本文整理汇总了Python中mvpa2.support.copy.copy函数的典型用法代码示例。如果您正苦于以下问题：Python copy函数的具体用法？Python copy怎么用？Python copy使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了copy函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _concat_results
def _concat_results(sl=None, dataset=None, roi_ids=None, results=None):
"""The simplest implementation for collecting the results --
just put them into a list
This this implementation simply collects them into a list and
uses only sl. for assigning conditional attributes. But
custom implementation might make use of more/less of them.
Implemented as @staticmethod just to emphasize that in
principle it is independent of the actual searchlight instance
"""
# collect results
results = sum(results, [])
if __debug__ and 'SLC' in debug.active:
debug('SLC', '') # just newline
resshape = len(results) and np.asanyarray(results[0]).shape or 'N/A'
debug('SLC', ' hstacking %d results of shape %s'
% (len(results), resshape))
# but be careful: this call also serves as conversion from parallel maps
# to regular lists!
# this uses the Dataset-hstack
result_ds = hstack(results)
if __debug__:
debug('SLC', " hstacked shape %s" % (result_ds.shape,))
if sl.ca.is_enabled('roi_feature_ids'):
sl.ca.roi_feature_ids = [r.a.roi_feature_ids for r in results]
if sl.ca.is_enabled('roi_sizes'):
sl.ca.roi_sizes = [r.a.roi_sizes for r in results]
if sl.ca.is_enabled('roi_center_ids'):
sl.ca.roi_center_ids = [r.a.roi_center_ids for r in results]
if 'mapper' in dataset.a:
# since we know the space we can stick the original mapper into the
# results as well
if roi_ids is None:
result_ds.a['mapper'] = copy.copy(dataset.a.mapper)
else:
# there is an additional selection step that needs to be
# expressed by another mapper
mapper = copy.copy(dataset.a.mapper)
# NNO if the orignal mapper has no append (because it's not a
# chainmapper, for example), we make our own chainmapper.
feat_sel_mapper = StaticFeatureSelection(
roi_ids, dshape=dataset.shape[1:])
if hasattr(mapper, 'append'):
mapper.append(feat_sel_mapper)
else:
mapper = ChainMapper([dataset.a.mapper,
feat_sel_mapper])
result_ds.a['mapper'] = mapper
# store the center ids as a feature attribute
result_ds.fa['center_ids'] = roi_ids
return result_ds示例2: _call
def _call(self, dataset):
"""Perform the ROI search.
"""
# local binding
nproc = self.nproc
if nproc is None and externals.exists('pprocess'):
import pprocess
try:
nproc = pprocess.get_number_of_cores() or 1
except AttributeError:
warning("pprocess version %s has no API to figure out maximal "
"number of cores. Using 1"
% externals.versions['pprocess'])
nproc = 1
# train the queryengine
self._queryengine.train(dataset)
# decide whether to run on all possible center coords or just a provided
# subset
if isinstance(self.__roi_ids, str):
roi_ids = dataset.fa[self.__roi_ids].value.nonzero()[0]
elif self.__roi_ids is not None:
roi_ids = self.__roi_ids
# safeguard against stupidity
if __debug__:
if max(roi_ids) >= dataset.nfeatures:
raise IndexError, \
"Maximal center_id found is %s whenever given " \
"dataset has only %d features" \
% (max(roi_ids), dataset.nfeatures)
else:
roi_ids = np.arange(dataset.nfeatures)
# pass to subclass
results, roi_sizes = self._sl_call(dataset, roi_ids, nproc)
if not roi_sizes is None:
self.ca.roi_sizes = roi_sizes
if 'mapper' in dataset.a:
# since we know the space we can stick the original mapper into the
# results as well
if self.__roi_ids is None:
results.a['mapper'] = copy.copy(dataset.a.mapper)
else:
# there is an additional selection step that needs to be
# expressed by another mapper
mapper = copy.copy(dataset.a.mapper)
mapper.append(StaticFeatureSelection(roi_ids,
dshape=dataset.shape[1:]))
results.a['mapper'] = mapper
# charge state
self.ca.raw_results = results
# return raw results, base-class will take care of transformations
return results示例3: test_ds_shallowcopy
def test_ds_shallowcopy():
# lets use some instance of somewhat evolved dataset
ds = normal_feature_dataset()
ds.samples = ds.samples.view(myarray)
# SHALLOW copy the beast
ds_ = copy.copy(ds)
# verify that we have the same data
assert_array_equal(ds.samples, ds_.samples)
assert_array_equal(ds.targets, ds_.targets)
assert_array_equal(ds.chunks, ds_.chunks)
# array subclass survives
ok_(isinstance(ds_.samples, myarray))
# modify and see that we actually DO change the data in both
ds_.samples[0, 0] = 1234
assert_array_equal(ds.samples, ds_.samples)
assert_array_equal(ds.targets, ds_.targets)
assert_array_equal(ds.chunks, ds_.chunks)
ds_.sa.targets[0] = 'ab'
ds_.sa.chunks[0] = 234
assert_array_equal(ds.samples, ds_.samples)
assert_array_equal(ds.targets, ds_.targets)
assert_array_equal(ds.chunks, ds_.chunks)
ok_(ds.sa.targets[0] == 'ab')
ok_(ds.sa.chunks[0] == 234)示例4: _sl_call
def _sl_call(self, dataset, roi_ids, nproc):
"""Classical generic searchlight implementation
"""
assert(self.results_backend in ('native', 'hdf5'))
# compute
if nproc is not None and nproc > 1:
# split all target ROIs centers into `nproc` equally sized blocks
nproc_needed = min(len(roi_ids), nproc)
roi_blocks = np.array_split(roi_ids, nproc_needed)
# the next block sets up the infrastructure for parallel computing
# this can easily be changed into a ParallelPython loop, if we
# decide to have a PP job server in PyMVPA
import pprocess
p_results = pprocess.Map(limit=nproc_needed)
if __debug__:
debug('SLC', "Starting off child processes for nproc=%i"
% nproc_needed)
compute = p_results.manage(
pprocess.MakeParallel(self._proc_block))
for iblock, block in enumerate(roi_blocks):
# should we maybe deepcopy the measure to have a unique and
# independent one per process?
compute(block, dataset, copy.copy(self.__datameasure),
iblock=iblock)
# collect results
results = []
if self.ca.is_enabled('roi_sizes'):
roi_sizes = []
else:
roi_sizes = None
for r, rsizes in p_results:
results += self.__handle_results(r)
if not roi_sizes is None:
roi_sizes += rsizes
else:
# otherwise collect the results in a list
results, roi_sizes = \
self._proc_block(roi_ids, dataset, self.__datameasure)
results = self.__handle_results(results)
if __debug__ and 'SLC' in debug.active:
debug('SLC', '') # just newline
resshape = len(results) and np.asanyarray(results[0]).shape or 'N/A'
debug('SLC', ' hstacking %d results of shape %s'
% (len(results), resshape))
# but be careful: this call also serves as conversion from parallel maps
# to regular lists!
# this uses the Dataset-hstack
result_ds = hstack(results)
if self.ca.is_enabled('roi_feature_ids'):
self.ca.roi_feature_ids = [r.a.roi_feature_ids for r in results]
if __debug__:
debug('SLC', " hstacked shape %s" % (result_ds.shape,))
return result_ds, roi_sizes示例5: _call
def _call(self, dataset):
analyzers = []
# create analyzers
for clf in self.clf.clfs:
if self.__analyzer is None:
analyzer = clf.get_sensitivity_analyzer(**(self._slave_kwargs))
if analyzer is None:
raise ValueError, \
"Wasn't able to figure basic analyzer for clf %r" % \
(clf,)
if __debug__:
debug("SA", "Selected analyzer %r for clf %r" % \
(analyzer, clf))
else:
# XXX shallow copy should be enough...
analyzer = copy.copy(self.__analyzer)
# assign corresponding classifier
analyzer.clf = clf
# if clf was trained already - don't train again
if clf.trained:
analyzer._force_train = False
analyzers.append(analyzer)
self.__combined_analyzer.analyzers = analyzers
# XXX not sure if we don't want to call directly ._call(dataset) to avoid
# double application of transformers/combiners, after all we are just
# 'proxying' here to combined_analyzer...
# YOH: decided -- lets call ._call
return self.__combined_analyzer._call(dataset)示例6: __getitem__
def __getitem__(self, key):
# if just one is requested return just one, otherwise return a
# NodeChain again
if isinstance(key, int):
return self._nodes[key]
else:
# operate on shallow copy of self
sliced = copy.copy(self)
sliced._nodes = self._nodes[key]
return sliced示例7: _sl_call
def _sl_call(self, dataset, roi_ids, nproc):
"""Classical generic searchlight implementation
"""
assert(self.results_backend in ('native', 'hdf5'))
# compute
if nproc is not None and nproc > 1:
# split all target ROIs centers into `nproc` equally sized blocks
nproc_needed = min(len(roi_ids), nproc)
nblocks = nproc_needed \
if self.nblocks is None else self.nblocks
roi_blocks = np.array_split(roi_ids, nblocks)
# the next block sets up the infrastructure for parallel computing
# this can easily be changed into a ParallelPython loop, if we
# decide to have a PP job server in PyMVPA
import pprocess
p_results = pprocess.Map(limit=nproc_needed)
if __debug__:
debug('SLC', "Starting off %s child processes for nblocks=%i"
% (nproc_needed, nblocks))
compute = p_results.manage(
pprocess.MakeParallel(self._proc_block))
for iblock, block in enumerate(roi_blocks):
# should we maybe deepcopy the measure to have a unique and
# independent one per process?
seed = mvpa2.get_random_seed()
compute(block, dataset, copy.copy(self.__datameasure),
seed=seed, iblock=iblock)
else:
# otherwise collect the results in an 1-item list
p_results = [
self._proc_block(roi_ids, dataset, self.__datameasure)]
# Finally collect and possibly process results
# p_results here is either a generator from pprocess.Map or a list.
# In case of a generator it allows to process results as they become
# available
result_ds = self.results_fx(sl=self,
dataset=dataset,
roi_ids=roi_ids,
results=self.__handle_all_results(p_results))
# Assure having a dataset (for paranoid ones)
if not is_datasetlike(result_ds):
try:
result_a = np.atleast_1d(result_ds)
except ValueError, e:
if 'setting an array element with a sequence' in str(e):
# try forcing object array. Happens with
# test_custom_results_fx_logic on numpy 1.4.1 on Debian
# squeeze
result_a = np.array(result_ds, dtype=object)
else:
raise
result_ds = Dataset(result_a)示例8: _precall
def _precall(self, ds):
# estimate the NULL distribution when functor is given
if not self.__null_dist is None:
if __debug__:
debug("STAT", "Estimating NULL distribution using %s" % self.__null_dist)
# we need a matching measure instance, but we have to disable
# the estimation of the null distribution in that child to prevent
# infinite looping.
measure = copy.copy(self)
measure.__null_dist = None
self.__null_dist.fit(measure, ds)示例9: as_descrete_time
def as_descrete_time(self, dt, storeoffset=False, offsetattr='offset'):
"""Convert `onset` and `duration` information into descrete timepoints.
Parameters
----------
dt : float
Temporal distance between two timepoints in the same unit as `onset`
and `duration`.
storeoffset : bool
If True, the temporal offset between original `onset` and
descretized onset is stored as an additional item.
offsetattr : str
The name of the attribute that is used to store the computed offset
in case the `storeoffset` is enabled.
Returns
-------
A copy of the original `Event` with `onset` and optionally `duration`
replaced by their corresponding descrete timepoint. The new onset will
correspond to the timepoint just before or exactly at the original
onset. The new duration will be the number of timepoints covering the
event from the computed onset timepoint till the timepoint exactly at
the end, or just after the event.
Note again, that the new values are expressed as #timepoint and not
in their original unit!
"""
dt = float(dt)
onset = self['onset']
out = copy(self)
# get the timepoint just prior the onset
out['onset'] = int(np.floor(onset / dt))
if storeoffset:
# compute offset
offset = onset - (out['onset'] * dt)
out[offsetattr] = offset
if out.has_key('duration'):
# how many timepoint cover the event (from computed onset
# to the one timepoint just after the end of the event
out['duration'] = int(np.ceil((onset + out['duration']) / dt) \
- out['onset'])
return out示例10: __copy__
def __copy__(self):
# XXX how do we safely and exhaustively copy a node?
return self.__class__([copy.copy(n) for n in self])示例11: test_subset
def test_subset():
data = np.array(
[[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
[32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
[48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]])
# float array doesn't work
sm = StaticFeatureSelection(np.ones(16))
assert_raises(IndexError, sm.forward, data)
# full mask
sm = StaticFeatureSelection(slice(None))
# should not change single samples
assert_array_equal(sm.forward(data[0:1].copy()), data[0:1])
# or multi-samples
assert_array_equal(sm.forward(data.copy()), data)
sm.train(data)
# same on reverse
assert_array_equal(sm.reverse(data[0:1].copy()), data[0:1])
# or multi-samples
assert_array_equal(sm.reverse(data.copy()), data)
# identical mappers
sm_none = StaticFeatureSelection(slice(None))
sm_int = StaticFeatureSelection(np.arange(16))
sm_bool = StaticFeatureSelection(np.ones(16, dtype='bool'))
sms = [sm_none, sm_int, sm_bool]
# test subsets
sids = [3, 4, 5, 6]
bsubset = np.zeros(16, dtype='bool')
bsubset[sids] = True
subsets = [sids, slice(3, 7), bsubset, [3, 3, 4, 4, 6, 6, 6, 5]]
# all test subset result in equivalent masks, hence should do the same to
# the mapper and result in identical behavior
for st in sms:
for i, sub in enumerate(subsets):
# shallow copy
orig = copy(st)
subsm = StaticFeatureSelection(sub)
# should do copy-on-write for all important stuff!!
orig += subsm
# test if selection did its job
if i == 3:
# special case of multiplying features
assert_array_equal(orig.forward1(data[0].copy()), subsets[i])
else:
assert_array_equal(orig.forward1(data[0].copy()), sids)
## all of the above shouldn't change the original mapper
#assert_array_equal(sm.get_mask(), np.arange(16))
# check for some bug catcher
# no 3D input
#assert_raises(IndexError, sm.forward, np.ones((3,2,1)))
# no input of wrong length
if __debug__:
# checked only in __debug__
assert_raises(ValueError, sm.forward, np.ones(4))
# same on reverse
#assert_raises(ValueError, sm.reverse, np.ones(16))
# invalid ids
#assert_false(subsm.is_valid_inid(-1))
#assert_false(subsm.is_valid_inid(16))
# intended merge failures
fsm = StaticFeatureSelection(np.arange(16))
assert_equal(fsm.__iadd__(None), NotImplemented)
assert_equal(fsm.__iadd__(Dataset([2, 3, 4])), NotImplemented)示例12: _call
def _call(self, dataset):
"""Perform the ROI search.
"""
# local binding
nproc = self.nproc
if nproc is None and externals.exists('pprocess'):
import pprocess
try:
nproc = pprocess.get_number_of_cores() or 1
except AttributeError:
warning("pprocess version %s has no API to figure out maximal "
"number of cores. Using 1"
% externals.versions['pprocess'])
nproc = 1
# train the queryengine
self._queryengine.train(dataset)
# decide whether to run on all possible center coords or just a provided
# subset
if isinstance(self.__roi_ids, str):
roi_ids = dataset.fa[self.__roi_ids].value.nonzero()[0]
elif self.__roi_ids is not None:
roi_ids = self.__roi_ids
# safeguard against stupidity
if __debug__:
qe_ids = self._queryengine.ids # known to qe
if not set(qe_ids).issuperset(roi_ids):
raise IndexError(
"Some roi_ids are not known to the query engine %s: %s"
% (self._queryengine,
set(roi_ids).difference(qe_ids)))
else:
roi_ids = self._queryengine.ids
# pass to subclass
results = self._sl_call(dataset, roi_ids, nproc)
if 'mapper' in dataset.a:
# since we know the space we can stick the original mapper into the
# results as well
if self.__roi_ids is None:
results.a['mapper'] = copy.copy(dataset.a.mapper)
else:
# there is an additional selection step that needs to be
# expressed by another mapper
mapper = copy.copy(dataset.a.mapper)
# NNO if the orignal mapper has no append (because it's not a
# chainmapper, for example), we make our own chainmapper.
#
# THe original code was:
# mapper.append(StaticFeatureSelection(roi_ids,
# dshape=dataset.shape[1:]))
feat_sel_mapper = StaticFeatureSelection(roi_ids,
dshape=dataset.shape[1:])
if 'append' in dir(mapper):
mapper.append(feat_sel_mapper)
else:
mapper = ChainMapper([dataset.a.mapper,
feat_sel_mapper])
results.a['mapper'] = mapper
# charge state
self.ca.raw_results = results
# store the center ids as a feature attribute
results.fa['center_ids'] = roi_ids
# return raw results, base-class will take care of transformations
return results示例13: __call__
#.........这里部分代码省略.........
if params.nproc is None and externals.exists('pprocess'):
import pprocess
try:
params.nproc = pprocess.get_number_of_cores() or 1
except AttributeError:
warning("pprocess version %s has no API to figure out maximal "
"number of cores. Using 1"
% externals.versions['pprocess'])
params.nproc = 1
# XXX I think this class should already accept a single dataset only.
# It should have a ``space`` setting that names a sample attribute that
# can be used to identify individual/original datasets.
# Taking a single dataset as argument would be cleaner, because the
# algorithm relies on the assumption that there is a coarse feature
# alignment, i.e. the SL ROIs cover roughly the same area
queryengines = self._get_trained_queryengines(
datasets, params.queryengine, params.radius, params.ref_ds)
# For surface nodes to voxels queryengines, roi_seed hardly makes sense
if isinstance(queryengines[params.ref_ds], SurfaceVerticesQueryEngine):
self.force_roi_seed = False
if not self.params.combine_neighbormappers:
raise NotImplementedError("Mapping from voxels to surface nodes is not "
"implmented yet. Try setting combine_neighbormappers to True.")
self.nfeatures = datasets[params.ref_ds].nfeatures
_shpaldebug("Performing Hyperalignment in searchlights")
# Setting up centers for running SL Hyperalignment
if params.sparse_radius is None:
roi_ids = self._get_verified_ids(queryengines) \
if params.mask_node_ids is None \
else params.mask_node_ids
else:
if params.queryengine is not None:
raise NotImplementedError(
"using sparse_radius whenever custom queryengine is "
"provided is not yet supported.")
_shpaldebug("Setting up sparse neighborhood")
from mvpa2.misc.neighborhood import scatter_neighborhoods
if params.mask_node_ids is None:
scoords, sidx = scatter_neighborhoods(
Sphere(params.sparse_radius),
datasets[params.ref_ds].fa.voxel_indices,
deterministic=True)
roi_ids = sidx
else:
scoords, sidx = scatter_neighborhoods(
Sphere(params.sparse_radius),
datasets[params.ref_ds].fa.voxel_indices[params.mask_node_ids],
deterministic=True)
roi_ids = [params.mask_node_ids[sid] for sid in sidx]
# Initialize projections
_shpaldebug('Initializing projection matrices')
self.projections = [
csc_matrix((self.nfeatures, self.nfeatures), dtype=params.dtype)
for isub in range(self.ndatasets)]
# compute
if params.nproc is not None and params.nproc > 1:
# split all target ROIs centers into `nproc` equally sized blocks
nproc_needed = min(len(roi_ids), params.nproc)
params.nblocks = nproc_needed \
if params.nblocks is None else params.nblocks
params.nblocks = min(len(roi_ids), params.nblocks)
node_blocks = np.array_split(roi_ids, params.nblocks)
# the next block sets up the infrastructure for parallel computing
# this can easily be changed into a ParallelPython loop, if we
# decide to have a PP job server in PyMVPA
import pprocess
p_results = pprocess.Map(limit=nproc_needed)
if __debug__:
debug('SLC', "Starting off %s child processes for nblocks=%i"
% (nproc_needed, params.nblocks))
compute = p_results.manage(
pprocess.MakeParallel(self._proc_block))
seed = mvpa2.get_random_seed()
for iblock, block in enumerate(node_blocks):
# should we maybe deepcopy the measure to have a unique and
# independent one per process?
compute(block, datasets, copy.copy(hmeasure), queryengines,
seed=seed, iblock=iblock)
else:
# otherwise collect the results in an 1-item list
_shpaldebug('Using 1 process to compute mappers.')
if params.nblocks is None:
params.nblocks = 1
params.nblocks = min(len(roi_ids), params.nblocks)
node_blocks = np.array_split(roi_ids, params.nblocks)
p_results = [self._proc_block(block, datasets, hmeasure, queryengines)
for block in node_blocks]
results_ds = self.__handle_all_results(p_results)
# Dummy iterator for, you know, iteration
list(results_ds)
_shpaldebug('Wrapping projection matrices into StaticProjectionMappers')
self.projections = [
StaticProjectionMapper(proj=proj, recon=proj.T) if params.compute_recon
else StaticProjectionMapper(proj=proj)
for proj in self.projections]
return self.projections示例14: _train
#.........这里部分代码省略.........
if len(sensitivity) > 1:
raise ValueError(
"RFE cannot handle multiple sensitivities at once. "
"'%s' returned %i sensitivities."
% (self._fmeasure.__class__.__name__,
len(sensitivity)))
if ca.is_enabled("sensitivities"):
ca.sensitivities.append(sensitivity)
if self._pmeasure:
# get error for current feature set (handles optional retraining)
error = np.asscalar(self._evaluate_pmeasure(wdataset, wtestdataset))
# Record the error
errors.append(error)
# Check if it is time to stop and if we got
# the best result
if self._stopping_criterion is not None:
stop = self._stopping_criterion(errors)
if self._bestdetector is not None:
isthebest = self._bestdetector(errors)
else:
error = None
nfeatures = wdataset.nfeatures
if ca.is_enabled("nfeatures"):
ca.nfeatures.append(wdataset.nfeatures)
# store result
if isthebest:
result_selected_ids = orig_feature_ids
if __debug__:
debug('RFEC',
"Step %d: nfeatures=%d error=%s best/stop=%d/%d " %
(step, nfeatures, error, isthebest, stop))
# stop if it is time to finish
if nfeatures == 1 or nfeatures <= self.nfeatures_min or stop:
break
# Select features to preserve
selected_ids = self._fselector(sensitivity)
if __debug__:
debug('RFEC_',
"Sensitivity: %s, nfeatures_selected=%d, selected_ids: %s" %
(sensitivity, len(selected_ids), selected_ids))
# Create a dataset only with selected features
wdataset = wdataset[:, selected_ids]
# select corresponding sensitivity values if they are not
# recomputed
if not self.__update_sensitivity:
if len(sensitivity.shape) >= 2:
assert(sensitivity.shape[0] == 1) # there must be only 1 sample
sensitivity = sensitivity[:, selected_ids]
else:
sensitivity = sensitivity[selected_ids]
# need to update the test dataset as well
# XXX why should it ever become None?
# yoh: because we can have __transfer_error computed
# using wdataset. See xia-generalization estimate
# in lightsvm. Or for god's sake leave-one-out
# on a wdataset
# TODO: document these cases in this class
if testdataset is not None:
wtestdataset = wtestdataset[:, selected_ids]
step += 1
# WARNING: THIS MUST BE THE LAST THING TO DO ON selected_ids
selected_ids.sort()
if self.ca.is_enabled("history") \
or self.ca.is_enabled('selected_ids'):
orig_feature_ids = orig_feature_ids[selected_ids]
# we already have the initial sensitivities, so even for a shared
# classifier we can cleanup here
if self._pmeasure:
self._pmeasure.untrain()
# charge conditional attributes
self.ca.errors = errors
self.ca.selected_ids = result_selected_ids
if __debug__:
debug('RFEC',
"Selected %d features: %s",
(len(result_selected_ids), result_selected_ids))
# announce desired features to the underlying slice mapper
# do copy to survive later selections
self._safe_assign_slicearg(copy(result_selected_ids))
# call super to set _Xshape etc
super(RFE, self)._train(dataset)示例15: __copy__
def __copy__(self):
return self.__class__([copy.copy(n) for n in self],
copy.copy(self._combine_axis),
copy.copy(self._a))