def test_save_results(self):
# test for 1d
# test for 2d
# test for 3d
# test for very large
nr_experiments = 10000
experiments = np.recarray((nr_experiments,),
dtype=[('x', float), ('y', float)])
outcome_a = np.random.rand(nr_experiments,1)
results = (experiments, {'a': outcome_a})
fn = u'../data/test.tar.gz'
save_results(results, fn)
os.remove(fn)
# ema_logging.info('1d saved successfully')
nr_experiments = 10000
nr_timesteps = 100
experiments = np.recarray((nr_experiments,),
dtype=[('x', float), ('y', float)])
outcome_a = np.zeros((nr_experiments,nr_timesteps))
results = (experiments, {'a': outcome_a})
save_results(results, fn)
os.remove(fn)
# ema_logging.info('2d saved successfully')
nr_experiments = 10000
nr_timesteps = 100
nr_replications = 10
experiments = np.recarray((nr_experiments,),
dtype=[('x', float), ('y', float)])
outcome_a = np.zeros((nr_experiments,nr_timesteps,nr_replications))
results = (experiments, {'a': outcome_a})
save_results(results, fn)
os.remove(fn)
# ema_logging.info('3d saved successfully')
nr_experiments = 500000
nr_timesteps = 100
experiments = np.recarray((nr_experiments,),
dtype=[('x', float), ('y', float)])
outcome_a = np.zeros((nr_experiments,nr_timesteps))
results = (experiments, {'a': outcome_a})
save_results(results, fn)
os.remove(fn)
def test_save_results(self):
# test for 1d
# test for 2d
# test for 3d
# test for very large
nr_experiments = 10000
experiments = np.recarray((nr_experiments, ),
dtype=[('x', float), ('y', float)])
outcome_a = np.random.rand(nr_experiments, 1)
results = (experiments, {'a': outcome_a})
fn = u'../data/test.tar.gz'
save_results(results, fn)
os.remove(fn)
# ema_logging.info('1d saved successfully')
nr_experiments = 10000
nr_timesteps = 100
experiments = np.recarray((nr_experiments, ),
dtype=[('x', float), ('y', float)])
outcome_a = np.zeros((nr_experiments, nr_timesteps))
results = (experiments, {'a': outcome_a})
save_results(results, fn)
os.remove(fn)
# ema_logging.info('2d saved successfully')
nr_experiments = 10000
nr_timesteps = 100
nr_replications = 10
experiments = np.recarray((nr_experiments, ),
dtype=[('x', float), ('y', float)])
outcome_a = np.zeros((nr_experiments, nr_timesteps, nr_replications))
results = (experiments, {'a': outcome_a})
save_results(results, fn)
os.remove(fn)
# ema_logging.info('3d saved successfully')
nr_experiments = 500000
nr_timesteps = 100
experiments = np.recarray((nr_experiments, ),
dtype=[('x', float), ('y', float)])
outcome_a = np.zeros((nr_experiments, nr_timesteps))
results = (experiments, {'a': outcome_a})
save_results(results, fn)
os.remove(fn)
def test_load_results(self):
# test for 1d
# test for 2d
# test for 3d
nr_experiments = 10000
experiments = np.recarray((nr_experiments,),
dtype=[('x', float), ('y', float)])
outcome_a = np.zeros((nr_experiments,1))
results = (experiments, {'a': outcome_a})
save_results(results, u'../data/test.tar.gz')
experiments, outcomes = load_results(u'../data/test.tar.gz')
logical = np.allclose(outcomes['a'],outcome_a)
os.remove('../data/test.tar.gz')
# if logical:
# ema_logging.info('1d loaded successfully')
nr_experiments = 1000
nr_timesteps = 100
nr_replications = 10
experiments = np.recarray((nr_experiments,),
dtype=[('x', float), ('y', float)])
outcome_a = np.zeros((nr_experiments,nr_timesteps,nr_replications))
results = (experiments, {'a': outcome_a})
save_results(results, u'../data/test.tar.gz')
experiments, outcomes = load_results(u'../data/test.tar.gz')
logical = np.allclose(outcomes['a'],outcome_a)
os.remove('../data/test.tar.gz')
def test_load_results(self):
# test for 1d
# test for 2d
# test for 3d
nr_experiments = 10000
experiments = np.recarray((nr_experiments, ),
dtype=[('x', float), ('y', float)])
outcome_a = np.zeros((nr_experiments, 1))
results = (experiments, {'a': outcome_a})
save_results(results, u'../data/test.tar.gz')
experiments, outcomes = load_results(u'../data/test.tar.gz')
logical = np.allclose(outcomes['a'], outcome_a)
os.remove('../data/test.tar.gz')
# if logical:
# ema_logging.info('1d loaded successfully')
nr_experiments = 1000
nr_timesteps = 100
nr_replications = 10
experiments = np.recarray((nr_experiments, ),
dtype=[('x', float), ('y', float)])
outcome_a = np.zeros((nr_experiments, nr_timesteps, nr_replications))
results = (experiments, {'a': outcome_a})
save_results(results, u'../data/test.tar.gz')
experiments, outcomes = load_results(u'../data/test.tar.gz')
logical = np.allclose(outcomes['a'], outcome_a)
os.remove('../data/test.tar.gz')
ParameterUncertainty((0.5,2),
"susceptible to immune population delay time region 2"),
ParameterUncertainty((0.01, 5),
"root contact rate region 1"),
ParameterUncertainty((0.01, 5),
"root contact ratio region 2"),
ParameterUncertainty((0, 0.15),
"infection ratio region 1"),
ParameterUncertainty((0, 0.15),
"infection rate region 2"),
ParameterUncertainty((10, 100),
"normal contact rate region 1"),
ParameterUncertainty((10, 200),
"normal contact rate region 2")]
if __name__ == "__main__":
ema_logging.log_to_stderr(ema_logging.INFO)
model = FluModel(r'./models/flu', "fluCase")
ensemble = ModelEnsemble()
ensemble.model_structure = model
ensemble.parallel = True #turn on parallel processing
nr_experiments = 1000
results = ensemble.perform_experiments(nr_experiments)
fh = r'./data/{} flu cases no policy.tar.gz'.format(nr_experiments)
save_results(results, fh)
