jupyter/net-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Analysis of Trial Data\n",
    "\n",
    "Using annalysis.py, and previous nb, analyze the following:\n",
    "1. Network weight convergence (4-30 trials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "## matplotlib notebook interacts negatively with imagemagick GIF writer\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from tqdm import tqdm\n",
    "\n",
    "mpl.style.use('mitch-exp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import annalysis as ann"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'annalysis' from 'annalysis.pyc'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reload(ann)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "002852_0414.csv          data 4-30/               data 5-30_4/\n",
      "Icon?                    data 4-30_2/             data 5_02_1/\n",
      "analysis-dev.ipynb       data 4-30_3/             fig/\n",
      "analysis.ipynb           data 4-30_4/             keras-matlab-test.ipynb\n",
      "annalysis.py             data 5-01_2/             keras-tutorial.ipynb\n",
      "annalysis.pyc            data 5-1/                net-analysis.ipynb\n",
      "cross_track_errors.csv   data 5-30_2/             trials/\n",
      "data/                    data 5-30_3/\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "P_trial_29_out_05-02-17_11-56-14.csv\r\n",
      "P_trial_30_meta_05-02-17_11-56-25.csv\r\n",
      "P_trial_30_out_05-02-17_11-56-25.csv\r\n",
      "P_trial_31_meta_05-02-17_11-56-37.csv\r\n",
      "P_trial_31_out_05-02-17_11-56-37.csv\r\n",
      "P_trial_32_meta_05-02-17_11-57-02.csv\r\n",
      "P_trial_32_out_05-02-17_11-57-02.csv\r\n",
      "P_trial_33_meta_05-02-17_11-57-05.csv\r\n",
      "P_trial_33_out_05-02-17_11-57-05.csv\r\n",
      "P_trial_34_meta_05-02-17_11-57-14.csv\r\n",
      "P_trial_34_out_05-02-17_11-57-14.csv\r\n",
      "P_trial_35_meta_05-02-17_11-57-46.csv\r\n",
      "P_trial_35_out_05-02-17_11-57-46.csv\r\n",
      "P_trial_36_meta_05-02-17_11-57-56.csv\r\n",
      "P_trial_36_out_05-02-17_11-57-56.csv\r\n",
      "P_trial_37_meta_05-02-17_11-58-15.csv\r\n",
      "P_trial_37_out_05-02-17_11-58-15.csv\r\n",
      "P_trial_38_meta_05-02-17_11-58-22.csv\r\n",
      "P_trial_38_out_05-02-17_11-58-22.csv\r\n",
      "P_trial_39_meta_05-02-17_11-58-42.csv\r\n",
      "P_trial_39_out_05-02-17_11-58-42.csv\r\n",
      "P_trial_40_meta_05-02-17_11-58-57.csv\r\n",
      "P_trial_40_out_05-02-17_11-58-57.csv\r\n",
      "P_trial_41_meta_05-02-17_11-59-03.csv\r\n",
      "P_trial_41_out_05-02-17_11-59-03.csv\r\n",
      "P_trial_42_meta_05-02-17_11-59-08.csv\r\n",
      "P_trial_42_out_05-02-17_11-59-08.csv\r\n",
      "P_trial_43_meta_05-02-17_11-59-22.csv\r\n",
      "P_trial_43_out_05-02-17_11-59-22.csv\r\n",
      "P_trial_44_meta_05-02-17_11-59-32.csv\r\n",
      "P_trial_44_out_05-02-17_11-59-32.csv\r\n",
      "P_trial_45_meta_05-02-17_11-59-44.csv\r\n",
      "P_trial_45_out_05-02-17_11-59-44.csv\r\n",
      "P_trial_46_meta_05-02-17_11-59-55.csv\r\n",
      "P_trial_46_out_05-02-17_11-59-55.csv\r\n",
      "P_trial_47_meta_05-02-17_12-00-15.csv\r\n",
      "P_trial_47_out_05-02-17_12-00-15.csv\r\n",
      "P_trial_48_meta_05-02-17_12-00-22.csv\r\n",
      "P_trial_48_out_05-02-17_12-00-22.csv\r\n",
      "P_trial_49_meta_05-02-17_12-00-34.csv\r\n",
      "P_trial_49_out_05-02-17_12-00-34.csv\r\n",
      "P_trial_50_meta_05-02-17_12-00-47.csv\r\n",
      "P_trial_50_out_05-02-17_12-00-47.csv\r\n",
      "P_trial_51_meta_05-02-17_12-00-55.csv\r\n",
      "P_trial_51_out_05-02-17_12-00-55.csv\r\n",
      "P_trial_52_meta_05-02-17_12-01-11.csv\r\n",
      "P_trial_52_out_05-02-17_12-01-11.csv\r\n",
      "P_trial_53_meta_05-02-17_12-01-15.csv\r\n",
      "P_trial_53_out_05-02-17_12-01-15.csv\r\n",
      "P_trial_54_meta_05-02-17_12-01-52.csv\r\n",
      "P_trial_54_out_05-02-17_12-01-52.csv\r\n",
      "P_trial_55_meta_05-02-17_12-02-00.csv\r\n",
      "P_trial_55_out_05-02-17_12-02-00.csv\r\n",
      "P_trial_56_meta_05-02-17_12-02-14.csv\r\n",
      "P_trial_56_out_05-02-17_12-02-14.csv\r\n",
      "P_trial_57_meta_05-02-17_12-02-32.csv\r\n",
      "P_trial_57_out_05-02-17_12-02-32.csv\r\n",
      "P_trial_58_meta_05-02-17_12-02-46.csv\r\n",
      "P_trial_58_out_05-02-17_12-02-46.csv\r\n",
      "P_trial_59_meta_05-02-17_12-02-50.csv\r\n",
      "P_trial_59_out_05-02-17_12-02-50.csv\r\n",
      "P_trial_60_meta_05-02-17_12-03-00.csv\r\n",
      "P_trial_60_out_05-02-17_12-03-00.csv\r\n",
      "P_trial_61_meta_05-02-17_12-03-17.csv\r\n",
      "P_trial_61_out_05-02-17_12-03-17.csv\r\n",
      "P_trial_62_meta_05-02-17_12-03-29.csv\r\n",
      "P_trial_62_out_05-02-17_12-03-29.csv\r\n",
      "P_trial_63_meta_05-02-17_12-03-58.csv\r\n",
      "P_trial_63_out_05-02-17_12-03-58.csv\r\n",
      "P_trial_64_meta_05-02-17_12-04-03.csv\r\n",
      "P_trial_64_out_05-02-17_12-04-03.csv\r\n",
      "P_trial_65_meta_05-02-17_12-04-03.csv\r\n",
      "P_trial_65_out_05-02-17_12-04-03.csv\r\n",
      "P_trial_66_meta_05-02-17_12-04-25.csv\r\n",
      "P_trial_66_out_05-02-17_12-04-25.csv\r\n",
      "P_trial_67_meta_05-02-17_12-04-28.csv\r\n",
      "P_trial_67_out_05-02-17_12-04-28.csv\r\n",
      "P_trial_68_meta_05-02-17_12-04-39.csv\r\n",
      "P_trial_68_out_05-02-17_12-04-39.csv\r\n",
      "P_trial_69_meta_05-02-17_12-05-04.csv\r\n",
      "P_trial_69_out_05-02-17_12-05-04.csv\r\n",
      "P_trial_70_meta_05-02-17_12-05-28.csv\r\n",
      "P_trial_70_out_05-02-17_12-05-28.csv\r\n",
      "P_trial_71_meta_05-02-17_12-05-29.csv\r\n",
      "P_trial_71_out_05-02-17_12-05-29.csv\r\n",
      "P_trial_72_meta_05-02-17_12-05-37.csv\r\n",
      "P_trial_72_out_05-02-17_12-05-37.csv\r\n",
      "P_trial_73_meta_05-02-17_12-05-46.csv\r\n",
      "P_trial_73_out_05-02-17_12-05-46.csv\r\n",
      "P_trial_74_meta_05-02-17_12-06-30.csv\r\n",
      "P_trial_74_out_05-02-17_12-06-30.csv\r\n",
      "P_trial_75_meta_05-02-17_12-06-39.csv\r\n",
      "P_trial_75_out_05-02-17_12-06-39.csv\r\n",
      "P_trial_76_meta_05-02-17_12-06-59.csv\r\n",
      "P_trial_76_out_05-02-17_12-06-59.csv\r\n",
      "P_trial_77_meta_05-02-17_12-07-16.csv\r\n",
      "P_trial_77_out_05-02-17_12-07-16.csv\r\n",
      "P_trial_78_meta_05-02-17_12-07-31.csv\r\n",
      "P_trial_78_out_05-02-17_12-07-31.csv\r\n",
      "P_trial_79_meta_05-02-17_12-07-51.csv\r\n",
      "P_trial_79_out_05-02-17_12-07-51.csv\r\n",
      "P_trial_80_meta_05-02-17_12-08-14.csv\r\n",
      "P_trial_80_out_05-02-17_12-08-14.csv\r\n",
      "P_trial_81_meta_05-02-17_12-08-27.csv\r\n",
      "P_trial_81_out_05-02-17_12-08-27.csv\r\n",
      "P_trial_82_meta_05-02-17_12-08-43.csv\r\n",
      "P_trial_82_out_05-02-17_12-08-43.csv\r\n",
      "P_trial_83_meta_05-02-17_12-08-55.csv\r\n",
      "P_trial_83_out_05-02-17_12-08-55.csv\r\n",
      "P_trial_84_meta_05-02-17_12-08-59.csv\r\n",
      "P_trial_84_out_05-02-17_12-08-59.csv\r\n",
      "P_trial_85_meta_05-02-17_12-09-20.csv\r\n",
      "P_trial_85_out_05-02-17_12-09-20.csv\r\n",
      "P_trial_86_meta_05-02-17_12-09-30.csv\r\n",
      "P_trial_86_out_05-02-17_12-09-30.csv\r\n",
      "P_trial_87_meta_05-02-17_12-09-36.csv\r\n",
      "P_trial_87_out_05-02-17_12-09-36.csv\r\n",
      "P_trial_88_meta_05-02-17_12-09-52.csv\r\n",
      "P_trial_88_out_05-02-17_12-09-52.csv\r\n",
      "P_trial_89_meta_05-02-17_12-09-58.csv\r\n",
      "P_trial_89_out_05-02-17_12-09-58.csv\r\n",
      "P_trial_90_meta_05-02-17_12-10-24.csv\r\n",
      "P_trial_90_out_05-02-17_12-10-24.csv\r\n",
      "P_trial_91_meta_05-02-17_12-10-35.csv\r\n",
      "P_trial_91_out_05-02-17_12-10-35.csv\r\n",
      "P_trial_92_meta_05-02-17_12-11-05.csv\r\n",
      "P_trial_92_out_05-02-17_12-11-05.csv\r\n",
      "P_trial_93_meta_05-02-17_12-11-22.csv\r\n",
      "P_trial_93_out_05-02-17_12-11-22.csv\r\n",
      "P_trial_94_meta_05-02-17_12-11-33.csv\r\n",
      "P_trial_94_out_05-02-17_12-11-33.csv\r\n",
      "P_trial_95_meta_05-02-17_12-11-48.csv\r\n",
      "P_trial_95_out_05-02-17_12-11-48.csv\r\n",
      "P_trial_96_meta_05-02-17_12-12-08.csv\r\n",
      "P_trial_96_out_05-02-17_12-12-08.csv\r\n",
      "P_trial_97_meta_05-02-17_12-12-16.csv\r\n",
      "P_trial_97_out_05-02-17_12-12-16.csv\r\n",
      "P_trial_98_meta_05-02-17_12-12-26.csv\r\n",
      "P_trial_98_out_05-02-17_12-12-26.csv\r\n",
      "P_trial_99_meta_05-02-17_12-12-43.csv\r\n",
      "P_trial_99_out_05-02-17_12-12-43.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls data\\ 5_02_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Network Weight Convergence\n",
    "\n",
    "Plot network weights for `data<x-xx>/` and study convergence properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "direct = 'data 5_02_1/'\n",
    "fpaths = os.listdir(direct)\n",
    "fpaths = [fpath for fpath in fpaths if 'out' in fpath and 'GPID' in fpath]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "159"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fpaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "trials = []\n",
    "trial_metas = []\n",
    "\n",
    "for fp in fpaths:\n",
    "    tr, trm = ann.load_data_from_fname(direct + fp)\n",
    "    tr.set_index('t')\n",
    "    trials.append(tr)\n",
    "    trial_metas.append(trm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GPID_trial_01'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpaths[0].split('_out')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 3/159 [00:02<02:19,  1.12it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-d56dce780848>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mfs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'out'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Weight'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mylabel\u001b[0;34m(s, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1576\u001b[0m             \u001b[0mwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1577\u001b[0m     \"\"\"\n\u001b[0;32m-> 1578\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1580\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mgca\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mfigure\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0ms\u001b[0m \u001b[0mgca\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m     \"\"\"\n\u001b[0;32m--> 950\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m \u001b[0;31m# More ways of creating axes:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mgca\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   1366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1367\u001b[0m         \u001b[0;31m# no axes found, so create one which spans the figure\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1368\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1370\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36madd_subplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1018\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1019\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1020\u001b[0;31m             \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1021\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1022\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axes/_subplots.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;31m# _axes_class is set in the subplot_class_factory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axes_class\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, rect, facecolor, frameon, sharex, sharey, label, xscale, yscale, axisbg, **kwargs)\u001b[0m\n\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_connected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m  \u001b[0;31m# a dict from events to (id, func)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m         \u001b[0;31m# funcs used to format x and y - fall back on major formatters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmt_xdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mcla\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    976\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    977\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspine\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 978\u001b[0;31m             \u001b[0mspine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    979\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mignore_existing_data_limits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/spines.pyc\u001b[0m in \u001b[0;36mcla\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_position\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m  \u001b[0;31m# clear position\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mis_frame_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36mcla\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    758\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_artist_props\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    759\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 760\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    762\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36mreset_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    772\u001b[0m         \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    773\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 774\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    775\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lastNumMajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36m_get_tick\u001b[0;34m(self, major)\u001b[0m\n\u001b[1;32m   2055\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2056\u001b[0m             \u001b[0mtick_kw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2057\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mYTick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2058\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2059\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_tickdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtickdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick1line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick1line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick2line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick2line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgridline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_gridline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36m_get_tick1line\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    555\u001b[0m                           \u001b[0mmarkeredgewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_width\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m                           zorder=self._zorder)\n\u001b[0;32m--> 557\u001b[0;31m         \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_yaxis_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwhich\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tick1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_artist_props\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mget_yaxis_transform\u001b[0;34m(self, which)\u001b[0m\n\u001b[1;32m    781\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mwhich\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tick1'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m             \u001b[0;31m# for cartesian projection, this is bottom spine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 783\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'left'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_spine_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    784\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mwhich\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tick2'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m             \u001b[0;31m# for cartesian projection, this is top spine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/spines.pyc\u001b[0m in \u001b[0;36mget_spine_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    404\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_spine_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m         \u001b[0;34m\"\"\"get the spine transform\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_position_is_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m         \u001b[0mwhat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_spine_transform\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/spines.pyc\u001b[0m in \u001b[0;36m_ensure_position_is_set\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    150\u001b[0m             \u001b[0;31m# default position\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_position\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'outward'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# in points\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_position\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/spines.pyc\u001b[0m in \u001b[0;36mset_position\u001b[0;34m(self, position)\u001b[0m\n\u001b[1;32m    394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 396\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    397\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36mreset_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    772\u001b[0m         \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    773\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 774\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    775\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminorTicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lastNumMajorTicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36m_get_tick\u001b[0;34m(self, major)\u001b[0m\n\u001b[1;32m   2055\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2056\u001b[0m             \u001b[0mtick_kw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2057\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mYTick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2058\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2059\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_tickdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtickdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick1line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick1line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick2line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick2line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgridline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_gridline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36m_get_tick1line\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    555\u001b[0m                           \u001b[0mmarkeredgewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_width\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m                           zorder=self._zorder)\n\u001b[0;32m--> 557\u001b[0;31m         \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_yaxis_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwhich\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tick1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_artist_props\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mget_yaxis_transform\u001b[0;34m(self, which)\u001b[0m\n\u001b[1;32m    781\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mwhich\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tick1'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m             \u001b[0;31m# for cartesian projection, this is bottom spine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 783\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'left'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_spine_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    784\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mwhich\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tick2'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m             \u001b[0;31m# for cartesian projection, this is top spine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/spines.pyc\u001b[0m in \u001b[0;36mget_spine_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    404\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_spine_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m         \u001b[0;34m\"\"\"get the spine transform\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_position_is_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m         \u001b[0mwhat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_spine_transform\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/spines.pyc\u001b[0m in \u001b[0;36m_ensure_position_is_set\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_ensure_position_is_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_position\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m             \u001b[0;31m# default position\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_position\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'outward'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# in points\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c7e10d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for tr, fp in tqdm(zip(trials, fpaths)):\n",
    "    fs = fp.split('out')[0]\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    plt.ylabel('Weight')\n",
    "\n",
    "    plt.subplot(3, 1, 1)\n",
    "    plt.plot(tr['t'], tr['wt1'])\n",
    "    plt.title(fs + ' Weights')\n",
    "    plt.ylabel('kP')\n",
    "\n",
    "    plt.subplot(3, 1, 2)\n",
    "    plt.plot(tr['t'], tr['wt2'])\n",
    "    plt.ylabel('kI')\n",
    "\n",
    "    plt.subplot(3, 1, 3)\n",
    "    plt.plot(tr['t'], tr['wt3'])\n",
    "    plt.ylabel('kD')\n",
    "\n",
    "    plt.xlabel('Time (s)')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('fig/' + fs + '_weights.pdf')\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### XTE for all network trials\n",
    "\n",
    "Fill numpy array with cross track error for each trial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.],\n",
       "       [ 2.,  0.],\n",
       "       [ 3.,  0.],\n",
       "       [ 4.,  0.],\n",
       "       [ 5.,  0.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctes = np.zeros((len(fpaths), 2))\n",
    "ctes[:, 0] = np.arange(len(fpaths)) + 1\n",
    "ctes[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# for trial in trials:\n",
    "#     trial['em_x'] = trial['wt6']\n",
    "#     trial['em_y'] = trial['wt7']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8363106252452712"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann.straight_mean_cte(trials[0], trial_metas[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "159it [00:00, 534.78it/s]\n"
     ]
    }
   ],
   "source": [
    "for i, (tr, trm) in tqdm(enumerate(zip(trials, trial_metas))):\n",
    "    ctes[i, 1] = ann.straight_mean_cte(tr, trm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean CTE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.379230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.957941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.705472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.182467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.870449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.888916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mean CTE\n",
       "count  159.000000\n",
       "mean     1.379230\n",
       "std      0.957941\n",
       "min      0.000000\n",
       "25%      0.705472\n",
       "50%      1.182467\n",
       "75%      1.870449\n",
       "max      4.888916"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctedf = pd.DataFrame(ctes)\n",
    "ctedf.columns = ['Trial No.', 'Mean CTE']\n",
    "ctedf = ctedf.set_index('Trial No.')\n",
    "ctedf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "ctedf['Smoothed Mean CTE'] = ctedf['Mean CTE'].rolling(min_periods=3, window=25).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "159"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ctedf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d18a810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VZucHGIyjkCzEMWmouU5sZoHZOSriF1/J76aXYSTI6qfHGOsI4FIAbQE4ALgAVAD4FcBq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\n+tFz6lainC37vUcIvydzc+rwgNR5KDdidepM/Ic/ex561iMMNosnK20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0nLpd+N1GUY7t\nVln43QbxZZiaHAtzuyB17tRZly0H4QlbFMQmfCRi29pVDr8bwiv2NdomlulIO3UWfhfr1AWkHmho\n0M5DbiVKKVIrV/G/nTTxTSZLxALe9Lo9mtMYVTvNksq8PE8Kv+eC1Nk1BeSGLoJTD/B6ZQ9IXXQe\nmYxFhc11HiaZU8+V/V44mVhWeRKoqgIhhLcQzlarns2pE71joZZT1523nkvnMrFOG0TZEWUtafMe\nfhfBCFsDxM0cZ7/z8LtHJ8zGUaQIJFZieotEo0ZlTaEV5ZJWRUtHB+/T/Dp1hVLKnujF0ERjHgEA\nSqkCYHQUVBzMEJ6Rem7HovjQDalHDEU50U6fq02A59cfQEIOwbOBHQoaC2ceaOgjEX4XHZwcerPR\nf2e670zVy20g8utjC2PcYTDboCDX0DtgVZmzhN+dc+ojGX6n8bhWix4Oa4tvNKqVWiaTyLS3AwCC\nDk5daWkxlQzKil+2JkdX4GEh9mFOY5QRVJkxVUdLl7ZckLoioX1FAVVVYxz7Dr9LS5Q0593D79p3\nnQSSsloB+ssz45tC3YkFJ2q91LPVqjuG33mr0TLj3OQUXJacumVj6qekLUv43S7CKG7m2DPhSN1j\nTp1HgiIRLkXMjMRiBlIXNh3plhb03/XrnAEBpTQrUh/N8LtCCFlMCGkA8DUAT1FKBwCAEFIPwGHV\nHR2T264y6z37YgwXxVATC6GuzCpax8Pv0oSfMa4CM8aVoz+u4NXNHebfYgtooDDh99EjynmUiZWI\ncjz8LuTTWPg9yHb/LkidXR8PYZp2qGJO3Sb8ziaUg1M3IVPhnJ2IcmLjmUIidfkZMpQerK/n6IIt\nRMoebfENjm2wPY/UivcBGHrxnohyzAGK96OAGgpOY1SVw+/xhIaAeeicycTmcA7isxf6tJuQupDW\n8Hw8dq6SY3Wbh0b4vVBIPfdnwpq5MKcemqA59Wy16o7PUE/vEFEWVZXD7+45dcsG2Zf4jDdJWZNT\nF3LqFqKcz/A7iUS08SVuiMWcurBBGfjN7zDwizsQf/FFT79hsXQa0CNEpnv2b1KUuxVaHvwAgDEA\nbgcAQsg1AN7W/xtRUw91W5SU+Hu6QhcnPOi2+wgtKzB9fJVt+JaH321CNhccrU2WJ1ZKk4XtYkOh\ngoTfw04otMBmF35HOKxN2EzGCG9KTGq78DsjynHH5DIQw2FzNMTkxMScum34XZh4tgeXwu/sXjr0\nuS9UmMtyGtIzFJnvzAIVGpJg8pAcqUts16SeTy/6xEna+x5y6gZSF9jvwcKF353GKEfqbOOXiJs5\nJ2HrwujVONoPh009GriAkF+kLiM4ac67zUNOlMs5p65vunhDl9zztOphfe7pKmhBPfyuZGHAO10f\nR+oiWYyNGb6xz5JTtyB1H13asjLlresWe+6ZQ13a90MhENb4x2MUhAMKPbXHCJkAE5/R13Zh00EH\nND+jfLjL029Yr0UANA6k3VFD6pTS5wHMBHAJgKMopVv0t3ZDE5H5ec5n4tEybQeg7N9v+x5rlMFy\nLd2DSSzb0oEnV2lMa7vQO+AcfgeAU2c1oDwaxra2fmzZLyABRYEKgt+0RfBooEn7fh4hTrkudqSM\na4yLSJ0Qawg+I4VybcLvGb2kjSlayZEOVZi07Pp4PttJSckWqbtIxMKGKCelDOQJPlLa7/IzVHUR\nCyZlChhIXWWaCg0OSF1nvhefcrL2vgf2u4HUhftBChd+dxqj/FrYOIgnBGW7kBFh8bnppZSaFfJ4\nRC1tILdIxKTUl/WYWZC66zzkOXX/Tp2qquEMufRoHkidh9+1TWLIY/jd6fpYesdoNaoYY4bn1DVA\n5BSRzJbaMH1WCEFrv+ct/C7KW3Omu06QJKUlRnMnrwpwKRbx0b7HuBsAy6lbSdTsXHJt7ONEiCtU\nWtB3nTql9ENK6ROU0lbhtWV605c9OZ+JD3NiubLXAxUVeHPbQZx9xxu4+fENvNZ8gdBuVTSn8DsA\nFIeDOHu+JqryxCqjDItmVLTUTcY/u0J4EBOwv6KhIAzjkTSx5aiYUwdsytosbRfNvcm57jshCPCc\nunH/Em+9hfZpR2Hosb+afz9pE37PUtKWNfzuUNJGbMrwtOONjkysGH5nxhYiZsypi5Nb2b8fmQMH\nQCoqeA2yJ2lSG6TupbY4X2MSscEJ+uY2kTB4EKGwkQbxi0wzGS1MGQyCBALGPGXRnLB2bL5R8iIV\n68PxyCamjnxvkoTKBNZwKKe6fd3k8HtwvFZNnBH0H/wYJ8qVVxjnxrTfLbwaB6Se8IHURRlawBdR\njhnnqOgWiJWYUjRezAi/67LiMlJn4XcRqetzlaXQ/Jo59eiA2kexTn0SIeQqva4chJByQsiDhJD1\nhJA7CSEO8dHCmhPLlb0eqKzEipZDUClw5NgyXHdyM+679lgsnFxjf8AsA+H8o5tACPDq5g70DumL\nQkbBiskL+GdenHlKXk59w4YN2T+Urwm7XUagYiYjdR5+l1t46rt0kajDc6aCs0xv2gxkMkhv0jr2\nbtiwwVikIQ/sLOIz2cLv8usyUU526qaceuHC7/IzZLnmgKAyx4g8zOyQenqjds8iC+Yb9dnxeNZ+\n2bwOWkTqNhGWXM1pjGYOaUg91KQ79XjCFDbPOT0lon0Im+8BTT3OaMFpiJBkM0u7S8nxOF0jr2dm\nf3vRDZC/D30+sVLLXKoBdOPzTw+/B/RyrGybP8fr09M7RqtRRdjY6ynLoHv43cp+d0Hq0v3LWldu\n17MC5lQrKSnh65XnOvWUeW0hpYZTD8RixgZMGCdsrmZyReoOa1+hSm39IvVvA/gOgLH6378EcDmA\nvQDOAXBLzmfiw5x25Cz8FqioQGu3Nrj/49Qj8YVPTMWsJmfROxJ2Dr8DwPjqGBY31yKlqHh2nVYH\nqioZvD/RcOpvHHE8BtK55y3TI9CvVzaRXCSbJfzOkYXEfNUnOlddqqnhoSu7nBCbNOl02jnsLRHX\nZBSUjf3uiNR5bb1MlBsZpC4/w4zOOWALL2CkhgAAwSACdbXaeYi8gn5tfAfr6rTNVHERIIUr7YwO\naGU9XH0NKGiXNqcxypF6k4HU+QIVDhsLrU9kKqJ9ALzKgfXOtuurnfWYWZC60zXK3/MdgmfHLRRS\nl9jviEQ0p6sorg7N8fosOfW0kVNnufQsqRw/OXV5LHtmv0saJGLki5SWCOqgHsPvQk4dgKmszVTS\nJtw3dp3q4cPeuwMK5sh4F9svj2L4/TgAH6OUPksIKQNwBYA7KKXnAjgWmmMfcVOFlpWm1/uM8Htr\ntzbpJtTEbD9rMpfwO7OLjtFyVk+t3oeMSrE3VIaOinpUhoG5wUEkwsV4KWGfs/disk5wIUzt68PQ\nY3+FqiMb2w5tzCQGvEkGFzDkRvXXMzrzPVBbw0NXJqet78QZKm5ubjY7UxeymgVtuHRoA2ANy7ON\niGP4XXjOBSTKyc+Ql/zVGBEiEakHKivtNQJ0XgFD6bxVZxYUlhHavDIjBZSJtRujNJ3WSu8IQbCR\n5dTjBlIXiaQ+kakJ7cPQI+CblxycOt9sFNu31HSch/IY9cmAp2K5YSGQulCnDui8GKbc5xJFcLo+\na526kVP3KhNrckpwR8u8UokJvuQQfgfMTj0QK7FVB3U9LkPqLKculLWZStpEpy7MVS/tbi2/KZbz\nOqUhRxGpZyilTEbsbGglbPcAAKW0B6NUp+5EiGGTOl5Sjq7+JMJBgjEVUdvPisbDui47y8XNtRhb\nGUXb4ThWtBzC8qiWwzqhNogLirVd89OpamRyZBlXVjpHEnK1oQceRO+NN2FYz2vLMouicefCBpwo\ngwub8LvusALVNUZJnN2g1F+rrKx0HLT8dYasJRTEe6nLhDh27hakbma/jxZRTn6GrOQv4OLUEQoZ\nlQdMAlW/fubUufBKlpAvE4EJ1AtNZQpY0mY3RsVrDJToIeCEQJSL5I7ULW1k9X/ZJpVtREVRn2zG\nHE2AKdRJjtVpHsoOynetulBuaHA9ChF+N3hC3Km7bP6crk9lRDnmJJW04bxZtMdvSZsbUtfHckAQ\nu3EzURZYNFaCB+hEuSxRV+txzWuinFPnY1cRnbrAf9m9x9PvmMwDuBlNolxIaN7yFQBvMsKcnk8f\nlbosp9wZI8q1B7TBPb46hmAge3dYIrBqnSwYILjgaC28+MTKVrxXqv3/x8eEcGxRHGP6O3GQRvDu\nB12Ox3CztrbcCC5uxohaTKrUaWKIr1nY78wpyOF3Vs5WU80RtClXzZC6nmdva2szh7Bs8koccUko\niD0XS+MWZsEgIJYq6kidL56qS0lbAZ26/AzZPQrUGOF3RuoCDDUw+d6rulNn7UZZh7KsSJ2x0MVO\ncQUMv9uN0QznDdTyigqZKJczUk8bjhAAD78z9TB230ilmQXtfkx9g8i15M1z3mke5h1+F5F6AUpg\n1cN6qrHKxqm7bP4cr4/l1Bn7XZSJZXPfb0mby/PmQmE8h+9ffAaQwu8lJab+59k4KACsOXWTUy8W\n+FbWnDqg97Lwac4lbSL7ffSQ+sMAthJCtgI4HsCdAEAIOQbAgwB25HwmPsxpR86c/X5Fe7ATakps\nP2cxNhCyDKyz549DJBTA8p2HsLu4BrHUMBbURxAMBXHGlmUAgKXLdlrV5zyYV6dO43FkOjqyfxDG\n4sfSFW7hd56rYvKFCqtTN+uos4nOSWBCTt0uV80meltbm0RQE5G67tT1BcoiFSugPjsjhJhC84cR\nwXf+ug53D9VhTdNsJNLSIjRCderyM8zYIfUKCanDQAnsXGSkznLk2cPvNkg9y0Lsx+zGKC9nq61D\nQHQqbBEMhwUN/hyResQcfuc5dT2EHqiqAghBpquLb2AdjXd9s0fqjvMwKTt1v+F3K1LRHeU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ocfaf+AL5lYKoffBfa7PMG0dpJBTUCCdYkS6jqpqqKxsdEcwlRVLTxmKmlj+ScHopyOIh5+Zw86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\nB+ER/kbZZR9QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d18af50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "y = np.ones(len(ctedf))*3.24\n",
    "ctedf['P Controller CTE'] = y\n",
    "\n",
    "ctedf.plot(figsize=(8, 6))\n",
    "plt.ylabel('Mean Cross-Track Error (CTE)')\n",
    "plt.savefig('fig/cte_neural_net_05_02_1.pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+m1Ht+Pj41PhZbKkGxqtBdHQ0k5iqCltbW84Ol3/79i0+//xzFBcXo7i4GDY2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V/+8pfIA5EFKs7GFvQiLBkdxeudhoHTLzZwvJjj5eII1i99FADQfcQZjvcdHhyToR/yJASoVqE/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\nbwDrvADLc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ZHdYOfgkFHk8yXmt4QeHEEW1esr3jbGE8LAEXhwtpseHMbAMf6lcfMGzlvG\nvTZVEKayhahcOJE8odqkGxjvb2E4dFER6ptLly7F0qVL0d7ejsWLF+PnP/85Fi9ejG3btkUeQFjQ\n7SAycChEta6lB0C4CsYsXjC6oh+cC9fCzC084zXBj25Zh7d+++8AgN7jP43maYcIPxeKZBzBgyMk\nGXsw+tnXSZkVsL1edBS5HZKXB4dddAsCA+e2227z4OAYxwJ8QlQ62HDKxCM+ikLXVAxvWoM1Tz2g\nv69pyGga6seGzHMAvHtROV2r9g9tfu0ZbHjpz8jkajHttK9G6k/G64HZmV1gYIjgRTIWpon7jNHM\noBJ5cFxCtHEhuhd2NybZlSWngVNQFVs3cYD1rkbx4OiPbMf5oCEqwEp/dQsb5ZiFxJQh9xlfkrEj\nRBWVZGz34MTl4KgAjsnnTW6g0IMT0MBxk0GB02d2zGTIkC3Pc3DY+4LmQdYgCNqMucAYWbJCVFsE\nHpyf/+rXxhh9D21CtMn7R7GI18fGsHjYqvw2xl1r3sBZYRg4ewgMHHYja22W7DpIfJ4Bbr7kQ/0V\n8+BMnz4dF154IVauXBn5B+PC04NDu4JW3cAZjlADBwBeGItm4AQhGYsw8MazWP3Uz6GoGez5if+L\n+u49HZ8JkyZur4MTP03cL4uKbkozXs98vsQZBEIDx4dkTDdvc3Ozw8Dhm3KOaJq5OIvc8nw9lJrG\ndkw67pMAgJV/+DHKRT0TcFRg1AB+JGP9kefgsFj5hx9jdNsmNE3eHz0fPDN06jivB3xGmh9EYUWr\nm7h1okEXGdI1vk0DkFwWleheoF3kyqI4RGXWPwJQ1znFmrwjzJ28N4j9v8kjTZxAocBGNw8OrLG6\neQyCpomTARG0KjUP+l0aB90LYbOosgCubGzCs13deKC9U88wLJWExQeD9kJzkwEV2aOFlfVEmHVw\njEc+TVxk4Ng9OMGMFXsdHP21uM02SwBWFMcwUC6bxk6+oSHQeFhYBpv12pCm4YT31+H6bVvN12jc\npEuOEJUH0bjAGPqi1i8sn8esiA+7gVNxkvFLL72EZcuWub5/2GGHRR5IUJgcHA8DZ2vbJADRauAA\nwJvFIraWy+jJZAOTdAGnBycMkbH/iZ9hw/L/RiZfwN7nXuvItgla6K9RUVCnqtheLmNHuYyCqgZy\nT9s8OC4KzYMPUfHnDzgnEDY2SxCniTtr3Nxwww2Oniu81T+sBUsTJwOn+4iPIlNTi02vPoMtby01\nP2fbnbPGjvHoRTLe4pHeWBoexIpFPwSgk5oP+8bDOPBff4LpH78KdZ1TBEe1g9WDDHSyZlnThDth\nEUahbxKosmtBUaAqCnaUy7aWHuRWZsN8Dbvth73O+Td0HnKK+VqXSw0cwNID2QaO6F7Y3QhRrfAJ\nUWVnX4IZF9+BX875GjbXNUfk4OiPrI4Q4bjBpdAfC2rY2Cjw4FC13aKmoQxxqi7gPR8osHSRODiR\nQ1TG9yxeYTQPzqxcHpc0NmFiJoMVxTHcvG0r5m0Q8yODEtzdC/3Z55giY5TQPETzEr8JZe/ZFj78\nDS1wiI6d58xKxgFE5uXBAYCPbngfJ61fZxqcX7nyCuPYwa8HzaFeYVQAjtAazw+izcRuXKp4rfE4\nTDpsHCunKOZvs2sLn2TiFeoPi1Bzz0MPPYRzzjnH9f077rgj8kCCghTSK4tqmAyciB4cDcCyCF4c\n2tUXXW4ev199+5GbMPDGs6ipb8E+512HXGNH4N8msNkk643JLQgPh/Xg1KkqcgifJs4TjAEnZyUo\nyVhU2RJwpmeK0sStjsSqQ8HZkv2Z2gZMOGQ2AGD10w/YPse6b0vM6XuFXSySsWH4uSwCA288i3f+\ndAeG3n8HWrmM2rYetH/gGOz/+f9Az9FnQwnIm2pVVaiKgs3lMoL4b2oa2tB58EcwkNGlUKsoaFQU\nDOYKuP3Y87H3uddi8of/BRMOm4t1PXtjTM2ioChQ1Ax6j/8MPvCZG9G65+GYNvdS7H3edcg1dbh2\nEgdYfoPcEBWPDlVFg6pioFy28RSIHE8enL9NORj5A08CAKycMgOXnXENlL1mhf493gsIWB4/r0J/\nBC8PDm+AmwZFlAWM5aNFDFGZxpzxPCoHp9vQk0eHduCY9etw87atWCMwioHwvdB4WJWM9eOQ8Vmj\nWGniZqE/470Mt4gDYg9OMaAHp8B8J1ShP585d0O5jPeYe61syDWId4gg4uCIwBuz/Ni2uXAdKfQ0\nJEhaKHD8ziFNczRtztveo7k0GkKVgTjggANw+eWX4xvf+AZeffVVDA4O2t6fPDlY0bo4MENULllU\n23N1KDVPgFYuYXjTmsi/88LoKI7N1+KQmjz+ODzs/wU4O2lbBk4w5dPKJbzx6+uwzyevR+NuH8Ce\nZ34LL//kMkArB262aXV2LkOFzk2YkFHxts8K2MRNgE2q6hui4vkcfJo84AxRFQQ3MN1oNLmr4Awc\n4/8rr7wSn3MLUTE3rQadhNegqqhX7N1u2RTJrsPmIJMvYMvbL2DHe2/ajjsqCD+w5xYkTdxr57H2\nbw9j7d8ehqJmUds+CV0z56HrsDnY7YTz0bbv0Xj9wWsxumWd43usHnQS/yaI90ZRsfc5/4b67um4\n7v0V+NbiH6FWWYOGQguumf1VrOiYghYALdP1Olf/APDp4iia3nsd+2XyqO/ZE5pWxvoX/oTWvY/8\n/+S9d5gcxbX+/+mesDnnoCyt8iqhgECASAIkgmUyl2jAILDBNli+ToAxGGGSbRAyF2EMGINNEEjk\noIhAWUgI5bxKuytpc5jU3z+6q6e7p3umZ1f29f39zvPsM9JMT091dXXVqfe85z3k9hvD8NvmUL/4\nRdi/3hSi8qbnkN1nJMH9KgnUOLL6eLw0KhHXiJOdWZ8FnX9j0eGKIjgynanZzDn1WgBqFr/CyX2q\n2d+zGqb/nCFjL6Fhxyoatq/S5ADiT/y2ISrtt3LkxA5OSxwEJ6q0rZq+OFuOizcf2EomdBXB0XVw\nuofgCEd4v4NTYzS3Do5TH+hp4nYcHO0YMS8JJEeyCVGJiIAx/O1W6NCYJu6Wt6N+LzECaLT7H3qY\nszMyk1LkduPgZJQNIJyeDe1H9NC7Lj2QmgXURzMtZfs52bhutEQi5MoymbJEQ9jsLFk5mMb+7lAU\nUiVVE8ltnxgtKQfn6quv5uDBg2RmZtoK/mVmxtdyORGWiGS8aMBEkD007VrXrQrOXSEaWytpR3f7\n7n83Euxk699/zfDvzyazYiDlEy/j4BevOypIW02oytZGwvrC4g7BURtZGw5T7PGQY3BwnAaWqPcj\nijLaITjWUg3GB1+YFcGxQtPCKWlsbCQl2xx3t0NwQOWTZKIiU81KOOZcnR4fpePU0iIHl/8j5tqM\nPCIjB8eISkiYl0Jr7NiNGqsSCdFet5c97/+JY5uX0ffCu8ko60+vKbey/R+xBTqN46BQr+eTeNEo\nHnMBGZqg5N6iPvz8ov8m++3fk3/eDHYX9iK/4TArPp1LSm4JqQU9qKgcRKCoF/U9hpEBdDbWsnPe\n72neu5Gaz1+kz4V3kVc1gTfPmUHvz+dw5PACQEWJhtzwGKn55ezcv4nWz+fgQU1CyJQkPikuYWsw\nyFSH8IQbsz4LvW0yqMBc7f7NSdfRlJZNx56vObD4b5y/8UNCY6bywknTyeoxhKweQ+gx+XpaDmxj\n+z9/Q6Cp3vH3xYRvF6JKJPQHiRAc9VWMY6e05HjzgREFioYau0gy1l5DltdkOThFcbhaVjOSw+OZ\nUx/EKhmr7/skY4hKtbghqngcnATPtjFNXITgE2nV+FDnlaCi4FYy92hTI2RkdqnYplM9vtyq8VRd\n/msk2cOmBbPw7/8WUPvhk4GTeH7S9ZR9OpfjmvipNctJv3aTrpY55G0XorKby02fdcHBSWrUf/vt\nt/Tt29fxb/PmzUk3IFkTD7ydg5MjyXw86HQAjqxe0K3fEUTjap/PdScJRyY2UyM5C3e0sHu+ytOo\nOP0a0op6MWPGDFffNarKClXXRJlUMtEsCwEb50iyq92EWMxzZTkmRRwMxTYtELARvrSSjK2OgRj4\nM2bM0MlpEctDIcJB4ly6mrFlkhQ75Nohp+PLzKP10A6adq2LuS6jiFvIsqMXzo+1V3VHrIux46bd\n69j0wo9QImFyB4zHmx5LojSOA0EwTpRB5U3Pocfk6wHY/d4f6VG7i9qsIoquexS5uDcVDYe4/O3f\ncuzbpRxa/ga75z9Jzl/u5oVX7mby+0+y+/2n2Tjndpr3blSvv/U42167n4OLX0GRZZ6afAsdvarx\npucw+NrfkZqvkvxbewzlvqn3EEpTr6NI9pAqSY7qp27N+izYZVAVDJvMzolX8I9RF7Jv8k2s6zeW\n1EAHh+Y/CSiElQhTN31G/jM3sO31Bziy5n1CLcfIrKhi6M1/JLNikOPv2yE4AvEToSCn3bEvI48t\noy7go0Gnk5aSHvO5tZSIE8k43nxgLKYoQlRdFfrzWtqj8wqTRHDEHGRXUsRqUR2c+DOvUx+Ie6CT\njHWnxFiLykIjsKAUEOUi2mZRJUMydhmiSsS/sbMbb71VPXcSc42uGWaDVGb1Gs6AS3+hh8nnnHod\nkkcdfd7cUv4yQS3uWTL2IjokmbCGsBjnQrvrsIr92XNwcPysqzycpBycm2++Oe7n3/3ud7vUiGQs\nHoJzuGIwNXnlBJuP0rDtq279ztFIhH2hEBmyzECHiqlWi0kTj8PXSGSNu9ZSu+Z9ZK+fvhf9hGHD\nq119L8rBiegcnJIEDo5Ab5ojET28kusCwYFolka+7NEnvbDhwdFj3BYI2Ewyjo/giPerq6v1B7k5\njtcPOELzfgnCksTRky4C4KC2C7GaKSxluXy7CunGtjR1g/0fbDlGw/ZVyB4vhdVnxXxeXR0dB4V6\noc34i0aPs27Cm5ZFw8411K55n+vfeZgx+74GQK7dw4MLHkVpPW76TruikNPRwtBda6hdvYBwZ2vM\neUNL/84Fmz4j5PHR//JfM/i6WaQV9aLtyG42/s+d+I4fYndhL5Zf/gD+7CK9P5yK5bo1Yx9ALIKT\n028M/afP5MiES3l9zMXsH6lWg79+xT9ob1DDfrr4W7CD41u/5PB7f+TZN++jb80m/Jn5DL7+UQqG\nn2n7+7ZCf5ZnxPrMZJRX0e+SnzLy7pfYc/r1PHfqtay+5Vl6nP09E9fOWgzWKbxh7QOj+Q0Qv3gO\nul2qQft/Vzdt0Y1X4tBkm0uSsVMfGNETwOSUCEdGPDHiNrnOonIZohLoU1sSISo3863VhowYARCT\nfBHPoiEg8/vppf0ZeOX9yF4/tWveJ1i/n4O5ZSwcPQ2QaD7/Tjq1AtEpOUVk9xlhG0600zprsWw4\n0wxctVgEB0KSh1DPapplr+mzZC0pB2ft2rWxJzA4Grt37+5SI5KxMOruPceAGIB6g5cMmQzAkbUf\noLhMm41nyaaLd49kHGv7PnmezoYjZFZU8c81h1x9J8rBiSI4RQkIhgK9aVaiDk6OLLl64ESWRp4s\nxyBY0DUdnBgHR3tdsGBBFCWxhIF0HRzttE71qHySxJL+JxPMK6Pj+CGOfbvU9rqM8G3QstMJ2Tiu\noo0dimIrgJiM1a1XC+gVjTw35rMFC6LIpI7gGPkskkz5pKvocfb3KBxxDgXDJlM8agqRcJC9H6i6\nIZFgJzM/eYbw339Jyas/J6ej2VSmAdxlspR4ZG788jWGb1uOJyWd9OLetNfXsOWVn9N2aAc9/v4L\n+tTvozWvjL4X/9iUJWdXLNetGfsAzAiOJHvpNeV2ANI3L+XStfMZtPodblj2CudsXaIvNroitfbM\nFng8lAfb+d1Hf6RlzXvIXj/9v/NTyiddHfP7esq5otYvkzw+U7gKouKYntRM+l78E4bd/EcKq89E\nkmTSt69g8KFthFIyKJ94GdV3Pk+uRna2FkJ04rxY+8BoxmchnmSCGxPIq44oiX5LcmwXG0Lnicwt\nB8euD3yofRhSFJ3HpJdXMCI4Wv9alYyN413n4BgRHJccJOM8J0pQJyICJ4PgSB4fvabcxku7stmd\nW4ZsQVHimTUMKnl8lIy9iMHX/g5PSgZHv1nE7vefpu59VZj001FT6TXl+3T0GEZOexO9138IQFH1\nObbZlnbX0WpxtE0oDeb5MpyayW/Pu5uMax7iL1N/TFiS/3UOTmVlpS7w99BDD8V8fvbZZ7Nw4UIG\nDhzYpQZ0xUQIwDhJ5mfls6L3aKRIhLp1H56Q39F5OD53Do5eeVd/eLT3uyiKFQ60sUtLKW4tHUdh\n9dkJvyMmkiPhsD6ZJApRCd5Ac0SJ9q2UmGQMcFz7jTzDrtw4hVlDVHbevTVN3ImDs2DBAn13atVI\nSLE4Y3qquAWa78wu5oWTVZj1wJJXHZWuTSEqy/Xb7WKN4llWfaBkrWH7SoKtDaQX9yajvMr0mcnB\n8YgyDdEeLz/lMnpMvp7yiZfR7+Kf0H+6SsQ8tPwNOo4dANQ+8igRUvduJFvT/Wmx9IPdxGW1Eo8H\nGYUJn86hbv0nNNdsZssrPyOooUFyWyP3ffA4vo4WcvqMIqX3CP27OV1ccK19AEYNnBClEy4hrbCS\n9voaMt77I1etfYcJX77OBZsXImHIBtJexT0U1+lVwgz6fC57PngGRYnQY/J19Jn6Q5Moo0A1mjJy\nGXHnXEb96BV29x9nalOHopA38GSqb3+OohHnEAl2cnD5P1n/pxtJf/sRfvveo1z0+i85tmU5Hl8q\nVZf/moLhZ+rOk1h8jBlA8frAaGZVb3sk061ZERwx0pLn4AhBSvcOTiLUya4PogRjIwIbdUqSSRMX\n64upFpVbBMfAQ4kiSPHNLYKTklvC0BufoHT8JYTTC3nirNvp8Ppdh6mMc2XhiHMYccfz9D5/Bt60\nLI5v/Yqd8x4DJULz3o2cvWUJIY+P0vEqX/H7y16mcq3a73mDJ3JMQ3SMPBxbB8fiaIv6Uq2Sh8bU\nLBTt+lPzK3j6O79mY8VgAPZWDOGVsd811zvMyHV1neDCwbnkkksYNmwY8+fPt00D37hxI0ePHmXh\nwoVUVnavUrJb08Mohk4tHjWFkMfL4L3r4hIEk7G1SRKNPZaHRzxYXQx/A9C0ax37F/4VSfbQ75J7\nKB5zQdzjdSg4Etbl8xORjEX9nBYlKiCVI7vj4BwzITixISprwcV4CI54QK2xd2OIyklrxhqi0ivY\nGs8lySyfcgdt/nT8276i/utPHK/LLnPKek0mBMfw+yHUey+0ZpI1JRKil39ggQAAIABJREFUfuPn\nABRpac2i/dWjowupLvKn3efMikFUnnEdAIe+epv6bxbRdmQ3jbvWcXDZa/r3jJBwpn7vzfe4zUWp\nBoEW1gUD7Hr3cb594UemZy+MQlZnKwO1CVE6/Vq9V+1CzG7NGJrIlWRyZJmWSITGjDwqTrsGgL0f\nzqZDSzLIlNSxGVEUR40qI1owwZ/CkVXz2f6PB4kEOykecwFVV9ynizKKcbpu4lX4MnLxpeewZuqP\nePKMW9hV0JM3R1wANzxJ1RX34c/Kp3nfJjb+eQb7P51LoLFWJxn3qd/D9n/8hgNL/44ke+j/nZ+S\nctKFgLHquT3nI26IysDBiSeZ4MaimzYzguM2MxTUZz5LlulQFBpdoBNuhf7s+kB8x1g0Ug9HGhAc\np3I6RmkH4YRHdXCc74fVjPNcGHXO8EhS3HvghODk9BtDyUkXUjLuYsonXcWwW54mo3wAHccPE2mu\n42BeOXNPvtp1mEqMj+NjL6bfxT8hJbeEtto9bHvtfra9fj9KRJ3xgsC1K98gu00lcxduXsr4veuQ\nGmtp2vM1Hl8qq/qqyUbGeSLdkkgieXzs7DWSTo/fhOAEZS9vX/4bPr71Oa677o/sufoRhn7vKepy\nS+l9dB+H356FHA7xbvUUvINPBUmmeMxURsz4H5dX6sLBmTp1KnfeeSf3338/+/bF6socOnSISy+9\nlLlz53Lvvfe6/uHuWIM2+eqTpCSTNkqNsw/d+NkJ+51NwQABRaHK63UVw9ZDNNr/nbgaydrBpX/n\npOxjAPSZ+kNKx3/H8VjBt6k1IDgis8rJRDZHc0QxOTjJhKjyDQiOWflXNfGZGyVjJw7OjBkzdHi1\nyRqispCM7epRVZx+DbVlVeS3HsP3wTOO1wTmEJUVwbGrKGx1sLpLjqtb9xEABcMnI3n95FVNYNTd\nr5A27QGG3PgEZRMvBU3vqT4SxpOSTr/pM5FkD4e+fIN9H/+ZnW89wsY/386WV/6bSLBTP7cRYdJV\njC1p225KNUTrUNnvysXiMfDrDwm2HEcpH8jqniqK0x0Hx0guFejN7lCInmffjMefpoo27lobLZor\niwU/albyrvE6x6eoS8XxrV+y+eWfEWxrIq9qPP2m/xSQ8AFbivuxb/AkIqEA+xe+hBzsYFn/8dz7\nnV/z6tjpyKX9CHW0sOfDZ/n2xXt09Ayi/DChelyz8K/s/fg5AORzbmXhgIn6+HNCDOKSjA0cHIXo\nve0KD8eJg5NMWrJQU3eD3oB7Do5dH2RYVIzBEFazKdWgWEJUthwcQ8kat3yaaLaoxgPS3o+Hshh5\nKcIqz7yBQdc8RO8L7qD3ebfTY/L1GtLyJd/8z51M7xvAF+zk84GnkmfD2bOzFCSOpefQOlGtfbj7\nvT+y8c8zOG7hrQYVhcxAG3d/OpvDK99h8MIX9PbVrVc3h2uq1GKfxg2CMZEks3Iww299hhUXz+SR\nc+/UFY5TJYl51edxvKgXKBHa/Om0lQ3Am5bF6D1r+e38WRz9ZhHjl72qtuX8HzD0pqfoM/UHeNOy\nXF0nuFh7y8vLef755xOe6KGHHuLLL790/cPdMSMRFiC3/0mQU0JJUy15+9wXpUxkncC3wSAj/X5G\n+PwsD3TGPV4n2Vriu8nsdpysZ3g/b7z/d3pfcAe9pnwff3Yh+z6dawqxGHdKDYqCpKjVb/NkD37M\nE7zRdJKxEtHVI93o4IAFwbEhGVuzFNwI/cVycDTUprGRXJ3IayWmWRAcbXJPycglq6gfmeVVVJx6\nJSgR7lo0l4c7mohndouhfk02u1i/xcHqUNQ09TRJspWjT2TtdXtpObCNzIoqhtzwGJl6qErR05pn\nA+80HCLw7TL65leQmldG68Ht7P/sxbjnNiE4wsGxtFFA/PF4EPFE/iCaipsa7OTAstfofd7tvHrS\ndxi9f4NJqDOjbADlp15BZuUQds1/ksYdq+K2X6QHe1IzKe07gpcHnMSy0ioKS/oRCXbqzoIYY8KB\nN5cQMaNwxsW/r9dHkSxTF4nQUrOZzS/ew5AbHyd/0Cn0OOtGvF/9kxdOVgVPD335JgeXvsp3dq+i\nc8rt7M2vZGTNJuat+5gd21fbSlWIZ8wYNjr81VtEQgH6XHAnL46/git2rAbqHTkfbtPEwVkywY2J\n57Y7HJwig/ioG4uiTvF/w64PrCniYE4Tj6LsqlnVtm11cAxhw64U20SSCShqtpEfcFJVs24oK8+8\ngYpTr0SJhKlb/zGRYCeKEqHt8C7qN3yqtrutnsu++IhXz7iJovPv4MihnbTX7o7bthRJ4uWxl4K2\nGahd877tceJaR9TuZO/6xWRl54A/iw5F4djmZfS+4A4OlA/kYHYxGUfrQZLxpmcj5RWzvaCENWNG\nMWTkeUja87ehYgh9TroIFv0NOa+cN0dOBWDQG7/hp52N/MmfxasdHbzaeYwUSe3vfl9/iLeiiqX9\nJ5BZUUWgqY69H7kXFE7o4EQiERQXE3RHRweRboh3JWMNhkUVINjWSNrO1ZxXv4uGE0AuNtr6YICR\nfj/DfL6EDk40TVqEqLT3u+/f6KJW4UA7fS68m7KTv0tqYQ92vvkI4UAbEK0LVBcO40nJwONPo1ZR\nKJckCj0eR+VQPUQVUXQV3lyXaeJGBMeOZGwNUVnhS4h1cPRMK63UhPj/zJkzeaugCIhW1rUT+gOo\n6TGce864jj0FPRliaO+E1e8w7NBWRw0IYXaLoTCdg2O4r9bf7y7RGKBu/UdkVlSRWV5FONDB/s//\nQv36T8juO4q8gSfTe+DJHMgtI2viZWo7A+3seOsRHWJ2MtsQVQyCk5gHUWoQlbQzowZS7Zr36Tfx\nUvblV6oT2+HdFEs+8gdNNBWYHXDZL9jyys9p0bQ3rJbTfyzPfXWUEXfMJbWggmPAPO2zSDjI3o/+\nrAskRhGcWAfHulBZQ3Hj/Cm8pxUebK/fx/Z//paB1zxE+SmXs7+kD41FvUlpPqqH/jwNh7n/gyf0\n7//18EEUh/lQ9HWWBcWqXb2AAYMm0tJ3NAsnXQuvPaA/S9adfzyhPz0NWPtumxLhaHoBWaVpHDq4\ng0RChkaLIjhmzkoyJS5KkiwI2x2hP+HMtppCVMJJjK0mbuXgGDdXfkkiTTInW7hx8Dyo80FYUcgc\nNpnqi3/C4iUvcuHOr1Tkx2HuMTpFRudm+xsPc3zLF459sLS4lJrKISzpP4GhNz7OrvlPOiZPANSX\nVbFkwMkQCrDvk+ccj1OIhto9mGsTRoIdHPt2KUUjz+WpybfSeVqYsQWVyL4UtgM/EyeJhDmw7O9M\nPF7D3ovuZc8pV5GxbRW1Z91E0Ouj7NvF+Pd9Q05OLqX1B2ltaiSlvJKI5kx2Kgq3L32Jde2tbGus\n58Cy14gE2m3ba2fdjZ6YzOdLBrjsugliq/CwWw9sZdC8R7gwJ5fnTrCTdcxhMrIzK8lYtKSrJGM7\nq9/wKZ2NRxhw2a/IGzCOITc9Qf3Xn9LZcJjijibmDxjD5z2qGVNWhSR7uCscorjlKBWHdnLssxfo\nOFoTc06dZGzKokouTVwlGQsEJ2puQlQBi4MjjmlQIqQjmwSyrFlUVgQnUtSLgWd9j839xgDg6Wyj\noW4v7XX7aNn/Lf99YB34/CYSsZ0Z4+YxCI7Nrlq0Q5y3uyEqgKPfLKRo5LmE2hrZ8+GzdB5XM+mO\nb/mC8NblfF7ek7XF/bi7aADZPYdxYNlrplCIk7lBcMLacamSKuJo59qL0KddHSow8htACQcpWv4P\nDp93B6+PUQmLfcRxnW0cWfM+vvRsikaey8ArH+Dbv95Le+0e/VyyN4We595KyUlT9fcioQDFdXuY\ndGQHy7evZsGOtUSC0f1xu06YV6/ReB9jOTjm53uCwcEBaNrzNXvef5q+F95No6b23GfpKyzRQn/W\nTLt4KrFNOrIUOzayP55D6/f+xK6qk8kdMI7g0Z1aO6PH/CI7h2mpaUypO6Kfy2jieanPKqJ86Dk8\nOXQSB0v6kQeMaj7Ksc1fcHzrl3Qc3a+GDuNsCqNZVOJVIF+OX4kxkcXpGsGJuHNw7KzMpjaaGcFR\nLVqqQfAkzZsrYbmSbCLlGvk8Tibmr5r0XHpfcCeyx8s7J13CBbtWxtXCSZUkwpLE0knXUjHyvITO\njbCgonDb0pf4pLOTlKGnM+DSX3B4xTyObV5GRrm6QVKUCMe3raBxx2rVeQbCK96msyFWLd16bq8k\n4ZNis2rr1n9M0chz2VnUG1BRsGBbI9mdbZSHOjnWWMuSz/9K2+Gd+NIzuOCbT3l/2NkMuuYhmtKy\nyOxopceSl2kwzEdGpXlQsxFTwgF6fDSbz9pipSoSWUIHZ+fOnZxxxhksWrQo7nGnn376vyVNHNA7\nxAhz58oqRtBwgh2cdpfxYLAhGZP8ZOBks2bN0ncszXs3smnuXQy88gHSi3vT85yoPtGL2qsSDhFs\nOYY/M5/DOSXIOSUM738SNYte5tCXb5pCW1myjALUVAymub0dAkdNaeLxQlQNOoLjMXCQnEMB0XMa\nhf5US9EXG0k/d7nHXGwz5bHHgdg08VRJYktxP8qn3oPk8SF3tPJfX79Hy6oFzDwWVc1NLSoGzKUY\nnCyIuhuLyaKyQeYEjC0e/hPh4IQ729g09y7Te2IcFMgevEqY4gPfsm/d50md19i2DJsdr7C2SIRU\nj4d0WabT5rnSJQkShKh0Re1vFzO81zBqswqpbWtkfdNxOo4fpG7dR4Q7WkCS8aRkkD/4FAZd8xA1\nC/9KsK0RJRKh59k3k17ci0gowLC0o7z+zG9pr93DO/n5jPan8HZ9LZGgOQirc41sEBwrl0SMuY2B\nAMP9fp2HY7S6dR+SWlBB+cTLGHxoG7lblumfWR3meJuCNkUhrCika7w1I96W3lzPhWvm8dcJV9D7\ngjs5Ou9htuUWEykaSF5nB4GmOsb4oCLYzlCfny9tUOUUJL4pG8j8c39ID18KBwF/qJNAWzP+7EJK\nx11E6biLom1vOUb9+k+oWfRSjLPjtSA4XeLgGHiBbsxat8jJjHOiMOHgHDL8VkAnBkef2WipBtXs\nsqggVg8spOmyxNu0pkkSCvD8aTfgTc0A4HhWIV/2GYP/sPMGJMWXwuNn3sbWPmOIhALseGtWQudm\n1qxZBB97nJRwgOA7j3Fo3zf0PPdWSsdfomc+CSscfiZKJMxh2UNByzH2OmiAGS0ApKHKsMTUotr3\nDaM+/BMnZeXx8b7NvLlvC+HOVu7LzuGWzCweaGygrVUt59SiRLh21Rt8WdKf45pDdO2qN1jV1mgq\nthnlUmL6ra7OowkdnD/84Q+8/vrrXHXVVSxevNj2mNNOO42//e1vXHnllV1qRLJm5eBA1Nk5foId\nnGQ62Krkqy+EJ6AdOTlmVdvO44fYNPduCqvPIiW/nJTcEsrzShjTUk/7tq94bsNiwp1tPFxQzBkl\nvbmv6jSaqs+m59nfI3/wJOrWf0hLzRba6/fTPHAi946bzu7CnqQDczYv5twvXiVNUa+kI44zcMyA\npulp4sYQFeY+sFYTlzw+cvuP5fW+Q6nNKWOIN5XQijeh9Yh+L4WDk5OTo3v4eqVZgRr50vjD5JuR\nPD7qN37OoEUvcnGan/mKOVwjoPVEISpxTGpcBCdqTiGqrqaKO5kYB0UeoYGTfEjWOKZFPaTmSGx/\ntCkK+ahhquOWz1KAfI+HoOJcyTxi4aClonDbFy8D8FZbK/MaLGdVIux46xEGXv0gOX1G0veiH5s+\nbq/bx463HuF7D/yUvxxWkQ2nMg0Q65ibxRvtOThfBDqp8vkY5PWRK0n6ZkrY/k9f4PzD2/hRay1P\nG943jqeIotgiXkZrVhRyNQTN+Bt+CaZu+ox3+4zleElf3rj+Kd7QPhMsLL2GdijISeEAofYWDq96\nlyMr5qFEwoQqh/DwuT8k7Evh+LavuGrbF1xXt5Pb6g7yRV4lBYMnkd1nJP6sAnyZefgz8yk/9Qqy\nelez442HCTTV6e0R5Ob2PqOpnnwTTZ88C61HkuLgFCdTMw33m0rrnAhQrjk4B00IThStkyybUCsH\nJ0WS+efIaQQ9Xi5fO58cWdKf7bbx0xl82jU8tmcd1y7/G2Bf6zBdkvhs4CS+7TGcYFsTR1bOo/KM\n63in+jz8X823/Y43PYfDl93HropBeDta2PD6A7pyeKI+EM61X5I4smo+LQe30XvKbUgeHy0Ht9F6\nYCuelDTyBk0kq+cwAG5Y8Tq/CCausWgMydkh+gVblnFeZhZrmxp1MVDrHA8q6dsfDjHl42d4Zfqv\nKa3dzZlbl7FEMQv9pZ7gjWJCB2fp0qU8/fTTfP7552zatImvvvqKI0eOoCgKpaWlTJgwgcGDB3Pv\nvfeybNmyRKc7IWaXJi6cnROP4LjvYPHQC9TCOsF3x2bPnh3zXjjQxpHV0Qfm0qxs7sjKZlZTI+FO\nlZdzLNBBz4aDVH/2HM9vXEifaXervI4KdbpUImHWaxOQ3NpAKCWDTwafzobyQdy9aC5VdbtMcHtq\nQSU9zrqRhu2rqFv3oUHJOAHJWHs1hqgk2cPg6x4hq8dQRDWoLGBnQSXt8x6goV29BuHVz549m+eE\nBknE7ED8ddK11GYV0XpwO7veeYJePi+kFZrTxIk6IlZhNjuzisIJE46rcZKPIRmT3IMpE91NxjMx\nDoSuSL1LXoPR3CI48YjGPbTspSPhsKP7qy8e2tftCJxWU8JBtr3+AGUTvkNKXjne9Gy8aVm01Gym\nZuFfiQQ79T7IkSTyPR7aIxGO2Dz3VgfHiLLEcHCk6AZpXSDAhJQUTvKn8GmndRFQKN23gazMLAun\nJ5ZTFs9E8cEsWabBsBj7kPAoEao/ncOnF/83mbJMZetxDjYc4WAkgj+7iJLcElrSssDrw+P14UnJ\noNc5t1A04hyOrHqXg+fcQsSXQsW3i1nxxiMU5eaRkp5OliTTdmiHubCsJJPVYwj9vjOTrMrBDLv1\nGXbOe5TGHasBFano9PjonHI7adlF1J5+Hcr7v0+Kg2PU5nJjbjk4dnOinYNjvNcxISrtVQ1RSaw5\n62a2DVUVrPflV5D9xsOkShLvDT2LyMlXIQFf9j2JjeWDKPzgWeptsnbTc4p5cbyaobT3w9kc27Kc\ngeMvYVdhL1J7DgdDaRhfVgFlE6ZTPOYC2vxpFLYcpe9bD/HFXnsOml0f3Kf1l3BGWw9sVYs0W+zw\ninl403N4recAJjQddjVOxbbBDsEBaI2IbMvovbIrqCzuaWXDYdY+cTWzsrKQ09PVYpuGudKqZ9bd\njaIrDs7DDz/Mxo0buf/++/ne975n+mzt2rVMnTqVjz/+uEsN6IrZITi5/2oEx8UD7YTgeLvv37Bh\nQ+LssBIbKFhMKsWyTOPONWx89vsUVp9FZuVgMisHkZpfTlbDYa7+5mPeWLGARRkFTL3s5+wr6MEv\nLvwZp21bhvf9OQRbjpE/9HT6TrsLT0o6eVUTaD24laYjuwkpCtkG3Ry74pSypO6djEJ/Pc7+Hlk9\nhhJoPsr5u1YxoOkIf+g/EcoG8MpJ3yXjU5UAJ1CbDRs24C8p47OqU2juMZL5R7bRd9uXlJQO44v+\nE0gJdvK1RrJtUdTxYCXJigXNrg6L1XS5fMv7dun/3UkTvy0jk7uzsrm4vpatNkiE0cQ4KBAaON1E\ncKIZdPYIDtir4I7VtKHWB51y8wwcHO3/bhwcgEigXRVhdDDRB71salAZLUiUJAnm0GkMB0dPL46w\nItDJhJQUJqTYOThO1cSjnydCbyBacFN1wGOLwabX7WXtE1fx+5w8rsrI4KcNx1mkcRDeKC0nXfbw\nWGsrz3R0kNVzGL2m3EZ6cW/6TP0hEWDSjq/wffwsGOpR2RLGlQjN+77hm+dm0PeSe8kbMI6qy3/N\npr/8mLZDO/BJsGDYOSjZKrm/s3wg35YOwLNrvYurVC1a9b57Ds70NLV211vaxsduTtRDVBF7BEeM\nOimvnAGTr6d53yYaD39DdrCdPufPYNvQM/GHAnhDAVb1GkXqpb/k6z3reV/Lmtv36VwuHDSeryuH\n0e8791J2ymU0bF9Jw45VyF4/BcMmkzVoIu3+NAbuWs2KbxYBMGbjpywZN52UCdNh1zq86TlUnnEt\nRaOmIGu1nvL2rOd3y1/mL/X7XfWT6INggVrqI1GtK4BQWyO9jx8Aj8c1ig0qf8ku6cTuXtkVVG5R\noo6QEg6Spr1vLdVwouU2XJOM58+fz/z58ykqKqJXr14A7N27l7q6ugTfPPEWL0T1r0Jw3HiQwpGJ\npomrdiIQnGnTpsVVLwVzHSphVjVjgfoI5Ef2pfJydhanp6TwYqiTttrd/PztB/lg3HTmDzuHRYNO\nY0TfcTTt3UDeAFVkruP4YVLzSukz7UdseuFujkciFHk8enVr657eyMQX/ShXTaBswnQi4RDb//lb\nng0cp9Lr5YEtKym96Q98OPRMRm9aAk01+qCfNm0anwZLeHP8ZcjAi31GIY2/jJ7aNV6z/G8s0Ui2\nei0qa7FNnQzs5uGOtt90PTbInOOD6eLeT0lLI1OWGebzJ3RwxDgo0lWMu4DgGHZMIouq1eY8bUrs\n7kzYOL8qercy4OzgRPkN6veNUvXdKdUg+iAannJeONsVRSfzGp0QJw5Om6KwQuO1jPenYGdip2xy\nagxjxE0tIRESzLaMT2uphqBNODRdkpBRKFIihDvbaNi+ksZd6yg/5XLKJl5G6Y6V/ODLV/iTdv/c\nqFKH2pvZ9vf76D31B5SMuYABl/6Cb567k460HN4aoYqLNu5cQ06/McyrPp8hu75OeI3Cohwcd2M1\nhPp8+g0Edy/weG4eCjCvvY0I9nNimTYm7BAcP5Ieksw+7WoyBp9K/uBTuS3USd/6fRSXDsATCvCz\nj//E0eaj/O3CmTT0GsH7vVTtptZP/odDX77JT3cvZ3nVKfxh7KWkF/cmvbg35adcbmrHkENbOW3R\nC7yk/X/Exk/5avQ0Av3H0uOsmygeMxVvagaKEuHopiUcXP4PftxaR35mVlLFNqdNm0ZguSrP4lbJ\nOIrEJD5WL1SKfYjKdakGxTwf2xbb5MQ7OEnPMnV1daxevZrVq1f/rzg3EEVp7BCcBhuovTuWHAfH\nvFOM6uB036ZNm5bwmGJDHSphIjWzyEHNOBLsIFu7NuEUtIYCXLfyDZ5689eM2L0Gjz+VvAHjiIQC\n7H7vT2z88+10NtaSWVFF6fhL9Pshij9al2jxf+HgHM4qovhClV+x/9PnaanZrCMqcu1ehq9+B4BN\n53yfTo9f7/uy0/6LN8dfhqREOP7FPxi3Zy2eSBjZ4+PkXasYtyWaGulUg8darTme6RWUrddjg8yl\nWhynZBxjUczVzRgT42CAhl7Uu4T9jdZhaJtTFhXED1GJxf+rTme8Qk8TtwtRdaNUw7Rp00iXJCal\npALOCA6YeQB24o1WDk6rorAmECCkKAz3+Wyv3bbYpsGpd+XgmBCcqAlqs1OxzVSiGT/5BgFPJRzk\nwJK/sXrWdIZ99DQeJaK3I7q4JOpzhb0fPkvrwe2k5pXR96IfsXz8dDr8qUS2r2THW7Mg2MnantU0\nF/ZIeI2gLjCFVrRRkikYfiYFQ8+IOT6jvIpeU25nb2YBEEUD8mUZn6Rm2QiH1TonpksSuZoOmJEX\nFuWRqO3p8PpJqzoZULPjAt4UtpQOIBIKcNkHTzHi4Gao28uDC2aRovGRrljzDk0rVUGCkKJw5vbl\nbH3yGja/NJNDX75BW+0e2o7sZv/Cl8j48208+N7vkdqiOj2+jibO2L4cgPJTLsebmsHx7SvZ8Oz3\n2fHmw7Qd2tGlYpvTpk1zLOfhZGKMuUGxjUKTevsM37NTnY4WVI7eA10ZXYotf2Hm4FhC/f8uBOc/\nyewcnDzpXxyiSoJkHJWDV1//fSGq2HTMIy7UjMWEIYi7IkOpovEI1370R/6eWkjBsMnUrvtQj9/v\nef9pBl71GyonX8euI1uo6mzS00HDlucmoiggSWrpAp+fx866DU9qBsc2L+PwCnXSMGrhnLT6HRoG\njGdffiU/nn4fme2NDA2F2dCSixwJ84PFL3DD0jeZWV5JszeVqf4c7uqop9YEiWqTuuW+RR/uxCYW\nGSuCYyfgGN15qP93O27KZI+u0+LGGdq5ciUv5xcyOVVd3L+OEyJyMjEp5coyXklSs0NsjnMq11Am\ne+jp9dIUibAlFCtkJywmRGXoL5H16H4qV224z0ffuS/wdUmZnh21JejchnYHJyQWwRES/wqtisKO\nUIhBPh99PF42Wa7Rr4eoomZEc7qD4Pj0cWQOj4p2phoWkgI7h0WJxAr9JaFkrISDbH/zYYbd8jT5\ng09lHSBHwnR8/hdC7U2kbPiEzjHT2DT6QtiRGMUp0Eq4HAuHCQLpJX3pM+2HZFYMAqBo9BR2z/8D\ngaY6yiddRcWkq5BkD3/qN4rH332YdFmiIQwFhg1alizTGA7HzIl2KeJgLq8gA6t6jUT2p9K8/1s2\nvzSTxQNHs2rABGZtXMbQtsPg83M4HObMplomv/ozhpf0ZvzRvTxi2fB4IyEa9nxN056v4ZOoGO6Y\n1DTIL7BIYcDFGz7i496jaW85zt6Pn6Nx5xpTO5Mptilsw4YNDNeuz02ICsx18xKZ0cG2y6pttUF5\n41UTF58Zz2UstmxU4Yb/BQTnP8HEQpyr1VdJRU0FDSiKacd2IiyZnbhei0mQUw1CZ901O0Kd0fxA\nnuwhpCgcNTh5Qh69UHYOlFnF3hoNnneHotC8bxN73n/aRE5s2L6S+o0L8fhSef/0G1GIKpaKCSUl\nrwwkWXf40iWJv0y4kt2Fveg4dpBd70aF0YRjkCpJZCoR7ljyFwgFOZxTwo7SKjIrBxMJBbjns2c5\nZcdXBFAf0KxQB/49X+OLhE07EuHgZMhdR3CiYQKz2Tmu1l2R2wezyhfdYyQaY5NSUrj2vQ+YnJrK\n8UiYW48dZX2cxd3JRNsKLffdak7lGsZpKdSrA51xidGih8X4N/ZWstmCAAAgAElEQVSFx4AeJWNP\n5uYz5MBB0mSZlZ2d/KzhOPM0ToadGZ0NoxNiFWyLFmlUr0hkB9qF0qLVxKMnTDpE5YTgWManE1cI\nHBwc7FW97X7LyTqPH2L3/Cf1/5+7ZTEBTT/Lu/Jd5EiYnVUn488pTngugRwfRKbnOTcz7JY/kVkx\niEBTPcHWBnL6jGL4bXMYdsvTVJ7+XyBJBNuaOFDYk7+Nna7fl3zDtQoxP+ucWCbHpohD9L57JTVN\nfEm/CQAc3bgQgOLGw1y59l069m3Un0FBWi8KtDO4bjcSBqfTgqpZzRjuFBZQFEqb68h55kY2PPv9\nGOcGDKhGEm7/7Nmz9XHoJkTlRX32QoqCG+w3aHCejCUrhImSGMZxaeeoWYVD7UJU/6scnP8ki6By\nbXJlmWxJ1jVITjT/BpLrYL0Wk/ZqraTdHcvJyYkrz27kZBgfjwCqMGKe7CFflk3Oj7Co0J/6zcaI\n0cFxbtPej+aQ2/8kDvQczpaS/hTt2wSoO/eSsRfS+/w7aNqzgWNLXyCrs4WCYWfy8eAz8IaCbH/j\nIT3TC4wIjvqA9K/fi/zszTxS2Z92j5crmprJCjQwPhLQH5YOLU4vkDzrIhNW1DRvoTUioz7c4SQf\nbicEx2uD4CSb3ljljSqKJHJwHs/NI9/jYUlnBz8+fozDXRzvom1iV+xUSsIpRCXCUyvi8G/A6OCr\nZqcx0pxkiE1kyUyuPcz2BHwlcIPgaCEq2bwoNVrr3RnMa0cyTjJEJfo823J+sYMVPWutYWS8F/kO\nYefoOTQER4Rrk6j6e2zzMvYvfInTBo7jijXv8qp2rkjTEU7dtUpTzX2C9rq9dDQcoe3QDo5t+YJQ\nm3mOKvbINKZmMees2ykrq0KJhDn01dscWPQyktdHrym3UTh8MuklfehsOMLOdx4nEuxg+E1PMn/4\nuWRuWgbbVpqcOYF6WefEaAaVeVzoPBJJoiU1m/WVQ1EiYY5+u0S9Ju04megYFSiQVQfHeD4nLRxb\nMVPhKMR5Zt3ojlktJyfHdYVzSI6DqB6nvnolbENodorndmniRuFQYyjKxMExhqj+/4zggLlcw79K\nAweSJRlbEBwxOE4AgjNr1qy4n5fY8G+Exasq7kFFOSIG9Mvo4LTH4TSF2ho5vOpdAN4ZPkUPUXV6\nfVRMuhqA7N7V/Gb6fSwcMJHsC+4E4NLlr9Cm6ZgIM4aoxG6gpeUYvev3MKx2Jy271vLAj9Xvi8nf\nGGpRz2FunzVMlezD7cjB0V6NvdlVHZyBLh2cHEmi3OMl7PVyzdH6Ljs3YHRwNATH4R47lWsYpzs4\n8QN91jpkKZbzJEs0TkF1CCKy7Mq5AfP4NQn96Rwc9f86BydifgZybJwCoWJrRoSi/3YXooqtR2U8\ntxUtsCqBgxnVMJqVqKwT7pNcJA4ufZU7332E7M4WHbUMKTB9/Xv4Au34swvVStdjLqDPtB8y+sev\nMui/fkfJSReSUV6F7E0ht6QfP734lxwqq6KzsZZNL/yIfR//mXCgjVBbIzvfnsWWV3/F/oUvsfHP\nt9O8dwOtB7dxysq31GuZdjfetGwKPLEIjnVOLLcR+RNtBjXMt6bvSURkDy071+rOmNgSyoYFVpwj\nT44qGYvRLu61k5qxKChpcq4N85uTuSmNY7VZs2ZFnScX60z0Wtz9RsiI4Ng6OLEor12ICsyh0sQI\nDqbf+o9AcMaPH8+KFStO5CkdzZhJ9a/SwIFuCv1ZdrD/ShNOnp3o2sGwyicY4PWy2cInMKYJi+HY\nZBB9S/SwHVk1nx4TL2N1rxFckl8BzbVsG3YWvsw8Wg/tIBxoh17Defr0m5CA03Z8xahvF8WcR68o\nLkmmHUCnopCuDXxZm3Ss3n2OTRVeUMMNOchkaFojUQKnO9OzqCyTge64GoaENa59okNUomp2a0E+\nyr49Cdsez0TbhEPeYiPyB/YpoLmSzCCfjw5FYUMCBEec1mMJUbVHIqTJctJE4wJtAQtkZLj+jpUH\nIcyK4KTpHBz1+WkwhMGtppOMMZ67ayEqawkYn2UcGdEHMN8LnySRI0k0Wn5PLLyiHW6Vge0sikor\n+muPhkPc8cId3KT4SMktISWvjJx+Y8jpO5qcvqPI6TsKUDW2DisKisdLzqFtLHz1PoKtVslIaNyx\nKqbAavWadznaawSby6oY/v3ZNG77gj371tHrWE0M6iWszEYDBwyIiySxtL8anmr4ZqH+uZErJsIw\nuryGJ3ZuSVSPyh7BUS0eytIVBAfMqdyJLBWzA5HIhDOXIUvIkkRAUUxhafs0cfvraFUUClHXHKOi\nvYmDY3Hw/6MQnEQ8kRNpwpmZlpbGn/LyAdgah/DYVUsGwREDTDxedgthVy1ecT2IL3S4XMt0OUMj\nphrNrtiilYMTz0KtDWRsWoQiySwcMYWAx8uW0Wp2Q83il9ny8s+YvOFDAOT6fdy67GXabRZUMaD9\nSKYHxOj4/P6hh4DYTKUcmxCVek3dQ3CiJGPLNSeTJp5g3AxwieAIB+fLA4lrTSUyO0fQzkTquJHH\nJPg36wKBhI5ibIhKfT1sqSXn1gRnaFdDg+vvOIWojJk1EFuFWkdwbJwCXQfHJgQB7nbHgmRsrUfl\nt4S/9PCD9rnVSbELU1mdbYHQWSUT3JiuIaS1Q7QnLRKkvW4vDdtXcmTlO2z7+69Z+/iV7Hznceo3\nfEbbkd0AKB4vp29fTuXr9s6Nk3VEwty16Hk89fvxZxdy4KSL+cn0+7lv6r2kp2UBsXNiuZYifiAl\nE39OCZ6UdFPb27OK2FY6gJRgJw1bluvfM+paCeK6SM4QHCLjMxMVv4vv4LQZ5lTdCUlQ4sH6W4ls\n5syZrupjYTnGjcgfRNud5biJNDs4EtE+tB5rRHtMCI72eYok6eNb8JCSFUy1WlIITllZGY8++ihn\nnHEGJSUlyN3QsuiuiYX8+5nqYP+4o53fNTlzVLpqbhcq8WnEgIScSJJxPP4NRBeLRpvFamFnB78E\nzkhJjclcydYGlFHorcEUokr8ICgr5yFVn83i/hPJa2ukIzOf1kM7aNimonmXLH+NS7Z9wSM1O0nL\nTLc9pzFEFZ0gFH2nkQJ0trRAeqa+mIh7o4eosH/4RGqiTuB0Cc8u7OhgqNcXk6lki+BYlYxdjJty\nj8e0g4/n4PTVHKFt7e4r6TqZdeKxK9MA9rszt+EpcA5RHQ5H6OPtioOj6akkQay2CxOADQfHwhuI\nhqhi2ygWCZMychcRHCelbasGk5UMLazAI7PbEpXWCxYKDo7u6HcdwRHPjB3/TFi4o4X6rz+h/utP\nALUMy2OllVyhBLnDRVkAo7UpCkWtx6j4y118UNib7445n5qqiWwqG0jaFQ/g/evMmDkxOyOXORMu\no3nQJEZp16pEwrS2NTKzrZEmrV/G7l3PV4b2iLuVZljExRzotXE6Avo9sW+7HYIhxkc8IrBAV5JB\ncBobGwlkZavnTiZE5fI3xGYu29FpEXOsZDp/u4ULCsbEj9hwl+DnZNm8D+70xOwsKQfnxRdfpGfP\nnrz99tscO3aMiGEhlCSJW2+9tUuN6IoZ9W6eaG7iyeampFNO3ViIqFCdtTCe0awEY+O/T0QccMaM\nGXERspw4CM7WUIiD4RDlHi9DvD5T2qvY1TUb+tNMMk7cqw31NUzc+zWreo/in6PVIn4Hlv5d/zwE\n9G44RHaoE4jv4KRKkmGiiZgcn2svvwIWLY4NUTkhOJadqx6icjlQXm5r5WWbCrb2CI64DvXVDfIn\n+DeianlanAWoj7Y77X3aafBefMHHROYawbFxcCYk4eAIv8lKMj4S7h6CUzCgP6yod/UdM4ITNWMh\n3FRJhd87DPC72CjYOTh2JGOjC5xcmrg1i0p9dU4TN48nOx6OTjLWziHur1Oa+HmpqawPBGx5XVEE\nx4IouVhvlHCQ3h0tkJKi63G5NePC2bL/W6a11VH1zUfcf8FPOFjUmyHX/55xns3Mnf1H/DnF5A4Y\nx1OTr6MtJQPCITpb6vGmZqooTmY+OzLz9XOfsWM5Txl+K2x5VoVsQkskoiPcxrnFmHZuZ3Yk22Sq\nkCeD4MyYMYPAS2p9NzchKh3FdrlaivttdTyEdSgKEUUhTctoduLfQJTflm1EcAznSZWkmLn838rB\nGTx4MAMHDqTdYReZl5fXpUZ0xd5pb2OI18ec1mY+7khud5CsCTXUNEmylbSH2BRxiE7wyRSmc7Lq\n6uq4n+dov+HEQ1rU0cHVGZlMTk1lU0t0qhfQo5GHkUyIClRy98UbP2JVbzX2nlm/n+MGCFj48gKt\nsBv8wulIMxLQMCM7g/r2hUWL9WNjEBzrwm3ZuSYbonIyO8e1KyGqKi3stF3TXHETosofMeKEOzhO\nWVTWUg3pksQwn4+QoorhJbKIBcEUu7Auh6g0jkV6RaXr75gWGeMipYd+pGiKeCTWybcLUfn1RT/6\nXlc5OFbir8+yAFlTkmMQHDsHx0LUdNKEApjoT+H5/ELea2/j+8ePxXwuHCsxY9g59/FMr0OVZEkR\nXUBOjgr9FbQ18OB7v+dHU+6Col5slioZ+/OzkTRkrw0YXvMNr7z7Bzq0cgeS7GV0bjGzeg5gZWo2\nAyWFkTWbTFmU4t8iy0zcv0aDg2MnN+Dk5Nkt8sYQvJN1hYNTXV2NUANyheBYdGYSWSDB3K1o72Vo\n62M8LR/haOfb9KnTZvXfysHZtm2bo3MDcP/993epEV2xVYEA04/W/cudG3DXyfYIjpjgu2+JyjTo\nISoHB2ehVlNncoqZhxMlGXcdwTkeCTPoyA4GHlF1cvqsfBNjIEyEdMQO0u6cAk4X1yHi10YH56ul\nS03vWUnGTgiO+F29kng3sT7hxBod166kiQ/0qcuHqOcU71jh4Ly9JlY/I1nrRBNf1CyxDo7arjF+\nP15J4ptg0JXelLXQajREpTk4yZKMtbGxfv8+198xLUyG96PE01j+DUTTxOPq4NhwesBtsU3zwiHM\nb+H3WENpVgfHjoNjRYF0NFGWY1TVq7Ux2Ndrv9ftDoIDhkriScoBWMOj4t7ntjdx3lsP0XJgKyFF\nRpI9dDbWEti9jpkfP811C36vOzcASiREqLmOQbU7GbVjBeP3ro9xA8Tot85PxihBp2kcaWPHwaGw\nc3DstHOmpKbynbQ0/f9dQXAWLFhg0MFJfHyyISrRbicEB8zlGuI7OOp7BbqzFLvOWOdyu3l0pC/K\nW0xkSc0wv//977nvvvtsy9QDfPZZbGXV/y+Ym3CDmB7sdgYnIkSVyMGJF6ICWNrZSVBRGOP366UZ\nIDrBNtvsXsHdbqJRgynv/XQ2v/zgSQq3Ljd9LvpBOFNtNiER6wAXv2skGa/5Sq25IhaW2DRxe2RC\nhKiSUfCMZ/HSxK38oHhjRmjgfK2hIU7HFsoy2bJMQyTCax992I2WR804UbnVwTk5ifAUGLJTtMuK\nF6I6NzWVT4tK9FCcnYkQ1dJvvnH1+2DNojIiONFFysq/Aft6d8LsScZRc7NACbFSK8nYysGx8j30\nxVNrX1wERxuLClEn1uogDdAWizKHfhdLSVTbK/7ibrR0SSJDK53QlOQzJzY4aZJapDfPcJ1FgVY2\nzb2b9X+8npUPX8T6P1xH5j/uZ9y+9TEqxhDtyxSLsyZMII0ZFiKt00ZPIHdOfJoKrS+NbRHjw4iy\nPJWbz1O5+eRq5+lKqYYFCxYkFB40mnV8JTKxKciyQV2EGZ1RO5FD63EFNsRtJwQnjPqceSRJH4uz\n8wrcNZ4kHZxnn32Wu+66i/r6eg4dOsTOnTtNf0OGDEnmdPh8Ph5++GGCwaBewPM/0dzsxkV9GGOI\nSvzb42LgJbJEIap4JGNQF7HVgQBeKVrDB2JF/iC6ewV3D5uCuiDktTcx6sCmGBE9MSlmxglRiQGt\nIzja//XdCRID+/QxvRdLMjabngWkh6jQvp/wkuKaHYLjt0C/7QnGjEQ0RCVIzE4OjkBv9oRCCceB\nW7MrmGe1VkuphpNTVAdneZz6U0Yzhqh8RBVUj9o4D1emZzDI5+MCw47WaoJknN2rp6vfB/NYMy5s\nephRMpZpiD47DToHJ/aeRNWGo+8lG6ISDkcMgmNBX6x8D8HTqtPkBewcHL+FgwPGMJX5eMEDy5Xl\nmPEnY5jXtPd0TpCLKU1oc9V2oV6acdHM1co9CFP7TGFgzxKUkPrslMv2KeJgUFa3XIsw4YinWxwM\n4zzYaeJbCScv1mSiVe53GwQHrShLqnYdHkmiyucjBbWvOxV3IqTCqqurk9TBUV/dOlFRBCeegxPN\njoqH4IgxX2CTeh8vYcQ4l/qBCo/76o5JOTjZ2dnMmzePl19+mQ8++IDFixfrf0uWLKGlpcX1uXr1\n6sXixYspLy/H6wCP/qeYm914PJLxiSi2OWPGjLifC+QjnhbQIpswlV2aeFOSHBwwiyxaH1Cx48mS\n3Ds4OoKjHZoqSVx47hTTse2WRcgRwREhqiQJdk7mRugvkVNc6fGQLsscCYf1SdlpfPXVdoS7QsGE\n48CtGeXgmx3GTLth4kqXJEb6/IQVhZVuERzt1YM5hGdXS66/NgcMiDMXiGr1Z19xhavfN14DmFEW\nI4ITLdMQ7ZMmrY3ZUmwOpGihcRyFiYb93DwznahjRVTMFmYNo1oLKQoRzNTeqrNf4CJNXL22WDVj\nCXN/l1rOpV+njZPohoNTZFMbz60ZHRyrEyfmEeOz4CTyp7ZZfRX9Yr07OgfHGqJyRHCEsxLbB+Ue\nD6mSxOFw2Jb/JeYgY+hzgNfXJfQGNJKxjiglPt6fZJheJxnLzu3TkV7ZXYiq0AYNiiI4Zv6Y8bNU\nSaLS400KMEjKs9i3bx833XST4+fJhKgyMzO59tprqays5Prrr0+mGf92S7Qbh+gDH7ZMeuBMMvai\nDnS78glWc50mHudcn3d08N/ZOaoejnY6Ea4yIjgR1Ak+W5ZdE96MAoOxELBqIlRkTzJW38uLcXDE\n7gs6mpu1Y9XviDIS2Q67C2sVZSu/oatmV4IjRmI8gX6DCE9tCwUThkAFgrM7FKI0wThwa64QHMMi\nM9bvxydJrA8EHIn2VjOmiRsncLFw5Biy23ppTlx/r3N8XSzm9Ulk5DiliRuRCLHot0bMz25zJEKW\nLJMpmZMLxCJlHecB1J25W35DSyRCiiYV0Kn1SUwtKovQnxgjjWlpFGOfReW3jEWIZm0VyDKiolyF\nx2PSOCrzeEyog5V/A7FZXVa7NSOTy9LT+Wdbmz5+ks2gAnOVauHgiExQUarBOCc6ifypbTY/i2HL\n/RHCHhmWhbfRkYOjml2auPFZNZpwKATJ15g9N9Dr5bMuEIxB7YOuhKhcc3BEmrhFn8Zo0Urh8R0c\na4jKjoRtx6c0Ojh9kgRDkkJwRo8eHffzs846y/W5Nm3axM6dOxMf+B9grkjG2kfGzAo9ROXwnSdy\n81hTUkZPF5BbIqG/HBcOzuZQkMPhMKUeD4O1hUQs/tZdvDiP28Jv8RAcPUQVD8GxkIzFMcY6JW+9\n9prpWLE79zg8tFbewYkKUdntYq1p4olQP0Ew3hYMJjxWEEB3hUMJx4FbM3NwEpdqmOhPLjwFIH5C\nxlxET1cJ1sZDH290V9bPYQKTiO787vntg67b4CT0p4cZkfQQpnVcNjrwcJwUscVC4/aZEU5TliFs\nZB1HxmwviI7lN5Z/AdiHqHRn2/Det1oYdITPr79XZXEmyyzzkDWDCuzDs0a7OC2dwT4/v87JZVau\nmlXbHQQnTZJ0J26P5jSI/jI+C0Lkzw7B0cskaP+3jnZjMWBIjOAEDWPHagJt3W0RnbUicUYEp8rn\nM2nwJGMzZ87sWqmGJENUTjo4YOXgxBKIhbVYeGN2CE56nM/SJIne/0oHJ5F99NFHJ/J0/zHmjmQc\ni+AkClGdlpKKV5JM9Yi6YmmShF+STKqQTrZIyzo7S1M11tPErZO7NkC7EqKy7mzFJG1H5hQmJvQc\nCxRq3PlY07ytC0lMmrglRGW3s+2K2ZHHrcROHfVzmHQE/2ZbKERYa5NHizFbTYj8WXeF3THjfXUS\n+hMF8jySxGRtvCwPuM9aNCoZR1P/FdoVhYCiamekYkZtsmSZUptFO0eS8UoSjZGI61Ib4FyqQd+F\nE1tJXJiTFo5eqsGK4CQRooLopiLLEDayhhB0vod2iJiD9mtjocBmcxTVe4q2Y7VGZB/jNzo45sWi\n1HKueAiO0zIj+mq9QUZgbxfGrTGDT2SK7QuFiSgKWZrmil3b7REc1cTz78TBEfOTGDNGB8ekg6OI\n88W2WyAMu6wIjgVlyTY4tVXdCFGp53Zuj9WSTbQQfZcZp312CsVusqhMToxlLrdzcP7lCA5AeXk5\n99xzD7Nnz2bu3Lmmv0QIT1ftlltuYdWqVaxatYqePXsyZ84cPf6ak5PDnDlzmDNnjp7dNWPGDObM\nmcO0aWrJgOrqaubMmWMqzjZr1izmzJmjkzanTZvmeN6IRuibds65jucVmSL5RUX6ecUtkiWJGbff\nbjrvi48+qut6XHrWWQnb+/bbbzu2V0+3zc5O2A/9broBgKmpKplzZNUAAIp69zadN72iAoBIWrqr\n/h179tl6W8OW/hVk0x75qthWu6LE3LcoB0fTtFAiVFdXM37SJECdnL57kSoieOrkyVRXV8c8bJ2W\n8yrp6jUO6tmLOXPmMG7kSAAy83K7NR68Gtl2wtixej8I6Pm3jz7KrFmzorsOWWbOs8/GnPeUsnIA\nDqSkMGfOHCSNXJsmSab+lYA+Gtpzw69+pbc5mfba3Tdj393503sd+yGkTShDfH6CisLW1FTXz9vP\nf/UrQHXwZ951t/oDGv+rXbumH954UwzvZkRObsx5Bf/GU1TI22+/7fq+yenRulW+tFT9vL995BFA\nXcT7lav3Ysypp5rOW9JffTZyJDl63ttv1xf+p5991tQPfq1G1ohx40z94DTOhDTDr378Y729fm3y\nn/XEE+Tk5OjOxYDevZk2bZrujP34tw8S0vge6ZKkn3f08OHIkkQYeMbQD9u0MXtWUbHe3ouGDQfg\nUKr62fDiYlN7hduZkZOj9++555+v3kZt7rKOM5G0UPuzn/KbwQOZ2XCcdX37JP28yVpfDuzVk4p0\nteRCj1EjCWuOdqYkMXv2bP28goNz6y9/EXPeR594AogivekZGab7VlhSDMAgLdElPS9PHXdXXam3\nd8wpp+jnFU7ndVdfE/O8XaTNMbvDIdP4FejhuJEjmTVrlgkVLPZ4mNi/HwCp2nncPsezZs2ih5Z8\nMXjAgIT9e/rJJ6sH+FNcPccXffe7an9r7R07aVLMecdoc3S6JJGvzWMnn3lmzHmHjR2rHqedq//Q\nYXp7h40eg9FOmniy3g9irvrlPfdywfDkkywUt3/jxo1TmpqalIMHDyrt7e3Krl27lF27dinNzc1K\nOBxW9u7d6/pc4u/0009XFEVRevXq5er4VatWJf0b3f17LCdPqSmvVK5KT3c8ZqDXq9SUVyqfFpWY\n3t9dVqHUlFcqPsvxZ6WkKjXllUpNeaVyZ2ZWwjbMmTPH8bPBXp/tb9v9pYCyubRcqSmvVPp4vMqC\nwmKlprxSGenzmY4b7fMr92RlK16XfXR7ZqZ+PTdmZJg+e7WgUKkpr9R/d2pqWsz3jf1RU16p/CE3\nTwGU+7NzlJrySuXmjEzlk2uvU2rKK5WfZ+UogHJlerrpO5ekmc87xudXasorlXcKixRAuSY9Q6kp\nr1Rm5eR2azzcmZml1JRXKjOzsvX3xLVlSpL+3k7t3qfYnOMb7fh8WVYAZXVJmVJTXqmUav8Xf+Ue\nj1JTXqmsKylLOA6S+Xspv0Dvt3zLbxr/viwu1Y+bp/Wj279cSVZqyiuVb0rLldHavXhXO8fnRSVK\nTXmlMtDrVZ7OzVdqyiuVLVqf3JCeEXOuCX71+28WFCXVB0O1Z6OmvFKZnhZ9fvNltW0bSsqUH2n3\n8yeG+wko/5NXEDNe/aDUlFcqO8sqYn5rudZXE/x+V22bq53/vNRU/b0d2phJtbT/o6JiBdCf19d+\n+StlhfZ7PTwe/fsZkqTUlFcqW0vLTb8lgbKxVB1jFdrx4lz3ac/Y3LwC03cqtbH3VXGp6TyiP+2u\naVecMZ/MXz9tPl1SXKI8YJgDxDVXeDz6OMjUrnmHzT0BlDTtc/G3XnuWxN87hUVKTXml8kyeOg4f\nzFbnh9NSUvTviDkHUH6jted7GZkxv7W0WB3XA7xe0/tnavPbS/lqH9+YkWFqkxiD/yxI7hmbM2eO\nMl57Nt5w8V3xO/dYxrrTn7Wddt/7pdYft2VkKj/Qzv9Tm+PGae0Uf3/U5nhAeTgn1/TZcMN6JNaP\n01JSlGXa/XfbP0khOL/73e+4/vrrKS8vZ/PmzfTt25e+ffuSlZXFzJkzee6555I53f8Zc1Pwy45k\nrP5ffG62YQaxoiIXiq7xyjQkU029E7VuF8DUtDQdHrcSR9cGAzzW3ORYmsJqx8LGEJX5MxEBiatk\nbOm3WJKxxJovzTo4dgiO0axKvALC7W5JVl0LxDAe7GLbTqFND+o9CxsItyJmbS3X0MdjhrxPVEFb\nEwcnzrgxhhOT4d+AMU08VuMjSjSW6a8hpEKMsr+NkJdIET8aCSfVB046OEYuiSCXWvWZ7OpRORGM\njed3HaKyqUdl5c9YReXE56++9aaenGDk4ThJ8SvAWi1sdJLfb5IpWKLd11gOjnatlvM4cQtTDaHy\n5EZKrAniqkoyVn/pWCSiz1PZkqyPgzI9g8p+trLeq0QhKjHfm3RwDP0pgm/WYIkX6OHxElEU9jmE\nqMT9ybY859Va6DDZENXs2bP1TKeUOOuTsGTD9Fa+YlwOjiy7KtUgzHhMvLlcfJYlyfTweEwipYks\nKQenoKDABA8b7bHHHmPy5MnJnO7/jEVJTs7dZUcyBmctnGEGsp9dqqfVNmzY4PiZ4K3EIxgb7V1N\njfrCtDR9co23yLmx40pikrGweGni1mPERJkqSRw/csR0rC4PKsAAACAASURBVPU8nZbT6rWoRJp4\nkjLlTibusbhrMurEEVEUk/PkRE43EsJFrzll6vW1ZGXEGwfJmPi9gKLE5bR0x8ExOvdWB0fwW/Jl\nmX6aE/eh5nj3t4mzC4JxXSSSVB84VhPXXk0cHMsEbKeFo4v82fxWnfYM1bp8lgTvTRA4dVKvEi3Y\nKxYv4ZyLtq7fulXPXDSqGafqYzz293Qejs9vkinYrBGQrRwcjz6nmU/mxMMRGZlN3ZxLwOjwS+Rr\n4cmjkXA0fV+W9HEQL4PK2F5h1jKQUSVjMzfEmYMTu8EB1bnxShI14XCMgyfGnhg/Yg4QopeC/G1H\nzo1nGzZs0J1ZN9pEXSUZC7NNEzckc7jJorI7lxsHp7+WjHAgCV2lpBycTsMEpygKWVlZ+v/9fj9V\nVVXJnO7/jLlJE7cjGYNzwc0hhl2q4BfEMxEXtbNEIn9WW9rZQWMkwlCfX0ePklUatVq8NHGr02en\nZOzo4IidDzConxqndtopO9VY0tPEkyTYOZmVZBzdNZvNycERWSFGp9AJ7eljEQ2LNw6SMdE2p0Kb\nwsTnAUVhdTAZeq8xTVwypNGr74nFY5jPT5osczgcZp22ANuligu+2tFIJKk+MKeJR983LlJ2OjjG\nNhrrUUUz8WLH0A+PH+O79bWOC63VBMk4RqfJuJhaEBzR1lPOPpujWnZSvhzbPrsFbI1ONE5hgEGm\noC4SIawoFHs8pvRvOwQHnBd44ag1J7lI25mdDs7RSEQ/d5bGi4L4GjjCjAu1lVMv5mwr+uCoZKy9\nWhMCrJsR8+9r35HMDs5qTVOq2BOr7uvGpk2bFiUwOyQ03JaRyS+zc0gl+TnQeiXxEJxEaeLWbM1k\nHZzB2pq5xwGps7OkHJympiZuuukmJEli1apVzJs3j4suuoiLLrqId999lwMHDrg+l8/nY+HChTz1\nlFrX9bXXXuPNN99Mpjn/NnNTsj262zG/LxAc2TAZZEuSrnYJUfg9nsWb1N2I/BktAHyk7ZZlSSKs\nKEnrL1gtXpq4tVVJITiGENUAjQToNkQVVTKW9HNA90NUepq4dj4hm+jkcFkdHKH1Y3QKnRwcPUVc\nSzs90Q6OUwaVMDF5rQ0Ekp58Fe14CecQlcjq2REKUhMO06EolHo8MSUMBIJTHw4n1QcdhknVDsHx\nS5KOYiYTorLTUjoUCbPCRRFSYXq4xarTZDjGqdjmmVOn6uPHGKKKlyWzPhggrCgM9fkYqfX7tmCQ\nMNFU7hIDiiNQaeu1RutjmU2EXZoSjCk3FkB9znySpCsiH4tETFXYxTgokxM7OMZNl9P8ZBX6a7JZ\nZMFYPiP+ZsRo+kZNpIlrfbXSMl6SnYenTZsWN0R1bXoGv8zJ5bbMLF4tKNLFHDtx9zvWceQ+TdwG\n6YlzLuvc3aHEHjeoC9mkSeVcPfvss9x8880sWrSIBx98kEWLFukhq4aGhqQmnv/X3pmHSVHde/9b\n3T0LwzLDMoDsgtHEBZAt0WsuKApXRa6Jz+MSo6jEmOuSxdfI1VwvahIV9QVj7kW8vG5xI64RIYAX\nRAKICjKKIiLryMAwCzMMDNNr9Xn/6DpVp07X2l29n8/z1DMzPdXVp3596tSvfms0Gi0Yl5abNHGz\nGgusoM9QzJG0cFU/BzE41i4q+xo4PMuCQVypZJg4LdxmRZtOweEXRGPlhYU3qdOLhlVw2pubMYLZ\nN8lFxX1OBIkLlFaLpQuy12niZn5tM9cmVXDaHSg4fOEwr1xUTi04NBZik8PqxSxWLiqq4IwrowpO\nDHEkFLnTy8oxKhDAZ1HtVk8fAlpduqjYpHb2Rk2QqDzskyS1ACVvQjdScIyUkFThLThaDZxkRYzO\nXZqB8tmOHZBUBUdTStS5aHAD6yIEO2JRnFlWjiu6JTKTvlHmVaMs4yR/ACf5/WhQFAVHFhxmrLQA\nnxcuKiBxQ6yWJNV61ybLuh5edB4McGLBAUCbgCSt0cop8N3E40jM0xqlnxZFteDYuJP1n6+3stA5\n9XU0qhaUZD/bKToXFfe/ieXleFDJSGyTZUyq0GpmO+9F5UTB0bfEAYxdbfx6bRmDg+T/sS1rnOLK\ngvPWW2/hkksuwd69e3Hw4EGMHj0a06dPx8yZM3HKKadgkxIEWmw46kWl/ORv5trNUHvvGYqpjQb3\n9TGo68DjVZAxZX04pO5vVqrfDR3xuBr8lWzF0v9tVeiPEjJQcPbu2KF7zc6CA7Al6n1JVWJThbfg\nmPm1bV1UNgpOAMAwv/6i9jrI2KzRJuWtYBc2R8JY0nXC9WewlbzVmzcXwEkDfHcryswu5Tx5NxXt\nX9PqMsgY0Bo38os6VR6o5YF/wlTr4DALt5lVIxVUd4uFCzXKKBOskvjfTz2lxeAwLm5arsDMBUHj\ncIardZgSUqDKwUk+5xYc/umY1tTywkUF6NeJE/E4QmBrB2lBxlTBsSooqHNRJcXg6B9E2HWFzlP2\n2lYtONxnmNXAYd+jBhkrytTReBy7mKKAbi04CxcuTKqxAwADfT483bsvyiQJ/9N5HNNbmvE188Dg\nvBeV/m+rVg3dmDlqdB4E+lhPKxeVUW83ut5mzEXFEwwGsXr1aixfvhzt7e3pHCqvYQPezDALMqY3\nQ7ZnH82g+jwSQZsswydJum65Rph1cAcYC46LhSUKLajTrJKtG+LM5/MWHH5BcRdkrCk43RWzOp38\nyUHGycftVCwQPaTkQoGpwi/wbhUcpxacQX4/yiQJh+SYaomwmgduoFkhnTbuhPfDIfyotcVVYB+F\nPTJ/8zjKzTmq2OxRFRz97UNzUcVdy4DKln8apdcmvX74IGNDF5VqwfFAwVE+j7rj+D5Uic9JUA7N\nPdUVT8iAxuAYuaiMgowBLZOK8o1y06Odr9lA44BqwdEfjFW6WKirzSsLDusypBlj1G3US/Kp84C6\nsJosWkLoqzHr/0dndg/15qwdR63ozlpwDBQKwLyKMcB0E+dcVMdIHN9EtRu2WwtOdXW1+n3Q9c0H\n4Ok+fdHf78eH4RD+eKwDjXEZP25txsZwCDFCsNtgjEbw89wqeLg704vKqJgroH+IYDPT2N+jRN9w\nlJeJGxeVawXnO9/5Dp555hm1gzgAPPDAA/jRj37k9lAFgxMLjl2aOLtc0wyq7dEoWpULyM5NxRbJ\n4qlxGYNDeburC4B59oFb6A07Scnj9nOm4GjBrUDiyXTs6WckXnMYgwNoF1R3yacGBXqWJq585/Sp\n2cwKVclNm95SYkG2U3B6Mzd1itU8cINmwfHmZmQGvRlYlcEHoC641JLDW3DYNHG3MqCLLT8v6Tyg\nCgxvVqfdpNmibF61+wCSLThqjBhzbKNg6C5CMG/ePMM0cbssmS2Mq/GwLKND2U+14DAKDs3K4a9f\nulrwQa2qi8oD6xagv0m2cdbmnj5JnQfUgnPYYQwOP+OpXhtQ56j2P7o2si54o35clQAGBwKIEoID\nFu0ieBdVRzyOnWlYcObNm6cpT8rPsWXlGF9egSZZxi/a29Tv6xghuOpIK0YfPoSdDpUEZ1lU2jVu\nlSYO6JVWMwuOVfKIUQq+Fa5icCZMmIC1a9eivb0dX3/9NUYpWS0bN27EE088AUmS8NZbb7k5ZEFA\nJ7zbXlSAdjOkClCiNH0AMiH4KhZFa1zGqShDP58fOx1XndGjXSzuLo6NkTCuP9KqmqnTpS0ex0gY\nBPExwwoRkrTAAFZBxom/KyQJPsU0SW8ujiw4NFXcJ3nWqkFNE5e0sSXGZXwOTmJwjPpReW3yZ2lW\nlCarm4IXyNCnYhspOMfjcTQpf+8ysOBUIqEERAhRb8huOCjLOMnvRwvnwqAJADS9OSmLivbMMgoy\n9tSCo6/TZBiDY3DzOCInp4lbZVEBwLeyjBZZRq3fr3ONNMaTFRzVgsMdi970/Nxy2FPy2ILDLBzU\nWsVmUQGJ+C6aCcp/vyzsymqWJk5hb6j3HzuKpcEubGUyCI0sWLRH0rdyLGn9A/QuKj8S8zlOCI4T\nkpaLij02nZt0LB9HwjrFjOJGAXVWB0dr1UCtfWbnwVqMzYKMzSzhQEIRdxMN6MqC88gjj2Du3LkY\nPnw4pk2bhqNHjwIA3nvvPUybNg133nmnm8MVDPTJzkmaOD+d+JvhaWVlCEgS9sRiCBGiWXBsUsWt\nmiymEoNDeT8cUoMK0+WzSAQRQpJMiDGDgDGeKKAr4ESf3rRmm8D+b74BYB6DY9QlSXNR+QzTcFMh\nyYJj4hawd1Fpcjey4KhFGJlFwatmm6tCQdzSdgR/Ujq0Zwr6nfIZKuxc3c3Ml32xKOKEYEQgoD4h\n9/Fr1hvAvQx+0X4El7Q0q9cahd5WfCYKjlpzRZJUW4XqovLASnGcCZgFzIv00c/qqWZ7EcyZMwdt\nRi4qAzcXD00X/4aJyTCy4GgxOPr3qx3Okyw43irkrAVHdVGpWVQS5syZg34+H3yShBZZtnw81BV5\n5P7HW93ZdaVBlrFUceVTtDRx7fyt4m8A6KwsqiJIEvWOdqbhopozZ46W1abMU9q82Y2lwwzePWnY\nTZyx0lqliQP6a8wsyNhKwTHKULPClYIzdOhQzJ8/X03/ZGloaEBlZaXBuwqfdIKM6aVOb4asewrQ\nnsLsUsU7OjpM/6cqOBl2N9hx/7EOjDl8KGkSsguKVSGriG4/asGhCo4Ev7II09di0D9dGikuapCx\nJKmLf7pP38kWHOjGRUktBke7JI0sOFbzwA0xAMtDwYzPGXr0qqQiapqs2CfYEIADsowAU0qhlnPV\nuZVBazyOrwyslLxlgl+UZSSsSz5J0uJkTG76qUADLmmdpjKTAGE+VihICDo6OtBBCKIk0XySuiec\nWCmXdJ1Ae1zGMubGbajgmMTg0Pkf4JZDrdCfNy4q9vtQXVSMW6+jo0N1TzXZdCxnzyG5Do4eOyVD\nSxPXXrNriMtaWWq4wqyNTAFDtxYcei2w9cKGqdak9B9c3QQZd5ckVJlkJGr72ruozFz9gLsMKsCl\nglNeXg7J5CYfCATQr18/Vx9eKDhKE1f+xQew0QuLCppmUH2pmDxbDZ7CjKDN13gkeFtBNF2MUs7Z\nBcVs4gP6iaxW2gW9cCUMru2ve023n4nrSyv2J6k3J7MATKfwcVUVnHWCQp92+HljXAdHMfMy0eg0\nNoPNcjObB/kKlVU37sZ7jBhbcABgj6KM0CacfZkUccA7GbCf2hXnHRcJ+EBjT4OMVctM4snbKAYH\n0BR/WlE5SIgqA60WTkJGTrpFrw6HcNbhRmxmAo5pRd3+Pr+6Vpkpc1odnGwGGSsuKsatd+uttzIB\nxtY3c/Yc+NHxorKrJmxkweJbqvDISFgzA5KE3sqYWTlRJd+tgkPnAatA8ZmX6cBbAo0UHDrm7j6f\n+oDmxIJjquCYrKNAhhWcjz/+GG+88QZGjBihe726uhqLFy/Ghg0bXH14oeAmyNjuaYcqONSCQxft\nWhsLDu3eytNLkuCTJByLxw19v/lAzEAhMcKojxNrwelenpCdUQqh2ROrVuyPSRNP14LDxVWZBRmb\nVcC2suCw+6qKK3NuZvMgX1GLqJnUGAGQlNFB43BGKU/FagaVcpPzSgasBYd3T1G0dg2JMVSaKCGp\nICOhWPmU+Bqz+JmYqghpBQmpDNq4asaqguNyLBEkiigGJEm1mNllUZnF4GTERSXTLCrFbeiTMHr0\naM2CY6PgsOeQnAhifxNnoXEpbGsEvl6V4fuUn3Q+s1mv/915HCuCQXzsst4UnQdsJhUbD5QuvJXT\nSDYEWimGShsLYpdpDA4Mf+f3c+uichVkfNddd2Hjxo3YvXs3mpub0atXL+zatQtDhgzBoUOHcN55\n57n68ELBWaG/BLz448zNUALwvQC14FAFR7Hg2MTgLFu2zPD1VIr8ZRu9iyp1BSd47Bh62uzHo1pw\nJEn1maedJs4prWZPzXYuKjYOhV74+hicZAuO2TzIV2ggLx+DAyQCnGt8PuyI6hUcqvCMU8oC1DJt\nGgDvZMBeq2YKjmrBUW7eo5SbR70HNw8gobxWIZH2XG4SP8Nne3URgjWKDFQLjt8HxLRMGrexHEDC\nTdXP78dJfj+a4nEti8rEKs1bcFJNdjDDMotK8mHZsmX4ruqisl7/rCw4bl1U6vkz16pdDA6QWB8q\nJUmdz6wF571QCO+FjKIIraHXAlWeekkSBvr9iBJiWfjQKU6CjIHEd1VFfzexhgL6rE2zGBz+M9j9\nMmrBaWhowNixY/Hwww9j//79OHToEFpaWvDoo49i/PjxaGxsdPXhhYITCw6NqJe5L4eN16jx+VDl\n8+FoPK4+GbaqMTipKTjpBBhnC9lAITGCVVKMKhnHlLR2VsO3teCoLiofUwfH7Rno4TPjzOIejOon\n9ZIkBBSLW1S3r4GCoz4Ra8ctOAVH+UljcFgr1x3tbbjhSGtSWu2aUAhhQnBhRSWG+/2q+7ZV2c8r\nGUR18834+qEKDr3OvqtYYHmlLFU6meOb1Wmi42QVHCoDqvQlWXBSUXC4TCrTGBzlJx+DQ+OUMlHo\njyo49IGlp8+Hvy9b5txFxVpwONHw+pitgkMtOIp8fEj0kooTYhkLRL/Hfh4qgqqCoxybxgIdlGVP\nLPqs3CKEmB7TLHjYaj+jkATAOsi43qXS5roOTnt7O+677z6ce+65OPXUU3Huuedi7ty5akZVMeLM\nRZXArM9JABL603RG5kvSinWl5qJKpchfttFZcCwuavbJVcuUSvxdKUmoUi5eoxgcs94qnaqLSrJs\nlOgG3oJjZpZtUZTXAcx3a+SeAuyyqLR9C9VFxaeJA8COWBSrw8lPrc3xOP4W7IJPknBzj566TuKA\nhy4q5nez2DB6E6LXGbXAfu2RgrNDsVb9a7dupjV2qIJBXZZBQhgXlT4GJ51SCFqgccIaQS0Upq0a\nOAtOT89jcBgXlbJOxqFdD98/80z0Vyzfti4qRhy8ZHh7g50dha4/9PrvxWRFWZ05nTE0HMGLNVt1\nUSljOiWFdgZWRE0UEp6gQwWny+R4bJwNv5bTOjuH5Jhry6StguMm9fvmm2929eGFQgQJKwStYWAE\n9UebBRn7oe+nQ9FicFILMk61yF82YUfmxIITZEycWpAx0L3MfQwO66IyqhSbCk7TxGmRsEFMZgqt\nWeJIwTGIaSi4IGPlvIwUHCv+pzORvn5ltyo1Fofe5LwLMtbGwlcxpqjtGnyJMvQnBwKuKsHa8T+d\nnQCA67v3UJUUfn5ShYcG8bJBxo3cHNNicNzPcbWasTIOOmv5lHjq7mEtOD5mfHbtP5zSxVwjR5jf\n6fXwi5/+1HEWVdQyBkcj6GAdVSsZK9c/nxVlBl2jaEkQL8IK6Dyg3wntEu9F/A2gL4pqde2eMHE9\n8Zi1arCqg7NXjuH/dR7HQ8fcZ5DaKjjXXnut44P94he/cD2AQsHOimMXZOyXoD5tsD1TOglBiBBU\n+XyWMT5mqbGFEIMTc6jdGzXRZF1UWqE/IwXH+JhamriHvaiUn/QGQFvY8U8eB5XxDvYnW3B4hdRI\nwdGyUrTjepUmni14C45Ty8LOWAwfhEKo8vkwRonFoQ8DnqXKM0Mxs+DQ76lG8uHUQAB+pYaV+9aj\nxtRFI9gYDqGXz4fruyea3yaliVMLjuqiiqsyaFDm2BCq4MBY2XYCnypuasExiMGhbQ6OxeOWVgw3\nsG5qVmmi10Oo9YiLLCpzF5WuLYADxZAPMna6BtMxqBYcL3oAcmniNEbMixo4gHMLDut6ssqUpfvF\nCdEFwhOY1zcDEiVI/hYMJr1uh62CM3bsWMRiMUfbmDFjXA+gULALNKZBxskuKmrBkTQLDncx0r+t\n4nDMiptVF0IMDvO7VQqmasFhFyMkFCS/JMGvuHzYm0vQ4qIA2BgcNkvF3fh5qMJGlV2zNPHmeBwR\nQtDP7wetEKWliOvngLEFJzmmwatCf9lCjcFJoVvy0yf0RQjpdeKVDNinerOu6myaOHVPeRV/Q/lv\nxVo1SHENJcfgJH7SQOeuOFFl0BBLyGSw8l6zmkxO4BUcuqaZNttklkK1yJ+H6xC9UfLXCr0eXnjy\nSdT6/ZAJSSriyMMGGSe5qBzexCm8guc0TIBa1YyyqFKFzgM6l79T5l0NnMRxNaxkw2ZHOXFRGe1j\nZ41PBdssqoMHD2LQoEFYuXIlmpubTfeTJAkzZszwbGD5hq0Fhz7tWCwGVHNv5i7G1ngcQ5BwYRn1\nMbGit3qxeDcpvMZpFhWVMf8EECZEDeI2C0AzTxNPvN7dIkvFLftlGRFCMDIQQC/JvIknQeKmMTwQ\nwEn+APbJMTVYlXdRGbVqoAXg7Bpi5jNJ8Q0u5un6cBhfRSM4XSmOecRjJZ69Vs1jcDQFRw0w9sg9\nRflHOIwvIhGcRZvJcv+nNy+20B+Ft+B4EYMz0M6CYxCDYxQQny70O+G/dxrjQ60VLQ5KZFilibNH\ndzI/qbKkWnAkZxYcavnpp2ZReScrOiZaY8erGJzEsQnKJMlawTEp4MdDs6iM9gkRgmp4q+DYWnBO\nPvlk/OxnP8NJJ52Erq4uPPDAA7jpppuSthtvvBEHDhzwbGD5RqoWHGoO9QOo9euzQSg0tsDKgmPW\nYNDpxZVLnGZRUcXDqseUWSq2aZAxU8nYKxdViBDURSLwSxK+X15h2eCQxuFQN1Uf0yBjmnGlzQGt\nDo62r1fNNrMF7w5wu3g9rcSoHGWyzrySgaMgY6YOzvfKvA0wZqFWHMCoknHiJ7Xo0WabQOJhKaxY\nCbtJkidZVIP8fpTD3IJDz559Oq52GIfiBnqT3snJmxb7mzV5MgB79xSgd0cmt2rQcFJoL8rF4Dl2\nUSnvq/EwrIDOA/66OuBRDA6gff/WCg77sGB+XvSB0+hY9BzStbCz2Co4sizj+eefx/jx47FmzRq8\n+uqreOmll3DmmWcm7Tt16lTvRpZnOLXgmPU58UNSLTgtBhYcwLofVXV1teHrBZEmzvxu5Z81clEB\n+ifaJAUH1hYctpKxmaUlFT5UCnKdW2Gt4PBxOEZVjIHkQn8BJNw6MiE6/7bZPMhX3NYY4Vka7MLf\nurrwNKMAeCUDXaE/k+uHrYNzeiAzFhwA+HsoiH3Kcfn5Sf/WYnCIKgNqJQQScywdK2WQEOyMRlEu\nSRhdXq5acJIf2pQbvE1AfLrslWM4p6kRvz3arnudKvx9lT5qdgHGgF4evOHErQVHDTKWeAXH+r38\n9+qFi4rOA9bd2ibLnlrS6PprFZ/kNE2cPnAa3QfsHlZTwVWa+Ntvv41zzz0Xzz33HBYsWIB3331X\nV9yvvb3d4t2FDf1yzYOMEyS5qJQ/A5JmweG73lKLjlWq+MKFCw1fp09O+a3gOJv8ZgoOu+jwC7dd\noT9dJWP1GOnzYTih4PxTRQUqTSoZA4l6FAAwWDGnm6WJh5GIBaiUJPiQaA4KJJv8zeZBvsK7qNwG\n50YB3H60DX9mFByvZMCqKaaVjJXvaWQggD5+PzricdUq5yVxAPd1HMWOaAQfcKnzfGG5ICE6GRxg\n3FRmGX1O+URR3L9fXmFrwSljXvO6TQPlgCwnXa9qnM/+egBOLTiMgsNnUbmMwaHjoedf4zCmhq9+\n7YUFh84D9jvyKv6GQo9tVeLDaZr4N7EYXjnRiYWdyU1+qexTKVJphqtKxpQ1a9ZgzZo1mDhxIl54\n4QUcOXIEP/nJT4SLCtYWHC3I2MSCY+Gi2rZtm+HrBWHBYearEwWHn+D6FEL9e5ymidMFOEasa1U4\nZWskjBAhOL2sXPWlW7moBnEWHF7BARKy6S4l0pF7GdTAAcznQb7CPx17sXR5JQMnMTi0KSit8eJ1\ngDHLB+EwPmhJjnPkb4xBEtfJ4GBMBioSgcZW1kQnfBwJ47ruPTCpvFyNB0zODFXWNF3GX3JbkUxB\nP6NWuTaaHdzQ2W+Nv/LStuBIzh4y+e/EC2WQzgO2dpKX8TeA9v1bW3CcpYkTAHd3GNfMy0SQsetC\nf5RLLrkE8+fPx2mnnYYxY8agd+/eng0qH0k1yJheeuVSck8dyhFVwTG34JgFcKsxOIVS6M9SwTHe\nx6pGgt1FIYOzAHl08YQBfKo87Y5XgkONnpp5F5VZDA6gV6KpBaeT+14LLZCfVW69ejLzSgZOYnCO\ncfL/OgPuKTv43mldhOhkQAONh/r9pv2snPJxOGGfmFBeoVo8zZttalAXVTYa/vJKv12bBkC/LpsV\nYwVSs+A4zWRlv8dgPO6JJZnOA9ay7VUNHApVnpxmUVmFIVhhF26QCq4UHEmScPXVV+Ozzz7D0qVL\ncdppp+GBBx7A8OHDC+7J0i1OLTj81KILfF+fH35JQntcBr9EUoXHKgbHVMEpBAuOQxcVvUj5IDXW\n9cMrKB+GwzgQi2Fd2Nz5wRaX8mJRYT8b0J7kjGNw9EHGZjE4gH6OqU/EnFm40BQc9iy9Wrg8U3DY\nGByTB4Q49DftTFpwzOB7QSUrOIk5NoSx4KSaKdgYl3EgFkO1z4czlOw1MwtOQGfB8T4Gxwz+Mw67\nteBwonGaBEExj8Fx7qLyKuuVzgO9i8pbBcdJkLHTGBwrwjYPq6ngSMEpKyvDz3/+c3zzzTd4+eWX\nUVNTgzvvvBPDhw/Hgw8+WNSxNxTVggMTBUeZ7Lx/ly4ONPWyWU6+CFpkewuOkQIZQCKVOEaIZ9VD\nM4FTFxWVcVKQsUUMTl00gnOaD2ONQcl/Cisbryw4ALCR6/xrFIOjuahoDI5SydjgRsAqOGZBm4X2\nIMEqt15ZcLySgS4GxyK+oCPHCo6RBYeVAbXgDA5oQcbp3CRoHM5ZStaYEwuOmvGXhZIG/Gc4icFh\nFQB+jXbtolJ+BlQXlTMrOrs+eJVtZuSi+jbmbQwOPV+nWVSpKjhUJl4qybYKzl133YX9+/dj4cKF\nCIfDuPHGGzFq1Cg8+eSTCHKVBd944w3PBpZvGFlw7u7ZC/+nZy8AmiD5py061U5S2zQkTz6tH5X5\n12EUWFkIVYwB54X+PoqEcViWsYGzxujTxN1/Pvt0l6ofLwAAIABJREFUnm6KOMvnkYiunLzRTeUE\nITgaj6NSkjDE70elUk/CaLHQW3Bo4TT9foUWZJwJF5VXMnASgwPob1w7c+Gi4sbGBxlrFhx/Wmni\nlE8iCTunT62Doz8WHY/fwIKTFRcV4V1UDoKMmd+t0sSdzlEq3zK4r2TsZF+n0HnAPvjt99xFZW/B\ncVoHx4o/dx7HvGMdWJtCV3UzbIOM582bh3g8jnfeeQfvvPMOCCH4yU9+krSfJEkYP368ZwPLN/gY\nnJ6ShF8qys0rJ05Y1MHRW3BaDCw41F3Rx+eDD4knignl5dgbi6n/q66uTipR7zR6P9c4dVFtiUQw\noSm5Iz0b25LKkylbKC/dIn8sUSRuBlMqE3WKzcZ2UI6hxleuPhG3myzIegtOcg0cwHge5DOZcFF5\nJQN9DI75NURvRvtjMdNsq0zCq1QhQnQyOCzLkAnBAJ9fje1Kp5bIx5xlkn9os4rByYqLirmeY4Q4\nKgCps+Bw5+PWgkM/t1ySUCZJqSk4HsmJzgN67Aghjlx2bqAWROetGlI7tz2xmC5b0gtsLTjt7e14\n8cUX0dHRgSlTpuD888833KZMmYIePXp4Orh8gldwahl30pTKCttKxqqCY3BziyFx0/NLEnr7fLim\nqgp/69cfj1TXqPsYFTerLoBGm4B+gUzFfGkVZOwE1oKTigXIik3MzcDsqZnG4dCYBqP4G0BfzVir\nYqzft+AK/WXAReVZoT9dDI69iyoTBf6cwN4Yu5RGtKwMYkjUwvFJEvoq60w6tUR2x2I4wtwk3cTg\nZMVFxVzPzXG+LrExrJvPKk3c6fpEY/kqFAUnTohtBhkb/+eVpYvOA3rsBjnmWS8wCnVRetGqIdvY\nWnC+/fZb3HTTTY4OtnXr1rQHlK/wLqr+TEDwBRXdVK3VLEK/Vs2gMp5+LXIcvX1+TCwvxwO9EorN\nKYEyw30pWkXM/JlQRsQdWnDM0AUZp7BwZyoGB9ACjQHzp2Yah3OmasExngM6FxXNSsmjxSIVdE/H\nHlrPvIC14FgpOPQBIhMF/pzAjtPs+mmQZQwJaMt5uvP8k0gEF3frBsBhDI7P2OKYCdgsqiYDi7gR\n7Dmkm0UFaEoezYo8RuxLILDfiddhBfTYXsffAJpy6KTHlN1+2cbWgnPVVVc5Pti0adPSGkw+Y2XB\nOa+iQlV8zJ52qD/brGYDNbM+XtNHbUzYn+lCbdRg0MuS35nEaSVjM6xaNTjhRIZcVACwLRrB0Xgc\nMiGm5nmaKn6mYsExV3C0dg09TergFFqzTVb39spF5ZUMnMbgvBsK4rNIBEuDXZ58rluiBuPkZXCQ\ni7tIV9asm8pJDE4208TZBxYnAcaA/hzSrYMDaDf9WhdrsE7B8fhaoPNin8c1cACnrRrizO/5o+DY\nWnB27drl+GCtra1pDSaf4ZshsspHT58PE8srACT33uEvPzMLDg0+rvH50BCLob/fjxqfD5UAQoBh\nzIFa5C/PY3BYF1Uqbop0g4w7dUHG7t9vhQzghrZW9PX5TDPZaMdn6qY0c1E5yaIqpPgbIDMuKq9k\nQO0xcZOgb8r6cBjrw+aNhjONkYLDy4Bv0puurekTVsExWdMMKxln4eYWR0Lx7+nzOQowBvTXvVWa\nuFMrIz0erT7vRMFhvxOvHkrpPFga7MIgvx8vnuj05LgsTurgeJEmnglSLvRXaiRbcHy612uVmxfv\nEZa5L9vOghMnBL862oYWZT963FtvvVW3/0h/ANdVdQcAdd98hbqowoTYdv01QheDk4IFhr34vKyx\nQNkSiWCVReT/Qe77sXNRsZWM+ZgGfh7kO6lkqNjhlQyoBScXgcNuYG+MdI7wMmAtOF7IeXs0qsZ/\n8WnqVOGiMTgVSMzZqI2i6CVU8XdqwWGVxKQ1mvndsYtKOUY/F4kemXBR0XnQGo/jD8c6UJ+Be8GK\nUBBfRSPYEjGvNVawLipBAu3pOiEyqnis5FLlzTIOKEZp4gDwZTQRJvZk53F8HImgWbkA+itPCKNH\nj1b3nVZZieW1/fGdsjLsjEbxStcJ9yeURahM0i0Axf/uFDZQl1+ss8Ehzn3gJAbHrJIxOw8KgUy4\nqLySAf1W8l3BiRncPHgZNDA3Ni/kLAN4LxREMB7Ht5zbQ60Do/zsmcUUcQpV/J27qDSsXFSOg4yV\n3eh9wK2LyqtYpWysB28GuzCtpRmHLc4xDvM6ZrlEKDgO4S04VPFYHurSdSK2qrEQt0hpXNLVhR80\nNeLx48cAaJaeAcoFtGzZMgDA1VVVeLZPP/T0+bAs2IXLWpuTupPnG3R0qT7dWXUTd0Img4yd0ByP\n654gzbLejOrg8BYcOg8KhUy4qLySQUxdkPP7+mEtODTWgZdBQ8xbBQcAfnW0HWc3NSa1QuAtOD1N\nGsNmEqpQOHdRWcXguJ+j1ILT10UMTiZcVPm0HhyWZYQJyaus3pSabZYiSS4qJYvqoFKYbrqSccAH\n5LEuqvZ4PMmiw8I+hTUrF25/5QKiE/m6qkQq/vzjxzBfUYbyHXqTS7U+Qrq9pDKZJu6EOBJpvMOU\nLBdnMThKkDEns3xa0JyQCReVZwqO8jPfLThGMTi8DFgroVdKPAEM48q0GBzaqiDxM5vJDk+fOI4G\nOYZNFi1aWHSF/gi/Rmu/Ow4yphYcNQbH/n2s4ulV5ms+rQez21pR7fPlVZCxsOA4hE8TpxO7RY5j\nLdMmgA8yZi8sN5YWasGhwczUFDlU+fulDASTZQovXVSp9JI6oXt/bi6+Q4zy6qjQn1rJWLioeLyS\nAVUcrNo05AOsW5XePHgZhKG5a9KpgeNoPKoFJ/G3OlezaAl7LxTCM8OGwpl6Y23BSUUJp8ej/QOd\nxOBkotBfPq0HO2MxtQp2viAUHIewFhwfNNNka1zWKzgWFhyjIn9mqDE4TJBxN0lCH78fYULy3i3F\n0iDHECEEu6KppTCyN8ZUrAC6SsY5erpgg0DtYnC6SxJ6KIo0/wRdaEHGOvO/Rzdez4KMlZ/59MRp\nBJsBZBZkDGhzLNNzXKuDk5ijvbKYIs7iZh6wSiK/CrPScvoQFlWDjBPrsxO3DDsGr4OMBcYIF5VD\nqJ++Uqk2HJAktMmJzuAHZRlfR6P4bllZUswEezG1OixKBWhPYwOoCbSjA0MUZeeQzDvC8pumeBwT\nmhpTvqjTroOTwTRxpxzUWXCsKxn39fnhkySciMeTFuPCSxPXyLc0ceoyzfc6UuzVTm/ARjI4IMsY\nh/TaNDjhgFrXKaHiaEX+sntxuZkH7HXPjzKVODF6vH4uYnDo9xIjxDO3aKGtB9lGKDgOYS04NMC4\nmZnUd7S3YVx5Ob7iqp2yC3yzKwsOdVElLqA5c+bg/IpEz6OGPE8LN8Is7sQJrFsp3SDjTJvvzaAK\nToyYl3SnNy8aWG4UtFlohf7YmerVjdcrGawLh/Hn48ewlMuEzDfYmzNt7mokg4NKoHEmSiGw7I7F\n8G0shmGBAMaWlWtBxllWFN3MA1ZJ9CIGhx6vh4s0cWrB8dLSVWjrQbYRCo5D2PgIGmDMupx2xKKG\npdzZi8ksRdwINQaHqZg8WLnxHcxAOe58hr0xphJDw2a55cpFRWNwzKw3gDbH+ucg7TZTxNN0L2aS\nICGYVwCB+kYxOEY0UBdVFpT498Mh3BDogalKo1kgv+er160aeEuwoywqan3L86y9YkLE4DiEteBo\nAcb2igYbddLswkXVEo8jTgj6+nzwI9FUjbqoGmTvy3HnM+lXMtbeFM3RTXavovzyRf9YqBu0j2rB\nSZ4vhddsUyPfmm0WCkbNao1ksFOZY9ko/LlGKWx5QUUlekm5cVG5mQf6Zpt6UkkT5+tpOVFwaOZU\ni4v7gB2ldi24RVhwHBJG4mm0QpLUkvvNDiY16991Y8GRkahuXOv3o9bnQ3V1Nbr7E19XIbqo0iGU\nphVAn0WVG+plGbPbWi17xfABjscNsnuqq6s9H1smyYSCU2gySBfW6kgtOEYy+DgSwXVHWvF5NPOz\nfFM4hGA8jtHl5WqrmGxbcNzMA7ZYYnI3cfqTOF4f+AclJwrOjlgU9xxtR52H30+pXQtuERYcF9AF\nepjfuQWH9e+6seAAWhxOrd+PhQsXYkhAcVGVmIKji8FJwfxOoLmpcuWiAoBVoRC+caPgGFhwFi5c\n6Pm4Mgl7Sl4pOIUmg3QxCjI2k8HacCiteDenhABsUEr3/5PShy+baeKAu3nABg+Y9Qt0Mz/5YASn\n1qsXu07gy6h3XelL7Vpwi1BwXEAvgKFKwTYnqdq6LCoXFhxAU4j6+/zYtm0bhqgWnFJ2UaV2k6Ru\nqlwqOHYkW3CS59e2bduyNRxPyEQl40KTQbrogowVGeaDDN5X3FS0qzifQZpp3MhAb8HRQy06buYn\ne7yOeDzpmNkiH+ZBPiMUHBcEVQsOVXCcxOAk3hMnxPWTFS1DPsDvx79ecgn6+3yQCUFjiVlw0o3B\nAbRUce+enbyHV3CMngpnzJiRreF4gj6LypsbYKHJIF2iOgtOYh7ngwzWhPUNZr3qr+QUNzJgnUJm\nvahcWXCYXXNZZiAf5kE+IxQcF9BCZYNTcFEdMahpYoeWSeXDFeedB58k4bAsW7Z7KEbSbbYJaMX+\nMp1Cmw78AmtkwSm0BU2XoeJRdk+hySBdjCw4+SCDQ0r9L0q208TdyIC1uJilibuptJ6Jon2pkA/z\nIJ8RCo4L6AVQpphk3QQZu3VPAfp2DY11dQBKL8AY4BScFG+SJwrARRWDfnxGfYAKzSTN3kyEiyo1\njNLE80UGa0JaDSGv+is5xY0MWMutqQXHxdqSibYLqZAv8yBfEQqOC9gFOkaIZU0TSquyz16L4FIz\n1HYNPj8+XbYcgL7kf6nA9ptJVUGh/Z/yvbEi+xRplJVSaEGFmXBRFZoM0oW1PlAFJ19kwLqpOvM4\nyNgqBofOy04XClomOoOnQr7Mg3xFpIm7gFVwWuN8sqExX0ajuKq1BV8bFAG0Q23X4PdjVPfuAErT\ngsMqNakqOE8cP46dsRg2cnED+UaQENDET6OslOrq6oIqz85+W16miReSDNKFXTmoApwvMvg0EsHn\nkQiOk3jWXeduZMCOjV+5P49GsLjzON53sTboLDg5VHDyZR7kK8KC4wL26brZhaKxMRLGkRQuArVd\ng8+HmRMnAii9FHEgYQWgC0qq5f6/ikXxf48fc9x9OFeEdBac5JMttMJemXBRFZoM0oW9mQaVdSRf\nZCADuLS1GVcfac36Z7uVAX04MkoTf+BYB9aHna8OrMLUkUOrcL7Mg3xFKDguYBdoN53BU4UqUbV+\nP7odTWjppZYiTmmNy4iVlakNEouVIHN+2a4rkgnoVSITUnLB8V5BLThxQpDf9sf8hrqpvFi5WaXT\nSSdxQW4QLioXsBYcL8ttmxFG4uKp8flQtn8/IEkl6aICgOuPHEFtzx45q0ScLdg5ZhQTUGjN9VIp\nomZHockgXUKEQCZE13y11GRghFsZUEXRi5mYL1lUYh5YIxQcF7CLtJvO4OnQLMuo8flQrWRulaKL\nClCamba353oYGUcXZGxgwSk0f3sqRdTsKDQZpEuQEMzpaNe5LEtNBka4lUFUteCkPxfzpQ6OmAfW\nCBeVC4JZdlEBekWqVZbzriNzNrn11ltzPYSMw84xo7oihSYDGu+Qanq/EYUmAy9Y0tWFvzMp2aUo\nAx63MqAuUj4GJxXyJU1czANrhILjAp0FJwsuqsTnaApOqbqnKKNHj871EDIOVXBCJo3/Ck0GmXBR\nFZoMMoGQgXsZUKXEi5moCzLOoQVHzANrhILjgmwHGQP6YoKlWAOHZdmyZbkeQsahCk6nyaJZaDKg\nV4yXCk6hySATCBm4lwFtm+ONiyo/YnDEPLAmpwrOZZddhk8++QTr1q3Dhg0bMH78+FwOx5ZsBxkD\nWi0cADhQ4hacUriY6Rwzy6AqNBnQNPFU0/uNKDQZZAIhA/cyoH3svFi52SDjo1mu4Mwi5oE1OVNw\nxo0bh1deeQWzZs3C5MmT8fDDD2PVqlUYMGBAroZkS24sONrnlLoFpxTMsVTBMevMXGgyEC6qzCBk\n4F4GqgXHkxgc7fdsNxllEfPAmpwpOPfccw9WrVqFHTt2AACWL1+OpqYm3Hbbbbkaki305hOMxw37\nBGUCNtanIVbaFpxSCKgL2VhwCk0GqXRqtqPQZJAJhAzcyyCaAQvOsXjck+OlipgH1uRMwbnwwgux\nZcsW3WubN2/GRRddlKMR2UMXaSdNNr2iSQQZq5RCSqTqojKx4BSaDDQXlUgT9xIhA/cyoBYcZ012\nrKHKUi7jbwAxD+zIiYLTu3dv1NTUoLGxUff64cOHMXLkyFwMyRH05pMt9xQgXFQspVDUyi4Gp9Bk\nIAr9ZQYhgxQK/SlT0BMXlaIk5TJFHBDzwI6cKDjdlcaRYa73RzgcRlVVVdL+N998MzZv3ozNmzdj\n2LBhWLRokWqaq66uxqJFi7Bo0SJUVyfaFN56661YtGgRZsyYASDhp1y0aJGub8e8efOwaNEi1Yc5\nY8YM2+OO++m16OzbF3sVJcyr41qNt5MQrAoGcWDUSDz61FOeHTdT4xXHTe+4H4ZDaOtehZGzri+I\n8dod99NIBPtiUdT+6PKCGK84bvEed2UoiD1xGTc+8nDaxx0+dSqO1daiVfm7kORQLMd1Csn21rt3\nb0IIIbNmzdK9/sgjj5CmpibL927evDnr482Hbd68eTkfQ643IQMhAyEDIQMhAyEDp1tOLDjt7e1o\nb2/HwIEDda8PHDgQe/bsycWQ8h63WmsxImQgZAAIGQBCBoCQASBk4IScaFavvfYaef3113Wvffnl\nl+TBBx+0fF+pWnBGjx6d8zHkehMyEDIQMhAyEDIQMnCx5eaDx40bR44dO0a++93vEgDk4osvJkeO\nHCEDBgywfF+pKjhiE5vYxCY2sYnN+ZazNPGtW7fi2muvxV/+8hesW7cOv/vd7zB9+nQ0NTXlakh5\nDQ3QKmWEDIQMACEDQMgAEDIAhAzsyGmrhnfffReTJk3C5MmTcd555yXVxRFoiIksZAAIGQBCBoCQ\nASBkAAgZ2CGabRYI27Zty/UQco6QgZABIGQACBkAQgaAkIEdEhK+qoJh8+bNmDhxYq6HIRAIBAKB\nII8RFpwCQaQDChkAQgaAkAEgZAAIGQBCBnYIBadAYKs+lipCBkIGgJABIGQACBkAQgZ2CAVHIBAI\nBAJB0SFicAqE6urqku8cK2QgZAAIGQBCBoCQASBkYIew4BQIYhILGQBCBoCQASBkAAgZAEIGdggF\np0CgXVVLGSEDIQNAyAAQMgCEDAAhAzuEglMg0DbypYyQgZABIGQACBkAQgaAkIEdBReD09zcjPr6\n+lwPI+v069cPra2tuR5GThEyEDIAhAwAIQNAyAAoXRm0trbi4osvdrRvzhtiic1+E01GhQyEDIQM\nhAyEDIQMnG/CRSUQCAQCgaDoEAqOQCAQCASCosMP4P5cD0LgjK1bt+Z6CDlHyEDIABAyAIQMACED\nQMjAioILMhYIBAKBQCCwQ7ioBAKBQCAQFB1CwREIBAKBQFB0CAUnT7j00kuxfPlyrF69Gps2bcLf\n//53nHXWWUn7zZ49G1u2bMH69evx3nvvYeTIkTkYbXa4/fbbQQjB5MmTda+XggyGDRuGJUuWYM2a\nNdi2bRu2bNmCKVOmqP8vdhmUl5dj/vz5qKurwwcffICPPvoIl19+uW6fYpRBWVkZHnroIUSjUQwf\nPjzp/07O+Z577sGnn36KTZs24Y033kBtbW02hu4ZVjK49tprsXr1aqxevRoff/wx/vrXvxrKqZhl\nwPL444+DEFKUMvCKnOeqiw2kpaWFXHPNNerfDz/8MGlubib9+/dXX5s5cyZpamoiAwYMIADIbbfd\nRnbv3k0qKipyPn6vt5NOOons37+fEELI5MmTS0oGffv2Jbt379ad92uvvUZuu+22kpHBgw8+SPbs\n2UN69OhBAJCxY8eSUChERo8eXbQyGD58OPnwww/J888/TwghZPjw4br/OznnO+64g3z11Veke/fu\nBAB57LHHyIYNG3J+bl7IoHv37iQajZLzzjuPACA+n4+89NJL5OuvvyaVlZUlIQN2GzNmDGlubjbc\np9Bl4OGW8wGIDSBvvvmm7u9+/foRQgj56U9/qr62efNm8vjjj6t/BwIBcvToUXLTTTflfPxeb2+8\n8Qa55ZZbkhScUpDBvHnzyMsvv6x7bejQoeoiVgoyWLp0KVmyZInutaamJvLrX/+6aGVwxhlnkFGj\nRpHJkycb3rTszlmSJNLY2Ehuv/12dZ/+/fsTQgi54IILcn5+6cqgsrKSvPjii7r9J0yYQAghqtJT\n7DKgmyRJZMOGDeS2225L2qcYZODVJlxUecIVV1yh+zsYDAIAKioqAAA1NTWYMGECtmzZou4Ti8Xw\n2Wef4aKLLsreQLPAjBkzEI1GsXLlSt3rpSKDK664Av/4xz90rx04cAD19fUlI4M333wTP/zhDzF4\n8GAAwLRp09C/f380NTUVrQy2b9+OPXv2GP7PyTmPHj0aAwcO1O1DW9sUilysZBAKhXDdddfpXuPX\nyWKXAeX222/H+vXr8eWXXyb9rxhk4BWBXA9AYMw555yDYDCIpUuXAgBOPvlkAEBjY6Nuv8OHDxdF\n7AGlqqoKf/zjHzF9+nR10aKUggyqqqowatQo+P1+vPTSSxgxYgS6urqwePFivP766yUhAwB44YUX\nUFVVhS+//BKNjY049dRT8dprr+H1119XY9OKXQYsTr53+rOU5HLOOeegsbER69evB1AaMhg0aBBm\nz56Nc845B5MmTUr6fynIwClCwclT7rvvPvzHf/wHWlpaAADdu3cHAITDYd1+4XAYVVVVWR9fpvj9\n73+PRYsW4fDhw0mBc6Ugg5qaGgDAH/7wB0ydOhV1dXWYOHEi1q1bh0AggAMHDgAobhkAiWDae++9\nFxMmTMCePXtw1lln4cILL4QsyyUxD3icnHOpyaWiogJ33303br/9dkQiEQClIYM///nPuOeee1Tr\nFU8pyMApwkWVhzz00EOor6/H/Pnz1ddOnDgBAElWjYqKCnR1dWV1fJli7Nix+P73v49FixYZ/r8U\nZCDLMgBg2bJlqKurAwBs3rwZb7/9Nu68886SkAEAPProo1i8eLFqqv/iiy8wc+ZM3HvvvSUjAxYn\n51xqcnn22Wfx17/+FW+99Zb6WrHL4LLLLkMsFsOKFStM9yl2GbhBKDh5xq9+9St873vfw4033qh7\nfd++fQCAgQMH6l4fOHCgrb+2UJgxYwa6deuG999/H2vXrsWSJUsAAE888QTWrl2LsrIyAMUtg5aW\nFoRCITQ0NOher6+vx8knn1wS86C2thZ9+vTB/v37da/v27cPV1xxRUnIgMfJOe/du9d2n2JhwYIF\naG9vx3333ad7vdhlcOmll2LEiBFYu3Yt1q5diyeeeAIAsGTJEqxduxa9evUqehm4JeeRzmJLbLNn\nzyarVq0i5eXlBAA5+eSTydSpU9X/f/LJJ+Sxxx5T/w4EAqS9vb2gM0estuHDhydlUZWCDFavXk2e\ne+453WvPPPMM2bp1a0nIQJIkEgwGyZw5c3Svr1ixQk11LWYZmGXP2J0zzZ6h5QQAkNra2oLMnrHK\nIHrwwQfJ888/r/49btw4Mm7cuJKSgdU+xSQDD7acD0BsALnqqqvI3r17yT//8z+T8ePHk/Hjx5Of\n//znZO7cueo+M2fOJIcPH1Zr4/zbv/1bwdf+sNqMFJxSkMFFF11E2trayIgRIwgAMmzYMNLW1kZu\nuOGGkpHBokWLyM6dO0nv3r0JAHL22WeTSCRCfvnLXxa9DMxubE7O+Y477iDbt28nVVVVBEiUHCjE\n+idmMrjrrrtIXV0dmTRpkrpOzp07l8yaNatkZOBkn2KRgQdbzgcgNoBEIhFiBKvgAAkrz6effkrW\nr19P/vd//5eMGjUq52PPxLZgwQKyadMmQgghdXV15LXXXispGVxzzTXqOW7atInMnj27pOZBt27d\nyLx589Rz/Oyzz8hvfvObopZBWVkZWbt2LamrqyOEELJp06ak+lhOzvnee+8lW7duVd9fW1ub83Pz\nQgaDBg0yXCMJIToFp5hlwG6vvvqqbp8nn3yyaGTg1Sa6iQsEAoFAICg6RJCxQCAQCASCokMoOAKB\nQCAQCIoOoeAIBAKBQCAoOoSCIxAIBAKBoOgQCo5AIBAIBIKiQyg4AoFAIBAIig6h4AgEgrznlltu\nwfbt20EIwaxZs3I9HIFAUAAIBUcgEFiyb98+bN++HXV1dairq0NjYyMIIUmvzZ071/ZY5eXl2Lt3\nL375y1+6GsPTTz+NSy65xNG+S5Yswa5du0AIwYIFC5L+f+WVV6Kurg7hcBjbt2/Hn/70J1djEQgE\nhUPOqw2KTWxiy99t3759ulLwc+fOTSoPP3fu3KSq20abz+cjH3zwAbn22mtdj4O27uCr1hpttIQ9\nIYRceumljs5LbGITW3FtAQgEAoEF69atQzAYtNxn9+7djo4Vj8cxZcoUD0ZlT11dHWpqavDcc89h\nzJgxaGxszMrnCgSC/EC4qAQCgSU33HADmpubLfd5+eWX8fLLL2Px4sWor68HIQTnn38+li5diq++\n+gqEEPz4xz9GXV0djh8/jrVr16rvraiowMMPP4wtW7bg008/xeeff46nnnoK1dXVaY27o6MDV111\nFaqrq/HSSy9BkiTb9wwZMgSvvPIK9u/fj927d2Pjxo244IIL0hqHQCDIDULBEQgEnnHzzTfjP//z\nPwEAd955J6677jqcfvrpeOeddyDLMs4++2xs2bJF956amhrceOONuPzyyzF+/HhMmjQJffr0wV/+\n8pe0x7N582b8+7//Oy644ALcc889lvv26dMHGzduhCRJGDVqFE455RQsXrwYq1atwkUXXZT2WAQC\nQXYRCo5AIMgIL7zwAjo6OgAkFJ81a9YY7tdicY+kAAAC+ElEQVTa2opzzz0XDQ0NAIBwOIxnn30W\nM2fORP/+/dMex4IFC/Duu+/i/vvvxw9+8APT/X7zm99gyJAh+O1vfwtZlgEAzz//PLZt24bHH388\n7XEIBILsIhQcgUCQEXbs2KH+3tLSgs7OTsP9ZFnGqaeeipUrV+KLL75AXV0d/uu//gsAMHLkSE/G\nMmvWLDQ2NuLVV19Fr169DPe58MILcfjwYVXRonzyyScYPXq0J8qWQCDIHkLBEQgEGcFMoeH5l3/5\nFyxfvhwrVqzAWWedhbPPPhs/+9nPACTic7ygvb0dV199NQYPHozFixcb7tOvXz+0t7cnvd7W1qb+\nXyAQFA5CwREIBDnluuuuQ2dnZ8br0WzatAn33XcfrrzySlWBYmltbUWfPn2SXqevtba2ZnR8AoHA\nW4SCIxAIckpFRQXi8bjutYEDB2bks+bNm4cVK1bgiSeeSFJmVq9ejQEDBmDo0KG61ydOnIht27bp\nMskyNT6BQOAdQsERCAQ5Zfny5aipqcHs2bMBAD169MCvf/3rjH3e9ddfj6NHjybF4ixYsAANDQ14\n7LHH4Pf7ASSsS2PGjMFdd92l7nf11VejsbERd999d8bGKBAIvCHn1QbFJjaxFcb20UcfkcbGRkII\nIdu3byf33nuv7v+PPvooqa+vV///7LPPqv/r06cPqaurI8ePHyfHjx8ndXV1pLa2lgAgv/vd70h9\nfT3Ztm0bWblyJbn//vsJIYTs2rWL3HHHHeSWW24h27dvJ4QQUl9fT5566inTMS5ZsoTs2rVL/Ywp\nU6Yk7fPDH/6QRKPRpErGQ4cOJa+++iqpr68nu3fvJhs3biRTp07V7TN9+nTS0dFBbrjhhpx/H2IT\nm9jMN0n5RSAQCAQCgaBoEC4qgUAgEAgERYdQcAQCgUAgEBQdQsERCAQCgUBQdAgFRyAQCAQCQdEh\nFByBQCAQCARFh1BwBAKBQCAQFB1CwREIBAKBQFB0CAVHIBAIBAJB0SEUHIFAIBAIBEXH/wcKBjv0\nOAchqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cb30810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.ones(len(ctedf))*3.32\n",
    "ctedf['P Controller CTE'] = y\n",
    "\n",
    "ctedf.plot(figsize=(8, 6))\n",
    "fig = plt.gcf()\n",
    "ax = plt.gca()\n",
    "fig.patch.set_facecolor('black')\n",
    "ax.set_facecolor('k')\n",
    "\n",
    "plt.title('Cross-Track Error Comparison', color='white')\n",
    "\n",
    "plt.ylabel('Mean Cross-Track Error (CTE)')\n",
    "\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('fig/video2.png', facecolor=fig.get_facecolor(), edgecolor='none', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean CTE</th>\n",
       "      <th>Smoothed Mean CTE</th>\n",
       "      <th>P Controller CTE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>300.000000</td>\n",
       "      <td>298.000000</td>\n",
       "      <td>3.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.902853</td>\n",
       "      <td>16.010089</td>\n",
       "      <td>2.786900e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.462428</td>\n",
       "      <td>13.649073</td>\n",
       "      <td>1.957257e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.123235</td>\n",
       "      <td>2.786900e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.111477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.786900e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.923976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.786900e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>30.999425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.786900e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72.515584</td>\n",
       "      <td>38.121612</td>\n",
       "      <td>2.786900e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mean CTE  Smoothed Mean CTE  P Controller CTE\n",
       "count  300.000000         298.000000      3.000000e+02\n",
       "mean    15.902853          16.010089      2.786900e+00\n",
       "std     21.462428          13.649073      1.957257e-14\n",
       "min      0.000000           1.123235      2.786900e+00\n",
       "25%      1.111477                NaN      2.786900e+00\n",
       "50%      2.923976                NaN      2.786900e+00\n",
       "75%     30.999425                NaN      2.786900e+00\n",
       "max     72.515584          38.121612      2.786900e+00"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctedf.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Add P-Controller CTEs\n",
    "\n",
    "From data 4-30_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "002852_0414.csv          data 4-30/               data 5-30_4/\n",
      "Icon?                    data 4-30_2/             data 5_02_1/\n",
      "analysis-dev.ipynb       data 4-30_3/             fig/\n",
      "analysis.ipynb           data 4-30_4/             keras-matlab-test.ipynb\n",
      "annalysis.py             data 5-01_2/             keras-tutorial.ipynb\n",
      "annalysis.pyc            data 5-1/                net-analysis.ipynb\n",
      "cross_track_errors.csv   data 5-30_2/             trials/\n",
      "data/                    data 5-30_3/\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "dir2 = 'data 5_02_1/'\n",
    "trials2 = []\n",
    "trial_metas2 = []\n",
    "fpaths = [fp for fp in os.listdir(dir2) if 'out' in fp and 'P_' in fp]\n",
    "\n",
    "for fp in fpaths:\n",
    "    tr, trm = ann.load_data_from_fname(dir2 + fp)\n",
    "    tr.set_index('t')\n",
    "    trials2.append(tr)\n",
    "    trial_metas2.append(trm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.],\n",
       "       [ 2.,  0.],\n",
       "       [ 3.,  0.],\n",
       "       [ 4.,  0.],\n",
       "       [ 5.,  0.]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctes2 = np.zeros((len(fpaths), 2))\n",
    "ctes2[:, 0] = np.arange(len(fpaths)) + 1\n",
    "ctes2[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>u</th>\n",
       "      <th>wt1</th>\n",
       "      <th>wt2</th>\n",
       "      <th>wt3</th>\n",
       "      <th>r</th>\n",
       "      <th>y</th>\n",
       "      <th>em_x</th>\n",
       "      <th>em_y</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>69.0</td>\n",
       "      <td>-0.036797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2299</td>\n",
       "      <td>1.4139</td>\n",
       "      <td>27.8422</td>\n",
       "      <td>30.7852</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>69.2</td>\n",
       "      <td>-0.037499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2261</td>\n",
       "      <td>1.4136</td>\n",
       "      <td>27.8934</td>\n",
       "      <td>30.9098</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>69.4</td>\n",
       "      <td>-0.038197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2223</td>\n",
       "      <td>1.4133</td>\n",
       "      <td>27.9445</td>\n",
       "      <td>31.0345</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>69.6</td>\n",
       "      <td>-0.038891</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2185</td>\n",
       "      <td>1.4130</td>\n",
       "      <td>27.9957</td>\n",
       "      <td>31.1591</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>69.8</td>\n",
       "      <td>-0.038990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2177</td>\n",
       "      <td>1.4127</td>\n",
       "      <td>28.0470</td>\n",
       "      <td>31.2837</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        t         u  wt1  wt2  wt3       r       y     em_x     em_y  \\\n",
       "345  69.0 -0.036797  NaN  NaN  NaN  1.2299  1.4139  27.8422  30.7852   \n",
       "346  69.2 -0.037499  NaN  NaN  NaN  1.2261  1.4136  27.8934  30.9098   \n",
       "347  69.4 -0.038197  NaN  NaN  NaN  1.2223  1.4133  27.9445  31.0345   \n",
       "348  69.6 -0.038891  NaN  NaN  NaN  1.2185  1.4130  27.9957  31.1591   \n",
       "349  69.8 -0.038990  NaN  NaN  NaN  1.2177  1.4127  28.0470  31.2837   \n",
       "\n",
       "     Unnamed: 9  \n",
       "345         NaN  \n",
       "346         NaN  \n",
       "347         NaN  \n",
       "348         NaN  \n",
       "349         NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trials2[0].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# for trial in trials:\n",
    "#     trial['em_x'] = trial['wt6']\n",
    "#     trial['em_y'] = trial['wt7']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30634916705708931"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann.straight_mean_cte(trials2[12], trial_metas2[12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "159it [00:00, 275.86it/s]\n"
     ]
    }
   ],
   "source": [
    "for i, (tr, trm) in tqdm(enumerate(zip(trials2, trial_metas2))):\n",
    "    ctes2[i, 1] = ann.straight_mean_cte(tr, trm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean CTE P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial No.</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>6.003525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>4.187467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>4.248851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>5.291408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Mean CTE P\n",
       "Trial No.            \n",
       "1.0          6.003525\n",
       "2.0               NaN\n",
       "3.0          4.187467\n",
       "4.0          4.248851\n",
       "5.0          5.291408"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctedf2 = pd.DataFrame(ctes2)\n",
    "ctedf2.columns = ['Trial No.', 'Mean CTE P']\n",
    "ctedf2 = ctedf2.set_index('Trial No.')\n",
    "ctedf2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "ctedf2['Smoothed Mean CTE P'] = ctedf2['Mean CTE P'].rolling(min_periods=3, window=25).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3297239328197805"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctedf2['Mean CTE P'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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3YhDhlilTAQCPvTI/QFP46rvvACIcxydOwzMjnw9tu3Zh9syZGDZsGPLy8lBQ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\npWsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d2a88d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ctedf2.plot()\n",
    "plt.ylabel('Mean Cross-Track Error (CTE)')\n",
    "plt.savefig('fig/cte_neural_net_P2.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean CTE P</th>\n",
       "      <th>Smoothed Mean CTE P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>170.000000</td>\n",
       "      <td>288.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.241917</td>\n",
       "      <td>3.024034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.988917</td>\n",
       "      <td>0.862883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.784560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.190729</td>\n",
       "      <td>5.506904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Mean CTE P  Smoothed Mean CTE P\n",
       "count  170.000000           288.000000\n",
       "mean     3.241917             3.024034\n",
       "std      1.988917             0.862883\n",
       "min      0.000000             0.784560\n",
       "25%           NaN                  NaN\n",
       "50%           NaN                  NaN\n",
       "75%           NaN                  NaN\n",
       "max      7.190729             5.506904"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctedf2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Weights across all trials\n",
    "\n",
    "Show the weights at the end of each trial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "wts = np.zeros((len(trials), 4))\n",
    "wts[:, 0] = np.arange(len(trials))\n",
    "\n",
    "for i, tr in enumerate(trials):\n",
    "    tr = tr.set_index('t')\n",
    "    wts[i, 1:] = tr[['wt1', 'wt2', 'wt3']].values[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wt1</th>\n",
       "      <th>wt2</th>\n",
       "      <th>wt3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.70230</td>\n",
       "      <td>0.010357</td>\n",
       "      <td>0.97499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.70207</td>\n",
       "      <td>0.010385</td>\n",
       "      <td>0.97433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.70825</td>\n",
       "      <td>0.010837</td>\n",
       "      <td>0.87869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.70797</td>\n",
       "      <td>0.011023</td>\n",
       "      <td>0.86405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.70519</td>\n",
       "      <td>0.010965</td>\n",
       "      <td>0.82796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           wt1       wt2      wt3\n",
       "trial                            \n",
       "0.0    0.70230  0.010357  0.97499\n",
       "1.0    0.70207  0.010385  0.97433\n",
       "2.0    0.70825  0.010837  0.87869\n",
       "3.0    0.70797  0.011023  0.86405\n",
       "4.0    0.70519  0.010965  0.82796"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wtsdf = pd.DataFrame(wts)\n",
    "wtsdf.columns = ['trial', 'wt1', 'wt2', 'wt3']\n",
    "wtsdf = wtsdf.set_index('trial')\n",
    "wtsdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wt1</th>\n",
       "      <th>wt2</th>\n",
       "      <th>wt3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.702300</td>\n",
       "      <td>0.010357</td>\n",
       "      <td>0.974990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.702185</td>\n",
       "      <td>0.010371</td>\n",
       "      <td>0.974660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.704207</td>\n",
       "      <td>0.010526</td>\n",
       "      <td>0.942670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.705148</td>\n",
       "      <td>0.010651</td>\n",
       "      <td>0.923015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.705156</td>\n",
       "      <td>0.010713</td>\n",
       "      <td>0.904004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            wt1       wt2       wt3\n",
       "trial                              \n",
       "0.0    0.702300  0.010357  0.974990\n",
       "1.0    0.702185  0.010371  0.974660\n",
       "2.0    0.704207  0.010526  0.942670\n",
       "3.0    0.705148  0.010651  0.923015\n",
       "4.0    0.705156  0.010713  0.904004"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wts_smooth = wtsdf[['wt1', 'wt2', 'wt3']].rolling(min_periods=1, window=25).mean()\n",
    "wts_smooth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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+FpXPIdH/Lrxe79aFurGyt66qil+uvY7s9z8AIHPkSMovu4R1Hg89A9suJdrf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\nNPEZDAaDwRCLCVAGg8FgsCV2GmbuGrq6bUgmia4afw6Qg7U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x9Dr1FL5racnI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c76bb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs = fp.split('out')[0]\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.ylabel('Weight')\n",
    "\n",
    "ax1 = plt.subplot(3, 1, 1)\n",
    "plt.plot(wts_smooth['wt1'])\n",
    "plt.title('Weights at $n^{th}$ Trial')\n",
    "plt.ylabel('$k_P$', rotation='horizontal')\n",
    "ax1.yaxis.labelpad = 15\n",
    "\n",
    "ax2 = plt.subplot(3, 1, 2)\n",
    "plt.plot(wts_smooth['wt2'])\n",
    "plt.ylabel('$k_I$', rotation='horizontal')\n",
    "ax2.yaxis.labelpad = 15\n",
    "\n",
    "ax3 = plt.subplot(3, 1, 3)\n",
    "plt.plot(wts_smooth['wt3'])\n",
    "plt.ylabel('$k_D$', rotation='horizontal')\n",
    "ax3.yaxis.labelpad = 15\n",
    "\n",
    "plt.xlabel('Trial $n$')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig/trial_weights2.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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d9h9iYjTJtQ/radMkkYNqYt26dVi3bl2Lx55++mk8/fTTXmUVFRWYOnVqb7jm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\nGIaRJJygGIZhGEnCCYphGIaRJP8fXyT4jnaaUwwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6a30d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs = fp.split('out')[0]\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "plt.ylabel('Weight')\n",
    "\n",
    "fig.patch.set_facecolor('black')\n",
    "\n",
    "ax1 = plt.subplot(3, 1, 1)\n",
    "plt.plot(wts_smooth['wt1'])\n",
    "plt.title('Weights at $n^{th}$ Trial', color='white')\n",
    "plt.ylabel('$k_P$', rotation='horizontal')\n",
    "ax1.yaxis.labelpad = 15\n",
    "\n",
    "ax2 = plt.subplot(3, 1, 2)\n",
    "plt.plot(wts_smooth['wt2'])\n",
    "plt.ylabel('$k_I$', rotation='horizontal')\n",
    "ax2.yaxis.labelpad = 15\n",
    "\n",
    "ax3 = plt.subplot(3, 1, 3)\n",
    "plt.plot(wts_smooth['wt3'])\n",
    "plt.ylabel('$k_D$', rotation='horizontal')\n",
    "ax3.yaxis.labelpad = 15\n",
    "\n",
    "plt.xlabel('Trial $n$')\n",
    "\n",
    "for ax in [ax1, ax2, ax3]:\n",
    "    ax.set_facecolor('k')\n",
    "    ax.spines['left'].set_color('white')\n",
    "    ax.spines['bottom'].set_color('white')\n",
    "    ax.xaxis.label.set_color('white')\n",
    "    ax.tick_params(axis='x', colors='white')\n",
    "    ax.yaxis.label.set_color('white')\n",
    "    ax.tick_params(axis='y', colors='white')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig/trial_weights2_blck.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train_bool</th>\n",
       "      <th>path_type</th>\n",
       "      <th>LOS_radius</th>\n",
       "      <th>em2vic_x</th>\n",
       "      <th>em2vic_y</th>\n",
       "      <th>theta_i</th>\n",
       "      <th>current_x</th>\n",
       "      <th>current_y</th>\n",
       "      <th>throttle</th>\n",
       "      <th>mem</th>\n",
       "      <th>step</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>-14.2612</td>\n",
       "      <td>20.0998</td>\n",
       "      <td>0.96022</td>\n",
       "      <td>-0.19736</td>\n",
       "      <td>0.038921</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train_bool  path_type  LOS_radius  em2vic_x  em2vic_y  theta_i  current_x  \\\n",
       "0           1          4          10  -14.2612   20.0998  0.96022   -0.19736   \n",
       "\n",
       "   current_y  throttle  mem    step  Unnamed: 11  Unnamed: 12  Unnamed: 13  \n",
       "0   0.038921         3  100  0.0001     0.000001          0.1          NaN  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trial_metas[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.69999  ,  0.0099977,  1.0025   ])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trials[-1][['wt1', 'wt2', 'wt3']].values[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}