def make_parcellation_from_files(betaFiles,
maskFile,
outFile,
nparcels,
method,
dry=False,
spatial_weight=10.):
if not op.exists(maskFile):
print 'Error, file %s not found' % maskFile
return
betaFiles = sorted(betaFiles)
for b in betaFiles:
if not op.exists(b):
raise Exception('Error, file %s not found' % b)
logger.info('Mask image: %s', op.basename(maskFile))
logger.info('Betas: %s ... %s', op.basename(betaFiles[0]),
op.basename(betaFiles[-1]))
logger.info("Method: %s, nb parcels: %d", method, nparcels)
logger.info('Spatial weight: %f', spatial_weight)
if not dry:
logger.info('Running parcellation ... ')
logger.info('Start date is: %s', strftime('%c', localtime()))
t0 = time()
v = logger.getEffectiveLevel() <= logging.INFO
lpa = fixed_parcellation(maskFile,
betaFiles,
nparcels,
nn=6,
method=method,
fullpath=outFile,
verbose=v,
mu=spatial_weight)
from pyhrf.ndarray import xndarray
c = xndarray.load(outFile)
if c.min() == -1:
c.data += 1
for i in np.unique(c.data):
# remove parcel with size < 2:
if i != 0 and len(c.data == 1) < 2:
c.data[np.where(c.data == i)] = 0
c.save(outFile)
logger.info('Parcellation complete, took %s',
format_duration(time() - t0))
return lpa
else:
logger.info('Dry run.')
def make_parcellation_from_files(betaFiles, maskFile, outFile, nparcels,
method, dry=False, spatial_weight=10.):
if not op.exists(maskFile):
print 'Error, file %s not found' %maskFile
return
betaFiles = sorted(betaFiles)
for b in betaFiles:
if not op.exists(b):
print 'Error, file %s not found' %b
return
pyhrf.verbose(1, 'Mask image: ' + op.basename(maskFile))
pyhrf.verbose(1, 'Betas: ' + op.basename(betaFiles[0]) + ' ... ' + \
op.basename(betaFiles[-1]))
pyhrf.verbose(1, "Method: %s, nb parcels: %d" %(method, nparcels))
pyhrf.verbose(1, 'Spatial weight: %f' %spatial_weight)
if not dry:
pyhrf.verbose(1, 'Running parcellation ... ')
pyhrf.verbose(1, 'Start date is: %s' %strftime('%c',localtime()))
t0 = time()
#lpa = one_subj_parcellation(maskFile, betaFiles, nparcels, 6,
# method, 10, 1, fullpath=outFile)
v = pyhrf.verbose.verbosity
lpa = fixed_parcellation(maskFile, betaFiles, nparcels, nn=6,
method=method, fullpath=outFile, verbose=v,
mu=spatial_weight)
from pyhrf.ndarray import xndarray
c = xndarray.load(outFile)
if c.min() == -1:
c.data += 1
for i in np.unique(c.data):
# remove parcel with size < 2:
if i!=0 and len(c.data==1) < 2:
c.data[np.where(c.data==i)] = 0
c.save(outFile)
pyhrf.verbose(1, 'Parcellation complete, took %s' \
%format_duration(time() - t0))
return lpa
else:
pyhrf.verbose(1, 'Dry run.')
def make_parcellation_from_files(betaFiles, maskFile, outFile, nparcels,
method, dry=False, spatial_weight=10.):
if not op.exists(maskFile):
print 'Error, file %s not found' % maskFile
return
betaFiles = sorted(betaFiles)
for b in betaFiles:
if not op.exists(b):
raise Exception('Error, file %s not found' % b)
logger.info('Mask image: %s', op.basename(maskFile))
logger.info('Betas: %s ... %s', op.basename(betaFiles[0]),
op.basename(betaFiles[-1]))
logger.info("Method: %s, nb parcels: %d", method, nparcels)
logger.info('Spatial weight: %f', spatial_weight)
if not dry:
logger.info('Running parcellation ... ')
logger.info('Start date is: %s', strftime('%c', localtime()))
t0 = time()
v = logger.getEffectiveLevel() <= logging.INFO
lpa = fixed_parcellation(maskFile, betaFiles, nparcels, nn=6,
method=method, fullpath=outFile, verbose=v,
mu=spatial_weight)
from pyhrf.ndarray import xndarray
c = xndarray.load(outFile)
if c.min() == -1:
c.data += 1
for i in np.unique(c.data):
# remove parcel with size < 2:
if i != 0 and len(c.data == 1) < 2:
c.data[np.where(c.data == i)] = 0
c.save(outFile)
logger.info(
'Parcellation complete, took %s', format_duration(time() - t0))
return lpa
else:
logger.info('Dry run.')
from nipy.labs.spatial_models.parcel_io import fixed_parcellation
# ------------------------------------
# Get the data (mask+functional image)
# take several experimental conditions
# time courses could be used instead
n_beta = [29]
data_dir = op.expanduser(op.join('~', '.nipy', 'tests', 'data'))
mask_image = op.join(data_dir, 'mask.nii.gz')
betas = [op.join(data_dir, 'spmT_%04d.nii.gz' % n) for n in n_beta]
missing_file = array(
[op.exists(m) == False for m in [mask_image] + betas]).any()
if missing_file:
import get_data_light
get_data_light.get_it()
# set the parameters
n_parcels = 500
mu = 10
nn = 6
write_dir = tempfile.mkdtemp()
verbose = 1
lpa = fixed_parcellation(mask_image, betas, n_parcels, nn, 'gkm',
write_dir, mu, verbose)
lpa = fixed_parcellation(mask_image, betas, n_parcels, nn, 'ward',
write_dir, mu, verbose)
lpa = fixed_parcellation(mask_image, betas, n_parcels, nn, 'ward_and_gkm',
write_dir, mu, verbose)
from get_data_light import DATA_DIR, get_second_level_dataset
# ------------------------------------
# Get the data (mask+functional image)
# take several experimental conditions
# time courses could be used instead
n_beta = [29]
mask_image = path.join(DATA_DIR, 'mask.nii.gz')
betas = [path.join(DATA_DIR, 'spmT_%04d.nii.gz' % n) for n in n_beta]
missing_file = array([not path.exists(m) for m in [mask_image] + betas]).any()
if missing_file:
get_second_level_dataset()
# set the parameters
n_parcels = 500
mu = 10
nn = 6
verbose = 1
# write directory
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
lpa = fixed_parcellation(mask_image, betas, n_parcels, nn, 'gkm', write_dir,
mu, verbose)
lpa = fixed_parcellation(mask_image, betas, n_parcels, nn, 'ward', write_dir,
mu, verbose)
lpa = fixed_parcellation(mask_image, betas, n_parcels, nn, 'ward_and_gkm',
write_dir, mu, verbose)
