feat: New plot for vaccination rates
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Benedikt Bastin
parent
3e19d3462a
commit
a3bf0654f9
63
plot.py
63
plot.py
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@ -14,6 +14,7 @@ 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 matplotlib.dates import date2num
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import matplotlib.ticker as mtick
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@ -67,7 +68,6 @@ 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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cumulative = np.cumsum(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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@ -80,7 +80,11 @@ def calculate_vaccination_data(data):
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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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mean_all_time = np.mean(data[start_of_vaccination_index:])
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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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def extrapolate(rate, to_be_vaccinated):
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@ -110,7 +114,7 @@ def calculate_vaccination_data(data):
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extrapolation_mean_seven_days = extrapolate(mean_seven_days, to_be_vaccinated)
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mean_vaccination_rates_daily = 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 * 0.7 - total
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vaccination_rate_needed_for_target = vaccinations_missing_until_target / days_until_target
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@ -131,6 +135,7 @@ def calculate_vaccination_data(data):
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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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@ -506,7 +511,7 @@ def plot_cumulative_two_vaccinations():
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ax.fill_between(dates, first_vaccinations_cumulative, label='Erstimpfungen', color='blue')
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ax.fill_between(dates, second_vaccinations_cumulative, label='Zweitimpfungen', color='lightblue')
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ax.set_ylim([0, first_vaccinations_cumulative.iloc[-1]])
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ax.set_ylim([0, first_vaccinations_cumulative[-1]])
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ax.legend(loc='upper left')
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ax.xaxis_date()
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@ -597,7 +602,7 @@ def plot_people_between_first_and_second():
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ax.grid()
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first_vaccinations_cumulative = data_first_vaccination['cumulative']
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second_vaccinations_cumulative = data_second_vaccination['cumulative']
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second_vaccinations_cumulative = np.nan_to_num(data_second_vaccination['cumulative'], nan=0)
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people_between = first_vaccinations_cumulative - second_vaccinations_cumulative
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@ -625,6 +630,54 @@ def plot_people_between_first_and_second():
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plot_people_between_first_and_second()
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def plot_vaccination_rate():
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archive_plot_filename = '{}/vaccination_rate'.format(archive_folder)
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latest_plot_filename = '{}/vaccination_rate'.format(site_folder)
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#if os.path.isfile(archive_plot_filename + '.pdf'):
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#print('Plot {} already exists'.format(archive_plot_filename))
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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 sowie durchschnittliche Impfrate\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.plot(dates, data_first_vaccination['daily'], label='Tägliche Erstimpfrate', color='blue', linewidth=0.5)
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ax.plot(dates, data_second_vaccination['daily'], label='Tägliche Zweitimpfrate', color='lightblue', linewidth=0.5)
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ax.plot(dates, data_first_vaccination['vaccination_rates_daily_rolling_average'], color='blue', linewidth=2, label='Erstimpfrate über sieben Tage')
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ax.plot(dates, data_second_vaccination['vaccination_rates_daily_rolling_average'], color='lightblue', linewidth=2, label='Zweitimpfrate über sieben Tage')
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ax.plot(dates, data_first_vaccination['mean_vaccination_rates_daily'], color='violet', label='Durchschnittliche Erstimpfrate\nbis zu diesem Tag (inkl.)')
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ax.plot(dates, data_second_vaccination['mean_vaccination_rates_daily'], color='magenta', label='Durchschnittliche Zweitimpfrate\nbis zu diesem Tag (inkl.)')
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ax.grid(True)
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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('Impfrate [Impfungen/Tag]')
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plt.savefig(archive_plot_filename + '.pdf')
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plt.savefig(archive_plot_filename + '.png')
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plt.savefig(latest_plot_filename + '.pdf')
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plt.savefig(latest_plot_filename + '.png')
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plt.close()
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print('Created plot {} as pdf and png'.format(archive_plot_filename))
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plot_vaccination_rate()
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def render_dashboard():
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dashboard_filename = 'site/index.xhtml'
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