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fc650e1207
...
548eebfc96
2 changed files with 118 additions and 128 deletions
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@ -22,8 +22,6 @@
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<p class="data-text">
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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).
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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>).
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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).
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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>).
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</p>
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<p class="data-text">
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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).
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244
plot.py
244
plot.py
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@ -52,158 +52,150 @@ plt.rcParams["figure.figsize"] = [11.69, 8.27]
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# Download
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def download_rki(filename_prefix):
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data_filename = '{}/{}_Impfquotenmonitoring.xlsx'.format(data_folder, filename_prefix)
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data_filename = '{}/{}_Impfquotenmonitoring.xlsx'.format(data_folder, filename_now)
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r = req.get('https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Daten/Impfquotenmonitoring.xlsx?__blob=publicationFile')
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r = req.get('https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Daten/Impfquotenmonitoring.xlsx?__blob=publicationFile')
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with open(data_filename, 'wb') as outfile:
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outfile.write(r.content)
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with open(data_filename, 'wb') as outfile:
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outfile.write(r.content)
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return data_filename
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#data_filename = 'data/20210118151908_Impfquotenmonitoring.xlsx'
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data_filename = download_rki(filename_now)
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rki_file = pd.read_excel(data_filename, sheet_name=None, engine='openpyxl')
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def parse_rki(filename):
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raw_data = rki_file['Impfungen_proTag']
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rki_file = pd.read_excel(filename, sheet_name=None, engine='openpyxl')
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impfungen = raw_data[:-1].dropna(subset=['Datum']).fillna(0)
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raw_data = rki_file['Impfungen_proTag']
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impfungen.drop(impfungen.tail(1).index,inplace=True) # remove Gesamt row
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impfungen = raw_data[:-1].dropna(subset=['Datum']).fillna(0)
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dates = impfungen['Datum']
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impfungen.drop(impfungen.tail(1).index,inplace=True) # remove Gesamt row
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start_of_reporting_date = dates.iloc[0].date()
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dates = impfungen['Datum']
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def calculate_vaccination_data(data):
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start_of_reporting_date = dates.iloc[0].date()
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total = int(np.sum(data))
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total_percentage = float(total) / einwohner_deutschland * 100
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def calculate_vaccination_data(data):
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to_be_vaccinated = einwohner_deutschland - total
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total = int(np.sum(data))
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total_percentage = float(total) / einwohner_deutschland * 100
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last_date = dates.iloc[-1].date()
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start_of_vaccination_index = (data != 0).argmax(axis=0)
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start_of_vaccination_date = dates[start_of_vaccination_index].date()
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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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to_be_vaccinated = einwohner_deutschland - total
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valid_data = data[start_of_vaccination_index:]
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last_date = dates.iloc[-1].date()
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start_of_vaccination_index = (data != 0).argmax(axis=0)
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start_of_vaccination_date = dates[start_of_vaccination_index].date()
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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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cumulative = np.concatenate(([math.nan] * (days_since_start_of_reporting - days_since_start_of_vaccination), np.cumsum(valid_data)))
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valid_data = data[start_of_vaccination_index:]
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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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cumulative = np.concatenate(([math.nan] * (days_since_start_of_reporting - days_since_start_of_vaccination), np.cumsum(valid_data)))
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vaccinations_by_week_map = map(lambda x: (Week.withdate(x[0]), x[1]), zip(dates, 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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vaccinations_by_week = {}
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vaccinations_by_week_map = map(lambda x: (Week.withdate(x[0]), x[1]), zip(dates, data))
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vaccinations_by_week = {}
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for w, v in vaccinations_by_week_map:
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if w in vaccinations_by_week:
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vaccinations_by_week[w] = vaccinations_by_week[w] + v
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else:
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vaccinations_by_week[w] = v
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def extrapolate(rate, to_be_vaccinated):
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days_extrapolated = int(np.ceil(to_be_vaccinated / rate))
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days_extrapolated_herd_immunity = int(np.ceil((einwohner_deutschland * herd_immunity - total) / rate))
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weeks_extrapolated = int(np.ceil(days_extrapolated / 7))
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weeks_extrapolated_herd_immunity = int(np.ceil(days_extrapolated_herd_immunity / 7))
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date_done = today + datetime.timedelta(days_extrapolated)
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date_herd_immunity = today + datetime.timedelta(days_extrapolated_herd_immunity)
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extrapolated_vaccinations = total + rate * range(-days_since_start_of_reporting, days_extrapolated - days_since_start_of_reporting)
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return {
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'rate': rate,
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'rate_int': int(np.round(rate)),
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'days_extrapolated': days_extrapolated,
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'days_extrapolated_herd_immunity': days_extrapolated_herd_immunity,
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'weeks_extrapolated': weeks_extrapolated,
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'weeks_extrapolated_herd_immunity': weeks_extrapolated_herd_immunity,
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'date_done': date_done,
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'date_done_str': date_done.strftime('%d. %B %Y'),
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'date_herd_immunity': date_herd_immunity,
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'date_herd_immunity_str': date_herd_immunity.strftime('%d. %B %Y'),
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'extrapolated_vaccinations': extrapolated_vaccinations
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}
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for w, v in vaccinations_by_week_map:
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if w in vaccinations_by_week:
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vaccinations_by_week[w] = vaccinations_by_week[w] + v
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else:
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vaccinations_by_week[w] = v
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extrapolation_mean_all_time = extrapolate(mean_all_time, to_be_vaccinated)
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extrapolation_last_rate = extrapolate(data.iloc[-1], to_be_vaccinated)
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extrapolation_mean_seven_days = extrapolate(mean_seven_days, to_be_vaccinated)
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def extrapolate(rate, to_be_vaccinated):
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days_extrapolated = int(np.ceil(to_be_vaccinated / rate))
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days_extrapolated_herd_immunity = int(np.ceil((einwohner_deutschland * 0.7 - total) / rate))
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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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weeks_extrapolated = int(np.ceil(days_extrapolated / 7))
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weeks_extrapolated_herd_immunity = int(np.ceil(days_extrapolated_herd_immunity / 7))
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vaccinations_missing_until_target = einwohner_deutschland * herd_immunity - total
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vaccination_rate_needed_for_target = vaccinations_missing_until_target / days_until_target
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vaccination_rate_needed_for_target_percentage = mean_all_time / vaccination_rate_needed_for_target * 100
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date_done = today + datetime.timedelta(days_extrapolated)
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date_herd_immunity = today + datetime.timedelta(days_extrapolated_herd_immunity)
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extrapolated_vaccinations = total + rate * range(-days_since_start_of_reporting, days_extrapolated - days_since_start_of_reporting)
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return {
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'daily': data,
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'cumulative': cumulative,
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'total': total,
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'total_percentage': total_percentage,
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'to_be_vaccinated': to_be_vaccinated,
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'last_date': last_date,
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'last_date_str': last_date.strftime('%d. %B %Y'),
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'days_since_start': days_since_start_of_vaccination + 1, # Shift from zero to one-based-index
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'start_of_vaccination_date': start_of_vaccination_date,
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'start_of_vaccination_date_str': start_of_vaccination_date.strftime('%d. %B %Y'),
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'vaccinations_by_week': vaccinations_by_week,
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'extrapolation_mean_all_time': extrapolation_mean_all_time,
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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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'vaccinations_last_day': data.iloc[-1],
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'vaccinations_last_day_percentage': data.iloc[-1] / einwohner_deutschland * 100,
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'vaccinations_last_day_vaccination_percentage': data.iloc[-1] / total * 100,
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'vaccinations_last_week': vaccinations_by_week[Week.thisweek() - 1],
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'vaccinations_last_week_percentage': vaccinations_by_week[Week.thisweek() - 1] / einwohner_deutschland * 100,
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'vaccinations_last_week_vaccination_percentage': vaccinations_by_week[Week.thisweek() - 1] / total * 100
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'rate': rate,
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'rate_int': int(np.round(rate)),
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'days_extrapolated': days_extrapolated,
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'days_extrapolated_herd_immunity': days_extrapolated_herd_immunity,
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'weeks_extrapolated': weeks_extrapolated,
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'weeks_extrapolated_herd_immunity': weeks_extrapolated_herd_immunity,
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'date_done': date_done,
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'date_done_str': date_done.strftime('%d. %B %Y'),
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'date_herd_immunity': date_herd_immunity,
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'date_herd_immunity_str': date_herd_immunity.strftime('%d. %B %Y'),
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'extrapolated_vaccinations': extrapolated_vaccinations
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}
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if 'Erstimpfung' in impfungen:
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raw_first_vaccinations = impfungen['Erstimpfung']
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elif 'Einmal geimpft' in impfungen:
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raw_first_vaccinations = impfungen['Einmal geimpft']
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elif 'Begonnene Impfserie' in impfungen:
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raw_first_vaccinations = impfungen['Begonnene Impfserie']
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if 'Zweitimpfung' in impfungen:
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raw_second_vaccinations = impfungen['Zweitimpfung']
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elif 'Vollständig geimpft' in impfungen:
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raw_second_vaccinations = impfungen['Vollständig geimpft']
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extrapolation_mean_all_time = extrapolate(mean_all_time, to_be_vaccinated)
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extrapolation_last_rate = extrapolate(data.iloc[-1], to_be_vaccinated)
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extrapolation_mean_seven_days = extrapolate(mean_seven_days, to_be_vaccinated)
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data_first_vaccination = calculate_vaccination_data(raw_first_vaccinations)
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data_second_vaccination = calculate_vaccination_data(raw_second_vaccinations)
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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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# Stand aus Daten auslesen
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#stand = dates.iloc[-1]
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#print_stand = stand.isoformat()
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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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vaccination_rate_needed_for_target_percentage = mean_all_time / vaccination_rate_needed_for_target * 100
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# Stand aus offiziellen Angaben auslesen
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stand = rki_file['Erläuterung'].iloc[1][0]
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return {
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'daily': data,
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'cumulative': cumulative,
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'total': total,
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'total_percentage': total_percentage,
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'to_be_vaccinated': to_be_vaccinated,
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'last_date': last_date,
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'last_date_str': last_date.strftime('%d. %B %Y'),
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'days_since_start': days_since_start_of_vaccination + 1, # Shift from zero to one-based-index
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'start_of_vaccination_date': start_of_vaccination_date,
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'start_of_vaccination_date_str': start_of_vaccination_date.strftime('%d. %B %Y'),
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'vaccinations_by_week': vaccinations_by_week,
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'extrapolation_mean_all_time': extrapolation_mean_all_time,
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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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'vaccinations_last_day': data.iloc[-1],
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'vaccinations_last_day_percentage': data.iloc[-1] / einwohner_deutschland * 100,
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'vaccinations_last_day_vaccination_percentage': data.iloc[-1] / total * 100,
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'vaccinations_last_week': vaccinations_by_week[Week.thisweek() - 1],
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'vaccinations_last_week_percentage': vaccinations_by_week[Week.thisweek() - 1] / einwohner_deutschland * 100,
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'vaccinations_last_week_vaccination_percentage': vaccinations_by_week[Week.thisweek() - 1] / total * 100
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}
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stand_regex = re.compile('^Datenstand: (\d\d.\d\d.\d\d\d\d, \d?\d:\d\d) Uhr$')
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m = stand_regex.match(stand)
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stand_date = datetime.datetime.strptime(m.groups()[0], '%d.%m.%Y, %H:%M')
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print_stand = stand_date.isoformat()
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if 'Erstimpfung' in impfungen:
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raw_first_vaccinations = impfungen['Erstimpfung']
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elif 'Einmal geimpft' in impfungen:
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raw_first_vaccinations = impfungen['Einmal geimpft']
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elif 'Begonnene Impfserie' in impfungen:
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raw_first_vaccinations = impfungen['Begonnene Impfserie']
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return dates, start_of_reporting_date, data_first_vaccination, data_second_vaccination, stand_date, print_stand
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if 'Zweitimpfung' in impfungen:
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raw_second_vaccinations = impfungen['Zweitimpfung']
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elif 'Vollständig geimpft' in impfungen:
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raw_second_vaccinations = impfungen['Vollständig geimpft']
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dates, start_of_reporting_date, data_first_vaccination, data_second_vaccination, stand_date, print_stand = parse_rki(filename=data_filename)
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data_first_vaccination = calculate_vaccination_data(raw_first_vaccinations)
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data_second_vaccination = calculate_vaccination_data(raw_second_vaccinations)
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# Stand aus Daten auslesen
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#stand = dates.iloc[-1]
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#print_stand = stand.isoformat()
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# Stand aus offiziellen Angaben auslesen
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stand = rki_file['Erläuterung'].iloc[1][0]
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stand_regex = re.compile('^Datenstand: (\d\d.\d\d.\d\d\d\d, \d?\d:\d\d) Uhr$')
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m = stand_regex.match(stand)
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stand_date = datetime.datetime.strptime(m.groups()[0], '%d.%m.%Y, %H:%M')
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print_stand = stand_date.isoformat()
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filename_stand = stand_date.strftime("%Y%m%d%H%M%S")
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@ -686,8 +678,8 @@ def plot_vaccination_done_days():
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)
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d = data_first_vaccination
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days_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily']))
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days_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
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days_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily']))
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days_remaining_rolling = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
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ax.set_xlim(start_of_reporting_date, today)
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ax.set_ylim(0, 2500)
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@ -730,8 +722,8 @@ def plot_vaccination_done_weeks():
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)
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d = data_first_vaccination
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weeks_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily'])) / 7
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weeks_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average'])) / 7
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weeks_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily'])) / 7
|
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weeks_remaining_rolling = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['vaccination_rates_daily_rolling_average'])) / 7
|
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ax.set_xlim(datetime.date(2021, 3, 1), today)
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ax.set_ylim(0, 52)
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|
|
@ -773,8 +765,8 @@ def plot_vaccination_done_dates():
|
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)
|
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d = data_first_vaccination
|
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|
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days_remaining_daily = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['mean_vaccination_rates_daily']))
|
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days_remaining_rolling = np.ceil((einwohner_deutschland * herd_immunity - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
|
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days_remaining_daily = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['mean_vaccination_rates_daily']))
|
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days_remaining_rolling = np.ceil((einwohner_deutschland * 0.7 - d['cumulative']) / (d['vaccination_rates_daily_rolling_average']))
|
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|
||||
dates_daily = [today + datetime.timedelta(days) for days in days_remaining_daily]
|
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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()]
|
||||
|
|
|
|||
Loading…
Reference in a new issue