benedikt/rki-impfparser
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2 changed files with 118 additions and 128 deletions
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2
dashboard_template.xhtml
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@ -22,8 +22,6 @@
<p class="data-text">
Gestern wurden <em>{{ '{:n}'.format(data_first_vaccination.vaccinations_last_day).replace('.', ' ') }}</em> Erstimpfungen vorgenommen (<em>{{ '{:.3n}'.format(data_first_vaccination.vaccinations_last_day_percentage) }} %</em> der Bevölkerung, <em>{{ '{:.3n}'.format(data_first_vaccination.vaccinations_last_day_vaccination_percentage) }} %</em> der verabreichten Erstimpfdosen).
Innerhalb der letzten Kalenderwoche sind <em>{{ '{:.9n}'.format(data_first_vaccination.vaccinations_last_week).replace('.', ' ') }}</em> Erstimpfungen erfolgt (<em>{{ '{:.3n}'.format(data_first_vaccination.vaccinations_last_week_percentage) }} %</em>, <em>{{ '{:.3n}'.format(data_first_vaccination.vaccinations_last_week_vaccination_percentage) }} %</em>).
Es wurden außerdem <em>{{ '{:n}'.format(data_second_vaccination.vaccinations_last_day).replace('.', ' ') }}</em> Zweitimpfungen vorgenommen (<em>{{ '{:.3n}'.format(data_second_vaccination.vaccinations_last_day_percentage) }} %</em> der Bevölkerung, <em>{{ '{:.3n}'.format(data_second_vaccination.vaccinations_last_day_vaccination_percentage) }} %</em> der verabreichten Erstimpfdosen).
Innerhalb der letzten Kalenderwoche sind <em>{{ '{:.9n}'.format(data_second_vaccination.vaccinations_last_week).replace('.', ' ') }}</em> Zweitimpfungen erfolgt (<em>{{ '{:.3n}'.format(data_second_vaccination.vaccinations_last_week_percentage) }} %</em>, <em>{{ '{:.3n}'.format(data_second_vaccination.vaccinations_last_week_vaccination_percentage) }} %</em>).
</p>
<p class="data-text">
In den letzten sieben Tagen wurden durchschnittlich <em>{{ '{:n}'.format(data_first_vaccination['extrapolation_mean_seven_days']['rate_int']).replace('.', ' ') }}</em> Erstimpfungen und <em>{{ '{:n}'.format(data_second_vaccination['extrapolation_mean_seven_days']['rate_int']).replace('.', ' ') }}</em> Zweitimpfungen pro Tag vorgenommen (<em>{{ '{:n}'.format(data_first_vaccination['extrapolation_mean_seven_days']['rate_int'] * 7).replace('.', ' ') }}</em>/<em>{{ '{:n}'.format(data_second_vaccination['extrapolation_mean_seven_days']['rate_int'] * 7).replace('.', ' ') }}</em> pro Woche).

84
plot.py
View file

@ -52,33 +52,28 @@ plt.rcParams["figure.figsize"] = [11.69, 8.27]
# Download
def download_rki(filename_prefix):
data_filename = '{}/{}_Impfquotenmonitoring.xlsx'.format(data_folder, filename_prefix)
data_filename = '{}/{}_Impfquotenmonitoring.xlsx'.format(data_folder, filename_now)
r = req.get('https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Daten/Impfquotenmonitoring.xlsx?__blob=publicationFile')
r = req.get('https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Daten/Impfquotenmonitoring.xlsx?__blob=publicationFile')
with open(data_filename, 'wb') as outfile:
with open(data_filename, 'wb') as outfile:
outfile.write(r.content)
return data_filename
#data_filename = 'data/20210118151908_Impfquotenmonitoring.xlsx'
data_filename = download_rki(filename_now)
rki_file = pd.read_excel(data_filename, sheet_name=None, engine='openpyxl')
def parse_rki(filename):
raw_data = rki_file['Impfungen_proTag']
rki_file = pd.read_excel(filename, sheet_name=None, engine='openpyxl')
impfungen = raw_data[:-1].dropna(subset=['Datum']).fillna(0)
raw_data = rki_file['Impfungen_proTag']
impfungen.drop(impfungen.tail(1).index,inplace=True) # remove Gesamt row
impfungen = raw_data[:-1].dropna(subset=['Datum']).fillna(0)
dates = impfungen['Datum']
impfungen.drop(impfungen.tail(1).index,inplace=True) # remove Gesamt row
start_of_reporting_date = dates.iloc[0].date()
dates = impfungen['Datum']
start_of_reporting_date = dates.iloc[0].date()
def calculate_vaccination_data(data):
def calculate_vaccination_data(data):
total = int(np.sum(data))
total_percentage = float(total) / einwohner_deutschland * 100
@ -108,9 +103,10 @@ def parse_rki(filename):
else:
vaccinations_by_week[w] = v
def extrapolate(rate, to_be_vaccinated):
days_extrapolated = int(np.ceil(to_be_vaccinated / rate))
days_extrapolated_herd_immunity = int(np.ceil((einwohner_deutschland * herd_immunity - total) / rate))
days_extrapolated_herd_immunity = int(np.ceil((einwohner_deutschland * 0.7 - total) / rate))
weeks_extrapolated = int(np.ceil(days_extrapolated / 7))
weeks_extrapolated_herd_immunity = int(np.ceil(days_extrapolated_herd_immunity / 7))
@ -142,7 +138,7 @@ def parse_rki(filename):
mean_vaccination_rates_daily = np.round(cumulative / range(1, len(cumulative) + 1))
vaccination_rates_daily_rolling_average = data.rolling(7).mean()
vaccinations_missing_until_target = einwohner_deutschland * herd_immunity - total
vaccinations_missing_until_target = einwohner_deutschland * 0.7 - total
vaccination_rate_needed_for_target = vaccinations_missing_until_target / days_until_target
vaccination_rate_needed_for_target_percentage = mean_all_time / vaccination_rate_needed_for_target * 100
@ -174,36 +170,32 @@ def parse_rki(filename):
'vaccinations_last_week_vaccination_percentage': vaccinations_by_week[Week.thisweek() - 1] / total * 100
}
if 'Erstimpfung' in impfungen:
if 'Erstimpfung' in impfungen:
raw_first_vaccinations = impfungen['Erstimpfung']
elif 'Einmal geimpft' in impfungen:
elif 'Einmal geimpft' in impfungen:
raw_first_vaccinations = impfungen['Einmal geimpft']
elif 'Begonnene Impfserie' in impfungen:
elif 'Begonnene Impfserie' in impfungen:
raw_first_vaccinations = impfungen['Begonnene Impfserie']
if 'Zweitimpfung' in impfungen:
if 'Zweitimpfung' in impfungen:
raw_second_vaccinations = impfungen['Zweitimpfung']
elif 'Vollständig geimpft' in impfungen:
elif 'Vollständig geimpft' in impfungen:
raw_second_vaccinations = impfungen['Vollständig geimpft']
data_first_vaccination = calculate_vaccination_data(raw_first_vaccinations)
data_second_vaccination = calculate_vaccination_data(raw_second_vaccinations)
data_first_vaccination = calculate_vaccination_data(raw_first_vaccinations)
data_second_vaccination = calculate_vaccination_data(raw_second_vaccinations)
# Stand aus Daten auslesen
#stand = dates.iloc[-1]
#print_stand = stand.isoformat()
# Stand aus Daten auslesen
#stand = dates.iloc[-1]
#print_stand = stand.isoformat()
# Stand aus offiziellen Angaben auslesen
stand = rki_file['Erläuterung'].iloc[1][0]
# Stand aus offiziellen Angaben auslesen
stand = rki_file['Erläuterung'].iloc[1][0]
stand_regex = re.compile('^Datenstand: (\d\d.\d\d.\d\d\d\d, \d?\d:\d\d) Uhr$')
m = stand_regex.match(stand)
stand_date = datetime.datetime.strptime(m.groups()[0], '%d.%m.%Y, %H:%M')
print_stand = stand_date.isoformat()
return dates, start_of_reporting_date, data_first_vaccination, data_second_vaccination, stand_date, print_stand
dates, start_of_reporting_date, data_first_vaccination, data_second_vaccination, stand_date, print_stand = parse_rki(filename=data_filename)
stand_regex = re.compile('^Datenstand: (\d\d.\d\d.\d\d\d\d, \d?\d:\d\d) Uhr$')
m = stand_regex.match(stand)
stand_date = datetime.datetime.strptime(m.groups()[0], '%d.%m.%Y, %H:%M')
print_stand = stand_date.isoformat()
filename_stand = stand_date.strftime("%Y%m%d%H%M%S")
@ -686,8 +678,8 @@ def plot_vaccination_done_days():
)
d = data_first_vaccination
days_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily']))
days_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
days_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily']))
days_remaining_rolling = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
ax.set_xlim(start_of_reporting_date, today)
ax.set_ylim(0, 2500)
@ -730,8 +722,8 @@ def plot_vaccination_done_weeks():
)
d = data_first_vaccination
weeks_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily'])) / 7
weeks_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average'])) / 7
weeks_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily'])) / 7
weeks_remaining_rolling = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['vaccination_rates_daily_rolling_average'])) / 7
ax.set_xlim(datetime.date(2021, 3, 1), today)
ax.set_ylim(0, 52)
@ -773,8 +765,8 @@ def plot_vaccination_done_dates():
)
d = data_first_vaccination
days_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily']))
days_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
days_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily']))
days_remaining_rolling = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
dates_daily = [today + datetime.timedelta(days) for days in days_remaining_daily]
dates_rolling = [today + datetime.timedelta(days) for days in days_remaining_rolling.dropna()]
@ -819,8 +811,8 @@ def plot_vaccination_done_dates_detail():
)
d = data_first_vaccination
days_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily']))
days_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
days_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily']))
days_remaining_rolling = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
dates_daily = [today + datetime.timedelta(days) for days in days_remaining_daily]
dates_rolling = [today + datetime.timedelta(days) for days in days_remaining_rolling.dropna()]