def test_random_state():
# Test that the random state controls reproducibility
sig_0 = simulate_pac_default(random_state=0)
sig_1 = simulate_pac_default(random_state=1)
sig_2 = simulate_pac_default(random_state=0)
assert_raises(AssertionError, assert_array_almost_equal, sig_0, sig_1)
assert_array_almost_equal(sig_0, sig_2)
def test_different_dimension_in_input():
# Test that 1D or 2D signals are accepted, but not 3D
for dim in [(4, -1), (-1, ), (1, -1)]:
fast_peak_locking(signal.reshape(*dim))
dim = (2, 2, -1)
assert_raises(ValueError, fast_peak_locking, signal.reshape(*dim))
def test_no_surrogate():
# Test the errors when n_surrogates == 0
for method in ALL_PAC_METRICS:
est = ComodTest(method=method, n_surrogates=0).fit(signal)
with assert_raises(ValueError):
est.comod_z_score_
with assert_raises(ValueError):
est.surrogate_max_
with assert_raises(ValueError):
est.plot(contour_level=0.01)
plt.close('all')
def test_plot_comodulogram():
# Smoke test with the standard plotting function
est = ComodTest().fit(signal)
est.plot()
# Smoke test with the special plotting functions
ax = plt.figure().gca()
for method in ALL_PAC_METRICS:
est = ComodTest(low_fq_range=[low_fq], method=method,
ax_special=ax).fit(signal)
# Test that it raises an error if ax_special is not None and low_fq_range
# has more than one element
func = partial(fast_comod, ax_special=ax)
assert_raises(ValueError, func)
plt.close('all')
def test_input_checking():
# test that we have a ValueError for bad parameters
func = partial(fast_comod, method='wrong')
assert_raises(ValueError, func)
func = partial(fast_comod, fs='wrong')
assert_raises(ValueError, func)
func = partial(fast_comod, low_sig='wrong')
assert_raises(ValueError, func)
func = partial(fast_comod, high_sig='wrong')
assert_raises(ValueError, func)
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_make_basis_new_sigdriv():
# Test that make_basis works the same with a new sigdriv,
# using stored orthonormalization transform.
model_params = {'ordar': 5, 'ordriv': 2, 'criterion': False}
for normalize in (True, False):
for ortho in (True, False):
for this_sigdriv, this_sigdriv_imag in ([_sigdriv, _sigdriv_imag],
[_noise, _noise[::-1]]):
dar = dar_no_fit(ortho=ortho,
normalize=normalize,
sigdriv=this_sigdriv,
sigdriv_imag=this_sigdriv_imag,
**model_params)
newbasis = dar.make_basis(sigdriv=this_sigdriv,
sigdriv_imag=this_sigdriv_imag)
assert_array_almost_equal(newbasis, dar.basis_)
# Different result if we change a parameter
dar_ortho = dar_no_fit(not ortho, normalize, **model_params)
dar_norma = dar_no_fit(ortho, not normalize, **model_params)
for dar2 in [dar_ortho, dar_norma]:
assert_raises(AssertionError, assert_array_almost_equal,
dar.basis_, dar2.basis_)
