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made major changes. Demonstration of Bay Area, Richmond Metro, and DC…
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|---|---|---|
| @@ -1,125 +1,31 @@ | ||
| import os, sys, numpy, glob, pylab, tabulate, datetime, pandas | ||
| from itertools import chain | ||
| from matplotlib.figure import Figure | ||
| from matplotlib.backends.backend_agg import FigureCanvasAgg | ||
| #!/usr/bin/env python3 | ||
|
|
||
| import os, sys, numpy, engine | ||
|
|
||
| engine.get_summary_demo_data( doShow = False ) | ||
| engine.get_summary_demo_data( prefix = 'richmond', state = 'Virginia', | ||
| regionName = 'Richmond Metropolitan Area', | ||
| list_of_counties = [ | ||
| "Richmond", | ||
| "Petersburg", | ||
| "Hopewell", | ||
| "Colonial Heights", | ||
| "Amelia", | ||
| "Caroline", | ||
| "Charles City", | ||
| "Chesterfield", | ||
| "Dinwiddie", | ||
| "Goochland", | ||
| "Hanover", | ||
| "Henrico", | ||
| "King William", | ||
| "New Kent", | ||
| "Powhatan", | ||
| "Prince George", | ||
| "Sussex" ], doShow = False ) | ||
|
|
||
|
|
||
| engine.get_summary_demo_data( prefix = 'dc', state = 'District of Columbia', | ||
| regionName = 'Washington DC', | ||
| list_of_counties = [ "District of Columbia" ], doShow = False ) | ||
|
|
||
| # | ||
| ## these are the 9 Bay Area counties | ||
| ## gotten from https://mtc.ca.gov/about-mtc/what-mtc/nine-bay-area-counties | ||
| _bay_area_counties = { | ||
| "Alameda", | ||
| "Contra Costa", | ||
| "Marin", | ||
| "Napa", | ||
| "San Francisco", | ||
| "San Mateo", | ||
| "Santa Clara", | ||
| "Solano", | ||
| "Sonoma" | ||
| } | ||
|
|
||
|
|
||
| def get_stat_line( line ): | ||
| line_split = list(map(lambda tok: tok.strip(), line.split(','))) | ||
| dstring = line_split[0] | ||
| county_name = line_split[1] | ||
| state_name = line_split[2] | ||
| cases_cumulative = int( line_split[-2] ) | ||
| death_cumulative = int( line_split[-1] ) | ||
| return { | ||
| 'date' : datetime.datetime.strptime( | ||
| dstring, '%Y-%m-%d' ).date( ), | ||
| 'county' : county_name, | ||
| 'state' : state_name, | ||
| 'cumulative cases' : cases_cumulative, | ||
| 'cumulative death' : death_cumulative } | ||
|
|
||
| all_counties_nytimes_covid19_data = list( | ||
| map(get_stat_line, | ||
| list( map(lambda line: line.strip(), filter( | ||
| lambda line: len( line.strip( ) ) != 0, | ||
| open( os.path.join( "covid-19-data", "us-counties.csv" ), "r" ).readlines())))[1:])) | ||
|
|
||
| def get_data_county( county_name ): | ||
| data_by_date = sorted(filter(lambda entry: entry['county'] == county_name and | ||
| entry['state'] == 'California', all_counties_nytimes_covid19_data ), key = lambda entry: entry['date'] ) | ||
| return data_by_date | ||
|
|
||
| # | ||
| ## now this creates a dictionary of incidents and deaths per county (in Bay Area) per date | ||
| all_data_bayarea = sorted( chain.from_iterable( | ||
| map( get_data_county, _bay_area_counties ) ), key = lambda entry: entry['date'] ) | ||
|
|
||
| # | ||
| ## now create a dictionary of cases, with key being the date, value being list of entries of counties for that date | ||
| all_data_bayarea_bydate = { } | ||
| for entry in all_data_bayarea: | ||
| mydate = entry[ 'date' ] | ||
| all_data_bayarea_bydate.setdefault( mydate, [] ).append( entry ) | ||
|
|
||
| # | ||
| ## now create a dictionary of cumulative deaths and cases by date | ||
| cases_deaths_bayarea_bydate = dict( | ||
| map(lambda mydate: ( mydate, { | ||
| 'cumulative cases' : sum( | ||
| map(lambda entry: entry['cumulative cases' ], all_data_bayarea_bydate[ mydate ] ) ), | ||
| 'cumulative death' : sum( | ||
| map(lambda entry: entry['cumulative death' ], | ||
| all_data_bayarea_bydate[ mydate ] ) ) } ), | ||
| all_data_bayarea_bydate ) ) | ||
|
|
||
| # | ||
| ## now create the dataframe to analyse | ||
| df_cases_deaths_bayeara = pandas.DataFrame({ | ||
| 'date' : sorted( cases_deaths_bayarea_bydate ), | ||
| 'cases' : list(map(lambda mydate: cases_deaths_bayarea_bydate[mydate][ 'cumulative cases' ], sorted( cases_deaths_bayarea_bydate ) ) ), | ||
| 'death' : list(map(lambda mydate: cases_deaths_bayarea_bydate[mydate][ 'cumulative death' ], sorted( cases_deaths_bayarea_bydate ) ) ) }) | ||
| df_cases_deaths_bayeara[ 'days_from_beginning' ] = list( | ||
| map(lambda mydate: ( mydate - min( cases_deaths_bayarea_bydate ) ).days, | ||
| df_cases_deaths_bayeara.date ) ) | ||
| first_date = min( df_cases_deaths_bayeara.date ) | ||
| last_date = max( df_cases_deaths_bayeara.date ) | ||
|
|
||
| # | ||
| ## pickle this pandas data | ||
| cur_date_str = datetime.datetime.now( ).date( ).strftime('%d%m%Y' ) | ||
| df_cases_deaths_bayeara.to_pickle( | ||
| 'covid19_bayeara_%s.pkl.gz' % cur_date_str ) | ||
|
|
||
| # | ||
| ## now make a plot, logarithmic | ||
| fig, ax = pylab.subplots( ) | ||
| fig.set_size_inches([ 12.0, 9.6 ]) | ||
| df_cases_deaths_bayeara.plot( 'days_from_beginning', 'cases', linewidth = 4.5, | ||
| ax = ax, logy = True, grid = True ) | ||
| df_cases_deaths_bayeara.plot( 'days_from_beginning', 'death', linewidth = 4.5, | ||
| ax = ax, logy = True, grid = True ) | ||
| ax.set_ylim( 1.0, 1.05 * df_cases_deaths_bayeara.cases.max( ) ) | ||
| ax.set_xlim( 0, df_cases_deaths_bayeara.days_from_beginning.max( ) ) | ||
| ax.set_xlabel( 'Days from First COVID-19 CASE (%s)' % first_date.strftime( '%d-%m-%Y' ), fontsize = 24, fontweight = 'bold' ) | ||
| ax.set_ylabel( 'Number of Cases/Deaths', fontsize = 24, fontweight = 'bold' ) | ||
| ax.set_title( '\n'.join([ | ||
| 'Bay Area Trend in COVID-19', | ||
| 'from %s through %s' % ( | ||
| first_date.strftime( '%d-%m-%Y' ), | ||
| last_date.strftime( '%d-%m-%Y' ) ) ]), fontsize = 24, fontweight = 'bold' ) | ||
| # | ||
| ## tick labels size 20, bold | ||
| for tick in ax.xaxis.get_major_ticks( ) + ax.yaxis.get_major_ticks( ): | ||
| tick.label.set_fontsize( 20 ) | ||
| tick.label.set_fontweight( 'bold' ) | ||
| # | ||
| ## legend size 24, bold | ||
| leg = ax.legend( ) | ||
| for txt in leg.texts: | ||
| txt.set_fontsize( 24 ) | ||
| txt.set_fontweight( 'bold' ) | ||
|
|
||
| # | ||
| ## save figures | ||
| fig.savefig( 'covid19_bayeara_%s.pdf' % cur_date_str, bbox_inches = 'tight' ) | ||
| fig.savefig( 'covid19_bayeara_%s.png' % cur_date_str, bbox_inches = 'tight' ) | ||
|
|
||
| # | ||
| ## now SHOW! | ||
| pylab.show( ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,131 @@ | ||
| import os, sys, numpy, glob, pylab, tabulate, datetime, pandas, titlecase | ||
| from itertools import chain | ||
| from matplotlib.figure import Figure | ||
| from matplotlib.backends.backend_agg import FigureCanvasAgg | ||
|
|
||
| # | ||
| ## these are the 9 Bay Area counties | ||
| ## gotten from https://mtc.ca.gov/about-mtc/what-mtc/nine-bay-area-counties | ||
| _bay_area_counties = { | ||
| "Alameda", | ||
| "Contra Costa", | ||
| "Marin", | ||
| "Napa", | ||
| "San Francisco", | ||
| "San Mateo", | ||
| "Santa Clara", | ||
| "Solano", | ||
| "Sonoma" | ||
| } | ||
|
|
||
| def _get_stat_line( line ): | ||
| line_split = list(map(lambda tok: tok.strip(), line.split(','))) | ||
| dstring = line_split[0] | ||
| county_name = line_split[1] | ||
| state_name = line_split[2] | ||
| cases_cumulative = int( line_split[-2] ) | ||
| death_cumulative = int( line_split[-1] ) | ||
| return { | ||
| 'date' : datetime.datetime.strptime( | ||
| dstring, '%Y-%m-%d' ).date( ), | ||
| 'county' : county_name, | ||
| 'state' : state_name, | ||
| 'cumulative cases' : cases_cumulative, | ||
| 'cumulative death' : death_cumulative } | ||
|
|
||
| all_counties_nytimes_covid19_data = list( | ||
| map(_get_stat_line, | ||
| list( map(lambda line: line.strip(), filter( | ||
| lambda line: len( line.strip( ) ) != 0, | ||
| open( os.path.join( "covid-19-data", "us-counties.csv" ), "r" ).readlines())))[1:])) | ||
|
|
||
| def get_data_county( county_name, state = 'California' ): | ||
| data_by_date = sorted(filter(lambda entry: county_name in entry['county'] and | ||
| entry['state'] == state, all_counties_nytimes_covid19_data ), | ||
| key = lambda entry: entry['date'] ) | ||
| return data_by_date | ||
|
|
||
| def get_summary_demo_data( prefix = 'bayarea', regionName = 'Bay Area', state = 'California', | ||
| list_of_counties = _bay_area_counties, doShow = True ): | ||
| # | ||
| ## now this creates a dictionary of incidents and deaths per county (in Bay Area) per date | ||
| all_data_region = sorted( chain.from_iterable( | ||
| map(lambda county: get_data_county( county, state = state ), | ||
| set( list_of_counties ) ) ), | ||
| key = lambda entry: entry['date'] ) | ||
| # | ||
| ## now create a dictionary of cases, with key being the date, value being list of entries of counties for that date | ||
| all_data_region_bydate = { } | ||
| for entry in all_data_region: | ||
| mydate = entry[ 'date' ] | ||
| all_data_region_bydate.setdefault( mydate, [] ).append( entry ) | ||
|
|
||
| # | ||
| ## now create a dictionary of cumulative deaths and cases by date | ||
| cases_deaths_region_bydate = dict( | ||
| map(lambda mydate: ( mydate, { | ||
| 'cumulative cases' : sum( | ||
| map(lambda entry: entry['cumulative cases' ], all_data_region_bydate[ mydate ] ) ), | ||
| 'cumulative death' : sum( | ||
| map(lambda entry: entry['cumulative death' ], | ||
| all_data_region_bydate[ mydate ] ) ) } ), | ||
| all_data_region_bydate ) ) | ||
|
|
||
| # | ||
| ## now create the dataframe to analyse | ||
| df_cases_deaths_region = pandas.DataFrame({ | ||
| 'date' : sorted( cases_deaths_region_bydate ), | ||
| 'cases' : list(map(lambda mydate: | ||
| cases_deaths_region_bydate[mydate][ 'cumulative cases' ], | ||
| sorted( cases_deaths_region_bydate ) ) ), | ||
| 'death' : list(map(lambda mydate: | ||
| cases_deaths_region_bydate[mydate][ 'cumulative death' ], | ||
| sorted( cases_deaths_region_bydate ) ) ) } ) | ||
| df_cases_deaths_region[ 'days_from_beginning' ] = list( | ||
| map(lambda mydate: ( mydate - min( cases_deaths_region_bydate ) ).days, | ||
| df_cases_deaths_region.date ) ) | ||
| first_date = min( df_cases_deaths_region.date ) | ||
| last_date = max( df_cases_deaths_region.date ) | ||
| # | ||
| ## pickle this pandas data | ||
| cur_date_str = datetime.datetime.now( ).date( ).strftime('%d%m%Y' ) | ||
| df_cases_deaths_region.to_pickle( | ||
| 'covid19_%s_%s.pkl.gz' % ( prefix, cur_date_str ) ) | ||
| # | ||
| ## now make a plot, logarithmic | ||
| fig, ax = pylab.subplots( ) | ||
| fig.set_size_inches([ 12.0, 9.6 ]) | ||
| df_cases_deaths_region.plot( 'days_from_beginning', 'cases', linewidth = 4.5, | ||
| ax = ax, logy = True, grid = True ) | ||
| df_cases_deaths_region.plot( 'days_from_beginning', 'death', linewidth = 4.5, | ||
| ax = ax, logy = True, grid = True ) | ||
| ax.set_ylim( 1.0, 1.05 * df_cases_deaths_region.cases.max( ) ) | ||
| ax.set_xlim( 0, df_cases_deaths_region.days_from_beginning.max( ) ) | ||
| ax.set_xlabel( 'Days from First COVID-19 CASE (%s)' % | ||
| first_date.strftime( '%d-%m-%Y' ), | ||
| fontsize = 24, fontweight = 'bold' ) | ||
| ax.set_ylabel( 'Cumulative Number of Cases/Deaths', fontsize = 24, fontweight = 'bold' ) | ||
| ax.set_title( '\n'.join([ | ||
| '%s Trend in COVID-19' % titlecase.titlecase( regionName ), | ||
| 'from %s through %s' % ( | ||
| first_date.strftime( '%d-%m-%Y' ), | ||
| last_date.strftime( '%d-%m-%Y' ) ) ]), | ||
| fontsize = 24, fontweight = 'bold' ) | ||
| # | ||
| ## tick labels size 20, bold | ||
| for tick in ax.xaxis.get_major_ticks( ) + ax.yaxis.get_major_ticks( ): | ||
| tick.label.set_fontsize( 20 ) | ||
| tick.label.set_fontweight( 'bold' ) | ||
| # | ||
| ## legend size 24, bold | ||
| leg = ax.legend( ) | ||
| for txt in leg.texts: | ||
| txt.set_fontsize( 24 ) | ||
| txt.set_fontweight( 'bold' ) | ||
| # | ||
| ## save figures | ||
| fig.savefig( 'covid19_%s_%s.pdf' % ( prefix, cur_date_str ), bbox_inches = 'tight' ) | ||
| fig.savefig( 'covid19_%s_%s.png' % ( prefix, cur_date_str ), bbox_inches = 'tight' ) | ||
| # | ||
| ## now SHOW! | ||
| if doShow: pylab.show( ) |