add_reg_names = ["tx", "ty", "tz", "rx", "ry", "rz"]
# write directory
swd = "/tmp"
########################################
# Design matrix
########################################
paradigm = np.vstack(([conditions, onsets])).T
paradigm = dm.EventRelatedParadigm(conditions, onsets)
X, names = dm.dmtx_light(
frametimes,
paradigm,
drift_model="Cosine",
hfcut=128,
hrf_model=hrf_model,
add_regs=motion,
add_reg_names=add_reg_names,
)
#######################################
# Get the FMRI data
#######################################
fmri_data = surrogate_4d_dataset(mask=mask, dmtx=X, seed=1)[0]
# if you want to save it as an image
# data_file = op.join(swd,'fmri_data.nii')
# save(fmri_data, data_file)
conditions = np.arange(20) % 2
onsets = np.linspace(5, (n_scans - 1) * tr - 10, 20) # in seconds
hrf_model = 'Canonical'
motion = np.cumsum(np.random.randn(n_scans, 6), 0)
add_reg_names = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz']
# write directory
swd = tempfile.mkdtemp()
########################################
# Design matrix
########################################
paradigm = dm.EventRelatedParadigm(conditions, onsets)
X, names = dm.dmtx_light(frametimes, paradigm, drift_model='Cosine', hfcut=128,
hrf_model=hrf_model, add_regs=motion,
add_reg_names=add_reg_names)
#######################################
# Get the FMRI data
#######################################
fmri_data = surrogate_4d_dataset(shape=shape, n_scans=n_scans)[0]
# if you want to save it as an image
data_file = op.join(swd, 'fmri_data.nii')
save(fmri_data, data_file)
########################################
# Perform a GLM analysis
def _run_interface(self, runtime):
session_info = self.inputs.session_info
functional_runs = self.inputs.session_info[0]['scans']
if isinstance(functional_runs, str):
functional_runs = [functional_runs]
nii = nb.load(functional_runs[0])
data = nii.get_data()
if isdefined(self.inputs.mask):
mask = nb.load(self.inputs.mask).get_data() > 0
else:
mask = np.ones(nii.shape[:3]) == 1
timeseries = data.copy()[mask,:]
del data
for functional_run in functional_runs[1:]:
nii = nb.load(functional_run)
data = nii.get_data()
npdata = data.copy()
del data
timeseries = np.concatenate((timeseries,npdata[mask,:]), axis=1)
del npdata
nscans = timeseries.shape[1]
if 'hpf' in session_info[0].keys():
hpf = session_info[0]['hpf']
drift_model=self.inputs.drift_model
else:
hpf=0
drift_model = "Blank"
reg_names = []
for reg in session_info[0]['regress']:
reg_names.append(reg['name'])
reg_vals = np.zeros((nscans,len(reg_names)))
for i in range(len(reg_names)):
reg_vals[:,i] = np.array(session_info[0]['regress'][i]['val']).reshape(1,-1)
frametimes= np.linspace(0, (nscans-1)*self.inputs.TR, nscans)
conditions = []
onsets = []
duration = []
for i,cond in enumerate(session_info[0]['cond']):
onsets += cond['onset']
conditions += [cond['name']]*len(cond['onset'])
if len(cond['duration']) == 1:
duration += cond['duration']*len(cond['onset'])
paradigm = dm.BlockParadigm(con_id=conditions, onset=onsets, duration=duration)
design_matrix, self._reg_names = dm.dmtx_light(frametimes, paradigm, drift_model=drift_model, hfcut=hpf,
hrf_model=self.inputs.hrf_model,
add_regs=reg_vals,
add_reg_names=reg_names
)
if self.inputs.normalize_design_matrix:
for i in range(len(self._reg_names)-1):
design_matrix[:,i] = (design_matrix[:,i]-design_matrix[:,i].mean())/design_matrix[:,i].std()
if self.inputs.plot_design_matrix:
pylab.pcolor(design_matrix)
pylab.savefig("design_matrix.pdf")
pylab.close()
pylab.clf()
glm = GLM.glm()
glm.fit(timeseries.T, design_matrix, method=self.inputs.method, model=self.inputs.model)
self._beta_file = os.path.abspath("beta.nii")
beta = np.zeros(mask.shape + (glm.beta.shape[0],))
beta[mask,:] = glm.beta.T
nb.save(nb.Nifti1Image(beta, nii.get_affine()), self._beta_file)
self._s2_file = os.path.abspath("s2.nii")
s2 = np.zeros(mask.shape)
s2[mask] = glm.s2
nb.save(nb.Nifti1Image(s2, nii.get_affine()), self._s2_file)
if self.inputs.save_residuals:
explained = np.dot(design_matrix,glm.beta)
residuals = np.zeros(mask.shape + (nscans,))
residuals[mask,:] = timeseries - explained.T
self._residuals_file = os.path.abspath("residuals.nii")
nb.save(nb.Nifti1Image(residuals, nii.get_affine()), self._residuals_file)
self._nvbeta = glm.nvbeta
self._dof = glm.dof
self._constants = glm._constants
self._axis = glm._axis
if self.inputs.model == "ar1":
self._a_file = os.path.abspath("a.nii")
a = np.zeros(mask.shape)
a[mask] = glm.a.squeeze()
nb.save(nb.Nifti1Image(a, nii.get_affine()), self._a_file)
self._model = glm.model
self._method = glm.method
return runtime
