def test_save_subject(self):
dataset = SubjectsDataset(self.subjects_list, transform=lambda x: x)
_ = len(dataset) # for coverage
subject = dataset[0]
output_path = self.dir / 'test.nii.gz'
paths_dict = {'t1': output_path}
with self.assertWarns(DeprecationWarning):
dataset.save_sample(subject, paths_dict)
nii = nib.load(str(output_path))
ndims_output = len(nii.shape)
ndims_subject = len(subject['t1'].shape)
assert ndims_subject == ndims_output + 1
rp_files = gfile('/data/romain/HCPdata/suj_274542/Motion_ms','^rp')
rp_files = gfile('/data/romain/HCPdata/suj_274542/mot_separate','^rp')
rpf = rp_files[10]
res = pd.DataFrame()
for rpf in rp_files:
dirpath,name = get_parent_path([rpf])
fout = dirpath[0] + '/check/'+name[0][3:-4] + '.nii'
t = RandomMotionFromTimeCourse(fitpars=rpf, nufft=True, oversampling_pct=0, keep_original=True, verbose=True)
dataset = ImagesDataset(suj, transform=t)
sample = dataset[0]
dicm = sample['T1']['metrics']
dicm['fname'] = fout
res = res.append(dicm, ignore_index=True)
dataset.save_sample(sample, dict(T1=fout))
fit_pars = sample['T1']['fit_pars']
plt.figure; plt.plot(fit_pars[3:].T)
plt.figure; plt.plot(fit_pars.T)
dic_no_mot ={ "noiseBasePars": (5, 20, 0),"swallowFrequency": (0, 1, 1), "suddenFrequency": (0, 1, 1),
"oversampling_pct":0.3, "nufft":True , "keep_original": True}
t = RandomMotionFromTimeCourse(**dic_no_mot)
dataset = ImagesDataset(suj, transform=t)
sample = dataset[0]
dico_params = {"maxDisp": (1, 6), "maxRot": (1, 6), "noiseBasePars": (5, 20, 0),
"swallowFrequency": (2, 6, 0), "swallowMagnitude": (1, 6),