def _run_samples_SAT(model, SAT, tmax, path, logging_prefix):
'''Run many simulations in parallel.'''
path_SAT = os.path.join(path, str(SAT))
os.makedirs(path_SAT, exist_ok=True)
logging_prefix_SAT = logging_prefix + f'SAT {SAT}, '
parameters = herd.Parameters(model=model, SAT=SAT)
samples = herd.samples.load(model=model, SAT=SAT)
return (delayed(_run_sample)(parameters, s, tmax, path_SAT, i,
logging_prefix_SAT)
for (i, s) in samples.iterrows())
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_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_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)
def get_susceptible_recruitment():
filename = 'susceptible_recruitment.csv'
try:
ser = pandas.read_csv(filename,
index_col=0, parse_dates=True,
header=None, squeeze=True)
except FileNotFoundError:
parameters = herd.Parameters()
t0 = parameters.start_time
susceptible_recruitment = SusceptibleRecruitment(parameters)
# Susceptible recruitment depends on time of year.
# 1 year of dates, points every day.
year = pandas.Timedelta(days=dates.DAYS_PER_YEAR)
start = (pandas.Timestamp(year=2001, month=1, day=1)
+ t0 * year)
end = start + year
dt = pandas.date_range(start, end, freq='D', closed='left')
# Convert to float year.
t = (dt - dt[0]) / year
v = susceptible_recruitment.pdf(t)
ser = pandas.Series(v, index=dt)
ser.to_csv(filename, header=False)
return ser
def run(model, SAT, tmax, nruns, store):
p = herd.Parameters(model=model, SAT=SAT)
logging_prefix = (', '.join((f'model {model}', f'SAT {SAT}')) + ', ')
df = run_many(p, tmax, nruns, logging_prefix=logging_prefix)
_prepend_index_levels(df, model=model, SAT=SAT)
hdfstore.put(df, min_itemsize=_min_itemsize)
#!/usr/bin/python3
import sys
from matplotlib import pyplot
sys.path.append('..')
import herd
from herd._initial_conditions import find_hazard_infection, plot
sys.path.pop()
models = ('acute', 'chronic')
for model in models:
params = herd.Parameters(model=model)
hazard_infection = find_hazard_infection(params)
plot(hazard_infection, params, show=False, label=model.capitalize())
pyplot.legend()
pyplot.show()
import run
sys.path.pop()
def make_plot(data, show=True):
(fig, ax) = pyplot.subplots()
seaborn.set_palette(seaborn.color_palette('deep', 6))
for (k, x) in data.items():
ax.step(365 * x.index, x, where='post', label=k)
ax.set_xlabel(data.index.name)
ax.set_ylabel('number')
ax.legend()
if show:
pyplot.show()
if __name__ == '__main__':
SAT = 1
model = 'chronic'
seed = 1
tmax = 10
debug = False
p = herd.Parameters(model=model, SAT=SAT)
t0 = time.time()
data = run.run_one(p, tmax, seed, debug=debug)
t1 = time.time()
print('Run time: {} seconds.'.format(t1 - t0))
make_plot(data)
def load_parameters_and_samples(model, SAT):
parameters = herd.Parameters(model=model, SAT=SAT)
samples = herd.samples.load(model=model, SAT=SAT)
return (parameters, samples)
