Resampling_SOM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outline\n",
    "## 1- Data Preprocessing and Cleaning\n",
    "###    1-1- Removing the repeated email_id subscriptions and using the latest one\n",
    "###    1-2- Finding Geo data related to Zip codes\n",
    "## 2- How to fill the empty tables?\n",
    "###    2-1 - Step wise Bootstraping and Resampling using SOM\n",
    "####       2-1-* it is good to decide how much of data we want to fill. There is an inverse relation between filling and accuracy\n",
    "## 3- Possible Products\n",
    "###   3-1-One dimensional histograms of distribution of (Specific and Vicinity) Demand based on Room, Size and Price ranges for a specific area\n",
    "###   3-2-Three dimensional histograms of distribution of (Specific  and Vicinity) Demand based on Room, Size and Price ranges all at once for a specific area\n",
    "###   3-3-Sensitivity analysis of Room,Size, Price on Demand for a specific area\n",
    "###   3-4-Sensitivity analysis of all the areas at once\n",
    "###   3-5- Next potential steps\n",
    "####     3-5-1- Region Clustering based ond the distribution of demands\n",
    "####     3-5-1- Time series analysis: Comparison of multi-dimensional demand for different months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "# import pandas.io.data\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import sys\n",
    "import sompy as SOM# from pandas import Series, DataFrame\n",
    "pd.__version__\n",
    "%matplotlib inline\n",
    "import  pysparse\n",
    "from pylab import matshow, savefig\n",
    "from scipy.linalg import norm\n",
    "import time\n",
    "# from IPython.html.widgets import *\n",
    "from ipywidgets import interact, HTML, FloatSlider\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>email_hash</th>\n",
       "      <th>room_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>price_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>size_max</th>\n",
       "      <th>zip</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>modified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1ea4c109f194b7d5a85a4f95b8898c7543e2d42b</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3232</td>\n",
       "      <td>47.0015</td>\n",
       "      <td>7.1091</td>\n",
       "      <td>2015-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fd8357409b8c4829e7d17af7e5f54f7c75ef6715</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1260</td>\n",
       "      <td>46.3829</td>\n",
       "      <td>6.2269</td>\n",
       "      <td>2015-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>634ff84744a1cab0da0ffe86677c1464414a4bc4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>8002</td>\n",
       "      <td>47.3606</td>\n",
       "      <td>8.5325</td>\n",
       "      <td>2015-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30379e6624190ee14e2acaa7320029737e65d733</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8134</td>\n",
       "      <td>47.3079</td>\n",
       "      <td>8.5218</td>\n",
       "      <td>2015-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c32c092483ce6037a19c02e56c150d331eb7fae3</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8000</td>\n",
       "      <td>47.3667</td>\n",
       "      <td>8.5500</td>\n",
       "      <td>2015-03-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 email_hash  room_min  room_max  price_min  \\\n",
       "0  1ea4c109f194b7d5a85a4f95b8898c7543e2d42b      30.0       NaN        NaN   \n",
       "1  fd8357409b8c4829e7d17af7e5f54f7c75ef6715      10.0      10.0      500.0   \n",
       "2  634ff84744a1cab0da0ffe86677c1464414a4bc4      40.0      60.0     2400.0   \n",
       "3  30379e6624190ee14e2acaa7320029737e65d733      30.0       NaN        NaN   \n",
       "4  c32c092483ce6037a19c02e56c150d331eb7fae3      40.0       NaN        NaN   \n",
       "\n",
       "   price_max  size_min  size_max   zip      lat     lon    modified  \n",
       "0        NaN       NaN       NaN  3232  47.0015  7.1091  2015-03-04  \n",
       "1      900.0       NaN       NaN  1260  46.3829  6.2269  2015-03-04  \n",
       "2     5000.0     100.0     200.0  8002  47.3606  8.5325  2015-03-04  \n",
       "3     2000.0       NaN       NaN  8134  47.3079  8.5218  2015-03-04  \n",
       "4     3500.0       NaN       NaN  8000  47.3667  8.5500  2015-03-04  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month = 3\n",
    "path= '/Users/SVM/Dropbox/Applications/realmatch360/Data/subscription/rent_unq_ids_2015_'+str(month)+'_01_ETH.csv'\n",
    "# subs = pd.read_csv(path,dtype={'price_min': np.float,'price_max': np.float,'size_min': np.float,'size_max': np.float})\n",
    "\n",
    "subs = pd.read_csv(path)\n",
    "# subs = subs.sort_values('zip')\n",
    "subs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "room_min 0.813728798186\n",
      "room_max 0.483533786848\n",
      "price_min 0.366968707483\n",
      "price_max 0.874427210884\n",
      "size_min 0.25452244898\n",
      "size_max 0.0848979591837\n"
     ]
    },
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_max</th>\n",
       "      <td>93600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size_min</th>\n",
       "      <td>280611</td>\n",
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       "    <tr>\n",
       "      <th>price_min</th>\n",
       "      <td>404583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_max</th>\n",
       "      <td>533096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_min</th>\n",
       "      <td>897136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_max</th>\n",
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       "    </tr>\n",
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      "text/plain": [
       "           count_complete\n",
       "size_max            93600\n",
       "size_min           280611\n",
       "price_min          404583\n",
       "room_max           533096\n",
       "room_min           897136\n",
       "price_max          964056"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valued_Data = subs[['room_min', 'room_max', 'price_min', 'price_max','size_min', 'size_max', 'lat', 'lon']].copy()\n",
    "\n",
    "#Taking out the extreme values as they have bad effects on SOM\n",
    "stat = valued_Data.describe(percentiles=[.001,.005,.02,.03,.04,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,.95,.99,.995,.999])\n",
    "\n",
    "\n",
    "for i in range(valued_Data.shape[1]-3):\n",
    "    mx = stat[valued_Data.columns[i]].ix['99.9%']\n",
    "    ind = valued_Data[valued_Data.columns[i]]>mx\n",
    "    valued_Data[valued_Data.columns[i]].ix[ind]=mx\n",
    "    mn = stat[valued_Data.columns[i]].ix['0.1%']\n",
    "    ind = valued_Data[valued_Data.columns[i]]<mn\n",
    "    valued_Data[valued_Data.columns[i]].ix[ind]=mn\n",
    "\n",
    "\n",
    "sz_complete = []\n",
    "ind_completes = {}\n",
    "ind_nons = {}\n",
    "for i in range(valued_Data.shape[1]-2):\n",
    "    Data = valued_Data.values[:,i]\n",
    "    \n",
    "    #empties\n",
    "    ind = valued_Data[valued_Data.columns[i]].isnull().values[:]\n",
    "    ind_nons[valued_Data.columns[i]]=set(np.arange(len(ind))[ind])\n",
    "    \n",
    "    #Completes\n",
    "    ind = np.bitwise_not(ind)\n",
    "    ind_completes[valued_Data.columns[i]]=set(np.arange(len(ind))[ind])\n",
    "    sz_complete.append(ind.sum())\n",
    "    print valued_Data.columns[i], ind.sum()/float(valued_Data.shape[0])\n",
    "sz_complete = pd.DataFrame(data=sz_complete,index=valued_Data.columns.values[:-2],columns=['count_complete'])\n",
    "sz_complete = sz_complete.sort_values('count_complete')\n",
    "sz_complete "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Procedure to fill in the gaps systematically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>price_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>room_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>30.0</td>\n",
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       "      <td>7.1091</td>\n",
       "      <td>47.0015</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>6.2269</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8.5325</td>\n",
       "      <td>47.3606</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>8.5218</td>\n",
       "      <td>47.3079</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>8.5500</td>\n",
       "      <td>47.3667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size_max  size_min  price_min  room_max  room_min  price_max     lon  \\\n",
       "0       NaN       NaN        NaN       NaN      30.0        NaN  7.1091   \n",
       "1       NaN       NaN      500.0      10.0      10.0      900.0  6.2269   \n",
       "2     200.0     100.0     2400.0      60.0      40.0     5000.0  8.5325   \n",
       "3       NaN       NaN        NaN       NaN      30.0     2000.0  8.5218   \n",
       "4       NaN       NaN        NaN       NaN      40.0     3500.0  8.5500   \n",
       "\n",
       "       lat  \n",
       "0  47.0015  \n",
       "1  46.3829  \n",
       "2  47.3606  \n",
       "3  47.3079  \n",
       "4  47.3667  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_to_fill = valued_Data[list(sz_complete.index.values)+['lon','lat']].copy()\n",
    "Data_to_fill.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "(0,) 18213\n",
      "(1,) 1392\n",
      "(2,) 10391\n",
      "(3,) 3132\n",
      "(4,) 332\n",
      "(5,) 470\n",
      "(0, 1) 186359\n",
      "(0, 2) 22682\n",
      "(0, 3) 26503\n",
      "(0, 4) 852\n",
      "(0, 5) 502\n",
      "(1, 2) 2519\n",
      "(1, 3) 110\n",
      "(1, 4) 346\n",
      "(1, 5) 3\n",
      "(2, 3) 2101\n",
      "(2, 4) 825\n",
      "(2, 5) 4365\n",
      "(3, 4) 3064\n",
      "(3, 5) 255\n",
      "(4, 5) 1\n",
      "(0, 1, 2) 121163\n",
      "(0, 1, 3) 40325\n",
      "(0, 1, 4) 3661\n",
      "(0, 1, 5) 2039\n",
      "(0, 2, 3) 85561\n",
      "(0, 2, 4) 2553\n",
      "(0, 2, 5) 2681\n",
      "(0, 3, 4) 5479\n",
      "(0, 3, 5) 3915\n",
      "(0, 4, 5) 91\n",
      "(1, 2, 3) 1051\n",
      "(1, 2, 4) 3161\n",
      "(1, 2, 5) 315\n",
      "(1, 3, 4) 319\n",
      "(1, 3, 5) 126\n",
      "(1, 4, 5) 0\n",
      "(2, 3, 4) 1566\n",
      "(2, 3, 5) 432\n",
      "(2, 4, 5) 25\n",
      "(3, 4, 5) 34\n",
      "(0, 1, 2, 3) 191390\n",
      "(0, 1, 2, 4) 23444\n",
      "(0, 1, 2, 5) 62813\n",
      "(0, 1, 3, 4) 39812\n",
      "(0, 1, 3, 5) 6201\n",
      "(0, 1, 4, 5) 206\n",
      "(0, 2, 3, 4) 17778\n",
      "(0, 2, 3, 5) 9905\n",
      "(0, 2, 4, 5) 204\n",
      "(0, 3, 4, 5) 469\n",
      "(1, 2, 3, 4) 450\n",
      "(1, 2, 3, 5) 279\n",
      "(1, 2, 4, 5) 172\n",
      "(1, 3, 4, 5) 134\n",
      "(2, 3, 4, 5) 0\n",
      "(0, 1, 2, 3, 4) 91292\n",
      "(0, 1, 2, 3, 5) 33713\n",
      "(0, 1, 2, 4, 5) 5086\n",
      "(0, 1, 3, 4, 5) 4008\n",
      "(0, 2, 3, 4, 5) 0\n",
      "(1, 2, 3, 4, 5) 0\n",
      "62\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "# #This is a list of possible combination of columns if it is not empty (i.e some rows)\n",
    "comb_list = []\n",
    "#This is to say for each the indices (rows) belong to whic unique combs\n",
    "comb_rows = []\n",
    "k = 0\n",
    "for i in range(1,6):\n",
    "    for r in itertools.combinations(range(6),i):\n",
    "        k = k+1\n",
    "        #At least it has len(r) nulls including the columns in r\n",
    "        fltr0 = Data_to_fill.ix[:,r].isnull().sum(axis=1)>=len(r)\n",
    "        #Among them we chose only those with exactly len(r) columns of null, which are necessarily those columns\n",
    "        fltr1 = Data_to_fill.ix[fltr0].isnull().sum(axis=1)==len(r)\n",
    "        print r, Data_to_fill.ix[fltr1.index].ix[fltr1].shape[0]\n",
    "        \n",
    "#         print Data_to_fill.values[:3,r]\n",
    "print k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# for i in range(1,6):\n",
    "#     for r in itertools.combinations(range(6),i):\n",
    "#         ind = Data_to_fill.isnull().values[:]\n",
    "#         sum_ind = ind.sum(axis=1)\n",
    "#         Data_tr = Data_to_fill.ix[sum_ind==0].values[:]\n",
    "    \n",
    "#         msz = [50,50]\n",
    "#         #Train a SOM based on Data_tr\n",
    "#         somP = SOM.SOM('som', Data_tr.astype(float), mapsize = msz,norm_method = 'var',initmethod='pca')\n",
    "#         somP.train(n_job = 1, shared_memory = 'no',verbose='off')\n",
    "#         bmus = somP.project_data(somP.data_raw)\n",
    "#         print 'size of training data is ',Data_tr.shape\n",
    "    \n",
    "    \n",
    "#         print \"combination is: \", r\n",
    "#         #At least it has len(r) nulls including the columns in r\n",
    "#         fltr0 = Data_to_fill.ix[:,r].isnull().sum(axis=1)>=len(r)\n",
    "#         #Among them we chose only those with exactly len(r) columns of null, which are necessarily those columns\n",
    "#         #Other wise, we are not sure if in addition to these r columns there is no null values\n",
    "#         fltr1 = Data_to_fill.ix[fltr0].isnull().sum(axis=1)==len(r)\n",
    "#         X_to_fill = Data_to_fill.ix[fltr1.index].ix[fltr1]\n",
    "#         if X_to_fill.shape[0]>1:\n",
    "#             ind_non_missing_dims = list(set(range(Data_to_fill.shape[1])).difference(r))\n",
    "#             for j, Target in enumerate(r[::-1]):\n",
    "#                 y_train = Data_tr[:,Target]\n",
    "            \n",
    "#                 #project hist consider the size of projection file\n",
    "#                 print 'Target is {} and ind_non_missing is {} and size of data to fill is {} '.format(Target,ind_non_missing_dims,X_to_fill.shape) \n",
    "#                 preds = fill_with_SOM(somP,bmus,X_to_fill.values[:],ind_non_missing_dims,Target,y_train,knn=1,which='SOM_hist',msz=msz)\n",
    "#                 X_to_fill.values[:,Target]= preds[:,0]\n",
    "#                 #now we use the estimated dimension for the projection of the next missing cols\n",
    "#                 ind_non_missing_dims.append(Target)\n",
    "            \n",
    "#         else:\n",
    "#             print 'Size of data to fill is {} '.format(X_to_fill.shape) \n",
    "            \n",
    "            \n",
    "            \n",
    "# #             preds = np.ones(ind_dim.sum())\n",
    "            \n",
    "#         Data_to_fill.values[X_to_fill.index]=X_to_fill.values[:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Data_to_fill.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# sz_complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# path= '/Users/SVM/Dropbox/Applications/realmatch360/Data/subscription/Filled_rent_unq_ids_2015_'+str(month)+'_01_ETH.csv'\n",
    "# Data_to_fill.to_csv(path_or_buf=path,index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>size_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>price_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>room_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>7.1091</td>\n",
       "      <td>47.0015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>6.2269</td>\n",
       "      <td>46.3829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8.5325</td>\n",
       "      <td>47.3606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>8.5218</td>\n",
       "      <td>47.3079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>8.5500</td>\n",
       "      <td>47.3667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size_max  size_min  price_min  room_max  room_min  price_max     lon  \\\n",
       "0     140.0      80.0     1200.0      50.0      30.0     2000.0  7.1091   \n",
       "1      35.0      30.0      500.0      10.0      10.0      900.0  6.2269   \n",
       "2     200.0     100.0     2400.0      60.0      40.0     5000.0  8.5325   \n",
       "3     100.0      75.0     1400.0      40.0      30.0     2000.0  8.5218   \n",
       "4     150.0     100.0     2500.0      50.0      40.0     3500.0  8.5500   \n",
       "\n",
       "       lat  \n",
       "0  47.0015  \n",
       "1  46.3829  \n",
       "2  47.3606  \n",
       "3  47.3079  \n",
       "4  47.3667  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path= '/Users/SVM/Dropbox/Applications/realmatch360/Data/subscription/Test_Filled_rent_unq_ids_2015_'+str(month)+'_01_ETH.csv'\n",
    "Filled_Data = pd.read_csv(path)\n",
    "Filled_Data = Filled_Data.dropna()\n",
    "Filled_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1102500, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>price_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>room_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>7.1091</td>\n",
       "      <td>47.0015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>6.2269</td>\n",
       "      <td>46.3829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8.5325</td>\n",
       "      <td>47.3606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>8.5218</td>\n",
       "      <td>47.3079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>8.5500</td>\n",
       "      <td>47.3667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size_max  size_min  price_min  room_max  room_min  price_max     lon  \\\n",
       "0     140.0      80.0     1200.0      50.0      30.0     2000.0  7.1091   \n",
       "1      35.0      30.0      500.0      10.0      10.0      900.0  6.2269   \n",
       "2     200.0     100.0     2400.0      60.0      40.0     5000.0  8.5325   \n",
       "3     100.0      75.0     1400.0      40.0      30.0     2000.0  8.5218   \n",
       "4     150.0     100.0     2500.0      50.0      40.0     3500.0  8.5500   \n",
       "\n",
       "       lat  \n",
       "0  47.0015  \n",
       "1  46.3829  \n",
       "2  47.3606  \n",
       "3  47.3079  \n",
       "4  47.3667  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove outliers based on a global statistics, calculated previously\n",
    "# \n",
    "\n",
    "for i in range(Filled_Data.shape[1]-2):\n",
    "    mx = stat[Filled_Data.columns[i]].ix['99.9%']\n",
    "    ind = Filled_Data[Filled_Data.columns[i]]>mx\n",
    "    Filled_Data[Filled_Data.columns[i]].ix[ind]=mx\n",
    "    mn = stat[Filled_Data.columns[i]].ix['0.1%']\n",
    "    ind = Filled_Data[Filled_Data.columns[i]]<mn\n",
    "    Filled_Data[Filled_Data.columns[i]].ix[ind]=mn\n",
    "\n",
    "Filled_Data = Filled_Data.dropna()\n",
    "print Data_to_fill.shape\n",
    "Filled_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Procedure to check the distribution of the filled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1102500, 8)\n",
      "(56230, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>price_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>room_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8.5325</td>\n",
       "      <td>47.3606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>200.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>9.0525</td>\n",
       "      <td>47.4519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>70.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>6.6987</td>\n",
       "      <td>46.5737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>70.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>6.6303</td>\n",
       "      <td>46.5224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>70.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>6.6322</td>\n",
       "      <td>46.5195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     size_max  size_min  price_min  room_max  room_min  price_max     lon  \\\n",
       "2       200.0     100.0     2400.0      60.0      40.0     5000.0  8.5325   \n",
       "91      200.0      20.0      400.0      40.0      10.0     1800.0  9.0525   \n",
       "679      70.0      40.0     1000.0      30.0      20.0     1300.0  6.6987   \n",
       "680      70.0      40.0     1000.0      30.0      20.0     1300.0  6.6303   \n",
       "681      70.0      40.0     1000.0      30.0      20.0     1300.0  6.6322   \n",
       "\n",
       "         lat  \n",
       "2    47.3606  \n",
       "91   47.4519  \n",
       "679  46.5737  \n",
       "680  46.5224  \n",
       "681  46.5195  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_Data_only_complete = subs[Filled_Data.columns.values].copy()\n",
    "# valued_Data = subs[['price_min','size_min','room_min','price_max','room_max', 'lat', 'lon']].copy()\n",
    "print original_Data_only_complete.shape\n",
    "\n",
    "#Taking out the extreme values as they have bad effects on SOM\n",
    "# stat = original_Data_only_complete.describe(percentiles=[.001,.005,.02,.03,.04,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,.95,.99,.995,.999])\n",
    "for i in range(original_Data_only_complete.shape[1]-2):\n",
    "    mx = stat[original_Data_only_complete.columns[i]].ix['99.9%']\n",
    "    ind = original_Data_only_complete[original_Data_only_complete.columns[i]]>mx\n",
    "    original_Data_only_complete[original_Data_only_complete.columns[i]].ix[ind]=mx\n",
    "    mn = stat[original_Data_only_complete.columns[i]].ix['0.1%']\n",
    "    ind = original_Data_only_complete[original_Data_only_complete.columns[i]]<mn\n",
    "    original_Data_only_complete[original_Data_only_complete.columns[i]].ix[ind]=mn\n",
    "\n",
    "original_Data_only_complete = original_Data_only_complete.dropna()\n",
    "print original_Data_only_complete.shape\n",
    "original_Data_only_complete.head()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x123a8de90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vvrqyc5arQ7H9rXjRrGHOUkv5UwI01/6EHtPPALvS/4NZRETgRuAeYDbwapPr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\n/05Yl+C1S3qdokjS9vXX3edXXy3JRP2NBa+TYTSbe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ofD/634tuO8HoNF6x+Pp5gcVzHL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uxcAOqENUHsXAUdahLU9cpy3A64DbCF08HwX+vN1lakKdZgD3Ee6Lfzp6kZ2Q2H4CYZrw54G7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\nkiRJKu3/AlcmeK1ydRAGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123a8d850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,18))\n",
    "k = 1\n",
    "for i, dim1 in enumerate(Filled_Data.columns.values[:-2]):\n",
    "    for dim2 in Filled_Data.columns.values[i+1:-2]:\n",
    "        plt.subplot(5,4,k)\n",
    "        k = k+1\n",
    "#         plt.title(dim1 +\" vs \" +dim2)\n",
    "        plt.xlabel(dim1)\n",
    "        plt.ylabel(dim2)\n",
    "# codebook_denorm.price_min.values[:] = np.around(codebook_denorm.price_min/100,decimals=0)*100\n",
    "# codebook_denorm.price_max.values[:] = np.around(codebook_denorm.price_max/100,decimals=0)*100\n",
    "        plt.plot(Filled_Data[dim1],Filled_Data[dim2],'r.',markersize=3.2,label='Filled Data')\n",
    "        plt.plot(original_Data_only_complete[dim1],original_Data_only_complete[dim2],'g.',markersize=.8,label='Original Sample')\n",
    "\n",
    "plt.tight_layout()\n",
    "font = {'size'   : 12}\n",
    "plt.rc('font', **font)\n",
    "plt.legend(loc='best',bbox_to_anchor = (1.51, 1.015),fontsize = 'medium')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "Tr_data_size = int(.2*Filled_Data.shape[0])\n",
    "ind_row_train = random.sample(range(Filled_Data.shape[0]),Tr_data_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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g70IaQHDffaBdmkRE5ABSyCCz0WrRyjuqyYYSFS9UwyoRETmc+oODIOh9vlPI8OX7Kd95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\nmS0zs2O629bMhpjZK2a2xsxeNrOCsGW3+Pv6yMxOj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TDXp84453b6/+42s2fxyrM7+yxI\nFL39rIor59zusNmE+rmN0n1Ln4kUbyKf32bOuSozWwjMJ4HPr0h/kJSVDJ1xzq1wzhU758Y758bh\n/UE03Tm3C1gAfM680cvHAROBd/0SqUozm+0nJi4Fmj/gF+ANFAfwWbwBuqLKvNF3vwmc55wLhC1a\nAFycaPF2YTEw0czGmlkGcLEfTzw8CKxyzt0d1rYAuNyfvoy256y35zlqnHPfcc6Ncc6Nxztnrznn\n/gvv4pyI8ZYBW83scL/pVGAliXl+twBzzCzLP8apwKoEjNVo+419NOOLxWdCm3j70WdYPLX//4/b\n73hP+DfkzS4EVvjTEWPv6/giSKTrUwdmluN/K4yZ5QKnAx/S+WdBvBzUZ1VfBRmm/WdVIv/cHvR9\nS18F6usQb6KeXzMbZn7XDTPLBj6FN45EIp9fkeTnEmD0yVi98EZrLgybvwVvlNiPgNPD2mfiXdDX\nAXeHtWcCT/jti4BDYxDjOmAz8L7/+k0ix9vNe5mPN8rwOuDmOP2fnwiE8EYPX+qf0/lAIfCqH98r\nwOADPc8xjP0UWp8ukbDxAtPwbtqXAU/jPV0iIePF6zr1EbAcb2Cn9ESKFXgU2AEE8JIiVwBDohVf\ntD8TOom333yG9eULb5CxrUAdsBP4WyL8zvQw9of936llwLN4fZu7jD3eLxLg+tRFbONovWZ92Bxf\nV59VcYgxKp9VcY43IX9uieJ9S5zjTdTzO9WPcZkf33f99oQ8v3rp1V9ezY/FEhERERERERE5KP2q\nu4SIiIiIiIiIxI+SDCIiIiIiIiISFUoyiIiIiIiIiEhUKMkgIiIiIiIiIlGhJIOIiIiIiIiIRIWS\nDCIiIiIiIiISFUoyiIiIiIiIiEhUKMkgIiIiIiIiIlGhJIOIiIiIiIiIRIWSDCIiIiIiIiISFUoy\niIiIiAxAZvYJM/so3nGIiEj/oiSDyABlZivM7OR4xyEiIvHhnHvTOXdEvOMQEZH+xZxz8Y5BRERE\nRPqQmaU650LxjkNERPofVTKIRIGZpcY7BhERETPbaGY3m9lKM9trZg+YWYaZnWJmW83sW2a2E3iw\nuS1s20PM7Ckz22Vmu83snrBlV5rZKn+ffzOzMT2IpcnMrjGztWZWaWY/MLPxZvZvM6sws8fMLM1f\nd7CZPecfe68/XeIvG+LHfrY/n2tm68zsP6N+AkVE5KApySBygPwbuW+Z2QdAtZlNMbPXzazczD40\ns3PD1h1kZg/7N08bzey7YcsuM7M3zewOf9v1Zna8377FzErN7NIexPN7M/u1mb1oZvvN7F9mVmRm\nd5rZPv/mcFq7+D/pT3/fzB43s4fMrMqPf0aUT5mIiPSNzwOfAiYAk4D/57cXA4OBMcCX/DYHYGYp\nwPPARn95CfCYv+x84GbgAmA48C/gzz2M5XRgOjAH+BZwnx/faGAqcIm/XgrwoN8+BqgFfgXgnCsH\nrgR+a2bDgbuA951zf+phDCIi0oeUZBA5OBcDZ+LddD0DvORPfwV4xMwO89e7F8gHDgXmApea2RVh\n+5kNLAMK8W7cHgOOxbtB/C/gXjPL6UE8nwW+AwwFgsDbwHv+/FPAnV1sey7wKFAAPAf8ugfHExGR\nxPMr59wO51wF8CNa/5APAd93zjU45wLttjkOGAl8yzlX75wLOufe8pddDfzYObfWOdcE/AQ4xsxG\n9yCWnzrnapxzHwErgFecc5udc/uBv+ElIHDO7XPOPeOcCzjnaoAfA6c078Q593fgL8A/gPnAl3t7\nUkREpG8oySBycO52zu3Au0nKdc791DnX6Jx7He8boUv8b4c+B9zsnKt1zm0GfomXPGi20Tn3sPMG\nSXkcOAS43b8R/DtewmBiD+J5xjm3zDkXxEt61DnnHgnb7zFdbPumc+5lf90/Akf35kSIiEjC2BY2\nvRkY5U/vds41dLLNIcBmP4nQ3ljgbr8qbh+wF68CoqQHsewKm64DytrN5wGYWbaZ3Wdmm8ysAngD\nGGxmFrb+b4EpwB/86gYREUlASjKIHJzmG7mRwNZ2yzbj3YANA9KBLRGWNWt/04Vzbk+7trwexNN+\nPxFv5jpRGjZdC2T5CRIREUku4RUGY4Ed/nRXo31vBcZ08rm/BbjaOVfov4Y45/Kcc4uiFC/AN4DD\ngFnOucFA89OPDFq6c9wPPARca2bjo3hsERGJIv0BIXJwmm/YdtD2pg68PqXbgT1AA96NXrOx/jIR\nEZFou87MSsysEK8L3WN+u3WxzbvATuAnZpZjZplmdoK/7D7gO2Z2JICZFZjZZ6Iccx5eMrzKj/u2\ndsu/CzThjc3wC+CP7aocREQkQSjJIBId7wC1/kCQaWY2FzgH+LNfevo48CMzyzOzscBX8bokdCZW\nN0692a9u3kREktOjwCvAemAd3rgM0EUlg3+tOhevmmALXmXDRf6yZ/HGYXjM78qwHG9chO60P15X\nlRR3ATl4ifm3gBebF/gDEd8E/Jffpe+neAmHm3sQg4iI9LGETDKY2XwzW+0/8ujbEZZPMrO3zKze\nzL4WYXmKmb1vZgv6JmIZoFpulvw+rucCZ+HdIN2LdzO0zl/lK3hdEDYA/wT+5Jz7fU/23cl8T7bp\nbp3u1u/J/kSSUnfXGX+de/zH5C0zs2O629Z/zN4rZrbGzF42swK//fNmttS/Li01s5CZacwTiaXF\nzrmj/K4NV/oDOb7hnGvz2Mn2bc65bc65/3DODXPOjXDO3RS27BHn3NHOucHOubHOuS92F4RzLtU5\ntyFs/mTn3MNh87c6577kT+90zs1zzuU75yY7537rb9/knHvfOTfUObfRX7fJOXeSc+7HB3eaREQk\nFsxLCCcOv8/dWuBUvBL0xcDFzrnVYesMwys3vwAod87d0W4fXwVmAoOcc+f1VewiIpL4enidORO4\n3jl3tpkdhzfI65yutjWznwJ7nXM/85MPQ5xzN7c79hS8AVoPQyQGzGwjcJVz7rV4xyIiIgNTIlYy\nzAbW+Y83asDrR3h++ArOuT3OuSVAY/uNzewQvG+Tf9cXwYqISNLp9jrjzz8M4Jx7Bygws6Jutj0f\nb1A6/H8viHDsS2jtHy8SC3327ZGZfcLM9ptZVdhrv5lV9VUMIiKSeNLiHUAEJbQdpX8b3k1dT90J\nfBMoiGZQIonAzFbgDSjZ0oR3Q3m1c+7P8YlKJOn05DoTaZ2SbrYtcs6VATjnSs1sRIRjfw5QhZ3E\njHOuz5664Jx7E8jvq+OJiEhySMQkwwEzs7OBMufcMn/gvYgD15lZYvURETl4j5rZo/EOQqQrzrlk\nHkz0QGJvc60xs9lAjXNuVacH0fVJRKRPJfm1SSQhJWJ3ie20/ab2EHr+qL8TgfPMbAPwZ2CemT0c\naUXn3IB4ff/73497DHqfep96n3qvCaYn15nttH0sbfM6XW1b6nepwMyKgV3t9nkx3rWpS/H+v+qv\nr4Hyu6bz2/9eOrexe4lIbCRiJcNiYKL/mL+deDdll3Sxfkv20Tn3HbznQWNmpwBfd85dGsNYRUQk\n+fTkOrMAuA543MzmABXOuTIz29PFtguAy/Eer3cZ8NfmnZmZ4T0O8BOxelMiIiKJLjs7u7S+vr4o\n3nFIdGRlZZXV1dUVt29PuCSDcy5kZtfjPd85BXjAOfeRmV3tLXb3+98UvYfXD7DJzG4EjnTOVccv\nchERSQY9uc445140s7PMbD1QA1zR1bb+rn8KPGFmVwKb8ZIKzU4GtjjnNvXFexQREUlE9fX1Raoi\n6T+aKzjbS7gkA4Bz7iVgUru2+8Kmy2hbxhppH28Ab8QkwCQyd+7ceIfQJ/Q++5eB8j5hYL3XRNLd\ndcafv76n2/rt+4DTOtnmDeCEA41XDp5+12JL5zd2dG5FJNnYQMwkmZkbiO9bRCRezAynwbW6peuT\niEjf0bWp7+k617909juUiAM/ioiIiIiIiERdamoqM2bMYPr06cyYMYMtW7awZMkSbrrpJgAeeugh\nvvKVrwBw++23c8cdd/Rq//n5kZ/s23zcKVOmMH36dO64445uByDdvHkzf/5z8j2lPiG7S4iIiIiI\niIhEW25uLu+//36btjFjxjBz5syo7N8b67nr4+7Zs4dLLrmEqqoqbrvttk73tXHjRh599FEuuaSr\n5yAkHlUyiIiIiIiIyIAQqXrgjTfe4Nxzz+1yuw0bNnDmmWcya9YsTjnlFNauXQvApk2bOOGEE5g2\nbRq33nprj2IYNmwY999/P/feey/gVSycfPLJHHvssRx77LEsWrQIgFtuuYU333yTGTNmcPfdd3e6\nXqJRJYOIiIiIiIjETyff/vdYL8Z5qKurY8aMGTjnGD9+PE899ZQfQtcxfOlLX+K+++5jwoQJvPvu\nu1xzzTX84x//4MYbb+S6667jC1/4Ar/5zW96HMe4ceNoampi9+7dFBUV8eqrr5KRkcH69eu55JJL\nWLx4MT/5yU/45S9/yYIFCwCor6+PuF6iUZJBREREREREBoScnJwO3SW6U1NTw1tvvcVnP/vZlkqI\nhoYGAP7973/z9NNPA/Bf//Vf3HzzzT3eb/O+gsEg119/PcuWLSM1NZV169ZFXL+n68WbkgwiIiIi\nIiISPwn+xImmpiaGDBkSMTlhZi1VEL15csaGDRtIS0tj+PDh3H777RQXF7N8+XJCoRDZ2dkRt7nz\nzjt7tF68aUwGERERERERGRAO5BGa+fn5jBs3jieffLKlbfny5QCceOKJLU+AeOSRR3p03N27d3PN\nNddwww03AFBZWcnIkSMBePjhhwmFQi3H3b9/f8t2na2XaJRkEBERERERkQGhu7EXOvOnP/2JBx54\ngGOOOYYpU6a0jJNw11138etf/5pp06axc+fOTrevr69veYTl6aefzvz58/ne974HwLXXXssf/vAH\npk+fztq1a8nNzQXg6KOPJiUlhenTp3P33Xdz3XXXRVwv0diBZHKSnZm5gfi+RUTixcxwzh3kqE79\nn65PIiJ9R9emvqfrXP/S2e+QKhlEREREREREJCqUZBARERERERGRqFCSQeQA3PLqLfz4Xz+Odxgi\nIiIiIiIJRUkGkQPwxuY3eGDpA/EOQ0REREREJKEoySByAOoa68jPzI93GCIiIiIiIglFSQaRA2B4\ng6hqdFwREREREZFWSjKI9FJDqIG0lDQGZQ6iOlgd73BERERERKSHtm/fzgUXXMDhhx/OYYcdxle/\n+lUaGxsjrrtz504uuuiibvd5zjnnUFVVdUDx3H777dxxxx0d2teuXcu8efOYPn06Rx11FF/+8pcP\naP899cYbb3DuuedGZV9KMoj00p7aPQzPHc6QrCGU15fHOxwREREREemhCy+8kAsvvJC1a9eydu1a\n9u/fz3e+850O64VCIUaOHMkTTzzR7T6ff/55Bg0aFNU4v/KVr/D1r3+dpUuXsnLlSm644Yao7j8S\nM4vKfpRkEOml3bW7GZYzjCHZQyivU5JBRERERCQZvPbaa2RnZ3PppZcC3h/Vd955Jw8++CD19fU8\n9NBDnH/++Zx66qmcdtppbN68malTpwJQV1fH5z73OaZMmcKFF17InDlzeP/99wEYN24c+/btY/Pm\nzRx55JF86UtfYsqUKcyfP59AIADA7373O2bPns306dP57Gc/S319fZexlpaWUlJS0jJ/1FFHAbB5\n82ZOPvlkjj32WI499lgWLVoEeJUIc+fO5YILLmDixInccsstPProoxx33HFMmzaNjRs3AnDFFVdw\nzTXXMGvWLCZPnswLL7zQ4di1tbVcddVVzJkzh5kzZ/Lcc8/16jwrySDSS1WBKgoyC1TJICIiIiKS\nRFauXMnMmTPbtOXn5zN27FjWr18PwNKlS3n66ad5/fXXgdZv93/zm99QWFjIihUr+OEPf9iSYAhf\nB2D9+vXccMMNrFixgoKCAp566ikAPv3pT/Puu++ydOlSJk+ezAMPdP2kuptuuol58+Zx9tlnc9dd\nd1FZWQlAUVERr776Ku+99x6PPfZYmwqH5cuXc//997Nq1Sr++Mc/sm7dOt555x2uuuoqfvWrX7Ws\nt3nzZhYvXszzzz/Pl7/8ZYLBYJtj/+hHP+LUU09l0aJFvPbaa3zjG9+grq6uZycZSOvxmiICtCYZ\n0lPTVckgIiIiIhIF8/80nz21e3q93bCcYbz0ny8d1LHDB3P/1Kc+RUFBQYd13nzzTW666SbAqyo4\n+uijI24/bty4luqHmTNnsmnTJsBLANx6661UVFRQU1PDGWec0WVMl19+OfPnz+ell17i2Wef5f77\n7+eDDz4gGAxy/fXXs2zZMlJTU1m3bl3LNrNmzWLEiBEATJgwgdNPPx2AqVOnsnDhwpb1mseZmDhx\nIhMmTGD16tVtjv3KK6/w3HPP8fOf/xyAYDDIli1bmDRpUpcxN1OSQaSXqgJVDMocRGZaJhX1FfEO\nR0REREQk6R1soqAnjjzySJ588sk2bVVVVWzdupWJEyeyZMkScnNze7Svzp4yl5mZ2TKdmpra0i3i\niiuuYMGCBUyZMoWHHnqIN954o9tjFBcXc/nll3P55ZczdepUVqxYwYIFCyguLmb58uWEQiGys7Mj\nHjslJaVlPiUlpc3gluGVF865DmMxOOd46qmnOOyww7qNMRJ1lxDppeYkQ15GHjUNNfEOR0RERERE\neuDUU0+lrq6OP/3pT4A3uOM3vvENrrjiCrKysrrc9sQTT+Txxx8HYNWqVXz44YcR1+ss+VBdXU1x\ncTENDQ088sgj3cb68ssvtyQGSktL2bdvHyUlJVRWVjJy5EgAHn74YUKhULf7au8vf/kLzjk+/vhj\nNm7c2KFC4YwzzuCee+5pmV+2bFmv9q8kg0gvNScZctJzqG2ojXc4IiIiIiLSQ8888wxPPPEEhx9+\nOJMnTyY7O5sf/ehH3W537bXXsmfPHqZMmcL3vvc9pkyZ0tKtIrwSoLMnNPzgBz9g9uzZnHTSSRxx\nxBHdHu+VV15hypQpTJ8+nTPPPJNf/OIXjBgxgmuvvZY//OEPTJ8+nbVr13ZaedHVkyLGjBnD7Nmz\nOfvss7nvvvvIyMhos/zWW2+loaGBo48+mqlTp/K9732v23jbHLuzTEt/ZmZuIL5viY7vv/59ZpXM\nwjnHezve4/Z5t8c7JJGEZ2Y456LzXKR+TNcnEZG+o2tT30vm61xTUxMNDQ1kZmayYcMGPvWpT7Fm\nzRrS0pJrBIIrrriCc889lwsvvPCg99XZ71BynRGRBFAdrCYvI49QU0jdJUREpNWvfw0/+xk8+STM\nmhXvaEREJIpqa2uZN28eDQ0NAPzv//5v0iUYoOsKh2hJyO4SZjbfzFab2Voz+3aE5ZPM7C0zqzez\nr4W1H2Jmr5nZSjP70My+0reRy0BQ11hHTnoOuRm56i4hkqS6u87469xjZuvMbJmZHdPdtmY2xMxe\nMbM1ZvaymRWELTvav26tMLMPzCyj/fGkH7j+etiyBXpZVioiIokvLy+PxYsXs2zZMpYtW9by5IZk\n8+CDD0aliqErCZdkMLMU4F7gDOAo4BIzm9xutb3ADcDP27U3Al9zzh0FHA9cF2FbkYNS21BLdlo2\nuem5qmQQSUI9uc6Y2ZnABOfcYcDVwP/1YNubgVedc5OA14Bb/G1SgT8CX3LOTQHmAg2xfI8SB+Hl\nv7t3xy8OERGROEu4JAMwG1jnnNvsnGsAHgPOD1/BObfHObcEL6kQ3l7qnFvmT1cDHwElfRO2DBS1\nDbXkpOdo4EeR5NXtdcaffxjAOfcOUGBmRd1sez7wkD/9EHCBP3068IFzboW/v/Kk7ZAqndsT9mz3\nGiWgRURk4ErEJEMJsDVsfhsHkCgws0OBY4B3ohKViK+usY7s9GxyM3KpCepGUiQJ9eQ609k6XW1b\n5JwrAy/pDYzw2w8HMLOXzOw9M/tmNN6EJJht21qnd+2KXxwiIiJxlnwjVfSAmeUBTwI3+hUNHdx2\n220t03PnzmXu3Ll9Epskv+ZKhlRLVXcJkU4sXLiQhQsXxjuMaDqQUZKaqxXSgBOBY4F64B9m9p5z\n7vVIG+n6lKRKS1un9+2DhgZIT49fPCLSQT+8NiWdrKysMr8yUPqBrKysskjtiZhk2A6MCZs/xG/r\nETNLw0sw/NE599fO1gu/iRPpjfAkg7pLiETW/o/j229PqEe99uQ6sx0YHWGdjC62LTWzIudcmZkV\nA81fZ28D/umcKwcwsxeBGUC3SQZJIpWVbed37YIS9dgUSSQJfm0aEOrq6orjHYPEXiJ2l1gMTDSz\nsf7o2xcDC7pYv/23Sw8Cq5xzd8cqQBnYgqEg6SnppKak0uSa4h2OiPReT64zC4BLAcxsDlDhd4Xo\natsFwOX+9GVAc6L7ZWCqmWX5ifBTgFUxeWcSP+2TDBUV8YlDREQkzhKuksE5FzKz64FX8JIgDzjn\nPjKzq73F7n6/xOY9IB9oMrMbgSOBacAXgA/NbCleqep3nHMvxeXNSL/V/HxZjd0mknx6cp1xzr1o\nZmeZ2XqgBriiq239Xf8UeMLMrgQ2Axf521SY2R14160m4AXn3N/67h1Ln2ifZNi/Pz5xiIiIxFnC\nJRkA/KTApHZt94VNl9G2jLXZv4HU2EYnIiLJrrvrjD9/fU+39dv3Aad1ss2jwKMHGq8kgfaVC1VV\n8YlDREQkzhKxu4SIiIhIclElg4iICKAkg8hBMTONyyAiIq1JBr87nSoZRERkoFKSQaQXmlwTFjbW\naE56DnUNdXGMSEREEkJzkmG035tTlQwiIjJAKckg0gv1jfVkp2e3zOek5+gxliIi0jHJoEoGEREZ\noJRkEOmF2oZastNakwxZaVkEQoE4RiQiIglBSQYRERFASQaRXqlrqCMnPadlPisti/rG+jhGJCIi\nCaH56RLqLiEiIgOckgwivVDbUNumu0RmaqaSDCIi0lrJcMgh3r+qZBARkQFKSQaRXqhtqCUnrW0l\nQ6BR3SVERAa0pqbWpEJzkkGVDCIiMkApySDSC3WNdW0qGdRdQkREqK4G5yAvD4YM8dpUySAiIgOU\nkgwivRBoDJCZmtkyrySDiIg0d5XYOyKPYI5/jaiujmNAIiIi8aMkg0gvBEIBMtNakwyZqZl6uoSI\nyEDnD/o4+aLdfOvj//Pa1F1CREQGqLR4ByCSTIKhoFfJ8OabEAyqkkFERKCyku35MK0mj3/uW+q1\nqZJBREQGKFUyiPRCoDFARk0dnHQSnHoqWfvrlGQQERnoKitZVgyz64eSkZ5FbTqqZBARkQFLSQaR\nXgiEAmSW7W2Zz9y5S0+XEBEZ6CorWTMMjmA4E4YexqbBtA4GKSIiMsAoySDSC8FQkIy9FS3zWbvL\nVckgIjLQVVayuQDGZo5g9OAxbB2e4SUYamvjHZmIiEifU5JBpBcCjQEya1qTCuouISIiVFSweTCM\nzR3F6EGj2TrcHyBYXSZERGQAUpJBpBcCoQCZtcGW+cyqGj1dQkRkoCsvZ3s+lAwew+iC0Wwt9MfV\nVpJBREQGICUZRHohGAqSEV7JUFGjSgYRkYFu3z4aUyBt6HCvkmGw364nTIiIyACkJINILwQaA2RW\n17XMZ1XXK8kgIjLAVVfsIi8IFBZ6lQx5/oCPqmQQEZEBKC3eAYgkk2AoSOb+tkkGPV1CRGRg21lT\nysgUYMgQhmYPZW9Wk7dASQYRERmAlGSQ5LdxI6SmwpgxMctUCQIAACAASURBVD9UIBQgo6qmZT6z\nRpUMIiIDXVn9XooMKCzEzCDFLxRVdwkRERmA1F1CktvatTBhAsyZA6FQzA8XaAyQGZZkyNpfR31I\nSQYRkYGsrLGC4mqgsBCA9JQ0gqlErGRwzvVtcCIiIn1MSQZJbq+95j2LfOdO+PjjmB8uGAqSUdn6\nzVTW/jp1lxARGeBK3X6KamhJMhRZPrty6ZBkePqjp8n5nxwaQg19H6SIiEgfUZJBktv69a3Ta9bE\n/HCBxnoyq2pb5jOr9HQJEZEBLRCgLKOB4rpUyM0FYGTqYHbm0aG7xCMfPsKcQ+awaNuiOAQqIiLS\nN5RkkOS2Y0frdFlZzA8XCNSQ2Yh3I5mdTVYD1Adqut1ORET6qbIySvOgKGMImAFQnFFIaR4dKhk2\nVWziC1O/wJKdS+IQqIiISN9QkkGS2549rdO7dsX8cMFgHRkhICcH8vLIaoRAsLbb7UREpJ/ato2y\nXCgaNKqlqThrmJdkCKtkaGxqxDCOHH4k6/aui0OgIiIifSMhkwxmNt/MVpvZWjP7doTlk8zsLTOr\nN7Ov9WZb6Wf6OMkQCNaRGQKysyE/n8wQ1CvJIJJ0enKtMLN7zGydmS0zs2O629bMhpjZK2a2xsxe\nNrMCv32smdWa2fv+6zexf4fSZ7ZvpywPioaNbWkqzh3RoZJhY/lGxg0Zx9iCsWyu3ByHQEVERPpG\nwiUZzCwFuBc4AzgKuMTMJrdbbS9wA/DzA9hW+pO9e1unq6pifrhgQ73XXcKvZMgIeW0ikjx6cq0w\nszOBCc65w4Crgf/rwbY3A6865yYBrwG3hO1yvXNuhv+6NnbvTvrctm0EUyFzVOtjlIvzR3VIMqzZ\nu4bJQyczMn8kO6t3xiFQERGRvpFwSQZgNrDOObfZOdcAPAacH76Cc26Pc24J0NjbbaWfCU8sRHhU\nWLQFGurbdJdIceD64NGZIhJVPblWnA88DOCcewcoMLOibrY9H3jIn34IuCBsfxaTdyJx51au8CYm\nTWppKy4Y1aG7xOo9q5k0bBIpDY00NTXpUZYiItJvJWKSoQTYGja/zW+L9baSbJxrm1joiyRDY31r\nd4m8PK+xqSnmxxWRqOrJtaKzdbratsg5VwbgnCsFRoStd6jfVeJ1M/vEwb8FSRT7Vy5lUAA4+uiW\ntuLCMR0rGfasYfL+TBg8mCHb91JRX9H3wYqIiPSBtHgHIHLAAgEIryLogyRDMBRsrWTwH1WmSgaR\nAeFAKhGav6reCYxxzpWb2QzgWTM70jlX3cW2kgx276Z0/TKKxhhMn97SnF0wjPo0OnSXmLQwE+rq\nGLNiK1sqtzAke0gcghYREYmtREwybAfGhM0f4rdFddvbbrutZXru3LnMnTu3NzFKImj3/PG+SDK4\nphApDi/JkJ3d3Bjz44okm4ULF7Jw4cJ4h9GZnlwrtgOjI6yT0cW2pWZW5JwrM7NiYBeAcy4IBP3p\n983sY+Bw4P1Iwen6lESeeYayHEfxkLEwaFBre34+AK56f0t2an9wP/kN3tzI/VC6ZxPTiqf1ccAi\nA1uCX5tE+o1ETDIsBiaa2Vi8b38uBi7pYv3wb5d6vG34TZwkqfZJhj4Y+NE1+VULOTmQlQWANalf\nrUh77f84vv322+MXTEc9uVYsAK4DHjezOUCFnzzY08W2C4DLgZ8ClwF/BTCzYcA+51yTmY0HJgIb\nOgtO16ck8vjjlOZB0RHHtm0fNIj8IFTXVpIP7KvbR2F2IezbB8DIaijduRam9H3IIgNZgl+bRPqN\nhEsyOOdCZnY98AremBEPOOc+MrOrvcXufn/wrfeAfKDJzG4EjnTOVUfaNk5vRWKtuXJhxAjv8ZV9\nUMlAc0IhO7slyaAxGUSSS0+uM865F83sLDNbD9QAV3S1rb/rnwJPmNmVwGbgIr/9ZOAHZhYEmoCr\nnXPqkJ/sQiFYtIiyKVA8/aS2ywoKKK4xdrKf/GCQNXvWMGnoJNi9HoDiathQvjXCTkVERJJfwiUZ\nAJxzLwGT2rXdFzZdRtsy1i63lX6quZJh5Mg+SzJYc0IhJwcyM71pjRAuknS6u87489f3dFu/fR9w\nWoT2p4GnDyZeSUAffwy1tZSNzGdO8cS2y1JSKA5lU5pXy+G7d7N6z2omD5sMexYBXpLhraodcQha\nREQk9hLx6RIiPdOcZBg6FNLSoKHBGwwylsKTDH4lQ0qTI9SkwR9FRAaUdesAKB01iKK8og6Li1MG\neU+Y2LXLG/Rx6CTYswfwx2So3dWX0YqIiPQZJRkkeTVXLuTntw64FetxGcK7S/gDP2Y0GcFQMLbH\nFRGRxLLDq0Qoy0+hOK+4w+KRGYXsyAd27WLV7lV+JYOXZCiuhp11u/syWhERkT6jJIMkr+ZKhvz8\nlpG8Y95lIkIlQ2aTEQjFuIJCREQSS3OSITvEiNwRHRYfnlXCmqHA7t1sqdzCmPRhUFcHQF4Q9jfW\n9GW0IiIifUZJBklezUmGvDzvj36A2tqYHa7JNbUdk8GvZMgMGYFGJRlERAYUP8nQkJ5KRmpGh8VH\n5k9g5QjYV7aJIdlDML+KAbzHYllI3exERKR/UpJBkld4ksH/g7/5W6JYCIaCZDTfE2ZltVYyNKJK\nBhGRgWbHDhxAenrExQXFY6nMhA9KP2Ba0bSWrhLNskMp1DbELjEuIiISL0oySPJqrloIGx8hlkmG\nQGOAzCbzZjIzwyoZUCWDiMhAs2MHlVkwOGdI5OWTJ1OyH35f9xYnjj6xQ5KhuDGL0urSPghURESk\nbynJIMmr+UkSWVl9X8mQmdlSyZDR2KRKBhGRgWb3bspyoWjQqMjLp07l0g/g2YKdnDn+9A5JhpHB\nDCUZRESkX1KSQZJXfb33b1hVQUwrGUIBMkNhlQzN3SUanJ4uISIy0JSXU5oHRYMPibx8/HgurptA\n5f848l74e2uSYfBgAIrr09i5f2cfBSsiItJ3lGSQ5BWHSobMRn8mvLtEg1N3CRGRgaSxEaqrKcs3\nioeMjryOGXztaxjAt78Nu/1HVo4dC0BxbYoqGUREpF9SkkGSV3OSoa8qGRoDZDS61mM2VzIE1V1C\nRGRAqagAoGxYNkV5xZ2vd/XVMGoUbNgAL7/stflJhpHVsLNalQwiItL/KMkgyau5u0QfVTIEQgEy\nw5MMzZUMwZAqGUREBhI/yVBamEFxV0mG1FQ4+2xv+r33vH/HjAGguMqpkkFERPolJRkkefVxJUMw\nFCSzoWMlQ0YwpEoGEZGBpLwcgLKCVIryirpe9+ST284ffjgAxRWNSjKIiEi/pCSDJK9ISYbm6oZY\nHK4xQEZDU4djZgYaNfCjiMhA0lzJkAdFud0kGU46qe38EUcAMKI8yK6aXbGITkREJK6UZJDk5ScU\n/hVcx9Ys/4/8WHeXCE8yZGZ6kwF1lxARGVD8Sobd2U0Mzx3e9bpjx0JRWCJi4kQA0qpraWxq7GQj\nERGR5KUkgyQvv5Lh5JXf4NaUhV5bjLtLZARD3kxmJmRkeJMBdZcQERlQ/EqGhrQUMlIzul9/+vTW\n6UGDvH9rakixFJpcUwwCFBERiR8lGSR5BQI0pMCE7BK2WJXXFuOnS2SGJxlaKhkaVckgIjKQ+JUM\nlprWs/Wvvda7Ztx9N+Tmem21tQzLGcae2j0xClJERCQ+enh1FElA9fVsKYBjBk/m4z3rvLZYD/wY\n6JhkyAg0Ut0Yu7EgREQkwVRUUJ8GmWmZPVv/3HOhuhrS0sA5SEmBYJDi3BGUVpcyIndEbOMVERHp\nQ6pkkOQVCLB5MIzNP4S01DQaU4j5mAwZQb//bEaG92iy1FQyGyHYoCSDiMiAUV7OnhwYll7Q823S\n/O91zFqqGYozhuoJEyIi0u8oySDJKxBgVy4U5RUxLH0we7OJcXeJ+raVDAAZGWSGIBCsjdlxRUQk\nwVRUsDsHhmcVHtj2OTkAjEwvZOf+nVEMTEREJP6UZJDkVV/vJRnyR1KUNZRducS2u0SwnsxGWioY\nAMjMJLMRAgElGUREBoyKCnbnwvAD7ebQXMmQWqBKBhER6XeUZJDkFQhQlgsjBo1kRNZQyvJoeeJE\nbA5XQ0aI1ioGvGlVMoiIDDAVFV53ibwDTDL4lQzFlsfOalUyiIhI/6IkgySvQMC7yRtUzLDsoezJ\nIcZJhlqvkqFdkiEjBIFg7CooREQkwVRWet0lBo86sO39SoaRLk+VDCIi0u8oySDJq76eqkwoyB9O\nQVYBVZnENMkQDNZ1rGTIyPAGftTTJUREBo7m7hKFow9s++ZKhsYsJRlERKTfUZJBklNjIzQ1UZkF\ng3ILGZQ9mMoYJxkCwVoyO+0uoSSDiMiA4XeXGD5s7IFt71cy5AegKlAVxcBERETiT0kGSU5+MqEq\nO4WCzAIKcgpjX8nQUB+xu0Rmo/fkCRERGQACAf5/9u48SNKsPu/99+SeWZlZlbV0Ve+z9CyAQAMM\nEtrscQiEEBJDXBOysGQtRNjoSkiyUPiCdB3SzA0bWcS9liwTuiAk+wKyjQS20FhCAmHcyCAEAzPD\nwDBL9yw93dXdtS9ZuS/n/vG+b2VWVVZVVub7ZmVXP5+Iicx8t/NmR0+fzCd/5xzKZRZGDJPjp3q7\nhlvJYIqaz0dERI4ehQxyYyo7X+pLUUMikmB0ZJy1RGt7ECqdhkt4lQwKGUREbg5rawAsZsNMjkz1\ndg23koFikWQ0SbGmsEFERI4OhQxyY/IqFkIGYwzZkfHgh0vUSzuHS8RizsSP9eDa3fTbvw3/7J/B\nxkbwbYmISGerqwBsJMKkY+neruFWMlAoMJOe0bwMIiJypAxlyGCM+UFjzFPGmGeMMe/e5ZjfNcZc\nMMY8Zoy5p237LxtjvmmMedwY85+MMbHB3bkMjBcmGOev8Gh6cgDDJSq7DpeoBh0yrKzAu94FH/oQ\n/OmfBtuWyE2gz36m47nGmJwx5jPGmKeNMZ82xoxuu94ZY0zeGPOu4N6ZBM4NGQiHMcb0do22S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YAyq1AQ+XUMggAuprZFDc8HzD1EgHsbqEdQtw/v7fB2BqqczCxrx/7YiIiAxIkKtLNHEm3/o5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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131c70d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize=(12,12))\n",
    "for i in range(Filled_Data.shape[1]-2):\n",
    "    ax = plt.subplot(3,2,i+1)\n",
    "    try:\n",
    "        Filled_Data.ix[ind_row_train,i].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        original_Data_only_complete.ix[:,i].plot(kind='kde',linewidth=.8,color='green',label='Original Sample')\n",
    "        plt.title(Filled_Data.columns.values[i])\n",
    "    except:\n",
    "        continue\n",
    "    \n",
    "\n",
    "font = {'size'   : 10}\n",
    "plt.rc('font', **font)\n",
    "plt.tight_layout()\n",
    "plt.legend(loc='best',bbox_to_anchor = (1.61, 1.015),fontsize = 'medium')\n",
    "plt.show()\n",
    "\n",
    "# path = '/Files/Research from 2014/Data_Python/Resamping/HITS_Pics/'+which+'_kde'+'_som_sz_'+str(msz0)+'_'+str(msz1)+'.png'\n",
    "\n",
    "# fig.savefig(path,transparent=False, dpi=200)\n",
    "# for i in range(codebook.shape[1]):\n",
    "#     ax1 = plt.subplot(3, 3, i)\n",
    "#     data.ix[:,i].plot('r-',kind='kde',linewidth=2,color='green')\n",
    "#     plt.title(sm.compname[0][i])\n",
    "#     font = {'size'   : 10}\n",
    "#     plt.rc('font', **font)\n",
    "#     ax1.set_ylabel(\"\")\n",
    "# plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demand distributions \n",
    "##### Here we divide the searches to \"Specific\" and \"Vicinity\" search, based on a threshold for the number of areas per unique email id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1102499, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>price_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>room_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>zip</th>\n",
       "      <th>email_hash</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>7.1091</td>\n",
       "      <td>47.0015</td>\n",
       "      <td>3232</td>\n",
       "      <td>1ea4c109f194b7d5a85a4f95b8898c7543e2d42b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>6.2269</td>\n",
       "      <td>46.3829</td>\n",
       "      <td>1260</td>\n",
       "      <td>fd8357409b8c4829e7d17af7e5f54f7c75ef6715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8.5325</td>\n",
       "      <td>47.3606</td>\n",
       "      <td>8002</td>\n",
       "      <td>634ff84744a1cab0da0ffe86677c1464414a4bc4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>8.5218</td>\n",
       "      <td>47.3079</td>\n",
       "      <td>8134</td>\n",
       "      <td>30379e6624190ee14e2acaa7320029737e65d733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>8.5500</td>\n",
       "      <td>47.3667</td>\n",
       "      <td>8000</td>\n",
       "      <td>c32c092483ce6037a19c02e56c150d331eb7fae3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size_max  size_min  price_min  room_max  room_min  price_max     lon  \\\n",
       "0     140.0      80.0     1200.0      50.0      30.0     2000.0  7.1091   \n",
       "1      35.0      30.0      500.0      10.0      10.0      900.0  6.2269   \n",
       "2     200.0     100.0     2400.0      60.0      40.0     5000.0  8.5325   \n",
       "3     100.0      75.0     1400.0      40.0      30.0     2000.0  8.5218   \n",
       "4     150.0     100.0     2500.0      50.0      40.0     3500.0  8.5500   \n",
       "\n",
       "       lat   zip                                email_hash  \n",
       "0  47.0015  3232  1ea4c109f194b7d5a85a4f95b8898c7543e2d42b  \n",
       "1  46.3829  1260  fd8357409b8c4829e7d17af7e5f54f7c75ef6715  \n",
       "2  47.3606  8002  634ff84744a1cab0da0ffe86677c1464414a4bc4  \n",
       "3  47.3079  8134  30379e6624190ee14e2acaa7320029737e65d733  \n",
       "4  47.3667  8000  c32c092483ce6037a19c02e56c150d331eb7fae3  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Complete_data = pd.concat((Filled_Data,subs[['zip','email_hash']]),join='inner',axis=1)\n",
    "print Complete_data.shape\n",
    "Complete_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#to add emails to the files.\n",
    "# path= '/Users/SVM/Dropbox/Applications/realmatch360/Data/subscription/Test_Filled_rent_unq_ids_with_emails_2015_'+str(month)+'_01_ETH.csv'\n",
    "# Complete_data.to_csv(path,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    82026.000000\n",
      "mean        13.440848\n",
      "std         22.126170\n",
      "min          1.000000\n",
      "10%          1.000000\n",
      "50%          3.000000\n",
      "60%          7.000000\n",
      "65%         10.000000\n",
      "70%         13.000000\n",
      "75%         16.000000\n",
      "80%         22.000000\n",
      "90%         38.000000\n",
      "max        146.000000\n",
      "dtype: float64\n",
      "threshold_for_area_search:  16.0\n",
      "(1102499, 11)\n"
     ]
    }
   ],
   "source": [
    "#To find those specific searches to those of area search\n",
    "gb_count =  Complete_data.groupby(by='email_hash').size()\n",
    "stat_emails = gb_count.describe(percentiles=[.1,.5,.6,.65,.7,.75,.8,.9])\n",
    "threshold_for_area_search = stat_emails.ix[\"75%\"]\n",
    "print stat_emails\n",
    "print 'threshold_for_area_search: ',threshold_for_area_search\n",
    "# gb_count['specific_area'] = np.ones(gb_count.shape)\n",
    "specific_search = gb_count[Complete_data['email_hash']].values\n",
    "specific_search[specific_search<=threshold_for_area_search]=1\n",
    "specific_search[specific_search>threshold_for_area_search]=0\n",
    "\n",
    "Complete_data['specific_search'] = specific_search\n",
    "print Complete_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>size_max</th>\n",
       "      <th>size_min</th>\n",
       "      <th>price_min</th>\n",
       "      <th>room_max</th>\n",
       "      <th>room_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>zip</th>\n",
       "      <th>email_hash</th>\n",
       "      <th>specific_search</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1102495</th>\n",
       "      <td>70.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>8.2454</td>\n",
       "      <td>47.2591</td>\n",
       "      <td>6287</td>\n",
       "      <td>92096d76b2ab3157cb425a221b8f142e10680eee</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102496</th>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>8.2715</td>\n",
       "      <td>47.2671</td>\n",
       "      <td>6288</td>\n",
       "      <td>92096d76b2ab3157cb425a221b8f142e10680eee</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102497</th>\n",
       "      <td>70.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>8.2985</td>\n",
       "      <td>47.2401</td>\n",
       "      <td>6289</td>\n",
       "      <td>92096d76b2ab3157cb425a221b8f142e10680eee</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102498</th>\n",
       "      <td>70.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>8.2367</td>\n",
       "      <td>47.2248</td>\n",
       "      <td>6294</td>\n",
       "      <td>92096d76b2ab3157cb425a221b8f142e10680eee</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102499</th>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>8.2280</td>\n",
       "      <td>47.2416</td>\n",
       "      <td>6295</td>\n",
       "      <td>92096d76b2ab3157cb425a221b8f142e10680eee</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         size_max  size_min  price_min  room_max  room_min  price_max     lon  \\\n",
       "1102495      70.0      50.0      800.0      40.0      20.0      900.0  8.2454   \n",
       "1102496      50.0      30.0      800.0      30.0      20.0      900.0  8.2715   \n",
       "1102497      70.0      50.0      800.0      40.0      20.0      900.0  8.2985   \n",
       "1102498      70.0      40.0      800.0      30.0      20.0      900.0  8.2367   \n",
       "1102499      50.0      30.0      800.0      30.0      20.0      900.0  8.2280   \n",
       "\n",
       "             lat   zip                                email_hash  \\\n",
       "1102495  47.2591  6287  92096d76b2ab3157cb425a221b8f142e10680eee   \n",
       "1102496  47.2671  6288  92096d76b2ab3157cb425a221b8f142e10680eee   \n",
       "1102497  47.2401  6289  92096d76b2ab3157cb425a221b8f142e10680eee   \n",
       "1102498  47.2248  6294  92096d76b2ab3157cb425a221b8f142e10680eee   \n",
       "1102499  47.2416  6295  92096d76b2ab3157cb425a221b8f142e10680eee   \n",
       "\n",
       "         specific_search  \n",
       "1102495                0  \n",
       "1102496                0  \n",
       "1102497                0  \n",
       "1102498                0  \n",
       "1102499                0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Complete_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **** A very important note regarding histograms\n",
    "#### unique number of emails for each zip codes is smaller than what we will see in the histogram. Because, we assume when a person says for example, room_min=1 and room max=5, then he creates demands for all the possible rooms in between (i.e. 1,2,3,4,5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def hist1d(Area):\n",
    "    q = Area\n",
    "#     q = 8134\n",
    "    # ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "    ind_q_vicinity = (Complete_data['zip']==q)\n",
    "    q_data_vicinity = Complete_data.ix[ind_q_vicinity,:]\n",
    "\n",
    "    fig = plt.figure(figsize=(12,3))\n",
    "    font = {'size'   : 8}\n",
    "    plt.rc('font', **font)\n",
    "    if q_data_vicinity.shape[0]>=1:\n",
    "    \n",
    "        rooms = []\n",
    "        sizes =[]\n",
    "        prices = []\n",
    "        print \"**************************************************************\"\n",
    "        print 'Number of unique vicinity search: {}.'.format(q_data_vicinity.shape[0])\n",
    "        for i in range(len(q_data_vicinity)):\n",
    "            rooms = rooms + list(np.arange(q_data_vicinity['room_min'].values[i]/10, q_data_vicinity['room_max'].values[i]/10+1))\n",
    "            sizes = sizes + list(np.around(np.linspace(q_data_vicinity['size_min'].values[i], q_data_vicinity['size_max'].values[i],num=10)))\n",
    "            prices= prices + list(np.around(np.linspace(q_data_vicinity['price_min'].values[i], q_data_vicinity['price_max'].values[i],num=10)))\n",
    "            \n",
    "            #An alternative not to use max values for rooms and min values for Pric\n",
    "#             rooms = rooms + list(np.arange(q_data_vicinity['room_min'].values[i]/10, q_data_vicinity['room_min'].values[i]/10+3))\n",
    "#             sizes = sizes + list(np.around(np.linspace(q_data_vicinity['size_min'].values[i], q_data_vicinity['size_min'].values[i]+100,num=10)))\n",
    "#             prices= prices + list(np.around(np.linspace(q_data_vicinity['price_max'].values[i]-500, q_data_vicinity['price_max'].values[i],num=10)))\n",
    "\n",
    "            \n",
    "            \n",
    "        ax = plt.subplot(1,3,1)\n",
    "        plt.title('Room')\n",
    "        \n",
    "\n",
    "        room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='white',linewidth=.5,edgecolor='black',rwidth=1)\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(1,3,2)\n",
    "        plt.title('Size')\n",
    "    \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        R = mx-mn\n",
    "        # stp = int(R/30)\n",
    "        stp = 20\n",
    "        size_bin = range(mn,mx+stp,stp)\n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='white',linewidth=.5,edgecolor='black')\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(1,3,3)\n",
    "        plt.title('Price')\n",
    "    \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "        stp = 200\n",
    "        price_bin = range(mn,mx+stp,stp)\n",
    "    \n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='white',linewidth=.5,edgecolor='black')\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough vicinity this area with the zip code {}.'.format(Area)\n",
    "        return\n",
    "    \n",
    "    ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    rooms = []\n",
    "    sizes =[]\n",
    "    prices = []\n",
    "    if q_data_specific.shape[0]>=1:\n",
    "        print 'Number of unique specific search: {}.'.format(q_data_specific.shape[0])\n",
    "        for i in range(len(q_data_specific)):\n",
    "            rooms = rooms + list(np.arange(q_data_specific['room_min'].values[i]/10, q_data_specific['room_max'].values[i]/10+1))\n",
    "            sizes = sizes + list(np.around(np.linspace(q_data_specific['size_min'].values[i], q_data_specific['size_max'].values[i],num=10)))\n",
    "            prices= prices + list(np.around(np.linspace(q_data_specific['price_min'].values[i], q_data_specific['price_max'].values[i],num=10)))\n",
    "            \n",
    "            #An alternative not to use max values for rooms and min values for Pric\n",
    "#             rooms = rooms + list(np.arange(q_data_specific['room_min'].values[i]/10, q_data_specific['room_min'].values[i]/10+3))\n",
    "#             sizes = sizes + list(np.around(np.linspace(q_data_specific['size_min'].values[i], q_data_specific['size_min'].values[i]+100,num=10)))\n",
    "#             prices= prices + list(np.around(np.linspace(q_data_specific['price_max'].values[i]-500, q_data_specific['price_max'].values[i],num=10)))\n",
    "            \n",
    "            \n",
    "        ax =plt.subplot(1,3,1)\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='red',linewidth=0.5,edgecolor='black',rwidth=1, align='mid')\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.ylabel('Number')\n",
    "        ax = plt.subplot(1,3,2)\n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='red',linewidth=0.5,edgecolor='black' )\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.ylabel('Number')\n",
    "        ax = plt.subplot(1,3,3)\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='red',linewidth=0.5,edgecolor='black' )\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.ylabel('Number')\n",
    "        plt.tight_layout()\n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough specific search for this area with the zip code {}.'.format(Area)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Histogram 3d "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hist3d(Area):\n",
    "    import numpy as np\n",
    "    q = Area\n",
    "    import itertools\n",
    "    room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "    \n",
    "    mn = int(stat['size_min'].ix['2%'])\n",
    "    mx = int(stat['size_max'].ix['99%'])\n",
    "    R = mx-mn\n",
    "    stp = 20\n",
    "    size_bin = range(mn,mx+stp,stp)\n",
    "\n",
    "    mn = int(stat['price_min'].ix['2%'])\n",
    "    mx = int(stat['price_max'].ix['99.5%'])\n",
    "    R = mx-mn\n",
    "    stp = 200\n",
    "    price_bin = range(mn,mx+stp,stp)\n",
    "    random_demand = np.zeros((0,6))\n",
    "    geo_info = []\n",
    "    H_all = []\n",
    "\n",
    "    ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    rooms = []\n",
    "    sizes =[]\n",
    "    prices = []\n",
    "    H_q = np.zeros((len(room_bin)-1, len(size_bin)-1, len(price_bin)-1))\n",
    "    \n",
    "    \n",
    "    if len(q_data_specific) >= 1:\n",
    "        print 'Number of specific search: {} for this area with the zip code {}.'.format(q_data_specific.shape[0],Area)\n",
    "        for i in range(len(q_data_specific)):\n",
    "            room_rng = list(np.arange(q_data_specific['room_min'].values[i]/10, q_data_specific['room_max'].values[i]/10+1))\n",
    "            size_rng = list(np.around(np.linspace(q_data_specific['size_min'].values[i], q_data_specific['size_max'].values[i],num=10)))\n",
    "            price_rng = list(np.around(np.linspace(q_data_specific['price_min'].values[i], q_data_specific['price_max'].values[i],num=10)))\n",
    "            iterables = [ room_rng, size_rng, price_rng ]\n",
    "            all_combs = np.zeros((len(room_rng)*len(size_rng)*len(price_rng),3))\n",
    "            for k,t in enumerate(itertools.product(*iterables)):\n",
    "                all_combs[k] = t \n",
    "        \n",
    "            H, edges = np.histogramdd(all_combs, bins = (room_bin, size_bin, price_bin))    \n",
    "            H_q = H_q + H\n",
    "        \n",
    "        lon = q_data_specific['lon'].values[i]\n",
    "        lat = q_data_specific['lat'].values[i]\n",
    "        geo_info.append([q,lon,lat])\n",
    "        H_all.append(H_q)\n",
    "\n",
    "\n",
    "        cmapname=\"RdYlBu_r\"\n",
    "        d = []\n",
    "        for i,room in enumerate(room_bin[:-1]):\n",
    "            for j, size in enumerate(size_bin[:-1]):\n",
    "                for k, price in enumerate(price_bin[:-1]):\n",
    "            \n",
    "                    if H_q[i,j,k]>0:\n",
    "                        d.append([room,size,price,H_q[i,j,k]])\n",
    "        d = np.asarray(d)\n",
    "            \n",
    "        from mpl_toolkits.mplot3d import Axes3D\n",
    "        from matplotlib import cm\n",
    "        import matplotlib.pyplot as plt\n",
    "\n",
    "        fig = plt.figure(figsize=(12,10))\n",
    "        ax = fig.gca(projection='3d',animated= False )\n",
    "\n",
    "        sc =ax.scatter3D(d[:,0], d[:,1], d[:,2],c=100*d[:,3]/float(H_q.sum()),s=d[:,3]/7,marker='o',edgecolor='gray',linewidth=.1,alpha=1,cmap=cmapname)\n",
    "        \n",
    "        \n",
    "        ax.set_xlim3d(left=room_bin[0],right=room_bin[-2])\n",
    "        ax.set_ylim3d(bottom=size_bin[0],top=size_bin[-2])\n",
    "        ax.set_zlim3d(bottom=price_bin[0],top=price_bin[-2])\n",
    "        \n",
    "        ax.view_init(elev=10., azim=127,)\n",
    "    \n",
    "        ax.set_xlabel('room')\n",
    "        ax.set_ylabel('size')\n",
    "        ax.set_zlabel('price',labelpad=.01)\n",
    "        mn = 100*np.min(d[:,3])/float(H_q.sum())\n",
    "        mx = 100*np.max(d[:,3])/float(H_q.sum())\n",
    "        plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=5),shrink=0.3,label='Percent')\n",
    "        font = {'size'   : 12}\n",
    "        plt.rc('font', **font)\n",
    "        plt.show()\n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough specific search for this area with the zip code {}.'.format(Area)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hist1d_Supply_Demand(Area):\n",
    "    q = Area\n",
    "#     q = 8134\n",
    "    # ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "    ind_q_vicinity = (Complete_data['zip']==q)\n",
    "    q_data_vicinity = Complete_data.ix[ind_q_vicinity,:]\n",
    "\n",
    "    fig = plt.figure(figsize=(12,9))\n",
    "    font = {'size'   : 8}\n",
    "    plt.rc('font', **font)\n",
    "    room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "    mn = int(stat['size_min'].ix['2%'])\n",
    "    mx = int(stat['size_max'].ix['99%'])\n",
    "    R = mx-mn\n",
    "        # stp = int(R/30)\n",
    "    stp = 20\n",
    "    size_bin = range(mn,mx+stp,stp)\n",
    "    \n",
    "    mn = int(stat['price_min'].ix['2%'])\n",
    "    mx = int(stat['price_max'].ix['99.5%'])\n",
    "    R = mx-mn\n",
    "    stp = 200\n",
    "    price_bin = range(mn,mx+stp,stp)\n",
    "    \n",
    "#     if q_data_vicinity.shape[0]>=1:\n",
    "    \n",
    "#         rooms = []\n",
    "#         sizes =[]\n",
    "#         prices = []\n",
    "#         print \"**************************************************************\"\n",
    "#         print 'Number of unique vicinity search: {}.'.format(q_data_vicinity.shape[0])\n",
    "#         for i in range(len(q_data_vicinity)):\n",
    "#             rooms = rooms + list(np.arange(q_data_vicinity['room_min'].values[i]/10, q_data_vicinity['room_max'].values[i]/10+1))\n",
    "#             sizes = sizes + list(np.around(np.linspace(q_data_vicinity['size_min'].values[i], q_data_vicinity['size_max'].values[i],num=10)))\n",
    "#             prices= prices + list(np.around(np.linspace(q_data_vicinity['price_min'].values[i], q_data_vicinity['price_max'].values[i],num=10)))\n",
    "  \n",
    "            \n",
    "#         ax = plt.subplot(2,3,1)\n",
    "#         plt.title('Room')\n",
    "        \n",
    "\n",
    "        \n",
    "#         a = plt.hist(rooms,bins=room_bin,alpha=1,color='white',linewidth=.5,edgecolor='black',rwidth=1)\n",
    "#         ax.yaxis.grid(True)\n",
    "    \n",
    "#         ax = plt.subplot(2,3,2)\n",
    "#         plt.title('Size')\n",
    "    \n",
    "        \n",
    "#         a = plt.hist(sizes,bins=size_bin,alpha=1,color='white',linewidth=.5,edgecolor='black')\n",
    "#         ax.yaxis.grid(True)\n",
    "    \n",
    "#         ax = plt.subplot(2,3,3)\n",
    "#         plt.title('Price')\n",
    "    \n",
    "        \n",
    "    \n",
    "#         a = plt.hist(prices,bins=price_bin,alpha=1,color='white',linewidth=.5,edgecolor='black')\n",
    "#         ax.yaxis.grid(True)\n",
    "#         plt.tight_layout()\n",
    "        \n",
    "        \n",
    "#     else:\n",
    "#         print \"\\n**************************************************************\"\n",
    "#         print 'Not enough vicinity this area with the zip code {}.'.format(Area)\n",
    "#         return\n",
    "    \n",
    "    ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    rooms = []\n",
    "    sizes =[]\n",
    "    prices = []\n",
    "    if q_data_specific.shape[0]>=1:\n",
    "        print 'Number of unique specific search: {}.'.format(q_data_specific.shape[0])\n",
    "        for i in range(len(q_data_specific)):\n",
    "            rooms = rooms + list(np.arange(q_data_specific['room_min'].values[i]/10, q_data_specific['room_max'].values[i]/10+1))\n",
    "            sizes = sizes + list(np.around(np.linspace(q_data_specific['size_min'].values[i], q_data_specific['size_max'].values[i],num=10)))\n",
    "            prices= prices + list(np.around(np.linspace(q_data_specific['price_min'].values[i], q_data_specific['price_max'].values[i],num=10)))\n",
    "            \n",
    "            #An alternative not to use max values for rooms and min values for Pric\n",
    "#             rooms = rooms + list(np.arange(q_data_specific['room_min'].values[i]/10, q_data_specific['room_min'].values[i]/10+3))\n",
    "#             sizes = sizes + list(np.around(np.linspace(q_data_specific['size_min'].values[i], q_data_specific['size_min'].values[i]+100,num=10)))\n",
    "#             prices= prices + list(np.around(np.linspace(q_data_specific['price_max'].values[i]-500, q_data_specific['price_max'].values[i],num=10)))\n",
    "            \n",
    "            \n",
    "        ax =plt.subplot(3,3,1)\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='red',linewidth=0.5,edgecolor='black',rwidth=1, align='mid')\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.ylabel('Number')\n",
    "        plt.title('Demand: Room')\n",
    "        ax = plt.subplot(3,3,2)\n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='red',linewidth=0.5,edgecolor='black' )\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.ylabel('Number')\n",
    "        plt.title('Demand: Size')\n",
    "        ax = plt.subplot(3,3,3)\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='red',linewidth=0.5,edgecolor='black' )\n",
    "        ax.yaxis.grid(True)\n",
    "        plt.ylabel('Number')\n",
    "        plt.title('Demand: Price')\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        \n",
    "        \n",
    "        ax = plt.subplot(3,3,7)\n",
    "#         a = plt.hist(rooms,bins=room_bin,alpha=.4,color='red',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "        DF = pd.DataFrame(data=rooms,columns=['room'])\n",
    "        DF['room'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        \n",
    "        ax = plt.subplot(3,3,8)\n",
    "        DF = pd.DataFrame(data=sizes,columns=['size'])\n",
    "#         a = plt.hist(sizes,bins=size_bin,alpha=.4,color='red',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "        DF['size'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        \n",
    "        \n",
    "        ax = plt.subplot(3,3,9)\n",
    "        DF = pd.DataFrame(data=prices,columns=['price'])\n",
    "#         a = plt.hist(prices,bins=price_bin,alpha=.4,color='red',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "        DF['price'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough specific search for this area with the zip code {}.'.format(Area)\n",
    "        \n",
    "    \n",
    "    \n",
    "    \n",
    "    ind_q_Supply = (listing['ZIP']==q)\n",
    "    q_data_Supply = listing.ix[ind_q_Supply]\n",
    "\n",
    "\n",
    "    if q_data_Supply.shape[0]>=1:\n",
    "    \n",
    "        rooms = []\n",
    "        sizes =[]\n",
    "        prices = []\n",
    "        print \"**************************************************************\"\n",
    "        print 'Number of unique Supply search: {}.'.format(q_data_Supply.shape[0])\n",
    "        \n",
    "            \n",
    "        ax = plt.subplot(3,3,4)\n",
    "        plt.title('Supply: Room')\n",
    "        \n",
    "        rooms = q_data_Supply['Rooms'].dropna().values[:]\n",
    "        room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='blue',linewidth=.5,edgecolor='black',rwidth=1)\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(3,3,5)\n",
    "        plt.title('Supply: Size')\n",
    "    \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        R = mx-mn\n",
    "        # stp = int(R/30)\n",
    "        stp = 20\n",
    "        size_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        \n",
    "        sizes = q_data_Supply['Living space'].dropna().values[:]\n",
    "        \n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='blue',linewidth=.5,edgecolor='black')\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(3,3,6)\n",
    "        plt.title('Supply: Price')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "        stp = 200\n",
    "        price_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        prices = q_data_Supply['Rent'].dropna().values[:]\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='blue',linewidth=.5,edgecolor='black')\n",
    "        \n",
    "        ax.yaxis.grid(True)\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "#         a = plt.hist(rooms,bins=room_bin,alpha=.4,color='blue',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "#         q_data_Supply['Rooms'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        \n",
    "    \n",
    "        ax = plt.subplot(3,3,7)\n",
    "        DF = pd.DataFrame(data=rooms,columns=['room'])\n",
    "        DF['room'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        mn = int(stat['room_min'].ix['2%']/10)\n",
    "        mx = int(stat['room_max'].ix['99%']/10+1)\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative Supply/Demand Distributions: Room')\n",
    "        \n",
    "        \n",
    "        ax = plt.subplot(3,3,8)\n",
    "        DF = pd.DataFrame(data=sizes,columns=['size'])\n",
    "        DF['size'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "#         a = plt.hist(sizes,bins=size_bin,alpha=.4,color='blue',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "#         q_data_Supply['Living space'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative Supply/Demand Distributions: Size')\n",
    "        \n",
    "        ax = plt.subplot(3,3,9)\n",
    "        DF = pd.DataFrame(data=prices,columns=['price'])\n",
    "        DF['price'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "#         a = plt.hist(prices,bins=price_bin,alpha=.4,color='blue',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "#         q_data_Supply['Rent'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative Supply/Demand Distributions: Price')\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough Supply this area with the zip code {}.'.format(Area)\n",
    "        return\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def histDemand(Area,generator):\n",
    "    generator(Area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique specific search: 1354.\n",
      "**************************************************************\n",
      "Number of unique Supply search: 140.\n"
     ]
    },
    {
     "data": {
      "image/png": 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OPWZWhpxzz61pWwALgIeBd5B1shYBt5NdpnispH2q6sysi0x2Svw/yJLDtWTf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\n6uRqZicQBj4od4hPEWlAyotEpJKU50g9Sa1my8LQjOPN7Pc5+8+NSt9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Tufvp1691X9++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\nooWSAS4Bx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Jbqz9df62Joz55J9eN76SVYvVqtWqeckrTbJo8+feDEE+GPP+Af//Bbmqj5\ny1+0HTNG85tZvCczla0wyTFcz7fu3aNYfT76aG3ff1/bzz7TNlVmU9GSkxP4HNxsiuXZvh0WLaJb\ntsZsRbRsGWNdCS0Wd/GlVy/Iykqp5Big3sN16sCGTTlsHnS8LrTY2C2LJTNw483d+PMkUO2tWi7/\n+pfOq559FmbO9FuaqDjoIPUm3bxZ190s3mOVrSCiciF0OewwrRq+cKEWjPjhB2jUCPr181bWZFDq\nU/R96PNz5kBJCe16NqROHbWOb94coT9X2XKzHVksmYarXfXrR2FhwNs4iXObiGRlBRUqP/wa3Xn8\ncZv/12LJBNx4rSTOV156SWO1unSpplYtlwMO0MJhIpqh0I3rT2GMCVi3Hn/cRoAkAqtsBRFTJtRa\ntTQYEuCYY7Q99dT0XK4p9SlaHvq8szqTNfDg0sQhEV0Jg+O2LJZM5MsvtT3kEL79Vl0zOnRQD9tU\nofQxbXCouhOvWKELRxaLpXqTZGWroADuvlv3q7VVy+W22zSD9HffwZ//nBbay+mn6+/T/PlabNri\nLZmpbK1cqW1VlC2Am27SeCfQ9pprvJEv2ZSmJgujbAVNHEuLG0dyJbRuhJZMprg4kOf94INTzoXQ\npfQx/S4b/vpXffHII/4JZLFYEs+vv2rgVL16sP/+Sbnlyy9rprsuXXRNutqTm6v1C+vUgbFj0yLz\nRO3aqhcCPPCAv7JURzJT2XJjk4IKAxcVBaw17opvpXTvrjFbl1yiK8JJGrg8x5U7lLmqpCTgdxyk\nbEW0bB1wgPopLV0Ke/Z4KqrFEg5jzEPGmOnGmIfLHW9hjJlijPncGDPcOVbPGDPRGDPDGHNuuesf\nMcbEH8C0eDHs2KFZdlq0SFllyx3nFi1C3V3q1dPYU2uRtliqL65V68ADk2JiyphYrfJ07QrPPaf7\nV10FU6f6K08U/PWv6rQ1cWKg5qzFGzJP2SouDllja9kyHRTat9cEYlEzcqQGQh5+uLdyJhM3SG3R\nIv18glm2DH7/XSPqW7cOxHlEsmzVqaOKbHGxFmK2WBKMMaY3UFdEBgO1jDEHBp2+CbgFGAnc6hy7\nBBgHDAYuNsbkOP00A8pWOo+VIEswkHKZCF1cZevbb0EaNIQLL9QD1rplsVRf3PEpSas/GWfVCuas\ns+D663U1/5RTwichSxGaNQv8DFjrlrdknrL1889QWKj1terWLT0cswthdaJRI2jdWgNLyg8GwRNH\nY8q4EUZ0Q7auhJbkMgD41NmfDBwcdK67iMwSkV3ANmNMffd6ERFgPuCapa8GHquSJO4zc/DBbNmi\n6w01a0aZeCeJNG2qlRq2b3fCWP/2N42UfvVVrYNhsViqHzNmaOuWr0kghYUBq9Ztt2WQVSuYf/9b\n4/o3b4bjjtOyOCnMddepY9LYsVoRyOINmadsucpEhw5lDn/9tba9eiVZnlTBnQnOn1/2uDswH6xz\n15YtoWFD2LSpkvmYzUhoSS55wDZnf6vz2iV4nHPPBV+/DcgzxjQCmgBhghejJGiBwvXY6d07qeVs\noqZvX21nz0at0cceqyZ+W+TYYql+/PGHpn03BgYOTPjtXKvW/vtr4dyMJDtbF7C6dtWVtzPOqOhB\nlEJ07Agnn6yK8sMPV369JToyV9kKiteCQGrmdMzc7gmulhmsbInAp46xYPhwQMfoqFwJrbJlSS5b\nAdcBuAEQXKE3OJ95Q+B353z5668CngSMs4Vk9OjRpVt+fn7Zkxs2aAKeOnWgR49gI1dK4noSua6O\nXH21tk89ZeMtLb6Tn59f5nmzVJGvvtLFlO7d1aMlgRQWaskpyGCrlkuDBvDee9C4MXz8Mdx8s98S\nReTGG7V9+mnr5OAVOX4LkHTcTIRBylZJScCydeCBId6TCbizrunTA8cWL4Z166B58zJZQ7p108X7\nxYtLdbCKWGXLklxmApcCE4ARwH+Dzi00xgwAFgH1RWSHMWYWMMIY8wbQE1iKxmrdA9QBOhpjThGR\nCeVvFHHSN22atgMGQE5OaQrdJCwix0UFZWvIEH12Fy6Ed97RfMAWi08MGTKEIUOGlL7+xz/+4Z8w\n1QH39z0JLoSvvKJWrf32s8MIoAkB3noLhg2D++/X0kFOXG+qceCB6uTw3ntw3302fssLrGULreW7\nYwe0aqUxDBnJ4MG69DR7NmxzvKs++kjbkSPVpOUQVfr3tm01u9kvv8BvvyVGZovFQUTmAXuMMdOB\nQhGZa4xxY6/uB+4CPgGcai+MAc4GpgEviEiRiJwvIkcB5wGfhVK0KuWzz7QdPpyiokAG+FRVtlxL\n/rx5jiHLGLj0Uj3oZtKyWCzVAzcsYPDghN7GxmqFYfBgNRuJaCaKFPYecNc1nnpKp3GWqmGVLawL\nIaBm7n791JfYXf167TVt3eLNDlG5EWZllcstbbEkFhG5WkQGi8jVzuu/Oe1aERkuIgNFZLJzbLuI\nHCsih4rIy+X6WS0i58UlhKtsDRvGokW6iNO+PbRoUYU/LIHk5Wk8RUEBfPONc/Dss7XoypQpAU8A\ni8WS3hQVBeJJE2zZGjtWk+507qwhSpYgbr9dUzN+/z08VrVcTImkd2844QTNm3bvvX5Lk/5klrJV\nUhKYPAQlyHCVLTdYPGMZOVLbt95SN6L589Wv+5hjylwWXGsrqoyE1pXQkgmsWaM/oPXrQ9++fPGF\nHk5Vq5bLYYdp6+qJ5OUFcjS/8IIvMlksFo/55htd/enQIaGrP0VF1qoVkVq1AuU1/vlPLTKdorge\n808/rYm8LfHjm7IVoQDphcaYH6pUVDQcq1ermr733ppSz8HNGJbxytbZZ2v76qtwzjm6f9ZZFdKo\nNWsGTZqot+GaNRH6s8qWJZNwtZXDDoOcnFJl69BD/RMpGkaM0Hby5KCDl1yi7X//q7Mni8UCVL14\nujEm2xjzstPHDUHv/doYs8sYk5h5mZvsyn3gE8Srr+qadqdO1qoVlpEj4aijtO7GPff4LU1YevbU\neLs9e1I+p0fK44uyVUkB0nfRAHfvWbxY2wMOKD1UWKjxCpDByTFcXJt/QYG6/tWrF0hLU45g61ZY\nXDdCq2xZMoEgF0Ig5ZNjuAwdqqFaX34Ju3Y5Bw89VCPb16+HDz/0VT6LJVXwqHj6ccASp49BTiH1\nTcAwYFbChJ80SVvXgyUBFBWpsQbg1lshJ/NSsEXP3U748H/+o+NsinLPPVoncuzYgBeYJXb8smyF\nLUAqIpuBxBQh+O47bd2gI2DBAjV2deqkWTkznmeegXPP1dX5Dz7QYschiCpJhqtsLV6c0nUlLJYq\nI1JG2Vq9Wq2+eXll1nZSksaN1T+/oCAoGakxcPHFum8TZVgsLlUtnt6lXB9TgYNEpEBEthKh5ESV\n2LYNZs5Unz5nMSgRvPaaWrU6dlSnGEsEevaEE0/U2mf33ee3NGFp3x6uukr3r722ktARS1j8UrYi\nFSBNHCEsW+7qc6q7+iSNhg21EmF+fsSMRa6yFTH3RV4etGmjg4mbmMRiqY6sXKlO7U2aQPfupS6E\nBx+suWJSnaOP1vbtt4MOnnce1Kihlq21a32Ry2JJMapcPL2SPhIzlc3PV7NT//76u5wAiooCdbWs\nVStKbr9d22eeSemUfzffrD9tM2ZoSL8ldvx6HCIVII2a4Ho35etxhCSEZStdXH1SDbcGcmkGs3D0\n6KGxcgsXqluSJe3Iz8+vWLzXUhbXqjV0KGRlpU1yDJeTT1b3n7ff1lS/2dlocObxx8OECfDii3DL\nLX6LabH4TbzF0zc6baiC6sujvXnMcx6XTz7RNoEuhGPHwvLlmn/DDf+2VEKvXpry7513tJhViha0\nysvTVPBXXql170eO1DxQ1ZWEzHlEJOkb0Bt42tl/Euhb7nw74JVK+pCYKC4WqVtXBEQ2bhQRkZIS\nkb331kNLl8bWXaaza5dIdrZIVpbIzp0RLrz5Zv2Ab701abJZEovz7Pkydvi9hR13Tj9dv+dPPy0i\nIr166cupU2P4YH2kpESkY8cQMn/8sR5s317HUIvFR/weeyLNXYBHUBfBumidPoBrgDOAbCAfXeA+\nEbjROT8RaB7Ux1QgO8y94//g3Id75sz4+4jA7t0ibdroLcaOTcgtqi9ff60fXJ06Ihs2+C1NWIqK\nRPr2VVGvvtpvaZKLF+OOLw4uEroA6aMAxpijgVeAYcaYNzy76c8/w86dulrrBGf98INabps00dwQ\nlujJzVVvzJKSSlwJba0tS3WnpKRMvNbmzRoLWrMmHHSQv6JFizFwyim6P3580IkRI9QVeNUqmDrV\nF9ksllQhzNwlpuLpwHtAd6ePL0XkV2NMjjHmU6AH8LExxruqn0uXqht/o0YJS7n8zDPqwNKjB5x5\nZkJuUX3p00f9uHftgocfrvx6n8jOhmef1faxx+Drr/2WKL3wLZpAKhYgvcppPxCRQSLSUkRO9eyG\nbrxWkAuhW0x94ECdbFhio08fbSM+dG769wULEi6PxeILCxbAb7+pUtKpE9OnaxDxgAFQp47fwkWP\nW+3htde0HA+gv6wXXaT7Y8b4IpfFkkqEmLvEVDxdRIpE5Bynj38HHTtcRBo77RzPBH73XW2PPTYh\ngVTbtgXqat19d3rEqKYct92m7RNPwObN/soSgd691Y2wpETzJxUU+C1R+pA5j4UbXORO/gnUlRk6\n1Ad5qgGushUxbqtzZ51x/vijTkgtlupGcEplY8qEb6UTXbvCIYdo6Zf//S/oxIUX6mrUW2/Bpk2+\nyWexWOLgnXe0PeGEhHT/0EOwcaMuWh91VEJuUf3p3x8OP1wH38ceq/x6Hxk9Gtq1g/nzNY7LEh2Z\no2y5lYv7qXW+pCRQ4+/ww32SKc1x65JFtGzl5ARcF776KuEyWSxJxw0+HzUKCHjbJTDDcsK47DJt\n//OfoINt2ujfVlCgUfAWiyU9WL8eZs2C2rUTkhxjwwZ48EHd//e/rYdQlXCtW48+Clu3+itLBOrV\n04TVWVlag8v1ELNEJnOULbcamzPxX7RIB4qWLaFLFx/lSmN69tTB9dtvtcJ4WPr313ZW4uo1Wiy+\nsHOnpjTNyoJhw9iwQZ+H2rUDX/t04tRTNfPUnDla5LiUSy7R9skndaXKYrGkPhMnanv44VC3rufd\n33abuhwffbQtn1NlBg3S+qZbtsC99/otTUQGDYKbblJ3+XPPTWndMGXIDGVr3TrdGjTQ6sWUtWrZ\n1Zj4qFdPs7kXFWlm97AMGKDt7NlJkctiSRrTpkFhoVrM99oLN1vswIFQq5avksVFbi5ccYXul/m9\nP+44aNtWczu//74vslkslhhJoAvhggUaxpmTk7IZy9OPf/9b2wcf1AxuKcwdd6h3008/aUlGuwYX\nmfRWtqINJHSXaPv1K43edD1/rAth1XD1qJkzI1zkLvF/9ZV9Ii3Vi3L1a9LZhdDlb39Ty9zEiYG8\nQuTkwFVX6f5DD/kmm8ViiZJt22DKFJ3zHHusp12LBBIlXHkl7L+/p91nLgMGqKmooEA/WElMjWsv\nqFlTY3vz8vS34p//9Fui1Ca9la1OnQKZdiJRLmJ9yxadFGVnJ7TGX0ZwyCHalnE5Kk/Llrpt3QrL\nliVFLoslKQQpWyKBXBnprGw1bx5IQHjffUEn/vQnrWQ5bZrN+2uxpDrvvqtW94EDoWlTT7t++23I\nz9cqOnfc4WnXlnvv1TT9H38MTz/ttzQR6dABxo1T77DRowNeq5aKpLeytXkznHxywFQejqAaOKBe\nMEVFMHiw1tiyxI+rbH3xRSUXDhyo7bRpCZXHkrkYYx4yxkw3xjxc7ngLY8wUY8znxpjhzrF6xpiJ\nxpgZxphznGPHGGNmGmO+MMZcU+kNf/4ZlixRBaR/f5Yu1XJUTZqU5uFJW66/XhejXntN/yZA3bDd\n2C07w7JYUptx47Q94wxPu92+Xa1aAHfeqXqBxUNatNCCVgDXXhvF5MpfjjhCU/6D1liz0SKhSW9l\n6+abobhYza7ffRf6mtWr1ZpSr15pcoy33tJTJ52UJDmrMV26qBl5zRqde4bFXeqfMiW2G+zerf/b\nnTvjltFS/THG9AbqishgoJYx5sCg0zcBtwAjgVudY5cA44DBwCXGmBxgPnCIiAwEjjfG1I94U9eq\nNWwY1KjBBx/oyyOPVEUlnWnfHs46Sxel3Bo6ANx4o46lH3xgF04sllRl40YNTM/O1qw3HnLbbfpb\n37dvIHupxWNOOUWDZ/fsURfQefP8ligiN94IF1ygdZmPOkrXIC1lSW9l61//0lWbHTtUcyqtxBnE\n669re8QRUKMGW7fCRx/poQSVncgosrLg4IN1P6Ir4YgR2n72WXRxW3v26BO8115aAKhRI13l+eOP\nKstsqZYMAJy0N0wGDg46111EZonILmCbo0QNAD4VEUGVrP1FZI3zGqAQiPxFLRev5SpbRx9d5b8l\nJbjtNn2+X3opKFa7WTO44Qbdv+GGlI4psFgylgkTdKXk8MM9dSGcM0fLQGVnq/El3ReVUprHHoPj\nj4fff1c3rBT20TMGnnsOjjlGHc5GjUr5/B5JJ72VLWM0HU7Xrmq9uvLKite4pvQzzwRg/Hidrw8d\nCq1aJVHWaozrSvj55xEu2ndfrYS3eXPl9bZ271bzwH336T+rTRv1PX/4YX2arcJlqUgesM3Z3+q8\ndgke59xzwddvC77eGHMksFJEQppTR48ezejbb2f0xInkA4wcyZYt+v3Pzi4tt5X2dOoE55wTwrp1\n7bWw9976HI8Z45t8lupPfn6+Pm/OZomS117T1pn3eEFhIVx6qa6vXHMN9O7tWdeWUGRnawaKs85S\nQ8Lxx8Of/xzaqJAC5OSouIceqpbPQYNg6VK/pUohRCQtNxXdYfFikdxcERB58cXA8fx8Pdawocju\n3SIi0q+fHnrlFbF4xIwZ+pnuv38lF151lV54/fXhrykpETnvPL2uRQuRL7/U43PmiDRvrscvvtgz\n2S2x4zx7vo8BwRtwBXCKs38i8Jegc58F7b8L1AP+BzRxjj0KdHP29wWmALXD3Ec/hK++0u9i+/Yi\nJSXy4ov6csgQTz9q31m+XCQ7W7cVK4JOvPaa/sF16ogsXeqbfJbMIhXHnmRtZeY8kVi9Wp/N2rVF\ntm6N7j1RcPPN2m27diI7dnjWraUyiotF7r9fpGbNwD9g8mS/pQrLtm36OwgiTZuKfPON3xJVHS/G\nnfS2bLkccIAW2wS4/HJ1VSsq0qproCuxtWvz9ddqBm/YUPNqWLyhf3/NU1bkKQAAG1BJREFUEbB0\naSVxW6ecou2ECeHdjx59VMuT16mj2XhcH8W+fdX/s3ZtXU1/7z1P/wZL2jMTGO7sjwCCK2gvNMYM\nMMbUBeqLyA7n/AhjTDbQE1jquBf+F/iTiEQ2n7ppB0eOBGMSFYvuOx07akhscbF6bZdy5plw9tnq\npH/WWTam0mJJFdzB6JhjNKmNB8yYAffco27FL7+ckPrIlnBkZWnGojlzoFcv+PFHDcu47LKUrCZc\nv7661I8aBb/9phauynLYZQRV1db82ii/ylNSInLZZapOZ2WJdOig+82bl67unHKKHrr22tg1W0tk\njjtOP9vnn49wUXGxSKtWeuGkSRXPT56sS+gg8vrroft4+GE936aNyPbtnshuiQ1SdHUZeASYDjzi\nvH7MaVui1qovgBHOsfrAe8DnwLnOsZuAn4DPnK1tiHvohzBggH4P335bNmzQr21OjsjGjR5/2CnA\nihUB69by5UEntmxRyx6IDB0qsnOnbzJaMoNUHXuSsVWY84SipESkc2d9JidOjOITrZwtW0TattUu\nb77Zky4t8VJQIPKvfwWsXG3biixa5LdUIfnjD5Fzz1UxQeSuu3QKmI54Me74PoDELXiogae4WOSG\nGwIT9mbNRGbNEhGR+fNFjNHv6Nq1sX7Ulsp4/HH9yE8/vZIL775bLzziiLLHly8Xady48hG9qEjk\nwAP1uv/7vyrLbYmdjJ/w/PqrDia1aons2CH33adfx6OP9uDDTVEuvFD/xvPPL3di2TJ19wWRgQNF\nfvrJD/EsGULGjz2V4fr0t2ghUlgYxScameJikRNO0C779tW5viUF+PZb/YeASIMGKetWWFIics89\n+nMJIiNHiqxf77dUsZPxytbcuSJ79oT4ZFavFpk6tdTyUVwscuih+tdedVVsH7IlOpYt08+3cWPV\nh8KycaNI3bp68bvv6rF16wIr5EcdVUkHIjJzpl6bm2s1Zx/I+AnPf/+r378jj5SiInWhB5H336/6\nZ5uqrFyplrusLJHvvy93cunSgMLVoIHIE0+UxshaLF6S8WNPZVxwgT6HN90UxadZOXfcod3l5ZWz\nalv8Z9cukVNPldL4vPx8vyUKy/vvB9bSmzUTeeMNVcTShYxXttzf9ksvFZk7N/wHdeedgX/yli0x\nfMKWqCkpCXhuTp1aycWPPKIX1q2rPp3uRK1v3+hdA086Sd9z6aVVFd0SIxk/4XG/e08+KRMm6G6H\nDunrIhEtf/qT/q3nnRfi5K+/ihx/vJT6jOy9t1qoFy9OupyW6kvGjz2R2LpVE9ZAiBWR2HnrLSmN\nyvj44yp3Z0kExcUil1yi/6j69TWRWIqydq3IsGGBn4ijjhJZtcpvqaIj45WtTp0C/zgQ6d9f5OWX\n1VdURA0kd92l54wR+eijKn7ilojceKN+1n/5SyUXFhWJnHNO2X/ewIE6YYuWpUsDgSRLllRJbkts\nZPyEp149EZDClT9Jly769X3iCQ8+2BTnhx8C1q2FC0NcUFIiMmGCSK9eZZ/tXr1EHnggPf1HLClF\nxo89kXjuOX3eBg2K8tMMz4wZgQTP999f5e4siaSoSOSMM6Q0/V8KmyCLi0WefloThLvOSbfdlvpG\nkIxXtkTUdfXqq9XM7f62N26sc3fXYAIaU2RJLHPnSqm7eKWr/CUlmgTjhht0ghaPf/mll+oNTzop\nLnkt8ZHxEx4Q6dlT7r9fd/fdN4w7czXkr3/Vv3nEiAhuICUlat6++OLAryrowsjRR4u88071NwNa\nEkLGjz3hKCkR6d1bn7Pg8jdxMHeuegyBWrPTyd0rYykoEBk1SkrdLGJZuPaB9esD+qE7Z3/ggdTN\nseTFuGO0n/TDGCPBsu/cqRlPn3wS5s8PXNexIzzyCBx9tA9CZhgiWrv4xx9hyhQYNizBN1y3Tv/B\nu3drRdmBAxN8QwuAMQYRMX7L4QdGI32Zdt7zjBx/EQUFmub2qKP8liw5bNqkxY5//12rLxxzTCVv\n+OMP+PBDeOUVeP99LckBWoj+nnvg2GMTLrOl+pDxY0+4+dq0aTBkCDRtCqtXa4mUOJg/X7OKb9oE\np52mtZGzs+OX2ZJEtm+HoUPh66+1VM7UqVCvnt9SReSLL+D//k9LCwA0bqzVm/7yF2je3F/ZgvFi\n3KkedbbQug8XXwzffAPLl+v3bPFi+P57q2glC2Pg/PN1/7nnknDDffaBv/9d96+6CkpKknBTS6bz\nDJdx1BsXUFAAf/tb5ihaoD+Gt9+u+9deq7pURGrXhpNOgrff1sWRBx+EVq10cD7uODjvPNiyJeFy\nWyzVmoce0vaKK+JWtD79FAYPVkXr6KN1fcQqWmmEW+CqfXuYO1e15cJCv6WKyMCBuk7w4Ydw0EH6\n3fvXv6BNG7joIpg1SxfxqwPVxrJlSQ1+/hnatdNBeu1aXWhLKDt3wn776c1eeAEuvDDBN7Rk+uoy\n6Lhz0UXw7LOZNyEpKICePbWI+d//DvfdF0cHTz0FN9+sVukuXbRgedu2CZHXUn3I9LEn5JxnxQro\n3Blq1FCrVhwmgRde0Bq5RUVar/zFF6FmzarLbPGB5cvhkENg40Y45RTVmuNUwJOJiFq6HnwQ3n03\noGR17aqGlHPP1cU+P7CWLUvK0bq1rvQXFsLTTyfhhnXrwr336v5NN2nJcoslgfRvu55XX4UxYzJP\n0QKdhL34ImRlwQMPwPTpcXRw9dXqs9S1KyxZAgMGqFuCxWKJjbvv1pnp2WfHrGjt2KHeKH/6kypa\n118PY8daRSut6dRJXbbr14cJEzSeY+VKv6WqFGPg0EPVCWLZMl3Ia9pUnSCuuUYdmY4/HsaP1zX2\ndMNatiye47qPN2qk8VsNGiT4hiI6oOTnwwknwFtv6ZMbLdu366/OXntBrVoJE7O6kPGry+vXw957\n+y2K79xyi87zmjaFOXPiNExt2aJuhm58wZtvwsiRnstqqR5k/NhTfs6zZAl066YrH0uWaAxzlOTn\nqzXr++8hNxeeeEKt9ZZqwsKF6g+6Zg3UqQN//StceaWuiKcJBQWqN44ZAx9/HLB21a2rXuhnngmj\nRiV+ccCLcccqW5aEcNhhuuJ9xx0wenQSbvjTT9CjB2zbBnfdpS5KlV0/Zgy8/LK6XoAqaH37qun9\nkktUW7RUwE547LgDuhJ+1FEa69G5M0yeHOfv+J496v47bhzk5MDzz2ssl8VSDjv2lBt7jj8eJk5U\nremZZ6LqZ906uPFGtWCBGpdffx0OOMBjgS3+s2mTZpsYPz5wrEcPned066YhGPvtF4j9SGHWr9fv\n6fjxGsvlkpeniteJJ+o6XZ063t/bKltpKnsmMH26Kly1asGiRWrZTjhvvgmnnqrLH/ffD9ddV9bC\nVVSkyyTPPlt2maR2bTW/bdoExcV6rH59uO02zYBgrV1lsBMeO+64/P67JsBasEAVrddeU1eQWNiy\nBeZ+VcKah9+g1sfvsB/L6HnGAWQ/9nASgj4t6YQde4LGngkT9PeuXj0NoGzZMuL7167V+Mr//EfX\nN2rV0jXJG25Ii5AeS1X46isNhnrvPY2TLU/NmmoV7dkTTj4ZjjwyMVqLR6xapUrXuHE6v3SpUweO\nOEIVr2OOUUXMC6yylaayZwoXXAAvvaQZZz77LEl+4E89paZy0CDR00/XX5IFC9S98Jdf9FzNmmrB\nuuwynR1mZakj8JQp8PjjukwPqiU+8YR1bQrCTnjsuBPM77/rD9uXX+pjdO65ms53v/3Cv2fNGn0c\n33lH0/66GeFd9mY9l9d4nqvO+o2GZx6lqaqspTnjsWOPM/asXw+9esGGDVrv5oorQr6npEQtz88+\nqwYw9zk7+WQNde7QIUnCW1KDXbtg3jyNj12yRIOjli1TTTyYunX1S3LeebqalpW66R2WLtU4r7ff\nVnd2l5wcFf2YY9TVsHPn2KJLgrHKVprKnils3KgLJevWwZ//rHpQvF/2mHjzTb3hxo0Vz3XuDJde\nqlHBTZqE7+OTTzSIf8kSfX3aaZpet5LVw0zATnjsuFOeggJ1GX7ggcCErndvXcfYf39dYdyxQ3/X\nv/gCZs8OvDc7W3WpfffVFfc5Mwv5aW0NAJrxK//mJs7nJbL2aqRR0i1aBLa99y67NWigiyu5udom\nZcCxJAs79ohOmA87TNN7DxmiC4RBk+HCQl3AeOcd3X7+WY9nZ2t45K23qieZxVLKjh0avDd1qvrq\nffVV4Fzr1nDOORoP36tXSmdP+fln/c6//bZ6V7mOSqAxxaNG6TZsWGxWL6tspansmcSsWfq7UFCg\nrsMPP6wrDgln2zYdNGbP1tlfx476lB14YPQTsIICrYj9j3/oD1y9eup3cdllmkwjEiUlGhe2aBF8\n+60u5YvoilHr1uog36+fd3buJGInPHbcCcfKlVqr+PXXNe9MOGrX1nivk09Wt4/gx0lEg/dvvW4X\nX85TV5aDzUyeksvpxYLYBKpfX4NS+vSBgw/Wbd99rRKWpmT82PPbbxqn9eWXWk9p9mz2NGjK/Pn6\nzEybBp9/XvbZa9dOQ5AvvFDXJyyWSlmxQlPGv/yyZjlzqVVLi2C1aKF52OvX13lR/fpl9xs21C9e\nx4667wObNmnUyKRJuna+aVPgnDH6s3DIIboNGKCihgtbS2tlyxjzENAX+FpErgk63gIYC9QCbheR\nz8K8P6GTnvz8fIYMGZKw/jPpHhMnqmt5QYG6FD73nJbW8fIeVSVi/6tXq5Xr7bf1dW4uHHusapH7\n7qva4/bt6ki8ZIkqWIsX62pR8D2ACnfYf38YNCiwtW0b/URQRN1IVq1St5I9e8hftIgh/fvr7LVN\nGy0g67ELQKpOeKIcU+4QkSnGmHrAa0Aj4FkRecUYkw38F2gHvC8iFSpIpaqylYznPBZ279b54OzZ\n+vhs2aK/w61bqxVr0CB9HUluEY0Bu/569f7NyhKuPG0jd540n7xtq+HXX/VE8LZ9u1Za3r1bzWSh\naNZMla727dU1sVYtfYZzcvTXNidHn/G8vMDWsKFqiDVr6vW1a5M/Y0ZKfebRkmrflVhIhbEnUeNM\nuH6D+pfVzQ5k+YYGfJs3iHlDr2HeD3l8913F2rX7769xKyecoLkQ4v0JSKXvipUlPAmTp6REXRHG\njlVT0dKllctCuXlOkyYajtGxY2Br0ULH0ho1tK1ZU8fX2rVLx1dycz2Zu+Tn5zNo0BC++UYVr0mT\n9Hep/DNTq5Y+N127qgNUq1a6tWwJ3btXfdxJho2hAsaY3kBdERlsjHnKGHOgiHztnL4JuAVYCHwA\nhFS2Ek0qKCnV5R7HHacrC2eeqc9tt27qzuC6A9erV/V7VJWI/bdpowEmn36qflKffKJL96+/HrnT\nvfeG7t31D953X/I/+oghgwbp7HPePN2WLtXtuef0Pa1a6Uy0WzcdkBo2VFv4rl3qV712rVrMVq3S\nFaddu8r+HZQb6GrX1oGuc+dA5qH99tNjjRqFVuz++EOXgTZt0oCc7dt127Yt/ATWZ2IcU6YAlwDj\ngPFAvjFmHHAssEREzjPGvGeMeVFENiT/r4mdVPvxz82F4cN1i0QkuY3R0kHHHqsuio8/bnh8fFNe\n/uhwLr5Y01R36RJhbaKkRL/DixapM//MmaoBbtigVTOrQnY2+Q0b6vPcp49uBx6YFqaDVPuupBOJ\nGmeAlhH6LaXNhrm6swV425VJJ4mDB+v63+DB+jPiBan0XbGyhCdh8mRlBRaCQecBa9ZobMjvv+uC\nsjs/cPbzZ8xgSNu28MMP6uqwcaNuM2fGfv/cXJ0gulvduqFfuy7j5beaNcmfNo0hy5fTr04d+vXO\n49YRjfmjbmO+Xt2UmYvrM3N2NnPmqAviggW6JQJflC1gAPCpsz8ZOBhwB5buInIVgDFmmzGmnojs\nCNGHJY047DAt+3DbbZpxfcIE3bKyVB/p3Fmtzs2ba9hFw4b6HLkLzj/+qO4R2dm6xeIFFM2169bp\nwnjE8kmHH67bDz/Ahx+qsrR6tS7B5+bqSnmnTgEFq3xM2MaNWnjZpaAAvv5aHexnzFBNdM0aTbET\nLXl5et/WrXXA+e47/SA3btQP7ZdfdLIZnLLHJTtbFa46dXRiWlwMW7dWUODShFjGlPrO9VeKiBhj\n5gNdnGNvOO+ZChwEvJ8k+S1haNBA3Y8vuEANzPn5mljrwQf1q96vnypdrmdLgwa6YJqTk0VOTlNy\n6g0ja/gwGI4+q2vWqOV540ZdQCgq0u++uxUV6aJC+YlEYaFuBQWw5w/WbV7H3HfXwLtrgIl041tq\n791Ig9WaNVNB6tXT5ywrS7fg/cqOFRXpvcpvRUVl3xeq/1DHRFT+zz/X8hiFhfrcu6vLtWoFVpmD\nt+C+jCkrpztuFBeX3Q+1ZWdXXM3Ozg58pgUFZfeLiipu/uP1OPMZ0B9oFaHfUprV302nHrXZbz9D\nnz76VevRI7oFS4ulytSvr4NtJNek0aMD9X5KStTrZsWKwLZ8uY697rNeWKgLvHv26OZ6Jrjt7t3w\n229Vk3vSpDIvawMDnY2GDWGvvdjWqzXfZXdn8bbWrNrRhDW7m7BmTxPWFjZjqQdDj1/KVh7glrTe\nCgRXeAi2G25zrrXKVjWgSRN4+mkNe3r1VTUWzZsX/WrCSy8lVr5OnbRqeaXsu68GoFWVmjUDcSQ3\n3KAD03ffqdL1ww+qAe7YoROS2rU1OUDLlqpYtW+vW/mYr+CBDnQy+f33gaxDy5apJe2HH7TvUElE\ncnJ01tq4sbojNmigg2yDBjohe+KJqv/t3hPtmLLVuTYPHV8gMM4EH3Ovs6QIPXtq/PbcuZps5/33\ndT0hOKSgcgzQ2tmqymieY3TpqxX1etHhlwXw0Uce9J1gpkzxW4J0xetxJvhYuH5L+XVbbtWkt1iS\nSVaWzllattQV91goKdGF3507da6yY0fZ/eDXf/yhi0nBW0mJKnJTp+qKxM6d6s++aRNs3qzb77/r\nAvPWrTRgFQOYzoAQonjitywiSd+AK4BTnP0Tgb8EnfssaP9doF6YPsRudrObP5sf44aXYwrwP6CJ\nc+xRoBtwL9DXOXYNcIwdd+xmt9TaquM4A1werl879tjNbv5vVR07/LJszQQuBSYAI9BgUZeFxpgB\nwCKgvoRxIZQUDNC3WCy+EdOYYoyZBYwwxrwB9ASWArNQZ7O5wFA0sL0MdtyxWDKaRI0zayP0C9ix\nx2JJZ3ypVCYi84A9xpjpQKGIzDXGPOacvh+4C/gEuNsP+SwWS3oRx5gyBjgbmAa8ICJFwHtAd6eP\nL0Xk16T+ERaLJaVJ1DhTrt8iEZmbxD/LYrEkmLSts2WxWCwWi8VisVgsqYwvli2LxWKxWCwWi8Vi\nqe6knbJljGlhjPnaGLPLGJMQ+Y0xBxljvjDGTDfGPJiA/rs6/U8zxjzvdf/l7nWNMWZGgvpua4z5\nxRjzmTHm40Tcw7nPucaYyc59PC9kY4wZZYyZ6mzrjDHHedx/rjHmfaf/t40xNbzs37lHtjFmnDFm\nijHm3x73XeGZM8Zcb4yZYYxxi3RaEky0/wdjzFnO+DLRKarqK6HGU2PM31Ndbgg9VqeL7FB2/E8X\nuUP9rqSL7OlCVcYSY8xQY8yXzm/NPh7IEvf44LUsTp9xP/OJkMfpN+bnOEGfTdzPZoLkKTM39Os7\nHBV+ZxGLIxtQTaAhWp8iK0H3aAbUdPbHAl097j87aP8F4MAEflYvAtMT1H9b4OUE/7/3AcYk8h7l\n7jcTqONxnycCtzr7NwPHJkDuU4Abnf1H0ZovXn6PSp85oCnwvnPu78DJyfr/ZPJWyf/hBuBktJzH\ndOf8qcD1KSB38Hj6CjA4HeR25Aseq5/Hqb2WJrKXjv/p8l1x5Cvzu5JOsqfLFsdYchpwnXP+M6AO\n0A94wgNZYh0fEiaL02esz3yi5YnlOU60LLE+m4n83pSZG/r92VS2pZ1lS0QKRGQrHqW+D3OPDSJS\n4LwsBIo97j+4vz3Az172H8Sf0Ic0kQxzVoCuTlD/o4BsZ/XiUWNiKWccG8aY9sCvIuJ1Vd+VQF1n\nPw/Y5HH/APsCC539BcAhXnUc9My59AXynf0paAFOS4Kp5P/gFkLtBCwUkRJS5H9TbjwtQmsI5Tuv\nU1ZuqDBWFwAdSBPZKTv+p8V3JYjg35V0kz3liWMsmQwcbIzJBXaJyC4RmQN09UCWWMeHhMniyBPr\nM59QeYjtOU60LBDbs5lIeYLnho+hipNfslRK2ilbQSQ8s4cxpgdaI2NpAvo+1hizCF3V8XzybYzJ\nAQ4TkXwSp5iuQ7/MQ4HhxphuCbhHc6CGiIwAdgPHJ+AeLicBbyeg3+XAIcaYb1Er5pcJuMcy4DBn\nfyiJLchri/+mBqH+Dw3LHWvog1whccdTYAvpJXfwWJ1DGsgeYvwvL2NKyu0Q/LsyAjiQ9JE9XYlm\nLHGPbQ96n2dzyBjHh0TLEusznxB54nyOE/nZxPNsJkqe4Lnhzgj3Tdr3JhLprGwlFGNMI+Ax4KJE\n9C8i74lId7S+xjEJuMW5hKgT5CUiUigiu51Vgw/Qgo1esxVNmwtq+u2SgHu4HAtMTEC/5wMTRaQb\n8KEx5pwE3OM9INcY8ynwB5DItOVbgQbOfgP0x9GSfEL9H4Inninzvyk3nm4jTeSGCmN1Mekhe/nx\nP5SMqSh3+d+V91HPgHT4zNOZaMeS4GcXPPL6iXN8SIgsEPcznwh54n2OE/LZVOHZTIQ8wXPDqUB7\nH2WplHRWtgwJstg4gXVjUT/w3xLQf82gl9tQi43X7Adcboz5COhqjLnS6xuYskHJA9EHz2u+BHo4\n+72AVQm4B8aY5sAeEfk9Ed0Dm539jSRgFVZESkTkKhE5HB08Jnl9DwLP2xwCVrQRaJFOS/KI9H9Y\njj7vWaTI/ybEeJoWckPIsTqL9JA9ePw/AHX3GeycS2W5Q/2urCA9PvN0JKaxxHGxr22MqWuMOQj4\nrsoCxDk+JEIWR564nvkEyRPXc5zAzyauZzNB8pSfG672UZbKSUZgmJcbatL9FHW9+xTol4B7nIFa\nBj5ztv4e938c6ls6FXg2CZ9ZohJkHAnMBT4H7kmg/Pc7n9XrQE6C7nEpcMX/t3cvKQjDUBRA7wZE\nx+Ju3IfrVNCJ4r4cpINKdWDtgwbOgUzT24akffRX1Pc2yXnYh0uSXcE29kP/1ySnhfuezLm0D2M8\n0k6SJWOizRuHtJ+oPtPudm5WkHuynvaQe8g0WavTXr5effbRPtx7yv3pvNJL9l7aP2tJkmPahe4t\nyWGBLLPXh6WzDH3OnvMVeUa5fprHRcdm9twsyvN2bbiGcfrW/NQYAACgQM+PEQIAAKyWYgsAAKCA\nYgsAAKCAYgsAAKCAYgsAAKCAYgsAAKCAYgsAAKDAC22zKaxuKpK/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19ca02510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(histDemand, Area=(8001,8009,1),\n",
    "        generator={'Supply':hist1d_Supply,'One dimensional': hist1d, 'Three dimensional': hist3d,'Suppply_Demand_normed':hist1d_Supply_Demand\n",
    "                   });\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sensitivity Analysis\n",
    "### Willingness to pay <-> (Area, Rooms, Size, Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def sensitivit_analysis(Area,Rooms,Size,Rent):\n",
    "    import itertools\n",
    "    import numpy as np\n",
    "\n",
    "    q = Area\n",
    "    house = [Rooms,Size,Rent]\n",
    "    howmanydemand= 0\n",
    "\n",
    "\n",
    "    ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    q_data_specific = Complete_data.ix[ind_q_specific,:]\n",
    "    rooms = []\n",
    "    sizes =[]\n",
    "    prices = []\n",
    "    if len(q_data_specific) >= 1:\n",
    "        for i in range(len(q_data_specific)):\n",
    "            room_rng = (q_data_specific['room_min'].values[i]/10, q_data_specific['room_max'].values[i]/10)\n",
    "            size_rng = (q_data_specific['size_min'].values[i], q_data_specific['size_max'].values[i])\n",
    "            price_rng = (q_data_specific['price_min'].values[i], q_data_specific['price_max'].values[i])\n",
    "            \n",
    "            if (house[0]>=room_rng[0]):\n",
    "                if (house[1]>=size_rng[0]):\n",
    "                    if (house[2]<=price_rng[-1]):\n",
    "                        howmanydemand+=1\n",
    "    \n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Total demand for this house is {} out of {} specific demands, which is {}% of the demand in this region.\\\n",
    "        '.format(howmanydemand,len(q_data_specific),np.around(100*howmanydemand/float(len(q_data_specific)),decimals=2))\n",
    "        print \"**************************************************************\"\n",
    "    else:\n",
    "        print 'Not enough data for this area with the zip code {}.'.format(Area)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**************************************************************\n",
      "Total demand for this house is 115 out of 1354 specific demands, which is 8.49% of the demand in this region.        \n",
      "**************************************************************\n"
     ]
    }
   ],
   "source": [
    "interact(sensitivit_analysis, Area=(8001,8012,1),Rooms=(1,10,1),Size=(20,400,20),Rent=(200,6000,100));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Complete_data_zip = Complete_data.copy()\n",
    "# Complete_data_zip.index = Complete_data_zip['zip']\n",
    "# print Complete_data_zip.shape\n",
    "# Complete_data_zip_specific = Complete_data_zip.ix[Complete_data_zip.specific_search==1]\n",
    "# print Complete_data_zip_specific.shape\n",
    "# zip_GB = Complete_data_zip_specific.groupby(by='zip')\n",
    "# long_lat_zip = zip_GB['zip','lon','lat'].first()\n",
    "\n",
    "# ind_zip = zip_GB.specific_search.sum()>20\n",
    "# total_specific_demand=zip_GB.specific_search.sum()[ind_zip]\n",
    "# # Complete_data_zip.ix[ind_zip].groupby(by='zip').filter(lambda x: x.specific_search.sum()>100).groupby(by='zip').specific_search.sum().shape\n",
    "# total_interest_in_property = Complete_data_zip_specific.ix[ind_zip].groupby(by='zip').apply(check_search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def sensitivit_analysis_all_f(Rooms,New_Rooms,Size,New_Size,Rent,New_Rent,Demand_Threshold):\n",
    "    cmapname=\"RdYlBu_r\"\n",
    "#     cmapname=\"B\"\n",
    "    import itertools\n",
    "    import numpy as np    \n",
    "    \n",
    "    def check_search(df):\n",
    "        a = df['room_min'].values[:]<=house[0]*10\n",
    "        b = df['size_min'].values[:]<=house[1]\n",
    "        c = df['price_max'].values[:]>=house[2]\n",
    "        return (a*b*c).sum()\n",
    "    \n",
    "    house = [Rooms,Size,Rent]\n",
    "    house1 = [New_Rooms,New_Size,New_Rent]\n",
    "    \n",
    "    geo_info = []\n",
    "    total_demand = []\n",
    "    percentofchange = []\n",
    "    \n",
    "    Complete_data_zip = Complete_data.copy()\n",
    "    Complete_data_zip.index = Complete_data_zip['zip']\n",
    "#     print Complete_data_zip.shape\n",
    "    Complete_data_zip_specific = Complete_data_zip.ix[Complete_data_zip.specific_search==1]\n",
    "    \n",
    "    long_lat_zip_all = Complete_data_zip.groupby(by='zip')['zip','lon','lat'].first()\n",
    "    \n",
    "#     print Complete_data_zip_specific.shape\n",
    "    zip_GB = Complete_data_zip_specific.groupby(by='zip')\n",
    "    long_lat_zip_specific = zip_GB['zip','lon','lat'].first()\n",
    "    \n",
    "    ind_zip = zip_GB.specific_search.sum()>Demand_Threshold\n",
    "    \n",
    "    \n",
    "    long_lat_zip_sel = long_lat_zip_specific.ix[ind_zip]\n",
    "    \n",
    "    total_specific_demand=zip_GB.specific_search.sum()[ind_zip]\n",
    "    total_demand = total_specific_demand.values[:]\n",
    "    \n",
    "    total_interest_in_property0 = Complete_data_zip_specific.ix[ind_zip].groupby(by='zip').apply(check_search)\n",
    "    \n",
    "    \n",
    "    house = house1\n",
    "    total_interest_in_property1 = Complete_data_zip_specific.ix[ind_zip].groupby(by='zip').apply(check_search)\n",
    "#     print total_interest_in_property1/(total_specific_demand.values[:]).astype(float)\n",
    "\n",
    "    percentofchange = 100*(total_interest_in_property1.values[:]-total_interest_in_property0.values[:])/(total_specific_demand.values[:]).astype(float)\n",
    "    percentofcoverage = 100*total_interest_in_property1.values[:]/(total_specific_demand.values[:]).astype(float)\n",
    "    \n",
    "    \n",
    "    #To Plot\n",
    "    fig = plt.figure(figsize=(20,12))\n",
    "    ax = fig.add_subplot(2,2,1)\n",
    "    sc = plt.scatter(long_lat_zip_all.lon,long_lat_zip_all.lat,c='None',s=20,marker='o',edgecolor='gray',linewidth=.3, cmap=cmapname ,alpha=.4)\n",
    "    \n",
    "    mn = np.min(percentofchange)  \n",
    "    mx = np.max(percentofchange)\n",
    "    mn = -50\n",
    "    mx = 50\n",
    "    sc = plt.scatter(long_lat_zip_sel.lon,long_lat_zip_sel.lat,c=percentofchange,s=20,vmin=mn,vmax=mx,marker='o',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "    \n",
    "    eps = .004\n",
    "    X_mn= valued_Data.lon.min()*(1-eps)\n",
    "    Y_mn= valued_Data.lat.min()*(1-eps)\n",
    "    X_mx= valued_Data.lon.max()*(1+eps)\n",
    "    Y_mx= valued_Data.lat.max()*(1+eps)\n",
    "    plt.xlim(X_mn,X_mx)\n",
    "    plt.ylim(Y_mn,Y_mx)\n",
    "    if mn == mx:\n",
    "        mx = mn + 6\n",
    "    plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.title(\"Percent of Change in Demand Coverage\")\n",
    "    plt.axis('off')\n",
    "    \n",
    "    \n",
    "    ax = fig.add_subplot(2,2,2)\n",
    "    sc = plt.scatter(long_lat_zip_all.lon,long_lat_zip_all.lat,c='None',s=20,marker='o',edgecolor='gray',linewidth=.3, cmap=cmapname ,alpha=.4)\n",
    "    \n",
    "    mn = np.min(percentofcoverage)  \n",
    "    mx = np.max(percentofcoverage)\n",
    "    mn = 0\n",
    "    mx =100\n",
    "    sc = plt.scatter(long_lat_zip_sel.lon,long_lat_zip_sel.lat,c=percentofcoverage,s=20,vmin=mn,vmax=mx,marker='o',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "    \n",
    "    eps = .004\n",
    "    X_mn= valued_Data.lon.min()*(1-eps)\n",
    "    Y_mn= valued_Data.lat.min()*(1-eps)\n",
    "    X_mx= valued_Data.lon.max()*(1+eps)\n",
    "    Y_mx= valued_Data.lat.max()*(1+eps)\n",
    "    plt.xlim(X_mn,X_mx)\n",
    "    plt.ylim(Y_mn,Y_mx)\n",
    "    if mn == mx:\n",
    "        mx = mn + 6\n",
    "    plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.title(\"New Percent of Demand Coverage\")\n",
    "    plt.axis('off')\n",
    "    \n",
    "    \n",
    "    \n",
    "    ax = fig.add_subplot(2,2,3)\n",
    "    sc = plt.scatter(long_lat_zip_all.lon,long_lat_zip_all.lat,c='None',s=20,marker='o',edgecolor='gray',linewidth=.3, cmap=cmapname ,alpha=.4)\n",
    "    \n",
    "    mn = np.min(total_demand)  \n",
    "    mx = np.max(total_demand)\n",
    "    sc = plt.scatter(long_lat_zip_sel.lon,long_lat_zip_sel.lat,c=total_demand,s=20,vmin=mn,vmax=mx,marker='o',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "    \n",
    "    eps = .004\n",
    "    X_mn= valued_Data.lon.min()*(1-eps)\n",
    "    Y_mn= valued_Data.lat.min()*(1-eps)\n",
    "    X_mx= valued_Data.lon.max()*(1+eps)\n",
    "    Y_mx= valued_Data.lat.max()*(1+eps)\n",
    "    plt.xlim(X_mn,X_mx)\n",
    "    plt.ylim(Y_mn,Y_mx)\n",
    "    if mn == mx:\n",
    "        mx = mn + 6\n",
    "    plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.title(\"Total Specific Demand\")\n",
    "    plt.axis('off')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    font = {'size'   : 12}\n",
    "    plt.rc('font', **font)\n",
    "    plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "global name 'Complete_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-ab264e5b1f78>\u001b[0m in \u001b[0;36msensitivit_analysis_all_f\u001b[0;34m(Rooms, New_Rooms, Size, New_Size, Rent, New_Rent, Demand_Threshold)\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mpercentofchange\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0mComplete_data_zip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mComplete_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m     \u001b[0mComplete_data_zip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mComplete_data_zip\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'zip'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;31m#     print Complete_data_zip.shape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: global name 'Complete_data' is not defined"
     ]
    }
   ],
   "source": [
    "interact(sensitivit_analysis_all_f,Rooms=(1,6,1),New_Rooms=(1,6,1),Size=(50,250,20),New_Size=(50,250,20),Rent=(1000,5000,200),New_Rent=(1000,5000,200),Demand_Threshold=(10,100,40));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Similar Analysis for Homegate Supply Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55361, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Living space</th>\n",
       "      <th>Year built</th>\n",
       "      <th>Last renovation</th>\n",
       "      <th>Rent</th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>645.0</td>\n",
       "      <td>8.041672</td>\n",
       "      <td>47.397999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>8.041403</td>\n",
       "      <td>47.396472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>58.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1116.0</td>\n",
       "      <td>8.050583</td>\n",
       "      <td>47.387093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>8.037885</td>\n",
       "      <td>47.387531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>8.031479</td>\n",
       "      <td>47.384245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ZIP  Rooms  Living space  Year built  Last renovation    Rent       lng  \\\n",
       "0  5000    1.0          28.0      1954.0              NaN   645.0  8.041672   \n",
       "1  5000    2.0           NaN         NaN           2016.0  1400.0  8.041403   \n",
       "2  5000    2.5          58.0         NaN              NaN  1116.0  8.050583   \n",
       "3  5000    3.0           NaN         NaN           2013.0  1250.0  8.037885   \n",
       "4  5000    3.5           NaN         NaN              NaN  1290.0  8.031479   \n",
       "\n",
       "         lat  \n",
       "0  47.397999  \n",
       "1  47.396472  \n",
       "2  47.387093  \n",
       "3  47.387531  \n",
       "4  47.384245  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = '/Users/SVM/Dropbox/Applications/Crawlers/homegate/DB/rental_DB/homegatedb__'+'all_latlng_complete'+'rent.csv'\n",
    "listing_with_latlong = pd.read_csv(path)\n",
    "listing_with_latlong.head()\n",
    "\n",
    "#Kinds of features we are interested in\n",
    "cols = ['ID', 'ZIP', 'Date','Type','Rooms','Floor',\n",
    "       'Living space','Floor space','Room height','Volume',\n",
    "       'Year built','Last renovation','Net rent','Additional expenses',\n",
    "       'Rent','Available','lng','lat']\n",
    "listing = listing_with_latlong[cols]\n",
    "\n",
    "#Types of listing we are interested in\n",
    "Types = ['Apartment','Attic compartment','Attic flat','Bachelor flat','Bifamiliar house','Cellar compartment','Chalet',\n",
    "       'Duplex','Farm house','Granny flat','Home','Roof flat','Row house','Rustic house','Single house','Studio','Terrace flat',\n",
    "       'Terrace house','Villa']\n",
    "id_type = []\n",
    "for i in range(listing.shape[0]):\n",
    "    if listing['Type'].values[i] in Types:\n",
    "        id_type.append(i)\n",
    "listing = listing.ix[id_type]\n",
    "\n",
    "cols = ['ZIP','Rooms',\n",
    "       'Living space',\n",
    "       'Year built','Last renovation',\n",
    "       'Rent','lng','lat']\n",
    "listing = listing[cols]\n",
    "listing['ZIP'].ix[:] = (listing['ZIP'].values[:]).astype(int) \n",
    "ind_by = listing['Rent']=='by request' \n",
    "listing['Rent'].ix[ind_by] =np.nan\n",
    "ind_by = listing['Rent']=='EUR 2635' \n",
    "listing['Rent'].ix[ind_by] =np.nan\n",
    "\n",
    "ind_by = listing['Rent']=='BGN 1630' \n",
    "listing['Rent'].ix[ind_by] =np.nan\n",
    "\n",
    "listing['Rent'] = listing['Rent'].values[:].astype(float) \n",
    "\n",
    "#Remove outliers based on a global statistics, calculated previously\n",
    "# \n",
    "Supply_stat = listing.describe(percentiles=[.001,.01,.2,.5,.99,.999])\n",
    "for f in ['Rooms','Living space','Rent']:\n",
    "    mx = Supply_stat[f].ix['99.9%']\n",
    "    ind = listing[f]>mx\n",
    "    listing[f].ix[ind]=mx\n",
    "    mn = Supply_stat[f].ix['0.1%']\n",
    "    ind = listing[f]<mn\n",
    "    listing[f].ix[ind]=mn\n",
    "\n",
    "\n",
    "print listing.shape\n",
    "listing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>ID</th>\n",
       "      <th>Address</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Living space</th>\n",
       "      <th>Floor space</th>\n",
       "      <th>Room height</th>\n",
       "      <th>...</th>\n",
       "      <th>Available</th>\n",
       "      <th>Public transport</th>\n",
       "      <th>Shopping</th>\n",
       "      <th>Kindergarten</th>\n",
       "      <th>Primary school</th>\n",
       "      <th>Secondary school</th>\n",
       "      <th>Motorway</th>\n",
       "      <th>URL</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>105742696</td>\n",
       "      <td>Sonnmattweg 17 5000 Aarau</td>\n",
       "      <td>5000</td>\n",
       "      <td>04/07/16</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>by agreement</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.homegate.ch/rent/105742696</td>\n",
       "      <td>47.397999</td>\n",
       "      <td>8.041672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>105785811</td>\n",
       "      <td>K�_ttigerstrasse 6 5000 Aarau</td>\n",
       "      <td>5000</td>\n",
       "      <td>04/07/16</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>01.08.2016</td>\n",
       "      <td>10.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.homegate.ch/rent/105785811</td>\n",
       "      <td>47.396472</td>\n",
       "      <td>8.041403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>105842788</td>\n",
       "      <td>G̦nhardweg 8 5000 Aarau</td>\n",
       "      <td>5000</td>\n",
       "      <td>04/07/16</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7</td>\n",
       "      <td>58.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>01.10.2016</td>\n",
       "      <td>200.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.homegate.ch/rent/105842788</td>\n",
       "      <td>47.387093</td>\n",
       "      <td>8.050583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>105849196</td>\n",
       "      <td>DAMMWEG 8a 5000 Aarau</td>\n",
       "      <td>5000</td>\n",
       "      <td>04/07/16</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>01.10.2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.homegate.ch/rent/105849196</td>\n",
       "      <td>47.387531</td>\n",
       "      <td>8.037885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105502493</td>\n",
       "      <td>W̦schnauring 23 5000 Aarau</td>\n",
       "      <td>5000</td>\n",
       "      <td>04/07/16</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>immediately</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.homegate.ch/rent/105502493</td>\n",
       "      <td>47.384245</td>\n",
       "      <td>8.031479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID                        Address   ZIP      Date       Type  Rooms  \\\n",
       "0  105742696      Sonnmattweg 17 5000 Aarau  5000  04/07/16  Apartment    1.0   \n",
       "1  105785811  K�_ttigerstrasse 6 5000 Aarau  5000  04/07/16  Apartment    2.0   \n",
       "2  105842788        G̦nhardweg 8 5000 Aarau  5000  04/07/16  Apartment    2.5   \n",
       "3  105849196          DAMMWEG 8a 5000 Aarau  5000  04/07/16  Apartment    3.0   \n",
       "4  105502493     W̦schnauring 23 5000 Aarau  5000  04/07/16  Apartment    3.5   \n",
       "\n",
       "  Floor  Living space  Floor space Room height    ...        Available  \\\n",
       "0     2          28.0          NaN         NaN    ...     by agreement   \n",
       "1     2           NaN          NaN         NaN    ...       01.08.2016   \n",
       "2     7          58.0          NaN         NaN    ...       01.10.2016   \n",
       "3     1           NaN          NaN         NaN    ...       01.10.2016   \n",
       "4     1           NaN          NaN         NaN    ...      immediately   \n",
       "\n",
       "   Public transport  Shopping Kindergarten Primary school Secondary school  \\\n",
       "0             700.0    1000.0        700.0          700.0           1300.0   \n",
       "1              10.0     150.0          NaN            NaN              NaN   \n",
       "2             200.0     800.0        500.0          400.0            400.0   \n",
       "3               NaN       NaN          NaN            NaN              NaN   \n",
       "4             400.0       NaN       1200.0         1200.0           1200.0   \n",
       "\n",
       "  Motorway                                    URL        lat       lng  \n",
       "0      NaN  http://www.homegate.ch/rent/105742696  47.397999  8.041672  \n",
       "1      NaN  http://www.homegate.ch/rent/105785811  47.396472  8.041403  \n",
       "2      NaN  http://www.homegate.ch/rent/105842788  47.387093  8.050583  \n",
       "3      NaN  http://www.homegate.ch/rent/105849196  47.387531  8.037885  \n",
       "4      NaN  http://www.homegate.ch/rent/105502493  47.384245  8.031479  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listing_with_latlong.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>ZIP</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Living space</th>\n",
       "      <th>Year built</th>\n",
       "      <th>Last renovation</th>\n",
       "      <th>Rent</th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>55361.000000</td>\n",
       "      <td>54295.000000</td>\n",
       "      <td>47373.000000</td>\n",
       "      <td>26240.000000</td>\n",
       "      <td>10042.000000</td>\n",
       "      <td>5.412800e+04</td>\n",
       "      <td>55361.000000</td>\n",
       "      <td>55361.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5623.628475</td>\n",
       "      <td>3.653740</td>\n",
       "      <td>93.991366</td>\n",
       "      <td>1984.204688</td>\n",
       "      <td>2011.182035</td>\n",
       "      <td>2.235992e+03</td>\n",
       "      <td>8.026778</td>\n",
       "      <td>47.095559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2822.045670</td>\n",
       "      <td>1.325539</td>\n",
       "      <td>47.324266</td>\n",
       "      <td>71.349375</td>\n",
       "      <td>12.734647</td>\n",
       "      <td>1.318378e+04</td>\n",
       "      <td>0.913221</td>\n",
       "      <td>0.463472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>5.968461</td>\n",
       "      <td>45.821860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1%</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1432.717000</td>\n",
       "      <td>1968.041000</td>\n",
       "      <td>3.625400e+02</td>\n",
       "      <td>6.017421</td>\n",
       "      <td>45.836494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1%</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1805.390000</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>6.800000e+02</td>\n",
       "      <td>6.121491</td>\n",
       "      <td>45.990131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>1950.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1964.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>1.356000e+03</td>\n",
       "      <td>7.136893</td>\n",
       "      <td>46.544679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6003.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>1.800000e+03</td>\n",
       "      <td>8.307027</td>\n",
       "      <td>47.293978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>9500.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>245.280000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.900000e+03</td>\n",
       "      <td>9.603502</td>\n",
       "      <td>47.696250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99.9%</th>\n",
       "      <td>9642.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>1.450000e+04</td>\n",
       "      <td>9.904930</td>\n",
       "      <td>47.747664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9658.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>1.380000e+06</td>\n",
       "      <td>10.370742</td>\n",
       "      <td>47.783855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ZIP         Rooms  Living space    Year built  \\\n",
       "count  55361.000000  54295.000000  47373.000000  26240.000000   \n",
       "mean    5623.628475      3.653740     93.991366   1984.204688   \n",
       "std     2822.045670      1.325539     47.324266     71.349375   \n",
       "min     1000.000000      1.000000      1.000000      1.000000   \n",
       "0.1%    1000.000000      1.000000     12.000000   1432.717000   \n",
       "1%      1000.000000      1.000000     24.000000   1805.390000   \n",
       "20%     1950.000000      2.500000     63.000000   1964.000000   \n",
       "50%     6003.000000      3.500000     88.000000   1996.000000   \n",
       "99%     9500.000000      7.500000    245.280000   2016.000000   \n",
       "99.9%   9642.000000     10.000000    450.000000   2017.000000   \n",
       "max     9658.000000     75.000000   3500.000000   9999.000000   \n",
       "\n",
       "       Last renovation          Rent           lng           lat  \n",
       "count     10042.000000  5.412800e+04  55361.000000  55361.000000  \n",
       "mean       2011.182035  2.235992e+03      8.026778     47.095559  \n",
       "std          12.734647  1.318378e+04      0.913221      0.463472  \n",
       "min        1014.000000  7.000000e+01      5.968461     45.821860  \n",
       "0.1%       1968.041000  3.625400e+02      6.017421     45.836494  \n",
       "1%         1987.000000  6.800000e+02      6.121491     45.990131  \n",
       "20%        2008.000000  1.356000e+03      7.136893     46.544679  \n",
       "50%        2013.000000  1.800000e+03      8.307027     47.293978  \n",
       "99%        2016.000000  6.900000e+03      9.603502     47.696250  \n",
       "99.9%      2017.000000  1.450000e+04      9.904930     47.747664  \n",
       "max        2022.000000  1.380000e+06     10.370742     47.783855  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Supply_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def hist1d_Supply(Area):\n",
    "    q = Area\n",
    "# #     q = 8134\n",
    "#     # ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "#     ind_q_vicinity = (Complete_data['zip']==q)\n",
    "#     q_data_vicinity = Complete_data.ix[ind_q_vicinity,:]\n",
    "\n",
    "    fig = plt.figure(figsize=(12,3))\n",
    "    font = {'size'   : 8}\n",
    "    plt.rc('font', **font)\n",
    "    ind_q_Supply = (listing['ZIP']==q)\n",
    "    q_data_Supply = listing.ix[ind_q_Supply]\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    if q_data_Supply.shape[0]>=1:\n",
    "    \n",
    "        rooms = []\n",
    "        sizes =[]\n",
    "        prices = []\n",
    "        print \"**************************************************************\"\n",
    "        print 'Number of unique Supply Ads: {}.'.format(q_data_Supply.shape[0])\n",
    "        \n",
    "            \n",
    "        ax = plt.subplot(1,3,1)\n",
    "        plt.title('Supply: Room')\n",
    "        \n",
    "        rooms = q_data_Supply['Rooms'].dropna().values[:]\n",
    "        room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='blue',linewidth=.5,edgecolor='black',rwidth=1,normed=True)\n",
    "        ax.yaxis.grid(True)\n",
    "        \n",
    "        ax = plt.subplot(1,3,2)\n",
    "        plt.title('Supply: Size')\n",
    "    \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        R = mx-mn\n",
    "        # stp = int(R/30)\n",
    "        stp = 20\n",
    "        size_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        \n",
    "        sizes = q_data_Supply['Living space'].dropna().values[:]\n",
    "        \n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='blue',linewidth=.5,edgecolor='black',normed=True)\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(1,3,3)\n",
    "        plt.title('Supply: Price')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "        stp = 200\n",
    "        price_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        prices = q_data_Supply['Rent'].dropna().values[:]\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='blue',linewidth=.5,edgecolor='black',normed=True)\n",
    "        \n",
    "        ax.yaxis.grid(True)\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        \n",
    "#         Data = rooms.astype(float)[:,np.newaxis]\n",
    "#         print Data.shape\n",
    "#         msz0 = 30\n",
    "#         msz1 = 30\n",
    "#         sm1 = SOM.SOM('hhhh', Data, mapsize = [msz0, msz1],norm_method = 'var',initmethod='pca')\n",
    "#         sm1.train(n_job = 1, shared_memory = 'no',verbose='final')\n",
    "#         cd = SOM.denormalize_by(sm1.data_raw,sm1.codebook)\n",
    "\n",
    "        \n",
    "#         a = plt.hist(rooms,bins=room_bin,alpha=.4,color='blue',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "#         q_data_Supply['Rooms'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        \n",
    "    \n",
    "        ax = plt.subplot(1,3,1)\n",
    "        DF = pd.DataFrame(data=rooms,columns=['room'])\n",
    "        DF['room'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        \n",
    "#         ax = plt.subplot(1,3,1)\n",
    "#         DF = pd.DataFrame(data=cd[:,0],columns=['room'])\n",
    "#         DF['room'].plot(kind='kde',linewidth=2,color='green',label='Filled Data')\n",
    "        \n",
    "        mn = int(stat['room_min'].ix['2%']/10)\n",
    "        mx = int(stat['room_max'].ix['99%']/10+1)\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative SupplyDistributions: Room')\n",
    "        \n",
    "        \n",
    "        ax = plt.subplot(1,3,2)\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        DF = pd.DataFrame(data=sizes,columns=['size'])\n",
    "        DF['size'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "#         a = plt.hist(sizes,bins=size_bin,alpha=.4,color='blue',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "#         q_data_Supply['Living space'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative Supply Distributions: Size')\n",
    "        \n",
    "        ax = plt.subplot(1,3,3)\n",
    "        \n",
    "        \n",
    "#         Data = prices.astype(float)[:,np.newaxis]\n",
    "# #         Data = np.concatenate((Data,Data,Data),axis=0)\n",
    "#         print Data.shape\n",
    "#         msz0 = 10\n",
    "#         msz1 = 10\n",
    "#         sm1 = SOM.SOM('hhhh', Data, mapsize = [msz0, msz1],norm_method = 'var',initmethod='pca')\n",
    "#         sm1.train(n_job = 1, shared_memory = 'no',verbose='final')\n",
    "#         cd = SOM.denormalize_by(sm1.data_raw,sm1.codebook)\n",
    "        \n",
    "#         DF = pd.DataFrame(data=cd,columns=['price'])\n",
    "#         DF['price'].plot(kind='kde',linewidth=2,color='green',label='Filled Data')\n",
    "        \n",
    "        \n",
    "        DF = pd.DataFrame(data=prices,columns=['price'])\n",
    "        DF['price'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "#         a = plt.hist(prices,bins=price_bin,alpha=.4,color='blue',linewidth=.5,edgecolor='black',normed='Yes')\n",
    "#         q_data_Supply['Rent'].plot(kind='kde',linewidth=2,color='blue',label='Filled Data')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative Supply Distributions: Price')\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough Supply this area with the zip code {}.'.format(Area)\n",
    "        return\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def hist1d_Supply_query(Area,Min_Rooms,Max_Rooms,Min_Size,Max_Size,Min_Rent,Max_Rent,Supply_Threshold):\n",
    "    q = Area\n",
    "# #     q = 8134\n",
    "#     # ind_q_specific = (Complete_data['zip']==q) & (Complete_data['specific_search']==1)\n",
    "#     ind_q_vicinity = (Complete_data['zip']==q)\n",
    "#     q_data_vicinity = Complete_data.ix[ind_q_vicinity,:]\n",
    "\n",
    "    fig = plt.figure(figsize=(12,3))\n",
    "    font = {'size'   : 8}\n",
    "    plt.rc('font', **font)\n",
    "    ind_q_Supply = (listing['ZIP']==q)\n",
    "    q_data_Supply = listing.ix[ind_q_Supply]\n",
    "    \n",
    "    \n",
    "    import itertools\n",
    "    import numpy as np    \n",
    "    \n",
    "    if Min_Rooms >= Max_Rooms:\n",
    "            Max_Rooms = Min_Rooms\n",
    "    if Min_Size>=Max_Size:\n",
    "            Max_Size= Min_Size\n",
    "    if Min_Rent>=Max_Rent:\n",
    "            Max_Rent= Min_Rent\n",
    "            \n",
    "    def check_search(df):\n",
    "        \n",
    "        amn = df['Rooms']>=Min_Rooms\n",
    "        amx = df['Rooms']<=Max_Rooms\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        bmn = df['Living space']>=Min_Size\n",
    "        bmx = df['Living space']<=Max_Size\n",
    "        \n",
    "        \n",
    "        cmn = df['Rent']>=Min_Rent\n",
    "        cmx = df['Rent']<=Max_Rent\n",
    "        \n",
    "        return amn&bmn&cmn&amx&bmx&cmx\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "        \n",
    "    if q_data_Supply.shape[0]>=1:\n",
    "    \n",
    "        rooms = []\n",
    "        sizes =[]\n",
    "        prices = []\n",
    "        print \"**************************************************************\"\n",
    "        print 'Number of unique Supply Ads: {}.'.format(q_data_Supply.shape[0])\n",
    "        \n",
    "            \n",
    "        ax = plt.subplot(1,3,1)\n",
    "        plt.title('Supply: Room')\n",
    "        \n",
    "        rooms = q_data_Supply['Rooms'].dropna().values[:]\n",
    "        room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='white',linewidth=.5,edgecolor='black',rwidth=1,normed=False)\n",
    "        ax.yaxis.grid(True)\n",
    "        \n",
    "        ax = plt.subplot(1,3,2)\n",
    "        plt.title('Supply: Size')\n",
    "    \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        R = mx-mn\n",
    "            # stp = int(R/30)\n",
    "        stp = 20\n",
    "        size_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        \n",
    "        sizes = q_data_Supply['Living space'].dropna().values[:]\n",
    "        \n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='white',linewidth=.5,edgecolor='black',normed=False)\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(1,3,3)\n",
    "        plt.title('Supply: Price')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "        stp = 200\n",
    "        price_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        prices = q_data_Supply['Rent'].dropna().values[:]\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='white',linewidth=.5,edgecolor='black',normed=False)\n",
    "    \n",
    "        ax.yaxis.grid(True)\n",
    "        plt.tight_layout()\n",
    "\n",
    "#         ax = plt.subplot(1,3,1)\n",
    "#         DF = pd.DataFrame(data=rooms,columns=['room'])\n",
    "#         DF['room'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        \n",
    "#         mn = int(stat['room_min'].ix['2%']/10)\n",
    "#         mx = int(stat['room_max'].ix['99%']/10+1)\n",
    "#         plt.xlim(mn,mx)\n",
    "#         plt.title('Relative SupplyDistributions: Room')\n",
    "        \n",
    "#         ax = plt.subplot(1,3,2)\n",
    "    \n",
    "#         DF = pd.DataFrame(data=sizes,columns=['size'])\n",
    "#         DF['size'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "#         mn = int(stat['size_min'].ix['2%'])\n",
    "#         mx = int(stat['size_max'].ix['99%'])\n",
    "#         plt.xlim(mn,mx)\n",
    "#         plt.title('Relative Supply Distributions: Size')\n",
    "        \n",
    "#         ax = plt.subplot(1,3,3)\n",
    "        \n",
    "        \n",
    "        \n",
    "#         DF = pd.DataFrame(data=prices,columns=['price'])\n",
    "#         DF['price'].plot(kind='kde',linewidth=2,color='red',label='Filled Data')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        plt.xlim(mn,mx)\n",
    "        plt.title('Relative Supply Distributions: Price')\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Not enough Supply this area with the zip code {}.'.format(Area)\n",
    "        return\n",
    "    \n",
    "    \n",
    "    q_data_Supply_bounded = q_data_Supply.ix[check_search(q_data_Supply)]\n",
    "    \n",
    "    if q_data_Supply_bounded.shape[0]>=1:\n",
    "    \n",
    "        rooms = []\n",
    "        sizes =[]\n",
    "        prices = []\n",
    "        print \"**************************************************************\"\n",
    "        print 'Number of unique Supply Ads based on your query: {}.'.format(q_data_Supply_bounded.shape[0])\n",
    "        \n",
    "            \n",
    "        ax = plt.subplot(1,3,1)\n",
    "        plt.title('No. of rooms')\n",
    "        \n",
    "        rooms = q_data_Supply_bounded['Rooms'].dropna().values[:]\n",
    "        room_bin = range(int(stat['room_min'].ix['2%']/10),int(stat['room_max'].ix['99.5%']/10+1))\n",
    "        a = plt.hist(rooms,bins=room_bin,alpha=1,color='red',linewidth=.5,edgecolor='black',rwidth=1,normed=False)\n",
    "        ax.yaxis.grid(True)\n",
    "        \n",
    "        ax = plt.subplot(1,3,2)\n",
    "        plt.title('Living space (m^2)')\n",
    "    \n",
    "        mn = int(stat['size_min'].ix['2%'])\n",
    "        mx = int(stat['size_max'].ix['99%'])\n",
    "        R = mx-mn\n",
    "            # stp = int(R/30)\n",
    "        stp = 20\n",
    "        size_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        \n",
    "        sizes = q_data_Supply_bounded['Living space'].dropna().values[:]\n",
    "        \n",
    "        a = plt.hist(sizes,bins=size_bin,alpha=1,color='red',linewidth=.5,edgecolor='black',normed=False)\n",
    "        ax.yaxis.grid(True)\n",
    "    \n",
    "        ax = plt.subplot(1,3,3)\n",
    "        plt.title('Monthly rent')\n",
    "        \n",
    "        mn = int(stat['price_min'].ix['2%'])\n",
    "        mx = int(stat['price_max'].ix['99.5%'])\n",
    "        R = mx-mn\n",
    "        stp = 200\n",
    "        price_bin = range(mn,mx+stp,stp)\n",
    "        \n",
    "        prices = q_data_Supply_bounded['Rent'].dropna().values[:]\n",
    "        a = plt.hist(prices,bins=price_bin,alpha=1,color='red',linewidth=.5,edgecolor='black',normed=False)\n",
    "    \n",
    "        ax.yaxis.grid(True)\n",
    "        plt.tight_layout()\n",
    "\n",
    "#         ax = plt.subplot(1,3,1)\n",
    "#         DF = pd.DataFrame(data=rooms,columns=['room'])\n",
    "#         DF['room'].plot(kind='kde',linewidth=2,color='green',label='Filled Data')\n",
    "        \n",
    "#         mn = int(stat['room_min'].ix['2%']/10)\n",
    "#         mx = int(stat['room_max'].ix['99%']/10+1)\n",
    "#         plt.xlim(mn,mx)\n",
    "#         plt.title('Relative SupplyDistributions: Room')\n",
    "        \n",
    "#         ax = plt.subplot(1,3,2)\n",
    "    \n",
    "#         DF = pd.DataFrame(data=sizes,columns=['size'])\n",
    "#         DF['size'].plot(kind='kde',linewidth=2,color='green',label='Filled Data')\n",
    "#         mn = int(stat['size_min'].ix['2%'])\n",
    "#         mx = int(stat['size_max'].ix['99%'])\n",
    "#         plt.xlim(mn,mx)\n",
    "#         plt.title('Relative Supply Distributions: Size')\n",
    "        \n",
    "#         ax = plt.subplot(1,3,3)\n",
    "        \n",
    "        \n",
    "        \n",
    "#         DF = pd.DataFrame(data=prices,columns=['price'])\n",
    "#         DF['price'].plot(kind='kde',linewidth=2,color='green',label='Filled Data')\n",
    "        \n",
    "#         mn = int(stat['price_min'].ix['2%'])\n",
    "#         mx = int(stat['price_max'].ix['99.5%'])\n",
    "#         plt.xlim(mn,mx)\n",
    "#         plt.title('Relative Supply Distributions: Price')\n",
    "        \n",
    "        path = '/Users/SVM/Dropbox/Applications/Crawlers/images/'\n",
    "        filename = path + 'ZIP_sensitivity_{}_Min_Rooms_{}_Max_Rooms_{}_Min_Size_{}_Max_Size_{}_Min_Rent_{}_Max_Rent_{}_.png'.format(Area,Min_Rooms,Max_Rooms,Min_Size,Max_Size,Min_Rent,Max_Rent)\n",
    "        fig.savefig(filename, dpi=200)\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        print \"\\n**************************************************************\"\n",
    "        print 'Based on your query there are not enough Supply for this area with the zip code {}.'.format(Area)\n",
    "        return\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************************************\n",
      "Number of unique Supply Ads: 188.\n",
      "**************************************************************\n",
      "Number of unique Supply Ads based on your query: 50.\n"
     ]
    },
    {
     "data": {
      "image/png": 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1ZMmSWhtdXpKXJKl6mflIU/EA8BTw6uJcaGtm/k5/IpM0rMo0uh4GXg48CdwIHAtMJ6O9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16330b850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(hist1d_Supply_query,Area=(8001,8008,1),Min_Rooms=(1,6,1),Max_Rooms=(10,11,1),Min_Size=(10,100,10),Max_Size=(400,450,10),Min_Rent=(100,200,100),Max_Rent=(1000,6000,100),Supply_Threshold=(0,1,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# def histsupply(Area,generator):\n",
    "#     generator(Area)\n",
    "    \n",
    "# interact(hist1d_Supply, Area=(8001,8009,1),\n",
    "#         generator={'Supply':hist1d_Supply\n",
    "#                    });"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************************************\n",
      "Number of unique Supply Ads: 140.\n"
     ]
    },
    {
     "data": {
      "image/png": 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ak2oCzQnnxTivSz6N2coR1KPAJkE3xpg4E/bzOLcC8Ch3scHtP58oSjX+xn0A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\nSimX/D9uKgcn6lNeugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x162df8250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(hist1d_Supply, Area=(8001,8009,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# interact(hist1d_Supply, Area=(8001,8009,1),\n",
    "#         generator={'Supply':hist1d_Supply\n",
    "#                    });"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4172\n"
     ]
    }
   ],
   "source": [
    "pathNY = '/Users/SVM/Dropbox/Applications/Crawlers/swiss-maps/topo/ch-plz.json'\n",
    "import fiona\n",
    "from shapely.geometry import MultiPoint\n",
    "CMAP = plt.get_cmap('RdYlBu_r')\n",
    "properties = []\n",
    "geometries = []\n",
    "zipids = []\n",
    "with fiona.open(pathNY) as f:\n",
    "    crs = f.crs\n",
    "    i = 0\n",
    "    for rec in f:\n",
    "        properties.append(rec['properties'].values())\n",
    "        zipids.append(int(rec['properties']['id']))\n",
    "        geometries.append(rec['geometry'])\n",
    "\n",
    "        i+=1\n",
    "print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11f73e790>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YwGXWyvMSEjgsFxICVK/Onm2LFuzZ+tKK5uhotv34caBGjbSPV6nCRXwbNvBK\n9fhxa/cvjYpifRlfyXTxJF3J6mGPPgq88QbzapOq2LlDVBQns1asAGbOZPBOKSl3N+krNpa5uz/8\nwD/WSpWABx/kV/362tv3hI8/ZjbH4MEcwrj3Xt7foAFL2A4YYGnznDZ/PgPmli3O7V0QF2ft+PeZ\nM8CFC9qRATTAe9zs2RyPLFHCfefcto2XoK1aAfv3p3/upNzdAgWS7+valV8JCcx++OEHoHt3jll2\n787AXyi9VQ3KJYcPA0uWcBhm3TqgWrWbHz961LdS7YoUAQoXvnloJjOXL1u7Q1mNGlwx3r07P2yC\ngtgZio8HPvgAuOce69rmbTpE42FffMG60TntIUdF8RL41Vf5i/v+++wdZueDI08eFj8bN455+XPn\nAp9+ygkzlTN79nBoYMsWDsWkDu4Ar66crVqYG3TpwgDftCmX52fl/Hnr04E7dAD+/W9gyBDg7ruB\n0qU5hNmypX/17HVHJw975x1OsJYpw/oWzk6siXCxyPr1wM8/MxDXr89f0NdfB265xX1tXLkS+O9/\nWbdD5UyDBsC5c8wTz6iC4ZkzHOqIifHc3Iy7JW1W06YNf/8yM2wYx+0nTPBO2zITH8+r2YAA7qda\nuTKLjy1fbnXLssdTpQpUNr37LnDkCIc+bruNRYuyEh3Nsftx4xgwvv6aY/jbtvE+dwZ3gJsd+MLu\nNL7g448ZDEeOzPiYggWBokUZcHxF4cL8KlMm62NDQnglkxvkzcvgDrDtK1fyathfaID3AmOYZfD+\n++w9rFjBnkV6zp3jhGliInvUL77IS2NP9vS2bgUaNvTc+f1Ju3YscJXZMMDffzOV0J3zMt5QqhQ7\nGlmpUMHzwyAHD7Lee0KCc20S4RxTjx7MaPIXGuC96LHHuBrw/vuZO5za/v1As2YsJzxnjncmPP/8\nEzhxgultKmfi45nrPn8+/48zEhjIwFS4sPfa5g6nTjk30RoWxk7Nrl3ub0NiIjOQ6tVjVlpgID94\nsnqtdes4Z7VsWXJpBX+gAd7LHn+ci59S50Fv2AC0b89efmiod9IWFy3ihOCQIdbmLdvB7t1MDezU\nif+/gwZlfGzlypzQ/ucf77XPHQICOH+QlYEDOcEZEsKg6g5ff82OT5487AhduQKUL88NdAYP5t9N\nQkLGz9+3j/n6LVv6WaaYiHj9iy/rn+LiRMqWFQkLu/n+Zs1Evv/eu2154QWRd9/17mvaVWioSKNG\nIv/8k/Wntjb3AAAUtklEQVSxx47xdyAmxvPtcqcNG9juDRucO37jRpEKFUSuXHH+Nc6cEenXT2TT\nJv4cHi4ycqRIlSoiP/wgsmWLSHT0zc+Jjhbp0EGkYkWRX39Ne86ffxYpXFhk8WLn25FbOWKn07FW\ne/Be1rs3F2EEBSXft2MHx95TFnHyhkceYX0cTY/MvvBw/juGhgJ9+nAzl6zky8fJP1/JoEkSFMQe\ns7NlCFq3ZubX+vXOv8bTTzMV88EHuUCwShUmKaxeDTzwAHvgBQve/JyCBTmvJcLNOFLbsIEbanfr\n5nw77EIXOnnRpUvAwoXAr7/ePATz6aecTPV2bZIWLZgfXK2a7jCVXePHszTt7t3ct9MZO3f63vh7\nklOnXCvD27Ejf+e7d8/62IQELgKbMIHDQR9+yOQEZ/5dd+/m0EvHjmkfi47mB6pfcqW7764v+OkQ\nzcsvizz33M33nT0rEhgocvmyNW2KihIBRB58kLeV865d45DF5s3OP+eXX/jvvXat59rlKZcvi9x5\np8h33zn/nPBwkTJlRPbvz/rYs2f5b7Nnj+tt+/prkVq1RA4fTvvYsWMipUqlHdrxRdAhmtxr1ixu\n+pHSiRO8tBw7lsuqva1IEU72GQO8+ab3X9+XxMZy0VlMDHuyQ4cy5c6VCeqlS4Fnn+WEuq8JC2MW\niytDHSVKcPenFi2YWZTeCt6ICA7LlC/PonnZSTB44gkO5Xz6adrHqlblOo8mTZxbiWsnGuC95PRp\njr2nLjjWogW3PFu+nAuOrFCuHPDWWyyrMGSIjsmnJzqa48IPPcRx4fr1uWBm9uysn5vkwgUufBs4\n0HPt9KR9+xgoXc1C6dePgTcqinVg7rsPGDGCQzKffcY0x+rVgeHDOTSTnTo9AQFcL5JetUuAc013\n3cX5kmvXXD+/r/LXkSmvGz6cNbPTWwkYF8dedNGi3m9XkqZNWSCrVy/2ogYPtq4tuY0IJ/wCAnjF\ndewY/x+dWdWZUtmynKTcs4e9SV+yZw+D8dat2Xv+gAH82rGDH3Qffsi1F/Xrs4Nz7BgnSr/4Ivsp\nwrfdxnP9+iuDeUrGMGWzaVO+xiuvZO81fI4r4znu+oIfjsE3aiTy0kvpP7ZokUj16iKJid5tU3oG\nDBAZPdrqVuQeiYkiw4aJ1K0rcuJEzs+3fbtI5cq+Nx7curXItGnuO19iosilSyLx8e475/nzIoMH\nM12yb9/00zOnThW57z73vaa3Qcfgc6dPP+VXZGTaxx54gKlzc+dyc+58+ayrU3LoEOvfKJo7l4ts\n1q0D7rgj5+e75x7Oe6xalfNzecv581xc1Lev+85pDIdm3Jk5VrYsM3COHmXWTOr5LoDDbEeOsPCf\nP9AA70EirCoYFsYxxurVmSKXmjEsXdCnD/Dkk1zy/tprXm8uJk3i5a0vTgB6SokSHFJ48EH3jN3+\n9BN/J5zZOCO32L2bQxsp9xbIzQoXZsLAunVpH0ta7ZpeR8uONMB7QEICNxbo3p2bED/8MGfyq1fn\nQo30tGrFnuKhQ3yueLma8tmzXKyzaZPv/CF7Q+fOHHffvp37qeZU8+acj+nc2fv/x9m1Z4/7K5h6\n2pEjaa+44uOZydO7t/9UT9V68G62cyd74tHRDJrHjrFA0+LF3Dghq9WLV6+yPszChew1eUtQEDdG\nyA01vHOjSZN4VTVvXuaFxJzVsiWH4Z54govccmuxtyef5LDhyJHc28BX5M8P9OzJ6p7LljG75vx5\n4PffuRmLr25R6Wo9eJ1kdZO4OJHevblQ4803OYnkyqRpYiJrZdSoIfLKK55rZ3oOHhQpXZoLTVTG\npkzhZLk7JCaK7Ngh8thjrJPy9tvuOa87RUaKFC3KyVBfs2WLSIECrGHz/fcib70lMmKEyMWLVrcs\nZ+DiJKv24N1k/XrudzpjBmtOOysujj2jMWM4nJNUs9qb+vRhmeKhQ737ur4mOhq49VbW6a9Vy33n\n/e039jRPn849PcuEBL7HkBDg88+tbk32XLzI1FYr94d1N93RySLz53MSzpX85qgo1s7Yt481qnfu\n9H5wB9juihW9/7q+plAhlnueO9e9561RgxODkye797w5sXcvs7l8NbgDXKdgp+CeHbrQyU0++IDp\nb0l7cmY1URkTwxV+Zctycw9vFxpLqVEjZnf07m1dG3xF796cS3nmGS6tdwdjOEfTtCkLv4WEuOe8\nOXH8OFC7ttWtUDmlPXg3KVKEe3GGhztXue7wYa4K/Oora4O7CPd69adtzHIif35e+ru7nEPt2pzg\n/uQT957XVWfOMMXwnXd0Exg70DF4N4mNZa99xgz2zDNz8SLwf//HlMjNm73TvozMmsUMiT17+CGl\nMjdgAIezRo1y/7kjI5l3/8ILLD7n7XTVyEim9BYuzEVNPXt69/VV1nQM3iILFnCDgwYNuJ1YUv2S\nkBDmPo8cycD+0UfJW7YtXmx1q1kDJyhIg7uzypbNfGu4nChWjGl8ixYBo0d75jUyIsIkgYoV+aGv\nwd0edAzeTVat4iKhBg24w8yNG8nlB2Jj2eMbP55DOM5ugOANbdpwgdNLL7GqZPnyzDxQN4uNBb78\nkgXZhg3z3OtUq8aKo95caSnCKpcnT3L1p5VDhsq9dIjGDZKGZ265hTU7ypThxGmtWml3oxHJPalw\nAFf3PfEEe20AA379+nwPPXpw0ZXiJtJbtgCPPspyv54Mgn//DdSsyUVV5coBjRvf/HjSn447fo9O\nnmSV0+PHmcnVqVPOz6k8R4doLJA/P7fjO3uWl/DGsO50eluN5abgDnBCeOZMpkpeucI/9vLlWa97\n7VqrW5c7REZyIvqpp7hnqKd7uJUqcQ/RceO4pD5lWuahQ0C9eu6ZAzhxgquXg4I46a/B3X60B6/S\nEGEQmTiR9cv90ebNLBK3ZAkXIt1/P5fsp97w2dP27WPdmurVGfTHj+emI59+ys2ka9XiRtPZWXh1\n7Ro/TI4c8b1aM/5Ke/AqxzZs4KrN1q2tbok1jh3jez97lusb/vyTQ27eDu4A53T++gt49VXu+HX9\nOouVjRsHtG3LSdHmzbkK2dXJ3yJFWCVzzhzPtF1ZT3vwKo2ICKB0aRZnSr3FoD+YMYN7p86bZ3VL\nMnf1KnD5MhAYyPmSEiU4l+JKRtTkydwucskSz7VTuY/24FWO7d7NydfixXn5HhVldYu8a9483xiP\nLlGC+8OWLMksroIFmUPvii+/BJ57zjPtU9bTAK/SmDiRG5Bs2sS9LZ95xuoWeVe1atzkw5fkz8+a\nNmXLZn1seDjLADdqxLIazZt7vn3KGhrgVRr79jHAP/wwc75nzwamTrW6Vd4THMx5CF+ybh2HW/r3\nz/y4334DKlRgWu9nnzGTxh+H4fyF0wHeGDPUGLPJcbuDMWadMeYnY8zdqY4raoxZYozZZIzJYtG+\nyo1mzeKq2+XLgffe48rKceOsbpV3xMQAX3wBJCZa3RLXfPABa9lktW5h2jSmRU6aBLRokfUGNMq3\nORXgjTH5ATQAIMaYggCeAdBBRNqJyJ5Uhz8FYA6AIACDjDG6WtbHNGvGnl1S6eNu3Zgj//rr1rbL\nG7ZvBw4eBFassLolztu2Dfj5Z+f20j18mIu1lH9wtgc/EMB0x+0WABIBrDTGfGOMKZTq2OYA1jjS\nZPYCcOPWCMoKAQHAgQPA9OlM2bOzb7/lQi9fWa5/+DA/gGfP5qrXrAwezNIUmsTmH7IM8I4eeBsR\nCQNgAJQDcAuAzgC2AXg21VMCAUQ4bkc4flY+rnRp9uxffZXL2+0gPp6LfSIiuJvS/Pnc+Pzll61u\nmfOGDGHAdnaf2A4duMbhzBmPNkvlEs4Mn/QDMDvFzxEANouIGGN+AvBqquPDARQHcNHxPTy9k4aG\nhv7vdnBwMIL9dcmkD5k8mdv73X8/hzG87fJlloS44w4WcsuJzZuTF3IVLMiSDdHRXCnqK0MYiYms\nj/P00wzYzuzKdfo0J1hPnOAqVpW7hYWFISwsLPsnyGrTVgBjAKxwfF0C8H8AFjoeexjAsFTHvwyg\nD4A8AMIA5E3nnO7bhVZ51ZUrIkWKiGzY4N3XXbpUxBiRfPlERo1Kvj82VuTAAdc2OL96VaRlS5Gp\nU0USEnifK8/PLWJjRUJCRDp0EClZUmTQIJEbNzI+futWkapVRV56SSQmxnvtVO4DT266bYzZKCJB\nxpiXAXQHcA3AIyISboyZICJDjDHFwB5/SQBTRGRGOucRV15X5R67dnFrubAwVp70lhEjkssn9O/P\nVMbff2dZ5suXuRGHs5k+7drx+6pVOb8SyC2SCsXFxnKoKb3smMBArn5NSNCS0L7K1ZWsWqpAueTk\nSS6Qefllbu3myQC5ahU3oUhIYDGtJk040btlCyd7q1fnOHqNGhyemD8fuO++rNt0++3cbKVhQ8+1\n3QpxcUCvXlyk1bcv8OKLNz++cCHw7LMcs3/+eUuaqHJIA7zyuEOHmJI3a5ZzqXnZsWgRg1DXrvxA\n2b+fddhT10ZP8s03rLDYqBEXaWVUd//cOS70OXWKO2vZTUwMMGYMC5Pt2JH28Vmz+OGWsgSx8h2u\nBnjNUVcuq1OHvWBPXeZv3szJzk6dGKyd0b8/0xuHDweOHmWvvn17Bv6Uk4nbtgH33GPP4A5wArV3\nb6ZNpqdwYWDvXu+2SVlHR+JUtjRrBqxf7/rzTp9mAbOMbNvGErb167s+jFC9OjcP79yZ5XPbtGGP\n/uOPOe7+0Uccs9+xg9/tqnx5XqkcOpT2sQ4duHtTnTrcR1jZnCszsu76gmbR+LzffhMpXZrZLc5K\nSBCpUkUEEGnaVOTZZ0UmTGAGy59/iowfL1Kxosh332W/XatWiYwcKXLtGn+eNk0kTx6+Tu/efO32\n7X0za8YV334rcuutzHpK7Z9/RNat47/1xx97v20q++DJLBp30TF4e5g5k5Unt21zbivCOXOA0aNZ\nDmD0aPaiP/qIwyv79rHHOW2a+/PQU47Hx8Ux5z23bZ3oCT16AF26sHJkerZvZz2aXbsynttQuYvW\ng1dec//9LEqWWaXJ7dtZb7xdO+5TO3YsN6QYNYppjSdOMKWvalWm8HlikVHKYJ4vn38Ed4CZNN99\nl/HjzZsDxYqxJryyJ+3BqxxZvJgrKXftAm69lUWvVq1iGuXSpcBjjzE/O39+1kzp2NHqFvuP69eZ\nPvr77+nXqQkL4//PgQNaMthXaJqk8rpu3ZiZUrcuUxkvXwYGDQK++orDBDqZZ52ePZmN9NRTN98f\nFcX9Xj/5BPjXv6xpm3Kdpkkqr3v6aearlygBLFvG2i5duvCxoUOtbZs/+/NPblwyfHjaxz7/nAvH\nNLjbm/bgVY6JAFu3ArfdxmEagBOoJ08yH11Zo3dv/vt37Ai0apW8bkEEqF2bY+/33mttG5VrdIhG\nKYVr17g/qzEcf69YkVU4p07lQqd+/bh9n79MONuFZtEopRAQwP109+4F/vgjeSJ86lRu79e/vwZ3\nf6A9eKX8xLx5HHsvW5YbmxQrZnWLlKt0iEYppWxKh2iUUkoB0ACvlFK2pQFeKaVsSgO8UkrZlAZ4\npZSyKQ3wSillUxrglVLKpjTAK6WUTWmAV0opm9IAr5RSNqUBXimlbEoDvFJK2ZQGeKWUsikN8Eop\nZVMa4JVSyqY0wCullE1pgFdKKZvSAK+UUjalAV4ppWxKA7xSStmUBnillLIpDfBKKWVTTgd4Y8xQ\nY8xGx+1wY8xPjq/AVMf1N8YccTw2xt0NVkop5RynArwxJj+ABinuOiAi7Rxf4ek85QPHY8Pc0kof\nExYWZnUTPMrO78/O7w3Q9+dvnO3BDwQwPcXPdxljNhhj3svg+KHGmDBjTLsctc5H2f2XzM7vz87v\nDdD352+yDPDGmLwA2ohIGADjuLuaiLQBEGiM6ZLqKYtEpB6AngDGGmMMlFJKeZ0zPfh+AGanvCPF\nsMxiAHVTPRbh+H4RwO8Ayue8mUoppVxlRCTzAzhRmjT+3gzACACfiUiiMeY/APaLyLwUxxcTkUhj\nTCEAmwA0E5GEVOfM/EWVUkqlS0ScHhXJ68TJ/jdR6sii2QBgpzEmEsAJAO84HpsgIkPA8ffO4HDO\ne6mDu6sNVEoplT1Z9uCVUkr5Jl3opJRSNuXVAG+Mud0Yc86xCGqlN1/bW4wx/Ywxax3vsYLV7XEn\nY0wnY8x6x9cZY0w3q9vkLsaYQsaYZY73tsgYk8/qNrmTMSaPMWaOMWadnRYgGmMqGGN2G2OuG2MC\nHPe9ZozZZIz51hiTx+o25kTq92eMyWuM2WqMiTDGVM3q+Vb04Fc7FkF1tuC1PcoYUxFMKe3geI9n\nrW6TO4nIKhFpKyJtAfwFYK3VbXKjzgC2O97bTsfPdtIdwF4RaQ+gkDGmntUNcpNLANoB2A4Axpiy\nAIJFpDWA/QAetLBt7nDT+xOReAAPAJjvzJOtCPDtHIukXrbgtT2tE4A8jh78BLuuATDGVAHwj4hc\nt7otbnQMQBHH7UDwD8tOqoIBDwD2AWhpYVvcRkRiReRqiruaAAhz3F4HoIXXG+VGKd6fSXHfhZQ/\nZ8bbAf4MgOoA2gJob4ypm8XxvqY8gHwi0gFANPhJa0c9ACyyuhFudhRAS2PMQQCNRWSr1Q1ys98A\ntHHcbgt+iNlRIIAIx+2rsM/7zFY2jFcDvIjEiUi0iCQC+BGpFknZwFUwjRQAfgJwl4Vt8aSuAJZY\n3Qg36w9giYjUBbDcGPOY1Q1ys6Xg0MwaADcA/GNxezzlKoDijtvFAaRXK8tveHuStWiKH1uBl8V2\nshVAfcfthuA6AVsxxpQHECMiV6xui5sZAJcdty8CKGFhW9xORBJFZIiIhABIALDK6ja5WdKQxU4k\nX6l0gGPs2gYM0g7LZDlM4+0hmtbGmF3GmM0ATovITi+/vkeJyD4AN4wx68GxQKcmQnzMA2CJCruZ\nDaC34//uEQCzLG6PWxljKjoyhNYC2GqXBABHVskasGO1CsAdADYaYzaBK/B/sLB5OZbq/a00xjQ1\nxnwPIATAdGNM10yfrwudlFLKnnShk1JK2ZQGeKWUsikN8EopZVMa4JVSyqY0wCullE1pgFdKKZvS\nAK+UUjalAV4ppWzq/wEe/wFf+23wrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124f58a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pathSwissborder = '/Users/SVM/Dropbox/Applications/Crawlers/swiss-maps/switzerland.geojson'\n",
    "from shapely.geometry import MultiPoint\n",
    "CMAP = plt.get_cmap('RdYlBu_r')\n",
    "properties = []\n",
    "swissborder = []\n",
    "zipids = []\n",
    "with fiona.open(pathSwissborder) as f:\n",
    "    crs = f.crs\n",
    "    i = 0\n",
    "    for rec in f:\n",
    "        swissborder.append(rec['geometry'])\n",
    "\n",
    "        i+=1\n",
    "print i\n",
    "# for g in swissborder[0]['coordinates'][:]:\n",
    "ar = np.asarray(swissborder[0]['coordinates'][0])\n",
    "plt.plot(ar[:,0],ar[:,1],'-')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(swissborder[0]['coordinates'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def spatial_query_supply(Min_Rooms,Max_Rooms,Min_Size,Max_Size,Min_Rent,Max_Rent,Supply_Threshold,whattoplot='percentofcoverage'):\n",
    "    \n",
    "    \n",
    "#     official_codes = pd.read_csv('/Users/SVM/Dropbox/Applications/realmatch360/Data/CH_Zip.csv')\n",
    "#     ind_canton = official_codes['admin code1']==canton_abrv\n",
    "#     zip_canton = official_codes.ix[ind_canton]['zip'].values[:]\n",
    "    \n",
    "    cmapname=\"RdYlBu_r\"\n",
    "#     cmapname=\"B\"\n",
    "    import itertools\n",
    "    import numpy as np    \n",
    "    \n",
    "    if Min_Rooms >= Max_Rooms:\n",
    "            Max_Rooms = Min_Rooms\n",
    "    if Min_Size>=Max_Size:\n",
    "            Max_Size= Min_Size\n",
    "    if Min_Rent>=Max_Rent:\n",
    "            Max_Rent= Min_Rent\n",
    "            \n",
    "    def check_search(df):\n",
    "        \n",
    "        amn = df['Rooms'].values[:]>=Min_Rooms\n",
    "        amx = df['Rooms'].values[:]<=Max_Rooms\n",
    "        \n",
    "        \n",
    "        \n",
    "        bmn = df['Living space'].values[:]>=Min_Size\n",
    "        bmx = df['Living space'].values[:]<=Max_Size\n",
    "        \n",
    "        \n",
    "        cmn = df['Rent'].values[:]>=Min_Rent\n",
    "        cmx = df['Rent'].values[:]<=Max_Rent\n",
    "        \n",
    "        return (amn*bmn*cmn*amx*bmx*cmx).sum()\n",
    "    \n",
    "   \n",
    "    Complete_data_zip = listing.copy()\n",
    "    Complete_data_zip.index = Complete_data_zip['ZIP']\n",
    "    geo_info = []\n",
    "    total_demand = []\n",
    "    percentofchange = []\n",
    "    \n",
    "#     print Complete_data_zip.shape\n",
    "#     Complete_data_zip_specific = Complete_data_zip\n",
    "    \n",
    "    long_lat_zip_all = Complete_data_zip.groupby(by='ZIP')['ZIP','lng','lat'].first()\n",
    "    \n",
    "    zip_GB = Complete_data_zip.groupby(by='ZIP')\n",
    "    long_lat_zip_specific = zip_GB['ZIP','lng','lat'].first()\n",
    "    \n",
    "    ind_zip = zip_GB.size()>Supply_Threshold\n",
    "#     print zip_GB.size()\n",
    "#     print ind_zip\n",
    "    long_lat_zip_sel = long_lat_zip_specific.ix[ind_zip]\n",
    "    \n",
    "    total_specific_supply=zip_GB.size()[ind_zip]\n",
    "    total_supply = total_specific_supply.values[:]\n",
    "    \n",
    "#     total_interest_in_property0 = Complete_data_zip_specific.ix[ind_zip].groupby(by='zip').apply(check_search)\n",
    "    \n",
    "#     print zip_GB.size()\n",
    "#     print Complete_data_zip.ix[ind_zip]\n",
    "    \n",
    "    total_interest_in_property1 = Complete_data_zip.ix[ind_zip].groupby(by='ZIP').apply(check_search)\n",
    "#     print total_interest_in_property1/(total_specific_demand.values[:]).astype(float)\n",
    "\n",
    "#     percentofchange = 100*(total_interest_in_property1.values[:]-total_interest_in_property0.values[:])/(total_specific_demand.values[:]).astype(float)\n",
    "    \n",
    "    total_cases = total_interest_in_property1.values[:]\n",
    "    percentofcoverage = 100*total_cases/(total_specific_supply.values[:]).astype(float)\n",
    "    \n",
    "    \n",
    "    \n",
    "    #To Plot\n",
    "    fig = plt.figure(figsize=(12,8))\n",
    "    \n",
    "    if whattoplot=='total_cases':\n",
    "        \n",
    "    \n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "        swissborderarr = np.asarray(swissborder[0]['coordinates'][0])\n",
    "        plt.plot(swissborderarr[:,0],swissborderarr[:,1],'-k')\n",
    "#         sc = plt.scatter(long_lat_zip_all.lng,long_lat_zip_all.lat,c='None',s=40,marker='.',edgecolor='gray',linewidth=.3, cmap=cmapname ,alpha=.4)\n",
    "        \n",
    "        \n",
    "        eps = .004\n",
    "        X_mn= swissborderarr[:,0].min()*(1-eps)\n",
    "        Y_mn= swissborderarr[:,1].min()*(1-eps)\n",
    "        X_mx= swissborderarr[:,0].max()*(1+eps)\n",
    "        Y_mx= swissborderarr[:,1].max()*(1+eps)\n",
    "    \n",
    "    \n",
    "    \n",
    "        md = np.median(total_cases)\n",
    "        sd = np.std(total_cases)\n",
    "        mn = md-2*sd\n",
    "        mx= md+2.5*sd\n",
    "        mn = np.min(total_cases)\n",
    "#         total_cases_canton = total_interest_in_property1.ix[zip_canton].values[:]\n",
    "    #     sc = plt.scatter(long_lat_zip_sel.lng.ix[zip_canton],long_lat_zip_sel.lat.ix[zip_canton],c=total_cases_canton,s=20,vmin=mn,vmax=mx,marker='o',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "    \n",
    "        sc = plt.scatter(long_lat_zip_sel.lng,long_lat_zip_sel.lat,c=total_cases,s=40,vmin=mn,vmax=mx,marker='.',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "    \n",
    "\n",
    "        plt.xlim(X_mn,X_mx)\n",
    "        plt.ylim(Y_mn,Y_mx)\n",
    "    \n",
    "    \n",
    "        ticklabels = np.round(np.linspace(mn,mx,5),decimals=3).astype(int).astype(str)\n",
    "        ticklabels[-1]=\">\"+ticklabels[-1]\n",
    "    \n",
    "        cbar = plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "        cbar.ax.set_yticklabels(ticklabels)\n",
    "    \n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.title(\"Total number of available cases\")\n",
    "        plt.axis('off')\n",
    "    \n",
    "    \n",
    "#     ax = fig.add_subplot(2,2,2)\n",
    "#     md = np.median(total_cases)\n",
    "#     sd = np.std(total_cases)\n",
    "#     mn = md-2*sd\n",
    "#     mx= md+2.5*sd\n",
    "#     mn = np.min(total_cases)\n",
    "#     import fiona\n",
    "#     CMAP = plt.get_cmap('RdYlBu_r')\n",
    "    \n",
    "#     for i,z in enumerate(zipids):\n",
    "# #         if z in zip_canton:\n",
    "#         try:\n",
    "#             val = total_interest_in_property1.ix[z]/mx\n",
    "#         except:\n",
    "#             val=-1\n",
    "#         try:\n",
    "#             if val>=0:\n",
    "#                 plot_multipolygon(ax, shape(geometries[i]),fcolor=CMAP(val),ecolor='none')\n",
    "#             else:\n",
    "#                 plot_multipolygon(ax, shape(geometries[i]),fcolor='none',ecolor='gray')\n",
    "#         except:\n",
    "#             continue\n",
    "# #         else:\n",
    "# #             continue\n",
    "# #             try:\n",
    "# #                 plot_multipolygon(ax, shape(geometries[i]),fcolor='none',ecolor='gray')\n",
    "# #             except:\n",
    "# #                 continue\n",
    "\n",
    "    \n",
    "   \n",
    "    \n",
    "#     ticklabels = np.round(np.linspace(mn,mx,5),decimals=3).astype(int).astype(str)\n",
    "#     ticklabels[-1]=\">\"+ticklabels[-1]\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "#     cbar = plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "#     cbar.ax.set_yticklabels(ticklabels)\n",
    "    \n",
    "#     plt.xticks([])\n",
    "#     plt.yticks([])\n",
    "#     plt.title(\"Total number of available cases\")\n",
    "#     plt.axis('off')\n",
    "    if whattoplot=='percentofcoverage':\n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "#         swissborderarr = np.asarray(swissborder[0]['coordinates'][0])\n",
    "# #         plt.plot(swissborderarr[:,0],swissborderarr[:,1],'-k')\n",
    "        \n",
    "        \n",
    "#         eps = .004\n",
    "#         X_mn= swissborderarr[:,0].min()*(1-eps)\n",
    "#         Y_mn= swissborderarr[:,1].min()*(1-eps)\n",
    "#         X_mx= swissborderarr[:,0].max()*(1+eps)\n",
    "#         Y_mx= swissborderarr[:,1].max()*(1+eps)\n",
    "        \n",
    "# #         sc = plt.scatter(long_lat_zip_all.lng,long_lat_zip_all.lat,c='None',s=20,marker='.',edgecolor='gray',linewidth=.3, cmap=cmapname ,alpha=.4)\n",
    "#         mn = np.min(percentofcoverage)  \n",
    "#         mx = np.max(percentofcoverage)\n",
    "#     #     mn = 0\n",
    "#     #     mx =100\n",
    "# #         percentofcoverage_canton = 100*total_cases_canton/(total_specific_supply.ix[zip_canton].values[:]).astype(float)\n",
    "#     #     sc = plt.scatter(long_lat_zip_sel.lng.ix[zip_canton],long_lat_zip_sel.lat.ix[zip_canton],c=percentofcoverage_canton,s=20,vmin=mn,vmax=mx,marker='o',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "#         sc = plt.scatter(long_lat_zip_sel.lng,long_lat_zip_sel.lat,c=percentofcoverage,s=40,vmin=mn,vmax=mx,marker='.',edgecolor='None', cmap=cmapname ,alpha=1)\n",
    "#         plt.xlim(X_mn,X_mx)\n",
    "#         plt.ylim(Y_mn,Y_mx)\n",
    "#         if mn == mx:\n",
    "#             mx = mn + 6\n",
    "#         plt.colorbar(sc,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "#         plt.xticks([])\n",
    "#         plt.yticks([])\n",
    "#         plt.title(\"Percent of available cases based on the selected query\")\n",
    "#         plt.axis('off')\n",
    "#         plt.close()\n",
    "        \n",
    "        \n",
    "    #     ax = fig.add_subplot(2,2,4)\n",
    "        md = np.median(total_cases)\n",
    "        sd = np.std(total_cases)\n",
    "        mn = md-2*sd\n",
    "        mx= 1\n",
    "        mn = 0\n",
    "        import fiona\n",
    "        CMAP = plt.get_cmap('RdYlBu_r')\n",
    "    \n",
    "        for i,z in enumerate(zipids):\n",
    "#         if z in zip_canton:\n",
    "            try:\n",
    "                nomin  = total_interest_in_property1.ix[z]\n",
    "                denom = total_specific_supply.ix[z]\n",
    "                val = nomin/denom.astype(float)\n",
    "            except:\n",
    "                val=-1\n",
    "            try:\n",
    "                if val>=0:\n",
    "                    cax = plot_multipolygon(ax, shape(geometries[i]),fcolor=CMAP(val),ecolor='none')\n",
    "                else:\n",
    "                    cax = plot_multipolygon(ax, shape(geometries[i]),fcolor='gray',ecolor='none',alpha=.1)\n",
    "            except:\n",
    "                continue\n",
    "#         else:\n",
    "#             continue\n",
    "#             try:\n",
    "#                 plot_multipolygon(ax, shape(geometries[i]),fcolor='none',ecolor='gray')\n",
    "#             except:\n",
    "#                 continue\n",
    "            \n",
    "    \n",
    "        mn = 0\n",
    "        mx = 100\n",
    "    \n",
    "        ticklabels = np.round(np.linspace(mn,mx,5),decimals=3).astype(int).astype(str)\n",
    "#         cbar = plt.colorbar(cax,ticks=np.round(np.linspace(mn,mx,5),decimals=3).astype(int),shrink=0.6)\n",
    "#         cbar.ax.set_yticklabels(ticklabels)\n",
    "    \n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "#         plt.title(\"Percent of available cases based on your query\")\n",
    "        plt.axis('off')\n",
    "    \n",
    "\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    font = {'size'   : 12}\n",
    "    plt.rc('font', **font)\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    path = '/Users/SVM/Dropbox/Applications/Crawlers/images/'\n",
    "    filename = path + 'swiss_sensitivity_{}_Min_Rooms_{}_Max_Rooms_{}_Min_Size_{}_Max_Size_{}_Min_Rent_{}_Max_Rent_{}_.png'.format(whattoplot,Min_Rooms,Max_Rooms,Min_Size,Max_Size,Min_Rent,Max_Rent)\n",
    "    fig.savefig(filename, dpi=200)\n",
    "#     return total_cases.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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KQUiOaWImvZ4ZtKG1We0AMoV5Kz1f9nr9N6yv7YwFMAKACGD7gsw46UuaEELIOYMCKkII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/kubX24oXGmX77hYd8DPb2uEABcYxkuLbq5IlswiHMrG8xtnQAADGRvyOPhuIWEkeKTt1kO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rx5Ea47rfP9PsRADllOfp5jnFYy1//wyAt1eUH7F6llPJGebCoMcNABtuv2lJcnzKO4zk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\n60xEqogI9S9+ef+suI2bqv60e3fDcOTx+MMALYFzvo+IpnJFWWfvaHm6YfN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