from nipy.labs import compute_mask_files from nibabel import load, save, Nifti1Image import get_data_light import nipy.labs.glm from nipy.modalities.fmri.design_matrix import make_dmtx from nipy.modalities.fmri.experimental_paradigm import \ load_protocol_from_csv_file from nipy.labs.viz import plot_map, cm ####################################### # Data and analysis parameters ####################################### # volume mask get_data_light.get_first_level_dataset() data_path = op.expanduser(op.join('~', '.nipy', 'tests', 'data', 's12069_swaloc1_corr.nii.gz')) paradigm_file = op.expanduser(op.join('~', '.nipy', 'tests', 'data', 'localizer_paradigm.csv')) # timing n_scans = 128 tr = 2.4 # paradigm frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans) conditions = ['damier_H', 'damier_V', 'clicDaudio', 'clicGaudio', 'clicDvideo', 'clicGvideo', 'calculaudio', 'calculvideo', 'phrasevideo', 'phraseaudio']
from nipy.modalities.fmri.glm import FMRILinearModel from nipy.modalities.fmri.design_matrix import make_dmtx from nipy.modalities.fmri.experimental_paradigm import \ load_paradigm_from_csv_file from nipy.labs.viz import plot_map, cm # Local import from get_data_light import DATA_DIR, get_first_level_dataset ####################################### # Data and analysis parameters ####################################### # volume mask # This dataset is large get_first_level_dataset() data_path = path.join(DATA_DIR, 's12069_swaloc1_corr.nii.gz') paradigm_file = path.join(DATA_DIR, 'localizer_paradigm.csv') # timing n_scans = 128 tr = 2.4 # paradigm frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans) # confounds hrf_model = 'canonical' drift_model = 'cosine' hfcut = 128
from nipy.modalities.fmri.glm import FMRILinearModel from nipy.modalities.fmri.design_matrix import make_dmtx from nipy.modalities.fmri.experimental_paradigm import \ load_paradigm_from_csv_file from nipy.labs.viz import plot_map, cm # Local import from get_data_light import DATA_DIR, get_first_level_dataset ####################################### # Data and analysis parameters ####################################### # volume mask # This dataset is large get_first_level_dataset() data_path = path.join(DATA_DIR, 's12069_swaloc1_corr.nii.gz') paradigm_file = path.join(DATA_DIR, 'localizer_paradigm.csv') # timing n_scans = 128 tr = 2.4 # paradigm frametimes = np.linspace(0.5 * tr, (n_scans - .5) * tr, n_scans) # confounds hrf_model = 'canonical with derivative' drift_model = "cosine" hfcut = 128