def to_image(self, path=None, data=None):
"""Write itself as a binary image, and returns it
Parameters
----------
path: string, path of the output image, if any
data: array of shape self.size,
data to put in the nonzer-region of the image
"""
if data is None:
wdata = np.zeros(self.shape, np.int8)
else:
wdata = np.zeros(self.shape, data.dtype)
wdata[self.ijk[:, 0], self.ijk[:, 1], self.ijk[:, 2]] = 1
if data is not None:
if data.size != self.size:
raise ValueError('incorrect data size')
wdata[wdata > 0] = data
nim = Nifti1Image(wdata, self.affine)
get_header(nim)['descrip'] = ('mask image representing domain %s' %
self.id)
if path is not None:
save(nim, path)
return nim
def to_image(self, path=None, data=None):
"""Write itself as a binary image, and returns it
Parameters
----------
path: string, path of the output image, if any
data: array of shape self.size,
data to put in the nonzer-region of the image
"""
if data is None:
wdata = np.zeros(self.shape, np.int8)
else:
wdata = np.zeros(self.shape, data.dtype)
wdata[self.ijk[:, 0], self.ijk[:, 1], self.ijk[:, 2]] = 1
if data is not None:
if data.size != self.size:
raise ValueError('incorrect data size')
wdata[wdata > 0] = data
nim = Nifti1Image(wdata, self.affine)
get_header(nim)['descrip'] = ('mask image representing domain %s'
% self.id)
if path is not None:
save(nim, path)
return nim
def parcellation_based_analysis(Pa,
test_images,
test_id='one_sample',
rfx_path=None,
condition_id='',
swd=None):
""" This function computes parcel averages and RFX at the parcel-level
Parameters
----------
Pa: MultiSubjectParcellation instance
the description of the parcellation
test_images: (Pa.nb_subj-) list of paths
paths of images used in the inference procedure
test_id: string, optional,
if test_id=='one_sample', the one_sample statstic is computed
otherwise, the parcel-based signal averages are returned
rfx_path: string optional,
path of the resulting one-sample test image, if applicable
swd: string, optional
output directory used to compute output path if rfx_path is not given
condition_id: string, optional,
contrast/condition id used to compute output path
Returns
-------
test_data: array of shape(Pa.nb_parcel, Pa.nb_subj)
the parcel-level signal average if test is not 'one_sample'
prfx: array of shape(Pa.nb_parcel),
the one-sample t-value if test_id is 'one_sample'
"""
nb_subj = Pa.nb_subj
# 1. read the test data
if len(test_images) != nb_subj:
raise ValueError('Inconsistent number of test images')
test = np.array(
[Pa.domain.make_feature_from_image(ti) for ti in test_images]).T
test_data = Pa.make_feature('', np.array(test))
if test_id is not 'one_sample':
return test_data
# 2. perform one-sample test
# computation
from ..utils.reproducibility_measures import ttest
prfx = ttest(test_data)
# Write the stuff
template = SubDomains(Pa.domain, Pa.template_labels)
template.set_roi_feature('prfx', prfx)
wim = template.to_image('prfx', roi=True)
hdr = get_header(wim)
hdr['descrip'] = 'parcel-based random effects image (in t-variate)'
if rfx_path is not None:
save(wim, rfx_path)
return prfx
def parcellation_based_analysis(Pa, test_images, test_id='one_sample',
rfx_path=None, condition_id='', swd=None):
""" This function computes parcel averages and RFX at the parcel-level
Parameters
----------
Pa: MultiSubjectParcellation instance
the description of the parcellation
test_images: (Pa.nb_subj-) list of paths
paths of images used in the inference procedure
test_id: string, optional,
if test_id=='one_sample', the one_sample statstic is computed
otherwise, the parcel-based signal averages are returned
rfx_path: string optional,
path of the resulting one-sample test image, if applicable
swd: string, optional
output directory used to compute output path if rfx_path is not given
condition_id: string, optional,
contrast/condition id used to compute output path
Returns
-------
test_data: array of shape(Pa.nb_parcel, Pa.nb_subj)
the parcel-level signal average if test is not 'one_sample'
prfx: array of shape(Pa.nb_parcel),
the one-sample t-value if test_id is 'one_sample'
"""
nb_subj = Pa.nb_subj
# 1. read the test data
if len(test_images) != nb_subj:
raise ValueError('Inconsistent number of test images')
test = np.array([Pa.domain.make_feature_from_image(ti)
for ti in test_images]).T
test_data = Pa.make_feature('', np.array(test))
if test_id is not 'one_sample':
return test_data
# 2. perform one-sample test
# computation
from ..utils.reproducibility_measures import ttest
prfx = ttest(test_data)
# Write the stuff
template = SubDomains(Pa.domain, Pa.template_labels)
template.set_roi_feature('prfx', prfx)
wim = template.to_image('prfx', roi=True)
hdr = get_header(wim)
hdr['descrip'] = 'parcel-based random effects image (in t-variate)'
if rfx_path is not None:
save(wim, rfx_path)
return prfx
def to_image(self, fid=None, roi=False, method="mean", descrip=None):
"""Generates a label image that represents self.
Parameters
----------
fid: str,
Feature to be represented. If None, a binary image of the MROI
domain will be we created.
roi: bool,
Whether or not to write the desired feature as a ROI one.
(i.e. a ROI feature corresponding to `fid` will be looked upon,
and if not found, a representative feature will be computed from
the `fid` feature).
method: str,
If a feature is written as a ROI feature, this keyword tweaks
the way the representative feature is computed.
descrip: str,
Description of the image, to be written in its header.
Notes
-----
Requires that self.dom is an ddom.NDGridDomain
Returns
-------
nim : nibabel nifti image
Nifti image corresponding to the ROI feature to be written.
"""
if not isinstance(self.domain, ddom.NDGridDomain):
print('self.domain is not an NDGridDomain; nothing was written.')
return None
if fid is None:
# write a binary representation of the domain if no fid provided
nim = self.domain.to_image(data=(self.label != -1).astype(np.int32))
if descrip is None:
descrip = 'binary representation of MROI'
else:
data = -np.ones(self.label.size, dtype=np.int32)
tmp_image = self.domain.to_image()
mask = tmp_image.get_data().copy().astype(bool)
if not roi:
# write a feature
if fid not in self.features:
raise ValueError("`%s` feature could not be found" % fid)
for i in self.get_id():
data[self.select_id(i, roi=False)] = \
self.get_feature(fid, i)
else:
# write a roi feature
if fid in self.roi_features:
# write from existing roi feature
for i in self.get_id():
data[self.select_id(i, roi=False)] = \
self.get_roi_feature(
fid, i)
elif fid in self.features:
# write from representative feature
summary_feature = self.representative_feature(
fid, method=method)
for i in self.get_id():
data[self.select_id(i, roi=False)] = \
summary_feature[self.select_id(i)]
# MROI object was defined on a masked image: we square it back.
wdata = -np.ones(mask.shape, data.dtype)
wdata[mask] = data
nim = Nifti1Image(wdata, get_affine(tmp_image))
# set description of the image
if descrip is not None:
get_header(nim)['descrip'] = descrip
return nim
def contrast(
self,
contrasts,
con_id="",
contrast_type=None,
output_z=True,
output_stat=False,
output_effects=False,
output_variance=False,
):
""" Estimation of a contrast as fixed effects on all sessions
Parameters
----------
contrasts : array or list of arrays of shape (n_col) or (n_dim, n_col)
where ``n_col`` is the number of columns of the design matrix,
numerical definition of the contrast (one array per run)
con_id : str, optional
name of the contrast
contrast_type : {'t', 'F', 'tmin-conjunction'}, optional
type of the contrast
output_z : bool, optional
Return or not the corresponding z-stat image
output_stat : bool, optional
Return or not the base (t/F) stat image
output_effects : bool, optional
Return or not the corresponding effect image
output_variance : bool, optional
Return or not the corresponding variance image
Returns
-------
output_images : list of nibabel images
The required output images, in the following order:
z image, stat(t/F) image, effects image, variance image
"""
if self.glms == []:
raise ValueError("first run fit() to estimate the model")
if isinstance(contrasts, np.ndarray):
contrasts = [contrasts]
if len(contrasts) != len(self.glms):
raise ValueError("contrasts must be a sequence of %d session contrasts" % len(self.glms))
contrast_ = None
for i, (glm, con) in enumerate(zip(self.glms, contrasts)):
if np.all(con == 0):
warn("Contrast for session %d is null" % i)
elif contrast_ is None:
contrast_ = glm.contrast(con, contrast_type)
else:
contrast_ = contrast_ + glm.contrast(con, contrast_type)
if output_z or output_stat:
# compute the contrast and stat
contrast_.z_score()
# Prepare the returned images
mask = self.mask.get_data().astype(np.bool)
do_outputs = [output_z, output_stat, output_effects, output_variance]
estimates = ["z_score_", "stat_", "effect", "variance"]
descrips = ["z statistic", "Statistical value", "Estimated effect", "Estimated variance"]
dims = [1, 1, contrast_.dim, contrast_.dim ** 2]
n_vox = mask.sum()
output_images = []
for (do_output, estimate, descrip, dim) in zip(do_outputs, estimates, descrips, dims):
if do_output:
if dim > 1:
result_map = np.tile(mask.astype(np.float)[:, :, :, np.newaxis], dim)
result_map[mask] = np.reshape(getattr(contrast_, estimate).T, (n_vox, dim))
else:
result_map = mask.astype(np.float)
result_map[mask] = np.squeeze(getattr(contrast_, estimate))
output = Nifti1Image(result_map, self.affine)
get_header(output)["descrip"] = "%s associated with contrast %s" % (descrip, con_id)
output_images.append(output)
return output_images
def as_volume_img(obj, copy=True, squeeze=True, world_space=None):
""" Convert the input to a VolumeImg.
Parameters
----------
obj : filename, pynifti or brifti object, or volume dataset.
Input object, in any form that can be converted to a
VolumeImg. This includes Nifti filenames, pynifti or brifti
objects, or other volumetric dataset objects.
copy: boolean, optional
If copy is True, the data and affine arrays are copied,
elsewhere a view is taken.
squeeze: boolean, optional
If squeeze is True, the data array is squeeze on for
dimensions above 3.
world_space: string or None, optional
An optional specification of the world space, to override
that given by the image.
Returns
-------
volume_img: VolumeImg object
A VolumeImg object containing the data. The metadata is
kept as much as possible in the metadata attribute.
Notes
------
The world space might not be correctly defined by the input
object (in particular, when loading data from disk). In this
case, you can correct it manually using the world_space keyword
argument.
For pynifti objects, the data is transposed.
"""
if hasattr(obj, 'as_volume_img'):
obj = obj.as_volume_img(copy=copy)
if copy:
obj = obj.__copy__()
return obj
elif isinstance(obj, basestring):
if not os.path.exists(obj):
raise ValueError("The file '%s' cannot be found" % obj)
obj = nib.load(obj)
copy = False
if isinstance(obj, SpatialImage):
data = obj.get_data()
affine = get_affine(obj)
header = dict(get_header(obj))
fname = obj.file_map['image'].filename
if fname:
header['filename'] = fname
elif hasattr(obj, 'data') and hasattr(obj, 'sform') and \
hasattr(obj, 'getVolumeExtent'):
# Duck-types to a pynifti object
data = obj.data.T
affine = obj.sform
header = obj.header
filename = obj.getFilename()
if filename != '':
header['filename'] = filename
else:
raise ValueError('Invalid type (%s) passed in: cannot convert %s to '
'VolumeImg' % (type(obj), obj))
if world_space is None and header.get('sform_code', 0) == 4:
world_space = 'mni152'
data = np.asanyarray(data)
affine = np.asanyarray(affine)
if copy:
data = data.copy()
affine = affine.copy()
if squeeze:
# Squeeze the dimensions above 3
shape = [
val for index, val in enumerate(data.shape)
if val != 1 or index < 3
]
data = np.reshape(data, shape)
return VolumeImg(data, affine, world_space, metadata=header)
def as_volume_img(obj, copy=True, squeeze=True, world_space=None):
""" Convert the input to a VolumeImg.
Parameters
----------
obj : filename, pynifti or brifti object, or volume dataset.
Input object, in any form that can be converted to a
VolumeImg. This includes Nifti filenames, pynifti or brifti
objects, or other volumetric dataset objects.
copy: boolean, optional
If copy is True, the data and affine arrays are copied,
elsewhere a view is taken.
squeeze: boolean, optional
If squeeze is True, the data array is squeeze on for
dimensions above 3.
world_space: string or None, optional
An optional specification of the world space, to override
that given by the image.
Returns
-------
volume_img: VolumeImg object
A VolumeImg object containing the data. The metadata is
kept as much as possible in the metadata attribute.
Notes
------
The world space might not be correctly defined by the input
object (in particular, when loading data from disk). In this
case, you can correct it manually using the world_space keyword
argument.
For pynifti objects, the data is transposed.
"""
if hasattr(obj, 'as_volume_img'):
obj = obj.as_volume_img(copy=copy)
if copy:
obj = obj.__copy__()
return obj
elif isinstance(obj, string_types):
if not os.path.exists(obj):
raise ValueError("The file '%s' cannot be found" % obj)
obj = nib.load(obj)
copy = False
if isinstance(obj, SpatialImage):
data = obj.get_data()
affine = get_affine(obj)
header = dict(get_header(obj))
fname = obj.file_map['image'].filename
if fname:
header['filename'] = fname
elif hasattr(obj, 'data') and hasattr(obj, 'sform') and \
hasattr(obj, 'getVolumeExtent'):
# Duck-types to a pynifti object
data = obj.data.T
affine = obj.sform
header = obj.header
filename = obj.getFilename()
if filename != '':
header['filename'] = filename
else:
raise ValueError('Invalid type (%s) passed in: cannot convert %s to '
'VolumeImg' % (type(obj), obj))
if world_space is None and header.get('sform_code', 0) == 4:
world_space = 'mni152'
data = np.asanyarray(data)
affine = np.asanyarray(affine)
if copy:
data = data.copy()
affine = affine.copy()
if squeeze:
# Squeeze the dimensions above 3
shape = [val for index, val in enumerate(data.shape)
if val !=1 or index < 3]
data = np.reshape(data, shape)
return VolumeImg(data, affine, world_space, metadata=header)
def to_image(self, fid=None, roi=False, method="mean", descrip=None):
"""Generates a label image that represents self.
Parameters
----------
fid: str,
Feature to be represented. If None, a binary image of the MROI
domain will be we created.
roi: bool,
Whether or not to write the desired feature as a ROI one.
(i.e. a ROI feature corresponding to `fid` will be looked upon,
and if not found, a representative feature will be computed from
the `fid` feature).
method: str,
If a feature is written as a ROI feature, this keyword tweaks
the way the representative feature is computed.
descrip: str,
Description of the image, to be written in its header.
Notes
-----
Requires that self.dom is an ddom.NDGridDomain
Returns
-------
nim : nibabel nifti image
Nifti image corresponding to the ROI feature to be written.
"""
if not isinstance(self.domain, ddom.NDGridDomain):
print('self.domain is not an NDGridDomain; nothing was written.')
return None
if fid is None:
# write a binary representation of the domain if no fid provided
nim = self.domain.to_image(
data=(self.label != -1).astype(np.int32))
if descrip is None:
descrip = 'binary representation of MROI'
else:
data = -np.ones(self.label.size, dtype=np.int32)
tmp_image = self.domain.to_image()
mask = tmp_image.get_data().copy().astype(bool)
if not roi:
# write a feature
if fid not in self.features:
raise ValueError("`%s` feature could not be found" % fid)
for i in self.get_id():
data[self.select_id(i, roi=False)] = \
self.get_feature(fid, i)
else:
# write a roi feature
if fid in self.roi_features:
# write from existing roi feature
for i in self.get_id():
data[self.select_id(i, roi=False)] = \
self.get_roi_feature(
fid, i)
elif fid in self.features:
# write from representative feature
summary_feature = self.representative_feature(
fid, method=method)
for i in self.get_id():
data[self.select_id(i, roi=False)] = \
summary_feature[self.select_id(i)]
# MROI object was defined on a masked image: we square it back.
wdata = -np.ones(mask.shape, data.dtype)
wdata[mask] = data
nim = Nifti1Image(wdata, get_affine(tmp_image))
# set description of the image
if descrip is not None:
get_header(nim)['descrip'] = descrip
return nim
def contrast(self,
contrasts,
con_id='',
contrast_type=None,
output_z=True,
output_stat=False,
output_effects=False,
output_variance=False):
""" Estimation of a contrast as fixed effects on all sessions
Parameters
----------
contrasts : array or list of arrays of shape (n_col) or (n_dim, n_col)
where ``n_col`` is the number of columns of the design matrix,
numerical definition of the contrast (one array per run)
con_id : str, optional
name of the contrast
contrast_type : {'t', 'F', 'tmin-conjunction'}, optional
type of the contrast
output_z : bool, optional
Return or not the corresponding z-stat image
output_stat : bool, optional
Return or not the base (t/F) stat image
output_effects : bool, optional
Return or not the corresponding effect image
output_variance : bool, optional
Return or not the corresponding variance image
Returns
-------
output_images : list of nibabel images
The required output images, in the following order:
z image, stat(t/F) image, effects image, variance image
"""
if self.glms == []:
raise ValueError('first run fit() to estimate the model')
if isinstance(contrasts, np.ndarray):
contrasts = [contrasts]
if len(contrasts) != len(self.glms):
raise ValueError(
'contrasts must be a sequence of %d session contrasts' %
len(self.glms))
contrast_ = None
for i, (glm, con) in enumerate(zip(self.glms, contrasts)):
if np.all(con == 0):
warn('Contrast for session %d is null' % i)
elif contrast_ is None:
contrast_ = glm.contrast(con, contrast_type)
else:
contrast_ = contrast_ + glm.contrast(con, contrast_type)
if output_z or output_stat:
# compute the contrast and stat
contrast_.z_score()
# Prepare the returned images
mask = self.mask.get_data().astype(np.bool)
do_outputs = [output_z, output_stat, output_effects, output_variance]
estimates = ['z_score_', 'stat_', 'effect', 'variance']
descrips = [
'z statistic', 'Statistical value', 'Estimated effect',
'Estimated variance'
]
dims = [1, 1, contrast_.dim, contrast_.dim**2]
n_vox = mask.sum()
output_images = []
for (do_output, estimate, descrip, dim) in zip(do_outputs, estimates,
descrips, dims):
if do_output:
if dim > 1:
result_map = np.tile(
mask.astype(np.float)[:, :, :, np.newaxis], dim)
result_map[mask] = np.reshape(
getattr(contrast_, estimate).T, (n_vox, dim))
else:
result_map = mask.astype(np.float)
result_map[mask] = np.squeeze(getattr(contrast_, estimate))
output = Nifti1Image(result_map, self.affine)
get_header(output)['descrip'] = (
'%s associated with contrast %s' % (descrip, con_id))
output_images.append(output)
return output_images
