fix: Added data about second vaccinations to data-text

dashboard_template.xhtml

@@ -22,6 +22,8 @@
 			<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).

plot.py

@@ -52,28 +52,33 @@ plt.rcParams["figure.figsize"] = [11.69, 8.27]
 
 # Download
 
-data_filename = '{}/{}_Impfquotenmonitoring.xlsx'.format(data_folder, filename_now)
+def download_rki(filename_prefix):
+	data_filename = '{}/{}_Impfquotenmonitoring.xlsx'.format(data_folder, filename_prefix)
 
-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)
 
-#data_filename = 'data/20210118151908_Impfquotenmonitoring.xlsx'
+	return data_filename
 
-rki_file = pd.read_excel(data_filename, sheet_name=None, engine='openpyxl')
+data_filename = download_rki(filename_now)
 
-raw_data = rki_file['Impfungen_proTag']
+def parse_rki(filename):
 
-impfungen = raw_data[:-1].dropna(subset=['Datum']).fillna(0)
+	rki_file = pd.read_excel(filename, sheet_name=None, engine='openpyxl')
 
-impfungen.drop(impfungen.tail(1).index,inplace=True) # remove Gesamt row
+	raw_data = rki_file['Impfungen_proTag']
 
-dates = impfungen['Datum']
+	impfungen = raw_data[:-1].dropna(subset=['Datum']).fillna(0)
 
-start_of_reporting_date = dates.iloc[0].date()
+	impfungen.drop(impfungen.tail(1).index,inplace=True) # remove Gesamt row
 
-def calculate_vaccination_data(data):
+	dates = impfungen['Datum']
+
+	start_of_reporting_date = dates.iloc[0].date()
+
+	def calculate_vaccination_data(data):
 
 		total = int(np.sum(data))
 		total_percentage = float(total) / einwohner_deutschland * 100
@@ -103,10 +108,9 @@ def calculate_vaccination_data(data):
 			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 * 0.7 - total) / rate))
+			days_extrapolated_herd_immunity = int(np.ceil((einwohner_deutschland * herd_immunity - total) / rate))
 
 			weeks_extrapolated = int(np.ceil(days_extrapolated / 7))
 			weeks_extrapolated_herd_immunity = int(np.ceil(days_extrapolated_herd_immunity / 7))
@@ -138,7 +142,7 @@ def calculate_vaccination_data(data):
 		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 * 0.7 - total
+		vaccinations_missing_until_target = einwohner_deutschland * herd_immunity - 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
 
@@ -170,32 +174,36 @@ def calculate_vaccination_data(data):
 			'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$')
-m = stand_regex.match(stand)
-stand_date = datetime.datetime.strptime(m.groups()[0], '%d.%m.%Y, %H:%M')
-print_stand = stand_date.isoformat()
+	stand_regex = re.compile('^Datenstand: (\d\d.\d\d.\d\d\d\d, \d?\d:\d\d) Uhr$')
+	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)
 
 filename_stand = stand_date.strftime("%Y%m%d%H%M%S")
 
@@ -678,8 +686,8 @@ def plot_vaccination_done_days():
 	)
 	d = data_first_vaccination
 
-	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']))
+	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']))
 
 	ax.set_xlim(start_of_reporting_date, today)
 	ax.set_ylim(0, 2500)
@@ -722,8 +730,8 @@ def plot_vaccination_done_weeks():
 	)
 	d = data_first_vaccination
 
-	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
+	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
 
 	ax.set_xlim(datetime.date(2021, 3, 1), today)
 	ax.set_ylim(0, 52)
@@ -765,8 +773,8 @@ def plot_vaccination_done_dates():
 	)
 	d = data_first_vaccination
 
-	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']))
+	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']))
 
 	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()]
@@ -811,8 +819,8 @@ def plot_vaccination_done_dates_detail():
 	)
 	d = data_first_vaccination
 
-	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']))
+	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']))
 
 	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()]