def fit_all_case_data(num_procs=4):
pool = multi.Pool(num_procs)
print(f"Pooling with {num_procs} processors")
case_counts = parse_tsv()
scenario_data = load_population_data()
age_distributions = load_distribution()
params = []
for region in case_counts:
if region in scenario_data:
params.append([
region, case_counts[region],
scenario_data.get(region, None),
age_distributions[scenario_data[region]['ages']], False
])
results = pool.map(fit_population, params)
results_dict = {}
for k, params in results:
if params is None:
results_dict[k] = None
elif np.isfinite(params['logInitial']):
results_dict[k] = params
else:
results_dict[k] = None
return results_dict
with open(output_json, "w+") as fd:
output = ScenarioArray(scenarios)
output.marshalJSON(fd)
if __name__ == '__main__':
generate('test.json', recalculate=False)
from scripts.test_fitting_procedure import generate_data, check_fit
from scripts.model import trace_ages, get_IFR
from matplotlib import pyplot as plt
case_counts = parse_tsv()
scenario_data = load_population_data()
age_distributions = load_distribution()
# region = 'JPN-Kagawa'
region = 'United States of America'
# region = 'Germany'
region = 'Switzerland'
region = 'USA-Texas'
age_dis = age_distributions[scenario_data[region]['ages']]
region, p, fit_params = fit_population(
(region, case_counts[region], scenario_data[region], age_dis, True))
model_data = generate_data(fit_params)
model_cases = model_data['cases'][7:] - model_data['cases'][:-7]
model_deaths = model_data['deaths'][7:] - model_data['deaths'][:-7]
model_time = fit_params.time[7:]
cases = cumulative_to_rolling_average(