def __init__(self, vg, white, pial, intermediate=None):
'''
Parameters
----------
volgeom: volgeom.VolGeom
Volume geometry
white: surf.Surface
Surface representing white-grey matter boundary
pial: surf.Surface
Surface representing pial-grey matter boundary
intermediate: surf.Surface (default: None).
Surface representing intermediate surface. If omitted
it is the node-wise average of white and pial.
This parameter is usually ignored, except when used
in a VolSurfMinimalLowresMapping.
Notes
-----
'pial' and 'white' should have the same topology.
'''
self._volgeom = volgeom.from_any(vg)
self._pial = surf.from_any(pial)
self._white = surf.from_any(white)
if not self._pial.same_topology(self._white):
raise Exception("Not same topology for white and pial")
#if intermediate is None:
# intermediate = (self.pial_surface * .5) + (self.white_surface * .5)
self._intermediate = surf.from_any(intermediate)
def test_volgeom_masking(self):
maskstep = 5
vg = volgeom.VolGeom((2 * maskstep, 2 * maskstep, 2 * maskstep), np.identity(4))
mask = vg.get_empty_array()
sh = vg.shape
# mask a subset of the voxels
rng = range(0, sh[0], maskstep)
for i in rng:
for j in rng:
for k in rng:
mask[i, j, k] = 1
# make a new volgeom instance
vg = volgeom.VolGeom(vg.shape, vg.affine, mask)
data = vg.get_masked_nifti_image(nt=1)
msk = vg.get_masked_nifti_image()
dset = fmri_dataset(data, mask=msk)
vg_dset = volgeom.from_any(dset)
# ensure that the mask is set properly and
assert_equal(vg.nvoxels, vg.nvoxels_mask * maskstep ** 3)
assert_equal(vg_dset, vg)
dilates = range(0, 8, 2)
nvoxels_masks = [] # keep track of number of voxels for each size
for dilate in dilates:
covers_full_volume = dilate * 2 >= maskstep * 3 ** .5 + 1
# constr gets values: None, Sphere(0), 2, Sphere(2), ...
for i, constr in enumerate([Sphere, lambda x:x if x else None]):
dilater = constr(dilate)
img_dilated = vg.get_masked_nifti_image(dilate=dilater)
data = img_dilated.get_data()
assert_array_equal(data, vg.get_masked_array(dilate=dilater))
n = np.sum(data)
# number of voxels in mask is increasing
assert_true(all(n >= p for p in nvoxels_masks))
# results should be identical irrespective of constr
if i == 0:
# - first call with this value of dilate: has to be more
# voxels than very previous dilation value, unless the
# full volume is covered - then it can be equal too
# - every next call: ensure size matches
cmp = lambda x, y:(x >= y if covers_full_volume else x > y)
assert_true(all(cmp(n, p) for p in nvoxels_masks))
nvoxels_masks.append(n)
else:
# same size as previous call
assert_equal(n, nvoxels_masks[-1])
# if dilate is not None or zero, then it should
# have selected all the voxels if the radius is big enough
assert_equal(np.sum(data) == vg.nvoxels, covers_full_volume)
def test_volgeom_masking(self):
maskstep = 5
vg = volgeom.VolGeom((2 * maskstep, 2 * maskstep, 2 * maskstep), np.identity(4))
mask = vg.get_empty_array()
sh = vg.shape
# mask a subset of the voxels
rng = range(0, sh[0], maskstep)
for i in rng:
for j in rng:
for k in rng:
mask[i, j, k] = 1
# make a new volgeom instance
vg = volgeom.VolGeom(vg.shape, vg.affine, mask)
data = vg.get_masked_nifti_image(nt=1)
msk = vg.get_masked_nifti_image()
dset = fmri_dataset(data, mask=msk)
vg_dset = volgeom.from_any(dset)
# ensure that the mask is set properly and
assert_equal(vg.nvoxels, vg.nvoxels_mask * maskstep ** 3)
assert_equal(vg_dset, vg)
dilates = range(0, 8, 2)
nvoxels_masks = [] # keep track of number of voxels for each size
for dilate in dilates:
covers_full_volume = dilate * 2 >= maskstep * 3 ** .5 + 1
# constr gets values: None, Sphere(0), 2, Sphere(2), ...
for i, constr in enumerate([Sphere, lambda x:x if x else None]):
dilater = constr(dilate)
img_dilated = vg.get_masked_nifti_image(dilate=dilater)
data = img_dilated.get_data()
assert_array_equal(data, vg.get_masked_array(dilate=dilater))
n = np.sum(data)
# number of voxels in mask is increasing
assert_true(all(n >= p for p in nvoxels_masks))
# results should be identical irrespective of constr
if i == 0:
# - first call with this value of dilate: has to be more
# voxels than very previous dilation value, unless the
# full volume is covered - then it can be equal too
# - every next call: ensure size matches
cmp = lambda x, y:(x >= y if covers_full_volume else x > y)
assert_true(all(cmp(n, p) for p in nvoxels_masks))
nvoxels_masks.append(n)
else:
# same size as previous call
assert_equal(n, nvoxels_masks[-1])
# if dilate is not None or zero, then it should
# have selected all the voxels if the radius is big enough
assert_equal(np.sum(data) == vg.nvoxels, covers_full_volume)
def __init__(self, vg, source, meta=None, src2nbr=None, src2aux=None):
"""Initialize a VolumeMaskDictionary
Parameters
----------
vg: volgeom.VolGeom or fmri_dataset-like or str
data structure that contains volume geometry information.
source: Surface.surf or numpy.ndarray or None
structure that contains the geometric information of
(the centers of) each mask. In the case of surface-searchlights this
should be a surface used as the center for searchlights.
meta: dict or None
Optional meta data stored with this instance (such as searchlight
radius and volumetric information). A use case is storing an instance
and loading it later, and then checking whether the meta information
is correct when it used to run a searchlight analysis.
src2nbr: dict or None
In a typical use case it contains a mapping from node center
indices to lists of voxel indices.
src2aux: dict or None
In a typical use case it can contain auxiliary information such as
distance of each voxel to each center.
"""
self._volgeom = volgeom.from_any(vg)
self._source = source
self._src2nbr = dict() if src2nbr is None else src2nbr
self._src2aux = dict() if src2nbr is None else src2aux
self._meta = meta
# this attribute is initially set to None
# upon the first call that requires an inverse mapping
# it is generated.
self._lazy_nbr2src = None
def __init__(self, vg, white, pial, intermediate=None):
'''
Parameters
----------
volgeom: volgeom.VolGeom
Volume geometry
white: surf.Surface
Surface representing white-grey matter boundary
pial: surf.Surface
Surface representing pial-grey matter boundary
intermediate: surf.Surface (default: None).
Surface representing intermediate surface. If omitted
it is the node-wise average of white and pial.
This parameter is usually ignored, except when used
in a VolSurfMinimalLowresMapping.
Notes
-----
'pial' and 'white' should have the same topology.
'''
self._volgeom = volgeom.from_any(vg)
self._pial = surf.from_any(pial)
self._white = surf.from_any(white)
if not self._pial.same_topology(self._white):
raise Exception("Not same topology for white and pial")
#if intermediate is None:
# intermediate = (self.pial_surface * .5) + (self.white_surface * .5)
self._intermediate = surf.from_any(intermediate)
def __init__(self, vg, source, meta=None, src2nbr=None, src2aux=None):
"""
Parameters
----------
vg: volgeom.VolGeom or fmri_dataset-like or str
data structure that contains volume geometry information.
source: Surface.surf or numpy.ndarray or None
structure that contains the geometric information of
(the centers of) each mask. In the case of surface-searchlights this
should be a surface used as the center for searchlights.
meta: dict or None
Optional meta data stored with this instance (such as searchlight
radius and volumetric information). A use case is storing an instance
and loading it later, and then checking whether the meta information
is correct when it used to run a searchlight analysis.
src2nbr: dict or None
In a typical use case it contains a mapping from node center
indices to lists of voxel indices.
src2aux: dict or None
In a typical use case it can contain auxiliary information such as
distance of each voxel to each center.
"""
self._volgeom = volgeom.from_any(vg)
self._source = source
self._src2nbr = dict() if src2nbr is None else src2nbr
self._src2aux = dict() if src2nbr is None else src2aux
self._meta = meta
# this attribute is initially set to None
# upon the first call that requires an inverse mapping
# it is generated.
self._lazy_nbr2src = None
def __init__(self, vg):
'''
Parameters
----------
vg: Volgeom.volgeom or str or NiftiImage
volume to be used as a surface
'''
self._vg = volgeom.from_any(vg)
n = self._vg.nvoxels
vertices = self._vg.lin2xyz(np.arange(n))
faces = np.zeros((0, 3), dtype=np.int)
# call the parent's class constructor
super(VolumeBasedSurface, self).__init__(vertices, faces, check=False)
def __init__(self, vg):
'''
Parameters
----------
vg: Volgeom.volgeom or str or NiftiImage
volume to be used as a surface
'''
self._vg = volgeom.from_any(vg)
n = self._vg.nvoxels
vertices = self._vg.lin2xyz(np.arange(n))
faces = np.zeros((0, 3), dtype=np.int)
# call the parent's class constructor
super(VolumeBasedSurface, self).__init__(vertices, faces, check=False)
def from_volume(v):
'''Makes a pseudo-surface from a volume.
Each voxels corresponds to a node; there is no topology.
A use case is mimicking traditional volume-based searchlights
Parameters
----------
v: str of NiftiImage
input volume
Returns
-------
s: surf.Surface
Surface with an equal number as nodes as there are voxels
in the input volume. The associated topology is empty.
'''
vg = volgeom.from_any(v)
vs = VolumeBasedSurface(vg)
return VolSurfMaximalMapping(vg, vs, vs, vs)
def from_volume(v):
'''Makes a pseudo-surface from a volume.
Each voxels corresponds to a node; there is no topology.
A use case is mimicking traditional volume-based searchlights
Parameters
----------
v: str of NiftiImage
input volume
Returns
-------
s: surf.Surface
Surface with an equal number as nodes as there are voxels
in the input volume. The associated topology is empty.
'''
vg = volgeom.from_any(v)
vs = VolumeBasedSurface(vg)
return VolSurfMaximalMapping(vg, vs, vs, vs)
def __init__(self, vg, source, src2nbr=None, src2aux=None):
"""
Parameters
----------
vg: volgeom.VolGeom or fmri_dataset-like or str
data structure that contains volume geometry information.
source: Surface.surf or numpy.ndarray or None
structure that contains the geometric information of
(the centers of) each mask. In the case of surface-searchlights this
should be a surface used as the center for searchlights.
"""
self._volgeom = volgeom.from_any(vg)
self._source = source
self._src2nbr = dict() if src2nbr is None else src2nbr
self._src2aux = dict() if src2nbr is None else src2aux
# this attribute is initially set to None
# upon the first call that requires an inverse mapping
# it is generated.
self._lazy_nbr2src = None
def train(self, dataset):
'''Train the query engine on a dataset'''
vg = self.voxsel.volgeom
# We are creating a map from big unmasked indices of voxels
# known to voxsel into the dataset's feature indexes.
# We verify that the current dataset has the necessary
# features (i.e. are not masked out) and that the volume
# geometry matches that of the original voxel selection
vg_ds = None
try:
vg_ds = volgeom.from_any(dataset)
except:
vg_ds = None
if vg_ds:
eps = .0001
if np.max(np.abs(vg_ds.affine - vg.affine)) > eps:
raise ValueError("Mismatch in affine matrix: %r !+ %r" %
(vg_ds.affine, vg.affine))
if not vg_ds.same_shape(vg):
raise ValueError("Mismatch in shape: (%s,%s,%s) != "
"(%s,%s,%s)" %
(vg_ds.shape[:3], vg.shape[:3]))
else:
warning("Could not find dataset volume geometry for %r" % dataset)
self._map_voxel_coord = map_voxel_coord = {}
long_is = vg.ijk2lin(dataset.fa[self.space].value)
long_is_invol = vg.contains_lin(long_is)
for i, long_i in enumerate(long_is):
if not long_is_invol[i]:
raise ValueError('Feature id %d (with voxel id %d)'
' is not in the (possibly masked) '
'volume geometry %r)' % (i, long_i, vg))
if long_i in map_voxel_coord:
map_voxel_coord[long_i].append(i)
else:
map_voxel_coord[long_i] = [i]
def train(self, dataset):
'''Train the query engine on a dataset'''
vg = self.voxsel.volgeom
# We are creating a map from big unmasked indices of voxels
# known to voxsel into the dataset's feature indexes.
# We verify that the current dataset has the necessary
# features (i.e. are not masked out) and that the volume
# geometry matches that of the original voxel selection
vg_ds = None
try:
vg_ds = volgeom.from_any(dataset)
except:
vg_ds = None
if vg_ds:
eps = .0001
if np.max(np.abs(vg_ds.affine - vg.affine)) > eps:
raise ValueError("Mismatch in affine matrix: %r !+ %r" %
(vg_ds.affine, vg.affine))
if not vg_ds.same_shape(vg):
raise ValueError("Mismatch in shape: (%s,%s,%s) != "
"(%s,%s,%s)" %
(vg_ds.shape[:3], vg.shape[:3]))
else:
warning("Could not find dataset volume geometry for %r" % dataset)
self._map_voxel_coord = map_voxel_coord = {}
long_is = vg.ijk2lin(dataset.fa[self.space].value)
long_is_invol = vg.contains_lin(long_is)
for i, long_i in enumerate(long_is):
if not long_is_invol[i]:
raise ValueError('Feature id %d (with voxel id %d)'
' is not in the (possibly masked) '
'volume geometry %r)' % (i, long_i, vg))
if long_i in map_voxel_coord:
map_voxel_coord[long_i].append(i)
else:
map_voxel_coord[long_i] = [i]
def __init__(self, vg, source, src2nbr=None, src2aux=None):
"""
Parameters
----------
vg: volgeom.VolGeom or fmri_dataset-like or str
data structure that contains volume geometry information.
source: Surface.surf or numpy.ndarray or None
structure that contains the geometric information of
(the centers of) each mask. In the case of surface-searchlights this
should be a surface used as the center for searchlights.
"""
self._volgeom = volgeom.from_any(vg)
self._source = source
self._src2nbr = dict() if src2nbr is None else src2nbr
self._src2aux = dict() if src2nbr is None else src2aux
# this attribute is initially set to None
# upon the first call that requires an inverse mapping
# it is generated.
self._lazy_nbr2src = None
def run_voxel_selection(radius,
volume,
white_surf,
pial_surf,
source_surf=None,
source_surf_nodes=None,
volume_mask=None,
distance_metric='dijkstra',
start_mm=0,
stop_mm=0,
start_fr=0.,
stop_fr=1.,
nsteps=10,
eta_step=1,
nproc=None,
outside_node_margin=None,
results_backend=None,
tmp_prefix='tmpvoxsel',
node_voxel_mapping='maximal'):
"""
Voxel selection wrapper for multiple center nodes on the surface
Parameters
----------
radius: int or float
Size of searchlight. If an integer, then it indicates the number of
voxels. If a float, then it indicates the radius of the disc
volume: Dataset or NiftiImage or volgeom.Volgeom
Volume in which voxels are selected.
white_surf: str of surf.Surface
Surface of white-matter to grey-matter boundary, or filename
of file containing such a surface.
pial_surf: str of surf.Surface
Surface of grey-matter to pial-matter boundary, or filename
of file containing such a surface.
source_surf: surf.Surface or None
Surface used to compute distance between nodes. If omitted, it is
the average of the gray and white surfaces.
source_surf_nodes: list of int or numpy array or None
Indices of nodes in source_surf that serve as searchlight center.
By default every node serves as a searchlight center.
volume_mask: None (default) or False or int
Mask from volume to apply from voxel selection results. By default
no mask is applied. If volume_mask is an integer k, then the k-th
volume from volume is used to mask the data. If volume is a Dataset
and has a property volume.fa.voxel_indices, then these indices
are used to mask the data, unless volume_mask is False or an integer.
distance_metric: str
Distance metric between nodes. 'euclidean' or 'dijksta' (default)
start_fr: float (default: 0)
Relative start position of line in gray matter, 0.=white
surface, 1.=pial surface
stop_fr: float (default: 1)
Relative stop position of line (as in see start)
start_mm: float (default: 0)
Absolute start position offset (as in start_fr)
stop_mm: float (default: 0)
Absolute start position offset (as in start_fr)
nsteps: int (default: 10)
Number of steps from white to pial surface
eta_step: int (default: 1)
After how many searchlights an estimate should be printed of the
remaining time until completion of all searchlights
nproc: int or None
Number of parallel threads. None means as many threads as the
system supports. The pprocess is required for parallel threads; if
it cannot be used, then a single thread is used.
outside_node_margin: float or None (default)
By default nodes outside the volume are skipped; using this
parameter allows for a marign. If this value is a float (possibly
np.inf), then all nodes within outside_node_margin Dijkstra
distance from any node within the volume are still assigned
associated voxels. If outside_node_margin is True, then a node is
always assigned voxels regardless of its position in the volume.
results_backend : 'native' or 'hdf5' or None (default).
Specifies the way results are provided back from a processing block
in case of nproc > 1. 'native' is pickling/unpickling of results by
pprocess, while 'hdf5' would use h5save/h5load functionality.
'hdf5' might be more time and memory efficient in some cases.
If None, then 'hdf5' if used if available, else 'native'.
tmp_prefix : str, optional
If specified -- serves as a prefix for temporary files storage
if results_backend == 'hdf5'. Thus can specify the directory to use
(trailing file path separator is not added automagically).
node_voxel_mapping: 'minimal' or 'maximal' or 'minimal_lowres'
If 'minimal' then each voxel is associated with at most one node.
If 'maximal' it is associated with as many nodes that contain the
voxel (default: 'maximal').
If 'minimal_lowres' then each voxel is associated with at most one
node, and each node that is mapped onto has a corresponding node
(at the same spatial location) in source_surf.
Returns
-------
sel: volume_mask_dict.VolumeMaskDictionary
Voxel selection results, that associates, which each node, the indices
of the surrounding voxels.
"""
vg = volgeom.from_any(volume, volume_mask)
mapper_dict = dict(maximal=volsurf.VolSurfMaximalMapping,
minimal=volsurf.VolSurfMinimalMapping,
minimal_lowres=volsurf.VolSurfMinimalLowresMapping)
mapper = mapper_dict[node_voxel_mapping]
vsm = mapper(vg,
white=white_surf,
pial=pial_surf,
intermediate=source_surf,
nsteps=nsteps,
start_fr=start_fr,
stop_fr=stop_fr,
start_mm=start_mm,
stop_mm=stop_mm)
sel = voxel_selection(vol_surf_mapping=vsm,
radius=radius,
source_surf=source_surf,
source_surf_nodes=source_surf_nodes,
distance_metric=distance_metric,
eta_step=eta_step,
nproc=nproc,
outside_node_margin=outside_node_margin,
results_backend=results_backend,
tmp_prefix=tmp_prefix)
return sel
def test_volgeom(self, temp_fn):
sz = (17, 71, 37, 73) # size of 4-D 'brain volume'
d = 2. # voxel size
xo, yo, zo = -6., -12., -20. # origin
mx = np.identity(4, np.float) * d # affine transformation matrix
mx[3, 3] = 1
mx[0, 3] = xo
mx[1, 3] = yo
mx[2, 3] = zo
vg = volgeom.VolGeom(sz, mx) # initialize volgeom
eq_shape_nvoxels = {
(17, 71, 37): (True, True),
(71, 17, 37, 1): (False, True),
(17, 71, 37, 2): (True, True),
(17, 71, 37, 73): (True, True),
(2, 2, 2): (False, False)
}
for other_sz, (eq_shape, eq_nvoxels) in eq_shape_nvoxels.iteritems():
other_vg = volgeom.VolGeom(other_sz, mx)
assert_equal(other_vg.same_shape(vg), eq_shape)
assert_equal(other_vg.nvoxels_mask == vg.nvoxels_mask, eq_nvoxels)
nv = sz[0] * sz[1] * sz[2] # number of voxels
nt = sz[3] # number of time points
assert_equal(vg.nvoxels, nv)
# a couple of hard-coded test cases
# last two are outside the volume
linidxs = [0, 1, sz[2], sz[1] * sz[2], nv - 1, -1, nv]
subidxs = ([(0, 0, 0), (0, 0, 1), (0, 1, 0), (1, 0, 0),
(sz[0] - 1, sz[1] - 1, sz[2] - 1)] +
[(sz[0], sz[1], sz[2])] * 2)
xyzs = ([(xo, yo, zo), (xo, yo, zo + d), (xo, yo + d, zo),
(xo + d, yo, zo),
(xo + d * (sz[0] - 1), yo + d * (sz[1] - 1), zo + d *
(sz[2] - 1))] + [(np.nan, np.nan, np.nan)] * 2)
for i, linidx in enumerate(linidxs):
lin = np.asarray([linidx])
ijk = vg.lin2ijk(lin)
ijk_expected = np.reshape(np.asarray(subidxs[i]), (1, 3))
assert_array_almost_equal(ijk, ijk_expected)
xyz = vg.lin2xyz(lin)
xyz_expected = np.reshape(np.asarray(xyzs[i]), (1, 3))
assert_array_almost_equal(xyz, xyz_expected)
# check that some identities hold
ab, bc, ac = vg.lin2ijk, vg.ijk2xyz, vg.lin2xyz
ba, cb, ca = vg.ijk2lin, vg.xyz2ijk, vg.xyz2lin
identities = [
lambda x: ab(ba(x)), lambda x: bc(cb(x)), lambda x: ac(ca(x)),
lambda x: ba(ab(x)), lambda x: cb(bc(x)), lambda x: ca(ac(x)),
lambda x: bc(ab(ca(x))), lambda x: ba(cb(ac(x)))
]
# 0=lin, 1=ijk, 2=xyz
identities_input = [1, 2, 2, 0, 1, 0, 2, 0]
# voxel indices to test
linrange = [0, 1, sz[2], sz[1] * sz[2]] + range(0, nv, nv // 100)
lin = np.reshape(np.asarray(linrange), (-1, ))
ijk = vg.lin2ijk(lin)
xyz = vg.ijk2xyz(ijk)
for j, identity in enumerate(identities):
inp = identities_input[j]
x = {0: lin, 1: ijk, 2: xyz}[inp]
assert_array_equal(x, identity(x))
# check that masking works
assert_true(vg.contains_lin(lin).all())
assert_false(vg.contains_lin(-lin - 1).any())
assert_true(vg.contains_ijk(ijk).all())
assert_false(vg.contains_ijk(-ijk - 1).any())
# ensure that we have no rounding issues
deltas = [-.51, -.49, 0., .49, .51]
should_raise = [True, False, False, False, True]
for delta, r in zip(deltas, should_raise):
xyz_d = xyz + delta * d
lin_d = vg.xyz2lin(xyz_d)
if r:
assert_raises(AssertionError, assert_array_almost_equal, lin_d,
lin)
else:
assert_array_almost_equal(lin_d, lin)
# some I/O testing
img = vg.get_empty_nifti_image()
img.to_filename(temp_fn)
assert_true(os.path.exists(temp_fn))
vg2 = volgeom.from_any(img)
vg3 = volgeom.from_any(temp_fn)
assert_array_equal(vg.affine, vg2.affine)
assert_array_equal(vg.affine, vg3.affine)
assert_equal(vg.shape[:3], vg2.shape[:3], 0)
assert_equal(vg.shape[:3], vg3.shape[:3], 0)
assert_true(len('%s%r' % (vg, vg)) > 0)
def run_voxel_selection(radius, volume, white_surf, pial_surf,
source_surf=None, source_surf_nodes=None,
volume_mask=None, distance_metric='dijkstra',
start_mm=0, stop_mm=0, start_fr=0., stop_fr=1.,
nsteps=10, eta_step=1, nproc=None,
outside_node_margin=None,
results_backend=None, tmp_prefix='tmpvoxsel',
node_voxel_mapping='maximal'):
"""
Voxel selection wrapper for multiple center nodes on the surface
Parameters
----------
radius: int or float
Size of searchlight. If an integer, then it indicates the number of
voxels. If a float, then it indicates the radius of the disc
volume: Dataset or NiftiImage or volgeom.Volgeom
Volume in which voxels are selected.
white_surf: str of surf.Surface
Surface of white-matter to grey-matter boundary, or filename
of file containing such a surface.
pial_surf: str of surf.Surface
Surface of grey-matter to pial-matter boundary, or filename
of file containing such a surface.
source_surf: surf.Surface or None
Surface used to compute distance between nodes. If omitted, it is
the average of the gray and white surfaces.
source_surf_nodes: list of int or numpy array or None
Indices of nodes in source_surf that serve as searchlight center.
By default every node serves as a searchlight center.
volume_mask: None (default) or False or int
Mask from volume to apply from voxel selection results. By default
no mask is applied. If volume_mask is an integer k, then the k-th
volume from volume is used to mask the data. If volume is a Dataset
and has a property volume.fa.voxel_indices, then these indices
are used to mask the data, unless volume_mask is False or an integer.
distance_metric: str
Distance metric between nodes. 'euclidean' or 'dijksta' (default)
start_fr: float (default: 0)
Relative start position of line in gray matter, 0.=white
surface, 1.=pial surface
stop_fr: float (default: 1)
Relative stop position of line (as in see start)
start_mm: float (default: 0)
Absolute start position offset (as in start_fr)
stop_mm: float (default: 0)
Absolute start position offset (as in start_fr)
nsteps: int (default: 10)
Number of steps from white to pial surface
eta_step: int (default: 1)
After how many searchlights an estimate should be printed of the
remaining time until completion of all searchlights
nproc: int or None
Number of parallel threads. None means as many threads as the
system supports. The pprocess is required for parallel threads; if
it cannot be used, then a single thread is used.
outside_node_margin: float or None (default)
By default nodes outside the volume are skipped; using this
parameter allows for a marign. If this value is a float (possibly
np.inf), then all nodes within outside_node_margin Dijkstra
distance from any node within the volume are still assigned
associated voxels. If outside_node_margin is True, then a node is
always assigned voxels regardless of its position in the volume.
results_backend : 'native' or 'hdf5' or None (default).
Specifies the way results are provided back from a processing block
in case of nproc > 1. 'native' is pickling/unpickling of results by
pprocess, while 'hdf5' would use h5save/h5load functionality.
'hdf5' might be more time and memory efficient in some cases.
If None, then 'hdf5' if used if available, else 'native'.
tmp_prefix : str, optional
If specified -- serves as a prefix for temporary files storage
if results_backend == 'hdf5'. Thus can specify the directory to use
(trailing file path separator is not added automagically).
node_voxel_mapping: 'minimal' or 'maximal' or 'minimal_lowres'
If 'minimal' then each voxel is associated with at most one node.
If 'maximal' it is associated with as many nodes that contain the
voxel (default: 'maximal').
If 'minimal_lowres' then each voxel is associated with at most one
node, and each node that is mapped onto has a corresponding node
(at the same spatial location) in source_surf.
Returns
-------
sel: volume_mask_dict.VolumeMaskDictionary
Voxel selection results, that associates, which each node, the indices
of the surrounding voxels.
"""
vg = volgeom.from_any(volume, volume_mask)
mapper_dict = dict(maximal=volsurf.VolSurfMaximalMapping,
minimal=volsurf.VolSurfMinimalMapping,
minimal_lowres=volsurf.VolSurfMinimalLowresMapping)
mapper = mapper_dict[node_voxel_mapping]
vsm = mapper(vg, white=white_surf, pial=pial_surf,
intermediate=source_surf, nsteps=nsteps, start_fr=start_fr,
stop_fr=stop_fr, start_mm=start_mm, stop_mm=stop_mm)
sel = voxel_selection(vol_surf_mapping=vsm, radius=radius,
source_surf=source_surf,
source_surf_nodes=source_surf_nodes,
distance_metric=distance_metric,
eta_step=eta_step, nproc=nproc,
outside_node_margin=outside_node_margin,
results_backend=results_backend,
tmp_prefix=tmp_prefix)
return sel
def test_volgeom(self, temp_fn):
sz = (17, 71, 37, 73) # size of 4-D 'brain volume'
d = 2. # voxel size
xo, yo, zo = -6., -12., -20. # origin
mx = np.identity(4, np.float) * d # affine transformation matrix
mx[3, 3] = 1
mx[0, 3] = xo
mx[1, 3] = yo
mx[2, 3] = zo
vg = volgeom.VolGeom(sz, mx) # initialize volgeom
eq_shape_nvoxels = {(17, 71, 37): (True, True),
(71, 17, 37, 1): (False, True),
(17, 71, 37, 2): (True, True),
(17, 71, 37, 73): (True, True),
(2, 2, 2): (False, False)}
for other_sz, (eq_shape, eq_nvoxels) in eq_shape_nvoxels.iteritems():
other_vg = volgeom.VolGeom(other_sz, mx)
assert_equal(other_vg.same_shape(vg), eq_shape)
assert_equal(other_vg.nvoxels_mask == vg.nvoxels_mask, eq_nvoxels)
nv = sz[0] * sz[1] * sz[2] # number of voxels
nt = sz[3] # number of time points
assert_equal(vg.nvoxels, nv)
# a couple of hard-coded test cases
# last two are outside the volume
linidxs = [0, 1, sz[2], sz[1] * sz[2], nv - 1, -1 , nv]
subidxs = ([(0, 0, 0), (0, 0, 1), (0, 1, 0), (1, 0, 0),
(sz[0] - 1, sz[1] - 1, sz[2] - 1)]
+ [(sz[0], sz[1], sz[2])] * 2)
xyzs = ([(xo, yo, zo), (xo, yo, zo + d), (xo, yo + d, zo),
(xo + d, yo, zo),
(xo + d * (sz[0] - 1), yo + d * (sz[1] - 1), zo + d * (sz[2] - 1))]
+ [(np.nan, np.nan, np.nan)] * 2)
for i, linidx in enumerate(linidxs):
lin = np.asarray([linidx])
ijk = vg.lin2ijk(lin)
ijk_expected = np.reshape(np.asarray(subidxs[i]), (1, 3))
assert_array_almost_equal(ijk, ijk_expected)
xyz = vg.lin2xyz(lin)
xyz_expected = np.reshape(np.asarray(xyzs[i]), (1, 3))
assert_array_almost_equal(xyz, xyz_expected)
# check that some identities hold
ab, bc, ac = vg.lin2ijk, vg.ijk2xyz, vg.lin2xyz
ba, cb, ca = vg.ijk2lin, vg.xyz2ijk, vg.xyz2lin
identities = [lambda x:ab(ba(x)),
lambda x:bc(cb(x)),
lambda x:ac(ca(x)),
lambda x:ba(ab(x)),
lambda x:cb(bc(x)),
lambda x:ca(ac(x)),
lambda x:bc(ab(ca(x))),
lambda x:ba(cb(ac(x)))]
# 0=lin, 1=ijk, 2=xyz
identities_input = [1, 2, 2, 0, 1, 0, 2, 0]
# voxel indices to test
linrange = [0, 1, sz[2], sz[1] * sz[2]] + range(0, nv, nv // 100)
lin = np.reshape(np.asarray(linrange), (-1,))
ijk = vg.lin2ijk(lin)
xyz = vg.ijk2xyz(ijk)
for j, identity in enumerate(identities):
inp = identities_input[j]
x = {0: lin,
1: ijk,
2: xyz}[inp]
assert_array_equal(x, identity(x))
# check that masking works
assert_true(vg.contains_lin(lin).all())
assert_false(vg.contains_lin(-lin - 1).any())
assert_true(vg.contains_ijk(ijk).all())
assert_false(vg.contains_ijk(-ijk - 1).any())
# ensure that we have no rounding issues
deltas = [-.51, -.49, 0., .49, .51]
should_raise = [True, False, False, False, True]
for delta, r in zip(deltas, should_raise):
xyz_d = xyz + delta * d
lin_d = vg.xyz2lin(xyz_d)
if r:
assert_raises(AssertionError,
assert_array_almost_equal, lin_d, lin)
else:
assert_array_almost_equal(lin_d, lin)
# some I/O testing
img = vg.get_empty_nifti_image()
img.to_filename(temp_fn)
assert_true(os.path.exists(temp_fn))
vg2 = volgeom.from_any(img)
vg3 = volgeom.from_any(temp_fn)
assert_array_equal(vg.affine, vg2.affine)
assert_array_equal(vg.affine, vg3.affine)
assert_equal(vg.shape[:3], vg2.shape[:3], 0)
assert_equal(vg.shape[:3], vg3.shape[:3], 0)
assert_true(len('%s%r' % (vg, vg)) > 0)
def test_voxel_selection_alternative_calls(self):
# Tests a multitude of different searchlight calls
# that all should yield exactly the same results.
#
# Calls differ by whether the arguments are filenames
# or data objects, whether values are specified explicityly
# or set to the default implicitly (using None).
# and by different calls to run the voxel selection.
#
# This method does not test for mask functionality.
# define the volume
vol_shape = (10, 10, 10, 3)
vol_affine = np.identity(4)
vol_affine[0, 0] = vol_affine[1, 1] = vol_affine[2, 2] = 5
# four versions: array, nifti image, file name, fmri dataset
volarr = np.ones(vol_shape)
volimg = nb.Nifti1Image(volarr, vol_affine)
# There is a detected problem with elderly NumPy's (e.g. 1.6.1
# on precise on travis) leading to segfaults while operating
# on memmapped volumes being forwarded to pprocess.
# Thus just making it compressed volume for those cases
suf = '.gz' \
if externals.exists('pprocess') and externals.versions['numpy'] < '1.6.2' \
else ''
fd, volfn = tempfile.mkstemp('vol.nii' + suf, 'test')
os.close(fd)
volimg.to_filename(volfn)
volds = fmri_dataset(volfn)
fd, volfngz = tempfile.mkstemp('vol.nii.gz', 'test')
os.close(fd)
volimg.to_filename(volfngz)
voldsgz = fmri_dataset(volfngz)
# make the surfaces
sphere_density = 10
# two versions: Surface and file name
outer = surf.generate_sphere(sphere_density) * 25. + 15
inner = surf.generate_sphere(sphere_density) * 20. + 15
intermediate = inner * .5 + outer * .5
nv = outer.nvertices
fd, outerfn = tempfile.mkstemp('outer.asc', 'test')
os.close(fd)
fd, innerfn = tempfile.mkstemp('inner.asc', 'test')
os.close(fd)
fd, intermediatefn = tempfile.mkstemp('intermediate.asc', 'test')
os.close(fd)
for s, fn in zip([outer, inner, intermediate],
[outerfn, innerfn, intermediatefn]):
surf.write(fn, s, overwrite=True)
# searchlight radius (in mm)
radius = 10.
# dataset used to run searchlight on
ds = fmri_dataset(volfn)
# simple voxel counter (run for each searchlight position)
m = _Voxel_Count_Measure()
# number of voxels expected in each searchlight
r_expected = np.array([[
18, 9, 10, 9, 9, 9, 9, 10, 9, 9, 9, 9, 11, 11, 11, 11, 10, 10, 10,
9, 10, 11, 9, 10, 10, 8, 7, 8, 8, 8, 9, 10, 12, 12, 11, 7, 7, 8, 5,
9, 11, 11, 12, 12, 9, 5, 8, 7, 7, 12, 12, 13, 12, 12, 7, 7, 8, 5,
9, 12, 12, 13, 11, 9, 5, 8, 7, 7, 11, 12, 12, 11, 12, 10, 10, 11,
9, 11, 12, 12, 12, 12, 16, 13, 16, 16, 16, 17, 15, 17, 17, 17, 16,
16, 16, 18, 16, 16, 16, 16, 18, 16
]])
params = dict(intermediate_=(intermediate, intermediatefn, None),
center_nodes_=(None, range(nv)),
volume_=(volimg, volfn, volds, volfngz, voldsgz),
surf_src_=('filename', 'surf'),
volume_mask_=(None, True, 0, 2),
call_method_=("qe", "rvs", "gam"))
combis = _cartprod(params) # compute all possible combinations
combistep = 17 #173
# some fine prime number to speed things up
# if this value becomes too big then not all
# cases are covered
# the unit test tests itself whether all values
# occur at least once
tested_params = dict()
def val2str(x):
return '%r:%r' % (type(x), x)
for i in xrange(0, len(combis), combistep):
combi = combis[i]
intermediate_ = combi['intermediate_']
center_nodes_ = combi['center_nodes_']
volume_ = combi['volume_']
surf_src_ = combi['surf_src_']
volume_mask_ = combi['volume_mask_']
call_method_ = combi['call_method_']
# keep track of which values were used -
# so that this unit test tests itself
for k in combi.keys():
if not k in tested_params:
tested_params[k] = set()
tested_params[k].add(val2str(combi[k]))
if surf_src_ == 'filename':
s_i, s_m, s_o = inner, intermediate, outer
elif surf_src_ == 'surf':
s_i, s_m, s_o = innerfn, intermediatefn, outerfn
else:
raise ValueError('this should not happen')
if call_method_ == "qe":
# use the fancy query engine wrapper
qe = disc_surface_queryengine(radius,
volume_,
s_i,
s_o,
s_m,
source_surf_nodes=center_nodes_,
volume_mask=volume_mask_)
sl = Searchlight(m, queryengine=qe)
r = sl(ds).samples
elif call_method_ == 'rvs':
# use query-engine but build the
# ingredients by hand
vg = volgeom.from_any(volume_, volume_mask_)
vs = volsurf.VolSurfMaximalMapping(vg, s_i, s_o)
sel = surf_voxel_selection.voxel_selection(
vs,
radius,
source_surf=s_m,
source_surf_nodes=center_nodes_)
qe = SurfaceVerticesQueryEngine(sel)
sl = Searchlight(m, queryengine=qe)
r = sl(ds).samples
elif call_method_ == 'gam':
# build everything from the ground up
vg = volgeom.from_any(volume_, volume_mask_)
vs = volsurf.VolSurfMaximalMapping(vg, s_i, s_o)
sel = surf_voxel_selection.voxel_selection(
vs,
radius,
source_surf=s_m,
source_surf_nodes=center_nodes_)
mp = sel
ks = sel.keys()
nk = len(ks)
r = np.zeros((1, nk))
for i, k in enumerate(ks):
r[0, i] = len(mp[k])
# check if result is as expected
assert_array_equal(r_expected, r)
# clean up
all_fns = [volfn, volfngz, outerfn, innerfn, intermediatefn]
map(os.remove, all_fns)
for k, vs in params.iteritems():
if not k in tested_params:
raise ValueError("Missing key: %r" % k)
for v in vs:
vstr = val2str(v)
if not vstr in tested_params[k]:
raise ValueError("Missing value %r for %s" %
(tested_params[k], k))
def test_voxel_selection(self):
'''Compare surface and volume based searchlight'''
'''
Tests to see whether results are identical for surface-based
searchlight (just one plane; Euclidean distnace) and volume-based
searchlight.
Note that the current value is a float; if it were int, it would
specify the number of voxels in each searchlight'''
radius = 10.
'''Define input filenames'''
epi_fn = os.path.join(pymvpa_dataroot, 'bold.nii.gz')
maskfn = os.path.join(pymvpa_dataroot, 'mask.nii.gz')
'''
Use the EPI datafile to define a surface.
The surface has as many nodes as there are voxels
and is parallel to the volume 'slice'
'''
vg = volgeom.from_any(maskfn, mask_volume=True)
aff = vg.affine
nx, ny, nz = vg.shape[:3]
'''Plane goes in x and y direction, so we take these vectors
from the affine transformation matrix of the volume'''
plane = surf.generate_plane(aff[:3, 3], aff[:3, 0], aff[:3, 1], nx, ny)
'''
Simulate pial and white matter as just above and below
the central plane
'''
normal_vec = aff[:3, 2]
outer = plane + normal_vec
inner = plane + -normal_vec
'''
Combine volume and surface information
'''
vsm = volsurf.VolSurfMaximalMapping(vg, outer, inner)
'''
Run voxel selection with specified radius (in mm), using
Euclidean distance measure
'''
surf_voxsel = surf_voxel_selection.voxel_selection(vsm,
radius,
distance_metric='e')
'''Define the measure'''
# run_slow=True would give an actual cross-validation with meaningful
# accuracies. Because this is a unit-test only the number of voxels
# in each searchlight is tested.
run_slow = False
if run_slow:
meas = CrossValidation(GNB(),
OddEvenPartitioner(),
errorfx=lambda p, t: np.mean(p == t))
postproc = mean_sample
else:
meas = _Voxel_Count_Measure()
postproc = lambda x: x
'''
Surface analysis: define the query engine, cross validation,
and searchlight
'''
surf_qe = SurfaceVerticesQueryEngine(surf_voxsel)
surf_sl = Searchlight(meas, queryengine=surf_qe, postproc=postproc)
'''
new (Sep 2012): also test 'simple' queryengine wrapper function
'''
surf_qe2 = disc_surface_queryengine(radius,
maskfn,
inner,
outer,
plane,
volume_mask=True,
distance_metric='euclidean')
surf_sl2 = Searchlight(meas, queryengine=surf_qe2, postproc=postproc)
'''
Same for the volume analysis
'''
element_sizes = tuple(map(abs, (aff[0, 0], aff[1, 1], aff[2, 2])))
sph = Sphere(radius, element_sizes=element_sizes)
kwa = {'voxel_indices': sph}
vol_qe = IndexQueryEngine(**kwa)
vol_sl = Searchlight(meas, queryengine=vol_qe, postproc=postproc)
'''The following steps are similar to start_easy.py'''
attr = SampleAttributes(
os.path.join(pymvpa_dataroot, 'attributes_literal.txt'))
mask = surf_voxsel.get_mask()
dataset = fmri_dataset(samples=os.path.join(pymvpa_dataroot,
'bold.nii.gz'),
targets=attr.targets,
chunks=attr.chunks,
mask=mask)
if run_slow:
# do chunkswise linear detrending on dataset
poly_detrend(dataset, polyord=1, chunks_attr='chunks')
# zscore dataset relative to baseline ('rest') mean
zscore(dataset,
chunks_attr='chunks',
param_est=('targets', ['rest']))
# select class face and house for this demo analysis
# would work with full datasets (just a little slower)
dataset = dataset[np.array(
[l in ['face', 'house'] for l in dataset.sa.targets],
dtype='bool')]
'''Apply searchlight to datasets'''
surf_dset = surf_sl(dataset)
surf_dset2 = surf_sl2(dataset)
vol_dset = vol_sl(dataset)
surf_data = surf_dset.samples
surf_data2 = surf_dset2.samples
vol_data = vol_dset.samples
assert_array_equal(surf_data, surf_data2)
assert_array_equal(surf_data, vol_data)
def test_voxel_selection_alternative_calls(self):
# Tests a multitude of different searchlight calls
# that all should yield exactly the same results.
#
# Calls differ by whether the arguments are filenames
# or data objects, whether values are specified explicityly
# or set to the default implicitly (using None).
# and by different calls to run the voxel selection.
#
# This method does not test for mask functionality.
# define the volume
vol_shape = (10, 10, 10, 3)
vol_affine = np.identity(4)
vol_affine[0, 0] = vol_affine[1, 1] = vol_affine[2, 2] = 5
# four versions: array, nifti image, file name, fmri dataset
volarr = np.ones(vol_shape)
volimg = nb.Nifti1Image(volarr, vol_affine)
# There is a detected problem with elderly NumPy's (e.g. 1.6.1
# on precise on travis) leading to segfaults while operating
# on memmapped volumes being forwarded to pprocess.
# Thus just making it compressed volume for those cases
suf = ".gz" if (externals.exists("pprocess") and externals.versions["numpy"] < "1.6.2") else ""
fd, volfn = tempfile.mkstemp("vol.nii" + suf, "test")
os.close(fd)
volimg.to_filename(volfn)
volds = fmri_dataset(volfn)
fd, volfngz = tempfile.mkstemp("vol.nii.gz", "test")
os.close(fd)
volimg.to_filename(volfngz)
voldsgz = fmri_dataset(volfngz)
# make the surfaces
sphere_density = 10
# two versions: Surface and file name
outer = surf.generate_sphere(sphere_density) * 25.0 + 15
inner = surf.generate_sphere(sphere_density) * 20.0 + 15
intermediate = inner * 0.5 + outer * 0.5
nv = outer.nvertices
fd, outerfn = tempfile.mkstemp("outer.asc", "test")
os.close(fd)
fd, innerfn = tempfile.mkstemp("inner.asc", "test")
os.close(fd)
fd, intermediatefn = tempfile.mkstemp("intermediate.asc", "test")
os.close(fd)
for s, fn in zip([outer, inner, intermediate], [outerfn, innerfn, intermediatefn]):
surf.write(fn, s, overwrite=True)
# searchlight radius (in mm)
radius = 10.0
# dataset used to run searchlight on
ds = fmri_dataset(volfn)
# simple voxel counter (run for each searchlight position)
m = _Voxel_Count_Measure()
# number of voxels expected in each searchlight
r_expected = np.array(
[
[
18,
9,
10,
9,
9,
9,
9,
10,
9,
9,
9,
9,
11,
11,
11,
11,
10,
10,
10,
9,
10,
11,
9,
10,
10,
8,
7,
8,
8,
8,
9,
10,
12,
12,
11,
7,
7,
8,
5,
9,
11,
11,
12,
12,
9,
5,
8,
7,
7,
12,
12,
13,
12,
12,
7,
7,
8,
5,
9,
12,
12,
13,
11,
9,
5,
8,
7,
7,
11,
12,
12,
11,
12,
10,
10,
11,
9,
11,
12,
12,
12,
12,
16,
13,
16,
16,
16,
17,
15,
17,
17,
17,
16,
16,
16,
18,
16,
16,
16,
16,
18,
16,
]
]
)
params = dict(
intermediate_=(intermediate, intermediatefn, None),
center_nodes_=(None, range(nv)),
volume_=(volimg, volfn, volds, volfngz, voldsgz),
surf_src_=("filename", "surf"),
volume_mask_=(None, True, 0, 2),
call_method_=("qe", "rvs", "gam"),
)
combis = _cartprod(params) # compute all possible combinations
combistep = 17 # 173
# some fine prime number to speed things up
# if this value becomes too big then not all
# cases are covered
# the unit test tests itself whether all values
# occur at least once
tested_params = dict()
def val2str(x):
return "%r:%r" % (type(x), x)
for i in xrange(0, len(combis), combistep):
combi = combis[i]
intermediate_ = combi["intermediate_"]
center_nodes_ = combi["center_nodes_"]
volume_ = combi["volume_"]
surf_src_ = combi["surf_src_"]
volume_mask_ = combi["volume_mask_"]
call_method_ = combi["call_method_"]
# keep track of which values were used -
# so that this unit test tests itself
for k in combi.keys():
if not k in tested_params:
tested_params[k] = set()
tested_params[k].add(val2str(combi[k]))
if surf_src_ == "filename":
s_i, s_m, s_o = inner, intermediate, outer
elif surf_src_ == "surf":
s_i, s_m, s_o = innerfn, intermediatefn, outerfn
else:
raise ValueError("this should not happen")
if call_method_ == "qe":
# use the fancy query engine wrapper
qe = disc_surface_queryengine(
radius, volume_, s_i, s_o, s_m, source_surf_nodes=center_nodes_, volume_mask=volume_mask_
)
sl = Searchlight(m, queryengine=qe)
r = sl(ds).samples
elif call_method_ == "rvs":
# use query-engine but build the
# ingredients by hand
vg = volgeom.from_any(volume_, volume_mask_)
vs = volsurf.VolSurfMaximalMapping(vg, s_i, s_o)
sel = surf_voxel_selection.voxel_selection(vs, radius, source_surf=s_m, source_surf_nodes=center_nodes_)
qe = SurfaceVerticesQueryEngine(sel)
sl = Searchlight(m, queryengine=qe)
r = sl(ds).samples
elif call_method_ == "gam":
# build everything from the ground up
vg = volgeom.from_any(volume_, volume_mask_)
vs = volsurf.VolSurfMaximalMapping(vg, s_i, s_o)
sel = surf_voxel_selection.voxel_selection(vs, radius, source_surf=s_m, source_surf_nodes=center_nodes_)
mp = sel
ks = sel.keys()
nk = len(ks)
r = np.zeros((1, nk))
for i, k in enumerate(ks):
r[0, i] = len(mp[k])
# check if result is as expected
assert_array_equal(r_expected, r)
# clean up
all_fns = [volfn, volfngz, outerfn, innerfn, intermediatefn]
map(os.remove, all_fns)
for k, vs in params.iteritems():
if not k in tested_params:
raise ValueError("Missing key: %r" % k)
for v in vs:
vstr = val2str(v)
if not vstr in tested_params[k]:
raise ValueError("Missing value %r for %s" % (tested_params[k], k))
def test_voxel_selection(self):
"""Compare surface and volume based searchlight"""
"""
Tests to see whether results are identical for surface-based
searchlight (just one plane; Euclidean distnace) and volume-based
searchlight.
Note that the current value is a float; if it were int, it would
specify the number of voxels in each searchlight"""
radius = 10.0
"""Define input filenames"""
epi_fn = pathjoin(pymvpa_dataroot, "bold.nii.gz")
maskfn = pathjoin(pymvpa_dataroot, "mask.nii.gz")
"""
Use the EPI datafile to define a surface.
The surface has as many nodes as there are voxels
and is parallel to the volume 'slice'
"""
vg = volgeom.from_any(maskfn, mask_volume=True)
aff = vg.affine
nx, ny, nz = vg.shape[:3]
"""Plane goes in x and y direction, so we take these vectors
from the affine transformation matrix of the volume"""
plane = surf.generate_plane(aff[:3, 3], aff[:3, 0], aff[:3, 1], nx, ny)
"""
Simulate pial and white matter as just above and below
the central plane
"""
normal_vec = aff[:3, 2]
outer = plane + normal_vec
inner = plane + -normal_vec
"""
Combine volume and surface information
"""
vsm = volsurf.VolSurfMaximalMapping(vg, outer, inner)
"""
Run voxel selection with specified radius (in mm), using
Euclidean distance measure
"""
surf_voxsel = surf_voxel_selection.voxel_selection(vsm, radius, distance_metric="e")
"""Define the measure"""
# run_slow=True would give an actual cross-validation with meaningful
# accuracies. Because this is a unit-test only the number of voxels
# in each searchlight is tested.
run_slow = False
if run_slow:
meas = CrossValidation(GNB(), OddEvenPartitioner(), errorfx=lambda p, t: np.mean(p == t))
postproc = mean_sample
else:
meas = _Voxel_Count_Measure()
postproc = lambda x: x
"""
Surface analysis: define the query engine, cross validation,
and searchlight
"""
surf_qe = SurfaceVerticesQueryEngine(surf_voxsel)
surf_sl = Searchlight(meas, queryengine=surf_qe, postproc=postproc)
"""
new (Sep 2012): also test 'simple' queryengine wrapper function
"""
surf_qe2 = disc_surface_queryengine(
radius, maskfn, inner, outer, plane, volume_mask=True, distance_metric="euclidean"
)
surf_sl2 = Searchlight(meas, queryengine=surf_qe2, postproc=postproc)
"""
Same for the volume analysis
"""
element_sizes = tuple(map(abs, (aff[0, 0], aff[1, 1], aff[2, 2])))
sph = Sphere(radius, element_sizes=element_sizes)
kwa = {"voxel_indices": sph}
vol_qe = IndexQueryEngine(**kwa)
vol_sl = Searchlight(meas, queryengine=vol_qe, postproc=postproc)
"""The following steps are similar to start_easy.py"""
attr = SampleAttributes(pathjoin(pymvpa_dataroot, "attributes_literal.txt"))
mask = surf_voxsel.get_mask()
dataset = fmri_dataset(
samples=pathjoin(pymvpa_dataroot, "bold.nii.gz"), targets=attr.targets, chunks=attr.chunks, mask=mask
)
if run_slow:
# do chunkswise linear detrending on dataset
poly_detrend(dataset, polyord=1, chunks_attr="chunks")
# zscore dataset relative to baseline ('rest') mean
zscore(dataset, chunks_attr="chunks", param_est=("targets", ["rest"]))
# select class face and house for this demo analysis
# would work with full datasets (just a little slower)
dataset = dataset[np.array([l in ["face", "house"] for l in dataset.sa.targets], dtype="bool")]
"""Apply searchlight to datasets"""
surf_dset = surf_sl(dataset)
surf_dset2 = surf_sl2(dataset)
vol_dset = vol_sl(dataset)
surf_data = surf_dset.samples
surf_data2 = surf_dset2.samples
vol_data = vol_dset.samples
assert_array_equal(surf_data, surf_data2)
assert_array_equal(surf_data, vol_data)
