def glm_fit(fMRI_path, DesignMatrix=None, output_glm=None, glm_info=None,
fit="Kalman_AR1", mask_url=None):
"""
Call the GLM Fit function with apropriate arguments
Parameters
----------
fMRI_path, string or list of strings,
path of the fMRI data file(s)
design_matrix, DesignMatrix instance, optional
design matrix of the model
output_glm, string, optional
path of the output glm .npz dump
glm_info, string,optional
path of the output configobj that gives dome infor on the glm
fit= 'Kalman_AR1', string to be chosen among
"Kalman_AR1", "Ordinary Least Squares", "Kalman"
that represents both the model and the fit method
mask_url=None string, path of the mask file
if None, no mask is applied
Returns
-------
glm, a nipy.neurospin.glm.glm instance representing the GLM
Note
----
either DesignMatrix or design_matrix_path have to be defined
fixme: mask should be optional
"""
import nipy.neurospin.glm
if fit == "Kalman_AR1":
model = "ar1"
method = "kalman"
elif fit == "Ordinary Least Squares":
method = "ols"
model="spherical"
elif fit == "Kalman":
method = "kalman"
model = "spherical"
# get the design matrix
if isinstance(DesignMatrix, basestring):
import nipy.neurospin.utils.design_matrix as dm
X = dm.dmtx_from_csv( DesignMatrix).matrix
#DM = dm.DesignMatrix()
#DM.read_from_csv(DesignMatrix)
#X = DM.matrix
else:
X = DesignMatrix.matrix
Y = load_image(fMRI_path, mask_url)
glm = nipy.neurospin.glm.glm()
glm.fit(Y.T, X, method=method, model=model)
if output_glm is not None:
glm.save(output_glm)
if glm_info is not None:
cobj = ConfigObj(glm_info)
cobj["DesignMatrix"] = X
cobj["mask_url"] = mask_url
cobj["GlmDumpFile"] = output_glm
cobj.write()
return glm
def glm_fit(fMRI_path, DesignMatrix=None, output_glm=None, outputCon=None,
fit="Kalman_AR1", design_matrix_path=None, data_scaling=True):
"""
Call the GLM Fit function with apropriate arguments
Parameters
----------
fMRI_path, string or list of strings,
path of the fMRI data file(s)
design_matrix, DesignMatrix instance, optional
design matrix of the model
output_glm, string, optional
path of the output glm .npz dump
outputCon, string,optional
path of the output configobj contrast object
fit= 'Kalman_AR1', string to be chosen among
"Kalman_AR1", "Ordinary Least Squares", "Kalman"
that represents both the model and the fit method
design_marix_path: string,
path of the design matrix .csv file
data_scaling: bool, Optional
scaling of the data to mean value
Returns
-------
glm, a nipy.neurospin.glm.glm instance representing the GLM
Note
----
either DesignMatrix or design_matrix_path have to be defined
"""
import nipy.neurospin.glm
if fit == "Kalman_AR1":
model = "ar1"
method = "kalman"
elif fit == "Ordinary Least Squares":
method = "ols"
model="spherical"
elif fit == "Kalman":
method = "kalman"
model = "spherical"
# get the design matrix
if isinstance(DesignMatrix, basestring):
import nipy.neurospin.utils.design_matrix as dm
X = dm.dmtx_from_csv( DesignMatrix).matrix
#X = dm.DesignMatrix().read_from_csv(DesignMatrix).matrix
else:
X = DesignMatrix.matrix
Y = load_texture(fMRI_path)
# data_scaling to percent of mean, and mean removal
if data_scaling:
# divide each voxel time course by its mean value, subtract 1,
# mulitply by 100 to deal with percent of average BOLD fluctuations
mY = np.repeat(np.expand_dims(Y.mean(-1), -1), Y.shape[-1], Y.ndim-1)
Y = 100* (Y/mY - 1)
# acveat:untested
glm = nipy.neurospin.glm.glm()
glm.fit(Y, X, method=method, model=model)
if output_glm is not None:
glm.save(output_glm)
if outputCon is not None:
cobj = ConfigObj(outputCon)
cobj["DesignMatrix"] = X
cobj["GlmDumpFile"] = output_glm
cobj.write()
return glm
def glm_fit(fMRI_path, DesignMatrix=None, output_glm=None, outputCon=None,
fit="Kalman_AR1", design_matrix_path=None):
"""
Call the GLM Fit function with apropriate arguments
Parameters
----------
fMRI_path, string or list of strings,
path of the fMRI data file(s)
design_matrix, DesignMatrix instance, optional
design matrix of the model
output_glm, string, optional
path of the output glm .npz dump
outputCon, string,optional
path of the output configobj contrast object
fit= 'Kalman_AR1', string to be chosen among
"Kalman_AR1", "Ordinary Least Squares", "Kalman"
that represents both the model and the fit method
design_marix_path: string,
path of the design matrix .csv file
Returns
-------
glm, a nipy.neurospin.glm.glm instance representing the GLM
Note
----
either DesignMatrix or design_matrix_path have to be defined
"""
import nipy.neurospin.glm
if fit == "Kalman_AR1":
model = "ar1"
method = "kalman"
elif fit == "Ordinary Least Squares":
method = "ols"
model="spherical"
elif fit == "Kalman":
method = "kalman"
model = "spherical"
# get the design matrix
if isinstance(DesignMatrix, basestring):
import nipy.neurospin.utils.design_matrix as dm
X = dm.DesignMatrix().read_from_csv(DesignMatrix).matrix
else:
X = DesignMatrix.matrix
Y = load_texture(fMRI_path)
glm = nipy.neurospin.glm.glm()
glm.fit(Y, X, method=method, model=model)
if output_glm is not None:
glm.save(output_glm)
if outputCon is not None:
cobj = ConfigObj(outputCon)
cobj["DesignMatrix"] = X
cobj["GlmDumpFile"] = output_glm
cobj.write()
return glm
def glm_fit(
fMRI_path, DesignMatrix, output_glm=None, glm_info=None, fit="Kalman_AR1", mask_url=None, data_scaling=True
):
"""
Call the GLM Fit function with apropriate arguments
Parameters
----------
fMRI_path: string or list of strings,
path of the fMRI data file(s)
design_matrix: DesignMatrix instance,
design matrix of the model
output_glm: string, optional
path of the output glm .npz dump
glm_info: string,optional
path of the output configobj that gives dome infor on the glm
fit: string, Optional,
to be chosen among 'Kalman_AR1', 'Ordinary Least Squares', 'Kalman'
that represents both the model and the fit method
mask_url: string, Optional,
path of the mask file
if None, no mask is applied
data_scaling: bool, Optional
scaling of the data to mean value
Returns
-------
glm, a nipy.neurospin.glm.glm instance representing the GLM
Note
----
either DesignMatrix or design_matrix_path have to be defined
fixme: mask should be optional
"""
import nipy.neurospin.glm
# get the model/fit methods
if fit == "Kalman_AR1":
model = "ar1"
method = "kalman"
elif fit == "Ordinary Least Squares":
method = "ols"
model = "spherical"
elif fit == "Kalman":
method = "kalman"
model = "spherical"
# get the design matrix
if isinstance(DesignMatrix, basestring):
import nipy.neurospin.utils.design_matrix as dm
X = dm.dmtx_from_csv(DesignMatrix).matrix
else:
X = DesignMatrix.matrix
# load the fMRI data
Y = load_image(fMRI_path, mask_url)
# data_scaling to percent of mean, and mean removal
if data_scaling:
# divide each voxel time course by its mean value, subtract 1,
# mulitply by 100 to deal with percent of average BOLD fluctuations
mY = np.repeat(np.expand_dims(Y.mean(-1), -1), Y.shape[-1], Y.ndim - 1)
Y = 100 * (Y / mY - 1)
# apply the GLM
glm = nipy.neurospin.glm.glm()
glm.fit(Y.T, X, method=method, model=model)
# Write outputs
if output_glm is not None:
glm.save(output_glm)
if glm_info is not None:
cobj = ConfigObj(glm_info)
cobj["DesignMatrix"] = X
cobj["mask_url"] = mask_url
cobj["GlmDumpFile"] = output_glm
cobj.write()
return glm
