def main():
for expt in session.query(models.Experiment):
print('In experiment "{}"...'.format(expt.id))
for odor_state in ODOR_STATES:
print('Odor state = "{}"'.format(odor_state))
trajs = session.query(models.Trajectory).\
filter_by(experiment=expt, odor_state=odor_state, clean=True)
for variable in QUANTITIES:
print('{}...'.format(variable))
tp_data = [
traj.timepoint_field(session, variable) for traj in trajs
]
n_data_points = np.sum([len(d) for d in tp_data])
window_len = N_LAGS / expt.sampling_frequency
acor, p_value, conf_lb, conf_ub = \
time_series.xcov_multi_with_confidence(tp_data, tp_data, 0, N_LAGS, normed=True)
time_vector = np.arange(len(acor)) / expt.sampling_frequency
file_name = '{}_{}_{}.pickle'.format(expt.id, odor_state,
variable)
tp_acor = models.TimepointAutocorrelation(
figure_root_path_env_var=figure_data_env_var,
directory_path=DIRECTORY_PATH,
file_name=file_name,
variable=variable,
experiment_id=expt.id,
odor_state=odor_state,
n_data_points=n_data_points,
n_trajectories=len(tp_data),
window_len=window_len)
tp_acor.data = {
'time_vector': time_vector,
'autocorrelation': acor,
'p_value': p_value,
'confidence_lower': conf_lb,
'confidence_upper': conf_ub
}
session.add(tp_acor)
commit(session)
def test_autocovariance_is_delta_function_for_white_noise(self):
xs = [np.random.normal(0, 3, np.random.randint(500, 1000)) for _ in range(100)]
acov, pv, conf_lb, conf_ub = \
time_series.xcov_multi_with_confidence(xs, xs, lag_backward=20, lag_forward=21, normed=True)
self.assertEqual(len(acov), 41)
self.assertAlmostEqual(acov[:20].mean(), 0, places=2)
self.assertAlmostEqual(acov[21:].mean(), 0, places=2)
self.assertAlmostEqual(acov[20], 1, places=5)
np.testing.assert_array_almost_equal(acov[:20], acov[21:][::-1])
np.testing.assert_array_almost_equal(pv[:20], pv[21:][::-1])
np.testing.assert_array_almost_equal(conf_lb[:20], conf_lb[21:][::-1])
np.testing.assert_array_almost_equal(conf_ub[:20], conf_ub[21:][::-1])
def main():
for expt in session.query(models.Experiment):
print('In experiment "{}"...'.format(expt.id))
for odor_state in ODOR_STATES:
print('Odor state = "{}"'.format(odor_state))
trajs = session.query(models.Trajectory).\
filter_by(experiment=expt, odor_state=odor_state, clean=True)
for variable in QUANTITIES:
print('{}...'.format(variable))
tp_data = [traj.timepoint_field(session, variable) for traj in trajs]
n_data_points = np.sum([len(d) for d in tp_data])
window_len = N_LAGS / expt.sampling_frequency
acor, p_value, conf_lb, conf_ub = \
time_series.xcov_multi_with_confidence(tp_data, tp_data, 0, N_LAGS, normed=True)
time_vector = np.arange(len(acor)) / expt.sampling_frequency
file_name = '{}_{}_{}.pickle'.format(expt.id, odor_state, variable)
tp_acor = models.TimepointAutocorrelation(figure_root_path_env_var=figure_data_env_var,
directory_path=DIRECTORY_PATH,
file_name=file_name,
variable=variable,
experiment_id=expt.id,
odor_state=odor_state,
n_data_points=n_data_points,
n_trajectories=len(tp_data),
window_len=window_len)
tp_acor.data = {'time_vector': time_vector,
'autocorrelation': acor,
'p_value': p_value,
'confidence_lower': conf_lb,
'confidence_upper': conf_ub}
session.add(tp_acor)
commit(session)
