thq = 0.95
verbose = 1
smin = 5
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
method = 'quick'
print 'method used:', method
# call the function
AF, BF = make_bsa_image(mask_images,
betas,
theta,
dmax,
ths,
thq,
smin,
write_dir,
method,
subj_id,
'%04d' % nbeta,
reshuffle=False)
# Write the result. OK, this is only a temporary solution
picname = path.join(write_dir, "AF_%04d.pic" % nbeta)
pickle.dump(AF, open(picname, 'w'), 2)
picname = path.join(write_dir, "BF_%04d.pic" % nbeta)
pickle.dump(BF, open(picname, 'w'), 2)
print "Wrote all the results in directory %s" % write_dir
if missing_file:
get_data_light.get_it()
# set various parameters
subj_id = ['%04d' %i for i in range(12)]
theta = float(stats.t.isf(0.01, 100))
dmax = 4.
ths = 0
thq = 0.95
verbose = 1
smin = 5
swd = tempfile.mkdtemp()
method = 'quick'
print 'method used:', method
# call the function
AF, BF = make_bsa_image(mask_images, betas, theta, dmax, ths, thq, smin, swd,
method, subj_id, '%04d' % nbeta, reshuffle=False)
# Write the result. OK, this is only a temporary solution
import pickle
picname = op.join(swd,"AF_%04d.pic" %nbeta)
pickle.dump(AF, open(picname, 'w'), 2)
picname = op.join(swd,"BF_%04d.pic" %nbeta)
pickle.dump(BF, open(picname, 'w'), 2)
print "Wrote all the results in directory %s" % swd
betas = [path.join(data_dir, 'spmT_%04d_subj_%02d.nii' % (nbeta, n))
for n in range(nbsubj)]
missing_file = array([not path.exists(m) for m in mask_images + betas]).any()
if missing_file:
get_second_level_dataset()
# set various parameters
subj_id = ['%04d' % i for i in range(12)]
threshold = float(stats.t.isf(0.01, 100))
sigma = 4.
prevalence_threshold = 2
prevalence_pval = 0.95
smin = 5
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
algorithm = 'density'
print('algorithm used:', algorithm)
# call the function
landmarks, individual_rois = make_bsa_image(
mask_images, betas, threshold, smin, sigma, prevalence_threshold,
prevalence_pval, write_dir, algorithm=algorithm,
contrast_id='%04d' % nbeta)
print("Wrote all the results in directory %s" % write_dir)
if missing_file:
get_second_level_dataset()
# set various parameters
subj_id = ['%04d' % i for i in range(12)]
threshold = float(stats.t.isf(0.01, 100))
sigma = 4.
prevalence_threshold = 2
prevalence_pval = 0.95
smin = 5
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
algorithm = 'density'
print('algorithm used:', algorithm)
# call the function
landmarks, individual_rois = make_bsa_image(mask_images,
betas,
threshold,
smin,
sigma,
prevalence_threshold,
prevalence_pval,
write_dir,
algorithm=algorithm,
contrast_id='%04d' % nbeta)
print("Wrote all the results in directory %s" % write_dir)
