771 lines
28 KiB
Python
771 lines
28 KiB
Python
#!/usr/bin/python
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# vim: set fileencoding=utf-8 :
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# Copyright (C) Benedikt Bastin, 2021
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# SPDX-License-Identifier: EUPL-1.2
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import datetime
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import re
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import requests as req
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import locale
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import os.path
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import shutil
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import math
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from matplotlib.dates import date2num
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import matplotlib.ticker as mtick
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locale.setlocale(locale.LC_ALL, 'de_DE.UTF-8')
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site_folder = 'site/'
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data_folder = 'data/'
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einwohner_deutschland = 83190556
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herd_immunity = 0.7
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today = datetime.date.today()
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print_today = today.isoformat()
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filename_now = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
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# https://www.tagesschau.de/ausland/europa/ursula-von-der-leyen-zu-corona-impfstoffen-101.html
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target_date_for_herd_immunity = datetime.date(2021, 9, 22)
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days_until_target = (target_date_for_herd_immunity - today).days - 21
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# DIN A4 Plots
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plt.rcParams["figure.figsize"] = [11.69, 8.27]
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# Download
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data_filename = '{}/{}_Impfquotenmonitoring.xlsx'.format(data_folder, filename_now)
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r = req.get('https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Daten/Impfquotenmonitoring.xlsx?__blob=publicationFile')
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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(subset=['Datum'])#.fillna(0)
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impfungen.drop(impfungen.tail(1).index,inplace=True) # remove Gesamt row
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dates = impfungen['Datum']
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start_of_reporting_date = dates.iloc[0].date()
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def calculate_vaccination_data(data):
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total = int(np.sum(data))
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total_percentage = float(total) / einwohner_deutschland * 100
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to_be_vaccinated = einwohner_deutschland - total
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last_date = dates.iloc[-1].date()
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start_of_vaccination_index = (data != 0).argmax(axis=0)
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start_of_vaccination_date = dates[start_of_vaccination_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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valid_data = data[start_of_vaccination_index:]
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cumulative = np.concatenate(([math.nan] * (days_since_start_of_reporting - days_since_start_of_vaccination), np.cumsum(valid_data)))
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mean_all_time = np.mean(valid_data)
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mean_seven_days = np.mean(data[-7:])
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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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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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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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mean_vaccination_rates_daily = np.round(cumulative / range(1, len(cumulative) + 1))
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vaccination_rates_daily_rolling_average = data.rolling(7).mean()
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vaccinations_missing_until_target = einwohner_deutschland * 0.7 - total
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vaccination_rate_needed_for_target = vaccinations_missing_until_target / days_until_target
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vaccination_rate_needed_for_target_percentage = mean_all_time / vaccination_rate_needed_for_target * 100
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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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'vaccination_rates_daily_rolling_average': vaccination_rates_daily_rolling_average,
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'vaccinations_missing_until_target': int(np.floor(vaccinations_missing_until_target)),
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'vaccination_rate_needed_for_target': int(np.floor(vaccination_rate_needed_for_target)),
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'vaccination_rate_needed_for_target_percentage': vaccination_rate_needed_for_target_percentage
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}
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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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#print_stand = stand.isoformat()
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# Stand aus offiziellen Angaben auslesen
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stand = rki_file['Erläuterung'].iloc[1][0]
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stand_regex = re.compile('^Datenstand: (\d\d.\d\d.\d\d\d\d, \d?\d:\d\d) Uhr$')
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m = stand_regex.match(stand)
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stand_date = datetime.datetime.strptime(m.groups()[0], '%d.%m.%Y, %H:%M')
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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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vaccination_per_mille_by_land = details_sheet['Impfungen pro 1.000 Einwohner'].iloc[0:17]
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vaccination_reason_age_by_land = details_sheet['Indikation nach Alter*'].iloc[0:17]
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vaccination_reason_job_by_land = details_sheet['Berufliche Indikation*'].iloc[0:17]
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vaccination_reason_medical_by_land = details_sheet['Medizinische Indikation*'].iloc[0:17]
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vaccination_reason_oldhome_by_land = details_sheet['Pflegeheim-bewohnerIn*'].iloc[0:17]
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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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'vaccination_reason_age_percentage': np.round(vaccination_reason_age_by_land[i] / total_vaccinations_by_land[i] * 100),
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'vaccination_reason_job': int(vaccination_reason_job_by_land[i]),
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'vaccination_reason_job_percentage': np.round(vaccination_reason_job_by_land[i] / total_vaccinations_by_land[i] * 100),
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'vaccination_reason_medical': int(vaccination_reason_medical_by_land[i]),
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'vaccination_reason_medical_percentage': np.round(vaccination_reason_medical_by_land[i] / total_vaccinations_by_land[i] * 100),
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'vaccination_reason_oldhome': int(vaccination_reason_oldhome_by_land[i]),
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'vaccination_reason_oldhome_percentage': np.round(vaccination_reason_oldhome_by_land[i] / total_vaccinations_by_land[i] * 100),
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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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'vaccination_reason_age_percentage': '{:n}'.format(np.round(vaccination_reason_age_by_land[i] / total_vaccinations_by_land[i] * 100)),
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'vaccination_reason_job': '{:n}'.format(int(vaccination_reason_job_by_land[i])).replace('.', ' '),
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'vaccination_reason_job_percentage': '{:n}'.format(np.round(vaccination_reason_job_by_land[i] / total_vaccinations_by_land[i] * 100)),
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'vaccination_reason_medical': '{:n}'.format(int(vaccination_reason_medical_by_land[i])).replace('.', ' '),
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'vaccination_reason_medical_percentage': '{:n}'.format(np.round(vaccination_reason_medical_by_land[i] / total_vaccinations_by_land[i] * 100)),
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'vaccination_reason_oldhome': '{:n}'.format(int(vaccination_reason_oldhome_by_land[i])).replace('.', ' '),
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'vaccination_reason_oldhome_percentage': '{:n}'.format(np.round(vaccination_reason_oldhome_by_land[i] / total_vaccinations_by_land[i] * 100))
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}
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for i in range(len(land_names) - 1):
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details_per_land[land_names[i]] = row_to_details(i)
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details_per_land_formatted[land_names[i]] = row_to_details_formatted(i)
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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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print('Archive folder {} already exists'.format(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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archive_plot_filename = '{}/extrapolated_to_{}_percent'.format(archive_folder, print_percentage)
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latest_plot_filename = '{}/extrapolated_to_{}_percent'.format(site_folder, print_percentage)
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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 übereinander), 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_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, 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.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 * data_first_vaccination['extrapolation_mean_all_time']['days_extrapolated']))
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ax2.grid(True)
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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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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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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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#ax2.axline((0, einwohner_deutschland * 0.7), slope=0, color='green')
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ax2.set_ylabel('Kumulierte 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_extrapolation_portion(0.1)
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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 übereinander)\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')
|
||
plt.close()
|
||
|
||
print('Created plot {} as pdf and png'.format(archive_plot_filename))
|
||
|
||
plot_vaccination_bar_graph_total_time()
|
||
|
||
def plot_vaccination_bar_graph_total_time_two_bars():
|
||
|
||
archive_plot_filename = '{}/vaccination_bar_graph_total_time_two_bars'.format(archive_folder)
|
||
latest_plot_filename = '{}/vaccination_bar_graph_total_time_two_bars'.format(site_folder)
|
||
|
||
if os.path.isfile(archive_plot_filename + '.pdf'):
|
||
print('Plot {} already exists'.format(archive_plot_filename))
|
||
return
|
||
|
||
fig, ax = plt.subplots(1)
|
||
|
||
|
||
plt.title(
|
||
'Tägliche Impfrate (Erst- und Zweitimpfung nebeneinander)\n'
|
||
'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
|
||
print_stand, print_today
|
||
)
|
||
)
|
||
|
||
ax.grid()
|
||
|
||
date_numbers = date2num(dates)
|
||
|
||
ax.bar(date_numbers - 0.2, data_first_vaccination['daily'], width=0.4, label='Tägliche Erstimpfungen', color='blue')
|
||
ax.bar(date_numbers + 0.2, data_second_vaccination['daily'], width=0.4, label='Tägliche Zweitimpfungen', color='lightblue')
|
||
|
||
ax.set_ylim([0, np.max(data_first_vaccination['daily']) + np.max(data_second_vaccination['daily'])])
|
||
|
||
ax.legend(loc='upper left')
|
||
ax.xaxis_date()
|
||
ax.get_yaxis().get_major_formatter().set_scientific(False)
|
||
|
||
ax.set_xlabel('Datum')
|
||
ax.set_ylabel('Tägliche Impfungen')
|
||
|
||
|
||
plt.savefig(archive_plot_filename + '.pdf')
|
||
plt.savefig(archive_plot_filename + '.png')
|
||
plt.savefig(latest_plot_filename + '.pdf')
|
||
plt.savefig(latest_plot_filename + '.png')
|
||
plt.close()
|
||
|
||
print('Created plot {} as pdf and png'.format(archive_plot_filename))
|
||
|
||
plot_vaccination_bar_graph_total_time_two_bars()
|
||
|
||
def plot_vaccination_bar_graph_compare_both_vaccinations():
|
||
|
||
archive_plot_filename = '{}/vaccination_bar_graph_compare_both_vaccinations'.format(archive_folder)
|
||
latest_plot_filename = '{}/vaccination_bar_graph_compare_both_vaccinations'.format(site_folder)
|
||
|
||
if os.path.isfile(archive_plot_filename + '.pdf'):
|
||
print('Plot {} already exists'.format(archive_plot_filename))
|
||
return
|
||
|
||
fig, ax = plt.subplots(1)
|
||
|
||
|
||
plt.title(
|
||
'Tägliche Impfrate (Erst- und Zweitimpfung um 21 Tage versetzt)\n'
|
||
'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
|
||
print_stand, print_today
|
||
)
|
||
)
|
||
|
||
ax.grid()
|
||
|
||
date_numbers_first = date2num(dates + datetime.timedelta(days=21))
|
||
date_numbers_second = date2num(dates)
|
||
|
||
ax.bar(date_numbers_first - 0.2, data_first_vaccination['daily'], width=0.4, label='Tägliche Erstimpfungen', color='blue')
|
||
ax.bar(date_numbers_second + 0.2, data_second_vaccination['daily'], width=0.4, label='Tägliche Zweitimpfungen', color='lightblue')
|
||
|
||
ax.set_ylim([0, np.max([np.max(data_first_vaccination['daily']), np.max(data_second_vaccination['daily'])])])
|
||
|
||
ax.legend(loc='upper left')
|
||
ax.xaxis_date()
|
||
ax.get_yaxis().get_major_formatter().set_scientific(False)
|
||
|
||
ax.set_xlabel('Datum')
|
||
ax.set_ylabel('Tägliche Impfungen')
|
||
|
||
|
||
plt.savefig(archive_plot_filename + '.pdf')
|
||
plt.savefig(archive_plot_filename + '.png')
|
||
plt.savefig(latest_plot_filename + '.pdf')
|
||
plt.savefig(latest_plot_filename + '.png')
|
||
plt.close()
|
||
|
||
print('Created plot {} as pdf and png'.format(archive_plot_filename))
|
||
|
||
plot_vaccination_bar_graph_compare_both_vaccinations()
|
||
|
||
def plot_cumulative_two_vaccinations():
|
||
archive_plot_filename = '{}/cumulative_two_vaccinations'.format(archive_folder)
|
||
latest_plot_filename = '{}/cumulative_two_vaccinations'.format(site_folder)
|
||
|
||
if os.path.isfile(archive_plot_filename + '.pdf'):
|
||
print('Plot {} already exists'.format(archive_plot_filename))
|
||
return
|
||
|
||
fig, ax = plt.subplots(1)
|
||
|
||
|
||
plt.title(
|
||
'Kumulative Impfrate (Erst- und Zweitimpfung)\n'
|
||
'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
|
||
print_stand, print_today
|
||
)
|
||
)
|
||
|
||
ax.grid()
|
||
|
||
first_vaccinations_cumulative = data_first_vaccination['cumulative']
|
||
second_vaccinations_cumulative = data_second_vaccination['cumulative']
|
||
|
||
ax.fill_between(dates, first_vaccinations_cumulative, label='Erstimpfungen', color='blue')
|
||
ax.fill_between(dates, second_vaccinations_cumulative, label='Zweitimpfungen', color='lightblue')
|
||
|
||
ax.set_ylim([0, first_vaccinations_cumulative[-1]])
|
||
|
||
ax.legend(loc='upper left')
|
||
ax.xaxis_date()
|
||
ax.get_yaxis().get_major_formatter().set_scientific(False)
|
||
|
||
ax.set_xlabel('Datum')
|
||
ax.set_ylabel('Kumulative Impfungen')
|
||
|
||
|
||
plt.savefig(archive_plot_filename + '.pdf')
|
||
plt.savefig(archive_plot_filename + '.png')
|
||
plt.savefig(latest_plot_filename + '.pdf')
|
||
plt.savefig(latest_plot_filename + '.png')
|
||
plt.close()
|
||
|
||
print('Created plot {} as pdf and png'.format(archive_plot_filename))
|
||
|
||
|
||
plot_cumulative_two_vaccinations()
|
||
|
||
def plot_cumulative_two_vaccinations_percentage():
|
||
archive_plot_filename = '{}/cumulative_two_vaccinations_percentage'.format(archive_folder)
|
||
latest_plot_filename = '{}/cumulative_two_vaccinations_percentage'.format(site_folder)
|
||
|
||
if os.path.isfile(archive_plot_filename + '.pdf'):
|
||
print('Plot {} already exists'.format(archive_plot_filename))
|
||
return
|
||
|
||
fig, ax = plt.subplots(1)
|
||
|
||
|
||
plt.title(
|
||
'Kumulative Impfrate (Erst- und Zweitimpfung) in Prozent der Bevökerung Deutschlands ({} Einwohner)\n'
|
||
'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
|
||
'{:n}'.format(einwohner_deutschland).replace('.', ' '),
|
||
print_stand, print_today
|
||
)
|
||
)
|
||
|
||
ax.grid()
|
||
|
||
first_vaccinations_cumulative = data_first_vaccination['cumulative'] / einwohner_deutschland
|
||
second_vaccinations_cumulative = data_second_vaccination['cumulative'] / einwohner_deutschland
|
||
|
||
ax.fill_between(dates, first_vaccinations_cumulative, label='Erstimpfungen', color='blue')
|
||
ax.fill_between(dates, second_vaccinations_cumulative, label='Zweitimpfungen', color='lightblue')
|
||
|
||
ax.set_ylim([0, 1])
|
||
|
||
ax.legend(loc='upper left')
|
||
ax.xaxis_date()
|
||
ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))
|
||
|
||
ax.set_xlabel('Datum')
|
||
ax.set_ylabel('Kumulative Impfungen')
|
||
|
||
|
||
plt.savefig(archive_plot_filename + '.pdf')
|
||
plt.savefig(archive_plot_filename + '.png')
|
||
plt.savefig(latest_plot_filename + '.pdf')
|
||
plt.savefig(latest_plot_filename + '.png')
|
||
plt.close()
|
||
|
||
print('Created plot {} as pdf and png'.format(archive_plot_filename))
|
||
|
||
|
||
plot_cumulative_two_vaccinations_percentage()
|
||
|
||
|
||
def plot_people_between_first_and_second():
|
||
archive_plot_filename = '{}/people_between_first_and_second'.format(archive_folder)
|
||
latest_plot_filename = '{}/people_between_first_and_second'.format(site_folder)
|
||
|
||
if os.path.isfile(archive_plot_filename + '.pdf'):
|
||
print('Plot {} already exists'.format(archive_plot_filename))
|
||
return
|
||
|
||
fig, ax = plt.subplots(1)
|
||
|
||
|
||
plt.title(
|
||
'Personen zwischen Erst- und Zweitimpfung\n'
|
||
'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
|
||
print_stand, print_today
|
||
)
|
||
)
|
||
|
||
ax.grid()
|
||
|
||
first_vaccinations_cumulative = data_first_vaccination['cumulative']
|
||
second_vaccinations_cumulative = np.nan_to_num(data_second_vaccination['cumulative'], nan=0)
|
||
|
||
people_between = first_vaccinations_cumulative - second_vaccinations_cumulative
|
||
|
||
ax.plot(dates, people_between, label='Personen zwischen Erst- und Zweitimpfung', color='darkblue')
|
||
|
||
ax.bar(dates, data_first_vaccination['daily'], color='blue', label='Erstimpfungen')
|
||
ax.bar(dates, -data_second_vaccination['daily'], color='lightblue', label='Zweitimpfungen')
|
||
|
||
ax.legend(loc='upper left')
|
||
ax.xaxis_date()
|
||
ax.get_yaxis().get_major_formatter().set_scientific(False)
|
||
|
||
ax.set_xlabel('Datum')
|
||
ax.set_ylabel('Personen zwischen Erst- und Zweitimpfung')
|
||
|
||
|
||
plt.savefig(archive_plot_filename + '.pdf')
|
||
plt.savefig(archive_plot_filename + '.png')
|
||
plt.savefig(latest_plot_filename + '.pdf')
|
||
plt.savefig(latest_plot_filename + '.png')
|
||
plt.close()
|
||
|
||
print('Created plot {} as pdf and png'.format(archive_plot_filename))
|
||
|
||
|
||
plot_people_between_first_and_second()
|
||
|
||
def plot_vaccination_rate():
|
||
|
||
archive_plot_filename = '{}/vaccination_rate'.format(archive_folder)
|
||
latest_plot_filename = '{}/vaccination_rate'.format(site_folder)
|
||
|
||
if os.path.isfile(archive_plot_filename + '.pdf'):
|
||
print('Plot {} already exists'.format(archive_plot_filename))
|
||
return
|
||
|
||
fig, ax = plt.subplots(1)
|
||
|
||
|
||
plt.title(
|
||
'Tägliche Impfrate sowie durchschnittliche Impfrate\n'
|
||
'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
|
||
print_stand, print_today
|
||
)
|
||
)
|
||
|
||
ax.plot(dates, data_first_vaccination['daily'], label='Tägliche Erstimpfrate', color='blue', linewidth=0.5)
|
||
ax.plot(dates, data_second_vaccination['daily'], label='Tägliche Zweitimpfrate', color='lightblue', linewidth=0.5)
|
||
|
||
ax.plot(dates, data_first_vaccination['vaccination_rates_daily_rolling_average'], color='blue', linewidth=2, label='Erstimpfrate über sieben Tage')
|
||
ax.plot(dates, data_second_vaccination['vaccination_rates_daily_rolling_average'], color='lightblue', linewidth=2, label='Zweitimpfrate über sieben Tage')
|
||
|
||
|
||
ax.plot(dates, data_first_vaccination['mean_vaccination_rates_daily'], color='violet', label='Durchschnittliche Erstimpfrate\nbis zu diesem Tag (inkl.)')
|
||
ax.plot(dates, data_second_vaccination['mean_vaccination_rates_daily'], color='magenta', label='Durchschnittliche Zweitimpfrate\nbis zu diesem Tag (inkl.)')
|
||
|
||
|
||
ax.grid(True)
|
||
|
||
|
||
ax.legend(loc='upper left')
|
||
ax.get_yaxis().get_major_formatter().set_scientific(False)
|
||
|
||
ax.set_xlabel('Datum')
|
||
ax.set_ylabel('Impfrate [Impfungen/Tag]')
|
||
|
||
plt.savefig(archive_plot_filename + '.pdf')
|
||
plt.savefig(archive_plot_filename + '.png')
|
||
plt.savefig(latest_plot_filename + '.pdf')
|
||
plt.savefig(latest_plot_filename + '.png')
|
||
plt.close()
|
||
|
||
print('Created plot {} as pdf and png'.format(archive_plot_filename))
|
||
|
||
plot_vaccination_rate()
|
||
|
||
def render_dashboard():
|
||
dashboard_filename = 'site/index.xhtml'
|
||
dashboard_archive_filename = 'site/archive/{}/index.xhtml'.format(filename_stand)
|
||
stylesheet_filename = 'site/rki-dashboard.css'
|
||
stylesheet_archive_filename = 'site/archive/{}/rki-dashboard.css'.format(filename_stand)
|
||
|
||
if os.path.isfile(dashboard_archive_filename):
|
||
print('Dashboard {} already exists'.format(dashboard_archive_filename))
|
||
return
|
||
|
||
from jinja2 import Template, Environment, FileSystemLoader, select_autoescape
|
||
env = Environment(
|
||
loader=FileSystemLoader('./'),
|
||
autoescape=select_autoescape(['html', 'xml', 'xhtml'])
|
||
)
|
||
|
||
german_text_date_format = '%d. %B %Y'
|
||
df = german_text_date_format
|
||
|
||
german_text_datetime_format = '%d. %B %Y, %H:%M:%S Uhr'
|
||
dtf = german_text_datetime_format
|
||
|
||
latest_dashboard_filename = site_folder + 'index.xhtml'
|
||
archive_dashboard_filename = archive_folder
|
||
|
||
template = env.get_template('dashboard_template.xhtml')
|
||
template.stream(
|
||
stand = stand_date.strftime(dtf),
|
||
filename_stand = filename_stand,
|
||
einwohner_deutschland = '{:n}'.format(einwohner_deutschland).replace('.', ' '),
|
||
herd_immunity = '{:n}'.format(int(herd_immunity * 100)),
|
||
target_date_for_herd_immunity = target_date_for_herd_immunity,
|
||
target_date_for_herd_immunity_str = target_date_for_herd_immunity.strftime('%d. %B %Y'),
|
||
days_until_target = days_until_target,
|
||
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
|
||
figures = [
|
||
{
|
||
'index': 1,
|
||
'filename': 'extrapolated_to_10_percent',
|
||
'caption': 'Tägliche Impfquote, kumulierte Impfungen und lineare Extrapolation bis 10 % der Bevölkerung Deutschlands'
|
||
},{
|
||
'index': 2,
|
||
'filename': 'extrapolated_to_70_percent',
|
||
'caption': 'Tägliche Impfquote, kumulierte Impfungen und lineare Extrapolation bis 70 % der Bevölkerung Deutschlands'
|
||
},{
|
||
'index': 3,
|
||
'filename': 'extrapolated_to_100_percent',
|
||
'caption': 'Tägliche Impfquote, kumulierte Impfungen und lineare Extrapolation bis 100 % der Bevölkerung Deutschlands'
|
||
},{
|
||
'index': 4,
|
||
'filename': 'vaccination_bar_graph_total_time',
|
||
'caption': 'Tägliche Impfrate (Erst- und Zweitimpfung übereinander)'
|
||
},{
|
||
'index': 5,
|
||
'filename': 'vaccination_bar_graph_total_time_two_bars',
|
||
'caption': 'Tägliche Impfrate (Erst- und Zweitimpfung nebeneinander)'
|
||
},{
|
||
'index': 6,
|
||
'filename': 'vaccination_bar_graph_compare_both_vaccinations',
|
||
'caption': 'Tägliche Impfrate (Erst- und Zweitimpfung nebeneinander)'
|
||
},{
|
||
'index': 7,
|
||
'filename': 'cumulative_two_vaccinations',
|
||
'caption': 'Kumulative Impfrate (Erst- und Zweitimpfung)'
|
||
},{
|
||
'index': 8,
|
||
'filename': 'cumulative_two_vaccinations_percentage',
|
||
'caption': 'Kumulative Impfrate (Erst- und Zweitimpfung) in Prozent der Bevölkerung Deutschlands'
|
||
},{
|
||
'index': 9,
|
||
'filename': 'people_between_first_and_second',
|
||
'caption': 'Anzahl der Personen zwischen Erst- und Zweitimpfung, also Personen, die die erste Impfung erhalten haben, die zweite aber noch nicht'
|
||
},{
|
||
'index': 10,
|
||
'filename': 'vaccination_rate',
|
||
'caption': 'Tägliche Impfrate sowie durchschnittliche Impfrate'
|
||
}
|
||
]
|
||
).dump('site/index.xhtml')
|
||
|
||
shutil.copyfile(dashboard_filename, dashboard_archive_filename)
|
||
shutil.copyfile(stylesheet_filename, stylesheet_archive_filename)
|
||
|
||
print('Created dashboard')
|
||
|
||
render_dashboard()
|