{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generational changes among the religious and non-religious\n",
    "\n",
    "Allen Downey\n",
    "\n",
    "Copyright 2020\n",
    "\n",
    "[MIT License](https://en.wikipedia.org/wiki/MIT_License)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In this notebook I use data from the GSS to explore differences in beliefs and attitudes between Christians and Nones (people with no religious affiliation) and look at generational changes in those differences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup\n",
    "\n",
    "If you are running this notebook in Colab, the following cell downloads the `empiricaldist` library.\n",
    "\n",
    "If you are running in another environment, you will need to install it yourself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If we're running in Colab, install empiricaldist\n",
    "import sys\n",
    "\n",
    "IN_COLAB = 'google.colab' in sys.modules\n",
    "\n",
    "if IN_COLAB:\n",
    "    !pip install empiricaldist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell downloads `utils.py`, which contains function I use in many data science projects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load some utility code\n",
    "import os\n",
    "\n",
    "file = 'utils.py'\n",
    "if not os.path.exists(file):\n",
    "    !wget https://github.com/AllenDowney/PoliticalAlignmentCaseStudy/raw/master/utils.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If everything we need is installed, the following cell should run without error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from empiricaldist import Cdf\n",
    "from utils import decorate\n",
    "from utils import underride\n",
    "from utils import values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data\n",
    "\n",
    "The following cell downloads an HDF5 file that contains the data we need.\n",
    "\n",
    "The HDF file is created by a notebook called `01_clean.ipynb`, which you should find in the same directory as this notebook, if you want to see the details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data file\n",
    "import os\n",
    "\n",
    "datafile = 'gss_eda.hdf5'\n",
    "if not os.path.exists(datafile):\n",
    "    !wget https://github.com/AllenDowney/PoliticalAlignmentCaseStudy/raw/master/gss_eda.hdf5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can read the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64814, 165)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gss = pd.read_hdf(datafile, 'gss')\n",
    "gss.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1972    1613\n",
       "1973    1504\n",
       "1974    1484\n",
       "1975    1490\n",
       "1976    1499\n",
       "1977    1530\n",
       "1978    1532\n",
       "1980    1468\n",
       "1982    1860\n",
       "1983    1599\n",
       "1984    1473\n",
       "1985    1534\n",
       "1986    1470\n",
       "1987    1819\n",
       "1988    1481\n",
       "1989    1537\n",
       "1990    1372\n",
       "1991    1517\n",
       "1993    1606\n",
       "1994    2992\n",
       "1996    2904\n",
       "1998    2832\n",
       "2000    2817\n",
       "2002    2765\n",
       "2004    2812\n",
       "2006    4510\n",
       "2008    2023\n",
       "2010    2044\n",
       "2012    1974\n",
       "2014    2538\n",
       "2016    2867\n",
       "2018    2348\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values(gss['year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For modeling purposes, I'll use data from 1998 to 2018 and respondents born during or after 1940.\n",
    "\n",
    "These choices are a compromise between using more data, so the results are more likely to be statistically valid, and using recent data, so the models are not too influenced by irrelevant history.\n",
    "\n",
    "I classify as \"Christian\" anyone who reports that their religious affiliation is Catholic, Protestant, or Christian."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_df(df):\n",
    "    # compute quadratic and cubic terms for the models\n",
    "    df['y2'] = (df['year']-2004)**2\n",
    "    df['c2'] = (df['cohort']-1970)**2\n",
    "    df['c3'] = (df['cohort']-1970)**3\n",
    "    \n",
    "    # classify religious affiliation\n",
    "    df['christian'] = df['relig'].isin([1,2,11])\n",
    "    df['none'] = (df['relig'] == 4)\n",
    "    \n",
    "    # select recent years and generations\n",
    "    recent = (df['year'] >= 1998)\n",
    "    notold = (df['cohort'] >= 1940)\n",
    "    return df[recent & notold]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For exploration, I'll use this subset of the data without resampling, which means it oversamples some groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25246, 170)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = prepare_df(gss)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For inference, I'll use 101 resamplings of the data, weighted to be representative of the adult population in the U.S."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import resample_rows_weighted\n",
    "\n",
    "dfs = None\n",
    "dfs = [resample_rows_weighted(df, df['wtssall'])\n",
    "       for i in range(101)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the groups\n",
    "\n",
    "To see what the groups look like, I select Christians and Nones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18900, 170)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "christian = df[df['christian']]\n",
    "christian.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4774, 170)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "none = df[df['none']]\n",
    "none.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the distribution of birth years looks like for the two groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cdf.from_seq(christian['cohort']).plot(label='Christian')\n",
    "Cdf.from_seq(none['cohort']).plot(label='None')\n",
    "\n",
    "decorate(xlabel='Birth year',\n",
    "         ylabel='CDF',\n",
    "         title='Distribution of birth year')\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Christians in this dataset come from earlier cohorts, so we have to control for that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cdf.from_seq(christian['year']).plot(label='Christian')\n",
    "Cdf.from_seq(none['year']).plot(label='None')\n",
    "\n",
    "decorate(xlabel='Interview year',\n",
    "         ylabel='CDF',\n",
    "         title='Distribution of interview year')\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, the fraction of Christians was declining over the observation period, so the Christians in the dataset were more likely to be observed earlier.  We'll have to control for that, too."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting variables\n",
    "\n",
    "The following is a list of the variables we'll explore.\n",
    "\n",
    "For each variable, I identify a response or list of responses to consider, and provide text that describes what that response means.\n",
    "\n",
    "I do my best to paraphrase the wording of the question accurately, but if you have any questions, you can [consult the documentation](https://gssdataexplorer.norc.org/projects/52787/variables/data_cart)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables = [\n",
    "    # allow to speak\n",
    "    ('spkmil', 1, 'Anti-democratic\\nmilitarist'),   \n",
    "    ('spkmslm', 1, 'Anti-U.S.\\nMuslim clergyman'),\n",
    "    ('spkath', 1, 'Opponent of churches\\nand religion'),\n",
    "    ('spkcom', 1, 'Communist'),\n",
    "    ('spkrac', 1, 'Racist'),\n",
    "    ('spkhomo', 1, 'Homosexual'),\n",
    "\n",
    "    # allow to teach at a college or university\n",
    "    ('colmil', 4, 'Anti-democratic\\nmilitarist'),\n",
    "    ('colmslm', 4, 'Anti-U.S.\\nMuslim clergyman'),\n",
    "    ('colath', 4, 'Opponent of churches\\nand religion'),\n",
    "    ('colcom', 5, 'Communist'),    # not fired\n",
    "    ('colrac', 4, 'Racist'),\n",
    "    ('colhomo', 4, 'Homosexual'),\n",
    "\n",
    "    # do not remove from library\n",
    "    ('libmil', 2, 'Anti-democratic\\nmilitarist'),\n",
    "    ('libmslm', 2, 'Anti-U.S.\\nMuslim clergyman'),\n",
    "    ('libath', 2, 'Opponent of churches\\nand religion'),\n",
    "    ('libcom', 2, 'Communist'),\n",
    "    ('librac', 2, 'Racist'),\n",
    "    ('libhomo', 2, 'Homosexual'),\n",
    "\n",
    "    # items related to sex\n",
    "    ('homosex', 1, 'Same-sex relations\\nalways wrong'),\n",
    "    ('premarsx', 1, 'Premarital sex\\nalways wrong'),\n",
    "    ('xmarsex', 1, 'Extramarital sex\\nalways wrong'),\n",
    "    ('teensex', 1, 'Teen sex\\nalways wrong'),\n",
    "    ('sexeduc', 1, 'Favor sex education\\nin schools'),\n",
    "    \n",
    "    # items related to abortion\n",
    "    ('abany', 1, 'A pregnant woman wants it\\nfor any reason'),\n",
    "    ('abdefect', 1, 'There is a strong chance\\nof a serious defect'),\n",
    "    ('abnomore', 1, 'She is married and\\ndoes not want more children'),\n",
    "    ('abhlth', 1, 'Her health is seriously endangered'),\n",
    "    ('abpoor', 1, 'She has very low income and\\ncannot afford more children'),\n",
    "    ('abrape', 1, 'She became pregnant\\nas a result of rape'),\n",
    "    ('absingle', 1, 'She is not married and\\ndoes not want to marry the man'),\n",
    "    \n",
    "    # other items related to public policy\n",
    "    ('cappun', 2, 'Oppose death penalty\\nfor murder'),\n",
    "    ('gunlaw', 1, 'Favor permit to buy gun'),\n",
    "    ('grass', 1, 'Marijuana should be\\nmade legal'),\n",
    "    ('divlaw', 1, 'Divorce should be\\neasier to obtain'),\n",
    "    ('prayer', 1, 'Approve SCOTUS ruling\\nprohibiting school prayer'),\n",
    "    ('letdie1', 1, 'Allow doctor to end life\\nof terminal patient'),\n",
    "    ('racopen', 2, 'Favor law barring\\nhousing discrimination'),\n",
    "    ('pornlaw', [2,3], 'Pornography should be legal'),\n",
    "    ('affrmact', [1,2], 'Favor affirmative action\\nfor blacks'),\n",
    "\n",
    "    # items related to spending\n",
    "    ('natroad', 1, 'Highways and bridges'),\n",
    "    ('natsoc', 1, 'Social Security'),\n",
    "    ('natmass', 1, 'Mass transportation'),\n",
    "    ('natpark', 1, 'Parks and recreation'),\n",
    "    ('natchld', 1, 'Assistance for child care'),\n",
    "    ('natsci', 1, 'Supporting scientific research'),\n",
    "    ('natenrgy', 1, 'Developing alternative\\nenergy sources'),\n",
    "    ('natspac', 1, 'Space exploration'),\n",
    "    ('natenvir', 1, 'Improving and protecting\\nthe environment'),\n",
    "    ('natheal', 1, \"Improving and protecting\\nthe nation's health\"),\n",
    "    ('natcity', 1, 'Solving the problems of\\nthe big cities'),\n",
    "    ('natcrime', 1, 'Halting the rising\\ncrime rate'),\n",
    "    ('natdrug', 1, 'Dealing with drug addiction'),\n",
    "    ('nateduc', 1, \"Improving the nation's\\neducation system\"),\n",
    "    ('natrace', 1, 'Improving the conditions\\nof Blacks'),\n",
    "    ('natarms', 1, 'The military, armaments\\nand defense'),\n",
    "    ('nataid', 1, 'Foreign aid'),\n",
    "    ('natfare', 1, 'Welfare'),\n",
    "\n",
    "    # confidence in institutions\n",
    "    ('confinan', 1, 'Banks and financial instituions'),\n",
    "    ('conbus', 1, 'Major companies'),\n",
    "    ('conclerg', 1, 'Organized religion'),\n",
    "    ('coneduc', 1, 'Education'),\n",
    "    ('confed', 1, 'Executive branch of\\nthe federal government'),\n",
    "    ('conlabor', 1, 'Organized labor'),\n",
    "    ('conpress', 1, 'Press'),\n",
    "    ('conmedic', 1, 'Medicine'),\n",
    "    ('contv', 1, 'Television'),\n",
    "    ('conjudge', 1, 'U.S. Supreme Court'),\n",
    "    ('consci', 1, 'Scientific community'),\n",
    "    ('conlegis', 1, 'Congress'),\n",
    "    ('conarmy', 1, 'Military'),\n",
    "\n",
    "    # religious beliefs\n",
    "    ('god', 6, 'Know God exists with no doubts'),\n",
    "    ('reborn', 1, 'Had a born again experience'),\n",
    "    ('savesoul', 1, 'Tried to convince others\\nto accept Jesus'),\n",
    "    ('bible', 1, 'Bible is the actual word of God\\nto be taken literally'),\n",
    "    ('postlife', 1, 'Believe there is a life after death'),\n",
    "\n",
    "    ('relpersn', [1,2], 'Considers self very or\\nmoderately religious'),\n",
    "    ('sprtprsn', [1,2], 'Considers self very or\\nmoderately spiritual'),\n",
    "    ('relexp', 1, 'Had a religious or spiritual\\nexperience that changed life'),\n",
    "    ('relactiv', [8,9,10,11], 'Church activities weekly or more\\nnot including services'),\n",
    "    ('pray', [1,2,3,4], 'Prayer weekly or more often'),\n",
    "    ('attend', [7,8], 'Attend religious services\\nweekly or more often'),\n",
    "    \n",
    "    # outlook on people\n",
    "    ('helpful', 1, 'People try to be helpful'),\n",
    "    ('fair', 2, 'People try to be fair'),\n",
    "    ('trust', 1, 'People can be trusted'),\n",
    "    ('fear', 2, 'Not afraid to walk alone at night'),\n",
    "\n",
    "    # miscellaneous\n",
    "    ('spanking', [1,2], 'Spanking sometimes\\nnecessary'),\n",
    "    \n",
    "    # gender roles and work\n",
    "    ('fepol', 1, 'Agree men are emotionally\\nmore suited for politics'),    \n",
    "    ('fejobaff', [1,2], 'Favor preferential hiring\\nand promotion of women'),    \n",
    "    ('fehire', [1,2], 'Favor special effort to hire\\nand promote qualified women'),    \n",
    "    ('fechld', [1,2], 'Working mother can have warm\\nsecure relationship with child'),    \n",
    "    ('fepresch', [1,2], 'Preschool child is likely\\nto suffer if mother works'),    \n",
    "    ('fefam', [1,2], 'Much better if man achieves and\\nwoman takes care of family'),    \n",
    "    ('discaffm', [1,2], 'Likely that a less qualified\\nwoman gets job or promotion'),    \n",
    "    ('discaffw', [1,2], 'Likely that a less qualified\\nman gets job or promotion'),    \n",
    "    ('meovrwrk', [1,2], 'Family life suffers because men\\nconcentrate too much on work'), \n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell makes maps from variable names to values and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_map = {}\n",
    "value_map = {}\n",
    "\n",
    "for varname, value, label in variables:\n",
    "    value_map[varname] = value\n",
    "    label_map[varname] = label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Period and cohort effects\n",
    "\n",
    "First I select the subset of one group where the dependent variable is valid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "natmass 1\n"
     ]
    }
   ],
   "source": [
    "varname = 'natmass'\n",
    "value = value_map[varname]\n",
    "print(varname, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "group = christian.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16692, 170)"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid = group.dropna(subset=[varname]).copy()\n",
    "valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the fraction of this variable that has the selected value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3707764198418404"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = np.atleast_1d(value)\n",
    "(valid[varname].isin(value)).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For logistic regression, we need the dependent variable to be 0 or 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10503\n",
       "1     6189\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid['y'] = (valid[varname].isin(value)).astype(int)\n",
    "valid['y'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the changes look like over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils import plot_series_lowess\n",
    "\n",
    "by_year = valid.groupby('year')['y'].mean()\n",
    "plot_series_lowess(by_year, 'C2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's what they look like by cohort."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_cohort = valid.groupby('cohort')['y'].mean()\n",
    "plot_series_lowess(by_cohort, 'C3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the model\n",
    "\n",
    "Now we can run logistic regression with year and cohort as explanatory variables.\n",
    "\n",
    "I consider two versions of this model, with and without quadratic terms.  It seems like the model with quadratic terms does a better job of capturing the period and cohort effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.656549\n",
      "         Iterations 5\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>  <td> 16692</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td> 16689</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     2</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 10 Jan 2020</td> <th>  Pseudo R-squ.:     </th> <td>0.004275</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>09:49:24</td>     <th>  Log-Likelihood:    </th> <td> -10959.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -11006.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>3.663e-21</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    2.8315</td> <td>    5.215</td> <td>    0.543</td> <td> 0.587</td> <td>   -7.390</td> <td>   13.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year</th>      <td>    0.0095</td> <td>    0.003</td> <td>    3.706</td> <td> 0.000</td> <td>    0.004</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cohort</th>    <td>   -0.0115</td> <td>    0.001</td> <td>   -9.511</td> <td> 0.000</td> <td>   -0.014</td> <td>   -0.009</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                16692\n",
       "Model:                          Logit   Df Residuals:                    16689\n",
       "Method:                           MLE   Df Model:                            2\n",
       "Date:                Fri, 10 Jan 2020   Pseudo R-squ.:                0.004275\n",
       "Time:                        09:49:24   Log-Likelihood:                -10959.\n",
       "converged:                       True   LL-Null:                       -11006.\n",
       "Covariance Type:            nonrobust   LLR p-value:                 3.663e-21\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.8315      5.215      0.543      0.587      -7.390      13.053\n",
       "year           0.0095      0.003      3.706      0.000       0.004       0.015\n",
       "cohort        -0.0115      0.001     -9.511      0.000      -0.014      -0.009\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "formula = ('y ~ year + cohort')\n",
    "model = smf.logit(formula, data=valid).fit()\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.655773\n",
      "         Iterations 5\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>  <td> 16692</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td> 16687</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 10 Jan 2020</td> <th>  Pseudo R-squ.:     </th> <td>0.005451</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>09:49:25</td>     <th>  Log-Likelihood:    </th> <td> -10946.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -11006.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>5.376e-25</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -35.1692</td> <td>    9.292</td> <td>   -3.785</td> <td> 0.000</td> <td>  -53.381</td> <td>  -16.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year</th>      <td>    0.0289</td> <td>    0.005</td> <td>    6.254</td> <td> 0.000</td> <td>    0.020</td> <td>    0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>y2</th>        <td>   -0.0023</td> <td>    0.000</td> <td>   -5.035</td> <td> 0.000</td> <td>   -0.003</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cohort</th>    <td>   -0.0118</td> <td>    0.001</td> <td>   -8.130</td> <td> 0.000</td> <td>   -0.015</td> <td>   -0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c2</th>        <td>-3.484e-05</td> <td> 8.19e-05</td> <td>   -0.425</td> <td> 0.671</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                16692\n",
       "Model:                          Logit   Df Residuals:                    16687\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Fri, 10 Jan 2020   Pseudo R-squ.:                0.005451\n",
       "Time:                        09:49:25   Log-Likelihood:                -10946.\n",
       "converged:                       True   LL-Null:                       -11006.\n",
       "Covariance Type:            nonrobust   LLR p-value:                 5.376e-25\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    -35.1692      9.292     -3.785      0.000     -53.381     -16.958\n",
       "year           0.0289      0.005      6.254      0.000       0.020       0.038\n",
       "y2            -0.0023      0.000     -5.035      0.000      -0.003      -0.001\n",
       "cohort        -0.0118      0.001     -8.130      0.000      -0.015      -0.009\n",
       "c2         -3.484e-05   8.19e-05     -0.425      0.671      -0.000       0.000\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = ('y ~ year + y2 + cohort + c2')\n",
    "model = smf.logit(formula, data=valid).fit()\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.655770\n",
      "         Iterations 5\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>  <td> 16692</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td> 16686</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     5</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 10 Jan 2020</td> <th>  Pseudo R-squ.:     </th> <td>0.005456</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>09:49:25</td>     <th>  Log-Likelihood:    </th> <td> -10946.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -11006.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>2.990e-24</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -34.0023</td> <td>    9.919</td> <td>   -3.428</td> <td> 0.001</td> <td>  -53.443</td> <td>  -14.562</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year</th>      <td>    0.0289</td> <td>    0.005</td> <td>    6.261</td> <td> 0.000</td> <td>    0.020</td> <td>    0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>y2</th>        <td>   -0.0024</td> <td>    0.000</td> <td>   -5.042</td> <td> 0.000</td> <td>   -0.003</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cohort</th>    <td>   -0.0124</td> <td>    0.002</td> <td>   -5.194</td> <td> 0.000</td> <td>   -0.017</td> <td>   -0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c2</th>        <td>-1.539e-05</td> <td>    0.000</td> <td>   -0.154</td> <td> 0.878</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c3</th>        <td>  1.77e-06</td> <td> 5.26e-06</td> <td>    0.336</td> <td> 0.737</td> <td>-8.55e-06</td> <td> 1.21e-05</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                16692\n",
       "Model:                          Logit   Df Residuals:                    16686\n",
       "Method:                           MLE   Df Model:                            5\n",
       "Date:                Fri, 10 Jan 2020   Pseudo R-squ.:                0.005456\n",
       "Time:                        09:49:25   Log-Likelihood:                -10946.\n",
       "converged:                       True   LL-Null:                       -11006.\n",
       "Covariance Type:            nonrobust   LLR p-value:                 2.990e-24\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    -34.0023      9.919     -3.428      0.001     -53.443     -14.562\n",
       "year           0.0289      0.005      6.261      0.000       0.020       0.038\n",
       "y2            -0.0024      0.000     -5.042      0.000      -0.003      -0.001\n",
       "cohort        -0.0124      0.002     -5.194      0.000      -0.017      -0.008\n",
       "c2         -1.539e-05      0.000     -0.154      0.878      -0.000       0.000\n",
       "c3           1.77e-06   5.26e-06      0.336      0.737   -8.55e-06    1.21e-05\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = ('y ~ year + y2 + cohort + c2 + c3')\n",
    "model = smf.logit(formula, data=valid).fit()\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following plot shows the data grouped by cohort and the model for someone interviewed in 2008.\n",
    "\n",
    "We can use it to confirm that the model is capturing the cohort effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.arange(1940, 2000)\n",
    "\n",
    "dfp = pd.DataFrame()\n",
    "dfp['cohort'] = xs\n",
    "dfp['year'] = 2008\n",
    "dfp['y2'] = (dfp['year']-2004)**2\n",
    "dfp['c2'] = (dfp['cohort']-1970)**2\n",
    "dfp['c3'] = (dfp['cohort']-1970)**3\n",
    "\n",
    "plot_series_lowess(by_cohort, 'C3')\n",
    "ys = model.predict(dfp)\n",
    "plt.plot(xs, ys, color='C7', label='Model at year 2008')\n",
    "\n",
    "plt.xlim(1938, 2002)\n",
    "decorate(xlabel='Birth year',\n",
    "         ylabel='Fraction',\n",
    "         title='Mean response by year of birth',\n",
    "         loc='lower left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('generation_by_cohort.png', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following plot shows the data grouped by year along with predictions for people born in 1968 and 1993.\n",
    "\n",
    "We can use it to confirm that the model captures the period effect, and we can see the generational difference as measured by the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NKBKiI9Jx2TU7VeVE9wn2tu9lT/se9rbvZV/7Pg6dOkTc/T7mFS+T0iaR4cvAIx6y/FlcX3x9f2LJ9p+ZaPoSUYYv45Kb5q7UB6mq0hvvPSsBNnQ1UNtSyzut71DbUttfG8z0ZTKvYB4LCxcyv3A+CwsXUpxVPCabIFP1nhrtRGS7qlYP1/lS2SRozGUpyS6hI9zRX7MCCEVClGSXpPR5h6Nm1xPr4eDJg/2Jqe9xKnyqv0xxVjFz8uZw87Sb2XFiBzNyZ5CXntefoPuS859W/+kw/4anXclmVxHpbxZMtnDyQm6tcO4NEE/EOdxxmHda3uHtlrepbanl3979N6KJKAD56fnML5jfn8QWFC4gPz1/WONMhZFsLRhPLGGZUWt1+WrW1a4DOONb6d2z707p8zaEGijKKjpjW3Ygm4bQ2eN+VJWGrgb2tu09IzHVddaR0AQAGb4MZufN5pbptzAnbw5z8uYwO282wcDpRPyPO/+RjnDHGbXJsZKch5PX42XmpJnMnDSTu2Y5l21G4hH2tu/lnZZ3+h+vHHulv3mxNLuU+QVO8lpQuIB5BfPI8mdd8djP52LeU+bcLGGZUasqv4oH5j9wRnPV3bPvTvkH6blqdoUZhew8sbM/Ke1r38fe9r2EoqH+ctNypjEnbw63Vd7Wn5zKcsrOSESDGQvJeaQEvIH+ZNSnK9rFrtZd/QmstrWW3xz5DeAMIJmRO6P/mAWFC5iTN2dEB3WMVGvBeGN9WMYMsKdtDz94+wd0RbsIRUOc6D5Ba2/rGSPtsv3Z/TWl5FrT5XyzH4lO+b6aXfIHad/6ZxZ/JqXPPdzaetuc5NVS6zQnttbS1tsGnB7UkZzEKnMrL/hFYrhYH9Ywnc8SljGOWCLGa42vsf7Qep4/8jw9sR4AJqVNYm7+XK6dei1V+VXMyZszZjv/BxrPH6SqSmNXY39f2MBBHblpuSwtWsqy4mXcUHwD04LTLnDGyzMRRwlawjoHS1jmUqgqbza/yfpD63nu8HO09baR48/hlopbuK3yNhZNXnTWIIHxZiJ9kPYN6ni75W22Hd/GlsYtNHU3AU5f2LLiZSwrWcb1RdeTl543wtGOfTZK0JhhsK99H+sPrefZQ89yLHSMNG8aK8tWcvuM21lRumJCXcRalV81bhPUQF6Pl1gi1t9Hd/esu5lbMJem7ia2NGzhN4d/w8/2/QyAuflznQRWvIwlU5eM+y8uY4HVsMyEcSx0jGcPPcv6Q+vZ174Pr3hZVrKMOyrv4OZpN5MdyB7pEE2KXagJNJaIsat1F1sat7ClcQs7Tuwglojh9/hZMmVJfwKbVzBvXF3UnCrWJHgOlrDMYFp7WvnNkd+w/uB6djbvBGDx5MXcPuN23jf9fRRkFIxwhOZKuthBJt3Rbnac2NGfwHa37QYgJ5DT3/+1rHgZ04PTx0Wf5nCzJkFjLqAr2sXGuo08c+gZtjRsIa5xZk2axaPXPMrairWU5ZSNdIgThqoSiyQId0cJd8fcx+nl3u4osXCcRELRuJJIuI+489Ck9bOW44oIeP0efAEvXp8HX8CDz+/B5/e62z3OT78Xn99D26E4kzKLUT8QSEB6jOz0IA2dgw/jz/Rnsrx0OctLlwPOSMTXG19nS+MWNjdspqauBoCirKL+5HV98fUUZhReqZd4QrEalhkXIvEIrxx7hfWH1vPi0RcJx8OUZJVw+4zb+6+JMsMjHk0QOtlLqC1MZ3svobZeuk5FzkpGfcuJ+Pk/Y3wBDx6vB49HEK/g8Uj/stcriEfwuNsHLqtCPBonFk0QjyaIRRLEonFnOZq44HP38yTICmaQkeMnIyfg/MwOJK0HyMg+vc+f5jQH1nfWs7lxM1sat/Ba42t0RJw5aWfnze5PYNVTq8n0Z17Waz5WWZPgOVjCmnjiiTjbmrbx7KFn+c2R39AZ6SQvLY9bK27ljhl3sGjyImumuUiaULo7I4TawoTae+ls6z1jubM9TE9H5Kzj0jJ9pGX5ScvwOcuZftKyfKT3Lff9zPCRlnW6TCDDh8eTur9RIqHEInHiMSeZ7WvZzy92P022J4fMRDa9nTEiXXGuzr6GjFgOvZ0Rujuj9Iacn7Hw4PfcDmT4CBamk1uYQXByBsHCDLILArT4GtjRs5XXTmxhR9MOIokIPvFx9eSrWVbiDJ9fULgAn2diNG5ZwjoHS1gTg6qyq3UXzxx6hl8f+jXNPc1k+jJZXb6a22fczvXF1+P3+Ec6zFEv0hOjrbGLtsYu2hu7aGvs5mRTF6GTYRKxMz8TfAEPOfnpZOelkZ2ffuZynrPsC4ydAQgXM4w/GonT0xmhpzN6xs9QWy+nWnrpaOmho7XnjNdMPEJOfhrZBWmEs0I0eevZG3+Hd6Nvciq9hbRMH8tLlnNj2Y0sL10+Ju6FeKksYZ2DJazx7dCpQ/0j/I50HMHv8bOidAW3zbiNlWUrbcjxOYS7o7Q1djtJqaGLtuNOggq1n573yuvzMKkok7yiTHIGJqT8dNIyfVZTPQ9NKKGTYSd5tfRwqrmHjr5k1tJDT2f0jPLx9DDNGfUczzhMa2YDBWVZXDNnASun38jc/Lnj6rW2hHUOlrDGn4MnD7KhbgMbjmzg3bZ3EYSlRUu5fcbtrC5fTW5a7kiHOGrEYwlaj4U4caTTrTE5j+5Tp5vvfH4PecVZ5BVnkl+cRX5xFnnFWQQLM1LaLDfRRXpidLT20NHcy8nmbtobumipD9HaGKJvlr+4xGjLbKQrp42Csizmzp7BexdVUzBp0sgGf5ksYZ2DJayxT1Wpba2lpq6GDUc2cLjjMABXT76a901/H7dV3saUzCkjG+QooKp0tPRy4nAHTYc6aDp8iua6EPGYc3d4X5qX/KLM/oTUn5gK0hFLTKNGPJ7g5PFuWupD1B9u5tChRrqOx/D1pveXiaR3kzFVqJhRxIwZpRRV5pJTkD5mamGWsM7BEtbYFEvE2HFiBxuObGDj0Y0c7zqOV7xcV3Qdq8tXs6p81YRPUr1dUSc5uY8Thzv6m5m8fg9TynOYUhlkakWQKdODlpjGuFMnu9j6zlvU7t3PifpO0k4GmdQzFa86/YT+HCibVUDprHyKZ+VSWJaNx3tlbuJ7sSxhnYMlrLEjHA+zpWELNXU1vHD0BU6GT5LmTeM9Je9hzfQ1rCxbOWGb+xLxBC31IafmdMhJUCeb3LvEC+RNzWRqZZCplblMrQiSX5qFd5R+WJnhcSx0jJcOb2Lbu29z4nAnhSfLKQ7NIDvs3OvQGxCKKidRPDOX4lm5FFXmEsgYHaMQx1TCEpG1wHcAL/B9Vf3WgP2fAx4CYkAz8KCqHnH3fRu4A/AAzwOP6nmCtYQ1unVFu3i5/mU21G3g5fqX6Y51k+3PZuW0lawuX83ykuUT8loVVaWtsYv63e3U726nYW87kV6nYyMjGGBqRdBNUE7tKW2UfBCZkdET62Hr8a1sqt/E6/t34GnKoqhzBhXdc8nuLERUEIH80uz+BFY8cxI5+ekXPnkKjJmEJSJeYC9wC1APbAXuV9VdSWVuBl5T1W4R+TRwk6reKyLvAf4GuNEt+grwRVV98VzPZwlr9GnvbefFoy+yoW4Dmxs2E01EyU/PZ1X5KtaUr2Fp0VL83ok3BL2jtac/QR3b0063e11TsDCdsqvyKavKo2hmLtl5aWOmr8JcearKgZMH2HRsE5vqN1Hb+C4FHWVUdM9jVnghma0FaNR5/2TnpVF2VR7l8wuYdlU+6dlX5v9uLN2aaSmwX1UPAojIk8BdQH/CUtUXkspvAT7atwtIBwKAAH6gKYWxmmFyvOs4NXU11NTVsL1pOwlNUJpdyv1X3c/q8tUsmrxowt00tCcU4diekxzd3Ub97nY6mp15tjJy/E6CuiqPsqo8goU2NN8MnYgwK28Ws/Jm8eCCBzkVPsXmxs28XP8yvzr2OO09JynsLuWaxHuZ0TOfAzuj7N58HASmVgSZNi+f6fMLmDI9Z9T2gQ2UyoRVChxNWq8Hrj9P+U8AzwKo6mYReQFoxElY/0dV3x14gIg8DDwMUF5ePkxhm4t18NRBNtZtZMORDdS21gIwa9IsPrnwk6yZvoaqvKoJVVOIReMc23uS+nfbqN/TTsvREAD+dC+lc/K4+qYyyq7KI78ka0K9Lia1ctNyWVuxlrUVa0lognda3uHlYy+zqX4Tz7X+mK/f9Q2W+W6iblcbdbWtbF9/mG3PHCYt00fZVfmUz8+nfF4+2Xkj03w4FKlsEvxd4FZVfchd/z1gqar+4SBlPwo8AqxU1bCIzMLp+7rXLfI88AVV3XSu57MmwStHVdnVtouaI05N6uCpgwBcXXg1q8pXsbp8NRW5FSMb5BXWG4py+J0WDr3ZQt2uNmLhOB6fUDwzl7IqpxY1lr7JmvGlubuZTH8mWf6s/m29XVHqd7dTV9tK3a42uk46F5Pnl2RRPi+f8nkFFM/Oxee/9BaRsdQkWA8kzzldBpx1S2QRWQN8CTdZuZvvBraoasgt8yywDDhnwjKpFU/E2XFiR39zX2NXI17xUj21mvuuuo9V01YxNWvqSId5RXW09HDozRYOvdlMw/5TaELJyg1QdX0RlYsKKZk9Cf8YumWRGb8mZ04+a1t6lp9Z105h1rVTnME/DV39ta+3Xqxn54aj+PweSubkcd37KyiqHPmRu6lMWFuB2SJSCRwD7gM+nFxARJYA3wXWquqJpF11wCdF5K9wmgRXAv+QwljNIKKJKFsbt/J83fNsrNtIW28bAU+A95S+h88s/gw3ld3EpPSxfSX+xVBVmus6+5NU67EuwPlGes2t5VQumsyU8hy7BsqMOSJCQWk2BaXZLLmlnGg4zrG97Rzd1UbdrraRDq9fyhKWqsZE5BHgOZxh7T9Q1VoR+RqwTVWfxhkJmA085bbl16nqncBPgVXA2zgDMH6tqr9KVazmtN5YL5sbNrOhbgMvHH2Bzkgnmb5Mbiy7kTXT17CidMWEGn4ejyU4tredQ2+2cPitFkLtYUSgeNYklv+3WVQuKiR38sR5PczE4E/zUrGwkIqFzrxeo+V6Xbtw2DjXSB17mQ1HNrCpfhM9sR6CgSA3TbuJW6bfwg0lN5DmTRvpMK+YeCxBXW0r+7Y2ceSdViK9cXwBD+XzCqhcVMj0hQVkZAdGOkxjRr2x1IdlRrFT4VO8VP8Szx95nlePvUokESE/PZ/3z3g/a6av4bqi6ybUNB2aUBoPnGTP600c2H6CcHeM9Gynjb9y0WTKrsobU1NoGNPnYqZTGe0sYU0gLT0tbKzbSE1dDa83vk5MYxRlFfGhqg+xZvoaFk9ePOGukWo9FmLv68fZu7WJUFsYX8BD5aLJzFk6lWnz8u22R2ZM29O2h3W16wgGghRlFdER7mBd7ToemP/AmExalrDGueNdx9lwZAMb6jbwRtMbKEp5Tjkfm/8xbpl+C/ML5k+4a4E623rZ+/px9m1tovVYF+IRyuflc8PvzKTi6kIC6fZvYcaHmroagoEgwbQgQP/PmroaS1hmdKjrqOP5I89TU1fD2y1vA86FvJ9a9CnWTF/D7EmzJ1yS6u2Ksn/7Cfa+fpzG/acAKJoR5Mb75jDr2ilk5FiflBl/GkINFGUVnbEtO5BNQ+isK4zGBEtY44Cqsv/k/v6a1N72vQDML5jPo9c8ypryNRPuQl5wBk8cerOFPa8dp662lURcySvK5Po7K5l9XRG5k+1WSGZ8K8kuoSPc0V+zAghFQpRkl4xgVJfOEtYlGumOTFVlV+uu/prU4Y7DCMKSKUv4s+v+jNXlq8fsm/JynWzqZtcrDeze0khPZ5TM3AALby6jamkRhdOyJ1zt0kxcq8tXs652HeDUrEKREB2RDu6effcIR3ZpbFj7JUjuyEx+E6S6I/Ncd5u4rug6bpl+CzdPu3nQK9ongngswcGdzdS+3MCxPe2IR6i8upB57y1h2rx8mwLeTFgj+eXahrWPAleyIzMSj7ClcQsb6zbywtEXaOttw+/xc0PJDXx60ae5edrNE+puEwMNrE3l5Kdz/Z0zmPueYrImTZxrx4w5l6r8qjE5wGIwlrAuQao7Mvsu5N14ZCObjm2iK9pFlj+LG0tvZNX0VawoXXHGTSwnmnPWplaUMLUMZCcAACAASURBVG2u1aaMGa8sYV2CVHRk9k12WFNXw+aGzf0X8q6tWMuq8lUsK15GwBvob44cDxcBXqxz1qaWF5OVa7UpY8Y7S1iXYLg6MhtDjWw8uvGMyQ5Lskr4UNWHWF2+miVTlpxxIe94uwhwKBLxBAd3tvDOpmNn1Kbmu7Upu9GsMROHJaxLUJVfxQPzHzijI/Pu2XcPKWkcPHmwf9BE8mSHDy18iNXlq5mbP/eco9jG20WA5xPujrLrlUbeeuEoofaw1aaMMZawLtVQOzJVldrWWmrqathwZAOHOw4DzmSHf3zNH1/UZIfj7SLAwZxq7ubNjfW8+2ojsXCc0jmTuPG+OUxfWGh9U8ZMcJawUiCWiPFG0xv9Namm7iZnssOiaj4898OXPNnhSF4EmMqhsapK4/5T7NxQx6G3WvB4hNnXTWXRqmlMLs8Zlucwxox9lrCGSTgeZnPDZmrqanjx6IucDJ8kzZvGe0rewx8u+UNWlq287OHnI3URYKr6zuLxBPu3neDNmqM013WSluXj2rXTWbiyzIakG2POYgnrMnRGOnm5/mVq6mp4+djL9MR6yPHncOO0G1ldvprlJcuHdbLDy+k7uxzD3XfW2xWl9uVjvP3iMbpOhskrymTlh6uoWlZkU8obY87JEtZlWFe7ju++9V0K0gt4/4z3s7p8NUuLluL3pm4eqZG4CHC4+s5ONnXz5saj7N7cSCySoOyqPG76SBXT5xfYaD9jzAVZwroM98y+h+Wly7m68OpxPY/U5fadNR3qYPuvDzv9U16hamkRi1ZPo6A0O1UhG2PGoZQmLBFZC3wH8ALfV9VvDdj/OeAhIAY0Aw+q6hF3XznwfWAaoMDtqno4lfFerJLskglxg9lL7Ttr2NfOtvWHOfpuO2lZPqpvr2DhyjIygzaVhzHm4qUsYYmIF3gcuAWoB7aKyNOquiup2A6gWlW7ReTTwLeBe919/wJ8U1WfF5FsIJGqWM35XUzfmapy9N02tq0/TOP+U2Tk+LnhnpksuLHUJkY0xlyWVH6CLAX2q+pBABF5ErgL6E9YqvpCUvktwEfdsvMAn6o+75YLpTBOMwQX6jtTVQ6/1cK29Yc5caST7Lw0Vtw7m3nLS/DZQApjzDBIZcIqBY4mrdcD15+n/CeAZ93lOcBJEfk5UAlsAP5cVePJB4jIw8DDAOXl5cMUtrkYiYRy4I0TbH/2CK3HQgQL07npI1VctawYr98z0uEZY8aRVCaswYZ9DTr5loh8FKgGVrqbfMAKYAlQB/wE+Djw/844mer3gO+BMx/WcARthiYeT7BvaxPbnz3CyaZu8ooyWfPxucy+bioeryUqY8zwS2XCqscZMNGnDDhrHLSIrAG+BKxU1XDSsTuSmhN/ASxjQMIyV148mmD3lkbeeO4IHS29FJRmc+snFzBjyWS7dZIxJqVSmbC2ArNFpBI4BtwHfDi5gIgsAb4LrFXVEwOOzRORyaraDKwCrsx0wmZQ8WiC2lcaeOO5I3SdDDOlIsh7PzSHioUFNuW8MeaKSFnCUtWYiDwCPIczrP0HqlorIl8Dtqnq08DfANnAU+6HXp2q3qmqcRH570CNODu2A/+cqljNuSXiCfa8dpzX/+sQobYwxbNyWf2xuZTNzbNEZYy5okR1fHT9VFdX67ZtVgkbLppQDuxo5rWnD3KyqZsp03NYdtdMS1TGmCETke2qWj1c57MLY8wZVJW62ja2/PIALUdD5BVncdsfLKRycaElKmPMiBpSwhKROcDngenJx6jqqhTFZUZAw76TbPnlARr3nyJYmO6M+ltaZIMpjDGjwlBrWE8B/xenHyl+gbJmjGmu62TLLw9SV9tKZm6AlffPYe7yErw+G55ujBk9hpqwYqr6TymNxFxx7ce7eO3pQxx44wRpWT5uuGcmC28qsyk+jDGj0lAT1q9E5DPAfwJ910qhqm0picqkVEdrD1ufOcyezY14A16qb69g8S3lpGVYl6YxZvQa6ifUA+7PzydtU2DG8IZjUqm3K8q2Zw7z9qZ6BOHqVdO45tbpdvd0Y8yYMKSEpaqVqQ7EpE48luDtF+vZtv4wkZ4YV72nmOvuqCQnP32kQzPGmCEb6ihBP/Bp4EZ304vAd1U1mqK4zDBQVQ7tbOHVn+/nVHMP0+bls/yDs2ziRGPMmDTUJsF/AvzAP7rrv+dueygVQZnLd+JIB688tY/G/afIK87i/X+4iOnzC0Y6LGOMuWRDTVjXqeqipPWNIvJmKgIyl6ezrZctvzzA3teayMjxs/LDVcxbXmx3UDfGjHlDTVhxEZmpqgcARGQGdj3WqBLpjbHjN3XseL4OFK65dTrXrp1OwEb+GWPGiaF+mn0eeEFEDuLMczUd+P2URWWGLJFQdr/ayJanD9LTEWH2dVNZ9jszCBZkjHRoxhgzrIY6SrBGRGYDVTgJa3fS3FVmhBzd1cZvf7aP1mNdFM3I5fZPL6SoMnekwzLGmJQ4b8ISkVWqulFE7hmwa6aIoKo/T2Fs5hzaj3fx25/u58g7rQQL07n1kwuYec1kuzmtMWZcu1ANayWwEfjAIPsUsIR1BUV6Y2x95jBv1RzFl+blPffM4uqby/D6bUCFMWb8O2/CUtW/cBe/pqqHkve5MwmbK0BV2be1id/+bD/dpyLMXV7Msrtm2h0qjDETylAHXfwMuGbAtp8C1w5vOGag1mMhNj25l4Z9J5lcnsNtn7J+KmPMxHShPqyrgPlA7oB+rCBg9/VJoXBPjNd/dZC3XzxGIMPLTR+pYu7yEpubyhgzYV2ohlUFvB+YxJn9WJ3AJy90chFZC3wH8ALfV9VvDdj/OZy7ZcSAZuBBVT2StD8IvAv8p6o+csHfZhzQhLLnteO8+vP99ISizF9RyrI7Z5Ce7R/p0IwxZkRdqA/rl8AvReQGVd18MScWES/wOHALUA9sFZGnVXVXUrEdQLWqdovIp4FvA/cm7f868NLFPO9Y1ny0k00/3svxg6eYWhnk/Y8sYsr04EiHZYwxo8JQ+7A+JSLvqupJABHJA/5OVR88zzFLgf2qetA95kngLqA/YanqC0nltwAf7VsRkWuBqcCvgeohxjkm9XZFee3pg9RuOkZ6tp9VH7uKq5YVI9b8Z4wx/YaasK7uS1YAqtouIksucEwpcDRpvR64/jzlPwE8CyAiHuDvcG6yu/pcB4jIw8DDAOXl5RcIZ/TRhPLuq41s/sUBwl1RFqwsY+kHKknPsuY/Y4wZaKgJyyMiearaDiAi+UM4drDqgQ5aUOSjOLWole6mzwDrVfXo+S6GVdXvAd8DqK6uHvTco9WJIx289OO9nDjcQfGsXG68bw6FZTkjHZYxxoxaQ01Yfwe8KiI/ddd/F/jmBY6pB6YlrZcBDQMLicga4EvAyqTbPd0ArBCRzwDZQEBEQqr650OMd9SK9MZ4/elDvPXCUdJzAqz5/XnMWTrV7lJhjDEXMNR7Cf6LiGwHbsapOd0zYPDEYLYCs90LjI8B9wEfTi7gNit+F1irqieSnu8jSWU+jjMwY8wnq0NvtbDpx3sInQyzYEUpy35nBmmZ1vxnjDFDMeS5J1S1VkSaca+/EpFyVa07T/mYiDwCPIczrP0H7jm+BmxT1aeBv8GpQT3l1jDqVPXOS/91Rqeuk2Fe/sleDuxoJr8kiw9+cgFFM+ziX2OMuRiieuGuHxG5E6dZsAQ4gTO9yLuqOj+14Q1ddXW1btu2baTDOEMiodRuOsbmXxwgEVeuu6OCxWvK8frs3n/GmPFPRLar6rCN8h5qDevrwDJgg6ouEZGbgfuHK4jxqKU+xIv/vpumQx2UXZXHyg9XMWlK5kiHZYwxY9ZQE1ZUVVtFxCMiHlV9QUT+OqWRjVHRSJxtzxxi5/NHCWT6bFCFMcYMk6EmrJMikg1sAv5dRE7g3E7JJKnb1cpLT+yho6WXq95TzPJ7ZtktlYwxZpgMNWHdBfQAfwJ8BMgFvpaqoMaa7o4Irzy1j31bm5g0NZPf+ZMllFbljXRYxhgzrlwwYbn3BPylqq4BEsC6lEc1RmhCeXdzI6/+bD/RSJzr7qjgmrXT8fm9Ix2aMcaMOxdMWKoaF5FuEclV1VNXIqix4vX/OsS29YcpmT2Jmz5SRV5R1kiHZIwx49ZQmwR7gbdF5Hmgq2+jqv5RSqIaI+a9t4ScgnTm3mA3qjXGmFQbasJ6xn2YJDn56cxbXjLSYRhjzIRwoRmHy1W1TlWt38oYY8yIutAtF37RtyAiP0txLMYYY8w5XShhJXfMzEhlIMYYY8z5XChh6TmWjTHGmCvqQoMuFolIB05NK8Ndxl1XVQ2mNDpjjDHGdd6Epap2BawxxphRwea5MMYYMyZYwjLGGDMmWMIyxhgzJljCMsYYMyakNGGJyFoR2SMi+0XkzwfZ/zkR2SUib4lIjYhMd7cvFpHNIlLr7rs3lXEaY4wZ/VKWsNxpSR4HbgPmAfeLyLwBxXYA1ap6NfBT4Nvu9m7gY6o6H1gL/IOITEpVrMYYY0a/VNawlgL7VfWgqkaAJ3Emguynqi+oare7ugUoc7fvVdV97nIDcAKYnMJYjTHGjHKpTFilwNGk9Xp327l8Anh24EYRWQoEgAOD7HtYRLaJyLbm5ubLDNcYY8xolsqENdgEUYPe3klEPgpUA38zYHsx8K/A76tq4qyTqX5PVatVtXryZKuAGWPMeDbU+bAuRT0wLWm9DGgYWEhE1gBfAlaqajhpexBnDq7HVHVLCuM0xhgzBqSyhrUVmC0ilSISAO4Dnk4uICJLgO8Cd6rqiaTtAeA/gX9R1adSGKMxxpgxImUJS1VjwCPAc8C7wH+oaq2IfE1E7nSL/Q2QDTwlIjtFpC+hfQi4Efi4u32niCxOVazGGGNGP1EdH7OGVFdX67Zt20Y6DGOMMS4R2a6q1cN1PrvThTHGmDEhlYMujDEGAE0oGo2jvXES4TiJ3hgajqPhOIloAhIKCUXdnyQUVU5vU4X46W3OfgUE8Qni8zgPr0Dfsrsdnwfxes4oh8+DJ+DFk+lz1s2YYAnLGDNkGlcSoQjxzgjxUJREZ4R4R4REd5REbxwNx0iE+xKTk5QSvXE0Eh++Ocs9gAjiESeBxROXdW7xe/Bk+JzkleHDk+HHk+nr3+bJ8Cctu4+cAJ6ATRd4pVnCMsag0Tix9jDxzoiTkDqixEMRJyF19v2MkuiODpocJM2LJ92LpPmcn+le/JPSnO1pXne/78xy7nYJeBGPgAdEBDzOQwTwSn9ywiMgbpnk2PtqX/EEGlOIJdD+h7OdvuW+7XF1kmtPjES3++iJkeiJEmvtQeuddY2edfnn6d85w4cvN4A3Ny3pEThj2ZNmH7HDyV5NYyYAVSXRFSXW2ku8rZdYaw+xtl7n0dpLojNy9kFewZsTcB75GQSmB/HmBPDkBPBmB/Dk+PEGneWRbFYTEehr/ksb3nNrNNGfyPqTWneUeGeU+Kmw8+iIEDkWIhGKnh1bmvesRObLT8c3OQN/YQaeTP/wBjzOWcIyZpzQhDrJqK2XWFsPsVZnOe7+1Ej8dGHBSTb5GaTPycNXkI4vLx1P0E1Q2X4kw3dWbWaiEb8Hrz+ANxi4YFmNJYh3RE4nslOnl2MdEaJN7c4Xg6QaqifTh29yJr7CjP6Hf3IGvoJ0xG9NjgNZwjJmjFFVEp0Rose7iR7vch5N3USbuiGW1ITl8zjf5gvSSZuZiy8/HW9BhrMtLx3x22CD4SR9r3d++jnLaDxBrD1MrLmbWEuP82juoXdfO4ntTUknw6mNTXaTWGEGvsmZ+Isy8eQEJuwXCUtYxoxiid6Yk4z6EtPxLmJN3SS6Y/1lPDkB/EWZZN9QjH9qJr4C5xu6Jzvg9P2YUUO8HvxuAhooEY4Ra+kl1tJNrLmHqJvQut84gYZP14492X78xVn4S7IJlGThL87GV5gxIf7WlrCMGSVip8JEj3YSqQ/1J6f4yf7bayJpXvxTM8lYUIi/KAt/USa+qVl4s6wfZDzwpPkIlGYTKM0+Y7uqkghFiZ5wv7g0dBFtCBF65RjEnfZF8Xv6k5i/OItASTb+osxx16xoCcuYEZAIx4jUh4gc7ex/JDrcgQ8ewTfZGeTgv95JTP6iLLyT0iZsU9BEJnJ68Ev6zNPz2Gos4SSxhi6ijSEiDSG6d5xAt7i1MQ/4JmcS6KuNlecQKM0e00nMEpYxKaZxJdrUdUZyip3o7u989xWkkz4jF/+0HALTcgiUZNvFrOaCxOchUJJNoCQbmAo4tbF4Wy/Rxi4iDSGiDV2ED52ie6c7X6BXnFpceZDA9CBp04NDGlAyWljCMmaYxTsjhA+fIlLnJKfosVD/9TyeTB+BaTlkLiwkMC0Hf1mONemZYSMibh9mBhkLCvu3x0MR5/14pIPwkQ5CWxrglWMAePPTSSvPITDdSWL+oqxR2x9mCcuYyxQPRQgfPNX/iJ3odnb4hEBJNllLi5ya07QcvPnp1qxnrjhvdoCMeQVkzCsAnObESEOIyJFOIkdO0XvgZH8tTAJep/nQrYEFynPwpI+OVDE6ojBmDIl3Rd3kdNJJUE1OgpKAh0BFLpnXTCFtRq417ZlRS3we0sqDpJUHYUWp05TYHiZS10H4cAeRIx10bqyj07ldIwUfmXtGjW2kWMIy5gLiXVEih071J6nocTdB+T0EKoJkLnETVGk24rUEZcYeEem/hixz8RTAHRh0tJPI4Q78ZdkXOMOVYQnLmAE0miB86BS9e9oIHzhF9HgXcDpBBRdNJm3GJAJllqDM+OVJ85E+K4/0WXkjHUo/S1jGALG2Xnr3tNG7p53wgZPOIAmfh7SKIMFbppM2M5dAWY418RkzgixhmQlJYwnChzvcJNVG7EQP4IyYyqyeSvpV+aTPyB3T16wYM96kNGGJyFrgO4AX+L6qfmvA/s8BDwExoBl4UFWPuPseAB5zi35DVdelMlYz/sVOhZ0Etbud8P6Tzs1gvULajFyylhaTXpXn3OLGRvEZMyqlLGGJiBd4HLgFqAe2isjTqrorqdgOoFpVu0Xk08C3gXtFJB/4C6Aa5/LK7e6x7amK14w/mlAidR307naSVF9flHdSGplLJpNelU/azEl40qwWZcxYkMoa1lJgv6oeBBCRJ4G7gP6EpaovJJXfAnzUXb4VeF5V29xjnwfWAj9OYbxmHNB4gvCBU/TUttBT2+rMUeQR0iqC5N5WSfpVefimZFotypgxKJUJqxQ4mrReD1x/nvKfAJ49z7GlAw8QkYeBhwHKy8vPOmE0GqW+vp7e3t6LCtxcWHp6OmVlZfj9I3+XBo0m6N3XTs87LfS824b2xJCAh/Sr8slYUEj6nLxRc+GjMebSpfK/eLCvsINMrg0i8lGc5r+VF3Osqn4P+B5AdXX1Wfvr6+vJycmhoqLCvlEPI1WltbWV+vp6KisrRySGRDhO7542et5poXd3OxqJI+k+MublkzG/kPQ5k2zAhDHjTCoTVj0wLWm9DGgYWEhE1gBfAlaqajjp2JsGHPvixQbQ29trySoFRISCggKam5uv6PMmemL0vNtKzzut9O5th1gCT5afzMWTyVhQSNqMXBt2bsw4lsqEtRWYLSKVwDHgPuDDyQVEZAnwXWCtqp5I2vUc8D9FpO+KtfcBX7yUICxZpcaVel0TPTF63mmh++0WwgdOQlzxBANkXTfVSVKVuaP2Rp3GmOGVsoSlqjEReQQn+XiBH6hqrYh8Ddimqk8DfwNkA0+5H4B1qnqnqraJyNdxkh7A1/oGYJjxT2MJene30b3jBD272yCuePPTyV5eQsaCQucCXktSxkw4Ke2JVtX1wPoB276StLzmPMf+APhB6qI72562PdTU1dAQaqAku4TV5aupyq+6kiFMWJpQIoedeXu632pBe2N4sv1kX19M5pIp+MuyrbZszARnQ6dce9r2sK52HcFAkKKsIjrCHayrXccD8x+wpJVC0eNddO84QffOZuKnwkjAQ8b8QjIXTyZtVh7itSRljHFYwnLV1NUQDAQJpgUB+n/W1NVcVsL68pe/TGFhIY8++igAX/rSl5g6dSp/9Ed/dPlBj1GxU2F6dp6ge0ezczGvB9Jn55F7WwXp8wrwBGx0nzHmbJawXA2hBoqyis7Ylh3IpiF01sDGi/KJT3yCe+65h0cffZREIsGTTz7J66+/flnnHIv6B0/sOEH40ClQCJTnMOnOmWRcXYg3e+xM022MGRmWsFwl2SV0hDv6a1YAoUiIkuySyzpvRUUFBQUF7Nixg6amJpYsWUJBQcHlhjsmqCqRQ6fo2tpE99stEEvgK8wguLqczMVT8BVmjHSIxpgxxBKWa3X5atbVOvfXzQ5kE4qE6Ih0cPfsuy/73A899BA/+tGPOH78OA8++OBln2+0i3dE6Hqjie6tx4m19iLpXrKqp5J17VQbPGGMuWSWsFxV+VU8MP+BM0YJ3j377mEZcHH33Xfzla98hWg0yhNPPDEM0Y4+Gld697TRtfU4vXvaIAGByiB5q8vJWFBo/VLGmMtmCStJVX5VSkYEBgIBbr75ZiZNmoTXO74+uGOtPXRta6JrWxOJzgiebD85K8rIrJ6Kf3LmSIdnjBlHLGFdAYlEgi1btvDUU0+NdCjDQhNKojdGvDPC8XXbQCC9Kp+s64pIvyrPpo03xqSEJawU27VrF+9///u5++67mT179kiHc1kSkTiJ7iiJ7hgkFBJK8NbpZF0zFW9u2kiHZ4wZ5yxhpdi8efM4ePDgSIdxyVSVRE+MRCjqzNAr4Mnw4cn04+1II7js7GldjDEmFSxhmUFpLEG8O0qiKwpxBZ8Hb24ankzf6SY/G+xnjLmCLGGZfqqKRuIkQlESPTEAJN2HN8+PpHltOLoxZkRZwjLOIAq3NqXRBHgET04Ab5bf5pcyxowalrAmsEQ0TqIrSqIrBqqI34s3Lx1Phs+m7zDGjDr29TnFvF4vixcvZtGiRVxzzTW8+uqrKXmeTZs2cc011+Dz+fjpT396xr4vfOELLFiwgAULFvDkk0+S6IkSbe4meryLx77yZRasXMKiNUv5xyf/GW+Wn47ODj7wgQ+waNEi5s+fzw9/+MOUxGyMMRfDalgplpGRwc6dOwF47rnn+OIXv8hLL700pGPj8fiQLzQuLy/nRz/6EX/7t397xvZnnnmGN954gx1v7KCnLcTNt65mzaIVBCfl8m+/epKG9uPs3rsHj8fDiRPOpM+PP/448+bN41e/+hXNzc1UVVXxkY98hEDAblBrjBk5EyZhvfwfe2k5GhrWcxZOy2bFh+YMuXxHRwd5eXmAM8Dhz/7sz3j22WcRER577DHuvfdeXnzxRf7yL/+S4uJidu7cyfr167ntttt473vfy6uvvkppaSm//OUvycg488axFRUVAHg8Z1aaa9+pZcX1y9HmMOnq4+r5C9mw/SXu/cj9fPdH3+eJJ57oP2bKlCkAiAidnZ2oKqFQiPz8fHy+CfNWMcaMUtYkmGI9PT0sXryYq666ioceeogvf/nLAPz85z9n586dvPnmm2zYsIHPf/7zNDY2AvD666/zzW9+k127dgGwb98+PvvZz1JbW8ukSZP42c9+dsHnTUTixFp7mF82h18/92t6E2FOerp5acvLHGtqREQ4cOAAP/nJT6iurua2225j3759ADzyyCO8++67lJSUsHDhQr7zne+clQiNMeZKS+nXZhFZC3wH8ALfV9VvDdh/I/APwNXAfar606R93wbuwEmqzwOPqqpeaiwXUxMaTslNgps3b+ZjH/sY77zzDq+88gr3338/Xq+XqVOnsnLlSrZu3UowGGTp0qVUVlb2n6OyspLFixcDcO2113L48OFBn0tV0XiC2KkwsRPdIMKtd9zGjoPvcOMHVjN58mRuuOGG/tpSOBwmPT2dbdu28fOf/5wHH3yQl19+meeee47FixezceNGDhw4wC233MKKFSsIBoODPq8xxlwJKfvaLCJe4HHgNmAecL+IzBtQrA74OPDEgGPfAyzHSWQLgOuAlamK9Uq54YYbaGlpobm5mfPl3qysrDPW09JO3/bI6/USi8XO2K8JJR6KEGvqRnvjkFC8uWn4i7PwTUrjsS8/xs6dO3n++edR1f5bRJWVlfHBD34QcO4o/9ZbbwHwwx/+kHvuuQcRYdasWVRWVrJ79+5heQ2MMeZSpbKdZymwX1UPqmoEeBK4K7mAqh5W1beAxIBjFUgHAkAa4AeaUhjrFbF7927i8TgFBQXceOON/OQnPyEej9Pc3MymTZtYunTpRZ2vrzYVPd5F/GQYPIKkefFOSsObE0A8Qjwep7W1FYC33nqLt956i/e9733/f3t3H2RVfd9x/P3ZJxZZqMA2kATRkMSio6ziahAVgtZNqpPKTmu1UrumWKsztdWOY+hQlGbasciMziRpZ+pQGhidsAa0Ag0mIChgeFgky5NoBLVlq+HJVZ4W9u693/5xfndzWXfh7nIv92G/r5k79+zvnvu753zn7P3uOee33x8AU6dOZfXq1QC88cYbXHppdBY6evRoXnvtNQD279/Pu+++y5gxYzIVBuec65NsXhL8MrAv5ecW4BvpvNHMNkhaA3xMVADoR2a2u+t6kh4AHoDoSzYfJe9hQXTJbsGCBZSWllJfX8+GDRuoqalBEk8//TQjR45M60zG4gk6PjlJoi0GFtX2e+udZv7orjtpbW1l+Yr/Zvbs2ezatYtYLMZNN90EwJAhQ3j++ec7LwnOmDGDadOm8eyzz1JVVcW8efMAmDVrFvfddx9XXnklZsacOXOorq7OUoSccy49OofbQmfuWLoT+JaZ3R9+vhe4zswe7mbdHwPLk/ewJH2N6N7XXWGVlcD3zGxtT59XW1trW7ZsOa1t9+7dXHbZZRnYm/yQOBUnfrQdO9kBEiWDyiitqshZNYpii69zLrMkvWVmtZnqL5tnWC3ARSk/jwI+SvO99cBGMzsGoCcwOwAAC19JREFUIGkFMAHoMWEVKzPDkonqVBxKROmQCkoGlfu8U865fiWb33hNwNclfUVSBXA3sDTN9/4vMFlSmaRyogEXn7skWMzMjPiJGB0HTtBxqA3rSEQDKUYOonTIAE9Wzrl+J2vfembWAfw18HOiZPOime2S9H1Jfwgg6VpJLcCdwL9L2hXevhjYC+wAtgHbzGxZtrY1n5gZ8eMxOvafIP7JSTAoHVoZJaowkMI55/qjrP4flpn9DPhZl7YnUpabiC4Vdn1fHPirbG5bvrGEkTgeI36sHeKhEO2wUIjWp/Vwzrn+U5opX1k8ERJVDBIWDUsfWuHzTznnXBeesHLE4gniR8OMvmbRRImDKygZkF6xW+ec62/8zn2WdZ1e5M216+n49CSx3xwncaydkoFllI24gPLqgeeUrNKdXqSxsbGzffXq1YwfP54rrriChoaGzgoar7zyCuPGjeOqq66itraW9evX93m7nHMuUzxhZVmylmDzW7/in2b+I3//+AwSx2KUDCynfOQgyoZVUlLefaKKx+Npf05yepF77rnntPbk9CLNzc1s2rSJuXPncuTIERKJBA0NDSxatIidO3dy8cUXs2DBAgBuueUWtm3bRnNzM/Pnz+f+++/vewCccy5D+s0lwU+X7aX9o+MZ7bPiS4O48DtfPet6Ha0nSZyI8dnBVoYOH0r5yEFQqvMyvcjbb7/N5MmTKSsro6ysjJqaGl599VWmTJnCgAEDOssx3XrrrTz11FNMnz6dqqqqzvcfP37c76U55/KCn2FliXUk6Gg9SVtbG9fceB3jptTy4Pce5onvz0ZlJVmfXiSppqaGFStWcOLECQ4dOsSaNWvYt28f1dXVxGIxktVBFi9ezL59v62k9fLLLzN27Fhuv/125s+fn8HIOOdc3/SbM6x0zoQywToSxI+2kzgRA8IlwW3NqKwk69OLdKeuro6mpiYmTpx42vQikli0aBGPPvoop06doq6u7rRJGuvr66mvr2ft2rXMmjWLVatWZSZAzjnXR36GlSHWERWkjf3mOIkTMUoGlVM+IpomJFnrL1vTi5zNzJkzu51e5Prrr2fdunVs3ryZSZMmdbanmjRpEnv37uXQoUO9+kznnMs0T1jn6LRE1RajpCpKVGUXVn6uKG2mpxdJx5mmFzlw4AAQTeQ4Z84cHnzwQQD27NnTmVC3bt1Ke3s7w4cPz/i2Oedcb/SbS4LZED/aTvyzU1Hl9KryqHRSlxp/2ZhepDtNTU3U19fT2trKsmXLePLJJ886vcjcuXNZvnw5iUSChx56iJtvvhmAJUuWsHDhQsrLyxk4cCCNjY0+8MI5l3NZm17kfMvF9CKJU3ESbR2UDu6fldN9ehHn3JkU0vQiRa9kQKlXpnDOufOk/50WOOecK0hFn7CK5ZJnvvG4OufOt6JOWJWVlRw+fNi/XDPMzDh8+DCVlZW53hTnXD9S1PewRo0aRUtLCwcPHsz1phSdyspKRo363FRmzjmXNUWdsMrLy0+rGOGcc65wFfUlQeecc8XDE5ZzzrmC4AnLOedcQSiaSheSDgL/k+vtOI+qAa9Imx6PVXo8TunzWKXn98xscKY6K5pBF2b2u7nehvNJ0pZMljwpZh6r9Hic0uexSo+kLWdfK31+SdA551xB8ITlnHOuIHjCKlzP5XoDCojHKj0ep/R5rNKT0TgVzaAL55xzxc3PsJxzzhUET1jOOecKgiesPCHpIklrJO2WtEvS34b2YZJWSnovPA8N7ZL0A0l7JG2XND6lr4aw/nuSGnK1T9mS4VjFJTWHx9Jc7VM29CFOYyVtkHRK0mNd+vq2pHdDDGfkYn+yKcOx+lDSjnBMZXRYdz7oQ6ymhd+77ZJ+Kakmpa/eHVdm5o88eABfBMaH5cHAr4HLgaeBGaF9BjAnLN8GrAAETAA2hfZhwPvheWhYHprr/cvHWIXXjuV6f/IoTl8ArgX+GXgspZ9SYC8wBqgAtgGX53r/8jFW4bUPgepc71MexWpi8jsI+IOU76peH1d+hpUnzOxjM9salo8Cu4EvA3cAC8JqC4CpYfkOYKFFNgIXSvoi8C1gpZl9YmatwErg2+dxV7Iug7Eqar2Nk5kdMLMmINalq+uAPWb2vpm1A4tCH0Ujg7Eqen2I1S/DdxHARiA5L1GvjytPWHlI0iXA1cAmYISZfQzRgUL0lx1EB8i+lLe1hLae2ovSOcYKoFLSFkkbJU2lSKUZp574MUXasQIw4BeS3pL0QLa2Mx/0IVbTia52QB+Oq6IpzVQsJFUBS4BHzOyIpB5X7abNztBedDIQK4DRZvaRpDHAakk7zGxvFjY3Z3oRpx676Katvx9TZ3JDOKa+AKyU9I6Zrc3ohuaB3sZK0hSihHVjsqmb1c54XPkZVh6RVE50ALxgZi+F5v3Jy1fh+UBobwEuSnn7KOCjM7QXlQzFCjNLPr8PvE7012LR6GWceuLHFGnHKvWYOgC8THTpq6j0NlaSxgHzgDvM7HBo7vVx5QkrTyj68+Q/gN1m9kzKS0uB5Ei/BuCVlPY/DyPgJgCfhdPwnwN1koaGUTp1oa1oZCpWIUYDQp/VwA3A2+dlJ86DPsSpJ03A1yV9RVIFcHfoo2hkKlaSBkkanFwm+v3bmfktzp3exkrSaOAl4F4z+3XK+r0/rnI94sQfnSNvbiQ6Hd4ONIfHbcBw4DXgvfA8LKwv4F+JRtnsAGpT+voLYE94fDfX+5avsSIavbSDaHTSDmB6rvctx3EaSfRX7xHg07A8JLx2G9FosL3AzFzvW77GimjE27bw2OWxMojOrFpT1t2S0levjisvzeScc64g+CVB55xzBcETlnPOuYLgCcs551xB8ITlnHOuIHjCcs45VxA8YTkXSDqWxjqPSLogg585T9LlmerPuWLmw9qdCyQdM7Oqs6zzIdH/cR3qRb+lZhY/1+3LFUllZtaR6+1wzs+wnOtC0jclvS5psaR3JL0QqmT8DfAlYI2kNWHdujAv0lZJPw311ZJzIj0haT3wuKTNKf1fIml7WH5dUm1PfUm6TtJL4fU7JLVJqpBUKen9Lts9WNIHoWwOkoaE7SiX9FVJr4aCrOskjQ3rfEfSJkm/krRK0ojQPlvSc5J+ASzMbsSdS48nLOe6dzXwCNE8P2OICpr+gKjW2RQzmxLKOf0D8PtmNh7YAvxdSh8nzexGM3sKqAgFdgHuAl5M/bAz9LWV39Y3vImozM+1wDeIKmR3smiqh9eB20PT3cASM4sBzwEPm9k1wGPAv4V11gMTzOxqoukdHk/p8hqi2m/3pBcy57LLq7U7173NZtYCIKkZuIToyz3VBKKE9maoVF0BbEh5vTFl+UXgT4B/IUpYd6XTl5l1KJqN9TKiIqrPAJOIJr9b1812zyNKOv8FfBf4y3DWNxH4aUpF7QHheRTQGIqVVgAfpPS11MzauvkM53LCE5Zz3TuVshyn+98VEU2W+ac99HE8ZbmRKGG8BJiZvdeLvtYRzdQaA1YBPyZKWI91XdHM3gyXHCcDpWa2U9IQ4FMzu6qbvn8IPGNmSyV9E5jdw/Y7l3N+SdC53jlKNC04RLOn3iDpawCSLpB0aXdvsmiOrTgwi9PPvJLO1NdaosuTG8zsIFGR0bFExVW7sxD4CfCf4bOPAB9IujP0LUk1Yd3fAf4vLDd07ci5fOIJy7neeQ5YIWlNSB73AT8Jgyg2EiWSnjQCf0aX+1cAZ+lrEzCCKHFBVCV7u/U8xPcFYChR0kqaBkyXlKwinpyKfDbRmd86IO2Rj87lgg9rd67ISPpjosES9+Z6W5zLJL+H5VwRkfRDovtdt+V6W5zLND/Dcs45VxD8HpZzzrmC4AnLOedcQfCE5ZxzriB4wnLOOVcQPGE555wrCP8PIJvO4D6XiCEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series_lowess(by_year, 'C2')\n",
    "\n",
    "xs = np.arange(1998, 2020)\n",
    "\n",
    "dfp = pd.DataFrame()\n",
    "dfp['year'] = xs\n",
    "dfp['cohort'] = 1968\n",
    "dfp['y2'] = (dfp['year']-2004)**2\n",
    "dfp['c2'] = (dfp['cohort']-1970)**2\n",
    "dfp['c3'] = (dfp['cohort']-1970)**3\n",
    "\n",
    "ys = model.predict(dfp)\n",
    "plt.plot(xs, ys, color='C4', label='Born 1968')\n",
    "\n",
    "dfp['cohort'] = 1993\n",
    "ys = model.predict(dfp)\n",
    "plt.plot(xs, ys, color='C6', label='Born 1993')\n",
    "\n",
    "decorate(xlabel='Interview year',\n",
    "         ylabel='Fraction',\n",
    "         title='Mean response by year of interview',\n",
    "         loc='lower left')\n",
    "plt.xlim(1996, 2020)\n",
    "\n",
    "plt.savefig('generation_by_year', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing generations\n",
    "\n",
    "Now let's see how things change from one generation to the next, controlling to period effects.\n",
    "\n",
    "I'll use the model to generate predictions for two hypothetical members of this group, born in 1968 and 1993, both interviewed in 2018.\n",
    "\n",
    "Here's a DataFrame that describes these hypothetical people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "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></th>\n",
       "      <th>cohort</th>\n",
       "      <th>year</th>\n",
       "      <th>y2</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1968</td>\n",
       "      <td>2018</td>\n",
       "      <td>196</td>\n",
       "      <td>4</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1993</td>\n",
       "      <td>2018</td>\n",
       "      <td>196</td>\n",
       "      <td>529</td>\n",
       "      <td>12167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cohort  year   y2   c2     c3\n",
       "0    1968  2018  196    4     -8\n",
       "1    1993  2018  196  529  12167"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pred = pd.DataFrame()\n",
    "df_pred['cohort'] = [1968, 1993]\n",
    "df_pred['year'] = 2018\n",
    "df_pred['y2'] = (df_pred['year']-2004)**2\n",
    "df_pred['c2'] = (df_pred['cohort']-1970)**2\n",
    "df_pred['c3'] = (df_pred['cohort']-1970)**3\n",
    "df_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here are the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.280852\n",
       "1    0.201243\n",
       "dtype: float64"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(df_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the model\n",
    "\n",
    "The following function encapsulates the steps in the previous section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_model(df, varname, value, formula):\n",
    "    \"\"\"Runs the model and generates predictions.\n",
    "    \n",
    "    df: DataFrame\n",
    "    varname: string variable name\n",
    "    value: value or list of values considered \"yes\"\n",
    "    formula: string patsy model\n",
    "    \n",
    "    returns: array of predicted values based on df_pred\n",
    "    \"\"\"\n",
    "    value = np.atleast_1d(value)\n",
    "    valid = df.dropna(subset=[varname]).copy()\n",
    "    valid['y'] = (valid[varname].isin(value)).astype(int)\n",
    "    model = smf.logit(formula, data=valid).fit(disp=0)\n",
    "    res = model.predict(df_pred)\n",
    "    return res.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on `formula`, we can run either the linear or quadratic version of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "#formula = 'y ~ year + cohort'\n",
    "#formula = 'y ~ year + y2 + cohort + c2'\n",
    "formula = 'y ~ year + y2 + cohort + c2 + c3'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the results for Christians and Nones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.28085169, 0.20124302])"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_model(christian, varname, value, formula)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.03442896, 0.03817344])"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_model(none, varname, value, formula)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing results\n",
    "\n",
    "The following function runs the analysis for the two groups and return an array with predictions for 4 hypothetical people:\n",
    "\n",
    "* Christian born in 1968\n",
    "* Christian born in 1993\n",
    "* None born in 1968\n",
    "* None born in 1993"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare(df, varname, value):\n",
    "    christian = df[df['christian']]\n",
    "    none = df[df['none']]\n",
    "\n",
    "    c = run_model(christian, varname, value, formula)\n",
    "    n = run_model(none, varname, value, formula)\n",
    "    return np.hstack([c, n]) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([28.08516892, 20.12430165,  3.44289599,  3.81734432])"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare(df, varname, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function runs the same analysis 101 times, using each of the resampled datasets.\n",
    "\n",
    "It computes the 5th, 50th, and 95th percentiles in each column and returns an array with one row for each percentile and one column for each of the 4 hypothetical people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_iter(dfs, varname, value):\n",
    "    t = [compare(df, varname, value) for df in dfs]\n",
    "    a = np.array(t)\n",
    "    percentiles = np.percentile(a, [5, 50, 95], axis=0)\n",
    "    return percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[28.65101432, 19.74193596,  2.2695887 ,  2.97257609],\n",
       "       [30.11273943, 21.91327412,  3.71919116,  3.89548952],\n",
       "       [31.78215012, 23.81139566,  5.04135907,  5.55335347]])"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentiles = compare_iter(dfs, varname, value)\n",
    "percentiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the results\n",
    "\n",
    "The following functions visualize the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_interval(y, interval, dy=0.16, **options):\n",
    "    \"\"\"Show a confidence interval.\n",
    "    \n",
    "    y: vertical coordinate\n",
    "    interval: triple of low, med, and high\n",
    "    dy: height of the error bars\n",
    "    options: passed to plt\n",
    "    \"\"\"\n",
    "    low, mid, high = interval\n",
    "    plt.hlines(y+dy, low, high, alpha=0.6, **options)\n",
    "    plt.vlines([low, high], y+dy, y+dy/2, alpha=0.6, **options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_map = {'Christian':'C0',\n",
    "             'None':'C1'}\n",
    "\n",
    "def plot_arrow(y, row, group):\n",
    "    \"\"\"Plot an arrow showing generational changes.\n",
    "    \n",
    "    y: vertical coordinate\n",
    "    row: x1, x2 pair\n",
    "    group: string group name\n",
    "    \"\"\"\n",
    "    color = color_map[group]\n",
    "    label1 = f'{group} born 1968'\n",
    "    label2 = f'{group} born 1993'\n",
    "    \n",
    "    x1, x2 = row    \n",
    "    dx = x2 - x1\n",
    "    plt.hlines(y, x1, x2, color=color, alpha=0.3)\n",
    "    \n",
    "    plt.plot(x1, y, 'o', color=color, label=label1)\n",
    "    \n",
    "    style = '>' if x2 > x1 else '<'\n",
    "    plt.plot(x2, y, style, color=color, label=label2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_percentiles(y, percentiles, dy=0.12):\n",
    "    \"\"\"Plot the results from the resampled analysis.\n",
    "    \n",
    "    y: vertical coordinate\n",
    "    percentiles: array with a row for each percentile and\n",
    "                 column for each hypothetical person\n",
    "    dy: vertical offset for the first group\n",
    "    \"\"\"\n",
    "    plot_interval(y+dy, percentiles[:, 2], color='C1')\n",
    "    plot_arrow(y+dy, percentiles[1, 2:4], group='None')\n",
    "    plot_interval(y-dy, percentiles[:, 0], color='C0')\n",
    "    plot_arrow(y-dy, percentiles[1, 0:2], group='Christian')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "def miniplot(percentiles):\n",
    "    \"\"\"Make a plot with just one variable\"\"\"\n",
    "    plot_percentiles(0, percentiles)\n",
    "    plt.ylim(-1.5, 1.5)\n",
    "    plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAfPUlEQVR4nO3de3RV1b328e+PEA0FBOTiS4ga8YImJNwVaIRgVJCqiPKiFouKYKlSZIiIeDq8UFtoY1vk1GLLqdbXUgpaiNZewMMlNqiAgZBAUFAEDaAIyiUSIAnz/WPvbEkkIVf2zPb5jMHIzlxrrzlXNuZxzbWYP3POISIi4psm4R6AiIjIySigRETESwooERHxkgJKRES8pIASEREvNQ33AOqiXbt2Lj4+PtzDEBGROsjOzt7rnGtfsb1RB1R8fDzvvvtuuIchIiJ1YGY7TtauKT4REfGSAkpERLykgBIRES816ntQIuKf4uJiCgoKOHLkSLiHIp6JiYkhLi6O6Ojoau2vgBKRelVQUEDLli2Jj4/HzMI9HPGEc459+/ZRUFDABRdcUK33aIpPROrVkSNHaNu2rcJJyjEz2rZtW6MrawWUiNQ7hZOcTE3/XiigRETESwooEYk4ZsbkyZND3z/99NM88cQTDdpnixYtGvT4ZcaMGUOHDh3o2rVrufYNGzbQr18/kpKSuOGGGzh48GBoW25uLv369SMxMZGkpKTQNNv8+fNJSkoiOTmZIUOGsHfv3tNyDtWlgBKR8MpdCL/pCk+0DnzNXVjnQ5555pksWrTIu1+4lSkpKan2vnfddRf//ve/v9E+duxYZs6cSV5eHsOHDyc9PT107DvuuIPnnnuOTZs2sXLlSqKjoykpKeGBBx5gxYoV5ObmkpyczG9/+9t6O6f6oIASkfDJXQh/nwgHPgFc4OvfJ9Y5pJo2bcq9997Lb37zm29s27FjB2lpaSQnJ5OWlsbHH38MBH7xT5w4kf79+9O5c2deeeWV0HvS09Pp06cPycnJPP7445X2O3nyZHr27ElaWhqff/45ADk5OfTt25fk5GSGDx/Ol19+CUBqaiqPPvooAwcO5Jlnnqmy/xMNGDCAs88++xvt77//PgMGDADgmmuu4W9/+xsAS5cuJTk5mW7dugHQtm1boqKicM7hnOOrr77COcfBgweJjY095c/2dFJAiUj4LJsOxUXl24qLAu11dP/99zNv3jwOHDhQrn3ChAmMHj2a3NxcRo0axcSJE0Pbdu/eTVZWFq+//jqPPPIIEPgFv3XrVtasWUNOTg7Z2dm8+eab3+jvq6++omfPnqxbt46BAwfy5JNPAjB69Gh+8YtfkJubS1JSUqgdYP/+/WRmZoamI0/Wf3V17dqV1157DYCXX36ZTz75BIAtW7ZgZgwePJiePXvyy1/+EoDo6GjmzJlDUlISsbGx5Ofnc88999Soz4amgBKR8DlQULP2GjjrrLMYPXo0s2fPLtf+9ttv8/3vfx+AH/zgB2RlZYW23XTTTTRp0oSEhAQ+++wzIBBQS5cupUePHvTs2ZP33nuPrVu3fqO/Jk2acOuttwJwxx13kJWVxYEDB9i/fz8DBw4E4M477ywXbmX7V9V/dT3//PM8++yz9OrVi0OHDnHGGWcAgSm+rKws5s2bR1ZWFosXL2bZsmUUFxczZ84c1q9fz65du0hOTmbGjBk16rOh6R/qikj4tIoLTu+dpL0eTJo0iZ49e3L33XdXus+Jjz6feeaZodfOudDXadOm8cMf/rBGfVfnkermzZuX+/5k/VfXpZdeytKlS4HAVdM//vEPAOLi4hg4cCDt2rUDYOjQoaxbt46zzjoLgAsvvBCAkSNHMnPmzBr12dB0BSUi4ZP2GEQ3K98W3SzQXg/OPvtsRo4cyR//+MdQW//+/fnrX/8KwLx580hJSanyGIMHD+b555+nsLAQgJ07d7Jnz55v7Hf8+PHQfaO//OUvpKSk0KpVK9q0acN//vMfAF566aXQ1VR9KxvT8ePHeeqppxg/fnxo/Lm5uRw+fJiSkhIyMzNJSEigU6dO5Ofnh+6VvfHGG1x22WUNMrba0hWUiIRP8sjA12XTA9N6reIC4VTWXg8mT55c7um02bNnM2bMGNLT02nfvj0vvPBCle+/9tpr2bx5M/369QMCj5P/+c9/pkOHDuX2a968OZs2baJXr160atWKBQsWAPDiiy8yfvx4Dh8+TOfOnU/Z36ncfvvtrFy5kr179xIXF8eTTz7JPffcw/z583n22WcBuPnmm0NXjW3atOHBBx+kT58+mBlDhw7le9/7HgCPP/44AwYMIDo6mvPPP58//elPdRpbfbOaXkb6pHfv3k4FC0X8snnzZu/+T1z8cbK/H2aW7ZzrXXFfTfGJiIiXFFAiIuIlBZSIiHhJASUiIl5SQImIiJcUUCIi4iUFlIhEHJXb+LrcxrFjx7j77rtJSkqiW7durFy5MvSeIUOG0K1bNxITExk/fjylpaWn5RyqSwElIuHzXAq8/iAc+rReD6tyG1+X25g7dy4AeXl5vPHGG0yePJnjx48DsHDhQjZs2MDGjRv5/PPPefnll+vhbOqPAkpEwufTPFj/EjzTrV6DSuU2vi63kZ+fT1paGgAdOnSgdevWlC1wULYeX0lJCceOHatxSfaGpoASkfAqPQYlR+o9qFRuI7AIb7du3Xj11VcpKSnho48+Ijs7O7QNAmv1dejQgZYtWzJixIga9dnQFFAi4oeyoMp+AV6pfPXx6lK5jUC5jTFjxhAXF0fv3r2ZNGkS/fv3p2nTr5dhXbJkCbt37+bo0aMsX768Rn02NC0WKyJ+iDoDrAl0HwUDp9bLIVVuIzDdeeJUZ//+/bn44ovLvTcmJoYbb7yRV199lWuuuaZG/TYkXUGJSHhFnQFNY6DHD+CBXLj+19DynHo5tMptwOHDh/nqq6+AQEmNpk2bkpCQQGFhIbt37wYC96D++c9/cumllzbI2GpLV1AiEj7/JwniLg9cMdVTKFX0bS+3sWfPHgYPHkyTJk3o1KkTL730EhC4Z3bjjTdy9OhRSktLueqqq0Kh5guV2xCReqVyG1IVldsQEZFGTwElIiJeUkCJiIiXFFAiIuIlBZSIiHhJASUiIl5SQIlIxPn000+57bbbuPDCC0lISGDo0KFs2bKFlStXcv3111frGI899hj/+7//W+n2jIwM8vPzq71/ddVkjHWxb98+Bg0aRIsWLZgwYUK5bQsWLCA5OZnExEQefvjhctsWLlxIQkICiYmJoSWjAB5++GESExO57LLLmDhxYo1XwjgZ/UNdEQmrjPU7SV/yPrv2FxHbuhlTBnfhph6dan085xzDhw/nzjvvDK0YkZOTU6O17UpLS5k+fXrV487I4PrrrychIQHglPufLqWlpURFRZ1yv5iYGH7605+yceNGNm7cGGrft28fU6ZMITs7m/bt23PnnXeybNky0tLS2Lp1KzNmzGDVqlW0adMmtHrFW2+9xapVq8jNzQUgJSWFzMxMUlNT63QuuoISkbDJWL+TaYvy2Lm/CAfs3F/EtEV5ZKzfWetjrlixgujo6HKrInTv3p0rr7wSgMLCQkaMGMGll17KqFGjQv+nHx8fz/Tp00lJSeHll1/mrrvuCi1d9Mgjj5CQkEBycjIPPfQQb731Fq+99hpTpkyhe/fufPjhh+X2nz59On369KFr167ce++9oT5SU1OZOnUql19+OZdcckloCaSKDh48yPDhw0lISGD8+PGh+k3z588nKSmJrl27MnXq1+sVtmjRgscee4wrrriCt99+m/j4eB5//HF69uxJUlIS77333jf6aN68OSkpKcTExJRr37ZtG5dccgnt27cH4Oqrrw6V7pg7dy73338/bdq0AQitpmFmHDlyhGPHjnH06FGKi4s555y6rwyigBKRsElf8j5FxeWruBYVl5K+5P1aH3Pjxo306tWr0u3r169n1qxZ5Ofns23bNlatWhXaFhMTQ1ZWFrfddluo7YsvvmDx4sVs2rSJ3NxcfvKTn9C/f39uvPFG0tPTycnJ4cILLyzXx4QJE1i7di0bN26kqKiI119/PbStpKSENWvWMGvWrHKlN060Zs0afvWrX5GXl8eHH37IokWL2LVrF1OnTmX58uXk5OSwdu1aMjIygMCyRV27dmX16tWhtQXbtWvHunXr+NGPfsTTTz9d7Z/fRRddxHvvvcf27dspKSkhIyMjVJ5jy5YtbNmyhe9+97v07ds3VDixX79+DBo0iI4dO9KxY0cGDx5cL6uJKKBEJGx27S+qUXt9uPzyy4mLi6NJkyZ0796d7du3h7ZVLH8BgbIdMTExjB07lkWLFvGd73znlH2sWLGCK664gqSkJJYvX86mTZtC226++WYAevXqVa7vimPs3LkzUVFR3H777WRlZbF27VpSU1Np3749TZs2ZdSoUaHSHVFRUdxyyy3ljlGdfk6mTZs2zJkzh1tvvZUrr7yS+Pj4UHmOkpIStm7dysqVK5k/fz5jx45l//79fPDBB2zevJmCggJ27tzJ8uXLT1ozq6YUUCISNrGtm9WovToSExPJzs6udPuJJS2ioqLKlVuvWP4CAuUq1qxZwy233EJGRgZDhgypsv8jR45w33338corr5CXl8e4ceM4cuTIN/qv2PeJKpbqMLMqHzqIiYn5xn2n6vRTmRtuuIHVq1fz9ttv06VLl1B5jri4OIYNG0Z0dDQXXHABXbp0YevWrSxevJi+ffvSokULWrRowXXXXcc777xToz5PRgElImEzZXAXmkWX/8XaLDqKKYO71PqYV111FUePHmXu3LmhtrVr15KZmVmr4xUWFnLgwAGGDh3KrFmzyMnJAaBly5YcOnToG/uXhVG7du0oLCystHR7VdasWcNHH33E8ePHWbBgASkpKVxxxRVkZmayd+9eSktLmT9/foOX7vjyyy/53e9+x9ixY4FAQcUVK1YAsHfvXrZs2ULnzp0577zzyMzMpKSkhOLiYjIzM+tlik9P8YlI2JQ9rVefT/GZGYsXL2bSpEnMnDmTmJgY4uPjmTVrFjt31vzhi0OHDjFs2DCOHDmCcy5U/O+2225j3LhxzJ49u1wItW7dmnHjxpGUlER8fDx9+vSpcZ/9+vXjkUceIS8vjwEDBjB8+HCaNGnCjBkzGDRoEM45hg4dyrBhw2p87BPFx8dz8OBBjh07RkZGBkuXLiUhIYEHHniADRs2AIHH5y+55BIgUBurbJ+oqCjS09Np27YtI0aMYPny5SQlJWFmDBkyhBtuuKFOYwOV2xCReqZyG1IVldsQEZFGTwElIiJeUkCJiIiXFFAiIuIlBZSIiHhJASUiIl5SQIlIxFG5jVOrTbmNHTt2kJaWRnJyMqmpqRQUFITae/XqRffu3UlMTOS5556rlzEqoETEG3sOHuEni/MY+szJV/mujrJyG6mpqXz44Yfk5+fz85//vFblNq6++upK96kYUKfa/3QpLS099U58XW6j4kKyZeU2li1bxqZNm/jss89YtmwZAA899BCjR48mNzeXxx57jGnTpgHQsWNH3nrrLXJycli9ejUzZ85k165ddT4XBZSIhF1ZMF35yxUsePcT8ncfrPWxVG6j4cpt5Ofnk5aWBsCgQYN49dVXATjjjDNCa/8dPXo0NN66UkCJSNhUDKajJccpLq3b6jYqt9Fw5Ta6desWCqvFixdz6NAh9u3bB8Ann3xCcnIy5557LlOnTiU2NrbafVZGASUiYTNh/nrmrfm4XoKpulRuo2pVldt4+umnyczMpEePHmRmZtKpU6fQtnPPPZfc3Fw++OADXnzxxRpNqVZGASUiYfPb7/dg1BXnE9O0CdFRduo3VIPKbVS/n8pUVm4jNjaWRYsWsX79en72s58B0KpVq3LvjY2NJTExsdLpy5pQQIlI2HRoGcNTN3XlzamDuLXPefUSVCq3UXeVldvYu3dv6P7SjBkzGDNmDAAFBQUUFRWF3rNq1Sq6dKl9yZQyKrchImFXFlQT0y5i9rIPWLfjy1ofS+U2qq+m5TZWrlzJtGnTMDMGDBjAs88+CwRWKJ88eXLoSu+hhx4iKSmpTmMDldsQkXqmchtSFZXbEBGRRk8BJSIiXlJAiUi9a8y3DqTh1PTvhQJKROpVTEwM+/btU0hJOc459u3b942VK6qip/hEpF7FxcVRUFDA559/Hu6hiGdiYmKIi4ur9v4KKBGpV9HR0VxwwQXhHoZEAE3xiYiIlxRQIiLiJQWUiIh4SQElIiJeUkCJiIiXFFAiIuIlBZSIiHhJASUiIl5SQImIiJcUUCIi4iUFlIiIeEkBJSIiXlJAiYiIlxRQIiLiJQWUiIh4SQElIiJeUkCJiIiXFFAiIuIlBZSIiHhJASUiIl5SQImIiJcUUCIi4iUFlIiIeEkBJSIiXlJAiYiIlxRQIiLiJQWUiIh4SQElIiJeUkCJiIiXFFAiIuIlBZSIiHhJASUiIl5SQImIiJcUUCIi4iUFlIiIeEkBJSIiXmoa7gGIyLfbnoNHmL1sK+s+3s8/H7gy3MOJaBnrd5K+5H127S8itnUzpgzuwk09OoV7WJVSQIlIWJQF08vZBRx3juJSF+4hRbSM9TuZtiiPouJSAHbuL2LaojwAb0NKASUip9WJwVR63FFy/Otgyt7xRRhHFtme+kd+KJzKFBWXkr7kfQWUiAjAhPnrWbv9C5wumE6rfYXHTtq+a3/RaR5J9SmgROS0+u33ezB72Qe88u4nlFaY2ut1/tlhHFlki23djJ0nCaPY1s3CMJrq0VN8InJadWgZw1M3deXNqYO4tc95xDRtQnSUhXtYEW/K4C40i44q19YsOoopg7uEaUSnpisoEQmLsqCamHYRs5d9wLodX4Z7SBGt7D5TY3qKz1wjngju3bu3e/fdd8M9DBERqQMzy3bO9a7Yrik+ERHxUq0DyswePeF1azO7r36GBGYWb2Yb6+t4IiKRYO6b25j75rZwDyOkocdTlyuoR0943Rqot4ASERGp1kMSZpYBnAvEAM8AnYFmZpYDbAKigAuD37/hnJtiZlOAkcCZwGLn3ONmFg/8C8gC+gM7gWHOuSIz6wU8DxwObhcRkW+x6j7FN8Y594WZNQPWAgOBCc657hCYkgO6nvD9tcDFwOWAAa+Z2QDg42D77c65cWa2ELgF+DPwAvBj51ymmaVXNhAzuxe4F+C8886r4emKiEhjUd0pvolmtgF4h8CV1MWn2P/a4J/1wDrg0hPe85FzLif4OhuIN7NWQGvnXGaw/aXKDuyc+4Nzrrdzrnf79u2rOXwREWlsTnkFZWapwNVAP+fcYTNbSWCqr8q3ATOcc7+vcKx44OgJTaVAs+D+jfd5dxGR0+SdbfvCPYSQd7bto2/ntg12/OpcQbUCvgyG06VA32B7sZlFB18fAlqe8J4lwBgzawFgZp3MrENlHTjn9gMHzCwl2DSqJichIvJtMG5A5wYNhJrq27kt4wZ0brDjV+ce1L+B8WaWC7xPYJoP4A9Arpmtc86NMrNVwUfD/xV8SOIy4G0zAygE7iBwxVSZu4HnzewwgYATEZEKGjIQfKOVJEREJKy0koSIiDQqWiy2tnIXQsZ9cLwYWsbCNU9C8shwj0pEJGLoCqo2chfC3ycGwgng0C5YPB5W/yG84xIRiSAKqNpYNh2KKxT+cqXwrynw+oNw6NPwjEtEJIIooGrjQEHl27JfgFfuPn1jERGJUAqo2mgVV8kGg153w4g/nc7RiIhEJAVUbaQ9BtHNyrdZFAxNh+t/DS3PCc+4REQiiJ7iq42yp/X0FJ+ISINRQNVW8kgFkohIA9IUn4iIeOnbHVBv/Xfgz7etbxGRRuDbHVAiIuItBZSIiHhJASUiIl5SQImIiJf0mPn2rPD1G59y6v1ERL6lvt0B1f/H4es7PiW8/YuIeO7bHVCgkBAR8ZTuQYmIiJcUUCIi4iUFlIiIeEkBJSIiXlJAiYiIlxRQIiLiJQWUiIh4SQElIiJeUkCJiIiXFFAiIuIlBZSIiHhJASUiIl5SQImIiJcUUCIi4iUFlIiIeEkBJSIiXlJAiYiIlxRQIiLiJQWUiIh4SQElIiJeUkCJiIiXFFAiIuIlBZSIiHhJASUiIl5SQImIiJcUUCIi4iUFlIiIeEkBJSIiXlJAiYiIlxRQIiLiJQWUiIh4SQElIiJeUkCJiIiXFFAiIuIlBZSIiHhJASUiIl5SQImIiJcUUCIi4iUFlIiIeEkBJSIiXlJAiYiIlxRQIiLiJQWUiIh4SQElIiJeUkCJiIiXFFAiIuIlBZSIiHhJASUiIl5SQImIiJcUUCIi4iUFlIiIeEkBJSIiXlJAiYiIlxRQIiLiJQWUiIh4SQElIiJeUkCJiIiXFFAiIuIlBZSIiHhJASUiIl5SQImIiJcUUCIi4iUFlIiIeEkBJSIiXlJAiYiIlxRQIiLiJQWUiIh4SQElIiJeUkCJiIiXFFAiIuIlBZSIiHhJASUiIl5SQImIiJcUUCIi4iUFlIiIeEkBJSIiXlJAiYiIlxRQIiLiJQWUiIh4SQElIiJeUkCJiIiXFFAiIuIlBZSIiHhJASUiIl5SQImIiJcUUCIi4iUFlIiIeEkBJSIiXlJAiYiIlxRQIiLiJQWUiIh4SQElIiJeUkCJiIiXFFAiIuIlc86Fewy1ZmafAzvCPY46aAfsDfcgGpjOMTLoHCODr+d4vnOufcXGRh1QjZ2Zveuc6x3ucTQknWNk0DlGhsZ2jpriExERLymgRETESwqo8PpDuAdwGugcI4POMTI0qnPUPSgREfGSrqBERMRLCigREfGSAipMzGy7meWZWY6ZvRvu8dQHM3vezPaY2cYT2s42szfMbGvwa5twjrGuKjnHJ8xsZ/CzzDGzoeEcY12Z2blmtsLMNpvZJjN7INgeMZ9lFecYMZ+lmcWY2Roz2xA8xyeD7ReY2erg57jAzM4I91gro3tQYWJm24Hezjkf/9FcrZjZAKAQ+H/Oua7Btl8CXzjnZprZI0Ab59zUcI6zLio5xyeAQufc0+EcW30xs45AR+fcOjNrCWQDNwF3ESGfZRXnOJII+SzNzIDmzrlCM4sGsoAHgAeBRc65v5rZc8AG59yccI61MrqCknrjnHsT+KJC8zDgxeDrFwn8Emi0KjnHiOKc2+2cWxd8fQjYDHQigj7LKs4xYriAwuC30cE/DrgKeCXY7vXnqIAKHwcsNbNsM7s33INpQOc453ZD4JcC0CHM42koE8wsNzgF2Ginvioys3igB7CaCP0sK5wjRNBnaWZRZpYD7AHeAD4E9jvnSoK7FOBxMCugwue7zrmewHXA/cGpI2mc5gAXAt2B3cCvwjuc+mFmLYC/AZOccwfDPZ6GcJJzjKjP0jlX6pzrDsQBlwOXnWy30zuq6lNAhYlzblfw6x5gMYG/PJHos+B8f9m8/54wj6feOec+C/4iOA7MJQI+y+A9i78B85xzi4LNEfVZnuwcI/GzBHDO7QdWAn2B1mbWNLgpDtgVrnGdigIqDMysefDGLGbWHLgW2Fj1uxqt14A7g6/vBF4N41gaRNkv7aDhNPLPMnhz/Y/AZufcr0/YFDGfZWXnGEmfpZm1N7PWwdfNgKsJ3GtbAYwI7ub156in+MLAzDoTuGoCaAr8xTn3szAOqV6Y2XwglcCS/p8BjwMZwELgPOBj4P865xrtQwaVnGMqgSkhB2wHflh2r6YxMrMU4D9AHnA82PwogXs0EfFZVnGOtxMhn6WZJRN4CCKKwMXIQufc9ODvn78CZwPrgTucc0fDN9LKKaBERMRLmuITEREvKaBERMRLCigREfGSAkpERLykgBIRES8poERqyMxKgytdbzSzl83sO2Eax6Rw9S1yOiigRGquyDnXPbia+TFgfHXfaGZR9TiOSYACSiKWAkqkbv4DXARgZncE6+/kmNnvy8LIzArNbLqZrQb6mVkfM3srWKdnjZm1DC7qmW5ma4MLlf4w+N5UM1tpZq+Y2XtmNs8CJgKxwAozWxHcd46ZvXti7Z9g+9Dge7PMbLaZvR5sbx5cEHWtma03s2Gn90cnUjUFlEgtBdczuw7IM7PLgFsJLALcHSgFRgV3bQ5sdM5dAawBFgAPOOe6EVh+pgi4BzjgnOsD9AHGmdkFwff3IHC1lAB0DvYxm8AaaoOcc4OC+/2Xc643kAwMNLNkM4sBfg9c55xLAdqfcAr/BSwP9jkISA8uvSXihaan3kVEKmgWLGEAgSuoPwL3Ar2AtYFl3mjG14uplhJYlBSgC7DbObcWoGyVcDO7Fkg2s7I10loBFxOYQlzjnCsI7pcDxBMoPlfRyGDplqZARwKB1gTY5pz7KLjP/OBYIbAG5I1m9lDw+xgCyxhtruHPQ6RBKKBEaq4oeJUUElx89EXn3LST7H/EOVdatisnL29gwI+dc0sqHDcVOHGdtFJO8t9t8GrrIaCPc+5LM/sTgcCxKs7DgFucc+9XsY9I2GiKT6R+LANGmFkHADM728zOP8l+7wGxZtYnuF/L4FThEuBHwRIQmNkl1ZhuOwS0DL4+C/gKOGBm5xCYeizrr3OwKB8EpiHLLAF+HAxXzKxHdU9W5HTQFZRIPXDO5ZvZTwhUSW4CFAP3Azsq7HfMzG4F/jtYAqGIwH2o/yEwdbcuGBifc+pS3H8A/mVmu51zg8xsPbAJ2AasCvZXZGb3Af82s70E7oGV+SkwC8gN9rkduL62PwOR+qbVzEUinJm1cM4VBkPoWWCrc+434R6XyKloik8k8o0LPlyxicDDF78P83hEqkVXUCIi4iVdQYmIiJcUUCIi4iUFlIiIeEkBJSIiXlJAiYiIl/4/INzwP5O0lXIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "miniplot(percentiles)\n",
    "plt.legend()\n",
    "\n",
    "plt.yticks([0], [varname])\n",
    "decorate(xlabel='Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Beliefs and attitudes\n",
    "\n",
    "Now let's see what the results look like for a variety of beliefs and attitudes.\n",
    "\n",
    "The following list contains variable names, the response I've selected, and a label that summarizes the selected response."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, here's a complete analysis of a single variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pray [1, 2, 3, 4]\n",
      "[88.64014414 79.02553422 42.42939496 27.36045101]\n",
      "[[87.36607046 77.5964954  40.73463178 23.39981756]\n",
      " [88.60220296 79.5213753  44.20904724 26.65050877]\n",
      " [89.62262558 81.53221359 48.17391681 28.93114145]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varname = 'pray'\n",
    "value = value_map[varname]\n",
    "print(varname, value)\n",
    "\n",
    "res = compare(df, varname, value)\n",
    "print(res)\n",
    "percentiles = compare_iter(dfs, varname, value)\n",
    "print(percentiles)\n",
    "miniplot(percentiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For testing, we can loop through the variables and run one analysis with the unresampled data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spkmil\n",
      "[73.5113101  76.76801946 82.09560968 78.39559141]\n",
      "spkmslm\n",
      "[45.49596897 36.16927047 59.53604459 53.6932326 ]\n",
      "spkath\n",
      "[79.22311623 78.04532283 89.69533261 89.5105957 ]\n",
      "spkcom\n",
      "[69.46353225 68.75260772 81.12576215 83.42409051]\n",
      "spkrac\n",
      "[60.3880369  49.91127651 68.47108633 58.15589464]\n",
      "spkhomo\n",
      "[90.06235488 90.4766765  94.4764429  95.82608918]\n",
      "colmil\n",
      "[59.49662759 66.43455177 66.02818803 68.87707294]\n",
      "colmslm\n",
      "[32.43295797 26.14526947 45.31159224 35.16720515]\n",
      "colath\n",
      "[64.03971692 72.31061918 75.72620643 83.06921543]\n",
      "colcom\n",
      "[63.78654967 68.70265262 77.15069651 79.11245972]\n",
      "colrac\n",
      "[45.40934808 39.49995269 48.12065303 40.06686363]\n",
      "colhomo\n",
      "[89.17096349 92.52793162 94.96915779 93.93005577]\n",
      "libmil\n",
      "[72.26322264 76.80931845 85.93400702 84.70593488]\n",
      "libmslm\n",
      "[48.76353068 40.22516432 64.43893098 56.1513263 ]\n",
      "libath\n",
      "[74.79277381 76.19890115 92.86989688 92.6605855 ]\n",
      "libcom\n",
      "[71.47928341 74.34902723 85.86445345 86.97307526]\n",
      "librac\n",
      "[61.67809039 51.13471775 76.10257378 60.81905199]\n",
      "libhomo\n",
      "[81.79362286 88.88577597 94.88162589 94.56903031]\n",
      "homosex\n",
      "[43.2499736  26.02727507 14.60015087  8.47780471]\n",
      "premarsx\n",
      "[22.26112763 15.61701899  4.74451967  2.38574064]\n",
      "xmarsex\n",
      "[83.39192428 82.43078978 54.62779781 58.98866977]\n",
      "teensex\n",
      "[68.41449398 49.65675108 41.25854856 27.52858109]\n",
      "sexeduc\n",
      "[91.70737342 94.98287385 96.2774079  98.25713522]\n",
      "abany\n",
      "[43.41944174 41.3995639  71.55856513 66.42643421]\n",
      "abdefect\n",
      "[73.02303301 66.27051086 91.30318802 81.33778165]\n",
      "abnomore\n",
      "[44.31492364 39.77807224 72.00750498 66.08859556]\n",
      "abhlth\n",
      "[87.46737603 86.47912608 94.85623463 93.26452543]\n",
      "abpoor\n",
      "[41.62665525 41.84029891 67.75769386 66.49518121]\n",
      "abrape\n",
      "[74.89404181 77.31786536 90.46982819 90.56697658]\n",
      "absingle\n",
      "[38.84668806 34.19344447 66.37167504 60.53935644]\n",
      "cappun\n",
      "[33.55463848 44.48520016 38.21351605 42.42618274]\n",
      "gunlaw\n",
      "[71.20383307 66.33977015 69.18452013 69.66204428]\n",
      "grass\n",
      "[61.68805191 71.01634253 82.78437414 84.89693807]\n",
      "divlaw\n",
      "[43.00281337 46.85598636 51.33716417 59.40353458]\n",
      "prayer\n",
      "[33.74359486 52.04324277 67.26735582 76.1386634 ]\n",
      "letdie1\n",
      "[69.6644715  74.86999269 88.79556461 86.20789208]\n",
      "racopen\n",
      "[80.26737897 81.45531965 79.09489328 80.21025033]\n",
      "pornlaw\n",
      "[64.52978902 76.56828694 85.97367299 88.22481399]\n",
      "affrmact\n",
      "[21.05093081 26.50331869 30.32874825 34.98033455]\n",
      "natroad\n",
      "[56.45389489 35.53284828 65.23946114 39.69656434]\n",
      "natsoc\n",
      "[67.37905989 44.79243358 64.64223973 45.29214691]\n",
      "natmass\n",
      "[35.22213294 28.7643498  48.52230686 36.32573053]\n",
      "natpark\n",
      "[34.17935857 38.2896629  39.46412034 45.04948144]\n",
      "natchld\n",
      "[59.37417463 62.79809711 67.03702883 65.23955519]\n",
      "natsci\n",
      "[39.80269003 36.90575408 51.24566019 50.13529848]\n",
      "natenrgy\n",
      "[52.86066667 53.78547176 65.54730071 65.26712737]\n",
      "natspac\n",
      "[19.89561017 19.58949264 32.77135355 37.94810993]\n",
      "natenvir\n",
      "[57.20770297 70.91138832 72.45425865 81.37103326]\n",
      "natheal\n",
      "[64.75055397 63.13098615 72.13970354 64.46942054]\n",
      "natcity\n",
      "[55.53992497 54.05057976 51.59353382 55.07975863]\n",
      "natcrime\n",
      "[73.94955158 67.76348809 57.31752779 54.03097305]\n",
      "natdrug\n",
      "[68.31794005 67.26020567 70.19711738 65.31999371]\n",
      "nateduc\n",
      "[73.99285499 72.16437619 84.32265258 81.23015693]\n",
      "natrace\n",
      "[49.89317856 56.11094299 62.69301732 67.82830273]\n",
      "natarms\n",
      "[39.83380456 28.84803579 25.28695122 17.01997032]\n",
      "nataid\n",
      "[ 9.82466168 21.19327073 10.08446096 22.11511565]\n",
      "natfare\n",
      "[19.93343815 21.64751293 25.48356417 27.32928223]\n",
      "confinan\n",
      "[12.66682956 24.24425984  6.07534072 17.24309586]\n",
      "conbus\n",
      "[19.64942601 24.8910571  14.9580066  17.64472432]\n",
      "conclerg\n",
      "[22.85102756 29.62101982  4.2986863   8.27954864]\n",
      "coneduc\n",
      "[21.84336117 35.87421951 16.83152194 28.55130699]\n",
      "confed\n",
      "[10.45491829 13.93303892  9.40292797  9.52391555]\n",
      "conlabor\n",
      "[11.18874318 20.61281316 10.28513127 22.33094665]\n",
      "conpress\n",
      "[ 8.81439129  9.70186571 12.6718666  11.3784002 ]\n",
      "conmedic\n",
      "[32.47554257 49.23012708 30.20770207 44.76007786]\n",
      "contv\n",
      "[ 9.85957672 13.9771673   3.98226901  5.73194279]\n",
      "conjudge\n",
      "[26.3352463  27.96629237 22.38261828 22.1787875 ]\n",
      "consci\n",
      "[37.83470996 44.01941385 52.78142307 61.74872794]\n",
      "conlegis\n",
      "[3.90103339 9.54029367 1.44883188 5.36324345]\n",
      "conarmy\n",
      "[59.34348941 60.82389205 49.22218759 44.3586916 ]\n",
      "god\n",
      "[70.05585786 54.54176727 23.1488361  13.84997176]\n",
      "reborn\n",
      "[57.94039213 47.78098959 19.08488879 13.20532487]\n",
      "savesoul\n",
      "[56.70785044 51.78148764 17.91740306 18.08368455]\n",
      "bible\n",
      "[40.7100707  32.53023435  9.78488653  7.88023483]\n",
      "postlife\n",
      "[86.60628194 91.07626079 55.77191022 62.86114651]\n",
      "relpersn\n",
      "[66.62937988 55.68363263 11.59321878  7.28325125]\n",
      "sprtprsn\n",
      "[76.74946413 54.58924942 48.13054167 34.58637659]\n",
      "relexp\n",
      "[62.6117107  56.19613869 17.96450418 21.2967375 ]\n",
      "relactiv\n",
      "[11.01806423  7.55599771  3.8962701   0.92942076]\n",
      "pray\n",
      "[88.64014414 79.02553422 42.42939496 27.36045101]\n",
      "attend\n",
      "[28.08516892 20.12430165  3.44289599  3.81734432]\n",
      "helpful\n",
      "[47.47359947 34.36248075 49.7537416  34.16047289]\n",
      "fair\n",
      "[53.61717119 34.47184959 55.82503034 41.38746425]\n",
      "trust\n",
      "[29.26894445 18.43385666 40.44124049 23.60146494]\n",
      "fear\n",
      "[67.25857509 61.96407852 72.89262483 63.31583672]\n",
      "spanking\n",
      "[72.47779753 74.91842981 57.12181834 65.55452896]\n",
      "fepol\n",
      "[13.88866324 18.70822545  8.55305731 11.82774957]\n",
      "fejobaff\n",
      "[37.32595866 41.64868146 32.2215146  44.18340857]\n",
      "fehire\n",
      "[73.35485936 68.97477718 74.32198819 81.83595582]\n",
      "fechld\n",
      "[77.03561326 78.57964449 78.92801115 82.26175663]\n",
      "fepresch\n",
      "[25.75030164 18.79883107 23.52021269 14.83169073]\n",
      "fefam\n",
      "[23.6351855  24.05805615 13.37111586 12.41761707]\n",
      "discaffm\n",
      "[47.60762486 44.9277582  39.21370124 44.4863117 ]\n",
      "discaffw\n",
      "[73.30302634 64.9975628  73.78844031 73.7674552 ]\n",
      "meovrwrk\n",
      "[50.94775015 41.15332679 51.50807705 37.09845044]\n"
     ]
    }
   ],
   "source": [
    "for varname, value, _ in variables:\n",
    "    print(varname)\n",
    "    res = compare(df, varname, value)\n",
    "    print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's how we run the complete analysis for all variables.\n",
    "\n",
    "It takes a few minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_results(variables, results=None):\n",
    "    if results is None:\n",
    "        results = {}\n",
    "\n",
    "    for varname, value, _ in variables:\n",
    "        if varname in results:\n",
    "            continue\n",
    "        print(varname)\n",
    "        percentiles = compare_iter(dfs, varname, value)\n",
    "        results[varname] = percentiles\n",
    "        \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to clear saved results\n",
    "# results = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sprtprsn\n",
      "relexp\n",
      "relactiv\n",
      "pray\n",
      "attend\n"
     ]
    }
   ],
   "source": [
    "results = generate_results(variables, results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function generates a plot for a collection of variable names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multiplot(results, varnames, **options):\n",
    "    \"\"\"Make a plot showing results for several variables.\n",
    "    \n",
    "    results: map from varname to array of percentiles\n",
    "    varnames: list of string varnames\n",
    "    loc: string location for the legend.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(8, 4.5))\n",
    "    \n",
    "    for i, varname in enumerate(varnames):\n",
    "        percentiles = results[varname]\n",
    "        plot_percentiles(i, percentiles)\n",
    "\n",
    "    # label the y axis\n",
    "    labels = [label_map[varname] for varname in varnames]\n",
    "    plt.yticks(range(len(varnames)), labels)\n",
    "\n",
    "    # make a legend with just the first four entries\n",
    "    ax = plt.gca()\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    \n",
    "    loc = options.pop('loc', 'best')\n",
    "    ax.legend(handles[:4], labels[:4], loc=loc)\n",
    "\n",
    "    # flip the axes so the results go from top to bottom\n",
    "    plt.gca().invert_yaxis()\n",
    "    \n",
    "    underride(options, xlabel='Percentage', legend=False)\n",
    "    decorate(**options)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Free speech\n",
    "\n",
    "There are always some people whose ideas are considered bad or dangerous by other people. \n",
    "\n",
    "* For instance, somebody who is against all churches and religion . . .\n",
    "If such a person wanted to make a speech in your (city/town/community) against churches and religion, should he be allowed to speak, or not?\n",
    "\n",
    "* Or consider a person who believes that Blacks are genetically inferior...\n",
    "If such a person wanted to make a speech in your community claiming that Blacks are inferior, should he be allowed to speak, or not?\n",
    "\n",
    "* Now, I should like to ask you some questions about a man who admits he is a Communist.\n",
    "Suppose this admitted Communist wanted to make a speech in your community. Should he be allowed to speak, or not?\n",
    "\n",
    "* Consider a person who advocates doing away with elections and letting the military run the country.\n",
    "If such a person wanted to make a speech in your community, should he be allowed to speak, or not?\n",
    "\n",
    "* And what about a man who admits that he is a homosexual?\n",
    "Suppose this admitted homosexual wanted to make a speech in your community. Should he be allowed to speak, or not?\n",
    "\n",
    "* Now consider a Muslim clergyman who preaches hatred of the United States.\n",
    "If such a person wanted to make a speech in your community preaching hatred of the United States, should he be allowed to speak, or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['spkmslm', 'spkrac', 'spkcom', 'spkmil', 'spkath', 'spkhomo']\n",
    "multiplot(results, varnames, title='Allow to speak')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are always some people whose ideas are considered bad or dangerous by other people. \n",
    "\n",
    "* For instance, somebody who is against all churches and religion . . .\n",
    "Should such a person be allowed to teach in a college or university, or not?\n",
    "\n",
    "* Now consider a Muslim clergyman who preaches hatred of the United States.\n",
    "Should such a person be allowed to teach in a college or university, or not?\n",
    "\n",
    "* Questions associated with this variable:\n",
    "Or consider a person who believes that Blacks are genetically inferior....\n",
    "Should such a person be allowed to teach in a college or university, or not?\n",
    "\n",
    "* Now, I should like to ask you some questions about a man who admits he is a Communist.\n",
    "Suppose he is teaching in a college. Should he be fired, or not?\n",
    "\n",
    "* Consider a person who advocates doing away with elections and letting the military run the country.\n",
    "Should such a person be allowed to teach in a college or university, or not?\n",
    "\n",
    "* And what about a man who admits that he is a homosexual?\n",
    "Should such a person be allowed to teach in a college or university, or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['colmslm', 'colrac', 'colcom', 'colmil', 'colath', 'colhomo']\n",
    "multiplot(results, varnames, title='Allow to teach at college or university');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are always some people whose ideas are considered bad or dangerous by other people. \n",
    "\n",
    "* libath: For instance, somebody who is against all churches and religion . . .\n",
    "If some people in your community suggested that a book he wrote against churches and religion should be taken out of your public library, would you favor removing this book, or not?\n",
    "\n",
    "* librac: Or consider a person who believes that Blacks are genetically inferior.\n",
    "If some people in your community suggested that a book he wrote which said Blacks are inferior should be taken out of your public library, would you favor removing this book, or not?\n",
    "\n",
    "* libcom: Now, I should like to ask you some questions about a man who admits he is a Communist.\n",
    "Suppose he wrote a book which is in your public library. Somebody in your community suggests that the book should be removed from the library. Would you favor removing it, or not?\n",
    "\n",
    "* libmil: Consider a person who advocates doing away with elections and letting the military run the country.\n",
    "Suppose he wrote a book advocating doing away with elections and letting the military run the country.  Somebody in your community suggests that the book be removed from the public library. Would you favor removing it, or not?\n",
    "\n",
    "* libhomo: And what about a man who admits that he is a homosexual?\n",
    "If some people in your community suggested that a book he wrote in favor of homosexuality should be taken out of your public library, would you favor removing this book, or not?\n",
    "\n",
    "* libmslm: Now consider a Muslim clergyman who preaches hatred of the United States.\n",
    "If some people in your community suggested that a book he wrote which preaches hatred of the United States should be taken out of your public library, would you favor removing this book, or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['libmslm', 'librac', 'libcom', 'libmil', 'libath', 'libhomo']\n",
    "multiplot(results, varnames, title='Keep book in library written by');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confidence in institutions\n",
    "\n",
    "\"I am going to name some institutions in this country. As far as the people running these institutions are concerned, would you say you have a great deal of confidence, only some confidence, or hardly any confidence at all in them?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['conbus', 'coneduc', 'conjudge', 'conmedic', 'consci', 'conarmy']\n",
    "multiplot(results, varnames, loc='upper right', title='Great deal of confidence in');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\"title='Great\", 'deal', 'of', 'confidence', \"in');\")"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wbDzpTd5pmerVq3te5z5jTlWZOnUqv/jFL0rUd3Gme2rUqJHvvbf+i+uGG25g5cqVAOzZs4cPPvgAcKbr5syZ46nXrVs3rr/++nznBgcH079/f959990KleDYFJUxxpjKqwwfm1CvXj2GDBnCX//6V09Zt27dWLRoEQALFiwgJqbo6evY2Fjmz59PRkYGAOnp6Rw+fPiCeufPn+ftt98G4B//+AcxMTHUqVOHunXrsn79egDeeustz2hOacuN6fz588ycOZOxY8cC8MMPP3D69GkAPv74YwIDA4mIiCAjI4PvvvsOcNbgfPjhh9xwww1lEtulshEcY4wxldPVUe7ms5PL7M7Cxx9/nBdffNHzfu7cuYwaNYq4uDjPIuOi3H777ezatYuuXbsCzu3gf//732nUqFG+ejVq1GDHjh107NiROnXqsHjxYgDefPNNzyLjFi1aXLS/i7nvvvtYu3YtR48eJSwsjKeffpqHHnqIhQsX8tJLLwEwaNAgz6jV4cOHiY2NpVq1ajRp0oS33noLcNYM9e/fn7Nnz5KTk8Ott97qSYoqCtuqwVRolXarBmPMJbGtGkxRSrJVg01RGWOMMcbvWIJjjDHGGL9jCY4xxhhj/I4lOMYYY4zxO5bgGGOMMcbvWIJjjDHGGL9jCY4xxhiTx6FDh7j33ntp2bIlERER9O3blz179rB27Vr69etXrDamTZvGv/71r0KPJyQksHPnzmLXL66SxHg5jh07Ru/evalZsybjxo3Ld2zx4sW0bduWNm3a5NvKApwdyCMiImjTpg0///nPPeWTJk2iTZs23HjjjYwfP77ET2L2xh70Z4wxptJK2JZO3IrdfHs8k8ahIUyMbc2A9k0uuT1VZeDAgYwYMcLzxOKUlJQS7e2Uk5PDjBkzio47IYF+/foREREBcNH65SUnJ4eAgICL1gsODuaZZ55h+/btbN++3VN+7NgxJk6cyJYtW2jYsCEjRoxg1apV9OnTh7179zJr1iw2btxI3bp1PU9P3rRpExs3biQ1NRWAmJgYEhMT6dWr12Vdi43gGGOMqZQStqUzNT6N9OOZKJB+PJOp8WkkbEu/5DbXrFlDUFBQvqfytmvXju7duwPOZpmDBw/mhhtuYNiwYZ6RhvDwcGbMmEFMTAxLly5l5MiRnq0XpkyZQkREBG3btuWJJ55g06ZNvPfee0ycOJF27drx1Vdf5as/Y8YMOnXqRGRkJGPGjPH00atXLyZPnkznzp1p1aqVZwuHgk6ePMnAgQOJiIhg7NixnD9/HoCFCxcSFRVFZGQkkyf/uF9XzZo1mTZtGl26dOGTTz4hPDycp556ig4dOhAVFcUXX3xxQR81atQgJiaG4ODgfOX79u2jVatWNGzYEIDbbruNd955B4B58+bxyCOPULduXQDP05xFhDNnznDu3DnOnj1LVlYWV111+U+mtgTHGGNMpRS3YjeZWTn5yjKzcohbsfuS29y+fTsdO3Ys9Pi2bdt4/vnn2blzJ/v27WPjxo2eY8HBwWzYsIF7773XU/af//yHZcuWsWPHDlJTU3nyySfp1q0b/fv3Jy4ujpSUFFq2bJmvj3HjxrF582a2b99OZmYmy5cv9xzLzs4mKSmJ559/nqefftprjElJSfzhD38gLS2Nr776ivj4eL799lsmT57M6tWrSUlJYfPmzSQkJADOtguRkZF89tlnnr21GjRowNatW/nlL3/Jc889V+zP77rrruOLL75g//79ZGdnk5CQwIEDBwBnE889e/Zwyy23cPPNN/PRRx8B0LVrV3r37s0111zDNddcQ2xsbKk8zdoSHGOMMZXSt8czS1ReGjp37kxYWBjVqlWjXbt27N+/33Ns6NChF9SvXbs2wcHBPPzww8THx3PllVdetI81a9bQpUsXoqKiWL16NTt27PAcGzRoEAAdO3bM13fBGFu0aEFAQAD33XcfGzZsYPPmzfTq1YuGDRsSGBjIsGHDWLduHQABAQHcfffd+dooTj/e1K1bl1deeYWhQ4fSvXt3wsPDCQx0VsNkZ2ezd+9e1q5dy8KFC3n44Yc5fvw4X375Jbt27eLgwYOkp6ezevVqT2yXwxIcY4wxlVLj0JASlRdHmzZt2LJlS6HHq1ev7nkdEBBAdna2532NGjUuqB8YGEhSUhJ33303CQkJ3HHHHUX2f+bMGX71q1/x9ttvk5aWxujRozlz5swF/RfsOy8RueB9UYt2g4ODL1h3U5x+CnPnnXfy2Wef8cknn9C6dWuuv/56AMLCwrjrrrsICgri2muvpXXr1uzdu5dly5Zx8803U7NmTWrWrMlPf/pTPv300xL16Y0lOMYYYyqlibGtCQnK/4c5JCiAibGtL7nNW2+9lbNnzzJv3jxP2ebNm0lMTLyk9jIyMjhx4gR9+/bl+eefJyUlBYBatWpx6tSpC+rnJjMNGjQgIyPDsy6nJJKSkvj66685f/48ixcvJiYmhi5dupCYmMjRo0fJyclh4cKF9OzZ85Ku6WJyFw//97//5eWXX+bhhx8GYMCAAaxZswaAo0ePsmfPHlq0aEGzZs1ITEwkOzubrKwsEhMTS2WKyu6iMsYYUynl3i1VmndRiQjLli3jscceY/bs2QQHBxMeHs7zzz9PenrJFy+fOnWKu+66izNnzqCqzJkzB4B7772X0aNHM3fu3HxJTGhoKKNHjyYqKorw8HA6depU4j67du3KlClTSEtLo0ePHgwcOJBq1aoxa9YsevfujarSt29f7rrrrhK3nVd4eDgnT57k3LlzJCQksHLlSiIiIpgwYQKff/454Nz+3qpVKwBiY2M9dQICAoiLi6N+/foMHjyY1atXExUVhYhwxx13cOedd15WbABSGveaG1NWoqOjNTk52ddhGC8u9fbcvi+sp0OzUMb3uZ5GtYMvWt9ULbt27SqVf70b/+Tt90NEtqhqdMG6NoJjjCmx3Ntzc+9gyb09F7hokrPzu5PsPXyKpVsOck/HMEt0jDFlwhIcY0yJFXZ77swPdtK03sUXeGblKKAsTj5giY4xpkzYImNjTIkVdhvusYxzJWonK0c5m32eBUn/ZtzCbaURmjHGADaCY4y5BI1DQ0j3kuQ0Dg2hY/N6xW4nKEAIEGFwdFPG97muNEM0xlRxluCYQolIDpCG83uyCxihqj/4NipTEUyMbZ1vDQ6U7PbcgolNo1o2NWWMKV2W4JiiZKpqOwARWQCMBf6Ye1Ccp0mJqp73UXymlM1btw+A0T1aFFnvcm7PjbimNh2a1/UkNsXt0xhjSsLW4JjiWg9cJyLhIrJLRF4GtgJNReR2EflERLaKyFIRqQkgIrNFZKeIpIrIc27ZPSKyXUQ+F5HLfxa38ZkB7ZuwccqtfD37Z2yccmuxnz3y4YTuzBwQaaM2psI6dOgQ9957Ly1btiQiIoK+ffuyZ88e1q5dS79+/YrVxrRp0/jXv/5V6PGEhAR27txZ7PrFVZIYL8exY8fo3bs3NWvWZNy4cfmOLV68mLZt29KmTRsmTZrkKf/mm2/o06cPbdu2pVevXhw8eNBT3rFjR9q1a0ebNm149dVXSyVGG8ExFyUigcBPgY/cotbAg6r6KxFpADwJ3Kaqp0VkMvBrEXkRGAjcoKoqIqHuudOAWFVNz1NWsL8xwBiAZs2ald2FGa8+3Xes3Pu7uUX9cu3T+IeyeKaSqjJw4EBGjBjBokWLAEhJSeH7778vdhs5OTnMmDGjyDoJCQn069ePiIgIgIvWLy85OTkXbNvgTXBwMM888wzbt29n+/btnvJjx44xceJEtmzZQsOGDRkxYgSrVq2iT58+PPHEEwwfPpwRI0awevVqpk6dyltvvcU111zDpk2bqF69OhkZGURGRtK/f38aN258WddiIzimKCEikgIkA/8G/uqWf6OquRuF3AxEABvduiOA5sBJ4AzwFxEZBOSu3dkIvCEiowGv/xWp6muqGq2q0Q0bNiyL6zKFGN2jRbknGze3qG/TU+aS7PzuJIuTD9D92TU8uSyNwyfPXPyki1izZg1BQUGMHTvWU9auXTu6d+8OOFsvDB48mBtuuIFhw4Z59ngKDw9nxowZxMTEsHTpUkaOHOl5QvGUKVOIiIigbdu2PPHEE2zatIn33nuPiRMn0q5dO7766qt89WfMmEGnTp2IjIxkzJgxnj569erF5MmT6dy5M61atWL9+vVer+HkyZMMHDiQiIgIxo4dy/nzziqChQsXEhUVRWRkJJMnT/bUr1mzJtOmTaNLly588sknhIeH89RTT9GhQweioqL44osvLuijRo0axMTEEBycP7Hct28frVq1Ivf/3bfddhvvvPMOADt37qRPnz4A9O7dm3fffReAK664wrP31dmzZz3xXi4bwTFF8azByeVu4nY6bxHwsareV/BkEekM9AHuBcYBt6rqWBHpAvwMSBGRdqpavkMGpkgVMtk4dQgSn4WDSTB2g6+jyS91CayaAScOQp0w6DMN2g7xdVRVRmk/U2n79u107Nix0OPbtm1jx44dNG7cmFtuuYWNGzcSExMDOKMaGzY4v58ffeQMeP/nP/9h2bJlfPHFF4gIx48fJzQ0lP79+9OvXz8GDx58QR/jxo1j2rRpADzwwAMsX77cs3VBdnY2SUlJfPjhhzz99NNep7WSkpLYuXMnzZs354477iA+Pp5u3boxefJktmzZQt26dbn99ttJSEhgwIABnD59msjIyHyjSA0aNGDr1q28/PLLPPfcc/zlL38p1ud33XXX8cUXX7B//37CwsJISEjg3Dnn8RE33XQT77zzDhMmTGDZsmWcOnWKY8eOUb9+fQ4cOMDPfvYzvvzyS+Li4i579AZsBMdcvk+BW0TkOgARuVJEWrnrcOqo6ofAY0DuYuWWqvqZqk4DjgJNfRW4qQROHYLlv4YXboJtb8GhNF9HlF/qEnh/PJw4AKjz/f3xTrkpV+X1TKXOnTsTFhZGtWrVaNeuHfv37/ccGzp06AX1a9euTXBwMA8//DDx8fFceeWVF+1jzZo1dOnShaioKFavXs2OHTs8xwYNGgRAx44d8/VdMMYWLVoQEBDAfffdx4YNG9i8eTO9evWiYcOGBAYGMmzYMNatc5ZBBgQEcPfdd+drozj9eFO3bl1eeeUVhg4dSvfu3QkPDycw0BlLee6550hMTKR9+/YkJibSpEkTz7GmTZuSmprKl19+yZtvvlmiKcHC2AiOuSyqekRERgILRaS6W/wkcAp4V0SCcUZ5/sc9Fici17tlq4DPyzlkUxnkjtikLIDz2c5Xrn9/5ru4ClrxG8gq8DygrExnRMdGccpVaT1TqU2bNkXu4J07lQJOYpCd/ePvZo0aNS6oHxgYSFJSEqtWrWLRokW8+OKLrF69utD2z5w5w69+9SuSk5Np2rQp06dP9+wwnrf/gn3n5Y6053tf1L6TwcHBF6y7KU4/hbnzzjs9I06vvfaap+3GjRsTHx8POFN977zzDnXq1Ml3buPGjWnTpg3r16/3OrpVEjaCYwqlqjW9lO1X1cgCZatVtZOqtnW/3lPV71S1s/s+SlXfdOsOct9HquoEtd1ejTdvPwhbXofsM/mTm4rm9BHv5ScOlm8cVVhQgBAcWI2hnZqxbnLvy75D79Zbb+Xs2bPMmzfPU7Z582YSExMvqb2MjAxOnDhB3759ef7550lJSQGgVq1anDp16oL6uclMgwYNyMjIKDLZKkxSUhJff/0158+fZ/HixcTExNClSxcSExM5evQoOTk5LFy4kJ49e17SNV3M4cOHAfjvf//Lyy+/zMMPPwzA0aNHPetrZs2axahRowA4ePAgmZmZnnM2btxI69bFe6ZWUWwExxhT8Qx+AxJ/74zg6HnIybMFRLMuPgvrAnXC3OkpL+WmzBV8plJpEBGWLVvGY489xuzZswkODiY8PJznn3+e9PT0Erd36tQp7rrrLs6cOYOqMmfOHADuvfdeRo8ezdy5c/MlMaGhoYwePZqoqCjCw8Pp1KlTifvs2rUrU6ZMIS0tjR49ejBw4ECqVavGrFmz6N27N6pK3759ueuuu0rcdl7h4eGcPHmSc+fOkZCQwMqVK4mIiGDChAl8/rkzOD9t2jRatWoFOLewT506FRGhR48evG3ZT8YAACAASURBVPTSS4CzQ/jjjz/uGWl64okniIqKuqzYwHlI22U3YkxZiY6O1uTkZF+HYXzl1PcXJjrTT/g6qh/lrsHJO00VFAJ3zrUpqku0a9cubrzxRl+HYSoob78fIrJFVaML1rUpKmNMxVXrKuj3R5iQCu0fgKuL+Ffdpj85X+Wp7RAnmanTFBDne2VKbnzxmRlTTmyKyhhT8eUmOhVR2yGVJ6ExpgqxBMcY4z/2V7Bn5FR0+zdAeIyvo7iAql5wJ5AxJV1SYwmOMcY/dHvU1xFUPuExFe5zCw4O9jz8zZIck0tVOXbs2AVPTi6KJTjGGP9Rwf5Ym5ILCwvj4MGDHDlSyC34psoKDg4mLKz4dyhagmOMMabCCAoK4tprr/V1GMYP2F1UxhhjjPE7luAYY4wxxu9YgmOMMcYYv2MJjjHGGGP8jiU4xhhjjPE7luAYY4wxxu9YgmOMMcYYv2MJjjHGGGP8jiU4xhhjjPE7luAYY4wxxu9YgmOMMcYYv2MJjjHGGGP8jiU4xhhjjPE7luAYY4wxxu9YgmOMMcYYv2MJjjHGGGP8jiU4xhhjjPE7gb4OwJjKLmFbOnErdvPt8Uwah4YwMbY1A9o38XVYPtH3hfV0aBbK+D7X06h2sK/DMcZUYZbgGHMZEralMzU+jcysHADSj2cyNT4NoEomOTu/O8new6dYuuUg93QMs0THGOMzluAYcxniVuz2JDe5MrNymPnBTprWC/FRVL6VlaOAsjj5gCU6xhifsTU4xlyGb49nei0/lnGunCOpeLJylLPZ51mQ9G/GLdzm63CMMVWMjeD4GRG5Gnge6AScBfYDj6nqHl/G5a8ah4aQ7iXJaRwaQsfm9XwQUcURFCAEiDA4uinj+1zn63CMMVWMJTh+REQEWAa8qar3umXtgKuAUk9wRCRQVbNLu93KZGJs63xrcABCggKYGNvah1H5VsHEplEtm5oyxpQ/S3D8S28gS1VfzS1Q1RRxxAE/BRSYqaqLRaQXMB04CkQCW4D7VVVFpC/wR/fYVqCFqvYTkelAYyAcOCoiDwCzgV5AdeAlVf2ziFwDLAZq4/ye/RLYBPwViHbjmK+qc8ru4yg989btA2B0jxb5ynMXEl/sLqrCzvc3EdfUpkPzupbYGGN8zhIc/5KbpBQ0CGgH3AQ0ADaLyDr3WHugDfAtsBG4RUSSgT8DPVT1axFZWKC9jkCMqmaKyBjghKp2EpHqwEYRWen2uUJVfysiAcCVbgxNVDUSQERCvV2E2+YYgGbNml3SB1GeBrRvUiXvmPLmwwndfR2CMcYAluBUFTHAQlXNAb4XkUScNTongSRVPQggIik4IzMZwD5V/do9fyFuwuF6T1VzF57cDrQVkcHu+zrA9cBmYL6IBAEJ7kjSPqCFiPwJ+ABY6S1YVX0NeA0gOjpaL/vqS8mn+45d1rk3t6hfitEYY4wpit1F5V924IyuFCRFnHM2z+scnKS3qPoApwu0/aiqtnO/rlXVlaq6DugBpANvichwVf0vzijSWuAR4C8X6ad8pS6BOZEwPdT5nrrEc2h0jxaXlaDc3KJ++UxPvRoDy38Npw6VfV/GGFOB2QiOf1kN/E5ERqvqPAAR6QT8FxgqIm8C9XASj4nADYW08wXOSEu4qu4HhhbR5wrglyKyWlWzRKQVTlLTAEhX1XkiUgPoICIfAudU9R0R+Qp443IvuNSkLoH3x0OWOzB14oDzHqDtEKCSrJ85lAZHdkPKAmg3DHpOglpX+zoqY4wpd5bg+BF3cfBA4HkRmQKcwb1NHKgJfI6zuHeSqh4SEa8Jjru25lfARyJyFEgqotu/4ExrbXXv4joCDMBZdDxRRLJwpryGA02A10Ukd+Rw6mVcbulaNePH5CZXVias+A2ENvdNTJcqx30Gz7a3LNExxlRZolphljiYCkREaqpqhpu0vATs9cUdT9HR0ZqcnFz2HU0Pxcn9ChIYtaLs+y8t82+/sEyqQbOb4cF/ln88xhhTxkRki6pGFyy3ERxTmNEiMgK4AtiGc1eV/6oT5kxLeStv1qX84ykNAVc4yU27YdBzsq+jMcaYcmUJjvHKHa2pFM+oKRV9puVfgwMQFOKUVzYFE5taV/k6ImOMKXeW4Bj/tulPzvdujxZdz11IzKoZcOKgM3LTZ9qP5Zfabnm7OgrCOltiY4yp8izBMSZX2yGFJzSVxdgNvo7AGGMqBEtwjP/bXwZ/9PdvgPCY0m/XGGNMqbAEx/i3sppCCo+peNNTxhhjPCzBMf7PEhFjjKlybKsGY4wxxvgdS3CMMcYY43cswTHGGGOM37EExxhjjDF+xxIcY4wxxvgdS3CMMcYY43cswTHGGGOM37EExxhjjDF+xxIcY4wxxvgdS3CMMcYY43cswTHGGGOM37EExxhjjDF+xxIcY4wxxvgdS3CMMcYY43cswTHGGGOM37EExxhjjDF+xxIcY4wxxvgdS3CMMcYY43eqbIIjIjkikpLna0o59fu/Bd5vKoU2w0Vk++W2U8I+14pI9GWcHyciO0QkrjTjMpVTwrZ0bpm9mmunfMAts1eTsC3dZ7H0fWE9Ty5L4/DJMz6LwRhz+QJ9HYAPZapqOx/0+7/A73LfqGq38upYRAJUNae8+ruIXwANVfWsrwMxvpWwLZ2p8WlkZjm/munHM5kanwbAgPZNyj2end+dZO/hUyzdcpB7OoYxvs/1NKodXO5xGGMuT1VOcC4gInWAJKC/qu4WkYXAalWdJyK3A08D1YGvgAdVNUNEOgEvADWAs0Af4G4gWlXHue0uB54D7gBCRCQF2KGqw0QkQ1Vrishi4E1V/dA95w3gfSABmA30cvt+SVX/7CX8QBF5E2gP7AGGq+oPIrIfmA/cDrwoIrWAMcAVwJfAA269N4CTQDRwNTBJVd92Y5kEPACcB/6pqrmjXfeIyMtAKPCQqq4v8HkK8CzwU0CBmaq6WETecz+vz0RklqouLuaPyPihuBW7PclNrsysHGZ+sJOm9UJ8ElNWjgLK4uQDlugYU0lV2Skq3EQjz9dQVT0BjAPeEJF7gbpuctMAeBK4TVU7AMnAr0XkCmAxMEFVbwJuAzIL69BNDDJVtZ2qDitweBEwFMBttw/wIfAQcEJVOwGdgNEicq2X5lsDr6lqW5xE5Vd5jp1R1RhVXQTEq2onN95dbvu5rgFigH44SRUi8lNgANDFPefZPPUDVbUz8BjwlJeYBgHtgNzPJk5ErlHV/nk+B0tuqrhvj3v/T+ZYxrlyjuRCWTnK2ezzLEj6N+MWbvN1OMaYEqjKIzhep6hU9WMRuQd4CecPM8DNQASw0RmU4ArgE5yk4jtV3eyeexLArVNS/wTmikh1nJGedaqa6Y4ctRWRwW69OsD1wNcFzj+gqhvd138HxuOMGoGThOWKFJGZOKMuNYEVeY4lqOp5YKeIXOWW3Qa8rqo/uNf4nzz1493vW4BwL9cUAyx0p8W+F5FEnCTtvSI+B1PFNA4NId1LktM4NISOzev5IKIfBQUIASIMjm7K+D7X+TQWY0zJVOUExysRqQbciDMSUw84CAjwsareV6BuW5ypl4KyyT86dtFxbVU9IyJrgVickZyFud0Aj6rqisLOzW2iiPen87x+Axigqp+LyEicqa9cedfDSJ7v3q4xb/0cvP8uXVKmZ6qWibGt863BAQgJCmBibGufxVQwsWlUy6amjKlsqvIUVWH+B2fq5j5gvogEAZ8Ct4jIdQAicqWItAK+ABq763AQkVoiEgjsB9qJSDURaQp0ztN+ltumN4uAB4Hu/DiysgL4Ze45ItJKRGp4ObeZiHR1X98HbCikj1rAd257BafJvFkJjBKRK93+S/JP6nXAUBEJEJGGQA+cNU7GT81bt4956/aV6JwB7Zswa1AUTUJDEKBJaAizBkVd8gLjS4khr4hrajO0UzPWTe7NzAGRltwYU0lV5RGc3MW+uT7CWYz7MNBZVU+JyDrgSVV9yh3tWOhOIeGW7xGRocCfRCQEZ9TnNmAjzhRSGrAd2Jqnn9eAVBHZ6mUdzkrgb8B7qpq7AOEvONM/W91Fu0dw1sQUtAsYISJ/BvYCrxRy3f8HfAZ848ZXq5B6AKjqRyLSDkgWkXM464L+t6hz8lgGdAU+xxkFmqSqh4p5rqlCBrRv4pM7prz5cEJ3X4dgjCkFolrY7IMxviEiY3Du9KJZs2Ydv/nmGx9HZIord+RkdI8WVToGY0z5EZEtqnrBc9mq8giOqaBU9TWckS6io6MtA69kPt13zOf939yivk9jMMb4niU4xphSU5xRk5CzR+jw9TwanUxlWedFtDz0IZ2+epGaZw+RUf1qNrccx1dX973kGG5uUd9Gb4wxlSfBEZFQ4Oeq+rL7vhfwhKr2u8T2GgLLcW75Hl/wIXWFnHNZfXppL0NVa5ZGW5VJwZ+l8S+FJhenDkHis5CyAPQ85JxjdGgyrJ8JWc5t4rXOfsete2dy6w2NoO2QcozaGONvKtNdVKHkf3jd5eoDfKGq7YuT3FwK946qCs1HMZb2z9JUZKcOwfJfwws3wdY3IfsM5Lhr6Ff8xpPceGRlwqoZ5R+nMcavVKYEZzbQ0n3qcO4GjTVF5G0R+UJEFrh3GSEiHUUkUUS2iMgKEbkmb0PuXUHPAn3d9kJE5HYR+UREtorIUhGp6da9w21/A86TeXPbqCEi80Vks4hsE5G73PKR7vnvAytFpKaIrHLbTcutVxQR+T+3z49FZKGIPJEbt4h8KiKpIrJMROqKyI0ikpTn3HARSS3qcxBno8zfuQ/emyAib4jIXBHZJCL7ch8qKCK93POXiMgeEZktIsNEJMm9lpZuvYYi8o77WWwWkVvc8unuZ7TWbXd8ET9L46/efhC2vO4kNuez8x87fcT7OScOln1cxhi/VpkSnCnAV+7j/Se6Ze1xtgmIAFrgPKsmCPgTMFhVO+Lc+v3bvA2pagowDVjsPs24Bt63YggG5gF34jyb5uo8zfwGZ5+qTkBvnG0Icp9P0xUYoaq3AmeAgW67vYE/5CZi3oizQ/fd7rUNwtkbKtffgMnudgxpwFOqugu4QkRy5wWGAkuK8TmEqmpPVf2D+/6CbRpcNwETgCic/ahaudsz/AV41K3zAjDH/Szudo/lugHn4YWdgafcuLz9LI2/GvwGdHwQAoMh4Ir8x+qEeT+nsHJjjCmmCj+FchFJqnoQwH2mTThwHIgEPnbziADgu4u0U9hWDDcAX6vqXrePv+PevoyzeWX/3NEVnKcVN3Nff5xnSwMBficiPXA2q2wCXAUU9jyYGOBdVc10+3zf/V4HJylJdOu9CSx1Xy8BhuAkJkPdr9YX+RwK7gHlbZsGgM2q+p0bw1c4z+oBJ8Hq7b6+DYjIk7fVFmdTT4AP3B3Dz4rIYffaTVVS6yro90foORkSf59vDQ59psH74/NPUwWFOOXGGHMZKnuCk3drgdztAgRnp+6u3k/xqrCtGNpR+DYFAtytqrsLnNOF/FsjDAMaAh1VNUuc3b2LejTqpWxvsBhYKiLxgKrqXhGJoujP4XSB9962aShYfj7P+/P8+PtTDeiam5R5GnESHm8/I1ORbfqT873bo0XXK6mCic7BpB8XEq+a4UxL1QlzkpvyWmBcVtdqjPG5yjRFdYqLPHXXtRtoKO62BSISJCJtLnJOUVsxXJu71gRnC4RcK4BH86z7aV9I23WAw25y0xtofpFYNgB3ikiwuw7oZwDuTuf/FZHcx6w+ACS6x77CSR7+jx9HZi7lc7hUK3F2Ycft64JNTAso7s/S+KPcRGesu5tI2yHwP9th+nHnu909ZYwpBZXmX9OqekxENorIdpydtz8opN45d5HsXHdaJxB4HthRRNtHpPCtGMYAH4jIUZzkI9I9/ozbbqqb5OzHWb9S0ALgfRFJBlJwkqairnOziLyHs73BNzjrgU64h0cAr4qzL9Q+nH2rci0G4oBrL/VzuAzjgZfcxc2BOHtQjS2scsGfpa3DqYD2F7aVmZ/ZvwHCY3wdhTGmDNhWDRWQiNRU1Qw3kVkHjFHVrRc7zx9FR0drcnKyr8OoenKnbqoCm54yplIT26qhUnlNRCJw1uq8WVWTG+ND9kffGFPJWYJTAanqz30dgzHGGFOZVaZFxsYYY4wxxWIJjjHGGGP8jiU4xhhjjPE7luAYY4wxxu9YgmOMMcYYv2MJjjHGGGP8jiU4xhhjjPE7luAYY4wxxu9YgmOMMcYYv2MJjjHGGGP8jiU4xhhjjPE7luAYY4wxxu9YgmOMMcYYv2MJjjHGGGP8jiU4xhhjjPE7luAYY4wxxu9YgmOMMcYYvxPo6wCMMcYYX0rYlk7cit18ezyTxqEhTIxtzYD2TXwdll/q+8J6OjQLZXyf62lUO7hM+7IExxhjTJWVsC2dqfFpZGblAJB+PJOp8WkAluSUgZ3fnWTv4VMs3XKQezqGlWmiYwmOMcaYKituxW5PcpMrMyuHmR/spGm9EB9F5d+ychRQFicfKNNEx9bgGGOMqbK+PZ7ptfxYxrlyjqTqycpRzmafZ0HSvxm3cFupt28jOIUQkTDgJSACJxFcDkxU1TL9rReRaGC4qo6/zHbeAJar6tuXeP5IIFpVx4nIWOAHVf1bEfVLJW5jjClPjUNDSPeS5DQODaFj83o+iKjqCAoQAkQYHN2U8X2uK/X2LcHxQkQEiAdeUdW7RCQAeA34LTCxQN1AVc0urb5VNRlILq32ilLc2FX11WLUKbe4jTGmtEyMbZ1vDQ5ASFAAE2Nb+zAq/1YwsWlUq2zW4NgUlXe3AmdU9XUAVc0B/gcYJSJXishIEVkqIu8DK0Wkmoi8LCI7RGS5iHwoIoMBRGSaiGwWke0i8pqbPCEia0Xk9yKSJCJ7RKS7W95LRJa7rz8UkRT364SIjBCRABGJc9tMFZFfuHVFRF4UkZ0i8gHQyNuFuf3+TkQSgQki0lBE3nHb2ywit3g5Z7qIPOG+7uT2+4kbx3YvcdcTkQS33qci0jZPO/PdGPaJiI32GGOKbd66fcxbt69U2xzQvgmzBkXRJDQEAZqEhjBrUFSpLjAui7grq4hrajO0UzPWTe7NzAGRZZbcgI3gFKYNsCVvgaqeFJF/A7njaF2Btqr6HzeZCQeicBKLXcB8t96LqjoDQETeAvoB77vHAlW1s4j0BZ4CbivQZ1/3vI7A60AC8BBwQlU7iUh1YKOIrATaA63dGK4CduaJoaBQVe3ptv0PYI6qbhCRZsAK4MYiPpvXgTGquklEZhdS52lgm6oOEJFbgb8B7dxjNwC9gVrAbhF5RVWziujPGGPK1ID2TeyOqXLy4YTu5daXJTjeCaAXKf9YVf/jvo4BlqrqeeCQiKzJc05vEZkEXAnUA3bwY4IT737fgpMgXdihSAPgLWCIqp4QkduBtrkjREAd4HqgB7DQHW36VkRWF3F9i/O8vg2IcAeWAGqLSK1CYgkFaqnqJrfoHzgJW0ExwN0AqrpaROqLSB332AeqehY4KyKHcZKxgwX6GQOMAWjWrFkRl2GMqWo+3XfM1yGU2Kf7jnFzi/q+DqPKsQTHux24f6BziUhtoCnwFdAROJ33sLdGRCQYeBlnse4BEZkO5B2PO+t+z8HLz8Jd+7MImKGq2/P09aiqrihQty/ekzJv8sZeDeiqqvlW2eVJePIVF7N9b/VyYzubp8zrdavqazhrnoiOji7uNVV8qUtg1Qw4cRDqhEGfadB2iK+jurhXYyCsM/ScBLWu9nU0pgob3aOFr0O4JDe3qF9pY6/MLMHxbhUwW0SGq+rf3ETjD8AbqvqDlz/+G4ARIvIm0BDohTO6kZvMHBWRmsBgoCR3Nc0GUlV1UZ6yFcAvRWS1qmaJSCsgHVgH/EJE/oYzTdbbjeFiVgLjgDgAEWmnqineKqrqf0XklIjcrKqfAvcW0uY6YBjwjIj0Ao66U3zFCMdPpS6B98dDlptHnjjgvIeKn+QcSoMjuyFlAbQbZomO8SlLFExxWYLjhaqqiAwEXhaR/8MZ5fgQ+N9CTnkH6ANsB/YAn+GskzkuIvOANGA/sLmEoTwB7BCR3IRjGvAXnOmsre6C5SPAAGAZzuLoNDeGxGL2MR54SURScX4f1gFji6j/EDBPRE4Da4ETXupMB1532/wBGFHMWPzXqhk/Jje5sjJhxW8gtLlvYiqJHPfpCNveskTHGFMpiKr/zAD4kojUVNUMEakPJAG3qOohX8dV2nKv0309BbhGVSeUVX/R0dGanOwHd59PD6XQZV2jVngpr0Dm335hmVSDZjfDg/8s/3iMMSYPEdmiqtEFy20Ep/QsdxfhXgE844/JjetnIjIV53fnG2Ckb8OpJOqEOdNS3sqbdSn/eC5VwBVOctNuGPSc7OtojDGmUJbglBJV7eXrGMqDqi4m/11Ypjj6TMu/BgcgKMQprwwKJja1rvJ1RMYYUyRLcIwpjk1/cr53e/TSzs9dSFwWd1FdbmwXc3WUexeVJTbGmMrDEhxjykvbIRX/jilvxm7wdQTGGFNiluAYU1z7K+gf+v0bIDzG11EYY0yFYgmOMcVRVtM/pSE8pmLHZ4wxPmAJjjHFZUmEMcZUGrabuDHGGGP8jiU4xhhjjPE7luAYY4wxxu9YgmOMMcYYv2MJjjHGGGP8jiU4xhhjjPE7luAYY4wxxu9YgmOMMcYYv2MJjjHGGGP8jiU4xhhjjPE7luAYY4wxxu9YgmOMMcYYv2MJjjHGGGP8jiU4xhhjjPE7luAYY4wxxu9YgmOMMcYYv2MJjjHGGGP8jiU4xhhjjPE7gb4OwBhjykvCtnTiVuzm2+OZNA4NYWJsawa0b+LrsEwF1veF9XRoFsr4PtfTqHawr8MxJWAJjjGmSkjYls7U+DQys3IASD+eydT4NABLckyhdn53kr2HT7F0y0Hu6RhmiU4lYgmOMaZKiFux25Pc5MrMymHmBztpWi/ER1GZyiArRwFlcfIBS3QqEVuDY4ypEr49num1/FjGuXKOxFRWWTnK2ezzLEj6N+MWbvN1OOYibATHGFMlNA4NId1LktM4NISOzev5ICJT2QQFCAEiDI5uyvg+1/k6HHMRFx3BEZEcEUkRkc9FZKuIdLvUzkRkrYhEX+r5l9HvdBF5wkt5QxH5TES2iUh3EflQRELLKaYZInLbReq8ISKDL+XcQtoLF5Gf53kfLSJz3de9ivOzFZHGIvJ2Sfs2xtcmxrYmJCggX1lIUAATY1v7KCJTWQQFCMGB1RjaqRnrJvdm5oBIGtWy6amKrjgjOJmq2g5ARGKBWUDPMo2q/PQBvlDVEe779eXVsapO88G54cDPgX+47SQDye6xXkAGsOkifX8LXJB0GVPR5S4ktruoTElEXFObDs3rMr7PdZbUVDIlnaKqDfwXQERqAu8CdYEg4ElVfVdEwoF/AhuAbkA6cJeqesaGRaQa8DpwAHgK+CsQDSgwX1Xn5O1URO4EngSuAI4Bw1T1exGZDjQDWrjfn1fV3BGJ3wDD3T6OAFsKtNkOeBYIEZEUoCuwy42jZmHXICKjgTFuLF8CD6jqDyLyBnDSPf9qYJKqvu32NQl4ADgP/FNVp7j1l6vq2yIyDbgTCMFJMH6hqlrYD6HAufuBN93zg4B7VPULEekJvOCeokAPYDZwo3u9bwLbgCeAccBYIEdE7gceBR7K7cPtM0NVa7o/3+WqGikiwcAr7jVnA79W1TUiMhLoD1wJtASWqeokEQngIj9rYy5m3rp9AIzu0aLE5w5o3+SyEprL6dtUTh9O6O7rEMwlKs4i4xB3iuoL4C/AM275GWCgqnYAegN/EBFxj10PvKSqbYDjwN152gsEFgB7VPVJoB3QRFUjVTUKJ/EpaANws6q2BxYBk/IcuwGIBToDT4lIkIh0BO4F2gODgE4FG1TVFGAasFhV2+VNwC5yDfGq2klVb8JJiB7Kc841QAzQDyeZQER+CgwAurjnPOvl+l5024zESXL6ealTlKPuz+EVnIQF9/sj7uhbdyATmAKsd6/Xk1io6n7gVWCOe6y4I1mPuOdHAfcBb7pJDzg/16FAFDBURJpSvJ81IjJGRJJFJPnIkSPFDMUYY4z5UUmnqLoCfxORSECA34lID5yRiSbAVe45X7sJBDgjJ+F52vszsERVf+u+3we0EJE/AR8AK73EEAYsFpFrcEZOvs5z7ANVPQucFZHDbgzdcUYNfnDjfq8Y11lQYdcQKSIzgVCckZ4Vec5JUNXzwE4Ryf0sbgNez41FVf/jpa/e7ijPlUA9YAfwfglijc8T5yD39UbgjyKyACcpO/hj/llqYoA/AbijRt8Ardxjq1T1/9u79yCpyjuN49/HAcKIyATFKyISL0lWCchgSMIqBFc3romkQnCzuDHR9ZJNRDflZdUNIiZVVtxoVhOjYvC2RsVLTHRNMIW6SFyFQZARlFyURBBvUYgYojj+9o/zjo5jz83p6TNz+vlUUT399jl9fu8cqvuZ9327zyYASauBPcn61dG5JiKuAq4CqK+vb3Mky6rXw0/9KbfjThi1Qy7HNrOu6dLHxCPi/4AdgWHAjHQ7LgWg54Hmv95fb7FbE+8OUg+RvaEPTM/5CvAx4AGyEYGrSxz6MrJRjgOAk1ocp71jdfeNsa3nvRb4Rqrl/HZqUYvb9qabBgKXA9PSc85t9ZxdqfXtOiPiQuBfyEaEHpb04S4+55uk/x9pZG5AqfI7UdPbdXXyXNv7sXI+XLI/zK7LblfOz7uiHnPCwaNyCxkTRu3g6am2XDER7v4mvPpc3pWYAV1cg5PeJGvI1sEMAV6IiK2SJpP9hd4ZPyZbD3KrpM+TjYS8ERG3S/o9WYBobQjZOhiAY0s83toi4FpJF5L18bNkI0flMBjYIKk/Wchb38H29wKzJP0krdUZ2moUpznMvJTWNU0Duv0pJUkfiohGoDGNvH2YbD3S4DZ2eZVsjVWztcA4YD5w6XHeLAAAD3JJREFUFNn6ntYWkf0O7pO0L9k6qDXAgW3UtCMdn2vrqpXz4a6ZsDXNsm56JrsPMHp6fnX1IIeMXui5RnhxDay4EcbMgEPOhMG75F2VVbHOBJzmRbiQ/cV+bEQ0pamPuyQ1ACuAJzt70Ii4WNIQ4AaytSrXpIXHAGeX2GU2WSBaDzwM7NXB8z8q6ZZU1x8o76ejvgU8kp63kbYDQ3Mtv0wLmhskvQHcA5zT4vGNkuam51oLLC1Tnael4NkErCZbNP0W8Kakx8jCRctvqroLuE3SUWSLjOcCP5O0BFgIvFbiGJcDV0hqJBvx+UpEvN7OVNjudHyurasWznkn3DTbugUWnAt1nf27w6wMmtKXJi6/wUHHcqd2Pqxjlrv6+vpoaGjoeMNqNruO0rOgguMWlGg36wHzDntvm7aBERPgq7+ofD1WNSQti4j3fMeev8nYrK8bMjyblirVPuLjla/HrGZAFm7GzIBDzsq7GqtSvhaVWV83ZRb0b3WxyP61WbtZJdUMgH4DYew/w6kr4ciLYfDOHe9n1gM8gmOWp4cuy24/ecr7f47mhcQL58CmddnIzZRZ3V9gXI7arHrscgAMPygbsXGosV7AAcesCEZPL+wnpqyPOHlx3hWYvYsDjlne1vbSN4a1i2HkxLyrMDN7XxxwzPLUm6d/Rk7s3fWZmbXDAccsbw4RZmZl509RmZmZWeE44JiZmVnhOOCYmZlZ4TjgmJmZWeE44JiZmVnhOOCYmZlZ4TjgmJmZWeE44JiZmVnhOOCYmZlZ4TjgmJmZWeE44JiZmVnhOOCYmZlZ4TjgmJmZWeE44JiZmVnhOOCYmZlZ4TjgmJmZWeE44JiZmVnhOOCYmZlZ4fTLuwAzs5buXL6eixas4dmNW9itrpYzDt+PqWN3z7ss68AR//UgB46oY+aUfdhp+4F5l2PmgGNmvcedy9dz9h2NbNnaBMD6jVs4+45GAIecXm71hj/z2xde5dZl6/jiuOEOOpY7Bxwz6zUuWrDm7XDTbMvWJr79P6vZY2htTlVZZ21tCiC4peEZBx3LndfgmFmv8ezGLSXb/7T5jQpXYt2xtSl4/c23uHHJH/nGTcvzLseqlEdwykTScOCHwEfJguPdwBkR0aOvzJLqgS9HxMxuPs+1wN0RcVtn2ltt8wBwekQ0dKcGs93qallfIuTsVlfLuD2H5lCRvR/9a0SNxLT6PZg5Ze+8y7Eq5RGcMpAk4A7gzojYB9gX2A74ToltyxoqI6Khu+EmT+X+fVjfdsbh+1Hbv+ZdbbX9azjj8P1yqsi6on+NGNhvG44eP4JFZ03m21P3Z6fBnp6yfPjNpTw+Dfw1Iq4BiIgmSf8GPC3pPGA68A/AQGCQpEOBHwCHAE+TBc15EXGbpFnAZ4Fa4CHgpIiINEryCDAZqAOOj4gHJU0iGz05UtI9wG6ppr2AmcB/AxcCk4APAD+MiCtTKLss1f40oI462VZt6eFjJF0KbA8cFxFLJA0F5gGjgL8AJ0bESkmzU50jgZeAf+rcr9n6grmLngLghINHdXnf5oXE7/dTVN05tnXPR3fdngP3/CAzp+ztUGO9ggNOefwNsKxlQ0T8WdIfgebx2U8AoyPiZUnTyN7cDwB2Ap4gCwIAP4iIOQCSbgCOBO5Kj/WLiIMkHQGcBxza6phHpP3GAdcAdwLHA5siYrykDwC/lnQvMBbYL9WwM7C6RQ1taa+2QRHxSUkHp+fZHzgfWB4RUyV9GrgeGJO2HwdMjIjSiy6sak0du7s/MdUH3XPq3+Zdgtm7OOCUh4DooP1XEfFy+nkicGtEvAU8J+n+FvtMlnQmsC0wFFjFOyHijnS7jCwgvfeA0o7ADcD0iNgk6TBgdApVAEOAfYCDgZsiogl4VtJ9nehne7XdBBARiyRtL6ku9fMLqf0+STtIGpK2/3lb4UbSicCJACNGjOhEWdbbPPzUn3I77oRRO+RybDPrXbwGpzxWAfUtGyRtD+wB/D41vdby4VJPImkgcDkwLSIOAOaSTWs1ez3dNlEinEqqAW4G5kTE4y2OdUpEjEn/9oqIe9NjpUJZSZ2orfVzBaX72bzdayUeyzaIuCoi6iOiftiwYZ0t0XqJEw4elVvImLHtI5yw7HMwuw4u2R9Wzs+lDuvjrpgId38TXn0u70qsGzyCUx4LgQslfTkirk9B43vAtRHxl2y5y7ssBo6VdB0wjGx9zE94JzC8JGk7YBrQ5qeXSrgQWBkRN7doWwB8TdJ9EbFV0r7AemARcJKk68mmySanGtrSUW1HA/dLmkg2JbZJ0iJgBnBBWiv0Upq660KXrC/KZQ3Myvnw4LdhaxoY3PQM3JXW34+eXvl6rO96rhFeXAMrboQxM+CQM2HwLnlXZV3kgFMGaRHw54HLJX2LbGTsHuCcNna5HZgCPA78hmzx8KaI2ChpLtAIrAWWdrGU04FVklak+7OAq8mmsx5NC4tfBKYCPyVbYNyYavjfDvrYUW2vSHqItMg4tc0GrpG0kmyR8bFd7I9Z5y2c8064abZ1Cyw4F+r2zKcm67ua0jd8LL/BQaeP0jsfgrFKkrRdRGyWtAOwBPhURHg8tJX6+vpoaPDX61gnzK6jzaVwxy2odDXWl8077L1t2gZGTICv/qLy9Vi7JC2LiPrW7R7Byc/daSHuAOAChxuzbhoyPJuWKtU+4uOVr8eKoWZAFm7GzIBDzsq7GusCB5ycRMSkvGswK5Qps7I1Ny2nqfrXZu1mXdU62AzeOe+KrIsccMys/B66LLv95CmVO2bzQuKFc2DTumzkZsqsyiwwzqO/1nN2OQCGH+Rg08c54JhZcYye7k9MWfedvDjvCqwMHHDMrGesraI3ibWLYeTEvKswsxYccMys/KptqmbkxOrrs1kv54BjZj3Db/hmliNfqsHMzMwKxwHHzMzMCscBx8zMzArHAcfMzMwKxwHHzMzMCscBx8zMzArHAcfMzMwKxwHHzMzMCscBx8zMzArHAcfMzMwKxwHHzMzMCkcRkXcNZm2S9CLwh3R3R+ClHMvJg/tcHaqtz9XWX3Cfe9KeETGsdaMDjvUZkhoioj7vOirJfa4O1dbnausvuM958BSVmZmZFY4DjpmZmRWOA471JVflXUAO3OfqUG19rrb+gvtccV6DY2ZmZoXjERwzMzMrHAccMzMzKxwHHOsTJP29pDWSfifp3/OupxIkrZXUKGmFpIa86+kJkuZJekHS4y3ahkr6laTfptsP5lljObXR39mS1qfzvELSEXnWWG6S9pB0v6QnJK2SdGpqL+R5bqe/hT3PkgZKWiLpsdTn81P7XpIeSef4FkkDKlqX1+BYbyepBvgN8HfAOmAp8KWIWJ1rYT1M0lqgPiIK++Vgkg4GNgPXR8T+qe27wMsRcWEKsx+MiLPyrLNc2ujvbGBzRPxnnrX1FEm7ArtGxKOSBgPLgKnAVyjgeW6nv9Mp6HmWJGBQRGyW1B9YDJwKfBO4IyJulnQF8FhE/KhSdXkEx/qCg4DfRcRTEfEGcDNwVM41WRlExCLg5VbNRwHXpZ+vI3tzKIQ2+ltoEbEhIh5NP78KPAHsTkHPczv9LazIbE53+6d/AXwauC21V/wcO+BYX7A78EyL++so+AtGEsC9kpZJOjHvYipo54jYANmbBbBTzvVUwjckrUxTWIWYqilF0khgLPAIVXCeW/UXCnyeJdVIWgG8APwK+D2wMSLeTJtU/HXbAcf6ApVoq4a51U9FxIHAZ4Cvp+kNK54fAR8CxgAbgO/lW07PkLQdcDtwWkT8Oe96elqJ/hb6PEdEU0SMAYaTjbp/pNRmlazJAcf6gnXAHi3uDweezamWiomIZ9PtC8BPyV40qsHzaR1D83qGF3Kup0dFxPPpzeEtYC4FPM9pXcbtwI0RcUdqLux5LtXfajjPABGxEXgAmADUSeqXHqr467YDjvUFS4F90or8AcA/Aj/PuaYeJWlQWqCIpEHAYcDj7e9VGD8Hjk0/Hwv8LMdaelzzm3zyeQp2ntMC1B8DT0TExS0eKuR5bqu/RT7PkoZJqks/1wKHkq09uh+Yljar+Dn2p6isT0gfqfw+UAPMi4jv5FxSj5I0imzUBqAf8JMi9lnSTcAkYEfgeeA84E5gPjAC+CPwxYgoxMLcNvo7iWzaIoC1wEnNa1OKQNJE4EGgEXgrNZ9Dti6lcOe5nf5+iYKeZ0mjyRYR15ANnMyPiDnpdexmYCiwHDgmIl6vWF0OOGZmZlY0nqIyMzOzwnHAMTMzs8JxwDEzM7PCccAxMzOzwnHAMTMzs8JxwDEz6wZJTenq0I9LulXStjnVcVpexzbrjRxwzMy6Z0tEjElXB38DOLmzO0qqKWMdpwEOOGaJA46ZWfk8COwNIOkYSUvS6M6VzWFG0mZJcyQ9AnxC0nhJD0l6LG0/OF248CJJS9PFGU9K+06S9ICk2yQ9KelGZWYCuwH3S7o/bfsjSQ2SVkk6v7lASUekfRdLulTS3al9ULoI5FJJyyUdVdlfnVl5OeCYmZVBuubOZ4BGSR8Bjia7YOoYoAmYkTYdBDweER8HlgC3AKdGxMfIvuJ+C3A8sCkixgPjgRMk7ZX2H0s2WvNRYFQ6xqVk1/mZHBGT03bnRkQ9MBo4RNJoSQOBK4HPRMREYFiLLpwL3JeOORm4KF0mxKxP6tfxJmZm1o5aSSvSzw+SXYfoRGAcsDS7NBG1vHMxySayCzEC7AdsiIilAM1X2ZZ0GDBaUvN1fIYA+5BNgS2JiHVpuxXASGBxibqmSzqR7HV+V7JAtA3wVEQ8nba5KdUK2fXOPifp9HR/INllFJ7o4u/DrFdwwDEz654taZTmbemCi9dFxNkltv9rRDQ1b0p2baLWBJwSEQtaPe8koOW1fJoo8TqeRntOB8ZHxCuSriULLGqnHwK+EBFr2tnGrM/wFJWZWfktBKZJ2glA0lBJe5bY7klgN0nj03aD01TXAuBrkvqn9n07MV30KjA4/bw98BqwSdLOZFNnzccbJWlkun90i/0XAKekcIaksZ3trFlv5BEcM7Myi4jVkv4DuFfSNsBW4OvAH1pt94ako4HLJNWSrb85FLiabOrp0RQ4XgSmdnDYq4BfSNoQEZMlLQdWAU8Bv07H2yLpX4FfSnqJbA1QswuA7wMr0zHXAke+39+BWd58NXEzsyoiabuI2JxCzA+B30bEJXnXZVZunqIyM6suJ6TFyavIFi9fmXM9Zj3CIzhmZmZWOB7BMTMzs8JxwDEzM7PCccAxMzOzwnHAMTMzs8JxwDEzM7PC+X9Pv7uy4105QgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['contv', 'conpress', 'conlegis', 'confed', 'conclerg', 'confinan', 'conlabor']\n",
    "multiplot(results, varnames, loc='upper right', title='Great deal of confidence in');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Allocation of resources\n",
    "\n",
    "\"We are faced with many problems in this country, none of which can be solved easily or inexpensively. I'm going to name some of these problems, and for each one I'd like you to tell me whether you think we're spending too much money on it, too little money, or about the right amount.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['nataid', 'natfare', 'natpark', 'natrace', 'natchld', 'natenvir']\n",
    "multiplot(results, varnames, title='We spend too little money on')\n",
    "\n",
    "plt.savefig('generation5.png', dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['natarms', 'natmass', 'natsci', 'natroad', 'natsoc', 'natcrime']\n",
    "multiplot(results, varnames, loc='upper right', title='We spend too little money on')\n",
    "\n",
    "plt.savefig('generation6.png', dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['natspac', 'natcity', 'natenrgy', 'natdrug', 'natheal', 'nateduc']\n",
    "multiplot(results, varnames, loc='upper right', title='We spend too little money on')\n",
    "\n",
    "plt.savefig('generation7.png', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Issues related to sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['premarsx', 'homosex', 'teensex', 'xmarsex', 'sexeduc']\n",
    "multiplot(results, varnames, loc='upper right')\n",
    "\n",
    "plt.savefig('generation8.png', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['trust', 'helpful', 'fair', 'fear']\n",
    "multiplot(results, varnames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Public policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['affrmact', 'cappun', 'divlaw', 'prayer']\n",
    "multiplot(results, varnames, loc='upper right', title='Law and public policy')\n",
    "\n",
    "plt.savefig('generation3.png', dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['grass', 'pornlaw', 'gunlaw', 'letdie1', 'racopen']\n",
    "multiplot(results, varnames, title='Law and public policy')\n",
    "\n",
    "plt.savefig('generation2.png', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abortion\n",
    "\n",
    "\"Please tell me whether or not you think it should be possible for a pregnant woman to obtain a legal abortion if ...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['abany', 'abnomore', 'absingle', 'abpoor', 'abdefect', 'abrape', 'abhlth']\n",
    "\n",
    "multiplot(results, varnames, title='Abortion should be legal if', loc='lower left')\n",
    "\n",
    "plt.savefig('generation4.png', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Religion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['bible', 'reborn', 'savesoul', 'god', 'postlife']\n",
    "multiplot(results, varnames, \n",
    "          loc='upper right', \n",
    "          title='Religious beliefs',\n",
    "          xlim=[0, 110])\n",
    "\n",
    "plt.savefig('generation1.png', dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['relactiv', 'attend', 'relpersn', 'sprtprsn', 'relexp', 'pray']\n",
    "multiplot(results, varnames, \n",
    "          loc='upper right', \n",
    "          title='Religious beliefs',\n",
    "          #xlim=[0, 110]\n",
    "         )\n",
    "\n",
    "plt.savefig('generation1b.png', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gender roles and work\n",
    "\n",
    "\"Tell me if you agree or disagree with this statement: Most men are better suited emotionally for politics than are most women.\"  Select \"agree\"\n",
    "\n",
    "    df.fepol.replace([0, 8, 9], np.nan, inplace=True)\n",
    "    df.fejobaff.replace([0, 8, 9], np.nan, inplace=True)\n",
    "    df.fehire.replace([0, 8, 9], np.nan, inplace=True)\n",
    "    df.fechld.replace([0, 8, 9], np.nan, inplace=True)\n",
    "    df.fepresch.replace([0, 8, 9], np.nan, inplace=True)\n",
    "    df.fefam.replace([0, 8, 9], np.nan, inplace=True)\n",
    "    df.discaffm.replace([0, 8, 9], np.nan, inplace=True)\n",
    "    df.discaffw.replace([0, 8, 9], np.nan, inplace=True)\n",
    "    df.meovrwrk.replace([0, 8, 9], np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['fepol', 'fefam', 'fepresch', 'fejobaff', 'discaffm', 'meovrwrk']\n",
    "multiplot(results, varnames, title='Agree or strongly agree');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 85)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['discaffw', 'fehire', 'fechld']\n",
    "multiplot(results, varnames, title='Agree or strongly agree', loc='lower left')\n",
    "\n",
    "plt.xlim(60,85);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Misc\n",
    "\n",
    "\"Do you strongly agree, agree, disagree, or strongly disagree that it is sometimes necessary to discipline a child with a good, hard spanking?\"\n",
    "\n",
    "\"Tell me if you agree or disagree with this statement: Most men are better suited emotionally for politics than are most women.\"  Select \"agree\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uueceOnbsSOXKlTnppJN48cUXD6ltJc0OdBhLpDjatGnj8+bNK+1miEg5s2zZsjI3EiBlR7zfDzPLcPc2+fNqikpEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOQpwREREJHKKFeCYWS8zczNrdrgbJAUzs1vN7OiY43+aWe0SruNeM7s9/PyimfUuyfJFRESOhOKO4FwNzAKuOtAKzKxi0bnKhzLQl1uB3ADH3Xu4++ZSbM+vzjMffM0zH3xd5soSkZJjZrn7OwGMGjXqsO+SXb169cNafo4bbriBBg0akJycnCd9wYIFtG/fnpSUFC6++GK2bt0KwJ49e7j++utJSUmhRYsWzJgxI/ea7t2706JFC5o3b86gQYPIzs4+In0oriIDHDOrDpwN3EhMgGNmFczsSTNbYmaTw9GE3uG5VWZ2t5nNAq4ws1PM7F0zyzCzD3NGgsysvpm9YWZzw9fZceq/zszSzextM1tpZoPN7E9mNt/MPjGzumG+gup40cweM7OPzezrgkYkwjoywv7cFJO+3cxGmNmnQHsza21mM8O875nZcXHKituvcHTkJTObEn5Hl5nZI2a2KGx75TBf17B/i8zseTOrYmZDgIbAdDObHvM91ws//8nMFoevW8O0RDNbZmbPhP2aYmZVw3MDw7YtCNt6dP5+xPSnq5lNijk+38wmFpRfROSIeDoNJv8Jtn1XosVWqVKFiRMnsnHjxhIt93DJysoqdt7rrruOd999d7/0AQMG8NBDD7Fo0SJ69erFyJEjAXjmmWcAWLRoEe+//z633XYb+/btA+C1115jwYIFLF68mB9++IEJEyaUQG9KTnFGcHoC77r7F8CPZtYqTL8MSARSgAFA+3zX7XL3NHd/Ffg78J/u3hq4HXgyzPMoMNrd2wKXA88W0IZk4HfAmcBfgJ/d/QxgNtAvzFNQHQDHAWnARUBBm2XcEF7bBhhiZseE6dWAxe7eDvgU+BvQO8z7fNie/Arr1ynAhcClwD+A6e6eAuwELjSzBOBFoE+YXgn4vbs/BqwHurh7l9jKzKw1cD3QDjgLGGhmOVtLnwY84e7Ngc1hewAmuntbd28BLCMIYAsyDTjdzOqHx9cDh/Y4TRGRQ/XdIpj/MjzaokQDnUqVKnHTTTcxevTo/c6tXr2arl27kpqaSteuXfnmm2+AIHAYMmQIHTp0oHHjxrnbLgCMHDmStm3bkpqayj333FNgvbfddhutWrWia9euuVsgZGZmctZZZ5GamkqvXr346aefAOjcuTN//vOf6dSpE48++mih9cfq2LEjdevW3S/9888/p2PHjgCcf/75vPHGGwAsXbqUrl27AtCgQQNq165NzkNcc/ajysrKYs+ePcXaP+tIKk6AczXwavj51fAYgoBhgrvvc/fvgOn5rhsPuSNAHYAJZpYJ/C9BwAFwHvB4mP4WUNPMasRpw3R33+buPwBbgLfD9EVAYhF1AKSH7VwKHFtAP4eY2QLgE+AEgsAAIBt4I/zclCDYej+s5y4g3q5uhfXrX+6+N2x7RSAnlF5EEDA2BVaGASXAS0DHAtqcIw2Y5O473H07MBE4Jzy30t0zw88ZYR0AyeFI1yKgL9C8oMI9eNz1y8A14Zqf9sC/imiTiMjhl70HsnaVeKBz8803M3bsWLZs2ZInffDgwfTr14+FCxfSt29fhgwZknvu22+/ZdasWUyePJlhw4YBwW7iK1asYM6cOWRmZpKRkZFnR/AcO3bsoFWrVnz22Wd06tSJ++67D4B+/frx8MMPs3DhQlJSUnLTATZv3szMmTNzp9Pi1V9cycnJvPXWWwBMmDCBNWuCTUxbtGjBm2++SVZWFitXriQjIyP3HAR7VTVo0IAaNWrQu3fZWrJZ6F5U4SjGuQR/DJ3gD7Kb2Z1AUaHajvC9ArDZ3VvGyVMBaO/uO4soa3fM530xx/sI+lBYHfmv36/dZtaZIChp7+4/m9kMIGcnw13unh1z7RJ3zz9alV/cfoXR7W4Ad99nZnv9l70ycvpyMCFwYdfE9j0byNmZ7kWgp7svMLPrgM5F1PECQWC5iyCwLf6YqIjI4Za9J3jPeAF+WAbXH9q/wWrWrEm/fv147LHHqFr1lw09Z8+ezcSJwQz9tddey5133pl7rmfPnlSoUIGkpCS+//57IAhwpkyZwhlnBIPq27dvZ8WKFbmjJTkqVKhAnz59ALjmmmu47LLL2LJlC5s3b87dYLN///5cccUVudfk5C+s/uJ6/vnnGTJkCCNGjOCSSy7hqKOOAoI1O8uWLaNNmzacdNJJdOjQgUqVfgkd3nvvPXbt2kXfvn2ZNm0a559//gHVezgVNYLTG/g/dz/J3RPd/QRgJcGIwSzg8nAtzrEU8AfS3bcCK83sCgALtAhPTwEG5+Q1s4IClEIVUUdx1AJ+CoObZgTTPPF8DtQ3s/ZhPZXNLN7Ix6H0aznBqNSp4fG1wMzw8zYg3gjXB0BPMzvazKoBvYAPi6inBvBtuO6nb1GNcvf1BFNkdxEERyIiZUfFo6BSArS+Hnq/WCJF3nrrrTz33HPs2LGjwDyx0zJVqlTJ/Zzzb1d3Z/jw4WRmZpKZmcmXX37JjTcWtiJg/3ILUq1atTzH8eovrmbNmjFlyhQyMjK4+uqrOeWUU4Bgum706NFkZmby5ptvsnnzZk477bQ81yYkJHDJJZfw5ptvHlCdh1tRAc7VwKR8aW8QrId5A1gLLCaYEvqUYPoonr7AjeEU0BKC9ScAQ4A2ZrbQzJYCgw64B0XXURzvApXMbCFwP8E01X7cfQ9B0PdwWE8mwdRYfgfdL3ffRbDGZUI4fbQPeDo8/XfgXzmLjGOu+Ywg6JhD8HN41t3nF1HVf4V53ycIqopjLLAmnOoTESl9OYHNGdfCLQvhov+BGgWtRDgwdevW5corr+S5557LTevQoQOvvhqs2hg7dixpaWmFltGtWzeef/55tm/fDsC6devYsGHDfvn27duXu27mlVdeIS0tjVq1alGnTh0+/DD49+rLL7+cO5pT0nLatG/fPh544AEGDQr+bP3888+5Ad77779PpUqVSEpKYvv27Xz77bdAsAbnn//8J82albEnybj7Qb+A6uH7McBXwG8OpTy9yvYLeBy4sZDzNwHzgHknnniiR9HfZ37lf5/5VZkrSyQqli5dWvzMT53t/vYf3bd+V6JtqFatWu7n7777zqtWrer33HOPu7uvXLnSu3Tp4ikpKX7uuef66tWr3d29f//+PmHChLhljBkzxpOTkz05OdnPOuss//LLL+PWedddd3mrVq28S5cuvmHDBnd3nz9/vrdr185TUlL80ksv9R9//NHd3Tt16uRz587Nvb6w+mNdddVV/pvf/MYrVarkxx9/vD/77LO5bTzttNP8tNNO86FDh/q+ffty+9ukSRNv1qyZd+3a1VetWpX7vbRp08ZTUlI8KSnJBw8e7Hv37i3mN3zw4v1+APM8zt8kC84dnHCtSm3gKOARd3/xoAuTMs3MMgjWVZ3v7ruLyt+mTRvPWWkfJTnPrRnYsXGZKkskKpYtW8bpp59e2s2QMire74eZZbh7m/x5C11kXBR373wo10v54cFt8SIiIuWC9qKKw8wuMbNh4eeeZpZ0EGVsLyB9SPjwvbGH2s4DaEvsAwE/Dt8Tzex3MXnamNljR6pNIiIih1O5DnDM7JBGoAri7m+5e84DAXsCBxzgFOIPQA93L/LOJSj5Prp7zqLoRILF4jnp89x9SNyLREREypkjHuCEIwfLzezZcFuBsWZ2npl9ZGYrzOzMMF9dC7ZPWGjBlgypYfq9ZvZ3M5sC/J+ZVTSzkeG2AwvN7D/i1FnNzN4JtyVYbGZ9wvTYkY024ZqinO0hHjezDsAlwEgzy7RgO4iCtoQ42cxmh+24v4C+Pw00Bt4ysz8Wt4/5yuhsZh+Y2SQzW2pmT5tZhfDc1RZs77DYzB4uoA05I0sPAeeE/fpjWO7kME91M3shLGuhmV0efs8vhmUvMrM/FvNHLiIicsQdlhGQYjgVuILgrpu5BCMJaQTBxJ8JRk3uA+a7e08zO5fgD33O82RaA2nuvtOCfaO2uHtbM6sCfGRmU9x9ZUx93YH17n4hgJnVKk4j3f1jM3sLmOzur4fXTgUGufsKM2tHsCXEuQTbMzzl7v9nZjcXUN4gM+tOsN3CRjP7W3H6GKeoMwlGlVYT3OJ+WTj19HB43U/AFDPr6e7pBXRvGHC7u18U9qtzzLn/IvhOU8JzdcJ2He/uyWFaie5iXp588vWmEivnrMbHFJ1RREQOWGkFOCvdfRGAmS0Bprq7h899SQzzpBHum+Tu08zsmJjA5K2YP/wXAKn2yyaatQi2WYgNcBYBo8JRjcnuXtRD8OKyvFtC5CTnPFnpbH7Z5+llgmCjKMXtY35z3P3rsE3jwnL2AjM82M6CcI1PR6CgAKcw5xGzsaq7/2RmXwONw6DsHYKHGf7qlOQdT2c1PkZ3UImIHCalFeAUtfUCxN9+IOee9tjHShrBJpvvFVSZu39hwYaUPYAHwxGeEUAWv0zTJRR0fYyitoQ40Hvui9vHourxAso6WJa/jjDIaQF0A24GrgRuKME6yw0FJSLR9t1333Hrrbcyd+5cqlSpQmJiImPGjGH9+vWMGjWKyZMnF1nG3XffTceOHTnvvPPink9PT6dJkyYkJSUVK39xzZgxo9htPBSbNm2id+/ezJ07l+uuu47HH38899z48eP5y1/+QnZ2NhdeeCGPPPJI7rnXXnuNe++9FzOjRYsWvPLKKwDceeedvPPOO+zbt4/zzz+fRx999JA37yzLi4w/INxCIJw+2ejBlgz5vQf83oItBzCzJhZsV5DLzBoS7ED+D2AUkLMj+iqCKR34ZfQlv9ztEbzwLSE+4pdRj2ItID6APuZ3ZrjmpwLQh2DbjE+BTmZWz8wqEjyFemYhZRS07QPsv9VEnXCtUgV3f4NgCqtVAdeKiBwx6fPXcfZD0zh52Duc/dA00uevO6Ty3J1evXrRuXNnvvrqK5YuXcpf//rXA9rbKTs7mxEjRhQarKSnp7N06S8PhS8q/5GSnZ1ddCaC7Rnuv/9+Ro0alSd906ZN3HHHHUydOpUlS5bw/fffM3XqVABWrFjBgw8+yEcffcSSJUsYM2YMAB9//DEfffQRCxcuZPHixcydO5eZMwv781U8ZTnAuZdwuwOCBbH9C8j3LLAU+MzMcraNyD8ylQLMsWB37/8HPBCm3wc8amYfEmxEGc+rwB1mNt/MTqHgLSFuAW42s7kE02Ql2cf8Zof5FxNMxU1y92+B4QS7ui8APnP3wjYGWQhkhQuv8y8YfgCoEy4oXgB0AY4HZoTf4YthXSIipSZ9/jqGT1zEus07cWDd5p0Mn7jokIKc6dOnU7ly5dytCgBatmzJOeecAwSbZfbu3ZtmzZrRt2/fnKe4k5iYyIgRI0hLS2PChAlcd911uVsvDBs2jKSkJFJTU7n99tv5+OOPeeutt7jjjjto2bIlX331VZ78I0aMoG3btiQnJ3PTTTfl1tG5c2eGDh3KmWeeSZMmTXK3cMhv69at9OrVi6SkJAYNGsS+ffsAGDduHCkpKSQnJzN06NDc/NWrV+fuu++mXbt2zJ49m8TERO655x5atWpFSkoKy5fvv5tPtWrVSEtLIyEh7+TH119/TZMmTahfvz4A5513Hm+88QYAzzzzDDfffDN16tQBoEGDBkCw79auXbvYs2cPu3fvZu/evRx77KFvt3HEp6jcfRWQHHN8Xbxz7v4jcfaTcvd78x3vI1iY/OdC6nyPYKQnf/qHQJM46S8Sbijp7h+x/23i3eNcsxKI3WX8ofx5wnyJMZ+L1cc4fnb3PvkT3f0V4JUi6qwevu8FuubLOiM8t534wZZGbUSkzBj53ufs3Jv336Y792Yz8r3P6XnG8QdV5uLFi2nduuDnms6fP58lS5bQsGFDzj77bD766KPc/agSEhKYNWsWAO+++y4AP/74I5MmTWL58uWYGZs3b6Z27dpccsklXHTRRfTu3Xu/OgYPHszdd98NBDuWT548mYsvvhgI9n2aM2cO//znP7nvvvv497//vd/1c+bMYenSpZx00kl0796diRMn0qFDB/9WlM0AACAASURBVIYOHUpGRgZ16tThggsuID09nZ49e7Jjxw6Sk5MZMWJEbhn16tXjs88+48knn2TUqFE8++yzxfr+Tj31VJYvX86qVato1KgR6enp7NkT7PT+xRdfAHD22WeTnZ3NvffeS/fu3Wnfvj1dunThuOOOw90ZPHhwiTzNuiyP4IiIiBRo/eb492EUlF4SzjzzTBo1akSFChVo2bIlq1atyj3Xp89+/+6kZs2aJCQkMGDAACZOnMjRRx9dZB3Tp0+nXbt2pKSkMG3aNJYsWZJ77rLLLgOgdevWeerO38bGjRtTsWJFrr76ambNmsXcuXPp3Lkz9evXp1KlSvTt25cPPvgAgIoVK3L55XlXaRSnnnjq1KnDU089RZ8+fTjnnHNITEykUqVgLCUrK4sVK1YwY8YMxo0bx4ABA9i8eTNffvkly5YtY+3ataxbt45p06bltu1QKMApZ9x9Rs6t3SIiv2YNa1c9oPTiaN68ORkZGQWer1KlSu7nihUrkpWVlXtcrVq1/fJXqlSJOXPmcPnll5Oenk737vtNAOSxa9cu/vCHP/D666+zaNEiBg4cyK5du/arP3/dsfIvzjWz3GmueBISEqhYsWKetOLUU5CLL76YTz/9lNmzZ9O0aVNOO+00ABo1asSll15K5cqVOfnkk2natCkrVqxg0qRJnHXWWVSvXp3q1avz29/+lk8++eSA6oxHAY6IiJRLd3RrStXKef8wV61ckTu6NT3oMs8991x2797NM888k5t2KItet2/fzpYtW+jRowdjxowhMzMTgBo1arBt27b98ucEM/Xq1WP79u2563IOxJw5c1i5ciX79u1j/PjxpKWl0a5dO2bOnMnGjRvJzs5m3LhxdOrU6aD6VJQNGzYA8NNPP/Hkk08yYMAAAHr27Mn06dMB2LhxI1988QWNGzfmxBNPZObMmWRlZbF3715mzpxZIlNUpXWbuIiIyCHJWWcz8r3PWb95Jw1rV+WObk0Pev0NBKMdkyZN4tZbb+Whhx4iISEh9zbxdesOfPHytm3buPTSS9m1axfuzujRowG46qqrGDhwII899lieIKZ27doMHDiQlJQUEhMTadu27QHX2b59e4YNG8aiRYvo2LEjvXr1okKFCjz44IN06dIFd6dHjx5ceul+S0APSGJiIlu3bmXPnj2kp6czZcoUkpKSuOWWW1iwYAEQ3P7epEmw1LVbt265eSpWrMjIkSM55phj6N27N9OmTSMlJQUzo3v37rlrjg6FFTZsJXKw2rRp4/PmzSvtZohIObNs2bIS+de7RFO83w8zy3D3NvnzaopKomPhazA6Ge6tHbwvfG3/PE+nweQ/wbbvjnz7RETkiNEUlUTDwtfg7SGwN7x7Ysua4Bgg9cpf8n23CH74HDLHQsu+0OlOqPGbI99eERE5rBTgSDRMHfFLcJNj70547/9B7ZPypmcHz2Rg/ssKdEREIkpTVBINW9bGT9/xQ8HXZO+BrF2Q8QK8fv3haZeIiJQKjeBINNRqFExLxUs/sV38ayoeBVYhHMEZGj+PiIiUSxrBkWjoejdUzvdwr8pVg/T8Kh4FlRLgjGvhloVw0f9AjUPf90RERMoOBTgSDalXwsWPQa0TAAveL34s7wJjgN+kKLARkUJ99913XHXVVZxyyikkJSXRo0cPvvjiC2bMmMFFFxXvQfJ333133H2icuTfTbyo/MV1IG08FJs2baJLly5Ur16dwYMH5zk3fvx4UlNTad68OXfeeWdu+urVq+natSupqal07tyZtWvX5qa3bt2ali1b0rx5c55++ukSaaOmqCQ6Uq/cP6DJb9CsI9MWETnsejz6Ia1OrM2QrqfRoGZC0RcUg7vTq1cv+vfvz6uvvgpAZmYm33//fbHLyM7OzrNxZTzp6elcdNFFJCUFezkXlf9Iyc7O3m/bhngSEhK4//77Wbx4MYsXL85N37RpE3fccQcZGRnUr1+f/v37M3XqVLp27crtt99Ov3796N+/P9OmTWP48OG8/PLLHHfccXz88cdUqVKF7du3k5yczCWXXELDhg0PqS8awRERkXJp6bdbGT9vDec8Mp27Ji1iw9ZdRV9UhOnTp1O5cmUGDRqUm9ayZUvOOeccINh6oXfv3jRr1oy+ffvm7vGUmJjIiBEjSEtLY8KECVx33XW5TygeNmwYSUlJpKamcvvtt/Pxxx/z1ltvcccdd9CyZUu++uqrPPlHjBhB27ZtSU5O5qabbsqto3PnzgwdOpQzzzyTJk2a8OGHH8btw9atW+nVqxdJSUkMGjSIffv2ATBu3DhSUlJITk5m6NBf1h1Wr16du+++m3bt2jF79mwSExO55557aNWqFSkpKSxfvny/OqpVq0ZaWhoJCXkDy6+//pomTZpQv359AM477zzeeOMNAJYuXUrXrl0B6NKlC2+++SYARx11VO7eV7t3785t76FSgCMiIuXW3mxnd9a+Egt0Fi9eTOvWrQs8P3/+fMaMGcPSpUv5+uuv+eijj3LPJSQkMGvWLK666qrctB9//JFJkyaxZMkSFi5cyF133UWHDh245JJLGDlyJJmZmZxyyil56hg8eDBz585l8eLF7Ny5k8mTJ+eey8rKYs6cOYwZM4b77rsvbhvnzJnDf//3f7No0SK++uorJk6cyPr16xk6dCjTpk0jMzOTuXPnkp6eDsCOHTtITk7m008/JS0tDQj2wvrss8/4/e9/z6hRo4r9/Z166qksX76cVatWkZWVRXp6OmvWBDeAtGjRIjfYmTRpEtu2bWPTpk0ArFmzhtTUVE444QSGDh16yKM3oABHREQiICfQGTvnGwaPm3/Y6jnzzDNp1KgRFSpUoGXLlqxatSr3XJ8+ffbLX7NmTRISEhgwYAATJ07k6KOPLrKO6dOn065dO1JSUpg2bRpLlizJPXfZZZcB0Lp16zx1529j48aNqVixIldffTWzZs1i7ty5dO7cmfr161OpUiX69u3LBx98AAQ7hl9++eV5yihOPfHUqVOHp556ij59+nDOOeeQmJhIpUrBaphRo0Yxc+ZMzjjjDGbOnMnxxx+fe+6EE05g4cKFfPnll7z00ksHNCVYEAU4IiJS7lWuaCRUqkDfdifx+O/OOOhymjdvTkZGRoHnc6ZSIAgMsrKyco+rVau2X/5KlSoxZ84cLr/8ctLT0+nevXuh9e/atYs//OEPvP766yxatIiBAwfm7jAeW3/+umOZ2X7Hhe07mZCQsN+6m+LUU5CLL76YTz/9lNmzZ9O0aVNOO+00ABo2bMjEiROZP38+f/nLXwCoVatWnmsbNmxI8+bNC5x+OxAKcEREpNzKCWz6tD2RD4Z24YGeyTSocfALjs8991x2797NM888k5s2d+5cZs6ceVDlbd++nS1bttCjRw/GjBlDZmYmADVq1GDbtm375c8JZurVq8f27dvz7DReXHPmzGHlypXs27eP8ePHk5aWRrt27Zg5cyYbN24kOzubcePG0alTp4PqU1E2bNgAwE8//cSTTz7JgAEDANi4cWPu+poHH3yQG264AYC1a9eyc+fO3Gs++ugjmjZtesjt0F1UIiJSLiUdV5NWJ9VhSNdTDymoiWVmTJo0iVtvvZWHHnqIhIQEEhMTGTNmDOvWrTvg8rZt28all17Krl27cHdGjx4NwFVXXcXAgQN57LHH8gQxtWvXZuDAgaSkpJCYmEjbtm0PuM727dszbNgwFi1aRMeOHenVqxcVKlTgwQcfpEuXLrg7PXr04NJLLz3gsmMlJiaydetW9uzZQ3p6OlOmTCEpKYlbbrmFBQsWAMHt702aNAGCW9iHDx+OmdGxY0eeeOIJINgh/Lbbbssdabr99ttJSUk5pLYBWGHDViIHq02bNj5v3rzSboaIlDPLli3j9NNPL+1mSBkV7/fDzDLcvU3+vJqiEhERkchRgCNl28d/C17ltXwRESkVCnBERKRM0dIJiedAfy8U4IiISJmRkJDApk2bFORIHu7Opk2b9ntycmF0F5WIiJQZjRo1Yu3atfzwww+l3RQpYxISEmjUqFGx8yvAERGRMqNy5cqcfPLJpd0MiQBNUYmIiEjkKMARERGRyFGAIyIiIpGjAEdEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOXoOjpR9q2Yd3rIT0w5f+SIiUioU4EjZ1uE/D2/5iWmHvw4RETniFOBI2acAREREDpDW4IiIiEjkKMARERGRyFGAIyIiIpGjAEdEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOQpwREREJHIU4IiIiEjkKMARERGRyFGAIyIiIpGjAEdEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOQpwREREJHIU4IiIiEjkKMARERGRyFGAIyIiIpGjAEdEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOQpwREREJHIU4IiIiEjkKMARERGRyFGAIyIiIpGjAEdEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOQpwREREJHIU4IiIiEjkKMARERGRyFGAIyIiIpGjAEdEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOQpwREREJHIU4IiIiEjkKMARERGRyFGAIyIiIpGjAEdEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOQpwREREJHIU4IiIiEjkKMARERGRyFGAIyIiIpGjAEdEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOQpwREREJHIU4IiIiEjkKMARERGRyFGAIyIiIpGjAEdEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOQpwREREJHIU4IiIiEjkKMARERGRyFGAIyIiIpGjAEdEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOQpwREREJHIU4IiIiEjkKMARERGRyFGAIyIiIpGjAEdEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOQpwREREJHIU4IiIiEjkKMARERGRyFGAIyIiIpGjAEdEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOQpwREREJHIU4IiIiEjkKMARERGRyFGAIyIiIpGjAEdEREQiRwGOiIiIRI4CHBEREYkcBTgiIiISOZVKuwEiIiJS9qTPX8fI9z5n/eadNKxdlTu6NaXnGccfdHk9Hv2QVifWZkjX02hQM6EEWxqfAhwRERHJI33+OoZPXMTOvdkArNu8k+ETFwEcdJCz9NutrNiwjQkZa7midaPDHugowBEREZE8Rr73eW5wk2Pn3mweeGcpJ9StetDl7s12wBk/b81hD3S0BkdERETyWL95Z9z0Tdv3lEj5e7Od3Vn7GDvnGwaPm18iZeanERwRERHJo2HtqqyLE+Q0rF2V1ifVPeTyK1c0KprRu80JDOl66iGXF48CHBEREcnjjm5N86zBAahauSJ3dGt6SOXmD2wa1NAaHBERETlCchYSl+RdVEnH1aTVSXUOe2CTw9z9sFcivz5t2rTxefPmlXYzREQk4swsw93b5E/XImMRERGJHAU4IiIiEjkKcERERCRyFOCIiIhI5CjAERERkchRgCMiIiKRowBHREREIqfMBzhm9v/MbImZLTSzTDNrV8LlJ5rZ4jjpbczssZKs60gws55mlhRzPMLMzivNNomIiBxpZfpJxmbWHrgIaOXuu82sHnDUkajb3ecB5fFJdT2BycBSAHe/u3SbIyIiZdEzH3wNwMCOjctEOSWtrI/gHAdsdPfdAO6+0d3XA5jZKjN72MzmhK9Tw/SLzexTM5tvZv82s2PD9HvN7Hkzm2FmX5vZkPyVmVnj8Lq2ZtbZzCYXda2Z/ZeZLTez981snJndHqfcK8xssZktMLMPwrQEM3vBzBaFdXYJ068zs3Qze9vMVprZYDP7U5jnEzOrG+Y7xczeNbMMM/vQzJqZWQfgEmBkONp1ipm9aGa9Y76zv5rZbDObZ2atzOw9M/vKzAbFtPcOM5sbjprdF6ZVM7N3wj4sNrM+JfDzFREROSzK9AgOMAW428y+AP4NjHf3mTHnt7r7mWbWDxhDMNozCzjL3d3MBgB3AreF+ZsBXYAawOdm9lROQWbWFHgVuN7dM82sc762xLu2BXA5cAbBd/kZkBGnH3cD3dx9nZnVDtNuBnD3FDNrBkwxsybhueSwzATgS2Cou59hZqOBnL7+HRjk7ivCabsn3f1cM3sLmOzur4f9yt+WNe7ePizrReDssJ4lwNNmdgFwGnAmYMBbZtYRqA+sd/cLw3JrxemniIhImVCmAxx3325mrYFzCIKL8WY2zN1fDLOMi3kfHX5uFOY7jmA6a2VMke+Eo0G7zWwDcGyYXh94E7jc3ZcU0Jx416YBb7r7TgAze7uAaz8CXjSz14CJYVoa8Lewn8vNbDWQE+BMd/dtwDYz2wLklLsISDWz6kAHYEJMAFOlgLrzeyumrOox9ewKg68Lwtf8MF91goDnQ2CUmT1MEEB9WMz6REREjrgyHeAAuHs2MAOYYWaLgP4EIw8AsTuF5nz+G/A/7v5WOApzb0ye3TGfs/ml/1uANQSjGQUFOPGu3W94pIA+DApHWS4EMs2sZRHXxta1L+Z4X1hvBWCzu7csTv0FlB1bbmzZBjzo7v+b/8Iw2OwBPGhmU9x9xEHULyIictiV6TU4ZtbUzE6LSWoJrI457hPzPjv8XAtYF37uX8yq9hAszu1nZr87gCbOAi4O19NUJwhg9mNmp7j7p+GC343ACcAHQN/wfBPgRODz4lTq7luBlWZ2RXi9mVmL8PQ2gmm0g/UecEPYH8zseDNrYGYNgZ/d/R/AKKDVIdQhIiJyWJX1EZzqwN/CqZMsgvUoN8Wcr2JmnxIEaleHafcSTN2sAz4BTi5ORe6+w8wuAt43sx0EozpFXTM3XPOygCDwmlfAdSPDQM2AqWH+5QRrXhaFfbsuvFOsOM2FIDh6yszuAioTrB9aEL4/Ey6E7l3cwmL6NMXMTgdmh23ZDlwDnBr2Yx+wF/j9gZYtIiJypJi7F52rDDKzVUAbd99Yyu2oHq4VOppgVOYmd/+sNNtUWszsJsIA9MQTT2y9evXqIq4QEZHSEpXbxM0sw93b5E8v01NU5cTfzSyT4A6qN36twQ2Au//d3du4e5v69euXdnNERORXrKxPURXI3RNLuw0A7n4ga3ZERETkCNAIjoiIiESOAhwplJmV21E+ERH59dIfr1JgZonAvwhuM+9AcFv7pUBD4AmCBw/+DAwMHwJ4LPA0kLOC6/fu/rGZXQMMIXig4afAH8LzzwFtCJ4N9Ly7jw7vqhpEcMfWUne/yszOJHgqclVgJ8FTnD83s+sIbnlPAKqFd6S97u5vhu0fS/BU6ZyHBoqISDn0ydebSqSMsxofUwKtKVkKcErPacDV7j4wfMLx5cD15Nt+ATgXeAyY6e69zKwiUD28lbsPcLa77zWzJwluHV8CHO/uyQAxW0MMA04Ob0XPSVsOdHT3LAt2HP9r2A6A9kCqu/9oZp2APwJvhls0dKD4zxgSEZEyqKTuejqr8TFlbqNNUIBTmla6e2b4OQNIpODtF84l2IMq58nOW8zsWqA1MDfMXxXYQLCtQ2Mz+xvwDsF+XgALgbFmlg6kh2m1gJfCZ/Q4wfN0crzv7j+Gdc40syfMrAFwGcHdYlkl8i2IiEipKYuBSUlRgFN68m/9cCwHtv2CAS+5+/D9TgRPNe5GsKHnlcANBFNOHQl2G/8vM2sO3E+w71WvcNpsRkwxO/IV+zLBCNFVYXkiIiJllhYZlx2Fbb8wlfDJwWZW0cxqhmm9w1EVzKyumZ1kZvWACu7+BvBfQCszqwCc4O7TCXZXr03wlOjYbS2uK6J9LwK3AhSyIamIiEiZoACnbOkL3GhmCwjW0lwapt8CdAm3dcgAmrv7UuAuYIqZLQTeB44DjifYmDSTICgZDlQE/hFePx8Y7e6bgUcINs78KMxTIHf/HlgGvFCC/RURETksyu1WDXJkhVtRLAJauXuR+3S1adPG582bd/gbJiIiv2raqkEOWniH1XLgb8UJbkREREqbFhlLkdz938CJpd0OERGR4tIIjoiIiESOAhwRERGJHAU4IiIiEjkKcEREROJZ+BqMToZ7awfvC18r7RYFnk6DyX+Cbd+VdkvKNC0yFhERyW/ha/D2ENi7MzjesiY4Bki9svTaBfDdIvjhc8gcCy37Qqc7ocZvSrdNZZACHBERkfymjvgluMmxdye89/+g9kml06ZY2XuC9/kvK9ApgKaoRERE8tuyNn76jh+ObDuKkr0HsnZBxgvw+vWl3ZoyRSM4IiIi+dVqFExLxUs/sd2Rb09BKh4FViEcwRla2q0pUzSCIyIikl/Xu6Fy1bxplasG6WVBxaOgUgKccS3cshAu+h+ocWxpt6pM0QiOiIhIfjkLiaeOCKarajUKgpvSXmAM8JsUaHRmMGKjoKZA2mxTDgtttikiIkeCNtsUERGRXw0FOCIiIhI5CnBEREQkchTgiIiISOQowBEREZHIUYAjIiIikaMAR0RERCJHAY6IiIhEjgIcEREpnz7+W/CKiqj1p5QpwBEREZHIUYAjIiIikaMAR0RERCJHAY6IiIhEjgIcERERiRwFOCIiIhI5CnBEREQkchTgiIiISOQowBEREZHIUYAjIiIikaMAR0RERCKnUmk3QERE5KCtmlXaLSg5q2ZBYlpptyIyFOCIiEj51OE/S7sFJSsxLXp9KkUKcEREpPxSQCAF0BocERERiRwFOCIiIhI5CnBEREQkchTgiIiISOQowBEREZHIUYAjIiIikaMAR0RERCJHAY6IiIhEjgIcERERiRwFOCIiIhI55u6l3QaJIDP7AVhd2u0opnrAxtJuxGGgfpUvUe0XRLdv6lfZcJK718+fqABHfvXMbJ67tyntdpQ09at8iWq/ILp9U7/KNk1RiYiISOQowBEREZHIUYAjAn8v7QYcJupX+RLVfkF0+6Z+lWFagyMiIiKRoxEcERERiRwFOCIiIhI5CnDkV8XMnjezDWa2OCatrpm9b2Yrwvc6pdnGg2FmJ5jZdDNbZmZLzOyWML1c983MEsxsjpktCPt1X5h+spl9GvZrvJkdVdptPRhmVtHM5pvZ5PC43PfLzFaZ2SIzyzSzeWFauf49BDCz2mb2upktD/87a1/e+2VmTcOfU85rq5ndWt77lUMBjvzavAh0z5c2DJjq7qcBU8Pj8iYLuM3dTwfOAm42syTKf992A+e6ewugJdDdzM4CHgZGh/36CbixFNt4KG4BlsUcR6VfXdy9ZcyzVMr77yHAo8C77t4MaEHwcyvX/XL3z8OfU0ugNfAzMIly3q8cCnDkV8XdPwB+zJd8KfBS+PkloOcRbVQJcPdv3f2z8PM2gv/5Hk8575sHtoeHlcOXA+cCr4fp5a5fAGbWCLgQeDY8NiLQrwKU699DM6sJdASeA3D3Pe6+mXLer3y6Al+5+2oi0i8FOCJwrLt/C0GgADQo5fYcEjNLBM4APiUCfQuncTKBDcD7wFfAZnfPCrOsJQjmypsxwJ3AvvD4GKLRLwemmFmGmd0UppX338PGwA/AC+GU4rNmVo3y369YVwHjws+R6JcCHJEIMbPqwBvAre6+tbTbUxLcPTscQm8EnAmcHi/bkW3VoTGzi4AN7p4Rmxwna7nqV+hsd28F/JZgqrRjaTeoBFQCWgFPufsZwA7K6bRNPOFar0uACaXdlpKkAEcEvjez4wDC9w2l3J6DYmaVCYKbse4+MUyORN8AwimBGQRrjGqbWaXwVCNgfWm16yCdDVxiZquAVwmmpsZQ/vuFu68P3zcQrOc4k/L/e7gWWOvun4bHrxMEPOW9Xzl+C3zm7t+Hx5HolwIcEXgL6B9+7g+8WYptOSjh+o3ngGXu/j8xp8p138ysvpnVDj9XBc4jWF80HegdZit3/XL34e7eyN0TCaYGprl7X8p5v8ysmpnVyPkMXAAsppz/Hrr7d8AaM2saJnUFllLO+xXjan6ZnoKI9EtPMpZfFTMbB3QG6gHfA/cA6cBrwInAN8AV7p5/IXKZZmZpwIfAIn5Z0/FngnU45bZvZpZKsMixIsE/yF5z9xFm1phg5KMuMB+4xt13l15LD56ZdQZud/eLynu/wvZPCg8rAa+4+1/M7BjK8e8hgJm1JFgQfhTwNXA94e8k5btfRwNrgMbuviVMK/c/L1CAIyIiIhGkKSoRERGJHAU4IiIiEjkKcERERCRyFOCIiIhI5CjAERH5/+3dX4jlYxzH8fd7UTutoRSyN4b8iTRsTBJlp6RWQpG54E67LC17sTdaN7jcC1pJ40JcSOJSMUqrdikzsmvtsFL+lNoLSpvVaDV9Xfyew7RNs2ekGXN8XlfnnL7PPM/vd3H69jy/M5+IGDhpcCIilqDOt6TlI+pb7We1q7GOnas1d8RalAYnImJpcy1x+RrgJPBIvwPVM/7FdewE0uBE9CkNTkRE//YDlwGoD6rTbXdnstfMqCfUZ9RPgJvUMfVj9fNWP9wCRPeoM+ph9eE2drP6ofq2elR93c7jwEZgn7qv1b6kfqrOqk/3Fqje0cYeUPeq77TPN6ivtDkPqnev7K2LWFlpcCIi+tAyorYAX6hXARN0wZLXAfPAA610A3Ckqm4EpoE3gSeq6lq6qIk54CHgeFWNAWPAVvWSNn4T3W7N1XQp1jdX1V66XKrxqhpvdbur6gZgFLhVHVXXA5PAlqq6BTh/wSXspouEGAPGgT0tTiFiIJ15+pKIiP+1IfVQe72fLvNrG3A9MNPFgDHE34GE83ShpwBXAseqagagl/Cu3g6Mqr3cqXOBy+mOwKar6sdWdwgYAQ4ssq771W103+MX0TVE64Bvq+q7VvNGWyt0uVB3qbva+/V0/4r/q2Xej4g1IQ1ORMTS5touzV9auOlrVfXkIvW/V9V8rxRYLA9HYEdVTZ3ydzcDC7On5lnky1TC1AAAASVJREFUe7rt9uwCxqrqF/VVuobFJa5D4N6q+nqJmoiBkSOqiIjl+wC4T70AQD1PvXiRuqPARnWs1Q23o64pYLt6Vvv8ij6Oi34Fhtvrc4DfgOPqhXRHZ735LlVH2vuJBeOngB2tOUPd1O/FRqxF2cGJiFimqvpSfQp4X10H/AE8BvxwSt1JdQJ4QR2ie/7mNrpU6hHgs9Zw/ATcc5ppXwbeVY9V1bh6EJilS7b+qM03pz4KvKf+TPcMUM+zwPPA4Tbn98Cd//QeRPzXJU08ImKAqGdX1YnWxLwIfFNVz632uiJWWo6oIiIGy9b2cPIs3cPLk6u8nohVkR2ciIiIGDjZwYmIiIiBkwYnIiIiBk4anIiIiBg4aXAiIiJi4KTBiYiIiIHzJy09a8ROtIaQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varnames = ['fepol', 'spanking']\n",
    "multiplot(results, varnames, title='Agree or strongly agree');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['confinan',\n",
       " 'conbus',\n",
       " 'conclerg',\n",
       " 'coneduc',\n",
       " 'confed',\n",
       " 'conlabor',\n",
       " 'conpress',\n",
       " 'conmedic',\n",
       " 'contv',\n",
       " 'conjudge',\n",
       " 'consci',\n",
       " 'conlegis',\n",
       " 'conarmy']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = []\n",
    "for varname, _, _ in variables:\n",
    "    if varname.startswith('con'):\n",
    "        t.append(varname)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}