diff --git a/dashboard_template.xhtml b/dashboard_template.xhtml
index 946337c..836d293 100644
--- a/dashboard_template.xhtml
+++ b/dashboard_template.xhtml
@@ -22,8 +22,6 @@
<p class="data-text">
Gestern wurden <em>{{ '{:n}'.format(data_first_vaccination.vaccinations_last_day).replace('.', ' ') }}</em> Erstimpfungen vorgenommen (<em>{{ '{:.3n}'.format(data_first_vaccination.vaccinations_last_day_percentage) }} %</em> der Bevölkerung, <em>{{ '{:.3n}'.format(data_first_vaccination.vaccinations_last_day_vaccination_percentage) }} %</em> der verabreichten Erstimpfdosen).
Innerhalb der letzten Kalenderwoche sind <em>{{ '{:.9n}'.format(data_first_vaccination.vaccinations_last_week).replace('.', ' ') }}</em> Erstimpfungen erfolgt (<em>{{ '{:.3n}'.format(data_first_vaccination.vaccinations_last_week_percentage) }} %</em>, <em>{{ '{:.3n}'.format(data_first_vaccination.vaccinations_last_week_vaccination_percentage) }} %</em>).
- Es wurden außerdem <em>{{ '{:n}'.format(data_second_vaccination.vaccinations_last_day).replace('.', ' ') }}</em> Zweitimpfungen vorgenommen (<em>{{ '{:.3n}'.format(data_second_vaccination.vaccinations_last_day_percentage) }} %</em> der Bevölkerung, <em>{{ '{:.3n}'.format(data_second_vaccination.vaccinations_last_day_vaccination_percentage) }} %</em> der verabreichten Erstimpfdosen).
- Innerhalb der letzten Kalenderwoche sind <em>{{ '{:.9n}'.format(data_second_vaccination.vaccinations_last_week).replace('.', ' ') }}</em> Zweitimpfungen erfolgt (<em>{{ '{:.3n}'.format(data_second_vaccination.vaccinations_last_week_percentage) }} %</em>, <em>{{ '{:.3n}'.format(data_second_vaccination.vaccinations_last_week_vaccination_percentage) }} %</em>).
</p>
<p class="data-text">
In den letzten sieben Tagen wurden durchschnittlich <em>{{ '{:n}'.format(data_first_vaccination['extrapolation_mean_seven_days']['rate_int']).replace('.', ' ') }}</em> Erstimpfungen und <em>{{ '{:n}'.format(data_second_vaccination['extrapolation_mean_seven_days']['rate_int']).replace('.', ' ') }}</em> Zweitimpfungen pro Tag vorgenommen (<em>{{ '{:n}'.format(data_first_vaccination['extrapolation_mean_seven_days']['rate_int'] * 7).replace('.', ' ') }}</em>/<em>{{ '{:n}'.format(data_second_vaccination['extrapolation_mean_seven_days']['rate_int'] * 7).replace('.', ' ') }}</em> pro Woche).
diff --git a/plot.py b/plot.py
index b1634c0..6a53770 100644
--- a/plot.py
+++ b/plot.py
@@ -52,158 +52,150 @@ plt.rcParams["figure.figsize"] = [11.69, 8.27]
# Download
-def download_rki(filename_prefix):
- data_filename = '{}/{}_Impfquotenmonitoring.xlsx'.format(data_folder, filename_prefix)
+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')
+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)
+with open(data_filename, 'wb') as outfile:
+ outfile.write(r.content)
- return data_filename
+#data_filename = 'data/20210118151908_Impfquotenmonitoring.xlsx'
-data_filename = download_rki(filename_now)
+rki_file = pd.read_excel(data_filename, sheet_name=None, engine='openpyxl')
-def parse_rki(filename):
+raw_data = rki_file['Impfungen_proTag']
- rki_file = pd.read_excel(filename, sheet_name=None, engine='openpyxl')
+impfungen = raw_data[:-1].dropna(subset=['Datum']).fillna(0)
- raw_data = rki_file['Impfungen_proTag']
+impfungen.drop(impfungen.tail(1).index,inplace=True) # remove Gesamt row
- impfungen = raw_data[:-1].dropna(subset=['Datum']).fillna(0)
+dates = impfungen['Datum']
- impfungen.drop(impfungen.tail(1).index,inplace=True) # remove Gesamt row
+start_of_reporting_date = dates.iloc[0].date()
- dates = impfungen['Datum']
+def calculate_vaccination_data(data):
- start_of_reporting_date = dates.iloc[0].date()
+ total = int(np.sum(data))
+ total_percentage = float(total) / einwohner_deutschland * 100
- def calculate_vaccination_data(data):
+ to_be_vaccinated = einwohner_deutschland - total
- total = int(np.sum(data))
- total_percentage = float(total) / einwohner_deutschland * 100
+ 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
- to_be_vaccinated = einwohner_deutschland - total
+ valid_data = data[start_of_vaccination_index:]
- 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
+ cumulative = np.concatenate(([math.nan] * (days_since_start_of_reporting - days_since_start_of_vaccination), np.cumsum(valid_data)))
- valid_data = data[start_of_vaccination_index:]
+ mean_all_time = np.mean(valid_data)
+ mean_seven_days = np.mean(data[-7:])
- cumulative = np.concatenate(([math.nan] * (days_since_start_of_reporting - days_since_start_of_vaccination), np.cumsum(valid_data)))
+ vaccinations_by_week_map = map(lambda x: (Week.withdate(x[0]), x[1]), zip(dates, data))
- mean_all_time = np.mean(valid_data)
- mean_seven_days = np.mean(data[-7:])
+ vaccinations_by_week = {}
- vaccinations_by_week_map = map(lambda x: (Week.withdate(x[0]), x[1]), zip(dates, data))
-
- vaccinations_by_week = {}
-
- for w, v in vaccinations_by_week_map:
- if w in vaccinations_by_week:
- vaccinations_by_week[w] = vaccinations_by_week[w] + v
- else:
- vaccinations_by_week[w] = v
-
- def extrapolate(rate, to_be_vaccinated):
- days_extrapolated = int(np.ceil(to_be_vaccinated / rate))
- days_extrapolated_herd_immunity = int(np.ceil((einwohner_deutschland * herd_immunity - total) / rate))
-
- weeks_extrapolated = int(np.ceil(days_extrapolated / 7))
- weeks_extrapolated_herd_immunity = int(np.ceil(days_extrapolated_herd_immunity / 7))
-
- date_done = today + datetime.timedelta(days_extrapolated)
- date_herd_immunity = today + datetime.timedelta(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,
- 'days_extrapolated_herd_immunity': days_extrapolated_herd_immunity,
- 'weeks_extrapolated': weeks_extrapolated,
- 'weeks_extrapolated_herd_immunity': weeks_extrapolated_herd_immunity,
- '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
- }
+ for w, v in vaccinations_by_week_map:
+ if w in vaccinations_by_week:
+ vaccinations_by_week[w] = vaccinations_by_week[w] + v
+ else:
+ vaccinations_by_week[w] = v
- 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)
+ def extrapolate(rate, to_be_vaccinated):
+ days_extrapolated = int(np.ceil(to_be_vaccinated / rate))
+ days_extrapolated_herd_immunity = int(np.ceil((einwohner_deutschland * 0.7 - total) / rate))
- mean_vaccination_rates_daily = np.round(cumulative / range(1, len(cumulative) + 1))
- vaccination_rates_daily_rolling_average = data.rolling(7).mean()
+ weeks_extrapolated = int(np.ceil(days_extrapolated / 7))
+ weeks_extrapolated_herd_immunity = int(np.ceil(days_extrapolated_herd_immunity / 7))
- 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_percentage = mean_all_time / vaccination_rate_needed_for_target * 100
+ date_done = today + datetime.timedelta(days_extrapolated)
+ date_herd_immunity = today + datetime.timedelta(days_extrapolated_herd_immunity)
+
+ extrapolated_vaccinations = total + rate * range(-days_since_start_of_reporting, days_extrapolated - days_since_start_of_reporting)
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'),
- 'vaccinations_by_week': vaccinations_by_week,
- '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,
- '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,
- 'vaccinations_last_day': data.iloc[-1],
- 'vaccinations_last_day_percentage': data.iloc[-1] / einwohner_deutschland * 100,
- 'vaccinations_last_day_vaccination_percentage': data.iloc[-1] / total * 100,
- 'vaccinations_last_week': vaccinations_by_week[Week.thisweek() - 1],
- 'vaccinations_last_week_percentage': vaccinations_by_week[Week.thisweek() - 1] / einwohner_deutschland * 100,
- 'vaccinations_last_week_vaccination_percentage': vaccinations_by_week[Week.thisweek() - 1] / total * 100
+ 'rate': rate,
+ 'rate_int': int(np.round(rate)),
+ 'days_extrapolated': days_extrapolated,
+ 'days_extrapolated_herd_immunity': days_extrapolated_herd_immunity,
+ 'weeks_extrapolated': weeks_extrapolated,
+ 'weeks_extrapolated_herd_immunity': weeks_extrapolated_herd_immunity,
+ '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
}
- if 'Erstimpfung' in impfungen:
- raw_first_vaccinations = impfungen['Erstimpfung']
- elif 'Einmal geimpft' in impfungen:
- raw_first_vaccinations = impfungen['Einmal geimpft']
- elif 'Begonnene Impfserie' in impfungen:
- raw_first_vaccinations = impfungen['Begonnene Impfserie']
- if 'Zweitimpfung' in impfungen:
- raw_second_vaccinations = impfungen['Zweitimpfung']
- elif 'Vollständig geimpft' in impfungen:
- raw_second_vaccinations = impfungen['Vollständig geimpft']
+ 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)
- data_first_vaccination = calculate_vaccination_data(raw_first_vaccinations)
- data_second_vaccination = calculate_vaccination_data(raw_second_vaccinations)
+ mean_vaccination_rates_daily = np.round(cumulative / range(1, len(cumulative) + 1))
+ vaccination_rates_daily_rolling_average = data.rolling(7).mean()
- # Stand aus Daten auslesen
- #stand = dates.iloc[-1]
- #print_stand = stand.isoformat()
+ vaccinations_missing_until_target = einwohner_deutschland * 0.7 - total
+ 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
- # Stand aus offiziellen Angaben auslesen
- stand = rki_file['Erläuterung'].iloc[1][0]
+ 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'),
+ 'vaccinations_by_week': vaccinations_by_week,
+ '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,
+ '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,
+ 'vaccinations_last_day': data.iloc[-1],
+ 'vaccinations_last_day_percentage': data.iloc[-1] / einwohner_deutschland * 100,
+ 'vaccinations_last_day_vaccination_percentage': data.iloc[-1] / total * 100,
+ 'vaccinations_last_week': vaccinations_by_week[Week.thisweek() - 1],
+ 'vaccinations_last_week_percentage': vaccinations_by_week[Week.thisweek() - 1] / einwohner_deutschland * 100,
+ 'vaccinations_last_week_vaccination_percentage': vaccinations_by_week[Week.thisweek() - 1] / total * 100
+ }
- stand_regex = re.compile('^Datenstand: (\d\d.\d\d.\d\d\d\d, \d?\d:\d\d) Uhr$')
- m = stand_regex.match(stand)
- stand_date = datetime.datetime.strptime(m.groups()[0], '%d.%m.%Y, %H:%M')
- print_stand = stand_date.isoformat()
+if 'Erstimpfung' in impfungen:
+ raw_first_vaccinations = impfungen['Erstimpfung']
+elif 'Einmal geimpft' in impfungen:
+ raw_first_vaccinations = impfungen['Einmal geimpft']
+elif 'Begonnene Impfserie' in impfungen:
+ raw_first_vaccinations = impfungen['Begonnene Impfserie']
- return dates, start_of_reporting_date, data_first_vaccination, data_second_vaccination, stand_date, print_stand
+if 'Zweitimpfung' in impfungen:
+ raw_second_vaccinations = impfungen['Zweitimpfung']
+elif 'Vollständig geimpft' in impfungen:
+ raw_second_vaccinations = impfungen['Vollständig geimpft']
-dates, start_of_reporting_date, data_first_vaccination, data_second_vaccination, stand_date, print_stand = parse_rki(filename=data_filename)
+data_first_vaccination = calculate_vaccination_data(raw_first_vaccinations)
+data_second_vaccination = calculate_vaccination_data(raw_second_vaccinations)
+
+# Stand aus Daten auslesen
+#stand = dates.iloc[-1]
+#print_stand = stand.isoformat()
+
+# Stand aus offiziellen Angaben auslesen
+stand = rki_file['Erläuterung'].iloc[1][0]
+
+stand_regex = re.compile('^Datenstand: (\d\d.\d\d.\d\d\d\d, \d?\d:\d\d) Uhr$')
+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")
@@ -686,8 +678,8 @@ def plot_vaccination_done_days():
)
d = data_first_vaccination
- days_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily']))
- days_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
+ 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)
@@ -730,8 +722,8 @@ def plot_vaccination_done_weeks():
)
d = data_first_vaccination
- weeks_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily'])) / 7
- weeks_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average'])) / 7
+ 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.date(2021, 3, 1), today)
ax.set_ylim(0, 52)
@@ -773,8 +765,8 @@ def plot_vaccination_done_dates():
)
d = data_first_vaccination
- days_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily']))
- days_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
+ 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']))
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()]
@@ -819,8 +811,8 @@ def plot_vaccination_done_dates_detail():
)
d = data_first_vaccination
- days_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily']))
- days_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
+ 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']))
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()]