scripts/irs_migration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IRS Migration Flows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook pulls data from the IRS Migration database and compares inflows, outflows, and netflows for domestic migration between New York City and counties throughout the US, and the New York Metropolitan Area and metros throughout the US, over of period of several years.\n",
    "\n",
    "Database: https://www.baruch.cuny.edu/confluence/display/geoportal/IRS+Migration+Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modules, Variables, Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import math\n",
    "from functools import reduce\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "from matplotlib import colors\n",
    "import numpy as np\n",
    "import sqlite3\n",
    "import os\n",
    "import pandas.io.formats.format as pf\n",
    "plt.style.use ('seaborn-whitegrid')\n",
    "\n",
    "# display numbers (floats) with thousand separator\n",
    "pd.options.display.float_format = '{:,.0f}'.format\n",
    "\n",
    "# found this hack to format integers for display with thousand separator\n",
    "# https://stackoverflow.com/questions/29663252/format-pandas-integers-for-display?answertab=active#tab-top\n",
    "class IntArrayFormatter(pf.GenericArrayFormatter):\n",
    "    def _format_strings(self):\n",
    "        formatter = self.formatter or '{:,d}'.format\n",
    "        fmt_values = [formatter(x) for x in self.values]\n",
    "        return fmt_values\n",
    "pf.IntArrayFormatter = IntArrayFormatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define paths and variables\n",
    "dir_path = os.getcwd()\n",
    "data_path=os.path.join(dir_path,'source_data')\n",
    "out_path=os.path.join(dir_path,'output')\n",
    "metro_file='counties_metros_2019.csv' # File is generated from metro_county_list.py\n",
    "db='irs_migration_county.sqlite'\n",
    "\n",
    "con = sqlite3.connect (os.path.join (data_path, db))\n",
    "cur=con.cursor()\n",
    "metros = pd.read_csv (os.path.join (data_path, metro_file),\n",
    "                      converters={'ccode': str, 'mcode': str},encoding='latin1')\n",
    "# project years\n",
    "years = ('2011_12', '2012_13', '2013_14', '2014_15', '2015_16', '2016_17', '2017_18')  \n",
    "\n",
    "# NYC counties\n",
    "nyc = ('36005', '36047', '36061', '36081', '36085')\n",
    "\n",
    "# NY Metro Counties\n",
    "nyma=('34003','34013','34017','34019','34023','34025','34027','34029','34031',\\\n",
    "      '34035','34037','34039','36005','36047','36059','36061',\\\n",
    "      '36079','36081','36085','36087','36103','36119','42103')\n",
    "\n",
    "# county fips and city names - counties home to the 10 largest cities in the US, population > 1 million\n",
    "big_cities={'06037':'Los Angeles Co, CA','17031': 'Cook Co, IL (Chicago)','48201': 'Harris Co, TX (Houston)',\n",
    "   '04013': 'Maricopa Co, AZ (Phoenix)','42101': 'Philadelphia Co, PA', '48029': 'Bexar Co, TX (San Antonio)', '06073': 'San Diego Co, CA',\n",
    "  '48113': 'Dallas Co, TX', '06085': 'Santa Clara Co, CA (San Jose)','36005':'New York City, NY','36047':'New York City, NY',\n",
    "       '36061':'New York City, NY','36081':'New York City, NY','36085':'New York City, NY'}\n",
    "\n",
    "# cbsa codes for 14 biggest metros, population > 4 million\n",
    "big_metros=['35620','31080','16980','19100','26420','47900','33100','37980','12060','38060','14460','41860','40140','19820']\n",
    "\n",
    "# codes for foreign migration\n",
    "foreign=('57005','57009','57001','57003','57007')\n",
    "\n",
    "# codes for suppressed flows\n",
    "suppressed=('58000','59000')\n",
    "msuppressed=('0','58','59')\n",
    "\n",
    "# plots colors\n",
    "#wccolors=['#98d669','#73a6dc','#e4e2e1']\n",
    "wccolors=['#59c134','#2250a9','#e4e2e1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uid</th>\n",
       "      <th>st_dest_abbrv</th>\n",
       "      <th>destination</th>\n",
       "      <th>origin</th>\n",
       "      <th>st_orig_abbrv</th>\n",
       "      <th>co_orig_name</th>\n",
       "      <th>returns</th>\n",
       "      <th>exemptions</th>\n",
       "      <th>income</th>\n",
       "      <th>disclosure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01001_01001</td>\n",
       "      <td>AL</td>\n",
       "      <td>01001</td>\n",
       "      <td>01001</td>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga County Non-migrants</td>\n",
       "      <td>17,696</td>\n",
       "      <td>41,757</td>\n",
       "      <td>971,428</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01001_01051</td>\n",
       "      <td>AL</td>\n",
       "      <td>01001</td>\n",
       "      <td>01051</td>\n",
       "      <td>AL</td>\n",
       "      <td>Elmore County</td>\n",
       "      <td>466</td>\n",
       "      <td>1,016</td>\n",
       "      <td>18,398</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01001_01101</td>\n",
       "      <td>AL</td>\n",
       "      <td>01001</td>\n",
       "      <td>01101</td>\n",
       "      <td>AL</td>\n",
       "      <td>Montgomery County</td>\n",
       "      <td>443</td>\n",
       "      <td>982</td>\n",
       "      <td>15,955</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01001_01021</td>\n",
       "      <td>AL</td>\n",
       "      <td>01001</td>\n",
       "      <td>01021</td>\n",
       "      <td>AL</td>\n",
       "      <td>Chilton County</td>\n",
       "      <td>72</td>\n",
       "      <td>192</td>\n",
       "      <td>2,228</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01001_01047</td>\n",
       "      <td>AL</td>\n",
       "      <td>01001</td>\n",
       "      <td>01047</td>\n",
       "      <td>AL</td>\n",
       "      <td>Dallas County</td>\n",
       "      <td>52</td>\n",
       "      <td>126</td>\n",
       "      <td>1,638</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           uid st_dest_abbrv destination origin st_orig_abbrv  \\\n",
       "0  01001_01001            AL       01001  01001            AL   \n",
       "1  01001_01051            AL       01001  01051            AL   \n",
       "2  01001_01101            AL       01001  01101            AL   \n",
       "3  01001_01021            AL       01001  01021            AL   \n",
       "4  01001_01047            AL       01001  01047            AL   \n",
       "\n",
       "                  co_orig_name  returns  exemptions  income  disclosure  \n",
       "0  Autauga County Non-migrants   17,696      41,757 971,428         nan  \n",
       "1                Elmore County      466       1,016  18,398         nan  \n",
       "2            Montgomery County      443         982  15,955         nan  \n",
       "3               Chilton County       72         192   2,228         nan  \n",
       "4                Dallas County       52         126   1,638         nan  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TEST - look at one year of the inflow data in the database\n",
    "inflow_temp = pd.read_sql_query (\"SELECT * from inflow_2011_12\", con)\n",
    "inflow_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_flows_by_city(year, city):\n",
    "    \"\"\" function to read in data from the database\n",
    "    and combine flows for places of ineterest\n",
    "\n",
    "    :rtype: dataframe\"\"\"\n",
    "\n",
    "    # read in inflow & outflow data and store it in a pandas dataframe\n",
    "    table1 = 'outflow_{}'.format (year)\n",
    "    table2 = 'inflow_{}'.format (year)\n",
    "\n",
    "    # SQL query to select \n",
    "    # flows between, but not within counties\n",
    "    df_out = pd.read_sql_query (\"SELECT * from {} where {}.origin!={}.destination\".format (table1, table1, table1), con)\n",
    "    df_in = pd.read_sql_query (\"SELECT * from {} where {}.origin!={}.destination\".format (table2, table2, table2), con)\n",
    "    \n",
    "    # most of the records for inflow will appear in the outflow table as well\n",
    "    # and be same in both tables\n",
    "    # make the uid be same for same records and set it as dataframe's index\n",
    "    df_out['uid'] = df_out.origin + \"_\" + df_out.destination\n",
    "    df_in['uid'] = df_in.origin + \"_\" + df_in.destination\n",
    "\n",
    "    df_in.set_index ('uid', inplace=True)\n",
    "    df_out.set_index ('uid', inplace=True)\n",
    "    \n",
    "    # select a subset of records between and not within selected counties\n",
    "    city_in = df_in[(df_in['destination'].isin (city)) & (~df_in['origin'].isin (city))]\n",
    "    city_out = df_out[(df_out['origin'].isin (city)) & (~df_out['destination'].isin (city))]\n",
    "      \n",
    "    to_city = city_in[['origin', 'co_orig_name', 'exemptions', 'st_orig_abbrv']].groupby (\n",
    "        ['origin', 'co_orig_name', 'st_orig_abbrv']).sum().sort_values ('exemptions', ascending=False).reset_index ()\n",
    "    \n",
    "    from_city = city_out[['destination', 'co_dest_name', 'st_dest_abbrv', 'exemptions']].groupby (\n",
    "        ['destination', 'co_dest_name', 'st_dest_abbrv']).sum().sort_values ('exemptions',\n",
    "                                                                              ascending=False).reset_index ()\n",
    "    \n",
    "    to_city.rename (columns={'origin': 'co_fips', 'co_orig_name': 'co_name', 'exemptions': 'inflow' + year,\n",
    "                             'st_orig_abbrv': 'state'}, inplace=True)\n",
    "    \n",
    "    from_city.rename (columns={'destination': 'co_fips', 'co_dest_name': 'co_name', 'st_dest_abbrv': 'state',\n",
    "                               'exemptions': 'outflow' + year}, inplace=True)\n",
    "\n",
    "    flows_city = to_city.merge (from_city, on=['co_fips', 'co_name', 'state'], how='outer')\n",
    "    flows_city.fillna(0, inplace=True)\n",
    "\n",
    "    # merge metro areas info to selected counties flows\n",
    "    flows_city = flows_city.merge (metros[['mcode', 'mname', 'ccode']], left_on='co_fips', right_on='ccode',how='left').drop('ccode',1)\n",
    "    \n",
    "    # label counties that are not part of the metro areas and are not supressed or foreign as 'non-metro' counties\n",
    "    flows_city.loc[(~flows_city['co_fips'].isin(suppressed)) & (~flows_city['co_fips'].isin(foreign)) & (flows_city['mcode'].isnull()),['mname']]='non-metro'\n",
    "    flows_city.loc[(flows_city['mname']=='non-metro'),['mcode']]='0'\n",
    "    \n",
    "    # label suppressed flows for metro areas so they don't vanish when grouping and summing totals\n",
    "    flows_city.loc[(flows_city['co_fips'].isin(suppressed)) & (~flows_city['co_fips'].isin(foreign)) & (flows_city['mcode'].isnull()),['mcode']]=flows_city['co_fips'].str[0:2]\n",
    "    flows_city.loc[(flows_city['mcode'].isin(['58','59'])),['mname']]=flows_city['co_name']\n",
    "    \n",
    "    # Add state code to county name\n",
    "    flows_city.loc[(~flows_city['co_fips'].isin(suppressed)) & (~flows_city['co_fips'].isin(foreign)),\n",
    "               ['co_name']]=flows_city['co_name']+', '+flows_city['state']\n",
    "\n",
    "    # add calculated columns\n",
    "    flows_city['netflow' + year] = flows_city['inflow' + year] - flows_city['outflow' + year]\n",
    "\n",
    "    return flows_city"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in Data and Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# empty lists to hold dataframes for each year\n",
    "city_flows_dfs = []\n",
    "metro_flows_dfs = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run the functions to get inflow/outflow data for New York city and for NY metro area\n",
    "# for each year and append the results to a list\n",
    "for year in years:\n",
    "    city_flows_dfs.append (get_flows_by_city (year, nyc))\n",
    "\n",
    "for year in years:\n",
    "    metro_flows_dfs.append (get_flows_by_city (year, nyma))\n",
    "\n",
    "# merge all years dfs for NYC (Metro) from the list into a single df\n",
    "city_flows=reduce(lambda x, y: pd.merge(x, y, on = ['co_fips', 'co_name', 'state', 'mcode', 'mname'], how='outer'), city_flows_dfs)\n",
    "metro_flows=reduce(lambda x, y: pd.merge(x, y, on = ['co_fips', 'co_name', 'state', 'mcode', 'mname'], how='outer'), metro_flows_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co_fips</th>\n",
       "      <th>co_name</th>\n",
       "      <th>state</th>\n",
       "      <th>inflow2011_12</th>\n",
       "      <th>outflow2011_12</th>\n",
       "      <th>mcode</th>\n",
       "      <th>mname</th>\n",
       "      <th>netflow2011_12</th>\n",
       "      <th>inflow2012_13</th>\n",
       "      <th>outflow2012_13</th>\n",
       "      <th>...</th>\n",
       "      <th>netflow2014_15</th>\n",
       "      <th>inflow2015_16</th>\n",
       "      <th>outflow2015_16</th>\n",
       "      <th>netflow2015_16</th>\n",
       "      <th>inflow2016_17</th>\n",
       "      <th>outflow2016_17</th>\n",
       "      <th>netflow2016_17</th>\n",
       "      <th>inflow2017_18</th>\n",
       "      <th>outflow2017_18</th>\n",
       "      <th>netflow2017_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36059</td>\n",
       "      <td>Nassau County, NY</td>\n",
       "      <td>NY</td>\n",
       "      <td>13,995</td>\n",
       "      <td>22,803</td>\n",
       "      <td>35620</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
       "      <td>-8,808</td>\n",
       "      <td>14,458</td>\n",
       "      <td>24,494</td>\n",
       "      <td>...</td>\n",
       "      <td>-8,416</td>\n",
       "      <td>12,884</td>\n",
       "      <td>26,345</td>\n",
       "      <td>-13,461</td>\n",
       "      <td>15,446</td>\n",
       "      <td>35,921</td>\n",
       "      <td>-20,475</td>\n",
       "      <td>14,062</td>\n",
       "      <td>30,584</td>\n",
       "      <td>-16,522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59000</td>\n",
       "      <td>Other flows - Different State</td>\n",
       "      <td>DS</td>\n",
       "      <td>13,850</td>\n",
       "      <td>16,808</td>\n",
       "      <td>59</td>\n",
       "      <td>Other flows - Different State</td>\n",
       "      <td>-2,958</td>\n",
       "      <td>14,062</td>\n",
       "      <td>17,381</td>\n",
       "      <td>...</td>\n",
       "      <td>-4,064</td>\n",
       "      <td>19,539</td>\n",
       "      <td>25,100</td>\n",
       "      <td>-5,561</td>\n",
       "      <td>19,978</td>\n",
       "      <td>27,303</td>\n",
       "      <td>-7,325</td>\n",
       "      <td>20,162</td>\n",
       "      <td>23,926</td>\n",
       "      <td>-3,764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36119</td>\n",
       "      <td>Westchester County, NY</td>\n",
       "      <td>NY</td>\n",
       "      <td>11,588</td>\n",
       "      <td>16,967</td>\n",
       "      <td>35620</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
       "      <td>-5,379</td>\n",
       "      <td>10,806</td>\n",
       "      <td>18,414</td>\n",
       "      <td>...</td>\n",
       "      <td>-4,938</td>\n",
       "      <td>9,771</td>\n",
       "      <td>18,833</td>\n",
       "      <td>-9,062</td>\n",
       "      <td>11,157</td>\n",
       "      <td>25,211</td>\n",
       "      <td>-14,054</td>\n",
       "      <td>9,902</td>\n",
       "      <td>19,826</td>\n",
       "      <td>-9,924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36103</td>\n",
       "      <td>Suffolk County, NY</td>\n",
       "      <td>NY</td>\n",
       "      <td>8,838</td>\n",
       "      <td>9,785</td>\n",
       "      <td>35620</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
       "      <td>-947</td>\n",
       "      <td>7,493</td>\n",
       "      <td>9,636</td>\n",
       "      <td>...</td>\n",
       "      <td>-2,334</td>\n",
       "      <td>6,593</td>\n",
       "      <td>9,608</td>\n",
       "      <td>-3,015</td>\n",
       "      <td>7,366</td>\n",
       "      <td>13,228</td>\n",
       "      <td>-5,862</td>\n",
       "      <td>6,907</td>\n",
       "      <td>11,997</td>\n",
       "      <td>-5,090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34017</td>\n",
       "      <td>Hudson County, NJ</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6,308</td>\n",
       "      <td>8,761</td>\n",
       "      <td>35620</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
       "      <td>-2,453</td>\n",
       "      <td>6,270</td>\n",
       "      <td>9,141</td>\n",
       "      <td>...</td>\n",
       "      <td>-2,564</td>\n",
       "      <td>5,282</td>\n",
       "      <td>9,862</td>\n",
       "      <td>-4,580</td>\n",
       "      <td>6,572</td>\n",
       "      <td>13,901</td>\n",
       "      <td>-7,329</td>\n",
       "      <td>5,990</td>\n",
       "      <td>11,474</td>\n",
       "      <td>-5,484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  co_fips                        co_name state  inflow2011_12  outflow2011_12  \\\n",
       "0   36059              Nassau County, NY    NY         13,995          22,803   \n",
       "1   59000  Other flows - Different State    DS         13,850          16,808   \n",
       "2   36119         Westchester County, NY    NY         11,588          16,967   \n",
       "3   36103             Suffolk County, NY    NY          8,838           9,785   \n",
       "4   34017              Hudson County, NJ    NJ          6,308           8,761   \n",
       "\n",
       "   mcode                                  mname  netflow2011_12  \\\n",
       "0  35620  New York-Newark-Jersey City, NY-NJ-PA          -8,808   \n",
       "1     59          Other flows - Different State          -2,958   \n",
       "2  35620  New York-Newark-Jersey City, NY-NJ-PA          -5,379   \n",
       "3  35620  New York-Newark-Jersey City, NY-NJ-PA            -947   \n",
       "4  35620  New York-Newark-Jersey City, NY-NJ-PA          -2,453   \n",
       "\n",
       "   inflow2012_13  outflow2012_13  ...  netflow2014_15  inflow2015_16  \\\n",
       "0         14,458          24,494  ...          -8,416         12,884   \n",
       "1         14,062          17,381  ...          -4,064         19,539   \n",
       "2         10,806          18,414  ...          -4,938          9,771   \n",
       "3          7,493           9,636  ...          -2,334          6,593   \n",
       "4          6,270           9,141  ...          -2,564          5,282   \n",
       "\n",
       "   outflow2015_16  netflow2015_16  inflow2016_17  outflow2016_17  \\\n",
       "0          26,345         -13,461         15,446          35,921   \n",
       "1          25,100          -5,561         19,978          27,303   \n",
       "2          18,833          -9,062         11,157          25,211   \n",
       "3           9,608          -3,015          7,366          13,228   \n",
       "4           9,862          -4,580          6,572          13,901   \n",
       "\n",
       "   netflow2016_17  inflow2017_18  outflow2017_18  netflow2017_18  \n",
       "0         -20,475         14,062          30,584         -16,522  \n",
       "1          -7,325         20,162          23,926          -3,764  \n",
       "2         -14,054          9,902          19,826          -9,924  \n",
       "3          -5,862          6,907          11,997          -5,090  \n",
       "4          -7,329          5,990          11,474          -5,484  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert all flow columns to integers, must fill with zeros first\n",
    "cols_to_int={c: 'int64' for c in city_flows.columns if c[-4].isdigit()}\n",
    "for k in cols_to_int.keys():\n",
    "    city_flows[k].fillna(0, inplace=True)\n",
    "city_flows=city_flows.astype(cols_to_int)\n",
    "city_flows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co_fips</th>\n",
       "      <th>co_name</th>\n",
       "      <th>state</th>\n",
       "      <th>mcode</th>\n",
       "      <th>mname</th>\n",
       "      <th>inflow2011_12</th>\n",
       "      <th>outflow2011_12</th>\n",
       "      <th>netflow2011_12</th>\n",
       "      <th>inflow2012_13</th>\n",
       "      <th>outflow2012_13</th>\n",
       "      <th>...</th>\n",
       "      <th>netflow2014_15</th>\n",
       "      <th>inflow2015_16</th>\n",
       "      <th>outflow2015_16</th>\n",
       "      <th>netflow2015_16</th>\n",
       "      <th>inflow2016_17</th>\n",
       "      <th>outflow2016_17</th>\n",
       "      <th>netflow2016_17</th>\n",
       "      <th>inflow2017_18</th>\n",
       "      <th>outflow2017_18</th>\n",
       "      <th>netflow2017_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36059</td>\n",
       "      <td>Nassau County, NY</td>\n",
       "      <td>NY</td>\n",
       "      <td>35620</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
       "      <td>13,995</td>\n",
       "      <td>22,803</td>\n",
       "      <td>-8,808</td>\n",
       "      <td>14,458</td>\n",
       "      <td>24,494</td>\n",
       "      <td>...</td>\n",
       "      <td>-8,416</td>\n",
       "      <td>12,884</td>\n",
       "      <td>26,345</td>\n",
       "      <td>-13,461</td>\n",
       "      <td>15,446</td>\n",
       "      <td>35,921</td>\n",
       "      <td>-20,475</td>\n",
       "      <td>14,062</td>\n",
       "      <td>30,584</td>\n",
       "      <td>-16,522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59000</td>\n",
       "      <td>Other flows - Different State</td>\n",
       "      <td>DS</td>\n",
       "      <td>59</td>\n",
       "      <td>Other flows - Different State</td>\n",
       "      <td>13,850</td>\n",
       "      <td>16,808</td>\n",
       "      <td>-2,958</td>\n",
       "      <td>14,062</td>\n",
       "      <td>17,381</td>\n",
       "      <td>...</td>\n",
       "      <td>-4,064</td>\n",
       "      <td>19,539</td>\n",
       "      <td>25,100</td>\n",
       "      <td>-5,561</td>\n",
       "      <td>19,978</td>\n",
       "      <td>27,303</td>\n",
       "      <td>-7,325</td>\n",
       "      <td>20,162</td>\n",
       "      <td>23,926</td>\n",
       "      <td>-3,764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36119</td>\n",
       "      <td>Westchester County, NY</td>\n",
       "      <td>NY</td>\n",
       "      <td>35620</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
       "      <td>11,588</td>\n",
       "      <td>16,967</td>\n",
       "      <td>-5,379</td>\n",
       "      <td>10,806</td>\n",
       "      <td>18,414</td>\n",
       "      <td>...</td>\n",
       "      <td>-4,938</td>\n",
       "      <td>9,771</td>\n",
       "      <td>18,833</td>\n",
       "      <td>-9,062</td>\n",
       "      <td>11,157</td>\n",
       "      <td>25,211</td>\n",
       "      <td>-14,054</td>\n",
       "      <td>9,902</td>\n",
       "      <td>19,826</td>\n",
       "      <td>-9,924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36103</td>\n",
       "      <td>Suffolk County, NY</td>\n",
       "      <td>NY</td>\n",
       "      <td>35620</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
       "      <td>8,838</td>\n",
       "      <td>9,785</td>\n",
       "      <td>-947</td>\n",
       "      <td>7,493</td>\n",
       "      <td>9,636</td>\n",
       "      <td>...</td>\n",
       "      <td>-2,334</td>\n",
       "      <td>6,593</td>\n",
       "      <td>9,608</td>\n",
       "      <td>-3,015</td>\n",
       "      <td>7,366</td>\n",
       "      <td>13,228</td>\n",
       "      <td>-5,862</td>\n",
       "      <td>6,907</td>\n",
       "      <td>11,997</td>\n",
       "      <td>-5,090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34017</td>\n",
       "      <td>Hudson County, NJ</td>\n",
       "      <td>NJ</td>\n",
       "      <td>35620</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
       "      <td>6,308</td>\n",
       "      <td>8,761</td>\n",
       "      <td>-2,453</td>\n",
       "      <td>6,270</td>\n",
       "      <td>9,141</td>\n",
       "      <td>...</td>\n",
       "      <td>-2,564</td>\n",
       "      <td>5,282</td>\n",
       "      <td>9,862</td>\n",
       "      <td>-4,580</td>\n",
       "      <td>6,572</td>\n",
       "      <td>13,901</td>\n",
       "      <td>-7,329</td>\n",
       "      <td>5,990</td>\n",
       "      <td>11,474</td>\n",
       "      <td>-5,484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  co_fips                        co_name state  mcode  \\\n",
       "0   36059              Nassau County, NY    NY  35620   \n",
       "1   59000  Other flows - Different State    DS     59   \n",
       "2   36119         Westchester County, NY    NY  35620   \n",
       "3   36103             Suffolk County, NY    NY  35620   \n",
       "4   34017              Hudson County, NJ    NJ  35620   \n",
       "\n",
       "                                   mname  inflow2011_12  outflow2011_12  \\\n",
       "0  New York-Newark-Jersey City, NY-NJ-PA         13,995          22,803   \n",
       "1          Other flows - Different State         13,850          16,808   \n",
       "2  New York-Newark-Jersey City, NY-NJ-PA         11,588          16,967   \n",
       "3  New York-Newark-Jersey City, NY-NJ-PA          8,838           9,785   \n",
       "4  New York-Newark-Jersey City, NY-NJ-PA          6,308           8,761   \n",
       "\n",
       "   netflow2011_12  inflow2012_13  outflow2012_13  ...  netflow2014_15  \\\n",
       "0          -8,808         14,458          24,494  ...          -8,416   \n",
       "1          -2,958         14,062          17,381  ...          -4,064   \n",
       "2          -5,379         10,806          18,414  ...          -4,938   \n",
       "3            -947          7,493           9,636  ...          -2,334   \n",
       "4          -2,453          6,270           9,141  ...          -2,564   \n",
       "\n",
       "   inflow2015_16  outflow2015_16  netflow2015_16  inflow2016_17  \\\n",
       "0         12,884          26,345         -13,461         15,446   \n",
       "1         19,539          25,100          -5,561         19,978   \n",
       "2          9,771          18,833          -9,062         11,157   \n",
       "3          6,593           9,608          -3,015          7,366   \n",
       "4          5,282           9,862          -4,580          6,572   \n",
       "\n",
       "   outflow2016_17  netflow2016_17  inflow2017_18  outflow2017_18  \\\n",
       "0          35,921         -20,475         14,062          30,584   \n",
       "1          27,303          -7,325         20,162          23,926   \n",
       "2          25,211         -14,054          9,902          19,826   \n",
       "3          13,228          -5,862          6,907          11,997   \n",
       "4          13,901          -7,329          5,990          11,474   \n",
       "\n",
       "   netflow2017_18  \n",
       "0         -16,522  \n",
       "1          -3,764  \n",
       "2          -9,924  \n",
       "3          -5,090  \n",
       "4          -5,484  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-order columns so flows are together\n",
    "first_cols=['co_fips','co_name','state','mcode','mname']\n",
    "reorder_cols=first_cols+[c for c in city_flows.columns if c not in first_cols]\n",
    "city_flows=city_flows[reorder_cols]\n",
    "city_flows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co_fips</th>\n",
       "      <th>co_name</th>\n",
       "      <th>state</th>\n",
       "      <th>inflow2011_12</th>\n",
       "      <th>outflow2011_12</th>\n",
       "      <th>mcode</th>\n",
       "      <th>mname</th>\n",
       "      <th>netflow2011_12</th>\n",
       "      <th>inflow2012_13</th>\n",
       "      <th>outflow2012_13</th>\n",
       "      <th>...</th>\n",
       "      <th>netflow2014_15</th>\n",
       "      <th>inflow2015_16</th>\n",
       "      <th>outflow2015_16</th>\n",
       "      <th>netflow2015_16</th>\n",
       "      <th>inflow2016_17</th>\n",
       "      <th>outflow2016_17</th>\n",
       "      <th>netflow2016_17</th>\n",
       "      <th>inflow2017_18</th>\n",
       "      <th>outflow2017_18</th>\n",
       "      <th>netflow2017_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59000</td>\n",
       "      <td>Other flows - Different State</td>\n",
       "      <td>DS</td>\n",
       "      <td>48,254</td>\n",
       "      <td>59,083</td>\n",
       "      <td>59</td>\n",
       "      <td>Other flows - Different State</td>\n",
       "      <td>-10,829</td>\n",
       "      <td>49,137</td>\n",
       "      <td>60,188</td>\n",
       "      <td>...</td>\n",
       "      <td>-17,940</td>\n",
       "      <td>65,648</td>\n",
       "      <td>89,912</td>\n",
       "      <td>-24,264</td>\n",
       "      <td>74,619</td>\n",
       "      <td>100,396</td>\n",
       "      <td>-25,777</td>\n",
       "      <td>68,261</td>\n",
       "      <td>87,702</td>\n",
       "      <td>-19,441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09001</td>\n",
       "      <td>Fairfield County, CT</td>\n",
       "      <td>CT</td>\n",
       "      <td>6,853</td>\n",
       "      <td>11,130</td>\n",
       "      <td>14860</td>\n",
       "      <td>Bridgeport-Stamford-Norwalk, CT</td>\n",
       "      <td>-4,277</td>\n",
       "      <td>7,374</td>\n",
       "      <td>12,111</td>\n",
       "      <td>...</td>\n",
       "      <td>-3,668</td>\n",
       "      <td>7,155</td>\n",
       "      <td>12,330</td>\n",
       "      <td>-5,175</td>\n",
       "      <td>8,309</td>\n",
       "      <td>17,577</td>\n",
       "      <td>-9,268</td>\n",
       "      <td>7,591</td>\n",
       "      <td>14,167</td>\n",
       "      <td>-6,576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade County, FL</td>\n",
       "      <td>FL</td>\n",
       "      <td>6,829</td>\n",
       "      <td>7,676</td>\n",
       "      <td>33100</td>\n",
       "      <td>Miami-Fort Lauderdale-Pompano Beach, FL</td>\n",
       "      <td>-847</td>\n",
       "      <td>6,642</td>\n",
       "      <td>6,784</td>\n",
       "      <td>...</td>\n",
       "      <td>-1,775</td>\n",
       "      <td>3,670</td>\n",
       "      <td>6,475</td>\n",
       "      <td>-2,805</td>\n",
       "      <td>4,741</td>\n",
       "      <td>7,223</td>\n",
       "      <td>-2,482</td>\n",
       "      <td>3,970</td>\n",
       "      <td>5,981</td>\n",
       "      <td>-2,011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06037</td>\n",
       "      <td>Los Angeles County, CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>6,707</td>\n",
       "      <td>9,010</td>\n",
       "      <td>31080</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA</td>\n",
       "      <td>-2,303</td>\n",
       "      <td>6,779</td>\n",
       "      <td>10,857</td>\n",
       "      <td>...</td>\n",
       "      <td>-2,966</td>\n",
       "      <td>6,728</td>\n",
       "      <td>11,929</td>\n",
       "      <td>-5,201</td>\n",
       "      <td>8,276</td>\n",
       "      <td>15,286</td>\n",
       "      <td>-7,010</td>\n",
       "      <td>6,797</td>\n",
       "      <td>12,297</td>\n",
       "      <td>-5,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12011</td>\n",
       "      <td>Broward County, FL</td>\n",
       "      <td>FL</td>\n",
       "      <td>5,828</td>\n",
       "      <td>7,570</td>\n",
       "      <td>33100</td>\n",
       "      <td>Miami-Fort Lauderdale-Pompano Beach, FL</td>\n",
       "      <td>-1,742</td>\n",
       "      <td>5,773</td>\n",
       "      <td>7,545</td>\n",
       "      <td>...</td>\n",
       "      <td>-2,473</td>\n",
       "      <td>3,378</td>\n",
       "      <td>7,723</td>\n",
       "      <td>-4,345</td>\n",
       "      <td>4,350</td>\n",
       "      <td>9,102</td>\n",
       "      <td>-4,752</td>\n",
       "      <td>3,834</td>\n",
       "      <td>7,315</td>\n",
       "      <td>-3,481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  co_fips                        co_name state  inflow2011_12  outflow2011_12  \\\n",
       "0   59000  Other flows - Different State    DS         48,254          59,083   \n",
       "1   09001           Fairfield County, CT    CT          6,853          11,130   \n",
       "2   12086          Miami-Dade County, FL    FL          6,829           7,676   \n",
       "3   06037         Los Angeles County, CA    CA          6,707           9,010   \n",
       "4   12011             Broward County, FL    FL          5,828           7,570   \n",
       "\n",
       "   mcode                                    mname  netflow2011_12  \\\n",
       "0     59            Other flows - Different State         -10,829   \n",
       "1  14860          Bridgeport-Stamford-Norwalk, CT          -4,277   \n",
       "2  33100  Miami-Fort Lauderdale-Pompano Beach, FL            -847   \n",
       "3  31080       Los Angeles-Long Beach-Anaheim, CA          -2,303   \n",
       "4  33100  Miami-Fort Lauderdale-Pompano Beach, FL          -1,742   \n",
       "\n",
       "   inflow2012_13  outflow2012_13  ...  netflow2014_15  inflow2015_16  \\\n",
       "0         49,137          60,188  ...         -17,940         65,648   \n",
       "1          7,374          12,111  ...          -3,668          7,155   \n",
       "2          6,642           6,784  ...          -1,775          3,670   \n",
       "3          6,779          10,857  ...          -2,966          6,728   \n",
       "4          5,773           7,545  ...          -2,473          3,378   \n",
       "\n",
       "   outflow2015_16  netflow2015_16  inflow2016_17  outflow2016_17  \\\n",
       "0          89,912         -24,264         74,619         100,396   \n",
       "1          12,330          -5,175          8,309          17,577   \n",
       "2           6,475          -2,805          4,741           7,223   \n",
       "3          11,929          -5,201          8,276          15,286   \n",
       "4           7,723          -4,345          4,350           9,102   \n",
       "\n",
       "   netflow2016_17  inflow2017_18  outflow2017_18  netflow2017_18  \n",
       "0         -25,777         68,261          87,702         -19,441  \n",
       "1          -9,268          7,591          14,167          -6,576  \n",
       "2          -2,482          3,970           5,981          -2,011  \n",
       "3          -7,010          6,797          12,297          -5,500  \n",
       "4          -4,752          3,834           7,315          -3,481  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ungrouped county-level flows for metro areas\n",
    "for k in cols_to_int.keys():\n",
    "    metro_flows[k].fillna(0, inplace=True)\n",
    "metro_flows=metro_flows.astype(cols_to_int)\n",
    "metro_flows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get subsets of domestic and foreign flows separately\n",
    "domestic_city_flows=city_flows[~city_flows['co_fips'].isin (foreign)].set_index('co_fips').copy()\n",
    "foreign_city_flows=city_flows[city_flows['co_fips'].isin (foreign)].set_index('co_fips').copy()\n",
    "\n",
    "domestic_metro_flows=metro_flows[~metro_flows['co_fips'].isin (foreign)].copy()\n",
    "foreign_metro_flows=metro_flows[metro_flows['co_fips'].isin (foreign)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mname</th>\n",
       "      <th>inflow2011_12</th>\n",
       "      <th>outflow2011_12</th>\n",
       "      <th>netflow2011_12</th>\n",
       "      <th>inflow2012_13</th>\n",
       "      <th>outflow2012_13</th>\n",
       "      <th>netflow2012_13</th>\n",
       "      <th>inflow2013_14</th>\n",
       "      <th>outflow2013_14</th>\n",
       "      <th>netflow2013_14</th>\n",
       "      <th>...</th>\n",
       "      <th>netflow2014_15</th>\n",
       "      <th>inflow2015_16</th>\n",
       "      <th>outflow2015_16</th>\n",
       "      <th>netflow2015_16</th>\n",
       "      <th>inflow2016_17</th>\n",
       "      <th>outflow2016_17</th>\n",
       "      <th>netflow2016_17</th>\n",
       "      <th>inflow2017_18</th>\n",
       "      <th>outflow2017_18</th>\n",
       "      <th>netflow2017_18</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>non-metro</td>\n",
       "      <td>1,633</td>\n",
       "      <td>2,471</td>\n",
       "      <td>-838</td>\n",
       "      <td>1,452</td>\n",
       "      <td>2,426</td>\n",
       "      <td>-974</td>\n",
       "      <td>1,111</td>\n",
       "      <td>1,556</td>\n",
       "      <td>-445</td>\n",
       "      <td>...</td>\n",
       "      <td>-372</td>\n",
       "      <td>850</td>\n",
       "      <td>2,115</td>\n",
       "      <td>-1,265</td>\n",
       "      <td>1,340</td>\n",
       "      <td>3,125</td>\n",
       "      <td>-1,785</td>\n",
       "      <td>1,209</td>\n",
       "      <td>2,181</td>\n",
       "      <td>-972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10420</th>\n",
       "      <td>Akron, OH</td>\n",
       "      <td>88</td>\n",
       "      <td>97</td>\n",
       "      <td>-9</td>\n",
       "      <td>134</td>\n",
       "      <td>106</td>\n",
       "      <td>28</td>\n",
       "      <td>60</td>\n",
       "      <td>36</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>67</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>42</td>\n",
       "      <td>-2</td>\n",
       "      <td>58</td>\n",
       "      <td>79</td>\n",
       "      <td>-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10500</th>\n",
       "      <td>Albany, GA</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10580</th>\n",
       "      <td>Albany-Schenectady-Troy, NY</td>\n",
       "      <td>2,102</td>\n",
       "      <td>3,147</td>\n",
       "      <td>-1,045</td>\n",
       "      <td>2,186</td>\n",
       "      <td>3,244</td>\n",
       "      <td>-1,058</td>\n",
       "      <td>1,661</td>\n",
       "      <td>2,722</td>\n",
       "      <td>-1,061</td>\n",
       "      <td>...</td>\n",
       "      <td>-865</td>\n",
       "      <td>1,648</td>\n",
       "      <td>3,167</td>\n",
       "      <td>-1,519</td>\n",
       "      <td>2,063</td>\n",
       "      <td>4,594</td>\n",
       "      <td>-2,531</td>\n",
       "      <td>1,739</td>\n",
       "      <td>3,480</td>\n",
       "      <td>-1,741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10740</th>\n",
       "      <td>Albuquerque, NM</td>\n",
       "      <td>149</td>\n",
       "      <td>132</td>\n",
       "      <td>17</td>\n",
       "      <td>104</td>\n",
       "      <td>157</td>\n",
       "      <td>-53</td>\n",
       "      <td>107</td>\n",
       "      <td>79</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>67</td>\n",
       "      <td>56</td>\n",
       "      <td>88</td>\n",
       "      <td>-32</td>\n",
       "      <td>76</td>\n",
       "      <td>177</td>\n",
       "      <td>-101</td>\n",
       "      <td>70</td>\n",
       "      <td>137</td>\n",
       "      <td>-67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mname  inflow2011_12  outflow2011_12  \\\n",
       "mcode                                                               \n",
       "0                        non-metro          1,633           2,471   \n",
       "10420                    Akron, OH             88              97   \n",
       "10500                   Albany, GA             24               0   \n",
       "10580  Albany-Schenectady-Troy, NY          2,102           3,147   \n",
       "10740              Albuquerque, NM            149             132   \n",
       "\n",
       "       netflow2011_12  inflow2012_13  outflow2012_13  netflow2012_13  \\\n",
       "mcode                                                                  \n",
       "0                -838          1,452           2,426            -974   \n",
       "10420              -9            134             106              28   \n",
       "10500              24              0               0               0   \n",
       "10580          -1,045          2,186           3,244          -1,058   \n",
       "10740              17            104             157             -53   \n",
       "\n",
       "       inflow2013_14  outflow2013_14  netflow2013_14  ...  netflow2014_15  \\\n",
       "mcode                                                 ...                   \n",
       "0              1,111           1,556            -445  ...            -372   \n",
       "10420             60              36              24  ...              67   \n",
       "10500              0               0               0  ...               0   \n",
       "10580          1,661           2,722          -1,061  ...            -865   \n",
       "10740            107              79              28  ...              67   \n",
       "\n",
       "       inflow2015_16  outflow2015_16  netflow2015_16  inflow2016_17  \\\n",
       "mcode                                                                 \n",
       "0                850           2,115          -1,265          1,340   \n",
       "10420             31               0              31             40   \n",
       "10500              0               0               0              0   \n",
       "10580          1,648           3,167          -1,519          2,063   \n",
       "10740             56              88             -32             76   \n",
       "\n",
       "       outflow2016_17  netflow2016_17  inflow2017_18  outflow2017_18  \\\n",
       "mcode                                                                  \n",
       "0               3,125          -1,785          1,209           2,181   \n",
       "10420              42              -2             58              79   \n",
       "10500               0               0              0               0   \n",
       "10580           4,594          -2,531          1,739           3,480   \n",
       "10740             177            -101             70             137   \n",
       "\n",
       "       netflow2017_18  \n",
       "mcode                  \n",
       "0                -972  \n",
       "10420             -21  \n",
       "10500               0  \n",
       "10580          -1,741  \n",
       "10740             -67  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group NYMA county-level flows by metro area to get metro-level flows for NYMA\n",
    "grouped_by_metro=domestic_metro_flows.groupby(['mcode','mname']).sum().reset_index()\n",
    "grouped_by_metro=grouped_by_metro.set_index('mcode')\n",
    "grouped_by_metro.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write Output to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add cummulative numbers for inflow, outflow, and netflow for city and metro \n",
    "# these are totals for all years\n",
    "domestic_city_flows['tot_inflow']=domestic_city_flows[[c for c in domestic_city_flows.columns if \n",
    "                                                       c.startswith('inflow')]].sum(axis=1)\n",
    "\n",
    "domestic_city_flows['tot_outflow']=domestic_city_flows[[c for c in domestic_city_flows.columns if \n",
    "                                                       c.startswith('outflow')]].sum(axis=1)\n",
    "\n",
    "domestic_city_flows['tot_netflow']=domestic_city_flows['tot_inflow']-domestic_city_flows['tot_outflow']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_by_metro['tot_inflow']=grouped_by_metro[[c for c in grouped_by_metro.columns if \n",
    "                                                       c.startswith('inflow')]].sum(axis=1)\n",
    "\n",
    "grouped_by_metro['tot_outflow']=grouped_by_metro[[c for c in grouped_by_metro.columns if \n",
    "                                                       c.startswith('outflow')]].sum(axis=1)\n",
    "\n",
    "grouped_by_metro['tot_netflow']=grouped_by_metro['tot_inflow']-grouped_by_metro['tot_outflow']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write the resulting data out to use for mapping in QGIS \n",
    "domestic_city_flows.to_csv(os.path.join(out_path,'data','irs_mig_flows_nyc.csv'))\n",
    "grouped_by_metro.to_csv(os.path.join(out_path,'data','irs_mig_flows_nyma.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Summary Tables and Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_total_flows(df):\n",
    "    inflow_cols=[c for c in df if c.startswith('inflow')]\n",
    "    in_mig=pd.DataFrame(df[inflow_cols].sum(axis=0), columns=['in_migration']).reset_index().rename(columns={'index':'years'})\n",
    "    in_mig['years']=in_mig.years.apply(lambda x :x[-7:])\n",
    "    outflow_cols=[c for c in df if c.startswith('outflow')]\n",
    "    out_mig=pd.DataFrame(df[outflow_cols].sum(axis=0), columns=['out_migration']).reset_index().rename(columns={'index':'years'})\n",
    "    out_mig['years']=out_mig.years.apply(lambda x :x[-7:])\n",
    "    df_total=pd.merge(in_mig,out_mig, on='years')\n",
    "    df_total['net_migration']=df_total['in_migration']-df_total['out_migration']\n",
    "    df_total.set_index('years',inplace=True)  \n",
    "    return df_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>in_migration</th>\n",
       "      <th>out_migration</th>\n",
       "      <th>net_migration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>years</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011_12</th>\n",
       "      <td>180,459</td>\n",
       "      <td>245,886</td>\n",
       "      <td>-65,427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012_13</th>\n",
       "      <td>173,813</td>\n",
       "      <td>257,056</td>\n",
       "      <td>-83,243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013_14</th>\n",
       "      <td>161,752</td>\n",
       "      <td>247,407</td>\n",
       "      <td>-85,655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014_15</th>\n",
       "      <td>140,397</td>\n",
       "      <td>202,264</td>\n",
       "      <td>-61,867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015_16</th>\n",
       "      <td>152,431</td>\n",
       "      <td>265,891</td>\n",
       "      <td>-113,460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016_17</th>\n",
       "      <td>178,653</td>\n",
       "      <td>361,137</td>\n",
       "      <td>-182,484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017_18</th>\n",
       "      <td>163,013</td>\n",
       "      <td>294,215</td>\n",
       "      <td>-131,202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         in_migration  out_migration  net_migration\n",
       "years                                              \n",
       "2011_12       180,459        245,886        -65,427\n",
       "2012_13       173,813        257,056        -83,243\n",
       "2013_14       161,752        247,407        -85,655\n",
       "2014_15       140,397        202,264        -61,867\n",
       "2015_16       152,431        265,891       -113,460\n",
       "2016_17       178,653        361,137       -182,484\n",
       "2017_18       163,013        294,215       -131,202"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# annual domestic migration for NYC by year\n",
    "total_city_dom=get_total_flows(domestic_city_flows)\n",
    "total_city_dom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>in_migration</th>\n",
       "      <th>out_migration</th>\n",
       "      <th>net_migration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>years</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011_12</th>\n",
       "      <td>246,580</td>\n",
       "      <td>343,076</td>\n",
       "      <td>-96,496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012_13</th>\n",
       "      <td>243,954</td>\n",
       "      <td>357,930</td>\n",
       "      <td>-113,976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013_14</th>\n",
       "      <td>219,767</td>\n",
       "      <td>348,356</td>\n",
       "      <td>-128,589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014_15</th>\n",
       "      <td>180,001</td>\n",
       "      <td>275,697</td>\n",
       "      <td>-95,696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015_16</th>\n",
       "      <td>211,775</td>\n",
       "      <td>374,922</td>\n",
       "      <td>-163,147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016_17</th>\n",
       "      <td>265,444</td>\n",
       "      <td>503,905</td>\n",
       "      <td>-238,461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017_18</th>\n",
       "      <td>225,293</td>\n",
       "      <td>397,116</td>\n",
       "      <td>-171,823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         in_migration  out_migration  net_migration\n",
       "years                                              \n",
       "2011_12       246,580        343,076        -96,496\n",
       "2012_13       243,954        357,930       -113,976\n",
       "2013_14       219,767        348,356       -128,589\n",
       "2014_15       180,001        275,697        -95,696\n",
       "2015_16       211,775        374,922       -163,147\n",
       "2016_17       265,444        503,905       -238,461\n",
       "2017_18       225,293        397,116       -171,823"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# annual domestic migration for NYMA by year\n",
    "total_metro_dom=get_total_flows(grouped_by_metro)\n",
    "total_metro_dom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def roundup(x):\n",
    "    return int(math.ceil(x / 100000.0)) * 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rounddown(x):\n",
    "    return int(math.floor(x / 100000.0)) * 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the max migration number for either nyma or nyc for consistent scale on yaxis\n",
    "# round up to next 100,000 \n",
    "y_limt=max(max(total_metro_dom.max()), max(total_city_dom.max()))\n",
    "y_limt=roundup(y_limt)\n",
    "y_limb=min(min(total_metro_dom.min()), min(total_city_dom.min()))\n",
    "y_limb=rounddown(y_limb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600000 -300000\n"
     ]
    }
   ],
   "source": [
    "print(y_limt,y_limb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# year by year plots\n",
    "def plot_migration(df, colors):\n",
    "    df=df.reset_index()\n",
    "    df.years=df.years.apply(lambda x: x.replace('_','-'))\n",
    "    df.set_index('years',inplace=True)\n",
    "    ax=df[['in_migration','out_migration']].plot(kind='bar', rot=0, legend=False, color=colors)\n",
    "    df['net_migration'].plot(kind='bar', ax=ax, rot=0, color=colors[2], width=0.2)\n",
    "    ax.legend (['Inflows','Outflows','Net Flow'],loc=8, bbox_to_anchor=(0.5, -0.3),ncol=3, fontsize=12, frameon=False)\n",
    "    ax.set_xlabel('')\n",
    "    ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}\".format(int(x))))\n",
    "    # set ylim to be consistent on the plots for easier comparisons\n",
    "    ax.set_ylim(top=y_limt,bottom=y_limb)\n",
    "    ax.yaxis.set_major_locator(plt.MaxNLocator(5))\n",
    "    # ticks labels size\n",
    "    plt.xticks(fontsize=12)\n",
    "    plt.yticks(fontsize=12)\n",
    "    plt.show()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56cdf4e7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56d0bfa5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot each\n",
    "pl1=plot_migration(total_city_dom, wccolors)\n",
    "f=pl1.get_figure()\n",
    "f.savefig(os.path.join(out_path, 'images','nyc_flows_yrly.png'),bbox_inches='tight')\n",
    "\n",
    "pl2=plot_migration(total_metro_dom, wccolors)\n",
    "f2=pl2.get_figure()\n",
    "f2.savefig(os.path.join(out_path, 'images','nyma_flows_yrly.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domestic_mig_nyc</th>\n",
       "      <th>domestic_mig_nyma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>in_migration</th>\n",
       "      <td>1,150,518</td>\n",
       "      <td>1,592,814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>out_migration</th>\n",
       "      <td>1,873,856</td>\n",
       "      <td>2,601,002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>net_migration</th>\n",
       "      <td>-723,338</td>\n",
       "      <td>-1,008,188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               domestic_mig_nyc  domestic_mig_nyma\n",
       "in_migration          1,150,518          1,592,814\n",
       "out_migration         1,873,856          2,601,002\n",
       "net_migration          -723,338         -1,008,188"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# put cummulative in one table \n",
    "cum_mig=pd.concat([total_city_dom.sum(),total_metro_dom.sum()], axis=1)\n",
    "cum_mig.columns=['domestic_mig_nyc','domestic_mig_nyma']\n",
    "cum_mig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>In Flows</th>\n",
       "      <th>Out Flows</th>\n",
       "      <th>Net Flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New York City</th>\n",
       "      <td>1,150,518</td>\n",
       "      <td>1,873,856</td>\n",
       "      <td>-723,338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York Metro</th>\n",
       "      <td>1,592,814</td>\n",
       "      <td>2,601,002</td>\n",
       "      <td>-1,008,188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                In Flows  Out Flows   Net Flow\n",
       "New York City  1,150,518  1,873,856   -723,338\n",
       "New York Metro 1,592,814  2,601,002 -1,008,188"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# format the table for output into latex document\n",
    "cum_mig_latex=cum_mig[['domestic_mig_nyc','domestic_mig_nyma']].T\n",
    "cum_mig_latex.rename(index={'domestic_mig_nyc':'New York City','domestic_mig_nyma':'New York Metro'}, \n",
    "                     columns={'in_migration':'In Flows','out_migration':'Out Flows','net_migration':'Net Flow'},inplace=True)\n",
    "\n",
    "cum_mig_latex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\\\begin{tabular}{lrrr}\\n\\\\toprule\\n{} &  In Flows &  Out Flows &   Net Flow \\\\\\\\\\n\\\\midrule\\nNew York City  & 1,150,518 &  1,873,856 &   -723,338 \\\\\\\\\\nNew York Metro & 1,592,814 &  2,601,002 & -1,008,188 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cum_mig_latex.to_latex()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NYC Inflows, Outflows, Netflows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co_name</th>\n",
       "      <th>tot_inflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>co_fips</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36059</th>\n",
       "      <td>Nassau County, NY</td>\n",
       "      <td>96,622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36119</th>\n",
       "      <td>Westchester County, NY</td>\n",
       "      <td>72,710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36103</th>\n",
       "      <td>Suffolk County, NY</td>\n",
       "      <td>50,090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34017</th>\n",
       "      <td>Hudson County, NJ</td>\n",
       "      <td>40,860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34003</th>\n",
       "      <td>Bergen County, NJ</td>\n",
       "      <td>30,801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06037</th>\n",
       "      <td>Los Angeles County, CA</td>\n",
       "      <td>30,314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09001</th>\n",
       "      <td>Fairfield County, CT</td>\n",
       "      <td>24,268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34013</th>\n",
       "      <td>Essex County, NJ</td>\n",
       "      <td>22,038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12086</th>\n",
       "      <td>Miami-Dade County, FL</td>\n",
       "      <td>19,382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17031</th>\n",
       "      <td>Cook County, IL</td>\n",
       "      <td>19,076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42101</th>\n",
       "      <td>Philadelphia County, PA</td>\n",
       "      <td>17,429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34023</th>\n",
       "      <td>Middlesex County, NJ</td>\n",
       "      <td>16,857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12011</th>\n",
       "      <td>Broward County, FL</td>\n",
       "      <td>15,124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11001</th>\n",
       "      <td>District of Columbia, DC</td>\n",
       "      <td>14,330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06075</th>\n",
       "      <td>San Francisco County, CA</td>\n",
       "      <td>13,887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25017</th>\n",
       "      <td>Middlesex County, MA</td>\n",
       "      <td>13,019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25025</th>\n",
       "      <td>Suffolk County, MA</td>\n",
       "      <td>12,274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34025</th>\n",
       "      <td>Monmouth County, NJ</td>\n",
       "      <td>11,506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36071</th>\n",
       "      <td>Orange County, NY</td>\n",
       "      <td>11,063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36087</th>\n",
       "      <td>Rockland County, NY</td>\n",
       "      <td>11,019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          co_name  tot_inflow\n",
       "co_fips                                      \n",
       "36059           Nassau County, NY      96,622\n",
       "36119      Westchester County, NY      72,710\n",
       "36103          Suffolk County, NY      50,090\n",
       "34017           Hudson County, NJ      40,860\n",
       "34003           Bergen County, NJ      30,801\n",
       "06037      Los Angeles County, CA      30,314\n",
       "09001        Fairfield County, CT      24,268\n",
       "34013            Essex County, NJ      22,038\n",
       "12086       Miami-Dade County, FL      19,382\n",
       "17031             Cook County, IL      19,076\n",
       "42101     Philadelphia County, PA      17,429\n",
       "34023        Middlesex County, NJ      16,857\n",
       "12011          Broward County, FL      15,124\n",
       "11001    District of Columbia, DC      14,330\n",
       "06075    San Francisco County, CA      13,887\n",
       "25017        Middlesex County, MA      13,019\n",
       "25025          Suffolk County, MA      12,274\n",
       "34025         Monmouth County, NJ      11,506\n",
       "36071           Orange County, NY      11,063\n",
       "36087         Rockland County, NY      11,019"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inflows to NYC\n",
    "domestic_city_flows.loc[~domestic_city_flows.index.isin(suppressed),['co_name','tot_inflow']].sort_values('tot_inflow', ascending=False).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co_name</th>\n",
       "      <th>tot_outflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>co_fips</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36059</th>\n",
       "      <td>Nassau County, NY</td>\n",
       "      <td>185,667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36119</th>\n",
       "      <td>Westchester County, NY</td>\n",
       "      <td>130,410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36103</th>\n",
       "      <td>Suffolk County, NY</td>\n",
       "      <td>71,696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34017</th>\n",
       "      <td>Hudson County, NJ</td>\n",
       "      <td>69,230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34003</th>\n",
       "      <td>Bergen County, NJ</td>\n",
       "      <td>56,269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06037</th>\n",
       "      <td>Los Angeles County, CA</td>\n",
       "      <td>51,895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34013</th>\n",
       "      <td>Essex County, NJ</td>\n",
       "      <td>45,641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09001</th>\n",
       "      <td>Fairfield County, CT</td>\n",
       "      <td>42,041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34023</th>\n",
       "      <td>Middlesex County, NJ</td>\n",
       "      <td>30,764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36071</th>\n",
       "      <td>Orange County, NY</td>\n",
       "      <td>27,110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42101</th>\n",
       "      <td>Philadelphia County, PA</td>\n",
       "      <td>26,874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12086</th>\n",
       "      <td>Miami-Dade County, FL</td>\n",
       "      <td>26,180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12011</th>\n",
       "      <td>Broward County, FL</td>\n",
       "      <td>25,767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34025</th>\n",
       "      <td>Monmouth County, NJ</td>\n",
       "      <td>23,713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34039</th>\n",
       "      <td>Union County, NJ</td>\n",
       "      <td>22,739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36087</th>\n",
       "      <td>Rockland County, NY</td>\n",
       "      <td>22,657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17031</th>\n",
       "      <td>Cook County, IL</td>\n",
       "      <td>20,438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12095</th>\n",
       "      <td>Orange County, FL</td>\n",
       "      <td>20,374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12099</th>\n",
       "      <td>Palm Beach County, FL</td>\n",
       "      <td>19,632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48453</th>\n",
       "      <td>Travis County, TX</td>\n",
       "      <td>18,560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         co_name  tot_outflow\n",
       "co_fips                                      \n",
       "36059          Nassau County, NY      185,667\n",
       "36119     Westchester County, NY      130,410\n",
       "36103         Suffolk County, NY       71,696\n",
       "34017          Hudson County, NJ       69,230\n",
       "34003          Bergen County, NJ       56,269\n",
       "06037     Los Angeles County, CA       51,895\n",
       "34013           Essex County, NJ       45,641\n",
       "09001       Fairfield County, CT       42,041\n",
       "34023       Middlesex County, NJ       30,764\n",
       "36071          Orange County, NY       27,110\n",
       "42101    Philadelphia County, PA       26,874\n",
       "12086      Miami-Dade County, FL       26,180\n",
       "12011         Broward County, FL       25,767\n",
       "34025        Monmouth County, NJ       23,713\n",
       "34039           Union County, NJ       22,739\n",
       "36087        Rockland County, NY       22,657\n",
       "17031            Cook County, IL       20,438\n",
       "12095          Orange County, FL       20,374\n",
       "12099      Palm Beach County, FL       19,632\n",
       "48453          Travis County, TX       18,560"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outflows from NYC\n",
    "domestic_city_flows.loc[~domestic_city_flows.index.isin(suppressed),['co_name','tot_outflow']].sort_values('tot_outflow', ascending=False).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>tot_netflow</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11001</th>\n",
       "      <td>District of Columbia, DC</td>\n",
       "      <td>2,922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25025</th>\n",
       "      <td>Suffolk County, MA</td>\n",
       "      <td>1,153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25017</th>\n",
       "      <td>Middlesex County, MA</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36109</th>\n",
       "      <td>Tompkins County, NY</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51013</th>\n",
       "      <td>Arlington County, VA</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49049</th>\n",
       "      <td>Utah County, UT</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12073</th>\n",
       "      <td>Leon County, FL</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17019</th>\n",
       "      <td>Champaign County, IL</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42027</th>\n",
       "      <td>Centre County, PA</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26161</th>\n",
       "      <td>Washtenaw County, MI</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55025</th>\n",
       "      <td>Dane County, WI</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47093</th>\n",
       "      <td>Knox County, TN</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05119</th>\n",
       "      <td>Pulaski County, AR</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18105</th>\n",
       "      <td>Monroe County, IN</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51540</th>\n",
       "      <td>Charlottesville city, VA</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09013</th>\n",
       "      <td>Tolland County, CT</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29510</th>\n",
       "      <td>St. Louis city, MO</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33009</th>\n",
       "      <td>Grafton County, NH</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22033</th>\n",
       "      <td>East Baton Rouge Parish, LA</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39153</th>\n",
       "      <td>Summit County, OH</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             co_name  tot_netflow\n",
       "co_fips                                          \n",
       "11001       District of Columbia, DC        2,922\n",
       "25025             Suffolk County, MA        1,153\n",
       "25017           Middlesex County, MA          604\n",
       "36109            Tompkins County, NY          369\n",
       "51013           Arlington County, VA          278\n",
       "49049                Utah County, UT          267\n",
       "12073                Leon County, FL          248\n",
       "17019           Champaign County, IL          202\n",
       "42027              Centre County, PA          175\n",
       "26161           Washtenaw County, MI          173\n",
       "55025                Dane County, WI          170\n",
       "47093                Knox County, TN          158\n",
       "05119             Pulaski County, AR          153\n",
       "18105              Monroe County, IN          146\n",
       "51540       Charlottesville city, VA          139\n",
       "09013             Tolland County, CT          125\n",
       "29510             St. Louis city, MO          115\n",
       "33009             Grafton County, NH          114\n",
       "22033    East Baton Rouge Parish, LA          105\n",
       "39153              Summit County, OH          105"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Net senders to NYC (NYC gains migrants)\n",
    "domestic_city_flows.loc[~domestic_city_flows.index.isin(suppressed),['co_name','tot_netflow']].sort_values('tot_netflow', ascending=False).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co_name</th>\n",
       "      <th>tot_netflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>co_fips</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36059</th>\n",
       "      <td>Nassau County, NY</td>\n",
       "      <td>-89,045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36119</th>\n",
       "      <td>Westchester County, NY</td>\n",
       "      <td>-57,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34017</th>\n",
       "      <td>Hudson County, NJ</td>\n",
       "      <td>-28,370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34003</th>\n",
       "      <td>Bergen County, NJ</td>\n",
       "      <td>-25,468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34013</th>\n",
       "      <td>Essex County, NJ</td>\n",
       "      <td>-23,603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36103</th>\n",
       "      <td>Suffolk County, NY</td>\n",
       "      <td>-21,606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06037</th>\n",
       "      <td>Los Angeles County, CA</td>\n",
       "      <td>-21,581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09001</th>\n",
       "      <td>Fairfield County, CT</td>\n",
       "      <td>-17,773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36071</th>\n",
       "      <td>Orange County, NY</td>\n",
       "      <td>-16,047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34023</th>\n",
       "      <td>Middlesex County, NJ</td>\n",
       "      <td>-13,907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34039</th>\n",
       "      <td>Union County, NJ</td>\n",
       "      <td>-12,318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34025</th>\n",
       "      <td>Monmouth County, NJ</td>\n",
       "      <td>-12,207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36087</th>\n",
       "      <td>Rockland County, NY</td>\n",
       "      <td>-11,638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12011</th>\n",
       "      <td>Broward County, FL</td>\n",
       "      <td>-10,643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12095</th>\n",
       "      <td>Orange County, FL</td>\n",
       "      <td>-10,369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12099</th>\n",
       "      <td>Palm Beach County, FL</td>\n",
       "      <td>-9,601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42101</th>\n",
       "      <td>Philadelphia County, PA</td>\n",
       "      <td>-9,445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34029</th>\n",
       "      <td>Ocean County, NJ</td>\n",
       "      <td>-8,923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48453</th>\n",
       "      <td>Travis County, TX</td>\n",
       "      <td>-7,548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48201</th>\n",
       "      <td>Harris County, TX</td>\n",
       "      <td>-7,309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         co_name  tot_netflow\n",
       "co_fips                                      \n",
       "36059          Nassau County, NY      -89,045\n",
       "36119     Westchester County, NY      -57,700\n",
       "34017          Hudson County, NJ      -28,370\n",
       "34003          Bergen County, NJ      -25,468\n",
       "34013           Essex County, NJ      -23,603\n",
       "36103         Suffolk County, NY      -21,606\n",
       "06037     Los Angeles County, CA      -21,581\n",
       "09001       Fairfield County, CT      -17,773\n",
       "36071          Orange County, NY      -16,047\n",
       "34023       Middlesex County, NJ      -13,907\n",
       "34039           Union County, NJ      -12,318\n",
       "34025        Monmouth County, NJ      -12,207\n",
       "36087        Rockland County, NY      -11,638\n",
       "12011         Broward County, FL      -10,643\n",
       "12095          Orange County, FL      -10,369\n",
       "12099      Palm Beach County, FL       -9,601\n",
       "42101    Philadelphia County, PA       -9,445\n",
       "34029           Ocean County, NJ       -8,923\n",
       "48453          Travis County, TX       -7,548\n",
       "48201          Harris County, TX       -7,309"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Net receivers from NYC (NYC loses migrants)\n",
    "domestic_city_flows.loc[~domestic_city_flows.index.isin(suppressed),['co_name','tot_netflow']].sort_values('tot_netflow', ascending=True).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co_name</th>\n",
       "      <th>tot_inflow</th>\n",
       "      <th>tot_outflow</th>\n",
       "      <th>tot_netflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>co_fips</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59000</th>\n",
       "      <td>Other flows - Different State</td>\n",
       "      <td>129,062</td>\n",
       "      <td>159,730</td>\n",
       "      <td>-30,668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58000</th>\n",
       "      <td>Other flows - Same State</td>\n",
       "      <td>8,152</td>\n",
       "      <td>9,396</td>\n",
       "      <td>-1,244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               co_name  tot_inflow  tot_outflow  tot_netflow\n",
       "co_fips                                                                     \n",
       "59000    Other flows - Different State     129,062      159,730      -30,668\n",
       "58000         Other flows - Same State       8,152        9,396       -1,244"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of other flows\n",
    "domestic_city_flows.loc[domestic_city_flows.index.isin(suppressed),['co_name','tot_inflow','tot_outflow','tot_netflow']].sort_values('tot_netflow', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get list of top inflow and outflow counties\n",
    "top_inflows=domestic_city_flows['tot_inflow'].sort_values(ascending=False).head(n=17).drop('59000')\n",
    "top_outflows=domestic_city_flows['tot_outflow'].sort_values(ascending=False).head(n=17).drop('59000')\n",
    "counties_for_plot=top_inflows.index.tolist()\n",
    "counties_for_plot.extend(top_outflows.index.tolist())\n",
    "counties_for_plot=set(counties_for_plot)\n",
    "len(counties_for_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co_name</th>\n",
       "      <th>tot_inflow</th>\n",
       "      <th>tot_outflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>co_fips</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36059</th>\n",
       "      <td>Nassau County, NY</td>\n",
       "      <td>96,622</td>\n",
       "      <td>185,667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36119</th>\n",
       "      <td>Westchester County, NY</td>\n",
       "      <td>72,710</td>\n",
       "      <td>130,410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36103</th>\n",
       "      <td>Suffolk County, NY</td>\n",
       "      <td>50,090</td>\n",
       "      <td>71,696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34017</th>\n",
       "      <td>Hudson County, NJ</td>\n",
       "      <td>40,860</td>\n",
       "      <td>69,230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34003</th>\n",
       "      <td>Bergen County, NJ</td>\n",
       "      <td>30,801</td>\n",
       "      <td>56,269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06037</th>\n",
       "      <td>Los Angeles County, CA</td>\n",
       "      <td>30,314</td>\n",
       "      <td>51,895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34013</th>\n",
       "      <td>Essex County, NJ</td>\n",
       "      <td>22,038</td>\n",
       "      <td>45,641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09001</th>\n",
       "      <td>Fairfield County, CT</td>\n",
       "      <td>24,268</td>\n",
       "      <td>42,041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34023</th>\n",
       "      <td>Middlesex County, NJ</td>\n",
       "      <td>16,857</td>\n",
       "      <td>30,764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36071</th>\n",
       "      <td>Orange County, NY</td>\n",
       "      <td>11,063</td>\n",
       "      <td>27,110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42101</th>\n",
       "      <td>Philadelphia County, PA</td>\n",
       "      <td>17,429</td>\n",
       "      <td>26,874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12086</th>\n",
       "      <td>Miami-Dade County, FL</td>\n",
       "      <td>19,382</td>\n",
       "      <td>26,180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12011</th>\n",
       "      <td>Broward County, FL</td>\n",
       "      <td>15,124</td>\n",
       "      <td>25,767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34025</th>\n",
       "      <td>Monmouth County, NJ</td>\n",
       "      <td>11,506</td>\n",
       "      <td>23,713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34039</th>\n",
       "      <td>Union County, NJ</td>\n",
       "      <td>10,421</td>\n",
       "      <td>22,739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36087</th>\n",
       "      <td>Rockland County, NY</td>\n",
       "      <td>11,019</td>\n",
       "      <td>22,657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17031</th>\n",
       "      <td>Cook County, IL</td>\n",
       "      <td>19,076</td>\n",
       "      <td>20,438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06075</th>\n",
       "      <td>San Francisco County, CA</td>\n",
       "      <td>13,887</td>\n",
       "      <td>17,783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25017</th>\n",
       "      <td>Middlesex County, MA</td>\n",
       "      <td>13,019</td>\n",
       "      <td>12,415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11001</th>\n",
       "      <td>District of Columbia, DC</td>\n",
       "      <td>14,330</td>\n",
       "      <td>11,408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          co_name  tot_inflow  tot_outflow\n",
       "co_fips                                                   \n",
       "36059           Nassau County, NY      96,622      185,667\n",
       "36119      Westchester County, NY      72,710      130,410\n",
       "36103          Suffolk County, NY      50,090       71,696\n",
       "34017           Hudson County, NJ      40,860       69,230\n",
       "34003           Bergen County, NJ      30,801       56,269\n",
       "06037      Los Angeles County, CA      30,314       51,895\n",
       "34013            Essex County, NJ      22,038       45,641\n",
       "09001        Fairfield County, CT      24,268       42,041\n",
       "34023        Middlesex County, NJ      16,857       30,764\n",
       "36071           Orange County, NY      11,063       27,110\n",
       "42101     Philadelphia County, PA      17,429       26,874\n",
       "12086       Miami-Dade County, FL      19,382       26,180\n",
       "12011          Broward County, FL      15,124       25,767\n",
       "34025         Monmouth County, NJ      11,506       23,713\n",
       "34039            Union County, NJ      10,421       22,739\n",
       "36087         Rockland County, NY      11,019       22,657\n",
       "17031             Cook County, IL      19,076       20,438\n",
       "06075    San Francisco County, CA      13,887       17,783\n",
       "25017        Middlesex County, MA      13,019       12,415\n",
       "11001    District of Columbia, DC      14,330       11,408"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_to_plot=domestic_city_flows.loc[counties_for_plot,['co_name','tot_inflow','tot_outflow']]\n",
    "df_to_plot.sort_values(by='tot_outflow',ascending=False, inplace=True)\n",
    "df_to_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56ca0ddcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Inflows and outflows on one chart\n",
    "axc=df_to_plot.set_index('co_name').plot(kind='barh',figsize=(8,7), color=wccolors)\n",
    "axc.legend(['Coming to NYC','Leaving from NYC'], loc='best',fontsize=12, frameon=False)\n",
    "axc.invert_yaxis()\n",
    "axc.set_ylabel('')\n",
    "axc.set_xlabel('Migrants')\n",
    "axc.get_xaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}\".format(int(x))))\n",
    "\n",
    "# ticks labels size\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.show()\n",
    "nyc_fig=axc.get_figure()\n",
    "nyc_fig.savefig(os.path.join(out_path, 'images','nyc_top_flows.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions for Showing Changes in Ranking Over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create array of top N geographies by flow type, with rows as ranks and columns as years\n",
    "# Returns 3 arrays with values for geographies (id codes), flow values, and place names\n",
    "# Must specify: number of places to rank, counties or metros, or sort by largest or smallest (True or False)\n",
    "def create_arrays(df,flowtype,nsize,gtype,largest):\n",
    "    geogs=[]\n",
    "    flows=[]\n",
    "    cols=[c for c in df if c.startswith(flowtype)]\n",
    "    for c in cols:\n",
    "        if gtype=='counties':\n",
    "            row=df.loc[~df.index.isin(suppressed),[c]]\n",
    "        elif gtype=='metros':\n",
    "            row=df.loc[~df.index.isin(msuppressed),[c]]   \n",
    "        if largest is True:\n",
    "            row=row[c].nlargest(nsize)\n",
    "        elif largest is False:\n",
    "            row=row[c].nsmallest(nsize)\n",
    "        idxs=list(row.index)\n",
    "        geogs.append(idxs)\n",
    "        vals=list(row.values)\n",
    "        flows.append(vals)\n",
    "    \n",
    "    if gtype=='counties':\n",
    "        fips=df.to_dict()['co_name']\n",
    "    elif gtype=='metros':\n",
    "        fips=df.to_dict()['mname']\n",
    "    labels=[]\n",
    "    for row in geogs:\n",
    "        line=[]\n",
    "        for uid in row:\n",
    "            if gtype=='counties':\n",
    "                if fips[uid]=='District of Columbia, DC':\n",
    "                    line.append('Washington\\n DC')\n",
    "                else:\n",
    "                    line.append(fips[uid].replace('County, ','\\n')) #creates short labels\n",
    "            elif gtype=='metros':\n",
    "                if '-' in fips[uid]:\n",
    "                    line.append(fips[uid].split('-')[0]) #creates short labels\n",
    "                else:\n",
    "                    line.append(fips[uid].split(',')[0])\n",
    "        labels.append(line)\n",
    "        \n",
    "    a_geogs=np.array(geogs).T\n",
    "    a_flows=np.array(flows).T\n",
    "    a_labels=np.array(labels).T\n",
    "\n",
    "    return a_geogs, a_flows, a_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create array showing how top N geographies have changed ranks over time, with rows as rank changes and \n",
    "# columns as years. Returns 1 array with values: 0 (no change), 1 (increased rank), and -1 (descreased rank)\n",
    "def rank_change(geoarray):\n",
    "    rowcount=geoarray.shape[0]\n",
    "    colcount=geoarray.shape[1]\n",
    "\n",
    "    # Create a number of blank lists\n",
    "    changelist = [[] for _ in range(rowcount)]\n",
    "\n",
    "    for i in range(colcount):\n",
    "        if i==0:\n",
    "            # Rank change for 1st year is 0, as there is no previous year\n",
    "            for j in range(rowcount):\n",
    "                changelist[j].append(0)\n",
    "        else:\n",
    "            col=geoarray[:,i] #Get all values in this col\n",
    "            prevcol=geoarray[:,i-1] #Get all values in previous col\n",
    "            for v in col:\n",
    "                array_pos=np.where(col == v) #returns array\n",
    "                current_pos=int(array_pos[0]) #get first array value\n",
    "                array_pos2=np.where(prevcol == v) #returns array\n",
    "                if len(array_pos2[0])==0: #if array is empty, because place was not in previous year\n",
    "                    previous_pos=current_pos+1\n",
    "                else:\n",
    "                    previous_pos=int(array_pos2[0]) #get first array value\n",
    "                if current_pos==previous_pos:\n",
    "                    changelist[current_pos].append(0)\n",
    "                    #print('Processed',v, current_pos)\n",
    "                    #print('No change in rank for',v)\n",
    "                elif current_pos<previous_pos: #Smaller value = higher rank\n",
    "                    changelist[current_pos].append(1)\n",
    "                    #print('Processed',v, current_pos)\n",
    "                    #print('Rank has increased for',v)\n",
    "                elif current_pos>previous_pos: #Larger value = smaller rank\n",
    "                    changelist[current_pos].append(-1)\n",
    "                    #print('Processed',v, current_pos)\n",
    "                    #print('Rank has decreased for',v)\n",
    "                else:\n",
    "                    pass\n",
    "\n",
    "    rankchange=np.array(changelist)                 \n",
    "    return rankchange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create grid plot based on an array that shows change in ranks and an array of cell labels\n",
    "def rank_grid(rank_change,labels):\n",
    "    alabels=labels\n",
    "    xlabels=[yr.replace('_','-') for yr in years]\n",
    "    ranklabels=['1st','2nd','3rd','4th','5th','6th','7th','8th','9th','10th',\n",
    "               '11th','12th','13th','14th','15th','16th','17th','18th','19th','20th']\n",
    "    nsize=rank_change.shape[0]\n",
    "    ylabels=ranklabels[:nsize]\n",
    "    \n",
    "    #mycolors = colors.ListedColormap(['lightblue','ghostwhite','palegreen'])\n",
    "    mycolors = colors.ListedColormap(['#2250a9','#f8f8ff','#59c134'])\n",
    "\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10,10))\n",
    "    im = ax.imshow(rank_change, cmap=mycolors)\n",
    "\n",
    "    # Show all ticks...\n",
    "    ax.set_xticks(np.arange(len(xlabels)))\n",
    "    ax.set_yticks(np.arange(len(ylabels)))\n",
    "    # ... and label them with the respective list entries\n",
    "    ax.set_xticklabels(xlabels)\n",
    "    ax.set_yticklabels(ylabels)\n",
    "\n",
    "    # Create white grid.\n",
    "    ax.set_xticks(np.arange(rank_change.shape[1]+1)-.5, minor=True)\n",
    "    ax.set_yticks(np.arange(rank_change.shape[0]+1)-.5, minor=True)\n",
    "    ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n",
    "    ax.grid(which=\"major\",visible=False)\n",
    "\n",
    "    cbar = ax.figure.colorbar(im, ax=ax, ticks=[1,0,-1], shrink=0.5)\n",
    "    cbar.ax.set_yticklabels(['Increased','No Change','Decreased'])\n",
    "\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    for i in range(len(ylabels)):\n",
    "        for j in range(len(xlabels)):\n",
    "            if rank_change[i,j] != 0:\n",
    "                text = ax.text(j, i, alabels[i, j],\n",
    "                           ha=\"center\", va=\"center\", color=\"w\", fontsize=10)\n",
    "            else:\n",
    "                text = ax.text(j, i, alabels[i, j],\n",
    "                           ha=\"center\", va=\"center\", color=\"k\", fontsize=10)\n",
    "                \n",
    "    #ax.set_title(\"Change in Rank Over Time\")\n",
    "    plt.xticks(fontsize=12)\n",
    "    plt.yticks(fontsize=12)\n",
    "    fig.tight_layout()\n",
    "    plt.show()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changes in Rank - NYC Flows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create arrays for inflows\n",
    "incounty_geogs, incounty_flows, incounty_labels = create_arrays(domestic_city_flows,'inflow',10,'counties',True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['36059', '36059', '36059', '36059', '36059', '36059', '36059'],\n",
       "       ['36119', '36119', '36119', '36119', '36119', '36119', '36119'],\n",
       "       ['36103', '36103', '36103', '36103', '36103', '36103', '36103'],\n",
       "       ['34017', '34017', '34017', '34017', '34017', '34017', '34017'],\n",
       "       ['34003', '34003', '34003', '34003', '06037', '06037', '06037'],\n",
       "       ['06037', '06037', '06037', '06037', '34003', '34003', '34003'],\n",
       "       ['12086', '12086', '09001', '48453', '09001', '09001', '09001'],\n",
       "       ['34013', '09001', '34013', '09001', '34013', '17031', '34013'],\n",
       "       ['09001', '34013', '12086', '34013', '17031', '34013', '17031'],\n",
       "       ['34023', '12011', '17031', '34023', '48453', '06075', '06075']],\n",
       "      dtype='<U5')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incounty_geogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  1,  0,  0],\n",
       "       [ 0,  0,  0,  0, -1,  0,  0],\n",
       "       [ 0,  0,  1,  1,  1,  0,  0],\n",
       "       [ 0,  1,  1, -1,  1,  1,  1],\n",
       "       [ 0, -1, -1, -1,  1, -1, -1],\n",
       "       [ 0,  1,  1,  1, -1,  1,  0]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create array for annual change in ranks\n",
    "incounty_rchange=rank_change(incounty_geogs)\n",
    "incounty_rchange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56d0c3dcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create grid for annual change in ranks for inflows\n",
    "incounty_rankplot=rank_grid(incounty_rchange,incounty_labels)\n",
    "inco_fig=incounty_rankplot.get_figure()\n",
    "inco_fig.savefig(os.path.join(out_path, 'images','nyc_inflow_grid.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create arrays for outflows\n",
    "outcounty_geogs, outcounty_flows, outcounty_labels = create_arrays(domestic_city_flows,'outflow',10,'counties',True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create array for annual change in ranks\n",
    "outcounty_rchange=rank_change(outcounty_geogs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56ce5d1da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create grid for annual change in ranks for outflows\n",
    "outcounty_rankplot=rank_grid(outcounty_rchange,outcounty_labels)\n",
    "outco_fig=outcounty_rankplot.get_figure()\n",
    "outco_fig.savefig(os.path.join(out_path, 'images','nyc_outflow_grid.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create arrays for negative netflows (sort is False to go from small to large)\n",
    "netcounty_geogs, netcounty_flows, netcounty_labels = create_arrays(domestic_city_flows,'netflow',10,'counties',False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create array for annual change in ranks\n",
    "netcounty_rchange=rank_change(netcounty_geogs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56cba9def0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create grid for annual change in ranks for negative net flows\n",
    "netcounty_rankplot=rank_grid(netcounty_rchange,netcounty_labels)\n",
    "netco_fig=netcounty_rankplot.get_figure()\n",
    "netco_fig.savefig(os.path.join(out_path, 'images','extras','nyc_netflow_grid.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56cc3b0898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Just a test to see grid of actual values\n",
    "alabels=incounty_labels\n",
    "ylabels=['1st','2nd','3rd','4th','5th','6th','7th','8th','9th','10th']\n",
    "xlabels=[yr.replace('_','-') for yr in years]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "im = ax.imshow(incounty_flows, cmap='BuGn')\n",
    "\n",
    "# Show all ticks...\n",
    "ax.set_xticks(np.arange(len(xlabels)))\n",
    "ax.set_yticks(np.arange(len(ylabels)))\n",
    "# ... and label them with the respective list entries\n",
    "ax.set_xticklabels(xlabels)\n",
    "ax.set_yticklabels(ylabels)\n",
    "\n",
    "# Create white grid.\n",
    "ax.set_xticks(np.arange(incounty_flows.shape[1]+1)-.5, minor=True)\n",
    "ax.set_yticks(np.arange(incounty_flows.shape[0]+1)-.5, minor=True)\n",
    "ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=5)\n",
    "ax.grid(which=\"major\",visible=False)\n",
    "\n",
    "cbar = ax.figure.colorbar(im, ax=ax)\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "for i in range(len(ylabels)):\n",
    "    for j in range(len(xlabels)):\n",
    "        text = ax.text(j, i, alabels[i, j],\n",
    "                       ha=\"center\", va=\"center\", color=\"k\", fontsize=10)\n",
    "\n",
    "ax.set_title(\"Population Inflows to NYC\")\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NYMA Inflows, Outflows, Netflows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mname</th>\n",
       "      <th>tot_inflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37980</th>\n",
       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
       "      <td>95,914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33100</th>\n",
       "      <td>Miami-Fort Lauderdale-Pompano Beach, FL</td>\n",
       "      <td>92,153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47900</th>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV</td>\n",
       "      <td>63,730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31080</th>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA</td>\n",
       "      <td>56,832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14460</th>\n",
       "      <td>Boston-Cambridge-Newton, MA-NH</td>\n",
       "      <td>53,223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14860</th>\n",
       "      <td>Bridgeport-Stamford-Norwalk, CT</td>\n",
       "      <td>50,082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39100</th>\n",
       "      <td>Poughkeepsie-Newburgh-Middletown, NY</td>\n",
       "      <td>50,002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45940</th>\n",
       "      <td>Trenton-Princeton, NJ</td>\n",
       "      <td>38,174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16980</th>\n",
       "      <td>Chicago-Naperville-Elgin, IL-IN-WI</td>\n",
       "      <td>34,771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10900</th>\n",
       "      <td>Allentown-Bethlehem-Easton, PA-NJ</td>\n",
       "      <td>33,432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41860</th>\n",
       "      <td>San Francisco-Oakland-Berkeley, CA</td>\n",
       "      <td>31,726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12060</th>\n",
       "      <td>Atlanta-Sandy Springs-Alpharetta, GA</td>\n",
       "      <td>31,599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36740</th>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL</td>\n",
       "      <td>26,723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45300</th>\n",
       "      <td>Tampa-St. Petersburg-Clearwater, FL</td>\n",
       "      <td>22,983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12580</th>\n",
       "      <td>Baltimore-Columbia-Towson, MD</td>\n",
       "      <td>20,679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12420</th>\n",
       "      <td>Austin-Round Rock-Georgetown, TX</td>\n",
       "      <td>18,490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26420</th>\n",
       "      <td>Houston-The Woodlands-Sugar Land, TX</td>\n",
       "      <td>17,111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19100</th>\n",
       "      <td>Dallas-Fort Worth-Arlington, TX</td>\n",
       "      <td>17,075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35300</th>\n",
       "      <td>New Haven-Milford, CT</td>\n",
       "      <td>16,761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41740</th>\n",
       "      <td>San Diego-Chula Vista-Carlsbad, CA</td>\n",
       "      <td>14,132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              mname  tot_inflow\n",
       "mcode                                                          \n",
       "37980   Philadelphia-Camden-Wilmington, PA-NJ-DE-MD      95,914\n",
       "33100       Miami-Fort Lauderdale-Pompano Beach, FL      92,153\n",
       "47900  Washington-Arlington-Alexandria, DC-VA-MD-WV      63,730\n",
       "31080            Los Angeles-Long Beach-Anaheim, CA      56,832\n",
       "14460                Boston-Cambridge-Newton, MA-NH      53,223\n",
       "14860               Bridgeport-Stamford-Norwalk, CT      50,082\n",
       "39100          Poughkeepsie-Newburgh-Middletown, NY      50,002\n",
       "45940                         Trenton-Princeton, NJ      38,174\n",
       "16980            Chicago-Naperville-Elgin, IL-IN-WI      34,771\n",
       "10900             Allentown-Bethlehem-Easton, PA-NJ      33,432\n",
       "41860            San Francisco-Oakland-Berkeley, CA      31,726\n",
       "12060          Atlanta-Sandy Springs-Alpharetta, GA      31,599\n",
       "36740                 Orlando-Kissimmee-Sanford, FL      26,723\n",
       "45300           Tampa-St. Petersburg-Clearwater, FL      22,983\n",
       "12580                 Baltimore-Columbia-Towson, MD      20,679\n",
       "12420              Austin-Round Rock-Georgetown, TX      18,490\n",
       "26420          Houston-The Woodlands-Sugar Land, TX      17,111\n",
       "19100               Dallas-Fort Worth-Arlington, TX      17,075\n",
       "35300                         New Haven-Milford, CT      16,761\n",
       "41740            San Diego-Chula Vista-Carlsbad, CA      14,132"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inflows to NYMA\n",
    "grouped_by_metro.loc[~grouped_by_metro.index.isin(msuppressed),['mname','tot_inflow']].sort_values('tot_inflow', ascending=False).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mname</th>\n",
       "      <th>tot_outflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33100</th>\n",
       "      <td>Miami-Fort Lauderdale-Pompano Beach, FL</td>\n",
       "      <td>161,220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37980</th>\n",
       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
       "      <td>143,776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31080</th>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA</td>\n",
       "      <td>92,117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14860</th>\n",
       "      <td>Bridgeport-Stamford-Norwalk, CT</td>\n",
       "      <td>88,573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39100</th>\n",
       "      <td>Poughkeepsie-Newburgh-Middletown, NY</td>\n",
       "      <td>84,840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47900</th>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV</td>\n",
       "      <td>81,548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36740</th>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL</td>\n",
       "      <td>70,024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12060</th>\n",
       "      <td>Atlanta-Sandy Springs-Alpharetta, GA</td>\n",
       "      <td>68,485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14460</th>\n",
       "      <td>Boston-Cambridge-Newton, MA-NH</td>\n",
       "      <td>62,579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10900</th>\n",
       "      <td>Allentown-Bethlehem-Easton, PA-NJ</td>\n",
       "      <td>61,651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45300</th>\n",
       "      <td>Tampa-St. Petersburg-Clearwater, FL</td>\n",
       "      <td>55,599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41860</th>\n",
       "      <td>San Francisco-Oakland-Berkeley, CA</td>\n",
       "      <td>50,568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45940</th>\n",
       "      <td>Trenton-Princeton, NJ</td>\n",
       "      <td>49,553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19100</th>\n",
       "      <td>Dallas-Fort Worth-Arlington, TX</td>\n",
       "      <td>40,860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16980</th>\n",
       "      <td>Chicago-Naperville-Elgin, IL-IN-WI</td>\n",
       "      <td>39,150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26420</th>\n",
       "      <td>Houston-The Woodlands-Sugar Land, TX</td>\n",
       "      <td>38,542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16740</th>\n",
       "      <td>Charlotte-Concord-Gastonia, NC-SC</td>\n",
       "      <td>38,111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12420</th>\n",
       "      <td>Austin-Round Rock-Georgetown, TX</td>\n",
       "      <td>33,931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12580</th>\n",
       "      <td>Baltimore-Columbia-Towson, MD</td>\n",
       "      <td>31,144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39580</th>\n",
       "      <td>Raleigh-Cary, NC</td>\n",
       "      <td>27,688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              mname  tot_outflow\n",
       "mcode                                                           \n",
       "33100       Miami-Fort Lauderdale-Pompano Beach, FL      161,220\n",
       "37980   Philadelphia-Camden-Wilmington, PA-NJ-DE-MD      143,776\n",
       "31080            Los Angeles-Long Beach-Anaheim, CA       92,117\n",
       "14860               Bridgeport-Stamford-Norwalk, CT       88,573\n",
       "39100          Poughkeepsie-Newburgh-Middletown, NY       84,840\n",
       "47900  Washington-Arlington-Alexandria, DC-VA-MD-WV       81,548\n",
       "36740                 Orlando-Kissimmee-Sanford, FL       70,024\n",
       "12060          Atlanta-Sandy Springs-Alpharetta, GA       68,485\n",
       "14460                Boston-Cambridge-Newton, MA-NH       62,579\n",
       "10900             Allentown-Bethlehem-Easton, PA-NJ       61,651\n",
       "45300           Tampa-St. Petersburg-Clearwater, FL       55,599\n",
       "41860            San Francisco-Oakland-Berkeley, CA       50,568\n",
       "45940                         Trenton-Princeton, NJ       49,553\n",
       "19100               Dallas-Fort Worth-Arlington, TX       40,860\n",
       "16980            Chicago-Naperville-Elgin, IL-IN-WI       39,150\n",
       "26420          Houston-The Woodlands-Sugar Land, TX       38,542\n",
       "16740             Charlotte-Concord-Gastonia, NC-SC       38,111\n",
       "12420              Austin-Round Rock-Georgetown, TX       33,931\n",
       "12580                 Baltimore-Columbia-Towson, MD       31,144\n",
       "39580                              Raleigh-Cary, NC       27,688"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outflows to NYMA\n",
    "grouped_by_metro.loc[~grouped_by_metro.index.isin(msuppressed),['mname','tot_outflow']].sort_values('tot_outflow', ascending=False).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mname</th>\n",
       "      <th>tot_netflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27060</th>\n",
       "      <td>Ithaca, NY</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45220</th>\n",
       "      <td>Tallahassee, FL</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44300</th>\n",
       "      <td>State College, PA</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39340</th>\n",
       "      <td>Provo-Orem, UT</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31540</th>\n",
       "      <td>Madison, WI</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16580</th>\n",
       "      <td>Champaign-Urbana, IL</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11460</th>\n",
       "      <td>Ann Arbor, MI</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30780</th>\n",
       "      <td>Little Rock-North Little Rock-Conway, AR</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14020</th>\n",
       "      <td>Bloomington, IN</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16820</th>\n",
       "      <td>Charlottesville, VA</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10420</th>\n",
       "      <td>Akron, OH</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43780</th>\n",
       "      <td>South Bend-Mishawaka, IN-MI</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12940</th>\n",
       "      <td>Baton Rouge, LA</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28940</th>\n",
       "      <td>Knoxville, TN</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29620</th>\n",
       "      <td>Lansing-East Lansing, MI</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11260</th>\n",
       "      <td>Anchorage, AK</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17860</th>\n",
       "      <td>Columbia, MO</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30100</th>\n",
       "      <td>Lebanon, NH-VT</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29200</th>\n",
       "      <td>Lafayette-West Lafayette, IN</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15940</th>\n",
       "      <td>Canton-Massillon, OH</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          mname  tot_netflow\n",
       "mcode                                                       \n",
       "27060                                Ithaca, NY          630\n",
       "45220                           Tallahassee, FL          374\n",
       "44300                         State College, PA          324\n",
       "39340                            Provo-Orem, UT          267\n",
       "31540                               Madison, WI          206\n",
       "16580                      Champaign-Urbana, IL          199\n",
       "11460                             Ann Arbor, MI          195\n",
       "30780  Little Rock-North Little Rock-Conway, AR          153\n",
       "14020                           Bloomington, IN          146\n",
       "16820                       Charlottesville, VA          144\n",
       "10420                                 Akron, OH          118\n",
       "43780               South Bend-Mishawaka, IN-MI          113\n",
       "12940                           Baton Rouge, LA          105\n",
       "28940                             Knoxville, TN           98\n",
       "29620                  Lansing-East Lansing, MI           96\n",
       "11260                             Anchorage, AK           72\n",
       "17860                              Columbia, MO           64\n",
       "30100                            Lebanon, NH-VT           55\n",
       "29200              Lafayette-West Lafayette, IN           50\n",
       "15940                      Canton-Massillon, OH           42"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Net senders to NYMA (NYMA gains migrants)\n",
    "grouped_by_metro.loc[~grouped_by_metro.index.isin(msuppressed),['mname','tot_netflow']].sort_values('tot_netflow', ascending=False).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mname</th>\n",
       "      <th>tot_netflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33100</th>\n",
       "      <td>Miami-Fort Lauderdale-Pompano Beach, FL</td>\n",
       "      <td>-69,067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37980</th>\n",
       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
       "      <td>-47,862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36740</th>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL</td>\n",
       "      <td>-43,301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14860</th>\n",
       "      <td>Bridgeport-Stamford-Norwalk, CT</td>\n",
       "      <td>-38,491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12060</th>\n",
       "      <td>Atlanta-Sandy Springs-Alpharetta, GA</td>\n",
       "      <td>-36,886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31080</th>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA</td>\n",
       "      <td>-35,285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39100</th>\n",
       "      <td>Poughkeepsie-Newburgh-Middletown, NY</td>\n",
       "      <td>-34,838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45300</th>\n",
       "      <td>Tampa-St. Petersburg-Clearwater, FL</td>\n",
       "      <td>-32,616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10900</th>\n",
       "      <td>Allentown-Bethlehem-Easton, PA-NJ</td>\n",
       "      <td>-28,219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16740</th>\n",
       "      <td>Charlotte-Concord-Gastonia, NC-SC</td>\n",
       "      <td>-25,914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19100</th>\n",
       "      <td>Dallas-Fort Worth-Arlington, TX</td>\n",
       "      <td>-23,785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26420</th>\n",
       "      <td>Houston-The Woodlands-Sugar Land, TX</td>\n",
       "      <td>-21,431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39580</th>\n",
       "      <td>Raleigh-Cary, NC</td>\n",
       "      <td>-19,017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41860</th>\n",
       "      <td>San Francisco-Oakland-Berkeley, CA</td>\n",
       "      <td>-18,842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47900</th>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV</td>\n",
       "      <td>-17,818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12420</th>\n",
       "      <td>Austin-Round Rock-Georgetown, TX</td>\n",
       "      <td>-15,441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34820</th>\n",
       "      <td>Myrtle Beach-Conway-North Myrtle Beach, SC-NC</td>\n",
       "      <td>-12,503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20700</th>\n",
       "      <td>East Stroudsburg, PA</td>\n",
       "      <td>-12,068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35840</th>\n",
       "      <td>North Port-Sarasota-Bradenton, FL</td>\n",
       "      <td>-12,006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15980</th>\n",
       "      <td>Cape Coral-Fort Myers, FL</td>\n",
       "      <td>-11,650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               mname  tot_netflow\n",
       "mcode                                                            \n",
       "33100        Miami-Fort Lauderdale-Pompano Beach, FL      -69,067\n",
       "37980    Philadelphia-Camden-Wilmington, PA-NJ-DE-MD      -47,862\n",
       "36740                  Orlando-Kissimmee-Sanford, FL      -43,301\n",
       "14860                Bridgeport-Stamford-Norwalk, CT      -38,491\n",
       "12060           Atlanta-Sandy Springs-Alpharetta, GA      -36,886\n",
       "31080             Los Angeles-Long Beach-Anaheim, CA      -35,285\n",
       "39100           Poughkeepsie-Newburgh-Middletown, NY      -34,838\n",
       "45300            Tampa-St. Petersburg-Clearwater, FL      -32,616\n",
       "10900              Allentown-Bethlehem-Easton, PA-NJ      -28,219\n",
       "16740              Charlotte-Concord-Gastonia, NC-SC      -25,914\n",
       "19100                Dallas-Fort Worth-Arlington, TX      -23,785\n",
       "26420           Houston-The Woodlands-Sugar Land, TX      -21,431\n",
       "39580                               Raleigh-Cary, NC      -19,017\n",
       "41860             San Francisco-Oakland-Berkeley, CA      -18,842\n",
       "47900   Washington-Arlington-Alexandria, DC-VA-MD-WV      -17,818\n",
       "12420               Austin-Round Rock-Georgetown, TX      -15,441\n",
       "34820  Myrtle Beach-Conway-North Myrtle Beach, SC-NC      -12,503\n",
       "20700                           East Stroudsburg, PA      -12,068\n",
       "35840              North Port-Sarasota-Bradenton, FL      -12,006\n",
       "15980                      Cape Coral-Fort Myers, FL      -11,650"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Net receivers from NYMA (NYMA loses migrants)\n",
    "grouped_by_metro.loc[~grouped_by_metro.index.isin(msuppressed),['mname','tot_netflow']].sort_values('tot_netflow', ascending=True).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mname</th>\n",
       "      <th>tot_inflow</th>\n",
       "      <th>tot_outflow</th>\n",
       "      <th>tot_netflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Other flows - Different State</td>\n",
       "      <td>437,112</td>\n",
       "      <td>564,810</td>\n",
       "      <td>-127,698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>non-metro</td>\n",
       "      <td>8,278</td>\n",
       "      <td>14,929</td>\n",
       "      <td>-6,651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Other flows - Same State</td>\n",
       "      <td>20,349</td>\n",
       "      <td>23,219</td>\n",
       "      <td>-2,870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               mname  tot_inflow  tot_outflow  tot_netflow\n",
       "mcode                                                                     \n",
       "59     Other flows - Different State     437,112      564,810     -127,698\n",
       "0                          non-metro       8,278       14,929       -6,651\n",
       "58          Other flows - Same State      20,349       23,219       -2,870"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_metro.loc[grouped_by_metro.index.isin(msuppressed),['mname','tot_inflow','tot_outflow','tot_netflow']].sort_values('tot_netflow', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mtop_inflows=grouped_by_metro['tot_inflow'].sort_values(ascending=False).head(n=20).drop('59').drop('58')\n",
    "mtop_outflows=grouped_by_metro['tot_outflow'].sort_values(ascending=False).head(n=20).drop('59')\n",
    "metros_for_plot=mtop_inflows.index.tolist()\n",
    "metros_for_plot.extend(mtop_outflows.index.tolist())\n",
    "metros_for_plot=set(metros_for_plot)\n",
    "len(metros_for_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mname</th>\n",
       "      <th>tot_inflow</th>\n",
       "      <th>tot_outflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33100</th>\n",
       "      <td>Miami-Fort Lauderdale-Pompano Beach, FL</td>\n",
       "      <td>92,153</td>\n",
       "      <td>161,220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37980</th>\n",
       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
       "      <td>95,914</td>\n",
       "      <td>143,776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31080</th>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA</td>\n",
       "      <td>56,832</td>\n",
       "      <td>92,117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14860</th>\n",
       "      <td>Bridgeport-Stamford-Norwalk, CT</td>\n",
       "      <td>50,082</td>\n",
       "      <td>88,573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39100</th>\n",
       "      <td>Poughkeepsie-Newburgh-Middletown, NY</td>\n",
       "      <td>50,002</td>\n",
       "      <td>84,840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47900</th>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV</td>\n",
       "      <td>63,730</td>\n",
       "      <td>81,548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36740</th>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL</td>\n",
       "      <td>26,723</td>\n",
       "      <td>70,024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12060</th>\n",
       "      <td>Atlanta-Sandy Springs-Alpharetta, GA</td>\n",
       "      <td>31,599</td>\n",
       "      <td>68,485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14460</th>\n",
       "      <td>Boston-Cambridge-Newton, MA-NH</td>\n",
       "      <td>53,223</td>\n",
       "      <td>62,579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10900</th>\n",
       "      <td>Allentown-Bethlehem-Easton, PA-NJ</td>\n",
       "      <td>33,432</td>\n",
       "      <td>61,651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45300</th>\n",
       "      <td>Tampa-St. Petersburg-Clearwater, FL</td>\n",
       "      <td>22,983</td>\n",
       "      <td>55,599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41860</th>\n",
       "      <td>San Francisco-Oakland-Berkeley, CA</td>\n",
       "      <td>31,726</td>\n",
       "      <td>50,568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45940</th>\n",
       "      <td>Trenton-Princeton, NJ</td>\n",
       "      <td>38,174</td>\n",
       "      <td>49,553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19100</th>\n",
       "      <td>Dallas-Fort Worth-Arlington, TX</td>\n",
       "      <td>17,075</td>\n",
       "      <td>40,860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16980</th>\n",
       "      <td>Chicago-Naperville-Elgin, IL-IN-WI</td>\n",
       "      <td>34,771</td>\n",
       "      <td>39,150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26420</th>\n",
       "      <td>Houston-The Woodlands-Sugar Land, TX</td>\n",
       "      <td>17,111</td>\n",
       "      <td>38,542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16740</th>\n",
       "      <td>Charlotte-Concord-Gastonia, NC-SC</td>\n",
       "      <td>12,197</td>\n",
       "      <td>38,111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12420</th>\n",
       "      <td>Austin-Round Rock-Georgetown, TX</td>\n",
       "      <td>18,490</td>\n",
       "      <td>33,931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12580</th>\n",
       "      <td>Baltimore-Columbia-Towson, MD</td>\n",
       "      <td>20,679</td>\n",
       "      <td>31,144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              mname  tot_inflow  tot_outflow\n",
       "mcode                                                                       \n",
       "33100       Miami-Fort Lauderdale-Pompano Beach, FL      92,153      161,220\n",
       "37980   Philadelphia-Camden-Wilmington, PA-NJ-DE-MD      95,914      143,776\n",
       "31080            Los Angeles-Long Beach-Anaheim, CA      56,832       92,117\n",
       "14860               Bridgeport-Stamford-Norwalk, CT      50,082       88,573\n",
       "39100          Poughkeepsie-Newburgh-Middletown, NY      50,002       84,840\n",
       "47900  Washington-Arlington-Alexandria, DC-VA-MD-WV      63,730       81,548\n",
       "36740                 Orlando-Kissimmee-Sanford, FL      26,723       70,024\n",
       "12060          Atlanta-Sandy Springs-Alpharetta, GA      31,599       68,485\n",
       "14460                Boston-Cambridge-Newton, MA-NH      53,223       62,579\n",
       "10900             Allentown-Bethlehem-Easton, PA-NJ      33,432       61,651\n",
       "45300           Tampa-St. Petersburg-Clearwater, FL      22,983       55,599\n",
       "41860            San Francisco-Oakland-Berkeley, CA      31,726       50,568\n",
       "45940                         Trenton-Princeton, NJ      38,174       49,553\n",
       "19100               Dallas-Fort Worth-Arlington, TX      17,075       40,860\n",
       "16980            Chicago-Naperville-Elgin, IL-IN-WI      34,771       39,150\n",
       "26420          Houston-The Woodlands-Sugar Land, TX      17,111       38,542\n",
       "16740             Charlotte-Concord-Gastonia, NC-SC      12,197       38,111\n",
       "12420              Austin-Round Rock-Georgetown, TX      18,490       33,931\n",
       "12580                 Baltimore-Columbia-Towson, MD      20,679       31,144"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_to_plot=grouped_by_metro.loc[metros_for_plot,['mname','tot_inflow','tot_outflow']]\n",
    "df_to_plot.sort_values(by='tot_outflow',ascending=False, inplace=True)\n",
    "df_to_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tSOfnDcCAfOeiKWcBn5HO21p7nnT8Sy6QNK7sq6n+jQI2krRy/nwgs2e1K7L9D9JFjOa04yGEEMKCrrWZ7CUljSus+wHpVvJbKR7hadsT8vxxpFvRFdm+VNJWkn4BfAvYgBSs9wKm2n4wL3eLpGvyatuTMqUP5u1BuiW/doVNXJlrov8haQypDKIemGi7dFEwDjikrF+fSdoV2CUH9b1J2b1KZtD8hcpBQD9JJ5Eyokvm9j4EpgL35OUmAk+UMoOS3iFdKGwB3Gf73bzc/5GCR0h14JsCz+b90QX4WmHb9+TM9aeSJuT2KhlNCsbfA16z/ZGkkcAfcs35RsAjzYzzAdufFPo+pjCuatu9C2beEXkvL9cP+IPtj3NbV5KC/HI7kUoo3s9tDJV0KfkOAjDG9pe5jReb6EO5hyVNJ+3Lz0l3SkpP9gwGRtr+EPhQ0uukC71zKjcF+U7EAaSyk3tb2IeWaiRdnJa0tlxkCnA7KRt+EemCchtgrznY9kxjhxzXii6EEDqyHkNua8XSLza/SIezcI5p5Nm9ml1mfjF58mQaGpqq7Gw7c1WTXcHUws+NpIekKpJ0HilAvI6UrV60sHz5eqUHrLoAD9ret9DO6sA7pCxqpXUgBcKlbHOTfZS0Gimr/H/AX0hByK553rWk7DWkW/RPAT+T1KWYzc4P6B1t+0BSRv8FUtB5G7BZYZtTyh6OLPatqNjH4ri6kB5Auypvd3Fg+cL8SeVjzQ8flkocns318qOB4aSgaSSA7fE5wN4deNz25Cp9K/my7HO1sRR9pX+k8RXHO9tdgoIuFLL6WSfSeVSt7ZaYWZNdlO+WHAh8mUtdAJYBjpJ0Ian+vpT5v5t0XgMpCy/px6Ts97BCm3uT7mR8kfs3NU8vXchCroGvog/pIcUmSdqdVGoD8I7tfoXZw4CrJT2ZuuqPChew1dorlZVMbG7bIYQQFjw9e/Zs7y60WENDQ5v2t76+vuq89nxP9o7AJbaHkzKo25MCpxdIwWA/mBkglALH0gN56+Z5/fLyS1Zof0Bephspiz26hf3ahPTipDOB+5gVYHexPch27/x1da4FfwW4OAekpVrzy4HXJS1HCoROsP1HUqnK2nmcLXVfHnPpgb+BhXn3AoPyA3qQAqnhTTWW+10aw6A87SVgWeAHzKo9hlR+chLNl4rU0ihgT0nL5s+HkoJkSAF4KYgeA/xI0tcBJB1Mujswgbaxf25/Fdvdnd7ushYp2Nzb9mmF/fqVh2hzhnk08LPC5HNJdyogldlMzMv2LnxVfKhY0qF5+82mkWzfXWivX9m8saR/P2eTauSblM/py0lv6WnuwiuEEEJYaLVnkH0GcKGkF0iZv78Aa+e3WfwQ+E3O6O1BCsKx/TLp9vytkp4HfgPsXuWBtjUl1ZOCsaNb8XaH+4B/AAYaSA/lvU/lkhRIDx12Aupznx4E7gB+lUsezgGekzQeOBF4vIm2vsL2i6Ta4wfzQ3hLFGZfSwqKn5L0Eqn2emBL2y7zALBE3sclo0ilIn+awzZbzfZDwG+BJ/N4l2VWWcJTwFqS/phr3oeQ3rDxEqksZ9cKD3bORtIZSq9rbK3BwMXFOxb5+F5Geii1JY4mPeBbciRwiaRXSLXj1Wr/AfbNNdZ/zefZjkDfskC3Uk12S96aM5xUXz2myvybclv1pLKhZ4BftqDdEEIIYaHVqbGx5q9ybnf5dv5e1bKAYf6l9B7wLW1flj//AtisWCI0l+1/i/Su7RNr0V6YXX19feM+Z0YVSQghLMgmjtinvbvQYvOiXKSurq5iSWr8xccwv3kVOCG/nq+R9IdfDq9h+yJln0Mb6Uj/+Tanrf9zbg8xpo5hQRvTgjYeiDGF5i2QQbZr9NcQw7yX367S6tfttaL9kc0vFUIIIYQwd9qzJjuEEEIIIYQFUgTZIYQQQggh1FgE2SGEEEIIIdRYBNkhhBBCCCHUWATZIYQQQggh1FgE2SGEEEIIIdRYBNkhhBBCCCHUWATZIYQQQggh1NgC+cdoQgjtp/+jfdq7C7X1fnt3oA10oDHdvPUz7d2FEEKYIwt1kC2pOzDedtc23MZuwN3Aj2z/vg23M5Q0lgtr0FZ32ni/VNluX2A04DxpEeADYLDthjbYXiPwddsftHD5lYC3gKG2B8/ltrszB/tY0rXArbYfmJvtF9rrAhwD9Cft78WAe4DTbH9ZWG6enMchhBDCgiLKRdrekcBNwM/buyMdxETbvfPXBsCfgMvau1PZocBdQH9JK7RHB2wPqlWAnV0FbAF833ZvoA8g4Nqy5eI8DiGEEFphoc5kN0XSssCVQG+gkZRhPcn2NEm/Bv4bmAJ8CAy0/c8KbawF9AXWABokbW77qTxvKPAp0AtYHXgBGGD7M0n9gPOA6cA4YDvgu7bfkHQoKeDpnLd9lO1XyrbbE7gUWBHoAlxm+zpJXYHrgW8BM4B64Me2Z9Rov0wGzgV2AFYGzrd9Vc6WXgDsDnwCjAXWs923mW11AlYA/lmYVnH8ktbJ/Vo6b3scsK/tyZI2IwXqS5GO2bG2H8pN/lrS5nlfXWD7yip96Qz8OG+7K3B4HiuSBpLOhxmkffsFcJDthtz2+cDiuV/32z40N9tF0tXApsCywPG278htngzsmcf5BnCk7XckPQJcATwLPATcD9SR/i2flvu4bp6/X1PHNmfT9wdWtv0pgO3PJR0BfKewXNXzOIQQQgiVRSa7ustIQVwvYBNgI+BYSasDPwP62N4EuA/YrEobRwCjbL8H3MpXs4B1wE5AT6A7sLekFYHhwAE5s/gwsCqApG2Ag4CtbH+bFLyNKDYoaRHgduBE23XANrnfm5MCwaULGUuAtWqxX/K8xYEPbG8J7AUMkbQEMCiPdQNS1rRHE+33kDRO0jjgHWAwcEkLxn8YcIPtzYG1gTWBXSQtCtwJnJEz44cBl+agGeBveT/9N3BRXr6SnYCvAQ8ANwBH5X1dsg3w07yNscCJefoxpNKLzYD1gN0l1eV5S5CC7o3zPjw/j3NA3r+b5mP1J76aWSaPcVQ+D58kXVjtB6wPbAVsXmUsJXXAS6UAu8T2u6VgP2vuPA4hhBBCmchkV7cz8B3bjcCXOeP4M1Ig9DzwnKTRwGjbD5avLGlx4GDgkDzpBuBxSavbfitPG1Oqe5X0IilruzXwsu3nAWzfIKlULrELKYB8QlJpU8uXlS6sQwpirysssyTwbWAMcHbOht4PXGJ7Qo32y7l5/l35+3OkoHspoB8wzPbkPNZrgKOrtD8xB5bkZQcA90las5nxnwBsL+n4vA9WIWWcewHTbY8CsF2fp5HbuDm3My73dxnSRUS5wcBNOWN/N3ANsDdwS55fb/sfhbHvkX8+COgn6SRShnnJ3K8PgSmFYHYc8I38866k7PazuY9dSAF+uamk+mmAicATpYBZ0juk86kpM2jmQruF5/Fsxg45rpnNhtByPYbc1sIlX2zTfrSP9hnTyLN71bzNyZMn09BQ80dr2lWMqWNozzFFkF1dZ1I5RPHzorZn5IzqJqQyjiGSxtg+vmz9fYDlgSskXZ6nNQI/BUrLTios3wh0Aqbl70WlW/5dgOG2T4CZJQyrAP8uLNsF+KQsUP1mnjZZ0tqkW//bAg9IOpyU1d49L343cF3VvVJlvxQ+TwKw3ZgDxEpjmt5E+7OxPSxfZKxH0+O/lXQ+3waMAroVtl3sL5I2AEolNlPL+5sfLtwkz7+aVBLTD9hY0p55+iKkjG4pyK50LAEeJZUCjcl926wwb2qVdboA59m+Kvd3cdK5VG5KvtgpmVphmaaMBXpKWtr2f0oTJa0K/B/pbsReNH8ehxAWID179qx5mw0NDW3SbnuKMXUMbT2m+vr6qvOiXKS6e0klAZ1ykHM4cL+kjYDxQIPtc4AhzCq9KBoMnGV7DdvdbXcn3XY/TNJSTWz3cWAdSRsC5KBuOVJgcy+wn6SV87JHAOVZdAOTJB2Q118997dO0mBSTfZ9OVC9F9jY9mmFhw1Pm5P90sw6o4ADJC2eSywGUhb4ViOpVBtsmh7/jqSSkNKbLzYjBasGGiVtn9vbmFTLXPXczw8XlvbH1aQ657/YXrVwLOtIQfeWTfR9OdK5cYLtPwKrkTLxXZoZ9r3AIEnL5M9nkEqIasr2O6SHGa8rbSt//1/gQ9uTmPPzOIQQQlioRSYblpL0Wdm0LUjlDJeT7tctRspEnmV7iqTbSLfyPyNlMGcrfciBeG9mZYdLhgGnkILMimx/JGk/YJikGaQH2KYBX9i+T9J5pGB/BunByT0KWVhy/35Aqjs+npRlPtX247nOuS/wsqQvgL9T/c0drdov1caTDSW9seKvwGfA66SHAyvpkfsJKRD+Mo/xY1LZSLXxnwSMkPQ56eHKPwNr2/5S0h7AJZIuID34uEfeT810GyQtRnqryCHF6bZfk3QLKZs9qtK6tj+WdA6ptOhz4B+ki6i1SeUd1VxLqsN/Suk1g3+niXOmBWO4Fng2XzCUOxI4lVSCM41UMnMn8KsWnscVHxQNIYQQFnadGhtblFAM80jOJJ4CnG77i5x5HQWsUlYa0GFI2gH4hu0b8+dLgcmlso/QtnIWv0eVILum6uvrG/c5s6nrhxDC/G7iiH1q3maUIXQMMabWq6+vp66urrzMF4hM9nzH9qeSpgDPSJpKqrPdp6MG2NlLwHE5s96F9ODoXP0xl9AqK5LKQuaJtvgF3V7iF07HEGMKIcyPIsieD9k+hZTNXiDYfhvYvr37sbCyfWt79yGEEEJY2MSDjyGEEEIIIdRYBNkhhBBCCCHUWATZIYQQQggh1FgE2SGEEEIIIdRYBNkhhBBCCCHUWATZIYQQQggh1FgE2SGEEEIIIdRYBNkhhBBCCCHUWPwxmhBCTfV/tE97d6G23m/vDrSBeTSmm7d+Zt5sKIQQ5kMRZLcTSd2BicCLeVIX4AvgF7Yfr7D8JsCJtveqMG8kcLvtoW3W4WZIWhYYYXvbKvNXBi4B1gMagUnA2bbvyvNPA54vfa5BfwYCZwANtnecwzZWAt633anCvL7AQ8COtu8vTL8C+MD26XOyzTns5xvAXkBX4ArbG7Ry/SWAk4FdgU6kc/FG4HygJ3BzXnQFYFng9fz5BttD5rL7IYQQwgIpguz2Ncl279IHSfsAQ4FvlS9o+1lSIDW/Wh7YtIn51wIP2N4XQNJ6wOOStrTdAGwLvFzD/gwATrJ9Yw3bLDcFuEHShrY/aMPttBlJnYA7gVeBLWxPlrQiMAroavtUoHdediCwl+1d26u/IYQQQkcRQfb8ZUXgnzAzU3op8DkpQ3kccJHtDSStAtwArAK8CXyj1ICkfsB5wHRgHLAd8F3bb0g6FDiSVIv/IXCU7VckDSVllnvntu4DjrY9VdJWwAXA10hB5Sm2x+SA61BgKeCTvPklJY0D6mxPLxvbynl+Z9szbL8saXfg35J+AmwCXCBpOvAScCWwdF5vHLBvDgAnAxfncXUFTgf2BnoB7wC7AWeSAv41JX0duC6315uURR9NCsCnSfoSuAvYCNgf6AacRbqr0Ny97gnAWOD6vN3ZSFoNuAroTsoQ32D7gnwX4zGgIc97HbjN9u8kbQE8AfSw/TdJp+T9cDFwDfBN4L9Ix30f2+9V6pik75Iy0D+y/UQTY9ialK3epXTMbH8o6cDctxBCCCHMgXjwsX0tKWlc/nqTFFSfU5i/AbCf7Q2BLwvTrwSesr0+cDSwLkDOQA4HDsgZ8oeBVfO8bYCDgK1sf5tUCjCi0OZmwPakco71gB/n9m4Hjsl9OAi4UdKaeZ31gb62vwccTM7MVwiwAY4FjgLek3SXpOOAv9l+1/aVwLPAcbZHAIeRAtLNgbWBNYFdcjuLA+/a3pR0oXEt8LPc52WBH9j+eaG9IcBlpIuKXqRgfqPcH4DFgHtsC3iLFJDvabuOFMg252hgHUlHVZh3E/Cw7V7Ad4ADJP0oz1sN+I3tdfJyO+fpOwHvki4iAHYH7gB+BDxpewtgLdJFwIGVOiTpe6Q7Irs2E2BD2h9jy4+Z7deKZTAhhBBCaJ3IZLev8nKR7YA7JfXKk96yXSnQ244cJNqeIOmhPH1r4GXbz+d5N0i6LM/bhRSwPiGp1M7yklZE1v4mAAAgAElEQVTIPw+1/VnuxzDgh8DfgAm2x+b2XpL0ONCXlBF+wfanLRmo7YckdQM2z/3cDThN0ra2yzPGJwDbSzoeWIeUse9amH9H/j4ReNH227nfr5PqhsvtDHzHdiPwpaSrSYH5uXn+Y/n7d3N7pbKVa4CzmxnX55L2Ax6W9EhpuqSlSIH1Dnm5T/Idg52Bp4BpwJN58XuAiyUtAuxIysRvn2vtvwE8Y/tpSVtJ+gWpnGgDUha93GrASOAq2y801fdsBjW+2B475LhaNhc6sB5DbpuHW3ux+UU6nNqPaeTZvZpfqA1MnjyZhoaGdtl2W4kxdQztOaYIsucjth+QNIFU6vAv4LMqizaSyg9KphW+lz+kNyN/7wIMt30CgKTOpOD132VtQAq6pud1Gsva6wwsSiodqdi/XAZyRv74DjCQVNbxU9t/Af4CnC3pWlJ2vDzIvoV0bt5Gqg3uVjauYlZ/aqU+VOhzY9nnRQufi+OotF/Jfd0kf7waeKU0z/Zzks7M/S4Fvp356rEobvdL29Py+v/OZTa7AcsAw4BTSRc6I2w3SjqPdF5cR7pDsWiF9kt97gfcJekPpQukJjwF/ExSl2I2W1IfUslQxWx5CKFj6tmzZ7tst6Ghod223VZiTB1DW4+pvr6+6rwoF5mPSFqHVAf712YWHQMcntfpBnwvT3+cVLqwYZ63J7AcKcC8F9gvv+UD4AjgwUKb+0paPL9p4iBSdvVJYF1Jm+b21idloR+p0KdpQBdJnWzfnctGetvuB3xEKkU5Jj9oh6SvAT2A5wrrlwLQHYEzbP8+f96MFPDPqXuBoyR1krQ4ad9VKoV4FFhf0kb588DSDNuDCmO6usK6F5LKPA7Iy/+HFMD+BGa+fWVAle0C/JGUNX8wr/sqcCKzsvY7ApfYHg68R9qflfbJu7lE5FhgeN7PVdl+knTBcHE+9kj6JnA5s94iEkIIIYRWiiC7fRVrsseR6p8Pt/1qM+v9BFhPUgPwO9KDgdj+CNgPGCbpOVJgNg34wvZ9pAci75f0AtAf2COXUECq8X2MdH/yMeD6/MaMvYHLJb1IepDu4Cr9+yfwNPBSruWeKWdsdwC2AF6XNJ6U8R1p+7q82N3AOZIOAk4CRuRtXgP8mVTqMqeOJpVdvJi/THq4cTa23yftl5vy/luzfJlq8n4cAPynMHl/4Pt5HE+TAumhVZq4ExCzgvB7SRcdpZrqM4AL87G7m3Q3oOo+sX0DKXi+CEDSn/Idhkr2JGXF6yU9T7r4ugP4VbX2QwghhNC0To2N5dUAoaOStAxwCnC67S8kbUwqt1ilEExXWm8oMN72hfOmp2Fek3QYqcZ/TFtup76+vnGfMye25SZCCHNh4oh92mW7UYbQMcSYWq++vp66urpK5ZtRk70gsf2ppCnAM5KmkuqV92kqwA4LjWnMXh7UZtrrl3hbiF84HUOMKYQwP4ogewFj+xRSNrs16wxsm96E+YXt69u7DyGEEMLCJGqyQwghhBBCqLEIskMIIYQQQqixCLJDCCGEEEKosQiyQwghhBBCqLEIskMIIYQQQqixCLJDCCGEEEKosQiyQwghhBBCqLEIskMIIYQQQqix+GM0IYSa6v9on/buQm29394daAPzYEw3b/1M228khBDmYwtEJltSd0nTJY0rfD0v6ZA22t5ASSOrzHtE0l5tsd2WkHSGpAGtXKe7pEZJh5ZNP1bS0Br0qa+k8XPbTi3al3R6HuvBZdOXkvRZ6bhW24+SVpJU8c/UF/dXS84DSctKeqgl/W4LzR13SctLekvST8rmbyTpY0m95m2PQwghhI5jgQiys0m2e5e+gH7ARZI2bO+OzUu2T7M9bA5WnUHaX6p1n+ZDfwcOLJu2J/BZ6cNc7MfWWB7YtI230Zyqx932v4EDgHMlrQMgaXHgRuA42y/O056GEEIIHcgCWy5i+21JrwHrAC9IOhXYD5gGvAocZftdSY8AV9i+HVIGsvRZUj/gPGA6MA7YDvhu3sTKkkYB3XKb/W03lLYvaRHgZmAqcBCwFHAp0AtYFHiQFKhMk9Qzz1sR6AJcZvs6SX3z9t8E1gUmAQNtN0j6LnBxXr4ROMf2HTmTOt72hdXarbLLJgEXATdL2sL2lOJMSYvlvmyT2/orcDRwCFBn+0BJiwIfAsfYvj738SLgBKCrpNuBtYGPgcNtv1rsb95Osf9vAGOBDYGTgH8AVwGLAROBNYBf5C52lXRr3k9LAIfZfqzKWMcAP5S0mu1/5GkHkYLHdSv0Yw/gLOALYOY98Dzey4DtgfeAfwGflG9M0pZ53y1FOpd+bXskcD2wpKRxQB2wJXAB8DVgCnAKcD/wLrCF7QmSfgkcYXuN3PYDhX38JPAd0jn5QN7HM6rsg5Imj7vtP0u6DBiWx3Em8KLt3zbTbgghhLBQW5Ay2bORtAUpoBubSwN2BvrY3hAYDwxtZv0VgeHAATkz/jCwamGRtUjBZC/gUeDYwrzFgD+QAq8DbE8DhgD1tuuAbwMrAb/IwfjtwIl53jbAsZI2z21tAlye+3197hPAr4GL8zqHANuW9b+5dis5i5TNPbvCvBNJFxN1tjcC3gHOBf4I7CipM+kC5HNS0AmwO3BH/nn13N/epIuP4bTMeNs9gXvytk7N++IyoHdhudWAIbn9a4DTm2hzKnAbsD+ApG7A0qTzYjaSvglcB+yZ9+ObhdlHki7i1iONuVuF9ZcnHbcDbW8M/AC4Km/zYPIdGGA50vE6Jo+vFPR3y2PfKTe5E7CYpHUkLQtsRLpgA+gB9CVdlOxMOuYt0dRxB/hV/n4dsCvw4xa2G0IIISy0FqRMdikjCGlcHwD7235L0s7A9bY/z/MvBU7O2dlqtgZetv08gO0bckav5GnbE/LP44A9CvMuIgVtPWyX6nd3BTYt1L8umb+vQwqOrivcsV+SFIg3AM8XMrLXAVfmC4Db8s+7kbKWJ5X1v6l2n6o0YNszJB0AjJN0b9nsXUmB4Pa5vcWA92z/XdJbpEzsTsA5wC8ldSIF2buQAuwXbD+R2xpKCjSXrdSPMqWx98p9HJ2/P1xWhz3R9tj88zjShUdThpH253mk0pFqpSHfJWVuX86fr2FWMLodcHPO/k6RdBMpwC3aAlgZuLNwHBrzcsX+bwZMKI3B9kuSHicFzSOAIyTdAPwX6SJle+AjYIztKbnte3Lm+lNJE4AVmtkH5G01ddzJd1v6k+4ebG77P021N3bIcS3ZbFjA9Rhy2zze4oJYvdTyMY08e/5+RGLy5Mk0NDQ0v2AHEmPqGNpzTAtSkF3KCFZSKqko6Uwae6c8vVNhXinwnlY2HVL9asnUws/lbQzPn39LCjRLfdi7VFIiabm8Xjfgk2Lfc/b0E2Dz3I+S0jam275G0j3ADqTg9vSyutou1dqVdEahX3eTgk0A8kXJj4EbmD3w7ELKso7ObXUllWVACgL75b7sAvQH9iUdk4mSVieVSRQ1kvZhtf1fUqqTrnQ8im1WPB6FCy+AQYVxPiOpi6Teua99mbVPyhW3O60V8yDttwbbm5UmSFqF9H6HVcuWK3+gsjOptOh+4FrSvn0kfx5MKl+5tbD8pMLP5fu1SU0c99L8v+XTa2JL2wwhzDs9e/Zs7y40qaGhYb7vY2vFmDqGth5TfX191XkLbLlImTHAIZKWyp+PBh61/SUp2NkEQNJ6zMpEPg6sU3pwUtKepExuxTdLlHkaOBVYW9Jhedq9wM8ldcoPj90NHAUYmJQzieSAdDwpMwzQu/Dw5uHAE7Y/lvQE8G3bQ/P05UhZzpKq7eaH+koPiZ5W3vlcnz4a+Flh8r3AUZIWy6UhvyVlrSGVcfQHOtt+B7gPOJ9ZpSIAG+WAFlK5wV9sf8Hs+38Vqpc4NABfStopL7spKbvd5PEoPgxr+9my2cNJZTyv2v6oShOPAutL2ih/HliYNxoYIGkJSUuQgvVyTwHfkrR17ndv4DVSgD0N6JKz/k8C6+ZxIWl90t2UR2xPBv5MKtu4L/+8BbAV6bjURJXjHkIIIYQ5sLAE2b8jlVQ8LakB2Jhcj0t6kGuHXHpwBimoIgdd+5Ee+HoO2JEUFH3Rkg3mwGggcIGkHqTAfinS/b8X8vfzc6nBD4BBkl4gBVGn2n48N/UucJakF4EfMuutGMcDZ0j6Kym7+WvbbxS231y7zTma2euPfwO8QXrg8WVSlvR/8rZeJgW7pdrge0klIsUguwH4laTnSRnjg/L0y0kPkZpUu1zxlXa5rn1PUsb+r3nb79LC41HFjaRAdmi1BWy/T7qAuCmfB2sWZl8DPEu6ePkz8HqV9fcknQfPkwL7A/Ox+ifpguwl0v7bG7g8H+ubgYNtv5qbGkEqAXrI9iTgeeDxfJ41SdK1ko5obrms/LiHEEIIYQ50amxsSWJ24SNpGdLbHU63/YWkjYFRwCqFOuu27kNf0ptONpgX25vfSboAuND2v3Jm/nlgLdsft3PX5muStic9H3B1W2+rvr6+cZ8zo6IkhHlt4oh92rsLTYoyhI4hxtR69fX11NXVVSzPXJBqsmvK9qeSpgDPSJpKqvndZ14F2KGiN4EH8/HoBAyKALtFVgRumlcbm99/2bdG/MLpGGJMIYT5UQTZTbB9Cimb3V7bfwSILHZm+wrgivbuR0dj+9bmlwohhBBCLS0sNdkhhBBCCCHMMxFkhxBCCCGEUGMRZIcQQgghhFBjEWSHEEIIIYRQYxFkhxBCCCGEUGMRZIcQQgghhFBjEWSHEEIIIYRQYxFkhxBCCCGEUGPxx2hCCDXV/9E+7d2F2nq/vTvQBhbSMd289TNt348QQsiaDLIljQFG2740f14HMHCO7ZPytG8AbwHfsP1JazYuqS9whe2v/FVDSWcAE2wPa02bhfV3ATazfdqcrN9M288CXwPWr/Zn1iV1B8bb7irpCGA52+fO4fb6AIfaPmJO+9xE23cA2wDdbH9RmN4IfN32B7XeZgv7dSywge2BFeZdC9xq+4E5bPt04CfA23nSosALwGm2XysstytwLLBcXmY8cKztt8ra6wy8Bpxc/tcVJV0OzLB9TP68Eunfy1Dbg5voY1/gYWCY7YPK5j0CbJLPrb7AaNK/S4AupHDj7Er7R9LmwCjSsZ2Rp90C7AGsZPs/edr/Ap/n6c2OK4QQQgiza65cZDTwvcLn3YB7gB8Upm0LPN7aALs5tk+b0wA76wOsUKv+lEjaDFgCmALs2JJ1bF89pwF2tj6w2lysX5GkVYCtgaeAAbVuv63YHjSnAXbB7233zl/rA38CHpK0DICk/sD5wCDbvUl/3n4c8LCkxcv6MwO4Gji0OF3SksD+wP8WJh8K3AX0l9Tc+flPYDdJXyu0uQawTtlyEwtj6QX8HLgxn6vlngYagQ1ze4uQ/o0/AuxUWG5b0r/1lo4rhBBCCAXNlYuMBn4lqXMOJHYDTgJuldTD9kTg+6TMWCnzdxKwGPAN4Abbp0rqClwPfAuYAdQDP87b6CrpVmBdUvB6mO3HJA0lZYIvlDQZOBfYAVgZON/2VZK6ABcAuwOfAGOB9YATgCOALpI+sX2ypFOB/YBpwKvAUbbfzVnBJ4HvAN2AB4DDS1m+CgYDI4EPgJ8BY/LYuwOPAQ1Ad2Bm9jFnTleyfZSkN4Cheb91I2UqT83LnUgKaP4DPAr8ENgKOANYVtL1tg+WdDhwNDAd+Fcey6t5n30K9AJWJ2VnB9j+rMpYDgceBG4HfiPpmkqZeUmHAkeSLso+BI7K+/B+oN728ZK2y+OqA5YFrgSWJh2vccC+tic3cSwXBS4Dtgfey+P6JG//EeAj0jlyFbAn6Q7I7ZJOIl30LQksRco0j6gy3qpsD5d0INCfFFieRToPJuT5jZLOBf4OLA58WdbEdcDpktaw/Waetg/wrG3ncXQmnfdHAl1J+7+pi6+PgImk8+DmPG1A/rnqXQ3bz0u6jBRs/6hs3gxJ9wF9Scflu6Tz5DbSv6M/SFoV+CbwBPBSc+MKIYQQwlc1mcm2/Srwb2BDScsDImU9/0T6hQw5yJbUCfgf4CDbmwCbA7/Mt8f/G1g6ZwRLBZtr5e+rAUPyvGuA0yt0ZXHgA9tbAnsBQyQtAQwiBXUbAFsAPXK/x5ICpd/nAPtgYGegj+0NSbf9hxba70EKOjbMy21TaX/kzOO+wI356/uS1issshrwG9vrkLKQ1XS1vRWwJXCspDUl7QgMzPunjhSgkksTTgMeywH2tsDxwPdsb0QKuO7M+5+87k5AT1Kwv3eVsSwCHJbHcQ8pqNqpwnLbkC4YtrL9bVJ2d0S+CDkAGCDpB6T92d/2v3K7N9jeHFgbWBPYJTdZ7VgeScrQrkcKtLuVdeXfttezfXmhb2sA2wF983E9mXRBMqeeB3pJWpG07x4vzrTdaPsm25+Wr2j7Q+APwMGFyYeTLjZKdiKVGT0A3AAclY9DU4YBBxY+78usgLvZsVSZN5p0vkO6cB5J+je9c75w/T5wr+1pLRxXCCGEEMq05MHH0i/k94D7cyZsJPATSSOARtuvAEjaDdg132rvCXQiZRf/ApydM5L3A5fYniBpNdKt7rF5W+OAQ6r04678/TlSoLYU0I+UCZ6ct38NKcNbbmfgetuf58+XAidLWix/vicHjZ9KmkD1MpODgZdtj8/bux84hllZ+WmkrHhz7gKw/bak9/L2+gF/sP1xbvtKUrBTbifSxcP7uY2hki4lBYUAY2x/mdt4sYmx/IBUvzvG9rR8N+FnpONdtAspUH5CUmna8pJWsP1PSYfl8fzK9qN5/gnA9pKOJwXOq5Ayt7ONn9mP5XbAzbanAFMk3UQuacgeKx+A7TclDQD2l7Q26cKua/lyrdAIfEG62wKtf/vOlaRM8BmkrPuqpAC2ZDBwU97fd5MuKvcGbmmizXuAqyR9k3QcXiFluJtTGkslY0gXN51JQfZO+Vi+AWxCKhUZ1YpxzWbskONa0L0Q5r0eQ25r7y600oszfxp5drVr5o5h8uTJNDQ0tHc3airG1DG055haGmQfCkwG7szTHgSuJQVGpVKRpYC/AiNIAdF1pNvcnWy/noOgvqRf4A/kkof/AFML22okBeaVTIKZt+3Jy00rW356lXW75LZLOpPGXlp3Unkf8sOKpVvyz5Kys0cAK+RgBFJWsm8uWQD40va0Kn34yliK22vlWKaUTetEejCv2lh2Z1aG9x3b/UiZ4yWBCXl/LgasLGl92y+VbW+47RNgZsnDKqQ7HJDqxf8FFOt/byHt39tI50e3srFVOpaULVO+H79S8iJpY1LAPgS4D/gzqZxkTvUhXYz9W9KrpKB9ttpvSbeRSkl+SgpIAa7OdffPSHqfdHG0S54+Pa+3BulCamNJe+b1FiGVdNySH+ac2R4pmMb2FKWHU39E2tdDWzGWF3Pd/Z8K0/vZfkfS66SHGqfZ/lueN4pUPrINMDNSbmpcIYR5o2fPnu3dhbnS0NDQ4cdQLsbUMbT1mOrr66vOa0mQ/TBwOSnjOBjA9iRJ9aTa3BPyct8ClgFOyYHBgXmdLpIGk2qL97d9b87KbUwKiubGKFK5wnBSUDqQWcH0NGYFnmOAQyTdkrPZRwOP2v6ykJ2dje2rScEOAJJ2INWZdys95Jlv9ZuUyW7JLfzmxnKlpAty+4c2MZarJF1q+/1cCvMhMKFaw7bvBu4ujGUdUiC1bqnmOE//Mykzf3hh9XuBayVdYvufpAuNY4B1JW2af96EVKd/jNObaHYEtsm1weuTAvDfNzP+0aRjeWP+vC/pjR1N2ZpUG3xxLnP4X9JFQavluvO1SBcGAL8GLpX0g3zXpQvwS6A38IrtQVWaupJUXrM16Rwv+THwF9szS5EkfQtokLRleXtKbw0pGZbHtiTp4qjJh2DzcRkM7Gj7ndzncqOBU5k9Iz0S+C3wbulOSQvGFUIIIYQKmr0d7vRat9fSj7O9QWQUKbB+JH9+gfRL+hVJDaTb0C+TbnEPIwU/L+fgfFnSQ25zayjpYce/kh7SmsKsW+QPATsqvWrsd6SM5NO5bxuT3o7QGoOB/yvug5y1Ppv0OrhFq63YErYfIgU4Tyq9InDZwlieAtaS9Efb95Mytw9JeokU+OzaxIOa1cYyohhgZ2cAB+Y6+lK/7gPOA+6X9ALpwcA9SGUZtwA/tf026QLnNEnfJj38OiKXq1xDuphau5k+XUO6YzA+L/96C8ZxC7BSPqYvk7LdK0haWtIqksblTG4l++b5f5X0POnCoG+p9Mj2zaRje4ukcaQHANcDti2V41Rxa27rYefXH+aypENJ9ewzOb0u8BZSNrsq20+SSmpGVrlT0iOPZZyk50jHq7/t55todjSpHKcYZD8L/BezZ76rjiuEEEII1XVqbKz4mucOoZRdtn1j/nwpMLlU2tCRSNoE2NL2ZfnzL0jv+d63fXvWcSm9beWnzu9+Dm2vvr6+cZ8zJ7Z3N0JY4EwcsU97d2GuRBlCxxBjar36+nrq6uoqljp39L/4+BJwXH7ArgvpjQpV/8DHfO5V4IRcq95IelXc4U2vEqpRerf0AxFgz3sdPRgoil84HUOMKYQwP+rQQXYuU9i+vftRC/m1cBVftxdaL5c53djsgiGEEEIIbaC1rygLIYQQQgghNCOC7BBCCCGEEGosguwQQgghhBBqLILsEEIIIYQQaiyC7BBCCCGEEGosguwQQgghhBBqLILsEEIIIYQQaiyC7BBCCCGEEGqsQ/8xmhDC/Kf/o33auwu19f7cN3Hz1s/MfSMhhBA6lMhkhxBCCCGEUGORyZ5PSToCGAwsCjQCzwEn2/57leXfAPay/WwNtr0S8L7tTq1cbygw3vaF+fOywD3Ae8D+wJXArbYfaGW7qwC3296yNevNS5JWBi4B1iMdr0nA2bbvmos2ewN3AB8De9p+Yw7bGQ8cZfuRZpZrBMYD0wuTn7U9qJbnVwghhLAwiCB7PiTpQmAjYFfbb0nqDBwAPClpM9v/aN8eNk/SN4AxwBPA0bZnAIPmpC3b7wDzbYCdXQs8YHtfAEnrAY9L2tJ2wxy2uTvwsO052m9z6Hu2P5iH2wshhBAWSBFkz2ckrQYcAaxu+98AOUAdJqkO+CXwk5xZHAtsCJxUWL8zMATYHFga6AQMsv14zjR/CvQCVgdeAAbY/kzSHsBZwBfAbAWkkk4F9gOmAa+SsqLvNjGGbsB9wHDbZxWmPwJcAdwJXA58B5gK/A04GJhcZfpKpAx5V0mnAz2AVYGVgXrgYeAgYE3geNu3tHS53K+TgT1J5VNvAP/P3p2H2zWefRz/nkSMUWMHURWCnxgqHLMi0sYQMbTGxlxqHmqst1VVQ2oOSlFKqKnGIq0x5ik4JBI5fq2xxgpqqiREzvvH82yWbe8zZZ+cnJP7c13nyt5rPesZ1trv6173vtfu/rbfyJn4s/P56gWMBo60Pa3CshcD5pHUw/Z02xMlbQn8N4/xM2AfYE5gYeBk2+dL2h34MTAdWDaf/92A1YD9gZ6S5rG9U7XrkM/re8DywPl5npcA8wLPAfNVu1YhhBBC6BgRZM961gIaSwF2mbuBEwvvJxQypyMKx/cB1rE9XdLRwNHAFnl/PTCIFNSNAbaT9A9SULZuDg7/rzSApD2AzYA1bP8vB68jgU2rzF/AQ6SSgxFV2qwDDARWsN0k6RTSzULPKtvfKDv+B8AA4FPgdeA/tjeQtBVwGnB1a9tJ2pUURK9pe5qkvUlZ6SF5/g22d5fUM6/7MODUCms6ArgCOEzSw/kcXJWD4N7Az4Ehtt+VtDZwFykgBtgQWMn2a5L+ABxtezdJywKL2j6wFdfhv7ZXAJD0NHCu7T9LWg94sMp1qOReScVykY1tv92G4xkz4si2NJ8t9BtxbWdPocz4zp5AB+j4NY0avnKHj1EyZcoUGhvb+yXYrKe7rQdiTV1FZ64pguxZU68q2+ci1fuWfC14sv2opGOAfST1IwWtHxWa3G57KoCk8aSs6g+A8bYn5jYXAsPz682AS23/L78/G/i1pDltf1phjjsBOwAHkILVYRXajCcF4WMk3QHcYPtxSQtW2d637Pi7bX+Q1/AGqSwF4IW8nra0GwqsCTwpCVKgP29xn6Q98/t5KqwFANv35Az+2sAGpJuaYyUNsv2EpKHA5jlwHgD0LhzeUCgBegr4SYUhql6H/P7BvM5FSDcml+d5PZxrslsrykVCqKJ///4zbazGxsaZOl5H627rgVhTV9HRa2poaKi6L35dZNbzGLCspO9U2LcRqca55OPyBpI2B/6e394MXEAqGSmZXHjdVNhXbFMsh+jJVwP7HqSbs8UkjS389cn7T7J9K6mGfH1Jh5XP0fb7pJrzI0hB9V8l7V9te/nxwNSy959VaNPadj2BU2wPsD0AWJ1UrlLat11h31rAgZK2LKz7H5K+JemPQJPth2wPt70B8Fdgt1wCNBZYkpThPqZsDtWuSfk8K12HUtvyz0K16xlCCCGEmSCC7FmM7deBc0ilDIuXtudygW2AU1roYjBwq+3zgSeBrUkBWnMeAFaUtEp+v3th3+3AzySV6noPBh6w/Uop+Mx/pZKOqXkd7wA7AidJGlgcLGd1RwOP2D6OlHVdo9r2FuY+o+4A9pL0jfz+eOAvhX2HSqqTNBdwC6kO+pbCuoeQ6qEHA4dIqstrnJdUE/4UKXCfRCr1uZOUISeXoLRWtevwlRsJ2++S6s/3ymOsRiqHCSGEEMJMFEH2LMj2/5Hqe2+WNEHSv4AfkeqsX2nh8AuAgbkU5ClSacRS+YHIauNNIpV1XCnpKdKDgSV/JtWCPy6pkfRA3k6tXMfDwLGkjPR3C7tuA54FJkh6kvTLIb9rZntHuhgYBTwm6VlSqcXued/BpIcGx5MeEh1PhXrs/CDkxqRa85dyecYYYJTtS0iB9WuAgUbge6Sge5k2zLMt1+GnwI75M/CbPCYAki5W+nnI9nhA0mzgnh8AACAASURBVMeFv0rfMoQQQggBqGtqamq5VQihW5A0GOhn+4KO6L+hoaGpvr6+I7ruFFGf2DXEmmZ93W09EGvqKmZGTXZ9fX3F/12RyGSHMHtZBLiysycRQgghdHfx6yIhzEZsX9PZcwghhBBmB5HJDiGEEEIIocYiyA4hhBBCCKHGIsgOIYQQQgihxiLIDiGEEEIIocYiyA4hhBBCCKHGIsgOIYQQQgihxiLIDiGEEEIIocbid7JDCDU17IE1OnsKtTWpc4a9aoMnOmfgEEIINRFBditI6gX8Gxhre7PC9juBYbbfkfQysK3tJ9s5xrHAONs3t/G4xYCzgBWAJmAyMLyt/bQwxgTgQNv3tbJ9L+AkYNM8pzrgGuD3tpvaMG4f4Hrb67Z50u3QzHUeCJxre6UWjm8Cvmn7nQ6a3xefEUlrAHva3ncG+1wS+B2wLvAZ0Au4DjjO9meFdosCrwIjbe83I2OGEEIIs4MoF2mdnwBjgdUl9S9sH1zDMQaRApy2uhh4zPbKtr8P7AGMLJvnzPYLYGlgNdurAOsD2wA/b0sntt+YWQF2Vu06zyqKn5EVge/OSGeSFgceAx4GZHtFYDVgeeCMsuZ7AjcDwyQtPCPjhhBCCLODyGS3zn6kTOwLwCHAvpIuzfvulTSk1FBSD2AEsDYwPymLu5fthyWNBD4EVgaWAJ4BdgV2A1YHTpP0OfAscF4+fjFS4LeD7SkV5rYYMI+kHran254oaUvgv3k+PwP2AeYEFgZOtn2+pN2BHwPTgWWBT4DdbDdKWgG4BJgXeA6YL/f1a2AF2zvl9z8A/mB71Qpz6gXMBUyz/YGkXcg3dZLuA54CfgAsCvzF9m8l9QUeBBqBvvm83GW7t6Tj8rbFgCWB14Gdbb+Zs7rn5zW+kPcfBjwJXJrXNx1oAPaxPb3CeYQK17m8Qb6Gk4EBwLeAO4GDC1nf30laG1gEOM32eZLmy/NbNm//iPQNiPO5eI8U2J4PXA6cTfqM9AJGA0eSrmHpMzIPcDywQP4c7kmVz1yVdZYcDdxg+6LSBtsfSzoQ2Law5h55/P2B3sDewMkt9B1CCCHM1iKT3YIccK5D+gr9MmBXSYvY3iM32cj2q4VD1gL6AOvYXiEfc3Rhfz2pjKI/KWjczvZ5pIDwSNs3kTK+l9leG1gGWArYvMoUjwAOBN6WdLOkI4EXbb8lqXfua0gOhHcATi0cuyFwUC6DGFOY55XARTkzfjYpaAW4CBhayGTuDVxQYU5nAosD70i6T9JJwFy2JxTaCFiPlDndQdLQvP27wAm2lwPeLOt3/Xy+lgf+R7rZmQO4EfhNnu85pAAY0k3E/LYHAKVC4aUrncRq17lSW9I1Hkwq0VmBFICWvGi7Po99Ri5B2Qx43/Y6eV1PkK5ZyX9tr2D7D6RguSH3sSrpJuSwss/IX4BjgQfz57Clz1w16wN3lG+0/WaeS8mmpBuuu3PfB+bzHkIIIYQq4j+ULdsPGGX7XeBdSS+RgsvfV2ps+1FJxwD7SOoHDCRlLktutz0VQNJ4Una53C+BwZKOApYjBVC9q4x3j6TvkbKYGwBbAMdKGmT7iRy8bi5pWVLwWeynwfZr+fVTwE9yYPl9UkaVnIGfkF+/LWkUsIuky4FNSNnN8jm9Riq5WAHYKP89Kukw23/MzS7M2d/3JV2X+5oATAMerbRW4D7bH+bXT+dzt3Ie87b8772l+QIPAcNztvgu4Czbz1fpuy3XeaTtjwHyedgaODfvuyr/O5aUyf+G7eslvSjpINJN08CyNT5YeD0UWFPSnvn9PFXm+4VWfOaqqSPVzJPXciSwU377HdK3Fu+Rzs2VtqdJugW4ENgOuLpSp2NGHNmKoUNL+o24tgN7H9+BfXeW2q9p1PCVa95na02ZMoXGxsZOG7/Wutt6INbUVXTmmiLIbkb+mn8XYGp+sBHgG6RM3ulVjtmclP09g1TD+hywc6HJ5MLr0kOB5a4mXZtrgb8D3wPqchnI8bnNG8DuwHGkbPRDfBlUXgzsJulNUjD3p7zvelIQ15q5FF9PK7w+j1TWMI1UavBxhXNwKnCx7YnAROA8STuTsqulILvYZw/g8/x6qu3ivqJK853G18/h5wC2X5JUCmoHAXdL2puU1d4yt70FOIW2Xedqc4f08CC2myRBum77kQL2c0lB+HukbydKiuewJylb3wggaUEKgXAlrfjMVfMI6dyMynM+DTgt99kE9MgPRg4BVpO0TT5uDuBQqgTZIXQX/ft33qMZjY2NnTp+rXW39UCsqavo6DU1NDRU3RflIs3bCXgX6GO7r+2+pHKD3qRM3ud8/WHFwcCtts8nfb2/NSlwasm0Ql+bAMfb/mt+vxbQ0/YttgfkvyGkYG0wcIikOgBJ8wL9SJnp1Uk/QHYiqXZ4aG5TdT45k9sA7JXbrkbOFuf9j5Dqm4+gcqkIpFrlE/JcyHNbKc+pZGdJPSQtBGwP3Nr86amqkRQcb5rHWjPPtykHt5cCd9r+Jak0YjXbxxbO47G0fJ3L7SBpLklzk+rGW5r7JqTs958Bk75tqHYN7gAOlVQnaS7STUCptKT4GSm+bu9n7iRge0m7lj4TkuaQtH3eP51UCvOQ7cUL56aeFHTPzIdSQwghhC4lguzm7QecafuLTKXt90l1v4eS6nfvl1T8abcLgIG5FOQp0kN0S+WHx5pzC/B7SbsBvwJuyn1cCNxPKjP4ipzx3ZhUS/xSLpMYQyp7uIQUWL9GCuwaSRnxSZX6KvNTYMc8/m/ysUWXAm/YfqbK8fuTMu3PSHqWlFldADig0GYe4HHSr1v80fboFuZUUT4H2wDHSXoaOBx4i/Qg5+WkYHOipIY8h3MqdNPSdS73CanEY3z+99IKbYpOJ5VyPJPbP0X1a3Aw6UHT8aQHY8fzZR198TPyGLC0pBtp5jMnaXVJYysNlMt61iY9gPp0Pv55Uj352qQM+558tY4f2/8iZbErnZsQQgghAHVNTa3+2eIQyA+83QRcUci0t7WP+0i/O319jeZ0GnC67f9IWgIYByydA+Wayr8uMsF2xXKhWZGkG2xv03LLGdfQ0NC0/YkvzIyhQuhwL9y0fcuNOkh3+9q+u60HYk1dxcwoF6mvr69U+hs12aH18oOMD5OC7Os6eTpFrwCjJX3Glz9fV/MAuytS+i3sS2bmmJ0ZmNRa/Aena+iOawohdH0RZIdWyw8yLlSDfgbO+Gy+0t+5fPnrHh3K9u4zY5xasf066TfFQwghhDATRU12CCGEEEIINRZBdgghhBBCCDUWQXYIIYQQQgg1FkF2CCGEEEIINRZBdgghhBBCCDUWQXYIIYQQQgg1FkF2CCGEEEIINRZBdgghhBBCCDUW/2M0IYSaGvbAGp09hdqa1NkT6ACzyJqu2uCJzp5CCCF0mAiya0RSX+AFYHze1BP4BDjM9sPt7HMNYE/b+9Zkkl/2OxQ4AlgQ6AVMAI6w/WqN+r8PONf29S206wNcb3vdCvvOBd6xfVyN5rQ7cD6whu0Jhe2j8hxGzmD/SwGn295mRvpppv+XgW8C37b9cWH77sClwHbF8y3pBmBD4Hu2P2mh3wdt71LYtjrpnPTNn+sJtnuXHXccsKjtA2dwaSGEEEK3FOUitTXZ9oD8tzJwBjByBvpbEfhuTWaWSRoGnArsZXsAsBIwFrhX0ly1HKsltt+oFGB3oDrgaklzd0DfSwLqgH6L3gF+UrZtV+A/xQ355mUD4LG8vyXbSdq5JjMMIYQQAhCZ7I62CPBm6Y2kvYGDgc9JgdGBtv8p6QfAmaTsdxPwe+Bx4HhgAUmX2t6jmeNHAh8CKwNLAM8AuxYzngUnAXvbfh7AdpOkk4F/A3NJmoOU8V02z/8jYJht5wx1A7A28C3gT8B3SBnT+YDtbZcy+T+WdDQwL3Cl7ZNyVvRBoBHoC+wG3GW7t6RvABcDq+RzNg14KJ+3NfKc5iR9W7Ak6RuC+yRtARyT931Cysg/WuV6jM7tTge+loGVtDhwLvA9Uob/GtvDJf0NuNX2nyWtAzwC9LP9oqRjgAWAHwOLS7rD9iaStgZ+S7qR/SjP9/GcAe4LLJbX8Tqws+03adkVwM7A5Xm+SwK9gefK2u2d13o9cIKkC203NdPvr4E/SHrY9kutmEcIIYQQWhCZ7NqaR9LY/PcKcDYpYEbSIOAoYCPbqwBXAX+TVAf8DjjTdj3wM2BQLt04lvRV/h4tHA9QD2wK9CcFcduVT07SInnfV8pXbDfZvtL2h8BmwPu217G9HPAEXw1I+9pejxTsnQrcZ3t14HbgoEK7b5CC8bWBnSVtlrd/Fzgh910MLH8HTAaWz3NXnvMcwI3Ab2x/HzgHGJD3LQsMB4bYXpUUXN4oab7ytWdNpMzu9rlkptxfgEvydVgT+JGk7fP4pflvCrwF/Ci/3xK4DtgLeCEH2MsDFwDb5Gt1LHBzvpEAWJ9U3rE88D+gteVAfwdWkbRYfr8LOeAuyefr56SA/Fbg23nOzbkf+CNwVT6+XPFzPVbS2DbMOYQQQpgtRSa7tibnEgwAJP2IFAivTAp0/mp7EoDtkZLOJgW91wLn5azs3cCvKvTd3PEAt9uemscdDyxcoY/p+d+qN1e2r5f0oqSDgGWAgUAxM3xj/veF0riF9wML7S62PQ34UNL1wGBSBntaWX8lPwJ+kTOukyTdlLevnOd1W/73XkmlmurBpIzwaOmLSo3ped7jqqzvTUl7ApdI+n5pew7MNwQWlnRC3tybFNCfBpyZA9BNgBOBwbme+1ukG5ENC8MMAkbbfjGPeY+kt0k3QpBuTD7Mr5+m8rWq5FNSdnoYqRRphzzutoU2W5G+Ebnd9jRJ1wC/AG5roe/fAj8EjgP+VrbvK59r+LImu1JHY0Yc2YqlhAD9Rlxbw97GV9w6avjKNRxj5pkyZQqNjY2dPY2a6W7rgVhTV9GZa4oguwPZvlvS86SsaE9SkFRUB/SyfaGkW4GNScH0cSpEjVnV4/PryYXtTUCdpC1JJScAb9geIumfpOzy3cWOJF1LKiVZl5QRPpeULX8PWKrQdGrZGj+rsvzPC697AKV2U3PwXUld4fW0wr91Ze1KffckBbM7FNaxBPCGpH8AffLmY8vmfKuk60hZ4M8KfdUB65YeFJS0KDDF9sc5e7sFKUN/OfAbYGvgplxyUxyiVPZT1INmrhWtdzlwgaRH01L8XtnY+wPzAM/n7XMCi0laEehH2eehdFAOyIeRyoHea8N8Qpil9e/fv7On0C6NjY1ddu6VdLf1QKypq+joNTU0NFTdF+UiHUjScqRM89OkjO+Okr6Z9+0BvEsKhh4BVs2/cLE36Vc/vkMKMEuBWdXjq41v+5bCg5ilgOp3wNmSlsn99Mx1xQNItb2bACNt/xkwKbDs2Y7l7yqpTtJCwPZ8mfGu5jZgT0k98jFb5e2NwFRJm+b5rknKbjeR6o43zuUZSBpCqkefx/aQwtpvqTDe4aQg/IcAObP8GHBY7mtBUllNaR43kkpTRtv+CPgncDRwQ95fvFajgU0kLZ37GkSqlR/Twjloke0xpCB6OGUP1ebP24ZAve2++a8P8ABwSJXPQ7HvF0k1/8NndJ4hhBDC7C6C7Nr6Su0q6av9vW3/0/ZdwAjgHknPkh76G2p7OqnW+nhJTwP3Ab+z/TIp6Fta0o0tHN9qtq8iBVFX5zk+C6xAqgOfSnoocB9Jz5AeUnyKVH7RVh+QsqKPAH+wfW8L7Y8jZZWfI9USj8/znQZsQ8ruP00Kjt8CPrE9kXRTco2kccAJwJZVHvj8CttTgJ/y1YzzMGDtXG4zBrja9pV5399IdeJ35fd3kILqR/L7icAUSY+Tbgz2J9WHTwBOBraw/UFzc5K0r6SLW5o7qXZcfP3GZT9SZr38xut4YJecmW+W7b+QPrchhBBCmAF1TU3N/ehACJ1P0mmk36D+Ty4HGQcsbfv9Tp5aTUman3RDsntnz6W9GhoamrY/8YWWG4Ywk7xw0/adPYV26W5f23e39UCsqauYGeUi9fX1Fcs+oyY7dAWvkB5u/IxUv7xXdwuwswGkrHeX1lWDmkriPzhdQ3dcUwih64sgO8zybJ9LehCzW7P9YGfPIYQQQgi1ETXZIYQQQggh1FgE2SGEEEIIIdRYBNkhhBBCCCHUWATZIYQQQggh1FgE2SGEEEIIIdRYBNkhhBBCCCHUWATZIYQQQggh1FgE2SGEEEIIIdRY/I/RhBBqatgDa3T2FGprUmdP4EtXbfBEZ08hhBBCK0UmO4QQQgghhBqLTPYMkNQL+Dcw1vZmedtA4FzbK0kaCUywfXo7+18D2NP2vjWacmvGHAjcBjhvmgN4B9jPdmMLx34x3+J5qNBuJC2cl+aO7wh5ToP5et7yEtvntLPPO4Fhtt+ZwelV6nskX863CegFvAD83PbbhXZnAAcBS9t+rZn+7ssvB9menrctCkyyXVehfRPwzY5YWwghhNAdRJA9Y34CjAVWl9S/pSC0HVYEvlvjPlvjBdsDSm8kHQWcQwrqmtNZ862VEe29IaqipfM1o74y3xxQ/xHYNr+fG9gVuB44EDi6hf7WBn4FnNghsw0hhBBmIxFkz5j9gGtIGcRDgKoZZ0n9gbOBRYCewDm2L8kZ25OAF4GVSBnJfUgZ8uOBBSRdansPSXsDBwOfA/8hBU7zArfaXiKPcwfwlu3dJM0FvAEsndufDGwMLAacavv8lhYoqQ5YGHizsG1PYH9SudG7eR7/K84XuAzoLekaYHlgblKW9cHWnJe8u+LxkuYETgE2zMc8DRxs+0NJLwNXAYOAhYBTgfWAeuAzYEvbb7S07grnYSgpAJ0T+BZwme3fSOoNXAosC0wHGkjX78/50HslDQG+AZyb19kEnGH78mrX3/bDbZ0jMDqvt2RH0mfzTOBOScfb/qSZ408AjpR0t+3H2jF+CCGEELIIsttJ0grAOsA2pMDqfkm/rtJ2DlI2cRfbT0laAHhU0sTcZC3gANtjJR0ODLe9oaRjgW1zgD0IOApYx/YkSbsDfyNljz+TtBIpoFoeWCH3+0NgjO0PcsD9ju11JdUDD+fgfUqFKfeTNDa//jYpkN8or2VDYDdgfdufSNoYuMl2/7L5DiRltUfYHiPpUOC4PKfWnpdqxx8NTAPqbTdJGk66gdg/Hze37bUl7UAKuFezPU7STcDuwPBK1wk4VNLOZdt2ASYAhwO72f6XpD7AvyWdDWwGzG97gKSewAWk0ow98jXaCHgfuA840vaN+fjHJf0rj/G160+6gWg1SfOQstb3FjbvD1xu+0lJb5KuW3M3VgaOBK6UtGpbxi8aM+LI9h4aWtBvxLU16ml8jfqZlXy5plHDV+7EedTGlClTaGys9Zejnae7rQdiTV1FZ64pguz22w8YZftd4F1JLwF7A49WaLsc0A+4RFJp2zzAqkAj8IrtUlD7FCkQLLcp8FfbkwBsj8xBXl/gJlKwN4GUzVxF0orAVsANhT5uLowxFzAfUCnILi8X2ZWUCV0K2BxYBniksJaFJC1cpZ8x+fVY4Gdl+1s6L9WOHwosCAzOx80JfFGHXFjzC6Ss/rjC+0rzLKlaLiJpC2CopGFAf6COdP4eAobnmua7gLNsP19hnXPbvhHA9huSbiBd03tp3fWvpHhTMAdwP/B/eb6rAasAV+f9lwGHSLrAdlO1Dm1fJGkTUtnJL1o5jxBmKf379+/sKcywxsbGbrGOku62Hog1dRUdvaaGhoaq+yLIbgdJ85EynFNzeQKkcoADgScrHNIT+KAscP028AGpDnZyoW0TKYCr1MenZdvqSOUFN5G+6u9DCvTeBjYhBd6/KrSfDJCzvwB1ki4GVs/7LwCeKx84lzWcQ8qQ9wT+YvuXeR098rj/rTDnz1pYV0vnpdrxPYFDbN+Wj+lNKicpmVplDqUxVgcuLqxvQHmbsvbzkUpSbgIeBC4BtgbqbL8kaRlgIKlE5W5Je9u+tWyd5YFtD9K1g9Zd/0qaqyE/gJTtb8jXeg7SddpM0ts0v/6fA88AX2T1c2Z7mu3xuYQIKpzbEEIIISQRZLfPTqRa5OVsfw4gaUHgFeCbFdobmCxpZ9tXSFqClLHcuoVxpvFlIHY7cL6ks3O5yB55Ds/nv36k8orhpPrrkcA/c6a9Ktt7Fd/nMg/Ktq1XWMcdwMWSzrL9JqkO/RBSmUpxvq3R3vNyB3CgpNF5zIuAj0nBYcuD2k8CzQbWZZYl3UQdY/tTSbuQvgnoKWk/YH1gJ9t35JuE1YBbSbXzvUg3Lp9J+kmhXGQb0ueo5vJncUdgqO17C9svB35he2OaWb/t/+YM+W2FzUNJGfldSOubBHzYAdMPIYQQuoX4nez22Q84sxRgA9h+n/QLHIeWN7b9Kal0Yy9JzwB3Ar9pxcNtjwFLS7rR9l3ACOAeSc+S6muH2p6ef3LtNuCjXE7yEKks4oZqHbegn6Sx+e8Z4CzgJ7bft30n6aHDu/K+YXlfU3G+rRlkBs7LCcDLpOzyRFLm9/A2r/LrDi2su/R3ASmrOwp4TlIjsEUedxngclKmeqKkBmAB0ucA4DpSCYdINw6H5HXeDRxfDIArkbR6oTa+LXYDJlbo/0RgUK7fb5bt+0kPTJacCywqaQKpzn2v5spOQgghhNldXVNT/HcyhFmVpBtsb9PZ82ithoaGpvr6+s6eRs1EfWLXEGua9XW39UCsqauYGTXZ9fX1Fcs8I5MdwixK0uKk+u8QQgghdDFRkx3CLMr268DrnT2PEEIIIbRdZLJDCCGEEEKosQiyQwghhBBCqLEIskMIIYQQQqixCLJDCCGEEEKosQiyQwghhBBCqLEIskMIIYQQQqixCLJDCCGEEEKosfid7BBCTQ17YI3OnkJtTaq+66oNnph58wghhNClRCY7hBBCCCGEGotMdjMknQNskN+uALwETM7v17E9ueKBM5mkFYAzgMWBOuBd4P9sP5r3XwKcbXtcC/28BvyPtMYmYE7gduBI29ObOW4h4DrbP6rBcpD0I+B02wNq0V8rx+wJHArsSPq/izmBW4BjbX8q6QrgSdtnzaw5VVPr8537XAYwML5s143AcOAzYCHb79dqzBBCCKE7iyC7GbYPLr2W9DKwk+0nO21C1d0EHGH7VgBJGwG3Seqbg6KNgXNa2dcOtsfmfuYEHgL2Bi5o5phFgNXbO/lZxEXAvMAg2x9K6g1cDVwI7NGpM/u6jjrfH1W6sZEU/38ihBBCaKP4j+cMkPRzYC9S1nNh4CTbf5K0F7AF0Av4HvAKMBI4AFgWOM32WbndVvn4JYDXgF1tvyVpPeD3wNzAYsBttveuMpXvAL1Lb2zfK2lHYLqkk4FvAX+V1KabhJzBfQhYPq/3B8DJpGD0c+C3tv8BXArML2ms7QGSVgTOBhYCegIjbF+WM9SnAVOAeYANgUuAfsB04HFgvzz8/JJuzPveA35u+/nyjHLxfc7EPwSsAhxFqqY9L1+HfwHLAAfYfqi4zpzF3Q5YzPbHee0fS9obWLv8vDSzvp7AWcAawDdy85/ZfizP8xt5Pa8A/W0vlfsbDbxse09J8wCvA0uSsupf+3zNwPle0/anla92CCGEEGopgux2kvQN4GfAZrbfywHorcCfcpP1gZWBN4GJwE+AjYDVgAcknZ3bbQislgPI00lB2o7AIcCvbD8kaX7gFUnnVSn5OAA4Px//EPAAcJXtD4GjJe1MIUPdhjUuDgwFjpS0CPBnYLDtf0v6LvCYpLVJmd4nc8DXC7gO+KntcZIWzO2ezd2uDPS1/ZqkPYC58nFzkLLGfXO7JYFhtsdI2h+4DFivFdMeZ3vHPI+XgT1s35kDzjurHFMPjC8F2CW23yR9S1A8J82tbx7gm8C6tqdLOgb4JfDjfPhctlfM/bwiaXngVVLwv0xuM5h0Deuo/vlq1/luxbmbX1LxMzLV9lqtOO4rxow4sq2HdFn9Rlzb2VNop/KqoFnLqOErt6n9lClTaGxs7KDZdI7utqbuth6INXUVnbmmCLLbKZcUbAFsIWlZYFUK2WRgjO3X4YtSkzttN0l6gZQJniu3u9328/n1RcBj+fUuwBBJvyZlkucu6784lyskXU8K7DcgZT+PkbSW7X+3cWl/lTSZ9FDsZ8Afbd8saUtSRv0WSaW204GVgOcLx/cHlgYuK7Sbi3R+XiJlbEsB3wPACZLuAe4m1WG/JKkf8LTtMbndJcAfJM3Xivk/mP8dAHxq+04A23dLeq7KMdNp/UPAVddn+yJJk4B9cnZ8ICkLXz43gL8Bm5HO3R3AWkodbgXc0IrPV4vz4evnuyUVy0VCmNn69+/fpvaNjY1tPmZW193W1N3WA7GmrqKj19TQ0FB1XwTZ7SRpSVLG8QJS8HQjsEmhydSyQz6r0tW0wusewOeS6oCHgSdJAdg1wLpAnaQDgJ/n9o8BfyBlfH8N3JX/fiPpXlL2vK0P6lXLePckZXu/yCbnTPfbpKxzsd27xWBN0neA94EfAF9ki22/UAhGBwH3SNoT+JRUjlJSR3oQc1r+t66wb86yeZb6n1bWjlKfkk4CNs/bbiSV8qwoaT7b/yvM+3vAH4FtWrM+SVsBpwNnkjLg/ySVoZTPjbz/18BzpGv2IbAp6TN0RCs+Xy3Oh7LzHUIIIYSZJ37Cr/3WIJWCDCeVIWxB+87nYEmL5df7kEoCFiFlYn9p+yZSCcVSQE/b59kekP/2Bd4C9pdUKkkgl3b0AZ7Om6aR6pJnxCPACrlWHEmrkYLIb+f+58g3BxNJteA75nZLAs+S6qS/QtJBpPKHO2wfBYwmZWABVpX0/fx6X+A+21NJddar5+O/S8reV/Is0JTLRJC0LukXYpps/7pwDo/P2f7rgD/n0hwkLUAKsN/O45Y0t77BwN9snw80AFuTguBKHiRloTfL674TOByYYPu/NP/5atf5DiGEEMLME0F2+91GCvgMNJIeLfbpPwAAIABJREFUPvxvLnVoi9eAqyQ1kn6C7zDb75AeWBuba2uPIAW5y5QfbPtd4IekEoWXc/s7gd/Zvj83uxG4RtIgST+WdEtbF2v7P8C2wAhJ40jZ32G5FOF1UkA/gVTWsiWwn6RnSD8BeHSh9KNoJKmO+VlJDfn1eXlfI6mU5BlShrf0Cx9nA0vm0o+LgHuqzPdTUgb6JElPAweTsu6fVFniPqTSjUfz+h4DxuXtxX6nNrO+80k3TeOBp0gPWy6dg+Hy+X1O+pbiPdvvkUpnFgVuyE2a+3y193wj6Q5JQ6qcg5a8Junjwt+m7ewnhBBC6PbqmpqaOnsOsy2lXxcZanvrzp5Ld5MD29OAU2xPyhnep4ElbX/UubPrPJL2A/5l++6O6L+hoaGpvr6+I7ruFFGf2DXEmmZ93W09EGvqKmZGTXZ9ff3XkmkQNdmhm8oPmb4K3CvpM1J99h6zc4CdTQXu6+xJhBBCCN1dBNmdyPbFwMWdPY/uyvbZpPKSkNm+pLPnEEIIIcwOoiY7hBBCCCGEGosgO4QQQgghhBqLIDuEEEIIIYQaiyA7hBBCCCGEGosgO4QQQgghhBqLIDuEEEIIIYQaiyA7hBBCCCGEGovfyQ4h1NSwB9bo7CnU1qTOGfaqDZ7onIFDCCHURGSyQwghhBBCqLHIZLeSpLWB3wOLkG5OXgWOsP1sjfrfnfS/TvhS2a5jbd9SizGqjNsHuN72uh01RmGsVYETgOWBj/Pf6bb/1opjXwa2tf1ka7a3c36LApNs11XYNxC4DXDeNAfwDrCf7cY2jjMSmGD79DYc8zI1WmeFvhcHTgJWA5qAKcBw2zeXtbsB2BD4nu1Paj2PEEIIoTuJILsVJM0FjAI2tv1U3rYzcJukpWx/XqOhHrQ9tEZ9tYrtN4CZEWCvAdwE7GP773nb8sD1kr5t+8KOnkMNvGB7QOmNpKOAc4DBnTelGSPpm8AjwDHAHrabJK0C3CXpE9t35XZ9gA2Ax4BdgQs6a84hhBBCVxBBduvMCywI9C5suxL4EOgpqQkYAawNzA/UAXvZfjhnLT8EVgaWAJ4BdrX9cWsHz1nuPYH5gA+AocD5wLKkzPpHwDDblnQf8CiwHvA94G5gb9vTJQ0FTiRl4v8H7Jv7m2C7dw56/wzMnddwse0/SpoDODWPO40UlO1PynqeCfwQ+BwYAxxq+6MKyzgROKkUYAPYfk7SLsDofJ4WBC4Evg18B3gF2N7224Vz0Rv4B/Co7V8WtvegHddA0k9IWdxPgFYXwUqqAxYG3ixs2zOflx7Au8CBeY0jc9t+pJu1Yj8jgO8DWwGfAqeQssU9gaeBg21/WHbMFqSgeM487yNIwe9zecxSYHwxMN722c0sZX/gIdt/KW2wPU7StsB/C+32BkYD1wMnSLrQdlMLpymEEEKYbUWQ3Qq2/5uzlrdLegt4GLgXuMb2p5LWAfoA6+Rg9mjgaGCL3EU9MAiYTgpEtwMurTDU+pLGFt4/Znvf/HpFoK/tD3MA9L7tdQAkXQAcCByU2/YDBpJuChqBDSVNBK4ANrL9dA4uTwb2K4x3JHCr7ZMlfQc4K/e9f17DKsBU4GpgB2CZvO5VSEH2xcBppOC93LrAYRXO7dP5JmUFUqb0Udun5CD278AuwBm5+QLAHcAttk8p62ot2ngNJP0DuARY1/ZESf9XYd5F/QrX59ukm6+NACRtCOwGrG/7E0kbkzL3/XP7eW2vmNuOBOoknZvnPMT2VEnHkm5i6nNGeTjpGu1fmoCkZYHhwEDb70pakXQjtQzpxuvnpCz0/MCWpAC8OauTymC+wvYDhTHnyP3uA9wF/AnYtNJxAGNGHNnCkKE1+o24tgN7H9+BfX/VqOErd/gYU6ZMobGxTVVbs7zutqbuth6INXUVnbmmCLJbyfaZki4iZRk3AH4J/FLSmrYflXQMsI+kUoBbzObebnsqgKTxpKxmJc2VizxTymjavl7Si5IOIgVXA0nZ65JbbU8HPpT0fB5vPVLG+uncx43AjZL6Fo67Cbhc0pqkwO3gHLD+CPiL7cm53Q55LY8Dv7b9WX7/B6C5+upeVbbPCTTZPlvS+pIOI2XpVyIFxCV/IQWh55R30M5r8ANSpndibnMhKYCtprxcZFfgTklLAZuTrsUjkkpNFpJUutYPlfV1GPAtYEBpXqRvChYEBuc+5gTeLjtuMLAYKftf2jY9jz0S+G0uAdkWGGX7/WbWUzq2pQegtyJl1m+3PU3SNcAvqBJkh1DUv3//lhvNoMbGxpkyzszU3dbU3dYDsaauoqPX1NDQUHVfBNmtIGk9UrbzNNLX/aMk/QqYQAqIJpMeWjwDuJn0tf3OhS4mF143kUoZ2uqL8hJJ+5G+vj8XuAp4D1iqhfE+y69LfdSRyie+KEWwPSpnSgeTSkB+K6meFNgWj/02KTDrWdyet/WStDopq13qdwAp+z8QKGbqS7XanwHPSToFWJOUXb6XFJQXz9WJpMzxqXyZtS/1szntuwbF/qcV+ruYlOWFVH/8HGVsXy7pHFIWvifpRuSX+fgepCx1qeSivDzoftI5GSlp7Xyj0hM4xPZtuY/epNKdop7AaNs7FOa6BPCG7c8lXZfXPQw4oHzOFTxGKrE5t7hR0j7AfLbPJGXS5wGeLwT/i0lasVYP/oYQQgjdTfyEX+tMAo6R9IPCtsVI5QvjSUHprbbPB54EtiYFQx1lE2Ck7T+Tfu1ii1aMNwbon8sLIGUnryg2kHQVsIPta0iB1Yek0pO7gWGS5srB4/nAT4Hbgf0k9crbDwDusv2k7QGlv9z9/5Ey/0MK4/UnZV9/Y3tKXtdZuT74bdJ5La7rcVJ5y3a5HKOoPdfgAWDF/KAfwO6lHbb3Kqyh4kN++eYL0jW4A/ippMXytn1JNczVPEkKbN8Hjsvb7gAOlDRnPp8XkX7Rpmg0sHGunyefz2dIQTDAecDBQA/bjzczfsmFwEBJO+UbL/KN1fHAeEnLkb69qbfdN//1IZ27Q1rRfwghhDBbiiC7FWz/kxS0Dc9lGhOBa0m/xmBSpnNgLkN4CngBWCoHSh3hdFJZxDPAg3nMZVpYw3+AnYDLcl3xYcCOZc1OAHaSNI4UlN9ECqYuBBry33jSw37nkDLLb5Gy042kzHPFwCuXqWwM7C3J+RxeDPzW9nm52fHA6Xldt5BKLJYp6+cd0g3AJZIWKuxq8zWwPYmU8b1S0lN89duASvpJGpv/ngHOAn5i+33bd5IeWrwr7xuW91V9ODDv+xmwv6R1Sef/ZdIDjxNJWfbDy46ZSPoW45p8nU4Atiw9SGt7HCl7/sWNgaQ+ec59KszhPdI3DNsAE/L5Ow/YMz9AuR9wk+3nyw49HthF6WcPQwghhFCmrqkpfiAghO4i16PfB6j4W9b5YcuDqvzyS800NDQ01dfXd+QQM1XUJ3YNsaZZX3dbD8SauoqZUZNdX19fsQw4MtkhdBOSjifVeR9UFmDPC9zd0QF2CCGEEL4UDz6G0E3YPhY4tsL2Tyirvw8hhBBCx4pMdgghhBBCCDUWQXYIIYQQQgg1FkF2CCGEEEIINRZBdgghhBBCCDUWQXYIIYQQQgg1FkF2CCGEEEIINRZBdgghhBBCCDUWv5MdQqipYQ+s0dlTqK1JnT2B9rtqgyc6ewohhDDbikx2CCGEEEIINRaZ7DDTSDoH2CC/XQF4CZic369je3LFA9s2xhrAnrb3ndG+Cn2+DEwlzbUJmBO4Ezjc9vSytn2A622vW6vxC31fBFxgu6ED+j4O2B/4vu23CtsnAAfavq+s/X3Aubavr/VcQgghhO4gguww09g+uPQ6B6472X6yxsOsCHy3xn1CYa6S5gTuJwWl5xYb2X4DqHmAnQ0GLuygvgG+AVwuaRPbTR04TgghhNDtRZAdZhmSpgI3A6sAOwH/A84GFgF6AufYvkTSQOAk4EVgJaAXsA/wb+B4YAFJl9reQ9LewMHA58B/SFnZf0oaCXwIrAwsATwD7Gr745bmaftTSQ8Cy0vqCzwINAJ9gd2Au2z3ztnhvsBiwJLA68DOtt+UtBwpYP4WMB040fZfJS1OCty/l9d1je3hkk4C+gBXSto193V+7r8OuMz2aXk+o4F/AGsBCwFH2b6pFZfgCmBt4HDg9Fa0DyGEEEIVEWSHWcmcwK22t5c0BzAO2MX2U5IWAB6VNDG3XQs4wPZYSYcDw21vKOlYYNscYA8CjiKVokyStDvwN0kr5j7qgUGkIHcMsB1waUuTzCUhWwDH5E3fBYbZfjAHuUXrA6va/lDSLcC+wG+Ba4CLbf9R0hLAfZJuA/4CjLB9q6S5gX9Iet72ryXtRM6oS7ofuNn2mfncPCDpVeAxYGngDtsHSdoGOAtoTZA9Bfgp8JCke2w/1YpjvmbMiCPbc1joAP1GXFtlz/gOHXfU8JU7tP9yU6ZMobGxcaaO2dG625q623og1tRVdOaaIsgOs5oH87/LAf2ASySV9s0DrErKGr9ie2ze/hSwe4W+NgX+ansSgO2Rks4mZX8Bbrc9FUDSeGDhZuZ1paTJpIeFPyMFyDfkoHoa8GiV4+6z/WF+/TSwsKSFSdn6i/O8XgX6SZoP2DC3OSEf0xsYAHwRLeV26wEb5+M/yJn5zUhB9mekTHbp3DS3rq+wPV7SMcBVkupbe1wIRf3795+p4zU2Ns70MTtad1tTd1sPxJq6io5eU0ND9cekIsgOs5pSuUZP4APbA0o7JH0b+IBU0lB8SLKJVDJRrifwadm2OlIZBpX6kLQlqeQE4A3bQ/Lr5urHp9qeVmVfpXlOK7wHQOlO4q28f13bn+Tti5IyzEU9+Pp6exTW9Wnhgcxq56Yq23+QtAmpVKc0v8WB5W2PzpvqSMF8CCGEECqIn/ALsyoDkyXtDJBLKiaQSjyaM40vg83bgR0lfTP3sQfwLvB81UHtW2wPyH9DqrWbETmz3UCq3y6t7WFSpv4x4LC8fcG8fat86DSgl+2PcrsDcrsFgF2Bu2o4zT2AzYFl8vulgT9J6iVpfmBZ4IUajhdCCCF0KxFkh1mS7U9JweVekp4h/WTeb2w/3MKhjwFLS7rR9l3ACOAeSc+Sgtqh5T+710mGAdtLGgfcCuyVfzpvGLB2Ll8ZA1xt+8p8zI3AFZI2Jj0Y+sPc7vG8b2RzA0rqI2lsrilvVi6x2Y1UJ4/tB/M8nwHGAhfantDGNYcQQgizjbqmpvilrhBmF7l2+6CcDa+5hoaGpvr67lPKHfWJXUOsadbX3dYDsaauYmbUZNfX11csy4xMdgizCUnzAnd3VIAdQgghhC/Fg48hzCbyw5RXdPY8QgghhNlBZLJDCCGEEEKosQiyQwghhBBCqLEIskMIIYQQQqixCLJDCCGEEEKosQiyQwghhBBCqLEIskMIIYQQQqixCLJDCCGEEEKosfid7BBCTQ17YI3OnkKbXLXBE509hRBCCN1QZLJDCCGEEEKoschkdwGS+gIvAOPzph7Ax8BZtq9txfFNwDeBocC2tofWaF4vA1OByYXNb9ge0sZ+7gSG2X6nsK0X8C6wvu1xeds+wAXAJrbvzNu2Bw63vVYbxlsKON32NvncTrDduw3Hrww8Axxt+5Rm2h0HLGr7QEn/AI6wPbG145T1dSwwzvbN7Tm+hb4fAeYF5gTEl5+zZ4EzgHuBH9p+MrdfFBgDHGz777WeTwghhNAdRJDddUy2PaD0RtKSwGhJn9u+oRPntVMp+JoBg8s32P5M0mhgI2Bc3jwUuBXYCrgzbxsEtDXQW5IUTLbX/sCVwIGSzrA9raUD2nrjUcEgoF0Bektsrwtf3MxNKH7O8vajgOskrQZ8BFwLXBIBdgghhFBdBNldlO1XcnbzSOAGScsB5wHzA4sBY4EdbE+pdLyktYFTgbly+7ts7ylpDuAPwHrAZ8CLwB62P27L/CStCJwLLAI0AWfYvlzSQOBs4H9Ab6AhH3KvpCG2Xy10cxswBDhL0jzAWsBA4HbggNzmh8COecytgd+SMv0fAYfZfjxnlNcB+gATgDWAxSXdAewD9JR0AbAmsABwVLUbF0nzAzvluQwAtgWuyfuK44wDni8c93Ju2xs4iXReVwJ6AfvYfljSN4FLgX6kLP5beb6TgNWB0yR9DtxDutYD8rm9DfiV7WmSpgAnAxuTruupts+vtJbWsn2hpB8Al5C+UXkfGD4jfYYQQgjdXQTZXds4YOX8+ufAZbavyKUWDcDmQLUs9yHAsbbvk9QbeElSPalsYCCwgu0mSacA3wceqdLPlZKK5SK7kwLDW4Ajbd8oqQ/wuKR/5TYrAUvbfgVA0u7ARsVykew24PeSegA/Ah6yPVHSJ5JWBd4hBa1PSVqeVEqyru0XJQ0CbpZUylgvCayUA9GBwLm2N8nZ27lJNxn7SvoxcHoz520X4J+2GyVdBhxKDrIrjHNclT7WAg6wPVbS4aSAdUPgHOBZ20MlLUa6hhNsnydpuzznm/K475Ku/Zz5XB9BCq7nAt6xvW6+ng9LurTazVYb7Eu6cVs1r6+pWsMxI46cwaFmrn4jWqq4Gt/C/q5oPKOGr9xysy5iypQpNDY2dvY0aqq7ram7rQdiTV1FZ64pguyurQn4JL/+JTA4f7W/HCmb2lyd8W7AEEm/ApYH5sntxwGfA2NypvcG248308/XykUkrQDMbftGANtvSLoB2JRU3/tqKcBuju1XJb1JCvK3AEblXaNImdr/AP/INwODgNG2X8zH3iPpbaA+H/NYM2UdnxYy12OBbzUzrX2Bi/LrK0g3AevYfrQV45S8Yntsfv0U6cYEUtZ+tTz/NyVdX+X4zYD1cqA7NWfhf0EKsgFKddtPkYLu+YAZDbJF+pZkbtI5vX8G+wudrH///p09hZppbGzsVuuB7rem7rYeiDV1FR29poaGhqr7Isju2tbgyzTb1aTreS2pRvl7QF0zxz5Aenjv9nzMWkCd7fclrUIqFxkE/FXSacB0UoAJ8KTtvZrpuyfpBqCoB6k0AtJDm//P3p3H2VzvDxx/jbElSrK0p9DbToaUm3KlLNcypK5Q6epKRaUQoiQVEUVJbkkpbbJnyZKEsoyWwXinkl8UKbI0xjbz++PzOePMOLNxxiy9n4/HPJzzXT7L93tu9/19n/f3ezJrHi7L2wIY5Jd9DDwM7AFmhKHPI0Gvk/DHzd+seIFf/rjvrzrQ12egAQ7jstmBIDszcwvO/Cf3Bxwl5Tk7lsb+BUg51+B5JrfvLz4g/c9BhvyNjtNw8ywKvCciV6rqjlNp1xhjjMnP7BF+eZSvwR6Ee/oDQFNgiKq+79/XxwWeofYtiQvQH/XZ5ouAirja5JbAYmClqg4G3gLqqep4Va3t/9ILsAE2AUdEpJ3v7wLgZmBhGtsfI2WQGGwe8B/cU0t2+mWf44LdBkFtLgaaisjlvs/GwMW4p2CkdjSd/pKpaougOc8C7gUmq+rFqlpeVcvjbsZsJyKXZNReJnwMdPXjPxdoy/FgOnjMC3A3XUaISBGgG2kf21MiIpHA+8BsVX1XVd/AXZi979cZY4wxJgQLsvOOM0Tka/+3DpgE9A96wsMAYLqIxAKv4r7OrxiqIVX9E3gWV8u8HugHrPDbz8M9um29iKzFBbJPZmWgqnoEiAYeFJFvgUW4C4BP09jlQ+AzEakeYt3nwGUcLxXBl2OswZVd7PfLNuKe+jHNz2kY0EpV94ZocyOQICKryWSW19+U2A4YkWquS3BZ7J6ZaScDvYDK/hx+BGzleDnQLFxpyp3AA7iSllj/p7ibKTOaw9ciUjeLYxqBKzd5JGjZ/UAp7OZHY4wxJk0RSUlp3r9kjDmNROQ+4CtV/cJnqD8HnlDVeWFq/2lcJn5TONoLJSYmJikqKirjDfMIq0/MG2xOuV9+mw/YnPKK01GTHRUVFTJhZzXZxuQeG4GxvgyjMPBhGAPsCOCn7AywjTHGGHOcBdnG5BKquhT3POzsaDuJ409FMcYYY0w2s5psY4wxxhhjwsyCbGOMMcYYY8LMgmxjjDHGGGPCzIJsY4wxxhhjwsyCbGOMMcYYY8LMgmxjjDHGGGPCzIJsY4wxxhhjwsyek22MCauOy+rl9BDCa1dODyC0KdetyekhGGOMSYdlso0xxhhjjAkzy2SfJv6nsh8EOuKOe2FgNvC4qh4SkUnAelUdGWLfr4FGqvrnaRxycP9dgFeAeqq6Pmj5HGCqqk7KiXH5McwFegNlgZdUtXp6xzKDtpKA9cCxVKuigfKB9jNo45TOlYgMBkqrao/g15nYbxJ+zv711UCUqv4VtM0BoLqq/pRq3/HAQVXt5d+XAH4HPlLVjn5ZJLDbt1sfaK+qLU9mjsYYY8zfgQXZp88rwDnADaq6V0TOBN4BXgNuT29HVa19GsaXkQjgXRGpp6oJOT2YAFVtASAiZcPU5D9V9ffUC0WkfCbHkxvOFbiLgheBuzOx7TzgyaD3NwGLgZtEpJCqHgHqAX+oapyI1A/3YI0xxpj8xoLs08AHaJ2A81V1H4Cq/iUi3YF/BG3aQERWAuVwGdWOfrskoIyq/i4i/YE7gaPAZqCLf/0KUAk4F9jv91URqQhMBEoBv+KC5bdVdZKIRANP4MqG9gMPq+rqNKaxGJd9HwmckFkVkZbAAL9NWeBNVR0kIo2A4cBWoDJwEOjig7XCft31QCTwFfCAqu4TkZ+AVUBNYDAwUFVr+L5KAluAy/0+7dM59lVwwea5vo8xqjoxre0zQ0TKAG8AFYA/gB24LPLgwLkCWgJtgUTceYkH7lTVuFPpOwteBO4QkZtV9aMMtl0MvCcipVR1N9AKeBsoAVzn198AfJydAzbGGGPyEwuyT48oYEMgwA5Q1R1AcAB0IfBP4BCwGmgHTA6sFJHWuKD6alXdIyKjcAGvAn+q6jV+u/F+eU+//1uq+ooPONcCb4tIZWA80EBVfxSRxsBMEZHU4/SSgDuAb0RkvqrOCRpXBPAILojcLCIXAP8nIi/6TeoCvVX1c39hMdkv64e7QIhS1SQReQYYBtzn91uvqv/27Q8Tkbqquha4DfjYH4M0D7qIFASmArer6joRORv4QkQ2quqXaez2qYgEl4tsUdW2qbYZgzufLUXkfCAGd1GU2vW48oxtIjLWz/fONAccXrt8X++KyGpV/TmtDVX1gL+4u05EZgFNgYeBS4DWHA+yn8tMx6tG9znVsZtMqDD6gyxtP+eZGgAkJCQQF3e6rvVOD5tT7pff5gM2p7wiJ+dkQfbpkUjmbjKdoarxACKyHpcRDtYE+FBV9wCo6sOBFSLyo4j0BCoCjXDB5DnAVbhsJD57vNjv0hhYrKo/+nVLROQ33AXBp6EGp6q/ikhXYKKI1AxaniQirYCWItIRqILLmJ/pN/lGVT/3rycCL4vIubhsb0ngRh8sFwZ+C+ry86D2J+IuMNYCdwGZieSuwGWbJwYF42cAVwJpBdkhy0VSaQHU8WP7VUSmprFdjKpu86/X4S6aThtV/cTXZ78tIv/MYPN5uM/Nb8BmVd3ta+4/FJGiQC1gaTYO12SzKlWqABAXF5f8Or+wOeV++W0+YHPKK7J7TjExMWmusyD79FgFVBGREqq6P7BQRC4EJnC83OFI0D5JuEA12FG/PLB/SVyQ2hzoBrwETMHdoHaZ355U7QSytJHBbXkFgEL+ZsIL/LLHgzdQ1dki8iHwVmC8vr78K2A6LjCeiLtZMNDv0aAmAsuO+TE8qKrzfDvFgaJB2x4Iej0RWCcirwElVfUzMhYJ7A2ukxaRcsBeERmCy9ICzFLVx0M1kIajhD6mqR0Meh3qfJ4U/43GEP/2l0Bdehr64y4oBgTt3x3o7t+uVdW7cUH2ZFxZyxwAVV3vA+zWwIrcVItvjDHG5Hb2CL/TQFV/wd3kOFFEzgLw/47D3Ux2ML39gywC2gXawNUqP4z7en+Sqr6OKx1pBUT6gH4FLvOLiFyG+9o/CVcC0FRELvfrGgMXA6tUtYWq1vZ/s0KM4xFcEH6Df18JOAtXNz0blxEtggtyAWoHZb67ASv90zcWAD1EpLCIFAD+BzwbauKquh1XQvMq7mbRzFDgoIh09nO8GFfWEaWqjwfNMSsBNrja5K6+zXNxtdepL1iyjarOChp7egE2qnoYV17TG5fFR1XHB+1/t1+2ATgbaIMPsr25uADd6rGNMcaYLLBM9ulzHzAIWCkiR3FB6AzcjYeZoqpzRaQqsMKXP2wA/ov7Kn+CL+WIAL4Aavjd7gBeF5H7gO24GwbjVXWjXzbN1y7HA61UdW8mxpEgIrfhgl6Ab3GB2SYROQTEAhtxpSuHcDcGPu1vAP2N409TeQp3I+VXuID8a1wAn5b/4WqsW6ezTfA4D4tIG+BFEekLFAIGqeqKdHZLXZMNLsiMD3rfC3hNRGJxNz5uTbU+XT4T3T2jANnrJu4RigHfqmqDzPYF4G+A7Y07fulZBDRR1Y1Byz7GfXbnZqVPY4wx5u8uIinptCXgTA4Qkcdwzzve5G/8+xZoniqQys7+G5GJ50vnJf7i5CtV/UJEiuBKZJ4IlL1kYv+CwDuq+u/sHGdOiImJSYqKisrpYYSN1SfmDTan3C+/zQdsTnnF6ajJjoqKClkOapns/O874H0RScSd72GnK8DOxzYCY/0PtBTG3YyaqQDbqwKMzpaRGWOMMSZXsCA7n1PVD4EPc7D/pUC+yWJD8pzqnsL+seEbjTHGGGNyI7vx0RhjjDHGmDCzINsYY4wxxpgwsyDbGGOMMcaYMLMg2xhjjDHGmDCzINsYY4wxxpgwsyDbGGOMMcaYMLMg2xhjjDHGmDCz52QbY8Kq47J6OT2ELJly3ZqcHoIxxph8yDLZxhhjjDHGhFm+zGSLSBJQRlV/D1rWBWivqi2zob9PgI7B/Z1CWyWBpf5tceBCQP37hcAGTmEeInI+8DNQVlV3+2XPAv2AyqqqfllfIEpV/32SUwn6PAr+AAAgAElEQVTuszSwS1Ujwn0eROQl4HdVHZyFfUoAo4CrgUT/97KqvhaOMWVFdn4ufftzgKmqOimdbfoBHfzbisAuYK9//29gIvClqj4StM9LQGlV7YAxxhhjTpAvg+wccGO4GlLVP4HaACLSCHhJVWsH1vug7FTa/1VEYoHrgBl+cUtgNtAaGOGXNQamnEpfudgw4ABQU1WTROQC4EsR+T9V/SSHx3baqeow3DFBRJbiPnNTA+tF5N/AOhFZrqrTReQ/QEPgmpwYrzHGGJMX/C2DbBE5G3gZF8wmAfOAAap6NHUWPPAeSADeACrhMp8xwD3A677ZT0WkBXAW8BJwrm/7eVV9ywfMTwM/AtWBQsA9qrriJKZwvoh8DFwCHMVl0eP8vF4Eavj2FwN9VPVoqv3nAY2AGSJSHiji9xsMjBCRwkAD4A5/DLoBDwDHgJ1AD1X9LoPj2M7PNx4IWfQqIlcDz/n+zwcWqmpXP6bFwFygPnAO0NcHeGcBrwG1gF/9/Jf79u4FugOHcefrHlXdGOr4+XkUAg6r6i9+vIHM/k+47PLa1O/9RU4/4CCwBHhQVQuKSDngVaAccB6wFbhVVX/z+68CavrjMz3U8QhxfFoCA4DCQFngTVUdlN5nyV8wvAlc4MdQNjN9pUdVt4lIZ+BdEYkAhgL/UNX4U23bGGOMya/yc5D9qYgcC3pfCvjWvx4D/IELRgsDs4De+GxeGtoCJVS1tohEAuOBy1X1Lh94/RP4E1fq0UdVp/mAZ7WIbPZt1AfuV9WvReQR4Bng+pOY2+XAv1X1exF50Y+9KzAaiFHVLn6Mk4CHcYFssHnAWP+6FTAHWAZU86UdVYGNPkBsDPQFrlHVXX6uM0SkGmkcRxF5A1di0EBVN4pI/zTm8SDwuKouFZHiwBYRifJtXg4sUNWeInIz8AIwHXgSF+BWBkoD64Dlfr4vAOV9tv524FogVJA9GJgK/C4iK4EVwPuq+mOaRxwQkarAcFwZzTYReQKI9Ks7AF+o6nAfiH4M3A4879evz0rpjW/jEeBOVd3sP0v/5883pP1ZehlX2jFIRCoCX2e2z/So6ici8irwEdBaVbekte2q0X3C0eVpU2H0BxlsEXtaxpFd5jxTI8X7hIQE4uLicmg02cPmlPvlt/mAzSmvyMk55ecg+5+harL92+a4TFwScEhExgMPkX6QvRx4xn+dvhB4QVW/T7XNFUBRVZ0G4DOkHwHNgE+BraoaCHrWAV1Ocm6rg/r+GmjnX7cErhKRrv79GWns/wVwiYiUwgXZw1T1iIgsAW4AquCCRPzY31fVXX5Ok3ygV560j+NmIDYoi/wqLghM7U6ghYgMwAXNZ+Dq0P8AjuAy2eCOVSn/ugnwkO9zl4hM9+M6JiIfAit9ln8BaZS7qOq3IiJAHVxgeiPwmIjcoqqz0zhmAE2BT1R1m38/Fhewo6ovikhDEXkY921HdVz2OuDzdNoNNcYkEWkFtBSRjrhzEgGc6TdJ67PUBHfRhb8IW5KVftMiIoVwJSK/Am1w5UUmD6hSpUqK93FxcScsy+tsTrlffpsP2JzyiuyeU0xMTJrr8nOQnZ4CuPKG4PeFgt5HAPiyCQBUdYvPDDbC1SsvEpFuqYKyyFTtpm77YNDypKB+grONdwfKFNJxJFQ7vv9bVDXOt1syxHjw5RyLcUFybY4HgB/jsr+1cMFyoM3DqZqI8HNK7zhGBC1PXa4SsAz37cJ84ANcdjaw32FVTQwxxzTbVtXOIlIdF2j2A24Xkedw5SUBdXHZ3v6qGoMr+xklIgNx5T+zQ/QX+BwcTbU8+ZsSERkOXIXL4H+KOw7B2x5I4xiEJCJnAl/hsvef+3ajg9oM+VkKMfa0jn1WvYibQ11cfXaX9G6mNMYYY/7u/q6P8FsA9BCRCBEpAnTDZafBPVmhrn/dMbCDr/d9A5fJfNS3UcevPoYLqjYBR3x9L/4r/puD2g5JVWsH/WUUYGc0r15B85oF9Ehj23m4MpClqhoI2ufiMtkX4wI8cAFwBxEp4+d0Fy7T/D1pH8dA6Ukt30aX1J37C4B6wKM+838R7skWkam3DTHuriJSQETOwWVVEZHSIvIz8IeqvgAMBOqp6tpUx/coIMAgn51FRAriMsXrfB/JnwFf/3y+X74AaCIiF/r3dweNqynu243JwG+47HhGc0lPJVx9/0B/IdcIV7ueUZvzcecBEbkEV8Z0Svw5bwF0UtVfcP+7GCsiNU+1bWOMMSa/+rsG2Q/gbgiL9X+Ku5EssO5lEVmHC7x+9cvfwgU4G0UkBjgbV5MM8CHwGS54iwYeFJFvgUXAEFX9NNtndHzsZ+Lm9K3/N3U9dsA83I14cwILVHUnLlu5xJdjoKoLcbXeS0RkA67Eo6XPMoc8jr60pCPwjj+Ol6Xu3D9F5VlcVnQ9LvO8Ahdop2cwLpO/CZd1jvXt/Y67IW+xPz/DgP+m0UZ73Pn7zs8pFneT4BC//lHcOfwaV1cd4/v4DugFLBCRtbjPR+DmvyHASH/eZ+HKizKaS0AzETkQ9LcNd/7mAJtEJA5X1rMxE23eD1T1+7xOUE22iAwRkSFp7hmCiNTD1brfEnjko6ouwX2upvobUY0xxhiTSkRS0gnVBMaYEETkMtwTV55S1UT/jcWjqlo/h4eWKSJSCeiqqv2yq4+YmJikqKio7Gr+tLP6xLzB5pT75bf5gM0przgdNdlRUVERodb9XWuyjTkZ23CPxosVkaO4H2z5T84OKUuE49++GGOMMSYbWZBtTCb52vV7cnocJ0tV52S8lTHGGGPC4e9ak22MMcYYY0y2sSDbGGOMMcaYMLMg2xhjjDHGmDCzINsYY4wxxpgwsyDbGGOMMcaYMLMg2xhjjDHGmDCzINsYY4wxxpgws+dkG2PCquOyejnW95Tr1uRY38YYY0wwy2QbY4wxxhgTZpbJPgUiEgk8CHTEHcvCwGzgcVU9JCKTgPWqOvIU+ugCtFfVlhls9z9gvKrGiMi/gPqq+vhJ9Hch8DRQB0gCEoBnVHVmlgefTUSkLjBVVcunsb4YMABo6xcVA5YDfVX115Ps8zJgpKrefDL7+zbmAr1VdeNJ7j8JuBqIUtW/gpYfAKqr6k/+/Z1Ad+AM3GcyMPc/02i3C9AT9xkuCHwBPKKqe/36ksAQoBGQiPtcvKSqr5/MPIwxxpi/A8tkn5pXgGuAG1S1NlAPEOC1HBjLjUCEf10PKJXVBkSkDLASWAzUUtVaQDfgfyJyY7gGmp38hc984FzgKlWtBlQA1gPzRCQivf3TcSnu3J40VW1xsgF2kPLAi2mtFJEBwN1AtP9M1gKO4C7+Qm1fD3gcuNGf75rAUdxnGxEpCnwGbAPq+Dajgf4i0vUU52KMMcbkW5bJPkkiUh7oBJyvqvsAVPUvEekO/CNo0wYishIohwv0Ovrt/gPcg8s0lgKGqeorPqvYFTgT2Au8GdTnRbjgpzwuoH5TVUeIyNPABcA7InIHLosZKSJ7VfUxHwzdh7uo+gPooaqbQkzrPmC5qk4OLFDVb0SkPbDHj6EhMAKXHT4MDFTV+X7cbXGZzkpAPHCnqsaJyHnAeKCyXz9eVcekM5/ywOdAnF93PS6w6+WPSWw6pyYaKAncr6qJfg6JwHAfYBcH9vtgtA0u23smLsM8XUQqA68DRf2YXgNe9f9eKCILVLWpiEQDT/hjuh94WFVXi8hgP+bzcYH5dqCzqv4qIj8B7YF1wGhcVrqE7+duVV2RzrwCXgTuEJGbVfWj4BUicibQHxcM7/RzPyIifYC2IlJYVQ+nau98P4diwG5VPSYijwPV/Pp/AwdU9bnADqq6VURuxX12jTHGGBOCBdknLwrYEAiwA1R1BxAc/FwI/BM4BKwG2onIdOC/QAtV/UNErgYW4rOHuACnvKru88FrwDvATFUdJSJnA8tE5GcfSHcCOqnqWhEZD5T2y68H7gQaqmq8iNwETAeqhJhTXWBe6oWqugxARM4FpgKtVXWViFQDPvPZUHDBcHVV3SYiY4F+vu9xwHeqGu3HvcKXTrweaj7Al8BFuAuSz0WkNjAYl13f4eeXlobAwkCAnWoew/w8LgWaAI1U9aCIdMCVQ0wH+gCzVXWYvzh4AXeBcDeuRKKpD8THAw1U9UcRaQzMFJFAprshcKU/f7NwFz1PBA2lPu6i6BpVTRSRfv5YtUpnXgG7/DF9V0RWq+rPQesqAwdVdXOqecfjPjuhzANWAD+JyLe4bzLmAXP9+rp+fQqqui6tAa4a3ScT08geFUZ/kA2tpndNl7vNeabGCcsSEhKIi4vLgdFkH5tT7pff5gM2p7wiJ+dkQfbJSyRz5TYzfJCDiKwHyqrqARFpCfxLRCoBtXEZ1oBvUwfvPkv5D+AmAFXd62t0mwPvpdP/v4CKwMrjMSDniEgpVd2dxTnVB75X1VV+DBtEZAWuVjcJiFHVbX7bdUA7/7oJ0DcwbqB6BvP5Eley8IXf/wbgE38BAzABaJbGGCP8WAAQkX/issbgvjG4T1Xn+Ix/JxGpiMsoB47/dOAtEbkKWAQ84APh4D4aA4tV9Uc/9iUi8hvuwgtgadD5+4pUpTuq+oWIDATuEZEK/vjtT2M+J1DVT/yxetvPLyCzn8ngto7gjkMf3MXg9bhvTxbjsthZbtPkHlWqnHgtHRcXF3J5XmZzyv3y23zA5pRXZPecYmJi0lxnQfbJWwVUEZESqpocIPkbByfgygLA1cMGJAERvkziC7/dclx2OPjGxgMh+ivA8Zrr4GWFMhhnJDBZVR/14yuAy6LuEZGvg7a7GxfcXg28FNyAiNyDK6nYTFAAm2oMh4GDQcuTgsZ7lJSB7+W4spX05nNIVY8GrQveNnm5z4hf4N8+jsvE9g6sV9VPcRcxiMhSoKiI1AFm4oLvT3A1x6/47ef4C58bccH9EyISCJ4DItM5DpD2cQiM+V+4so/n/Tg2AZ3Jmv648zUgaNlGoJCIVArOZvu66mm4c/waKY9XaeB3VZ2Fy3a/IyJDcZnt+30f96fuXERa474dybm0tTHGGJOLWYbqJKnqL7igZKKInAXg/x0H/KGqB9PZvS7ua/+huCCvpd8/Mp3+9hMU8PjyijtwZSbgAs9CIV4vAG4TkfP9++64LCWqWjvoby2u9riRiHQK3CDoA8whuO/MvwAq+ywvvlzkOmBpOnMFlxG+K2jci3HZ9fTmE+wT4CZ/cQLQJei4tAiawyxcqc5fIvKCiCR/OyAi9YHLgWN+zGtVdRQuwI7GBc6IyBTg36r6Hq5GfR/uxsngY7oYaOovFvDlIhfjLrwy40ZcScorwNrg/jPL11bfhrugOMMvOwQMB14XkXJ+bEVwFxNnquovIY5XoF79oqDmqwFbcXX4HwFni0jfwOfTz3sUrmbeGGOMMSFYkH1q7sNlD1f6rPAq//7uDPb7BPe0BsUFKpfggu6KGezXCbhBRGJx9d3TgEl+3TRc+cBNwBJcEDhWVT/BBV4Lfc1tR6CdqqbOxOLLRxoBNwPrfT8vA11VdaGq/g7cAoz166YAd6nqdxmMuwcu6/8trr73WVWNyWA+weOKxZWbLBaRtbibEkPy2e9muEz5MhH5WkT+D3dB0weYAbwLlBaRONz5OgCUEpESwFO48olvcOdzOrDMb5cgIqtx5+w+YJovARoGtAo88i4TxuMuZmJxZTU/AJeJSAERqZvqG4Y0qariguwCQcuewQXGC3w73+Ay6W3SaGMSMBaYKyIqIpuAe4GmqnrMB/NNcIF3rD+HHwFDVXViJudrjDHG/O1EJCWdEGsZY3KQiHykp/A87pwUExOTFBWVurom77L6xLzB5pT75bf5gM0przgdNdlRUVEhHw9smWxjchFf028ZYmOMMSaPsxsfjclFVHU77tnaxhhjjMnDLJNtjDHGGGNMmFmQbYwxxhhjTJhZkG2MMcYYY0yYWZBtjDHGGGNMmFmQbYwxxhhjTJjZ00WMMcYYk291XFYv+xrfBVOuW5N97Zs8zTLZxhhjjDGnwbFjx3jjjTdo164dbdq0oUWLFowYMYLDhw+HrY82bdqwb9++sLS1dOlSXnzxxSzts2rVKipXrsyKFStSLB8yZAhjx47l559/Jioqik8//TTF+s8++4wGDRqwc+dO+vXrh4jw5Zdfpthm27ZtVK5cmSFDhqRYPnnyZESEr7/O1A8mnzaWyTbGhFW2Zo2ymWWkjDHZafDgwezdu5c333yTEiVKEB8fT+/evXnssccYMWJEWPqYOXNmWNoBiI2NZe/evVner1ChQjz66KPMmjWLUqVKpVh38cUXM2jQIAYOHMjs2bMpVaoUe/bsYeDAgQwfPpxy5coBcMEFFzBz5kyuvvrq5H1nzJjBueeee0J/7733Hq1ateLNN9+kdu3aWR5vdrEg2xhjjDEmm23bto3Zs2ezfPlyihcvDkCxYsV48sknWbduHQD79+/nySefZNOmTURERNCwYUMefvhhChYsSI0aNbjrrrtYuXIl8fHx9OjRg/nz5/Pdd99RtmxZxo8fT7FixRARvvjiC5YuXcrChQspUKAAW7dupWjRogwfPpwKFSqwdetWBgwYwN69eylTpgxJSUm0bt06xc+Pf/PNN7z33nscO3aMEiVK0KtXL15++WU+/vhjIiMjueyyyxg0aBBlypQ5Ya6XXnopNWvWZMCAAYwfP/6E9dHR0SxfvpzBgwczZswYnnjiCdq2bUvDhg2Tt2nRogVTp04lISGBokWLAjBv3jyaN29OYmJi8narVq1i79699OnThxtvvJFff/2V888/Pzwn7RRZkH2KRKQQ8H/A16ra/BTauQwYqao3i8gFwFRVbZDFNiYBNwK7/KICQHFgvKo+d7Jjy0L/dXHjLh9i3U/AIeAgkAQUARKB3qo6/yT7Wwq8pKpTM7HtpcCTQAPgCFAI+BAYrKpHTqb/cBGRekBXVe2eQ/2vBIoBhQEBYv2qDcDzwKfADaq61m9fGlgFPKCqH5/+ERtjTN6zYcMGKlasmBxgB5QpU4amTZsCMHToUEqWLMns2bM5cuQI9957LxMnTqRbt24cPnyY0qVLM3XqVCZMmMDAgQOZN28eZcqUoX379ixevJhWrVqlaHvNmjXMmTOH8847j6eeeooJEyYwfPhw+vbtS5s2bejYsSM//PADN998M61bt06xb61atejQoQN79uyhV69efPTRR3z++edMnTqVYsWKMXbsWPr168frr78ecr4DBw6kbdu2vP3223Tu3PmE9YMHD6ZNmzb079+f33//nVGjRqVYX6pUKa688kqWLFlCixYtWLt2LRUqVODss89mz549ydtNmTKFVq1aUa5cOa6++mrefvtt+vTpk/kTk42sJvvUtQO+BuqKSJWMNk7HpbgAB1X9JasBdpDRqlrb/9UEGgFPiEjlUxhbuHTy47pSVasCg4E3srtTEbkQ+BJYAYiqVgPqAJVxQWROqwZclFOdq2oDVa0NtAAOBn1+OqnqOqAv8KGInCMiBYEPgIkWYBtjTOYVKFAgRQY2lGXLltG5c2ciIiIoXLgwHTp0YNmyZcnrA8H4JZdcwhVXXEG5cuUoUKAAF110UciyjmrVqnHeeecBULVqVfbu3cvevXv59ttvueWWWwCoUKFCipKM9MbWrl07ihUrBsAdd9zBl19+mWY9ebFixRg1ahSjR4/mu+++O2F98eLFGTp0KLNnz2bUqFEULHhi3rdNmzbMmjULcKUibdu2TbF+165dLF68mOjoaMBlyD/88EPi4+MznM/pYJnsU3cv8B7wA/Ag0F1EGuEyrNUBgt/7YPd1oCgQAbwGvOr/vVBEFgD3AOtVtbiIDAbKA+fjAvHtQGdV/TWT47vI97PfjyUaeAJ3gbUfeFhVV/t+SqtqD79d8nufMf4C+AdwCbAI6KaqiSJyL9AL2MvxDGiGRCQCuAzYHbRsEHAbcBT4DuihqjtE5DxgPC4oTsRl5scE7VcQmILLUN+pqkdTddcP+EhV/xdYoKoHRKQH0D6ona7Aff7Y/OH73yQiZwMvA7VxWfh5wABVPSoih4CZQC2gE1AWGA4cw118NQGuVdWfQrUP/AUMAc4WkTdU9S4R6QY84NvY6bcrBsxW1Yv9WBcAO1T1ThEpAvwCXO63HwbchPvMPKeqr2TqpKRBVV8VkWuBibjP+Z/AM6fSpjHG/N3UrFmTH3/8kQMHDqTIZu/cuZNBgwYxZswYEhMTiYiISF6XmJjI0aPH/y+tUKFCIV+nJVBmARAREUFSUhKRkZEAJCUlJa8LLEtPRmMLpVq1atx777088sgj1KxZ84T1F198MYUKFUq+EEjthhtuYMiQIfz666+sWbOGwYMHs379+uT1H3zwAQD33ntv8pgOHDjA9OnT6dSpU4Zzym4WZJ8CEakKXAPcDMQAn4nIYxns1gcXLA3zweMLuADyblwg3lREyqfapyFwparuE5FZQHdcoBxKLxHpDJzl/5YD/1LV7T7AHw80UNUfRaQxMFNEJBPTrYDLihcH4oDrRWQPLhtdywfDJxZepfSOiCQAgbsWFgCtAETkLqA5UE9V//JB/iSgGTAO+E5Vo33Au0JE5vo2CuPKPrYDPVX1+H81jmsIDEq90F+ojPX9Xw/cCTRU1XgRuQmYDlQBxuCC4hq+v1lAb1wwWxh3Pm8VkXP9nBqr6jcicqdvM832VbWKiDwOtPcBdmNc5vgaVd0lIl2AGbhs9xERqY4LdCsDVf1UbgBWqepeH3D/rqoNRCTKH6s3VDUh/VOToe64i4YrgeppHGcAVo3OHV/TnYwKoz8IsTTT146nxZxnapzS/gkJCcTFxYVpNLmDzSn3y2/zCZaVeV177bX07NmTHj16UKxYMeLj43nhhRcoVqwYW7ZsoWbNmowZM4auXbty9OhRJk6ciIgk97F582bOOusstm/fzl9//ZW8fN++fezYsSPFdr/88gsHDhxIXhZ4//PPP1O5cmXGjRtH06ZN2blzJytWrKBq1apcdtllKeaze/dufv/9d+Li4qhYsSKTJ0+mYsWKFC1alHfffZdq1arxww8/pJjj1q1bOXToUHI7DRo04JNPPmHmzJm0a9cuRfs7d+4kMTHxhGP4559/8ttvv/HDDz9Qt25devToQa1atdi8eTO7du1i//79rF+/nilTptC9e3duuOGG5H2nTJnCa6+9xpVXXklERESOfvYsyD419wJzVPUP4A8R2QJ0w2V90zIdeEtErsJlhB/wGeH0+lmqqoHn8XwFlEpn29GqOlJEzgTex9VBL/XrGgOLVfVHAFVdIiK/AVHpde7NVtVEYJ+IfO/HUAf4RFV3+G0m4ILitHRS1bW+/nwRro79R7+uOfCGqv7l378IPCYihXHZ4L5+zHuBwDcE4Mo9SgAV0gn8InAZaPx+fXBZZ4DzcMHqv4CKwMqgc3GOiJTyY/uHb/+Qv5h4CBdkA3zu/70O2Kiq3/ixvikigYx7eu0Hawa8r6q7fBuTRORF3LcZ0/1Y1gOLgVoiUg1oA3wU1Ebg1vJ1uNr3M4FTDbIFd5yL4j4vn51ie+YkBd+YdDLi4uJOuY3cxuaU++XkfKZUyZ6nBp3MnF544QXGjRvHE088QWRkJIcPH6ZJkyb07NmTwoUL89xzzzF06FD69u3LkSNHaNiwIX379qVw4cIAVKpUiVKlSrF161bOPPPM5P7POusszjvvvOT3lSpVYvv27RQvXjx5WVxcXPL7sWPH8thjj7FkyRLKlSvHJZdcwuWXX07RokVTzKlVq1b07t2bqVOn8thjLoc4cOBAEhMTufTSSxk3btwJWeh9+/ZRpEiRFO289NJLtG7dmjJlyqRYXqJECQoUKHDCcSxZsiRly5alSpUqdOnShY4dO/Lss89yxRVXUKZMGQoWLMiOHTuIjIykW7duyccHoHfv3sydO5fffvuNRo0aZftnLyYmJs11FmSfJB/E3o4Lun7yi8/CfbW/EhfYBSSffVWdIyKVcDco3oCrl84oyD0Y9DoJiPA3Gb4W1G6KZ9b4bPDtuKxzL2AUEElQsOkVwN0EmJTWmNMag38dvE/63xsdH9sWP7ZPRWSVqq4OMbYCuM9nhG83OEi+HPjdv53st/kf0NrfNDo3qJ0WuPPRCJjj+x8BjPBtJfm+IoHJqvqoX14AuADY49enHlvw93QHguYffDzAlbeQQfvBIoHUBW4Rvr/pwFN+v4XAb0BTXOA9IGj7g36eST6gTz2mLPE3Ok7DfY6KAu+JyJVBF1fGGGMyoWDBgjzwwAM88MADIdefc845PP986FuFVDX5dbNmzWjW7HhOa8yYMSds165dO9q1a5e8PPj97Nmzefzxx6lQoQL79++ndevWVKpUiSNHUj4HoE6dOixZsiT5/YMPPsiDDz6Y7hzr16/PnDlzUiwrW7bsCc+8Brjooov46quvTlg+bNiw5NdRUVEp5t6zZ8/k102aNDlh37PPPjvdwPd0shsfT14nXAnBBapa3j9R43JcOcU/gEtEpKyvPe4Q2ElEpgD/VtX3cPW5+3ClGEdJGbilS1XXBt2gFvKhkKq6B3gEeNLf/LcYaOqDVHxpwsW4J0XsAqJEJEJESgAtMzGMT4CbRCRw016XLIx/JfAWMM4HnPOB//iLF3A1yctU9RAu632XH/PZfh6V/HarcaUgFUXkv/6m0dpBf78ATwO3isgdIhLp2ykoIrf6NhJxZR63iUjguT/dfT/4dT38sSmC+7ZiYYhprQCuEJGavo+bgZK4AD299oPP/Xygg4iU8W3chfucfY+7WKiAOzeLcMf/IVwpzR/pHvCT5I/X+7hvMt5V1Tf8GN8PHEtjjDF5S/ny5enVqxfR0dF07NiR//73v1SsWDGnh5XvWJB98u4FRqnqscACVf0TV7/bFncz41rcUy22BO33FNBJRL7BBbfTgWXARiBBRFZzipnHYKr6Dq5efKSqbsQF9qCXP2gAABmiSURBVNNEZD2u3KGVL8F4Bxdob8ZlfDMsB1DVWFwZx2IRWYvLcmZFf9zNj//F3Qy6CFgtInG4UpRASUcPoIqIfIsLZJ9V1eTLVF9v3AUYISIVQoxzG3A1cC3wlYjE4oLWtsDVqrpbVT/B3bC40PfTEWjnS0QewN3QGOv/FBe4p+5nN+7GzbdEZB0uy3wUiM+g/S+By0VkmqouBEYDS0RkA66Ou6WqJvpynXnAfl9OshxXtvMRmSAiX/tvQLJiBK7c5JGgZff7fu3mR2OMyYOaN2/OrFmzmDFjBrNnz6Zjx445PaR8KSL47lJjzMkTkbOAgbhnb8eLSB3gY9y3HTn+PzQReRpXsrIpu/qIiYlJiorKTIl/3pDf6mLB5pRX5Lc55bf5gM0przgdNdlRUVEhk6NWk21MmPinvxwG1ojIEdwjBW/NJQF2BPBTdgbYxhhjjDnOgmxjwkhVB+Ky2bmKD/T/l+GGxhhjjAkLq8k2xhhjjDEmzCyTbYwxxph8q0LbUD8yFS6x/DD91ow3M39Llsk2xhhjjMlm27Zt48orr8z2fmJjY9N8DvfJmDZtGo0aNaJr165hazMtY8eO5ZprrmHXrl0plrds2ZJVq1Yxf/586tWrx44dKX+m4bnnnqNz584cO3aMxo0bU7t2bf76668U20ybNg0RYf78+SmW9+zZk/r163Pw4EHCzYJsY4wxxph8okaNGil+nOZUzZgxg169evH666+Hrc30HDhwgEcffZRQT79r1qwZN954I/37909ev3btWmbOnMnzzz9PZKT7+YZzzjmHhQtT/pzFjBkzKF26dIplO3fuZM2aNdSuXZsZM2aEfS5WLmKMMcYYk4MOHz7MyJEjWbNmDceOHaNq1aoMHDiQ4sWL8+mnn/Lqq69y+PBhdu/eTXR0NA899BCPPPII1apV4z//+Q8AU6ZMYfXq1dx222089dRTzJkzh379+lG8eHFUlR07diAiDB8+nDPPPJPPPvuMkSNHJv+s+cqVKxkyZEiKx90988wzxMbGsm3bNvbs2cOmTZv4888/+fnnn2nUqBHdu3fnySefZNOmTURERNCwYUMefvhhChYsSI0aNbjrrrtYuXIl8fHx9OjRg/nz5/Pdd99RtmxZxo8fT7FixU44Fq1bt+abb75h4sSJIbPnAwcOpG3btkyZMoXo6Gj69evHs88+S7ly5VK0MWvWLKKjowHYvn078fHxXH755Sna+uCDD7jmmmto2rQpL774Ih06dCAiImw/VWKZbGOMMcaYnDRhwgQiIyOZNm0as2bNomzZsowcOZKkpCQmTpzIsGHDmDZtGu+//z4TJkxg9+7d3HLLLUyfPj25jenTp3PrrSfWh69fv57XX3+duXPnsn37dubPn8+ePXvo27cvI0aMYObMmdSvX5+dO3eesO+AAQOoXr06ffv2pUuXLgAkJCTw8ccf06dPH4YOHUrJkiWZPXs2H330EarKxIkTAXfhULp0aaZOnUp0dDQDBw7kscceY+7cuRw4cIDFixef0B9AkSJFeP755xk3bhwbNmw4YX2xYsUYNWoUY8aMoX///jRv3pzrrrsuxTbXX389mzZt4rfffgNg5syZyQF3wNGjR/nggw9o3bo1jRs35o8//mDZsmXpnKWssyDbGGOMMSYHLV26lCVLlhAdHU2bNm1YtGgRP/zwAxEREYwfP54NGzbw0ksvMWzYMJKSkjh48CD169fn0KFDxMbG8v3337N7926uueaaE9pu2LAhhQsXplChQlxxxRXs3buXtWvXUqFCBSpXrgxA27ZtKV68eKbGGvyDY8uWLaNz585ERERQuHBhOnTokCJQbdq0KQCXXHIJV1xxBeXKlaNAgQJcdNFF7N27N80+RCQ5Wx8fH3/C+mrVqtGxY0e2bNnCgw8+eML6QoUK0bRpU+bMmQPAvHnzaNmyZYptFi9eTGJiYvLxadGiBW+99VamjkFmWbmIMSasOi6rl9NDCK9dMOW6NTk9CmNMPpaYmMiAAQO4/vrrAfjrr784dOgQ8fHxtG3bliZNmlC3bl1uvvlmFi1aRFJSEhEREbRv356ZM2dSqFAh2rdvH7LUoWjRosmvIyIiSEpKIjIy8oSa5wIFMpd3DS7xSExMTNFnYmIiR48eTX5fqFChkK8z4/bbb2f58uU8/fTTIddffPHFXHDBBRQsGDqUjY6O5oknnuCcc87hsssuo2TJkinWT5kyhYSEBG666SbAZd537drF5s2bqVSpUpbGmhbLZBtjjDHG5KBrr72Wd955h8OHD5OYmMigQYMYNWoUW7du5cCBAzz00EM0btyYVatWJW8DLgO9ZMkSFixYQLt27TLdX506dfjpp5/YtMn9CPCCBQvYt29fluuRr732Wt5++22SkpI4fPgwH3zwAQ0aNMhSG+l59tln+eyzz9i6dWuW961VqxYJCQlMnjyZtm3bpli3ZcsW1qxZw7Rp01iyZAlLlixh+fLl1KtXL6zZbMtk5zARKQ/8AMT6RZFAPPCwqq7IYN9JwHpVHSkijwPfqOpMERkCfK+q4f3e4ySIyIXA00AdIAlIAJ5R1ZkZ7NcFaK+qLdPbLgvjSALKqOrvqZa3BpqoaqafdyQi/YAO/m1FYBcQ+N7rZlX9IQxDzlYicjfuFyAfV9WngpZHAD8Be1S1tt/ueWCL36Qg8CPwhKp+dXpHbYwxeVt8fPwJj/F77733uO+++xg+fDht27bl2LFjVKlShX79+lGsWDEaNWpE8+bNKVy4MFdccQUVK1Zk69atXHLJJZQpU4aqVaty9OjRFDf+ZaRkyZKMGjWKRx99lAIFClC9enUKFixIkSJFsjSfgQMHMnToUFq1asWRI0do2LAh3bt3z1Ib6SlVqhTDhg3j7rvvPqn927Rpw5tvvknDhg1TLH/33Xdp0qQJl156aYrl999/P/fccw+9evWiVKlSJz3ugIhQj0gxp48PsteravGgZbcCT6tqut9XpAqylwIvqerUbBxulohIGWAt7mfG31bVJBGpBSwEOqnqwnT27cJpCLLD0O5SctlxzwwfPA8CElRVgpY3BqYAO4KC7JaqGh20TVPgbaCOqv4c3G5MTEzS83+F7z+wuUV+KheJi4tL8fSA/MDmlPvlt/lA3p7TgQMHGDduHD179uSMM85gw4YN3HPPPUyYMIGqVavm9PDCKrvPU0xMDFFRUSG/ArBMdu50LvArgIgUAEYDVwMlgAjg7uAst4jcD9QFRojIMaANx4PvBGAU0AQoDgwGbgFqAL8ArVT1LxFpCIwAigGHgYGqOt8Hu12BM4G9qvpPEekK3IcrN/oD6KGqm0LM4z5guapODixQ1W9EpD2wx489ZL/BjaQOZIPfZ3Z+vqmnRaSeH/dAVZ0THMyLyNXAc0AR4Hxgoapm+en7IlIDGAuUwmXvn1PVd0RkPXC/qn4mIrcDrwLnqOohf8G0AvgOGIk7zwBDVXWGiJQExgE1/fLZuCAZXBZ9FHCDH/ezqjohE0P9CqgoIlep6mq/7E5cAN0krZ1UdYGIzAbuwV1AGWOMyUOKFy+eXMddsGBBChYsyAsvvBDWx9cZC7JzizNE5Gv/+hxcoNTGv68PXABco6qJvlShH9AqsLOqviwit+ACz+ki0iao7SK4rORVIvIo8BpQBRfErwHaiMgCYCrQWlVXiUg14DMfkAJUA8qr6j4RuR4XiDVU1XgRuQmY7ttMrS4wL/VCVV0GICLnZtBvZmQ4P1xmFuBHVb1HRKr7fiqnautBXPnEUhEpDmwRkShVjcnsYESkEDALeFBVZ4nIRcBqEdkMTAOaA58BzXDB8T/8RUMzoI8/HsP9BcSVwF3ADOBl4FdV7SgiRYE5wEPAC7gLlO2q2kBE6gNLReQNVT2SiSFPBm73YyyOu5h7gHSCbO8boHGoFatG98lEt3lLhdHZ+bPMJ5rzTI1sazshIYG4uLhsaz8n2Jxyv/w2H8j7c2rWrBnNmjVLsSyvzymUnJyTBdm5w0FVrR14IyJNgBkiUkNVvxCRgcA9IlIBaATsz2L7H/l/fwBiVXW772cLLttaH1fDvQpAVTeIyArfVxLwraru8238C1eHvFIkucrgHBEppaq7U/WbSPo312bUb7jmFzDe97NeRDYCqZ91dCfQQkQGAJWBM3DZ8ayoAhRQ1Vm+r20iMh1oirsYmSQi/YEGuAD5RuAIEKequ0TkA2C8iEQDizieKW6Gu2hBVRNE5FWgu28DIFDjvg4oigu8034+0nFvAzEi0gu42Y/xWCb2S8LdO2CyQXZ+tZmXv+JOi80p98tv8wGbU15xOspF0mJPF8mFVHUR8D1wlYj8C/jYr5qJCxSz+n3OoaDXobKbkZwY1BYAAs/bOZBq28mqWttfGNTBBX97ROTroL+6wJe4zGgKInKPiDyciX4Dkkg558JZnF9AcPBYIMS2y4AWwCZgCLAdiBCR7kHzei2d9iGdOfkbBUsArYGNuPN6k38/Fdy3EkAtYDEu6/2NiBQO0W7q43TQ/xvYJlOfEX9BEou7CLgDmJSZ/YB6HL9Z1xhjjDGpWJCdC4nIFUB5XM3sjcBsVX0FdxNhNC7gSu0oJwanmfUFUFlErvL9VwOuA5aG2HYBcJuInO/fd8cFhAQCb/+3Fldz3EhEOvmnViAiUbgANjYL/e7CZ3FFpCrH65Kzqotvow4uG78qsMLXPNcDHlXVacBFfptIVR0fNK+MbnHeCBTwTy3Bl4u0xWWlwZV+DAM+UdX1QFnck0qm++1XA9VV9Q2gG1DGb7MA6Om3KQr8F3cDaTi8BfQFzkijtj4FEWmFC8r/F6b+jTHGmHzHguzc4YzgLDAuq9lNVb/DZa4biUgsrhTgB+Ayf0NksFnAsyJyZ1Y790/cuAUY6/uZAtzl+0+97SfAcGChiHwLdATaqeoJ5R2+fKQRrgxhvW/7ZaCrqi7MQr9DgZv8jYNDcBnnk3G5iHyFq9vuEFzeoqp/As8C63w//XA3IlbMSgeqeghXB97bH58FwKBAHToumK7M8QB5EfB/qvqLf98bdx6/Aj4FHlPVbUAP4EI/tm+B9bjzkCYRifSfqdrpbYerFa+DC7ZD+WfQ5/Mr3A2PTVR1VwbtGmOMMX9b9gg/Y/IxERkO/E9Vvz8d/cXExCQF/+RuXmf1iXmDzSn3y2/zAZtTXpGTj/CzTLYx+ZQv0dl8ugJsY4wxxhxnTxcxJp/yJTwZ3ahpjDHGmGxgmWxjjDHGGGPCzIJsY4wxxhhjwsxufDTGhE1MTIz9B8UYY8zfSlo3PlqQbYwxxhhjTJhZuYgxxhhjjDFhZkG2McYYY4wxYWaP8DPGnDIR+RfuFzOL4H6Rsquq7svZURljjDE5x2qyjTGnRETKABuAf6jqZv8rkyVU9b4cHldnoA+QBMQDD6jqWhHpD9yJSzK8DTzpnymeev//4H7mvhCwyO9/RESK4Z4/fiXu28BHVXWG36c+8BJQHPgF6Kyqv2bD3KKByapawr/Ps3MSkRrAWOBs4Bhwj6rG5PE5tQWeBBKB3cB/gZ+A54Fmfk4jVXV8GvuHnLv/39pbwKW+7W6qutLvE/YLXf+DVpOAWFUdKSKRac1BRCoBrwOlgQPAHaq6KUSbWZ5Dev2GYU5nAC8DVwERwCrgflU9mN5YU7V5Up+1zH7GszKfVOumAb+oag//PtfPJ605ich9wN3AGUAM7rNxKLfPycpFjDGn6iZgjapu9u9fATr5/1DmCBERYATQTFVrA0OBaSLSArj1/9u7/6CpqjqO429MKTSIwcYQQadffBOVdATGnILI0oYpFUqlicxfEQk1aFPa2AhD0pSVlomCojaJlkoWP0QHizQDB4ZMKcc+JmqCZFL5C0dEYPrjnAcvyz77LLv7sLv0ec08M7v33r17PvvcvXvu2bPnAMcCRwKjgdPKPP5IUiVpFBBAX+CCvHo6sEnS4cAngFkRMTAiegLzgal53XxSpaPR2d4P/JBUIaCdM+UPvqXA5ZKOAb4D3NLmmXqRPozH5WNvEXAV8GVgcM4zHJgaESPKPL5S9lnAA5KGABOAOyJi/1zRuAn4jKQAngS+V2eOw4HfAZ8tLK6U4RZgdi7bNGB+J+eAWjJU9drVmOkSUgVqaP7rBXyrUlnL7Ho6u3msVXuM15CnY903gY+ULG7pPJ1liohxwFeBjwNHkP5HHe/zls7kSraZ1WsQsK5wfz3QB+jdnOIA8DpwXqF1cjXQn3SCvFXSq5I2kz7UJ5R5/CnAQkkbJW0H5hS2GwtcDyDpGeBe0ol4OPCypOV5uxuAEyLiwEaFyh8e84ALC4vHtnGmE4G1kpbk+wvz87ZzpreQLoDeke+/Hdicy3OTpK2SXgB+2UmmstkjYl/gU4VMDwN/J7XudseF7mRSy98dJWXbJUNEHAJ8IN9H0t059zHFHdaRodrXrpZMfwAuk7Rd0jbgz8BhXZS1VC3HWrXH+O7mISI+mss5u7CsHfJ0lulM4EeS/pvf55OAm9shkyvZZlavfUhdMkpt29MF6SDpaUl3wY6vHq8gVeAOZtcLgoFldlHuwmFgF+t2Wi5pC7AROKSeLCXm5L81VZaVKrdrVqbBwHMRcUNErCZ98O3bRVmLWi6TpE2kSsCKiNgATAEu6qKsRZ1t905gH0kby6xr+IWupCmSbq2ybINI3RK2l1lXVGuGal+7isplkrRU0uMAEXEYMJVUwatU1lK1HGt1ZyqXJyIGAD8BPs/O5+CWz5P3We64GwwcFBH3RMQaUqv0i+2QyZVsM6vXM8CAwv1DgBckvdqk8uwQEQcAtwPvI/XnK70g6EH5i4FK23W2rtzFRmf73225T+JWSTfuRlmr3a4pmUj9qMcA10kaRuqbvYTUJ7ctM+U+5pcCQyQNAGYCvyK1cNeTqVK599SFbr2vaa0Zqj3GaxYRxwIPAFdLWtxFWUs14n9Wd6aI2A/4BXCBdv2NQdvlKdiP1MXjdGAY0I/0vmr5TK5km1m9lgLH5b7CkFrxFjSxPABExKHACtJJcbSkF9n1gmAAqXWiVKXtOlu30/L8gXcg8GxdQd50FjA8Ih4mVUR75dvrK5S1qBUzbQAek7QSQNICUmV0e4WyFrVippOA5ZLW5vuzSP05/1GhrEWdlft5oEdE9Cuzbk9d6FZ6TQ8u6Z5SLl+tGap939YkIsaTvkW5WNJ3qyhrqVqOte7INAx4D3BFPjdMAs6IiLltmqfDBuBOSS/nluZ5wIdog0yuZJtZXSQ9D5xN+qHTY8BRwNebWaaI6A3cRzoxj5f0Wl61gNTP84CIeCup4vqbMrtYCJwcEQflisPEwnYL8n0iYiCp/99i0qgEB0bE8Xm7c4AHc+W+bpJGSDoy/5huDPBavv3rds0E3A28O7ciEhEjSS1HP27jTA8BoyLiXfn+qcBTuTznRMS+EdEXGN9JprLHqKStwF2FTEOBIaTjfE9d6JbNIGk98ARwRi7bSaQLpb8UH1xHhmpfu90WEZ8m/TD1xGI3hS7KWqqWY63ac1HVJD0oaZCko/O5YTZwm6Tz2jFPwXzg9Ijold/np5L677d8Jo+TbWZ1yz9cW9LlhnvOFNKQTmMjDafW4QTgTmAV0JN0Ev05QERMAoblD6Q1ETEDWEb6qnIl8P28j2nAtRHxKKnV9RsdrZb5V/BX524q/yH9YKdbSVqUuyi0XSZJz0UajvCavP/XSaNy/LGNMy2LiB8A90XEFtIQfqcAAt4LPJIzzZF0fy7PjPzYSyv9P4HzgbkR8VfSxcgXJL2U99FxodsTWNvITAXXdpYB+BxwfUR8m/RDz9M6+mjnVtXzJK2uMUOl561Xx0g9cyOiY9lySZO7KOsS0mgqC6nhWOvi/9xd2jXPNaQuIn/K5XmINxtyWjqTx8k2M2NH6/dPJZ3V7LI0ijO1h9x6e66ki5tdlu4QETNJ47rvMm52u4qILwHrJN3T7LI0wt6WB1ojk7uLmJklR1Pn2MItyJnaQ5C6LOx18tf7T+9NFexsK2k8573F3pYHWiCTW7LNzMzMzBrMLdlmZmZmZg3mSraZmZmZWYN5dBEzM7MmiDT99e+B8ZJuKyxfQxpBoY+kcd3wvP2AT5aZWc/MGsgt2WZmZs3zN9Lwd8COGSMPAOiOCnY2FDi5m/ZtZplbss3MzJrnEWBwRPTNE2BMAG4BDo2I5yT1j4gRpJkjXyHNcrcZmA4sIo3ru4Q0Rvi0vM/9SWP9biFNs72ONM70KklfAS4BPhgRE4F/AxcBbwBPA2d2jG9tZvVxS7aZmVlz3UmaOKkHMAJYUbJ+NnCWpI+RJmnp0J80U+HlwBHAhLzNQuC0vM1g4Ny83zER0R+YCSyTdB2pFf1KSR8mzbrYpzsCmv0/ciXbzMysuW4lTRU+EnigzPoBkh7Nt4vrn5K0Jd9+FrgqIn4GjCbNgAnwhKRXJG0D/gm8rWTfFwIjI+J+4HjSdOhm1gCuZJuZmTWRpCdJ/bC/Bswrs8m6iBiSbx9XWF6sEM8Fzs4zYW4gTRUOaarpUtt58/N/IjBd0qj8mLG1ZDCzXbmSbWZm1ny3AYMkPV5m3fnAjRHxW1K3jzfKbHMzsDIilgO9gQEVnmstcFRETAVWAfdGxDJS95PFdWQwswLP+GhmZtbCImIycLukjRFxGbBF0oxml8vMKvPoImZmZq3tX8DSiNgEvAR8scnlMbMquCXbzMzMzKzB3CfbzMzMzKzBXMk2MzMzM2swV7LNzMzMzBrMlWwzMzMzswZzJdvMzMzMrMFcyTYzMzMza7D/AVVVPoYc0hrZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56cdd2da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axm=df_to_plot.set_index('mname').plot(kind='barh',figsize=(8,7), color=wccolors)\n",
    "axm.legend(['Coming to NYMA','Leaving from NYMA'], loc='best',fontsize=12, frameon=False)\n",
    "axm.invert_yaxis()\n",
    "axm.set_ylabel('')\n",
    "axm.set_xlabel('Migrants')\n",
    "axm.get_xaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}\".format(int(x))))\n",
    "\n",
    "# ticks labels size\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.show()\n",
    "nyma_fig=axm.get_figure()\n",
    "nyma_fig.savefig(os.path.join(out_path, 'images','nyma_top_flows.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changes in Rank - NYMA Flows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create arrays for inflows\n",
    "inmetro_geogs, inmetro_flows, inmetro_labels = create_arrays(grouped_by_metro,'inflow',10,'metros',True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['33100', '33100', '37980', '37980', '37980', '37980', '37980'],\n",
       "       ['37980', '37980', '33100', '33100', '33100', '33100', '33100'],\n",
       "       ['47900', '47900', '31080', '47900', '47900', '47900', '47900'],\n",
       "       ['31080', '31080', '47900', '31080', '31080', '31080', '31080'],\n",
       "       ['14460', '14460', '14860', '14860', '14860', '14460', '14460'],\n",
       "       ['39100', '39100', '39100', '39100', '14460', '39100', '14860'],\n",
       "       ['14860', '14860', '14460', '12420', '39100', '14860', '39100'],\n",
       "       ['12060', '12060', '45940', '14460', '45940', '45940', '41860'],\n",
       "       ['10900', '45940', '16980', '45940', '16980', '41860', '45940'],\n",
       "       ['45940', '10900', '12060', '10900', '12420', '16980', '16980']],\n",
       "      dtype='<U5')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inmetro_geogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  1,  0,  0,  0,  0],\n",
       "       [ 0,  0, -1,  0,  0,  0,  0],\n",
       "       [ 0,  0,  1,  1,  0,  0,  0],\n",
       "       [ 0,  0, -1, -1,  0,  0,  0],\n",
       "       [ 0,  0,  1,  0,  0,  1,  0],\n",
       "       [ 0,  0,  0,  0,  1,  1,  1],\n",
       "       [ 0,  0, -1,  1, -1, -1, -1],\n",
       "       [ 0,  0,  1, -1,  1,  0,  1],\n",
       "       [ 0,  1,  1, -1,  1,  1, -1],\n",
       "       [ 0, -1, -1,  1, -1, -1,  0]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create array for annual change in ranks\n",
    "inmetro_rchange=rank_change(inmetro_geogs)\n",
    "inmetro_rchange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vvZHrrgulQ4c2fPnl5+pHIfYjN3buP0PVin6U9fMG4JZutfly9QEAfpl7IwDtm1Xivee68d5z3fjkxaupU60M1Sv58elLVzP9/zry+cvXMOxmw9RR7fj6tV48fscVALz/fHfqVS9bPAeWDy1atOTQoYOcOXMGgA8/fI+BA+8A/lqJzekcioqKokuXK7n99v9w1VVtef31Vxg2bCjt2rVk/PinAXJcNS7p1I/s1BN36ocUl9zeWk0ChlmWdSTj778BVYwxU4wx840xS40xO40xPxhjqgIYY7oYYzYaYzYYY2blYc5M4eErCQ3tTu/ePRk69A5ef/1N/P3L0KfP9SxdupJevfqwZMknOe67Y8c25s17j6VLV3L99Tfx6aeL2bZtK2vXrmbdugjmzHmXs2fjAJg6dQo9elzNsmU/MmPGLEaNGpFtvA4drmL58nD69RvA1KlTAEhMTGTx4s/54Yc1hIVNd9vesnby8MOP8+23y3n99emEhb2V1zaoH4Vg2S9/0KtDdQBaNizP7ztPur3esGYAj73+K4MnrOKHiMNcd5XrVmzNyv489dZvDJu8lkdvb8bkeZu4dcwK+l9Tt8iPoaDdfPOtfPHFEpxOJxER6+nY0f02c07nEMD+/fsIC5vDZ599zcSJ45k69TXWrPmVBQvmFMdhFBj1Izv1xJ36IcUhV7dWM26HRgEYYxzAa8CXQDLQBWhtWVasMeZLYLgxZjKwGLjDsqwfjDG3A/fltdistxLPe/XVqbRpEwJAlSpVMleDLlStWnUef3w0/v5lOHz4Tzp27MT27dsICWmLh4cHAQEBNGvmWkXZunUL4eErMz/vcObM6RxrAVeA+eqrLwBo1qw5vr6+AHh5ube2atWqvPDCJObPn4PD4SAlJSWvbXCrQf0oGF+uOchzD4Rw6Fg8ETuis71+7NQ5JgxrTUJiKpXLlyYyI+gdOhbP2YQUklPSOBGTRMzZZACcTmeR1l8YBgwYxOjRI6hbtx6dOnXJ9npO5xBA3br1CAwMxNfXl+DgypQvXx4Ah8NRpPUXNPUjO/XEnfohxSFPq2PGGH9gEdAAGJbxdLhlWbEZjzcA5YErgBTLsn4AsCzrQyAuXxXn4FJO9hEjhjFr1jxmz55P1arVcDqdNGpkiIhYT3p6OvHx8ezcuR1wfdZh1KhHWb48nPffX5S5PJ7Vhg2RAPz88zqaNm120TomThzP4MFDmDdvId269SjUH/TqR+4dOhaPXylP7rq+IZ+vOpDt9SkPtmXMmxE8+WYEx08ncv7QLoO89rfq1atHfHw8M2ZMY9Cgwdlez+kcgsv3h4/6kZ164k79kOKQ6yBnjKkF/ASkAT0syzqT8dK5LJs5gfNn5oVnaGpu5ywIgwbdSZcuV9K9eyfOno3jyJHDtGzZittu60enTu24886BVKoUDMCYMWP59NNFhIZ256abrqVZs+bZxlu4cD7XXNON7777hjFjxl50/ltv7cdjj42mZ88u/PDDck6ePFHgx5gb6kd236w9RNWKpYk6fDbba5+vOsCnU69m0ZSe+Jf2onL50sVQYdHr128Af/xxiIYNG2V7Ladz6HKnfmSnnrhTP6SoOXKzEmKMKQtsAhZYljUxy/PPAhUtyxqZ9e/Ao7huxd5nWda3xpibgC+Auhd+azUyMtIZEhJCUlJ+DqdohIZ2Z/r0MIxpXCjjZ9yNBFA/MmTtSf2+iwptHrvYu6R/5mM7nCNFwW7XTWFTP7JTT9z5+kJkZCQhISFaErSx3P7fj4wEagN9jTF9szy/JqeNLctKMcbcAoQZY6YAG4HjeapURERERNzkakWuMNlpRa6w6bfG7LQi504rctnpunGnfmSnnrjTitzlwTb/soOIiIiIuFOQExEREbEpBTkRERERm1KQExEREbEpBTkRERERm1KQExEREbEpBTkRERERm1KQExEREbEpBTkRERERm1KQExEREbEpBTkRERERm1KQExEREbEpBTkRERERm1KQExEREbEpBTkRERERm1KQExEREbEpBTkRERERm1KQExEREbEpBTkRERERm1KQExEREbEpBTkRERERm1KQExEREbEpBTkRERERm1KQExEREbEpBTkRERERm1KQExEREbEpBTkRERERm/Iq7gIu5Otb3BWULOpHdnuX9C/uEkoUnSPZqSfu1I/s1BO5XGhFTkRERMSmFOREREREbKrE3VpNSiruCopf1iV/9cMla08GrW5XfIWUEB90jch8rH64ZO2Jrhu9j+REPXGn28uXB63IiYiIiNiUgpyIiIiITSnIiYiIiNiUgpyIiIiITSnIiYiIiNiUgpyIiIiITSnIiYiIiNiUgpyIiIiITSnIiYiIiNiUgpyIiIiITSnIiYiIiNiUgpyIiIiITSnIiYiIiNiUgpyIiIiITSnIiYiIiNiUgpyIiIiITSnIiYiIiNiUgpyIiIiITSnIiYiIiNiUgpyIiIiITSnIiYiIiNiUgpyIiIiITSnIiYiIiNiUgpyIiIiITSnIiYiIiNiUgpyIiIiITSnIiYiIiNiUgpyIiIiITeUryBljRhpjthljthpjvjDGBF/CPl8bY4bmdq7evXsSEbEegOTkZIKDA3n99VcyX7/mmm5s3rzpksaKioqia9cO2Z5/+eUXM+fIjdmzZ5GSkpLr/fJD/XDXJLANoxpPLtAxb6wxhLeu/A5vh0+Bjjujw/cFOt7fGdtiBvXLNgXA0+HF7Kt+5PoagzNfH99iJrX8G17SWBV9qzKx1dxsz99Y867MOXKjZ5W+eDo8c71ffum6cad+ZKeeiN3kOcgZY0KAJ4CrLMtqDuwGni+owi50zTW9WLduDQBr164hNLQ33333DQCJiYn88cchWrRoma85/u//nqJdu/a53m/q1CmkpaXla+7cUj8KX6fga/k5ehkdg3sVdyl5svn0r5iA1gA0DmzN5tM/06p8JwC8HT5U8K3Mwfjd+Zrjq0ML2Bu3Pdf73VxrKB4UfZDTdeNO/chOPbn8GGPqGGN+Ke46/okx5iNjTPe87OuV10kty4o0xjS0LCvFGFMKqA7sN8bMB8oD9YGvgTeABUA14ABw0VW7nFx9dSgvvPA8jzzyOEuXfsvQocMYN24MMTExbNjwO126dOOzzz4hLOwtnE4nAB9++AlOp5PBgweQnp5OSkoK06eHUaZMWaKjo+nX7xaOHj1C8+YtePvtdxg2bCj9+w/k6NGjLF36LQkJCezbt5fHHx/DkCFDiYhYzyOPPESZMmUJDg7G17cUnTp14dixo9x550AWL/6cMWMe56ef1gIwYMAgRo58mGHDhuLr68uBA1EcPXqEd96ZT+vWbfLaevUjF5oHtad/nRGkpCcTlxrDLOs5PD28GN1kCg488HR4MXf3CxxK2Ou2X5PANhxL/IMfjnzGg42fY/WxrwEY1yKMA2d3UdO/PqU9/Xljx1OcSDpK31r30rZCd+JSTuPjWYrFUWFEnbW4v9F4yngHAvDunlfc5qnpV58hDZ7AgYOzKTHM3PUcXh7eF63tUm09/St9a93Lt3++T6vynfjx6BfcXncUpT39qVumMTtifqd9xZ6EVuuHAwcAb2wfAw5HthrOpSUQ4F2Ox5q+TJBPRQ7G72H27sk80OgZfo5eRpBPBVqVvwofj1JULl2Drw69y+pjX1O/bFOGNhhDYlo8scmnSU5PxordQKBPBUY1mcxr2/+PO+o9gglw/WD86fhSvj/8EQ80eoZUZzKVfKsS5FORsF0TiTpr5akPWem6UT/UE7nc5DnIAWSEuFuA2UASMAEYC/hZltUMwBizBPjFsqzxxpgGwMa8zNWqVWssaydOp5O1a1fz3HNT6NnzGlauXMGWLZvp1etadu/exeeff4Ofnx8PPfQAy5cvJSgoiMDAQBYs+IAdO7YTGxtLmTJliYuLZdaseQQGBtK0aQOOHz/uNl9MTAxff72UPXt2c+utNzJkyFBGjRrO3LkLadq0GRMmjOXw4T+5++57eeGF51m48CO+/fZroqL2s3r1L6SmptKzZ2e6d+8JQK1atXnrrZnMmfMOc+bMYvr0sDz3Xf24dMMaPc3EjfdxOjmaa6sN5JZa97I95jcSUs8yfed4avjVpbSXf7b9elS5hR+PfsGRcwdITU+mftlm7I3bBsDeuG0s3Pca/euMoGNwbzad+omW5a5i3Ia78PLw5qWQDwG4udbdbDsTwYojn1KlVE0eMBOYuOm+LLWNZdau5/kzYT/dq9zEjTWHsCt280Vru1RRZy2q+dUBXCtyH+9/i62n19O8XHtq+Tdk0+mfqVq6Fi9vfYTk9CTubfhfWpTvSHxqXLYazqUlUNrTn5m7niMh9Syvt1tCgHc5t/n8PMvw4tbRVClVkyeav8bqY19zT4P/MsN6hj8T9tG/zgjK+QQTfvRL+ta6lzd3jKV1+c5UKlWNCRvvxtPhyTMtZ7PtTAQAJxKPMGf3C/Socgs9q/Rl7p4X89yL83TdqB/qyb+XMSYcVwZpDgQA/SzLOmCMGQfcgisTvQ0sBb4CTgLfAt8B0wBHxnP3AGeBmUBNoALwXUbOuRUYA6QAUcAQoCwwJ2M7gNGWZW0xxjwEDAOOkMdFLshnkAOwLOtz4HNjzH24Dn5txp/zrsF1CxbLsvYYY1bmZR4PDw9atGjJsmXfU7lyFXx9fenVqw/fffc1mzdvYuTIh4mLi2PYsLvw9y/Drl07ufLKjvTu3Yc9e3bzn//cjLe3N089NQ6AunXrUa6c6wdRcHAwCQkJbvO1bNkKgBo1apKYmAjAkSOHadq0GQCdO3dh0aKP3PbZuXMHnTp1weFw4O3tTfv2Hdixw3XbqVUr1y2umjVr8vPP6/LSAvUjl8p6B3EuNZ7TydEA7Ij5nQF1H+LD/dOoUroWjzd7hbT0VD4/6P7ZL3+vsrQqfxUBPuXoXW0Apb3K0Ktaf962ngHIXBk6mXSMIJ8KVPerw964bThJJyU9if1xOwCo5d+AZkHt6FApNGPcALd5qvvV5e4GYwDXZ9iOnjvIplM//WNtueHEyYGzu2lZ7ipikk+S6kxh4+mfaFO+M7X8G/H9nx9RqmIPRphnSUw7RzW/2uyO3fK3NRxP/JP41DgAYlJO4eNRym2+qPhdmX3x9nB9rrCcbyX+TNgHwM6YjXSs5H6burpfHayYDQCkOdPYE7eV6v71Mvr813iNAvJ3K+s8XTfqh3pScq2MWOYM9Khw8Q3/2YGQkJA6//D6esuyHjHGTAZuN8YsBfoAVwK+wAvAMqAKEGJZVnLGbdl7LMvaboy5F3gSeAfXItWwjLuSfwDjgduB1y3L+sgYMwRXYPwv8INlWW8bYxoC84wx1wMPA1cA6UBkXg84P5+Ra2CM6ZzlqblAbaAcrqR6nhMy7tu4pOZ1zp49Q3nppSn07t0HgE6dOrNhw+8AeHp6MmnSMyxc+BFhYbMpVao0TqeTVavCqVKlKt98s4ynnhrHhAlPA+BwOP52nr97vUaNmpkXy6+//nW73cPDg/T0dBo3bpK51J2SksIvv/xEgwYNL2m+vFA//llcyhlKe/kT5ON6Y2gSFMLRcwdpEhTCmeQTvLhlFJ8fnEv/ug+67dcpuA/hR7/kxS2jeGnraCZsGEqLcldS1jsoYwun2/Z/xO+jXtmmOHDg5fCmdhkDwOGEKL774wMmbR7OtB3/Zd3x79z2O5JwgLetZ5i0eTgf7p/GhlNrL1pbbm058ys31xrKxlM/AWDFbKROmcYApDvT+E/t+5m2Yyyzdk0iOT0JB46/rcF5wXFnk8PLJ5OOUd2vLgANA5r/tanTicPh4M+EKEyA6weZp8OThgEtOHru4KXNl0e6btypH9mpJ8Uj0KMCr8YPz9cfXDnkn2zI+N9DQCnA4Ap3aZZlJViW9TCud7P9lmUlZ2zbBJiRsaJ3D66Pip0C2hlj3gdexxUCAR4DuhpjVgFX4QppVwD3ZOz/Dq6c1BjYZllWkmVZKUDuv/2SIT8rclWBD40xrSzLOgHcAWzFteyY1ffA/cCTxphaQA/gy7xMePXVoTz44H3MnbsQAB8fH4KCgmjZsjUBAQF07NiJDh3a4O/vT1BQOY4cOcwNN9zE4MEDmD79f3h4ePL00xPyery88cYMHnjGh536AAAgAElEQVTgHvz9y+Dj40O1atUB6NSpCzfffB3Llv3I6tXhdOvWkeTkZG67rX+hfj5B/XB3RbkrmdR6Qebfp+8cz+xdU3i06VTSnU7iU2OZaU3ECYxuMoU+1QeS7kzns4Oz3cbpUeVmZmSsvgEkpyex/sSP9KxyS47zHkrYy8ZT63iu1TziUs+Q5kwlzZnK5wfncX+jcfSs2pfSnv58euAdt/3m7nmREWYiHg7X71Ozdk3ibErMP9aWW1tP/8r9jcYxY6freNKcqSSkxhF11uJcWjy7Yjcxpc1CktISiU+NJcinIpEnVxdYDfN2v8T9jcaTlHaOVGcKp5Jcq6M7YzfwZPM3mLR5OE0CQ5jYag5eDm9+iV5RIJ+F+ye6btypH9mpJ5e1C39D3AmMMMZ4AJ64bqWOxBXAzrOAIZZlHTTGdMKVf4YCZyzLeiDjY2P3G2McuPLOs5ZlHTfGzAT6ZszxnmVZH2T8v3sMA/YBTY0xpYFkoDXwXl4OyHH+w5p5YYwZATyEa5XtcMbjZ4CtlmW9krFNJWAeri8//IErPC6wLGt+1rEiIyOdISEhJCXluZxCFxb2Frfd1p9KlSrxzDPj8PHxYezYvF+sf8fX96/H6odL1p4MWt2uUObIqwDvcrSveDUrjnyCl8ObqW0/ZvLmEZxMOlZoc37QNSLzcUnrR1ahVfvxy4nlxKWcoV+d4aSmp7Ikn+H072Ttia4bvY/kRD1x5+sLkZGRhISElIhlvMjISGfGqlqePe4flu14jDF1gI+ARGC4ZVk7jTHDgSqWZT1rjPkvcBOuu5RvA+HAR5ZldcjYPwR4FTK/an8v4J0xZhwQj+uzclcDbYBJuBa14nCt4IHrM3JBuG61PmtZ1pfGmAHAU0A0rtXBCZZlhef2mPP7ZYe3cR10VkMv2CYauCE/85QUwcGVueGGXvj7lyEwMJDZsxdcfKfLmPrhEpdyhvplm9KtygJwOvnx6BeFGuLsJCblJP+9YjqJaQkkpJ4lzJpY3CUVO1037tSP7NSTgmVZVhTQ4YLnwrI8fgHXZ+Oy6pDl9Uigew5DX5HDc3/i+qLEhbLd0rEs62Pg478p+5Lla0WuINlhRa6o2OW3xqJUklfkioNdVuSKkl1W5IqK3keyU0/c/VtW5C53+ie6RERERGxKQU5ERETEphTkRERERGxKQU5ERETEphTkRERERGxKQU5ERETEphTkRERERGxKQU5ERETEphTkRERERGxKQU5ERETEphTkRERERGxKQU5ERETEphTkRERERGxKQU5ERETEphTkRERERGxKQU5ERETEphTkRERERGxKQU5ERETEphTkRERERGxKQU5ERETEphTkRERERGxKQU5ERETEphTkRERERGxKQU5ERETEphTkRERERGxKQU5ERETEphTkRERERGzKq7gLuJCvb3FXULKoH9l90DWiuEsoUdSP7HTduFM/slNP5HKhFTkRERERm1KQExEREbGpEndrNSmpuCsoflmX/NUPl6w9qd93UfEVUkLsXdI/87H64ZK1J7pu9D6SE/XEnW4vXx60IiciIiJiUwpyIiIiIjalICciIiJiUwpyIiIiIjalICciIiJiUwpyIiIiIjalICciIiJiUwpyIiIiIjalICciIiJiUwpyIiIiIjalICciIiJiUwpyIiIiIjalICciIiJiUwpyIiIiIjalICciIiJiUwpyIiIiIjalICciIiJiUwpyIiIiIjalICciIiJiUwpyIiIiIjalICciIiJiUwpyIiIiIjalICciIiJiUwpyIiIiIjalICciIiJiUwpyIiIiIjalICciIiJiU7YIcqtWhXPnnQMLdMxXXnmJOnWqkpiYWKDj1q5dpUDHy4n6kbP3nutGi4blAfD28mDje7cw7GaT+foHk7rTuE7gJY1VvZIfn7x4dbbnH7i1ceYcuTEwtB5eno5c75dXVzarxBuPdSjQMe/v25if59yIj3fBvm38MvfGAh3v7+i6cad+ZKeeiB0VyDuyMeYWY0xcxuO6xphPMx7XMcacLYg5CtpHH71Pv34DWbToo+IupUS4HPqxZuMx2jWpCEC7JhVZvfEYPUKqAuDj7UHVCn7sjIrJ1xwzP9vJ5t2ncr3fiP80wcOj6IJcYbi5ay2+XnuIGzvXKu5SSozL4bopSOpHduqJFDav/A5gjGkIvAKc/ylVGzB/v0fBWbFiORMnjsPXtxQVKlRg5sy5pKSkMHjwANLT00lJSWH69DCaN7/Cbb9Vq8KpV68+9903nLvvHsyQIUMBCA3tTsuWrdi2bStxcbG8//5iateuzZQpz/Pll0uoWLESCQkJPPPM87Rq1Zrhw+/l5MmTALz22jS3ebZu3cJjj43G6XRm1pacnHzR2tSPvPdj3cZjjOzflDlf7qJbSFUWLd/Hk0NaUMbPm+b1gli/LZprO9bgzj4NMs/WkVN/wuFwMO2JDjgcDrw9PRgXFkn8uRTKB/oS9lQnKpUrhXUghqdn/MbUUe34eu0hKgWVoltIVUr7elKrShlmfbaTT3+MokXD8ky8rw3xiSmcPJNEUkoaEdtPUCmoFNMe78jwF9fx36EtaZsROL9ac5D5X+9m6qh2JKekUz3Yn+BypXjyzfVs23cmz734O51aVuaxQc1JSknjTFwyY96MwNvLI9vx7zroHnivbFaJg0fP8sHSvbz2yJV8+mMUAO8/350d+8/QqFYgZfy8GPnyzxyOTmBkv6aEXlmdU7FJlPb15PUPtrJt/xlefKgt5cr6AjBx9ga3eRrVCuSZYa3Bgau26RH4XEJt+fVvv27UD/VE7C1fK3LGGD/gPeCxjL97ArOB+saYpRmbeRpjwowxvxtj9hpjbstXxRmcTicPPXQ/H330GStWrKJLl268+OIkfvttPYGBgXz55Xe89to0YmNjs+07b95s7r57GI0aGXx8fFm//tfM19q2bc93362gZ89QFi36kM2bN7Fs2XesWxfB4sWfc/ToEQCmTp1Cjx5Xs2zZj8yYMYtRo0a4zfHgg/fxxhtvsXx5OL17X8err069pNrUj7zbtv809aqXBaB900qs3xbNT5uP0alFMFc2D2b1hqPUrVaWeyet4Y7x4ez/M44uravQomF54uJTuOf5NTw3ewNl/Vy/35Qp7cWTb67nP0/9QMcrgqkQ6Os2X1k/b+6bvJYHpqzlgVsbA/D8AyE8+eZ6Bk9YxcFjrsXoxT/sJ/pMIqNf/ZkebatSs7I/t435gQFPr+TGLrVoVMt1u/fP6ATufm417367h4G96uerF39n8ogQHnzpJwaNC2f9tmhG9mv6t8efVf/Qeny8Yj/7D8eRnJpGyyy3lzftPsWQZ1exbtMxbuxSi8Z1AunWpgp9n1zB8BfXEVyuFAAP3taEnzYf544J4Yx9+zeeH97GbY4pD7blmVm/c8f4cMIjj/BAX3NJteWHrhv1Qz0Ru8vvu+LMjD+bASzLSjPGDAOmW5bV2xhTBygFLLcsa7gxpi+u1btP8zkvJ06cICAggOrVqwPQuXNXJkx4milTprJnz27+85+b8fb25qmnxrntd/r0aZYu/Zbo6OPMmPEmsbExvP32dNq3vxKAVq1aA1CzZk2OHj3Kzp07aNu2PZ6enpQuXZqQkLaA67eg8PCVLF78MQBnzpx2m2fnzh2MHv0gACkpKTRs2Ijevfv8Y23qR/764XTCzqgzdGtThegziSSnprPq96P0aFuVJnWCmP/1bnqX9uLlh9uTcC6VejXKssE6yarfj1Cnahlm/rcTqanpvPXJDgAOHYsnNj4FgJMxSZTy8XSbb8d+14rZ4RMJ+Ga8Vrl8KXYfcr1pRmw/wQ2da7rt06BGABHbTwCQmuZk466TNKwZAMD2/a6eHTmRQEjjCvnqRU7KB/hyNiGVY6fOAbB+WzRPDL6CF9/dlOPxnxfg7033NlWoEOjLXdc1oKyfN3de14BNb6x3q/vwiXNUCipFgxoBbNp9ivR0J0nJaWzZ43rd1A6k4xXBXJ/RkwB/nwt6U5aJD7jCnbenB/sPx/3tf5uCoutG/VBPxO7yHOSMMQ8CqZZlzc0IbH8n2bKs88FtIxCc1zmzqlixIrGxsRw5coSqVauyZs0qGjZsxKpV4VSpUpVvvlnGL7/8zIQJT7Ns2Y+Z+3344XsMHXovL7zwMgAJCQk0blyX6OhoABwO988xNW3ajBkz3sxcot64cUPG8Tfm9tsHM3DgII4fP868ebPd9mvUyDBnzrvUqlWLn35ax9GjRy5am/qR/36s3XSMEbc14as1BwH4bccJRvZvSnq6k7R0Jw8PbEaX+78B4N1nu4IDOjQPJvp0IkMnrqa1qcATd1zBk2+ux+n857mcZN/gyIlzNKgRwJ4/YmnV6K8wlp7uxMPhYM8fsfynZ13mfbULL08HbUxFPvsxim5Uueh8+XUqNokyfl5UKleK6NOJtG9Wif2H43I8/jsmhGfud0u32iz+YT8vLtgMQCkfT1bNvJ7yAa4Vygvr3nUwliHXNcThcAWypvWCANj7ZyyfrzrNV2sOUiHQl/7X1HPbb9/hOJ54Y31mkK1UrvRFa8svXTfqh3oidpefFbmhgJ8xZiPgA5TOeDz1gu1Ssjx28tdn6XJlxYplXHVV28y/L1jwATNmvMPAgbficHhQrlw53nlnPg6Hg8GDBzB9+v/w8PDk6acnuI0zb95s5s5dmPl3Pz8/brnlNubOfSfHeZs3v4Jrr72Orl07UKFCRby9vfH29mbMmLEMH34vc+bMIi4ulnHjnnXbb9q0t7n33iGkpaUBMHPmHMqXr/CPtakfee/HeWs3HeOFh9rx+P9ctzBSUtOJi09h277TnE1IIXLnSb58JZRzSanEnE2mcvnS/BBxmGmPd2TojQ1JT3fy5sfb8zz/hFmRvDiyHQmJqaSkpmeufv224wRzxnfhjvHhdGgWzOIXe+Lj5cE36/4olM/CAXRuVZnPX74m8++PvP4LT8/4jRljrsKZDjHxyTw5bT1O+Mfj739NPR5/469bQonJaXz/8x8MCHUPYuftOhhD+O9H+PSlqzkdm0xqmpPUtHRmfLKDFx9qx8Be9ShT2ptpH29z229C2O+88nB7PDO+FPLUWxGciUsusP82oOtG/VBP5PLjcBbAMkDGitxWy7LKGGM6A3MsyzJZn79wuwvHiIyMdIaEhJCUlO9yCtTx48dZsuQTHnjgQZKSkmjduhnff7+SWrUK75t7vlk+iqV+uGTtSf2+iwp1rvwY3KcB3647xKnYJB4b1Jzk1HSmL8pf+MjJ3iX9Mx+XtH5UCPTl2o41eP/7vfh4efD9tN7cMWEVR04kFOq8WXui60bvIzlRT9z5+kJkZCQhISEl4iv1kZGRzlfjh+drjMf9w0rM8RSVgv3ksMt2INEYsx4YUAjjF6mKFSvy228RvPtuOxwOB3ffPazQ32xKMvXjn504k8j8Z7qSkJhKXEIK/zdtfXGXVOROxSbRokF5/jO1Lk6cfLxif6GHuJJO14079SM79UTyqkBW5ApCSV2RKw4l+bfG4mKXFbmiUpJX5IpLSV6RKw56H8lOPXGnFbnLgy3+ZQcRERERyU5BTkRERMSmFOREREREbEpBTkRERMSmFOREREREbEpBTkRERMSmFOREREREbEpBTkRERMSmFOREREREbEpBTkRERMSmFOREREREbEpBTkRERMSmFOREREREbEpBTkRERMSmFOREREREbEpBTkRERMSmFOREREREbEpBTkRERMSmFOREREREbEpBTkRERMSmFOREREREbEpBTkRERMSmFOREREREbEpBTkRERMSmFOREREREbEpBTkRERMSmFOREREREbMqruAu4kK9vcVdQsqgf2e1d0r+4SyhR1I/sdN24Uz+yU0/kcqEVORERERGbUpATERERsakSd2s1Kam4Kyh+WZf81Q+XrD0ZtLpd8RVSQnzQNSLzsc4RF1037nTNZKfrxp1uL18etCInIiIiYlMKciIiIiI2pSAnIiIiYlMKciIiIiI2pSAnIiIiYlMKciIiIiI2pSAnIiIiYlMKciIiIiI2pSAnIiIiYlMl7l92EBERkX+neVdGXHyjf7B1a2QBVWIfWpETERERsSkFORERERGbUpATERERsSkFORERERGbUpATERERsSkFORERERGbUpATERERsSkFORERERGbUpATERERsSkFORERERGbUpATERERsSkFORERERGbUpATERERsSkFORERERGbUpATERERsSkFORERERGbUpATERERsSkFORERERGbyleQM8a8aow5aIzZmPHnY2NMXWPMpxmv1zHGnC2YUi/dqlXh1KwZTGhod665phvdu1+FZe3M1RhffLGEw4cPF1KFRe/f2JMmgW14u8NSxrUIY3yLmTzbcg7VStd226ZFuY70rNI3274TW82lom/VoiqVxoGtqenfoFDnuJRzYNmy75k9e1a2fbt27UBUVFSh1pfVmjWr2bJlc6HPo57kzqVcUxfTtkJ3gnwqFlKFRevf+L4qJU9+V+SuAgZaltUq488AoDZg8l9a/nTv3pPly8NZsWIVY8c+y1NPPZGr/adPf4O4uNhCqq54/Bt7sv3Mb0zaPJznNz/ApwdnMajew26vbz79MyuPLimm6v7SrfJNlPOpVOjzXOwc6NXrWoYNu7/Q67iYBQvmcuRI0fxwU09y52LX1MVcW30gfp7+hVRd0fs3vq9KyeKV1x2NMb5Aa+BJY0x9YBfwGDAbqG6MWQo8AHgaY8KA9kAg8KRlWZ/mu/JcOHPmNLVr12Hjxg08+ugoPD09KVWqFDNmvENwcDB33NGfmJgYEhPPMXnyVBIS4tm8eSP33juElSvXMmPGmyxe/BFeXl507tyVyZNf4vnnnyUqaj/R0cc5ePAAL7/8OqGhvYvysPLl39gTf68ATiQeYVyLMOJSTuPvFcBP0UupUqoWH0VNp3+dEbQs15GTScco6x0EQFmvQB5qMglvhw9Hzh2gaVBbHou4lcaBbRhQZwTpznSOJf7BnN1T6BTch5AKXSntWYay3oF8dnA2ESd+pHlQe/rXGUFKejJxqTHMsp6jdplG3F53FKnOFLaeXk/L8h2pW8bwZ8I+TiYdK5J+nD8HQkO7U6lSJU6fPk3//rezd+9uJk16kQkTxrJ8+ffUqFGTEydOAHDixAnuumsQSUlJNGpkCA9fyfbte1i9ehXPPDMWT09P6tWrz1tvzeTDD9/n66+/IDY2lpMnT/D00xPo2/c2VqxYzsSJ4/D1LUWFChWYOXMumzZtZNy4MXh7+9Cz5zUsX/49Gzf+TuPGTalVq1aR9EM9yb3z11Rt/0YMbfB/pDvTSUlP4p3dk4lNPs3opi/g51kGHw9fPtg/DV/P0tQu04gRZiLPbhpG72oD6Bjci3RnKjtiNvDR/uncVvs+KpWqRoB3eSr6VuG9fa+z+fQvxX2ol+Tf+L4qxS/PQQ6oBqwExgHbgCeAz4H7gDcty+ptjKkDlAKWW5Y13BjTF3gFKPQgFx6+ktDQ7iQnJ7Fly2aWLPmGBx+8j7ffnk3Llq346qsvGDPmMcaPn8ixY0f59tsVREcfZ/fuXfTpcz0tWrRi+vQwdu2y+PTTRYSH/4SXlxcDB97Gt99+DYCvry9ffvkdK1Ys5403Xi3xF9e/sSdNg9oyrkUY3h7e1PRvyMtbH6GGf33WHV/KbyfD6Vr5BgBq+NWncWBrxm24i1KefrzWznWK3lzrHn47sYoVRz6heVB7rih3JQD3NXyaiZvuIzblNP1qD6dr5RtJc6ZSytOPF7Y8RFnvcjzfej6/n1zNsEZPM3HjfZxOjubaagO5pda9bDi1Bm8PHyb8fjcAlUpV5+foZYUe4nI6B7Zt28qAAYO4+ea+vPvufAC2bdvK2rWrWbcugrNnz9K8eUMAXnppMjfddAsPPPAgK1YsZ8WKZTidTh588D5WrlxLcHAwzz47nnffnY+3tzdnz57l22+XEx0dTefO7bnhhpt46KH7WblyLdWrV2f69Dd48cVJ9OlzA4mJiaxZ8ysAUVH76d9/YJEEFvUkd3K6pu5rNJZ3dk3mQPwuQip0ZXC9R/nkwCyCvCswZctDBHiXo2rpWmw8tY4DZ3cxd/cLVCtdmw6VruHZjfeQ5kzjkaZTaV2+MwCp6SlM3fowzYPac32NwSU6yP0b31elZMlzkLMsaz9w3fm/G2NeAcYDdS7YNDnLCtxGIDivc+ZG9+49WbjwIwB27bLo1q0jTqeTli1bAdC5c1fGjXuKpk2b8cADDzFkyO2kpKTw0EOj3caxrJ20b98Bb29vADp16sL27dsAaNWqNQA1a9YkMTGxKA4rX/6NPdl+5jfe3DkWgKqlazOx1RyOnjvEkXMH3Lar4V+PfXE7cOLkXFo8h+L3AlDdry5rjn0DgBWzEYAA73IE+VRkdJMXAPDx8GXL6V85lvgHO2J+x4mT2JRTxKfGEehTkXOp8ZxOjgZgR8zvDKj7EBtOrclWQ1HI6Rxo0KAhjRq5fxpi+/ZthIS0xcPDg4CAAJo1uwKAnTt3MHjwXQB07twFgOjoaI4ePcIdd/QHIDHxHFdf3Yt69erTpUs3PDw8qFy5MuXKlePo0aMEBARQvXr1jDG6MmHC0/Tpc0O2GoqKepI7OV1TDhwciN8FwM6YDQysO5I/E/ax7MhiRjaehKeHF0v//NhtnGp+ddgTt5U0ZxoAVswGavjVAyDqrAXAqaRjeHv4FNWh5cm/8X1VSpY8f0bOGNPCGHPnBU87gJQLnsv6d2fGNkUqOLgyAHXr1sv8sPCaNato2LARW7du4ezZOD7//Btmz17Ao4+OAsDDw4P09HSMaUxExK+kpqbidDpZu3Y1DRs2AsDhKPJDKTD/xp7EJJ/MfOx0pru9diThAPXLNsOBA1+PUlT3qwvAofi9NAxw/cBuENAcgLiUM5xKPs6r2x5n0ubhfH5wHttifgOgbpnGAAR4l8fP05/TSdGU9vInyKcCAE2CQjh67mBGDc6/6iEdjyK+NM6fA+D6b5tVo0aGiIj1pKenEx8fz86d2wFo1qw5v/76MwC//upaJalYsSLVq9fgk0++YPnycMaMGUu3bj0A2LAhEoBjx44RGxtLtWrViI2N5ciRI8Bf59yFNZw/14qaepI756+p44l/Zn5Zp0lgG44kHKSmX31Ke/rx8rZHCbOe5a4Grs+OOUnH4fDgcEIU9cs2wwNPwPWFnyPnrw2cOcxW8v0b31el+OXn1mo6MM0YszZjdW4EsBmIArwLoLZ8Ob/c7enpydmzcbz00mu0aNGSRx4ZidPpxMvLi7CwOVSrVo3Jkyfy/vvv4u3tw4QJzwHQocNV3HvvEL7+ehm33dafHj06kZ6ezlVXdeamm25h8+ZNxXyEufdv7Mn520DpznRKe/rx3r7/Zd5OzepA/C5+PfEDk1ov4HTyCWJSTgPw1aH5jGj8HFdWuoYzySdIc6bixMm7e1/lyeb/w4EH59Liedt6hoq+VQjyqcDTV8zAz8ufuXtewkk6s3dN4dGmU0l3OolPjWWmNZEa/vXd5t8Tt5WBdUdyPPEwh89FFVo/cjoHFi6cn227li1bcdtt/ejUqR1Vq1ajUiXXQvoTTzzFPffcySefLKJq1Wp4e3vj4eHBK6+8wS23XI/TmU7ZsgHMmfMuhw4d5Nixo1x77dXExsYwbdoMPD09mTHjHQYOvBWHw4Ny/8/encfZWPd/HH+dM7sZZobZDMOMZS5r1KCkJqHF3UYKqZCStO8KWUqhaJGUpKSb3NLt7pfbnUi2khhLDC47k2XMjNn3mXN+f5xJMyjLzJi56v18PHp0luv6Xp/vZ65zvOd7nUNgIDNmzCI+fluZ47dvfzkjR75AZGQUzZo1r7R+qCfn70yvqYNZuxjY5Dls2Ch2FjNj1zhS85O4veFgrg65iSJnIQsOTAdgV8YvDDXGMH7rY6xLWsaYth9hs9kx0zezIWUFDf2aVtncLsTf8X1Vqhdb6VWB82UYxj3AC4Ab8CtwP5AFfA/kA32AraZp+pVsHwls++1+aXFxcc6YmBjy8y+4nL8ML6/fb6sfLqV70m9V+4t23LaBV5JRmMa+rO20CujAbQ0G8uovD59x29jQmwn3iWTegamVXtfc2PUnb1/Mc+SbbxYTFBRMu3bt+e67Zbz++mssWbL8jNvOnj2LXbt2Mm7chItSW1W9bqprT6rqNVOdVdXrprry8oK4uDhiYmKqxZJfXFycs1WrmHKNsW1b9ZnPxVKeFTlM0/wn8M8zPNWm1O2Toc00zQOl74tUd8fzjjDEeIliZzF23Ph076SqLqlKRUZGMWTIINzc3HE4ipk8eUpVl1Tl1BMRqUrlCnIif3VHcg8wevP957TtqsRFlVxN1WvWrDkrV649p2379x9YucVUE+qJiFQl/RNdIiIiIhalICciIiJiUQpyIiIiIhalICciIiJiUQpyIiIiIhalICciIiJiUQpyIiIiIhalICciIiJiUQpyIiIiIhalICciIiJiUQpyIiIiIhalICciIiJiUQpyIiIiIhalICciIiJiUQpyIiIiIhalICciIiJiUQpyIiIiIhalICciIiJiUQpyIiIiIhalICciIiJiUQpyIiIiIhalICciIiJiUQpyIiIiIhalICciIiJiUQpyIiIiIhblXtUFiIiIiFQGwzA6A/8BWpummVDy2ARgp2mas85xjAeBewAH4AGMME1zhWEYs4B5pml+UwmlnzOtyImIiMhfWQHwiWEYtvPd0TCMvsB1QFfTNDvjCnSfGYYRVLElXrhqtyLn5VXVFVQv6sfp5saur+oSqhWdI6dTT8rSa+Z0Okf+VpbjWrh6BJha+gnDMJ4B+gJFwCrTNIedsu8Q4GnTNAsBTNPcbxhGW9M0UwzDABhiGMbzgD8w1DTNnw3DGA+0A2oCO0zTvM8wjDFAFOR79eAAACAASURBVBACNASeMk1ziWEYNwMvA+lAKvCLaZpjSsaILan7TdM0v/ijyWlFTkRERP7qhgJPGYbR9LcHDMNoDfQGriz5r2lJsCotHNhX+gHTNFNK3Y0zTbML8C4w0DCMWkCqaZrXlYx5hWEY9Uq2zTdNszvwREktbsAUoLtpmtcCuSV1dQeiTNPsBFwLjDAMI+CPJqYgJyIiIn9pJeHrSWAWv2efZsBPpmkWmqbpBFYDLU/Z9SAQUfoBwzCuNwwjrORuXMn/jwE1cIWxEMMwPgemA364PlcHsKnk/wmANxAMZJimmVjy+OqS/7cGYgzDWAF8U7J/wz+aW7W7tJqfX9UVVL3SS/7qh4t6Upb6cbrSPem3qn3VFVJNlL6cqn64qCdl/d0uuZum+bVhGD2BgcDzwE7gGcMw3IFiXJcyZ5+y28fAS4Zh3G2aZpFhGNHATCCm5HnnKdt3ByJM0+xjGEYw0BOw/cG2x4GahmEEm6aZBFwBHCip63vTNB80DMMOvMQpq4KlaUVORERE/i6epOQSpmmaW4H5wA/Az7hC1H9Kb2ya5jzgJ2CNYRirgE+Ae0zTPP4H4/8MNDIM4ydgAa4AFn6mDU3TdACPAosNw1gGNAAKga+BLMMwVuNa8XOappn5RxOqdityIiIiIhXBNM0VwIpS9zModZnSNM03gTfPMsZbwFtneHxgqdvf4LoMCnCm5d4fSm27E+hccrctcJVpmvmGYfwTSCi5zPv0n9VUmoKciIiISNXIBH4yDCMH14rgv853AAU5ERERkSpgmuZUTvkrUc6XPiMnIiIiYlEKciIiIiIWpSAnIiIiYlEKciIiIiIWpSAnIiIiYlEKciIiIiIWpSAnIiIiYlEKciIiIiIWpSAnIiIiYlEKciIiIiIWpSAnIiIiYlEKciIiIiIWpSAnIiIiYlEKciIiIiIWpSAnIiIiYlEKciIiIiIWpSAnIiIiYlEKciIiIiIWpSAnIiIiYlEKciIiIiIWpSAnIiIiYlEKciIiIiIWpSAnIiIiYlHVMsitXLmCiIgQrruuM9dffy2xsVcwbdq7FTJ2dHQkeXl5ZR6bPXsWI0e+UCHjn82zzz7JoUOHzns/9aQs9eN06knFau5/Ge9fsYSRl3zAS5dMZ0ybmYT7NDyvMdrV6UyAZ1AlVXj+Ss9pxCXvM7btx1wf3rtCxn6nw1d42DzLPBYbejN9Ix+tkPHP5t5GT1PHK7TCxz2X8+CSwI50Cet52r5j235MkFfdCq/pjzTzv5QI3yYX7XhSPbiXZ2fDMFoD7wL+QDEwBDgBTDJNs5dhGJHANtM0/c537M6du/DZZ/MAyM/Pp3Vrg3797iUgIKA8JVe5SZPevuB91ZOy1I/TqScVa3vaBt7dOQKA1oGX06/RE0yKf/qc97+xXl8+3j2eNJIrq8TzVnpO7jYPJrdfwJrExeQUZ1VxZeXz2b43K23ss50Hv6SurbRjn49rQm9lbdK3JGTvqepS5CK64CBnGEYN4FvgftM0FxuGcRswB3gIMCqoPgAyMzNxc3Nj27atvPTSi7i5ueHt7c20aTNwOBz079+XVat+AiA29gpmz56Hn58fAwb0Iz8/n+hogxUrlrN9u+vkfuyxoRw4sB+A+fMXnjxOUlISd97Zg1GjXubqq2N59NGH2LNnNw6HgzFjxnHNNZ1ZtWolo0ePwM3NjUaNGvPee9P5/PM5LFr0FRkZGaSkJDN8+Ch69uzFqFEjWLlyOQ6Hg9697+Kxx57kuus6M3XqB4SF1eWhh+4nJSUFgDffnEKrVq3VkwvsifqhnlQ2X/daJOcdpaFvNAObPIfD6aDQkc+M3a+SUZDK4y3GU8PND0+7F3P3T8HLzYeGftEMNcYyZssD3BDeh44h1+NwFrEjfRPz9k+lV8PBBHuHU8ujNkFeYfxz31v8kvrTRZkPgI9bDRxOBxG+Tegb9UiZOdmw81jzVxm9eRDgWl16d8cI8otzeKT5ODxsnhzNPUiLgHY8vf52AAY1fYFg73AA3tr+3Mnj1PQI4OkWk1hwcDo70zcyqOmLhPlEYMfO/APvsyN9I838L6NP5FAcTgeJeb8yc/drdArpTkydWHzc/Kjp4c+/D33E+uTv6R05lJYB7bBh58ekb/nm8OeMvOQDPt49ntSCZB6Mfgk/D38AZu+ZRELO3grr2W/nwchLPiCzMBVf91r8mLSEMO8GzDswld6RQ2kT2JGU/ERqerh+garp7n/Gnp3PnFsFdKB35FAKHQVkFqXzofkyDf2iuSvqMYqchWxL/Zk2tTsS5WdwOGcfKfmJFTZnqd7KsyJ3PbDXNM3FJff/DzgAfAnUMwxjCa4VOjfDMD4AOuBauXveNM0vzzb4ihXLue66ztjtdjw8PHjrrXd5/vmneP/9j2jTpi1ff/0Vw4Y9zfjxk864/8SJr3LrrT0YMuRhli1byrJl3558buDA++nU6SoeeGAg3323FIDExETuuONW3njjbTp0uJwPP3yfoKAgpk+fSUpKCt26xbJx4zYefngwy5evISQkhDFjXmL27Fl4eHiQlZXF4sVLSUpK4qqrOnDLLbcxZ85sli1bRXh4OLNnzypT3+uvv8a113blwQeHsmfPbgYPvo/vv1+jnpxHT9QP9eRcelIeLQLaMfKSD/CwexDh25Q3tj3J4OgRzNj1KgezdxFTJ5Z7Gj3FgoMfEuBRh9e2PkItj0Dq+jRg84kfOJi1i493jyfcpyFXBHdjzOZBFDuLebLF61xa+yoAihyFvL7tCVoFdOCm+vdUepD7bU5OHBQ7ivh0zxvc2/ip0+Y0Z987Z9z/tgaD2JC8kmVHF9AqoAOtAy8/+dyKY19hZmxhSPRoWgW4Hvf3rM2zLScze++b7M2Mp1vdXmQWpjFj1zj83P0Z1eZDno/rw+Cmwxm7ZTAZhanc2fAhYkNvodhZhLdbDcZvfYSaHoG8cuks4pJXcXXoTby85UFS85OIDbv5lPruIz5tPcuOfkmYdwRDjFGM3TK4QnpW+jyo79uYH44vYUPKCmJDXTXUr9GYZv6XMnLTALzdavBm+y//tGfnOueNKat4IHo4YzcPJrUgiRvD+9Kjwf1sOrEaD7snozbeB0Cwdz3WJn2rEPc3U54gFw0cMwxjJtAGSAOeBx4AppqmeUPJpVVvYKlpmg8ZhtETmIQr7P2p0peIfjN06AO0adMWgKuuij3j53OcTicAO3fu4J57BpRse3WZbS67LAaAsLAwcnJyAFi69BvCwuricDgA2LZtKz/8sJqff14HQFFREcePH+fYsaPcfbfrMyV5ebl07Xo9jRo15uqrr8FutxMaGkpgYCBJSUl89tk8Ro16kWPHjnHDDd3L1LBt21ZWrFjOF1/8C4C0tNSztUQ9UT/+tB/qScUrfUmtrk9DxradiQ0bB7N3AbAzfRN9ox7lcM4+vj36BY82G4eb3Z0lh/9VZpzwGpHsydxGsbMYADN9E/VrNALgQJYJwIn8RDzsZT9jVtlz+s3g6JGnzelUNmwA1KsRxerE/wJgpm8us82+rJ0ApBek4OXmDbg+P5ZWkIy95CPZEb5NMPzb0qRmKwDsNjdqedQmwDOIx5uPB8DT7sXW1HUk5v3KjvSNOHGSUXiC7KJMankG8O6O4fSNfAR/zzpsOfFjmRoa+DahZUB7rgi+DnCtoJXXmc6DY7kJHM09WGa7+r6N2Je5AydOcouzScje+4c9q+UReM5z9vcMIrcom9SCJAB2pG+kT9QjbDqx+rQa5O+nPEHOA/gHcK1pmutKLq0uBgacsl1BqRW4zUDIhR6wbt1wtm79hdatL2H16pU0bRqNt7c3x48fp7i4mMzMzJOXflq2bMW6dWtp06Yt69aV/Q3XZrOdNvY99wzg7rv706/fnaxZ8zOG0Yx69eozbNhwcnNzmTDhVYKDg6lXrz4LFnyFv78/ixb9H76+fiQkHGLTpjjAtUKRkZFB7dq1mTx5IrNnf47T6eTSS1ty5519Tx7PMJpx11330LdvP44fP84nn3yknlRAT9QP9aSypBe4LucezztMhG8TErL30Nz/Mo7mHCKiRmN83GrwRvxTBHjWYUzbmWz6eQ1OHNhsdo7kHOAf9e/GjhsOimnmfymrExfT0K8pTpwXbQ5/JLUg6bQ5FToK8PeojQ07Pm41Tl4yTcjeS9NarTmYvYsmtVqVHch5+lxWJ/6X1cf/yxPNJ/DSpgEcyTnAifxEvkqYhYfdix4N7iOzMJUTBceZHP8MucXZXFY7ljxHDkFeYUT5NQOglkdtarj5klWYwS31B/DuzhHYsPF6u3+xNun3leMjOQfYl/k/fkxaQi2PQK4Nu61Ce/XbeeCarqPMc0dzDnJDeB9s2PC0e1GvRhRw5p5lFqad85xT85PwcfclwLMOaQUpNA+I4VjuoZIafu+5Ewd2Tn+dyl9beYLcEWCHaZrrAEzT/MowjI+ARqdsV1jqthMu/CybNm0GTz75KE6nE3d3dz74YCZhYWF07XodnTq1p3HjJjRu7PrGzrPPvsCgQfeyYMF86tYNx8PD46zjN2/egrvuuofnnnuKt956l6FDB9Ot2zVkZmbw4IMPY7fbmTTpHXr0uAmn00HNmrWYOXM2CQmHSEw8xo03diUjI50pU6bh5eVFYGBtOnRoS0BAIN26XU+DBg1OHmvYsBE89ND9zJz5IZmZGYwcOUY9qYCeqB/qSUX67ZKaw+nAx60G/9z3NgezdjGwyXPYsFHsLGbGrnGk5idxe8PBXB1yE0XOQhYcmA7AroxfGGqMYfzWx1iXtIwxbT/CZrNjpm9mQ8oKGvo1rdT6z9WMXa+eNqf0whS2pq1j3KWfkpiXQGLurwB8nTCLoc1e5vLgbqQVJFPsLDrr+Idz9rPm+P+4t/HTzNrzBoOjR/DSJdPxcfdl6ZEFOHEye+9knm/1Njbs5BZn8745miCvMAI86zC89TRquPvy8Z6JFDkLySrKYPxlc8guymRr6jqS84+dPNZ/Dn3Cg9Ej6VK3Jz5uvnx5cEa5+3Om8+C3y6mlHczexbrk7xh36aekFiSTXpj6hz07nzk7cfDRrtd4qsXrOJxOsosymG6Opb5v4zLH35O5jb5Rj3I87whHcg+Ue95iDTbnGX6DOheGYYQB24HrTNOMMwwjFlgA3A7MNE3TOPVbq3/2Lda4uDhnTEwM+fkXNpFTffPNYoKCgmnXrj3ffbeM119/jSVLllfM4KeYPXsWu3btZNy4CRUynpfX77crqh+gnpxK/TjdX6Un/Va1r5AxrWxu7PqTtyuyH20DrySjMI19WdtpFdCB2xoM5NVfHq6w8UuLDb2ZcJ9I5h2YWiHjVVZPzuZ8elbRc/4zc2PXExcXR0xMTLVYxouLi3O2ahVTrjG2bas+87lYLnhFzjTNY4Zh9ACmGYbhC+TjCnHbgTzDMH4G+lRMmecvMjKKIUMG4ebmjsNRzOTJU6qqlGpDPSlL/TideiJnczzvCEOMlyh2FmPHjU/3nvmLM/I79Uwq0wWvyFW0il6Rs7LKWm2xMvWkLPXjdFqRK6uqVp+qM/WkLK3I/TVUy3/ZQURERETOTkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsyr2qCxAREREBaNF3frn2nz+ycQVVYh1akRMRERGxKAU5EREREYtSkBMRERGxKAU5EREREYtSkBMRERGxKAU5EREREYtSkBMRERGxKAU5EREREYtSkBMRERGxqGr3Lzt4eVV1BdWL+nE69aQs9eN0c2PXV3UJ1Yr6cTr1RP4qtCInIiIiYlEKciIiIiIWVe0urebnV3UFVa/0pTL1w6V0Txr3LN8/qvxXsHdh75O3+61qX4WVVB+lL5XpHCl7jqgfLupJWaX7IdalFTkRERERi1KQExEREbEoBTkRERERi1KQExEREbEoBTkRERERi1KQExEREbEoBTkRERERi1KQExEREbEoBTkRERERi1KQExEREbEoBTkRERERi1KQExEREbEoBTkRERERi1KQExEREbEoBTkRERERi1KQExEREbEoBTkRERERi1KQExEREbEoBTkRERERi1KQExEREbEoBTkRERERi1KQExEREbEoBTkRERERi1KQExEREbEoBTkRERERi1KQExEREbEoBTkRERERi1KQExEREbGoCw5yhmH0Nwxjc6n/9huGUWgYxuWGYXxQsk1nwzC2VVy5v1u5cgURESFcd11nunW7hs6dr8Q0d5bZ5ttvv+Gjjz48bd/Y2Cs4cOBAZZR1RqtXr2Lr1l8q9Rjqx/m5vGUwP8+6lTmvdGbuuM58Mb4LjerVPK8xrr+8HiGB3pVUYdW7pX5/3rv8f3jYPM9733Z1OhPgGYS/Rx3uazKsEqo7u9I/4zkvd2bBhK70/0eTChl75fSb8PQo+/bZ69pInru3dYWMfzYjB7WlblCNCh/3XF4XsZeG0fe6Rqftu2BCV+oFV3xNf6R9iyCMhv7lGkPniPwVuF/ojqZpzgZmAxiG4QGsAiYAzYH6FVLdWXTu3IXPPpsHwNKl3/LCC8+ycOGik89ff/2NF6OMs/r004/p3bsvrVtfUqnHUT/Oz9pfjvPEmz8BcFWbUF4c2IbBr6455/0H3NyUPR9kcDw1r7JKrFKdQm5kbdK3dAy5nlWJi86+Qyk31uvLx7vHcyT3IJ/smVhJFZ5d6Z+xp7udpe91Z+GKg2TmFFZZTRVh3MebK23ss70uVm06VmnHPh93do1i0ZoEzIPp5RpH54hY3QUHuVMMA44Di4EfAH/DMD4BPgX8DMOYBzQDvIHBpmmurqDjnpSWlkrDhpFcd11ngoODSU1NpXfvu9i7dzfjxk1g1KgRLF36DfXrR5CcnAxAcnIyAwb0Iz8/n+hogxUrlrN9+x5WrVrJ6NEjcHNzo1Gjxrz33nQ+/3wOixZ9RUZGBikpyQwfPoqePXuxbNlSxo4diZeXN3Xq1GH69I/ZsmUzI0cOw8PDky5durF06Tds3ryRZs1a0KBBg4qeuvpRAfz9PPn1eDYtogIYPfhSih1O8gscDJ+2gZT0PN59tiM1fT3w9nRj4qe/4OPtTouoACY90YE+w7+n/z+acPNVDShyOFgfn8zrn/3C431aEhHqSx1/L8KDa/Dqx5tZvTmxqqd6Tpr7X0Zi3q98d/TfPNzsZVYlLmLkJR+cDGdd695OgGcd/u/QpzzeYjw13PzwtHsxd/8UvNx8aOgXzVBjLO+ZoxhqjGH05kFMuGwuO9I30sC3KU6cTI5/htzi7Is2J18fd4odToyG/jx3b+syP2O7Dd55piN3vPAd4FpdemLyWnLyinjr6Svw9LCz/3AmHVuH0OXh/wHwypAY6of6AjB0wg8nj1O7lhcfvNCJtz/fxs/bk3jloRgi69bEboc352xjXXwSHVoG88zdrSh2ODl0LJuR72/gttiGdOsQjl8NDwJrevHu/HiW/HSYZ+5uxRWtQ7DbbHy9+hCzFu1mziudeemDOI6n5jHhkXYE1vQCYOxHm9h1qHzBprTfXhdzXunMiYx8/H09+Xr1ISLD/Xjjs608c3crYi8N42hyLrVruVZuA2t6nrFn5zPnTm1CebpfK/ILi0nLLGDYu+tpERXA8/0vobDIwQ9bEom9NIyWjQLZnZDB0eScCpmvzhGxonIHOcMwgoBngBjTNBMMwxgF3GGa5n2GYXTGtTr3lmma6wzDeAoYA3Qt73EBVqxYznXXdaagIJ+tW39h4cL/Eh+/jT59+nHbbT2ZPXsWAPHx21izZhU//LCerKwsWrVqCsDEia9y6609GDLkYZYtW8qyZd/idDp5+OHBLF++hpCQEMaMeYnZs2fh4eFBVlYWixcvJSkpiauu6sDNN9/KI488yPLla6hXrx5Tp77DhAnj6N79ZvLy8li9eh0ABw7sp3fvvpUeWtSP89PxkhDmvNIZTw87zRoG8MC41bz2cDtefG8DOw6k0a1DOCPua8M78+IJDvSm/+iV1PH3JircjxVxR9m+P42XPogjKrwm/+gUwZ0vfkdRsZNpw67k2nZ1ASgoLGbQK6vp1CaUB26NtkyQuzasB98f+4qjuQcpchTQuGbLM24X4lOfAI86vLb1EWp5BFLXpwGbT/zAwaxdfLx7PEWO31c1fNx9+THpWz7dO4lHmr1C29pXsjZpaaXO47efsdPppLDIycszNjJyUNvTfsbjZ2054/4P39GCpesOM+ebvXRqE8pVbcNOPjf/u/3E7Ujm9cfac1WbUACC/L2ZPrwT42ZuZsvuE/S7oTGpGQW8+N73BNT05PNx19L9iSW89nA7+gxfTkp6Pk/d1YpeXaIoKnJQw9ud/mNWUruWFwtf78ayn4/Qs3MkfUd8z/ETufTqElm2vl7N+fGX48xdspfIun5MfKw9fYZ/XyE9K/26iG7gz9erDvHtusP0utZVQ3SDWrRvEUyP55bh6+3Od9P+8ac9O9c5f7f+CK8OjaHP8O9JPJHLwJub8uidLVi+4QheHm70GuYKUhGhvixak1DuEKdzRKyuIlbkHgS+Mk1z3x88v9c0zXUltzcDgyrgmEDZS4m7dplcc01HmjRpSnS0UWa77dvjiYlph91up1atWrRs6fqMws6dO7jnngEAXHXV1QAkJSVx7NhR7r67NwB5ebl07Xo9jRo15uqrr8FutxMaGkpgYCDHjh2jVq1a1KtXr2SMWEaNGk737jefVsPFoH6cn9KXVKLCa7JgQhdsNhs7DqQB8HN8Es/dewm7EzL45//28PbTV+DubufT/+4uM07j+jXZvCuFomInAOu3J9M0wvXZnfh9rrGOJufg6el2saZWLr7uNWlb+0pqeQZyQ3gffNz9uD68d5ltbNgAOJyzj2+PfsGjzcbhZndnyeF//enYB7JMAFLyEvGwe1XOBEop/TP+zWuPtD/tZ3wqm2t6NKlfk39/fwCA9duTymyzbW8qAElpefh4ud5KYy8N43hqHna7awCjoT/tWwTRJro2AO5uNur4exEc6M2UZzsC4O3pxprNiRw6lsXP8Uk4nZCSnk96dgG1/b14YvJanrunNUGB3qzcWPayptHQn46tQ7jpqggAavme/+cZT3Wm18WBo1nsO5xZZrumEf5s3ZOK0wlZuUUnL3GeqWfnM+fgAG+ycopIPJELuH5Gz97TmuUbjrDvSNkaKoLOEbG6ighyfYDH/+T50h80cELJnwAVLCQk9ORtu73sB0yjow3ee28KDoeD3Nxcdu7cDkDLlq1Yt24tbdq0Zd061ws5KCiIevXqs2DBV/j7+7No0f/h6+tHQsIhNm2KAyAxMZGMjAzCw8PJyMjg6NGj1K1bl9WrV9K0afRpNdjtdhwOR2VM+w+pH+cnOc31ObeExCyMhv6YB9O5vGUw+49kEt3AH19vDx54dQ3Bgd58Mb4L3284itPpxG6zsffXTO6/1cDNbqPY4aRDiyAWrjhIs8gAnFU8rwvRKaQ7K479H3P3TwHA0+7FOx2+IiF7HwGeQRzJPUikXzNSC44TUaMxPm41eCP+KQI86zCm7Uw2/bwGJw5stjN8l8pZ9R05fiL3tJ9xfqGDOv5e2O02fL3diSi5HLbrUAaXGnXYcSCNS6PrlBnHeYa5/HvFARZ+f5B3n+vI7c8vY9/hTI6l5PL+lzvw8nTjkTuacyIjn2MpuQwZ/wNZOYV0bR9OTl4R4UE1aNU4EHAFHz8fD9IzC+jesxlPvPkTNht8M+VGFq05dPJ4ew9n8J+VqXy9+hB1/L3o3e30LyGUx2+vCwDHKfPddziTATc1xWZzBY0mEbWAM/fsfOacmJqHXw13ggO9SUrNo0PJzwjA6XCWqgfslfKnic4RsZZyBTnDMAKBJsCPpR4uAjzKM+65+u1SopubG1lZmUyc+CaffTbrtO3atGlLr1530qlTe+rWDSc4OASAZ599gUGD7mXBgvnUrRuOh4cHdrudSZPeoUePm3A6HdSsWYuZM2eTkHCIxMRj3HhjVzIy0pkyZRpubm5MmzaDvn1vx2azExgYyIwZs4iPL/tF3fbtL2fkyBeIjIyiWbPm6sdF6sfZ/HZJxeFw4uvjzmufbGHHgTTGDL4Mmw2Ki5288N56Ek/k8nifFvS8tiGFRQ7e/jwegI07U5j0RAcGjF3F4h8TmD++C3abjQ07kvl23WGaRQZU2dzK49qw25hmjj55v8CRz8/J33MiP5GBTZ7nRH4iJwpcKw/HchO4veFgrg65iSJnIQsOTAdgV8YvDDXG8NHu16pkDn9m+LQNp/2Mk9Py+GFLIgtf78bBY1kcPJoFwAf/3sHkJy/npk4RJJ7Ipajo7L+A7Pk1g69WHmTkoLaMnbGJ1x5ux9xxnfHz8WDON3txOuGVjzYxc+RV2G02snIKeXbKz4QH1SAo0JvPxl5DzRoejP5wIwVFDtKyClj05vWkZxewZvMxjiT9filx2oIdTHikPX2vb4SfjwdT/hVf7v6c6XVx+ymX6wB2HEhj8Y8J/OeNbiSeyCMlPe8Pe3Y+c3Y4nAyftoFpw67E6YD07AKen/Iz0Q3KfkN1y64Unrv3EhKOZ7P314pdqdM5IlZiO9NvDOfKMIz2wOemaTYp9VgT4H/AVmAKMNU0zVYlz3Uufb+0uLg4Z0xMDPn5F1zOefvmm8UEBQXTrl17vvtuGa+//hpLliw/47azZ89i166djBs3odLr8ip1xUn9cCndk8Y951+UY1Znexf+fqmz36r2VVhJ9TE3dv3J2xV1jnS+LIyUjHy27knlyktCePiO5twzamWFjH2qXtdG0qh+Td74bGuFjFf6HLmYr5nz6VlFz/lsKqMnVj9H4uLiiImJqaS1zfMTm8B9zQAAIABJREFUFxfn7D1ub7nGmD+ycbWZz8VSrhU50zTX41qRK/3YHqBpqYdalXpuRen7VS0yMoohQwbh5uaOw1HM5MlTqrqkKqV+iJSVcDybCY+2p7jYiZvdxssfbarqkqq9v1vP/m7zleqnov76EUtq1qw5K1euPadt+/cfWLnFVAPqh0hZe3/N5M4XzrwqXdG+LPnAvNWdT8/+CnPWOSJVTf9El4iIiIhFKciJiIiIWJSCnIiIiIhFKciJiIiIWJSCnIiIiIhFKciJiIiIWJSCnIiIiIhFKciJiIiIWJSCnIiIiIhFKciJiIiIWJSCnIiIiIhFKciJiIiIWJSCnIiIiIhFKciJiIiIWJSCnIiIiIhFKciJiIiIWJSCnIiIiIhFKciJiIiIWJSCnIiIiIhFKciJiIiIWJSCnIiIiIhFKciJiIiIWJSCnIiIiIhFKciJiIiIWJSCnIiIiIhFKciJiIiIWJSCnIiIiIhFuVd1Aafy8qrqCqoX9eN0exf2ruoSqpW5seuruoRqR+dIWerH6dQT+avQipyIiIiIRSnIiYiIiFhUtbu0mp9f1RVUvdKXU9UPl9I96beqfdUVUk2UvpzauOf8Kqyk+ih9qUznSNlzRO8jLnofKUsfy/hr0IqciIiIiEUpyImIiIhYlIKciIiIiEUpyImIiIhYlIKciIiIiEUpyImIiIhYlIKciIiIiEUpyImIiIhYlIKciIiIiEUpyImIiIhYlIKciIiIiEUpyImIiIhYlIKciIiIiEUpyImIiIhYlIKciIiIiEUpyImIiIhYlIKciIiIiEUpyImIiIhYlIKciIiIiEUpyImIiIhYlIKciIiIiEUpyImIiIhYlIKciIiIiEUpyImIiIhYlIKciIiIiEUpyImIiIhYlIKciIiIiEW5V3UBlWnSpIlMnfo2O3fux9vbm23btpKamsrVV8cSHR3JL7/sxNvb+5zHy8vLY+7cfzJo0AOVWHXl+rv35O5GTxLl14wAzzp42r05nneYzMJU3tnx4gWNF1GjMb4etdiZvqmCK734Lm8ZzLvPdWR3QgY2G3i42Rk2dT37Dmee8xjXX16PzbtSOJ6aV4mVVi6dI6cbNuwZNm6MIzHxGDk5OURFNSI4OJi5c7+4oPFKv+9UpVsiBtA6oAMOHOB08q8D09iftfOCx+vVcDBXBt9AakHyycc+3z+FvZnbK6Lck55s8Tpvb3++QscU6ypXkDMMoycwFnAAJ4DBQG3gftM0HzIMozMw1TTNVuUt9ELMmzeHO+/sy/z58+jffyALF35JWFjYBb95HDt2jFmzPrJMaDmTv3tP5ux7G4DY0JsJ94lk3oGp5RqvQ3AX0gpSLP2HdGlrfznOE2/+BMBVbUJ5cWAbBr+65pz3H3BzU/Z8kGHpIKdz5HQTJ04GYPbsWezatZNx4yaUa7zyvu9UhHo1ooipHcuYLfcD0NA3moeM0by48e5yjbv48Fy+O/rviijxDynEnZ+SLDIf2A7YAA/gbdM051dlXX/GMIwbgb6maQ4827YXHOQMw/AB/gm0MU1zj2EYTwFTgC+A+hc6bkVZuXIFjRo1ZvDgh7jvvnvo2vU6PvtsFp6enrRte9nJ7eLjt/H880/jcDhIT09j8uQpdOx4JS1bNqVjx07s3m0SEhLKvHlfMnHiq+zYsZ1XX32ZAQMG8fjjQ8nLy+PEiRSGDx/Frbf2qMIZn516cmbN/S/jrqjHKHIWsvzoQpLzE+kTORSH00Fi3q/M3P0anUK607b2lXjavQn1qc/XCbPZmrqO2NCbKXIUciBrJz5ufvSOHEqho4DMonQ+NF+moV80t0QMoMhRSIh3OGuTlvJVwidVPeVz4u/nya/Hs2kRFcDowZdS7HCSX+Bg+LQNpKTn8e6zHanp64G3pxsTP/0FH293WkQFMOmJDvQZ/j39/9GEm69qQJHDwfr4ZF7/7Bce79OSiFBf6vh7ER5cg1c/3szqzYlVPdWz0jlyupUrVzBy5DA8PDy5//4HiYhowOjRI3Bzc6NRo8a89950Pv98DkuWLCYnJ4d9+/byzDPDTnvfSU9PZ+zYkXh5eVOnTh2mT/+YLVs2M3nyRDw9PTlwYD933NGHF14YUaH1ZxScoI53KJ3DbmXLibUczN7FS5sGAtDM/zJ6NXT9cupp9+Z9cwxFjkIeaz6OlPxEQr3rszczno/3TDynYw2JHk1ND3/83P2ZFP80d0U9Sh2vUPw8/NlyYi1fHPyAIdGjKXIWEOxVlwDPID7YNZYDWSadw26lW91e2G1uxKWs5MuDM5h2xTc8/NONdKt7B7GhN+HEgZm+hbn7pxDmHcHg6JG4293JL87n3Z3D8bL78GD0S7jZ3cAJn+6dxKHs3RXaTwtYbppmXwDDMPyAlYZh7DJNc3MV11Vu5VmRc8OVbP1L7vsB3sDLgL9hGJ8AnwJ+hmHMA5qVPD/YNM3V5TjuOfnkk4+4774HiI428PT04vDhX7n33oGEhYXRvn2Hk9tt3x7PxImTadWqNfPmzWX27E/o2PFK9u/fxzffLCciIoLOnTuxYcN6hg0bQXz8VkaMGMV33y3jiSee4ZprOrN27Y+88sroah9a1JM/5mH3ZNTG+wCY3G4BY7cMJqMwlTsbPkRs6C0UO4uo4ebHhG2PE+YdwbOt3mRV4iJWJS4irSCFvZnbebvDfxi7eTCpBUncGN6XHg3uZ9OJ1QR5hfFCXD887B68d8X/qvUf0h0vCWHOK53x9LDTrGEAD4xbzWsPt+PF9zaw40Aa3TqEM+K+NrwzL57gQG/6j15JHX9vosL9WBF3lO3703jpgziiwmvyj04R3PnidxQVO5k27EqubVcXgILCYga9sppObUJ54NZoSwQ50DlyJnl5eaxevQ6n00nr1gbLl68hJCSEMWNeYvbsWXh4eJCens6iRUvYs2c3t99+C/37Dzz5vtOuXXuaNWvE8uVrqFevHlOnvsOECePo3v1mDh06yIYNv5Cfn09UVHiFB7nMonQmxz/D9eG9ub3BYAocefzrwDTWJ39P/RqNeG/nKNIKkrktYiCXB3Xlh+PfEObTgPFbHyO/OI+3O/wHf486pBemlBn3H/X60TH4egASsvfw6d5JAMSnred/hz8nyKsuuzO3MWP3q3jYPJl6xX/54uAHACTnHWXm7vFcG9aDLmE9WXBwOrdGDGBYXD+KHAXc3ehJvOw+J491TdgtfLrnDfZkbnOFPdzo1+gJvkqYxS+pa7k8qBuRfgZdwnqy5Mg84lJW0dA3mgejRzJy04AK7aeVmKaZZRjGdOAOwzD6ALG4vjPwpmmaXxiGcTnwDq6Mcxi4G/gfkAQEAjcB04CmJfuNNE1zhWEYdwCPlOwHcEfJ7X+VbOcBPGSa5lbDMB4D+gFOYJ5pmlMMw2gOfAxkl/yXei7zueAgV9KIh4AfDcNIwRXsOgFXAXeYpnlfyXJmfeAt0zTXlazajQG6Xuhxz0VqaipLliwmKek406a9S0ZGOu+/P5VGjRqftm14eD3Gj38Fb28fsrIyqVmzFgBBQUFEREQAEBERQV5e2UtFdevWZfz4ccyaNRObzUZhYWFlTqnc1JM/dzT3IAC1PAIJ8Azi8ebjAfC0e7E1dR2Jeb9yIHsXACn5iXjYPcvsX9MjgNyibFILkgDYkb6RPlGPsOnEahKy9+KgmHxHMQWO/Is4q/NX+tJqVHhNFkzogs1mY8eBNAB+jk/iuXsvYXdCBv/83x7efvoK3N3tfPrfsr/dN65fk827UigqdgKwfnsyTSNcv/PF73ONdTQ5B09Pt4s1tXLTOXK66GgDgKSkJI4dO8rdd/cGIC8vl65dr6dRo8a0adMWgPr1T3/PSE5OplatWtSrVw+Aq66KZdSo4XTvfjOtWrXG3d0dd3d3fHx8qGih3vXJLcrmw12vABDl15znW73N9rQ4UguOM6Dxs+QV51LbK5hdGVsASMz9lbziHADSCpJP+xnDH19aPZrjOn+yizJoXLMFLQPakVuUhbvN4+Q2B7J+P3+ia7UhxLseCdl7KSw5J/65760yY043X+am+vdwV9Rj7M7Yis0G4TUasjvjFwDWJS8D4N5GT5+8tH8wexd1vEIvsGuV7uD8kY0blneMc9wuEXgG2GSaZifDMLyBnwzDWAp8iOuy5g7DMB4GmpfsM9c0zYWGYQwFkk3TvN8wjDrAKqAlEA3cZJpmTklQvAFIA9JxhbYWQC3DMFoAfXDlJSewzDCMJcArwCjTNJcahjGs1HH/VHkurbYGRgEtTNPcaxjG48CXwNunbLrXNM11Jbc3A4Mu9Jjn6vPP/8nAgfczfvwbAOTk5NCsWRSNGz+Mw+Eos+0zzzzOrFlzaNasOS+/PJqDBw8AYLPZTh0Wu91+cv+xY19i0KDB3HBDdz799BM++2xWpc6pvNSTP+d0ugJHZmEaJwqOMzn+GXKLs7msdix5jhyCvMJcL7dTOJxObNjJLEzDx92XAM86pBWk0DwghmO5h34b/eJNpAIlp7n+0E1IzMJo6I95MJ3LWwaz/0gm0Q388fX24IFX1xAc6M0X47vw/YajOJ1O7DYbe3/N5P5bDdzsNoodTjq0CGLhioM0iwywaDd0jpyJ3e76iw+CgoKoV68+CxZ8hb+/P4sW/R++vn4kJBz60/eNoKAgMjIyOHr0KHXr1mX16pU0bRoNnPn9piI18G1Kt/BeTNr2NIXOAo7lHiK3OAuHs5jBTUfy5Poe5BXn8JAxmt8WWJzl+Dk5SvaNDb2Z7KJMZu4eT6h3fbrU7Xlym1PHT8z7lfAakbjbPChyFvJE8wnM3jv55PNd6vbg493jKXQW8EKrKTSt1YbDOftpXLMl29J+plPIjfi61+Jwzn6MWpey8YRrRS6toOwqYnURExMTeREP1xCYA9xrGMaKksc8Sh4PNU1zB4BpmtMADMMAMEu2aw1cXbJyB+BeEuiOA58ahpGF6yrkWlwreU2Br4BCYBzQquQ435XsHwg0wRUGfy557AcqO8jhSpo/mKa5t+T+e8BbQJ1Ttiu9LOPk9yXHSvPJJx/x8cefnbxfo0YNevTohbu7O++/PxXX6qXLXXfdwx133EZISCj16tUnJSX5TEMCEBISQkFBASNGDOP22+/k6acfJzQ0jPr1I/50v+pAPTk3TpzM3juZ51u9jQ07ucXZvG+Odv0hfQb7s3bQL+pxjuTs56Ndr/FUi9dxOJ1kF2Uw3RxLfd/TVzyrs98urTocTnx93Hntky3sOJDGmMGXYbNBcbGTF95bT+KJXB7v04Ke1zaksMjB25/HA7BxZwqTnujAgLGrWPxjAvPHd8Fus7FhRzLfrjtMs8iAKp5h+f3dz5EzsdvtTJr0Dj163ITT6aBmzVrMnDmbhIRDZ9z+sstiePHF5zCM5kybNoO+fW/HZrMTGBjIjBmziI/fVuk1r0/5nvAakbx86SfkFedit9mYu28KucXZrDm+mJfbfkJ2USbphScI9AyusONuS1vPY81fpVmtS8l35HIsN+EPx88sTOPrhNm81GY64GRjyuqTK7oAh7L38Mpln5JZmMqJ/CT2Zmxjbv4U7m86nB4NBlHgyOO9naPYmLKawdEjuCnibtxt7ny4a1yFzceKDMOoievLmR8B35um+aBhGHbgJWAfcMQwjKamae4uWRnbVbLrb6seO4FfTdN8reT7AiOAIlxf/mxQss1SXHmnM3DUNM3rDcPoCLwGPAnEA91N03SWXK3cWjJuR+AboP25zsf222+Z58swjC64ruVebppmomEYvYCJuC6d3mua5g2nfmv1z77FGhcX54yJiSHfOlcVKo2X1++31Q+X0j3pt+qcz++/rLmx60/ebtyz2n7x6qLau7D3yds6R8qeI3ofcdH7SFlzY9cTFxdHTExMpS+wVKVTvrVajGsR6x1gITAZV2jyAxaapvmyYRjtgTdxBbejwABgCa7Pt+00DMMLmIFrVa0Wrs/LfYTrs3AGv3++7Ufgg5LHfUuO/bJpmt8ahvEc0APwwrUK9xgQUrJtIa7P4+VV6rdWTdNcbhjGG8AKwzAKcP31I7cB+cBowzD+jetbrCIiIiJVwjTNFbhC0pk8fYbt1wNXn/Jw51LP5wP9zzBW7zM8BtDtDMd4A3jjlIeP4vrixXkp198jZ5rme7guqZ6qaanbJ1ffSppZJX+nnIiIiMhfjf6JLhERERGLUpATERERsSgFORERERGLUpATERERsSgFORERERGLUpATERERsSgFORERERGLUpATERERsSgFORERERGLUpATERERsSgFORERERGLUpATERERsSgFORERERGLUpATERERsSgFORERERGLUpATERERsSgFORERERGLUpATERERsSgFORERERGLUpATERERsSgFORERERGLUpATERERsSgFORERERGLUpATERERsSgFORERERGLUpATERERsSgFORERERGLsjmdzqquAYC4uDhnTExMVZchIiLytxEXF0dMTIytquuQC6cVORERERGLUpATERERsSj3qi7gVPn5VV1B1fPy+v12v1Xtq66QamRu7PqTt9WTsv1o3HN+FVZSfexd2PvkbZ0jes2ciV43ZZV+zYh1aUVORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsSkFORERExKIU5EREREQsyr08OxuG8RjwKJAL7AAeARoD95um+ZBhGJ2BqaZptipvoX9m0qSJTJ36Njt37sfb25vrruvM1KkfMH/+PMLCwhg8+KHzGu+jjz5kwID78PDwqKSKL567Gz1JlF8zAjzr4Gn35njeYTILU3lnx4sXNF5Ejcb4etRiZ/qmCq704qhXoxH9oh7D080bb7cabD7xAzvS4uha93be3TmizLb3NnqaxYfnkJKfWEXVXhwvDmxDq8aBBAd44+PlxqHEbE5k5PPYG2svaLzoBv74+3mwfntyBVd6cfxVz5FbIgbQOqADDhzgdPKvA9PYn7Xzgsfr1XAwVwbfQGrB7z/nz/dPYW/m9ooo96QnW7zO29ufr9Axy0uvGalOLjjIGYZxLTAMuMI0zV8Nw7gX+BBYBNSvoPrOybx5c7jzzr7Mnz+P/v0Hlnu8119/jXvu6f+XCHJz9r0NQGzozYT7RDLvwNRyjdchuAtpBSmWDHI13Px4rPmrvB3/PMfyErBh54kWE0grOPOb52f73rzIFVaN8bO2ANDr2kga1a/JG59tLdd4N3asT1JaniX/UPqrniP1akQRUzuWMVvuB6ChbzQPGaN5cePd5Rp38eG5fHf03xVR4h+qbiEO9JqR6qU8K3IxwDLTNH8tuf9vYBZwJeBrGMYnwKeAn2EY84BmgDcw2DTN1eU4bhkrV66gUaPGDB78EPfdd88fBrmRI19kzZpVOJ0OHn/8aXr1upPrrutMmzZtiY/fRmZmBnPmfMHy5ctITDzGvff25Ysv/sOwYc/w449rAOjTpx933XUP3bt35eefN/PTT2vp2fMmfv01iaNHj/LQQ/fTu/ddLFmymJycHPbt28szzwyrkHBZkZr7X8ZdUY9R5Cxk+dGFJOcn0idyKA6ng8S8X5m5+zU6hXSnbe0r8bR7E+pTn68TZrM1dR2xoTdT5CjkQNZOfNz86B05lEJHAZlF6XxovkxDv2huiRhAkaOQEO9w1iYt5auET6p6ygDEBF3D9rQNHMtLAMCJg/d3jiba/xK6hPXg+Vbv4O8RyMYTq/ny4AxGXvIBH+8eT1ZRBg8Zo6nhVhObDd7fOYYCRz6Dmr6Ah90TP3d/Fh76iA0pK7m09lXc0XAIOcVZZBdlkpC9my8PzuDuRk9i1GoDwI/Hl/DNkXlV2YqzurxlMM/3v4TCIgfzvt3HkeQcnrm7FcUOJ4eOZTPy/Q3cFtuQa2Lq4uPlRoMwPz78907WbEmkV5dICoscxO9LpWYND57u14r8wmLSMgsY9u56WkQFMOT2ZhQUOYgI8eW/PyQwbcGOqp4y8Nc9RzIKTlDHO5TOYbey5cRaDmbv4qVNAwFo5n8ZvRo+AICn3Zv3zTEUOQp5rPk4UvITCfWuz97MeD7eM/GcjjUkejQ1Pfzxc/dnUvzT3BX1KHW8QvHz+P/27js8qir/4/h70ikJhBpa6BzASAtFBGkKqygKosiKBXWxIrqCwlIFddVd9CcoCkqxwtpWXREFRUKRHkT6ASJVAiSBhBDSM78/ZowJJBjCkGT083oenmfKuad5zr3fe86dWImfTqzh4wMzeaDZJLKcGVQPrEXlgGrM3D2Z/actPcJu5JpaA/Fx+BKdsJxPD7zF61d8w8Nrr+WaWrfQreb1OMnBJv3E/H3TCQuqx7Bm4/Hz8SM9O51Xd40l0Kcc9zebgK+PLzjhnZipHEzZ47G+LMyfdc5I6buYQG4dMMIYU99aewC4B9czd68CXay197i3VusC/2etXWeM+TvwNHD1xVX7N/Pmzeaee/5Gs2aGgIBA1q9fd06axYu/5sCBfURF/UBaWhrdul3B1Vf3BqB9+45MnfoKEyeO46OPFvDkk2N4/vlneO+9/7Bo0UL279/HihVrycrKolevrvTo0YsqVapy6NAhliz5hrp167FpUzSbNm3kppsGAJCUlMTChYvZu3cPN9/cr8wFcgD+PgFM3HQPAC+1/4TJPw3jVOZJbq3/IN1q9iPbmUV534q8sG0EYUH1GBXxMiuOLWTFsYUkZiQQk7yDVzp+zuTNwziZEce1tQfTP/w+fjyxkmqBYYyJvh1/H39mXPF1mQnkQgOqczz1l3yfpeekkpWTib9PIC9vH4WPw4dXOy3k0wNv5abpH34v0QkrWBr7X1pWbk/jkMtIyjjBV4ffZ2fSJpqGtOKW+vcTnbCSuxuPYuLmezmVeYJHmj8DQNsqXakeVJuJm+/B1+HLpNaz2Z64gUNnYkq0/Rcq0N+XgaOXAvDdjOu4bez3JCSl8/e/RjCwV0OysnIILu/PPVNW0KBWRd4c25VPl+3n0+/3E5eYxpY9J4ia2Zfbxi7j2IlUht7QlOG3tuT7jUeoXb081z++hAB/H9bM6VdmLkp/1DGSnJXES9tH0qf2IG4OH0ZGThof7n+dDfHLqFu+ETN2TSQxI56b6g2lU7Wr+eH4N4SVC+f5rY+Snp3GKx0/p5J/VZIyE/Ll27fO7XSu3geAQyl7eSdmKgDbEzfw9S8LqBZYiz3J23hrz3P4OwJ47Yqv+PjATADi02KZs+d5eob1p1fYAD45MIsb693N6OjbycrJYEijxwn0KZdbVvewfryz99/sTd7mCvbw5fZGj/HFobfZcnINnapdQ4OKhl5hA1h85D9EJ6ygfoVm3N9sPON/vNsj/fh7/oxzRkpfsQM5a+1KY8xk4DNjTA4wFzgBZJyVNMZa+2t0tRm4t7hlnu3kyZMsXryIuLjjvP76q5w6lcQbb5y7dbht21Y2bYqmd+8eAGRmZnLw4AEA2rRpC0C9evU4evRovuN27dpJly5X4XA48Pf3p2PHK9i5cwc33TSAb75ZxNq1qxk5cjRLl37L2rWrmTlzDkuWfEPr1m0AqFu3HmlpaZ5qrkfFprraH+IfSuWAaoxo8TwAAT6BbD25jmNph9mfshuAhPRj+PsE5Ds+2L8yqVkpnMyIA2Bn0iZua/gIP55YyaGUGHLIJj0nm4yc9BJs1fnFp8XSoGLzfJ9VD6pN80rtOJQSQ5YzE5yQ7czOl6ZWufpEHf0fADsSNwKu56gGhN9Lj7CbACe+Dj9C/EM5k53CqcwTAOxK+pHKAVWpU74B1r0Vne3MZm/yNupUaFTmA7mfjyQDULVSINVDg5g+qjMAQQG+rNp8jINHT7NzXyIAR+LPEBjgm+/4KiGBnD6TxbETqQCs3x7HqDsu5/uNR9h9IInsHCep6dmkZeTv79L0Rx0jNYPqkpqVwpu7XYFjw4oteCriFXYkRnMy4zh3Nx5FWnYqVQKrs/uUa9vwWOph0rLPAJCYEX/OOQAK31qNPeM6v6RknaJxcEsuq9ye1KzT+Dl+e1xl/+nfzi/NQlpTI6gOh1JiyHSfM97/+f/y5TnLTuH6unfw14aPsufUVhwOqF2+PntObQFgXfx3gOu5xV8f/TiQspuqgTWL2WsX7s84Z6T0FftXq8aYYGC5tbadtbY98IX7qxNnJc3M89oJOIpb5tkWLHifoUPv46uvlvDll9+wcuU6li5dQnx83Fl1bU737j359tsoFi/+noEDB9GwYSMAHI5zq+Pj40NOTg7Nm7fI3VbNzMxk7drVNGnSlH79+vPhh/MJCQnhL3+5ji+//Jz09HTCwsIKzbOscTqdACRnJnIi4zgvbR/Js1se5POD89ietNGd6NzjcpxOHPiQnJlIOb8KVA6oCkCLypEcTT1IoQeWAT+eWEXrKp2pEVQHAF+HL3c0epzkzETOV+dfzuyjUXBLAJpXasvghsO5tcGDrDy2iDfsJHYkRuPAwanME5TzLU+wf2UAmgRf7j5+PyakTW6ZTUNa5emrssuZ4+qTE6fSOZqQygPP/8CQCVG8/slO1m477kpTQL/lOJ34OFzHVSzvR/XQIAA6Xladfe4LnbNsDpE/7BgJr9DUtc3rcAVjR1MPkpp9mhxnNsOajmfW7inM2j3ZfWPmOn8V9N+2qHLcx3areQMpWcnM2DWBrw5/QKBvUG6as/M/lnaY2uUb5AZ7j7V4gdCA6rnf96rVn7l7nueZLQ/QoGIzmoa05pcz+2gcfBkAXWpcS5/ag/i55vg1AAAej0lEQVTlzD5MiOsGvX6FZiRm5F9FvJT+jHNGSt/FbK3WBpYaY1paa08B44AFuAK3EvmVwLx5s5k7973c9+XLl6d//4HMmzc7X7rrr+/HihVR9Op1FSkpp7nxxgEEBwcXmm+XLldx0019WbJkGStWRNG9e2cyMjIYOHAQbdu2AyA9PY2ePa8mNDQUPz8/rrvu+kvTyEvMiZN3Y17iqYhXcOBDanYKb9hJVAsMKzD9vtM7ub3hCI6c2cfs3f/k7y3/RY7TSUrWKWbZydSt0LiEW1B0qdkpzLRPM6zZOBz4UM63PJtOrOTImX20qNS20OO+ODSPB5pNpGuN63Di5K3dz9I4+DLubjKSxIyhJKQfI9i/Mk6cvL333zwVMY3UrNM4HA6Oph7kxxOraFEpkslt5uDn8Gdt3HfsP21LsOUXx+mEZ2b/yJzxXfFxODh9JpNR09dTu1r5AtNviznJmLtbEXP4FGNf38jro6/EmQNJKRk8NX09zcIrlXALiu6POkY2JCyjdvkGTGk7j7TsVHwcDub/PJ3U7BRWHV/ElDbzSMlKJinzRL7g6WJtS9zAoy2eo3lIW9JzUjmaeqjQ/JMzE/ny0LtMaD0LcLIpYWXuij/AwZS9PNPuHZIzT3IiPY6YU9uYnz6d+5qOpX/4vWTkpDFj10Q2JaxkWLNxXF9vCH4OP97c/azH2lNUf6Y5I6XP4byIMN8YMxzXnxzxAVbh+lMkdYCvga3AdPL8+ZHz/TmS6OhoZ2RkJOllZyeu1AQG/vb69hUdSq8iZcj8bhtyX5flPrmx3lAWHf6ALGcmD5spbD25lpXHF3m8nLz90XjARx7P3xvFfDYo97XGiPfMmZKkeZNfzGeDiI6OJjIysuxvI0mhLurvyFlrXwPOfihtL9A0z/uIPOmj8r4X+aNJy05hStt5pGenEZ8ey5q4b0u7SlLGaIyIiCddVCAnIvktOfIxS458XNrVkDJMY0REPEn/iy4RERERL6VATkRERMRLKZATERER8VIK5ERERES8lAI5ERERES+lQE5ERETESymQExEREfFSCuREREREvJQCOREREREvpUBORERExEspkBMRERHxUgrkRERERLyUAjkRERERL6VATkRERMRLKZATERER8VIK5ERERES8lAI5ERERES+lQE5ERETESymQExEREfFSCuREREREvJQCOREREREvpUBORERExEspkBMRERHxUgrkRERERLyUAjkRERERL6VATkRERMRLKZATERER8VIOp9NZ2nUAIDo62hkZGVna1RAREfnTiI6OJjIy0lHa9ZDi04qciIiIiJdSICciIiLipfxKuwJnS08v7RqUvsDA3143HvBR6VWkDIn5bFDua/VJ/v64fUWHUqxJ2TG/24bc1xojmjMFydsnutbkv9aI99KKnIiIiIiXUiAnIiIi4qUUyImIiIh4KQVyIiIiIl5KgZyIiIiIl1IgJyIiIuKlFMiJiIiIeCkFciIiIiJeSoGciIiIiJdSICciIiLipRTIiYiIiHgpBXIiIiIiXkqBnIiIiIiXUiAnIiIi4qUUyImIiIh4KQVyIiIiIl5KgZyIiIiIl1IgJyIiIuKlFMiJiIiIeCkFciIiIiJeSoGciIiIiJdSICciIiLipRTIiYiIiHgpBXIiIiIiXkqBnIiIiIiXUiAnIiIi4qUUyImIiIh4qT9UIDd69Eh69+5Bq1bNadIknN69e3D77bcWO79t27aycuUKD9aw5Nw/oDlr5vQjwN/1n/iDZ3rQqE4wI267jL/+pfEF5ze4dyP8fB2ermapO7ufmoVXokPLagAsn3V97udFFeDvw6BrGnq8npdCv7p3MaPT1/g7AgAY32omtcvVZ2D9YVxd6+YLzq9X2AB8Hb6ermaZcfZYuRB9OtWhRmgQ1SoHMfn+dpegdpdW03ohzB7XlQ+m9OCzf13DY4Mv44qI6kx74opz0o6/tw21qpUv+UqWoB07ttO///X06dOTLl06MGXKJKKilnHnnYPPSTtq1OMcPHiwFGopfxZFOiMZYxzGmHeMMaPc732NMa8YY3YZY/YaYx7Mk3aiMeYm9+u3fz2mJLz44kt8+20Uo0aNYfDg2/n22yjmz/+42Pl99tmn7Nq1w4M1LDk3dQtn4apD9Osa7pH8HrqlBT4+f7xA7ux+urZzXZrUq1Ts/KpXDmLQNY08Vb1LqkuNa1kTt4TONfp4JL+bwofiwx83kLuYOXX3DU2pWN6f+MQ0Jr256RLU7tIJLu/PtJFX8OzczQyZGMXAMUsx9SvRsE5IgemfnbuZ2PgzJVzLkpOYmMiddw5m6tRXWLJkGStWrGX79q3s2WMLTD916iuEh3vmPCxSEL/fS2CMaQHMADoBW90fPwA0AyKAYGCNMWaTtXY90AsoM9HP8uVRjB8/Gn//AO67737q1Qtn0qRx+Pr60qhRY2bMmMWCBR+wePEizpw5w88/xzBy5Giuvro37733NgEBAbRp046kpCQmTx5PYGAQVatWZdasufz002ZeeulFAgIC2L9/H7fcchtjxowr7SbT6bLqHDx6mvmLY3j58U58umx/gelG3XE5HVtWx+EDc/+3m69XH+aDZ3qwc18izcIrUbG8H8P/vYYurWpSvXIQ00d25sEXfuAfQ1vTvoVr1erLlQf5fPkB3pvcnX5PfEtbU5XZ47rSYej/qBEaxPOPdGDhyoN0j6xFuUBfwsMq8uZ/dxVap5J0dj+t+ukYA3s1IDMrh+0/n8xN1yw8hLH3tMHH4SCkgj9TZv/IJpvA0hnXEb0rnoZ1gklITOfhf63mkVtb0qReCMMHteSTpfuY8kAkgf4+VA4O5LWPtvPt+iOl2OLftKjUjmNph1ka+18ebj6FFccWFpjutgaP0LxSW3wcDhYdns+6+KWMbzWTA6d3U69CY8r5VmDazjFEhHakUkBVHm3xHC/veJIhjR7HhLQGYPXxxaw6/jVjW73O2E1DaBp8OU9G/B8PrOlDaEA17m82ntVxS2hT5UoCfIKoWa4uXx56t9A6lYaC5tQHz/Rgwsxofv4lmb/+pTHVKwcx8787eXVUZ4Ir+BMU4MuL72yhXJAfLRtWZupjHXnilXVMHdGJW8Ys5av/68P67XGYBpVwOuGB53/g9JnM0m7qOXp3qs2arcfZH3sagJwcJ6Omraedqcrg3g2ZO+EqqlYKZOmGWKZ/uD23X5JOZ/CvRzsSUsEfHPDktPWkZWQXOCd6tq/F44MjOH0mk6SUDHbtT2L6h9vPOde8vXBPaXaFqx5ffkGPHr1o0qQpAL6+vsyZ8y5r1qxm7ty3uPHG64iLO07fvv2YMOFpevfuwWuvzSQ0tArDhg0lKSkRp9PJnDnvEhRUjhEjHiItLY0TJxIYO3YiN97Yn0WLFjJlykRCQioRGhpKREQrJkx4mtGjR7J69SoAbrvtdoYPf6w0u0LKiN8N5IBHgNlA3rXhAcCb1tos4KQx5j/AHcaYDkB74N/GmGx32iuNMauBmsA24HZrbYrHWlAEaWlprFy5DqfTyeWXG77/fhU1atTg6acn8O67b+Pv709SUhILFy5m79493HxzP+66ayh33jmUsLAw2rfvQPPmjfj++1XUqVOH116bxgsvPMt1193AwYMH2LhxC+np6TRsWLtMBHKDejfiw+/2se9IMhlZ2bRuWuWcNN3bhVGvRgUGjf2eAH8fPn3xalZtPgbAT3tO8OzczYwcEkG/q8KZ9d9dDB/UkhEvraFn+1rUq1mBgaOX4ufr4MN/9mL1luMkJmdQq2o5urUNIzY+lYjGoVzeOJQl634BXHf190xZQYNaFXlzbNcyEcid3U9hVcvx6ff7iUtMY8ueE7npmtarxD/n/cTug0n0uyqcW65uyCabQL2aFbhjYhSxCal89M9etGoSyoyPd9AsvBKvfbSDK1vVYM4XlnXb42hnqvLY4MvKTCDXM6w/y45+QWzqAbJyMmgcfNk5aVqHXkmNoNpM/ulv+DsCmNx2HltPrgMgJnk77/38MoMaPETnGn/hy0PvMCD8Pl7dOY62VbpSPag2Ezffg6/Dl0mtZ7M9cQOnM5OoEliTVlU6k5B+jEbBzWlYsSUbEqIAKO9bkRe2jSAsqB6jIl4uU4FcUeYUQP2wilQPDeKuScupWimIhrUrEhUdy459iUyYGU1mZk5u2orl/fly5UEmz07g5cc70aNdGAtXHSqpJhVZjdByHDqW/5R9Ji2LzKwcAvx9efD5H/DxcbDyrRuY/uH23DQP39KC7zYcYcHiGDpfXoNWTasQn5h2zpxYujGWife15ZYxS0lISuflxzsBFHqu2X0wqUTbf7bY2CM0bJh/1b1ixYoEBASQlpbGxx9/TnZ2Nk2bhjNhwtO5aV588TluuOFGhg17kGXLvmfDhvXUqFGTxx4bSffuPVizZjXPPDOJ66/vxxNPjGD58jXUrFmTu+8eAsCiRQvZv38fK1asJSsri169utKjRy8iIi4vyeZLGfS7gZy1djiAMSbv/ks9IO8Z5zDQylo7whhzK/CatfYz9xZrHaAnkA6sB24G3vNQ/YukWTMDQFxcHEePxjJkyCAA0tJSufrqPjRq1JjWrdsAULduPdLS0vIdHx8fT0hICHXq1AGga9duTJw4luuuu4GIiMvx8/PDz8+PcuXKlWCrChZSwZ8e7cKoWimQu/s2Ibi8P3f2bXJOOhNeiYjGoXzwTA8A/Hx9qFPD9VzLjn2u1agj8alUrxyU77gmdUPYsCMegKxsJ5t3J9C0XghL1v5Cj8hatDNVmfXZLrq2rkk7U5UxMzbSvW0YO/cluvM8Q2BA6W+/FdZPB4+ee49x9EQqwwe1JD0jmwrl/HJXTU4mZxCbkApAbPwZAv3ztyvuZBqP3NqSW69pCE7w8ysbj6RW8AumTZUrCQkI5S+1b6OcX0X61B50Trp6FRrTMLg541vNBMDP4Ue1oFoA7D/t2kZKSD9G5YCq+Y6rU74BNulHALKd2exN3kadCo3YmLCMNlWupFlIK7489C4RlTvRLKQVb+5+ltZVOrM/ZXdunv4+AZes/ReqKHPq14cO9hw6xftf7+WVJ67Az8+Hd746/wrSdve8iE04d/yUFb/EnSGiUWi+z+rWqECHy6qz52ASGVmu4DQ7Oydfmka1g/l46T4A1mw9DrietTt7TlQJCeR0aiYJSekAbNgZT/XKQYWea0o7kAsPr8/mzfm3x/ft28eqVSu47LIIAgMDAfDzy3953b3bcvfd9wLQs2cvwPWs3fPPP8vbb8/B4XCQmZlJXFwcISEh1KxZE4CuXa/i6NGj7Nq1ky5drsLhcODv70/Hjlewc+cOBXJS7B87+ADOPO8dQHYhaT+31p6x1mbjWpGrUcwyi83Hx9XMatWqUadOXT755Au+/TaK0aPH0b17TwAcjnOf//Lx8SEnJ4dq1apx6tQpYmNjAVi5cjlNmzYr9LjS1L97fT5euo+hk1dwzzMrufmppVzVJowqIYH50sX8kszabccZMiGKOyZGseiHQxxyBzFO57n55uQ48XE42Hv4VO5Wh5+vg3amGvtjk/l2/S/06xbO6dQslm+KpXfHOgT4+xKf6AqKnRSQaSkqrJ+cTidnPwo48b62TPvPNp6cvh57ICn3v7mzgI7KcYJ7uPH32yP4LGo/o6atZ82245SVodKlxnVEHf0fL2x9lBe3jWDij0NpFdqJEP/8F+sjZw6wIzGaZ7c8yHNbHmJt3LccT/vF/e25bXc6nTgcDn45sx8T4rox8nX40jSkFUdTD7Ixfjldql9LalYKP51cTftqPfD3CSApM6GwLMuEwsZKTo6TGqGuG52Ixq6+axZeiQpB/vztuVU8OX09k/7WFsA9rs4dAAWNobJm2cYjdGsXRnhYBcA178fd05qTp9ILPFf8au/hZFo1ca1cdmhZjafubFXgnEhISqNCOf/cc1TbZlXcxxd8riltffvewJIl3xATEwNAZmYmo0c/QbVq1c57PWjevAUbN24AYOXKFYwbN5rJkydwxx13MW/ee3Tv3hOn00mNGjVITk4mLi4OgHXr1uYe/+u2amZmJmvXrs7d3pU/t6JsrRbkIFA7z/vauFblCpL3oQ8nv928ljgfHx+mTp1G//7X43TmEBwcwpw573LoUMG/KGrXLpJ//ONJjGnB66+/xeDBN+Nw+BAaGspbb73N9u3bSrgFv2/QNY0YOW1d7vu0jGy+WXP4nF9SLt1whE4R1fnPcz0pH+THknW/kJKWVWi+G3fGM2fCVQyZEMUVl9Xg4xd6EeDnw1c/HGb7z65VhUB/X1ZvOcaplEyycnJYFh17aRrpAYX1U3aOkzv7NiHm8Knc775YcYBZ/+hKfGIaRxNSCQ0ufLUoISkNfz8fnrqzFV//cIiJ97UlbmAasfFnCA0OLPS4ktQz7CZet5Ny32fkpLM+fhk9w27Kl27TiRW0rNyOia3fJMi3PBvio0jLLvwh9l2nfuSpiGk8u+VBWlSKZHKbOfg5/Fkb913uCp6/TwDbEjeQkpVMjjOLH0+sujSN9KDCxsrRhFSevr8dsfGpHDvhWpndH5vMiNtaMqBnfTKzcnhlgWurcdOuBKY+1pFxb2wslTZcjNOpWTw5bT3/fLg9DoeDiuX8WLohlpjDp+jYsnqhx73x6U5eGN6Bm7rXByeMmbGBNk2rnDMnnE6Y/NYm5oy/iuQzmfj4wP7Y0yzbGFvouaY0hYSEMHv2Ozz88DBycnI4fTqZvn37YUwLVq0q/K8cPPXUWB544F4WLHgfh8PBzJlz2LBhHU88MYKaNcOoW7ceCQnx+Pj48Morr9G/f19CQiqRk5NDkyZN6dv3BlasiKJ7985kZGQwcOAg2rb1vl9Ai+c5inpHaIx5G9hmrZ1qjHkU6IPrWbmKwBrgQWvtcmPMd8Aca+2CvMecncfZ+UdHRzsjIyNJT/dAq7xcYJ7rfeMBH5VeRcqQmM9+2/pTn+Tvj9tXdCjFmpQd87ttyH2tMeJdc+bBm5sz93+7ycjK4aXHO7Fq81E+izrg8XLy9klZvtb861/P89hjTxAYGMjQoXdwzTV9uOOOuzxeTmAgREdHExkZWUb2C6Q4irsi9wbQGPgJCABmWWuXu7/7H/C8MabsPOQiIiJlVkpqFp+8eDVp6dkcPp7CV2XwRx8lKTg4mG7drqBcufLUr9+AW2+9rbSrJGVYkQM5a+3QPK+zgMcLSTcdmO5++05heYiIiAC89/Ve3vt6b2lXo8x46KHhPPTQ8NKuhniJsvEzOhERERG5YArkRERERLyUAjkRERERL6VATkRERMRLKZATERER8VIK5ERERES8lAI5ERERES+lQE5ERETESymQExEREfFSCuREREREvJQCOREREREvpUBORERExEspkBMRERHxUgrkRERERLyUAjkRERERL6VATkRERMRLKZATERER8VIK5ERERES8lAI5ERERES+lQE5ERETESymQExEREfFSCuREREREvJQCOREREREvpUBORERExEspkBMRERHxUgrkRERERLyUAjkRERERL+VwOp2lXQcAoqOjnZGRkaVdDRERkT+N6OhoIiMjHaVdDyk+v9KuQF7R0dGlXQURERERr1FmVuRERERE5MLoGTkRERERL6VATkRERMRLKZATERER8VJl6scOxWGMuQN4EnACZ4AR1tqNxph/AHfjauP7wGRrrTPPcX2Af1lr25yVnwN4G9hqrZ36O2XXA9YCra218e7POgCvABUAX+BFa+37nmhrUXiyPwrL6zxlVwZWAPf+ms4Y0wp4A6gI5ABjrbVfe7DJv8vDfTIceMidVwwwzFp7/DxlnzNG8nzXEIgG+pyvXz3N03PG/V1/4D1rbfDvlF3QGLkcWAPszZP0NmutvYhmFpmHx8dLwK3ACfdH1lp723nKLqg/ygH/BrrgOo+8Za39t6faWxQe7pPLgVeBSkA28IC1ttBftp3dJ+73UWcluxx40lr78kU3tog81SfGmLuAJ/JkXQmoC9S11h4rpOyCrjUtgTdxnVudwBhr7WIPNlm8hFevyBljDK4T3rXuSfIs8F9jTF9gEBAJRAA9cZ1cMcaUM8Y8C3zIWYGsMaYFsBS4pQhl34XrZFM7z2cO4FNgkrs+1wEvG2OaXmRTi8ST/VFYXucpuy+wDjBnffU+8G9rbWvgTuAjY0yAB5pbJB7uk0hgFHCltTYC2AM8c56yzxkjeb4LwtU3JdYX7nI9Omfc3zcFpgLn/RMG5xkjVwLzrbVt8vwrqSDO0/1xJTA4TzvOF8QV1h8vAlWA9kAH4BFjzBUX19Ki8/CcKQ8swRXItMU1Xz44T9nn9Im1NjHv2ADmAKtwBYclwpN9Yq19N09bOgBHgeHnCeIKO4+8Dsx153MvrnOr1y/OyIXz6kAOSAf+Zq2Ndb/fCIThmkjzrbUp1to0YB5whzvNX3Dd5d5dQH6PALOBj89XqDGmNtDfnVdegbjuxr4DsNYeBuJw3W2VBE/2R4F5nScIG+HOM/asz9sBX7hfNwYScd2VlxSP9Yl7FaGptTbJHYjVARIKKvQ8Y+RXM3Ct/MYX8v2l4tE5475Qv0/+FYbCFDZGrgRaGGM2GWPWG2NuvsA2XQyP9YcxJhBoCzxljNlqjPnUGBN+nrLP6Q/3zeCdwERrbba1NglXcLDrItt5ITw5RvoAMdbaRe73/8MV+BSmsDECgDGmCTAeuNNam3lBrbo4nr7W/Go0cNxaO6ugL3/nPOILhLpfBwNpRW+O/JF4dfRurd0P7IfcE+DLuE4UtYC8S8yHcQdT1trPgc+NMT0KyG+4O68+v1PuEeBmd9q8n6fhulvE/d39uCbY2gtrWfF4sj8Ky8tam1FI2de60579eZYxxmGMiQEaAI9Za0sskLsEYyTTvY04G9fJfWIh5RY4Rtzv/wb4W2vfMsaMK3bjisHT/QHMcv/bUoSyCxwjQAqwwJ1PM2C5MeZgSWw3e7g/agPf4wo0tuNavf3CGNMu71ZbnrIL6o/quM4Z1xhjZgOVgXnW2mkX0cwL4uE+aQYcNcbMAVrjupF76jxlFzZGfvUc8Kq19uAFNOmiXYJ5gzGmGjAS12peYeUWeh7BtfDwvTHm70ANXCvBWRfUMPlD8PYVOQCMMRWAj4AmwN9wtSvvidNBya4CYYwZA0wG+llrU0u4bI/1RwF5XTBrrdNa29idxxhjTK/i5HMxPNkn1trPrbXVgKeBxcaYIs8jY0w74EH3v1Ljif4wxjwMZFlr515MXay1D1trX3evQO3EtRXV72LyvFCe6A9r7T5rbV9r7TZ34DYV1yp0gwuoij+ulZbGQC9cKzEPum8eSpSH5ow/0Bd401rbHtd26CL36uWF1qcerv4osaC2gDp48lpzP/CFtfbnYtQjCNc8GWqtrQt0A2a5+0j+ZLw+kHNvXazGNXl6WmsTgYPkf56gNq47peKWsTnPv/a/kzbQGLMA+CvQ2Vr7U3HLLQ5P9kcheWGMWZSnP248z/EBxpjBvwY61tp9wHe4tp9KjKf6xBjTxBjTNc9Hc4H6QOgFjJG7gBBgtTFms7vcD87Xj57mwTEyFOjgbscioJy7/bUvYIz4GmPGGWPy/kjCAZTYtpkHx0crY8ydZ33sADKL2h+4HsXIBN611ua4n5taCHS+sFZdHA+OkSPATmvtOgBr7Re4AtVGF9Anv7oF+Mxam3yBzfGIS3CtuQ3XVmzeMop6HokAyltrFwJYa9fiWgXuVMSy5Q/Eq7dW3Sf/KOAda+3kPF99AUwyxrwJZOG64Lxd3HJsAb/SO4/3gfK4HohPKW6ZxeHJ/jhPXlhr+xalPtbaDON62NcHmO9+3qMn8FpRjvcED4+RWsACY0wb6/rl2BBgm7U2ASjSGLHWPg48nqd++4EhJbGN6C7PY/1hre2YJ98GuPri134o6hjJdl/E04CXjDH1gYG4VqMuOQ+PjxxgujFmlfum5SFgi/tZ2QuZM1/ieq5qlDGmItAb18P1JcLDffI1rv+ukdbaaGNMN1wrWPuKeh7JozvwyQUe4xGevtYYY0Jxreqtzvv5BVxr9gKVjDFXWmtXG2MaAy2BH4t4vPyBeHUgBwzHtSIywBgzIM/nV+P6heV6XL8K/AJ491JXxhjTGddd427ghzzPNIy2JfOzcE/2R6F5uQOXohoAzDDGPIXrQvdkSQUtbh7rE2vtSmPMc0CUMSYL12pDiW95XaQyNWfchgAzjTFDca3WPO7eYi0Jnhwf24wxjwJfGmN8ca3M/LUYdRoGTDPG7MDVH/OttSUZwHiyT466t4Vfd29LpgM3u58nvlBNcT+nVgo8PW+aALHF/cGGtTbRXY9p7m3WLOB+a21McfIT76b/16qIiIiIl/L6Z+RERERE/qwUyImIiIh4KQVyIiIiIl5KgZyIiIiIl1IgJyIiIuKlFMiJiIiIeCkFciIiIiJe6v8BjpTCrIsEt2gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56cd1e9a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create grid for annual change in ranks for inflows\n",
    "inmetro_rankplot=rank_grid(inmetro_rchange,inmetro_labels)\n",
    "inmet_fig=inmetro_rankplot.get_figure()\n",
    "inmet_fig.savefig(os.path.join(out_path, 'images','nyma_inflow_grid.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create arrays for outflows\n",
    "outmetro_geogs, outmetro_flows, outmetro_labels = create_arrays(grouped_by_metro,'outflow',10,'metros',True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create array for annual change in ranks\n",
    "outmetro_rchange=rank_change(outmetro_geogs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56ca08aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create grid for annual change in ranks for outflows\n",
    "outmetro_rankplot=rank_grid(outmetro_rchange,outmetro_labels)\n",
    "outmet_fig=outmetro_rankplot.get_figure()\n",
    "outmet_fig.savefig(os.path.join(out_path, 'images','nyma_outflow_grid.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create arrays for netflows\n",
    "netmetro_geogs, netmetro_flows, netmetro_labels = create_arrays(grouped_by_metro,'netflow',10,'metros',False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create array for annual change in ranks\n",
    "netmetro_rchange=rank_change(netmetro_geogs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56cd6ef128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create grid for annual change in ranks for netflows\n",
    "netmetro_rankplot=rank_grid(netmetro_rchange,netmetro_labels)\n",
    "netmet_fig=netmetro_rankplot.get_figure()\n",
    "netmet_fig.savefig(os.path.join(out_path, 'images','extras','nyma_netflow_grid.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Net Totals Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lr}\n",
      "\\toprule\n",
      "{} &  tot\\_netflow \\\\\n",
      "co\\_name                 &              \\\\\n",
      "\\midrule\n",
      "Nassau County, NY       &      -89,045 \\\\\n",
      "Westchester County, NY  &      -57,700 \\\\\n",
      "Hudson County, NJ       &      -28,370 \\\\\n",
      "Bergen County, NJ       &      -25,468 \\\\\n",
      "Essex County, NJ        &      -23,603 \\\\\n",
      "Suffolk County, NY      &      -21,606 \\\\\n",
      "Los Angeles County, CA  &      -21,581 \\\\\n",
      "Fairfield County, CT    &      -17,773 \\\\\n",
      "Orange County, NY       &      -16,047 \\\\\n",
      "Middlesex County, NJ    &      -13,907 \\\\\n",
      "Union County, NJ        &      -12,318 \\\\\n",
      "Monmouth County, NJ     &      -12,207 \\\\\n",
      "Rockland County, NY     &      -11,638 \\\\\n",
      "Broward County, FL      &      -10,643 \\\\\n",
      "Orange County, FL       &      -10,369 \\\\\n",
      "Palm Beach County, FL   &       -9,601 \\\\\n",
      "Philadelphia County, PA &       -9,445 \\\\\n",
      "Ocean County, NJ        &       -8,923 \\\\\n",
      "Travis County, TX       &       -7,548 \\\\\n",
      "Harris County, TX       &       -7,309 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "domestic_city_flows.sort_values(by='tot_netflow',inplace=True)\n",
    "citytop20net=domestic_city_flows[domestic_city_flows.index != '59000'][['co_name','tot_netflow']].head(n=20)\n",
    "citytop20net.set_index('co_name',inplace=True)\n",
    "print(citytop20net.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lr}\n",
      "\\toprule\n",
      "{} &  tot\\_netflow \\\\\n",
      "co\\_name                  &              \\\\\n",
      "\\midrule\n",
      "District of Columbia, DC &        2,922 \\\\\n",
      "Suffolk County, MA       &        1,153 \\\\\n",
      "Middlesex County, MA     &          604 \\\\\n",
      "Tompkins County, NY      &          369 \\\\\n",
      "Arlington County, VA     &          278 \\\\\n",
      "Utah County, UT          &          267 \\\\\n",
      "Leon County, FL          &          248 \\\\\n",
      "Champaign County, IL     &          202 \\\\\n",
      "Centre County, PA        &          175 \\\\\n",
      "Washtenaw County, MI     &          173 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "citybot10net=domestic_city_flows[['co_name','tot_netflow']].tail(n=10)\n",
    "citybot10net.set_index('co_name',inplace=True)\n",
    "citybot10net.sort_values(by='tot_netflow', ascending=False, inplace=True)\n",
    "print(citybot10net.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lr}\n",
      "\\toprule\n",
      "{} &  tot\\_netflow \\\\\n",
      "mname                                         &              \\\\\n",
      "\\midrule\n",
      "Miami-Fort Lauderdale-Pompano Beach, FL       &      -69,067 \\\\\n",
      "Philadelphia-Camden-Wilmington, PA-NJ-DE-MD   &      -47,862 \\\\\n",
      "Orlando-Kissimmee-Sanford, FL                 &      -43,301 \\\\\n",
      "Bridgeport-Stamford-Norwalk, CT               &      -38,491 \\\\\n",
      "Atlanta-Sandy Springs-Alpharetta, GA          &      -36,886 \\\\\n",
      "Los Angeles-Long Beach-Anaheim, CA            &      -35,285 \\\\\n",
      "Poughkeepsie-Newburgh-Middletown, NY          &      -34,838 \\\\\n",
      "Tampa-St. Petersburg-Clearwater, FL           &      -32,616 \\\\\n",
      "Allentown-Bethlehem-Easton, PA-NJ             &      -28,219 \\\\\n",
      "Charlotte-Concord-Gastonia, NC-SC             &      -25,914 \\\\\n",
      "Dallas-Fort Worth-Arlington, TX               &      -23,785 \\\\\n",
      "Houston-The Woodlands-Sugar Land, TX          &      -21,431 \\\\\n",
      "Raleigh-Cary, NC                              &      -19,017 \\\\\n",
      "San Francisco-Oakland-Berkeley, CA            &      -18,842 \\\\\n",
      "Washington-Arlington-Alexandria, DC-VA-MD-WV  &      -17,818 \\\\\n",
      "Austin-Round Rock-Georgetown, TX              &      -15,441 \\\\\n",
      "Myrtle Beach-Conway-North Myrtle Beach, SC-NC &      -12,503 \\\\\n",
      "East Stroudsburg, PA                          &      -12,068 \\\\\n",
      "North Port-Sarasota-Bradenton, FL             &      -12,006 \\\\\n",
      "Cape Coral-Fort Myers, FL                     &      -11,650 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "grouped_by_metro.sort_values(by='tot_netflow',inplace=True)\n",
    "metrotop20net=grouped_by_metro[grouped_by_metro.index != '59'][['mname','tot_netflow']].head(n=20)\n",
    "metrotop20net.set_index('mname',inplace=True)\n",
    "print(metrotop20net.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lr}\n",
      "\\toprule\n",
      "{} &  tot\\_netflow \\\\\n",
      "mname                                    &              \\\\\n",
      "\\midrule\n",
      "Ithaca, NY                               &          630 \\\\\n",
      "Tallahassee, FL                          &          374 \\\\\n",
      "State College, PA                        &          324 \\\\\n",
      "Provo-Orem, UT                           &          267 \\\\\n",
      "Madison, WI                              &          206 \\\\\n",
      "Champaign-Urbana, IL                     &          199 \\\\\n",
      "Ann Arbor, MI                            &          195 \\\\\n",
      "Little Rock-North Little Rock-Conway, AR &          153 \\\\\n",
      "Bloomington, IN                          &          146 \\\\\n",
      "Charlottesville, VA                      &          144 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "metrobot10net=grouped_by_metro[['mname','tot_netflow']].tail(n=10)\n",
    "metrobot10net.set_index('mname',inplace=True)\n",
    "metrobot10net.sort_values(by='tot_netflow', ascending=False, inplace=True)\n",
    "print(metrobot10net.to_latex())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flows between NYC, suburban counties, and outside areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to label counties in metro as 'suburban' and others as 'distant'\n",
    "def is_suburban(col, metro):\n",
    "    if col in metro:\n",
    "        return 'suburban'\n",
    "    else:\n",
    "        return 'distant'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distant    460\n",
       "suburban    18\n",
       "Name: cnty_status, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domestic_city_flows['cnty_status']=domestic_city_flows.apply(lambda row: 'suburban' if row.name in nyma else 'distant', axis=1)\n",
    "domestic_city_flows['cnty_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>inflow2011_12</th>\n",
       "      <th>inflow2012_13</th>\n",
       "      <th>inflow2013_14</th>\n",
       "      <th>inflow2014_15</th>\n",
       "      <th>inflow2015_16</th>\n",
       "      <th>inflow2016_17</th>\n",
       "      <th>inflow2017_18</th>\n",
       "      <th>outflow2011_12</th>\n",
       "      <th>outflow2012_13</th>\n",
       "      <th>outflow2013_14</th>\n",
       "      <th>outflow2014_15</th>\n",
       "      <th>outflow2015_16</th>\n",
       "      <th>outflow2016_17</th>\n",
       "      <th>outflow2017_18</th>\n",
       "      <th>tot_inflow</th>\n",
       "      <th>tot_outflow</th>\n",
       "      <th>tot_netflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnty_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>distant</th>\n",
       "      <td>117,478</td>\n",
       "      <td>115,168</td>\n",
       "      <td>105,707</td>\n",
       "      <td>91,814</td>\n",
       "      <td>100,791</td>\n",
       "      <td>118,956</td>\n",
       "      <td>106,935</td>\n",
       "      <td>155,147</td>\n",
       "      <td>161,670</td>\n",
       "      <td>154,740</td>\n",
       "      <td>126,574</td>\n",
       "      <td>166,241</td>\n",
       "      <td>220,222</td>\n",
       "      <td>176,213</td>\n",
       "      <td>756,849</td>\n",
       "      <td>1,160,807</td>\n",
       "      <td>-403,958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suburban</th>\n",
       "      <td>62,981</td>\n",
       "      <td>58,645</td>\n",
       "      <td>56,045</td>\n",
       "      <td>48,583</td>\n",
       "      <td>51,640</td>\n",
       "      <td>59,697</td>\n",
       "      <td>56,078</td>\n",
       "      <td>90,739</td>\n",
       "      <td>95,386</td>\n",
       "      <td>92,667</td>\n",
       "      <td>75,690</td>\n",
       "      <td>99,650</td>\n",
       "      <td>140,915</td>\n",
       "      <td>118,002</td>\n",
       "      <td>393,669</td>\n",
       "      <td>713,049</td>\n",
       "      <td>-319,380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             inflow2011_12  inflow2012_13  inflow2013_14  inflow2014_15  \\\n",
       "cnty_status                                                               \n",
       "distant            117,478        115,168        105,707         91,814   \n",
       "suburban            62,981         58,645         56,045         48,583   \n",
       "\n",
       "             inflow2015_16  inflow2016_17  inflow2017_18  outflow2011_12  \\\n",
       "cnty_status                                                                \n",
       "distant            100,791        118,956        106,935         155,147   \n",
       "suburban            51,640         59,697         56,078          90,739   \n",
       "\n",
       "             outflow2012_13  outflow2013_14  outflow2014_15  outflow2015_16  \\\n",
       "cnty_status                                                                   \n",
       "distant             161,670         154,740         126,574         166,241   \n",
       "suburban             95,386          92,667          75,690          99,650   \n",
       "\n",
       "             outflow2016_17  outflow2017_18  tot_inflow  tot_outflow  \\\n",
       "cnty_status                                                            \n",
       "distant             220,222         176,213     756,849    1,160,807   \n",
       "suburban            140,915         118,002     393,669      713,049   \n",
       "\n",
       "             tot_netflow  \n",
       "cnty_status               \n",
       "distant         -403,958  \n",
       "suburban        -319,380  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incols=[c for c in domestic_city_flows.columns if c.startswith('inflow')]\n",
    "outcols=[c for c in domestic_city_flows.columns if c.startswith('outflow')]\n",
    "all_cols=['cnty_status']+incols+outcols\n",
    "by_status=domestic_city_flows[all_cols].groupby('cnty_status').sum()\n",
    "by_status['tot_inflow']=by_status[incols].sum(axis=1)\n",
    "by_status['tot_outflow']=by_status[outcols].sum(axis=1)\n",
    "by_status['tot_netflow']=by_status['tot_inflow']-by_status['tot_outflow']\n",
    "by_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56cc145cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inflows and outflows to NYC from suburban counties accounted for more than third of all domestic NYC flows \n",
    "ax=by_status[['tot_inflow','tot_outflow']].plot.pie(subplots=True, legend=False, autopct='%1.1f%%',figsize=(8,4),colormap='Paired')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f56cc472cf8>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56cc143828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inflows to NYC by year\n",
    "by_status[incols].T.plot(kind='bar', rot=30, colormap='Paired')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f56cc0502e8>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56cc2fd860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outflows from NYC by year\n",
    "by_status[outcols].T.plot(kind='bar', rot=30, colormap='Paired')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with other big cities and metros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare cumulative flows for selected big (population over 1M ) cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# empty list to hold the records of the dataframe we'll build from querying the database\n",
    "df_lines=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get cumulative total domestic inflows and outflows for big cities\n",
    "for cnty in big_cities.keys():\n",
    "    \n",
    "    # exclude NYC: it consists of multiple counties and we already have the data\n",
    "    if cnty not in nyc:        \n",
    "        # yearly outflows\n",
    "        all_outs=[]        \n",
    "        # yearly inflows\n",
    "        all_ins=[]        \n",
    "        for year in years:\n",
    "            table1 = 'outflow_{}'.format (year)\n",
    "            table2 = 'inflow_{}'.format (year)\n",
    "\n",
    "            cur.execute(\"SELECT SUM (exemptions) as outflow{0} from {1} where {2}.origin='{3}' and {4}.destination!='{5}' and {6}.destination not in {7} \".format (year, table1,  table1, cnty, table1, cnty, table1, foreign))    \n",
    "            outs=cur.fetchone()[0]\n",
    "\n",
    "            cur.execute(\"SELECT SUM (exemptions) as inflow{0} from {1} where {2}.origin!='{3}' and {4}.destination='{5}' and {6}.origin not in {7} \".format (year, table2, table2, cnty, table2, cnty, table2, foreign))    \n",
    "            ins=cur.fetchone()[0]\n",
    "            \n",
    "            all_outs.append(outs)\n",
    "            all_ins.append(ins)\n",
    "        # sum yearly data\n",
    "        total_out=sum(all_outs)\n",
    "        total_in=sum(all_ins)\n",
    "        df_line=(cnty,total_in,total_out)\n",
    "        df_lines.append(df_line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dataframe from the list \n",
    "df_cities=pd.DataFrame(df_lines, columns=['county','tot_inflow','tot_outflow'])\n",
    "df_cities['tot_netflow']=df_cities['tot_inflow']-df_cities['tot_outflow']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add city names column\n",
    "city_names=pd.DataFrame.from_dict(big_cities, orient='index')\n",
    "df_cities=df_cities.merge(city_names, left_on='county', right_index=True, how='left').rename(columns={0:'city_name'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# append NYC records\n",
    "nyc_tot=pd.DataFrame(domestic_city_flows[['tot_inflow','tot_outflow','tot_netflow']].sum()).T\n",
    "nyc_tot['city_name']='New York City, NY'\n",
    "df_cities=df_cities.append(nyc_tot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city_name</th>\n",
       "      <th>tot_inflow</th>\n",
       "      <th>tot_outflow</th>\n",
       "      <th>tot_netflow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maricopa Co, AZ (Phoenix)</td>\n",
       "      <td>1,057,753</td>\n",
       "      <td>876,468</td>\n",
       "      <td>181,285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bexar Co, TX (San Antonio)</td>\n",
       "      <td>554,313</td>\n",
       "      <td>495,341</td>\n",
       "      <td>58,972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harris Co, TX (Houston)</td>\n",
       "      <td>1,140,861</td>\n",
       "      <td>1,204,817</td>\n",
       "      <td>-63,956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dallas Co, TX</td>\n",
       "      <td>849,630</td>\n",
       "      <td>916,209</td>\n",
       "      <td>-66,579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>San Diego Co, CA</td>\n",
       "      <td>795,260</td>\n",
       "      <td>863,590</td>\n",
       "      <td>-68,330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Philadelphia Co, PA</td>\n",
       "      <td>317,213</td>\n",
       "      <td>406,107</td>\n",
       "      <td>-88,894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Santa Clara Co, CA (San Jose)</td>\n",
       "      <td>450,721</td>\n",
       "      <td>568,066</td>\n",
       "      <td>-117,345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cook Co, IL (Chicago)</td>\n",
       "      <td>874,722</td>\n",
       "      <td>1,224,927</td>\n",
       "      <td>-350,205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Los Angeles Co, CA</td>\n",
       "      <td>1,381,584</td>\n",
       "      <td>1,828,981</td>\n",
       "      <td>-447,397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York City, NY</td>\n",
       "      <td>1,150,518</td>\n",
       "      <td>1,873,856</td>\n",
       "      <td>-723,338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       city_name  tot_inflow  tot_outflow  tot_netflow\n",
       "3      Maricopa Co, AZ (Phoenix)   1,057,753      876,468      181,285\n",
       "5     Bexar Co, TX (San Antonio)     554,313      495,341       58,972\n",
       "2        Harris Co, TX (Houston)   1,140,861    1,204,817      -63,956\n",
       "7                  Dallas Co, TX     849,630      916,209      -66,579\n",
       "6               San Diego Co, CA     795,260      863,590      -68,330\n",
       "4            Philadelphia Co, PA     317,213      406,107      -88,894\n",
       "8  Santa Clara Co, CA (San Jose)     450,721      568,066     -117,345\n",
       "1          Cook Co, IL (Chicago)     874,722    1,224,927     -350,205\n",
       "0             Los Angeles Co, CA   1,381,584    1,828,981     -447,397\n",
       "0              New York City, NY   1,150,518    1,873,856     -723,338"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reorder columns; remove county column\n",
    "df_cities=df_cities[['city_name','tot_inflow','tot_outflow','tot_netflow']]\n",
    "df_cities.sort_values(by='tot_netflow',ascending=False, inplace=True)\n",
    "df_cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56cc40bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot domestic migration for 10 big cities\n",
    "axbc=df_cities[['city_name','tot_inflow','tot_outflow']].set_index('city_name').plot(kind='barh', figsize=(8,5), color=wccolors)\n",
    "axbc.legend(['In Flows','Out Flows'], fontsize=12, frameon=False)\n",
    "axbc.invert_yaxis()              \n",
    "axbc.get_xaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}\".format(int(x))))\n",
    "axbc.set_ylabel('')\n",
    "# ticks labels size\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.show()\n",
    "big_c_fig=axbc.get_figure()\n",
    "big_c_fig.savefig(os.path.join(out_path, 'images','extras','big_cities_flows.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare cumulative flows for selected largest metros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdf_lines=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101 seconds passed\n"
     ]
    }
   ],
   "source": [
    "# running this cell takes awhile\n",
    "import time\n",
    "start = time.time()\n",
    "\n",
    "for m in big_metros:\n",
    "    all_outs=[]\n",
    "    all_ins=[]\n",
    "    for year in years:\n",
    "        \n",
    "        table1 = 'outflow_{}'.format (year)\n",
    "        table2 = 'inflow_{}'.format (year)\n",
    "        \n",
    "        outflows=pd.read_sql_query (\"SELECT * from {} where {}.origin!={}.destination\".format (table1, table1, table1), con)\n",
    "        \n",
    "        outflows=outflows.merge(metros[['mcode', 'mname', 'ccode']], left_on='origin', right_on='ccode',\n",
    "                                   how='left').drop ('ccode', 1).rename(columns={'mname':'mname_orig',\n",
    "                                                                                'mcode':'mcode_orig'})\n",
    "        outflows=outflows.merge(metros[['mcode', 'mname', 'ccode']], left_on='destination', right_on='ccode',\n",
    "                                   how='left').drop ('ccode', 1).rename(columns={'mname':'mname_dest',\n",
    "                                                                                'mcode':'mcode_dest'})\n",
    "        \n",
    "        inflows=pd.read_sql_query (\"SELECT * from {} where {}.origin!={}.destination\".format (table2, table2, table2), con)\n",
    "        \n",
    "        inflows=inflows.merge(metros[['mcode', 'mname', 'ccode']], left_on='origin', right_on='ccode',\n",
    "                                   how='left').drop ('ccode', 1).rename(columns={'mname':'mname_orig',\n",
    "                                                                                'mcode':'mcode_orig'})\n",
    "        inflows=inflows.merge(metros[['mcode', 'mname', 'ccode']], left_on='destination', right_on='ccode',\n",
    "                                   how='left').drop ('ccode', 1).rename(columns={'mname':'mname_dest',\n",
    "                                                                                'mcode':'mcode_dest'})\n",
    "        \n",
    "        # select & sum flows from counties that are not in the same metro and are not foreign\n",
    "        tot_out=outflows.loc[(outflows['mcode_orig']==m) & (outflows['mcode_dest']!=m) & (~outflows['destination'].isin(foreign)), 'exemptions'].sum()\n",
    "        \n",
    "        # select & sum flows to metro from counties that are not in the same metro and are not foreign\n",
    "        tot_in=inflows.loc[(inflows['mcode_orig']!=m) & (inflows['mcode_dest']==m) & (~inflows['origin'].isin(foreign)), 'exemptions'].sum()\n",
    "        \n",
    "        all_outs.append(tot_out)\n",
    "        all_ins.append(tot_in)\n",
    "        \n",
    "    total_out=sum(all_outs)\n",
    "    total_in=sum(all_ins)\n",
    "    df_line=(m,total_in,total_out)\n",
    "    mdf_lines.append(df_line)\n",
    "    \n",
    "end = time.time()\n",
    "print(int(end - start), 'seconds passed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('35620', 1592814.0, 2601002.0),\n",
       " ('31080', 1646413.0, 2165662.0),\n",
       " ('16980', 981499.0, 1390289.0),\n",
       " ('19100', 1517908.0, 1228907.0),\n",
       " ('26420', 1226126.0, 1031959.0),\n",
       " ('47900', 1298600.0, 1459732.0),\n",
       " ('33100', 1023229.0, 1173300.0),\n",
       " ('37980', 761484.0, 860254.0),\n",
       " ('12060', 1376434.0, 1219202.0),\n",
       " ('38060', 1079198.0, 852295.0),\n",
       " ('14460', 709090.0, 826530.0),\n",
       " ('41860', 965639.0, 1032336.0),\n",
       " ('40140', 1142343.0, 1025256.0),\n",
       " ('19820', 518550.0, 587585.0)]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf_lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mcode</th>\n",
       "      <th>tot_inflow</th>\n",
       "      <th>tot_outflow</th>\n",
       "      <th>tot_netflow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35620</td>\n",
       "      <td>1,592,814</td>\n",
       "      <td>2,601,002</td>\n",
       "      <td>-1,008,188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31080</td>\n",
       "      <td>1,646,413</td>\n",
       "      <td>2,165,662</td>\n",
       "      <td>-519,249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16980</td>\n",
       "      <td>981,499</td>\n",
       "      <td>1,390,289</td>\n",
       "      <td>-408,790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19100</td>\n",
       "      <td>1,517,908</td>\n",
       "      <td>1,228,907</td>\n",
       "      <td>289,001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26420</td>\n",
       "      <td>1,226,126</td>\n",
       "      <td>1,031,959</td>\n",
       "      <td>194,167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47900</td>\n",
       "      <td>1,298,600</td>\n",
       "      <td>1,459,732</td>\n",
       "      <td>-161,132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33100</td>\n",
       "      <td>1,023,229</td>\n",
       "      <td>1,173,300</td>\n",
       "      <td>-150,071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37980</td>\n",
       "      <td>761,484</td>\n",
       "      <td>860,254</td>\n",
       "      <td>-98,770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12060</td>\n",
       "      <td>1,376,434</td>\n",
       "      <td>1,219,202</td>\n",
       "      <td>157,232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38060</td>\n",
       "      <td>1,079,198</td>\n",
       "      <td>852,295</td>\n",
       "      <td>226,903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14460</td>\n",
       "      <td>709,090</td>\n",
       "      <td>826,530</td>\n",
       "      <td>-117,440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>41860</td>\n",
       "      <td>965,639</td>\n",
       "      <td>1,032,336</td>\n",
       "      <td>-66,697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>40140</td>\n",
       "      <td>1,142,343</td>\n",
       "      <td>1,025,256</td>\n",
       "      <td>117,087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19820</td>\n",
       "      <td>518,550</td>\n",
       "      <td>587,585</td>\n",
       "      <td>-69,035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mcode  tot_inflow  tot_outflow  tot_netflow\n",
       "0   35620   1,592,814    2,601,002   -1,008,188\n",
       "1   31080   1,646,413    2,165,662     -519,249\n",
       "2   16980     981,499    1,390,289     -408,790\n",
       "3   19100   1,517,908    1,228,907      289,001\n",
       "4   26420   1,226,126    1,031,959      194,167\n",
       "5   47900   1,298,600    1,459,732     -161,132\n",
       "6   33100   1,023,229    1,173,300     -150,071\n",
       "7   37980     761,484      860,254      -98,770\n",
       "8   12060   1,376,434    1,219,202      157,232\n",
       "9   38060   1,079,198      852,295      226,903\n",
       "10  14460     709,090      826,530     -117,440\n",
       "11  41860     965,639    1,032,336      -66,697\n",
       "12  40140   1,142,343    1,025,256      117,087\n",
       "13  19820     518,550      587,585      -69,035"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metros=pd.DataFrame(mdf_lines, columns=['mcode','tot_inflow','tot_outflow'])\n",
    "df_metros['tot_netflow']=df_metros['tot_inflow']-df_metros['tot_outflow']\n",
    "df_metros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mcode</th>\n",
       "      <th>tot_inflow</th>\n",
       "      <th>tot_outflow</th>\n",
       "      <th>tot_netflow</th>\n",
       "      <th>mname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19100</td>\n",
       "      <td>1,517,908</td>\n",
       "      <td>1,228,907</td>\n",
       "      <td>289,001</td>\n",
       "      <td>Dallas-Fort Worth-Arlington, TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38060</td>\n",
       "      <td>1,079,198</td>\n",
       "      <td>852,295</td>\n",
       "      <td>226,903</td>\n",
       "      <td>Phoenix-Mesa-Chandler, AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26420</td>\n",
       "      <td>1,226,126</td>\n",
       "      <td>1,031,959</td>\n",
       "      <td>194,167</td>\n",
       "      <td>Houston-The Woodlands-Sugar Land, TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12060</td>\n",
       "      <td>1,376,434</td>\n",
       "      <td>1,219,202</td>\n",
       "      <td>157,232</td>\n",
       "      <td>Atlanta-Sandy Springs-Alpharetta, GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>40140</td>\n",
       "      <td>1,142,343</td>\n",
       "      <td>1,025,256</td>\n",
       "      <td>117,087</td>\n",
       "      <td>Riverside-San Bernardino-Ontario, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>41860</td>\n",
       "      <td>965,639</td>\n",
       "      <td>1,032,336</td>\n",
       "      <td>-66,697</td>\n",
       "      <td>San Francisco-Oakland-Berkeley, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19820</td>\n",
       "      <td>518,550</td>\n",
       "      <td>587,585</td>\n",
       "      <td>-69,035</td>\n",
       "      <td>Detroit-Warren-Dearborn, MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37980</td>\n",
       "      <td>761,484</td>\n",
       "      <td>860,254</td>\n",
       "      <td>-98,770</td>\n",
       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14460</td>\n",
       "      <td>709,090</td>\n",
       "      <td>826,530</td>\n",
       "      <td>-117,440</td>\n",
       "      <td>Boston-Cambridge-Newton, MA-NH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33100</td>\n",
       "      <td>1,023,229</td>\n",
       "      <td>1,173,300</td>\n",
       "      <td>-150,071</td>\n",
       "      <td>Miami-Fort Lauderdale-Pompano Beach, FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47900</td>\n",
       "      <td>1,298,600</td>\n",
       "      <td>1,459,732</td>\n",
       "      <td>-161,132</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16980</td>\n",
       "      <td>981,499</td>\n",
       "      <td>1,390,289</td>\n",
       "      <td>-408,790</td>\n",
       "      <td>Chicago-Naperville-Elgin, IL-IN-WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31080</td>\n",
       "      <td>1,646,413</td>\n",
       "      <td>2,165,662</td>\n",
       "      <td>-519,249</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35620</td>\n",
       "      <td>1,592,814</td>\n",
       "      <td>2,601,002</td>\n",
       "      <td>-1,008,188</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mcode  tot_inflow  tot_outflow  tot_netflow  \\\n",
       "3   19100   1,517,908    1,228,907      289,001   \n",
       "9   38060   1,079,198      852,295      226,903   \n",
       "4   26420   1,226,126    1,031,959      194,167   \n",
       "8   12060   1,376,434    1,219,202      157,232   \n",
       "12  40140   1,142,343    1,025,256      117,087   \n",
       "11  41860     965,639    1,032,336      -66,697   \n",
       "13  19820     518,550      587,585      -69,035   \n",
       "7   37980     761,484      860,254      -98,770   \n",
       "10  14460     709,090      826,530     -117,440   \n",
       "6   33100   1,023,229    1,173,300     -150,071   \n",
       "5   47900   1,298,600    1,459,732     -161,132   \n",
       "2   16980     981,499    1,390,289     -408,790   \n",
       "1   31080   1,646,413    2,165,662     -519,249   \n",
       "0   35620   1,592,814    2,601,002   -1,008,188   \n",
       "\n",
       "                                           mname  \n",
       "3                Dallas-Fort Worth-Arlington, TX  \n",
       "9                      Phoenix-Mesa-Chandler, AZ  \n",
       "4           Houston-The Woodlands-Sugar Land, TX  \n",
       "8           Atlanta-Sandy Springs-Alpharetta, GA  \n",
       "12          Riverside-San Bernardino-Ontario, CA  \n",
       "11            San Francisco-Oakland-Berkeley, CA  \n",
       "13                   Detroit-Warren-Dearborn, MI  \n",
       "7    Philadelphia-Camden-Wilmington, PA-NJ-DE-MD  \n",
       "10                Boston-Cambridge-Newton, MA-NH  \n",
       "6        Miami-Fort Lauderdale-Pompano Beach, FL  \n",
       "5   Washington-Arlington-Alexandria, DC-VA-MD-WV  \n",
       "2             Chicago-Naperville-Elgin, IL-IN-WI  \n",
       "1             Los Angeles-Long Beach-Anaheim, CA  \n",
       "0          New York-Newark-Jersey City, NY-NJ-PA  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add metro names to cbsa codes and merge\n",
    "metro_names=metros[['mname','mcode']].drop_duplicates()\n",
    "df_metros=df_metros.merge(metro_names, on='mcode')\n",
    "df_metros.sort_values(by='tot_netflow', ascending=False, inplace=True)\n",
    "df_metros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56ca184b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot domestic migration for big metros\n",
    "axbm=df_metros[['mname','tot_inflow','tot_outflow']].set_index('mname').plot(kind='barh', figsize=(8,6), color=wccolors)\n",
    "axbm.legend(['In Flows','Out Flows'], fontsize=12, frameon=False)\n",
    "axbm.get_xaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}\".format(int(x))))\n",
    "axbm.invert_yaxis() \n",
    "axbm.set_ylabel('')\n",
    "# ticks labels size\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.show()\n",
    "big_m_fig=axbm.get_figure()\n",
    "big_m_fig.savefig(os.path.join(out_path, 'images','extras','big_metros_flows.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enter info for other counties / metros to check out their stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "other_county=['06037'] # LA county\n",
    "other_metro=['06037','06059'] #LA metro\n",
    "\n",
    "#other_county=['17031'] # Cook county IL\n",
    "#other_metro=['17031','17037','17043','17063','17089','17093','17097','17111','17197',\n",
    "#             '18073','18089','18111','18127','55059'] #Chicago metro\n",
    "\n",
    "#other_county=['42101'] #Philly county\n",
    "#other_metro=['10003','24015','34005','34007','34015','34033','42017','42029','42045',\n",
    "#             '42091','42101'] #Philly metro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "co_flows_dfs=[]\n",
    "met_flows_dfs=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "for year in years:\n",
    "    co_flows_dfs.append (get_flows_by_city (year, other_county))\n",
    "\n",
    "for year in years:\n",
    "    met_flows_dfs.append (get_flows_by_city (year, other_metro))\n",
    "\n",
    "# merge all years dfs from the list into a single df\n",
    "co_flows=reduce(lambda x, y: pd.merge(x, y, on = ['co_fips', 'co_name', 'state', 'mcode', 'mname'], how='outer'), co_flows_dfs)\n",
    "met_flows=reduce(lambda x, y: pd.merge(x, y, on = ['co_fips', 'co_name', 'state', 'mcode', 'mname'], how='outer'), met_flows_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co_fips</th>\n",
       "      <th>co_name</th>\n",
       "      <th>state</th>\n",
       "      <th>inflow2011_12</th>\n",
       "      <th>outflow2011_12</th>\n",
       "      <th>mcode</th>\n",
       "      <th>mname</th>\n",
       "      <th>netflow2011_12</th>\n",
       "      <th>inflow2012_13</th>\n",
       "      <th>outflow2012_13</th>\n",
       "      <th>...</th>\n",
       "      <th>netflow2014_15</th>\n",
       "      <th>inflow2015_16</th>\n",
       "      <th>outflow2015_16</th>\n",
       "      <th>netflow2015_16</th>\n",
       "      <th>inflow2016_17</th>\n",
       "      <th>outflow2016_17</th>\n",
       "      <th>netflow2016_17</th>\n",
       "      <th>inflow2017_18</th>\n",
       "      <th>outflow2017_18</th>\n",
       "      <th>netflow2017_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06059</td>\n",
       "      <td>Orange County, CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>30,298</td>\n",
       "      <td>37,081</td>\n",
       "      <td>31080</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA</td>\n",
       "      <td>-6,783</td>\n",
       "      <td>32,180</td>\n",
       "      <td>37,902</td>\n",
       "      <td>...</td>\n",
       "      <td>-4,827</td>\n",
       "      <td>27,833</td>\n",
       "      <td>35,765</td>\n",
       "      <td>-7,932</td>\n",
       "      <td>34,036</td>\n",
       "      <td>45,600</td>\n",
       "      <td>-11,564</td>\n",
       "      <td>27,374</td>\n",
       "      <td>36,837</td>\n",
       "      <td>-9,463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06071</td>\n",
       "      <td>San Bernardino County, CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>28,641</td>\n",
       "      <td>40,010</td>\n",
       "      <td>40140</td>\n",
       "      <td>Riverside-San Bernardino-Ontario, CA</td>\n",
       "      <td>-11,369</td>\n",
       "      <td>27,414</td>\n",
       "      <td>39,172</td>\n",
       "      <td>...</td>\n",
       "      <td>-11,047</td>\n",
       "      <td>20,116</td>\n",
       "      <td>37,393</td>\n",
       "      <td>-17,277</td>\n",
       "      <td>23,687</td>\n",
       "      <td>49,355</td>\n",
       "      <td>-25,668</td>\n",
       "      <td>20,850</td>\n",
       "      <td>41,482</td>\n",
       "      <td>-20,632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06065</td>\n",
       "      <td>Riverside County, CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>15,565</td>\n",
       "      <td>21,758</td>\n",
       "      <td>40140</td>\n",
       "      <td>Riverside-San Bernardino-Ontario, CA</td>\n",
       "      <td>-6,193</td>\n",
       "      <td>14,967</td>\n",
       "      <td>22,447</td>\n",
       "      <td>...</td>\n",
       "      <td>-5,518</td>\n",
       "      <td>11,135</td>\n",
       "      <td>20,479</td>\n",
       "      <td>-9,344</td>\n",
       "      <td>12,623</td>\n",
       "      <td>28,643</td>\n",
       "      <td>-16,020</td>\n",
       "      <td>10,815</td>\n",
       "      <td>23,567</td>\n",
       "      <td>-12,752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06073</td>\n",
       "      <td>San Diego County, CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>8,982</td>\n",
       "      <td>9,834</td>\n",
       "      <td>41740</td>\n",
       "      <td>San Diego-Chula Vista-Carlsbad, CA</td>\n",
       "      <td>-852</td>\n",
       "      <td>8,989</td>\n",
       "      <td>10,322</td>\n",
       "      <td>...</td>\n",
       "      <td>-1,595</td>\n",
       "      <td>7,649</td>\n",
       "      <td>9,381</td>\n",
       "      <td>-1,732</td>\n",
       "      <td>9,444</td>\n",
       "      <td>11,685</td>\n",
       "      <td>-2,241</td>\n",
       "      <td>7,535</td>\n",
       "      <td>9,766</td>\n",
       "      <td>-2,231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06111</td>\n",
       "      <td>Ventura County, CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>8,593</td>\n",
       "      <td>10,502</td>\n",
       "      <td>37100</td>\n",
       "      <td>Oxnard-Thousand Oaks-Ventura, CA</td>\n",
       "      <td>-1,909</td>\n",
       "      <td>8,195</td>\n",
       "      <td>11,280</td>\n",
       "      <td>...</td>\n",
       "      <td>-1,727</td>\n",
       "      <td>7,399</td>\n",
       "      <td>10,776</td>\n",
       "      <td>-3,377</td>\n",
       "      <td>8,346</td>\n",
       "      <td>14,612</td>\n",
       "      <td>-6,266</td>\n",
       "      <td>7,083</td>\n",
       "      <td>11,729</td>\n",
       "      <td>-4,646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  co_fips                    co_name state  inflow2011_12  outflow2011_12  \\\n",
       "0   06059          Orange County, CA    CA         30,298          37,081   \n",
       "1   06071  San Bernardino County, CA    CA         28,641          40,010   \n",
       "2   06065       Riverside County, CA    CA         15,565          21,758   \n",
       "3   06073       San Diego County, CA    CA          8,982           9,834   \n",
       "4   06111         Ventura County, CA    CA          8,593          10,502   \n",
       "\n",
       "   mcode                                 mname  netflow2011_12  inflow2012_13  \\\n",
       "0  31080    Los Angeles-Long Beach-Anaheim, CA          -6,783         32,180   \n",
       "1  40140  Riverside-San Bernardino-Ontario, CA         -11,369         27,414   \n",
       "2  40140  Riverside-San Bernardino-Ontario, CA          -6,193         14,967   \n",
       "3  41740    San Diego-Chula Vista-Carlsbad, CA            -852          8,989   \n",
       "4  37100      Oxnard-Thousand Oaks-Ventura, CA          -1,909          8,195   \n",
       "\n",
       "   outflow2012_13  ...  netflow2014_15  inflow2015_16  outflow2015_16  \\\n",
       "0          37,902  ...          -4,827         27,833          35,765   \n",
       "1          39,172  ...         -11,047         20,116          37,393   \n",
       "2          22,447  ...          -5,518         11,135          20,479   \n",
       "3          10,322  ...          -1,595          7,649           9,381   \n",
       "4          11,280  ...          -1,727          7,399          10,776   \n",
       "\n",
       "   netflow2015_16  inflow2016_17  outflow2016_17  netflow2016_17  \\\n",
       "0          -7,932         34,036          45,600         -11,564   \n",
       "1         -17,277         23,687          49,355         -25,668   \n",
       "2          -9,344         12,623          28,643         -16,020   \n",
       "3          -1,732          9,444          11,685          -2,241   \n",
       "4          -3,377          8,346          14,612          -6,266   \n",
       "\n",
       "   inflow2017_18  outflow2017_18  netflow2017_18  \n",
       "0         27,374          36,837          -9,463  \n",
       "1         20,850          41,482         -20,632  \n",
       "2         10,815          23,567         -12,752  \n",
       "3          7,535           9,766          -2,231  \n",
       "4          7,083          11,729          -4,646  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co_flows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get subsets of domestic flows \n",
    "domestic_co_flows=co_flows[~co_flows['co_fips'].isin (foreign)].set_index('co_fips').copy()\n",
    "\n",
    "#group by metro area to get metro to metro flows\n",
    "met_flows=met_flows.groupby(['mname','mcode']).sum().reset_index()\n",
    "met_flows=met_flows.set_index('mcode')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add cummulative numbers for inflow, outflow, netflow for city and metro \n",
    "# these are totals for all years\n",
    "domestic_co_flows['tot_inflow']=domestic_co_flows[[c for c in domestic_co_flows.columns if \n",
    "                                                       c.startswith('inflow')]].sum(axis=1)\n",
    "\n",
    "domestic_co_flows['tot_outflow']=domestic_co_flows[[c for c in domestic_co_flows.columns if \n",
    "                                                       c.startswith('outflow')]].sum(axis=1)\n",
    "\n",
    "domestic_co_flows['tot_netflow']=domestic_co_flows['tot_inflow']-domestic_co_flows['tot_outflow']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>in_migration</th>\n",
       "      <th>out_migration</th>\n",
       "      <th>net_migration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>years</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011_12</th>\n",
       "      <td>211,644</td>\n",
       "      <td>252,314</td>\n",
       "      <td>-40,670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012_13</th>\n",
       "      <td>212,627</td>\n",
       "      <td>261,257</td>\n",
       "      <td>-48,630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013_14</th>\n",
       "      <td>193,376</td>\n",
       "      <td>245,689</td>\n",
       "      <td>-52,313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014_15</th>\n",
       "      <td>157,365</td>\n",
       "      <td>197,551</td>\n",
       "      <td>-40,186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015_16</th>\n",
       "      <td>188,147</td>\n",
       "      <td>254,455</td>\n",
       "      <td>-66,308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016_17</th>\n",
       "      <td>229,735</td>\n",
       "      <td>340,934</td>\n",
       "      <td>-111,199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017_18</th>\n",
       "      <td>188,690</td>\n",
       "      <td>276,781</td>\n",
       "      <td>-88,091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         in_migration  out_migration  net_migration\n",
       "years                                              \n",
       "2011_12       211,644        252,314        -40,670\n",
       "2012_13       212,627        261,257        -48,630\n",
       "2013_14       193,376        245,689        -52,313\n",
       "2014_15       157,365        197,551        -40,186\n",
       "2015_16       188,147        254,455        -66,308\n",
       "2016_17       229,735        340,934       -111,199\n",
       "2017_18       188,690        276,781        -88,091"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# annual domestic migration for county by year\n",
    "total_co_dom=get_total_flows(domestic_co_flows)\n",
    "total_co_dom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co_name</th>\n",
       "      <th>tot_inflow</th>\n",
       "      <th>tot_outflow</th>\n",
       "      <th>tot_netflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>co_fips</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36061</th>\n",
       "      <td>New York County, NY</td>\n",
       "      <td>25,200</td>\n",
       "      <td>15,921</td>\n",
       "      <td>9,279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17031</th>\n",
       "      <td>Cook County, IL</td>\n",
       "      <td>21,030</td>\n",
       "      <td>12,990</td>\n",
       "      <td>8,040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36047</th>\n",
       "      <td>Kings County, NY</td>\n",
       "      <td>16,266</td>\n",
       "      <td>8,525</td>\n",
       "      <td>7,741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36081</th>\n",
       "      <td>Queens County, NY</td>\n",
       "      <td>8,115</td>\n",
       "      <td>4,499</td>\n",
       "      <td>3,616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25025</th>\n",
       "      <td>Suffolk County, MA</td>\n",
       "      <td>4,175</td>\n",
       "      <td>2,440</td>\n",
       "      <td>1,735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34017</th>\n",
       "      <td>Hudson County, NJ</td>\n",
       "      <td>3,637</td>\n",
       "      <td>2,005</td>\n",
       "      <td>1,632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25017</th>\n",
       "      <td>Middlesex County, MA</td>\n",
       "      <td>5,976</td>\n",
       "      <td>4,360</td>\n",
       "      <td>1,616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06075</th>\n",
       "      <td>San Francisco County, CA</td>\n",
       "      <td>18,369</td>\n",
       "      <td>16,906</td>\n",
       "      <td>1,463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42101</th>\n",
       "      <td>Philadelphia County, PA</td>\n",
       "      <td>3,834</td>\n",
       "      <td>2,525</td>\n",
       "      <td>1,309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11001</th>\n",
       "      <td>District of Columbia, DC</td>\n",
       "      <td>4,847</td>\n",
       "      <td>3,558</td>\n",
       "      <td>1,289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12086</th>\n",
       "      <td>Miami-Dade County, FL</td>\n",
       "      <td>8,327</td>\n",
       "      <td>7,073</td>\n",
       "      <td>1,254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36059</th>\n",
       "      <td>Nassau County, NY</td>\n",
       "      <td>3,164</td>\n",
       "      <td>2,024</td>\n",
       "      <td>1,140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34003</th>\n",
       "      <td>Bergen County, NJ</td>\n",
       "      <td>3,291</td>\n",
       "      <td>2,159</td>\n",
       "      <td>1,132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24031</th>\n",
       "      <td>Montgomery County, MD</td>\n",
       "      <td>4,048</td>\n",
       "      <td>3,194</td>\n",
       "      <td>854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36119</th>\n",
       "      <td>Westchester County, NY</td>\n",
       "      <td>2,797</td>\n",
       "      <td>1,946</td>\n",
       "      <td>851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36103</th>\n",
       "      <td>Suffolk County, NY</td>\n",
       "      <td>2,515</td>\n",
       "      <td>1,722</td>\n",
       "      <td>793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17043</th>\n",
       "      <td>DuPage County, IL</td>\n",
       "      <td>2,693</td>\n",
       "      <td>1,996</td>\n",
       "      <td>697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09001</th>\n",
       "      <td>Fairfield County, CT</td>\n",
       "      <td>2,813</td>\n",
       "      <td>2,122</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36005</th>\n",
       "      <td>Bronx County, NY</td>\n",
       "      <td>1,664</td>\n",
       "      <td>995</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12011</th>\n",
       "      <td>Broward County, FL</td>\n",
       "      <td>5,211</td>\n",
       "      <td>4,545</td>\n",
       "      <td>666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          co_name  tot_inflow  tot_outflow  tot_netflow\n",
       "co_fips                                                                \n",
       "36061         New York County, NY      25,200       15,921        9,279\n",
       "17031             Cook County, IL      21,030       12,990        8,040\n",
       "36047            Kings County, NY      16,266        8,525        7,741\n",
       "36081           Queens County, NY       8,115        4,499        3,616\n",
       "25025          Suffolk County, MA       4,175        2,440        1,735\n",
       "34017           Hudson County, NJ       3,637        2,005        1,632\n",
       "25017        Middlesex County, MA       5,976        4,360        1,616\n",
       "06075    San Francisco County, CA      18,369       16,906        1,463\n",
       "42101     Philadelphia County, PA       3,834        2,525        1,309\n",
       "11001    District of Columbia, DC       4,847        3,558        1,289\n",
       "12086       Miami-Dade County, FL       8,327        7,073        1,254\n",
       "36059           Nassau County, NY       3,164        2,024        1,140\n",
       "34003           Bergen County, NJ       3,291        2,159        1,132\n",
       "24031       Montgomery County, MD       4,048        3,194          854\n",
       "36119      Westchester County, NY       2,797        1,946          851\n",
       "36103          Suffolk County, NY       2,515        1,722          793\n",
       "17043           DuPage County, IL       2,693        1,996          697\n",
       "09001        Fairfield County, CT       2,813        2,122          691\n",
       "36005            Bronx County, NY       1,664          995          669\n",
       "12011          Broward County, FL       5,211        4,545          666"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domestic_co_flows.loc[~domestic_co_flows.index.isin(suppressed),['co_name','tot_inflow','tot_outflow','tot_netflow']].sort_values('tot_netflow', ascending=False).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co_name</th>\n",
       "      <th>tot_inflow</th>\n",
       "      <th>tot_outflow</th>\n",
       "      <th>tot_netflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>co_fips</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>06071</th>\n",
       "      <td>San Bernardino County, CA</td>\n",
       "      <td>163,681</td>\n",
       "      <td>273,400</td>\n",
       "      <td>-109,719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065</th>\n",
       "      <td>Riverside County, CA</td>\n",
       "      <td>88,663</td>\n",
       "      <td>152,573</td>\n",
       "      <td>-63,910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06059</th>\n",
       "      <td>Orange County, CA</td>\n",
       "      <td>204,063</td>\n",
       "      <td>256,951</td>\n",
       "      <td>-52,888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32003</th>\n",
       "      <td>Clark County, NV</td>\n",
       "      <td>37,664</td>\n",
       "      <td>76,006</td>\n",
       "      <td>-38,342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06111</th>\n",
       "      <td>Ventura County, CA</td>\n",
       "      <td>53,476</td>\n",
       "      <td>76,841</td>\n",
       "      <td>-23,365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06029</th>\n",
       "      <td>Kern County, CA</td>\n",
       "      <td>29,832</td>\n",
       "      <td>45,915</td>\n",
       "      <td>-16,083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04013</th>\n",
       "      <td>Maricopa County, AZ</td>\n",
       "      <td>25,627</td>\n",
       "      <td>39,633</td>\n",
       "      <td>-14,006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06073</th>\n",
       "      <td>San Diego County, CA</td>\n",
       "      <td>57,443</td>\n",
       "      <td>68,104</td>\n",
       "      <td>-10,661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48085</th>\n",
       "      <td>Collin County, TX</td>\n",
       "      <td>2,958</td>\n",
       "      <td>9,055</td>\n",
       "      <td>-6,097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48453</th>\n",
       "      <td>Travis County, TX</td>\n",
       "      <td>11,313</td>\n",
       "      <td>17,187</td>\n",
       "      <td>-5,874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53033</th>\n",
       "      <td>King County, WA</td>\n",
       "      <td>15,004</td>\n",
       "      <td>20,540</td>\n",
       "      <td>-5,536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06085</th>\n",
       "      <td>Santa Clara County, CA</td>\n",
       "      <td>18,568</td>\n",
       "      <td>23,369</td>\n",
       "      <td>-4,801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48201</th>\n",
       "      <td>Harris County, TX</td>\n",
       "      <td>11,117</td>\n",
       "      <td>15,895</td>\n",
       "      <td>-4,778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48439</th>\n",
       "      <td>Tarrant County, TX</td>\n",
       "      <td>5,004</td>\n",
       "      <td>8,748</td>\n",
       "      <td>-3,744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48113</th>\n",
       "      <td>Dallas County, TX</td>\n",
       "      <td>8,152</td>\n",
       "      <td>11,623</td>\n",
       "      <td>-3,471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06067</th>\n",
       "      <td>Sacramento County, CA</td>\n",
       "      <td>11,648</td>\n",
       "      <td>14,939</td>\n",
       "      <td>-3,291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06001</th>\n",
       "      <td>Alameda County, CA</td>\n",
       "      <td>18,355</td>\n",
       "      <td>21,624</td>\n",
       "      <td>-3,269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06019</th>\n",
       "      <td>Fresno County, CA</td>\n",
       "      <td>9,423</td>\n",
       "      <td>12,593</td>\n",
       "      <td>-3,170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41051</th>\n",
       "      <td>Multnomah County, OR</td>\n",
       "      <td>4,727</td>\n",
       "      <td>7,845</td>\n",
       "      <td>-3,118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04015</th>\n",
       "      <td>Mohave County, AZ</td>\n",
       "      <td>1,716</td>\n",
       "      <td>4,753</td>\n",
       "      <td>-3,037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           co_name  tot_inflow  tot_outflow  tot_netflow\n",
       "co_fips                                                                 \n",
       "06071    San Bernardino County, CA     163,681      273,400     -109,719\n",
       "06065         Riverside County, CA      88,663      152,573      -63,910\n",
       "06059            Orange County, CA     204,063      256,951      -52,888\n",
       "32003             Clark County, NV      37,664       76,006      -38,342\n",
       "06111           Ventura County, CA      53,476       76,841      -23,365\n",
       "06029              Kern County, CA      29,832       45,915      -16,083\n",
       "04013          Maricopa County, AZ      25,627       39,633      -14,006\n",
       "06073         San Diego County, CA      57,443       68,104      -10,661\n",
       "48085            Collin County, TX       2,958        9,055       -6,097\n",
       "48453            Travis County, TX      11,313       17,187       -5,874\n",
       "53033              King County, WA      15,004       20,540       -5,536\n",
       "06085       Santa Clara County, CA      18,568       23,369       -4,801\n",
       "48201            Harris County, TX      11,117       15,895       -4,778\n",
       "48439           Tarrant County, TX       5,004        8,748       -3,744\n",
       "48113            Dallas County, TX       8,152       11,623       -3,471\n",
       "06067        Sacramento County, CA      11,648       14,939       -3,291\n",
       "06001           Alameda County, CA      18,355       21,624       -3,269\n",
       "06019            Fresno County, CA       9,423       12,593       -3,170\n",
       "41051         Multnomah County, OR       4,727        7,845       -3,118\n",
       "04015            Mohave County, AZ       1,716        4,753       -3,037"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domestic_co_flows.loc[~domestic_co_flows.index.isin(suppressed),['co_name','tot_inflow','tot_outflow','tot_netflow']].sort_values('tot_netflow', ascending=True).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "met_flows['tot_inflow']=met_flows[[c for c in met_flows.columns if \n",
    "                                                       c.startswith('inflow')]].sum(axis=1)\n",
    "\n",
    "met_flows['tot_outflow']=met_flows[[c for c in met_flows.columns if \n",
    "                                                       c.startswith('outflow')]].sum(axis=1)\n",
    "\n",
    "met_flows['tot_netflow']=met_flows['tot_inflow']-met_flows['tot_outflow']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>in_migration</th>\n",
       "      <th>out_migration</th>\n",
       "      <th>net_migration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>years</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011_12</th>\n",
       "      <td>251,730</td>\n",
       "      <td>292,514</td>\n",
       "      <td>-40,784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012_13</th>\n",
       "      <td>252,253</td>\n",
       "      <td>307,376</td>\n",
       "      <td>-55,123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013_14</th>\n",
       "      <td>229,651</td>\n",
       "      <td>289,383</td>\n",
       "      <td>-59,732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014_15</th>\n",
       "      <td>183,600</td>\n",
       "      <td>230,591</td>\n",
       "      <td>-46,991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015_16</th>\n",
       "      <td>225,079</td>\n",
       "      <td>302,225</td>\n",
       "      <td>-77,146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016_17</th>\n",
       "      <td>278,311</td>\n",
       "      <td>412,448</td>\n",
       "      <td>-134,137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017_18</th>\n",
       "      <td>225,789</td>\n",
       "      <td>331,125</td>\n",
       "      <td>-105,336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         in_migration  out_migration  net_migration\n",
       "years                                              \n",
       "2011_12       251,730        292,514        -40,784\n",
       "2012_13       252,253        307,376        -55,123\n",
       "2013_14       229,651        289,383        -59,732\n",
       "2014_15       183,600        230,591        -46,991\n",
       "2015_16       225,079        302,225        -77,146\n",
       "2016_17       278,311        412,448       -134,137\n",
       "2017_18       225,789        331,125       -105,336"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# annual domestic migration for metro by year\n",
    "total_met_dom=get_total_flows(met_flows)\n",
    "total_met_dom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mname</th>\n",
       "      <th>tot_inflow</th>\n",
       "      <th>tot_outflow</th>\n",
       "      <th>tot_netflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35620</th>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
       "      <td>92,117</td>\n",
       "      <td>56,832</td>\n",
       "      <td>35,285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16980</th>\n",
       "      <td>Chicago-Naperville-Elgin, IL-IN-WI</td>\n",
       "      <td>38,735</td>\n",
       "      <td>25,191</td>\n",
       "      <td>13,544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14460</th>\n",
       "      <td>Boston-Cambridge-Newton, MA-NH</td>\n",
       "      <td>18,983</td>\n",
       "      <td>13,628</td>\n",
       "      <td>5,355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37980</th>\n",
       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
       "      <td>14,020</td>\n",
       "      <td>9,747</td>\n",
       "      <td>4,273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47900</th>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV</td>\n",
       "      <td>28,420</td>\n",
       "      <td>24,530</td>\n",
       "      <td>3,890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33100</th>\n",
       "      <td>Miami-Fort Lauderdale-Pompano Beach, FL</td>\n",
       "      <td>21,102</td>\n",
       "      <td>18,719</td>\n",
       "      <td>2,383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12580</th>\n",
       "      <td>Baltimore-Columbia-Towson, MD</td>\n",
       "      <td>6,911</td>\n",
       "      <td>5,370</td>\n",
       "      <td>1,541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14860</th>\n",
       "      <td>Bridgeport-Stamford-Norwalk, CT</td>\n",
       "      <td>3,878</td>\n",
       "      <td>2,797</td>\n",
       "      <td>1,081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19820</th>\n",
       "      <td>Detroit-Warren-Dearborn, MI</td>\n",
       "      <td>8,950</td>\n",
       "      <td>7,988</td>\n",
       "      <td>962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33460</th>\n",
       "      <td>Minneapolis-St. Paul-Bloomington, MN-WI</td>\n",
       "      <td>9,474</td>\n",
       "      <td>8,534</td>\n",
       "      <td>940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25540</th>\n",
       "      <td>Hartford-East Hartford-Middletown, CT</td>\n",
       "      <td>2,711</td>\n",
       "      <td>1,819</td>\n",
       "      <td>892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41180</th>\n",
       "      <td>St. Louis, MO-IL</td>\n",
       "      <td>5,806</td>\n",
       "      <td>4,922</td>\n",
       "      <td>884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39300</th>\n",
       "      <td>Providence-Warwick, RI-MA</td>\n",
       "      <td>2,418</td>\n",
       "      <td>1,625</td>\n",
       "      <td>793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33340</th>\n",
       "      <td>Milwaukee-Waukesha, WI</td>\n",
       "      <td>3,093</td>\n",
       "      <td>2,462</td>\n",
       "      <td>631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35300</th>\n",
       "      <td>New Haven-Milford, CT</td>\n",
       "      <td>2,184</td>\n",
       "      <td>1,581</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38300</th>\n",
       "      <td>Pittsburgh, PA</td>\n",
       "      <td>3,354</td>\n",
       "      <td>2,838</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11460</th>\n",
       "      <td>Ann Arbor, MI</td>\n",
       "      <td>2,096</td>\n",
       "      <td>1,611</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29620</th>\n",
       "      <td>Lansing-East Lansing, MI</td>\n",
       "      <td>897</td>\n",
       "      <td>428</td>\n",
       "      <td>469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18140</th>\n",
       "      <td>Columbus, OH</td>\n",
       "      <td>4,178</td>\n",
       "      <td>3,715</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40380</th>\n",
       "      <td>Rochester, NY</td>\n",
       "      <td>1,513</td>\n",
       "      <td>1,056</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              mname  tot_inflow  tot_outflow  \\\n",
       "mcode                                                                          \n",
       "35620         New York-Newark-Jersey City, NY-NJ-PA      92,117       56,832   \n",
       "16980            Chicago-Naperville-Elgin, IL-IN-WI      38,735       25,191   \n",
       "14460                Boston-Cambridge-Newton, MA-NH      18,983       13,628   \n",
       "37980   Philadelphia-Camden-Wilmington, PA-NJ-DE-MD      14,020        9,747   \n",
       "47900  Washington-Arlington-Alexandria, DC-VA-MD-WV      28,420       24,530   \n",
       "33100       Miami-Fort Lauderdale-Pompano Beach, FL      21,102       18,719   \n",
       "12580                 Baltimore-Columbia-Towson, MD       6,911        5,370   \n",
       "14860               Bridgeport-Stamford-Norwalk, CT       3,878        2,797   \n",
       "19820                   Detroit-Warren-Dearborn, MI       8,950        7,988   \n",
       "33460       Minneapolis-St. Paul-Bloomington, MN-WI       9,474        8,534   \n",
       "25540         Hartford-East Hartford-Middletown, CT       2,711        1,819   \n",
       "41180                              St. Louis, MO-IL       5,806        4,922   \n",
       "39300                     Providence-Warwick, RI-MA       2,418        1,625   \n",
       "33340                        Milwaukee-Waukesha, WI       3,093        2,462   \n",
       "35300                         New Haven-Milford, CT       2,184        1,581   \n",
       "38300                                Pittsburgh, PA       3,354        2,838   \n",
       "11460                                 Ann Arbor, MI       2,096        1,611   \n",
       "29620                      Lansing-East Lansing, MI         897          428   \n",
       "18140                                  Columbus, OH       4,178        3,715   \n",
       "40380                                 Rochester, NY       1,513        1,056   \n",
       "\n",
       "       tot_netflow  \n",
       "mcode               \n",
       "35620       35,285  \n",
       "16980       13,544  \n",
       "14460        5,355  \n",
       "37980        4,273  \n",
       "47900        3,890  \n",
       "33100        2,383  \n",
       "12580        1,541  \n",
       "14860        1,081  \n",
       "19820          962  \n",
       "33460          940  \n",
       "25540          892  \n",
       "41180          884  \n",
       "39300          793  \n",
       "33340          631  \n",
       "35300          603  \n",
       "38300          516  \n",
       "11460          485  \n",
       "29620          469  \n",
       "18140          463  \n",
       "40380          457  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "met_flows.loc[~met_flows.index.isin(msuppressed),['mname','tot_inflow','tot_outflow','tot_netflow']].sort_values('tot_netflow', ascending=False).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mname</th>\n",
       "      <th>tot_inflow</th>\n",
       "      <th>tot_outflow</th>\n",
       "      <th>tot_netflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40140</th>\n",
       "      <td>Riverside-San Bernardino-Ontario, CA</td>\n",
       "      <td>376,083</td>\n",
       "      <td>596,864</td>\n",
       "      <td>-220,781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29820</th>\n",
       "      <td>Las Vegas-Henderson-Paradise, NV</td>\n",
       "      <td>48,888</td>\n",
       "      <td>94,968</td>\n",
       "      <td>-46,080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19100</th>\n",
       "      <td>Dallas-Fort Worth-Arlington, TX</td>\n",
       "      <td>26,429</td>\n",
       "      <td>51,732</td>\n",
       "      <td>-25,303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37100</th>\n",
       "      <td>Oxnard-Thousand Oaks-Ventura, CA</td>\n",
       "      <td>60,616</td>\n",
       "      <td>83,273</td>\n",
       "      <td>-22,657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38060</th>\n",
       "      <td>Phoenix-Mesa-Chandler, AZ</td>\n",
       "      <td>40,513</td>\n",
       "      <td>62,342</td>\n",
       "      <td>-21,829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12540</th>\n",
       "      <td>Bakersfield, CA</td>\n",
       "      <td>34,078</td>\n",
       "      <td>50,996</td>\n",
       "      <td>-16,918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41740</th>\n",
       "      <td>San Diego-Chula Vista-Carlsbad, CA</td>\n",
       "      <td>96,706</td>\n",
       "      <td>112,063</td>\n",
       "      <td>-15,357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38900</th>\n",
       "      <td>Portland-Vancouver-Hillsboro, OR-WA</td>\n",
       "      <td>14,527</td>\n",
       "      <td>27,983</td>\n",
       "      <td>-13,456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12420</th>\n",
       "      <td>Austin-Round Rock-Georgetown, TX</td>\n",
       "      <td>16,574</td>\n",
       "      <td>28,906</td>\n",
       "      <td>-12,332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42660</th>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA</td>\n",
       "      <td>32,034</td>\n",
       "      <td>43,651</td>\n",
       "      <td>-11,617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26420</th>\n",
       "      <td>Houston-The Woodlands-Sugar Land, TX</td>\n",
       "      <td>20,023</td>\n",
       "      <td>30,265</td>\n",
       "      <td>-10,242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19740</th>\n",
       "      <td>Denver-Aurora-Lakewood, CO</td>\n",
       "      <td>16,259</td>\n",
       "      <td>24,340</td>\n",
       "      <td>-8,081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41860</th>\n",
       "      <td>San Francisco-Oakland-Berkeley, CA</td>\n",
       "      <td>77,705</td>\n",
       "      <td>85,555</td>\n",
       "      <td>-7,850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40900</th>\n",
       "      <td>Sacramento-Roseville-Folsom, CA</td>\n",
       "      <td>25,253</td>\n",
       "      <td>31,919</td>\n",
       "      <td>-6,666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14260</th>\n",
       "      <td>Boise City, ID</td>\n",
       "      <td>2,604</td>\n",
       "      <td>8,820</td>\n",
       "      <td>-6,216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41940</th>\n",
       "      <td>San Jose-Sunnyvale-Santa Clara, CA</td>\n",
       "      <td>28,552</td>\n",
       "      <td>33,897</td>\n",
       "      <td>-5,345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12060</th>\n",
       "      <td>Atlanta-Sandy Springs-Alpharetta, GA</td>\n",
       "      <td>18,242</td>\n",
       "      <td>23,076</td>\n",
       "      <td>-4,834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34980</th>\n",
       "      <td>Nashville-Davidson--Murfreesboro--Franklin, TN</td>\n",
       "      <td>4,390</td>\n",
       "      <td>9,191</td>\n",
       "      <td>-4,801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29420</th>\n",
       "      <td>Lake Havasu City-Kingman, AZ</td>\n",
       "      <td>2,801</td>\n",
       "      <td>7,520</td>\n",
       "      <td>-4,719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41700</th>\n",
       "      <td>San Antonio-New Braunfels, TX</td>\n",
       "      <td>6,281</td>\n",
       "      <td>10,959</td>\n",
       "      <td>-4,678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                mname  tot_inflow  \\\n",
       "mcode                                                               \n",
       "40140            Riverside-San Bernardino-Ontario, CA     376,083   \n",
       "29820                Las Vegas-Henderson-Paradise, NV      48,888   \n",
       "19100                 Dallas-Fort Worth-Arlington, TX      26,429   \n",
       "37100                Oxnard-Thousand Oaks-Ventura, CA      60,616   \n",
       "38060                       Phoenix-Mesa-Chandler, AZ      40,513   \n",
       "12540                                 Bakersfield, CA      34,078   \n",
       "41740              San Diego-Chula Vista-Carlsbad, CA      96,706   \n",
       "38900             Portland-Vancouver-Hillsboro, OR-WA      14,527   \n",
       "12420                Austin-Round Rock-Georgetown, TX      16,574   \n",
       "42660                     Seattle-Tacoma-Bellevue, WA      32,034   \n",
       "26420            Houston-The Woodlands-Sugar Land, TX      20,023   \n",
       "19740                      Denver-Aurora-Lakewood, CO      16,259   \n",
       "41860              San Francisco-Oakland-Berkeley, CA      77,705   \n",
       "40900                 Sacramento-Roseville-Folsom, CA      25,253   \n",
       "14260                                  Boise City, ID       2,604   \n",
       "41940              San Jose-Sunnyvale-Santa Clara, CA      28,552   \n",
       "12060            Atlanta-Sandy Springs-Alpharetta, GA      18,242   \n",
       "34980  Nashville-Davidson--Murfreesboro--Franklin, TN       4,390   \n",
       "29420                    Lake Havasu City-Kingman, AZ       2,801   \n",
       "41700                   San Antonio-New Braunfels, TX       6,281   \n",
       "\n",
       "       tot_outflow  tot_netflow  \n",
       "mcode                            \n",
       "40140      596,864     -220,781  \n",
       "29820       94,968      -46,080  \n",
       "19100       51,732      -25,303  \n",
       "37100       83,273      -22,657  \n",
       "38060       62,342      -21,829  \n",
       "12540       50,996      -16,918  \n",
       "41740      112,063      -15,357  \n",
       "38900       27,983      -13,456  \n",
       "12420       28,906      -12,332  \n",
       "42660       43,651      -11,617  \n",
       "26420       30,265      -10,242  \n",
       "19740       24,340       -8,081  \n",
       "41860       85,555       -7,850  \n",
       "40900       31,919       -6,666  \n",
       "14260        8,820       -6,216  \n",
       "41940       33,897       -5,345  \n",
       "12060       23,076       -4,834  \n",
       "34980        9,191       -4,801  \n",
       "29420        7,520       -4,719  \n",
       "41700       10,959       -4,678  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "met_flows.loc[~met_flows.index.isin(msuppressed),['mname','tot_inflow','tot_outflow','tot_netflow']].sort_values('tot_netflow', ascending=True).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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