def main(configpath):
# Load the model configuration file and the data (observed cases)
config = load_config(configpath)
data = load_data(config)
outpath = config['outpath']
useworldfile = config['worldfile']
if (not useworldfile):
data = generate_hos_actual(data, config)
else:
data = generate_zero_columns(data, config)
# Run the forward model to obtain a prior ensemble of models
save_input_data(configpath, outpath, data)
prior, time, prior_param, fwd_args = run_prior(config)
# Calibrate the model (as calibration data, we either use 'hospitalized' or 'dead')
calibration_mode = get_calibration_modes(config['calibration_mode'])[0]
if calibration_mode == 'dead':
data_index = I_DEAD
output_index = O_DEAD
elif calibration_mode == 'hospitalized':
data_index = I_HOS
output_index = O_HOS
elif calibration_mode == 'hospitalizedcum':
data_index = I_HOSCUM
output_index = O_HOSCUM
elif calibration_mode == 'ICU':
data_index = I_ICU
output_index = O_ICU
else:
raise NotImplementedError
plotplume = config['plot']['hindcast_plume']
p = calibrate_with_data(
prior, time, data, data_index,
output_index) # Posterior probability for each ensemble member
integrated_parameter_values = integrate_parameters(prior_param, p)
np.savetxt(outpath + "/integrated_parameter_values.csv",
integrated_parameter_values,
delimiter=";")
integrated_results = integrate_calibration(prior,
p) # Mean posterior model
# Create output of the calibration phase
plot_results(prior,
time,
configpath,
outpath,
config,
data,
integrated_results,
calibration_mode,
prior=True,
plot_plume=plotplume)
# Based on the model performance in the past, run models again, forecasting the future
forecast = rerun_model_with_alpha_uncertainty(prior_param, p, config,
fwd_args)
plot_results(forecast,
time,
configpath,
outpath,
config,
data,
cal_mode=config['calibration_mode'],
prior=False,
plot_plume=True)
plot_posterior(forecast, config, configpath, outpath, data, ['time'])
hospital_forecast_to_txt(forecast, time, configpath, outpath, config)
def main(configpath):
# Load the model configuration file and the data (observed cases)
config = load_config(configpath)
data = load_data(config)
base_filename = (os.path.split(configpath)[-1]).split('.')[0]
useworldfile = config['worldfile']
if (not useworldfile):
data = generate_hos_actual(data, config)
else:
data = generate_zero_columns(data, config)
# Run the forward model to obtain a prior ensemble of models
save_input_data(base_filename, data)
ndata = np.size(data[:, 0])
prior, time, prior_param, fwd_args = run_prior(config, ndata)
# Calibrate the model (as calibration data, we either use 'hospitalized' or 'dead')
calibration_mode = get_calibration_modes(config['calibration_mode'])[0]
if calibration_mode == 'dead':
data_index = I_DEAD
output_index = O_DEAD
elif calibration_mode == 'hospitalized':
data_index = I_HOS
output_index = O_HOS
elif calibration_mode == 'hospitalizedcum':
data_index = I_HOSCUM
output_index = O_HOSCUM
elif calibration_mode == 'ICU':
data_index = I_ICU
output_index = O_ICU
else:
raise NotImplementedError
plotplume = config['plot']['hindcast_plume']
p = calibrate_with_data(
prior, time, data, data_index,
output_index) # Posterior probability for each ensemble member
integrated_results = integrate_calibration(prior,
p) # Mean posterior model
# Create output of the calibration phase
plot_results(prior,
time,
configpath,
config,
data,
integrated_results,
calibration_mode,
prior=True,
plot_plume=plotplume)
# Based on the model performance in the past, run models again, forecasting the future
forecast = rerun_model_with_alpha_uncertainty(prior_param, p, config,
fwd_args)
base = (os.path.split(configpath)[-1]).split('.')[0]
outbase = os.path.join(os.path.split(os.getcwd())[0], 'output', base)
plot_results(forecast,
time,
configpath,
config,
data,
cal_mode=config['calibration_mode'],
prior=False,
plot_plume=True)
plot_posterior(forecast, config, outbase, data, ['time'])
try:
hospital_forecast_to_txt(forecast, time, configpath, config, data)
except:
pass