step_2a_RiskToSQL-Josh.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from math import sin, cos, sqrt, atan2, radians\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats, integrate\n",
    "sns.set(color_codes=True)\n",
    "from sqlalchemy import create_engine\n",
    "conn = create_engine(\"sqlite:///volcano_app/volcano.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#erupt_df = pd.read_csv(\"clean_eruptions.csv\")\n",
    "#pop_df = pd.read_csv(\"pops.csv\")\n",
    "erupt_df = pd.read_sql(\"SELECT * FROM eruptions\", conn)\n",
    "# erupt_df = erupt_df.rename(columns = {\"volcano_name\": \"Volcano Name\", \"start_year\":\"Start Year\",\"latitude\":\"Latitude\", \"longitude\":\"Longitude\"})\n",
    "pop_df = pd.read_sql(\"SELECT * FROM worldcities\",conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "years = []\n",
    "lats = []\n",
    "longs = []\n",
    "for name in erupt_df[\"volcano_name\"].unique():\n",
    "    temp = erupt_df[erupt_df[\"volcano_name\"]==name]\n",
    "    x = temp[temp[\"start_year\"] == temp[\"start_year\"].max()].iloc[0][[\"start_year\", \"latitude\", \"longitude\"]]\n",
    "    year = x[0]\n",
    "    lat = x[1]\n",
    "    long = x[2]\n",
    "    \n",
    "    years.append(year)\n",
    "    lats.append(lat)\n",
    "    longs.append(long)\n",
    "comp_df = pd.DataFrame(data={\"start_year\":years, \"lat\":lats, \"lng\":longs })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>start_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-77.53</td>\n",
       "      <td>167.17</td>\n",
       "      <td>1972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-76.28</td>\n",
       "      <td>-112.08</td>\n",
       "      <td>-5550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-76.05</td>\n",
       "      <td>-136.00</td>\n",
       "      <td>-8350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-74.35</td>\n",
       "      <td>164.70</td>\n",
       "      <td>1892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-74.33</td>\n",
       "      <td>-99.42</td>\n",
       "      <td>-210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     lat     lng  start_year\n",
       "0 -77.53  167.17        1972\n",
       "1 -76.28 -112.08       -5550\n",
       "2 -76.05 -136.00       -8350\n",
       "3 -74.35  164.70        1892\n",
       "4 -74.33  -99.42        -210"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pop_df.head()\n",
    "pop_df = pop_df[[\"city\", \"lat\", \"lng\", \"pop\", \"country\"]]\n",
    "# pop_df = pop_df.rename(columns = {\"city\":\"City\", \"lat\":\"Latitude\", \"lng\":\"Longitude\", \"pop\":\"Population\", \"country\":\"Country\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def lat_long_dist(lat1,lon1,lat2,lon2):\n",
    "    R = 6373.0\n",
    "    dlon = lon2 - lon1\n",
    "    dlat = lat2 - lat1\n",
    "    a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n",
    "    c = 2 * atan2(sqrt(a), sqrt(1 - a))\n",
    "    distance = R * c\n",
    "    return distance   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "years = []\n",
    "dists = []\n",
    "for _, pop_row in pop_df.iterrows():\n",
    "    min_dist = np.inf\n",
    "    min_time = 0\n",
    "    for _, comp_row in comp_df.iterrows():\n",
    "        dist = lat_long_dist(pop_row[\"lat\"], pop_row[\"lng\"], comp_row[\"lat\"], comp_row[\"lng\"])\n",
    "        if dist < min_dist:\n",
    "            min_dist = dist\n",
    "            min_time = comp_row[\"start_year\"]\n",
    "    years.append(min_time)\n",
    "    dists.append(min_dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pop_df['time'] = years\n",
    "pop_df['time'] = 2020 - pop_df['time']\n",
    "pop_df[\"dist\"] = dists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7322 entries, 0 to 7321\n",
      "Data columns (total 7 columns):\n",
      "city       7322 non-null object\n",
      "lat        7322 non-null float64\n",
      "lng        7322 non-null float64\n",
      "pop        7322 non-null float64\n",
      "country    7322 non-null object\n",
      "time       7322 non-null float64\n",
      "dist       7322 non-null float64\n",
      "dtypes: float64(5), object(2)\n",
      "memory usage: 400.5+ KB\n"
     ]
    }
   ],
   "source": [
    "pop_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_df = pop_df[pop_df[\"pop\"] > 1000 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jbui/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "new_df[\"risk\"] = np.log(np.log( new_df[\"pop\"] + 2 )/( np.log(new_df[\"time\"] + 2)*np.square( new_df[\"dist\"] + 2 )))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jbui/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "min_thing = new_df[\"risk\"].min()\n",
    "max_thing = new_df[\"risk\"].max()\n",
    "new_df[\"risk\"] = new_df[\"risk\"].map(lambda x: (x-min_thing)*10/(max_thing-min_thing))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[\"risk\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a115b78d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.distplot(new_df[\"risk\"],bins=100, fit=stats.norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a115b78d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.distplot(new_df[\"risk\"],bins=100, fit=stats.gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.008462635085641157, pvalue=0.70613338779940471)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = stats.gamma.fit(new_df[\"risk\"])\n",
    "stats.kstest(new_df[\"risk\"], lambda x : stats.gamma.cdf(x, *params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.056261401023143698, pvalue=2.1124283959489008e-19)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = stats.norm.fit(new_df[\"risk\"])\n",
    "stats.kstest(new_df[\"risk\"], lambda x : stats.norm.cdf(x, *params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#We can use a KS test to determine the goodness of fit. But.... I don't really know how to use that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7.2834667647082458, -0.4423556277303623, 0.45627193334610117)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.gamma.fit(new_df[\"risk\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pdf for the gamma distribution is $f(x; \\alpha, \\beta) = \\frac{\\beta^{\\alpha}}{\\Gamma(\\alpha)}x^{\\alpha-1}e^{-\\beta x}$. If we write $x = \\log y$ and note that $dx = dy/y$, we get $f(y; \\alpha,\\beta) = \\frac{\\beta^{\\alpha}}{\\Gamma(\\alpha)} y^{-(\\beta + 1)} (\\log y)^{\\alpha - 1}  $."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_df.to_csv(\"finalized_data/risk_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6902 entries, 0 to 7321\n",
      "Data columns (total 8 columns):\n",
      "city       6902 non-null object\n",
      "lat        6902 non-null float64\n",
      "lng        6902 non-null float64\n",
      "pop        6902 non-null float64\n",
      "country    6902 non-null object\n",
      "time       6902 non-null float64\n",
      "dist       6902 non-null float64\n",
      "risk       6902 non-null float64\n",
      "dtypes: float64(6), object(2)\n",
      "memory usage: 485.3+ KB\n"
     ]
    }
   ],
   "source": [
    "new_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['berkeleydata', 'eruptions', 'risk', 'risk_stats', 'tempinfo', 'worldcities']\n"
     ]
    }
   ],
   "source": [
    "print(conn.table_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'city', 'lat', 'lng', 'pop', 'country', 'time', 'dist', 'risk'], dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "risk_df = pd.read_sql(\"SELECT * FROM risk\", conn)\n",
    "risk_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'gamma_fit', 'gaussian_fit', 'kde_fit', 'risk'], dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7.2834667647076516, -0.44235562773034021, 0.45627193334610117)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.gamma.fit(risk_df[\"risk\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'year', 'log_CO2_277_3', 'volcanic', 'temperature_C', 'fit'], dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berkeley_df = pd.read_sql(\"SELECT * FROM berkeleydata\", conn)\n",
    "berkeley_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def pdf(x):\n",
    "    alpha = 7.28\n",
    "    loc = -0.442\n",
    "    beta = 1/0.456\n",
    "    y = x  - loc\n",
    "    return beta**(alpha) * y**(alpha - 1)*np.exp(-beta * y) /gamma(alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEFCAYAAAAYKqc0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a112605f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-0.4,10,0.1)\n",
    "y = pdf(x)\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEFCAYAAAAYKqc0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nISl3IYTwQ1LuQgjhh6TchRDCD/1/SWolqb7uYJAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a895438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(risk_df[\"risk\"],bins=100, fit=stats.gamma)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD3CAYAAADmBxSSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108f12240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(risk_df[\"risk\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = len(risk_df[\"risk\"])\n",
    "h = n**(-1/5)\n",
    "def k(x): return np.exp(- x**2 / (2*h**2) )/(n*np.sqrt(2* np.pi * h**2))\n",
    "def pdf_k(x): return np.sum( k(x - risk_df[\"risk\"]) )\n",
    "#def pdf_k(x): return sum([ k(x - risk_df['risk'][j] ) for j in range(len(risk_df[\"risk\"])) ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD3CAYAAADmBxSSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt0VPW9///nXDK5zOQKk4QkJCTB4Q4hIF4woEVU7EVK\nOQL5mrNsvwtaq71YDsvWX0s5lAXoWfbbpX6Rar8LPSoaLR6ltVWLF4IIAikBEu6EXIHcSTKTZCaT\nvX9/BAaswEAykz2X92OtlEz27NnvT6a+8pnP3vvz0amqqiKEECKk6LUuQAghhO9JuAshRAiScBdC\niBAk4S6EECFIwl0IIUKQUesCLmpq6hzwvomJMbS1dfmwmsAnbQ594dZekDbfKKs19qrbQqLnbjQa\ntC5hyEmbQ1+4tRekzT59XW9PUBSFVatWcezYMUwmE2vWrCErK8uz/fXXX+edd95Bp9Px6KOPctdd\nd6GqKrNmzWLUqFEA5OXlsXz5cr80QAghxNd5Dfdt27bhcrkoLi6mrKyM9evX88ILLwDQ2trK5s2b\neffdd3E6nXzzm9/kzjvvpKamhgkTJrBx40a/N0AIIcTXeR2WKS0tpaCgAOjvgZeXl3u2JSUl8d57\n7xEREUFzczNxcXHodDoqKipoaGigqKiIpUuXUllZ6b8WCCGE+BqvPXe73Y7FYvE8NhgMuN1ujMb+\nXY1GI6+99hrPPfccRUVFAFitVpYtW8a8efPYt28fK1asYMuWLdc8TmJizKDGnq51YiFUSZtDX7i1\nF6TNvuI13C0WCw6Hw/NYURRPsF/00EMP8eCDD7J06VJ2797NlClTMBj6g3r69Ok0NDSgqio6ne6q\nxxnMGXKrNXZQV9sEI2lz6Au39oK0eSD7Xo3XYZn8/HxKSkoAKCsrw2azebZVVlby2GOPoaoqERER\nmEwm9Ho9zz//PK+88goAR48eJS0t7ZrBLoQQwre89tznzp3Lzp07Wbx4MaqqsnbtWjZt2kRmZiZz\n5sxh7NixLFq0CJ1OR0FBATNmzGDMmDGsWLGC7du3YzAYWLdu3VC0RQghxAW6QJnydzAfxeSj3CWu\n3j7KTjZTeaaDbqebmCgjqUkxZI+II8NqQa8P3k9Q4fY+h1t7Qdo8kH2vJmDuUBWDo6gqJQfO8M72\nSuzdvVd8TnSkgZy0eEanxzM6I57RafFEmsLvphEhwoGEewjodffx0l+PsO9oI9GRBr55WxaTc4cR\nF2Ois7uXM80OTta3c7KunYrTrVScbgXAaNAzYVQid988kvFZiXJeRIgQIuEe5HrdCn94+yBHqtuw\nZcTzwwcmkhgb6dmeAoxOj2fWlDQAOrtcnKrv4ETdeQ5WtnDgVP/XNJuV798/lpioCI1aIoTwJQn3\nIKaoKv/v/cMcqW5j6k3D+dEDE4jwcq9AbIyJvJuGk3fTcP7trtFUnevgzW0nKD3eROP5blYsmYol\nWgJeiGAXEhOHhau/7apmz5FGRmfEX1ewX8mo1DhWFE7lzrw0ahvtPL/lIH2K4odqhRBDScI9SB2t\nauXdHadJjI3kJwsmDSjYLzLo9Tx07ximj7FyvK6d/yk57cNKhRBakHAPQk5XH//1eimqqrLs2+OJ\njTEN+jX1Oh3fv38c1oQoPviyhpqG8LocTYhQI+EehLbuPE1jaxf33ZrJmMxEn71udKSRonvGoKgq\nr354jAC5BUIIMQAS7kGmrsnOR3trSU6K4Tszs33++hNzhjF9jJVTZzooPdbk89cXQgwNCfcg8+bH\nJ+hTVB5ZMJnICP/cgLRgdi56nY7/2VGJokjvXYhgJOEeRI5UtXK4qo0J2UlMH5fit+OkJsUwc1Iq\nZ1u62H34nN+OI4TwHwn3IKGqKu+U9C96smBWjt+P952Z2Rj0Ot7fVY0iY+9CBB0J9yBRUdXKqTMd\n5NusZI+I8/vxhsVHccv4FM62dHHwZIvfjyeE8C0J9yDx0Z5aAL55W5aXZ/rOfbdkAvC3L6uH7JhC\nCN+QcA8C9U12yk+3YhuZMCS99osyrBYm5w7jZF07J+rOD9lxhRCDJ+EeBLaV1gFw780jh/zY99/a\n/0nh77trhvzYQoiBk3APcE5XH18ebmBYXCRTRg8f8uPflBFPblocZSebqW92eN9BCBEQJNwD3D+P\nN9Hj6uO2iSM0WUVJp9Mx70Lv/QMZexciaEi4B7jPD50FYOakVM1qyLtpOCOGxbC7ooHWjh7N6hBC\nXD8J9wDW2tHD0eo2bsqIJyUxRrM69Dod992SSZ+i8tHeWs3qEEJcPwn3AFZ6rAkVuHW8/+5GvV63\nTUglMTaS7WVnrrpGqxAicHgNd0VRWLlyJYsWLaKoqIjq6q+Ou77++ut873vfY+HChXz66acA9PT0\n8JOf/ITCwkKWLl1Ka2urf6oPcf883oQOmGqzal0KRoOeudNH4uzt49N/1mldjhDCC6/hvm3bNlwu\nF8XFxSxfvpz169d7trW2trJ582befPNNXn75ZVatWoWqqrzxxhvYbDY2b97M/Pnz2bBhg18bEYo6\nulwcrztPTnocCZZI7zsMgdl5acREGtlWWoert0/rcoQQ1+A13EtLSykoKAAgLy+P8vJyz7akpCTe\ne+89IiIiaG5uJi4uDp1O95V9Zs2axa5du/xUfug6cKIZVYX8AOi1XxQdaeSu/HQ6u3rZcfCs1uUI\nIa7B6wLZdrsdi8XieWwwGHC73RiN/bsajUZee+01nnvuOYqKijz7xMbGAmA2m+ns9L6qT2JiDMZB\nLBVntcYOeN9AVFHdf0fo3beOwjrccsXnaNHmRfeO5R97a/lwTw0L5tgw+Wna4asJtffZm3BrL0ib\nfcVruFssFhyOSzevKIriCfaLHnroIR588EGWLl3K7t27v7KPw+EgLs77LfNtbV03WruH1RpLU1Po\nLAvX61YoO9HIiGExRKjqFdumZZu/MS2DD76s4e1/HOOeIbxrNtTeZ2/Crb0gbR7IvlfjdVgmPz+f\nkpISAMrKyrDZbJ5tlZWVPPbYY6iqSkREBCaTCb1eT35+Ptu3bwegpKSEadOmDajwcHWqvh1Xr8KE\nUUlal3JF99+aRZTJwPu7quhxubUuRwhxBV577nPnzmXnzp0sXrwYVVVZu3YtmzZtIjMzkzlz5jB2\n7FgWLVqETqejoKCAGTNmMGnSJJ544gmWLFlCREQEzzzzzFC0JWRUVPVfXTQhOzDD3RIdwX0zMnn3\n89P8Y18d3759lNYlCSH+hU4NkFWQB/NRLNQ+yq1+eS+1jXae+3kBUaYr//3Vus3dTjdPbNxFn6Ly\n1I9uwxId4fdjat3moRZu7QVp80D2vRq5iSnAdHa5qD7Xyej0+KsGeyCIjjTyzduy6Ha6+ftumXNG\niEAj4R5gjlS3oRK4QzKX+0Z+OomxkWwrraOt06l1OUKIy0i4B5jDVW1AcIR7hNHAA3dk0+tW+MvO\n01qXI4S4jIR7gDlee55Ik4HMlCtf2x5oZk5KJTUphpIDZ2loHfjlrEII35JwDyAdDhfnWrsYnR6P\nQR8cb41Br2fBrBwUVeV/dlRqXY4Q4oLgSJAwcXGdUtvIBI0ruTHTxljJSo1lz5FGahrC60oHIQKV\nhHsAOV7bDoAtI17jSm6MTqdj4excALZsl967EIFAwj2AHK87j9GgIyfN+3QNgWb8qETGZSVyqLKF\nU/XtWpcjRNiTcA8Q3U43NQ2djBoRR8QgJlDTik6n41sX7lSV1ZqE0J6Ee4A4Vd+OqoItI7jG2y83\nNjOBkckWSo810dIua60KoSUJ9wBx3HMyNbjG2y+n0+mYO30kiqrysazWJISmJNwDxPHadnTA6PTg\n7bkD3DI+BUt0BJ8fPIu7T9G6HCHCloR7AHD3KZw+20FGsoWYqMCdT+Z6RBj13DYhFXt3LwdPtWhd\njhBhS8I9ANQ22ul1K4xOD94hmcvNnJQKwM5DshSfEFqRcA8AlWc6AILyEsgryUyJJTPZwsFTLXQ4\nXFqXI0RYknAPAKfO9F8XnhsiPXeA2yeNoE9RKT3WqHUpQoQlCfcAUFnfgTnKSEpitNal+Mz0MVYA\n9h1r0rgSIcKThLvGOrpcNJ7vJjstDp1Op3U5PpMUF0X2iDiO1ZzH3t2rdTlChB0Jd41dHG/PTQud\nIZmLpo+xoqgq+49L712IoSbhrrHKi+PtIXIy9XLTLgzNlEq4CzHkJNw1dqq+v+eeHYLhnpwYQ4bV\nwuGqNpy9fVqXI0RY8XrHjKIorFq1imPHjmEymVizZg1ZWVme7S+//DLvv/8+ALNnz+axxx5DVVVm\nzZrFqFGjAMjLy2P58uX+aUEQUxSV02c7GDEsBnNUhNbl+MXk3GHUNdk5Wt3GlNHDtS5HiLDhNdy3\nbduGy+WiuLiYsrIy1q9fzwsvvABAbW0tW7du5e2330an01FYWMjdd99NdHQ0EyZMYOPGjX5vQDA7\n0+Kgx9UXMte3X8nk3GH8bXc1BytbJNyFGEJeh2VKS0spKCgA+nvg5eXlnm2pqan86U9/wmAwoNfr\ncbvdREZGUlFRQUNDA0VFRSxdupTKSlnA4UpC+WTqRbnpcURHGjl0qgVVVbUuR4iw4bXnbrfbsVgu\nLdZsMBhwu90YjUYiIiJISkpCVVWefvppxo8fT3Z2Ns3NzSxbtox58+axb98+VqxYwZYtW655nMTE\nGIyDmMfcao0d8L5aqW/pX1B6+sQRA6o/WNqcPzaZnQfO4FR1jEweXM3B0mZfCbf2grTZV7yGu8Vi\nweFweB4rioLReGk3p9PJk08+idls5re//S0AEydOxGDoD+rp06fT0NCAqqrXvI67ra1rwI2wWmNp\nagq+tTsPn24hMsJAtIEbrj+Y2jwmPZ6dB86wfV8N987IHPDrBFObfSHc2gvS5oHsezVeh2Xy8/Mp\nKSkBoKysDJvN5tmmqio//vGPGTNmDKtXr/YE+vPPP88rr7wCwNGjR0lLSwupG3R8odvp5kyTg+wR\nsRj0oX3R0qScJACZJVKIIeS15z537lx27tzJ4sWLUVWVtWvXsmnTJjIzM1EUhT179uByudixYwcA\nv/jFL1i2bBkrVqxg+/btGAwG1q1b5/eGBJvTZztQgZwQHm+/KN4SSWaKhRN153G6+og0Bd8ygkIE\nG6/hrtfrWb169Vd+lpub6/n+0KFDV9zvxRdfHGRpoS3UZoL0ZlLOMGoa7BytkUsihRgKoT0eEMBO\nn71w89KI8Aj3idn9QzPlla0aVyJEeJBw10jVuU7iLSYSYyO1LmVI5KbHE2UyUH5axt2FGAoS7hpo\ntztp63SSnRoevXYAo0HPuKxEGtq6aRzElVFCiOsj4a6B0+f6L3salRpe1/NOzBkGQPlpGZoRwt8k\n3DVQdWG8fVSYjLdfJOPuQgwdCXcNVIVpz92aEE1KUgxHatpw9ylalyNESJNwH2KqqlJ1rpNhcZHE\nmU1alzPkJmYn4XT1cbKuXetShAhpEu5DrK3TSYfDxagwOpl6uYt3qx6Sq2aE8CsJ9yF2+uyFIZkR\n4TUkc9GYkYkYDToqZNxdCL+ScB9iVefC82TqRZEmA7aRCdQ02mm3O7UuR4iQJeE+xML1ZOrlJmbL\nJZFC+JuE+xBSVZWqsx0kJ0SH7LJ612PihXF3CXch/EfCfQg1tffg6HGH7Xj7RenDzSTGRlJxuhVF\nkdWZhPAHCfchVO0ZkgnP8faLdDodE7KTsHf3Ut0QXgszCDFUJNyHUF2jHYCRyRYvzwx9l+5WlUsi\nhfAHCfchVNfUH+4ZVrPGlWhv/KgkdDo4JOPuQviFhPsQqm9yYImOCMs7U/+VJTqCnBFxVNZ30NXT\nq3U5QoQcCfch0uNy03i+mwyrWdaTvWBizjAUVeVwVZvWpQgRciTch0h9swOADKuMt1/kGXeXoRkh\nfE7CfYjUN10IdzmZ6pE9Ig5zlJHy0y2oqlwSKYQvSbgPkYtXyqTLyVQPvV7H+FFJtHY4OdsiqzMJ\n4UtGb09QFIVVq1Zx7NgxTCYTa9asISsry7P95Zdf5v333wdg9uzZPPbYY/T09LBixQpaWlowm808\n9dRTJCUl+a8VQeDilTLpwyXcLzcxJ4m9Rxspr2whTX43QviM1577tm3bcLlcFBcXs3z5ctavX+/Z\nVltby9atW3nzzTcpLi7m888/5+jRo7zxxhvYbDY2b97M/Pnz2bBhg18bEehUVaWuyYE1IYook9e/\np2FF5pkRwj+8hntpaSkFBQUA5OXlUV5e7tmWmprKn/70JwwGA3q9HrfbTWRk5Ff2mTVrFrt27fJT\n+cGhw+HC3t0rJ1OvIDE2kgyrmWO153H19mldjhAhw2s30m63Y7FcCiWDwYDb7cZoNBIREUFSUhKq\nqvL0008zfvx4srOzsdvtxMb2z59iNpvp7PR+i3liYgxGo2HADbFaA3e+lrrWbgBso5J8Wmcgt/lG\n3DxhBP/z2UnOdTiZNjblms8NlTZfr3BrL0ibfcVruFssFhwOh+exoigYjZd2czqdPPnkk5jNZn77\n299+bR+Hw0FcnPe5VNraBn5CzWqNpakpcOcoKT/RBECS2eSzOgO9zTciN7W/87Bzfz2Zw2Ku+rxQ\navP1CLf2grR5IPtejddhmfz8fEpKSgAoKyvDZrN5tqmqyo9//GPGjBnD6tWrMRgMnn22b98OQElJ\nCdOmTRtQ4aGiXqYduKabMhIwReg5eKpZLokUwke89tznzp3Lzp07Wbx4MaqqsnbtWjZt2kRmZiaK\norBnzx5cLhc7duwA4Be/+AVLlizhiSeeYMmSJURERPDMM8/4vSGBrK7JgdGgJzkxWutSAlKEUc+k\nnGGUHmuivskh9wII4QNew12v17N69eqv/Cw3N9fz/aFDh66437PPPjvI0kKDoqicaXGQNjwGg15u\nK7iam8cmU3qsib1HGyXchfABSRs/a2jrotetyJUyXkzOHUaEUc++Y40yNCOED0i4+5ln2gEJ92uK\nMhmZnDOMsy1dnnl4hBADJ+HuZ5453JPlZKo308cmA7DvaKPGlQgR/CTc/axOeu7Xbcro/qGZvUdl\naEaIwZJw97O6JjuW6AjiZYEOr6JMRibn9g/N1DTYtS5HiKAm4e5HTlcfTW2yQMeNmDlxBACfHzqr\ncSVCBDcJdz+qb3agAukyJHPdJuYkERcTwe6Kc/S6Fa3LESJoSbj7kSyIfeOMBj23TUzF0ePmwMlm\nrcsRImhJuPvRpXCXnvuNmDlJhmaEGCwJdz+6eI27LEJxYzKsFrJHxHHoVAuN57u1LkeIoCTh7kd1\nTXaGx0cRHSkLdNyou6dnoAKflNZpXYoQQUnC3U/aHS46u3oZKfOkDMjNY5OJt5jYcfAM3U631uUI\nEXQk3P3Es2aqjLcPiNGg566p6XQ7+2TsXYgBkHD3k7pGuVJmsO6cmo4pQs8HX9bQ65Yl+IS4ERLu\nfiJXygxeXIyJOfkZtHU62V52RutyhAgqEu5+cnGBjpQkWaBjMO67JZPICAPv76rGKQtoC3HdJNz9\nQFFUzjQ7SBsmC3QMVmyMibunZ9DucPHu9pNalyNE0JDk8YPG8930uhU5meoj99+aRZzZxNsfn6C1\no0frcoQIChLufuA5mSpzuPtEdKSRhbNzcbr6ePuzU1qXI0RQkHD3AzmZ6nu3T0rFlpnAl4cbOFbT\npnU5QgQ8CXc/kKX1fE+v0/HD705GB7z20XHcfTJjpBDX4jXcFUVh5cqVLFq0iKKiIqqrq7/2nNbW\nVu655x6cTicAqqpSUFBAUVERRUVFPPPMM76vPIDVNtkxRxlJsMgCHb5ky0xk9tR06psdbNsn0xII\ncS1eJz3Ztm0bLpeL4uJiysrKWL9+PS+88IJn+44dO3jmmWdobr40PWtNTQ0TJkxg48aN/qk6gF1c\noMM2MkEW6PCDBbNy2He0kfc+P82McckkxUVpXZIQAclrz720tJSCggIA8vLyKC8v/+oL6PVs2rSJ\nhIQEz88qKipoaGigqKiIpUuXUllZ6eOyA9eZlv4FOmRIxj8s0RH82125OHv7ePPjE1qXI0TA8tpz\nt9vtWCyXgspgMOB2uzEa+3edOXPm1/axWq0sW7aMefPmsW/fPlasWMGWLVuueZzExBiMRsON1n/Z\nMWMHvK8vlVW2AjA2Z5jfawqUNg8lqzWW+XfZ2H24kX3Hmqht6SZ/bLLWZflNuL7H4cYfbfYa7haL\nBYfD4XmsKIon2K9m4sSJGAz9QT19+nQaGhpQVfWawxRtbV3XW/PXWK2xNDV1Dnh/XzpS2QJAfLTR\nrzUFUpuHyuVtXvyN0fznpr383z+X8bv/PYOIQXQMAlW4v8fhYjBtvtYfBa/DMvn5+ZSUlABQVlaG\nzWbzesDnn3+eV155BYCjR4+SlpYWNuPPntkgZYEOvxqZbGHOtAwa27r5+5c1WpcjRMDx2nOfO3cu\nO3fuZPHixaiqytq1a9m0aROZmZnMmTPnivssW7aMFStWsH37dgwGA+vWrfN54YGqXhboGDLzC7LZ\nc7SB93dVc+uEVJITZB4fIS7ymkB6vZ7Vq1d/5We5ublfe94nn3zi+T4+Pp4XX3zRB+UFlw6Hi46u\nXvJGx2tdSliIjjSy+Bs38cetFWz+x3F+tnBy2HxCFMIbuYnJhzx3psq0A0NmxrhkxmUlcvBUC+Wn\nW7UuR4iAIeHuQ5cW6JDLIIeKTqdj0TdGA/DujtOoqqpxRUIEBgl3H6q7MO2AzAY5tDJTYplms3L6\nbAeHKqX3LgRIuPtUbZMdo0FPqizQMeS+c0c2AO99Xim9dyGQcPeZPkXhTLOD9OFmWaBDAyOTLUwf\nY+X02U4qqqT3LoSkkI80tvUv0CEnU7Vz/21ZAHy0p1bjSoTQnoS7j9ReOJk6UsbbNTMqNY4xIxMo\nP93quXJJiHAl4e4jnnBPlnDX0j0zRgLw0V7pvYvwJuHuIxcvg0yXcNfUlNHDSUmMZnfFOdrtTq3L\nEUIzEu4+UttkJ95iIi5GFujQkl6nY+7NI3H3qZQcPKt1OUJoRsLdBxw9vbR2OGVIJkDcNiGVyAgD\nJWVnUBS5LFKEJwl3H6iTk6kBJTrSyC3jU2jp6JEpCUTYknD3gYsnUzOk5x4w7pyaBsBn++s1rkQI\nbUi4+8DFy+5kWCZwjEqNY1RqLAdONdPa0aN1OUIMOQl3H6htdGDQ60hNitG6FHGZO6emo6pQcuCM\n1qUIMeQk3AepT1Goa7KTPtyM0SC/zkAyY1wyUSYDOw6elROrIuxIGg3SmeYuet0Ko0aE36K+gS7K\nZOTW8Sm0dTo5dGFtWyHChYT7IFWd7QAgKzVO40rElczOSwdkaEaEHwn3Qapq6F+1fFSq9NwDUVZq\nLFkpsRw42cJ5uWNVhBEJ90GqOtuJQa+T1ZcC2Ky8NBRV5XO5Y1WEEQn3QXD3KdQ22km3mokwyq8y\nUN06PgVThJ6SA2dQZCEPESa8JpKiKKxcuZJFixZRVFREdXX1157T2trKPffcg9PZ/7G3p6eHn/zk\nJxQWFrJ06VJaW0PzLsEzzQ7cfYoMyQS46EgjM8al0Nzew5GqNq3LEWJIeA33bdu24XK5KC4uZvny\n5axfv/4r23fs2MEPfvADmpubPT974403sNlsbN68mfnz57NhwwbfVx4Aqs5dHG+Xk6mBbvaU/jtW\nt8uJVREmvIZ7aWkpBQUFAOTl5VFeXv7VF9Dr2bRpEwkJCVfcZ9asWezatcuXNQeM6gvhniU994CX\nkxZHutXM/uNNdDhcWpcjhN8ZvT3BbrdjsVw6WWgwGHC73RiN/bvOnDnzivvExvYHntlsprOz02sh\niYkxGI2G6y78X1mtQx+wtc0OjAYdU8enEjGI2gdKizZrbTBtvn9mNi+9W86B060suOsmH1blP/Ie\nhwd/tNlruFssFhwOh+exoiieYL+efRwOB3Fx3oct2tq6vD7naqzWWJqavP8B8SVnbx+n69vJSo3l\n/CBqHygt2qy1wbZ5UlYiRoOev31RxR0TUtDpdD6szvfkPQ4Pg2nztf4oeB2Wyc/Pp6SkBICysjJs\nNpvXA+bn57N9+3YASkpKmDZt2vXWGjSqznbQp6iMTo/XuhRxnSzREdw81kpDaxfHa89rXY4QfuU1\n3OfOnYvJZGLx4sWsW7eOX/3qV2zatImPP/74qvssWbKEEydOsGTJEoqLi3nsscd8WnQgOFnfDiDh\nHmRmyYlVESa8Dsvo9XpWr179lZ/l5uZ+7XmffPKJ5/vo6GieffZZH5QXuE7V9087kCvhHlRsIxNI\nTYph39EmCu/uxRIdoXVJQviF3HkzAKqqcrK+nWFxUSTGRmpdjrgBOp2OWVPScPcpfFF+TutyhPAb\nCfcBaGjrxt7dy+gM6bUHo5mTUjEZ9Xy0twZ3n6J1OUL4hYT7AJysk/H2YBYbY2J2XjqtHU7pvYuQ\nJeE+AMdq+29hl3APXvfdkonRoOf9XVXSexchScL9BqmqytHqNsxRRkamyEyQwSoxNpJZU0bQdL5H\nFtEWIUnC/QY1ne+mpcPJ2KxE9AF+E4y4tu/MzCY60si7O07T0SVTEojQIuF+g47W9N/8MjYzUeNK\nxGDFmU3ML8imy+mm+OMTWpcjhE9JuN+gI9X94+3jsiTcQ8E38tMZlRrLrooGdlfIyVUROiTcb4Cq\nqhypbiPebGLEsBityxE+YNDr+eEDE4g0GXjlw2OemT6FCHYS7jegrslBh8PFuFGJAT/plLh+KYkx\n/O/7x+Fy9fF/3j7A2RaH952ECHAS7jfgwMn+BUkm5wzTuBLha9PHJlM410aHw8W61/7puZdBiGAl\n4X4DDp5qQaeDiRLuIWnOtAwenjeWrh43T23+Jx98WSNrroqgJeF+nTq7XJyqb2d0erxMNhXCZk1J\n4/FFUzBHR/DWpyf5P8VltHU6tS5LiBsm4X6dyitbUYEpo4drXYrwswmjklj9gxlMzh1GRVUbK//f\nl+w50qB1WULcEAn361R2cbw9V4ZkwkGc2cTPFk6m6N4x9PYpbHyvghf/UkFXT6/WpQlxXbzO5y7A\n6erjwKlmkhOjSR9u1rocMUR0Oh13TU1nXFYiL/3lMLsrGjhee54fPTBR5hUSAU967tfhwKlmXL0K\nM8YlyyWhIS3rAAAQZ0lEQVSQYSg1KYZfPZTPA3dkc77TxX+9sd/zSU6IQCXhfh32HmkEYMbYFI0r\nEVoxGvQ8cEc2P104CR3w/JZD7D/RpHVZQlyVhLsX3U43BytbGDEshnSrDMmEu8m5w1m+OA+jUcfG\n9ypkoW0RsCTcvdh/oolet8KMcSkyJCMAuCkjgUe/OwlFUXluy0Eaz3drXZIQXyPh7sWOA2cBuG1i\nqsaViEAyKWcY/37vGBw9bv7vO4dw9vZpXZIQX+E13BVFYeXKlSxatIiioiKqq6u/sv2tt95iwYIF\nPPjgg3z66acAnD9/nltuuYWioiKKiop45ZVX/FO9n51r7eJY7XnGZSWSnBCtdTkiwBRMSWN2Xhq1\njXb++4NjqHI3qwggXi+F3LZtGy6Xi+LiYsrKyli/fj0vvPACAE1NTbz66qts2bIFp9NJYWEhM2fO\n5PDhw3zrW9/iN7/5jd8b4E87Dp4BoGDKCI0rEYGq8G4bNQ12dlWc46aMeO6cmq51SUIA19FzLy0t\npaCgAIC8vDzKy8s92w4ePMjUqVMxmUzExsaSmZnJ0aNHKS8vp6Kigoceeoif/vSnNDY2+q8FfuLu\nU/ji0DnMUUam2axalyMCVIRRz4/nT8QcZWTztuNUnevQuiQhgOvoudvtdiyWS2uFGgwG3G43RqMR\nu91ObGysZ5vZbMZut5OTk8PEiRO5/fbb2bp1K2vWrOHZZ5+95nESE2MwGg0DbojVGuv9STdgx/56\n2h0uvl2QQ9qIBJ++tq/4us3BIBDbbLXG8h8PTec//7SbP249zB8en40lxuSz1w430mbf8BruFosF\nh+PS/NaKomA0Gq+4zeFwEBsby+TJk4mO7h+jnjt3rtdgB2hr67rh4i+yWmNpavLtIgt//uQ4OuD2\ncck+f21f8EebA10gtzlreAzfun0Uf/2iiqde2ctPvjdp0FdXBXJ7/UXafOP7Xo3XYZn8/HxKSkoA\nKCsrw2azebZNnjyZ0tJSnE4nnZ2dnDp1CpvNxq9//Ws+/PBDAHbt2sWECRMGVLhWTta3U3mmgymj\nh5OSJCsuiesz/45sxmUlUnaymQ/21GhdjghzXnvuc+fOZefOnSxevBhVVVm7di2bNm0iMzOTOXPm\nUFRURGFhIaqq8vjjjxMZGcny5ct58skneeONN4iOjmbNmjVD0Raf+cfeWgDm3jxS40pEMNHrdSz7\nzgRWbdrDls8qyU2LxzYyMIf0ROjTqQFy/dZgPor58qNcc3s3v9y4m3SrmVXfvzlgb1ySj6+B63jt\neZ7evJ9YcwSrvj+DePPAxt+Dpb2+JG2+8X2vRm5i+hd/392/+s69M0YGbLCLwGYbmcD37syh3e7i\nxa0VKEpA9J9EmJFwv0xbp5MdB8+QnBDNLeNlkjAxcPfNyCRv9HCOVLfx3x/KDU5i6Em4X+bvu6tx\n96l88/YsDHr51YiB0+l0LP32eLJSYik5cIZ3Siq1LkmEGUmwC87bnWw/cIbh8VHcNkHmkRGDFx1p\n5PEHp5CSGM37u6r56xdVWpckwoiE+wV/21VNr1vh/tuyMBrk1yJ8I85sYvniPIbFRfJOSSVvf3ZS\nhmjEkJAUAxrbuvh0fz3WhCjumCTzyAjfGh4fzS//1zRSEqP5++4a/vvDY7j7FK3LEiFOwh14p6SS\nPkXle7Nzpdcu/GJYfBS/fGgaI5MtbC87w++Ly+jocmldlghhYZ9klWc62HOkkewRsdw8NlnrckQI\nizeb+NVD+UyzWTlac57fvbyXYzVtWpclQlRYh7uiqLz20TEAHrxrtFzXLvwuymTkke9OZH5BNq2d\nTp7evJ+3Pjkpi30InwvrcP90fz1V5zq5dXwKYzITtS5HhAm9Tsd3Zmbzq4emYU2M5oM9Nfx/L+3m\ny8MNcrJV+EzYhntrRw/vlJwiJtLIojk3aV2OCEOj0+P5z+/PYN6tmXQ4XPxxawVr/ruUspPNEvJi\n0LxOHBaKFEXlpb8cptvZx8Pzxg547g8hBivSZODf7hzN7Lx0/vzpSfYda+LZPx9kZLKFJfeO5aYR\nFrmhTgxIWIb7X7+o4ljtefJtVgomy6WPQnvJCdH8+LuTqGuy87dd1Xx5pIGnX91HUlwkc/IzKJiS\nhiU6QusyRRAJu3Dfc6SBdz8/TVJcJA/PGysnUUVAybBaWPadCTxwRzafVzSwbU8Nb392ivd2nub2\niSO4e1oGacPNWpcpgkBYhXt5ZQt/+usRokwGfrZwivSERMBKSYrhRwsmM+/mDEoOnOXj0jo+21/P\nZ/vrGZeVyB2TR5BvsxIZMfClKUVoC5tw33OkgZf+chi9Xsej353EyGSL952E0FhMVAT33ZLJ3Jsz\nKDvRzD/21XGkuo0j1W1ERxqYPiaZvJuGMzYzkejIsPnPWVyHkP9/g7tP4c+fneKjvbVEmgz87HuT\nGZsllz2K4GLQ65k2JplpY5JpaO1iZ/lZdh46x46DZ9lx8CwGvY7MFAsZVgupSTHExpiIjYkgJspI\ndKSRmMj+f6NMBhmKDBMhHe6Hq1p57aPjnGvtYsSwGB6ZP5EMq/TYRXBLSYphwaxc5t+Rw8n6dspP\nt1JxuoXaRjunz157RR+dDqJN/UEfHWkk3mLCmhBNckI01oQorAnRWBOi5VNACAjJd7CmoZP3Pj/N\n/hPN6HTwjfx0Ft6ZS5QpJJsrwpRer8M2MgHbyAQWzMrB3adwrrWL5vM9dHa56OzupavHTbez/6vL\neen7bqeblo5u6prsV3zt2JgIrAnRDI+PQq/T0aeoKBeuvdfRP1+9JTqCpLhI0odbyEmLI04uKQ4o\nIZV2NQ2dbN1ZxT+PNwH9N4n8r7k2slKvvs6gEKHCaNCTYbXc0KfTHpebpvM9NLZ103T+0lfj+W6q\nz3VSeabjul9reHwUozPiuSk9ntz0eDKsFvR6GQLSStCHu6Kq7D18ji0fH6eiqn8Sppy0OObfkc2E\n7CQZXxTiGqJMRkYmW654gUGfotBu75+50qDXobsY1CqoqkpHVy8t7T3UNHRSebaDU/Xt7K5oYHdF\nw4XXNjAqNZZh8VEMi4si3hJJXIyJeLOJOHMEcWaTfJr2I6+/WUVRWLVqFceOHcNkMrFmzRqysrI8\n29966y3efPNNjEYjjzzyCHfddRetra38x3/8Bz09PSQnJ7Nu3Tqio6N9Xryiqqx5ZR9V5/rHGceM\nTOD+27KYKKEuxKAZ9HqS4qKuuj3eEsnIZAt5Nw0H+v97PNfSxcn6dk7WtXOyvp2jNeeveQxThN4T\n+PGWSEamxmE26RmeEI01PorhCdFyuecAeQ33bdu24XK5KC4upqysjPXr1/PCCy8A0NTUxKuvvsqW\nLVtwOp0UFhYyc+ZMNmzYwLe+9S0WLFjAiy++SHFxMQ8//LDPi9cBw+KiuCkzkZkTUshMkeEXIbSi\n1+lIG24mbbiZWVPSAHD19tHW6aSlo4d2h4uOy77auy59X3Wukz6lwzOkerk4s4lESyTmaCMxURFY\novr/jYzQYzToMRj0RBh0GAx6dLr+OnS6/vMClx7rPOcK9Jdt0+l0mIx6IiL0mIwGTBf+jTDqPa9/\nox1FVe0/P6EoKorS/0fP81jlws9V+lQVVVExx179D+hgeA330tJSCgoKAMjLy6O8vNyz7eDBg0yd\nOhWTyYTJZCIzM5OjR49SWlrKD3/4QwBmzZrF73//e/+Eu07HowsmYbXG0tR07asEhBBDzxRhICUp\nhpSkmGs+T1FV7N299On0nKhquTD230NzezfN53s42+rA1avd6lW6C/+jo/+PAnDh30vBr14I8Bud\n8i0pLor/euQ2n482eA13u92OxXJpPM5gMOB2uzEajdjtdmJjL/WWzWYzdrv9Kz83m810dnoP3sTE\nGIzGgX/8slrDr9cubQ594dTelAv/2q4y/Xavuw97Vy/27l46u1w4XX24+5T+L7dKb5+CqqoXes58\n5V9VucLPVJU+RcXVq+Dq7cPV24fzwpertw+nqw+Xu/8PSv/zufQ9cDHFL8a5XqdDr+//Muh1X3n8\nr9sMnu/15GbEk5wc5/Pfp9dwt1gsOBwOz2NFUTAajVfc5nA4iI2N9fw8KioKh8NBXJz3wtvaugZS\nP0BY9tylzaEv3NoL19fmKD1EWULnssvBvM/X+uPvdS7R/Px8SkpKACgrK8Nms3m2TZ48mdLSUpxO\nJ52dnZw6dQqbzUZ+fj7bt28HoKSkhGnTpg2ocCGEEAPjtec+d+5cdu7cyeLFi1FVlbVr17Jp0yYy\nMzOZM2cORUVFFBYWoqoqjz/+OJGRkTzyyCM88cQTvPXWWyQmJvLMM88MRVuEEEJcoFMDZMmXwXz8\nlI+v4SHc2hxu7QVp80D2vRpZ4kUIIUKQhLsQQoQgCXchhAhBEu5CCBGCJNyFECIEBczVMkIIIXxH\neu5CCBGCJNyFECIESbgLIUQIknAXQogQJOEuhBAhSMJdCCFCkIS7EEKEoKANd0VRWLlyJYsWLaKo\nqIjq6mqtS/K73t5eVqxYQWFhIQsXLuTjjz/WuqQh09LSwuzZszl16pTWpQyJP/7xjyxatIgFCxbw\n9ttva12O3/X29rJ8+XIWL15MYWFhyL/PBw4coKioCIDq6mqWLFlCYWEhv/3tb1EU3ywnGLThfvnC\n3cuXL2f9+vVal+R3W7duJSEhgc2bN/PSSy/xu9/9TuuShkRvby8rV64kKso/CwkHmi+//JL9+/fz\nxhtv8Oqrr3Lu3DmtS/K77du343a7efPNN3n00Uf5wx/+oHVJfvPSSy/x61//GqfTCcC6dev4+c9/\nzubNm1FV1WedtqAN92st3B2q7rvvPn72s595HhsMA19zNpg89dRTLF68mOTkZK1LGRKff/45NpuN\nRx99lB/96EfceeedWpfkd9nZ2fT19aEoCna73bOUZyjKzMzkueee8zyuqKhgxowZAMyaNYsvvvjC\nJ8cJ2t/gtRbuDlVmsxnob/tPf/pTfv7zn2tckf+98847JCUlUVBQwIsvvqh1OUOira2NM2fOsHHj\nRurq6njkkUf44IMP0Ol0WpfmNzExMdTX1zNv3jza2trYuHGj1iX5zb333ktdXZ3nsaqqnvfWbDbT\n2embxUqCtud+rYW7Q9nZs2f593//dx544AG+/e1va12O323ZsoUvvviCoqIijhw5whNPPEFTU5PW\nZflVQkICd9xxByaTiZycHCIjI2ltbdW6LL96+eWXueOOO/jwww957733+OUvf+kZtgh1ev2lGHY4\nHMTFxfnmdX3yKhq41sLdoaq5uZkf/OAHrFixgoULF2pdzpB4/fXXee2113j11VcZN24cTz31FFar\nVeuy/GratGns2LEDVVVpaGigu7ubhIQErcvyq7i4OGJj+5eMi4+Px+1209fXp3FVQ2P8+PF8+eWX\nAJSUlDB9+nSfvG7QdnWvtHB3qNu4cSMdHR1s2LCBDRs2AP0nZ8LlRGO4uOuuu9i7dy8LFy5EVVVW\nrlwZ8udXHn74YZ588kkKCwvp7e3l8ccfJyYmRuuyhsQTTzzBb37zG37/+9+Tk5PDvffe65PXlSl/\nhRAiBAXtsIwQQoirk3AXQogQJOEuhBAhSMJdCCFCkIS7EEKEIAl3IYQIQRLuQggRgv5/iazmpZel\n1IMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a3cd198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0.,10,0.001)\n",
    "y = np.array( [pdf_k(x[j]) for j in range(len(x))] )\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.array(risk_df[\"risk\"].sort_values())\n",
    "y1 = pdf(x)\n",
    "y2 = [pdf_k( x[j] ) for j in range(len(x))] \n",
    "y3 = stats.norm.pdf(x, 2.88, 1.24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD3CAYAAADmBxSSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a490320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y1, x, y3, x, y2)\n",
    "#plt.plot(x,y2)\n",
    "#plt.plot(x,y3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "risk_stats_df = pd.DataFrame({\"risk\":x, \"kde_fit\":y2, \"gaussian_fit\":y3, \"gamma_fit\":y1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "risk_stats_df.to_sql('risk_stats',conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'gamma_fit', 'gaussian_fit', 'kde_fit', 'risk'], dtype='object')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "riskstats_df = pd.read_sql(\"SELECT * FROM risk_stats\", conn)\n",
    "riskstats_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD3CAYAAADmBxSSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKt7dhpfgqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a11299048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.003355\n",
       "1       0.005530\n",
       "2       0.005697\n",
       "3       0.006247\n",
       "4       0.006963\n",
       "5       0.007155\n",
       "6       0.007386\n",
       "7       0.007477\n",
       "8       0.008309\n",
       "9       0.008429\n",
       "10      0.008924\n",
       "11      0.008987\n",
       "12      0.009524\n",
       "13      0.010723\n",
       "14      0.011386\n",
       "15      0.012610\n",
       "16      0.013340\n",
       "17      0.013420\n",
       "18      0.013922\n",
       "19      0.014000\n",
       "20      0.014329\n",
       "21      0.014801\n",
       "22      0.016413\n",
       "23      0.018200\n",
       "24      0.018803\n",
       "25      0.019004\n",
       "26      0.021701\n",
       "27      0.021751\n",
       "28      0.021814\n",
       "29      0.021890\n",
       "          ...   \n",
       "6872    0.007864\n",
       "6873    0.007852\n",
       "6874    0.007817\n",
       "6875    0.007804\n",
       "6876    0.007339\n",
       "6877    0.007051\n",
       "6878    0.006990\n",
       "6879    0.006818\n",
       "6880    0.005897\n",
       "6881    0.005694\n",
       "6882    0.005380\n",
       "6883    0.004605\n",
       "6884    0.004472\n",
       "6885    0.004324\n",
       "6886    0.004135\n",
       "6887    0.003964\n",
       "6888    0.003949\n",
       "6889    0.003807\n",
       "6890    0.003802\n",
       "6891    0.003546\n",
       "6892    0.003505\n",
       "6893    0.003168\n",
       "6894    0.002871\n",
       "6895    0.002100\n",
       "6896    0.001226\n",
       "6897    0.001100\n",
       "6898    0.000814\n",
       "6899    0.000884\n",
       "6900    0.000743\n",
       "6901    0.000339\n",
       "Length: 6902, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        ,   0.09522548,   0.10197757, ...,   8.74323855,\n",
       "         8.8537744 ,  10.        ])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(risk_df[\"risk\"].sort_values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}