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

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

from matplotlib.dates import date2num
import matplotlib.ticker as mtick

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")

# 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)

dates = impfungen['Datum']

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

def calculate_vaccination_data(data):
	cumulative = np.cumsum(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

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

	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))

	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'),
		'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
	}


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 plot_extrapolation_portion(percentage):

	print_percentage = int(percentage * 100)
	archive_plot_filename = '{}/extrapolated_to_{}_percent'.format(archive_folder, print_percentage)
	latest_plot_filename = '{}/extrapolated_to_{}_percent'.format(site_folder, print_percentage)

	if os.path.isfile(archive_plot_filename + '.pdf'):
		print('Plot {} already exists'.format(archive_plot_filename))
		return

	fig, ax = plt.subplots(1)


	plt.title(
		'Tägliche Impfrate (Erst- und Zweitimpfung), kumulierte Impfungen und lineare Extrapolation bis {:n} % der Bevölkerung Deutschlands\n'
		'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'
		'Erstimpfungen: {:n} ({:n} %), Durchschnittliche Impfrate: {:n} Impfungen/Tag (läuft seit {:n} Tagen)\n'
		'Zweitimpfungen: {:n} ({:n} %), Durchschnittliche Impfrate: {:n} Impfungen/Tag (läuft seit {:n} Tagen)'.format(
			print_percentage,
			print_stand, print_today,
			data_first_vaccination['total'], np.round(data_first_vaccination['total_percentage'], 2), data_first_vaccination['extrapolation_mean_all_time']['rate'], data_first_vaccination['days_since_start'],
			data_second_vaccination['total'], np.round(data_second_vaccination['total_percentage'], 2), data_second_vaccination['extrapolation_mean_all_time']['rate'], data_second_vaccination['days_since_start']
		)
	)

	ax2 = ax.twinx()

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

	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.)')

	ax2.set_ylim([0, einwohner_deutschland * percentage])
	ax2.set_xlim(xmax=dates[0] + datetime.timedelta(days=percentage * data_first_vaccination['extrapolation_mean_all_time']['days_extrapolated']))
	ax2.grid(True)

	ax2.plot(dates, data_first_vaccination['cumulative'], color='red', label='Kumulierte Erstimpfungen')
	ax2.plot(dates, data_second_vaccination['cumulative'], color='indianred', label='Kumulierte Zweitimpfungen')

	ax2.plot(data_first_vaccination['extrapolation_mean_all_time']['dates'], data_first_vaccination['extrapolation_mean_all_time']['extrapolated_vaccinations'], color='orange', label='Extrap. kumulierte Erstimpfungen (Ø gesamt)\n{:n} Impfungen/Tag'.format(data_first_vaccination['extrapolation_mean_all_time']['rate_int']))
	ax2.plot(data_first_vaccination['extrapolation_mean_seven_days']['dates'], data_first_vaccination['extrapolation_mean_seven_days']['extrapolated_vaccinations'], color='goldenrod', label='Extrap. kumulierte Erstimpfungen (Ø 7 Tage)\n{:n} Impfungen/Tag'.format(data_first_vaccination['extrapolation_mean_seven_days']['rate_int']))
	ax2.plot()

	ax2.plot(data_second_vaccination['extrapolation_mean_all_time']['dates'], data_second_vaccination['extrapolation_mean_all_time']['extrapolated_vaccinations'], color='orange', label='Extrap. kumulierte Zweitimpfungen (Ø gesamt)\n{:n} Impfungen/Tag'.format(data_second_vaccination['extrapolation_mean_all_time']['rate_int']))
	ax2.plot(data_second_vaccination['extrapolation_mean_seven_days']['dates'], data_second_vaccination['extrapolation_mean_seven_days']['extrapolated_vaccinations'], color='goldenrod', label='Extrap. kumulierte Zweitimpfungen (Ø 7 Tage)\n{:n} Impfungen/Tag'.format(data_second_vaccination['extrapolation_mean_seven_days']['rate_int']))
	#ax2.plot()

	ax.legend(loc='upper left')
	ax.get_yaxis().get_major_formatter().set_scientific(False)

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

	ax2.legend(loc='lower right')
	ax2.get_yaxis().get_major_formatter().set_scientific(False)

	# Estimated percentage for herd immunity
	#ax2.axline((0, einwohner_deutschland * 0.7), slope=0, color='green')

	ax2.set_ylabel('Kumulierte Impfungen')

	plt.savefig(archive_plot_filename + '.pdf')
	plt.savefig(archive_plot_filename + '.png')
	plt.savefig(latest_plot_filename + '.pdf')
	plt.savefig(latest_plot_filename + '.png')
	plt.close()

	print('Created plot {} as pdf and png'.format(archive_plot_filename))


plot_extrapolation_portion(0.1)
#plot_extrapolation_portion(0.7)
plot_extrapolation_portion(1.0)



def plot_vaccination_bar_graph_total_time():

	archive_plot_filename = '{}/vaccination_bar_graph_total_time'.format(archive_folder)
	latest_plot_filename = '{}/vaccination_bar_graph_total_time'.format(site_folder)

	if os.path.isfile(archive_plot_filename + '.pdf'):
		print('Plot {} already exists'.format(archive_plot_filename))
		return

	fig, ax = plt.subplots(1)


	plt.title(
		'Tägliche Impfrate (Erst- und Zweitimpfung 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')


	plt.savefig(archive_plot_filename + '.pdf')
	plt.savefig(archive_plot_filename + '.png')
	plt.savefig(latest_plot_filename + '.pdf')
	plt.savefig(latest_plot_filename + '.png')
	plt.close()

	print('Created plot {} as pdf and png'.format(archive_plot_filename))

plot_vaccination_bar_graph_total_time()

def plot_vaccination_bar_graph_total_time_two_bars():

	archive_plot_filename = '{}/vaccination_bar_graph_total_time_two_bars'.format(archive_folder)
	latest_plot_filename = '{}/vaccination_bar_graph_total_time_two_bars'.format(site_folder)

	if os.path.isfile(archive_plot_filename + '.pdf'):
		print('Plot {} already exists'.format(archive_plot_filename))
		return

	fig, ax = plt.subplots(1)


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

	ax.grid()

	date_numbers = date2num(dates)

	ax.bar(date_numbers - 0.2, data_first_vaccination['daily'], width=0.4, label='Tägliche Erstimpfungen', color='blue')
	ax.bar(date_numbers + 0.2, data_second_vaccination['daily'], width=0.4, label='Tägliche Zweitimpfungen', color='lightblue')

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

	ax.legend(loc='upper left')
	ax.xaxis_date()
	ax.get_yaxis().get_major_formatter().set_scientific(False)

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


	plt.savefig(archive_plot_filename + '.pdf')
	plt.savefig(archive_plot_filename + '.png')
	plt.savefig(latest_plot_filename + '.pdf')
	plt.savefig(latest_plot_filename + '.png')
	plt.close()

	print('Created plot {} as pdf and png'.format(archive_plot_filename))

plot_vaccination_bar_graph_total_time_two_bars()

def plot_vaccination_bar_graph_compare_both_vaccinations():

	archive_plot_filename = '{}/vaccination_bar_graph_compare_both_vaccinations'.format(archive_folder)
	latest_plot_filename = '{}/vaccination_bar_graph_compare_both_vaccinations'.format(site_folder)

	if os.path.isfile(archive_plot_filename + '.pdf'):
		print('Plot {} already exists'.format(archive_plot_filename))
		return

	fig, ax = plt.subplots(1)


	plt.title(
		'Tägliche Impfrate (Erst- und Zweitimpfung um 21 Tage versetzt)\n'
			'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
			print_stand, print_today
		)
	)

	ax.grid()

	date_numbers_first = date2num(dates + datetime.timedelta(days=21))
	date_numbers_second = date2num(dates)

	ax.bar(date_numbers_first - 0.2, data_first_vaccination['daily'], width=0.4, label='Tägliche Erstimpfungen', color='blue')
	ax.bar(date_numbers_second + 0.2, data_second_vaccination['daily'], width=0.4, label='Tägliche Zweitimpfungen', color='lightblue')

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

	ax.legend(loc='upper left')
	ax.xaxis_date()
	ax.get_yaxis().get_major_formatter().set_scientific(False)

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


	plt.savefig(archive_plot_filename + '.pdf')
	plt.savefig(archive_plot_filename + '.png')
	plt.savefig(latest_plot_filename + '.pdf')
	plt.savefig(latest_plot_filename + '.png')
	plt.close()

	print('Created plot {} as pdf and png'.format(archive_plot_filename))

plot_vaccination_bar_graph_compare_both_vaccinations()

def plot_cumulative_two_vaccinations():
	archive_plot_filename = '{}/cumulative_two_vaccinations'.format(archive_folder)
	latest_plot_filename = '{}/cumulative_two_vaccinations'.format(site_folder)

	if os.path.isfile(archive_plot_filename + '.pdf'):
		print('Plot {} already exists'.format(archive_plot_filename))
		return

	fig, ax = plt.subplots(1)


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

	ax.grid()

	first_vaccinations_cumulative = data_first_vaccination['cumulative']
	second_vaccinations_cumulative = data_second_vaccination['cumulative']

	ax.fill_between(dates, first_vaccinations_cumulative, label='Erstimpfungen', color='blue')
	ax.fill_between(dates, second_vaccinations_cumulative, label='Zweitimpfungen', color='lightblue')

	ax.set_ylim([0, first_vaccinations_cumulative.iloc[-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('Tägliche Impfungen')


	plt.savefig(archive_plot_filename + '.pdf')
	plt.savefig(archive_plot_filename + '.png')
	plt.savefig(latest_plot_filename + '.pdf')
	plt.savefig(latest_plot_filename + '.png')
	plt.close()

	print('Created plot {} as pdf and png'.format(archive_plot_filename))


plot_cumulative_two_vaccinations()

def plot_cumulative_two_vaccinations_percentage():
	archive_plot_filename = '{}/cumulative_two_vaccinations_percentage'.format(archive_folder)
	latest_plot_filename = '{}/cumulative_two_vaccinations_percentage'.format(site_folder)

	if os.path.isfile(archive_plot_filename + '.pdf'):
		print('Plot {} already exists'.format(archive_plot_filename))
		return

	fig, ax = plt.subplots(1)


	plt.title(
		'Kumulative Impfrate (Erst- und Zweitimpfung) in Prozent der Bevökerung Deutschlands ({} Einwohner)\n'
		'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
			'{:n}'.format(einwohner_deutschland).replace('.', ' '),
			print_stand, print_today
		)
	)

	ax.grid()

	first_vaccinations_cumulative = data_first_vaccination['cumulative'] / einwohner_deutschland
	second_vaccinations_cumulative = data_second_vaccination['cumulative'] / einwohner_deutschland

	ax.fill_between(dates, first_vaccinations_cumulative, label='Erstimpfungen', color='blue')
	ax.fill_between(dates, second_vaccinations_cumulative, label='Zweitimpfungen', color='lightblue')

	ax.set_ylim([0, 1])

	ax.legend(loc='upper left')
	ax.xaxis_date()
	ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))

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


	plt.savefig(archive_plot_filename + '.pdf')
	plt.savefig(archive_plot_filename + '.png')
	plt.savefig(latest_plot_filename + '.pdf')
	plt.savefig(latest_plot_filename + '.png')
	plt.close()

	print('Created plot {} as pdf and png'.format(archive_plot_filename))


plot_cumulative_two_vaccinations_percentage()

def render_dashboard():
	dashboard_filename = 'site/index.xhtml'
	dashboard_archive_filename = 'site/archive/{}/index.xhtml'.format(filename_stand)
	stylesheet_filename = 'site/rki-dashboard.css'
	stylesheet_archive_filename = 'site/archive/{}/rki-dashboard.css'.format(filename_stand)

	if os.path.isfile(dashboard_archive_filename):
		print('Dashboard {} already exists'.format(dashboard_archive_filename))
		return

	from jinja2 import Template, Environment, FileSystemLoader, select_autoescape
	env = Environment(
		loader=FileSystemLoader('./'),
		autoescape=select_autoescape(['html', 'xml', 'xhtml'])
	)

	german_text_date_format = '%d. %B %Y'
	df = german_text_date_format

	german_text_datetime_format = '%d. %B %Y, %H:%M:%S Uhr'
	dtf = german_text_datetime_format

	latest_dashboard_filename = site_folder + 'index.xhtml'
	archive_dashboard_filename = archive_folder

	template = env.get_template('dashboard_template.xhtml')
	template.stream(
		stand = stand_date.strftime(dtf),
		filename_stand = filename_stand,
		einwohner_deutschland = '{:n}'.format(einwohner_deutschland).replace('.', ' '),
		herd_immunity = '{:n}'.format(int(herd_immunity * 100)),
		data_first_vaccination = data_first_vaccination,
		data_second_vaccination = data_second_vaccination,
		#details_per_land = dict(sorted(details_per_land_formatted.items(), key=lambda item: item[0])),
		#details_total = details_total_formatted
	).dump('site/index.xhtml')

	shutil.copyfile(dashboard_filename, dashboard_archive_filename)
	shutil.copyfile(stylesheet_filename, stylesheet_archive_filename)

	print('Created dashboard')

render_dashboard()