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 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 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):
'''