feat: Modified to parse some information of v2 data format
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2 changed files with 220 additions and 110 deletions
238
plot.py
238
plot.py
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@ -38,59 +38,82 @@ r = req.get('https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Daten/
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with open(data_filename, 'wb') as outfile:
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outfile.write(r.content)
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#data_filename = 'data/20210118151908_Impfquotenmonitoring.xlsx'
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rki_file = pd.read_excel(data_filename, sheet_name=None, engine='openpyxl')
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raw_data = rki_file['Impfungen_proTag']
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impfungen = raw_data[:-1].dropna()
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impfungen = raw_data[:-1].dropna(subset=['Datum'])#.fillna(0)
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dates = impfungen['Datum']
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daily = impfungen['Gesamtzahl Impfungen']
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cumulative = np.cumsum(impfungen['Gesamtzahl Impfungen'])
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total_vaccinations = int(np.sum(daily))
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total_vaccinations_percentage = float(total_vaccinations) / einwohner_deutschland
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start_of_reporting_date = dates.iloc[0].date()
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mean_vaccinations_daily = np.mean(daily)
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mean_vaccinations_daily_int = int(np.round(mean_vaccinations_daily))
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def calculate_vaccination_data(data):
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cumulative = np.cumsum(data)
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to_be_vaccinated = einwohner_deutschland - total_vaccinations
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days_extrapolated = int(np.ceil(to_be_vaccinated / mean_vaccinations_daily))
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total = int(np.sum(data))
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total_percentage = float(total) / einwohner_deutschland * 100
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extrapolated_dates = np.array([dates[0] + datetime.timedelta(days=i) for i in range(days_extrapolated)])
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mean_all_time = np.mean(data)
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mean_seven_days = np.mean(data[-7:])
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mean_vaccinations_daily_done = extrapolated_dates[-1]
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mean_vaccinations_daily_herd_immunity = extrapolated_dates[int(np.ceil(days_extrapolated * herd_immunity))]
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to_be_vaccinated = einwohner_deutschland - total
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days_extrapolated_with_todays_rate = int(np.ceil(to_be_vaccinated / daily.iloc[-1]))
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last_date = dates.iloc[-1].date()
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start_of_vaccination_date = dates[data.first_valid_index()].date()
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days_since_start_of_vaccination = (last_date - start_of_vaccination_date).days
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days_since_start_of_reporting = (last_date - start_of_reporting_date).days
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last_date = dates.iloc[-1]
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def extrapolate(rate, to_be_vaccinated):
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days_extrapolated = int(np.ceil(to_be_vaccinated / rate))
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extrapolated_dates = np.array([dates[0] + datetime.timedelta(days=i) for i in range(days_extrapolated)])
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last_date_day_rate = daily.iloc[-1]
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last_date_day_rate_done = dates[0] + datetime.timedelta(days=days_extrapolated_with_todays_rate)
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last_date_day_rate_herd_immunity = dates[0] + datetime.timedelta(days=int(np.ceil(days_extrapolated_with_todays_rate * herd_immunity)))
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date_done = extrapolated_dates[-1]
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date_herd_immunity = extrapolated_dates[int(np.ceil(days_extrapolated * herd_immunity))]
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extrapolated_vaccinations = total + rate * range(-days_since_start_of_reporting, days_extrapolated - days_since_start_of_reporting)
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return {
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'rate': rate,
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'rate_int': int(np.round(rate)),
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'days_extrapolated': days_extrapolated,
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'dates': extrapolated_dates,
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'date_done': date_done,
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'date_done_str': date_done.strftime('%d. %B %Y'),
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'date_herd_immunity': date_herd_immunity,
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'date_herd_immunity_str': date_herd_immunity.strftime('%d. %B %Y'),
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'extrapolated_vaccinations': extrapolated_vaccinations
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}
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extrapolated_vaccinations = mean_vaccinations_daily * range(1, days_extrapolated + 1)
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extrapolation_mean_all_time = extrapolate(mean_all_time, to_be_vaccinated)
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extrapolation_last_rate = extrapolate(data.iloc[-1], to_be_vaccinated)
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extrapolation_mean_seven_days = extrapolate(mean_seven_days, to_be_vaccinated)
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days_since_start = (dates.iloc[-1].date() - dates[0].date()).days
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mean_vaccination_rates_daily = np.round(cumulative / range(1, len(cumulative) + 1))
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mean_vaccinations_last_seven_days = np.mean(daily[-7:])
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mean_vaccinations_last_seven_days_int = int(np.round(mean_vaccinations_last_seven_days))
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days_extrapolated_last_seven_days = int(np.ceil(to_be_vaccinated / mean_vaccinations_last_seven_days))
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extrapolated_vaccinations_last_seven_days = total_vaccinations + mean_vaccinations_last_seven_days * range(-days_since_start, days_extrapolated - days_since_start)
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mean_vaccinations_last_seven_days_done = dates.iloc[-1] + datetime.timedelta(days=days_extrapolated_last_seven_days)
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mean_vaccinations_last_seven_days_herd_immunity = dates.iloc[-1] + datetime.timedelta(days=int(np.ceil(days_extrapolated_last_seven_days * herd_immunity)))
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return {
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'daily': data,
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'cumulative': cumulative,
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'total': total,
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'total_percentage': total_percentage,
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'to_be_vaccinated': to_be_vaccinated,
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'last_date': last_date,
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'last_date_str': last_date.strftime('%d. %B %Y'),
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'days_since_start': days_since_start_of_vaccination + 1, # Shift from zero to one-based-index
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'start_of_vaccination_date': start_of_vaccination_date,
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'start_of_vaccination_date_str': start_of_vaccination_date.strftime('%d. %B %Y'),
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'extrapolation_mean_all_time': extrapolation_mean_all_time,
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'extrapolation_last_rate': extrapolation_last_rate,
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'extrapolation_mean_seven_days': extrapolation_mean_seven_days,
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'mean_vaccination_rates_daily': mean_vaccination_rates_daily
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}
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mean_vaccinations_daily_up_to_date = np.round(cumulative / range(1, len(cumulative) + 1))
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data_first_vaccination = calculate_vaccination_data(impfungen['Erstimpfung'])
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data_second_vaccination = calculate_vaccination_data(impfungen['Zweitimpfung'])
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# Stand aus Daten auslesen
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#stand = dates.iloc[-1]
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@ -107,11 +130,14 @@ print_stand = stand_date.isoformat()
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filename_stand = stand_date.strftime("%Y%m%d%H%M%S")
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'''
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# Infos der einzelnen Länder
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details_sheet_name = (set(rki_file.keys()) - {'Erläuterung', 'Impfungen_proTag'}).pop()
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details_sheet = rki_file[details_sheet_name]
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regionalcodes = details_sheet['RS'].iloc[0:17]
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land_names = details_sheet['Bundesland'].iloc[0:17]
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total_vaccinations_by_land = details_sheet['Impfungen kumulativ'].iloc[0:17]
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@ -125,8 +151,37 @@ vaccination_reason_oldhome_by_land = details_sheet['Pflegeheim-bewohnerIn*'].ilo
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details_per_land = {}
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details_per_land_formatted = {}
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# Regionalcodes der Länder zu Abkürzung und Name (Plus gesamt)
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laendernamen = [
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('SH', 'Schleswig-Holstein'),
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('HH', 'Hamburg'),
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('NI', 'Niedersachsen'),
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('HB', 'Bremen'),
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('NW', 'Nordrhein-Westfalen'),
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('HE', 'Hessen'),
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('RP', 'Rheinland-Pfalz'),
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('BW', 'Baden-Württemberg'),
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('BY', 'Bayern'),
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('SL', 'Saarland'),
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('BE', 'Berlin'),
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('BB', 'Brandenburg'),
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('MV', 'Mecklenburg-Vorpommern'),
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('SN', 'Sachsen'),
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('ST', 'Sachsen-Anhalt'),
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('TH', 'Thüringen'),
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('𝚺', 'Gesamt')
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]
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def row_to_details(i):
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regionalcode = regionalcodes[i] if i != 16 else 16
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print(laendernamen[regionalcode])
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shortname, name = laendernamen[regionalcode]
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return {
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'name': name,
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'shortname': shortname,
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'total_vaccinations': int(total_vaccinations_by_land[i]),
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'total_vaccinations_percentage': vaccination_per_mille_by_land[i] / 10,
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'vaccination_reason_age': int(vaccination_reason_age_by_land[i]),
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@ -140,7 +195,15 @@ def row_to_details(i):
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}
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def row_to_details_formatted(i):
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regionalcode = regionalcodes[i] if i != 16 else 16
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print(laendernamen[regionalcode])
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shortname, name = laendernamen[regionalcode]
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return {
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'name': name,
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'shortname': shortname,
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'total_vaccinations': '{:n}'.format(int(total_vaccinations_by_land[i])).replace('.', ' '),
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'total_vaccinations_percentage': '{:.3n}'.format(np.round(vaccination_per_mille_by_land[i] / 10, 2)),
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'vaccination_reason_age': '{:n}'.format(int(vaccination_reason_age_by_land[i])).replace('.', ' '),
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@ -162,6 +225,13 @@ for i in range(len(land_names) - 1):
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details_total = row_to_details(16)
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details_total_formatted = row_to_details_formatted(16)
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'''
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archive_folder = site_folder + 'archive/' + filename_stand
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if os.path.isdir(archive_folder):
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@ -169,6 +239,9 @@ if os.path.isdir(archive_folder):
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else:
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os.mkdir(archive_folder)
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def plot_extrapolation_portion(percentage):
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print_percentage = int(percentage * 100)
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@ -183,26 +256,38 @@ def plot_extrapolation_portion(percentage):
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plt.title(
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'Tägliche Impfquote, kumulierte Impfungen und lineare Extrapolation bis {:n} % der Bevölkerung Deutschlands\n'
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'Erstellung: {}, Datenquelle: RKI, Stand: {}\n'
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'Impfungen gesamt: {:n} ({:n} %), Durchschnittliche Impfrate: {:n} Impfungen/Tag'.format(
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'Tägliche Impfrate (Erst- und Zweitimpfung), kumulierte Impfungen und lineare Extrapolation bis {:n} % der Bevölkerung Deutschlands\n'
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'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'
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'Erstimpfungen: {:n} ({:n} %), Durchschnittliche Impfrate: {:n} Impfungen/Tag (läuft seit {:n} Tagen)\n'
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'Zweitimpfungen: {:n} ({:n} %), Durchschnittliche Impfrate: {:n} Impfungen/Tag (läuft seit {:n} Tagen)'.format(
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print_percentage,
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print_today, print_stand,
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total_vaccinations, np.round(total_vaccinations_percentage * 100, 2), mean_vaccinations_daily_int
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print_stand, print_today,
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data_first_vaccination['total'], np.round(data_first_vaccination['total_percentage'], 2), data_first_vaccination['extrapolation_mean_all_time']['rate'], data_first_vaccination['days_since_start'],
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data_second_vaccination['total'], np.round(data_second_vaccination['total_percentage'], 2), data_second_vaccination['extrapolation_mean_all_time']['rate'], data_second_vaccination['days_since_start']
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)
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)
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ax2 = ax.twinx()
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ax.bar(dates, daily, label='Tägliche Impfungen', color='blue')
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ax.plot(dates, mean_vaccinations_daily_up_to_date, color='violet', label='Durchschnittliche Impfquote\nbis zu diesem Tag (inkl.)')
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ax.bar(dates, data_first_vaccination['daily'], label='Tägliche Erstimpfungen', color='blue')
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ax.bar(dates, data_second_vaccination['daily'], label='Tägliche Zweitimpfungen', color='lightblue')
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ax.plot(dates, data_first_vaccination['mean_vaccination_rates_daily'], color='violet', label='Durchschnittliche Erstimpfrate\nbis zu diesem Tag (inkl.)')
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ax.plot(dates, data_second_vaccination['mean_vaccination_rates_daily'], color='magenta', label='Durchschnittliche Zweitimpfrate\nbis zu diesem Tag (inkl.)')
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ax2.set_ylim([0, einwohner_deutschland * percentage])
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ax2.set_xlim(xmax=dates[0] + datetime.timedelta(days=percentage * days_extrapolated))
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ax2.set_xlim(xmax=dates[0] + datetime.timedelta(days=percentage * data_first_vaccination['extrapolation_mean_all_time']['days_extrapolated']))
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ax2.grid(True)
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ax2.plot(dates, cumulative, color='red', label='Kumulierte Impfungen')
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ax2.plot(extrapolated_dates, extrapolated_vaccinations, color='orange', label='Extrap. kumulierte Impfungen (Ø gesamt)\n{:n} Impfungen/Tag'.format(mean_vaccinations_daily_int))
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ax2.plot(extrapolated_dates, extrapolated_vaccinations_last_seven_days, color='goldenrod', label='Extrap. kumulierte Impfungen (Ø 7 Tage)\n{:n} Impfungen/Tag'.format(mean_vaccinations_last_seven_days_int))
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ax2.plot(dates, data_first_vaccination['cumulative'], color='red', label='Kumulierte Erstimpfungen')
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ax2.plot(dates, data_second_vaccination['cumulative'], color='indianred', label='Kumulierte Zweitimpfungen')
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ax2.plot(data_first_vaccination['extrapolation_mean_all_time']['dates'], data_first_vaccination['extrapolation_mean_all_time']['extrapolated_vaccinations'], color='orange', label='Extrap. kumulierte Erstimpfungen (Ø gesamt)\n{:n} Impfungen/Tag'.format(data_first_vaccination['extrapolation_mean_all_time']['rate_int']))
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ax2.plot(data_first_vaccination['extrapolation_mean_seven_days']['dates'], data_first_vaccination['extrapolation_mean_seven_days']['extrapolated_vaccinations'], color='goldenrod', label='Extrap. kumulierte Erstimpfungen (Ø 7 Tage)\n{:n} Impfungen/Tag'.format(data_first_vaccination['extrapolation_mean_seven_days']['rate_int']))
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ax2.plot()
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ax2.plot(data_second_vaccination['extrapolation_mean_all_time']['dates'], data_second_vaccination['extrapolation_mean_all_time']['extrapolated_vaccinations'], color='orange', label='Extrap. kumulierte Zweitimpfungen (Ø gesamt)\n{:n} Impfungen/Tag'.format(data_second_vaccination['extrapolation_mean_all_time']['rate_int']))
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ax2.plot(data_second_vaccination['extrapolation_mean_seven_days']['dates'], data_second_vaccination['extrapolation_mean_seven_days']['extrapolated_vaccinations'], color='goldenrod', label='Extrap. kumulierte Zweitimpfungen (Ø 7 Tage)\n{:n} Impfungen/Tag'.format(data_second_vaccination['extrapolation_mean_seven_days']['rate_int']))
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#ax2.plot()
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ax.legend(loc='upper left')
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@ -211,7 +296,7 @@ def plot_extrapolation_portion(percentage):
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ax.set_xlabel('Datum')
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ax.set_ylabel('Tägliche Impfungen')
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ax2.legend(loc='center right')
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ax2.legend(loc='lower right')
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ax2.get_yaxis().get_major_formatter().set_scientific(False)
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# Estimated percentage for herd immunity
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@ -229,10 +314,54 @@ def plot_extrapolation_portion(percentage):
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plot_extrapolation_portion(0.1)
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plot_extrapolation_portion(0.7)
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#plot_extrapolation_portion(0.7)
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plot_extrapolation_portion(1.0)
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def plot_vaccination_bar_graph_total_time():
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archive_plot_filename = '{}/vaccination_bar_graph_total_time'.format(archive_folder)
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latest_plot_filename = '{}/vaccination_bar_graph_total_time'.format(site_folder)
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if os.path.isfile(archive_plot_filename + '.pdf'):
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print('Plot {} already exists'.format(archive_plot_filename))
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return
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fig, ax = plt.subplots(1)
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plt.title(
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'Tägliche Impfrate (Erst- und Zweitimpfung)\n'
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'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
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print_stand, print_today
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)
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)
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ax.grid()
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ax.bar(dates, data_first_vaccination['daily'], label='Tägliche Erstimpfungen', color='blue')
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ax.bar(dates, data_second_vaccination['daily'], label='Tägliche Zweitimpfungen', color='lightblue', bottom=data_first_vaccination['daily'])
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ax.set_ylim([0, np.max(data_first_vaccination['daily']) + np.max(data_second_vaccination['daily'])])
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ax.legend(loc='upper left')
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ax.get_yaxis().get_major_formatter().set_scientific(False)
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ax.set_xlabel('Datum')
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ax.set_ylabel('Tägliche Impfungen')
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plt.savefig(archive_plot_filename + '.pdf')
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plt.savefig(archive_plot_filename + '.png')
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plt.savefig(latest_plot_filename + '.pdf')
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plt.savefig(latest_plot_filename + '.png')
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plt.close()
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print('Created plot {} as pdf and png'.format(archive_plot_filename))
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plot_vaccination_bar_graph_total_time()
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def render_dashboard():
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dashboard_filename = 'site/index.xhtml'
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dashboard_archive_filename = 'site/archive/{}/index.xhtml'.format(filename_stand)
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@ -264,21 +393,10 @@ def render_dashboard():
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filename_stand = filename_stand,
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einwohner_deutschland = '{:n}'.format(einwohner_deutschland).replace('.', ' '),
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herd_immunity = '{:n}'.format(int(herd_immunity * 100)),
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total_vaccinations = '{:n}'.format(total_vaccinations).replace('.', ' '),
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total_vaccinations_percentage = '{:.3n}'.format(total_vaccinations_percentage * 100),
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days_since_start = days_since_start,
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last_date = last_date.strftime(df),
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last_date_day_rate = '{:n}'.format(last_date_day_rate).replace('.', ' '),
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mean_vaccinations_daily = '{:n}'.format(mean_vaccinations_daily_int).replace('.', ' '),
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mean_vaccinations_daily_herd_immunity = mean_vaccinations_daily_herd_immunity.strftime(df),
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mean_vaccinations_daily_done = mean_vaccinations_daily_done.strftime(df),
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last_date_day_rate_herd_immunity = last_date_day_rate_herd_immunity.strftime(df),
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last_date_day_rate_done = last_date_day_rate_done.strftime(df),
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mean_vaccinations_last_seven_days = '{:n}'.format(mean_vaccinations_last_seven_days_int).replace('.', ' '),
|
||||
mean_vaccinations_last_seven_days_herd_immunity = mean_vaccinations_last_seven_days_herd_immunity.strftime(df),
|
||||
mean_vaccinations_last_seven_days_done = mean_vaccinations_last_seven_days_done.strftime(df),
|
||||
details_per_land = dict(sorted(details_per_land_formatted.items(), key=lambda item: item[0])),
|
||||
details_total = details_total_formatted
|
||||
data_first_vaccination = data_first_vaccination,
|
||||
data_second_vaccination = data_second_vaccination,
|
||||
#details_per_land = dict(sorted(details_per_land_formatted.items(), key=lambda item: item[0])),
|
||||
#details_total = details_total_formatted
|
||||
).dump('site/index.xhtml')
|
||||
|
||||
shutil.copyfile(dashboard_filename, dashboard_archive_filename)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue