feat: New plot for vaccination rates

plot.py

@@ -14,6 +14,7 @@ import requests as req
 import locale
 import os.path
 import shutil
+import math
 
 from matplotlib.dates import date2num
 import matplotlib.ticker as mtick
@@ -67,7 +68,6 @@ 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
@@ -80,7 +80,11 @@ def calculate_vaccination_data(data):
 	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:])
+	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:])
 
 	def extrapolate(rate, to_be_vaccinated):
@@ -110,7 +114,7 @@ def calculate_vaccination_data(data):
 	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
@@ -131,6 +135,7 @@ def calculate_vaccination_data(data):
 		'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
@@ -506,7 +511,7 @@ def plot_cumulative_two_vaccinations():
 	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.set_ylim([0, first_vaccinations_cumulative[-1]])
 
 	ax.legend(loc='upper left')
 	ax.xaxis_date()
@@ -597,7 +602,7 @@ def plot_people_between_first_and_second():
 	ax.grid()
 
 	first_vaccinations_cumulative = data_first_vaccination['cumulative']
-	second_vaccinations_cumulative = data_second_vaccination['cumulative']
+	second_vaccinations_cumulative = np.nan_to_num(data_second_vaccination['cumulative'], nan=0)
 
 	people_between = first_vaccinations_cumulative - second_vaccinations_cumulative
 
@@ -625,6 +630,54 @@ def plot_people_between_first_and_second():
 
 plot_people_between_first_and_second()
 
+def plot_vaccination_rate():
+
+	archive_plot_filename = '{}/vaccination_rate'.format(archive_folder)
+	latest_plot_filename = '{}/vaccination_rate'.format(site_folder)
+
+	#if os.path.isfile(archive_plot_filename + '.pdf'):
+		#print('Plot {} already exists'.format(archive_plot_filename))
+		#return
+
+	fig, ax = plt.subplots(1)
+
+
+	plt.title(
+		'Tägliche Impfrate sowie durchschnittliche Impfrate\n'
+		'Datenquelle: RKI, Stand: {}. Erstellung: {}, Ersteller: Benedikt Bastin, Lizenz: CC BY-SA 4.0\n'.format(
+			print_stand, print_today
+		)
+	)
+
+	ax.plot(dates, data_first_vaccination['daily'], label='Tägliche Erstimpfrate', color='blue', linewidth=0.5)
+	ax.plot(dates, data_second_vaccination['daily'], label='Tägliche Zweitimpfrate', color='lightblue', linewidth=0.5)
+
+	ax.plot(dates, data_first_vaccination['vaccination_rates_daily_rolling_average'], color='blue', linewidth=2, label='Erstimpfrate über sieben Tage')
+	ax.plot(dates, data_second_vaccination['vaccination_rates_daily_rolling_average'], color='lightblue', linewidth=2, label='Zweitimpfrate über sieben Tage')
+
+
+	ax.plot(dates, data_first_vaccination['mean_vaccination_rates_daily'], color='violet', label='Durchschnittliche Erstimpfrate\nbis zu diesem Tag (inkl.)')
+	ax.plot(dates, data_second_vaccination['mean_vaccination_rates_daily'], color='magenta', label='Durchschnittliche Zweitimpfrate\nbis zu diesem Tag (inkl.)')
+
+
+	ax.grid(True)
+
+
+	ax.legend(loc='upper left')
+	ax.get_yaxis().get_major_formatter().set_scientific(False)
+
+	ax.set_xlabel('Datum')
+	ax.set_ylabel('Impfrate [Impfungen/Tag]')
+
+	plt.savefig(archive_plot_filename + '.pdf')
+	plt.savefig(archive_plot_filename + '.png')
+	plt.savefig(latest_plot_filename + '.pdf')
+	plt.savefig(latest_plot_filename + '.png')
+	plt.close()
+
+	print('Created plot {} as pdf and png'.format(archive_plot_filename))
+
+plot_vaccination_rate()
 
 def render_dashboard():
 	dashboard_filename = 'site/index.xhtml'