ipc_microsim_tool/ipc_microsim_tool.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import patsy\n",
    "import random\n",
    "import statsmodels.formula.api as smf\n",
    "from mpltools import style\n",
    "from ipc_microsim_tool import IPCmicrosimTool as imt\n",
    "import matplotlib.pyplot as plt\n",
    "style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "pop = imt.population()\n",
    "print pop.country\n",
    "print pop.description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['WORLD', 'More developed regions', 'Less developed regions',\n",
       "       'Least developed countries',\n",
       "       'Less developed regions, excluding least developed countries',\n",
       "       'Less developed regions, excluding China', 'High-income countries',\n",
       "       'Middle-income countries', 'Upper-middle-income countries',\n",
       "       'Lower-middle-income countries', 'Low-income countries',\n",
       "       'Sub-Saharan Africa', 'AFRICA', 'Eastern Africa', 'Burundi',\n",
       "       'Comoros', 'Djibouti', 'Eritrea', 'Ethiopia', 'Kenya', 'Madagascar',\n",
       "       'Malawi', 'Mauritius', 'Mayotte', 'Mozambique', 'R\\xc3\\xa9union',\n",
       "       'Rwanda', 'Seychelles', 'Somalia', 'South Sudan', 'Uganda',\n",
       "       'United Republic of Tanzania', 'Zambia', 'Zimbabwe',\n",
       "       'Middle Africa', 'Angola', 'Cameroon', 'Central African Republic',\n",
       "       'Chad', 'Congo', 'Democratic Republic of the Congo',\n",
       "       'Equatorial Guinea', 'Gabon', 'Sao Tome and Principe',\n",
       "       'Northern Africa', 'Algeria', 'Egypt', 'Libya', 'Morocco', 'Sudan',\n",
       "       'Tunisia', 'Western Sahara', 'Southern Africa', 'Botswana',\n",
       "       'Lesotho', 'Namibia', 'South Africa', 'Swaziland', 'Western Africa',\n",
       "       'Benin', 'Burkina Faso', 'Cabo Verde', \"C\\xc3\\xb4te d'Ivoire\",\n",
       "       'Gambia', 'Ghana', 'Guinea', 'Guinea-Bissau', 'Liberia', 'Mali',\n",
       "       'Mauritania', 'Niger', 'Nigeria', 'Saint Helena', 'Senegal',\n",
       "       'Sierra Leone', 'Togo', 'ASIA', 'Eastern Asia', 'China',\n",
       "       'China, Hong Kong SAR', 'China, Macao SAR',\n",
       "       \"Dem. People's Republic of Korea\", 'Japan', 'Mongolia',\n",
       "       'Republic of Korea', 'Other non-specified areas',\n",
       "       'South-Central Asia', 'Central Asia', 'Kazakhstan', 'Kyrgyzstan',\n",
       "       'Tajikistan', 'Turkmenistan', 'Uzbekistan', 'Southern Asia',\n",
       "       'Afghanistan', 'Bangladesh', 'Bhutan', 'India',\n",
       "       'Iran (Islamic Republic of)', 'Maldives', 'Nepal', 'Pakistan',\n",
       "       'Sri Lanka', 'South-Eastern Asia', 'Brunei Darussalam', 'Cambodia',\n",
       "       'Indonesia', \"Lao People's Democratic Republic\", 'Malaysia',\n",
       "       'Myanmar', 'Philippines', 'Singapore', 'Thailand', 'Timor-Leste',\n",
       "       'Viet Nam', 'Western Asia', 'Armenia', 'Azerbaijan', 'Bahrain',\n",
       "       'Cyprus', 'Georgia', 'Iraq', 'Israel', 'Jordan', 'Kuwait',\n",
       "       'Lebanon', 'Oman', 'Qatar', 'Saudi Arabia', 'State of Palestine',\n",
       "       'Syrian Arab Republic', 'Turkey', 'United Arab Emirates', 'Yemen',\n",
       "       'EUROPE', 'Eastern Europe', 'Belarus', 'Bulgaria', 'Czech Republic',\n",
       "       'Hungary', 'Poland', 'Republic of Moldova', 'Romania',\n",
       "       'Russian Federation', 'Slovakia', 'Ukraine', 'Northern Europe',\n",
       "       'Channel Islands', 'Denmark', 'Estonia', 'Faeroe Islands',\n",
       "       'Finland', 'Iceland', 'Ireland', 'Isle of Man', 'Latvia',\n",
       "       'Lithuania', 'Norway', 'Sweden', 'United Kingdom',\n",
       "       'Southern Europe', 'Albania', 'Andorra', 'Bosnia and Herzegovina',\n",
       "       'Croatia', 'Gibraltar', 'Greece', 'Holy See', 'Italy', 'Malta',\n",
       "       'Montenegro', 'Portugal', 'San Marino', 'Serbia', 'Slovenia',\n",
       "       'Spain', 'TFYR Macedonia', 'Western Europe', 'Austria', 'Belgium',\n",
       "       'France', 'Germany', 'Liechtenstein', 'Luxembourg', 'Monaco',\n",
       "       'Netherlands', 'Switzerland', 'LATIN AMERICA AND THE CARIBBEAN',\n",
       "       'Caribbean', 'Anguilla', 'Antigua and Barbuda', 'Aruba', 'Bahamas',\n",
       "       'Barbados', 'British Virgin Islands', 'Caribbean Netherlands',\n",
       "       'Cayman Islands', 'Cuba', 'Cura\\xc3\\xa7ao', 'Dominica',\n",
       "       'Dominican Republic', 'Grenada', 'Guadeloupe', 'Haiti', 'Jamaica',\n",
       "       'Martinique', 'Montserrat', 'Puerto Rico', 'Saint Kitts and Nevis',\n",
       "       'Saint Lucia', 'Saint Vincent and the Grenadines',\n",
       "       'Sint Maarten (Dutch part)', 'Trinidad and Tobago',\n",
       "       'Turks and Caicos Islands', 'United States Virgin Islands',\n",
       "       'Central America', 'Belize', 'Costa Rica', 'El Salvador',\n",
       "       'Guatemala', 'Honduras', 'Mexico', 'Nicaragua', 'Panama',\n",
       "       'South America', 'Argentina', 'Bolivia (Plurinational State of)',\n",
       "       'Brazil', 'Chile', 'Colombia', 'Ecuador',\n",
       "       'Falkland Islands (Malvinas)', 'French Guiana', 'Guyana',\n",
       "       'Paraguay', 'Peru', 'Suriname', 'Uruguay',\n",
       "       'Venezuela (Bolivarian Republic of)', 'NORTHERN AMERICA', 'Bermuda',\n",
       "       'Canada', 'Greenland', 'Saint Pierre and Miquelon',\n",
       "       'United States of America', 'OCEANIA', 'Australia/New Zealand',\n",
       "       'Australia', 'New Zealand', 'Melanesia', 'Fiji', 'New Caledonia',\n",
       "       'Papua New Guinea', 'Solomon Islands', 'Vanuatu', 'Micronesia',\n",
       "       'Guam', 'Kiribati', 'Marshall Islands',\n",
       "       'Micronesia (Fed. States of)', 'Nauru', 'Northern Mariana Islands',\n",
       "       'Palau', 'Polynesia', 'American Samoa', 'Cook Islands',\n",
       "       'French Polynesia', 'Niue', 'Samoa', 'Tokelau', 'Tonga', 'Tuvalu',\n",
       "       'Wallis and Futuna Islands'], dtype=object)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.get_WPP_countries()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Madagascar',\n",
       " 'Malawi',\n",
       " 'Mauritius',\n",
       " 'Mayotte',\n",
       " 'Mali',\n",
       " 'Mauritania',\n",
       " 'Maldives',\n",
       " 'Malaysia',\n",
       " 'Malta',\n",
       " 'Martinique',\n",
       " 'Marshall Islands']"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.get_WPP_countries('ma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mauritius - Medium variant'"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.get_WPP_projection('Mauritius', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mauritius</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>493254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>506431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>521190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>537048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>553623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Mauritius\n",
       "1950     493254\n",
       "1951     506431\n",
       "1952     521190\n",
       "1953     537048\n",
       "1954     553623"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.projection.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mauritius</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2096</th>\n",
       "      <td>972115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2097</th>\n",
       "      <td>966812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2098</th>\n",
       "      <td>961666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2099</th>\n",
       "      <td>956691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100</th>\n",
       "      <td>951897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Mauritius\n",
       "2096     972115\n",
       "2097     966812\n",
       "2098     961666\n",
       "2099     956691\n",
       "2100     951897"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.projection.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mauritius'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'WPP 2015 - Medium variant'"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>access_ele</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      access_ele\n",
       "2014           1\n",
       "2015           1\n",
       "2016           1\n",
       "2017           1\n",
       "2018           1"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ram = imt.resource_access()\n",
    "ram.period = (2014, 2030)\n",
    "ram.description = 'Mauritius - electricity'\n",
    "ram.RAM = pd.DataFrame({'access_ele': 1.0}, range(ram.period[0], ram.period[1]+1))\n",
    "ram.RAM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>access_ele</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      access_ele\n",
       "2026           1\n",
       "2027           1\n",
       "2028           1\n",
       "2029           1\n",
       "2030           1"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ram.RAM.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hhkey</th>\n",
       "      <th>income</th>\n",
       "      <th>kwhpc</th>\n",
       "      <th>wgt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>18.709273</td>\n",
       "      <td>51.004929</td>\n",
       "      <td>101.734508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>13.633015</td>\n",
       "      <td>66.802184</td>\n",
       "      <td>143.742106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9.210717</td>\n",
       "      <td>40.396657</td>\n",
       "      <td>114.716599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>56.405275</td>\n",
       "      <td>116.014927</td>\n",
       "      <td>113.544390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>14.247824</td>\n",
       "      <td>38.253699</td>\n",
       "      <td>150.369788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hhkey     income       kwhpc         wgt\n",
       "0      1  18.709273   51.004929  101.734508\n",
       "1      2  13.633015   66.802184  143.742106\n",
       "2      3   9.210717   40.396657  114.716599\n",
       "3      4  56.405275  116.014927  113.544390\n",
       "4      5  14.247824   38.253699  150.369788"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FILE = 'data/example_aztlan_seed.tab.txt'\n",
    "data = pd.read_csv(FILE, sep='\\t', index_col=False, na_values='')\n",
    "ms = imt.microsim(data)\n",
    "ms.dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hhkey', 'income', 'kwhpc', 'wgt']"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.seedvars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hhkey</th>\n",
       "      <th>income</th>\n",
       "      <th>kwhpc</th>\n",
       "      <th>wgt</th>\n",
       "      <th>newvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>18.709273</td>\n",
       "      <td>51.004929</td>\n",
       "      <td>101.734508</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>13.633015</td>\n",
       "      <td>66.802184</td>\n",
       "      <td>143.742106</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9.210717</td>\n",
       "      <td>40.396657</td>\n",
       "      <td>114.716599</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>56.405275</td>\n",
       "      <td>116.014927</td>\n",
       "      <td>113.544390</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>14.247824</td>\n",
       "      <td>38.253699</td>\n",
       "      <td>150.369788</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hhkey     income       kwhpc         wgt  newvar\n",
       "0      1  18.709273   51.004929  101.734508     1.5\n",
       "1      2  13.633015   66.802184  143.742106     1.5\n",
       "2      3   9.210717   40.396657  114.716599     1.5\n",
       "3      4  56.405275  116.014927  113.544390     1.5\n",
       "4      5  14.247824   38.253699  150.369788     1.5"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.dataset['newvar'] = 1.5\n",
    "ms.dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hhkey</th>\n",
       "      <th>income</th>\n",
       "      <th>kwhpc</th>\n",
       "      <th>wgt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>18.709273</td>\n",
       "      <td>51.004929</td>\n",
       "      <td>101.734508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>13.633015</td>\n",
       "      <td>66.802184</td>\n",
       "      <td>143.742106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9.210717</td>\n",
       "      <td>40.396657</td>\n",
       "      <td>114.716599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>56.405275</td>\n",
       "      <td>116.014927</td>\n",
       "      <td>113.544390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>14.247824</td>\n",
       "      <td>38.253699</td>\n",
       "      <td>150.369788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hhkey     income       kwhpc         wgt\n",
       "0      1  18.709273   51.004929  101.734508\n",
       "1      2  13.633015   66.802184  143.742106\n",
       "2      3   9.210717   40.396657  114.716599\n",
       "3      4  56.405275  116.014927  113.544390\n",
       "4      5  14.247824   38.253699  150.369788"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.__reset__()\n",
    "ms.dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Elasticity calculator started - please be patient\n",
      "Replicating observations, 11280 to 464778...\n",
      "Fitting models...\n",
      "Quantile\telasticity\tse_elast\tintercept\tse_intercept\n",
      "0.1\t0.398640\t0.001902\t2.070578\t0.004844\n",
      "0.2\t0.393800\t0.001311\t2.292835\t0.003348\n",
      "0.3\t0.394674\t0.001207\t2.436981\t0.003087\n",
      "0.4\t0.387523\t0.001195\t2.585914\t0.003060\n",
      "0.5\t0.375112\t0.001243\t2.736595\t0.003186\n",
      "0.6\t0.368819\t0.001130\t2.872029\t0.002895\n",
      "0.7\t0.369336\t0.001127\t3.001140\t0.002886\n",
      "0.8\t0.379222\t0.001295\t3.122457\t0.003309\n",
      "0.9\t0.394856\t0.001866\t3.321080\t0.004746\n"
     ]
    }
   ],
   "source": [
    "ms.elast_calc('hhkey', 'kwhpc', 'income', 'wgt', stub='kwhpc', parts=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hhkey</th>\n",
       "      <th>income</th>\n",
       "      <th>kwhpc</th>\n",
       "      <th>wgt</th>\n",
       "      <th>quantkwhpc</th>\n",
       "      <th>elastkwhpc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>18.709273</td>\n",
       "      <td>51.004929</td>\n",
       "      <td>101.734508</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.368819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>13.633015</td>\n",
       "      <td>66.802184</td>\n",
       "      <td>143.742106</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.379222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9.210717</td>\n",
       "      <td>40.396657</td>\n",
       "      <td>114.716599</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.375112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>56.405275</td>\n",
       "      <td>116.014927</td>\n",
       "      <td>113.544390</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.394856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>14.247824</td>\n",
       "      <td>38.253699</td>\n",
       "      <td>150.369788</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.387523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hhkey     income       kwhpc         wgt  quantkwhpc  elastkwhpc\n",
       "0      1  18.709273   51.004929  101.734508         0.6    0.368819\n",
       "1      2  13.633015   66.802184  143.742106         0.8    0.379222\n",
       "2      3   9.210717   40.396657  114.716599         0.5    0.375112\n",
       "3      4  56.405275  116.014927  113.544390         0.9    0.394856\n",
       "4      5  14.247824   38.253699  150.369788         0.4    0.387523"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ms.dataset.to_csv('newfile.txt', '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>hhkey</th>\n",
       "      <th>income</th>\n",
       "      <th>kwhpc</th>\n",
       "      <th>wgt</th>\n",
       "      <th>quantkwhpc</th>\n",
       "      <th>elastkwhpc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.466055</td>\n",
       "      <td>48.970023</td>\n",
       "      <td>77.499222</td>\n",
       "      <td>0.723647</td>\n",
       "      <td>0.381338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11.269757</td>\n",
       "      <td>64.137027</td>\n",
       "      <td>109.499733</td>\n",
       "      <td>0.872327</td>\n",
       "      <td>0.406476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7.614056</td>\n",
       "      <td>38.784982</td>\n",
       "      <td>87.388708</td>\n",
       "      <td>0.558331</td>\n",
       "      <td>0.383330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>46.627525</td>\n",
       "      <td>111.386365</td>\n",
       "      <td>86.495744</td>\n",
       "      <td>1.008531</td>\n",
       "      <td>0.439620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>11.777990</td>\n",
       "      <td>36.727520</td>\n",
       "      <td>114.548562</td>\n",
       "      <td>0.513623</td>\n",
       "      <td>0.386870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  hhkey     income       kwhpc         wgt  quantkwhpc  \\\n",
       "0           0      1  15.466055   48.970023   77.499222    0.723647   \n",
       "1           1      2  11.269757   64.137027  109.499733    0.872327   \n",
       "2           2      3   7.614056   38.784982   87.388708    0.558331   \n",
       "3           3      4  46.627525  111.386365   86.495744    1.008531   \n",
       "4           4      5  11.777990   36.727520  114.548562    0.513623   \n",
       "\n",
       "   elastkwhpc  \n",
       "0    0.381338  \n",
       "1    0.406476  \n",
       "2    0.383330  \n",
       "3    0.439620  \n",
       "4    0.386870  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FILE = 'data/example_aztlan_w_elast.tab.txt'\n",
    "data = pd.read_csv(FILE, sep='\\t', index_col=False, na_values='')\n",
    "ms = imt.microsim(data)\n",
    "ms.dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    11280.000000\n",
       "mean         0.401532\n",
       "std          0.015938\n",
       "min          0.378095\n",
       "25%          0.387018\n",
       "50%          0.403671\n",
       "75%          0.407931\n",
       "max          0.470317\n",
       "Name: elastkwhpc, dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.dataset['elastkwhpc'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1hrghAYqNawK7FnwPgKlpDai6Alz2dvt+Hb6nlFJKKaXUQSGiA5QxZs9P4vEg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PAAAg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\nNIBSFEVRFEVRFEVpIf8PiakiPjiPaEgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1090c7890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "ms.visualize(ms.dataset, 'income', 'elastkwhpc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78       99\n",
       "1547    100\n",
       "6239    100\n",
       "6334    100\n",
       "1624    101\n",
       "Name: cem, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msdts = ms.dataset.sort('income')\n",
    "msdts['cem'] = (((msdts.income * msdts.wgt).cumsum() / (msdts.income *\n",
    "msdts.wgt).sum()) * 100).astype(int) + 1\n",
    "msdts.cem.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hhkey</th>\n",
       "      <th>income</th>\n",
       "      <th>cem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11275</th>\n",
       "      <td>11276</td>\n",
       "      <td>6.549277</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11276</th>\n",
       "      <td>11277</td>\n",
       "      <td>6.071800</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11277</th>\n",
       "      <td>11278</td>\n",
       "      <td>7.342415</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11278</th>\n",
       "      <td>11279</td>\n",
       "      <td>1.445418</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11279</th>\n",
       "      <td>11280</td>\n",
       "      <td>22.524087</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       hhkey     income  cem\n",
       "11275  11276   6.549277   11\n",
       "11276  11277   6.071800   10\n",
       "11277  11278   7.342415   15\n",
       "11278  11279   1.445418    1\n",
       "11279  11280  22.524087   60"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msdts['cem'][-1:] = 100\n",
    "ms.dataset['cem'] = msdts['cem']\n",
    "ms.dataset[['hhkey', 'income', 'cem']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cem</th>\n",
       "      <th>income</th>\n",
       "      <th>meanelast</th>\n",
       "      <th>meankwhpc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cem</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>96</td>\n",
       "      <td>86.738293</td>\n",
       "      <td>0.404155</td>\n",
       "      <td>161.651200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97</td>\n",
       "      <td>97.233829</td>\n",
       "      <td>0.421202</td>\n",
       "      <td>170.206306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98</td>\n",
       "      <td>113.328776</td>\n",
       "      <td>0.423841</td>\n",
       "      <td>174.856580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>155.644612</td>\n",
       "      <td>0.413995</td>\n",
       "      <td>226.627580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>100</td>\n",
       "      <td>324.308197</td>\n",
       "      <td>0.398940</td>\n",
       "      <td>291.313339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cem      income  meanelast   meankwhpc\n",
       "cem                                        \n",
       "96    96   86.738293   0.404155  161.651200\n",
       "97    97   97.233829   0.421202  170.206306\n",
       "98    98  113.328776   0.423841  174.856580\n",
       "99    99  155.644612   0.413995  226.627580\n",
       "100  100  324.308197   0.398940  291.313339"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggr = ms.dataset.groupby('cem')\n",
    "newdf = pd.DataFrame({'cem': aggr['cem'].first(), 'income': aggr['income'].mean(),\n",
    "'meankwhpc': aggr['kwhpc'].mean(), 'meanelast': aggr['elastkwhpc'].mean()})\n",
    "newdf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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W7OuTin+Nxb6+Qlbs/zbFvr5gMJjvSxhRxf7vJ42PNRai8fDvUuxrZH2FbbjroysUAADI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j6K/uufUW99T+h4VVOP2ngW9uc/hu7twvrHuybVi6fqzIQI+5YPYf/gblRRMSobWRcbX4t1/S1T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\nog/8f9P6cmsC3vuyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108557090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "ms.visualize(newdf, 'cem', subplotcols=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key\n",
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "3    3\n",
       "4    4"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grwt = ms.growth(1000)\n",
    "grwt.dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>partes</th>\n",
       "      <th>grt20072008</th>\n",
       "      <th>grt20082009</th>\n",
       "      <th>grt20092010</th>\n",
       "      <th>grt20102011</th>\n",
       "      <th>grt20112012</th>\n",
       "      <th>grt20122013</th>\n",
       "      <th>grt20132014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.292686</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.102545</td>\n",
       "      <td>-0.616508</td>\n",
       "      <td>0.119817</td>\n",
       "      <td>-0.922023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.723832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.002871</td>\n",
       "      <td>-0.261410</td>\n",
       "      <td>0.001830</td>\n",
       "      <td>-0.095768</td>\n",
       "      <td>-0.315862</td>\n",
       "      <td>0.361479</td>\n",
       "      <td>-0.005498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.067021</td>\n",
       "      <td>-0.156985</td>\n",
       "      <td>-0.045783</td>\n",
       "      <td>-0.002918</td>\n",
       "      <td>-0.117650</td>\n",
       "      <td>-0.107455</td>\n",
       "      <td>0.030462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  partes  grt20072008  grt20082009  grt20092010  grt20102011  \\\n",
       "0    0       1     0.000000     0.000000     0.000000     0.000000   \n",
       "1    1       2     0.292686     0.000000     0.000000     0.000000   \n",
       "2    2       3     0.102545    -0.616508     0.119817    -0.922023   \n",
       "3    3       4    -0.002871    -0.261410     0.001830    -0.095768   \n",
       "4    4       5    -0.067021    -0.156985    -0.045783    -0.002918   \n",
       "\n",
       "   grt20112012  grt20122013  grt20132014  \n",
       "0     0.000000     0.000000     0.000000  \n",
       "1     0.000000     0.000000     0.000000  \n",
       "2     0.000000     0.000000    -0.723832  \n",
       "3    -0.315862     0.361479    -0.005498  \n",
       "4    -0.117650    -0.107455     0.030462  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FILE = 'data/example_aztlan_growth.tab.txt'\n",
    "grwt.load_csv(FILE, '\\t')\n",
    "grwt.dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eJ2xw5DiFntN4PBChc1DUmWP8/OwXgOh6mOAQaNXxbFcpD9d1ee+993jyySeJ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\nR8nIyOCVV14hIyODC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T17BKaG38+NMfDDH+PMvg97Sn9MpyScZW9CSTFm6IWYiMgq4uHA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\n/wWPrNNjyRW0cQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1089da1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "toviz = [col for col in grwt.dataset.columns if 'grt' in col]\n",
    "plt.figure(figsize=(12,12))\n",
    "ms.visualize(grwt.dataset, 'key', toviz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grt20072008    0.034193\n",
       "grt20082009    0.069243\n",
       "grt20092010    0.032252\n",
       "grt20102011   -0.035453\n",
       "grt20112012    0.019849\n",
       "grt20122013   -0.036412\n",
       "grt20132014   -0.111413\n",
       "dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grwt.dataset[toviz].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0099999999999999672"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grwt.add_log()\n",
    "grwt.dataset.log.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FCC/wqwJR3+RB0RU0y2RfRxGiW/CvAtm3B236HLRAmZclhDf4TYGo6hso61/Rpjzo6yhC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1085074d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3))\n",
    "ms.visualize(grwt.dataset, 'key', 'log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NJzAf7sQU/h0++dB7meGo8VhzHsFKvYJWy6IVaAa4SKaezNtddsoK9srrdDrJy8vrsffr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\ngDTeOl0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1084f7990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grwt.add_log(average=0.025)\n",
    "plt.figure(figsize=(8,3))\n",
    "ms.visualize(grwt.dataset, 'key', ['log','log1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cyMHmcjoz3hhjWL58OcnJydx5552dzmI+3oYr87ZOv4+IBE+bBSYkJITZs2eT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1083ece10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grwt.add_log(stub='ln', average=0.033)\n",
    "plt.figure(figsize=(8,6))\n",
    "ms.visualize(grwt.dataset, 'key', ['log','log1', 'ln'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ln         0.033\n",
       "flipped    0.033\n",
       "dtype: float64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grwt.add_log(stub='flipped', average=0.033, flip=True)\n",
    "grwt.dataset[['ln', 'flipped']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nQQVGJEoZY+CiQRCtu5GqyiE2DpOY5HUSETkPKjAiUcxkDMAW7/Y6hjeKd0O/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\n3/n/96ZCbauDNGIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103610050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3))\n",
    "ms.visualize(grwt.dataset, 'key', ['ln', 'flipped'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ln           0.033\n",
       "flipped      0.033\n",
       "alpha100     0.033\n",
       "alpha1000    0.033\n",
       "dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grwt.add_log(stub='alpha100', average=0.033, flip=True, alpha=100)\n",
    "grwt.add_log(stub='alpha1000', average=0.033, flip=True, alpha=1000)\n",
    "grwt.dataset[['ln', 'flipped', 'alpha100', 'alpha1000']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cvVeKGCGuiLq6I8aUOaimLTCfeQTz/Tflqj8fIAWMED5KRTdxFjE6839WxxHC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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1085571d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "ms.visualize(grwt.dataset, 'key', ['ln', 'flipped', 'alpha100', 'alpha1000'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log1    0.025\n",
       "log2    0.015\n",
       "dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grwt.add_log(average=0.025, shift=-0.01)\n",
    "grwt.dataset[['log1', 'log2']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KT4eNhg0bxsmTJykpKaGhoYGdO3eSltb0ngdpaWls374dgAMHDhAVFUVsbGyb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\nImIral5ERETEVtS8iIiIiK38/8HPdYYwY7kXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1089b2590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3))\n",
    "ms.visualize(grwt.dataset, 'key', ['log1', 'log2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ipc_microsim_tool.py:727: FutureWarning: sort is deprecated, use sort_values(inplace=True) for for INPLACE sorting\n",
      "  X.sort(ascending=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pwr       0.01\n",
       "pwr1      0.01\n",
       "pwr2      0.05\n",
       "pwr3      0.05\n",
       "pwr4      0.05\n",
       "pshift    0.03\n",
       "dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grwt.add_power()\n",
    "grwt.add_power(power=3)\n",
    "grwt.add_power(power=3, average=0.05)\n",
    "grwt.add_power(power=3, average=0.05, flip=True)\n",
    "grwt.add_power(power=3, average=0.05, flip=True, alpha=100)\n",
    "grwt.add_power(stub='pshift', power=3, average=0.05, shift=-0.02, flip=True,\n",
    "alpha=100)\n",
    "grwt.dataset[['pwr', 'pwr1', 'pwr2', 'pwr3', 'pwr4', 'pshift']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L3oPqcRNUULDuKETkA2Tpx1Bt2kNFROmO4hOMvrdBlsyHlBTrjkLk89hAEXkBSd8O7N8D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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1090c1710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "ms.visualize(grwt.dataset, 'key', ['pwr', 'pwr1', 'pwr2', 'pwr3', 'pwr4', 'pshift'],\n",
    "subplotcols=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ipc_microsim_tool.py:329: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  sorted_dt = dt.sort(prvinc)\n",
      "ipc_microsim_tool.py:339: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  == X[3].parts] = X[3].parts - 1\n",
      "ipc_microsim_tool.py:376: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  self.dataset.sort(key, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Simulation sim1 results written'"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simname = 'sim1'\n",
    "period = (2014, 2030)\n",
    "income = ('income', 'inc', 'random', grwt)\n",
    "resources = [('kwhpc', 'ele', 'elastkwhpc')]\n",
    "weight = 'wgt'\n",
    "key = 'hhkey'\n",
    "ms.simulate(simname, period, income, resources, weight, key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unnamed: 0', 'hhkey', 'income', 'kwhpc', 'wgt', 'quantkwhpc', 'elastkwhpc']"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.seedvars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Unnamed: 0', u'hhkey', u'income', u'kwhpc', u'wgt', u'quantkwhpc',\n",
       "       u'elastkwhpc', u'inc2014', u'ele2014', u'inc2015', u'ele2015',\n",
       "       u'inc2016', u'ele2016', u'inc2017', u'ele2017', u'inc2018', u'ele2018',\n",
       "       u'inc2019', u'ele2019', u'inc2020', u'ele2020', u'inc2021', u'ele2021',\n",
       "       u'inc2022', u'ele2022', u'inc2023', u'ele2023', u'inc2024', u'ele2024',\n",
       "       u'inc2025', u'ele2025', u'inc2026', u'ele2026', u'inc2027', u'ele2027',\n",
       "       u'inc2028', u'ele2028', u'inc2029', u'ele2029', u'inc2030', u'ele2030'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sim1']"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.results.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['name', 'reselast0', 'period', 'dataset', 'growth', 'income', 'resvar0']"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.results['sim1'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>grwtpatt</th>\n",
       "      <th>mean_inc</th>\n",
       "      <th>mean_ele</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2014</td>\n",
       "      <td>none</td>\n",
       "      <td>13.135075</td>\n",
       "      <td>44.219901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2015</td>\n",
       "      <td>grt20092010</td>\n",
       "      <td>13.524350</td>\n",
       "      <td>44.799302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2016</td>\n",
       "      <td>flipped</td>\n",
       "      <td>13.896788</td>\n",
       "      <td>45.358621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2017</td>\n",
       "      <td>grt20092010</td>\n",
       "      <td>14.327106</td>\n",
       "      <td>45.959655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2018</td>\n",
       "      <td>pwr1</td>\n",
       "      <td>14.610118</td>\n",
       "      <td>46.211045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>flipped</td>\n",
       "      <td>15.013402</td>\n",
       "      <td>46.788042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2020</td>\n",
       "      <td>flipped</td>\n",
       "      <td>15.428808</td>\n",
       "      <td>47.372526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2021</td>\n",
       "      <td>pwr3</td>\n",
       "      <td>15.719131</td>\n",
       "      <td>48.054122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>2022</td>\n",
       "      <td>alpha1000</td>\n",
       "      <td>16.227263</td>\n",
       "      <td>48.686495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>2023</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>16.458650</td>\n",
       "      <td>49.025149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>2024</td>\n",
       "      <td>pwr1</td>\n",
       "      <td>16.775949</td>\n",
       "      <td>49.289042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>2025</td>\n",
       "      <td>pwr3</td>\n",
       "      <td>17.111418</td>\n",
       "      <td>50.016121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>2026</td>\n",
       "      <td>pshift</td>\n",
       "      <td>17.155383</td>\n",
       "      <td>50.385229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>2027</td>\n",
       "      <td>pwr3</td>\n",
       "      <td>17.546257</td>\n",
       "      <td>51.167857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>2028</td>\n",
       "      <td>grt20122013</td>\n",
       "      <td>17.205158</td>\n",
       "      <td>50.585367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>2029</td>\n",
       "      <td>grt20122013</td>\n",
       "      <td>16.944344</td>\n",
       "      <td>50.027017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2030</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>17.196713</td>\n",
       "      <td>50.389418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year     grwtpatt   mean_inc   mean_ele\n",
       "2014  2014         none  13.135075  44.219901\n",
       "2015  2015  grt20092010  13.524350  44.799302\n",
       "2016  2016      flipped  13.896788  45.358621\n",
       "2017  2017  grt20092010  14.327106  45.959655\n",
       "2018  2018         pwr1  14.610118  46.211045\n",
       "2019  2019      flipped  15.013402  46.788042\n",
       "2020  2020      flipped  15.428808  47.372526\n",
       "2021  2021         pwr3  15.719131  48.054122\n",
       "2022  2022    alpha1000  16.227263  48.686495\n",
       "2023  2023  grt20112012  16.458650  49.025149\n",
       "2024  2024         pwr1  16.775949  49.289042\n",
       "2025  2025         pwr3  17.111418  50.016121\n",
       "2026  2026       pshift  17.155383  50.385229\n",
       "2027  2027         pwr3  17.546257  51.167857\n",
       "2028  2028  grt20122013  17.205158  50.585367\n",
       "2029  2029  grt20122013  16.944344  50.027017\n",
       "2030  2030  grt20112012  17.196713  50.389418"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.results['sim1']['dataset']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Simulation sim1 results overwritten'"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grwt.dataset.drop('partes', axis=1, inplace=True)\n",
    "ms.simulate(simname, period, income, resources, weight, key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>grwtpatt</th>\n",
       "      <th>mean_inc</th>\n",
       "      <th>mean_ele</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2014</td>\n",
       "      <td>none</td>\n",
       "      <td>13.135075</td>\n",
       "      <td>44.219901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2015</td>\n",
       "      <td>alpha100</td>\n",
       "      <td>13.530998</td>\n",
       "      <td>44.787637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2016</td>\n",
       "      <td>pwr2</td>\n",
       "      <td>14.888101</td>\n",
       "      <td>46.016438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2017</td>\n",
       "      <td>alpha1000</td>\n",
       "      <td>15.368015</td>\n",
       "      <td>46.621498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2018</td>\n",
       "      <td>grt20102011</td>\n",
       "      <td>14.966859</td>\n",
       "      <td>46.015880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>flipped</td>\n",
       "      <td>15.367717</td>\n",
       "      <td>46.586747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2020</td>\n",
       "      <td>pwr2</td>\n",
       "      <td>16.976449</td>\n",
       "      <td>47.896692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2021</td>\n",
       "      <td>grt20082009</td>\n",
       "      <td>18.723941</td>\n",
       "      <td>49.506214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>2022</td>\n",
       "      <td>alpha1000</td>\n",
       "      <td>19.326054</td>\n",
       "      <td>50.156721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>2023</td>\n",
       "      <td>pshift</td>\n",
       "      <td>19.254872</td>\n",
       "      <td>50.448119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>2024</td>\n",
       "      <td>grt20102011</td>\n",
       "      <td>18.780383</td>\n",
       "      <td>49.804736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>2025</td>\n",
       "      <td>log</td>\n",
       "      <td>18.987497</td>\n",
       "      <td>50.014348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>2026</td>\n",
       "      <td>grt20092010</td>\n",
       "      <td>19.505547</td>\n",
       "      <td>50.661995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>2027</td>\n",
       "      <td>grt20072008</td>\n",
       "      <td>20.817719</td>\n",
       "      <td>51.553049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>2028</td>\n",
       "      <td>grt20102011</td>\n",
       "      <td>20.357300</td>\n",
       "      <td>50.905958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>2029</td>\n",
       "      <td>pshift</td>\n",
       "      <td>20.278454</td>\n",
       "      <td>51.204296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2030</td>\n",
       "      <td>log</td>\n",
       "      <td>20.502146</td>\n",
       "      <td>51.419666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year     grwtpatt   mean_inc   mean_ele\n",
       "2014  2014         none  13.135075  44.219901\n",
       "2015  2015     alpha100  13.530998  44.787637\n",
       "2016  2016         pwr2  14.888101  46.016438\n",
       "2017  2017    alpha1000  15.368015  46.621498\n",
       "2018  2018  grt20102011  14.966859  46.015880\n",
       "2019  2019      flipped  15.367717  46.586747\n",
       "2020  2020         pwr2  16.976449  47.896692\n",
       "2021  2021  grt20082009  18.723941  49.506214\n",
       "2022  2022    alpha1000  19.326054  50.156721\n",
       "2023  2023       pshift  19.254872  50.448119\n",
       "2024  2024  grt20102011  18.780383  49.804736\n",
       "2025  2025          log  18.987497  50.014348\n",
       "2026  2026  grt20092010  19.505547  50.661995\n",
       "2027  2027  grt20072008  20.817719  51.553049\n",
       "2028  2028  grt20102011  20.357300  50.905958\n",
       "2029  2029       pshift  20.278454  51.204296\n",
       "2030  2030          log  20.502146  51.419666"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.results['sim1']['dataset']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Simulation sim1 results overwritten'"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income = ('income', 'inc', 'grt20112012', grwt)\n",
    "ms.simulate(simname, period, income, resources, weight, key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>grwtpatt</th>\n",
       "      <th>mean_inc</th>\n",
       "      <th>mean_ele</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2014</td>\n",
       "      <td>none</td>\n",
       "      <td>13.135075</td>\n",
       "      <td>44.219901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2015</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>13.336674</td>\n",
       "      <td>44.538705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2016</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>13.563643</td>\n",
       "      <td>44.865550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2017</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>13.816994</td>\n",
       "      <td>45.199565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2018</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>14.100234</td>\n",
       "      <td>45.542708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>14.416631</td>\n",
       "      <td>45.892105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2020</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>14.771195</td>\n",
       "      <td>46.249028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2021</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>15.169867</td>\n",
       "      <td>46.617020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>2022</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>15.618922</td>\n",
       "      <td>46.995719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>2023</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>16.128293</td>\n",
       "      <td>47.381974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>2024</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>16.709865</td>\n",
       "      <td>47.776361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>2025</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>17.378663</td>\n",
       "      <td>48.184566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>2026</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>18.153066</td>\n",
       "      <td>48.593599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>2027</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>19.055925</td>\n",
       "      <td>49.014723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>2028</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>20.114543</td>\n",
       "      <td>49.447448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>2029</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>21.364513</td>\n",
       "      <td>49.888582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2030</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>22.848817</td>\n",
       "      <td>50.343344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year     grwtpatt   mean_inc   mean_ele\n",
       "2014  2014         none  13.135075  44.219901\n",
       "2015  2015  grt20112012  13.336674  44.538705\n",
       "2016  2016  grt20112012  13.563643  44.865550\n",
       "2017  2017  grt20112012  13.816994  45.199565\n",
       "2018  2018  grt20112012  14.100234  45.542708\n",
       "2019  2019  grt20112012  14.416631  45.892105\n",
       "2020  2020  grt20112012  14.771195  46.249028\n",
       "2021  2021  grt20112012  15.169867  46.617020\n",
       "2022  2022  grt20112012  15.618922  46.995719\n",
       "2023  2023  grt20112012  16.128293  47.381974\n",
       "2024  2024  grt20112012  16.709865  47.776361\n",
       "2025  2025  grt20112012  17.378663  48.184566\n",
       "2026  2026  grt20112012  18.153066  48.593599\n",
       "2027  2027  grt20112012  19.055925  49.014723\n",
       "2028  2028  grt20112012  20.114543  49.447448\n",
       "2029  2029  grt20112012  21.364513  49.888582\n",
       "2030  2030  grt20112012  22.848817  50.343344"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.results['sim1']['dataset']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['log',\n",
       " 'log1',\n",
       " 'ln',\n",
       " 'flipped',\n",
       " 'alpha100',\n",
       " 'alpha1000',\n",
       " 'log2',\n",
       " 'pwr',\n",
       " 'pwr1',\n",
       " 'pwr2',\n",
       " 'pwr3',\n",
       " 'pwr4',\n",
       " 'pshift']"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "todrop = [pat for pat in grwt.dataset if not 'grt' in pat and pat is not grwt.key]\n",
    "todrop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'key', u'grt20072008', u'grt20082009', u'grt20092010', u'grt20102011',\n",
       "       u'grt20112012', u'grt20122013', u'grt20132014'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grwt.dataset.drop(todrop, axis=1, inplace=True)\n",
    "grwt.dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>grwtpatt</th>\n",
       "      <th>mean_inc</th>\n",
       "      <th>mean_ele</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2014</td>\n",
       "      <td>none</td>\n",
       "      <td>13.135075</td>\n",
       "      <td>44.219901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2015</td>\n",
       "      <td>grt20072008</td>\n",
       "      <td>13.945927</td>\n",
       "      <td>44.972731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2016</td>\n",
       "      <td>grt20082009</td>\n",
       "      <td>15.330969</td>\n",
       "      <td>46.449784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2017</td>\n",
       "      <td>grt20092010</td>\n",
       "      <td>15.746268</td>\n",
       "      <td>47.052027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2018</td>\n",
       "      <td>grt20102011</td>\n",
       "      <td>15.346951</td>\n",
       "      <td>46.448222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>15.573186</td>\n",
       "      <td>46.777749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2020</td>\n",
       "      <td>grt20122013</td>\n",
       "      <td>15.241837</td>\n",
       "      <td>46.291444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2021</td>\n",
       "      <td>grt20132014</td>\n",
       "      <td>14.060584</td>\n",
       "      <td>44.465783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>2022</td>\n",
       "      <td>grt20072008</td>\n",
       "      <td>14.983143</td>\n",
       "      <td>45.246351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>2023</td>\n",
       "      <td>grt20082009</td>\n",
       "      <td>16.523991</td>\n",
       "      <td>46.773755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>2024</td>\n",
       "      <td>grt20092010</td>\n",
       "      <td>16.954116</td>\n",
       "      <td>47.384560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>2025</td>\n",
       "      <td>grt20102011</td>\n",
       "      <td>16.535351</td>\n",
       "      <td>46.785985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>2026</td>\n",
       "      <td>grt20112012</td>\n",
       "      <td>16.776762</td>\n",
       "      <td>47.115835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>2027</td>\n",
       "      <td>grt20122013</td>\n",
       "      <td>16.443894</td>\n",
       "      <td>46.658567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>2028</td>\n",
       "      <td>grt20132014</td>\n",
       "      <td>15.239554</td>\n",
       "      <td>44.859005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>2029</td>\n",
       "      <td>grt20072008</td>\n",
       "      <td>16.297752</td>\n",
       "      <td>45.673384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2030</td>\n",
       "      <td>grt20082009</td>\n",
       "      <td>18.019213</td>\n",
       "      <td>47.255200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year     grwtpatt   mean_inc   mean_ele\n",
       "2014  2014         none  13.135075  44.219901\n",
       "2015  2015  grt20072008  13.945927  44.972731\n",
       "2016  2016  grt20082009  15.330969  46.449784\n",
       "2017  2017  grt20092010  15.746268  47.052027\n",
       "2018  2018  grt20102011  15.346951  46.448222\n",
       "2019  2019  grt20112012  15.573186  46.777749\n",
       "2020  2020  grt20122013  15.241837  46.291444\n",
       "2021  2021  grt20132014  14.060584  44.465783\n",
       "2022  2022  grt20072008  14.983143  45.246351\n",
       "2023  2023  grt20082009  16.523991  46.773755\n",
       "2024  2024  grt20092010  16.954116  47.384560\n",
       "2025  2025  grt20102011  16.535351  46.785985\n",
       "2026  2026  grt20112012  16.776762  47.115835\n",
       "2027  2027  grt20122013  16.443894  46.658567\n",
       "2028  2028  grt20132014  15.239554  44.859005\n",
       "2029  2029  grt20072008  16.297752  45.673384\n",
       "2030  2030  grt20082009  18.019213  47.255200"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simname = 'sim2'\n",
    "income = ('income', 'inc', 'order', grwt)\n",
    "ms.simulate(simname, period, income, resources, weight, key)\n",
    "ms.results['sim2']['dataset']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sim2', 'sim1']"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.results.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sim2'"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.cursim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014    13.135075\n",
       "2015    13.945927\n",
       "2016    15.330969\n",
       "2017    15.746268\n",
       "2018    15.346951\n",
       "2019    15.573186\n",
       "2020    15.241837\n",
       "2021    14.060584\n",
       "2022    14.983143\n",
       "2023    16.523991\n",
       "2024    16.954116\n",
       "2025    16.535351\n",
       "2026    16.776762\n",
       "2027    16.443894\n",
       "2028    15.239554\n",
       "2029    16.297752\n",
       "2030    18.019213\n",
       "dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.mean('inc', 'wgt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014    13.168533\n",
       "2015    13.981450\n",
       "2016    15.370021\n",
       "2017    15.786377\n",
       "2018    15.386044\n",
       "2019    15.612854\n",
       "2020    15.280661\n",
       "2021    14.096399\n",
       "2022    15.021308\n",
       "2023    16.566081\n",
       "2024    16.997302\n",
       "2025    16.577470\n",
       "2026    16.819496\n",
       "2027    16.485780\n",
       "2028    15.278373\n",
       "2029    16.339266\n",
       "2030    18.065112\n",
       "dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.mean('inc', 'wgt', nozero=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "There were zeroes in incxxxx\n",
      "Ge(1) did not consider those obs.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ipc_microsim_tool.py:476: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  == np.inf] = 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2014    0.358208\n",
       "2015    0.413545\n",
       "2016    0.435598\n",
       "2017    0.416442\n",
       "2018    0.434503\n",
       "2019    0.432726\n",
       "2020    0.413939\n",
       "2021    0.452402\n",
       "2022    0.514825\n",
       "2023    0.536216\n",
       "2024    0.513686\n",
       "2025    0.532373\n",
       "2026    0.532590\n",
       "2027    0.507600\n",
       "2028    0.548020\n",
       "2029    0.617247\n",
       "2030    0.636914\n",
       "dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.inequality_ge('inc', 'wgt', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "There were zeroes in incxxxx\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ipc_microsim_tool.py:482: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  == np.inf] = 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2014    0.618484\n",
       "2015    0.953791\n",
       "2016    0.963800\n",
       "2017    0.808664\n",
       "2018    0.898244\n",
       "2019    0.985938\n",
       "2020    0.692153\n",
       "2021    0.793192\n",
       "2022    1.223512\n",
       "2023    1.219467\n",
       "2024    1.019149\n",
       "2025    1.122334\n",
       "2026    1.251653\n",
       "2027    0.864033\n",
       "2028    0.984311\n",
       "2029    1.524991\n",
       "2030    1.501470\n",
       "dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.inequality_ge('inc', 'wgt', 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "There were zeroes in incxxxx\n",
      "Ge(2) did not consider those obs.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2014    0.615642\n",
       "2015    0.950098\n",
       "2016    0.960081\n",
       "2017    0.805339\n",
       "2018    0.894692\n",
       "2019    0.982163\n",
       "2020    0.689124\n",
       "2021    0.789907\n",
       "2022    1.219133\n",
       "2023    1.215099\n",
       "2024    1.015289\n",
       "2025    1.118212\n",
       "2026    1.247203\n",
       "2027    0.860568\n",
       "2028    0.980540\n",
       "2029    1.519846\n",
       "2030    1.496385\n",
       "dtype: float64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.inequality_ge('inc', 'wgt', 2, nozero=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ipc_microsim_tool.py:505: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  self.dataset.sort(curvar, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2014    0.439520\n",
       "2015    0.456018\n",
       "2016    0.471638\n",
       "2017    0.467786\n",
       "2018    0.472887\n",
       "2019    0.469347\n",
       "2020    0.476378\n",
       "2021    0.496661\n",
       "2022    0.512832\n",
       "2023    0.526399\n",
       "2024    0.521924\n",
       "2025    0.526567\n",
       "2026    0.523644\n",
       "2027    0.528173\n",
       "2028    0.546284\n",
       "2029    0.562039\n",
       "2030    0.573644\n",
       "dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.inequality_gini('inc', 'wgt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p0(3.1)</th>\n",
       "      <th>p1(3.1)</th>\n",
       "      <th>p2(3.1)</th>\n",
       "      <th>pgap(3.1)</th>\n",
       "      <th>pge2(3.1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.063151</td>\n",
       "      <td>0.018115</td>\n",
       "      <td>0.008808</td>\n",
       "      <td>0.286855</td>\n",
       "      <td>0.056225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.059418</td>\n",
       "      <td>0.017327</td>\n",
       "      <td>0.008509</td>\n",
       "      <td>0.291606</td>\n",
       "      <td>0.057952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.057368</td>\n",
       "      <td>0.017742</td>\n",
       "      <td>0.009153</td>\n",
       "      <td>0.309264</td>\n",
       "      <td>0.066971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.057368</td>\n",
       "      <td>0.017226</td>\n",
       "      <td>0.009020</td>\n",
       "      <td>0.300274</td>\n",
       "      <td>0.068496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.061553</td>\n",
       "      <td>0.019203</td>\n",
       "      <td>0.009986</td>\n",
       "      <td>0.311965</td>\n",
       "      <td>0.068567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.061553</td>\n",
       "      <td>0.019155</td>\n",
       "      <td>0.009991</td>\n",
       "      <td>0.311196</td>\n",
       "      <td>0.068999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.076064</td>\n",
       "      <td>0.027068</td>\n",
       "      <td>0.014246</td>\n",
       "      <td>0.355861</td>\n",
       "      <td>0.073091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>0.108574</td>\n",
       "      <td>0.037465</td>\n",
       "      <td>0.018915</td>\n",
       "      <td>0.345064</td>\n",
       "      <td>0.064276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>0.103877</td>\n",
       "      <td>0.036053</td>\n",
       "      <td>0.018228</td>\n",
       "      <td>0.347076</td>\n",
       "      <td>0.064521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>0.100205</td>\n",
       "      <td>0.035453</td>\n",
       "      <td>0.018458</td>\n",
       "      <td>0.353808</td>\n",
       "      <td>0.070672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>0.096938</td>\n",
       "      <td>0.034413</td>\n",
       "      <td>0.018060</td>\n",
       "      <td>0.355003</td>\n",
       "      <td>0.072445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>0.104987</td>\n",
       "      <td>0.037395</td>\n",
       "      <td>0.019684</td>\n",
       "      <td>0.356183</td>\n",
       "      <td>0.073125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>0.100762</td>\n",
       "      <td>0.036697</td>\n",
       "      <td>0.019530</td>\n",
       "      <td>0.364192</td>\n",
       "      <td>0.075679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>0.112846</td>\n",
       "      <td>0.046063</td>\n",
       "      <td>0.025818</td>\n",
       "      <td>0.408195</td>\n",
       "      <td>0.088748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>0.156990</td>\n",
       "      <td>0.060421</td>\n",
       "      <td>0.033663</td>\n",
       "      <td>0.384869</td>\n",
       "      <td>0.087609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>0.147193</td>\n",
       "      <td>0.058118</td>\n",
       "      <td>0.032531</td>\n",
       "      <td>0.394845</td>\n",
       "      <td>0.088892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>0.140919</td>\n",
       "      <td>0.056754</td>\n",
       "      <td>0.032264</td>\n",
       "      <td>0.402745</td>\n",
       "      <td>0.093559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       p0(3.1)   p1(3.1)   p2(3.1)  pgap(3.1)  pge2(3.1)\n",
       "2014  0.063151  0.018115  0.008808   0.286855   0.056225\n",
       "2015  0.059418  0.017327  0.008509   0.291606   0.057952\n",
       "2016  0.057368  0.017742  0.009153   0.309264   0.066971\n",
       "2017  0.057368  0.017226  0.009020   0.300274   0.068496\n",
       "2018  0.061553  0.019203  0.009986   0.311965   0.068567\n",
       "2019  0.061553  0.019155  0.009991   0.311196   0.068999\n",
       "2020  0.076064  0.027068  0.014246   0.355861   0.073091\n",
       "2021  0.108574  0.037465  0.018915   0.345064   0.064276\n",
       "2022  0.103877  0.036053  0.018228   0.347076   0.064521\n",
       "2023  0.100205  0.035453  0.018458   0.353808   0.070672\n",
       "2024  0.096938  0.034413  0.018060   0.355003   0.072445\n",
       "2025  0.104987  0.037395  0.019684   0.356183   0.073125\n",
       "2026  0.100762  0.036697  0.019530   0.364192   0.075679\n",
       "2027  0.112846  0.046063  0.025818   0.408195   0.088748\n",
       "2028  0.156990  0.060421  0.033663   0.384869   0.087609\n",
       "2029  0.147193  0.058118  0.032531   0.394845   0.088892\n",
       "2030  0.140919  0.056754  0.032264   0.402745   0.093559"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.poverty('inc', 'wgt',[3.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ipc_microsim_tool.py:413: FutureWarning: icol(i) is deprecated. Please use .iloc[:,i]\n",
      "  cmp1 = pop.projection.icol(0).loc[period[0]:period[1]]\n",
      "ipc_microsim_tool.py:414: FutureWarning: icol(i) is deprecated. Please use .iloc[:,i]\n",
      "  cmp2 = ram.RAM.icol(0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2014    760.660509\n",
       "2015    776.443173\n",
       "2016    804.619145\n",
       "2017    817.535865\n",
       "2018    809.296343\n",
       "2019    817.144433\n",
       "2020    810.602765\n",
       "2021    780.382461\n",
       "2022    795.717879\n",
       "2023    824.123726\n",
       "2024    836.303803\n",
       "2025    826.992993\n",
       "2026    833.937680\n",
       "2027    826.793191\n",
       "2028    795.652394\n",
       "2029    810.666607\n",
       "2030    839.116172\n",
       "dtype: float64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.totaldemand('ele', pop, ram, 1.13, 1e6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "-c:1: FutureWarning: icol(i) is deprecated. Please use .iloc[:,i]\n",
      "ipc_microsim_tool.py:469: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  == np.inf] = 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "There were zeroes in elexxxx\n",
      "Ge(0) did not consider those obs.\n",
      "\n",
      "There were zeroes in elexxxx\n",
      "Ge(1) did not consider those obs.\n",
      "\n",
      "There were zeroes in incxxxx\n",
      "Ge(0) did not consider those obs.\n",
      "\n",
      "There were zeroes in incxxxx\n",
      "Ge(1) did not consider those obs.\n"
     ]
    }
   ],
   "source": [
    "ms.add_results(pop.projection.icol(0).loc[2014:2030], 'PopMedvarWPP')\n",
    "ms.add_results(ram.RAM)\n",
    "ms.add_results(ms.totaldemand('ele', pop, ram, 1.13), 'eleGWh')\n",
    "ms.add_results(ms.inequality_gini('ele', 'wgt'), 'eleGini')\n",
    "ms.add_results(ms.inequality_ge('ele', 'wgt', 0), 'eleTheilL')\n",
    "ms.add_results(ms.inequality_ge('ele', 'wgt', 1), 'eleTheilT')\n",
    "ms.add_results(ms.inequality_gini('inc', 'wgt'), 'Gini')\n",
    "ms.add_results(ms.inequality_ge('inc', 'wgt', 0), 'TheilL')\n",
    "ms.add_results(ms.inequality_ge('inc', 'wgt', 1), 'TheilT')\n",
    "ms.add_results(ms.poverty('inc', 'wgt',[1.9, 3.1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulations!\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n"
     ]
    }
   ],
   "source": [
    "simname = 'mult'\n",
    "income = ('income', 'inc', 'random', grwt)\n",
    "totaldemand = pd.DataFrame()\n",
    "avgincome = pd.DataFrame()\n",
    "gini = pd.DataFrame()\n",
    "pov310 = pd.DataFrame()\n",
    "print 'Running simulations!'\n",
    "for sim in range(1000):\n",
    "    print '.',\n",
    "    ms.simulate(simname, period, income, resources, weight, key)\n",
    "    out = pd.DataFrame(ms.totaldemand('ele', pop, ram, 1.13))\n",
    "    totaldemand = pd.concat((totaldemand, out.T), ignore_index=True)\n",
    "    out = pd.DataFrame(ms.mean('inc', 'wgt'))\n",
    "    avgincome = pd.concat((avgincome, out.T), ignore_index=True)\n",
    "    out = pd.DataFrame(ms.inequality_gini('inc', 'wgt'))\n",
    "    gini = pd.concat((gini, out.T), ignore_index=True)\n",
    "    out = pd.DataFrame(ms.poverty('inc', 'wgt', [3.1])['p0(3.1)'])\n",
    "    pov310 = pd.concat((pov310, out.T), ignore_index=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "totaldemand.to_csv('result/1000simstd.csv', '\\t')\n",
    "avgincome.to_csv('result/1000simsavg.csv', '\\t')\n",
    "gini.to_csv('result/1000simsgini.csv', '\\t')\n",
    "pov310.to_csv('result/1000simspov310.csv', '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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