{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Urban scaling and coronavirus #\n",
    "\n",
    "This notebook is a replication attempt of Stier et al.'s preprint on Arxiv [1]. This notebook brings together MSA definitions and census data to allow demographic calculations for MSAs in relation to the coronavirus outbreak. The MSAs are a county-level unit delineated by the Census Bureau (see https://www.census.gov/programs-surveys/metro-micro/about/delineation-files.html). The coronavirus outbreak data are provided by USAFacts (https://usafacts.org/visualizations/coronavirus-covid-19-spread-map/, last downloaded March 25th, 2020 14:34 EDT).\n",
    "\n",
    ".. [1] Andrew J Stier, Marc G. Berman, and Luis M. A. Bettencourt. March 23rd, 2020. COVID-19 attack rate increases with city size. arXiv:2003.10376v1 [q-bio.PE] https://arxiv.org/abs/2003.10376v1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import key packages\n",
    "import numpy as np\n",
    "import pandas\n",
    "import datetime #the covid dataset uses datetimes as column indices\n",
    "import string\n",
    "import scipy.stats\n",
    "import matplotlib.pyplot as plt\n",
    "import ipywidgets as widgets\n",
    "\n",
    "#Load the datasets: the MSA delineations, the ACS 2018 population estimates, and the USAFacts Coronavirus dataset\n",
    "msas = pandas.read_excel(r'data/USCensus/Delineations/list1_Sep_2018.xls',header=2)\n",
    "pop = pandas.read_excel(r'data/USCensus/Population/co-est2018-alldata.xls')\n",
    "\n",
    "#Load the different Coronavirus datasets\n",
    "edt = datetime.timezone(datetime.timedelta(hours = -4),name = 'US Eastern Daylight Time')\n",
    "\n",
    "#I've created a standardized notation for up-to-date coronavirus datasets\n",
    "## These are dictionaries with the following keyterms:\n",
    "    #cases - the cases per day per county dataset\n",
    "    #deaths - deathes per day per county\n",
    "    #credit - attribution\n",
    "    #daterange - the range of dates that can be queried. If dates are out of range, won't work\n",
    "    #dateformat - how to format a date into a column name for a particular day's data. Lambda function\n",
    "    #get_county_cases - a lambda function that takes cases or deaths and a row of the MSAS dataset to return the matching row\n",
    "    \n",
    "\n",
    "#This is teh USAFacts dataset\n",
    "## This seems to be updated by 1 am EDT\n",
    "latestdate = datetime.datetime.combine(datetime.datetime.now(tz=edt).date(),datetime.time()) + [datetime.timedelta(days = -2) if datetime.datetime.now(tz=edt).time() < datetime.time(1, 0) else datetime.timedelta(days = -1)][0]\n",
    "usafacts = {'cases' : pandas.read_csv('https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_confirmed_usafacts.csv'),\n",
    "            'deaths': pandas.read_csv('https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_deaths_usafacts.csv'),\n",
    "            'credit': 'USAFacts. https://usafacts.org/visualizations/coronavirus-covid-19-spread-map/',\n",
    "            'daterange' : pandas.date_range(datetime.datetime(2020, 1, 22, 0, 0), latestdate),\n",
    "            'dateformat_cases' : lambda x: '{dt.month}/{dt.day}/{short}'.format(dt=x,short=str(x.year)[:2]),\n",
    "            'dateformat_deaths': lambda x: '{dt.month}/{dt.day}/{dt.year}'.format(dt=x),\n",
    "            'get_county' : lambda data, m: data.countyFIPS == int(m['FIPS State Code']*1000 + m['FIPS County Code'])\n",
    "           }\n",
    "\n",
    "#This is the dataset made by Tom Quisel, CSBS, and 1point3acres\n",
    "## This seems to update as of 2 am eastern the next day\n",
    "## Note that county names do not include the designation for counties/parishes/etc.\n",
    "### in that state, so they must be removed from the MSAs data labels\n",
    "latestdate = datetime.datetime.combine(datetime.datetime.now(tz=edt).date(),datetime.time()) + [datetime.timedelta(days = -2) if datetime.datetime.now(tz=edt).time() < datetime.time(2, 0) else datetime.timedelta(days = -1)][0]\n",
    "acres = {'cases' : pandas.read_csv('https://raw.githubusercontent.com/tomquisel/covid19-data/master/data/csv/timeseries_confirmed.csv'),\n",
    "         'deaths' : pandas.read_csv('https://raw.githubusercontent.com/tomquisel/covid19-data/master/data/csv/timeseries_death.csv'),\n",
    "         'credit' : 'Tom Quisel, CSBS (Conference of State Bank Supervisors), and 1point3acres. https://github.com/tomquisel/covid19-data, https://www.csbs.org/information-covid-19-coronavirus, and https://coronavirus.1point3acres.com/en',\n",
    "         'daterange' : pandas.date_range(datetime.datetime(2020, 3, 14, 0, 0), latestdate),\n",
    "         'dateformat_cases' : lambda x: x.strftime('%Y-%m-%d'),\n",
    "         'dateformat_deaths': lambda x: x.strftime('%Y-%m-%d'),\n",
    "         'get_county' : lambda data, m: (data['County_Name'] == ' '.join(m['County/County Equivalent'].split(' ')[:-1])) & (data['State_Name'] == m['State Name'])\n",
    "       }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the data are loaded, pick a dataset and dates to run the COVID-19 regression on. (This is a linear regression of normalized, natural log active case counts (i.e., confirmed tests with deaths removed). These assumptions are all replicating reference [1], above.\n",
    "\n",
    "Note: this takes a few minutes to run! Set output to True to see all the places that can't be included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4715929b60544c686e323b982004c17",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Select(description='Dataset:', options=('USAFacts', '1point3acres'), value='USAFacts')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#First, select a dataset to explore.\n",
    "dataset_select = widgets.Select(\n",
    "    options=['USAFacts','1point3acres'],\n",
    "    value='USAFacts',\n",
    "    description='Dataset:',\n",
    "    disabled=False)\n",
    "\n",
    "dataset_select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change the dates to your window of interest, then click \"Run Interact\" to run the regression.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cec863558b5241a39ec7c9e96bf90c3a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(SelectionRangeSlider(description='Dates:', index=(0, 0), layout=Layout(width='500px'), o…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#Assemble the state, metro/micro areas, pop data, and covid cases\n",
    "## First  define a selection slider for the analysis\n",
    "if dataset_select.value == 'USAFacts':\n",
    "    dataset = usafacts\n",
    "if dataset_select.value == '1point3acres':\n",
    "    dataset = acres\n",
    "\n",
    "date_range_slider = widgets.SelectionRangeSlider(options=[(d.strftime('%m/%d'),d) for d in dataset['daterange']],\n",
    "    #index=(min([d for d in data['daterange']]),max([d for d in data['daterange']])),\n",
    "    description='Dates:',\n",
    "    orientation='horizontal',\n",
    "    layout={'width': '500px'}\n",
    ")\n",
    "\n",
    "\n",
    "def coallate_analyze(dateTuple, msas, pop, dataset, remove_deaths = True, filtering = 'None', chatty=False):\n",
    "    \"\"\"Utility for ipywidgets.\n",
    "    \n",
    "    This function takes as input the US Census MSA definitions and 2018 ACS data, the USAFacts\n",
    "    coronavirus case and death counts at the county level, and the start and end dates for the \n",
    "    statistical regression in keeping with the methodology outlined in [1]. It tabulates these data\n",
    "    and outputs a graph.\n",
    "    \n",
    "    If chatty is True, lots of output about which cities aren't included.\n",
    "    \n",
    "    \"\"\"\n",
    "    global df #to remain accessible after the function is run\n",
    "    dateStart = datetime.datetime.combine(dateTuple[0],datetime.time())\n",
    "    dateEnd = datetime.datetime.combine(dateTuple[1],datetime.time())\n",
    "    output = [] #to store the actual data to be exported and used\n",
    "    popnotfound = [] #to store fips of counties that can't be found\n",
    "    covidnotfound = [] #to store fips of counties without cases\n",
    "    covidnotincl = [] #to store CBSA of metros excluded\n",
    "    days = (dateEnd - dateStart).days+1 #number of days to be included in the analysis\n",
    "    col_names = ['CBSA','Title','MetroMicro','Pop2018','COVIDEnd','AttackRate','r']\n",
    "    #Leaving out Puerto Rico because it is not in the census data; I'm sorry! I hope to remedy this.\n",
    "    for cbsa in msas.loc[(msas['FIPS State Code'] != 72)]['CBSA Code'][:-4].unique():\n",
    "        #Get the MSA information\n",
    "        #cbsa = '10740' #ABQ metro CBSA code for testing\n",
    "        counties = msas.loc[msas['CBSA Code'] == cbsa]\n",
    "        if chatty == True:\n",
    "            print('Starting on ' + str(counties.iloc[0]['CBSA Title']))\n",
    "        #Add teh metro area name and its metro/micro status to the output\n",
    "        row = [cbsa,counties.loc[counties.index[0]]['CBSA Title'],counties.loc[counties.index[0]]['Metropolitan/Micropolitan Statistical Area']]\n",
    "        #for all state and county codes, go through and select the relevant pop data\n",
    "        pop_total = 0 #to sum the population\n",
    "        covid_last = 0 #the total number of cases in the last day of the series\n",
    "        covid_series = [0]*days #This stores just cases, not people who died\n",
    "        #Loop through every constituent county to get the population as well as the COVID cases\n",
    "        for ci in counties.index:\n",
    "            #Get teh FIPS code for working with census data\n",
    "            county = counties.loc[ci]\n",
    "            s, c = (county['FIPS State Code'],county['FIPS County Code'])\n",
    "            fips = int(s*1000 + c) #str(int(s)) + '0'*(3-len(str(int(c))))+str(int(c))\n",
    "            #Calculate the population from this county towards the MSA\n",
    "            if any((pop.STATE == int(s)) & (pop.COUNTY == int(c))):\n",
    "                pop_total += int(pop.loc[(pop.STATE == int(s)) & (pop.COUNTY == int(c))]['POPESTIMATE2018'])\n",
    "            else: #If the fips code doesn't exist, skip this county but record why.\n",
    "                if chatty == True:\n",
    "                    print(str(fips) + ' was not found in the ACS 2018 data.')\n",
    "                popnotfound.append(fips)\n",
    "            #Go through the chosen COVID-19 data and add the confirmed case numbers\n",
    "            #If the county is in the data, add it to the total for this MSA\n",
    "            if any(dataset['get_county'](dataset['cases'], county)):\n",
    "                #Remove deaths if called for by the methodology\n",
    "                if remove_deaths == True:\n",
    "                    #When data contain duplicates; it is assumed that together these are the true total.\n",
    "                    covid_last += int(sum(dataset['cases'].loc[dataset['get_county'](dataset['cases'], county)][dataset['dateformat_cases'](dateEnd)]))-int(sum(dataset['deaths'].loc[dataset['get_county'](dataset['deaths'], county)][dataset['dateformat_deaths'](dateEnd)]))\n",
    "                    #Add the total number of active cases to each day\n",
    "                    for i,d in zip(range(days),pandas.date_range(dateStart,dateEnd)):\n",
    "                        covid_series[i] += int(sum(dataset['cases'].loc[(dataset['get_county'](dataset['cases'], county))][dataset['dateformat_cases'](d)])) - int(sum(dataset['deaths'].loc[dataset['get_county'](dataset['deaths'], county)][dataset['dateformat_deaths'](d)]))\n",
    "                #Otherwise, don't remove the deaths, just count active cases\n",
    "                else:\n",
    "                    #When data contain duplicates; it is assumed that together these are the true total.\n",
    "                    covid_last += int(sum(dataset['cases'].loc[dataset['get_county'](dataset['cases'], county)][dataset['dateformat_cases'](dateEnd)]))\n",
    "                    #Add the total number of active cases to each day\n",
    "                    for i,d in zip(range(days),pandas.date_range(dateStart,dateEnd)):\n",
    "                        covid_series[i] += int(sum(dataset['cases'].loc[(dataset['get_county'](dataset['cases'], county))][dataset['dateformat_cases'](d)])) \n",
    "            else:\n",
    "                if chatty == True:\n",
    "                    print(str(fips) + ' was not found in the COVID data.')\n",
    "                covidnotfound.append(fips)\n",
    "        row.append(pop_total) \n",
    "        row.append(covid_last)\n",
    "        #Now filter the data using any of the predefined criteria\n",
    "        if filtering == 'Chicago':\n",
    "            if any(pandas.isna(covid_series)) or (covid_series[-1] <= 3) or any([cs <= 0 for cs in covid_series]):\n",
    "                if chatty == True:\n",
    "                    print(row[1] + ' had too few cases for inclusion')\n",
    "                covidnotincl.append(cbsa)\n",
    "                row.append(np.nan)\n",
    "                row.append(np.nan)\n",
    "            else:\n",
    "                #normalize the covid_series so that the March 13th data is 1\n",
    "                covid_series = [cs * 1. / covid_series[0] for cs in covid_series]\n",
    "                #run a regression\n",
    "                slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(range(days),[np.log(cs) for cs in covid_series])\n",
    "                row.append(slope)\n",
    "                row.append(r_value)\n",
    "        elif filtering == 'None':\n",
    "                #run a regression\n",
    "                slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(range(days),[np.log(cs) for cs in covid_series])\n",
    "                row.append(slope)\n",
    "                row.append(r_value)\n",
    "        output.append(row)\n",
    "    df = pandas.DataFrame(output,columns=col_names)\n",
    "    print('\\n \\n \\n Data are gathered for cases from ' + dateStart.strftime(\"%B %d, %Y\") +' to ' + dateEnd.strftime(\"%B %d, %Y\") + '! Run the next box when ready.')\n",
    "\n",
    "#Run with some interactability for the dates\n",
    "print('Change the dates to your window of interest, then click \"Run Interact\" to run the regression.')\n",
    "widgets.interact_manual(coallate_analyze, dateTuple = date_range_slider, msas = widgets.fixed(msas), pop = widgets.fixed(pop), dataset = widgets.fixed(dataset), remove_deaths = True, filtering = widgets.Select(options=['None','Chicago']), chatty = False)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8d35a636fd64e699ff79f0e85d7035f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Output(),), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def plot_some_figures(df):\n",
    "    \"\"\"\n",
    "    Make plots.\n",
    "    \"\"\"\n",
    "    ####################################################\n",
    "    #Now that the data are collated, generate the graphs\n",
    "    #Plot the chart from figure 1a from the arxiv preprint\n",
    "    fig, ((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)    \n",
    "    fig.set_figheight(12)\n",
    "    fig.set_figwidth(12)\n",
    "    points, = ax1.plot([np.log(x) for x in df.Pop2018],[np.log(y) for y in df.AttackRate],'bo')\n",
    "    #Now run a linear regression to calculate any potential power law behavior\n",
    "    #Only include those where an attack rate could be esitmated and its a Metropolitan Area (not Micro)\n",
    "    which = df.index[(pandas.isna(df.AttackRate)==False) & (df.MetroMicro == 'Metropolitan Statistical Area')] \n",
    "    global slope, intercept\n",
    "    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress([np.log(x) for x in df.Pop2018[which]],[np.log(y) for y in df.AttackRate[which]])\n",
    "    line, = ax1.plot([np.log(x) for x in df.Pop2018],[np.log(x)*slope + intercept for x in df.Pop2018],'k-')\n",
    "    ax1.set_xlabel('log(MSA Population)')\n",
    "    ax1.set_ylabel('log(Estimated attack rate)')\n",
    "    ax1.set_title('Attack rate vs. pop (r = {:0.3f}, p = {:0.3f}, exponent {:1.4f})'.format(r_value,p_value,slope))\n",
    "    # Look at how well correlation describes the growth curves for the selected dates\n",
    "    ax2.hist(df.r[which][pandas.isna(df.r[which]) == False],bins=np.arange(0.0,1.10,.10))\n",
    "    ax2.set_title('Correlations for city-by-city exponential growth')\n",
    "    # Make a q-q plot\n",
    "    #Calculate teh residual distribution of the data\n",
    "    residuals = [np.log(y) - (intercept + slope*np.log(x)) for x,y in zip( df.Pop2018[which], df.AttackRate[which])]\n",
    "    (osm, osr), (qqslope, qqintercept, qqr) = scipy.stats.probplot(residuals,fit=True, plot=ax3)    \n",
    "    ax3.set_title('Q-Q plot against normal (slope = %f, r = %f)' % (qqslope, qqr))\n",
    "    plt.show\n",
    "    \n",
    "widgets.interact(plot_some_figures,df = widgets.fixed(df))\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fec4cc6586634d50abda25613cebf317",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Select(description='Pick a city to examine specifics', options=('Abilene, TX', 'Akron, O…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def lookup_a_city(df,pickacity):\n",
    "    w = df.index[df.Title == pickacity]\n",
    "    if len(w) > 1:\n",
    "        print('Uh-oh, more than one city by that name!')\n",
    "    else:\n",
    "        w = df.index[df.Title == cityselector.value][0]\n",
    "        print(str(df.Title[w]))\n",
    "        print(str(df.MetroMicro[w]))\n",
    "        print('Population (2018 ACS estimate): %d ' % (df.Pop2018[w]))\n",
    "        print('Covid cases by ' + date_range_slider.value[1].strftime(\"%B %d, %Y\") + ': ' + str(df.COVIDEnd[w]))\n",
    "        if pandas.isna(df.AttackRate[w]):\n",
    "            print('There was not sufficient data (or another error occurred) to estimate a growth rate')\n",
    "        else:\n",
    "            print('COVID-19 attack rate (from regression): %f' % (df.AttackRate[w]))\n",
    "            print('Correlation for that regression: %f' % (df.r[w]))\n",
    "            print('Subsequent R: %f' % ((df.AttackRate[w]*4.5) + 1))\n",
    "            print('Residual for the power-law regression: %f' % (np.log(df.AttackRate[w]) - (intercept + slope*np.log(df.Pop2018[w]))))\n",
    "\n",
    "cityselector = widgets.Select(options = df.Title[df.MetroMicro == 'Metropolitan Statistical Area'],description = 'Pick a city to examine specifics')\n",
    "widgets.interact(lookup_a_city,df = widgets.fixed(df),pickacity = cityselector, output = False)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output(outputs=({'output_type': 'stream', 'text': '\\n \\n \\n Data are gathered for cases from March 14, 2020 to March 30, 2020! Run the next box when ready.\\n', 'name': 'stdout'}, {'output_type': 'display_data', 'data': {'text/plain': '      CBSA                              Title                     MetroMicro  \\\\\\n0    10100                       Aberdeen, SD  Micropolitan Statistical Area   \\n1    10140                       Aberdeen, WA  Micropolitan Statistical Area   \\n2    10180                        Abilene, TX  Metropolitan Statistical Area   \\n3    10220                            Ada, OK  Micropolitan Statistical Area   \\n4    10300                         Adrian, MI  Micropolitan Statistical Area   \\n..     ...                                ...                            ...   \\n921  49660  Youngstown-Warren-Boardman, OH-PA  Metropolitan Statistical Area   \\n922  49700                      Yuba City, CA  Metropolitan Statistical Area   \\n923  49740                           Yuma, AZ  Metropolitan Statistical Area   \\n924  49780                     Zanesville, OH  Micropolitan Statistical Area   \\n925  49820                         Zapata, TX  Micropolitan Statistical Area   \\n\\n     Pop2018  COVIDEnd  AttackRate         r  \\n0      43191         3         NaN       NaN  \\n1      73901         1         NaN       NaN  \\n2     171451        11         NaN       NaN  \\n3      38247         3         NaN       NaN  \\n4      98266        15         NaN       NaN  \\n..       ...       ...         ...       ...  \\n921   538952       160    0.315987  0.992818  \\n922   174848        15         NaN       NaN  \\n923   212128         6         NaN       NaN  \\n924    86183         2         NaN       NaN  \\n925    14190         0         NaN       NaN  \\n\\n[926 rows x 7 columns]', '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>CBSA</th>\\n      <th>Title</th>\\n      <th>MetroMicro</th>\\n      <th>Pop2018</th>\\n      <th>COVIDEnd</th>\\n      <th>AttackRate</th>\\n      <th>r</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>0</th>\\n      <td>10100</td>\\n      <td>Aberdeen, SD</td>\\n      <td>Micropolitan Statistical Area</td>\\n      <td>43191</td>\\n      <td>3</td>\\n      <td>NaN</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>1</th>\\n      <td>10140</td>\\n      <td>Aberdeen, WA</td>\\n      <td>Micropolitan Statistical Area</td>\\n      <td>73901</td>\\n      <td>1</td>\\n      <td>NaN</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>2</th>\\n      <td>10180</td>\\n      <td>Abilene, TX</td>\\n      <td>Metropolitan Statistical Area</td>\\n      <td>171451</td>\\n      <td>11</td>\\n      <td>NaN</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>3</th>\\n      <td>10220</td>\\n      <td>Ada, OK</td>\\n      <td>Micropolitan Statistical Area</td>\\n      <td>38247</td>\\n      <td>3</td>\\n      <td>NaN</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>4</th>\\n      <td>10300</td>\\n      <td>Adrian, MI</td>\\n      <td>Micropolitan Statistical Area</td>\\n      <td>98266</td>\\n      <td>15</td>\\n      <td>NaN</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>...</th>\\n      <td>...</td>\\n      <td>...</td>\\n      <td>...</td>\\n      <td>...</td>\\n      <td>...</td>\\n      <td>...</td>\\n      <td>...</td>\\n    </tr>\\n    <tr>\\n      <th>921</th>\\n      <td>49660</td>\\n      <td>Youngstown-Warren-Boardman, OH-PA</td>\\n      <td>Metropolitan Statistical Area</td>\\n      <td>538952</td>\\n      <td>160</td>\\n      <td>0.315987</td>\\n      <td>0.992818</td>\\n    </tr>\\n    <tr>\\n      <th>922</th>\\n      <td>49700</td>\\n      <td>Yuba City, CA</td>\\n      <td>Metropolitan Statistical Area</td>\\n      <td>174848</td>\\n      <td>15</td>\\n      <td>NaN</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>923</th>\\n      <td>49740</td>\\n      <td>Yuma, AZ</td>\\n      <td>Metropolitan Statistical Area</td>\\n      <td>212128</td>\\n      <td>6</td>\\n      <td>NaN</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>924</th>\\n      <td>49780</td>\\n      <td>Zanesville, OH</td>\\n      <td>Micropolitan Statistical Area</td>\\n      <td>86183</td>\\n      <td>2</td>\\n      <td>NaN</td>\\n      <td>NaN</td>\\n    </tr>\\n    <tr>\\n      <th>925</th>\\n      <td>49820</td>\\n      <td>Zapata, TX</td>\\n      <td>Micropolitan Statistical Area</td>\\n      <td>14190</td>\\n      <td>0</td>\\n      <td>NaN</td>\\n      <td>NaN</td>\\n    </tr>\\n  </tbody>\\n</table>\\n<p>926 rows × 7 columns</p>\\n</div>'}, 'metadata': {}}))\n"
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\n",
          "text/plain": "<Figure size 864x864 with 4 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "02a53efdd21d46f1a3a4d1a07cc133f4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_519815054a0b4f3fadb34f12edf5f522",
        "IPY_MODEL_15fe8832f0b44d6bb611b0397e4754c8"
       ],
       "layout": "IPY_MODEL_3dddcf38518e4162bcb8eda87f3b4f39"
      }
     },
     "0347bdca7676423b94527aa329b85f64": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "03a8fd4b6a9d43048e298d4724ac8f3f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_3c231dd044784f2fbcfc656b222c2b7c",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\n \n \n Data are gathered for cases from March 13, 2020 to March 23, 2020! Run the next box when ready.\n"
        }
       ]
      }
     },
     "043268d8a1554259bb091f8029b4c73a": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_3b84b7b540b147fb976657d33a414826",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Albany, GA\nMetropolitan Statistical Area\nPopulation (2018 ACS estimate: 149917 \nCovid cases by March 19, 2020: 19\nCOVID-19 attack rate (from regression): 0.472420\n"
        },
        {
         "ename": "TypeError",
         "evalue": "cannot convert the series to <class 'float'>",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-61-9b207d895cca>\u001b[0m in \u001b[0;36mlookup_a_city\u001b[1;34m(df, pickacity)\u001b[0m\n\u001b[0;32m     13\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'COVID-19 attack rate (from regression): %f'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAttackRate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Correlation for that regression: %f'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Residual for the power-law regression: %f'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAttackRate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mintercept\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mslope\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPop2018\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    110\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    111\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mconverter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 112\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"cannot convert the series to {converter}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    114\u001b[0m     \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mf\"__{converter.__name__}__\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mTypeError\u001b[0m: cannot convert the series to <class 'float'>"
         ]
        }
       ]
      }
     },
     "044a319e290d4ac287a470a7ec9ef7c0": {
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      "model_name": "VBoxModel",
      "state": {
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       "children": [
        "IPY_MODEL_51316b0c0bd44fd98c1b63384c06ccc5"
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     },
     "05201612afab45509beba912fe8e3dd5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "05310a24a6924161bfe28c601b6853f9": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
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      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_0c8f02da573749e08f71eba59369f9d3",
        "IPY_MODEL_84a59d9f09ed438cb2627833335c3926",
        "IPY_MODEL_cb001109b05444ecb86385c931e8e517",
        "IPY_MODEL_b99926e5ffeb456598180b190c349eae"
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       "layout": "IPY_MODEL_e03500cf871e4e0ab602272e4a9ceaba"
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     },
     "07094c2490964710bd7f3a82c9e4e011": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
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     "0847fc2452e84bbd9d844a0b640ad7bb": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "084be3b4e77742749707dfb3dd39ce9c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "0a7795e0ecf1446f99adf5e98d470cef": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_87459ad315c14f8cacb82276806590c4",
        "IPY_MODEL_b75b14e65651447fb2504fb082c7fe6d"
       ],
       "layout": "IPY_MODEL_9ce753d9bb5949a9a3677e0f249b31f1"
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      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_3b746947815a4239a60b1cd159dcc515",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Abilene, TX\nMetropolitan Statistical Area\nPopulation (2018 ACS estimate: 171451 \nCovid cases by March 19, 2020: 0\nThere was not sufficient data (or another error occurred) to estimate a growth rate\n"
        }
       ]
      }
     },
     "0bacf1f2c8d74863a888bb5e5b045d31": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_1f029f354ec041b68df096ec5d181e93",
        "IPY_MODEL_707c606871f241c0b98de9b99100595e",
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        "IPY_MODEL_cbeb2c4c8828467b84d7dbf902379599",
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      }
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     "0bf52a79fd4a4fe4b3bc0f0b604e886d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "0c8f02da573749e08f71eba59369f9d3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DatePickerModel",
      "state": {
       "description": "Start date",
       "disabled": false,
       "layout": "IPY_MODEL_5fbce1e6c67e4b158be9ab673a3ce0ae",
       "style": "IPY_MODEL_aafe87fef9a54296b8abd938b08cb217",
       "value": {
        "date": 10,
        "month": 2,
        "year": 2020
       }
      }
     },
     "0d258e40087f419cafe8e9ed9cd1557f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
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     },
     "0e0310e6580d404c9b4ced1e7b2d41c3": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_80b6f3c5d4e948f296ba83d4bba4e066",
       "outputs": [
        {
         "ename": "TypeError",
         "evalue": "unsupported operand type(s) for -: 'datetime.date' and 'datetime.datetime'",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-18-eb78f8c10d5c>\u001b[0m in \u001b[0;36mcoallate_analyze\u001b[1;34m(dateStart, dateEnd, msas, pop, covid, deaths, output)\u001b[0m\n\u001b[0;32m     19\u001b[0m     \u001b[0mcovidnotfound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[0mcovidnotincl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m     \u001b[0mdays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdateEnd\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mdateStart\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdays\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m     \u001b[0mcol_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'CBSA'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Title'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'MetroMicro'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Pop2018'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'COVIDEnd'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'AttackRate'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'r'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[1;31m#Leaving out Puerto Rico because it is not in the census data; I'm sorry! I hope to remedy this.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'datetime.date' and 'datetime.datetime'"
         ]
        }
       ]
      }
     },
     "0ede70ebd3f242a4b198c2e42d0de2a6": {
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      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "0f98ea4fd82049e59f22ddf4ea838209": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "0fb24961bf4b4d14b9be818152261808": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
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         "text": "Abilene, TX\nMetropolitan Statistical Area\nPopulation (2018 ACS estimate: 171451 \nCovid cases by March 19, 2020: 0\n"
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        "Hilton Head Island-Bluffton, SC",
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        "Homosassa Springs, FL",
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       "layout": "IPY_MODEL_b2002be59cd7406b9e63400d93bc08a0"
      }
     },
     "1961036d6fe64ae48b31f5cbe2c9a22b": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_7474fb6718f94753bc82b5f58f4eaff7",
       "outputs": [
        {
         "ename": "KeyError",
         "evalue": "datetime.date(2020, 3, 19)",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2645\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2646\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
          "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
          "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
          "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
          "\u001b[1;31mKeyError\u001b[0m: datetime.date(2020, 3, 19)",
          "\nDuring handling of the above exception, another exception occurred:\n",
          "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-73-8922b9659080>\u001b[0m in \u001b[0;36mcoallate_analyze\u001b[1;34m(dateStart, dateEnd, msas, pop, covid, deaths, output)\u001b[0m\n\u001b[0;32m     42\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfips\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mcovid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountyFIPS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m                 \u001b[1;31m#This data sometimes contains duplicates; it is assumed that together these are the true total.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m                 \u001b[0mcovid_last\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcovid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcovid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountyFIPS\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mfips\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdateEnd\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeaths\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeaths\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountyFIPS\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mfips\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdateEnd\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0md\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdate_range\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdateStart\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdateEnd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m                     \u001b[0mcovid_series\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcovid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcovid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountyFIPS\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mfips\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeaths\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeaths\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountyFIPS\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mfips\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2798\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2799\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2800\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2801\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2646\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2648\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2650\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
          "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
          "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
          "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
          "\u001b[1;31mKeyError\u001b[0m: datetime.date(2020, 3, 19)"
         ]
        }
       ]
      }
     },
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      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
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       "children": [
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       "children": [
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       "disabled": false,
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      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DatePickerModel",
      "state": {
       "description": "Start date",
       "disabled": false,
       "layout": "IPY_MODEL_d9ef5ab5401844da9c2ebd1de04ec90a",
       "style": "IPY_MODEL_a68c96c7eb5c4b5994f77b2764b321c5",
       "value": {
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       }
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      "model_module": "@jupyter-widgets/base",
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      "state": {
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       "outputs": [
        {
         "ename": "TypeError",
         "evalue": "'<=' not supported between instances of 'list' and 'int'",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-17-14e369976e99>\u001b[0m in \u001b[0;36mcoallate_analyze\u001b[1;34m(dateStart, dateEnd, msas, pop, covid, deaths, output)\u001b[0m\n\u001b[0;32m     52\u001b[0m         \u001b[0mrow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcovid_last\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m         \u001b[1;31m#Now calculate the r\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 54\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcovid_series\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcovid_series\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcovid_series\u001b[0m\u001b[1;33m<=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     55\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0moutput\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     56\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' had too few cases for inclusion'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mTypeError\u001b[0m: '<=' not supported between instances of 'list' and 'int'"
         ]
        }
       ]
      }
     },
     "2213b2a2dba34974940dd8f0ef778c3b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "22162bb424774ab0aabf041758c793e4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_85c4ebbc9eda43898ed7071a0a7a6813"
       ],
       "layout": "IPY_MODEL_a44f92779d32431da0007d7755a31167"
      }
     },
     "2468113bd1be43b69cacad17350a8f6a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_9f6dd6036bc2435ea2d1c88d43bf5768",
        "IPY_MODEL_9c59458108ab40c6b4204503b118040d"
       ],
       "layout": "IPY_MODEL_8cc4da71687643db8c0f6e697b7a43b0"
      }
     },
     "24d47f3cac9b4b4e930abb997f556a7d": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_cabb4806dafc400f9269755dc295524f",
       "outputs": [
        {
         "name": "stderr",
         "output_type": "stream",
         "text": "C:\\Users\\drewc\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:64: RuntimeWarning: divide by zero encountered in log\nC:\\Users\\drewc\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2449: RuntimeWarning: invalid value encountered in subtract\n  X -= avg[:, None]\nC:\\Users\\drewc\\Anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n  return (a < x) & (x < b)\nC:\\Users\\drewc\\Anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n  return (a < x) & (x < b)\nC:\\Users\\drewc\\Anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n  cond2 = cond0 & (x <= _a)\n"
        },
        {
         "data": {
          "image/png": 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          "text/plain": "<Figure size 720x360 with 2 Axes>"
         },
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        {
         "name": "stdout",
         "output_type": "stream",
         "text": "New Orleans-Metairie, LA\nMetropolitan Statistical Area\nPopulation (2018 ACS estimate): 1270399 \nCovid cases by March 20, 2020: 449\nCOVID-19 attack rate (from regression): 0.436933\nCorrelation for that regression: 0.980658\nSubsequent R: 5.412963\nResidual for the power-law regression: 0.659197\n"
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      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "35df78b266be4f59ac9009b1d684cd34": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_509aedf638604e0585832f6aad70e7f7",
       "outputs": [
        {
         "data": {
          "image/png": 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u28Ue27h7qRP6A8BLzGy7Eq89TNiJw4eZjSckDQ9ViLV4Wj2x1LuecU8QrsC6YtOmFcVaj9uA3WPPVxCqiEJAYVu8gqGNTusxl3BQuDY+0d1Xufub3X2Kux9A2OZ/ic9jZrsQDpw/LxF3pe1UbDSV99liu8Ri2IZQBfJwiflWAHsXelZF9qb09voHMDrq/FCwT2zeI4Cvx367AP9rZXrmEl0pFk0rua1LGLQ93H2xu+/l7pMJbVq6CFUaBXsSqkvKWUqoFnpV9L6lwJsI1RzXVYh/pOLfUwchoX84euwSTSso/p2U+47/GU3ujM1b6uIRM3s9oT3a2wi1BdsREuJafstQdOyJYtxEaCNXF3e/yd1PIDQu/zWhRKVUzO8gVIPOixIGCMeV9YSmJoXj17YeGrOXcjZh3faOjmHvIrYvuvsv3P3gaN2c0ESnnHLfQ/G2gcHfYXFStZShSdUDhFLJ+HF5grvHL5Dq2Q+rHbtriWkQd/8noeAkfhx+gFClHo97a3f/E4QRCwi/058A37ShQ3bsEvt/GgPHrlrOdeVUjKmKits4KoB5hNi5h8HHxmIrSsz7mLs/WZhQ4aJ3JOeOI4C3xo7RryNs/+9WiKt/HaLOPrtR+VhatUTvRELR4SxCkfK+hAP0HxnoMfcYoZ1CXPG0CYTqwScJB7uvFF6IrpB/DHzLzKZGpX8HFu1oKwjF1d8zs3p6/U4gHGyeMbOXEHbkSnFeCuxuZu82szHR4zVmtmfR+3D3RwhFz+ea2aRo3sKP9BfAqRaGydgqWt8/u/vKOmKvOZZhrGd8PTYTDuALzWyChaFPPs7gHqf1uJxwECq4BNjLzE4ys3GE9gy3RaU+Q0TrOY6wb442s3ElSiZLlchhZntG6zDWzN5FuED4VtF7301oB1JcgnwB8FozOzL6vI8STlZ3RKUN7zCzbaL9802EE9s1sc92Mzu0wnY51swOttB79kuE/aHUlf+1hN/cRywMD3B6NP2a4hmjA/rFwH+a2XgzO4hwQXV+NMvuhAND4bcL8BbgEjPbzszeFG3f0WbWQziZ/K7oY4ZsazPrMLP3R/u9mdn+hI5E/xObZ79oW00hNDn4bdF3fgjh91POUsIx5vaohPVa4H2Ek+yaMu8pu5/XYT8zmxsdQD9KOG7dCPyZkLB9OtpHDyVsy1/G3lvyO47ifYgwpMwoM3sP5S8SJhASszWE/f8LDL6ifwyYXpRwxl0IfMzMXh4lOV8B/p+7b6pnI0S/oR4z2zZK3tYS9svi+V5FaC5xYvx7iY7rPwT+r0XD6FgY/uhNZT5yAlHD+ijp+FTsM/Yws8OjY+kGwrFuSCwx5X5rlxOOqe+M9vm3E85tl0bv+xMh6dqfUMW7gqhmhYGLi78Aa83sM2a2dfR97mVmr6kQTyXVjt21xFRK8XH4POBMi3rkm9m2ZtYd/W+E0rwfEaofHyFst7gPmdnOUYyfZWDkhJGc68rGVIPHgJ2t/GgEEC7mPxcdp2YSmrX8tMK87zWzWRZ6wH6uxLxvJZSc/aFo+iXAJAvDo4wys3mEUskbajhWnkLIqQrH6GWE0r7CECw/Bz4e/XamEi4M4nHtTyhNLC6pHqxUfa4P1P1eSSjGLZ7+NkKR6mhCnfEt0QYoNCI9gdC+4xlC+7ptCI39nyMU8/4rgzsDbE2oZnqIgV65pXrdziF8wUN6fVK63chUwgnieULpx/uLlndgNP1pBtr97EHoZLGGkJheA+xbZvu8hJDdPxYt4+LYa6cRrqqeItaTygfaHuxWtKxS08rGwuAGvcNZz/j2n0RIdNYQrrK+QFGv22qxxl7bnlDdtXVs2pGEtp3rozinx147Dzgv9vyn0fLjj1Nir+9EOBEO+XzCiXkN4YR8PTCnxDx3EmsXUvTaXEKbtrVRnIWOL1MIiccz0Wt/A/4t9r6dCfv2kIa7sXUq9AR8nrB/v7zC7+5VhPYy6wmdPl4Ve+2zxDprEPbBX0frvBp4Z4Xlxr/zKYSSsuei9boReGPR/CW3NSEJv5Kwbxf2uc8yuN3I9dGynyIkeuNjr42L9pEdKsRaaOvzxei5EdppfT82z3Tq2M+LfzclPvMsBve6/StRB4To9dnRfvAsoSr6rbV+x4S2n/dH2/qb0XKGtNEjtNn5UbSfPUIYamglUZtnQmnJ9dE63hxNu5bBvW6/QPgdryEapqfU9ip+b9G2GBt9x09HsdxE1F6Kwb1uz4r2kXjP2yti3/NXCG1d1xI6anykzLafTdjnnyecTz4R+4y9CQlWYX+6lFjPznp+a4T2tcuj73A5Q3um/i+De1QuBu4ommcqIaF+NNo+N8a+n7MoakNZJsaajt21xlTiM/YiFJDEf5PvJhy71kb7x4+j6WcQSqXGxmJaw0BHnJUM9Lp9hnDO64wtt+ZzHUW/v3IxVXsvYf+8LPrMJ8psg60IhUhrCefoj8demxZt82mxaR+P5ltLKNncqmh5vwO+VOazXh+tx/OEZK2w7aoeK4uWcy2D2+gZ8LXo/U9F/8e/0+9R5jcVf1g0s0jDmNlXgMfd/Zy0Y0lCVHo4293PLPP6Twknrc8lGliLMrMPEzqifDrtWOIsDNC9m7u/axjv/Sn6jlOn72GAmf0C+JW7/3qEy1lJSD5+35DApCGikvKlhIKADZXmzfLAvNKi3P2zaceQJHcfbjV3W3L376Qdg0jeuXu5triSA+7+OEWdBctp6XvdioiIiMjwqepWREREJKdUoiciIiKSU2qjl5Ltt9/ep0+fnnYYIiIiiVm+fPkT7l5y4GxpDiV6KZk+fTrLli1LOwwREZHEmFnlMd+k4VR1KyIiIpJTSvREREREckqJnoiIiEhOKdETERERySkleiIiIiI5pURPREREJKeU6ImIiIjklBI9ERERkZxSoiciIiKSU0r0RERERHJKiZ6IiIhITinRExEREckpJXoi0rZ6e2H6dOjoCH97e9OOSESksUanHYCISBp6e2H+fFi3LjxftSo8B+jpSS8uEZFGUomeiLSlBQsGkryCdevCdBGRvFCiJyJtafXq+qaLiGSREj0RaUvTptU3XUQki5ToiUhbWrgQOjsHT+vsDNNFRPJCiZ6ItKWeHli0CLq6wCz8XbRIHTFEJF/U61ZE2lZPjxI7Eck3leiJiIiI5JQSPREREZGcUqInIiIiklNK9ERERERySomeiEiO6P69IhKnXrciIjmh+/eKSDGV6ImI5ITu3ysixZToiYjkhO7fKyLFlOiJNIjaRknadP9eESmmRK8CM3uJmV1tZndHfyeVmW+zmd0SPX6TdJySvkLbqFWrwH2gbZSSPUmS7t8rIsWU6FX278D/uPsM4H+i56Wsd/d9o8fxyYUnrUJto6QV6P69IlLM3D3tGFqWmd0FHOruj5jZy4Br3X2PEvM97+7b1LPsOXPm+LJlyxoVqqSsoyOU5BUzgy1bko9HRKQVmdlyd5+TdhztRCV6le3g7o8ARH9fWma+cWa2zMxuNLMTyy3MzOZH8y1bs2ZNM+KVlKhtlIiItKK2T/TM7Pdm9vcSjxPqWMy06ArlncA5ZvaKUjO5+yJ3n+Puc6ZMmdKQ+KU1qG2UiIi0orYfMNndjyz3mpk9ZmYvi1XdPl5mGQ9Hf+8zs2uBVwH3NiNeaU2FNlALFoShLKZNC0me2kaJiEia2r5Er4rfACdH/58MLCmewcwmmdlW0f/bAwcBtycWobSMnh5YuTK0yVu5UkmeiIikT4leZV8F3mhmdwNvjJ5jZnPM7L+jefYElpnZrcAfgK+6uxI9ERERSV3bV91W4u5PAkeUmL4MeF/0/5+AVyYcmoiIiEhVKtETERERySkleiIiUjPd6k8kW5ToiYi0mN5e2H77MOC2Wfi/FRIq3epPJHuU6ImItJDeXnjPe+DJJwemPfkknHpq+gmVbvUnkj1K9EREWsiCBfDii0Onb9yYfkK1enV900UkfUr0RERaSKWkKe2ESrf6E8keJXoiIi2kUtKUdkKlW/2JZI8SPRGRFrJwIYwdO3T6mDHpJ1Q9PbBoEXR1hU4iXV3hue4CI9K6NGCyiEgLKSRNZ5wx0CFj8mT49rdbI6Hq6WmNOESkNkr0RERajJIpEWkUVd2KiIiI5JQSPRGRNqG7Woi0H1Xdioi0gcJdLQoDHhfuagGqJhbJM5XoiYi0Ad3VQqQ9KdETEWkDuquFSHtSoici0gZ0VwuR9qRET0SkDeiuFiLtSYmeiEgb0F0tRNqTet2KiLQJDcQs0n5UoiciIiKSU0r0RERERHJKiZ5IzuluCCIi7UuJnkiOFe6GsGoVuA/cDUHJXjraKelup3UVaWVK9ERyTHdDGLlGJSylku53vzv0gB3ucls1mdIFhkjrUKInkmNZvBtCKyUvjUxYSiXd7uHvcJbbysmULjBEWod54UgjiZozZ44vW7Ys7TAk56ZPDwlAsa4uWLky6WiqKyQv8SShszO98d4auf06OgYSu3LqWW4rf7fl1tUMtmxJPh5pHWa23N3npB1HO1GJnkiOZe1uCK1WEtTIEtFabjVWz3JbubRWt1sTaR1K9ERyLGt3Q2i15KWRCUuppHsky23lZCprFxgieaZETzKtldpztaqenlCVt2VL+NuqSR60XvLSyIQlnnRDSLxHstxWTqaydoEhkmdK9CSzWrkxugxPqyUvjU5YCkm3O5x//siW2+rJVJYuMETyTJ0xUqLOGCPXyo3RZfh6e0ObvNWrQ0newoVKEkTyQp0xkqcSPcmsVmvPJY2hkqBsUfMJkdamRE8yq9Xac4m0m2Y0n1DiKNJYSvQks1qtPZe0ByUiAxo9HI7a3Yo0nhI9yaxWb4wu+aNEZLBGN59otXEURfJAnTFSos4YItmjDkCDNXp76I4a+afOGMlTiZ6ISI3UAWiwRjefULtbkcZToiciUiMlIoM1uvmE2t2KNJ4SPRGRGikRGaqRw+Go3a1I441OOwARkawoJBwa0Ll5enq0PUUaSSV6IiJ10IDOrUtD3zSGtmO+KNETkUzSyUjiNPRNY2g75o+GV0mJhlcRGb7CySg+5lpnp9pztTMNfdMYzd6OGl4leUr0UqJET2T4dFKXYhqDrzGavR2V6CVPVbcikjkazy5ZWagm19A3jaHtmD9K9EQkc3QySk5W2mxp6JvG0HbMHyV6IpI5OhklJyv3n9UYfI2h7Zg/aqOXErXRExmZ3l6NZ5cEtX2TRlIbveRpwGQRySQNrJuMadNKd3xRNblINqjqVkQkRa3e0UHV5CLZpkRPRCQlWejooDZbItmmRK8CM+s2sxVmtsXMyrYpMLOjzewuM7vHzP49yRhFktTqpU9Zk6WODo247Zv2H5HkKdGr7O/AXOC6cjOY2Sjge8AxwCzgX8xsVjLhiSQnC6VPaRluAtNO4wFq/xFJhxK9Ctz9Dne/q8ps+wP3uPt97v4i8EvghOZHJ5KsrJQ+JW0kCUwWxwMcblKr/UckHUr0Rm4n4IHY8wejaUOY2XwzW2Zmy9asWZNIcCKN0k6lT/UYSQKTtY4OI0lqtf+IpKPtEz0z+72Z/b3Eo9ZSOSsxreTghO6+yN3nuPucKVOmDD9okRRksfQpCSNJYLLW0WEkSa32H5F0tH2i5+5HuvteJR5LalzEg8Ausec7Aw83PlKRdGWt9CkpI01gRtLRIenODSNJarX/iKSj7RO9BrgJmGFmLzezscA7gN+kHJNIw2Wt9CkpaSUwaXRuGElSq/1HJB1K9Cows7ea2YPAgcBlZva7aPpUM7scwN03AacDvwPuAH7l7ivSilmkmRo1zEaejCSBGUmJXBqdG0aa1Gr/GaChZiQputdtSnSvW5H2ViiRiydrnZ21J4lp3YNW9xgeuZF+91mme90mT4leSpToibS36dNL30O2qyuUdjX7/a2k3ZLHPH139VKilzxV3YpI2ypXfZZEtdpIhxvJS+eGdhxIWUPNSJKU6IlIWyqXYHzwg8kkHo3orduszg1Jth9rx4GUNdSMJEmJnoi0pXIJxqJF5ROPRiZAjSiRa0bnhqRL2NqxdCsvpbGSDUr0RHKs1Xr2tVI85RKJzZtLTy8kPI1KgJIcbqSe7Z50CVs7lm5pqBlJlLvrkcJjv/32c5FmuuAC985O924GCKMAACAASURBVJCWhEdnZ5iueNy7ugbHUniMGlXf9K6usA5dXe5mA89bRb3b3az0epq1RnySbcAyb4FzcDs9VKInkrCkSrVare1Tq8VTrvps/vzS05Mq6Wu0erd70iVsKt0SaS4leiIJSrL9UxJtn+pJWlutLVa5BOPcc0tP7+oqvZxRo1orgS1W73ZPo/2YBlIWaR6No5cSjaPXnpIcP6vZn/XBD8J55w0etLfSoK9ZHzus3CC3xUleQbMHLq7VcLZ7u41rJ8nROHrJU4meSIKSLNVqZslMb+/QJA8ql2RlvadhuRLAciV9rdKZYDjbXSVsIvmhRE8kQUm2f2pm26cFC0rffgvKJ615aItVKgFq9QQ2D9s9Da3UQ1xkJFR1mxJV3banvNzjstx9ViE7VbGNlHRVp6pWmysvv9NWpKrb5KlETyRBzShdSaPkoVwJpFnrlGTVqhHbL17St3BhSMKa9X204y3DktZqPcRFRiTt8V3a9aFx9KQR0hqD7AMfGPyZhccRRzT3cxut0dsvie+j3Ph/XV2N+4xGaOWxBatJeizBdoLG0Uv8oRI9kQxLq+Th8stLT7/nnuZ+bqMNZ/tVKgFM4vtotWFqSsl6qWM73q2j4MUXX+Syyy7jlFNO4fzzz087HGkAtdFLidroSSOUayvX7KE90vrcRqt3Paq13arWdrERbeqyMExNFmKspB3a6G3YsIGrrrqKvr4++vr6eOGFF0rO1+gcQW30kqcSPZEMS6vkIS8lHvWuR7USu0ptF2st3arWZrDVe/lC40sdk26HmqeeyuvXr+fiiy/mne98J2PGjMHMMDO23nprTjjhBC644IIhSd4hhxzCd7/7XR5//PGUopaGSrvuuF0faqM3clluA9QoabXRy8v9SRt9H9hSyyv3nlJt6mqNp9X3/Ua2I8zLvtZMF1zgvssuzzv8yjs7u93MHKj6OOKII/y8887zxx57LLFYURu9xB+pB9CuDyV6I6OD/4C0TvqtnmzUqp71qCWBKV5eqfnLNezPSkeLahr5+8zLNmmUtWvX+oUXXuhz586tKZkD/KijjvIf/vCH/vjjj6cdvhK9FB5qo5cStdEbmay3AZJsGk7brXr21by0fYTGjfWXp21Sj7Vr1/Kb3/yGxYsXs2TJkhrfdQzQDZxAV9dLWvJYqDZ6yRuddgAiw5GFnoeSP4VEpZ4EZuHC0slhqTZ106aVTgqz1vYRwjZpRJu2PG2TUp555hmWLFlCX18fl112WU3vectb3sK8efN4y1vewuTJk0omwjoWSoESPcmkvB/8pXXVm8DUkxzWkxS2i7xsk6eeeopf//rX9PX1ceWVV9b0nhNPPJHu7m7e/OY3M3HixJLz6Fgo1eQm0TOzDmAfYCqwHljh7o+lG5U0S14O/tIeak0Oh1NimHdZ2yZPPPEEl1xyCX19fVx99dU1veekk05i3rx5HHfccUyYMKGuz9OxUKrJfBs9M3sF8BngSOBuYA0wDtgdWAf8APiZu7dUaw610Rs53e9TRNLy2GOP9Sd011xzTdX5zYzu7m66u7s55phjGD9+fMNiydKxUG30kpeHRO9C4PvAH71oZczspcA7gafd/WdpxFeOEr32kqUDsYgMeOSRR7joootYvHgxS5cureEdYzjwwHl86lPdHH300Wy99dZNjzFLlOglL/NVt+7+LxVeexw4J8FwRIYo7qlZGDAXlOyJtIoHH3yQiy66iL6+Pm644Yaq848bN47u7m62334e5513FOvXj+t/7dZbw+9dOZ60gtzcGcPMOs3s82b2w+j5DDN7c9pxiaR1P1rJt6TvFtEqnz1Sq1ev5lvf+hYHHnhg/10izIxddtmFj370o0OSvPHjx3PKKadw2WWXsWHDhv6xydavX8/Pf/5zLr74+EFJHuj3La0l8yV6MT8BlgMHRs8fBPqAS1OLSAQNBdNoqgZPt5Q4KyXU999/P4sXL6avr4+bbrqp6vwTJ06ku7ubyZO7+eUvD+eBB8bUtH/p9y2tLvNt9ArMbJm7zzGzv7r7q6Jpt7r7PmnHVora6LUPDe7cOO1ws/lapLlPtdr+fM899/QndDfffHPV+SdNmtTfKeKQQw5hzJgx/a81e0BsURu9NOSm6hZ40cy2JtzypdAb94XKbxFpvizchD4rVA0epFmKNJLPHkmV71133cWXv/xl9tlnn0FVrjNmzODMM88ckuRNmTKFD3zgA1xzzTVs3Lixv8r1qaee4gc/+AFHHnnkoCQPhrd/6fctLS/te7A16gEcBSwlDK/SC6wEDks7rnIP3eu2Plm/r2rW42+kkWwLM6/5vrEj/axqall2sz4/zfu/Dveza73/7YoVK/yss87y2bNn13Qf1x133NE//OEP+9KlS33Tpk0jWrd696/4uun3XRt0r9vEH6kH0NCVgcnAccCbge3TjqfSQ4le7Rp5g3RJ10i/y3qSjGbuN7UsO+3Pb5ZSn23m/oEPVH7f4O9ui8OtDp/30aNn1pTQ7bTTTv7Rj37Ub7jhBt+8eXNT1i3NBLpdKNFL/pF6AA1bEfifWqa1ykOJXu108M2PkX6X1RKceMnKqFHN229qWY9m77dpliJ94ANDS79KJZpbtmzxm2++2c8880yHGTUldF1dXf6JT3zCb7zxRt+yZUtyK+W6qEyCEr3kH5nvjGFm44BO4A/AoYBFL00ErnD3PVMKrSJ1xqhdR0c45BYzgy0tdb8TqaYR32W5XrelGtKX0oj9ppb1yPN+O7QDggPLmTixj8mT+7j//vtrWMquQDc77jiPhx/eDzOr+o4kqFd3c6kzRvLy0Bnj/YRhVWZGfwuPJcD3UoxLGqTczbkbedPuLI8LlgWF7VvuurKe77KnJ/Rm3LIl/I3fC7VaklfvZ9W7jPj0JPbbpLk7f/7zn1m16pNAF+G62ginktewdu3XhiR5hc4SX/7yzWy99RYGCu/upbPzq3zjG3NaJsmD8vuXSGalXaTYqAfw4bRjqOehqtvaNbs6RdU1pVWqGqyn2rDU9m3Gti7XkD6PbfSabfPmzX799df7GWec4TvttFNNVa4w07fd9vN+2223la1yVacFQVW3iT9SD6ChKwN7AW8D/rXwSDumcg8levVp5glCbQCHqpSk1JvAlNu+hW3c7J6oHR0D/0+e3NgLhLR63TbKpk2bfOnSpX766af7DjvsUFNCN3v2bJ879ywfN25FJpNYSZcSveQfmW+jV2BmXyS00ZsFXA4cA1zv7vPSjKsctdFrHXluSzVclQaBhfoGiG3k9q3UfqpUG71Ro2Dz5sHLaMcBljdv3sx1111HX18ffX19PPHEE1Xfs/fee9Pd3c28efOYOXPmkNfVlk2GQ230UpB2ptmoB/A3QkORW6PnOwC/TTuucg+V6LUOlegNVWk8sXrHGmvU9q21urRQgjZ5cunPrfTZrV4CV83GjRv96quv9vnz5/ukSZNqKqF79atf7Weffbb/4x//SDt8aQOoRC/xR+oBNGxF4C/R3+WEHrcGrEg7rnIPJXqto9njnWUxcaiUnNWbuI1k+45kuJRKVcalktIstal78cUX/YorrvD3vve9PnHixJoSute85jX+ta99ze+99960w5c2pkQvhfwo7QAatiJwLrAdcBpwN/BX4Cdpx1XuoUSvtTQjIctS4lCskW30Csurd/tW68RRrSSxUueMUslhK5bsvvDCC37ppZf6ySef7OPHj68poXvta1/r3/zmN33lypXpBS5ShhK95B+5aKNnoW/+zu7+QPR8OjDR3W9LM65K1EYv/7J+s/Nq7eGa3T6r3PYrVm57lnu/GZx//tB402yruWHDBq666qr+NnQvvFD9Nt0HHXQQ3d3dnHTSSey8887NDVCkQdRGL3m5SPSgf+fZL+04aqVEL//UyWNkym2/uEodK0p1zjCD006Dc88dOn8Sifn69eu54oorWLx4MX19fWzatKnqew455BDmzZvH3LlzmTp1amMCkarU2aQ5lOglLw8DJhfcaGavSTsIkYIsDJjbygNFl9tOo0aFhK2rq3Lv2Z6e8HpX18D8559fOsmDcCLv7Bw8rbMzTK/XP//5TxYvXszb3/52Ojo6MDPMjM7OTk466SQuvPDCIUne4Ycfzve//30effTRQdUu1157LaeffrqSvAQVLhJWrQoXG6tWheet9PsQqVnadceNegC3A5uAe4HbCL1wb0s7rnIPtdHLv3rasqXRaaPV2xCmEV+938Nzzz3nF154oZ900kk1tZ8DfK+93uhjxy5yeLwlt7u0ZnvNvEBt9BJ/5KnqtqvUdHevoZVP8lR12x5qqf4pVcWYxFhvWWhD2CrVZ2vXruW3v/0tfX19LFmypKb3HH300XR3d3PCCScwefLk/ulZ2O7tTs0umkdVt8nLTaKXNUr0pCCtE79OZkM988wz/OY3v6Gvr49LL720pvccd9xxdHd3c/zxxzNp0qSq82u7tz4l482jRC95o9MOQKTdrV5d3/RGmTat9MmsldoQNstTTz3Fr3/9axYvXswVV1xR03tOOOEE5s2bx1ve8ha23XbbYX92O2/3rFi4sHQp+3Daa4qkTYmeSMrSOvG3w8nsiSee4JJLLqGvr4+rr766pvfMnTuX7u5ujjvuOCZMmNDwmNphu2ddoXnAGWfAk0+G/7feOr14REYiN4memR3j7lcUTTvN3c9LKyaRWqR14i+czFqhDdxIPf7441x88cX09fVxzTXX1PSe7u5uuru7OfbYYxk/fnyTIxyQp+2ed+vXD/z/5JPhdwr6riRbctNGz8z+BHzO3a+Jnn8GONTdj0k3stLURk/iWqXTQat79NFHueiii+jr62Pp0qVV5x89ejTd3d3MmzePo48+ms7i8VNEylA7veZQG73k5WkcveOBr5jZ681sIbB/NG3YzKzbzFaY2RYzK7tjmtlKM/ubmd1iZsre6tTKY7klpacnnDy2bAl/2z3Je+ihh/j2t7/NwQcf3D8GnZnxspe9jNNPP71EkrcV48e/i499bAnr16/vH1Zg48aN/OIXv2Du3LmpJXnav7MprbazIo2Wm6pbd3/CzI4Hfg8sB+b5yIsr/w7MBX5Qw7yHufsTI/y8tlM8tEhhYFJQstMOVq9e3X+XiBtvvLHq/J2dnf1VrmvWHMmHPrRV/77zz3/CD34A++3XOvuO9u/sUqcZyYvMV92a2XOEgUgt+juWMHCyA+7uExvwGdcCn3T3kqV1ZrYSmFNPoqeq20DVI+3h/vvv70/obrrppqrzT5gwoT+hO/zwwxk7duyQebKw72QhRiktrfEt805Vt8nLfImeuze+W1z9HLjKzBz4gbsvKjWTmc0H5gNM02UhoOqRvLnnnnv6E7qbb7656vyTJk1i3rx5dHd3c+ihhzJmzJiaPysL+04WYmwHw2kDq04zkheZT/QKzOytwDXu/mz0fDtCZ4xfV3nf74EdS7y0wN1rGwIfDnL3h83spcDVZnanu19XPFOUAC6CUKJX47JzTdUj2XTXXXfR19fH4sWLufXWW6vOv/322/d3injDG97A6NEjP/RkYd/JQox5N5Lq854eJXaSA2nfg61RD+CWEtP+2qBlX0uomq1l3rMI1bwV59O9boNWv99qu1uxYoWfddZZPnv27Jru47rDDjv46aef7kuXLvVNmzY1NbZa9p0k7yFc6rO0f6dP961tLehet4k/Ug+gYSsCt5WY9rcGLbtsogeMBybE/v8TcHS1ZSrRG5DkyViG2rJli992223++c9/3mfOnFlTQjd16lQ/44wz/Prrr/fNmzenFnulfSfJJKvSZ2n/TpeZl0z0zNKOrD0p0Uv+kfnOGAVm9mPgGeB7hJPRh4FJ7n7KCJb5VuA7wJRo2be4+5vMbCrw3+5+rJntClwSvWU08At3rzrUrTpjSNLcnVtuuaW/Dd3dd99d9T3Tpk3rb0O3//7709GRnRGZkuwIoU4Xram3F04+GTZvHvqavpt0qDNG8vKU6I0HPg8cSeiBexXwZXf/Z6qBlaFET5rF3Vm+fHl/G7r77ruv6nte/vKX97ehmzNnDmaWQKTN1dERym6KmYXxCrP6WVKbUr1mC9R7Nj1K9JKXm84YUUL372nHIZIUd+cvf/kLfX199PX1sbqGrpy77bZb/7Al++67by4SunIqdYRo9J1I1Omi9SxYUDrJGzVKSZ60l9wkemY2Bfg0MBsYV5ju7oenFpRIA2zZsoUbb7yxP6F76KGHqr5njz326E/oXvnKV+YmoasnQSt3D+Fjj238IMYLF8Kpp8LGjQPTxoxp/v2Kpbxy1z1btijJk/aSm0QP6AX+H/Bm4DTgZGBNqhGJ1GHLli1cf/31/W3oHn300arvmTVrVn9CN3v27ASiTE+9w2SUGwetVEnPunVh+kgSgOJcOie5dWaplFUkyFMbveXuvp+Z3ebue0fTlrr7IWnHVora6LWvzZs3c9111/W3oVuzpvr1yN57793fKWLmzJkJRNl6GtXhoRnt6dQZo/XozhatSW30kpenEr1CpckjZnYc8DCwc4rxSJvbtGkT1157bX+V69NPP131Pa961avo7u7mpJNOYvfdd08gyuxo1F0mmlHSoztgtB7d2UIkyFOi92Uz2xb4BGFIlInAR9MNSdrBxo0bueaaa/oTurVr11Z9z5w5c/p7ue66664JRJl9jUrQyrXdG0l7OlUTtibd2UIEsjMoVnVPu/uz7v53dz/M3fcDnko7KMmPF198kcsvv5xTTz2VbbbZBjPDzBg7dixHH300P/rRj4YkeQcccADf+MY3uP/++wcNYHnTTTfx6U9/WkleHRYuDAlZ3HAStJ6eUH3X1RWqa7u6Rl6d16jY2kVvb6ju7ugIf3t7045IJL/y1EbvZnd/dbVprUJt9FrXhg0buOqqq/rb0G3YsKHqew466CC6u7uZO3cuu+yySwJRtqdGD4vSSK0cWytR27n2pjZ6yct8omdmBwKvI1TT/t/YSxOBt7r7PqkEVoUSvfStX7+eK6+8sr/KddOmTVXf84Y3vKE/oZs6dWoCUUojKRlLnzqutDclesnLQxu9scA2hHWZEJu+FpiXSkTSUtatW8fll1/en9DVcnFz2GGH0d3dzVvf+lZ23HHHBKKUZqt3eBZpDnVcEUlW5hM9d18KLDWz9e7+tfhrZtYNVL+hp+TCCy+8wN13383VV1/Nxz/+8Zrec+SRR/YndFOmTGlyhJKmZo2fV4lKEIdSxxWRZGU+0Yt5B/C1omlnAn0pxCJN9MILL3DXXXdx++23c/vtt7NixQpuv/127r77bjaXuns5cPTRRzNv3jxOPPFEJk+enHDE0gqSLklSCWJpzej1LCLl5aGN3jHAscDbCHfGKJgIzHL3/VMJrAq10atuw4YN3HXXXf2JXCGpu+eee9gSjWzb0dHBbrvtxqxZs5g9ezazZs1izz33ZPr06UyaNCnlNZBWUq5t2OTJsM02jS91U1u08lTS2b7URi95eUj09gH2Bf4T+ELspeeAP7h79VFqU6BEb8D69eu58847B5XO3X777dx77739Cd2oUaOYMWMGs2bNGpTU7b777owbN67KJ4iU7u05dmy4S0b8HrWN6gHajDtwZIGSOKlEiV7yMp/oFZjZGHffWH3O1tCOid66deu44447hlS53nffff0dJEaPHs2MGTP6E7lCUjdjxgy22mqrlNdAsq44CXn+eXjyyaHzNaLUrR1L9DR0ilSjRC95eUr0ZgBnA7OA/iIed2/JEWnznOg9//zz3HnnnUOqXFeuXNmf0I0ZM4bdd999UOncrFmzmDFjBmPHjk15DaRdNLPULemkpxVK0toxuZX6KNFLXp46Y/wE+CJhLL3DgFMBSzWinHvuuef6S+jiSd3K2BF97Nix7LHHHuy///6ccsop/UndbrvtxpgxY9ILXpqqFZKOWjSzB2iS91ptlY4fGjpFpPXkqURvubvvZ2Z/c/dXRtP+6O6vTzu2UrJUord27VruuOOO/mSu8Hd17Og9duxYZs6cOaTK9RWveAWjR+fpekKqyVL1XZZiraRVStJaJQ5pXSrRS16ezsAbzKwDuNvMTgceAl6ackyZ8uyzzw5pP7dixQoefPDB/nnGjRvHzJkzOfjggwcldbvuuqsSOgHSGa9uuJIsdWumVilJ09ApIq0nTyV6rwHuALYDvkQYXuXr7n5jqoGVkWaJ3tNPP92f0MWTuoceeqh/nnHjxrHnnnv2J3OFvy9/+csZNWpUKnFLNrRrb9M0tVJJWlaq7SUdKtFLXm4SvaxJItG799576evr44477mC77bbrT+oeeeSR/nk6OzvZc889ByVzs2fPpqurSwmdDMv225fuyTp5MjzxRPLxtIO8VEE3khLO1qREL3mqa8uJn/70p5xzzjnceuutJV8fP348s2bN4qijjhqU1HV1ddHR0ZFwtK1HJwXJsrxUQTdKq3ROEWkFKtFLSSNL9O6++2523333IdN32GEHuru7efvb387rXvc6JXRlqDSksVR1K2lrpapsGUwleslTopeSRiZ67s6VV17JxIkTOfDAA5XQ1UknhcbS9pS06WKjdSnRS17mq27N7DtA2WzV3T+SYDipMDOOOeaYtMPIrFbpsZgX6nkpaWvm+IgiWZOHop9lwHLC3TBeDdwdPfYFNqcYl2REuYO/TgrD09MTqr27ukIJSleXqsElWQsXhouLOF1sSLvKfKLn7j9z958BM4DD3P077v4d4AhCsicZ0Nsbqvw6OsLf3t7kPlsnhcbr6QnVtFu2hL9K8rItzd/ncOhiQ2RA5hO9mKnAhNjzbaJp0uIKnSFWrQrtago95JI6meikILXIWrLTKGn/PodLFxsiQW46Y5jZqcBZwB+iSYcAZ0WlfS0nS7dAazY13pdW1849s/X7lEZSZ4zk5SbRAzCzHYEDoqd/dvdH04ynEiV6A9RDTlpdOyc7+n1KIynRS15uqm7NzIAjgX3cfQkw1sz2TzksqYE6Q0irV4u2c89s/T5Fsi03iR5wLnAg8C/R8+eA76UXjtRKnSHaWxbagLVzsqPfp0i25SnRO8DdPwRsAHD3p4Gx6YYktVBniPa2YMHgtm8Qni9YkE48pbRzsqPfp0i25SnR22hmo4gGTzazKYBakGSEesi1ryxUizY72Wn1qmv9PkWyK/N3xoj5L+AS4KVmthCYB3w+3ZBEpJqs3MWgp6c5CU5xj95C1XXhM0VERiI3JXru3gt8GjgbeAQ40d1/lW5UIlJNO1eLQjaqrkUku3KT6JnZ+e5+p7t/z92/6+53mNn5acclIpW1exuwLFRdi0h25anqdnb8SdReb7+UYhGROjSrWjQLslJ1LSLZlPkSPTM708yeA/Y2s7Vm9lz0/HFgScrhiYhU1O5V1yLSXJlP9Nz9bHefAHzd3Se6+4ToMdndz0w7PhGRStq96lpEmivziV6Bu59pZpPMbH8ze0PhkXZcedPqw0CINFOz9n8NX9JcOm5JO8tNGz0zex9wBrAzcAvwWuB/gcPTjCtPNAyEtDPt/9mk703anXmpu1VnkJn9DXgNcKO772tmM4H/cPe3pxxaSXPmzPFly5alHUZd2vnG7iLa/7NJ31trMbPl7j4n7TjaSW6qboEN7r4BwMy2cvc7gT1SjilXNAyEtLN23f+zXu3Zrt+bSEGeEr0HzWw74NfA1Wa2BHg45ZhypZ1v7C7Sjvt/odpz1SpwH6j2zFKy147fm0hcbhI9d3+ruz/j7mcRbn32I+DEdKPKFw0DIe2sHff/PNy1ox2/N5G43CR6AFGv272B54AHgb1SDilXNAyEtLN23P/zUO3Zjt+bSFyeOmN8CTgFuA/YEk12d2/JXrdZ7IwhMhK9vaEkaPXqUG22cKFOtq1OHRmk0dQZI3l5KtF7G/AKdz/E3Q+LHi2Z5Im0mzy09WpHrVrtmfUOIiJJylOi93dgu7SDEElSLSe8Vjgp5qGtVztqxWpPXTSI1CdPVbdzCPe2/TvwQmG6ux+fWlAVqOpWRqp4IFgIpS3xE3Et88SX16yq1Y6OcFIuZhbuBiFSK1UnZ5uqbpOXp0RvBfAD4G8MtNHD3ZeOYJlfB94CvAjcC5zq7s+UmO9o4NvAKOC/3f2r1ZatRE9GqpYTXq0nxVIJoRmcdhqce24ysYrUQhcN2aZEL3l5qrp9wt3/y93/4O5LC48RLvNqYC933xv4B3Bm8QxmNgr4HnAMMAv4FzObNcLPFamqVOJUPL3WXpOlqlbd4bzzGlMl1qptvSR7NC6eSH3ylOgtN7OzzexAM3t14TGSBbr7Ve6+KXp6I+E+usX2B+5x9/vc/UXgl8AJI/lckVqMGlV9eq0nxXIJoXtj2tG1YlsvySZdNIjUJ0+J3quA1wJfAb4ZPb7RwOW/B7iixPSdgAdizx+Mpg1hZvPNbJmZLVuzZk0DQ5M8qbXzxObN1afXelKsVBrSqDHTenpCNe2WLeGvkjwZDl00iNRndNoBNIq7Hzac95nZ74EdS7y0wN2XRPMsADYBpU65ViqcMjEuAhZBaKM3nHgl34rbyhV6FMLQE1lXV/l2bwWF91TrZLFwIbz73aXbPqlKTFpNT48SO5FaZT7RM7N3ufsFZvbxUq+7+7cqvd/dj6yy/JOBNwNHeOmeKw8Cu8Se74zusSvDVGkYklLJWaketcWldbWcFHt64IYbQpu8+F6uKjERkWzLQ9Xt+OjvhBKPbUay4Kg37WeA4919XZnZbgJmmNnLzWws8A7gNyP5XGlf9dxyqtFVWOeeC+efryoxEZE8ydPwKge5+w3VptW5zHuArYAno0k3uvtpZjaVMIzKsdF8xwLnEIZX+bG7Vy0D0fAqUoqGIRGRPNPwKsnLQ4lewXdqnFYzd9/N3Xdx932jx2nR9IcLSV70/HJ3393dX1FLkidSjnoUCrTG3UxEJB8yn+hFw6l8AphiZh+PPc4ilLCJ2+ZUSAAAIABJREFUZIZ6FEoat/hSYimSX5mvujWzQ4BDgdOA82IvPQf81t3vTiOualR1KyKlJF19X89t8kRGSlW3yct8oldgZl3uvir6vwPYxt3XphxWWUr0RKSUkdziazj3K1a7UEmSEr3kZb7qNuZsM5toZuOB24G7zOxTaQclIlKP4d7iq9Yq3+Jq2nK30mvUQNkikq48JXqzohK8E4HLgWnAu9MNSUSkPsPtkFNpDMaCUsmglRryHQ2ULZIXeUr0xpjZGEKit8TdN1LmDhUiIq1quB1yahmDsVQy6D402VNPb5H8yFOi9wNgJWEA5evMrAto2TZ6IiLlDOe+wLVU+ZZLBt3V01skr3KT6Ln7f7n7Tu5+bHSrstXAsO5/KyKSNbVU+ZZLBgsdL+pJLOM0PItI68p8omdm58T+P6Pwf5Ts/XcqQYmIJKyWKt9mDMidxrh/IlK7zA+vYmY3u/uri/8v9byVaHgVEUnDcIZgqUTDs0g9NLxK8kanHUADWJn/RUSkSE9PY9vf1dIJRETSk/mqW6DDzCaZ2eTY/y8xs5egW6CJiDTVcMf9E5Fk5CHR2xZYDiwDJgI3R8+XAxNSjEtEJPea0e5PRBon81W37j497RhERNpVoRq4ke3+RKRxMp/omdl0d19Z4XUDdnL3B5OLSkSkfTS63Z+INE7mEz3g62bWASwhVNeuAcYBuxHG0TsC+CKgRE9ERETaSuYTPXfvNrNZQA/wHuBlwHrgDuAyYKG7b0gxRBEREZFUZD7RA3D324EFVWcUERERaSO5SPQAzGxuicnPAn9z98eTjkdEREQkbblJ9ID3AgcCf4ieHwrcCOxuZv/p7uenFZiIiIhIGvKU6G0B9nT3xwDMbAfg+8ABwHWAEj0RERFpK3kYMLlgeiHJizwO7O7uTwEbU4pJREREJDV5KtH7o5ldCvRFz+cB15nZeOCZ9MISERERSUeeEr0PAXOBgwEDfgZc5O5OGE9PREREpK3kJtFzdzez64EXAQf+EiV5IiIiIm0pN230zOxtwF8IVbZvA/5sZvPSjUpEREQkPbkp0SMMmPyawph5ZjYF+D2wONWoRERERFKSmxI9oKNoYOQnydf6iYiIiNQlTyV6V5rZ74ALo+dvBy5PMR4RERGRVOUm0XP3T5nZScBBhF63i9z9kpTDEhEREUlNrqo23f0id/+4u39MSZ5I/vX2wvTp0NER/vb2ph2RiEhryXyJnpk9RxhOZchLhFFXJiYckogkoLcX5s+HdevC81WrwnOAnp704hIRaSWZL9Fz9wnuPrHEY4KSPJH8WrBgIMkrWLcuTBcRkSDziZ6ItKfVq+ubLiLSjpToiWREs9qjZbWd27Rp9U0XEWlHSvREMqDQHm3VKnAfaI820qSsWctNwsKF0Nk5eFpnZ5guIiKB6Xaw6ZgzZ44vW7Ys7TAkI6ZPD0lYsa4uWLmy9ZablN7e0CZv9epQkrdwoTpiiLQyM1vu7nPSjqOdKNFLiRI9qUdHRyhxK2YGW7a03nJFREpRopc8Vd2KZECz2qOpnZuISL4p0RPJgGa1R2vEcrPamSNt2m4ikgQleiIZ0NMDixaFtnNm4e+iRSNvjzbS5Wa5M0eatN1EJClqo5cStdGTPMh6Z460aLtJu1IbveSpRE9Ehk2DFg+PtpuIJEWJnogMmzpzDI+2m4gkRYmeiAxbLZ051OlgKA32LCJJUaInIsNWrTOHOh2U1qzONSIixdQZIyXqjCFZMZK7T6jTgYjEqTNG8kanHYCItK5Cidy6deF5oUQOakv21OlARCRdqroVkbIWLBhI8grWrQvTa6FOByIi6VKiJyJljbRETp0ORETSpURPRMoaaYmcOh2IiKRLiV4FZvZ1M7vTzG4zs0vMbLsy8600s7+Z2S1mph4WkhuNKJHr6QkdL7ZsCX+V5ImIJEeJXmVXA3u5+97AP4AzK8x7mLvvq95EkicqkRMRyTb1uq3A3a+KPb0RmJdWLCJp6elRYiciklUq0avde4AryrzmwFVmttzM5pdbgJnNN7NlZrZszZo1TQlSRHQ3DhGRgrYv0TOz3wM7lnhpgbsvieZZAGwCyp0uDnL3h83spcDVZnanu19XPJO7LwIWQRgwuSErICKDjHTsPxGRPNGdMaows5OB04Aj3H1dDfOfBTzv7t+oNJ/ujCHSHLobh0jr0p0xkqeq2wrM7GjgM8Dx5ZI8MxtvZhMK/wNHAX9PLkoRidPdOEREBijRq+y7wARCdewtZnYegJlNNbPLo3l2AK43s1uBvwCXufuV6YQrIrobh4jIgLZvo1eJu+9WZvrDwLHR//cB+yQZl4iUt3Dh4DZ6oLtxiEj7UomeiOSKxv4TERmgEj0RyR2N/SciEqhET6SNabw5EZF8U4meSJvSeHMiIvmnEj2RNrVgweAOCxCeL1iQTjwiItJ4SvRE2pTGmxMRyT8leiJtSuPNiYjknxI9kTa1cGEYXy5O482JiOSLEj2RNqXx5kRE8k+9bkXamMabExHJN5XoiYiIiOSUEj0RERGRnFKiJyIiIpJTSvREREREckqJnoiIiEhOKdETERERySkleiIiIiI5pURPREREJKeU6ImIiIjklBI9ERERkZxSoiciIiKSU0r0RERERHJKiZ6IiIhITinRExEREckpJXoiIiIiOaVET0RERCSnlOiJiIiI5JQSPREREZGcUqInIiIiklNK9ERERERySomeiIiISE4p0RMRERHJKSV6IiIiIjmlRE9EREQkp5ToiYiIiOSUEj0RERGRnFKiJyIiIpJTSvREREREckqJnoiIiEhOKdETabDeXpg+HTo6wt/e3rQjEhGRdjU67QBE8qS3F+bPh3XrwvNVq8JzgJ6e9OISEZH2pBI9kQZasGAgyStYty5MFxERSZoSPZEGWr26vukiIiLNpERPpIGmTatvuoiISDMp0RNpoIULobNz8LTOzjBdREQkaUr0RBqopwcWLYKuLjALfxctUkcMERFJh3rdijRYT48SOxERaQ0q0RMRERHJKSV60hY0iLGIiLQjVd1K7mkQYxERaVcq0ZPc0yDGIiLSrpToVWBmXzKz28zsFjO7ysymlpnvZDO7O3qcnHScUpkGMRYRkXalRK+yr7v73u6+L3Ap8IXiGczsJcAXgQOA/YEvmtmkZMOUSjSIsYiItCslehW4+9rY0/GAl5jtTcDV7v6Uuz8NXA0cnUR8UhsNYiwiIu1KiV4VZrbQzB4AeihRogfsBDwQe/5gNK3Usuab2TIzW7ZmzZrGByslaRBjERFpV+ZeqpCqfZjZ74EdS7y0wN2XxOY7Exjn7l8sev+ngK3c/cvR888D69z9m5U+d86cOb5s2bIRxy8iIpIVZrbc3eekHUc7afvhVdz9yBpn/QVwGaE9XtyDwKGx5zsD1444MBEREZERUtVtBWY2I/b0eODOErP9DjjKzCZFnTCOiqaJiIiIpKrtS/Sq+KqZ7QFsAVYBpwGY2RzgNHd/n7s/ZWZfAm6K3vOf7v5UOuGKiIiIDGj7NnppURs9ERFpN2qjlzxV3YqIiIjklBI9ERERkZxSoiciIiKSU0r0RERERHJKiZ6IiIhITinRExEREckpJXoiIiIiOaVET0RERCSnlOiJiIiI5JQSPREREZGcUqInIiIiklNK9EREROrQ2wvTp0NHR/jb25t2RCLljU47ABERkazo7YX582HduvB81arwHKCnJ724RMpRiZ6IiEiNFiwYSPIK1q0L00VakRI9ERGRGq1eXd90kbQp0RMREanRtGn1TRdJmxI9ERGRGi1cCJ2dg6d1dobpIq1IiZ6IiEiNenpg0SLo6gKz8HfRInXEkNalXrciIiJ16OlRYifZoRI9ERERkZxSoiciIiKSU0r0RERERHJKiZ6IiIhITinRExEREckpJXoiIiIiOaVET0RERCSnlOiJiIiI5JQSPREREZGcUqInIiIiklNK9ERERERySomeiIiISE6Zu6cdQ1syszXAqrTjaJDtgSfSDqKJ8rx+WrfsyvP6ad2yq9r6dbn7lKSCESV60gBmtszd56QdR7Pkef20btmV5/XTumVX3tcvi1R1KyIiIpJTSvREREREckqJnjTCorQDaLI8r5/WLbvyvH5at+zK+/pljtroiYiIiOSUSvRE/n979x6vWVnXffzzjRGRg3IaiaODiucMdUTyUKT2BGaCqQGZTUbxWFZ4CjHrkUpLs9TKwkhsxiIEEYLMUkIRLcUGIQ4OCinBKIfxAIKaivyeP9a14WZz75nZe/bet/uaz/v1ul97rWudfmvda8/9nWuttW9Jkjpl0JMkSeqUQU+zkuRdSW5OcsVI265Jzktydfu5yyRrnKsZ9u0FSa5McmeSJf0nA2bYvzcnuSrJZUnOTrLzJGucqxn27Q/afl2a5ENJ9ppkjXM1bt9Gpr0qSSXZfRK1zYcZ3rsTk3yxvXeXJnnWJGucq5neuyS/keSz7d+WP55UfVtihvft9JH37Nokl06yRg0Mepqt1cCh09pOAM6vqgOA89v4UrSae+/bFcDPABcuejXzbzX33r/zgMdU1WOBzwGvWeyi5slq7r1vb66qx1bVgcD7gf+36FXNj9Xce99Isi/wE8B1i13QPFvNmP0D3lpVB7bXBxa5pvmymmn7luTHgcOBx1bVo4E/mUBd82E10/atqo6ces+A9wFnTaIw3ZNBT7NSVRcCX53WfDiwpg2vAY5Y1KLmybh9q6p1VfXZCZU0r2bYvw9V1R1t9JPAPote2DyYYd++PjK6A7Aknzyb4XcO4K3A8SzR/Zqykf1b8mbYt18F3lhV327z3Lzohc2Djb1vSQL8LHDaohalsQx6mg97VNUNAO3nAydcj+bml4B/mXQR8ynJG5JcD7yQpdujdy9JngN8sar+a9K1LKBfb5fe37VUbweZwcOApyW5KMlHkzxx0gUtgKcBN1XV1ZMuRAY9SUCS1wJ3AKdOupb5VFWvrap9Gfbr1yddz3xIsj3wWjoKrmOcBDwEOBC4AfjTyZYzr5YBuwAHA78FnNF6wHpyNPbmfd8w6Gk+3JRkT4D2c0leithaJVkFPBt4YfX7hzX/AXjepIuYJw8B9gf+K8m1DJfbP53kByda1Tyqqpuq6ntVdSfwN8BBk65pHq0HzqrBp4A7gSX7MM10SZYx3Nd8+qRr0cCgp/lwLrCqDa8CzplgLZqFJIcCrwaeU1XfnHQ98ynJASOjzwGumlQt86mqLq+qB1bViqpawRAcHl9VN064tHkz9R/H5rkMD0X14h+BpwMkeRiwLfDliVY0v54JXFVV6yddiAYGPc1KktOATwAPT7I+yTHAG4GfSHI1w1OAb5xkjXM1bt+SPDfJeuBHgH9O8sHJVjl3M7x3bwd2As5rfxLhHRMtco5mOi+TXJHkMuD/AMdNtMg5mmHfujHD/v1xksvbe/fjwMsnWuQczbBv7wIe3P4syXuAVUuxJ30j5+VReNn2+4pfgSZJktQpe/QkSZI6ZdCTJEnqlEFPkiSpUwY9SZKkThn0JEmSOmXQk7TZkty+hcufmeTBbfjaJB+bNv3S9mcnSLJ9klPbn9m4IsnHk+w4Mu9zk1SSR2xke9+bWmeS97ZvlZg3SX4xyds3Mc8hSZ48Mv6SJL8wx+39UJLVc1lW0tbJoCdpUSR5NLBNVX1+pHmnJPu26Y+ctshxDN+X+UNV9RjgGOC7I9OPBj7O8He7ZvKtqjqwLf8d4CVbuh9zcAhwV9CrqndU1bvnsqKquhzYJ8l+81SbpM4Z9CTNWgZvbj1llyc5srX/QJK/SnJlkvcn+UCS57fFXsi9vzXlDODINjz9+zH3BL44NVJVn62qb7ft7Ag8hSH8bSzojfoY8NC2/Cta7VckeVlrW5HkqiRrklzWeh+3b9OuTbJ7G16Z5IIxx+Sn2xfVX5Lk35LskWQFQ7h8eetZfFqSE5O8qi1zYJJPtu2dnWSX1n5Bkjcl+VSSzyV52sim/mkW+yxpK2fQkzQXP8PwhfM/zPCVR29uX1v1M8AK4IeAX2b4RpEpTwEunraeM9syAD/NEGKmvAt4dZJPJHn9tK80OwL416r6HPDVJI/fWLHt+zcPAy5P8gTgxcCTGL5Y/leSPK7N+nDg5Kp6LPB14Nc2ehTu6ePAwVX1OIZvPDi+qq4F3gG8tfUsfmzaMu8GXt22dznwupFpy6rqIOBl09rXAqPBT5JmZNCTNBdPBU5rXzx/E/BR4Imt/b1VdWf77tWPjCyzJ7Bh2nq+CnwtyVHAOuCu79utqkuBBwNvBnYF/nPk8u7RDGGK9vPoGeq8X5JLGcLRdcAprcazq+obVXU7cBZ3B6frq+rf2/Dft3k31z7AB5NcDvwW8OiNzZzkAcDOVfXR1rQG+NGRWc5qPy9mCM9Tbgb2mkVdkrZiyyZdgKQlKbNsB/gWsN2Y9tOBvwR+cfqEkSB2VpI7gWcluZnhS+Efk6SAbYBKcvyY7wz9VlUdeI8Ck43VOH35qfE7uPs/xuP2AeAvgLdU1blJDgFO3Mh2Nse328/vcc9/q7djOJaStEn26EmaiwuBI5Nsk2Q5Q0/UpxguXz6v3au3B8ODCFPW0e6Rm+Zs4I+BD442JnnKyD1r2wKPAv4HeD7w7qp6UFWtqKp9gS+w+b1vFwJHtKd6dwCey3D/HsB+SaYuN0897AFwLfCENvy8Gdb7AO6+p3DVSPttwE7TZ66qWxl6M6d6E1/E0DO6KQ8DrtiM+STJoCdpTs4GLgP+C/gww/1oNwLvA9YzBJG/Bi4Cbm3L/DP3DH4AVNVtVfWmqvrOtEkPAT7aLoVewnD59X0MAezsafO+D/i5zSm8qj4NrGYIphcB76yqS9rkdcCqJJcxXC4+qbX/HvBn7c/BfG+GVZ8IvLfN8+WR9n8Cnjv1MMa0ZVYx3N94GcM9j7+/Gbvw4wzHUpI2Kfe+0iFJc5dkx6q6PcluDGHqKVV1Y5L7Mdyz95SqmiksTUx7Qvb97U+xfF9Kcl+GXr+nVtUdk65H0vc/79GTNN/en2RnYFvgD1pPH1X1rSSvA/ZmeDBCs7cfcIIhT9LmskdPkiSpU96jJ0mS1CmDniRJUqcMepIkSZ0y6EmSJHXKoCdJktQpg54kSVKnDHqSJEmdMuhJkiR1yqAnSZLUKYOeJElSpwx6kiRJnTLoSZIkdcqgJ0mS1CmDniRJUqcMepIkSZ0y6EmSJHXKoCdJktQpg54kSVKnDHqSJEmdMuhJkiR1yqAnSZLUKYOeJElSpwx6kiRJnTLoSZIkdcqgJ0mS1CmDniRJUqcMetIWSnJtkmfOcdmnJfnsfNe0iW3ukeTCJLcl+dMF3taVSQ6Zp3UdkmT9fKxrE9t5R5LfXejt9CLJvyRZtZnzzvl3ZbEkWZGkkiybdC3SfDDoaclL8nNJ1ia5PckN7YPnqZOua5z2AfLQqfGq+lhVPXyRyzgW+DJw/6p65UJuqKoeXVUXACQ5McnfL+T25kNVvaSq/gAWL1wuFePew6o6rKrWTKqmLbUUwqe0JQx6WtKSvAJ4G/CHwB7AfsBfAYfPYV33+h98p/+rfxDwmaqq2S7Y6fHQEuN5KM1CVfnytSRfwAOA24EXbGSe+zIEwS+119uA+7ZphwDrgVcDNwJ/N66tzfts4FLgFuA/gMeObONa4Jlt+CDgE22+G4C3A9u2aRcCBXyj1X3k1PZG1vVI4IK2/JXAc0amrQb+Evhn4DbgIuAhbVqAtwI3A7cClwGPGXM8VgPfBb7TanjmbI/RDMf5V4B1ra7PAI8fPTbAoW2b323b/S/gBcDF09bzSuAfZ9jGVC2/zdAjeS3wwjbticBNwLKR+Z8HXLqRc+Op7b28Bbge+MWRY/R6YAfgW8Cdrebbgb2AbwK7jaznCcAG4D5jtvEDwAnAfwNfAc4Adm3TjgQ+z9CzCnBYO8bL23gBv9nm+TLwZuAHRtb7O8D/tPf83cAD2rQVbdlVwHVt2dduZk0zLjvuPWztFwC/3IYfAny4rffLwKnAzuN+V8Ycq92AfwK+Dvxnew8+PjK9gJcCVwNfaG1PbvPe2n4+ubX/OHD5yLL/BnxqZPzjwBEMv/N3tvf5duD4TR0/X76W2mviBfjyNddX++C5g5EP9zHz/D7wSeCBwHKGD/Y/aNMOacu/iSHs3G+Gtse3D9MnAdu0D4BruTsM3fXhxfChfzCwrH1grANeNlJPAQ8dGT+EFvSA+wDXMASZbYGnMwSnh7fpq4GvMoTJZe1D9D1t2k8CFwM7M4S+RwJ7znBMVgOvn+sxGrO+FwBfZAhbAR4KPGjMsTkR+PuR5e7b9ueRI22XAM+boe6pWt7Slv0xhtA8dXw+Axw2Mv/ZwCtnWNd+7dge3Y77bsCB048P04J4a/sA8Ksj428F/mKG7bysHdt9Ws1/DZw2Mv3Utr3dGEL2s6edKx8Bdm31fo67A9UvtXPlwcCOwFnc/Z+SFW3Zv2E4f38Y+PbUcd5YTZux7D3ew9Z2wUhdDwV+oq13OcN/bt42Mu9d58OYY/We9toeeBRD+J4e9M5rx+N+7efXgBcx/D4c3cZ3A7ZjCG+7t2k3tuO7U1v2W7SwPr2mTR0DX76W2mviBfjyNdcX8ELgxk3M89/As0bGfxK4tg0fwtBDsd3I9HFtJ9GCz0jbZ4Efa8Mb+/B6GXD2yPjGgt7T2gfSD4xMPw04sQ2vBt45Mu1ZwFVt+OkMQeDg0eVnqGk19wx6szpGY9b3QeC4GabddWwYHxJOAt7Qhh/dPqjvO8O6DmEIejuMtJ0B/G4bfjVwahvelaHnbaaw+5rR92Wm48P4oHck8O9teJv2nh00w7rWAc8YGd+ToUdsWRvfmaHX6HLgr6ctW8ChI+O/Bpzfhs8Hfm1k2sOn1svdQWWfkemfAo7aVE2bsey49/ACWtAbs/9HAJeMOx+mzbdNq+HhI23jevSePjL+IkZ66VrbJ7i7Z/ZjwM8w/E58qJ0rhzL09l02U02bOga+fC21l/c5aCn7CrB7kmVVdccM8+zFcHlryv+0tikbqup/py0zve1BwKokvzHStu209QCQ5GEMPU4rGXomljH0tG2OvYDrq+rOafXuPTJ+48jwNxl6c6iqDyd5O8Ol3f2SnA28qqq+vpnbne0xGrUvQ1icizXAaUl+h+GD+4yq+naS/Rh66ACoqh3b4Neq6hsz1Pr3wLokOwI/C3ysqm4ASHL7yDKP2sKazwHekeTBwMOAW6vqUzPM+yDg7CSj7+n3GO4n/WJV3ZLkvcArGC41T3f9yPDovo57z5a19U4Ze65soqZNLbtRSR4I/DnDf1p2YrhM/LXNWHR5q390f68fM99o2/RjAPf8ffkod1/u/2ir48cYeuc+uhk1zekYSN9vfBhDS9kngP9l6DWYyZcYPtim7NfaptSYZaa3Xc/Q67TzyGv7qjptzLInAVcBB1TV/Rkuw2YT+zFa675JRn8v92O4LLpJVfXnVfUEhp6xhwG/NYvtzvYYjbqe4d6sTZZ4r4aqTzL0GD4N+DmGe6aoquuqasep18giuyTZYVytVfVFhnPiuQyh8e9GtrPjyOu6Laz5fxl6h144fTtjXM9wOXn03Nmu1UqSAxkuw57GEJCm23fcvjL+PbuD4T7FTdloTZuwqXPhj9o8j23n/8+zeef/Bob69xlp23fMfKPbn34M4J6/L1NB70fb8EcZgt6Pcc+gt6l9kpY0g56WrKq6Ffh/wF8mOSLJ9knuk+SwJH/cZjsN+J0ky5Ps3uaf7Z/4+BvgJUmelMEOSX4qyU5j5t2J4Wby25M8AvjVadNvYrivapyLGO45O77txyHATzPct7RRSZ7Y6rtPW8f/MvTSbI4tPUbvBF6V5Ant+Dw0yfQPYBj2fcW0IAvDgwRvB+6oqo9vxvZ+L8m2SZ7G8JDMe6et63jghxju0ZvJqcAzk/xskmVJdmuha1zNuyV5wJiafxF4Dhs/Vu8A3jB1PNoxPrwNb9eW/W3gxcDeSX5t2vK/lWSXJPsCxwGnt/bTgJcn2b/1YP4hcPpGerY3q6bNMNN7OGUnhocabkmyN5v5n42q+h7DfYYntt/jRwC/sInFPgA8rP15pWVJjmTorX1/m/4fDJe0D2K4xHslQzB8EsO9g6P7NNPvpLTkGfS0pFXVWxgue/0OQ6/A9cCvA//YZnk9sJbhKdTLgU+3ttlsYy3DU6VvZ7j8cw3Dh/w4r2LombqNISCePm36icCaJLck+dlp2/kOQ3A4jOFJv78CfqGqrtqMMu/ftvc1hstXXwH+ZDOWgy08RlX1XuANwD8w7Pc/MtwjN91UIPtKkk+PtP8d8Bg23jM25UaGffwSQ1h7ybTjczbt0uS0S7zTa76O4R7HVzI8EHIpw0330+e7iiFUfb69Z3u19n9neFrz01V17Ubq/TPgXOBDSW5jeAjiSW3aHzHc/3dSVX2boffr9UkOGFn+HIZL/5cyPG19Smt/F8PxuhD4AkOwH721YGM2VtOmzPQeTvk9hoeXbm31nrWZ64Xh9/YB3P0E/GkMl1nHqqqvMAT9VzKc78czPMzy5Tb9Gwzn8pXtdwuGHt//qaqbR1b1Rwz/0bklyatmUa+0JKTKXmtJk5PkfgxPNT++qq6eh/X9N/B/q+rftri4jW/nw8A/VNU7F2j9xXALwDULsf7vd0neBPxgVa2adC3SUmaPnqRJ+1XgP+cp5D2P4Z6rD29xVRvfzhMZeq6m99hqjpI8Islj2+X/g4Bj2Pjld0mbwaduJU1MkmsZbtbf2AM1m7uuCxju0XrRtCeX51WSNQz1HldVty3UdrZCOzFcrt2LoYf3TxkuXUvaAl66lSRJ6pSXbiVJkjrlpdsJ2X333WvFihWTLkOSpEVz8cUXf7mqlk+6jq2JQW9CVqxYwdq1ayddhiRJiybJ9G8z0QLz0q0kSVKnDHqSJEmdMuhJkiR1yqAnSZLUKYOeJEnVSZnXAAASdklEQVRSpwx6kiRJnTLoSZIkdcqgJ0mS1CmDniRJUqcMepIkSZ0y6EmSJHXKoCdJktQpg54kSVKnDHqSJEmdMuhJkiR1yqAnSZLUqWWTLkDzY8UJ/zzpEubk2jf+1KRLkCSpW/boSZIkdcqgJ0mS1CmDniRJUqcMepIkSZ0y6EmSJHXKoCdJktQpg54kSVKnDHpjJHlXkpuTXDHS9uYkVyW5LMnZSXYemfaaJNck+WySn5xM1ZIkSfdk0BtvNXDotLbzgMdU1WOBzwGvAUjyKOAo4NFtmb9Kss3ilSpJkjSeQW+MqroQ+Oq0tg9V1R1t9JPAPm34cOA9VfXtqvoCcA1w0KIVK0mSNAOD3tz8EvAvbXhv4PqRaetb270kOTbJ2iRrN2zYsMAlSpKkrZ1Bb5aSvBa4Azh1qmnMbDVu2ao6uapWVtXK5cuXL1SJkiRJACybdAFLSZJVwLOBZ1TVVJhbD+w7Mts+wJcWuzZJkqTp7NHbTEkOBV4NPKeqvjky6VzgqCT3TbI/cADwqUnUKEmSNMoevTGSnAYcAuyeZD3wOoanbO8LnJcE4JNV9ZKqujLJGcBnGC7pvrSqvjeZyiVJku5m0Bujqo4e03zKRuZ/A/CGhatIkiRp9rx0K0mS1CmDniRJUqcMepIkSZ0y6EmSJHXKoCdJktQpg54kSVKnDHqSJEmdMuhJkiR1yqAnSZLUKYOeJElSpwx6kiRJnTLoSZIkdcqgJ0mS1CmDniRJUqcMepIkSZ0y6EmSJHXKoCdJktQpg54kSVKnDHqSJEmdMuhJkiR1yqAnSZLUKYOeJElSpwx6kiRJnTLoSZIkdcqgJ0mS1CmDniRJUqcMepIkSZ0y6EmSJHXKoCdJktQpg54kSVKnDHqSJEmdMuhJkiR1yqAnSZLUKYOeJElSpwx6kiRJnTLoSZIkdcqgJ0mS1CmDniRJUqcMepIkSZ0y6EmSJHXKoCdJktQpg54kSVKnDHqSJEmdMuhJkiR1yqAnSZLUKYOeJElSpwx6kiRJnTLoSZIkdcqgN0aSdyW5OckVI227JjkvydXt5y6tPUn+PMk1SS5L8vjJVS5JknQ3g954q4FDp7WdAJxfVQcA57dxgMOAA9rrWOCkRapRkiRpowx6Y1TVhcBXpzUfDqxpw2uAI0ba312DTwI7J9lzcSqVJEmamUFv8+1RVTcAtJ8PbO17A9ePzLe+td1LkmOTrE2ydsOGDQtarCRJkkFvy2VMW42bsapOrqqVVbVy+fLlC1yWJEna2hn0Nt9NU5dk28+bW/t6YN+R+fYBvrTItUmSJN2LQW/znQusasOrgHNG2n+hPX17MHDr1CVeSZKkSVo26QK+HyU5DTgE2D3JeuB1wBuBM5IcA1wHvKDN/gHgWcA1wDeBFy96wZIkSWMY9MaoqqNnmPSMMfMW8NKFrUiSJGn2vHQrSZLUKYOeJElSpwx6kiRJnTLoSZIkdcqgJ0mS1CmDniRJUqcMepIkSZ0y6EmSJHXKoCdJktQpg54kSVKnDHqSJEmdMuhJkiR1yqAnSZLUKYOeJElSpwx6kiRJnTLoSZIkdcqgJ0mS1CmDniRJUqcMepIkSZ0y6EmSJHXKoCdJktQpg54kSVKnDHqSJEmdMuhJkiR1yqAnSZLUKYOeJElSpwx6kiRJnTLoSZIkdcqgJ0mS1CmDniRJUqcMepIkSZ0y6EmSJHXKoCdJktQpg54kSVKnDHqSJEmdMuhJkiR1yqAnSZLUKYOeJElSpwx6kiRJnTLoSZIkdcqgJ0mS1CmDniRJUqcMepIkSZ0y6EmSJHXKoCdJktQpg54kSVKnDHqSJEmdMuhJkiR1yqA3S0lenuTKJFckOS3Jdkn2T3JRkquTnJ5k20nXKUmSZNCbhSR7A78JrKyqxwDbAEcBbwLeWlUHAF8DjplclZIkSQOD3uwtA+6XZBmwPXAD8HTgzDZ9DXDEhGqTJEm6i0FvFqrqi8CfANcxBLxbgYuBW6rqjjb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          "text/plain": "<Figure size 576x1080 with 2 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
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     "39245713b3064ab6b208dd6027d8601a": {
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     "39c8b20e3f884c249597e5e4579517ae": {
      "model_module": "@jupyter-widgets/base",
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     "3b1da1899a8d4faab14a0a69be35fd0b": {
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      "state": {
       "layout": "IPY_MODEL_cebfaf42769e4ccead710aa2f8e81d7d",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Albuquerque, NM\nMetropolitan Statistical Area\nPopulation (2018 ACS estimate): 915927 \nCovid cases by March 24, 2020: 50\nCOVID-19 attack rate (from regression): 0.198350\nCorrelation for that regression: 0.980226\nSubsequent R: 5.411019\nResidual for the power-law regression: -0.207218\n"
        }
       ]
      }
     },
     "3b746947815a4239a60b1cd159dcc515": {
      "model_module": "@jupyter-widgets/base",
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     "3b84b7b540b147fb976657d33a414826": {
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      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
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     "3c231dd044784f2fbcfc656b222c2b7c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "3c296776d234484cb2db36a27fec798b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "3d005ed67e194f0c9277289fc6414482": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
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     "3dddcf38518e4162bcb8eda87f3b4f39": {
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      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "3eee4b2dabc746f4ada2c17755fb7ca6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DatePickerModel",
      "state": {
       "description": "End date",
       "disabled": false,
       "layout": "IPY_MODEL_e9b305c59ed84abe84dfb28e95304adb",
       "style": "IPY_MODEL_8d52cd9c38f6409eb64509e84b8d77e9",
       "value": {
        "date": 19,
        "month": 2,
        "year": 2020
       }
      }
     },
     "3f223aeadd4b44d3b8284385001bf79f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8976eaa092004e89b13e3dc0ebca9bce",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 864x864 with 4 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "3f6655141f1e418292164c9a6f1ce1e7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SelectModel",
      "state": {
       "_options_labels": [
        "Abilene, TX",
        "Akron, OH",
        "Albany, GA",
        "Albany-Lebanon, OR",
        "Albany-Schenectady-Troy, NY",
        "Albuquerque, NM",
        "Alexandria, LA",
        "Allentown-Bethlehem-Easton, PA-NJ",
        "Altoona, PA",
        "Amarillo, TX",
        "Ames, IA",
        "Anchorage, AK",
        "Ann Arbor, MI",
        "Anniston-Oxford, AL",
        "Appleton, WI",
        "Asheville, NC",
        "Athens-Clarke County, GA",
        "Atlanta-Sandy Springs-Alpharetta, GA",
        "Atlantic City-Hammonton, NJ",
        "Auburn-Opelika, AL",
        "Augusta-Richmond County, GA-SC",
        "Austin-Round Rock-Georgetown, TX",
        "Bakersfield, CA",
        "Baltimore-Columbia-Towson, MD",
        "Bangor, ME",
        "Barnstable Town, MA",
        "Baton Rouge, LA",
        "Battle Creek, MI",
        "Bay City, MI",
        "Beaumont-Port Arthur, TX",
        "Beckley, WV",
        "Bellingham, WA",
        "Bend, OR",
        "Billings, MT",
        "Binghamton, NY",
        "Birmingham-Hoover, AL",
        "Bismarck, ND",
        "Blacksburg-Christiansburg, VA",
        "Bloomington, IL",
        "Bloomington, IN",
        "Bloomsburg-Berwick, PA",
        "Boise City, ID",
        "Boston-Cambridge-Newton, MA-NH",
        "Boulder, CO",
        "Bowling Green, KY",
        "Bremerton-Silverdale-Port Orchard, WA",
        "Bridgeport-Stamford-Norwalk, CT",
        "Brownsville-Harlingen, TX",
        "Brunswick, GA",
        "Buffalo-Cheektowaga, NY",
        "Burlington, NC",
        "Burlington-South Burlington, VT",
        "California-Lexington Park, MD",
        "Canton-Massillon, OH",
        "Cape Coral-Fort Myers, FL",
        "Cape Girardeau, MO-IL",
        "Carbondale-Marion, IL",
        "Carson City, NV",
        "Casper, WY",
        "Cedar Rapids, IA",
        "Chambersburg-Waynesboro, PA",
        "Champaign-Urbana, IL",
        "Charleston, WV",
        "Charleston-North Charleston, SC",
        "Charlotte-Concord-Gastonia, NC-SC",
        "Charlottesville, VA",
        "Chattanooga, TN-GA",
        "Cheyenne, WY",
        "Chicago-Naperville-Elgin, IL-IN-WI",
        "Chico, CA",
        "Cincinnati, OH-KY-IN",
        "Clarksville, TN-KY",
        "Cleveland, TN",
        "Cleveland-Elyria, OH",
        "Coeur d'Alene, ID",
        "College Station-Bryan, TX",
        "Colorado Springs, CO",
        "Columbia, MO",
        "Columbia, SC",
        "Columbus, GA-AL",
        "Columbus, IN",
        "Columbus, OH",
        "Corpus Christi, TX",
        "Corvallis, OR",
        "Crestview-Fort Walton Beach-Destin, FL",
        "Cumberland, MD-WV",
        "Dallas-Fort Worth-Arlington, TX",
        "Dalton, GA",
        "Danville, IL",
        "Daphne-Fairhope-Foley, AL",
        "Davenport-Moline-Rock Island, IA-IL",
        "Dayton-Kettering, OH",
        "Decatur, AL",
        "Decatur, IL",
        "Deltona-Daytona Beach-Ormond Beach, FL",
        "Denver-Aurora-Lakewood, CO",
        "Des Moines-West Des Moines, IA",
        "Detroit-Warren-Dearborn, MI",
        "Dothan, AL",
        "Dover, DE",
        "Dubuque, IA",
        "Duluth, MN-WI",
        "Durham-Chapel Hill, NC",
        "East Stroudsburg, PA",
        "Eau Claire, WI",
        "El Centro, CA",
        "Elizabethtown-Fort Knox, KY",
        "Elkhart-Goshen, IN",
        "Elmira, NY",
        "El Paso, TX",
        "Enid, OK",
        "Erie, PA",
        "Eugene-Springfield, OR",
        "Evansville, IN-KY",
        "Fairbanks, AK",
        "Fargo, ND-MN",
        "Farmington, NM",
        "Fayetteville, NC",
        "Fayetteville-Springdale-Rogers, AR",
        "Flagstaff, AZ",
        "Flint, MI",
        "Florence, SC",
        "Florence-Muscle Shoals, AL",
        "Fond du Lac, WI",
        "Fort Collins, CO",
        "Fort Smith, AR-OK",
        "Fort Wayne, IN",
        "Fresno, CA",
        "Gadsden, AL",
        "Gainesville, FL",
        "Gainesville, GA",
        "Gettysburg, PA",
        "Glens Falls, NY",
        "Goldsboro, NC",
        "Grand Forks, ND-MN",
        "Grand Island, NE",
        "Grand Junction, CO",
        "Grand Rapids-Kentwood, MI",
        "Grants Pass, OR",
        "Great Falls, MT",
        "Greeley, CO",
        "Green Bay, WI",
        "Greensboro-High Point, NC",
        "Greenville, NC",
        "Greenville-Anderson, SC",
        "Gulfport-Biloxi, MS",
        "Hagerstown-Martinsburg, MD-WV",
        "Hammond, LA",
        "Hanford-Corcoran, CA",
        "Harrisburg-Carlisle, PA",
        "Harrisonburg, VA",
        "Hartford-East Hartford-Middletown, CT",
        "Hattiesburg, MS",
        "Hickory-Lenoir-Morganton, NC",
        "Hilton Head Island-Bluffton, SC",
        "Hinesville, GA",
        "Homosassa Springs, FL",
        "Hot Springs, AR",
        "Houma-Thibodaux, LA",
        "Houston-The Woodlands-Sugar Land, TX",
        "Huntington-Ashland, WV-KY-OH",
        "Huntsville, AL",
        "Idaho Falls, ID",
        "Indianapolis-Carmel-Anderson, IN",
        "Iowa City, IA",
        "Ithaca, NY",
        "Jackson, MI",
        "Jackson, MS",
        "Jackson, TN",
        "Jacksonville, FL",
        "Jacksonville, NC",
        "Janesville-Beloit, WI",
        "Jefferson City, MO",
        "Johnson City, TN",
        "Johnstown, PA",
        "Jonesboro, AR",
        "Joplin, MO",
        "Kahului-Wailuku-Lahaina, HI",
        "Kalamazoo-Portage, MI",
        "Kankakee, IL",
        "Kansas City, MO-KS",
        "Kennewick-Richland, WA",
        "Killeen-Temple, TX",
        "Kingsport-Bristol, TN-VA",
        "Kingston, NY",
        "Knoxville, TN",
        "Kokomo, IN",
        "La Crosse-Onalaska, WI-MN",
        "Lafayette, LA",
        "Lafayette-West Lafayette, IN",
        "Lake Charles, LA",
        "Lake Havasu City-Kingman, AZ",
        "Lakeland-Winter Haven, FL",
        "Lancaster, PA",
        "Lansing-East Lansing, MI",
        "Laredo, TX",
        "Las Cruces, NM",
        "Las Vegas-Henderson-Paradise, NV",
        "Lawrence, KS",
        "Lawton, OK",
        "Lebanon, PA",
        "Lewiston, ID-WA",
        "Lewiston-Auburn, ME",
        "Lexington-Fayette, KY",
        "Lima, OH",
        "Lincoln, NE",
        "Little Rock-North Little Rock-Conway, AR",
        "Logan, UT-ID",
        "Longview, TX",
        "Longview, WA",
        "Los Angeles-Long Beach-Anaheim, CA",
        "Louisville/Jefferson County, KY-IN",
        "Lubbock, TX",
        "Lynchburg, VA",
        "Macon-Bibb County, GA",
        "Madera, CA",
        "Madison, WI",
        "Manchester-Nashua, NH",
        "Manhattan, KS",
        "Mankato, MN",
        "Mansfield, OH",
        "McAllen-Edinburg-Mission, TX",
        "Medford, OR",
        "Memphis, TN-MS-AR",
        "Merced, CA",
        "Miami-Fort Lauderdale-Pompano Beach, FL",
        "Michigan City-La Porte, IN",
        "Midland, MI",
        "Midland, TX",
        "Milwaukee-Waukesha, WI",
        "Minneapolis-St. Paul-Bloomington, MN-WI",
        "Missoula, MT",
        "Mobile, AL",
        "Modesto, CA",
        "Monroe, LA",
        "Monroe, MI",
        "Montgomery, AL",
        "Morgantown, WV",
        "Morristown, TN",
        "Mount Vernon-Anacortes, WA",
        "Muncie, IN",
        "Muskegon, MI",
        "Myrtle Beach-Conway-North Myrtle Beach, SC-NC",
        "Napa, CA",
        "Naples-Marco Island, FL",
        "Nashville-Davidson--Murfreesboro--Franklin, TN",
        "New Bern, NC",
        "New Haven-Milford, CT",
        "New Orleans-Metairie, LA",
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\n",
          "text/plain": "<Figure size 864x864 with 4 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "519815054a0b4f3fadb34f12edf5f522": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SelectModel",
      "state": {
       "_options_labels": [
        "Abilene, TX",
        "Akron, OH",
        "Albany, GA",
        "Albany-Lebanon, OR",
        "Albany-Schenectady-Troy, NY",
        "Albuquerque, NM",
        "Alexandria, LA",
        "Allentown-Bethlehem-Easton, PA-NJ",
        "Altoona, PA",
        "Amarillo, TX",
        "Ames, IA",
        "Anchorage, AK",
        "Ann Arbor, MI",
        "Anniston-Oxford, AL",
        "Appleton, WI",
        "Asheville, NC",
        "Athens-Clarke County, GA",
        "Atlanta-Sandy Springs-Alpharetta, GA",
        "Atlantic City-Hammonton, NJ",
        "Auburn-Opelika, AL",
        "Augusta-Richmond County, GA-SC",
        "Austin-Round Rock-Georgetown, TX",
        "Bakersfield, CA",
        "Baltimore-Columbia-Towson, MD",
        "Bangor, ME",
        "Barnstable Town, MA",
        "Baton Rouge, LA",
        "Battle Creek, MI",
        "Bay City, MI",
        "Beaumont-Port Arthur, TX",
        "Beckley, WV",
        "Bellingham, WA",
        "Bend, OR",
        "Billings, MT",
        "Binghamton, NY",
        "Birmingham-Hoover, AL",
        "Bismarck, ND",
        "Blacksburg-Christiansburg, VA",
        "Bloomington, IL",
        "Bloomington, IN",
        "Bloomsburg-Berwick, PA",
        "Boise City, ID",
        "Boston-Cambridge-Newton, MA-NH",
        "Boulder, CO",
        "Bowling Green, KY",
        "Bremerton-Silverdale-Port Orchard, WA",
        "Bridgeport-Stamford-Norwalk, CT",
        "Brownsville-Harlingen, TX",
        "Brunswick, GA",
        "Buffalo-Cheektowaga, NY",
        "Burlington, NC",
        "Burlington-South Burlington, VT",
        "California-Lexington Park, MD",
        "Canton-Massillon, OH",
        "Cape Coral-Fort Myers, FL",
        "Cape Girardeau, MO-IL",
        "Carbondale-Marion, IL",
        "Carson City, NV",
        "Casper, WY",
        "Cedar Rapids, IA",
        "Chambersburg-Waynesboro, PA",
        "Champaign-Urbana, IL",
        "Charleston, WV",
        "Charleston-North Charleston, SC",
        "Charlotte-Concord-Gastonia, NC-SC",
        "Charlottesville, VA",
        "Chattanooga, TN-GA",
        "Cheyenne, WY",
        "Chicago-Naperville-Elgin, IL-IN-WI",
        "Chico, CA",
        "Cincinnati, OH-KY-IN",
        "Clarksville, TN-KY",
        "Cleveland, TN",
        "Cleveland-Elyria, OH",
        "Coeur d'Alene, ID",
        "College Station-Bryan, TX",
        "Colorado Springs, CO",
        "Columbia, MO",
        "Columbia, SC",
        "Columbus, GA-AL",
        "Columbus, IN",
        "Columbus, OH",
        "Corpus Christi, TX",
        "Corvallis, OR",
        "Crestview-Fort Walton Beach-Destin, FL",
        "Cumberland, MD-WV",
        "Dallas-Fort Worth-Arlington, TX",
        "Dalton, GA",
        "Danville, IL",
        "Daphne-Fairhope-Foley, AL",
        "Davenport-Moline-Rock Island, IA-IL",
        "Dayton-Kettering, OH",
        "Decatur, AL",
        "Decatur, IL",
        "Deltona-Daytona Beach-Ormond Beach, FL",
        "Denver-Aurora-Lakewood, CO",
        "Des Moines-West Des Moines, IA",
        "Detroit-Warren-Dearborn, MI",
        "Dothan, AL",
        "Dover, DE",
        "Dubuque, IA",
        "Duluth, MN-WI",
        "Durham-Chapel Hill, NC",
        "East Stroudsburg, PA",
        "Eau Claire, WI",
        "El Centro, CA",
        "Elizabethtown-Fort Knox, KY",
        "Elkhart-Goshen, IN",
        "Elmira, NY",
        "El Paso, TX",
        "Enid, OK",
        "Erie, PA",
        "Eugene-Springfield, OR",
        "Evansville, IN-KY",
        "Fairbanks, AK",
        "Fargo, ND-MN",
        "Farmington, NM",
        "Fayetteville, NC",
        "Fayetteville-Springdale-Rogers, AR",
        "Flagstaff, AZ",
        "Flint, MI",
        "Florence, SC",
        "Florence-Muscle Shoals, AL",
        "Fond du Lac, WI",
        "Fort Collins, CO",
        "Fort Smith, AR-OK",
        "Fort Wayne, IN",
        "Fresno, CA",
        "Gadsden, AL",
        "Gainesville, FL",
        "Gainesville, GA",
        "Gettysburg, PA",
        "Glens Falls, NY",
        "Goldsboro, NC",
        "Grand Forks, ND-MN",
        "Grand Island, NE",
        "Grand Junction, CO",
        "Grand Rapids-Kentwood, MI",
        "Grants Pass, OR",
        "Great Falls, MT",
        "Greeley, CO",
        "Green Bay, WI",
        "Greensboro-High Point, NC",
        "Greenville, NC",
        "Greenville-Anderson, SC",
        "Gulfport-Biloxi, MS",
        "Hagerstown-Martinsburg, MD-WV",
        "Hammond, LA",
        "Hanford-Corcoran, CA",
        "Harrisburg-Carlisle, PA",
        "Harrisonburg, VA",
        "Hartford-East Hartford-Middletown, CT",
        "Hattiesburg, MS",
        "Hickory-Lenoir-Morganton, NC",
        "Hilton Head Island-Bluffton, SC",
        "Hinesville, GA",
        "Homosassa Springs, FL",
        "Hot Springs, AR",
        "Houma-Thibodaux, LA",
        "Houston-The Woodlands-Sugar Land, TX",
        "Huntington-Ashland, WV-KY-OH",
        "Huntsville, AL",
        "Idaho Falls, ID",
        "Indianapolis-Carmel-Anderson, IN",
        "Iowa City, IA",
        "Ithaca, NY",
        "Jackson, MI",
        "Jackson, MS",
        "Jackson, TN",
        "Jacksonville, FL",
        "Jacksonville, NC",
        "Janesville-Beloit, WI",
        "Jefferson City, MO",
        "Johnson City, TN",
        "Johnstown, PA",
        "Jonesboro, AR",
        "Joplin, MO",
        "Kahului-Wailuku-Lahaina, HI",
        "Kalamazoo-Portage, MI",
        "Kankakee, IL",
        "Kansas City, MO-KS",
        "Kennewick-Richland, WA",
        "Killeen-Temple, TX",
        "Kingsport-Bristol, TN-VA",
        "Kingston, NY",
        "Knoxville, TN",
        "Kokomo, IN",
        "La Crosse-Onalaska, WI-MN",
        "Lafayette, LA",
        "Lafayette-West Lafayette, IN",
        "Lake Charles, LA",
        "Lake Havasu City-Kingman, AZ",
        "Lakeland-Winter Haven, FL",
        "Lancaster, PA",
        "Lansing-East Lansing, MI",
        "Laredo, TX",
        "Las Cruces, NM",
        "Las Vegas-Henderson-Paradise, NV",
        "Lawrence, KS",
        "Lawton, OK",
        "Lebanon, PA",
        "Lewiston, ID-WA",
        "Lewiston-Auburn, ME",
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        "Longview, WA",
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        "Lubbock, TX",
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        "Macon-Bibb County, GA",
        "Madera, CA",
        "Madison, WI",
        "Manchester-Nashua, NH",
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        "Mansfield, OH",
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        "Norwich-New London, CT",
        "Ocala, FL",
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         "text": "2    Abilene, TX\nName: Title, dtype: object\n2    Metropolitan Statistical Area\nName: MetroMicro, dtype: object\nPopulation (2018 ACS estimate: 171451 \nCovid cases by March 19, 2020: 2    0\nName: COVIDEnd, dtype: int64\nThere was not sufficient data (or another error occurred) to estimate a growth rate\n"
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         "text": "5    Akron, OH\nName: Title, dtype: object\n5    Metropolitan Statistical Area\nName: MetroMicro, dtype: object\nPopulation (2018 ACS estimate: 704845 \nCovid cases by March 19, 2020: 5    6\nName: COVIDEnd, dtype: int64\n"
        },
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          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-48-64a79af19e4b>\u001b[0m in \u001b[0;36mlookup_a_city\u001b[1;34m(df, pickacity)\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Population (2018 ACS estimate: %d '\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPop2018\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Covid cases by '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdateEnd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%B %d, %Y\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m': '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOVIDEnd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAttackRate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'There was not sufficient data (or another error occurred) to estimate a growth rate'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1477\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1478\u001b[0m         raise ValueError(\n\u001b[1;32m-> 1479\u001b[1;33m             \u001b[1;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1480\u001b[0m             \u001b[1;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1481\u001b[0m         )\n",
          "\u001b[1;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
         ]
        }
       ]
      }
     },
     "5fbce1e6c67e4b158be9ab673a3ce0ae": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "5ff4cd5f01f64f2fa3d08de36414203a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "60a3428f4e85417ebaca3b398eff225b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "61e66c301a734f4bbf3a81f53340c966": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "639d5ef1e2a349dfbd5e2e873ba7b19e": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_9ac1888049aa4bbfaca04c402de735e7",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 864x864 with 4 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "63b0b798a8094fdb9068e63134e7bc79": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "63bf647d1f7b4c7faa6be5b20b77e62d": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_11d372bb19d242d0adeda1c6ab312066",
       "outputs": [
        {
         "ename": "ValueError",
         "evalue": "not enough values to unpack (expected 4, got 2)",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-25-ad0508741e63>\u001b[0m in \u001b[0;36mplot_some_figures\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;31m#Now that the data are collated, generate the graphs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;31m#Plot the chart from figure 1a from the arxiv preprint\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0max1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0max2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0max3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0max4\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_figheight\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_figwidth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mValueError\u001b[0m: not enough values to unpack (expected 4, got 2)"
         ]
        }
       ]
      }
     },
     "643fc2ce93d44aa5b1baa65ce836e26e": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8eed77a545b3491fbcda80afb1b96c38",
       "outputs": [
        {
         "ename": "NameError",
         "evalue": "name 'pd' is not defined",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-16-7bbc87c7d5b6>\u001b[0m in \u001b[0;36mcoallate_analyze\u001b[1;34m(dateStart, dateEnd, msas, pop, covid, deaths, output)\u001b[0m\n\u001b[0;32m     52\u001b[0m         \u001b[0mrow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcovid_last\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m         \u001b[1;31m#Now calculate the r\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 54\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcovid_series\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcovid_series\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcovid_series\u001b[0m\u001b[1;33m<=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     55\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0moutput\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     56\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' had too few cases for inclusion'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
         ]
        }
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     "64f9eaa649ac4307b44c8a38205081de": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
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       "_dom_classes": [
        "widget-interact"
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       "children": [
        "IPY_MODEL_cafbacfb09aa4ccaaef48052a438b7bd"
       ],
       "layout": "IPY_MODEL_8145eaeb9762493f9bd57b5bba1f9f28"
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     "651a5e2a7ed14b51bacd66227eddac7f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DatePickerModel",
      "state": {
       "description": "End date",
       "disabled": false,
       "layout": "IPY_MODEL_75875ca5d8d7416493016b7ef1d6e069",
       "style": "IPY_MODEL_a731f49e195b401881989e112fcf7330",
       "value": {
        "date": 22,
        "month": 2,
        "year": 2020
       }
      }
     },
     "651abe01a3de496b98f647cd8d052fe3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "layout": "IPY_MODEL_9a6922fb937a412d9e48c1ea190bba8d"
      }
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     "6674273f2f2c43c093999e822603462a": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_b5668ba684774e27af28ca874346d01e",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Abilene, TX\nMetropolitan Statistical Area\nPopulation (2018 ACS estimate: 171451 \nCovid cases by March 19, 2020: 0\nThere was not sufficient data (or another error occurred) to estimate a growth rate\n"
        }
       ]
      }
     },
     "67533dc21874402a91b544db165612a1": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_4433ff19039b475f9d8b8a9610ae60a6",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Aberdeen, SD had too few cases for inclusion\nAberdeen, WA had too few cases for inclusion\nAbilene, TX had too few cases for inclusion\nAda, OK had too few cases for inclusion\nAdrian, MI had too few cases for inclusion\nAlamogordo, NM had too few cases for inclusion\nAlbemarle, NC had too few cases for inclusion\nAlbert Lea, MN had too few cases for inclusion\nAlbertville, AL had too few cases for inclusion\nAlexander City, AL had too few cases for inclusion\nAlexandria, LA had too few cases for inclusion\nAlexandria, MN had too few cases for inclusion\nAlice, TX had too few cases for inclusion\nAlma, MI had too few cases for inclusion\nAlpena, MI had too few cases for inclusion\nAltoona, PA had too few cases for inclusion\nAltus, OK had too few cases for inclusion\nAmarillo, TX had too few cases for inclusion\nAmericus, GA had too few cases for inclusion\nAmes, IA had too few cases for inclusion\nAmsterdam, NY had too few cases for inclusion\nAndrews, TX had too few cases for inclusion\nAngola, IN had too few cases for inclusion\nAnniston-Oxford, AL had too few cases for inclusion\nAppleton, WI had too few cases for inclusion\nArcadia, FL had too few cases for inclusion\nArdmore, OK had too few cases for inclusion\nArkadelphia, AR had too few cases for inclusion\nAsheville, NC had too few cases for inclusion\nAshland, OH had too few cases for inclusion\nAshtabula, OH had too few cases for inclusion\nAstoria, OR had too few cases for inclusion\nAtchison, KS had too few cases for inclusion\nAthens, OH had too few cases for inclusion\nAthens, TN had too few cases for inclusion\nAthens, TX had too few cases for inclusion\n"
        },
        {
         "name": "stderr",
         "output_type": "stream",
         "text": "C:\\Users\\drewc\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:63: RuntimeWarning: divide by zero encountered in log\nC:\\Users\\drewc\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2449: RuntimeWarning: invalid value encountered in subtract\n  X -= avg[:, None]\nC:\\Users\\drewc\\Anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n  return (a < x) & (x < b)\nC:\\Users\\drewc\\Anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n  return (a < x) & (x < b)\nC:\\Users\\drewc\\Anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n  cond2 = cond0 & (x <= _a)\n"
        },
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Atlantic City-Hammonton, NJ had too few cases for inclusion\nAtmore, AL had too few cases for inclusion\nAuburn, IN had too few cases for inclusion\nAuburn, NY had too few cases for inclusion\nAuburn-Opelika, AL had too few cases for inclusion\nAugusta-Richmond County, GA-SC had too few cases for inclusion\nAugusta-Waterville, ME had too few cases for inclusion\nAustin, MN had too few cases for inclusion\nBainbridge, GA had too few cases for inclusion\nBakersfield, CA had too few cases for inclusion\nBangor, ME had too few cases for inclusion\nBaraboo, WI had too few cases for inclusion\nBardstown, KY had too few cases for inclusion\nBarnstable Town, MA had too few cases for inclusion\nBarre, VT had too few cases for inclusion\nBartlesville, OK had too few cases for inclusion\nBatavia, NY had too few cases for inclusion\nBatesville, AR had too few cases for inclusion\nBattle Creek, MI had too few cases for inclusion\nBay City, MI had too few cases for inclusion\nBay City, TX had too few cases for inclusion\nBeatrice, NE had too few cases for inclusion\nBeaumont-Port Arthur, TX had too few cases for inclusion\nBeaver Dam, WI had too few cases for inclusion\nBeckley, WV had too few cases for inclusion\nBedford, IN had too few cases for inclusion\nBeeville, TX had too few cases for inclusion\nBellefontaine, OH had too few cases for inclusion\nBemidji, MN had too few cases for inclusion\nBennettsville, SC had too few cases for inclusion\nBerlin, NH had too few cases for inclusion\nBig Rapids, MI had too few cases for inclusion\nBig Spring, TX had too few cases for inclusion\nBig Stone Gap, VA had too few cases for inclusion\nBillings, MT had too few cases for inclusion\nBinghamton, NY had too few cases for inclusion\nBismarck, ND had too few cases for inclusion\nBlackfoot, ID had too few cases for inclusion\nBlacksburg-Christiansburg, VA had too few cases for inclusion\nBloomington, IL had too few cases for inclusion\nBloomington, IN had too few cases for inclusion\nBloomsburg-Berwick, PA had too few cases for inclusion\nBluefield, WV-VA had too few cases for inclusion\nBlytheville, AR had too few cases for inclusion\nBogalusa, LA had too few cases for inclusion\nBoise City, ID had too few cases for inclusion\nBonham, TX had too few cases for inclusion\nBoone, NC had too few cases for inclusion\nBorger, TX had too few cases for inclusion\nBoulder, CO had too few cases for inclusion\nBowling Green, KY had too few cases for inclusion\nBozeman, MT had too few cases for inclusion\nBradford, PA had too few cases for inclusion\nBrainerd, MN had too few cases for inclusion\nBranson, MO had too few cases for inclusion\nBrenham, TX had too few cases for inclusion\nBrevard, NC had too few cases for inclusion\nBrookhaven, MS had too few cases for inclusion\nBrookings, OR had too few cases for inclusion\nBrookings, SD had too few cases for inclusion\nBrownsville, TN had too few cases for inclusion\nBrownsville-Harlingen, TX had too few cases for inclusion\nBrownwood, TX had too few cases for inclusion\nBrunswick, GA had too few cases for inclusion\nBucyrus-Galion, OH had too few cases for inclusion\nBuffalo-Cheektowaga, NY had too few cases for inclusion\nBurley, ID had too few cases for inclusion\nBurlington, IA-IL had too few cases for inclusion\nBurlington, NC had too few cases for inclusion\nButte-Silver Bow, MT had too few cases for inclusion\nCadillac, MI had too few cases for inclusion\nCalhoun, GA had too few cases for inclusion\nCalifornia-Lexington Park, MD had too few cases for inclusion\nCambridge, MD had too few cases for inclusion\nCambridge, OH had too few cases for inclusion\nCamden, AR had too few cases for inclusion\nCampbellsville, KY had too few cases for inclusion\nCañon City, CO had too few cases for inclusion\nCape Girardeau, MO-IL had too few cases for inclusion\nCarbondale-Marion, IL had too few cases for inclusion\nCarlsbad-Artesia, NM had too few cases for inclusion\nCarroll, IA had too few cases for inclusion\nCarson City, NV had too few cases for inclusion\nCasper, WY had too few cases for inclusion\nCedar City, UT had too few cases for inclusion\nCedar Rapids, IA had too few cases for inclusion\nCedartown, GA had too few cases for inclusion\nCelina, OH had too few cases for inclusion\nCentral City, KY had too few cases for inclusion\nCentralia, IL had too few cases for inclusion\nCentralia, WA had too few cases for inclusion\nChambersburg-Waynesboro, PA had too few cases for inclusion\nChampaign-Urbana, IL had too few cases for inclusion\nCharleston, WV had too few cases for inclusion\nCharleston-Mattoon, IL had too few cases for inclusion\nCharlottesville, VA had too few cases for inclusion\nChattanooga, TN-GA had too few cases for inclusion\nCheyenne, WY had too few cases for inclusion\nChico, CA had too few cases for inclusion\nChillicothe, OH had too few cases for inclusion\nClarksburg, WV had too few cases for inclusion\nClarksdale, MS had too few cases for inclusion\nClarksville, TN-KY had too few cases for inclusion\nClearlake, CA had too few cases for inclusion\nCleveland, MS had too few cases for inclusion\nCleveland, TN had too few cases for inclusion\nClewiston, FL had too few cases for inclusion\nClinton, IA had too few cases for inclusion\nClovis, NM had too few cases for inclusion\nCoeur d'Alene, ID had too few cases for inclusion\nCoffeyville, KS had too few cases for inclusion\nColdwater, MI had too few cases for inclusion\nCollege Station-Bryan, TX had too few cases for inclusion\nColumbia, MO had too few cases for inclusion\nColumbus, GA-AL had too few cases for inclusion\nColumbus, IN had too few cases for inclusion\nColumbus, MS had too few cases for inclusion\nColumbus, NE had too few cases for inclusion\nColumbus, OH had too few cases for inclusion\nConcord, NH had too few cases for inclusion\nConnersville, IN had too few cases for inclusion\nCookeville, TN had too few cases for inclusion\nCoos Bay, OR had too few cases for inclusion\nCordele, GA had too few cases for inclusion\nCorinth, MS had too few cases for inclusion\nCornelia, GA had too few cases for inclusion\nCorning, NY had too few cases for inclusion\nCorpus Christi, TX had too few cases for inclusion\nCorsicana, TX had too few cases for inclusion\nCortland, NY had too few cases for inclusion\nCorvallis, OR had too few cases for inclusion\nCoshocton, OH had too few cases for inclusion\nCraig, CO had too few cases for inclusion\nCrawfordsville, IN had too few cases for inclusion\nCrescent City, CA had too few cases for inclusion\nCrossville, TN had too few cases for inclusion\nCullman, AL had too few cases for inclusion\nCullowhee, NC had too few cases for inclusion\nCumberland, MD-WV had too few cases for inclusion\nDalton, GA had too few cases for inclusion\nDanville, IL had too few cases for inclusion\nDanville, KY had too few cases for inclusion\nDanville, VA had too few cases for inclusion\nDaphne-Fairhope-Foley, AL had too few cases for inclusion\nDavenport-Moline-Rock Island, IA-IL had too few cases for inclusion\nDayton, TN had too few cases for inclusion\nDayton-Kettering, OH had too few cases for inclusion\nDecatur, AL had too few cases for inclusion\nDecatur, IL had too few cases for inclusion\nDecatur, IN had too few cases for inclusion\nDefiance, OH had too few cases for inclusion\nDel Rio, TX had too few cases for inclusion\nDeming, NM had too few cases for inclusion\nDeRidder, LA had too few cases for inclusion\nDes Moines-West Des Moines, IA had too few cases for inclusion\nDickinson, ND had too few cases for inclusion\nDixon, IL had too few cases for inclusion\nDodge City, KS had too few cases for inclusion\nDothan, AL had too few cases for inclusion\nDouglas, GA had too few cases for inclusion\nDover, DE had too few cases for inclusion\nDublin, GA had too few cases for inclusion\nDuBois, PA had too few cases for inclusion\nDubuque, IA had too few cases for inclusion\nDuluth, MN-WI had too few cases for inclusion\nDumas, TX had too few cases for inclusion\nDuncan, OK had too few cases for inclusion\nDurango, CO had too few cases for inclusion\nDurant, OK had too few cases for inclusion\nDyersburg, TN had too few cases for inclusion\nEagle Pass, TX had too few cases for inclusion\nEaston, MD had too few cases for inclusion\nEau Claire, WI had too few cases for inclusion\nEffingham, IL had too few cases for inclusion\nEl Campo, TX had too few cases for inclusion\nEl Centro, CA had too few cases for inclusion\nEl Dorado, AR had too few cases for inclusion\n"
        },
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Elizabeth City, NC had too few cases for inclusion\nElizabethtown-Fort Knox, KY had too few cases for inclusion\nElk City, OK had too few cases for inclusion\nElkhart-Goshen, IN had too few cases for inclusion\nElkins, WV had too few cases for inclusion\nElko, NV had too few cases for inclusion\nElmira, NY had too few cases for inclusion\nEl Paso, TX had too few cases for inclusion\nEmporia, KS had too few cases for inclusion\nEnid, OK had too few cases for inclusion\nEnterprise, AL had too few cases for inclusion\nErie, PA had too few cases for inclusion\nEscanaba, MI had too few cases for inclusion\nEspañola, NM had too few cases for inclusion\nEufaula, AL-GA had too few cases for inclusion\nEugene-Springfield, OR had too few cases for inclusion\nEureka-Arcata, CA had too few cases for inclusion\nEvanston, WY had too few cases for inclusion\nEvansville, IN-KY had too few cases for inclusion\nFairbanks, AK had too few cases for inclusion\nFairfield, IA had too few cases for inclusion\nFairmont, MN had too few cases for inclusion\nFairmont, WV had too few cases for inclusion\nFallon, NV had too few cases for inclusion\nFargo, ND-MN had too few cases for inclusion\nFaribault-Northfield, MN had too few cases for inclusion\nFarmington, MO had too few cases for inclusion\nFarmington, NM had too few cases for inclusion\nFayetteville, NC had too few cases for inclusion\nFayetteville-Springdale-Rogers, AR had too few cases for inclusion\nFergus Falls, MN had too few cases for inclusion\nFernley, NV had too few cases for inclusion\nFindlay, OH had too few cases for inclusion\nFitzgerald, GA had too few cases for inclusion\nFlagstaff, AZ had too few cases for inclusion\nFlint, MI had too few cases for inclusion\nFlorence, SC had too few cases for inclusion\nFlorence-Muscle Shoals, AL had too few cases for inclusion\nForest City, NC had too few cases for inclusion\nForrest City, AR had too few cases for inclusion\nFort Dodge, IA had too few cases for inclusion\nFort Leonard Wood, MO had too few cases for inclusion\nFort Madison-Keokuk, IA-IL-MO had too few cases for inclusion\nFort Morgan, CO had too few cases for inclusion\nFort Payne, AL had too few cases for inclusion\nFort Polk South, LA had too few cases for inclusion\nFort Smith, AR-OK had too few cases for inclusion\nFort Wayne, IN had too few cases for inclusion\nFrankfort, IN had too few cases for inclusion\nFrankfort, KY had too few cases for inclusion\nFredericksburg, TX had too few cases for inclusion\nFreeport, IL had too few cases for inclusion\nFremont, NE had too few cases for inclusion\nFremont, OH had too few cases for inclusion\nFresno, CA had too few cases for inclusion\nGadsden, AL had too few cases for inclusion\nGaffney, SC had too few cases for inclusion\nGainesville, GA had too few cases for inclusion\nGainesville, TX had too few cases for inclusion\nGalesburg, IL had too few cases for inclusion\nGallup, NM had too few cases for inclusion\nGarden City, KS had too few cases for inclusion\nGardnerville Ranchos, NV had too few cases for inclusion\nGeorgetown, SC had too few cases for inclusion\nGettysburg, PA had too few cases for inclusion\nGillette, WY had too few cases for inclusion\nGlasgow, KY had too few cases for inclusion\nGlens Falls, NY had too few cases for inclusion\nGloversville, NY had too few cases for inclusion\nGoldsboro, NC had too few cases for inclusion\nGranbury, TX had too few cases for inclusion\nGrand Forks, ND-MN had too few cases for inclusion\nGrand Island, NE had too few cases for inclusion\nGrand Junction, CO had too few cases for inclusion\nGrand Rapids, MN had too few cases for inclusion\nGrants, NM had too few cases for inclusion\nGrants Pass, OR had too few cases for inclusion\nGreat Bend, KS had too few cases for inclusion\nGreat Falls, MT had too few cases for inclusion\nGreen Bay, WI had too few cases for inclusion\nGreeneville, TN had too few cases for inclusion\nGreensboro-High Point, NC had too few cases for inclusion\nGreensburg, IN had too few cases for inclusion\nGreenville, MS had too few cases for inclusion\nGreenville, NC had too few cases for inclusion\nGreenville, OH had too few cases for inclusion\nGreenville-Anderson, SC had too few cases for inclusion\nGreenwood, SC had too few cases for inclusion\nGrenada, MS had too few cases for inclusion\nGulfport-Biloxi, MS had too few cases for inclusion\nGuymon, OK had too few cases for inclusion\nHagerstown-Martinsburg, MD-WV had too few cases for inclusion\nHailey, ID had too few cases for inclusion\nHammond, LA had too few cases for inclusion\nHanford-Corcoran, CA had too few cases for inclusion\nHannibal, MO had too few cases for inclusion\nHarrison, AR had too few cases for inclusion\nHarrisonburg, VA had too few cases for inclusion\nHartford-East Hartford-Middletown, CT had too few cases for inclusion\nHastings, NE had too few cases for inclusion\nHays, KS had too few cases for inclusion\nHelena, MT had too few cases for inclusion\nHelena-West Helena, AR had too few cases for inclusion\nHenderson, NC had too few cases for inclusion\nHereford, TX had too few cases for inclusion\nHermiston-Pendleton, OR had too few cases for inclusion\nHickory-Lenoir-Morganton, NC had too few cases for inclusion\nHillsdale, MI had too few cases for inclusion\nHilo, HI had too few cases for inclusion\nHilton Head Island-Bluffton, SC had too few cases for inclusion\nHinesville, GA had too few cases for inclusion\nHobbs, NM had too few cases for inclusion\nHolland, MI had too few cases for inclusion\nHomosassa Springs, FL had too few cases for inclusion\nHood River, OR had too few cases for inclusion\nHope, AR had too few cases for inclusion\nHot Springs, AR had too few cases for inclusion\nHoughton, MI had too few cases for inclusion\nHudson, NY had too few cases for inclusion\nHuntingdon, PA had too few cases for inclusion\nHuntington, IN had too few cases for inclusion\nHuntington-Ashland, WV-KY-OH had too few cases for inclusion\nHuntsville, TX had too few cases for inclusion\nHutchinson, KS had too few cases for inclusion\nHutchinson, MN had too few cases for inclusion\nIdaho Falls, ID had too few cases for inclusion\nIndiana, PA had too few cases for inclusion\nIndianola, MS had too few cases for inclusion\nIron Mountain, MI-WI had too few cases for inclusion\nIthaca, NY had too few cases for inclusion\nJackson, MI had too few cases for inclusion\nJackson, OH had too few cases for inclusion\nJackson, TN had too few cases for inclusion\nJackson, WY-ID had too few cases for inclusion\nJacksonville, IL had too few cases for inclusion\nJacksonville, NC had too few cases for inclusion\nJacksonville, TX had too few cases for inclusion\nJamestown, ND had too few cases for inclusion\nJamestown-Dunkirk-Fredonia, NY had too few cases for inclusion\nJanesville-Beloit, WI had too few cases for inclusion\nJasper, AL had too few cases for inclusion\nJasper, IN had too few cases for inclusion\nJefferson, GA had too few cases for inclusion\nJefferson City, MO had too few cases for inclusion\nJennings, LA had too few cases for inclusion\nJesup, GA had too few cases for inclusion\nJohnson City, TN had too few cases for inclusion\nJohnstown, PA had too few cases for inclusion\nJonesboro, AR had too few cases for inclusion\nJoplin, MO had too few cases for inclusion\nJuneau, AK had too few cases for inclusion\nKahului-Wailuku-Lahaina, HI had too few cases for inclusion\nKalamazoo-Portage, MI had too few cases for inclusion\nKalispell, MT had too few cases for inclusion\nKankakee, IL had too few cases for inclusion\nKapaa, HI had too few cases for inclusion\nKearney, NE had too few cases for inclusion\nKeene, NH had too few cases for inclusion\nKendallville, IN had too few cases for inclusion\nKennett, MO had too few cases for inclusion\nKennewick-Richland, WA had too few cases for inclusion\nKerrville, TX had too few cases for inclusion\nKetchikan, AK had too few cases for inclusion\nKey West, FL had too few cases for inclusion\nKill Devil Hills, NC had too few cases for inclusion\nKilleen-Temple, TX had too few cases for inclusion\nKingsport-Bristol, TN-VA had too few cases for inclusion\nKingsville, TX had too few cases for inclusion\nKinston, NC had too few cases for inclusion\nKirksville, MO had too few cases for inclusion\nKlamath Falls, OR had too few cases for inclusion\nKnoxville, TN had too few cases for inclusion\nLaconia, NH had too few cases for inclusion\nLa Crosse-Onalaska, WI-MN had too few cases for inclusion\nLafayette-West Lafayette, IN had too few cases for inclusion\n"
        },
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "La Grande, OR had too few cases for inclusion\nLaGrange, GA-AL had too few cases for inclusion\nLake Charles, LA had too few cases for inclusion\nLake City, FL had too few cases for inclusion\nLake Havasu City-Kingman, AZ had too few cases for inclusion\nLakeland-Winter Haven, FL had too few cases for inclusion\nLamesa, TX had too few cases for inclusion\nLancaster, PA had too few cases for inclusion\nLaramie, WY had too few cases for inclusion\nLaredo, TX had too few cases for inclusion\nLas Cruces, NM had too few cases for inclusion\nLas Vegas, NM had too few cases for inclusion\nLaurel, MS had too few cases for inclusion\nLaurinburg, NC had too few cases for inclusion\nLawrence, KS had too few cases for inclusion\nLawrenceburg, TN had too few cases for inclusion\nLawton, OK had too few cases for inclusion\nLebanon, MO had too few cases for inclusion\nLebanon, PA had too few cases for inclusion\nLevelland, TX had too few cases for inclusion\nLewisburg, PA had too few cases for inclusion\nLewisburg, TN had too few cases for inclusion\nLewiston, ID-WA had too few cases for inclusion\nLewiston-Auburn, ME had too few cases for inclusion\nLewistown, PA had too few cases for inclusion\nLexington, NE had too few cases for inclusion\nLiberal, KS had too few cases for inclusion\nLima, OH had too few cases for inclusion\nLincoln, IL had too few cases for inclusion\nLincoln, NE had too few cases for inclusion\nLock Haven, PA had too few cases for inclusion\nLogan, UT-ID had too few cases for inclusion\nLogansport, IN had too few cases for inclusion\nLondon, KY had too few cases for inclusion\nLongview, TX had too few cases for inclusion\nLongview, WA had too few cases for inclusion\nLos Alamos, NM had too few cases for inclusion\nLubbock, TX had too few cases for inclusion\nLudington, MI had too few cases for inclusion\nLufkin, TX had too few cases for inclusion\nLumberton, NC had too few cases for inclusion\nLynchburg, VA had too few cases for inclusion\nMacomb, IL had too few cases for inclusion\nMacon-Bibb County, GA had too few cases for inclusion\nMadera, CA had too few cases for inclusion\nMadison, IN had too few cases for inclusion\nMadisonville, KY had too few cases for inclusion\nMagnolia, AR had too few cases for inclusion\nMalone, NY had too few cases for inclusion\nMalvern, AR had too few cases for inclusion\nManchester-Nashua, NH had too few cases for inclusion\nManhattan, KS had too few cases for inclusion\nManitowoc, WI had too few cases for inclusion\nMansfield, OH had too few cases for inclusion\nMarietta, OH had too few cases for inclusion\nMarinette, WI-MI had too few cases for inclusion\nMarion, IN had too few cases for inclusion\nMarion, NC had too few cases for inclusion\nMarion, OH had too few cases for inclusion\nMarquette, MI had too few cases for inclusion\nMarshall, MN had too few cases for inclusion\nMarshall, MO had too few cases for inclusion\nMarshalltown, IA had too few cases for inclusion\nMartin, TN had too few cases for inclusion\nMartinsville, VA had too few cases for inclusion\nMaryville, MO had too few cases for inclusion\nMason City, IA had too few cases for inclusion\nMayfield, KY had too few cases for inclusion\nMaysville, KY had too few cases for inclusion\nMcAlester, OK had too few cases for inclusion\nMcAllen-Edinburg-Mission, TX had too few cases for inclusion\nMcComb, MS had too few cases for inclusion\nMcMinnville, TN had too few cases for inclusion\nMcPherson, KS had too few cases for inclusion\nMeadville, PA had too few cases for inclusion\nMedford, OR had too few cases for inclusion\nMenomonie, WI had too few cases for inclusion\nMerced, CA had too few cases for inclusion\nMeridian, MS had too few cases for inclusion\nMexico, MO had too few cases for inclusion\nMiami, OK had too few cases for inclusion\nMichigan City-La Porte, IN had too few cases for inclusion\nMiddlesborough, KY had too few cases for inclusion\nMidland, MI had too few cases for inclusion\nMidland, TX had too few cases for inclusion\nMilledgeville, GA had too few cases for inclusion\nMinden, LA had too few cases for inclusion\nMineral Wells, TX had too few cases for inclusion\nMinot, ND had too few cases for inclusion\nMissoula, MT had too few cases for inclusion\nMitchell, SD had too few cases for inclusion\nMoberly, MO had too few cases for inclusion\nMobile, AL had too few cases for inclusion\nMonroe, LA had too few cases for inclusion\nMonroe, MI had too few cases for inclusion\nMontrose, CO had too few cases for inclusion\nMorehead City, NC had too few cases for inclusion\nMorgan City, LA had too few cases for inclusion\nMorgantown, WV had too few cases for inclusion\nMorristown, TN had too few cases for inclusion\nMoscow, ID had too few cases for inclusion\nMoses Lake, WA had too few cases for inclusion\nMoultrie, GA had too few cases for inclusion\nMountain Home, AR had too few cases for inclusion\nMountain Home, ID had too few cases for inclusion\nMount Airy, NC had too few cases for inclusion\nMount Gay-Shamrock, WV had too few cases for inclusion\nMount Pleasant, MI had too few cases for inclusion\nMount Pleasant, TX had too few cases for inclusion\nMount Sterling, KY had too few cases for inclusion\nMount Vernon, IL had too few cases for inclusion\nMount Vernon, OH had too few cases for inclusion\nMuncie, IN had too few cases for inclusion\nMurray, KY had too few cases for inclusion\nMuscatine, IA had too few cases for inclusion\nMuskegon, MI had too few cases for inclusion\nMuskogee, OK had too few cases for inclusion\nMyrtle Beach-Conway-North Myrtle Beach, SC-NC had too few cases for inclusion\nNacogdoches, TX had too few cases for inclusion\nNapa, CA had too few cases for inclusion\nNatchez, MS-LA had too few cases for inclusion\nNatchitoches, LA had too few cases for inclusion\nNew Bern, NC had too few cases for inclusion\nNewberry, SC had too few cases for inclusion\nNew Castle, IN had too few cases for inclusion\nNew Castle, PA had too few cases for inclusion\nNew Haven-Milford, CT had too few cases for inclusion\nNew Philadelphia-Dover, OH had too few cases for inclusion\nNewport, OR had too few cases for inclusion\nNewport, TN had too few cases for inclusion\nNew Ulm, MN had too few cases for inclusion\nNiles, MI had too few cases for inclusion\nNogales, AZ had too few cases for inclusion\nNorfolk, NE had too few cases for inclusion\nNorth Platte, NE had too few cases for inclusion\nNorth Vernon, IN had too few cases for inclusion\nNorth Wilkesboro, NC had too few cases for inclusion\nNorwalk, OH had too few cases for inclusion\nNorwich-New London, CT had too few cases for inclusion\nOcala, FL had too few cases for inclusion\nOcean City, NJ had too few cases for inclusion\nOdessa, TX had too few cases for inclusion\nOgdensburg-Massena, NY had too few cases for inclusion\nOil City, PA had too few cases for inclusion\nOkeechobee, FL had too few cases for inclusion\nOlean, NY had too few cases for inclusion\nOneonta, NY had too few cases for inclusion\nOntario, OR-ID had too few cases for inclusion\nOpelousas, LA had too few cases for inclusion\nOrangeburg, SC had too few cases for inclusion\nOshkosh-Neenah, WI had too few cases for inclusion\nOskaloosa, IA had too few cases for inclusion\nOthello, WA had too few cases for inclusion\nOttawa, IL had too few cases for inclusion\nOttawa, KS had too few cases for inclusion\nOttumwa, IA had too few cases for inclusion\nOwatonna, MN had too few cases for inclusion\nOwensboro, KY had too few cases for inclusion\nOxford, MS had too few cases for inclusion\nOzark, AL had too few cases for inclusion\nPaducah, KY-IL had too few cases for inclusion\nPahrump, NV had too few cases for inclusion\nPalatka, FL had too few cases for inclusion\nPalestine, TX had too few cases for inclusion\nPalm Bay-Melbourne-Titusville, FL had too few cases for inclusion\nPampa, TX had too few cases for inclusion\nPanama City, FL had too few cases for inclusion\nParagould, AR had too few cases for inclusion\nParis, TN had too few cases for inclusion\nParis, TX had too few cases for inclusion\nParkersburg-Vienna, WV had too few cases for inclusion\nParsons, KS had too few cases for inclusion\nPayson, AZ had too few cases for inclusion\nPearsall, TX had too few cases for inclusion\nPecos, TX had too few cases for inclusion\nPella, IA had too few cases for inclusion\nPensacola-Ferry Pass-Brent, FL had too few cases for inclusion\n"
        },
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Peoria, IL had too few cases for inclusion\nPeru, IN had too few cases for inclusion\nPierre, SD had too few cases for inclusion\nPinehurst-Southern Pines, NC had too few cases for inclusion\nPittsburg, KS had too few cases for inclusion\nPlainview, TX had too few cases for inclusion\nPlatteville, WI had too few cases for inclusion\nPlattsburgh, NY had too few cases for inclusion\nPlymouth, IN had too few cases for inclusion\nPocatello, ID had too few cases for inclusion\nPoint Pleasant, WV-OH had too few cases for inclusion\nPonca City, OK had too few cases for inclusion\nPontiac, IL had too few cases for inclusion\nPoplar Bluff, MO had too few cases for inclusion\nPortales, NM had too few cases for inclusion\nPort Angeles, WA had too few cases for inclusion\nPort Lavaca, TX had too few cases for inclusion\nPort St. Lucie, FL had too few cases for inclusion\nPortsmouth, OH had too few cases for inclusion\nPottsville, PA had too few cases for inclusion\nPrescott Valley-Prescott, AZ had too few cases for inclusion\nPrice, UT had too few cases for inclusion\nPrineville, OR had too few cases for inclusion\nProvo-Orem, UT had too few cases for inclusion\nPueblo, CO had too few cases for inclusion\nPullman, WA had too few cases for inclusion\nPunta Gorda, FL had too few cases for inclusion\nQuincy, IL-MO had too few cases for inclusion\nRacine, WI had too few cases for inclusion\nRapid City, SD had too few cases for inclusion\nRaymondville, TX had too few cases for inclusion\nReading, PA had too few cases for inclusion\nRed Bluff, CA had too few cases for inclusion\nRedding, CA had too few cases for inclusion\nRed Wing, MN had too few cases for inclusion\nRexburg, ID had too few cases for inclusion\nRichmond, IN had too few cases for inclusion\nRichmond-Berea, KY had too few cases for inclusion\nRio Grande City-Roma, TX had too few cases for inclusion\nRoanoke, VA had too few cases for inclusion\nRoanoke Rapids, NC had too few cases for inclusion\nRochelle, IL had too few cases for inclusion\nRockford, IL had too few cases for inclusion\nRockingham, NC had too few cases for inclusion\nRockport, TX had too few cases for inclusion\nRock Springs, WY had too few cases for inclusion\nRocky Mount, NC had too few cases for inclusion\nRolla, MO had too few cases for inclusion\nRoseburg, OR had too few cases for inclusion\nRoswell, NM had too few cases for inclusion\nRuidoso, NM had too few cases for inclusion\nRussellville, AR had too few cases for inclusion\nRuston, LA had too few cases for inclusion\nRutland, VT had too few cases for inclusion\nSafford, AZ had too few cases for inclusion\nSaginaw, MI had too few cases for inclusion\nSt. George, UT had too few cases for inclusion\nSt. Joseph, MO-KS had too few cases for inclusion\nSt. Marys, GA had too few cases for inclusion\nSt. Marys, PA had too few cases for inclusion\nSalem, OH had too few cases for inclusion\nSalina, KS had too few cases for inclusion\nSalinas, CA had too few cases for inclusion\nSalisbury, MD-DE had too few cases for inclusion\nSan Angelo, TX had too few cases for inclusion\nSan Antonio-New Braunfels, TX had too few cases for inclusion\nSandpoint, ID had too few cases for inclusion\nSandusky, OH had too few cases for inclusion\nSanford, NC had too few cases for inclusion\nSan Luis Obispo-Paso Robles, CA had too few cases for inclusion\nSanta Maria-Santa Barbara, CA had too few cases for inclusion\nSault Ste. Marie, MI had too few cases for inclusion\nSavannah, GA had too few cases for inclusion\nSayre, PA had too few cases for inclusion\nScottsbluff, NE had too few cases for inclusion\nScottsboro, AL had too few cases for inclusion\nScottsburg, IN had too few cases for inclusion\nScranton--Wilkes-Barre, PA had too few cases for inclusion\nSearcy, AR had too few cases for inclusion\nSebastian-Vero Beach, FL had too few cases for inclusion\nSebring-Avon Park, FL had too few cases for inclusion\nSedalia, MO had too few cases for inclusion\nSelinsgrove, PA had too few cases for inclusion\nSelma, AL had too few cases for inclusion\nSeneca, SC had too few cases for inclusion\nSeneca Falls, NY had too few cases for inclusion\nSevierville, TN had too few cases for inclusion\nSeymour, IN had too few cases for inclusion\nShawano, WI had too few cases for inclusion\nShawnee, OK had too few cases for inclusion\nShelby, NC had too few cases for inclusion\nShelbyville, TN had too few cases for inclusion\nShelton, WA had too few cases for inclusion\nSherman-Denison, TX had too few cases for inclusion\nShow Low, AZ had too few cases for inclusion\nSidney, OH had too few cases for inclusion\nSierra Vista-Douglas, AZ had too few cases for inclusion\nSikeston, MO had too few cases for inclusion\nSilver City, NM had too few cases for inclusion\nSioux City, IA-NE-SD had too few cases for inclusion\nSnyder, TX had too few cases for inclusion\nSomerset, KY had too few cases for inclusion\nSomerset, PA had too few cases for inclusion\nSonora, CA had too few cases for inclusion\nSouth Bend-Mishawaka, IN-MI had too few cases for inclusion\nSpartanburg, SC had too few cases for inclusion\nSpearfish, SD had too few cases for inclusion\nSpencer, IA had too few cases for inclusion\nSpirit Lake, IA had too few cases for inclusion\nSpokane-Spokane Valley, WA had too few cases for inclusion\nSpringfield, IL had too few cases for inclusion\nSpringfield, OH had too few cases for inclusion\nStarkville, MS had too few cases for inclusion\nState College, PA had too few cases for inclusion\nStatesboro, GA had too few cases for inclusion\nStaunton, VA had too few cases for inclusion\nSteamboat Springs, CO had too few cases for inclusion\nStephenville, TX had too few cases for inclusion\nSterling, CO had too few cases for inclusion\nSterling, IL had too few cases for inclusion\nStevens Point, WI had too few cases for inclusion\nStillwater, OK had too few cases for inclusion\nStorm Lake, IA had too few cases for inclusion\nSturgis, MI had too few cases for inclusion\nSulphur Springs, TX had too few cases for inclusion\nSummerville, GA had too few cases for inclusion\nSumter, SC had too few cases for inclusion\nSunbury, PA had too few cases for inclusion\nSusanville, CA had too few cases for inclusion\nSweetwater, TX had too few cases for inclusion\nSyracuse, NY had too few cases for inclusion\nTahlequah, OK had too few cases for inclusion\nTalladega-Sylacauga, AL had too few cases for inclusion\nTallahassee, FL had too few cases for inclusion\nTaos, NM had too few cases for inclusion\nTaylorville, IL had too few cases for inclusion\nTerre Haute, IN had too few cases for inclusion\nTexarkana, TX-AR had too few cases for inclusion\nThe Dalles, OR had too few cases for inclusion\nThe Villages, FL had too few cases for inclusion\nThomaston, GA had too few cases for inclusion\nThomasville, GA had too few cases for inclusion\nTiffin, OH had too few cases for inclusion\nTifton, GA had too few cases for inclusion\nToccoa, GA had too few cases for inclusion\nToledo, OH had too few cases for inclusion\nTopeka, KS had too few cases for inclusion\nTraverse City, MI had too few cases for inclusion\nTrenton-Princeton, NJ had too few cases for inclusion\nTroy, AL had too few cases for inclusion\nTruckee-Grass Valley, CA had too few cases for inclusion\nTullahoma-Manchester, TN had too few cases for inclusion\nTupelo, MS had too few cases for inclusion\nTwin Falls, ID had too few cases for inclusion\nUkiah, CA had too few cases for inclusion\nUnion, SC had too few cases for inclusion\nUnion City, TN had too few cases for inclusion\nUrbana, OH had too few cases for inclusion\nUtica-Rome, NY had too few cases for inclusion\nUvalde, TX had too few cases for inclusion\nVan Wert, OH had too few cases for inclusion\nVermillion, SD had too few cases for inclusion\nVernal, UT had too few cases for inclusion\nVernon, TX had too few cases for inclusion\nVicksburg, MS had too few cases for inclusion\nVictoria, TX had too few cases for inclusion\nVidalia, GA had too few cases for inclusion\nVincennes, IN had too few cases for inclusion\nVineland-Bridgeton, NJ had too few cases for inclusion\nVineyard Haven, MA had too few cases for inclusion\nWabash, IN had too few cases for inclusion\nWaco, TX had too few cases for inclusion\nWahpeton, ND-MN had too few cases for inclusion\nWalla Walla, WA had too few cases for inclusion\nWapakoneta, OH had too few cases for inclusion\nWarner Robins, GA had too few cases for inclusion\nWarren, PA had too few cases for inclusion\nWarrensburg, MO had too few cases for inclusion\nWarsaw, IN had too few cases for inclusion\nWashington, IN had too few cases for inclusion\nWashington, NC had too few cases for inclusion\n"
        },
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Washington Court House, OH had too few cases for inclusion\nWaterloo-Cedar Falls, IA had too few cases for inclusion\nWatertown, SD had too few cases for inclusion\nWatertown-Fort Atkinson, WI had too few cases for inclusion\nWatertown-Fort Drum, NY had too few cases for inclusion\nWauchula, FL had too few cases for inclusion\nWausau-Weston, WI had too few cases for inclusion\nWaycross, GA had too few cases for inclusion\nWeatherford, OK had too few cases for inclusion\nWeirton-Steubenville, WV-OH had too few cases for inclusion\nWenatchee, WA had too few cases for inclusion\nWest Plains, MO had too few cases for inclusion\nWest Point, MS had too few cases for inclusion\nWheeling, WV-OH had too few cases for inclusion\nWhitewater, WI had too few cases for inclusion\nWichita, KS had too few cases for inclusion\nWichita Falls, TX had too few cases for inclusion\nWilliamsport, PA had too few cases for inclusion\nWilliston, ND had too few cases for inclusion\nWillmar, MN had too few cases for inclusion\nWilmington, NC had too few cases for inclusion\nWilmington, OH had too few cases for inclusion\nWilson, NC had too few cases for inclusion\nWinchester, VA-WV had too few cases for inclusion\nWinfield, KS had too few cases for inclusion\nWinnemucca, NV had too few cases for inclusion\nWinona, MN had too few cases for inclusion\nWisconsin Rapids-Marshfield, WI had too few cases for inclusion\nWoodward, OK had too few cases for inclusion\nWooster, OH had too few cases for inclusion\nWorthington, MN had too few cases for inclusion\nYankton, SD had too few cases for inclusion\nYork-Hanover, PA had too few cases for inclusion\nYuba City, CA had too few cases for inclusion\nYuma, AZ had too few cases for inclusion\nZanesville, OH had too few cases for inclusion\nZapata, TX had too few cases for inclusion\n"
        },
        {
         "ename": "IndexingError",
         "evalue": "Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match).",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mIndexingError\u001b[0m                             Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-5-af9dff0420d7>\u001b[0m in \u001b[0;36mcoallate_analyze_plot\u001b[1;34m(dateStart, dateEnd, msas, pop, covid, deaths, output)\u001b[0m\n\u001b[0;32m     82\u001b[0m     \u001b[0max1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Attack rate correlation (%f, p %f) with population size as power law (exponent %f)'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mr_value\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mp_value\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mslope\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m     \u001b[1;31m# Look at how well correlation describes the growth curves for the selected dates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m     \u001b[0max2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwhich\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m     \u001b[0max2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Correlations for city-by-city exponential growth'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    906\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    907\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 908\u001b[1;33m             \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    909\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    910\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36mcheck_bool_indexer\u001b[1;34m(index, key)\u001b[0m\n\u001b[0;32m   2315\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2316\u001b[0m             raise IndexingError(\n\u001b[1;32m-> 2317\u001b[1;33m                 \u001b[1;34m\"Unalignable boolean Series provided as \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2318\u001b[0m                 \u001b[1;34m\"indexer (index of the boolean Series and of \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2319\u001b[0m                 \u001b[1;34m\"the indexed object do not match).\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mIndexingError\u001b[0m: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match)."
         ]
        }
       ]
      }
     },
     "67c72e9d0985479a91060fceb6fdb92b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "67d94bc6451c4311acc80b7705ac9b09": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "681740e22de445cfa2f08a26be32b2bf": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "68c67be065714608aad4cbdf687cbc36": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "692b35e39248447fb36df2aef58ee19b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "6a06f76b20e4437780015cb0a2bf76e9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_a25b53a315aa4b53b67cb45ff5de6c2f",
        "IPY_MODEL_2505b79b27574e46ac2179ffade7500d"
       ],
       "layout": "IPY_MODEL_b4a6f1bf664c434baf9fa9fc98cf8491"
      }
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     "6ab81bf4b50e486a8f8d737084920bd0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "6b2f94bcde70441cbada9495be1641ec": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_4a410dc64bd1444a9d0957f484c87277",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 864x864 with 4 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "6ccae7397ae64f65a9f336b51e8d404a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "6ceb0d9ff3c94566b1d843e38772be38": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_2213b2a2dba34974940dd8f0ef778c3b",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Abilene, TX\nMetropolitan Statistical Area\nPopulation (2018 ACS estimate): 171451 \nCovid cases by March 19, 2020: 0\nThere was not sufficient data (or another error occurred) to estimate a growth rate\n"
        }
       ]
      }
     },
     "6d46f1d47b354408b9ed8ff26a54d9ea": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_a2509990656f41f8b9c25b6cef767326",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 864x864 with 4 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "6da01dea9f904de9a94173fb52e2db67": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SelectModel",
      "state": {
       "_options_labels": [
        "Abilene, TX",
        "Akron, OH",
        "Albany, GA",
        "Albany-Lebanon, OR",
        "Albany-Schenectady-Troy, NY",
        "Albuquerque, NM",
        "Alexandria, LA",
        "Allentown-Bethlehem-Easton, PA-NJ",
        "Altoona, PA",
        "Amarillo, TX",
        "Ames, IA",
        "Anchorage, AK",
        "Ann Arbor, MI",
        "Anniston-Oxford, AL",
        "Appleton, WI",
        "Asheville, NC",
        "Athens-Clarke County, GA",
        "Atlanta-Sandy Springs-Alpharetta, GA",
        "Atlantic City-Hammonton, NJ",
        "Auburn-Opelika, AL",
        "Augusta-Richmond County, GA-SC",
        "Austin-Round Rock-Georgetown, TX",
        "Bakersfield, CA",
        "Baltimore-Columbia-Towson, MD",
        "Bangor, ME",
        "Barnstable Town, MA",
        "Baton Rouge, LA",
        "Battle Creek, MI",
        "Bay City, MI",
        "Beaumont-Port Arthur, TX",
        "Beckley, WV",
        "Bellingham, WA",
        "Bend, OR",
        "Billings, MT",
        "Binghamton, NY",
        "Birmingham-Hoover, AL",
        "Bismarck, ND",
        "Blacksburg-Christiansburg, VA",
        "Bloomington, IL",
        "Bloomington, IN",
        "Bloomsburg-Berwick, PA",
        "Boise City, ID",
        "Boston-Cambridge-Newton, MA-NH",
        "Boulder, CO",
        "Bowling Green, KY",
        "Bremerton-Silverdale-Port Orchard, WA",
        "Bridgeport-Stamford-Norwalk, CT",
        "Brownsville-Harlingen, TX",
        "Brunswick, GA",
        "Buffalo-Cheektowaga, NY",
        "Burlington, NC",
        "Burlington-South Burlington, VT",
        "California-Lexington Park, MD",
        "Canton-Massillon, OH",
        "Cape Coral-Fort Myers, FL",
        "Cape Girardeau, MO-IL",
        "Carbondale-Marion, IL",
        "Carson City, NV",
        "Casper, WY",
        "Cedar Rapids, IA",
        "Chambersburg-Waynesboro, PA",
        "Champaign-Urbana, IL",
        "Charleston, WV",
        "Charleston-North Charleston, SC",
        "Charlotte-Concord-Gastonia, NC-SC",
        "Charlottesville, VA",
        "Chattanooga, TN-GA",
        "Cheyenne, WY",
        "Chicago-Naperville-Elgin, IL-IN-WI",
        "Chico, CA",
        "Cincinnati, OH-KY-IN",
        "Clarksville, TN-KY",
        "Cleveland, TN",
        "Cleveland-Elyria, OH",
        "Coeur d'Alene, ID",
        "College Station-Bryan, TX",
        "Colorado Springs, CO",
        "Columbia, MO",
        "Columbia, SC",
        "Columbus, GA-AL",
        "Columbus, IN",
        "Columbus, OH",
        "Corpus Christi, TX",
        "Corvallis, OR",
        "Crestview-Fort Walton Beach-Destin, FL",
        "Cumberland, MD-WV",
        "Dallas-Fort Worth-Arlington, TX",
        "Dalton, GA",
        "Danville, IL",
        "Daphne-Fairhope-Foley, AL",
        "Davenport-Moline-Rock Island, IA-IL",
        "Dayton-Kettering, OH",
        "Decatur, AL",
        "Decatur, IL",
        "Deltona-Daytona Beach-Ormond Beach, FL",
        "Denver-Aurora-Lakewood, CO",
        "Des Moines-West Des Moines, IA",
        "Detroit-Warren-Dearborn, MI",
        "Dothan, AL",
        "Dover, DE",
        "Dubuque, IA",
        "Duluth, MN-WI",
        "Durham-Chapel Hill, NC",
        "East Stroudsburg, PA",
        "Eau Claire, WI",
        "El Centro, CA",
        "Elizabethtown-Fort Knox, KY",
        "Elkhart-Goshen, IN",
        "Elmira, NY",
        "El Paso, TX",
        "Enid, OK",
        "Erie, PA",
        "Eugene-Springfield, OR",
        "Evansville, IN-KY",
        "Fairbanks, AK",
        "Fargo, ND-MN",
        "Farmington, NM",
        "Fayetteville, NC",
        "Fayetteville-Springdale-Rogers, AR",
        "Flagstaff, AZ",
        "Flint, MI",
        "Florence, SC",
        "Florence-Muscle Shoals, AL",
        "Fond du Lac, WI",
        "Fort Collins, CO",
        "Fort Smith, AR-OK",
        "Fort Wayne, IN",
        "Fresno, CA",
        "Gadsden, AL",
        "Gainesville, FL",
        "Gainesville, GA",
        "Gettysburg, PA",
        "Glens Falls, NY",
        "Goldsboro, NC",
        "Grand Forks, ND-MN",
        "Grand Island, NE",
        "Grand Junction, CO",
        "Grand Rapids-Kentwood, MI",
        "Grants Pass, OR",
        "Great Falls, MT",
        "Greeley, CO",
        "Green Bay, WI",
        "Greensboro-High Point, NC",
        "Greenville, NC",
        "Greenville-Anderson, SC",
        "Gulfport-Biloxi, MS",
        "Hagerstown-Martinsburg, MD-WV",
        "Hammond, LA",
        "Hanford-Corcoran, CA",
        "Harrisburg-Carlisle, PA",
        "Harrisonburg, VA",
        "Hartford-East Hartford-Middletown, CT",
        "Hattiesburg, MS",
        "Hickory-Lenoir-Morganton, NC",
        "Hilton Head Island-Bluffton, SC",
        "Hinesville, GA",
        "Homosassa Springs, FL",
        "Hot Springs, AR",
        "Houma-Thibodaux, LA",
        "Houston-The Woodlands-Sugar Land, TX",
        "Huntington-Ashland, WV-KY-OH",
        "Huntsville, AL",
        "Idaho Falls, ID",
        "Indianapolis-Carmel-Anderson, IN",
        "Iowa City, IA",
        "Ithaca, NY",
        "Jackson, MI",
        "Jackson, MS",
        "Jackson, TN",
        "Jacksonville, FL",
        "Jacksonville, NC",
        "Janesville-Beloit, WI",
        "Jefferson City, MO",
        "Johnson City, TN",
        "Johnstown, PA",
        "Jonesboro, AR",
        "Joplin, MO",
        "Kahului-Wailuku-Lahaina, HI",
        "Kalamazoo-Portage, MI",
        "Kankakee, IL",
        "Kansas City, MO-KS",
        "Kennewick-Richland, WA",
        "Killeen-Temple, TX",
        "Kingsport-Bristol, TN-VA",
        "Kingston, NY",
        "Knoxville, TN",
        "Kokomo, IN",
        "La Crosse-Onalaska, WI-MN",
        "Lafayette, LA",
        "Lafayette-West Lafayette, IN",
        "Lake Charles, LA",
        "Lake Havasu City-Kingman, AZ",
        "Lakeland-Winter Haven, FL",
        "Lancaster, PA",
        "Lansing-East Lansing, MI",
        "Laredo, TX",
        "Las Cruces, NM",
        "Las Vegas-Henderson-Paradise, NV",
        "Lawrence, KS",
        "Lawton, OK",
        "Lebanon, PA",
        "Lewiston, ID-WA",
        "Lewiston-Auburn, ME",
        "Lexington-Fayette, KY",
        "Lima, OH",
        "Lincoln, NE",
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        "Logan, UT-ID",
        "Longview, TX",
        "Longview, WA",
        "Los Angeles-Long Beach-Anaheim, CA",
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        "Lubbock, TX",
        "Lynchburg, VA",
        "Macon-Bibb County, GA",
        "Madera, CA",
        "Madison, WI",
        "Manchester-Nashua, NH",
        "Manhattan, KS",
        "Mankato, MN",
        "Mansfield, OH",
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        "Missoula, MT",
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        "Napa, CA",
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        "New Haven-Milford, CT",
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        "Ocala, FL",
        "Ocean City, NJ",
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        "Oklahoma City, OK",
        "Olympia-Lacey-Tumwater, WA",
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        "Orlando-Kissimmee-Sanford, FL",
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        "Port St. Lucie, FL",
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        "Provo-Orem, UT",
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        "Sacramento-Roseville-Folsom, CA",
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        "Santa Maria-Santa Barbara, CA",
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        "Savannah, GA",
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         "text": "Albany, GA\nMetropolitan Statistical Area\nPopulation (2018 ACS estimate: 149917 \nCovid cases by March 19, 2020: 19\nCOVID-19 attack rate (from regression): 0.472420\nCorrelation for that regression: 0.885526\nResidual for the power-law regression: 0.796683\n"
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\n",
          "text/plain": "<Figure size 864x864 with 4 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "82e95944ab3649699aed6afc40f48f67": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "83a40881d5774f20b2eb997024bb12c9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DatePickerModel",
      "state": {
       "description": "End date",
       "disabled": false,
       "layout": "IPY_MODEL_215d35c2f21c4988a4871c99ecde2d68",
       "style": "IPY_MODEL_8827405248de47048b3649f383d6d3e4",
       "value": {
        "date": 19,
        "month": 2,
        "year": 2020
       }
      }
     },
     "8480fe73bbef46f2b5ce3d266c37be67": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "CheckboxModel",
      "state": {
       "description": "output",
       "disabled": false,
       "layout": "IPY_MODEL_2b9787b274f24cf7902cd8460b118685",
       "style": "IPY_MODEL_9efa8d8cbdd248e882958235462da967",
       "value": false
      }
     },
     "84864e3acafb4dafb0264a0fff74d1fb": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_e359ea53103a4faab22b1ab0277c8f44",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\n \n \n Data are gathered for cases from March 10, 2020 to March 24, 2020! Run the next box when ready.\n"
        }
       ]
      }
     },
     "84a52a26d2374da79fc626b8c5270b15": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "84a59d9f09ed438cb2627833335c3926": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DatePickerModel",
      "state": {
       "description": "End date",
       "disabled": false,
       "layout": "IPY_MODEL_013a2638d0ef45779c44ba8caf322319",
       "style": "IPY_MODEL_0fb24961bf4b4d14b9be818152261808",
       "value": {
        "date": 20,
        "month": 2,
        "year": 2020
       }
      }
     },
     "84afe593bf4c4b918cc626003aff9613": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "857e1843463d45ea99924af358305ed6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ButtonStyleModel",
      "state": {}
     },
     "85c4ebbc9eda43898ed7071a0a7a6813": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_7ae40173bfb9464280c34409d8ce6e02",
       "outputs": [
        {
         "ename": "TypeError",
         "evalue": "'float' object cannot be interpreted as an integer",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-22-ffa3afef1a38>\u001b[0m in \u001b[0;36mplot_some_figures\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m     19\u001b[0m     \u001b[0max1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Attack rate correlation (%f, p %f) with population size as power law (exponent %f)'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mr_value\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mp_value\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mslope\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[1;31m# Look at how well correlation describes the growth curves for the selected dates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m     \u001b[0max2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwhich\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwhich\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m.10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m     \u001b[0max2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Correlations for city-by-city exponential growth'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
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        "Abilene, TX",
        "Akron, OH",
        "Albany, GA",
        "Albany-Lebanon, OR",
        "Albany-Schenectady-Troy, NY",
        "Albuquerque, NM",
        "Alexandria, LA",
        "Allentown-Bethlehem-Easton, PA-NJ",
        "Altoona, PA",
        "Amarillo, TX",
        "Ames, IA",
        "Anchorage, AK",
        "Ann Arbor, MI",
        "Anniston-Oxford, AL",
        "Appleton, WI",
        "Asheville, NC",
        "Athens-Clarke County, GA",
        "Atlanta-Sandy Springs-Alpharetta, GA",
        "Atlantic City-Hammonton, NJ",
        "Auburn-Opelika, AL",
        "Augusta-Richmond County, GA-SC",
        "Austin-Round Rock-Georgetown, TX",
        "Bakersfield, CA",
        "Baltimore-Columbia-Towson, MD",
        "Bangor, ME",
        "Barnstable Town, MA",
        "Baton Rouge, LA",
        "Battle Creek, MI",
        "Bay City, MI",
        "Beaumont-Port Arthur, TX",
        "Beckley, WV",
        "Bellingham, WA",
        "Bend, OR",
        "Billings, MT",
        "Binghamton, NY",
        "Birmingham-Hoover, AL",
        "Bismarck, ND",
        "Blacksburg-Christiansburg, VA",
        "Bloomington, IL",
        "Bloomington, IN",
        "Bloomsburg-Berwick, PA",
        "Boise City, ID",
        "Boston-Cambridge-Newton, MA-NH",
        "Boulder, CO",
        "Bowling Green, KY",
        "Bremerton-Silverdale-Port Orchard, WA",
        "Bridgeport-Stamford-Norwalk, CT",
        "Brownsville-Harlingen, TX",
        "Brunswick, GA",
        "Buffalo-Cheektowaga, NY",
        "Burlington, NC",
        "Burlington-South Burlington, VT",
        "California-Lexington Park, MD",
        "Canton-Massillon, OH",
        "Cape Coral-Fort Myers, FL",
        "Cape Girardeau, MO-IL",
        "Carbondale-Marion, IL",
        "Carson City, NV",
        "Casper, WY",
        "Cedar Rapids, IA",
        "Chambersburg-Waynesboro, PA",
        "Champaign-Urbana, IL",
        "Charleston, WV",
        "Charleston-North Charleston, SC",
        "Charlotte-Concord-Gastonia, NC-SC",
        "Charlottesville, VA",
        "Chattanooga, TN-GA",
        "Cheyenne, WY",
        "Chicago-Naperville-Elgin, IL-IN-WI",
        "Chico, CA",
        "Cincinnati, OH-KY-IN",
        "Clarksville, TN-KY",
        "Cleveland, TN",
        "Cleveland-Elyria, OH",
        "Coeur d'Alene, ID",
        "College Station-Bryan, TX",
        "Colorado Springs, CO",
        "Columbia, MO",
        "Columbia, SC",
        "Columbus, GA-AL",
        "Columbus, IN",
        "Columbus, OH",
        "Corpus Christi, TX",
        "Corvallis, OR",
        "Crestview-Fort Walton Beach-Destin, FL",
        "Cumberland, MD-WV",
        "Dallas-Fort Worth-Arlington, TX",
        "Dalton, GA",
        "Danville, IL",
        "Daphne-Fairhope-Foley, AL",
        "Davenport-Moline-Rock Island, IA-IL",
        "Dayton-Kettering, OH",
        "Decatur, AL",
        "Decatur, IL",
        "Deltona-Daytona Beach-Ormond Beach, FL",
        "Denver-Aurora-Lakewood, CO",
        "Des Moines-West Des Moines, IA",
        "Detroit-Warren-Dearborn, MI",
        "Dothan, AL",
        "Dover, DE",
        "Dubuque, IA",
        "Duluth, MN-WI",
        "Durham-Chapel Hill, NC",
        "East Stroudsburg, PA",
        "Eau Claire, WI",
        "El Centro, CA",
        "Elizabethtown-Fort Knox, KY",
        "Elkhart-Goshen, IN",
        "Elmira, NY",
        "El Paso, TX",
        "Enid, OK",
        "Erie, PA",
        "Eugene-Springfield, OR",
        "Evansville, IN-KY",
        "Fairbanks, AK",
        "Fargo, ND-MN",
        "Farmington, NM",
        "Fayetteville, NC",
        "Fayetteville-Springdale-Rogers, AR",
        "Flagstaff, AZ",
        "Flint, MI",
        "Florence, SC",
        "Florence-Muscle Shoals, AL",
        "Fond du Lac, WI",
        "Fort Collins, CO",
        "Fort Smith, AR-OK",
        "Fort Wayne, IN",
        "Fresno, CA",
        "Gadsden, AL",
        "Gainesville, FL",
        "Gainesville, GA",
        "Gettysburg, PA",
        "Glens Falls, NY",
        "Goldsboro, NC",
        "Grand Forks, ND-MN",
        "Grand Island, NE",
        "Grand Junction, CO",
        "Grand Rapids-Kentwood, MI",
        "Grants Pass, OR",
        "Great Falls, MT",
        "Greeley, CO",
        "Green Bay, WI",
        "Greensboro-High Point, NC",
        "Greenville, NC",
        "Greenville-Anderson, SC",
        "Gulfport-Biloxi, MS",
        "Hagerstown-Martinsburg, MD-WV",
        "Hammond, LA",
        "Hanford-Corcoran, CA",
        "Harrisburg-Carlisle, PA",
        "Harrisonburg, VA",
        "Hartford-East Hartford-Middletown, CT",
        "Hattiesburg, MS",
        "Hickory-Lenoir-Morganton, NC",
        "Hilton Head Island-Bluffton, SC",
        "Hinesville, GA",
        "Homosassa Springs, FL",
        "Hot Springs, AR",
        "Houma-Thibodaux, LA",
        "Houston-The Woodlands-Sugar Land, TX",
        "Huntington-Ashland, WV-KY-OH",
        "Huntsville, AL",
        "Idaho Falls, ID",
        "Indianapolis-Carmel-Anderson, IN",
        "Iowa City, IA",
        "Ithaca, NY",
        "Jackson, MI",
        "Jackson, MS",
        "Jackson, TN",
        "Jacksonville, FL",
        "Jacksonville, NC",
        "Janesville-Beloit, WI",
        "Jefferson City, MO",
        "Johnson City, TN",
        "Johnstown, PA",
        "Jonesboro, AR",
        "Joplin, MO",
        "Kahului-Wailuku-Lahaina, HI",
        "Kalamazoo-Portage, MI",
        "Kankakee, IL",
        "Kansas City, MO-KS",
        "Kennewick-Richland, WA",
        "Killeen-Temple, TX",
        "Kingsport-Bristol, TN-VA",
        "Kingston, NY",
        "Knoxville, TN",
        "Kokomo, IN",
        "La Crosse-Onalaska, WI-MN",
        "Lafayette, LA",
        "Lafayette-West Lafayette, IN",
        "Lake Charles, LA",
        "Lake Havasu City-Kingman, AZ",
        "Lakeland-Winter Haven, FL",
        "Lancaster, PA",
        "Lansing-East Lansing, MI",
        "Laredo, TX",
        "Las Cruces, NM",
        "Las Vegas-Henderson-Paradise, NV",
        "Lawrence, KS",
        "Lawton, OK",
        "Lebanon, PA",
        "Lewiston, ID-WA",
        "Lewiston-Auburn, ME",
        "Lexington-Fayette, KY",
        "Lima, OH",
        "Lincoln, NE",
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        "Logan, UT-ID",
        "Longview, TX",
        "Longview, WA",
        "Los Angeles-Long Beach-Anaheim, CA",
        "Louisville/Jefferson County, KY-IN",
        "Lubbock, TX",
        "Lynchburg, VA",
        "Macon-Bibb County, GA",
        "Madera, CA",
        "Madison, WI",
        "Manchester-Nashua, NH",
        "Manhattan, KS",
        "Mankato, MN",
        "Mansfield, OH",
        "McAllen-Edinburg-Mission, TX",
        "Medford, OR",
        "Memphis, TN-MS-AR",
        "Merced, CA",
        "Miami-Fort Lauderdale-Pompano Beach, FL",
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        "Midland, TX",
        "Milwaukee-Waukesha, WI",
        "Minneapolis-St. Paul-Bloomington, MN-WI",
        "Missoula, MT",
        "Mobile, AL",
        "Modesto, CA",
        "Monroe, LA",
        "Monroe, MI",
        "Montgomery, AL",
        "Morgantown, WV",
        "Morristown, TN",
        "Mount Vernon-Anacortes, WA",
        "Muncie, IN",
        "Muskegon, MI",
        "Myrtle Beach-Conway-North Myrtle Beach, SC-NC",
        "Napa, CA",
        "Naples-Marco Island, FL",
        "Nashville-Davidson--Murfreesboro--Franklin, TN",
        "New Bern, NC",
        "New Haven-Milford, CT",
        "New Orleans-Metairie, LA",
        "New York-Newark-Jersey City, NY-NJ-PA",
        "Niles, MI",
        "North Port-Sarasota-Bradenton, FL",
        "Norwich-New London, CT",
        "Ocala, FL",
        "Ocean City, NJ",
        "Odessa, TX",
        "Ogden-Clearfield, UT",
        "Oklahoma City, OK",
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        "Omaha-Council Bluffs, NE-IA",
        "Orlando-Kissimmee-Sanford, FL",
        "Oshkosh-Neenah, WI",
        "Owensboro, KY",
        "Oxnard-Thousand Oaks-Ventura, CA",
        "Palm Bay-Melbourne-Titusville, FL",
        "Panama City, FL",
        "Parkersburg-Vienna, WV",
        "Pensacola-Ferry Pass-Brent, FL",
        "Peoria, IL",
        "Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",
        "Phoenix-Mesa-Chandler, AZ",
        "Pine Bluff, AR",
        "Pittsburgh, PA",
        "Pittsfield, MA",
        "Pocatello, ID",
        "Portland-South Portland, ME",
        "Portland-Vancouver-Hillsboro, OR-WA",
        "Port St. Lucie, FL",
        "Poughkeepsie-Newburgh-Middletown, NY",
        "Prescott Valley-Prescott, AZ",
        "Providence-Warwick, RI-MA",
        "Provo-Orem, UT",
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        "Punta Gorda, FL",
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        "Riverside-San Bernardino-Ontario, CA",
        "Roanoke, VA",
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        "Rochester, NY",
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        "Rocky Mount, NC",
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        "St. Louis, MO-IL",
        "Salem, OR",
        "Salinas, CA",
        "Salisbury, MD-DE",
        "Salt Lake City, UT",
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        "San Antonio-New Braunfels, TX",
        "San Diego-Chula Vista-Carlsbad, CA",
        "San Francisco-Oakland-Berkeley, CA",
        "San Jose-Sunnyvale-Santa Clara, CA",
        "San Luis Obispo-Paso Robles, CA",
        "Santa Cruz-Watsonville, CA",
        "Santa Fe, NM",
        "Santa Maria-Santa Barbara, CA",
        "Santa Rosa-Petaluma, CA",
        "Savannah, GA",
        "Scranton--Wilkes-Barre, PA",
        "Seattle-Tacoma-Bellevue, WA",
        "Sebastian-Vero Beach, FL",
        "Sebring-Avon Park, FL",
        "Sheboygan, WI",
        "Sherman-Denison, TX",
        "Shreveport-Bossier City, LA",
        "Sierra Vista-Douglas, AZ",
        "Sioux City, IA-NE-SD",
        "Sioux Falls, SD",
        "South Bend-Mishawaka, IN-MI",
        "Spartanburg, SC",
        "Spokane-Spokane Valley, WA",
        "Springfield, IL",
        "Springfield, MA",
        "Springfield, MO",
        "Springfield, OH",
        "State College, PA",
        "Staunton, VA",
        "Stockton, CA",
        "Sumter, SC",
        "Syracuse, NY",
        "Tallahassee, FL",
        "Tampa-St. Petersburg-Clearwater, FL",
        "Terre Haute, IN",
        "Texarkana, TX-AR",
        "The Villages, FL",
        "Toledo, OH",
        "Topeka, KS",
        "Trenton-Princeton, NJ",
        "Tucson, AZ",
        "Tulsa, OK",
        "Tuscaloosa, AL",
        "Twin Falls, ID",
        "Tyler, TX",
        "Urban Honolulu, HI",
        "Utica-Rome, NY",
        "Valdosta, GA",
        "Vallejo, CA",
        "Victoria, TX",
        "Vineland-Bridgeton, NJ",
        "Virginia Beach-Norfolk-Newport News, VA-NC",
        "Visalia, CA",
        "Waco, TX",
        "Walla Walla, WA",
        "Warner Robins, GA",
        "Washington-Arlington-Alexandria, DC-VA-MD-WV",
        "Waterloo-Cedar Falls, IA",
        "Watertown-Fort Drum, NY",
        "Wausau-Weston, WI",
        "Weirton-Steubenville, WV-OH",
        "Wenatchee, WA",
        "Wheeling, WV-OH",
        "Wichita, KS",
        "Wichita Falls, TX",
        "Williamsport, PA",
        "Wilmington, NC",
        "Winchester, VA-WV",
        "Winston-Salem, NC",
        "Worcester, MA-CT",
        "Yakima, WA",
        "York-Hanover, PA",
        "Youngstown-Warren-Boardman, OH-PA",
        "Yuba City, CA",
        "Yuma, AZ"
       ],
       "description": "Pick a city to examine specifics",
       "index": 5,
       "layout": "IPY_MODEL_ae4472704cac49179f84949f83b5daf6",
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       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Seattle-Tacoma-Bellevue, WA\nMetropolitan Statistical Area\nPopulation (2018 ACS estimate): 3939363 \nCovid cases by March 24, 2020: 1918\nCOVID-19 attack rate (from regression): 0.146250\nCorrelation for that regression: 0.995194\nSubsequent R: 5.478372\nResidual for the power-law regression: -0.594302\n"
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       "style": "IPY_MODEL_81bedead599143bb96c9cf5d5edd6c47",
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\n",
          "text/plain": "<Figure size 576x1080 with 2 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "8edbc08430de4f06987bed80c5c2e802": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "8eed77a545b3491fbcda80afb1b96c38": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "901306021b904038ae65bea80e17f3c7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DatePickerModel",
      "state": {
       "description": "Start date",
       "disabled": false,
       "layout": "IPY_MODEL_27796861c6c6487cbe867905f8db5d95",
       "style": "IPY_MODEL_5864d2f3fb3e4927bd57e2d29a2f1ab7",
       "value": {
        "date": 13,
        "month": 2,
        "year": 2020
       }
      }
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        "Albuquerque, NM",
        "Alexandria, LA",
        "Allentown-Bethlehem-Easton, PA-NJ",
        "Altoona, PA",
        "Amarillo, TX",
        "Ames, IA",
        "Anchorage, AK",
        "Ann Arbor, MI",
        "Anniston-Oxford, AL",
        "Appleton, WI",
        "Asheville, NC",
        "Athens-Clarke County, GA",
        "Atlanta-Sandy Springs-Alpharetta, GA",
        "Atlantic City-Hammonton, NJ",
        "Auburn-Opelika, AL",
        "Augusta-Richmond County, GA-SC",
        "Austin-Round Rock-Georgetown, TX",
        "Bakersfield, CA",
        "Baltimore-Columbia-Towson, MD",
        "Bangor, ME",
        "Barnstable Town, MA",
        "Baton Rouge, LA",
        "Battle Creek, MI",
        "Bay City, MI",
        "Beaumont-Port Arthur, TX",
        "Beckley, WV",
        "Bellingham, WA",
        "Bend, OR",
        "Billings, MT",
        "Binghamton, NY",
        "Birmingham-Hoover, AL",
        "Bismarck, ND",
        "Blacksburg-Christiansburg, VA",
        "Bloomington, IL",
        "Bloomington, IN",
        "Bloomsburg-Berwick, PA",
        "Boise City, ID",
        "Boston-Cambridge-Newton, MA-NH",
        "Boulder, CO",
        "Bowling Green, KY",
        "Bremerton-Silverdale-Port Orchard, WA",
        "Bridgeport-Stamford-Norwalk, CT",
        "Brownsville-Harlingen, TX",
        "Brunswick, GA",
        "Buffalo-Cheektowaga, NY",
        "Burlington, NC",
        "Burlington-South Burlington, VT",
        "California-Lexington Park, MD",
        "Canton-Massillon, OH",
        "Cape Coral-Fort Myers, FL",
        "Cape Girardeau, MO-IL",
        "Carbondale-Marion, IL",
        "Carson City, NV",
        "Casper, WY",
        "Cedar Rapids, IA",
        "Chambersburg-Waynesboro, PA",
        "Champaign-Urbana, IL",
        "Charleston, WV",
        "Charleston-North Charleston, SC",
        "Charlotte-Concord-Gastonia, NC-SC",
        "Charlottesville, VA",
        "Chattanooga, TN-GA",
        "Cheyenne, WY",
        "Chicago-Naperville-Elgin, IL-IN-WI",
        "Chico, CA",
        "Cincinnati, OH-KY-IN",
        "Clarksville, TN-KY",
        "Cleveland, TN",
        "Cleveland-Elyria, OH",
        "Coeur d'Alene, ID",
        "College Station-Bryan, TX",
        "Colorado Springs, CO",
        "Columbia, MO",
        "Columbia, SC",
        "Columbus, GA-AL",
        "Columbus, IN",
        "Columbus, OH",
        "Corpus Christi, TX",
        "Corvallis, OR",
        "Crestview-Fort Walton Beach-Destin, FL",
        "Cumberland, MD-WV",
        "Dallas-Fort Worth-Arlington, TX",
        "Dalton, GA",
        "Danville, IL",
        "Daphne-Fairhope-Foley, AL",
        "Davenport-Moline-Rock Island, IA-IL",
        "Dayton-Kettering, OH",
        "Decatur, AL",
        "Decatur, IL",
        "Deltona-Daytona Beach-Ormond Beach, FL",
        "Denver-Aurora-Lakewood, CO",
        "Des Moines-West Des Moines, IA",
        "Detroit-Warren-Dearborn, MI",
        "Dothan, AL",
        "Dover, DE",
        "Dubuque, IA",
        "Duluth, MN-WI",
        "Durham-Chapel Hill, NC",
        "East Stroudsburg, PA",
        "Eau Claire, WI",
        "El Centro, CA",
        "Elizabethtown-Fort Knox, KY",
        "Elkhart-Goshen, IN",
        "Elmira, NY",
        "El Paso, TX",
        "Enid, OK",
        "Erie, PA",
        "Eugene-Springfield, OR",
        "Evansville, IN-KY",
        "Fairbanks, AK",
        "Fargo, ND-MN",
        "Farmington, NM",
        "Fayetteville, NC",
        "Fayetteville-Springdale-Rogers, AR",
        "Flagstaff, AZ",
        "Flint, MI",
        "Florence, SC",
        "Florence-Muscle Shoals, AL",
        "Fond du Lac, WI",
        "Fort Collins, CO",
        "Fort Smith, AR-OK",
        "Fort Wayne, IN",
        "Fresno, CA",
        "Gadsden, AL",
        "Gainesville, FL",
        "Gainesville, GA",
        "Gettysburg, PA",
        "Glens Falls, NY",
        "Goldsboro, NC",
        "Grand Forks, ND-MN",
        "Grand Island, NE",
        "Grand Junction, CO",
        "Grand Rapids-Kentwood, MI",
        "Grants Pass, OR",
        "Great Falls, MT",
        "Greeley, CO",
        "Green Bay, WI",
        "Greensboro-High Point, NC",
        "Greenville, NC",
        "Greenville-Anderson, SC",
        "Gulfport-Biloxi, MS",
        "Hagerstown-Martinsburg, MD-WV",
        "Hammond, LA",
        "Hanford-Corcoran, CA",
        "Harrisburg-Carlisle, PA",
        "Harrisonburg, VA",
        "Hartford-East Hartford-Middletown, CT",
        "Hattiesburg, MS",
        "Hickory-Lenoir-Morganton, NC",
        "Hilton Head Island-Bluffton, SC",
        "Hinesville, GA",
        "Homosassa Springs, FL",
        "Hot Springs, AR",
        "Houma-Thibodaux, LA",
        "Houston-The Woodlands-Sugar Land, TX",
        "Huntington-Ashland, WV-KY-OH",
        "Huntsville, AL",
        "Idaho Falls, ID",
        "Indianapolis-Carmel-Anderson, IN",
        "Iowa City, IA",
        "Ithaca, NY",
        "Jackson, MI",
        "Jackson, MS",
        "Jackson, TN",
        "Jacksonville, FL",
        "Jacksonville, NC",
        "Janesville-Beloit, WI",
        "Jefferson City, MO",
        "Johnson City, TN",
        "Johnstown, PA",
        "Jonesboro, AR",
        "Joplin, MO",
        "Kahului-Wailuku-Lahaina, HI",
        "Kalamazoo-Portage, MI",
        "Kankakee, IL",
        "Kansas City, MO-KS",
        "Kennewick-Richland, WA",
        "Killeen-Temple, TX",
        "Kingsport-Bristol, TN-VA",
        "Kingston, NY",
        "Knoxville, TN",
        "Kokomo, IN",
        "La Crosse-Onalaska, WI-MN",
        "Lafayette, LA",
        "Lafayette-West Lafayette, IN",
        "Lake Charles, LA",
        "Lake Havasu City-Kingman, AZ",
        "Lakeland-Winter Haven, FL",
        "Lancaster, PA",
        "Lansing-East Lansing, MI",
        "Laredo, TX",
        "Las Cruces, NM",
        "Las Vegas-Henderson-Paradise, NV",
        "Lawrence, KS",
        "Lawton, OK",
        "Lebanon, PA",
        "Lewiston, ID-WA",
        "Lewiston-Auburn, ME",
        "Lexington-Fayette, KY",
        "Lima, OH",
        "Lincoln, NE",
        "Little Rock-North Little Rock-Conway, AR",
        "Logan, UT-ID",
        "Longview, TX",
        "Longview, WA",
        "Los Angeles-Long Beach-Anaheim, CA",
        "Louisville/Jefferson County, KY-IN",
        "Lubbock, TX",
        "Lynchburg, VA",
        "Macon-Bibb County, GA",
        "Madera, CA",
        "Madison, WI",
        "Manchester-Nashua, NH",
        "Manhattan, KS",
        "Mankato, MN",
        "Mansfield, OH",
        "McAllen-Edinburg-Mission, TX",
        "Medford, OR",
        "Memphis, TN-MS-AR",
        "Merced, CA",
        "Miami-Fort Lauderdale-Pompano Beach, FL",
        "Michigan City-La Porte, IN",
        "Midland, MI",
        "Midland, TX",
        "Milwaukee-Waukesha, WI",
        "Minneapolis-St. Paul-Bloomington, MN-WI",
        "Missoula, MT",
        "Mobile, AL",
        "Modesto, CA",
        "Monroe, LA",
        "Monroe, MI",
        "Montgomery, AL",
        "Morgantown, WV",
        "Morristown, TN",
        "Mount Vernon-Anacortes, WA",
        "Muncie, IN",
        "Muskegon, MI",
        "Myrtle Beach-Conway-North Myrtle Beach, SC-NC",
        "Napa, CA",
        "Naples-Marco Island, FL",
        "Nashville-Davidson--Murfreesboro--Franklin, TN",
        "New Bern, NC",
        "New Haven-Milford, CT",
        "New Orleans-Metairie, LA",
        "New York-Newark-Jersey City, NY-NJ-PA",
        "Niles, MI",
        "North Port-Sarasota-Bradenton, FL",
        "Norwich-New London, CT",
        "Ocala, FL",
        "Ocean City, NJ",
        "Odessa, TX",
        "Ogden-Clearfield, UT",
        "Oklahoma City, OK",
        "Olympia-Lacey-Tumwater, WA",
        "Omaha-Council Bluffs, NE-IA",
        "Orlando-Kissimmee-Sanford, FL",
        "Oshkosh-Neenah, WI",
        "Owensboro, KY",
        "Oxnard-Thousand Oaks-Ventura, CA",
        "Palm Bay-Melbourne-Titusville, FL",
        "Panama City, FL",
        "Parkersburg-Vienna, WV",
        "Pensacola-Ferry Pass-Brent, FL",
        "Peoria, IL",
        "Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",
        "Phoenix-Mesa-Chandler, AZ",
        "Pine Bluff, AR",
        "Pittsburgh, PA",
        "Pittsfield, MA",
        "Pocatello, ID",
        "Portland-South Portland, ME",
        "Portland-Vancouver-Hillsboro, OR-WA",
        "Port St. Lucie, FL",
        "Poughkeepsie-Newburgh-Middletown, NY",
        "Prescott Valley-Prescott, AZ",
        "Providence-Warwick, RI-MA",
        "Provo-Orem, UT",
        "Pueblo, CO",
        "Punta Gorda, FL",
        "Racine, WI",
        "Raleigh-Cary, NC",
        "Rapid City, SD",
        "Reading, PA",
        "Redding, CA",
        "Reno, NV",
        "Richmond, VA",
        "Riverside-San Bernardino-Ontario, CA",
        "Roanoke, VA",
        "Rochester, MN",
        "Rochester, NY",
        "Rockford, IL",
        "Rocky Mount, NC",
        "Rome, GA",
        "Sacramento-Roseville-Folsom, CA",
        "Saginaw, MI",
        "St. Cloud, MN",
        "St. George, UT",
        "St. Joseph, MO-KS",
        "St. Louis, MO-IL",
        "Salem, OR",
        "Salinas, CA",
        "Salisbury, MD-DE",
        "Salt Lake City, UT",
        "San Angelo, TX",
        "San Antonio-New Braunfels, TX",
        "San Diego-Chula Vista-Carlsbad, CA",
        "San Francisco-Oakland-Berkeley, CA",
        "San Jose-Sunnyvale-Santa Clara, CA",
        "San Luis Obispo-Paso Robles, CA",
        "Santa Cruz-Watsonville, CA",
        "Santa Fe, NM",
        "Santa Maria-Santa Barbara, CA",
        "Santa Rosa-Petaluma, CA",
        "Savannah, GA",
        "Scranton--Wilkes-Barre, PA",
        "Seattle-Tacoma-Bellevue, WA",
        "Sebastian-Vero Beach, FL",
        "Sebring-Avon Park, FL",
        "Sheboygan, WI",
        "Sherman-Denison, TX",
        "Shreveport-Bossier City, LA",
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        "Sioux City, IA-NE-SD",
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        "Springfield, MO",
        "Springfield, OH",
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        "Staunton, VA",
        "Stockton, CA",
        "Sumter, SC",
        "Syracuse, NY",
        "Tallahassee, FL",
        "Tampa-St. Petersburg-Clearwater, FL",
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        "Texarkana, TX-AR",
        "The Villages, FL",
        "Toledo, OH",
        "Topeka, KS",
        "Trenton-Princeton, NJ",
        "Tucson, AZ",
        "Tulsa, OK",
        "Tuscaloosa, AL",
        "Twin Falls, ID",
        "Tyler, TX",
        "Urban Honolulu, HI",
        "Utica-Rome, NY",
        "Valdosta, GA",
        "Vallejo, CA",
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        "Virginia Beach-Norfolk-Newport News, VA-NC",
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        "Waterloo-Cedar Falls, IA",
        "Watertown-Fort Drum, NY",
        "Wausau-Weston, WI",
        "Weirton-Steubenville, WV-OH",
        "Wenatchee, WA",
        "Wheeling, WV-OH",
        "Wichita, KS",
        "Wichita Falls, TX",
        "Williamsport, PA",
        "Wilmington, NC",
        "Winchester, VA-WV",
        "Winston-Salem, NC",
        "Worcester, MA-CT",
        "Yakima, WA",
        "York-Hanover, PA",
        "Youngstown-Warren-Boardman, OH-PA",
        "Yuba City, CA",
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        "Albuquerque, NM",
        "Alexandria, LA",
        "Allentown-Bethlehem-Easton, PA-NJ",
        "Altoona, PA",
        "Amarillo, TX",
        "Ames, IA",
        "Anchorage, AK",
        "Ann Arbor, MI",
        "Anniston-Oxford, AL",
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        "Asheville, NC",
        "Athens-Clarke County, GA",
        "Atlanta-Sandy Springs-Alpharetta, GA",
        "Atlantic City-Hammonton, NJ",
        "Auburn-Opelika, AL",
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        "Austin-Round Rock-Georgetown, TX",
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        "Baltimore-Columbia-Towson, MD",
        "Bangor, ME",
        "Barnstable Town, MA",
        "Baton Rouge, LA",
        "Battle Creek, MI",
        "Bay City, MI",
        "Beaumont-Port Arthur, TX",
        "Beckley, WV",
        "Bellingham, WA",
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        "Billings, MT",
        "Binghamton, NY",
        "Birmingham-Hoover, AL",
        "Bismarck, ND",
        "Blacksburg-Christiansburg, VA",
        "Bloomington, IL",
        "Bloomington, IN",
        "Bloomsburg-Berwick, PA",
        "Boise City, ID",
        "Boston-Cambridge-Newton, MA-NH",
        "Boulder, CO",
        "Bowling Green, KY",
        "Bremerton-Silverdale-Port Orchard, WA",
        "Bridgeport-Stamford-Norwalk, CT",
        "Brownsville-Harlingen, TX",
        "Brunswick, GA",
        "Buffalo-Cheektowaga, NY",
        "Burlington, NC",
        "Burlington-South Burlington, VT",
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        "Carbondale-Marion, IL",
        "Carson City, NV",
        "Casper, WY",
        "Cedar Rapids, IA",
        "Chambersburg-Waynesboro, PA",
        "Champaign-Urbana, IL",
        "Charleston, WV",
        "Charleston-North Charleston, SC",
        "Charlotte-Concord-Gastonia, NC-SC",
        "Charlottesville, VA",
        "Chattanooga, TN-GA",
        "Cheyenne, WY",
        "Chicago-Naperville-Elgin, IL-IN-WI",
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        "Cincinnati, OH-KY-IN",
        "Clarksville, TN-KY",
        "Cleveland, TN",
        "Cleveland-Elyria, OH",
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        "College Station-Bryan, TX",
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        "Columbia, MO",
        "Columbia, SC",
        "Columbus, GA-AL",
        "Columbus, IN",
        "Columbus, OH",
        "Corpus Christi, TX",
        "Corvallis, OR",
        "Crestview-Fort Walton Beach-Destin, FL",
        "Cumberland, MD-WV",
        "Dallas-Fort Worth-Arlington, TX",
        "Dalton, GA",
        "Danville, IL",
        "Daphne-Fairhope-Foley, AL",
        "Davenport-Moline-Rock Island, IA-IL",
        "Dayton-Kettering, OH",
        "Decatur, AL",
        "Decatur, IL",
        "Deltona-Daytona Beach-Ormond Beach, FL",
        "Denver-Aurora-Lakewood, CO",
        "Des Moines-West Des Moines, IA",
        "Detroit-Warren-Dearborn, MI",
        "Dothan, AL",
        "Dover, DE",
        "Dubuque, IA",
        "Duluth, MN-WI",
        "Durham-Chapel Hill, NC",
        "East Stroudsburg, PA",
        "Eau Claire, WI",
        "El Centro, CA",
        "Elizabethtown-Fort Knox, KY",
        "Elkhart-Goshen, IN",
        "Elmira, NY",
        "El Paso, TX",
        "Enid, OK",
        "Erie, PA",
        "Eugene-Springfield, OR",
        "Evansville, IN-KY",
        "Fairbanks, AK",
        "Fargo, ND-MN",
        "Farmington, NM",
        "Fayetteville, NC",
        "Fayetteville-Springdale-Rogers, AR",
        "Flagstaff, AZ",
        "Flint, MI",
        "Florence, SC",
        "Florence-Muscle Shoals, AL",
        "Fond du Lac, WI",
        "Fort Collins, CO",
        "Fort Smith, AR-OK",
        "Fort Wayne, IN",
        "Fresno, CA",
        "Gadsden, AL",
        "Gainesville, FL",
        "Gainesville, GA",
        "Gettysburg, PA",
        "Glens Falls, NY",
        "Goldsboro, NC",
        "Grand Forks, ND-MN",
        "Grand Island, NE",
        "Grand Junction, CO",
        "Grand Rapids-Kentwood, MI",
        "Grants Pass, OR",
        "Great Falls, MT",
        "Greeley, CO",
        "Green Bay, WI",
        "Greensboro-High Point, NC",
        "Greenville, NC",
        "Greenville-Anderson, SC",
        "Gulfport-Biloxi, MS",
        "Hagerstown-Martinsburg, MD-WV",
        "Hammond, LA",
        "Hanford-Corcoran, CA",
        "Harrisburg-Carlisle, PA",
        "Harrisonburg, VA",
        "Hartford-East Hartford-Middletown, CT",
        "Hattiesburg, MS",
        "Hickory-Lenoir-Morganton, NC",
        "Hilton Head Island-Bluffton, SC",
        "Hinesville, GA",
        "Homosassa Springs, FL",
        "Hot Springs, AR",
        "Houma-Thibodaux, LA",
        "Houston-The Woodlands-Sugar Land, TX",
        "Huntington-Ashland, WV-KY-OH",
        "Huntsville, AL",
        "Idaho Falls, ID",
        "Indianapolis-Carmel-Anderson, IN",
        "Iowa City, IA",
        "Ithaca, NY",
        "Jackson, MI",
        "Jackson, MS",
        "Jackson, TN",
        "Jacksonville, FL",
        "Jacksonville, NC",
        "Janesville-Beloit, WI",
        "Jefferson City, MO",
        "Johnson City, TN",
        "Johnstown, PA",
        "Jonesboro, AR",
        "Joplin, MO",
        "Kahului-Wailuku-Lahaina, HI",
        "Kalamazoo-Portage, MI",
        "Kankakee, IL",
        "Kansas City, MO-KS",
        "Kennewick-Richland, WA",
        "Killeen-Temple, TX",
        "Kingsport-Bristol, TN-VA",
        "Kingston, NY",
        "Knoxville, TN",
        "Kokomo, IN",
        "La Crosse-Onalaska, WI-MN",
        "Lafayette, LA",
        "Lafayette-West Lafayette, IN",
        "Lake Charles, LA",
        "Lake Havasu City-Kingman, AZ",
        "Lakeland-Winter Haven, FL",
        "Lancaster, PA",
        "Lansing-East Lansing, MI",
        "Laredo, TX",
        "Las Cruces, NM",
        "Las Vegas-Henderson-Paradise, NV",
        "Lawrence, KS",
        "Lawton, OK",
        "Lebanon, PA",
        "Lewiston, ID-WA",
        "Lewiston-Auburn, ME",
        "Lexington-Fayette, KY",
        "Lima, OH",
        "Lincoln, NE",
        "Little Rock-North Little Rock-Conway, AR",
        "Logan, UT-ID",
        "Longview, TX",
        "Longview, WA",
        "Los Angeles-Long Beach-Anaheim, CA",
        "Louisville/Jefferson County, KY-IN",
        "Lubbock, TX",
        "Lynchburg, VA",
        "Macon-Bibb County, GA",
        "Madera, CA",
        "Madison, WI",
        "Manchester-Nashua, NH",
        "Manhattan, KS",
        "Mankato, MN",
        "Mansfield, OH",
        "McAllen-Edinburg-Mission, TX",
        "Medford, OR",
        "Memphis, TN-MS-AR",
        "Merced, CA",
        "Miami-Fort Lauderdale-Pompano Beach, FL",
        "Michigan City-La Porte, IN",
        "Midland, MI",
        "Midland, TX",
        "Milwaukee-Waukesha, WI",
        "Minneapolis-St. Paul-Bloomington, MN-WI",
        "Missoula, MT",
        "Mobile, AL",
        "Modesto, CA",
        "Monroe, LA",
        "Monroe, MI",
        "Montgomery, AL",
        "Morgantown, WV",
        "Morristown, TN",
        "Mount Vernon-Anacortes, WA",
        "Muncie, IN",
        "Muskegon, MI",
        "Myrtle Beach-Conway-North Myrtle Beach, SC-NC",
        "Napa, CA",
        "Naples-Marco Island, FL",
        "Nashville-Davidson--Murfreesboro--Franklin, TN",
        "New Bern, NC",
        "New Haven-Milford, CT",
        "New Orleans-Metairie, LA",
        "New York-Newark-Jersey City, NY-NJ-PA",
        "Niles, MI",
        "North Port-Sarasota-Bradenton, FL",
        "Norwich-New London, CT",
        "Ocala, FL",
        "Ocean City, NJ",
        "Odessa, TX",
        "Ogden-Clearfield, UT",
        "Oklahoma City, OK",
        "Olympia-Lacey-Tumwater, WA",
        "Omaha-Council Bluffs, NE-IA",
        "Orlando-Kissimmee-Sanford, FL",
        "Oshkosh-Neenah, WI",
        "Owensboro, KY",
        "Oxnard-Thousand Oaks-Ventura, CA",
        "Palm Bay-Melbourne-Titusville, FL",
        "Panama City, FL",
        "Parkersburg-Vienna, WV",
        "Pensacola-Ferry Pass-Brent, FL",
        "Peoria, IL",
        "Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",
        "Phoenix-Mesa-Chandler, AZ",
        "Pine Bluff, AR",
        "Pittsburgh, PA",
        "Pittsfield, MA",
        "Pocatello, ID",
        "Portland-South Portland, ME",
        "Portland-Vancouver-Hillsboro, OR-WA",
        "Port St. Lucie, FL",
        "Poughkeepsie-Newburgh-Middletown, NY",
        "Prescott Valley-Prescott, AZ",
        "Providence-Warwick, RI-MA",
        "Provo-Orem, UT",
        "Pueblo, CO",
        "Punta Gorda, FL",
        "Racine, WI",
        "Raleigh-Cary, NC",
        "Rapid City, SD",
        "Reading, PA",
        "Redding, CA",
        "Reno, NV",
        "Richmond, VA",
        "Riverside-San Bernardino-Ontario, CA",
        "Roanoke, VA",
        "Rochester, MN",
        "Rochester, NY",
        "Rockford, IL",
        "Rocky Mount, NC",
        "Rome, GA",
        "Sacramento-Roseville-Folsom, CA",
        "Saginaw, MI",
        "St. Cloud, MN",
        "St. George, UT",
        "St. Joseph, MO-KS",
        "St. Louis, MO-IL",
        "Salem, OR",
        "Salinas, CA",
        "Salisbury, MD-DE",
        "Salt Lake City, UT",
        "San Angelo, TX",
        "San Antonio-New Braunfels, TX",
        "San Diego-Chula Vista-Carlsbad, CA",
        "San Francisco-Oakland-Berkeley, CA",
        "San Jose-Sunnyvale-Santa Clara, CA",
        "San Luis Obispo-Paso Robles, CA",
        "Santa Cruz-Watsonville, CA",
        "Santa Fe, NM",
        "Santa Maria-Santa Barbara, CA",
        "Santa Rosa-Petaluma, CA",
        "Savannah, GA",
        "Scranton--Wilkes-Barre, PA",
        "Seattle-Tacoma-Bellevue, WA",
        "Sebastian-Vero Beach, FL",
        "Sebring-Avon Park, FL",
        "Sheboygan, WI",
        "Sherman-Denison, TX",
        "Shreveport-Bossier City, LA",
        "Sierra Vista-Douglas, AZ",
        "Sioux City, IA-NE-SD",
        "Sioux Falls, SD",
        "South Bend-Mishawaka, IN-MI",
        "Spartanburg, SC",
        "Spokane-Spokane Valley, WA",
        "Springfield, IL",
        "Springfield, MA",
        "Springfield, MO",
        "Springfield, OH",
        "State College, PA",
        "Staunton, VA",
        "Stockton, CA",
        "Sumter, SC",
        "Syracuse, NY",
        "Tallahassee, FL",
        "Tampa-St. Petersburg-Clearwater, FL",
        "Terre Haute, IN",
        "Texarkana, TX-AR",
        "The Villages, FL",
        "Toledo, OH",
        "Topeka, KS",
        "Trenton-Princeton, NJ",
        "Tucson, AZ",
        "Tulsa, OK",
        "Tuscaloosa, AL",
        "Twin Falls, ID",
        "Tyler, TX",
        "Urban Honolulu, HI",
        "Utica-Rome, NY",
        "Valdosta, GA",
        "Vallejo, CA",
        "Victoria, TX",
        "Vineland-Bridgeton, NJ",
        "Virginia Beach-Norfolk-Newport News, VA-NC",
        "Visalia, CA",
        "Waco, TX",
        "Walla Walla, WA",
        "Warner Robins, GA",
        "Washington-Arlington-Alexandria, DC-VA-MD-WV",
        "Waterloo-Cedar Falls, IA",
        "Watertown-Fort Drum, NY",
        "Wausau-Weston, WI",
        "Weirton-Steubenville, WV-OH",
        "Wenatchee, WA",
        "Wheeling, WV-OH",
        "Wichita, KS",
        "Wichita Falls, TX",
        "Williamsport, PA",
        "Wilmington, NC",
        "Winchester, VA-WV",
        "Winston-Salem, NC",
        "Worcester, MA-CT",
        "Yakima, WA",
        "York-Hanover, PA",
        "Youngstown-Warren-Boardman, OH-PA",
        "Yuba City, CA",
        "Yuma, AZ"
       ],
       "description": "Pick a city to examine specifics",
       "index": 2,
       "layout": "IPY_MODEL_2df105d3d5be4810b3e2f688d4b73a5d",
       "style": "IPY_MODEL_d2ba2bda874a4fe0a68de7a51d8ef175"
      }
     },
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      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_319fb3319b9d4507a093570e4254ecc0",
       "outputs": [
        {
         "ename": "TypeError",
         "evalue": "'float' object cannot be interpreted as an integer",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-21-bb3e563e3126>\u001b[0m in \u001b[0;36mplot_some_figures\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m     19\u001b[0m     \u001b[0max1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Attack rate correlation (%f, p %f) with population size as power law (exponent %f)'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mr_value\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mp_value\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mslope\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[1;31m# Look at how well correlation describes the growth curves for the selected dates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m     \u001b[0max2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwhich\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwhich\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m.10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m     \u001b[0max2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Correlations for city-by-city exponential growth'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
         ]
        }
       ]
      }
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      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
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     "a8f9212b72284a5aaa73907a41a40d6b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DatePickerModel",
      "state": {
       "description": "End date",
       "disabled": false,
       "layout": "IPY_MODEL_9351216bb88b40028bf8f8c54d6e71ad",
       "style": "IPY_MODEL_b9614fa9c97745018f64a385b4565c5c",
       "value": {
        "date": 19,
        "month": 2,
        "year": 2020
       }
      }
     },
     "a9a91bf723c44203852ec77b9e839dbc": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_d3526693e0fb4748a0cfcc94c0323569",
        "IPY_MODEL_6674273f2f2c43c093999e822603462a"
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       "layout": "IPY_MODEL_b2c26d7d5b5740639efab6f960e59b31"
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      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "aab1eda0ca554152b4136aca0870d871": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
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     "aab8ba05e8a94b73bba39d9015f5bcdb": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_fa7698647ccd44d696772af497ecbcbb",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "88    Birmingham-Hoover, AL\nName: Title, dtype: object\n88    Metropolitan Statistical Area\nName: MetroMicro, dtype: object\nPopulation (2018 ACS estimate: 1088090 \nCovid cases by March 19, 2020: 88    44\nName: COVIDEnd, dtype: int64\n"
        },
        {
         "ename": "NameError",
         "evalue": "name 'pd' is not defined",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-46-fa582c3f2a8f>\u001b[0m in \u001b[0;36mlookup_a_city\u001b[1;34m(df, pickacity)\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Population (2018 ACS estimate: %d '\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPop2018\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Covid cases by '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdateEnd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%B %d, %Y\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m': '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOVIDEnd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAttackRate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'There was not sufficient data (or another error occurred) to estimate a growth rate'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
         ]
        }
       ]
      }
     },
     "aafe87fef9a54296b8abd938b08cb217": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
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      }
     },
     "ae354b930abe47c99317d9dd9dc95b72": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
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     "ae4472704cac49179f84949f83b5daf6": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "b1329926232441cbbe8a935c02b2d00e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_63bf647d1f7b4c7faa6be5b20b77e62d"
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       "layout": "IPY_MODEL_bb06ca9cf93a49b089ee7a579b53fd1d"
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     "b2002be59cd7406b9e63400d93bc08a0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b232d1ed7e9d447bbaeb21309f0cb789": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "b2aca5c1a83b43ab8d3310c0ea389db1": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b2c26d7d5b5740639efab6f960e59b31": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "b4a6f1bf664c434baf9fa9fc98cf8491": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "b5668ba684774e27af28ca874346d01e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "b75b14e65651447fb2504fb082c7fe6d": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_ec27779331eb42379da8c6c01b3c2186",
       "outputs": [
        {
         "ename": "NameError",
         "evalue": "name 'which' is not defined",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-45-036ebe79b72b>\u001b[0m in \u001b[0;36mlookup_a_city\u001b[1;34m(df, pickacity)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mlookup_a_city\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpickacity\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTitle\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mpickacity\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwhich\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Uh-oh, more than one city by that name!'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mNameError\u001b[0m: name 'which' is not defined"
         ]
        }
       ]
      }
     },
     "b85fa0d667fd459888c7d034b0b62887": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "b86221e458f642069f8ff9556cc9e069": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "b87bdcaff16f441f8eacf52b0d19c263": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b9614fa9c97745018f64a385b4565c5c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "b99926e5ffeb456598180b190c349eae": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_4c3d5aa8faf84b97836c11e72afee40c",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Data are gathered! Run the next box when ready.\n"
        }
       ]
      }
     },
     "bb06ca9cf93a49b089ee7a579b53fd1d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "bbc2b03320204ee88397cfbdceae5f96": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SelectModel",
      "state": {
       "_options_labels": [
        "Abilene, TX",
        "Akron, OH",
        "Albany, GA",
        "Albany-Lebanon, OR",
        "Albany-Schenectady-Troy, NY",
        "Albuquerque, NM",
        "Alexandria, LA",
        "Allentown-Bethlehem-Easton, PA-NJ",
        "Altoona, PA",
        "Amarillo, TX",
        "Ames, IA",
        "Anchorage, AK",
        "Ann Arbor, MI",
        "Anniston-Oxford, AL",
        "Appleton, WI",
        "Asheville, NC",
        "Athens-Clarke County, GA",
        "Atlanta-Sandy Springs-Alpharetta, GA",
        "Atlantic City-Hammonton, NJ",
        "Auburn-Opelika, AL",
        "Augusta-Richmond County, GA-SC",
        "Austin-Round Rock-Georgetown, TX",
        "Bakersfield, CA",
        "Baltimore-Columbia-Towson, MD",
        "Bangor, ME",
        "Barnstable Town, MA",
        "Baton Rouge, LA",
        "Battle Creek, MI",
        "Bay City, MI",
        "Beaumont-Port Arthur, TX",
        "Beckley, WV",
        "Bellingham, WA",
        "Bend, OR",
        "Billings, MT",
        "Binghamton, NY",
        "Birmingham-Hoover, AL",
        "Bismarck, ND",
        "Blacksburg-Christiansburg, VA",
        "Bloomington, IL",
        "Bloomington, IN",
        "Bloomsburg-Berwick, PA",
        "Boise City, ID",
        "Boston-Cambridge-Newton, MA-NH",
        "Boulder, CO",
        "Bowling Green, KY",
        "Bremerton-Silverdale-Port Orchard, WA",
        "Bridgeport-Stamford-Norwalk, CT",
        "Brownsville-Harlingen, TX",
        "Brunswick, GA",
        "Buffalo-Cheektowaga, NY",
        "Burlington, NC",
        "Burlington-South Burlington, VT",
        "California-Lexington Park, MD",
        "Canton-Massillon, OH",
        "Cape Coral-Fort Myers, FL",
        "Cape Girardeau, MO-IL",
        "Carbondale-Marion, IL",
        "Carson City, NV",
        "Casper, WY",
        "Cedar Rapids, IA",
        "Chambersburg-Waynesboro, PA",
        "Champaign-Urbana, IL",
        "Charleston, WV",
        "Charleston-North Charleston, SC",
        "Charlotte-Concord-Gastonia, NC-SC",
        "Charlottesville, VA",
        "Chattanooga, TN-GA",
        "Cheyenne, WY",
        "Chicago-Naperville-Elgin, IL-IN-WI",
        "Chico, CA",
        "Cincinnati, OH-KY-IN",
        "Clarksville, TN-KY",
        "Cleveland, TN",
        "Cleveland-Elyria, OH",
        "Coeur d'Alene, ID",
        "College Station-Bryan, TX",
        "Colorado Springs, CO",
        "Columbia, MO",
        "Columbia, SC",
        "Columbus, GA-AL",
        "Columbus, IN",
        "Columbus, OH",
        "Corpus Christi, TX",
        "Corvallis, OR",
        "Crestview-Fort Walton Beach-Destin, FL",
        "Cumberland, MD-WV",
        "Dallas-Fort Worth-Arlington, TX",
        "Dalton, GA",
        "Danville, IL",
        "Daphne-Fairhope-Foley, AL",
        "Davenport-Moline-Rock Island, IA-IL",
        "Dayton-Kettering, OH",
        "Decatur, AL",
        "Decatur, IL",
        "Deltona-Daytona Beach-Ormond Beach, FL",
        "Denver-Aurora-Lakewood, CO",
        "Des Moines-West Des Moines, IA",
        "Detroit-Warren-Dearborn, MI",
        "Dothan, AL",
        "Dover, DE",
        "Dubuque, IA",
        "Duluth, MN-WI",
        "Durham-Chapel Hill, NC",
        "East Stroudsburg, PA",
        "Eau Claire, WI",
        "El Centro, CA",
        "Elizabethtown-Fort Knox, KY",
        "Elkhart-Goshen, IN",
        "Elmira, NY",
        "El Paso, TX",
        "Enid, OK",
        "Erie, PA",
        "Eugene-Springfield, OR",
        "Evansville, IN-KY",
        "Fairbanks, AK",
        "Fargo, ND-MN",
        "Farmington, NM",
        "Fayetteville, NC",
        "Fayetteville-Springdale-Rogers, AR",
        "Flagstaff, AZ",
        "Flint, MI",
        "Florence, SC",
        "Florence-Muscle Shoals, AL",
        "Fond du Lac, WI",
        "Fort Collins, CO",
        "Fort Smith, AR-OK",
        "Fort Wayne, IN",
        "Fresno, CA",
        "Gadsden, AL",
        "Gainesville, FL",
        "Gainesville, GA",
        "Gettysburg, PA",
        "Glens Falls, NY",
        "Goldsboro, NC",
        "Grand Forks, ND-MN",
        "Grand Island, NE",
        "Grand Junction, CO",
        "Grand Rapids-Kentwood, MI",
        "Grants Pass, OR",
        "Great Falls, MT",
        "Greeley, CO",
        "Green Bay, WI",
        "Greensboro-High Point, NC",
        "Greenville, NC",
        "Greenville-Anderson, SC",
        "Gulfport-Biloxi, MS",
        "Hagerstown-Martinsburg, MD-WV",
        "Hammond, LA",
        "Hanford-Corcoran, CA",
        "Harrisburg-Carlisle, PA",
        "Harrisonburg, VA",
        "Hartford-East Hartford-Middletown, CT",
        "Hattiesburg, MS",
        "Hickory-Lenoir-Morganton, NC",
        "Hilton Head Island-Bluffton, SC",
        "Hinesville, GA",
        "Homosassa Springs, FL",
        "Hot Springs, AR",
        "Houma-Thibodaux, LA",
        "Houston-The Woodlands-Sugar Land, TX",
        "Huntington-Ashland, WV-KY-OH",
        "Huntsville, AL",
        "Idaho Falls, ID",
        "Indianapolis-Carmel-Anderson, IN",
        "Iowa City, IA",
        "Ithaca, NY",
        "Jackson, MI",
        "Jackson, MS",
        "Jackson, TN",
        "Jacksonville, FL",
        "Jacksonville, NC",
        "Janesville-Beloit, WI",
        "Jefferson City, MO",
        "Johnson City, TN",
        "Johnstown, PA",
        "Jonesboro, AR",
        "Joplin, MO",
        "Kahului-Wailuku-Lahaina, HI",
        "Kalamazoo-Portage, MI",
        "Kankakee, IL",
        "Kansas City, MO-KS",
        "Kennewick-Richland, WA",
        "Killeen-Temple, TX",
        "Kingsport-Bristol, TN-VA",
        "Kingston, NY",
        "Knoxville, TN",
        "Kokomo, IN",
        "La Crosse-Onalaska, WI-MN",
        "Lafayette, LA",
        "Lafayette-West Lafayette, IN",
        "Lake Charles, LA",
        "Lake Havasu City-Kingman, AZ",
        "Lakeland-Winter Haven, FL",
        "Lancaster, PA",
        "Lansing-East Lansing, MI",
        "Laredo, TX",
        "Las Cruces, NM",
        "Las Vegas-Henderson-Paradise, NV",
        "Lawrence, KS",
        "Lawton, OK",
        "Lebanon, PA",
        "Lewiston, ID-WA",
        "Lewiston-Auburn, ME",
        "Lexington-Fayette, KY",
        "Lima, OH",
        "Lincoln, NE",
        "Little Rock-North Little Rock-Conway, AR",
        "Logan, UT-ID",
        "Longview, TX",
        "Longview, WA",
        "Los Angeles-Long Beach-Anaheim, CA",
        "Louisville/Jefferson County, KY-IN",
        "Lubbock, TX",
        "Lynchburg, VA",
        "Macon-Bibb County, GA",
        "Madera, CA",
        "Madison, WI",
        "Manchester-Nashua, NH",
        "Manhattan, KS",
        "Mankato, MN",
        "Mansfield, OH",
        "McAllen-Edinburg-Mission, TX",
        "Medford, OR",
        "Memphis, TN-MS-AR",
        "Merced, CA",
        "Miami-Fort Lauderdale-Pompano Beach, FL",
        "Michigan City-La Porte, IN",
        "Midland, MI",
        "Midland, TX",
        "Milwaukee-Waukesha, WI",
        "Minneapolis-St. Paul-Bloomington, MN-WI",
        "Missoula, MT",
        "Mobile, AL",
        "Modesto, CA",
        "Monroe, LA",
        "Monroe, MI",
        "Montgomery, AL",
        "Morgantown, WV",
        "Morristown, TN",
        "Mount Vernon-Anacortes, WA",
        "Muncie, IN",
        "Muskegon, MI",
        "Myrtle Beach-Conway-North Myrtle Beach, SC-NC",
        "Napa, CA",
        "Naples-Marco Island, FL",
        "Nashville-Davidson--Murfreesboro--Franklin, TN",
        "New Bern, NC",
        "New Haven-Milford, CT",
        "New Orleans-Metairie, LA",
        "New York-Newark-Jersey City, NY-NJ-PA",
        "Niles, MI",
        "North Port-Sarasota-Bradenton, FL",
        "Norwich-New London, CT",
        "Ocala, FL",
        "Ocean City, NJ",
        "Odessa, TX",
        "Ogden-Clearfield, UT",
        "Oklahoma City, OK",
        "Olympia-Lacey-Tumwater, WA",
        "Omaha-Council Bluffs, NE-IA",
        "Orlando-Kissimmee-Sanford, FL",
        "Oshkosh-Neenah, WI",
        "Owensboro, KY",
        "Oxnard-Thousand Oaks-Ventura, CA",
        "Palm Bay-Melbourne-Titusville, FL",
        "Panama City, FL",
        "Parkersburg-Vienna, WV",
        "Pensacola-Ferry Pass-Brent, FL",
        "Peoria, IL",
        "Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",
        "Phoenix-Mesa-Chandler, AZ",
        "Pine Bluff, AR",
        "Pittsburgh, PA",
        "Pittsfield, MA",
        "Pocatello, ID",
        "Portland-South Portland, ME",
        "Portland-Vancouver-Hillsboro, OR-WA",
        "Port St. Lucie, FL",
        "Poughkeepsie-Newburgh-Middletown, NY",
        "Prescott Valley-Prescott, AZ",
        "Providence-Warwick, RI-MA",
        "Provo-Orem, UT",
        "Pueblo, CO",
        "Punta Gorda, FL",
        "Racine, WI",
        "Raleigh-Cary, NC",
        "Rapid City, SD",
        "Reading, PA",
        "Redding, CA",
        "Reno, NV",
        "Richmond, VA",
        "Riverside-San Bernardino-Ontario, CA",
        "Roanoke, VA",
        "Rochester, MN",
        "Rochester, NY",
        "Rockford, IL",
        "Rocky Mount, NC",
        "Rome, GA",
        "Sacramento-Roseville-Folsom, CA",
        "Saginaw, MI",
        "St. Cloud, MN",
        "St. George, UT",
        "St. Joseph, MO-KS",
        "St. Louis, MO-IL",
        "Salem, OR",
        "Salinas, CA",
        "Salisbury, MD-DE",
        "Salt Lake City, UT",
        "San Angelo, TX",
        "San Antonio-New Braunfels, TX",
        "San Diego-Chula Vista-Carlsbad, CA",
        "San Francisco-Oakland-Berkeley, CA",
        "San Jose-Sunnyvale-Santa Clara, CA",
        "San Luis Obispo-Paso Robles, CA",
        "Santa Cruz-Watsonville, CA",
        "Santa Fe, NM",
        "Santa Maria-Santa Barbara, CA",
        "Santa Rosa-Petaluma, CA",
        "Savannah, GA",
        "Scranton--Wilkes-Barre, PA",
        "Seattle-Tacoma-Bellevue, WA",
        "Sebastian-Vero Beach, FL",
        "Sebring-Avon Park, FL",
        "Sheboygan, WI",
        "Sherman-Denison, TX",
        "Shreveport-Bossier City, LA",
        "Sierra Vista-Douglas, AZ",
        "Sioux City, IA-NE-SD",
        "Sioux Falls, SD",
        "South Bend-Mishawaka, IN-MI",
        "Spartanburg, SC",
        "Spokane-Spokane Valley, WA",
        "Springfield, IL",
        "Springfield, MA",
        "Springfield, MO",
        "Springfield, OH",
        "State College, PA",
        "Staunton, VA",
        "Stockton, CA",
        "Sumter, SC",
        "Syracuse, NY",
        "Tallahassee, FL",
        "Tampa-St. Petersburg-Clearwater, FL",
        "Terre Haute, IN",
        "Texarkana, TX-AR",
        "The Villages, FL",
        "Toledo, OH",
        "Topeka, KS",
        "Trenton-Princeton, NJ",
        "Tucson, AZ",
        "Tulsa, OK",
        "Tuscaloosa, AL",
        "Twin Falls, ID",
        "Tyler, TX",
        "Urban Honolulu, HI",
        "Utica-Rome, NY",
        "Valdosta, GA",
        "Vallejo, CA",
        "Victoria, TX",
        "Vineland-Bridgeton, NJ",
        "Virginia Beach-Norfolk-Newport News, VA-NC",
        "Visalia, CA",
        "Waco, TX",
        "Walla Walla, WA",
        "Warner Robins, GA",
        "Washington-Arlington-Alexandria, DC-VA-MD-WV",
        "Waterloo-Cedar Falls, IA",
        "Watertown-Fort Drum, NY",
        "Wausau-Weston, WI",
        "Weirton-Steubenville, WV-OH",
        "Wenatchee, WA",
        "Wheeling, WV-OH",
        "Wichita, KS",
        "Wichita Falls, TX",
        "Williamsport, PA",
        "Wilmington, NC",
        "Winchester, VA-WV",
        "Winston-Salem, NC",
        "Worcester, MA-CT",
        "Yakima, WA",
        "York-Hanover, PA",
        "Youngstown-Warren-Boardman, OH-PA",
        "Yuba City, CA",
        "Yuma, AZ"
       ],
       "description": "Pick a city to examine specifics",
       "index": 0,
       "layout": "IPY_MODEL_30b2b5fc18ee43fb9bd4856c2f6031b1",
       "style": "IPY_MODEL_5e6a7aa7f35246b99b3834f122542eeb"
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     "bd1f98c81c4544a18132ed8b55485648": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "bdafe84bc3b14b278f3edf3b171996d3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "be45703b0ebf426eb2c357469df0c01f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "bea14207399941799612fed811378a87": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "c0c04975bef0443c8b2ae83f9fdb2cfe": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_4754f75f40954519be91e71269eb0605",
       "outputs": [
        {
         "ename": "AttributeError",
         "evalue": "'DataFrame' object has no attribute 'ArrackRate'",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-30-91f3ccbdf111>\u001b[0m in \u001b[0;36mplot_some_figures\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[1;31m# Make a q-q plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[1;31m#Calculate teh residual distribution of the data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m     \u001b[0mresiduals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mintercept\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mslope\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPop2018\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwhich\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mArrackRate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwhich\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     26\u001b[0m     \u001b[1;33m(\u001b[0m\u001b[0mosm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mosr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mqqslope\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mqqintercept\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mqqr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprobplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresiduals\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m     \u001b[0max3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Q-Q plot against normal (slope = %f, r = %f)'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mqqslope\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mqqr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5272\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5273\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5274\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5276\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'ArrackRate'"
         ]
        }
       ]
      }
     },
     "c20d477f0f4d46c79a6bf16bcc1095a1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_dbbfe9e6cb2b4e1699a4f545abe7f9c9"
       ],
       "layout": "IPY_MODEL_736c87afa7594e129a876920cd573474"
      }
     },
     "c24759ff4475486fbddc39d122aecd99": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_ca125259124f4085b32bd5ab1fef467e",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 864x864 with 4 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
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        "Chambersburg-Waynesboro, PA",
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        "College Station-Bryan, TX",
        "Colorado Springs, CO",
        "Columbia, MO",
        "Columbia, SC",
        "Columbus, GA-AL",
        "Columbus, IN",
        "Columbus, OH",
        "Corpus Christi, TX",
        "Corvallis, OR",
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        "Cumberland, MD-WV",
        "Dallas-Fort Worth-Arlington, TX",
        "Dalton, GA",
        "Danville, IL",
        "Daphne-Fairhope-Foley, AL",
        "Davenport-Moline-Rock Island, IA-IL",
        "Dayton-Kettering, OH",
        "Decatur, AL",
        "Decatur, IL",
        "Deltona-Daytona Beach-Ormond Beach, FL",
        "Denver-Aurora-Lakewood, CO",
        "Des Moines-West Des Moines, IA",
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        "Dothan, AL",
        "Dover, DE",
        "Dubuque, IA",
        "Duluth, MN-WI",
        "Durham-Chapel Hill, NC",
        "East Stroudsburg, PA",
        "Eau Claire, WI",
        "El Centro, CA",
        "Elizabethtown-Fort Knox, KY",
        "Elkhart-Goshen, IN",
        "Elmira, NY",
        "El Paso, TX",
        "Enid, OK",
        "Erie, PA",
        "Eugene-Springfield, OR",
        "Evansville, IN-KY",
        "Fairbanks, AK",
        "Fargo, ND-MN",
        "Farmington, NM",
        "Fayetteville, NC",
        "Fayetteville-Springdale-Rogers, AR",
        "Flagstaff, AZ",
        "Flint, MI",
        "Florence, SC",
        "Florence-Muscle Shoals, AL",
        "Fond du Lac, WI",
        "Fort Collins, CO",
        "Fort Smith, AR-OK",
        "Fort Wayne, IN",
        "Fresno, CA",
        "Gadsden, AL",
        "Gainesville, FL",
        "Gainesville, GA",
        "Gettysburg, PA",
        "Glens Falls, NY",
        "Goldsboro, NC",
        "Grand Forks, ND-MN",
        "Grand Island, NE",
        "Grand Junction, CO",
        "Grand Rapids-Kentwood, MI",
        "Grants Pass, OR",
        "Great Falls, MT",
        "Greeley, CO",
        "Green Bay, WI",
        "Greensboro-High Point, NC",
        "Greenville, NC",
        "Greenville-Anderson, SC",
        "Gulfport-Biloxi, MS",
        "Hagerstown-Martinsburg, MD-WV",
        "Hammond, LA",
        "Hanford-Corcoran, CA",
        "Harrisburg-Carlisle, PA",
        "Harrisonburg, VA",
        "Hartford-East Hartford-Middletown, CT",
        "Hattiesburg, MS",
        "Hickory-Lenoir-Morganton, NC",
        "Hilton Head Island-Bluffton, SC",
        "Hinesville, GA",
        "Homosassa Springs, FL",
        "Hot Springs, AR",
        "Houma-Thibodaux, LA",
        "Houston-The Woodlands-Sugar Land, TX",
        "Huntington-Ashland, WV-KY-OH",
        "Huntsville, AL",
        "Idaho Falls, ID",
        "Indianapolis-Carmel-Anderson, IN",
        "Iowa City, IA",
        "Ithaca, NY",
        "Jackson, MI",
        "Jackson, MS",
        "Jackson, TN",
        "Jacksonville, FL",
        "Jacksonville, NC",
        "Janesville-Beloit, WI",
        "Jefferson City, MO",
        "Johnson City, TN",
        "Johnstown, PA",
        "Jonesboro, AR",
        "Joplin, MO",
        "Kahului-Wailuku-Lahaina, HI",
        "Kalamazoo-Portage, MI",
        "Kankakee, IL",
        "Kansas City, MO-KS",
        "Kennewick-Richland, WA",
        "Killeen-Temple, TX",
        "Kingsport-Bristol, TN-VA",
        "Kingston, NY",
        "Knoxville, TN",
        "Kokomo, IN",
        "La Crosse-Onalaska, WI-MN",
        "Lafayette, LA",
        "Lafayette-West Lafayette, IN",
        "Lake Charles, LA",
        "Lake Havasu City-Kingman, AZ",
        "Lakeland-Winter Haven, FL",
        "Lancaster, PA",
        "Lansing-East Lansing, MI",
        "Laredo, TX",
        "Las Cruces, NM",
        "Las Vegas-Henderson-Paradise, NV",
        "Lawrence, KS",
        "Lawton, OK",
        "Lebanon, PA",
        "Lewiston, ID-WA",
        "Lewiston-Auburn, ME",
        "Lexington-Fayette, KY",
        "Lima, OH",
        "Lincoln, NE",
        "Little Rock-North Little Rock-Conway, AR",
        "Logan, UT-ID",
        "Longview, TX",
        "Longview, WA",
        "Los Angeles-Long Beach-Anaheim, CA",
        "Louisville/Jefferson County, KY-IN",
        "Lubbock, TX",
        "Lynchburg, VA",
        "Macon-Bibb County, GA",
        "Madera, CA",
        "Madison, WI",
        "Manchester-Nashua, NH",
        "Manhattan, KS",
        "Mankato, MN",
        "Mansfield, OH",
        "McAllen-Edinburg-Mission, TX",
        "Medford, OR",
        "Memphis, TN-MS-AR",
        "Merced, CA",
        "Miami-Fort Lauderdale-Pompano Beach, FL",
        "Michigan City-La Porte, IN",
        "Midland, MI",
        "Midland, TX",
        "Milwaukee-Waukesha, WI",
        "Minneapolis-St. Paul-Bloomington, MN-WI",
        "Missoula, MT",
        "Mobile, AL",
        "Modesto, CA",
        "Monroe, LA",
        "Monroe, MI",
        "Montgomery, AL",
        "Morgantown, WV",
        "Morristown, TN",
        "Mount Vernon-Anacortes, WA",
        "Muncie, IN",
        "Muskegon, MI",
        "Myrtle Beach-Conway-North Myrtle Beach, SC-NC",
        "Napa, CA",
        "Naples-Marco Island, FL",
        "Nashville-Davidson--Murfreesboro--Franklin, TN",
        "New Bern, NC",
        "New Haven-Milford, CT",
        "New Orleans-Metairie, LA",
        "New York-Newark-Jersey City, NY-NJ-PA",
        "Niles, MI",
        "North Port-Sarasota-Bradenton, FL",
        "Norwich-New London, CT",
        "Ocala, FL",
        "Ocean City, NJ",
        "Odessa, TX",
        "Ogden-Clearfield, UT",
        "Oklahoma City, OK",
        "Olympia-Lacey-Tumwater, WA",
        "Omaha-Council Bluffs, NE-IA",
        "Orlando-Kissimmee-Sanford, FL",
        "Oshkosh-Neenah, WI",
        "Owensboro, KY",
        "Oxnard-Thousand Oaks-Ventura, CA",
        "Palm Bay-Melbourne-Titusville, FL",
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        "Peoria, IL",
        "Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",
        "Phoenix-Mesa-Chandler, AZ",
        "Pine Bluff, AR",
        "Pittsburgh, PA",
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        "Pocatello, ID",
        "Portland-South Portland, ME",
        "Portland-Vancouver-Hillsboro, OR-WA",
        "Port St. Lucie, FL",
        "Poughkeepsie-Newburgh-Middletown, NY",
        "Prescott Valley-Prescott, AZ",
        "Providence-Warwick, RI-MA",
        "Provo-Orem, UT",
        "Pueblo, CO",
        "Punta Gorda, FL",
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        "Raleigh-Cary, NC",
        "Rapid City, SD",
        "Reading, PA",
        "Redding, CA",
        "Reno, NV",
        "Richmond, VA",
        "Riverside-San Bernardino-Ontario, CA",
        "Roanoke, VA",
        "Rochester, MN",
        "Rochester, NY",
        "Rockford, IL",
        "Rocky Mount, NC",
        "Rome, GA",
        "Sacramento-Roseville-Folsom, CA",
        "Saginaw, MI",
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        "Dover, DE",
        "Dubuque, IA",
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        "Durham-Chapel Hill, NC",
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        "Eau Claire, WI",
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        "Elizabethtown-Fort Knox, KY",
        "Elkhart-Goshen, IN",
        "Elmira, NY",
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        "Erie, PA",
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        "Evansville, IN-KY",
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        "Farmington, NM",
        "Fayetteville, NC",
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        "Flagstaff, AZ",
        "Flint, MI",
        "Florence, SC",
        "Florence-Muscle Shoals, AL",
        "Fond du Lac, WI",
        "Fort Collins, CO",
        "Fort Smith, AR-OK",
        "Fort Wayne, IN",
        "Fresno, CA",
        "Gadsden, AL",
        "Gainesville, FL",
        "Gainesville, GA",
        "Gettysburg, PA",
        "Glens Falls, NY",
        "Goldsboro, NC",
        "Grand Forks, ND-MN",
        "Grand Island, NE",
        "Grand Junction, CO",
        "Grand Rapids-Kentwood, MI",
        "Grants Pass, OR",
        "Great Falls, MT",
        "Greeley, CO",
        "Green Bay, WI",
        "Greensboro-High Point, NC",
        "Greenville, NC",
        "Greenville-Anderson, SC",
        "Gulfport-Biloxi, MS",
        "Hagerstown-Martinsburg, MD-WV",
        "Hammond, LA",
        "Hanford-Corcoran, CA",
        "Harrisburg-Carlisle, PA",
        "Harrisonburg, VA",
        "Hartford-East Hartford-Middletown, CT",
        "Hattiesburg, MS",
        "Hickory-Lenoir-Morganton, NC",
        "Hilton Head Island-Bluffton, SC",
        "Hinesville, GA",
        "Homosassa Springs, FL",
        "Hot Springs, AR",
        "Houma-Thibodaux, LA",
        "Houston-The Woodlands-Sugar Land, TX",
        "Huntington-Ashland, WV-KY-OH",
        "Huntsville, AL",
        "Idaho Falls, ID",
        "Indianapolis-Carmel-Anderson, IN",
        "Iowa City, IA",
        "Ithaca, NY",
        "Jackson, MI",
        "Jackson, MS",
        "Jackson, TN",
        "Jacksonville, FL",
        "Jacksonville, NC",
        "Janesville-Beloit, WI",
        "Jefferson City, MO",
        "Johnson City, TN",
        "Johnstown, PA",
        "Jonesboro, AR",
        "Joplin, MO",
        "Kahului-Wailuku-Lahaina, HI",
        "Kalamazoo-Portage, MI",
        "Kankakee, IL",
        "Kansas City, MO-KS",
        "Kennewick-Richland, WA",
        "Killeen-Temple, TX",
        "Kingsport-Bristol, TN-VA",
        "Kingston, NY",
        "Knoxville, TN",
        "Kokomo, IN",
        "La Crosse-Onalaska, WI-MN",
        "Lafayette, LA",
        "Lafayette-West Lafayette, IN",
        "Lake Charles, LA",
        "Lake Havasu City-Kingman, AZ",
        "Lakeland-Winter Haven, FL",
        "Lancaster, PA",
        "Lansing-East Lansing, MI",
        "Laredo, TX",
        "Las Cruces, NM",
        "Las Vegas-Henderson-Paradise, NV",
        "Lawrence, KS",
        "Lawton, OK",
        "Lebanon, PA",
        "Lewiston, ID-WA",
        "Lewiston-Auburn, ME",
        "Lexington-Fayette, KY",
        "Lima, OH",
        "Lincoln, NE",
        "Little Rock-North Little Rock-Conway, AR",
        "Logan, UT-ID",
        "Longview, TX",
        "Longview, WA",
        "Los Angeles-Long Beach-Anaheim, CA",
        "Louisville/Jefferson County, KY-IN",
        "Lubbock, TX",
        "Lynchburg, VA",
        "Macon-Bibb County, GA",
        "Madera, CA",
        "Madison, WI",
        "Manchester-Nashua, NH",
        "Manhattan, KS",
        "Mankato, MN",
        "Mansfield, OH",
        "McAllen-Edinburg-Mission, TX",
        "Medford, OR",
        "Memphis, TN-MS-AR",
        "Merced, CA",
        "Miami-Fort Lauderdale-Pompano Beach, FL",
        "Michigan City-La Porte, IN",
        "Midland, MI",
        "Midland, TX",
        "Milwaukee-Waukesha, WI",
        "Minneapolis-St. Paul-Bloomington, MN-WI",
        "Missoula, MT",
        "Mobile, AL",
        "Modesto, CA",
        "Monroe, LA",
        "Monroe, MI",
        "Montgomery, AL",
        "Morgantown, WV",
        "Morristown, TN",
        "Mount Vernon-Anacortes, WA",
        "Muncie, IN",
        "Muskegon, MI",
        "Myrtle Beach-Conway-North Myrtle Beach, SC-NC",
        "Napa, CA",
        "Naples-Marco Island, FL",
        "Nashville-Davidson--Murfreesboro--Franklin, TN",
        "New Bern, NC",
        "New Haven-Milford, CT",
        "New Orleans-Metairie, LA",
        "New York-Newark-Jersey City, NY-NJ-PA",
        "Niles, MI",
        "North Port-Sarasota-Bradenton, FL",
        "Norwich-New London, CT",
        "Ocala, FL",
        "Ocean City, NJ",
        "Odessa, TX",
        "Ogden-Clearfield, UT",
        "Oklahoma City, OK",
        "Olympia-Lacey-Tumwater, WA",
        "Omaha-Council Bluffs, NE-IA",
        "Orlando-Kissimmee-Sanford, FL",
        "Oshkosh-Neenah, WI",
        "Owensboro, KY",
        "Oxnard-Thousand Oaks-Ventura, CA",
        "Palm Bay-Melbourne-Titusville, FL",
        "Panama City, FL",
        "Parkersburg-Vienna, WV",
        "Pensacola-Ferry Pass-Brent, FL",
        "Peoria, IL",
        "Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",
        "Phoenix-Mesa-Chandler, AZ",
        "Pine Bluff, AR",
        "Pittsburgh, PA",
        "Pittsfield, MA",
        "Pocatello, ID",
        "Portland-South Portland, ME",
        "Portland-Vancouver-Hillsboro, OR-WA",
        "Port St. Lucie, FL",
        "Poughkeepsie-Newburgh-Middletown, NY",
        "Prescott Valley-Prescott, AZ",
        "Providence-Warwick, RI-MA",
        "Provo-Orem, UT",
        "Pueblo, CO",
        "Punta Gorda, FL",
        "Racine, WI",
        "Raleigh-Cary, NC",
        "Rapid City, SD",
        "Reading, PA",
        "Redding, CA",
        "Reno, NV",
        "Richmond, VA",
        "Riverside-San Bernardino-Ontario, CA",
        "Roanoke, VA",
        "Rochester, MN",
        "Rochester, NY",
        "Rockford, IL",
        "Rocky Mount, NC",
        "Rome, GA",
        "Sacramento-Roseville-Folsom, CA",
        "Saginaw, MI",
        "St. Cloud, MN",
        "St. George, UT",
        "St. Joseph, MO-KS",
        "St. Louis, MO-IL",
        "Salem, OR",
        "Salinas, CA",
        "Salisbury, MD-DE",
        "Salt Lake City, UT",
        "San Angelo, TX",
        "San Antonio-New Braunfels, TX",
        "San Diego-Chula Vista-Carlsbad, CA",
        "San Francisco-Oakland-Berkeley, CA",
        "San Jose-Sunnyvale-Santa Clara, CA",
        "San Luis Obispo-Paso Robles, CA",
        "Santa Cruz-Watsonville, CA",
        "Santa Fe, NM",
        "Santa Maria-Santa Barbara, CA",
        "Santa Rosa-Petaluma, CA",
        "Savannah, GA",
        "Scranton--Wilkes-Barre, PA",
        "Seattle-Tacoma-Bellevue, WA",
        "Sebastian-Vero Beach, FL",
        "Sebring-Avon Park, FL",
        "Sheboygan, WI",
        "Sherman-Denison, TX",
        "Shreveport-Bossier City, LA",
        "Sierra Vista-Douglas, AZ",
        "Sioux City, IA-NE-SD",
        "Sioux Falls, SD",
        "South Bend-Mishawaka, IN-MI",
        "Spartanburg, SC",
        "Spokane-Spokane Valley, WA",
        "Springfield, IL",
        "Springfield, MA",
        "Springfield, MO",
        "Springfield, OH",
        "State College, PA",
        "Staunton, VA",
        "Stockton, CA",
        "Sumter, SC",
        "Syracuse, NY",
        "Tallahassee, FL",
        "Tampa-St. Petersburg-Clearwater, FL",
        "Terre Haute, IN",
        "Texarkana, TX-AR",
        "The Villages, FL",
        "Toledo, OH",
        "Topeka, KS",
        "Trenton-Princeton, NJ",
        "Tucson, AZ",
        "Tulsa, OK",
        "Tuscaloosa, AL",
        "Twin Falls, ID",
        "Tyler, TX",
        "Urban Honolulu, HI",
        "Utica-Rome, NY",
        "Valdosta, GA",
        "Vallejo, CA",
        "Victoria, TX",
        "Vineland-Bridgeton, NJ",
        "Virginia Beach-Norfolk-Newport News, VA-NC",
        "Visalia, CA",
        "Waco, TX",
        "Walla Walla, WA",
        "Warner Robins, GA",
        "Washington-Arlington-Alexandria, DC-VA-MD-WV",
        "Waterloo-Cedar Falls, IA",
        "Watertown-Fort Drum, NY",
        "Wausau-Weston, WI",
        "Weirton-Steubenville, WV-OH",
        "Wenatchee, WA",
        "Wheeling, WV-OH",
        "Wichita, KS",
        "Wichita Falls, TX",
        "Williamsport, PA",
        "Wilmington, NC",
        "Winchester, VA-WV",
        "Winston-Salem, NC",
        "Worcester, MA-CT",
        "Yakima, WA",
        "York-Hanover, PA",
        "Youngstown-Warren-Boardman, OH-PA",
        "Yuba City, CA",
        "Yuma, AZ"
       ],
       "description": "Pick a city to examine specifics",
       "index": 35,
       "layout": "IPY_MODEL_94d0f3186c21446ab626d5420ae23988",
       "style": "IPY_MODEL_5c12f87b9c9143909f261921f3af7c94"
      }
     },
     "c84b66aceb8e4ef6a12c831748bb2537": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "c92b9660b5bf401d934107bd1466284b": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8edbc08430de4f06987bed80c5c2e802",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 576x1080 with 2 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "c943ba24627a46599c60235283262df9": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "c9a244f145204205959aa8fff4453980": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DatePickerModel",
      "state": {
       "description": "Start date",
       "disabled": false,
       "layout": "IPY_MODEL_4aa535da3a6e46a48cc6602d9f35278b",
       "style": "IPY_MODEL_e2da301cb8954fbaaa8c3c45193ae8c3",
       "value": {
        "date": 13,
        "month": 2,
        "year": 2020
       }
      }
     },
     "ca125259124f4085b32bd5ab1fef467e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "cabb4806dafc400f9269755dc295524f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "cafbacfb09aa4ccaaef48052a438b7bd": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_a8a724134bb140a1a6c4cb51df0dac10",
       "outputs": [
        {
         "data": {
          "image/png": 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        "Chambersburg-Waynesboro, PA",
        "Champaign-Urbana, IL",
        "Charleston, WV",
        "Charleston-North Charleston, SC",
        "Charlotte-Concord-Gastonia, NC-SC",
        "Charlottesville, VA",
        "Chattanooga, TN-GA",
        "Cheyenne, WY",
        "Chicago-Naperville-Elgin, IL-IN-WI",
        "Chico, CA",
        "Cincinnati, OH-KY-IN",
        "Clarksville, TN-KY",
        "Cleveland, TN",
        "Cleveland-Elyria, OH",
        "Coeur d'Alene, ID",
        "College Station-Bryan, TX",
        "Colorado Springs, CO",
        "Columbia, MO",
        "Columbia, SC",
        "Columbus, GA-AL",
        "Columbus, IN",
        "Columbus, OH",
        "Corpus Christi, TX",
        "Corvallis, OR",
        "Crestview-Fort Walton Beach-Destin, FL",
        "Cumberland, MD-WV",
        "Dallas-Fort Worth-Arlington, TX",
        "Dalton, GA",
        "Danville, IL",
        "Daphne-Fairhope-Foley, AL",
        "Davenport-Moline-Rock Island, IA-IL",
        "Dayton-Kettering, OH",
        "Decatur, AL",
        "Decatur, IL",
        "Deltona-Daytona Beach-Ormond Beach, FL",
        "Denver-Aurora-Lakewood, CO",
        "Des Moines-West Des Moines, IA",
        "Detroit-Warren-Dearborn, MI",
        "Dothan, AL",
        "Dover, DE",
        "Dubuque, IA",
        "Duluth, MN-WI",
        "Durham-Chapel Hill, NC",
        "East Stroudsburg, PA",
        "Eau Claire, WI",
        "El Centro, CA",
        "Elizabethtown-Fort Knox, KY",
        "Elkhart-Goshen, IN",
        "Elmira, NY",
        "El Paso, TX",
        "Enid, OK",
        "Erie, PA",
        "Eugene-Springfield, OR",
        "Evansville, IN-KY",
        "Fairbanks, AK",
        "Fargo, ND-MN",
        "Farmington, NM",
        "Fayetteville, NC",
        "Fayetteville-Springdale-Rogers, AR",
        "Flagstaff, AZ",
        "Flint, MI",
        "Florence, SC",
        "Florence-Muscle Shoals, AL",
        "Fond du Lac, WI",
        "Fort Collins, CO",
        "Fort Smith, AR-OK",
        "Fort Wayne, IN",
        "Fresno, CA",
        "Gadsden, AL",
        "Gainesville, FL",
        "Gainesville, GA",
        "Gettysburg, PA",
        "Glens Falls, NY",
        "Goldsboro, NC",
        "Grand Forks, ND-MN",
        "Grand Island, NE",
        "Grand Junction, CO",
        "Grand Rapids-Kentwood, MI",
        "Grants Pass, OR",
        "Great Falls, MT",
        "Greeley, CO",
        "Green Bay, WI",
        "Greensboro-High Point, NC",
        "Greenville, NC",
        "Greenville-Anderson, SC",
        "Gulfport-Biloxi, MS",
        "Hagerstown-Martinsburg, MD-WV",
        "Hammond, LA",
        "Hanford-Corcoran, CA",
        "Harrisburg-Carlisle, PA",
        "Harrisonburg, VA",
        "Hartford-East Hartford-Middletown, CT",
        "Hattiesburg, MS",
        "Hickory-Lenoir-Morganton, NC",
        "Hilton Head Island-Bluffton, SC",
        "Hinesville, GA",
        "Homosassa Springs, FL",
        "Hot Springs, AR",
        "Houma-Thibodaux, LA",
        "Houston-The Woodlands-Sugar Land, TX",
        "Huntington-Ashland, WV-KY-OH",
        "Huntsville, AL",
        "Idaho Falls, ID",
        "Indianapolis-Carmel-Anderson, IN",
        "Iowa City, IA",
        "Ithaca, NY",
        "Jackson, MI",
        "Jackson, MS",
        "Jackson, TN",
        "Jacksonville, FL",
        "Jacksonville, NC",
        "Janesville-Beloit, WI",
        "Jefferson City, MO",
        "Johnson City, TN",
        "Johnstown, PA",
        "Jonesboro, AR",
        "Joplin, MO",
        "Kahului-Wailuku-Lahaina, HI",
        "Kalamazoo-Portage, MI",
        "Kankakee, IL",
        "Kansas City, MO-KS",
        "Kennewick-Richland, WA",
        "Killeen-Temple, TX",
        "Kingsport-Bristol, TN-VA",
        "Kingston, NY",
        "Knoxville, TN",
        "Kokomo, IN",
        "La Crosse-Onalaska, WI-MN",
        "Lafayette, LA",
        "Lafayette-West Lafayette, IN",
        "Lake Charles, LA",
        "Lake Havasu City-Kingman, AZ",
        "Lakeland-Winter Haven, FL",
        "Lancaster, PA",
        "Lansing-East Lansing, MI",
        "Laredo, TX",
        "Las Cruces, NM",
        "Las Vegas-Henderson-Paradise, NV",
        "Lawrence, KS",
        "Lawton, OK",
        "Lebanon, PA",
        "Lewiston, ID-WA",
        "Lewiston-Auburn, ME",
        "Lexington-Fayette, KY",
        "Lima, OH",
        "Lincoln, NE",
        "Little Rock-North Little Rock-Conway, AR",
        "Logan, UT-ID",
        "Longview, TX",
        "Longview, WA",
        "Los Angeles-Long Beach-Anaheim, CA",
        "Louisville/Jefferson County, KY-IN",
        "Lubbock, TX",
        "Lynchburg, VA",
        "Macon-Bibb County, GA",
        "Madera, CA",
        "Madison, WI",
        "Manchester-Nashua, NH",
        "Manhattan, KS",
        "Mankato, MN",
        "Mansfield, OH",
        "McAllen-Edinburg-Mission, TX",
        "Medford, OR",
        "Memphis, TN-MS-AR",
        "Merced, CA",
        "Miami-Fort Lauderdale-Pompano Beach, FL",
        "Michigan City-La Porte, IN",
        "Midland, MI",
        "Midland, TX",
        "Milwaukee-Waukesha, WI",
        "Minneapolis-St. Paul-Bloomington, MN-WI",
        "Missoula, MT",
        "Mobile, AL",
        "Modesto, CA",
        "Monroe, LA",
        "Monroe, MI",
        "Montgomery, AL",
        "Morgantown, WV",
        "Morristown, TN",
        "Mount Vernon-Anacortes, WA",
        "Muncie, IN",
        "Muskegon, MI",
        "Myrtle Beach-Conway-North Myrtle Beach, SC-NC",
        "Napa, CA",
        "Naples-Marco Island, FL",
        "Nashville-Davidson--Murfreesboro--Franklin, TN",
        "New Bern, NC",
        "New Haven-Milford, CT",
        "New Orleans-Metairie, LA",
        "New York-Newark-Jersey City, NY-NJ-PA",
        "Niles, MI",
        "North Port-Sarasota-Bradenton, FL",
        "Norwich-New London, CT",
        "Ocala, FL",
        "Ocean City, NJ",
        "Odessa, TX",
        "Ogden-Clearfield, UT",
        "Oklahoma City, OK",
        "Olympia-Lacey-Tumwater, WA",
        "Omaha-Council Bluffs, NE-IA",
        "Orlando-Kissimmee-Sanford, FL",
        "Oshkosh-Neenah, WI",
        "Owensboro, KY",
        "Oxnard-Thousand Oaks-Ventura, CA",
        "Palm Bay-Melbourne-Titusville, FL",
        "Panama City, FL",
        "Parkersburg-Vienna, WV",
        "Pensacola-Ferry Pass-Brent, FL",
        "Peoria, IL",
        "Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",
        "Phoenix-Mesa-Chandler, AZ",
        "Pine Bluff, AR",
        "Pittsburgh, PA",
        "Pittsfield, MA",
        "Pocatello, ID",
        "Portland-South Portland, ME",
        "Portland-Vancouver-Hillsboro, OR-WA",
        "Port St. Lucie, FL",
        "Poughkeepsie-Newburgh-Middletown, NY",
        "Prescott Valley-Prescott, AZ",
        "Providence-Warwick, RI-MA",
        "Provo-Orem, UT",
        "Pueblo, CO",
        "Punta Gorda, FL",
        "Racine, WI",
        "Raleigh-Cary, NC",
        "Rapid City, SD",
        "Reading, PA",
        "Redding, CA",
        "Reno, NV",
        "Richmond, VA",
        "Riverside-San Bernardino-Ontario, CA",
        "Roanoke, VA",
        "Rochester, MN",
        "Rochester, NY",
        "Rockford, IL",
        "Rocky Mount, NC",
        "Rome, GA",
        "Sacramento-Roseville-Folsom, CA",
        "Saginaw, MI",
        "St. Cloud, MN",
        "St. George, UT",
        "St. Joseph, MO-KS",
        "St. Louis, MO-IL",
        "Salem, OR",
        "Salinas, CA",
        "Salisbury, MD-DE",
        "Salt Lake City, UT",
        "San Angelo, TX",
        "San Antonio-New Braunfels, TX",
        "San Diego-Chula Vista-Carlsbad, CA",
        "San Francisco-Oakland-Berkeley, CA",
        "San Jose-Sunnyvale-Santa Clara, CA",
        "San Luis Obispo-Paso Robles, CA",
        "Santa Cruz-Watsonville, CA",
        "Santa Fe, NM",
        "Santa Maria-Santa Barbara, CA",
        "Santa Rosa-Petaluma, CA",
        "Savannah, GA",
        "Scranton--Wilkes-Barre, PA",
        "Seattle-Tacoma-Bellevue, WA",
        "Sebastian-Vero Beach, FL",
        "Sebring-Avon Park, FL",
        "Sheboygan, WI",
        "Sherman-Denison, TX",
        "Shreveport-Bossier City, LA",
        "Sierra Vista-Douglas, AZ",
        "Sioux City, IA-NE-SD",
        "Sioux Falls, SD",
        "South Bend-Mishawaka, IN-MI",
        "Spartanburg, SC",
        "Spokane-Spokane Valley, WA",
        "Springfield, IL",
        "Springfield, MA",
        "Springfield, MO",
        "Springfield, OH",
        "State College, PA",
        "Staunton, VA",
        "Stockton, CA",
        "Sumter, SC",
        "Syracuse, NY",
        "Tallahassee, FL",
        "Tampa-St. Petersburg-Clearwater, FL",
        "Terre Haute, IN",
        "Texarkana, TX-AR",
        "The Villages, FL",
        "Toledo, OH",
        "Topeka, KS",
        "Trenton-Princeton, NJ",
        "Tucson, AZ",
        "Tulsa, OK",
        "Tuscaloosa, AL",
        "Twin Falls, ID",
        "Tyler, TX",
        "Urban Honolulu, HI",
        "Utica-Rome, NY",
        "Valdosta, GA",
        "Vallejo, CA",
        "Victoria, TX",
        "Vineland-Bridgeton, NJ",
        "Virginia Beach-Norfolk-Newport News, VA-NC",
        "Visalia, CA",
        "Waco, TX",
        "Walla Walla, WA",
        "Warner Robins, GA",
        "Washington-Arlington-Alexandria, DC-VA-MD-WV",
        "Waterloo-Cedar Falls, IA",
        "Watertown-Fort Drum, NY",
        "Wausau-Weston, WI",
        "Weirton-Steubenville, WV-OH",
        "Wenatchee, WA",
        "Wheeling, WV-OH",
        "Wichita, KS",
        "Wichita Falls, TX",
        "Williamsport, PA",
        "Wilmington, NC",
        "Winchester, VA-WV",
        "Winston-Salem, NC",
        "Worcester, MA-CT",
        "Yakima, WA",
        "York-Hanover, PA",
        "Youngstown-Warren-Boardman, OH-PA",
        "Yuba City, CA",
        "Yuma, AZ"
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        "Albany-Schenectady-Troy, NY",
        "Albuquerque, NM",
        "Alexandria, LA",
        "Allentown-Bethlehem-Easton, PA-NJ",
        "Altoona, PA",
        "Amarillo, TX",
        "Ames, IA",
        "Anchorage, AK",
        "Ann Arbor, MI",
        "Anniston-Oxford, AL",
        "Appleton, WI",
        "Asheville, NC",
        "Athens-Clarke County, GA",
        "Atlanta-Sandy Springs-Alpharetta, GA",
        "Atlantic City-Hammonton, NJ",
        "Auburn-Opelika, AL",
        "Augusta-Richmond County, GA-SC",
        "Austin-Round Rock-Georgetown, TX",
        "Bakersfield, CA",
        "Baltimore-Columbia-Towson, MD",
        "Bangor, ME",
        "Barnstable Town, MA",
        "Baton Rouge, LA",
        "Battle Creek, MI",
        "Bay City, MI",
        "Beaumont-Port Arthur, TX",
        "Beckley, WV",
        "Bellingham, WA",
        "Bend, OR",
        "Billings, MT",
        "Binghamton, NY",
        "Birmingham-Hoover, AL",
        "Bismarck, ND",
        "Blacksburg-Christiansburg, VA",
        "Bloomington, IL",
        "Bloomington, IN",
        "Bloomsburg-Berwick, PA",
        "Boise City, ID",
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        "Boulder, CO",
        "Bowling Green, KY",
        "Bremerton-Silverdale-Port Orchard, WA",
        "Bridgeport-Stamford-Norwalk, CT",
        "Brownsville-Harlingen, TX",
        "Brunswick, GA",
        "Buffalo-Cheektowaga, NY",
        "Burlington, NC",
        "Burlington-South Burlington, VT",
        "California-Lexington Park, MD",
        "Canton-Massillon, OH",
        "Cape Coral-Fort Myers, FL",
        "Cape Girardeau, MO-IL",
        "Carbondale-Marion, IL",
        "Carson City, NV",
        "Casper, WY",
        "Cedar Rapids, IA",
        "Chambersburg-Waynesboro, PA",
        "Champaign-Urbana, IL",
        "Charleston, WV",
        "Charleston-North Charleston, SC",
        "Charlotte-Concord-Gastonia, NC-SC",
        "Charlottesville, VA",
        "Chattanooga, TN-GA",
        "Cheyenne, WY",
        "Chicago-Naperville-Elgin, IL-IN-WI",
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        "Cincinnati, OH-KY-IN",
        "Clarksville, TN-KY",
        "Cleveland, TN",
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        "Coeur d'Alene, ID",
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        "Columbia, MO",
        "Columbia, SC",
        "Columbus, GA-AL",
        "Columbus, IN",
        "Columbus, OH",
        "Corpus Christi, TX",
        "Corvallis, OR",
        "Crestview-Fort Walton Beach-Destin, FL",
        "Cumberland, MD-WV",
        "Dallas-Fort Worth-Arlington, TX",
        "Dalton, GA",
        "Danville, IL",
        "Daphne-Fairhope-Foley, AL",
        "Davenport-Moline-Rock Island, IA-IL",
        "Dayton-Kettering, OH",
        "Decatur, AL",
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        "Deltona-Daytona Beach-Ormond Beach, FL",
        "Denver-Aurora-Lakewood, CO",
        "Des Moines-West Des Moines, IA",
        "Detroit-Warren-Dearborn, MI",
        "Dothan, AL",
        "Dover, DE",
        "Dubuque, IA",
        "Duluth, MN-WI",
        "Durham-Chapel Hill, NC",
        "East Stroudsburg, PA",
        "Eau Claire, WI",
        "El Centro, CA",
        "Elizabethtown-Fort Knox, KY",
        "Elkhart-Goshen, IN",
        "Elmira, NY",
        "El Paso, TX",
        "Enid, OK",
        "Erie, PA",
        "Eugene-Springfield, OR",
        "Evansville, IN-KY",
        "Fairbanks, AK",
        "Fargo, ND-MN",
        "Farmington, NM",
        "Fayetteville, NC",
        "Fayetteville-Springdale-Rogers, AR",
        "Flagstaff, AZ",
        "Flint, MI",
        "Florence, SC",
        "Florence-Muscle Shoals, AL",
        "Fond du Lac, WI",
        "Fort Collins, CO",
        "Fort Smith, AR-OK",
        "Fort Wayne, IN",
        "Fresno, CA",
        "Gadsden, AL",
        "Gainesville, FL",
        "Gainesville, GA",
        "Gettysburg, PA",
        "Glens Falls, NY",
        "Goldsboro, NC",
        "Grand Forks, ND-MN",
        "Grand Island, NE",
        "Grand Junction, CO",
        "Grand Rapids-Kentwood, MI",
        "Grants Pass, OR",
        "Great Falls, MT",
        "Greeley, CO",
        "Green Bay, WI",
        "Greensboro-High Point, NC",
        "Greenville, NC",
        "Greenville-Anderson, SC",
        "Gulfport-Biloxi, MS",
        "Hagerstown-Martinsburg, MD-WV",
        "Hammond, LA",
        "Hanford-Corcoran, CA",
        "Harrisburg-Carlisle, PA",
        "Harrisonburg, VA",
        "Hartford-East Hartford-Middletown, CT",
        "Hattiesburg, MS",
        "Hickory-Lenoir-Morganton, NC",
        "Hilton Head Island-Bluffton, SC",
        "Hinesville, GA",
        "Homosassa Springs, FL",
        "Hot Springs, AR",
        "Houma-Thibodaux, LA",
        "Houston-The Woodlands-Sugar Land, TX",
        "Huntington-Ashland, WV-KY-OH",
        "Huntsville, AL",
        "Idaho Falls, ID",
        "Indianapolis-Carmel-Anderson, IN",
        "Iowa City, IA",
        "Ithaca, NY",
        "Jackson, MI",
        "Jackson, MS",
        "Jackson, TN",
        "Jacksonville, FL",
        "Jacksonville, NC",
        "Janesville-Beloit, WI",
        "Jefferson City, MO",
        "Johnson City, TN",
        "Johnstown, PA",
        "Jonesboro, AR",
        "Joplin, MO",
        "Kahului-Wailuku-Lahaina, HI",
        "Kalamazoo-Portage, MI",
        "Kankakee, IL",
        "Kansas City, MO-KS",
        "Kennewick-Richland, WA",
        "Killeen-Temple, TX",
        "Kingsport-Bristol, TN-VA",
        "Kingston, NY",
        "Knoxville, TN",
        "Kokomo, IN",
        "La Crosse-Onalaska, WI-MN",
        "Lafayette, LA",
        "Lafayette-West Lafayette, IN",
        "Lake Charles, LA",
        "Lake Havasu City-Kingman, AZ",
        "Lakeland-Winter Haven, FL",
        "Lancaster, PA",
        "Lansing-East Lansing, MI",
        "Laredo, TX",
        "Las Cruces, NM",
        "Las Vegas-Henderson-Paradise, NV",
        "Lawrence, KS",
        "Lawton, OK",
        "Lebanon, PA",
        "Lewiston, ID-WA",
        "Lewiston-Auburn, ME",
        "Lexington-Fayette, KY",
        "Lima, OH",
        "Lincoln, NE",
        "Little Rock-North Little Rock-Conway, AR",
        "Logan, UT-ID",
        "Longview, TX",
        "Longview, WA",
        "Los Angeles-Long Beach-Anaheim, CA",
        "Louisville/Jefferson County, KY-IN",
        "Lubbock, TX",
        "Lynchburg, VA",
        "Macon-Bibb County, GA",
        "Madera, CA",
        "Madison, WI",
        "Manchester-Nashua, NH",
        "Manhattan, KS",
        "Mankato, MN",
        "Mansfield, OH",
        "McAllen-Edinburg-Mission, TX",
        "Medford, OR",
        "Memphis, TN-MS-AR",
        "Merced, CA",
        "Miami-Fort Lauderdale-Pompano Beach, FL",
        "Michigan City-La Porte, IN",
        "Midland, MI",
        "Midland, TX",
        "Milwaukee-Waukesha, WI",
        "Minneapolis-St. Paul-Bloomington, MN-WI",
        "Missoula, MT",
        "Mobile, AL",
        "Modesto, CA",
        "Monroe, LA",
        "Monroe, MI",
        "Montgomery, AL",
        "Morgantown, WV",
        "Morristown, TN",
        "Mount Vernon-Anacortes, WA",
        "Muncie, IN",
        "Muskegon, MI",
        "Myrtle Beach-Conway-North Myrtle Beach, SC-NC",
        "Napa, CA",
        "Naples-Marco Island, FL",
        "Nashville-Davidson--Murfreesboro--Franklin, TN",
        "New Bern, NC",
        "New Haven-Milford, CT",
        "New Orleans-Metairie, LA",
        "New York-Newark-Jersey City, NY-NJ-PA",
        "Niles, MI",
        "North Port-Sarasota-Bradenton, FL",
        "Norwich-New London, CT",
        "Ocala, FL",
        "Ocean City, NJ",
        "Odessa, TX",
        "Ogden-Clearfield, UT",
        "Oklahoma City, OK",
        "Olympia-Lacey-Tumwater, WA",
        "Omaha-Council Bluffs, NE-IA",
        "Orlando-Kissimmee-Sanford, FL",
        "Oshkosh-Neenah, WI",
        "Owensboro, KY",
        "Oxnard-Thousand Oaks-Ventura, CA",
        "Palm Bay-Melbourne-Titusville, FL",
        "Panama City, FL",
        "Parkersburg-Vienna, WV",
        "Pensacola-Ferry Pass-Brent, FL",
        "Peoria, IL",
        "Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",
        "Phoenix-Mesa-Chandler, AZ",
        "Pine Bluff, AR",
        "Pittsburgh, PA",
        "Pittsfield, MA",
        "Pocatello, ID",
        "Portland-South Portland, ME",
        "Portland-Vancouver-Hillsboro, OR-WA",
        "Port St. Lucie, FL",
        "Poughkeepsie-Newburgh-Middletown, NY",
        "Prescott Valley-Prescott, AZ",
        "Providence-Warwick, RI-MA",
        "Provo-Orem, UT",
        "Pueblo, CO",
        "Punta Gorda, FL",
        "Racine, WI",
        "Raleigh-Cary, NC",
        "Rapid City, SD",
        "Reading, PA",
        "Redding, CA",
        "Reno, NV",
        "Richmond, VA",
        "Riverside-San Bernardino-Ontario, CA",
        "Roanoke, VA",
        "Rochester, MN",
        "Rochester, NY",
        "Rockford, IL",
        "Rocky Mount, NC",
        "Rome, GA",
        "Sacramento-Roseville-Folsom, CA",
        "Saginaw, MI",
        "St. Cloud, MN",
        "St. George, UT",
        "St. Joseph, MO-KS",
        "St. Louis, MO-IL",
        "Salem, OR",
        "Salinas, CA",
        "Salisbury, MD-DE",
        "Salt Lake City, UT",
        "San Angelo, TX",
        "San Antonio-New Braunfels, TX",
        "San Diego-Chula Vista-Carlsbad, CA",
        "San Francisco-Oakland-Berkeley, CA",
        "San Jose-Sunnyvale-Santa Clara, CA",
        "San Luis Obispo-Paso Robles, CA",
        "Santa Cruz-Watsonville, CA",
        "Santa Fe, NM",
        "Santa Maria-Santa Barbara, CA",
        "Santa Rosa-Petaluma, CA",
        "Savannah, GA",
        "Scranton--Wilkes-Barre, PA",
        "Seattle-Tacoma-Bellevue, WA",
        "Sebastian-Vero Beach, FL",
        "Sebring-Avon Park, FL",
        "Sheboygan, WI",
        "Sherman-Denison, TX",
        "Shreveport-Bossier City, LA",
        "Sierra Vista-Douglas, AZ",
        "Sioux City, IA-NE-SD",
        "Sioux Falls, SD",
        "South Bend-Mishawaka, IN-MI",
        "Spartanburg, SC",
        "Spokane-Spokane Valley, WA",
        "Springfield, IL",
        "Springfield, MA",
        "Springfield, MO",
        "Springfield, OH",
        "State College, PA",
        "Staunton, VA",
        "Stockton, CA",
        "Sumter, SC",
        "Syracuse, NY",
        "Tallahassee, FL",
        "Tampa-St. Petersburg-Clearwater, FL",
        "Terre Haute, IN",
        "Texarkana, TX-AR",
        "The Villages, FL",
        "Toledo, OH",
        "Topeka, KS",
        "Trenton-Princeton, NJ",
        "Tucson, AZ",
        "Tulsa, OK",
        "Tuscaloosa, AL",
        "Twin Falls, ID",
        "Tyler, TX",
        "Urban Honolulu, HI",
        "Utica-Rome, NY",
        "Valdosta, GA",
        "Vallejo, CA",
        "Victoria, TX",
        "Vineland-Bridgeton, NJ",
        "Virginia Beach-Norfolk-Newport News, VA-NC",
        "Visalia, CA",
        "Waco, TX",
        "Walla Walla, WA",
        "Warner Robins, GA",
        "Washington-Arlington-Alexandria, DC-VA-MD-WV",
        "Waterloo-Cedar Falls, IA",
        "Watertown-Fort Drum, NY",
        "Wausau-Weston, WI",
        "Weirton-Steubenville, WV-OH",
        "Wenatchee, WA",
        "Wheeling, WV-OH",
        "Wichita, KS",
        "Wichita Falls, TX",
        "Williamsport, PA",
        "Wilmington, NC",
        "Winchester, VA-WV",
        "Winston-Salem, NC",
        "Worcester, MA-CT",
        "Yakima, WA",
        "York-Hanover, PA",
        "Youngstown-Warren-Boardman, OH-PA",
        "Yuba City, CA",
        "Yuma, AZ"
       ],
       "description": "Pick a city to examine specifics",
       "index": 0,
       "layout": "IPY_MODEL_7f6a0810219e452daa2f8fe7ea1239f3",
       "style": "IPY_MODEL_7a0163b8455442e1a3f46c46f0b000a4"
      }
     },
     "d428658c4e75402bb243ebda9f3cf18d": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_a00ebcaa43d148dd8369468d94932ece",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\n \n \n Data are gathered! Run the next box when ready.\n"
        }
       ]
      }
     },
     "d507aff5681f469e87ce2ff0e4f5f210": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_dda226a7aed445e4aa7cc7eded207081",
       "outputs": [
        {
         "ename": "AttributeError",
         "evalue": "'DataFrame' object has no attribute 'countyFIPS'",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    254\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m<ipython-input-4-18dcb5c906d6>\u001b[0m in \u001b[0;36mcoallate_analyze_plot\u001b[1;34m(dateStart, dateEnd, msas, pop, covid, deaths)\u001b[0m\n\u001b[0;32m     39\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfips\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' was not found in the ACS data.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m                 \u001b[0mpopnotfound\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfips\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfips\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mcovid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountyFIPS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     42\u001b[0m                 \u001b[1;31m#This data sometimes contains duplicates; it is assumed that together these are the true total.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m                 \u001b[0mcovid_last\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcovid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcovid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountyFIPS\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mfips\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdateEnd\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeaths\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeaths\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountyFIPS\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mfips\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdateEnd\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5272\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5273\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5274\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5276\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'countyFIPS'"
         ]
        }
       ]
      }
     },
     "d53823377b0440bf8d9dba845b324255": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DatePickerModel",
      "state": {
       "description": "End date",
       "disabled": false,
       "layout": "IPY_MODEL_1fc69e9c30fd496480eda98223629ff9",
       "style": "IPY_MODEL_9c9b6e52e6a549b39e65d8f7720f338a",
       "value": {
        "date": 24,
        "month": 2,
        "year": 2020
       }
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      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
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     "d79d3d71cae2404ba45601afa521d001": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SelectModel",
      "state": {
       "_options_labels": [
        "Abilene, TX",
        "Akron, OH",
        "Albany, GA",
        "Albany-Lebanon, OR",
        "Albany-Schenectady-Troy, NY",
        "Albuquerque, NM",
        "Alexandria, LA",
        "Allentown-Bethlehem-Easton, PA-NJ",
        "Altoona, PA",
        "Amarillo, TX",
        "Ames, IA",
        "Anchorage, AK",
        "Ann Arbor, MI",
        "Anniston-Oxford, AL",
        "Appleton, WI",
        "Asheville, NC",
        "Athens-Clarke County, GA",
        "Atlanta-Sandy Springs-Alpharetta, GA",
        "Atlantic City-Hammonton, NJ",
        "Auburn-Opelika, AL",
        "Augusta-Richmond County, GA-SC",
        "Austin-Round Rock-Georgetown, TX",
        "Bakersfield, CA",
        "Baltimore-Columbia-Towson, MD",
        "Bangor, ME",
        "Barnstable Town, MA",
        "Baton Rouge, LA",
        "Battle Creek, MI",
        "Bay City, MI",
        "Beaumont-Port Arthur, TX",
        "Beckley, WV",
        "Bellingham, WA",
        "Bend, OR",
        "Billings, MT",
        "Binghamton, NY",
        "Birmingham-Hoover, AL",
        "Bismarck, ND",
        "Blacksburg-Christiansburg, VA",
        "Bloomington, IL",
        "Bloomington, IN",
        "Bloomsburg-Berwick, PA",
        "Boise City, ID",
        "Boston-Cambridge-Newton, MA-NH",
        "Boulder, CO",
        "Bowling Green, KY",
        "Bremerton-Silverdale-Port Orchard, WA",
        "Bridgeport-Stamford-Norwalk, CT",
        "Brownsville-Harlingen, TX",
        "Brunswick, GA",
        "Buffalo-Cheektowaga, NY",
        "Burlington, NC",
        "Burlington-South Burlington, VT",
        "California-Lexington Park, MD",
        "Canton-Massillon, OH",
        "Cape Coral-Fort Myers, FL",
        "Cape Girardeau, MO-IL",
        "Carbondale-Marion, IL",
        "Carson City, NV",
        "Casper, WY",
        "Cedar Rapids, IA",
        "Chambersburg-Waynesboro, PA",
        "Champaign-Urbana, IL",
        "Charleston, WV",
        "Charleston-North Charleston, SC",
        "Charlotte-Concord-Gastonia, NC-SC",
        "Charlottesville, VA",
        "Chattanooga, TN-GA",
        "Cheyenne, WY",
        "Chicago-Naperville-Elgin, IL-IN-WI",
        "Chico, CA",
        "Cincinnati, OH-KY-IN",
        "Clarksville, TN-KY",
        "Cleveland, TN",
        "Cleveland-Elyria, OH",
        "Coeur d'Alene, ID",
        "College Station-Bryan, TX",
        "Colorado Springs, CO",
        "Columbia, MO",
        "Columbia, SC",
        "Columbus, GA-AL",
        "Columbus, IN",
        "Columbus, OH",
        "Corpus Christi, TX",
        "Corvallis, OR",
        "Crestview-Fort Walton Beach-Destin, FL",
        "Cumberland, MD-WV",
        "Dallas-Fort Worth-Arlington, TX",
        "Dalton, GA",
        "Danville, IL",
        "Daphne-Fairhope-Foley, AL",
        "Davenport-Moline-Rock Island, IA-IL",
        "Dayton-Kettering, OH",
        "Decatur, AL",
        "Decatur, IL",
        "Deltona-Daytona Beach-Ormond Beach, FL",
        "Denver-Aurora-Lakewood, CO",
        "Des Moines-West Des Moines, IA",
        "Detroit-Warren-Dearborn, MI",
        "Dothan, AL",
        "Dover, DE",
        "Dubuque, IA",
        "Duluth, MN-WI",
        "Durham-Chapel Hill, NC",
        "East Stroudsburg, PA",
        "Eau Claire, WI",
        "El Centro, CA",
        "Elizabethtown-Fort Knox, KY",
        "Elkhart-Goshen, IN",
        "Elmira, NY",
        "El Paso, TX",
        "Enid, OK",
        "Erie, PA",
        "Eugene-Springfield, OR",
        "Evansville, IN-KY",
        "Fairbanks, AK",
        "Fargo, ND-MN",
        "Farmington, NM",
        "Fayetteville, NC",
        "Fayetteville-Springdale-Rogers, AR",
        "Flagstaff, AZ",
        "Flint, MI",
        "Florence, SC",
        "Florence-Muscle Shoals, AL",
        "Fond du Lac, WI",
        "Fort Collins, CO",
        "Fort Smith, AR-OK",
        "Fort Wayne, IN",
        "Fresno, CA",
        "Gadsden, AL",
        "Gainesville, FL",
        "Gainesville, GA",
        "Gettysburg, PA",
        "Glens Falls, NY",
        "Goldsboro, NC",
        "Grand Forks, ND-MN",
        "Grand Island, NE",
        "Grand Junction, CO",
        "Grand Rapids-Kentwood, MI",
        "Grants Pass, OR",
        "Great Falls, MT",
        "Greeley, CO",
        "Green Bay, WI",
        "Greensboro-High Point, NC",
        "Greenville, NC",
        "Greenville-Anderson, SC",
        "Gulfport-Biloxi, MS",
        "Hagerstown-Martinsburg, MD-WV",
        "Hammond, LA",
        "Hanford-Corcoran, CA",
        "Harrisburg-Carlisle, PA",
        "Harrisonburg, VA",
        "Hartford-East Hartford-Middletown, CT",
        "Hattiesburg, MS",
        "Hickory-Lenoir-Morganton, NC",
        "Hilton Head Island-Bluffton, SC",
        "Hinesville, GA",
        "Homosassa Springs, FL",
        "Hot Springs, AR",
        "Houma-Thibodaux, LA",
        "Houston-The Woodlands-Sugar Land, TX",
        "Huntington-Ashland, WV-KY-OH",
        "Huntsville, AL",
        "Idaho Falls, ID",
        "Indianapolis-Carmel-Anderson, IN",
        "Iowa City, IA",
        "Ithaca, NY",
        "Jackson, MI",
        "Jackson, MS",
        "Jackson, TN",
        "Jacksonville, FL",
        "Jacksonville, NC",
        "Janesville-Beloit, WI",
        "Jefferson City, MO",
        "Johnson City, TN",
        "Johnstown, PA",
        "Jonesboro, AR",
        "Joplin, MO",
        "Kahului-Wailuku-Lahaina, HI",
        "Kalamazoo-Portage, MI",
        "Kankakee, IL",
        "Kansas City, MO-KS",
        "Kennewick-Richland, WA",
        "Killeen-Temple, TX",
        "Kingsport-Bristol, TN-VA",
        "Kingston, NY",
        "Knoxville, TN",
        "Kokomo, IN",
        "La Crosse-Onalaska, WI-MN",
        "Lafayette, LA",
        "Lafayette-West Lafayette, IN",
        "Lake Charles, LA",
        "Lake Havasu City-Kingman, AZ",
        "Lakeland-Winter Haven, FL",
        "Lancaster, PA",
        "Lansing-East Lansing, MI",
        "Laredo, TX",
        "Las Cruces, NM",
        "Las Vegas-Henderson-Paradise, NV",
        "Lawrence, KS",
        "Lawton, OK",
        "Lebanon, PA",
        "Lewiston, ID-WA",
        "Lewiston-Auburn, ME",
        "Lexington-Fayette, KY",
        "Lima, OH",
        "Lincoln, NE",
        "Little Rock-North Little Rock-Conway, AR",
        "Logan, UT-ID",
        "Longview, TX",
        "Longview, WA",
        "Los Angeles-Long Beach-Anaheim, CA",
        "Louisville/Jefferson County, KY-IN",
        "Lubbock, TX",
        "Lynchburg, VA",
        "Macon-Bibb County, GA",
        "Madera, CA",
        "Madison, WI",
        "Manchester-Nashua, NH",
        "Manhattan, KS",
        "Mankato, MN",
        "Mansfield, OH",
        "McAllen-Edinburg-Mission, TX",
        "Medford, OR",
        "Memphis, TN-MS-AR",
        "Merced, CA",
        "Miami-Fort Lauderdale-Pompano Beach, FL",
        "Michigan City-La Porte, IN",
        "Midland, MI",
        "Midland, TX",
        "Milwaukee-Waukesha, WI",
        "Minneapolis-St. Paul-Bloomington, MN-WI",
        "Missoula, MT",
        "Mobile, AL",
        "Modesto, CA",
        "Monroe, LA",
        "Monroe, MI",
        "Montgomery, AL",
        "Morgantown, WV",
        "Morristown, TN",
        "Mount Vernon-Anacortes, WA",
        "Muncie, IN",
        "Muskegon, MI",
        "Myrtle Beach-Conway-North Myrtle Beach, SC-NC",
        "Napa, CA",
        "Naples-Marco Island, FL",
        "Nashville-Davidson--Murfreesboro--Franklin, TN",
        "New Bern, NC",
        "New Haven-Milford, CT",
        "New Orleans-Metairie, LA",
        "New York-Newark-Jersey City, NY-NJ-PA",
        "Niles, MI",
        "North Port-Sarasota-Bradenton, FL",
        "Norwich-New London, CT",
        "Ocala, FL",
        "Ocean City, NJ",
        "Odessa, TX",
        "Ogden-Clearfield, UT",
        "Oklahoma City, OK",
        "Olympia-Lacey-Tumwater, WA",
        "Omaha-Council Bluffs, NE-IA",
        "Orlando-Kissimmee-Sanford, FL",
        "Oshkosh-Neenah, WI",
        "Owensboro, KY",
        "Oxnard-Thousand Oaks-Ventura, CA",
        "Palm Bay-Melbourne-Titusville, FL",
        "Panama City, FL",
        "Parkersburg-Vienna, WV",
        "Pensacola-Ferry Pass-Brent, FL",
        "Peoria, IL",
        "Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",
        "Phoenix-Mesa-Chandler, AZ",
        "Pine Bluff, AR",
        "Pittsburgh, PA",
        "Pittsfield, MA",
        "Pocatello, ID",
        "Portland-South Portland, ME",
        "Portland-Vancouver-Hillsboro, OR-WA",
        "Port St. Lucie, FL",
        "Poughkeepsie-Newburgh-Middletown, NY",
        "Prescott Valley-Prescott, AZ",
        "Providence-Warwick, RI-MA",
        "Provo-Orem, UT",
        "Pueblo, CO",
        "Punta Gorda, FL",
        "Racine, WI",
        "Raleigh-Cary, NC",
        "Rapid City, SD",
        "Reading, PA",
        "Redding, CA",
        "Reno, NV",
        "Richmond, VA",
        "Riverside-San Bernardino-Ontario, CA",
        "Roanoke, VA",
        "Rochester, MN",
        "Rochester, NY",
        "Rockford, IL",
        "Rocky Mount, NC",
        "Rome, GA",
        "Sacramento-Roseville-Folsom, CA",
        "Saginaw, MI",
        "St. Cloud, MN",
        "St. George, UT",
        "St. Joseph, MO-KS",
        "St. Louis, MO-IL",
        "Salem, OR",
        "Salinas, CA",
        "Salisbury, MD-DE",
        "Salt Lake City, UT",
        "San Angelo, TX",
        "San Antonio-New Braunfels, TX",
        "San Diego-Chula Vista-Carlsbad, CA",
        "San Francisco-Oakland-Berkeley, CA",
        "San Jose-Sunnyvale-Santa Clara, CA",
        "San Luis Obispo-Paso Robles, CA",
        "Santa Cruz-Watsonville, CA",
        "Santa Fe, NM",
        "Santa Maria-Santa Barbara, CA",
        "Santa Rosa-Petaluma, CA",
        "Savannah, GA",
        "Scranton--Wilkes-Barre, PA",
        "Seattle-Tacoma-Bellevue, WA",
        "Sebastian-Vero Beach, FL",
        "Sebring-Avon Park, FL",
        "Sheboygan, WI",
        "Sherman-Denison, TX",
        "Shreveport-Bossier City, LA",
        "Sierra Vista-Douglas, AZ",
        "Sioux City, IA-NE-SD",
        "Sioux Falls, SD",
        "South Bend-Mishawaka, IN-MI",
        "Spartanburg, SC",
        "Spokane-Spokane Valley, WA",
        "Springfield, IL",
        "Springfield, MA",
        "Springfield, MO",
        "Springfield, OH",
        "State College, PA",
        "Staunton, VA",
        "Stockton, CA",
        "Sumter, SC",
        "Syracuse, NY",
        "Tallahassee, FL",
        "Tampa-St. Petersburg-Clearwater, FL",
        "Terre Haute, IN",
        "Texarkana, TX-AR",
        "The Villages, FL",
        "Toledo, OH",
        "Topeka, KS",
        "Trenton-Princeton, NJ",
        "Tucson, AZ",
        "Tulsa, OK",
        "Tuscaloosa, AL",
        "Twin Falls, ID",
        "Tyler, TX",
        "Urban Honolulu, HI",
        "Utica-Rome, NY",
        "Valdosta, GA",
        "Vallejo, CA",
        "Victoria, TX",
        "Vineland-Bridgeton, NJ",
        "Virginia Beach-Norfolk-Newport News, VA-NC",
        "Visalia, CA",
        "Waco, TX",
        "Walla Walla, WA",
        "Warner Robins, GA",
        "Washington-Arlington-Alexandria, DC-VA-MD-WV",
        "Waterloo-Cedar Falls, IA",
        "Watertown-Fort Drum, NY",
        "Wausau-Weston, WI",
        "Weirton-Steubenville, WV-OH",
        "Wenatchee, WA",
        "Wheeling, WV-OH",
        "Wichita, KS",
        "Wichita Falls, TX",
        "Williamsport, PA",
        "Wilmington, NC",
        "Winchester, VA-WV",
        "Winston-Salem, NC",
        "Worcester, MA-CT",
        "Yakima, WA",
        "York-Hanover, PA",
        "Youngstown-Warren-Boardman, OH-PA",
        "Yuba City, CA",
        "Yuma, AZ"
       ],
       "description": "Pick a city to examine specifics",
       "index": 5,
       "layout": "IPY_MODEL_1a9ae8216c5745619207acb63f98e236",
       "style": "IPY_MODEL_bdafe84bc3b14b278f3edf3b171996d3"
      }
     },
     "d7dfc21723484175b5d47f438b9637a1": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "d9b685d2d3cf498781032522ad3d3caa": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "d9ef5ab5401844da9c2ebd1de04ec90a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "da210575b4a146e8ad5b7bd4f086a754": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "da505b4c29b24b95943c7b257e2ae69f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "dbbfe9e6cb2b4e1699a4f545abe7f9c9": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_63b0b798a8094fdb9068e63134e7bc79",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 864x864 with 4 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "dc2ba935d0a94394ab8e03b1154e3241": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "dd6e6eda2b934c2f8c2fa7873d59c242": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DatePickerModel",
      "state": {
       "description": "End date",
       "disabled": false,
       "layout": "IPY_MODEL_e7b8a0553570463f86fade33575c6055",
       "style": "IPY_MODEL_b85fa0d667fd459888c7d034b0b62887",
       "value": {
        "date": 19,
        "month": 2,
        "year": 2020
       }
      }
     },
     "dda226a7aed445e4aa7cc7eded207081": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
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\n",
          "text/plain": "<Figure size 864x864 with 4 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "e68090163591470c963e6265ee78d1d2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "e69cf2f8b28040d1a671f7e40647c5b4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DatePickerModel",
      "state": {
       "description": "Start date",
       "disabled": false,
       "layout": "IPY_MODEL_d342107221b3479a90194b88c27559e6",
       "style": "IPY_MODEL_7a7046c3b328452a86a9571033de82b1",
       "value": {
        "date": 13,
        "month": 2,
        "year": 2020
       }
      }
     },
     "e7312049d994464581d1884b5eee7563": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
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        "Anchorage, AK",
        "Ann Arbor, MI",
        "Anniston-Oxford, AL",
        "Appleton, WI",
        "Asheville, NC",
        "Athens-Clarke County, GA",
        "Atlanta-Sandy Springs-Alpharetta, GA",
        "Atlantic City-Hammonton, NJ",
        "Auburn-Opelika, AL",
        "Augusta-Richmond County, GA-SC",
        "Austin-Round Rock-Georgetown, TX",
        "Bakersfield, CA",
        "Baltimore-Columbia-Towson, MD",
        "Bangor, ME",
        "Barnstable Town, MA",
        "Baton Rouge, LA",
        "Battle Creek, MI",
        "Bay City, MI",
        "Beaumont-Port Arthur, TX",
        "Beckley, WV",
        "Bellingham, WA",
        "Bend, OR",
        "Billings, MT",
        "Binghamton, NY",
        "Birmingham-Hoover, AL",
        "Bismarck, ND",
        "Blacksburg-Christiansburg, VA",
        "Bloomington, IL",
        "Bloomington, IN",
        "Bloomsburg-Berwick, PA",
        "Boise City, ID",
        "Boston-Cambridge-Newton, MA-NH",
        "Boulder, CO",
        "Bowling Green, KY",
        "Bremerton-Silverdale-Port Orchard, WA",
        "Bridgeport-Stamford-Norwalk, CT",
        "Brownsville-Harlingen, TX",
        "Brunswick, GA",
        "Buffalo-Cheektowaga, NY",
        "Burlington, NC",
        "Burlington-South Burlington, VT",
        "California-Lexington Park, MD",
        "Canton-Massillon, OH",
        "Cape Coral-Fort Myers, FL",
        "Cape Girardeau, MO-IL",
        "Carbondale-Marion, IL",
        "Carson City, NV",
        "Casper, WY",
        "Cedar Rapids, IA",
        "Chambersburg-Waynesboro, PA",
        "Champaign-Urbana, IL",
        "Charleston, WV",
        "Charleston-North Charleston, SC",
        "Charlotte-Concord-Gastonia, NC-SC",
        "Charlottesville, VA",
        "Chattanooga, TN-GA",
        "Cheyenne, WY",
        "Chicago-Naperville-Elgin, IL-IN-WI",
        "Chico, CA",
        "Cincinnati, OH-KY-IN",
        "Clarksville, TN-KY",
        "Cleveland, TN",
        "Cleveland-Elyria, OH",
        "Coeur d'Alene, ID",
        "College Station-Bryan, TX",
        "Colorado Springs, CO",
        "Columbia, MO",
        "Columbia, SC",
        "Columbus, GA-AL",
        "Columbus, IN",
        "Columbus, OH",
        "Corpus Christi, TX",
        "Corvallis, OR",
        "Crestview-Fort Walton Beach-Destin, FL",
        "Cumberland, MD-WV",
        "Dallas-Fort Worth-Arlington, TX",
        "Dalton, GA",
        "Danville, IL",
        "Daphne-Fairhope-Foley, AL",
        "Davenport-Moline-Rock Island, IA-IL",
        "Dayton-Kettering, OH",
        "Decatur, AL",
        "Decatur, IL",
        "Deltona-Daytona Beach-Ormond Beach, FL",
        "Denver-Aurora-Lakewood, CO",
        "Des Moines-West Des Moines, IA",
        "Detroit-Warren-Dearborn, MI",
        "Dothan, AL",
        "Dover, DE",
        "Dubuque, IA",
        "Duluth, MN-WI",
        "Durham-Chapel Hill, NC",
        "East Stroudsburg, PA",
        "Eau Claire, WI",
        "El Centro, CA",
        "Elizabethtown-Fort Knox, KY",
        "Elkhart-Goshen, IN",
        "Elmira, NY",
        "El Paso, TX",
        "Enid, OK",
        "Erie, PA",
        "Eugene-Springfield, OR",
        "Evansville, IN-KY",
        "Fairbanks, AK",
        "Fargo, ND-MN",
        "Farmington, NM",
        "Fayetteville, NC",
        "Fayetteville-Springdale-Rogers, AR",
        "Flagstaff, AZ",
        "Flint, MI",
        "Florence, SC",
        "Florence-Muscle Shoals, AL",
        "Fond du Lac, WI",
        "Fort Collins, CO",
        "Fort Smith, AR-OK",
        "Fort Wayne, IN",
        "Fresno, CA",
        "Gadsden, AL",
        "Gainesville, FL",
        "Gainesville, GA",
        "Gettysburg, PA",
        "Glens Falls, NY",
        "Goldsboro, NC",
        "Grand Forks, ND-MN",
        "Grand Island, NE",
        "Grand Junction, CO",
        "Grand Rapids-Kentwood, MI",
        "Grants Pass, OR",
        "Great Falls, MT",
        "Greeley, CO",
        "Green Bay, WI",
        "Greensboro-High Point, NC",
        "Greenville, NC",
        "Greenville-Anderson, SC",
        "Gulfport-Biloxi, MS",
        "Hagerstown-Martinsburg, MD-WV",
        "Hammond, LA",
        "Hanford-Corcoran, CA",
        "Harrisburg-Carlisle, PA",
        "Harrisonburg, VA",
        "Hartford-East Hartford-Middletown, CT",
        "Hattiesburg, MS",
        "Hickory-Lenoir-Morganton, NC",
        "Hilton Head Island-Bluffton, SC",
        "Hinesville, GA",
        "Homosassa Springs, FL",
        "Hot Springs, AR",
        "Houma-Thibodaux, LA",
        "Houston-The Woodlands-Sugar Land, TX",
        "Huntington-Ashland, WV-KY-OH",
        "Huntsville, AL",
        "Idaho Falls, ID",
        "Indianapolis-Carmel-Anderson, IN",
        "Iowa City, IA",
        "Ithaca, NY",
        "Jackson, MI",
        "Jackson, MS",
        "Jackson, TN",
        "Jacksonville, FL",
        "Jacksonville, NC",
        "Janesville-Beloit, WI",
        "Jefferson City, MO",
        "Johnson City, TN",
        "Johnstown, PA",
        "Jonesboro, AR",
        "Joplin, MO",
        "Kahului-Wailuku-Lahaina, HI",
        "Kalamazoo-Portage, MI",
        "Kankakee, IL",
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        "Kennewick-Richland, WA",
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        "Kingston, NY",
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        "Lake Charles, LA",
        "Lake Havasu City-Kingman, AZ",
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        "Las Cruces, NM",
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        "Longview, WA",
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        "Lubbock, TX",
        "Lynchburg, VA",
        "Macon-Bibb County, GA",
        "Madera, CA",
        "Madison, WI",
        "Manchester-Nashua, NH",
        "Manhattan, KS",
        "Mankato, MN",
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        "Morgantown, WV",
        "Morristown, TN",
        "Mount Vernon-Anacortes, WA",
        "Muncie, IN",
        "Muskegon, MI",
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        "Napa, CA",
        "Naples-Marco Island, FL",
        "Nashville-Davidson--Murfreesboro--Franklin, TN",
        "New Bern, NC",
        "New Haven-Milford, CT",
        "New Orleans-Metairie, LA",
        "New York-Newark-Jersey City, NY-NJ-PA",
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        "North Port-Sarasota-Bradenton, FL",
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        "Ocean City, NJ",
        "Odessa, TX",
        "Ogden-Clearfield, UT",
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        "Pittsburgh, PA",
        "Pittsfield, MA",
        "Pocatello, ID",
        "Portland-South Portland, ME",
        "Portland-Vancouver-Hillsboro, OR-WA",
        "Port St. Lucie, FL",
        "Poughkeepsie-Newburgh-Middletown, NY",
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        "Providence-Warwick, RI-MA",
        "Provo-Orem, UT",
        "Pueblo, CO",
        "Punta Gorda, FL",
        "Racine, WI",
        "Raleigh-Cary, NC",
        "Rapid City, SD",
        "Reading, PA",
        "Redding, CA",
        "Reno, NV",
        "Richmond, VA",
        "Riverside-San Bernardino-Ontario, CA",
        "Roanoke, VA",
        "Rochester, MN",
        "Rochester, NY",
        "Rockford, IL",
        "Rocky Mount, NC",
        "Rome, GA",
        "Sacramento-Roseville-Folsom, CA",
        "Saginaw, MI",
        "St. Cloud, MN",
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        "St. Joseph, MO-KS",
        "St. Louis, MO-IL",
        "Salem, OR",
        "Salinas, CA",
        "Salisbury, MD-DE",
        "Salt Lake City, UT",
        "San Angelo, TX",
        "San Antonio-New Braunfels, TX",
        "San Diego-Chula Vista-Carlsbad, CA",
        "San Francisco-Oakland-Berkeley, CA",
        "San Jose-Sunnyvale-Santa Clara, CA",
        "San Luis Obispo-Paso Robles, CA",
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/dvfPCKVo+bZ1m0TvyQ2EL9CDopOZjD2AH8f20fUJF4yeUeCyuxBa7P9RTGwV9DzhWD+yhWmyj3HZn79Cvm9mE1p34/tJD3e/Nce81xB+DR4UfUeczYrvh9a8D6wfnYTF4y1kX8bdr3T3bQgt0JtQ+IXbxWyjuNkUdrH8Sstx9xcIJ5s7Eb7Xb46GZ1+on5H3eOru7xH2iQMIiX5T5wTxZbn7rBJjXkz4/h2dvZ4cZhNKbeL7zqpRrETf9ccQfiG5Msf8BR33onHLCHX3rWkxpla0ti9cHE2zRbT/H0lh+//HhPgHxIatn2O6+Ppby1+yk/OnyJ2ct/aa2k0lW85HEs7QNieUhGxFqI17BvheNM2HhDqquOxhPQk1QPMI9Um/yIyIWh3+APyfmfWLWtm3z/qyf53w88bVZtaW3mJ6EkpmPjez3oRkoqU4HwA2MbOjzGyV6LGtmW2Wa+Huvgy4i3DhR29CfRVm1tXMRptZr+hLeT5hOxZqmJntGPW68XPgr+4+m1A7t4mFrsi6mNlhhPfmgdi8R5rZ5mbWg1CbeJe7r7Tu6FeItwn1kAXz0G3V6nkeOXsciVpO7gEuNLPVzOw7hJO1nAdJM/uBma0T/b854SfNx7MmO4DQcvhE1vBNCElVZn8F+B9CPWOm68+JrbzMC6L3cCfChWV35nldZd8WhAuHTjGz/mbWj3DCmYn/ScJ+9WML3fdlWmf/HL3WGjNblfDriZnZqraiJ5dMV6Ln51nvtcBZFvUiY2a9zOyQ6H+LYriBcIHqB4T9NO4kMxsQfe7OBm6Phk8Gvm+hC8huhGPBX919Rp44CoqpAB8CA+KvP4ebCI0Aa5nZNwjlRhNzTWjBqoRaY6JtGz9mXUM4Vv6Pu3+VNfsehJa3zD76PqEE7upoWbXRsrsANdGyV4nNP5RQxpDdCpVq0a9Z5xKO4yMt/LK5ipnta2a/jCa7lfAe9DWztaPp29qd3++BE8xsu+h9Ws3MhptZrl8KexKOzwuj9zy7ISbX91vGXwk11D+LXseuhGPNba0FGH2vbBe9r1+y4oLlQpS6ja4HTjOzbaLts7GZZSdNEF57fdbJB4TPyVXAMndvsXvSSEvH05sIF/X9N9ExOo8GYE8L3Yx2MbM+Fus2NivmPmbWK0fMRxN6m2tpW10LjM9sj2gbj4j+XzWa92zCdWn9zezErPlPj44f6wM/YcVx71bgp2a2gYUGt18At0c5RGvyxlSAfO9hRk+ii+QtNLgUdIIY5RT3AOdHn+NvsCInzKe1/OU5QmPqUEL5y+tE1QyEqof4a8r3mWxfSdfJFPog1JJelmP4oYSfELoQamAzV8XfF40fQajL+pxQL7464aelBYSfMb5HrJaPUMR/BeEMKtObS67eWoYQ3ph9c8S0KyvXmvUjJDALCS2Wx2ctb/to+GfAlb6iFuxBwpnhPEKis1UL2yjTxeTVsWFdo233GSuuZN4xGlcXxVOXZ3kTWdFby8JoW2wQG78jodb3i+hvvMb2SVb01jKfcCX12i3EfhJwTda6L8o3fYn7Um/CLwhfRvvGqKxtuDD2/Mboff6SUAL1K7JqKAl1kz8vYL3ZNaOPA8flmXZXQu8C4wi/NswCjqrwtjDgl4QSjk+j/+P13d+K9oOvgL8B38p6PZ71eDI2/l1grxbiPIpQWzmf0Hrzh2j4Twi/vGR6tegXfV4yF0DPYEVvLZ8Tft3oEVvuCdG6PyXWg1Ke96vZPpkvptbmJXwmH4zW+Ume19uN0FAwP9r/TomNa/a5ZcWxKf6YEY0bGD3P9ASVeeTs+YeVayiPzrHsibHxVwM/LsfntD0ehBbMadH+Pzd6XzK9NqxKaJH8IHpcSfTZJ/cxfqVh0fB9CMfdTC8sdwI9s7c3oZXurej9eYbQoBGvmz0hmv9zwvdes/URWr2fIhyP3wAOaGHfbZqXcIL2arTeTC8Zq+fZXtnLadM2yrPMEwh1+AsJF29/K8e26UPoJeMz4G9Zn4VG4IJW1rErrRxPCY1184FJBcS8E+GEKPPZH5Nn+/yB8N39Oc17gnqb8Mt4S+uoAU6Jts0CwnHqF9G4y4GHYtNuSTieDIqeOyt6a5lHKHupjS333Cjuj4m6v43G1RPLS6JhT7Lios2WYmpt3pXew6zx3yR8fywk5HGn0nz/btofcmyrvoTPbibHuZSoxj62PTbOmidv/hKNf55Y72uExs83s6bJzi9b3AblfFi0MukEolbdOe7+/4qY90nCxR/XFzh9N8LV9nt4VOvXkUWtpv8g/IS3NMf4XQnbb0D2uI7GzAYAd7p7W66FKHTZMwgHxseSXnZnF/2i9BQhmWqprlikLCyUFH5E6IHs7QSW9y6hXK2sxwsz+zMwudDvxyKW74RE/Z1yLD/tzOxSQscNYyodS3vpmJ23S8W5+9eEn5U6BXdfQig16PQ8lDUlnphLebn7R2gflsr6IfBSQon5QYRWzz+XHFXL69mW0MVmm292JblFpSxdCb9kbksocexUd1xXci4iIiIVFf0qZrR8UW+hy3qS0Dh0lDfv8SZRZjaJEO9P3D2Jnqwk6Emope9H+CXlMkL5cqehshYRERERkZSodD/nIiIiIiIS6VRlLWuvvbbX19dXOgwRkTZ7+eWXP3H3vpWOoz3pmC0i1aqUY3anSs7r6+uZNm1apcMQEWkzM6uqvseToGO2iFSrUo7ZKmsREREREUkJJeciIiIiIimh5FxEREREJCWUnIuIiIiIpISScxERERGRlFByLiIiIiKSEkrORURERERSQsm5iIiIiEhKKDkXEREREUkJJecikgoNDVBfDzU14W9DQ6UjkmKY2aZm9vfYY76ZnWxmvc3sUTN7O/q7VqVjFRFJIyXnIlJxDQ0wdizMnAnu4e/YsUrQq5G7/8vdt3L3rYBtgEXAvcCZwOPuPgh4PHouIiJZlJyLSMWNGweLFjUftmhRGC5VbQ/gXXefCYwAJkXDJwEjKxaViEiKKTkXkYqbNattw6VqHA7cGv2/rrt/ABD9XadiUYmIpFiXSgcgIlJXF0pZcg2X6mRmXYH9gbPaON9YYCxAnXYAEcmh/swH23V9My4Z3q7rU8u5iFTc+PHQo0fzYT16hOFStfYF/ubuH0bPPzSz9QCivx/lmsndJ7j7EHcf0rdv33YKVUQkPZSci0jFjR4NEybAwIFgFv5OmBCGS9U6ghUlLQD3A2Oi/8cAU9o9IhGRKqCyFhFJhdGjlYx3FGbWA9gLOD42+BLgDjM7FpgFHFKJ2ERE0k7JuYiIJMrdFwF9sobNI/TeIiIiLVBZi4iIiIhISig5FxERERFJCSXnIiIiIiIpoeRcRERERCQllJyLiIiIiKSEknMRERERkZRQci4iIiIikhJKzkVEREREUkLJuYiIiIhISig5FxERERFJCSXnIiIiIiIpoeRcRERERCQllJyLiIiIiKSEknORlGtogPp6qKkJfxsaKh2RiIiIlEuXSgcgIvk1NMDYsbBoUXg+c2Z4DjB6dOXiEhERkfJQy7lIio0btyIxz1i0KAwXERGRjkfJuUiKzZrVtuEiIiJS3ZSci6RUQ0OoM8+lrq59YxEREZH2oeRcJIUytebLl688rkcPGD++/WMSERGR8lNyLpJCuWrNAWprYcIEXQwqIiLSUSk5F0mhfDXljY1KzKuVusQUEZFCKDkXSaF8NeWqNa9OmTKlmTPBfUWXmErQRUQkm5JzkRQaPz7Ulse1tdZcLbXpoS4xRUSkUErORVJo9OhQWz5wIJiFv22pNVdLbbqoS0wRESlU6pJzMzvEzF43s0YzG9LCdPuY2b/M7B0zO7M9YxRJSkut26NHw4wZoc58xoy21ZqrpTZdVKYkIiKFSl1yDrwGHAg8nW8CM6sFrgb2BTYHjjCzzdsnPJFklKt1u6EhLCuXcrbUqowmvyTKlEREpHNIXXLu7m+6+79amWwo8I67T3f3JcBtwIjyRyeSnHK0bmcS/nzK1VKrMpqWlVqmJCIinUfqkvMC9Qdmx57PiYatxMzGmtk0M5v28ccft0twIoUoRx1yvv7RIfmW2nhL+ZgxlSmjqabW+lLKlEREpPOoSHJuZo+Z2Ws5HoW2fluOYZ5rQnef4O5D3H1I3759iw9aJGHlqENuKbFPsqU2u6U8151MM/GUK4FWa72IiHREFUnO3X1Pdx+c4zGlwEXMAdaPPR8AvJ98pCLlU4465HyJ/cCBybbUttRCH9e7d/kSaF30KiIiHVG1lrW8BAwysw3MrCtwOHB/hWMSaZNy1CEnnfDna/UutPTm66/Ll0Cre0IREemIUpecm9kBZjYH2B540Mwejob3M7OpAO6+DPhf4GHgTeAOd3+9UjGLFCvpOuQkE/4TT4Sjjsrd6l1o6c3ChbmHJ5FAq3tCERHpiFKXnLv7ve4+wN27ufu67r53NPx9dx8Wm26qu2/i7hu5uzokk9Rrr4sXk0j4Gxrg2mtDUh6XafXO1ULfFkkk0OqeML3MbE0zu8vM3jKzN81sezPrbWaPmtnb0d+1Kh2niEgapS45F+mIqu3ixXHjVk7MM2bNWrmFvrY297R9+pQvgVb3hKn2G+Ahd/8GsCXhF84zgcfdfRDwePRcRESyKDkXaQfVdvFiS2UnmVbveAv9pEm5k/Df/Ka8CbS6J0wfM1sD2Bm4AcDdl7j754R7UUyKJpsEjKxMhCIi6abkXITyl5xU28WL+cpOzHK3erfUiq0EutPZEPgYuNHMXjGz681sNWBdd/8AIPq7Tq6ZdW8KEenslJxLp9ceJSfVdvFirnpuMzjhhPzJdbUm4dV0I6Mq0QXYGrjG3b8FfEkbSlh0bwoR6eyUnEun1x4lJ9V28WKulvCbb4bf/a7SkSWr2q4FqBJzgDnu/tfo+V2EZP1DM1sPIPr7UYXiExFJNSXn0um1R8lJqRcvVqJ1t1pbwtui2q4FqAbuPheYbWabRoP2AN4g3ItiTDRsDFDoTedERDqVLpUOQKTS6upCi2mu4UnK1F+3VaZ1N5NEZlp3M8uU4lXbtQBV5EdAQ3STuOnA9wmNQXeY2bHALOCQCsYnIpJaajmXTi/tJSdq3S2f9rwW4JVXXmHDDTfEzPB8/VR2EO7+96hufAt3H+nun7n7PHffw90HRX8/rXScIiJppORcOr2095et1t3yKfeJ2cyZM9l5550xM7beemv+85//APD1118nswIREelwlJyLkO766mrr6aWalOPE7LPPPuPwww/HzKivr+eZZ55pGnf33Xfj7qy66qoJRC8iIh2RknORmDR2q9eeZTdpfP2lKOT1JHFi9vXXX3PaaadhZvTu3Zvbb7+9adxvf/tbGhsbcXcOPPDAIl+JiIh0FrogVCSS1gsvM+seNy6UstTVhcQ86ZjS+vqLVe7X09jYyJVXXslPf/rTlcadccYZXHDBBXTr1q30FYmISKdiHf3CpLghQ4b4tGnTKh2GpFR9fe5eWwYODC2qHV1He/3lej133XUXhxyyckcjo0eP5qqrrmLNNdcsfuEtMLOX3X1IWRaeUjpmi0gu9Wc+2K7rm3HJ8DbPU8oxW2UtIpH2uPAyzWUjSb/+Sr/WJF/PM888Q58+fTCzZon5rrvuyqxZs3B3brnllrIl5iIi0nkoOReJlPvCy7TfjTLJ15+G11rq63nrrbfYYostMDN23nlnPv009Pw3aNAg/v73v+PuPPHEE6y//voJRSwiIqLkXKRJuS+8THt/5Um+/jS81mJez9y5cxk2bBhmxmabbcY///lPALp3784jjzyCu/Pvf/+bLbfcsoyRi4hIZ6bkXCqi0iUPuZS7v/O091ee5OvP95pmzmy/973Q17Nw4ULGjh2LmbHeeuvxpz/9qWncpEmTaGxsZNGiRey1117lC1ZERCSi5FzaXRpKHvIpZ3/nrZVZtHbC0h4nNEm9/nyv1ax93/d8r2fZsmVceOGFmBk9e/bk97//fdM848ePZ9myZbg73/ve9zCz8gUoIiKSRcm5tLs0lDxUQktlFq2dsKT5hCaXXK/VLMQe157vu7tz/fXXY2asssoqnHfeeU3jTjrpJL788kvcnbPPPpva2tr2CUpERCSLknNpd2kv7yiXlsosWjthqbYTmlyvNV+vraC6kn0AACAASURBVOV+3//0pz/RpUsXampqOO6445qGjxgxgo8++gh356qrrqJH9tmEiIhIBSg5l3bX1l400lifXqx8ZRatnbC05YSmHNurmGVmv9aBA3NPl1RvOHF/+9vfqK+vx8wYNmwYy5cvB2Cbbbbh3//+N+7OfffdR9++fZNfuYiISAmUnEu7a0svGtVWzlGs1k5YCj2hKcf2SmqZ5e4NZ8aMGey4446YGdtssw0zozsQrbvuujz33HO4O9OmTWPQoEHJrFBERKQMlJxLu2tLryDVVs5RrNYS10IT23Jsr6SWWY7ecD777DMOP/xwzIwNNtiAZ599tmncvffei7szd+5ctt9+++JXIiIi0o6UnEtFFNorSGepT28tcS00sS3H9kpymUn0BrN48WJOPfVUzIzevXtz++23N4276qqraGxsxN0ZOXJk2xcuIiJSYV0qHYBIS+rqQhlFruEdzejRLSerrY2H8myvNLwHjY2NXHHFFZx66qkrjVtjjbOYP/98Bg7sypprhpMXERGRaqWWc0m1ctcpdzTl2F6VfA/uuOMOzIza2tpmiflRRx3FhAmf06OHM3/+L4CuHfZ6BBER6VyUnEuqlfuunR1NObZXe78HzzzzDL1798bMOOyww5qG77bbbsyePRt356abbmL8+F6d4noEERHpXFTWIqlXSDmHrFCO7VXu9+DNN9/k0EMP5bXXXms2fJNNNuHOO+9kiy22WGmeznI9goiIdC5qORdJoTT07V7uGD744AP22WcfzIzNN9+8KTFfbbXVeOyxx3B3/vWvf+VMzAF698693I54PYKIiHQeSs5FUiYNfbuXK4aFCxfygx/8ADOjX79+PPzww03jbr75ZhobG1m4cCF77LFHiycHDQ0wf/7Ky+/atfy18Gk4cRIRkY5Lybl0GtWSVJWzb/dCt0GSMSxbtozzzz8fM6Nnz57ccMMNTeMuvvhili1bhrtz5JFHYlFXK62dHIwbB0uXrryunj3LW36ThhMnERHp2JScS6dQTUlVuWqp27INSo3B3bn++usxM1ZZZRUuuOCCpnEnnXQSX375Je7OmWeeSW1t7Urzt3ZykC+OTz8tLL5idZabYomISOUUnZybWY2ZfcvMhpvZ7ma2bpKBiSSpmpKqfDXTpdZSt2UbFBvD1KlTqa2tpaamhuOOO65p+MiRI/noo49wd6666ip6ZPfNmKW1k4NybaPW6CJUEREptzYn52a2kZlNAN4BLgGOAE4EHjWzF8zs+2amFnlJlWpKqsrVr3hbtkFbYnj55Zepq6vDzBg+fDiNjY0AdO26Lb/+9du4O/feey99+/YtONbWku9K9b1eqZMCERHpPIpJoi8CbgE2cve93f1Idz/Y3bcA9gd6AUclGaRIqaopqSpXv+Jt2QatxfCf//yHHXbYATNjyJAhzJ49GwCz9YAXAGfJkhc599yNiyodai35rlT/97oploiIlFubk3N3P8Ldn3Z3zzHuI3e/wt0nJROeyAqlXNBZbUnV6NEwYwY0Noa/SSSdbd0G2THsu++nHHrooZgZG264Ic8//3zTtPfddx8DBzru7wPbNQ0vtnSokOS7HNsoibhERERKUUrNeQ8zO8fMfh89H2Rm+yUXmsgKpV7QqaSquG2wePFiTjnlFMyMPn36cOeddzaNu/rqq2lsbMTdGTFiROKlQ5VIvguR1rhERKRjKKU2/Ebga2D76PkcQsmLSOKSuKBTSVVh26CxsZHLLrsMM6N79+5cfvnlTePOPvtsvv76a9ydE088sanrQ6iu0iEREZG0KiU538jdfwksBXD3rwBreRaR4lTTBZ2tSWt/67fddhtmRm1tLaeddlrT8DFjxvDFF1/g7owfP56uXbvmnL/aSodERETSqJTkfImZdQccQi8uhJZ0kcSlpVW21MQ6bf2tP/3006y55pqYGUcccUTT8D322IM5c+bg7kycOJE11lij1WWpdEgyzGyGmf3TzP5uZtOiYb3N7FEzezv6u1al4xQRSaNSkvPzgYeA9c2sAXgcOCOJoESypaFVNonEOg39rb/xxhsMHjwYM2OXXXbhiy++AGDTTTfl1Vdfxd157LHH6N+/f5uXrdIhidnN3bdy9yHR8zOBx919EOH74szKhSYikl5FJ+fu/ghwIHA0cCswxN2fSCgukWbS0CqbRGKdrwxn5szylrl88MEH7L333pgZ3/zmN3n99dcBWH311Xn88cdxd9566y3++7//O/mViwQjgExPXpOAkRWMRUQktUrpreVxd5/n7g+6+wPu/omZPZ5kcFI+aa17bkmlW2WTqHtvqQwn6TKXBQsWcMwxx2Bm9OvXj0ceeaRp3C233EJjYyMLFixg9913L31lFVSN+3In4MAjZvaymY2Nhq3r7h8ARH/XyTWjmY01s2lmNu3jjz9up3BFRNKjmDuErmpmvYG1zWytqI6wt5nVA/2SDlCSl7a652qRRN17rvKcbIsWwZgxxSWbS5cu5dxzz8XMWGONNbjxxhubxl166aUsW7YMd2f06NHNelqpVtqXU+s77r41sC9wkpntXOiM7j7B3Ye4+5C23FVWRKSjKKbl/HjgZeAb0d/MYwpwdakBmdkhZva6mTWa2ZAWplvpgiMpTBrqnqtBdovssGGl171nl+fks3x54cmmuzNhwgTMjK5du/Lzn/+8adyPfvQjFi1ahLvzs5/9jNra2sKDrQLal9PJw92ocPePgHuBocCHFm4hS/T3o8pFKCKSXsXcIfQ37r4BcJq7b+juG0SPLd39qgRieo1Qy/50AdNmX3AkBehI3RKWS64W2UmTQot2qXXv8fKcgQNbnz5fsvnggw9SU1NDTU0Nxx9/fNPwAw44gI8//hh358orr6R79+5tC7CKaF9OHzNbzcx6Zv4Hvks4rt8PjIkmG0No0BERkSxdip3R3X9rZoOBzYFVY8NvKiUgd38T6BA/uadVXV1INnMNlyBfi+zUqSGxTsr48eEkIHtd2TLJ5rRp0zjggAOYM2dOs/FDhw5l8uTJbLTRRskFVwW0L6fSusC90TG8CzDZ3R8ys5eAO8zsWGAWcEgFYxQRSa2ik3MzOw/YlZCcTyXUFv4FKCk5b4PMBUcOXOfuE/LEORYYC1Cnb2wgd0Kom8U0114tsplW93HjwrJrakJJS3PTWWWVUZj9tdnQ/v37c8899zB06NBkg6oi2pfTx92nA1vmGD4P2KP9IxIRqS6l9HN+MOFAO9fdv084GHcrZEYze8zMXsvxGNGG9Rd0wZEuLlpZGrolTLtCLv5MqpeQeJnL2LGZWvR5hI+YARuxZMmKxHzKlCm4O3PmzOnUiTloXxYRkY6n6JZz4Ct3bzSzZWa2BuHing0LmdHd9yxhvZllNF1wZGaZC44KqVMXQvKiBCa/1lpkMzXpmfGZCzeh+O16442L+f3vz8T9NyuNu+aaazj++ONV7pWD9mUREelISmk5n2ZmawK/J/TW8jfgxUSiakULFxyJJKK1FtmkeglpbGzk17/+NWbGMcd0Z9myeGI+DljCwIHOCSecoMRcRESkEygqObeQJVzs7p+7+7XAXsCYqLylJGZ2gJnNAbYHHjSzh6Ph/cxsajTZusBfzOwfhBOCB939oVLXLenW3jebaemmR6XWpN96662YGbW1tZx++umxMUcDXxAuqbgIWEU9j4iIiHQiRZW1uLub2X3ANtHzGUkF5O73EvrFzR7+PjAs+j/nBUfScZWjjKQUxfQS8tRTT7H//vszf/78ZsP32msvJk6cyA479FPPIyIiIp1cKWUtL5jZtolFItKCtN1sJtedPnP1EvL666+z+eabY2bsuuuuTYn5Zpttxj//+U/cnUceeYR+/foVvEwRERHpuEpJzncDnjezd83s1ehuna8mFZhIXGtlJJUoeclXk/7+++/z3e9+FzNj8ODBvPnmmwD07NmTP//5z7g7b7zxBoMHDy54mSIiItI5lJKc7wtsBOwO/A+wX/RXcmjv5LGjaalrw1x382ztlvdJiNekv/rqfB577PuYGf379+fRRx9tmq6hoQF3Z/78+ey2224FLzO7zl1EREQ6vqKTc3efmeuRZHAdRaWSx46kpZKPSpW8LF26lHPOOQczo1evXkycOLFp3C9/+UuWLVuGuzNq1KhE1qcTPBERkY6vlJZzKVDa6qWrUUslH+11N08Ad+e6667DzOjatSsXXXRRbOxPWH/9Rdxyi3P66adTW1ub2HrTdoKnEwUREZHyUHLeDtozeezI8pV8FHI3z1L98Y9/xMyoqanhhBNOaBq+7bYH0b37J4SuD69g9uzuZUma03SCl7YTBRERkY6k6OTczPbNMeyEXNN2du2RPHZm5erl5KWXXmLAgAGYGfvvv3/T8O222453330Xd+ejj+7iq6/6NJuvHElzmk7w0nSiICIi0tGU0nJ+jpntnnliZmcAI0oPqeNRF3nlVUgvJ4WWYUyfPp3tttsOM2Po0KG89957AAwYMIAXX3wRd+eFF15gww03BNovaU7TCV6aThREREQ6mlKS8/2BX5jZTmY2HhgaDZMs6iKv/Frq5aS1Mox58+Zx0EEHYWZstNFGvPjii03z/vGPf8TdmT17Nttuu3K3/u2VNKfpBC9NJwoiIiIdTSm9tXxCSMavBvoBB7v70qQC62jURV7l5C7D+Iof/vDHmBlrr70299xzT9O4a6+9lsbGRtyd/fbbr8Vlt1fSnKYTvDSdKIiIiHQ0bU7OzWyBmc03swXAO8AmwCHAfDOb3/LcIu1vRbnFcuCXgAE9WLDgt03TnHPOOSxZsgR35/jjj8fMClp2eybNaTnBS9OJgoiISEfTpa0zuHvPcgQiUi59+kzmk09WzhxXW+37fPDBb+jZs7RdevTozpeYlvs1NzSEXzxmzQrlMuPHd75tLCIinVMpvbUcYGa9Ys/XNLORyYQlcepTuu2efPJJ1lhjDcwsKzH/LvAePXo41133h5ITc0meumoUEZHOrJQLQs9z9y8yT9z9c+C80kOSuGpLVCp5IvHaa6+x2WabYWbstttuLFiwAIDNN9+cSy55jYEDHbOHGTiwX6cvw0jzCZ+6ahQRkc6szWUtMbkS+1KWJzm0lKikLbnMnEhk4s2cSED5Yn3vvfcYM2YMjz/+eLPhvXr1YsqUKeyyyy5Nw844ozwxVJtKvE9toa4aRUSkMyul5Xyamf2fmW1kZhua2eXAy0kFJkE1JSrt1eI5f/58jj76aMyMAQMGNEvMJ0+ejLvz+eefN0vMZYW0t0yrq0YREenMSknOfwQsAW4H7gQWAyclEZSsUE2JSjlPJJYuXcq4ceMwM3r16sWkSZOaxv36179m+fLluDtHHHFE6Svr4NJ+wjdsWOgFJk5dNYqISGdRSj/nX7r7me4+xN23cfez3P3LJIOT6upTOukTCXfnmmuuwczo2rUrv/jFL5rGnXzyyXz11Ve4O6eeeio1NaWcZ3YuaT7ha2iASZPC9RUZZjBmTDpKbkRERMqtlN5a+prZr8xsqpn9OfNIMjiprj6lkzqRuP/++zEzampqOPHEE5uGH3zwwXzyySe4O5dffjmrrrpqm5ab5osg21OaT/hyldy4w9SplYlHRESkvZXS3NgAvAVsAFwAzABeSiAmyZKWm8+0ppQTiRdffJH+/ftjZowYMaJp+Pbbb8/06dNxd+6880769OlTVGzV1utNOaX5hC/tJTciIiLlVkpy3sfdbwCWuvtT7n4M8O2E4pIq1ZYTiXfffZehQ4diZmy33Xa8//77AKy//vq89NJLuDvPPfccG2ywQclxpf0iyPaW1hO+NJfciIiItIdSkvOl0d8PzGy4mX0LGJBATNKBffLJJxx44IGYGRtvvDEvvRR+bKmpqeGBBx7A3Zk1axZDhgxJdL1qka0OaS65ERERaQ+lJOcXRXcIPRU4DbgeODmRqKRD+eqrr/jRj36EmdG3b1/uvffepnETJkygsbGR5cuXM3z48LLFoBbZ6pDmkhsREZH2UEpy/pm7f+Hur7n7bu6+DfBpUoFJehVyYeXy5cu59NJLMTN69OjBVVdd1TTu3HPPZcmSJbg7xx13HJbdb14ZqEW2eqS15EZERKQ9lJKc/7bAYdKBtHRhpbvT0NCAmdGlSxfOPPPMpvmOPfZY5s+fj7tzwQUXsMoqq7Rr3GqRrT7qXUdERDqjLm2dwcy2B3YA+prZKbFRawC1SQUm6ZT7wso/c9RR+3Pkkc27ud9777258cYbWW+99doxwvxGj1YyXi0yJ4GZfS1zEgh6D0VEpGMrpuW8K7A6IbHvGXvMBw5OLjRJoxUXUL4GfAMwYA8y958aPHgwr7/+Ou7OQw89lJrEXKpLKb3rqMU9Hcys1sxeMbMHoucbmNlfzextM7vdzLpWOkYRkTRqc8u5uz8FPGVmX7n7L+PjzOwQ4O2kgpN0ee+99+jW7XssXpx9r6k1WXfdKcydu3NF4pKOp9jeddTinio/Ad4k/KoKcClwubvfZmbXAscC11QqOBGRtCql5vzwHMPOKmF5kkLz589nzJgxmBkDBgzISsxvA5wePT7jssuUmEtyiu1dR/3Zp4OZDQCGE3rxwsJV37sDd0WTTAJGViY6EZF0a3Nybmb7mtlvgf5mdmXsMRFYlniE0u6WLFnC2WefjZnRq1cvbrrppqZxl112GTfdtJyBAx2zw3RhpZRFsb3rqD/71LgC+BnQGD3vA3zu7pnviDlA/0oEJiKSdsW0nL8PTAMWAy/HHvcDeycXmrQk6bpad+fqq6/GzOjWrRsXX3xx07hTTjmFr776CnfnlFNO4aijatTVnZRVsb3rqD/7yjOz/YCP3P3l+OAck3qe+cea2TQzm/bxxx+XJUYRkTQrpub8H8A/zGyyuy9tdQZJXJJ1tVOmTGHkyJV/XT700EO55ppr6N27d4nRSjVqaAilILNmhcR2/Pj2Pwkrpned8eObfzZA/dlXwHeA/c1sGLAqoeb8CmBNM+sStZ4PIDT0rMTdJwATAIYMGZIzgRcR6chKqTmvN7O7zOwNM5ueeSQWmeRVal3tX//6V9Zbbz3MrFlivsMOO/Cf//wHd+f2229XYl4G1dCTSEt92aed+rOvPHc/y90HuHs94dqkP7v7aOAJVvToNQaYUqEQRURSrZTk/EbClfbLgN2Am4CbkwhKWlZMXe0777zDtttui5nx7W9/m7lz5wIwcOBApk2bhrvz7LPPUl9fn3zAArRf0lvqCUC1X1SpO4ym1hnAKWb2DqEG/YYKxyMikkqlJOfd3f1xwNx9prufT7gaX8qs0Lrajz/+mJEjR2JmDBo0iGnTpgFQW1vLgw8+iLszY8YMttlmmzJHLNA+SW8xJwDZyfzMmbmn00WV0lbu/qS77xf9P93dh7r7xu5+iLt/Xen4RETSqJTkfLGZ1QBvm9n/mtkBwDoJxSUtaKkni6VLlzJ16lTMjHXWWYcpU1b8cnz99dfT2NjIsmXLGDZsWDtHLe3Rk0i+E4AxY3In6LmSect16R66qFJERKQ9lJKcnwz0AH4MbAMcSagjlDLLrqutq2vklFOe4dlnT2S99dZj+PDhTdOef/75LF26FHfn2GOPxfJlXlJ27dGTSL5Ef/ny3C3ouZJ595UTdF1UKSIi0j6KTs7d/SV3X+juc9z9++5+kLu/kGRwkt+oUc6UKf/g9NPPwL2eiy7amYkTJ7LXXnsxZcoUFi9ejLtz3nnn0aVLmzvlkTIotu/utmgp0c9VQpMvmXcv/0WV1XBxrIiISHtT1lZlpk+fzq233srkyZN54403qK2tZe+99+biiy9mxIgRrL766pUOUfLIJLfl7KIwV1eCcdnJeF1d7hrzgQPDxZTlkmR3oCIiIh2JkvMq8OGHH3LHHXcwefJkXngh/Dix44478rvf/Y6DDz6Yvn37VjhCKVQxfXe3dfkQasyXL195fHbLeqX6BW/p4lgl5yIi0pmVUnMuZTR//nwmTZrE3nvvTb9+/fjxj3/MokWLuOSSS5gxYwbPPPMMP/zhD5WYdyKFloGMHg2TJhVWQtPWfsGTKkVpj4tjRUREqlGbW87N7Lfkue0ygLv/uKSIOrHFixczdepUJk+ezAMPPMDXX39NfX09Z555JkcccQSDBw+udIhSIW0tA2lLCU2hrflJlqLkK6dRjzAiItLZFdNyPg14mXBb5q2Bt6PHVkCOH9KlJcuXL+exxx7jmGOOYd111+Wggw7imWeeYezYsTz//PNMnz6d8ePHKzHv5IrpIz3pm/Ek2U97e1wcKyIiUo3a3HLu7pMAzOxoYDd3Xxo9vxZ4JNHoOih356WXXmLy5MncfvvtzJ07l549e3LggQcyatQodt9996J7WGloKO8Fh1IZaSgDSTKG9rg4VkREpBqVckFoP6An8Gn0fPVomOTx5ptvNvW08u6779K1a1eGDx/OqFGjGD58ON27dy9p+eoBo2NqaAg13oVc4FlOSZeilPviWBERkWpUygWhlwCvmNlEM5sI/A34RakBmdmvzOwtM3vVzO41szXzTLePmf3LzN4xszNLXW+5zJ49m1//+tdsvfXWbL755lx00UXU19dzww038OGHH3LPPfdw8MEHl5yYQ/vcHl7aV+aEK1di3t5lICpFERERKb+iW87d/UYz+xOwXTToTHefm0BMjwJnufsyM7sUOAs4Iz6BmdUCVwN7AXOAl8zsfnd/I4H1l2zevHncddddTJ48maeffhqAoUOHcvnll3PYYYex3nrrlWW9aSh9kGTlOuECqK0tz42BWqJSFBERkfIrOjm3cB/4PYEN3f1CM6szs6Hu/mIpAbl7vG79BeDgHJMNBd5x9+lRLLcBI4CKJedffvkl999/P5MnT+ahhx5i2bJlbLrpplx44YUcccQRbLzxxmWPQT1gdDz5TqwaGyuTFKsURUREpLxKqTn/HdAI7A5cCCwA7ga2TSCujGOA23MM7w/Mjj2fw4oW/GbMbCwwFqAu4Sx16dKlPPLII0yePJn77ruPRYsW0b9/f04++WRGjRrFVlttRTiHaR+VuqGMlI9OuERERDqXUpLz7dx9azN7BcDdPzOzroXMaGaPAf+VY9Q4d58STTMOWAbkus1Jrow3Z9/r7j4BmAAwZMiQvP2zF2r58uXcfffdPPHEE9x5553MmzePtdZaiyOPPJJRo0ax0047UVNTmXs7qeyg49EJl4iISOdSSnK+NKr9dgAz60toSW+Vu+/Z0ngzGwPsB+zh7rkS6jnA+rHnA4D3C1l3Mdydm266iaOPPrppWPfu3RkxYgSjRo1i7733pmvXgs5Lyk5lBx2LTrhEREQ6l1KS8yuBe4F1zGw8oTb8nFIDMrN9CBeA7uLuOS6FA+AlYJCZbQC8BxwOjCp13bkMHz6cqVOnNhs2bNgwbrvtNnr27FmOVYo0oxMuERGRzqOU3loazOxlYA9CmclId38zgZiuAroBj0b12i+4+wlm1g+43t2HRT25/C/wMFAL/MHdX09g3c0sW7asKTHfcsstue222/jGN76R9GpERERERIDSemu52d2PAt7KMaxo7p6zWxN3fx8YFns+FZiaa9qkdOnShdxVNSIiIiIiySvlysVvxp9E9efblBaOiIiIiEjn1ebk3MzOMrMFwBZmNt/MFkTPPwKmJB6hiIiIiEgn0ebk3N0vdveewK/cfQ137xk9+rj7WWWIUUrU0AD19VBTE/425OqcUkREREQqrpQLQs8ys7WAQcCqseFPJxGYJKOhoXk/2TNnhuegHkBERERE0qbomnMz+wHwNKHHlAuiv+cnE5YkZdy45jewgfB83LjKxCMiIiIi+ZVyQehPgG2Bme6+G/At4ONEopLEzJrVtuEiIiIiUjmlJOeL3X0xgJl1c/e3gE2TCUuSUlfXtuEiIiIiUjmlJOdzzGxN4D7CDYOmAO8nE5YkZfx46NGj+bAePcJwEREREUmXUi4IPSD693wzewLoBTyUSFSSmMxFn+PGhVKWurqQmOtiUBEREZH0KaXlHDNby8y2ABYAc4DBiUQliRo9GmbMgMbG8LcaEnN1/ygiIiKdUdEt52b2c+BoYDrQGA12YPfSw5LOTN0/ioiISGdVdHIOHAps5O5LkgpGBFru/lHJuYiIiHRkpZS1vAasmVQg0rm0VLai7h9FRESksyql5fxi4BUzew34OjPQ3fcvOSrp0ForW6mrC8OyqftHkfQzs1UJN6jrRviOucvdzzOzDYDbgN7A34Cj9MuriMjKSmk5nwRcClwCXBZ7iLSotbuWqvtHkar2NbC7u28JbAXsY2bfJnxfXO7ug4DPgGMrGKOISGqV0nL+ibtfmVgk0mm0Vrai7h9Fqpe7O7AwerpK9Mh0FjAqGj4JOB+4pr3jExFJu1KS85fN7GLgfpqXtfyt5KikQyukbGX0aCXjItXKzGqBl4GNgauBd4HP3X1ZNMkcoH+eeccCYwHqVMsmIp1QKcn5t6K/344NU1eK0qrx45vXnIPKVkQ6EndfDmwV3UX6XmCzXJPlmXcCMAFgyJAhOacREenISrlD6G5JBiKdh8pWRDoHd//czJ4kNOKsaWZdotbzAcD7FQ1ORCSl2pycm9mR7n6LmZ2Sa7y7/1/pYUlHp7IVkY7JzPoCS6PEvDuwJ+Fi0CeAgwk9towBplQuShGR9Cqm5Xy16G/PHOP0E6SISOe2HjApqjuvAe5w9wfM7A3gNjO7CHgFuKGSQYqIpFWbk3N3vy769zF3fzY+zsy+k0hUIiJSldz9VVZckxQfPh0Y2v4RiYhUl1L6Of9tgcNERERERKQAxdScbw/sAPTNqjtfA6hNKjARERERkc6mmJrzrsDq0bzxuvP5hIt9RERERESkCMXUnD8FPGVmE919JoCZ1QCru/v8pAMUEREREeksSqk5v9jM1jCz1YA3gH+Z2ekJxSUiIiIi0umUkpxvHrWUjwSmAnXAUYlEkNuQYQAAIABJREFUJZJiDQ1QXw81NeFvQ0OlIxIREZGOoug7hAKrmNkqhOT8Kndfambq51w6tIYGGDsWFi0Kz2fODM9BN1USERGR0pXScn4dMINwU6KnzWwg4aJQkQ5r3LgViXnGokVhuIiIiEipik7O3f1Kd+/v7sPc3YFZwG7JhSaSPrNmtW24iIiISFu0OTk3syti//8k83+UoF+fUFwiiUqqTryurm3DRURERNqimJbznWP/j8kat0UJsYiURaZOfOZMcA9/jzwS1l677Un6+PHQo0fzYT16hOEiIiIipSomObc8/4ukUq46cYB580LS3pYEffRomDABBg4Es/B3wgRdDCoiIiLJKKa3lhozW4uQ2Gf+zyTptYlFJpKQlurBMxdztiW5Hj1aybiIiIiURzHJeS/gZVYk5H+LjVNXipI6dXWhlCUfXcwpIiIiadHmshZ3r3f3Dd19gxyPDcsRpEgpF3TmqhOP08WcIiIikhbF9NZS38p4M7MBxQYkki3XBZ1tqRXP1In36bPyOF3MKSIiImlSzAWhvzKzu83se2b2TTNbx8zqzGx3M/s58CywWcJxSieWxI1/Ro+GTz6BW27RxZwiIiKSXm2uOXf3Q8xsc2A0cAywHvAV8CbwIDDe3RcnGqV0akne+EcXc4qIiEiaFXNBKO7+BqAblku7yHdBp2rFRUREpKMpKjkHMLMDcwz+Avinu39UfEgizY0fH2rM46UtqhUXERGRjqjo5Bw4FtgeeCJ6vivwArCJmV3o7jeXGJsIsKIMZdy4UMpSVxcSc5WniIiISEdTSnLeCGzm7h8CmNm6wDXAdsDTgJJzSYxqxUVERKQzKCU5r88k5pGPgE3c/VMzW1rsQs3sV8D/AEuAd4Hvu/vnOaabASwAlgPL3H1IsesUEREREUmDYrpSzHjGzB4wszFmNga4H3jazFYDVkqm2+BRYLC7bwH8GzirhWl3c/etlJiLiIiISEdQSnJ+EnAjsBXwLWAScJK7f+nuuxW7UHd/xN2XRU9fAHRDIymbUu48KiIiIpK0osta3N3N7C+E8hMHXnR3Tyyy4Bjg9nwhAI+YmQPXufuEXBOZ2VhgLECd+t6TmMydRzO9wGTuPAqqbxcREZHKKLrl3MwOBV4EDgYOBf5qZgcXOO9jZvZajseI2DTjgGVAvrbM77j71sC+wElmtnOuidx9grsPcfchffv2bcMrlI4uiTuPioiIiCSplAtCxwHbZvo0N7O+wGPAXa3N6O57tjQ+qmHfD9gjX2u8u78f/f3IzO4FhhJ6iREpSJJ3HhURERFJQik15zVZNxuaV+LyADCzfYAzgP3dfVGeaVYzs56Z/4HvAq+Vum7pXPJVOan6SURERCqllGT6ITN72MyONrOjgQeBqQnEdBXQE3jUzP5uZtcCmFk/M8ssf13gL2b2D0JpzYPu/lAC65ZOZPz4cKfRON15VERERCqplAtCTzezg4DvAAZMcPd7Sw3I3TfOM/x9YFj0/3Rgy1LXJZ2b7jwqIiIiaVNKzTnufjdwd0KxiLQ73XlUJFlmtj5wE/BfhDtJT3D335hZb0LvW/XADOBQd/+sUnGKiKRVm8tazGyBmc3P8VhgZvPLEaSIiFSNZcCp7r4Z8G1Cb1qbA2cCj7v7IODx6LmIiGRpc8u5u/csRyAiIlL93P0D4IPo/wVm9ibQHxgB/5+9+w6Tqjz/P/7+CKLBrqA/G6DGbqJJiJoYKxY0tqhRERUrlmhMNEYTEjUa8tWYxJjEhoolrr2ioljRmMSCvSsiIDZAbASVdv/+eM7AMMzsDsvuzNndz+u65po5de5zZmf2Ps+5z3PYJpvtKmAk6eJ/MzMrstC9q5iZmZUjqRfpDtJPACtliXshgV+xwjIDJY2SNGrSpEm1CtXMLDecnJuZWYuTtCTpmqSfRUTVJY++cZyZdXROzs3MrEVJWpSUmDdExK3Z6A8lrZxNXxmYWGl5M7OOzMm5mZm1GEkCLgdejYi/FE0aBgzIXg8A7qh1bGZmbcFCdaVoZmZWYgvgIOBFSc9l434NnA3cKOlwYDzw4zrFZ2aWa07OzcysxUTEY6Qb05XTp5axmJm1RS5rMTMzMzPLCSfnZmZmZmY54eTczMzMzCwnnJybmZmZmeWEk3MzMzMzs5xwcm5mZmZmlhNOzs3MzMzMcsLJuZmZmZlZTjg5NzMzMzPLCSfnZmZmZmY54eTczMzMzCwnnJybmZmZmeWEk3MzMzMzs5xwcm5mZmZmlhNOzs3MzMzMcsLJuZmZmZlZTjg5NzMzMzPLCSfnZmZmZmY54eTczMzMzCwnnJybmZmZmeWEk3MzMzMzs5xwcm5mZmZmlhNOzs3MzMzMcsLJuZmZmZlZTjg5NzMzMzPLCSfnZmZmZmY54eTczMzMzCwnnJybmZmZmeWEk3MzMzMzs5xwcm5mZmZmlhNOzs3MzMzMcsLJuZmZtShJQyVNlPRS0bjlJd0v6c3sebl6xmhmlldOzs3MrKVdCfQtGXcq8GBErA08mA2bmVkJJ+dmZtaiIuJRYErJ6D2Aq7LXVwF71jQoM7M2wsm5mZnVwkoR8T5A9rxineMxM8ul3CXnks6S9IKk5yTdJ2mVCvMNyGoX35Q0oNZxmplZy5M0UNIoSaMmTZpU73DMzGoud8k5cG5EfDMiNgHuAk4rnUHS8sDpwGbApsDpvrjIzCzXPpS0MkD2PLHcTBExJCJ6R0Tv7t271zRAM7M8yF1yHhGfFQ0uAUSZ2XYC7o+IKRHxMXA/8198ZGZm+TEMKJzlHADcUcdYzMxyq3O9AyhH0mDgYOBTYNsys6wKvFM0PCEbZ2ZmdSbpOmAboJukCaQznWcDN0o6HBgP/Lh+EZqZ5VddknNJDwD/r8ykQRFxR0QMAgZJ+hVwHOmHfZ5VlFm2XAs7kgYCAwF69OjR/KDNzKwqEdGvwqQ+NQ3EzKwNqktyHhHbVznrtcDdzJ+cTyC1yhSsBoys8F5DgCEAvXv3LpvAm5mZmZnlQe5qziWtXTS4O/BamdlGADtKWi67EHTHbJyZmZmZWZuVx5rzsyWtC8wGxgFHA0jqDRwdEUdExBRJZwFPZcucGRGlN7wwMzMzM2tTcpecR8TeFcaPAo4oGh4KDK1VXGZmZmZmrS13ZS1mZmZmZh2Vk3MzMzMzs5xwcm5mZmZmlhNOzs3MzMzMcsLJuZmZmZlZTjg5NzMzMzPLCSfnZmZmZmY54eTczMzMzCwnnJybmZmZmeWEk3MzMzMzs5xwcm5mZmZmlhNOzs3MzMzMcsLJuZmZmZlZTjg5NzMzMzPLCSfnZmZmZmY50bneAVjH1tAAgwbB+PHQowcMHgz9+9c7KjMzM6tWr1PvrncI7YqTc6ubhgYYOBCmTUvD48alYXCCbmZmZh2Ty1qsbgYNmpuYF0yblsabmZmZdUROzq1uxo9fsPFmZmZm7Z2Tc6ubHj0WbLyZmZlZe+fk3Opm8GDo2nXecV27pvFmZmZmHZGTc6ub/v1hyBDo2ROk9DxkiC8GNTMzs47LvbVYXfXv72TczMzMrMAt52ZmZmZmOeHk3MzMzMwsJ5ycm5mZmZnlhJNzMzMzM7Oc8AWhZmZmZq2s16l31zsEayPccm5mZjUhqa+k1yWNlnRqveMxM8sjJ+dmZtbqJHUCLgB2BjYA+knaoL5RmZnlj5NzMzOrhU2B0RExJiKmA9cDe9Q5JjOz3OlQNedPP/30ZEnj6h1Hphswud5BlMhjTOC4FlQe48pjTNC24upZj0Ba0KrAO0XDE4DNSmeSNBAYmA1OlfR6M94rr59rS2jP2wbte/u8bW2UzmnW9jX7N7tDJecR0b3eMRRIGhURvesdR7E8xgSOa0HlMa48xgSOq8ZUZlzMNyJiCDBkod6ofe4/oH1vG7Tv7fO2tV213j6XtZiZWS1MAFYvGl4NeK9OsZiZ5ZaTczMzq4WngLUlrSGpC7A/MKzOMZmZ5U6HKmvJmYU6bdtK8hgTOK4Flce48hgTOK6aiYiZko4DRgCdgKER8XIrvV27239F2vO2QfvePm9b21XT7VPEfCV/ZmZmZmZWBy5rMTMzMzPLCSfnZmZmZmY54eS8lUkaKmmipJeKxi0v6X5Jb2bPy+Ukrh9LelnSbEl16RKpQlznSnpN0guSbpO0bE7iOiuL6TlJ90lapd4xFU37haSQ1K2WMVWKS9IZkt7N9tVzknbJQ1zZ+OOzW8q/LOmPeYhL0g1F+2qspOdqHVdbIKlv9tmNlnRqmemLZftytKQnJPWqfZTNU8W2nSjplew36EFJbaYf/Ka2rWi+fbLfsTbVRV812ydp3+zze1nStbWOsbmq+LvsIelhSc9mf5s1/61vrsb+p2bTJelv2ba/IOnbrRZMRPjRig9gK+DbwEtF4/4InJq9PhU4JydxrQ+sC4wEeudof+0IdM5en5Oj/bV00eufAhfXO6Zs/Oqki+7GAd1ysq/OAH5Rj7+pJuLaFngAWCwbXjEPcZVM/zNwWj33XR4fpItK3wLWBLoAzwMblMxzbOF7Seod5oZ6x92C27Yt0DV7fUx72rZsvqWAR4HH6/X/qBU/u7WBZ4HlsuGa/+604rYNAY7JXm8AjK133AuwfU39Fu8C3EO6Z8PmwBOtFYtbzltZRDwKTCkZvQdwVfb6KmDPmgZF+bgi4tWIaM7d+FpMhbjui4iZ2eDjpP6R8xDXZ0WDS1Dmhiq1jilzHvDLWsdT0EhcdVUhrmOAsyPiq2yeiTmJC0gtNcC+wHU1Dapt2BQYHRFjImI6cD3pt7VY8W/tzUCfbJ/mXZPbFhEPR8S0bLAuv4vNVM3nBnAWqSHry1oG1wKq2b4jgQsi4mOoz+9OM1WzbQEsnb1ehjZ0L4Mq/nftAVwdyePAspJWbo1YnJzXx0oR8T5A9rxineNpSw4jHbnmgqTBkt4B+gOn5SCe3YF3I+L5esdSxnHZqcCh9SjlqmAdYMus5OERSd+td0AltgQ+jIg36x1IDq0KvFM0PCEbV3ae7AD/U2CFmkS3cKrZtmKHk6PfxSY0uW2SvgWsHhF31TKwFlLNZ7cOsI6kf0t6XFLfmkW3cKrZtjOAAyVNAIYDx9cmtJpY0O9lszk5tzZD0iBgJtBQ71gKImJQRKxOium4esYiqSswiBwcJJRxEbAWsAnwPqlUIw86A8uRTlGeDNyYs5bVfrjVvJJyn1Pp2aJq5smjquOWdCDQGzi3VSNqOY1um6RFSGf/TqpZRC2rms+uM6m0ZRvSd/yyelxL1QzVbFs/4MqIWI1UBvLP7DNtD2r2e9Jedlhb82HhVEj23FZOadWNpAHArkD/yIq/cuZaYO86x7AWsAbwvKSxpNPcz0j6f3WNCoiIDyNiVkTMBi4lnR7NgwnArdlpyieB2UDNL6ItR1JnYC/ghnrHklMTSNdXFKzG/KfQ58yT7c9lyGHJVRnVbBuSticdkO9eKM1qA5ratqWAjYCR2e/Y5sCwNnRRaLV/l3dExIyIeBt4nZSs510123Y4cCNARPwXWJyc/Ka2gKq+ly3ByXl9DAMGZK8HAHfUMZbcy075nUL6BzStqflrRVLxj+nuwGv1igUgIl6MiBUjoldE9CL9kHw7Ij6oZ1ww5yC04EdA2avh6+B2YDsASeuQLnKaXNeI5toeeC0iJtQ7kJx6Clhb0hqSupAu+BxWMk/xb+0+wEM5Pbgv1eS2ZaUfl5B+F9tSA0+j2xYRn0ZEt6LfscdJ2ziqPuEusGr+Lm8nXdCLUo9a6wBjahpl81SzbeOBPgCS1icl55NqGmXrGQYcnPXasjnwaaFEucXV6irYjvognZJ+H5hBSpYOJ9U8Pgi8mT0vn5O4fpS9/gr4EBiRk7hGk+q8nsseNe0VpZG4biElmS8AdwKr1jumkuljqU9vLeX21T+BF7N9NQxYOSdxdQGuyT7HZ4Dt8hBXNv5K4Ohax9OWHqTT5m+QepAYlI07k5TMQUoMbsp+Q54E1qx3zC24bQ9kv9OF38Vh9Y65pbatZN6RtKHeWqr87AT8BXgl+13cv94xt+C2bQD8m9STy3PAjvWOeQG2rdz/iKMLv8PZ53ZBtu0vtubfpbI3NDMzMzOzOnNZi5mZmZlZTjg5NzMzMzPLCSfnZmZmZmY54eTczMzMzCwnnJybmZmZmeWEk3PLBUlTF3L5myWtmb0eK+lfJdOfk/RS9rqrpAZJL0p6SdJjkpYsmvdHkkLSeo2836zCOiXdlN2ds8VIOkTSP5qYZxtJ3y8aPlrSwc18v29IurI5y5qZmVnLcXJubZ6kDYFOEVF8E4elJBXuDLh+ySInAB9GxDciYiNSX6Yziqb3Ax4j3WChki8iYpNs+emkvlBrbRtgTnIeERdHxNXNWVFEvAisJqlHC8VmZmZmzeDk3HIlu/PWuVmL9IuS9svGLyLpQkkvS7pL0nBJ+2SL9Wf+u6zeCOyXve5HurlAwcrAu4WBiHg9sltfZy3oW5AS9saS82L/Ar6eLX9iFvtLkn6Wjesl6TVJV0l6IWvl75pNG5vdIQ5JvSWNLLNPdpP0hKRnJT0gaSVJvUgHBD/PWvC3lHSGpF9ky2wi6fHs/W6TtFw2fqSkcyQ9KekNSVsWvdWdC7DNZmZm1gqcnFve7AVsAmxMun35udmt3/cCegHfAI4Avle0zBbA0yXruTlbBmA3UuJZMBQ4RdJ/Jf1e0tpF0/YE7o2IN4Apkr7dWLCSOgM7Ay9K+g5wKLAZsDlwZHaLbYB1gSER8U3gM+DYRvfCvB4DNo+IbwHXA7+MiLHAxcB5WQv+v0qWuRo4JXu/F4HTi6Z1johNgZ+VjB8FFCfrZmZmVmNOzi1vfgBcFxGzIuJD4BHgu9n4myJidkR8ADxctMzKwKSS9UwBPpa0P/AqMK0wISKeA9YEzgWWB54qKn3pR0qAyZ77VYjza5KeIyW044HLsxhvi4j/RcRU4FbmJrvvRMS/s9fXZPNWazVghKQXgZOBDRubWdIywLIR8Ug26ipgq6JZbs2enyYd8BRMBFZZgLjMzMyshXWudwBmJbSA4wG+ABYvM/4G4ALgkNIJRcnzrZJmA7tImghsB2wkKYBOQEj6ZURE6XtGxCbzBCg1FmPp8oXhmcw9SC63DQB/B/4SEcMkbQOc0cj7VOOr7HkW8/4GLE7al2ZmZlYnbjm3vHkU2E9SJ0ndSS2+T5JKO/bOas9XIl0MWfAqWc13iduAPwIjikdK2qKoBrsLsAEwDtgHuDoiekZEr4hYHXib6lu5HwX2zHqDWQL4EakeHaCHpEIpTuGCU4CxwHey13tXWO8yzK2RH1A0/nNgqdKZI+JT0lmDQqv9QaQzEE1ZB3ipivnMzMyslTg5t7y5DXgBeB54iFRf/QFwCzCBlDxeAjwBfJotczfzJusARMTnEXFOREwvmbQW8EhWJvIsqTTlFlLSfFvJvLcAB1QTeEQ8A1xJOph4ArgsIp7NJr8KDJD0AqmU5qJs/O+A87OuH2dVWPUZwE3ZPJOLxt8J/KhwQWjJMgNI9fovkGr4z6xiE7Yl7UszMzOrE81/tt4snyQtGRFTJa1ASoC3iIgPJH2NVIO+RURUSnDrJutZ5a6s28VckrQYqXX9BxExs97xmJmZdVSuObe25C5JywJdgLOyFnUi4gtJpwOrki7OtAXXAzjVibmZmVl9ueXczMzMzCwnXHNuZmZmZpYTTs7NzMzMzHLCybmZmZmZWU44OTczMzMzywkn52ZmZmZmOeHk3MzMzMwsJ5ycm5mZmZnlhJNzMzMzM7OccHJuZmZmZpYTTs7NzMzMzHLCybmZmZmZWU44Oa9A0jaSJtQ7jqZI6i/pvnrHUU/VfFaSrpO0ZxXrCklfb7nozPJJ0kqSXpW0WL1jMTOzuXKZnEs6RNKLkqZJ+kDShZKWaWKZxST9n6Txkr6Q9KakX0hSDeK9UtLvW/t9yomIhojYcWHX056TUknfBDYG7qh3LC1JyTmSPsoef6z09y5p2+w79Uk2722SVi2avrykGyRNzh4NkpYumv6wpEmSPpP0vKQ9StZ/vKS3s+mjJP2g5L0flvSppLFlYvu+pCclfS7pheJl60lSryzuaZJek7R9I/OuKukOSVMkTZB0dMn07SQ9k+2fMZIGlkw/QNI4Sf+TdLuk5YumrS/poWz/jZb0owoxnJ59j7cvGb999t7/k/SOpH0BIuJD4GFgYLn1mZlZfeQuOZd0EnAOcDKwDLA50Au4T9KijSx6E9AH2AVYCjgIOAr4c2vGawtOUucav+VRQENERI3ft7UNBPYkHXh8E9iVtK3lvALsFBHLAqsAbwIXFU3/PbAcsCawFrAScEbR9BOAlSNi6ex9r5G0MoCkzYCzgX1I39nLgdskdcqW/R8wlPSdnkeWhA4DzgWWBf4I3ClpuWp3QtG6JKklf9OuA54FVgAGATdL6l5h3muAt0n77YfAHyRtm8W1KHAbcAlp/+wH/EXSxtn0DbNpB2XLTwMuzKZ1Jh1U3gUsz9x9v07xm0tai7T/3y8ZvwFwbRb/MsAmwNNFszRQ+W/GzMzqISJy8wCWBqYC+5aMXxKYCAyosFwf4Etg9ZLxmwGzgDUrLDcW+BUpcfkYuAJYPJu2DTChaN71gZHAJ8DLwO7Z+IHADGB6FvudFd7rfOAd4DPSP8cti6Z9Dbgqi+FV4Jcl730q8BbweRbrj4qmHQI8VjQcwNGk5Otj4AJA2bSvA48AnwKTgRuy8Y9my/0v24b9ysR/CPAY8KdsvW8DOxdNX4WUZE0BRgNHFk07A7iZlMB8BhyRjbspG/c58CKwTvZ5TMz21Y5F6zg02zefA2OAo4qmzfNZlYl9DPCDouGy+6Fo/309e70McDUwCRgH/AZYpGh//Bv4e7ae14A+RespJKnvA++Skt9OLfx9+Q8wsGj4cODxKpZbDPg/4JWicfcAxxYN/wQYUWH5TUnft02z4f2AJ4umL5Htx5VLltseGFsyblfg5ZJxbwCHV7kPRgKDs8/ii8Jn1wL7dh3gK2CponH/Ao4uM++S2fZ2Lxo3BPhn9nqlbHrXoulPAf2y138Ari2athbp92QpYCPSd1JF0+8DziqJ4R5Sw8RYYPui8deWzluyXGfSwUDPlvzb9MMPP/zwo/mPvLWcfx9YHLi1eGRETCX986lUvrED8EREvFOy3BPABFLyXkl/YCfSP8R1SAnYPLKWrztJ/xRXBI4HGiStGxFDSK1Pf4yIJSNitwrv8xSp1Wp50j/MmyQtnk07nXR2YM1sWw4sWfYtYEtSwvc7ilotK9gV+C6pRXXfbPsAzsq2YTlgNVJiSURslU3fONuGGyqsdzPgdaAbqYXz8qIyiutI+3oVUgveHyQV7/c9SAn6sqT9BbAb8M8snmeBEaSzOasCZ5JaEwsmZtu1NClRP0/StxvZBwBIWgJYI4u7oOx+KOPvpH2+JrA1cHD23gWbkRL/bqTP8NaicoSrgJmkA4Fvkf52j6gQ4wFZuUmlR48K8W0IPF80/Hw2rixJPSR9Qkpif0H6DAsuAHaVtFzWar036TtXvPxdkr4EniAlxaOySfcAnSRtlrWWHwY8B3xQKZbi1WaP0nEbVbFswUGkg+SlSAdR864sxV1p395VYZ0bAmMi4vOicZX2r0qe59mGSOUj1wGHSuok6XtAT9LBbuG95nyOEfEWKTlfh/n3zTzrzrbvx8D0iBheZt7Ns3lelPS+pGuKS2YiYibpYHrjMsuamVkd5C057wZMzv5hlHofqHRKuRslp3OrXA7gHxHxTkRMIbXA9Sszz+ak1rGzI2J6RDxEOs1cbt6yIuKaiPgoImZGxJ9JrZfrZpP3Bf4QER9HxATgbyXL3hQR70XE7CxxfpPUelnJ2RHxSUSMJ9WUbpKNn0FKClaJiC8j4rGKayhvXERcGhGzSMnnysBKklYHfgCckq33OeAyUtJU8N+IuD3bhi+ycf+KiBHZ530T6XM6OyJmANcDvSQtm+2DuyPirUgeISXXW1YR87LZc3GS1eR+yJLM/YBfRcTnETGWVCJVvE0Tgb9GxIzsc3kd+KGklYCdgZ9FxP8iYiJwHrB/uQAj4tqIWLaRx/gK27YkqdW+4FNgyaIDptL3GR+prKUb6SD0taLJzwBdgI+yxyyy0oqi5XclJcC7kFrVZ2eTPgduISWbX5EOVAZGRDVlRP8BVpHUT9KikgaQDpS7VrFswZUR8XL23ZpROjEidm1k3+5aYZ2l+5ZseKky6/+c1HL/W0mLZweNe5dsw3XAaaT98y9gUFFjQmPv9Rrp7+zkbP/sSDpQ7AogaUlSy/vPKmzHaqS/2b2BtUln6UoPRj9n7vfEzMzqLG/J+WSgW4Wa5JVJ5QVImlr06JEtV6klec5yFRS3to8jtfyWWgV4pygZKcy7apl5y5J0klLPCJ9mrZfLkJKkOeuvEBOSDpb0XKG1j9Rq1o3Kilssp5H++UMqlxHwpKSXJR1Wbfyl642IadnLJbP4p5S0Mpbun3m2KfNh0esvSAdms4qGC+tH0s6SHs8uuPuElCA2tg8KPsmei5OqavZDN1KyWtwSW7pN75YkoIW/n57AosD7RZ/ZJaSzLi1pKulMQsHSwNSmkuLsQPQq4I6i79pNpHKSpbL1vEUqOSpddkZE3APsJGn3bPQRpNbyDUn77EDgLknlvkul6/uIdFblRNLfQ1/gAdJZmGqV+9taWKX7lmz48zLzQjoDt0YWy0Wks0MTACStB9xAOvPShbSffinph029V3awsSepjv0D4CTgRubun9+RymferhDXF8AVEfFGdgbyD6TvTrGlmPs9MTOzOstbcv5fUsvSXsUjs9KEnUl1wmSlF4XHeNI/882yFtzi5TYFepBqqispXqYH8F6Zed4DVi+52KwHqZYYUj1pRZK2BE4htZAvl7VefsrcU9bvk1q45otJUk/gUuA4YIVs2Zcof7q7URHxQUQcGRGrkC4Cu1BKswBqAAAgAElEQVQt00PLe8DykooT4OL9A03so8YodfV2C6nefaVsHwynin0QEf8jJZrrFI2rZj9MZm4Le0HpNq1a0kpd+Pt5h/R33K2ohXbpiChbcqLUHebURh6VylpeZt5yhI2zcdXoTDpYKCSFGwOXZC39U4GLmT+JK11+raJl78wSwNkRcS/pb/r71QQSEY9ExHcjYnlSK++6wJNVbgc0/f27p5F9e0+FxV4G1iz5m664fyNiXNZC3z0iNiNdRFrYho2A17OzRLMj4nXgbtJvWuG95nyOktYknVl7I1v3CxGxdUSsEBE7kcqsCuvuA/xUqVerD0i/HTdKOiWb/kJj+yc7OPs685ZHmZlZHeUqOY+IT0ktQX+X1Dc7jduL1Ko3mbm1yqXLPQA8CNwiacOsrnPzbP6rs3+GlfxE0mpZHeavSS1cpZ4gXSz5yyymbUj10tdn0z8k/cOsZClS/fEkoLOk05i3pexG4FdZve+qpES8oHBxXeGswaEsWD3uHJJ+LKlwEPBxtt5CS3VT21BRdnr+P8D/Zaf1v0m6OLHs59UMXUjJyiRgpqSdqXz9QTnDSaUAQJP7AYCsBf9GYLCkpbKDpBOZtzV5RVJitGhW97s+MDwi3ieV3fxZ0tKSFpG0lqStKSNSd5hLNvKoVNZyNXCiUjd+q5BaVa8sN6OkvSStm8XSHfgL8GzWig7pmogjJH1N0tdINdzPZ8uul525+Fq2rQcCW5EdLGfL/lDSmkp2IB0MvZQtv4jS9RWLpkEtLqlLUWzfyta7NOkAbEJEjMim9VLqHrBXhX3QpIjYuZF9u3OFZd4g1c2fnsX7I1KPOLeUm1+pu8OlJHXJ9s+OpH0M6XqKtZW6U5RSzyq7MjchbgB2k7Rl1hBxJnBr4UyUpG9mMXSV9AvS2cArs2X7kH4PNske75EOOC/Ipl9BqnVfU1JXUiNBcZ39pqSLdOer1Tczs/rIVXIOEBF/JCXJfyKdQn6bVF+5fdYKWsnepPrqe0k9Sfw3e91UH77XkhKpMdljvv7KI2I6sDuppWsyqRb34Igo1OxeDmyQlTDcXuY9RpAumnuDVPrwJfOeij+TdJr6bdJZgJtJLa9ExCukWuf/khLob5DqW5vju8ATkqaSelY5oeh0+BnAVdk27NuMdfcjXdT6HqnbuNMj4v5mxjmPLEn5KSlZ/hg4gBR/tYYA/YtauRvbD8WOJx2UjSHVU19L6hKw4AlSHe9k0vUK+2RlGjC3hKHQE9DNVC69aq5LSBcqv0hKhO+m6CLarGW4UJe/Kun7UOgZZzZQ3F/2YaTPbwLp7MCapB5pIJ2hOINU+zyJ1K3ifhHxTDb9atKB6khSbzx/I/WmU/h+bEUqrxhOOrvwBek7V/BL0j58h7SPiuNanfSdKT5jUSv7A71Jn9/ZpM+3cJDcX1JxK/pOpL+Tj0m9JfUtzBvpAs/DSPvlM9JBzS2k3w0i4uVsmQbSPl4KOLZo3QeRzkRMJCXjO0RE4ffho+xM0AcR8QHpIPPj7OwHETGU9Pk8QdqPX5G+SwX9SWdJzMwsJwpd7OWWUj3w74AtGmlBLLfcVaSEZJcsuS43z1jgiKzlPTckHQPsHxFlW1ptwUm6FrgxIsodPDVnfYeQ/nZyccOc9krSb4BJEXFJkzPbApG0IulA4VsR8WW94zEzs6TWN4NZYBExVNIMUv1q1ck56SK1E4FvA4+3RmwtRalbxDVJreNrk8oT/lHXoNqZiDig3jHYgouIutx5tyOI1IvQ+vWOw8zM5pX75BwgIv7ZjGVmkO402hZ0IZUjrEHqNeF6SrqxMzPLK0lDSXX0EyNivmtispKy80kXGU8DDikqizIzsyK5L2sxM7N8k7QVqUvIqysk57uQruHYhXTzrvOzXm3MzKxE7i4INTOztiUiHgWmNDLLHqTEPSLicWBZNX6XYzOzDqtNlLW0lG7dukWvXr3qHYaZ2QJ7+umnJ0dEY3c7zrNVmbeHqgnZuPnu7CxpIFkvW0ssscR31ltvvZoEaGbWkhbmN7tDJee9evVi1KhR9Q7DzGyBSWrLfZGXu2FY2ZrKiBhC6v6U3r17h3+zzawtWpjfbJe1mJlZa5vAvHdjXo3yd2M2M+vwnJybmVlrGwYcnN0hdXPg0+xOumZmVqJDlbWYmVnLk3QdsA3QTdIE4HRgUYCIuJh0d9hdgNGkrhQPrU+kZmb55+TczMwWSkT0a2J6AD+pUThmZm2ay1rMzMzMzHLCybmZmZmZWU44OTczMzMzywkn52ZmZmZmOeHk3MzMzMwsJ5ycm5mZmZnlhJNzM7Namjix3hGYmVmOOTk3M6uVP/0J1lkHXnut3pGYmVlOOTk3M6uFwYPh5JOhb19Ya616R2NmZjnl5NzMrDVFwOmnw29+AwceCNdcA4suWu+ozMwspzrXOwAzs3YrAn79azj7bDj0ULj0UujUqd5RmZlZjjk5NzNrDRFw0klw3nlw9NFwwQWwiE9WmplZ4/yfwsyspc2eDccfnxLzn/4ULrzQibmZmVXF/y3MzFrS7Nlw1FGppfzkk+GvfwWp3lGZmVkb4eTczKylzJoFhx0Gl10GgwbBOec4MTczswXimnMzs5YwcyYcfDBcdx2ceSb89rf1jsjMzNogJ+dmZgtrxgw44AC4+ebUM8spp9Q7IjMza6OcnJuZLYyvvoJ994Vhw+Avf4Gf/7zeEZmZWRvm5NzMrLm++AL23hvuuQf+8Q/4yU/qHZGZmbVxTs7NzJpj2jTYYw948EEYMgSOPLLeEZmZWTvg5NzMbEFNnQq77QaPPgpXXAEDBtQ7IjMzayecnJuZLYjPPoNddoHHH4drroF+/eodkZmZtSNOzs3MqvXxx9C3LzzzDFx/PeyzT70jMjOzdsbJuZlZNT76CHbcEV58MXWZuMce9Y7IzMzaISfnZmZNmTgRdtgBXn8d7rgDdt653hGZmVk75eTczKwx778P228Pb78Nd92VXpuZmbUSJ+dmZpW8+y5st116vuce2HrrekdkZmbtnJNzM7Nyxo9PifnEiTBiBGyxRb0jMjOzDmCRer65pL6SXpc0WtKpZaafJ+m57PGGpE+Kps0qmjastpGbWbs2ZgxstRVMngz33+/E3MzMaqZuLeeSOgEXADsAE4CnJA2LiFcK80TEz4vmPx74VtEqvoiITWoVr5l1EG++mVrMp02Dhx6Cb3+73hGZmVkHUs+W802B0RExJiKmA9cDjfVN1g+4riaRmVnH9OqrqcX8q6/g4YedmJuZWc3VMzlfFXinaHhCNm4+knoCawAPFY1eXNIoSY9L2rPSm0gamM03atKkSS0Rt5m1Ry++mC74jICRI+Gb36x3RGZm1gHVMzlXmXFRYd79gZsjYlbRuB4R0Rs4APirpLXKLRgRQyKid0T07t69+8JFbGbt07PPwrbbwqKLwiOPwAYb1DsiMzProOqZnE8AVi8aXg14r8K8+1NS0hIR72XPY4CRzFuPbmZWnaeeSjXmSywBjz4K665b74jMzKwDq2dy/hSwtqQ1JHUhJeDz9boiaV1gOeC/ReOWk7RY9robsAXwSumyZmaN+s9/0k2FllsuJeZrlT0BZ2ZmVjN1660lImZKOg4YAXQChkbEy5LOBEZFRCFR7wdcHxHFJS/rA5dImk06wDi7uJcXM7MmPfoo7LILrLJK6pVltdXqHZGZmVl9b0IUEcOB4SXjTisZPqPMcv8BvtGqwZlZ+/Xgg7DbbtCzZ0rMV1653hGZmZkBdb4JkZlZzd17L+y6ayphGTnSibmZmeWKk3Mz6zjuvBP22APWWy/1Y77SSvWOyMzMbB5Ozs2sY7j1VthrL9h441TK0q1bvSMyMzObj5NzM2v/rr8e9t0XvvtduP/+1DuLmZlZDjk5N7P27eqroX9/2GILGDECllmm3hGZmZlV5OTczNqvyy+HQw6BbbaB4cNhqaXqHZGZmVmjnJybWft00UVwxBGw445w113pDqBmZmY55+TczNqf88+HY49NXSbefjt87Wv1jsjMzKwqTs7NrH354x/hZz9LPbPccgssvni9IzIzM6uak3Mzaz9+/3s45RTYb7/UQ0uXLvWOaI6GBujVCxZZJD03NNQ7IjMzy6PO9Q7AzGyhRcDpp8NZZ8FBB8HQodA5Pz9vDQ0wcCBMm5aGx41Lw5A6kjEzMytwy7mZtW0RcOqpKTE/7DC44opcJeYAgwbNTcwLpk1L483MzIo5OTeztisCTjwx1Zkfcwxceil06tQiq27JMpTx4xdsvJmZdVxOzs2sbZo9G447Dv76VzjhBLjggpRJt4BCGcq4cSn/L5ShNDdB79Fjwca3RZL6Snpd0mhJp5aZ3kPSw5KelfSCpF3qEaeZWd45OTeztmf2bDjqKLjwQvjlL+G880BqsdW3dBnK4MHQteu847p2TePbA0mdgAuAnYENgH6SNiiZ7TfAjRHxLWB/4MLaRmlm1jY4OTeztmXWLDj0ULjsMvjtb+Hss1s0MYeWL0Pp3x+GDIGePVOoPXum4XZ0MeimwOiIGBMR04HrgT1K5glg6ez1MsB7NYzPzKzNcHJuZm3HzJmpN5arr4Yzz0yPKhPzBakhb40ylP79YezY1Og/dmy7SswBVgXeKRqekI0rdgZwoKQJwHDg+NqEZmbWtjg5N7O2Yfp02H9/uO46OOec1GpepQWtIW/vZSitoNwRUpQM9wOujIjVgF2Af0qa73+QpIGSRkkaNWnSpFYI1cws35ycm1n+ffUV7LNPuuPneeelOvMFsKA15B2gDKWlTQBWLxpejfnLVg4HbgSIiP8CiwPdSlcUEUMiondE9O7evXsrhWtmll/56gzYzKzUF1/AXnvBvfemC0CPOWaBV9GcGvL+/Z2ML4CngLUlrQG8S7rg84CSecYDfYArJa1PSs7dNG5mVsIt52aWX9Omwe67w4gR6QLQZiTm0DG6MqyniJgJHAeMAF4l9crysqQzJe2ezXYScKSk54HrgEMiorT0xcysw3PLuZnl09SpsOuu8K9/wZVXwsEHN3tVgwenGvPi0hbXkLesiBhOutCzeNxpRa9fAbaodVxmZm2NW87NLH8+/RR22gkeeyxdtbkQiTm4htzMzNoOJ+dmli8ffww77ABPPgk33JB6aGlCNd0ktvOuDM3MrJ1wcm5m+fHRR9CnDzz/fOqZZe+950yqlIAvaDeJZmZmeebk3MzyYeJE2HZbeOUVHjrhDnr9dPc5ifixx1ZOwBe0m0QzM7M88wWhZlZ/77+fWszHjuWBn9/NHn/rMyfhHjcOLr44JeXFCgl4c7pJNDMzyyu3nJtZfU2YAFtvnbLpe+7hiOv6zNcSXqnDvfHj3U2imZm1L3VNziX1lfS6pNGSTi0z/RBJkyQ9lz2OKJo2QNKb2WNAbSM3sxYxblxKzD/8EO67D7beeoFavHv0SN0hdu0673h3k2hmZm1V3ZJzSZ2AC4CdgQ2AfpI2KDPrDRGxSfa4LFt2eeB0YDNgU+B0ScvVKHQzawlvvQVbbQVTpsD998P3vw9UbvGW5h0uJODuJtHMzNqTeracbwqMjogxETEduB7Yo8pldwLuj4gpEfExcD/Qt5XiNLOW9sYbsPXWfDVlKj9c/EEW2XzTOT2wVGoJP/roygm4u0k0M7P2op7J+arAO0XDE7JxpfaW9IKkmyWtvoDLImmgpFGSRk2aNKkl4jazZmpogO1XeYX3192aSe9N53tfjmT4B9+epwcWKN8SfuGFTsDNzKz9q2dvLSozrvSyrzuB6yLiK0lHA1cB21W5bBoZMQQYAtC7d+8Kl5WZWWtraIC/HfECd365PbPoxNYxkldnzlvJVuiBxcm3mZl1VPVsOZ8ArF40vBrwXvEMEfFRRHyVDV4KfKfaZc0sX679xTMM/3JbptOFrXmEVyl3iYm7QDQzs46tnsn5U8DaktaQ1AXYHxhWPIOklYsGdwdezV6PAHaUtFx2IeiO2Tgzy5HCXT0305Nc80EfprIkW/Eob7JOxWXcBaKZmXVkdStriYiZko4jJdWdgKER8bKkM4FRETEM+Kmk3YGZwBTgkGzZKZLOIiX4AGdGxJSab4SZzaNwx87x42H55eHzz6H39H9zDzszmW5sy8OMp2fF5d0FopmZdXR1vUNoRAwHhpeMO63o9a+AX1VYdigwtFUDNLNGlUvGp09P0z76CLbiEe7mh7zLqvThQd5ltXmWX3RRWHrp1Jtioc9y15qbmVlHVtfk3MzaroaG1LtK4W6eH3007/Q+PMAwdmcsvejDg3zA3Co1ycm4mZlZOU7OzaxZBg2am5iX6ss93MaPeIN12J4HmMSKc6b17Jl6YzEzM7P51fOCUDNrwyr1qrIbw7idPXmFDdiWh+dJzF1TbmZm1jgn52bWLOV6Vdmbm7mFvXmOTejDg3y+6AqssEL5u3qamZnZ/Jycm1mzDB6cWsIL9uc6rmd/num8KTtyP8v0XI4rroDJk31XTzMzs2q55tzMmqWQaA8aBNuMu4rLOYzJ623JZk/dxadLLlnf4MzMzNooJ+dm1mz9+0P/Ly5L3bb06cNKd9wxb3O6mZmZLRCXtZhZ811wARx5JOy0E9x5pxNzMzOzheTk3Mya57zz4LjjYPfd4fbbYfHF6x2RmZlZm+fk3MwWSEMDnL3cOXDiiQzvujfX7XUTLLZYvcMyMzNrF1xzbmZVa2iAtw49i9NmnMa19OPgaVez2LGdmd3ZPbGYmZm1BLecm1l1Iphy7G84bcZpXMkADuKfzKIz06alHlvMzMxs4Tk5N7OyGhqgVy9YZBHotkLw966ncPxng7mUIziMocym05x5K90t1MzMzBaMy1rMbD4NDal3xGnTAILfTvkZx/M3LuBYjufvRMlxfbm7hZqZmdmCc8u5mc1RaC0/8MCUmIvZXMixnMDf+As/5zj+MV9i3rVruluomZmZLTwn52YdVCERl6Bz5/R80EEwblyavgizuJQjOYaL+T9O5ST+DGiedfTsCUOG+GJQMzOzluKyFrMOaN6yFZg1Kz1HpOdOzOQKDuUgruF3nMYZnEG5xHzs2FpFbGZm1jE4OTfrgAYNmpuYl+rMDK7hQPbjRgbxe/7A/F2xuJTFzMysdbisxawDKZSyFEpXSi3KdG5gP/bjRn7BuXMS8xVWSA/JpSxmZmatyS3nZu1cQ0NqKR83LiXXhdKVUovxJTezD7tyNz/lfP7OT+na1Ym4mZlZLTk5N2unGhrghBPgo4/mjquUmC/OF9zOnuzEfRzNxVzCUfTsmUpXnJibmZnVjpNzs3ao9ILPxnTlf9zJ7mzDw5y8wuVsef5hXOyE3MzMrC6cnJu1Mw0NMGDA3B5YGrMkn/PAYj9ksxn/hquu5twDD2z9AM3MzKwiXxBq1g4U91l+0EHVJeZL8yn3L7IT353xH7j22nTnITMzM6srt5ybtXGlJSyV6soLJFg2pvBwl53YaNbzLHLjjbDXXq0fqJmZmTXJLedmbVxjfZaXWmEFuPHCyUzZpA8b8wKdbr/VibmZmVmOuOXcrI0bP77peTp1gquugv7bfwjbbw+jR8OwYbDTTq0foJmZmVXNLedmbVhDAyzSxLe4a9csMd/2PdhmGxgzBu6+24m5mZlZDtU1OZfUV9LrkkZLOrXM9BMlvSLpBUkPSupZNG2WpOeyx7DaRm5Wf4Va83IXf0rpec7dPLd6B7beGiZMgHvvhe22q22wZmZmVpW6lbVI6gRcAOwATACekjQsIl4pmu1ZoHdETJN0DPBHYL9s2hcRsUlNgzars+K7fVYyp4Sl0Ff52LGw1bYwZQrcdx9873u1CNXMzMyaoZ4t55sCoyNiTERMB64H9iieISIejojCpW6PA6vVOEaz3Ci0lDeWmAPMnl2UmI8eDVttBZ98Ag8+6MTczMws5xYoOZe0nKRvttB7rwq8UzQ8IRtXyeHAPUXDi0saJelxSXtWWkjSwGy+UZMmTVq4iM3qqNpeWXr0yF689loqZZk2DR5+GHr3btX4zMzMbOE1WdYiaSSwezbvc8AkSY9ExIkL+d4qM65sD82SDgR6A1sXje4REe9JWhN4SNKLEfHWfCuMGAIMAejdu3cTPUCb5Vc1vbJ07QqDBwMvvwx9+qROz0eOhI02au3wzMzMrAVU03K+TER8BuwFXBER3wG2b4H3ngCsXjS8GvBe6UyStgcGAbtHxFeF8RHxXvY8BhgJfKsFYjLLrTkt4hV06pRd/LnR86lXlkUWgUcecWJuZmbWhlSTnHeWtDKwL3BXC773U8DaktaQ1AXYH5in1xVJ3wIuISXmE4vGLydpsex1N2ALoPhCUrN2Z/Dg1DJezpzuEtd7GrbdFhZfPCXm661X2yDNzMxsoVSTnJ8JjADeioinsjKSNxf2jSNiJnBctu5XgRsj4mVJZ0raPZvtXGBJ4KaSLhPXB0ZJeh54GDi7pJcXs3anf//UMt4z61C0U6f0PKe7xK8/kUpZll4aHn0U1l67fsGamZlZszRZcx4RNwE3FQ2PAfZuiTePiOHA8JJxpxW9Lls+ExH/Ab7REjGYtQWFLhTHj0/lLddcU9QjC8Bjj8EOu8CKK8JDDzVdA2PWwiT1Bc4HOgGXRcTZZebZFziDdH3R8xFxQE2DNDNrA5psOZe0TnYDoJey4W9K+k3rh2ZmMG8XihHpeeDANB5IF3z27QurrJJKWZyYW40V3bdiZ2ADoJ+kDUrmWRv4FbBFRGwI/KzmgZqZtQHVlLVcSvpBnQEQES+Q6sPNrAbKdaE4bVoaz/33wy67pNqWkSNh1cZ6IzVrNU3etwI4ErggIj4GKL6OyMzM5qomOe8aEU+WjJvZGsGY2bwaGirfdGjDccNht91SbfnIkfD//l9NYzMrUs19K9YB1pH07+z+FH3Lrcj3pjCzjq6a5HyypLXI+iCXtA/wfqtGZdbBNTRAt25w4IHlp+/B7dzGnrDhhqnGvHv32gZoNq9q7lvRGVgb2AboB1wmadn5FooYEhG9I6J3d/9dm1kH1OQFocBPSDfxWU/Su8DbQIWUwcwWVqHGvNLdQPfhJq7lAD5d6zt0e/BeWHa+/Mas1qq5b8UE4PGImAG8Lel1UrL+VG1CNDNrG5psOc9qCLcHugPrRcQPImJsq0dm1kGVqzEvOIAGrmd/Pl5nM7o9c58Tc8uLJu9bAdwObAtz7k+xDjCmplGambUBTbacSzqtZBiAiDizlWIy63AKXSVWqi8HGMCVDOUwnlhsa7739J2w5JK1C9CsERExU1LhvhWdgKGF+1YAoyJiWDZtR0mvALOAkyPio/pFbWaWT9WUtfyv6PXiwK6kmwaZWQtoqowF4EiGMISjeHCRHZh04e18b8kKtwo1q5Mq7lsRwInZw8zMKqjmJkR/Lh6W9CfmP11pZs3UWBkLwE/4B//geO5fdBcmX3IL/Q5dvHbBmZmZWU1V03JeqiuwZksHYtYRNdZVIsDP+Qt/4STe+c4e7PDvG2CxxWoXnJmZmdVcNTXnLzK3S6xOpAtDXW9uthAaGuCEE+CjRipuT+FszuZX3N11H37432th0UVrF6CZmZnVRTUt57sWvZ4JfBgRvgmRWTM1XWMenMaZ/I4zuLFTP2ZedDUs2pyTXGZmZtbWVPyPL2n57OXnJZOWlkRETGm9sMzap4YGGDAAZs2qNEcwmEH8mv/j5iUGMPOiyzngoE61DNHMzMzqqLHmuKdJ5SyV7vzmunOzJhR3kShBlN4zcR7BuZzML/gzDBzIPhddBItUcxNfMzMzay8qJucRsUYtAzFrb0rLVxpLzMVszucEjucfvL7Dcax78d9SNm9mZmYdSlWFrJKWI91meU4fbhHxaGsFZdYeNNVFYoGYzUUcw1EM4dWdT2T9u//kxNzMzKyDavKcuaQjgEdJd3f7XfZ8RuuGZdY2NTRAr14pt26si8SCRZjFUB3OUQyBU091Ym5mZtbBVVPQegLwXWBcRGwLfAuY1KpRmbVBhTKWapJygE7M5NpOB3NIXAmnnw5/+IMTczMzsw6umrKWLyPiS0lIWiwiXpO0bqtHZtaGNN0Ly7w6M4ObuvRnz+k3weDB8Otft26AZmZm1iZUk5xPkLQscDtwv6SPgfdaNyyzfFuwXljmtXaPr3iw+/6s/vTt8Kc/wUkntV6gZmZm1qY0mZxHxI+yl2dIehhYBri3VaMyy6lyd/asNjHv2RPGvvYl7LMP3H03/P3vcNxxrROomZmZtUmN3YTobuBa4PaI+B9ARDxSq8DM8qbpO3tW1rUrnH3aNNh9T7j/frjkkrQyMzMzsyKNXRA6BNgVGCvpBkl7SupSo7jMcqfarhFL9ewJQ/82lf2v2RUeeACGDnVibmZmZmVVTM4j4o6I6Af0AG4FBgDjJQ2VtEOtAjTLg4aG6nthgdRSfs01qeRl7Aufsd8VfeGRR+Cf/4RDD229QM3MzKxNa7IrxYj4IiJuyGrPdyR1peiac+swCuUs1VphBRgyBPr3Bz75BHbcER5/HK6/PhtpZmZmVl41NyFaSdLxkv5N6rHlPuA7rR6ZWU40Vc6ySPYt6tkztZZPnpzl4FOmwPbbwzPPwE03wY9/XJN4zczMrO1q7ILQI4F+wLqkspZfRsS/axWYWR40Vc5yzTUVGsMnTYIddoDXXoPbboMf/rDVYjQzM7P2o7GuFL8PnA08EBGzaxSPWS6U6zKxVM+eFRLzDz6APn1gzBi4886UpJuZmZlVobELQg+NiPtaMzGX1FfS65JGSzq1zPTFsp5iRkt6QlKvomm/ysa/Lmmn1orROp5CjXljiXnXrunGnvN5913YZhsYOxaGD3dibmZmZgukyZrz1iKpE3ABsDOwAdBP0gYlsx0OfBwRXwfOA87Jlt0A2B/YEOgLXJitz2yhVdNl4pwLPouNHw9bbw3vvQcjRsC227ZajGZmZtY+1S05BzYFRkfEmIiYDlwP7FEyzx7AVdnrm6QwGjQAACAASURBVIE+kpSNvz4ivoqIt4HR2frMFtr48Y1PL1vO8vbbKTGfPBnuuw9+8INWi8/MzMzar8YuCF2+sQUjYspCvveqwDtFwxOAzSrNExEzJX0KrJCNf7xk2VUXMh4zGhpS7yuzZpWfXracZfRo2G47mDoVHnwQvuPOjMzMzKx5Grsg9GkgAJFuRPRx9npZYDywxkK+t8qMiyrnqWbZtAJpIDAQoEePHgsSn3UwhVrzSon5CivA+eeXtJq/9lpKzGfMgIcfho03rkmsZmZm1j41dkHoGhGxJjAC2C0iukXECsCupK4VF9YEYPWi4dWA9yrNI6kzsAwwpcplC9sxJCJ6R0Tv7t27t0DY1p40NECvXiDBgQeWrzXv1Kmk//KCl15KpSyzZjkxNzMzsxZRTc35dyNieGEgIu4Btm6B934KWFvSGpK6kC7wHFYyzzBgQPZ6H+ChiIhs/P5Zby5rAGsDT7ZATNZBNDRAt24pIW+sH3OA2bPL1Jg/91zqlaVTJ3jkEdhoo9YK1czMzDqQxspaCiZL+g1wDal05ECgkU7mqpPVkB9HapnvBAyNiJclnQmMiohhwOXAPyWNJrWY758t+7KkG4FXgJnATyKiQjGC2bwK5StN9chSMF811KhRsOOOsOSS8NBD8PWvt3iMZmZm1jFVk5z3A04HbiMl549m4xZa1iI/vGTcaUWvvwTK3vM8IgYD5XqaNquooQEGDKhcV15qvgtAH38cdtoJll8+lbL06tUaYZqZmVkH1WRynvXKcoKkJSNiag1iMmsVTV3wWer/t3fncVqX9f7HX29AUFxKwYxQGI9padZBnaz0HFFAwSVwQaMGBUVxyeWkdqSDxwrjpFJx8qeluAE2JbmTishuZpq478lRBklUxCULN+Dz++P7Hb0Z5p65h7n3eT8fj/txf5fr/t6fa9bPXPP5Xlfnzk3mM7/vPjj4YNhuuyQx32GHFl9vZmZm1lat1pxL2kfSMyQlJEj6V0m/KnhkZnmWy+JCjbp3h2nTMhLz+fOTEfPevZMacyfmZmZmVgC53BA6GRhMWmceEY8D+xUyKLN8q69v/cbPTul3Q9++TUbMZ8+GQw+FHXdMEvPenlLfzMzMCiOXmnMi4uVkYc6P+eZLqxiN5SzZdO7cZJQ80x13wFFHwa67wpw54Ok4zczMrIByGTl/WdI+QEjqKulc4NkCx2WWF403gGYrZ9mgfCXTrbfCkUfCV76SlLU4MTczM7MCyyU5PwX4LtCbZPGffum+WVnL5QbQ9cpXMs2YAUcfDXvtBXPnJrOzmJmZmRVYi2UtkjoDx0ZEc+mLWVlr7QbQvn2zJOa/+U0y3L7vvnDnnbDllgWL0czMzCxTiyPn6cI+w4oUi1netHYD6Abzlze67jo47rhk9c9Zs5yYm5mZWVHlckPonyRdBswA/tl4MCIeKVhUZu2Qyw2gzZazXHklnHJKsvrnbbfBZpsVNE4zMzOzpnJJzvdJnydkHAtgQP7DMWu/lspZunfPkphfeimcdVYyZeJNN8GmmxY8TjMzM7Omclkh9IBiBGKWL8uWZT/XbGL+s5/B978PRxwBN9wAXbsWND4zMzOzbHJZIXQ7SddImpXu7yZpTOFDM9s4ffo0f7zZG0AnTkwS82OOSWZocWJuZmZmJZTLVIpTgdnA59L9vwL/UaiAzNpr4sSkfCXTBjeARsAPfwjnnw8jRyaF6ptsUtQ4zczMzJrKJTnvGRG/B9YBRMQavEKolbG6uqR8pW9fkJLn9cpZIuAHP4AJE+D442HqVOiS02K5ZmZmZgWVS0byT0k9SG4CRdLXgXcKGpVZO9XVZZnDPALOOQcmT4aTT4Zf/Qo65fI3qpmZmVnh5ZKcnw3MBHaS9CdgW2B4QaMyK4R16+CMM5KE/Iwz4Je/TIbWzczMzMpELrO1PCKpP/AFQMDzEfFRwSMzy6d165I5zK+6Khk5nzTJibmZmZmVnazJuaQjs5zaRRIRcUuBYjLLr7VrYcwYmDYtmQT9wgudmJuZmVlZaqnY9pvpYwxwDVCXPq4GRhY+NLO2q6+HmpqkjLymBn47fQ0ce2ySmP/4x/CTnzgxNzMzs7KVdeQ8Io4HkHQHsFtErEj3ewGXFyc8s9zV18PYsZ+sDvq3ho/Y9ITvwNqb4Kc/hXHjShugmZmZWStymaaipjExT70G7FKgeMw22vjxnyTmXfmAGzmaI9fexIVb/8KJuZmZmVWEXJLzhZJmSxotaRRwJ7CgwHGZtdmyZcnzprzHrRzB4dzOd7mMH779vdIGZtYBSBoi6XlJSyRl/WtY0nBJIam2mPGZmVWKXGZrOV3SEcB+6aEpEXFrYcMya5v6+qTOvOva1dzOMAYyj5OYwtWcRN8+pY7OrLpJ6kxS7nggsBx4SNLMiHimSbstgTOBB4sfpZlZZWgxOU9/4M6OiEGAE3IrS4215puu/Qd3cBj/zh85nuuYzii6d4eJE0sdoVnV2xtYEhEvAki6ARgGPNOk3YXAJcC5xQ3PzKxytFjWEhFrgdWSPlWkeMzabPx46Lz678xmMP/GfYzkN0xnFJ07w5QpWVYKNbN86g28nLG/PD32MUl7ADtExB0tXUjSWEmLJS1euXJl/iM1MytzuawQ+j7wpKQ5wD8bD0bEmQWLyixH9fXwTsNbzGEIe/III7iBm9MFbNetc2JuViTNzU8aH5+UOgGTgdGtXSgipgBTAGpra6OV5mZmVSeX5PzO9GFWVurrYdxJq5jLQXyZJzmKm/kDQz8+38e15mbFshzYIWN/e+CVjP0tgd1JJhgA+CwwU9LQiFhctCjNzCpALsn5DODzJKMg/xcR7xc2JLPc/GLc69zx3oF8gec5nNuYxSEfn3OtuVlRPQTsLGlH4G/ACOA7jScj4h2gZ+O+pIXAuU7Mzcw2lLXmXFIXSZeQjIhMA34DvCzpEkmbtOdNJW0jaY6kF9LnrZtp00/SnyU9LekJSd/KODdV0kuSHksf/doTj1WgFSu4fvn+7MwLHMYd6yXm4Fpzs2KKiDXA6cBs4Fng9xHxtKQJkoa2/GozM8vU0sj5JJJ/Re4YEe8CSNoK+Fn6OKsd7zsOmBcRF6Xz4Y4DzmvSZjVwXES8IOlzwMOSZkfE2+n570fETe2IwSrV8uUwYAB99QoHxyzupf96p/v2dWJuVmwRcRdwV5NjF2Rpu38xYjIzq0QtzdZyGHBSY2IOEBF/B06FJsOUbTeMZDSe9Pnwpg0i4q8R8UK6/QrwOrBtO9/XKl1DA/TvD6++yp/+ezaLu6+fmLucxczMzCpZS8l5RMQGd8qn0yu29w767SJiRXq9FcBnWmosaW+gK/B/GYcnpuUukyV1a2c8VglefBH69+fDV1cxbPO5DLlwXzbbDHr0ACkZMXc5i5mZmVWylpLzZyQd1/SgpJHAc61dWNJcSU818xjWlgAl9QKuB46PiHXp4R8AXwS+CmzDhiUxma/3nLnV4IUXoH9/Plj1Lgesm8/MV/cmAlatgvfeg+uvh6VLnZibmZlZZWup5vy7wC2STgAeJhkt/yqwGXBEaxdOVxVtlqTXJPWKiBVp8v16lnZbkUzjeH5EPJBx7RXp5geSrqOF1eY8Z24VeO45GDAAPvqIYVsu4P4VX1nv9OrVyUJETszNzMys0mUdOY+Iv0XE14AJwFJgGTAhIvaOiL+1831nAqPS7VHA7U0bSOoK3ApMj4gbm5zrlT6LpF79qXbGY+XqqaeSGvN162DhQu559SvNNlu2rMhxmZmZmRVAq/OcR8R8YH6e3/ci4PeSxpAk/UcDSKoFTomIE4FjgP2AHpJGp68bHRGPAfWStiVZle4x4JQ8x2fl4NFH4cADoVs3mD8fvvAF+vRJ7gltygsOmZmZWTXIZRGivIuIVcDAZo4vBk5Mt39DMrd6c68fUNAArfQeeggOOgi22ipJzHfaCUhmYhk7NillaeQZWszMzKxatHRDqFlp3H8/DBoEW28N9977cWIOSV35lCnJzCyeocXMzMyqTUlGzs2yuvdeOPRQ6NUrGTHffvsNmtTVORk3MzOz6uSRcysf8+bBkCFJQr5oUbOJuZmZmVk1c3Ju5WH2bDjssKSEZeHCZOTczMzMrINxcm6l94c/wNCh8MUvwoIFsN12zTarr4eaGujUKXmury9qlGZmZmYF55pzK61bboFvfQv69UtGz7fZptlm9fXrz9LS0JDsg+vPzczMrHp45NxK54Yb4Jhj4KtfhblzsybmkKwAmjl9InyyMqiZmZlZtXBybqUxfXoy5L3vvsmI+ac+1WLzbCuAemVQMzMzqyZOzq34rrkGRo+GAw6Au+6CLbds9SXZVgD1yqBmZmZWTZycW3H9+tdw4okweHByI+jmm+f0sokTk5VAM3llUDMzM6s2Ts6teH75SzjtNPjmN+G222CzzXJ6WX39JzXnnTsnx7wyqJmZmVUjz9ZixTFpEvznf8KRR8Lvfgddu+b0sqaztKxd+8mIuRNzMzMzqzYeObfCmzgxScxHjEhmaMkxMQfP0mJmZmYdi5NzK5wIuOACOP98OPZYuP562GSTNl3Cs7SYmZlZR+Lk3AojAsaNgwsvhDFj4LrroEvbq6g8S4uZmZl1JE7OLf8i4Oyz4ZJL4NRTkzs3G+/kbCPP0mJmZmYdiZNzy6916+D00+F//xfOOgsuvxw6bfyXWV1dktv37QuSZ2kxMzOz6ubZWix/1q2Dk0+Gq69ObgC96KIko85R45SJy5bBNtskx958Mylh8ewsZmZm1hE4Obf8WLsWTjgBpk9PbgCdMKHNiXnmlImrVn1yrqEhOQdO0M3MzKy6uazF2m/NGhg5MknMJ0xIbgJtQ2IOzU+ZmMnTJ5qZmVlH4JFza58PP4TvfAduvhkuvjgpZ9kIuUyN6OkTzczMrNp55Nw23gcfwPDhSWI+eXKriXl9PdTUJPeH9uyZPBq3cxlo9/SJZmZmVu08cm4b57334KijYNasZEaW005rsXlLNeWZ29l4+kQzMzPrCDxybm23ejUMHQp33w1XXbVBYt7cCPnIkS3XlDclQY8enj7RzMzMOhaPnFvb/OMfcNhh8Mc/wtSpcNxxwCfTIDY0JAl1RNI8l1HxbN54o/3hmpmZmVUSJ+eWu3fegUMOgQcfTLLxESOADUtWGhPz9nB9uZmZmXVETs4tN2+9BYMHw6OPwowZSb15qrVpENvK9eVmZmbWUbnm3Fr3xhswcCA8/jjccgscddR6deUNDW2/ZI8en9SUZ267vtzMzMw6Mo+cW8tefx0GDYIXXoDbb4chQzYoY2mL7t2dfJuZmZllU5KRc0nbSJoj6YX0eess7dZKeix9zMw4vqOkB9PXz5DUtXjRdyArVsD++8OSJcz9jzuoOWUInTrBqFGtJ+aN85Z7VNzMzMwsd6UqaxkHzIuInYF56X5z3ouIfuljaMbxi4HJ6evfAsYUNtwOaPly6N8fli1jzjl3M+zSgTQ0JDd7rl2b/WWNSfj11ydt33gjeaxbB0uXOjE3MzMza0mpkvNhwLR0expweK4vlCRgAHDTxrzectDQkCTmr70G99zDSdfvl1MJS9++TsLNzMzM2qNUyfl2EbECIH3+TJZ2m0paLOkBSY0JeA/g7YhYk+4vB3pneyNJY9NrLF65cmW+4q9eL74I++0Hb74Jc+fCPvuwbFnrL/MMK2ZmZmbtV7AbQiXNBT7bzKnxbbhMn4h4RdK/APMlPQn8vZl2WWfWjogpwBSA2traPMzAXcX++lcYMADeew/mz4c99gCSOcebm5Glc+dkpLxPnyQx92i5mZmZWfsULDmPiEHZzkl6TVKviFghqRfwepZrvJI+vyhpIbAHcDPwaUld0tHz7YFX8t6BjuaZZ5LpEteuhYUL4ctf/vjUxIkbzs7iWVfMzMzM8q9UZS0zgVHp9ijg9qYNJG0tqVu63RPYF3gmIgJYAAxv6fXWBk88kczKAhsk5pAk4FOmJDXlnnXFzMzMrHBKlZxfBBwo6QXgwHQfSbWSrk7b7AoslvQ4STJ+UUQ8k547Dzhb0hKSGvRrihp9NXnkETjgAOjaFRYtgt12a7ZZXV1yo6dv+DQzMzMrnJIsQhQRq4CBzRxfDJyYbt8PfLlpm/Tci8DehYyxQ/jLX2DwYNhqq6TGfKedSh2RmZmZWYdWqpFzK7X7709W/txmG7j3Xuof2ImaGujUCWpq4LTTWG+/vr604ZqZmZl1BCUZObcSW7QIDj0UeveGefOoX7T9ejd8NjTAr3/9SfOGhuSGUHA5i5mZmVkheeS8o5k7Fw4+OJn/cOFC2H57xo+n1UWGVq+G8W2ZBNPMzMzM2szJeUcyaxYcdhh8/vNJYt6rF0BOiwy1pZ2ZmZmZbRwn5x3FzJlw+OHJbCwLFsBnPlmUtU+f3C6RazszMzMz2zhOzjuCm26Co46Cfv248ZR51OzVY70bPSdOTBYVakn37kk7M7PmSBoi6XlJSySNa+b82ZKekfSEpHmS+pYiTjOzcufkvNr99rcwYgTsvTczTpzD6O9tTUMDRKx/o2fTRYZOPdWLDplZbiR1Bi4HDgZ2A74tqemiCY8CtRHxFeAm4JLiRmlmVhk8W0s1mzYNTjgB/u3f4M47OW/3LTa48bPxRk8vLGRm7bA3sCRdgwJJNwDDgMaF44iIBRntHwBGFjVCM7MK4ZHzanXVVXD88cnqn3fdBVtskfWGTt/oaWbt1Bt4OWN/eXosmzHArOZOSBorabGkxStXrsxjiGZmlcHJeTW6/PKkXmXwYPjDH2DzzYHsN3T6Rk8zayc1cyyabSiNBGqBSc2dj4gpEVEbEbXbbrttHkM0M6sMTs6rzeTJcPrpMHQo3HYbbLbZx6eau/HTN3qaWR4sB3bI2N8eeKVpI0mDgPHA0Ij4oEixmZlVFCfn1eTii+Hss5OZWW68Ebp1W+90Xd2GN376Rk8zy4OHgJ0l7SipKzACmJnZQNIewJUkifnrJYjRzKwi+IbQanHhhXDBBfDtb8P06dCl+U9tXZ2TcTPLr4hYI+l0YDbQGbg2Ip6WNAFYHBEzScpYtgBulASwLCKGlixoM7My5eS80kUkSflPfgLHHQfXXgudO5c6KjPrYCLiLuCuJscuyNgeVPSgzMwqkJPzShYB550HkybBiSfClVdCJ1cqmZmZmVUqZ3KVKgK+970kMT/tNCfmZmZmZlXA2VwlWrcuSch/+cskQb/sMifmZmZmZlXAGV2lWbsWTjoJrrgCxo2Dn/88mXrFzMzMzCqek/NKsmYNjB6d3PT5wx/C//yPE3MzMzOzKuIbQivFRx/ByJHw+98nM7OMH1/qiMzMzMwsz5ycV4IPP4QRI+DWW5MbQM89t9QRmZmZmVkBODkvd++/D8OHw513JjeAnnlmqSMyMzMzswJxzXk5e+89GDYsScyvuKLNiXl9PdTUJBO51NQk+2ZmZmZWvpycl6t//hMOPRTmzIFrroGTT262WbYEvL4exo6FhoZkSvSGhmTfCbqZmZlZ+XJZSzl6990kMf/Tn2D69ORG0GY0JuCrVyf7jQk4JPeLNh5vtHp1cryuroCxm5mZmdlG88h5uXnnHTjoILj/fv542u+oOX9k1rKUlhLwZcuav3y242ZmZmZWek7Oi6jVGvA334RBg+Dhh1l0+o0MufaYFstSWkrA+/Rp/ly242ZmZmZWek7Oi6TVGvA33oCBA+GJJ+CWWxh12xFZR8UbtZSAT5wI3buvf7x79+S4mZmZmZWnkiTnkraRNEfSC+nz1s20OUDSYxmP9yUdnp6bKumljHP9it+LtmmpBIXXXoP994fnnoOZM+Gww3IqS2kpAa+rgylToG/fZBHRvn2Tfdebm5mZmZWvUo2cjwPmRcTOwLx0fz0RsSAi+kVEP2AAsBq4J6PJ9xvPR8RjRYm6HbIl2x81vJIk5i+9lEyZOHgwkFtZSmsJeF0dLF0K69Ylz07MzczMzMpbqZLzYcC0dHsacHgr7YcDsyJidSvtylZzyfb2vMx9XfrD8uVw990wYMDH53ItS3ECbmZmZlY9SpWcbxcRKwDS58+00n4E8LsmxyZKekLSZEndsr1Q0lhJiyUtXrlyZfuiboemyXZflnKv+tN7k9fhnnvg3/99vfYuSzEzMzPreAo2z7mkucBnmzk1vpljLV2nF/BlYHbG4R8ArwJdgSnAecCE5l4fEVPSNtTW1kZb3jufGpPq8eNhk4YlLOw8gJ7d3qXronlQW5v1NU7GzczMzDqOgiXnETEo2zlJr0nqFREr0uT79RYudQxwa0R8lHHtFenmB5KuA87NS9AFVlcHdbXPJ+UrH3wAcxdAv7K/l9XMzMzMiqRUZS0zgVHp9ijg9hbafpsmJS1pQo8kkdSrP1WAGPPv6aehf39YswYWLnRibmZmZmbrKVVyfhFwoKQXgAPTfSTVSrq6sZGkGmAHYFGT19dLehJ4EugJ/KQIMbfP448ns7J06gSLFsHuu5c6IjMzMzMrMwUra2lJRKwCBjZzfDFwYsb+UqB3M+0GND1W1h5+GA48EDbfHObPh513LnVEZmZmZlaGvEJooT3wQLLy51Zbwb33OjE3MzMzs6ycnBfSfffBQQdBz55JYr7jjqWOyMzMzMzKmJPzQlm4EIYMgc99Lqkxz7bkp5mZmZlZysl5IcyZA4cckqwctHAh9N6gbN7MzMzMbANOzvPtrrvgm99MassXLoTPNrcOk5mZmZnZhpyc59Ntt8Hhh8OXvpTMyrLttqWOyMzMzMwqiJPzfLnxRjj6aNhzT5g3D3r0KHVEZmZmZlZhnJznw29/CyNGwNe/DvfcA5/+dKkjMjMzM7MK5OS8vaZOhZEjoX9/mDUrmc/czMzMzGwjODlvjylT4PjjYdAguOMO2GKLUkdkZmZmZhXMyXkL6uuhpgY6dUqe6+szTl52GZx8cjJl4syZ0L17iaI0MzMzs2rRpdQBlKv6ehg7FlavTvYbGpJ9gLrXfgHnnAPDhsGMGdCtW+kCNTMzM7Oq4eQ8i/HjP0nMG61eDX87/afw9n8lM7PU18Mmm5QmQDMzMzOrOi5ryWLZsqZHggv4Mf/59n9BXV0yQ4sTczMzMzPLIyfnWfTpk7kXTGQ8P+ZH3Lj5aJg2Dbr4nw5mZmZmll9OzrOYOLHxHs/gZ5zLf/FTrukylg9/fQ107lzq8MzMzMysCnn4N4u6OiCCj047k9HvXsbULU9n019dSt1IlTo0MzMzM6tSTs5bULfXc/DR1XDOOYyeNAnkxNzMzMzMCsfJeUt23RUefxx23tmJuZmZmZkVnJPz1uyyS6kjMDMzM7MOwjeEmpmZmZmVCSfnZmZmZmZlwsm5mZmZmVmZcHJuZmZmZlYmnJybmZmZmZUJJ+dmZmZmZmXCybmZmZmZWZlwcm5mZmZmViacnJuZWbtJGiLpeUlLJI1r5nw3STPS8w9Kqil+lGZm5c/JuZmZtYukzsDlwMHAbsC3Je3WpNkY4K2I+DwwGbi4uFGamVUGJ+dmZtZeewNLIuLFiPgQuAEY1qTNMGBaun0TMFCSihijmVlF6FLqAIrp4YcffkNSQ6njSPUE3ih1EEXQUfoJ7ms1Kqd+9i11AC3oDbycsb8c+Fq2NhGxRtI7QA+afHwljQXGprsfSHqqIBGXr3L6misW97lj6Gh9/sLGvrBDJecRsW2pY2gkaXFE1JY6jkLrKP0E97UadZR+5kFzI+CxEW2IiCnAFOiYH3/3uWNwn6ufpMUb+1qXtZiZWXstB3bI2N8eeCVbG0ldgE8BbxYlOjOzCuLk3MzM2ushYGdJO0rqCowAZjZpMxMYlW4PB+ZHxAYj52ZmHV2HKmspM1NKHUCRdJR+gvtajTpKP9slrSE/HZgNdAaujYinJU0AFkfETOAa4HpJS0hGzEfkcOmO+PF3nzsG97n6bXR/5YELMzMzM7Py4LIWMzMzM7My4eTczMzMzKxMODkvIUmTJD0n6QlJt0r6dKljKgRJR0t6WtI6SVU5jVJrS5dXC0nXSnq92ueelrSDpAWSnk2/ds8qdUzVrLXvH0ndJM1Izz8oqab4UeZXDn0+W9Iz6e+HeZLKeZ77nOT6c1LScElR6b8vcumvpGPSz/PTkn5b7BjzLYev6z7pz9ZH06/tQ0oRZz619ntRiUvTj8kTkvZs9aIR4UeJHsBBQJd0+2Lg4lLHVKB+7koyGf9CoLbU8RSgf52B/wP+BegKPA7sVuq4CtTX/YA9gadKHUuB+9kL2DPd3hL4a7V+Tkv9yOX7BzgNuCLdHgHMKHXcRejzAUD3dPvUjtDntN2WwL3AA5X8+yLHz/HOwKPA1un+Z0oddxH6PAU4Nd3eDVha6rjz0O8Wfy8ChwCzSNZ6+DrwYGvX9Mh5CUXEPRGxJt19gGRu4KoTEc9GxPOljqOAclm6vCpExL10gLmpI2JFRDySbr8LPEuywqXlXy7fP8OAaen2TcBASc0talQpWu1zRCyIiNXpbjX8fsj15+SFwCXA+8UMrgBy6e9JwOUR8RZARLxe5BjzLZc+B7BVuv0pNlwPoeLk8HtxGDA9Eg8An5bUq6VrOjkvHyeQ/GVllae5pcudyFWJtIRiD+DB0kZStXL5/vm4TTqg8Q7QoyjRFUZbf2aMofJ/P7TaZ0l7ADtExB3FDKxAcvkc7wLsIulPkh6QNKRo0RVGLn3+ETBS0nLgLuCM4oRWUm3OETzPeYFJmgt8tplT4yPi9rTNeGANUF/M2PIpl35WsZyWJbfKI2kL4GbgPyLi76WOp0rl8v1Tbd9jOfdH0kigFuhf0IgKr8U+S+oETAZGFyugAsvlc9yFpLRlf5L/jPxR0u4R8XaBYyuUXPr8bWBqRPxc0jdI1j7YPSLWFT68kmnzzy8n5wUWEYNaOi9pFHAYMDDS4qRK1Fo/q1wuS5dbhZG0CUliXh8Rt5Q6niqWy/dPY5vlkrqQ/Du8ksurDrIrqgAABehJREFUcvqZIWkQMB7oHxEfFCm2Qmmtz1sCuwML04qlzwIzJQ2NiMVFizJ/cv26fiAiPgJekvQ8SbL+UHFCzLtc+jwGGAIQEX+WtCnQE6j0kp6WtDlHcFlLCaX/wjoPGJpRW2iVJ5ely62CpPXM1wDPRsQvSh1Plcvl+2cmMCrdHg7Mr+TBDHLoc1ricSXJ74dqSFxa7HNEvBMRPSOiJiJqSOrsKzUxh9y+rm8jufEXST1JylxeLGqU+ZVLn5cBAwEk7QpsCqwsapTFNxM4Lp215evAOxGxoqUXODkvrctIRgvmSHpM0hWlDqgQJB2R1pd9A7hT0uxSx5RPaQ1s49LlzwK/j4inSxtVYUj6HfBn4AuSlksaU+qYCmRf4FhgQPq9+Vg1TPlVjrJ9/0iaIGlo2uwaoIekJcDZQEVPV5pjnycBWwA3pl9/Ff0Hf459rho59nc2sErSM8AC4PsRsao0Ebdfjn0+BzhJ0uPA74DRFf6HdrO/FyWdIumUtMldJH90LQGuIpl9quVrVvjHxMzMzMysanjk3MzMzMysTDg5NzMzMzMrE07OzczMzMzKhJNzMzMzM7My4eTczMzMzKxMODm3kpDUI2OKulcl/S3dfjudVqqYsfTLnCZP0lBJGzVVm6Sl6Xy1RSdptKTPZexfLWm3UsdlZmZmuXNybiUREasiol9E9AOuACan2/2AvC/jm64qmE0/4OPkPCJmRsRF+Y6hCEYDHyfnEXFiRBT1Dx0zMzNrHyfnVo46S7pK0tOS7pG0GYCknSTdLelhSX+U9MX0eF9J8yQ9kT73SY9PlfQLSQuAiyVtLulaSQ9JelTSsHQVswnAt9KR+2+lI9CXpdfYTtKtkh5PH/ukx29L43ha0tjWOiTpeEl/lbQo7Vvj9adKGp7R7h/p8xZpXx6R9KSkYenxGknPNv34pNeoBerTfmwmaaGk2mZiGSnpL2m7KyV1Th9TJT2Vvt/32vH5MzMzs43k5NzK0c7A5RHxJeBt4Kj0+BTgjIjYCzgX+FV6/DJgekR8BagHLs241i7AoIg4BxhPsuz3V0mWTJ4EbAJcAMxIR/JnNInlUmBRRPwrsCfQuPLnCWkctcCZknpk64ykXsCPSVadPBDYLYePwfvAERGxZxrrzyUp28cnIm4CFgN1aT/eyxLLrsC3gH3T/1SsBepI/nvQOyJ2j4gvA9flEKOZmZnlWUv/6jcrlZci4rF0+2GgRtIWwD4kS1k3tuuWPn8DODLdvh64JONaN0bE2nT7IGCopHPT/U2BPq3EMgA4DiC9zjvp8TMlHZFu70CSMGdbdvlrwMKIWAkgaQbJHw0tEfA/kvYjKfPpDWyXntvg49PKtTINBPYCHko/jpsBrwN/AP5F0v8D7gTuacM1zczMLE+cnFs5+iBjey1JAtkJeDsd7W1NZGz/M2NbJKPMz2c2lvS1tgQnaX9gEPCNiFgtaSFJop9rTJnWkP4HKx0Z75oerwO2BfaKiI8kLc14j+Y+PjmHD0yLiB9scEL6V2Aw8F3gGOCENlzXzMzM8sBlLVYRIuLvwEuSjoYkkU2TSYD7gRHpdh1wX5bLzAbOaCwPkbRHevxdYMssr5kHnJq27yxpK+BTwFtpYv5F4OuthP8gsH86Q80mwNEZ55aSjGQDDCMpsyF9j9fTxPwAoG8r79FaPzL7M1zSZ9I+bZPW7PcEOkXEzcB/k5TwmJmZWZE5ObdKUgeMkfQ4Se33sPT4mcDxkp4AjgXOyvL6C0mS3yckPZXuAywAdmu8IbTJa84CDpD0JEkJyZeAu4Eu6ftdCDzQUtARsQL4EfBnYC7wSMbpq4D+kv5CUv7SONJfD9RKWpz2+7mW3iM1Fbii8YbQLLE8A5wP3JPGPwfoRVI2s1DSY+l1NhhZNzMzs8JTRLb/tptZIUgaDdRGxOmljsXMzMzKi0fOzczMzMzKhEfOzczMzMzKhEfOzczMzMzKhJNzMzMzM7My4eTczMzMzKxMODk3MzMzMysTTs7NzMzMzMrE/weZ82H1VZBqZwAAAABJRU5ErkJggg==\n",
          "text/plain": "<Figure size 864x864 with 4 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "fe179ddd8a484aa28907231a9adfbfb3": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_2f1149aa67bc49c0a07d036ddd18b7fa",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<Figure size 864x864 with 4 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "feb655faff88493c99ed91423a250df0": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "fedef828885f4f3fa7b9c8a620690319": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_e5c6315dfa1640ecb54d290a0e8c3fb2"
       ],
       "layout": "IPY_MODEL_d19a8fee09154fb7ba6d5c1964ad2694"
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}