def _run_interface(self, runtime):
beta_nii = nb.load(self.inputs.beta)
if isdefined(self.inputs.mask):
mask = nb.load(self.inputs.mask).get_data() > 0
else:
mask = np.ones(beta_nii.shape[:3]) == 1
glm = GLM.glm()
nii = nb.load(self.inputs.beta)
glm.beta = beta_nii.get_data().copy()[mask,:].T
glm.nvbeta = self.inputs.nvbeta
glm.s2 = nb.load(self.inputs.s2).get_data().copy()[mask]
glm.dof = self.inputs.dof
glm._axis = self.inputs.axis
glm._constants = self.inputs.constants
reg_names = self.inputs.reg_names
self._stat_maps = []
self._p_maps = []
self._z_maps = []
for contrast_def in self.inputs.contrasts:
name = contrast_def[0]
_ = contrast_def[1]
contrast = np.zeros(len(reg_names))
for i, reg_name in enumerate(reg_names):
if reg_name in contrast_def[2]:
idx = contrast_def[2].index(reg_name)
contrast[i] = contrast_def[3][idx]
est_contrast = glm.contrast(contrast)
stat_map = np.zeros(mask.shape)
stat_map[mask] = est_contrast.stat().T
stat_map_file = os.path.abspath(name + "_stat_map.nii")
nb.save(nb.Nifti1Image(stat_map, nii.get_affine()), stat_map_file)
self._stat_maps.append(stat_map_file)
p_map = np.zeros(mask.shape)
p_map[mask] = est_contrast.pvalue().T
p_map_file = os.path.abspath(name + "_p_map.nii")
nb.save(nb.Nifti1Image(p_map, nii.get_affine()), p_map_file)
self._p_maps.append(p_map_file)
z_map = np.zeros(mask.shape)
z_map[mask] = est_contrast.zscore().T
z_map_file = os.path.abspath(name + "_z_map.nii")
nb.save(nb.Nifti1Image(z_map, nii.get_affine()), z_map_file)
self._z_maps.append(z_map_file)
return runtime
def _run_interface(self, runtime):
beta_nii = nb.load(self.inputs.beta)
if isdefined(self.inputs.mask):
mask = nb.load(self.inputs.mask).get_data() > 0
else:
mask = np.ones(beta_nii.shape[:3]) == 1
glm = GLM.glm()
nii = nb.load(self.inputs.beta)
glm.beta = beta_nii.get_data().copy()[mask,:].T
glm.nvbeta = self.inputs.nvbeta
glm.s2 = nb.load(self.inputs.s2).get_data().copy()[mask]
glm.dof = self.inputs.dof
glm._axis = self.inputs.axis
glm._constants = self.inputs.constants
reg_names = self.inputs.reg_names
self._stat_maps = []
self._p_maps = []
self._z_maps = []
for contrast_def in self.inputs.contrasts:
name = contrast_def[0]
_ = contrast_def[1]
contrast = np.zeros(len(reg_names))
for i, reg_name in enumerate(reg_names):
if reg_name in contrast_def[2]:
idx = contrast_def[2].index(reg_name)
contrast[i] = contrast_def[3][idx]
est_contrast = glm.contrast(contrast)
stat_map = np.zeros(mask.shape)
stat_map[mask] = est_contrast.stat().T
stat_map_file = os.path.abspath(name + "_stat_map.nii")
nb.save(nb.Nifti1Image(stat_map, nii.get_affine()), stat_map_file)
self._stat_maps.append(stat_map_file)
p_map = np.zeros(mask.shape)
p_map[mask] = est_contrast.pvalue().T
p_map_file = os.path.abspath(name + "_p_map.nii")
nb.save(nb.Nifti1Image(p_map, nii.get_affine()), p_map_file)
self._p_maps.append(p_map_file)
z_map = np.zeros(mask.shape)
z_map[mask] = est_contrast.zscore().T
z_map_file = os.path.abspath(name + "_z_map.nii")
nb.save(nb.Nifti1Image(z_map, nii.get_affine()), z_map_file)
self._z_maps.append(z_map_file)
return runtime
def glm_nipy(fmri_data, contrasts=None, hrf_model='Canonical',
drift_model='Cosine', hfcut=128,
residuals_model='spherical', fit_method='ols',
fir_delays=[0],
rescale_results=False, rescale_factor=None):
"""
Perform a GLM analysis on fMRI data using the implementation of Nipy.
Args:
fmri_data (pyhrf.core.FmriData): the input fMRI data defining the
paradigm and the measured 3D+time signal.
contrasts (dict): keys are contrast labels and values are arithmetic
expressions involving regressor names. Valid names are:
* names of experimental conditions as defined in fmri_data
* constant
hrf_model: "Canonical", "Canonical with Derivative", "FIR"
residuals_model: "spherical", "ar1"
fit_method: "ols", "kalman" (If residuals_model is "ar1" then method
is set to "kalman" and this argument is ignored)
fir_delays: list of integers indicating the delay of each FIR coefficient
(in terms of scans). Eg if TR = 2s. and we want a FIR
duration of 20s.: fir_delays=range(10)
Returns:
(glm instance, design matrix, dict of contrasts of objects)
Examples:
>>> from pyhrf.core import FmriData
>>> from pyhrf.glm import glm_nipy
>>> g,dmtx,con = glm_nipy(FmriData.from_vol_ui())
>>> g,dmtx,con = glm_nipy(FmriData.from_vol_ui(), \
contrasts={'A-V':'audio-video'})
"""
paradigm = fmri_data.paradigm.to_nipy_paradigm()
# BOLD data
Y = fmri_data.bold.T
n_scans = Y.shape[1]
# pyhrf.verbose(1, 'Input BOLD: nvox=%d, nscans=%d' %Y.shape)
# Design matrix
frametimes = np.linspace(0, (n_scans-1)*fmri_data.tr, n_scans)
design_matrix = dm.make_dmtx(frametimes, paradigm,
hrf_model=hrf_model,
drift_model=drift_model, hfcut=hfcut,
fir_delays=fir_delays)
ns, nr = design_matrix.matrix.shape
pyhrf.verbose(2, 'Design matrix built with %d regressors:' %nr)
for rn in design_matrix.names:
pyhrf.verbose(2, ' - %s' %rn)
# ax = design_matrix.show()
# ax.set_position([.05, .25, .9, .65])
# ax.set_title('Design matrix')
# plt.savefig(op.join(output_dir, 'design_matrix.png'))
# GLM fit
my_glm = glm.glm()
pyhrf.verbose(2, 'Fit GLM - method: %s, residual model: %s' \
%(fit_method,residuals_model))
my_glm.fit(Y.T, design_matrix.matrix, method=fit_method,
model=residuals_model)
from pyhrf.tools import map_dict
from pyhrf.paradigm import contrasts_to_spm_vec
if rescale_results:
# Rescale by the norm of the HRF:
# from nipy.modalities.fmri.hemodynamic_models import _hrf_kernel, \
# sample_condition
# oversampling = 16
# hrfs = _hrf_kernel(hrf_model, fmri_data.tr, oversampling,
# fir_delays=fir_delays)
# hframetimes = np.linspace(0, 32., int(32./fmri_data.tr))
# hr_regressor, hr_frametimes = sample_condition(
# (np.array([0]),np.array([0]),np.array([1])),
# hframetimes, oversampling)
# from scipy.interpolate import interp1d
# for i in xrange(len(hrfs)):
# f = interp1d(hr_frametimes, hrfs[i])
# hrfs[i] = f(hframetimes).T
# n_conds = len(fmri_data.paradigm.stimOnsets)
# for i in xrange(n_conds * len(hrfs)):
# h = hrfs[i%len(hrfs)]
# my_glm.beta[i] = my_glm.beta[i] * (h**2).sum()**.5
#my_glm.variance = np.zeros_like(my_glm.beta)
if 1:
if rescale_results and rescale_factor is None:
#Rescale by the norm of each regressor in the design matrix
dm_reg_norms = (design_matrix.matrix**2).sum(0)**.5
pyhrf.verbose(2,'GLM results (beta and con effects) are '\
'rescaled by reg norm. Weights: %s ' \
%str(dm_reg_norms))
for ib in xrange(my_glm.beta.shape[0]):
my_glm.beta[ib] = my_glm.beta[ib] * dm_reg_norms[ib]
#my_glm.nvbeta[ib,:] = my_glm.nvbeta[ib,:] * dm_reg_norms[ib]**2
else:
pyhrf.verbose(2,'GLM results (beta and con effects) are '\
'rescaled by input scale factor.')
# Use input rescale factors:
for ib in xrange(rescale_factor.shape[0]):
my_glm.beta[ib] = my_glm.beta[ib] * rescale_factor[ib]
#TOCHECK: nvbeta seems to be a covar matrix between reg
# -> we dont get position-specific variances ...
#my_glm.nvbeta[ib,:] = my_glm.nvbeta[ib,:] * rescale_factor[ib]**2
if contrasts is not None:
con_vectors = contrasts_to_spm_vec(design_matrix.names, contrasts)
# if rescale_results:
# for con_vec in con_vectors.itervalues():
# con_vec *= dm_reg_norms
contrast_result = map_dict(my_glm.contrast, con_vectors)
else:
contrast_result = None
return my_glm, design_matrix, contrast_result
#actually: not possible to compute PPM from glm results
#Should relaunch estimation with propoer model under SPM
#def PPMcalculus_glmWN(beta, var_beta, dm, threshold_value):
'''
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'])
else:
duration += cond['duration']
if conditions:
paradigm = BlockParadigm(con_id=conditions, onset=onsets, duration=duration)
else:
paradigm = None
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:
if pylab_available:
pylab.pcolor(design_matrix)
pylab.savefig("design_matrix.pdf")
pylab.close()
pylab.clf()
else:
Exception('Pylab not available for saving design matrix image')
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
def glm_nipy(fmri_data, contrasts=None, hrf_model='Canonical',
drift_model='Cosine', hfcut=128,
residuals_model='spherical', fit_method='ols',
fir_delays=[0],
rescale_results=False, rescale_factor=None):
"""
Perform a GLM analysis on fMRI data using the implementation of Nipy.
Args:
fmri_data (pyhrf.core.FmriData): the input fMRI data defining the
paradigm and the measured 3D+time signal.
contrasts (dict): keys are contrast labels and values are arithmetic
expressions involving regressor names. Valid names are:
* names of experimental conditions as defined in fmri_data
* constant
hrf_model: "Canonical", "Canonical with Derivative", "FIR"
residuals_model: "spherical", "ar1"
fit_method: "ols", "kalman" (If residuals_model is "ar1" then method
is set to "kalman" and this argument is ignored)
fir_delays: list of integers indicating the delay of each FIR coefficient
(in terms of scans). Eg if TR = 2s. and we want a FIR
duration of 20s.: fir_delays=range(10)
Returns:
(glm instance, design matrix, dict of contrasts of objects)
Examples:
>>> from pyhrf.core import FmriData
>>> from pyhrf.glm import glm_nipy
>>> g,dmtx,con = glm_nipy(FmriData.from_vol_ui())
>>> g,dmtx,con = glm_nipy(FmriData.from_vol_ui(), \
contrasts={'A-V':'audio-video'})
"""
paradigm = fmri_data.paradigm.to_nipy_paradigm()
# BOLD data
Y = fmri_data.bold.T
n_scans = Y.shape[1]
# Design matrix
frametimes = np.linspace(0, (n_scans - 1) * fmri_data.tr, n_scans)
design_matrix = dm.make_dmtx(frametimes, paradigm,
hrf_model=hrf_model,
drift_model=drift_model, hfcut=hfcut,
fir_delays=fir_delays)
ns, nr = design_matrix.matrix.shape
logger.info('Design matrix built with %d regressors:', nr)
for rn in design_matrix.names:
logger.info(' - %s', rn)
# GLM fit
my_glm = glm.glm()
logger.info('Fit GLM - method: %s, residual model: %s', fit_method,
residuals_model)
my_glm.fit(Y.T, design_matrix.matrix, method=fit_method,
model=residuals_model)
from pyhrf.tools import map_dict
from pyhrf.paradigm import contrasts_to_spm_vec
if rescale_results:
if 1:
if rescale_results and rescale_factor is None:
# Rescale by the norm of each regressor in the design matrix
dm_reg_norms = (design_matrix.matrix ** 2).sum(0) ** .5
logger.info('GLM results (beta and con effects) are '
'rescaled by reg norm. Weights: %s ',
str(dm_reg_norms))
for ib in xrange(my_glm.beta.shape[0]):
my_glm.beta[ib] = my_glm.beta[ib] * dm_reg_norms[ib]
else:
logger.info('GLM results (beta and con effects) are '
'rescaled by input scale factor.')
# Use input rescale factors:
for ib in xrange(rescale_factor.shape[0]):
my_glm.beta[ib] = my_glm.beta[ib] * rescale_factor[ib]
# TOCHECK: nvbeta seems to be a covar matrix between reg
# -> we dont get position-specific variances ...
#my_glm.nvbeta[ib,:] = my_glm.nvbeta[ib,:] * rescale_factor[ib]**2
if contrasts is not None:
con_vectors = contrasts_to_spm_vec(design_matrix.names, contrasts)
# if rescale_results:
# for con_vec in con_vectors.itervalues():
# con_vec *= dm_reg_norms
contrast_result = map_dict(my_glm.contrast, con_vectors)
else:
contrast_result = None
return my_glm, design_matrix, contrast_result
# actually: not possible to compute PPM from glm results
# Should relaunch estimation with propoer model under SPM
# def PPMcalculus_glmWN(beta, var_beta, dm, threshold_value):
'''
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'])
else:
duration += cond['duration']
if conditions:
paradigm = BlockParadigm(con_id=conditions,
onset=onsets,
duration=duration)
else:
paradigm = None
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:
import pylab
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
def glm_nipy(fmri_data,
contrasts=None,
hrf_model='Canonical',
drift_model='Cosine',
hfcut=128,
residuals_model='spherical',
fit_method='ols',
fir_delays=[0],
rescale_results=False,
rescale_factor=None):
"""
Perform a GLM analysis on fMRI data using the implementation of Nipy.
Args:
fmri_data (pyhrf.core.FmriData): the input fMRI data defining the
paradigm and the measured 3D+time signal.
contrasts (dict): keys are contrast labels and values are arithmetic
expressions involving regressor names. Valid names are:
* names of experimental conditions as defined in fmri_data
* constant
hrf_model: "Canonical", "Canonical with Derivative", "FIR"
residuals_model: "spherical", "ar1"
fit_method: "ols", "kalman" (If residuals_model is "ar1" then method
is set to "kalman" and this argument is ignored)
fir_delays: list of integers indicating the delay of each FIR coefficient
(in terms of scans). Eg if TR = 2s. and we want a FIR
duration of 20s.: fir_delays=range(10)
Returns:
(glm instance, design matrix, dict of contrasts of objects)
Examples:
>>> from pyhrf.core import FmriData
>>> from pyhrf.glm import glm_nipy
>>> g,dmtx,con = glm_nipy(FmriData.from_vol_ui())
>>> g,dmtx,con = glm_nipy(FmriData.from_vol_ui(), \
contrasts={'A-V':'audio-video'})
"""
paradigm = fmri_data.paradigm.to_nipy_paradigm()
# BOLD data
Y = fmri_data.bold.T
n_scans = Y.shape[1]
# Design matrix
frametimes = np.linspace(0, (n_scans - 1) * fmri_data.tr, n_scans)
design_matrix = dm.make_dmtx(frametimes,
paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
hfcut=hfcut,
fir_delays=fir_delays)
ns, nr = design_matrix.matrix.shape
logger.info('Design matrix built with %d regressors:', nr)
for rn in design_matrix.names:
logger.info(' - %s', rn)
# GLM fit
my_glm = glm.glm()
logger.info('Fit GLM - method: %s, residual model: %s', fit_method,
residuals_model)
my_glm.fit(Y.T,
design_matrix.matrix,
method=fit_method,
model=residuals_model)
from pyhrf.tools import map_dict
from pyhrf.paradigm import contrasts_to_spm_vec
if rescale_results:
if 1:
if rescale_results and rescale_factor is None:
# Rescale by the norm of each regressor in the design matrix
dm_reg_norms = (design_matrix.matrix**2).sum(0)**.5
logger.info(
'GLM results (beta and con effects) are '
'rescaled by reg norm. Weights: %s ', str(dm_reg_norms))
for ib in xrange(my_glm.beta.shape[0]):
my_glm.beta[ib] = my_glm.beta[ib] * dm_reg_norms[ib]
else:
logger.info('GLM results (beta and con effects) are '
'rescaled by input scale factor.')
# Use input rescale factors:
for ib in xrange(rescale_factor.shape[0]):
my_glm.beta[ib] = my_glm.beta[ib] * rescale_factor[ib]
# TOCHECK: nvbeta seems to be a covar matrix between reg
# -> we dont get position-specific variances ...
#my_glm.nvbeta[ib,:] = my_glm.nvbeta[ib,:] * rescale_factor[ib]**2
if contrasts is not None:
con_vectors = contrasts_to_spm_vec(design_matrix.names, contrasts)
# if rescale_results:
# for con_vec in con_vectors.itervalues():
# con_vec *= dm_reg_norms
contrast_result = map_dict(my_glm.contrast, con_vectors)
else:
contrast_result = None
return my_glm, design_matrix, contrast_result
# actually: not possible to compute PPM from glm results
# Should relaunch estimation with propoer model under SPM
# def PPMcalculus_glmWN(beta, var_beta, dm, threshold_value):
'''
