benedikt/rki-impfparser
1
0
Fork 0
Code Releases Activity

feat: New plot for remaining weeks until target, annotation for target

Browse source
This commit is contained in:
Benedikt Bastin 2021-04-14 13:33:24 +02:00
parent c1f4b57bc0
commit a1a4348ea1
1 changed files with 51 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

51
plot.py
View file

@ -736,7 +736,9 @@ def plot_vaccination_done_days():
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_xlim(start_of_reporting_date, today)
ax.set_ylim(0, 2500)
ax.axhline(days_until_target, label='Impfziel Ende Sommer')
ax.plot(dates, days_remaining_daily, label='Durchschnitt Gesamt', linewidth=0.5)
ax.plot(dates, days_remaining_rolling, label='Durchschnitt 7 Tage', linewidth=2)
@ -757,6 +759,49 @@ def plot_vaccination_done_days():
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 Erstimpfungen 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
)
)
d = data_first_vaccination
weeks_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily'])) / 7
weeks_remaining_rolling = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['vaccination_rates_daily_rolling_average'])) / 7
ax.set_xlim(datetime.datetime(2021, 3, 1), today)
ax.set_ylim(0, 52)
ax.axhline(days_until_target / 7, label='Impfziel Ende Sommer')
ax.plot(dates, weeks_remaining_daily, label='Durchschnitt Gesamt', linewidth=0.5)
ax.plot(dates, weeks_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('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'
@ -788,7 +833,9 @@ def plot_vaccination_done_dates():
#print(dates_rolling)
ax.set_xlim(start_of_reporting_date, today)
ax.set_ylim(today, today + datetime.timedelta(int(np.max(days_remaining_rolling) * 1.05)))
ax.axhline(target_date_for_herd_immunity - datetime.timedelta(21), label='Impfziel Ende Sommer')
ax.plot(dates, dates_daily, label='Durchschnitt Gesamt', linewidth=0.5)
ax.plot(dates[6:], dates_rolling, label='Durchschnitt 7 Tage', linewidth=2)
@ -885,6 +932,10 @@ def render_dashboard():
'caption': 'Lineare Extrapolation bis 70 % der Bevölkerung anhand der Erstimpfungen der durchschnittlichen Impfrate (Anzahl Tage, Gesamt und 7 Tage)'
},{
'index': 10,
'filename': 'vaccination_done_weeks',
'caption': 'Lineare Extrapolation bis 70 % der Bevölkerung anhand der Erstimpfungen der durchschnittlichen Impfrate (Anzahl Wochen, Gesamt und 7 Tage)'
},{
'index': 11,
'filename': 'vaccination_done_dates',
'caption': 'Lineare Extrapolation bis 70 % der Bevölkerung anhand der Erstimpfungen der durchschnittlichen Impfrate (Datum, Gesamt und 7 Tage)'
}