def test_reduce_shape():
dyna_corrs_reduced_1 = timecorr(sim_1,
rfun='PCA',
weights_function=laplace['weights'],
weights_params=laplace['params'])
dyna_corrs_reduced_3 = timecorr(sim_3,
rfun='PCA',
weights_function=laplace['weights'],
weights_params=laplace['params'])
assert np.shape(dyna_corrs_reduced_1) == np.shape(sim_1)
assert np.shape(dyna_corrs_reduced_3) == np.shape(sim_3)
def test_exclude_timepoints_neg():
dyna_corrs_reduced_3 = timecorr(sim_3,
rfun='PCA',
weights_function=laplace['weights'],
weights_params=laplace['params'],
exclude_timepoints=-3)
assert np.shape(dyna_corrs_reduced_3) == np.shape(sim_3)
def test_nans():
sim_3[0][0] = np.nan
dyna_corrs_reduced_3 = timecorr(sim_3,
rfun='PCA',
weights_function=laplace['weights'],
weights_params=laplace['params'])
assert np.shape(dyna_corrs_reduced_3) == np.shape(sim_3)
assert isinstance(data_dl, list)
Test_dl = data_dl[0].shape[0]
Test_pdf = data_pdf[0].shape[0]
Test_npa = data_npa[0].shape[0]
#Test returns the shape of the weights_function
# Test_npl= data_npl[0].shape[0]
# Test_rand= data_rn[0].shape[0]
assert isinstance(Test_pdf, int)
dl_tester = gaussian_weights(Test_dl, params=gaussian_params)
pdf_tester = gaussian_weights(Test_pdf, params=gaussian_params)
npa_tester = gaussian_weights(Test_npa, params=gaussian_params)
# thrid_tester = gaussian_weights(T3, params=gaussian_params)
# fourth_tester = gaussian_weights(T4, params=gaussian_params)
assert isinstance(npa_tester, np.ndarray)
# assert npa_tester.shape == data_npa.shape
# assert dl_tester.shape > data_dl.shape
#unsure how to test 'across' mode
corrs = timecorr(numpy_array,
weights_function=gaussian_weights,
weights_params=gaussian_params,
cfun=isfc)
#assert()
#assert len(corrs.get_time_data()[0]) == len(numpy_array)