information.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from res_ind_lib import *\n",
    "\n",
    "#Imports the function to plot render the policy experiments\n",
    "from policy_assessment import *\n",
    "from apply_policy import apply_policy\n",
    "\n",
    "\n",
    "from fancy_plots import *\n",
    "\n",
    "#ask jupyter to display the plots within this notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": 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>gdp_pc_pp</th>\n",
       "      <th>pop</th>\n",
       "      <th>prepare_scaleup</th>\n",
       "      <th>borrow_abi</th>\n",
       "      <th>avg_prod_k</th>\n",
       "      <th>T_rebuild_K</th>\n",
       "      <th>pi</th>\n",
       "      <th>income_elast</th>\n",
       "      <th>rho</th>\n",
       "      <th>shareable</th>\n",
       "      <th>max_increased_spending</th>\n",
       "      <th>protection</th>\n",
       "      <th>fa_glofris</th>\n",
       "      <th>tau_tax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>9960.940726</td>\n",
       "      <td>2897366</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.162500</td>\n",
       "      <td>0.185280</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20</td>\n",
       "      <td>0.073680</td>\n",
       "      <td>0.186757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>7488.011760</td>\n",
       "      <td>21471618</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.432397</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20</td>\n",
       "      <td>0.017033</td>\n",
       "      <td>0.106132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>18087.196262</td>\n",
       "      <td>41446246</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.283333</td>\n",
       "      <td>0.269550</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20</td>\n",
       "      <td>0.072058</td>\n",
       "      <td>0.145281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>7526.836419</td>\n",
       "      <td>2976566</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.366696</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20</td>\n",
       "      <td>0.038001</td>\n",
       "      <td>0.268470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>42834.016760</td>\n",
       "      <td>23129300</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.273602</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20</td>\n",
       "      <td>0.041090</td>\n",
       "      <td>0.157776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              gdp_pc_pp       pop  prepare_scaleup  borrow_abi  avg_prod_k  \\\n",
       "country                                                                      \n",
       "Albania     9960.940726   2897366         0.000000    0.162500    0.185280   \n",
       "Angola      7488.011760  21471618         0.000000    0.200000    0.432397   \n",
       "Argentina  18087.196262  41446246         0.666667    0.283333    0.269550   \n",
       "Armenia     7526.836419   2976566         0.400000    0.437500    0.366696   \n",
       "Australia  42834.016760  23129300         0.800000    0.916667    0.273602   \n",
       "\n",
       "           T_rebuild_K   pi  income_elast   rho  shareable  \\\n",
       "country                                                      \n",
       "Albania              3  0.2           1.5  0.06        0.8   \n",
       "Angola               3  0.2           1.5  0.06        0.8   \n",
       "Argentina            3  0.2           1.5  0.06        0.8   \n",
       "Armenia              3  0.2           1.5  0.06        0.8   \n",
       "Australia            3  0.2           1.5  0.06        0.8   \n",
       "\n",
       "           max_increased_spending  protection  fa_glofris   tau_tax  \n",
       "country                                                              \n",
       "Albania                      0.05          20    0.073680  0.186757  \n",
       "Angola                       0.05          20    0.017033  0.106132  \n",
       "Argentina                    0.05          20    0.072058  0.145281  \n",
       "Armenia                      0.05          20    0.038001  0.268470  \n",
       "Australia                    0.05          20    0.041090  0.157776  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the excel data file into a dataframe (table)\n",
    "macro=pd.read_csv(\"intermediate/macro.csv\", \n",
    "                 index_col=economy, #tells Pandas to use the first column (Provinces) as the index of the table\n",
    "                ).dropna() \n",
    "\n",
    "# macro=def_ref_values(macro)\n",
    "\n",
    "#Displays the first rows of that dataframe\n",
    "macro.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": 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></th>\n",
       "      <th>n</th>\n",
       "      <th>c</th>\n",
       "      <th>axfin</th>\n",
       "      <th>gamma_SP</th>\n",
       "      <th>k</th>\n",
       "      <th>fa</th>\n",
       "      <th>v</th>\n",
       "      <th>shew</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>income_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Albania</th>\n",
       "      <th>nonpoor</th>\n",
       "      <td>0.8</td>\n",
       "      <td>11349.246839</td>\n",
       "      <td>0.092387</td>\n",
       "      <td>1.094243</td>\n",
       "      <td>61811.780436</td>\n",
       "      <td>0.041340</td>\n",
       "      <td>0.31325</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>0.2</td>\n",
       "      <td>4407.716271</td>\n",
       "      <td>0.075853</td>\n",
       "      <td>0.623028</td>\n",
       "      <td>21560.668539</td>\n",
       "      <td>0.036298</td>\n",
       "      <td>0.63000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Angola</th>\n",
       "      <th>nonpoor</th>\n",
       "      <td>0.8</td>\n",
       "      <td>8854.573906</td>\n",
       "      <td>0.201269</td>\n",
       "      <td>1.160476</td>\n",
       "      <td>20523.147061</td>\n",
       "      <td>0.019437</td>\n",
       "      <td>0.50500</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2021.763175</td>\n",
       "      <td>0.091791</td>\n",
       "      <td>0.358097</td>\n",
       "      <td>4494.564269</td>\n",
       "      <td>0.100576</td>\n",
       "      <td>0.63000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <th>nonpoor</th>\n",
       "      <td>0.8</td>\n",
       "      <td>21573.503341</td>\n",
       "      <td>0.060371</td>\n",
       "      <td>1.171127</td>\n",
       "      <td>80282.025353</td>\n",
       "      <td>0.018010</td>\n",
       "      <td>0.30500</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        n             c     axfin  gamma_SP             k  \\\n",
       "country   income_cat                                                        \n",
       "Albania   nonpoor     0.8  11349.246839  0.092387  1.094243  61811.780436   \n",
       "          poor        0.2   4407.716271  0.075853  0.623028  21560.668539   \n",
       "Angola    nonpoor     0.8   8854.573906  0.201269  1.160476  20523.147061   \n",
       "          poor        0.2   2021.763175  0.091791  0.358097   4494.564269   \n",
       "Argentina nonpoor     0.8  21573.503341  0.060371  1.171127  80282.025353   \n",
       "\n",
       "                            fa        v  shew  \n",
       "country   income_cat                           \n",
       "Albania   nonpoor     0.041340  0.31325   0.0  \n",
       "          poor        0.036298  0.63000   0.0  \n",
       "Angola    nonpoor     0.019437  0.50500   0.0  \n",
       "          poor        0.100576  0.63000   0.0  \n",
       "Argentina nonpoor     0.018010  0.30500   0.8  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_info = pd.read_csv(\"intermediate/cat_info.csv\", index_col=[economy,\"income_cat\"])\n",
    "\n",
    "cat_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": 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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>fa</th>\n",
       "      <th>shew</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>hazard</th>\n",
       "      <th>rp</th>\n",
       "      <th>income_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Afghanistan</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">earthquake</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">20</th>\n",
       "      <th>nonpoor</th>\n",
       "      <td>0.015826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>0.015826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">50</th>\n",
       "      <th>nonpoor</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>0.031746</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <th>nonpoor</th>\n",
       "      <td>0.051516</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             fa  shew\n",
       "country     hazard     rp  income_cat                \n",
       "Afghanistan earthquake 20  nonpoor     0.015826     0\n",
       "                           poor        0.015826     0\n",
       "                       50  nonpoor     0.031746     0\n",
       "                           poor        0.031746     0\n",
       "                       100 nonpoor     0.051516     0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hazard_ratios = pd.read_csv(\"intermediate/hazard_ratios.csv\", index_col=event_level+[\"income_cat\"])\n",
    "\n",
    "hazard_ratios.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "macro variables are: gdp_pc_pp, pop, prepare_scaleup, borrow_abi, avg_prod_k, T_rebuild_K, pi, income_elast, rho, shareable, max_increased_spending, protection, fa_glofris, tau_tax\n",
      "micro variables are: n, c, axfin, gamma_SP, k, fa, v, shew\n",
      "hazard variables are: fa, shew\n"
     ]
    }
   ],
   "source": [
    "print(\"macro variables are: \"+\", \".join(macro.columns))\n",
    "print(\"micro variables are: \"+\", \".join(cat_info.columns))\n",
    "print(\"hazard variables are: \"+\", \".join(hazard_ratios.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default_rp detected, droping rp\n"
     ]
    }
   ],
   "source": [
    "outputs = [\"risk\", \"resilience\", \"risk_to_assets\"]\n",
    "\n",
    "info = pd.DataFrame( index=outputs)\n",
    "ref=compute_resilience(macro,cat_info)[outputs].dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# (compute_resilience(macro,cat_info,hazard_ratios, return_stats=True) / compute_resilience(macro,cat_info,hazard_ratios, verbose_replace=True, return_stats=True)).dropna().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## averages and bias in macro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "macro[\"fap\"] = cat_info.fa.unstack().poor\n",
    "macro[\"far\"] = cat_info.fa.unstack().nonpoor\n",
    "\n",
    "macro[\"v_p\"] = cat_info.v.unstack().poor\n",
    "macro[\"v_r\"] = cat_info.v.unstack().nonpoor\n",
    "\n",
    "macro[\"cp\"] = cat_info.c.unstack().poor\n",
    "macro[\"cr\"] = cat_info.c.unstack().nonpoor\n",
    "\n",
    "\n",
    "x=cat_info[[\"c\",\"n\"]].prod(axis=1)\n",
    "macro[\"share1\"]=x.ix[:,\"poor\"]/x.sum(level=\"country\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prout\n"
     ]
    }
   ],
   "source": [
    "\n",
    "v,pv,fa,pe= compute_v_fa(macro)\n",
    "\n",
    "macro[\"v_moy\"] = v\n",
    "macro[\"fa_moy\"]= fa\n",
    "\n",
    "macro[\"v_bias\"]  = pv\n",
    "macro[\"fa_bias\"]= pe\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Albania                      9960.940726\n",
       "Angola                       7488.011760\n",
       "Argentina                   18087.196262\n",
       "Armenia                              NaN\n",
       "Australia                   42834.016760\n",
       "Austria                     44056.254417\n",
       "Azerbaijan                  16593.188760\n",
       "Bangladesh                   2853.447740\n",
       "Belarus                     17054.558334\n",
       "Belgium                     40608.972009\n",
       "Benin                        1733.309399\n",
       "Bolivia                      5934.387978\n",
       "Bosnia and Herzegovina       9387.350031\n",
       "Botswana                    15246.608797\n",
       "Brazil                      14555.082195\n",
       "Bulgaria                    15695.017794\n",
       "Burkina Faso                 1630.444497\n",
       "Burundi                       746.960151\n",
       "Cambodia                     2943.524668\n",
       "Cameroon                     2739.343945\n",
       "Canada                      41898.882359\n",
       "Central African Republic      584.375944\n",
       "Chad                         2021.593471\n",
       "Chile                       21714.031556\n",
       "China                       11524.567507\n",
       "Colombia                    12025.385372\n",
       "Congo, Dem. Rep.              783.259254\n",
       "Congo, Rep.                          NaN\n",
       "Costa Rica                  13430.743207\n",
       "Croatia                     20048.987159\n",
       "                                ...     \n",
       "Romania                     18184.109733\n",
       "Russian Federation          23564.207736\n",
       "Rwanda                       1426.366288\n",
       "Senegal                      2170.077207\n",
       "Serbia                      12892.496301\n",
       "Sierra Leone                 1494.648548\n",
       "Slovak Republic             25758.862635\n",
       "Slovenia                    27368.172026\n",
       "South Africa                12454.190922\n",
       "Spain                       31682.644741\n",
       "Sri Lanka                    9425.737383\n",
       "Sudan                        3264.772978\n",
       "Swaziland                    6470.884190\n",
       "Sweden                      43540.494392\n",
       "Syrian Arab Republic         4959.392523\n",
       "Tajikistan                   2431.659069\n",
       "Tanzania                     2364.702757\n",
       "Thailand                    13931.811471\n",
       "Togo                         1345.914276\n",
       "Turkey                      18567.134710\n",
       "Uganda                       1620.576939\n",
       "Ukraine                      8508.014787\n",
       "United Kingdom              36931.474226\n",
       "United States               51340.487628\n",
       "Uruguay                     18965.816535\n",
       "Uzbekistan                   5002.149120\n",
       "Venezuela, RB               17614.598210\n",
       "Vietnam                      5124.603520\n",
       "Yemen, Rep.                  3832.284740\n",
       "Zambia                       3799.540436\n",
       "dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_p,v_r, far, fap, cp, cr = unpack(macro.v_moy,macro.v_bias,macro.fa_moy,macro.fa_bias,0.2,macro.share1)\n",
    "\n",
    "macro.v_p - v_p\n",
    "macro.v_r - v_r\n",
    "macro.cp / cp\n",
    "# macro.v_p - v_p\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on  fa_bias\n",
      "default_rp detected, droping rp\n"
     ]
    }
   ],
   "source": [
    "for v in macro:\n",
    "    df_ = macro.dropna().copy()\n",
    "    try : \n",
    "        progress_reporter(v)\n",
    "        df_[v] = macro[v].mean()\n",
    "        \n",
    "        #bias and mean to poor and rich\n",
    "        \n",
    "        cat_info.v=cat_info.v.unstack().assign(nonpoor = (df_.v_moy)/(0.2* df_.v_bias+.8)).stack()\n",
    "        cat_info.v=cat_info.v.unstack().assign(poor = lambda df:df.nonpoor*df_.v_bias).stack()\n",
    "\n",
    "        cat_info.fa=cat_info.fa.unstack().assign(nonpoor = (df_.fa_moy)/(0.2* df_.fa_bias+.8)).stack()\n",
    "        cat_info.fa=cat_info.fa.unstack().assign(poor = lambda df:df.nonpoor*df_.fa_bias).stack()\n",
    "\n",
    "\n",
    "    except TypeError:\n",
    "        pass\n",
    "    \n",
    "    g=compute_resilience(df_,cat_info)[outputs]\n",
    "    info[v] = (((g - ref)/ref).abs()).median()\n",
    "\n",
    "# info = info.T.sort_values(\"resilience\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "for v in cat_info:\n",
    "#     for i in [\"poor\", \"nonpoor\"]:\n",
    "        c_ = cat_info.dropna().copy()\n",
    "        try : \n",
    "            c_[v] = cat_info[v].mean()\n",
    "#           c_[v] = cat_info[v].unstack().assign(**{i:lambda df:df[i].mean()}).stack()\n",
    "        except TypeError:\n",
    "            pass\n",
    "\n",
    "        g=compute_resilience(macro,c_,hazard_ratios)[outputs]\n",
    "#         info[(v,i)] = (((g - ref)/ref).abs()).median()        \n",
    "        info[v] = (((g - ref)/ref).abs()).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "if False:\n",
    "    for v in hazard_ratios:\n",
    "#     for i in [\"poor\", \"nonpoor\"]:\n",
    "        h_ = hazard_ratios.copy()\n",
    "        try : \n",
    "            h_[v] = hazard_ratios[v].mean()\n",
    "        except TypeError:\n",
    "            pass\n",
    "\n",
    "        g=compute_resilience(macro,cat_info,h_)[outputs]\n",
    "        info[v] = (((g - ref)/ref).abs()).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "info=info.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "info = info.rename(index=pd.read_csv(\"inputs/inputs_info.csv\", index_col=0).descriptor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## save info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "with pd.ExcelWriter(\"results/information.xlsx\", engine='xlsxwriter') as writer:\n",
    "    \n",
    "    workbook=writer.book\n",
    "    \n",
    "    info.to_excel(writer,sheet_name=\"information\")\n",
    "    worksheet=writer.sheets[\"information\"]\n",
    "    #width of the columns\n",
    "    worksheet.set_column(0, 0, 20)\n",
    "    worksheet.set_column(1, 1, 20)\n",
    "    worksheet.set_column(2, 2, 20)\n",
    "    worksheet.set_column(3, 3, 20)\n",
    "\n",
    "    #filter\n",
    "    worksheet.autofilter('A1:D1')\n",
    "\n",
    "    #freeze panes\n",
    "    writer.sheets[\"information\"].freeze_panes(1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## barpot info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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5kmuvvZb6+no2btzIrFmzmDJlSrPtlyxZwsiRIzu4SusI/h2WdRn+HVZ+tPQ7rHXr1nHb\nbbc1e5cgQETw3e9+l0996lMMGDCgvcu0tuHfYZnZ3m/58uU89NBDjesvvfQS/fv3d1jtpTzCspKT\n9H3gPWTXqJT+vTYiZrXxcTzCygn/pYsupegRlq9hWclFxP9X6hrMrPNzYFmXMaj8EIZUPV/qMqwI\ng8oPKXUJ1gl5StC6Er/ZzTof33RhZmZ7FweWmZnlggPLzMxywYFlZma54MAyM7NccGCZmVkuOLDM\nzCwXHFhmZpYLDiwzM8sFB5aZmeWCA8vMzHLBgWVmZrngwDIzs1xwYJmZWS44sMzMLBccWGZmlgsO\nLDMzywUHlpmZ5YIDy7qMwQPLkVSS/wYPLC/16ZvlniKi1DWYdQhJ8cKoI0py7CFVz+P/18yapWIb\n9mjPKsyseX/4wx9YtGgRb775Jv/4j//YbJvq6mruu+++xvXx48czfPjwjirRrNNxYFm7kXQN8P8i\n4odpfQawPiL+q0m78cDVwDpgJHAnUAVcAfQGzoqIFyUNBn4GHAD8HZia9lkMVEbEVkn9gEUN6x1w\nmrtl2LBhHH/88Vx//fUttjn44IO5+OKLkcSGDRu44YYbGDZsGFLRX0jN9iq+hmXt6XbgIwXrH0nb\nmjMauBg4CriILHBOAH4KTE9trgdmRsTRwH8D10fEBmAecEZq8zHgrs4cVgAVFRX07du31TY9evRo\nDKe33nrLQWVdnkdY1m4iYqGkgyQdArwdeC0iqlto/kRErAKQ9Bfgd2l7FTAhLZ8InJ2Wbwa+lZZ/\nCnwJ+BXZqOszbXkepVRdXc2cOXN4/fXXOfvssx1a1qU5sKy93QmcDxxCy6MrgM0Fy/UF6/Vse582\nvWshACLiEUnvTFOL3SLi2T2uupOoqKjg0ksvZfXq1cyePZvKykq6d+9e6rLMSsJTgtbe7iCbpjuX\nLLz2xCPABWn5E8D8gsduJpsm/NkeHqNTOvDAA+nZsyerVq0qdSlmJePAsnaVRjv9gL9FxKvF7tbC\n9suBqZIWAheS3ZTR4FZgAPDL3a21s1m3bh319fWNy6tXr2bAgAElrsqsdPw7LNsrSDoP+FBETGml\nTaf5HdYDDzzAkiVLWL9+Pf369WPs2LGMHz+e5cuXU1NTw4QJE1i8eDELFiyge/fuSGL8+PEMGzas\nJPWbtaOiL8w6sCz3JF0HTAROj4gVrbTrNIFlZo38w2HrfCSNJLvW1PDJLeDNiDhxT/qNiMv3tDYz\n6/w8wrIuY/DA8ni55pWSHHtQ+SG8tLKmJMc26+Q8JWjWDL/ZzTqfogPLdwmamVkuOLDMzCwXHFhm\nZpYLDiwzM8sFB5aZmeWCA8vMzHLBgWVmZrngwDIzs1xwYJmZWS44sMzMLBccWGZmlgsOLDMzywUH\nlpmZ5YIDy8zMcsGBZWZmueDAMjOzXHBgmZlZLjiwzMwsFxxY1mUMHliOpF36b/DA8lKXbWaJIqLU\nNZh1CEnxwqgjdmmfIVXP4/9HzNqVim240xGWpK2SnpZUJWmOpLI9q62x3ymSrt+F9h+WNLwtjr0r\nJM2UdE5HH3dPSJom6RM7aTND0ufb4diDJV2wB/tPkXRIwfqNpXjdzazz6VFEm40RMRZA0s+By4Br\n2uj4u/LV9SzgPmBZGx17rxURP27P/iV1j4itLTx8GPBx4Lbd7P6TwBLgFYCIuHg3+2kXNTU1zJ49\nm7q6OiorK5k4ceIObdatW8cPfvADDjzwQAAOPfRQzjjjDLZs2cLMmTMb29XW1jJ69GhOO+20Dqvf\nLM929RrWo0BFw4qkL0p6XNJCSTMKtt8j6Yk0KvtMwfapkpZLegx4T9rWV9ILkrqn9X6F62nbicBk\n4FtptHeYpDGSHk3HvktS/6bFSjo/1fCMpIfStm6Svp22L5R0Wdr+VUl/lrRY0g3NnbyksZIeSuf2\nG0kHN9NmsKSqgvUvSPrXtDxP0vdSPYsljWtm/6NSHU+n+g5P2z+fal4s6YqC9v9H0qLU56y0rXH0\nJOkz6TV6RtKdkno3d24F/c2U9KN0jssknZ62T0kj7AeB36dtDc/jIknnpy6uAU5K9V+Rnu9vpXNa\nKOmzBcf6v+l8npH0DUnnAscBt6T9e6fnrOEL0wWp/WJJ3yzoZ72kf0v9PyLpoNbOcU/MnTuXyZMn\nM336dNasWcOKFSuabbf//vszbdo0pk2bxhlnnAFAz549G7dNmzaN/v37c+SRR7ZXqWZ7nWICS5B9\nqwZOBX6V1j8AVEbE8cAxwHGSTkr7TI2IccA44ApJ+6VpnquAE4GTgKMAImIDMA84I+37MeCuwm/w\nEfFoOu6XImJsRLwI/CKtH032jfyqZmr/KvDBiDiGLPAALgYGA6PTvrem7ddHxAkRMRp4m6QzCjuS\n1AO4Hjg3ndtM4BstPGetjRz7pHouA37WzOOfA76XRrXHAX9LH9hTyJ7PE4HPpsA+CvgnYELq84pm\n+rsrIo5Pjy8DPt1KbQ0Gp3M8E/ixpJ5p+zHAORFxirJp0tERMQr4APCdFOBfAean1+nadLx1EXEC\ncDxwcQr1icCHgHGptm9FxF3AE8DH0/5vNhQkqRz4JjABOBoYJ6nhNd0XeCS9nvOBxlBsSxs2bGDz\n5s1UVGTf2caMGcOyZbs34F+zZg2bNm1i0KBBbVmi2V6tmCnBPpKeBg4FngUeSNs/CHwgPSayD41K\nYAFwpaSzUrtD0/ZyYF5EvAYg6fa0HeCnwJfIQmkq0Dgqa46y62j9I2JB2jQLuKOZpguAWZLuAO5O\n294P/CjSlfSIWJe2nyrpS8DbgP3IQnBuQV/DgJHAA5JEFvYrW6uzBbel485Po8myiKgtePxR4J8l\nvQO4OyJWpC8C9zR8gEu6CziZLBjvjIi1Tc6l0GhJXwcGkL1G9xdR4x2pvxWS/gI0XEN6ICJeT8sn\nFZzLqjSCHQesb9LXB4FRBSOwMrLX/f3AzIjY3KR20fxF2HFs//65NT0HvwK2RMSvU7unUt9trra2\nlrKybZdwy8rKWL++6elm1q1bx49//GN69+7NKaecskMwLVmyhBEjRrRHmWZ7rWICa1NEjE1TSfeT\njQy+T/ahck1E3FTYWNJ44H3ACRGxWdI8oGEaqtm7QSLiEUnvTPt2i4hnd/N8mvZ7aZp2OxN4StKx\nzbWT1Av4ATA2IlYqm95sOnUmYElEvKfJvocC95KFxw1kIde9oEnTfgpHX2qyTkTcpmzK9ExgrqTP\nFbRtul/QwnNaYCYwOSKWSJoCjN9J+9Zq3NjKPi3VIWB6RDyw3cZshLWrWjrGWwXLWynufd1u+vXr\nx5VXXkmfPn2oqanhl7/8JZdddhk9e/ZsbLN06VLOPvvsElZplj9FTwmmb/dXAF+U1I0svD4laV8A\nSQPTtYP+wNoUVsOBd6V+/gycnKYH9wHOb3Kcm4H/pvlpMsi+uZelWmqBtZIawuMi4OEdCpeGRMQT\nETEDWEU22nsAmKZt18z2IwuVANZI6guc18zxlwMHSXpX2q+HpKMi4m8RcUyawroReDW12y8F4ZlN\n+vlo2v8ksqmy7b6iSzosIl6MiOvJRg+jyKa5Ppyu6ewLnJ22zQPOk7R/wbk01Rd4JT3nFzbzeHPO\nV+ZwspsoljfTZj7w0XSN6iDgvcDjFLxOyf3ApWlKFUmVkt5G9jpMldSnSe21TfZv8DjZ+2f/9Npd\nADxU5Pm0ibKyMmprtw2Ga2tr6dev3w7tunfvTp8+fQAoLy9n//33Z82aNY2Pv/rqq9TX11Ne7t94\nme2KYr6JNn7bjoiFkhYBF0TErZKOBB7NZshYD3wC+C3wOUlLyT7oHk37viLpKuAxYC2wsMlxbgW+\nDvyyhTp+CdwkaTpZoEwhu77SB3iBbCqxqW9Laph2fDAiFqe6jgAWS9oC3BQRP5T0E2ApUEP24bjd\n+UfEW5LOA65XdoNHd+B7ZNOk2xpH1En6Gtm1mL8BzzWp6c00jdqjhZo/IukislFDDfDvEbFO2R2a\nT6R6boyIRQCS/h14WFId8AzwqSb9/Ws6n1VkXxp2/ITd0ctpn37AtIjYkl7jwvO8J4X3IqCe7Hri\nKkmvAVslPQP8PCKulfRO4Ok0lboKOCsi7pc0BnhS0mbg18C/kE3v3iBpE/Butj3/r0j6CttCam5E\n3NdQThHntMf69u1Lr169qK6uZuDAgSxatIgTTjhhh3abNm2iT58+SGLt2rW89tpr7Lfftu8SVVVV\njBw5siNKNturdJofDqcw+FBETCl1Le0lTY9+ISKeLnUtLZE0E7g3Iu7eaeOcURv8cHjlypXMmTOH\nuro6hg4dyqRJkwBYvnw5NTU1TJgwgeeee4558+bRvXt3JHHKKadQWVnZ2Md1113HhRdeyAEHHNA2\nJ2aWb0X/cLhTBJak64CJwOkR0fx9wnsBSX8AvtjJA+tnwH0OrIz/0oVZu8tXYJl1BAeWWadUdGCV\n9G4qs440qPwQhlQ9v8v7mFnn4BGWdSV+s5t1Pm33x2/NzMw6AweWmZnlggPLzMxywYFlZma54MAy\nM7NccGCZmVkuOLDMzCwXHFhmZpYLDiwzM8sFB5aZmeWCA8vMzHLBgWVmZrngwDIzs1xwYJmZWS44\nsMzMLBccWGZmlgsOLDMzywUHlpmZ5YIDy7qMwQPLkdTif4MHlpe6RDNrhSKi1DWYdQhJ8cKoI1p8\nfEjV8/j/B7MOp2Ib9mjPKiwfJA0G7ouIUaWupViSpgDHRcT0Zh5bEBEn7ekxampqmD17NnV1dVRW\nVjJx4sQd2lRXV3Pfffc1ro8fP57hw4ezZcsWZs6c2bi9traW0aNHc9ppp+1pWWZdlgPLGuzW0EJS\nt4ioL1jvHhFb266sVjVbc1uEFcDcuXOZPHkyFRUV3HrrraxYsYKhQ4du1+bggw/m4osvRhIbNmzg\nhhtuYNiwYfTs2ZNp06Y1trvxxhs58sgj26Issy7L17CswT6SbpH0rKQ7JPWWdKqkpyUtkvQTSfsA\nSHpR0jclPQmcJ2mepO9Kehy4XNJgSQ9KWijpAUmHSuom6YW0/wBJdZJOSusPSzq8uaIkjZP0iKSn\nJC2QVFnw8KB07OWS/rVgn/V7+mRs2LCBzZs3U1FRAcCYMWNYtmzZDu169OiBlM1ovPXWW43Lhdas\nWcOmTZsYNGjQnpZl1qV5hGUNhgFTI+IxST8BvgBMA06JiL9ImgVcAlyX2q+OiOMAJF0C7BMRx6f1\nXwEzI+IWSVOB6yPibEnLJB0JDAGeAt6bQu7QiPhLC3U9B5wUEfWSTgWuAc5Lj40DRgBvAk9Iui8i\nnmY3R4uFamtrKSsra1wvKytj/frmc7C6upo5c+bw+uuvc/bZZ+8QWkuWLGHEiBF7WpJZl+cRljV4\nOSIeS8u3AqcCLxQEySzg5IL2tzfZv3D9ROC2tHwz8J60vAAYn/q5BngvWeg80UpdA4D/kVQFfBc4\nquCxByJiXUS8CdwNNEwFFn0Rty1UVFRw6aWX8tnPfpYFCxawdev2M6JLly5l5MiRHVmS2V7JgWUN\nmo5K1u2k/cZW1lsa4fyRbSH1a7IwmgDMb+U4Xwf+kG4I+RDQu5XjRAvbd1lZWRm1tbWN67W1tfTr\n16/VfQ488EB69uzJqlWrGre9+uqr1NfXU17uW+bN9pQDyxoMlnRCWv442ajnnZKGpG0XAQ8V2dcj\nwAVp+RNsC6THgXcD9RGxBVhINu34x1b6KgOq0/LUJo99IF0P6wOcRTaCgzYYYfXt25devXpRXV1N\nRLBo0SKGDx++Q7t169ZRX1/fuLx69WoGDBjQ+HhVVZVHV2ZtxNewrMEy4DJJM4GlZNNvj5FNx3Un\nC7Afp7YtjWwaXA7MlPRF4O+koImILZJeBh5N7eYDH4uIqlbq+jYwS9K/AHObPPY42VRgBXBzRDzT\nQj275fTTT2fOnDnU1dUxdOjQxjsEly9fTk1NDRMmTODll19mwYIFdO/eHUmcccYZ9OnTp7GPZ599\nlgsvvLAtyjHr8vzDYesy/MNhs06p6BkRTwmamVkueErQOgVJnwSuYPvpvD8195csdteg8kMYUvV8\nq4+bWeflKUHrSvxmN+t8PCVoZmZ7FweWmZnlggPLzMxywYFlZma54MAyM7NccGCZmVkuOLDMzCwX\nHFhmZpaZy+phAAASkklEQVQLDiwzM8sFB5aZmeWCA8vMzHLBgWVmZrngwDIzs1xwYJmZWS44sMzM\nLBccWGZmlgsOLDMzywUHlpmZ5YIDy7qMwQPLGTywvNRlmNluUkSUugazDiEpAPyeN+tUVGxDj7Cs\nTUm6XNKzkm4udS1mtnfxCMvalKTngFMjYmWpa2mquRFWTU0Ns2fPpq6ujsrKSiZOnNjsvvPnz2fh\nwoV069aNiRMncvjhh7NlyxZmzpzZ2Ka2tpbRo0dz2mmntfOZmO1Vih5h9WjPKqxrkfQjYAjwG0m3\nAmcBvYA3gKkR8b8t7Dcltd0XGAr8J9ATuAh4Ezg9ItZJOhr4EdAH+AvwKeAA4M6IODb1NRS4vWF9\nZ+bOncvkyZOpqKjg1ltvZcWKFQwdOnS7Nn//+9959tlnueyyy6itreUXv/gF06dPp2fPnkybNq2x\n3Y033siRRx5Z1HNlZrvOU4LWZiLiEqAamAD8EDgpBccM4Jqd7D6CLLSOB/4d2BARY4HHgP+T2swC\nvhQRRwNLgBkR8QKwTtLo1GYq8LNi6t2wYQObN2+moqICgDFjxrBs2bId2i1fvpwRI0bQrVs3BgwY\nwAEHHEB1dfV2bdasWcOmTZsYNGhQMYc2s93gEZa1NaX/BgC/kFQJBDt/r82LiE3AJknrgPvS9ipg\nlKQyoH9ELEjbZwF3pOWfAlMlfQH4KDCumEJra2spKytrXC8rK2P9+vXNtnvHO97RuN6vX78d2i1Z\nsoQRI0YUc1gz200eYVlba7hA9HXgDxExCvgQ0Hsn+21u0kfDej3bwq6lue67gNOBM4EnI2Ltrha9\np5YuXcrIkSM7+rBmXYoDy9paQ6j0J5sehGyabo9ERC3wmqT3pE0XAQ+nxzYD95Nd35rZfA87Kisr\no7a2tnG9traWfv36Ndvu9ddfb7Hdq6++Sn19PeXl/o2XWXtyYFlbaxhhfQv4pqSn2PX3WUu3rn4S\n+I6khcAY4GsFj90KbAV+V+xB+vbtS69evaiuriYiWLRoEcOHD9+h3bBhw1i6dClbt25l7dq1vPba\na43XvQCqqqo8ujLrAL6t3fYK6fpVWUTMaKXNDre1r1y5kjlz5lBXV8fQoUOZNGkSkN1oUVNTw4QJ\nE4DstvZnnnmG7t27N97W3uC6667jwgsv5IADDmiPUzPb2xV9W7sDy3JP0t1kt9O/LyJea6Wd/9KF\nWefjwLLOR9IHgf9g25SfgBci4twOOr4Dy6zzcWCZNTV4YHkAvLSyptSlmNk2DiyzZvjNbtb5+I/f\nmpnZ3sWBZWZmueDAMjOzXHBgmZlZLjiwzMwsFxxYZmaWCw4sMzPLBQeWmZnlggPLzMxywYFlZma5\n4MAyM7NccGCZmVkuOLDMzCwXHFhmZpYLDiwzM8sFB5aZmeWCA8vMzHLBgWVmZrngwLIu46qrrip1\nCWa2B3Y7sCSdJale0hF70Mexkr7XwmMvStp/F/qaIun6Yrd3NpI+JOnLafnDkoYXPDZP0tgOqKFD\njlMsSeWS7mir/q6++uq26srMSqDHHuz7MWA+cAGwW58EEfEU8FRLD+9Ol23YV4eKiHuBe9PqWcB9\nwLKOOr6kkoy2JXWPiK3NPRYRNcBH2uvYNTU1zJ49m7q6OiorK5k4cWJ7HcrM2sBufUhJ2hd4D/Bp\nssBq2H6bpEkF6zMlnSOpl6SfSVos6SlJE9Lj4yXdm5b3l3S/pCpJNwEq6OdCSX+W9LSkH0lS2j5V\n0nJJj6V6dlb3YEkPSloo6QFJh6bt56fjPiPpobTtqIJjLpR0eEu1SOqWznWxpEWSrmhy3G6SXkjL\nAyTVSToprT8s6fCGkaCkE4HJwLfSMYakbj6SjrtMUrPnKumLkh5P9c4o2H6PpCfSOX6mYPt6Sd+R\n9AxwYsH2qZK+W7D+GUn/2eRY5zVsk3SFpL+k5cMkLUjLX001L5Z0Q8G+8yR9V9LjwOXpubtW0p8k\nrZB0TsHrVZWWp0i6S9Jv0mv+HwX9fbrhfSDpRknXtfwu2Gbu3LlMnjyZ6dOns2bNGlasWFHMbmZW\nIrv7rfrDwG8jYgWwWtIxafvtwEcBJO0DvA+YC1wG1EfEaODjwCxJPdM+DaOfGcD8iBgF3AMMSv0M\nT32+OyLGAvXAhZIOAa4i+6A9CTiqiLqvB2ZGxNHAf6d1gK8CH4yIY8jCAuBzwPfSMY8D/tZSLcDR\nQEVEjI6IMcDMwoNGRD2wTNKRZMH6FPDe9BwcGhF/2dY0HgV+BXwpIsZGxAvpse4RcQLwD+m8tyPp\nA0BlRBwPHAMc1xCKwNSIGAeMA66QtF/avi/waEQcExF/KujuDuBDkro37A/8rMkh55M976R/V0sq\nB94LPNzwfEfECel1f5ukMwr23ycijo+IhmA8JCLeA3wI+I+CdoWj4zHA+cBo4KOSKtIx/wU4nuy5\nHU4RNmzYwObNm6moqMg6HjOGZcs6bEBrZrthdwPrAuCXafl2shAC+A0wIYXVJOCPEbGZ7APtFoCI\nWA78FWh67evkgja/Btam7acCY4En0kjgfcAQ4ARgXkS8FhF1qY6dORG4LS3fzLZR2Z/IQvQzbJsm\nfRT4Z2XXld6ZzqOlWl4ADkujhNOA9c0cez4wPp3nNWQf7OOAJ4qoG+Du9O9TwOBmHv8g8AFJTwNP\nA8OAyvTYlZIWAo8BhxZsryvot1FEbAQeBM6UNAzoERFLm7R5FegrqS/wDrIvAOPTec1PzU5No57F\nwCnAiIIumr5es1O/zwFvb+E5eDAiNqTXYml6Ho4HHoqI19PU4p0t7Lud2tpaysrKGtfLyspYv765\nl83MOotdvoaVvp2/DxgpKYDuZN+CvxQRm9OU2kSykchtLXVTzKEK/p0VEf/cpI4PF9lPoWavZUXE\nJZLGAWcCT0kaGxG3KZtqPBOYK2laS7WkesYApwHTyK67fLpJk/nAJUA52Yjuy8AEtn2478zm9O9W\nmn/dBFwTETc1qWs82et1Qnp95gG908NvRkRL1/d+CvwT2XW0mS20eYRs9LUsncengXcBn5fUC/gB\nMDYiVqYpyt4F+25s4fwazqU5hW3q2fY87Or7wMxyaHdGWOcDv4iIwyJiSEQMBl4smH66g+xD7CTg\nt2nbfLKpM5TdVfgOYHmTfv9Y0GYSMCBtfxA4T9JB6bH9JA0C/gycnNb3SXXtzCNsu+b2iVQXkoZE\nxBMRMQNYBbxD0mER8WJEXE82RTe6pVokHUA2ZXcPWRgdw44eB95NNjW6BVhIFm5/bKbteqCsme0N\nmvuAvh/4lLLri0gamOrsD6xNYTWcLFBa6weAiHic7HW6gJa/eCwAvkg2BbiQbBS1OSLWk4VTAGvS\nKOy8Vs6nmPNryRNk74P+knoA5xazU1lZGbW1tY3rtbW19OvXbxcOa2YdbXcC66Nk15gK3c22IPgd\n2bTXA2mqDuCHQPc0NXQbMCUi3mrSx9VkHzxVZHfJvQyNU0T/AvxO0qLU/yER8QrZtZzHyILn2SJq\nvxyYmqbHLgQabo74droxYDHwp4hYTHaTw5I09TeCLKSbrQWoAB5KbW8GvtL0wCmkXiabaiTV3Dci\nqpqp85fAl5TdoDKEHUeGO4yKIuIBsmm5R9N53An0JfvSsI+kpcA3Co7fXD9N1+9Iz8frzdTYcA6H\nkk391qfzm5/qeR24iWzq7jdkgV3scYu5qzPScVaSndfj6dgvAi3V26hv37706tWL6upqIoJFixYx\nfHhRl7/MrETU8oyQdXXK7uD8r4iYV+paWiNp34jYmG4SuQf4aUTMaabddjOgK1euZM6cOdTV1TF0\n6FAmTZrUdBcza39Fz6g4sGwHkvqTjVieiYiPlbqenZH0beD9QC/gdxFxZQvtWrlkZ2Yl4sAya8qB\nZdYpFR1Y/luC1mXMmDFj543MrNPyCMu6Er/ZzTofj7DMzGzv4sAyM7NccGCZmVkuOLDMzCwXHFhm\nZpYLDiwzM8sFB5aZmeWCA8vMzHLBgWVmZrngwDIzs1xwYJmZWS44sMzMLBccWGZmlgsOLDMzywUH\nlpmZ5YIDy8zMcsGBZWZmueDAMjOzXHBgWZdx1VVXlboEM9sDiohS12DWISSF3+9mnY6KbegRluWG\npPWlrsHMSqdHqQswK4YksQvfxIpRU1PD7Nmzqauro7KykokTJ7Zl92bWxjzCsk5L0mBJyyTNAqqA\nPpL+TdJCSY9IOqig3YNp+wOSDi2m/7lz5zJ58mSmT5/OmjVrWLFiRXuejpntIQeWdXZDge9HxEiy\nEdYjEXE0MB/4bGpzPTAzbf/vtN6qDRs2sHnzZioqKgAYM2YMy5Yta4/6zayNOLCss3spIp5Iy5sj\n4tdp+SngnWn5ROC2tHwzcNLOOq2traWsrKxxvaysjPXrfYnMrDNzYFlnt7Fg+a2C5a1suwbb9NY/\n3wpothdyYFlnpxaWCz0CXJCWP0E2XdiqsrIyamtrG9dra2vp16/f7tZoZh3AgWWdXbSwXOhyYKqk\nhcCFwBU767Rv37706tWL6upqIoJFixYxfPjwNijXzNqLfzhsXUbTHw6vXLmSOXPmUFdXx9ChQ5k0\naVIJqzPrsor+uYoDy7oM/6ULs07Jf+nCzMz2Lg4s6zJmzJhR6hLMbA94StC6Er/ZzTofTwmamdne\nxYFlZma54MAyM7NccGCZmVkuOLDMzCwXHFhmZpYLDiwzM8sFB5aZmeWCA8vMzHLBgWVmZrngwDIz\ns1xwYJmZWS44sMzMLBccWGZmlgsOLDMzywUHlpmZ5YIDy8zMcsGBZWZmueDAsi7jqquuKnUJZrYH\nFBGlrsE6CUlbgUXAPsCzwJSIeLMN+58CHBcR01tpMx7YEhGPpvVpwMaIuKUNjh9+v5t1Oiq2YY/2\nrMJyZ2NEjAWQdAvwOeB7bXyMnSXGBGAD8ChARPy4jY/fqKamhtmzZ1NXV0dlZSUTJ05sr0OZWRvw\nlKC1ZD4wFEDS5yVVSVos6Yq0bbCk5yTdIulZSXdI6p0ee1HS/mn5WEnzmnYu6UxJj0l6StLvJB0k\naTBZSF4p6WlJ75E0Q9Ln0z5HS3pU0kJJd0nqn7bPk/RNSX+WtEzSe4o5wblz5zJ58mSmT5/OmjVr\nWLFiRRs8bWbWXhxYVkgAknoAk4AqSWOBKcA44ETgs5LGpPbDgO9HxFHAeuDStL3pKKq5UdX8iHhX\nRBwL3A58OSJeAm4AvhsRYyPiT032mQV8KSKOBpYAMwoe6x4RJwD/AFy1sxPdsGEDmzdvpqKiAoAx\nY8awbNmyne1mZiXkwLJCfSQ9DTwO/BX4KXAScE9EvBkRG4G7gfem9i9HxGNp+ZbUFoqbk36HpPsl\nLQa+CIxorbGkMqB/RCxIm2YBJxc0uTv9+xQweGcHr62tpaysrHG9rKyM9evXF1G2mZWKr2FZoU0N\n17AaSEVfD4VtI6k6tn0Z6t1C2+uB70TE3HSjxYwW2m1XTiuPbU7/bsXva7O9kkdYVqi5QJgPnCWp\nt6R9gbPTNoBBkk5Iyx8v2P4icGxaPreFY5UBK9PylILt69Nj24mIWuC1gutTFwEP78J5bH/wsjJq\na2sb12tra+nXr9/OdjOzEnJgWaEdrjVFxDPAz4EnyO7cuzEiFqWHlwOXSXoWGEB2/Qnga8B1kh4n\nG20152rgfyQ9Afy9YPu9wNkNN100qemTwHckLQTGpOM0V/dO713v27cvvXr1orq6mohg0aJFDB8+\nfGe7mVkJ+XdYtlvSHX33RcSoUtdSrKa/w1q5ciVz5syhrq6OoUOHMmnSpBJWZ9ZlFX3dwYFluyUF\n1r0RMbrUtRTLPxw265QcWGZNObDMOqWiA8vXsKzLmDGjmBsRzayz8gjLuhK/2c06H4+wzMxs7+LA\nMjOzXHBgmZlZLjiwzMwsFxxYZmaWCw4sMzPLBQeWmZnlggPLzMxywYFlZma54MAyM7NccGCZmVku\nOLDMzCwXHFhmZpYLDiwzM8sFB5aZmeWCA8vMzHLBgWVmZrngwDIzs1xwYJmZWS44sMzMLBccWGZm\nlgsOLDMzywUHlpmZ5YIDy8zMcsGBZWZmueDAMjOzXHBgmZlZLjiwzMwsFxxYZmaWCw4sMzPLBQeW\nmZnlggPLzMxywYFlZma54MAyM7NccGCZmVkuOLDMzCwXHFhmZpYLDiwzM8sFB5aZmeWCA8vMzHLB\ngWVmZrngwDIzs1xwYJmZWS44sMzMLBccWGZmlgsOLDMzywUHlpmZ5YIDy8zMcsGBZWZmueDAMjOz\nXHBgmZlZLjiwzMwsFxxYZmaWCw4sMzPLBQeWmZnlggPLzMxywYFlZma50KPUBZh1IJW6ADPbfR5h\nmZlZLjiwzMwsFxxYZmaWCw4sMzPLBQeWmZnlggPLzMxy4f8HJ3fUKQXtF9kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xae9a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_barh(info.sort_values(\"resilience\", ascending=True).resilience.tail(10), color=\"#de2d26\")\n",
    "plt.savefig(\"img/ser_info.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tep07d6ZHjx4A9O3bl/e9732sWLGiYfsbb7xBXV0dffv23ZbumFmB5nzCbPgU\nHRHPSpoHnBsRd0r6KPBkNstFDXA+8Afgi5IWkb2pPZn2fV3S1WQ3Q6wku3hd6E7gO8BdJfpxF3Cb\npMvIwmMs2fWQHmQ3UIwrss+1kuqnDv8cEfNTvz4CzJe0DrgtIm6W9HNgEVBF9ka40flHxHuSzgRu\nlNSbbHT0Y7Kpzg2VI2olfZvs2skysov7hd5NU6FdSvT5U5IuIBsNVAH/ExGr0p2ST6X+3BoR8wAk\n/Q/wuKRashsLPtuovW+m83mT7APCpu++m3o17dMLuCgi1qXnuPA8H0hBPQ+oI7v+96akt4D1kuYC\nv4yI6yXtBcxJ06FvAqdGxMOShgJPS1oL/A74OtkU7c8krQEOZ8Pj/7qkr7EhkB6KiGn13WnGOW2i\nZ8+edOvWjcrKSj70oQ8xb948Dj300E3qrVmzhh49eiCJlStX8tZbb7Hrrhs+ByxYsID9999/a7pg\nZiUoXXppd+mN/5MRMba9+9Ja0hTnlyNiTnv3pRRJE4AHI+L+JitvZyTVB11D2fLly5k6dSq1tbUM\nHDiQMWPGALB06VKqqqoYPXo0zz33HNOnT6dz585I4qijjqKioqKhjRtuuIHzzjuP3XbbrY3PyGyH\nUHS6PhfhJOkG4ASy25ZfaO/+tBZJjwJfyXk4/QKY1lHCyczaXX7DyawtOJzMcqloOLXoXU1medav\n357t3QUzayaPnKwj8YvdLH+Kjpz8q+RmZpY7DiczM8sdh5OZmeWOw8nMzHLH4WRmZrnjcDIzs9xx\nOJmZWe44nMzMLHccTmZmljsOJzMzyx2Hk5mZ5Y7DyczMcsfhZGZmueNwMjOz3HE4mZlZ7jiczMws\ndxxOZmaWOw4nMzPLHYeTdRjl5f0pL+/f3t0ws2ZQRLR3H8zahKQA8GveLFdUrNAjJ0PSAEkL2rsf\nW0LSWEk3ltg2s637Y2Ytq0t7d8ByY6uGE5I6RURdwXrniFjfct3arKJ9joiRzW2gqqqKKVOmUFtb\nS0VFBSeccMImdSorK5k2bVrD+qhRoxg0aBDr1q1jwoQJDeXV1dUMGTKE448/fotOwsyKiAj/6+D/\ngAHAc8AkYDFwD9AdOAaYA8wDfg7slOq/DHwPeBr4FDAduA6YDfx7au/PwLPAI0A52Sj9pbR/H6AW\nGJnWHwc+XKJvI4AngGeAmUBFKh8LTEnHXgp8s2CfmhJtRfaS3+C2226LZcuWRUTEpEmT4vnnn4/G\n3nvvvaj+Hpb1AAASx0lEQVSrq4uIiJqamrj22msb1gvdcsst8corr2xSbmabVfR9ydN6Vm9f4CcR\nsR9QDXwZmACcFRFDgZ2Aiwvq/zMihkfEPWl9p4g4JCKuA24EJkTEgcCvgRsjG10tkfRR4AiysPm4\npK5AeUS8WKJfz5GF2MHAeOCagm0jgNOAocBZkoal8maNAlevXs3atWvp168fAEOHDmXJkiWb1OvS\npQtSNi3+3nvvNSwXWrFiBWvWrKF/f99wYdYSPK1n9V6NiFlp+U7gG2QjnfrQmAhcAtyQ1u9utH/h\n+mFkoQFwB/D9tDwTGAXsTRYyXwD+Ajy1mX71AX4lqYIsdApfs49ExCoASfcDI8lGekUvsDZWXV1N\nWVlZw3pZWRk1NTVF61ZWVjJ16lTefvttTjvttE0CauHChQwePLg5hzWzZvDIyeo1Hm2saqL+O5tZ\nLzVy+QvwcbIRz+/Igmc0MGMzx/kO8GhEHAB8kmy6sdRxokT5NuvXrx+XXHIJF154ITNnzmT9+o0v\nqy1atIj999+/pQ9r1mE5nKzeAEmHpuVPk41m9pK0Tyq7AHismW09AZybls9nQ/jMBg4H6iJiHdk1\nqYvIQquUMqAyLY9rtO1YSX0k9QBOJRuZQTNHTmVlZVRXVzesV1dX06tXr83us/vuu9O1a1fefPPN\nhrI33niDuro6+vbt25zDmlkzOJys3hLgUkmLyUY015GFwW8kzQPWA7ekuqVGLPUuB8ZJehY4D7gC\nIAXSq8CTqd4MoGdEbO429muB70l6hk1fr7OB+8lC7t6ImFuiP0X17NmTbt26UVlZSUQwb948Bg0a\ntEm9VatWUVdX17D8z3/+kz59+jRsX7BggUdNZi3MX8K1DqPYl3CXL1/O1KlTqa2tZeDAgYwZMwaA\npUuXUlVVxejRo5k/fz4zZ86kc+fOSGLUqFHsu+++DW3ccMMNnHfeeey2225tfEZmO4SiMx0OJ+sw\n/AsRZrlUNJx8t57lgqTPkE3/FSbHXyPisvbpkZm1J4+crMMoL+8fAMuWvdreXTGzDTytZx2eX+xm\n+eMffjUzs+2Dw8nMzHLH4WRmZrnjcDIzs9xxOJmZWe44nMzMLHccTmZmljsOJzMzyx2Hk5mZ5Y7D\nyczMcsfhZGZmueNwMjOz3HE4mZlZ7jiczMwsdxxOZmaWOw4nMzPLHYeTmZnljsPJzMxyx+FkHcbV\nV1/d3l0ws2ZSRLR3H8zahKTw690sd1SssEtb98J2fJIuB74IPBMRFzRzn7HA8Ii4rMi2mRExsoW7\nSVVVFVOmTKG2tpaKigpOOOGElj6EmW0lT+tZa7gY+LfmBlOBosOa1ggmgIceeoiTTz6Zyy67jBUr\nVvDCCy+0xmHMbCs4nKxFSfopsA/we0lflfSEpGckzZRU0cTu/SVNl7RU0jcL2qxJf3eR9CdJT0ua\nJ+nkVL6zpGmS5kqaL+mspvq5evVq1q5dS79+/QAYOnQoS5Ys2drTNrMW5mk9a1ERcbGk44HRwHvA\nDyOiTtIxwDXAmZvZfQQwGHgXeErStIiYw4YR1bvAqRGxWtJuwCzgt8AJQGVEnAQgqVdT/ayurqas\nrKxhvaysjJqami07WTNrNQ4naw1K//oAv0ojpqDp19sjEbEKQNL9wEhgDhsumAq4RtKRQB3wIUnv\nBxYAP5R0DfBQRMxs6RMys7blaT1rDfUjne8Aj0bEAcAnge7N3K/xev3f84DdgYMi4iDgTaB7RDwP\nDCMLqf+W9PWmOlhWVkZ1dXXDenV1Nb16NTngMrM24nCy1lA/0ukNVKblcc3Y71hJfST1AE4F6kdA\nhe29maYJjwL6A0jqC/wrIn4NXEsWVJvVs2dPunXrRmVlJRHBvHnzGDRoULNOzsxan6f1rDXUj3R+\nAExMI5mHmrHfbOB+oB9wR0TMbdTencCDkuYBTwP1dzAcAFwrqQ5YR3a3YJNOPPFEpk6dSm1tLQMH\nDmTgwIHN2c3M2oC/hGsdhr+Ea5ZLRb+E62k9MzPLHU/rWZuSdBzwfTZM1Ql4KSLOaO1jjx8/vrUP\nYWYtxNN61pH4xW6WP57WMzOz7YPDyczMcsfhZGZmueNwMjOz3HE4mZlZ7jiczMwsdxxOZmaWOw4n\nMzPLHYeTmZnljsPJzMxyx+FkZma543AyM7PccTiZmVnuOJzMzCx3HE5mZpY7DiczM8sdh5OZmeWO\nw8nMzHLH4WQdxtVXX93eXTCzZlJEtHcfzNqEpPDr3Sx3VKxwq0dOkk6VVCfpI9vQxsGSflxi28uS\n3rcFbY2VdGNzy/NG0iclfTUtnyJpUMG26ZKGtUEf2uQ4zSWpr6R72rsfZtb2umzDvucAM4BzgW9t\nTQMR8QzwTKnNW9NkC7bVpiLiQeDBtHoqMA1Y0lbHl9QuU7ySOkfE+mLbIqIK+FRrHbuqqoopU6ZQ\nW1tLRUUFJ5xwQmsdysy20Fa9IUnaBTgC+BxZONWXT5Y0pmB9gqTTJXWT9AtJ8yU9I2l02j5K0oNp\n+X2SHpa0QNJtFAz1JJ0n6W+S5kj6qSSl8nGSlkqalfrTVL8HSPqzpGclPSKpPJWflY47V9JjqWy/\ngmM+K+nDpfoiqVM61/mS5km6otFxO0l6KS33kVQraWRaf1zSh+tHeJIOA04GfpCOsU9q5lPpuEsk\nFT1XSV+RNDv1d3xB+QOSnkrn+PmC8hpJP5Q0FzisoHycpOsK1j8v6UeNjnVmfZmkKyS9mJb3ljQz\nLX8j9Xm+pJ8V7Dtd0nWSZgOXp8fuekl/lfSCpNMLnq8FaXmspPsk/T49598vaO9z9a8DSbdKuqH0\nq2CDhx56iJNPPpnLLruMFStW8MILLzRnNzNrA1v7afkU4A8R8QLwT0kHpfK7gbMBJO0EHA08BFwK\n1EXEEODTwERJXdM+9aOa8cCMiDgAeADon9oZlNo8PCKGAXXAeZI+CFxN9qY6EtivGf2+EZgQEQcC\nv07rAN8AjouIg8iCAeCLwI/TMYcDy0r1BTgQ6BcRQyJiKDCh8KARUQcskfRRshB9Bvh4egzKI+LF\nDVXjSeC3wFURMSwiXkrbOkfEocC/p/PeiKRjgYqIOAQ4CBheH4DAuIgYAYwArpC0ayrfBXgyIg6K\niL8WNHcP8ElJnev3B37R6JAzyB530t9/SuoLfBx4vP7xjohD0/O+s6RPFOy/U0QcEhH1IfjBiDgC\n+CTw/YJ6haPeocBZwBDgbEn90jG/DhxC9tgOohlWr17N2rVr6devX9bw0KEsWdJmA1Uza8LWhtO5\nwF1p+W6ywAH4PTA6BdMY4C8RsZbszWsSQEQsBf4ONL5WdWRBnd8BK1P5McAw4Kn0Cf9oYB/gUGB6\nRLwVEbWpH005DJiclu9gw2jrr2SB+Xk2THU+CfyXsutAe6XzKNWXl4C906f/44GaIseeAYxK53kN\n2Zv4COCpZvQb4P709xlgQJHtxwHHSpoDzAH2BSrStislPQvMAsoLymsL2m0QEe8AfwZOkrQv0CUi\nFjWq8wbQU1JPYE+ysB+VzmtGqnZMGs3MB44CBhc00fj5mpLafQ54f4nH4M8RsTo9F4vS43AI8FhE\nvJ2mB+8tse9GqqurKSsra1gvKyujpqbY02Zm7WGLrzmlT91HA/tLCqAz2afbqyJibZoWO4FshDG5\nVDPNOVTB34kR8V+N+nFKM9spVPTaU0RcLGkEcBLwjKRhETFZ2XThScBDki4q1ZfUn6HA8cBFZNdJ\nPteoygzgYqAv2Ujtq8BoNryRN2Vt+rue4s+bgGsi4rZG/RpF9nwdmp6f6UD3tPndzdy+djvwn2TX\nvSaUqPME2ahqSTqPzwEfA74kqRtwEzAsIpanacbuBfu+U+L86s+lmMI6dWx4HLb0dWBmObc1I6ez\ngF9FxN4RsU9EDABeLphCuofsDWsk8IdUNoNs+gtld/ftCSxt1O5fCuqMAfqk8j8DZ0raI23bVVJ/\n4G/AkWl9p9SvpjzBhmtk56d+IWmfiHgqIsYDbwJ7Sto7Il6OiBvJptmGlOqLpN3Ipt0eIAueg9jU\nbOBwsunNdcCzZEH2lyJ1a4CyIuX1ir0ZPwx8Vtn1QCR9KPWzN7AyBdMgsvDYXDsARMRssufpXEp/\nyJgJfIVsGu9ZstHR2oioIQuiAFak0dWZmzmf5pxfKU+RvQ56S+oCnNGcncrKyqiurm5Yr66uplev\nXltwWDNrTVsTTmeTXRMqdD8b3vT/SDZ19UiabgO4GeicpncmA2Mj4r1GbXyL7E1mAdndaq9CwzTP\n14E/SpqX2v9gRLxOdu1lFlnILG5G3y8HxqUprvOA+hsXrk0X7ecDf42I+WQ3ICxM03eDyQK5aF+A\nfsBjqe4dwNcaHzgF0qtk04WkPveMiAVF+nkXcJWym0f2YdMR3yajnYh4hGxq7cl0HvcCPck+IOwk\naRHw3YLjF2un8fo96fF4u0gf68+hnGz6ti6d34zUn7eB28im335PFs7NPW5z7q6MdJzlZOc1Ox37\nZaBUfxv07NmTbt26UVlZSUQwb948Bg1q1uUqM2sD/hKulaTsTsr/jYjp7d2XzZG0S0S8k27geAC4\nPSKmFqm30Szm8uXLmTp1KrW1tQwcOJAxY8Y03sXMWl/RmRKHk21CUm+ykcjciDinvfvTFEnXAv8G\ndAP+GBFXlqjnX4gwyx+Hk3VsDiezXGrZny8y296MHz++6UpmlgseOVlH4he7Wf545GRmZtsHh5OZ\nmeWOw8nMzHLH4WRmZrnjcDIzs9xxOJmZWe44nMzMLHccTmZmljsOJzMzyx2Hk5mZ5Y7DyczMcsfh\nZGZmueNwMjOz3HE4mZlZ7jiczMwsdxxOZmaWOw4nMzPLHYeTmZnljsPJOoyrr766vbtgZs2kiGjv\nPlhOSFoPzAN2AhYDYyPi3RZsfywwPCIu20ydUcC6iHgyrV8EvBMRk1rg+OHXu1nuqFhhl7buheXa\nOxExDEDSJOCLwI9b+BhNpcNoYDXwJEBE3NLCx29QVVXFlClTqK2tpaKighNOOKG1DmVmW8jTelbK\nDGAggKQvSVogab6kK1LZAEnPSZokabGkeyR1T9telvS+tHywpOmNG5d0kqRZkp6R9EdJe0gaQBaI\nV0qaI+kISeMlfSntc6CkJyU9K+k+Sb1T+XRJ35P0N0lLJB3RnBN86KGHOPnkk7nssstYsWIFL7zw\nQgs8bGbWEhxOVkgAkroAY4AFkoYBY4ERwGHAhZKGpvr7Aj+JiP2AGuCSVN54dFRstDQjIj4WEQcD\ndwNfjYhXgJ8B10XEsIj4a6N9JgJXRcSBwEJgfMG2zhFxKPDvwNVNnejq1atZu3Yt/fr1A2Do0KEs\nWbKkqd3MrI04nKxQD0lzgNnA34HbgZHAAxHxbkS8A9wPfDzVfzUiZqXlSakulJhDbmRPSQ9Lmg98\nBRi8ucqSyoDeETEzFU0Ejiyocn/6+wwwoKmDV1dXU1ZW1rBeVlZGTU1NM7ptZm3B15ys0Jr6a071\npObkTIP6EVItGz74dC9R90bghxHxULoJYnyJeht1ZzPb1qa/6/Hr2my755GTFSr25j8DOFVSd0m7\nAKelMoD+kg5Ny58uKH8ZODgtn1HiWGXA8rQ8tqC8Jm3bSERUA28VXE+6AHh8C85j44OXlVFdXd2w\nXl1dTa9evZrazczaiMPJCm1ybSgi5gK/BJ4iu4Pu1oiYlzYvBS6VtBjoQ3a9CODbwA2SZpONoor5\nFvAbSU8B/ygofxA4rf6GiEZ9+gzwQ0nPAkPTcYr1u8n7xXv27Em3bt2orKwkIpg3bx6DBg1qajcz\nayP+npNtlXRn3bSIOKC9+9Jcjb/ntHz5cqZOnUptbS0DBw5kzJgx7dg7sw6r6EyHw8m2SgqnByNi\nSHv3pbn8JVyzXHI4WcfmcDLLpaLh5GtO1mGMH9+cGwLNLA88crKOxC92s/zxyMnMzLYPDiczM8sd\nh5OZmeWOw8nMzHLH4WRmZrnjcDIzs9xxOJmZWe44nMzMLHccTmZmljsOJzMzyx2Hk5mZ5Y7DyczM\ncsfhZGZmueNwMjOz3HE4mZlZ7jiczMwsdxxOZmaWOw4nMzPLHYeTmZnljsPJzMxyx+FkZma543Ay\nM7PccTiZmVnuOJzMzCx3HE5mZpY7DiczM8sdh5OZmeWOw8nMzHLH4WRmZrnjcDIzs9xxOJmZWe44\nnMzMLHccTmZmljsOJzMzyx2Hk5mZ5Y7DyczMcsfhZGZmueNwMjOz3HE4mZlZ7jiczMwsdxxOZmaW\nOw4nMzPLHYeTmZnljsPJzMxyx+FkZma543AyM7PccTiZmVnuOJzMzCx3HE5mZpY7DiczM8sdh5OZ\nmeWOw8nMzHLH4WRmZrnjcDIzs9xxOJmZWe44nMzMLHccTmZmljtd2rsDZm1I7d0BM2sej5zMzCx3\nHE5mZpY7DiczM8sdh5OZmeWOw8nMzHLH4WRmZrnz/wEH+z4kMTlYmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xae9a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_barh(info.sort_values(\"risk\", ascending=True).risk.tail(10), color=\"#756bb1\", xlabel=\"Average error (%)\")\n",
    "plt.savefig(\"img/risk_info.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G5s2bR21tLcOHD2fy5MmdWJ1Zl1VxpsPhZK2SwunWiBjd2bU0l/9C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\nSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb696ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_barh(info.sort_values(\"risk_to_assets\", ascending=True).risk_to_assets.tail(10), color=\"#3182bd\")\n",
    "plt.savefig(\"img/risk_to_assets_info.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "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": "3.4.4"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": true
  },
  "toc_position": {
   "height": "626px",
   "left": "0px",
   "right": "1388px",
   "top": "107px",
   "width": "212px"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}