#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)