def simulate_monte_carlo(seed):
from variables import allow_set_variable, get_variable, set_variable
with allow_set_variable():
set_variable('random_seed', seed)
print(seed)
df = simulate_individuals(skip_cache=True)
df['run'] = seed
return df
def run_monte_carlo(scenario_name):
from scenarios import SCENARIOS
from variables import allow_set_variable
for scenario in SCENARIOS:
if scenario.id == scenario_name:
break
else:
raise Exception('Scenario not found')
with allow_set_variable():
scenario.apply()
print(scenario.id)
with multiprocessing.Pool(processes=8) as pool:
dfs = pool.map(simulate_monte_carlo, range(1000))
df = pd.concat(dfs)
df.index.name = 'date'
df = df.reset_index()
df['scenario'] = scenario.id
df.to_csv('reina_%s.csv' % scenario.id, index=False)
return df
with multiprocessing.Pool(processes=8) as pool:
dfs = pool.map(simulate_monte_carlo, range(1000))
df = pd.concat(dfs)
df.index.name = 'date'
df = df.reset_index()
df['scenario'] = scenario.id
df.to_csv('reina_%s.csv' % scenario.id, index=False)
return df
if __name__ == '__main__':
if False:
from variables import allow_set_variable, get_variable, set_variable
with allow_set_variable():
set_variable('simulation_days', 50)
df = simulate_individuals()
exit()
if False:
from scenarios import SCENARIOS
for scenario in SCENARIOS:
df = run_monte_carlo(scenario.id)
print(df[df.date == df.date.max()])
last = df[df.date == df.date.max()]
print(last.dead.describe(percentiles=[.25, .5, .75]))
exit()
if False:
sample_model_parameters('symptom_severity', 90)
exit()
