Deliverable_Explanation_423.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimation Example\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deliverables:\n",
    "\n",
    "Notes:\n",
    "- iron-fortified millet: current baseline - 47 mg/kg, varieties get up to 77 mg/kg\n",
    "- What is FCT units (iron is mg/100g)\n",
    "- Calculate cost of subsidy (multiply amount of subsidy by amount of households, do this later)\n",
    "\n",
    "INTRO\n",
    "\n",
    "1. Show Iron deficiency histogram - what percentage is not hitting RDI, and show mean (whole population of Uganda, households with at least 1 child (00-3, 04-08))\n",
    "2. Show FCT showing iron content of beans and millet\n",
    "3. Show elasticities of beans and millet\n",
    "\n",
    "BEANS\n",
    "\n",
    "4. Simulate fortification of beans ONLY (change the iron percentage in FCT) (check out on harvest plus site the iron content and compare to fct)\n",
    "5. Show iron deficiency histogram and compare to intro's\n",
    "6. Change prices to simulate subsidies of fortified beans ONLY (40% subsidy)\n",
    "7. Create iron deficiency histogram and compare to intro's\n",
    "8. Compare 5 and 7\n",
    "\n",
    "MILLET\n",
    "\n",
    "7. Simulate fortification of millet ONLY (change the iron percentage in FCT)\n",
    "8. Show iron deficiency histogram and compare to intro's\n",
    "9. Change prices to simulate subsidies of fortified millet ONLY\n",
    "10. Create iron deficiency histogram and compare to intro's\n",
    "11. Compare 8 and 10\n",
    "\n",
    "\n",
    "COMPARISON\n",
    "\n",
    "10. Create overlaid histogram (beans and millet) and compare means and percentage not hitting RDI\n",
    "\n",
    "--------------------------------------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "In this document we&rsquo;ll work through a simple approach to estimating\n",
    "Constant Frisch Elasticity (CFE) demand systems.\n",
    "\n",
    "There are two prerequisites; the first is the `CFEDemands` python\n",
    "module.  You may have this already, but if not it  can be installed with a simple:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second prerequisite is a dataset including detailed information on\n",
    "household expenditures and characteristics.  The structure of that\n",
    "dataset is detailed in the next section.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the case in which the data are saved in a google sheet; we&rsquo;ll\n",
    "turn data from these into `pandas` dataframes.  \n",
    "There are limits on the allowable size of google sheets (5 million\n",
    "cells when I last checked), so if your dataset is larger than this you&rsquo;ll need some other\n",
    "mechanism to get your data into such dataframes.  Even in this latter\n",
    "case you&rsquo;ll want your dataframes to have a structure that matches the\n",
    "description given below.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Structure of the Spreadsheet\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example spreadsheet with data for Uganda can be found at\n",
    "[https://docs.google.com/spreadsheets/d/1yVLriVpo7KGUXvR3hq_n53XpXlD5NmLaH1oOMZyV0gQ/](https://docs.google.com/spreadsheets/d/1yVLriVpo7KGUXvR3hq_n53XpXlD5NmLaH1oOMZyV0gQ/)\n",
    "\n",
    "This spreadsheet consists of several worksheets, labeled &ldquo;RDI&rdquo;, &ldquo;FCT&rdquo;,\n",
    "&ldquo;Household Characteristics&rdquo;, &ldquo;Prices&rdquo;, and a series of different years of food &ldquo;Expenditures&rdquo;.\n",
    "\n",
    "Each worksheet has the initial three columns labeled &ldquo;i&rdquo;, &ldquo;t&rdquo;, and\n",
    "&ldquo;m&rdquo;; together these identify an observation uniquely.  The column &ldquo;i&rdquo;\n",
    "should include a household id; the column &ldquo;t&rdquo; should include an\n",
    "indicator for the year or period (for those of you with\n",
    "cross-sectional data these will all take a common value, e.g., 2019);\n",
    "the column &ldquo;m&rdquo; should be an indicator of &ldquo;market&rdquo;, which may be a\n",
    "region identifier, or may take a single value (e.g., &ldquo;Uganda&rdquo;).\n",
    "\n",
    "The triple (i,t,m) will serve as the index of some constructed\n",
    "dataframes.  It is important not to have **too** many different values\n",
    "for &ldquo;m&rdquo; (unless you have lots of data and RAM to play with).\n",
    "\n",
    "Beyond the first three columns, the &ldquo;Expenditures&rdquo; worksheet for a given year should\n",
    "have one column for each category of expenditures (e.g., &ldquo;Beans&rdquo;, &ldquo;Beef&rdquo;, &ldquo;Beer&rdquo;,&#x2026;).\n",
    "\n",
    "Beyond the first three columns, the &ldquo;Household Characteristics&rdquo; should\n",
    "have columns corresponding to different elements of the vector of\n",
    "observable characteristics $z$.  If the data permit, I recommend\n",
    "constructing observables that match what I have in these data:\n",
    "namely numbers of different children by age and sex, along with the\n",
    "logarithm of total household size (&ldquo;log HSize&rdquo;).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From Sheet to DataFrame to Regression Object\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by defining a dictionary that contains the spreadsheet key. \n",
    "One is provided for the Ugandan example that I wish to work\n",
    "through.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Uganda_Data = '1yVLriVpo7KGUXvR3hq_n53XpXlD5NmLaH1oOMZyV0gQ'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the spreadsheet defined , grab it and define a couple of\n",
    "dataframes. (Sheets should be shared with `students@eep153.iam.gserviceaccount.com`):\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_182/3389232646.py:18: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  x = x.replace(0,np.nan) # Replace zeros with missing\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from eep153_tools.sheets import read_sheets\n",
    "\n",
    "# Change 'Uganda_Data' to key of your own sheet in Sheets, above\n",
    "x = read_sheets(Uganda_Data,sheet='Expenditures (2019-20)')\n",
    "x.columns.name = 'j'\n",
    "\n",
    "p = read_sheets(Uganda_Data,sheet='Prices').set_index(['t','m'])\n",
    "p.columns.name = 'j'\n",
    "p = p.xs('2019-20',level='t')\n",
    "\n",
    "d = read_sheets(Uganda_Data,sheet=\"HH Characteristics\")\n",
    "d.columns.name = 'k'\n",
    "\n",
    "# x may have duplicate columns\n",
    "x = x.T.groupby('j').sum().T\n",
    "x = x.replace(0,np.nan) # Replace zeros with missing\n",
    "\n",
    "# Take logs of expenditures; call this y\n",
    "y = np.log(x.set_index(['i','t','m']))\n",
    "\n",
    "d.set_index(['i','t','m'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives a dataframe of household characteristics $d$ with a simple structure.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>k</th>\n",
       "      <th>F 00-03</th>\n",
       "      <th>F 04-08</th>\n",
       "      <th>F 09-13</th>\n",
       "      <th>F 14-18</th>\n",
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      "text/plain": [
       "k                                                 F 00-03  F 04-08  F 09-13  \\\n",
       "i                                t       m                                    \n",
       "00c9353d8ebe42faabf5919b81d7fae7 2018-19              1.0      0.0      1.0   \n",
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       "k                                                 F 51+  M 00-03  M 04-08  \\\n",
       "i                                t       m                                  \n",
       "00c9353d8ebe42faabf5919b81d7fae7 2018-19            1.0      0.0      0.0   \n",
       "                                 2019-20 Eastern    1.0      0.0      0.0   \n",
       "02dd448165ce46279ca601a02865d543 2018-19            0.0      0.0      0.0   \n",
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       "\n",
       "k                                                 M 09-13  M 14-18  M 19-30  \\\n",
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       "\n",
       "k                                                 M 31-50  M 51+  log HSize  \n",
       "i                                t       m                                   \n",
       "00c9353d8ebe42faabf5919b81d7fae7 2018-19              0.0    0.0   2.079442  \n",
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       "039a11571b874a88b7a6c200469fe4f3 2018-19              1.0    0.0   1.609438  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "k                                                  F 00-03  F 04-08  F 09-13  \\\n",
       "i                                t       m                                     \n",
       "110f81c0d9254505838dcaf2497d130a 2019-20 Northern      0.0      0.0      1.0   \n",
       "118e5671a1e0437baebdd563bf3e43df 2019-20 Northern      0.0      1.0      0.0   \n",
       "2302300051007                    2019-20 Northern      0.0      0.0      1.0   \n",
       "2302300080102                    2019-20 Northern      0.0      0.0      1.0   \n",
       "2302300080104                    2019-20 Northern      0.0      1.0      0.0   \n",
       "...                                                    ...      ...      ...   \n",
       "_47                              2019-20 Northern      1.0      1.0      0.0   \n",
       "_49                              2019-20 Northern      0.0      0.0      0.0   \n",
       "_60                              2019-20 Northern      0.0      0.0      0.0   \n",
       "_66                              2019-20 Northern      0.0      0.0      0.0   \n",
       "e07bc322c4884559b4b8ca75c945dd3e 2019-20 Northern      1.0      1.0      1.0   \n",
       "\n",
       "k                                                  F 14-18  F 19-30  F 31-50  \\\n",
       "i                                t       m                                     \n",
       "110f81c0d9254505838dcaf2497d130a 2019-20 Northern      0.0      0.0      0.0   \n",
       "118e5671a1e0437baebdd563bf3e43df 2019-20 Northern      2.0      0.0      1.0   \n",
       "2302300051007                    2019-20 Northern      2.0      1.0      1.0   \n",
       "2302300080102                    2019-20 Northern      1.0      0.0      0.0   \n",
       "2302300080104                    2019-20 Northern      0.0      1.0      0.0   \n",
       "...                                                    ...      ...      ...   \n",
       "_47                              2019-20 Northern      0.0      2.0      0.0   \n",
       "_49                              2019-20 Northern      0.0      1.0      0.0   \n",
       "_60                              2019-20 Northern      0.0      1.0      0.0   \n",
       "_66                              2019-20 Northern      0.0      1.0      0.0   \n",
       "e07bc322c4884559b4b8ca75c945dd3e 2019-20 Northern      0.0      0.0      1.0   \n",
       "\n",
       "k                                                  F 51+  M 00-03  M 04-08  \\\n",
       "i                                t       m                                   \n",
       "110f81c0d9254505838dcaf2497d130a 2019-20 Northern    1.0      0.0      2.0   \n",
       "118e5671a1e0437baebdd563bf3e43df 2019-20 Northern    0.0      1.0      0.0   \n",
       "2302300051007                    2019-20 Northern    0.0      0.0      1.0   \n",
       "2302300080102                    2019-20 Northern    1.0      0.0      0.0   \n",
       "2302300080104                    2019-20 Northern    0.0      1.0      1.0   \n",
       "...                                                  ...      ...      ...   \n",
       "_47                              2019-20 Northern    0.0      0.0      2.0   \n",
       "_49                              2019-20 Northern    0.0      2.0      0.0   \n",
       "_60                              2019-20 Northern    0.0      2.0      0.0   \n",
       "_66                              2019-20 Northern    1.0      1.0      0.0   \n",
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       "\n",
       "k                                                  M 09-13  M 14-18  M 19-30  \\\n",
       "i                                t       m                                     \n",
       "110f81c0d9254505838dcaf2497d130a 2019-20 Northern      2.0      1.0      0.0   \n",
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       "2302300051007                    2019-20 Northern      0.0      0.0      0.0   \n",
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       "2302300080104                    2019-20 Northern      1.0      0.0      1.0   \n",
       "...                                                    ...      ...      ...   \n",
       "_47                              2019-20 Northern      0.0      0.0      1.0   \n",
       "_49                              2019-20 Northern      1.0      0.0      0.0   \n",
       "_60                              2019-20 Northern      0.0      0.0      0.0   \n",
       "_66                              2019-20 Northern      0.0      0.0      1.0   \n",
       "e07bc322c4884559b4b8ca75c945dd3e 2019-20 Northern      1.0      0.0      0.0   \n",
       "\n",
       "k                                                  M 31-50  M 51+  log HSize  \n",
       "i                                t       m                                    \n",
       "110f81c0d9254505838dcaf2497d130a 2019-20 Northern      0.0    1.0   2.079442  \n",
       "118e5671a1e0437baebdd563bf3e43df 2019-20 Northern      0.0    0.0   1.791759  \n",
       "2302300051007                    2019-20 Northern      0.0    1.0   1.945910  \n",
       "2302300080102                    2019-20 Northern      0.0    0.0   1.098612  \n",
       "2302300080104                    2019-20 Northern      0.0    0.0   1.791759  \n",
       "...                                                    ...    ...        ...  \n",
       "_47                              2019-20 Northern      0.0    0.0   1.945910  \n",
       "_49                              2019-20 Northern      1.0    0.0   1.609438  \n",
       "_60                              2019-20 Northern      0.0    0.0   1.098612  \n",
       "_66                              2019-20 Northern      0.0    0.0   1.386294  \n",
       "e07bc322c4884559b4b8ca75c945dd3e 2019-20 Northern      0.0    0.0   1.945910  \n",
       "\n",
       "[662 rows x 15 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_d = d[(d['F 00-03']!=0) | (d['M 00-03']!=0) | (d['F 04-08']!=0) | (d['M 04-08']!=0) | (d['F 09-13']!=0) | (d['M 09-13']!=0)]\n",
    "filtered_d = filtered_d.xs('2019-20',level='t',drop_level=False)\n",
    "filtered_d = filtered_d.xs('Northern',level='m',drop_level=False)\n",
    "filtered_d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also a dataframe of log expenditures $y$ with a similarly simple\n",
    "structure.  Note that any expenditures of zero are dropped and\n",
    "replaced with a missing value indicator.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Bongo</th>\n",
       "      <th>Bread</th>\n",
       "      <th>Butter, etc.</th>\n",
       "      <th>Cabbages</th>\n",
       "      <th>Cake</th>\n",
       "      <th>Cassava</th>\n",
       "      <th>...</th>\n",
       "      <th>Sugarcane</th>\n",
       "      <th>Sweet Bananas</th>\n",
       "      <th>Sweet Potatoes</th>\n",
       "      <th>Tea</th>\n",
       "      <th>Tomatoes</th>\n",
       "      <th>Waragi</th>\n",
       "      <th>Water</th>\n",
       "      <th>Wheat (flour)</th>\n",
       "      <th>Yam</th>\n",
       "      <th>Yogurt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <th>t</th>\n",
       "      <th>m</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00c9353d8ebe42faabf5919b81d7fae7</th>\n",
       "      <th>2019-20</th>\n",
       "      <th>Eastern</th>\n",
       "      <td>8.188689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.244228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.294050</td>\n",
       "      <td>5.298317</td>\n",
       "      <td>6.907755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>062da72d5d3a457e9336b62c8bb9096d</th>\n",
       "      <th>2019-20</th>\n",
       "      <th>Eastern</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.214608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0d0e29faff394154a69562b4527b48b8</th>\n",
       "      <th>2019-20</th>\n",
       "      <th>Eastern</th>\n",
       "      <td>6.907755</td>\n",
       "      <td>8.411833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.294050</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.684612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0e03e253c35d4333a1ffad2df9d38850</th>\n",
       "      <th>2019-20</th>\n",
       "      <th>Eastern</th>\n",
       "      <td>7.937375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.070906</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.517193</td>\n",
       "      <td>5.298317</td>\n",
       "      <td>6.214608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013000201</th>\n",
       "      <th>2019-20</th>\n",
       "      <th>Central</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.769956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.294050</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.600902</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.991465</td>\n",
       "      <td>7.649693</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "j                                                    Beans      Beef  \\\n",
       "i                                t       m                             \n",
       "00c9353d8ebe42faabf5919b81d7fae7 2019-20 Eastern  8.188689       NaN   \n",
       "062da72d5d3a457e9336b62c8bb9096d 2019-20 Eastern       NaN       NaN   \n",
       "0d0e29faff394154a69562b4527b48b8 2019-20 Eastern  6.907755  8.411833   \n",
       "0e03e253c35d4333a1ffad2df9d38850 2019-20 Eastern  7.937375       NaN   \n",
       "1013000201                       2019-20 Central       NaN       NaN   \n",
       "\n",
       "j                                                     Beer  Biscuits  Bongo  \\\n",
       "i                                t       m                                    \n",
       "00c9353d8ebe42faabf5919b81d7fae7 2019-20 Eastern       NaN       NaN    NaN   \n",
       "062da72d5d3a457e9336b62c8bb9096d 2019-20 Eastern       NaN       NaN    NaN   \n",
       "0d0e29faff394154a69562b4527b48b8 2019-20 Eastern       NaN       NaN    NaN   \n",
       "0e03e253c35d4333a1ffad2df9d38850 2019-20 Eastern       NaN       NaN    NaN   \n",
       "1013000201                       2019-20 Central  9.769956       NaN    NaN   \n",
       "\n",
       "j                                                    Bread  Butter, etc.  \\\n",
       "i                                t       m                                 \n",
       "00c9353d8ebe42faabf5919b81d7fae7 2019-20 Eastern       NaN           NaN   \n",
       "062da72d5d3a457e9336b62c8bb9096d 2019-20 Eastern  6.214608           NaN   \n",
       "0d0e29faff394154a69562b4527b48b8 2019-20 Eastern       NaN           NaN   \n",
       "0e03e253c35d4333a1ffad2df9d38850 2019-20 Eastern       NaN           NaN   \n",
       "1013000201                       2019-20 Central       NaN           NaN   \n",
       "\n",
       "j                                                 Cabbages  Cake   Cassava  \\\n",
       "i                                t       m                                   \n",
       "00c9353d8ebe42faabf5919b81d7fae7 2019-20 Eastern  7.244228   NaN  8.699515   \n",
       "062da72d5d3a457e9336b62c8bb9096d 2019-20 Eastern       NaN   NaN       NaN   \n",
       "0d0e29faff394154a69562b4527b48b8 2019-20 Eastern       NaN   NaN  8.294050   \n",
       "0e03e253c35d4333a1ffad2df9d38850 2019-20 Eastern       NaN   NaN  8.070906   \n",
       "1013000201                       2019-20 Central       NaN   NaN  8.294050   \n",
       "\n",
       "j                                                 ...  Sugarcane  \\\n",
       "i                                t       m        ...              \n",
       "00c9353d8ebe42faabf5919b81d7fae7 2019-20 Eastern  ...        NaN   \n",
       "062da72d5d3a457e9336b62c8bb9096d 2019-20 Eastern  ...        NaN   \n",
       "0d0e29faff394154a69562b4527b48b8 2019-20 Eastern  ...        NaN   \n",
       "0e03e253c35d4333a1ffad2df9d38850 2019-20 Eastern  ...        NaN   \n",
       "1013000201                       2019-20 Central  ...        NaN   \n",
       "\n",
       "j                                                 Sweet Bananas  \\\n",
       "i                                t       m                        \n",
       "00c9353d8ebe42faabf5919b81d7fae7 2019-20 Eastern            NaN   \n",
       "062da72d5d3a457e9336b62c8bb9096d 2019-20 Eastern            NaN   \n",
       "0d0e29faff394154a69562b4527b48b8 2019-20 Eastern            NaN   \n",
       "0e03e253c35d4333a1ffad2df9d38850 2019-20 Eastern            NaN   \n",
       "1013000201                       2019-20 Central       7.600902   \n",
       "\n",
       "j                                                 Sweet Potatoes       Tea  \\\n",
       "i                                t       m                                   \n",
       "00c9353d8ebe42faabf5919b81d7fae7 2019-20 Eastern        8.294050  5.298317   \n",
       "062da72d5d3a457e9336b62c8bb9096d 2019-20 Eastern             NaN       NaN   \n",
       "0d0e29faff394154a69562b4527b48b8 2019-20 Eastern             NaN       NaN   \n",
       "0e03e253c35d4333a1ffad2df9d38850 2019-20 Eastern        8.517193  5.298317   \n",
       "1013000201                       2019-20 Central             NaN  5.991465   \n",
       "\n",
       "j                                                 Tomatoes  Waragi  Water  \\\n",
       "i                                t       m                                  \n",
       "00c9353d8ebe42faabf5919b81d7fae7 2019-20 Eastern  6.907755     NaN    NaN   \n",
       "062da72d5d3a457e9336b62c8bb9096d 2019-20 Eastern       NaN     NaN    NaN   \n",
       "0d0e29faff394154a69562b4527b48b8 2019-20 Eastern  6.684612     NaN    NaN   \n",
       "0e03e253c35d4333a1ffad2df9d38850 2019-20 Eastern  6.214608     NaN    NaN   \n",
       "1013000201                       2019-20 Central  7.649693     NaN    NaN   \n",
       "\n",
       "j                                                 Wheat (flour)       Yam  \\\n",
       "i                                t       m                                  \n",
       "00c9353d8ebe42faabf5919b81d7fae7 2019-20 Eastern            NaN  8.006368   \n",
       "062da72d5d3a457e9336b62c8bb9096d 2019-20 Eastern            NaN       NaN   \n",
       "0d0e29faff394154a69562b4527b48b8 2019-20 Eastern            NaN       NaN   \n",
       "0e03e253c35d4333a1ffad2df9d38850 2019-20 Eastern            NaN       NaN   \n",
       "1013000201                       2019-20 Central            NaN       NaN   \n",
       "\n",
       "j                                                 Yogurt  \n",
       "i                                t       m                \n",
       "00c9353d8ebe42faabf5919b81d7fae7 2019-20 Eastern     NaN  \n",
       "062da72d5d3a457e9336b62c8bb9096d 2019-20 Eastern     NaN  \n",
       "0d0e29faff394154a69562b4527b48b8 2019-20 Eastern     NaN  \n",
       "0e03e253c35d4333a1ffad2df9d38850 2019-20 Eastern     NaN  \n",
       "1013000201                       2019-20 Central     NaN  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimation\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let $y_{i}^j$ be log expenditures on food $j$ by household $i$ at a particular time.  We want to estimate a regression that takes the form\n",
    "$$\n",
    "      y^j_{i} = A^j(p) + \\gamma_j'd_i + \\beta_j w_i + \\zeta^j_i.\n",
    "$$\n",
    "So, a first step is to turn our dataframe for `y` into a series, so we can put it on the left-hand side of this regression.  Then we need to make our dataframe $d$ conform:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.stack()\n",
    "\n",
    "d = d.stack()\n",
    "\n",
    "# Check that indices are in right places!\n",
    "assert y.index.names == ['i','t','m','j']\n",
    "assert d.index.names == ['i','t','m','k']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Estimation\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just one line to set up the regression:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cfe import Regression\n",
    "\n",
    "result = Regression(y=y,d=d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then one line to predict expenditures and estimate most of the things we need:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "i                                 t        m         j             \n",
       "00c9353d8ebe42faabf5919b81d7fae7  2019-20  Eastern   Beans             3734.056145\n",
       "                                                     Beef              9831.715556\n",
       "                                                     Biscuits          1312.459857\n",
       "                                                     Bread             3780.272722\n",
       "                                                     Cabbages          1250.071808\n",
       "                                                                          ...     \n",
       "e07bc322c4884559b4b8ca75c945dd3e  2019-20  Northern  Sweet Bananas     2513.237637\n",
       "                                                     Sweet Potatoes    7513.732200\n",
       "                                                     Tea                207.521279\n",
       "                                                     Tomatoes          1410.956143\n",
       "                                                     Waragi            4469.686843\n",
       "Length: 94325, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.predicted_expenditures()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compare predicted log expenditures with actual:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='yhat', ylabel='y'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df = pd.DataFrame({'y':y,'yhat':result.get_predicted_log_expenditures()})\n",
    "df.plot.scatter(x='yhat',y='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That&rsquo;s all there is to estimation!  Note that we didn&rsquo;t estimate\n",
    "demands for all goods&#x2014;lots of goods didn&rsquo;t have enough observations,\n",
    "and were automatically dropped.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parameters\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (Relative) Income Elasticity\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the parameters have an interesting interpretation.  First among these are the $\\beta$ coefficients, which govern how *income elastic* different goods are (higher values means more elastic).  These are also called *Frisch elasticities*.\n",
    "Here we use a plot to visualize, with confidence intervals.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = result.graph_beta()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To just see the values of $\\beta$:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "j\n",
       "Beans             0.316913\n",
       "Beef              0.423841\n",
       "Biscuits          0.781522\n",
       "Bread             0.677132\n",
       "Cabbages          0.403810\n",
       "Cassava           0.142661\n",
       "Chapati           0.634176\n",
       "Cooking Oil       0.416759\n",
       "Dodo              0.369812\n",
       "Eggs              0.345383\n",
       "Fish (dried)      0.446381\n",
       "Fish (fresh)      0.886778\n",
       "Goat              0.467223\n",
       "Ground Nuts       0.426204\n",
       "Maize             0.406007\n",
       "Mangos            0.463788\n",
       "Matoke            0.443616\n",
       "Milk (fresh)      0.834115\n",
       "Millet            0.360086\n",
       "Onions            0.340940\n",
       "Oranges           0.756571\n",
       "Other Fruits      0.658992\n",
       "Other Veg.        0.585797\n",
       "Peas              0.277380\n",
       "Pork              0.422006\n",
       "Rice              0.531964\n",
       "Salt              0.145142\n",
       "Sim Sim           0.563487\n",
       "Soda              0.586076\n",
       "Sorghum           0.686320\n",
       "Sweet Bananas     0.591652\n",
       "Sweet Potatoes    0.325598\n",
       "Tea               0.421568\n",
       "Tomatoes          0.488395\n",
       "Waragi            0.401824\n",
       "Name: beta, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.get_beta()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Demand and Household Composition\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We&rsquo;re particularly interested in the effects of household composition on demand.  These are captured by the $\\gamma_j$ parameters in the regression.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>k</th>\n",
       "      <th>F 00-03</th>\n",
       "      <th>F 04-08</th>\n",
       "      <th>F 09-13</th>\n",
       "      <th>F 14-18</th>\n",
       "      <th>F 19-30</th>\n",
       "      <th>F 31-50</th>\n",
       "      <th>F 51+</th>\n",
       "      <th>M 00-03</th>\n",
       "      <th>M 04-08</th>\n",
       "      <th>M 09-13</th>\n",
       "      <th>M 14-18</th>\n",
       "      <th>M 19-30</th>\n",
       "      <th>M 31-50</th>\n",
       "      <th>M 51+</th>\n",
       "      <th>log HSize</th>\n",
       "      <th>Constant</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Beans</th>\n",
       "      <td>-0.129845</td>\n",
       "      <td>0.024727</td>\n",
       "      <td>0.064567</td>\n",
       "      <td>-0.028448</td>\n",
       "      <td>-0.002598</td>\n",
       "      <td>0.029310</td>\n",
       "      <td>0.084417</td>\n",
       "      <td>-0.071322</td>\n",
       "      <td>0.020194</td>\n",
       "      <td>0.036715</td>\n",
       "      <td>0.049218</td>\n",
       "      <td>0.042955</td>\n",
       "      <td>0.011466</td>\n",
       "      <td>0.107460</td>\n",
       "      <td>0.426698</td>\n",
       "      <td>8.409504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beef</th>\n",
       "      <td>-0.121810</td>\n",
       "      <td>0.046408</td>\n",
       "      <td>0.033906</td>\n",
       "      <td>0.022340</td>\n",
       "      <td>0.084701</td>\n",
       "      <td>0.093669</td>\n",
       "      <td>0.222188</td>\n",
       "      <td>-0.008614</td>\n",
       "      <td>0.026050</td>\n",
       "      <td>0.016648</td>\n",
       "      <td>0.055342</td>\n",
       "      <td>0.080857</td>\n",
       "      <td>0.196879</td>\n",
       "      <td>0.148855</td>\n",
       "      <td>0.122587</td>\n",
       "      <td>9.262532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biscuits</th>\n",
       "      <td>-0.068814</td>\n",
       "      <td>-0.131646</td>\n",
       "      <td>-0.041326</td>\n",
       "      <td>0.101954</td>\n",
       "      <td>-0.122081</td>\n",
       "      <td>0.295563</td>\n",
       "      <td>0.287443</td>\n",
       "      <td>0.270660</td>\n",
       "      <td>-0.033024</td>\n",
       "      <td>-0.136711</td>\n",
       "      <td>-0.181973</td>\n",
       "      <td>-0.019032</td>\n",
       "      <td>-0.115969</td>\n",
       "      <td>-0.374188</td>\n",
       "      <td>0.279835</td>\n",
       "      <td>6.792856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bread</th>\n",
       "      <td>-0.015413</td>\n",
       "      <td>0.046979</td>\n",
       "      <td>-0.053303</td>\n",
       "      <td>0.016995</td>\n",
       "      <td>0.054328</td>\n",
       "      <td>0.073295</td>\n",
       "      <td>0.134078</td>\n",
       "      <td>-0.061955</td>\n",
       "      <td>0.100468</td>\n",
       "      <td>0.014863</td>\n",
       "      <td>0.043759</td>\n",
       "      <td>0.054900</td>\n",
       "      <td>0.172740</td>\n",
       "      <td>0.106329</td>\n",
       "      <td>0.212862</td>\n",
       "      <td>8.285099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabbages</th>\n",
       "      <td>0.000047</td>\n",
       "      <td>-0.052952</td>\n",
       "      <td>0.034075</td>\n",
       "      <td>0.024258</td>\n",
       "      <td>-0.010929</td>\n",
       "      <td>0.031573</td>\n",
       "      <td>0.057126</td>\n",
       "      <td>-0.049127</td>\n",
       "      <td>0.035070</td>\n",
       "      <td>0.004137</td>\n",
       "      <td>0.052812</td>\n",
       "      <td>0.084709</td>\n",
       "      <td>0.018162</td>\n",
       "      <td>0.054391</td>\n",
       "      <td>0.203897</td>\n",
       "      <td>7.044567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cassava</th>\n",
       "      <td>0.024785</td>\n",
       "      <td>0.087112</td>\n",
       "      <td>0.099496</td>\n",
       "      <td>0.046304</td>\n",
       "      <td>0.021227</td>\n",
       "      <td>0.049439</td>\n",
       "      <td>0.138227</td>\n",
       "      <td>0.008328</td>\n",
       "      <td>0.137877</td>\n",
       "      <td>0.190429</td>\n",
       "      <td>0.158452</td>\n",
       "      <td>0.067733</td>\n",
       "      <td>0.103726</td>\n",
       "      <td>0.134321</td>\n",
       "      <td>0.157361</td>\n",
       "      <td>8.437182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chapati</th>\n",
       "      <td>-0.060105</td>\n",
       "      <td>-0.020808</td>\n",
       "      <td>0.063681</td>\n",
       "      <td>0.090755</td>\n",
       "      <td>-0.024146</td>\n",
       "      <td>0.013664</td>\n",
       "      <td>-0.073183</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.047077</td>\n",
       "      <td>-0.045219</td>\n",
       "      <td>-0.043024</td>\n",
       "      <td>0.076034</td>\n",
       "      <td>0.138217</td>\n",
       "      <td>0.134401</td>\n",
       "      <td>0.209899</td>\n",
       "      <td>7.644030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cooking Oil</th>\n",
       "      <td>-0.088183</td>\n",
       "      <td>-0.049166</td>\n",
       "      <td>-0.050817</td>\n",
       "      <td>0.017481</td>\n",
       "      <td>0.005940</td>\n",
       "      <td>-0.005681</td>\n",
       "      <td>-0.081625</td>\n",
       "      <td>-0.112399</td>\n",
       "      <td>-0.077558</td>\n",
       "      <td>-0.039460</td>\n",
       "      <td>-0.007269</td>\n",
       "      <td>-0.042908</td>\n",
       "      <td>-0.006277</td>\n",
       "      <td>0.036738</td>\n",
       "      <td>0.419226</td>\n",
       "      <td>7.262257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dodo</th>\n",
       "      <td>-0.068655</td>\n",
       "      <td>0.003535</td>\n",
       "      <td>0.112343</td>\n",
       "      <td>0.053806</td>\n",
       "      <td>0.044161</td>\n",
       "      <td>0.075993</td>\n",
       "      <td>0.195738</td>\n",
       "      <td>-0.022117</td>\n",
       "      <td>0.035598</td>\n",
       "      <td>0.080625</td>\n",
       "      <td>0.009036</td>\n",
       "      <td>0.032212</td>\n",
       "      <td>0.084695</td>\n",
       "      <td>0.194343</td>\n",
       "      <td>0.140041</td>\n",
       "      <td>7.233421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eggs</th>\n",
       "      <td>-0.105953</td>\n",
       "      <td>-0.048813</td>\n",
       "      <td>0.019172</td>\n",
       "      <td>-0.039684</td>\n",
       "      <td>0.033600</td>\n",
       "      <td>-0.005183</td>\n",
       "      <td>0.098584</td>\n",
       "      <td>0.026998</td>\n",
       "      <td>-0.031245</td>\n",
       "      <td>-0.056543</td>\n",
       "      <td>0.002635</td>\n",
       "      <td>-0.032299</td>\n",
       "      <td>0.055478</td>\n",
       "      <td>-0.025709</td>\n",
       "      <td>0.309056</td>\n",
       "      <td>7.730907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fish (dried)</th>\n",
       "      <td>0.046656</td>\n",
       "      <td>-0.007218</td>\n",
       "      <td>0.040495</td>\n",
       "      <td>0.008603</td>\n",
       "      <td>-0.004083</td>\n",
       "      <td>0.033960</td>\n",
       "      <td>0.204012</td>\n",
       "      <td>0.039705</td>\n",
       "      <td>0.040072</td>\n",
       "      <td>0.025001</td>\n",
       "      <td>-0.074135</td>\n",
       "      <td>0.066381</td>\n",
       "      <td>0.064574</td>\n",
       "      <td>0.142760</td>\n",
       "      <td>0.172011</td>\n",
       "      <td>8.630962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fish (fresh)</th>\n",
       "      <td>0.024146</td>\n",
       "      <td>0.076684</td>\n",
       "      <td>0.126765</td>\n",
       "      <td>0.053754</td>\n",
       "      <td>0.065049</td>\n",
       "      <td>0.053777</td>\n",
       "      <td>0.037531</td>\n",
       "      <td>0.087181</td>\n",
       "      <td>0.054395</td>\n",
       "      <td>0.144390</td>\n",
       "      <td>0.035447</td>\n",
       "      <td>0.059293</td>\n",
       "      <td>0.171567</td>\n",
       "      <td>0.207033</td>\n",
       "      <td>0.069131</td>\n",
       "      <td>7.951865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goat</th>\n",
       "      <td>-0.003827</td>\n",
       "      <td>-0.050402</td>\n",
       "      <td>-0.200800</td>\n",
       "      <td>0.039682</td>\n",
       "      <td>-0.100133</td>\n",
       "      <td>0.075203</td>\n",
       "      <td>0.007360</td>\n",
       "      <td>-0.006511</td>\n",
       "      <td>-0.096499</td>\n",
       "      <td>-0.154284</td>\n",
       "      <td>-0.024807</td>\n",
       "      <td>0.003631</td>\n",
       "      <td>0.089071</td>\n",
       "      <td>0.221986</td>\n",
       "      <td>0.531004</td>\n",
       "      <td>9.160922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ground Nuts</th>\n",
       "      <td>0.013237</td>\n",
       "      <td>0.015216</td>\n",
       "      <td>0.068062</td>\n",
       "      <td>0.040570</td>\n",
       "      <td>0.074938</td>\n",
       "      <td>0.091543</td>\n",
       "      <td>0.212355</td>\n",
       "      <td>0.037590</td>\n",
       "      <td>0.003487</td>\n",
       "      <td>0.005814</td>\n",
       "      <td>0.046110</td>\n",
       "      <td>0.054597</td>\n",
       "      <td>0.111010</td>\n",
       "      <td>0.137234</td>\n",
       "      <td>0.108330</td>\n",
       "      <td>7.741600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maize</th>\n",
       "      <td>-0.021131</td>\n",
       "      <td>0.057953</td>\n",
       "      <td>0.096844</td>\n",
       "      <td>0.097528</td>\n",
       "      <td>-0.010593</td>\n",
       "      <td>-0.022914</td>\n",
       "      <td>0.165180</td>\n",
       "      <td>0.052915</td>\n",
       "      <td>-0.007556</td>\n",
       "      <td>0.093869</td>\n",
       "      <td>0.103813</td>\n",
       "      <td>0.046180</td>\n",
       "      <td>0.044127</td>\n",
       "      <td>0.138631</td>\n",
       "      <td>0.462311</td>\n",
       "      <td>8.415360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mangos</th>\n",
       "      <td>-0.061576</td>\n",
       "      <td>0.004513</td>\n",
       "      <td>0.017853</td>\n",
       "      <td>0.062891</td>\n",
       "      <td>0.076866</td>\n",
       "      <td>0.004491</td>\n",
       "      <td>0.062067</td>\n",
       "      <td>0.129027</td>\n",
       "      <td>-0.020133</td>\n",
       "      <td>0.007101</td>\n",
       "      <td>0.062352</td>\n",
       "      <td>-0.030811</td>\n",
       "      <td>0.075669</td>\n",
       "      <td>0.106466</td>\n",
       "      <td>0.202535</td>\n",
       "      <td>7.684994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matoke</th>\n",
       "      <td>-0.035113</td>\n",
       "      <td>-0.032396</td>\n",
       "      <td>0.018301</td>\n",
       "      <td>0.000948</td>\n",
       "      <td>-0.062374</td>\n",
       "      <td>0.078486</td>\n",
       "      <td>0.166959</td>\n",
       "      <td>-0.061651</td>\n",
       "      <td>-0.010736</td>\n",
       "      <td>0.020587</td>\n",
       "      <td>0.016185</td>\n",
       "      <td>0.067388</td>\n",
       "      <td>0.158698</td>\n",
       "      <td>0.145267</td>\n",
       "      <td>0.363288</td>\n",
       "      <td>9.267410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milk (fresh)</th>\n",
       "      <td>0.025052</td>\n",
       "      <td>0.005336</td>\n",
       "      <td>0.034501</td>\n",
       "      <td>0.037748</td>\n",
       "      <td>0.057253</td>\n",
       "      <td>0.148313</td>\n",
       "      <td>0.200898</td>\n",
       "      <td>-0.014421</td>\n",
       "      <td>-0.018812</td>\n",
       "      <td>0.009104</td>\n",
       "      <td>-0.015815</td>\n",
       "      <td>0.064984</td>\n",
       "      <td>0.069364</td>\n",
       "      <td>0.136029</td>\n",
       "      <td>0.189380</td>\n",
       "      <td>8.328761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Millet</th>\n",
       "      <td>-0.141622</td>\n",
       "      <td>0.004160</td>\n",
       "      <td>0.019282</td>\n",
       "      <td>-0.021144</td>\n",
       "      <td>0.093478</td>\n",
       "      <td>0.090660</td>\n",
       "      <td>0.187800</td>\n",
       "      <td>-0.133507</td>\n",
       "      <td>-0.026901</td>\n",
       "      <td>0.071256</td>\n",
       "      <td>0.014302</td>\n",
       "      <td>-0.031229</td>\n",
       "      <td>0.153994</td>\n",
       "      <td>0.237257</td>\n",
       "      <td>0.325276</td>\n",
       "      <td>7.951785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Onions</th>\n",
       "      <td>-0.071736</td>\n",
       "      <td>-0.031344</td>\n",
       "      <td>-0.007109</td>\n",
       "      <td>0.002546</td>\n",
       "      <td>0.084256</td>\n",
       "      <td>-0.018113</td>\n",
       "      <td>0.008946</td>\n",
       "      <td>-0.075256</td>\n",
       "      <td>-0.041738</td>\n",
       "      <td>-0.010124</td>\n",
       "      <td>-0.019404</td>\n",
       "      <td>0.012160</td>\n",
       "      <td>0.131983</td>\n",
       "      <td>0.082035</td>\n",
       "      <td>0.116020</td>\n",
       "      <td>6.407458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oranges</th>\n",
       "      <td>0.031446</td>\n",
       "      <td>0.079108</td>\n",
       "      <td>0.053201</td>\n",
       "      <td>0.055743</td>\n",
       "      <td>0.339324</td>\n",
       "      <td>0.120602</td>\n",
       "      <td>0.056272</td>\n",
       "      <td>0.092388</td>\n",
       "      <td>0.182142</td>\n",
       "      <td>0.208820</td>\n",
       "      <td>0.187388</td>\n",
       "      <td>0.063023</td>\n",
       "      <td>0.024345</td>\n",
       "      <td>0.194713</td>\n",
       "      <td>0.001166</td>\n",
       "      <td>7.178517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other Fruits</th>\n",
       "      <td>-0.132696</td>\n",
       "      <td>-0.002710</td>\n",
       "      <td>0.052436</td>\n",
       "      <td>-0.005487</td>\n",
       "      <td>0.017435</td>\n",
       "      <td>0.126434</td>\n",
       "      <td>0.206581</td>\n",
       "      <td>0.017880</td>\n",
       "      <td>-0.056318</td>\n",
       "      <td>-0.022153</td>\n",
       "      <td>0.024694</td>\n",
       "      <td>0.044509</td>\n",
       "      <td>0.231017</td>\n",
       "      <td>0.175866</td>\n",
       "      <td>0.221067</td>\n",
       "      <td>7.579703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other Veg.</th>\n",
       "      <td>-0.068586</td>\n",
       "      <td>-0.024274</td>\n",
       "      <td>-0.002683</td>\n",
       "      <td>-0.025444</td>\n",
       "      <td>0.044153</td>\n",
       "      <td>0.112520</td>\n",
       "      <td>0.228836</td>\n",
       "      <td>0.034050</td>\n",
       "      <td>0.015050</td>\n",
       "      <td>0.048114</td>\n",
       "      <td>0.002747</td>\n",
       "      <td>0.033640</td>\n",
       "      <td>0.081002</td>\n",
       "      <td>0.142732</td>\n",
       "      <td>0.218348</td>\n",
       "      <td>7.263056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peas</th>\n",
       "      <td>-0.040790</td>\n",
       "      <td>-0.094772</td>\n",
       "      <td>-0.015547</td>\n",
       "      <td>-0.018121</td>\n",
       "      <td>-0.125731</td>\n",
       "      <td>0.044672</td>\n",
       "      <td>0.028875</td>\n",
       "      <td>-0.060701</td>\n",
       "      <td>-0.148844</td>\n",
       "      <td>-0.015911</td>\n",
       "      <td>0.029012</td>\n",
       "      <td>-0.143846</td>\n",
       "      <td>-0.141362</td>\n",
       "      <td>-0.024874</td>\n",
       "      <td>0.638375</td>\n",
       "      <td>7.986032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pork</th>\n",
       "      <td>-0.033656</td>\n",
       "      <td>0.014086</td>\n",
       "      <td>0.023963</td>\n",
       "      <td>-0.139544</td>\n",
       "      <td>0.101726</td>\n",
       "      <td>0.073433</td>\n",
       "      <td>0.018390</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>-0.008243</td>\n",
       "      <td>-0.052288</td>\n",
       "      <td>-0.058001</td>\n",
       "      <td>0.026973</td>\n",
       "      <td>0.080138</td>\n",
       "      <td>0.110900</td>\n",
       "      <td>0.243813</td>\n",
       "      <td>9.049802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rice</th>\n",
       "      <td>0.006396</td>\n",
       "      <td>0.043405</td>\n",
       "      <td>0.043743</td>\n",
       "      <td>0.072528</td>\n",
       "      <td>0.023635</td>\n",
       "      <td>0.083692</td>\n",
       "      <td>0.142684</td>\n",
       "      <td>0.018691</td>\n",
       "      <td>0.086074</td>\n",
       "      <td>0.068868</td>\n",
       "      <td>0.042560</td>\n",
       "      <td>0.080745</td>\n",
       "      <td>0.063892</td>\n",
       "      <td>0.177189</td>\n",
       "      <td>0.253933</td>\n",
       "      <td>8.462596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salt</th>\n",
       "      <td>-0.041373</td>\n",
       "      <td>0.005510</td>\n",
       "      <td>-0.015191</td>\n",
       "      <td>-0.037283</td>\n",
       "      <td>-0.015544</td>\n",
       "      <td>0.032865</td>\n",
       "      <td>0.059353</td>\n",
       "      <td>-0.018579</td>\n",
       "      <td>0.008263</td>\n",
       "      <td>0.010975</td>\n",
       "      <td>0.007117</td>\n",
       "      <td>-0.009195</td>\n",
       "      <td>-0.006319</td>\n",
       "      <td>0.023656</td>\n",
       "      <td>0.439357</td>\n",
       "      <td>5.763618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim Sim</th>\n",
       "      <td>-0.030669</td>\n",
       "      <td>-0.135316</td>\n",
       "      <td>0.040586</td>\n",
       "      <td>-0.009371</td>\n",
       "      <td>0.050600</td>\n",
       "      <td>-0.010580</td>\n",
       "      <td>0.142336</td>\n",
       "      <td>0.056733</td>\n",
       "      <td>0.025250</td>\n",
       "      <td>0.020941</td>\n",
       "      <td>0.046041</td>\n",
       "      <td>0.087579</td>\n",
       "      <td>0.193124</td>\n",
       "      <td>0.066712</td>\n",
       "      <td>0.280677</td>\n",
       "      <td>7.920279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soda</th>\n",
       "      <td>-0.064028</td>\n",
       "      <td>-0.131735</td>\n",
       "      <td>0.022161</td>\n",
       "      <td>0.057778</td>\n",
       "      <td>0.066158</td>\n",
       "      <td>0.065022</td>\n",
       "      <td>0.037868</td>\n",
       "      <td>0.037801</td>\n",
       "      <td>-0.049805</td>\n",
       "      <td>0.110753</td>\n",
       "      <td>0.012316</td>\n",
       "      <td>0.079333</td>\n",
       "      <td>0.197485</td>\n",
       "      <td>0.166200</td>\n",
       "      <td>-0.039856</td>\n",
       "      <td>7.937324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sorghum</th>\n",
       "      <td>-0.019363</td>\n",
       "      <td>-0.078712</td>\n",
       "      <td>0.050082</td>\n",
       "      <td>-0.002172</td>\n",
       "      <td>-0.009680</td>\n",
       "      <td>-0.034687</td>\n",
       "      <td>-0.181918</td>\n",
       "      <td>-0.114714</td>\n",
       "      <td>-0.040236</td>\n",
       "      <td>0.074912</td>\n",
       "      <td>-0.033217</td>\n",
       "      <td>0.026380</td>\n",
       "      <td>-0.168734</td>\n",
       "      <td>0.118833</td>\n",
       "      <td>0.370152</td>\n",
       "      <td>7.894543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweet Bananas</th>\n",
       "      <td>-0.052151</td>\n",
       "      <td>-0.013603</td>\n",
       "      <td>-0.076310</td>\n",
       "      <td>-0.063958</td>\n",
       "      <td>0.033555</td>\n",
       "      <td>0.077992</td>\n",
       "      <td>0.224438</td>\n",
       "      <td>0.128711</td>\n",
       "      <td>0.056851</td>\n",
       "      <td>0.006153</td>\n",
       "      <td>0.073467</td>\n",
       "      <td>0.045048</td>\n",
       "      <td>0.141334</td>\n",
       "      <td>0.176413</td>\n",
       "      <td>0.300664</td>\n",
       "      <td>7.765022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweet Potatoes</th>\n",
       "      <td>0.098161</td>\n",
       "      <td>0.089491</td>\n",
       "      <td>0.194222</td>\n",
       "      <td>0.064835</td>\n",
       "      <td>0.005813</td>\n",
       "      <td>0.099382</td>\n",
       "      <td>0.340967</td>\n",
       "      <td>0.109800</td>\n",
       "      <td>0.114679</td>\n",
       "      <td>0.174715</td>\n",
       "      <td>0.115915</td>\n",
       "      <td>0.060316</td>\n",
       "      <td>0.025699</td>\n",
       "      <td>0.069652</td>\n",
       "      <td>0.145980</td>\n",
       "      <td>8.358587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tea</th>\n",
       "      <td>-0.042382</td>\n",
       "      <td>-0.035185</td>\n",
       "      <td>0.013232</td>\n",
       "      <td>0.022740</td>\n",
       "      <td>0.065259</td>\n",
       "      <td>0.130721</td>\n",
       "      <td>0.176237</td>\n",
       "      <td>-0.080094</td>\n",
       "      <td>-0.017011</td>\n",
       "      <td>0.016910</td>\n",
       "      <td>-0.018131</td>\n",
       "      <td>0.020682</td>\n",
       "      <td>0.143988</td>\n",
       "      <td>0.170532</td>\n",
       "      <td>0.168847</td>\n",
       "      <td>5.385922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomatoes</th>\n",
       "      <td>-0.041588</td>\n",
       "      <td>-0.030377</td>\n",
       "      <td>-0.030979</td>\n",
       "      <td>-0.000128</td>\n",
       "      <td>0.048132</td>\n",
       "      <td>0.027279</td>\n",
       "      <td>-0.014251</td>\n",
       "      <td>-0.075214</td>\n",
       "      <td>-0.023519</td>\n",
       "      <td>-0.027247</td>\n",
       "      <td>-0.044792</td>\n",
       "      <td>-0.002956</td>\n",
       "      <td>0.074617</td>\n",
       "      <td>0.091042</td>\n",
       "      <td>0.252019</td>\n",
       "      <td>7.241197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waragi</th>\n",
       "      <td>-0.343352</td>\n",
       "      <td>0.045994</td>\n",
       "      <td>-0.196042</td>\n",
       "      <td>-0.104741</td>\n",
       "      <td>-0.104941</td>\n",
       "      <td>-0.171669</td>\n",
       "      <td>-0.255223</td>\n",
       "      <td>0.067177</td>\n",
       "      <td>-0.158311</td>\n",
       "      <td>-0.166235</td>\n",
       "      <td>-0.132163</td>\n",
       "      <td>-0.053405</td>\n",
       "      <td>0.094746</td>\n",
       "      <td>0.221683</td>\n",
       "      <td>0.475116</td>\n",
       "      <td>8.328901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "k                F 00-03   F 04-08   F 09-13   F 14-18   F 19-30   F 31-50  \\\n",
       "j                                                                            \n",
       "Beans          -0.129845  0.024727  0.064567 -0.028448 -0.002598  0.029310   \n",
       "Beef           -0.121810  0.046408  0.033906  0.022340  0.084701  0.093669   \n",
       "Biscuits       -0.068814 -0.131646 -0.041326  0.101954 -0.122081  0.295563   \n",
       "Bread          -0.015413  0.046979 -0.053303  0.016995  0.054328  0.073295   \n",
       "Cabbages        0.000047 -0.052952  0.034075  0.024258 -0.010929  0.031573   \n",
       "Cassava         0.024785  0.087112  0.099496  0.046304  0.021227  0.049439   \n",
       "Chapati        -0.060105 -0.020808  0.063681  0.090755 -0.024146  0.013664   \n",
       "Cooking Oil    -0.088183 -0.049166 -0.050817  0.017481  0.005940 -0.005681   \n",
       "Dodo           -0.068655  0.003535  0.112343  0.053806  0.044161  0.075993   \n",
       "Eggs           -0.105953 -0.048813  0.019172 -0.039684  0.033600 -0.005183   \n",
       "Fish (dried)    0.046656 -0.007218  0.040495  0.008603 -0.004083  0.033960   \n",
       "Fish (fresh)    0.024146  0.076684  0.126765  0.053754  0.065049  0.053777   \n",
       "Goat           -0.003827 -0.050402 -0.200800  0.039682 -0.100133  0.075203   \n",
       "Ground Nuts     0.013237  0.015216  0.068062  0.040570  0.074938  0.091543   \n",
       "Maize          -0.021131  0.057953  0.096844  0.097528 -0.010593 -0.022914   \n",
       "Mangos         -0.061576  0.004513  0.017853  0.062891  0.076866  0.004491   \n",
       "Matoke         -0.035113 -0.032396  0.018301  0.000948 -0.062374  0.078486   \n",
       "Milk (fresh)    0.025052  0.005336  0.034501  0.037748  0.057253  0.148313   \n",
       "Millet         -0.141622  0.004160  0.019282 -0.021144  0.093478  0.090660   \n",
       "Onions         -0.071736 -0.031344 -0.007109  0.002546  0.084256 -0.018113   \n",
       "Oranges         0.031446  0.079108  0.053201  0.055743  0.339324  0.120602   \n",
       "Other Fruits   -0.132696 -0.002710  0.052436 -0.005487  0.017435  0.126434   \n",
       "Other Veg.     -0.068586 -0.024274 -0.002683 -0.025444  0.044153  0.112520   \n",
       "Peas           -0.040790 -0.094772 -0.015547 -0.018121 -0.125731  0.044672   \n",
       "Pork           -0.033656  0.014086  0.023963 -0.139544  0.101726  0.073433   \n",
       "Rice            0.006396  0.043405  0.043743  0.072528  0.023635  0.083692   \n",
       "Salt           -0.041373  0.005510 -0.015191 -0.037283 -0.015544  0.032865   \n",
       "Sim Sim        -0.030669 -0.135316  0.040586 -0.009371  0.050600 -0.010580   \n",
       "Soda           -0.064028 -0.131735  0.022161  0.057778  0.066158  0.065022   \n",
       "Sorghum        -0.019363 -0.078712  0.050082 -0.002172 -0.009680 -0.034687   \n",
       "Sweet Bananas  -0.052151 -0.013603 -0.076310 -0.063958  0.033555  0.077992   \n",
       "Sweet Potatoes  0.098161  0.089491  0.194222  0.064835  0.005813  0.099382   \n",
       "Tea            -0.042382 -0.035185  0.013232  0.022740  0.065259  0.130721   \n",
       "Tomatoes       -0.041588 -0.030377 -0.030979 -0.000128  0.048132  0.027279   \n",
       "Waragi         -0.343352  0.045994 -0.196042 -0.104741 -0.104941 -0.171669   \n",
       "\n",
       "k                  F 51+   M 00-03   M 04-08   M 09-13   M 14-18   M 19-30  \\\n",
       "j                                                                            \n",
       "Beans           0.084417 -0.071322  0.020194  0.036715  0.049218  0.042955   \n",
       "Beef            0.222188 -0.008614  0.026050  0.016648  0.055342  0.080857   \n",
       "Biscuits        0.287443  0.270660 -0.033024 -0.136711 -0.181973 -0.019032   \n",
       "Bread           0.134078 -0.061955  0.100468  0.014863  0.043759  0.054900   \n",
       "Cabbages        0.057126 -0.049127  0.035070  0.004137  0.052812  0.084709   \n",
       "Cassava         0.138227  0.008328  0.137877  0.190429  0.158452  0.067733   \n",
       "Chapati        -0.073183  0.000267  0.047077 -0.045219 -0.043024  0.076034   \n",
       "Cooking Oil    -0.081625 -0.112399 -0.077558 -0.039460 -0.007269 -0.042908   \n",
       "Dodo            0.195738 -0.022117  0.035598  0.080625  0.009036  0.032212   \n",
       "Eggs            0.098584  0.026998 -0.031245 -0.056543  0.002635 -0.032299   \n",
       "Fish (dried)    0.204012  0.039705  0.040072  0.025001 -0.074135  0.066381   \n",
       "Fish (fresh)    0.037531  0.087181  0.054395  0.144390  0.035447  0.059293   \n",
       "Goat            0.007360 -0.006511 -0.096499 -0.154284 -0.024807  0.003631   \n",
       "Ground Nuts     0.212355  0.037590  0.003487  0.005814  0.046110  0.054597   \n",
       "Maize           0.165180  0.052915 -0.007556  0.093869  0.103813  0.046180   \n",
       "Mangos          0.062067  0.129027 -0.020133  0.007101  0.062352 -0.030811   \n",
       "Matoke          0.166959 -0.061651 -0.010736  0.020587  0.016185  0.067388   \n",
       "Milk (fresh)    0.200898 -0.014421 -0.018812  0.009104 -0.015815  0.064984   \n",
       "Millet          0.187800 -0.133507 -0.026901  0.071256  0.014302 -0.031229   \n",
       "Onions          0.008946 -0.075256 -0.041738 -0.010124 -0.019404  0.012160   \n",
       "Oranges         0.056272  0.092388  0.182142  0.208820  0.187388  0.063023   \n",
       "Other Fruits    0.206581  0.017880 -0.056318 -0.022153  0.024694  0.044509   \n",
       "Other Veg.      0.228836  0.034050  0.015050  0.048114  0.002747  0.033640   \n",
       "Peas            0.028875 -0.060701 -0.148844 -0.015911  0.029012 -0.143846   \n",
       "Pork            0.018390 -0.058333 -0.008243 -0.052288 -0.058001  0.026973   \n",
       "Rice            0.142684  0.018691  0.086074  0.068868  0.042560  0.080745   \n",
       "Salt            0.059353 -0.018579  0.008263  0.010975  0.007117 -0.009195   \n",
       "Sim Sim         0.142336  0.056733  0.025250  0.020941  0.046041  0.087579   \n",
       "Soda            0.037868  0.037801 -0.049805  0.110753  0.012316  0.079333   \n",
       "Sorghum        -0.181918 -0.114714 -0.040236  0.074912 -0.033217  0.026380   \n",
       "Sweet Bananas   0.224438  0.128711  0.056851  0.006153  0.073467  0.045048   \n",
       "Sweet Potatoes  0.340967  0.109800  0.114679  0.174715  0.115915  0.060316   \n",
       "Tea             0.176237 -0.080094 -0.017011  0.016910 -0.018131  0.020682   \n",
       "Tomatoes       -0.014251 -0.075214 -0.023519 -0.027247 -0.044792 -0.002956   \n",
       "Waragi         -0.255223  0.067177 -0.158311 -0.166235 -0.132163 -0.053405   \n",
       "\n",
       "k                M 31-50     M 51+  log HSize  Constant  \n",
       "j                                                        \n",
       "Beans           0.011466  0.107460   0.426698  8.409504  \n",
       "Beef            0.196879  0.148855   0.122587  9.262532  \n",
       "Biscuits       -0.115969 -0.374188   0.279835  6.792856  \n",
       "Bread           0.172740  0.106329   0.212862  8.285099  \n",
       "Cabbages        0.018162  0.054391   0.203897  7.044567  \n",
       "Cassava         0.103726  0.134321   0.157361  8.437182  \n",
       "Chapati         0.138217  0.134401   0.209899  7.644030  \n",
       "Cooking Oil    -0.006277  0.036738   0.419226  7.262257  \n",
       "Dodo            0.084695  0.194343   0.140041  7.233421  \n",
       "Eggs            0.055478 -0.025709   0.309056  7.730907  \n",
       "Fish (dried)    0.064574  0.142760   0.172011  8.630962  \n",
       "Fish (fresh)    0.171567  0.207033   0.069131  7.951865  \n",
       "Goat            0.089071  0.221986   0.531004  9.160922  \n",
       "Ground Nuts     0.111010  0.137234   0.108330  7.741600  \n",
       "Maize           0.044127  0.138631   0.462311  8.415360  \n",
       "Mangos          0.075669  0.106466   0.202535  7.684994  \n",
       "Matoke          0.158698  0.145267   0.363288  9.267410  \n",
       "Milk (fresh)    0.069364  0.136029   0.189380  8.328761  \n",
       "Millet          0.153994  0.237257   0.325276  7.951785  \n",
       "Onions          0.131983  0.082035   0.116020  6.407458  \n",
       "Oranges         0.024345  0.194713   0.001166  7.178517  \n",
       "Other Fruits    0.231017  0.175866   0.221067  7.579703  \n",
       "Other Veg.      0.081002  0.142732   0.218348  7.263056  \n",
       "Peas           -0.141362 -0.024874   0.638375  7.986032  \n",
       "Pork            0.080138  0.110900   0.243813  9.049802  \n",
       "Rice            0.063892  0.177189   0.253933  8.462596  \n",
       "Salt           -0.006319  0.023656   0.439357  5.763618  \n",
       "Sim Sim         0.193124  0.066712   0.280677  7.920279  \n",
       "Soda            0.197485  0.166200  -0.039856  7.937324  \n",
       "Sorghum        -0.168734  0.118833   0.370152  7.894543  \n",
       "Sweet Bananas   0.141334  0.176413   0.300664  7.765022  \n",
       "Sweet Potatoes  0.025699  0.069652   0.145980  8.358587  \n",
       "Tea             0.143988  0.170532   0.168847  5.385922  \n",
       "Tomatoes        0.074617  0.091042   0.252019  7.241197  \n",
       "Waragi          0.094746  0.221683   0.475116  8.328901  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.get_gamma()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Welfare\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another import quantity we estimate is $w_i$.  This is a welfare measure for the household.  Here&rsquo;s a histogram:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Density'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = result.get_w().plot.hist(bins=100,density=True)\n",
    "result.get_w().plot.kde(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving Result\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can save the estimated result, using something like\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_pickle('uganda_estimates.rgsn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_pickle('../Project3/uganda_estimates.rgsn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can subsequently load this using\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cfe\n",
    "result = cfe.regression.read_pickle('uganda_estimates.rgsn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Demand and Utility\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having estimated the demand system, we can examine the implied demand curves.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Budgets\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get food budget for all households, then find median budget:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "xhat = result.predicted_expenditures()\n",
    "\n",
    "# Total food expenditures per household\n",
    "xbar = xhat.groupby(['i','t','m']).sum()\n",
    "\n",
    "# Reference budget\n",
    "xref = xbar.quantile(0.5)  # Household at 0.5 quantile is median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Reference Prices\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose reference prices.  Here we&rsquo;ll choose a particular year, and average prices across markets.  If you wanted to focus on particular market you&rsquo;d do this differently.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reference prices chosen from a particular time; average across place.\n",
    "# These are prices per kilogram:\n",
    "pbar = p.mean()\n",
    "pbar = pbar[result.beta.index] # Only use prices for goods we can estimate\n",
    "\n",
    "#Finally, define a function to change a single price in the vector $p$:\n",
    "def my_prices(p0,p=pbar,j='Millet'):\n",
    "    \"\"\"\n",
    "    Change price of jth good to p0, holding other prices fixed.\n",
    "    \"\"\"\n",
    "    p = p.copy()\n",
    "    p.loc[j] = p0\n",
    "    return p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Demand as a function of prices\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let&rsquo;s take a look at demand schedules for households with different budgets.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Quantities of Peas Demanded')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "use = 'Peas'  # Good we want demand curve for\n",
    "\n",
    "# Vary prices from 50% to 200% of reference.\n",
    "scale = np.linspace(.5,2,20)\n",
    "\n",
    "# Demand for Millet for household at median budget\n",
    "plt.plot([result.demands(xref,my_prices(pbar[use]*s,pbar))[use] for s in scale],scale)\n",
    "\n",
    "# Demand for Millet for household at 25% percentile\n",
    "plt.plot([result.demands(xbar.quantile(0.25),my_prices(pbar[use]*s,pbar))[use] for s in scale],scale)\n",
    "\n",
    "# Demand for Millet for household at 75% percentile\n",
    "plt.plot([result.demands(xbar.quantile(0.75),my_prices(pbar[use]*s,pbar))[use] for s in scale],scale)\n",
    "\n",
    "plt.ylabel(f\"Price (relative to base of {pbar[use]:.2f})\")\n",
    "plt.xlabel(f\"Quantities of {use} Demanded\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Engel Curves\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Engel curves trace out how demand changes with budget:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Engel Curves')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "\n",
    "ax.plot(np.log(scale*xref),[result.expenditures(s*xref,pbar)/(s*xref) for s in scale])\n",
    "ax.set_xlabel(f'log budget (relative to base of {xref:.0f}')\n",
    "ax.set_ylabel(f'Expenditure share')\n",
    "ax.set_title('Engel Curves')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Indirect Utility\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also describe an indirect utility function that corresponds to these demands:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Indirect Utility Function')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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REREZOF+gQ0LKRRxILf9d2pF0La4UV50TramDGu19m6C9bxPl0KfGxtIIHTceDG5ERESNmF4vOJVdgISUXCQk52JfSm61hzztrMzR9t/DnR3+PeTp4WjN36XdYQxuREREjciV4lIcTM3DvpRcJKTkYn9qLgqKDCe1VamAQHcH3OPnpOxRa9HUniM86wEGNyIiogYsU1uIfcnlIS0hJRfHM/Or/DbN1socHZo1QWgzJ4T6O6O9bxMe8qynGNyIiIgaiNIyPU5mlR/23JeSi4Tki8jQFlWp89JYI9TfGWF+Tgj1c0JrDwdYNOJJbU0JgxsREZGJyi8qwYHUPCQkX0RCai4OpubhcqVBBOZmKgR5OiL035AW6ucEryY2RuqYbheDGxERkYnQFpZgb9JF7DqTg11JOTiWkQ+pNCOHg7UF7mnmpOxNa+fbBHZqft03FHwliYiI6qmaBDU/F9t/f5vmhDA/Z7R0s+fktg0YgxsREVE9UZOg1rypHTo1dym/BDjDzdHaOM2SUTC4ERERGYn2Sgn2JP8b1M7k4HgmgxpdH4MbERHRHcKgRreLwY2IiKiOMKhRbWNwIyIiqiUlZXocTMvDjlPnsf3vCzh8Ng+Vz8POoEa3g8GNiIjoFokIUnKuYMff5UEt/nQOLukMTx/V3NUOnVowqFHtYHAjIiK6CdrCEsSfvoDtf1/Ajr/PI+1iocF6J1tL3N+yKbq2dEXXlq7w1HCyW6o9DG5ERETXUVqmx6Gzedh+qjyoHUwzPPxpaa5CqJ8TurZsigdaNsXdXo6cR43qDIMbERFRJak5V7D97/PY8fd5xP2Tg4JKhz9bNLUrD2qtXNExwIVnJqA7hu80IiJq9PKLShD3Tw52/H0eO/+5gJScKwbrm9haostdrnigpSvub9kU3jzXJxmJmbEbuBnp6el44okn4OLiAltbW7Rv3x4JCQnKehFBdHQ0vLy8YGNjg27duuHYsWMG29DpdJg0aRJcXV1hZ2eHQYMG4ezZswY1ubm5iIqKgkajgUajQVRUFPLy8gxqUlNTMXDgQNjZ2cHV1RWTJ09GcXFxnT12IiKqPSKCYxlafPLn3xiyOA4d5mzE+P8mYPnuVKTkXIGFmQr3BTjjxd6t8OvzXZDwei98NuIeDLu3GUMbGZXJ7HHLzc1Fly5d0L17d/z+++9wc3PD6dOn0aRJE6Vm3rx5WLBgAb755hu0atUKb731Fnr16oXExEQ4ODgAAKZMmYK1a9di5cqVcHFxwfTp0zFgwAAkJCTA3NwcADBixAicPXsWGzZsAAA888wziIqKwtq1awEAZWVl6N+/P5o2bYqdO3ciJycHo0ePhohg4cKFd/aJISKiGikqKUP8mRz8eeIcNp/IRoa2yGB9c1e7fwcUNEWnFi6w5+FPqodUIpWnAqyfXnnlFfz111/YsWNHtetFBF5eXpgyZQpmzJgBoHzvmru7O9577z08++yz0Gq1aNq0Kb777jsMGzYMAJCRkQFfX1+sX78ekZGROHHiBIKCgrBr1y507NgRALBr1y6Eh4fj5MmTCAwMxO+//44BAwYgLS0NXl5eAICVK1dizJgxyM7OhqOj4w0fT35+PjQaDbRabY3qiYjo5mUXFGHLyWxsOpGNnX9fQGFJmbLO2tIM99/VFA+2dkPXlq7wdbY1YqdkKoz9/W0y/51Ys2YNIiMj8dhjj2Hbtm3w9vbGhAkTMG7cOABAUlISsrKy0Lt3b+U2arUaERERiIuLw7PPPouEhASUlJQY1Hh5eSE4OBhxcXGIjIxEfHw8NBqNEtoAoFOnTtBoNIiLi0NgYCDi4+MRHByshDYAiIyMhE6nQ0JCArp3716lf51OB51Op1zPz8+v1eeHiIjK/xN/IrMAf544h00ns3EoLc9gvYejNR5s44aebdzQuYUrrC3NjdMo0S0ymeB25swZLF68GNOmTcOrr76KPXv2YPLkyVCr1Rg1ahSysrIAAO7u7ga3c3d3R0pKCgAgKysLVlZWcHJyqlJTcfusrCy4ublVuX83NzeDmsr34+TkBCsrK6Wmsrlz52L27Nm38MiJiOh6dKVliD+dgz9PZGPzyWyk5xnOqxbio0GP1u7o0cYNd3s5QqXiVB1kukwmuOn1eoSFheGdd94BAHTo0AHHjh3D4sWLMWrUKKWu8gdSRG74Ia1cU139rdRcbebMmZg2bZpyPT8/H76+vtfti4iIqnfhkg6bT2bjzxPnsOPvC7hSXPkQqCt6tHHHg63d4M4zFVADYjLBzdPTE0FBQQbL2rRpg5iYGACAh4cHgPK9YZ6enkpNdna2snfMw8MDxcXFyM3NNdjrlp2djc6dOys1586dq3L/58+fN9jO7t27Ddbn5uaipKSkyp64Cmq1Gmq1+qYeMxERlRMRJJ4rwJ8nsrHpxDkcTMszOFm7u6MaD7Z2Vw6B2ljxECg1TCYT3Lp06YLExESDZadOnYKfnx8AICAgAB4eHti4cSM6dOgAACguLsa2bdvw3nvvAQBCQ0NhaWmJjRs3YujQoQCAzMxMHD16FPPmzQMAhIeHQ6vVYs+ePbjvvvsAALt374ZWq1XCXXh4ON5++21kZmYqITE2NhZqtRqhoaF1/EwQETUOpWV67Em6iD+OZWHTiaqHQIO9HdGjtTt6tnFHsDcPgVLjYDLBberUqejcuTPeeecdDB06FHv27MGXX36JL7/8EkD5ocspU6bgnXfeQcuWLdGyZUu88847sLW1xYgRIwAAGo0GY8eOxfTp0+Hi4gJnZ2e8+OKLaNu2LXr27AmgfC9enz59MG7cOHzxxRcAyqcDGTBgAAIDAwEAvXv3RlBQEKKiovD+++/j4sWLePHFFzFu3DiOECUiug2lZXrsTrqIdUcy8cfRLORc/t/8mGoLM3S5yxU92rihR2t3eGh4CJQaH5MJbvfeey9Wr16NmTNnYs6cOQgICMBHH32EkSNHKjUvv/wyCgsLMWHCBOTm5qJjx46IjY1V5nADgA8//BAWFhYYOnQoCgsL0aNHD3zzzTfKHG4AsHz5ckyePFkZfTpo0CB8+umnynpzc3OsW7cOEyZMQJcuXWBjY4MRI0Zg/vz5d+CZICJqWErL9Nh1pjysxR4zDGtNbC3RO8gdvYI8cP9dPARKZDLzuDU0xp4HhojImP4X1jLwx7FzuHhVWHOytUTk3R7o19YT4S1cYGluUif5oQbO2N/fJrPHjYiITFtpmR7xZ3Kw/khmtWGtT3B5WOvUnGGN6FoY3IiIqM6UlOkRf7oirGUh90qJss7ZzgqRd3ugf1tPdGruDAuGNaIbYnAjIqJaVVKmR9zpHKw/nIk/jmchr5qwNiDEEx0DGNaIbhaDGxER3baKsLbucAZij58zCGsudlaIDC7fs8awRnR7GNyIiOiW6PWCXUk5+OVAepWw5mr/v8Og9zGsEdUaBjciIropZ85fwqr96Vh9IN1gUlxXeytlgEHHABeYm3FCXKLaxuBGREQ3pL1SgrWHM7Bq/1nsT81TljtYW2BAiCcGtfPGfQHODGtEdYzBjYiIqlVSpsf2U+exan86Np44h+JSPQDATAVEtGqKR+7xQa8gd1hbclJcojuFwY2IiAwcy9AiJiEdaw6l48Kl/8211trDAUPu8cFDHbzg5sDTTREZA4MbEREhu6AIvx7IQMz+sziZVaAsd7GzwkPtvTEk1Bt3e2mM2CERAQxuRESNVlFJGTYeP4dV+89i+98XUKYvPwOilbkZega5Ycg9PnigVVOexYCoHmFwIyJqREQECSm5iNl/Fr8dzkRBUamyrkOzJhhyjw8GhnhBY2tpxC6J6FoY3IiIGoG0i1ewan86Vh04i5ScK8py7yY2eLiDNx65xxvNm9obsUMiqgkGNyKiBqq4VI/Y41n4764U7DpzUVlua2WOvsGeGBLqjU4BLjDjFB5EJoPBjYiogcnUFmLF7lSs2JuG8wU6AIBKBXRu4YIh9/igT7AHbK3455/IFPGTS0TUAOj1grjTOfhuVzI2nchWBho0dVBj+H3NMOxeX3g3sTFyl0R0uxjciIhMmPZKCX7efxbLd6XgzIXLyvJOzZ0R1ckfve9256hQogaEwY2IyAQdTdfiu/gU/HooHUUl5Wc0sFdbYMg93hjZyQ+t3B2M3CER1QUGNyIiE1FUUoZ1hzPx3a4UHEzLU5a39nBAVLgfBrf3hp2af9aJGjJ+womI6rnUnCtYvjsFP+5LQ+6VEgCApbkK/dp6IqqTH0L9nKBScWQoUWPA4EZEVA+V6QVbE7Px3a4UbDt1HlI+1gDeTWwwomMzDA3zRVMHtXGbJKI7jsGNiKgeuXBJhx/3pWH5rlSk5xUqyyNaNUVUJz90b+0Gc867RtRoMbgRERmZiGB/ai6+i0/B+iNZKC4rH2zQxNYSQ8N8MeK+ZvB3tTNyl0RUHzC4EREZSZle8PvRTHyx7QyOpGuV5e18myCqkx8GhHjC2tLciB0SUX3D4EZEdIcVlZQhZv9ZLNl+Bsn/njdUbWGGh9p74YlOfgjxaWLcBomo3mJwIyK6Q/KLSrB8Vyq+2pmEC5fKT0XVxNYSo8P9MbqzP5ztrIzcIRHVdwxuRER1LDu/CF//lYzlu1JQoCsFAHhprPF01+Z4/D5fnjeUiGqMfy2IiOpI8oXL+GL7GcQknFUGHLR0s8f4iBYY1N6Lp6IiopvG4EZEVMuOnNXi822n8fvRTPx7rnfc06wJnut2F3q0doMZp/MgolvE4EZEVAtEBHGnc/D5ttPY8fcFZfmDrd0wPqIF7vXn2Q2I6PYxuBER3YYyvSD2WBYWbzuNw2fLp/QwN1NhYIgnno1ogTaejkbukIgaEgY3IqJboCstw+r96fhi+xkkXbgMoHxKj8fv9cXTXZvD19nWyB0SUUPE4EZEdBMKikrw/e7yKT2yC8qn9NDYWGJUuB9Gd/aHqz3PH0pEdYfBjYioBs4X6LD0ryR8tysFBUXlU3p4OFrj6a4BePy+ZrBX888pEdU9/qUhIrqOnEs6fLblNP67OwXFpeVTejRvaofxES0wuL03rCw4pQcR3TkMbkRE1bikK8X/7TiDJdvP4HJxGYDyc4g+F9ECvYPcOaUHERkFgxsR0VV0pWX4fncqPt38D3IuFwMA7vZyxMt9WuOBlq6c0oOIjIrBjYgI5dN6/HIgHQs2nkJ6XiEAwN/FFi9GBqJfsCf3sBFRvcDgRkSNmojgzxPZeP+PRCSeKwAAuDmo8ULPlhga5svTUhFRvWKyf5Hmzp0LlUqFKVOmKMtEBNHR0fDy8oKNjQ26deuGY8eOGdxOp9Nh0qRJcHV1hZ2dHQYNGoSzZ88a1OTm5iIqKgoajQYajQZRUVHIy8szqElNTcXAgQNhZ2cHV1dXTJ48GcXFxXX1cImoDuxJuohHP4/H09/uQ+K5AjhaW2BGn9bY9lJ3jOzox9BGRPWOSf5V2rt3L7788kuEhIQYLJ83bx4WLFiATz/9FHv37oWHhwd69eqFgoICpWbKlClYvXo1Vq5ciZ07d+LSpUsYMGAAysrKlJoRI0bg4MGD2LBhAzZs2ICDBw8iKipKWV9WVob+/fvj8uXL2LlzJ1auXImYmBhMnz697h88Ed22E5n5eHLpHgz9Ih4JKbmwtjTDc91aYMfLD+K5bi1gY2Vu7BaJiKonJqagoEBatmwpGzdulIiICHnhhRdERESv14uHh4e8++67Sm1RUZFoNBr5/PPPRUQkLy9PLC0tZeXKlUpNenq6mJmZyYYNG0RE5Pjx4wJAdu3apdTEx8cLADl58qSIiKxfv17MzMwkPT1dqVmxYoWo1WrRarU1ehxarVYA1LieiG5fyoXL8sKK/eL/ym/iN+M3aT5zncxcdViytIXGbo2ITISxv79Nbo/b888/j/79+6Nnz54Gy5OSkpCVlYXevXsry9RqNSIiIhAXFwcASEhIQElJiUGNl5cXgoODlZr4+HhoNBp07NhRqenUqRM0Go1BTXBwMLy8vJSayMhI6HQ6JCQk1P6DJqLbcr5Ahzd+PYoeC7bil4MZEAEGhHhi07QIvPNwW7g7Whu7RSKiGjGpwQkrV67E/v37sXfv3irrsrKyAADu7u4Gy93d3ZGSkqLUWFlZwcnJqUpNxe2zsrLg5uZWZftubm4GNZXvx8nJCVZWVkpNZTqdDjqdTrmen59/3cdKRLcvv6gES7afwVc7k3Dl37nYHmjVFC9HBiLYW2Pk7oiIbp7JBLe0tDS88MILiI2NhbX1tf93XHmOJRG54bxLlWuqq7+VmqvNnTsXs2fPvm4fRFQ7ikrK8F18Cj7b+g/yrpQAKJ88d0afQHRu4Wrk7oiIbp3JHCpNSEhAdnY2QkNDYWFhAQsLC2zbtg2ffPIJLCwslD1glfd4ZWdnK+s8PDxQXFyM3Nzc69acO3euyv2fP3/eoKby/eTm5qKkpKTKnrgKM2fOhFarVS5paWm38CwQ0fWUlunx4940dJ+/FW+vP4G8KyW4y80enz8Ril8mdGZoIyKTZzLBrUePHjhy5AgOHjyoXMLCwjBy5EgcPHgQzZs3h4eHBzZu3Kjcpri4GNu2bUPnzp0BAKGhobC0tDSoyczMxNGjR5Wa8PBwaLVa7NmzR6nZvXs3tFqtQc3Ro0eRmZmp1MTGxkKtViM0NLTa/tVqNRwdHQ0uRFQ7RAQbjmahz8c78HLMYWRqi+Clsca8R0Ow4YWu6BPswTMeEFGDYDKHSh0cHBAcHGywzM7ODi4uLsryKVOm4J133kHLli3RsmVLvPPOO7C1tcWIESMAABqNBmPHjsX06dPh4uICZ2dnvPjii2jbtq0y2KFNmzbo06cPxo0bhy+++AIA8Mwzz2DAgAEIDAwEAPTu3RtBQUGIiorC+++/j4sXL+LFF1/EuHHjGMiI7rDT5y/hjV+P4q9/cgAATraWeL77XXiikx+sLTmtBxE1LCYT3Gri5ZdfRmFhISZMmIDc3Fx07NgRsbGxcHBwUGo+/PBDWFhYYOjQoSgsLESPHj3wzTffwNz8f3/gly9fjsmTJyujTwcNGoRPP/1UWW9ubo5169ZhwoQJ6NKlC2xsbDBixAjMnz//zj1YokauqKQMn235B19sO4PiMj3UFmZ45oHmGPdAczhaWxq7PSKiOqESETF2E41Rfn4+NBoNtFot99IR3aQtJ7PxxpqjSLtYfk7R7oFNMXtQMJq52Bq5MyJq6Iz9/d2g9rgRUcOWkVeIOWuPY8Ox8sFBnhprzBoYhMi7+Rs2ImocGNyIqN4rKdNj6V9J+GjT37hSXAZzMxXG3h+AF3q0hJ2af8aIqPHgXzwiqtf2JV/Ea6uPIvFc+TmHw/yc8NbDwWjtwZ8YEFHjw+BGRPXSxcvFmLv+BH5KOAugfLTozH5t8Og9PjAz42FRImqcGNyIqF7R6wU/7EvDextOKmc9GH6fL16ObA0nOysjd0dEZFwMbkRUbxzPyMfrvxzB/tQ8AEAbT0e8NTgYoX5O178hEVEjweBGREZ3SVeKBbGn8E1cEvQC2FmZY1rvQIwO94OFucmc4IWIqM4xuBGR0YgI1h/JwpzfjuFcvg4A0L+tJ/4zIAgeGmsjd0dEVP8wuBGRUSRfuIw31hzD9lPnAQD+LraY/VAwIlo1NXJnRET1F4MbEd1RRSVlWLz1NBZvO43iUj2sLMwwoVsLjI9owXOLEhHdAIMbEd0x20+dxxu/HkVyzhUAQNeWrpjzUDACXO2M3BkRkWlgcCOiOpd7uRj/+fUofjucCQBwd1TjjQF3o19bnqqKiOhmMLgRUZ36658LmPbjQZzL18FMBYzpHICpvVrCwdrS2K0REZkcBjciqhPFpXp8EJuIL3ecgQjQvKkdPh7WAW19NMZujYjIZDG4EVGt+yf7El5YeQDHMvIBACM6NsPr/dvA1op/coiIbgf/ihJRrRERLN+dirfWHUdRiR5OtpZ4b0gIet/tYezWiIgaBAY3IqoVOZd0mBFzBJtOnANQPmJ0/mPt4O7IiXSJiGoLgxsR3bbtp85j+k+HcL5ABytzM7zcJxBPdQmAmRlHjBIR1SYGNyK6ZUUlZZi3IRFf/5UEAGjpZo+PH++AIC9HI3dGRNQwMbgR0S05da4Ak1ccwMmsAgDA6HA/zOzXhmc/ICKqQwxuRHRTRATfxqfgnfUnoCvVw9XeCvMeDcGDrd2N3RoRUYPH4EZENXa+QIeXfz6ELYnlJ4bvFtgU7z/aDk0d1EbujIiocWBwI6Ia2XzyHF766TByLhfDysIMr/Vrg1HhfjxlFRHRHcTgRkTXVVRShrnrT2BZfAoAoLWHAz5+vAMCPRyM3BkRUePD4EZE13Q8Ix8vrDyAv7MvAQDG3h+AlyIDOQCBiMhIGNyIqAq9XvD1X0mYtyERxWV6NHVQY/5j7RDRqqmxWyMiatQY3IjIQHZ+Eab/dAg7/r4AAOjZxh3vDWkLF3sOQCAiMjYGNyJSxB7LwoyYw8i9UgJrSzP8Z0AQRtzXjAMQiIjqCQY3IkJJmR5vrzuBb+KSAQBBno74ZHh73OXGAQhERPUJgxtRI6ctLMHzy/dj5z/lh0affaA5pvVuBbUFByAQEdU3DG5EjVjyhct4atlenDl/GbZW5vhoWHv0vtvD2G0REdE1MLgRNVLxp3Pw3PIE5F0pgZfGGktGh+FuL42x2yIioutgcCNqhH7Ym4rXVh9FqV7QzrcJlowKhZuDtbHbIiKiG2BwI2pEyvSCd38/gSU7kgAAA9t54f1HQzihLhGRiWBwI2okLulK8cKKA/jzZDYAYErPlnihR0tO9UFEZEIY3IgagbO5V/D0sn04mVUAtYUZ5j/WDgPbeRm7LSIiukkMbkQNXEJKLp79bh8uXCpGUwc1lowKQ3vfJsZui4iIbgGDG1ED9suBdLwccxjFpXoEeTri/0aHwauJjbHbIiKiW8TgRtQA6fWCDzedwsLN/wAAege548Nh7WGn5keeiMiU8a84UQNTWFyG6T8dxPojWQCA8REt8HJkIMzMOAiBiMjUmRm7gZqaO3cu7r33Xjg4OMDNzQ2DBw9GYmKiQY2IIDo6Gl5eXrCxsUG3bt1w7NgxgxqdTodJkybB1dUVdnZ2GDRoEM6ePWtQk5ubi6ioKGg0Gmg0GkRFRSEvL8+gJjU1FQMHDoSdnR1cXV0xefJkFBcX18ljJ6qpLG0Rhn4Rj/VHsmBprsL7j4bglb6tGdqIiBoIkwlu27Ztw/PPP49du3Zh48aNKC0tRe/evXH58mWlZt68eViwYAE+/fRT7N27Fx4eHujVqxcKCgqUmilTpmD16tVYuXIldu7ciUuXLmHAgAEoKytTakaMGIGDBw9iw4YN2LBhAw4ePIioqChlfVlZGfr374/Lly9j586dWLlyJWJiYjB9+vQ782QQVePIWS0e+mwnjqRr4WRrieVPd8JjYb7GbouIiGqTmKjs7GwBINu2bRMREb1eLx4eHvLuu+8qNUVFRaLRaOTzzz8XEZG8vDyxtLSUlStXKjXp6eliZmYmGzZsEBGR48ePCwDZtWuXUhMfHy8A5OTJkyIisn79ejEzM5P09HSlZsWKFaJWq0Wr1daof61WKwBqXE90PesPZ0jg6+vFb8Zv0vODrZJy4bKxWyIiapCM/f1tMnvcKtNqtQAAZ2dnAEBSUhKysrLQu3dvpUatViMiIgJxcXEAgISEBJSUlBjUeHl5ITg4WKmJj4+HRqNBx44dlZpOnTpBo9EY1AQHB8PL63/zYEVGRkKn0yEhIaHafnU6HfLz8w0uRLdLRPDp5r/x3PL9KCrRI6JVU8RM6IxmLrbGbo2IiOqASQY3EcG0adNw//33Izg4GACQlVX+Q2x3d3eDWnd3d2VdVlYWrKys4OTkdN0aNze3Kvfp5uZmUFP5fpycnGBlZaXUVDZ37lzlN3MajQa+vjyERbenqKQM0348hPmxpwAAYzr746vRYXC0tjRyZ0REVFdMMrhNnDgRhw8fxooVK6qsq3z6HhG54Sl9KtdUV38rNVebOXMmtFqtcklLS7tuT0TXc+GSDiP/bzdWH0iHuZkKbw0ORvSgu2FhbpIfaSIiqiGT+ys/adIkrFmzBlu2bIGPj4+y3MPDAwCq7PHKzs5W9o55eHiguLgYubm51605d+5clfs9f/68QU3l+8nNzUVJSUmVPXEV1Go1HB0dDS5Et+JkVj4e+vQvJKTkwtHaAsuevA9PdPIzdltERHQHmExwExFMnDgRq1atwubNmxEQEGCwPiAgAB4eHti4caOyrLi4GNu2bUPnzp0BAKGhobC0tDSoyczMxNGjR5Wa8PBwaLVa7NmzR6nZvXs3tFqtQc3Ro0eRmZmp1MTGxkKtViM0NLT2HzzRvzafPIchi+KQnlcIfxdbrH6+C+5v6WrstoiI6A5RiYgYu4mamDBhAr7//nv8+uuvCAwMVJZrNBrY2JSfwue9997D3LlzsXTpUrRs2RLvvPMOtm7disTERDg4OAAAnnvuOfz222/45ptv4OzsjBdffBE5OTlISEiAubk5AKBv377IyMjAF198AQB45pln4Ofnh7Vr1wIonw6kffv2cHd3x/vvv4+LFy9izJgxGDx4MBYuXFijx5Ofnw+NRgOtVsu9b1QjK/ek4tXVR6AXoFNzZyweGQonOytjt0VE1KgY/fvbKGNZbwGAai9Lly5VavR6vcyaNUs8PDxErVbLAw88IEeOHDHYTmFhoUycOFGcnZ3FxsZGBgwYIKmpqQY1OTk5MnLkSHFwcBAHBwcZOXKk5ObmGtSkpKRI//79xcbGRpydnWXixIlSVFRU48dj7OHEZFpW7E4Rvxm/id+M3+Tlnw6JrqTM2C0RETVKxv7+Npk9bg2N0RM7mYwf9qZiRswRAMBTXQLwnwFtbjjghoiI6oaxv79N5jduRI3Rj3vT8Mqq8tD2ZBd/hjYiokaOwY2onvppXxpmrDoMkfI52t4YEMTQRkTUyDG4EdVDPyecxcsx5aFtdLgfZg1kaCMiIgY3onpn1f6zeOnnQxABojr5IXrQ3QxtREQEgMGNqF5ZfeAspv9UHtqe6NQMcx5iaCMiov9hcCOqJ345kI7pP5aHtpEdm2HOoGCGNiIiMsDgRlQP/HowHdN+PAi9AMPva4Y3HwqGmRlDGxERGWJwIzKyNYcyMPWHitDmi7cHM7QREVH1GNyIjGjtoQxMWXkAegGGhfni7cFtGdqIiOiaGNyIjOS3wxmY8u+etqFhPpj7CEMbERFdH4MbkRGsO5yJF1YeRJle8GioD959JIShjYiIbojBjegO+/1IJiavPIAyvWDIPT54bwhDGxER1QyDG9EdtOFoJiatKA9tj9zjjXmPhsCcoY2IiGroloJbdHQ0UlJSarsXogZtw9EsTPz+AEr1goc7eOP9R9sxtBER0U25peC2du1atGjRAj169MD333+PoqKi2u6LqEH541gWJn6/H6V6weD2Xpj/GEMbERHdvFsKbgkJCdi/fz9CQkIwdepUeHp64rnnnsPevXtruz8ikxd7LAvPLy8PbQ+198IHQ9sztBER0S255d+4hYSE4MMPP0R6ejq+/vprpKeno0uXLmjbti0+/vhjaLXa2uyTyCRtOn4Oz/+7p21gOy98wD1tRER0G257cIJer0dxcTF0Oh1EBM7Ozli8eDF8fX3xww8/1EaPRCbpzxPn8NzyBJSUCQaEeOLDoe1gYc7xQEREdOtu+VskISEBEydOhKenJ6ZOnYoOHTrgxIkT2LZtG06ePIlZs2Zh8uTJtdkrkcnYfPIcnvvvfpSUCfq39cRHw9oztBER0W1TiYjc7I1CQkJw4sQJ9O7dG+PGjcPAgQNhbm5uUHP+/Hm4u7tDr9fXWrMNSX5+PjQaDbRaLRwdHY3dDtWiLSez8ex3CSgu06NfWw98/HgHWDK0ERE1CMb+/ra4lRs99thjeOqpp+Dt7X3NmqZNmzK0UaOzJfF/oa1vMEMbERHVrlv6RhERODk5VVleWFiIOXPm3HZTRKZoX/JFJbT1udsDnwxnaCMiotp1S4dKzc3NkZmZCTc3N4PlOTk5cHNzQ1lZWa012FAZe1cr1a6MvEIM+nQnLlwqRs827lj8xD0MbUREDZCxv79veY+bSlV1SoNDhw7B2dn5tpsiMiWFxWV45rt9uHCpGK09HPDJ8PYMbUREVCdu6jduTk5OUKlUUKlUaNWqlUF4Kysrw6VLlzB+/Phab5KovhIRzIg5jKPp+XCytcSSUWGwtbqln44SERHd0E19w3z00UcQETz11FOYPXs2NBqNss7Kygr+/v4IDw+v9SaJ6qsvtp/BmkMZsDBTYdHIUPg62xq7JSIiasBuKriNHj0aABAQEIDOnTvD0tKyTpoiMgVbTmbjvQ0nAQCzBgYhvIWLkTsiIqKGrsbBLT8/X/kRXocOHVBYWIjCwsJqa/lje2roTp+/hMkrD0AEGH6fL57o5GfsloiIqBGocXBzcnJSRpI2adKk2sEJFYMWOKqUGrL8ohKM+3YfCopKEebnhNmDgqv9PBAREdW2Gge3zZs3KyNGt2zZUmcNEdVnZXrBCysO4Mz5y/DUWGPxE6GwsuAIUiIiujNqHNwiIiKq/TdRYzI/NhFbEs9DbWGGL6PC0NRBbeyWiIioEalxcDt8+HCNNxoSEnJLzRDVZ78eTMfiracBAPMeDUFbH80NbkFERFS7ahzc2rdvD5VKhRudaIG/caOG6Gi6FjNiyv/z8mxEczzU/trn6SUiIqorNQ5uSUlJddkHUb11vkCHZ77dh6ISPSJaNcXLka2N3RIRETVSNQ5ufn6c7oAan+JSPSYsT0CGtgjNXe3wyfAOMDfjCFIiIjKOGge3NWvWoG/fvrC0tMSaNWuuWzto0KDbboyoPoheewx7k3PhoLbAl6PCoLHhpNNERGQ8NQ5ugwcPRlZWFtzc3DB48OBr1vE3btRQ/HdXCr7fnQqVCvh4eHvc5WZv7JaIiKiRq3Fw0+v11f6bqCHadSYH0WuOAQBeigzEg63djdwRERERcEszh3777bfQ6XRVlhcXF+Pbb7+97aaIjOls7hVMWL4fpXrBwHZeeC6ihbFbIiIiAnCLwe3JJ5+EVqutsrygoABPPvnkbTdFZCxXikvxzLcJuHi5GHd7OWLekBCezoqIiOqNWwpuFeckrezs2bPQaBrXpKSLFi1CQEAArK2tERoaih07dhi7JbpFIoKXfj6M45n5cLGzwpejwmBjZW7stoiIiBQ1/o0bAHTo0AEqlQoqlQo9evSAhcX/bl5WVoakpCT06dOn1pusr3744QdMmTIFixYtQpcuXfDFF1+gb9++OH78OJo1a2bs9ugmLdp6GusOZ8LCTIXFT4TCu4mNsVsiIiIycFPBbfDgwRARHDx4EJGRkbC3/98oOysrK/j7+2PIkCG13mR9tWDBAowdOxZPP/00AOCjjz7CH3/8gcWLF2Pu3LlG7o5uxqbj5zA/NhEAMPuhu3FfgLOROyIiIqrqpoLbrFmzAADNmzfHsGHDoFY33hNsFxcXIyEhAa+88orB8t69eyMuLq5KvU6nMxjQkZ+fX+c9Us38k12AKT8chAgwsmMzjOzIyaaJiKh+uqnfuJmZmcHc3BxPPvkkbG1tYW5uDnNzczg5OaFTp05YtWpVXfVZ71y4cAFlZWVwdzecJsLd3R1ZWVlV6ufOnQuNRqNcfH1971SrdB3aKyUY920CLulKcV+AM2YNvNvYLREREV3TTe1xW7VqVbWDEvLy8rBnzx488cQTWLZsGR577LFaa7C+q/x8XGvgxsyZMzFt2jTlen5+PsObkZXpBZNWHkDShcvwbmKDRSPvgZXFLY3XISIiuiNu+jdu1zJ69GgEBQVh/vz5jSK4ubq6wtzcvMretezs7Cp74QBArVY36kPL9dF7G05i+6nzsLY0wxdRoXC15+tDRET1W63uXujduzdOnTpVm5ust6ysrBAaGoqNGzcaLN+4cSM6d+5spK6oplYfOIsvt58BALz/aDsEezeuaWyIiMg03dQetxspLCyEtbV1bW6yXps2bRqioqIQFhaG8PBwfPnll0hNTcX48eON3Rpdx+GzeZgRcwQA8Hz3FhjYzsvIHREREdVMrQa3JUuWoEOHDrW5yXpt2LBhyMnJwZw5c5CZmYng4GCsX78efn4clVhfZRcU4ZlvE1BcqkeP1m6Y3ivQ2C0RERHVmEpEpKbFV/+4/mparRb79u3D6dOnsWPHjkYV3m5Vfn4+NBoNtFotHB0djd1Oo1BapsfjX+7CvpRctGhqh9XPd4GjtaWx2yIiIhNi7O/vm9rjduDAgWqXOzo6ok+fPpgwYQL3NlG99fVfSdiXkgsHtQWWjApjaCMiIpNzU8Fty5YtddUHUZ1KvnAZH8SWD5x5fUAbNG9qf4NbEBER1T+ctIoaPL1eMCPmMHSlenS5ywVDwzh/HhERmSYGN2rwVuxNxe6ki7CxNMfch0OqnSCZiIjIFDC4UYOWqS3E3PUnAQAvRgaimYutkTsiIiK6dQxu1GCJCF5bfRSXdKXo0KwJxnT2N3ZLREREt4XBjRqsNYcysPlkNqzMzTBvSAjMzXiIlIiITBuDGzVIOZd0iF5zDAAw8cG70NLdwcgdERER3T4GN2qQotceR+6VErT2cMD4iBbGboeIiKhWMLhRg7Px+DmsPZQBMxUw79EQWFnwbU5ERA0Dv9GoQckvKsHrv5SfQH5c1+YI8Wli3IaIiIhqEYMbNShz15/AuXwd/F1sMaVnK2O3Q0REVKsY3KjBiPvnAlbsSQMAvDskBDZW5kbuiIiIqHYxuFGDUFhchldWlR8iHdmxGTo1dzFyR0RERLWPwY0ahA9iE5F68Qo8NdZ4pW9rY7dDRERUJxjcyOQdSM3F138lAQDefjgYDtaWRu6IiIiobjC4kUkrLtVjRsxh6AUY3N4LD7Z2N3ZLREREdYbBjUzaZ1v+walzl+BiZ4U3Bt5t7HaIiIjqFIMbmayTWflYtPUfAED0oLvhbGdl5I6IiIjqFoMbmaQyvWDGz4dRUibo2cYdA0I8jd0SERFRnWNwI5P09c4kHDqrhYO1Bd5+OBgqlcrYLREREdU5BjcyOckXLuODjYkAgNf6tYG7o7WROyIiIrozGNzIpIgIXll1GEUlenRu4YJh9/oauyUiIqI7hsGNTMqKPWnYdeYirC3N8O4jITxESkREjQqDG5mMTG0h5q4/AQB4sXcgmrnYGrkjIiKiO4vBjUyCiOD11UdRoCtFe98meLJLgLFbIiIiuuMY3MgkrDmUgT9PZsPSXIV5j4bA3IyHSImIqPFhcKN6L+eSDrPXHgcATOzeEq3cHYzcERERkXEwuFG9N3vtcVy8XIzWHg54rlsLY7dDRERkNAxuVK9tOn4Oaw5lwEwFvDckBFYWfMsSEVHjxW9Bqrfyi0rw+i9HAQBPd22Odr5NjNsQERGRkTG4Ub01d/1JZOUXwd/FFlN7tjJ2O0REREbH4Eb1UtzpC1ixJxUA8O6QENhYmRu5IyIiIuNjcKN6p7C4DDNXHQEAjOzYDJ2auxi5IyIiovqBwY3qnQUbE5GScwWeGmu80re1sdshIiKqNxjcqF45mJaHr3YmAQDefjgYDtaWRu6IiIio/mBwo3pDRPDGr0ehF2Bwey882Nrd2C0RERHVKwxuVG/8eSIbh89qYWNpjtcHBBm7HSIionqHwY3qBRHBR3+eAgCM6uwHV3u1kTsiIiKqf0wiuCUnJ2Ps2LEICAiAjY0NWrRogVmzZqG4uNigLjU1FQMHDoSdnR1cXV0xefLkKjVHjhxBREQEbGxs4O3tjTlz5kBEDGq2bduG0NBQWFtbo3nz5vj888+r9BQTE4OgoCCo1WoEBQVh9erVtf/AG5GNx8/haHo+bK3M8ewDPK0VERFRdSyM3UBNnDx5Enq9Hl988QXuuusuHD16FOPGjcPly5cxf/58AEBZWRn69++Ppk2bYufOncjJycHo0aMhIli4cCEAID8/H7169UL37t2xd+9enDp1CmPGjIGdnR2mT58OAEhKSkK/fv0wbtw4/Pe//8Vff/2FCRMmoGnTphgyZAgAID4+HsOGDcObb76Jhx9+GKtXr8bQoUOxc+dOdOzY0ThPkgkTEXy06W8AwOjO/nC2szJyR0RERPWTSirvbjIR77//PhYvXowzZ84AAH7//XcMGDAAaWlp8PLyAgCsXLkSY8aMQXZ2NhwdHbF48WLMnDkT586dg1pdfiju3XffxcKFC3H27FmoVCrMmDEDa9aswYkTJ5T7Gj9+PA4dOoT4+HgAwLBhw5Cfn4/ff/9dqenTpw+cnJywYsWKGvWfn58PjUYDrVYLR0fHWnlOTNWGo1kY/98E2FmZY+eMB+HE4EZERPWUsb+/TeJQaXW0Wi2cnZ2V6/Hx8QgODlZCGwBERkZCp9MhISFBqYmIiFBCW0VNRkYGkpOTlZrevXsb3FdkZCT27duHkpKS69bExcXV6mNsDPR6wUebyn/bNqaLP0MbERHRdZhkcDt9+jQWLlyI8ePHK8uysrLg7m44fYSTkxOsrKyQlZV1zZqK6zeqKS0txYULF65bU7GN6uh0OuTn5xtcCPjjWBZOZhXAXm2BcV2bG7sdIiKies2owS06Ohoqleq6l3379hncJiMjA3369MFjjz2Gp59+2mCdSqWqch8iYrC8ck3FkeLaqKnu/ivMnTsXGo1Gufj6+l6ztrEo39tW/tu2J7v4o4kt97YRERFdj1EHJ0ycOBGPP/74dWv8/f2Vf2dkZKB79+4IDw/Hl19+aVDn4eGB3bt3GyzLzc1FSUmJsnfMw8Ojyl6x7OxsALhhjYWFBVxcXK5bU3kv3NVmzpyJadOmKdfz8/MbfXj7/WgWEs8VwEFtgafv5942IiKiGzFqcHN1dYWrq2uNatPT09G9e3eEhoZi6dKlMDMz3FkYHh6Ot99+G5mZmfD09AQAxMbGQq1WIzQ0VKl59dVXUVxcDCsrK6XGy8tLCYjh4eFYu3atwbZjY2MRFhYGS0tLpWbjxo2YOnWqQU3nzp2v2b9arTb4bV1jp9cLPv533rYn7w+AxpantiIiIroRk/iNW0ZGBrp16wZfX1/Mnz8f58+fR1ZWlsFer969eyMoKAhRUVE4cOAA/vzzT7z44osYN26cMupjxIgRUKvVGDNmDI4ePYrVq1fjnXfewbRp05TDnOPHj0dKSgqmTZuGEydO4Ouvv8ZXX32FF198UbmvF154AbGxsXjvvfdw8uRJvPfee9i0aROmTJlyR58XU7buSCZOnbsEB2sLjL0/wNjtEBERmQYxAUuXLhUA1V6ulpKSIv379xcbGxtxdnaWiRMnSlFRkUHN4cOHpWvXrqJWq8XDw0Oio6NFr9cb1GzdulU6dOggVlZW4u/vL4sXL67S008//SSBgYFiaWkprVu3lpiYmJt6TFqtVgCIVqu9qds1BKVleunxwVbxm/GbfLgx0djtEBER1Zixv79Ndh43U2fseWCM6deD6Xhh5UE4Wltg5ysPwtGah0mJiMg0GPv72yQOlVLDUaYXfPJn+UjSp7s2Z2gjIiK6CQxudEetPZSB0+cvQ2NjiSe7+Bu7HSIiIpPC4EZ3TGmZXtnbNq5rABy4t42IiOimMLjRHbPmUAbOXLiMJraWGN3Z39jtEBERmRwGN7ojDPe2NefeNiIiolvA4EZ3xC8HM5CccwVO3NtGRER0yxjcqM6VlumxcHP53rZnHmgBe7VRT9hBRERkshjcqM6tOpCOlJwrcLazwqhwP2O3Q0REZLIY3KhOlVy1t+3ZB5rDjnvbiIiIbhmDG9Wp1fvTkXaxEK72Voji3jYiIqLbwuBGdaakTI+FWyr2trWArRX3thEREd0OBjeqMzEJZ//d26bGE524t42IiOh2MbhRnSgu1WPh5n8AAOMjmsPGytzIHREREZk+BjeqEz8nnEV6XiGaOnBvGxERUW1hcKNaV1yqx2dbyve2PRfRAtaW3NtGRERUGxjcqNb9uC8N6XmFcHNQY0THZsZuh4iIqMFgcKNapSstU/a2TejGvW1ERES1icGNatWPe9OQqS2Ch6M1Hr+Pe9uIiIhqE4Mb1ZqikjJ8tuU0AGBCd+5tIyIiqm0MblRrftibhqz8InhqrDHsXl9jt0NERNTgMLhRrSgqKcOirf/+tq37XVBbcG8bERFRbWNwo1qxYk8qzuXr4KWxxtAwH2O3Q0RE1CAxuNFtK9/bVv7btucf5N42IiKiusLgRrdt+e5UnC/QwbuJDR4L5W/biIiI6gqDG92WwuIyLP53b9vEB++ClQXfUkRERHWF37J0W5bvTsGFSzr4ONng0VD+to2IiKguMbjRLbtSXIrPt5XvbZv04F2wNOfbiYiIqC7xm5Zu2X93peDCpWI0c7bFI/dwbxsREVFdY3CjW3KluBRfbDsDoPy3bdzbRkREVPf4bUu35Nv4FORcLoafiy0e6eBt7HaIiIgaBQY3ummXdaX4cnv53rZJD7aEBfe2ERER3RH8xqWbtiw+GRcvFyPA1Q6D23sZux0iIqJGg8GNbsolg71td3FvGxER0R3Eb126KSt2pyLvSgmau9phUDvubSMiIrqTGNyoxkQEP+5LAwA83bU597YRERHdYfzmpRo7fFaLv7MvQW1hhgHtPI3dDhERUaPD4EY19nPCWQBAn2APOFpbGrkbIiKixofBjWqkqKQMvx5MBwA8Fupr5G6IiIgaJwY3qpFNJ84hv6gUXhprhLdwMXY7REREjRKDG9VIxWHSR+7xgbmZysjdEBERNU4mF9x0Oh3at28PlUqFgwcPGqxLTU3FwIEDYWdnB1dXV0yePBnFxcUGNUeOHEFERARsbGzg7e2NOXPmQEQMarZt24bQ0FBYW1ujefPm+Pzzz6v0ERMTg6CgIKjVagQFBWH16tW1/ljri3P5Rdh+6jwAYEgoTyZPRERkLCYX3F5++WV4eVWdP6ysrAz9+/fH5cuXsXPnTqxcuRIxMTGYPn26UpOfn49evXrBy8sLe/fuxcKFCzF//nwsWLBAqUlKSkK/fv3QtWtXHDhwAK+++iomT56MmJgYpSY+Ph7Dhg1DVFQUDh06hKioKAwdOhS7d++u2wdvJKsPpEMvQJifEwJc7YzdDhERUeMlJmT9+vXSunVrOXbsmACQAwcOGKwzMzOT9PR0ZdmKFStErVaLVqsVEZFFixaJRqORoqIipWbu3Lni5eUler1eRERefvllad26tcH9Pvvss9KpUyfl+tChQ6VPnz4GNZGRkfL444/X+LFotVoBoPRWX+n1enlw/hbxm/GbrNyTYux2iIiIjMrY398ms8ft3LlzGDduHL777jvY2tpWWR8fH4/g4GCDvXGRkZHQ6XRISEhQaiIiIqBWqw1qMjIykJycrNT07t3bYNuRkZHYt28fSkpKrlsTFxd3zf51Oh3y8/MNLqbgYFoeTp+/DGtLM/Rry7nbiIiIjMkkgpuIYMyYMRg/fjzCwsKqrcnKyoK7u7vBMicnJ1hZWSErK+uaNRXXb1RTWlqKCxcuXLemYhvVmTt3LjQajXLx9TWNKTUqBiX0DfaEA+duIyIiMiqjBrfo6GioVKrrXvbt24eFCxciPz8fM2fOvO72VKqqox1FxGB55Rr5d2BCbdRUd/8VZs6cCa1Wq1zS0tKu+1jqg6KSMqw5lAEAeJSDEoiIiIzOwph3PnHiRDz++OPXrfH398dbb72FXbt2GRziBICwsDCMHDkSy5Ytg4eHR5XBAbm5uSgpKVH2jnl4eFTZK5adnQ0AN6yxsLCAi4vLdWsq74W7mlqtrtJ/fbfx+DkUFJXCu4kNwptz7jYiIiJjM2pwc3V1haur6w3rPvnkE7z11lvK9YyMDERGRuKHH35Ax44dAQDh4eF4++23kZmZCU/P8t9ixcbGQq1WIzQ0VKl59dVXUVxcDCsrK6XGy8sL/v7+Ss3atWsN7j82NhZhYWGwtLRUajZu3IipU6ca1HTu3PkWn4n66ad/D5MOuccbZpy7jYiIyPiMMiTiNiUlJVUZVVpaWirBwcHSo0cP2b9/v2zatEl8fHxk4sSJSk1eXp64u7vL8OHD5ciRI7Jq1SpxdHSU+fPnKzVnzpwRW1tbmTp1qhw/fly++uorsbS0lJ9//lmp+euvv8Tc3FzeffddOXHihLz77rtiYWEhu3btqvFjMPaolBvJzCuUgFd+E78Zv0nyhUvGboeIiKheMPb3t0kMTqgJc3NzrFu3DtbW1ujSpQuGDh2KwYMHY/78+UqNRqPBxo0bcfbsWYSFhWHChAmYNm0apk2bptQEBARg/fr12Lp1K9q3b48333wTn3zyCYYMGaLUdO7cGStXrsTSpUsREhKCb775xmDvX0Ow6sBZ6AW4z98Zfi6cu42IiKg+UIlUOm0A3RH5+fnQaDTQarVwdHQ0djsGRAQ9FmzDmfOXMW9ICIbeaxojYImIiOqasb+/G8weN6o9+1PzcOb8ZdhYmqNfCOduIyIiqi8Y3KgKZe62th6wVxt1/AoRERFdhcGNDBSVlOE3zt1GRERULzG4kYE/jmWhQFc+d1unAM7dRkREVJ8wuJGBisOkQ0J9OHcbERFRPcPgRoqMvELs/Kf8fKyP3sPDpERERPUNgxspVh9IhwjQMcAZzVxsjd0OERERVcLgRgDK526rOEzKQQlERET1E4MbAQD2p+Yi6cJl2FqZo19bzt1GRERUHzG4EQDgp33/zt0W7Ak7zt1GRERULzG4EQqLy/Db4UwAwGNhPExKRERUXzG4Ef44loVLulL4OtvgPn9nY7dDRERE18DgRv+bu+0ezt1GRERUnzG4NXLpeYX463T53G1DOHcbERFRvcbg1sitSjgLEaBTc2f4OnPuNiIiovqMwa0RExH8vL/8MOljob5G7oaIiIhuhMGtEduXkouUnCuwszJH37Yexm6HiIiIboDBrRH7+d+52/q19YStFeduIyIiqu8Y3BqpK8WlWHekfO42nuKKiIjINDC4NVIbjpbP3dbM2Rb3BXDuNiIiIlPA4NZIXX1CeZWKc7cRERGZAga3Ruhs7hXEnc4BADxyj7eRuyEiIqKaYnBrhFbtTwcAdG7hAh8nzt1GRERkKhjcGhm9XgwOkxIREZHpYHBrZPYmX0TqxSuwV1ugTzDnbiMiIjIlDG6NTMXetv6cu42IiMjkMLg1Ipd1V83dFsbDpERERKaGwa0R2XA0C1eKy+DnYoswPydjt0NEREQ3icGtEfkpIQ0A8Og9nLuNiIjIFDG4NRJpF69g15mLUKmARzialIiIyCQxuDUSMfvLByV0aeEK7yY2Ru6GiIiIbgWDWyOg14sS3Dh3GxERkelicGsEdiddRNrFQtirLRB5N+duIyIiMlUMbo1AxdxtA0I8YWNlbuRuiIiI6FYxuDVwl3Wl+P3ov3O38TApERGRSWNwa+DWH8nEleIyBLjaIZRztxEREZk0BrcG7uoTynPuNiIiItPG4NaApeZcwe6k8rnbHu7gbex2iIiI6DYxuDVgP/87Bcj9d7nCi3O3ERERmTwGtwZKrxfEJHDuNiIioobEpILbunXr0LFjR9jY2MDV1RWPPPKIwfrU1FQMHDgQdnZ2cHV1xeTJk1FcXGxQc+TIEURERMDGxgbe3t6YM2cORMSgZtu2bQgNDYW1tTWaN2+Ozz//vEovMTExCAoKglqtRlBQEFavXl37D/g27ErKQXpeIRw4dxsREVGDYTLBLSYmBlFRUXjyySdx6NAh/PXXXxgxYoSyvqysDP3798fly5exc+dOrFy5EjExMZg+fbpSk5+fj169esHLywt79+7FwoULMX/+fCxYsECpSUpKQr9+/dC1a1ccOHAAr776KiZPnoyYmBilJj4+HsOGDUNUVBQOHTqEqKgoDB06FLt3774zT0YN/Lzv37nb2nnB2pJztxERETUIYgJKSkrE29tb/u///u+aNevXrxczMzNJT09Xlq1YsULUarVotVoREVm0aJFoNBopKipSaubOnSteXl6i1+tFROTll1+W1q1bG2z72WeflU6dOinXhw4dKn369DGoiYyMlMcff7zGj0mr1QoApbfalF9YLIGvrxe/Gb/JvuSLtb59IiKixqouv79rwiT2uO3fvx/p6ekwMzNDhw4d4Onpib59++LYsWNKTXx8PIKDg+Hl5aUsi4yMhE6nQ0JCglITEREBtVptUJORkYHk5GSlpnfv3gb3HxkZiX379qGkpOS6NXFxcdd8DDqdDvn5+QaXuvL7kSwUlejR3NUO9zRrUmf3Q0RERHeWSQS3M2fOAACio6Px+uuv47fffoOTkxMiIiJw8eJFAEBWVhbc3d0Nbufk5AQrKytkZWVds6bi+o1qSktLceHChevWVGyjOnPnzoVGo1Euvr6+N/Uc3IyKuduGcO42IiKiBsWowS06Ohoqleq6l3379kGv1wMAXnvtNQwZMgShoaFYunQpVCoVfvrpJ2V71YUUETFYXrlG/h2YUBs11wtJM2fOhFarVS5paWnXrL0dyRcuY0/yRZipgCH3cDQpERFRQ2JhzDufOHEiHn/88evW+Pv7o6CgAAAQFBSkLFer1WjevDlSU1MBAB4eHlUGB+Tm5qKkpETZO+bh4VFlr1h2djYA3LDGwsICLi4u162pvBfuamq12uAQbV1JuXgFTR3UaOPpCA+NdZ3fHxEREd05Rg1urq6ucHV1vWFdaGgo1Go1EhMTcf/99wMASkpKkJycDD8/PwBAeHg43n77bWRmZsLT0xMAEBsbC7VajdDQUKXm1VdfRXFxMaysrJQaLy8v+Pv7KzVr1641uP/Y2FiEhYXB0tJSqdm4cSOmTp1qUNO5c+fbeDZqR0Srpoh/5UFcvFJ842IiIiIyLUYZEnELXnjhBfH29pY//vhDTp48KWPHjhU3Nze5eLF81GRpaakEBwdLjx49ZP/+/bJp0ybx8fGRiRMnKtvIy8sTd3d3GT58uBw5ckRWrVoljo6OMn/+fKXmzJkzYmtrK1OnTpXjx4/LV199JZaWlvLzzz8rNX/99ZeYm5vLu+++KydOnJB3331XLCwsZNeuXTV+PMYelUJEREQ3z9jf3yYT3IqLi2X69Oni5uYmDg4O0rNnTzl69KhBTUpKivTv319sbGzE2dlZJk6caDD1h4jI4cOHpWvXrqJWq8XDw0Oio6OVqUAqbN26VTp06CBWVlbi7+8vixcvrtLPTz/9JIGBgWJpaSmtW7eWmJiYm3o8xn7hiYiI6OYZ+/tbJVLptAF0R+Tn50Oj0UCr1cLR0dHY7RAREVENGPv72ySmAyEiIiIiBjciIiIik8HgRkRERGQiGNyIiIiITASDGxEREZGJYHAjIiIiMhEMbkREREQmgsGNiIiIyEQwuBERERGZCAY3IiIiIhPB4EZERERkIiyM3UBjVXGK2Pz8fCN3QkRERDVV8b1trFO9M7gZSUFBAQDA19fXyJ0QERHRzSooKIBGo7nj96sSY0XGRk6v1yMjIwMODg5QqVTGbsek5Ofnw9fXF2lpaXB0dDR2O3QVvjb1F1+b+ouvTf1V3WsjIigoKICXlxfMzO78L864x81IzMzM4OPjY+w2TJqjoyP/yNVTfG3qL7429Rdfm/qr8mtjjD1tFTg4gYiIiMhEMLgRERERmQgGNzI5arUas2bNglqtNnYrVAlfm/qLr039xdem/qqPrw0HJxARERGZCO5xIyIiIjIRDG5EREREJoLBjYiIiMhEMLgRERERmQgGN6p127dvx8CBA+Hl5QWVSoVffvnFYL2IIDo6Gl5eXrCxsUG3bt1w7NgxgxqdTodJkybB1dUVdnZ2GDRoEM6ePWtQk5ubi6ioKGg0Gmg0GkRFRSEvL8+gJjU1FQMHDoSdnR1cXV0xefJkFBcXG9QcOXIEERERsLGxgbe3N+bMmWO0c9DVtRu9NmPGjIFKpTK4dOrUyaCGr03tmzt3Lu699144ODjAzc0NgwcPRmJiokENPzfGUZPXhp8b41i8eDFCQkKUyXHDw8Px+++/K+sb7GdGiGrZ+vXr5bXXXpOYmBgBIKtXrzZY/+6774qDg4PExMTIkSNHZNiwYeLp6Sn5+flKzfjx48Xb21s2btwo+/fvl+7du0u7du2ktLRUqenTp48EBwdLXFycxMXFSXBwsAwYMEBZX1paKsHBwdK9e3fZv3+/bNy4Uby8vGTixIlKjVarFXd3d3n88cflyJEjEhMTIw4ODjJ//vy6e4KM6EavzejRo6VPnz6SmZmpXHJycgxq+NrUvsjISFm6dKkcPXpUDh48KP3795dmzZrJpUuXlBp+boyjJq8NPzfGsWbNGlm3bp0kJiZKYmKivPrqq2JpaSlHjx4VkYb7mWFwozpVORzo9Xrx8PCQd999V1lWVFQkGo1GPv/8cxERycvLE0tLS1m5cqVSk56eLmZmZrJhwwYRETl+/LgAkF27dik18fHxAkBOnjwpIuUhxczMTNLT05WaFStWiFqtFq1WKyIiixYtEo1GI0VFRUrN3LlzxcvLS/R6fS0+E/XPtYLbQw89dM3b8LW5M7KzswWAbNu2TUT4ualPKr82Ivzc1CdOTk7yf//3fw36M8NDpXRHJSUlISsrC71791aWqdVqREREIC4uDgCQkJCAkpISgxovLy8EBwcrNfHx8dBoNOjYsaNS06lTJ2g0GoOa4OBgeHl5KTWRkZHQ6XRISEhQaiIiIgwmV4yMjERGRgaSk5Nr/wkwAVu3boWbmxtatWqFcePGITs7W1nH1+bO0Gq1AABnZ2cA/NzUJ5Vfmwr83BhXWVkZVq5cicuXLyM8PLxBf2YY3OiOysrKAgC4u7sbLHd3d1fWZWVlwcrKCk5OTtetcXNzq7J9Nzc3g5rK9+Pk5AQrK6vr1lRcr6hpTPr27Yvly5dj8+bN+OCDD7B37148+OCD0Ol0APja3AkigmnTpuH+++9HcHAwAH5u6ovqXhuAnxtjOnLkCOzt7aFWqzF+/HisXr0aQUFBDfozY3FT1US1RKVSGVwXkSrLKqtcU119bdTIvz8WvVE/DdGwYcOUfwcHByMsLAx+fn5Yt24dHnnkkWvejq9N7Zk4cSIOHz6MnTt3VlnHz41xXeu14efGeAIDA3Hw4EHk5eUhJiYGo0ePxrZt25T1DfEzwz1udEd5eHgAqPo/jOzsbOV/Hx4eHiguLkZubu51a86dO1dl++fPnzeoqXw/ubm5KCkpuW5NxSGOyv87aow8PT3h5+eHv//+GwBfm7o2adIkrFmzBlu2bIGPj4+ynJ8b47vWa1Mdfm7uHCsrK9x1110ICwvD3Llz0a5dO3z88ccN+jPD4EZ3VEBAADw8PLBx40ZlWXFxMbZt24bOnTsDAEJDQ2FpaWlQk5mZiaNHjyo14eHh0Gq12LNnj1Kze/duaLVag5qjR48iMzNTqYmNjYVarUZoaKhSs337doNh27GxsfDy8oK/v3/tPwEmJicnB2lpafD09ATA16auiAgmTpyIVatWYfPmzQgICDBYz8+N8dzotakOPzfGIyLQ6XQN+zNzU0MZiGqgoKBADhw4IAcOHBAAsmDBAjlw4ICkpKSISPkQbY1GI6tWrZIjR47I8OHDqx2i7ePjI5s2bZL9+/fLgw8+WO0Q7ZCQEImPj5f4+Hhp27ZttUO0e/ToIfv375dNmzaJj4+PwRDtvLw8cXd3l+HDh8uRI0dk1apV4ujo2CCHzotc/7UpKCiQ6dOnS1xcnCQlJcmWLVskPDxcvL29+drUseeee040Go1s3brVYEqJK1euKDX83BjHjV4bfm6MZ+bMmbJ9+3ZJSkqSw4cPy6uvvipmZmYSGxsrIg33M8PgRrVuy5YtAqDKZfTo0SJSPrXBrFmzxMPDQ9RqtTzwwANy5MgRg20UFhbKxIkTxdnZWWxsbGTAgAGSmppqUJOTkyMjR44UBwcHcXBwkJEjR0pubq5BTUpKivTv319sbGzE2dlZJk6caDAcW0Tk8OHD0rVrV1Gr1eLh4SHR0dENdtj89V6bK1euSO/evaVp06ZiaWkpzZo1k9GjR1d53vna1L7qXhMAsnTpUqWGnxvjuNFrw8+N8Tz11FPi5+cnVlZW0rRpU+nRo4cS2kQa7mdGJdIAp1MmIiIiaoD4GzciIiIiE8HgRkRERGQiGNyIiIiITASDGxEREZGJYHAjIiIiMhEMbkREREQmgsGNiIiIyEQwuFGjo1Kp8MsvvwAAkpOToVKpcPDgQaP2VFuu99i2bt0KlUqFvLy8Ornv//znP3jmmWfqZNuVffPNN2jSpMkdua/aUlvPf12/jrXN398fH330kbHbuGlXrlzBkCFD4OjoaFLPNzV8DG5kMsaMGYPBgwfX6jZ9fX2RmZmJ4ODgWt1uZTXtvVu3bpgyZUqV5b/88gtUKpVyPTo6Gu3bt69Sl5mZib59+1a77c6dOyMzMxMajQZA7Yafc+fO4eOPP8arr76qLBszZgxUKpVycXFxQZ8+fXD48OFauc/aVh9DfHXvh8qvY10wtXBYF5YtW4YdO3YgLi7ums93ZmYmRowYgcDAQJiZmVX72b3aypUroVKpqvwtiI6ONvisqFQq5STpFS5duoSJEyfCx8cHNjY2aNOmDRYvXlzt/YgI+vbta/AfuQr+/v5V7uuVV1654fNB9QeDGzVq5ubm8PDwgIWFRbXrRQSlpaV3uKtb5+HhAbVaXe06KysreHh4GATA2vLVV18hPDy8ysmS+/Tpg8zMTGRmZuLPP/+EhYUFBgwYUOv3b0x3+j1Sl68j/c/p06fRpk0bBAcHX/P51ul0aNq0KV577TW0a9fuuttLSUnBiy++iK5du1a7/u6771Y+K5mZmThy5IjB+qlTp2LDhg3473//ixMnTmDq1KmYNGkSfv311yrb+uijj677/pgzZ47Bfb3++uvX7Z3qFwY3MlndunXD5MmT8fLLL8PZ2RkeHh6Ijo42qPn777/xwAMPwNraGkFBQdi4caPB+msdTvzjjz8QFhYGtVqNHTt2QEQwb948NG/eHDY2NmjXrh1+/vlng20dO3YM/fv3h6OjIxwcHNC1a1ecPn0a0dHRWLZsGX799Vflf7hbt2695cf9zTffYPbs2Th06JCyvW+++QYAqv0fdoWr96Js3boVTz75JLRarbKN6OhozJkzB23btq1y29DQULzxxhvX7GnlypUYNGhQleVqtRoeHh7w8PBA+/btMWPGDKSlpeH8+fNVeqpw8OBBqFQqJCcnGzzmZs2awdbWFg8//DBycnKq3Ndbb70FNzc3ODg44Omnn8Yrr7xSZa/k0qVL0aZNG1hbW6N169ZYtGiRsi4gIAAA0KFDB6hUKnTr1q3ax3o775Gr5eTkYPjw4fDx8YGtrS3atm2LFStWKOvHjBmDbdu24eOPP1Zeo+TkZIPnTKvVwsbGBhs2bDDY9qpVq2BnZ4dLly4BANLT0zFs2DA4OTnBxcUFDz30kMHze7Xk5GR0794dAODk5ASVSoUxY8YAKA8qkydPhpubG6ytrXH//fdj796913yMFQoKCjBixAjY29vDy8sLCxcuNFi/YMECtG3bFnZ2dvD19cWECROU3oHy0DNw4EA4OTnBzs4Od999N9avX6+sP378OPr16wd7e3u4u7sjKioKFy5cuG5PMTExuPvuu6FWq+Hv748PPvhAWdetWzd88MEH2L59+3XfC/7+/vj4448xatSo6+4BLSsrw8iRIzF79mw0b9682hoLCwvls+Lh4YGmTZsarI+Pj8fo0aPRrVs3+Pv745lnnkG7du2wb98+g7pDhw5hwYIF+Prrr6/Zj4ODg8F92dvbX7OW6qGbPrspkZGMHj1aHnroIeV6RESEODo6SnR0tJw6dUqWLVsmKpVKOclwWVmZBAcHS7du3eTAgQOybds26dChgwCQ1atXi4hIUlKSAJADBw6IyP9Owh4SEiKxsbHyzz//yIULF+TVV1+V1q1by4YNG+T06dOydOlSUavVsnXrVhEROXv2rDg7O8sjjzwie/fulcTERPn666/l5MmTUlBQIEOHDpU+ffpIZmamZGZmik6nq/YxRkREyAsvvFBl+erVq6Xi43rlyhWZPn263H333cr2rly5IiJSo8eWm5srOp1OPvroI3F0dFS2UVBQIGlpaWJmZiZ79uxR7vvQoUOiUqnk9OnT1fZ88eJFUalUsmvXruu+XgUFBfLss8/KXXfdJWVlZVV6qnDgwAEBIElJSSIismvXLlGpVDJ37lxJTEyUjz/+WJo0aSIajUa5zX//+1+xtraWr7/+WhITE2X27Nni6Ogo7dq1U2q+/PJL8fT0lJiYGDlz5ozExMSIs7OzfPPNNyIismfPHgEgmzZtkszMTMnJyan28d7qe6TyYz179qy8//77cuDAATl9+rR88sknYm5urjyPeXl5Eh4eLuPGjVNeo9LS0irbGTJkiDzxxBMGPQ4ZMkSGDx8uIiKXL1+Wli1bylNPPSWHDx+W48ePy4gRIyQwMLDa92FpaanExMQIAElMTJTMzEzJy8sTEZHJkyeLl5eXrF+/Xo4dOyajR48WJyenaz5XIiJ+fn7i4OCgvH4Vj/Pqk4F/+OGHsnnzZjlz5oz8+eefEhgYKM8995yyvn///tKrVy85fPiwnD59WtauXSvbtm0TEZGMjAxxdXWVmTNnyokTJ2T//v3Sq1cv6d69+zV72rdvn5iZmcmcOXMkMTFRli5dKjY2NsqJ43NycmTcuHESHh5+3ffC1a712RUReeONN2Tw4MEiUvVzISIya9YssbW1FU9PT/H395dhw4ZV+bw9++yzEhYWJmfPnhW9Xi+bN28We3t72bFjh1Jz+fJladOmjfzyyy8iYvj3oIKfn594eHiIs7OztGvXTt56661r/j2i+onBjUxGdcHt/vvvN6i59957ZcaMGSIi8scff4i5ubmkpaUp63///fcahZuKP3wiIpcuXRJra2uJi4szuK+xY8cqX44zZ86UgIAAKS4urlHv11KT4CZS/of+6lBSoabBTURk6dKlBuGnQt++fQ2+NKdMmSLdunW7Zs8VQSs1NdVg+ejRo8Xc3Fzs7OzEzs5OAIinp6ckJCQoNTUJbsOHD5c+ffoYbHvYsGEGvXfs2FGef/55g5ouXboYPEe+vr7y/fffG9S8+eabEh4eLiJVn69rudX3SHWPtbJ+/frJ9OnTlevVvR8qb2fVqlVib28vly9fFhERrVYr1tbWsm7dOhER+eqrryQwMFD0er2yDZ1OJzY2NvLHH39c9zFe3eulS5fE0tJSli9friwrLi4WLy8vmTdv3jUfk5+fX7WvX9++fa95mx9//FFcXFyU623btpXo6Ohqa//zn/9I7969DZalpaUpwbM6I0aMkF69ehkse+mllyQoKEi5/sILL0hERMQ1e6zsWp/dnTt3ire3t5w/f15Eqv9bsH79evn555/l8OHDsnHjRomIiBB3d3e5cOGCUqPT6WTUqFECQCwsLMTKykq+/fZbg+0888wzMnbsWOV6dcFtwYIFsnXrVjl06JAsWbJEXF1dDW5D9V/1P+whMhEhISEG1z09PZGdnQ0AOHHiBJo1awYfHx9lfXh4eI22GxYWpvz7+PHjKCoqQq9evQxqiouL0aFDBwDlh/e6du0KS0vLW3oc9cm4cePw1FNPYcGCBTA3N8fy5csNDiNVVlhYCACwtrausq579+7KD6gvXryIRYsWoW/fvtizZw/8/Pxq1M+JEyfw8MMPGywLDw83ODyYmJiICRMmGNTcd9992Lx5MwDg/PnzSEtLw9ixYzFu3DilprS09JZ/5H+z75HKysrK8O677+KHH35Aeno6dDoddDod7OzsbqqP/v37w8LCAmvWrMHjjz+OmJgYODg4oHfv3gCAhIQE/PPPP3BwcDC4XVFREU6fPl3j+zl9+jRKSkrQpUsXZZmlpSXuu+8+nDhx4rq3rfy5Cw8PNxhpumXLFrzzzjs4fvw48vPzUVpaiqKiIly+fBl2dnaYPHkynnvuOcTGxqJnz54YMmSI8tlPSEjAli1bqj3cd/r0abRq1arK8hMnTuChhx4yWNalSxd89NFHKCsrg7m5+Q2fj5ooKCjAE088gSVLlsDV1fWadVcPKGrbti3Cw8PRokULLFu2DNOmTQMAfPLJJ9i1axfWrFkDPz8/bN++HRMmTICnpyd69uyJNWvWYPPmzThw4MB1e5o6dary75CQEDg5OeHRRx/Fe++9BxcXl9t8xHQnMLiRSasclFQqFfR6PYDyH41XVtMfdF/95VmxvXXr1sHb29ugrmIggI2NTc2bvg5HR0dotdoqy/Py8uDo6Fgr93EjAwcOhFqtxurVq6FWq6HT6TBkyJBr1ld8IeXm5lb5XY6dnR3uuusu5XpoaCg0Gg2WLFmCt956C2Zm5T+zvfq1KikpMdhGda9jdSq/tlffruI1XLJkCTp27GhQd6tf0jf7Hqnsgw8+wIcffoiPPvpI+X3XlClTUFxcfFN9WFlZ4dFHH8X333+Pxx9/HN9//z2GDRumDLjR6/UIDQ3F8uXLq9y28ut1PRXPZ3XP860MlKi4TUpKCvr164fx48fjzTffhLOzM3bu3ImxY8cq74Wnn34akZGRWLduHWJjYzF37lx88MEHmDRpEvR6PQYOHIj33nuvyn14enpe87Fc7/1SW06fPo3k5GQMHDhQWVbxXrGwsEBiYiJatGhR5XZ2dnZo27Yt/v77bwDl/zl69dVXsXr1avTv3x9Aeeg6ePAg5s+fj549e2Lz5s04ffp0lZHiQ4YMQdeuXa/5u9pOnToBAP755x8GNxPB4EYNVlBQEFJTU5GRkQEvLy8A5T/wvZXtqNVqpKamIiIiotqakJAQLFu2DCUlJdXudbOyskJZWdkN76t169b4/fffqyzfu3cvAgMDb3p713OtbVhYWGD06NFYunQp1Go1Hn/8cdja2l5zOy1atICjoyOOHz9e7d6Nq6lUKpiZmSl76SqCQ2ZmJpycnACgynQcQUFB2LVrl8GyytcDAwOxZ88eREVFKcuu/tG2u7s7vL29cebMGYwcObLa3qysrADglp7XmrxHKtuxYwceeughPPHEEwDKv9D//vtvtGnTxqCnmvQzcuRI9O7dG8eOHcOWLVvw5ptvKuvuuece/PDDD3Bzc6tx+K/uubjrrrtgZWWFnTt3YsSIEQDKQ/a+fftuOA1Gda9f69atAZS/TqWlpfjggw+UIP/jjz9W2Yavry/Gjx+P8ePHY+bMmViyZAkmTZqEe+65BzExMfD397/m6PDKgoKCsHPnToNlcXFxaNWqVa3tbQPKP8+VR4e+/vrrKCgowMcffwxfX99qb6fT6XDixAllBGpJSQlKSkqU56eCubm5EgRfeeUVPP300wbr27Ztiw8//NAgOFZWsYfuWiGX6h8GN2qwevbsicDAQIwaNQoffPAB8vPz8dprr930dhwcHPDiiy9i6tSp0Ov1uP/++5Gfn4+4uDjY29tj9OjRmDhxIhYuXIjHH38cM2fOhEajwa5du3DfffchMDAQ/v7++OOPP5CYmAgXFxdoNJpqA96ECRPw6aef4vnnn8czzzwDGxsbbNy4EV999RW+++47pc7f3x9JSUk4ePAgfHx84ODgcM09O9fi7++PS5cu4c8//0S7du1ga2urBLSnn35aCRB//fXXdbdjZmaGnj17YufOnVXmp9LpdMjKygJQvkfu008/xaVLl5Qvkrvuugu+vr6Ijo7GW2+9hb///rvKYdnJkyejc+fOmDdvHgYPHozY2NgqoygnTZqEcePGISwsDJ07d8YPP/yAw4cPG4zgi46OxuTJk+Ho6Ii+fftCp9Nh3759yM3NxbRp0+Dm5qaM0PTx8YG1tXWND6PW5D1S2V133YWYmBjExcXByckJCxYsQFZWlkFw8/f3x+7du5GcnAx7e3s4OztXe/8RERFwd3fHyJEj4e/vr+xFAcpD3fvvv4+HHnoIc+bMgY+PD1JTU7Fq1Sq89NJLBj8lqODn5weVSoXffvsN/fr1g42NDezt7fHcc8/hpZdegrOzM5o1a4Z58+bhypUrGDt27HWfn7/++kt5/TZu3IiffvoJ69atA1Ae/EtLS7Fw4UIMHDgQf/31Fz7//HOD20+ZMgV9+/ZFq1atkJubi82bNyvP0/PPP48lS5Zg+PDheOmll+Dq6op//vkHK1euxJIlS6oNYtOnT8e9996LN998E8OGDUN8fDw+/fRTg1HGNVXxH41Lly7h/PnzOHjwIKysrBAUFARra+sqc0RW7BG7evmLL76IgQMHolmzZsjOzsZbb72F/Px85X3j6OiIiIgIvPTSS7CxsYGfnx+2bduGb7/9FgsWLAAAZYRoZc2aNVNGTMfHx2PXrl3o3r07NBoN9u7di6lTp2LQoEFo1qzZTT92MhLj/byO6OZUNzih8o+BH3roIRk9erRyPTExUe6//36xsrKSVq1ayYYNG27qB/wV9Hq9fPzxxxIYGCiWlpbStGlTiYyMVEa2iZSPvuzdu7fY2tqKg4ODdO3aVRkZlp2dLb169RJ7e3sBIFu2bLnm49y3b59ERkaKm5ubODo6SlhYmKxYscKgpqioSIYMGSJNmjQRAMpouJt9bOPHjxcXFxcBILNmzTK4j65duxr8WPt6NmzYIN7e3spoUZHy1wuAcnFwcJB7771Xfv75Z4Pb7ty5U9q2bSvW1tbStWtX+emnnwwGJ4iU/8Dex8dHbGxsZODAgTJ//vwqAyvmzJkjrq6uYm9vL0899ZRMnjxZOnXqZFCzfPlyad++vVhZWYmTk5M88MADsmrVKmX9kiVLxNfXV8zMzK75w/RbfY9Uvl1OTo489NBDYm9vL25ubvL666/LqFGjDN7jiYmJ0qlTJ7GxsVGek2vd/0svvSQA5I033qjSc2ZmpowaNUpcXV1FrVZL8+bNZdy4caLVaqt9jBXPp4eHh6hUKuUzVVhYKJMmTVK206VLF4MRyNXx8/OT2bNny9ChQ8XW1lbc3d3lo48+MqhZsGCBeHp6io2NjURGRsq3335r8BgnTpwoLVq0ELVaLU2bNpWoqCiDH+6fOnVKHn74YWnSpInY2NhI69atZcqUKQYDMir7+eefJSgoSCwtLaVZs2by/vvvG6yv6eCEq9/jFRc/P79r1lc3OGHYsGHi6ekplpaW4uXlJY888ogcO3bMoCYzM1PGjBkjXl5eYm1tLYGBgfLBBx9c9zFe/fdARCQhIUE6duwoGo1G2casWbOUgS1kGlQidXBgn4hMmoigdevWePbZZ5UfR9+ovlOnTpgyZQqGDx9+Bzq8sV69esHDw8NgTyURkanjoVIiMpCdnY3vvvsO6enpePLJJ2t0G5VKhS+//NJop7O6cuUKPv/8c0RGRsLc3BwrVqzApk2bqky4TERk6rjHjYgMqFQquLq64uOPP1Z+hF7fFRYWYuDAgdi/fz90Oh0CAwPx+uuv45FHHjF2a0REtYrBjYiIiMhE8FylRERERCaCwY2IiIjIRDC4EREREZkIBjciIiIiE8HgRkRERGQiGNyIiIiITASDGxEREZGJYHAjIiIiMhEMbkREREQm4v8Bm7fQsbWVFzQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "\n",
    "ax.plot(scale*xref,[result.indirect_utility(s*xref,pbar) for s in scale])\n",
    "ax.set_xlabel(f'Indirect Utility (Budget relative to base of {xref:.0f}')\n",
    "ax.set_ylabel(f'Utility')\n",
    "ax.set_title('Indirect Utility Function')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  },
  "org": null
 },
 "nbformat": 4,
 "nbformat_minor": 4
}