def run_population_size(model, SAT, population_size, tmax, nruns, hdfstore):
if population_size == 1000:
_copy_run(model, SAT, population_size, nruns, hdfstore)
else:
p = herd.Parameters(model=model, SAT=SAT)
p.population_size = population_size
logging_prefix = (', '.join(
(f'model {model}', f'SAT {SAT}',
f'population_size {population_size}')) + ', ')
df = run.run_many(p, tmax, nruns, logging_prefix=logging_prefix)
run._prepend_index_levels(df,
model=model,
SAT=SAT,
population_size=population_size)
hdfstore.put(df, min_itemsize=run._min_itemsize)
def run_start_time(model, SAT, start_time, tmax, nruns, hdfstore):
if start_time == 0.5:
_copy_run(model, SAT, start_time, nruns, hdfstore)
else:
p = herd.Parameters(model=model, SAT=SAT)
p.start_time = start_time
logging_prefix = (', '.join(
(f'model {model}', f'SAT {SAT}', f'start_time {start_time}')) +
', ')
df = run.run_many(p, tmax, nruns, logging_prefix=logging_prefix)
run._prepend_index_levels(df,
model=model,
SAT=SAT,
start_time=start_time)
hdfstore.put(df, min_itemsize=run._min_itemsize)
def run_initial_conditions(model, SAT, initial_conditions, tmax, nruns,
hdfstore):
if initial_conditions == SAT:
_copy_run(model, SAT, initial_conditions, nruns, hdfstore)
else:
p = herd.Parameters(model=model,
SAT=SAT,
_initial_conditions=initial_conditions)
logging_prefix = (', '.join(
(f'model {model}', f'SAT {SAT}',
f'initial_conditions {initial_conditions}')) + ', ')
df = run.run_many(p, tmax, nruns, logging_prefix=logging_prefix)
run._prepend_index_levels(df,
model=model,
SAT=SAT,
initial_conditions=initial_conditions)
hdfstore.put(df, min_itemsize=run._min_itemsize)
def run_birth_seasonality(model, SAT, birth_seasonality_scaling, tmax, nruns,
hdfstore):
p = herd.Parameters(model=model, SAT=SAT)
p.birth_seasonal_coefficient_of_variation *= birth_seasonality_scaling
bscov = p.birth_seasonal_coefficient_of_variation
if birth_seasonality_scaling == 1:
_copy_run(model, SAT, bscov, nruns, hdfstore)
else:
logging_prefix = (', '.join(
(f'model {model}', f'SAT {SAT}',
f'birth_seasonality_scaling {birth_seasonality_scaling}')) + ', ')
df = run.run_many(p, tmax, nruns, logging_prefix=logging_prefix)
run._prepend_index_levels(
df,
model=model,
SAT=SAT,
birth_seasonal_coefficient_of_variation=bscov)
hdfstore.put(df, min_itemsize=run._min_itemsize)
data_mean = get_mean(data)
for (j, (k, x)) in enumerate(data_mean.items()):
ax[j].step(365 * x.index, x, where='post', color='black', alpha=0.6)
ax[j].set_ylabel(k.replace(' ', '\n'))
ax[-1].set_xlabel(data.index.names[1])
for ax_ in ax:
yl = ax_.get_ylim()
if yl[0] < 0:
ax_.set_ylim(ymin=0)
if show:
pyplot.show()
if __name__ == '__main__':
SAT = 1
model = 'chronic'
nruns = 100
tmax = 10
p = herd.Parameters(model=model, SAT=SAT)
t0 = time.time()
data = run.run_many(p, tmax, nruns)
t1 = time.time()
print('Run time: {} seconds.'.format(t1 - t0))
make_plots(data)