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 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 = False

# 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

data_filename = '{}/{}_Impfquotenmonitoring.xlsx'.format(data_folder, filename_now)

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)

#data_filename = 'data/20210118151908_Impfquotenmonitoring.xlsx'

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

raw_data = rki_file['Impfungen_proTag']

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

impfungen.drop(impfungen.tail(2).index,inplace=True) # remove Gesamt row

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))
		extrapolated_dates = np.array([dates[0] + datetime.timedelta(days=i) for i in range(days_extrapolated)])

		date_done = extrapolated_dates[-1]
		date_herd_immunity = extrapolated_dates[int(np.ceil(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,
			'dates': extrapolated_dates,
			'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  = np.round(cumulative / range(1, len(cumulative) + 1))
	vaccination_rates_daily_rolling_average = data.rolling(7).mean()

	vaccinations_missing_until_target = einwohner_deutschland * 0.7 - total
	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
	}


data_first_vaccination = calculate_vaccination_data(impfungen['Erstimpfung'])
data_second_vaccination = calculate_vaccination_data(impfungen['Zweitimpfung'])

# 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()

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


'''

# Infos der einzelnen Länder
details_sheet_name = (set(rki_file.keys()) - {'Erläuterung', 'Impfungen_proTag'}).pop()

details_sheet = rki_file[details_sheet_name]

regionalcodes = details_sheet['RS'].iloc[0:17]
land_names = details_sheet['Bundesland'].iloc[0:17]

total_vaccinations_by_land = details_sheet['Impfungen kumulativ'].iloc[0:17]
vaccination_per_mille_by_land = details_sheet['Impfungen pro 1.000 Einwohner'].iloc[0:17]

vaccination_reason_age_by_land = details_sheet['Indikation nach Alter*'].iloc[0:17]
vaccination_reason_job_by_land = details_sheet['Berufliche Indikation*'].iloc[0:17]
vaccination_reason_medical_by_land = details_sheet['Medizinische Indikation*'].iloc[0:17]
vaccination_reason_oldhome_by_land = details_sheet['Pflegeheim-bewohnerIn*'].iloc[0:17]

details_per_land = {}
details_per_land_formatted = {}

# Regionalcodes der Länder zu Abkürzung und Name (Plus gesamt)
laendernamen = [
	('SH', 'Schleswig-Holstein'),
	('HH', 'Hamburg'),
	('NI', 'Niedersachsen'),
	('HB', 'Bremen'),
	('NW', 'Nordrhein-Westfalen'),
	('HE', 'Hessen'),
	('RP', 'Rheinland-Pfalz'),
	('BW', 'Baden-Württemberg'),
	('BY', 'Bayern'),
	('SL', 'Saarland'),
	('BE', 'Berlin'),
	('BB', 'Brandenburg'),
	('MV', 'Mecklenburg-Vorpommern'),
	('SN', 'Sachsen'),
	('ST', 'Sachsen-Anhalt'),
	('TH', 'Thüringen'),
	('𝚺', 'Gesamt')
]

def row_to_details(i):
	regionalcode = regionalcodes[i] if i != 16 else 16

	print(laendernamen[regionalcode])

	shortname, name = laendernamen[regionalcode]

	return {
		'name': name,
		'shortname': shortname,
		'total_vaccinations': int(total_vaccinations_by_land[i]),
		'total_vaccinations_percentage': vaccination_per_mille_by_land[i] / 10,
		'vaccination_reason_age': int(vaccination_reason_age_by_land[i]),
		'vaccination_reason_age_percentage': np.round(vaccination_reason_age_by_land[i] / total_vaccinations_by_land[i] * 100),
		'vaccination_reason_job': int(vaccination_reason_job_by_land[i]),
		'vaccination_reason_job_percentage': np.round(vaccination_reason_job_by_land[i] / total_vaccinations_by_land[i] * 100),
		'vaccination_reason_medical': int(vaccination_reason_medical_by_land[i]),
		'vaccination_reason_medical_percentage': np.round(vaccination_reason_medical_by_land[i] / total_vaccinations_by_land[i] * 100),
		'vaccination_reason_oldhome': int(vaccination_reason_oldhome_by_land[i]),
		'vaccination_reason_oldhome_percentage': np.round(vaccination_reason_oldhome_by_land[i] / total_vaccinations_by_land[i] * 100),
	}

def row_to_details_formatted(i):
	regionalcode = regionalcodes[i] if i != 16 else 16

	print(laendernamen[regionalcode])

	shortname, name = laendernamen[regionalcode]

	return {
		'name': name,
		'shortname': shortname,
		'total_vaccinations': '{:n}'.format(int(total_vaccinations_by_land[i])).replace('.', ' '),
		'total_vaccinations_percentage': '{:.3n}'.format(np.round(vaccination_per_mille_by_land[i] / 10, 2)),
		'vaccination_reason_age': '{:n}'.format(int(vaccination_reason_age_by_land[i])).replace('.', ' '),
		'vaccination_reason_age_percentage': '{:n}'.format(np.round(vaccination_reason_age_by_land[i] / total_vaccinations_by_land[i] * 100)),
		'vaccination_reason_job': '{:n}'.format(int(vaccination_reason_job_by_land[i])).replace('.', ' '),
		'vaccination_reason_job_percentage': '{:n}'.format(np.round(vaccination_reason_job_by_land[i] / total_vaccinations_by_land[i] * 100)),
		'vaccination_reason_medical': '{:n}'.format(int(vaccination_reason_medical_by_land[i])).replace('.', ' '),
		'vaccination_reason_medical_percentage': '{:n}'.format(np.round(vaccination_reason_medical_by_land[i] / total_vaccinations_by_land[i] * 100)),
		'vaccination_reason_oldhome': '{:n}'.format(int(vaccination_reason_oldhome_by_land[i])).replace('.', ' '),
		'vaccination_reason_oldhome_percentage': '{:n}'.format(np.round(vaccination_reason_oldhome_by_land[i] / total_vaccinations_by_land[i] * 100))
	}


for i in range(len(land_names) - 1):

	details_per_land[land_names[i]] = row_to_details(i)
	details_per_land_formatted[land_names[i]] = row_to_details_formatted(i)

details_total = row_to_details(16)
details_total_formatted = row_to_details_formatted(16)

'''






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=0.5)
	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), today, '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_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 (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 = list(data_first_vaccination['vaccinations_by_week'].values())
	s = list(data_second_vaccination['vaccinations_by_week'].values())

	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

		rh1 = nan_to_zero(r1.get_height())
		rh2 = nan_to_zero(r2.get_height())

		h1 = math.floor(rh1 / 1000)
		h2 = math.floor(rh2 / 1000)
		hg = math.floor((rh1 + rh2) / 1000)

		if h1 > 30:
			plt.text(x, h1 * 500, f'{h1:5n} k'.replace('.', ' '), ha='center', va='center', color='white')

		if h2 > 30:
			plt.text(x, h1 * 1000 + h2 * 500, f'{h2:5n} k'.replace('.', ' '), ha='center', va='center', color='black')

		plt.text(x, hg * 1000, f'{hg:5n} k'.replace('.', ' '), ha='center', va='bottom')

		if i == 12:
			# Woche der AstraZeneca-Aussetzung
			plt.annotate('AZ-Stopp', (x, hg * 1000 + 50000),
				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 * 1000 + 50000),
				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, 2))
	ax.get_yaxis().get_major_formatter().set_scientific(False)

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

	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_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))

	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 Erstimpfungen 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
		)
	)
	d = data_first_vaccination

	days_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily']))
	days_remaining_rolling = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))

	ax.set_ylim(0, 2500)

	ax.plot(dates, days_remaining_daily, label='Durchschnitt Gesamt', linewidth=0.5)
	ax.plot(dates, days_remaining_rolling, label='Durchschnitt 7 Tage', linewidth=2)

	ax.grid(True)


	ax.legend(loc='upper right')
	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_dates():

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

	fig, ax = plt.subplots(1)


	plt.title(
		'Lin. Extrapolation der Erstimpfungen 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
		)
	)
	d = data_first_vaccination

	#print(d['cumulative'])
	#print(np.sum(d['daily']))

	#print((einwohner_deutschland - d['cumulative'])[:-1] - d['to_be_vaccinated'])


	days_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily']))
	days_remaining_rolling = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))

	dates_daily = [today + datetime.timedelta(days) for days in days_remaining_daily]
	dates_rolling = [today + datetime.timedelta(days) for days in days_remaining_rolling.dropna()]

	#print(dates_rolling)

	ax.set_ylim(today, today + datetime.timedelta(int(np.max(days_remaining_rolling) * 1.05)))

	ax.plot(dates, dates_daily, label='Durchschnitt Gesamt', linewidth=0.5)
	ax.plot(dates[6:], dates_rolling, label='Durchschnitt 7 Tage', linewidth=2)

	ax.grid(True)


	ax.legend(loc='upper right')

	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 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_total_time_by_week',
				'caption': 'Wöchentliche Impfrate (Erst- und Zweitimpfung übereinander)'
			},{
				'index': 3,
				'filename': 'vaccination_bar_graph_total_time_two_bars',
				'caption': 'Tägliche Impfrate (Erst- und Zweitimpfung nebeneinander)'
			},{
				'index': 4,
				'filename': 'vaccination_bar_graph_compare_both_vaccinations',
				'caption': 'Tägliche Impfrate (Erst- und Zweitimpfung nebeneinander)'
			},{
				'index': 5,
				'filename': 'cumulative_two_vaccinations',
				'caption': 'Kumulative Impfrate (Erst- und Zweitimpfung)'
			},{
				'index': 6,
				'filename': 'cumulative_two_vaccinations_percentage',
				'caption': 'Kumulative Impfrate (Erst- und Zweitimpfung) in Prozent der Bevölkerung Deutschlands'
			},{
				'index': 7,
				'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': 8,
				'filename': 'vaccination_rate',
				'caption': 'Tägliche Impfrate sowie durchschnittliche Impfrate'
			},{
				'index': 9,
				'filename': 'vaccination_done_days',
				'caption': 'Lineare Extrapolation bis 70 % der Bevölkerung anhand der Erstimpfungen der durchschnittlichen Impfrate (Anzahl Tage, Gesamt und 7 Tage)'
			},{
				'index': 10,
				'filename': 'vaccination_done_dates',
				'caption': 'Lineare Extrapolation bis 70 % der Bevölkerung anhand der Erstimpfungen 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()