pyhrf.validation.valid_sandbox_parcellation.FeatureExtractionTest(methodName='runTest')¶Bases: unittest.case.TestCase
setUp()¶Hook method for setting up the test fixture before exercising it.
tearDown()¶Hook method for deconstructing the test fixture after testing it.
test_feature_extraction()¶test_generate_features()¶pyhrf.validation.valid_sandbox_parcellation.ParcellationTest(methodName='runTest')¶Bases: unittest.case.TestCase
save_parcellation_outputs(pobj, mask)¶setUp()¶Hook method for setting up the test fixture before exercising it.
tearDown()¶Hook method for deconstructing the test fixture after testing it.
test_gmm_from_forged_features()¶Test spatial Ward with uncertainty on forged features
test_hemodynamic_parcellation_GMM_2D_high_SNR()¶test GMM-based parcellation on features extracted from a 2D artificial fMRI data set, at high SNR
test_hemodynamic_parcellation_wpu_2D_high_SNR()¶test WPU on features extracted from a 2D artificial fMRI data set, at high SNR
test_mixtdist()¶Check that merge is in favour of non-activ at the same feature level, starting from singleton clusters.
test_parcellation_history()¶test_parcellation_mmp_act_level_1D()¶Test the ability of MMP to ‘jump’ non-activating positions (1D case).
test_parcellation_mmp_act_level_2D()¶Test the ability of MMP to ‘jump’ non-activating positions (2D case).
test_parcellation_spatialWard_2()¶Test WPU on a simple case.
test_parcellation_spatialWard_400_nonoise()¶test_parcellation_spatialWard_400_variance()¶test_parcellation_spatialWard_5_sklearn()¶test_parcellation_spatialWard_act_level_1D()¶Test the ability of WPU to ‘jump’ non-activating positions (1D case).
test_parcellation_spatialWard_act_level_2D()¶Test the ability of WPU to ‘jump’ non-activating positions (2D case).
test_parcellation_spatialWard_variance_1D()¶Test the ability of WPU to ‘jump’ non-activating positions (1D case).
test_parcellation_spatialWard_variance_2D()¶Test the sensibility to variance (2D case).
test_render_ward_tree()¶test_spatialward_against_modelbasedspatialward()¶Check that pyhrf’s spatial Ward parcellation is giving the same results as scikit’s spatial Ward parcellation
test_spatialward_against_ward_sk()¶Check that pyhrf’s spatial Ward parcellation is giving the same results as scikit’s spatial Ward parcellation
test_spatialward_from_forged_features()¶Test spatial Ward on forged features
test_uspatialward_formula()¶Check that pyhrf’s Uncertain spatial Ward parcellation is giving the same results as Uncertain spatial Ward parcellation modified formula
test_uward_tree_save()¶test_ward_distance_1D_v1()¶test_ward_distance_1D_v2()¶test_ward_distance_2D()¶test_ward_tree_save()¶test_wpu_from_forged_features()¶Test spatial Ward with uncertainty on forged features
pyhrf.validation.valid_sandbox_parcellation.StatTest(methodName='runTest')¶Bases: unittest.case.TestCase
setUp()¶Hook method for setting up the test fixture before exercising it.
test_gmm_known_alpha0()¶Test biGMM update with posterior weights equal to 0
test_gmm_known_weights_difvars_noise()¶Test biGMM fit with known post weights, from biGMM samples (no noise) 1D case.
test_gmm_known_weights_difvars_noisea()¶Test biGMM fit with known post weights, from biGMM samples (no noise) 1D case.
test_gmm_known_weights_noise()¶Test biGMM fit with known post weights, from biGMM samples (no noise) 1D case.
test_gmm_known_weights_noisea()¶Test biGMM fit with known post weights, from biGMM samples (no noise) 1D case.
test_gmm_known_weights_simu_1D()¶Test biGMM fit with known post weights, from biGMM samples (no noise) 1D case.
test_gmm_likelihood()¶Test the log likelihood computation
test_informedGMM_parameters()¶Check that merge is in favour of non-activ at the same feature level, starting from singleton clusters.
test_norm_bc()¶pyhrf.validation.valid_sandbox_parcellation.create_features(size='2D', feat_contrast='high', noise_var=0.0, n_features=2)¶pyhrf.validation.valid_sandbox_parcellation.simulate_fmri_data(scenario='high_snr', output_path=None)¶