#import nipy.neurospin.spatial_models.parcellation as Pa from nipy.neurospin.spatial_models.parcel_io import one_subj_parcellation import get_data_light get_data_light.getIt() # ------------------------------------ # 1. Get the data (mask+functional image) # take several experimental conditions # time courses could be used instead nbeta = [29] data_dir = op.expanduser(op.join('~', '.nipy', 'tests', 'data')) MaskImage = op.join(data_dir,'mask.nii.gz') betas = [op.join(data_dir,'spmT_%04d.nii.gz'%n) for n in nbeta] # set the parameters nbparcel = 500 mu = 10 nn = 6 write_dir = tempfile.mkdtemp() verbose = 1 lpa = one_subj_parcellation(MaskImage, betas, nbparcel, nn, 'gkm', write_dir, mu, verbose) lpa = one_subj_parcellation(MaskImage, betas, nbparcel, nn, 'ward', write_dir, mu, verbose) lpa = one_subj_parcellation(MaskImage, betas, nbparcel, nn, 'ward_and_gkm', write_dir, mu, verbose)
import os.path import tempfile from nipy.neurospin.spatial_models.parcel_io import one_subj_parcellation import get_data_light data_dir = get_data_light.get_it() # ------------------------------------ # Get the data (mask+functional image) # take several experimental conditions # time courses could be used instead n_beta = [29] mask_image = os.path.join(data_dir, 'mask.nii.gz') betas = [os.path.join(data_dir, 'spmT_%04d.nii.gz' % n) for n in n_beta] # set the parameters n_parcels = 500 mu = 10 nn = 6 write_dir = tempfile.mkdtemp() verbose = 1 lpa = one_subj_parcellation(mask_image, betas, n_parcels, nn, 'gkm', write_dir, mu, verbose) lpa = one_subj_parcellation(mask_image, betas, n_parcels, nn, 'ward', write_dir, mu, verbose) lpa = one_subj_parcellation(mask_image, betas, n_parcels, nn, 'ward_and_gkm', write_dir, mu, verbose)