def test_fiac(): dataset = datasets.fetch_fiac_first_level(data_dir=tmpdir) assert_true(isinstance(dataset.func1, _basestring)) assert_true(isinstance(dataset.func2, _basestring)) assert_true(isinstance(dataset.design_matrix1, _basestring)) assert_true(isinstance(dataset.design_matrix2, _basestring)) assert_true(isinstance(dataset.mask, _basestring))
def test_fiac(): dataset = datasets.fetch_fiac_first_level(data_dir=tmpdir) assert_true(isinstance(dataset.func1, _basestring)) assert_true(isinstance(dataset.func2, _basestring)) assert_true(isinstance(dataset.design_matrix1, _basestring)) assert_true(isinstance(dataset.design_matrix2, _basestring)) assert_true(isinstance(dataset.mask, _basestring))
def report_flm_fiac(): # pragma: no cover data = nistats_datasets.fetch_fiac_first_level() fmri_img = [data['func1'], data['func2']] from nilearn.image import mean_img mean_img_ = mean_img(fmri_img[0]) design_files = [data['design_matrix1'], data['design_matrix2']] design_matrices = [pd.DataFrame(np.load(df)['X']) for df in design_files] fmri_glm = FirstLevelModel(mask_img=data['mask'], minimize_memory=True) fmri_glm = fmri_glm.fit(fmri_img, design_matrices=design_matrices) n_columns = design_matrices[0].shape[1] contrasts = { 'SStSSp_minus_DStDSp': _pad_vector([1, 0, 0, -1], n_columns), 'DStDSp_minus_SStSSp': _pad_vector([-1, 0, 0, 1], n_columns), 'DSt_minus_SSt': _pad_vector([-1, -1, 1, 1], n_columns), 'DSp_minus_SSp': _pad_vector([-1, 1, -1, 1], n_columns), 'DSt_minus_SSt_for_DSp': _pad_vector([0, -1, 0, 1], n_columns), 'DSp_minus_SSp_for_DSt': _pad_vector([0, 0, -1, 1], n_columns), 'Deactivation': _pad_vector([-1, -1, -1, -1, 4], n_columns), 'Effects_of_interest': np.eye(n_columns)[:5] } report = make_glm_report( fmri_glm, contrasts, bg_img=mean_img_, height_control='fdr', ) output_filename = 'generated_report_flm_fiac.html' output_filepath = os.path.join(REPORTS_DIR, output_filename) report.save_as_html(output_filepath) report.get_iframe()
def test_fiac(): # Create dummy 'files' fiac_dir = os.path.join(tst.tmpdir, 'fiac_nistats', 'nipy-data-0.2', 'data', 'fiac') fiac0_dir = os.path.join(fiac_dir, 'fiac0') os.makedirs(fiac0_dir) for session in [1, 2]: # glob func data for session session + 1 session_func = os.path.join(fiac0_dir, 'run%i.nii.gz' % session) open(session_func, 'a').close() sess_dmtx = os.path.join(fiac0_dir, 'run%i_design.npz' % session) open(sess_dmtx, 'a').close() mask = os.path.join(fiac0_dir, 'mask.nii.gz') open(mask, 'a').close() dataset = datasets.fetch_fiac_first_level(data_dir=tst.tmpdir) assert_true(isinstance(dataset.func1, _basestring)) assert_true(isinstance(dataset.func2, _basestring)) assert_true(isinstance(dataset.design_matrix1, _basestring)) assert_true(isinstance(dataset.design_matrix2, _basestring)) assert_true(isinstance(dataset.mask, _basestring))
from nilearn import plotting from nilearn.image import mean_img import nibabel as nib from nistats.first_level_model import FirstLevelModel from nistats import datasets # write directory write_dir = path.join(getcwd(), 'results') if not path.exists(write_dir): mkdir(write_dir) ######################################################################### # Prepare data and analysis parameters # -------------------------------------- data = datasets.fetch_fiac_first_level() fmri_img = [data['func1'], data['func2']] mean_img_ = mean_img(fmri_img[0]) design_files = [data['design_matrix1'], data['design_matrix2']] design_matrices = [pd.DataFrame(np.load(df)['X']) for df in design_files] ######################################################################### # GLM estimation # ---------------------------------- # GLM specification fmri_glm = FirstLevelModel(mask=data['mask'], minimize_memory=True) ######################################################################### # GLM fitting fmri_glm = fmri_glm.fit(fmri_img, design_matrices=design_matrices)
from nilearn import plotting from nilearn.image import mean_img import nibabel as nib from nistats.glm import FirstLevelGLM from nistats import datasets # write directory write_dir = path.join(getcwd(), 'results') if not path.exists(write_dir): mkdir(write_dir) # Data and analysis parameters data = datasets.fetch_fiac_first_level() fmri_img = [data['func1'], data['func2']] mean_img_ = mean_img(fmri_img[0]) design_files = [data['design_matrix1'], data['design_matrix2']] design_matrices = [pd.DataFrame(np.load(df)['X']) for df in design_files] # GLM specification fmri_glm = FirstLevelGLM(data['mask'], standardize=False, noise_model='ar1') # GLM fitting fmri_glm.fit(fmri_img, design_matrices) # compute fixed effects of the two runs and compute related images n_columns = design_matrices[0].shape[1] def pad_vector(contrast_, n_columns): return np.hstack((contrast_, np.zeros(n_columns - len(contrast_))))