rki-impfparser/plot.py

#!/usr/bin/python
# vim: set fileencoding=utf-8 :

# Copyright (C) Benedikt Bastin, 2021
# SPDX-License-Identifier: EUPL-1.2

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

import datetime
import re
import requests as req
import locale
import os.path
import shutil
import math
from functools import reduce

from matplotlib.dates import date2num, DateFormatter, WeekdayLocator
import matplotlib.dates as mdates
import matplotlib.ticker as mtick
from matplotlib.patches import Rectangle

from isoweek import Week

locale.setlocale(locale.LC_ALL, 'de_DE.UTF-8')

site_folder = 'site/'
data_folder = 'data/'

einwohner_deutschland = 83190556
herd_immunity = 0.7


today = datetime.date.today()
print_today = today.isoformat()

filename_now = datetime.datetime.now().strftime("%Y%m%d%H%M%S")

force_renew_plots = False
force_renew_dashboard = True

# https://www.tagesschau.de/ausland/europa/ursula-von-der-leyen-zu-corona-impfstoffen-101.html
target_date_for_herd_immunity = datetime.date(2021, 9, 22)
days_until_target = (target_date_for_herd_immunity - today).days - 21


# DIN A4 Plots
plt.rcParams["figure.figsize"] = [11.69, 8.27]


# Download

def download_rki(filename_prefix):
	data_filename = '{}/{}_Impfquotenmonitoring.xlsx'.format(data_folder, filename_prefix)

	r = req.get('https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Daten/Impfquotenmonitoring.xlsx?__blob=publicationFile')

	with open(data_filename, 'wb') as outfile:
		outfile.write(r.content)

	return data_filename

data_filename = download_rki(filename_now)

def parse_rki(filename):

	rki_file = pd.read_excel(filename, sheet_name=None, engine='openpyxl')

	raw_data = rki_file['Impfungen_proTag']

	impfungen = raw_data[:-1].dropna(subset=['Datum']).fillna(0)

	# Filter out rows without date in first column
	impfungen['Datum'] = pd.to_datetime(impfungen['Datum'], errors='coerce', dayfirst=True)
	impfungen = impfungen[pd.notna(impfungen['Datum'])]


	dates = impfungen['Datum']

	start_of_reporting_date = dates.iloc[0].date()

	def calculate_vaccination_data(data):

		total = int(np.sum(data))
		total_percentage = float(total) / einwohner_deutschland * 100

		to_be_vaccinated = einwohner_deutschland - total

		last_date = dates.iloc[-1].date()
		start_of_vaccination_index = (data != 0).argmax(axis=0)
		start_of_vaccination_date = dates[start_of_vaccination_index].date()
		days_since_start_of_vaccination = (last_date - start_of_vaccination_date).days
		days_since_start_of_reporting = (last_date - start_of_reporting_date).days

		valid_data = data[start_of_vaccination_index:]

		cumulative = np.concatenate(([math.nan] * (days_since_start_of_reporting - days_since_start_of_vaccination), np.cumsum(valid_data)))

		mean_all_time = np.mean(valid_data)
		mean_seven_days = np.mean(data[-7:])

		vaccinations_by_week_map = map(lambda x: (Week.withdate(x[0]), x[1]), zip(dates, data))

		vaccinations_by_week = {}

		for w, v in vaccinations_by_week_map:
			if w in vaccinations_by_week:
				vaccinations_by_week[w] = vaccinations_by_week[w] + v
			else:
				vaccinations_by_week[w] = v

		def extrapolate(rate, to_be_vaccinated):
			days_extrapolated = int(np.ceil(to_be_vaccinated / rate))
			days_extrapolated_herd_immunity = int(np.ceil((einwohner_deutschland * herd_immunity - total) / rate))

			weeks_extrapolated = int(np.ceil(days_extrapolated / 7))
			weeks_extrapolated_herd_immunity = int(np.ceil(days_extrapolated_herd_immunity / 7))

			date_done = today + datetime.timedelta(days_extrapolated)
			date_herd_immunity = today + datetime.timedelta(days_extrapolated_herd_immunity)

			extrapolated_vaccinations = total + rate * range(-days_since_start_of_reporting, days_extrapolated - days_since_start_of_reporting)

			return {
				'rate': rate,
				'rate_int': int(np.round(rate)),
				'days_extrapolated': days_extrapolated,
				'days_extrapolated_herd_immunity': days_extrapolated_herd_immunity,
				'weeks_extrapolated': weeks_extrapolated,
				'weeks_extrapolated_herd_immunity': weeks_extrapolated_herd_immunity,
				'date_done': date_done,
				'date_done_str': date_done.strftime('%d. %B %Y'),
				'date_herd_immunity': date_herd_immunity,
				'date_herd_immunity_str': date_herd_immunity.strftime('%d. %B %Y'),
				'extrapolated_vaccinations': extrapolated_vaccinations
			}


		extrapolation_mean_all_time = extrapolate(mean_all_time, to_be_vaccinated)
		extrapolation_last_rate = extrapolate(data.iloc[-1], to_be_vaccinated)
		extrapolation_mean_seven_days = extrapolate(mean_seven_days, to_be_vaccinated)

		mean_vaccination_rates_daily  = data.expanding().mean() #np.round(cumulative / range(1, len(cumulative) + 1))
		vaccination_rates_daily_rolling_average = data.rolling(7).mean()

		vaccinations_missing_until_target = einwohner_deutschland * herd_immunity - total
		vaccination_rate_needed_for_target = vaccinations_missing_until_target / days_until_target
		vaccination_rate_needed_for_target_percentage = mean_all_time / vaccination_rate_needed_for_target * 100

		return {
			'daily': data,
			'cumulative': cumulative,
			'total': total,
			'total_percentage': total_percentage,
			'to_be_vaccinated': to_be_vaccinated,
			'last_date': last_date,
			'last_date_str': last_date.strftime('%d. %B %Y'),
			'days_since_start': days_since_start_of_vaccination + 1, # Shift from zero to one-based-index
			'start_of_vaccination_date': start_of_vaccination_date,
			'start_of_vaccination_date_str': start_of_vaccination_date.strftime('%d. %B %Y'),
			'vaccinations_by_week': vaccinations_by_week,
			'extrapolation_mean_all_time': extrapolation_mean_all_time,
			'extrapolation_last_rate': extrapolation_last_rate,
			'extrapolation_mean_seven_days': extrapolation_mean_seven_days,
			'mean_vaccination_rates_daily': mean_vaccination_rates_daily,
			'vaccination_rates_daily_rolling_average': vaccination_rates_daily_rolling_average,
			'vaccinations_missing_until_target': int(np.floor(vaccinations_missing_until_target)),
			'vaccination_rate_needed_for_target': int(np.floor(vaccination_rate_needed_for_target)),
			'vaccination_rate_needed_for_target_percentage': vaccination_rate_needed_for_target_percentage,
			'vaccinations_last_day': data.iloc[-1],
			'vaccinations_last_day_percentage': data.iloc[-1] / einwohner_deutschland * 100,
			'vaccinations_last_day_vaccination_percentage': data.iloc[-1] / total * 100,
			'vaccinations_last_week': vaccinations_by_week[Week.thisweek() - 1],
			'vaccinations_last_week_percentage': vaccinations_by_week[Week.thisweek() - 1] / einwohner_deutschland * 100,
			'vaccinations_last_week_vaccination_percentage': vaccinations_by_week[Week.thisweek() - 1] / total * 100
		}

	if 'Erstimpfung' in impfungen:
		raw_first_vaccinations = impfungen['Erstimpfung']
	elif 'Erstimpfungen' in impfungen:
		raw_first_vaccinations = impfungen['Erstimpfungen']
	elif 'Einmal geimpft' in impfungen:
		raw_first_vaccinations = impfungen['Einmal geimpft']
	elif 'mindestens einmal geimpft' in impfungen:
		raw_first_vaccinations = impfungen['mindestens einmal geimpft']
	elif 'Begonnene Impfserie' in impfungen:
		raw_first_vaccinations = impfungen['Begonnene Impfserie']

	if 'Zweitimpfung' in impfungen:
		raw_second_vaccinations = impfungen['Zweitimpfung']
	if 'Zweitimpfungen' in impfungen:
		raw_second_vaccinations = impfungen['Zweitimpfungen']
	elif 'Vollständig geimpft' in impfungen:
		raw_second_vaccinations = impfungen['Vollständig geimpft']
	elif 'vollständig geimpft' in impfungen:
		raw_second_vaccinations = impfungen['vollständig geimpft']
	elif 'vollständig geimpt' in impfungen:
		raw_second_vaccinations = impfungen['vollständig geimpt'] # sic!

	data_first_vaccination = calculate_vaccination_data(raw_first_vaccinations)
	data_second_vaccination = calculate_vaccination_data(raw_second_vaccinations)

	# Stand aus Daten auslesen
	#stand = dates.iloc[-1]
	#print_stand = stand.isoformat()

	# Stand aus offiziellen Angaben auslesen
	stand = rki_file['Erläuterung'].iloc[1][0]

	stand_regex = re.compile('^Datenstand: (\d\d.\d\d.\d\d\d\d, \d?\d:\d\d) Uhr$')
	m = stand_regex.match(stand)
	stand_date = datetime.datetime.strptime(m.groups()[0], '%d.%m.%Y, %H:%M')
	print_stand = stand_date.isoformat()

	return dates, start_of_reporting_date, data_first_vaccination, data_second_vaccination, stand_date, print_stand

dates, start_of_reporting_date, data_first_vaccination, data_second_vaccination, stand_date, print_stand = parse_rki(filename=data_filename)

filename_stand = stand_date.strftime("%Y%m%d%H%M%S")

print(f"Effective {stand_date}, last reported date {dates.iloc[-1].date()}")


archive_folder = site_folder + 'archive/' + filename_stand

if os.path.isdir(archive_folder):
	print('Archive folder {} already exists'.format(archive_folder))
else:
	os.mkdir(archive_folder)

def check_recreate_plot(plot_name):

	archive_plot_filename = '{}/{}'.format(archive_folder, plot_name)

	if os.path.isfile(archive_plot_filename + '.pdf') and not force_renew_plots:
		print('Plot {} already exists'.format(plot_name))
		return False

	return True

def save_plot(plot_name):

	folders = [archive_folder, site_folder]
	file_formats = ['pdf', 'png', 'svg']
	file_template = '{folder}/{plot_name}.{format}'

	for folder in folders:
		for format in file_formats:
			plt.savefig(file_template.format(folder=folder, plot_name=plot_name, format=format))

	print('Created plot {} as {}'.format(plot_name, file_formats))

def labeled_timeperiod(ax, start, end, text, color='lightgrey', y_height = 100):
	centre = start + (end - start) / 2
	ax.axvspan(start, end, color=color, alpha=0.2)
	ax.text(centre, ax.get_ylim()[1] * (y_height / 100), text, bbox={
		'boxstyle': 'square',
		'fc': color,
		'ec': 'black'
	}, ha='center')


def add_annotations(ax):
	labeled_timeperiod(ax, datetime.date(2021, 3, 15), datetime.date(2021, 3, 19), 'AZ-Stopp', 'silver')
	labeled_timeperiod(ax, datetime.date(2021, 3, 29), datetime.date(2021, 4, 6), 'AZ-Stopp u. 60', 'lightgrey', 96)
	labeled_timeperiod(ax, datetime.date(2021, 4, 6), today, 'Arztpraxen impfen', 'lightgreen')


def plot_vaccination_bar_graph_total_time():

	plot_name = 'vaccination_bar_graph_total_time'
	if not check_recreate_plot(plot_name):
		return

	fig, ax = plt.subplots(1)


	plt.title(
		'Tägliche Impfrate (Erst- und Zweitimpfung übereinander)\n'
			'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
			print_stand, print_today
		)
	)

	ax.grid()

	ax.bar(dates, data_first_vaccination['daily'], label='Tägliche Erstimpfungen', color='blue')
	ax.bar(dates, data_second_vaccination['daily'], label='Tägliche Zweitimpfungen', color='lightblue', bottom=data_first_vaccination['daily'])

	ax.set_ylim([0, np.max(data_first_vaccination['daily']) + np.max(data_second_vaccination['daily'])])

	ax.legend(loc='upper left')
	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()

def plot_vaccination_bar_graph_last_three_weeks():

	plot_name = 'vaccination_bar_graph_last_three_weeks'
	if not check_recreate_plot(plot_name):
		return

	fig, ax = plt.subplots(1)


	plt.title(
		'Tägliche Impfrate der letzten drei Wochen (Erst- und Zweitimpfung übereinander)\n'
			'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
			print_stand, print_today
		)
	)

	ax.grid()

	ax.bar(dates, data_first_vaccination['daily'], label='Tägliche Erstimpfungen', color='blue')
	ax.bar(dates, data_second_vaccination['daily'], label='Tägliche Zweitimpfungen', color='lightblue', bottom=data_first_vaccination['daily'])

	ax.set_xlim([today - datetime.timedelta(22), today])

	ax.legend(loc='upper left')
	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_last_three_weeks()

def plot_vaccination_bar_graph_total_time_by_week():

	plot_name = 'vaccination_bar_graph_total_time_by_week'
	if not check_recreate_plot(plot_name):
		return

	fig, ax = plt.subplots(1)


	plt.title(
		'Wöchentliche Impfrate in Tausend (Erst- und Zweitimpfung übereinander)\n'
			'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
			print_stand, print_today
		)
	)

	ax.grid()

	w = [w.day(3) for w in data_first_vaccination['vaccinations_by_week'].keys()]

	f = np.floor(np.array(list(data_first_vaccination['vaccinations_by_week'].values())) / 1000)
	s = np.floor(np.array(list(data_second_vaccination['vaccinations_by_week'].values())) / 1000)

	bar1 = ax.bar(w, f, label='Wöchentliche Erstimpfungen', color='blue', width=6.8)
	bar2 = ax.bar(w, s, label='Wöchentliche Zweitimpfungen', color='lightblue', width=6.8, bottom=f)

	i = 0

	nan_to_zero = lambda x: 0 if math.isnan(x) else x

	for r1, r2 in zip(bar1, bar2):

		x = r1.get_x() + r1.get_width() / 2.0

		h1 = nan_to_zero(r1.get_height())
		h2 = nan_to_zero(r2.get_height())
		hg = h1 + h2

		if h1 > 30:
			plt.text(x, h1 * 0.5, f'{h1:5n}'.replace('.', ' '), ha='center', va='center', color='white')

		if h2 > 30:
			plt.text(x, h1 + h2 * 0.5, f'{h2:5n}'.replace('.', ' '), ha='center', va='center', color='black')

		plt.text(x, hg, f'{hg:5n}'.replace('.', ' '), ha='center', va='bottom')

		if i == 12:
			# Woche der AstraZeneca-Aussetzung
			plt.annotate('AZ-Stopp', (x, hg + 60),
				xytext=(x, ax.get_ylim()[1]),
				arrowprops={
					'arrowstyle': '->'
				},
				bbox={
					'boxstyle': 'square',
					'fc': 'white',
					'ec': 'black'
				},
				ha='center')

		if i == len(bar1) - 1:
			plt.annotate('Diese Woche', (x, hg + 60),
				xytext=(x, ax.get_ylim()[1]),
				arrowprops={
					'arrowstyle': '->',
					'relpos': (0, 0)
				},
				bbox={
					'boxstyle': 'square',
					'fc': 'white',
					'ec': 'black'
				},
				ha='left')

		i = i + 1


	ax.legend(loc='upper left')
	ax.get_xaxis().set_major_formatter(DateFormatter('%Y-w%W'))
	ax.get_xaxis().set_major_locator(WeekdayLocator(3, 3))
	ax.get_yaxis().get_major_formatter().set_scientific(False)

	ax.set_xlabel('Datum')
	ax.set_ylabel('Wöchentliche Impfungen (in Tausend)')

	save_plot(plot_name)
	plt.close()

plot_vaccination_bar_graph_total_time_by_week()

def plot_vaccination_bar_graph_total_time_two_bars():

	plot_name = 'vaccination_bar_graph_total_time_two_bars'
	if not check_recreate_plot(plot_name):
		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()