def export_latest_data(d_ref_states, d_states_data: dict):
d_states_latest = helper.extract_latest_data(d_ref_states, d_states_data)
# # d_states_latest = dict(d_ref_states)
# for code in d_states_latest.keys():
# assert code in d_states_data.keys()
# l_state = d_states_data[code]
# d_latest = l_state[-1]
# d_states_latest[code]['Date_Latest'] = d_latest['Date']
# for key in ('Cases', 'Deaths', 'Cases_New', 'Deaths_New', 'Cases_Per_Million', 'Deaths_Per_Million'):
# d_states_latest[code][key] = d_latest[key]
with open('data/de-states/de-states-latest.tsv', mode='w', encoding='utf-8', newline='\n') as fh:
csvwriter = csv.DictWriter(fh, delimiter='\t', extrasaction='ignore',
fieldnames=('State', 'Code', 'Population', 'Pop Density',
'Date_Latest',
'Cases', 'Deaths',
'Cases_New', 'Deaths_New',
'Cases_Per_Million',
'Deaths_Per_Million')
)
csvwriter.writeheader()
for code in sorted(d_states_latest.keys()):
d = d_states_latest[code]
d['Code'] = code
if code == 'DE-total': # DE as last row
d_de = dict(d)
continue
csvwriter.writerow(
d
)
del d, code
# add # to uncomment the DE total sum last line
d_de['State'] = '# Deutschland'
csvwriter.writerow(d_de)
del d_de
def export_latest_data(d_districts_data: dict):
d_districts_latest = helper.extract_latest_data(d_ref_landkreise,
d_districts_data)
d_for_export_V1 = d_districts_latest
l_for_export_V2 = []
for lk_id, d in d_districts_latest.items():
# V1: dict (lk_id) -> dict
# V2: list of ficts
# d_for_export_V1[lk_id] = d
d["Landkreis"] = get_lk_name_from_lk_id(lk_id)
d["Bundesland"] = d["BL_Name"]
del d["BL_Name"]
# divi data is not returned by helper.extract_latest_data and mostly not available at latest day, so using the date of the previous day instead
if 'DIVI_Intensivstationen_Covid_Prozent' in d_districts_data[lk_id][
-1]:
d['DIVI_Intensivstationen_Covid_Prozent'] = d_districts_data[
lk_id][-1]['DIVI_Intensivstationen_Covid_Prozent']
d['DIVI_Intensivstationen_Betten_belegt_Prozent'] = d_districts_data[
lk_id][-1]['DIVI_Intensivstationen_Betten_belegt_Prozent']
elif 'DIVI_Intensivstationen_Covid_Prozent' in d_districts_data[lk_id][
-2]:
d['DIVI_Intensivstationen_Covid_Prozent'] = d_districts_data[
lk_id][-2]['DIVI_Intensivstationen_Covid_Prozent']
d['DIVI_Intensivstationen_Betten_belegt_Prozent'] = d_districts_data[
lk_id][-2]['DIVI_Intensivstationen_Betten_belegt_Prozent']
d_for_export_V2 = d
d_for_export_V2['LK_ID'] = lk_id
l_for_export_V2.append(d_for_export_V2)
# Export as JSON
helper.write_json('data/de-districts/de-districts-results.json',
d_for_export_V1,
sort_keys=True)
helper.write_json(
filename='data/de-districts/de-districts-results-V2.json',
d=l_for_export_V2,
sort_keys=True)
# Export as CSV
with open('data/de-districts/de-districts-results.tsv',
mode='w',
encoding='utf-8',
newline='\n') as fh_csv:
csvwriter = csv.DictWriter(
fh_csv,
delimiter='\t',
extrasaction='ignore',
fieldnames=[
'Landkreis', 'Bundesland', 'Population', 'Cases', 'Deaths',
'Cases_Per_Million', 'Deaths_Per_Million',
'DIVI_Intensivstationen_Covid_Prozent',
'DIVI_Intensivstationen_Betten_belegt_Prozent'
])
csvwriter.writeheader()
for lk_id, d in d_for_export_V1.items():
csvwriter.writerow(d)
def extract_latest_date_data():
"""
for all countries in json: extract latest entry
write to data/int/countries-latest-all.tsv and data/int/countries-latest-all.json
"""
d_countries_latest = helper.extract_latest_data(d_countries_ref,
d_countries_timeseries)
l_for_export = []
with open('data/int/countries-latest-all.tsv',
mode='w',
encoding='utf-8',
newline='\n') as fh:
csvwriter = csv.DictWriter(
fh,
delimiter='\t',
extrasaction='ignore',
fieldnames=[
'Country', 'Population', 'Date', 'Cases', 'Deaths',
'Cases_Per_Million', 'Deaths_Per_Million',
'Cases_Last_Week_Per_Million', 'Deaths_Last_Week_Per_Million',
'Continent', 'Code', 'DoublingTime_Cases_Last_Week_Per_100000'
])
# 'Cases_Last_Week',
csvwriter.writeheader()
for country in sorted(d_countries_latest.keys(), key=str.casefold):
# l_time_series = d_countries_timeseries[country]
# d = l_time_series[-1] # last entry (=>latest date)
# pop = read_population(country)
d2 = d_countries_latest[country]
d2['Country'] = country
# d2['Code'] = read_country_code(d2['Country'])
# d2['Continent'] = read_continent(d2['Country'])
# d2['Population'] = pop
# if d2['Cases_Per_Million']:
# d2['Cases_Per_Million'] = round(
# d['Cases_Per_Million'], 0)
# if d2['Deaths_Per_Million']:
# d2['Deaths_Per_Million'] = round(
# d['Deaths_Per_Million'], 0)
# if d2['Cases_Last_Week_Per_Million']:
# d2['Cases_Last_Week_Per_Million'] = round(
# d['Cases_Last_Week_Per_Million'], 0)
# if d2['Deaths_Last_Week_Per_Million']:
# d2['Deaths_Last_Week_Per_Million'] = round(
# d['Deaths_Last_Week_Per_Million'], 0)
csvwriter.writerow(d2)
l_for_export.append(d2)
# JSON export
helper.write_json(filename='data/int/countries-latest-all.json',
d=l_for_export)
# for selected countries write to separate file, for Gnuplot plotting
with open('data/int/countries-latest-selected.tsv',
mode='w',
encoding='utf-8',
newline='\n') as fh:
csvwriter = csv.DictWriter(fh,
delimiter='\t',
extrasaction='ignore',
fieldnames=[
'Country', 'Date', 'Population',
'Cases', 'Deaths', 'Cases_Per_Million',
'Deaths_Per_Million'
])
csvwriter.writeheader()
for country in sorted(d_selected_countries.keys(), key=str.casefold):
l_time_series = d_countries_timeseries[country]
d = l_time_series[-1] # last entry for this country
d2 = d
d2["Country"] = country
d2['Population'] = d_selected_countries[country]['Population']
csvwriter.writerow(d2)