def test_seed(self):
# test seed gen, in base class
s1 = seed_generator(0)
s2 = seed_generator(0)
self.assertNotEqual(s1, s2)
s3 = seed_generator(23)
self.assertEqual(s3, 23)
def obs_generator(self, field_list, ensemble_size, obs_size):
"""
apply field model and generate observable raw data
:param parameters: dict of parameters
:param ensemble_size: number of realizations in ensemble
:param obs_size: size of observable
:return: numpy ndarray
"""
# coordinates
raw_arr = np.zeros((ensemble_size, obs_size))
coo_x = np.linspace(0., 2. * np.pi, obs_size)
for i in range(ensemble_size):
pars = field_list[0].report_parameters(i)
# double check parameter keys
assert (pars.keys() == field_list[0].field_checklist.keys())
# extract parameters
par_a = pars['a']
par_b = pars['b']
par_s = pars['random_seed']
# get thread-time dependent random number
np.random.seed(seed_generator(par_s))
raw_arr[i, :] = np.multiply(
np.cos(coo_x),
np.random.normal(loc=par_a, scale=par_b, size=obs_size))
return raw_arr