DHS Raw Logistic Regression PK More Features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width: 100% !important;} </style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width: 100% !important;} </style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For the Individual/ Women's Survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_stata('dhs_data/raw/PKIR61FL.DTA') # Pakistan 2012-2013 -- filename decoded: PK = Pakistan, IR = Women's Survey, 61 = DHS 6, FL = flat-ascii .DTA = Stata-compatible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13558, 4107)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    13558\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.duplicated().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset.columns = dataset.columns.str.strip() #strip whitespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "columns = dataset.columns.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# households = pd.read_sas('dhs_data/raw/PKHR61FL.SAS')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Codebook  "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From DHS 6 Recode Codebook, available from http://dhsprogram.com/pubs/pdf/DHSG4/Recode6_DHS_22March2013_DHSG4.pdf\n",
    "v044/ HV044 - household selected for DV module\n",
    "\n",
    "\n",
    "v447a - woman's age\n",
    "d105a through d105n - various forms of DV reported\n",
    "d107 - any severe violence\n",
    "d108 - any sexual violence\n",
    "v511 - age at first cohabitation\n",
    "v106 - highest level education attended\n",
    "v107 - highest year of education at level in v106\n",
    "\n",
    "PCA-related\n",
    "v113 drink water source\n",
    "v116 toilet facility\n",
    "v119 electricity\n",
    "v120 radio\n",
    "v121 television\n",
    "v122 refrigerator\n",
    "v123 bicycle\n",
    "v124 motorcycle/scooter\n",
    "v125 car/truck\n",
    "v127 floor type\n",
    "v128 wall type\n",
    "v129 roof type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wealth and Assets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cooking fuel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wood                       6041\n",
       "natural gas                4923\n",
       "animal dung                 757\n",
       "lpg                         547\n",
       "straw/shrubs/grass          514\n",
       "not a dejure resident       425\n",
       "charcoal                    242\n",
       "biogas                       53\n",
       "coal, lignite                23\n",
       "electricity                  22\n",
       "99                            4\n",
       "kerosene                      4\n",
       "no food cooked in house       3\n",
       "Name: v161, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v161.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset[\"wood\"] = dataset.v161.map( lambda x: 1 if x == \"wood\" else 0)\n",
    "dataset[\"natural_gas\"] = dataset.v161.map( lambda x: 1 if x == \"natural gas\" else 0)\n",
    "dataset[\"animal_dung\"] = dataset.v161.map( lambda x: 1 if x == \"animal dung\" else 0)\n",
    "dataset[\"lpg\"] = dataset.v161.map( lambda x: 1 if x == \"lpg\" else 0)\n",
    "dataset[\"grass\"] = dataset.v161.map( lambda x: 1 if x == \"straw/shrubs/grass\" else 0)\n",
    "dataset[\"charcoal\"] = dataset.v161.map( lambda x: 1 if x == \"charcoal\" else 0)\n",
    "dataset[\"biogas\"] = dataset.v161.map( lambda x: 1 if x == \"biogas\" else 0)\n",
    "dataset[\"kerosene\"] = dataset.v161.map( lambda x: 1 if x == \"kerosene\" else 0)\n",
    "dataset[\"electric_power\"] = dataset.v161.map( lambda x: 1 if x == \"electricity\" else 0)\n",
    "dataset[\"coal\"] = dataset.v161.map( lambda x: 1 if x == \"coal, lignite\" else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Water source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "piped into dwelling                                     4351\n",
       "tube well or borehole                                   2457\n",
       "hand pump                                               2392\n",
       "public tap/standpipe                                    1039\n",
       "filtration plant                                         487\n",
       "not a dejure resident                                    425\n",
       "river/dam/lake/ponds/stream/canal/irrigation channel     377\n",
       "protected well                                           365\n",
       "unprotected spring                                       329\n",
       "bottled water                                            295\n",
       "unprotected well                                         211\n",
       "piped to yard/plot                                       211\n",
       "cart with small tank                                     185\n",
       "protected spring                                         181\n",
       "tanker truck                                             173\n",
       "other                                                     55\n",
       "rainwater                                                 21\n",
       "99                                                         4\n",
       "Name: v113, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v113.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "safe_water = (\"piped into dwelling\",\"tube well or borehole\",\"hand pump\",\"public tap/standpipe\",\"filtration plant\",\"protected well\",\"tanker truck\",\"piped to yard/plot\",\"protected spring\")\n",
    "unsafe_water = (\"river/dam/lake/ponds/stream/canal/irrigation channel\",\"unprotected spring\",\"unprotected well\",\"cart with small tank\",\"rainwater\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset[\"safe_water\"] = dataset.v113.map( lambda x: 1 if x in safe_water else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[\"unsafe_water\"] = dataset.v113.map( lambda x: 1 if x in unsafe_water else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Toilet facility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no                       9959\n",
       "yes                      1215\n",
       "not a dejure resident     425\n",
       "9.0                        25\n",
       "Name: v160, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v160.value_counts() #shared?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset[\"shared_toilet\"] = dataset.v160.map( lambda x: 1 if x == (\"yes\" or \"not a dejure resident\") else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flush to piped sewer system              3847\n",
       "flush to pit latrine                     2838\n",
       "flush to septic tank                     2164\n",
       "no facility/bush/field                   1934\n",
       "pit latrine with slab                     846\n",
       "pit latrine without slab/open pit         472\n",
       "flush to somewhere else                   471\n",
       "not a dejure resident                     425\n",
       "ventilated improved pit latrine (vip)     295\n",
       "flush, don't know where                   143\n",
       "bucket toilet                              59\n",
       "hanging toilet/latrine                     35\n",
       "other                                      23\n",
       "99                                          6\n",
       "Name: v116, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v116.value_counts() #type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[\"improved_sani\"] = dataset.v116.map( lambda x: 1 if x == ('flush to piped sewer system' or 'flush to pit latrine' or 'flush to septic tank' or \"flush, don't know where\" or \"ventilated improved pit latrine (vip)\" or \"flush to somewhere else\") else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset[\"unimproved_sani\"] = dataset.v116.map( lambda x: 1 if x == ('pit latrine with slab' or 'pit latrine without slab/open pit' or 'hanging toilet/latrine' or 'bucket toilet')else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[\"open_defec\"] = dataset.v116.map( lambda x: 1 if x == (\"no facility/bush/field\") else 0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dejure resident?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[\"dejure_res\"] = dataset.v116.map( lambda x: 1 if x != (\"not a dejure resident\") else 0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "asset_components = (\"electricity\",\"radio\",\"tv\",\"refrigerator\",\"bicycle\",\"motorcycle\",\"car/truck\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yes                      12467\n",
       "no                         663\n",
       "not a dejure resident      425\n",
       "9                            3\n",
       "Name: v119, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v119.value_counts() #electricity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[asset_components[0]] = dataset.v119.map( lambda x: 1 if x == \"yes\" else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no                       10717\n",
       "yes                       2404\n",
       "not a dejure resident      425\n",
       "9                           12\n",
       "Name: v120, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v120.value_counts() #radio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[asset_components[1]] = dataset.v120.map( lambda x: 1 if x == \"yes\" else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yes                      8458\n",
       "no                       4669\n",
       "not a dejure resident     425\n",
       "9                           6\n",
       "Name: v121, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v121.value_counts() #television"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset[asset_components[2]] = dataset.v121.map( lambda x: 1 if x == \"yes\" else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no                       6690\n",
       "yes                      6432\n",
       "not a dejure resident     425\n",
       "9                          11\n",
       "Name: v122, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v122.value_counts() #refrigerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[asset_components[3]] = dataset.v122.map( lambda x: 1 if x == \"yes\" else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no                       9513\n",
       "yes                      3605\n",
       "not a dejure resident     425\n",
       "9                          15\n",
       "Name: v123, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v123.value_counts() #bicycle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[asset_components[4]] = dataset.v123.map( lambda x: 1 if x == \"yes\" else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no                       8435\n",
       "yes                      4683\n",
       "not a dejure resident     425\n",
       "9                          15\n",
       "Name: v124, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v124.value_counts() #motorcycle/scooter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[asset_components[5]] = dataset.v124.map( lambda x: 1 if x == \"yes\" else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no                       11600\n",
       "yes                       1519\n",
       "not a dejure resident      425\n",
       "9                           14\n",
       "Name: v125, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v125.value_counts() #car/truck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[asset_components[6]] = dataset.v124.map( lambda x: 1 if x == \"yes\" else 0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Floor, wall, roof types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "earth, sand               5433\n",
       "cement                    4407\n",
       "chips/terrazzo             824\n",
       "bricks                     737\n",
       "marble                     700\n",
       "carpet                     488\n",
       "not a dejure resident      425\n",
       "mats                       226\n",
       "ceramic tiles              177\n",
       "dung                       109\n",
       "wood planks                 18\n",
       "99                          10\n",
       "parquet, polished wood       2\n",
       "other                        1\n",
       "palm, bamboo                 1\n",
       "Name: v127, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v127.value_counts() #floor type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hard_floor = (\"cement\",\"chips/terrazzo\",\"bricks\",\"marble\",\"carpet\",\"ceramic tiles\",\"wood planks\",\"parquet, polished wood\")\n",
    "dirt_floor = (\"earth, sand\",\"mats\",\"dung\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[\"hard_floor\"] = dataset.v127.map( lambda x: 1 if x in hard_floor else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[\"dirt_floor\"] = dataset.v127.map( lambda x: 1 if x in dirt_floor else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cement                      3918\n",
       "baked bricks                2456\n",
       "unbaked bricks/mud          1889\n",
       "bricks                      1289\n",
       "mud/stones                   868\n",
       "no walls                     711\n",
       "cement blocks                571\n",
       "stone with mud               556\n",
       "not a dejure resident        425\n",
       "stone with lime / cement     229\n",
       "covered adobe                193\n",
       "bamboo/sticks/mud            165\n",
       "uncovered adobe               91\n",
       "dirt                          64\n",
       "cane / palm / trunks          63\n",
       "tent                          22\n",
       "bamboo with mud               18\n",
       "other                         13\n",
       "99                             8\n",
       "wood planks / shingles         3\n",
       "reused wood                    3\n",
       "carton/plastic                 2\n",
       "cardboard                      1\n",
       "Name: v128, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v128.value_counts() #wall type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sturdy_walls = (\"cement\",\"baked bricks\",\"bricks\",\"cement blocks\",\"stone with lime / cement\",\"reused wood\")\n",
    "dirt_walls = (\"unbaked bricks/mud\",\"mud/stones\",\"stone with mud\",\"covered adobe\",\"dirt\",\"bamboo with mud\")\n",
    "no_walls = (\"no walls\",\"cane / palm / trunks\",\"tent\",\"wood planks / shingles\",\"carton/plastic\",\"cardboard\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[\"sturdy_walls\"] = dataset.v128.map( lambda x: 1 if x in sturdy_walls else 0) \n",
    "dataset[\"dirt_walls\"] = dataset.v128.map( lambda x: 1 if x in dirt_walls else 0) \n",
    "dataset[\"no_walls\"] = dataset.v128.map( lambda x: 1 if x in no_walls else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wood/ t iron/mud               3655\n",
       "cement/rcc                     3530\n",
       "rustic mat                     1515\n",
       "wood planks                    1248\n",
       "reinforced brick cement/rcc    1060\n",
       "not a dejure resident           425\n",
       "iron sheets/asbestos            416\n",
       "palm / bamboo                   406\n",
       "sod/grass                       382\n",
       "calamine / cement fiber         354\n",
       "thatch / palm leaf              171\n",
       "other                           170\n",
       "ceramic tiles                    66\n",
       "cardboard                        50\n",
       "no roof                          48\n",
       "roofing shingles                 31\n",
       "metal                            22\n",
       "99                                9\n",
       "Name: v129, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v129.value_counts() #roof type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sturdy_roof = (\"cement/rcc\", \"reinforced brick cement/rcc\", \"ceramic tiles\", \"roofing shingles\", \"metal\")\n",
    "mid_roof = (\"wood/ t iron/mud\", \"wood planks\", \"iron sheets/asbestos\", \"calamine / cement fiber\")\n",
    "bad_roof = (\"rustic mat\", \"palm / bamboo\", \"sod/grass\", \"thatch / palm leaf\", \"cardboard\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset[\"sturdy_roof\"] = dataset.v129.map( lambda x: 1 if x in sturdy_roof else 0) \n",
    "dataset[\"mid_roof\"] = dataset.v129.map( lambda x: 1 if x in mid_roof else 0) \n",
    "dataset[\"bad_roof\"] = dataset.v129.map( lambda x: 1 if x in bad_roof else 0) \n",
    "dataset[\"no_roof\"] = dataset.v129.map( lambda x: 1 if x == \"no roof\" else 0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recode education variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no education    7625\n",
       "secondary       2415\n",
       "primary         1831\n",
       "higher          1687\n",
       "Name: v106, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v106.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0    2494\n",
       "2.0    1038\n",
       "4.0     933\n",
       "3.0     860\n",
       "6.0     356\n",
       "1.0     252\n",
       "Name: v107, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.v107.value_counts() #years of ed achieved at highest level of ed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>v107</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v106</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no education</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary</th>\n",
       "      <td>41</td>\n",
       "      <td>187</td>\n",
       "      <td>220</td>\n",
       "      <td>233</td>\n",
       "      <td>1150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary</th>\n",
       "      <td>161</td>\n",
       "      <td>172</td>\n",
       "      <td>612</td>\n",
       "      <td>137</td>\n",
       "      <td>1333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>higher</th>\n",
       "      <td>50</td>\n",
       "      <td>679</td>\n",
       "      <td>28</td>\n",
       "      <td>563</td>\n",
       "      <td>11</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "v107          1.0  2.0  3.0  4.0   5.0  6.0\n",
       "v106                                       \n",
       "no education    0    0    0    0     0    0\n",
       "primary        41  187  220  233  1150    0\n",
       "secondary     161  172  612  137  1333    0\n",
       "higher         50  679   28  563    11  356"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(dataset.v106,dataset.v107)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset['no_edu'] = dataset.v106.map( lambda x: 1 if x == \"no education\" else 0) \n",
    "dataset['primary_edu'] = dataset.v106.map( lambda x: 1 if x == \"primary\" else 0) \n",
    "dataset['secondary_edu'] = dataset.v106.map( lambda x: 1 if x == \"secondary\" else 0) \n",
    "dataset['higher_edu'] = dataset.v106.map( lambda x: 1 if x == \"higher\" else 0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean out households/women not selected for DV module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "woman not selected                          9750\n",
       "woman selected and interviewed              3687\n",
       "woman selected, but privacy not possible     100\n",
       "woman selected, but not interviewed           21\n",
       "Name: v044, dtype: int64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"v044\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dv = dataset[dataset.v044 == 'woman selected and interviewed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3687, 4144)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add child marriage variable -- All under 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ANTARES/anaconda2/envs/snakes/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "dv[\"child_marriage\"] = dv.v511.map( lambda x: 1 if (x < 18) else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2125\n",
       "1    1562\n",
       "Name: child_marriage, dtype: int64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv[\"child_marriage\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split Under 16 v. 16-18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ANTARES/anaconda2/envs/snakes/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "dv[\"1617_marriage\"] = dv.v511.map( lambda x: 1 if (x < 18 and x>=16) else 0) #16 is age of consent in Pak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2840\n",
       "1     847\n",
       "Name: 1617_marriage, dtype: int64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv[\"1617_marriage\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ANTARES/anaconda2/envs/snakes/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "dv[\"u16_marriage\"] = dv.v511 < 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      3687\n",
       "unique        2\n",
       "top       False\n",
       "freq       2972\n",
       "Name: u16_marriage, dtype: object"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv.u16_marriage.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ANTARES/anaconda2/envs/snakes/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "dv[\"u16_marriage\"] = dv.u16_marriage.map({True: 1, False: 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2972\n",
       "1     715\n",
       "Name: u16_marriage, dtype: int64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv[\"u16_marriage\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Domestic violence module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     2531\n",
       "yes    1154\n",
       "9.0       2\n",
       "Name: d104, dtype: int64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv.d104.value_counts() #ever any emotional violence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dv = dv[dv[\"d104\"] != 9.0]\n",
    "dv = dv[dv[\"d106\"] != 9.0]\n",
    "dv = dv[dv[\"d107\"] != 9.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dv[\"ever_emo_violence\"] = dv.d104.map(lambda x: 1 if x == \"yes\" else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "never                                           3268\n",
       "sometimes                                        168\n",
       "yes, but not in the last 12 months               165\n",
       "often                                             83\n",
       "yes, but frequency in last 12 months missing       1\n",
       "9.0                                                0\n",
       "Name: d105j, dtype: int64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv[\"d105j\"].value_counts()  #physical violence, types A-J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "never                                           3074\n",
       "sometimes                                        296\n",
       "yes, but not in the last 12 months               195\n",
       "often                                            116\n",
       "yes, but frequency in last 12 months missing       2\n",
       "9.0                                                1\n",
       "no response given                                  1\n",
       "Name: d105a, dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv.d105a.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from string import ascii_lowercase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "physical_violence = (\"sometimes\",\"often\",\"yes, but not in the last 12 months\",\"yes, but frequency in last 12 months missing\")\n",
    "\n",
    "# dv[col_name] = dv[col_name].map( lambda x: 1 if x in last_twelve else 0) \n",
    "\n",
    "for letter in ascii_lowercase[0:10]:\n",
    "    col_name = \"d105{}\".format(letter)\n",
    "    col_name_temp = str(col_name) + \"_temp\"\n",
    "    dv[col_name_temp] = dv[col_name].map( lambda x: 1 if x in physical_violence else 0) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3076\n",
       "1     609\n",
       "Name: d105a_temp, dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv.d105a_temp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dv.dtypes;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dv.reset_index(inplace=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>caseid</th>\n",
       "      <th>v000</th>\n",
       "      <th>v001</th>\n",
       "      <th>v002</th>\n",
       "      <th>v003</th>\n",
       "      <th>v004</th>\n",
       "      <th>v005</th>\n",
       "      <th>v006</th>\n",
       "      <th>v007</th>\n",
       "      <th>...</th>\n",
       "      <th>d105a_temp</th>\n",
       "      <th>d105b_temp</th>\n",
       "      <th>d105c_temp</th>\n",
       "      <th>d105d_temp</th>\n",
       "      <th>d105e_temp</th>\n",
       "      <th>d105f_temp</th>\n",
       "      <th>d105g_temp</th>\n",
       "      <th>d105h_temp</th>\n",
       "      <th>d105i_temp</th>\n",
       "      <th>d105j_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1 2  2</td>\n",
       "      <td>PK6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2152140</td>\n",
       "      <td>11</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1 5  2</td>\n",
       "      <td>PK6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2152140</td>\n",
       "      <td>11</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>1 8  8</td>\n",
       "      <td>PK6</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2152140</td>\n",
       "      <td>11</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>110  2</td>\n",
       "      <td>PK6</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2152140</td>\n",
       "      <td>11</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>119  2</td>\n",
       "      <td>PK6</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2152140</td>\n",
       "      <td>11</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 4159 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index           caseid v000  v001  v002  v003  v004     v005  v006  v007  \\\n",
       "0      0           1 2  2  PK6     1     2     2     1  2152140    11  2012   \n",
       "1      2           1 5  2  PK6     1     5     2     1  2152140    11  2012   \n",
       "2      6           1 8  8  PK6     1     8     8     1  2152140    11  2012   \n",
       "3      9           110  2  PK6     1    10     2     1  2152140    11  2012   \n",
       "4     16           119  2  PK6     1    19     2     1  2152140    11  2012   \n",
       "\n",
       "      ...      d105a_temp  d105b_temp  d105c_temp  d105d_temp  d105e_temp  \\\n",
       "0     ...               0           0           0           0           0   \n",
       "1     ...               0           0           0           0           0   \n",
       "2     ...               0           0           0           0           0   \n",
       "3     ...               0           0           0           0           0   \n",
       "4     ...               0           0           0           0           0   \n",
       "\n",
       "  d105f_temp d105g_temp d105h_temp  d105i_temp  d105j_temp  \n",
       "0          0        NaN        NaN         NaN           0  \n",
       "1          0        NaN        NaN         NaN           0  \n",
       "2          0        NaN        NaN         NaN           0  \n",
       "3          0        NaN        NaN         NaN           0  \n",
       "4          0        NaN        NaN         NaN           0  \n",
       "\n",
       "[5 rows x 4159 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dv[\"physical_dv\"] = dv[\"d105a_temp\"] + dv[\"d105b_temp\"] + dv[\"d105c_temp\"] + dv[\"d105d_temp\"] + dv[\"d105e_temp\"] + dv[\"d105f_temp\"] + dv[\"d105j_temp\"]\n",
    "dv[\"physical_dv\"] = dv[\"physical_dv\"].map( lambda x: 1 if x >= 1 else 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2644\n",
       "1    1041\n",
       "Name: physical_dv, dtype: int64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv[\"physical_dv\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All ages, all assets in this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# # Our main analysis will be on women ages 20-24 at time of survey\n",
    "# df2024 = dv.copy(deep=\"True\")\n",
    "# df2024 = df2024[df2024.v447a.isin(range(20,25))];\n",
    "# df2024.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dv[\"age\"] = dv[\"v447a\"] #woman's age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# df2024[\"interviewed\"]= df2024[\"v044\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# df2024[\"any_abuse\"]= df2024[\"d107\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# df2024.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# df2024[\"v511\"].describe() #age at first cohabitation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# READY, SET...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# fuel_cols = [\"wood\",\"natural_gas\",\"animal_dung\",\"lpg\",\"grass\",\"charcoal\",\"biogas\",\"kerosene\",\"electric_power\",\"coal\"]\n",
    "wash_cols = [\"improved_sani\",\"unimproved_sani\",\"open_defec\",\"safe_water\",\"unsafe_water\", \"shared_toilet\"]\n",
    "hh_cols = [\"sturdy_walls\",\"dirt_walls\",\"no_walls\",\"sturdy_roof\",\"mid_roof\",\"bad_roof\",\"no_roof\", \"hard_floor\", \"dirt_floor\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dv_cols = [\"physical_dv\",\"age\",\"ever_emo_violence\",\"u16_marriage\", \"child_marriage\", \"no_edu\", \"primary_edu\", \"secondary_edu\", \"higher_edu\"]\n",
    "asset_cols = list(asset_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clean_cols = dv_cols + asset_cols + wash_cols + hh_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df =dv[clean_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>physical_dv</th>\n",
       "      <th>age</th>\n",
       "      <th>ever_emo_violence</th>\n",
       "      <th>u16_marriage</th>\n",
       "      <th>child_marriage</th>\n",
       "      <th>no_edu</th>\n",
       "      <th>primary_edu</th>\n",
       "      <th>secondary_edu</th>\n",
       "      <th>higher_edu</th>\n",
       "      <th>safe_water</th>\n",
       "      <th>...</th>\n",
       "      <th>shared_toilet</th>\n",
       "      <th>sturdy_walls</th>\n",
       "      <th>dirt_walls</th>\n",
       "      <th>no_walls</th>\n",
       "      <th>sturdy_roof</th>\n",
       "      <th>mid_roof</th>\n",
       "      <th>bad_roof</th>\n",
       "      <th>no_roof</th>\n",
       "      <th>hard_floor</th>\n",
       "      <th>dirt_floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   physical_dv   age  ever_emo_violence  u16_marriage  child_marriage  no_edu  \\\n",
       "0            0  25.0                  0             0               1       1   \n",
       "1            0  31.0                  1             0               0       0   \n",
       "2            0  30.0                  1             0               0       0   \n",
       "3            0  46.0                  0             0               0       0   \n",
       "4            0  39.0                  1             0               1       1   \n",
       "\n",
       "   primary_edu  secondary_edu  higher_edu  safe_water     ...      \\\n",
       "0            0              0           0           1     ...       \n",
       "1            1              0           0           1     ...       \n",
       "2            0              0           1           1     ...       \n",
       "3            0              1           0           1     ...       \n",
       "4            0              0           0           1     ...       \n",
       "\n",
       "   shared_toilet  sturdy_walls  dirt_walls  no_walls  sturdy_roof  mid_roof  \\\n",
       "0              0             1           0         0            0         1   \n",
       "1              1             1           0         0            0         1   \n",
       "2              0             1           0         0            0         1   \n",
       "3              0             1           0         0            1         0   \n",
       "4              0             1           0         0            0         1   \n",
       "\n",
       "   bad_roof  no_roof  hard_floor  dirt_floor  \n",
       "0         0        0           1           0  \n",
       "1         0        0           1           0  \n",
       "2         0        0           1           0  \n",
       "3         0        0           1           0  \n",
       "4         0        0           1           0  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['physical_dv', 'age', 'ever_emo_violence', 'u16_marriage',\n",
       "       'child_marriage', 'no_edu', 'primary_edu', 'secondary_edu',\n",
       "       'higher_edu', 'safe_water', 'unsafe_water', 'no_walls', 'dirt_walls',\n",
       "       'mid_roof', 'bad_roof', 'sturdy_roof', 'no_roof', 'sturdy_walls',\n",
       "       'dirt_floor', 'hard_floor', 'electricity', 'radio', 'tv',\n",
       "       'refrigerator', 'bicycle', 'motorcycle', 'car/truck', 'open_defec',\n",
       "       'unimproved_sani', 'wood', 'natural_gas', 'animal_dung', 'lpg', 'grass',\n",
       "       'charcoal', 'biogas', 'kerosene', 'electric_power', 'coal',\n",
       "       'improved_sani', 'unimproved_sani', 'open_defec', 'safe_water',\n",
       "       'unsafe_water', 'shared_toilet', 'sturdy_walls', 'dirt_walls',\n",
       "       'no_walls', 'sturdy_roof', 'mid_roof', 'bad_roof', 'no_roof',\n",
       "       'hard_floor', 'dirt_floor'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clean_cols2 = dv_cols + hh_cols\n",
    "df2 = dv[clean_cols2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Physical abuse ever, age 16 and below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X, y = df.drop(['physical_dv',\"ever_emo_violence\", 'child_marriage'], axis=1),df['physical_dv']\n",
    "\n",
    "X_train, X_holdout, y_train, y_holdout = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3685 2579 1106\n"
     ]
    }
   ],
   "source": [
    "print(len(X),len(X_train),len(X_holdout))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "\n",
    "models = {}\n",
    "models['LogisticRegression'] = LogisticRegression()\n",
    "models['K-Nearest Neighbors'] = KNeighborsClassifier(n_neighbors=6)\n",
    "models['Gaussian'] = GaussianNB()\n",
    "# models['SVC'] = SVC()\n",
    "models['Decision Tree'] = DecisionTreeClassifier()\n",
    "models['Random Forest'] = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A+vbtizVr1uDu3bto2bIl+Hw+YmJiEBISgsWLF6Nt27YwMTHBF198gS+//BL9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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f1835c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(X_train,y_train)\n",
    "y_pred = model.predict(X_holdout)\n",
    "fpr, tpr, thresholds = roc_curve(y_holdout, model.predict_proba(X_holdout)[:,1])\n",
    "plt.plot(thresholds, tpr, label=\"TPR\")\n",
    "plt.plot(thresholds, 1-fpr, label=\"FPR\")\n",
    "\n",
    "plt.title('Determining the Trade-off between True and False Positives')\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Positivity Rate')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "Accuracy: 0.72\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.72      0.98      0.83       796\n",
      "          1       0.37      0.02      0.04       310\n",
      "\n",
      "avg / total       0.62      0.72      0.61      1106\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "lr = model.fit(X_train,y_train)\n",
    "y_pred = model.predict(X_holdout)\n",
    "\n",
    "print(\"Logistic Regression\")\n",
    "print(\"Accuracy: {:.2f}\".format(accuracy_score(y_holdout, y_pred)))\n",
    "print(classification_report(y_holdout, model.predict(X_holdout)))\n",
    "#     print(\"\")\n",
    "#what is the difference between score and accuracy_score in scikit-learn?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred_lower_threshold = lr.decision_function(X_holdout) > -.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression, Lower Threshold\n",
      "Accuracy: 0.65\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.77      0.73      0.75       796\n",
      "          1       0.38      0.42      0.40       310\n",
      "\n",
      "avg / total       0.66      0.65      0.65      1106\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Logistic Regression, Lower Threshold\")\n",
    "print(\"Accuracy: {:.2f}\".format(accuracy_score(y_holdout, y_pred_lower_threshold)))\n",
    "print(classification_report(y_holdout, y_pred_lower_threshold))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_even_lower_threshold = lr.decision_function(X_holdout) > -.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression, Lower Threshold\n",
      "Accuracy: 0.59\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.80      0.57      0.66       796\n",
      "          1       0.37      0.64      0.46       310\n",
      "\n",
      "avg / total       0.68      0.59      0.61      1106\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Logistic Regression, Lower Threshold\")\n",
    "print(\"Accuracy: {:.2f}\".format(accuracy_score(y_holdout, y_pred_even_lower_threshold)))\n",
    "print(classification_report(y_holdout, y_pred_even_lower_threshold))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VsVrdr88+m0BKSjJffz3rfjflvqiO9+zfTNyv6udO37Pc3TvJWL8O2Viy5prB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yTvcrnZtfkcxw9pFRz0eh1jb0kic3RMhxDoUly8l7OCPaUHAAjytvBU5qPE\nBmjLlTiayrHlLsPVol0ddOhCWbRzIGW12o9dXIQ/T9yZwQApdC+EEEL0iAS5fk5paaF2xzYALbhN\nmkLg+Al4RUZ9y54dOVpcrPngCOUldQD4B3pz+5zBRA6wdNi2wdnIG9nvcKb6PAAx/tE8PexRgry1\nbRvsx6kq+ARV0daCK6yP5+3dA3ApenQ6yBobz5yJiRgN+h59byGEEEJIkOv3LvzfK57XxpBQQuff\n0+1juN0Kn606Tt7ZSk9bXHIw02anY/bpOHO0orGS146+RVljBQAZIWk8lvEdzEYzqqpQXbyZuvLd\nAKjo2XIuhe1nQwEdYUFmHp89mJSYoG73UwghhBDtSZDr55rOnPa8jnjokR4do7Sopl2IS0wNZca8\njE6fpztfk88/ji6h3qlNgJg8YDz3pNyJQW/A7WzAlreClvo8AFrcPry7L4WimkAAJmVG8cC0FHy8\n5cdOCCGE6A3yF7W/0+lAVfHNGII5PqFHh/h6yauUweFMnpHaaYg7UHaEd05+iEtxoUPH/JTZ3BIz\nEZ1OR0vDBWy5y3E7awEorrOydH8KDQ4vAnxNLJyZxojUsB71TwghhBCd6zNBbunSpSxatAibzUZa\nWhrPP/88mZmZnW67adMm3n//fU6ePInD4SAlJYXvf//7TJw48Sr3uu/wyxjaK8cZNT6+wyK/qqqy\nKX8rH5/fAIBJb+LRjAcZFjYEgHrbQaqKNoCqzXDdnT+ATafiUVQ9w5JDWJiVjsXv0hUjhBBCCNEz\nfeJJ8/Xr1/Pyyy/zzDPPsGrVKtLS0nj88cepukRN0H379jFhwgTeeOMNVq1axc0338xTTz1FTk7O\nVe759UFVL12E3q24eS9nhSfEBXj58+zIpxgWNgRVcVFZsJaqwrWgunEpBj46MoiNOYmYjCYemTmI\nZ+7JlBAnhBBCXCF94orckiVLuP/++5k7dy4AL7zwAlu3bmXFihU88cQTHbb/2c9+1u79s88+y+bN\nm9myZQtpaWlXpc99QeOpnHZVG3qipdnFnq25gHaX1vtrkxuaXE28mf1PcuxaEfsovwieznyMEB8r\nLkcNttzlOBq1ZUrsjWbeP5ROeb0fydGBPH7nYCKsvpfVNyGEEEJ8s2se5JxOJ8ePH+fJJ5/0tOl0\nOsaPH8/hw4e7dAxVVWloaMBi6bhMxvXGVVdLyd//hrOivF31BoMlsPvHcrrZ8FE2tvJ6AEaOi8e3\n9epZZZOd146+RUmDVhM1zZrC40O/i4/Rh+a6XGx5K1BcjQCcLreyMnsQTsXEvEkJZI2Lx6DvExd7\nhRBCiOvaNQ9ydrsdt9tNaGhou/aQkBByc3O7dIw333yTxsZGZs2a1e3zG/rZOmbn/vtnuOvrO7QH\n3XQTemPXv4vbrfD5JycpKaoBYMjIaMbdkoROpyOvppC/HnqLWoe2ntzEATfznfT56HV6akp3UVX0\nOaCiqrD1XBzbzsUSGeLHk3OGkBTd/UDZFRfHqb+N141Mxqx/kfHqf2TM+pcrNU7XPMhdrjVr1vC3\nv/2N1157jeDg4G7vHxjocwV6dWWUb9naLsRF3Tkbo68P4bfdijm86+uyqYrKxx8cJveMDYDBw6KZ\n++BI9Hod+y4c4Y/7F+Fwawv5fidzLnPSpqO4HeQfX4a97CgATU4DK48O4owtmNkTEnlk9mDMXlf+\nx6k/jZfQyJj1LzJe/Y+M2Y3tmgc5q9WKwWDAZrO1a6+srOxwle5frVu3jl/84hf88Y9/ZOzYsT06\nf21tE2630qN9ryanvYozf/yz533YvPlY52jPFDYBTfaGLh1HVVV2bDrL0QNFAMQlBTM1K5Xq6gY2\nF2zno1NrUFEx6o08NuRBRkUOo7y0gLIzH+Js1hYALqvz5YND6agGCz96MIPM5BCaGlpoamjp3S/9\nNQaDnsBAn34zXkLGrL+R8ep/ZMz6l4vj1duueZAzmUxkZGSwe/dupk2bBmhhY/fu3Tz00EOX3G/t\n2rU8//zzvPrqq0yePLnH53e7FVyuvv8LUL1vv+e1zmgkaNbsHvX7wK58Du/V6rKGRwcwfe5gXG43\nH+WsYduFXQD4m/x4MnMhSZZ4aitPUpm3GlXRQtrR4jDWnBjIsJQoHp4xCH8f01X936+/jJdoI2PW\nv8h49T8yZje2ax7kABYuXMhzzz3HkCFDGDp0KG+//TbNzc3Mnz8fgFdeeYXy8nJ+97vfAdrt1Oee\ne46f//znDB061HM1z2w24+/vf82+R29TVZWyxW/SmHMSpanJ0574u1fQ9WAywbGDF9i7TXvu0Brq\nyx33ZuLWu3kzeynHKrWlWyJ8w3g68zFCfaxUF39Bbdl2ABQFNp5K5GhZLAtnpTE2I6LTRYOFEEII\ncfX0iSCXlZWF3W7nT3/6EzabjfT0dN58803PM282m42SkhLP9suWLcPtdvPiiy/y4osvetrnzp3L\nSy+9dNX7f6VUrlpB7a6d7doMliCMPZide/ZkOds/05YRCQj0Zvb9w2jWN/LawcUU1WtLiKQEJfHE\n0Icx63SUn3uflrpzANS3mFh+JA2fgHhefGwwIRbzZX4zIYQQQvQGnfpNq8HeAOz2hj53Sbp66xbs\nGzfgrKjwtPlmDME7Jhb/UaPxSUru1vEKzlex4aNsFEXF7Gti3ndHUG+q5rWji6lu0Wat3hQ5kgVp\n96A02yg79yGqS2svrA5gZXY608cO5vYxseiv0VU4o1GP1erXJ8dLdE7GrH+R8ep/ZMz6l4vj1evH\n7fUjistWtWEdrsq2IvZekVHEPPujHh2r9EING1cdQ1FUvLwNzL4vkyIln7cOLqXF7QAgK/F2shJu\no9GejS1/DTq0Ulv7CiLJrszk2QeGEBN+/dyyFkIIIa4XEuT6INWtBSnvhEQCRo3GMmVqj45TWVHP\n+uXZuJwKBqOeWXcP5aTrGMtOrEZFxaAz8N30exkTPoyK/PU02w+gA1xuHWtPDCQqdgw/vyMJUzfW\npxNCCCHE1SNBrg/zTUsneNYdPdq3trqJtR8epaXZhU4Ht92Vzu6W7Wwp1CYv+Bp9+N7QR0jyDyPv\n+FsYXNoziNVN3nx6JpM5t4xlUJy1176LEEIIIXqfBLk+xL5pI9WbP8ddU3NZx2lscLDmgyM01mu3\nTifNHMjnLRs4UnEMgFBzMP8+7DH8XY3kHn0NL30zAOdsQRQ6J/H9+4fia5YfDSGEEKKvk7/WfUjV\nurW46+s8742B3S931dLsZO2HR6it1sLZyCkxrHOsIr9OWzsuyRLPE0MepvrCYZqqt+Kl1+a6fFUQ\nR1LqTB5Oj+yFbyKEEEKIq0GC3DVkW72ydXkRLUxdDHHm5IH4jxyFZfKUbh3P6XSz/qNjVJZrVR5S\nR4ewVl1OVZ0dgFHhw/hOylxOZ68iyHAevQ5aXAYOlI9g5rRpBPl7996XE0IIIcQVJ0HuKqs/fAjb\nR8twNzVe8hZq4PgJBE25pVvHdbsVNq0+TmmRdszodF/WG5fT3KxdmZsRfys3BWSSc+ANgn1qAbA1\n+NDoO4v7szJkcV8hhBCiH5IgdxU57XaK//LHDu2msHD8R4wEwBhkJXDs+G4dV1VVtq4/Rf65KgAs\n8QY+D1iJoijodXoeSJ2H2aajpnIJwT4uAPJrIkjOuI/oMJnQIIQQQvRXEuSuooZDB9q9t06fiSks\nHMukyeiMPRsKVVXZ+flZTh8vA8A7ws2usA2oqJgNZhak3I/t5ElirCfQmUBRocQxgrETszAZDZf9\nnYQQQghx7UiQu4rK3/un53XSK3/sUamtf3VgVz7ZBy4AoAtycCjmC1S9SrDZym1Bd1B94kvSQrRa\ntE1OE6awOxiXmHnZ5xVCCCHEtSdB7iqxf76p3fveCHHHDl5g3/Y8ABS/Zk4lbUcxuIn1jyGhdjgB\nykZCQ5oAqHNaSRy8AF//4Ms+rxBCCCH6BglyV0ntzm2e12EPfOeyj3fmRBnbPzsDgMvczNmUnbiN\nTgb6D8I/P4jRCdvwMmq191qMaaQPm4deb7rs8wohhBCi75AgdxU4KypoKSz0vA+advtlHa/gfCVb\n1uYA4DI5OJ/6FS6vFhL0mURdaGH8wOMAKKoOv8jpxEbdJLNShRBCiOuQBLmroG7/Ps9r64xZlxWq\nSotq2LjyOIqi4jY4yUvdi9PcRHh1JmMCK0lK0JYfceFLdOr9mP1jL7v/QgghhOibJMhdBarb5Xkd\nPDOrx8epLK9n3fJsXC4FRecmP2U/Lr8mwkoGMz/xAhafFgD05hjiB96HweR/2X0XQgghRN8lQe4K\nU1WVytUrAdB5mzEEBPToOLXVTaz98CiOFhcqCoUDD9Hi10xqRTJZaQUYW0tt+YfejDXmNnQ6WVpE\nCCGEuN5JkLvCKj9e5XmttjT36BiN9S188sFhGhscAFxIyqbR3MTUlghGpxZpG+mMhMTdhV/wkMvu\nsxBCCCH6BwlyV4iqKJS++Q/q9u7xtEU8+ni3j9PS7GT1B4eoq9Zum5bEncDh3cwDfr7EWLT14Qxe\nVsKS7sPLJ6J3Oi+EEEKIfkGC3BXSUlTYLsSZIiKwTJjYrWM4nW5WfrCfGpsW4sqjz2D2a+HBCDd+\n3lqbOXAgofHz0Bt9eq/zQgghhOgXJMhdKW6356Xf8BFEPta9q3Fut8LyD3dTU6pNlKgMyyMmuJFp\nUXXo9do2lsgpBEZOlqVFhBBCiBuUBLmrIHjWHRh8/bq8vaIovLfsS+qLtIBWH3KBUfFVZITUA6Az\neBMaPw8fS+oV6a8QQggh+gcJcr2o6fx5yt9djLuhAdXp+vYdOuFW3CxesRFnvq/WEFbMLYOKCPfT\nbqWazOGEJt2HyVtKbQkhhBA3OglyvaRq/VpsKz/q9DNDQGCXjmGzV/PBB7sw1GghzjKgkNFpBZiN\n2m1aX+sQgmNnozd49U6nhRBCCNGvSZDrJZVrP2n33jp9JgDmpCS8wsO/df/cC8Ws/TAbo8MXUElO\nOU9a0oXWT3UEDbidgLCb5Xk4IYQQQnhIkOsFjadPoTq0Nd50RiPxL/wGr4iuLwWy53gO+9YXYnR7\nYzI5GTL8GNHBdQDojX6EJtyNOSDhSnRdCCGEEP2YBLleUPPlVs/riEce7VaIW7ZlJxX7mzAoJvwD\n6hg56hgB3k4AvHwHEJp4L0avrt2aFUIIIcSNRYLcZVKam6jbs9vz3n/0mC7t1+xw8dqqDXjn+aFX\njURFl5CZcQ6jXtGOEzoK64AZ6PQyREIIIYTonKSEy9Rw/LjntW/6YPSmb5+IcL6kmn+u/4ywinB0\nOoW09NMkxZVqH+oMBMfegX/I8CvVZSGEEEJcJyTIXa6vLfwbMu/ub9xUVVXW7TnLgSP7CbdH4e3V\nwogRJwgJ0p6HM5gshCXdi5dv9BXtshBCCCGuDxLkepEpJPSSnzldCm9+eoCqovOEV0dhDaph5PAT\nmFufhzMHJBKScDcGo+/V6q4QQggh+jkJcldBbaODV1dvw1xdTXBtOPGxxQxOO4derwIQGDEBS9Qt\n6HT6a9xTIYQQQvQnEuQuk+2TVd/4eVFFPf+3bhNRdTr8m6wMHXKKmAHlAOj0XoTEz8E3KP1qdFUI\nIYQQ1xkJcj3krquj+G9/xlla6mnTm83ttjl81sZb2zaSWBuIVWdk1M1HsARq9VKN3iGEJd2HyRx2\nVfsthBBCiOuHBLkeqD9ymOI//1+7Nsst09B7ewPapIYNe/L4NGczA6sHEBXQxIjMbLy8tPqrPpY0\nQuLnoDd4X/W+CyGEEOL6IUGuB6o/39TufdgDCwiaegugTWpYsvEYJ6t2kVKRyKC4Mgal5KFV1tJh\nibqFwIgJUmpLCCGEEJdNglw3uartNJ5sWzsu5sfP4Zs6CIDaBgd//HgvNbpDDCpPZcTQs0RGVAKg\nN/gQkjAfn8Dka9JvIYQQQlx/JMh1U/2hQ57XlilTPSGuqLyeVz/ZQVPUV4wuzGTszUfx92sCwOQT\nRVjivRi9g65Jn4UQQghxfZIg103lS9/xvA65ay4Ah8/Y+McXX6JLOEAmfkwddQKjUSu15Rc8DGts\nFnq96Vp0VwghhBDXMQly3eCqqW733hBoYcNX+aw69iWmpONM8/ZltL9W6UFRdJiDbyU4brw8DyeE\nEEKIK0JWoO2Guj1feV5bbp/Jm+tOsOrsBsKi83jEFMpofy2wNTd7sXvvMAJCR0uIE0IIIcQVI1fk\nushpq6Bi2Qee96tsPuQ4tzEGHyYHeePjoz0PV2W3YG+cxNhpcfgFyPIiQgghhLhyJMh1Ud2B/Z7X\nFb7R1ChupqlWhqSfwWDQSm3VNQ1i4Og78Q+UeqlCCCGEuPIkyHWV243dHE5e8DCq/aIYH3+W+Fit\nqoOiGgiMyiIuasQ17qQQQgghbiQS5L6FqqoU5dn54riRhpgszN4tjB1+BGtQHQB6UxCRyffj5RNx\njXsqhBBCiBuNBLlLUFWVgvNV7N+ZT3lxLWAm2FrNyGEn8PbWSm2ZAwcSGj8PvdHn2nZWCCGEEDck\nCXL/QlVV8s5WcmBnPhWldRdbGRiXR+qgQnSt83wDIydjiZwis1KFEEIIcc1IkGuVf66SPV+ep6He\nQXOj09Puo9YzOu0IgQnu1hYjYUn34GNJvTYdFUIIIYRoJUEOKCuuZeOq47hdiqfNgUq6ey+pN9eh\nD/ECQG+yEpGyAJN38LXqqhBCCCGExw0f5Gqrm9jwUTZul4LRqMcnwp/ac9lM8T2AdaoVnbcW4kym\neCLSH0Rv8LrGPRZCCCGE0NzQQa65ycmaD4/S1HorNYQSYnZvJXS4GeOYUABURcWrPpbIyQ/L83BC\nCCGE6FNu6CD38QeHqapoAMAUHUDq3qX43R6CIUFb0FdtdOPTnE7I+DkS4oQQQgjR5/SZWqtLly7l\n1ltvJTMzk/vuu4+jR49+4/Z79uxh/vz5DB06lBkzZrBq1apun/PUMW1BX1OgieSC9/C/J9IT4lwt\nXkQN+z7h0x7A4CPLiwghhBCi7+kTQW79+vW8/PLLPPPMM6xatYq0tDQef/xxqqqqOt2+qKiIp556\nirFjx/Lxxx/z8MMP8/zzz7Nz584enT+dLSRND0BvMQGgNIeQeNOP8PIP6/F3EkIIIYS40vpEkFuy\nZAn3338/c+fOJTk5mRdeeAGz2cyKFSs63f79998nJiaGH//4xyQlJbFgwQJmzJjBkiVLunVenU5h\n8KAzxI5T0Zn0qC4FXWUUcaOfQKe/oe86CyGEEKIfuOZBzul0cvz4ccaNFgPtiQAAEHRJREFUG+dp\n0+l0jB8/nsOHD3e6z5EjRxg/fny7tokTJ15y+0u5efRREhNKAFDrnBjyIom97Qn0JpmZKoQQQoi+\n75pfdrLb7bjdbkJDQ9u1h4SEkJub2+k+FRUVhISEdNi+vr4eh8OBl1fXglhIcC0AjdUq4foJhN07\nHZ3xmmdb0QmDQd/uv6LvkzHrX2S8+h8Zs/7lSo3TNQ9y19Ko6b+/1l0Q3RQYKBNP+hsZs/5Fxqv/\nkTG7sV3zGG+1WjEYDNhstnbtlZWVHa7SXRQWFkZlZWWH7f39/bt8NU4IIYQQor+75kHOZDKRkZHB\n7t27PW2qqrJ7925GjBjR6T7Dhw9vtz3Azp07GT58+BXtqxBCCCFEX3LNgxzAwoULWb58OatXr+bc\nuXP88pe/pLm5mfnz5wPwyiuv8JOf/MSz/QMPPEBhYSG///3vOX/+PEuXLmXjxo08+uij1+orCCGE\nEEJcdX3iGbmsrCzsdjt/+tOfsNlspKen8+abbxIcrBWnt9lslJSUeLaPiYnh9ddf56WXXuLdd98l\nMjKSX//61x1msgohhBBCXM90qqqq17oTQgghhBCi+/rErVUhhBBCCNF9EuSEEEIIIfopCXJCCCGE\nEP2UBDkhhBBCiH5KgpwQQgghRD8lQU4IIYQQop+6roPc0qVLufXWW8nMzOS+++7j6NGj37j9nj17\nmD9/PkOHDmXGjBmsWrXqKvVUQPfGa9OmTTz22GOMGzeOUaNG8cADD7Bjx46r2FsB3f8du+jAgQNk\nZGQwb968K9xD8XXdHS+Hw8Grr77KrbfeytChQ5k2bRorV668Sr0V0P0x++STT5gzZw7Dhw9n4sSJ\n/OxnP6O6uvoq9fbGtn//fp566ikmTZpEWloamzdv/tZ9eiV3qNepdevWqUOGDFFXrVqlnj17Vv3v\n//5vdcyYMWplZWWn2xcWFqrD///27j0oqroN4Ph3F2QUuQgogqETlKViwqIVmMIoMuaF8m5jiTJe\nm9GSkdFBG2TkNmAqIpABNoqm6XgBhdJsKkplvGaKg4ZoCGgJiCgionLeP3zdtxV7c2m57PJ8Zvhj\nfzznd57DM+w++zt79nh6KnFxcUpRUZGydetWpV+/fsrhw4dbOPP2Sd96RUdHK+np6cq5c+eU4uJi\nZc2aNYq7u7tSUFDQwpm3X/rW7Inbt28rI0aMUGbNmqWMGzeuhbIVTanX/PnzlalTpyp5eXlKWVmZ\ncubMGeX06dMtmHX7pm/NTp48qfTt21fZunWrUlpaqpw6dUoZO3assnDhwhbOvH3Kzc1VEhISlEOH\nDil9+vRRvvvuu/8bb6i+w2QbucmTJyuRkZHaxw0NDcrQoUOV1NTUZ8bHx8crY8eO1RkLCQlRZs+e\n3ax5isf0rdezjBkzRklOTm6O9MQzNLVmISEhyrp165T169dLI9eC9K1Xbm6u8vrrryvV1dUtlaJ4\nir4127hxoxIQEKAztmXLFsXPz6850xTP8Oqrr/5jI2eovsMkT60+ePCA8+fP4+Pjox1TqVQMHjyY\nM2fOPHObX3/9tdEtvoYMGfK38cJwmlKvpymKwt27d7G1tW2uNMVfNLVmu3fvprS0lAULFrREmuK/\nmlKvH374gf79+5OWloavry8jR44kLi6O+/fvt1Ta7VpTaubp6cn169fJzc0FHt/e8sCBA/j5+bVI\nzkI/huo72sS9Vg2tqqqKR48e0bVrV51xBwcHrly58sxtysvLcXBwaBRfU1NDfX09FhYWzZZve9eU\nej0tPT2d2tpaRo0a1Rwpiqc0pWa///47a9euZdu2bajVJvkess1qSr1KSko4efIkFhYWJCcnU1VV\nRUREBNXV1cTExLRE2u1aU2rm5eXFqlWrCAkJob6+nocPHzJ8+HDCw8NbImWhJ0P1HfJsKoze/v37\nSUlJYd26ddjb27d2OuIZGhoaCA0NZeHChfTq1Qt4vIoq2i5FUVCr1axevZrXXnsNX19fwsLCyMzM\npL6+vrXTE89w6dIloqOjWbhwIXv27GHjxo2UlpZKI2fiTLKRs7Ozw8zMjIqKCp3xysrKRu9unujW\nrRuVlZWN4q2srGQ1rpk1pV5P5OTkEB4ezrp16/D29m7ONMVf6Fuzu3fvkp+fT2RkJO7u7ri7u5OS\nkkJBQQH9+/fn2LFjLZV6u9TU58Tu3bvTuXNn7ZibmxuKovDHH380a76iaTVLTU3Fy8uL4OBgXnnl\nFd566y1WrFjB7t27G80jWp+h+g6TbOQ6dOiAu7s7eXl52jFFUcjLy0Oj0TxzG09PT514gCNHjuDp\n6dmsuYqm1QsgOzub5cuXs2bNGnx9fVsiVfFf+tbMysqK7OxsMjMzycrKIisri/feew83NzeysrLw\n8PBoyfTbnab8j3l5eXHjxg3u3bunHbty5QpqtRonJ6dmz7m9a0rN6urqMDfX/cSUWq1GpVLJCngb\nZKi+wywiIiLCgHm1GZ07dyYxMRFnZ2c6dOhAQkICFy9eJDo6mk6dOrF69WqysrIICAgAoFevXmzY\nsIE7d+7g7OzM119/zaZNmwgPD6dnz56tfDSmT9967d+/n7CwMMLCwvDx8aG2tpba2loaGhpkBbWF\n6FMzlUqFvb29zk9+fj5Xr17lww8/bPTiIwxP3/8xV1dX9uzZQ35+Pr1796aoqIioqCgCAgIYMWJE\nKx9N+6Bvzerq6khLS8POzo4uXbpw6dIlYmJi6NGjBzNmzGjlozF9tbW1FBUVUV5ezo4dOxgwYAAd\nO3bkwYMHWFtbN1vfYbLPnqNHj6aqqorExEQqKiro27cv6enp2s9QVVRUcP36dW28i4sLqampxMbG\nsmXLFpycnIiKimp0RYloHvrWa+fOnTx69IiVK1eycuVK7fi4ceOIjY1t8fzbI31rJlqXvvWytLTk\niy++ICoqikmTJtGlSxdGjRrFokWLWusQ2h19azZ+/Hhqa2vZtm0b8fHxWFtb4+Pjw+LFi1vrENqV\n/Px8goKCUKlUqFQq4uLigP+9LjVX36FSZL1VCCGEEMIomeRn5IQQQggh2gNp5IQQQgghjJQ0ckII\nIYQQRkoaOSGEEEIIIyWNnBBCCCGEkZJGTgghhBDCSEkjJ4QQQghhpKSRE0IIIYQwUtLICSGEEEIY\nKWnkhBBtyueff8706dMNNt/PP/9MUFAQAwcOxNPTk8DAQFJTU3n48KHB9vFPysrK6NOnDydOnACg\nurqamTNnMmDAAKZMmUJSUhL+/v7PNdfTsZcuXSI3N7dZ8hZCtH1yiy4hRJvx5ZdfEh0dzaBBg8jI\nyPjX8x05coR58+YRGhqKr68vHTp04NSpU8TGxuLv709MTIwBsv5niqJw8+ZNbG1tMTc3Z8uWLaxa\ntYrt27fj6OiIlZUVdXV12NnZ/eNc9+7d04n19/dn/PjxLFiwoLkPQwjRBpm3dgJCCHHjxg3Cw8M5\nfvw4rq6uBpt3x44d+Pn5MXPmTO1Yz549qaurIzIykmXLlmFlZWWw/f0dlUqFg4OD9nF1dTVdu3bF\n3d1dO9apU6fnmqtTp046sfJeXIj2TU6tCiFa3fnz57GwsGDfvn0MGDDAYPOq1WoKCgr4888/dcbH\njx9PdnY2lpaWAEyfPp2YmBgWL16Mp6cnfn5+pKam6mxTVFTE3Llz0Wg0DBkyhNDQUCoqKnRiNm/e\nzNtvv42HhweBgYHk5OQAuqdWw8LCSEpK4tq1a/Tt25fMzEySkpIYPny4dp7KykqWLFmCt7c3gwYN\nYv78+Vy9ehWA9evXa2OHDx/OtWvXSE5OJigoiNjYWAICAnRyqqmpwcPDQ06/CmGipJETQrS6YcOG\nkZiYiIuLi0HnnTFjBpWVlfj7+zNz5kySk5M5ceIE5ubmuLq6olb/7ylw+/bt2NrasnfvXkJCQkhJ\nSSE9PR14vGL4/vvv4+rqyt69e0lNTaWmpoapU6dSV1cHQFpaGgkJCcydO5fs7GymTp3K0qVLOX78\nOPB4VQ5g+fLlBAcH4+zszJEjRxg9erTO7x89ekRwcDCXL19mw4YN7Ny5k4aGBubMmYOiKKhUKm3s\nrl27cHJyIjg4mKSkJCZMmEBpaSmnT5/WHldOTg62trb4+voa9G8rhGgb5NSqEMJkaTQa9uzZw6ZN\nm8jNzeXYsWMoioKjoyMrVqzQuWjAzc2N8PBwAFxdXSkqKiIjI4PZs2ezbds2nJ2dCQsL08avXbsW\nHx8fDhw4wLhx48jIyGDGjBlMmDABgA8++ID79+9rL6p4cgrUysqKzp07o1arsbe3b5Tz0aNHKSws\n5ODBg/Tq1QuAqKgoNm3axK1bt3Ri7e3tUavVWFpaYmNjg42NDf369SMrKwsvLy8AMjMzeffdd7XN\nnxDCtMiKnBDC6MyZMweNRoNGo8HLy4vs7Oy/jX3ppZeIjIzkp59+Iicnh2XLltGxY0c+/vhjCgsL\ntXFvvPGGznYajYby8nJu3bpFQUEBhYWF2n1qNBoGDx5MfX09ly9fpqqqivLycjw8PHTmmDVrFoMH\nD9br2AoLC7GxsdE2cQCOjo4sWbLkuS6GmDhxIgcOHODBgwcUFxfzyy+/aJtLIYTpkRU5IYTRiY6O\n5v79+9rHf72Q4Il79+6xevVqJk2aRJ8+fYDHq25ubm4EBgYybNgwDh8+TO/evQEwN9d9OmxoaADA\nzMyMhoYG3nzzTSIiIhrtx9rautG2/8a/nSswMJD4+Hh+/PFHLl68iIeHh0EvIBFCtC2yIieEMDqO\njo707NlT+/PkooW/6tixI9nZ2ezYsaPR7ywtLTEzM6Nr167asfz8fJ2YU6dO4eLigrW1Nb179+by\n5cs4OTlp92ljY0N0dDS//fYbVlZWODo6cu7cOZ05PvroI+Li4vQ6tpdffpnbt29TUlKiHbt58ybe\n3t6cPXu2UfzTp0ytra0ZMWIE3377LQcPHpTVOCFMnDRyQgiTpFKpWLx4MV999RURERGcPXuWsrIy\njh49yoIFC+jRowcjR47Uxp88eZKkpCSKi4vZtWsX27dvZ86cOQBMmzaNO3fuEBoayoULF7hw4QKL\nFi0iPz9fu6I3d+5cNm/ezL59+ygpKSEjI4Pvv//+ub/o9wkfHx/c3d1ZsmQJZ8+epbCwkKVLlzb6\nupInLC0tKS4uprKyUjs2ceJEDh06RElJCWPGjGnKn08IYSTk1KoQwmRNnjwZR0dHMjIymDdvHjU1\nNXTr1g1/f39WrVqFhYWFNtbf35+ioiLeeecdunfvzrJly5gyZQoALi4ubN26lU8//ZRp06Zhbm6O\nl5cXmzdv1n5u7cnFDYmJiZSXl/Piiy+SkJDAoEGDKCsre+6LDVQqFZ999hmxsbHMmjULlUqFt7c3\naWlpmJmZNYoPCgoiPj6ewsJCMjMzgcfNoJ2dHQMHDmyR78kTQrQeubODEKLdmz59Oi4uLsTGxrZ2\nKgZx9+5dhg4dSkpKCt7e3q2djhCiGcmKnBBCmIjbt2+Tl5fHN998wwsvvCBNnBDtgDRyQghhIh4+\nfMgnn3yCg4MDCQkJrZ2OEKIFyKlVIYQQQggjJVetCiGEEEIYKWnkhBBCCCGMlDRyQgghhBBGSho5\nIYQQQggjJY2cEEIIIYSRkkZOCCGEEMJISSMnhBBCCGGkpJETQgghhDBS/wGMRZKpfLVvAQAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x138320160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (7,4))\n",
    "\n",
    "for name,model in models.items():\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred = model.predict(X_holdout)\n",
    "    fpr, tpr, thresholds = roc_curve(y_holdout, model.predict_proba(X_holdout)[:,1])\n",
    "#     plt.plot(fpr, tpr)\n",
    "    plt.plot(fpr, tpr, label=name)\n",
    "    \n",
    "#     print('Model: '+name)\n",
    "#     print(\"Score: \" + str(model.score(X_holdout,y_holdout)))\n",
    "#     print(\"Accuracy: {:.2f}\".format(accuracy_score(y_holdout, y_pred)))\n",
    "#     print(classification_report(y_holdout, model.predict(X_holdout)))\n",
    "#     print(\"\")\n",
    "#what is the difference between score and accuracy_score in scikit-learn?\n",
    "\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.xlabel('1 - Specificity')\n",
    "plt.ylabel('Sensitivity')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def joel_pr_curve(truthvec, scorevec, model):\n",
    "    threshvec = np.unique(scorevec)\n",
    "    numthresh = len(threshvec)\n",
    "    tpvec = np.zeros(numthresh)\n",
    "    fpvec = np.zeros(numthresh)\n",
    "    fnvec = np.zeros(numthresh)\n",
    "\n",
    "    for i in range(numthresh):\n",
    "        thresh = threshvec[i]\n",
    "        tpvec[i] = sum(truthvec[scorevec>=thresh])\n",
    "        fpvec[i] = sum(1-truthvec[scorevec>=thresh])\n",
    "        fnvec[i] = sum(truthvec[scorevec<thresh])\n",
    "    recallvec = tpvec/(tpvec + fnvec)\n",
    "    precisionvec = tpvec/(tpvec + fpvec)\n",
    "    return (recallvec, precisionvec, threshvec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "joel_recall_vec, joel_prec_vec, joel_thresh_vec = joel_pr_curve(y_holdout,pred_probs[:,1], \"KNN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1130b6a90>]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A5Kdz+Oml/ahb59D/2jqf6OVsopeziW7OJ3odyUyqVEgPR0VFBeFwmHvuuYf6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\nUiAVEREREVP9P6fC2hFCtNHjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x138ba4dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(joel_recall_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=6, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=6)\n",
    "neigh.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pred_probs = neigh.predict_proba(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.66666667,  0.16666667,  0.5       , ...,  0.16666667,\n",
       "        0.5       ,  0.5       ])"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_probs[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def pr_curve(truthvec, scorevec, model):\n",
    "    threshvec = np.unique(scorevec)\n",
    "    numthresh = len(threshvec)\n",
    "    tpvec = np.zeros(numthresh)\n",
    "    fpvec = np.zeros(numthresh)\n",
    "    fnvec = np.zeros(numthresh)\n",
    "\n",
    "    for i in range(numthresh):\n",
    "        thresh = threshvec[i]\n",
    "        tpvec[i] = sum(truthvec[scorevec>=thresh])\n",
    "        fpvec[i] = sum(1-truthvec[scorevec>=thresh])\n",
    "        fnvec[i] = sum(truthvec[scorevec<thresh])\n",
    "    recallvec = tpvec/(tpvec + fnvec)\n",
    "    precisionvec = tpvec/(tpvec + fpvec)\n",
    "    plt.plot(precisionvec,recallvec)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.title(model)\n",
    "    plt.xlabel(\"Precision\")\n",
    "    plt.ylabel(\"Recall\")\n",
    "    return (recallvec, precisionvec, threshvec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  0.84193548,  0.56129032,  0.28387097,  0.09354839,\n",
       "        0.02580645,  0.00322581])"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joel_recall_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.33333333,  0.5       ])"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joel_thresh_vec[(joel_recall_vec>0.1) & (joel_recall_vec<0.8)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.        ,  0.8516129 ,  0.55483871,  0.26774194,  0.08709677,\n",
       "         0.01935484,  0.00322581]),\n",
       " array([ 0.28028933,  0.2979684 ,  0.32089552,  0.33739837,  0.375     ,\n",
       "         0.42857143,  0.5       ]),\n",
       " array([ 0.        ,  0.16666667,  0.33333333,  0.5       ,  0.66666667,\n",
       "         0.83333333,  1.        ]))"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7yUwPHnFM5xcAAMIvYHHBmd+Kaq/qvD6TqwEAwFyEX8DiMl2JoY855hgAYHeEX8DigmMP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\nOCfD9ps3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130ec940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pr_curve(y_holdout, pred_probs[:,1], \"KNN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pred probs: [[ 0.69670291  0.30329709]\n",
      " [ 0.81821125  0.18178875]\n",
      " [ 0.61590039  0.38409961]\n",
      " ..., \n",
      " [ 0.6911732   0.3088268 ]\n",
      " [ 0.73194058  0.26805942]\n",
      " [ 0.60461613  0.39538387]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([ 1.        ,  1.        ,  1.        , ...,  0.00645161,\n",
       "         0.00645161,  0.00322581]),\n",
       " array([ 0.28028933,  0.28054299,  0.2807971 , ...,  0.66666667,\n",
       "         1.        ,  1.        ]),\n",
       " array([ 0.0607746 ,  0.06664006,  0.06862366, ...,  0.58579874,\n",
       "         0.61015366,  0.6527882 ]))"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MqAgAKp9PLHsoKChQixYt9Ktf/UqjR4/+2fGHDh3SqFGjdOedd+rll1/WqlWr9PTTTysu\nLk7dunWrgooBVBcvvPCCioqK1LZtW5WUlGjRokXauHGj/vCHP1xyNwQrWLhwoebNm6devXopNDRU\n69ev14IFC9SjRw+uiAPwWz4Rfnv27On+RO7lrMKYM2eOEhIS9Kc//UnSmY3t169frxkzZhB+AXjo\n3Lmz/vGPf+jrr79WcXGxGjVqpD//+c+66667zC7NdElJSQoMDNT06dN1+vRpxcTEaOTIkXrkkUfM\nLg0AKo1PhN8rtXHjxnJ3L+revbv7wx4AcNbAgQM1cOBAs8vwSddee63eeecds8sAgCrlE2t+r9TJ\nkycVHR3t0RcdHa3Tp09fcj9IAAAAWFu1vPLrLYZhsL0Nqo1/Ld2hfy7cJkm6pkFtvfL7XiZXBABA\n9VMtw29sbKwyMzM9+jIzMxUeHn5FH2Cx2WzKzS2U0+nydonwMQ6HXRERIdV6vq9vU88dfutFhig7\nO9/kinyXP8w3Lh/zbS3Mt7WcnW9vqpbht02bNuXu/LRy5Uq1adPmip/L6XSprIxvHquozvN9IqfQ\nfbxi01HdN6CFidVUD9V5vnHlmG9rYb5RUT6x5regoEDp6enatu3MVa2DBw8qPT1dR48elSRNnjxZ\nTzzxhHv8HXfcoYMHD2rSpEnas2ePZs+erUWLFum+++4zpX6gKtQK8/yrxtL1h0yqBACA6ssnwu+W\nLVs0aNAgDR48WDabTRMnTtRtt92mKVOmSJIyMjLcQViSEhISNHXqVK1atUqDBg3SzJkzNW7cuHI7\nQAD+JCjQoTpRoe72+0t3mlgNAADVk+Vvb5ydnc+fTSwgIMCuyMiwaj/fRzPz9dTba9ztx+9oozpR\noYqKCDaxKt/jL/ONy8N8WwvzbS1n59ubfOLKL4DLUy/a8wfApPc36Mmpq5VzuvxtagEAQHmEX6Ca\n6ZZS16NdUubS4Qx2fgAA4HIQfoFq5r4BLTT+N53UqmmUu+/1eZs1f/keLV53UKVlThOrAwDAt1XL\nrc4AK7PbbKoXHabbejTVlj1ZkqSiEqc++XafJMlht6lPuwQTKwQAwHdx5ReophJiw5WaGK3wkEAF\nBTrc/RGhl3+jFwAArIYrv0A1FRhg1++HtJYkffjVbi1cvV+SPJZDAAAAT1z5BfzAZ2v2u4+Da/Bv\nWgAALobwC/iB83frZucHAAAujvAL+IHQoHNXewMdNhMrAQDAtxF+AT+QmhgtSaoTGaK4yNCfGQ0A\ngHURfoFqzjAM7TmSK0kqLHHqRE6hyRUBAOC7CL9ANXcyp9AdeHPzS/TkW6t1hHW/AABcEOEXqOaC\nagQo5Lw1vy7DUEFRmYkVAQDguwi/QDVXK6yGXn6oqyJCA919NQL51gYA4EL4DQn4gZCgACU1jPzv\nsUPxsWEmVwQAgG8i/AJ+YuPuDElSYbHT5EoAAPBdhF/AT5SUutzHLtclBgIAYGGEX8BPhAWf+dBb\nStNoBQbwrQ0AwIXwGxLwA0cy8pX/3x0e2ifFmlwNAAC+i/AL+IH1O05Kkmw2qc01MSZXAwCA7yL8\nAn7g++1nwm/zhNqKCKthcjUAAPguwi9QzWXkFGr/8TxJUjuWPAAAcEmEX6Ca+2bzUfcxSx4AALg0\nwi9Qza3YdC78rk0/YWIlAAD4vgCzCwBwdRrGhSs7r1iS9OFXu1Vc4lRCXLg6JseZXBkAAL6HK79A\nNXfHL5p5tD/5dp/+/vEWHc7IN6kiAAB8F+EXqObqRIbqpi6NVDM0UA67TZJkt9kUERpocmUAAPge\nwi/gB27vlaj/G9NDdaNDJUl2u02vz9usV/+1kSvAAACch/AL+AmXy9CRk2eCbpnTpR2HTmnT7kyt\n2HjE5MoAAPAdhF/AT9jtNt03oIXaJ8WqSb0Id3/T+hGXOAsAAGsh/AJ+pHtqPf3uthQlxp8JvDUC\n7GqdyN6/AACcRfgF/IzLMLT+v7c7Lilz6cf9WSZXBACA7yD8An4mK7fIve+vJE3/dJuJ1QAA4FsI\nv4CfiYoIVt92Ce52/dgwE6sBAMC3EH4BP2O32fSLjufC765Dp7R1L0sfAACQCL+AX8o8VeTRXpt+\n3KRKAADwLYRfwA8lN4pU99R67nZxqcvEagAA8B2EX8AP2W02JZ63v2+/Dg1MrAYAAN9B+AX81A87\nMyRJsbWD1aReTZOrAQDANxB+AT90urBUm3ZnSpLiIkNls9lMrggAAN9A+AX80I/7zu3usHVvlnJO\nF19iNAAA1kH4BfxQ/WjPvX2fnLpaGacKTaoGAADfQfgF/FBCXLiubxvvbheVODX+n+vdSyEAALAq\nwi/gp27vlag6UaHu9qn8Er2z4EcTKwIAwHyEX8BPhQYH6PdDUtWySZS7L7eg1MSKAAAwH+EX8GN1\nIkP18OAUdzu5YW0TqwEAwHyEX8DP7Tl8yn3csA77/QIArI3wC/i5XeeF39jaISZWAgCA+Qi/gJ/L\nLypzH89evENHMvJNrAYAAHMRfgE/Fx/juefv09PWaOOuDJOqAQDAXIRfwM91S62n1onRHn0ffr3b\npGoAADAX4Rfwc3abTSMHtFD3lHruvsMnWfoAALAmwi9gAbXCaujuXzb36Pv0233mFAMAgIkIv4BF\nlDkNBTjOfcvPW75H2w9km1gRAABVj/ALWERocID+eEcbj753F+8wqRoAAMxB+AUspFlCLQ3s2tjd\nZu0vAMBqCL+AhdhsNl3fNt7d7pZS18RqAACoeoRfwGLW/HjcfdwwjtsdAwCsJcDsAgBUrfyiUvfx\n0cx8ncgplF1SdK1g2Ww28woDAKAKEH4Bizl/x4evNhzRVxuOSJJaJ0brkSGtzSoLAIAqwbIHwGKS\nGtS+YP/G3ZlVXAkAAFWPK7+AxSQ3itRfRnZU9uliSdKUDze5H1ux8YgCA+xKTYxWaHCgWSUCAFBp\nCL+ABTWqW1ONVP7Dbv/4LF2S1KVlHf3m5pZVXRYAAJWOZQ+AxaUmRpfrW7X1uAqLy0yoBgCAysWV\nX8Difj+ktYpLnMorKNGf3lzl7v/bvM0acUOS6kaFmlgdAADexZVfAAqq4VBM7RDF1Q5x923bn61P\nVu4zrygAACoB4ReA230DkhUecu6Dbg3rhJtYDQAA3kf4BeCW1DBS3VPqudu1w4NMrAYAAO8j/ALw\ncCQz332842COiZUAAOB9hF8AHlo0inQfpzQtvxMEAADVGeEXgIftB85d7Y2tHWxiJQAAeB/hF4CH\nTefd5njX4VMmVgIAgPcRfgF46Nn63AfePvp6jw6fPG1iNQAAeBfhF4CHxvUi3MenC0v1wsx1Kirh\nbm8AAP/gM+F39uzZ6tOnj1JTUzV06FBt2rTpkuP/85//6NZbb1WbNm3UvXt3Pfnkk8rJ4ZPpwNVK\nbhSpmqHn9votKXPpof9drhmfpZtYFQAA3uET4XfhwoWaMGGCxowZo/nz5ys5OVkPPPCAsrKyLjh+\n/fr1Gjt2rIYOHaoFCxZoypQp2rx5s5555pkqrhzwP3G1Q/TKw911TUItj/7lG49o/faTJlUFAIB3\n+ET4nTFjhoYNG6ZBgwYpMTFRzz33nIKDg/XRRx9dcPzGjRuVkJCg4cOHKz4+Xu3atdOwYcN+9mox\ngMtjt9v00KBWuueGJI/+1+dv1vYD2SoucZpUGQAAV8f08FtaWqqtW7eqS5cu7j6bzaauXbtqw4YN\nFzynTZs2Onr0qL7++mtJUkZGhj7//HP16tWrSmoGrKB2eJB6t43XsD7XePRPfO8HTZ574e9NAAB8\nXYDZBWRnZ8vpdComJsajPzo6Wnv37r3gOe3atdOkSZP06KOPqqSkRGVlZerTp0+Flj04HKbnf1SB\ns/PMfF+5m7o21tZ9Wdqy59wypF2HTuml977XmCGtFR4SeImzzcF8WwvzbS3Mt7VUxjybHn4rYteu\nXRo/frxGjx6tbt266eTJk5o4caKeeeYZjR8//oqeKyIipJKqhC9ivitm3P9015GTp/XQS1+6+9IP\n5OiTVfv10O2tTazs0phva2G+rYX5RkWZHn4jIyPlcDiUkZHh0Z+ZmVnuavBZU6dOVbt27XTfffdJ\nkpo3b66//OUvGj58uB599NGLnnchubmFcjpdFf8CUC04HHZFRIQw31chvIZdaXe304vvfu/u++zb\nffpF23jFRvrWLyHm21qYb2thvq3l7Hx7k+nhNzAwUC1bttSqVavUt29fSZJhGFq1apVGjBhxwXOK\niooUGOj5p1a73S6bzSbDMK7o9Z1Ol8rK+OaxCub76jRLqK3H7mijye+fW/O7/UC2ImsGmVjVxTHf\n1sJ8WwvzjYryiQUzI0eO1L/+9S99/PHH2r17t/7yl7+oqKhIgwcPliRNnjxZTzzxhHv89ddfr0WL\nFmnOnDk6ePCg1q9fr/Hjx6t169aKjY0168sALKFl4yjd1KWRu13CLx8AQDVi+pVfSRowYICys7M1\nZcoUZWRkqEWLFpo2bZqioqIkndnN4ejRo+7xt912mwoKCvTee+/ppZdeUs2aNdWlSxc99thjZn0J\ngKXkFZS4j9sn8Q9OAED1YTOudJ2An8nOzufPJhYQEGBXZGQY8+0l908498G3d8b2MbGSC2O+rYX5\nthbm21rOzrc3+cSyBwDVh8X/vQwAqOYIvwCuSEmp55WW3PySi4wEAMD3EH4BXJG9R3M92st+OGxS\nJQAAXDnCL4Ar0rR+hJo3qO1u1wqvYWI1AABcGcIvgCtSI9Chlk2i3O1/fr5dE95d77EDBAAAvorw\nC+CKHcss8GjvOHRKe47kXmQ0AAC+g/AL4IoN7tlUN3dtrFphZ5Y8BAbYFRURrNOFpSZXBgDApRF+\nAVyx6FrB+kWHBOUVnAm7pWUu/eWd7/TIlBX6YcdJk6sDAODiCL8AKsTlMuRw2Dz6DEPatj/bpIoA\nAPh5PnF7YwDVT63wIL3wQCcdycjXknUH9eO+M6G3sKTM5MoAALg4wi+ACourHaK42iHKzS9xh99T\n3PQCAODDWPYA4KrZzlv9sGVPlvKL+OAbAMA3EX4BXLWzuz6c9fq8zTp1utikagAAuDjCL4CrltI0\nWud/9C39QI7+Nn+zth/IVkmp07S6AAD4KcIvgKtms9n0h2FtlBAb5u7bfThXE9/7QX+bv9nEygAA\n8ET4BeAVLZtE6c/3dlBkzSCP/i17srT78CmTqgIAwBPhF4DXBAY49Nffdlba3e08+sfPWq+Vm4/K\nMAyTKgMA4AzCLwCvCgp0qFlCbXW6to5H//QF27TzEFeAAQDmIvwCqBS392zqsQZYkgqKuAEGAMBc\nhF8AlSKmdoie/3UnxdYOdvcF/OR2yAAAVDXCL4BKdTKnyH3csE5NEysBAIDwC6CSBQU63Mff7zxp\nYiUAABB+AVSyGoHnfsyEBgWYWAkAAIRfAJUsLjLEfdy2WayJlQAAQPgFUIlOF5Zq9+Fcd3vv0dxL\njAYAoPIRfgFUmrDgc8scAhx21Y8Ju8RoAAAqH+EXQKXJOFUk2393N7u+bbzCQwLNLQgAYHmEXwCV\n5ovvDsowJLvNpn4dE8wuBwAAwi+AypFXUKKl3x+SJF13bZxiaoX8zBkAAFQ+wi+ASrFi01H3cVZu\nsYmVAABwDuEXQKUoLXO5j3cczJHLMEysBgCAMwi/ACpF+yTPPX3/881eFZWUmVQNAABnEH4BVIr4\nn2xr9p+V+/ThV7tNqgYAgDMIvwAqhc1m081dG3v0hQWz1RkAwFyEXwCV5raeTXVbjybu9t5judq6\nL8vEigAAVkf4BVCpsvLO7fSwZU+W/vl5uonVAACsjvALoFJ1T62npvUj3O2TOUUmVgMAsDrCL4BK\nlVi/lob3a+5ud/jJLhAAAFQlwi+ASvflf+/0JkknTxWpuMRpYjUAACsj/AKodCWl5254sf9Ynr7e\ncNjEagAAVkb4BVDpOibHebTf/3KXSZUAAKyO8Aug0nVIjtN9/ZPNLgMAAMIvgKrRuWUdj/bqrcdY\n+wsAqHKEXwBVIjDA4dGe+smPmr9ij0nVAACsivALoMq0bRbj0f5i7UGVOV0XGQ0AgPcRfgFUmdG3\np+p/BrXy6Cti6QMAoAoRfgFUqeYNaruP2yfFKjwk0MRqAABWQ/gFUKU++mq3+/gX7RNMrAQAYEWE\nXwBV6pt9i1VxAAAgAElEQVTNR93HdaPDTKwEAGBFhF8Appn75S4VlZSZXQYAwEIIvwCqVEjQuS3P\nVm09pq9+OGJiNQAAqyH8AqhSv+qV6NFOP5BtUiUAACsi/AKoUte3S9CN1zV0t+tEhppYDQDAagi/\nAKrcqfxi93G9GMIvAKDqEH4BVLm16Sfdx9clx5lYCQDAagi/AKrc+bc0PnD8tImVAACshvALoEq5\nXIZHe932EyZVAgCwIsIvgCplt9vUtlmMu52bX2JiNQAAqyH8AqhysbVD3McdWPMLAKhChF8AVarM\n6dLa9HNLHU4XlppYDQDAagi/AKpUXkGpsvPObXX27hc79MOOk5c4AwAA7yH8AqhSkTWDNLBrY4++\n1+ZtVkZOoTkFAQAshfALoMrd1LmRkhrU9uj78ofDJlUDALASwi+AKhdUw6EnhrdT3HkffMsrYNcH\nAEDlI/wCME3T+hHu4192bGhiJQAAqyD8AjDF8awCrdl2XJLUpWUdNYgLN7kiAIAVEH4BmOKTb/fJ\n+O/N3gID+FEEAKga/MYBYIoT5+3usHzjUX2x9qAMw7jEGQAAXD3CLwBT9Gkb79F+f+lOHcsqMKka\nAIBVEH4BmKJd81hd18Lz1sYxtUIuMhoAAO8g/AIwRY1Ah+7r38Kj78vvD5lUDQDAKgi/AEyTfbrY\no/31hiMmVQIAsArCLwDT1I0KVYekWHe7R2o9E6sBAFgB4ReAqY5nn9v1obCkzMRKAABW4DPhd/bs\n2erTp49SU1M1dOhQbdq06ZLjS0pK9Morr6hPnz5KSUlR3759NW/evCqqFoC3HD9vh4flLHsAAFSy\nALMLkKSFCxdqwoQJeuGFF5SSkqKZM2fqgQce0Oeff66oqKgLnvPII48oOztbf/3rX9WwYUOdPHlS\nLperiisHcLVu75WoOUt3SpJKnXwPAwAql0+E3xkzZmjYsGEaNGiQJOm5557TV199pY8++ki/+c1v\nyo1fvny51q9fryVLligiIkKSVL9+/SqtGYB3hAaf+zE0oHMjEysBAFiB6cseSktLtXXrVnXp0sXd\nZ7PZ1LVrV23YsOGC5yxbtkytWrXS22+/rZ49e+qGG27QxIkTVVxcfMHxAHyTYRj6dNV+d3vDrgw5\n+QsOAKASmR5+s7Oz5XQ6FRMT49EfHR2tjIyMC55z8OBBrVu3Tjt37tTrr7+up556SosWLdJzzz1X\nFSUD8BKny1Bu/rl/tO4+nKuZn29X5qkiE6sCAPgzn1j2cKUMw5DdbtfkyZMVFhYmSUpLS9Mjjzyi\nZ599VjVq1Ljs53I4TM//qAJn55n59i0BAXY9OaKD/jxtjbvvm01Htf9Ynp4Y3k4RYZf/vXw+5tta\nmG9rYb6tpTLm2fTwGxkZKYfDUe4qb2ZmZrmrwWfFxsaqTp067uArSU2bNpVhGDp27JgaNmx42a8f\nEcHtVK2E+fY9kZFhGtG/hWZ9ts3dd/DEaT3+xrd65+l+Cg+tWACWmG+rYb6thflGRZkefgMDA9Wy\nZUutWrVKffv2lXTmyu6qVas0YsSIC57Trl07LVq0SIWFhQoJOfN//r1798put6tu3bpX9Pq5uYVy\n8glzv+dw2BUREcJ8+6h+7eNVM9ihN+Zvcfc5nS7l5RWqtLj0ip+P+bYW5ttamG9rOTvf3mR6+JWk\nkSNHKi0tTa1atXJvdVZUVKTBgwdLkiZPnqwTJ05o4sSJkqSBAwfq73//u9LS0jR69GhlZWVp0qRJ\nuv32269oyYN05hdsWRnfPFbBfPuuDklxGvHL5pr1xQ5JUmxkiBZ8u1/tmseqQVx4hZ6T+bYW5tta\nmG9UlE+E3wEDBig7O1tTpkxRRkaGWrRooWnTprn3+M3IyNDRo0fd40NDQ/XOO+9o3Lhx+tWvfqXa\ntWurf//++v3vf2/WlwDAC37YeW750+GT+Tp8cq9W/3hcL/62s4lVAQD8ic0wDMPsIsyUnZ3Pvxwt\nICDArsjIMObbxy3feEQfr9ijnNMlHv1TH++tgCv40APzbS3Mt7Uw39Zydr69+pxefTYAuAo9W9dX\nz9b1dSKnUGPfXOXut9lMLAoA4FfYJwSAzwkPDvRoO+z8qAIAeAe/UQD4nB92nnQft2oSZWIlAAB/\nQ/gF4HPO/+BbvWjvrvUCAFgb4ReAzzn/9saL1x3Up9/uk8van80FAHgJ4ReAz0mMj/Boz1u+R3sO\n55pUDQDAnxB+Afic69vG69rGkR59x7MLTKoGAOBPCL8AfE58bLj+eEdbj77QIHZmBABcPcIvAJ+U\ncapQgQFnfkQ1jAtX62YxJlcEAPAHhF8APmnOkp0qLXPJbrPp1wOvlZ07XQAAvIDwC8DnbNqd4d7u\nrG/7BDWICze5IgCAvyD8AvApJaVOzV68Q5JUK7yGBvVoYnJFAAB/QvgF4FMWrt6vkzln9vlt1yxW\nIXzQDQDgRYRfAD6jzOnS52sOuNvLfjisPUfY3xcA4D2EXwA+w26zqWn9cze4sEmqGRpoXkEAAL9D\n+AXgM+x2m/q2T3C3g2o4FFs7xMSKAAD+hvALwKd8u+WY+7ioxKnsvGITqwEA+BvCLwCf0q55rPs4\nITZMtcJrmFgNAMDfEH4B+JT120+6j++5MZmbWwAAvIrwC8BnuAxDG3ZluNtxkaz3BQB412VvoJmW\nlnbZT/riiy9WqBgA1rb/WJ5H+9OV+3RXv+YmVQMA8EeXHX4PHTpUmXUAgOpGhXq0G9apaVIlAAB/\nddnhd9asWZVZBwAo53SxAhw2lTkNNU+opa4pdc0uCQDgZy47/B45cuSyn7R+/foVKgaAdbkMQzM/\nS1eZ01CAw6Z7+/NhNwCA9112+O3Tp49sP/OLyDAM2Ww2bdu27aoLA2Atyzcc0Y5DpyRJN3dtrHrR\nYSZXBADwR5cdfv/5z39WZh0ALKygqEz/XLTd3f5FhwYmVgMA8GeXHX6vu+66yqwDgIXlFZZ4tAuK\nyhQSdNk/ngAAuGwV+u1SXFysDz74QDt27JDT6XT3l5SUaMuWLVq0aJHXCgTg/6JqBnm0l35/SB2T\n49Sobk3W/QIAvKpC4XfcuHH6+OOPde2112rz5s1q27at9u/fr8zMTI0cOdLLJQLwd4EBDo/252sO\n6PM1B3RLt8Ya1KOpSVUBAPxRhe7wtnTpUr344ov64IMPFB8frxdeeEHLli1T3759VVpa6u0aAVhA\nqyZR5fq+2XzUhEoAAP6sQuE3NzdX7dq1kyRdc801+vHHHxUYGKgHH3xQy5Yt82qBAKzh90Nba9wD\nndQsoZa776dXhAEAuFoVCr9RUVHKzMyUJDVu3Fg7duyQJEVGRiojI8N71QGwDLvNpvoxYWpSL8Ld\n99O1wAAAXK0Khd+ePXvqueee086dO9W+fXt9+umn2rx5s2bPnq26dbkjE4CKW3neUodBPZqYWAkA\nwB9VKPz+6U9/UlxcnL777jv17dtXiYmJGjJkiGbNmqUxY8Z4u0YAFuEyDOUXlbnbjetGXGI0AABX\nrkK7PUREROiNN95wt6dOnaoNGzYoPj5ecXFxXisOgLUcPpnv0Q4MqNC/zwEAuKgK/WYpKipSWlqa\n3nrrLUmSzWbTY489ptdee00lJSU/czYAXNja9OPu4/8Z1EouwzCxGgCAP6pQ+J0wYYLWrVuntm3b\nuvvS0tK0Zs0avfLKK14rDoC1bNh57gOzf/94ix6c9JU+W7PfxIoAAP6mQuF3yZIleumllzxuedyv\nXz+NHz9eCxYs8FpxAKyltMzl0Xa6DG0/kGNSNQAAf1Sh8Jufn6+IiPIfRImKitKpU6euuigA1vT7\noa01vF9zhQSd+TiC3WbTzd0am1sUAMCvVCj8tmnTRtOmTZPLde4qjWEYmjlzplJSUrxWHABrqRMZ\nqjKnS4XFZ3Z8uKlLIyXWr/UzZwEAcPkqtNvDo48+qnvvvVdr1qxRq1atJElbt25VTk6O3nnnHa8W\nCMA6jmcX6KOv97jbyzcd0ZL1hxTgsKlFo0j99paWsttsJlYIAKjuKnTlNzU1VZ988oluuukmlZSU\nyOVyaeDAgfrss8/UunVrb9cIwCL2H8tTmfPcX5ROnS5RYXGZ8gpK9d22E8rNZzcZAMDVqdCVX0lK\nSEjQY4895s1aAFhc22axurNvM2XlFUmSTuWXaPXWc9uf1Q7ndscAgKtT4fD79ddfa/r06dqzZ48+\n+OADzZs3Tw0bNtStt97qzfoAWEhggF39OjZwt4tLnO7w26pplFllAQD8SIWWPaxcuVIPP/yw6tev\nr9zcXLlcLpWVlSktLU0ff/yxt2sEYFH/+mqX+7h7Sj0TKwEA+IsKhd/XXntNjz32mCZMmCCHwyHp\nzIfgHn30UU2fPt2rBQKwJsMw9OX3h93t9kmxJlYDAPAXFQq/27dvV58+fcr133jjjTpw4MBVFwUA\nKzYddR93T6knh71CP64AAPBQod8mNWvW1IkTJ8r179q1S7VqsScngKtzNDNf7y3ZIUmqExWq4f2a\nm1wRAMBfVCj83nzzzfrrX/+q9PR02Ww25efna/ny5XrhhRc0YMAAb9cIwELKnC79/eOtKik9s+XZ\ng7dcq6AaDpOrAgD4iwrt9vD73/9ex44d06BBgyRJt912mwzDUO/evfXoo496tUAA1rLvaJ4OnTzt\nbp8NwQAAeEOFwm9gYKAmT56sMWPGaNu2bXK5XGrevLnq16+vyZMn68knn/R2nQAsolHdmh7tuMgQ\nkyoBAPijy172UFxcrOeff16dOnVS9+7dNWnSJDVo0EA33nijBgwYoGPHjunmm2/Wu+++W5n1AvBz\ne4/m6uwdjFs0iuTGFgAAr7rsK78vvfSS5s6dq1tuuUU1atTQnDlzFB4ergcffFDjxo3TnDlz1LBh\nQ82cObMy6wXgx04Xluqt/2yVYUghQQEa2T/Z7JIAAH7mssPvl19+qaeeekp33nmnJKl3794aP368\njh49qg8//FD333+/HnnkEdWoUaPSigXgvwzD0DsLtik7r1iSNLJ/smJrs+QBAOBdlx1+MzIy1L17\nd3e7R48eOnz4sBYvXqx//OMf6tSpU6UUCMAalqw/pA27MiRJvdvUV8fkOJMrAgD4o8te81taWqrQ\n0FB32+FwKCgoSE899RTBF8BVOXjitOYs2elu39G3mYnVAAD82VXfMik1NdUbdQCwsB92nvRor/7x\nuI5nFZhUDQDAn11R+LWd/Qj2z/QBwJWIjgj2aM/4LF0vzFyn0jKnXIZhUlUAAH90Rfv8jhs3TkFB\n57YdKi0t1aRJkxQWFuYx7sUXX/ROdQAs4ZqEWooIDVRuQam7r6C4TA++/LUcdptu75WoGzs1NLFC\nAIC/uOzw27FjR5086fmnybZt2yo7O1vZ2dleLwyAddSJDNX/Ptxdm/dk6v8+3OTxmNNlaO6yXTp4\n4rScLpcCHXZ1bBGn1MQYk6oFAFRnNsOw9t8Us7PzVVbG7VP9XUCAXZGRYcy3jzMMQ5v3ZOpkTpHe\nX7pTTteFfzzVCquhV0Z3v+BjEvNtNcy3tTDf1nJ2vr36nF59NgC4CjabzX1F95r4Wpq7bJcyThUq\nNDhQ+4/lucedyi8xq0QAQDVH+AXgkxrVranH72zrbqe9tUrHswslScP7NTerLABANXfVW50BQGVb\nufmoO/hG1gzS9W3jTa4IAFBdEX4B+LwPv9rtPg6u4ZDdzhaLAICKIfwC8Hnnr/E9mlmgBav2mVYL\nAKB6I/wC8Hn9OjTwaJ/MKTKpEgBAdUf4BeDzhvZJ9GinJkabVAkAoLoj/ALweZt2Z7qP60SFKqUp\n4RcAUDGEXwA+LbegRK99tNnd/uOwNgoM4EcXAKBi+A0CwKet3XbCox1dK9ikSgAA/oDwC8Cn7TiY\n49F2XeSWxwAAXA7CLwCfFhYS6NFmj18AwNXg9sYAfNrOQ55Xfv/4xkpJUlZusWJqBev6dvHq36mR\nGaUBAKohrvwCqFaycouVlVssSco4VaQl6w6ZXBEAoDrxmfA7e/Zs9enTR6mpqRo6dKg2bdp0Weet\nX79eLVu21G233VbJFQIwwwM3Xav+nRuqX4cGuuG6M/+dLzqCD8ABAC6fTyx7WLhwoSZMmKAXXnhB\nKSkpmjlzph544AF9/vnnioqKuuh5eXl5Gjt2rLp06aLMzMyLjgNQfTWqW1ON6tZ0tw3D0KLvDrrb\nw/pcY0ZZAIBqyieu/M6YMUPDhg3ToEGDlJiYqOeee07BwcH66KOPLnneX/7yF918881q06ZNFVUK\nwGwnsgs92ucHYwAAfo7p4be0tFRbt25Vly5d3H02m01du3bVhg0bLnreRx99pEOHDunhhx+uijIB\n+IDThaV66u017vaY21MV4DD9xxgAoBoxfdlDdna2nE6nYmJiPPqjo6O1d+/eC56zb98+vfLKK3rv\nvfdkt1/dLz4Hvzgt4ew8M9/V23tLdshlnNnnt0m9CHVoEXfBccy3tTDf1sJ8W0tlzLPp4fdKuVwu\n/fGPf9To0aPVsGFDSWfWAFZURESIt0pDNcB8V1+GYWj11uPudv24cEVGhl3yHObbWphva2G+UVGm\nh9/IyEg5HA5lZGR49GdmZpa7GixJ+fn52rJli9LT0/X8889LOhOIDcNQq1atNH36dHXq1OmyXz83\nt1BOp+vqvgj4PIfDroiIEOa7Gispc3q0T+cXKzs7/4JjmW9rYb6thfm2lrPz7U2mh9/AwEC1bNlS\nq1atUt++fSWducKzatUqjRgxotz48PBwffrppx59s2fP1po1a/Taa68pPj7+il7f6XSprIxvHqtg\nvqsvuzzv7BZTK+Rn55L5thbm21qYb1SU6eFXkkaOHKm0tDS1atXKvdVZUVGRBg8eLEmaPHmyTpw4\noYkTJ8pms+maazy3NoqOjlZQUJASExPNKB9AFcg5XezRXrr+kNo0i1HLxhffDhEAgJ/yifA7YMAA\nZWdna8qUKcrIyFCLFi00bdo09x6/GRkZOnr0qMlVAjCT3W5TSJBDhcXnlj9s3p1J+AUAXBGbcTWf\nFvMD2dn5/NnEAgIC7IqMDGO+q7nj2QVKe2u1u90+KVa/uy2l3Djm21qYb2thvq3l7Hx7E/uEAKg2\niks8P/R2U5dGJlUCAKiufGLZAwD8nIycQr0yd6MkyWG36ZEhqWpcN8LkqgAA1Q1XfgH4vKKSMr00\n5wedyi+RJPXv3FCtmkSbXBUAoDriyi8An7f7SK4yThW528s3HlVsrTP7PibG11L9GO+uBwMA+C/C\nLwCf1yy+lkc7N79E//gsXZIU4LDr1dHdFBx05seZ3WYrdz4AAGcRfgH4vMAAu1onRmvj7sxyj5U5\nXXr41RWSzqwFvqV7E93Ws2lVlwgAqCYIvwB8ns1m0yNDWqu4xClDhpb9cFj/Wra73Diny1D6/mwT\nKgQAVBeEXwDVRlANhySpX4cGiqkVolOni5V+IEff7zgpSbJJanNNjA6dOO31fSEBAP6B3R4AVDsB\nDrs6JsepR+v6Hld6DUlzlu7Uk1NXa+naA+YVCADwWVz5BVBt2W1nbnlcUFxW7rFX3/9BdaNCNbhn\nU3VIjjOhOgCAL+L2xtwe0RK4Hab/Ki1z6Xh2gfIKSjVpzg8XHDOwa2O1ahKl5g1qV3F1qAp8f1sL\n820t3N4YAH4iMMCuhNhwtWgUqdGDU/SL9gnlxnz67T5NmP29cv97kwwAgHURfgH4jbbNY3VXv+Ya\n1KOJwkMCyz3+2OsrtWVP+e3SAADWQfgF4HcG90rUnHEDdEv3Jh79Tpeh3AKu/gKAlRF+Afitwb2a\nakjvRI++1tfEmFQNAMAXEH4B+C27zVbuSu+SdYdMqgYA4AsIvwD8Wmiw59rfetGhJlUCAPAFhF8A\nfisrt0jzl+9xtwf1aKLrWtQxsSIAgNkIvwD81vtLd3q0m8XXUnZesUnVAAB8AeEXgF8yDEOrtx73\n6Jv0/gb96e/fKiOn0KSqAABmI/wC8FuJ8RHl+pwuQ6t+PH6B0QAAKyD8AvBLNptNf763o1749XXq\nllLX47Ef92aZVBUAwGyEXwB+y263KT42XPVjPO8LP7hXU5MqAgCYjfALwK/tOnxK/1q226Pv02/3\nm1QNAMBshF8Afi2/sLRc346DOTIMw4RqAABmI/wC8Gutr4nRs/d19OjrmBwnm81mUkUAADMRfgH4\nNZfL0Fcbjnj03daTNb8AYFWEXwB+7aPlu/XVD4fd7ZH9kxVZM8jEigAAZiL8AvBr+47mebRnfJau\nz9cckIs1vwBgSYRfAH7thusalOubu2yX9h7NNaEaAIDZCL8A/Nq1jaPUrVXdcv3zvt5jQjUAALMR\nfgH4tQCHXb8eeK2GXJ/o0R9cw2FSRQAAMxF+Afi9nNPFWvPjcXe7TlSofn3TtSZWBAAwS4DZBQBA\nZTqcka9X525QZm6xJCm5YW2NuCFJp4tKVVRSpqiIYJMrBABUJcIvAL9VXOLUpPe+V27Bubu8pR/I\n0VNvr3G3r28brxE3JJlRHgDABCx7AOC3Sp0uFZY4LzkmK7eoiqoBAPgCrvwC8FvhIYGa8GAX7T9+\nZq/f3PwSzfgs3f14k3oRGtk/2azyAAAmIPwC8GuRNYPcd3SbNOcHj8eeuKutagSy6wMAWAnLHgBY\nQlFJmbbtz3a3B3RuRPAFAAsi/AKwhPO3OpOkrhe48QUAwP+x7AGA31u4er8+/Gq3u31923jVjwkz\nsSIAgFm48gvAr23bl+URfCVp2Q+HtXVflkkVAQDMRPgF4NcCAx0KcNjK9dcI4McfAFgRP/0B+LVr\n4mtp8u+6letP35+tEzmFJlQEADAT4ReAJc1fsVdvzNtsdhkAgCpG+AXg98JDAtUhKbZc/4ETp/X9\njpMmVAQAMAvhF4Dfs9lseui2FE0c1aXcY7n5JSZUBAAwC+EXgGXE1g5RdESQR1+H5DiTqgEAmIHw\nC8Ayikudyswt9uib+Vm6SdUAAMxA+AVgGRfa3iw+lptdAICVEH4BWMa/fnKziwCHXSdzCvWflXtV\nWuY0qSoAQFXi9sYALCErt0ifrzng0VfmdGnV1uOSpMZ1I5SaGG1GaQCAKsSVXwCWEFkzSH3bJ6hR\n3ZoKC/b8d39CbLiSGtQ2qTIAQFXiyi8AS7DZbBrer7m+33FSb8zf4vFYbn6xnpq22qPP5TKUc7pE\nCbFhGt6vuZIaRlZluQCASkL4BWAp67eflMswPPpyC0ovOv7QyXxNfO8H3dy1cbnHXIahomKn2jaP\n0bWNo7xdKgCgEhB+AVjK7b2aKrZ2sIpLL/4Bt0XfHSzX98m3+y46fun3h/TMyA4KDQ706I+OCJLD\nzuoyAPAlhF8AlhIVEaxBPZpeckxq02jNXLRdJ7ILFRLkKBdgi0udKi1zefQ9P2NdueepExWqcQ9c\nRwAGAB9iM4yf/P3PYrKz81X2k19i8D8BAXZFRoYx3xZR2fNdWFymR1/7RiWX8dwN48IVXMPh0Vcz\ntIZ+dX2i6kSGer02K+L721qYb2s5O99efU6vPhsAWEBIUIAmjOqivUdzZbPZ3P0ncwo1Z8lOj7EH\nTpy+4HM0rBOum7s1qdQ6AQDlEX4BoAJqhwepbbNYjz7DMM7sHbzlmCJrBqtGoF0ypK37slRUcm6N\ncUJsmHq0rl/VJQMARPgFAK+x2Wzq36mR+ndq5O776OvdWr/jpMe4hNhw1Q4PquryAAAi/AJApdp9\n+FS5vtU/HteZ1RI2lZY5lZ1XrK6t6ur6dglVXh8AWA3hFwAq0ahbW+nNf29R+oEcj/6zt1U+a/eR\nXAU47IqpFSxJMiQF1XCoab0Ij3XFAICrQ/gFgEoUEVZDo25tpXe/2K5120+qfkyYAgPsyjxVpNOF\nnjfX+Mdn6eXOv+G6BhrWp1lVlQsAfo/wCwCVLCKshh66LcWjLze/RGlTV6uwuOyS5y767qDWpp+Q\nJIUHB+qeG5PVtH5EpdUKAP6O8AsAJogIq6GXH+qqzNwij/435m/RsawCj76s3GL3/27anUH4BYCr\nQPgFAJOEBAUoITbco+8PQ1trxaaj2nkop9w64VZNo9SvY4OqLBEA/A733AQAHxJTO0QDuzZSVl6x\nR/+dv2imR4e0VlhwoEmVAYB/IPwCgI9xuaQT2YUefamJ0ez6AABeQPgFAB+z+0j5vYFrhnDFFwC8\ngTW/AOAjDMPQhp0ZenfxDo/+wT2bKpTlDgDgFYRfAPABx7MK9N6Sndq8J9Pd16JRpO7q11zxMWEm\nVgYA/oXwCwAmKi5x6tNV+7TouwMqcxqSpMiaQbqjbzN1SIplnS8AeBnhFwBMYBiG1m8/qfe/3One\nx9dht+nGTg01sEtjBdVwmFwhAPgnnwm/s2fP1vTp05WRkaHk5GQ9/fTTSk1NveDYxYsXa86cOdq2\nbZtKSkrUrFkzPfzww+revXsVVw0AV+7A8Ty9v3Snxz6+rZpE6a5+zVU3KtTEygDA//nEbg8LFy7U\nhAkTNGbMGM2fP1/Jycl64IEHlJWVdcHxa9euVbdu3fT2229r/vz56tSpk0aNGqX09PQqrhwArsyi\n7w7o2X+s9Qi+owen6NGhrQm+AFAFbIZhGGYXMXToUKWmpurpp5+WdObPgb169dKIESP0m9/85rKe\nY+DAgRowYIAeeuihK3rt7Ox8lZW5rrhmVC8BAXZFRoYx3xbhy/N9/4Qvy/Xd3LWx+zivsFRBgXZ1\nT6mn+J/c/Q0X5svzDe9jvq3l7Hx79Tm9+mwVUFpaqq1bt+rBBx9099lsNnXt2lUbNmy4rOcwDEP5\n+fmqVatWZZUJAFctI6fwgv2ffLuvXN+P+7L13P3XVXJFAGA9poff7OxsOZ1OxcTEePRHR0dr7969\nl/Uc06ZNU0FBgfr373/Fr+9w+MTKD1Sys/PMfFuDr853dO0QpTSNdm9nFn7ejStOF5Z6jD2ZU6iA\nAN+q31f56nyjcjDf1lIZ82x6+L1an3zyid544w39/e9/V1RU1BWfHxERUglVwVcx39bii/P919+V\n/7GGGxMAAB0+SURBVGCu0+nSoD994tHXo0281//U5+98cb5ReZhvVJTp4TcyMlIOh0MZGRke/ZmZ\nmeWuBv/UggUL9Mwzz+j//u//1Llz5wq9fm5uoZxO1gz5O4fDroiIEObbIqrbfB86cbpcX93IEGVn\n55tQTfVT3eYbV4f5tpaz8+1NpoffwMBAtWzZUqtWrVLfvn0lnVnDu2rVKo0YMeKi53366ad6+umn\n9corr6hnz54Vfn2n08WCeQthvq2lusz3rkOnyvU1i69VLWr3JdVlvuEdzDcqyvTwK0kjR45UWlqa\nWrVqpZSUFM2cOVNFRUUaPHiwJGny5Mk6ceKEJk6cKOnMUoe0tDQ99dRTSklJcV81Dg4OVng4n44G\nUH0cOnFac5ft8uiLqRWsqIggkyoCAP/mE+F3wIABys7O1pQpU5SRkaEWLVpo2rRp7jW8GRkZOnr0\nqHv83Llz5XQ69fzzz+v555939w8aNEgvvvhildcPAFeqtMypf36+Xd9uOaaf7jfZIC5csxfvlGRo\n675stW8eqxs6NVRcbdY4AsDV8ol9fs3EPoHWwL6Q1lId5nv1j8c09T8/Xvb43m3jdc8NSZVYUfVV\nHeYb3sN8W4tf7vMLAFbUqkm02lwTo+zTxeUe238sr1xfSJCjKsoCAL9H+AUAE4SHBGrMr1Iv+Ni/\nv9mrf3/juc/5Z6sPaM2Px93tsOBA3T+ghRrVrVmpdQKAvyH8AoCPOV1Q+v/t3XlUk1feB/BvFgKB\nsIMsAlWKgkUUXKqg1ZZqrbWd0R7b0lns2OmcsTNd9NRTpdNxwa3YuuHU1yLM0XrQLuNCXV57tNN2\nppba4lsXnLqgUgFBCISwZSN53j8osQwgBLORfD/ncBJu7vPkF+8hfL3cPLfb9vpGXaf7567VMfwS\nEVmI4ZeIyMk8nnYPFN4euHRDBZFIhB9+VHV6XCIWIW1kOKaNjXJQhUREAxfDLxGRk/FXeCJxaBCu\nVzXi3NU6c7tUIsbU0ZF4dEIMgv29HFghEdHAxfBLROQkBEHAf8pUOPrNj51mez09JHgoZTAeuT8a\nAQpe/5eI6G4w/BIROYHvLtbgfw6WdGkPUMgwfVw0FHIP1Kg0DL9ERHeJ4ZeIyMHKqhu7Db4A0NCs\nx8dfXDV/v/L5+xE9iDtZEhH1l9jRBRARuTt/H08EKGS991PI4OvtYYeKiIhcF2d+iYgcLNDXE+/8\naRJ0BiOOF5fjf7+5AZ3B2KmPRCxCbIQfPv78ag9nua22QYOmVj0eTxuCSUkRtiqbiGhAYvglInIC\nYrEIWr0RB/99vdvHjSYB319RWnTO/CM/wEsmhfcddoczGE24J9wP/j69zzwTEbkChl8iIicRoJBh\n2tgoXKlUW3xsd1siA8C7B873eqyXTIL1L6ZBIeeSCiJyfQy/REROQiQS4VfTh/fr2Gs3G7F292mY\nBMHiY7V6I+obtQy/ROQWGH6JiFxAbKQfNr08CQ3NeohEPfc7eb4Kn35b3qX9elUjYsK4VTIRuT6G\nXyIiF+HrLYOvd/drd69XNeLAv6+h5Fp9l8fuHzEIaSP5wTgicg8Mv0RELu78tTps+uhsl/YHRkXg\nV9OHw9Oj5w/EERG5Gl7nl4jIxdWoNN22h/h7MfgSkdth+CUicnFBvt1viVyr1tq5EiIix+OyByIi\nF1XToMG/z97EF99Xdvv4zAkxdq6IiMjxGH6JiFzQZ6crUHD8cpf2+0cMwuwHYhEe5O2AqoiIHI/L\nHoiIXIxG19Zt8AWAKaMjGXyJyK1x5peIyAWYTAJ++FGFr0uqcPpybbd95j0aj/uGBNm5MiIi58Lw\nS0Q0gFUqW/B1SRW+uXALqiZdp8fCAuVIGxmO1MRwhATIHVQhEZFzYfglIhqA9AYjtu47hwtlqi6P\nPZQyGGkjwxEb6QfRnbZ7IyJyQwy/REQDUF2jttvgCwC/nRFv52qIiAYOhl8iogFI3azv8bGahtub\nWgT7eUIi5mebiYg6MPwSEQ1ABqOpx8eWbi8y3w8L8kbW8/fDQ8oATEQE8FJnREQurbGl5xliIiJ3\nxJlfIqIBKDRAjvEJg/DdxZpuH48K9cHY+EGYNDKcs75ERD/D8EtENAAIgoDK2hYUX6rB6cu1qKxt\n6dJnSLgvxsaHYlz8IIRxIwsiom4x/BIROSmjyYTSCjW+v6LEmVIlalSaTo+LAMRF+WNs/CCMHR6K\nYH8vxxRKRDSAMPwSETmZFq0Bmz8+i6uVjXfsN2JIIPx9PPFjdRN+rG7q5awCymuaUVHbgsfThuCJ\ntCFcDkFEbonhl4jIyXx9vrrX4AsA/+nhOr+9Ofx1GbT6NqTEhZjbPDwkiI3wg1jMTTGIyLUx/BIR\nOZmUYSE4d60OF67XAwAiQ3z6NUt7p9ngE8UVOFFc0antoTGD8dtHuEEGEbk2hl8iIicTEiDHa88k\n3/V5PjtdgYLjl/vc39uTvxKIyPXxnY6IyEWljxmMGpUGx4vLe+wTE6ZAUmwwkmKDMSzK347VERE5\nBsMvEZGLulzecMfg++c5IzE2fpAdKyIicjyGXyIiF2JoM6G0Uo3/lNWjuIcNMABgUKAcw6MD7FgZ\nEZFzYPglInIBzRoDNn54BmU9fMhNBGBswiAkDgnEiCFBGBQgt2+BREROguGXiMgF/PvczR6DLwD8\n/vERSBsZYceKiIicE8MvEdEAIwgCatVaXLqhwqUbDbh0Q4W6Rl2P/UcODUJyXKgdKyQicl4Mv0RE\nTs5oMqG8phlXKtQorVCjtFINVVP3YTfQ1xMJMYGIjwlAQkwAQgPkEIm4cQURUQeGXyIiJ6PRteHq\nTTWulLcH3Ws3G6EzGLvtG+znififwm58TCBC/b0YdomI7oDhl4jIgQRBQJ1ai9JKNa5Uts/sVtQ2\nQxC67x8R7I1hUf6IG9w+sxvCD64REVmE4ZeIyI40ujaUVTfh2k01rlY24lpVIxpb9N32lUrEGBrh\ni7gofwwbHIC4KH8o5B52rpiIyLUw/BIR2YHeYMTWfedwoUzVYx9fbw/EDfbHsKj2oHtPmC88pGI7\nVklE5Pr4rkpEZAfHi8vvGHwBYGryYAT5euH/Ltdi74kruF7VaKfqiIjcB2d+iYhsTKnWYN+X13rt\nd/jrsk7fv1Xwf1jzhwmQSG7PUxjaTAgPkkMi5twFEVF/MPwSEdlYgMITSbHBOH+tzuJj/7LjVJe2\nwSE+WD5/PKQSBmAiIksx/BIR2ZhUIsaip0d3amtq1WPxtq9haDNZfD51Dx+QIyKi3jH8EhHZWW2D\nBu99csHi4Bvs54V7B/sh4+FhnPUlIuonhl8iIjsymQQs2V7Ua7+0keFIjgtBTJgCIQFyiLlxBRGR\nVTD8EhHZgCAIUDXpcFPZgoraFtxUtqBS2X7bm7BAOX77SDw8ZRI7VEpE5F4YfomIrESnN2LXpxfx\nzYVbfT4m2M8TkSEKDA71weAQH0SG+CAmTMGrORAR2QjDLxHRXWrVtqG6vhVHisrw/RVlr/1/OyMe\nw6L8ER7kzbW7RER2xvBLRNQHRpMJygYtqupbUV3Xiur62189bU/cnUfGR+OhlME2rJSIiO6E4ZeI\n6A40uja888H3uF7V1Kf+njIJwoO8ERHkjfAgb4QHt9+GBXpzDS8RkRNg+CUi6kZjqx7vFV7ADz/e\neUviDqPuDcZzjyYgQCGDiFdmICJyWgy/REQ/MbSZUN+oRa1ag40fnu3zcePiQzH/sRGQe/ItlYjI\n2fGdmojcRpvRBFWTDsoGDZRqLWrVWtSpNT/datHQpINg4TlXzB+PmDBfm9RLRETWx/BLRC5BEAQ0\nawyob9ShsVUPTVsNKqobUafWoL5RB6Vai/omLQQL0q2PlxQhAXKE+Hsh1F+OYH8vhAZ4Idi/vc3T\ng2t4iYgGGoZfInJ6giCgRdsGVZMO9Y1a1P90+/PvVU06i7cLlntKEPJTkDXfBty+z2UMRESuh+/s\nRORQ3QVbVZMW9Y0/C7j9CLYAoJB7INDXE0G+ngj52cxtiL8cIQFe8PaU8sNpRERuhuGXiBxq677z\nOFPa+8YQvblvSCBSE8Pbg26gHLExQdC06NDWj9BMRESui+GXiBymzWjCpfIGq5xr5NBgTEqKAABI\npWJ4yaTQtOiscm4iInIdDL9EZFcNzTocKfoRjS16NLboodG13fU5n0gbgmnjoqxQHRERuTqGXyKy\nm2aNAUvfK4LeYN2lCH4+MkglYquek4iIXBPDLxFZjaHNiGZNG1o0BjRrDGjRtt82awxo0bShVq2x\nevAF2q/aQERE1BcMv0TUZw3NOmw/WILLFWpHlwIAGJcwCLMm3oN7wrnJBBER9Q3DLxH12Uf/LHWa\n4AsAxRdr8MtJQ1DToOnULpWIoDMBanUr2owW7tkmCDC0maDVG6GQeyAsyNuKFRMRkaMx/BJRn6UM\nD8U3/7nl6DI6+Wv+tzY9f0Z6HB65P8amz0FERPbD8EtEfTY+YRDGL03vc/8TxeXYc+KKDSuyPVUz\nL5dGRORKGH6JqM90eiP2/+saLt5QQWcwQmcwQm8wQqc3wSRYuLzASXjKJPCSSeAlk7bfevz0vacU\nw6L88WDyYEeXSEREVsTwS0R9VnDiMr46V+XoMvps8yuT4SERw9NDArGY2xgTEZEThd+CggLk5+dD\nqVQiISEBb775JkaNGtVj/1OnTiE7OxtXrlxBZGQkFixYgDlz5tixYqKBSxAEGE0C2owmtBk7bm/f\nNxoFGIwmGH9q67g/kILvg2OiEOTnxe2NiYioE5EgOP5vlUePHsWSJUuwatUqJCUlYdeuXTh27BiO\nHTuGoKCgLv0rKirwxBNP4Nlnn8XcuXNRVFSEtWvXIjc3F5MmTbLouVWqFv5ydANSqRiBgT4uM96C\nIODQyTIc/Oq6o0txGKlEDA+pGDJp+23Hl0wqgZenBKGB3tDr22Aa+MNNvRCLAZlMyvF2Exxv9yKR\niLDkufutek6nmPnduXMnnnnmGcyePRsAsHLlSnzxxRfYt28f/vCHP3Tpv3fvXkRFReH1118HAMTG\nxuL06dPYuXOnxeGXaCCqqG1x6+ALwDxbrenx82j19iyHiIhsZImVz+fw8GswGHDhwgX88Y9/NLeJ\nRCKkpaXhzJkz3R5z9uxZpKWldWqbPHky1q1bZ9NaiZxFZIg3JtwXhlM2vuyYWCSCVCqCVCyGVCKC\nVCpuvy8VQyoWQeSk62hFovaZ4TajCY7/2xbZGsfbvXC83ceP1U02Oa/Dw69KpYLRaERISEin9uDg\nYFy/3v3MVm1tLYKDg7v0b25uhl6vh0wm6/PzSyRiy4umAadjnF1lvKUQ489PJuHPTyY5uhSnJJGI\n4ecnR2OjBkYj/y7q6jje7oXj7T7KqhqRtfM7q5/X4eHX0fz85I4ugeyI4+1eON7uhePtXjjeri8w\n0AcH1v/C6ud1+DRYYGAgJBIJlEplp/a6urous8EdQkNDUVdX16W/QqGwaNaXiIiIiNyLw8Ovh4cH\nEhMTUVRUZG4TBAFFRUVISUnp9pjk5ORO/QHg5MmTSE5OtmmtRERERDSwOTz8AsDvfvc7fPzxxzh4\n8CCuXr2K5cuXQ6vV4sknnwQAbNiwAUuW3P6sX0ZGBsrLy/H222/j2rVrKCgowKeffor58+c76iUQ\nERER0QDgFGt+H3vsMahUKuTk5ECpVGLEiBHIy8szX+NXqVSiqur2xfWjoqKQm5uLdevWYffu3QgP\nD8fq1au7XAGCiIiIiOjnnGKTCyIiIiIie3CKZQ9ERERERPbA8EtEREREboPhl4iIiIjcBsMvERER\nEbkNhl8iIiIichsMv0RERETkNlw6/BYUFCA9PR2jRo3C008/jXPnzt2x/6lTp/Dkk08iKSkJM2bM\nwIEDB+xUKVmDJeN9/PhxPP/880hNTcXYsWORkZGBr776yo7V0t2y9Oe7w+nTp5GYmIg5c+bYuEKy\nJkvHW6/XY9OmTUhPT0dSUhIefvhh7N+/307V0t2ydLw/+eQT/PKXv0RycjImT56MN954Aw0NDXaq\nlvqruLgYCxYswAMPPICEhAR89tlnvR5jjazmsuH36NGjeOutt/DKK6/gwIEDSEhIwAsvvID6+vpu\n+1dUVGDBggWYOHEiCgsLMW/ePLz55ps4efKknSun/rB0vL/77jtMmjQJO3bswIEDBzBhwgQsWLAA\nFy9etHPl1B+WjneHpqYmLF26FKmpqXaqlKyhP+P96quv4tSpU1i7di0+/fRTbNy4EUOHDrVj1dRf\nlo736dOnsXTpUjz99NM4cuQIcnJycP78eSxbtszOlZOlWltbMWLECCxfvhwikajX/lbLaoKLeuqp\np4RVq1aZvzeZTMIDDzwg5Obmdtt//fr1wuOPP96pbdGiRcILL7xg0zrJOiwd7+7MmjVLePfdd21R\nHllZf8d70aJFwpYtW4StW7cKs2fPtnWZZCWWjveXX34pjB8/XlCr1fYqkazI0vHOz88Xpk+f3qlt\n9+7dwtSpU21ZJllZfHy8cOLEiTv2sVZWc8mZX4PBgAsXLnSa3RGJREhLS8OZM2e6Pebs2bNdtkee\nPHlyj/3JefRnvP+bIAhoaWmBv7+/rcokK+nveO/btw8VFRV46aWX7FEmWUl/xvvzzz/HyJEjsWPH\nDkyZMgUzZsxAdnY2dDqdvcqmfurPeCcnJ6OqqgpffvklAECpVOLYsWOYOnWqXWom+7FWVpNasyhn\noVKpYDQaERIS0qk9ODgY169f7/aY2tpaBAcHd+nf3NwMvV4PmUxms3rp7vRnvP9bXl4eWltbMXPm\nTFuUSFbUn/EuKyvDpk2bsGfPHojFLvl/fpfVn/EuLy9HcXExZDIZ3n33XahUKqxYsQJqtRpr1661\nR9nUT/0Z7zFjxuDtt9/GokWLoNfr0dbWhvT0dC57cEHWymr8LUBu79ChQ9i2bRu2bNmCoKAgR5dD\nVmYymbB48WK8/PLLiImJAdA+00+uSxAEiMVibNiwAUlJSZgyZQoyMzNx8OBB6PV6R5dHVlZaWoo1\na9bg5Zdfxv79+5Gfn4+KigqGX+qRS4bfwMBASCQSKJXKTu11dXVd/jfZITQ0FHV1dV36KxQKzvo6\nuf6Md4cjR45g2bJl2LJlCyZOnGjLMslKLB3vlpYWlJSUYNWqVUhMTERiYiK2bduGH374ASNHjsSp\nU6fsVTr1Q3/fz8PCwuDj42Nui42NhSAIqK6utmm9dHf6M965ubkYM2YM5s+fj+HDh2PSpElYvnw5\n9u3b1+U8NLBZK6u5ZPj18PBAYmIiioqKzG2CIKCoqAgpKSndHpOcnNypPwCcPHkSycnJNq2V7l5/\nxhsADh8+jL/85S/YuHEjpkyZYo9SyQosHW+FQoHDhw/j4MGDKCwsRGFhITIyMhAbG4vCwkKMHj3a\nnuWThfrz8z1mzBjU1NRAo9GY265fvw6xWIzw8HCb10z915/x1mq1kEo7r+IUi8UQiUT8K4+LsVZW\nk6xYsWKFFetyGj4+PsjJyUFERAQ8PDywefNmXLp0CWvWrIFcLseGDRtQWFiI6dOnAwBiYmKwfft2\nNDU1ISIiAkePHsXOnTuxbNkyREdHO/jVUG8sHe9Dhw4hMzMTmZmZSE1NRWtrK1pbW2EymTjTPwBY\nMt4ikQhBQUGdvkpKSnDjxg28+OKLXX5pkvOx9Od76NCh2L9/P0pKSjBs2DBcvXoVq1evxvTp0zFt\n2jQHvxrqjaXjrdVqsWPHDgQGBiIgIAClpaVYu3YtIiMj8dxzzzn41dCdtLa24urVq6itrcWHH36I\nUaNGwcvLCwaDAb6+vjbLai77rv/YY49BpVIhJycHSqUSI0aMQF5ennlNp1KpRFVVlbl/VFQUcnNz\nsW7dOuzevRvh4eFYvXp1l08VknOydLw/+ugjGI1GZGVlISsry9w+e/ZsrFu3zu71k2UsHW8a2Cwd\nb29vb/z973/H6tWrMXfuXAQEBGDmzJlYuHCho14CWcDS8Z4zZw5aW1uxZ88erF+/Hr6+vkhNTcVr\nr73mqJdAfVRSUoJ58+ZBJBJBJBIhOzsbwO3fxbbKaiKBfxMgIiIiIjfhkmt+iYiIiIi6w/BLRERE\nRG6D4ZeIiIiI3AbDLxERERG5DYZfIiIiInIbDL9ERERE5DYYfomIiIjIbTD8EhEREZHbYPglIiIi\nIrfhstsbExE5Snp6Om7evGn+XiQSwdvbG/fddx9effVVjBs3zibPm5mZicrKSrz//vu99v3b3/6G\nAwcO4LPPPrNJLUREzorbGxMRWVl6ejpmzpyJ559/HgAgCAIaGhqwYcMGFBUV4dixYwgPD7f68zY3\nN8NkMsHPz6/XvhqNBlqtFoGBgVavg4jImXHZAxGRDcjlcgQHByM4OBghISGIi4vDypUrodVqcfz4\ncZs8p0Kh6FPw7aiPwZeI3BHDLxGRnUgkEgCATCZDeno6srOzMWvWLKSmpqK4uBgAsGPHDkybNg3J\nycmYM2cODh061OkcN27cwIsvvohx48ZhwoQJeO2111BfXw+gfdnDvHnzzH3z8/Mxffp0JCUl4eGH\nH8a2bdvMj23duhXp6enm76urq7F48WJMnjwZKSkp+P3vf49Lly6ZH8/MzERmZiays7ORlpaG5ORk\nLFiwALW1tdb/hyIisiGGXyIiO7h16xaysrLg7e2NqVOnAgD27NmDv/71r8jLy8Po0aOxceNGfPjh\nh1i2bBkOHTqEefPmYeXKldi7dy8AoKmpCb/+9a/R1taG3bt3Y9euXbhx4wYWLlzY5fn++c9/Ijc3\nF1lZWTh+/DgWL16M7du3m8O0SCSCSCQCALS0tCAjIwM1NTXYvn07PvjgA8jlcvzmN79BVVWV+ZyH\nDx9GY2MjCgoKkJeXh5KSEmzevNnW/3RERFbFD7wREdnAe++9h/z8fACA0WiEwWDAvffei5ycHPN6\n36lTp2LixIkA2tfg7tq1Cxs3bsSUKVMAANHR0aioqMCOHTvw7LPP4siRI2hpacGmTZugUCgAAGvW\nrMGRI0dgMBg6PX95eTlkMhkiIyMRHh6OmTNnIiwsDJGRkV1qLSwshFqtRk5ODgICAgAAGzZswLRp\n01BQUIDFixcDAPz8/JCVlQWJRIKhQ4di1qxZ+Ne//mWDfz0iItth+CUisoGMjAzzEgSxWAx/f39z\nYO1wzz33mO+XlpZCp9OZg2YHk8kEg8EAvV6PK1euYMiQIZ3OM3z4cAwfPrzL8//iF7/A/v37MWPG\nDMTFxSEtLQ0zZszo9oN2HeftCL4A4OnpiVGjRuHy5cvmtujoaPPSDQDw9fXtErqJiJwdwy8RkQ34\n+/sjOjr6jn08PT3N9zsuvLN582bExsZ26evh4QGptO9v2YGBgSgsLMT333+PkydP4quvvsL777+P\nV155BX/605869e3poj8mk6nTc8pksi59eMEgIhpouOaXiMgJxMbGQiqV4ubNm4iOjjZ/ff7558jL\ny4NIJEJcXBzKysrQ3NxsPu7ChQtIS0vDrVu3Op3v0KFDKCgoQEpKCl566SV88MEHeOqpp3D06NEu\nzx0fH4+ysjLzB+cAQKfToaSkBMOGDbPdiyYicgCGXyIiJ6BQKJCRkYHNmzfjk08+QXl5Of7xj3/g\nnXfeQVhYGADgiSeeQEBAAF5//XVcunQJJSUlWLFiBRISEsx9Ouh0Oqxfvx6FhYWorKxEcXExvv32\nW4wZM6bLc3ecd+HChTh//jwuXryIxYsXQ6PR4JlnnrHL6ycishcueyAisrKOqyhY2ueNN95AUFAQ\ncnJyUFNTg4iICCxcuNC8WYaXlxfy8vLw1ltvISMjA3K5HA8++CCWLFnS5Vxz586FWq3Gtm3bUF1d\nDT8/Pzz66KNd1hQD7cF79+7dyM7Oxvz58wEAY8eOxd69e7v9gBwR0UDGHd6IiIiIyG1w2QMRERER\nuQ2GXyIiIiJyGwy/REREROQ2GH6JiIiIyG0w/BIRERGR22D4JSIiIiK3wfBLRERERG6D4ZeIiIiI\n3AbDLxERERG5DYZfIiIiInIbDL9ERERE5Db+Hw1WByYcC2sAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134ad70b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_probs = LogisticRegression().fit(X_train,y_train).predict_proba(X_holdout)\n",
    "print(\"pred probs:\",pred_probs)\n",
    "pr_curve(y_holdout, pred_probs[:,1], \"Logistic Regression\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "joel_model = GaussianNB().fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joel_model.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "joel_model.predict_proba?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method BaseNB.predict_proba of GaussianNB()>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GaussianNB().predict_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method BaseNB.predict_proba of GaussianNB()>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GaussianNB().fit(X_train,y_train).predict_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('probability of one: 0.303297085228', 'actual prediction: 1'),\n",
       " ('probability of one: 0.181788754697', 'actual prediction: 0'),\n",
       " ('probability of one: 0.384099611721', 'actual prediction: 1'),\n",
       " ('probability of one: 0.0985918184869', 'actual prediction: 0'),\n",
       " ('probability of one: 0.261350133835', 'actual prediction: 0'),\n",
       " ('probability of one: 0.441839956624', 'actual prediction: 1'),\n",
       " ('probability of one: 0.238537637074', 'actual prediction: 0'),\n",
       " ('probability of one: 0.295146376246', 'actual prediction: 0'),\n",
       " ('probability of one: 0.329618962365', 'actual prediction: 0'),\n",
       " ('probability of one: 0.376985196302', 'actual prediction: 1'),\n",
       " ('probability of one: 0.188276374011', 'actual prediction: 0'),\n",
       " ('probability of one: 0.284758822124', 'actual prediction: 1'),\n",
       " ('probability of one: 0.238019658706', 'actual prediction: 0'),\n",
       " ('probability of one: 0.363012353499', 'actual prediction: 1'),\n",
       " ('probability of one: 0.24769094384', 'actual prediction: 0'),\n",
       " ('probability of one: 0.297415825474', 'actual prediction: 0'),\n",
       " ('probability of one: 0.131124859433', 'actual prediction: 1'),\n",
       " ('probability of one: 0.320669087471', 'actual prediction: 0'),\n",
       " ('probability of one: 0.141059027396', 'actual prediction: 1'),\n",
       " ('probability of one: 0.202229800676', 'actual prediction: 0'),\n",
       " ('probability of one: 0.351296881583', 'actual prediction: 1'),\n",
       " ('probability of one: 0.279215324718', 'actual prediction: 0'),\n",
       " ('probability of one: 0.323984681083', 'actual prediction: 1'),\n",
       " ('probability of one: 0.112603787419', 'actual prediction: 0'),\n",
       " ('probability of one: 0.284582223924', 'actual prediction: 0'),\n",
       " ('probability of one: 0.454050846821', 'actual prediction: 1'),\n",
       " ('probability of one: 0.398628945447', 'actual prediction: 0'),\n",
       " ('probability of one: 0.375575007511', 'actual prediction: 1'),\n",
       " ('probability of one: 0.355541860955', 'actual prediction: 1'),\n",
       " ('probability of one: 0.205467579134', 'actual prediction: 0'),\n",
       " ('probability of one: 0.191393559354', 'actual prediction: 0'),\n",
       " ('probability of one: 0.269245791431', 'actual prediction: 0'),\n",
       " ('probability of one: 0.292194466557', 'actual prediction: 1'),\n",
       " ('probability of one: 0.253574515673', 'actual prediction: 0'),\n",
       " ('probability of one: 0.298659394853', 'actual prediction: 1'),\n",
       " ('probability of one: 0.245887406773', 'actual prediction: 0'),\n",
       " ('probability of one: 0.376429543336', 'actual prediction: 0'),\n",
       " ('probability of one: 0.211182602544', 'actual prediction: 1'),\n",
       " ('probability of one: 0.232912602081', 'actual prediction: 0'),\n",
       " ('probability of one: 0.381944234169', 'actual prediction: 0'),\n",
       " ('probability of one: 0.300483975167', 'actual prediction: 0'),\n",
       " ('probability of one: 0.258482478959', 'actual prediction: 0'),\n",
       " ('probability of one: 0.310527334624', 'actual prediction: 0'),\n",
       " ('probability of one: 0.317875535736', 'actual prediction: 1'),\n",
       " ('probability of one: 0.225707520564', 'actual prediction: 0'),\n",
       " ('probability of one: 0.268467040023', 'actual prediction: 1'),\n",
       " ('probability of one: 0.296771922271', 'actual prediction: 0'),\n",
       " ('probability of one: 0.493057876517', 'actual prediction: 1'),\n",
       " ('probability of one: 0.325040980271', 'actual prediction: 1'),\n",
       " ('probability of one: 0.299254163199', 'actual prediction: 1'),\n",
       " ('probability of one: 0.284840219267', 'actual prediction: 0'),\n",
       " ('probability of one: 0.290075042858', 'actual prediction: 0'),\n",
       " ('probability of one: 0.292126051266', 'actual prediction: 0'),\n",
       " ('probability of one: 0.274214794414', 'actual prediction: 1'),\n",
       " ('probability of one: 0.165552135685', 'actual prediction: 0'),\n",
       " ('probability of one: 0.369479420517', 'actual prediction: 1'),\n",
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       " ('probability of one: 0.285906476787', 'actual prediction: 0'),\n",
       " ('probability of one: 0.369976222316', 'actual prediction: 0'),\n",
       " ('probability of one: 0.21037163798', 'actual prediction: 0'),\n",
       " ('probability of one: 0.114753240618', 'actual prediction: 0'),\n",
       " ('probability of one: 0.0829238158188', 'actual prediction: 0'),\n",
       " ('probability of one: 0.205282698164', 'actual prediction: 0'),\n",
       " ('probability of one: 0.297970369484', 'actual prediction: 1'),\n",
       " ('probability of one: 0.373407179736', 'actual prediction: 0'),\n",
       " ('probability of one: 0.283101499014', 'actual prediction: 1'),\n",
       " ('probability of one: 0.20814336354', 'actual prediction: 0'),\n",
       " ('probability of one: 0.160831418018', 'actual prediction: 0'),\n",
       " ('probability of one: 0.315269930598', 'actual prediction: 0'),\n",
       " ('probability of one: 0.364847077776', 'actual prediction: 0'),\n",
       " ('probability of one: 0.356600890474', 'actual prediction: 0'),\n",
       " ('probability of one: 0.112146085202', 'actual prediction: 0'),\n",
       " ('probability of one: 0.354576785175', 'actual prediction: 0'),\n",
       " ('probability of one: 0.133716874868', 'actual prediction: 0'),\n",
       " ('probability of one: 0.387651328176', 'actual prediction: 1'),\n",
       " ('probability of one: 0.309604676105', 'actual prediction: 1'),\n",
       " ('probability of one: 0.295859002131', 'actual prediction: 0'),\n",
       " ('probability of one: 0.153092082698', 'actual prediction: 0'),\n",
       " ('probability of one: 0.217856226223', 'actual prediction: 1'),\n",
       " ('probability of one: 0.212935458467', 'actual prediction: 0'),\n",
       " ('probability of one: 0.419971424667', 'actual prediction: 0'),\n",
       " ('probability of one: 0.39008936765', 'actual prediction: 0'),\n",
       " ('probability of one: 0.445556120974', 'actual prediction: 0'),\n",
       " ('probability of one: 0.389494926147', 'actual prediction: 0'),\n",
       " ('probability of one: 0.394479760264', 'actual prediction: 0'),\n",
       " ('probability of one: 0.208514886393', 'actual prediction: 0'),\n",
       " ('probability of one: 0.480425745866', 'actual prediction: 0'),\n",
       " ('probability of one: 0.267166625908', 'actual prediction: 0'),\n",
       " ('probability of one: 0.236166362554', 'actual prediction: 0'),\n",
       " ('probability of one: 0.173335501811', 'actual prediction: 0'),\n",
       " ('probability of one: 0.4355292408', 'actual prediction: 1'),\n",
       " ('probability of one: 0.215805285662', 'actual prediction: 0'),\n",
       " ('probability of one: 0.346764116748', 'actual prediction: 1'),\n",
       " ('probability of one: 0.2904404523', 'actual prediction: 0'),\n",
       " ('probability of one: 0.275420152664', 'actual prediction: 0'),\n",
       " ('probability of one: 0.341533953067', 'actual prediction: 0'),\n",
       " ('probability of one: 0.411391215742', 'actual prediction: 1'),\n",
       " ('probability of one: 0.153407295748', 'actual prediction: 1'),\n",
       " ('probability of one: 0.288376107486', 'actual prediction: 0'),\n",
       " ...]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(\"probability of one: \" + str(pred[1]), 'actual prediction: ' + str(y_pred)) for pred, y_pred in zip(pred_probs,y_pred)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-225-d33f6b922a55>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-225-d33f6b922a55>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    joel_arr = np.array([[pred[1][i],y_pred[i] for i in range(len(y_pred))])\u001b[0m\n\u001b[0m                                                 ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "joel_arr = np.array([[pred[1][i],y_pred[i] for i in range(len(y_pred))]) "
   ]
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  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "joel_df = pd.DataFrame([pred_probs[i][1] for i in range(len(pred_probs))], columns=['pred_proba'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "joel_df['y_pred'] = y_pred"
   ]
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  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "      <td>0.542967</td>\n",
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       "<p>1106 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "      pred_proba  y_pred\n",
       "428     0.060775       0\n",
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       "...          ...     ...\n",
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       "472     0.483151       0\n",
       "762     0.486044       0\n",
       "674     0.486526       1\n",
       "47      0.493058       1\n",
       "106     0.497901       0\n",
       "201     0.504532       0\n",
       "1080    0.504816       1\n",
       "117     0.506585       0\n",
       "908     0.508246       0\n",
       "457     0.523870       1\n",
       "98      0.530282       0\n",
       "776     0.532699       1\n",
       "764     0.541836       0\n",
       "398     0.542967       0\n",
       "129     0.550054       0\n",
       "284     0.550618       1\n",
       "596     0.554110       0\n",
       "550     0.554990       0\n",
       "1029    0.556872       0\n",
       "792     0.564415       1\n",
       "826     0.569332       0\n",
       "839     0.585799       1\n",
       "460     0.610154       0\n",
       "583     0.652788       0\n",
       "\n",
       "[1106 rows x 2 columns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joel_df.sort_values(by='pred_proba')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.        ,  0.87741935,  0.87741935,  0.87419355,  0.87419355,\n",
       "         0.87419355,  0.87419355,  0.87419355,  0.87096774,  0.74516129,\n",
       "         0.74193548,  0.74193548,  0.74193548,  0.74193548,  0.73225806,\n",
       "         0.73225806,  0.73225806,  0.73225806,  0.73225806,  0.72903226,\n",
       "         0.61612903,  0.61612903,  0.61612903,  0.61612903,  0.61290323,\n",
       "         0.60967742,  0.60967742,  0.60967742,  0.43225806,  0.42903226,\n",
       "         0.42903226,  0.42903226,  0.42580645,  0.42580645,  0.42258065,\n",
       "         0.41935484,  0.41935484,  0.26774194,  0.26774194,  0.26129032,\n",
       "         0.26129032,  0.26129032,  0.25806452,  0.25806452,  0.25483871,\n",
       "         0.25483871,  0.25483871,  0.25483871,  0.2483871 ,  0.16129032,\n",
       "         0.15806452,  0.15806452,  0.15806452,  0.09677419,  0.09354839,\n",
       "         0.09354839,  0.09354839,  0.07741935,  0.07419355,  0.04516129,\n",
       "         0.04516129,  0.04516129,  0.00645161]),\n",
       " array([ 0.28028933,  0.29153269,  0.29184549,  0.29139785,  0.29171152,\n",
       "         0.29202586,  0.29234088,  0.29329004,  0.29284165,  0.30841121,\n",
       "         0.30748663,  0.30831099,  0.30872483,  0.30913978,  0.30800543,\n",
       "         0.30884354,  0.30926431,  0.30968622,  0.31010929,  0.30958904,\n",
       "         0.33275261,  0.33391608,  0.33450088,  0.33508772,  0.3374778 ,\n",
       "         0.33629893,  0.3368984 ,  0.3375    ,  0.35078534,  0.34908136,\n",
       "         0.35092348,  0.3537234 ,  0.352     ,  0.35294118,  0.35120643,\n",
       "         0.34946237,  0.35040431,  0.34583333,  0.34728033,  0.34177215,\n",
       "         0.34322034,  0.34468085,  0.34482759,  0.34632035,  0.34497817,\n",
       "         0.34649123,  0.34801762,  0.34955752,  0.34529148,  0.3875969 ,\n",
       "         0.38582677,  0.38888889,  0.392     ,  0.37974684,  0.37179487,\n",
       "         0.37662338,  0.38157895,  0.53333333,  0.53488372,  0.56      ,\n",
       "         0.58333333,  0.60869565,  0.5       ]),\n",
       " array([ 0.        ,  0.02      ,  0.03333333,  0.04285714,  0.05      ,\n",
       "         0.05333333,  0.05714286,  0.09      ,  0.1       ,  0.125     ,\n",
       "         0.13333333,  0.14206349,  0.14444444,  0.15      ,  0.16666667,\n",
       "         0.17      ,  0.175     ,  0.18166667,  0.2       ,  0.2       ,\n",
       "         0.225     ,  0.23333333,  0.24285714,  0.25      ,  0.26333333,\n",
       "         0.26666667,  0.29      ,  0.3       ,  0.325     ,  0.33333333,\n",
       "         0.35      ,  0.35833333,  0.36666667,  0.375     ,  0.38333333,\n",
       "         0.38833333,  0.4       ,  0.42      ,  0.425     ,  0.43333333,\n",
       "         0.43333333,  0.45      ,  0.46333333,  0.46666667,  0.46666667,\n",
       "         0.475     ,  0.48      ,  0.48333333,  0.5       ,  0.53333333,\n",
       "         0.55      ,  0.56666667,  0.6       ,  0.625     ,  0.63333333,\n",
       "         0.65      ,  0.7       ,  0.75      ,  0.8       ,  0.81111111,\n",
       "         0.83333333,  0.9       ,  1.        ]))"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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RfoEYc+ZevycIvwAAtArhF4gxqUkepSS6JbHyCwBAaxF+gRjEjg8AALQN4ReIQaG+X444\nBgCgdQi/QAzKy2LlFwCAtiD8AjEo1PZQVetXbX2DydUAABA7CL9ADDpzxwdWfwEAaDnCLxCDCL8A\nALQN4ReIQdnpiXI6HJK46Q0AgNYg/AIxyO1yKjsjURIrvwAAtAbhF4hR7PULAEDrEX6BGNWZ7c4A\nAGg1wi8Qo0Irv96KOgWDhsnVAAAQGwi/QIwKhd9A0FDpSW56AwCgJQi/QIwKHXEsseMDAAAtRfgF\nYhR7/QIA0HqEXyBGpSZ5lJLolkT4BQCgpQi/QAxjuzMAAFqH8AvEsLzwdmf0/AIA0BKEXyCGhW56\nY+UXAICWIfwCMSzU9lBV61dNXYPJ1QAAYH2EXyCGnbnjg7eC1V8AAM6F8AvEMLY7AwCgdQi/QAzL\nTk+U0+GQxE1vAAC0BOEXiGFul1PZGYmSWPkFAKAlCL9AjOt8aruzE4RfAADOifALxDgOugAAoOUI\nv0CMC4Xfkoo6BYOGydUAAGBthF8gxoXCbyBoqPQkN70BAPBVCL9AjAud8iax4wMAAOdC+AViHHv9\nAgDQcoRfIMalJnmUkuiWRPgFAOBcCL9AHMg7td3Z60UHTK4EAABrI/wCceDg8arw4+OlNSZWAgCA\ntRF+gTiQ3yUt/PhP63eYWAkAANZG+AXiwIRLe4QfJya4TawEAABrI/wCcWD0kAvUq0u6JCkxwWVy\nNQAAWBfhF4gT3fNSJUmHi6vOcSUAAPZF+AXiRI+8xr7fY6U18vkDJlcDAIA1EX6BONGzc2P4NQzp\nSEm1ydUAAGBNhF8gTvTofHrHh4MnaH0AAKA5hF8gTmSmJigjxSOJ8AsAwNkQfoE4Elr9PUT4BQCg\nWYRfII6Ebno7VFwtwzBMrgYAAOsh/AJxJHTTW1WtX+VVPpOrAQDAegi/QBzpecZNb4fY7xcAgCYI\nv0AcuSAnVU6HQxJ9vwAANIfwC8QRj9upC3JSJEkHWfkFAKAJwi8QZ9jxAQCAsyP8AnGmR16qJOlo\nSY0aAkGTqwEAwFoIv0CcCd30FggaOlpSY3I1AABYC+EXiDOhvX4lWh8AAPgywi8QZ7LSE5Wa5JbE\nMccAAHwZ4ReIMw6HI7z6y44PAABEIvwCcYgdHwAAaB7hF4hDoZveKqp9qqzmmGMAAEIIv0Acirjp\njdYHAADCCL9AHOqelyrHqce0PgAAcBrhF4hDiR6XOmdzzDEAAF9G+AXiVM9TJ70dOlFtciUAAFgH\n4ReIU6EdHw57qxUIcswxAACShcLvqlWrNH78eBUWFmratGnasmXLV17/6quv6oYbbtCwYcM0ZswY\nPfjggyovL++gagHr63nqpreGQFDHS2tNrgYAAGuwRPhdv369Fi9erNmzZ2vNmjUqKCjQ7bffrtLS\n0mav/+STT/TAAw9o2rRpev311/X0009r69atevjhhzu4csC6Qiu/Ejs+AAAQYonwu2LFCk2fPl2T\nJ09W3759tXDhQiUlJWn16tXNXr9582b16NFDt9xyi7p3765LLrlE06dPP+dqMWAnOZlJSkpwSeKY\nYwAAQkwPv36/X9u3b9fIkSPDYw6HQ6NGjdKmTZuafc6wYcN09OhRvfvuu5Ikr9erN998U1deeWWH\n1AzEAueZxxwTfgEAkCS5zS6grKxMgUBAubm5EeM5OTnat29fs8+55JJL9OSTT2ru3Lny+XxqaGjQ\n+PHj29T24HKZnv/RAULzbLf5zu+Spt2HK3SouEput32+drvOt10x3/bCfNtLe8yz6eG3LXbv3q3H\nH39c99xzj0aPHq3i4mI98cQTevjhh/X444+36rUyMpLbqUpYkd3me0DvHL396WGVVtbLk+hRWkqC\n2SV1KLvNt90x3/bCfKOtTA+/WVlZcrlc8nq9EeMlJSVNVoNDli1bpksuuUTf+973JEn9+/fXggUL\ndMstt2ju3LlnfV5zKitrFQiwDVS8c7mcyshItt1856SdDrtbd51QQa8sE6vpOHadb7tivu2F+baX\n0HxHk+nh1+PxaNCgQSoqKtKECRMkSYZhqKioSDNmzGj2OXV1dfJ4PBFjTqdTDodDhmG06v0DgaAa\nGvjLYxd2m+8LTp3yJkkHjp1Uv+6ZJlbT8ew233bHfNsL8422skTDzKxZs/Tyyy9r7dq12rNnjxYs\nWKC6ujpNnTpVkrRkyRLdf//94eu//vWva8OGDXrxxRd18OBBffLJJ3r88cc1dOhQ5eXlmfVlAJaT\nnOhWbmaSJG56AwBAssDKryRNnDhRZWVlevrpp+X1ejVw4EAtX75c2dnZkhp3czh69Gj4+ilTpqim\npkYvvPCCfvnLXyo9PV0jR47UvHnzzPoSAMvq2TlN3oo69voFAECSw2htn0CcKSur5tcmNuB2O5WV\nlWrL+V7z3l6t+2C/EjxOPfPjK+V0OMwuqd3Zeb7tiPm2F+bbXkLzHU2WaHsA0H56njrpzecPqric\nY44BAPZG+AXiXMQxx/T9AgBsjvALxLnOnZKVcOqAC256AwDYHeEXiHNOp0Pd8xr7pQi/AAC7I/wC\nNtAjr7H1gR0fAAB2R/gFbCDU91tcXqfa+gaTqwEAwDyEX8AGeuadvuntsLfaxEoAADAX4RewAXZ8\nAACgEeEXsIG0ZI+y0hMlSQfp+wUA2BjhF7CJ0GEXrPwCAOyM8AvYxJk7Ptj8VHMAgI0RfgGb6NG5\nca/f2vqASirrTK4GAABzEH4Bmzhzx4dDJ9jxAQBgT4RfwCa6ZKfI7XJIkg6eOGlyNQAAmIPwC9iE\n2+VUt5xTxxwXs/ILALAnwi9gIz3Y8QEAYHOEX8BGQjs+HC+rUb0/YHI1AAB0PMIvYCO5mUmSJMOQ\nvOW1JlcDAEDHI/wCNrJlT0n48c4vyk2sBAAAcxB+AZs6SN8vAMCGCL+Ajdw0oV/4cdfsFBMrAQDA\nHIRfwEa8FadPduuel2piJQAAmIPwC9jI4TP2931m7TZV1fpNrAYAgI5H+AVsJCnBFX5c7wuomB0f\nAAA2Q/gFbGToRbka3Ds7/HGXLPp+AQD2QvgFbMTpcOjaEfnhj/ceqTCxGgAAOh7hF7CZi3p2UnJi\nY/vD5t0l57gaAID4QvgFbMbtcmpw7xxJ0uY9XhmGYXJFAAB0HMIvYEND+zWGX29FnQ57q89xNQAA\n8YPwC9jQkD45cjgaH2/e7TW3GAAAOhDhF7Ch9JQE9e2eKYm+XwCAvRB+AZsa2rex9WHP4QqdrPGZ\nXA0AAB2D8AvY1LB+uZIkQ9LWvaz+AgDsgfAL2FS33FTlZiZJkjbR+gAAsAnCL2BTDodDQ/s2rv5u\n31eihkDQ5IoAAGh/hF/AxoZe1Nj3W1sf0K6D5SZXAwBA+yP8AjY2oGeWEj2c9gYAsA/CL2BjHrdT\ng3pnS2rc75fT3gAA8Y7wC9hc6LS3E+W1OlZaY3I1AAC0L8IvYHOFfXN16rA3Wh8AAHGP8AvYXGZq\ngnp3y5AkbeKoYwBAnCP8Agif9rb7UIWqav0mVwMAQPsh/ALQ0FOnvQUNQ9v20foAAIhfhF8A6tk5\nTVnpiZLo+wUAxDfCL4DG095Orf5u3VOiQJDT3gAA8YnwC0CSNOzUlmc19Q3afajC5GoAAGgfhF8A\nkqSC/CwluBu/JdD6AACIV4RfAJKkBI9LF1946rS3PWx5BgCIT4RfAGGh096OltToeBmnvQEA4g/h\nF0BYYd/c8GNaHwAA8YjwCyAsKz1RvbqkS5I2c9obACAOEX4BRAi1Puw6WK6augaTqwEAILoIvwAi\nhPb7DQQNbd9fanI1AABEF+EXQIReXdOVmZYgSdr0Oa0PAID4QvgFEMHpcGho38bWh617SxQMGiZX\nBABA9BB+ATQx9NSuD1W1fu05wmlvAID4QfgF0MTFF2bL7eK0NwBA/CH8AmgiMcGlgb2yJHHaGwAg\nvhB+ATRr2Kktzw4XV8tbXmtyNQAARAfhF0CzIk5720PrAwAgPhB+ATQrJzNJPfLSJEmbOO0NABAn\nCL8AzmrYRY2tD599Uabaek57AwDEPsIvgLMKbXnWEDD07/1lJlcDAMD5I/wCOKveF2QoPcUjSdpM\n6wMAIA4QfgGcldPpUOGp09627PEqaHDaGwAgthF+AXylUOtDZY1f+45WmlwNAADnh/AL4CsN6p0t\nl9MhidPeAACxj/AL4CslJ7pVkN9JkrSFvl8AQIwj/AI4p8J+ja0PX5yoUmllncnVAADQdoRfAOc0\ntB+nvQEA4gPhF8A5de6UrG65qZLY8gwAENsIvwBaZGi/0JZnJVrz3l6VVND+AACIPYRfAC0S2vJM\nktZ9sF+PrPxIB46dNLEiAABaj/ALoEX69cjUiIu7KCnBJUk6WePXky/+S1W1fpMrAwCg5SwTflet\nWqXx48ersLBQ06ZN05YtW77yep/Pp6eeekrjx4/XkCFDNGHCBL3yyisdVC1gP06HQ3ddP0jP/PhK\nfa2gsySppr5B9b6AyZUBANBybrMLkKT169dr8eLFevTRRzVkyBCtXLlSt99+u958801lZ2c3+5w5\nc+aorKxMv/jFL5Sfn6/i4mIFg8EOrhywp71HKsKPvRW1yslMMrEaAABazhIrvytWrND06dM1efJk\n9e3bVwsXLlRSUpJWr17d7PXvvfeePvnkEy1btkxXXHGFunXrpqFDh2r48OEdXDlgTzde1Tf8uLic\nG98AALHD9PDr9/u1fft2jRw5MjzmcDg0atQobdq0qdnnvPPOOxo8eLD+8Ic/aNy4cbrmmmv0xBNP\nqL6+vqPKBmxtxMAuSnA3fvsoO0n4BQDEDtPbHsrKyhQIBJSbmxsxnpOTo3379jX7nIMHD+rjjz9W\nQkKCfvvb36qsrEw///nPVVFRoV/84hcdUTZgaw6HQ1npiTpeVquyk/yjEwAQO0wPv21hGIacTqeW\nLFmi1NTGjffnz5+vOXPm6Oc//7kSEhJa/Foul+mL3+gAoXlmvqMnOyOpMfxW+eR2W+vPlfm2F+bb\nXphve2mPeTY9/GZlZcnlcsnrjTw1qqSkpMlqcEheXp66dOkSDr6S1KdPHxmGoWPHjik/P7/F75+R\nkdy2whGTmO/o6ZKbqh0HylRZ41NWVuq5n2AC5ttemG97Yb7RVqaHX4/Ho0GDBqmoqEgTJkyQ1Liy\nW1RUpBkzZjT7nEsuuUQbNmxQbW2tkpMb/+fft2+fnE6nunbt2qr3r6ysVSDALhHxzuVyKiMjmfmO\norTExm8fxWW1KiurNrmaSMy3vTDf9sJ820tovqPJ9PArSbNmzdL8+fM1ePDg8FZndXV1mjp1qiRp\nyZIlOnHihJ544glJ0re+9S397ne/0/z583XPPfeotLRUTz75pP7zP/+zVS0PkhQIBNXQwF8eu2C+\noycztfHvWlWtXzW1fiV4XCZX1BTzbS/Mt70w32grS4TfiRMnqqysTE8//bS8Xq8GDhyo5cuXh/f4\n9Xq9Onr0aPj6lJQUPfvss3rsscd04403qlOnTrr22mt17733mvUlALaTnZ4YflxeVa/OWSkmVgMA\nQMs4DMMwzC7CTGVl1fzL0QbcbqeyslKZ7yjaf6xSj6z4WJJ0/83DNSA/y+SKTmO+7YX5thfm215C\n8x1N3CoJoE2y0k+f6lbKdmcAgBhB+AXQJukpHrmcDklir18AQMwg/AJoE+epgy4kqayS8AsAiA2E\nXwBtFgq/pRxxDACIEYRfAG0WXvml7QEAECMIvwDajPALAIg1hF8AbRba8aGy2qcGTloCAMQAwi+A\nNgsddGFIqqjymVsMAAAtQPgF0GZZZ5zyRusDACAWEH4BtNmZ4ZcdHwAAsYDwC6DNMtMS5Gg854KV\nXwBATCD8Amgzl9OpTmns+AAAiB2EXwDn5fRBF4RfAID1EX4BnJfTe/3S8wsAsD7CL4DzkkXbAwAg\nhhB+AZyXrIzG8Ft+0qdg0DC5GgAAvhrhF8B5CbU9BA1DlTUcdAEAsDbCL4Dzkn3qiGOJ1gcAgPUR\nfgGcl4iDLioJvwAAayP8AjgvoX1+JXZ8AABYn7ulF86fP7/FL7po0aI2FQMg9njcTmWkeFRZ46ft\nAQBgeS0Ov4cOHWrPOgDEsKz0JMIvACAmtDj8Pvfcc+1ZB4AYlpWeqAPHT3LKGwDA8locfo8cOdLi\nF+3WrVvtvxx6AAAgAElEQVSbigEQmzjlDQAQK1ocfsePHy+Hw/GV1xiGIYfDoR07dpx3YQBix+nw\n6wt/HwAAwIpaHH7/+7//uz3rABDDQuG3IRBUVa1f6SkJJlcEAEDzWhx+L7/88vasA0AMy04/c7uz\nesIvAMCyWhx+z1RfX6+XXnpJu3btUiAQCI/7fD5t27ZNGzZsiFqBAKwvK+P0KW+lJ+uV3yXdxGoA\nADi7NoXfxx57TGvXrtXFF1+srVu3avjw4Tpw4IBKSko0a9asKJcIwOqy0iJXfgEAsKo2nfD21ltv\nadGiRXrppZfUvXt3Pfroo3rnnXc0YcIE+f3+aNcIwOISE1xKTWr8tzQ7PgAArKxN4beyslKXXHKJ\nJKlfv37697//LY/Ho7vuukvvvPNOVAsEEBvCOz5UsvILALCuNoXf7OxslZSUSJIuvPBC7dq1S5KU\nlZUlr9cbveoAxIys9Ma+Xw66AABYWZvC77hx47Rw4UJ9/vnnuvTSS/Xaa69p69atWrVqlbp27Rrt\nGgHEgNN7/RJ+AQDW1abwe99996lz58768MMPNWHCBPXt21ff/va39dxzz2n27NnRrhFADDgz/BqG\nYXI1AAA0r027PWRkZOiZZ54Jf7xs2TJt2rRJ3bt3V+fOnaNWHIDYEQq/9f6AausDSklq07cXAADa\nVZtWfuvq6jR//nz9/ve/lyQ5HA7NmzdPS5culc/ni2qBAGJD5EEX7PgAALCmNoXfxYsX6+OPP9bw\n4cPDY/Pnz9fGjRv11FNPRa04ALEjK529fgEA1tem8Pu///u/+uUvfxlx5PE3vvENPf7443r99dej\nVhyA2BHa7UFixwcAgHW1KfxWV1crIyOjyXh2drYqKirOuygAsSc50aXEBJckVn4BANbVpvA7bNgw\nLV++XMFgMDxmGIZWrlypIUOGRK04ALHD4XCE+37p+QUAWFWbbseeO3eubr31Vm3cuFGDBw+WJG3f\nvl3l5eV69tlno1oggNiRlZ6ooyU1tD0AACyrTSu/hYWFWrduna677jr5fD4Fg0F961vf0htvvKGh\nQ4dGu0YAMYKDLgAAVtfmjTh79OihefPmRbMWADEuHH4rCb8AAGtq08qvJL377ruaOXOmxowZo8OH\nD2vp0qX661//Gs3aAMSY0I4PNfUNqvcFTK4GAICm2hR+33//ff3oRz9St27dVFlZqWAwqIaGBs2f\nP19r166Ndo0AYkTEXr9VrP4CAKynTeF36dKlmjdvnhYvXiyXq3Fro7lz52ru3Ln64x//GNUCAcSO\niFPeKtnxAQBgPW0Kv5999pnGjx/fZPyb3/ymvvjii/MuCkBsOnPllx0fAABW1Kbwm56erhMnTjQZ\n3717tzIzM8+7KACxKS3ZI7er8dsKOz4AAKyoTeF30qRJ+sUvfqGdO3fK4XCourpa7733nh599FFN\nnDgx2jUCiBGRB10QfgEA1tOmrc7uvfdeHTt2TJMnT5YkTZkyRYZh6KqrrtLcuXOjWiCA2JKVnqgT\n5bWEXwCAJbUp/Ho8Hi1ZskSzZ8/Wjh07FAwG1b9/f3Xr1k1LlizRgw8+GO06AcSIrIzGld9SjjgG\nAFhQi9se6uvr9cgjj2jEiBEaM2aMnnzySfXs2VPf/OY3NXHiRB07dkyTJk3S888/3571ArC4rLTG\n8FvOyi8AwIJavPL7y1/+Un/5y190/fXXKyEhQS+++KLS0tJ011136bHHHtOLL76o/Px8rVy5sj3r\nBWBxoR0fKmv88jcE5XG3+SwdAACirsXh9+2339bPfvYzfec735EkXXXVVXr88cd19OhR/c///I9u\nu+02zZkzRwkJCe1WLADrC53yJknlVfXK65RsYjUAAERqcfj1er0aM2ZM+OOxY8fq8OHD+tvf/qY/\n/elPGjFiRLsUCCC2ZGeccdDFScIvAMBaWvz7SL/fr5SUlPDHLpdLiYmJ+tnPfkbwBRAWedAFN70B\nAKzlvJvxCgsLo1EHgDiRkZIgl9Mhib1+AQDW06rw63A4WjQGwL6cToc6pTX2/pdVEn4BANbSqn1+\nH3vsMSUmnv6Vpt/v15NPPqnU1NSI6xYtWhSd6gDEpKz0JJVU1rPyCwCwnBaH38suu0zFxcURY8OH\nD1dZWZnKysqiXhiA2BXq+y0l/AIALKbF4fe5555rzzoAxJFQ+C2vIvwCAKyF3ecBRN2Z4TcQDJpc\nDQAApxF+AURdKPwahlRR5TO5GgAATiP8Aoi67DNOeeOmNwCAlRB+AUTdmQddEH4BAFZC+AUQdWnJ\nnvDj0kpOeQMAWAfhF0DUvfLe3vDjP7+9Wyvf3KmGADe+AQDMR/gFEHUf7jwe8fG7m47owLGTJlUD\nAMBphF8AUffdbwzQwF5ZEWO9uqabVA0AAKcRfgFE3aUD8iLC7yX98+R28e0GAGA+fhoBiLrPvigL\n9/12zU7RHZMuNrkiAAAaEX4BRN2r7+8PP77xqr5K9LjMKwYAgDMQfgFE3ZktD5mpCSZWAgBAJMIv\ngKjr0y0j/JhVXwCAlRB+AURdvT8QfpyQQPgFAFiHZcLvqlWrNH78eBUWFmratGnasmVLi573ySef\naNCgQZoyZUo7Vwigpc4Mv0ms/AIALMQS4Xf9+vVavHixZs+erTVr1qigoEC33367SktLv/J5J0+e\n1AMPPKCRI0d2UKUAWqLedzr80vYAALASS4TfFStWaPr06Zo8ebL69u2rhQsXKikpSatXr/7K5y1Y\nsECTJk3SsGHDOqhSAC1R7z99lLHHY4lvMwAASLJA+PX7/dq+fXvE6q3D4dCoUaO0adOmsz5v9erV\nOnTokH70ox91RJkAWiHU9pDgccrpcJhcDQAAp7nNLqCsrEyBQEC5ubkR4zk5Odq3b1+zz9m/f7+e\neuopvfDCC3I6zy+/uzh1yhZC88x8d4yGhsaV30SPS253x/+ZM9/2wnzbC/NtL+0xz6aH39YKBoP6\nyU9+onvuuUf5+fmSJMMw2vx6GRnJ0SoNMYD57hiGs3G1NyXJo6ysVNPqYL7thfm2F+YbbWV6+M3K\nypLL5ZLX640YLykpabIaLEnV1dXatm2bdu7cqUceeURSYyA2DEODBw/WH//4R40YMaLF719ZWatA\nIHjuCxHTXC6nMjKSme8OUnmyXpLkcTlUVlbd4e/PfNsL820vzLe9hOY7mkwPvx6PR4MGDVJRUZEm\nTJggqXElt6ioSDNmzGhyfVpaml577bWIsVWrVmnjxo1aunSpunfv3qr3DwSC4V/RIv4x3x2jztcg\nSUrwuEz982a+7YX5thfmG21leviVpFmzZmn+/PkaPHiwhgwZopUrV6qurk5Tp06VJC1ZskQnTpzQ\nE088IYfDoX79+kU8PycnR4mJierbt68Z5QP4ktBWZ2xzBgCwGkuE34kTJ6qsrExPP/20vF6vBg4c\nqOXLlys7O1uS5PV6dfToUZOrBNBSod0eCL8AAKtxGOdzt1gcKCur5tcmNuB2O5WVlcp8d5BHV36k\nfUdPasTFXXTX9YM6/P2Zb3thvu2F+baX0HxHE/uEAIi60CEXiRxwAQCwGH4yAYi6UM9vAm0PAACL\nIfwCiLpQz29SAuEXAGAthF8AUefjhjcAgEURfgFEVTBoyHfqJhTaHgAAVkP4BRBVoZYHiZVfAID1\nEH4BRJWP8AsAsDDCL4Coilj55YY3AIDFEH4BRFWdj5VfAIB1EX4BRJXPf/rEJcIvAMBqCL8Aoiry\nhje+xQAArIWfTACiip5fAICVEX4BRBVbnQEArIzwCyCq6rnhDQBgYYRfAFEVWvl1SPK4+RYDALAW\nfjIBiKpQ+E1McMnhcJhcDQAAkQi/AKIqHH5peQAAWBDhF0BUhXp+Cb8AACsi/AKIqtDKbwLhFwBg\nQYRfAFFVf+qEtyT2+AUAWBDhF0BU+cI9v3x7AQBYDz+dAERVqOeXtgcAgBURfgFEVd0ZW50BAGA1\nhF8AUeVjqzMAgIURfgFEFfv8AgCsjPALIKrY5xcAYGWEXwBRVU/PLwDAwgi/AKImEAyqIWBIYuUX\nAGBNhF8AUVPvC4YfE34BAFZE+AUQNaGWB4m2BwCANRF+AUSN78zwywlvAAAL4qcTgKip850Zfln5\nBQBYD+EXQNTQ9gAAsDrCL4CoiWx7IPwCAKyH8AsgauoJvwAAiyP8AoiaM8NvAuEXAGBBhF8AUVN/\nxg1vSfT8AgAsiPALIGrq/Y2HXLicDrldfHsBAFgPP50ARE2o7YGWBwCAVRF+AURNKPxywAUAwKr4\nCQUgakI9v+z0AACwKsIvgKgJr/xysxsAwKIIvwCi5nTbA+EXAGBNhF8AUUP4BQBYHeEXQNT46PkF\nAFgc4RdA1NTR8wsAsDjCL4CoCR1ywcovAMCqCL8AosZHzy8AwOIIvwCiJrTPbwKHXAAALIqfUACi\npqa+QZL06vv7VV3nN7kaAACaIvwCiIpt+0oiPi6rrDepEgAAzs5tdgEAYpthGHrlvb16vehAeOzb\nV/VVj85pJlYFAEDzWPkFcF4++aw4Ivj2viBd117Ry8SKAAA4O8IvgPOSlOCS0+EIf7zv6Elt3u01\nsSIAAM6O8AvgvAzuk6MlPxqt/DPaHH79P1v0xj8PqKKKvl8AgLUQfgGct8zUBBX2y4kYe/nve7T6\nvb0mVQQAQPMIvwCi4msDOis1KfIe2oH5WSZVAwBA8wi/AKKia3aKOqUnhj+eMq6PRg7uamJFAAA0\nRfgFEBV7DlfocHG1JOnSAXmaNOpCcwsCAKAZhF8AUdG3e6a6ZqdIkrbuLdHx0hqTKwIAoCnCL4Co\nSPC4dOf1F8vldMjnD2rZuu1qCATNLgsAgAiEXwBRc2HXDE0e21tS436/697fb25BAAB8CeEXQFRd\nO6KX+vfsJEl6rWi/Pj9Ubm5BAACcgfALIKqcTodu/9ZAJSe6ZRjSH9b9W7X1DWaXBQCAJMIvgHaQ\nm5msGVf3lyR5K+q06m+7TK4IAIBGhF8A7eKKQV11xcVdJEkfbDumD3ccN7kiAAAIvwDa0Xev7q+c\njMaDL/77zc9UWllnckUAALsj/AJoNylJHt3+rYvlkFRT36Dlr/1bQcMwuywAgI0RfgG0qwH5Wbr2\nil6SpJ1flOv/fXjQ5IoAAHZG+AXQ7iaP7a1eXdIlSavf3aMvjp80uSIAgF0RfgG0O7fLqTuvv1gJ\nbqcCQUO/f3W7fP6A2WUBAGyI8AugQ1yQk6rpEy6SJB0tqdHL7+wxuSIAgB0RfgF0mKuGddOwfrmS\npLc+PaQte0pMrggAYDeEXwAdxuFwaNa1BcpI8UiSnl2/Q5XVPpOrAgDYiWXC76pVqzR+/HgVFhZq\n2rRp2rJly1mv/dvf/qbbbrtNI0eO1KWXXqqbbrpJ//jHPzqwWgBtlZGaoNuuGyhJqqz2acUbO2Ww\n/RkAoINYIvyuX79eixcv1uzZs7VmzRoVFBTo9ttvV2lpabPXf/TRRxo9erT+8Ic/aM2aNRoxYoTu\nvvtu7dy5s4MrB9AWhX1zNf6S7pKkTbu9enfzEZMrAgDYhSXC74oVKzR9+nRNnjxZffv21cKFC5WU\nlKTVq1c3e/2DDz6o73//+xo8eLDy8/M1d+5cXXjhhXr77bc7uHIAbfXtr/fTBTkpkqQ/v/W5jpXW\nmFwRAMAOTA+/fr9f27dv18iRI8NjDodDo0aN0qZNm1r0GoZhqLq6WpmZme1VJoAoS/S4dOekQXI5\nHfL5g1r26nY1BIJmlwUAiHNuswsoKytTIBBQbm5uxHhOTo727dvXotdYvny5ampqdO2117b6/V0u\n0/M/OkBonplva+nbI1M3XtVXL729W/uPndS6D/br21/vd96vy3zbC/NtL8y3vbTHPJsefs/XunXr\n9Mwzz+h3v/udsrOzW/38jIzkdqgKVsV8W893rr1Y/z5Qrq17vHrtg/0aPayHBvXJicprM9/2wnzb\nC/ONtjI9/GZlZcnlcsnr9UaMl5SUNFkN/rLXX39dDz/8sH7961/riiuuaNP7V1bWKsCvWuOey+VU\nRkYy821Rt00s0M/+8E/V1DXoV89/rMfuuEIpSW3/9sR82wvzbS/Mt72E5juaTA+/Ho9HgwYNUlFR\nkSZMmCCpsYe3qKhIM2bMOOvzXnvtNT300EN66qmnNG7cuDa/fyAQVEMDf3nsgvm2pszUBM28ZoD+\n71+3y1tRpxVv7NCdkwad9+sy3/bCfNsL8422skTDzKxZs/Tyyy9r7dq12rNnjxYsWKC6ujpNnTpV\nkrRkyRLdf//94evXrVunBx54QPfff7+GDBkir9crr9erqqoqs74EAOfp8oFdNHJQV0nSP7cf1z//\nfczkigAA8cj0lV9JmjhxosrKyvT000/L6/Vq4MCBWr58ebiH1+v16ujRo+Hr//KXvygQCOiRRx7R\nI488Eh6fPHmyFi1a1OH1A4iO717dX58fKpe3ok7Pbdili7p3Uk5mktllAQDiiMOw+dFKZWXV/NrE\nBtxup7KyUpnvGLDrYLmeeOFTGYY0oGcn/fQ7w+V0Olr1Gsy3vTDf9sJ820tovqPJEm0PABDSv2cn\nXTeylyTps4PlevPDL0yuCAAQTwi/ACzn+tG91fuCdEnSmvf26sCxkyZXBACIF4RfAJbjdjl1x6RB\nSvA4FQga+v2r21XvD5hdFgAgDhB+AVhS1+wUfWfCRZKkY6U1+svbu02uCAAQDwi/ACxr3NBuGn5R\n42E37/zrsDbv9p7jGQAAfDXCLwDLcjgcmnVtgTJTEyRJf1q/Q5XVPpOrAgDEMsIvAEtLT0nQ968b\nKEmqrPHr2fU7ZPMdGgEA54HwC8DyBvfJ0YRLe0iStuwp0d//ddjkigAAsYrwCyAmfPuqvuqe27jR\n+Utv79bRkmqTKwIAxCLCL4CYkOBx6Y5JF8vtcsjXENSyV/+thgCnOwEAWofwCyBm5HdJ18hBXSVJ\nB46f1PqiA9pxoEzVdX6TKwMAxAq32QUAQEvV1PlVtP1Y+OO1/9gnScrNTNIv/88os8oCAMQQVn4B\nxAyHw6HkxKb/Zq+tbzChGgBALGLlF0DMSE5065f/Z5SKy2slQ3r42Q8lNe4GAQBAS7DyCyCmJHpc\n6pGXFjFWSPgFALQQ4RdATNq8p/GoY4ekwX2yzS0GABAzCL8AYtLWPSWSpD7dMpSekmByNQCAWEH4\nBRBzquv82n24UpJU2JeWBwBAyxF+AcSc7ftKFTQMSVJh31yTqwEAxBLCL4CYs+VUy0NmWoLyu6Sd\n42oAAE4j/AKIKUHD0Na9jeF3SJ8cORwOkysCAMQSwi+AmLL/6EmdrGk8zpgtzgAArUX4BRBTtpza\n4szldGhQb7Y4AwC0DuEXQEwJ9fte1COz2aOOAQD4KoRfADGjotqn/cdOSmKXBwBA2xB+AcSM0MEW\nEvv7AgDahvALIGZsObXLQ25mki7ISTG5GgBALCL8AogJDYGgtu9rDL+FfdniDADQNoRfADFhz+EK\n1dYHJNHvCwBoO8IvgJiw+VS/r8ftVEF+J5OrAQDEKsIvgJgQutltYK8sJXhcJlcDAIhVhF8Aluet\nqNVhb7UkdnkAAJwfwi8Ay4vY4owjjQEA54HwC8DyQqe6dctNVW6nZJOrAQDEMsIvAEvz+QPacaBM\nEqu+AIDzR/gFYGmfHSyXryEoiX5fAMD5I/wCsLQtuxtbHpITXerXI9PkagAAsc5tdgEA8GXHy2q0\nbW+ptu0t0fb9jS0Pgy7MltvFv9cBAOeH8AvAMj7ccVz/96/bm/3cx58Va+nqLUpL9oT/S032KDXJ\no7Rkt1LPGHe7CckAgOYRfgFYxmsf7P/Kz//rc2+LXicpwaX01ASlJLqVmuQ+FZA94YCcmuSOCNBp\nyR6lJLrldDqi8FUAAKyM8AvAMqaN76dnX9+h8iqf+nTLUFZ6YvhzgYChmjq/quoaVF3rV1WtX4Gg\n0ezr1PkCqvPVtuq9HZJSkiJXkFPP+DgUoL/8uaQElxwOQjMAxAqHYRjN//SwibKyajWcupMc8cvt\ndiorK5X5jiOGYajOF1B1nV/VtQ2qOhWIq+v8qqlvUENQKimv0cka/+nP1fpVU9egaH7TczkdpwPz\nqUD85QCddkaADn3OQ2tG1PD3216Yb3sJzXdUXzOqrwYAHcThcCg50a3kRLdyv7QJxFf9cAwGDdXU\nn149DgXmqlMBujr8cSgwN6iqzq96X6DZOgJBQ5XVPlVW+1pVf6LHpdRkt9JOBeLTgbnpWChApyZ5\naM0AgPNE+AVgK06nI7wS26UVz/M3BE+tMoeCccOXPvaruu50gK469bmGQPPrzPX+gOr9AZVW1req\n/pREd2T/8qmwHL4BMDlypTmN1gwAiED4BYAW8Lid6pSWqE5piee++BTDMFTvD0SsIFfXRgbo0Mpz\n9RkBurrWf9bWjJr6BtXUN0jlLe9pdjkdEf3LZwbj1NBOGV+6KTAt2S2P29Xi9wCAWEH4BYB24nA4\nlJTgVlJC09aMrxI0DNWEbuw7MxiHepsjQvTpAF33Va0ZNX5V1vhbVX+Cx6nM1ATdNOEiDb8or1XP\nBQCrIvwCgMU4HW1rzWgIBCNWkCN7mk+H5VCoDo2drTXD5w+quLxOy1/boYXfS1Nup+TofIEAYCLC\nLwDECbfLqcy0RGW2sjXD5w82G5TLqnx6vWi/ausb9IvnP9EPpwxR3+4cMQ0gthF+AcDGHA6HEhNc\nSkxwKSczqcnnkxNdevmdPSqv8umJFz7Vzd/or6uGdTehUgCIDjaaBACc1bUjeun/TB6sRI9LDQFD\n//3mZ1rxxk752V8VQIwi/AIAvtJlBZ31s5mXqnNWY8/ve5uP6IkXPlXZydZt0wYAVkD4BQCcU4+8\nND1869dU2DdHkrT3SKUWrvhIuw6Wm1wZALQO4RcA0CIpSR7NvrFQ14++UJJUWe3Tky/+S299ckiG\nEc1DowGg/RB+AQAt5nQ4NHlsH90zdYiSElwKBA2t+tsu/fH1HfL5m99nGACshPALAGi14f3z9P/d\n+jVdkJMiSfpg2zEtev5TeStafvIcAJiB8AsAaJMLclL10Myv6ZL+jae/HTh+Uo+s+Fg79peaXBkA\nnB3hFwDQZsmJbv1gymBNHddHDklVtX796qVNenPjF/QBA7Akwi8A4Lw4HQ59a9SFmvPtoUpJdMsw\npL+8s1u/f3W76n30AQOwFk54AwBERWHfHD0862v6zStbdai4Wh/uOKEj3mp9b+JApSZ72u193S6H\n6oNSRUWNGgKnV5vTkz1KTuTHHIBIDsPmv5cqK6tWAycVxT2326msrFTm2yaYb3PV+wL60xs79OGO\nE6bWkeB26qFbv6YeeWmm1oHo4u+3vYTmO5poewAARFVigkt3XT9I077eTw6HeXX4GoKqrW8wrwAA\nlsTvgwAAUedwOPTNEfm6ZECejnirz/v1qmv92nWwXJ8dLNeJsrNvp+Z2OdWve4b69+ykof1y1fuC\njPN+bwDxhfALAGg3nTslq3On5FY/r6La1xh2vyjTZwfLdbi4+QCd4HGqf49OGlbQWb3yUpXfOV0e\nN7/UBHB2hF8AgOlKK+v02cHyU4G3XMdKa5q9LjnRpYt6dNKAnp3UP7+TenVJV1Kimx5QAC1G+AUA\ndCjDMOStqNNnX5Trs4Nl2nWwXMXldc1em5rkVv+ejWF3QH6WenZOk9NpYiMxgJhH+AUAtCvDMHSs\ntCbcs7vrYLlKK+ubvTYjxaP++VmNYbdnJ3XLS5XTzLvmAMQdwi8AIKqChqEj3upwC8Oug+WqqPY1\ne21WemK4hWFAz07qmp0iB2EXQDsi/AIAzkswaOjgiarwqu6ug+WqqvU3e21uZtLpsJufpbzMJMIu\ngA5F+AUAtEpDIKgvjlc19ut+Ua5dhyrOup9ul+yUcAvDgPxOys5I6uBqASAS4RcA8JX8DUHtP1Z5\n6ga1cu0+VKF6f6DZa7vnpoZbGPr37KROaYkdXC0AfDXCLwAggs8f0J4jleF9dvccqZS/mS3EHJJ6\ndkk7tRtDlvr3zFR6SkLHFwwArUD4BQCbq/M1aPfhivDNafuOVqohYDS5zulwqFfX9HDPbv8emUpJ\n8phQMQC0HeEXAGymps6vzw9V6LNTuzEcOHZSQaNp2HU5HerdLSPcs9u3e6aSE/mxASC28V0MAOJc\nVa0/vO3YZwfLdPB4lZpGXcnjdqpvt4zGNob8LPXplqFEj6vD6wWA9mSZ8Ltq1Sr98Y9/lNfrVUFB\ngR566CEVFhae9fqNGzfqiSee0Oeff65u3brp7rvv1pQpUzqwYgCwpoqq+tNHBR8s1+Hi6mavS/S4\n1K9HZvgEtd4XZMjjdnZwtQDQsSwRftevX6/Fixfr0Ucf1ZAhQ7Ry5UrdfvvtevPNN5Wdnd3k+kOH\nDunuu+/Wd77zHf3qV79SUVGRHnroIXXu3FmjR4824SsAAHP5/AGt/cc+bfrcq2OlNc1ek5zo0kU9\nGrcc69+zk3p1SZfbRdgFYC+WCL8rVqzQ9OnTNXnyZEnSwoUL9fe//12rV6/WHXfc0eT6F198UT16\n9NB9990nSerTp48++eQTrVixgvALwJa27CnRmxu/iBhLTXKHWxgG9Oyknp3T5HRyoAQAezM9/Pr9\nfm3fvl133XVXeMzhcGjUqFHatGlTs8/ZvHmzRo0aFTE2ZswYLVq0qF1rBQCruvjCLF1W0FmGFD5Q\noltuqpycngYAEUwPv2VlZQoEAsrNzY0Yz8nJ0b59+5p9TnFxsXJycppcX1VVJZ/Pp4SElu8z6eJX\nfrYQmmfm2x7sON8ZaYm658az3ycRz+w433bGfNtLe8yz6eHXbBkZyWaXgA7EfNsL820vzLe9MN9o\nK9P/2ZSVlSWXyyWv1xsxXlJS0mQ1OCQvL08lJSVNrk9LS2vVqi8AAADsxfTw6/F4NGjQIBUVFYXH\nDMNQUVGRhg8f3uxzhg0bFnG9JL3//vsaNmxYu9YKAACA2GZ6+JWkWbNm6eWXX9batWu1Z88eLViw\nQKK06AQAAA0vSURBVHV1dZo6daokacmSJbr//vvD19900006ePCgnnzySe3du1erVq3Shg0b9L3v\nfc+sLwEAAAAxwBI9vxMnTlRZWZmefvppeb1eDRw4UMuXLw/v8ev1enX06NHw9T169NCyZcu0aNEi\nPffcc+ratasee+yxJjtAAAAAAGdyGEYzB7oDAAAAccgSbQ8AAABARyD8AgAAwDYIvwAAALANwi8A\nAABsg/ALAAAA2yD8AgAAwDbiOvyuWrVK48ePV2FhoaZNm/b/t3fvMU3dbxjAn4KgIHdcuGS4wRBx\niBZ0ERBhqRDG2EUW3bpsYdH5By5eIJLNug0RAYWNW52EQVmmBNRlgpVLXJgzLhLChtnFmsmGkQAK\nAooMKDdpf38s9BcGm2ttC9Lnk5DQw/ec856+afv08D0tfv31138d39jYiNdeew3+/v6IiopCZWWl\nkSolfdCm33V1ddi2bRuCg4OxZs0aiMViXL582YjV0qPS9vE96cqVK/Dz80NsbKyBKyR90rbfY2Nj\nyM3NhUgkgr+/PzZu3IiKigojVUuPStt+nzt3Dq+++iqEQiFCQ0Oxf/9+3L9/30jVkq6ampoQHx+P\nDRs2wNfXFxcuXHjoOvrIavM2/NbW1uLIkSPYvXs3Kisr4evri+3bt+PevXszju/o6EB8fDyCgoIg\nl8sRFxeHjz76CPX19UaunHShbb9//PFHrF+/HsXFxaisrMS6desQHx+P69evG7ly0oW2/Z40MDCA\nffv2ITg42EiVkj7o0u89e/agsbERGRkZ+Oabb5CTkwNPT08jVk260rbfV65cwb59+/D666+jpqYG\nUqkUV69eRXJyspErJ20plUqsWLECBw4cgEAgeOh4vWU19Ty1ZcsW9aFDhzS3VSqVesOGDeqioqIZ\nx2dlZalfeumlKcsSExPV27dvN2idpB/a9nsmMTEx6mPHjhmiPNIzXfudmJiozs/PVx89elS9adMm\nQ5dJeqJtvy9duqR+7rnn1P39/cYqkfRI236XlJSoIyMjpywrLS1Vh4eHG7JM0rPly5erv/32238d\no6+sNi/P/I6Pj+PatWtTzu4IBAKEhITg559/nnGdX375ZdrXI4eGhv7jeJo7dOn336nVagwNDcHe\n3t5QZZKe6NrvM2fOoKOjAzt37jRGmaQnuvT74sWLWLlyJYqLixEWFoaoqChkZmZidHTUWGWTjnTp\nt1AoRGdnJy5dugQA6O3txfnz5xEeHm6Umsl49JXVFuizqLmir68PExMTWLJkyZTlzs7OuHnz5ozr\n9PT0wNnZedr4wcFBjI2NwdLS0mD10qPRpd9/J5PJoFQqER0dbYgSSY906Xdraytyc3NRXl4OM7N5\n+Z5/3tKl3+3t7WhqaoKlpSWOHTuGvr4+pKSkoL+/HxkZGcYom3SkS78DAwPxySefIDExEWNjY3jw\n4AFEIhGnPcxD+spqfBUgk1dVVYWCggLk5+fDyclptsshPVOpVEhKSsKuXbuwdOlSAH+d6af5S61W\nw8zMDNnZ2fD390dYWBgkEgnOnj2LsbGx2S6P9KylpQXp6enYtWsXKioqUFJSgo6ODoZf+kfzMvw6\nOjrC3Nwcvb29U5bfvXt32rvJSU888QTu3r07bbyNjQ3P+s5xuvR7Uk1NDZKTk5Gfn4+goCBDlkl6\nom2/h4aGoFAocOjQIfj5+cHPzw8FBQX47bffsHLlSjQ2NhqrdNKBrs/nLi4uWLx4sWaZl5cX1Go1\nurq6DFovPRpd+l1UVITAwEBs3boVPj4+WL9+PQ4cOIAzZ85M2w493vSV1eZl+LWwsICfnx8aGho0\ny9RqNRoaGhAQEDDjOkKhcMp4AKivr4dQKDRorfTodOk3AFRXV+PDDz9ETk4OwsLCjFEq6YG2/bax\nsUF1dTXOnj0LuVwOuVwOsVgMLy8vyOVyrF692pjlk5Z0eXwHBgaiu7sbw8PDmmU3b96EmZkZXF1d\nDV4z6U6Xfo+MjGDBgqmzOM3MzCAQCPhfnnlGX1nNPCUlJUWPdc0ZixcvhlQqhZubGywsLJCXl4fm\n5makp6fDysoK2dnZkMvliIyMBAAsXboUhYWFGBgYgJubG2pra/Hll18iOTkZHh4es3w09DDa9ruq\nqgoSiQQSiQTBwcFQKpVQKpVQqVQ80/8Y0KbfAoEATk5OU34UCgXa2tqwY8eOaS+aNPdo+/j29PRE\nRUUFFAoFli1bhhs3biAtLQ2RkZGIiIiY5aOhh9G23yMjIyguLoajoyMcHBzQ0tKCjIwMuLu74513\n3pnlo6F/o1QqcePGDfT09OD06dNYtWoVFi1ahPHxcdja2hosq83bZ/0XX3wRfX19kEql6O3txYoV\nKyCTyTRzOnt7e9HZ2akZ/+STT6KoqAiHDx9GaWkpXF1dkZaWNu2qQpqbtO33V199hYmJCaSmpiI1\nNVWzfNOmTTh8+LDR6yftaNtverxp229ra2t88cUXSEtLw+bNm+Hg4IDo6GgkJCTM1iGQFrTtd2xs\nLJRKJcrLy5GVlQVbW1sEBwdj7969s3UI9B8pFArExcVBIBBAIBAgMzMTwP9fiw2V1QRq/k+AiIiI\niEzEvJzzS0REREQ0E4ZfIiIiIjIZDL9EREREZDIYfomIiIjIZDD8EhEREZHJYPglIiIiIpPB8EtE\nREREJoPhl4iIiIhMBsMvEREREZmMefv1xkREs0UkEuH27dua2wKBANbW1nj22WexZ88erF271iD7\nlUgkuHXrFk6cOPHQsZ999hkqKytx4cIFg9RCRDRX8euNiYj0TCQSITo6Gtu2bQMAqNVq3L9/H9nZ\n2WhoaMD58+fh6uqq9/0ODg5CpVLBzs7uoWOHh4cxMjICR0dHvddBRDSXcdoDEZEBWFlZwdnZGc7O\nzliyZAm8vb1x8OBBjIyMoK6uziD7tLGx+U/Bd7I+Bl8iMkUMv0RERmJubg4AsLS0hEgkQmZmJmJi\nYhAcHIympiYAQHFxMSIiIiAUChEbG4uqqqop22hra8OOHTuwdu1arFu3Dnv37sW9e/cA/DXtIS4u\nTjO2pKQEkZGR8Pf3x8aNG1FQUKD529GjRyESiTS3u7q6kJSUhNDQUAQEBODdd99Fc3Oz5u8SiQQS\niQSZmZkICQmBUChEfHw8enp69H9HEREZEMMvEZER3LlzB6mpqbC2tkZ4eDgAoLy8HB9//DFkMhlW\nr16NnJwcnD59GsnJyaiqqkJcXBwOHjyIkydPAgAGBgbw1ltv4cGDBygtLcXx48fR1taGhISEafv7\n7rvvUFRUhNTUVNTV1SEpKQmFhYWaMC0QCCAQCAAAQ0NDEIvF6O7uRmFhIU6dOgUrKyu8/fbb6Ozs\n1Gyzuroaf/75J8rKyiCTyaBQKJCXl2fou46ISK94wRsRkQF8/vnnKCkpAQBMTExgfHwczzzzDKRS\nqWa+b3h4OIKCggD8NQf3+PHjyMnJQVhYGADAw8MDHR0dKC4uxptvvomamhoMDQ0hNzcXNjY2AID0\n9HTU1NRgfHx8yv7b29thaWkJd3d3uLq6Ijo6Gi4uLnB3d59Wq1wuR39/P6RSKRwcHAAA2dnZiIiI\nQFlZGZKSkgAAdnZ2SE1Nhbm5OTw9PRETE4Pvv//eAPceEZHhMPwSERmAWCzWTEEwMzODvb29JrBO\neuqppzS/t7S0YHR0VBM0J6lUKoyPj2NsbAx//PEHnn766Snb8fHxgY+Pz7T9v/LKK6ioqEBUVBS8\nvb0REhKCqKioGS+0m9zuZPAFgIULF2LVqlX4/fffNcs8PDw0UzcAwNbWdlroJiKa6xh+iYgMwN7e\nHh4eHv86ZuHChZrfJz94Jy8vD15eXtPGWlhYYMGC//6U7ejoCLlcjp9++gn19fW4fPkyTpw4gd27\nd+O9996bMvafPvRHpVJN2aelpeW0MfzAICJ63HDOLxHRHODl5YUFCxbg9u3b8PDw0PxcvHgRMpkM\nAoEA3t7eaG1txeDgoGa9a9euISQkBHfu3JmyvaqqKpSVlSEgIAA7d+7EqVOnsGXLFtTW1k7b9/Ll\ny9Ha2qq5cA4ARkdHoVAosGzZMsMdNBHRLGD4JSKaA2xsbCAWi5GXl4dz586hvb0dX3/9NT799FO4\nuLgAAF5++WU4ODjg/fffR3NzMxQKBVJSUuDr66sZM2l0dBRZWVmQy+W4desWmpqa8MMPPyAwMHDa\nvie3m5CQgKtXr+L69etISkrC8PAw3njjDaMcPxGRsXDaAxGRnk1+ioK2Y/bv3w8nJydIpVJ0d3fD\nzc0NCQkJmi/LWLRoEWQyGY4cOQKxWAwrKys8//zz+OCDD6Zta/Pmzejv70dBQQG6urpgZ2eHF154\nYdqcYuCv4F1aWorMzExs3boVALBmzRqcPHlyxgvkiIgeZ/yGNyIiIiIyGZz2QEREREQmg+GXiIiI\niEwGwy8RERERmQyGXyIiIiIyGQy/RERERGQyGH6JiIiIyGQw/BIRERGRyWD4JSIiIiKTwfBLRERE\nRCaD4ZeIiIiITAbDLxERERGZjP8BWj5VwWMBbKwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x138d18400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_probs = RandomForestClassifier().fit(X_train,y_train).predict_proba(X_holdout)\n",
    "pr_curve(y_holdout, pred_probs[:,1], \"Random Forest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.        ,  0.39677419,  0.39354839,  0.39354839,  0.39354839,\n",
       "         0.39354839,  0.36129032,  0.36129032]),\n",
       " array([ 0.28028933,  0.34647887,  0.34659091,  0.34757835,  0.34857143,\n",
       "         0.35260116,  0.34674923,  0.34890966]),\n",
       " array([ 0.        ,  0.2       ,  0.25      ,  0.28571429,  0.33333333,\n",
       "         0.5       ,  0.66666667,  1.        ]))"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j3G2eLqdW6LdZ6LdZ6LdZnP12J9/ayAegwTif9JadW6hys/+ODADwI4RfANWK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\nmrLwJnfPjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1133bb860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_probs = DecisionTreeClassifier().fit(X_train,y_train).predict_proba(X_holdout)\n",
    "pr_curve(y_holdout, pred_probs[:,1], \"Decision Tree\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Emotional abuse model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X, y = df.drop(['12mo_physical_dv',\"ever_emo_violence\"], axis=1),df['ever_emo_violence']\n",
    "\n",
    "X_train, X_holdout, y_train, y_holdout = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "479 335 144\n"
     ]
    }
   ],
   "source": [
    "print(len(X),len(X_train),len(X_holdout))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "\n",
    "models = {}\n",
    "models['LogisticRegression'] = LogisticRegression()\n",
    "models['K-Nearest Neighbors'] = KNeighborsClassifier(n_neighbors=6)\n",
    "models['Gaussian'] = GaussianNB()\n",
    "models['SVC'] = SVC()\n",
    "models['Decision Tree'] = DecisionTreeClassifier()\n",
    "models['Random Forest'] = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: SVC\n",
      "Score: 0.701388888889\n",
      "Accuracy: 0.70\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.70      1.00      0.82       101\n",
      "          1       0.00      0.00      0.00        43\n",
      "\n",
      "avg / total       0.49      0.70      0.58       144\n",
      "\n",
      "\n",
      "Model: K-Nearest Neighbors\n",
      "Score: 0.680555555556\n",
      "Accuracy: 0.68\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.71      0.93      0.80       101\n",
      "          1       0.36      0.09      0.15        43\n",
      "\n",
      "avg / total       0.60      0.68      0.61       144\n",
      "\n",
      "\n",
      "Model: Decision Tree\n",
      "Score: 0.701388888889\n",
      "Accuracy: 0.70\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.71      0.98      0.82       101\n",
      "          1       0.50      0.05      0.09        43\n",
      "\n",
      "avg / total       0.65      0.70      0.60       144\n",
      "\n",
      "\n",
      "Model: Random Forest\n",
      "Score: 0.701388888889\n",
      "Accuracy: 0.70\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.71      0.98      0.82       101\n",
      "          1       0.50      0.05      0.09        43\n",
      "\n",
      "avg / total       0.65      0.70      0.60       144\n",
      "\n",
      "\n",
      "Model: LogisticRegression\n",
      "Score: 0.701388888889\n",
      "Accuracy: 0.70\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.70      1.00      0.82       101\n",
      "          1       0.00      0.00      0.00        43\n",
      "\n",
      "avg / total       0.49      0.70      0.58       144\n",
      "\n",
      "\n",
      "Model: Gaussian\n",
      "Score: 0.590277777778\n",
      "Accuracy: 0.59\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.71      0.71      0.71       101\n",
      "          1       0.31      0.30      0.31        43\n",
      "\n",
      "avg / total       0.59      0.59      0.59       144\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ANTARES/anaconda2/envs/snakes/lib/python3.5/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/ANTARES/anaconda2/envs/snakes/lib/python3.5/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "for name,model in models.items():\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred = model.predict(X_holdout)\n",
    "    print('Model: '+name)\n",
    "    print(\"Score: \" + str(model.score(X_holdout,y_holdout)))\n",
    "    print(\"Accuracy: {:.2f}\".format(accuracy_score(y_holdout, y_pred)))\n",
    "    print(classification_report(y_holdout, model.predict(X_holdout)))\n",
    "    print(\"\")\n",
    "#what is the difference between score and accuracy_score in scikit-learn?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>physical_dv</th>\n",
       "      <th>age</th>\n",
       "      <th>ever_emo_violence</th>\n",
       "      <th>u16_marriage</th>\n",
       "      <th>child_marriage</th>\n",
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       "      <th>higher_edu</th>\n",
       "      <th>electricity</th>\n",
       "      <th>...</th>\n",
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       "      <th>charcoal</th>\n",
       "      <th>biogas</th>\n",
       "      <th>kerosene</th>\n",
       "      <th>electric_power</th>\n",
       "      <th>coal</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   physical_dv   age  ever_emo_violence  u16_marriage  child_marriage  no_edu  \\\n",
       "0            0  25.0                  0             0               1       1   \n",
       "1            0  31.0                  1             0               0       0   \n",
       "2            0  30.0                  1             0               0       0   \n",
       "3            0  46.0                  0             0               0       0   \n",
       "4            0  39.0                  1             0               1       1   \n",
       "\n",
       "   primary_edu  secondary_edu  higher_edu  electricity    ...      \\\n",
       "0            0              0           0            1    ...       \n",
       "1            1              0           0            1    ...       \n",
       "2            0              0           1            1    ...       \n",
       "3            0              1           0            1    ...       \n",
       "4            0              0           0            1    ...       \n",
       "\n",
       "   natural_gas  animal_dung  lpg  grass  charcoal  biogas  kerosene  \\\n",
       "0            0            0    1      0         0       0         0   \n",
       "1            0            0    0      0         0       0         0   \n",
       "2            0            0    1      0         0       0         0   \n",
       "3            1            0    0      0         0       0         0   \n",
       "4            1            0    0      0         0       0         0   \n",
       "\n",
       "   electric_power  coal  Intercept  \n",
       "0               0     0          1  \n",
       "1               0     0          1  \n",
       "2               0     0          1  \n",
       "3               0     0          1  \n",
       "4               0     0          1  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ANTARES/anaconda2/envs/snakes/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "df[\"Intercept\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['physical_dv', 'age', 'ever_emo_violence', 'u16_marriage',\n",
       "       'child_marriage', 'no_edu', 'primary_edu', 'secondary_edu',\n",
       "       'higher_edu', 'electricity', 'radio', 'tv', 'refrigerator', 'bicycle',\n",
       "       'motorcycle', 'car/truck', 'improved_sani', 'unimproved_sani',\n",
       "       'open_defec', 'safe_water', 'unsafe_water', 'shared_toilet',\n",
       "       'sturdy_walls', 'dirt_walls', 'no_walls', 'sturdy_roof', 'mid_roof',\n",
       "       'bad_roof', 'no_roof', 'hard_floor', 'dirt_floor', 'wood',\n",
       "       'natural_gas', 'animal_dung', 'lpg', 'grass', 'charcoal', 'biogas',\n",
       "       'kerosene', 'electric_power', 'coal', 'Intercept'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'> <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "X, y = df.drop(['physical_dv',\"ever_emo_violence\",\"u16_marriage\",'motorcycle','car/truck'], axis=1),df['physical_dv']\n",
    "print(type(X), type(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = sm.Logit(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.567899\n",
      "         Iterations: 35\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:            physical_dv   No. Observations:                 3685\n",
      "Model:                          Logit   Df Residuals:                     3649\n",
      "Method:                           MLE   Df Model:                           35\n",
      "Date:                Thu, 27 Oct 2016   Pseudo R-squ.:                 0.04602\n",
      "Time:                        23:20:55   Log-Likelihood:                -2092.7\n",
      "converged:                      False   LL-Null:                       -2193.7\n",
      "                                        LLR p-value:                 2.331e-25\n",
      "===================================================================================\n",
      "                      coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
      "-----------------------------------------------------------------------------------\n",
      "age                 0.0046      0.005      0.967      0.334        -0.005     0.014\n",
      "child_marriage      0.1306      0.079      1.657      0.098        -0.024     0.285\n",
      "no_edu              0.0238        nan        nan        nan           nan       nan\n",
      "primary_edu         0.0126        nan        nan        nan           nan       nan\n",
      "secondary_edu      -0.3011        nan        nan        nan           nan       nan\n",
      "higher_edu         -0.8067        nan        nan        nan           nan       nan\n",
      "electricity         0.0678      0.179      0.378      0.705        -0.283     0.419\n",
      "radio              -0.0557      0.105     -0.531      0.595        -0.262     0.150\n",
      "tv                 -0.0250      0.096     -0.260      0.795        -0.213     0.163\n",
      "refrigerator       -0.0890      0.103     -0.863      0.388        -0.291     0.113\n",
      "bicycle             0.2104      0.088      2.386      0.017         0.038     0.383\n",
      "improved_sani      -0.3356      0.108     -3.113      0.002        -0.547    -0.124\n",
      "unimproved_sani    -0.3997      0.174     -2.298      0.022        -0.740    -0.059\n",
      "open_defec         -0.1506      0.130     -1.161      0.246        -0.405     0.104\n",
      "safe_water          0.2622      0.278      0.943      0.346        -0.283     0.808\n",
      "unsafe_water        0.6514      0.308      2.118      0.034         0.049     1.254\n",
      "shared_toilet       0.3776      0.123      3.072      0.002         0.137     0.618\n",
      "sturdy_walls       -0.0494      0.252     -0.196      0.844        -0.543     0.444\n",
      "dirt_walls         -0.0087      0.242     -0.036      0.971        -0.483     0.466\n",
      "no_walls            0.3966      0.268      1.479      0.139        -0.129     0.922\n",
      "sturdy_roof         0.3891      0.356      1.093      0.275        -0.309     1.087\n",
      "mid_roof            0.0571      0.356      0.160      0.872        -0.640     0.754\n",
      "bad_roof            0.1421      0.366      0.389      0.698        -0.574     0.859\n",
      "no_roof             0.0736      0.629      0.117      0.907        -1.159     1.306\n",
      "hard_floor          0.0090      1.728      0.005      0.996        -3.379     3.397\n",
      "dirt_floor          0.4164      1.729      0.241      0.810        -2.973     3.806\n",
      "wood               -0.6600      1.734     -0.381      0.703        -4.058     2.738\n",
      "natural_gas        -0.4950      1.734     -0.285      0.775        -3.894     2.904\n",
      "animal_dung        -0.5540      1.741     -0.318      0.750        -3.966     2.858\n",
      "lpg                -0.8232      1.747     -0.471      0.638        -4.248     2.601\n",
      "grass              -0.4364      1.739     -0.251      0.802        -3.845     2.972\n",
      "charcoal           -0.4718      1.752     -0.269      0.788        -3.906     2.963\n",
      "biogas             -1.7648      2.038     -0.866      0.387        -5.760     2.230\n",
      "kerosene          -17.3321   6549.043     -0.003      0.998     -1.29e+04  1.28e+04\n",
      "electric_power    -20.1956   2.58e+04     -0.001      0.999     -5.05e+04  5.05e+04\n",
      "coal                0.5014      1.881      0.267      0.790        -3.185     4.187\n",
      "Intercept          -1.0715        nan        nan        nan           nan       nan\n",
      "===================================================================================\n",
      "age                1.004567e+00\n",
      "child_marriage     1.139540e+00\n",
      "no_edu             1.024100e+00\n",
      "primary_edu        1.012643e+00\n",
      "secondary_edu      7.399765e-01\n",
      "higher_edu         4.463213e-01\n",
      "electricity        1.070138e+00\n",
      "radio              9.457778e-01\n",
      "tv                 9.753464e-01\n",
      "refrigerator       9.148727e-01\n",
      "bicycle            1.234227e+00\n",
      "improved_sani      7.149046e-01\n",
      "unimproved_sani    6.705479e-01\n",
      "open_defec         8.601851e-01\n",
      "safe_water         1.299840e+00\n",
      "unsafe_water       1.918239e+00\n",
      "shared_toilet      1.458748e+00\n",
      "sturdy_walls       9.517616e-01\n",
      "dirt_walls         9.913722e-01\n",
      "no_walls           1.486722e+00\n",
      "sturdy_roof        1.475661e+00\n",
      "mid_roof           1.058737e+00\n",
      "bad_roof           1.152695e+00\n",
      "no_roof            1.076346e+00\n",
      "hard_floor         1.009025e+00\n",
      "dirt_floor         1.516559e+00\n",
      "wood               5.168520e-01\n",
      "natural_gas        6.095405e-01\n",
      "animal_dung        5.746553e-01\n",
      "lpg                4.390191e-01\n",
      "grass              6.463716e-01\n",
      "charcoal           6.239045e-01\n",
      "biogas             1.712276e-01\n",
      "kerosene           2.970070e-08\n",
      "electric_power     1.695004e-09\n",
      "coal               1.651037e+00\n",
      "Intercept          3.425032e-01\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ANTARES/anaconda2/envs/snakes/lib/python3.5/site-packages/statsmodels/base/model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/Users/ANTARES/anaconda2/envs/snakes/lib/python3.5/site-packages/statsmodels/base/model.py:971: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return np.sqrt(np.diag(self.cov_params()))\n",
      "/Users/ANTARES/anaconda2/envs/snakes/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/Users/ANTARES/anaconda2/envs/snakes/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/Users/ANTARES/anaconda2/envs/snakes/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1814: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    }
   ],
   "source": [
    "results = model.fit()\n",
    "predict = results.predict()\n",
    "print(results.summary())\n",
    "print (np.exp(results.params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'> <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "X, y = df2.drop(['physical_dv',\"ever_emo_violence\",\"u16_marriage\"], axis=1),df['physical_dv']\n",
    "print(type(X), type(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = sm.Logit(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.574918\n",
      "         Iterations 6\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:            physical_dv   No. Observations:                 3685\n",
      "Model:                          Logit   Df Residuals:                     3670\n",
      "Method:                           MLE   Df Model:                           14\n",
      "Date:                Thu, 27 Oct 2016   Pseudo R-squ.:                 0.03423\n",
      "Time:                        18:58:57   Log-Likelihood:                -2118.6\n",
      "converged:                       True   LL-Null:                       -2193.7\n",
      "                                        LLR p-value:                 6.613e-25\n",
      "==================================================================================\n",
      "                     coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
      "----------------------------------------------------------------------------------\n",
      "age                0.0035      0.005      0.768      0.443        -0.006     0.013\n",
      "child_marriage     0.0996      0.078      1.278      0.201        -0.053     0.252\n",
      "no_edu            -0.9665      0.309     -3.126      0.002        -1.572    -0.361\n",
      "primary_edu       -0.9908      0.311     -3.191      0.001        -1.599    -0.382\n",
      "secondary_edu     -1.3715      0.305     -4.502      0.000        -1.969    -0.774\n",
      "higher_edu        -1.9650      0.322     -6.098      0.000        -2.597    -1.333\n",
      "sturdy_walls      -0.0042      0.243     -0.017      0.986        -0.480     0.472\n",
      "dirt_walls         0.0300      0.235      0.128      0.898        -0.431     0.491\n",
      "no_walls           0.4077      0.265      1.541      0.123        -0.111     0.926\n",
      "sturdy_roof        0.4211      0.348      1.211      0.226        -0.260     1.103\n",
      "mid_roof           0.1731      0.346      0.501      0.617        -0.504     0.851\n",
      "bad_roof           0.2308      0.355      0.649      0.516        -0.466     0.927\n",
      "no_roof            0.1423      0.617      0.231      0.818        -1.067     1.352\n",
      "hard_floor        -0.4848      0.488     -0.994      0.320        -1.441     0.471\n",
      "dirt_floor        -0.0121      0.487     -0.025      0.980        -0.966     0.942\n",
      "==================================================================================\n",
      "age               1.003549\n",
      "child_marriage    1.104714\n",
      "no_edu            0.380425\n",
      "primary_edu       0.371266\n",
      "secondary_edu     0.253729\n",
      "higher_edu        0.140152\n",
      "sturdy_walls      0.995841\n",
      "dirt_walls        1.030498\n",
      "no_walls          1.503372\n",
      "sturdy_roof       1.523698\n",
      "mid_roof          1.188989\n",
      "bad_roof          1.259587\n",
      "no_roof           1.152965\n",
      "hard_floor        0.615800\n",
      "dirt_floor        0.988004\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "results = model.fit()\n",
    "predict = results.predict()\n",
    "print(results.summary())\n",
    "print (np.exp(results.params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1gblTGmL+kr3eHSQfQ6K4KJCD+/z5c2bNmsXatWvR0tIqqTIVG4IgSKuO80Cp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\ny5b+VK1ajT//DOHhw4cMHTqcu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rbqgumRkZGIosiiRYtYuHBhgXoFQSAhIQFbW1uioqJo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NzdXQy6eoYYNmwwqanJJCQkYmJijLW1DfHxt5k3T1d5qVevd1iyZAHffDNPr9/Wrb/Svn1H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8sw07d+7E/PmL0aZNK5lr3XeMep3gmpiYQFlZGb1796Yc+gkhYDAYGDRoEAARTRgAeHt7\nU876I0eOpDmAW1tbIz8/H05OThAKhfD09MSPP/7YIPQ430s0qrTyzs4fqHJatDADh2OFfv164vTp\nU2jRoiVVrrEcxGlgMKCh8X2uMmlqWkBHRwebN6/HtWvXQAhB5n/5yoqzoWfdCwoaxmAwmSh4eRkd\nOsSAz+fD0tJSamDFlClTcOPGDQgEAlhaWn6yXjXV97ox6pCM9NbTM4CiohKGDPkFDAaDJq9mLdsD\nENOEAbx7UbC339uodhD4tlkUKkMoJDRZaWlpY9261bh5Mw4AqElTenoaJk2ajtatOWCxWFi0aD7u\n30/8IrpVY/u/IxYFaeWFQgIWiwU1NQ1aujjf3r27MWzYSLRvLzoNTeZa9/2izhPc4uJipKamUkYg\nLS2NogkzMDCAvb09bty4gWXLlsHAwADh4eF48eIFFYi2e/duyMnJgc1mIygoCIcOHcL48ePh4OBA\nvVhRUVGQl5eHrq4uxo8fj4iICAQFBckcwBsYqqqqMDExRXp6GhwcnEAIQU5ODvT06KsWdVmNrzcQ\ngvz84mpXLpr6qsPHUFJSgvT0NPz3XYjk5OdQU9OiBVZs2bIZmpqaEOq4Q5D7DILCLJh6TBDdm60I\nBXUjvM99gcHTpsHU1BTz588Hm82mAj7Nzc2hr6+Pc+fOYfbs2WCz2ViwYAE0NDRkelVHfCwQxtra\nBnfu3EJAwDAcjcuEIPcZyoqyoNpcFLGd/yoWYDABBhNz586V2cEGhFi3WCyRcr15k14laGnr1nDo\n6zfHH38swM6dfyM19RXCwrYCEAUttWnDwYUL5zB16lSZbjUBpKWloU+f7pCXl4eNjR3Gjp0Iff3m\n1ElnTk6uVF6Za933izpPcB88eEA7HSskJATAh9OxnJyc8PTpU/z1118Sp2NtpbYG/vnnH0yZMgVv\n377F8uXLIRAIUF5ejj59+gAA9XUl3kJYuHAhAKC0tBT9+/evr+eWQQqKi4uRnp6OH3/sSaNj4XI5\nAESySUxMpK3Gs1gsxMbGoksX0VGylelY6hMVFR9fuWiqqw4fw4MHD/Dbb+MovVqzJhQA8OOPPTBr\n1jwMHDgU165dxb17dyHMOwllbXMYu42CnOKHwBdBcQ7YKvr4+++/IRAI4O7ujitXrtBWLrKysuDg\n4IC//voLTCYT3t7eOHnypGzloo548uQRTV7iQBixvOzs2uL58xQcORKNgsJCKGubw6jdKDCYIpOb\n++IqdCy6oijnlcwONjAqy0p8oqNYVgEBwxAbew337ydi+vSJMDdvg9GjxyM19RVSU18BANLSUmFm\nZi7TrSYAa2tb/PHHPJiatgCPl42tW8MxadIY/PPPXlpAriQay7VOdpIZakxvaHwWTVh1KC4uhqqq\nKnR1daGpqUlNbtPS0pCdnY0OHTqAy+VSp2MNGTIEKSkpAEB9XXXu3Bm9e/em6vTx8cGjR4/g4uJS\n1ybLUA02bFgDDw9PaGlp4+bNOMTEHAVAoK+vj4SE2+jQoSMiI8NRVlYGX9+uWLNmDZo3b06txquq\nqqJfv35YunQp1NXVpdKxyFA7SFIZaWqqIDe3qMok2dHRGY8fP0SZkIWyomzwnp6BDrc72ErNUFac\nA6GgCM1ad0XsoaVUmSFDhlSh6AsLC6uWok+G2kGSJkyarADRyqG8vDyYbGWw5FWoyW1ZcQ4qSguh\noscFU9UYV3bNACCzgw2FuuhWaTnw9EUaQtZvh6qxC1jyqqgoLQCfXwCBug3uxR2nysh0q3HQrp3k\nymtrWFraoF+/njh//gzlWtdUIDvJrP7q/hTUO4uCvb09li1bhlatWiErKwvr1q1DQEAAjh07Vm80\nYvWF750mLDs7EwsWzEZeXi7KhSzIaxhBtaUvQg+IP2C0AQ0OwiIisG3bFjg7OyMiIoL6YAGAWbNm\ngcVi1UjHIkP9wNraFoMHD8ehG9lQUtUE7+kZpF3fhJZegSj/j36KyaYbkobUrab6XjeFNtjZ2aF1\n6wUoLi7C+r1xeJ/9qIqsWAqqgASNWOMGcH7fq0x2dnZgsUbg4PUsSrfyk0+ipVcgSvlvVf+eJQAA\nIABJREFUwQMDajqtaGUaS17fOk1YXcs3a6YOU9MWePMmHS4uLlRAroHBB87ixnKtk51kJsJXc5LZ\nx+Dp6Un938LCAnZ2dvD29saJEydgZmZW37f7LHzvNGHr168FANy8eRPTV19Cs+ZtquRp1rwN8t4l\nU6tMlSEvL485c+ZQq/EyNBzatXOHvLw8jibegopuGyg2M8GLc0vBf5sIeVW9L9qW5ORkVFRIN9qA\naBCpzWDxrepW9+4il52bN29CXuM1tM3cGk1WH8P3RhNWnbx0dJrhSMKlRtetj+Fbpwmra/mioiK8\nfp2Ofv36wtpaFJD76FEiXF1FbnKN6VonO8nsQ3pjoMF5cNXU1NCyZUukpqbC1dW1XmjE6gvfO02Y\nGI31dSXD54HFVgJbRRdlRTwoa5sDAIRldFk2lG4NGjcPSnrW1V43VeZh3YpF1bf9O9MtabKqKC2k\n5W0sO/g90YTVdpWpMXXrY/jeacLWrVuFdu3aIz8/H3l5uTh+/F8IhUKoqmri3LnL8PDoiLVr1yI/\nn48ff5S51n3PaPAJblFREVJTU+Hn5wcTExPo6Ojgxo0bVPTpx76udu3ahbCwMGRmZmL//v20KOPP\nRUPRhPXr9xMcHZ0xd+6CTypfH22oS3mxASnmpSA9NhzG7mOhrC1abX93dy8KM5MASF/BlcSuXbsQ\nGRmJ7OxscLlczJ49W2YwGhDC8lKUFWdDTtEJbGUtsBRUISh4Q12vSbcyMzMRGRmJzMxMlJWVVdla\n/RjUtE2hYmhT7XX50tvfNJXRu3dv4e/fC8uWLYOXV5eP6pY0WRVnJ4OhbADg8+xgdHQ0Zs2ahfPn\nz8PQ0LDB+qCp27HagMvlYtSosRg2bJTUusVoTN0CRHa3mPccZp2Dq1yrT5qwGzeuY/Pm9Xj16hXK\ny8tw4sR5aGio17p8fbShruXfvcvA3LnByMsvAJOtBLZqc6i29MWKfY/+y6EFos7Bxs0R2Lr14651\nxcXFYDKZEAqF6N+/v2zcqgFLlszH3bt3sH//v43dlFqh3ie4ISEh8PHxgaGhITIyMrBu3TrIycnB\n19cXADBs2DBs2rQJpqamMDIyqvHr6vHjx9i8eTOMjIxgbW0NGxsbjBo1CidPnqwSJSkNRblv8L78\nKjTNOtT3Y9YIMQfwtwHGf7+aERMTg2XLlmHRokWwtbXF9u3b6ySrpoT79xMRGxuLAQMGQUWl7oNT\nQ2HDhjXQ09NDRSkfJTkvwXt6BgwGC2qGIp5NzVae4CWfw/nz52vUrblz56KgoADjx4/H2bNnkZWV\nhZCQEHh7e0uV1bVr13D8+HHcv38fKSkpMDAwALSrD3ISFPGQ9DgWP/3UFcXFRdDV1Ufnzl0wevT4\nhumYRkJNei4O4MzJ4aGsMANvXl6UIqvzUGvRAUlJSZ9lB8XsADJ8OhpLt6pHw8u0oCAf8+YFw8zM\nHIGBM8Fmy0NRsemf5LVgwZ9ISLiNeWFnwCjLh7qJM9hKmrQ8tXWtc3JywsyZM7FgwYKvftz6EhC9\nk1+Pral37oaMjAwEBgaie/fumD59OrS0tLB3715oaopewNGjR2Pw4MGYO3cu+vfvj9LSUqlfV97e\n3ggLCwMg+uqOiIjAggULoKioiIMHD9aqLcV5b5D38mp9P6IMUrBt2zYMGDAAffr0gbm5eZ1l1ZRw\n7949bNu2BXx+4cczf0FkZmZg+/ZI8B7sx9s7u8GSV4Fph0lgyYv88bRad4KSnvVHdYvBYIDFYiEy\nMhKmpqY4cuRIjbI6duwYYmJioKamBn19fal5xHif/wapV9eiuDAfAwcOxrRp/0OXLt2QnZ1Vfx3R\nBNC8uQEuXYqlMRxIIjMzAwsWzMbixfOQ//ycVFk1a9ke/FdXP9sO9unTB4mJiZ+8eitD4+lWddC3\n74eWnX6v12esjMePH6GkpASjR09Ajx690LXrj2CxWA16z/pE+fs88J6eRVlx7ifX8S2NWzJURb2v\n4IaGhn40z+TJkzF58uRqr8vLyyMoKAh79uzB2rVrqa9kAGjfvj3u3r1bL239VJSWvoeCQs20H98T\nysrK8PDhQ4wdO5ZKYzAYTUJWnwbS2A2QCvHKxaLtt6QGBAKAqpETruyKqrYOBoOB/Pz8OunV9OnT\nsXjxYrBYLIwbNw7JyclS8xFC8O7uHsir6sHe0QWDBg2tw9M1HRBCUFZWRpu8iFFRUQFCCHVYTXWr\nbAsW/AkANcpLh9MVchqtalxlqo0dZDAYUtvaWJDso68FjaVb1dfFbPCFspwcHgA0qV2qOoF8np2u\nbtxydXX9SsctGSqjyVqg3NxcVFRUSKUUe/HiBQCRf+/q1atx7tw5ZGVlQU1NDVwuFzNmzMDSpUvx\nnp8JgIGnx2YCANjKmmjlE4T8tFvISNwP0zaOuHcvgar76dMkrFmzElOnzkCbNhZgMhlYvXoF+Hw+\nhgwZgQMH9iI19RU6dOiIfv0GAABOnDiGK1cuo7i4CK1amaF//4EQCEqRm5uDu3fvoLi4AIGBgejb\ntz98fH6gPUtKyjOEhi7HiBGj4ewsfcuXyWRAVVURhYXvsWTJQujo6NC2ehcvno+3b9/gjz/mwdDQ\nCABw69ZN/P13BObOXQgDAwO8f1+I6OjDSEp6gpycHMjLy4PD4cDPzx/a2tpITk4Cn5cKBXk2AKAw\nJx2CMlGEvKCkAERYTmvT8ePHERkZiZcvX4LBYEBPTw/l5eU1yqo+kJT0CKWl0gPiJPtJKPw0w8dk\nMnD6dAwOHToEAPD3/4m6Ji8vjxYtWmLqVPpkhBCCWbP+B3Nzc4wZM75e2lBTHWJZVYeargF0vQoK\nCkJ8fDzOnz9Pk9W6deuwYcMGiu+6ticxFWc9hYCfAaN2I5GTnYRbt+IgJycHJrPqRtHHnvPGjevY\nsWMbpk//H27fvolbt26ioqICTk7OGDBgEMrLBTh4cB/u3LkDAPDw8ISfXz9aHWfOnEJiYgIyMjIg\nEAhgYGCAbt26w8HBidaGIUOGwMvLG61ameHkyRhkZWVi1KixMDIywdy5wfj5535gMJi4dOk8eLwc\nBAXNhpKSEubODcaYMWPg4OACoZDg9et0nDt3Bs+eJSM/Pw9KSsowNjZGQSE9Cr/gxUUI8tPBlFdB\neUkOunY9jZEjR+Lo0aO4efMmkpKSAIhWbH/++WeUl5djypQpKCsrA5PJhLOzM8zNzSl5SfPB9fHx\ngYWFBX799VeEhIQg+VES2Cra0LPpDWVtc/Df3gfv6RmUFWVDTkEN5841h42NLdXGf/7ZioSEO/jj\nj3nYs2cXUlKeQVFRCZ6eXvD17Unl4/F41faRsbEx+Hw+Dh8+iIcP76OkpAT6+s3h4/MD3NxERxZX\nVFQgKCgQdnZtMWTIcFo/vX//HjNnToeXlw/69fOHqqoi8vIKERNzDDdvxiM3NwdqaupwdnbBTz/1\noU2oy8vLcfjwQcTHx6G8vAwWFlwMHBgAQOQ/LWn3xahOt97npKD43T2Ul+TCySkKhoaG8Pf3x9Ch\nog+4/Px8hIWF4eLFiygvL0dgYCBcXFwQGBiIbdu24cyZM2jdujXi4+MxdOhQrFq1ClFRUYiLi4OC\nggIcHR1x48YNDB48GG5ubsh8dBxv7kQBDAaMXEZARY9DtUVYXop7ty+jd+/TyM/Ph5KSEoyMTODn\n1xcmJqYAgOfPn+Hy5Qt49uwZCgoKoKamDgcHR/Tu/TPYbJGNX7VqBZ49ewoAGDVqCADAza09hgwZ\nDiaTgXfv0rFv3368ePEcFRUVaNGiJXr18oO5eesq/SMNTCYDy5YthqamNkaPHkelL15c83ilr98c\nOTk8nD59EsnJScjO5lUZrwDg33+jUfD8HAAG0mPD/qudgWacHpBXE/m156XdRkBAAB49egQGgwEX\nFxf8/vvvaN1a9Ay5ubkoLy/H9OnTcezYMSxcuBC3b9+Gjo4OteP8uWAwGHjw4C7k5RWq7SdJO/j6\ndTr+/HMhxo2bCFtbkXtMauorhIQsgYmJKYKCZlNl169fg5KSYsycOQuqqoo4duwELl26gKysTKio\nqKJtWwf06tUHSkrKtHveuXMLp0+fxNu3b6GgIA9raxsMHhwANluJZovv3k3A0aOHkZ2dBV1dPfTs\n2Rs5OTwIBKVV9Kehx61PRZOd4NYGc+fOxZkzZzB48GCYm5sjLy8Pt2/fxvPnzzFhwgQUFhYiIyMD\ns2bNAiEEysrK6Ny5Mw4dEmLW/QPYHr6Ctq2npqYIBoMBJyd7ikh969Zw8Hg8bNkSBl9fXwwfPgza\n2trw8emI1atX49ixf9GpUyd07NgRjx49Qnj4RgCAgYE+vL1FlGm7du1CUtIjLFmykNb+q1cvQFVV\nFVOnToKCgnQFkMT1650QExODzp07AhAZ1nfv3oLFYoHFIlT69euXoK2tjUGDRCcenTp1CtnZmejf\n3x/6+vp4/fo1oqKisHnzeqq+iROB+Ph4DBsWg/VzfqGePziYh/j4YqoN165dQ2BgIDw8PODv7w9A\ndArX8+fP6y7ASji6Uvp2rwg1Xas/GBnpQyAQICYmBrNmzUKzZs0AAKmpqdi4cSPatrWmjCwgooEq\nKMjHr7+OoOTdkBDLqj5Qne9mbXw6z+9bViUtJCQE224x8OcETyxfHotp0yaBzWajS5cumDdvHjQ0\nNKTUJB0FBaLVpRMnjkJXVxczZgQiMTERR44cgaUlBwkJCTA0NMTMmTNx6dIlnDt3Bl260A9FWLRo\nLjp37gxzc3OUlZUhJiYGkZHhCAsLg5eXF+1+6empePDgHgICAqCpqQkHBweoqakBAO7duwuBQIDh\nw4dDXl4eXbv6UBRpZmZmlNz//vtvCIXlGDw4ADo6Onj27Bn27t0LLoeNvRKrtOPHp+D8+WQYGehg\n4MAZqKiowMaNG1FeTv+QBICLFy8CAAwNDWFlZYXr168jPj4ejx49QqtWIl7W6uT16tUrzJgxAwMG\nDEDv3r0RGRmJvPu7MHX+fKxadR5Txw8FIQSbN29GTMy/mDLlw4t19uwJJCYysXVrOBwcHODv3w9X\nrlzB8eP/omVLE2oH7vXr19X2kaamJvz8/JCWlobBgwfD2NgYJ0+exI4d22BqaoQhQ0QTq0uXuuPs\n2bPw8mpPm6QePnwYFRUVmDhxHKysrEAIwahRo3Dnzh388ssvMDMzQ1JSEvbs2QNAiPXr11Nlf//9\nd1y8eB4//fQT2rZtixs3bmD79kgwGAy0bGlK2UpJSNOta9euYeTILfDw8MAPP4gWKFJSUpCQkEBN\ncNPS0nD+/Hl4enri+fPn6N27Ny5duoShQ4fCw8OjimwiIiKQlZUFBoOBsWPHYsuWLSCE4NatWzh9\n+jQmj/8V58+fx8OHD5H7IAqHLl6kdCcwMBBnCrKrjHk6Os2oZ4qNvQwVFRWMGDECmpqauHfvHqKj\no6GgwMbq1aIT95SV2Thx4gT279+PKVOmwMjICKamprC3t0dsbCz+/PNP2NjYYOrUqWAymYiOjsa6\ndauwe/du2Nraoja4fr3jZ41X/fr1kzpeKSgogMMxg4GBPnbu3Ilx48ZTFKQeHh7Q0tLC4cOHERwc\nCRWuJ2bMmIH3798jKioKAQEBOHToEG3cr6iowMiRI+Hk5ISgoCCcO3cOeXl5tXpGoH7HLUII1q9f\nDYGghOqf7du3g8lk4s2b13Bzc4KKigoIIZg5czp++eUXeHt7Yt26ddi7dzc8PDwwevQovHjxAlFR\nUcjLy0FUVBTlehIdHY3IyHDY29tjyJDB4PF42L59O5Yvf43Dhw9TAZFXr15FZORmtGnTBiNGDEde\nXh527tyJ5s2bQ0lJUar+1IT6HLfqBNJEIRAIiJWVFTl79iwtfebMmWTChAmEEEKcnZ3JokWLqq1j\n7NixxMfHp0p6dHQ04XK55PXr17T0uLg4wuVySXx8PJU2ePBgwuVyyb59+2h5eTwesbGxIePGjaOl\nh4aGEg6HQ4KCgqi0vXv3Ei6XS54/f06llZWVETc3NxIcHFxt+yvj5MmThMvlkpSUFEIIIefPnye2\ntrZkwoQJZPr06VS+Xr16kcmTJ1N/l5aWVqkrMTGRcDgccuTIkRqfPygoiNaHS5YsIc7OzrS6aiOr\nrwmRkZFV3o8XL14QDodDdu7cScs7f/584ujoKLWPmyIkZSUpW0lZrVu3jnC5XKnlq9MpQggZP348\n4XA4pF27duT3338np0+fJmvXriXW1tZk4MCBdWpndHQ04XA4ZPTo0bT0AQMGEC6XSxYsWEClVVRU\nEC8vLzJkyBBa3soyKS8vJz179iTDhw+npXM4HGJlZUXplRjp6emEw+EQZ2dnkpubK/XaoUOHqr0f\nIYQcP36ccLlccuvWLSqtY8eOhMPhkKysLCotNTWVcLlcYmFhQWuXhYUF4XA4lG49efKESvf19aX6\nqvL76u3tTbhcLklMTKTSrl69SjgcDmnbti159+4dlS62T5X1nsvlkiVLltCeZ+zYscTW1pbqj5r6\naNu2bYTL5ZJjx45RaeXl5WTAgAHE0dGRFBUV0dp18eJFWvnRo0eTLl26UH8fPnyYWFlZkTt37tDy\n7dmzh3C5XJKQkEAIIeTx48eEw+FUGRsCAwMJl8sl69atI7WFNHtXGQKBgPpXrFuvX78mtra2pE+f\nPqRt27ZkwoQJJC4ujnA4HNKjRw+yZs0aSsf++ecfYmFhQWxsbEhaWhohRNT/np6eVWzOx8Y8QqS/\nh5s3byaWlpbk7du3VJr4vXnw4AEtb9euXavoXWlpKencuTP59ddfa7y3JBp6vBLXL/neEkJIUVER\ncXFxIXPnzqWl83g84uzsTObMmUMIEcmLw+EQDodDQkNDqXyNPW6NHTuW9O/fn/p78uTJZPLkycTa\n2ppcuXKFEELIw4cPCYfDIRcuXKDmIqNGjaLVs3PnTsLlckl0dDQhRDTnaN++PenVqxetjy9evEg4\nHA5NL3r37k08PT1JYWEhlXb9+nXC4XCqtf9NEY1zQHAtwGazYW1tjdjYWCqNEILY2FiKjFlNTQ2J\niYnIzMxs0LbIy8vDz8+PlhYbG4vy8nJqFUKMYcOGVSnfvXt3EUH/0aNU2pUrV5CXl4devXrVuh3O\nzs7Ulz4A3Lp1C3Z2dmjfvj2VxufzkZycDCcnJ1r7xSgvL0deXh6MjY2hrq6OR48eoS5QV1dHSUkJ\nrly5QqXVRlZfO1q2bAlLS0ucOHGCShMKhTh9+jR8fHyalA9kTWhIWRUXi1b67e3tsXz5cnTp0gWT\nJ0/Gb7/9hoSEBNo9awMGg4G+ffvS0uztRdt2kulMJhM2NjZIT0+n5ZWUSUFBAfLz8+Hs7Cz1nXd1\nda32IJpu3bpRK/k1QfJ+AoEAubm5sLOzAyGEuqdQKERWVhaUlJRoLj0mJiZo0aIFrT7yn48hh8PB\nxYsXkZubCz09PaioqIDBYEhd8ZVE69ataXRH4r5zc3OjBQuK25iWllaljoCAgCp/CwQCXL9+nZYu\nrY8uX74MHR0d9OjRg0pjsVgYOnQoiouLcfPmTao9mpqaiImJofIVFBTg+vXrFPsOIFrZMzMzQ8uW\nLZGbm0v92rVrB0II4uLiAACXLl0Cg8HA4MGDae0ZNmwY1ae1hTR7VxnibX82mw0rKytcvHgRioqK\naNWqFfLy8lBaWkrTrb59+9LcdpydnQGIXFKMjY1p9aqqqtLe69qMeZLvYUlJCXJzc+Hg4AChUPhR\ne//48WO8evUKPXr0oPVxYWEh3N3dqXGmNmis8er69evg8/lVngEQvevi94TNZlPv7MCBAwE0jXHL\nyckJDx8+xPv37wEAt2/fhpeXFzgcDq0vmUwmHB0dcf36dZSXl1eZe/Tv3x8qKirULtCDBw/A4/Ew\naNAgWh97eXnBzMyMypeVlYUnT57Az88PKiofDhRxd3en3Du+FjRpF4Xhw4cjODgYNjY2FIXH+/fv\n8fPPPwMQbUMFBwejU6dOsLa2hpeXF3r37g0TE5N6bYe+vn6VgAnx1pypqSktXUtLq8pWrJqaGry9\nvXHs2DH89ttvAICjR49CX18fbm5utW6HtrY2WrRogVu3bqF///64ffs22rVrB2dnZyxatAjp6el4\n9uwZCCGU0QSA0tJShIWF4dChQ8jIyKCMPIPBAJ/Pr31HABg0aBBOnjyJMWPGQE9PDx4eHujevftH\nZfUtoHv37li9ejUyMzOhp6eHuLg48Hg82iD8NUAsK2tra5SXl2PevHn1IitFRZGLT+X++OmnnxAa\nGoqEhAS4u7tXU1o6DAwMaH+L3Qakpefn59PSLly4gLCwMDx+/BgCgYBKl+YTbGRkVG0baromifz8\nfKxbtw4nTpwAj8ej0iX1jMfjoaKiQmoAVnUuHElJSXjy5An2798P4MPEVzyxqg6V+0i8/di8eXNa\nurhPCwoKaOlMJrOKLW3ZsiWAD/ZPDGl99ObNGyq/JMzMzEAIoepgsVjo2rUrjh8/jrKyMrDZbJw6\ndQoVFRXo3r07Ve7Vq1d4/vy51HeIwWBQff7mzRswmcwqtlns0lEXVGfvJE/sJIRg+/btiIqKQmpq\nKu7du4f9+/dTwX+EEPz888949uwZAFH/p6SkUOXV1UW8s5KTCclrku91bca8t2/fYs2aNbhw4QKt\nbG3s/cuXLwEAM2fOlHqdyWSCz+dT70xNaKzx6uXLlyCEUC4kkmAwGDRe4jZt2iA+Ph43btxoMuOW\ns7MzysvLkZCQgObNmyMnJwfOzs5ITk7G7du3AYgmuObm5lBXV8ebNyJ+5sq6xmazYWJiQl1/8+bN\nfy469HyASCfF8Qzi/JU/uAGRDtV1Uawx0aQnuL6+vsjNzcXatWuRnZ0NS0tLbNmyheKn6969O1xc\nXHDmzBlcu3YNkZGRiIiIwPr162kGqDKq8y+s7ujR2vjHfgx9+vTBqVOncPfuXbRp0wYXLlyosjpS\nGzg5OeHGjRsoLS3FgwcPMGnSJFhYWEBdXR23bt1CSkoKlJWVYWVlRZVZuHAhDh8+jGHDhqFt27aU\ncZo2bRqEwrqRcIv9m65evYrLly/j8uXLiI6Ohp+fH/73v/9VK6tvAb6+vggNDcXJkycxdOhQnDhx\nAurq6ujQ4cvyLH8uxHq1YsUKFBcXIykpiSarmo7grQl6eqJgKmnBhkDVCVRtUB1tkbRJquTq3K1b\ntzBhwgS4urpi/vz50NXVhZycHA4ePIjjx49XKauoWD0rSk3XJDFlyhQkJiZi5MiR4HK5UFFRgVAo\nxMiRI2ulZ9JWFxkMBrZu3Yrz58/jxIkTyM/PByEENjY2+OOPP2qsT1ofAdX3aV1XNyVR2z6qDj16\n9MDevXtx+fJldO7cGSdPnoSZmRk4HIkAK6EQFhYWCA4OltrWyhP6+kBN9m7p0qUAgE2bNmHt2rXw\n9/fHlClTcPv2bZw8eRI8Ho8KvpW0gywWS6qOVTcuST7rx8Y8oVCI4cOHg8/nY8yYMWjVqhWUlJSQ\nkZGBoKCgj8pYfD0oKIjW95JQVlaWmi4NjTFeCYVCMBgM/PXXX7R4CTEkPy6NjY1x9+7dJjVu2djY\nQEFBAbdu3ULz5s2pDwVnZ2dERUVBIBDgzp071DHDMlSPJj3BBURbYjVNBHV0dDBw4EAMHDgQOTk5\n8PPzQ1hYWI0TXPEXc0FBAc3ZvPKqRE0Qr1i8evWKtq2Uk5NTZSUJADw9PaGpqYmjR4/C1tYW79+/\nr5N7ghjOzs44dOgQjh8/DkII2rZtCwaDAUdHR8pgODg40Izl6dOnqQmoGAKBoM6rt2LIycmhU6dO\n6NSpEwBg3rx52LdvHyZMmPBJk/amhuoGGmNjY9jZ2SEmJgYBAQE4c+YMfvjhh4+upDVFBAQEIC0t\nDdHR0di7dy/tWl30QBLW1tbYt28fMjIyaOniv7/koHH69GkoKioiMjKSNqAdOHCgQe5XUFCAGzdu\nYMqUKRg//gPLyatXr2j5tLW1wWKxUFJSlQ3k3bt3VdIIITAyMsLs2bMxe7YogtrHxwdmZmYNftqS\nUChEWloabSVHzNxQm1VtQ0NDPH36tEq6OCBVsg4XFxfo6uoiJiYGjo6OiIuLo/UjINotS0pK+uiu\nl5GREYRCIVJTU2mrVZ8aCFuTvTMxMcHp06fh5uaGRYtER1P7+vpizpw5VCCjNFl/qo4BNY95T58+\nxatXr7B8+XLa+FLZpaQ6iFeCVVRU6rzbIg0NOV5VZ6dNTU1BCIGWllatnoHNZuP8+fOf+IT1Dzab\nDTs7O9y8eROGhoaU+4aTkxMEAgGOHj2K7OxsKhBcrEcvXrygzUXKysqQnp6O9u1FjCWGhoYghODF\nixdo164d7Z4vXryg5kLifyvbLnG+rwlN1gf3YxAKhSgspBPxa2lpQU9Pj9qOVFZWrpIH+KAAkv5E\nQqEQ+/btq/X93d3dwWKxsGPHDlr69u3bpeZnsVjo0aMHYmJENFQWFhawsLCo9f3EEPs1RUREgMPh\nUNstzs7OiI2NxcOHD2nbPQCoYwgl8c8//3zSSp206FLxc0huA3/NUFISneYjbcXR19cXiYmJOHjw\nIHJzc7869wRJmJiYgM/n0yYhmZmZOHv27CfV17lzZ8jLyyM6OpqWLt6uFRvaLwHxKqXkO56ent5g\nA5l4tbSynm3bto02EDOZTIrVQewLCAB37typ8mEgZkeQZAeQRF0ivT8VO3fupP29a9cusNnsWk0c\nvLy8kJ2dTfOtraiowI4dO6CiokIN0IDoWbt164YLFy7gyJEjVdwTANHq5bt376Ta6dLSUmoi2bFj\nRxBCpNrmup4OVht7x2Qyq6yMnjhxAhkZGVBUVKyiY/n5+Z+kY7UZ86p7D2v77DY2NjA1NUVkZCTl\nUy+JnJycOrW5IccrJSUlEEKq2OkOHTpAVVUVYWFhUv3U6/oMjQEnJyfcu3cP8fHxVP9oamrCzMwM\nERER/7E9iSa+7u7ukJOTq/K+79+/H4WFhfD29gYgkq22tjb27NmDsrIyKt+lS5dWnGxsAAAgAElE\nQVSQkpJCfcDp6urC0tIShw4dor1v165do9xsvhY0+RXc6lBUVISOHTvixx9/BIfDgYqKCq5du4YH\nDx4gKCgIgGhF6cSJE1i2bBlsbW2hrKwMb29vtG7dGm3btsXKlSuRl5cHDQ0NHD9+vE7b9VpaWhg5\nciTCw8MxduxYdOzYEY8fP8aVK1eqXanq06cPduzYgfj4ePz++6edUmNqagodHR28fPmSFkTh4uKC\nFStWgMFgVDEY3t7e+Pfff6GqqorWrVsjISEBN27ckMr197EtrNmzZyM/Px/t2rVD8+bN8fr1a+za\ntQuWlpYwNzf/pGdqarC2tgYhBKtWrYKvry/YbDZ8fHygqKiI7t27IyQkBCEhIWjWrFm9rHI0Fnr0\n6IEVK1Zg4sSJGDJkCEpKSrBnz54qflZJSUnUxPDVq1coLCzEpk2bAIgCY8QGVEdHB+PGjcO6desw\ncuRIdO7cmfId7dmzJ2xsbOrUvs/ZMvfy8sLff/+NkSNHomfPnsjOzkZUVBRatGhB8czWJ1RVVeHi\n4oItW7agrKwM+vr6uHbtGl6/fl3lOf744w8MGzYMI0aMgJeXFxW0paioSAWWiNG2bVscO3YM6enp\n+OGHH6CiooL8/HycPn0aFhYWGDFiRL0/ixjy8vK4cuUKgoKCYGdnR23Rjxs3rlY8oQMGDMDevXsR\nFBSE+/fvUzRhd+/exR9//FFlq9vX1xc7d+7EunXrYGFhUSXor3fv3jhx4gTmz5+PuLg4ODo6oqKi\nAikpKTh58iS2bt0Ka2trcLlc9OjRA7t370ZBQQEcHBxw48YNpKam1vmdqo298/b2xsaNGxEcHAxH\nR0ckJSXh6NGjMDU1hba2NjIzMzFx4kRq4r1y5cpP8mWszZhnZmYGU1NThISEICMjA6qqqjh16lS1\nu3WV+4PBYGDx4sUYM2YMevbsiZ9//hn6+vrIyMhAXFwcVFVVKd2vDRpyvLK0tASLxUJERAT4fD7k\n5eXh5uYGLS0tzJ8/HzNnzoSfnx969OgBLS0tvHnzBpcuXYKTkxO1G9JU4ezsjLCwMLx9+5bWP87O\nzti7dy+MjY2pQFEtLS2MHTsWGzZswMiRI+Hj40PRhNnZ2eGnn0R87nJycpgxYwZmzZqFwYMHo0eP\nHsjOzsaOHTtgYmKC4cOHU/eZPn06xo0bh4EDB6Jv377Iy8vDrl270KZNG6kfPk0VX+0EV1FREQEB\nAbh27RrOnDkDoVCIFi1aYP78+RgwQHQIw6BBg/DkyRMcOnQI27dvh6GhITUYr1ixAvPmzUNERATU\n1NTg7+8PV1dX/Prrr1XuVd2X77Rp06CgoIA9e/YgPj4e9vb22Lp1K8aMGSO1jLW1Ndq0aYPnz5+j\nZ8+eUmqsHZydnXHq1Cla5Km1tTWUlJQgFAqrbF3Onj0bLBYLx44dQ2lpKZycnKjBv3I7q+NFFaN3\n797Yu3cv9uzZg4KCAipKetKkSZ/8PE0Ntra2mDp1Kvbs2YOrV69CKBTi3LlzMDQ0hL6+PhwcHJCQ\nkAB/f/+v6mjLymjWrBk2btyIpUuXYsWKFTA2NkZgYCBevnxJG3wfPXqEtWvX0sqK/+7Tpw+lUwAw\nYcIEaGhoYOfOnVi6dCl0dXUxYcIETJgwoc7tq+tqm2R+Nzc3/PnnnwgPD8fSpUthbGyM33//Henp\n6VUmuDXdpyZO4MrpK1euxOLFixEVFQVCCDp06ICIiAh4enpWadvMmTOxevVqnD9/Hmw2G3379sWz\nZ8+QkJBAq9/GxgajRo3Ctm3bsGHDBgCi1coWLVrAx8enzu2u6VkqX2OxWNiyZQvmzZuHv/76Cyoq\nKpg0aRImViK0rO5eCgoK2LFjB1auXIkjR46gsLAQrVq1wtKlS9GnT58q+R0dHWFgYIB3797RmBck\n77Nx40Zs27YNhw8fxtmzZ6GoqEgNzpLuCEuXLoW2tjaOHj2Kc+fOwd3dHZs3b0anTp3q9F7Vxt6N\nHTsWJSUlOHbsGE6ePAlra2tERERQEzixjoldgXr16iWVEaA6Wyz+tzZjnpycHMLCwrBkyRKEh4dD\nQUEBXbp0QUBAgNRjpaX1haurK/bs2YONGzdi165dKC4uho6ODuzt7an71AUNNV7p6OhgwYIFCA8P\nx+zZs1FRUYF//vkHWlpa6NmzJ/T19REeHo6tW7dCIBBAX18fTk5OVQLI6mpnvgQcHBzAYrGgrKwM\nLpdLpTs7O2Pfvn203Q8AmDRpErS0tLBr1y4sW7YMGhoa+OWXXzBt2jTaGOXn5wclJSWEh4dj5cqV\nUFJSQteuXREYGEgLvvP09MSaNWuwevVqrFq1Cqampli6dCnOnTtHsZ98DWCQz1kmkaHO8PPzQ7Nm\nzfD33383dlNkkEGGJoSJEyfi2bNnOHXqVGM3BcHBwTh16hQVWS2DDDLI8LWhTj64mzdvRr9+/eDo\n6Ij27dtj4sSJVZyOg4ODweVyab/Ro0fT8ggEAixYsADt2rWDg4MDfvvtNxqtDiDyUwoMDISTkxNc\nXFzwxx9/fFVL49Jw//59PH78WOrqRX1DJquvC1FRUejVqxecnJzg5OSEX375BZcvX6blWbNmDTp0\n6AB7e3uMGDGiShCATFZfBh+TVW30is/n02Q1cuRIXLx4kRb8IZNV/aA+5CXTrS8D2bglQ32iTiu4\no0ePRo8ePWBra4vy8nKEhoYiOTkZMTExFE1McHAweDweli1bRvn3yMvL03jz5s2bhytXrmDZsmVQ\nVVXFwoULwWQysXv3birPqFGjwOPxsHDhQpSVlSE4OBi2trZYsWJFfT37F0NycjIePHiAv//+G/n5\n+Thz5gyNaFkoFH7U8V1ZWblO9CwyWX1duHjxIphMJlq2bAlCyH9HKkbiyJEjMDc3R3h4OLZs2YKQ\nkBAYGRlh9erVePr0KWJiYqh36WuQVWlp6UfZOzQ0NJo0M8XHZFUbvbK3t4ecnBwGDBiA/Px8HDp0\nCIQQnDp1iuJvbUxZfUsruNXJa9u2bWjZsiWWLFmC3NxcGu0am82Gnp4eZXO/Bt36UmiI8UoM2bgl\nQ73ic45B4/F4hMPhkJs3b1JpQUFBZOLEidWW4fP5xNrampw+fZpKS0lJIRwOhzpW8tmzZ4TD4ZCH\nDx9SeS5fvkwsLS1JZmYmlbZz507i7e1NbG1tib+/P+1YSmm4ceMG8fPzIzY2NqRr164kOjq6TnWc\nPn2ajBgxgri5uRFHR0cyYMAAsmDBgo+WX7duHbG0tCQ9evSg+urWrVvEysqK9OnTh6xfv55YWFhQ\nP/HxgeJf5eMlS0tLSWhoKPH29iY2NjbE1dWVtGvXrsY2SMrqyJEjpFevXsTS0pJYWVkRLy8v4uHh\nQTsS9GOy6tatG7GxsSGdOnWqlazqQ16///77Z8nqypUrdW6DGGJ5dezYsU7lK8vKx8eHzJgxo1Z1\nuLq6kgMHDpAjR44QKysrYm1tTVxcXIiXlxdxd3cnFhYWJCQkhBBSs6x27dpF/Pz8iLW1NbGwsKC9\nSw0lq+p0S3wEr7Sf+P23t7evtW5Jg6Ru1fU5PkW3CKHLysXFhXC5XEpWlXWLz+cTLpdL3N3diZ2d\nHXF2diYBAQFNSlZjxowhjo6OUst/C7rl7OxM2VpJ2yv5Ex8n29R0q7HtoKWlpdT+qm68EuNrHLe+\nljmGNNSnHayLbokhlpe9vX21trAmSOvLuuKzJrgvX74kXC6XJCcnU2lBQUHExcWFuLu7k27dupF5\n8+bRziiPjY0lXC6X8Pl8Wl3e3t5k27ZthBBCDhw4QFxdXWnXy8vLiZWVFTlz5gwhRHTGu42NDTl0\n6BB59uwZmTNnDnFxcSE8Hk9qW9PS0kjbtm1JSEgISUlJoc5ptrKyqnUdS5YsIVu2bCH3798nr169\nIhMmTCAWFhZkw4YNtSovRkFBAfnhhx/IyJEjibe3N7G2tiZ//fUXOXDgABkzZgxxcHAgp06dItev\nX6d+4jPKCSFk3LhxZMCAASQ2Npbs2LGDWFtbk9WrV9fYBrGsDh8+TCwtLcnOnTvJ5MmTiZ2dHeFw\nOMTW1pZYWFjQzvqWJqu0tDTC4XDIkCFDSEpKCpkxYwaxsLAgV69erVZW9SGv4OBgakL3KbIKDQ0l\nlpaWxNrautZtqCyvXr16EQ6HU6fykrJ6/fo12bhx40fbUFFRQY4dO0ZsbW3Jv//+S7hcLrGwsCCX\nLl0ikZGRxM3NjQwYMIBYWFiQcePGEUJE54RL0ytPT09ibW1NQkJCyKZNm4i9vT2xsrKi5NUQsqpJ\nt5KTk2nvtfg3adIkMmfOHLJ7927y6NGjetOtujxHZXnVRrcqy8rS0pL4+/sTJycn0rZtW8Llckn7\n9u1pRr06G9jUZPWt61ZoaCjhcrmke/fuxN7entja2hIul0tNfvfu3dvk5NUU7OCIESNI+/btiZWV\nVbXjluR4JU1eX8O41RR0qynYwdrqliTEH0I7d+4k6enptHGLy+V+dIIrrS8ldau2+OQJrlAoJGPG\njCEBAQG09OPHj5Pz58+Tp0+fkrNnzxJfX1/i7+9PhEIhIYSQo0ePEltb2yr19evXj6xYsYIQQkhY\nWBj58ccfq+Rxd3cnUVFRhBBC/P39yaJFi2jt8fT0JOHh4VLbu3z5ctKzZ09amqurK+nUqVOt66gM\nf39/4urqSjZs2FCn8tOmTSNr1qwh69atI/b29nV6jkuXLhEXFxeSn59PteFj5SVlFRkZSbp06UII\n+SCrlStXknbt2hELCwvStWvXGmW1fPlyYmdnR5OVg4MDGTVqFC2fpKxq287K95GUl7+/P+natSt1\nn7rKihBC7OzsSP/+/WvdBjHE8vL09CRubm61Ll9ZVuLnqK4fkpKSSNu2bYmVlRVxcXGhJrQdO3Yk\nXC6XZGVlEUII2bFjB/Hy8iIWFhZk0KBBhJDq9crDw4O0a9eOEPJBr6ZNm0aTV33LipBvX7eqk1WX\nLl1oNnD27NnEysqKWFhYUAPntyYrQr5e3fra5NUU7OD3Mm41Bd1qCnZQ3I661CEpLzHE41ZtVnCl\n9WVl3aoNPnmCO3fuXOLj40MyMjJqzJeamko4HA6JjY0lhNTPBFcgEBArK6sqnTRz5kwyYcIE6m/x\nC/+9oz5lFRAQQNq3b0+TlYeHB3F2dqblkzQUMnnVDmVlZSQ1NZU8fPiQhIaGEjc3N/Ls2TNy584d\n2gRXjClTppBp06YRQqrXKwcHB9K3b19CyAe9OnjwIE1enyKrTt1/IT8N+b3aX2DwIvItozpZSUNt\nbWBDyYoQQjr3m1rtz8XLr8o2+reGr0lez549q/GXl5f3eZ3xlaAxx6266Nb3Pm41BAICAsiff/5J\nS6usW7XBJ/HgLly4EJcvX8auXbuo8+erg4mJCTQ1NZGamgo3Nzfo6OigrKwMhYWFNN41Ho9HnWGv\no6NTxYm9oqIC+fn50NHRQW5uLioqKqSeeS8ZcclgMFBQUIKKitof4CAJFosJdXWlT67jc8vXRx0r\nV4bg2rUr2Lw5Emy2CnJzi6rkEQgEePDgPgBARUUVp06dRVGRAKmpbyAQCBATcxq+vl0BAFlZWRAI\nBDRZFRcXo6SkBAKBAPLy8jRZAfgi8moKfV0fbdDQ0IGJiQmMjVvh9u0ERERsxZAhw0AIwfPnaWCx\nlKi8795lgsPhIDe3CIqKqigrK8ObN1kwNNSl8rx//76KXmlra6OwsBACgQAsFuvTZKXaAkS3+kMu\ncvm3pb5rYjSFvv7cOqTJaubMWVXyqapqoVmzZnj8OBkcji0lq/T0DKirq0NdXSTThpIVACiZdqr2\nOcqZt5CdXQA5OelBQU2hrxtKt+oiry+lWwPGLoSKTvWH5rTRLsZfi+dUe70p9PWXGLfEqI286jpu\n1UW3ZONW/bVBjKysLGhra9PySOqWZJB+TajzBHfhwoU4d+4cdu7cSZ1ZXBPevXuHvLw86OqKXjQb\nGxuwWCzExsaiS5cuAERnhL958wYODg4ARKf3FBQU4NGjR7CysgIAxMbGghACe3v7Op1GU1EhRHn5\npwmtvuporDaEhobg6tXL2L17F1RVtaotn5iYiP+FRkNZTQuFhYU4Hp+Bs0/jIawQgICBmUsjqQlu\nWVkZdToQIJJVZWoVSVl9ieesz/JNqQ1CoRClpaXQ0zOAlpY24uPj0KpVawBAUVEhHj58AD8/f5SX\nC9G6NQcsFgtxcTfg5yc6ueb58+eoqKiAgYEBgA96lZqaSt3nc2RVEwhBrfqgqfR1Xeo4fPgADh06\niHfv3oLBAFq1MkNJyXvo6upR9WzZEoajRw+jsJAPDscSeXn50NTUpslq1qwgPHnyCGVlAupUrsqy\nWrNmDYRCIdq3b0/ZvvqWVW374GvVrfqSV3Dw//DkySMIBA0rLzVdM6gaVl9GriLhm9UtoPbjlhiZ\nmRnIz68qL0lbWNdxqy5zDODr7eum1ob6Rp0muPPnz8fx48exadMmKCkpITs7GwCgpqYGBQUFFBcX\nY/369ejWrRt0dHSQmpqKv/76C/9v77zDo6ryP/zemUx6T4AQklCUEAwkECSEQECaaFxXXAUXWQUX\nCypggf0BgiR0Qm+RFpQquohSFHRFpXekQ+gloSWT3tvc3x/jXDKkw4S08z6Pj2TmnnPvPZ8595x7\nyufbpEkTOnfuDOhDWr766qtMmzYNe3t7bGxsmDx5MgEBAUpEkyeeeILOnTszbtw4wsPDycvLY9Kk\nSbzwwgvUq1ePvLw81Gq1cn4DhUeB6zqzZk1nx45fmDVrLlZWViQkJFBQoMPGxhYLCwuysrL46qtl\ndO3ag8TEBCw0atJv7Mbcth71vbshqfTRT/ISO5B25zSHDh1SwoTWr1/fSKvmzZtz+fJloqOji2gF\n+hjaQq/SGTr0XZKSErh3Lw4LC3Ps7R24dSuWuXP10av69evPkiWLWLhwrlG69etXKyEsO3QIZsaM\nafz663YOHTpEZmYmZmZmSgx3Q71asWIFkiQRGBhITk4OHh4e2NjYAEKr8nDs2FF69w6ldetWqFQ6\nFi2K5PLli/Tt+0+ysrIYOXI4ly5dYPjwEciyjsjI+ajVKgIC9CE3bWxs8fDw5OjRQwwb9gkeHm6E\nhYWhUqmUkYknnngCBwcHLl68iKWlJSNGjGDy5Mm4ubmJelVBTKXXkSOHGDVqFM7OzkKvSqIi7ZaL\niwuxsTEsXrwQT08vOnTQzyjZ2NjywgsvMX/+HNzd6z9UuyX6GFVLvXr1ivgWJyQkYGtrW+7RW6hg\noIf169eTmprKgAED6NSpE506dSIkJITt27cD+tCOFy5cYODAgXTv3p233noLrVbL9OnTjXwtR44c\niY2NDQMHDuSVV17h9u3bTJgwwehcYWFhJCcn07dvX15//XUkSVLibWs0Gnx9fTlw4IByvCzLHDhw\nQHlDq+ts2vQd6elpDBnyNp06deKFF3rx0kvP8/vvvwKgUqm4fPkyH330PmFhn5F8cRsFOek08O+n\ndG4BXFs8j6TSKFrpdDqsrKyMztWoUSM0Gk2xWoHQqzzcuhVLSkoqBQUFSJJEWloqDg6OtGrVGoAB\nAwbSuHFTzMzMQFKhsW2Ik89LxFu0Z9Kqo0xadZQLmU+QlJbDH3/8QX5+Ph06dMDR0ZEtW7Yo55k9\nezaJiYkUFBSgUqno2rUrAOPHjweEVuXBxsaGH374jo8/HsrYsWOxsrLG2lr/gqBSqTh37gygH4la\nvXolXbt2R5JU7N+/F9CPvsfE3KR9+yC++iqK8ePH07ZtW3Q6Hb///jsAV65cISkpCUdHR/Ly8pgz\nZw4dO3bkzp07xMfHA0Kr8mIqvQIDg/jiiy+EXpVIRdqtV199kY8//oDExATGjg3XPxv/YsiQoVhb\nWz90uyW0qlratGljVPYA+/bto02bNhXKp0IjuF26dCnWhPm5554D9LHHO3TowOnTp5k7d65iSD9i\nxAgjQ/qZM2eSkZHB6tWrFRPmsLAwIxPm8PBwHBwcmD9/vmLCPG3aNMWEedCgQYwZM4ZWrVrRunVr\nVq1aRXZ2thJnevbs2YwYMaJChVGbCAoKpmfP3vj6+mJjY05ExAyuXLlC9+49Ab1WAQHtOH/+LAMH\n/psNe++So43m7vGvafLMSCSV/qehvbANWZenaDV27Fiio6OZOXMmr7zyCgcOHGDnzp14enoyZ86c\nYrUCoVdZ/PDDNgDMzFQ4Odlw7Voszz/fk+joaPz99ZXa27sF1tbW3DVvj6Nb8yJ5FORlE5+fSfv2\nT7NmzRpAP+02aNAg/vOf//D++++zZcsWcnJyCA8Pp3///gDs2bOH9957j1GjRlGvXr1yaVWXGT1a\nv/7RzEyFg4MV3323iRMnjtO6tR8JCVry8/NZvnw1Tz55X6Nbt2I5e/YUPXr0Ijr6PDqdjgkTpuDg\nYI+Tk76z1aVLF+7cucPMmTOxs7PDysqK5ORkoqKiCA4OpqCgAD8/P06ePEnPnvp6XB6t4uLigODH\nW0jVCFPpNXnyNBYunENERARQeXrVZSrSbk2YMJWGDRuyfPliwsPHsm7dd8pA2pIlC436GA/TbpW3\nbhl+D4JHY9SoUUpZ/vOf/2TdunVGev3yyy8sW7asQnlWqIO7fPlyo7+nTZtGcHAwZ86c4emn9dM5\nq1ev5oMPPqBbt24AzJgxg+DgYHbs2EFoaCjp6els3LiRuXPnEhgYCMDUqVMJDQ3l1KlT+Pn5ceXK\nFfbu3cv333+vrMEdN26cUSMcGhpKUlISCxYsQKvV0rJlS6KionB2dgYoMrVQ15g1awFwv8P0+ecT\ninSYNmz4hkGDBuPt7cMPx3JxavMaV3+dSPrds9i5+1OQl01qzBHsm3ZTtJo9ezahoaH89ttvrFmz\nRlkIPn/+/BK1AoReFSQtLQ1JkrC3tzf6/PLli2TlXiDJwhZr1ydxbdEbtbl+c1BOSizIsrIkAaBj\nx464urpy+PBhfvnlF2xtbbG2tlY6twDBwcFIkqQ0xEKrsrl69TJDhvyb3NxcrK2tmTp1Jl5eTThz\n5hSSJCllZcDZ2UWZcktMTMDMTIONja3RMQ0aNCAwMJADBw4QHR2NJElMmTKF4GB951StVuPg4GBU\n/uXR6s6dO1CPUlGrVZiZFT+hp1arjP7/MDxqHo+a/sqVy7zzziBFr4iIWTRr1ozTp08iSRL16rka\n3b+LiwtJSYmYmalISUlCo9Fgb2+vL8u/qCy9wLLUe1FJUola6c9btWX9KHnMm7dISWdvb0V4+CSe\nfbY7ly5doE0b/cjpd999w7///bYy+xQePonQ0F7s27eLnj2fJSMjnZ9+2sKUKRGP1G6Vu24JTELh\nsvTw8GDZsmVMmzaNNWvW4ObmxuTJk5W6VV4eykXBgKERdnR0BCAmJgatVktQUJByjK2tLf7+/pw4\ncYLQ0FBOnz5NQUEBHTve34HdrFkz3N3dOX78OH5+fpw4cQIHBwflhwdFG2GAAQMGMGDAgGKvbdq0\naY9ya7WOBztMt2/fIjExgXbtAklLSwVArbHE0tGLrKQb2Ln7k5MSi6zTobFvpORj0Kp///4MHDiQ\njRs3MmPGjDK1AqFXeZFlmXnzZuHn14amTZspn3foEIynpxcb9t7F0soWbfTP3Dr8JZ6dPkSSJPJz\n0kAyDkcJ4O7uTlBQECNGjGDp0qVs2rTJ6PviGuKytOreb3Sx3xmQJGptIwz6erBu3X+BPLZs+ZEp\nU8JYvDgKtVpS8ix8/5IEKpW+Y6JWq5TyefDcDRs2ZNasWYpOffr0KfNaylOvXhyxucT0kgSOjtbK\nSHJJFN7l/LA8ah4Pm75NG1+2bNlCWloav/zyC5MmhbF27Vrs7PT5PXj/5uZmqFT6wQEbGwvl3KtX\nrzbKtzL06tZ/QrHfKddmYVamVobrfRSqg96ynIckSXh6uuHkZENMTAwJCQl0795VKQMnJxv8/f25\ndOk8ffu+THT0KQoKCujRo6uSz8O2W6LNenw8WLfat2/P999//0h5PnQHV5Zlpk6dSrt27XjySf3O\nbq1WiyRJxVprGBrPhIQENBqNkUXYg8dotdoiIyDFNcKC8lFchykxMUEZaTJ0cAHUFrYU5KQBkJ+T\nhqRSo1IbL+oWWlUu4eHhXLt2jS++WGH0eY8evTh+/Bg/HMvF1q05FvYNufZ7BFkJV7B2fbKKrrZ4\nzM1rfyNcr54DAE899RQXLpxn06YNvP3228iyTH5+Jk5OXsqxaWkptGzZEicnGxo3bkReXh4ajYyt\n7f1zV8Qq0ZTIMiQnZ2JpWbwVU3WyEapOFnxVpVduTn6tt+BTq1XY2VkyceIk/P3b4OLSkKSkDK5d\ni0GSJMzMrI3KwM7Ogdu375KUlMHNm7fRaDTodGqjPCur3Qp9faT+HosxXkjVXue7NcsrtDFKYDoe\nuoMbHh7O5cuXWb9+vSmvx+TU5Gk1U12DocO0bNmXyqhS4ZGmR7k+U1PTy/pRr6Gw/2O9ekU9pgvn\nrbF2Rm1uTW5GAtauT2JmYQey7pE8pk1Fbm7tbYRXrfqSXbv+4MaN61haWtK6tR+pqWmkp2dia+uM\ni4sLo0ePITr6PIBiOZScnEJSUgYeHk1Rq9Vs3/4rJ078yY4d/yM7O5vs7GyaNdO/gBpspw4fPsy3\n337Lzp070el0FBQU4O3t/VD3Wxq12SZszZqv2L37D27cuIGVlSWtWvmRmZlpZME3derEInqlp6cZ\n2U7t27eXc+dOsW3btirVSyfLtdomzEB4eDhXr17liy9WKHkUFMjF5ivLoNPpy6WgQIcs89DPhYqi\nbhiCuoTvLHUqVKqSZ7NqS7tlqmswNSYN9ODq6oosy2i1WqPGMiEhgZYtWyrHPGqgh4pQHaZZqvIa\nCmtV2Le4aVNPZaSpcN4FOelYOOiPM7OwQ9YVoCvINcqzMjtMNbmsHzX9xIkT2bNnF2vXrsXT07PM\nvPOykinIzcTM0g4ACwcPkKRH8pg2FbXZB3fz5h/o2bM3Q4YMRZLymT49gqgo5bwAACAASURBVFu3\nYhk06G3y83X07dufqKgleHv7MHToJ6xd+xU3b95g9uxF5OfrsLCw5oUXXmLq1IlYWFjy6aef8vXX\nXxMTE8PixYsJCQlRLN3ef/996tWrx+jRo4mMjESSJBYsWGC0gVNQOlu3bqJnz968//4wI73eeutt\nQG/BV5xec+boLfoMtlNTp07E2tpa6PUYmDVrOvv37+WLL5YbtSPOzi7IskxiYiLOzvcDASQmJuDt\n3QIAFxdX8vPzyMhIN5pFqooXfZVKwsnJBkvL0tdV1+R2y9R5mBKTBnrw9PTE1dWVgwcP4uPjA0B6\nejonT57k9ddfB0wT6KEiVPU0S1VO9cyaNZ3du3f9ZZjtZDSiZhhp+v33XbRooX/5KMjLJjv5Jo5N\n9Au5LRw8kFQq8lJvKekqu8NUU8v6UdMPHvwm58+fw9zcnL59+9Ky5VMMGvQ2LVr4KP6PUVFLuXr1\nCvEnjhFXkIdKbYbG2gmbevoHu1pjiaWLN6NGjWLUqFEUFBRgaWmJn5+fkf9jUFAQgwbpN9xIkoRK\npeK5555TNgQKSicg4Gl+/fUXvv12HXZ2dnh5NebWrVisrPSN6YABA/n111+4du0KI0cOw9+/LXPn\nRip+xQCDB7/Lli369WXTp08nJCSEsLAwXn/9dWWz7dChQ+nXrx86nY4ZM2bQu3dvnnnmGYYPH260\ngVNQOqbUS5IkoVclU1qgB3f3Rjg7u3Ds2GHF9SIjI51z587yj3/0A6BFCx/UajVHjhzGw+N+0Juq\neNHX6eS/lrkUFPt9TW+3TH0NpsakgR4ABg4cyOLFi/Hy8qJRo0bMnz8fNzc3evToAZgm0ENFqOpp\nlqq6hgcNs+Pi4o0MswH69u3PV1+t4J//HEB+ZiJ3Y/ZjZumArZu+wqs1lth7BpIWc1AJ9FCZWj3M\nfZo6fVVdw9mzZ5AkiZycXHJzc9m/fx/79+9j1KixvPjiy+h0sHv3TmJiboKk0i9HkFTIsg7jxV/6\nf0uSdP+TB6LyGP6WJEk5TqerPtFnqjuFbaecnGw4deo8r732DyPHC2/vFty7dxczMzPu3r3DN9+s\n491338feXr9u99q1qwBs3fozHh4NlHSFN9tevnwZR0dHDh06pHxv8El+cAOnoGRMqdfu3buNZh6F\nXqalrEAPoB9xX7XqSxo18qRhQ3eiohZTv359QkL0m8qKC/RQ2e1WSch/LSeprct/KiMPU1KhDu43\n33yDJEm88cYbRp9PmzZN2T36zjvvkJ2dzfjx40lLS+Ppp59m+XLjRdafffYZarWa4cOHk5ubq7wN\nF2b27NlMnDiRt956C5VKRe/evRk7duzD3medY/PmjUiSxAcfvGv0+Zgx43n++b8B+pGL7Oxsvvlm\nHanp6Vi7PEGjDoMVD1yAek+9SG5WitCqktm79yhQ1AfXy6spgBIp8OWX+7IvxhFHt+YU5GUXsXXL\nTrjEooULjGZHHrTgO3jwoJEFn8EHNz4+XowyVZDSHC+6du1Ow4bu3L4dy5IlkYwc+RFLl36FJEkk\nJGiLtQqrqg2ctd0mzJBWlmXmz5+Nv38bmje/vzEzOLgT3bv3wN29EbGxsSxevJD//OcjoqJWIUkS\nycmJ1WZzdG22CSup3Ro3LpzQUH27NXDgW+Tm5jBr1lTS0tJp06YNc+cuxMrKQjn+009HsmjRPNFu\n1XEq1MGNjo7m6NGjREVFcfbsWeLj44mMjFRGZwHGjBnDDz/8oPy9b98+Jk+eXMRDt6wRI1mWjUae\nHvxbUDp79hzh5MnjrF+/hosXo4mPj2fatFl07nzfOmXq1Als3/6j/g9ZJjP+EnFnNuPRYbBxZmUU\nu9DK9JRk69aihQ/7Yu4Cxdu6IcsmseATlI/SHC8MNGv2BM2aPclrr/Xh+PFjSgjY6kJdsAkzEBYW\nxvXr11i/fr3R/fbt+7Ly73bt/AgIaE2vXr24ePEMQUFBilVYdaA224Q9TB/j8OFDLFw4x6iPkZur\nKfUlAES7VReo8BrczMxMWrZsyauvvsqwYcOKPaZLly5Mnz5d+bE8aJExZcoU9uzZw8KFC5VIZsOG\nDTPy7xwxYgQJCQmsXLlSiTIyfvx4sVi/AmRlZeHt3YL+/V8rUaugoGBefPFl5nx7Avv6zYzC9ALE\nn9tCTspNlq1YLLR6TJRm62ZnZw/cVY590NYNSVUtRplquw8ulO14URgvL08cHR25fTuWwMBA6tev\nR35+HllZmVW+Eaau2IQV1kujsSnV5cPW1hlHR0fOn79EixatFauwauFQUsttwuLiEmnWrLnSx0hP\nzzG635ycPDp27MTnn08o1MfQGB0TETGVgwf3iT5GHafCHdwuXbrQpUsXoOjaPgPm5uZFGlEDpopk\nJiiboKBgOnfujJOTTYlaaTTm2NnZodJYYWZh3DEqLpKZ0KryKWlUsCZRm31wly5dysqVK0lKSvor\nnPhsRo4cSdOmTY2Omz9/Phs2bCAtLQ1fX19SUlJo0sQDJycbgoKeRq1WM378GE6fPk1ubi4BAQHF\nboR5++23OX78OCqVCj8/P5NvhIHabxP23/9+TXJyMg4ODsyaNYP33huKl1djo+Oiopawdesm0tPT\naNGiJcnJKTg5uRhZhX388cdVrldttwlr374jHTt2Utotna6oJZiZmQY7OwejdIZjMjLS+fHHzUya\nNE20W9WE3Nxczp49XeL3arWKHj26mPy8jxTJrCQOHz5McHAw9vb2BAUF8fHHHyvRzs6cOWOySGaC\nR+f48WMcP36UzDwVOfFPFQn9WlIkM6FV5VCWPU7hoBxQ1NZN+OBWfh7r1n1NZmYm4eGTaNLEk8jI\nL3jjjTdZu/ZbHBwcyMrK4pNPhnHxYjQffzwSWZZZuHAuKpWKFi1a/1UuEh4enuzfv59Ro0bh7OxM\nWFgYNjY2RhthHBwcOHLkCOHh4WRnZzN58mTc3NxEA1wBNm36jqysLMaPn0CzZo1ZtCiSjz56n9Wr\n12Nnp9dr5MjhXLp0geHDRyDLOiIj56NWq5TlJDY2tkKvasTx48d48cVnsbOzIyCgvdGGwAsXoiko\nKKB9+0DleNFuVS1nz57m/+Z8j52LV7HfpyXc5ERN6OCGhITw7LPP4uHhwc2bN5kzZw7vvvsu3377\nLZIkodVqH+ti/dqwMcIU1wCgUhlvJDFsrEhOTmbhN/vJunuySOjXxx3JrDaU9cOmL2zr5uDgatTp\n8vLyxMXFhUuXLgD6B3lxtm7CB7fy84iLu4ckSUyYMF75TKfTsW7dGt599wN0Ov0DXaPRMGvWdFxc\n6tG1a3f+97+f2b17Nz169CIjI52YmJsEBgbxxRdfKCOCe/fuNRplSkpKokuXLkyePBmVSkXHjh3Z\nt2+f2BBYAeLi4pAkiUmTwpXPdDod69ev4913P0ClUnHu3Bk0Gg1z5kQY6bV//14jvTp16iT0qmJq\n0gZO6a8NgXVhA2dpeajVKuxcvHB0a/7Q53gYTN7BDQ0NVf7dvHlzvL296dWrF4cOHSIoKMjUpyuT\n2rAxwlR52NpaFLux4siRI1g43sLZK6DKQ7/WhrJ+mPQffvghO3fuxNbWlp49eypemwYLvjFjxqDV\natm48b8AxAFqc1sjWzdJZYbawoHhw4ej0Who27YtGRkZRvY4rq6uuLi48Morr2BlZUX79u25du2a\nye1xajN79hwBitpO9er1HAAJCVry8/NZvny14tUJcOtWLGfPnqJHj15ER59Hp9MxefI0I5uw7t27\nG40yOTo6Gm2eKSgowM/PT4wyVQBT6jV37lyjwRmh1+OnJm3gFIEeTJf3w1ApSxQK4+npiZOTEzdv\n3iQoKOixRzKr6RsjTGmg/OBifQOpqVlA8aFfH3cks9pQ1g+T/rfffgMgJSUF0O8UliRJscfJyckj\nOLgz1tbW/L5zD7IuD3O7BjTwe0WxdYs/twVdQS49e/Tg4MGDHD58GHt7e7Zt26acZ8SIETg7O+Pj\n48PRo0fZvXs3Hh4eTJw4scL3Kyh9Q+CDI0TOzi4kJCQox1SXUSZ9vnVjlKk4m7CUlEQkSaJePVej\nMnBxcSEpKREzMxUpKUnCJuwx5lHazOODVOcNnCLQgx5DH+NxU+kd3Lt375KcnKyMDj3uSGY1dWNE\nZeTx4GL9wnlD8aFfH2ckszc/GEdeXgHF7YfLTE9hytiPcXNrWGoe1aGsHyZ94VGmjh3bERExh+Dg\nEEA/1W/YWPHSS69wKrlxkamewhsCFy1aBNz3wL116xYuLi7FbqwweOCmp6djZVW9wizWBGrDhsC6\nbhNmZ6fP88EyMDc3Q6XSj/oKm7Cqy+PBmccHuXv3brEbOM+ePYG7e1Uv1RKBHgyfVwUPZRN28+ZN\nZVd+TEwM0dHRODg44ODgwKJFi+jduzeurq7cvHmTmTNn0qRJEzp37gw83khmW7ZuIyExrcTC9fL0\noG2bNhUtghpDVlYWd+/ews5OPz1y+/YtLl26iL29Pfb2Dnz11TK6du1BYmICuam3SLm0DXPbekah\nXx9nJLMkq/ZQwvMwJeMMKSkpZXZwazNiQ2D14eTJ40yeHMa9e3fR6XRER59VPKadnV3Q6XT8/e+9\njSLK2dra8fzzLwDg4uJKfn4eU6dOZPfunYoZfVxcnNEok1arZcSIEezcuROVSkWvXr2ETVgF0584\n8Sdr167m6NHDZGdnM3ZsmJFN2NKly9HpdHTq1EnRKygomOzsbFq0aEFSUgZqtSU5ObkEBgaSl5en\nBA4oPCpoY2NDTEwM7dq1Q6VS8eyzzzJ69GhhE1bBPLKysrh9O1Z5qbh48Qp2ds5KuxUVtZRu3Xrg\n4uJCbGwMkZEL8PT0olWrtsoGzhdf7MPUqVMfS7RUQfWlwh3cM2fO8OabbyphPiMiIgDo06cP4eHh\nXLhwgc2bN5Oamkr9+vXp3LkzH330ERqNRsnjcUUyi1iyEcuGHUr83qVgE5G1uIMbHX2O4cOHKFot\nWjQPgOeee4ERI0Zz+fJlfv55G6mpKehUVtg19MXF+1kjL1wRyax6YNhYkZKSTOR/D1SLDYGlUdt9\ncNevX01qagphYWFMmDDBaBrVy8sTCwsLGjZ0Z/HiKGRZJjMzg9df74u/fxvMzFT4+hpG0HcpXp2f\nffYZd+/eLTLKdPbsWcWr8+OPP6agoEDYhFWA9PRMtFotFhaW5OTk4ODgaJSHlZU1Go2GN98cTJ8+\n/wAgNzeP/v3/wcsv9yU/X8evv/4CyAwaNIhnnnmGiRMn8s477xiNCm7bto28vDymTJmCp6cnY8aM\n4cMPPxQ2YRXM48yZM0bt1oIFc4H77dalS5fYvv0n0tPTcHGpR4cOQQwePARQK+caOvQTVCqVaLfq\nOBXu4KpUKp555pkSo4ysWLFC8X/UarXcvHmTtLS0Ig3r44hkZmVtj5VDgxK/N0+/UaH8ahoqlYpO\nnUJKjGQ2Z85CoqKW8P33G0hNTycvIwFdfjY84IcrIplVPYaNFcePH8PCsXG12BBYGrXZBzc8PJxT\np06ybNkymjRpQlhYGDpdDtbWZlhY6EedmjdvzoULF7h+/SKNGjVi8eIFNGzYkL//PRRzc3M0Gn39\nUKlUitdn4dHewhiOEXXq4di7dzexsTHMmjWXIUPeJi0tjcTEBGxsbBW9PDw82bBhPc2be9OwoTtR\nUYupX78+ISFdychI53//205QUEe+++472rVrx1tvvcXw4cPx8fFRHBT+/PNPAgICWLVqFeHh4bz2\n2mtERETQu3dvMSpYAcrbbm3duonERC2xsTFkZKTj5ORklI8si2ipdZ0KD10YIpmFhYUV+0BetmwZ\n69atY9KkSWzYsAErKysGDx5Mbu79jUpTpkxh1y79yMW6deuIi4srEmlrxIgRXL16lZUrV7J06VKO\nHj3K+PHjHzydoBQMkcxK0mrt2pVs3Phf+vf/F84t+yCpzbl1KApZl68cY4hkJrSqXhTeEAhUyYbA\n0jD44Jb0n2HTQWpqVqnHVVb6R8njm2++IT09nTfeeIOQEP066TFjxvDdd5uUYxo3bookSXzwwQe8\n9NJLnD59hrCwKWRk5JGUlMHBg0eRZZkuXZ5h+PDhvPHGG3h6euLm5sbx48cBOHHiBPb29jz11FO8\n9dZbDBkyhJCQEFQqFSdPnjSpXrWZzZs3kpmZwQcfvAvA5Mlh9OnzPL///qtyjFarJTc3h7Fj/8O/\n/z2AjIwMZs1agEajUXxVw8Im0a1bN4YPH87o0aOxtLSkd+/eAMryn6VLl9KsWTPeeustFi9ejCRJ\nPPfcc1Vy3zWV8rZb//d/Y1m+fBVWVlZ8+ulQ8vLylGMWLJjNvn17RbtVxzF5JLPVq1fzwQcf0K1b\nNwBmzJhBcHAwO3bsIDQ0VEQye4yUFclsw4ZvGDRoMN7ePvxwLBenNq9x9deJpN89i527v4hkVo2p\n6g2BZVGbfXAftJ3y8fFRRpkM+QQGBtOli7FX56xZ0xWvzri4eDQac0aNGsv06VOVvPv27Wu0pMTV\n1bVI6NA//vjD5EtKajOlbeCE4n1Vs7OzadTIA0DxVXVycubzzz/n888/B/RaZWXpX5IMy3/s7e2N\n9AoODiY11Tg4i6B0yttudeqk13DcuAm8+GJvdu/eqXgW//TTFhHJTGBaF4WYmBi0Wq2R362trS3+\n/v6cOHGC0NBQTp8+LSKZVQNu375FYmIC7doFKtGx1BpLLB29yEq6gZ27v9i49Bg5fPggq1ev4MaN\n68iyzJ49u6hXr4HRhsCUlBR27fqD9IwMVGoNGmsnow2Bdh5Pk3ptFwEBARQUFGBpaYmfn5/Rxoqg\noCAGDRpEbm4ukiShUql47rnnxAPdxNQkr06oOzZhoPcmLXyvhlFYAG9vvXf7K6/8nVOn/qRdu/ao\n1Sok6dHObUrqsk2Yod0KDOygfObgYI+vbyvOnz9N7969uXRJP+LeocP9fohot+omJu3garVaJEkq\nMt1ZeKNLQkJCtfETrO0bYR5MV/hBUdj7MTMz/f7xFrYU5KQBVLuNS2p1yQ/26lTWD5P+ypWLnDhx\nXJmS+/HHzfz00xZCQ//Gf/4zhj17dhEbG4NKpUJlZonKzOKvNWaFRzhkZDCa1ntwBMTwt2EDB5S9\nHl7w6Li7N8LBwZHY2BgCAp5WXBT0awer1quzLtmEAVhbm5d6r05O3jg5OZGYGIeTkw2NGzciLy8P\nlcrYy7Sqlv/UZZuw69czkCSJZs08jcrAza0+aWkpODnZkJOTgUajwd3d+KVdRDKrnPTlyaOqXg4r\n3Qe3OqPR1N0HRWHvx6SkGuB/Kkk4ONTeRnj48A8ZPvxDAHx8fIps3szOzmL06NH4+vry6bxd2Ll4\nFllOkhZ7DIem3Ti8LRK474NbeFru4MGDxfrgilCiFePkyeOsX7+GixejkWWZc+fOGG2EAZSNMOnp\nabRo0ZKUlBRcXPSdnRYtfFCr1YwdO5ro6HNK6NfilpS8/fbbHD9+HJVKhZ+fX6WEVa4LNmEXLpxH\nlmWOHPmTgADjqJrLli1my5YfSEtLx8fHh6SkZKys7EhKysDDoylqtZqhQ4dx/nzVagW13yascHow\nDlCUlqZfEpKcnIlafb8McnPzUalUJCVlkJGRA+jX1ldVBC0DIpKZ6fJ+GEzawXV1dUWWZWXtmIGE\nhARatmypHPM4I5mVRl5e3X1QmJvr1zddvRqjPDQACnLSsXBwB6rZxiVZJiUls0S9qlNZP+o1AGRm\n5ir3evv2LbRaLU895U9qqn50XSwnqVqSk5NxcXHl3//+N9OnTycxMdHIY3rkyOFcunSB4cNHIMs6\nIiPno1arlOUJNja2eHh4cuTIIUaNGoWzszNhYWHY2NgYLSlxcHDgyJEjhIeHk52dzeTJk3FzczP5\ny0httglLSEjC2dmFAQPeZN682Wi1Ws6fjy5TK3//duTn67CwsMbDw5PDh6uHVrXdJuxBCgcocnBw\nRpZl4uO1ODjcd01ISEjA27sF+fk6HB2dycvLIzU11ahjJSKZVU768uRRKyKZeXp64urqysGDB/Hx\n8QEgPT2dkydP8vrrrwOPP5JZadTmjTDFUfhBUb9+Q5ydXTh8+BDe3nqtCvKyyU6+iWOTYKD6bVwq\nKCj7wV4dyto0Wt2/17i4eCRJwsHBieTkFOWY6rycpLYv/3FycmTLlh/YulW/zOOnn7awbdtWZUnJ\nuXNn0Gg0zJkTgatrPbp168Evv2zn0KF99Oz5LBkZ6cTE3KRDh4588cUXyqjg3r17jUbck5KS6NKl\nC5MnT0alUtGxY0f27dsnRtwrgL29PVu3biqy/Mfgq1pYKxeXenTt2p3//e9n9u/fq2xaiom5SWBg\nkNCqinF3b4SzswvHjh3mySf10RwzMtI5d+4s//hHP+D+7MiRI4fx8HgREJHMHkf60vKoFZHMGjZs\nyMCBA1m8eDFeXl40atSI+fPn4+bmpky3Ps5IZnWd0iKZNWjgRr9+/Vm16kv++c8B5GcmcjdmP2aW\nDti66Sv8445kJqg91GYfXIAePboQHR0NFF1SEhMTQ35+Pt99953yog9w794dLl06T9++LxMdfQqd\nTsfChfONZrK6d+9uNOLu6OjI8uXLle8LCgrw8/MTI+4VoG3bduzZc6RYF4Xbt2+Rn5/P8uWrlQ4T\nwK1bsZw9e4oePXoRHX0enU7H5MnT8PC476sutKocyttuNWrkWcSzGPSzIy+88BLz58/B3b2+aLfq\nMCaNZDZt2jTeeecdsrOzGT9+PGlpaTz99NMsX74cc/P7I0uPK5JZXae0SGaffRbGgAEDyc7O5ptv\n1pGano61yxM06jAYSXX/ZyEimVU9zs4uyLJcdEqtui4n4b4PbknUhGm18qYH4+U/167F/LW5xNqo\nDOzsHLh9+y5JSRncvHkbjUaDTqc2yrPqNnDWnY0whV0UCm+2LXz/Li4uJCUlYmamIiUlCY1Gg729\nvVF+VaVVbXdRuHQpmg8/fLdIuxUa+jfGjQtn4MC3yM3NYdasqaSlpdOmTRvmzl2IlZWFksenn45k\n0aJ5ot2q41S4gxsYGKiMXBTHokWLiIyMVP7et28f77//Ptu2bVM+W7x4Mb/88gvZ2dkEBAQwYsQI\nXFxclO9zc3OZO3cue/bsUWKEf/LJJ1hZ1YDNUNWItm3bceDAMZyc9HHXH5w6+PLLZaxcGaX/Q5bJ\njL/E7SMrafLMSOWYxMu/kZN0HVmtE1pVEYWn5WrKcpK6vPynoEAuNl9Zvr/0pKBAhyxX3dRdYeqy\ni0LhzbaF79/c3AyVSu9zbGNjYbJzm4La7qJQeHakOBYtWsSXX94fKT98+BCjR48w6mPMnx/FH3/8\nJvoYdZxKcVFo3rw5q1atUpYxqNX3RykMkc4iIiJo1KgR8+bNY/DgwWzbtk0Z5Z0yZQp79uxRYrRP\nnDiRYcOG8fXXX1fG5dZpmjV7gsGDhzDn2xPY12+GJN1/4068/AfJ1/dj1ziEtXOHCa0qkaysLGJj\nY1Cr9esEb9+OLTItt2zZF+Tk6HcIx/2VTnvhF+zc/ZTlJKnXdxMYGEhWVhYWFhb4+voaTcsFBwcr\nkQXz8vJQq9X07NlTTMuZkMIj7s7O9xvVxET9RhigWtmE1XYXhcJ5gPEGzsKbbdXq+52bu3fjaNGi\nxV8bhGzJy8vj9u14I+upKpsdqUMuCsWlz8rKpVmzJ1i0aKlRH8NQJqtXr2Tt2rWEh0+mefOmot2q\nw1RKB9fMzKzIdI0BU0Q6E5gOtVqNnZ0dKo0VZhbG3sRJ1/bi0rwHknVDvL29hVaVyIPLSRYsmAsY\nLyc5cGAfp06dQCdLWDk3xtUnFHOb+42nfplCPvn5+ajVaqysrEhMTCQ3N1d5sNevX59jx47pjzcz\nw9LSkri4uKIXJHho3N0bYWlpxaBB/ZWNTYaGOCDgaY4fP0Z2djayDC+++Bw6nX52ZPDgwUYj7r6+\nvqSkpNCuXTt0Oh0hISE899xzlbSBs+5shCm8gdOw2XbhwnkcOXIIuK9VbGwMQ4d+ypNPtkCtVjNp\nUjinT58kLS2Nli1bFjs78tFHH3Hw4EFyc3Np2bJlpWhVl1wUikuv08l/tVsORp8bjvv2268ZOHAw\nwcH6aGii3aq7VIr77vXr1wkJCaFnz56MHDmSO3fuAGVHOgPKjHQmMC0xMTGMGzcK7elvuHN8PXlZ\nyQDkZSZSkJOOtev9jRdCq8rDsBFm//6jnD9/nv37j7J792E+++z+urGAgKdxd29E/Xb/xrPjEKyc\nvFCbWyvfJ984gG2jQP78809OnDjB9u3bSUhIYMeOHYDe0WTr1q3MnDmTEydO8Oeff7J27VpOnDjB\nqVOnHvs912SysrK4dOki58+fB+5vhLl37y4Avr6tUKlU/Otfg9A0aIe5vQcqczu2nshn0qqjTJi/\nDhnIzM5n/Pjx5OXl8f7779O2bVulgV2/fj3m5ubUq1eP8PBwrl69yujRo8VGmApi0OrixQvA/dkR\ng1b9+vXn5MnjNGjgxoIFS+nQoeNfm5dWA/pNS82bt+CPP37jX//6F9OmTePq1atoNBplE+ETTzxB\ngwYN+O233/jkk08YN24cZ86cwcHBQWhVCcTExNCnz/P06/cSEyd+rmhZOEKnAdFu1V1MPoLr7+/P\n9OnTadq0KfHx8SxcuJABAwbw448/mizSmamo7VZGZaX38/PjyScnkJmZwaJvD5GtPUfM/sU06TqC\n/L/sp9QWtlAo0llVaaW/h9obyaw8eahUEvHxceTdW0fSOUssnRrj6vM8GitH5YXE3N5dOf5hwmQL\nykdZGzj9/Npw9eoVNm/+nty/NnC6t3tdGXFPOP0Nri16k5F4gxkzZpCbm0t+fj59+vQBUEaZDFOp\nEydOBCAnJ4d+/fpVzU3XUMo7O3L69ElGjhyGv39b5s6NVIJyANy9ewc/P3+++uorcnNz6dixI3v2\n7DEaFYyPj6dt27bMnDkTlUpFt27d+Pnnn8WooInx9W3N2LFheHk1yYFGZAAAEhZJREFUJiFBy5df\nLmPo0HdZvfpbEhMTkCSpyAxy1fUxRCSzR837UTB5BzckJET5t7e3N35+fnTr1o3t27fTrFkzU5/u\nkahLkcyKS//883of4iNHjmDucAuXZkFc+20aaXdOYm5b/5HOZ3JqeSSz8uQRFNQea2sL1v52Cytb\nJxIu/lrkhUSlMU4nXh4rJ4/27dtz+PDxEtcJqlQS2dlZaDTmqDTWqM1tFHcSw8uITX0fVLYe7Fmn\n39T5xhtvcOXKFeD+KFOPHj146aWXlHy7d+/OuXPnaN++/UPdc12ksE1YSRtuAwKe5sKFaGxsbIiN\njeHLL5fx3nsf0qCBG7dv3yIpKZH58yMJDGyrpHnjjTeMXh51Oh1Lliwp0fZNYBo6dCj8gv4kLVu2\n4tVX/8bvv/9K48ZNqu7CikFEMjNd3g9DpYfqtbOzo0mTJty8eZPAwECTRDozFXUpkllp6Q1RRtQa\nKzQ29cjLSMDa5QlAb0VVmKrSqi5FMispj1atAsjNlTHbn4VNveZYOnpW2xeS2u6DW1b6oKD2+Pn5\nkpGRQcSXu2vA7EjdHmUyzGY1btwYrVZLVNRShg59l6+/3lDISsx4qUFV6VXbbcIqmt7R0R4vr8bc\nvh1L+/btkWWZlJQkGja871lcVe2WiGSmp1ZEMiuOjIwMbt68ycsvv2yySGemoi5ZGZWW3vCD1OXn\nkJepxcyyHRprZ9QWtmRqLyFZNwSqViv9ddadSGbliQhT3AuJLs/4QVJVD/a65INb0ssIwJ9/HsPc\nwaNaz47UNZuw0mazDHTqFEi3bt04cGCXMvNoCDxQ1dR2m7CKps/IyODWrVheffUVfH29cXV15dy5\nk8poe9X2MUQkM8PnVYHJO7gRERF0794dd3d37t27x8KFCzEzMyM0NBTAJJHOBKYhMnI+nTqFkJiY\nQF76PW5f34kkqbFz1+/6dWoaQsKl37Fr3JkLFy4IraoZxb2Q5KbeVr4XL4+PJ4/yvDxW59mRumQT\nVv48VHh6enHhwmV8fFojyzI3btyqFjOPdd0mbOHCuXTu3AU3t4bEx8ezfPkSVCo1nTo9Q1JSBv36\n9Scy8gtcXd1o3rypaLfqMCbv4N67d48RI0aQnJyMs7Mz7dq149tvv8XJyQmgwpHOMjMzUalU6HQ6\n+vXrx7hx48SP0ETExd1jwoRxJCcnUSCZY+PaHK/OQ1Gb60cHnJ98Bl1BLsnX99Kv326hVRUTGTmf\n+vXrU5CTRlbidRIu/lrMC8lv/P7772W+PB4+fJjt27ej1WqxtrYu7bQCE/CosyPr1q1jyZIlxMXF\nsWHDBiO3BVNQl2zCypNHZmYmMTEx9O79gmIldujQQdq109e10vSKi4tjxYoVxMXFkZeXV2TZwqNS\n123C7t69x+eff0ZKSjK2trY0a/Ykw4eP4OLFSwD4+PjSqVMIEyeOJycnR7RbdRiTd3DnzJlT5jHD\nhg1j2LBhJX5vbm7O559/Trt27Rg1ahQTJkygdevWrFq1irfffpuff/65RJ9dQfmZMGEqAMePH2PS\nqqM4ujUvcoxri2cxc2iqbIR5EKHV4yMu7h7bt28lOSWNFAs7rJybFHkhyUy5W+bLY0xMDKtXr8bc\n3JxOnTrh4uIitDIxppwdOX/+PEuXLqVRo0b4+vrSqlUroZeJiYycT2BgEKmpKSQnJ7Nt21ZApkGD\nBhw/fozOnbsQFbUUH5/mpb48jh8/ntTUVN5//3127NhBfHw8ERERdOvWTWhlIiZMmMrx48f4vznf\no3HxIgZY/NN14HqhoxqS6xzEyZ8XFJuHaLfqBpW+BvdRWLlyJa+99ppinTNhwgR27tzJxo0beeed\nd6r46gSFEVpVPoYHe0kvIwC2jdqxZ936EvMwNzcnLS2NN954g3HjxgH6dWJdu3YVWpkQU86OLFmy\nBEmS8PHxISwsDGdnZ1G3TIxer7Ekp6Si0lihsXXDtkkoc74zhIx1IUfjVubL4//+9z/UajUrVqwg\nJCSEqKgo/vGPfwitKgE7F68Sn4PlRbRbtZtq28HNy8vj7NmzvPfee8pnkiQRHBysGDYLqgdCq5qD\n0OrxYKrZkdGjR/PNN9+wYMECZbQQEHqZmPK8PAIlagX6epSSkiK0qiGIZ2Htp9p2cJOSkigoKCg2\nKMS1a9dMco6E+NucOlVy5BKVSsLW1pL09Gx0OrnC+T9q+sd1DZcuXSAt4WaJeZT2HTwerQAuXDhH\nTk7xdiM1pawfNY+aopU2TtQtqDl6nTt3hnv37hT7XU0p60fNo6Zopb0n6lZZWkH10EuSJM6cOYG5\nuUWx39eEsjZFHo9atx6WatvBNQU7Nswt44iXyvi+btCjRxc+/LCqrwK2zi5ND6EVVB+tfv/v9DKO\nEHpB9dFL1K2yqS5a/bE+rIwjhF7VRSsQdas8VJVeVRM/rRw4OTmhVquLmGRXShABwSMhtKo5CK1q\nFkKvmoPQqmYh9Kr9VNsOrkajwdfXlwMHDiifybLMgQMHKiWIgODhEVrVHIRWNQuhV81BaFWzEHrV\nftTh4eHhVX0RJWFjY8OCBQto2LAhGo2GefPmceHCBaZMmYKVVdXENhYUj9Cq5iC0qlkIvWoOQqua\nhdCrdlOt1+CGhoaSlJTEggUL0Gq1tGzZkqioKOFPVw0RWtUchFY1C6FXzUFoVbMQetVuJFmWH27b\nnEAgEAgEAoFAUA2ptmtwBQKBQCAQCASCh0F0cAUCgUAgEAgEtQrRwRUIBAKBQCAQ1CpEB1cgEAgE\nAoFAUKsQHVyBQCAQCAQCQa1CdHAFAoFAIBAIBLWKau2DWxbr1q1jxYoVaLVafHx8GDduHH5+fiUe\nf+jQISIiIrh06RLu7u4MGTKEzMzMcufx66+/sn79es6fP09ubi7NmzfnqaeeYufOneW+BgPHjh3j\nzTffxNvbm1dffbVC95Gbm0tkZCRbt24lPj4ea2trJEkiMzOzXOm3bNnCihUruHHjBubm5lhbW5Of\nn49WqyUyMpIePXqUeu3FlePLL79c5j0/ql7+/v4cPXr0obUaOnQoN27cqNA1GDDo5erqqoR3fBit\n6tevT0BAAMeOHSt3HlWhl6hbNaduVQetRN16fHpVh+egqFvlP0dteA7WlLpVBLmG8tNPP8mtWrWS\nf/jhB/ny5cvy559/Lrdv315OSEgo9viYmBi5TZs2ckREhHzlyhV57dq1so+Pj/zUU0+VO48pU6bI\nUVFR8unTp+UbN27IH3zwgezt7S1HRkaWK72B1NRUuWfPnvLgwYPlbt26Veg+ZFmWhwwZIr/22mvy\ngQMH5DVr1si+vr7yvHnzypX+6NGjcsuWLeW1a9fKsbGx8ooVK+SgoCD5tddek318fOQdO3aUeu3F\nleNTTz0l7927t9R0j6rXmDFjZG9vbzkiIuKhtJozZ47csmVL2dfXt0JlLcv39fr73/8ut2jR4qG1\nunXrlvzFF19U6BqqQi9Rt2pO3aoOWom69fj0qg7PQVG36tZzsKbUreKosR3cvn37ypMmTVL+1ul0\nckhIiLxs2bJij58xY4b8t7/9zeizwMBA+Zlnnil3HsVdQ2BgoBwZGVmh9J988ok8f/58eeHChbK/\nv3+F7mPXrl1y+/bt5ZSUFOUaKpJ+xYoVcq9evYw+W7Nmjdy1a1e5RYsWZf7wiivHTz75RH777bdL\nTfeoevXt21d+9tlnlfNUVCtZlmU/Pz+5X79+5b4GAwa9QkJC5KCgoHKnf1Arw31Ud71E3ao5das6\naCXLom4Vvp/K1Ks6PAdF3apbz0HDdVR3vYqjRq7BzcvL4+zZs3Ts2FH5TJIkgoODOXHiRLFpTp48\nSXBwsFEeqampJCUllTuP4q5BpVLh4OBQ7vQbN24kNjaWoUOHUlBQQFZWVoXu448//qBVq1YsX76c\nkJAQTp48yZ07d8jJySlX+jZt2nDnzh127doFgFar5eeff6Zr165l3jMULUeAzp07l3rPj6qXIX1I\nSIhyfEW0Av2US3Z2Ni1atCj3NcB9vd577z3i4+OxsbEpd/rCWnXp0oVnn32WU6dO8fTTT5c7j8et\nl6hbNaduVQetQNStwlSmXtXhOSjqVt16DtaUulUSNbKDm5SUREFBAa6urkafu7i4oNVqi00THx+P\ni4uLUR6yLJOdnU1ubm658njwGvLz88nNzeX5558vV/rr168zd+5cZs2ahUqlIjs7G6BC9xETE8PR\no0e5dOkSU6ZMQZIkTp48yYQJE8qVPiAggJkzZ/LJJ5/QqlUrOnfujL29PePHjy/znqFoORrOl56e\nblSOhXlUvQzpGzVqZHSe8moFEBkZCUCvXr3KfQ2F9UpJSUGn06FWq8udvrBWkZGRDB06FFmW2bRp\nU7nzeNx6ibpVc+pWddAKRN168HyVpVd1eA6KulW3noM1pW6VRI3s4FYHfv31VwA+/fRTnJ2dyzxe\np9MxcuRIhg0bhpeXFwCyLFf4vLIso1KpmD17Nj4+PgAMHDiQTZs2lUv4y5cvM2XKFIYNG8b333/P\nihUriI2NLfcPryaydetWVq1ahSRJ2NvblytNcXpVlMJatW7dmqCgICRJYteuXeWupHVRL1G3ag6i\nbtUcTKWVqFuPh+rwHKzpdatGdnCdnJyU3baFSUhIKPKmYqBevXokJCQY5SFJEpaWlpibm5crDwM/\n/fQTM2fORK1W4+bmVq5ryMjI4MyZM0yaNAlfX198fX1ZuXIlAP379+fQoUPlvo8GDRpgY2OjlIO1\ntTWyLHP37t0y0y9btoyAgADeeustvL296dSpE2FhYWzcuLHUey58/sLlaDifra2tUTkW5lH1MqS/\ndeuW0XnKq9X48eOZM2dOha7hQb2eeeYZQP+G3KpVK0Wv8mpluA+VSmWkVVl5PG69RN2qOXWrOmgl\n6tbj06s6PAdF3apbz0HDfVT3ulUSNbKDq9Fo8PX15cCBA8pnsixz4MAB2rZtW2yaNm3aGB2v0Wiw\nt7fHycmp3HkA/Pjjj4wdO5a5c+fSqlWrcl+Dra0tP/74I5s2bWLz5s1s3ryZ/v37Y2FhwQsvvIC/\nv3+5riEgIIC4uDiysrKUcti9ezcqlQo3N7cy02dnZ2NmZuwOp1KpkCSpxHsuzIPlCLBv3z7atGlT\nYppH1cuQfs+ePcp5KqLVnDlz6N69e4Wu4UG9tmzZgouLCw4ODmzevBl/f/8KaWW4D09PTwDloVXd\n9BJ1q+bUreqglahbj0+v6vAcFHWrbj0HDfdR3etWSajDw8PDK5SimmBjY8OCBQto2LAhGo2GefPm\nceHCBaZMmYKVlRWzZ89m8+bNylojLy8vlixZQlpaGg0bNmTbtm3s2LGD7Oxs3N3dy5XH1q1bGTNm\nDGPGjKFjx45YWlqyZMkSnJycsLKyKjW9JEk4Ozsb/XfmzBlu3LjBmTNn8PT0LNc1NG3alO+//54z\nZ87QvHlzcnNz2bBhA08//TQBAQFlps/Ozmb58uU4OTnh6OjImTNnmDBhAq6uriQkJODn54elpSV5\neXnY2dmVqxxXrlzJ+PHjlUpQGXpdv36dAwcO0KtXL9zc3CqsVWZmpqJXea6hOL1OnTrFuXPn8PPz\nK1Pv4rS6cuUKW7duJTs7Gy8vr3KVQ1XoJepWzalb1UErUbcen17V4Tko6lbdeg7WlLpVHDU20ENo\naChJSUksWLAArVZLy5YtiYqKUtaqaLVa7ty5oxzv4eHBsmXLmDZtGmvWrMHNzY2pU6eSkZFR7jz+\n+9//UlBQwMSJE5k4cSKgf5OJiIigoKCgzPTF4eDgwODBg8t9DdbW1nz55ZdMnjyZV199FUdHR4KC\ngrhx4wYvv/xymelffvllMjMz+frrr5kxYwYWFhYkJSUpb1cREREA9OnTh2nTppWrHCdPnlxkx2Nl\n6NWnTx+2b9/OmjVrHkorgNatW5f7Gh6kefPmnDhx4pG0+vvf/46bm1u586gKvUTdqjl1q7poBaJu\nPS69qvo5CKJulVer2vIcrAl1qzgk+WFWIQsEAoFAIBAIBNWUGrkGVyAQCAQCgUAgKAnRwRUIBAKB\nQCAQ1CpEB1cgEAgEAoFAUKsQHVyBQCAQCAQCQa1CdHAFAoFAIBAIBLUK0cEVCAQCgUAgENQqRAdX\nIBAIBAKBQFCrEB1cgUAgEAgEAkGtQnRwBQKBQCAQCAS1CtHBFQgEAoFAIBDUKkQHVyAQCAQCgUBQ\nq/h/UeLUxJkWmMoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x138213b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "asset_cols = [\"safe_water\", \"unsafe_water\", \"no_walls\", \"dirt_walls\", \"mid_roof\", \"bad_roof\", \"sturdy_roof\", \"no_roof\", \"sturdy_walls\" ,\"dirt_floor\",\"hard_floor\", \"electricity\",\"radio\",\"tv\",\"refrigerator\",\"bicycle\",\"motorcycle\",\"car/truck\", \"open_defec\", \"improved_sani\", \"shared_toilet\",\"unimproved_sani\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "asset_df = dv[asset_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose number of components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(asset_df)\n",
    "X_scaled = scaler.transform(asset_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eig_vals, Q = np.linalg.eig(np.cov(X_scaled, rowvar=0)*99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  5.55546485e+02,   1.93868426e+02,   1.79776555e+02,\n",
       "         1.52101590e+02,   1.43270922e+02,   6.68129391e+00,\n",
       "         2.98031450e+00,   7.77565999e-01,   2.10919813e+01,\n",
       "         1.10021170e+02,   9.95348555e+01,   1.02535210e+02,\n",
       "         4.49802233e+01,   9.12216512e+01,   5.22701422e+01,\n",
       "         5.70359109e+01,   5.92616212e+01,   6.82431026e+01,\n",
       "         7.43068910e+01,   8.03636424e+01,   8.27216511e+01,\n",
       "         6.14140446e-15])"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eig_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ordered = sorted(zip(eig_vals, Q.T), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ordered;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = eig_vals[0]/sum(eig_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b = eig_vals[1]/sum(eig_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c = eig_vals[2]/sum(eig_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d =eig_vals[3]/sum(eig_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "e = eig_vals[4]/sum(eig_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5620898384193912"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+b+c+d+e #explains this percentage of the variance.... we need more assets in the dataset! Possibility to link to household survey in future iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=5)\n",
    "pca.fit(asset_df)\n",
    "pca.components_;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# print(\"pca.components_.shape: {}\".format(pca.components_.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import StandardScaler\n",
    "# from sklearn.decomposition import PCA\n",
    "# from sklearn.pipeline import Pipeline\n",
    "\n",
    "# import numpy as np\n",
    "\n",
    "# data = asset_df\n",
    "\n",
    "# pipeline = Pipeline([('scaling', StandardScaler()), ('pca', PCA(n_components=5))])\n",
    "\n",
    "# pipeline.fit_transform(data).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original shape: (3685, 22)\n",
      "Reduced shape: (3685, 5)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "# keep the first two principal components of the data pca = PCA(n_components=2)\n",
    "# fit PCA model to breast cancer data pca.fit(X_scaled)\n",
    "    # transform data onto the first two principal components\n",
    "X_pca = pca.transform(X_scaled)\n",
    "print(\"Original shape: {}\".format(str(X_scaled.shape))) \n",
    "print(\"Reduced shape: {}\".format(str(X_pca.shape)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x14de97cf8>"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/n3v37hEUFIS1tTUNGjSgR48euLm5qX4/6372zJkzsbCwYNy4cdy6dYt58+bx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\nIiIiIiKqt/8HbF8+DDFsMYEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1485bec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(pca.components_, cmap='viridis')\n",
    "plt.yticks([0, 1, 2, 3, 4], [\"First component\", \"Second component\", \"Third component\", \"Fourth component\", \"Fifth component\"])\n",
    "plt.colorbar()\n",
    "plt.xticks(range(len(asset_df.columns)), asset_df.columns, rotation=60, ha='left')\n",
    "plt.xlabel(\"Feature\")\n",
    "plt.ylabel(\"Principal components\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['safe_water', 'unsafe_water', 'no_walls', 'dirt_walls', 'mid_roof',\n",
       "       'bad_roof', 'sturdy_roof', 'no_roof', 'sturdy_walls', 'dirt_floor',\n",
       "       'hard_floor', 'electricity', 'radio', 'tv', 'refrigerator', 'bicycle',\n",
       "       'motorcycle', 'car/truck', 'open_defec', 'improved_sani',\n",
       "       'shared_toilet', 'unimproved_sani'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asset_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df3 = df.drop(fuel_cols, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pca_df = df3.drop(asset_cols, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.40881635, -1.16081644, -0.97057093, -2.08542245, -0.54873631])"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_components = pd.DataFrame(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3685, 9)"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result_df = pd.concat([pca_df, df_components], axis=1, join='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>physical_dv</th>\n",
       "      <th>age</th>\n",
       "      <th>ever_emo_violence</th>\n",
       "      <th>u16_marriage</th>\n",
       "      <th>child_marriage</th>\n",
       "      <th>no_edu</th>\n",
       "      <th>primary_edu</th>\n",
       "      <th>secondary_edu</th>\n",
       "      <th>higher_edu</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.408816</td>\n",
       "      <td>-1.160816</td>\n",
       "      <td>-0.970571</td>\n",
       "      <td>-2.085422</td>\n",
       "      <td>-0.548736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.865141</td>\n",
       "      <td>-1.006029</td>\n",
       "      <td>-1.192600</td>\n",
       "      <td>-1.881813</td>\n",
       "      <td>0.665299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.672377</td>\n",
       "      <td>-1.532594</td>\n",
       "      <td>-0.866967</td>\n",
       "      <td>-0.549028</td>\n",
       "      <td>-0.281227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.959533</td>\n",
       "      <td>-0.531786</td>\n",
       "      <td>1.190145</td>\n",
       "      <td>-0.278186</td>\n",
       "      <td>-0.707814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.933334</td>\n",
       "      <td>-1.004626</td>\n",
       "      <td>-0.941950</td>\n",
       "      <td>-2.073635</td>\n",
       "      <td>0.822657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   physical_dv   age  ever_emo_violence  u16_marriage  child_marriage  no_edu  \\\n",
       "0            0  25.0                  0             0               1       1   \n",
       "1            0  31.0                  1             0               0       0   \n",
       "2            0  30.0                  1             0               0       0   \n",
       "3            0  46.0                  0             0               0       0   \n",
       "4            0  39.0                  1             0               1       1   \n",
       "\n",
       "   primary_edu  secondary_edu  higher_edu         0         1         2  \\\n",
       "0            0              0           0 -0.408816 -1.160816 -0.970571   \n",
       "1            1              0           0 -0.865141 -1.006029 -1.192600   \n",
       "2            0              0           1 -1.672377 -1.532594 -0.866967   \n",
       "3            0              1           0 -1.959533 -0.531786  1.190145   \n",
       "4            0              0           0 -0.933334 -1.004626 -0.941950   \n",
       "\n",
       "          3         4  \n",
       "0 -2.085422 -0.548736  \n",
       "1 -1.881813  0.665299  \n",
       "2 -0.549028 -0.281227  \n",
       "3 -0.278186 -0.707814  \n",
       "4 -2.073635  0.822657  "
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moment of truth! Let's see if \"child marriage\" influences coefficients of LR model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'> <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "X, y = result_df.drop(['physical_dv',\"ever_emo_violence\",\"u16_marriage\",\"child_marriage\"], axis=1),df['physical_dv']\n",
    "print(type(X), type(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = sm.Logit(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.575614\n",
      "         Iterations 6\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:            physical_dv   No. Observations:                 3685\n",
      "Model:                          Logit   Df Residuals:                     3675\n",
      "Method:                           MLE   Df Model:                            9\n",
      "Date:                Fri, 28 Oct 2016   Pseudo R-squ.:                 0.03306\n",
      "Time:                        01:27:51   Log-Likelihood:                -2121.1\n",
      "converged:                       True   LL-Null:                       -2193.7\n",
      "                                        LLR p-value:                 9.306e-27\n",
      "=================================================================================\n",
      "                    coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
      "---------------------------------------------------------------------------------\n",
      "age               0.0044      0.005      0.945      0.345        -0.005     0.013\n",
      "no_edu           -0.9905      0.181     -5.458      0.000        -1.346    -0.635\n",
      "primary_edu      -0.9993      0.182     -5.477      0.000        -1.357    -0.642\n",
      "secondary_edu    -1.3486      0.176     -7.675      0.000        -1.693    -1.004\n",
      "higher_edu       -1.9221      0.209     -9.191      0.000        -2.332    -1.512\n",
      "0                 0.0927      0.020      4.744      0.000         0.054     0.131\n",
      "1                 0.0104      0.028      0.375      0.708        -0.044     0.065\n",
      "2                 0.0601      0.029      2.070      0.038         0.003     0.117\n",
      "3                 0.0198      0.032      0.615      0.538        -0.043     0.083\n",
      "4                -0.1226      0.036     -3.430      0.001        -0.193    -0.053\n",
      "=================================================================================\n",
      "age              1.004375\n",
      "no_edu           0.371393\n",
      "primary_edu      0.368131\n",
      "secondary_edu    0.259607\n",
      "higher_edu       0.146297\n",
      "0                1.097161\n",
      "1                1.010457\n",
      "2                1.061905\n",
      "3                1.019981\n",
      "4                0.884637\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "results = model.fit()\n",
    "predict = results.predict()\n",
    "print(results.summary())\n",
    "print (np.exp(results.params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o0KEPlLGE3wrKZsmSJVKfPn0kX19fydfXVxo2bJiUlJT0qM2qUmp7jLyXbt26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mgUFBQgJSUZ5Y8nwLt3b/Hixd9o37411SOlSdMgM7jdunXDwYMHWZckfD6fPSf2ubdu3Tq0a9cO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14d872588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_df.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'> <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "X, y = result_df.drop(['physical_dv',\"ever_emo_violence\",\"child_marriage\"], axis=1),df['physical_dv']\n",
    "print(type(X), type(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = sm.Logit(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.574040\n",
      "         Iterations 6\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:            physical_dv   No. Observations:                 3685\n",
      "Model:                          Logit   Df Residuals:                     3674\n",
      "Method:                           MLE   Df Model:                           10\n",
      "Date:                Fri, 28 Oct 2016   Pseudo R-squ.:                 0.03571\n",
      "Time:                        01:28:11   Log-Likelihood:                -2115.3\n",
      "converged:                       True   LL-Null:                       -2193.7\n",
      "                                        LLR p-value:                 1.586e-28\n",
      "=================================================================================\n",
      "                    coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
      "---------------------------------------------------------------------------------\n",
      "age               0.0043      0.005      0.934      0.350        -0.005     0.013\n",
      "u16_marriage      0.3129      0.091      3.433      0.001         0.134     0.492\n",
      "no_edu           -1.0677      0.183     -5.826      0.000        -1.427    -0.709\n",
      "primary_edu      -1.0576      0.184     -5.761      0.000        -1.417    -0.698\n",
      "secondary_edu    -1.3879      0.176     -7.870      0.000        -1.734    -1.042\n",
      "higher_edu       -1.9346      0.209     -9.242      0.000        -2.345    -1.524\n",
      "0                 0.0909      0.020      4.643      0.000         0.053     0.129\n",
      "1                 0.0130      0.028      0.468      0.640        -0.041     0.067\n",
      "2                 0.0592      0.029      2.036      0.042         0.002     0.116\n",
      "3                 0.0178      0.032      0.553      0.580        -0.045     0.081\n",
      "4                -0.1325      0.036     -3.684      0.000        -0.203    -0.062\n",
      "=================================================================================\n",
      "age              1.004329\n",
      "u16_marriage     1.367352\n",
      "no_edu           0.343806\n",
      "primary_edu      0.347290\n",
      "secondary_edu    0.249591\n",
      "higher_edu       0.144479\n",
      "0                1.095194\n",
      "1                1.013085\n",
      "2                1.060985\n",
      "3                1.017987\n",
      "4                0.875884\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "results = model.fit()\n",
    "predict = results.predict()\n",
    "print(results.summary())\n",
    "print (np.exp(results.params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'> <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "X, y = result_df.drop(['physical_dv',\"ever_emo_violence\",\"u16_marriage\"], axis=1),df['physical_dv']\n",
    "print(type(X), type(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = sm.Logit(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.575350\n",
      "         Iterations 6\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:            physical_dv   No. Observations:                 3685\n",
      "Model:                          Logit   Df Residuals:                     3674\n",
      "Method:                           MLE   Df Model:                           10\n",
      "Date:                Fri, 28 Oct 2016   Pseudo R-squ.:                 0.03351\n",
      "Time:                        01:28:44   Log-Likelihood:                -2120.2\n",
      "converged:                       True   LL-Null:                       -2193.7\n",
      "                                        LLR p-value:                 1.544e-26\n",
      "==================================================================================\n",
      "                     coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
      "----------------------------------------------------------------------------------\n",
      "age                0.0047      0.005      1.009      0.313        -0.004     0.014\n",
      "child_marriage     0.1084      0.078      1.394      0.163        -0.044     0.261\n",
      "no_edu            -1.0541      0.187     -5.630      0.000        -1.421    -0.687\n",
      "primary_edu       -1.0554      0.187     -5.646      0.000        -1.422    -0.689\n",
      "secondary_edu     -1.3920      0.179     -7.797      0.000        -1.742    -1.042\n",
      "higher_edu        -1.9447      0.210     -9.271      0.000        -2.356    -1.534\n",
      "0                  0.0910      0.020      4.642      0.000         0.053     0.129\n",
      "1                  0.0114      0.028      0.412      0.680        -0.043     0.066\n",
      "2                  0.0584      0.029      2.012      0.044         0.002     0.115\n",
      "3                  0.0205      0.032      0.639      0.523        -0.043     0.084\n",
      "4                 -0.1247      0.036     -3.483      0.000        -0.195    -0.055\n",
      "==================================================================================\n",
      "age               1.004674\n",
      "child_marriage    1.114548\n",
      "no_edu            0.348514\n",
      "primary_edu       0.348052\n",
      "secondary_edu     0.248572\n",
      "higher_edu        0.143032\n",
      "0                 1.095220\n",
      "1                 1.011503\n",
      "2                 1.060175\n",
      "3                 1.020756\n",
      "4                 0.882796\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "results = model.fit()\n",
    "predict = results.predict()\n",
    "print(results.summary())\n",
    "print (np.exp(results.params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:snakes]",
   "language": "python",
   "name": "conda-env-snakes-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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