def filter_and_mask(imgs, mask_img_, parameters,
memory_level=0, memory=Memory(cachedir=None),
verbose=0,
confounds=None,
copy=True):
imgs = _utils.check_niimg(imgs, atleast_4d=True, ensure_ndim=4)
# Check whether resampling is truly necessary. If so, crop mask
# as small as possible in order to speed up the process
if not _check_same_fov(imgs, mask_img_):
parameters = copy_object(parameters)
# now we can crop
mask_img_ = image.crop_img(mask_img_, copy=False)
parameters['target_shape'] = mask_img_.shape
parameters['target_affine'] = mask_img_.get_affine()
data, affine = filter_and_extract(imgs, _ExtractionFunctor(mask_img_),
parameters,
memory_level=memory_level,
memory=memory,
verbose=verbose,
confounds=confounds, copy=copy)
# For _later_: missing value removal or imputing of missing data
# (i.e. we want to get rid of NaNs, if smoothing must be done
# earlier)
# Optionally: 'doctor_nan', remove voxels with NaNs, other option
# for later: some form of imputation
return data, affine
def filter_and_mask(imgs,
mask_img_,
parameters,
memory_level=0,
memory=Memory(cachedir=None),
verbose=0,
confounds=None,
copy=True):
imgs = _utils.check_niimg(imgs, atleast_4d=True, ensure_ndim=4)
# Check whether resampling is truly necessary. If so, crop mask
# as small as possible in order to speed up the process
if not _check_same_fov(imgs, mask_img_):
parameters = copy_object(parameters)
# now we can crop
mask_img_ = image.crop_img(mask_img_, copy=False)
parameters['target_shape'] = mask_img_.shape
parameters['target_affine'] = get_affine(mask_img_)
data, affine = filter_and_extract(imgs,
_ExtractionFunctor(mask_img_),
parameters,
memory_level=memory_level,
memory=memory,
verbose=verbose,
confounds=confounds,
copy=copy)
# For _later_: missing value removal or imputing of missing data
# (i.e. we want to get rid of NaNs, if smoothing must be done
# earlier)
# Optionally: 'doctor_nan', remove voxels with NaNs, other option
# for later: some form of imputation
return data
def filter_and_mask(imgs,
mask_img_,
parameters,
memory_level=0,
memory=Memory(location=None),
verbose=0,
confounds=None,
sample_mask=None,
copy=True,
dtype=None):
"""Extract representative time series using given mask.
Parameters
----------
imgs : 3D/4D Niimg-like object
Images to be masked. Can be 3-dimensional or 4-dimensional.
For all other parameters refer to NiftiMasker documentation.
Returns
-------
signals : 2D numpy array
Signals extracted using the provided mask. It is a scikit-learn
friendly 2D array with shape n_sample x n_features.
"""
imgs = _utils.check_niimg(imgs, atleast_4d=True, ensure_ndim=4)
# Check whether resampling is truly necessary. If so, crop mask
# as small as possible in order to speed up the process
if not _check_same_fov(imgs, mask_img_):
parameters = copy_object(parameters)
# now we can crop
mask_img_ = image.crop_img(mask_img_, copy=False)
parameters['target_shape'] = mask_img_.shape
parameters['target_affine'] = mask_img_.affine
data, affine = filter_and_extract(imgs,
_ExtractionFunctor(mask_img_),
parameters,
memory_level=memory_level,
memory=memory,
verbose=verbose,
confounds=confounds,
sample_mask=sample_mask,
copy=copy,
dtype=dtype)
# For _later_: missing value removal or imputing of missing data
# (i.e. we want to get rid of NaNs, if smoothing must be done
# earlier)
# Optionally: 'doctor_nan', remove voxels with NaNs, other option
# for later: some form of imputation
return data
def save(self):
if self.id and not getattr(self,'_TrackHistory__copied', False):
old = copy_object(self)
old._TrackHistory_current=False
old._save()
self.id = None
self._TrackHistory_current=True
self._TrackHistory_revision += 1
super(TrackHistory,self).save()
print self.__class__.objects.all()
def save(self):
if self.id and not getattr(self, '_TrackHistory__copied', False):
old = copy_object(self)
old._TrackHistory_current = False
old._save()
self.id = None
self._TrackHistory_current = True
self._TrackHistory_revision += 1
super(TrackHistory, self).save()
print self.__class__.objects.all()
def deep_copy(obj, copy_related = True):
copied_obj = copy_object(obj)
copied_obj.id = None
if hasattr(copied_obj,'clone'):
copied_obj.clone()
rename_unique(copied_obj)
rename_unique_together(copied_obj)
copied_obj.save()
for original, copy in zip(obj._meta.many_to_many, copied_obj._meta.many_to_many):
# get the managers of the fields
source = getattr(obj, original.attname)
destination = getattr(copied_obj, copy.attname)
# copy m2m field contents
for element in source.all():
destination.add(element)
# save for a second time (to apply the copied many to many fields)
if hasattr(copied_obj,'clone'):
copied_obj.clone()
copied_obj.save()
if (copy_related):
# clone related objects
links = [rel.get_accessor_name() for rel in obj._meta.get_all_related_objects()]
for link in links:
for original in getattr(obj, link).all():
copied_related = deep_copy(original)
for field in copied_related._meta.fields:
#set foreign key to copied_obj
if (getattr(copied_related, field.name) == obj):
setattr(copied_related, field.name, copied_obj)
if hasattr(copied_related,'clone'):
copied_related.clone()
rename_unique(copied_related)
copied_related.save()
return copied_obj
