def test_epochs_deconv():
"""Test epochs deconvolution"""
tmin, tmax = -0.5, 1.5
event_dict = dict(foo=999)
events = np.array([np.arange(0, 21000, 1000, int),
999 * np.ones(21, int)]).T
for fi, fname in enumerate(fnames):
if fi == 0:
n_jobs = 1
else:
n_jobs = 0
raw = read_raw(fname)
epochs = Epochs(raw, events, event_dict, tmin, tmax)
a = raw.info['sample_fields']
b = epochs.info['data_cols']
assert_equal(len(a), len(b))
assert_true(all(aa == bb for aa, bb in zip(a, b)))
data = epochs.get_data('ps')
assert_raises(RuntimeError, Epochs, raw, events, 'test', tmin, tmax)
fit, times = epochs.deconvolve()
assert_array_equal(data, epochs.get_data('ps'))
assert_equal(fit.shape, (len(epochs), len(times)))
fit, times = epochs.deconvolve(spacing=[-0.1, 0.4, 1.0],
bounds=(0, np.inf),
n_jobs=n_jobs)
assert_equal(fit.shape, (len(epochs), len(times)))
assert_equal(len(times), 3)
if fi == 0:
if _has_joblib():
assert_raises(ValueError, epochs.deconvolve, n_jobs=-1000)
def test_epochs_deconv():
"""Test epochs deconvolution"""
tmin, tmax = -0.5, 1.5
event_dict = dict(foo=999)
events = np.array([np.arange(0, 21000, 1000, int),
999 * np.ones(21, int)]).T
for fi, fname in enumerate(fnames):
if fi == 0:
n_jobs = 1
else:
n_jobs = 0
raw = read_raw(fname)
epochs = Epochs(raw, events, event_dict,
tmin, tmax)
a = raw.info['sample_fields']
b = epochs.info['data_cols']
assert_equal(len(a), len(b))
assert_true(all(aa == bb for aa, bb in zip(a, b)))
data = epochs.get_data('ps')
assert_raises(RuntimeError, Epochs, raw, events, 'test', tmin, tmax)
fit, times = epochs.deconvolve()
assert_array_equal(data, epochs.get_data('ps'))
assert_equal(fit.shape, (len(epochs), len(times)))
fit, times = epochs.deconvolve(spacing=[-0.1, 0.4, 1.0],
bounds=(0, np.inf), n_jobs=n_jobs)
assert_equal(fit.shape, (len(epochs), len(times)))
assert_equal(len(times), 3)
if fi == 0:
if _has_joblib():
assert_raises(ValueError, epochs.deconvolve, n_jobs=-1000)
