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

fix: Replaced magic number herd immunity with variable

Browse source
This commit is contained in:
Benedikt Bastin 2021-04-29 13:28:55 +02:00
parent 548eebfc96
commit e13e8f1df6
1 changed files with 10 additions and 11 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

21
plot.py
View file

@ -103,10 +103,9 @@ def calculate_vaccination_data(data):
else: else:
vaccinations_by_week[w] = v vaccinations_by_week[w] = v
def extrapolate(rate, to_be_vaccinated): def extrapolate(rate, to_be_vaccinated):
days_extrapolated = int(np.ceil(to_be_vaccinated / rate)) days_extrapolated = int(np.ceil(to_be_vaccinated / rate))
days_extrapolated_herd_immunity = int(np.ceil((einwohner_deutschland * 0.7 - total) / rate)) days_extrapolated_herd_immunity = int(np.ceil((einwohner_deutschland * herd_immunity - total) / rate))
weeks_extrapolated = int(np.ceil(days_extrapolated / 7)) weeks_extrapolated = int(np.ceil(days_extrapolated / 7))
weeks_extrapolated_herd_immunity = int(np.ceil(days_extrapolated_herd_immunity / 7)) weeks_extrapolated_herd_immunity = int(np.ceil(days_extrapolated_herd_immunity / 7))
@ -138,7 +137,7 @@ def calculate_vaccination_data(data):
mean_vaccination_rates_daily = np.round(cumulative / range(1, len(cumulative) + 1)) mean_vaccination_rates_daily = np.round(cumulative / range(1, len(cumulative) + 1))
vaccination_rates_daily_rolling_average = data.rolling(7).mean() vaccination_rates_daily_rolling_average = data.rolling(7).mean()
vaccinations_missing_until_target = einwohner_deutschland * 0.7 - total vaccinations_missing_until_target = einwohner_deutschland * herd_immunity - total
vaccination_rate_needed_for_target = vaccinations_missing_until_target / days_until_target 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 vaccination_rate_needed_for_target_percentage = mean_all_time / vaccination_rate_needed_for_target * 100
@ -678,8 +677,8 @@ def plot_vaccination_done_days():
) )
d = data_first_vaccination d = data_first_vaccination
days_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily'])) days_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily']))
days_remaining_rolling = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['vaccination_rates_daily_rolling_average'])) days_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
ax.set_xlim(start_of_reporting_date, today) ax.set_xlim(start_of_reporting_date, today)
ax.set_ylim(0, 2500) ax.set_ylim(0, 2500)
@ -722,8 +721,8 @@ def plot_vaccination_done_weeks():
) )
d = data_first_vaccination d = data_first_vaccination
weeks_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily'])) / 7 weeks_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - 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 weeks_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average'])) / 7
ax.set_xlim(datetime.date(2021, 3, 1), today) ax.set_xlim(datetime.date(2021, 3, 1), today)
ax.set_ylim(0, 52) ax.set_ylim(0, 52)
@ -765,8 +764,8 @@ def plot_vaccination_done_dates():
) )
d = data_first_vaccination d = data_first_vaccination
days_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily'])) days_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily']))
days_remaining_rolling = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['vaccination_rates_daily_rolling_average'])) days_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
dates_daily = [today + datetime.timedelta(days) for days in days_remaining_daily] 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()] dates_rolling = [today + datetime.timedelta(days) for days in days_remaining_rolling.dropna()]
@ -811,8 +810,8 @@ def plot_vaccination_done_dates_detail():
) )
d = data_first_vaccination d = data_first_vaccination
days_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily'])) days_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily']))
days_remaining_rolling = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['vaccination_rates_daily_rolling_average'])) days_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
dates_daily = [today + datetime.timedelta(days) for days in days_remaining_daily] 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()] dates_rolling = [today + datetime.timedelta(days) for days in days_remaining_rolling.dropna()]