def combine():
with h5.HDFStore('samples.h5', mode='a') as store:
# (model, SAT, sample) that are already in `store`.
store_idx = store.get_index().droplevel(_index).unique()
for model in os.listdir(_path):
path_model = os.path.join(_path, model)
for SAT in map(int, sorted(os.listdir(path_model))):
path_SAT = os.path.join(path_model, str(SAT))
# Sort in integer order.
for filename in sorted(os.listdir(path_SAT),
key=_get_sample_number):
sample = _get_sample_number(filename)
if (model, SAT, sample) not in store_idx:
path_sample = os.path.join(path_SAT, filename)
recarray = numpy.load(path_sample)
df = pandas.DataFrame.from_records(recarray,
index=_index)
run._prepend_index_levels(df,
model=model,
SAT=SAT,
sample=sample)
print('Inserting '
+ ', '.join((f'model={model}',
f'SAT={SAT}',
f'sample={sample}'))
+ '.')
store.put(df, min_itemsize=run._min_itemsize)
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)