1018 lines
34 KiB
Python
1018 lines
34 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 functools import reduce
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from matplotlib.dates import date2num, DateFormatter, WeekdayLocator
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import matplotlib.dates as mdates
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import matplotlib.ticker as mtick
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from matplotlib.patches import Rectangle
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from isoweek import Week
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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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force_renew_plots = False
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force_renew_dashboard = True
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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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def download_rki(filename_prefix):
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data_filename = '{}/{}_Impfquotenmonitoring.xlsx'.format(data_folder, filename_prefix)
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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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return data_filename
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data_filename = download_rki(filename_now)
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def parse_rki(filename):
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rki_file = pd.read_excel(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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# Filter out rows without date in first column
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impfungen['Datum'] = pd.to_datetime(impfungen['Datum'], errors='coerce', dayfirst=True)
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impfungen = impfungen[pd.notna(impfungen['Datum'])]
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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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vaccinations_by_week_map = map(lambda x: (Week.withdate(x[0]), x[1]), zip(dates, data))
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vaccinations_by_week = {}
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for w, v in vaccinations_by_week_map:
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if w in vaccinations_by_week:
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vaccinations_by_week[w] = vaccinations_by_week[w] + v
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else:
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vaccinations_by_week[w] = v
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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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days_extrapolated_herd_immunity = int(np.ceil((einwohner_deutschland * herd_immunity - total) / rate))
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weeks_extrapolated = int(np.ceil(days_extrapolated / 7))
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weeks_extrapolated_herd_immunity = int(np.ceil(days_extrapolated_herd_immunity / 7))
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date_done = today + datetime.timedelta(days_extrapolated)
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date_herd_immunity = today + datetime.timedelta(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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'days_extrapolated_herd_immunity': days_extrapolated_herd_immunity,
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'weeks_extrapolated': weeks_extrapolated,
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'weeks_extrapolated_herd_immunity': weeks_extrapolated_herd_immunity,
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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 = data.expanding().mean() #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 * herd_immunity - 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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'vaccinations_by_week': vaccinations_by_week,
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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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'vaccinations_last_day': data.iloc[-1],
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'vaccinations_last_day_percentage': data.iloc[-1] / einwohner_deutschland * 100,
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'vaccinations_last_day_vaccination_percentage': data.iloc[-1] / total * 100,
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'vaccinations_last_week': vaccinations_by_week[Week.thisweek() - 1],
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'vaccinations_last_week_percentage': vaccinations_by_week[Week.thisweek() - 1] / einwohner_deutschland * 100,
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'vaccinations_last_week_vaccination_percentage': vaccinations_by_week[Week.thisweek() - 1] / total * 100
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}
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if 'Erstimpfung' in impfungen:
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raw_first_vaccinations = impfungen['Erstimpfung']
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elif 'Erstimpfungen' in impfungen:
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raw_first_vaccinations = impfungen['Erstimpfungen']
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elif 'Einmal geimpft' in impfungen:
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raw_first_vaccinations = impfungen['Einmal geimpft']
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elif 'mindestens einmal geimpft' in impfungen:
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raw_first_vaccinations = impfungen['mindestens einmal geimpft']
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elif 'Begonnene Impfserie' in impfungen:
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raw_first_vaccinations = impfungen['Begonnene Impfserie']
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if 'Zweitimpfung' in impfungen:
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raw_second_vaccinations = impfungen['Zweitimpfung']
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if 'Zweitimpfungen' in impfungen:
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raw_second_vaccinations = impfungen['Zweitimpfungen']
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elif 'Vollständig geimpft' in impfungen:
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raw_second_vaccinations = impfungen['Vollständig geimpft']
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elif 'vollständig geimpft' in impfungen:
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raw_second_vaccinations = impfungen['vollständig geimpft']
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elif 'vollständig geimpt' in impfungen:
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raw_second_vaccinations = impfungen['vollständig geimpt'] # sic!
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data_first_vaccination = calculate_vaccination_data(raw_first_vaccinations)
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data_second_vaccination = calculate_vaccination_data(raw_second_vaccinations)
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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{2}.\d{2}.(\d{2}|\d{4}), \d{1,2}:\d{2}) Uhr$')
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m = stand_regex.match(stand)
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stand_date_part = m.groups()[0].replace('.21,', '.2021,')
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stand_date = datetime.datetime.strptime(stand_date_part, '%d.%m.%Y, %H:%M')
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print_stand = stand_date.isoformat()
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return dates, start_of_reporting_date, data_first_vaccination, data_second_vaccination, stand_date, print_stand
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dates, start_of_reporting_date, data_first_vaccination, data_second_vaccination, stand_date, print_stand = parse_rki(filename=data_filename)
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filename_stand = stand_date.strftime("%Y%m%d%H%M%S")
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print(f"Effective {stand_date}, last reported date {dates.iloc[-1].date()}")
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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 check_recreate_plot(plot_name):
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archive_plot_filename = '{}/{}'.format(archive_folder, plot_name)
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if os.path.isfile(archive_plot_filename + '.pdf') and not force_renew_plots:
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print('Plot {} already exists'.format(plot_name))
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return False
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return True
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def save_plot(plot_name):
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folders = [archive_folder, site_folder]
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file_formats = ['pdf', 'png', 'svg']
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file_template = '{folder}/{plot_name}.{format}'
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for folder in folders:
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for format in file_formats:
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plt.savefig(file_template.format(folder=folder, plot_name=plot_name, format=format))
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print('Created plot {} as {}'.format(plot_name, file_formats))
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def labeled_timeperiod(ax, start, end, text, color='lightgrey', y_height = 100):
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centre = start + (end - start) / 2
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ax.axvspan(start, end, color=color, alpha=0.2)
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ax.text(centre, ax.get_ylim()[1] * (y_height / 100), text, bbox={
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'boxstyle': 'square',
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'fc': color,
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'ec': 'black'
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}, ha='center')
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def add_annotations(ax):
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labeled_timeperiod(ax, datetime.date(2021, 3, 15), datetime.date(2021, 3, 19), 'AZ-Stopp', 'silver')
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labeled_timeperiod(ax, datetime.date(2021, 3, 29), datetime.date(2021, 4, 6), 'AZ-Stopp u. 60', 'lightgrey', 96)
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labeled_timeperiod(ax, datetime.date(2021, 4, 6), today, 'Arztpraxen impfen', 'lightgreen')
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def plot_vaccination_bar_graph_total_time():
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plot_name = 'vaccination_bar_graph_total_time'
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if not check_recreate_plot(plot_name):
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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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add_annotations(ax)
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save_plot(plot_name)
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plt.close()
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plot_vaccination_bar_graph_total_time()
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def plot_vaccination_bar_graph_last_three_weeks():
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plot_name = 'vaccination_bar_graph_last_three_weeks'
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if not check_recreate_plot(plot_name):
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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 der letzten drei Wochen (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_xlim([today - datetime.timedelta(22), today])
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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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add_annotations(ax)
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save_plot(plot_name)
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plt.close()
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plot_vaccination_bar_graph_last_three_weeks()
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def plot_vaccination_bar_graph_total_time_by_week():
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plot_name = 'vaccination_bar_graph_total_time_by_week'
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if not check_recreate_plot(plot_name):
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return
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fig, ax = plt.subplots(1)
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plt.title(
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'Wöchentliche Impfrate in Tausend (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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w = [w.day(3) for w in data_first_vaccination['vaccinations_by_week'].keys()]
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f = np.floor(np.array(list(data_first_vaccination['vaccinations_by_week'].values())) / 1000)
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s = np.floor(np.array(list(data_second_vaccination['vaccinations_by_week'].values())) / 1000)
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bar1 = ax.bar(w, f, label='Wöchentliche Erstimpfungen', color='blue', width=6.8)
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bar2 = ax.bar(w, s, label='Wöchentliche Zweitimpfungen', color='lightblue', width=6.8, bottom=f)
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i = 0
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nan_to_zero = lambda x: 0 if math.isnan(x) else x
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for r1, r2 in zip(bar1, bar2):
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x = r1.get_x() + r1.get_width() / 2.0
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h1 = nan_to_zero(r1.get_height())
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h2 = nan_to_zero(r2.get_height())
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hg = h1 + h2
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if h1 > 30:
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plt.text(x, h1 * 0.5, f'{h1:5n}'.replace('.', ' '), ha='center', va='center', color='white')
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if h2 > 30:
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plt.text(x, h1 + h2 * 0.5, f'{h2:5n}'.replace('.', ' '), ha='center', va='center', color='black')
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plt.text(x, hg, f'{hg:5n}'.replace('.', ' '), ha='center', va='bottom')
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if i == 12:
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# Woche der AstraZeneca-Aussetzung
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plt.annotate('AZ-Stopp', (x, hg + 60),
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xytext=(x, ax.get_ylim()[1]),
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arrowprops={
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'arrowstyle': '->'
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},
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bbox={
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'boxstyle': 'square',
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'fc': 'white',
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'ec': 'black'
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},
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ha='center')
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if i == len(bar1) - 1:
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plt.annotate('Diese Woche', (x, hg + 60),
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xytext=(x, ax.get_ylim()[1]),
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arrowprops={
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'arrowstyle': '->',
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'relpos': (0, 0)
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},
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bbox={
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'boxstyle': 'square',
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'fc': 'white',
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'ec': 'black'
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},
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ha='left')
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i = i + 1
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ax.legend(loc='upper left')
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ax.get_xaxis().set_major_formatter(DateFormatter('%Y-w%W'))
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ax.get_xaxis().set_major_locator(WeekdayLocator(3, 3))
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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('Wöchentliche Impfungen (in Tausend)')
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save_plot(plot_name)
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plt.close()
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plot_vaccination_bar_graph_total_time_by_week()
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def plot_vaccination_bar_graph_total_time_two_bars():
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plot_name = 'vaccination_bar_graph_total_time_two_bars'
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if not check_recreate_plot(plot_name):
|
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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')
|
||
|
||
add_annotations(ax)
|
||
|
||
save_plot(plot_name)
|
||
plt.close()
|
||
|
||
plot_vaccination_bar_graph_total_time_two_bars()
|
||
|
||
def plot_vaccination_bar_graph_compare_both_vaccinations():
|
||
|
||
plot_name = 'vaccination_bar_graph_compare_both_vaccinations'
|
||
if not check_recreate_plot(plot_name):
|
||
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')
|
||
|
||
save_plot(plot_name)
|
||
plt.close()
|
||
|
||
plot_vaccination_bar_graph_compare_both_vaccinations()
|
||
|
||
def plot_vaccination_bar_graph_compare_both_vaccinations_six_weeks():
|
||
|
||
plot_name = 'vaccination_bar_graph_compare_both_vaccinations_six_weeks'
|
||
if not check_recreate_plot(plot_name):
|
||
return
|
||
|
||
fig, ax = plt.subplots(1)
|
||
|
||
|
||
plt.title(
|
||
'Tägliche Impfrate (Erst- und Zweitimpfung um 42 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=42))
|
||
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')
|
||
|
||
save_plot(plot_name)
|
||
plt.close()
|
||
|
||
plot_vaccination_bar_graph_compare_both_vaccinations_six_weeks()
|
||
|
||
def plot_cumulative_two_vaccinations():
|
||
|
||
plot_name = 'cumulative_two_vaccinations'
|
||
if not check_recreate_plot(plot_name):
|
||
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')
|
||
|
||
add_annotations(ax)
|
||
|
||
save_plot(plot_name)
|
||
plt.close()
|
||
|
||
|
||
plot_cumulative_two_vaccinations()
|
||
|
||
def plot_cumulative_two_vaccinations_percentage():
|
||
|
||
plot_name = 'cumulative_two_vaccinations_percentage'
|
||
if not check_recreate_plot(plot_name):
|
||
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, symbol=' %'))
|
||
|
||
ax.set_xlabel('Datum')
|
||
ax.set_ylabel('Kumulative Impfungen')
|
||
|
||
add_annotations(ax)
|
||
|
||
save_plot(plot_name)
|
||
plt.close()
|
||
|
||
|
||
plot_cumulative_two_vaccinations_percentage()
|
||
|
||
|
||
def plot_people_between_first_and_second():
|
||
|
||
plot_name = 'people_between_first_and_second'
|
||
if not check_recreate_plot(plot_name):
|
||
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')
|
||
|
||
add_annotations(ax)
|
||
|
||
save_plot(plot_name)
|
||
plt.close()
|
||
|
||
|
||
plot_people_between_first_and_second()
|
||
|
||
def plot_vaccination_rate():
|
||
|
||
plot_name = 'vaccination_rate'
|
||
if not check_recreate_plot(plot_name):
|
||
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]')
|
||
|
||
add_annotations(ax)
|
||
|
||
save_plot(plot_name)
|
||
plt.close()
|
||
|
||
plot_vaccination_rate()
|
||
|
||
def plot_vaccination_done_days():
|
||
|
||
plot_name = 'vaccination_done_days'
|
||
if not check_recreate_plot(plot_name):
|
||
return
|
||
|
||
fig, ax = plt.subplots(1)
|
||
|
||
|
||
plt.title(
|
||
'Lin. Extrapolation der Erst- und Zweitimpfungen bis 70 % der Bevölkerung anhand der durchschn. Impfrate (Anzahl Tage, Gesamt und 7 Tage)\n'
|
||
'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
|
||
print_stand, print_today
|
||
)
|
||
)
|
||
|
||
ax.set_xlim(start_of_reporting_date, today)
|
||
ax.set_ylim(0, 2500)
|
||
ax.axhline(days_until_target, label='Impfziel Ende Sommer')
|
||
|
||
for d, c, l in [(data_first_vaccination, 'blue', 'Erstimpfungen'), (data_second_vaccination, 'lightblue', 'Zweitimpfungen')]:
|
||
|
||
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.plot(dates, days_remaining_daily, label=f'{l} Durchschnitt Gesamt', linewidth=0.5, color=c)
|
||
ax.plot(dates, days_remaining_rolling, label=f'{l} Durchschnitt 7 Tage', linewidth=2, color=c)
|
||
|
||
ax.grid(True)
|
||
|
||
d = datetime.date(2021, 3, 1)
|
||
ax.add_patch(Rectangle((d, 0), today - d, 52 * 7, edgecolor='darkgrey', lw=3, fill=False, label='Detailansicht (s.u.)'))
|
||
|
||
ax.legend(loc='lower left')
|
||
ax.get_yaxis().get_major_formatter().set_scientific(False)
|
||
|
||
ax.set_xlabel('Datum')
|
||
ax.set_ylabel('Tage, bis 70 % erreicht sind')
|
||
add_annotations(ax)
|
||
|
||
save_plot(plot_name)
|
||
plt.close()
|
||
|
||
plot_vaccination_done_days()
|
||
|
||
def plot_vaccination_done_weeks():
|
||
|
||
plot_name = 'vaccination_done_weeks'
|
||
if not check_recreate_plot(plot_name):
|
||
return
|
||
|
||
fig, ax = plt.subplots(1)
|
||
|
||
|
||
plt.title(
|
||
'Lin. Extrapolation der Erst- und Zweitimpfungen bis 70 % der Bevölkerung anhand der durchschn. Impfrate (Anzahl Wochen, Gesamt und 7 Tage)\n'
|
||
'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
|
||
print_stand, print_today
|
||
)
|
||
)
|
||
|
||
ax.set_xlim(datetime.date(2021, 3, 1), today)
|
||
ax.set_ylim(0, 52)
|
||
ax.axhline(days_until_target / 7, label='Impfziel Ende Sommer')
|
||
|
||
for d, c, l in [(data_first_vaccination, 'blue', 'Erstimpfungen'), (data_second_vaccination, 'lightblue', 'Zweitimpfungen')]:
|
||
|
||
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.plot(dates, weeks_remaining_daily, label=f'{l} Durchschnitt Gesamt', linewidth=0.5, color=c)
|
||
ax.plot(dates, weeks_remaining_rolling, label=f'{l} Durchschnitt 7 Tage', linewidth=2, color=c)
|
||
|
||
ax.grid(True)
|
||
|
||
|
||
ax.legend(loc='lower left')
|
||
ax.get_yaxis().get_major_formatter().set_scientific(False)
|
||
|
||
ax.set_xlabel('Datum')
|
||
ax.set_ylabel('Wochen, bis 70 % erreicht sind')
|
||
|
||
add_annotations(ax)
|
||
|
||
save_plot(plot_name)
|
||
plt.close()
|
||
|
||
plot_vaccination_done_weeks()
|
||
|
||
def plot_vaccination_done_dates():
|
||
|
||
plot_name = 'vaccination_done_dates'
|
||
if not check_recreate_plot(plot_name):
|
||
return
|
||
|
||
fig, ax = plt.subplots(1)
|
||
|
||
|
||
plt.title(
|
||
'Lin. Extrapolation der Erst- und Zweitimpfungen bis 70 % der Bevölkerung anhand der durchschn. Impfrate (Datum, Gesamt und 7 Tage)\n'
|
||
'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
|
||
print_stand, print_today
|
||
)
|
||
)
|
||
|
||
for d, c, l in [(data_first_vaccination, 'blue', 'Erstimpfungen'), (data_second_vaccination, 'lightblue', 'Zweitimpfungen')]:
|
||
|
||
days_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily']))
|
||
#days_remaining_daily[np.isnan(days_remaining_daily)] = 10000
|
||
days_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
|
||
#days_remaining_rolling[np.isnan(days_remaining_rolling)] = 10000
|
||
|
||
dates_daily = [today + datetime.timedelta(days) for days in days_remaining_daily.dropna()]
|
||
dates_rolling = [today + datetime.timedelta(days) for days in days_remaining_rolling.dropna()]
|
||
|
||
offset = len(dates) - len(dates_daily)
|
||
ax.plot(dates[offset:], dates_daily, label=f'{l} Durchschnitt Gesamt', linewidth=0.5, color=c)
|
||
|
||
offset = len(dates) - len(dates_rolling)
|
||
ax.plot(dates[offset:], dates_rolling, label=f'{l} Durchschnitt 7 Tage', linewidth=2, color=c)
|
||
|
||
ax.set_xlim(start_of_reporting_date, today)
|
||
ax.set_ylim(today, datetime.date(2025, 7, 1))
|
||
|
||
ax.axhline(target_date_for_herd_immunity - datetime.timedelta(21), label='Impfziel Ende Sommer')
|
||
ax.add_patch(Rectangle((datetime.date(2021, 4, 6), today), today - datetime.date(2021, 4, 6), target_date_for_herd_immunity - today, edgecolor='darkgrey', lw=3, fill=False, label='Detailansicht (s.u.)'))
|
||
ax.grid(True)
|
||
|
||
|
||
ax.legend(loc='lower left')
|
||
|
||
ax.set_xlabel('Datum')
|
||
ax.set_ylabel('Datum, an dem 70 % erreicht sind')
|
||
|
||
add_annotations(ax)
|
||
|
||
save_plot(plot_name)
|
||
plt.close()
|
||
|
||
plot_vaccination_done_dates()
|
||
|
||
def plot_vaccination_done_dates_detail():
|
||
|
||
plot_name = 'vaccination_done_dates_detail'
|
||
if not check_recreate_plot(plot_name):
|
||
return
|
||
|
||
fig, ax = plt.subplots(1)
|
||
|
||
|
||
plt.title(
|
||
'Lin. Extrapolation der Erst- und Zweitimpfungen bis 70 % der Bevölkerung anhand der durchschn. Impfrate (Detail, Datum, Gesamt und 7 Tage)\n'
|
||
'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
|
||
print_stand, print_today
|
||
)
|
||
)
|
||
|
||
for d, c, l in [(data_first_vaccination, 'blue', 'Erstimpfungen'), (data_second_vaccination, 'lightblue', 'Zweitimpfungen')]:
|
||
|
||
days_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily']))
|
||
days_remaining_daily[np.isnan(days_remaining_daily)] = 10000
|
||
days_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
|
||
days_remaining_rolling[np.isnan(days_remaining_rolling)] = 10000
|
||
|
||
dates_daily = [today + datetime.timedelta(days) for days in days_remaining_daily.dropna()]
|
||
dates_rolling = [today + datetime.timedelta(days) for days in days_remaining_rolling.dropna()]
|
||
|
||
offset = len(dates) - len(dates_daily)
|
||
ax.plot(dates[offset:], dates_daily, label=f'{l} Durchschnitt Gesamt', linewidth=0.5, color=c)
|
||
|
||
offset = len(dates) - len(dates_rolling)
|
||
ax.plot(dates[offset:], dates_rolling, label=f'{l} Durchschnitt 7 Tage', linewidth=2, color=c)
|
||
|
||
ax.set_xlim(datetime.date(2021, 4, 6), today)
|
||
ax.set_ylim(today, target_date_for_herd_immunity)
|
||
ax.axhline(target_date_for_herd_immunity - datetime.timedelta(21), label='Impfziel Ende Sommer')
|
||
|
||
ax.grid(True)
|
||
|
||
|
||
ax.legend(loc='lower left')
|
||
|
||
ax.set_xlabel('Datum')
|
||
ax.set_ylabel('Datum, an dem 70 % erreicht sind')
|
||
|
||
add_annotations(ax)
|
||
|
||
save_plot(plot_name)
|
||
plt.close()
|
||
|
||
plot_vaccination_done_dates_detail()
|
||
|
||
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) and not force_renew_dashboard:
|
||
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': 'vaccination_bar_graph_total_time',
|
||
'caption': 'Tägliche Impfrate (Erst- und Zweitimpfung übereinander)'
|
||
},{
|
||
'index': 2,
|
||
'filename': 'vaccination_bar_graph_last_three_weeks',
|
||
'caption': 'Tägliche Impfrate der letzten drei Wochen (Erst- und Zweitimpfung übereinander)'
|
||
},{
|
||
'index': 3,
|
||
'filename': 'vaccination_bar_graph_total_time_by_week',
|
||
'caption': 'Wöchentliche Impfrate in Tausend (Erst- und Zweitimpfung übereinander)'
|
||
},{
|
||
'index': 4,
|
||
'filename': 'vaccination_bar_graph_total_time_two_bars',
|
||
'caption': 'Tägliche Impfrate (Erst- und Zweitimpfung nebeneinander)'
|
||
},{
|
||
'index': 5,
|
||
'filename': 'vaccination_bar_graph_compare_both_vaccinations',
|
||
'caption': 'Tägliche Impfrate (Erst- und Zweitimpfung um 21 Tage versetzt)'
|
||
},{
|
||
'index': 6,
|
||
'filename': 'vaccination_bar_graph_compare_both_vaccinations_six_weeks',
|
||
'caption': 'Tägliche Impfrate (Erst- und Zweitimpfung um 42 Tage versetzt)'
|
||
},{
|
||
'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'
|
||
},{
|
||
'index': 11,
|
||
'filename': 'vaccination_done_days',
|
||
'caption': 'Lineare Extrapolation der Erst- und Zweitimpfungen bis 70 % der Bevölkerung anhand der durchschnittlichen Impfrate (Anzahl Tage, Gesamt und 7 Tage)'
|
||
},{
|
||
'index': 12,
|
||
'filename': 'vaccination_done_weeks',
|
||
'caption': 'Lineare Extrapolation der Erst- und Zweitimpfungen bis 70 % der Bevölkerung anhand der durchschnittlichen Impfrate (Anzahl Wochen, Gesamt und 7 Tage)'
|
||
},{
|
||
'index': 13,
|
||
'filename': 'vaccination_done_dates',
|
||
'caption': 'Lineare Extrapolation der Erst- und Zweitimpfungen bis 70 % der Bevölkerung anhand der durchschnittlichen Impfrate (Datum, Gesamt und 7 Tage)'
|
||
},{
|
||
'index': 14,
|
||
'filename': 'vaccination_done_dates_detail',
|
||
'caption': 'Lineare Extrapolation der Erst- und Zweitimpfungen bis 70 % der Bevölkerung anhand der durchschnittlichen Impfrate (Datum, Gesamt und 7 Tage)'
|
||
}
|
||
]
|
||
).dump('site/index.xhtml')
|
||
|
||
shutil.copyfile(dashboard_filename, dashboard_archive_filename)
|
||
shutil.copyfile(stylesheet_filename, stylesheet_archive_filename)
|
||
|
||
print('Created dashboard')
|
||
|
||
render_dashboard()
|