs\n4hVneW/fceRbczS7akLqtq/hSoqsAPyiBW20qlSF03/sgdOwKLX4ByVHSvBMNTm2cQWk7Yynlm0j\nkyF129dws6orUDzCAn0WRobg9J+U4lRKwasuEYUoIUFRzhyLpQumYMDuRt2+44I/1/KqCbH3sqw4\nsE5836GTEYVxbp13Af63pSfuPvFa9I0rxmPthnpVFafS3qeWiJKmlARFuSqvGQx63HvTNBz8yobO\nLFs1Ee+9vHvR1FB2ekFeDt7Y0oSa5z9AZ7cdeh0E14BbTIaI3eQSodcBOTl6OF3KvDtY/qttcLrP\nti2TKxakYgAnoghK2IddzhwLi8kYcz9yra+aiPdeBpe2RVeLi1W/5bqZ46DX6SLO1zfogl0kMS4o\nWB1OaIvhcEX5OfD4/OgfTO8QdnjwDqfk4lTKaxERZZSSEhTlWjuthJuSTJDyXoolu+n1gN8HlEZt\nMRt+vvWbDwneHEULPt5o0OPdPUcFg35wx7ccgx6v/Pkg6vYdhytGYE0XJU+zKK9FRKQImSw8Ijcl\n3ZRkgth7KZrs5geeuPcKXDC+OOL3FX4+oU1gRuSa0G93D7lZCr4Pi+ddgFc3NKDhK5vgMQ6XBzde\ncz5uvWESuvoc6Ol3YtXLO+X9pUik5GmW7PkEE1HW09JNiVziJS5GB+9osW6OxCr55VlN+OEt04cc\n4/X6BBPJvnFFpdwvWzIlT7Mos1VERJQWciUuRt8cSblZij4mVgU+j9cnui2rnKxmA5wuryqmWRjA\niYiynBJyBMTm4vcdOikpAW44RhaYceVFX8PieRegd8CtimkWZbeOiIhSTgk5AvGKCC2YUwWjQY9d\nDW0p6YlPqRoVWi6WZzXJfv5UUObqdCIiSrtMVlaMV4GvpMiKuxdNxa8f+jpqf3Qt5K6t0nS0SxXV\n18IxgBMRUcaJlb4Nn4u3mIyorCjAvNmVsj5/ukoFy4lD6EREpAiJzMXf8+2pMOr12HHwBGw9Toyw\n6OD0Ai732TI0OsQuShNNycvFYmEAJyIiRUhkLj7WsT39TjS39aKyogBdfQ4s++UHkoK4kpeLxaKu\n1hIRkeYlsl4/+tjCfDMuOr8UAGA2GVAaZ/lZWbHyl4vFIlsA7+vrw8qVK9Hf3w+3242HH34Yl1xy\niVynJyIiSojYGvd5l4/DTV+fqIrlYrHI1urf/va3mDVrFu644w4cPnwYDz74IP785z/LdXoiIqKE\nic2rK3WbUKlkC+B33HEHTKbA2jmv1wuzWV3JAEREpD1KWOOeKkm9ij/+8Y/43e9+F/G9p59+GtOm\nTUNnZydWrlyJRx55JO55amtrsXr16mSaQEREJFmseXWxmu1Kp/P7/VKz7ONqamrCihUr8NBDD+Hq\nq69O6hwtLS2YO3cu6urqMGbMGLmaRkREFOL1+rBuU+OQjVMyNbSeTOyT7Xbjyy+/xAMPPIAXXngB\nF154oVynJSIikl2sjVMAhEqqKp1sAfz555+Hy+XCU089BQDIz8/HmjVr5Do9ERGRLMQ2TtnV0IYl\n8yepYjhdthYyWBMRkRrE2zilq9epin3j1Z1DT0RElKB4G6eopaQqAzgREWUVqRunKJ06WklERCSj\nRDZOUSoGcCIiyjpaKPCirtYSERHJKJGNU5SGc+BEREQqxABORESkQgzg9P/au5+QKP44jONvWWOV\nNiFC6A/UIZCgiMiwIKUQuniICBYXLJDYwqA/KGQUpnuQjQ4egiC89AeJiNhzRIcoFA8SetiDRgtZ\nF2NOsbth4u7ndwiFQsgfrfN1xud1HGaWzz4M+zDDl/2KiEgAqcBFREQCSAUuIiISQOtu6V2pVAJg\nbm7O8SQiIiL+WOq8pQ5cjXVX4J7nAdDR0eF4EhEREX95nseePXtWdW5F9wOvhPn5ebLZLPX19UQi\nkf99/dJ+quIv5e6OsndDubsTxuxLpRKe53HgwAFqampWdc26ewKvqanhyJEj//QZq90MXSpLubuj\n7N1Q7u6EMfvVPnkv0SI2ERGRAFKBi4iIBJAKXEREJIAiqVQq5XqISjt69KjrETYk5e6OsndDubuj\n7NfhKnQRERH5O71CFxERCSAVuIiISACpwEVERAJIBS4iIhJAKnAREZEACkWB5/N5kskkHR0ddHZ2\nLm+IMjU1RTweJ5FI8ODBA8dThk+pVGJwcJBEIsHZs2d5+/YtoNz9lMvlaGxs5OfPn4CyX2v5fJ6u\nri7OnTtHe3s7k5OTgHL3Q7lcpr+/n/b2ds6fP8/s7KzrkdyzEHjy5Indu3fPzMxevHhhd+/eNTOz\n06dP2+zsrJXLZUsmk5bNZl2OGTqZTMYGBgbMzGxubs4eP35sZsrdL/l83i5evGjHjh2z+fl5M1P2\na+3+/fvL93kul7MzZ86YmXL3w+vXr+3mzZtmZjY5OWldXV2OJ3IvFE/gDQ0NFItFAAqFAtXV1RQK\nBRYWFti9ezdVVVU0NzczPj7ueNJwGR0dZfv27Vy6dIm+vj5aW1uVu0/MjDt37tDT00NtbS2AsvdB\nZ2cniUQC+PUGKhqNKneffPjwgZaWFgAOHTpENpt1PJF76243sr95+fIlT58+/e1Yf38/Y2NjtLW1\n8f37d549e0ahUCAWiy2fs3nzZr5+/er3uKGxUu5bt24lGo0yPDzMxMQEt27dYmhoSLlX2ErZ79y5\nk7a2Nvbt27d8TPd8Za2Uezqd5uDBg3iex40bN7h9+7Zy98mfOUciERYXF6muDlyNVUzgvnk8Hice\nj/927MqVKySTSRKJBNPT01y9epXnz58vP5UDFItF6urq/B43NFbKvbu7m5MnT1JVVUVTUxOfP38m\nFosp9wpbKftTp06RyWTIZDJ4nseFCxcYHh5W9hW0Uu4AMzMz9PT00NvbS1NTE4VCQbn74M/flnK5\nvKHLG0KyiK2uro4tW7YAsG3bNorFIrFYjE2bNvHlyxfMjNHR0X/eZ1x+19jYyLt37wCYnp5mx44d\nyt0nb968YWRkhJGREerr63n06JGy98GnT5+4fv06Q0NDnDhxAkC5++Tw4cO8f/8e+LVosKGhwfFE\n7oXiv9C/fftGX18fP378YHFxkWvXrnH8+HGmpqZIp9OUSiWam5vp7u52PWqoLCwsMDAwQC6Xw8xI\npVLs379fufustbWVV69eEY1Glf0au3z5MjMzM+zatQv4Vd4PHz5U7j4ol8ukUik+fvyImZFOp9m7\nd6/rsZwKRYGLiIhsNKF4hS4iIrLRqMBFREQCSAUuIiISQCpwERGRAFKBi4iIBJAKXEREJIBU4CIi\nIhor1+0AAAAOSURBVAGkAhcREQmg/wAWDvuoNSJAnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e032a07b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.mlab import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "X_std = StandardScaler().fit_transform(extractData_all[:,1:4])\n",
    "pca_results = PCA(X_std) \n",
    "\n",
    "print(pca_results.fracs) # Gives same values as above, therefore the extra normalization step not necessary\n",
    "print(pca_results.Y)\n",
    "\n",
    "\n",
    "f2, (ax1, ax2, ax3) = plt.subplots(3,1, figsize=(8, 16))\n",
    "ax1.scatter(pca_results.Y[:,0],pca_results.Y[:,1])\n",
    "ax2.scatter(pca_results.Y[:,0],pca_results.Y[:,2])\n",
    "ax3.scatter(pca_results.Y[:,1],pca_results.Y[:,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Hmm, What exactly does this mean..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "Do the same PCA as above, but this time maintaining the categories.\n",
    "\n",
    "- One way would be to use the IDs from each seperate group "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34048, 3)\n"
     ]
    }
   ],
   "source": [
    "a=extractData_car[:,1:4]\n",
    "print(np.shape(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34048, 3)\n",
      "(12720, 3)\n",
      "(3831, 3)\n",
      "(3958, 3)\n",
      "(1389, 3)\n"
     ]
    }
   ],
   "source": [
    "combArray = np.vstack((extractData_car[:,1:4], extractData_pub[:,1:4],\n",
    "                            extractData_cyc[:,1:4], extractData_ped[:,1:4],\n",
    "                            extractData_oth[:,1:4]))\n",
    "\n",
    "iCar_i = 0\n",
    "iCar_f = extractData_car[:,1].size\n",
    "iPub_i = iCar_f# + 1\n",
    "iPub_f = iPub_i + extractData_pub[:,1].size\n",
    "iCyc_i = iPub_f# + 1\n",
    "iCyc_f = iCyc_i + extractData_cyc[:,1].size\n",
    "iPed_i = iCyc_f# + 1\n",
    "iPed_f = iPed_i + extractData_ped[:,1].size\n",
    "iOth_i = iPed_f# + 1\n",
    "iOth_f = iOth_i + extractData_oth[:,1].size\n",
    "\n",
    "\n",
    "print(np.shape(combArray[iCar_i:iCar_f,:]))\n",
    "print(np.shape(combArray[iPub_i:iPub_f,:]))\n",
    "print(np.shape(combArray[iCyc_i:iCyc_f,:]))\n",
    "print(np.shape(combArray[iPed_i:iPed_f,:]))\n",
    "print(np.shape(combArray[iOth_i:iOth_f,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34048, 3)\n",
      "(12720, 3)\n",
      "(3831, 3)\n",
      "(3958, 3)\n",
      "(1389, 3)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(extractData_car[:,1:4]))\n",
    "print(np.shape(extractData_pub[:,1:4]))\n",
    "print(np.shape(extractData_cyc[:,1:4]))\n",
    "print(np.shape(extractData_ped[:,1:4]))\n",
    "print(np.shape(extractData_oth[:,1:4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55946, 3)\n",
      "34048\n",
      "55946\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(combArray))\n",
    "print( extractData_car[:,0].size)\n",
    "test = extractData_car[:,0].size +  extractData_ped[:,0].size +  extractData_pub[:,0].size + extractData_cyc[:,0].size +  extractData_oth[:,0].size\n",
    "print(test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Matplotlib PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.mlab import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X_std = StandardScaler().fit_transform(combArray)\n",
    "pca_results = PCA(X_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Plot the categories with respect to principal components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19e0318b208>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAOQCAYAAAAZiVmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEVHIMayJiIhCjmFNREQUcgxrIiKikPt/IbGBhYWPwAcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e042fd9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f2, (ax1, ax2, ax3) = plt.subplots(3,1, figsize=(8, 16))\n",
    "\n",
    "ax1.scatter(pca_results.Y[iCar_i:iCar_f,0],pca_results.Y[iCar_i:iCar_f,1], color='blue', alpha=0.05)\n",
    "ax1.scatter(pca_results.Y[iPub_i:iPub_f,0],pca_results.Y[iPub_i:iPub_f,1], color='magenta', alpha=0.05)\n",
    "ax1.scatter(pca_results.Y[iCyc_i:iCyc_f,0],pca_results.Y[iCyc_i:iCyc_f,1], color='green', alpha=0.05)\n",
    "ax1.scatter(pca_results.Y[iPed_i:iPed_f,0],pca_results.Y[iPed_i:iPed_f,1], color='yellow', alpha=0.05)\n",
    "#ax1.scatter(pca_results.Y[iOth_i:iOth_f,0],pca_results.Y[iOth_i:iOth_f,1], color='cyan')\n",
    "\n",
    "ax2.scatter(pca_results.Y[iCar_i:iCar_f,0],pca_results.Y[iCar_i:iCar_f,2], color='blue', alpha=0.05)\n",
    "ax2.scatter(pca_results.Y[iPub_i:iPub_f,0],pca_results.Y[iPub_i:iPub_f,2], color='magenta', alpha=0.05)\n",
    "ax2.scatter(pca_results.Y[iCyc_i:iCyc_f,0],pca_results.Y[iCyc_i:iCyc_f,2], color='green', alpha=0.05)\n",
    "ax2.scatter(pca_results.Y[iPed_i:iPed_f,0],pca_results.Y[iPed_i:iPed_f,2], color='yellow', alpha=0.05)\n",
    "#ax2.scatter(pca_results.Y[iOth_i:iOth_f,0],pca_results.Y[iOth_i:iOth_f,2], color='cyan')\n",
    "\n",
    "ax3.scatter(pca_results.Y[iCar_i:iCar_f,1],pca_results.Y[iCar_i:iCar_f,2], color='blue', alpha=0.05)\n",
    "ax3.scatter(pca_results.Y[iPub_i:iPub_f,1],pca_results.Y[iPub_i:iPub_f,2], color='magenta', alpha=0.05)\n",
    "ax3.scatter(pca_results.Y[iCyc_i:iCyc_f,1],pca_results.Y[iCyc_i:iCyc_f,2], color='green', alpha=0.05)\n",
    "ax3.scatter(pca_results.Y[iPed_i:iPed_f,1],pca_results.Y[iPed_i:iPed_f,2], color='yellow', alpha=0.05)\n",
    "#ax3.scatter(pca_results.Y[iOth_i:iOth_f,1],pca_results.Y[iOth_i:iOth_f,2], color='cyan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.75, 0.75)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfQAAAHdCAYAAADrdYt0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e042996d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\")\n",
    "#f3, ((ax1), (ax2), (ax3)) = plt.subplots(3,1, figsize=(8, 16))\n",
    "f3, (ax3) = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Set up the figure\n",
    "\n",
    "# # Draw the multiple density plots for Subplot 1\n",
    "# ax1 = sns.kdeplot(pca_results.Y[iCar_i:iCar_f,0], pca_results.Y[iCar_i:iCar_f,1],cmap=\"Blues\", \n",
    "#                  shade=True, shade_lowest=False, alpha=0.5)\n",
    "# ax1 = sns.kdeplot(pca_results.Y[iPub_i:iPub_f,0], pca_results.Y[iPub_i:iPub_f,1],cmap=\"Purples\", \n",
    "#                  shade=True, shade_lowest=False, alpha=0.5)\n",
    "# ax1 = sns.kdeplot(pca_results.Y[iCyc_i:iCyc_f,0], pca_results.Y[iCyc_i:iCyc_f,1],cmap=\"Greens\", \n",
    "#                  shade=True, shade_lowest=False, alpha=0.5)\n",
    "# ax1 = sns.kdeplot(pca_results.Y[iPed_i:iPed_f,0], pca_results.Y[iPed_i:iPed_f,1],cmap=\"Oranges\", \n",
    "#                  shade=True, shade_lowest=False, alpha=0.5)\n",
    "# ax1.set_xlim((-2,2))\n",
    "# ax1.set_ylim((-2,2))\n",
    "\n",
    "\n",
    "# # Draw the multiple density plots for Subplot 2\n",
    "# ax2 = sns.kdeplot(pca_results.Y[iCar_i:iCar_f,0], pca_results.Y[iCar_i:iCar_f,2],cmap=\"Blues\", \n",
    "#                  shade=True, shade_lowest=False, alpha=0.5)\n",
    "# ax2 = sns.kdeplot(pca_results.Y[iPub_i:iPub_f,0], pca_results.Y[iPub_i:iPub_f,2],cmap=\"Purples\", \n",
    "#                  shade=True, shade_lowest=False, alpha=0.5)\n",
    "# ax2 = sns.kdeplot(pca_results.Y[iCyc_i:iCyc_f,0], pca_results.Y[iCyc_i:iCyc_f,2],cmap=\"Greens\", \n",
    "#                  shade=True, shade_lowest=False, alpha=0.5)\n",
    "# ax2 = sns.kdeplot(pca_results.Y[iPed_i:iPed_f,0], pca_results.Y[iPed_i:iPed_f,2],cmap=\"Oranges\", \n",
    "#                  shade=True, shade_lowest=False, alpha=0.5)\n",
    "# ax2.set_xlim((-2,2))\n",
    "# ax2.set_ylim((-2,2))\n",
    "\n",
    "\n",
    "# Draw the multiple density plots for Subplot 3\n",
    "ax3 = sns.kdeplot(pca_results.Y[iCar_i:iCar_f,1], pca_results.Y[iCar_i:iCar_f,2],cmap=\"Blues\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax3 = sns.kdeplot(pca_results.Y[iPub_i:iPub_f,1], pca_results.Y[iPub_i:iPub_f,2],cmap=\"Purples\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax3 = sns.kdeplot(pca_results.Y[iCyc_i:iCyc_f,1], pca_results.Y[iCyc_i:iCyc_f,2],cmap=\"Greens\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax3 = sns.kdeplot(pca_results.Y[iPed_i:iPed_f,1], pca_results.Y[iPed_i:iPed_f,2],cmap=\"Oranges\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax3.set_xlim((-1,1.5))\n",
    "ax3.set_ylim((-0.75,0.75))\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Scikit learn PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.81990156  0.15559012  0.02450832]\n",
      "[ 370.95907945  161.59806381   64.13600507]\n",
      "[[-0.32628696 -0.44681302 -0.54293516]\n",
      " [-0.6920605  -0.67715733 -0.69866943]\n",
      " [-0.86418923 -0.75052827 -0.61133579]\n",
      " ..., \n",
      " [-1.00404381 -0.68537539 -0.71041155]\n",
      " [-0.98252772 -0.70941172 -0.76038703]\n",
      " [-0.53068982 -0.07984258 -0.17286925]]\n",
      "[[ 0.517265    0.60485641  0.60546317]\n",
      " [ 0.85581636 -0.36880516 -0.36271354]\n",
      " [-0.00390833 -0.7057843   0.70841601]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQ8AAAEDCAYAAAAr7YFFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngAnxAGBCPACY/A/s/OjP+BaWBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e032a0e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA #as sklearnPCA\n",
    "\n",
    "X_std = StandardScaler().fit_transform(combArray)\n",
    "Y = PCA(X_std)\n",
    "\n",
    "pca = PCA()\n",
    "pca.fit(X_std)\n",
    "pca_score = pca.explained_variance_ratio_\n",
    "V = pca.components_\n",
    "\n",
    "print(pca.explained_variance_ratio_)  \n",
    "print(pca.singular_values_)\n",
    "\n",
    "f4, (ax) = plt.subplots(figsize=(4, 4))\n",
    "ax.imshow(V, cmap='Greys')\n",
    "\n",
    "print(Y.n_components)\n",
    "print(V)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This graph above is the key to understanding all of the plots below. Add the labels and triple check to be sure that you know which PC represents which variable what amount.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Y.n_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19e045d6c88>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAOQCAYAAAAZiVmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlekAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e04536c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f2, (ax1, ax2, ax3) = plt.subplots(3,1, figsize=(8, 16))\n",
    "\n",
    "ax1.scatter(Y.n_components[iCar_i:iCar_f,0],Y.n_components[iCar_i:iCar_f,1], color='blue', alpha=0.05)\n",
    "ax1.scatter(Y.n_components[iPub_i:iPub_f,0],Y.n_components[iPub_i:iPub_f,1], color='magenta', alpha=0.05)\n",
    "ax1.scatter(Y.n_components[iCyc_i:iCyc_f,0],Y.n_components[iCyc_i:iCyc_f,1], color='green', alpha=0.05)\n",
    "ax1.scatter(Y.n_components[iPed_i:iPed_f,0],Y.n_components[iPed_i:iPed_f,1], color='yellow', alpha=0.05)\n",
    "#ax1.scatter(pca_results.Y[iOth_i:iOth_f,0],pca_results.Y[iOth_i:iOth_f,1], color='cyan')\n",
    "\n",
    "ax2.scatter(Y.n_components[iCar_i:iCar_f,0],Y.n_components[iCar_i:iCar_f,2], color='blue', alpha=0.05)\n",
    "ax2.scatter(Y.n_components[iPub_i:iPub_f,0],Y.n_components[iPub_i:iPub_f,2], color='magenta', alpha=0.05)\n",
    "ax2.scatter(Y.n_components[iCyc_i:iCyc_f,0],Y.n_components[iCyc_i:iCyc_f,2], color='green', alpha=0.05)\n",
    "ax2.scatter(Y.n_components[iPed_i:iPed_f,0],Y.n_components[iPed_i:iPed_f,2], color='yellow', alpha=0.05)\n",
    "#ax2.scatter(pca_results.Y[iOth_i:iOth_f,0],pca_results.Y[iOth_i:iOth_f,2], color='cyan')\n",
    "\n",
    "ax3.scatter(Y.n_components[iCar_i:iCar_f,1],Y.n_components[iCar_i:iCar_f,2], color='blue', alpha=0.05)\n",
    "ax3.scatter(Y.n_components[iPub_i:iPub_f,1],Y.n_components[iPub_i:iPub_f,2], color='magenta', alpha=0.05)\n",
    "ax3.scatter(Y.n_components[iCyc_i:iCyc_f,1],Y.n_components[iCyc_i:iCyc_f,2], color='green', alpha=0.05)\n",
    "ax3.scatter(Y.n_components[iPed_i:iPed_f,1],Y.n_components[iPed_i:iPed_f,2], color='yellow', alpha=0.05)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification using SciKit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin, use the raw, un-reduced data with a Gaussian Process classifier, as it appears to work the best for 'circular' types of data distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Switch out the sample data with the trajectory stats data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference for mode specific indexes\n",
    "\n",
    "    iCar_i:iCar_f\n",
    "    iPub_i:iPub_f\n",
    "    iCyc_i:iCyc_f\n",
    "    iPed_i:iPed_f\n",
    "    iOth_i:iOth_f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choose the data to fit/test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "##### Use the original values of speed and distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Combarray already has removed the id number, so indexes not the same as extractData\n",
    "X=np.stack((combArray[:,0],combArray[:,2]), axis=-1)\n",
    "y=np.zeros(combArray[:,0].size)\n",
    "\n",
    "y[iCar_i:iCar_f]=1  # Swap the indexes here to change the mode that's being trained/tested.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "##### Alternatively, use the PCA output data (first two principal components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Combarray already has removed the id number, so indexes not the same as extractData\n",
    "# pca_results.Y or Y.n_components (Results from different PCA functions)\n",
    "X=np.copy(pca_results.Y[:,0:2])\n",
    "y=np.zeros(combArray[:,0].size)\n",
    "\n",
    "y[iCyc_i:iCyc_f]=1  # Swap the indexes here to change the mode that's being trained/tested.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "##### Some housekeeping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Because my PC froze using the analysis below with the full data set, I suspect that virtual memory may have been limiting. So, the cell below is just to remove any variables I'm not currently needing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# # del allCoords\n",
    "# # del allIDs\n",
    "# # del arrays\n",
    "# # del arrays2\n",
    "# # del arrays3\n",
    "# del avgSpeed\n",
    "# del blankCount\n",
    "# del burrShapes\n",
    "# del burr_name_list\n",
    "# del burroughPops\n",
    "# del burrough_array\n",
    "# del carCount\n",
    "# del combArray\n",
    "# del coords_if\n",
    "# del countNotNone\n",
    "# del currdist\n",
    "# del cycCount\n",
    "# del duration\n",
    "# del eucdist\n",
    "# # del extractData_all\n",
    "# # del extractData_car\n",
    "# # del extractData_cyc\n",
    "# # del extractData_nil\n",
    "# # del extractData_oth\n",
    "# # del extractData_ped\n",
    "# # del extractData_pub\n",
    "# # del f\n",
    "# # del f2\n",
    "# # del f3\n",
    "# # del filepath\n",
    "# # del filepath2\n",
    "# # del filepath3\n",
    "# # del i\n",
    "# # del iCar_f\n",
    "# # del iCar_i\n",
    "# # del iCyc_f\n",
    "# # del iCyc_i\n",
    "# # del iOth_f\n",
    "# # del iOth_i\n",
    "# # del iPed_f\n",
    "# # del iPed_i\n",
    "# # del iPub_f\n",
    "# # del iPub_i\n",
    "# del iTot\n",
    "# del ids\n",
    "# del j\n",
    "# del json_data\n",
    "# del json_filepath\n",
    "# del k\n",
    "# del k2\n",
    "# del k3\n",
    "# del lenArray\n",
    "# del lenArray2\n",
    "# del lenArray3\n",
    "# del mode\n",
    "# del nB\n",
    "# del nBurroughs\n",
    "# del nTraj\n",
    "# del ncoords\n",
    "# del numIds\n",
    "# del othCount\n",
    "# del pedCount\n",
    "# del pubCount\n",
    "# del purpose\n",
    "# del sumdist\n",
    "# del tempArray\n",
    "# del totNCoords\n",
    "# del trip_final\n",
    "# del v\n",
    "# del v2\n",
    "# del v3\n",
    "# del x1\n",
    "# del x2\n",
    "# del y1\n",
    "# del y2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# PyInt_ClearFreeList()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.19058023 -0.08912072]\n",
      " [-1.27111798 -0.24104881]\n",
      " [-0.97219883  0.39479847]\n",
      " [-0.71030447 -0.41666273]]\n",
      "<class 'sklearn.preprocessing.data.StandardScaler'>\n",
      "()\n",
      "<class 'sklearn.preprocessing.data.StandardScaler'>\n",
      "()\n",
      "[ -1.36555884e-15  -4.75507097e-16]\n",
      "----\n",
      "[[-0.7591318  -0.13044489]\n",
      " [-0.81048387 -0.35282013]\n",
      " [-0.61988854  0.57786159]\n",
      " [-0.45290077 -0.60986404]]\n",
      "----\n",
      "[[-0.7591318  -0.13044489]\n",
      " [-0.81048387 -0.35282013]\n",
      " [-0.61988854  0.57786159]\n",
      " [-0.45290077 -0.60986404]]\n",
      "0.932034496626\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAAI6CAYAAADc9LdlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e04299278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "# from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from matplotlib.colors import ListedColormap\n",
    "# Import a sample dataset to make sure the process runs smoothly in it first\n",
    "from sklearn.datasets import make_circles\n",
    "\n",
    "h = .02  # step size in the mesh\n",
    "\n",
    "# clf = GaussianProcessClassifier(1.0 * RBF(1.0))\n",
    "clf = MLPClassifier(alpha=1)\n",
    "\n",
    "# X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\n",
    "#                            random_state=1, n_clusters_per_class=1)\n",
    "# First use the sample dataset. Then replace with my data.\n",
    "# ds= make_circles(noise=0.2, factor=0.5, random_state=1)\n",
    "# preprocess dataset, split into training and test part\n",
    "# X, y = ds\n",
    "\n",
    "#scaler = StandardScaler().fit_transform(X)\n",
    "\n",
    "scaler_test = StandardScaler().fit_transform(X)\n",
    "scaler = StandardScaler()\n",
    "print(X[1:5,:])\n",
    "print(type(scaler))\n",
    "print(np.shape(scaler))\n",
    "X_sc = scaler.fit_transform(X)\n",
    "print(type(scaler))\n",
    "print(np.shape(scaler))\n",
    "print(scaler.mean_)\n",
    "print(\"----\")\n",
    "print(X_sc[1:5,:])\n",
    "print(\"----\")\n",
    "print(scaler_test[1:5,:])\n",
    "\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "    train_test_split(X_sc, y, test_size=.4, random_state=42)\n",
    "    \n",
    "fig5, ax = plt.subplots(1,figsize=(10, 10))\n",
    "# Set the meshgrid boundaries for the plot and decision boundaries    \n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                     np.arange(y_min, y_max, h))    \n",
    "\n",
    "# just plot the dataset first\n",
    "cm = plt.cm.RdBu\n",
    "cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "score = clf.score(X_test, y_test)\n",
    "\n",
    "# Plot the training points\n",
    "# ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n",
    "#            edgecolors='k', alpha=0.05)\n",
    "# and testing points\n",
    "# ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.1,\n",
    "#            edgecolors='k')\n",
    "ax.set_xlim(xx.min(), xx.max())\n",
    "ax.set_ylim(yy.min(), yy.max())\n",
    "ax.set_xticks(())\n",
    "ax.set_yticks(())\n",
    "\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "if hasattr(clf, \"decision_function\"):\n",
    "    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
    "else:\n",
    "    Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
    "    \n",
    "    \n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
    "\n",
    "# # Plot also the training points\n",
    "# ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n",
    "#            edgecolors='k', alpha=0.05)\n",
    "# # and testing points\n",
    "ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n",
    "           edgecolors='k', alpha=0.05)\n",
    "\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Notes and things to try:\n",
    "    - Doing this on PCA data\n",
    "    - Creating density plot of the test results.\n",
    "    - Look at fit on a log scale (?)\n",
    "    - Compare multiple fit methods (as in the example)\n",
    "        - Compare them quantitatively \n",
    "    - Can we increase the number of categories to 4, instead of doing sequential trainings?\n",
    "        - Apparently all of the classifiers work for multi-class out of the box.. Just add more labels to y[]\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Seperate the test results into two groups based on the color in y. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Test classification ')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgIAAAHzCAYAAABSY3XtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e045f5c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the test results into groups a and b.\n",
    "group_a = X_test[y_test.astype(bool), :]             # Filter the test results with the boolean array\n",
    "group_b = X_test[np.invert(y_test.astype(bool)), :]  # Get the remaining results of the test matrix\n",
    "\n",
    "sns.set(style=\"ticks\")\n",
    "\n",
    "# Set up the figure\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Draw the two density plots \n",
    "ax = sns.kdeplot(group_b[:,1], group_b[:,0],cmap=\"Blues\",     # I changed the order to be consistent with scatter plots earlier in notebook\n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax = sns.kdeplot(group_a[:,1], group_a[:,0],cmap=\"Greens\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "\n",
    "\n",
    "ax.set_xlim((-1.5,1.5))\n",
    "ax.set_ylim((-1.5,1.5))\n",
    "\n",
    "plt.xlabel('Distance of commute')\n",
    "plt.ylabel('Average Speed during commute')\n",
    "plt.title('Test classification ')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Training classification ')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgIAAAHzCAYAAABSY3XtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wdxw4Rxpf44KwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e04506358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the test results into groups a and b.\n",
    "group_a = X_train[y_train.astype(bool), :]             # Filter the test results with the boolean array\n",
    "group_b = X_train[np.invert(y_train.astype(bool)), :]  # Get the remaining results of the test matrix\n",
    "\n",
    "sns.set(style=\"ticks\")\n",
    "\n",
    "# Set up the figure\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Draw the two density plots \n",
    "ax = sns.kdeplot(group_b[:,1], group_b[:,0],cmap=\"Blues\",     # I changed the order to be consistent with scatter plots earlier in notebook\n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax = sns.kdeplot(group_a[:,1], group_a[:,0],cmap=\"Greens\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "\n",
    "\n",
    "ax.set_xlim((-1.5,1.5))\n",
    "ax.set_ylim((-1.5,1.5))\n",
    "\n",
    "plt.xlabel('Distance of commute')\n",
    "plt.ylabel('Average Speed during commute')\n",
    "plt.title('Training classification ')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Apply the model to new data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Apply the scaler to the new data (stored in the variable X from the functuon: X = preprocessing.StandardScaler().fit(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_new=np.stack((extractData_nil[:,1],extractData_nil[:,3]), axis=-1) # Indexes different than during classification because I'm getting the values straight from the extractData variable \n",
    "X_new_scaled = scaler.transform(X_new)\n",
    "y_new = clf.predict(X_new_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgIAAAHzCAYAAABSY3XtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAFkYRAADAwv4Pp7oVo0blSngAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e0670f160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the test results into groups a and b.\n",
    "group_a = X_new_scaled[y_new.astype(bool), :]             # Filter the test results with the boolean array\n",
    "group_b = X_new_scaled[np.invert(y_new.astype(bool)), :]  # Get the remaining results of the test matrix\n",
    "\n",
    "sns.set(style=\"ticks\")\n",
    "\n",
    "# Set up the figure\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Draw the two density plots \n",
    "ax = sns.kdeplot(group_b[:,1], group_b[:,0],cmap=\"Blues\",     # I changed the order to be consistent with scatter plots earlier in notebook\n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax = sns.kdeplot(group_a[:,1], group_a[:,0],cmap=\"Greens\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "\n",
    "\n",
    "ax.set_xlim((-1.5,1.5))\n",
    "ax.set_ylim((-1.5,1.5))\n",
    "\n",
    "plt.xlabel('Distance of commute')\n",
    "plt.ylabel('Average Speed during commute')\n",
    "plt.title('Classifier prediction of new data ')\n",
    "print(np.shape(group_a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-label classification with Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "##### Reference for mode specific indexes\n",
    "\n",
    "    iCar_i:iCar_f\n",
    "    iPub_i:iPub_f\n",
    "    iCyc_i:iCyc_f\n",
    "    iPed_i:iPed_f\n",
    "    iOth_i:iOth_f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choose the data to fit/test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "##### Use the original values of speed and distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "\n",
    "# Combarray already has removed the id number, so indexes not the same as extractData\n",
    "X=np.stack((combArray[:,0],combArray[:,2]), axis=-1)\n",
    "y=np.zeros(combArray[:,0].size)\n",
    "\n",
    "# Set different classes\n",
    "y[iCar_i:iCar_f]=1  \n",
    "y[iPub_i:iPub_f]=2\n",
    "y[iCyc_i:iCyc_f]=3\n",
    "y[iPed_i:iPed_f]=4\n",
    "y[iOth_i:iOth_f]=5\n",
    "\n",
    "\n",
    "# print(y[0:30])\n",
    "# print(type(y))\n",
    "# MultiLabelBinarizer().fit(y.astype(int))\n",
    "# print(y[0:30,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.68345323741\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAAI6CAYAAADc9LdlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAIABJREFUeJzs3cuSHMl97/mvu0dEZtYFQF940Rmb\nWc1SS72NHmgeYtZ6I+5ncWRHFJskblWVmRHh7rPwRHezJfKIniUUAvb9mJUVQCKzffkz9/8l1For\nkiRJ+lF86QNIkiR9aQxIkiRJv2BAkiRJ+gUDkiRJ0i8YkCRJkn7BgCRJkvQLBiRJkqRfMCBJkiT9\nggFJkiTpFwxIkiRJv2BAkiRJ+oXh7/3A6XTid7/7Hb/61a9IKf13nEmSJOlZ5Jz54Ycf+Md//Ef2\n+/1/+XN/d0D63e9+xz//8z//vR+TJEl6Mf/yL//CP/3TP/2X//3fHZB+9atfAfD//Ov/5M2ceSCw\njgeeDnt++OZX3Hz7W2KK5BhJwRsmSZL0ct7nhf/3z//fj/nlv+rvDkifntV+PS+8WVe+Z+CPeWYK\nA0McmYaRGBO1VpYYifHv/k9IkiQ9q7+3LKi7SHu9/J5Z+VCfWIeBFAL18r+HEIil9H69JEnSi+kO\nSDe7iV2cODDwhsiSoKRIij9PaIFa61/9DkmSpC9R9/vX2/PMUFZOAEyEJXMmsPuLf1UJIVx3QkmS\npM+s+wZpD9wAd8CRQhkn7sYdy7r++G9KdMySJEnanv4EExOFyAykOJFDoBAIeWEF5hAs0JYkSZvU\nnWBCqRxJBBJjAGqEAGuKpDQQfVqTJEkb1R2Q4jBwV9oF1AdgN0SWUiAM1h1JkqRN62/zD3AsmYVK\nqoGFAARqsu5IkiRtW3eaSXFHiCMfCjwQOANlGkhpfMbjSZIkfX7dT2x/WFeWAkcOTHcjcRjJYWT0\neU2SJG1c9w1SzisrKztm3s4rt0OkBIdCSpKk7esOSL9J8BsCiZWBgRMVV9NKkqSvQXdAmsaRATgA\n+/nIOReSN0iSJOkr0N/FNi8kMq9oweg1gZxdTitJkravvye/ZqCykNmz8lBndjGQS36+00mSJL2A\n/kGRtQArZ+AhV5a444ZIqD6zSZKkbeu+QTpffjJQKIQaqGWlGpAkSdLGdQekI/AEnIBp2HGXoJzO\nFJyDJEmStq07IL0Gvge+J5JzZZhnBmBdn57tcJIkSS+hf9UIsABPFCLwMUQOw8hureR1fbYDSpIk\nfW5XbZattCrvpZ5gzcwBxmEgUa1FkiRJm9U/B4l2i3QEEoHdeuRxWaghMQClOhNJkiRtU/8uNuCR\ndov0KkyMcWCcz+T66fbIYm1JkrRNVz2xDbRC7Xd1JdTKTQwsZWEJgRSv+mpJkqQX051iHmlzkB4J\nTECtgVArS6kQXVsrSZK2qzsg3dJqkN5ROQw7ZgrvQmTa3xK9PZIkSRvWnWQ+9aiNwONaSDFw2o2M\nQ/f2EkmSpC9Cd0AKwB2Jb4gcOfE+jNzs7snF7jVJkrRt3QFpYiATWKgMJIbDgTFGgu39kiRp47oD\n0kyhsDJSWShMVPJytrtfkiRt3pUBqa0buWWC5UxdZnI1IUmSpG3rrqj+SOSGNjDydppY88IfcuYW\nV4xIkqRt6w5It7yikHngSJ0zDBOHmp/zbJIkSS+i+4ntTAImdrziPZUaB4ZaKBYhSZKkjeu+QdqF\niQGYCWQqaUich5HRIZGSJGnjugPSD/UMjFQGMgvv4wD7O2LwBkmSJG1bd0DaUygceU+mAg8EXu1v\nnvFokiRJL6P7PeyGyg1w4Mz93Y60PxB8XpMkSV+B7kRz5ImRD/xmSowhciCwLPNznk2SJOlFdAek\nX4WFPZW4FkqEXSiUbJu/JEnavit2scFEppQjOWcWYBx9YpMkSdvXnWhihT1wAuJ84jQv1GpAkiRJ\n23dFDRJ8BAowxIEpVKiuGZEkSdvXHZCGy4cPAKcnlmUhuGpEkiR9BboDUgJugFsgMPM6z5wfHqje\nIkmSpI3rDkgVmGnPbAuBXQ2MZYGyPtvhJEmSXkJ3QPpAq0PKwGsidZ6peYVSvUWSJEmbdlUN0p7E\nLQOFPXE+seZMqBXjkSRJ2rLuXWwDE0cKj0QqI0uFISUyleTCWkmStGHdN0g/EDiRqUSOJD6WgTCM\nlNiduSRJkr4I3Wlmz45IIlFJVM4hEQLcDgYkSZK0bd03SIVIJQGBgZUlDCQi2X1skiRp464eFLly\n4t/Ysb+95zaN5GybvyRJ2rbugLRSmQnASEkjhwDnXIlO05YkSRvXHZD2FA4cKSy8mVrnWg2Zgh1s\nkiRp265YNXLklsw9gRQrDymwj5HgECRJkrRx3QFppvKRwgMDJQ6EGMhpYJ8SuZTnPKMkSdJn1V+k\nPX1q8I+MzERmwrgnhADO0pYkSRvWPbRonWf2VHZUQh54t5wYgVArWIckSZI2rPsG6RUQyVQy5Xhk\nzJWynFlDIMXur5UkSXpx3UlmiTcM3HIicq4rAxVKgZie83ySJEmfXXdAarVGiRtuSWlPiYEwDlYf\nSZKkzet/C8uFIwsnMu9zgAxLgRisP5IkSdvW/8TGSntMK2QyRwq5rpTqHZIkSdq2KwJS4COBJyr3\n0y2hFu5CJObVOUiSJGnTutv8FwACJyI3u4FziFArqWZCAeL0XGeUJEn6rLpvkCYSNwS+YaWenyBU\nQmiPbqniLZIkSdqs7hukX08rr9fMkcrjXFhyhVoZiDgqUpIkbVl3QPrzvAArM5DYUWMEKhUoIRAd\nFilJkjbqiic2GGm1SI8ETsOOwzBR6kp1WKQkSdqw7hukQAAiOxKFSh4G5hSpIXp7JEmSNu2KgFQJ\nFE4UPhC4S4kQIiFafSRJkrat+6pnphVi74BbdqRlZl4XSvB5TZIkbVt3QNoDCfgGuCGzrAtLLSSf\n1yRJ0sb1L6u9/H4EMgvn8wzrmeqqEUmStHHdNUgPtC62M7CSeD1AXRbyfCKOOwu1JUnSZl3V5r8D\nClDYE+pEjImJQCj52Q4oSZL0uV11zVOACjySmdMIw0SgMtTqqhFJkrRZ3QHp6fKzAjsC7yrsxx2V\nQAiBFp0kSZK2pzsgHWhPbJ/qkO4nOJcMIbACMViDJEmStqk7xSQGXjNxTwBOjDmzLGcASvx0iyRJ\nkrQ9V1zzrDwwk0n8lltO60qqmXOAGLub4yRJkl5cd5J5oj2zHcmsQJoGBp/VJEnSV+CqSdqRtpPt\niZlYKp/61hwWKUmStuyqK59Am4c0UUg1U2OAGK0/kiRJm9YdkAqtgw3gdpioS2YulXxd5pIkSXpx\n3Wlmd/lwBWIaWKmMJGp1QKQkSdq2q2qQDrRbpFMt7Kc9kUw0IEmSpI3rDkgn2hTtB+BpWblNlTln\nl9RKkqTN627z/9Pl9y1wJrCslSWvpBCwRFuSJG1Z93XPG+DN5eNP9cgxwD4msi9skiRp47pvkI7A\nHymswMSO85AYd3syJiRJkrRtV7X574A74AMru2lHJRCcpi1JkjauO81E9hxonWwDmfV85lgLKabn\nO50kSdIL6A5IM5kzI4/ALYH1vFAsz5YkSV+B7oCU2LMSeCDwkco0TKS8UJyDJEmSNq47IGWgsuMG\neAR2qVBKJrioVpIkbVx3QJqAeLk9+g54//YdYc3eIEmSpM3rDkgfgBOVA5CAMQK1kCqUYkiSJEnb\ndcUNUqHwxAAcSIwxsabIGAKh5mc8oiRJ0ud1RRfbmR2ZQBsaOex3DOMEYB2SJEnatO6A9D0r39NG\ncS8EPuaVmEYAqu3+kiRpw7oDUgVOwAKMQF0q0/nMvC7UaECSJEnb1R2Qhp/97CikcmJHJS9nb5Ak\nSdKmdQektmYkkIBd2JPiQF4XphhJJVOtQ5IkSRvVHZDOtDb/M60oexpG9gSWWhgIzkOSJEmbdVUN\nUgDeAO85UoA5AHG83B75zCZJkrbpqlUjkdbivwKPITIPO8ZhJAdIsfurJUmSXlR3ilmAPwG/B34A\nwrBnSYmVSo7Dc51PkiTps7vqiW0Cvr/8nOcTIQVS8OZIkiRtW3eauQO+o3WzTcCrvMAyE4HoqhFJ\nkrRh3QFpBT5evuANMM4nOD6R19lVI5IkadOueg87APPlS5Y1c7+cWefTsxxMkiTppVxVgzTTntce\ngcOQyKUyVli8QZIkSRvWHZAKbQfbE/AA1DhwroG5ZKKF2pIkacOumqT9J9oz2wi8XzOHmEi1UFw1\nIkmSNqw7IAXghnaDNAH/MA3U5UyJiUNM1GInmyRJ2qbugHRDuzn68YtCm6YdhjYkMrqLTZIkbVR3\nQIrAHvg18GfgOEzsdjvSjy9rwWc2SZK0Sd07QRbg3eX3Hni3rtzu9pTYQlENEIILayVJ0vZcdYP0\nLZBoi2vLciaHQA5D+3tIz3RESZKkz6v7BmlHC0YTrWD7XCFeapDWmEjRVn9JkrRN3Slmoc1C2tPC\n0u00UQLUYW84kiRJm3bVLrYMvKUFpUMaWalELMyWJEnb1h2Q9pefiba0NofAMIwE5x9JkqSN6w5I\nJ+ADLRx9C/zr23dMcaA4/0iSJG1cd0B6oN0e3V++5LtUyadHqq39kiRp47oD0o6JE5E7WpH2oVZO\nTw+U/q+UJEn6InSnmUKiMvEnAkdgipE1jAx2sEmSpI3rTjMTFQic2AHwEAL7w43rRSRJ0uZdUYNU\nOFMZSJyA83RDSgFLtCVJ0tZdsWqkrRJ5ZKGSqPs9YRgIwSc2SZK0bd2rRt5SqQRmVhZGXu/3fCiZ\nvTVIkiRp467oYkvcAb9mJKUBaoUM1UGRkiRp47oD0isKewJ/ZmSKEx9S4nBzQ6zVQm1JkrRp3QFp\npnAEAkAsrCUSY2IAp2lLkqRN6w5IgcwNcE8mrCv7PHOej5RaCThNW5IkbVd3kXZkpQATK5EbpiFS\n1sxcq8MiJUnSpl0xKBL2rMxAqTDkTK0ZDEeSJGnjrkgzIwsDBSjliXdzJg4jIaZnO5wkSdJLuGJQ\nJBwITJelI/PxkadQSN4gSZKkjetOMwuF+dLqHwks68yOiTWvz3k+SZKkz647IN3QFtYuLPyZyt0w\nMlBJeaUU2/wlSdJ2dQekQiFSOFP4HrgjMK9L+0LnIEmSpA3rDkgnKkdaN9sZqKGylEJ0Wa0kSdq4\n7jSzB+6AkTZM6SFEhqGNVarBQZGSJGm7rpqDNAFvgB1AGhiAc60kW/0lSdKGXVGD1ByBFbgNmaUs\nBNv8JUnSxnWvGqnAR+AJmBjgcEvJhZpXGMZnO6AkSdLndsUcJJhpBdoMN4xxINRKtcVfkiRtXHdA\neqA9sy3Ax/XE07oQSiXb4i9JkjauOyBlWji6BSIzsRRiLQwV8ro82wElSZI+t+6A9B1wT3tmG7nc\nJoXIbRqIZaXU+kxHlCRJ+ryuWlY7Aa9oISlX2A+RcrlFqj61SZKkjbrqiS3Rbo52QEqBISTWslK9\nPZIkSRt21Q3SmdbmPzIyLivzmkkE1ojDIiVJ0mZddYP0ySMrOVRyXngohZp2z3A0SZKkl9E9KPJE\nm6JdgSOVIcMcE+MwgU9skiRpw7oD0g2ti20FXl8uotZaSTGSamEuheTaEUmStEHdCeZEWzUSgUwh\nAnFdiXklhECwi02SJG1Ud0AagTe0Fv8ZyGFHZoCSySUT8JlNkiRtU3dAmvhpFhLAQyns4sSpRGqt\nlODzmiRJ2qbuGqQPQLj8PgAfayWnBAzkmGzzlyRJm3VVm3+kTdI+Ax/WyjkMHMeBNE5/+8OSJElf\nsKsGRb6hTdHeAQuZ0ziwnwxHkiRp27oD0re0Fn9oK0fGWhjqmSnavSZJkratOyCttE62mTYs8j4s\nDPls75okSdq87oD0GrijPa+NwON5ZoyRWrxBkiRJ29YdkMrlw7e0lSPDGMmB1tomSZK0Yd1t/u9p\ntUe/Bx6A8dW3TDWSfWOTJEkb132DVIFH2k1SJXKzP5BTJASvkCRJ0rZ1B6SFVoMUgSOV8zTAtCca\nkCRJ0sZ1B6SBtrD2BghE1mmEcecNkiRJ2ryrntg+lRsNFE5PR3It1GoRkiRJ2rbugDTTdrAB7Ej8\nmsA0H6nrTLbVX5IkbVh3QIKRDz/29CcClaFWhlJJJXuTJEmSNqs7IE0EJkbeAhORh7wSQyTWwgDU\nmp/vlJIkSZ9R9xykf2fmxEpm4BGYwkCp9ac5kV4gSZKkjeq+QXpF4jUjt0RWzixkUgiUEKm1UsMV\nr3eSJEkvqPsGKQGQOVKo7AglsBIIMbKEQIoGJEmStE1XzEEKjBRW4ANn8jpzDIElReIwPuMRJUmS\nPq/ugPTAygR8D/wW4PgANVOvaYyTJEn6AnSnme+B28sX3AF1PhGWhWj3miRJ2rjugBRo+9gqbeXI\nN3EgLEeqQyIlSdLGdRdp/+lnXzABNQTCmm3vlyRJm9d9g3QDfANk4BWQ88w5BurQnbkkSZK+CFcF\npE8fPgJPIRL3d0TnH0mSpI3rTjOf6o8K8AR8jDvGae8LmyRJ2rzugHRH62L7DjgDN4cDlUKANknb\nZbWSJGmjuguG3tGKsz/SbpFKKcSYKHm9TNm+3DDF5FRtSZK0KVdVVJ+Bx8ufH4BbAkOthPDjylpy\nyWQgGpIkSdJGdKeWSJuFdA+MwEIk8ZfhCC4726qzkSRJ0nZ0B6QV2NFukSpQ5iNzzpS8kkv+ixqk\nYD2SJEnakO6A9A1woK0c2QNhXdmVwlRhVyohL5RPU7XDX/8eSZKkL83Vbf6J9sR2z8pSMvXS6D8Q\nCbXVH5WQ/voXSZIkfWGuGhS5o03STkCdV855YQ2BFShUaqnMIdrFJkmSNqW7i+0tLRg9XH6/SpEl\nJUIaqLWyXv6d4UiSJG3NVV1sw+XnATiGxDSMAIQQiCGQL78lSZK2pDsgPdBmIP2BNjSyTntiyT/+\n/yttSKQkSdLWXPX+daYVa+8BUmSucKqFOQQnaEuSpM3qTjC3tCLt74EjEHc7hjQS0khMg5OzJUnS\nZnWnmCMtIM1cKr3XlZngrZEkSdq87jST+OmJbQXGdSWvC7m4VkSSJG1bd0CaabdIAzABKQ3clEyY\nzz9N0JYkSdqg7oB0oK0bicAdMK0zKa+kUqg/62aTJEnamu6A9BZ4oj2zJWDHSi0rU83UakCSJEnb\n1R2QBlow+gdasfZyXkilstRKuG56gCRJ0ovqTjLf00LSW9qy2uOSCSExAyF1bzCRJEl6cd0B6Xz5\n8Aj8Eag18CEU6u7GGUiSJGnTupNMBQKtzX8EnkKlxEQwHEmSpI27KiDNtDqkCuxi5DavlDw/19kk\nSZJeRHdA2tPWjQxABnKtxBAZSqHW+lznkyRJ+uy6q6kzbYJ2AE7AGAM5RUIN5LySLv+uhkCIiRDC\nMxxXkiTpv1/3DdKHy88D8AbIVHIYqFR2tTLSapOmWqnr6q2SJEnajO6ANBIYidzQVo7k21cMMRJr\n/Q+3RVOAUl0/IkmStqE7IL1n4kThSCvUHmvmlPNf7WIL3iBJkqSN6K5BWsh8ILGQOQNjSOS8Wmsk\nSZI2r/sG6Y7KLYEnWpH2PsKhFM75P+5hywDB+UiSJGkbulPLgcg9gf+DkQnIDw+camEJsPLTc9oK\nrDE6XVuSJG3GFU9slTOFFfgGeHs6UmpgSgM1jZwvRdkxRJLPbpIkaUOu6GJbuaWQyCzAA4U0JtLl\nKS3FRHL+kSRJ2qCrJmlD5Z5WY3SOB+53N6zrgv1qkiRpy7oD0o4WkhZandE4JEoplADRWyNJkrRh\n3QHpibakdrn83tfAx5wJaXqus0mSJL2I7oA00W6OKnAHzMsTS0pM4/hcZ5MkSXoR3QGpXj58T1s1\nEoYBbOWXJElfge42/z/R6pCegPfAN/eveZfn5zqXJEnSi7nqie1MS1i3wLIsBCzOliRJ29cdkDIw\nAo+0GqQ4n4gZiktpJUnSxl1VNBQJbZUIMA4jY5kppTzPySRJkl5Id0B6ZMd74o9zkPYJ6noCx0RK\nkqSN6y7STtxwZmDmPRNwXCoh/PR1uWRCrRCAkBweKUmSNqM7IL1jT2Jk5UThzJnIuL8FoKwLU61t\nD1uFlZUcE8kxAJIkaQOuSCyRTKQy8QAMQ+RYKrVmJviLJbUDkMpKtYBbkiRtwBVt/it3VCoDKxDS\nyE1ZCGv+8d/UWsklU3ImlkIu+a9/oSRJ0hfiihqkQKWQOAEQ84lSgNIGAJRaCTmz+/SBCgsrOUSf\n2iRJ0hetO6mMFConDszcADGOjDmTP7X5l8LPt7KtVKYQid4iSZKkL1x3QHrigR1nEoEb4BAhJoix\ntf7H+tM8pEwlx0gIgaHWn0KUJEnSF6j7ie2GhcqRQJt89JgXIglSgDQwl4VaK5VADXawSZKk7egO\nSK9Y2ANHoAA1RM4xMsSxdbClkXAZg/RzOWBYkiRJX7TupDLQdrBBC0G1rNRaKKF9ZQ2J9RdTtVcg\nx+5MJkmS9FlcUYPUbo8CcAvc5sApVFJsd0YxRmoamWNgCTCHQHFYpCRJ2oDu65wA7C4//wa8SiMs\nK/VnmSuE8OP6EReNSJKkrei+zrkBZuCR9nT2oUSmYfSGSJIkbV53mlmATLsZGoD/NRfi3d3f/pAk\nSdIGdAekdxwITPwaSLQFtcTd/+5jkiRJX7zuGqQ3lznZfyCzI/Pdqx1TcACkJEnavu4bpCOZE5Uj\nIwMwhEBZ23BISZKkLesOSJ9i0CvaapExJUpen+dUkiRJL+iqgHSgLa3NfNrBFtoUbUmSpA27apL2\nI5UzZ07AUwiM4/R8J5MkSXoh3QFpYuE7nrilkoCb3Z5QsQZJkiRtXndAumUGKo+0Nv/l+ESgUkt+\ntsNJkiS9hO6ANAJvgD3wASBn9lTqMj/T0SRJkl5Gd0BaadO0AV4DeV2pFQYqpTgPSZIkbVd3QArA\nmbasdgLenwrUSggRMCBJkqTt6g5ID7RW/zPtJmmfK+dSIUQqtvpLkqTt6g5Ie36ahRSAWE+cc6bE\nQAzdXytJkvTiupPMQqtDysAtUJgpZSHHwWGRkiRp07oDUuan+qMVeEUr1E7R2yNJkrRt3WnmhhaS\nRtpU7ZvpBs5nB0VKkqTNu+q659MMpN8Du2lkCrCuLqyVJEnb1h2Q0uX3nva89rBm4jASa/EWSZIk\nbVp3QHpPa/U/0ELSu9PCME0MtO42SZKkrbqqzX8PfKTdJo0pkUOkxkC0i02SJG1Yd0AaaEXan+Zm\n72JgzivFLjZJkrRxV7X5L/w0TTtOA4RAjMNznU2SJOlFdAekSJuB9HT5Cfsd4zA+17kkSZJeTHdA\nGmk1SAMwAykMnAef1yRJ0vZdlWjGy08AfjifOaTpWQ4lSZL0kroD0hPwB+BE28V2EzNpnsnr7Bwk\nSZK0ad0BaUcbEPkd7altWjJrXZgqVKdpS5KkDbvqie2R1skGkCiwLNRaGankkq8/nSRJ0gu4KiC9\nos1AAihpYB8T8RKMgvO0JUnSRl21iy3SutjeA2ncUYeRgUApmYrTtCVJ0jZ1B6SFNiyy0mqQ3p7O\nhNjmIGUqKaa/8WlJkqQvV/fY6x1t/tE7WljapchaMzFUShyue7uTJEl6Qd055kirPwqXn7zMhDiS\n00h0orYkSdqwq5bVBuAOeAMclyNryOSYiMH6I0mStF3dAWmlTdE+Ax+Au+nAGgIp+rgmSZK27ao0\nc778DkBKA9XKI0mS9BV4loC0A+ZpIrmLTZIkfQW6A1IFJlqx9gfgPE2Mo8XZkiRp+66qQQq0Au0/\nAWl3S6jlb39IkiRpA7oD0j3tae0M/AaA7HoRSZL0VegOSDvgQOtkG4Hp8cjiglpJkvQV6A5Ij8AD\nLRwtQH58T83eIEmSpO3rDkh74JZWh7QAKe1o29kkSZK27aoi7ZW2bqQAQ14oeX2uc0mSJL2Y7oB0\noj2zHWm1SE9lpYTu3beSJElfjO6AlGkhaabVIWUig0tqJUnSV6A7ID1dfi+0Vv9DiuTzmVot1JYk\nSdvWHZDeAOny5wfgZr9jrJlaLdSWJEnbdlWR9g1tYOT3wDwv7GKA4jRtSZK0bd0B6VM5dvj0OyRy\nzhRf2CRJ0sZ1B6RIe2KLtDZ/UmKplZDsZJMkSdvWHZACUC+/T8CZQN0fSLH7KyVJkr4I3dc9b2k3\nSPny53pz4G6cnutckiRJL+aqZbUjbVBkpK0eYXWStiRJ2r7ugHQL3AETLSzdrQt5OT/XuSRJkl7M\nFZO0JzKtDmkB0jKzuotNkiR9BboDUmHkzC2BxASUJTMv2UnakiRp865aNVIJzJebpDlk7sZEKd4i\nSZKkbbuiJz+SgY/E1vK/FsYCdV2e62ySJEkvorvN/wMTT0Tec+Y3wDENDHnmtIwMu2c8oSRJ0md2\nxdjrwD0JSLwF7lNizAsFd7FJkqRt6w5IlULgTGRlYGA5PVJzYRgcFilJkratuwbpDUe+YSFR28qR\nEDmGRE3jMx5PkiTp87viia1yuoSjmZVcC3k38sp1I5IkaeOuWlY7AIUTt8BxhTDtn+1gkiRJL+WK\nSdon4Mj3l1ukw5TIOT/fySRJkl5Id0D69DY3A4/A7bRjMiBJkqSvQHdAugO+oQWkM7Dmwliqt0iS\nJGnzugPSp41rt8AeOC0zdYitOEmSJGnDugNSBEZgAu6BD+ePzAXcVStJkrbuqhukDCTac1ukEIgM\nNVOK07QlSdJ2XdXmX4D18iU36ZYxwkCAakCSJEnb1R2QFlpASsA7IKUItVDzQsnrc51PkiTps+ue\npP17WgfbAy1lpcOB3RAZa6DWwrKupOGKQd2SJEkvpPsG6UBkTyACH4CyuyGFkUqlxshAtRZJkiRt\nUndAemJiZuQM/CswHG7IKbGESIqJBLRHOEmSpG3pDki3DOzZsWPie+Dd6YkaEjGlZzyeJEnS53dF\nm38mUIgEvgPuS2YoK3ltk7RXIATDkiRJ2p7ugLQnXJ7RzpyBcp6p68JIYS6FHCMhOFZbkiRtT3dA\nKjxRePyxDW6ohY/LzBoDNbU6JEmSpC3qDkjQltVm2lTtYRg4UNqzWrjqayVJkl5Ud5I5A3+g7WML\nwDIvDLlwygvRgCRJkjasO8m8Bt7QJmkHINZCjoFKsPZIkiRtWndAGoE9MAEfgTTtiNOeYJu/JEna\nuO6A9Kn26EzbV/JUKyUmYnTHthNRAAAUTklEQVS9iCRJ2rbugJRo4WgP7ID3aeA87EneIEmSpI27\nYtVI8wj8G7DevmK33z/LoSRJkl5Sd0C6od0izbQntpvbu+c6kyRJ0ovqLhj6M60OaQEOwJBn8roS\nU7KLTZIkbdpVXWwTrcX/FhiWTMorJefnOpskSdKLuKpIO9CKtFdgYiFSSVRqrc91PkmSpM+uOyB9\noNUfrbQbpOXde5blTFgXcl4ppTzXGSVJkj6rq4q0I/BAq0Macoa8kqjsaiWWTDYkSZKkDeoOSHe0\nVSMHLjdJAYY0UmIkhMAAxGI9kiRJ2p7ugFRpLXCvL39/qoU1DX8xSXuo1VskSZK0Od0BaabVIRUu\n3WxxZPzFv7FUW5IkbVF3QFouH36kzUPa1cw8z5SfdbDlEEix+z8hSZL0Iq6qQVqBj1yKtNdIWU5w\nCUgrUKJ72SRJ0vZ0B6R3tGW1O1pA2gdY5oVTzcwxUNPg7ZEkSdqk7lUje1o4urt8yRoqA5FY+YtC\nbUmSpK25ag7St7SQVIFYI2OK1LI+19kkSZJexFWrRjItHL0CTo8P5ABjiE7RliRJm3ZVm/9C28d2\nA5w5EqjENFBt8JckSRvWHZDOP/t9At4c7kg5M+dCi02SJEnb1B2QPpVhfxoOeT/tGEoml8XuNUmS\ntGnd7WYD7Z7ogVaofc4LS6mEYDiSJEnb1p1mTrTntXvaqpG388zu7g4GW/wlSdK2dQekQOtkW2kz\nkXIpnEIgpl9uZJMkSdqWq7rYTrRW/7dAGkfOcWBI3iBJkqRtu7LNP/wYkN4Pew673bMdTJIk6aVc\nUVEdORM4EdkB+2XmPJ8cEilJkjbvqoC0Z+BE5ADczEeYZ2JZqdVBkZIkabu6A9KOyJnKdPn7IUZY\nZkKp1Jqf6XiSJEmfX3dAeqAQiaysBGCcJqYagErwBkmSJG3YFTdIkDhzS5uHtKaBTzMiq6tGJEnS\nhnUHpJGV18D7y8/t4ZYlRGpM4DRtSZK0YVc8scH/pM1B2gMf10yOiRwT0V1skiRpw7qnOu6BG1rC\negQqcLPfuahWkiRtXneaicA3tIT1A3CgQK5k5yBJkqSN6w5Id7RVIwPwGpjCp841O9gkSdK2XfUe\ndkurQboBSq6wzmAHmyRJ2rjugHS+/NzTIlEJkUIgBgOSJEnatu6ANNAKtQH+BITdREgWaEuSpO27\n6gbp7eX3ANTbe1LsboqTJEn6YnQHpEdgAWZawfaSC8T0XOeSJEl6Md0B6QBMQKL1rU0xEVgp7mGT\nJEkb1x2QZqDQCrR3wFoyMURSXp2FJEmSNu2KXWztJ9DCUsqVWCoJCNWAJEmStqs7IAXaDdIb2kTt\n0+MDgUwIgeCwSEmStGHdAemeVoO00J7Ylsf3LLXNQKoOi5QkSRt21S62b2kF2gvwzbowzwsrlWA3\nmyRJ2rCrntiOtNujEXjMCykFFiLBadqSJGnDrhp9nWhDIj8Ay+EG0kgyG0mSpI27qs0/0yZpvwNe\nHe6otbqrVpIkbd5VAWmm3R7dA48lM8YIwfojSZK0bd0BaaIVaK+0GiQIrM9zJkmSpBfVHZCWy0+m\nBaVdSkSLsyVJ0legOyDtgdeXnw9AJVIIhiRJkrR5V60aibRJ2iMwRjivPrJJkqTt6w5IK61hrdIC\n0kAg5vm5ziVJkvRiugNSpbX4/5nLqpFSuKuBxVskSZK0cVcNigQ4Xb4k1gopQc3Xn0qSJOkFdQek\nRLtFisB7IO4mViCGqzOXJEnSi+pOMx9oT2y3tLBUSuUcg4tqJUnS5nUHpEwrzl5oxdpvU2TcHYjR\nGyRJkrRt3WlmoBVof5qmHUoFZyBJkqSvQHdAKrR1I3+kdbENpyeWYoG2JEnavqsCUmTiBviBwHrM\nxHkml/J8p5MkSXoBQ+8HF25ZqEDmjsQcJlLYsauZZ5geIEmS9GK6k8xEAgIVmIisFQJ7SvYGSZIk\nbVt3QJrJLBQiiUrlFBLjMLA6B0mSJG1cd5p5YOXEE4XIRCAMiSWvEJyDJEmStq07IL0ic0ugsvIR\nCLVSh8Bu8AZJkiRtW3eaWVk5sjJSeE9gvBm5nZyDJEmStq87IH0L/N9ApXDmyKv7iRgNSJIkafu6\nA1IEnoDXtEnaSwlUZyBJkqSvQPccpBNtF9sReAVMdSWVzFIzMbYRAMm9bJIkaYOuSjBvaAFpBWKF\nus7cLAvDujLllbIu1Fqf5aCSJEmfy1XLan8AHoAbIOaFsUIKkVALIQQmoOb1mY4qSZL0eXQHpCMQ\naM9rM5AohEuNdpuv/dN/oHiLJEmSNuSqIu17WkiagKUGal6pZWX5WSBKtfrMJkmSNqU7IO1otUeV\nVqx9XjMTkVoq+wp5zQDkgMXakiRpU666QQJ4R6tH+jAvnCOENBBDZKKw5kyOrh6RJEnbcsWyWnik\nhaMM3N7fUYYda4SVwhpgjZFkQJIkSRtzxaoRONDqjz4AdW1zj0IaYZgIaST6tCZJkjaoO8EEWv3R\njtbJFmuk/GySdgYIBiRJkrQ9VwSkxKfNazvgfC7ky8yjlfa85g2SJEnaoisSTOJMa/V/AkgDJQTm\nGChpsPZIkiRtVndAapONJgrtuS2UmRICMQ7ETxMjJUmSNqg7IH2kcmbmA+1J7dtw5pzz851MkiTp\nhVwxKDKTaOHoDgglk3BitiRJ2r7ugPRIZKG1+g/AMg7sXCkiSZK+At0B6ZYdMDEDD8CuQKG6mFaS\nJG1ed0DKHEi84kwiAnMplDRhebYkSdq6K3axFSIQODAAlQohEOxgkyRJG9cdkAYKA4U/X/6+rCuV\n8jc/I0mStAVX7GIrHCmcORGAwzhgk78kSfoaDL0fnFkYeOQfKAzAYym8TtMzHk2SJOlldN8gfUfm\nDZU3wDvgfdoTo/VHkiRp+7oDUmHmV5fBkL8Hdq9fE4PLaSVJ0vZ1J5oD7eZopi2sjaHYwSZJkr4K\n3QFpASZaSEpAium5ziRJkvSiugPS8fLzRAtLuzU7RVuSJH0VrnpiW2ltcLfA+fT4XGeSJEl6Ud0B\nqQLfAP8nkIH75cz59PRc55IkSXoxV6waac9rA61Q++5wYMgz1Wc2SZK0cVdM0m7PbIVWrH0mEkvA\neCRJkrbuqhqkBXgLBOBxXQlDItrqL0mSNq47IL2jdbFBe2or88wSoBQX1kqSpG3r3sV2os0/eqQ9\nt6XDnn2aKCVDdKK2JEnaru4k85qJ79lTaXVIEIkhMNRK9hZJkiRtWHdAemTmLSdugP8L+Pj0RCmf\n1o1Yqi1JkrarOyBNl5/XtDb/21jJtbACwaW1kiRpw7qTzI6BN8D58vepBnLOZIKdbJIkadO6i7T/\nTCTSWv0jQC2tONsCbUmStHHdaWbgQOCeRJuFVIaBFCDW/HynkyRJegFXFGkXTgR+T3tmm8aRVFZC\nxXUjkiRp07oD0sIb/p2JSGzvdLmSCpT6qZNNkiRpm66YpB058Yo7CoFWzLQC1Q42SZK0cVe0+VdG\nHgnADnhaFuYQiam77luSJOmL0B2QvmXm15z5A60G6WOoME7Umq1BkiRJm9YdkCKPHIB72tPabnfP\noWTSmgl5cWmtJEnarCue2AqRE98BR2AfAzEXUskMBILt/pIkaaOuKBh65MjKO2APxLyQqJRaWUoh\nxEgphXrZy5ZiepYDS5Ik/Xe7ogYJ/get/igAJS/UkimX+qNc2lPbrhSmnGGdyT67SZKkDei+QfpI\nC0avgBOwns8UoMY2AynVwhBHAEII7T9UMjUE5yRJkqQv2hU1SJAuPzfAMD+xACUO5JqJ4T8+qQ1A\ntTZJkiR94bpvkAotHGVaJ9uflpU0TkxDghKI/JVbIicASJKkL1z3DdKZkfeXNSMfgbi/ZZomYhwg\n/udfW2ul+rwmSZK+cN0B6ZbIPSMfaXOQwjiRLzXYISTW/+SqaAnBbjZJkvTF6w5IT5cG/ol2g3R/\n/5pPl0MhBEocmENgpbJSmUMguIZEkiRtQHdiOXJg5okdrQ7pkAJrzgyXEBRj5Of5yxW2kiRpK7pz\nSyDw74y8p7X7z6cT0TlHkiTpK9AdkE4U3rBwvvSrHWIkL0cX1UqSpM27Yg7SzHsCgUQAdkNkqNU5\nR5IkafO6A9IDgRtgIlOBp3UhpIngDZIkSdq4K+Yg/Q/+nch7CgX4mCb2uz31rw2IlCRJ2ograpDO\nwDfsGKhAGNNlEKT9apIkadu608wbCoGFhbv2JaVyjoH0V6ZoS5IkbUV3mjmw446J5fIl708Lwdsj\nSZL0FbiiBqkSqEBpf5ojOTsHSZIkbV93QJoJQOaWI5nKboz/yfY1SZKk7eleNZL4SODEkYU9EIiY\nkCRJ0teg+wapcuYHPhBoC2un+UTJ6/OdTJIk6YV0B6SPvCLxiltaQCqnj2SnaEuSpK9Ad0C654Yb\nviPQXtZ2Q7hMRJIkSdq27oA0cmLPwpE37IBxv6cUu9gkSdL2dQekhcKZlYHAWyAc9gzD+IxHkyRJ\nehndASm20mwqR56AJQyEmJ7vZJIkSS/kikGRZwInnjjzCOyngeqeWkmS9BXoDkiBBwJvyST2QDnP\nsC7UaqG2JEnatu6A9FvOvL5M0x6B+enIVDPVVn9JkrRx3QGpUFlYGakkIOXMWANlWZ7xeJIkSZ9f\nd0D6wEBg5ddABoiFEBNjrbb7S5KkTesOSN+TecOOhbbQrY57Sq2EGAEDkiRJ2q4rBkVWFgoFCEAq\nmVMpxJio2M4mSZK2qzsgHVkYyByAM7CWTEqJlUoM3V8rSZL04rqTTOaGSqs/ugHOGdaayXEgBG+Q\nJEnSdl3VxfYEPy6rrbsDOU0AZIu0JUnShnUHpIGVlUQBvgECC/taiOcTU15hncnFmUiSJGl7ugNS\novLNpRj7ETiMI0Mpbap2rQwEhlK8TZIkSZvTHZDeU3hgZkcr0r4dd1AzIQRCbaEoAdHJ2pIkaWO6\nA1Ilk4GPtCC0rPOPzf0B97FJkqTtGno/+FtaupppRdohBOolGJUQSZd/VwlORZIkSZvSfYO0AhOw\np6WsPByINTD/bA7SCuBMJEmStDFXBKQbCiMJ+DNwjANPKZBTIgdYApSYiNGAJEmStqX7ia1Fo4GZ\nhW+AzEqaDoQ0Am0+kk9rkiRpi7oD0iOR9VJztAJxmSn8VHskSZK0Vd3vX+8Y+cAtD8ATENP4Y5G2\nJEnSll2xrHbPI3c8AX8E9sNAdXK2JEn6CnQHpBsyRxZmBm4u/1t1Sa0kSfoKdAekSOW3FG5YuQWO\n68J4KdCWJEnasu6AdEtiIPD/t3cvu20cWxiFV1V1k7o5Smx4kkGMzP2q57HyBn6FM4gZ5GJaEsnu\nqjqDZnJiRgasomKpifUBAnhptmr4Y/euXS+Ac2CoUKs9SJIkaf6O6EEaOOcDF0APLEplLOPjrUyS\nJOmJNAekM9Zkbhj2N7kddqTgJn9JkjR/zXOQCplfmc5iuwNSzaRkQJIkSfPXXEG64SUvuGZgCknn\nZ+f2IEmSpJPQHJA2JD7sA9IlkEI0IEmSpJPQHJAC8IHMsH9KtyzVgCRJkk7CEdv8M99wRwYqcLvb\nEjxqRJIknYAjdrHdEdnyDeN0WC2DAUmSJJ2E5oAEO35kwzXTVrg0wljLY61LkiTpyRwRkKBSuQIS\nEPqO4BwkSZJ0ApoD0ndEBs7ogAxsIsRoQJIkSfPXHJAK0BPZklgDZ/0C3MUmSZJOwFEBaQDGfWt2\nHzuqTdqSJOkENAekCHRUNozTTQrUYkCSJEnz1xyQfgYGdiyYdrGFPhHiUT3fkiRJz8IRj9jOeU/P\n70yP20qMxBAeb2WSJElPpDkgvQduuGbDgsAUkoIBSZIknYAjJml/y5bIL1xPAankR1yWJEnS02kO\nSB2JwrckBhIwfrxlGIdHXJokSdLTOKIHqacHOm54AfQlk4YteRwfb3WSJElPoDkg9UBg5CWZCizq\nSAD6WsjFM9kkSdJ8HVFBWvOKNVeE/Y0CNSRiCIRaqLVSnKwtSZJmqGv94ZIbej6SgTWw6xZcdh21\nVnLJ9LWSaqWE/QiA2PyvJEmSvqrmClJizZrMmkwGlssFtVbGPLIMkQ6IIdAR6HMhu8tNkiTNRHNA\n+o5Ez5YRyEBaLNiVTL1nYGQIgWhfkiRJmonmgLSmck0iAjvgjo4hRhYx3Xt98CBbSZI0E80B6Yol\ngQsqYWrTTtCnns89SKs4ZVuSJM1Dc0AaqVSgY0kEYi2kGBnvCUK1VspnKkuSJEnPTfPWsh1L1mxZ\nEsmwb8QuxJTY5kyaTmcDKjklUmzOYpIkSV9Vc2r5g8JAYKCyALa3N4x5IIRA6jpq6qmpg25Bsnok\nSZJmpDkgLbgAXhIYOQMWeSD8bTBkCIEQ7DuSJEnz0xyQNnSMBJaEqTG7i4RoIJIkSfPXHJAu2RKo\nZCpL4HaEGJyWLUmS5u+IXWxbCnckBn4HFhfnFGcdSZKkE9Bc8rliS+Q3euAOKLsMHk4rSZJOQHMF\n6QVrvmeaop2BYdwQ3covSZJOQHOi6f/2dwkMu+HPmdqSJEmz1hyQLoAlU/UoAiXn/WxtSZKkeWsO\nSAG43b8uQOoXxDw+yqIkSZKeUnNA+pkpIFVgC7y6vCQPA7mUf1xbayWXTCkj5Z7vJUmSnpPmXWyv\nmNJVZmrUDjUQyz8rSLlkUiks/3xPJtdCTM5MkiRJz1NzBekjU/VoYOpF6ikMJX+yk63USirlkxSW\ngL6UeytNkiRJz8GDyzg5ZwD+23Vc7T9bAatSGEsmDdu/zmArdaT/TA4a60iIVpEkSdK/5488AP/P\nL1/qwQlltVoB8J8ffvjk85+4hbtbuHv/0FtKkiT9q1arFW/evPni60OtDxt/vdlsePfuHa9fvyal\n9OAFSpIkfS05Z1arFW/fvuXs7OyLf/fggCRJknTqPBtEkiTpgAFJkiTpgAFJkiTpgAFJkiTpgAFJ\nkiTpgAFJkiTpgAFJkiTpgAFJkiTpgAFJkiTpgAFJkiTpwP8A/8tjedXybZsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e020f5be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "# from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from matplotlib.colors import ListedColormap\n",
    "# Import a sample dataset to make sure the process runs smoothly in it first\n",
    "from sklearn.datasets import make_circles\n",
    "\n",
    "h = .02  # step size in the mesh\n",
    "\n",
    "# clf = GaussianProcessClassifier(1.0 * RBF(1.0))\n",
    "clf = MLPClassifier(alpha=1)\n",
    "\n",
    "# X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\n",
    "#                            random_state=1, n_clusters_per_class=1)\n",
    "# First use the sample dataset. Then replace with my data.\n",
    "# ds= make_circles(noise=0.2, factor=0.5, random_state=1)\n",
    "# preprocess dataset, split into training and test part\n",
    "# X, y = ds\n",
    "\n",
    "#scaler = StandardScaler().fit_transform(X)\n",
    "\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_sc = scaler.fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "    train_test_split(X_sc, y, test_size=.4, random_state=42)\n",
    "    \n",
    "fig5, ax = plt.subplots(1,figsize=(10, 10))\n",
    "# Set the meshgrid boundaries for the plot and decision boundaries    \n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                     np.arange(y_min, y_max, h))    \n",
    "\n",
    "# just plot the dataset first\n",
    "cm = plt.cm.RdBu\n",
    "cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "score = clf.score(X_test, y_test)\n",
    "\n",
    "# Plot the training points\n",
    "# ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n",
    "#            edgecolors='k', alpha=0.05)\n",
    "# and testing points\n",
    "# ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.1,\n",
    "#            edgecolors='k')\n",
    "ax.set_xlim(xx.min(), xx.max())\n",
    "ax.set_ylim(yy.min(), yy.max())\n",
    "ax.set_xticks(())\n",
    "ax.set_yticks(())\n",
    "\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "if hasattr(clf, \"decision_function\"):\n",
    "    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
    "else:\n",
    "    Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
    "    \n",
    "    \n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
    "\n",
    "# # Plot also the training points\n",
    "# ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n",
    "#            edgecolors='k', alpha=0.05)\n",
    "# # and testing points\n",
    "ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n",
    "           edgecolors='k', alpha=0.05)\n",
    "\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "##### Separate them into groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-0.91260043 -0.64377867]\n",
      "  [-0.26173869 -0.73136079]\n",
      "  [-0.46076253 -0.43411075]\n",
      "  ..., \n",
      "  [ 0.20623628  0.16013933]\n",
      "  [-0.18105335  0.04965057]\n",
      "  [ 1.62091924  2.16830955]]]\n"
     ]
    }
   ],
   "source": [
    "# Split the test results into groups a and b.\n",
    "# group_a = X_test[y_test.astype(bool), :]             # Filter the test results with the boolean array\n",
    "# group_b = X_test[np.invert(y_test.astype(bool)), :]  # Get the remaining results of the test matrix\n",
    "\n",
    "group_a = X_test[np.where(y_test == 1), :]  \n",
    "print(group_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13664, 2)\n",
      "(5021, 2)\n",
      "(1521, 2)\n",
      "(1594, 2)\n"
     ]
    }
   ],
   "source": [
    "# Split the test results into groups a-d (e)\n",
    "group_a = np.squeeze(X_test[np.where(y_test == 1), :])    # Car\n",
    "group_b = np.squeeze(X_test[np.where(y_test == 2), :])    # Public transit\n",
    "group_c = np.squeeze(X_test[np.where(y_test == 3), :])    # Cyclists\n",
    "group_d = np.squeeze(X_test[np.where(y_test == 4), :])    # Pedestrian\n",
    "#group_e = np.squeeze(X_test[np.where(y_test == 5), :])     # other combination\n",
    "\n",
    "\n",
    "print(np.shape(group_a))\n",
    "print(np.shape(group_b))\n",
    "print(np.shape(group_c))\n",
    "print(np.shape(group_d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13664, 2)\n",
      "(5021, 2)\n",
      "(1521, 2)\n",
      "(1594, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgIAAAHzCAYAAABSY3XtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKClMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e0666e208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\")\n",
    "\n",
    "# Set up the figure\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Draw the two density plots \n",
    "ax = sns.kdeplot(group_a[:,1], group_a[:,0],cmap=\"Blues\",     # I changed the order to be consistent with scatter plots earlier in notebook\n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax = sns.kdeplot(group_b[:,1], group_b[:,0],cmap=\"Purples\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax = sns.kdeplot(group_c[:,1], group_c[:,0],cmap=\"Greens\",    \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax = sns.kdeplot(group_d[:,1], group_d[:,0],cmap=\"Oranges\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "\n",
    "ax.set_xlim((-1.5,1.5))\n",
    "ax.set_ylim((-1.5,1.5))\n",
    "\n",
    "plt.xlabel('Distance of commute')\n",
    "plt.ylabel('Average Speed during commute')\n",
    "plt.title('Test classification ')\n",
    "\n",
    "print(np.shape(group_a))\n",
    "print(np.shape(group_b))\n",
    "print(np.shape(group_c))\n",
    "print(np.shape(group_d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### As above, apply the model to new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_new=np.stack((extractData_nil[:,1],extractData_nil[:,3]), axis=-1) # Indexes different than during classification because I'm getting the values straight from the extractData variable \n",
    "X_new_scaled = scaler.transform(X_new)\n",
    "y_new = clf.predict(X_new_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Classifier prediction of new data ')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgIAAAHzCAYAAABSY3XtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19e04f4ebe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the test results into groups a-d (e)\n",
    "group_a = np.squeeze(X_new_scaled[np.where(y_new == 1), :])    # Car\n",
    "group_b = np.squeeze(X_new_scaled[np.where(y_new == 2), :])    # Public transit\n",
    "group_c = np.squeeze(X_new_scaled[np.where(y_new == 3), :])    # Cyclists\n",
    "group_d = np.squeeze(X_new_scaled[np.where(y_new == 4), :])    # Pedestrian\n",
    "#group_e = np.squeeze(X_new_scaled[np.where(y_new == 5), :])     # other combination\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "sns.set(style=\"ticks\")\n",
    "\n",
    "# Set up the figure\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Draw the two density plots \n",
    "ax = sns.kdeplot(group_a[:,1], group_a[:,0],cmap=\"Blues\",     # I changed the order to be consistent with scatter plots earlier in notebook\n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax = sns.kdeplot(group_b[:,1], group_b[:,0],cmap=\"Purples\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax = sns.kdeplot(group_c[:,1], group_c[:,0],cmap=\"Greens\",    \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "ax = sns.kdeplot(group_d[:,1], group_d[:,0],cmap=\"Oranges\", \n",
    "                 shade=True, shade_lowest=False, alpha=0.5)\n",
    "\n",
    "ax.set_xlim((-1.5,1.5))\n",
    "ax.set_ylim((-1.5,1.5))\n",
    "\n",
    "plt.xlabel('Distance of commute')\n",
    "plt.ylabel('Average Speed during commute')\n",
    "plt.title('Classifier prediction of new data ')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(168994, 2)\n",
      "(31387, 2)\n",
      "(0, 2)\n",
      "(17998, 2)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(group_a))\n",
    "print(np.shape(group_b))\n",
    "print(np.shape(group_c))\n",
    "print(np.shape(group_d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "If this analysis can be trusted, it gives us a lot more data to play with. 163000 more cars, 43000 more public transit, and 11.5k more pedestrians \n",
    "(surprisingly there are no categorized bikes... which is a problem. )\n",
    "\n",
    "    - In order to access the trajectories to use in a visualization, I would need to go back and make sure the IDs are being  sliced and recombined in all the same ways as the other data, namely:\n",
    "        - extractData_xxx variables still have ID number in the first column.\n",
    "        - Column was clipped into combArray - using the transit-mode-specific indexes (i.e. iCar_i:iCar_f)\n",
    "        - However, X_new was taken directly from extractData_nil to be used in the training data, so the indexes obtained using the mask [np.where(y_new == i), :] (where i =1,2,3,4,5) should work equally well to extract the relevant IDs from extractData_nil[:,0].\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(168994,)\n",
      "(31387,)\n",
      "(0,)\n",
      "(17998,)\n",
      "-----------\n",
      "[ 3374507.  3444521.  2690388.  3152793.  2336903.  2545586.  2007630.\n",
      "  1618457.  3024731.  2278777.  2184542.  1645367.  1687684.  2337205.\n",
      "  2518262.  1608749.  3544147.  2766019.  2816285.  1723073.]\n",
      "-----------\n",
      "[ 1692997.  1998815.  3288563.  2665391.  3586292.  2432316.  2033432.\n",
      "  2871054.  3271392.  3258002.  1965619.  2819478.  2493213.  3113859.\n",
      "  2320433.  2361267.  2634379.  2884813.  3265894.  1973045.]\n",
      "-----------\n",
      "[]\n",
      "-----------\n",
      "[ 3070237.  3131106.  2463135.  2892612.  1622841.  3566304.  2513709.\n",
      "  2734281.  3170066.  3059946.  2813933.  3447139.  1801808.  2291356.\n",
      "  2692990.  3194369.  3127780.  1723274.  2825734.  2824888.]\n"
     ]
    }
   ],
   "source": [
    "a_ids=np.squeeze(extractData_nil[np.where(y_new == 1),0])\n",
    "b_ids=np.squeeze(extractData_nil[np.where(y_new == 2),0])\n",
    "c_ids=np.squeeze(extractData_nil[np.where(y_new == 3),0])\n",
    "d_ids=np.squeeze(extractData_nil[np.where(y_new == 4),0])\n",
    "\n",
    "print(np.shape(a_ids))\n",
    "print(np.shape(b_ids))\n",
    "print(np.shape(c_ids))\n",
    "print(np.shape(d_ids))\n",
    "print(\"-----------\")\n",
    "print(a_ids[0:20])\n",
    "print(\"-----------\")\n",
    "print(b_ids[0:20])\n",
    "print(\"-----------\")\n",
    "print(c_ids[0:20])\n",
    "print(\"-----------\")\n",
    "print(d_ids[0:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "##### Make a hexbinned 2d histrogram using the newly 'obtained'  classified data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'allIDs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-90-355333e67e94>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Find the overlap between the list of coordinates and the ids classified in a specific group.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m    \u001b[1;31m# Beginining with Pedestrians (group d) because the number is smaller, and it's easier to judge the output plot.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mmask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mallIDs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma_ids\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0massume_unique\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0msharedIDs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mallIDs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msharedIDs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'allIDs' is not defined"
     ]
    }
   ],
   "source": [
    " # Find the overlap between the list of coordinates and the ids classified in a specific group.\n",
    "    # Beginining with Pedestrians (group d) because the number is smaller, and it's easier to judge the output plot.\n",
    "mask=np.isin(allIDs, a_ids, assume_unique=False)\n",
    "sharedIDs=allIDs[mask]\n",
    "print(np.shape(sharedIDs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Apply the mask to the coordinates file as well. A little bit trickier in 2D but worked like this earlier in the notebook\n",
    "print(np.shape(np.squeeze(mask)))\n",
    "curr_coords = allCoords[np.squeeze(mask.astype(bool)), :]\n",
    "print(np.shape(curr_coords))\n",
    "\n",
    "#define x1 and y1 here from the allCoords set. \n",
    "x1=np.copy(curr_coords[:,0]) # With the above note in mind, this may be an exception??\n",
    "y1=np.copy(curr_coords[:,1])\n",
    "\n",
    "# Remove the nans from the array\n",
    "x1 = x1[~np.isnan(x1)]\n",
    "y1 = y1[~np.isnan(y1)]\n",
    "\n",
    "\n",
    "# Create linear and log histograms to test the output.\n",
    "xmin = -74\n",
    "xmax = -73.4\n",
    "ymin = 45.3\n",
    "ymax = 45.8\n",
    "\n",
    "fig, axs = plt.subplots(ncols=1, nrows=2, sharex=True, figsize=(10, 16))\n",
    "fig.subplots_adjust(hspace=0.5, left=0.07, right=0.93)\n",
    "ax = axs[0]\n",
    "hb = ax.hexbin(x1, y1, gridsize=1000, cmap='inferno',extent=(xmin, xmax, ymin, ymax),marginals=True)\n",
    "ax.axis([xmin, xmax, ymin, ymax])\n",
    "ax.set_title(\"Classified pedestrians - Hexagon binning\")\n",
    "cb = fig.colorbar(hb, ax=ax)\n",
    "cb.set_label('counts')\n",
    "ax.axis([xmin, xmax, ymin, ymax])\n",
    "\n",
    "ax = axs[1]\n",
    "hb = ax.hexbin(x1, y1, gridsize=1000, bins='log', cmap='inferno',extent=(xmin, xmax, ymin, ymax),marginals=True)\n",
    "ax.axis([xmin, xmax, ymin, ymax])\n",
    "ax.set_title(\"Classified pedestrians - With a log color scale  \")\n",
    "cb = fig.colorbar(hb, ax=ax)\n",
    "cb.set_label('log10(N)')\n",
    "ax.axis([xmin, xmax, ymin, ymax])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "As an alternative way to visualize the seperation between modes of transit, try using andrews_curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export the categorized data into a format to be useful elsewhere. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ideally, it would have the same formatting as the list of trajectories which I'm already using - i.e. A dict  object. \n",
    "However ... the dict already contains all of those points. Therefore, it may be more straightforward to use the list of IDs and corresponding labels, when reading through the existing dict (should take far less space in memory)\n",
    "The list could then be used to:\n",
    "    - Perform summary statistics for each borough and populate a dataframe\n",
    "    - Create density plots/hexbins of the seperate data sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# group_a    # Car\n",
    "# group_b    # Public transit\n",
    "# group_c    # Cyclists\n",
    "# group_d    # Pedestrian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save the data\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/categorized_cars.npy', \n",
    "        group_a)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/categorized_public.npy', \n",
    "        group_b)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/categorized_bikes.npy', \n",
    "        group_c)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/categorized_pedestrian.npy', \n",
    "        group_d)\n",
    "\n",
    "# ID lists for the groups\n",
    "\n",
    "\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/cat_ids_cars.npy', \n",
    "        a_ids)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/cat_ids_public.npy', \n",
    "        b_ids)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/cat_ids_bikes.npy', \n",
    "        c_ids)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/cat_ids_pedestrian.npy', \n",
    "        d_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the saved data\n",
    "a_ids = np.load('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/cat_ids_cars.npy')\n",
    "b_ids = np.load('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/cat_ids_public.npy')\n",
    "c_ids = np.load('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/cat_ids_bikes.npy')\n",
    "d_ids = np.load('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/cat_ids_pedestrian.npy')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "168994\n"
     ]
    }
   ],
   "source": [
    "#print(np.shape(group_a))\n",
    "# print(np.shape(group_b))\n",
    "# print(np.shape(group_c))\n",
    "# print(np.shape(group_d))\n",
    "\n",
    "print(np.size(a_ids))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each mode of transit, create hexbin density plots of the island of Mtl to compare the categorized vs Scikitlearn categorized data. Despite the numbers being far different, the spatial distributions should be similar\n",
    "- Or alternatively a contour density plot may show this better.\n",
    " This would be an interesting test, because the spatial localization was not used in the analysis to label them (althought distance was...)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Added these values in manually, since they're set to zero in the next cell\n",
    "n_cyc_traj = 3831\n",
    "n_car_traj = 34048\n",
    "n_pub_traj = 12720\n",
    "\n",
    "n_ped_traj = 3958\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below is still necessary, because we have to calculate the cummulative lengths of all trajectories for each mode of transit, in order to preallocate to the correct size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "blankCount=0\n",
    "countNotNone=0\n",
    "cycCount=0\n",
    "pedCount=0\n",
    "pubCount=0\n",
    "carCount=0\n",
    "othCount=0\n",
    "\n",
    "cycCatCount=0\n",
    "pedCatCount=0\n",
    "pubCatCount=0\n",
    "carCatCount=0\n",
    "\n",
    "for nTraj in range(0,numIds):\n",
    "    if trip_final[\"features\"][nTraj][\"geometry\"] is not None: \n",
    "        tempArray=[]\n",
    "        tempArray=np.asarray(trip_final[\"features\"][nTraj][\"geometry\"][\"coordinates\"][0][:])\n",
    "\n",
    "        if mode[nTraj]==\"cyclist\":\n",
    "            cycCount=cycCount+np.size(tempArray)/2\n",
    "        if mode[nTraj]==\"pedestrian\":\n",
    "            pedCount=pedCount+np.size(tempArray)/2\n",
    "        if mode[nTraj]==\"publicTransit\":\n",
    "            pubCount=pubCount+np.size(tempArray)/2\n",
    "        if mode[nTraj]==\"Automobile\": # this capitalization will surely be confusing down the line..\n",
    "            carCount=carCount+np.size(tempArray)/2  \n",
    "        if mode[nTraj] is None:     #==\"None\":\n",
    "            # If ID on one of the lists\n",
    "            tempID=trip_final[\"features\"][nTraj][\"properties\"][\"id_trip\"]\n",
    "            if sum(np.isin(a_ids,tempID)):    # Cars\n",
    "                carCatCount=carCatCount+np.size(tempArray)/2\n",
    "            elif sum(np.isin(b_ids,tempID)):    # Public transit\n",
    "                pubCatCount=pubCatCount+np.size(tempArray)/2\n",
    "            elif sum(np.isin(c_ids,tempID)):    # Cyclists\n",
    "                cycCatCount=cycCatCount+np.size(tempArray)/2\n",
    "            elif sum(np.isin(d_ids,tempID)):    # Pedestrians\n",
    "                pedCatCount=pedCatCount+np.size(tempArray)/2\n",
    "            else:    # Otherwise... (don't know what this will mean)\n",
    "                print('Not on any list')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preallocate the arrays before the big loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preallocate numpy arrays for the labelled arrays\n",
    "cycTrajs_lab=np.empty((int(cycCount),2))\n",
    "pedTrajs_lab=np.empty((int(pedCount),2))\n",
    "pubTrajs_lab=np.empty((int(pubCount),2))\n",
    "carTrajs_lab=np.empty((int(carCount),2))\n",
    "\n",
    "# Preallocate numpy arrays for the categorized arrays (using SciKitLearn)\n",
    "# cycTrajs_cat=np.empty((np.size(c_ids),2))\n",
    "# pedTrajs_cat=np.empty((np.size(d_ids),2))\n",
    "# pubTrajs_cat=np.empty((np.size(b_ids),2))\n",
    "# carTrajs_cat=np.empty((np.size(a_ids),2))\n",
    "\n",
    "# This also has to count the total length of all the trajectories..\n",
    "cycTrajs_cat=np.empty((int(cycCatCount),2))\n",
    "pedTrajs_cat=np.empty((int(pedCatCount),2))\n",
    "pubTrajs_cat=np.empty((int(pubCatCount),2))\n",
    "carTrajs_cat=np.empty((int(carCatCount),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(303969, 2) (94793, 2) (693886, 2) (4477188, 2)\n",
      "(0, 2) (271093, 2) (1307717, 2) (14950375, 2)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(cycTrajs_lab),np.shape(pedTrajs_lab),np.shape(pubTrajs_lab),np.shape(carTrajs_lab))\n",
    "print(np.shape(cycTrajs_cat),np.shape(pedTrajs_cat),np.shape(pubTrajs_cat),np.shape(carTrajs_cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cyc_rowInd=0\n",
    "ped_rowInd=0\n",
    "car_rowInd=0\n",
    "pub_rowInd=0\n",
    "\n",
    "cyc_cat_rowInd=0\n",
    "ped_cat_rowInd=0\n",
    "car_cat_rowInd=0\n",
    "pub_cat_rowInd=0\n",
    "\n",
    "for nTraj in range(0,numIds):\n",
    "    if trip_final[\"features\"][nTraj][\"geometry\"] is not None: \n",
    "        tempArray=[]\n",
    "        tempArray=np.asarray(trip_final[\"features\"][nTraj][\"geometry\"][\"coordinates\"][0][:]) # Load data as array instead of as a list.\n",
    "       \n",
    "        if mode[nTraj]==\"cyclist\":\n",
    "            currEnd=int(cyc_rowInd+np.size(tempArray)/2)\n",
    "            cycTrajs_lab[cyc_rowInd:currEnd, :]=tempArray\n",
    "            cyc_rowInd=int(currEnd) \n",
    "        if mode[nTraj]==\"pedestrian\":\n",
    "            currEnd=int(ped_rowInd+np.size(tempArray)/2)\n",
    "            pedTrajs_lab[ped_rowInd:currEnd, :]=tempArray\n",
    "            ped_rowInd=int(currEnd) \n",
    "        if mode[nTraj]==\"publicTransit\":\n",
    "            currEnd=int(pub_rowInd+np.size(tempArray)/2)\n",
    "            pubTrajs_lab[pub_rowInd:currEnd, :]=tempArray\n",
    "            pub_rowInd=int(currEnd) \n",
    "        if mode[nTraj]==\"Automobile\": # this capitalization will surely be confusing down the line..\n",
    "            currEnd=int(car_rowInd+np.size(tempArray)/2)\n",
    "            carTrajs_lab[car_rowInd:currEnd, :]=tempArray\n",
    "            car_rowInd=int(currEnd) \n",
    "        if mode[nTraj] is None:     #==\"None\":\n",
    "\n",
    "            # If ID on one of the lists\n",
    "            tempID=trip_final[\"features\"][nTraj][\"properties\"][\"id_trip\"]\n",
    "            if sum(np.isin(a_ids,tempID)):    # Cars\n",
    "                currEnd=int(car_cat_rowInd+np.size(tempArray)/2)\n",
    "                carTrajs_cat[car_cat_rowInd:currEnd, :]=tempArray\n",
    "                car_cat_rowInd=int(currEnd) \n",
    "            elif sum(np.isin(b_ids,tempID)):    # Public transit\n",
    "                currEnd=int(pub_cat_rowInd+np.size(tempArray)/2)\n",
    "                pubTrajs_cat[pub_cat_rowInd:currEnd, :]=tempArray\n",
    "                pub_cat_rowInd=int(currEnd) \n",
    "            elif sum(np.isin(c_ids,tempID)):    # Cyclists\n",
    "                currEnd=int(cyc_cat_rowInd+np.size(tempArray)/2)\n",
    "                cycTrajs_cat[cyc_cat_rowInd:currEnd, :]=tempArray\n",
    "                cyc_cat_rowInd=int(currEnd) \n",
    "            elif sum(np.isin(d_ids,tempID)):    # Pedestrians\n",
    "                currEnd=int(ped_cat_rowInd+np.size(tempArray)/2)\n",
    "                pedTrajs_cat[ped_cat_rowInd:currEnd, :]=tempArray\n",
    "                ped_cat_rowInd=int(currEnd) \n",
    "            else:    # Otherwise... (don't know what this will mean)\n",
    "                print('Hope this doesnt print too many times...')\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Save the resulting arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save the labelled data (seperated by mode of transit)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/lab_coords_cars.npy', \n",
    "        carTrajs_lab)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/lab_coords_public.npy', \n",
    "        pubTrajs_lab)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/lab_coords_bikes.npy', \n",
    "        cycTrajs_lab)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/lab_coords_pedestrian.npy', \n",
    "        pedTrajs_lab)\n",
    "\n",
    "# Save the categorized data (seperated by mode of transit)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/cat_coords_cars.npy', \n",
    "        carTrajs_cat)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/cat_coords_public.npy', \n",
    "        pubTrajs_cat)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/cat_coords_bikes.npy', \n",
    "        cycTrajs_cat)\n",
    "np.save('E:/Documents/Professional/Jupyter notebooks/Projects/monTrajet/cat_coords_pedestrian.npy', \n",
    "        pedTrajs_cat)"
   ]
  },
  {
   "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": "3.6.3"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "position": {
    "height": "797px",
    "left": "164px",
    "right": "20px",
    "top": "108px",
    "width": "800px"
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}