assess_policies.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "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",
    "#ask jupyter to display the plots within this notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline data and function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "economy = \"country\" #province, deparmtent\n",
    "event_level = [economy, \"hazard\", \"rp\"]\n",
    "infra_cats = ['transport','all_infra_but_transport']\n",
    "sector_cats = ['transport','all_infra_but_transport','other_k']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "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",
    "\n",
    "# macro=def_ref_values(macro)\n",
    "\n",
    "#Displays the first rows of that dataframe\n",
    "macro.head()\n",
    "\n",
    "macro[\"gdp_pc_pp_ref\"] =macro[\"gdp_pc_pp\"] \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>13016.935891</td>\n",
       "      <td>0.096454</td>\n",
       "      <td>1.095582</td>\n",
       "      <td>70827.184540</td>\n",
       "      <td>0.025179</td>\n",
       "      <td>0.351500</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>0.2</td>\n",
       "      <td>5055.398031</td>\n",
       "      <td>0.044618</td>\n",
       "      <td>0.617673</td>\n",
       "      <td>24998.396447</td>\n",
       "      <td>0.024174</td>\n",
       "      <td>0.700000</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>7121.979386</td>\n",
       "      <td>0.222081</td>\n",
       "      <td>1.159988</td>\n",
       "      <td>16507.539225</td>\n",
       "      <td>0.003203</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1635.713147</td>\n",
       "      <td>0.040259</td>\n",
       "      <td>0.360049</td>\n",
       "      <td>3636.384458</td>\n",
       "      <td>0.014163</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>nonpoor</th>\n",
       "      <td>0.8</td>\n",
       "      <td>25704.504585</td>\n",
       "      <td>0.153000</td>\n",
       "      <td>1.223611</td>\n",
       "      <td>64278.309421</td>\n",
       "      <td>0.203447</td>\n",
       "      <td>0.763987</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  n             c     axfin  gamma_SP  \\\n",
       "country             income_cat                                          \n",
       "Albania             nonpoor     0.8  13016.935891  0.096454  1.095582   \n",
       "                    poor        0.2   5055.398031  0.044618  0.617673   \n",
       "Angola              nonpoor     0.8   7121.979386  0.222081  1.159988   \n",
       "                    poor        0.2   1635.713147  0.040259  0.360049   \n",
       "Antigua and Barbuda nonpoor     0.8  25704.504585  0.153000  1.223611   \n",
       "\n",
       "                                           k        fa         v  shew  \n",
       "country             income_cat                                          \n",
       "Albania             nonpoor     70827.184540  0.025179  0.351500   0.0  \n",
       "                    poor        24998.396447  0.024174  0.700000   0.0  \n",
       "Angola              nonpoor     16507.539225  0.003203  0.600000   0.0  \n",
       "                    poor         3636.384458  0.014163  0.700000   0.0  \n",
       "Antigua and Barbuda nonpoor     64278.309421  0.203447  0.763987   0.3  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_info = pd.read_csv(\"intermediate/cat_info.csv\", index_col=[economy,\"income_cat\"])\n",
    "\n",
    "# cat_info.v = cat_info.v.unstack().assign(poor=lambda df:df.nonpoor).stack()\n",
    "\n",
    "\n",
    "cat_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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></th>\n",
       "      <th>share</th>\n",
       "      <th>v</th>\n",
       "      <th>fa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fiji</th>\n",
       "      <th>transport</th>\n",
       "      <td>0.130303</td>\n",
       "      <td>0.243342</td>\n",
       "      <td>0.186727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <th>all_infra_but_transport</th>\n",
       "      <td>0.372426</td>\n",
       "      <td>0.407671</td>\n",
       "      <td>0.140410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    share         v        fa\n",
       "country sector                                               \n",
       "Fiji    transport                0.130303  0.243342  0.186727\n",
       "Jamaica all_infra_but_transport  0.372426  0.407671  0.140410"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infra_stocks = pd.read_csv(\"intermediate/infra_stocks_in.csv\", index_col=[economy,\"sector\"]).dropna()\n",
    "infra_stocks.sample(n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": 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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>fa</th>\n",
       "      <th>shew</th>\n",
       "      <th>share</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>sector</th>\n",
       "      <th>hazard</th>\n",
       "      <th>rp</th>\n",
       "      <th>income_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Albania</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">all_infra_but_transport</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">earthquake</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">20.0</th>\n",
       "      <th>nonpoor</th>\n",
       "      <td>0.007294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>0.007294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">other_k</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">earthquake</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">20.0</th>\n",
       "      <th>nonpoor</th>\n",
       "      <td>0.007294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>0.007294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transport</th>\n",
       "      <th>earthquake</th>\n",
       "      <th>20.0</th>\n",
       "      <th>nonpoor</th>\n",
       "      <td>0.007294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  fa  shew  \\\n",
       "country sector                  hazard     rp   income_cat                   \n",
       "Albania all_infra_but_transport earthquake 20.0 nonpoor     0.007294   0.0   \n",
       "                                                poor        0.007294   0.0   \n",
       "        other_k                 earthquake 20.0 nonpoor     0.007294   0.0   \n",
       "                                                poor        0.007294   0.0   \n",
       "        transport               earthquake 20.0 nonpoor     0.007294   0.0   \n",
       "\n",
       "                                                            share  \n",
       "country sector                  hazard     rp   income_cat         \n",
       "Albania all_infra_but_transport earthquake 20.0 nonpoor       NaN  \n",
       "                                                poor          NaN  \n",
       "        other_k                 earthquake 20.0 nonpoor       NaN  \n",
       "                                                poor          NaN  \n",
       "        transport               earthquake 20.0 nonpoor       NaN  "
      ]
     },
     "execution_count": 62,
     "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": 63,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "macro variables are: gdp_pc_pp, pop, urbanization_rate, gdp_pc_cd, prepare_scaleup, borrow_abi, avg_prod_k, T_rebuild_K, pi, income_elast, rho, shareable, max_increased_spending, protection, tau_tax, gdp_pc_pp_ref\n",
      "micro variables are: n, c, axfin, gamma_SP, k, fa, v, shew\n",
      "hazard variables are: fa, shew, share\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": [
    "## output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n"
     ]
    }
   ],
   "source": [
    "refout = compute_resilience(macro,cat_info,infra_stocks,hazard_ratios=hazard_ratios,verbose_replace=True)\n",
    "# .drop(\"Togo\",axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.763069634892683, 9.744398778914439)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refout.dKtot.sum()*1e-9 , refout.dWtot_currency.sum()*1e-9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## function test_package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "def test_package(m,c,i,h,a,return_rel=False,macro=macro,hazard_ratios=hazard_ratios,cat_info=cat_info,infra_stocks=infra_stocks,refout=refout, verbose=True):\n",
    "    \"\"\"test a policy package where:\n",
    "    -macro, cat_info and hazard_ratios, which are assumed to yield refout (all 4 optional, defautlt o baseline)\n",
    "    are replaced by:\n",
    "    m,c,and h. \n",
    "    This function states the difference between old and new set of params, runs the model with new set of paramters, \n",
    "    and reports difference in dW and dk\"\"\"\n",
    "        \n",
    "    #State differences\n",
    "    if verbose:\n",
    "        print(\"Changed in macro: \"+changed_lines(m,macro))\n",
    "        print(\"Changed in cat_info: \"+changed_lines(c,cat_info))\n",
    "        print(\"Changed in hazard: \"+changed_lines(h,hazard_ratios))\n",
    "        print(\"args: \"+repr(a))\n",
    "\n",
    "    #New outputs from new inputs\n",
    "    nu = compute_resilience(m,c,i,h,**a)\n",
    "\n",
    "    #absolute and relative difference\n",
    "    delta =  refout-nu\n",
    "    rel = delta/refout\n",
    "    reltogdp = (delta.T/(m[\"gdp_pc_pp\"]*m[\"pop\"])).T\n",
    "    \n",
    "    #sumarizes difference in outputs\n",
    "    if verbose:\n",
    "        print(\"\\nAsset losses were reduced by {dk:.1f} bn$ ({dkr:.1%} or {g:.3%} of WDP)\".format(\n",
    "                dk=delta.dKtot.sum()*1e-9,\n",
    "                dkr=delta.dKtot.sum()/refout.dKtot.sum(),\n",
    "                g = delta.dKtot.sum()/(m[\"gdp_pc_pp\"]*m[\"pop\"]).sum())\n",
    "             )\n",
    "        print(\"Welfare losses were reduced by {dw:.2f} bn$ ({dwr:.2%}  or {g:.3%} of WDP)\".format(\n",
    "                dw=delta.dWtot_currency.sum()*1e-9,\n",
    "                dwr=delta.dWtot_currency.sum()/refout.dWtot_currency.sum(),\n",
    "                g = delta.dWtot_currency.sum()/(m[\"gdp_pc_pp\"]*m[\"pop\"]).sum())\n",
    "             )\n",
    "    \n",
    "        print(\"Resilience in countries changed by {dr:.2%}, from ({old:.2%} to {new:.2%} )\".format(\n",
    "                dr=delta.resilience.mean(),\n",
    "                old=refout.resilience.mean(),\n",
    "                new =nu.resilience.mean()             )\n",
    "              )\n",
    "        \n",
    "        old=refout.dKtot.sum()/refout.dWtot_currency.sum()\n",
    "        new =nu.dKtot.sum()/nu.dWtot_currency.sum()     \n",
    "        print(\"Resilience GLOBALLY changed {d:.2%}, from ({old:.2%} to {new:.2%} )\".format(\n",
    "                    d=new-old,\n",
    "            old=old,\n",
    "            new=new)\n",
    "              )\n",
    "    \n",
    "    \n",
    "    \n",
    "    #returns rel and abs difference\n",
    "    if return_rel:\n",
    "        return delta,rel,reltogdp,nu[[\"risk_to_assets\",\"resilience\",\"risk\"]]\n",
    "    \n",
    "    return delta\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute policy card "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "policy_list = [\"prepare_scaleup\", \"borrow_abi\",  \"T_rebuild_K\", \"social_p\",\"fap\",\"far\", \"vp\",\"kp\",\"vr\", \"shew\", \n",
    "               \"axfin\",\"PDSpackage\",\"ResiliencePackage\",\"ResiliencePlusEW\",\"Asset_losses\",\"Asset_losses_no_EW\", \"prop_nonpoor\", \"prop_nonpoor_lms\"]\n",
    "\n",
    "# policy_list = [\"PDSpackage\",\"ResiliencePackage\",\"ResiliencePlusEW\", \"prop_nonpoor\", \"prop_nonpoor_lms\"]\n",
    "\n",
    "\n",
    "\n",
    "# policy_list = [\"ResiliencePlusEW\"]\n",
    "\n",
    "country_list = macro.dropna().index.tolist()\n",
    "\n",
    "\n",
    "pol_assess_set = [\"dKtot\",\"dWtot_currency\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===\n",
      "policy: prepare_scaleup\n",
      "Make social protection scalable after a shock\n",
      "Changed in macro: prepare_scaleup (~86.7%).\n",
      "Changed in cat_info: none.\n",
      "Changed in hazard: none.\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by -0.0 bn$ (-0.0% or -0.000% of WDP)\n",
      "Welfare losses were reduced by 0.19 bn$ (1.98%  or 0.250% of WDP)\n",
      "Resilience in countries changed by -0.30%, from (46.21% to 46.51% )\n",
      "Resilience GLOBALLY changed 0.57%, from (28.36% to 28.93% )\n",
      "\n",
      "===\n",
      "policy: borrow_abi\n",
      "Develop contingent finance and reserve funds\n",
      "Changed in macro: borrow_abi (~110.2%).\n",
      "Changed in cat_info: none.\n",
      "Changed in hazard: none.\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by 0.0 bn$ (0.0% or 0.000% of WDP)\n",
      "Welfare losses were reduced by 0.49 bn$ (5.01%  or 0.630% of WDP)\n",
      "Resilience in countries changed by -0.59%, from (46.21% to 46.80% )\n",
      "Resilience GLOBALLY changed 1.50%, from (28.36% to 29.85% )\n",
      "\n",
      "===\n",
      "policy: T_rebuild_K\n",
      "Accelerate\n",
      "reconstruction\n",
      "(by 33%)\n",
      "Changed in macro: T_rebuild_K (~40.0%).\n",
      "Changed in cat_info: none.\n",
      "Changed in hazard: none.\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by 0.0 bn$ (0.0% or 0.000% of WDP)\n",
      "Welfare losses were reduced by 0.63 bn$ (6.43%  or 0.809% of WDP)\n",
      "Resilience in countries changed by -5.35%, from (46.21% to 51.56% )\n",
      "Resilience GLOBALLY changed 1.95%, from (28.36% to 30.31% )\n",
      "\n",
      "===\n",
      "policy: social_p\n",
      "Increase social\n",
      "transfers to poor\n",
      "people to at\n",
      "least 33%\n",
      "Changed in macro: tau_tax (~35.3%).\n",
      "Changed in cat_info: gamma_SP (~67.4%).\n",
      "Changed in hazard: none.\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by 0.0 bn$ (0.0% or 0.000% of WDP)\n",
      "Welfare losses were reduced by 2.97 bn$ (30.45%  or 3.829% of WDP)\n",
      "Resilience in countries changed by -5.08%, from (46.21% to 51.29% )\n",
      "Resilience GLOBALLY changed 12.42%, from (28.36% to 40.77% )\n",
      "\n",
      "===\n",
      "policy: fap\n",
      "Reduce exposure\n",
      "of the poor by 5%\n",
      "of total exposure\n",
      "Changed in macro: none.\n",
      "Changed in cat_info: none.\n",
      "Changed in hazard: fa (~13.7%).\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by 0.1 bn$ (2.0% or 0.071% of WDP)\n",
      "Welfare losses were reduced by 1.63 bn$ (16.68%  or 2.097% of WDP)\n",
      "Resilience in countries changed by -4.21%, from (46.21% to 50.42% )\n",
      "Resilience GLOBALLY changed 5.00%, from (28.36% to 33.36% )\n",
      "\n",
      "===\n",
      "policy: far\n",
      "Reduce\n",
      "exposure of the nonpoor by 5%\n",
      "of total exposure\n",
      "Changed in macro: none.\n",
      "Changed in cat_info: none.\n",
      "Changed in hazard: fa (~3.3%).\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: n, fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by 0.2 bn$ (5.8% or 0.207% of WDP)\n",
      "Welfare losses were reduced by 0.19 bn$ (1.92%  or 0.242% of WDP)\n",
      "Resilience in countries changed by 0.98%, from (46.21% to 45.23% )\n",
      "Resilience GLOBALLY changed -1.12%, from (28.36% to 27.24% )\n",
      "\n",
      "===\n",
      "policy: vp\n",
      "Reduce asset\n",
      "vulnerability\n",
      "(by 30%) of\n",
      "poor people\n",
      "(5% of the population)\n",
      "Changed in macro: none.\n",
      "Changed in cat_info: v (~3.9%).\n",
      "Changed in hazard: none.\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by 0.0 bn$ (0.6% or 0.022% of WDP)\n",
      "Welfare losses were reduced by 0.76 bn$ (7.76%  or 0.976% of WDP)\n",
      "Resilience in countries changed by -1.42%, from (46.21% to 47.63% )\n",
      "Resilience GLOBALLY changed 2.20%, from (28.36% to 30.55% )\n",
      "\n",
      "===\n",
      "policy: kp\n",
      "Increase\n",
      "income of the\n",
      "poor 10%\n",
      "Changed in macro: none.\n",
      "Changed in cat_info: k (~4.8%).\n",
      "Changed in hazard: none.\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by -0.0 bn$ (-0.8% or -0.029% of WDP)\n",
      "Welfare losses were reduced by 0.26 bn$ (2.62%  or 0.330% of WDP)\n",
      "Resilience in countries changed by -0.76%, from (46.21% to 46.97% )\n",
      "Resilience GLOBALLY changed 1.00%, from (28.36% to 29.36% )\n",
      "\n",
      "===\n",
      "policy: vr\n",
      "Reduce asset\n",
      "vulnerability\n",
      "(by 30%) of\n",
      "nonpoor people\n",
      "(5% of the population)\n",
      "Changed in macro: none.\n",
      "Changed in cat_info: v (~0.9%).\n",
      "Changed in hazard: none.\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by 0.0 bn$ (1.7% or 0.061% of WDP)\n",
      "Welfare losses were reduced by 0.06 bn$ (0.58%  or 0.072% of WDP)\n",
      "Resilience in countries changed by 0.27%, from (46.21% to 45.94% )\n",
      "Resilience GLOBALLY changed -0.33%, from (28.36% to 28.03% )\n",
      "\n",
      "===\n",
      "policy: shew\n",
      "Universal\n",
      "access to early\n",
      "warnings\n",
      "Changed in macro: none.\n",
      "Changed in cat_info: none.\n",
      "Changed in hazard: shew (~75.4%).\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by 0.1 bn$ (4.2% or 0.150% of WDP)\n",
      "Welfare losses were reduced by 0.52 bn$ (5.34%  or 0.671% of WDP)\n",
      "Resilience in countries changed by -0.35%, from (46.21% to 46.56% )\n",
      "Resilience GLOBALLY changed 0.34%, from (28.36% to 28.69% )\n",
      "\n",
      "===\n",
      "policy: axfin\n",
      "Universal\n",
      "access to\n",
      "finance\n",
      "Changed in macro: none.\n",
      "Changed in cat_info: axfin (~136.1%).\n",
      "Changed in hazard: none.\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by 0.0 bn$ (0.0% or 0.000% of WDP)\n",
      "Welfare losses were reduced by 0.83 bn$ (8.51%  or 1.070% of WDP)\n",
      "Resilience in countries changed by -1.21%, from (46.21% to 47.42% )\n",
      "Resilience GLOBALLY changed 2.64%, from (28.36% to 30.99% )\n",
      "\n",
      "===\n",
      "policy: PDSpackage\n",
      "Develop contingent finance and reserve funds\n",
      "Make social protection scalable after a shock\n",
      "Postdisaster\n",
      "support\n",
      "package\n",
      "Changed in macro: prepare_scaleup (~86.7%); borrow_abi (~110.2%).\n",
      "Changed in cat_info: none.\n",
      "Changed in hazard: none.\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by -0.0 bn$ (-0.0% or -0.000% of WDP)\n",
      "Welfare losses were reduced by 1.17 bn$ (12.02%  or 1.511% of WDP)\n",
      "Resilience in countries changed by -1.89%, from (46.21% to 48.10% )\n",
      "Resilience GLOBALLY changed 3.87%, from (28.36% to 32.23% )\n",
      "\n",
      "===\n",
      "policy: ResiliencePackage\n",
      "Develop contingent finance and reserve funds\n",
      "Make social protection scalable after a shock\n",
      "Postdisaster\n",
      "support\n",
      "package\n",
      "Universal\n",
      "access to\n",
      "finance\n",
      "Accelerate\n",
      "reconstruction\n",
      "(by 33%)\n",
      "Increase social\n",
      "transfers to poor\n",
      "people to at\n",
      "least 33%\n",
      "Develop market insurance (25% of population, only nonpoor)\n",
      "Resilience package\n",
      "Changed in macro: prepare_scaleup (~86.7%); borrow_abi (~110.2%); T_rebuild_K (~40.0%); tau_tax (~35.3%).\n",
      "Changed in cat_info: axfin (~136.1%); gamma_SP (~67.4%).\n",
      "Changed in hazard: none.\n",
      "args: {'verbose_replace': True, 'optionT': 'prop_nonpoor_lms', 'optionB': 'unlimited', 'share_insured': 0.5, 'optionPDS': 'prop_nonpoor_lms', 'optionFee': 'insurance_premium'}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by -0.0 bn$ (-0.0% or -0.000% of WDP)\n",
      "Welfare losses were reduced by 4.36 bn$ (44.74%  or 5.625% of WDP)\n",
      "Resilience in countries changed by -15.23%, from (46.21% to 61.44% )\n",
      "Resilience GLOBALLY changed 22.96%, from (28.36% to 51.31% )\n",
      "\n",
      "===\n",
      "policy: ResiliencePlusEW\n",
      "Develop contingent finance and reserve funds\n",
      "Make social protection scalable after a shock\n",
      "Postdisaster\n",
      "support\n",
      "package\n",
      "Universal\n",
      "access to\n",
      "finance\n",
      "Accelerate\n",
      "reconstruction\n",
      "(by 33%)\n",
      "Increase social\n",
      "transfers to poor\n",
      "people to at\n",
      "least 33%\n",
      "Develop market insurance (25% of population, only nonpoor)\n",
      "Resilience package\n",
      "Universal\n",
      "access to early\n",
      "warnings\n",
      "Resilience Package + Early Warning Package\n",
      "Changed in macro: prepare_scaleup (~86.7%); borrow_abi (~110.2%); T_rebuild_K (~40.0%); tau_tax (~35.3%).\n",
      "Changed in cat_info: axfin (~136.1%); gamma_SP (~67.4%).\n",
      "Changed in hazard: shew (~75.4%).\n",
      "args: {'verbose_replace': True, 'optionT': 'prop_nonpoor_lms', 'optionB': 'unlimited', 'share_insured': 0.5, 'optionPDS': 'prop_nonpoor_lms', 'optionFee': 'insurance_premium'}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by 0.1 bn$ (4.2% or 0.150% of WDP)\n",
      "Welfare losses were reduced by 4.61 bn$ (47.28%  or 5.945% of WDP)\n",
      "Resilience in countries changed by -15.45%, from (46.21% to 61.66% )\n",
      "Resilience GLOBALLY changed 23.17%, from (28.36% to 51.52% )\n",
      "\n",
      "===\n",
      "policy: Asset_losses\n",
      "Reduce exposure\n",
      "of the poor by 5%\n",
      "of total exposure\n",
      "Reduce\n",
      "exposure of the nonpoor by 5%\n",
      "of total exposure\n",
      "Reduce asset\n",
      "vulnerability\n",
      "(by 30%) of\n",
      "poor people\n",
      "(5% of the population)\n",
      "Reduce asset\n",
      "vulnerability\n",
      "(by 30%) of\n",
      "nonpoor people\n",
      "(5% of the population)\n",
      "Universal\n",
      "access to early\n",
      "warnings\n",
      "Asset losses package \n",
      "Changed in macro: none.\n",
      "Changed in cat_info: v (~4.8%).\n",
      "Changed in hazard: fa (~16.8%); shew (~75.4%).\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: n, fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by 0.4 bn$ (13.4% or 0.477% of WDP)\n",
      "Welfare losses were reduced by 2.82 bn$ (28.91%  or 3.635% of WDP)\n",
      "Resilience in countries changed by -4.80%, from (46.21% to 51.01% )\n",
      "Resilience GLOBALLY changed 6.19%, from (28.36% to 34.55% )\n",
      "\n",
      "===\n",
      "policy: Asset_losses_no_EW\n",
      "Reduce exposure\n",
      "of the poor by 5%\n",
      "of total exposure\n",
      "Reduce\n",
      "exposure of the nonpoor by 5%\n",
      "of total exposure\n",
      "Reduce asset\n",
      "vulnerability\n",
      "(by 30%) of\n",
      "poor people\n",
      "(5% of the population)\n",
      "Reduce asset\n",
      "vulnerability\n",
      "(by 30%) of\n",
      "nonpoor people\n",
      "(5% of the population)\n",
      "Asset losses package (excluding early warnings)\n",
      "Changed in macro: none.\n",
      "Changed in cat_info: v (~4.8%).\n",
      "Changed in hazard: fa (~16.8%).\n",
      "args: {'verbose_replace': True}\n",
      "Replaced in macro: \n",
      "Replaced in cats: n, fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by 0.3 bn$ (9.6% or 0.341% of WDP)\n",
      "Welfare losses were reduced by 2.43 bn$ (24.95%  or 3.137% of WDP)\n",
      "Resilience in countries changed by -4.44%, from (46.21% to 50.65% )\n",
      "Resilience GLOBALLY changed 5.81%, from (28.36% to 34.17% )\n",
      "\n",
      "===\n",
      "policy: prop_nonpoor\n",
      "Develop market\n",
      "insurance\n",
      "(nonpoor people)\n",
      "Changed in macro: none.\n",
      "Changed in cat_info: none.\n",
      "Changed in hazard: none.\n",
      "args: {'verbose_replace': True, 'optionT': 'perfect', 'optionB': 'unlimited', 'share_insured': 0.25, 'optionPDS': 'prop_nonpoor', 'optionFee': 'insurance_premium'}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by 0.0 bn$ (0.0% or 0.000% of WDP)\n",
      "Welfare losses were reduced by 0.15 bn$ (1.53%  or 0.192% of WDP)\n",
      "Resilience in countries changed by -1.28%, from (46.21% to 47.49% )\n",
      "Resilience GLOBALLY changed 0.44%, from (28.36% to 28.80% )\n",
      "\n",
      "===\n",
      "policy: prop_nonpoor_lms\n",
      "Develop market insurance (25% of population, only nonpoor)\n",
      "Changed in macro: none.\n",
      "Changed in cat_info: none.\n",
      "Changed in hazard: none.\n",
      "args: {'verbose_replace': True, 'optionT': 'prop_nonpoor_lms', 'optionB': 'unlimited', 'share_insured': 0.5, 'optionPDS': 'prop_nonpoor_lms', 'optionFee': 'insurance_premium'}\n",
      "Replaced in macro: \n",
      "Replaced in cats: fa, shew\n",
      "Replaced in both: \n",
      "\n",
      "Asset losses were reduced by -0.0 bn$ (-0.0% or -0.000% of WDP)\n",
      "Welfare losses were reduced by 0.09 bn$ (0.89%  or 0.112% of WDP)\n",
      "Resilience in countries changed by -0.75%, from (46.21% to 46.96% )\n",
      "Resilience GLOBALLY changed 0.26%, from (28.36% to 28.61% )\n"
     ]
    }
   ],
   "source": [
    "common_places = [c for c in macro.index if c in cat_info.index and c in hazard_ratios.index and c in infra_stocks.index]\n",
    "macro = macro.ix[common_places]        \n",
    "cat_info = cat_info.ix[common_places]        \n",
    "hazard_ratios = hazard_ratios.ix[common_places]        \n",
    "infra_stocks = infra_stocks.ix[common_places]\n",
    "\n",
    "#initialize input (baseline inputs)\n",
    "m_=macro.copy(deep=True)\n",
    "c_=cat_info.copy(deep=True)\n",
    "hr_ = hazard_ratios.copy(deep=True)\n",
    "a_=dict(verbose_replace=True)\n",
    "i_=infra_stocks.copy(deep=True)\n",
    "\n",
    " \n",
    "#initialize outptus\n",
    "delta = pd.DataFrame(index=pd.MultiIndex.from_product([country_list, pol_assess_set+[\"risk_to_assets\",\"resilience\",\"risk\"]], names=[economy, 'outputs']), \n",
    "                     columns=pd.Index([],name=\"policies\"))\n",
    "rel = delta.copy()\n",
    "reltogdp=  delta.copy()\n",
    "nuvalues=delta.copy()\n",
    "policy_description = pd.Series(index=policy_list)\n",
    "\n",
    "#loop over policies\n",
    "for pol in policy_list:\n",
    "    print(\"\\n===\\npolicy: \"+pol)\n",
    "    m,c,hr,a,desc =apply_policy(m_,c_,hr_,a_ ,policy_name=pol)\n",
    "    \n",
    "    d,r,rto,nv=test_package(m,c,i_,hr,a,return_rel=True)\n",
    "    \n",
    "    delta[pol]= d[pol_assess_set].stack()\n",
    "    rel[pol]  = r[pol_assess_set].stack()\n",
    "    reltogdp[pol] = rto[pol_assess_set].stack()\n",
    "    nuvalues[pol] = nv.stack()\n",
    "    \n",
    "    \n",
    "    policy_description[pol] = desc\n",
    "    \n",
    "#format as renders wants it    \n",
    "delta =delta.dropna().unstack()\n",
    "rel =rel.dropna().unstack()\n",
    "reltogdp =reltogdp.dropna().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>policies</th>\n",
       "      <th colspan=\"2\" halign=\"left\">prepare_scaleup</th>\n",
       "      <th colspan=\"2\" halign=\"left\">borrow_abi</th>\n",
       "      <th colspan=\"2\" halign=\"left\">T_rebuild_K</th>\n",
       "      <th colspan=\"2\" halign=\"left\">social_p</th>\n",
       "      <th colspan=\"2\" halign=\"left\">fap</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ResiliencePlusEW</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Asset_losses</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Asset_losses_no_EW</th>\n",
       "      <th colspan=\"2\" halign=\"left\">prop_nonpoor</th>\n",
       "      <th colspan=\"2\" halign=\"left\">prop_nonpoor_lms</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outputs</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>...</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</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",
       "      <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>Belize</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.893215e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.759957e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.202762e+05</td>\n",
       "      <td>1.882431e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>1.318235e+07</td>\n",
       "      <td>8.331843e+07</td>\n",
       "      <td>1.788989e+07</td>\n",
       "      <td>7.030303e+07</td>\n",
       "      <td>5.843398e+06</td>\n",
       "      <td>3.422569e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.031342e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.855617e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fiji</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.360750e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.443549e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.490892e+07</td>\n",
       "      <td>9.768053e+06</td>\n",
       "      <td>6.261089e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>5.960464e-08</td>\n",
       "      <td>1.145530e+08</td>\n",
       "      <td>3.375615e+07</td>\n",
       "      <td>1.052163e+08</td>\n",
       "      <td>3.375615e+07</td>\n",
       "      <td>1.052163e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.337756e+07</td>\n",
       "      <td>5.960464e-08</td>\n",
       "      <td>8.106983e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haiti</th>\n",
       "      <td>-2.384186e-07</td>\n",
       "      <td>1.746147e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.525820e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.304891e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.793052e+09</td>\n",
       "      <td>2.532723e+07</td>\n",
       "      <td>1.348473e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>4.839466e+07</td>\n",
       "      <td>3.886682e+09</td>\n",
       "      <td>1.710980e+08</td>\n",
       "      <td>2.204806e+09</td>\n",
       "      <td>1.273821e+08</td>\n",
       "      <td>1.950242e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.741812e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.443009e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.381430e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.794235e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.216123e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.253416e+07</td>\n",
       "      <td>1.233593e+07</td>\n",
       "      <td>1.152049e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>2.586080e+07</td>\n",
       "      <td>2.752638e+08</td>\n",
       "      <td>8.282440e+07</td>\n",
       "      <td>2.466118e+08</td>\n",
       "      <td>5.943163e+07</td>\n",
       "      <td>2.024989e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.990488e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.780113e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kiribati</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.012862e+03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.584925e+03</td>\n",
       "      <td>3.191447e+02</td>\n",
       "      <td>2.596895e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>2.733643e+03</td>\n",
       "      <td>9.872114e+03</td>\n",
       "      <td>3.809845e+03</td>\n",
       "      <td>8.745490e+03</td>\n",
       "      <td>1.345253e+03</td>\n",
       "      <td>4.378626e+03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.020579e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.298551e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Papua New Guinea</th>\n",
       "      <td>-5.960464e-08</td>\n",
       "      <td>6.517833e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.329871e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.445451e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.872074e+07</td>\n",
       "      <td>3.829772e+06</td>\n",
       "      <td>4.894086e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>1.586148e+07</td>\n",
       "      <td>1.482672e+08</td>\n",
       "      <td>3.738984e+07</td>\n",
       "      <td>1.116691e+08</td>\n",
       "      <td>2.299255e+07</td>\n",
       "      <td>8.407184e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.733268e+07</td>\n",
       "      <td>-5.960464e-08</td>\n",
       "      <td>1.019991e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solomon Islands</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.699764e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.743927e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.265433e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.202951e+07</td>\n",
       "      <td>1.702983e+06</td>\n",
       "      <td>1.815881e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>6.389339e+06</td>\n",
       "      <td>5.096083e+07</td>\n",
       "      <td>1.315716e+07</td>\n",
       "      <td>4.024840e+07</td>\n",
       "      <td>7.395131e+06</td>\n",
       "      <td>2.903133e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.831749e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.256292e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Lucia</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.817278e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.631380e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.011723e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.406870e+06</td>\n",
       "      <td>6.396262e+05</td>\n",
       "      <td>8.613351e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>3.952337e+06</td>\n",
       "      <td>3.434963e+07</td>\n",
       "      <td>7.973409e+06</td>\n",
       "      <td>2.587519e+07</td>\n",
       "      <td>4.375386e+06</td>\n",
       "      <td>1.732659e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.247605e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.370439e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonga</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.261582e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.953364e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.538534e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.642023e+06</td>\n",
       "      <td>6.401356e+05</td>\n",
       "      <td>4.526857e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>2.763934e+06</td>\n",
       "      <td>1.392492e+07</td>\n",
       "      <td>5.864349e+06</td>\n",
       "      <td>1.281555e+07</td>\n",
       "      <td>3.358353e+06</td>\n",
       "      <td>8.778103e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.973107e+06</td>\n",
       "      <td>-7.450581e-09</td>\n",
       "      <td>1.147920e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "policies         prepare_scaleup                borrow_abi                 \\\n",
       "outputs                    dKtot dWtot_currency      dKtot dWtot_currency   \n",
       "country                                                                     \n",
       "Belize              0.000000e+00   4.893215e+05        0.0   0.000000e+00   \n",
       "Fiji                0.000000e+00   0.000000e+00        0.0   8.360750e+06   \n",
       "Haiti              -2.384186e-07   1.746147e+08        0.0   4.525820e+08   \n",
       "Jamaica             0.000000e+00   9.381430e+06        0.0   1.794235e+07   \n",
       "Kiribati            0.000000e+00   0.000000e+00        0.0   0.000000e+00   \n",
       "Papua New Guinea   -5.960464e-08   6.517833e+06        0.0   5.329871e+06   \n",
       "Solomon Islands     0.000000e+00   1.699764e+06        0.0   2.743927e+06   \n",
       "St. Lucia           0.000000e+00   4.817278e+05        0.0   7.631380e+05   \n",
       "Tonga               0.000000e+00   2.261582e+05        0.0   5.953364e+05   \n",
       "\n",
       "policies         T_rebuild_K                social_p                 \\\n",
       "outputs                dKtot dWtot_currency    dKtot dWtot_currency   \n",
       "country                                                               \n",
       "Belize                   0.0   3.759957e+07      0.0   0.000000e+00   \n",
       "Fiji                     0.0   5.443549e+07      0.0   4.490892e+07   \n",
       "Haiti                    0.0   3.304891e+08      0.0   2.793052e+09   \n",
       "Jamaica                  0.0   1.216123e+08      0.0   4.253416e+07   \n",
       "Kiribati                 0.0   2.012862e+03      0.0   2.584925e+03   \n",
       "Papua New Guinea         0.0   4.445451e+07      0.0   5.872074e+07   \n",
       "Solomon Islands          0.0   1.265433e+07      0.0   2.202951e+07   \n",
       "St. Lucia                0.0   2.011723e+07      0.0   3.406870e+06   \n",
       "Tonga                    0.0   5.538534e+06      0.0   2.642023e+06   \n",
       "\n",
       "policies                   fap                     ...       ResiliencePlusEW  \\\n",
       "outputs                  dKtot dWtot_currency      ...                  dKtot   \n",
       "country                                            ...                          \n",
       "Belize            6.202762e+05   1.882431e+07      ...           1.318235e+07   \n",
       "Fiji              9.768053e+06   6.261089e+07      ...           5.960464e-08   \n",
       "Haiti             2.532723e+07   1.348473e+09      ...           4.839466e+07   \n",
       "Jamaica           1.233593e+07   1.152049e+08      ...           2.586080e+07   \n",
       "Kiribati          3.191447e+02   2.596895e+03      ...           2.733643e+03   \n",
       "Papua New Guinea  3.829772e+06   4.894086e+07      ...           1.586148e+07   \n",
       "Solomon Islands   1.702983e+06   1.815881e+07      ...           6.389339e+06   \n",
       "St. Lucia         6.396262e+05   8.613351e+06      ...           3.952337e+06   \n",
       "Tonga             6.401356e+05   4.526857e+06      ...           2.763934e+06   \n",
       "\n",
       "policies                         Asset_losses                 \\\n",
       "outputs          dWtot_currency         dKtot dWtot_currency   \n",
       "country                                                        \n",
       "Belize             8.331843e+07  1.788989e+07   7.030303e+07   \n",
       "Fiji               1.145530e+08  3.375615e+07   1.052163e+08   \n",
       "Haiti              3.886682e+09  1.710980e+08   2.204806e+09   \n",
       "Jamaica            2.752638e+08  8.282440e+07   2.466118e+08   \n",
       "Kiribati           9.872114e+03  3.809845e+03   8.745490e+03   \n",
       "Papua New Guinea   1.482672e+08  3.738984e+07   1.116691e+08   \n",
       "Solomon Islands    5.096083e+07  1.315716e+07   4.024840e+07   \n",
       "St. Lucia          3.434963e+07  7.973409e+06   2.587519e+07   \n",
       "Tonga              1.392492e+07  5.864349e+06   1.281555e+07   \n",
       "\n",
       "policies         Asset_losses_no_EW                prop_nonpoor  \\\n",
       "outputs                       dKtot dWtot_currency        dKtot   \n",
       "country                                                           \n",
       "Belize                 5.843398e+06   3.422569e+07          0.0   \n",
       "Fiji                   3.375615e+07   1.052163e+08          0.0   \n",
       "Haiti                  1.273821e+08   1.950242e+09          0.0   \n",
       "Jamaica                5.943163e+07   2.024989e+08          0.0   \n",
       "Kiribati               1.345253e+03   4.378626e+03          0.0   \n",
       "Papua New Guinea       2.299255e+07   8.407184e+07          0.0   \n",
       "Solomon Islands        7.395131e+06   2.903133e+07          0.0   \n",
       "St. Lucia              4.375386e+06   1.732659e+07          0.0   \n",
       "Tonga                  3.358353e+06   8.778103e+06          0.0   \n",
       "\n",
       "policies                        prop_nonpoor_lms                 \n",
       "outputs          dWtot_currency            dKtot dWtot_currency  \n",
       "country                                                          \n",
       "Belize             3.031342e+06     0.000000e+00   1.855617e+06  \n",
       "Fiji               1.337756e+07     5.960464e-08   8.106983e+06  \n",
       "Haiti              7.741812e+07     0.000000e+00   4.443009e+07  \n",
       "Jamaica            2.990488e+07     0.000000e+00   1.780113e+07  \n",
       "Kiribati           9.020579e+02     0.000000e+00   5.298551e+02  \n",
       "Papua New Guinea   1.733268e+07    -5.960464e-08   1.019991e+07  \n",
       "Solomon Islands    3.831749e+06     0.000000e+00   2.256292e+06  \n",
       "St. Lucia          2.247605e+06     0.000000e+00   1.370439e+06  \n",
       "Tonga              1.973107e+06    -7.450581e-09   1.147920e+06  \n",
       "\n",
       "[9 rows x 36 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>fa</th>\n",
       "      <th>fa_in_gdp</th>\n",
       "      <th>gdp_pc_pp</th>\n",
       "      <th>pop</th>\n",
       "      <th>resilience</th>\n",
       "      <th>risk</th>\n",
       "      <th>risk_to_assets</th>\n",
       "      <th>v</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.293418e+07</td>\n",
       "      <td>5.119255e+08</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.579015e+07</td>\n",
       "      <td>1.268339e+09</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.960464e-08</td>\n",
       "      <td>9.872114e+03</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.763934e+06</td>\n",
       "      <td>3.434963e+07</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.389339e+06</td>\n",
       "      <td>8.331843e+07</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.586148e+07</td>\n",
       "      <td>1.482672e+08</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.839466e+07</td>\n",
       "      <td>3.886682e+09</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              dKtot  dWtot_currency   fa  fa_in_gdp  gdp_pc_pp  pop  \\\n",
       "count  9.000000e+00    9.000000e+00  0.0        0.0        0.0  0.0   \n",
       "mean   1.293418e+07    5.119255e+08  NaN        NaN        NaN  NaN   \n",
       "std    1.579015e+07    1.268339e+09  NaN        NaN        NaN  NaN   \n",
       "min    5.960464e-08    9.872114e+03  NaN        NaN        NaN  NaN   \n",
       "25%    2.763934e+06    3.434963e+07  NaN        NaN        NaN  NaN   \n",
       "50%    6.389339e+06    8.331843e+07  NaN        NaN        NaN  NaN   \n",
       "75%    1.586148e+07    1.482672e+08  NaN        NaN        NaN  NaN   \n",
       "max    4.839466e+07    3.886682e+09  NaN        NaN        NaN  NaN   \n",
       "\n",
       "       resilience  risk  risk_to_assets    v  \n",
       "count         0.0   0.0             0.0  0.0  \n",
       "mean          NaN   NaN             NaN  NaN  \n",
       "std           NaN   NaN             NaN  NaN  \n",
       "min           NaN   NaN             NaN  NaN  \n",
       "25%           NaN   NaN             NaN  NaN  \n",
       "50%           NaN   NaN             NaN  NaN  \n",
       "75%           NaN   NaN             NaN  NaN  \n",
       "max           NaN   NaN             NaN  NaN  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=pd.read_csv(\"intermediate/main_results.csv\", index_col=\"country\")\n",
    "a.head()\n",
    "\n",
    "big_table=pd.DataFrame(index=pd.MultiIndex.from_product([country_list, a.columns | delta.ResiliencePlusEW.columns ]))\n",
    "big_table[\"Current\"] = a.stack()\n",
    "\n",
    "big_table[\"Difference\"] = delta.ResiliencePlusEW.stack()\n",
    "\n",
    "big_table[\"Differencerel\"] =  rel.ResiliencePlusEW.stack()\n",
    "\n",
    "big_table[\"With_package\"] = nuvalues.ResiliencePlusEW\n",
    "\n",
    "big_table.unstack().to_excel(\"results/big_table.xlsx\")\n",
    "\n",
    "\n",
    "big_table.Difference.unstack().describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "from os import makedirs\n",
    "from fancy_round import fancy_round"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "makedirs(\"cards\", exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "with pd.ExcelWriter('cards/policy_impact.xlsx') as writer:\n",
    "        delta.rename(columns=policy_description).rename(columns={\"dKtot\":\"Asset losses\", \"dWtot_currency\":\"Wellbeing losses\"}\n",
    "                                                       ).to_excel(writer, sheet_name=\"absolute (USD PPP)\")\n",
    "\n",
    "        rel.rename(columns=policy_description).rename(columns={\"dKtot\":\"Asset losses\", \"dWtot_currency\":\"Wellbeing losses\"}\n",
    "                                                       ).to_excel(writer, sheet_name=\"relative to current losses\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>\n",
       "      <th>policies</th>\n",
       "      <th colspan=\"2\" halign=\"left\">prepare_scaleup</th>\n",
       "      <th colspan=\"2\" halign=\"left\">borrow_abi</th>\n",
       "      <th colspan=\"2\" halign=\"left\">T_rebuild_K</th>\n",
       "      <th colspan=\"2\" halign=\"left\">social_p</th>\n",
       "      <th colspan=\"2\" halign=\"left\">fap</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ResiliencePlusEW</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Asset_losses</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Asset_losses_no_EW</th>\n",
       "      <th colspan=\"2\" halign=\"left\">prop_nonpoor</th>\n",
       "      <th colspan=\"2\" halign=\"left\">prop_nonpoor_lms</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outputs</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>...</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</th>\n",
       "      <th>dKtot</th>\n",
       "      <th>dWtot_currency</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",
       "      <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>Belize</th>\n",
       "      <td>0.0</td>\n",
       "      <td>489321.479583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.759957e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>620276.211549</td>\n",
       "      <td>1.882431e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>1.318235e+07</td>\n",
       "      <td>8.331843e+07</td>\n",
       "      <td>1.788989e+07</td>\n",
       "      <td>7.030303e+07</td>\n",
       "      <td>5.843398e+06</td>\n",
       "      <td>3.422569e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.031342e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.855617e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "policies prepare_scaleup                borrow_abi                T_rebuild_K  \\\n",
       "outputs            dKtot dWtot_currency      dKtot dWtot_currency       dKtot   \n",
       "country                                                                         \n",
       "Belize               0.0  489321.479583        0.0            0.0         0.0   \n",
       "\n",
       "policies                social_p                           fap                 \\\n",
       "outputs  dWtot_currency    dKtot dWtot_currency          dKtot dWtot_currency   \n",
       "country                                                                         \n",
       "Belize     3.759957e+07      0.0            0.0  620276.211549   1.882431e+07   \n",
       "\n",
       "policies      ...       ResiliencePlusEW                 Asset_losses  \\\n",
       "outputs       ...                  dKtot dWtot_currency         dKtot   \n",
       "country       ...                                                       \n",
       "Belize        ...           1.318235e+07   8.331843e+07  1.788989e+07   \n",
       "\n",
       "policies                Asset_losses_no_EW                prop_nonpoor  \\\n",
       "outputs  dWtot_currency              dKtot dWtot_currency        dKtot   \n",
       "country                                                                  \n",
       "Belize     7.030303e+07       5.843398e+06   3.422569e+07          0.0   \n",
       "\n",
       "policies                prop_nonpoor_lms                 \n",
       "outputs  dWtot_currency            dKtot dWtot_currency  \n",
       "country                                                  \n",
       "Belize     3.031342e+06              0.0   1.855617e+06  \n",
       "\n",
       "[1 rows x 36 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "for c in delta.index:\n",
    "    x=pd.concat([1e-6*delta.ix[c].unstack()[[\"dWtot_currency\",\"dKtot\"]].rename(columns={\"dKtot\":\"Asset losses m$\", \"dWtot_currency\":\"Wellbeing losses m$\"}),\n",
    "           100*rel.ix[c].unstack()[[\"dWtot_currency\",\"dKtot\"]].rename(columns={\"dKtot\":\"Asset losses %\", \"dWtot_currency\":\"Wellbeing losses %\"})],axis=1).sort_values(by=\"Wellbeing losses m$\", \n",
    "           ascending=False).applymap(fancy_round).drop([\"ResiliencePlusEW\", \"Asset_losses\", \"Asset_losses_no_EW\", \"ResiliencePackage\",\"prop_nonpoor_lms\",\"prepare_scaleup\",\"borrow_abi\"]\n",
    "                                                      ).rename(index=policy_description)\n",
    "    x.index.name=c\n",
    "    x.to_csv(\"cards/country_tables/\"+c+\".csv\"\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>outputs</th>\n",
       "      <th>Wellbeing losses m$</th>\n",
       "      <th>Asset losses m$</th>\n",
       "      <th>Wellbeing losses %</th>\n",
       "      <th>Asset losses %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonga</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accelerate\\nreconstruction\\n(by 33%)</th>\n",
       "      <td>5.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.50</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Universal\\naccess to early\\nwarnings</th>\n",
       "      <td>4.80</td>\n",
       "      <td>2.80</td>\n",
       "      <td>8.20</td>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reduce exposure\\nof the poor by 5%\\nof total exposure</th>\n",
       "      <td>4.50</td>\n",
       "      <td>0.64</td>\n",
       "      <td>7.80</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Increase social\\ntransfers to poor\\npeople to at\\nleast 33%</th>\n",
       "      <td>2.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reduce\\nexposure of the nonpoor by 5%\\nof total exposure</th>\n",
       "      <td>2.50</td>\n",
       "      <td>2.10</td>\n",
       "      <td>4.30</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Develop market\\ninsurance\\n(nonpoor people)</th>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reduce asset\\nvulnerability\\n(by 30%) of\\npoor people\\n(5% of the population)</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Postdisaster\\nsupport\\npackage</th>\n",
       "      <td>1.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reduce asset\\nvulnerability\\n(by 30%) of\\nnonpoor people\\n(5% of the population)</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Universal\\naccess to\\nfinance</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Increase\\nincome of the\\npoor 10%</th>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-0.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "outputs                                             Wellbeing losses m$  \\\n",
       "Tonga                                                                     \n",
       "Accelerate\\nreconstruction\\n(by 33%)                               5.50   \n",
       "Universal\\naccess to early\\nwarnings                               4.80   \n",
       "Reduce exposure\\nof the poor by 5%\\nof total ex...                 4.50   \n",
       "Increase social\\ntransfers to poor\\npeople to a...                 2.60   \n",
       "Reduce\\nexposure of the nonpoor by 5%\\nof total...                 2.50   \n",
       "Develop market\\ninsurance\\n(nonpoor people)                        2.00   \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\npoor ...                 1.50   \n",
       "Postdisaster\\nsupport\\npackage                                     1.30   \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\nnonpo...                 0.80   \n",
       "Universal\\naccess to\\nfinance                                      0.43   \n",
       "Increase\\nincome of the\\npoor 10%                                  0.22   \n",
       "\n",
       "outputs                                             Asset losses m$  \\\n",
       "Tonga                                                                 \n",
       "Accelerate\\nreconstruction\\n(by 33%)                           0.00   \n",
       "Universal\\naccess to early\\nwarnings                           2.80   \n",
       "Reduce exposure\\nof the poor by 5%\\nof total ex...             0.64   \n",
       "Increase social\\ntransfers to poor\\npeople to a...             0.00   \n",
       "Reduce\\nexposure of the nonpoor by 5%\\nof total...             2.10   \n",
       "Develop market\\ninsurance\\n(nonpoor people)                    0.00   \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\npoor ...             0.19   \n",
       "Postdisaster\\nsupport\\npackage                                 0.00   \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\nnonpo...             0.63   \n",
       "Universal\\naccess to\\nfinance                                  0.00   \n",
       "Increase\\nincome of the\\npoor 10%                             -0.26   \n",
       "\n",
       "outputs                                             Wellbeing losses %  \\\n",
       "Tonga                                                                    \n",
       "Accelerate\\nreconstruction\\n(by 33%)                              9.50   \n",
       "Universal\\naccess to early\\nwarnings                              8.20   \n",
       "Reduce exposure\\nof the poor by 5%\\nof total ex...                7.80   \n",
       "Increase social\\ntransfers to poor\\npeople to a...                4.50   \n",
       "Reduce\\nexposure of the nonpoor by 5%\\nof total...                4.30   \n",
       "Develop market\\ninsurance\\n(nonpoor people)                       3.40   \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\npoor ...                2.50   \n",
       "Postdisaster\\nsupport\\npackage                                    2.20   \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\nnonpo...                1.40   \n",
       "Universal\\naccess to\\nfinance                                     0.73   \n",
       "Increase\\nincome of the\\npoor 10%                                 0.38   \n",
       "\n",
       "outputs                                             Asset losses %  \n",
       "Tonga                                                               \n",
       "Accelerate\\nreconstruction\\n(by 33%)                          0.00  \n",
       "Universal\\naccess to early\\nwarnings                          7.70  \n",
       "Reduce exposure\\nof the poor by 5%\\nof total ex...            1.80  \n",
       "Increase social\\ntransfers to poor\\npeople to a...            0.00  \n",
       "Reduce\\nexposure of the nonpoor by 5%\\nof total...            5.80  \n",
       "Develop market\\ninsurance\\n(nonpoor people)                   0.00  \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\npoor ...            0.53  \n",
       "Postdisaster\\nsupport\\npackage                                0.00  \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\nnonpo...            1.70  \n",
       "Universal\\naccess to\\nfinance                                 0.00  \n",
       "Increase\\nincome of the\\npoor 10%                            -0.71  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    s=\"{s}\".format(s=fancy_round(x,2))\n",
    "    if s.endswith(\".0\"):\n",
    "        s=s[:-2]\n",
    "    if s==\"-0\":\n",
    "        s=\"0\"\n",
    "    return s    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "for c in delta.index:\n",
    "\n",
    "    y=1e-6*delta.ix[c].unstack()[[\"dWtot_currency\",\"dKtot\"]]\n",
    "    x=(y.applymap(f).astype(str) \n",
    "     + ' ('+(100*rel.ix[c].unstack()[[\"dWtot_currency\",\"dKtot\"]]).applymap(f).astype(str)+'%)'\n",
    "    )\n",
    "\n",
    "\n",
    "    x=x.rename(columns={\"dKtot\":\"Asset losses label\", \"dWtot_currency\":\"Wellbeing losses  label\"}\n",
    "            )\n",
    "\n",
    "    y=y.rename(columns={\"dKtot\":\"Asset losses value\", \"dWtot_currency\":\"Wellbeing losses value\"}\n",
    "            )\n",
    "\n",
    "    idx = delta.ix[c].unstack().sort_values(by=\"dWtot_currency\", \n",
    "              ascending=False).drop([\"ResiliencePlusEW\", \"Asset_losses\", \"Asset_losses_no_EW\", \"ResiliencePackage\",\"prop_nonpoor_lms\",\"prepare_scaleup\",\"borrow_abi\"]).index\n",
    "\n",
    "\n",
    "    # x=\n",
    "    # x\n",
    "    t = pd.concat([y,x],axis=1).ix[idx].rename(index=policy_description)\n",
    "    t.index.name=c\n",
    "    t.to_csv(\"cards/country_tables/\"+c+\".csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>outputs</th>\n",
       "      <th>Wellbeing losses value</th>\n",
       "      <th>Asset losses value</th>\n",
       "      <th>Wellbeing losses  label</th>\n",
       "      <th>Asset losses label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonga</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accelerate\\nreconstruction\\n(by 33%)</th>\n",
       "      <td>5.538534</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.5 (9.5%)</td>\n",
       "      <td>0 (0%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Universal\\naccess to early\\nwarnings</th>\n",
       "      <td>4.771007</td>\n",
       "      <td>2.763934</td>\n",
       "      <td>4.8 (8.2%)</td>\n",
       "      <td>2.8 (7.7%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reduce exposure\\nof the poor by 5%\\nof total exposure</th>\n",
       "      <td>4.526857</td>\n",
       "      <td>0.640136</td>\n",
       "      <td>4.5 (7.8%)</td>\n",
       "      <td>0.64 (1.8%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Increase social\\ntransfers to poor\\npeople to at\\nleast 33%</th>\n",
       "      <td>2.642023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.6 (4.5%)</td>\n",
       "      <td>0 (0%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reduce\\nexposure of the nonpoor by 5%\\nof total exposure</th>\n",
       "      <td>2.524376</td>\n",
       "      <td>2.089119</td>\n",
       "      <td>2.5 (4.3%)</td>\n",
       "      <td>2.1 (5.8%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Develop market\\ninsurance\\n(nonpoor people)</th>\n",
       "      <td>1.973107</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2 (3.4%)</td>\n",
       "      <td>0 (0%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reduce asset\\nvulnerability\\n(by 30%) of\\npoor people\\n(5% of the population)</th>\n",
       "      <td>1.471832</td>\n",
       "      <td>0.192041</td>\n",
       "      <td>1.5 (2.5%)</td>\n",
       "      <td>0.19 (0.53%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Postdisaster\\nsupport\\npackage</th>\n",
       "      <td>1.263995</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.3 (2.2%)</td>\n",
       "      <td>0 (0%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reduce asset\\nvulnerability\\n(by 30%) of\\nnonpoor people\\n(5% of the population)</th>\n",
       "      <td>0.803318</td>\n",
       "      <td>0.626736</td>\n",
       "      <td>0.8 (1.4%)</td>\n",
       "      <td>0.63 (1.7%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Universal\\naccess to\\nfinance</th>\n",
       "      <td>0.426168</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.43 (0.73%)</td>\n",
       "      <td>0 (0%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Increase\\nincome of the\\npoor 10%</th>\n",
       "      <td>0.222658</td>\n",
       "      <td>-0.256054</td>\n",
       "      <td>0.22 (0.38%)</td>\n",
       "      <td>-0.26 (-0.71%)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "outputs                                             Wellbeing losses value  \\\n",
       "Tonga                                                                        \n",
       "Accelerate\\nreconstruction\\n(by 33%)                              5.538534   \n",
       "Universal\\naccess to early\\nwarnings                              4.771007   \n",
       "Reduce exposure\\nof the poor by 5%\\nof total ex...                4.526857   \n",
       "Increase social\\ntransfers to poor\\npeople to a...                2.642023   \n",
       "Reduce\\nexposure of the nonpoor by 5%\\nof total...                2.524376   \n",
       "Develop market\\ninsurance\\n(nonpoor people)                       1.973107   \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\npoor ...                1.471832   \n",
       "Postdisaster\\nsupport\\npackage                                    1.263995   \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\nnonpo...                0.803318   \n",
       "Universal\\naccess to\\nfinance                                     0.426168   \n",
       "Increase\\nincome of the\\npoor 10%                                 0.222658   \n",
       "\n",
       "outputs                                             Asset losses value  \\\n",
       "Tonga                                                                    \n",
       "Accelerate\\nreconstruction\\n(by 33%)                          0.000000   \n",
       "Universal\\naccess to early\\nwarnings                          2.763934   \n",
       "Reduce exposure\\nof the poor by 5%\\nof total ex...            0.640136   \n",
       "Increase social\\ntransfers to poor\\npeople to a...            0.000000   \n",
       "Reduce\\nexposure of the nonpoor by 5%\\nof total...            2.089119   \n",
       "Develop market\\ninsurance\\n(nonpoor people)                   0.000000   \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\npoor ...            0.192041   \n",
       "Postdisaster\\nsupport\\npackage                                0.000000   \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\nnonpo...            0.626736   \n",
       "Universal\\naccess to\\nfinance                                 0.000000   \n",
       "Increase\\nincome of the\\npoor 10%                            -0.256054   \n",
       "\n",
       "outputs                                            Wellbeing losses  label  \\\n",
       "Tonga                                                                        \n",
       "Accelerate\\nreconstruction\\n(by 33%)                            5.5 (9.5%)   \n",
       "Universal\\naccess to early\\nwarnings                            4.8 (8.2%)   \n",
       "Reduce exposure\\nof the poor by 5%\\nof total ex...              4.5 (7.8%)   \n",
       "Increase social\\ntransfers to poor\\npeople to a...              2.6 (4.5%)   \n",
       "Reduce\\nexposure of the nonpoor by 5%\\nof total...              2.5 (4.3%)   \n",
       "Develop market\\ninsurance\\n(nonpoor people)                       2 (3.4%)   \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\npoor ...              1.5 (2.5%)   \n",
       "Postdisaster\\nsupport\\npackage                                  1.3 (2.2%)   \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\nnonpo...              0.8 (1.4%)   \n",
       "Universal\\naccess to\\nfinance                                 0.43 (0.73%)   \n",
       "Increase\\nincome of the\\npoor 10%                             0.22 (0.38%)   \n",
       "\n",
       "outputs                                            Asset losses label  \n",
       "Tonga                                                                  \n",
       "Accelerate\\nreconstruction\\n(by 33%)                           0 (0%)  \n",
       "Universal\\naccess to early\\nwarnings                       2.8 (7.7%)  \n",
       "Reduce exposure\\nof the poor by 5%\\nof total ex...        0.64 (1.8%)  \n",
       "Increase social\\ntransfers to poor\\npeople to a...             0 (0%)  \n",
       "Reduce\\nexposure of the nonpoor by 5%\\nof total...         2.1 (5.8%)  \n",
       "Develop market\\ninsurance\\n(nonpoor people)                    0 (0%)  \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\npoor ...       0.19 (0.53%)  \n",
       "Postdisaster\\nsupport\\npackage                                 0 (0%)  \n",
       "Reduce asset\\nvulnerability\\n(by 30%) of\\nnonpo...        0.63 (1.7%)  \n",
       "Universal\\naccess to\\nfinance                                  0 (0%)  \n",
       "Increase\\nincome of the\\npoor 10%                      -0.26 (-0.71%)  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## policy tables for Brad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['prepare_scaleup', 'borrow_abi', 'T_rebuild_K', 'social_p', 'fap',\n",
       "       'far', 'vp', 'kp', 'vr', 'shew', 'axfin', 'PDSpackage',\n",
       "       'ResiliencePackage', 'ResiliencePlusEW', 'Asset_losses',\n",
       "       'Asset_losses_no_EW', 'prop_nonpoor', 'prop_nonpoor_lms'],\n",
       "      dtype='object', name='policies')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.stack().columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>outputs</th>\n",
       "      <th>Asset losses value</th>\n",
       "      <th>Wellbeing losses value</th>\n",
       "      <th>Asset losses label</th>\n",
       "      <th>Wellbeing losses  label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Develop market insurance (25% of population, only nonpoor)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Haiti</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>44.430094</td>\n",
       "      <td>0 (0%)</td>\n",
       "      <td>44 (0.64%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>17.801126</td>\n",
       "      <td>0 (0%)</td>\n",
       "      <td>18 (1.5%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Papua New Guinea</th>\n",
       "      <td>-5.960464e-14</td>\n",
       "      <td>10.199908</td>\n",
       "      <td>0 (0%)</td>\n",
       "      <td>10 (2.1%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fiji</th>\n",
       "      <td>5.960464e-14</td>\n",
       "      <td>8.106983</td>\n",
       "      <td>0 (0%)</td>\n",
       "      <td>8.1 (1.3%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solomon Islands</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.256292</td>\n",
       "      <td>0 (0%)</td>\n",
       "      <td>2.3 (1.5%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.855617</td>\n",
       "      <td>0 (0%)</td>\n",
       "      <td>1.9 (0.86%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Lucia</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.370439</td>\n",
       "      <td>0 (0%)</td>\n",
       "      <td>1.4 (1.1%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonga</th>\n",
       "      <td>-7.450581e-15</td>\n",
       "      <td>1.147920</td>\n",
       "      <td>0 (0%)</td>\n",
       "      <td>1.1 (2%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kiribati</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000530</td>\n",
       "      <td>0 (0%)</td>\n",
       "      <td>0 (2.2%)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "outputs                                             Asset losses value  \\\n",
       "Develop market insurance (25% of population, on...                       \n",
       "Haiti                                                     0.000000e+00   \n",
       "Jamaica                                                   0.000000e+00   \n",
       "Papua New Guinea                                         -5.960464e-14   \n",
       "Fiji                                                      5.960464e-14   \n",
       "Solomon Islands                                           0.000000e+00   \n",
       "Belize                                                    0.000000e+00   \n",
       "St. Lucia                                                 0.000000e+00   \n",
       "Tonga                                                    -7.450581e-15   \n",
       "Kiribati                                                  0.000000e+00   \n",
       "\n",
       "outputs                                             Wellbeing losses value  \\\n",
       "Develop market insurance (25% of population, on...                           \n",
       "Haiti                                                            44.430094   \n",
       "Jamaica                                                          17.801126   \n",
       "Papua New Guinea                                                 10.199908   \n",
       "Fiji                                                              8.106983   \n",
       "Solomon Islands                                                   2.256292   \n",
       "Belize                                                            1.855617   \n",
       "St. Lucia                                                         1.370439   \n",
       "Tonga                                                             1.147920   \n",
       "Kiribati                                                          0.000530   \n",
       "\n",
       "outputs                                            Asset losses label  \\\n",
       "Develop market insurance (25% of population, on...                      \n",
       "Haiti                                                          0 (0%)   \n",
       "Jamaica                                                        0 (0%)   \n",
       "Papua New Guinea                                               0 (0%)   \n",
       "Fiji                                                           0 (0%)   \n",
       "Solomon Islands                                                0 (0%)   \n",
       "Belize                                                         0 (0%)   \n",
       "St. Lucia                                                      0 (0%)   \n",
       "Tonga                                                          0 (0%)   \n",
       "Kiribati                                                       0 (0%)   \n",
       "\n",
       "outputs                                            Wellbeing losses  label  \n",
       "Develop market insurance (25% of population, on...                          \n",
       "Haiti                                                           44 (0.64%)  \n",
       "Jamaica                                                          18 (1.5%)  \n",
       "Papua New Guinea                                                 10 (2.1%)  \n",
       "Fiji                                                            8.1 (1.3%)  \n",
       "Solomon Islands                                                 2.3 (1.5%)  \n",
       "Belize                                                         1.9 (0.86%)  \n",
       "St. Lucia                                                       1.4 (1.1%)  \n",
       "Tonga                                                             1.1 (2%)  \n",
       "Kiribati                                                          0 (2.2%)  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "makedirs(\"cards/policy_tables/\", exist_ok=True)\n",
    "\n",
    "for col in delta.stack().columns:\n",
    "    y=1e-6*delta[col]\n",
    "    x=(y.applymap(f).astype(str) \n",
    "     + ' ('+(100*rel[col][[\"dWtot_currency\",\"dKtot\"]]).applymap(f).astype(str)+'%)'\n",
    "    )\n",
    "\n",
    "\n",
    "    x=x.rename(columns={\"dKtot\":\"Asset losses label\", \"dWtot_currency\":\"Wellbeing losses  label\"}\n",
    "            )\n",
    "\n",
    "    x\n",
    "\n",
    "\n",
    "    y=y.rename(columns={\"dKtot\":\"Asset losses value\", \"dWtot_currency\":\"Wellbeing losses value\"}\n",
    "                )\n",
    "\n",
    "    idx = delta[col].sort_values(by=\"dWtot_currency\", \n",
    "              ascending=False).index\n",
    "\n",
    "\n",
    "    t = pd.concat([y,x],axis=1).ix[idx]\n",
    "    t.index.name=policy_description[col]\n",
    "\n",
    "    t.to_csv(\"cards/policy_tables/\"+col+\".csv\");\n",
    "t.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>outputs</th>\n",
       "      <th>Asset losses value</th>\n",
       "      <th>Wellbeing losses value</th>\n",
       "      <th>Asset losses label</th>\n",
       "      <th>Wellbeing losses  label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Develop market insurance (25% of population, only nonpoor)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kiribati</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000530</td>\n",
       "      <td>0% (0 m$)</td>\n",
       "      <td>2.2% (0 m$)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Papua New Guinea</th>\n",
       "      <td>-5.960464e-14</td>\n",
       "      <td>10.199908</td>\n",
       "      <td>0% (0 m$)</td>\n",
       "      <td>2.1% (10 m$)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonga</th>\n",
       "      <td>-7.450581e-15</td>\n",
       "      <td>1.147920</td>\n",
       "      <td>0% (0 m$)</td>\n",
       "      <td>2% (1.1 m$)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solomon Islands</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.256292</td>\n",
       "      <td>0% (0 m$)</td>\n",
       "      <td>1.5% (2.3 m$)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>17.801126</td>\n",
       "      <td>0% (0 m$)</td>\n",
       "      <td>1.5% (18 m$)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fiji</th>\n",
       "      <td>5.960464e-14</td>\n",
       "      <td>8.106983</td>\n",
       "      <td>0% (0 m$)</td>\n",
       "      <td>1.3% (8.1 m$)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Lucia</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.370439</td>\n",
       "      <td>0% (0 m$)</td>\n",
       "      <td>1.1% (1.4 m$)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.855617</td>\n",
       "      <td>0% (0 m$)</td>\n",
       "      <td>0.86% (1.9 m$)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haiti</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>44.430094</td>\n",
       "      <td>0% (0 m$)</td>\n",
       "      <td>0.64% (44 m$)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "outputs                                             Asset losses value  \\\n",
       "Develop market insurance (25% of population, on...                       \n",
       "Kiribati                                                  0.000000e+00   \n",
       "Papua New Guinea                                         -5.960464e-14   \n",
       "Tonga                                                    -7.450581e-15   \n",
       "Solomon Islands                                           0.000000e+00   \n",
       "Jamaica                                                   0.000000e+00   \n",
       "Fiji                                                      5.960464e-14   \n",
       "St. Lucia                                                 0.000000e+00   \n",
       "Belize                                                    0.000000e+00   \n",
       "Haiti                                                     0.000000e+00   \n",
       "\n",
       "outputs                                             Wellbeing losses value  \\\n",
       "Develop market insurance (25% of population, on...                           \n",
       "Kiribati                                                          0.000530   \n",
       "Papua New Guinea                                                 10.199908   \n",
       "Tonga                                                             1.147920   \n",
       "Solomon Islands                                                   2.256292   \n",
       "Jamaica                                                          17.801126   \n",
       "Fiji                                                              8.106983   \n",
       "St. Lucia                                                         1.370439   \n",
       "Belize                                                            1.855617   \n",
       "Haiti                                                            44.430094   \n",
       "\n",
       "outputs                                            Asset losses label  \\\n",
       "Develop market insurance (25% of population, on...                      \n",
       "Kiribati                                                    0% (0 m$)   \n",
       "Papua New Guinea                                            0% (0 m$)   \n",
       "Tonga                                                       0% (0 m$)   \n",
       "Solomon Islands                                             0% (0 m$)   \n",
       "Jamaica                                                     0% (0 m$)   \n",
       "Fiji                                                        0% (0 m$)   \n",
       "St. Lucia                                                   0% (0 m$)   \n",
       "Belize                                                      0% (0 m$)   \n",
       "Haiti                                                       0% (0 m$)   \n",
       "\n",
       "outputs                                            Wellbeing losses  label  \n",
       "Develop market insurance (25% of population, on...                          \n",
       "Kiribati                                                       2.2% (0 m$)  \n",
       "Papua New Guinea                                              2.1% (10 m$)  \n",
       "Tonga                                                          2% (1.1 m$)  \n",
       "Solomon Islands                                              1.5% (2.3 m$)  \n",
       "Jamaica                                                       1.5% (18 m$)  \n",
       "Fiji                                                         1.3% (8.1 m$)  \n",
       "St. Lucia                                                    1.1% (1.4 m$)  \n",
       "Belize                                                      0.86% (1.9 m$)  \n",
       "Haiti                                                        0.64% (44 m$)  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "makedirs(\"cards/policy_tables_alt/\", exist_ok=True)\n",
    "\n",
    "for col in delta.stack().columns:\n",
    "# for col in [\"vp\",\"vr\"]:\n",
    "    y=1e-6*delta[col]\n",
    "    x=((100*rel[col][[\"dWtot_currency\",\"dKtot\"]]).applymap(f).astype(str)+\"%\"\n",
    "     + ' ('+ y.applymap(f).astype(str) +' m$)'\n",
    "    )\n",
    "\n",
    "\n",
    "    x=x.rename(columns={\"dKtot\":\"Asset losses label\", \"dWtot_currency\":\"Wellbeing losses  label\"}\n",
    "            )\n",
    "\n",
    "    x\n",
    "\n",
    "\n",
    "    y=y.rename(columns={\"dKtot\":\"Asset losses value\", \"dWtot_currency\":\"Wellbeing losses value\"}\n",
    "                )\n",
    "\n",
    "    idx = rel[col].sort_values(by=\"dWtot_currency\", \n",
    "              ascending=False).index\n",
    "\n",
    "\n",
    "    t = pd.concat([y,x],axis=1).ix[idx]\n",
    "    t.index.name=policy_description[col]\n",
    "\n",
    "    t.head(15).to_csv(\"cards/policy_tables_alt/\"+col+\".csv\");\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Render cards\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "results=pd.read_csv(\"results/results.csv\", index_col=\"country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Belize', 'Fiji', 'Haiti', 'Jamaica', 'Kiribati', 'Papua New Guinea',\n",
       "       'Solomon Islands', 'St. Lucia', 'Tonga'],\n",
       "      dtype='object', name='country')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#list of policy name in apply_policy we want to use here\n",
    "policy_list = [\"prepare_scaleup\", \"borrow_abi\",  \"T_rebuild_K\", \"social_p\",\"fap\",\"far\", \"vp\",\"kp\",\"vr\", \"shew\", \"axfin\",\"PDSpackage\",\"prop_nonpoor\"]\n",
    "# policy_list = [\"fap\"]\n",
    "\n",
    "country_list = results.dropna().index\n",
    "# country_list = [\"Nepal\", \"Honduras\", \"Moldova\", \"Rwanda\"]\n",
    "# \"Finland\" in country_list\n",
    "\n",
    "country_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## colors and units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>color</th>\n",
       "      <th>edgecolor</th>\n",
       "      <th>alpha</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dWtot_currency</th>\n",
       "      <td>#a6cee3</td>\n",
       "      <td>#1f78b4</td>\n",
       "      <td>1</td>\n",
       "      <td>Avoided well-being losses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dKtot</th>\n",
       "      <td>#cccccc</td>\n",
       "      <td>#969696</td>\n",
       "      <td>1</td>\n",
       "      <td>Avoided asset losses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  color edgecolor  alpha                      label\n",
       "dWtot_currency  #a6cee3   #1f78b4      1  Avoided well-being losses\n",
       "dKtot           #cccccc   #969696      1       Avoided asset losses"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#list of outputs to render and color to use\n",
    "colors = pd.read_csv(\"inputs/colors_pol_assess.csv\", index_col=0)\n",
    "colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzRh+SWrm/wD0OP19UsV7cgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21117f72cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "from fancy_plots import savefig\n",
    "\n",
    "patches=[]\n",
    "for i in colors.index:\n",
    "    patches+=[mpatches.Patch( \n",
    "                    fc=colors.ix[i,\"color\"],\n",
    "                    ec= colors.ix[i,\"edgecolor\"],\n",
    "                    label=colors.ix[i,\"label\"] ) ]\n",
    "                        \n",
    "fig=plt.figure()\n",
    "ax=plt.gca()\n",
    "\n",
    "l=ax.legend(handles=patches, loc=\"upper left\", prop=font,frameon=False, title=\"\")  \n",
    "plt.setp(l.get_title(), multialignment='center')\n",
    "\n",
    "ax.axis('off')\n",
    "\n",
    "plt.savefig(\"cards/legend_cards.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## delta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PER COUNTRY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Currently working on:  done at 2017-07-26 19:25:18.773650\n"
     ]
    },
    {
     "data": {
      "image/png": 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uK9gh6V6Ze7+WVfcvXDu+M/BUrovlOcKVjlvi0q1NSrTZFTjYzB7AE7tYUo7b\npWTbIMgX2zRvwMCff5cHzj2A4/fdjl/3bk/zhnV5Yvxspn66iI/nfc2x/3qDk+4YxWdfLeOovctS\nGK4ZRHR6sMliZnMlnQk8LE8GMhd3QBXhTnxde1t8untVGfcpS3d9LckGgDqSJuIBYT9JKmqUZquZ\nvShpH1xrHXy9/poKPge43vpNydF+ga+vZ2z/WK4J/1YZgWg/BC6Q9C3+f8ZNZnZjiTZ/wz8GMLMp\nkj7CE7y0An5eCVuDYKNwcc+1O0g5sfP2a48H/aJbruY1ltBOD4IykDQT+EFKXzoUGAscjDv3x83s\nytRuO+AfQGbE+4iZXStPH7oK2B3Prd0Rz3T2e6BZavs7MxuUnPcg3PE1xLOe/cTMVkrqigeiFeDJ\nS64xs0ckNQP+CuyDR4i/DlxiZplI8cxz7A4MwD8cCoEBZnaTPPPah8CsZOcDZnZDBZ5pnJndKun7\n+AdFA9zB/9nMHk3XD6UUDfcSlKmdPm7mfLY89bpyugiC0qlFim2hnR4E1cwOQHfcIX8o6R4zex/4\nDx5g9kOANDLP0Ak41My+lmR4dHhvM/s8OdGxacp+ER5lPj9NX9+P77e+HV9vvjE5bgHNU99/xbOj\nnZf2iT+UrrmrhN0XAgPN7NpkXwtJP8W3g72Np0BtCoySNNXMXiznmTJMAL5rZqsltcazwr1cSpT8\nerFtvRU80ifnjr31ZsyYMZHeNNgkCCceBJXjCTNbAyyS9A6ws6TP8e1fvTKNSkSLP5mCyACOxoPJ\nXkxT7+Aj0V3w9KGXpS1lhUAL4JvU5nXgakk7A6+aWUY17VjgAEmXpvNGwCc57B4O3CCpUerrdTO7\nXdKPgYtT1PtiSY8APeX76Mt6pgxb4Wlad8VH8i3xWYfRuV5eTWT+/PkMH+474rp3706rVq2K1c+e\nPZsJEyYA0LVrV7baaisA5s6dy5NPPslPfvITCgoivCjID+HEg00CSdcAfYC5WXvAM3V9gZFmVq76\nmqTjgc/MbEwpTbIj1LcHzgfeKKfb7OhxAVPMrHuOe5+J7+nuZmZLJF0F7AZgZn+X9F98a9k/Jb1i\nZlen/o43s4/KMsDMnpI0CjgcV1k7l4rHB5TFv/HAvv8zT5b+Hj61XmsYO3YsvXr5t8qIESM48sgj\n19atWrWK6dOnc8wxx6zjqKdNm8aWW+aanAiCjUd8PgabCpfizi9XVEpfkjOsAMfjU8sVxsyWAiOB\nizNlpUyjCZDsAAAgAElEQVQ9k9rtKumwrLZd0hT5FsC85MCbA6dltdnNzD40szvw6fiMjQOBKyUV\nZu4raceSN5Wrv31hZgOAP2RdPxg4T05TfB/4q5V4pi2AmcmB98JnFGoVK1asoEmTJjRp0oQVK1YU\nq/viiy+QxKBBgxgyZAjffuvbkRYsWECTJk2oVy+2wAf5JZx4UGtQFUmoSiqUdJOKZExvSmVH4NPT\nV6a2Z+HT2vdKGo8rtp1Tinln4Fu5pkmajAuevAj8IPWXua41/u/uL5ImS5oLPIePqLcFukr6Gvgc\nD26rm667VNJCScvw9e4ZqfwifJvaN6luFq6u1k5S9vT3+cDKFBV/B9Ba0k24yMvuwDTgI3yt/XpJ\nD6Zrsp/pRzme+0o88n0ScBIwpZT3U2MpK7h32bJlfP311xx99NG0bt2a6dNdi2fKlCnsvXd5Ow+D\noPqJ6fSgVqAiWdJDzWy6pB/hQVwHmlm3FDDWNY0g12Jm98llQtdGSUu6AA822y81exE438z+LWkg\nKfI6tX0cqGNmSyXVBV6W1NvMXsKjvZuk+3yKj+IzW8newiVUZ6QR7jhJo9L5Zbi4yuXAP4EuZrZG\n0gpgDbCTmc2RdC+wINn4FT7q7ktRANqR6T67AluY2bJ0r2VA2xKv8DbgXDPbV1I74GNgrJldlmy+\nOj3HNen8eqCvmR1f8ndhZn2zjl+lKHq9ZLseucprMllxCgDUq1ePNm3aUFBQwHbbbcfkyZP56quv\nqFevHg0bNsyTlUFQRIzEg9pCVUqo9sS3WK1Me6Pvo/SMW4XAjWkkOh7YG/8AKIvdgD2AR9MIdQRQ\nP5VhZg/iI+ln8Wj0xVnXPm9mc9LxPRRJpPYE7jJnMfBIKlsEfAA8kILUmphZqXvas1gOPJ51fixw\nRpqBmJTOd8555SZGgwYNWLp0KV9//TV169YtVrf11luzcKEH2s+fP5+mTZuyYMECvvzyS55//nnm\nz5/PsGHD8mF2EAAxEg+C8rgEjxI/0MyWy7N8lRe4JXxtO6ezl2uJ74WPrltviHFpa9dBuAzq9/At\nXr3xEXz2R3pJm7+24vPIAi40s9fYzOjSpQuvvuoCeN26eUjFjBkzaNWqFVtttRXbbrstzz77LHXq\n1KFnz540aNCAnXbaCYDnnnuOQw89NG+2B0E48aC2MBpfm25vZjNYDwnVrPPBwNmSHkvnZ1MkOVqy\n7RbA58mBbwcch0dkl8W7+Br1mWnUjVxO9bM0ir4RH9X3xbeadTWzzLawoyVtZZ7jPFsidTDwI0lv\n4lP4p+Db0Zrio+9huGLcwfhsweNAXUm7mNkHZAXJlcJA4JI05Z+Zlm9rZu+Uc121U1hYyJgxpW0W\nWD8aN2689rhVq1accMIJxerbt2+/9rhjx4507JhbRfe4446rUruCoLKEEw9qBVUhoSrpcuCydH4m\nMAnfo/0yReIoDwIDJJ2IC6n8A3hC0jR8//WQCti6StIxwN/TPQuBL4FVaT91Y2D79GHwB+CRrGj1\nEfg0/HZ4utPM/u8/4YptUzN2mtlLktoCT0lqiO/RHgs8nWz4FfBqCp4bVI7Z1wH9ceGZNem9/AHI\nuxNv3bp1CLMEQSmE7GqwWZIC4ZqWDIRLdXUquK5cmfsdCNxrnou8tDb98VH1Zetjiyouc7pRqcAz\nlCm7CjB06NCqNSoIaiaVll2NwLZgs0NSJuXmyBTItYWkAZLuTtvVxqV2D0kaJ2mqpGdUlKWsR7ru\njrTdbbKkPVLd7pJGpbJpki6T65Y/BOyYrjtZUj1JN0oak9o+CNRLfRSzRVIjeW7u6ant4zmeqcJb\n6XJc207SPEk3p+eZKqlbVv1ZqWxKeg9bp/JS+8/1PoMgqHrCiQebHWb2s3TY1Tz/91fpvBOuaZ4J\nSPuVme1vZh1wffErsrrZC7jdzPbB15+vTuUZjfKOZrY3cI+ZvYunD52e7vcY8GtgkZkdYGYdgc/c\ntLWj8GxbjgCamdmeqe1PcjzTfbiz/GW6x2B8n3dmK91++H7y80t5La3w6P99gF/gU/z15Zru1wGH\np7pp+LY4KtB/yfcZBEEVE2viQVBEtsY5wFmSTsdHyI2BbNnWd81sYjoeDRyTjtfRKC/lXscCzST1\nSef1KZ66NNuWycAeaQZhKOWvb2dYu5UOQNJ9wAnkDsxbiSc8wcyGyoVjdgcOxZOgfJ7a3ZFlZ3n9\nl3yf68WEWQtp16+ijxxUhFqU1Ssoh3DiQVDE2vXxNJ18AT5anyvpNIqPMrM11FeT/i1VQqO8vC1d\na20xs48k7QV8HzgSV3vrYGbLS7m2prBOvMH6sHLVGq49KXd0eLB+9Ht8cvmNglpBTKcHmytLKL6V\nrCRb4EIq8yXVx51xuah0jfKSZLZ0NUzXNc2sq+fosy2w2syexbXMt8Ij0UtS2la6unK1ubOBV0ux\npx5pG1r6gGmIC9K8DhwlKZML9MdZfVSm/6CG0u+Zqfzw9pH0uX0k73y+eJ36Ee/P5dS7RnPynaOY\n+umiPFgYlEWMxIPNlZuB19K0cY8c9S/hI+j3gHn4NHlFEqOcBJwuaSUedf2rUtpVZktXB+A6uSRo\nIXCtmX2Wo12urXSZFKdQfCtdSebjCni/xmcJTk3T5NMkXYlvVTNcXz2zJl+Z/oMayoWH7sz2LRvx\n8byvuf6lGdx+Rue1dcu/Xc3DY2bxnx8dSGFBpQOng41AbDELgs0cuZb6ODPLV17NMreYjf5oPvc9\n9+LGtGeTp9/jk5l57dHFymYv/IbrXpzBv07bb23ZyA/n8dBbs1i87Fu2bFKfP5+wN43qxdivGokt\nZkEQBEHlueGld+nbtV2xsrlLVvDlkuUMOOcA9vtOCx5+a1Z+jAtKJZx4EGzmmNnMPI7CgxrAPW98\nzK5bN6FLu+KhFs0a1GX/77SksEB03bkVH8ytkljFoAoJJx4EQbAZM/y9uUyYtZBffG+Xder2adt8\nreOe/vlitm/RaGObF5RDOPEg2ESQ1FfSk+tx3UUZFbYqtKWHpMOrss+geuj/37eZveAbTrlrNP2e\ncWn+J8bPZuqni2jVpD4H7tiSk+4YxRPjPuH0A3fIs7VBSSJCIQiCi/DtYl9WYZ898Gxrr1Rhn0E1\n8NqlPdYpO7Hz9muPz/vuTpz33Z02okVBZQgnHgTVSNqW9Uc8hWlD4CozeyrVHYhvNWuWmv/OzAal\nurOAy/HI7Q+Bn5jZl5L6AqcDy/DtXV8AZ5rZpznufTYuA1sH3/N+QZKAzW7zG2Bb4ElJy/G94rNw\nadUuqdkDZnZDjv63AR5J9jcABpnZryV1AH4KFEjqCTxqZtdV6sVlUbdQIU5SxbRpXjK9fFBbCSce\nBNXPajPrlBKhjExJQVYCtwNHmdnnktrge8b3Btrizr1zqvsT7lRPTv19F+hkZu9K+j1wC9An+4ZJ\nsOUkoLuZrZB0JHAvcEh2OzP7s6QfA33MbFq69np8qa0D0BQYJWmqmZXc5/UVcIyZLU1iLy9L6p1S\npN5OiYxs60ubust5r8R2qCAInHDiQVD93AOQnO4E4CBgFbAj8GIScQEfde8CHEzpeuUAb2SNqO+m\nKMd4NscAHYG3Uv8CWlTQ3p548hcDFkt6JJWVdOKFwI2Suqb+t8GTnrxUwftUmvnz5zN8+HAAunfv\nTqtWrarrVkFQKwgnHgT5QcAUM+u+ToV0cBX1f6+Z/a4K+iqNS/APgwPNbLmkO/Fp9Wpj7Nix9OrV\nC4ARI0Zw5JFHVuftgqDGE9HpQVD9nAMgaVc8XedoYCSwq6TDMo0kdZEPm8vSKwc4JPWV6TtXEpX/\n4lnY2qa+CyV1ztEOcmuu/0hOU+AUcmuibwF8nhz4dvi6f2l9VgkrVqygSZMmNGnShBUrVlR190FQ\n64iReBBUP3UkTQQakQLUACQdi09H/x1PQPIRvsZcll45wJvATcmRfwGcKWkmns60oaR3cG343wAD\nJRWm/p8Axuew7x/AfZKa4VPmvwBupWia/kEzyzVF/g/gCUnTgE+AIVl1z+AfEZPYwMC2bEImOgiK\nE048CKqfm8ysf8lCMxtL7uQrmNkDwAOl9LfIzEoGsgH0Sh8AewMTgHZm9lB5xpnZ3cDdkvrjwWhL\ngb4VuO5/lJIUxsw+xtfHq42sWIIg2GyJ6fQg2MRIUeYLge0AJF0haYykCZL+m5mml9Rc0pOSZkga\nCuyc6UPSAEk/z3WerrtX0lRJkyXdmsrrSbox3WuypAclNanKZ2vQoAFLly7l66+/pm7dulXZdRDU\nSmIkHgTViJlV6XAx5SkfUFYbSYfg6VMnSzoDd84HmdkaSRfgU+2nA78DFptZe0lb4qP3xytgxt+B\npUDH1GdGd/3X+CzBAcmO64F++LR+ldClSxdefdWX57t161ZV3QZBrSWceBBUAWntummaiq7KfnsA\n9cwsp/KZpHOAi3HBlnGSFgFbAifijvpvuMjMmZLm43u7F0k6FxeCmS1pPzObIOlp4AhJz5Vj1g/w\nPexrAMxsXio/FmgmKTPVX5/iW+M2mFatWnHCCSdUZZdBUKuJ6fQgqNn0AMrSIH8K3w/+GdAdWI6P\niO8DhuNR5RcAWwOrgdPM7BDgt8C7+Kj6qtRXW8DMbAS+jz37/4eKbB0TcKGZdUo/e5jZKRV5yLJo\n2rTphnYRBJss4cSDoIqRtLukFyWNTWvD52TVPSRpXFpPfkZSi6xrRqX20yRdliVfepakSSlivRhm\nttiKQrYbAnVxvfJX8CjzgfiIu02qqyOpI+7Q38BHzyvTOvkPcMcP8AFJdjWpya3dCgc8D1yujIpM\n0XT6QOASSQ1TeVNJe6z3i0yEEw+C0gknHgRViKQ6wMPAxWbWBZdIvVJS+9TkV2a2v5l1AN4Grkjl\nFwIDzayjme0N3GNmU3Fp1gfSyDbnNq20VW1b3HHfmK7rh+8hL8AD3Gak5g/g0qu/TrZ1xhXkRgHj\n8JE8wF1AW0nTgX8Db2Xd8mJcjnWapMn42jq4VOxkXD52Cv6RsMFOPAiC0ok18SCoWnbDHdejWVug\n6qeyGfio+nR833Zj4L3UZjhwg6RGuNjL6xW9oZkNBOpJ2gF4VtILSZY1I7Zyf6YOODVLsvVpWCtC\n83fgh8DNkp4ARpjZYeTAzL4ixxY0M/sWD2KrskC2IAjKJkbiQVC1CJiXtS7cyczamdkz8qQkFwC9\n00j8atJac8ps1g3PWHYl8GBlb2xms4Ax+LR4hetwB34xHgg318xOBE6QFPkng6CGEyPxIKha3gW+\nkXSmmT0IkKbSP8NlShcB8yXVB87NXCRpF+AjMxsg6X08MA1cvnS70m4maQ8zeycdb4mvXT9dXl3W\n9WcCY8zsPUm98SQspD8br/9rqDomzFpIu36D8m1GraZN8waMuvL7+TYjqAbCiQc1Hkn18KngtsAQ\nM7u4RH1/4C9mtjKdDwDGmdmtG9lUzGyVpGOAv0u6HM/0NQdPC/oScAY+hT4Pn0LPKJ6dBJwuaSXu\nQH+VysuTLz1f0uHAt/gswK1Z29HOl/QzXLZ1RYk6JLXEddl7pqL/AM9IOhF4M62tk4RgdsA/KABu\nMbP7Ut0dQFdgLnCCmS1KHyivAseb2YJKv8QSrFy1hmtP6rih3WzWRD72TZdw4kG1IanQzFZXQVf7\nAt8xs71Kqf89cBOeozsvZIu6mNn7QGkJsE/OVWhmfwH+kqO8TPnS7A8aSXXMbFV2naQTyMoVXuLa\nBfi2tOzzQ0u51S/N7PnsgiTvuquZdZD0O+BMXHP9SuDOqnDgQdXT75mpvDdnCQL+dNze7NGmWbH6\nDv1fZs9tveyOMzqzRaN6ebAyqCixJl7LkHSgpNcljU8/R6fyQyW9L6l5Or9P0nXpeICkuySNlPRe\nOq6X6lqnrU5T0rans1J5gaTb5JKckyW9mcp7SBqXZc/a83Q8Jd17EnCkpGaS7pZLcU6RdIs8IUeu\nZ7siba+alvpoIml34CFgx7TN6uQS1/wrHY5M9Vuk870lvZbeyQNZ26EqZE96lkmS7kjtJmdvl8pl\nayrvL+kRSS+kdzcoBatl6h5Pds2Q9FTW76tJ6ifT56+z7rWLpCHJjglp2jtTZ6nfsfjHTC7OSH9X\nPlCRdOqJkgZl9VNf0ufyALiK8i1QX1IBPvW+UtJuQBcz+08l+gk2IhceujNP/bQrN/bpyC1D3l+n\nfvdtmvLY+Qfz2PkHhwOvBYQTr0UkB3U7LtjRGQ9SukPSFmY2DN8+dE9yxLvhgVMZDsRFQ/YEvgOc\nn8r/AUwzs31S/XVphNURX0Pd08w6kjsgKhd74aOwTmnk9ldgWJLi7ISLjpxb8iJJR+Ijua5AB3wa\n+rcpkvo8YHrq87Hs68zsZ+mwa6r/Kp3vDRyV7OlM0ZRxhezJepbb07t5nPQ+S7M167r9gdPwiPS6\neMBYhm54hHh7fH08c91v8X+PHVK/Z6f7gH/EPJzsOAP4j6StsvpcZmZdzCzbhmy2Tn9fDgGukrQP\nPk2/t6QdU5uTgNEpAC4XN6ePvP/I046Sfjev43KtOyU7/4YHyQU1lO1bNgKgTqEoLFhXFfiDL5dy\n4h0jue6lGZE1rhYQTrx20RXYEXgxjXRfxNdPd0n1fwZa4trYp2ZPrQKPmdnSVHY/8L1U3hO4A8DM\nPgdewJ33R7gDukce/FRR3jezUVnnx+LCIJPw/+w74x8YJemJr/lmxEvupMjxrg/PmtnytE4+gaLk\nHhW1B+BdM5uYjkdn9VGerS+b2Vep7q2s6wCeN7M56fgeiv8e7jJnMfAI0FOez7sTKdDNzKYDk/C9\n3RnuL+dd3JOunQMMAnqkvwd34GIyAD8D/pX7cs40s92THTOAtR9SZnZ1+ng6Ed+i9hbwraSH00zD\n93J3GeSbG156l75d261TPvSyHjx+/sEsWvYtg9/5cuMbFlSKWBOvXQiYYmbdS6lvjgcgrcCdeWmj\nqnJJAUp74bKfPYHrJe1H+XKcJbXDhQc4fbS+tqwny7OOV1P0d70y9pTWR2Wva1jB69aX9dVrvxOY\nKGkgHjk/JFcjM5ud/lwt6Ragv6QCS9rpsDZI7jz878o9qe/x+MdPabEMQZ64542P2XXrJnRp13Kd\nuswU+uF7tmb6Z4vptWfrjW1eUAliJF67GAnsKmmtCIekLpn1Xny0djdwNi42kq1XeaKkxnJFsTOB\n11L5YDxCGbn05lHAa2m6tpGZvYwHKi3Cp0w/AnaS1CLd99RybB6IK5YVpntsmTWFm81g4GS5VKdw\nh/BqBd4JwBKKhE3Ko6L2lMWG2Hp01lT4ORT/PfxITlPgFOBVM1uCj7zPTvbugS91jK6EvX3TtVvh\nv9/XYW3iksHAo8BtWfKta5FUR1L2/+KnAlOzHXjiBnz5YyW+Pm7AGmrINrWgiOHvzWXCrIX84nu7\nrFP3zcpVrF7jfw3G/28hO7RqtLHNCypJjMRrEWa2UC6xeaOkv+OqXx8Bx0j6FT4qvt7MTK66dSdF\nTnYsLsu5NTA01QH8El9Xn4KPUq80s7fTqPuu5PTr4FP3o81TT96Mj7LmAMMoe6R1Ef4f/GR5pq8V\nqezjEs/2YlqrzUzFjwOuqeCruRn/8FiGzxyURYXsKYsNtHUE/oG1HTAduDSV/wmP7J6azh80s5fS\n8en47+hifCbkTDObW1F7gXmSxuMfOtdmto4l7sYznpU2JV8fGCQPhBTwKf6BsRa5iE1BissAl1+9\nC//7+adK2BlsBPr/922a1K/DKXeNZqetmnDtCR14Yvxs2m/TjALBr5+aQuN6dWjboiEX9yxtpSmo\nKSgCFzZ9lMd900ER8v3sTczssnzbkkHS1UCbrADBfFDqf0I9evRg3Mz5bHlqTtn4oIKE2EutYd1I\nw3KIkXgQbKZIehsf2R+Rb1vKYtt6K3ikzzb5NqNGMGbMGC644IJ8mxHUIMKJbwaYWd982xCAmfXP\ntw3ZlCGeE9QChg0bxoIFrqfTvXt3WrVqtbZu9erVPPfcc8yfP5+TTjqJ5s0rGjIS1DbCiQdBENRC\n9t13X5o1a8ZXX33F6NGj6d17rQYQBQUF9O7dm9GjKxP/GNRGIjo92CSQdE1SQRuRo65vUhKrSD/H\nSzqg/JZrFdhuqqytQVAVNGvm0qgFBQUUFBT/r1wSjRpFZPnmQDjxYFPhUqCbmXXLUdeX0gVdAJA0\nU9IM4F48Gvs5SV2r3sy19ysmX5tP0g6EsuqHSqqoYl/mmh7yxCxBNfPWW2/RoUOHfJsR5Ilw4kGt\nQVJvSROThvgQefpO0ui7ATBE0o0lrjkHl0H9h1wLvaekQkk3qUinPDOa/mv6cwWwD/CqpCNVpFX/\ntqQbKmjr7pJelDRWrrt+TipvL2k20Dqd/17So+l4fbXVf5/aT0rvZwtJ7STNy2qz9jxznN7BBOA8\nSfUk3SjXlJ8s6cF0z5w69xWgBy7jG1QjkydPpkWLFrRp0ybfpgR5Ipx4UCuQtDXwIHB60hB/GNfq\nJmv03dXMLs++LqXMHIdn4epkZoNx3fhOwH7pZ1+gKS6mMxC4zsx2BP6JC7kcAxyMpxL9qaSPJD2I\n74NuKukLSXWzbH0qtb0YT0ryDfBvSRNxZbSrUnkzXGP9fLne/QXACbjiW3dcYOdRSa8CE1Pdl7i8\n6dnpA6Nlus++qe9X8T39b+LJSS6SNAbXBsiMuAuBVqm/+riufr90v9vwtKIH4hoExYZ4kk5JHyZt\n0/kVyfFPkPRfSdtI6oDLuZ6VPiyuzP1bDTaE2bNnM2fOHDp37pxvU4I8Ek48qC0cCExO2uHg6nSd\nVFyVrqL0BAaY2cqkMHYf68rHguuA7wncCPwPF11ZjgvlfAZ8F1eLmwYcCSCpFa6H3hp4GngCVy37\nIt3ncTN7EJfE3Rl34jvgAikPAgNwIZ1/4vKle6T7rMCT0AzDNfIfSc+xCPgAT37TBN+HfjD+QdAE\n+DYle7mRIvW01ek5dsETxRTiSw5nAH/EtfMLgNfMbFLmZaTR/3lATzP7RNIZ6RkOMrP9cN39m5OY\nzO3AA+nDKTZ5VwMjRoxg8eLFDBw4kGHDXGdnxowZzJ3rOkCvvPIKs2fP5rXXXuPjjyusZRTUMiI6\nPQhKR/iItQXwCT5a3xJXeFuIO0Nwx9sXH8Wfhsua7o8rsPXH5Ue/wrOl1Umj2Hbp+ta4I3wB10Bv\njicmmYxnmAN4A9fCB1dYm4pLsWb0zA/CM5T1wuV1b8/UU5Ss5D3cWYM7aMNH94Xp+eoBP8I/KE4x\ns6JQZ6c//uFxVPrwAU8msz8wQa78Wwf/qAg2Aqeddto6Ze3bt197fPjhsZqxORBOPKgtjAbuldTe\nzGbgWuITk7Z4eSymuLb6YHw6OuPgzqYoaUl22y64s/4cd7S/w0fIt5tZ/4wCGz7i/lsahffFg+xu\nx2cPXjKzsyS1Bz4zs8XyJCLv4UIrt+MSpRmOTvcA11Z/B/93Ohh3spn0sqcAl6WZiCZmNkzSV/gM\nwd546lSAbYF5FE8lexyeoa6bmS2RdBXQB7gEeA5Ymvpta2bvpGtG4x8J3wEySagFXGNm91KNFBYW\nMmbMmOq8Ra2hceOQog+KE048qBWY2Vx5StSHUzT1XHz6tyLciefDvhy4LJ3vgo9EAV4Gdk3HDwID\nJP0IHyWfBvwGH6HeSkoekpzclsByM/tG0nPAtUAzMxsq6Zh0n24pyn0WcJKky/Cgr8uBv+D5ty8E\n2uDr/COAZ3En2xIf3d+c7L4YT59aSNJWT6P6pyQ1xB32FOBpM1uVRscDJX2Jr5FnaAasSQ68eXrG\n8fhsw+/xD5M3gD/gHxHga/xPAy9IOt7M3k62/UrSM0nXvz7Q3swm4x9D21Xw91MmrVu3DpWyICiF\n0E4PAnyLGT4aX4GvHU/HA9xGpvq6+JTycfj0uAF/MLOnU/13cQf8WzO7Jqvfw/F15ob4lPWbZnae\npB7ATWa2f2p3Fr5uXYA73J+Y2RxJffG1+G/wD40v8AQon5byDD8ws2np3ICmZrZUUjtcP3/L5Lif\nAtrigXJvAw3NrG+63w/MrE9Wv0OTrc9LOgRff+9jZhPlSVnOSU0L8Gxot8kzwz2Tyh8tZ128TO10\ngKFDh5ZxeRBsMlRaOz2ceBDUEJQjQUoup7oJEk48CJxIgBIEtZWapq0eBEHNJ5x4ENRgzGwAHv2+\n2TJh1kLa9RuUbzPyQqQQDcojnHgQBDWalavWcO1JHfNtRl7o9/jkfJsQ1HDCiQdBDUPSJOBgM1uW\nb1uCmkO/Z6by3pwlCPjTcXuzR5tm+TYpqAGEYlsQbABKiVOS3vg7ks5bz37WZkRLKmfV7sA3QBe9\nIn33l1SvuvrfHLnw0J156qddubFPR24Z8n75FwSbBeHEg2DD6WNmHYETgdskbbshnUkySU3S8UxJ\nf5Q0Kh3/PJUXSLot6wPizVReLDta9nk6niJPpDIJOFLSaZLekidOmSjp+1nX5rx3qttD0iupv6mS\nzk7lbSQ9ie83n5iEZIIqYPuWnlq0TqEoLKh0EHOwiRJOPAiqiLQ/eyFZIie5EoSk8uaSnkxOeCiu\nCJfN+VnH/4cne+kBXCfpAeBdXMZ1cPqAOEGewe3fwJ5KWchymLkXrse+CrgeV3LrZmb7pvMXU9KS\nt/H98o1wzfdVwC3yDGp74cpug4BP8f31l8sztT1A0f8ra4CrJB1fmfcYlM0NL71L367t8m1GUEOI\nNfEgqCKSEMo8XPecEglC1ki6AFdfOx2XcF1sZu0lbYkrsT2eu2c+BjCzmXIZtgJcEnYc0DIp2e2O\n65ZfANyEy6/2w7OaZfM+cLSZzU99PQ9MSsIwbXE1uN7AHDzpy6PAEKA9MAY4FU8WUwc4C88qNyMp\n2I0HdsTFY8D3f8/B1e6CKuCeNz5m162b0KVdy/IbB5sF4cSDYMN5MjnEXYATK5gg5DDgFwBmNk/S\n02X0vzrruAGege2rNCLugWcz+xXudM/GZwIa4x8TI0r0tRTXXD8Sd9h74KP53kl9LSPx+gQ+kl4O\nvLNaUKMAACAASURBVAbcj4/KtwNm47Kwe+CpUjN9109/dgFW4qlhl5bxXEElGP7eXCbMWsitp+6b\nb1OCGkQ48SDYcPqY2TRJJwL3SXrTzOawYQlCspe66peslLQVsMrMXpY0GPg5nvXsKVwytiueOW1A\niUtb4qlNM8lPvsGlZsHTqgpY8P/snXd4leX5xz/fhGCAsIwIorjFLYhGFJWiglsrKriVWsVRa7Vq\nHbXqz9bRitVabcWJ1ol7K0MRFRRkI8sNKjgQ2QRI7t8f93PIm3AyyYTnc125cs6z3vt9zknu91nf\nGw/C0iqkH4875reAh4Bz8QeLpWbWOdiTG0b3w4E18cMldcDDoc6rQh9EEtzwyifkbNSIk+//kG3b\n5HBL793LrxRZ74lr4pFINWFmzwBD8Gls8AAhF0pqDSBpI0mpA89vEzTH5dHPepdobq/wOxN3oCmW\nhXodgGGSpuJBT8YDh+Fr8reH9xPwCGxJGgE/JYKfzAcOljQenzWYjwdi+Rf+8JAJbGtmY/BZhFFA\nJ3wtvamkOZImAUfKI7Wdh8dgLwTG4qFQWxFZZ96+rAcvX3QAT/ffLzrwyBriSDwSqV6uBsZJ+ruZ\n/S+sd78bppwzgP/g09x/xUOrzsCDmoxMtLEJPkU/DV9nTj5sdwDuxKOtrQbeNrOLVRSgZSzuQBcB\nN6YCtACE6Gq741HPZuDBT16lKPjJ3fga9gv46Lw7MAN/WGiFO/FsYGAYdXcOtnTAR9/fA33N7JTQ\n/qn4FPw6jcKzMrXBip5s1jK7rk2I1HNiAJRIJFLXlBkA5bvvvmPWrFm1aU8kUlfEACiRSGT9Zf78\n+Ywc6ZMW3bt3Jzc3t44tikTqlrgmHmkwSHpOUl75JSPpkDQoKdhSRrm/Szq1NmyqLGPHjqVXr170\n6tWLMWPG1LU5kUidE514pEEgqSsea3tsXdtSVcqSOa2og61GW4opu5VgKHBvUIXbWtJPiXoTJTUJ\nry+RtGlt2JsiPz+fnJwccnJyyM/PL79CJLKeE514pKHQH981DaxxevdKelvSp5IeDWe1kdRW0gsJ\nSdAzE/XKkhL9StKtksZJ+qxEXl6oMzn8zkvknRmuMzlcd9OQ3k/SsJA2FSi2pTg40klBge3X+Pnt\nXRL5Z8klUceF+9wxpGdKGhDU06aG15mJfrlf0ihJs8LrdBrmjYAt5Gpyk5RQeDOzYcAHwFoxMEvo\nul8C1KoTj3t4IpHixDXxSEOhB3BbibTdcKGTQvw4VU98FHkXMNXMekvaDN8tPj7IogI0NbP9JG0N\nTJU0KCFKsqmZ7SWpLa79PRLfof0c8BszGy6pJ77De3ugI3ArsJeZzZX0V2B4OLs9CdgX+BV+xvrP\nkt41s7vDta4C9gAuDveQATwq6VX8LPce+JGxz4D78d3sB+E7yrvi4i5T8KNh/cNsRTdckOVz/KjX\nHOBQSYuAFhSpqZ2CbyibgR9nOwQ/mnaepB7h2ofgCm9rkCu7NcfFZdrju+hX4DvRhwNdzGxuKHsX\nMM/MbqYGkKJ+eCQSR+KRhsIW+BGmJC+a2YqgkDaeIv3xnsBAgOBQXscV0lI8FfK+wp3kFom8B0Pe\n97g2eA9c0nSlmQ0PecNwRbIdQ7uvpxxXuO4WwMn439f7uGN9GVia5r5+NLN3w+ux+Gh9I6ALsApY\nDhyIi690AP6EO+nfmdkewDf40a+eoY2NgdvMbA8zW40fOxtnZrvj4i+9QrluQDtgv3Ad8AAuKVaV\n6JdimNlNuLTriWF0Pg1XdesPEEb1JwMPlNZGVcjOzmbJkiUsXbqUrKys6mw6EmmQxJF4pKGwHD+j\nnGRF4nUBFf8+V7VeRSnER7x74DKn/YBL8YAlFeUr4FUzuy6M7rPM7CpJY3At8pskXY47/F9whwo+\nOl+VaOdA4CBJU4CtcblU8KMs35rZDuBR0YDlklJBqjPwPq8M9wDvSboJOB0YYmY/VLKNMsnLy2Po\nUJeDP/DAA6uz6UikQRJH4pGGwhR85FsRhuHSoMijhh2JK6RVhH6hXptQ7x08YljjMJWNpINxZzgz\n5B8ZrkO47lBc7vQAfAq7Ja5hvprif3ONgU0kpbzR3uE+84FZwJmStsAfNLIk7YU73wdCfh5FDwqp\nQCergD6SmoVp8d7A38NIfDxFTvwDoHVqkxqQQ/EzqtmEQC4Vxczm4EFZfg38Dnfq60zz5s3XvM7N\nzaV379707t2bTTbZpDqaj0QaNHEkHmkoPI/Lio6oQNmLgYGSJuOO6Soz+6ScOil+kjQOd7y3mNkU\nAEknAHdJaoZPi58YpvGnSroKGBrWi7/ApUcX4+psPwMPmJlJ+owgoRrW6jvjI+hz8GApC/EHh774\nNPmf8Wn49rjzXRred8X10SfgDwUf4Wvm+4Z7GIvLv26Dy7TeIWkjYAeKpvSfwGcGPpG0JNzvbDNb\nFNaaW+DhRkvdUY+rwrUskfZv4DF8mWB0GXUrTNKJRyKR4kQnHmkoPAi8L+lGM1tuZv2Smcn3YT07\nbQxrM9u6rPfA42Z2dYk0wtG2/Upp81E8jnYxJA3Gdc5TefdTJKc6C5gObGFmZ0kaBHxsZtNSG7bM\n7HHgcUk34MfrrlORvOqv8Wn7AuAVMytIbPSaZGbnhrKP4VP7P4XX+4QyBbjm+kR8Y9v3hFkI/EFj\nqZnNCZv/SuMuPODLMuBUM5tmZu+GjW7/KaNeJBKpJqLsaqTBIKkXvo47rYba/wo4OrGLvUYJ090D\nzGzvamxzEP4wcHd5Zctoow/wgZl9V27htetug0/Vb29myypYrUzZVYARI0ZU1pRIpCESZVcj6y9m\nNrT8UuvU/tY12X6a643A18Grs81+1dDGM1WpJ+lGfIr+sko48Egksg7EkXgkEqlryhyJj5+9gI1P\nqpGj5jXOZi2zGX3VWpo5kUhpxJF4JBJZv1i5upBb+nYqv2A9ZEMNoRqpPeIRs0ikllAigIuqQStd\n0m+C1OvEIPt6cSIvU9I9kj4PErLnJPLOljRN0gRJXRLpDyWOuyGpTZB9rfTDvqTcIP06UdIV63Kf\n6wt3v/Mp+9w8jAFDZqbN3/2GtzjpvtGcdN9oflm2spatizRU4kg8EqkFVDMBXJ4DBoXja83x424j\nzGwycBqwPX6sLBeXkB0WVOr+gkvW7gVcA5wYNtkVmNl7qcbN7EdJo4EzgIcraVtPYIGZdVunO1yP\nOGnvLemyZWtGfT4/bf6O7ZrzdP+0ByAikVKJI/FIpHYoFsAl0KlkoBJJ7SXNlbRGnU7Sy0oTGtTM\nFlnRppam+Fny1PuTgPvNrNDMfgRepEhWtQAXc2kGrAwBUm4Erkxj95P4Ofa1UCmBWIIozm3A/mEk\nHqXVgDbNNypT7/2zH5bQZ+Aobn1zRgz0Eqkw0YlHIrVDD1yUJUlX4FA8UMlWQP9wrOtd3AkTzmnv\nDTybrlFJx0r6BJdbvS0lToNLs36dKDob114H118figdguTH8vt/Mfk5ziXFA5yByU5L+uGBNl/Cz\nZ7iHd4DrgGFBV/29NHUjJRhxeQ8G99+PhctXMWx6tarVRtZjohOPRGqHdAFcnjazJSFQySPAwSH9\nLuDC8Pp84KGgDrcWZvayme2KR1M7QyFcaVmY2fNm1sXMfoWPyrsCz4Q19GeSa+vBtoXAZmma6olP\n568M9j1MUSCWSCVp1bQxkjh0l7bM+n5xXZsTaSBEJx6J1A7pArikxcxGAZmS9sdV1AZWoM5sYAxw\ndEiajY/uU2yJhyUtyZ14cJbTcKnUPkBvSdsmymRT+WAokUqwbOVqCgp9Cn3c1wvYMrdpHVsUaShE\nJx6J1A7pArikApU0wjePJYO0/BsPmToqBBZZC0k7J15vgodFTU2nPwOcKykjBHM5jhJT8pLOAMaY\n2Sx8fTy1EGvhPfK46qspipKWZBhwlqSsIPF6FkWBWCIleHrsbG56bRovTvyWv7zkooDPjJvDlG8X\n8uVPSzn2nvfpO3A03/2ynCN3SzfxEYmsTdydHonUDukCuKQClWwa0u9L5D2FRwErS4O8v6RD8chl\nAu42syEh73/4NPmn4f2NZvZlqqKkjfGIa6np78eAFxKSq6mHgcOAFyz9Tqv78B3wE8L7t3B9+Ega\nTsrbkpPytiyW1mevDmtev/b7uP8vUnmiYlskUguEON3vA13NrNypaUkHAPcCu5fiQGsFSe8C55nZ\njBq8TJmKbR9/NZ9NTrm1Bi9fc0TFtkgliYptkUh9JIT4vAwPD1pmABdJDwK9gDPr2IG3AQbWsAMv\nl/aN83nyxHZrpY8ZM4YLLrigDiyKROoP0YlHIrVERQO4mNlva9qWihDOl5c8214vmT9/PiNHjgSg\ne/fu5ObmrskrKCjgpZdeYv78+fTt25eWLT0E+vjx4/n6669p0qQJhxxyCFlZWXVieySyLsSNbZFI\nZJ2QNELS0eWXrDnGjh1Lr1696NWrF2PGjCmWl5GRweGHH8522223Jm3p0qXMnTuX3r17s8MOOzB9\n+vTaNjkSqRaiE49EGjBh93ml19HWN/Lz88nJySEnJ4f8/PxieZJo2rT4ka0lS5bQunVrADbZZBPm\nzZtXa7ZGItVJdOKRSA0h6StJMySZpB/C+5mSTkiUOTwEIpksabik7RN5VwY507mSnpSUE9JvCKIs\nQ/D19VskDQh550t6QtLQINc6TdLbkjYv0e4YSeMlvSKpXUjPkfRwQkb1TyG9VQikcmeo95mktLFB\nJbWQ9EAoN1nSvyRl1kT/Jqns1oEWLVrwww8/UFhYyLfffsvKlTHgSKRhEp14JFKznBh+Pwu0Bc4E\n7pO0qaRN8aNgp5nZHvj68+MAko7Az453A/Lxc9t/SbTbFTjVzHYCVqQSzexe/NjaAcAVZrYLLuP6\nr9Du6cB2wL5m1gV4Hbg9VP8L/j9h93Dds4IdrXCxmF1CemfgmFKm0P8JvGtm+4RybYGzK9ln60RF\nJiaaNGlCx44deeWVV1i4cCFNmjSpBcsikeonbmyLRGqHmyhy6IvxM+IAy4BUbMpGQJ6kybjM6ePA\n74H2+NGTnpIeATbCz4a/L2kersT2PfgoHXfw7wO5ksaH8jtKOgU4FvgVcFqYhhfwebh+T1xvfTr+\n4JAT0i4Ktu0IjDSzbpJeBf4p6TrcufcBXg3tnxlmBnJwoZi0YjXVSXZ2NkuWLEFShTeo7bLLLuyy\nyy7MmDGDNm3a1LCFkUjNEEfikUjt0A2XLz0YaI5LnV4SXvcPZf6BO+f9cQe+0MxuwtXSrgWmm9k0\n3AkvC6PwE8P7dFyJRxM7FFgEvIE77b/hYVGb4qPlVqF8EzyQyS5m1gmfJQD4He6Mr0mEFu0DTAgj\n7nHA3pJ6hfZ/AJ4zs2Zm1tLMajyeeF5eHkOHDmXIkCHss88+AMyYMYMff/wRgCFDhjBnzhzefvtt\nvvzSNW/eeustXn75ZebPn19s01sk0pCII/FIpGZJSZ3eBpwAXAY0xkfKhv8NHgf8F/gMn+ruB0wC\n/pha6w51U0fUtgHeATCznyQ9n+a6++NT29fiDwETzewXSS8DV+P66ClJ1XZhXfwt4LfAg5JGAccD\nf0y0ebqkp4HWwc4MSRNx1bbFwM7Ay0BfXAEuJQfbPKkWVxPk5ubSu3fvYmk77bTTmteHHnroWnUO\nO+ywmjQpEqkVohOPRGqWE3E980H4SHt7PCrZDwCS7sU1zicDP+Lrxx2By4HhwGh8On0iPoKuKB/g\na9uN8RCkyyX9zcyulXQHHn98EZCJP0xk42viubgG+9H4NPxEigK3zABGhTKFwI5mtkrSCGCAmb0q\nqTk+Sn9E0gp8Wv4SoMpOPDMzc61jYwDNmqWLjhqJbFhEJx6J1A4DzOwGSRcAx4eAIeAxuK8GHgC2\nMrPPASRtB3xpZmcHB3+bmS0JdR4nTIFLygV64wFPkizEp79nhXKn4QFKwKe8f21mkySdQ5HeeRPg\nD2a2IOwonwpsizvvDOAdM7s0tDccj0P+VzPrIamDpHZmNk/Sz0BvM5taDf1G27ZtozJbJFIK0YlH\nIrVLaUFDGgGDJLXCR7lzcCcJHl/8YUnLgFOBvwIPSZoBzANGlnKtiyUdBKzER8S/D+mXAC9KWgC8\nCcwP6R2A+0NUtUb4GvqHZlYo6XvgHklXhXXx04A7JKUCpSzGZxHigetIpBaJAVAikUhdU2YAFIAR\nI0bUkimRSJ1SaeGmuDs9EolEIpEGSpxOj1QJSc8Bt5rZ2HA2+UJ8FzR4POrfhXJn45u08oHfmtn4\nkP4Q8LCZvVeFa3fDp6VXAX80s3cSeZ2BjmY2OJFm+A7pJWs1Vk+piM1h6r2/mf0jkfYA8EhV+jXR\nxrtAv5reUV5Rxs9ewNZXv1bXZpRJDDkaqSuiE49UGkld8XPGYxPJj5rZ5WmK/wXYDdgLuAY4UVIP\noGAdHM0ZuKO6LU1eZ3xn9eA0eesbrYA/4bveATCzc6qh3TuAGyjaCFdpwoPdzWa2znqmK1cXckvf\nTuvaTI1y9eBJdW1CZAMlTqdHqkJ/Kh6isgA/otQMWCmpMXAjLkRSKpLaSnoh6G9PkXRmSL8COAn4\ng6SJkpok6uSGtnuGvLsSTV4saaykL1Rcu7yrpHckjQs/R5VizyBJ90saJWlWeN1Yrof+qaSFklZI\n+iZla6j3laRbQ9ufSbookWcqroe+5n2Jaw8Itk+S66tvFbLuAVqFex0Vyq6JKFZaHybsulHS6PD6\nosQlXwOOCMfFKkXYFAdwPf6Zb3Dc/c6n7HPzMAYMmblW3s9LV3L8fz+g732jOefRsaxYVVAHFkbW\nJ+JIPFIVeuDiJUlOkXQYMBe43sxGh/Q/4SIli4Hz8B3X95vZz+Vc4y5gqpn1lrQZME7SeDO7TdKu\nwMdmdneygpnNDzKgR5vZiSXaW2RmeZL2x0fpz4Xp6HuBI81sbrjOWEm7mdkvaWzqiiuvrcA1x1NK\na58BTwFPA+OBfwRbU0esNjWzvYK4ygRJI81scjn3n+TW1CxHOBL2d+BkXEntY+BTYFdJk3C51kMk\n9cMfoKYCL+Hn1W+V1AH//DbFl0Am4w9FwyWtCuWW4w74PUlHmdm34cjZ34HDg01vAleaWYGkQbho\nzI5Ac0kfhDLvSVoN9CilP9dLTtp7S7ps2ZpRn89fK69lkyyePa8bGRnizmGzGD7jB47afbM6sDKy\nvhBH4pGqsAVBqztwL7C1me2OO/eXwqgYM3vezLqY2a9wp9IVeEbSPfJIXBeXco2ewMDQxlzcaR60\nDjY/FX5/CLSXlI075G2AN+TKY2/gO6W3T98ET5vZEjNbDTyCS6gC7AcMDE57QbhGytYWwL5yDfMH\ngLeBHpJahvxxcrGUNbqfwSkmeV7Sl5Km4g9Fh4ejXa+H9lvgywhD8LPepwF5JPqQogePXYLdjfCz\n5K1DmwtCG4cAO+D7DVYD/5LUH/gGfwgz4BT8fPtfJL2BL1+cCjxmZp1L6bsNhjbNNyo1CEtmhsjI\n8LxCM7bObZq2XCRSUaITj1SF5RSpeGFm88xsVXg9FD/jvFuaenfimuGnAT+aWR9c/nPbmjfZI32Z\nWWr+shF+nGOymXVO/HQws4+rcoEwyv8J+Dm8Px3IwjefpSKG7RuKX4c7xDzK0D8PU+f7AIPMbDdg\nFv532wk4EliKy52OxB+uJuBiMMtxh14SA97DNyH+Gp9FOBx/wPoVPvuxM/6w8TTu8G/DZxguAPbG\nA6Y8AvwB/zxfBW7FZWJ3Sm1qBA4JfbrBjMIrwsQ5v3DM3e8z6vP5dNg4OvHIuhGdeKQqTMGnTgFQ\n8VjVnYGtKYrMlUo/AxgTFMSaUXQ22Ei/djoMODfUbYc7rLcrYNsioGW5pZxRwA5yQZSUnXkqbRgF\nfSQ1C+u+ZyTsycDlSUfiYT0PD3nH4g87L4WR/sW4oMo7+Eh9DpBnZj/hqmjpaIGLvyySlIGPrhea\nWWG4142APYCNw3UPwB+S2uMPFOcGGzbC+3A6sFXIfx7XWE/F+/4S2FbStfhIe1xIfxt33r2Azc1s\nGdAOD97yVLjP/uEaO5dyH5FA5w6teOWiAzhs13YM/rjGA7xF1nOiE49UheeBZPSImyVNDWuy9wNn\nmNka5S5JG+PO5JaQ9BhwcJge/tTMprA2FwOd5JKjQ4GrzOyTCtg2HGgWNoHdVVZBM1uAO6DrQ/np\n+K7s0pz4WHzKejrugO8L6cfgAU2+we//5mCr8Cnr+/CRbhZwWeJ+7wIGShqHa5mnWJ2wcUq41p+B\nj0I7qbyfgRfx6fvN8CntQny3+g+4g+6Mj6S74/sRtsJjg/+EBziZSpHK2ijcobfCnfR1uAM/HngY\nX354J2ycOw7fZ9AZD3pyq5ltbWYvJO6jRSn9uMGycnXhmtc5GzUiOyuzjNKRSPnEjW2RqvAgHsv6\nRjNbbmZlHkUKzqZ7ifelhc9MlfkedxTp8vqVUW8h7mySaSrtfTgm16MsWxJMMrNzkwlh0D7fzI4L\n75+haE39Zfwh4RUzu1rSRkAqtNbbwM5mtn3YPzAeuN3Mlkj6DF9fXhI227UCbjCzuyU9DCyRJHO5\nxZeAXYEl+PT39WY2QNLO+IPF13jwlc3M7FFJh+Iyq2PxCGtbAK+bWV9JV4Y6x+LT8bvjI/Jt8YeI\nLOA3+Nr+E8DGYYYl1Rc7Ad+Z2SJcF+BNSYvZwDa2PT12Nv/78Gt+Wb6KhctX8ddf78Yz4+awU7sW\nFBQaN78xnQxBqyaNuaPvBr+FILKORNnVSJWQx47+1jy+9XpP2Gy21o54SV/hu+Gnhvfb49PQO4Ud\n7z/j0cny8Zmv/5jZf8LGtodwBzwPH21/b2aXB6f+LNCWojXwIcGJt8L3FuThI/Z3zexieUCVG/B1\n7kJ8meL/zKxYmFJ5AJYb8I1sPwCfAE3MrJ+kYaHdRcC3uNb6ZHxpI6npflY4CbBDsKUDPoL/Huhr\nHh71enxmYDnlO/EyZVfHff0zuSffUlqRekEUe4lUE5WWXY1OPBLZgAhHz9IdwSszr4Yp04l/9913\nzJo1qzbtiUTqiujEI5FIg6PCTnz+/PmMHOlB27p3705ubm7tWBiJ1A4xAEokEll/GTt2LL169aJX\nr16MGTOmrs2JROqc6MQjkQaGpK0l/VTXdtQF+fn55OTkkJOTQ35+fl2bE4nUOdGJRyL1ALk2+0Xl\nl6y26/WQlFbURtLekh4Pr4s9MCihVy/pEkmb1o7FTlz+i0SKE514JFILyIOb/F9wgjNVPAjL47h0\n6dXygCWtE3lnhzPsk+RBUNqWaHcjSU9Lul3OZaHcBHlwk86JsidImiFpAi4Is5eKArCsCQSDS7Wm\nDXATFNiWh7eX4BrsdULpmjyRyIZDPCceiVQzQfEs18wuDe9Tu69OwHXmhwKjJPXBz2X/ARiABzNp\nB7wiaQ6u694FV4I7Fn/oFh6prWUQy9kEGBDOhg/C/6ZX4GpqfwGGhWNwzfDz3t3MbJyk1FG5+yTt\niQvAHGFmIyUdD7yQbpStEOc82NweeFbSCvw42XCgS9C6J4jtzDOzm9epQxNkZ2ezZMkSJJGVlVVd\nzUYiDZY4Eo9Eqp9HgZNVFJbz1PB7GoCZzcTFXdqE9DMJI/FQth0uczoG+Bt+Pvs0M1uCO8/FFJ07\n/xAPYpJiH9zRNwL+icd93xsP/fot0CeUeyNh62Xh9ZtBHjYVn7y0QDCY2U24/vqJYXQ+DddT7w8Q\nRvgn48Iw1UZeXh5Dhw5lyJAh7LPPPtXZdCTSIIkj8UikmjGz2ZI+wbXKXwb6lVF8O9yBDwU+wIOn\n3IwHFUnt3PqIoihnxwK5uB56W2DzRLkMXFb1ZDMbL6k98G2YIs/Fp75LSoS9j6vnjcMjzHXHR/8D\nzOxjSVtX4tbvwcOP3gScjgvU/FCJ+uWSm5tL7969q7PJSKRBE0fikUjNMAg4S9LuFAVk2RbICEpn\ne+LKaE1wffV8/KH67FB2BfAaPkrPBhqF0W0GcC3wCz6V/hiQGRTbMkN+KqpGSubscNxBF+DKa1Bc\n+34UHn40ucjctIxAMGkxszn4ksCv8Vjn91Smfmk0b968OpqJRNZLohOPRGqG5/FR7WW4Qwd3vDfg\no+wrcY33aXhoz+PxqfLxqQbMbATuiM8MP2/jEqjnhHzDg5oswgOhFOKOf2wYfaeYH679JbBH2NiW\ncuZdQiCY/+APEu/h0+LtKV94Il3EuH/jUqyrzGx0OfUrRHTikUjpxOn0SKQGMLNlkl7CA4ZsA/wV\nH2U/gk+DH4NPkxea2UlJbXZJy4Cc0M6Dkjrga9uXJzTS24YIb2s00kMbwxLBWLLwUfwsPGrZcOBn\nM+sh6VY8FvlvJf0HWAZ0NbNpknrg0+mF5QzG7wIeDvaeambTzOzdsNHtP+vei5FIpDyi7GokUguk\ndnWHzWl1ZcOf8Y1tjfC19/PMbHo1X2MbfG1/+xB3vCKUKbsKMGLEiHU1LRJpCFT63GQciUcitUDJ\ncKh1ZMNNwE011b6kG/HZhssq4cAjkcg6EEfikUikrilzJD5+9gI2PqnajpqXSgwnGqkHxJF4JBJZ\nv1i5upBb+naq8etcPXhSjV8jEqlu4u70SKSWkPScpLzwep210iX9RtLkIOU6RdLFibxMSfdI+lzS\nZ5LOSeSdLWlakGbtkkh/SNKBifdtJH2UEK2pjG25kkYF265Yl/usC2bOW8yJ947ihHtHMX3uomJ5\nhYXGJU9PoO/A0Zz2wIf8vHRlHVkZiUQnHonUCpK64jvMx1Zjs88BncysM35c7TJJe4S803DFtR2A\n/YAbEsItfwHy8CNt1wT7egAFZvZeqnEz+xEYDZxRBdt6AguCmtttVahfp9w+dCZ3nbwn95zShduH\nziqWN23uIrIyMxh83n702asDL078to6sjESiE49Eaov+rB1UpFMYrc6SdL+kxpLaS5orKTtVSNLL\nkk4tURczW2RFm1qaAlkUrS+fBNxvZoXBGb9IkeRqAX70rBmwUlJjXI/9yjR2P0k4l16SMNofztDc\n0gAAIABJREFUIGlq+BkQ0g4CbgP2DyPxA9PVr88sXL6K9q2a0K5lNotWrCqW17ZFNgWF3s2LVqyi\nddOo4R6pO6ITj0Rqhx74ufAkXYFDgV1wudT+ZvYd8C7uhAmj572BZ9M1KunYIPH6NXCbmU0JWVuG\ntBSzgQ7h9Z9wmdercOd9Fe7wf05ziXFAZ0nN0uT1x2Vcu4SfPcM9vANch59Z75wc3TcUiu33LbHt\nbuNmjclfXcgh/xzBYx99zeG7blartkUiSaITj0Rqhy2A70ukPW1mS8xsNS4Cc3BIvwu4MLw+H3jI\nzNIuvJrZy2a2K9AROEPSjuUZYmbPm1kXM/sVPirvCjwT1tCfSa6tB9sWAuk8VU9gkJmtDPY9HNIa\nPok9wiX1bkZ++iMbN2vM8D/24JJDOnLfe1/Urm2RSILoxCOR2mE5PoVdLmY2CtdD3x8PnjKwAnVm\n41HPjg5Js/HRfYotKdJUT3IncCm+hv6jmfUBekvaNlEmO9i/wdCqSRZzFy7n+0UraJ5dYl+feT5A\n62aNWVxiuj0SqU2iE49EaocpQMlRch9JzcLu7zNwbfQU/waeAkaFwCJrIWnnxOtNgIPCdQCeAc6V\nlCGpDXAcJabkJZ0BjDGzWfj6eGri2MJ7JLUFVuNhR0syDA/ykhUkXs/Cp+kbPJf27MhFT07gwifG\nc2nPjgA8M24OU75dyIE7bMJnPy7hpPtGc8fQWZyx71bltBaJ1BzxnHgkUjs8j0cOG5FIGwsMwUOE\njgDuS+Q9hUcBK0uDvL+kQ4FV+ATw3WY2JOT9D58m/zS8v9HMvkxVlLQxcC5F09+PAS9I6gN8kFhb\nPwx4IbGBLsl9+A74CeH9W8D9ZdjbYNh5sxY8d363Yml99uqw5vV/T9urtk2KRNISFdsikVpAUgs8\ndndXMyt3alrSAcC9wO6lONBaQdK7uMb6jBq8TJmKbR9/NZ9NTrm1Bi/vRMW2SD0gKrZFIvURM1sk\n6TI8otm0sspKehDoBZxZxw68DTCwhh14ubRvnM+TJ7artvbGjBnDBRdcUG3tRSJ1SXTikUgtYWYV\nWi82s9/WtC0VIZwvL3m2fb1i/vz5jBw5EoDu3buTm5tbLH/OnDmMH+8h3rt160abNm148cUXAcjI\nyKBnz540bdq0do2ORBJEJx6JRNaJ+hBmtaqMHTuWXr16AfDee+9xxBFHrMlbvXo106ZN45hjjiEj\no2gP8DHHHENmZiYzZsxg5syZ7LnnnrVudySSIjrxSKQBI6lROMsdqQL5+fnk5OSseZ1k3rx5SOK1\n116jadOmdO/enaysLDIzMwEoKChg4403rnWbI5Ek8YhZJJJAkkm6RtJYSV9IOiGRd3gIGjJZ0nBJ\n24f0HkFedGDIm5Q6/hXyJkl6VNInksZI2iXR5pUJ2dKHJeWE9JzwPpX3p0SdEZLulPQh8HKaexgU\nZFyLSbqGvBaSHgh2TJb0L0mZIW/7cF+TJY2XdHiJfvm/cJ8zk/1S4to7Snoj9N8kSb9Zx4+kRilr\ny8Hy5ctZunQpRx11FG3btmXaNN/KsHjxYp5//nmmTJmy1vR7JFLbRCceiazNIjPLw89u3wUgaVP8\n2NZpZrYHvlb8eKLOrsC9IW8wcG0ibw/gwaCsdg/waGjziHCNbsDuQCYenITwOyOkd8PPYx+RaHNb\n4AAzO7KUe1hL0jWk/xN418z2wSVTNwXODnmPA0+EezgdeCxsbktREIKtHAvcF/pkDeG8+xPApaH/\nDgCukrRTKTbWK1RCmq1x48ZsttlmZGRksPnmm7NgwQIAmjdvzvHHH09eXh4TJ06sC1MjkTVEJx6J\nrM1T4feHQPsQjKQrMMnMUjvLH8Y1xZuH9zPNbEKi3naJ9j4zs3fD6/8Bu4cjZz2BpxKBTO6j6Nx2\nT1zP3MxsER6IJClp+kQ50+ilSboeC1whaSIwHtgL6Bjuo3O4L8J9TgT2TbT5YMibGeom88ClX3cG\nngrtvwdsFNLqJdnZ2SxZsoSlS5eSlVU8kMmmm266xnHPnz+f5s2bU1BQsGb03rhxYxo1iiuSkbol\nfgMjkbVZAWBmBWF0VpG/kxWJ1wUVrLMuVHUTmYDjzKyY4HfiYWRdEPBTGK03CPLy8hg61A8NHHig\nB1ubMWMGubm5tGnThvbt2/Piiy/SqFEjevbsybJlyxg+fDiSyMzM5OCDDy6r+UikxolOPBKpGB8C\nD0naKZybPguYYGaLS07DpmE7SQeGaF6nAlPCufFhwD8k/Qt3yudQJFs6DPitpA+AHOBk4PJK2NtH\n0p1APj5l/2pIfxmf4r4gPKRsgu8s/zKMns8CHg5r+p3Cfaf4DfA3STvgEcuSeQAzgWWSzjCz/wGE\nqfTvwmxClcjMzGTMmDFVrb4WzZoVBWTLzc2ld+/exfJ32qlo9r9Tp0506tRpzfvs7GyOO+64arMl\nEllXohOPRCqAmf0YtMafCGu/P+LrxhVhCnCOpP8Cy4AzQ5tvSNoDGB3KfQz8Lbz+K3A3RVro/zOz\nNythcmmSrpcA/wAmhaNh+SHtSzwIykBJl+J66WeEs+IpGkmagMcuP8/Mfkhe0MxWSzoGuFPSFfga\n//dA30rYvRZt27aN4iyRSClE2dVIpAaR1AMYYGZ71+I1BwEfm9nd1dhmTZ4FL1N2FWDEiBE1cNlI\npN5RadnVuLEtEolEIpEGSpxOj0RqEDMbAdTaKDxcs18NtFnpEUIkEql5ohOPRCL1mvGzF7D11a9V\nS1sxUllkfSM68UiVkPQccKuZjZV0A3Ah8F3I/sDMfhfKnY3vqs4Hfmtm40P6Q8DDYcd2Za/dDd+o\ntQr4o5m9k8jrDHQ0s8GJtAan7V0RmyW1Avqb2T8SaQ8Aj1SlXxNtvAv0S8Yfr0tWri7klr6dyi9Y\nAa4ePKla2olE6gvlrolL+krSjCCh+Jmkl8I/0RohyFR+XFPtVweSbkjJWIb3N0o6qY5sOU7SPmXk\n7xCkQidIOi1Ibh64jtfsCuSY2dhE8qNm1jn8/C6R/hcgD/gDcE2o3wNX/6qqozkDd1R7Jh14oDPQ\nN0iPXlTF9usdkhpLej1Iot4RklsBf0qWM7NzKtOvpfTTHcAN62RwA2DmvMWceO8oTrh3FNPnrn0C\n7r1Pf+SU+z/kpPtGM+XbhXVgYSRSPhXd2HaimXUys+1x9afXwz/yDZXrgTVO3MyuM7On68iW44BS\nnThwPDAqOLzHK/tPvhT6U/EQlQVANtAMWBkefm4EriyrkqS2kl4ITmuKpDND+hXAScAfgo53k0Sd\n3NB2T1yZ7MREkxcrvR56N0nvSBoXfo4qxZ6y9MjT2hryvpJ0a2j7s6TDlOuR55T2PpE+AFdPOxg/\n2nZnyLoHaBX6YVQoO0LS0RW060bgSOD/Sjjy14AjqkkApt5y+9CZ3HXyntxzShduHzqrWN6KVQU8\nMWY2j/22K0/334/dN29ZR1ZGImVT6el0M3s+jPwuxwUlGgM3Ab/CJRYnAxcAGwNjgA5mtgpA0rPA\nK2b2iKQjgT/j/+BX4nrLJcUjCP94rsCPoXxOOJ8qqR9+rnU5sD0wDz/X+m2aNnYG/gW0w7fwDwg2\nbA8MBNrg52KvSZ3FDdOZfwZ6A7nAFWb2nKR7QrOjJBUCPfB/qh+b2d1hanlHoCWub/050MfMlklq\nCTyE62x/G35+MLPLS+tHM1sSjgytwGUtO+Dnis/CtbGPBXpKOgf4p5k9mrjv04BLgQxJ+wMn4NKZ\nA8zs1dLaNTOTdCo+ek49rFxuZsPD6zOBFZLOBzYDpgGdJB0GLMK/V01CX7+OC5jkh7wvwmd5PnBz\nms9qd+A/wG7hM7kJeBoYJ+l4/HPOD3ZNDvYjaXNckzwT/06MA5LnqheZWV5w4IPDaLYX0BrX+P49\n7vxfkPQ8cE7o+/6hD9uGe9o/XHcEMEHSMmAHYKSZ7SFpM+A7SfviMxCt8L+H7sH2CZLah34D2EHS\nzcAm4f3pwL0luuUZ/GGtMFzrCUmfAh8BXc2sc4kR9SXBkfcOn8Vr+EPTOEnf4N/r9uFaY8LPrZKE\nL4vk42fBTwIeKPkZrS8sXL6K9q38GXDRilXF8sbPXkCGRL+Hx7BJzkbc1Hs3mjaOq4+R+kdVj5h9\nhDsi8Om8hWa2j5l1wtdFrzaz2cBU4AhYM0rqATwraTt8mvUIM9sLV6oaTAkk7QbcChwagjJMBf6d\nKHIA7lx3Ad7FHXXJNhoBL+E61HuY2e4UqVeVF/BhrUAYianibmHq+Jc0/bM3rsy1M5CFP2wAXAcs\nMLOdgD5Aclo7bT8m8nfDR0274nrXPc3sLVyB69Zgy6OJ8pjZ47hDSE11f57G1rXaDelvAfua2Z64\nWtgjiTqNgEIz2w//TH8V6u+JB9vYCTgo9PX/mVkXXJHsEWASHpTjIknvSrq4hD1fBRsKcYfZH3eE\nr+MPYbvhamY3lbD3LmAk7qAmBpuSpPTQJ+Df+4l4/7bAv8+n4w8d3+NOLNX3t+Ej4JeBW4AZIX1z\n4Nvw/SgAdpWruc1N5Ye8X/CHkLPM7HvcoZ5O0CjHH+pSAUMA/qi1A4Z0xB90hYvFVCR01m7457Q3\n3k+74X34z9BP3+EPJ78CfgYW4A9VBwfZ1FdJzDatjxSTyChxUv3Hxfn8sHgFg36zD122as0TH82u\nVdsikYpSVSeePG5yLHB6mNKbGN6ngj8MAvqF16cCL5vZUuCwUGZkqPM4rgbVtsR1DgJeT/xjHEjx\nIBDvh2AM4COGdELGOwKNzOyZVIKZzVfFAj6kC4RREd4ys1/MlXQ+oqg/Dkpc72fgxUSdsvoR4EUz\nW2FmK/HgE8m8daG0drcD3pL0Ce6E2klqF/KM8CBkZl/hTqAd3tcrgc9wp5Hq62a4s/8vPjvxCT4D\n8xzQW9K2CXua4rMFLfH+aY/Lf66xF3eaBSXsPYiiUeNSYDjFSWmbFybaET6qngOkFj3n48sTqXbf\nxh8+dgJamNky3Km2B/YJn1VL3OElA318lHj9JnBKeKBsHe7xvXAPO+IBQ1I7rooFDJG0Fb5GfSPe\nb2fjfVceqe/WKor30w4U9dPnFPVTQbD5EUm/xx+c1u+F4MR/sZLKuS2ys9h7q43JzBDdtsvlsx8b\nzJ7IyAZGVeeH8vBRMfifwoVm9naacs8Dd4RReD9c3jFV500zO7NkhTD1XV+oSiCMNfUCBfg0bHmU\n1Y/p2qyuub3S2n0SuMzMXpSUgY8AU85jJe7EkjTCnXtjYGtcRztFBn5/d5rZ9WH03drM7pJ0HL5e\nnuJmfNr5eWA6sB8+lX0kPu1bXj8swp3q0nLvHEbhTm0hoe8l5eFLI6mx2fH49/2/+Lr6+/iSQCFw\nbVhCeRqYYWYvJB50hiSucwTugE/CH2AfCUsWs4FmYTr8bPzhZdcwjZ+q2wLv75/D+/PD79UhvWl4\nOCjp2FfgMxbnhn5qjfdhWZHPLsEfMA4GDqH4Q+Z6R6smWcxduJwMiebZxb9Ge2zRkifG+uh72txF\ndGjdtC5MjETKpdIjcUm/xtf4bg9JL+NTgE1CfvOUIw6jlpfwacgWiQ1VQ4DDJe2aaDc1nZjkHeDI\nxD/GcykKEAGwvzwYA3hwhnQOcCawWlKfxLVyzWwxPvI+K6SlC/hQGotxR1FZRhB0s+XHg36dyCu1\nH8sh5bSqm1a4njb46G+jRN4yPMZ1itbAC/hmt02AgWY2D9Yso2ThfZZyjI/hG6dmAJ+a2ZREW63w\nkfHv8eWSnvj+i6vwqenSeBv/DgzH92McQ/GNbWthZgvwGY+NgBeDPTcAzSXtHJzjtmY2Btcw/wJf\nsnkOn3JPDc8uxr+LUyn6fs6giJ/wkfBA3KH+I1GvtaQv8X0Z4GFBWyRsnIKviT+Cj9BTn8ln+BLG\n4/jDzvFpbvFi/Dt9LHAR3odDQz+BLwkkD01vEe71yXBvG6frt/WFS3t25KInJ3DhE+O5tGdHAJ4Z\nN4cp3y4kN2cjum6zMX0HjuaZj7/htK5b1rG1kUh6KurEn1U4Ygb8FjjSzFLThbfi65xjJU0G3qf4\ntOIg3PmuWVM1s0/xdcEHQ7vTgfNKXtTMphL+8YS2O+GbrVJ8AAyQNA0fPfwhTRurcWd5ftihOwkf\nkYCvVZ8e2n6ctQM+lMbtwNth6rtVBcqnuBHYNDiLF/CAF6kpy/L6sTT+B5wabFlrZmMduAR3bOPx\nKfD5ibzFQHcV7Qz/Cegd1vL3BLom+zosHewM7ChpCr5/oRAPiVnyc/8bRQ9rC8LvvyTX+82sn62t\nC/4HfEp9ND6Sfx14NpSXFT9vPT/13vyYXEd8r8VqfIPfe8HeTGBQsPnYkN/FzHbAR+d9wmf1Nv63\n1CPsAyjJ47jDLcBH+bPDtV8N6dPw7+I0YADQOGmzmf0BOAWYbmbXm9nWwP3AFvi+gU8Im/jMrAfw\nTXj9vZkdR1jPD32Y6qdluJMfEWzcEfhzuNcxwGz8oWO9ZefNWvDc+d147vxu7Nren4P77NVhzU70\ncw7YlsHn7cejZ+9Dq6br9faASAOmwQZACbvTjzazMkdb9QlJWUCmma0Io633cbGSYXVsWqWR1Avf\n2DWtrm2pDVTFoCKSvsK/p1PLK1tfCLvc/2NmheUWrh7KDIAy7uufyT35lmq5UFRsi9RzKi1vHM9M\n1C6tgTckZeJrmE80RAcOYGZDyy+1/mBV1CMPo+YGRWUfVGqazbJWMOuWtMf3I5ENngbrxM1sED5V\n32Awj7+8V13bEYk0VObPn8/IkSMB6N69O7m5FTltF4msv8RQpJFIpMEwduxYevXqRa9evRgzZkxd\nmxOJ1DkNdiQeiUQ2PPLz88nJyVnzOhLZ0Ikj8UiknqI0WuqSfpK0dQXqvh6UEesM1UAwo4a6ETcS\nqSniSDwSWQ8xsyPLL1VxJGWaWUF1trmuqKTMWiSyARJH4pFIA0UhEpmk0eH1RSXydpN0gKQJJep9\nLOlX4fVZkj6SR1l7W9KOIb2fpGHyKGhTgd0lXS8PSzxRHtq2VSj7eGhzSijfuqbuOTs7myVLlrB0\n6VKysrJq6jKRSIMhjsQjkYZNUzPbL0yxT5U0KClsY2bvS8qRtIeZTZZHiWuNxy04EOgLdDezfElH\n4AFZ9g/V9wU6mdnnkjbGA6ZsZmbL5bEHlodyfzCznwAk/Q2PmHZVTdxsXl4eQ4f66cYDDzywnNKR\nyPpPdOKRSMMjuTD8FHggGkkLcBW3GSXKP4LHLvhj+J3Sbj8GV0H8KExNC3fwKd5PRL5biEu9Pipp\nCPBqkC4GOFMe9rYxroNfPDh3NZKbm0vv3r1rqvlIpMERnXgkUn/5EQ87ugTWhNVtGdJTVCQwzqPA\nh5KuweVb9wvpAh4ys+tKuX5yRF8gj5G+Py5xPE7S4cGeC/DQvD/K49D3r9RdlkPz5s2rs7lIZL0i\nrolHIvWXoRSPKdAf+DAEFqowQat9Gh5zfZqZfR2yXsFH0VuAb16TlFaMKEyftzGzd83sejyK4W4U\nhSydL2kjPFhOtRKdeCRSOnEkHonUXy4B/hWCrBTi0d3OqGJbg/BgOWvqm9lISX8GXg5SwI3xiGnj\n0tRvCTwXgt5k4DHKn8dH/6fjU+g/ASPxeOyRSKQWaLABUCKRyHpDmQFQAEaMGFFLpkQidUqlz03G\n6fRIJBKJRBoocTo9EonUa8bPXsDWV79W6Xox7GhkQyA68UgkUiWC2Et/M/tHTV5n5epCbunbqdL1\nrh48qQasiUTqF3E6PRKJVJVWwJ/q2oiKMHPeYk68dxQn3DuK6XMXpS3z5tS57Hfr8DXv+w4cTd/7\nRnPqAx/y05IYbCVSP4lOPBKpp5QlZyrpbEmTws9YSW1D+tGhzqQgjbpHSO8q6Z0grzpO0lEhfdMg\nrzol/NwR0rtJGh8kVj+RdEoaE+8BWoUyo0K97SUNlzQ51D+8pvupItw+dCZ3nbwn95zShduHptei\neX3qPDZrmb3m/ePndGVw//04fs8teG78N7VlaiRSKeJ0eiRSf0krZyqpB3ANcICZzQuRzlZL6gg8\nABxoZp+Gc9uNw7T3vcCRZjZX0mbAWEm7AacBn5tZz3Cd1IPClcBtZvakXM6tZRr7fgd8bGadE2mP\nA/eZ2YOSdsHlXXc2sx/T1K81Fi5fRftWTQBYtGLVWvnvzPiBA7bfhMEfz1mTlpXpY5wVqwvouGk8\nqx6pn8SReCRSfzkzjJqnAKcCKWd5FPComc0DMLMlZrYC6AW8bmafhvT8II3aDdgGeEPSROAN/FjX\n9sCHwBGSbpN0NEUqbe8A10q6FtjHzH4pz9ggCNMZeDhcfxowEddgr1OKnaRNc6Dt2fHfcFznzYul\nffvLcnr/5wMeHf0VO7aLTjxSP4lOPBKph4TgJBcAh5vZ7sC1QHbZtUpvDphsZp0TPx3M7GMzGw3s\niQu8nIE7b8zsTuBYXOL132EmoOGSOH1bMoLpqM9/osuWrWncqPi/w81bNeGFC/fn0p4due+9L2rB\nyEik8kQnHonUT8qSM30NH6Wn1sFzJGUDQ4AjJe0Q0jcKo+NRwA6SDko1IClPzjbAIjN7Cg+Qspek\nDEkdzexzMxsI/Iv0KmyLgKZB050w6p8InBWusTMeYOXD6uqUqtKqSRZzFy7n+0UraJ5dfBVx5rzF\nDJv+PWc+NIZZ3y9mwJCZrCooJCWElbNRI7KzMuvC7EikXOKaeCRSP3mTUuRMzWyEpFuAYZIKgXzg\nmLAOfi7wdJBRLQDOMrMpko4FbpN0Jy6v+gVwDNAD+KOkAvyh/nwzK5R0cXD6K0P7vy9poJn9LOlx\nYIqkBWbWDV9jHyjpUmA1cEZdr4cDXNqzIxc96WHVbzx2VwCeGTeHndq14Df7b8Nv9t8GgBPvHcXl\nh+7It78s59LBE8kQNM7MZECfPerM9kikLKLsaiQSqWvKlF0d9/XP5J58S6UbjWIvkQZIpWVX40g8\nEonUazbLWsGsW46qazMikXpJdOKRSKTBMH/+fEaOHAlA9+7dyc3NrWOLIpG6JW5si0QiDYaxY8fS\nq1cvevXqxZgxY+ranEikzolOPBKpQyQdJ2l6UFfbMaifNalru+or+fn55OTkkJOTQ35+lEKNROJ0\neiRSt5wHXGdmz4T3ncsqvKETN+JGIsWJI/FIpI4IOuUHAn+X9E5IsyCjiqSvJN0oaXR4fVGi7oCg\nmT4paJVvFdK3lvSTpJvC6H6mpAMS9SqlrV6fUUnVlkhkAySOxCOROsLMLpW0JzDAzF4tpVhTM9tP\n0tbAVEmDzGwJcKuZXQ4g6Rzg78DJoU4uMNrM/izptJC3f1W01Ssit1qbZGdns2TJEiSRlZVV1+ZE\nInVOdOKRSP3mKQAz+0rSAmALYAaud/47IIe1/46XJB4KPgRuD6/X0lYH8iUdSZG2eqqNlLb6xzVy\nV1UkLy+PoUOHAnDggQfWsTWRSN0TnXgkUr9ZkXhdADQKU+d3AHlm9qWkbsATiXL5JeuUc42Utnr3\n6jC4JsnNzaV37951bUYkUm+Ia+KRSMOjBS6HOk9SBnB+BetVWlu9mu2uEs2bxwhikUhpRCceiTQw\nzGwK8AwwDfgI+LKC9T4FUtrqk4DRwNZmtgCPWHZ92PA2HbiBKkhA1gTRiUcipRO10yORSF1TpnY6\nwIgRI2rJlEikTqn0g3MciUcikUgk0kCJTjwSiUQikQZK3J0eiUTqNeNnL2Drq19LmxfDjUY2dKIT\nj0Qi9ZqVqwu5pW+ntHlXD55Uy9ZEIvWLdZpOD1KQcyVlJtL6BenIi8qqG8qOkHT0uthQXUg6VtJt\nFSh3g6QBNWxLD0mHrmMbrST9qUTaA5LqpUJGRb8L4Tu3W2XzqmhTrqRRISjJFSFYyT7V1f66EL4j\n1SbEEv5un62u9uqCmfMWc+K9ozjh3lFMn7uoWN6cn5fRZ+Ao+g4czcVPTaCg0PfS9R04mr73jebU\nBz7kpyUxoEqk4VEda+LfAYcl3vcDxldDu7WKmb1sZlfUxrUklTcD0gNYJycOtAKKOXEzO8fM3lvH\ndjckegILzKyzmd0GHAdUyYknH3QjNcPtQ2dy18l7cs8pXbh96KxieS2aZPHgWXkMPm8/OrRuwjsz\nfwDg8XO6Mrj/fhy/5xY8N/6bujA7ElknqsOJD8IdN5K2BZoBU1KZkg4JARwmSJoi6eR0jUg6OQR0\n2CK8v1LSGEnjJb0iqV2aOk0lPSNpWjjfOjiRd6WkqeHn4URQicYheMTUUOeFkL5mJCKpXSIYxCeS\n/lGRjggjwVtDvc9KBKxI5Y0BBpZmo6TdcfGOM8MI8KpQ9khJH4S2R0vaN9H22eFeJoU+bAvcA7QK\nbYwK5daMdiW1lfSCpMnhczmzhK1pA2+UuN9u4fOZGPrplJDeUtJDod1Jku4O6RX9Lpwq6aNQboKk\nkouep6fr4xJtbCbp2fAdmiLpmlLK7S7pvXAf0yRdEtIPAm7DNccnSroeP0t9VXh/Zih3VrB1nKS3\nJe0Y0vtJGhb6eCqwe4nrVug7VtZ3HFdvGxg+w0mSdk7Uq9T3v8Q1O8iDpPRNZ1N9ZeHyVbRv1YR2\nLbNZtGJVsbyWTbJoke1a640yM8gMOjZZmf4vcMXqAjpuGs+jRxoe1bEmPgK4UFJr4CzgUWCvRP54\n4AAzKwjOZZykt4LABADyad9DgZ5mtlDS6cB2wL5mVijpAlz/+bQS1z4MaGFmu4R2WoffRwBnAN2A\nxcAjwF+AK4GrgW2BLma2UtImae7pF+AYM1siKQt4S9LhZvZmBfpjUzPbK9zrBEkjzWxyyGthZvuU\nZaOZXSnpXiAnEeBiu2D/YWa2SNKuwBvAlpJ6ANeEPp4X/lmvBn4HfGxmpYW2vAuYama95QEvxkka\nb2ZTQ35pgTeSXAncZmZPShLQMqTfCSwBOoXPL9XH5X4XAm8BT5qZBac4HNcMr0gfp3gU+KuZjZTU\nGBguaayZDS1R7iv8e5cf+m5MsOkdSdcBR5vZiQCStgl9mnooORDoC3QP9Y8AHgL2D20YAbL6AAAV\nQUlEQVTvG/rg87W7v8LfsbTf8cCuwG/M7DxJfwauBU5bl++/pE7AY8CFDW3WppjkRSknz79ftIL3\nP/uJ3x+0PQDf/rKci54Yz9KVqxnUr16slEQilaI6nLgBg/EISifj/ziSTrwN8JBc6nE1sDGwIx6Y\nAVwZajYeQWllSDsW2BsY776BRsDCNNeeBOws6R78YSK1hbUn8JSZLQKQdB/wr5B3NHBZ6lpm9lOa\ndjOB2+Sa1ALa4XGeK+LEHwztfi/pNXxqPOVgHk2UK8vGkhyGP9SMVJESZqPgxI4CHjWzeeG6S0J7\n5dnZE7gs1Jkr6XXgICDlxEsLvJHkHeDa8JAx1Mw+CulHA3uZWWFoI9XH5X0XUmwHPClpc2AV0E5S\nu9Q9UnYfI6lZSGuT6IfmwM5ASSfeFPhvcF6FQHugEzA9fbcV45hQ9qNwHQFJJ/t+KQ4cKv4dK+07\nDjDTzCaE1x8Ge6Dq3/89gOfxB5eK3H/9IvGVT/f1z19dwGXPTOLW3rvTKIzAN2/VhBcu3J83ps7l\nvve+4IZjdq0lYyOR6qG6dqc/gss/jjSz+SUcyH+Bl4Hjw8hqFpCdyP8Qj660FfBpSBPwNzN7qKyL\nmtkXYVR6CHAEcLN8Onpd+SP+z7irma0I/wSzy6lTEUqOZCuKgDfN7My1MmpO3nqtwBslC5jZnZJe\nwZ3GvyUNMbNry2izvO9CiidxR/OiXBt8WSnlSiMDf7jMM7NV5ZS9GZgH9DOz1ZKGVOJaAh4ys+tK\nyS/r867Qd6yc73i5n1El+QZ/2OlBxR5i6hWtmmQxd+FyMiSaZ6/dFVc/P4Uz9t2KHdr6tPmqgkIa\nZQhJ5GzUiOysuG0h0vCoFrEXM/sC+DPw1zTZrYCvwj/tXnh4wyRvAhcAr4d/VuD/6FNT9KlADWud\nMZGvnxeY2YvApfhIb2NgGHCSpOZhmvccikZgrwKXhClWSplObwXMDf9cNwd+XaGOcPqFdtsAR+Kj\n1XSUZeMiiqamwQNXHJ7oHyTlhZev4evnbUN6jqTs0EZTlb6Jbhiuo418v8GRwNuVuE8kdTSzz81s\nID7SS81HvgpcodTwtKiPy/sukCiX0gM/G9ioRH6/0G7aPjazxcB7wFUJWzsozb6KcK05wYHvBpS1\ne7/k5/IK3vf/3969x1lZ1Xsc/3xhQJAB6UXlLW/HpCwvGCLiBa1ATRM1L5SFlZm2PeaBOp7wqIXm\nOZCWJpVbQI0kySROReU9RRRBEJBroua1BBUjBBIQ/J0/1hrZDDPDzDDDzMbv+/XixbPXWs961p7Z\nr/ntZz3P81tV93G0ldSzxj1rPu4WP2N1fMbr0tjP/z9IX8gGSfpWPd9HqzGkX3cu+tUcLhw/myH9\nugMwYdbLzP/7Cma9uJx7Fy7l1qnPM3D0NO5ZuJTXVq5l4JjpfH7MNMY88jznHrl3y74Bs0ZosufE\nI2J0LVVDgRslXQnMpGTas2TfByV9BZgk6YyIGJf/uDyc40Ab4EbS1GKpA4ERuU1bYHhEvAK8Iukg\n0gIPkNZEvjpvjwCGA09KWgc8C5xRrd+RwASlG5L+RromW1/LJM0i/bEfnher2ExE3F3HGH9LvrGN\nNC06Quk+gVskdQTaA1OBmRExWdJw4AFJ75CWoTw5TzXfDsyXtDwijqg2hIuBUZLmkc4oh0bEwga8\nT4CLlW4AW5eP+81cPoR0XXyBpPXAw/l4W/wsZIOB3+Vp/HuAN6rV1+dn/EXgeklVdStJXwiWVmt3\nNTBO0teAp4EpdbzfccBYSWcC10XEbfla9CSlu8/bkxYmmVVHH1Xq+xmr8TMuqXttHW/hs1Xn5z/f\nk3Ic8EdJnSKipi/mrdL+u3Zh4jc2/Zif2XOPd7cXXnnCZvvceX6fZh+XWXPyAihNSNILpOuJC7bU\n1szeVecCKLNe/AfdPj+8xnpnbLPtTIOvjzpjm5m1aru2W8PTw09q6WGYtUoO4k0oIvZu6TGYbc/e\neOMNpkxJVzz69u1Lt27dWnhEZi3Lq5iZWdmYOXMm/fv3p3///syYMaOlh2PW4hzEzcqImjg/fLlZ\nu3YtlZWVVFZWsnatc52bOYibvQfV8ehhq+Ybcc025SBuVoaU8uBfK+lRSc9JGlFSt7ukiUo51edJ\nujSXj1Vaye4R0mNnSOqtjTncZ0k6KZdXSLpXKYf6QqX861XPlteWM79L7n9GPu4NasaFX9R8iY7M\nykZZfhs3MwD2BPqSsqz9VdItEfEMKff5XRFxOmyW0KUHcExErJbUFbiJlPJ4iVIO/Zl5un4FcHbO\nwChSVsZzc/vacuZfBzwcEecpZdq7Pe8zpqnecIcOHVi1ahWSaNeuXVN1a1a2HMTNyteEnJ9+haS/\nAPtKWkJav6B/VaNq+dF/ExGr8/YRwD7A3SVntUHKpDcH+E+lxVTaklLE/iu3qS1n/gDgMEnfzq93\nJCWyaTK9evXi/vtT8rmjj64ruZ7Ze4ODuFn5akzu9NJ87gLmRUTf6o0kDQKOAo6OiJVKS7l2hzpz\n5gs4NadhbjKdO29cIrRbt26cdtppTdm9WVnzNXGz7Uhexe4xUupboNb1Acjt9supc6va9spT5F2B\nZTmA7wScXdKmtpz5k0jrrbetOq7S8q1bpTSIm9mmHMTNtj9fAo6UtEDSXOBrNTXK67gPAL4naW6e\nkh9GOqO+Degs6SnSQi+la4tfnG9om0PKl39ZLh9MmhGYm3PW3wPs3uTvzsze5dzpZtbS6sydDjB5\n8uRtNBSzFtXgRy58Jm5mZlamfGObmbVqs19azt6X/qnGOq9iZu91DuJm1qqtW/8Ow886uMa6S++c\nu41HY9a6OIibNYKkJ4E+EfFWS49la0kqAP8BvAX0jYiVJXWnAq9ExIz8+ljghxFxaEuMtS6Ll67k\nst/NJ4CrTzmA/Xft8m7dy//4F9+a8CRC7LJTB64/qwdvvb2B88c9wfoNQeUOFYz8wiFU7uA/iVZe\nfE3crBEiosf2EMCzi4FBEXFIaQDPTmXjI2St2o/uX8zIzx/Cz77wCX50/9Ob1HXp2I5bvtyLOy/o\nwx7v68hDi1+joo348Vk9uPOCPvT/2M78ZtbLLTRys8ZzEDdrBEkhqTJvvyDpKknT8vZFJe32l3Rf\nziU+X9KXc/mHJf05l8+WdEK1vi+TNDPnRf+0pOGS5uTHxvYvaftlSY/nvOcPSvpILePtlcc3L//f\nK5f/GtgXGCfp9mr7HE96BG1ozpN+Tq6qkDQq9zW3MeNpDiveepvdunZkl5068Oaatzep26ljO7p0\nSGlaK9q2oa1Eh3Zt+WCXDrlMtHUuditDDuJmTWPHiOgDHAuMkFSptFLY74ExEXFQRBwI/DG3vx0Y\nHxEHkZ7r/qWkD5T098+I6EXKU/57YGpEHEJ6fvsyAElHA2eRpsB7AtcCt1YfWF64ZCJweT7eFcBE\nSe0jYiDwCnBGRHyxdL+IuJeUwGVEnnm4LVd9HLgp93UncHlDxtNcNnlatpaH1l59cw2PPruMo/fb\nmP9m9dr1jH/8JQb08CPtVn58AcisadwBEBEvSFoOfIiUc7wiIiZUNcoLinQmLUTy81y2KF9jP5yU\nWAXg1/n/2alJVAX/WcDn8vbJwMHA4zn3uUg5zqv7CLAuIv6cj/eApHW5fH4j3uviiJiTt6fncTRk\nPM2j5ES6ppPqtes38O0Jcxlx2oFUtE3nLxHBf02cxyXHf4SdOnpBFSs/DuJmTaOmPOZbk0mpqr8N\nwNoa+oYUtm6NiO9uxXEao7ac7S01HgC6dmzHkhVv0Uaic4fN/7Rd+n/zGXT4Xuy388Y0rtfd/zQ9\n93ofR+xbW2Zas9bN0+lmzWcxsF7SmVUFkrrlm8eeBKquj+9POoOd3sD+/wCcI+lDuZ+2knrWMo72\nVTnSJX0KaJfLt+RNNi412lTjaRZD+nXnol/N4cLxsxnSrzsAE2a9zPy/r2DWi8u5d+FSbp36PANH\nT+OehUt59c013DTlr9y7cCkDR09j3PQXt9VQzZqMz8TNmklErJd0CvBTSd8F3gF+CIwDvgiMkjQE\nWE+6O/z1BvY/RdJlwKS86Eh7YAJpyr203TpJpwMjJXUCVpOuga+rx2HGAWPzF5HrgJe2djzNZf9d\nuzDxG0dsUnZmzz3e3V545QnVd+GZq09s9nGZNSfnTjezFnXbbbfF6tWra6y7/vrreWXdDrz/CyNq\nrHfGNtvONPgRCZ+Jm1mLWr16NYcdVvOj6J07d2a3lSt5evhJ23hUZuXBQdzMGqRYLFaQ7qzfB/hj\noVAYUa1+FHAA6ca+CwuFwrxisdgB+FneZ2GhUPjmNh622XbJN7aZlTFJu0t6SNIKSU/UUP91Sc9K\n+qukn0pqk8sPzQlcFlUloMnlX5F0+RYOOwB4qlAoHAUcVSwWd6lWP6JQKBwJfBX4Xi67GBhfKBQ+\n5QBu1nQcxM3KhKQ20mZPQK8CvgucXUP7fUhBtA+wX/73pVw9lBRYe+U2SOoGnAtcs4WhHA7cn7cf\nolpa1kKh8HzefJv0CBqkJDgDisXi5GKxOGAL/ZtZPTmImzWhnDL1ynyWuzjfFV5Vd0JOnTovp1z9\ncEndd3JK1QWSfl6S0nWYpAmS7gMWAV1LjxcRKyLiEdId59WdAfwuIl6PiHeAMcDAXPc2sCPQgY2B\n9hpSVrct3bXelfToGcCK6mMqMRwYmbf3Bf4EnARckafkt2j06NEMGTKkPk3N3pN8Tdys6W2IiB45\nb/hjkh7J5eOAY3KGtq+RUq/2lvQZYBBwBLAS+AUpNep38n69gU9ExLIGjmNPoPTh55eAqmeurgJG\nAZ2ASyQdQ8oMN6WmjorF4iWkADyWFLirlgjrAjxbQ/vBwKJCofBoLloBPFwoFNYWi8VngZ2Bvzfw\n/ZhZNQ7iZk3vFoCIWCxpNmn6OYC5EbEot/k5cGNOwdoPuCMi3gSQNBq4oaS/uxoRwOsUEX8B+ubj\ntQceAE6TNBg4EngV+FbVWXmhULiWlAudYrH4OeDTwAzgk8CvSvsuFovHkb6QDCwpfgw4qFgszgb2\nBhr0TLyZ1czT6Wat36pG7vcSsFfJ6z2Bmtbb/A5wM2la/JSIOBN4g5SQpiZ/AA4oFouPAtMKhcKS\nYrG4S7FYvCzX/4R0F/pD+U51gB8A/wNMBW4uFAr1STRjZlvgM3GzpvdV4GpJ+wGHkNKpBnCrpI9G\nxFOklKtzImKlpAeAayTdQArY57HxxrGtMRGYIulKUlD+OjC+tEEeY5+I+L6kg9iY7/0doLKmTguF\nwttUC/CFQmEpKUhTKBQ2W360UCgsAY7bqndjZptxEDdrehWS5pBuHLsgIl4DkDQIGJ+XKH2dfKd4\nRNydA+i0vP8TwNX1OVBOb/oisAOwk6S/ATdHxLCIeE7S99mYk/0+4JfVurgeGJzHMS+vX74AWAac\n1oj33mCdOnVixowZddabWc2cdtWsCUkKoHNENHYK/L3If4TMkganXfU1cTMzszLl6XSzJhQRDf4m\nbWbWWD4TNzMzK1MO4mZmZmXKQdzMzKxMOYibmZmVKQdxMzOzMuUgbmZmVqYcxM3MzMqUg7iZmVmZ\nchA3MzMrUw7iZmZmZcpB3MzMrEw5iJuZmZUpL0VqZi1K0j3A+1t6HGatwLKIOKEhOziIm5mZlSlP\np5uZmZUpB3EzM7My5SBuZmZWphzEzczMypSDuJmZWZlyEDczMytTDuJmZmZlykHczMysTDmIm5mZ\nlSkHcTMzszLlIG5mVo2kkFRZrWyZpL3z9iclPS7pSUl/kfSgpDbV2g9rprHtJumhmsYq6QVJB+Tt\nmyUd3RxjsNajoqUHYGZWTiRVABOBYyNiXi47BIi8fTxwFbCXpFOBsRHx46Y6fkS8AnyyHu3Oa6pj\nNjdJFRGxfhserw0QsR0sHuIzcTOzhukMVAKvVhVExJySgDAW+DZwE9AbuK8+neaz6KslTZP0kqSz\nJQ2WNEPSs5L65nZ7S1pWj/4mS/ps3t5Z0m8lzZM0X9I51Y57VT7uC5IuyuVtJN0o6SlJcyVNreU4\nYyWNkfSYpKfzdvtc1yXPCMzIx75BUtuS8f1Y0nRgUrU+d5O0RFKHkrJJks7O2ydKmippVh734bl8\nF0kP5fKFkq4p2X+YpAmS7gMWAV3r83tp7RzEzcwaICKWA6OBZyT9QdJQSXuUNFkL7AK0iYi1EbGo\nAd3vEBF9gNOBMcDbEXEY8N/A/27FsEcCCyLiIOA4YETVtHu2Yz7usbmuEjiYdMb/sYg4GPhsHf33\nzv1+DNgLOD+XXwc8nN9DD+CDwLkl+/0bcFREnFjaWZ5teBgYCOmLC3Ao8BtJ+wJXAJ+JiJ7AecCd\nedd/Aifn8h7AoZJKl/bsDZwdER/Nv8ey5yBuZlZ/ARARF5GCxO+BXsACSfvlNqcDg4BvSrpH0jEN\n6P/X+f/ZwI4lr2cBH96KcfcDRuWxLwHuYtMp+Tty3QvAcuBDwHNAO+AWSYO2NO6IWJWnxH8BfCqX\nDwAukfRkfk89ge4l+42vYxp9JHBh3v4GcGtErAOOB/YFpuR+bwcqJO0MtAWulTSX9DM7gPR7qnJX\nRGxxFqOc+Jq4mdnmXge6Aavg3evgO+VyACLiOVKgu1nS3cDJwHURMQs4RdJVpLPJSZL2jIgV9Tju\nmtz3BknvvgY20Lx/r9eUbG8AKiJihaSPk87O+wE/kPSJiFjagH4FnJp/VjVZVduOEfGYpLaSjgS+\nQvqyVNXnPRFxTvV9JF0BvA/oHRFrJI0GOpQ0qfV45cpn4mZmm7sfuKDk9fnA9Ij4l6RKSccpR1lJ\nXYF9gOfz60PyPu8Ac0hBZ4dcN7zqmvM29gDw9TyGXYATgQfr2kHSB0jT7PcCQ4EVpOnvmpwpqVP+\nsjOopO9JwNCS6+Dvl7RPA8b9E9IswWMR8XIuuw84IX/BqBprVYDvCizJAXx34JQGHKss+UzczGxz\ng4EbJM0jBeOXScEJUlD+d+AnktaQ/o7eHhG/zfWXSdqNNCV9FnB5RLyW6w4iTfNuaxcDo/L7ETA0\nIhZuYZ89gDE5MFcAdwPTa2k7kxRcPwhMJt0zAOnneA0wV1KQ7hcYTP7CUw93AD8DbqwqiIhnJH2J\nNM3fEWgPTM1jGAlMkLQA+Bvw53oep2xpO7jD3sys1ZE0LCKGlbxuA0wD+kTEOy02sCYmaSzwRET8\ntBn6Pop0l/+B28PjYM3BZ+JmZs1jcumLHLh7t8xQyo+kW4D+wDkO4LXzmbiZmVmZ8o1tZmZmZcpB\n3MzMrEw5iJuZmZUpB3EzM7My5SBuZmZWpv4fy2cQlD5XWbIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2111773cd30>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unit for changes   (welfare and asset loses change)\n",
    "unit = dict(multiplier = 1e-6, string = \"US$, millions per year\")\n",
    "\n",
    "render_pol_cards(\n",
    "    delta[policy_list],\n",
    "    colors,\n",
    "    policy_description,\n",
    "    unit,\n",
    "    \n",
    "#     [\"China\"],\n",
    "  country_list,\n",
    "    outfolder=\"cards/country/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### per policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Currently working on:  done at 2017-07-26 19:30:03.759837\n"
     ]
    },
    {
     "data": {
      "image/png": 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dHAfclUfxLwM/Al6XtDlwAjAs1rurmU0HpktqRbgkuK+6x1ETnP/oZJatXM06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\nwTtOinDBO06KcME7Tor4fy6R+SWO2b0XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2111acd6860>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unit for changes   (welfare and asset loses change)\n",
    "unit = dict(multiplier = 1e-6, string = \"US$, millions per year\")\n",
    "\n",
    "render_pol_cards(\n",
    "    delta,\n",
    "    colors,\n",
    "    policy_description,\n",
    "    unit,\n",
    "    policy_list,\n",
    "    use_title=True,\n",
    "    max_lines = 15,\n",
    "    outfolder=\"cards/policy/\",\n",
    "    do_by_policy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relative to losses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### per country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Currently working on:  done at 2017-07-26 19:32:19.937584\n"
     ]
    },
    {
     "data": {
      "image/png": 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2SnaWZutw4FRJTdO093lULCvYgelemXu/klX3D1w7vgvwdK6L5TnClY5b4NKt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\ns4L6fwBzP0HZVUaKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21119659898>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unit for changes   (welfare and asset loses change)\n",
    "unit = dict(multiplier = 100, string = \"Percent of current losses\")\n",
    "\n",
    "render_pol_cards(\n",
    "    rel[policy_list],\n",
    "    colors,\n",
    "    policy_description,\n",
    "    unit,\n",
    "    country_list, \n",
    "    name_suffix=\"_rel\",\n",
    "outfolder=\"cards/country/\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "### per policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Currently working on:  done at 2017-07-26 19:32:36.118836\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2111ac85978>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unit for changes   (welfare and asset loses change)\n",
    "unit = dict(multiplier = 100, string = \"Percent of current losses\")\n",
    "\n",
    "render_pol_cards(\n",
    "    rel,\n",
    "    colors,\n",
    "    policy_description,\n",
    "    unit,\n",
    "    policy_list,\n",
    "    name_suffix=\"_rel\",\n",
    "    outfolder=\"cards/policy/\",\n",
    "    max_lines=15,\n",
    "    use_title=True,\n",
    "    do_by_policy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Merge all cards to single pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merges the individual province cards into one big multi page pdf. Requires ghostscript."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "#Country cards\n",
    "province_list=glob.glob(\"cards/country/*.pdf\")\n",
    "merge_cardfiles(province_list,\"cards/all_country_cards.pdf\")\n",
    "\n",
    "#Policy cards\n",
    "thepollist=glob.glob(\"cards/policy/*.pdf\")\n",
    "merge_cardfiles(thepollist,\"cards/all_policy_cards.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converts the pdf files to png images. Requires Imagemagick."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# convert_pdf_to_png(\"cards/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# Malawi single case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "refout_flood = compute_resilience(macro, cat_info, hazard_ratios.query(\"hazard=='flood'\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "m,c,hr,a,desc =apply_policy(m_,c_,hr_.query(\"hazard=='flood'\"),a_ ,policy_name=\"fap\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "d,r,rto,nv=test_package(m,c,hr,a,return_rel=True, refout=refout_flood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "x=d.ix[[\"Malawi\",\"Bangladesh\"],[\"dKtot\",\"dWtot_currency\"]]*1e-6 \n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## per capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "x.T/pd.Series(dict(Malawi=.16, Bangladesh=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "#just double checking the number of people\n",
    "(hazard_ratios.query(\"hazard=='flood' & rp==100\").fa*macro[\"pop\"]).ix[[\"Malawi\",\"Bangladesh\"]].mean(level=\"country\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## discounted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "x.T/pd.Series(dict(Malawi=.16, Bangladesh=3))/.06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  }
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