def perm_hist(subj):
conf = AnalysisConfiguration()
data_dir = os.environ.get('DATA_DIR') or '/home/user/data'
sub_dir = _opj(data_dir,conf.study_name,'sub{:0>3d}'.format(subj))
directory = _opj(data_dir,'LP/sub{:0>3d}/results/'.format(subj))
print conf.dir_name()
for pair in conf.conditions_to_compare:
#print _opj(directory,conf.dir_name(),'{}*{}{}*.p'.format(conf.mask_name,pair[0],pair[1]))
files = sorted(glob(_opj(directory,conf.dir_name(),'{}*{}{}*.p'.format(conf.mask_name,pair[0],pair[1]))))
plt.figure()
plt.subplot(211)
plt.title('sub{:0>3d}-{}{}'.format(subj,pair[0],pair[1]))
print pair, " ", len(files)
all_maps = []
for f in files[:-1]:
f_h = file(f,'r')
m = pickle.load(f_h)
all_maps.append(m)
if 'perm' in f:
color = 'black'
line_width = 1
else:
color = 'crimson'
line_width = 2
plt.hist(np.transpose(m),bins=20,histtype='step',color=[color], lw = line_width)
perms = vstack(all_maps)
real_f = files[-1]
f_h = file(real_f,'r')
real_map = pickle.load(f_h)
color = 'crimson'
line_width = 2
plt.hist(np.transpose(real_map),bins=20,histtype='step',color=[color], lw = line_width)
percentiles = np.zeros((1,len(real_map.samples[0])))
for i,vox in enumerate(real_map.samples[0]):
percentiles[0,i]=percentileofscore(perms[:,i].samples.flat,vox)
plt.subplot(212)
print len(percentiles[0])
plt.hist(percentiles[0],bins=20,histtype='step')
real_map.samples=percentiles
nii = real_f.replace("_sl_map.p", "-acc.nii.gz")
nii_file = nib.load(nii)
perc_results = map2nifti(real_map, imghdr=nii_file.header)
perc_nii_filename =real_f.replace("_sl_map.p", "-percentiles_sub{:0>3d}.nii.gz".format(subj))
perc_results.to_filename(perc_nii_filename)
thr_prc_filename = perc_nii_filename.replace(".nii.gz","_p0.01.nii.gz")
thr = fsl.maths.Threshold(in_file=perc_nii_filename, thresh=100,
out_file=thr_prc_filename)
thr.run()
mni_thr_filename = thr_prc_filename.replace(".nii.gz","_mni.nii.gz")
apply_warp(sub_dir,thr_prc_filename, mni_thr_filename)
plt.show()
#plt.savefig('/tmp/sub{:0>3d}_{}{}'.format(subj,pair[0],pair[1]))
raw_input()
def main():
conf = AnalysisConfiguration()
data_dir = os.environ.get('DATA_DIR') or '/home/user/data'
op = OpenFMRIData(data_dir, conf.study_name)
analyzer = OpenFMRIAnalyzer(op, conf)
all_subject_dirs = op.all_subjects_dirs_with_raw()
for subject in all_subject_dirs:
analyzer.extract_brain(subject)
for subject in all_subject_dirs:
analyzer.anatomical_registration(subject)
for subject in all_subject_dirs:
#for task in conf.mvpa_tasks:
#subject.remove_volumes_from_model(1, "", task, conf.num_of_volumes_to_delete)
analyzer.motion_correction(subject)
analyzer.functional_registration(subject)
if conf.func_seg:
analyzer.functional_segmentation(subject)
else:
analyzer.segmentation(subject)
analyzer.generate_functional_gm_masks(subject)
#analyzer.warp_standard_mask(subject)
for subject in all_subject_dirs:
# DO SL
out_dir = _opj(subject.path(),'results',conf.dir_name())
if not os.path.exists(out_dir):
os.makedirs(out_dir)
run_searchlight(op, subject, conf, out_dir)
# run_searchlight(op.study_dir(), subject.subcode(), mask_name, k, [['G1','G4']], out_dir,flavor)
#Group Level
output_dir = _opj(op.study_dir(), 'results', "{}".format(conf.dir_name()))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
files = glob(_opj(op.study_dir(), "**", 'results', conf.dir_name(), '*acc_mni.nii.gz'))
print files
generate_group_level_map( files, output_dir)
def main():
conf = AnalysisConfiguration()
data_dir = os.environ.get('DATA_DIR') or '/home/daniel/fsl-analysis/data'
op = OpenFMRIData(data_dir, conf.study_name)
analyzer = OpenFMRIAnalyzer(op, conf)
all_subject_dirs = op.all_subjects_dirs_with_raw()
for subject in all_subject_dirs:
analyzer.extract_brain(subject)
for subject in all_subject_dirs:
analyzer.anatomical_registration(subject)
for subject in all_subject_dirs:
#for task in conf.mvpa_tasks:
#subject.remove_volumes_from_model(1, "", task, conf.num_of_volumes_to_delete)
analyzer.motion_correction(subject)
analyzer.functional_registration(subject)
if conf.func_seg:
analyzer.functional_segmentation(subject)
else:
analyzer.segmentation(subject)
analyzer.generate_functional_gm_masks(subject)
#analyzer.warp_standard_mask(subject)
for subject in all_subject_dirs:
# DO SL
out_dir = _opj(subject.path(),'results',conf.dir_name())
if not os.path.exists(out_dir):
os.makedirs(out_dir)
run_searchlight(op, subject, conf, out_dir)
