def high_variance_confounds(niimgs, n_confounds=10, percentile=1.,
detrend=True, mask_img=None):
""" Return confounds signals extracted from input signals with highest
variance.
Parameters
==========
niimgs: niimg
4D image.
mask_img: niimg
If provided, confounds are extracted from voxels inside the mask.
If not provided, all voxels are used.
n_confounds: int
Number of confounds to return
percentile: float
Highest-variance signals percentile to keep before computing the
singular value decomposition.
mask_img.sum() * percentile must be greater than n_confounds.
detrend: bool
If True, detrend signals before processing.
Returns
=======
v: numpy.ndarray
highest variance confounds. Shape: (number of scans, n_confounds)
Notes
======
This method is related to what has been published in the literature
as 'CompCor' (Behzadi NeuroImage 2007).
The implemented algorithm does the following:
- compute sum of squares for each signals (no mean removal)
- keep a given percentile of signals with highest variance (percentile)
- compute an svd of the extracted signals
- return a given number (n_confounds) of signals from the svd with
highest singular values.
See also
========
nilearn.signal.high_variance_confounds
"""
niimgs = check_niimgs(niimgs)
if mask_img is not None:
mask_img = check_niimg(mask_img)
sigs = masking.apply_mask(niimgs, mask_img)
else:
sigs = niimgs.get_data()
# Not using apply_mask here saves memory in most cases.
sigs = np.reshape(sigs, (-1, sigs.shape[-1])).T
return signal.high_variance_confounds(sigs, n_confounds=n_confounds,
percentile=percentile,
detrend=detrend)
def smooth(niimgs, fwhm):
"""Smooth images by applying a Gaussian filter.
Apply a Gaussian filter along the three first dimensions of arr.
In all cases, non-finite values in input image are replaced by zeros.
Parameters
==========
niimgs: niimgs or iterable of niimgs
One or several niimage(s), either 3D or 4D.
fwhm: scalar or numpy.ndarray
Smoothing strength, as a Full-Width at Half Maximum, in millimeters.
If a scalar is given, width is identical on all three directions.
A numpy.ndarray must have 3 elements, giving the FWHM along each axis.
If fwhm is None, no filtering is performed (useful when just removal
of non-finite values is needed)
Returns
=======
filtered_img: nibabel.Nifti1Image or list of.
Input image, filtered. If niimgs is an iterable, then filtered_img is a
list.
"""
# Use hasattr() instead of isinstance to workaround a Python 2.6/2.7 bug
# See http://bugs.python.org/issue7624
if hasattr(niimgs, "__iter__") \
and not isinstance(niimgs, basestring):
single_img = False
else:
single_img = True
niimgs = [niimgs]
ret = []
for img in niimgs:
img = check_niimg(img)
affine = img.get_affine()
filtered = masking._smooth_array(img.get_data(), affine,
fwhm=fwhm, ensure_finite=True,
copy=True)
ret.append(nibabel.Nifti1Image(filtered, affine))
if single_img:
return ret[0]
else:
return ret