def main(configpath):
# Load the model configuration file and the data (observed cases)
config = load_config(configpath)
data = load_data(config)
useworldfile = config['worldfile']
if (not useworldfile):
data = generate_hos_actual(data, config)
else:
data = generate_zero_columns(data, config)
plot_confidence(configpath, config, data, config['startdate'])
plot_confidence_alpha(configpath, config, data, config['startdate'])
def main(configpath):
# Load the model configuration file and the data (observed cases)
config = load_config(configpath)
data = load_data(config)
useworldfile = config['worldfile']
if (not useworldfile):
data = generate_hos_actual(data, config)
else:
data = generate_zero_columns(data, config)
base = (os.path.split(configpath)[-1]).split('.')[0]
outpath = os.path.join(os.path.split(os.getcwd())[0], 'output', base)
plot_confidence(outpath, config, data, config['startdate'])
def main(action, configpath):
if action == 1:
scrape_and_post_nl_data()
elif action == 2:
config = load_config(configpath)
# Check if the IC data file is defined in config, otherwise use '../res/icdata_main.txt'
try:
ic_data_file = config['icdatafile']
except KeyError:
print("No icdatafile in config, using '../res/icdata_main.txt'.")
ic_data_file = '../res/icdata_main.txt'
# Input argument is the path to the config file
get_and_save_nl_data(config['startdate'], config['country'], ic_data_file)
def preprocess(configpath, save_plots=True):
# This function will
# 1) scrape the data from the RIVM and NICE sites and post it to the database,
# 2) pull from the database and generate the dataNL file and the icdata file (in the res directory), and
# 3) generate the icufrac file and ICanalysis plots (in the output directory)
config = load_config(configpath)
output_base_filename = (os.path.split(configpath)[-1]).split('.')[0]
# Check if the IC data file is defined in config, otherwise use '../res/icdata_main.txt'
try:
ic_data_file = config['icdatafile']
except KeyError:
print("No icdatafile in config, using '../res/icdata_main.txt'.")
ic_data_file = '../res/icdata_main.txt'
scrape_and_post_nl_data()
get_and_save_nl_data(config['startdate'], config['country'], ic_data_file)
save_and_plot_IC(config, output_base_filename, int(save_plots))
print("Preprocessing complete.")
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]
run_esmda_model(base_filename, config, data)
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)
# concatenate additional column for actual observed hospitalized based
t_obs = data[:, I_TIME]
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, data)
calibration_modes = get_calibration_modes(config['calibration_mode'])
observation_errors = get_calibration_errors(config['observation_error'])
calibration_mode = calibration_modes[0]
found = False
for i, calmode in enumerate(s_calmodes):
if (calmode == calibration_mode):
print('ensemble fit on : ', calibration_modes[0], ' with error ',
observation_errors[0])
y_obs = data[:, i_calmodes[i]]
output_index = [o_calmodes[i]]
error = np.ones_like(y_obs) * observation_errors[0]
found = True
if not found:
raise NotImplementedError
for ical, calibration_mode in enumerate(calibration_modes):
if (ical > 0):
print('additional ensemble fit on : ', calibration_modes[ical],
' with error ', observation_errors[ical])
for i, calmode in enumerate(s_calmodes):
if (calmode == calibration_mode):
y_obs2 = data[:, i_calmodes[i]]
output_index2 = [o_calmodes[i]]
error2 = np.ones_like(y_obs2) * observation_errors[ical]
y_obs = np.append(y_obs, y_obs2)
# output_index = [output_index[0], O_ICU]
output_index = np.append(output_index, output_index2)
error = np.append(error, error2)
found = True
if not found:
raise NotImplementedError
# Load inversion method and define forward model
try:
ni = config['esmda_iterations']
except KeyError:
ni = 8
im = InversionMethods(ni=ni)
forward = base_seir_model
np.random.seed(12345)
# Parse the configuration json
m_prior, fwd_args = parse_config(config)
m_prior = reshape_prior(m_prior)
# Estimated uncertainty on the data
# sigma = np.sqrt(y_obs).clip(min=10)
# error = config['observation_error']
# if calibration_mode == S_HOS or calibration_mode == S_HOSCUM:
# error = 2 * error
# sigma = error * np.ones_like(y_obs)
sigma = error
add_arg = [fwd_args]
kwargs = {
't_obs': t_obs,
'output_index': output_index,
'G': map_model_to_obs,
'print_results': 1
}
results = im.es_mda(forward, m_prior, y_obs, sigma, *add_arg, **kwargs)
plot_prior_and_posterior(results, fwd_args, config, configpath, t_obs,
data, calibration_mode, output_index)
base = (os.path.split(configpath)[-1]).split('.')[0]
outpath = os.path.join(
os.path.split(os.getcwd())[0], 'output', base + '_output.h5')
save_results(results, fwd_args, config, outpath, data, mode='esmda')
# Check if we want to apply a 'hammer'
try:
hammer_icu = config['hammer_ICU']
hammer_alpha = config['hammer_alpha']
assert len(hammer_alpha) == 2
hammered_results = apply_hammer(results['fw'][-1],
results['M'][-1],
fwd_args,
hammer_icu,
hammer_alpha,
data_end=data[-1, 0])
# TODO: Add hammer results to h5 file and plotting
add_hammer_to_results(hammered_results, outpath, mode='esmda')
except KeyError as e:
pass # Don't apply hammer, you're done early!
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
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
import os
import locale
import matplotlib.patches as mpatches
from src.io_func import load_config
country = 'OPT'
save_dirs = ['output/OPT/final-plots']
configpath = 'configs/OPT.json'
config = load_config(configpath)
pop = config['population']
sigma = config['sigma']
locale.setlocale(locale.LC_ALL, "nl_NL")
today = datetime.today().strftime("%d-%B-%Y").lower()
span = datetime.today() - datetime.strptime('1-maart-2020', "%d-%B-%Y")
short = span.days + 7
long = span.days + 100
labels = ['S', 'E', 'REM', 'I', 'REC', 'HOS', 'D']
dfs = []
for label in labels:
data_phl = pd.read_csv('output/{}/{}_{}_prob.csv'.format(
country, country, label),
header=None,
names=[
'time', label, label + '5', label + '30',
label + '50', label + '70', label + '95'
],
def main(configpath, save_plots=True):
# Load the model configuration file and the data (observed cases)
config = load_config(configpath)
output_base_filename = (os.path.split(configpath)[-1]).split('.')[0]
save_and_plot_IC(config, output_base_filename, save_plots)