{
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   "source": [
    "# Introduction\n",
    "My wife and I are both interested in our genealogy and genealogical record, and subsequently have an [Ancestry account](www.ancestry.com). We both have family that reaches *really* far back (...in American terms), and have put in a lot of time into tracking and researching our ancestral records.  \n",
    "\n",
    "In reviewing the record, I became interested in the possibilities of analyzing the raw data that might be available within our respective genealogies. I discovered you can extract a [GEDCOM](https://en.wikipedia.org/wiki/GEDCOM) (**Ge**nealogical **D**ata **Com**munication) file, and since I was trying to improve my Python data munging skills, I thought - why not analyze my ancestry?\n",
    "\n",
    "\n",
    "There are a few Python packages out there that will load a GEDCOM file directly into Python, but I ended up using the GNU program [GRAMPS](https://gramps-project.org/) to convert it into a series of CSVs.  GRAMPS technically does about 80% of what I wanted to do - and is ironically built with Python - but just using it wouldn't be nearly as fun.\n",
    "\n",
    "# Questions to Explore\n",
    "- What were the family names of my ancestors?\n",
    "- Which states contained the most births or deaths?\n",
    "- How mobile were my ancestors over time?\n",
    "- How has the average life span changed over time?\n",
    "- What was the migratory pattern of my ancestors across states?\n",
    "\n",
    "I have also posted a summary at the bottom of the notebook with answers to these questions without the related code.\n",
    "\n",
    "# Caveats\n",
    "In addition to the data being **really** messy, there are a few restrictions that may impact some of the analysis:\n",
    "* The basic Ancestry account will only pull records for those residing in the United States.\n",
    "* While my maternal side of the family in the US goes back to the colonial times, my paternal side of the family are relatively recent immigrants from Italy.  This causes an imbalance in the data, so that the majority of the data is from my maternal side of the family.\n",
    "- In the context of the time vs. quality tradeoff, time took precedent in my analyses due to the level and depth of data cleaning necessary. For example, \"somewhere in the delta\" being listed as a birth place may be important to examine and adjust within a professional setting, looking at the state and country of birth. However, for my interest-based purposes, I decided against completing an in-depth and time-consuming data munging of all available data points. \n",
    "\n",
    "With that all said, let's get started!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from chorogrid import Colorbin, Chorogrid  # For plotting choropleth maps\n",
    "from colour import Color  # Easily creating colors for the maps\n",
    "\n",
    "plt.style.use('seaborn-deep')  # Keeps plot aesthetics consistent\n",
    "pd.set_option('display.max_columns', None)  # Displays all columns for wide data frames\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part I\n",
    "## The Data\n",
    "We have four CSVs at our disposal:\n",
    "- **Individual Detail**: The bulk of our data - one row for every individual with various information shown below\n",
    "- **Marriage Detail**: The husband/wife, date, place of marriage, and a \"family\" key\n",
    "- **Family Detail**: The \"family\" key and children\n",
    "- **Place Detail**: The \"place\" key and address.  In the end, I did not end up using this, but it would be useful if I wanted to look at locations of marriage or burial.\n",
    "\n",
    "Since some of the columns in the tables had either little or no data, I included lines to drop any columns without data, and manually viewed each table with the Pandas .info() command to see which columns contained too few records to be of use.\n",
    "\n",
    "You'll also see as we go on that this data is *messy*.  It has a lot of missing records, and some of the data is clearly in the wrong place.  The formatting is also very inconsistent.  For example, I have seen all of the following formats for dates: '29 Dec, 1865', '12/29/1865', '1865-12-29', '1860s', and 'about Dec 1865'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 489 entries, 0 to 488\n",
      "Data columns (total 13 columns):\n",
      "Person           489 non-null object\n",
      "Surname          483 non-null object\n",
      "Given            486 non-null object\n",
      "Suffix           20 non-null object\n",
      "Gender           489 non-null object\n",
      "Birth date       460 non-null object\n",
      "Birth place      400 non-null object\n",
      "Birth source     132 non-null object\n",
      "Death date       308 non-null object\n",
      "Death place      250 non-null object\n",
      "Death source     36 non-null object\n",
      "Burial place     21 non-null object\n",
      "Burial source    20 non-null object\n",
      "dtypes: object(13)\n",
      "memory usage: 49.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Individual Detail\n",
    "df = pd.read_csv('Macaluso_Tree.csv', nrows=489)\n",
    "df.dropna(axis = 'columns', how='all', inplace=True)  # Drops any column that doesn't contain any data\n",
    "df.info()\n",
    "\n",
    "# Marriage Detail\n",
    "Marriage = pd.read_csv('Macaluso_Tree.csv', skiprows=490, nrows=87)\n",
    "Marriage.dropna(axis='columns', how='all', inplace=True)\n",
    "\n",
    "# Family Detail\n",
    "Family = pd.read_csv('Macaluso_Tree.csv', skiprows=579, nrows=407)\n",
    "Family.dropna(axis='columns', how='all', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Too few sources to bother with\n",
    "df.drop(['Birth source', 'Death source', 'Burial source'], axis=1, inplace=True)\n",
    "\n",
    "# Create a new column for full name.  Correcting often misspelled last name.\n",
    "df.replace(to_replace='MacAluso', value='Macaluso', inplace=True)  # No, it's not Scottish or Irish\n",
    "df[\"Name\"] = df[\"Given\"].map(str) + \" \" + df[\"Surname\"]\n",
    "\n",
    "# Renaming the columns to make them more intuitive\n",
    "Family.columns = ['Family', 'Person']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to combine the three tables and tie the parents to the children, I had to join the Marriage data frame to the Individual data frame before combining it all together.\n",
    "\n",
    "Here's a quick look at the information I'll be adding to the main data frame for the parents.  You can also note the 'about 1842' under the Father Birth date column, with no country listed for the first record under Father Birth place, and many missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Husband</th>\n",
       "      <th>Father Birth date</th>\n",
       "      <th>Father Birth place</th>\n",
       "      <th>Father Death date</th>\n",
       "      <th>Father Death place</th>\n",
       "      <th>Father Burial place</th>\n",
       "      <th>Father Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>[P481]</td>\n",
       "      <td>about 1842</td>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Willett Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>[P141]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>John McKee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>[P130]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Daniel McDonald Sr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>[P192]</td>\n",
       "      <td>1650</td>\n",
       "      <td>Lambeth, London, England</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>John Rasbury</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>[P430]</td>\n",
       "      <td>1645</td>\n",
       "      <td>Anne Arundel County, Maryland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Anne Arundel County, Maryland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan Bagley</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   Husband Father Birth date             Father Birth place Father Death date  \\\n",
       "80  [P481]        about 1842                       New York               NaN   \n",
       "81  [P141]               NaN                            NaN               NaN   \n",
       "82  [P130]               NaN                            NaN               NaN   \n",
       "83  [P192]              1650       Lambeth, London, England               NaN   \n",
       "84  [P430]              1645  Anne Arundel County, Maryland               NaN   \n",
       "\n",
       "               Father Death place Father Burial place         Father Name  \n",
       "80                            NaN                 NaN       Willett Green  \n",
       "81                            NaN                 NaN          John McKee  \n",
       "82                            NaN                 NaN  Daniel McDonald Sr  \n",
       "83                            NaN                 NaN        John Rasbury  \n",
       "84  Anne Arundel County, Maryland                 NaN          nan Bagley  "
      ]
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   "source": [
    "Father_Info = df.drop(['Surname', 'Given', 'Suffix', 'Gender'], axis=1)\n",
    "\n",
    "# Columns will be renamed in the final join\n",
    "Father_Info.columns = ['Husband', 'Father Birth date', 'Father Birth place',\n",
    "                       'Father Death date', 'Father Death place',\n",
    "                       'Father Burial place', 'Father Name']\n",
    "\n",
    "Father = pd.concat([Marriage['Husband']], axis=1)  # Inner join to limit the list to only husbands\n",
    "\n",
    "Father = Father.merge(Father_Info, on='Husband')\n",
    "\n",
    "Father.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I'm doing the same thing for the mother.  Even though these are more recent dates, you can see that the data isn't much cleaner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wife</th>\n",
       "      <th>Mother Birth date</th>\n",
       "      <th>Mother Birth place</th>\n",
       "      <th>Mother Death date</th>\n",
       "      <th>Mother Death place</th>\n",
       "      <th>Mother Burial place</th>\n",
       "      <th>Mother Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>[P478]</td>\n",
       "      <td>about 1884</td>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mabel Greene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>[P480]</td>\n",
       "      <td>about 1843</td>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sarah Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>[P140]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Margaret McKee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>[P27]</td>\n",
       "      <td>about 1856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>about 1916</td>\n",
       "      <td>Tacoma, Pierce County, Washington, USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gecomina Damico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>[P129]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emma Flora McDonald</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Wife Mother Birth date Mother Birth place Mother Death date  \\\n",
       "77  [P478]        about 1884           New York               NaN   \n",
       "78  [P480]        about 1843           New York               NaN   \n",
       "79  [P140]               NaN                NaN               NaN   \n",
       "80   [P27]        about 1856                NaN        about 1916   \n",
       "81  [P129]               NaN                NaN               NaN   \n",
       "\n",
       "                        Mother Death place Mother Burial place  \\\n",
       "77                                     NaN                 NaN   \n",
       "78                                     NaN                 NaN   \n",
       "79                                     NaN                 NaN   \n",
       "80  Tacoma, Pierce County, Washington, USA                 NaN   \n",
       "81                                     NaN                 NaN   \n",
       "\n",
       "            Mother Name  \n",
       "77         Mabel Greene  \n",
       "78          Sarah Green  \n",
       "79       Margaret McKee  \n",
       "80      Gecomina Damico  \n",
       "81  Emma Flora McDonald  "
      ]
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   ],
   "source": [
    "Mother_Info = df.drop(['Surname', 'Given', 'Suffix', 'Gender'], axis=1)\n",
    "\n",
    "# Columns will be renamed in the final join\n",
    "Mother_Info.columns = ['Wife', 'Mother Birth date', 'Mother Birth place', 'Mother Death date',\n",
    "                       'Mother Death place', 'Mother Burial place', 'Mother Name']\n",
    "\n",
    "Mother = pd.concat([Marriage['Wife']], axis=1)\n",
    "\n",
    "Mother = Mother.merge(Mother_Info, on='Wife')\n",
    "\n",
    "Mother.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Merge the Family data frame to assign the family ID for other joins\n",
    "df = pd.merge(df, Family, on='Person', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Merge the Marriage data frame to assign the mother and father\n",
    "Marriage.columns = ['Family', 'FatherKey', 'MotherKey', 'Date', 'Place', 'Source']\n",
    "df = pd.merge(df, Marriage, on='Family', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to finish up the merges, and get a look at a few descriptors and a slice of our data frame.\n",
    "\n",
    "I'm going to use a neat library [found from this Reddit thread](https://www.reddit.com/r/pystats/comments/4owh98/what_are_your_favorite_smaller_or_lesserknown/) that visualizes missing values.  We're using this instead of df.info() (which is preferred over df.describe() in this case since we're mostly looking for non-null counts and most columns are strings or inconsistently formatted dates) to give us a more intuitive idea of the missing values at hand. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Merge the parental info on the data frame\n",
    "df = pd.merge(df, Father, how ='left', left_on='FatherKey', right_on='Husband')\n",
    "df = pd.merge(df, Mother, how ='left', left_on='MotherKey', right_on='Wife')\n",
    "df.drop(['Husband', 'Wife', 'MotherKey', 'FatherKey', 'Suffix', 'Date', 'Place', 'Source'], \n",
    "        axis=1, inplace=True)\n",
    "df.drop_duplicates(inplace=True)  # To account for dupliciate rows later discovered\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
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EkH9IEaQEvJCf09Qep6pkmY9/Al8DkwEfAe+W9o8EtiBqlXcG1jazBc1sHjOb0syOJpxb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\nshvlowgOkScsDraUjRRIkODZUdKy7DkphLClfPmQ181sz9j7HS7vOHK5m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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17ceb2956a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno as msno\n",
    "\n",
    "msno.matrix(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Renaming columns to remove spaces for dot notation\n",
    "df.columns = ['PersonKey', 'Surname', 'Given', 'Gender', 'BirthDate', 'BirthPlace',\n",
    "              'DeathDate', 'DeathPlace', 'BurialPlace', 'Name', 'Family',\n",
    "              'FatherBirthDate', 'FatherBirthPlace', 'FatherDeathDate','FatherDeathPlace',\n",
    "              'FatherBurialPlace', 'FatherName', 'MotherBirthDate', 'MotherBirthPlace',\n",
    "              'MotherDeathDate', 'MotherDeathPlace',\n",
    "              'MotherBurialPlace', 'MotherName']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a good look at some more recent data with a couple of great uncles, a great aunt, and my grandpa on my father's side.  You can also see how recent my Italian family's immigration is under the Father/Mother Birth Place columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PersonKey</th>\n",
       "      <th>Surname</th>\n",
       "      <th>Given</th>\n",
       "      <th>Gender</th>\n",
       "      <th>BirthDate</th>\n",
       "      <th>BirthPlace</th>\n",
       "      <th>DeathDate</th>\n",
       "      <th>DeathPlace</th>\n",
       "      <th>BurialPlace</th>\n",
       "      <th>Name</th>\n",
       "      <th>Family</th>\n",
       "      <th>FatherBirthDate</th>\n",
       "      <th>FatherBirthPlace</th>\n",
       "      <th>FatherDeathDate</th>\n",
       "      <th>FatherDeathPlace</th>\n",
       "      <th>FatherBurialPlace</th>\n",
       "      <th>FatherName</th>\n",
       "      <th>MotherBirthDate</th>\n",
       "      <th>MotherBirthPlace</th>\n",
       "      <th>MotherDeathDate</th>\n",
       "      <th>MotherDeathPlace</th>\n",
       "      <th>MotherBurialPlace</th>\n",
       "      <th>MotherName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>[P28]</td>\n",
       "      <td>Macaluso</td>\n",
       "      <td>Frank</td>\n",
       "      <td>male</td>\n",
       "      <td>1913-11-11</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1999-04-21</td>\n",
       "      <td>Anderson Island, Pierce, Washington, United St...</td>\n",
       "      <td>Tacoma, Pierce County, Washington, USA</td>\n",
       "      <td>Frank Macaluso</td>\n",
       "      <td>[F0005]</td>\n",
       "      <td>1884-04-21</td>\n",
       "      <td>Alimena, Sicilia, Italy</td>\n",
       "      <td>16 February 1976</td>\n",
       "      <td>Tacoma, Pierce, Washington, United States of A...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Joseph Macaluso</td>\n",
       "      <td>1892</td>\n",
       "      <td>Italy</td>\n",
       "      <td>1965-03-03</td>\n",
       "      <td>Tacoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Francesca Romano</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>[P9]</td>\n",
       "      <td>Macaluso</td>\n",
       "      <td>James Joseph</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>about 1969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>James Joseph Macaluso</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>[P34]</td>\n",
       "      <td>Macaluso</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>male</td>\n",
       "      <td>about 1934</td>\n",
       "      <td>Tacoma, Pierce County, Washington, USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Joseph Macaluso</td>\n",
       "      <td>[F0005]</td>\n",
       "      <td>1884-04-21</td>\n",
       "      <td>Alimena, Sicilia, Italy</td>\n",
       "      <td>16 February 1976</td>\n",
       "      <td>Tacoma, Pierce, Washington, United States of A...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Joseph Macaluso</td>\n",
       "      <td>1892</td>\n",
       "      <td>Italy</td>\n",
       "      <td>1965-03-03</td>\n",
       "      <td>Tacoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Francesca Romano</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>[P30]</td>\n",
       "      <td>Macaluso</td>\n",
       "      <td>Mary</td>\n",
       "      <td>female</td>\n",
       "      <td>about 1917</td>\n",
       "      <td>Washington</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mary Macaluso</td>\n",
       "      <td>[F0005]</td>\n",
       "      <td>1884-04-21</td>\n",
       "      <td>Alimena, Sicilia, Italy</td>\n",
       "      <td>16 February 1976</td>\n",
       "      <td>Tacoma, Pierce, Washington, United States of A...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Joseph Macaluso</td>\n",
       "      <td>1892</td>\n",
       "      <td>Italy</td>\n",
       "      <td>1965-03-03</td>\n",
       "      <td>Tacoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Francesca Romano</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PersonKey   Surname         Given  Gender   BirthDate  \\\n",
       "304     [P28]  Macaluso         Frank    male  1913-11-11   \n",
       "305      [P9]  Macaluso  James Joseph    male         NaN   \n",
       "306     [P34]  Macaluso        Joseph    male  about 1934   \n",
       "307     [P30]  Macaluso          Mary  female  about 1917   \n",
       "\n",
       "                                 BirthPlace   DeathDate  \\\n",
       "304                              Washington  1999-04-21   \n",
       "305                                     NaN  about 1969   \n",
       "306  Tacoma, Pierce County, Washington, USA         NaN   \n",
       "307                              Washington         NaN   \n",
       "\n",
       "                                            DeathPlace  \\\n",
       "304  Anderson Island, Pierce, Washington, United St...   \n",
       "305                                                NaN   \n",
       "306                                                NaN   \n",
       "307                                                NaN   \n",
       "\n",
       "                                BurialPlace                   Name   Family  \\\n",
       "304  Tacoma, Pierce County, Washington, USA         Frank Macaluso  [F0005]   \n",
       "305                                     NaN  James Joseph Macaluso      NaN   \n",
       "306                                     NaN        Joseph Macaluso  [F0005]   \n",
       "307                                     NaN          Mary Macaluso  [F0005]   \n",
       "\n",
       "    FatherBirthDate         FatherBirthPlace   FatherDeathDate  \\\n",
       "304      1884-04-21  Alimena, Sicilia, Italy  16 February 1976   \n",
       "305             NaN                      NaN               NaN   \n",
       "306      1884-04-21  Alimena, Sicilia, Italy  16 February 1976   \n",
       "307      1884-04-21  Alimena, Sicilia, Italy  16 February 1976   \n",
       "\n",
       "                                      FatherDeathPlace FatherBurialPlace  \\\n",
       "304  Tacoma, Pierce, Washington, United States of A...               NaN   \n",
       "305                                                NaN               NaN   \n",
       "306  Tacoma, Pierce, Washington, United States of A...               NaN   \n",
       "307  Tacoma, Pierce, Washington, United States of A...               NaN   \n",
       "\n",
       "          FatherName MotherBirthDate MotherBirthPlace MotherDeathDate  \\\n",
       "304  Joseph Macaluso            1892            Italy      1965-03-03   \n",
       "305              NaN             NaN              NaN             NaN   \n",
       "306  Joseph Macaluso            1892            Italy      1965-03-03   \n",
       "307  Joseph Macaluso            1892            Italy      1965-03-03   \n",
       "\n",
       "    MotherDeathPlace MotherBurialPlace        MotherName  \n",
       "304           Tacoma               NaN  Francesca Romano  \n",
       "305              NaN               NaN               NaN  \n",
       "306           Tacoma               NaN  Francesca Romano  \n",
       "307           Tacoma               NaN  Francesca Romano  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[304:307]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part II\n",
    "## The Questions\n",
    "\n",
    "### Question 1: Family Names\n",
    "\n",
    "Let's pause here and take a more detailed look at family names.  You can see the name \"Joseph\" listed multiple times, and since this timeframe is roughly as far back as records are available for my father's side, I know \"William\" and \"Bryan\" are very popular names on my mother's side.  \n",
    "\n",
    "Below, I'm going to put together plots of the highest occurring names.  I'm going to do this by creating a flattened list out of the given names column to look for *all names* - including middle names.  This will be more of a [bag of words](https://en.wikipedia.org/wiki/Bag-of-words_model) approach, as I'm not interested in assigning any kind of weights for middle names. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "John         36\n",
       "William      34\n",
       "Elizabeth    26\n",
       "Sarah        19\n",
       "Mary         17\n",
       "Thomas       16\n",
       "James        16\n",
       "Joseph       14\n",
       "Ann          12\n",
       "Margaret      9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting to account for middle names listed within the 'Given' column\n",
    "family_names = [str(name).split() for name in df['Given'].dropna()]\n",
    "\n",
    "# Flattening, removing abbreviations, and transforming into a series to speed things up\n",
    "family_names = [item for sublist in family_names for item in sublist]\n",
    "family_names = pd.Series([word if len(word) > 2 else np.NaN for word in family_names]).dropna()\n",
    "\n",
    "family_names = family_names.value_counts()\n",
    "\n",
    "# Top 10 names\n",
    "family_names[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So William can definitely be considered a family name, but where is Bryan?  Or am I just biased because that's one of my middle names?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bryan    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "family_names[family_names.index == 'Bryan']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, that's a lot lower than I would have thought.  What about the histogram I mentioned?\n",
    "\n",
    "I'll just use the top 20 names here since, theoretically, there wouldn't be more names than that available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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D8sxBeeagPHMgtY7fRCdJkiRJhVmYSZIkSVJhFmaSJEmSVJiFmSRJkiQVZmEmSZIkSYVZ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\nlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17ceb75e7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = family_names[:20].plot(kind=\"barh\", figsize=(10, 7), title=\"Family Names\")\n",
    "ax.set_xlabel(\"Occurrences\")\n",
    "\n",
    "plt.gca().invert_yaxis()  # For descending order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When I initially received these results, my data frame contained duplicates, and I saw the name \"Keturah\" appear with 6 instances.  Given its unique nature and the fact that I have never heard this name in the family before, I decided to take a look at these records to rule out duplicates and conduct further examination: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PersonKey</th>\n",
       "      <th>Surname</th>\n",
       "      <th>Given</th>\n",
       "      <th>Gender</th>\n",
       "      <th>BirthDate</th>\n",
       "      <th>BirthPlace</th>\n",
       "      <th>DeathDate</th>\n",
       "      <th>DeathPlace</th>\n",
       "      <th>BurialPlace</th>\n",
       "      <th>Name</th>\n",
       "      <th>Family</th>\n",
       "      <th>FatherBirthDate</th>\n",
       "      <th>FatherBirthPlace</th>\n",
       "      <th>FatherDeathDate</th>\n",
       "      <th>FatherDeathPlace</th>\n",
       "      <th>FatherBurialPlace</th>\n",
       "      <th>FatherName</th>\n",
       "      <th>MotherBirthDate</th>\n",
       "      <th>MotherBirthPlace</th>\n",
       "      <th>MotherDeathDate</th>\n",
       "      <th>MotherDeathPlace</th>\n",
       "      <th>MotherBurialPlace</th>\n",
       "      <th>MotherName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>[P363]</td>\n",
       "      <td>Peddicord</td>\n",
       "      <td>Keturah</td>\n",
       "      <td>female</td>\n",
       "      <td>1706</td>\n",
       "      <td>St Annes Parish, Anne Arundel, Maryland, Unite...</td>\n",
       "      <td>1759-12-00</td>\n",
       "      <td>Baptism St Annes Parish, Anne Arundel, Marylan...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Keturah Peddicord</td>\n",
       "      <td>[F0056]</td>\n",
       "      <td>1669</td>\n",
       "      <td>Carroll's Manor, Frederick, Maryland, United S...</td>\n",
       "      <td>1732</td>\n",
       "      <td>Baltimore, Maryland, USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>John Peddicord</td>\n",
       "      <td>1677</td>\n",
       "      <td>Elk Ridge, Baltimore, Maryland, United States</td>\n",
       "      <td>1727</td>\n",
       "      <td>Baltimore, Baltimore, Maryland, United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sarah Dorsey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>[P415]</td>\n",
       "      <td>Shipley</td>\n",
       "      <td>Keturah Roturah</td>\n",
       "      <td>female</td>\n",
       "      <td>1717-11-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Keturah Roturah Shipley</td>\n",
       "      <td>[F0066]</td>\n",
       "      <td>1726-09-20</td>\n",
       "      <td>Parish, Anne Arundel, Maryland, USA</td>\n",
       "      <td>1789-06-12</td>\n",
       "      <td>Shipley Advent, Anne Arundel, Maryland, United...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>George Shipley</td>\n",
       "      <td>1694-01-01</td>\n",
       "      <td>Ann Arundel Parish, Baltimore, Maryland, Unite...</td>\n",
       "      <td>1762-02-22</td>\n",
       "      <td>Baltimore, Baltimore, Maryland, United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Katherine Ogg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PersonKey    Surname            Given  Gender   BirthDate  \\\n",
       "358    [P363]  Peddicord          Keturah  female        1706   \n",
       "450    [P415]    Shipley  Keturah Roturah  female  1717-11-22   \n",
       "\n",
       "                                            BirthPlace   DeathDate  \\\n",
       "358  St Annes Parish, Anne Arundel, Maryland, Unite...  1759-12-00   \n",
       "450                                                NaN         NaN   \n",
       "\n",
       "                                            DeathPlace BurialPlace  \\\n",
       "358  Baptism St Annes Parish, Anne Arundel, Marylan...         NaN   \n",
       "450                                                NaN         NaN   \n",
       "\n",
       "                        Name   Family FatherBirthDate  \\\n",
       "358        Keturah Peddicord  [F0056]            1669   \n",
       "450  Keturah Roturah Shipley  [F0066]      1726-09-20   \n",
       "\n",
       "                                      FatherBirthPlace FatherDeathDate  \\\n",
       "358  Carroll's Manor, Frederick, Maryland, United S...            1732   \n",
       "450                Parish, Anne Arundel, Maryland, USA      1789-06-12   \n",
       "\n",
       "                                      FatherDeathPlace FatherBurialPlace  \\\n",
       "358                           Baltimore, Maryland, USA               NaN   \n",
       "450  Shipley Advent, Anne Arundel, Maryland, United...               NaN   \n",
       "\n",
       "         FatherName MotherBirthDate  \\\n",
       "358  John Peddicord            1677   \n",
       "450  George Shipley      1694-01-01   \n",
       "\n",
       "                                      MotherBirthPlace MotherDeathDate  \\\n",
       "358      Elk Ridge, Baltimore, Maryland, United States            1727   \n",
       "450  Ann Arundel Parish, Baltimore, Maryland, Unite...      1762-02-22   \n",
       "\n",
       "                                  MotherDeathPlace MotherBurialPlace  \\\n",
       "358  Baltimore, Baltimore, Maryland, United States               NaN   \n",
       "450  Baltimore, Baltimore, Maryland, United States               NaN   \n",
       "\n",
       "        MotherName  \n",
       "358   Sarah Dorsey  \n",
       "450  Katherine Ogg  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Given'].str.contains(\"Keturah\") == True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After brief research, I learned that Keturah is the name of [Abraham's second wife](https://en.wikipedia.org/wiki/Keturah). Given the colonial time period from which this data was drawn, the appearance of this unique name makes sense.\n",
    "\n",
    "So what about last names?  My last name is out of the running because of the nature of the data, but I would expect my mother's maiden name, Johnson, to have the highest count. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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YICVJkiRJLTFASpIkSZJaYoCUJEmSJLXEAClJkiRJakl36QI0tpYtW0Zf32r6+9eWLmVS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\nSY38f96qg4kiLxfGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cebcd7390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Frequency of surnames\n",
    "Surname = df[['Surname', 'PersonKey']].dropna()\n",
    "Surname = Surname.groupby('Surname').count()\n",
    "Surname = Surname.sort_values(by='PersonKey', ascending=0)\n",
    "ax = Surname[:15].plot(kind='barh', figsize=(10, 7), legend=False, title='Last Names')\n",
    "ax.set_xlabel(\"Occurrences\")\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I didn't expect to see Flint appear more frequently than Johnson, but it makes sense considering that it belongs to my great grandmother's line which dates back to the colonial times.  The surname Rasberry is a similar story, and is my direct grandma's maiden name.  However, I don't recognize any of the others listed after my last name (Macaluso). \n",
    "\n",
    "\n",
    "### Question 2: Which states contained the most births or deaths?\n",
    "\n",
    "In order to begin answering this question, we first have to clean up our data.  Specifically, we need to extract the state names from our \"Birth Place\" and \"Burial Place\" columns.\n",
    "\n",
    "I'm going to begin by [plugging in a dictionary of US states I found on GitHub](https://gist.github.com/rogerallen/1583593) \n",
    "\n",
    "**Side Note**: We're going to generate some state-wide [choropleth maps](https://en.wikipedia.org/wiki/Choropleth_map) using this data later on.  I'm keeping it separate so the maps can be compared side-by-side more easily.  These will provide a better visualization than bar graphs, but this section is still worth looking over since it includes the data engineering needed to extract the states from our initial data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Creating a dictionary of states and their codes to extract places into states\n",
    "states = {\n",
    "    'Alabama': 'AL',\n",
    "    'Alaska': 'AK',\n",
    "    'Arizona': 'AZ',\n",
    "    'Arkansas': 'AR',\n",
    "    'California': 'CA',\n",
    "    'Colorado': 'CO',\n",
    "    'Connecticut': 'CT',\n",
    "    'Delaware': 'DE',\n",
    "    'Florida': 'FL',\n",
    "    'Georgia': 'GA',\n",
    "    'Hawaii': 'HI',\n",
    "    'Idaho': 'ID',\n",
    "    'Illinois': 'IL',\n",
    "    'Indiana': 'IN',\n",
    "    'Iowa': 'IA',\n",
    "    'Kansas': 'KS',\n",
    "    'Kentucky': 'KY',\n",
    "    'Louisiana': 'LA',\n",
    "    'Maine': 'ME',\n",
    "    'Maryland': 'MD',\n",
    "    'Massachusetts': 'MA',\n",
    "    'Michigan': 'MI',\n",
    "    'Minnesota': 'MN',\n",
    "    'Mississippi': 'MS',\n",
    "    'Missisippi': 'MS',  # to account for spelling errors\n",
    "    'Missouri': 'MO',\n",
    "    'Montana': 'MT',\n",
    "    'Nebraska': 'NE',\n",
    "    'Nevada': 'NV',\n",
    "    'New Hampshire': 'NH',\n",
    "    'New Jersey': 'NJ',\n",
    "    'New Mexico': 'NM',\n",
    "    'New York': 'NY',\n",
    "    'North Carolina': 'NC',\n",
    "    'North Dakota': 'ND',\n",
    "    'Ohio': 'OH',\n",
    "    'Oklahoma': 'OK',\n",
    "    'Oregon': 'OR',\n",
    "    'Pennsylvania': 'PA',\n",
    "    'Rhode Island': 'RI',\n",
    "    'South Carolina': 'SC',\n",
    "    'South Dakota': 'SD',\n",
    "    'Tennessee': 'TN',\n",
    "    'Texas': 'TX',\n",
    "    'Utah': 'UT',\n",
    "    'Vermont': 'VT',\n",
    "    'Virginia': 'VA',\n",
    "    'Washington': 'WA',\n",
    "    'West Virginia': 'WV',\n",
    "    'Wisconsin': 'WI',\n",
    "    'Wyoming': 'WY',\n",
    "}\n",
    "\n",
    "# Reverses the above dictionary to extract state abbreviations using the same function\n",
    "states_reverse = dict(zip(states.values(),states.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to use the dictionary to pass through the individual elements of the column. This will allow us to check for a match, and extract the state if a match exists.\n",
    "\n",
    "We'll begin by creating a function that will return a series of the passed column containing the state after using a string split and strip within a list comprehension.\n",
    "\n",
    "Then, we'll add these series as columns to our data frame.\n",
    "\n",
    "Finally, we'll account for instances where a state abbreviation was extracted by converting it to the full state name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BirthPlace</th>\n",
       "      <th>BirthState</th>\n",
       "      <th>DeathPlace</th>\n",
       "      <th>DeathState</th>\n",
       "      <th>FatherBirthState</th>\n",
       "      <th>MotherBirthState</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>Mississippi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Carolina, USA</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Winston, Mississippi, United States</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>,, Sc</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Davie, North Carolina, United States</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>St Stephen, Greene, Alabama, United States</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             BirthPlace      BirthState  \\\n",
       "0                           Mississippi     Mississippi   \n",
       "1                   South Carolina, USA  South Carolina   \n",
       "2                                 ,, Sc  South Carolina   \n",
       "3  Davie, North Carolina, United States  North Carolina   \n",
       "\n",
       "                                   DeathPlace   DeathState FatherBirthState  \\\n",
       "0                                         NaN          NaN      Mississippi   \n",
       "1         Winston, Mississippi, United States  Mississippi              NaN   \n",
       "2                                         NaN          NaN              NaN   \n",
       "3  St Stephen, Greene, Alabama, United States      Alabama              NaN   \n",
       "\n",
       "  MotherBirthState  \n",
       "0      Mississippi  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3              NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting the state from various addresses\n",
    "def state_extract(dict1, dict2, column):\n",
    "    \"\"\"\n",
    "    Extracts the state from the place columns if any part of the record exists \n",
    "    within the state dictionary or reverse state dictionary\n",
    "    \n",
    "    dict1 is the regular state dictionary, dict2 is the reverse state dictionary for state abbreviations\n",
    "    \"\"\"\n",
    "    return pd.Series([[state.strip() for state in cell.split(',')\n",
    "               if state.strip() in dict1\n",
    "                   or state.strip().upper() in dict2]  # dict2 for state abbreviations\n",
    "              for cell in column.fillna(\"null\")])\n",
    "    \n",
    "    \n",
    "# Running functions to extract the states into series\n",
    "BirthState = state_extract(states, states_reverse, df['BirthPlace'])\n",
    "DeathState = state_extract(states, states_reverse, df['DeathPlace'])\n",
    "\n",
    "FatherBirthState = state_extract(states, states_reverse, df['FatherBirthPlace'])\n",
    "FatherDeathState = state_extract(states, states_reverse, df['FatherDeathPlace'])\n",
    "\n",
    "MotherBirthState = state_extract(states, states_reverse, df['MotherBirthPlace'])\n",
    "MotherDeathState = state_extract(states, states_reverse, df['MotherDeathPlace'])\n",
    "\n",
    "\n",
    "# Assigns series generated from the function to columns and removes brackets surrounding values\n",
    "df['BirthState'] = BirthState.apply(lambda s: s[-1] if s else np.NaN)\n",
    "df['DeathState'] = DeathState.apply(lambda s: s[-1] if s else np.NaN)\n",
    "\n",
    "df['FatherBirthState'] = FatherBirthState.apply(lambda s: s[-1] if s else np.NaN)\n",
    "df['FatherDeathState'] = FatherDeathState.apply(lambda s: s[-1] if s else np.NaN)\n",
    "\n",
    "df['MotherBirthState'] = MotherBirthState.apply(lambda s: s[-1] if s else np.NaN)\n",
    "df['MotherDeathState'] = MotherDeathState.apply(lambda s: s[-1] if s else np.NaN)\n",
    "\n",
    "\n",
    "# Converts from abbreviated state to full state name in title case\n",
    "df['BirthState'] = df['BirthState'].str.upper().replace(states_reverse).str.title()\n",
    "df['DeathState'] = df['DeathState'].str.upper().replace(states_reverse).str.title()\n",
    "\n",
    "df['FatherBirthState'] = df['FatherBirthState'].str.upper().replace(states_reverse).str.title()\n",
    "df['FatherDeathState'] = df['FatherDeathState'].str.upper().replace(states_reverse).str.title()\n",
    "\n",
    "df['MotherBirthState'] = df['MotherBirthState'].str.upper().replace(states_reverse).str.title()\n",
    "df['MotherDeathState'] = df['MotherDeathState'].str.upper().replace(states_reverse).str.title()\n",
    "\n",
    "df[['BirthPlace', 'BirthState', 'DeathPlace', 'DeathState', 'FatherBirthState', 'MotherBirthState']].ix[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Adjust for \"Missisippi\" spelling error\n",
    "df.replace(\"Missisippi\", \"Mississippi\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Adding abbreviated columns for choropleth maps later\n",
    "df['BirthStateAbbrev'] = df['BirthState'].replace(states)\n",
    "df['DeathStateAbbrev'] = df['DeathState'].replace(states)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are a couple of quick plots to visualize the birth and death states.  You can see the prominence of Mississippi from my mother's side, and just how recent Texas is in my family's history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Sg8XxT2DjiFi5Yts2gxDPfsAPM/OAzLwYSOD1LCLJkCRJUvNxjsAAZOZLEXE1cDLw98x8\naDF3/X/ADODCiDgWeANwCMXk4YGYBUyOiDdQTCA+AlgTeM0A25UkSdIwY4/AwF0BbAlcXrW9cuWf\nVy3dWS7luSvFakB/Bb4BXAS8NMBYjgGepphv8FuK5wucC7QNsF1JkiQNM/YIDFBm3kCNybiZuWHF\n6x0ryyJidWD9zHx7xbbDgSfK+lOAKeXrqdXtZ+beFa+PrXj9JPDegX0iSZIkNQMTgfq5NiL+h2Ke\nwSbA/wDfrm9IkiRJahYODaqDzHyGYpWhA4D7gAuAMzLzvLoGJkmSpKZhj0CdZOYvgV/WOw5JkiQ1\nJ3sEJEmSpCZkIiBJkiQ1IYcGqYcHn5hb7xCWqQefmMvEegchaVh54MnmOo82mgeenMtW9Q5CGgJM\nBNTDOw85n46OF+jsXFjvUJbIqFEjGTt2xSWOfSLQ2jph6QUmqenscGjjnkf7e65clgYa41Z4XpcW\nh4mAepg0aRKzZ89nwYLG/APRm+WWG8lqq608JGOXNLw08nl0KJwrh0KM0nDgHAFJkiSpCZkISJIk\nSU3IRECSJElqQs4RUA/t7e0NPYmsW2vrBFpaWuodhiT10Mjn0WaYLLy0Gd/ANWKMzXhdYSKgHn52\nzn6sP250vcPo0yMz58EnT6etzYU/JTWeX5y9HxuOG1PvMCQtpodnzoVPNd91hYmAelh/3Gg2XX/V\neochSUPWhuPGsJnnUUkNzjkCkiRJUhMyEZAkSZKakImAJEmS1IRMBCpExMKI6IyIdWqUfb4sP2op\nHPftEbHUpsyXcW+3tNqXJEnS0GMi0NPLwM41tn8YWJrrW3UtxbYlSZKkVzER6OkWqhKBiBgDbAv8\ntS4RSZIkSYPM5UN7ugY4KSJGZ+a8ctv7KRKElbsrRcTywPHAx4A1gMeB72bmBWX5NOBKYE/gCWAW\ncG9mHlrRxrUUycWNlQFExFuA7wNbU/QUTAX2ycynImIv4DPltgMp/g0vyszDKvY/qiwbAXxt4D8S\nSZIkDTf2CPR0N8VF/U4V23YBfkFxYd3tCOC9ZdkmwCXAWRGxekWdTwHvpLhwv6KsC0BEjAXeXW6n\navt1wPXAZsC7gNeXx+s2uTzmZOAg4NCIeEe5/+eAQ8pjvhPYF4cdSZIkqYqJQG3XUg4PiogWiovx\na6rq/A3YNzPbM/MRijv4y1NcoHf7UWbek5l3Az8H1oiIbcuyXYDMzPuq2l0ROC4zv5OZ0zPz9nLf\n1oo6I4HPZuYDmXk5cBcwqSzbDzglM3+TmX8v31cmMJIkSZKJQC+uAd4bESMp7qrfnZnPVlbIzGuB\nlSLipIi4DphGced9VEW1RyrqzwF+A3y03PRR4CfVB87Mp4BLI+KLETElItqBw6vafSoz51e876BI\nQgA2p0gMutu7F6isK0mSJJkI9OL35fe3Ah8Crq6uEBHfBi4DXgKmAG+i5533F6veXwHsFhGrUCQY\nPRKBiFibYnjSDsAdwP8AJ1dVe6lGzCN6eQ3FSkiSJEnSf5gI1JCZncCvKJKAD1AjEQD2Bw7KzK9n\n5k+BMeX2vobhXAusRnGH/67MnFajzoeBWZm5c2aemZl/oJgjsLjDe/7BK8OEiIj1gVUXc19JkiQ1\nCVcN6t21wMXAQ5n5aI3yWcAHI+JOYG3gNIqhQa/prcHMfDEirgEOA77eS7VZwLoRsSPFcKOPAbsC\nf17MuM8Ezo6Iu4D7y7g6F3NfSZIkNQl7BF6tcnWd31IkSlf3Ur4PsBXFHfiLKJYK/TPQVqNupSuB\nFuCqXsqvAn4E/BRoB7YHvgRsVi5Z2mfc5eThoykSglvKzzG7l/0kSZLUpOwRqJCZoypez6fiuQHl\nth0rXt9GkQhUOrGifMNeDrMWcGtmzqyoO5VyMnBmLqR4BsCBVfudUX6fUn7VjKt8fzpweq24JEmS\nJDARWGYi4vUUY/e/waufCSBJkiQtcw4NWnY2AC4EbsnMKxZVWZIkSVqa7BFYRjLzBmB0veOQJEmS\nwB4BSZIkqSnZI6AeHpk5r94hLNIjM+fR22xsSaq3h2fOrXcIkpbAwzPnMr7eQdSBiYB62O0LF9LR\n8QKdnQvrHUqvNgRaWyfUOwxJqunDBzbueXTUqJGMHbtiw8YHjR+j8Q1co8U4nua8rjARUA+TJk1i\n9uz5LFhQ/19MSRqKGvk8utxyI1lttZUbNj5o/BiNb+CGQozNwDkCkiRJUhMyEZAkSZKakImAJEmS\n1IScI6Ae2tvbG2byzpJotIlHS8LYF09r6wRaWlqW6jGkwdDI59HB+p3191Ea+kwE1MMl5+3D+LV9\n9pkay4zH58HHz6StbWK9Q5EW6fJz92HdtcfUO4ylZvrjc+ET/j5KQ52JgHoYv/ZoNtpglXqHIUlD\n1rprj2ETz6OSGpxzBCRJkqQmZCIgSZIkNSETAUmSJKkJNUwiEBELI+JHNbbvFRHTBtDu6IjYo+L9\ntIjYcwnbWCcizo+IGRExLyL+EhG79zemxTzmwojYrny9xDFLkiRJfWmYRKD0yYjYvsb2rgG0+SVg\n7/7uHBEbA3cArwU+AkwAzgV+EBFfGkBcS2Ib4MpldCxJkiQ1gUZbNegR4OyI2DIzFwxSmyMGuP85\nwF8z8yMV2y6MiBWA70bEhZnZMcBj9CkzZy3N9iVJktR8Gi0R+CbF3fYvA9+rVSEi1gZOBd4BLAR+\nDByemS9HxF7AZ4GngR2AE4Gjy/06M3NU2cwbIuIPwNbAvcBnMvPvvRxrR2CnGqFcCNwJzCvrvgX4\nftlmFzAV2Cczn6oR1xeAnwCHA58H1gJuBw7NzH/UiGMacHRmXhoRNwH/B2xXfs0ADs7M35V1NwdO\nASYDywPtwGczM2v9PCVJktScGm1o0OPAMcA3I2K96sKIWB64CVgReBvwUeD9wAkV1SYDdwNvBqYA\nJwO3AWtW1NmXItGYADwHnNdLPFuU3++oLsjMFzPztsxcGBFjgeuA64HNgHcBrweO6CWu31IkKF8C\nDgHagOnA9RGxYi+xVPo6cDnQCvwNOB8gIkYA1wIPlbFvC4wCjl+MNiVJktREGq1HAOAM4DPl9w9V\nlb2X4u75NuVwnHsi4kDg2oj4RllnIfDdzPw3QETMA17KzGcq2jknM68ry88AruglllXL73MWEfOK\nwHGZeWr5fnpE/ByYVFGnOq6DgK9m5q/K95+luIDfHbhgEcf7VWZeVu73beBvEbEm0EHRo3JOZr5Q\nlk+h6GGRJEmS/qPhEoHyDvsBwK0RsXNV8abA/VVj8m+j+Bwble+f7r7Y7sPDFa/nACv0Uq97bP5q\nFa9rxfxURFwaEV8EtgI2B7YEfl9R7emKJGANisnHf65oY0FE3EHRo7AoD1S87v5ZLJ+Zz0fEecBe\nEbENxc9ra+DJxWhTkiRJTaTRhgYBkJm3AxdT9AqsXFH0Yo3qoygmBI/qo061zsUM5c7y+8TqgohY\nKSJ+FxETImIcxbCfHSiGEf0PxZCkSi/28rrSKF75HH15qca2ERGxcnn8T1LMfTgKewMkSZJUQ0Mm\nAqWvUiQBh1dsS2CTiFi1Yttk4GWKYTW19Hvp0cx8Fvgd8MUaxfsCb6UY278LMCszd87MMzPzDxRz\nBGquWFT2aDxFMV8AgIhYjiLhuK+/8QLbU8yF2D4zT87MG4H1eotDkiRJzavhhgZ1y8znIuKrFKvz\nPFJu/j+KYT2XRcQRwOoUvQaXZ2ZHRNRqaj4wLiLWy8xH+xHKl4DfR8RVFKsQ/QvYGTiOYoz/nIiY\nBawbETsC04CPAbtSMfSnhlOA4yLiCeBB4GvAa+jf8wK6L/RnAaOBXcthRu8CDmTRcxwkSZLUZBqp\nR6DHnfvMvIhiDkBX+X4hxUU4wB8plg69mmIJzt5cTTHc5p8RsXqt4/QlM++luPPfBVxDMVzoExRL\ng55ZVrsK+BHwU4rlOrenSCA2K1c6quVkiknB51MM5xlHcSf/ubK8qyLW6tfVun8+f6RIUM4G7gL2\npFiqdI2IWGtJPrckSZKGtxFdXQN5aK+Go+8ftW3XRhusUu8wpFd5cNoctn7r92hr6zFlp9+WW24k\nq622MrNnz2fBgoWD1u6yMAxiH9ZDFk85cnLXJsP4PHr/tDm84W3fHdTfx0qN/v/b+Aau0WMcIvEN\n+DzaSD0CkiRJkpYREwFJkiSpCZkISJIkSU3IRECSJElqQg27fKjqZ8bj8+odgtTDjMfnsXW9g5AW\n0/TH59Y7hKVq+uNzeUO9g5A0YCYC6uEzn7+Ijo4X6OxsvFnyfRk1aiRjx65o7MvYsop9a6C1dcJS\na18aTJ8+oHHPo4PxO/sG/H2UhgMTAfUwadKkhl0uqy+NvtRXX4xdGl4a+Tzq76ykbs4RkCRJkpqQ\niYAkSZLUhEwEJEmSpCbkHAH10N7e3rCT3Poy1Cfcvu1tb653GJIGSSOfR4fCubIRYmxtnUBLS0td\nji0tKyYC6uGsC/Zm3Nqj6x1GU5n5+DzGjr2YjTbavN6hSBoEP/jB3qw9buV6h6F+enzmfD72sbNo\na5tY71CkpcpEQD2MW3s062+4Sr3DkKQha+1xK7Oh51FJDc45ApIkSVITMhGQJEmSmpCJgCRJktSE\nhuUcgYhYFTgS2AX4b+AR4HzgjMzsqmNofYqILYGVMvP2iHg7cGNmjhqEdg/IzHMHHqEkSZKGi2HX\nIxARrwXaga2BvYHNgWOArwOn1y+yxXI1sHH5+g/AWgNtMCK2A84eaDuSJEkaXoZjj8DxwAvAuzPz\n5XLboxHxAvCLiDgjMx+sX3h9GtH9IjMXAE8PQpsjgYbtBZEkSVJ9DKtEICJagI8Dh1UkAQBk5nUR\n8Q6KpGBV4ARgZ2AF4FrgkMz8Vzkk5xKKhOKbwKrAz4F9M/PliDia4q59B/Bp4EXgpMw8sSKOI4HP\nAysBtwAHZeaMsmx14CxgJ2A+cFFmfjMibgLWAy6OiO2BKcBNmTmy3O/15X5vBWYBJ2fmmWXdG7vr\nlXUvprj4Pxa4sdzWCeyQmbcM4EcsSZKkYWK4DQ16PbAycEetwsycWiYIvwC2AN4HvBPYDLi4ouo4\nYDfg3RTzDHYD9qwo/yjwPNAGnAgcHxEbAUTEwcAngU8AbwKeAn4XEd1j/a+hmLfwNuBjwD4R8YXy\nOI8Bh5ZfUN7Jj4jXAL+jSD4mAQcB34mI95V1ervjP72MvQtYE7itl3qSJElqMsOqR4Di7j3AnN4q\nRMQEiovwTTLzoXLb7sC9EdE9Pn854ODMvA+4JyKup7gA/2FZ/izw5XLi8UkR8TVgG+BB4MvAAZl5\na9n2AcBMYKeImE6RHGyQmdPL8v2B0WVvRCfQkZlzI6Iy7PcA/wXsnZnPA/eVCUdnXz+MzOyKiOfK\n18/0VVeSJEnNZbglArMoxtmv1kedzYB/dScBAJmZETG7LOtOIirnEXQAy1e8n1a1+tBcYPmIWBlY\nB7gyIirLVwA2AVYEnutOAspj/3IxPtcmwP1lEtC93xSAciiTJEmStESGWyLwEMWF/ETgL9WFEfEL\n4KJe9h1VfgH/maxbaUTF65dq7D+CV36eHwHuryp/DujvRfvLfZTVGha03CL2kSRJUpMbVnMEMrMT\n+AlwUES8KsmJiA8CH6S4QF+tYhgQEbE5MAbIAR5/DsVKP2tl5sOZ+TAwg2IeQQAPAK+NiLUrjn1I\nRPy8fNvbWP8HgI0iYoWK/U6KiNMok5KyN6LbhhWvXTFIkiRJPQy3HgEonhnwJ+C3EXEsxQTcHShW\nCTotM++LiN8Al5bj7EdSrMYzNTPvGYShNqcA342IZygSiyOBycB9mTknIm4ELoqIwyjG/X8V+Fa5\n73xg04ioHtr0W+BJ4PyI+A5FUvE5isnG/6RYuegbEXE+xUTmNl5JauYDRMTWwD8z898D/HySJEka\nBoZVjwBAZj4FvAV4GPgRcDfFKjzfBA4vq+1Rlt8A/Kass8sADlt51/0k4ALgB8CdwHiKZxp0zz3Y\nHZgH3F7Gd15mnleWnUOxItAFVZ+pE/gQxQPG7gROpVgi9frMnAvsR7FS0T+ACcCZFbvfXX7OPwDv\nHcBnlCRJ0jAyoqvLkSN6tSOOfXPX+huuUu8wmsojD89h1/edyUYbbc6CBQvrHc4SWW65kay22srM\nnj3f2JcwYspNAAAgAElEQVShYRD7iEXXHLqOPupNXRt6Hh2yHn54Dm956/G0tU2sWd7ov3+NHh80\nfoxDJL4Bn0eHXY+AJEmSpEUzEZAkSZKakImAJEmS1IRMBCRJkqQmZCIgSZIkNaHh+BwBDdDMx+fV\nO4Sm489cGl4enzm/3iFoAPz3U7MwEVAPB332Yjo6XqCzs/GWy+rLqFEjGTt2xSEb+5Zbbsn8+S/X\nOxRJg2D//Rv3PDoUzpWNEGNr64S6HFdalkwE1MOkSZMadt3cvjT6mr99WW65kbS0tJgISMNEI59H\nh8K5cijEKA0HzhGQJEmSmpCJgCRJktSEHBqkHtrb2+noeIFNN22lpaWl3uFI0pDTfR4drPHtra0T\nPB9LGnQmAurhOxfvDcAXPnwWbW0T6xyNJA09p1y4N2uuM3pQ2nrysXnsjedjSYPPREA9rD5If7wk\nqVmtuc5o1n39KvUOQ5L65BwBSZIkqQmZCEiSJElNyERAkiRJakImAkBELIyI7erZ9tKMQZIkSarm\nZOGlb03guUGsJ0mSJA2YicBSlplPD2Y9SZIkaTCYCCyGiPgAcCywGfAwcGRmXl2W3QTclJnHle/X\nA6YB62fm9IhYCGyfmbdExI7AycCmwOPACZl5frlfZb1xwBnAjsBKwD+BgzPztor2dwNOBNYGbgD2\nyMx/lW3tBxwGbAh0AFeW+3ct1R+UJEmShgznCCxCefH+M+ASYAvgh8CVEdHWx249LrgjYiRwFcVF\n+SbAkcDZEbFpjf1/BIwA3gRsBcwAzqmqcwTwcWA7YBLFhT/lPIPTgK8BGwP7A/sCH1rkh5UkSVLT\nsEdg0Q4EfpqZZ5bvT42INwKHA5/uZZ8RNbatArwWeDozZwBXRMRM4Ikada8GfpaZMwEi4lzguqo6\nR2XmX8ryyymSAYB5wL6ZeU35fnpE/BVoBX7R90eVJElSszARWLTNgHOrtt0G7L0kjWTm7Ig4B7gw\nIo4CfglclJlzalQ/D/hEREymGEY0kVf33nQBD1a87wCWL49zZ0S8EBHHUFz8TwA2Aq5fknglSZI0\nvDk0aNFerLFtVPkFPYcBLVdjGwCZeRDFxfkPgDcCf4yI91TWiYgRFGP+vwQ8CpwA7FmjuZeq3o8o\n938P8Bfgv4FfU8wluK1WPJIkSWpeJgKLlsCbq7ZtW26H4oJ8TEXZ62s1EhH/HRFnAQ9m5vcy803A\njcDOVVU3B94GvCMzv5+ZvwHGLUG8+wE/zMwDMvPiMs7XU3u4kiRJkpqUQ4Ne8aaIWLFq21TgVODW\niPgTxR32DwK7AO8q67QDe0bElRQX28f20v5zwK7AiIg4GViHYiLw/1bV+xfQCXwqIq6l6Dk4BiAi\nWso6fV3UzwImR8QbKHomjqB4RsFr+thHkiRJTcYegUIX8H2KC/3Kr3GZ+WdgD+AA4G5gL+CjmTm1\n3PcU4E6KpOFy4LgabZOZL1MkEVsCdwE/AS7IzB9W1Xu8PNZXgH8AXwUOBhYAbZV1e3EM8DRwO/Bb\n4HmKOQ59rXIkSZKkJmOPAJCZoxZRfhXF0p+1ymZT9BBUGlVRXvn6L8BbFxVDZl4IXFhV5cpa7Zf1\nj614/STw3lrHkCRJkrrZIyBJkiQ1IRMBSZIkqQmZCEiSJElNyERAkiRJakImApIkSVITctUg9fDM\nY/OKF9vUNw5JGqqe7D6PDlZbbxq05iTpP0wE1MM39r6Yjo4X2HTT1nqHIklD0pf2K86jnZ0LB97Y\nm6C1dcLA25GkKiYC6mHSpEnMnj2fBQsG4Q+YJDUhz6OShgLnCEiSJElNyERAkiRJakIODVIP7e3t\nAxrb2to6gZaWlkGOSpKGjurzqOdFSY3IREA97HPZAYxZd0y/9p07fS4ncxJtbRMHOSpJGjq+OWUf\nXjd+NACzZszjMM70vCip4ZgIqIcx645hlY1fW+8wJGnIet340ay50Sr1DkOS+uQcAUmSJKkJmQhI\nkiRJTchEQJIkSWpCzhFYSiLiJuAm4FHgmMzcICK2B27MzJERsR4wDVg/M6cP8FhHA2/PzB0HGrck\nSZKagz0Cy0ZXxffu1zOANcvvA3UisOsgtCNJkqQmYY9AnWTmQuDpQWrreeD5wWhLkiRJzcFEoE6q\nhwZFxEJgD+BrwMbAn4E9MvPRsv5mwCnAZKADOD8zv1WWHQ1sn5k7RMRywLnAh4EVgBuBAzJz5jL9\ngJIkSWpoDg2qr66q98cABwFbA/8FfBsgIl4H3AI8BrwR+AJwcEQcWqOtg4G3Ae8EJgKjKRIISZIk\n6T9MBOprRNX7kzNzambeQ3FXf1K5/dPAfGD/LPwSOBL4So021wNeAKZn5v3AZ4DvL43gJUmSNHSZ\nCDSWBytedwDLl683Bf5SzivodhuwZkSMrWrjfGAt4MmI+C3wfuC+pRSvJEmShigTgcbyUtX77h6D\nF2vUHVX1HYCyN2F94FPATOC7wG8HL0RJkiQNByYC9VU9R6A3CUyMiMqL/snAM5k5u7JiROwB7JyZ\nP8vMvYH3Am+NiNUHJWJJkiQNC64aVF/VcwR6cznFROIfRMRJQJTvz6pRdxXgGxHxLMWqRLtTTDJ+\ndqDBSpIkafiwR2DpWZy7/V29vH6VzJwH7ARsBNwJnAGckpnH1ah+NnAJcCnwT2BLih6Cxe19kCRJ\nUhOwR2ApycwdK95OKbdNpRzTXz4fYFRF/eqx/lO69yvf3wVs38uxjq143QUcUX5JkiRJNdkjIEmS\nJDUhEwFJkiSpCZkISJIkSU3IRECSJElqQiYCkiRJUhNy1SD1MHf63IHtu8UgBiNJQ9CsGfNe/bqt\njsFIUi9MBNTDRXucS0fHC3R2LlzynbeA1tYJgx+UJA0h397rolfOo22eFyU1JhMB9TBp0iRmz57P\nggX9SAQkSZ5HJQ0JzhGQJEmSmpCJgCRJktSETAQkSZKkJuQcAfXQ3t7e/8nCA9TaOoGWlpZlflxJ\nGkxL+zzquVLSYDARUA/7Xvo9Ro9fY5kfd96MpzmJQ2hrm7jMjy1Jg2mfy77CmHVXWyptz50+m5M5\n2nOlpAEzEVAPo8evwaobr1PvMCRpyBqz7mqssvGyv6EiSUvCOQKSJElSEzIRkCRJkpqQiYAkSZLU\nhIZNIhAR10XEhVXbPhkRCyPiqKrt34yIOwdwrL0iYlof5RdHxEX9bb+qrQ0iYqfBaEuSJEnqNmwS\nAeBW4I1V27YHHgd2qNr+ZuDmAR6vq4+yQ4BDB9h+tx/S83NJkiRJAzLcEoHNImKlim07ACcBb46I\n11RsfzMwdWkFkplzM3PuIDU3YpDakSRJkv5jOC0f2g68DEwEbo2IdYB1gQuArwNvAW6MiE2AVYFb\nImJz4BRgMrB82cZnMzMBIuK7wGfK+n8CDszMe8rjjYyIo4GDKH6OF2TmV8r9Lga6MnOfss7GQAfw\naeBF4KTMPLGsOwL4HrBv2e5p5TH3BfYG3g5sFxHbZ+aOEbE2cCrwDmAh8GPg8Mx8OSL2KvedChxY\nxnVRZh428B+vJEmShpNh0yOQmS9TXKx3D6PZHrgjM58HbuGV4UFvBv4B/Au4FngI2ALYFhgFHA8Q\nEbsAnwV2A1qBJ4DKcf/rApuU++0PHBYR7+4lvI8CzwNtwInA8RGxUVn2dWB34BPAO4EPABuUZYcA\ntwMnA7tGxPLATcCKwNvKdt8PnFBxrMllXJMpkpRDI+Idvf3cJEmS1JyGTSJQuoVXEoEdKC6aoZgP\nUJkITKW4mD6X4m76I5n5N2AKxUU/wHrAv4HHMnMaxUX5lyqO9RKwb2Y+mJlXAXcBW/YS17PAlzPz\n4cw8CXgO2KYsOwD4Rmb+v8y8C9iL8t+lHF70EjAvM/8FvBdYC/h0Zt6TmTdT3Pn/QsWQqJEUvRoP\nZOblZVyTFv2jkyRJUjMZbolA5YThHXhlQvDNwMSIaKG4gz+17Ck4D9grIi6MiN9TDMsZVe5zBfAC\nMC0ibqW4QO8eFgTwVGa+WPF+DrBCL3FNy8zKycVzgeUj4nXAOOCO7oLMvB+Y3Us7mwL3Z2ZHxbbb\nKIYAdfcwPJWZ8yvKOyiGPUmSJEn/MdwSgduAcRExkeIC+w8AmflPigv17YDNgakRsTLFBfgngXuB\no4AvdzeUmU9RXHh/EPg7cDhwe0R0X+x31jh+bxN7X+ql7oJe9uutnRdrbBtV1u9OYHo7liRJkvQf\nwyoRKO/y/41izP6fq+7Y30ox+fb+zJxFMYdgTWD7zDw5M2+kGA40AiAi3kcxxOY3mXkgsBUQwIRB\njHcOMJNigjPlcTekmJzcrbInIYFNIqKyfDLFJOmHBisuSZIkDX/DadWgbrcAn6dYWafSzRQTgS8p\n388CRlNMwr0DeBfFePs5ZflI4KSIeBL4K/ApYD5wP0WvwmA5E/hWRMwoYzqd4uK/OwGYD2wcEasD\n/wdMAy6LiCOA1YEzgMszsyMiBjEsSZIkDWfDqkegdCuwEj0fGHYzxQThmwEy84/AccDZFBNq9wS+\nAKwREWtl5nXAkRQJxb0UK/TsXN7Fr6WvB4z1Vfck4Gfl1w0UKxl18coQnwspJgn/JjMXAjuX2/9I\nsXTo1RSJz+IcS5IkSQJgRFeX14n1FBHvoVjmdFb5/r+Ap4ANMnN6PWKafMLnu1bdeJ1lftx/PfAY\nx2z1CdraJi66cg3LLTeS1VZbmdmz57NgwcJBjm7pMvb6MPb6KGMf1nOXJp+0a9cqG6+xVNqe88DT\nHLPFIcP6XNnoMRrfwDV6jEMkvgGfR4fj0KChZn/gwIj4avn+OIr5DXVJAiRJktQchuPQoKHmQIrV\ng/5AseoRwK71C0eSJEnNwB6BOsvMJ/DCX5IkScuYPQKSJElSE7JHQD3Mm/F0/Y67VV0OLUmDau70\n3h4QP0htb7HUmpfUREwE1MMP9zyCjo4X6OxcxrPkt4LW1kF7Xpsk1c1Fe5yw9M6jW3iulDQ4TATU\nw6RJkxp2uSxJGgo8j0oaCpwjIEmSJDUhEwFJkiSpCZkISJIkSU3IOQLqob29vT6ThSu0tk6gpaWl\nbseXpIGoPo96TpPUiEwE1MN+U85k9Lpr1e3486Y/wYnsQ1vbxLrFIEkDse+lJzB6/BpAsTTySRzo\nOU1SwzERUA+j112LVTdav95hSNKQNXr8Gqy68fh6hyFJfXKOgCRJktSETAQkSZKkJmQiIEmSJDUh\n5wg0gIhYEzgO+ACwKvAQcAlwWmZ2RsRewDGZuUEv+18MdGXmPssoZEmSJA1xJgJ1FhHrALcB9wIf\nAR4H3gicAOxAkRwAdPXRzCFLM0ZJkiQNPyYC9XcWRQ/ATpnZfbH/aET8EfhnRBwAPN9XA5k5dynH\nKEmSpGHGRKCOImIN4IPA+yqSAAAyc0ZEXAJ8DjgNGBkRRwMHUfy7XZCZXynbedXQoIj4AHAssBnw\nMHBkZl69bD6VJEmShgInC9fX1uX3O3op/z2wBfAaYF1gE2BbYH/gsIh4d/UOEbEj8DOKOQZbAD8E\nroyItkGNXJIkSUOaPQL19dry++xeymdX1HsJ2DczXwQejIivAVsCv6va50Dgp5l5Zvn+1Ih4I3A4\n8OlBi1ySJElDmj0C9fVc+X3NXsrHld9nAU+VSUC3OcAKNfbZDPhT1bbbyu2SJEkSYCJQb3cAC4GJ\nvZRvA/ydojegs0b5iBrbXqyxbVT5JUmSJAEmAnWVmc8CVwNHRsSrLuojYjywL3D+kjYLvLlq27bl\ndkmSJAlwjkAjOBS4FfhNRHwLmE7RE3ACcGNmnls+UGxxnQrcGhF/An5NsSrRLsC7BjdsSZIkDWX2\nCNRZZj5BcQc/gcuB+yieMnwOsHMfu9Z8wFhm/hnYAzgAuBvYC/hoZk4dxLAlSZI0xNkj0ADKIUKH\nll+1yqcAU6q27Vjxeu+qsquAqwY/UkmSJA0X9ghIkiRJTchEQJIkSWpCDg2SJKlKRKxL8fyVW4Ax\nmfl0nUOSpEFnIiBJUikiWoBLgY9RPOdlE+CkiBgD7JaZHfWMT5IGk4mAepg3/Yn6H3+ruoYgqXl9\nE9gS2BG4rtx2BnAx8H3gC4vTyLwZT7/6tec0SQ3IREA9XLjXwXR0vEBn58L6BLAVtLZOqM+xJTW7\nTwIHZObNEdEFUL7ej6KnYLESgR/u+ZVXzqOe0yQ1qAEnAhHxmsz892AEo8YwadIkZs+ez4IFdUoE\nJKl+1gYerLF9OvDaxW3E86ikoaDfqwZFxOcjYhowPyI2jIhzI+KbgxibJEnL2j3AO2ts/0RZJknD\nRr8SgYj4FMVYySnAS+Xme4FvRMRhgxSbJEnL2jHA6RFxCkWv+V4R8RPgaOC79QxMkgZbf3sEDgcO\nzcxjgE6AzDwDOBDYf3BCkyRp2crM64DdgG0o/r59GdgQ+Hhm/qyesUnSYOvvHIGgWFu52k3A2f0P\nR42gvb19QJOFW1sn0NLSMshRSdLSFxHbATdk5vVV21eIiN0WNxkY6Hl0sHlellRLfxOBJymSgWlV\n2ycDMwcUkepuv0suZMz4tfu179wZj3Mi0NY2cXCDkqRl4yZgTeCZqu2bAz8CFisR2G/KWYweP26Q\nQ+ufeTNmciJ7e16W1EN/E4EfAGdHxBeBEUBExLuBbwOnDVZwqo8x49dm1Y1eX+8wJGmZiIj/AU4u\n344AnoyIWlX/vLhtjh4/jlU3Wn/gwUnSUtSvRCAzT4iIVYGfACsAvwIWAOfhZCpJ0tByFvAcxby5\ni4AvAnMqyruAecCNyz40SVp6+v0cgcz8ekR8m6K7dCRwX2Z2RMSaFEOHJElqeJm5gOJhYZQPEfuJ\nz8eR1Az6lQhERCewZmY+A9xRsX194B/A6EGJTpKkZSgzp0TE6hGxCTCq3DwCeA0wKTO/U7/oJGlw\nLXYiEBH7ALuXb0cAV0fES1XVxgGzlySAiFhI0e26XmY+VlX2eeAc4JjMPG5J2l0WIuJoYPvM3KEO\nx/4IcHNmPlu+PyAzz13WcUjScBIRnwYuBLqX2BlB8TcK4BHAREDSsLEkzxH4BcVJ8NHy/WPl6+6v\nR4DfAR/uRxwvAzvX2P5hoDHWXutd16KrDK6IWBe4ClipfL8dLtsqSYPhGxTz31op5glMovhbNJPi\noWKSNGwsdo9AZj4H7ANQrqZwaGZ2DFIct1AkAud0b4iIMcC2wF8H6RjDyUhenYBUv5ck9c+GwK6Z\neV9E3AWsnpm/jIjlga9TLCEqScNCf1cN2rvW9ohooRhD+YclbPIa4KSIGJ2Z88pt76dIEFauaH95\n4HjgY8AawOPAdzPzgrJ8R4ol4DYty07IzPPLso8DxwLrAQ8B38jMa8qytwDfB7amuKCeCuyTmU+V\n5TtRdAdvCtwPHJaZ3atHtETEWcAewAvA8Zl5arnfTcBN3cOaImI9imcvrJ+Z0xcR0zoUidE7gKeA\nS4BvZWYX8HB57GnlkK2Ly306gR3KY1xI8VyH54ErgS+VE+IkSb37d/kF8CDwBuB6ivlwG9crKEla\nGpZkaNB/RMTWEXFnRLwcEZ3dXxQXwrWeOLwod1NcuO9UsW0XiuFIIyq2HQG8tyzbhOLi+KxyYtdI\niuEyV5ZlR1I862DTiFidYkWI75RlFwM/johVI2IscB3FiX4z4F3A68tjERGtwLXA/wJbUHQZ/yIi\n1ihjmgy8CGxFkUycHL0sQF3qKtvtNaay3s+BJ4Atgc8An6S4GwXwxrKdSeVn3q18vyZwO3AmMLeM\n90Nl+X59xCRJKtwBfLZ8fTfF3wQoVsirnhcnSUNaf5cPPY3iuQEHA6cCXwI2Ag6kuDPeH9dSDA/6\n37Jn4V1le7tX1PkbxaPf2wEi4vsUYzY3Ae4BXgs8nZkzgCsiYibFxfQGFJ/18bLs5LLL90VgFeC4\n7rv4wPSI+DnFRTYUw6F+n5nfK98fHxErAd0X7I9l5uHdP5eIOIriAjwX8XnX7i2msmdj3cx8Y1n3\nwYj4MkXi8x1eeeLls5n5QkQ8B1Cu4tS9etNfgBmZOS0i3scSTuKWpCZ1DHB9RMyiOOceHRH/BMZT\n3HiRpGGjXz0CFENoDsrM84C/A3dn5mEUd9E/1882rwHeW97Zf2fZ5rOVFTLzWmCliDgpIq6jGALT\nBYzKzNkUQ2kujIhHIuJMoCMz52Tm3ygeenZDRNxbJhCPZOaL5fCfSyPiixExJSLagcN5Zdm4oLio\nrozj6My8v3w7repzzKF4yFqf+oqJomfivyJibvcXRU/HqhGx2qLaBk6gSKCeiYgfUw5FWoz9JKmp\nZeatFEOArs7MWcDbgBuA44AD6hmbJA22/iYCIynutAM8AEwoX19DMZSlP35ffn8rxXCWq6srlA8w\nu4yie3YK8CYqhg5l5kEUKz38gGL4zB8j4j1l2c7ltp8CHwD+EhFbRMQ4iu7fHSi6hCsfNQ/FikZ9\n6ayxrTum6gm8r+qB6S2mst69FD0LW5ZfEyh6PiqfdllTZv6Y4u7VVyme6fDTiGi45VclqdFExEXA\n3Mx8GCAz78nM/8/encdrOtePH3+NZfhWRAuKsfOecRrmGCMUBmmhZKtfSnbZQoWKxBCFJEqyZCeh\nQt+sKUu2zDeUJe/sxr4NZ4yRZub8/vhch3vOMnPOzJlzz33u1/PxOI+5ruvzuT7X+z48rnO9789y\n7U954/CldQ1OkvrZ7A4NeojywH4R8CBlGM0vKcNsFpqdBjNzWkRcSUkCPgv8sJtqewB7ZubvACJi\nter4kIhYkjIv4JvVMJ4fRcTVwBYR8TiwW2YeRHnYP6zq6v0UZTLty9VDOVW7+/POw/xDlPH/1JTf\nCpzUi4/1FrBIzf5KNW3ETGK6F1iWMvRnUlV/U2BHytCrdmacOzFDwlElTJdUE6VPj4jvADsAh/Ui\nZklqKtWCER335x2BuyKi86p4Iyi91ZI0aMxuIvBz4MxqTuxvgX9GxBTgY8AdcxDPHyiTZh/JzCe6\nKX8Z+FxE3EUZY38i5SF4IeAVYGtKUvATYBnKA/xvgVeBvSLiVeBCyioQy1GWJv0AsGw1Lv8xyopE\nWwN3Vtc8Fbg/Ir4B/G9VvhplUvSIWXye8cAOEXEx5cH9iJqynmK6C7gBeBK4MCIOARan9HJcl5nt\nETG5amNUNY51MkBEtFLmSgynTKLeh/Iehs2qdiVJXbVT5gN0bP+smzqvAz8eqIAkaSDM1tCgzPwV\n8GXKRNkHKavafJzykrG+zhGo/Tb7WkpyclkP5btQHu7vo3TTXkx5YG/NzP8Cn6MMo/kHZXWfMzLz\nzGoewFbAtsD9lETmu5l5PWXy1wWULt/xwFjK5OcREbFg1T28DbAr5Zv6rYHPZuZzvfg8J1AewG+i\nPOy/PTxnJjH9OTOnV59lCCWxupSystH+1bkvVzFfXBPX9cBtlFWV9gSeA26sjj3Vca4kaUaZeVtm\nzpeZ81Huu0t17Nf8LJqZR8yqLUlqJEPa2/v+HqqI2AG4ODP/0+n4u4Gv1azAowb08WOObF9s5ZVm\nXbEbrz78CIetuQGtraP7OapZW2CB+Vh88XczceJkpk6d119IPSNjrw9jr48q9iGzrtm4Pnbswe2L\nrbx8vcMA4NWHH+fw1k+9fV9uhP935vUYjW/OzesxNkh8c3wf7fXQoIj4APCuavds4L6IeKlTtVGU\nsf0mApKkhlHN29odOCYzX6rebn865eWWzwNHZKZvFZY0qPRljsBmlDGUHRNVx3dTZwhw1ZyHJUnS\nwIiIUcBfKXPNfg68REkCvkAZ4vka5QWVr2Xm/9YtUEnqZ71OBDLzvGr1nfmAv1DGtr9SU6WdMpnq\n3n6MT5Kkue37lDlqX8rMqdWy0l8EzsvMbwNUCzscSFk0QpIGhT6tGpSZNwNExEbArZk5da5EJUnS\nwFkf2Kzmb9qm1b+1bxL+K/AjJGkQ6VMiEBELAJ8G/tJxw4yIPXhnDOVPqlWEJElqFO+l/A3rsD4w\nlbJMdIdJvPPGeUkaFPoyWXgJynKUQXl774MRcShlbfzxwMLA3yJivcy8fy7EqgEyacLTc3bumv0Y\njCTNfU8DKwATqv1Ngb9l5uSaOuvWlM/S6xOe6b/o5tDrE56B1npHIWle1JcegcOA/wKrZWZGxHuA\ng4GbM3MjgOpFXuMoE6zUoH610260tU1h2rTZWC5rTWhpGdn/QUnS3HMZ5W30e1F6uIdR83b7iFia\n8qXXFb1t8Fc7fn3276P9rdX7sqTu9SUR2JzyjoCs9jcB/gc4o6bOpZS3A6uBjRkzZp5dN1eS5oIj\nKZOA76n2/0BZNYiI+B5lMvHDwFG9bdD7qKRG0JdE4MNA1uyvT1kp6M81x54BFu2HuCRJGhCZ+Rqw\nQUS0ANMz8181xQ8ABwFnZ+brdQlQkuaSviQCrwLvA56s9jcGHszM2glWAbzYT7FJkjRgupvflpmX\n1SMWSRoIfUkE/gJ8HdgtIjagvEX47W7SiJgP+A4zrrKgBjR+/Hja2qYwfHgLQ4cOrXc4kjRgImII\nsCOwFmX465Da8szcpTftdNxHeztHoKVlpPdbSQOuL4nA4cAtEfEKsAjwOOWNi0TEF4FDKKsufLSf\nY9QA+9o5FwJw7Oen09o6us7RSNKAOh74JvBPYOLsNrLbOb9ikWFL96rupAlP82PwfitpwPXlzcL/\njoiPUFYEmg78JjNfrYqXBx4BdvQ9Ao1vkWHL1jsESaqXHYBdMvOcOWlkkWFLs9jKK/VPRJI0l/T1\nzcIvAL/o5vhx/RaRJEn18z/ADfUOQpIGQp8SgVoRsSLl1VHdjaE8bw7jkiSpHq4FPgecXO9AJGlu\nm61EICJ2An4FzNdNcTtgIiBJaggRcVjN7ovACRGxHvAQMK22bmYeOZCxSdLcNLs9At8HTgO+VzNP\nQDUi4mzKyhPtdOoxqY5tlJmusCRJ9bdzp/2ngXWrn1rtlJePSdKgMLuJwNLA8SYBM7UfZTlVgC8B\nB1CWo+tICl6pR1CSpBll5gq9qVctky1Jg8bsJgL3ACOAx/oxlkElMycBkwAi4jVgWmb6sjVJmodF\nxPKurV4AACAASURBVKPAWpn5SqfjSwP/AD5Ql8AkaS7odSJQvUSsw2XAryJiHN2PoXTISy9ExN6U\nV9e/H7gT2C8zH6hec38XZTnW30TEwsB9wO8y8zsRsQzwM2AjymTt+4CvZ+YdVbvfBPYHlqSshf2N\nzLx9gD+eJDWEiPh/wKeq3eWBX0TElE7VlqcMDZKkQaMvPQI30nW8+6nd1GsH5p+DmJpCRGxFeQnb\nbsDDlDGqf4mIVTLz/oj4MXBcRPwBOAz4L2VuBsCvgeeAtSn/DY+jrHCxVkSMAX4IfB54kDIk6WLA\nlwNIUvduA/bgnb9vywJv1ZS3A69T5n1J0qDRl0SgV2Mo1WsHAT/IzGuq/e9FxObAlykTsX8AbAuc\nRXmo3ygzO/4w/Q64ODOfA4iIU4HfVmXLUXponszMJyPiEOCyiBiSmX6bJUmdZOYEYGOAiLgB2Doz\nZ/utwpLUKPryZuEnOrYj4ixg/2ocPDXH30d5cN2y3yIcvEZQlqg7vubYQsCqAJn5n2ro0PXAaR3D\nfiqnAF+OiHWqdkbzTi/M1cC/gAci4i7gCuAMkwBJmrXM3KinsohYJjOfGsh4JGlu6sscgY8BHe9L\n3xG4KyLaOlUbAXyin2Ib7BYA9gE6z6d4rWZ7FDAV+FhEzJ+Z06pVK26gzA24lPKg/27gIoDMnAyM\niYixlJfi7ALsERGjM/P5ufh5JKnhRcQKwE+AkbzzBcsQyhc1SzAHL+KUpHlNX25o7cA5Nfs/66bO\n68CP5ySgJpLAsMx8tONARJxDGc9/dUQsBxwBbA8cA3wXOJryx2k9YPHMfK06bz+qsa3VS3A2yMxj\ngBuroUEvVudcNjAfTZIa1i8oPbOXUuZYHQ8EsBVlHoEkDRp9GRp0G9WbhCNiOrBUZr4wtwJrAicA\nv4yIR4A7gL2BrYFxVfkpwA2ZeUlEvAVcFBGXABMpcwC+HBFXAutQJhPPV/UWvAkcERHPUXoONgEW\nBu4dsE8mSY3rY8DnM/PGiPg0cHlm3hkRRwGbAWfUNzxJ6j+z+3KUKyhLXmo2ZeavKQ/wR1Me0tcH\nNsvMxyPii5SlQfev6l5Oeag/PTOfBPYFDqYsG3ogZYjRNKA1M++irER0MGWuwIHAdpn58AB+PElq\nVAsBj1TbCaxebZ9H+eJFkgaN2R3ruBHQeY1l9SAzzwXO7eb4ScBJ3Ry/BLik07HNarZPpevSrRfX\nlJ8PnD9nUUtSU3oc+AgwgZIIjKqOzw8sUqeYJGmumN1E4Bzg2Ig4Eng4M//TfyFJklQ35wLnR8QO\nwJXADRHxBPBJypuFJWnQmN1EYHPKCkLbAkTEDIWZ6QvFJEmN6BhKj/eQam7AD4BDKT0E29c1Mknq\nZ7ObCBzVr1FIkjQPqN65cmLN/jGU5ECSBp3ZSgSqMe+SJA06EbEGZbGG4cAXKG93vz8zb6prYJLU\nz/ryQrG33yZcbfekPTN3nfPQJEkaWBExGriVsqzzaMoqQq3AiRGxZWZeVc/4JKk/9aVHYAXeecvi\nipQXjGkQmjThybKx5pr1DUSSBt6xwPGZeWhETALIzN2r7XFArxKBSROe7vUFJ014GrzdSqqDvrxQ\nbKOa7bEd2xGxIPAJyjsJ/pyZb/ZngBp4p+/0FdrapjB8eEu9Q5GkgbYW5QWPnf0C+FpvG/nVTrvR\n1jaFadOmz7rymtDSMrLXAUpSf+nTHIGI2AvYudo9A7gIuBlYozr2dERs7MurGtuYMWOYOHEyU6f2\n4g+YJA0ubwGLdnN8GDC5t414H5XUCHr9ZuGIOBA4DrgLuAX4AXAdZbjQ+sCGwPPAj/o/TEmSBsTl\nwNERsVi13x4Rwykvf/xj/cKSpP7Xlx6B3YFdq7feEhEXAX8DPpeZt1XHvgn8tt+jlCRpYBwIXA28\nRPmy7C5KD8E/gIPqGJck9bu+JALLUR78AcjM8RHxX6B2GNDDwPv7KTbVyfjx43s/tnUuaWkZydCh\nQ+t2fUnNKTPbIuJTwBaUhTHeAu4DrsnMXt8UO99HvadJmhf1JREYSnnbYq23gP/W7LfTh+FGmjft\nce5lLDpshbpdv23CYxwDtLaOrlsMkppLRCxC+cZ/O0oC0OEh4ALgRuCN3rb3tXPOZ5FhywJlJbZj\n8Z4mad4zu28W1iC26LAVWHzlEfUOQ5IGRES8n7LwxTDgMuA04FXgvZR3CRwMfDEi1s/M13rT5iLD\nlmWxlVeZSxFLUv/oayJwQETUrpqwILBfRLxS7b+nf8KSJGnA/IDSm92SmRM6F0bEMpR5AwcAhw1w\nbJI01/QlEXgS+GKnY89SXr3euZ4kSY1ic2Cf7pIAgMx8KiIOBX6MiYCkQaQvLxRbfi7GIUlSvSwJ\n3DuLOv8Alh2AWCRpwDhHoJ9ExON0/SPRDtwKTANuyMwjZ6PdDYG/ZOb8PZQfDoytffOzJKlPulsM\no7MplOGwkjRomAj0n3ZgP+CSTsffokw+m123Ah/qxbUlSZKkXjMR6F9tmflC54MRMdsNZuZUoEub\nkqR+1XkxjM5cDEPSoGMiUAcRsRPwbWB5yotqDsjMv1ZljwEXAztQJmMfQBkaNF9VPgI4HVgTuB34\nV6e2d6vOWRFoq9raNzPtNZCk7nW3GEZP9SRp0DARGGBVEvBzYE/gTmAX4KqIWDUzn62qfRn4BDA/\n8D6qoT8RMRS4ErgJ2BXYBDgJuKUq3wA4EfgKcDewFnAhcD1w+dz/dJLUeFwMQ1KzMhHoX6dGxC9q\n9tspq1HU2hc4MTMvrPYPriYEfx34XnXsgsx8AN6eLNxhU0pisFdmvgn8OyLGAh+syl8Hds3MK6r9\nJyPibqAFEwFJkiTVmK/eAQwy3wfWqPkZlZmdV6IYQekJqHV7dbzD4z20PwJ4qEoCOozv2MjMu4B/\nRsS4iLg0Ih4E1qb0LEiSJElvs0egf72YmY/Oos6b3Rybnxkf1rur02FIp/23OjYi4lOUFYrOBa4C\nxgG/nEU8kiRJakL2CAy8BNbpdGwd4MFenHsfsGpELFJzbM2a7d2AMzNzr8w8u7rWSnRNHiRJktTk\n7BEYeCcAZ0bEv4C/USb9rg58tRfnXk9ZteLMiDiMkkB8EbijKn8ZWC8iPkKZn3AwsBSwUL9+AkmS\nJDU8ewT6z8yW53y7LDMvBQ4BjqS8sn4DYNPMfGhW7VTvFNicMmH478AewMk1VcZR3jlwO3At8AZl\naFBr3z6KJEmSBjt7BPpJZq44k7KNO+2fzIwP8D22k5k3UTN/IDOfoCwt2t25zwGf6X3UkiRJalb2\nCEiSJElNyERAkiRJakImApIkSVITMhGQJEmSmpCJgCRJktSEXDVIXbRNeKz+129dpa4xSNKcmDTh\nyRm311yrjtFIUvdMBNTFaTtuRVvbFKZNm16fAFpXoaVlZH2uLUn94PSdvvrOfXTNtbynSZonmQio\nizFjxjBx4mSmTq1TIiBJDc77qKRG4BwBSZIkqQmZCEiSJElNyERAkiRJakLOEVAX48ePp61tCsOH\ntzB06NB6hyNJDafjPtrTogstLSO9v0qqOxMBdbHPuX8B4KgtptPaOrrO0UhS49nj3N+x6LDluy1r\nm/A4x4D3V0l1ZyKgLt67rGv4S9KcWHTY8iy+8vB6hyFJM+UcAUmSJKkJmQhIkiRJTaghhgZFxOPA\nsjWHpgKPAKdm5kn1iGlWIuJsoD0zd5nL15kOjM3Mm+fmdSRJkjS4NEqPQDuwH7BU9bMC8CPg+IjY\nvp6BzQOWAm6rdxCSJElqLA3RI1Bpy8wXavbPi4jtgK2BC+oUU911+p1IkiRJvdJIiUB3pgJvAUTE\n94E9gXcBNwNfz8wJVdl04KvAd4FVgDuBr2bmExGxIXAOcCxwKLAY8Htg18z8b0S8FzgL2JjSM3El\nsFdV7wlgdGbeU13ng8AzQHQEGBGLAs8Dn87Mm6pj7wFeBDbJzNsi4hBgN2Bp4CXgtMw8sqp7A/An\nYIPqZwKwb2ZeV/PZxmbmzRHxYeBnVazvAu6v6tpjIEmSpBk0ytCgGUTEAhGxNbApcEVE7AtsB3wJ\n+Cjlwfu6iJi/5rRxwNeBNYEPAEfVlH0Y2Ab4JLBVtb1DVXYksASwLjAWWAM4NDOfAm4Btq1pZ1vg\nrsx8tONAZrYB11B6Ljp8DnihSgJ2oAx72oWSpBwBjIuIUTX1DwEuBFqAe4DTe/jVXAAMqX4HoyhJ\nwyk91JUkSVITa6RE4NSImBQRk4A3gbOBEzLzIuAg4KDM/Gtm/pvyjf37gE/XnP+TzLwpMx8AfgmM\nqSlbgPLN+QOZ+SfKg3tH+XLA68ATmflPysP+2VXZRcAXatr5QnWss99QEowO2wCXVNtPADtn5o2Z\n+WRmng48R3no73BlZp6fmY9REphhEbFUN9e5rPocD2Xmg9XnbOmmniRJkppcIw0N+j7lQRdKIvBs\nZrZHxLuBZYCLI6K9pv7ClG/YOzxcs90GLNip/Z7KTwIuB16MiOuB3wK/rsouBU6KiNUpD+8fB77S\nTez/C/wqItYG7qUkKBsCZOZNEbF2RPwQGAG0AksCtb0ZD3WKjW7iBzgV+FJErAcMB0bTWMmeJEmS\nBkgjJQIv1g65qdHxGbYF/t2p7JWa7bc6lQ2p3cnMqd2VZ+YNETEM+DywOXAaZQjRDpn5cpUcbAM8\nC9yemc92DjAz34iIP1b1lqEkMX8HiIjdgBOAMyhJxgHAjZ2a6C72GeKPiCHA9cCiwMXAH4CFgN91\njkeSJElq+G+LM/M14AXgQ5n5aJUsTAB+TM2k3dkVEd8A1qqG5nyJMpZ/m5oqFwFbUJKE38ykqd8A\nnwW2pDyod9gDOCIzD8jMCynJy5J0etDvpL2bY6sB61MmIB+TmVdT5j5IkiRJXTRSj8DMnAD8MCJe\nBJIyjGg94MF+aHsZ4GsRsTPlIX1b4K6a8sspvQQrATvPpJ2rKasTLUMZQtThZeATEfEHyrf5R1P+\nuyw0k7a6SxJeBaYBX67aWpsyQZqIGJqZnXsVJEmS1MQapUegu2/Aax1PGVpzGuUhfRjwqaq3oDfn\nz8z3KasDXQHcTVmW8+2XmGXm65SH/Nsz86WeGqkexC8HJmTmvTVF+1MSgHsoQ4PuocyFaJ1J7O2d\ntzPzacok6W8D9wHfAfalLLHa2rkBSZIkNbch7e1z8owsgIi4BTgjM8+tdyz9YdPjft8OcNCo99Ha\nOrre4fTaAgvMx+KLv5uJEyczder0eofTJ8ZeH8ZeH1XsMxv+2PDGHntq++IrD++2bOLDD3JI6/C6\n3V8b4f+deT1G45tz83qMDRLfHN9HB8vQoLqIiLGUYT4jKCsISZIkSQ3BRGDO7ECZKLx7Zr5R72Ak\nSZKk3jIRmAOZuUu9Y5AkSZJmR6NMFpYkSZLUj0wEJEmSpCbk0CB18dqTD5WNUR+tbyCS1KDaJjw+\n87LW7lcUkqSBZCKgLn6x48a0tU1h+PCWeociSQ3ptB23oa1tCtOmdbPsYOtwWlpGDnxQktSJiYC6\nGDNmzDy7bq4kNQLvo5IagXMEJEmSpCZkIiBJkiQ1IRMBSZIkqQk5R0BdjB8/vsskt5aWkQwdOrSO\nUUlS4+h8H/UeKmleZCKgLn523m18aFi8vf/shOSrQGvr6PoFJUkNZO9zr2XRYSsD0DbhYY7Ge6ik\neY+JgLr40LBguVVa6x2GJDWsRYetzPtW/ki9w5CkmXKOgCRJktSETAQkSZKkJmQiIEmSJDUh5whU\nIuJxYNlqtx14A/gHcGRmXteL8zcEbshMkytJkiTN83xofUc7sB+wFLA08FHgVuDKiNi4D21IkiRJ\n8zx7BGbUlpkvVNvPAd+JiA8BPwXWqF9YkiRJUv8yEZi104GbImJF4GXgZGALYBLwe+CgzPxP55Mi\n4mPAMcCalJ6Cm4BdgP8CLwCrZ+YDEbEA8BpwXGYeUZ17IfBIZh4WEbsBBwArAm3AxcC+mdkeEWdX\nl2ul9GR8DHipmxi/nZlv9u+vRZIkSY3MoUGz9gAwBFgNOBNYBFgX2BJYi/LQPYOIWBT4I3ANMALY\nFFgZODgzXwH+Doytqo8BFqY8xHfYBLg6IjYATgK+C6wC7AHsCny+pu72wCHA5pn5CHAW8J5OMf58\nDj6/JEmSBiF7BGbtterfkZQH8Pdl5iSAiNgDuDsivtXpnP+hTDL+abX/ZET8jvLQD3AdJRE4BdgA\nuBpYPyKGAKsDQ4G/AaOAXTLzipp27gZagMurY+Mz86oqnhWrGBfvLsaOY5IkSZKJwKwtWv37T2B+\n4JmI6Fxn5dqdzHw+Is6LiG9SHuZXo8wxuKWqci2we7W9AaWnYe2q7kbA9Zk5HbgrIqZExDjKw//I\n6lrX1Fzu8ZrtEZRenp5ivLtXn1iSJEmDnonArK1BGeO/MvAqMJoyVKjW08A6HTsRsTQwHvg/4E+U\neQafpaxEBHA7sHBErA6sB+xEWaHo45RhQb+r2vkUcBlwLnAVMA74Zadr1479X2AWMUqSJEmAcwR6\nYxfKmP5rgMUAMvPRzHwUeDdwPLBQp3O2BF7OzC0y8+eZeSuwEtXDeWZOA24A9gGez8wXKb0Fm1B6\nCDq+8d8NODMz98rMs4GsbacbCby3lzFKkiSpidkjMKP3RsSSlAftD1AexL8IfCIzMyKuAX4dEfsC\n0ynf9L+UmW2dhuK8DCxbvX/gsaqNrYE7a+pcR1mW9IJq/6/AccD9mflMTTvrRcRHKL0SB1NWB+r2\noT4zH4yIa3uKcXZ/KZIkSRp87BGY0YnAM8BTlCE9qwAbZWbH2P7tgUeB6ykP8v8CtuumnUsoD/iX\nUoYIjQW+BYyIiAWrOtcCC1ISAIC7KG8zvrqmnXGUpUZvr+q/QRka1DqTz9DbGCVJktTE7BGoZOYK\nvajzCvCVHspuokwmpprou0/1U+tnNfUf7ahf7U+lLE1a2+ZzwGdmEs/OfYlRkiRJ6mCPgCRJktSE\nTAQkSZKkJmQiIEmSJDUhEwFJkiSpCTlZWF08OyG77o9ao07RSFLjaZvw8IzbrUvVMRpJ6p6JgLrY\nb4f1aGubwrRp08uBUWvQ0jKyvkFJUgM5ZcdPvXMfbV3Ke6ikeZKJgLoYM2YMEydOZurU6fUORZIa\nkvdRSY3AOQKSJElSEzIRkCRJkpqQiYAkSZLUhJwjoC7Gjx8/42ThftDSMpKhQ4f2W3uSNC+b1X3U\ne6KkeYGJgLq46Kw7WG7Y8H5r74kJDwLQ2jq639qUpHnZj867hSWGRbdlL0xI9sR7oqT6MxFQF8sN\nG86qK7XWOwxJalhLDAuWXmVUvcOQpJlyjoAkSZLUhEwEJEmSpCZkIiBJkiQ1oUE9RyAizgZ2BNqB\nIZ2K24GNMvPmAQ9MkiRJqrPB3iOwH7AU8CHgG8AEYMmaY7fVLzRJkiSpfgZ1j0BmTgImAUTEa8C0\nzHyxvlFJkiRJ9TeoE4HeiIi9gYOA9wN3Avtl5gNV2QTgKGBnYA3gAWCXzPxHRKwEPAhsBxxL6WH4\nE7BDZr5WnT8WOB5YDfg3MC4zL6/KlgPOANYFJgMXAQdk5vSIGAWcUl3zZeDUzPxhdd5CwE+ALwHT\ngauB/TPz1ap8GPALYBPgOeBs4OjMbO/3X54kSZIa1mAfGjRTEbEVcAiwF7Am8DfgLxGxSE21ccAP\ngNWBN4ATa8rmBw4EvgBsRHmo/0bV9tLAFZSH/RZKQnBeRKxTnXsKMLFqdyvKg/0uVdkFlKRkOPA1\n4JCI+ERVdlx1zieBjSkJzG+qaw6prjmBkkTsCnwV+PZs/YIkSZI0aDV7j8BBwA8y85pq/3sRsTnw\nZeC06tiZmXklQEScAJzfqY1DM/OuqvwiYEx1fB/gqszsaOexiFgL2B+4A1gOuBWYkJmPRcRnKN/+\nAywPvAQ8lZkTqiTgkYh4D7AnsHpmZnXNHYEXIiKqNpfIzH2qdh6OiO8Ap1J6LSRJkiTARGAEcEJE\nHF9zbCFglZr9h2u224AFa/bbZ1I+AtgsIibVlC8A3F9tHwucCXwhIq4GfpOZ91RlR1OGJO0TEX8E\nzsvMFyNijar98dW3/7VxrAqsCCzV6ZrzAQtFxCLVnAlJkiSp6ROBBSjf3HdeQvS1mu23OpV1Xoa0\np/IFKOPzj+10zlsAmXl+RFxHGRb0WeB3EXFUZh6ZmT+qehe2ArYAboiIXYH7qjY+Cvyn03WfpyQD\n9wJbdxPn60iSJEmVpp4jACQwLDMf7fgBDgPW7qe2V8nMx2ra3pYyuZiI+CHwwcw8NTM/CxwBbBMR\nC0fEScCUzPxpZm5ESSi2pvQ+TAM+UNNmx7yFD1TXXB54oaZ8VcokZScLS5Ik6W3N3iNwAvDLiHiE\nMm5/b8oD9+G9PL/zt+61fgE8EBHjKJN/1wWOBLavylcDTo6IfSlDez4D3JWZb0bEhsCHIuJQ4L3A\n+sBFmfla9ZK00yJiT8qcghOBJTPziYh4CngGuLA6932U+QF/7OXnkSRJUpNo6h6BzPw1pQfgaMqQ\nmvWBzTLziarKrL5F77E8Mx8DPkcZ2nNvdZ39MvO3VZXdgReBG4FbgMeAb1Zl21ISgDuBqyjLkv6o\nKvtGdc7vKJONJwObV9ecVl1zQUpicwlwOfCtWXwOSZIkNZmm6RHIzHOBc7s5fhJwUg/nLNtp/8/A\n0Gr7EcryobXl3++0fz1lWdLu2n6Rsuxod2UPA5/qoewNSs/F3j2UP0qVGEiSJEk9aeoeAUmSJKlZ\nmQhIkiRJTchEQJIkSWpCJgKSJElSE2qaycLqvScmPNjv7Y1Ya2S/tilJ87IXJuTMy0a1DmA0ktQ9\nEwF1sd0u69DWNoVp06b3S3sj1hpJS4uJgKTmcfAOH+/5Pjqq1XuipHmCiYC6GDNmDBMnTmbq1P5J\nBCSp2XgfldQInCMgSZIkNSETAUmSJKkJmQhIkiRJTcg5Aupi/PjxtLVNYfjwFoYOHVrvcCSp4XTc\nR3tadKGlZaT3V0l1ZyKgLq465XYApm03ndbW0XWORpIaz9nn3sEyw4Z3W/ZUtUSz91dJ9WYioC5W\n+nDUOwRJamjLDBvOyiv7rgBJ8zbnCEiSJElNyERAkiRJakImApIkSVITGnSJQERMj4gLujm+Y0Q8\nNsCx3BARhw3kNSVJkqTeGHSJQGW7iBjbzfH2gQ5EkiRJmhcN1kTgceAXEeGqSJIkSVI3BuuD8qHA\nL4GDgB91VyEilgFOATYBngfOAX4ALA68AKyemQ9UycRrwHGZeUR17oXAI5nZp2E/EbEVcBSwPHAv\n8O3MvLkqu6E6tjkwP9AC7Ap8C1iyKvtmZt5a1f8I8DNgHeAJ4GeZ+cuIWLj6PDtm5uVV3QWA54Av\nZOYNfYlZkiRJg9Ng7RF4GhgHHBoRy/VQ5/fAs8AawE7AdsAhmfkK8HdgbFVvDLAw8LGaczcBru5L\nQBGxBiXZOBIYCVwAXBURK9ZU2wn4MrAVsApwHLAnEMAtwCVVWwsDVwE3Ax8BDgS+HxFfycw3gcuB\nbWva3RR4C7ixLzFLkiRp8BqsiQCUb8sfqv6dQURsAiybmXtk5sPVt/IHAd+sqlzHO4nABpSH/o9G\nxJDqgX4o8Lc+xnMAcHpmXpyZj2bmycA1wF41df6YmX/LzLspvQbTgScz80lKL8f2ETEfJVl4PjPH\nVW1dCfywJv6LgM9GRMf7678AXJqZzpGQJEkSMHiHBpGZ0yNiL+CvEbFFp+LhwAciYlLNsfmAhSJi\nceBaYPfq+AbAmcDawChgI+D6zJzex5BGAF+IiD1rji1ISQY6PF6zfS1lONB9EXE3cAVwRvW5RgCj\nOsU/P+Vbf4A/VdufjoirgC2BzfoYryRJkgaxQZsIAGTm7RFxNqVX4LiaogWAfwFbAEM6nfYacDuw\ncESsDqxHGbJzK/BxyrCg33V3vWrc/lOZ+Wp1aAgwteaaxwLndTptSs32mzWxT6H0QmwIfK6KYc+I\nGF21dT2wdzfxk5nTIuK3wDaUhOC1zLyju5glSZLUnAbz0KAO3wHeTRlH3yGBZYGXqqE1jwIrUcbv\nt2fmNOAGYB/KEJwXKWP0N6H0ENR+i1/rBmDjmv33Ai/VXHOFjutV19wT+Ex3DUXEOhFxSGbelJkH\nUnox/oeSjCSwKvB4TVvrAfvVNPFrysTjLajmFkiSJEkdBnWPAEBmvhIR3wF+xTtDb66jrLRzYUQc\nQlkp6DTguppx9NcBP6VM6gX4K6VX4f7MfKaHy90E7B0R91ImF68G/KUq+ylwc0T8H3Al5QH9G5Sh\nRt2ZAhweEc9Tvv0fS0lo/gE8Q5kMfXpEHE9JYk4CflzzuW+JiMnAjpTkQZIkSXrbYOwR6DIhNjPP\nAm7rKKvG93cMC7oDuBT4I7B/zWnXUsbw/7Xavwt4g5mvFvQNylCcvwPfB3bJzIera/4N+CplOM/9\nwG7AlzqWA+0cd2b+A9iZMon5X8B3ga9k5r8z83Xg05SVhe6mJDE/y8xjOsVzKWWo0t0ziVmSJElN\naND1CGTm/D0c/3in/ccpY+97audRygTcjv2pwCKzuPZTzGRSbmZeQg/DdDJz426O/ZoyxKe7+vfw\nzspGPVmqp/MlSZLU3AZdIiCIiI8CawGfp7yYTJIkSZqBicDg9GnKG4kPrt5BIEmSJM3ARGAQyswj\ngCPqHYckSZLmXYNxsrAkSZKkWTARkCRJkpqQQ4PUxSPPJABLsHqdI5GkxvTUhAdnWrZW68gBjEaS\numcioC4223td2tqmMHy4Cw5J0uzYecd1aGubwrRp07uUrdU6kpYWEwFJ9WcioC7GjBnDxImTmTq1\n6x8wSdKseR+V1AicIyBJkiQ1IRMBSZIkqQk5NEhdjB8/vsvY1paWkQwdOrSOUUlS4+juPtoX3nMl\nDQQTAXVx649vIZZc9e39fP7fsBu0to6uY1SS1Dh+f8YdLL90zNa5jz9dVm7znitpbjMRUBex5KqM\nGjaq3mFIUsNafulgxIqt9Q5DkmbKOQKSJElSEzIRkCRJkpqQiYAkSZLUhOo+RyAiFgAOBb4Kivjx\nJgAAIABJREFULA08B/wOODwzX++na2wL3JiZL0XE4cDYzNyoD+cPAfYDdgZWAV4A/gCMy8yJ/RFj\nN9c8G2jPzF2qmDfMzI3nxrUkSZLUfOaFHoHjgK2AXYFVKQ/bnwR+3R+NR8SywCXAu2oOt/exmd8C\n+wNHAS3AjsB6wDURMRDru/0Y2HoAriNJkqQmUfceAcpD9c6ZeWO1/2RE7AH8NSKWzMzn57D9+ej7\ng//bIuIrwGbAiMx8vDr8eERsDjxC6ck4cw5jnKnMfAN4Y25eQ5IkSc1lXkgEpgMbR8T/ZmbHA/vt\nlG/eXwKIiIWAI4HtgPcBfwb2ycynImI54DFg+cx8sqpfO5TmUUoi8FhE7Fy1PzQiTqY8xE8Bjs3M\nn/YQ347AZTVJAACZ+UJEbAw8VF1zEeAkYHNgseq6383MK6ry6cAPgL2BWzNzy4hYl9Ij0go8DxyX\nmad1DqB2OFNE7AjsBNwE7EP5b3hWZh5Q1V0QOBb4IrAE8DTww8w8o4fPJ0mSpCY0LwwNOoky/v7x\niDglIrYG3pWZD2bmtKrOacCWwPbAOsCCwBU1bczsG/+1q3/HABdX2+sBbwKjgGOAn0RET29+WQMY\n311BZo7PzFdrPscqwCeA1YCbgTOqORAdPgusC3w3IoZTEpobKYnAEVUcn+8hjtrPuB5lGNV6wNeB\n/SNik6rsYOAzlOFWqwLnACdHxAd7aFeSJElNqO6JQGYeBXwFeBLYnTIe/5mI2AkgIhajJAB7Z+bN\nmXlfVT8iYtOqmSEzucSL1b8vZeZ/qu2nMvPAzHwsM08EXgVW7+H8xYDXevFRbgT2yMx7M/MR4ATg\n/cCSNXVOzcyHM/PB6rPelZnfz8yHMvM84OfAt3txrfmA3avzLgT+QUl0AO4Bdq2SlMcpic6ClKRA\nkiRJAuaBRAAgMy/KzPUpQ1m+DNwH/CoiWikPsEOAO2vqTwQSGDGbl3ys0/5rwMI91H0ZWLwXbZ5P\nSU5OiohrgVur4/PX1HmiZnsE8LdObdxG7z7T85k5uWa/jfKwT2b+AXhXRBwfEX+kfNb2TnFIkiSp\nydU1EYiIkRFxfMd+Zk7MzN8AYylj2zemDOHpzvzVT3fDgmY192FaN8d66lX4OzC6u4KIODoi9q12\nz6es7vMKcAplrkBnb/aw3aHjM83KW90cG1LFdFQVy1vAucBHmXmPiSRJkppQvXsEFgC+FRFr1B7M\nzP9SVsl5gbIyzzTK3AAAIuL9lPH4D/LOQ/EiNU2sWLPdzpw9CF8AbBkRy9cejIilKZN136omCm8H\nfDEzj6gmCL+/qtrTtZOaz1RZrzo+J/YAvp6Zh2TmpbzzezEZkCRJ0tvqumpQZt5dDV+5IiIOpgyN\nWYqyKs5CwO8zc3JEnEGZ8Po1YCJlVZwngOuBqcAE4KCIOALYkPJt/F3VZTqG0KwRES/PRowXVyv1\n/DkivgP8H2X4znHA/cDZlGTjdWDb6hrDKeP9qT5Hd04B9ouIoykTetcD9qIkF3PiZeBzEXEX5QVt\nJ1bx9RSHJEmSmlC9ewSgLHN5PnA48C/gj5RvsTeoGQd/IPAnykTiv1Ie7jfNzP9WS47uQlkd6H5g\nG8qLvwDIzJcp3+pfQnlpWXdm9Z6BLSnDbI6qrvEL4Fpgs8x8q+rB2B7Ytio/nrJU6LOUFYG6XCMz\nJ1BWEfo08E/gEOCb1aThvqptexfKakj3AWdRVkq6syYOSZIkiSHt7bP9ri0NUlfve2X7qGGj3t6/\nZ8I9zP+5d9Pa2u1UiXnGAgvMx+KLv5uJEyczder0eofTJ8ZeH8ZeH1Xsg3q44mlHXNc+YsXZ+/7l\nX4/ezQrrDp1r99xG+H9nXo/R+ObcvB5jg8Q3x/fReaFHQJIkSdIAMxGQJEmSmpCJgCRJktSETAQk\nSZKkJmQiIEmSJDWhur5HQPOmfP7fXfZXc/VRSeq1x5+e/XdDPv50sgIj+zEaSeqeiYC6+NhBH6et\nbQrTppXlslajlZYW/yhJUm9tvfs6M9xH+2IFRnrPlTQgTATUxZgxY+bZdXMlqRF4H5XUCJwjIEmS\nJDUhEwFJkiSpCTk0SF2MHz+etrYpDB/ewtChQ+sdjiQ1nI77aG/nCLS0jPR+K2nAmQioi9t+eiUA\n03aaTmvr6DpHI0mN5/qf3cbKH45e1X34mYSv4v1W0oAzEVAXsdSK9Q5Bkhrayh8ORi7nssuS5m3O\nEZAkSZKakImAJEmS1IRMBCRJkqQm5ByB2RAR7wIOBrYFlgMmAzcCh2fmA3WI53Bgw8zceKCvLUmS\npMZkj0AfRcS7gduA/wccCATwSWAScFtELFeHsH4MbF2H60qSJKlB2SPQd4cDHwBGZOak6tgEYJeI\nWAb4FrD/QAaUmW8AbwzkNSVJktTYTAT6ICKGADsCx9QkAbW+Crxa1V0fOAFoAR4CjsjM39e0tRPw\nbWB54D7ggMz8a1X2GHAxsAPwbGaOjojRwMnAGsBdwJ+BDTJzo2po0NjM3Kg6fzfgAGBFoK1qa9/M\nbO+/34YkSZIamUOD+mYl4IPALd0VZubzmfmfiFgS+F/gLOAjwLHA2RHxMXg7Cfg5cDTlwf7PwFUR\n8aGa5r4MfALYKSIWBa4Gxlf1L6LMUah9sG+v2t4AOBH4LrAKsAewK/D5OfzskiRJGkTsEeibD1Ae\nuF/pOBARmwCX19R5Avg98KfM/GV17NGIWBP4BnArsC9wYmZeWJUfHBEbAl8Hvlcdu6Bj4nFEfI0y\nB2H/6lv9h6qkYqluYnwd2DUzr6j2n4yIuyk9E5d3U1+SJElNyESgbyYCQ4DFao7dSvmWHmAbYC9g\nBLBFRNQOH1oAyGp7BDCuU9u3V8c7PF6zPRK4q9PQntuBrToHmJl3RcSUiBhHefgfCawMXDPzjyZJ\nkqRmYiLQNw8DLwPrAX8HyMw3gUcBIuIFSqIwP3A+ZejPkJrz/1v9+2Y3bc9f/dBNnamd2qGbfaoY\nPgVcBpwLXEVJOH7ZXV1JkiQ1L+cI9EFmTqOM+/9GRLynmyrLUIYOJbBqZj6WmY9m5qOUb++/0tEU\nsE6nc9cBHuzh0vcDozodW6uHursBZ2bmXpl5dnWtleghcZAkSVJzskeg78YBH6e8M+AISs/AB4Hd\ngZ2BCynfwO8fET+gfDO/NqV3YKeqjROAMyPiX8DfKJN5V6esOtSdi4AfRsRPgVOAsZT3GHQ3afll\nYL2I+AglKTmYMpdgodn9wJIkSRp87BHoo8ycAmwInAccSln68xpKb8DWmblTZj4JfBb4DHAvcCTw\nzcz8TdXGpcAh1fF/ABsAm2bmQ9VlZljmMzMnA5+r6v2TkjBcALzVTYjjgBcocwiupbxf4JdA65x/\nekmSJA0W9gjMhsycChxf/fRU5y/0PHyHzDyZ8l6A7spWrN2PiOWBBTJzdM2xk4Fnq/pH1Jz7HCUB\nkSRJknpkItAY3gtcHxHbU94lsBawPfClukYlSZKkhuXQoAaQmf8A9gF+RJlQ/EPKUCOXBJUkSdJs\nsUegQWTmWZQViyRJkqQ5Zo+AJEmS1IRMBCRJkqQm5NAgdZHPPQrACJatcySS1Jgefib7VHc0a8zF\naCSpeyYC6mK9b25OW9sUhg9vqXcoktSQPrHferS1TWHatOmzrDuaNWhpGTkAUUnSjEwE1MWYMWOY\nOHEyU6fO+g+YJKkr76OSGoFzBCRJkqQmZCIgSZIkNSETAUmSJKkJOUdAXYwfP/7tycJDhw6tdziS\n1HA67qMdk4VbWkZ6P5U0zzERUBe3n3QxANN2mE5r6+g6RyNJjee2428glloVgHzu37Ar3k8lzXNM\nBNRFfGi5eocgSQ0tllqVUcNWr3cYkjRTzhGQJEmSmpCJgCRJktSETAQkSZKkJmQi0AsRMT0ipkXE\nMt2U7VmVH1btnx0RZw18lD2bF2OSJElSfTlZuPf+C2wBnNLp+JZA7Tvk9xuwiHpvXoxJkiRJdWQi\n0Hs30ykRiIhFgHWBuzuOZeakgQ9t5ubFmCRJklRfJgK9dwVwfES8JzNfr45tTkkQ3t1RKSLOBtoz\nc5eIeC9wFrAx0A5cCeydmZMiYhjwK2A94A3gYuBbmTk1IoYABwJ7Ah8Cbgf2z8z7qmtMB8Zm5s3V\n/o7AuMxcISI2BM4Brga+DBwNrNYR01z63UiSJKnBOEeg9+4FngY+XXNsK+ByYEgP5xwJLEHpNRgL\nrAF8ryo7GZgErA58HtgG2K0qOxz4FmVITyvwJHBNRPzPTOJrr9leDlioOvc3s/xkkiRJajomAn3z\nB8rwICJiKLAppaegJ8sBrwNPZOY/gW2Bs2vKXgMmZOYdwGbAVVXZ14FDM/PKzExgd2AasH0v42wH\njsnMxzJzQm8/nCRJkpqHiUDfXAF8JiLmAz4B3JuZL82k/kmUoT8vRsTlwNrAQ1XZcZQH+xcj4tfA\n8pn5ZEQsAbwPuLOjkcycCvwfMKIPsT7Rh7qSJElqMiYCfXNL9e/HKcN5LptZ5cy8ARgG7AW8CZxG\nGb9PZv66KvsO8B7g0og4sqrXnfmrn+50meuRmW/NLDZJkiQ1NxOBPsjMaZQJv58HPsssEoGI+Aaw\nVmaen5lfAnahzAUgIo4ClsrM0zNzC+D7wDaZ2QY8D6xT084CwGjgwerQW8AiNZdaqR8+niRJkpqI\nqwb13R8o4/wfycxZDb9ZBvhaROwMvEKZI3BXVTYcODki9qG8h2CzmrITgCMj4lngYeC7lMm/l1Tl\n44F9IyIpKwLtRM89CZIkSVIX9gj0Tu2KPNdSEqjLeiiv9X3KcKIrKO8aeBfvTPjdE3gOuBG4DXgK\n2L8q+wlwBnA6ZW7AhynLhb5cle8LvJ+yktGB1XUkSZKkXrNHoBcyc/6a7cnUvDegOrZxzfbONdtT\ngK9VP53bfAn4Yg/Xmw4cVv10V34PMKbT4TOrspvoNJegNiZJkiQJ7BGQJEmSmpKJgCRJktSETAQk\nSZKkJmQiIEmSJDUhJwuri3y2rIo6nJF1jkSSGlM+9+8Ztkewdh2jkaTumQioi3X3/3+0tU1h+PCW\neociSQ1pvQM3oq1tCtOmTWcEa9PS4hcrkuY9JgLqYsyYMUycOJmpU6fXOxRJakjeRyU1AucISJIk\nSU3IRECSJElqQiYCkiRJUhNyjoAkSf1s/Pjxb08WBmhpGcnQoUPrHJUkzchEQJKkfnbbT/+XWGpF\nAPK5R2EnaG0dXd+gJKkTEwFJkvpZLLUircuuVu8wJGmmnCMgSZIkNSETAUmSJKkJmQhIkiRJTaip\n5whExFLAkcBngcWAR4BzgBMzc1pE7AiMy8wVujn3cGBsZm7Ui+ucDbRn5i79Gb8kSZI0u5q2RyAi\nlgHuBJYDtgVGUJKCrwNX1FRtn0kzMyuTJEmS5lnN3CNwMqUH4NOZ2fFA/0RE3AHcHxF7AW/ULTpJ\nkiRpLmrKRCAilgA+B2xWkwQAkJkTIuIcYHfgpJpzhgCXACsBY7tpczfgAGBFoA24GNi3pv33RsRF\nwBbAS8B3M/Oi6tyFKL0R2wHvA/4M7JOZT0XEcsBjlOFLvwA+AJwJnEEZxjQCuAH4UmZOjogFgWOB\nLwJLAE8DP8zMM2bz1yVJkqRBqFmHBq1Z/ft/PZTfAqwBLFRz7ERgdeCTmdlWWzkiNqjKvwusAuwB\n7Ap8vqbalsB4oIWSJJwVEYtUZadV5dsD/7+9O4+Sq6oTOP6NiYAgCjJCQIEg6g+SAYwYwqKiIqLj\nAQEdFVEZtsFBBGVwwUFZFA9qUDaBcSGKuIyMggwKKKIiGDTILvpTwyayyBJEMKzp+ePeSipNN1m6\n6XqV9/2c06e77nv16levX933fu8utSXwdBbtngTwYUrysg9wIPC9WrY9sFUtBzgUeAOwC/BiSrJw\nUkQ8d5j3KkmSpBZqZYsA5a47wNxhlnfKVwfGRcQHgTcDW2fm3UOs/wCwd2Z2Lt5viYgrKRf9Z9ey\nWZn5OYCI+CRwCLBRRPyRkgDskJkX1+W7A3+OiO2BP9TnH5WZ1wHXRcTxwDcz86K6/oXARnW9q4AL\nM3N2XXYMcDglKbhrCfaNJEmSWqCtLQL31t8Th1m+Ttd66wBHAw8Ddw61cmZeAVwTEUdExJkR8Xtg\nC2B812pzutbvtCisRLlAH0cZuNxZPhdISrefjhu7/p4H3Dzo8Yr1uecAK0fEjIg4tz5vYFAskiRJ\narm2JgKXA/OBzYdZ/jLgGuAR4HFgB8qF9GFDrRwROwC/AdYCfkhpPfjloNUeH+Kp44CHholhPIte\nvD82aPn8YWL5JPD1GvvXgOn1dSRJkqQFWpkI1O49ZwEfq4OAF4iIdSn9+79Yi+7IzJ8CHwQOiYgX\nDLHJfYCvZOZ/ZOZMyt38DVmyC/A5lCRhy64Y1qCMNfh9LVqaaUr3Aw7IzI9m5plAZxyCyYAkSZIW\naGUiUB1EGStwXkRsExHrRsQuwEXARZl5SvfK9aL6Msq0o4PdA2wdEf8cEVMoA3Qnsuhg4yFl5oOU\nGYBOiohtI2JT4AxK158L62pLcxF/D7BjRGwQES8HTqckEouNRZIkSe3R2kQgM2+n3IVP4BuUu+9H\nASdTpvgcyoHAayNi50HlRwB/BWYBF1C+f+AUYOqThNB9l/8Q4MfA/wK/AB4Ets/MR4dYd6jH3fYC\nXgJcB5xGmaHo14uJRZIkSS0zbmDAL8fVEwzMnfsgjz025DCExpow4WmsvvoqGPvYMvbeWA5iX667\nK55/8FcHpq43GYArb7mep71+ElOnDjcsbWz1w7HT9BiNb+SaHmOfxDfierS1LQKSJElSm5kISJIk\nSS1kIiBJkiS1kImAJEmS1EITeh2AJEnLm7zjhkX+3phJvQtGkoZhIiBJ0ijb+gM7cv/983j88fls\nzCSmTNmk1yFJ0hOYCEiSNMqmTZvW2GkHJanDMQKSJElSC5kISJIkSS1kIiBJkiS1kGMEJEkaZbNn\nz14wWBhgypRNWGGFFXoclSQtykRAkqRRNuv4bxNrrwdA3n4LvBumTt28x1FJ0qJMBCRJGmWx9npM\nXT96HYYkPSnHCEiSJEktZCIgSZIktZCJgCRJktRCjhHoEhGrAR8DdgHWAm4CvgickJkDi3nutsBP\nM/NpEbE+cCMwKTNviYjpwBnA84ADMvO0UY77RuDwzDx9NLcrSZKk5ZeJQBURzwF+BdwK7ElJArYA\nTgI2BA5cgs10koU/AxOBu+rjjwB/AF4D3DtqQS/0MuCBp2C7kiRJWk6ZCCz0aWAe8LrMfLSW3RwR\n84CzI+KEzPzTkmwoM+cDf+0qejbw88z886hGvPD17nkqtitJkqTll4kAEBErAG8D/rMrCQAgM8+N\niO0oScFk4HPA1sDTgdnAvpmZg7Y3CbgBmAR8DdgWeFVEfDwzx9cuSJ8BdgJWAs4BDszM+2oXo68C\n5wG7AZ8CJlNaEp4H7AjcA3w0M8+or7ega1BErAocD7wRWK3G8ZHM/P4o7S5JkiQtBxwsXGwIrAJc\nPtTCzPw58Bjlgn0OsCmwFTCe0pIw2AALuwntAswCZlC6CwGcXbfxL8BrgY2BmV3PXx9YEXgp8K1a\n9l5K4jEF+C5war3oH+x44EV1u5OBi4EvRYRJnyRJkhYwEShWq7//9iTrPAM4BTgkM2/KzKsod/un\nDLP+OIDMvA94BHggM++KiE2BVwC7Z+YVmXk58E7gTRHxovrcAeCYzLwxM2+tZVdn5rGZeRPwcWDl\nYV77Z8B+mXltZs6htGCsQRn8LEmSJAEmAh33UC7cVx9uhcz8B3AqsEdEfDkiLgGOo7QKLI2NgPvq\nRXpn2wnMpbQMdNw86Hl/7Fr/7/XPpw+x/a8DERHHR8QFwKW1fGnjlCRJ0nLMRKCYQ2kN2HyohRFx\ndkS8kdI1Zzfgd5S78h9chtd6aJjy8XRdrGfmI4OWD34MtdVhkK8Dn6WMKTiZMlZAkiRJWoSJAJCZ\njwPfBg4Y3Jc+InakDNB9AbA28KraReciSl/+oS7GYeEYgSe8HLBaVzcg6iDkVeuyZVbHDOwGvDUz\nj6wDhNeoi4eLU5IkSS3kANKFjqB8j8AFEXEk5fsEXk2Z3ec4SmvAM4FdI+JyYHvKAN7hxhUMeeGd\nmRkR5wOnR8T7KMnYSZTpRa+vswYtq4co3yfwloi4h9IN6cS6bMURbFeSJEnLGVsEqsy8E9iGMt3m\nGcC1wEHAYZQBwpcBRwFfAK4G3g3sD6wZEWsPscmBYf4GeFd9nQsp04ReS5ldaGl0z0w0UN/Do5SB\nx28BfkuZqegTwO3A1KXcviRJkpZj4wYGhuvBohYbmDv3QR57bH6v41gqEyY8jdVXXwVjH1vG3hvL\nQezLdXfFCz54/MDU9QOAK29Oxm2/GVOnDjkMbcz1w7HT9BiNb+SaHmOfxDfietQWAUmSJKmFTAQk\nSZKkFjIRkCRJklrIRECSJElqIRMBSZIkqYX8HgFJkkZZ3n7LIn9vxGY9jEaShmYiIEnSKNvqoLdz\n//3zePzx+WzEZkyZskmvQ5KkJzARkCRplE2bNq2x849LUodfKCZJkiS1kIOFJUmSpBYyEZAkSZJa\nyERAkiRJaiETAUmSJKmFTAQkSZKkFjIRkCRJklrIRECSJElqIRMBSZIkqYVMBCRJkqQWMhGQJEmS\nWmhCrwNQc0TEisDJwK7AP4BjM/NzvY1qaBGxDnAC8GpKrN8BDs3MRyJiEvAlYCvgJuADmfnjHoU6\nrIj4AXBnZu5VH0+i4XFHxArA54HdgIeB0zLzv+qySTQ4/oh4PnAK8ErgHuD4zDy+LptEA2Ovn8nL\ngfdm5sW1bBJPEmtEvJbyP3oBMAvYNzNvHNvIh419S+BYYFPgVmBGZn6labGPRFPr0WU5lsYorsbX\n5RGxIfAFYBtK3XFSZs6oyxoRY0cTzysRsTPwPWAAGFd/fzcz39qE+GqMjT23RcQewEwW3X/jgPmZ\nOSEiNgC+uKzx2SKgbjOAlwKvAvYHDo+IXXsa0fC+C6xEqZjfDuwIfKIu+z5wG7A5cAZwVr0IbIyI\neDvwhkHFZ9PwuCkn7O2A7YF3APtGxL51WdP3+5nA3ynH+PuBoyPiTXVZ42KvF27fAiYPWjTscRIR\n6wJnAV8BXgbcXdcfU0PFHhFrAT8ELgJeAhwBnBgRb6jL16MBsY+CxtWjy3IsjaFG1+URMQ74AXAn\n5bh9D3BYrcMbEWNXrE09r0wGzgEm1p+1gX3qsqbsvyaf277Nwv02EVgf+BNwXF0+ov+xLQICICJW\nBvYGdsjMq4GrI+IzwAGUTL4xIiKALYC1MvPuWvZx4LMRcT6wATA9Mx8CjomI7YC9gKN6FXO3iFgd\n+Azw666y11Dugm7Z8Lj3Al6Tmb+pZTOA6RHxJxq83yNiNWA6sHdmzgHm1GNlu4i4n4bFHhEbA98c\nonxxx8m+wOzMPK6uvydwR0S8snMXuFexAzsDt2fmx+rjORHxaspJ9zzKhUFPYx+pJtajIziWxiK2\nfqjL1wKuBPbPzAcpx+1PgJdHxJ0NibHp55WNgesy867uwhpfz/df089tmfkw8NeueA+tfx46GvvQ\nFgF1bEZJDGd1lV1CuXhqmjuA13dOHF2eDWwJXFE/EB2XUJrMmmIGcDrwu66y6TQ/7pcD92XmJZ2C\nzPxMZu5D8/f7POBBYM+ImFAvQLahnOCbGPu2wE9qDOO6yhd3nEwHFlw0Z+Y84ArG9r0MF/t5wJ5D\nrP/s+rsJsY9UE+vRZT2WxkLj6/LMvCMzd6tJABGxDfAK4GdNibFq8nllMvCHIcqbEl/fnNtq0vIh\n4MOZ+SijsA9tEVDH2sDdmflYV9mdwEoRsUZm3tOjuJ4gM/8GdPeJHke54/YTyvu4bdBT7gQa0UWl\nZu+vADYBTu1a1Oi4qxcAN0XEu4CPAitQ+i0eTcPjz8yHI+IA4CRKt6DxwMzMnBkRJ9Cw2DNzwbFR\ncpYFFrefe/5/GC72zLwFuKVr2ZqUriAfr0U9j30UNK4eHcGx9JTrt7o8Im4C1gXOpbTwHEcDYuyD\n80oAr4+I/6LUvWdSPvdNia+fzm37A3/JzLPq4xHHZyKgjpUpA2S6dR6vOMaxLK3PAlOBacDBDP0+\nev4eaj/dUylNzA8POikPt/97HneXZwIvBv4d+DdKBfTflAF+/RD/xpR+qjMoJ8wTaxN/P8TesbhY\n++K9RMRKlL7ht1EGuUGfxL4Y/VSPNnF/N70u35XSR/sUysDSnu/Dpp9X6tifZ1BaZf+V0o3lhFrW\n8/iqfjq37Q0c0/V4xPGZCKjjIZ544HQe/2OMY1liEfFp4EDgrZl5fUQ8BDxn0Gor0oz3cASlD/SF\nQyxrctwdjwGrArtl5q0AEbE+5Q7Fj4A1Bq3fmPhrn8m9gefX/pZX1sFUh1HuPjY29kEWd5wM9zme\n+xTHtcQiYhVKQvZCYJuuJu3Gx74E+qkebVSd0w91eWZeARARBwPfoAxsX33QamMd4xE0+LySmbfU\n1rD7atE1ETGeMqh1Jr3ff9An57aImAY8D/ifruIR/48dI6COvwD/FBHdx8REYF7XB7hRIuJE4APA\n7pnZmV3kL5S4u00Ebh/L2IbxNmDniPh7RPwd2B14Zx2seivNjbvjduChTkVZJaUJssn7HcosLn+s\nSUDHlcB6ND/2bouLtdHvJSJWpZxYJwOvzswbuhY3OvYl1E/1aGP2d5Pr8ohYs2t2sY7rKd1Hbqf3\nMTb+vDLEsf87ykxRd9CA+Oifc9sOwMW1S13HiOMzEVDHVcCjlIExHa8AZvcmnCcXEYdTmvHelpln\ndi26DHhpbS7teHkt77VtKV1SNqs/51CmJdsM+BXNjbvjMkpf5xd2lU2mzFt8GbB5g+O/DXhhRHS3\ngm4M3EjzY++2uOP7svoYWDCLzVQa8F5q/++zgEnAKzPz94NWaWzsS6Gf6tFG1JV9UJdvAHwvItbu\nKnsZZRaXS+h93dHo80pEvC4i7q7dATumUqYH/gW933/QP+e26cClg8pG/DkZNzAwMAqVa2vsAAAE\ngUlEQVSxaXkQEadQZlLZi5IJfxXYIzO/38u4BqvT4V0DfIryxT3d7gKuBq6jzEW9E3AoMGVQtt9z\nETETGMjMveodxMbHHRHnUJoh96f0ozydMkXZKZT/ybU0MP6IeBblLtSPKQPANgJOo8R4Gs2OfT7w\nqsy8eHHHSW3Ovh44kjKg8XDgRZn50gbEvi/lONmRMhtQxyOZObdpsS+rJtejS3MsjVE8ja/L636a\nBdxLGbewAaVL0NE15kbVHU07r0TEMymf64sp54oNKV/O9fn604j91w/ntoi4kTJb0He6ykb8P7ZF\nQN0OBn5D+cKfE4GPNeHkNYSdKMfuYZQ7vbdRmsFuy8z5lPnKJ1K+RfMdwM5NuKB7MjXuN9H8uHen\nfJHJLygXOCdk5hdq/DvR0Pgz837Kl8WsTZln+1jgqMz8ctNjp3yLJLD44yQzb6YMaNyL8j5XA3YZ\n64C7DLAw/l0p01eey8LP7W2UQcNNjH1ZNbkeXeJjaYw0vi7v2k8PAr+kDG4/LjNPanrd0YT/cWY+\nQOnS8lxKy9iXgFMz89iG7b9+OLetyaAxU6PxP7ZFQJIkSWohWwQkSZKkFjIRkCRJklrIRECSJElq\nIRMBSZIkqYVMBCRJkqQWMhGQJEmSWshEQJIkSWohEwFJkiSphUwEJEmSpBYyEZDUUxHxnIjYq9dx\nSFK/sh7VsjIRkNRrM4B39joISepj1qNaJiYCknptXK8DkKQ+Zz2qZTJuYGCg1zFI6qGIWAU4Bngz\nsCrwG+DgzLwiIrYCPglsDjwK/B9wSGbeW597IzAzM4/q2t6CsojYAzisbuMwYF3gOuB9mTkrImYC\ne9SnDmTm+Kf+HUvS6LIeVb+yRUDSmcAOwLuBzYAbgB9FxBbAT4FrgenAW+rvCyJiae4+rQfsB7wD\nmAo8CHytLjsI+A7wS2DiiN+JJPWG9aj60oReByCpdyLixcDrge0z8ye17D3AvcCHgKsz8/119YyI\n3YCrKCe885fwZSYA+2XmtXX7xwJnRcRamXlnRMwDHsnMu0btjUnSGLEeVT+zRUBqt02AAeBXnYLM\nfCQzDwE2Bi7tXjkzrwH+Vp+3NH7f9fff6u8VljpaSWoe61H1LRMBqd0efZJlwzVbj1vM857Q0piZ\nQ63v4DZJywPrUfUtuwZJ7fa7+nsapR8rETEemAM8D3ige+WI2Ax4FvDbWvRIfdxZ/ixgraWMwRkL\nJPUz61H1LRMBqcUy848RcRbwhYjYH7gNOJTS3Lw1cGlEnACcTBmEdiJlNoyL6iZmAW+LiO9SmqqP\n5MnvcnV038V6AFgnIiZl5k0jf1eSNHasR9XP7BokaU/gYsqsE7Mpd7C2z8zZlMFsmwNXAN8GLqnL\nHq/P/ShwJfBj4EeUWSsW6Q87jO67V18DVgGuiwhnvJDUj6xH1Zf8HgFJkiSphWwRkCRJklrIRECS\nJElqIRMBSZIkqYVMBCRJkqQWMhGQJEmSWshEQJIkSWohEwFJkiSphUwEJEmSpBYyEZAkSZJayERA\nkiRJaiETAUmSJKmF/h8hFMvgL8KLegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cebd8b4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, sharey=True)\n",
    "sns.countplot(y='BirthState', data=df, ax=axs[0])\n",
    "sns.countplot(y='DeathState', data=df, ax=axs[1])\n",
    "fig.set_size_inches(8,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a few flag variables that we'll use later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Creates binary columns to show if the individual died where they were born and if they're currently alive\n",
    "df['BirthState'].fillna(np.NaN)\n",
    "df['DiedWhereBorn'] = np.where(df['BirthState'] == df['DeathState'], 1,\n",
    "                               np.where(df['DeathState'].isnull(), np.NaN, 0))  # Also add NaN\n",
    "df['IsAlive'] = np.where(df['DeathDate'].isnull(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Creates binary columns for if the individual was born in their mother or father's states.  \n",
    "# Will be used to calculate mobility\n",
    "df['BornInFatherState'] = np.where(df['BirthState'] == df['FatherBirthState'], 1,\n",
    "                                   np.where(df['FatherBirthState'].isnull(), np.NaN, 0))\n",
    "df['BornInMotherState'] = np.where(df['BirthState'] == df['MotherBirthState'], 1,\n",
    "                                   np.where(df['MotherBirthState'].isnull(), np.NaN, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17cebf26b70>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/8K88+HbjT6C6t8YHdrUoSZI0P8x2jcbBwImZua3VkJnjEfEu4OquVFaLiN2owsvLgK3A\nJzLzbXXf3sDHgEOAW4HXZ+bF3Ty+JEmavdmOaNwL7DtF+6OpwkA3fRg4Gnge8HLgpIg4qe77PLCO\nKvhcCKyJiFVdPr4kSZql2Y5ofBb4SES8BvhW3fYM4K+Bf+lGYQARsRQ4ETgqM6+t284FVkfEjcA+\nwOrM3AK8OyKOrrc/q1s1SJKk2Ztt0PgLqjUZFwOTbe1rgD/f1aLaHAbcnZlXtBoy8xyAiHgLcF0d\nMlquoJpGkSRJDTDbj4nfBPxORDyJBz699YeZ+ZNuFkc1PXNrRPwh1cLT3YBPAu8EVlJNm7RbDzh1\nIklSQ8z6Y+IBMvPHwI+7VMtUfhV4EvBK4HiqcPG3wH1UH+rWuR5kKzAykwMMDg4wODiwy4V2Ghqa\n7fIXqb8MDQ0yPNx/r3fPUS0UvT5HdylozIHtwCOAl2Xm7QAR8XiqW51/BVjesf0IVQiZtmXLljAw\n0P2gMTq6qOv7lJpodHQRS5cu6XUZM+Y5qoWi1+do04PGHcCWVsioJdX0yFqg8+ZgK+rnTNuGDZuK\njGiMjW3u+j6lJhob28zGjZt6XcaMeY5qoSh5jk4nwDQ9aFwF7B4RT8zMG+u2/anumXEV8JaIGMnM\n1hTKYcDlMznAxMQkExOTO99whsbHJ7q+T6mJxscn2L69/17vnqNaKHp9jjY6aGTmjyPii8AFEXEy\n1RqNN1NdvnoZ1WetXBARZwPHUF1ie3yPypUkSR36YTXUHwA3Uo1UXAB8ODP/OjMnqMLFCuAaqpt5\nHdsxzSJJknqo0SMaAJl5L9UoxfFT9N0MHDnHJUmSpGnqhxENSZLUpwwakiSpGIOGJEkqxqAhSZKK\nMWhIkqRiDBqSJKkYg4YkSSrGoCFJkooxaEiSpGIMGpIkqRiDhiRJKsagIUmSijFoSJKkYgwakiSp\nGIOGJEkqxqAhSZKKMWhIkqRiDBqSJKkYg4YkSSrGoCFJkooxaEiSpGIMGpIkqRiDhiRJKsagIUmS\nijFoSJKkYgwakiSpGIOGJEkqxqAhSZKKMWhIkqRiDBqSJKkYg4YkSSrGoCFJkooZ7nUBMxERXwTW\nZ+aJ9eO9gY8BhwC3Aq/PzIt7VqAkSXqQvhnRiIiXAs/vaP4csA44GLgQWBMRq+a6NkmSNLW+CBoR\nsRQ4B/h2W9tRwL7Aq7LybuBK4MTeVClJkjr1y9TJucCngMe0ta0GrsvMLW1tV1BNo0iSpAZo/IhG\nPXLxbODsjq6VVNMm7dYDTp1IktQQjQ4aETECnA+cnJlbO7oXA51tW4GRuahNkiTtXNOnTv4SuDoz\nvzpF3xZgWUfbCHDfTA4wODjA4ODA7Kp7GENDjc5wUtcMDQ0yPNx/r3fPUS0UvT5Hmx40XgLsFRH3\n1o9HACLid4G/Avbv2H4FcMdMDrBs2RIGBrofNEZHF3V9n1ITjY4uYunSJb0uY8Y8R7VQ9PocbXrQ\nOAL4lbbH5wCTwGnA3sBfRMRI27TKYcDlMznAhg2bioxojI1t7vo+pSYaG9vMxo2bel3GjHmOaqEo\neY5OJ8A0Omhk5m3tj+uRjcnMvCUifgrcBlwQEWcDxwDPAI6fyTEmJiaZmJjsUsUPGB+f6Po+pSYa\nH59g+/b+e717jmqh6PU52reTlJk5AbyIarrkGuDlwLGZeXtPC5MkSfdr9IhGp8w8oePxzcCRPSpH\nkiTtRN+OaEiSpOYzaEiSpGIMGpIkqRiDhiRJKsagIUmSijFoSJKkYgwakiSpGIOGJEkqxqAhSZKK\nMWhIkqRiDBqSJKkYg4YkSSrGoCFJkooxaEiSpGIMGpIkqRiDhiRJKsagIUmSijFoSJKkYgwakiSp\nGIOGJEkqxqAhSZKKMWhIkqRiDBqSJKkYg4YkSSrGoCFJkooxaEiSpGIMGpIkqRiDhiRJKsagIUmS\nijFoSJKkYgwakiSpGIOGJEkqxqAhSZKKGe51ATsTEY8GPgwcCdwH/BPwlszcFhF7Ax8DDgFuBV6f\nmRf3qFRJktShH0Y0PgvsDhwKvBR4IXB23fd5YB1wMHAhsCYiVvWiSEmS9FCNHtGIiACeCeyVmXfW\nbWcA742ILwP7AKszcwvw7og4GjgROKtXNUuSpAc0fUTj58Bvt0JGmz2AZwHX1SGj5QqqaRRJktQA\njR7RyMx7gPvXXETEAPBa4BJgJdW0Sbv1gFMnkiQ1RKODxhTeCxwEPAN4A7C1o38rMDKTHQ4ODjA4\nONCd6toMDTV9sEjqjqGhQYaH++/17jmqhaLX52jfBI2IeA/wOuD3M/MHEbEFWNax2QjVlSnTtmzZ\nEgYGuh80RkcXdX2fUhONji5i6dIlvS5jxjxHtVD0+hzti6AREecBrwL+IDM/VzevBfbv2HQFcMdM\n9r1hw6YiIxpjY5u7vk+picbGNrNx46ZelzFjnqNaKEqeo9MJMI0PGhFxJvBK4CWZuaat6yrgzREx\nkpmtKZTDgMtnsv+JiUkmJia7U2yb8fGJru9TaqLx8Qm2b++/17vnqBaKXp+jjQ4aEbEfcDrwV8A3\nI2Kvtu5LgduACyLibOAYqrUbx891nZIkaWpNXw11DFWNp1NdYbKOampkXWZOAMdSTZdcA7wcODYz\nb+9RrZIkqUOjRzQy8z3Aex6m/yaqW5NLkqQGavqIhiRJ6mMGDUmSVIxBQ5IkFWPQkCRJxRg0JElS\nMQYNSZJUjEFDkiQVY9CQJEnFGDQkSVIxBg1JklSMQUOSJBVj0JAkScUYNCRJUjEGDUmSVIxBQ5Ik\nFWPQkCRJxRg0JElSMQYNSZJUjEFDkiQVY9CQJEnFGDQkSVIxBg1JklSMQUOSJBVj0JAkScUYNCRJ\nUjEGDUmSVIxBQ5IkFWPQkCRJxRg0JElSMQYNSZJUjEFDkiQVY9CQJEnFDPe6gF0VESPAR4DjgPuA\n92Xm+3tblSRJgvkxonEu8DTgOcDJwJkRcVxPK5IkSUCfB42IWAy8AnhdZn43Mz8PnAO8treVSZIk\n6POgATyFavrnyra2K4DVvSlHkiS16/egsRK4MzO3t7WtB3aPiOU9qkmSJNX6fTHoYmBrR1vr8ch0\ndjA4OMDg4EBXiwIYGhrknrvWdn2/UpPcc9dahoYGGR7uv/+zDA0Ncvfau3pdhlTU3Wvv6vk5OjA5\nOdmzg++qiPhd4MOZ+ei2ticDNwDLM/PunhUnSZL6fupkLfDIiGj/PVYAmw0ZkiT1Xr8Hje8AvwSe\n1db2bODq3pQjSZLa9fXUCUBE/A1wKHAisAq4APjj+lJXSZLUQ/2+GBTgDVR3Bv0acA/wdkOGJEnN\n0PcjGpIkqbn6fY2GJElqMIOGJEkqxqAhSZKKMWhIkqRiDBqSJKmY+XB5qxaQiBihupz5OOA+4H2Z\n+f4dbHsQ8DfAgcD3gddk5nVzVau0kNXn6jXAKZl52Q628RxdABzRUL85F3ga8BzgZODMiDiuc6OI\nWAx8Ebi03v5K4IsRsWjuSpUWpjpkfAbY/2G28RxdIAwa6hv1P0yvAF6Xmd+tb8x2DvDaKTZ/KXBf\nZr45K6cC9wK/N3cVSwtPROwHXAXss5NNPUcXCIOG+slTqKb7rmxruwJYPcW2q+u+dt8ADilTmqTa\nEcAlVOfawMNs5zm6QLhGQ/1kJXBnZm5va1sP7B4RyzPzro5tv9/x/PXAAYVrlBa0zDy/9XNEPNym\nnqMLhCMa6ieLga0dba3HI9PctnM7Sb3hObpAGDTUT7bw0H+EWo/vm+a2ndtJ6g3P0QXCoKF+shZ4\nZES0v25XAJsz8+4ptl3R0bYCuKNgfZKmz3N0gTBoqJ98B/gl8Ky2tmcDV0+x7VXAb3S0HVq3S+o9\nz9EFwsWg6huZuTkiPgWcHxEnAquANwJ/DBARewH3ZOYW4P8C74qIDwAfBV5NNSf8Tz0pXpLn6ALl\niIb6zRuAa4GvAecBb6/vpwHVkOvvA2TmvcALgMOp7k74TOD5mbl5ziuWFq7JjseeowvQwORk5+tA\nkiSpOxzRkCRJxRg0JElSMQYNSZJUjEFDkiQVY9CQJEnFGDQkSVIxBg1JklSMQUOSJBVj0JAkScX4\nWSfSPBERtwKPa2vaBqwHvkh1q/a76u0mgOMz81OzPM7jgVuA5wB7AJ8H9svMbNvm1cBHgE9m5iva\n2oeBu4EzqW47/XVg78z82Wxqma2IOLOuYRIYqL9vAC4G3piZfoKo1CWOaEjzxyTwXqqP2l4BBHAK\ncCRwWUQ8ot5uBfCPXTgWwKXAOA/9FM7fAn5Wf2/3DGARcFHHfnrhNh74Wz0W+J36+5oe1iTNOwYN\naX7ZlJn/VX/9NDO/APwm1RvonwPUfVt38TgD9b7GqEYmDm111KMWRwHvAFZGxIFtzzsMWJ+Z39/F\n43fDeGb+ov57rMvMq6n+Rs+MiP17XZw0Xzh1Is1zmXlbRKwBXgac0Tl1EhEnUL3B7k01JfK3wHmZ\nOVn3HwB8GFgNrAPe3XGIS4D/2fb4UKp/W/438CaqUY3v1X2HA1/teP4LIuI1wH8HbgROy8wvtToj\n4jTgVVQjDwmcm5mfrvuOqPf3NuA04ObMfGZEPBp4f33sceAbVFMiN+7kz7WZjlGWiDiEKjQdDPwS\n+DfgTZm5oe6/heojz38HeFT9tzgLuKrt8WD9vFdl5qad1CDNK45oSAvD94B9I2JJe2NEvBI4h2q9\nwv7A6cCbgXfV/aNUQWIj8HTgNcDbefCb8VeBJ0XEsvrxbwKX16MmXwF+u97XAFUI+UrbcweAPwVO\nBn4N+DHwjxGxuH7OX1GFjFPq/g8BH6nXgLQMUb3Jrwb+pH7uf1AFjGdThZtfAN+KiJU7+gNFxPL6\nd7syM39Qtz2Tah3J9+r9/279/aL692k5BXht/bt+q247lepj0Z8O/AFwLPD6HR1fmq8c0ZAWhrvr\n73t0tJ8OnJ2Z/1w/vjUi9qB6Mz+DahRkMdUIyP8DfhQRpwL/0raPbwJbqNZpfIHqzfbCuu8i4KSI\nWAQ8qT5+54jGn2Xm5QARcRbwImD/iPgB1Zv1SzPzy/W2t0TEPlRh6Py2fbw3M2+q9/GK+jh/mJkT\nddtJVNM5J1GNNgA8PiLGqMLOINXakc11/S1vAL6bmafWjzMiXgZ8h2q0pFXXlzLz660nRQTADzLz\n7XXTTRHxFdqmmKSFwqAhLQytgNEKHETEI4FVwLsi4p1t2w4CuwH7UI8y1CGj5ZvUazQAMnNbRHwD\nODQivgU8Ffijuvs/6u+HAk8GbsjMn7ftaxL4SdvjjfW+F1GNsOwOfDoi2kdQhoDdImKkbR/tUyIH\nAcuBe+o3/JaRuoaWtcARPBA0ltd1XxwRR2fmN4ADeWDhauv3/c+IuKfuawWN9t+h5Ucdj+/hoUFP\nmvcMGtLC8HTgJ5l5X9ubb2vq9FSq6ZFOt1G9iXdOsf5yim0vAZ4PPA9Yl5k3AGTmpoi4kuoN/Uk8\neNqkZXyKttabP8DvUa3NeJDM3Nr2u2xu6xqkepN/IW2BqNYemLZn5i1tj2+kml55LvA6qnUdnc9v\nr6/977B5im2mWnC7o/1J85ZrNKR5LiJWAcfwwHQGUF19QrV24QmZeXPri+oS1NYIx3d48PoL6v7O\ny1K/SrVY8iiqe1G0+wrwNKpRjc6+h/MjYDvw+I76XkB9Bc0OfB94PHBP23N+BryHar3GzgzywL+N\n/0l1pcz9IuIpwChwwwx+F2nBckRDml9+NSL2qn9eBDyF6oqJm6iuwuj0HuAdEXEb8O/19h8B1mTm\nLyPiH6iu6PhMRPw5sBT44BT7uY7qBmG/B7yyo++ieh/DVPfdaLfD/+Fn5lhEnF/Xdy/VlM2Rdc3t\nUz2d+7iQag3HZyPizcAYcAbV2ovT27YbavtbQRUeXg3sS7U2A6q/2eUR8WGqv8sK4DzgWuBrO6pd\n0gMc0ZDmlzdSXYK6jmpE4L1UN6B6dmbeV29z/2hEZr6f6k31FOAHwAeoFlm+pu6/j2qUYhtwBfD3\nVG/0D1JfCvsfVAtHL+7oux7YBFyRmVs6njrVDbva206lCjZn1fX9BXB6Zr5jR/uo7+1xOHAn1RqK\nbwErgee2372Uan3Kurav66hGL/4oM/+t3te3qQLKwXX/P9R/h+dlZmvKp5c3HZMab2By0nNEkiSV\n4YiGJElqYshMAAAAQUlEQVQqxqAhSZKKMWhIkqRiDBqSJKkYg4YkSSrGoCFJkooxaEiSpGIMGpIk\nqRiDhiRJKsagIUmSijFoSJKkYv4/nlDvpzFEk4EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cebdf0c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "sns.countplot(x='DiedWhereBorn', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I filtered the data set to identify any records that did not include the United States to check if this dictionary contained all available countries.  This list ended up being more comprehensive, but I left it for future use with other GEDCOM files. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countries = {\n",
    "    # Europe\n",
    "    'England': 'EN',\n",
    "    'Scotland': 'SC',\n",
    "    'Wales': 'WL',\n",
    "    'Ireland': 'IR',\n",
    "    'Rebpulic of Ireland': 'IR',\n",
    "    'Great Britain': 'GB',\n",
    "    'Britain': 'GB',\n",
    "    'United Kingdom': 'UK',\n",
    "    'Italy': 'IT',\n",
    "    'ITA': 'IT',\n",
    "    'Germany': 'DE',\n",
    "    'France': 'FR',\n",
    "    'Denmark': 'DR',\n",
    "    'Sweden': 'SW',\n",
    "    'Norway': 'NR',\n",
    "    'Canada': 'CN',\n",
    "    'Netherlands': 'ND',\n",
    "    'Spain': 'SP',\n",
    "    'Belgium': 'BG',\n",
    "    'Poland': 'PL',\n",
    "    'Austria': 'AR',\n",
    "    'Portugal': 'PR',\n",
    "    'Russia': 'RU',\n",
    "    'Hungary': 'HR',\n",
    "    'Slovakia': 'SL',\n",
    "    'Greece': 'GR',\n",
    "    \n",
    "    # North America\n",
    "    'United States': 'US',\n",
    "    'USA': 'US',\n",
    "    'US': 'US',\n",
    "    'Mexico': 'MX'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17cebf0ae80>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YMCVJkiRJRRgwJUmSJElFGDAlSZIkSUUYMCVJkiRJRRgwJUmSJElFGDCbFBGXRMTnB2zb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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cec4c5898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extracting the country from birth addresses\n",
    "BirthCountry = pd.Series([[state.strip() for state in cell.split(',') \n",
    "                  if state.strip() in countries] \n",
    "                 for cell in df.BirthPlace.fillna(\"null\")])\n",
    "\n",
    "df['BirthCountry'] = BirthCountry.apply(lambda s: s[-1] if s else np.NaN)\n",
    "df['BirthCountry'] = np.where(df['BirthCountry'] == 'USA', 'United States', df['BirthCountry'])\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.set_size_inches(10, 5)\n",
    "sns.countplot(y='BirthCountry', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3: How mobile were my ancestors over time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll begin with some initial plots of the states before moving into time series plots.  Here, we will examine two angles:\n",
    "\n",
    "**1)** The ratio of people that never moved - e.g. they died in the same state they were born in\n",
    "\n",
    "**2)** The ratio of people that were born in the same state as their parents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x17cebdf00f0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sddxeVM8SjYixwHqZeRxwfUQcDDxZHfPbgQ5u8uSp9PRMn9PPKPWro6Odrq5R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\nkiRJkqTaWXxKkiRJkmr3f/Op6LFzsLEoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cebfeadd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DiedWhereBornRatio = df['DiedWhereBorn'].groupby(df['BirthState']).mean()\n",
    "DiedWhereBornRatio.plot(kind=\"barh\", figsize=(10, 6), \n",
    "                        title=\"Died in Same State as Birth\").set_xlabel(\"Ratio\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x17cebc57390>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wKSI2BT4M7ArsGRHrZubjfQ2urW3MgC5K6i/HmprFsaZmcaypWRxrmt+YfA6f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\npKpr6OsB9BcR8QKwYvmxFXgXeAw4KTPv6ML1mwF3Z+ZcH+ibmpoYOnR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6AAAC\n1UlEQVS/AP4+M5dWea0xwFOZ+UREHAj8FvibzFzQgSFLkpqcI5+SJDWnVcBNwLbAduV/uwNfBcYB\nV1ZzkYgYDNwObFM23QdsDzzXwfFKkpqcI5+SJDWvNzLzpYrXfwEmR8RI4FhgfBXX6ElRyAKQmSvK\n60iSVBOLT0mSup9lwJvQNrJ5BXAwsBXFtNwfZeaFEbEz8DRF8fm7iLgUmAH8Dnh3Zi6IiM2ALwGf\nBXYAngAuy8yfNfieJEmbOKfdSpLUTUREr3L95gnAlLL5NuDtwKHAe4CvA1+MiCOABcC+QA9gLP8/\nVXdVxWX/AzgROAcYBvwc+Gl5viRJbXqsWrVqw70kSVKXEhG/Aw6gGOVs1Q94Bvg+xdrPPsDngJsz\n808V5z4P/FtmXl6Ofs4HDsrMmZUbDgH9gT8AYzJzesX5PwN2yMwP1PEWJUldjNNuJUlqXrcBX6SY\n6bQvMBG4G/hqZrYASyPi28CREbEfMATYg2JzoV5VXH8YxSjofe3aZwD/0iF3IElqGhafkiQ1r8WZ\nOb/8+qlyRPNuYAUwPiL6A/cCfYGfUoyI/icwq8rr91hHe0/KNaWSJLWy+JQkqZvIzHsi4ipgQkT8\nAtgc2BPYNjMXAkTEQIrHs7QWlutbn/NY2W8UcEdF+2iK6biSJLWx+JQkqXv5MvBpYDJwctl2UkRM\nBQZTTJftTTEaCvDX8v/DImJO+XUPgMx8IiKmAddGxNnAk8BxwCeBo+p9I5KkrsXdbiVJ6kYycxlw\nBkWhORY4DzgX+G/gBuAe4CfAyLL/K2X714F/Ki9TORp6DHArcD3wKDAGGJuZt9b5ViRJXYy73UqS\nJEmS6s6RT0mSJElS3Vl8SpIkSZLqzuJTkiRJklR3Fp+SJEmSpLqz+JQkSZIk1Z3FpyRJkiSp7iw+\nJUmSJEl1Z/EpSZIkSao7i09JkiRJUt1ZfEqSJEmS6s7iU5IkSZJUd/8HfpWuhBSW5H0AAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cec4dbe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FatherStateBirthRatio = df['BornInFatherState'].groupby(df['BirthState']).mean()\n",
    "MotherStateBirthRatio = df['BornInMotherState'].groupby(df['BirthState']).mean()\n",
    "\n",
    "BirthRatioByState = pd.concat([FatherStateBirthRatio, MotherStateBirthRatio], axis=1)\n",
    "BirthRatioByState.columns = ['Father', 'Mother']\n",
    "\n",
    "ax = BirthRatioByState.plot(kind=\"barh\", figsize=(10, 6),\n",
    "                            title=\"Born in Same State as Parents\")\n",
    "ax.set_xlabel(\"Ratio\")\n",
    "ax.set_ylabel(\"State\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before generating the time series plots, we are going to discretize the birth years into decades.  This will assist us in making the plots more consistent by taking care of the binary variables (Ex. born in the same state as their parents or not) which aren't suitable for time series graphs on their own."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BirthDate</th>\n",
       "      <th>BirthYear</th>\n",
       "      <th>BirthDecade</th>\n",
       "      <th>DeathDate</th>\n",
       "      <th>DeathYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1894</td>\n",
       "      <td>1894.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1810</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1853</td>\n",
       "      <td>1853.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1795</td>\n",
       "      <td>1795.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1772</td>\n",
       "      <td>1772.0</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>1841</td>\n",
       "      <td>1841.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1759</td>\n",
       "      <td>1759.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>1808</td>\n",
       "      <td>1808.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BirthDate  BirthYear  BirthDecade DeathDate  DeathYear\n",
       "0      1894     1894.0       1890.0       NaN        NaN\n",
       "1      1810     1810.0       1800.0      1853     1853.0\n",
       "2      1795     1795.0       1790.0       NaN        NaN\n",
       "3      1772     1772.0       1770.0      1841     1841.0\n",
       "4      1759     1759.0       1750.0      1808     1808.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Binning the birth years into decades\n",
    "\n",
    "Years_int = np.arange(1500, 2020)  # To extract the birth and death years\n",
    "Years = [str(s) for s in Years_int]  # Label argument to pass\n",
    "\n",
    "decades = np.arange(1600, 2020, 10)\n",
    "decades_labels = [s for s in decades]\n",
    "del decades_labels[-1]  # Removes the last record to use as the labels in the pd.cut\n",
    "\n",
    "\n",
    "# Extracting the birth year from the date\n",
    "BirthYear = pd.Series([[date.strip() for date in cell.split(' ')\n",
    "                  if date.strip() in Years]\n",
    "                 for cell in df['BirthDate'].fillna(\"null\").str.replace('-', ' ')])\n",
    "\n",
    "# Applying the bins\n",
    "df['BirthYear'] = BirthYear.apply(lambda s: s[-1] if s else np.NaN)\n",
    "df['BirthYear'] = pd.to_numeric(df['BirthYear'])\n",
    "df['BirthDecade'] = pd.cut(df['BirthYear'], decades, labels=decades_labels).astype(float)\n",
    "\n",
    "# Extracting the death year from the date\n",
    "DeathYear = pd.Series([[date.strip() for date in cell.split(' ')\n",
    "                  if date.strip() in Years]\n",
    "                 for cell in df['DeathDate'].fillna(\"null\").str.replace('-', ' ')])\n",
    "\n",
    "# Appending to the data frame as a numeric column\n",
    "df['DeathYear'] = DeathYear.apply(lambda s: s[-1] if s else np.NaN)\n",
    "df['DeathYear'] = pd.to_numeric(df['DeathYear'])\n",
    "\n",
    "df[['BirthDate', 'BirthYear', 'BirthDecade', 'DeathDate', 'DeathYear']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our time series plots, we are going to be layering a [LOWESS](https://en.wikipedia.org/wiki/Local_regression) line on top of the regular line plots in order to get a better idea of the trends.  This is preferable over regular linear plots since a lot of our data isn't very linear - our $R^2$ values would be very low.  [Time series decomposition](https://en.wikipedia.org/wiki/Decomposition_of_time_series) is a popular choice for an exploratory analysis of time series, but we're just looking for a general trend over time.\n",
    "\n",
    "We'll be using [Seaborn's regression plots](http://seaborn.pydata.org/generated/seaborn.regplot.html) which give the option of a LOWESS line.  We'll be plotting these on top of the regular pandas plots. \n",
    "\n",
    "Let's start with a plot of the ratio of those that died in the same state they were born in by decade:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x17cebd700f0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tKS3WwxJCCCGOE67OeSBBU0w585ncLheLSycYNKUm0UKfNIIQQkSN1+fly5tv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\nyAr+f4LfDABiW7WeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cebca4748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "died_where_born_ratio_ts = df['DiedWhereBorn'].groupby(df['BirthDecade']).mean().dropna()\n",
    "\n",
    "died_where_born_ratio = died_where_born_ratio_ts.reset_index()\n",
    "died_where_born_ratio.columns = ['BirthDecade', 'Ratio']\n",
    "died_where_born_ratio.dropna(inplace = True)\n",
    "\n",
    "\n",
    "ax = died_where_born_ratio_ts.plot(figsize=(10, 5))\n",
    "# Plotting a LOWESS line on top with seaborn's regplot() command\n",
    "sns.regplot(x='BirthDecade', y='Ratio', data=died_where_born_ratio, lowess=True, color='Green')\n",
    "\n",
    "ax.set_ylim([-0.01, 1.01])\n",
    "ax.set_title('Mobility: Died in the Same State as Birth by Decade')\n",
    "ax.set_xlabel(\"Decade Born\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So this indicates two different periods of increased mobility over time, e.g. people more frequently spending their final days in a different state than the one they were born in as we get closer to the modern day.\n",
    "\n",
    "If that is mobility over their entire lifetime, let's take a look at a slice of their lives.  Since the only reliable geographical data points we have are birth places and death places, let's compare the state children are born in with that of their parents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x17ceb745828>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vpjMQwOgwCQQCxJsmRUlxEb9RfbaPdzr24/e7cbk9tAF1viqWJRbIzX6U+Wwf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\nHWYsfAl/Rd1NWTZl1s6uN6ro7e2tAH4F+I3+/v7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uBfs4Pf0qzZV+Hmt9046OabezJ0TV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\nJElqwaJKkiRJklqwqJIkSZKkFv4fgpuOZjbfoAcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cebd014a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "born_in_father_state_ratio = df['BornInFatherState'].groupby(df['BirthDecade']).mean()\n",
    "born_in_mother_state_ratio = df['BornInMotherState'].groupby(df['BirthDecade']).mean()\n",
    "BirthRatio = pd.concat([born_in_father_state_ratio, born_in_mother_state_ratio], axis=1)\n",
    "BirthRatio.columns = ['Father', 'Mother']\n",
    "\n",
    "# Split our data between mother and father to create our flags\n",
    "birth_ratio_linear = BirthRatio\n",
    "linear_father = birth_ratio_linear[['Father']]\n",
    "linear_father.columns = ['Ratio']\n",
    "linear_father['Parent'] = 'Father'\n",
    "\n",
    "linear_mother = birth_ratio_linear[['Mother']]\n",
    "linear_mother.columns = ['Ratio']\n",
    "linear_mother['Parent'] = 'Mother'\n",
    "\n",
    "# Recombine, drop NA, reset index to convert it to a data frame\n",
    "birth_ratio_linear = linear_father.append(linear_mother)\n",
    "birth_ratio_linear.dropna(inplace=True)\n",
    "birth_ratio_linear.reset_index(inplace=True)\n",
    "\n",
    "\n",
    "ax = BirthRatio.dropna().plot(title=\"Children Born in the Same State as Parents\", figsize=(10, 5))\n",
    "\n",
    "sns.regplot(x=\"BirthDecade\", y=\"Ratio\",\n",
    "                data=birth_ratio_linear[birth_ratio_linear['Parent'] == 'Mother'],\n",
    "               lowess=True, color='LightGreen')\n",
    "\n",
    "sns.regplot(x=\"BirthDecade\", y=\"Ratio\",\n",
    "                data=birth_ratio_linear[birth_ratio_linear['Parent'] == 'Father'],\n",
    "               lowess=True, color='LightBlue')\n",
    "\n",
    "ax.set_ylabel(\"Ratio\")\n",
    "ax.set_xlabel(\"Decade\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see a large upward trend after 1800.  We can likely infer from this that there was a lot of transience in the colonial period for my family, and that my ancestors began to stay in areas longer after the establishment of the US.\n",
    "\n",
    "### Question 4: How has the average life span changed over time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Using static year instead of datetime.date.today().year since data is static\n",
    "df['LifeSpan'] = df['DeathYear'] - df['BirthYear']\n",
    "\n",
    "df['CurrentAge'] = (df[(df['DeathYear'].isnull()) & \n",
    "                      (df['BirthYear'] >= (2016-100))].DeathYear.fillna(2016) \n",
    "                   - \n",
    "                   df[(df['DeathYear'].isnull()) & \n",
    "                      (df['BirthYear'] >= (2016-115))].BirthYear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x17cec544cc0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jhrmp3kDjlQ/yaHYMTg+0p2CDzWph8fTuBXVdSYwOI9hu7Is8J5hGCl3XeXv3\nBQASokNZODW5R4+XIClAOZwOvrbpUZ7aso/3zu6WAd+dGJOC5ZNY/CAx5bcw+sLXqNGm8vKxzf5u\nmuiD0xdqvZevNB7Jl8Vi4a4VRqnWptaOgOkZ2HSwiOO5xoSEK+ek8IXlt7BkoZ24eCMl8P19xQFf\nlc/fckvq+fk/jvCzvx0h2xwLEB0RzANrJ/HjLyxm+awx2Ky924V5zgy3dbh4Z29+v7VZDB5P6W81\nNoZR4cFdLjNhTBQTzAplWw4VDnqqlPCvDqeb375ygvOlRm/Mmvlp3sqoPWGzWnlgzSTA2M+8/uH5\n/mxml1xut3eer5mZ8URHdP0Z7w6r1cJoc1xS0QircHcir5rCCuM137RwHHZbz/YZEiQFqM35G6nP\nnklMzWriy+8CkAHfPnLrsomsn4/NbZS2tGAjomk2WzeF8z9/O8zR7CqZH2MI8kwOmxQT1u0J8yan\nxzLN7HV6/0AhDS3+7Rm4UNbAS9uMMUdjkyK5f7Xilsz1fG3+o3z3vhXend1fNpwZcTusrnROm80p\nqeY3Lx/nR3895A2aI0Lt3L0qk58+vJjV89IIsvdt1zUueRTzlZGite1IsbdCmhgaKmpbvAc+81TS\nVZddM9/oNaisc3hPXIjhz+V288c3T3Em3/gNWTI9hftWT+x1ytrk9Fhv+ubWI0Xez99AOZFXQ73Z\ny93TuZG6kpYw8ircGb1Ixkmw6MjgXr2PEiQFqOMXiglrMVJygttTsHXEADJLvEfGqImMqjcmBWsP\nLaIp6iBui9EFfragjt+8cpzv/mkfWw8X0dYeeAP6xeXcuu7doXUn1c7XXSuNcSZt7S7e2eO/noHW\nNidPvn4Kl1snJMjGw7dNI8h+sWRr7KgQvnznDGxWC20dLn73yolBS90IRJ602ccP/pzXj27n2bfP\n8+O/HuNoThVgzAty+7Lx/M+XlnDTwvRelb+9ktuWT8CCcbb57T0X+m29YuAdPlflvXyteWPmqyRi\nIo0TE5sOSkXDkUDXdZ59T/P2Ns7OSuAzH52MtZcBkse9N2QRbLei60YRB30AT8R6Uu2iIoKZMSH+\nGktfm6d4Q3ltS0AWORoIWkEdOcVGFsJHFoy7ZF/cXX4PkpRSwUqpJ5RSNUqpUqXUj3zuy1BKbVJK\nNSmlTiql1vqzrYOpIT/zkuthzUbAJLPEG0LrZ2BzGV/6xoQPqEvaQOjMN1i/bBzR5g6xvKaF5zee\n45v/t4vo4Kf8AAAgAElEQVSXt+fK2eIAV1De6K025qlA1l0ZKVHenoGth4upaRj8tFRd13nufY2K\nOmNM1SfWTWJ0fMRly2WlRvOJdUbqRkVdK39449SITQPanL+RnLpsIurnknjhYcKbpgNgs+l8dFE6\nP3t4CeuXjb+scll/SE2IYNE0Iz/9w6MlVNWPvKpPQ5Wn9HfmmChiR4VcdVm7zcqqOUY58DP5tSN+\nQs3hTtd1XtyW4w0yJo+L4Uu3T+t1aq6vuKhQbl6cDsC5wjr2nxmYlOmG5naOmSeKlkxP6XGKWFc8\ncyXpujHJ8kjgOfkVEWrn+jm9K5/u9yAJ+A2wGlgL3A88pJR6yLzvDaAEmAc8D7ymlOrZiLsh6Gx+\nLRUVlx4UhLVMIitmoswSj1HGcvMB4wcwNtbFrfPn8uj8b/Krtb/k9mVZ/O+XlvDQLVMZl2wEUc0O\nJ+/szedbv9/NH986xYWyBn82f1ANpeIfnlQ7i8WobNdTd6yYgMVifD7e2DnwOeOd7TpRxt7T5QAs\nnpbMkqsMtF05O5XrZxs/2ifP1/Dqh3mD0sZAk1uXjcUdTHT1WixY0XHSGL2XyctO8bHrM4kMCxrQ\n51+/bDxWiwWXW+etXRcG9LlE/6htbPNW/JqrulfKd+WcVO+B5uZDI7Mc+FDaF/TFO3vzeX+/0WOY\nnjKKr941s1c9CFdy48JxJMYYE5m/uC0HR3v/Fwvae6oMl3nibNmMvqfawcWeJBgZKXd5JQ3edO21\n88cSGty7E21+DZKUUrHAZ4HPa5p2SNO0bcDPgYVKqVXAeOCLmuGnwB5z+WFL13Ve3WEcMEWE2cma\naOSkhjnG8+MlvyLUHurP5gUE30nBPrF6Dt9Z/u/cmnWb972x26wsnp7C9x9cwLfvn8PsrAQsgMut\ns/dUOY89c5CfvnCYw+cqh/UZfIfTwde2fpW/bPuALSe0gC/+4flBGz86ivDQnh8cj46PYKm5Q9l5\nopTS6sHbEZRWN/P8Jg2ApNgwPrFOXTP3/f61k8hKjQaMHfuBEVjIITNmIhENc7G6je9uZepz1CW8\nj0rKGJTnT44NZ+kMI5jddaKMcpl0NOAdNlOo4Nqpdh5R4cEsnGqMJ9lzsmzEzY/lrQZ74PdsOZ7D\nbw7+JqD3Bb217Ugxr3xgHD+Njg/n0Xtm9XsvdJDdxn2rJwJGwL6hn9O7dV1nh9kLljkmijEJl2cj\n9EZMZDDh5ntRVDX8e1M9Fe1Cg22snt/7vhV/9yQtA+o0TdvpuUHTtP/RNO3zwCLgsKZpvt/incDi\nQW7joDqRV+Od1fmji9L5+JIlAOi6hZxC2YG7dd1bjWp0fDjzrnIm0WKxoMbF8i8fm8mPv7CI1XPT\nCA4yPvLnCuv43asn+H9/3Mvmg4UDcjbI397L3Uj92bnEVt1MfNm92NvjA7b4R1uHi+yiOsCYGLK3\nbls6HrvNgq7DazsGpzepw+ni96+for3Djc1q4Uu3Te/Wjtlus/LIHdO94yX+suE0RQM8GDjQrBq7\nltjGZQC0hxbSHlYw6D3mty7NwGa14NZ13tg1+D2Qomc8QVJaYiTJseHdftyaeUY58Hanmx3HBm+u\nm0DgSWuNL7+ThPK7SS76AhcqagNyX9Bb+8+U8/z7xomq+KgQvnHvbKKuUPWwr2ZnJTB9glksaH8B\n5bX9d2x2oazRW6a7Pwo2eFgsFm8Z8+Hek1RU0eQd17pqbioRvTjp6uHvIGkCcEEp9Uml1BmlVK5S\n6j+UUhZgNEaqna9yoEch4VCaaFTXdV4z026iI4K5YW4a6SmjvNWwjmZXXe3hI8KRc5XefNqPLkrv\n9kDM5LhwHlg3iV98eSl3X5/pzWOvqGvlb5uz+cYTu3lxaw7V9YH/OemOZkcHm7fhLf5hwUJ400wg\nMIt/ZBfW4XQZvXo9HY/kKz46lFVzjJ+Ig2crBiW18p9bc7xV6u5ZlUV6SvdnRY+JDOHLd8zAbrPQ\n3uHmt68eH1FnuU/mNEC7seOeMdPCo/O/yeOrfjeoPeYJ0WGsNFMf950qlzErAayxpR2twDiZcrUT\nZF1JTxnFpDSj53br4SJcbne/ty9Q5dZlY3VGENJqFLgJ6kggueghDmnD45jiRF41f3rrNDowKjyI\nb9w3h7iogfsNsVgs3L9mEjarBadL5x+bs/tt3Z5epGC7leum9GxOn2vxpNwN9yDJMxYpyG5l3YJx\nfVqXv4OkSGAS8AXgQeAbwFeBR4FwoPNI+zbg6qM0O9mwT+PxAz8fEl3Lh89Vkl9uTJZ4y5IMQoJs\nWC0WZmYalU1O5FUP6/Swa9F1nbfNru34qJ5PCgYQERrETYvS+dnDi/nC+qlkmAe0rW1O3ttfwLef\n3MOTb5wkr6RhyOZw1za28dPnD9NSZ/wgeqr+RTTOAD0wi3+cumCMRwoJspFppqD11s1L0gkJNnLQ\nPakXA+WQVsnWw8UAzMqM95Yb7onM1Gg+sc4IZivrHPzhzZFRyEHXde+8VkkxYfzs7kcuSZsdTDcv\nziDIbkUHv4xnE91zNOfi1A7zuplq58szuWx1QxtHzg2PAKE7MmMmEtYyCQvGSUUdF1Y9mPzjGfxj\nSzZO19ANGLOL6nji1RO43DphITa+fs9sUuK638PYWylx4axbYHyejuVWczy375+n9g4X+8xxrfNU\nUr+nCqaaqXvVDY6AmXi9v5XXtHhT11fMGuPtZNB1nfq2OnJqs9lTsqvb6+v/kkE94wRGAR/XNK0I\nQCmVDjwCbAQ61z0MAXrUrxlbegfW+FHkWnaxrWgTt2bd1g/N7n9ut87r5s45PiqUG+alYTfnApmr\nEtlxvJSm1g7OlzWixsX4s6l+cyK3mvwyI4i8eUk6oSF2bOZgXFsPq7/Y7VaWzRzD0hmjOVdYz/v7\n8zmkVeLWdfafqWD/mQqskZVURm6jNeIsWHTevfAWv179RECPCyupauZ//3bEO2bLlnSOGstJ4srv\nxO6MJTN4CTdm3uj9bAUKz3ikyemxhHbaMfR0G8dFhXLTwnG8vuM8p87XkF1Ux5Q+pPBdSVVdK0+/\newYwSnt/4bZpBPWyRPUN89IoqGhi66EiTp2v4bUdedxr5r0PV2fza7lgfp9vXJSOzWrp8fe4vyTG\nhrF6Xhrv7SvgoFZJUVUTGSlRfmnLcNbb32uPI2Y2RXJsGOmjR/V4zpsFU5OI3xpKdYODLYeKWHSV\n4irDyY2ZN/L6xnrcQEdQFbXJr5NUfj90hLPxQCH5ZY18+c4ZxFyjUmB39HUb90RBeSO/fuk47U43\nQXYrj94zm8y0vp1k64nbV0xgz6ky6pra+fvmbGZkJvRpDrf9Zyu8wcv1c8b0+37aN8uhrLaFiWm9\nO5YczG18NZ7Ap6KlgsqWCipaKnjzwHHOBOfTbq2judXKc69UU95cTlVrJW2ui/0u+qzunYj0d5BU\nCjg8AZJJw0ipKwamdVo+xXxMj0TVrKQt6gwlbQXExvbPILj+tv1wkbcL9P4bJ5OUePHDvGzOWP7v\ntZN0ON2cLaxj0axUfzXTr97dfwQwUpTWXz/xkjlToqK6N/FoVxbFRbJoVipl1c28tSOPTfvzaW1z\n4W5KJL7pHpxBtTQmbibPdpY9VR9w55Q7+/xaBoKWX8OP/nqIRnMy1U/cOJn116/jrbPv8sLzLtxO\nGyuiPsPoxL7PudCfahsd3on5rpuWcsXvaE+28cdvnMKWQ8U0trTz6o7z/O/stF5PItgVl8vNT54/\nTIvDidUC//bJ+YxL7X2aIMBX7plDWU0Lp8/XsGFPPtMyE1k+Z/h+17e8dhKAiLAgbllhTHnQl+9x\nXz1w01S2HynG0e7ird35/OfnFvmtLcNdb7Zzi6ODk3lGj/Oy2anExUVe4xFdu3X5BJ7ZcBqtsI6a\n5g4ye3mgOJS0OIKxNqXhRidjPHxhxWdYOno1v/n7SY7nVKEV1vH9p/bz7U8tYFo/zMkDA/9dLqlq\n4uf/OEpLmxOb1cJ3Pr2ABVMHN+iNBT63fjq/+Nthymtb+eB4KXevntTr9e05VQZASnw4i2alYbX2\n3z4LYFrwxbE5tc0dfT4eHohtrOs6dY46yprKKG8up7yp3Pvfe5vP7e2uLiaPN2P9gn4oZOnvIGkv\nEKqUytI0zTNQYipwwbzvO0qpEE3TPOHfMmBHT55Ax4VFDyKq/CZGB4+mtjbwcjGdLjfPv3MaMM6Q\nzcmMu6ydUzNiOZZTzd4Tpdy2NMMPrfSvc4V1nDRnS1933Vhamhy0YJzJiIoKo6GhFVcfUwZCrPCx\nlRP46MJxPPbWCxTkRmB3xmDviCW25G6aRx3h8IXTrEr5SD+8ov51PLeK37x8nPYONxYLPHjTFFbN\nTcXR5GZt2kc4P/UUO4+Xsvt4GR+/obFPZ7v62+4TF897TEiJvOyz39ttfMuSdP6+ORstv5at+/O7\nXQmrO17elsMZM0XwtuUTSIsL65fflodvm8b3/7Kf2sY2fvXPw0SF2RiX3P0xTkNFaXUz+80DglVz\nUulo6yAsxN4v3+O+WLtgLG/tusCB0+UcOFFC1iCelR4J+vJ7vfdUmTctbHpGbK+/b9dNTuRv71tp\nd7p5Zcs5Hlrf+Vzs8LP/dLl3zOfnl95EZko06PDoPTN5eXsuG3bnU9vYxnd/v4v71kxk3YKxvT6p\n1J/75CupaXDw388epM6c+/ChW6eSNXqUX47vZo6PZWJaNNlF9fxz0znmZsX3ajxUZV0rx82e0qXT\nU6ivH5hCXTGRwdQ1tXPuQg3X9XBcn0dvtnFTexMVLeXmXwUVzeWUm5eNXiAj6KlsqaDd3UXg4yd+\nDZI0TTunlNoAPKOUegSjWMO3gceAD4FC874fAuuBBRhjl7qtOW4PkTXLCG3JIqp5Ek5n4OXefnis\nhPJaYyLD25aNB53L2jkzM4FjOdUUVzVTUtVMUoz/zrj6w5tmKmJYiJ2Vs8Zc9v64XO5+27bBdiuL\nZkazr+OXhDVPIaZ6HXZnNBGNc8jZ40YbU0vmmMA5eNpzqoynNpzB5dax26x8cf005qnES96PhVOT\n2Xm8lBaHk8NaBfNUkh9bfKkTZvAbExlMUkzYFbdjT7fx9bPH8N6+Amob23hpWw7TM+L65czc6Qs1\n3jl11NgYbl6U3m+fvcjQIB65Yzo/e+Ew7R1ufvXiMf7zwQUDPl/QYHt3bwE6YLNaWDUn1buj7c/v\ncW+sWzCWzQeLaG1z8sr2HL5x3xy/tWU46812PmBO3Bk7KoSxSZG9/pyEBtlYPD2FD46WsOdUGXet\nzCQqYmCqoAUKzxiN6Mjgy967u1ZkkpEcxV82nMbR7uKFjefILqzjwZsm93puGRiY77LD6WDDuY1s\n3mKjrdk4Bnpg7SSum5Ls19+N+9dM4rFnDtDW4eLvm7P5Yi8C7w+PlqADFmDxtJQBez2pCRHUNbVT\nWNHU5+dobmuhtLHMG/h4gh3v9VbjemVLBS3Owa/OHGoLJSk8mcTwRBLDkkgMTyIxLJHE8O4f//i7\nJwngAeC3GD1ELcBvNE17AkAptR74C3AQyAFu75Sad01rliZwYqub5mYrL2+7wJys5D6VA+xvHU43\nb5llZ1MTIq5YzWRWZjzPmZePZVex1hwwOBIUlDdy3DyQXj0vrd8HM3ZlTfo6NuS9SY7lNI6wPOKq\nbia8aTrNzVZ+8txhbluWwc2LM/q9O7ynNu4v4B9bjU7YsBA7/3LXDNS4y9O+poyL9Z5B2nOqPGCC\nJF3XvUUbpmXE9WtKXJDdxm3LxvPMu2cprmxm3+lyFvdxDEJDc7u3ilJkWBAP3Tq13z8DmWOi+eRH\nFE+/c5aqegdPvnGSR++Z1S8zxgeChpZ2dpm9hwunJnsrTQaCiNAgPrJgLK/vPM+pC7VoBbVdfp/E\n4Opwurz7gLkTE7td1fRK1sxL44OjJThdOh8cLebWpeP7o5kByelye4sKXOm9m6cSSU1cwBOvnqC4\nqpn9Zyooqmzmy3dMZ3R8YAxRcDgd/Os736UpdzYhbUYasm3McZbOWuLnlhljfVbOSWX7kWL2nS5n\n1ZxUJo3tfhqnW9fZaVa1mzo+bkAr86UmRnLqQu0Vq3i63C6qHFVmcGP2+ngDH0/wY/yvb6sfsHZe\nSZg97GLAE55kXk6kqMhCbp5OiB7Dt+5cwYyxGUQG9XzcYmd+D5I0TWvE6B16sIv78oBVfVn/d6//\nNh9EFvDzvx+hobmdV7bn8qkbJ/dllf3qw2MlVDcYXca3Lx9/xQOuuKhQxiVFUmDWfx9JQZJnXqTg\nICtr+zApWE+E2kN5fNXv2Jy/kbz6HMbPzyCqOYt/bjmPo93FazvOc+J8DV+4ZSoJfujV03Wdl7fn\n8u4+ozpYdEQwX793NmOTus7Tt1otLJqawnv7CziWU0VTa0dA9E6UVLdQ12R0rU8d3//FFZbOSOHd\nfQWU17Tw2o48FkxJwt7LwaZuXefPG05T32y097M3TxmwndnymWPIL2tk6+FiTl+o5ZXtedxzQ+BV\nJeyN7YeL6TDPYH7kur6VZx0IaxeMZdPBQpodTl77MI9vPzC3X4N30XOnztfS1uECjEJGfZWaGMmU\n9FjO5Ney9UgxNy1K7/XvQqA7W1BLa5vx3s2ZlHDF5VLiwvnup+bxzLtn2X+mgpKqZn747EE+d/PU\nHpdb70+6rnOusI5nth6go+xmb3njxui91IW+z+b8CdySud5v7fO4c8UEDpwpp9nh5IVN5/j+gwu6\nfQLtbH6tt9jS8n6cG8lD13Ua2xuoaKmgLugUJfbj5Dlr+d6HG6l3Vl1MgWsxChy49cHtlbukxyc8\nmaQwz2UjCPLclxSWRERQ5GW/x82ODv5t525GO13MzkpgSebMfmub34OkwTAzM56FU5PZd7qc7UdL\nWDJ9dEDkmrd1uLyzAqcnj7rmmIlZWQkUVDRxrrCO1jbnoPSo+FtZTYs3zWLlrFRGDdDkcF0JtYde\n9uM7JT2RP711itziBnKK6vn+0/v5xDrF4mmDN2DU6XLz7Ltn2XXSGNORHBvG1++dTeI1grVF05J5\nb38BLrfOwbMVXB8ARQFOn6/xXu7LJLJXYrNauWP5eJ584xRV9Q4+PFbCDXN7F2hv3F/oHTi+dv5Y\nZmdd+YCjP9y3eiJFFU2cK6rnvf0FjEuJZNEgD0zubx1OF1sPG8kA0zJirxjU+1NYiJ2PLkrnpe25\nnCuq59SFGqaPD6xiJyPNoXPGPiAyLIhJY/tn3712/ljO5NdS39TOQa1iyH+3ruSwWeo8LMTO5Gv0\nioYG2/ni+mlkpkbz4tYcHO0unnjtBDctHMedKycMam+2261z+Fwl7+4r4HxpA57DVd3ipDF6L/Vx\nW8ASOPP+RYYFceeKCTy38RyFFU1sP1rc7X3NTrNnPTzEzpyJ3d+vtLnafFLcLu/xKW8p897vcPlM\nYWJWSD99sttP1WN2q/1igBOWSFJ4svln9AB5L4clMSo4qk8norYcLMLRbpwIuGVJRj+9AsPwP8o2\n3XdDFidyq2lpc/Ls+2f5/oML/H7maOvhIu9Z6TtWTLjmh2T2xATe2n0Bl1vn5PkaFkwOjJSpgfTu\n3nzv2IWPXOf/3rOkmDD+/YG5vL07nzd3nae1zcWf3jrNidxqPrFOER46sF+ptg4Xv3/9pDf1JCNl\nFF+7Z1a3ZhYflzyKtMQIiiqb2X2qLCCCJE+qXVpipHc+g/42f3IS4/bmU1DexFu7LrB0+mjvPErd\nlVfSwCsf5ALGCY2PXZ85EE29hN1m5Ut3zOCxZw5Q29jGM++cZUx8xJAu5LDnVDkNLca8XYHYi+Rx\nw9w03j9QSENzO699eL7fU0FF9zldbu9E6nMmJvTbgfrMrHiSYsKoqGtl88GiYRkkuXWdI9mVgJGy\n351jHovFwtr5Y0lPHsXv3zhJfVO7N1B5+LbpAz5+q73Dxa6TZby/v4AKc6w2QFCQTlXkDpqi9uO2\nXyzQEEjz/q2cncoHR0soqGjitQ/zWDA56ZondlscHRzSjG20aFoyViuXBDyVLRVUeMf2XBoM1bXV\nDcbL8rJgIT4sgaTwJNJiUokNiich1KenxycQigmJxWoZ+GNsR7uTTQcLAaPA2YQx/Tt1w4gJkqIj\nQ/jYqkz++p5GcWUz7+8v4ObFGX5rT2ubk3f3GqlSWWnRzJhw7bPo6SmjiI4Ipr65naPZVcM+SKpp\ncLDb7C1ZOiNlQPN0e8JmtXLbsvFMy4jjj28ZPRR7T5eTXVTPQ7dO7VEuck80tXbw65ePkVvcABhn\n4h+5Y0aPehQXT0vhpe255BTVU1HX6tcCIE6XG63A+JGfNn7gxn1YLRbuWpnJ4y8eo765nc2HCnv0\n3W9xOHnyjZO43DohwTYevm3aoFUHjI4I5it3zuAnzx+m3enmt6+c4D8fnD+oPar9xe0zeWxqYgTT\nBiC9sr+EBNu4eVE6f9+SzfnSBo7lVDO7B2d4Rf85V1hHs8OYO6Y/076sFgs3zEvjH1uyyStpILek\nPqAK8vSH86UN1JvpzHN6WN1z0tgY/uvBBfz+jVOcK6zjbEEdP3jmAI/cPr3PE353pam1g22Hi9h8\nqIhG80QKQHxUCOuuG8eCqXF8Z9ffaai7GCBlxUxkTfq6fm9Lb1mtFu5fO4mfvnDYm677qRsno+s6\nDe31XRY3OFZ0ntPB+bSF1LKnsJlv/qFq0NPdIoNGkeTt3Unu8nJyeArxYQnYrXbsdiuxsRHU1jb7\nvRja9iMl3t+HWwbgmH7EBElgzL67+0QZOcX1vLnrAgumJPvtIHHTgUKaWo0fgjuXX7sXCYwf9ZmZ\n8ew4XsqJvGrcbt3vhQMGkic1zGKBmxam+7s5l8lKi+YHn72OFzadY/fJMqobHPzsb4e5eXEG65dm\n9GtPZU2Dg1/88yil1UaFmOumJPH5W6b2+DkWTk3m5e256MC+U2V+HbCcW1zvHWcwbQBS7XxNHx/H\npLExnCus4929BVw/J7VbBVx0XefZ94wCCgCf+ogieRBmc/c1fnQUn/qI4ql3zlDd4ODJN07x9XuH\nXiGHk3nV3s9vX0oMD5br54zhvf1GdcTXduQxMyu+zwUDRM8dOmecZQ8NtjElvX9/J5bNGM1rO/Jo\na3ex5WARmeuHV5B02Hzv7DYr03txUiI6MoRv3jebl7fnsvFAIbWNbfz0hcN8fM1EVs1J7ZfvcFVd\nKxsPFPLh8RLaOy4ecI9NiuSmheOYP/niOFLfccITorNYk77Ob5O767pOU0ejdxJT30pupaM1iupK\n2Zldx89LW6htr7pkItPLeI7Er7JIT3nS3ZK9wY4n1S2Zg8cclJbamJiUxvfvX01kUOClPXdHh9Pl\nPfGWlRqNGtf/J6hHVJBktVj41I2KHzx9gA6nm+ff13j0nlmDvrNuau3g/QPGhp2aEcvk9O6fRZ+d\nlcCO46U0tXaQU1w/YL0W/tbQ0s6HR0sAWDA5adAPTLsrLMTO52+ZyszMeJ59T6O1zcnbuy9w6nwN\nX7h1ar+0u6SqmV/88yi15pwQq+el8fE1E3t1wBYXFcpkc8Dy7lPl3LIkw28Hq6cu1AJgt1mYOMCf\nY4vFwsdWZvLj5w/R0ubkvX0F3LXy2ilzO46XesvnLp2RMqhjz3wtmzma/PJGthwq4kx+LS9ty+W+\n1RP90pbeen+/kRIRHRE8JFKbguw2bl2SwV/f1yisaOKQVjnse+8DjVvXvQf6s7IS+r0HNzzUzrLp\no9lyuIgDZyu4e1VWQFVb7Ksj5nikqRmxvR7DbLdZuW/1RCaMieLpd87S1uHi+Y3nyC1u4FM3qksm\nde+JgvJG3ttXwP4zFbh13Xv71IxYblw4rssU167GCfe35o5mM9ipNP57g59KKlqNIKjSDIpana1X\nXpHnHNxVFumNuNA4M70tmaSwrnt/EsOTiAuNu2K6W0xNLhuK8mmuthFhD4zqhb2x83ipd8jKLUvS\nB+RYZkQFSWCMfbhx4Tg27Mnn5Pka9p+pYOHUrstuD5T39hV4q83csXxCjx47NSMOu82K0+XmWG7V\nsA2SNh8spN3sxvVnWmR3XTclmcwx0fz5bWMm9/OlDfzX0we4f81Els0c3esvb05xPb9+6Zi3O/mu\nlRP46KK+/RgsnpbCmfxaymtauFDWyPjR/ZvD212nzfFIE9Nier2j7YmstGhmZcZzLLeaTQcKWT0v\njZjIKx8QFVc28bdN5wCj8tMDa3s/k3p/uPeGLIoqmtAK69h4oJCMlFEs8lPQ1lMF5Y2cyTeC4hvm\npQXUZMZXs2zmaN7Zm09VvYPXd+Qxb1LisO69DzR5xRfTxeb142TQvlbPT2PL4SJcbp3tR4q5Y0XP\n9smBqrS6mbIao+e2PybSvm5KMqmJkTzx6gnKalrYc6qMwoomvnzndJJju3cyUNd1TufX8t7efO9J\nMgCLxTgZetPCdNJT+nfMpVt3U+uopaq1ksrWCqpazP+tlVR2ulzVWumX+XxsegipUWNIibhyultS\neDIJYYkE2/qeap2aaARGjnYXNQ1txEcHxlCGnnC63LxjDlkZlxTJjAkDU1xnxAVJYFS/2H+mnMo6\nB3/fks30CXGDNneSZ0wEGAMpe5rbGxJsY2pGLMdzqzmWU83d1wfOoMX+0uJwsuVQMWC8R4FYAasr\n8dGh/NvH5/Duvnxe33Getg4XT797luN51Xz6xsk9Lrl9LKeK379+knanG4sFPn3jZFbMGtPnds5T\niTy3UaPD6Wb3yTK/BEnNjg6zYpFx5vBqdF2nw9WB7nO2sbfuXJnJ8dxq2p1u3tp9gU+uU10u197h\n4sk3TtHudGO3WXj4tml9mlixP9htVr50+3Qee/YANQ1tPP3uWUbHR/T7QcVA8KREBNutrAqAgiHd\nZbdZWb90PE+9c4bS6pZ+mWtLdJ+nql2Q3cr0bozb7Y2UuHBmTIjnRF41248Wc8uSjCETxF+NpwfO\ngtEL1x9SEyL43qfn89Q7ZzikVVJU2cRjzxzkoVumXnXMnsvt5uDZSt7dZxTQ8Qi2W1k+awzrFoy9\nZiCL4A4AACAASURBVHVWX22uNqpbq6hsMQMcz5/neksFVa1VVLZWUN1ahUt39el190aILQS7K5og\nVzQR1jhWTFaMjjR7gMKTiAtJ5C+vFuFsiWSRGscjd8wYtLalJVw8piquahqSQdK+0+Xesuk3D2BG\nzIgMkkKCbHxyneKXLx4b9LmT3tmT78277e0Zq1lZCRzPraakqtnvg+8HwrYjxoz3YHz4hxKr1cLN\nizOYmhHHH988RXltK4e0SvJKGvj8zVOY0s2xN7tOlPL0O2dx6zpBdisP3zaNORP750xqmFlmdP+Z\nCvafKefeG7IuG9uk6zou3UW7q50Odzvtrg7zfzsd7o7Lbm9ztRl/TgcOl8PnchttLgdtTuO/53pp\nbT1nQytxWzooLQzhdyVOHE6Hsayrzbzc5r3uEWwNJsgWTLA1iGBbCMG2YIKsQQTbggm2hXgvB1mD\nCTH/B9uCzOshBNmCqB7TSHl1B+dO27kQPp6YiAhCrMGEBYUTbg8nPCiCXUeqOVXbhM0ayp1LphA6\nqpWmDgvh9vBBqdhzJVERwXz5DqOQQ4fTze9ePc73HlzQreqG/lLT4GC/WcZ/6czRATE/V08snp7M\nhr35lNe08MbO832aa0t0n67r3qpf08fHDehJirXz0ziRV01jSwf7z5SzdEb/z1Uz2Dylv7PSovu1\ncmhYiJ1Hbp/O+/sLeXl7Lq1tTn7zynFuWZLO7csuPaZpa3ex43gJGw8Uesd1glEue828NFbNNab1\naHO1UdJUTHVrFVWtVVQ7qqhqraS6tdq4zVFFdWuV9/6G9sGfxBQgyBrkncg0qfMcPuHJl1yPCo7m\nRF4Nv3rpGABLreO4Z+HFk9qHz1VCSxt2jB7rwZQSH47NasHl1imqbGZm5tAqSuN262zYY8yfmRIX\nPmC9zDBCgySA6RMGf+6kmgYH244YPSTzJyf1upTvrMx4njMvH8seXhPLtnW42HjA6GmbPC6GrAGo\nojMYxo+O4j8fnM8LW87w4fFCyhub+NE/t7NiTgprFoxGtzjpcDvpcHfgdHXQ4XbidHfQ7m5n75li\nPjxehNvmIjhIZ/WC0ZzqyOfoyQ6c7ovLdrg76HC1X1zPFe7zXG/33tZOfUsr5RGNuHGy9VkLWF2X\nBUM6fe+5uSrzWLmyqvsPaXe30+5up/nai16dmWV37vhVljFTtT88Bhy7eHOYPcwbTIXbw4kIivBe\nDg8KJ9we4f1v3GcsGxkUSVRwFKOCoxkVPMq8PIrI4FE9CrzGj47i0zcq/rLhDNUNbTz5+km+cd/s\ngC3ksOWQkcpkwSjYMNTYrFZuXzaeP7x5ioq6VnafLOuXHl1xdYUVTd4D6/5IF7uaaePjGB0fTml1\nC5sOFrJkekrAFxa5mtrGNm9PfX+dXPNlsVi4ceE4MlJG8eQbJ2lo6eDt3fnkFNcxaW4JBS3nqboQ\nR25BO/+fvTuPbus+7/z/xkpw30VSpEhJpHS1S5Zt2bJkx7vjek2TtGna5pdJ2yS/NL/peDKd02l6\npk066bTJZNJ2kkzSTJq02TOJt9ppLXmLZVuKZVu75CuSkkhRFElxX7Hj98e9ACFZCxcQAMnP6xwd\nYrkAvuIFwPvc7/N9nqFgP0HnEEHvMC7fGBWVYfy54/zv7j7+2+O99Pn7GAkOp3yMU+V1eu2mpZVU\n5FoNTCtyKy/q52MFP1ZZ6+m8LzY1lrOlqYKDLb3s3n+WWzfVUFNu/WF59bDVG6mkwJv2Sp9ul5Oq\nsjw6e8c4d2H02g/IMm+dvJBIJX1ge8OcpkAv2iAJ0t876ZnXzxCOWKlTj+6ceVWxsiIf9UsKaO8Z\n5WDLwgqSXj18PlH+84Hty61UKzt4CEWCiQN963qIqCOMb9xF3+AQ/pB1cB+OhghGQpPBwrsCiuC7\nAoxQNEgoknxfaPL2aIhwNJx47osCkaRtL70vMcWflM22qwX+bCq975JSvPcdTN3v9yL2MqAx/9U3\nk4tNhCeYCE/Q5+9LyfM5cFDgLaTQU0hRThGFdvB0aUBV5C2iwFtIkbeYwtJCjA0jvH1slENnB/nC\n4+coXnGSRrskbqYqPl1qIhDmZbsAy5ZVFVNeu5Btbly7hGf2nuHchTH+5bXTbF9fvSBSsrJZfBbJ\n5XSkLF3sShwOB3ddX8f3d52kvXuU5o75XRQp3hsJYOvq2f3uApEA/RN99Pv76ff32f/syxN9jKzq\n5Uh7G4PBfl7oGyH4/DARh/1HxcNkAYO4C5e+QuoVeYupzLODntwlVORWJIKfytwlVORVssQOimbb\nyPRaPnRXE0dP9xGOxPjR88089hubGR4LJnod7thYk5ETXHWV+XaQNOtTjmkVi8V45vUzAFQU++a8\npsCiDpLS2TupZ3CCPfaZg+3rq1laMbuKIpubKmjvGeXk2UEmAuEZV65JDkKCkQDBiJU6FYqECEQC\nBCMBAvEAJRIgYP8M2rMNgUggMfMQsB8fjAQJRpOfywpuwok0rXenawXtbfpHx4gUhsAR5rnnIoSi\noWv/J2Recsa85Lp95Ofk4nP5yHHlkOO2fua6c63rLh+5Hh95Ph9j/gkCYeu9aL3vkt9PgUTgPPle\nnkwFzGYxYowEhxkJDtM5dm56D7Yno3f3gKPHhdvpJNedx/ryDZTnVlDqK6U0p4wSXyllvjJKfWWU\n5pRaP31llOSUpGQh8JW8evh8InU2m5vHXovT4eDRnSv52hNH6BsO8MqhTu66vi7Tw1rQ4mtq1tSX\npCVF85YN1fz8l6eYCIR5/s2z8ztIsn93dZX5LEk6MeEP+xnw99Pn72PADnQSlyeSLicCon7GQlOc\naZjD2jtep5fy3Arrn6/8XTM+FbkVdjBUSUVeJTmu7KlQuKQ0j/u2TRYLO9jcS9fAeKKi384MpXbW\n2segnX3jRKLRrM1EuNTh1j7O9ljvyftvqp/z1OdFHSRB+nonPf3qaSLRGC6ng4d3rpgMTiIBa52G\nvR4jcTkaTNw2ud4jQCBq/eyKDmPmnCZKiH+/6wlKilx2EONPBDL+eJATtm4LRPyJwCUeAPkjWTiN\nED+pk9keZQuCx+mx/sXX7ji9eFzWz1jUxYWBEM6Ym9ryIiqKCi5a82P9nNze7fQk1vd4L1nv43V5\nrUDHDnKsy1aQc/HlHH51rJ8f7jqFAwdf/H+3U1F89c/bbBvXxWIxayYwaTYyGAnw1N5mXj54ligh\nfvf+Ro62n2fviXbCBNi8uoBVy/MYC40xHhpnPHzxz7HwOOOhMcZDY0mXx5kIj899muKV/p9ECEUj\nhIJD7D3/2pQfV+AptIKpRABV+q5gavKy9bPYW4LLefWjokg0muiEvqKmiFVznM4817aurqChqpC2\n7hGeef0MOzfVpKUq42J0vm+Mc73WGe6tRnrKrvu8bm7dVMOu/Wd5+2QvfUP+rFzQHovFGA+PM+gf\nYCAwwGBggAH/5M8LY738a/cJgrmjnHSF+OWP/Qza2121ZHUa5bhyKPdVUJFXSbmvPBEAVfgqqMit\ntK9bt1fmVlLgKZzX6Y8Pbl/O60e7GBgJ8KMXmnHZB/ar6ooz1t6kttIq3hCOROkZmEikAWazWCzG\nM3vPAFBc4E3LWq5FGyTFg5RAxM8Dt5fxxR+3MBoN8uVne/ngXQ2JAOLSxej+8MS7Fpb77UXpiYDH\nXqgef8xYYJzuoREiBUGc7gi7fhrCH/bP/mDKPlnSenb2vw+5mANHIhDwON247WDD7fLgcboTQcNF\n9zndiW2s+92JICVxn9PDhD/G4eYBxsZjOHDjwIkz5saJG2fuCL99600U+woSz+txuhNBzuRz2tdd\nk6/xrrE63Vf9wxKLxfjTb/2K7v5x1laW8scPXZeW321zWzsOHFSV5l4zQEoFh8OBx+XB4/KAZ/IP\nwUdvq+b4sb1MBMK88MsIY6PVVFHN6mVF/OcHr59RnnMsFmMiPMF4PHAKjzMWGmU8NM5IcCQxYzQS\nHGE4OMxwcJhR+6d1eeSiy+kIuEZDI4yGRjg70j7lxzhwUJxTfNVgqr/PzYnRQTzOQjZv3MpIcHjO\nU1vmksPh4H23reRv/+8hhsaCvPT2Od570/ydHctmyZXZtl6lalqq3XV9Hbv3nyUai/HigY45rR4b\ni8UYC48x4O+fDHiSfvb7+63Ax76eHAxdc3bcnnjrGgfSUNG60FuEz+VjNDSK2+nC4/Lgwo3b6ea9\nyx/gzoa7EzNAFbkV5HsK5u33wEzkeF385p1NfOOpYxcVsLh5feYKJsTLgAOcuzA2L4Iks32Q1nPW\n+rX7bqzH4577k1QLPkja+s2tjAXGL5qtiQc5Fx2A2O+Pl4fgO4/PwUDiM4JRFuUMydVmKDwuL8Gg\ng97+IA7cNFSWsKS4cHL249JZi6TH5nhyKC7IJxyI4WQyYPC6JoOYeEDhcXkuCjAS111ee7vJ+651\nlny2grdF+OJTuznVMpnqNFZwmP6Kp6jIu3/OG+aBddC3fX0VT+45zTttA/QP+ykrmtszp9FoLNEv\nZ12aF6teKt/n4Z4bl/L0q+2MjVof0IhzjDMlTxCMbsDnnP7vwuFw2IUa8iB3dn8Ao7EoY6HRyYAq\nMMxoyPo5bAdab3XvZ9/513EEinAGygk6Rgi4+nDn+BkPjxOOhmc1hiuJEWMwMMhgYJDTV9vQ/l59\ndR+wD1wOVyIFMHlmqiy3jNrSajwRH/nuQgrtNViFOUWJtVpF3mLczsz+ydq4soym2mJazg3xi31t\n3H7d0oyXhl+I4kFSU10xxVfpZZZqlSW5bFlVwYHmXl452MnDO1Zcc7YwFosxFhplIDDAgL//olmd\nAX9/YrYnHgAlb5ON6eRF3mLKfGX2v3LKcssp9ZVR7rN+lvnKJy/nllOaU4rX5cUf9vPYS5+mdcia\nKYlEojQWr+JzO76QNWskM2ljUxGOgm5io9YamqgjyE+6/zvbw3+bkd9PZXEuXreTYDjKud4xbkj7\nCKYvPouU73Nz+3XpKZ6z4L/dD3QdyPQQ0s7rtMoh+9w5eJ1WmWSf24fXlWOv87D+Wde9idu9Lm9i\ne6/LLpnsnLzstYOb5Md5XF5ynJPP4XF53rWtx+m55ozG5767n/bxUSqKffzVb9w85TzT2aZiZYrX\n46JwxTEujB6naHAn/txTDJe+Ag44NTSVyg6pcfP6ap7cc5oY8KsT3dx/U8Ocvl5b90iiMe76KZZD\nn1OVx4m4wBWxUg/6lzyJf7yF59t2pSVQvRqnw2kXcShiKZfvLRQ/MGkZbKb0wn0UDF8PwIOblvG+\nnU2MhkasM9JJZ6b7/f3W2Wv7snXw1p+4PBgYnLMZrEgsQq9dxncm8tx5dvEKO4iy/10uoEougFFk\nF8DI9+Tjc+fOONhyOBy879YVfOnHBxmdCPH8mx08OM/aFGS7viE/p8+PAKlvIBuJRpiITDARmmAi\nPG4XYZn8OR6aIFp9gbYzJ4lEA/zxc7uprvBY99nbjASHE0FO/DM1VycjZsOFm/I8K4gp8ZUmfpbk\nlFIeD35yyijPnQx+SnNKrRn3GfC5fXzljq/yUsduOgPtLM2p5466exQg2V5o3835ksepGv0EDpyM\nFxzj3MiJjP2tcTod1FTk09Y1Qsc8qHB3qnOY43YD4ntuXJa2k1MLPkhKt0vXZYyNQyjowOXwsqqm\ngnxvLjlua21GjssKXHxJC9bj6ze89mUrQLG298a3c3rJcftwRN184Z8OEYu4ufeGFfzme9bidXkz\n2sdlJo6e7k80mEvHQrxs0Viyimfyn8aff3FQtLI4fQ2Cl5Tk0lRXTEvHEHuPds15kHTsdD9gLYRf\nU3/1JrLp0DbazEDFSUovPMhIyd7EvkhnoDob8QOT59t20dLfSvvbEQYHXDz72llW15axYWU5hd4i\nGoqWT/k5I9EIw8GhxALu5MAqfiZ8MugaTGw3GhqZu/+obTw8znh4nJ7x7lk9j8fpIdedR647F5/b\nR559efK23MnrnlxyXfZ1j3VbpLqb7t4I39l/gNyarZTkFZDrziPHnsF2O9yJ1Nx4mq3H6cHlcC2q\nNKPLiUQjjIXGGAuME4wE8Yfja2WttPbXj3fQ4zpN1BGiO6ebH7+zJ3F/fG2t1XctkFhnG09tD0YC\nVjATencANGGnyk+JnQV8vB2YehbqnPA6vXYQU5YIcpIDn/iMbElOKT09Dn62+zyeWAF/8qHtrEvz\niSif28dDTY/MyxOXc611sJlQTg/9lU+TO76KobIXgcz+ramzg6T5UOEuXtHO53WltWjOgg+S1lSs\nwY0nUSkrXkHLl7juI9dt/cxx53DQHOJcTwBXzMsj21dRX1l6UQDjs7f1uSeDnPjjL50xaT03xBe+\n9xZg1XJ//3saU/7/u6HBz+HWPppPB/HdNT/P2Dxrv/mL8tOzEC9b3N1wL8+eepqWwebEbU12Ced0\numV9NS0dQ3RcGONszyjLlhRc+0EzdPyMFSStWFpIni/zXz+NJat4puBpJgreuej2dAaqs+Vz+6wz\nkY3Qu3qCz3/3TUYnQnzz6WP8+UdvpGKahWhcTldijdF02l2HIiEGAgO0dnfyNz97laBjhDWNXhqX\ney4OsC6ZzcrEYvJQNEQoODS7ppT2eus9/zq9h7kvCpwm1xC6nO5LgirrPndiG7cVZOFI/J1x4ICk\nyw676o3DcYXLOOKbX/Q88UfHiBGNRQhHI0RiESLRMOFYmEj8eixCOBomEg0TiUWty/b9ydtZt8cf\nH7FvDyduvyY7TfPNqdcfyXo+l8+uKFmaCGri6abvui1nMvDJdedOObD+zuET5MbC5PvcGPXztzrf\nQtRYsgqA8aJDjBdNNt7L5N+aePGG7oFxQuFIWtb4zESH3e4G4I6tteT70teQPPNHKXPsxB+emNYZ\njaG1AT77rV8xHgxz4UQ+n7pp5r2THn/lFAC5Oa45K3+7uamCw619dPaO0TM4MSeV+ebSybODnOyw\nDlTu27Ysaz+kcyF5FuDUUAsri5sy0uPmhjVL+MHuk0SiMfYe7WLZnXPzpR0IRmi293VWpNqRPYFq\nqlQU5/Lxh9fxlZ8cYswf5mtPHuVPf2drWj5XHpeHJXlLeK55gLLIOlxOB3921/ZrrnObCE8wEh4i\n6vXT0dvFwPhgYs3VcKLQxXDi8kX3BYYYDg5P7cA7S4SjYTs9Kzsqjcn0xIOdeKGS5FS2xG2XVIks\n8VnBzlyKRmOJA8ktTRXzpqTzYpGNf2vq7OINsRic7xunvqowY2O5mvhaJI/byb03prdYzoIPkqYr\nVb2TTrQNJBao37etfs76PGxuLOd79uVDzfOvseyze9sAyMtxc/uWy6+7WMgSswAZVJDrYVNjOQea\ne9l3vIsP3N44Jx2szbODRKLWWpd0p4FcSbYEqqm0YUU5j966gif2nKata4Qf7G7mo/evSctrj06E\neM3uB7dt7ZIpFQLJdedS6MuntDSfOu/KaafoxCsKvjuQsopcjNjFLkaCw4yHJ/DbzYDjKVjx6+OJ\n1CzrtmxcVC9X5nK4Ej3W4mmTuZ68d6VS5nkuvj758+L79h7uZ9+RftyxHP7rR3ZQX142q/Vsc63l\n3FCiEft1KV7LJbOXjX9r4jNJYFW4y8Ygqbt/nP3v9ABWy57i/Lnr63c52flpz7DZ9k6KxWI8scea\nRSrI9XDPDXMXuJQV+ahfUkB7zyiHWudXkNTWNcKRU1bX6btvqJtxQ1yZvVs2VHOguZfB0SAn2gfm\nZKYnnmrn87pYubQo5c8/U9kQqKbaA7cs51TnMIda+3jlUCeNtUXcumnuqwG9dOAcQTvISVfz2OSK\nglX51Sl73lAkhD8ycXFgddFaFz9P7TU51d2LwxXk7puqcLqsFLSQ3Tw7FA0RstPTJm8LT/G+EJFo\n5KL7IrEIsVi8qEYsUWAjfluM2EWXL3fbpfdfyulw4nK4cDvdOO2fbodr8rLTjdPhxG2n/7kcbvuy\nE5fTPXm7/bjk5/K43BTm5eOIuPE4PInUda+d/v764V66e4OUFRTw23eus1PdvZOp8om1vPFiRJP3\npTp4WVvo553De4nGYhw+HmTdXdl3AJksXhHQ63ayPsOVQ+Xysu1vTUmBl7wcN+OBMB292Vm84Rf7\n2ojFwOV0cH8GWi7oqPQynA4HH3mvwee+s59QOMr3nzN57Dc2Tzkv+MipflrstKL7b66f84P/zU0V\ntPeMYrYPMhEIz5tg49l91iyS1+Pk7jkMJOXaNjVWJL4s9x3tmpMg6ZgdJK2pL100xTkyxelw8PsP\nrePz393PhUE/3991kvolhTRUz92BXigc5cW3OgBY21CalWclpyPeW6vQe+WAfkvhKH/+j28QC0H9\naB0fvmd1GkeYOsmB01wW/rlaNdJxf4hDu1+lIRrjgTUNPLoq9Wt4p6OsyMdWo5I33+lhz+FOHr11\nRdaWe4/FYokgaf2KMjU5lilxOBzUVubT3DGUlcUb+of9vH60C7BO5M51i5LL0ZHKFdRVFiQaBR49\n3c8bJ3qm9LhYLMYT9lqk4nwvd26d+yocW+xme5FojKN29bBsd75vjLfsKdTbt9TOWTqiTI3H7eTG\ntVZn+zdPXiAQSu0aj8HRQOJLeN3yzFe1WwzyfR7+8H0b8bidhMJRvvbEEcb8c5dCtu94F0NjQcBa\nX7gY1C0pSHxuXjxwli+99vc80/o0/rD/Go/MLg6HVcQhk5VRD7X2JdJxrzeyI13snhusv98TgQiv\nHenK8GiurOPCWKJJ6Val2sk0xFPuzmVhGfB//VU7kWgMhwN+bfvcVt69EgVJV/HgLcupLLEi1x+9\n0DylA4y3T16grXsk8fh0nNFpqC5M5GkebJ5Z/5F0+9d97cSwplDTlZYjV7d9vZWqFAhGONB8IaXP\nHU+1A5QKkkb1VYX87r0GAL1Dfr71L8eJxlLfAykWi7Fr/1kAasrz2LCyPOWvka3u314LRIlGHew/\n5Ofv3v4yj7306XkXKGXa26b1nVNelENDlsxCNtUWJ2Zfn3+rY04+O6lwwJ5FcjocbG6aXRNrWVxq\nK6ziDX3DASYC2dPva2gsyCuHOgHYtraKqtK8jIxDQdJV5HhciQOM4bEgP3+59arbR6Mxntxj9Z8v\nL8rhts3p6QjsdDjY1GgdlBw51Uc0mp1f5HF9Q372HrPOyu3YWENpYfo6qsuVNdUVU1FsnRTYe3R2\nfWgudey0VcSktDCH6rLMfNktVjs31fCeLdZ30eHWvkS/iVQ6dro/MVN437Z6nIuoF9CRkT2MFR4G\nIH94K17/UloGm3m+bVeGRzZ/BEKRxPrUrauXZE0vKYfDwd12T5bu/vFEn7dsE0+1W72sWFkZMi3x\nCncA53qzJ+Vu1/52QnZK7gM3Z2YWCRQkXdOGleXctK4KgJcPdibWGl3OGye6E2+yh3aswONO3693\ni332aHQiRGvnLHp/pMG/vTE5hXr/zZpFyhZOh4Ob11vv9WOn+xOpU7MVi8U43mYdXKxfXpY1B0CL\nyYfvXs2KGuuM+FN7TnPUPiBNlefesDpuFuV52G6/hxaL1sFmhkpfJuoI4MBJeff7cUS986YhcTY4\neqo/UfAjW1Lt4ratraIozwo8dr95NsOjebfewQnae6xUKVW1k+m6uMJd5lPu/GE/j594muf2WxMO\nmxpLqZvD3o3XoiBpCj50ZxN5djGEf3ruHcKRd5eoDUeiPPmqtVOXlOayY2PqqixNxbrlZYnF8PFe\nCdloOEumUOXy4il30ViMN46nZjbpXO8YQ6NWwLVuhdYjZYLH7eRTj26kINdDDPjm08foHUxNn56z\nPaMcO2PNFN65tW5R9ToDq0lkxDPEQOWzALjDZZReeIAVRfOnIXGmvX3SWp9alOehqbY4w6O5mMft\n5PbrrPYUR0/18zevfDWr1p0dSEqx37pKQZJMT0Guh+ICa7lGpos3+MN+Hnvp0/zglf1EI9bfkROe\nH2f0s6YgaQrivZOARO+kS71+tIueAeug49GdK9LeyC3H60osiD/UktqzxKm0+82ziSnUX8vgFKpc\nXk15PsvtHPx4SuRsHU9KUVnXoPVImVJe7OPjD6/DAYlGs6Hw7At07LK/Dz1uJ7dvXXy9zu5uuJem\nklWMFx5hrOAQAPmjmygc3ZLhkc0P4UiUgy3xVLvKOenRNlvbN1aAw/qsvHl0NKvWncVT7RqqCikv\nnr/93SRz6ux1SZlOt3u+bRet/WcoGLoJAH9uK6dC+zKauqwgaYpu27w0cYbr6dfO0JN0FjYUjvIv\nr1mzSLUV+Wxbm5l0k/iCzc7esYvGly3G/WFefNsqEbylqYJlGZxClSuLzyad6RrhfN/svzTjswz1\nSwooSnMjOLlYvNEskGg0OxsDIwH22TOOOzZUU5S3+PZvvEnkH239DDdvc5BfYJ0E+skLp+nqH8/w\n6LLfO20DiQXjW7Ms1S5uf9/LjOUfAyB/ZAvOSG5WrDsbGQ9ysmMQgOtWq2CDzEy2VLhrHWwmb3QD\nrqiVYTRcugcgo6nLCpKmKN47yeV0JHonxXtLvHKok77hAACP3royY2fCNjdOVpQ6lIVV7l460MFE\nwDob90CGyjnKtW1bV5VYeD/b2aRQOIp51gqS1qmqXVZ44Jblie+KVw51sudw54yf68W3OxJlm+dT\nI+tUizeJfGzbf+A/vn8bLqeDQCjCN546mpg5l8t7y54Jyctxs6Y+O9NxWwebGSneB4Az5qWo/w4g\nswdvYGWNxAvuKdVOZipe4W54PMRwitYiz0RjySryR6wZ+JCnh4DP6qW5sjhzqcsKkqbhcr2TAqFI\nolpUQ3UhWzN4NqesyEe9PTtzqDW7gqRAKJIoEbymvoTGLMs7l0nF+d5Eme59x7pnVfa29dwQwZB1\nkDgXDWpl+uKNZuPtDb6/6yRtXSPTfp5AMMLLB84B1sxwTXn+NR6xOKyoKeL977HSs9u7R/n5L69e\nFXUxi0ZjifLVm5sqsrbJdGPJKkK+84wVHAGgYPh6PIHqjB68wWSq3ZKSXGor9fmTmcmW4g0bQfNH\nDAAAIABJREFUC28lx28dY48VHgIHNJWs4u6GezM2puz8Rspiyb2TvvvcEf785z9JVAH79dtWZrxy\nVzzlzmwfzKqa93sOdTIybvWZeuCW5ZkdjFzT9g1WymjvkP+qFR2v5ZjdH8ntcrKqToFxtkhFo9lX\nj5xnzG99xyyW5rFTde+2ZWywTzTs2n+Ww63Zu040k1rODTFs/13Itqp2yeLrzobKdxN1BHHgpGbg\n17mr/p6MjSkQjCS+X69bXZHxYw+Zv5ZWTBbQ6sjguqQ3T1jvZ4cjxo6NNfzR1s/wlTu+is+dubV2\nCpKmKcfj4jfvsnL6AwEnPWes9RuO/B6almW+UtuWVVaQFInGOJolPR3CkSj/Zi/uXlFTyLqG7Eyp\nkEnXraokx2tVl5lNyl28iezqZcV409BYWaauvqqQj9w3s0az0WiMXfutz/Ty6kJWLyuZs3HOR06H\ng997cF2idPS3nz3O4Gggw6PKPm/ZDWS9HmdWN5mOrzv79LaPU9tkZWnExio5YA5mbExHT/cnUjmv\nU6qdzILP606c/M9UhbtoNMbrR61jjU0rK/jM9n/Pg40PZzRAAgVJM9Lp3J+Ydo/rLv4FL7TvztCI\nJjVUF1JsL44/mCXrkvYe66LfXrP1wPblOuM1D+R4XNxg99zYf6JnRlXQRidCnDlvpXEp1S477dhY\nw+0zaDR7oPkCFwatyl73bavXZ/oyivO9/P6D6wAYGQ/xf56ZehC6GMRisUTp740ry8nJ8pMo8XVn\nn3v0w1TZDbF/+lJrxjI24ql22Vg2Xeaf2gq7eENvZtLtjp/pZ2DEOk7csbEmI2O4HAVJM9A62Mxg\n+XNEnVYFOX9uK4Hctowv4gTrDOYme1H2kVN9RKOZ/aMcjcb4xT7rjPPSivzETJdkv5s3WLOk44Hw\njNKF3mkbIP7uW6cgKWv91gwazcZnhsuLcrhhjc5iX8mGleW8d5uVY3/8zADP/erd7SMWq7bukUTB\no+vnURNUt8vJb9+zCrD6/j1l90dMp3AkymF73fGWVRVZWTZd5pf4mrZzF8YSRcnS6dUj5wGrb1M2\nHScqSJqBxpJVRN1jXKj+EaNFb9K/5CkgsxU4km2x1yWNToRo7Zz5epLZ8of9/MMrv6DbLoN7z7al\niappkv3W1pdSYjeZi0+DT0c8X74wz8OyKpV7z1bTbTTbcm6I1nPDANx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Qe491cf/NDXRc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cebc96b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lifespan = df[df['LifeSpan'].isnull() == False][['LifeSpan', 'BirthDecade']]\n",
    "avg_lifespan_decade = lifespan['LifeSpan'].groupby(lifespan['BirthDecade']).mean()\n",
    "avg_lifespan_decade = avg_lifespan_decade.dropna().iloc[:-1]  # Removing the last record to prevent skew\n",
    "\n",
    "ax = avg_lifespan_decade.plot(figsize=(10, 5))\n",
    "# Plotting a LOWESS line on top with seaborn's regplot() command\n",
    "sns.regplot(x='BirthDecade', y='LifeSpan', data=avg_lifespan_decade.reset_index(),\n",
    "            lowess=True, color='Green')\n",
    "ax.set_ylim([0,80])\n",
    "ax.set_title('Life Span by Decade')\n",
    "ax.set_ylabel(\"Life Span\")\n",
    "ax.set_xlabel(\"Decade Born\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surprisingly, there isn't a large gradual trend upward like you would expect.  That being said, most charts you see (like the one below) often start in the 20th century, which is where most modern medicine advancements were made (namely the discovery of [penecillin](https://en.wikipedia.org/wiki/Penicillin) in 1928).\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/1/1a/Life-expectancy-us-1900-2011.png\">\n",
    "\n",
    "Note that the dip you see in 1918 is from the [Spanish flu pandemic](https://en.wikipedia.org/wiki/1918_flu_pandemic) that had a devastating effect on the world population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choropleth Maps\n",
    "\n",
    "[Choropleth maps](https://en.wikipedia.org/wiki/Choropleth_map) are just maps with areas shaded in according to the attributes.  They won't directly answer a lot of our original questions, but it's another way to look at the data.  We're going to be using the [chorogrid](https://github.com/Prooffreader/chorogrid) package, and will be borrowing a lot of code from the [blog post](http://prooffreaderplus.blogspot.com/2015/08/a-python-script-to-make-choropleth-grid.html) the creator of the chorogrid package wrote.  We'll specifically be looking at the following:\n",
    "\n",
    "- Birth\n",
    "- Average Life Span\n",
    "- Migratory Patterns (Average Birth Year)\n",
    "- Never Moved Away\n",
    "- Least Desirable (Emigrated From)\n",
    "- Most Desirable (Immigrated To)\n",
    "- Death\n",
    "- Ancestral Countries\n",
    "\n",
    "We're keeping this section separate for two reasons: there is a lot of setup in the functions we have to define, and we want to be able to easily compare the maps.\n",
    "\n",
    "Before the actual maps, we're going to define some functions.  The first section is taken directly from [this blog post](http://bsou.io/posts/color-gradients-with-python) to create color gradients (like the one listed below) for use in the maps.  We're doing this so we don't have to manually define each individual color in the gradients for the maps.\n",
    "<img src=\"http://seaborn.pydata.org/_images/color_palettes_43_0.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def hex_to_RGB(hex):\n",
    "  \"\"\" \n",
    "  \"#FFFFFF\" -> [255,255,255] \n",
    "  \"\"\"\n",
    "  # Pass 16 to the integer function for change of base\n",
    "  return [int(hex[i:i+2], 16) for i in range(1,6,2)]\n",
    "\n",
    "def RGB_to_hex(RGB):\n",
    "  \"\"\" \n",
    "  [255,255,255] -> \"#FFFFFF\" \n",
    "  \"\"\"\n",
    "  # Components need to be integers for hex to make sense\n",
    "  RGB = [int(x) for x in RGB]\n",
    "  return \"#\"+\"\".join([\"0{0:x}\".format(v) if v < 16 else\n",
    "            \"{0:x}\".format(v) for v in RGB])\n",
    "\n",
    "def color_dict(gradient):\n",
    "  \"\"\" \n",
    "  Takes in a list of RGB sub-lists and returns dictionary of\n",
    "  colors in RGB and hex form for use in a graphing function\n",
    "  defined later on \n",
    "  \"\"\"\n",
    "  return {\"hex\":[RGB_to_hex(RGB) for RGB in gradient],\n",
    "      \"r\":[RGB[0] for RGB in gradient],\n",
    "      \"g\":[RGB[1] for RGB in gradient],\n",
    "      \"b\":[RGB[2] for RGB in gradient]}\n",
    "\n",
    "def linear_gradient(start_hex, finish_hex=\"#FFFFFF\", n=10):\n",
    "  \"\"\" \n",
    "  Returns a gradient list of (n) colors between\n",
    "  two hex colors. start_hex and finish_hex\n",
    "  should be the full six-digit color string,\n",
    "  inlcuding the number sign (\"#FFFFFF\") \n",
    "  \"\"\"\n",
    "  # Starting and ending colors in RGB form\n",
    "  s = hex_to_RGB(start_hex)\n",
    "  f = hex_to_RGB(finish_hex)\n",
    "  # Initilize a list of the output colors with the starting color\n",
    "  RGB_list = [s]\n",
    "  # Calcuate a color at each evenly spaced value of t from 1 to n\n",
    "  for t in range(1, n):\n",
    "    # Interpolate RGB vector for color at the current value of t\n",
    "    curr_vector = [\n",
    "      int(s[j] + (float(t)/(n-1))*(f[j]-s[j]))\n",
    "      for j in range(3)\n",
    "    ]\n",
    "    # Add it to our list of output colors\n",
    "    RGB_list.append(curr_vector)\n",
    "\n",
    "  return color_dict(RGB_list)\n",
    "\n",
    "\n",
    "mapGreen = linear_gradient('#bfe0be', '#2a6828', 6)['hex']\n",
    "mapRed = linear_gradient('#e59292', '#5b0505', 6)['hex']\n",
    "mapBlue = linear_gradient('#3ca9f2', '#2c3e50', 6)['hex']\n",
    "mapPurple = linear_gradient('#c295d8', '#5f018e', 6)['hex']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These functions are going to be used to decide how to discretize the values in the map.  We have one that begins at 0 (for maps using percentages), and another that begins at the minimum value (for maps not starting at 0, such as average birth year)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mapLegendScale(column):\n",
    "    \"\"\"\n",
    "    Returns a list of values to discretize the legend in the choropleth maps\n",
    "    \"\"\"\n",
    "    maxVal = column.max()\n",
    "    legend = [int(x) for x in list(np.arange(0, maxVal+1, maxVal/6))]\n",
    "    return legend\n",
    "\n",
    "def mapLegendScaleMinStart(column):\n",
    "    \"\"\"\n",
    "    Returns a list of values to discretize the legend in the choropleth maps\n",
    "    \n",
    "    Starts with the minimum rather than 0\n",
    "    \"\"\"\n",
    "    maxVal = column.max()\n",
    "    minVal = column.min()\n",
    "    legend = [int(x) for x in list(np.arange(minVal, maxVal+1, (maxVal-minVal)/6))]\n",
    "    return legend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our final function will create the actual map.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def createMapUSA(column, colors, title, legendTitle, legendScaleMin=0):\n",
    "    \"\"\"\n",
    "    Creates and plots a choropleth map for the USA based off of the given inputs\n",
    "    \n",
    "    Must come from the usaMap data frame\n",
    "    \"\"\"\n",
    "    # Creating the color bin per the chorogrid documentation\n",
    "    mapBin = Colorbin(usaMap[column].dropna(), colors, proportional=True, decimals=None)\n",
    "    \n",
    "    # Creates the intervals for the legend using the previously defined functions\n",
    "    # Will use the appropriate function given the input\n",
    "    if legendScaleMin == 1:\n",
    "        mapBin.fenceposts = mapLegendScaleMinStart(usaMap[column].dropna())\n",
    "    else:\n",
    "        mapBin.fenceposts = mapLegendScale(usaMap[column].dropna())\n",
    "        if legendScaleMin != 0:\n",
    "            print('Warning: Expecting binary input.  Assuming 0.')\n",
    "    mapBin.recalc(False)\n",
    "    \n",
    "    # Applies colors to the states\n",
    "    statesMap = list(usaMap.dropna(subset=[column]).State)\n",
    "    colors_by_state = mapBin.colors_out\n",
    "    font_colors_by_state = mapBin.complements\n",
    "    legend_colors = mapBin.colors_in\n",
    "    legend_labels = mapBin.labels\n",
    "    \n",
    "    # Creates the actual plot\n",
    "    cg = Chorogrid('usa_states.csv', statesMap, colors_by_state)\n",
    "    cg.set_title(title)\n",
    "    cg.set_legend(mapBin.colors_in, mapBin.labels, title=legendTitle)\n",
    "    cg.draw_map(spacing_dict={'legend_offset': [-275, -200]})\n",
    "    cg.done(show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before plotting the maps, we have to put our data together.  We're going to make a new data frame (usaMap) with all of the data we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>BirthCount</th>\n",
       "      <th>DeathCount</th>\n",
       "      <th>DiedWhereBornPct</th>\n",
       "      <th>AvgYearBorn</th>\n",
       "      <th>AvgLifeSpan</th>\n",
       "      <th>EmigratedPct</th>\n",
       "      <th>ImmigratedCount</th>\n",
       "      <th>BirthPct</th>\n",
       "      <th>DeathPct</th>\n",
       "      <th>ImmigratedPct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1832.777778</td>\n",
       "      <td>56.00</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.472527</td>\n",
       "      <td>1.702128</td>\n",
       "      <td>4.123711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CT</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>62.5</td>\n",
       "      <td>1727.025000</td>\n",
       "      <td>51.25</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.989011</td>\n",
       "      <td>11.914894</td>\n",
       "      <td>3.092784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1675.000000</td>\n",
       "      <td>70.00</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.274725</td>\n",
       "      <td>0.425532</td>\n",
       "      <td>1.030928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1793.857143</td>\n",
       "      <td>62.80</td>\n",
       "      <td>71.428571</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.923077</td>\n",
       "      <td>1.276596</td>\n",
       "      <td>3.092784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1735.000000</td>\n",
       "      <td>41.00</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.274725</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  BirthCount  DeathCount  DiedWhereBornPct  AvgYearBorn  AvgLifeSpan  \\\n",
       "0    AL         9.0         4.0               NaN  1832.777778        56.00   \n",
       "1    CT        40.0        28.0              62.5  1727.025000        51.25   \n",
       "2    DE         1.0         1.0               NaN  1675.000000        70.00   \n",
       "3    GA         7.0         3.0               NaN  1793.857143        62.80   \n",
       "4    IA         1.0         NaN               NaN  1735.000000        41.00   \n",
       "\n",
       "   EmigratedPct  ImmigratedCount   BirthPct   DeathPct  ImmigratedPct  \n",
       "0     66.666667              4.0   2.472527   1.702128       4.123711  \n",
       "1      7.500000              3.0  10.989011  11.914894       3.092784  \n",
       "2    100.000000              1.0   0.274725   0.425532       1.030928  \n",
       "3     71.428571              3.0   1.923077   1.276596       3.092784  \n",
       "4    100.000000              NaN   0.274725        NaN            NaN  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating one data frame for the USA maps from different groupbys\n",
    "birthStates = df['PersonKey'].groupby(df['BirthStateAbbrev']).count()\n",
    "birthStates = birthStates.reset_index()\n",
    "birthStates.columns = ['State', 'BirthCount']  # Renaming for the merge\n",
    "\n",
    "deathStates = df['PersonKey'].groupby(df['DeathStateAbbrev']).count()\n",
    "deathStates = deathStates.reset_index()\n",
    "deathStates.columns = ['State', 'DeathCount']  # Renaming for the merge\n",
    "\n",
    "timeStates = df['BirthYear'].groupby(df['BirthStateAbbrev']).mean()\n",
    "timeStates = timeStates.reset_index()\n",
    "timeStates.columns = ['State', 'AvgYearBorn']  # Renaming for the merge\n",
    "\n",
    "lifeSpanStates = df['LifeSpan'].groupby(df['BirthStateAbbrev']).mean().dropna()\n",
    "lifeSpanStates = lifeSpanStates.reset_index()\n",
    "lifeSpanStates.columns = ['State', 'AvgLifeSpan']  # Renaming for the merge\n",
    "\n",
    "\n",
    "# Gathering a percentage of residents from the state that moved\n",
    "staticStates = df[df['DiedWhereBorn'] == 1]['PersonKey'].groupby(df['BirthStateAbbrev']).count() \\\n",
    "    / df['PersonKey'].groupby(df['BirthStateAbbrev']).count() * 100\n",
    "staticStates = staticStates.reset_index().dropna()\n",
    "staticStates.columns = ['State', 'DiedWhereBornPct']  # Renaming for the merge\n",
    "\n",
    "emigratedFromStates = df[df['DiedWhereBorn'] == 0]['PersonKey'].groupby(df['BirthStateAbbrev']).count() \\\n",
    "    / df['PersonKey'].groupby(df['BirthStateAbbrev']).count() * 100\n",
    "emigratedFromStates = emigratedFromStates.reset_index().dropna()\n",
    "emigratedFromStates.columns = ['State', 'EmigratedPct']\n",
    "\n",
    "immigratedToStates = df[df['DiedWhereBorn'] == 0]['PersonKey'].groupby(df['DeathStateAbbrev']).count()\n",
    "immigratedToStates = immigratedToStates.reset_index().dropna()\n",
    "immigratedToStates.columns = ['State', 'ImmigratedCount']\n",
    "\n",
    "\n",
    "usaMap = pd.merge(birthStates, deathStates, how='outer')\n",
    "usaMap = usaMap.merge(staticStates, how='outer').merge(timeStates, how='outer') \\\n",
    "               .merge(lifeSpanStates, how='outer').merge(emigratedFromStates, how='outer') \\\n",
    "               .merge(immigratedToStates, how='outer')\n",
    "\n",
    "# Applying percentages to normalize our maps\n",
    "usaMap['BirthPct'] = usaMap['BirthCount']/usaMap['BirthCount'].sum() * 100\n",
    "usaMap['DeathPct'] = usaMap['DeathCount']/usaMap['DeathCount'].sum() * 100\n",
    "usaMap['ImmigratedPct'] = usaMap['ImmigratedCount']/usaMap['ImmigratedCount'].sum() * 100\n",
    "\n",
    "usaMap.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start off with the basics - which states were most of my ancestors born in?  This will also be useful since it can be used as a reference for the other maps to compare the magnitudes of things like movement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: The following ids in the csv are not included: {'OR', 'MI', 'NV', 'NE', 'AK', 'IN', 'WI', 'CO', 'DC', 'LA', 'OH', 'UT', 'HI', 'IL', 'CA', 'AR', 'NM', 'ME', 'MN', 'ID', 'SD', 'WV', 'FL', 'ND', 'WY', 'NH', 'MT', 'OK', 'RI', 'AZ', 'KY'}\n"
     ]
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       "5-10</text>\n",
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       "0-5</text>\n",
       "<text id=\"legendtitle\" style=\"stroke-opacity:1;letter-spacing:0px;stroke-linecap:butt;stroke-width:1px;stroke-linejoin:miter;font-style:normal;text-anchor:left;line-height:125%;font-size:12px;font-family:sans-serif;stroke:none;font-weight:normal;word-spacing:0px;fill-opacity:1\" x=\"0\" y=\"0\">\n",
       "Ancestors Born %</text>\n",
       "</g>\n",
       "<text id=\"title\" style=\"stroke-opacity:1;letter-spacing:0px;stroke-linecap:butt;stroke-width:1px;stroke-linejoin:miter;fill:#000000;font-style:normal;text-anchor:middle;line-height:125%;font-size:21px;font-family:sans-serif;stroke:none;font-weight:normal;word-spacing:0px;fill-opacity:1\" x=\"489.5\" y=\"45\">\n",
       "Birth</text>\n",
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "createMapUSA('BirthPct',  # Column to plot\n",
    "             mapGreen,  # Colors to use\n",
    "             'Birth',  # Title\n",
    "             'Ancestors Born %',  # Legend Title\n",
    "             legendScaleMin=0)  # Binary for if the lowest value should be the min value or 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see the heavy numbers in Mississippi here.  Additionally, you can see a lot of very light states with a small amount of people.  Keep these in mind for the rest of our plots.\n",
    "\n",
    "Next is the average life span.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: The following ids in the csv are not included: {'OR', 'MI', 'NV', 'NE', 'MO', 'AK', 'IN', 'WI', 'CO', 'DC', 'LA', 'OH', 'UT', 'HI', 'IL', 'CA', 'AR', 'NM', 'ME', 'MN', 'ID', 'SD', 'WV', 'FL', 'VT', 'ND', 'WY', 'NH', 'KS', 'MT', 'TX', 'OK', 'RI', 'AZ', 'KY'}\n"
     ]
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       "<IPython.core.display.SVG object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "createMapUSA('AvgLifeSpan',  # Column to plot\n",
    "             mapPurple,  # Colors to use\n",
    "             'Average Life Span',  # Title\n",
    "             'Life Span (Yrs.)',  # Legend Title\n",
    "             legendScaleMin=1)  # Binary for if the lowest value should be the min value or 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one was a little skewed in the states with a lower population.  It's interesting to see the coastal states having a higher life span (with the exception of a handful).  For fun, I also included a map with the modern life expectancy from Wikipedia below to compare our plot to.\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/2/2a/US_states_by_life_expectancy.svg/1024px-US_states_by_life_expectancy.svg.png\" style=\"width: 500px\">\n",
    "\n",
    "### Question 5: What was the migratory pattern of my ancestors?\n",
    "\n",
    "Next is the mobility.  This will be a map of the average year of birth per state with the darker shades representing earlier years and the lighter shades representing more recent years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: The following ids in the csv are not included: {'OR', 'MI', 'NV', 'NE', 'AK', 'IN', 'WI', 'CO', 'DC', 'LA', 'OH', 'UT', 'HI', 'IL', 'CA', 'AR', 'NM', 'ME', 'MN', 'ID', 'SD', 'WV', 'FL', 'ND', 'WY', 'NH', 'MT', 'OK', 'RI', 'AZ', 'KY'}\n"
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       "<rect height=\"12.0\" id=\"legendbox5\" style=\"fill:#2c3e50;stroke-width:0.5px;stroke:#303030;fill-rule:evenodd;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\" width=\"12.0\" x=\"0\" y=\"83.5\"/>\n",
       "<text id=\"legendlabel0\" style=\"stroke-opacity:1;letter-spacing:0px;stroke-linecap:butt;stroke-width:1px;stroke-linejoin:miter;font-style:normal;text-anchor:left;line-height:125%;font-size:12px;font-family:sans-serif;stroke:none;font-weight:normal;word-spacing:0px;fill-opacity:1;alignment-baseline:middle\" x=\"16.0\" y=\"22.5\">\n",
       "1936-1989</text>\n",
       "<text id=\"legendlabel1\" style=\"stroke-opacity:1;letter-spacing:0px;stroke-linecap:butt;stroke-width:1px;stroke-linejoin:miter;font-style:normal;text-anchor:left;line-height:125%;font-size:12px;font-family:sans-serif;stroke:none;font-weight:normal;word-spacing:0px;fill-opacity:1;alignment-baseline:middle\" x=\"16.0\" y=\"36.5\">\n",
       "1882-1936</text>\n",
       "<text id=\"legendlabel2\" style=\"stroke-opacity:1;letter-spacing:0px;stroke-linecap:butt;stroke-width:1px;stroke-linejoin:miter;font-style:normal;text-anchor:left;line-height:125%;font-size:12px;font-family:sans-serif;stroke:none;font-weight:normal;word-spacing:0px;fill-opacity:1;alignment-baseline:middle\" x=\"16.0\" y=\"50.5\">\n",
       "1828-1882</text>\n",
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       "1774-1828</text>\n",
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       "1720-1774</text>\n",
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       "1666-1720</text>\n",
       "<text id=\"legendtitle\" style=\"stroke-opacity:1;letter-spacing:0px;stroke-linecap:butt;stroke-width:1px;stroke-linejoin:miter;font-style:normal;text-anchor:left;line-height:125%;font-size:12px;font-family:sans-serif;stroke:none;font-weight:normal;word-spacing:0px;fill-opacity:1\" x=\"0\" y=\"0\">\n",
       "Avg Birth Year</text>\n",
       "</g>\n",
       "<text id=\"title\" style=\"stroke-opacity:1;letter-spacing:0px;stroke-linecap:butt;stroke-width:1px;stroke-linejoin:miter;fill:#000000;font-style:normal;text-anchor:middle;line-height:125%;font-size:21px;font-family:sans-serif;stroke:none;font-weight:normal;word-spacing:0px;fill-opacity:1\" x=\"489.5\" y=\"45\">\n",
       "Migratory Patterns</text>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "createMapUSA('AvgYearBorn',  # Column to plot\n",
    "             mapBlue[::-1],  # Colors to use\n",
    "             'Migratory Patterns',  # Title\n",
    "             'Avg Birth Year',  # Legend Title\n",
    "             legendScaleMin=1)  # Binary for if the lowest value should be the min value or 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is my favorite map because it shows my ancestral migratory patterns.  You can see the spread from the traditional colonies to the rest of the country over time.  Additionally, we can see that some of our coastal states (with the same life span as the south from our earlier map) were some of the earliest settlements.\n",
    "\n",
    "Next up, let's look at the states without movement - those where the person was born and died in the same state:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: The following ids in the csv are not included: {'OR', 'GA', 'MI', 'NE', 'MO', 'IA', 'AK', 'IN', 'WI', 'CO', 'DC', 'LA', 'OH', 'OK', 'UT', 'HI', 'IL', 'PA', 'CA', 'AR', 'NM', 'DE', 'ME', 'MN', 'ID', 'RI', 'SD', 'SC', 'AL', 'WV', 'FL', 'VT', 'NY', 'ND', 'WY', 'NH', 'KS', 'TN', 'MT', 'TX', 'NV', 'NJ', 'AZ', 'KY'}\n"
     ]
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       "0-11</text>\n",
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       "Died Where Born %</text>\n",
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       "Never Moved Away</text>\n",
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       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "createMapUSA('DiedWhereBornPct',  # Column to plot\n",
    "             mapPurple,  # Colors to use\n",
    "             'Never Moved Away',  # Title\n",
    "             'Died Where Born %',  # Legend Title\n",
    "             legendScaleMin=0)  # Binary for if the lowest value should be the min value or 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see a lot of the earlier colonies with a very high percentage listed here - Massachusetts, Connecticut, Maryland, and Virginia.  In addition, you can see that my family started to settle in Mississippi, as well as the recent immigration to Washington (on my father's side). \n",
    "\n",
    "Next are the least desirable states.  These are the states where the person died in a different state than the one they were born in.  Since these are percentages, they should be compared to the birth map to give an idea of the actual magnitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: The following ids in the csv are not included: {'OR', 'MI', 'NV', 'NE', 'AK', 'IN', 'WI', 'CO', 'DC', 'LA', 'OH', 'UT', 'HI', 'IL', 'PA', 'CA', 'AR', 'NM', 'ME', 'MN', 'ID', 'SD', 'WV', 'FL', 'VT', 'ND', 'WY', 'NH', 'WA', 'KS', 'MT', 'TX', 'OK', 'RI', 'AZ', 'KY'}\n"
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       "0-16</text>\n",
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       "Emigrated From %</text>\n",
       "</g>\n",
       "<text id=\"title\" style=\"stroke-opacity:1;letter-spacing:0px;stroke-linecap:butt;stroke-width:1px;stroke-linejoin:miter;fill:#000000;font-style:normal;text-anchor:middle;line-height:125%;font-size:21px;font-family:sans-serif;stroke:none;font-weight:normal;word-spacing:0px;fill-opacity:1\" x=\"489.5\" y=\"45\">\n",
       "Least Desirable States</text>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "createMapUSA('EmigratedPct',  # Column to plot\n",
    "             mapRed,  # Colors to use\n",
    "             'Least Desirable States',  # Title\n",
    "             'Emigrated From %',  # Legend Title\n",
    "             legendScaleMin=0)  # Binary for if the lowest value should be the min value or 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that there was a lot of movement in New Jersey, Delaware, Iowa, Missouri, and the deep south.  I don't know anything about Missouri and Iowa, but our counts were relatively low in the birth map.  The deep south is a similar story, which could mean that they moved to and married into my family in Mississippi.\n",
    "\n",
    "On the other side of the coin, let's examine the states most people moved to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: The following ids in the csv are not included: {'OR', 'MI', 'NE', 'IA', 'AK', 'IN', 'WI', 'CO', 'DC', 'UT', 'HI', 'AR', 'NM', 'ME', 'MN', 'ID', 'SD', 'WV', 'VT', 'NY', 'ND', 'WY', 'NH', 'KS', 'MT', 'NV', 'RI', 'AZ', 'KY'}\n"
     ]
    },
    {
     "data": {
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       "Most Desirable States</text>\n",
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      "text/plain": [
       "<IPython.core.display.SVG object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "createMapUSA('ImmigratedPct',  # Column to plot\n",
    "             mapGreen,  # Colors to use\n",
    "             'Most Desirable States',  # Title\n",
    "             'Immigrated To %',  # Legend Title\n",
    "             legendScaleMin=0)  # Binary for if the lowest value should be the min value or 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mississippi is the clear winner here.\n",
    "\n",
    "Lastly, we'll wrap up the USA maps by completing the circle of life - which states my ancestors passed away in: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: The following ids in the csv are not included: {'OR', 'MI', 'NE', 'IA', 'AK', 'IN', 'WI', 'CO', 'DC', 'UT', 'HI', 'AR', 'NM', 'ME', 'MN', 'ID', 'SD', 'WV', 'VT', 'NY', 'ND', 'WY', 'NH', 'KS', 'MT', 'NV', 'RI', 'AZ', 'KY'}\n"
     ]
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       "Ancestors Died %</text>\n",
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       "<text id=\"title\" style=\"stroke-opacity:1;letter-spacing:0px;stroke-linecap:butt;stroke-width:1px;stroke-linejoin:miter;fill:#000000;font-style:normal;text-anchor:middle;line-height:125%;font-size:21px;font-family:sans-serif;stroke:none;font-weight:normal;word-spacing:0px;fill-opacity:1\" x=\"489.5\" y=\"45\">\n",
       "Death</text>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "createMapUSA('DeathPct',  # Column to plot\n",
    "             mapRed,  # Colors to use\n",
    "             'Death',  # Title\n",
    "             'Ancestors Died %',  # Legend Title\n",
    "             legendScaleMin=0)  # Binary for if the lowest value should be the min value or 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This map may be relatively uninteresting since it very similar to the previous maps we've seen.\n",
    "\n",
    "**For fun** here is a map from [mapchart](www.mapchart.net) that I filled in with places I've been. It can be compared to the ancestral maps.  Since the legend is small in the embedded image, here it is: \n",
    "\n",
    "*Green*: Resided In\n",
    "\n",
    "*Orange*: Visited\n",
    "\n",
    "*Blue*: Traveled Through (on the ground)\n",
    "\n",
    "<img src=\"http://i.imgur.com/81GoIyw.png\", style=\"width: 900px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ancestral Countries\n",
    "\n",
    "Here, I take a closer look at ancestral countries outside of the US.  Since my ancestors who immigrated to the US only came from Europe and Canada, we're going to use a European map.  The [chorogrid](https://github.com/Prooffreader/chorogrid) package has other continents in case you want to re-appropriate this and incorporate other countries.\n",
    "\n",
    "The counts are lower here since most lines didn't have enough data to be traced back to their immigration to the US, and should be taken with a grain of salt. \n",
    "\n",
    "Additionally, I made a manual adjustment by assigning Italy as half of my ancestral home.  This wasn't represented in the data since there were no flags for which side of the family a particular ancestor belonged to, so it would assume full balance between the two if I didn't adjust for it manually. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BirthCountry</th>\n",
       "      <th>BirthCount</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>England</td>\n",
       "      <td>13</td>\n",
       "      <td>GB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>2</td>\n",
       "      <td>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2</td>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Scotland</td>\n",
       "      <td>5</td>\n",
       "      <td>GB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wales</td>\n",
       "      <td>1</td>\n",
       "      <td>GB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BirthCountry  BirthCount Country\n",
       "1      England          13      GB\n",
       "2       France           2      FR\n",
       "3        Italy           2      IT\n",
       "4     Scotland           5      GB\n",
       "6        Wales           1      GB"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euroMap = df['BirthCountry'].groupby(df['BirthCountry']).count()\n",
    "euroMap.name = 'BirthCount'\n",
    "euroMap = euroMap.reset_index()\n",
    "\n",
    "# Removing non-European countries\n",
    "euroMap = euroMap[(euroMap['BirthCountry'] != 'United States') & \n",
    "                    (euroMap['BirthCountry'] != 'Canada')]\n",
    "\n",
    "def countryAbbrev(country):\n",
    "    \"\"\"\n",
    "    Manually converting country names according to chorogrid's European country \n",
    "    name abbreviations due to dictionary containing multiple abbreviations per country\n",
    "    \"\"\"\n",
    "    if country == 'England' or country == 'Scotland' or country == 'Wales':\n",
    "        return 'GB'\n",
    "    if country == 'France':\n",
    "        return 'FR'\n",
    "    if country == 'Italy':\n",
    "        return 'IT'\n",
    "    else:\n",
    "        return np.NaN\n",
    "    \n",
    "euroMap['Country'] = euroMap['BirthCountry'].apply(lambda country: countryAbbrev(country))\n",
    "\n",
    "euroMap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that Scotland, England, and Wales were assigned Great Britain, so now we have to group these again in order to aggregate them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>BirthCount</th>\n",
       "      <th>AncestorPct</th>\n",
       "      <th>AdjustedPct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FR</td>\n",
       "      <td>2</td>\n",
       "      <td>8.695652</td>\n",
       "      <td>4.761905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GB</td>\n",
       "      <td>19</td>\n",
       "      <td>82.608696</td>\n",
       "      <td>45.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IT</td>\n",
       "      <td>2</td>\n",
       "      <td>8.695652</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country  BirthCount  AncestorPct  AdjustedPct\n",
       "0      FR           2     8.695652     4.761905\n",
       "1      GB          19    82.608696    45.238095\n",
       "2      IT           2     8.695652    50.000000"
      ]
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     "execution_count": 46,
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   ],
   "source": [
    "# Re-aggregating after England/Scotland/Wales were assigned GB\n",
    "euroMap = euroMap.groupby('Country').sum().reset_index()\n",
    "\n",
    "euroMap['AncestorPct'] = euroMap['BirthCount']/euroMap['BirthCount'].sum() * 100\n",
    "\n",
    "# Adjusting for under-represented Italian ancestry\n",
    "nonITCount = euroMap[euroMap['Country'] != 'IT']['BirthCount'].sum()\n",
    "euroMap['AdjustedPct'] = np.where(euroMap['Country'] == 'IT', 50,\n",
    "                                  (euroMap['BirthCount']/nonITCount * 100)/2)\n",
    "\n",
    "euroMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "13-27</text>\n",
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       "0-13</text>\n",
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       "% Ancestry</text>\n",
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       "Ancestral Countries</text>\n",
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     "metadata": {},
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   ],
   "source": [
    "mapBin = Colorbin(euroMap['AdjustedPct'], mapGreen, proportional=False, decimals=None)\n",
    "mapBin.fenceposts = mapLegendScale(euroMap['AncestorPct'])\n",
    "mapBin.recalc(False)\n",
    "\n",
    "cg = Chorogrid('europe_countries.csv', euroMap.Country, mapBin.colors_out, 'abbrev')\n",
    "cg.set_title('Ancestral Countries')\n",
    "cg.set_legend(mapBin.colors_in, mapBin.labels, title='% Ancestry')\n",
    "cg.draw_map(spacing_dict={'legend_offset':[-200,-100], 'margin_top': 50})\n",
    "cg.done(show=True)"
   ]
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   "source": [
    "That's about what I would expect with the exception of the lack of Ireland.  Keep in mind this doesn't account for movement within Europe - only what countries in Europe my ancestors immigrated from originally. "
   ]
  },
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   "metadata": {},
   "source": [
    "# To do\n",
    "\n",
    "Since this notebook is already on the long side, here are a couple of notes things for the future:\n",
    "\n",
    "- **Map surnames to country of origin**: Our ancestral countries map was relatively sparse due to missing data, so it would be interesting to see what it looked like if we used surnames as a proxy for countries.  The caveat here is that several records have the wife's name overwritten with the husband's name, so we're still missing data if we try this.\n",
    "- **Get genealogy test to compare to countries of origin**: Maybe after grad school when I have more of a disposable income!\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "# Summary\n",
    "\n",
    "Here's a quick summary with outputs of plots that answer our original questions.  Above is the code that generated them along with commentary and a few tangents.\n",
    "\n",
    "### What were the family names of my ancestors?\n",
    "\n",
    "Including middle names:\n",
    "<img src=\"http://i.imgur.com/uL5FkuZ.png\">\n",
    "\n",
    "<img src=\"http://imgur.com/zZH7Tlg.png\">\n",
    "\n",
    "### Which states contained the most births or deaths?\n",
    "\n",
    "<img src=\"http://i.imgur.com/k0ngPRz.png\">\n",
    "\n",
    "<img src=\"http://i.imgur.com/G7ZQGyD.png\">\n",
    "\n",
    "### How mobile were my ancestors over time?\n",
    "\n",
    "<img src=\"http://i.imgur.com/fEVMDX4.png\">\n",
    "\n",
    "<img src=\"http://i.imgur.com/evyigCT.png\">\n",
    "\n",
    "### How has the average life span changed over time?\n",
    "\n",
    "<img src=\"http://i.imgur.com/LG24tit.png\">\n",
    "\n",
    "### What was the migratory pattern of my ancestors across states?\n",
    "\n",
    "<img src=\"http://imgur.com/62zXFyX.png\">\n",
    "\n",
    "<img src=\"http://i.imgur.com/PqwfXfi.png\">\n",
    "\n",
    "<img src=\"http://i.imgur.com/EBJ2xtX.png\">\n",
    "\n",
    "<img src=\"http://i.imgur.com/tcNlh39.png\">"
   ]
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