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+import numpy as np
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+import pandas as pd
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+from datetime import timedelta
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+
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+from src.plotter import Plotter
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+
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+def transform_general_to_SIR(plotter:Plotter, dataset_path='datasets/COVID-19-Todesfaelle_in_Deutschland/', plot_name='', plot_title='', sample_rate=1, exclude=[], plot_size=(12,6), yscale_log=False, plot_legend=True):
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+ """Function to generate the SIR split from the data in the COVID-19-Todesfaelle_in_Deutschland dataset.
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+
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+ Args:
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+ plotter (Plotter): Plotter object to plot dataset curves.
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+ dataset_path (str, optional): Path to the dataset directory. Defaults to 'datasets/COVID-19-Todesfaelle_in_Deutschland/'.
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+ plot_name (str, optional): Name of the plot file. Defaults to ''.
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+ plot_title (str, optional): Title of the plot. Defaults to ''.
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+ sample_rate (int, optional): Sample rate used to sample the timepoints. Defaults to 1.
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+ exclude (list, optional): List of groups that are to excluded from the plot. Defaults to [].
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+ plot_size (tuple, optional): Size of the plot in (x, y) format. Defaults to (12,6).
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+ yscale_log (bool, optional): Controls if the y axis of the plot will be scaled in log scale. Defaults to False.
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+ plot_legend (bool, optional): Controls if the legend is to be plotted. Defaults to True.
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+ """
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+ # read the data
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+ df = pd.read_csv(dataset_path + 'COVID-19-Todesfaelle_Deutschland.csv')
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+
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+ df = df.drop(df.index[1200:])
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+
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+ # population of germany at the end of 2019
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+ N = 83100000
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+ S, I, R = np.zeros(df.shape[0]), np.zeros(df.shape[0]), np.zeros(df.shape[0])
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+
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+ # S_0 = N - I_0
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+ S[0] = N - df['Faelle_gesamt'][0]
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+ # I_0 = overall cases at the day - overall death cases at the day
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+ I[0] = df['Faelle_gesamt'][0] - df['Todesfaelle_gesamt'][0]
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+ # R_0 = overall death cases at the day
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+ R[0] = df['Todesfaelle_gesamt'][0]
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+
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+ # the recovery time is 14 days
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+ recovery_queue = np.zeros(14)
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+
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+ for day in range(1, df.shape[0]):
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+ infections = df['Faelle_gesamt'][day] - df['Faelle_gesamt'][day-1]
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+ deaths = df['Todesfaelle_neu'][day]
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+ recoveries = recovery_queue[0]
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+
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+ S[day] = S[day-1] - infections
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+ I[day] = I[day-1] + infections - deaths - recoveries
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+ R[day] = R[day-1] + deaths + recoveries
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+
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+ # update recovery queue
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+ if I[day] < 0:
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+ recovery_queue[-1] -= I[day]
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+ I[day] = 0
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+
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+ recovery_queue[:-1] = recovery_queue[1:]
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+ recovery_queue[-1] = infections
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+
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+ t = np.arange(0, df.shape[0], 1)
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+ if plotter != None:
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+ # plot graphs
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+ plots = []
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+ labels = []
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+
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+ if 'S' not in exclude:
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+ plots.append(S)
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+ labels.append('S')
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+
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+ if 'I' not in exclude:
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+ plots.append(I)
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+ labels.append('I')
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+
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+ if 'R' not in exclude:
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+ plots.append(R)
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+ labels.append('R')
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+
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+ plotter.plot(t, plots, labels, plot_name, plot_title, plot_size, y_log_scale=yscale_log, plot_legend=plot_legend, xlabel='time / days', ylabel='amount of poeple')
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+
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+ COVID_Data = np.asarray([t[0::sample_rate],
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+ S[0::sample_rate],
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+ I[0::sample_rate],
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+ R[0::sample_rate]])
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+
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+ np.savetxt(f"datasets/SIR_RKI_{sample_rate}.csv", COVID_Data, delimiter=",")
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+
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+
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+
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+def get_state_cases(county_id, state_id):
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+ id = county_id // 1000
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+ return id == state_id
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+
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+def state_based_data(plotter:Plotter, state_name:str, time_range=1200, sample_rate=1, dataset_path='datasets/state_data/Aktuell_Deutschland_SarsCov2_Infektionen.csv'):
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+ """Transforms the RKI infection cases dataset to a SIR dataset.
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+
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+ Args:
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+ plotter (Plotter): Plotter object to plot dataset curves.
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+ state_name (str): Name of the state that is to be singled out in the new dataset.
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+ time_range (int, optional): Number of days that will be looked at in the new dataset. Defaults to 1200.
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+ sample_rate (int, optional): Sample rate used to sample the timepoints. Defaults to 1.
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+ dataset_path (str, optional): Path to the CSV file, where the data is stored. Defaults to 'datasets/state_data/Aktuell_Deutschland_SarsCov2_Infektionen.csv'.
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+ """
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+ df = pd.read_csv(dataset_path)
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+
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+ state_lookup = {'Schleswig Holstein' : (1, 2897000),
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+ 'Hamburg' : (2, 1841000),
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+ 'Niedersachsen' : (3, 7982000),
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+ 'Bremen' : (4, 569352),
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+ 'Nordrhein-Westfalen' : (5, 17930000),
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+ 'Hessen' : (6, 6266000),
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+ 'Rheinland-Pfalz' : (7, 4085000),
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+ 'Baden-Württemberg' : (8, 11070000),
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+ 'Bayern' : (9, 13080000),
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+ 'Saarland' : (10, 990509),
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+ 'Berlin' : (11, 3645000),
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+ 'Brandenburg' : (12, 2641000),
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+ 'Mecklenburg-Vorpommern' : (13, 1610000),
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+ 'Sachsen' : (14, 4078000),
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+ 'Sachsen-Anhalt' : (15, 2208000),
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+ 'Thüringen' : (16, 2143000)}
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+ state_ID, N = state_lookup[state_name]
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+
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+ # single out a state
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+ state_IDs = df['IdLandkreis'] // 1000
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+ state_df = df.loc[state_IDs == state_ID]
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+
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+ # sort entries by state
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+ state_df = state_df.sort_values('Refdatum')
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+ state_df = state_df.reset_index(drop=True)
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+
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+
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+ # collect cases
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+ infections = np.zeros(time_range)
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+ dead = np.zeros(time_range)
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+ recovered = np.zeros(time_range)
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+ entry_idx = 0
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+ day = 0
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+ date = state_df['Refdatum'][entry_idx]
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+ # check for each date all entries
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+ while day < time_range:
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+ # use the date sorted characteristic and take all entries with current date
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+ while state_df['Refdatum'][entry_idx] == date:
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+ # TODO use further parameters
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+ infections[day] += state_df['AnzahlFall'][entry_idx]
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+ dead[day] += state_df['AnzahlTodesfall'][entry_idx]
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+ recovered[day] += state_df['AnzahlGenesen'][entry_idx]
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+ entry_idx += 1
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+ # move day index by difference between the current and next date
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+ day += (pd.to_datetime(state_df['Refdatum'][entry_idx])-pd.to_datetime(date)).days
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+ date = state_df['Refdatum'][entry_idx]
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+
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+ S = np.zeros(time_range)
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+ I = np.zeros(time_range)
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+ S[0] = N - infections[0]
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+ I[0] = infections[0]
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+
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+ for day in range(1, time_range):
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+ S[day] = S[day-1] - infections[day]
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+ I[day] = I[day-1] + infections[day] - I[day-1]/14
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+
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+ t = np.arange(0, time_range, 1)
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+
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+ plotter.plot(t, [S, I], ['S', 'I'], state_name.replace(' ', '_').replace('-', '_'), state_name+' SI', (6,6), xlabel='time / days', ylabel='amount of people')
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+
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+ COVID_Data = np.asarray([t[0::sample_rate],
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+ S[0::sample_rate],
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+ I[0::sample_rate]])
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+
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+ np.savetxt(f"datasets/SIR_RKI_{state_name.replace(' ', '_').replace('-', '_')}_{sample_rate}.csv", COVID_Data, delimiter=",")
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+
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+
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+
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+
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phillip.rothenbeck