def __init__(self, fmri_data, design_matrices, mask='compute',
m=0.2, M=0.9, threshold=.5):
"""Load the data
Parameters
----------
fmri_data : Image or str or sequence of Images / str
fmri images / paths of the (4D) fmri images
design_matrices : arrays or str or sequence of arrays / str
design matrix arrays / paths of .npz files
mask : str or Image or None, optional
string can be 'compute' or a path to an image
image is an input (assumed binary) mask image(s),
if 'compute', the mask is computed
if None, no masking will be applied
m, M, threshold: float, optional
parameters of the masking procedure. Should be within [0, 1]
Notes
-----
The only computation done here is mask computation (if required)
Examples
--------
We need the example data package for this example
>>> from nipy.utils import example_data
>>> from nipy.modalities.fmri.glm import FMRILinearModel
>>> fmri_files = [example_data.get_filename('fiac', 'fiac0', run)
... for run in ['run1.nii.gz', 'run2.nii.gz']]
>>> design_files = [example_data.get_filename('fiac', 'fiac0', run)
... for run in ['run1_design.npz', 'run2_design.npz']]
>>> mask = example_data.get_filename('fiac', 'fiac0', 'mask.nii.gz')
>>> multi_session_model = FMRILinearModel(fmri_files, design_files, mask)
>>> multi_session_model.fit()
>>> z_image, = multi_session_model.contrast([np.eye(13)[1]] * 2)
The number of voxels with p < 0.001
>>> np.sum(z_image.get_data() > 3.09)
671
"""
# manipulate the arguments
if isinstance(fmri_data, basestring) or hasattr(fmri_data, 'get_data'):
fmri_data = [fmri_data]
if isinstance(design_matrices, (basestring, np.ndarray)):
design_matrices = [design_matrices]
if len(fmri_data) != len(design_matrices):
raise ValueError('Incompatible number of fmri runs and '
'design matrices were provided')
self.fmri_data, self.design_matrices = [], []
self.glms, self.means = [], []
# load the fmri data
for fmri_run in fmri_data:
if isinstance(fmri_run, basestring):
self.fmri_data.append(load(fmri_run))
else:
self.fmri_data.append(fmri_run)
# set self.affine as the affine of the first image
self.affine = self.fmri_data[0].get_affine()
# load the designs
for design_matrix in design_matrices:
if isinstance(design_matrix, basestring):
loaded = np.load(design_matrix)
self.design_matrices.append(loaded[loaded.files[0]])
else:
self.design_matrices.append(design_matrix)
# load the mask
if mask == 'compute':
mask = compute_mask_sessions(
fmri_data, m=m, M=M, cc=1, threshold=threshold, opening=0)
self.mask = Nifti1Image(mask.astype(np.int8), self.affine)
elif mask == None:
mask = np.ones(self.fmri_data[0].shape[:3]).astype(np.int8)
self.mask = Nifti1Image(mask, self.affine)
else:
if isinstance(mask, basestring):
self.mask = load(mask)
else:
self.mask = mask
def __init__(self,
fmri_data,
design_matrices,
mask='compute',
m=0.2,
M=0.9,
threshold=.5):
"""Load the data
Parameters
----------
fmri_data : Image or str or sequence of Images / str
fmri images / paths of the (4D) fmri images
design_matrices : arrays or str or sequence of arrays / str
design matrix arrays / paths of .npz files
mask : str or Image or None, optional
string can be 'compute' or a path to an image
image is an input (assumed binary) mask image(s),
if 'compute', the mask is computed
if None, no masking will be applied
m, M, threshold: float, optional
parameters of the masking procedure. Should be within [0, 1]
Notes
-----
The only computation done here is mask computation (if required)
Examples
--------
We need the example data package for this example
>>> from nipy.utils import example_data
>>> from nipy.modalities.fmri.glm import FMRILinearModel
>>> fmri_files = [example_data.get_filename('fiac', 'fiac0', run)
... for run in ['run1.nii.gz', 'run2.nii.gz']]
>>> design_files = [example_data.get_filename('fiac', 'fiac0', run)
... for run in ['run1_design.npz', 'run2_design.npz']]
>>> mask = example_data.get_filename('fiac', 'fiac0', 'mask.nii.gz')
>>> multi_session_model = FMRILinearModel(fmri_files, design_files, mask)
>>> multi_session_model.fit()
>>> z_image, = multi_session_model.contrast([np.eye(13)[1]] * 2)
The number of voxels with p < 0.001
>>> np.sum(z_image.get_data() > 3.09)
671
"""
# manipulate the arguments
if isinstance(fmri_data, basestring) or hasattr(fmri_data, 'get_data'):
fmri_data = [fmri_data]
if isinstance(design_matrices, (basestring, np.ndarray)):
design_matrices = [design_matrices]
if len(fmri_data) != len(design_matrices):
raise ValueError('Incompatible number of fmri runs and '
'design matrices were provided')
self.fmri_data, self.design_matrices = [], []
self.glms, self.means = [], []
# load the fmri data
for fmri_run in fmri_data:
if isinstance(fmri_run, basestring):
self.fmri_data.append(load(fmri_run))
else:
self.fmri_data.append(fmri_run)
# set self.affine as the affine of the first image
self.affine = self.fmri_data[0].get_affine()
# load the designs
for design_matrix in design_matrices:
if isinstance(design_matrix, basestring):
loaded = np.load(design_matrix)
self.design_matrices.append(loaded[loaded.files[0]])
else:
self.design_matrices.append(design_matrix)
# load the mask
if mask == 'compute':
mask = compute_mask_sessions(fmri_data,
m=m,
M=M,
cc=1,
threshold=threshold,
opening=0)
self.mask = Nifti1Image(mask.astype(np.int8), self.affine)
elif mask == None:
mask = np.ones(self.fmri_data[0].shape[:3]).astype(np.int8)
self.mask = Nifti1Image(mask, self.affine)
else:
if isinstance(mask, basestring):
self.mask = load(mask)
else:
self.mask = mask