def test_SurfaceTransform_outputs():
output_map = dict(out_file=dict(), )
outputs = SurfaceTransform.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_SurfaceTransform_outputs():
output_map = dict(out_file=dict(),
)
outputs = SurfaceTransform.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_SurfaceTransform_inputs():
input_map = dict(
args=dict(argstr='%s', ),
environ=dict(
nohash=True,
usedefault=True,
),
hemi=dict(
argstr='--hemi %s',
mandatory=True,
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
out_file=dict(
argstr='--tval %s',
genfile=True,
),
reshape=dict(argstr='--reshape', ),
reshape_factor=dict(argstr='--reshape-factor', ),
source_annot_file=dict(
argstr='--sval-annot %s',
mandatory=True,
xor=['source_file'],
),
source_file=dict(
argstr='--sval %s',
mandatory=True,
xor=['source_annot_file'],
),
source_subject=dict(
argstr='--srcsubject %s',
mandatory=True,
),
source_type=dict(
argstr='--sfmt %s',
requires=['source_file'],
),
subjects_dir=dict(),
target_ico_order=dict(argstr='--trgicoorder %d', ),
target_subject=dict(
argstr='--trgsubject %s',
mandatory=True,
),
target_type=dict(argstr='--tfmt %s', ),
terminal_output=dict(
mandatory=True,
nohash=True,
),
)
inputs = SurfaceTransform.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_SurfaceTransform_inputs():
input_map = dict(ignore_exception=dict(nohash=True,
usedefault=True,
),
hemi=dict(mandatory=True,
argstr='--hemi %s',
),
out_file=dict(argstr='--tval %s',
genfile=True,
),
source_subject=dict(mandatory=True,
argstr='--srcsubject %s',
),
reshape=dict(argstr='--reshape',
),
args=dict(argstr='%s',
),
target_ico_order=dict(argstr='--trgicoorder %d',
),
target_type=dict(argstr='--tfmt %s',
),
reshape_factor=dict(argstr='--reshape-factor',
),
source_type=dict(requires=['source_file'],
argstr='--sfmt %s',
),
terminal_output=dict(mandatory=True,
nohash=True,
),
environ=dict(nohash=True,
usedefault=True,
),
subjects_dir=dict(),
source_file=dict(mandatory=True,
argstr='--sval %s',
xor=['source_annot_file'],
),
target_subject=dict(mandatory=True,
argstr='--trgsubject %s',
),
source_annot_file=dict(mandatory=True,
argstr='--sval-annot %s',
xor=['source_file'],
),
)
inputs = SurfaceTransform.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def main(subject, sourcedata, derivatives, smoothed, n_jobs=5):
os.environ['SUBJECTS_DIR'] = op.join(derivatives, 'freesurfer')
source_layout = BIDSLayout(sourcedata, validate=False, derivatives=False)
fmriprep_layout = BIDSLayout(op.join(derivatives, 'fmriprep'),
validate=False)
if smoothed:
bold_layout = BIDSLayout(op.join(derivatives, 'smoothed'),
validate=False)
bold = bold_layout.get(subject=subject, extension='func.gii')
else:
bold = fmriprep_layout.get(subject=subject, extension='func.gii')
bold = sorted([e for e in bold if 'fsaverage6' in e.filename],
key=lambda x: x.run)
fmriprep_layout_df = fmriprep_layout.to_df()
fmriprep_layout_df = fmriprep_layout_df[~fmriprep_layout_df.subject.isnull(
)]
fmriprep_layout_df['subject'] = fmriprep_layout_df.subject.astype(int)
fmriprep_layout_df = fmriprep_layout_df[np.in1d(fmriprep_layout_df.suffix,
['regressors'])]
fmriprep_layout_df = fmriprep_layout_df[np.in1d(
fmriprep_layout_df.extension, ['tsv'])]
fmriprep_layout_df = fmriprep_layout_df.set_index(['subject', 'run'])
events_df = source_layout.to_df()
events_df = events_df[events_df.suffix == 'events']
events_df['subject'] = events_df['subject'].astype(int)
events_df = events_df.set_index(['subject', 'run'])
tr = source_layout.get_tr(bold[0].path)
if smoothed:
base_dir = op.join(derivatives, 'glm_stim1_surf_smoothed',
f'sub-{subject}', 'func')
else:
base_dir = op.join(derivatives, 'glm_stim1_surf', f'sub-{subject}',
'func')
if not op.exists(base_dir):
os.makedirs(base_dir)
for b in bold:
run = b.entities['run']
hemi = b.entities['suffix']
# print(run)
confounds_ = fmriprep_layout_df.loc[(subject, run), 'path'].iloc[0]
confounds_ = pd.read_csv(confounds_, sep='\t')
confounds_ = confounds_[to_include].fillna(method='bfill')
pca = PCA(n_components=7)
confounds_ -= confounds_.mean(0)
confounds_ /= confounds_.std(0)
confounds_pca = pca.fit_transform(confounds_[to_include])
events_ = events_df.loc[(subject, run), 'path']
events_ = pd.read_csv(events_, sep='\t')
events_['trial_type'] = events_['trial_type'].apply(
lambda x: 'stim2' if x.startswith('stim2') else x)
frametimes = np.arange(0, tr * len(confounds_), tr)
X = make_first_level_design_matrix(
frametimes,
events_,
add_regs=confounds_pca,
add_reg_names=[f'confound_pca.{i}' for i in range(1, 8)])
Y = surface.load_surf_data(b.path).T
Y = (Y / Y.mean(0) * 100)
Y -= Y.mean(0)
fit = run_glm(Y, X, noise_model='ols', n_jobs=n_jobs)
r = fit[1][0.0]
betas = pd.DataFrame(r.theta, index=X.columns)
stim1 = []
for stim in 5, 7, 10, 14, 20, 28:
stim1.append(betas.loc[f'stim1-{stim}'])
result = pd.concat(stim1, 1).T
print(result.shape)
pes = nb.gifti.GiftiImage(header=nb.load(b.path).header,
darrays=[
nb.gifti.GiftiDataArray(row)
for ix, row in result.iterrows()
])
fn_template = op.join(
base_dir,
'sub-{subject}_run-{run}_space-{space}_desc-stims1_hemi-{hemi}.pe.gii'
)
space = 'fsaverage6'
pes.to_filename(fn_template.format(**locals()))
transformer = SurfaceTransform(source_subject='fsaverage6',
target_subject='fsaverage',
hemi={
'L': 'lh',
'R': 'rh'
}[hemi])
transformer.inputs.source_file = pes.get_filename()
space = 'fsaverage'
transformer.inputs.out_file = fn_template.format(**locals())
# Disable on MAC OS X (SIP problem)
transformer.run()
def main(subject, sourcedata, trialwise, clip=(-100, 100)):
derivatives = op.join(sourcedata, 'derivatives')
if trialwise:
layout = BIDSLayout(op.join(derivatives, 'glm_stim1_trialwise_surf'),
validate=False)
else:
layout = BIDSLayout(op.join(derivatives, 'glm_stim1_surf'),
validate=False)
for hemi in ['L', 'R']:
pes = layout.get(subject=subject, suffix=hemi)
print(pes)
df = []
for pe in pes:
d = pd.DataFrame(
np.clip(surface.load_surf_data(pe.path).T, clip[0], clip[1]))
df.append(d)
d['run'] = pe.run
d['number'] = np.log([5, 7, 10, 14, 20, 28])
df = pd.concat(df).set_index(['run', 'number'])
mask = ~df.isnull().any(0)
print('fitting {} time series'.format(mask.sum()))
for run in df.index.unique('run'):
train = df.drop(run)
test = df.loc[run]
model = GaussianReceptiveFieldModel()
costs, parameters, predictions = model.optimize(
train.index.get_level_values('number').values,
train.loc[:, mask].values)
base_dir = op.join(derivatives, 'modelfit_surf_cv',
f'sub-{subject}', 'func')
if not op.exists(base_dir):
os.makedirs(base_dir)
parameters.columns = df.loc[:, mask].columns
pars_df = pd.DataFrame(columns=df.columns)
pars_df = pd.concat((pars_df, parameters))
par_fn = op.join(
base_dir,
f'sub-{subject}_space-fsaverage6_desc-pars_hemi-{hemi}_cvrun-{run}.func.gii'
)
nb.gifti.GiftiImage(
header=nb.load(pe.path).header,
darrays=[
nb.gifti.GiftiDataArray(data=p.astype(float))
for _, p in pars_df.iterrows()
]).to_filename(par_fn)
transformer = SurfaceTransform(source_subject='fsaverage6',
target_subject='fsaverage',
hemi={
'L': 'lh',
'R': 'rh'
}[hemi])
transformer.inputs.source_file = par_fn
transformer.inputs.out_file = par_fn.replace(
'fsaverage6', 'fsaverage')
# Disable on MAC OS X (SIP problem)
transformer.run()
r2 = get_rsq(test.loc[:, mask].values,
predictions.values[:len(test), :]).to_frame('r2').T
r2.columns = test.loc[:, mask].columns
r2_df = pd.DataFrame(columns=test.columns)
r2_df = pd.concat((r2_df, r2), axis=0)
r2_fn = op.join(
base_dir,
f'sub-{subject}_space-fsaverage6_desc-r2_hemi-{hemi}_cvrun-{run}.func.gii'
)
nb.gifti.GiftiImage(
header=nb.load(pe.path).header,
darrays=[
nb.gifti.GiftiDataArray(data=r.astype(float))
for _, r in r2_df.iterrows()
]).to_filename(r2_fn)
transformer.inputs.source_file = r2_fn
transformer.inputs.out_file = r2_fn.replace(
'fsaverage6', 'fsaverage')
# Disable on MAC OS X (SIP problem)
transformer.run()
def main(subject, sourcedata, trialwise, clip=(-100, 100)):
derivatives = op.join(sourcedata, 'derivatives')
if trialwise:
layout = BIDSLayout(op.join(derivatives, 'glm_stim1_trialwise_surf'),
validate=False)
else:
layout = BIDSLayout(op.join(derivatives, 'glm_stim1_surf'),
validate=False)
for hemi in ['L', 'R']:
pes = layout.get(subject=subject, suffix=hemi)
print(pes)
if trialwise:
paradigm = np.log(
pd.Series(
np.repeat([5, 7, 10, 14, 20, 28], 6).tolist() * len(pes)))
else:
paradigm = np.log(pd.Series([5, 7, 10, 14, 20, 28] * len(pes)))
df = []
for pe in pes:
d = pd.DataFrame(
np.clip(surface.load_surf_data(pe.path).T, clip[0], clip[1]))
df.append(d)
df = pd.concat(df)
mask = ~df.isnull().any(0)
# mask = mask & (np.random.rand(df.shape[1]) < 0.001)
print('fitting {} time series'.format(mask.sum()))
model = GaussianReceptiveFieldModel()
costs, parameters, predictions = model.optimize(
paradigm.values.ravel(), df.loc[:, mask].values)
if trialwise:
base_dir = op.join(derivatives, 'modelfit_trialwise_surf',
f'sub-{subject}', 'func')
else:
base_dir = op.join(derivatives, 'modelfit_surf', f'sub-{subject}',
'func')
if not op.exists(base_dir):
os.makedirs(base_dir)
parameters.columns = df.loc[:, mask].columns
pars_df = pd.DataFrame(columns=df.columns)
pars_df = pd.concat((pars_df, parameters))
par_fn = op.join(
base_dir,
f'sub-{subject}_space-fsaverage6_desc-pars_hemi-{hemi}.func.gii')
nb.gifti.GiftiImage(header=nb.load(pe.path).header,
darrays=[
nb.gifti.GiftiDataArray(data=p.astype(float))
for _, p in pars_df.iterrows()
]).to_filename(par_fn)
transformer = SurfaceTransform(source_subject='fsaverage6',
target_subject='fsaverage',
hemi={
'L': 'lh',
'R': 'rh'
}[hemi])
transformer.inputs.source_file = par_fn
transformer.inputs.out_file = par_fn.replace('fsaverage6', 'fsaverage')
# Disable on MAC OS X (SIP problem)
transformer.run()
r2 = get_rsq(df.loc[:, mask].values, predictions).to_frame('r2').T
r2.columns = df.loc[:, mask].columns
r2_df = pd.DataFrame(columns=df.columns)
r2_df = pd.concat((r2_df, r2), axis=0)
r2_fn = op.join(
base_dir,
f'sub-{subject}_space-fsaverage6_desc-r2_hemi-{hemi}.func.gii')
nb.gifti.GiftiImage(header=nb.load(pe.path).header,
darrays=[
nb.gifti.GiftiDataArray(data=r.astype(float))
for _, r in r2_df.iterrows()
]).to_filename(r2_fn)
transformer.inputs.source_file = r2_fn
transformer.inputs.out_file = r2_fn.replace('fsaverage6', 'fsaverage')
# Disable on MAC OS X (SIP problem)
transformer.run()
