def test_high_sig_identical():
# Test that we have the same result with high_sig=low_sig and high_sig=None
for method in ALL_PAC_METRICS:
if method in BICOHERENCE_PAC_METRICS:
continue
comod_0 = fast_comod(method=method)
comod_1 = fast_comod(high_sig=signal, method=method)
assert_array_equal(comod_0, comod_1)
def test_fake_parallel():
# Test that _FakeParallel does nothing but unrolling the generator
generator = (twice(b) for b in range(10))
results = _FakeParallel(n_jobs=1)(generator)
reference = [twice(b) for b in range(10)]
assert_array_equal(np.array(results), np.array(reference))
# test fake parameters
_FakeParallel(n_jobs=1, foo='bar', baz=42)
def _compare_values(v, v2):
if isinstance(v, np.ndarray):
assert_array_equal(v, v2)
elif isinstance(v, dict):
for key, value in v.items():
_compare_values(v[key], v2[key])
elif isinstance(v, np.random.RandomState):
for s, s2 in zip(v.get_state(), v2.get_state()):
_compare_values(s, s2)
else:
assert_equal(v, v2)
def test_surrogates():
# Test the surrogates comodulogram
for method in ALL_PAC_METRICS:
msg = 'with method=%s' % method
if method in BICOHERENCE_PAC_METRICS or method == 'jiang':
continue
n_surrogates = 10
est = ComodTest(method=method, n_surrogates=n_surrogates).fit(signal)
assert_array_equal(est.comod_.shape, (n_low, n_high), err_msg=msg)
assert_array_equal(est.surrogates_.shape,
(n_surrogates, n_low, n_high),
err_msg=msg)
# z-score
z_score = est.comod_z_score_
assert_array_equal(z_score.shape, (n_low, n_high), err_msg=msg)
if method != 'jiang': # 'jiang' method does not estimate CFC but CFD
assert_greater(z_score[1, 1], z_score[-1, -1], msg=msg)
# surrogate_max
surrogate_max = est.surrogate_max_
assert_array_equal(surrogate_max.shape, (n_surrogates, ))
assert_greater(est.comod_[1, 1], surrogate_max.max(), msg=msg)
assert_greater(surrogate_max.max(), est.comod_[-1, -1], msg=msg)
# Smoke test with contours in the plotting function
est.plot(contour_level=0.01, contour_method='comod_max')
est.plot(contour_level=3, contour_method='z_score')
plt.close('all')
def test_mask_iterator():
# Test the mask iterator length and the shape of the elements
fs = 200.
tmin, tmax = -0.05, 0.2
n_points = 200
events = np.arange(0, n_points, int(fs * 0.3))
masks = MaskIterator(events, tmin, tmax, n_points, fs)
masks_list = [m for m in masks]
# test that the length is correct
n_masks_0 = len(masks)
n_masks_1 = len(masks_list)
assert_equal(n_masks_0, n_masks_1)
# test that the masks are of the correct shape
for mask in masks_list:
assert_array_equal(mask.shape, (1, n_points))
def test_comod_correct_maximum():
# Test that the PAC is maximum at the correct location in the comodulogram
for method in ALL_PAC_METRICS:
est = ComodTest(method=method, progress_bar=True).fit(signal)
comod = est.comod_
# test the shape of the comodulogram
assert_array_equal(comod.shape, (n_low, n_high))
# the bicoherence metrics fail this test with current parameters
if method in BICOHERENCE_PAC_METRICS or method == 'jiang':
continue
low_fq_0, high_fq_0, max_pac = est.get_maximum_pac()
assert_equal(low_fq_0, low_fq)
assert_equal(high_fq_0, high_fq)
assert_equal(max_pac, comod.max())
assert_true(np.all(comod > 0))
def test_amplitude():
# Test that the amplitude parameters change the amplitude
sig_0 = simulate_pac_default(high_fq_amp=0, low_fq_amp=0, noise_level=0)
zeros = np.zeros_like(sig_0)
assert_array_equal(sig_0, zeros)
sig_1 = simulate_pac_default(high_fq_amp=1, low_fq_amp=0, noise_level=0)
sig_2 = simulate_pac_default(high_fq_amp=2, low_fq_amp=0, noise_level=0)
assert_almost_equal(sig_1.std() * 2, sig_2.std(), decimal=7)
sig_1 = simulate_pac_default(high_fq_amp=0, low_fq_amp=0, noise_level=1)
sig_2 = simulate_pac_default(high_fq_amp=0, low_fq_amp=0, noise_level=2)
assert_almost_equal(sig_1.std(), 1, decimal=1)
assert_almost_equal(sig_2.std(), 2, decimal=1)
sig_1 = simulate_pac_default(high_fq_amp=0, low_fq_amp=1, noise_level=0)
sig_2 = simulate_pac_default(high_fq_amp=0, low_fq_amp=2, noise_level=0)
assert_almost_equal(sig_1.std(), 1, decimal=0)
assert_almost_equal(sig_2.std(), 2, decimal=0)
def test_dehumming_improve_snr():
# Test that dehumming removes the added noise
rng = np.random.RandomState(0)
fs = 250.
enf = 50.
clean = rng.randn(512)
noisy = clean.copy()
# add ENF noise
time = np.arange(512) / float(fs)
for harmonic in (1, 2):
noisy += np.sin(time * 2 * np.pi * enf * harmonic) / harmonic
for dim in [(1, 512), (512, ), (512, 1)]:
dehummed = dehummer(
noisy.reshape(*dim), fs, enf=enf)
assert_greater(norm(noisy - clean), norm(dehummed.ravel() - clean))
assert_array_equal(dehummed.shape, dim)
# test that the dehumming does not accept 2d arrays
assert_raises(ValueError, dehummer, noisy.reshape(2, 256), fs)
def test_amplitude():
# Test that the amplitude parameters change the amplitude
sig_0 = simulate_pac_default(high_fq_amp=0, low_fq_amp=0, noise_level=0)
zeros = np.zeros_like(sig_0)
assert_array_equal(sig_0, zeros)
sig_1 = simulate_pac_default(high_fq_amp=1, low_fq_amp=0, noise_level=0)
sig_2 = simulate_pac_default(high_fq_amp=2, low_fq_amp=0, noise_level=0)
# with a driver equal to zero at all time, the sigmoid modulates at 0.5
assert_almost_equal(sig_1.std(), 0.5, decimal=7)
assert_almost_equal(sig_2.std(), 1, decimal=7)
sig_1 = simulate_pac_default(high_fq_amp=0, low_fq_amp=0, noise_level=1)
sig_2 = simulate_pac_default(high_fq_amp=0, low_fq_amp=0, noise_level=2)
assert_almost_equal(sig_1.std(), 1, decimal=1)
assert_almost_equal(sig_2.std(), 2, decimal=1)
sig_1 = simulate_pac_default(high_fq_amp=0, low_fq_amp=1, noise_level=0)
sig_2 = simulate_pac_default(high_fq_amp=0, low_fq_amp=2, noise_level=0)
assert_almost_equal(sig_1.std(), 1, decimal=0)
assert_almost_equal(sig_2.std(), 2, decimal=0)
def test_signal_unchanged():
# Test that signal has not been changed during the test
assert_array_equal(signal_copy, signal)