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, 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 flat_surface2xy(surface):
'''Returns a tuple with x and y coordinates of a flat surface
Parameters
----------
surface: Surface
flat surface
Returns
-------
x: np.ndarray
x coordinates
y: np.ndarray
y coordinates
Notes
-----
If the surface is not flat (any z coordinate is non-zero), an exception
is raised.
'''
s = surf.from_any(surface)
v = s.vertices
if any(v[:, 2] != 0):
raise ValueError("Expected a flat surface with z=0 for all nodes")
x = v[:, 0]
y = v[:, 1]
return x, y
def build_connectivity_matrix(source_surface_fn, nodes=None):
"""
Given the brain surface (intermediate) saved in `source_surface_fn` and
a subset of nodes, returns a sparse adjacency matrix to be used
by the Ward algorithm as implemented in scikit learn.
"""
surface = surf.from_any(source_surface_fn)
triangles = surface.faces
nvertices = surface.nvertices
e = triangles2edges(triangles)
conn = np.zeros((nvertices, nvertices), dtype=bool)
# set adjacent nodes to 1
for idx in e:
conn[idx[0], idx[1]] = 1
conn[idx[1], idx[0]] = 1
# set diagonal to 1 -- useless but for completeness
conn[np.diag_indices_from(conn)] = 1
# get only nodes we have
if nodes is not None:
connectivity2 = conn[nodes, :][:, nodes]
else:
connectivity2 = conn
# check it's really symmetric
for x in xrange(len(connectivity2)):
assert(np.array_equal(connectivity2[x, :], connectivity2[:, x]))
# make it a sparse matrix for ward
connectivity2_sparse = sparse.csr_matrix(connectivity2)
return connectivity2_sparse
def flat_surface2xy(surface):
'''Returns a tuple with x and y coordinates of a flat surface
Parameters
----------
surface: Surface
flat surface
Returns
-------
x: np.ndarray
x coordinates
y: np.ndarray
y coordinates
Notes
-----
If the surface is not flat (any z coordinate is non-zero), an exception
is raised.
'''
s = surf.from_any(surface)
v = s.vertices
if any(v[:, 2] != 0):
raise ValueError("Expected a flat surface with z=0 for all nodes")
x = v[:, 0]
y = v[:, 1]
return x, y
def build_connectivity_matrix(source_surface_fn, nodes=None):
"""
Given the brain surface (intermediate) saved in `source_surface_fn` and
a subset of nodes, returns a sparse adjacency matrix to be used
by the Ward algorithm as implemented in scikit learn.
"""
surface = surf.from_any(source_surface_fn)
triangles = surface.faces
nvertices = surface.nvertices
e = triangles2edges(triangles)
conn = np.zeros((nvertices, nvertices), dtype=bool)
# set adjacent nodes to 1
for idx in e:
conn[idx[0], idx[1]] = 1
conn[idx[1], idx[0]] = 1
# set diagonal to 1 -- useless but for completeness
conn[np.diag_indices_from(conn)] = 1
# get only nodes we have
if nodes is not None:
connectivity2 = conn[nodes, :][:, nodes]
else:
connectivity2 = conn
# check it's really symmetric
for x in xrange(len(connectivity2)):
assert (np.array_equal(connectivity2[x, :], connectivity2[:, x]))
# make it a sparse matrix for ward
connectivity2_sparse = sparse.csr_matrix(connectivity2)
return connectivity2_sparse
def __init__(self, surface, curvature=None, min_nsteps=500,
range_='2_98%', threshold=None, color_map=None,
max_deformation=.5):
'''
Parameters
----------
surface: surf.Surface
a flat surface
curvature: str or np.ndarray
(Filename of) data representing curvature at each node.
min_steps: int
Minimal side of output plots in pixel
range_: str or float or tuple
If a tuple (a,b), then this tuple is returned.
If a float a, then (-a,a) is returned.
"R(a)", where R(a) denotes the string representation
of float a, is equivalent to range_=a.
"R(a)_R(b)" is equivalent to range_=(a,b).
"R(a)_R(b)%" indicates that the a-th and b-th
percentile of xs is taken to define the range.
"R(a)%" is equivalent to "R(a)_R(100-a)%"
threshold: str or float or tuple
Indicates which values will be shown. Syntax as in range_
color_map: str
colormap to use
max_deformation: float
maximum deformation to make a non-flat surface flat.
The normals of each face must have a dot product with the average
face normal that is not less than (1-max_deformation); otherwise
an exception is raised. The rationale for this option is that
certain surfaces may be almost flat, and projecting the vertices
on a truly flat surface should be fine. On the other hand, surfaces
that are definitly not flat (such as full cortical surface models)
should cause an error to be raised when it is attempted to flatten
them
'''
self._surface = surf.from_any(surface)
if curvature is None:
self._curvature = None
else:
self._curvature = curvature_from_any(curvature)
if self._surface.nvertices != len(self._curvature):
raise ValueError("Surface has %d vertices, but curvature "
"has %d values" %
(self._surface.nvertices,
self._curvature.size))
self._min_nsteps = min_nsteps
self._range_ = range_
self._threshold = threshold
self._color_map = color_map
self._max_deformation = max_deformation
self._grid_def = None
self._underlay = None
def flat_surface2xy(surface, max_deformation):
'''Returns a tuple with x and y coordinates of a flat surface
Parameters
----------
surface: Surface
flat surface
max_deformation: float
maximum deformation to make a non-flat surface flat.
The normals of each face must have a dot product with the average
face normal that is not less than (1-max_deformation); otherwise
an exception is raised. The rationale for this option is that certain
surfaces may be almost flat, and those can be made flat without
problem; but surfaces that are not flat, such as inflated surfaces,
should not be flattable.
Returns
-------
x: np.ndarray
x coordinates
y: np.ndarray
y coordinates
Notes
-----
If the surface is not flat (any z coordinate is non-zero), an exception
is raised.
'''
s = surf.from_any(surface)
face_normals = s.face_normals
msk = np.all(np.logical_not(np.isnan(face_normals)), 1)
avg_face_normal = s.nanmean_face_normal
deformations = abs(
1 - abs(np.dot(avg_face_normal[np.newaxis], face_normals[msk].T)))
too_deformed = np.nonzero(deformations > max_deformation)[0]
if len(too_deformed) > 0:
raise ValueError('Surface is not sufficiently flat with '
'max_deformation=%.3f' % max_deformation)
# find rotation so that surface is more or less orthogonal to
# the unit vector (0,0,1)
v = s.vertices
z_axis = np.asarray([0, 0, 1.])
r = vector_alignment_find_rotation(avg_face_normal, z_axis)
# apply rotation
v_rotated = r.dot(v.T)
# discard z-coordinate
x = v_rotated[0]
y = v_rotated[1]
return x, y
def flat_surface2xy(surface, max_deformation):
'''Returns a tuple with x and y coordinates of a flat surface
Parameters
----------
surface: Surface
flat surface
max_deformation: float
maximum deformation to make a non-flat surface flat.
The normals of each face must have a dot product with the average
face normal that is not less than (1-max_deformation); otherwise
an exception is raised. The rationale for this option is that certain
surfaces may be almost flat, and those can be made flat without
problem; but surfaces that are not flat, such as inflated surfaces,
should not be flattable.
Returns
-------
x: np.ndarray
x coordinates
y: np.ndarray
y coordinates
Notes
-----
If the surface is not flat (any z coordinate is non-zero), an exception
is raised.
'''
s = surf.from_any(surface)
face_normals = s.face_normals
msk = np.all(np.logical_not(np.isnan(face_normals)), 1)
avg_face_normal = s.nanmean_face_normal
deformations = abs(
1 - abs(np.dot(avg_face_normal[np.newaxis], face_normals[msk].T)))
too_deformed = np.nonzero(deformations > max_deformation)[0]
if len(too_deformed) > 0:
raise ValueError('Surface is not sufficiently flat with '
'max_deformation=%.3f' % max_deformation)
# find rotation so that surface is more or less orthogonal to
# the unit vector (0,0,1)
v = s.vertices
z_axis = np.asarray([0, 0, 1.])
r = vector_alignment_find_rotation(avg_face_normal, z_axis)
# apply rotation
v_rotated = r.dot(v.T)
# discard z-coordinate
x = v_rotated[0]
y = v_rotated[1]
return x, y
def get_node2voxels_mapping(self):
n2v = super(VolSurfMinimalLowresMapping, self).\
get_node2voxels_mapping()
# set low and high res intermediate surfaces
lowres = surf.from_any(self._intermediate)
highres = (self.pial_surface * .5) + \
(self.white_surface * .5)
high2high_in_low = lowres.vonoroi_map_to_high_resolution_surf(highres)
n_in_low2v = dict()
ds = []
for n, v2pos in n2v.iteritems():
(n_in_low, d) = high2high_in_low[n]
if v2pos is None:
continue
ds.append(d)
if not n_in_low in n_in_low2v:
# not there - just set the dictionary
n_in_low2v[n_in_low] = v2pos
else:
# is there - see if it is none
cur = n_in_low2v[n_in_low]
if cur is None and not v2pos is None:
# also overwrite (v2pos can also be None, that's fine)
n_in_low2v[n_in_low] = v2pos
elif v2pos is not None:
# update
for v, pos in v2pos.iteritems():
# minimal mapping, so voxel should not be there already
assert(not v in n_in_low2v[n_in_low])
cur[v] = pos
if __debug__ and 'SVS' in debug.active:
ds = np.asarray(ds)
mu = np.mean(ds)
n = len(ds)
s = np.std(ds)
debug('SVS', 'Reassigned %d nodes by moving %.2f +/- %.2f to low-res',
(n, mu, s))
return n_in_low2v
def get_node2voxels_mapping(self):
n2v = super(VolSurfMinimalLowresMapping, self).\
get_node2voxels_mapping()
# set low and high res intermediate surfaces
lowres = surf.from_any(self._intermediate)
highres = (self.pial_surface * .5) + \
(self.white_surface * .5)
high2high_in_low = lowres.vonoroi_map_to_high_resolution_surf(highres)
n_in_low2v = dict()
ds = []
for n, v2pos in n2v.iteritems():
(n_in_low, d) = high2high_in_low[n]
if v2pos is None:
continue
ds.append(d)
if not n_in_low in n_in_low2v:
# not there - just set the dictionary
n_in_low2v[n_in_low] = v2pos
else:
# is there - see if it is none
cur = n_in_low2v[n_in_low]
if cur is None and not v2pos is None:
# also overwrite (v2pos can also be None, that's fine)
n_in_low2v[n_in_low] = v2pos
elif v2pos is not None:
# update
for v, pos in v2pos.iteritems():
# minimal mapping, so voxel should not be there already
assert(not v in n_in_low2v[n_in_low])
cur[v] = pos
if __debug__ and 'SVS' in debug.active:
ds = np.asarray(ds)
mu = np.mean(ds)
n = len(ds)
s = np.std(ds)
debug('SVS', 'Reassigned %d nodes by moving %.2f +/- %.2f to low-res',
(n, mu, s))
return n_in_low2v
def __init__(self,
surface,
curvature=None,
min_nsteps=500,
range_='2_98%',
threshold=None,
color_map=None):
'''
Parameters
----------
surface: surf.Surface
a flat surface
curvature: str or np.ndarray
(Filename of) data representing curvature at each node.
min_steps: int
Minimal side of output plots in pixel
range_: str or float or tuple
If a tuple (a,b), then this tuple is returned.
If a float a, then (-a,a) is returned.
"R(a)", where R(a) denotes the string representation
of float a, is equivalent to range_=a.
"R(a)_R(b)" is equivalent to range_=(a,b).
"R(a)_R(b)%" indicates that the a-th and b-th
percentile of xs is taken to define the range.
"R(a)%" is equivalent to "R(a)_R(100-a)%"
threshold: str or float or tuple
Indicates which values will be shown. Syntax as in range_
color_map: str
colormap to use
'''
self._surface = surf.from_any(surface)
if curvature is None:
self._curvature = None
else:
self._curvature = curvature_from_any(curvature)
if self._surface.nvertices != self._curvature.size:
raise ValueError(
"Surface has %d vertices, but curvature %d" %
(self._surface.nvertices, self._curvature.size))
self._min_nsteps = min_nsteps
self._range_ = range_
self._threshold = threshold
self._color_map = color_map
self._grid_def = None
self._underlay = None
def get_surface(self, *args):
'''
Wizard-like function to get a surface
Parameters
----------
*args: list of str
parts of the surface file name or description, such as
'pial' (for pial surface), 'wm' (for white matter), or
'lh' (for left hemisphere').
Returns
-------
surf: surf.Surface
'''
return surf.from_any(self.get_surface_file(*args))
def get_surface(self, *args):
'''
Wizard-like function to get a surface
Parameters
----------
*args: list of str
parts of the surface file name or description, such as
'pial' (for pial surface), 'wm' (for white matter), or
'lh' (for left hemisphere').
Returns
-------
surf: surf.Surface
'''
return surf.from_any(self.get_surface_file(*args))
def __init__(self, surface, curvature=None, min_nsteps=500,
range_='2_98%', threshold=None, color_map=None):
'''
Parameters
----------
surface: surf.Surface
a flat surface
curvature: str or np.ndarray
(Filename of) data representing curvature at each node.
min_steps: int
Minimal side of output plots in pixel
range_: str or float or tuple
If a tuple (a,b), then this tuple is returned.
If a float a, then (-a,a) is returned.
"R(a)", where R(a) denotes the string representation
of float a, is equivalent to range_=a.
"R(a)_R(b)" is equivalent to range_=(a,b).
"R(a)_R(b)%" indicates that the a-th and b-th
percentile of xs is taken to define the range.
"R(a)%" is equivalent to "R(a)_R(100-a)%"
threshold: str or float or tuple
Indicates which values will be shown. Syntax as in range_
color_map: str
colormap to use
'''
self._surface = surf.from_any(surface)
if curvature is None:
self._curvature = None
else:
self._curvature = curvature_from_any(curvature)
if self._surface.nvertices != self._curvature.size:
raise ValueError("Surface has %d vertices, but curvature %d" %
(self._surface.nvertices, self._curvature.size))
self._min_nsteps = min_nsteps
self._range_ = range_
self._threshold = threshold
self._color_map = color_map
self._grid_def = None
self._underlay = None
def voxel_selection(vol_surf_mapping,
radius,
source_surf=None,
source_surf_nodes=None,
distance_metric='dijkstra',
eta_step=10,
nproc=None,
outside_node_margin=None,
results_backend=None,
tmp_prefix='tmpvoxsel'):
"""
Voxel selection for multiple center nodes on the surface
Parameters
----------
vol_surf_mapping: volsurf.VolSurfMapping
Contains gray and white matter surface, and volume geometry
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
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.
distance_metric: str
Distance metric between nodes. 'euclidean' or 'dijksta' (default)
eta_step: int
Report progress every eta_step (default: 10).
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 True 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).
Returns
-------
sel: volume_mask_dict.VolumeMaskDictionary
Voxel selection results, that associates, which each node, the indices
of the surrounding voxels.
"""
# construct the intermediate surface, which is used
# to measure distances
intermediate_surf = (vol_surf_mapping.pial_surface * .5) + \
(vol_surf_mapping.white_surface * .5)
if source_surf is None:
source_surf = intermediate_surf
else:
source_surf = surf.from_any(source_surf)
if _debug():
debug(
'SVS', "Generated high-res intermediate surface: "
"%d nodes, %d faces" %
(intermediate_surf.nvertices, intermediate_surf.nfaces))
debug(
'SVS', "Mapping source to high-res surface:"
" %d nodes, %d faces" %
(source_surf.nvertices, source_surf.nfaces))
if distance_metric[0].lower() == 'e' and outside_node_margin:
# euclidean distance: identity mapping
# this is *slow*
n = source_surf.nvertices
xyz = source_surf.vertices
src2intermediate = dict((i, tuple(xyz[i])) for i in xrange(n))
else:
# find a mapping from nodes in source_surf to those in
# intermediate surface
src2intermediate = source_surf.map_to_high_resolution_surf(\
intermediate_surf)
# if no sources are given, then visit all ndoes
if source_surf_nodes is None:
source_surf_nodes = np.arange(source_surf.nvertices)
n = len(source_surf_nodes)
if _debug():
debug('SVS', "Performing surface-based voxel selection"
" for %d centers" % n)
# visit in random order, for for better ETA estimate
visitorder = list(np.random.permutation(len(source_surf_nodes)))
# construct mapping from nodes to enclosing voxels
n2v = vol_surf_mapping.get_node2voxels_mapping()
if __debug__:
debug('SVS', "Generated mapping from nodes" " to intersecting voxels")
# build voxel selector
voxel_selector = VoxelSelector(radius,
intermediate_surf,
n2v,
distance_metric,
outside_node_margin=outside_node_margin)
if _debug():
debug('SVS', "Instantiated voxel selector (radius %r)" % radius)
# structure to keep output data. Initialize with None, then
# make a sparse_attributes instance when we know what the attributes are
node2volume_attributes = None
attribute_mapper = voxel_selector.disc_voxel_indices_and_attributes
srcs_order = [source_surf_nodes[node] for node in visitorder]
src_trg_nodes = [(src, src2intermediate[src]) for src in srcs_order]
if nproc is not None and nproc > 1 and not externals.exists('pprocess'):
raise RuntimeError("The 'pprocess' module is required for "
"multiprocess searchlights. Please either "
"install python-pprocess, or reduce `nproc` "
"to 1 (got nproc=%i) or set to default None" %
nproc)
if nproc is None:
if externals.exists('pprocess'):
try:
import pprocess
nproc = pprocess.get_number_of_cores() or 1
if _debug():
debug("SVS", 'Using pprocess with %d cores' % nproc)
except:
if _debug():
debug("SVS", 'pprocess not available')
if nproc is None:
# importing pprocess failed - so use a single core
nproc = 1
debug("SVS", 'Using %d cores - pprocess not available' % nproc)
# get the the voxel selection parameters
parameter_dict = vol_surf_mapping.get_parameter_dict()
parameter_dict.update(dict(radius=radius,
outside_node_margin=outside_node_margin,
distance_metric=distance_metric),
source_nvertices=source_surf.nvertices)
init_output = lambda: volume_mask_dict.VolumeMaskDictionary(
vol_surf_mapping.volgeom, intermediate_surf, meta=parameter_dict)
if nproc > 1:
if results_backend == 'hdf5':
externals.exists('h5py', raise_=True)
elif results_backend is None:
if externals.exists(
'h5py') and externals.versions['hdf5'] >= '1.8.7':
results_backend = 'hdf5'
else:
results_backend = 'native'
if _debug():
debug('SVS', "Using '%s' backend" % (results_backend, ))
if not results_backend in ('native', 'hdf5'):
raise ValueError('Illegal results backend %r' % results_backend)
import pprocess
n_srcs = len(src_trg_nodes)
blocks = np.array_split(np.arange(n_srcs), nproc)
results = pprocess.Map(limit=nproc)
reducer = results.manage(pprocess.MakeParallel(_reduce_mapper))
if __debug__:
debug('SVS', "Starting %d child processes", (len(blocks), ))
for i, block in enumerate(blocks):
empty_dict = init_output()
src_trg = []
for idx in block:
src_trg.append(src_trg_nodes[idx])
if _debug():
debug('SVS',
" starting block %d/%d: %d centers" %
(i + 1, nproc, len(src_trg)),
cr=True)
reducer(empty_dict,
attribute_mapper,
src_trg,
eta_step=eta_step,
proc_id='%d' % (i + 1, ),
results_backend=results_backend,
tmp_prefix=tmp_prefix)
if _debug():
debug('SVS', '')
debug('SVS', 'Started all %d child processes' % (len(blocks)))
tstart = time.time()
node2volume_attributes = None
for i, result in enumerate(results):
if result is None:
continue
if results_backend == 'hdf5':
result_fn = result
result = h5load(result_fn)
os.remove(result_fn)
if node2volume_attributes is None:
# first time we have actual results.
# Use as a starting point
node2volume_attributes = result
if _debug():
debug('SVS', '')
debug(
'SVS', "Merging results from %d child "
"processes using '%s' backend" %
(len(blocks), results_backend))
else:
# merge new with current data
node2volume_attributes.merge(result)
if _debug():
debug('SVS',
" merged result block %d/%d" % (i + 1, nproc),
cr=True)
if _debug():
telapsed = time.time() - tstart
debug('SVS', "")
debug(
'SVS', 'Merged results from %d child processed - '
'took %s' % (len(blocks), seconds2prettystring(telapsed)))
else:
empty_dict = init_output()
node2volume_attributes = _reduce_mapper(empty_dict,
attribute_mapper,
src_trg_nodes,
eta_step=eta_step)
debug('SVS', "")
if _debug():
if node2volume_attributes is None:
msgs = [
"Voxel selection completed: none of %d nodes have "
"voxels associated" % len(visitorder)
]
else:
nvox_selected = np.sum(node2volume_attributes.get_mask() != 0)
vg = vol_surf_mapping.volgeom
msgs = [
"Voxel selection completed: %d / %d nodes have "
"voxels associated" %
(len(node2volume_attributes.keys()), len(visitorder)),
"Selected %d / %d voxels (%.0f%%) in the mask at least once" %
(nvox_selected, vg.nvoxels_mask,
100. * nvox_selected / vg.nvoxels_mask)
]
for msg in msgs:
debug("SVS", msg)
if node2volume_attributes is None:
warning('No voxels associated with any of %d nodes' % len(visitorder))
return node2volume_attributes
def voxel_selection(vol_surf_mapping, radius, source_surf=None, source_surf_nodes=None,
distance_metric='dijkstra',
eta_step=10, nproc=None,
outside_node_margin=None,
results_backend=None, tmp_prefix='tmpvoxsel'):
"""
Voxel selection for multiple center nodes on the surface
Parameters
----------
vol_surf_mapping: volsurf.VolSurfMapping
Contains gray and white matter surface, and volume geometry
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
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.
distance_metric: str
Distance metric between nodes. 'euclidean' or 'dijksta' (default)
eta_step: int
Report progress every eta_step (default: 10).
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 True 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).
Returns
-------
sel: volume_mask_dict.VolumeMaskDictionary
Voxel selection results, that associates, which each node, the indices
of the surrounding voxels.
"""
# construct the intermediate surface, which is used
# to measure distances
intermediate_surf = (vol_surf_mapping.pial_surface * .5) + \
(vol_surf_mapping.white_surface * .5)
if source_surf is None:
source_surf = intermediate_surf
else:
source_surf = surf.from_any(source_surf)
if _debug():
debug('SVS', "Generated high-res intermediate surface: "
"%d nodes, %d faces" %
(intermediate_surf.nvertices, intermediate_surf.nfaces))
debug('SVS', "Mapping source to high-res surface:"
" %d nodes, %d faces" %
(source_surf.nvertices, source_surf.nfaces))
if distance_metric[0].lower() == 'e' and outside_node_margin:
# euclidean distance: identity mapping
# this is *slow*
n = source_surf.nvertices
xyz = source_surf.vertices
src2intermediate = dict((i, tuple(xyz[i])) for i in range(n))
else:
# find a mapping from nodes in source_surf to those in
# intermediate surface
src2intermediate = source_surf.map_to_high_resolution_surf(\
intermediate_surf)
# if no sources are given, then visit all ndoes
if source_surf_nodes is None:
source_surf_nodes = np.arange(source_surf.nvertices)
n = len(source_surf_nodes)
if _debug():
debug('SVS',
"Performing surface-based voxel selection"
" for %d centers" % n)
# visit in random order, for for better ETA estimate
visitorder = list(np.random.permutation(len(source_surf_nodes)))
# construct mapping from nodes to enclosing voxels
n2v = vol_surf_mapping.get_node2voxels_mapping()
if __debug__:
debug('SVS', "Generated mapping from nodes"
" to intersecting voxels")
# build voxel selector
voxel_selector = VoxelSelector(radius, intermediate_surf, n2v,
distance_metric,
outside_node_margin=outside_node_margin)
if _debug():
debug('SVS', "Instantiated voxel selector (radius %r)" % radius)
# structure to keep output data. Initialize with None, then
# make a sparse_attributes instance when we know what the attributes are
node2volume_attributes = None
attribute_mapper = voxel_selector.disc_voxel_indices_and_attributes
srcs_order = [source_surf_nodes[node] for node in visitorder]
src_trg_nodes = [(src, src2intermediate[src]) for src in srcs_order]
if nproc is not None and nproc > 1 and not externals.exists('pprocess'):
raise RuntimeError("The 'pprocess' module is required for "
"multiprocess searchlights. Please either "
"install python-pprocess, or reduce `nproc` "
"to 1 (got nproc=%i) or set to default None"
% nproc)
if nproc is None:
if externals.exists('pprocess'):
try:
import pprocess
nproc = pprocess.get_number_of_cores() or 1
if _debug() :
debug("SVS", 'Using pprocess with %d cores' % nproc)
except:
if _debug():
debug("SVS", 'pprocess not available')
if nproc is None:
# importing pprocess failed - so use a single core
nproc = 1
debug("SVS", 'Using %d cores - pprocess not available' % nproc)
# get the the voxel selection parameters
parameter_dict = vol_surf_mapping.get_parameter_dict()
parameter_dict.update(dict(radius=radius,
outside_node_margin=outside_node_margin,
distance_metric=distance_metric),
source_nvertices=source_surf.nvertices)
init_output = lambda: volume_mask_dict.VolumeMaskDictionary(
vol_surf_mapping.volgeom,
intermediate_surf,
meta=parameter_dict)
if nproc > 1:
if results_backend == 'hdf5':
externals.exists('h5py', raise_=True)
elif results_backend is None:
if externals.exists('h5py') and externals.versions['hdf5'] >= '1.8.7':
results_backend = 'hdf5'
else:
results_backend = 'native'
if _debug():
debug('SVS', "Using '%s' backend" % (results_backend,))
if not results_backend in ('native', 'hdf5'):
raise ValueError('Illegal results backend %r' % results_backend)
import pprocess
n_srcs = len(src_trg_nodes)
blocks = np.array_split(np.arange(n_srcs), nproc)
results = pprocess.Map(limit=nproc)
reducer = results.manage(pprocess.MakeParallel(_reduce_mapper))
if __debug__:
debug('SVS', "Starting %d child processes", (len(blocks),))
for i, block in enumerate(blocks):
empty_dict = init_output()
src_trg = []
for idx in block:
src_trg.append(src_trg_nodes[idx])
if _debug():
debug('SVS', " starting block %d/%d: %d centers" %
(i + 1, nproc, len(src_trg)), cr=True)
reducer(empty_dict, attribute_mapper, src_trg,
eta_step=eta_step, proc_id='%d' % (i + 1,),
results_backend=results_backend, tmp_prefix=tmp_prefix)
if _debug():
debug('SVS', '')
debug('SVS', 'Started all %d child processes' % (len(blocks)))
tstart = time.time()
node2volume_attributes = None
for i, result in enumerate(results):
if result is None:
continue
if results_backend == 'hdf5':
result_fn = result
result = h5load(result_fn)
os.remove(result_fn)
if node2volume_attributes is None:
# first time we have actual results.
# Use as a starting point
node2volume_attributes = result
if _debug():
debug('SVS', '')
debug('SVS', "Merging results from %d child "
"processes using '%s' backend" %
(len(blocks), results_backend))
else:
# merge new with current data
node2volume_attributes.merge(result)
if _debug():
debug('SVS', " merged result block %d/%d" % (i + 1, nproc),
cr=True)
if _debug():
telapsed = time.time() - tstart
debug('SVS', "")
debug('SVS', 'Merged results from %d child processed - '
'took %s' %
(len(blocks), seconds2prettystring(telapsed)))
else:
empty_dict = init_output()
node2volume_attributes = _reduce_mapper(empty_dict,
attribute_mapper,
src_trg_nodes,
eta_step=eta_step)
debug('SVS', "")
if _debug():
if node2volume_attributes is None:
msgs = ["Voxel selection completed: none of %d nodes have "
"voxels associated" % len(visitorder)]
else:
nvox_selected = np.sum(node2volume_attributes.get_mask() != 0)
vg = vol_surf_mapping.volgeom
msgs = ["Voxel selection completed: %d / %d nodes have "
"voxels associated" %
(len(node2volume_attributes.keys()), len(visitorder)),
"Selected %d / %d voxels (%.0f%%) in the mask at least once" %
(nvox_selected, vg.nvoxels_mask,
100. * nvox_selected / vg.nvoxels_mask)]
for msg in msgs:
debug("SVS", msg)
if node2volume_attributes is None:
warning('No voxels associated with any of %d nodes' %
len(visitorder))
return node2volume_attributes
def flat_surface2grid_mask(surface, min_nsteps, max_deformation):
'''Computes a mask and corresponding coordinates from a flat surface
Parameters
----------
surface: Surface
flat surface
min_nsteps: int
minimum number of pixels in x and y direction
max_deformation: float
maximum deformation to make a non-flat surface flat.
The normals of each face must have a dot product with the average
face normal that is not less than (1-max_deformation); otherwise
an exception is raised. The rationale for this option is that
certain surfaces may be almost flat, and projecting the vertices
on a truly flat surface should be fine. On the other hand, surfaces
that are definitly not flat (such as full cortical surface models)
should cause an error to be raised when it is attempted to flatten
them
Returns
-------
x: np.ndarray
x coordinates of surface
y: np.ndarray
y coordinates of surface
m: np.ndarray
mask array of size PxQ, with min(P,Q)==min_nsteps.
m[i,j]==True iff the position at (i,j) is 'inside' the flat surface
xi: np.ndarray
vector of length Q with interpolated x coordinates
yi: np.ndarray
vector of length P with interpolated y coordinates
Notes
-----
The output of this function can be used with scipy.interpolate.griddata
'''
surface = surf.from_any(surface)
x, y = flat_surface2xy(surface, max_deformation)
xmin = np.min(x)
xi, yi = unstructured_xy2grid_xy_vectors(x, y, min_nsteps)
delta = xi[1] - xi[0]
vi2xi = (x - xmin) / delta
# compute paths of nodes on the border
pths = surface.nodes_on_border_paths()
# map x index to segments that cross the x coordinate
# (a segment is a pair (i,j) where nodes i and j share a triangle
# and are on the border)
xidx2segments = dict()
for pth in pths:
# make a tour across pairs (i,j)
j = pth[-1]
for i in pth:
pq = vi2xi[i], vi2xi[j]
p, q = min(pq), max(pq)
# always go left (p) to right (q)
for pqs in np.arange(np.ceil(p), np.ceil(q)):
# take each point in between
ipqs = int(pqs)
# add to xidx2segments
if not ipqs in xidx2segments:
xidx2segments[ipqs] = list()
xidx2segments[ipqs].append((i, j))
# take end point from last iteration as starting point
# in next iteration
j = i
# space for the mask
yxshape = len(yi), len(xi)
msk = np.zeros(yxshape, dtype=np.bool_)
# see which nodes are *inside* a surface
# (there can be multiple surfaces)
for ii, xpos in enumerate(xi):
if not ii in xidx2segments:
continue
segments = xidx2segments[ii]
for jj, ypos in enumerate(yi):
# based on PNPOLY (W Randoph Franklin)
# http://www.ecse.rpi.edu/~wrf/Research/Short_Notes/pnpoly.html
# retrieved Apr 2013
c = False
for i, j in segments:
if ypos < (y[j] - y[i]) * (xpos - x[i]) / (x[j] - x[i]) + y[i]:
c = not c
msk[jj, ii] = np.bool(c)
return x, y, msk, xi, yi
def __init__(self,
surface,
curvature=None,
min_nsteps=500,
range_='2_98%',
threshold=None,
color_map=None,
max_deformation=.5):
'''
Parameters
----------
surface: surf.Surface
a flat surface
curvature: str or np.ndarray
(Filename of) data representing curvature at each node.
min_steps: int
Minimal side of output plots in pixel
range_: str or float or tuple
If a tuple (a,b), then this tuple is returned.
If a float a, then (-a,a) is returned.
"R(a)", where R(a) denotes the string representation
of float a, is equivalent to range_=a.
"R(a)_R(b)" is equivalent to range_=(a,b).
"R(a)_R(b)%" indicates that the a-th and b-th
percentile of xs is taken to define the range.
"R(a)%" is equivalent to "R(a)_R(100-a)%"
threshold: str or float or tuple
Indicates which values will be shown. Syntax as in range_
color_map: str
colormap to use
max_deformation: float
maximum deformation to make a non-flat surface flat.
The normals of each face must have a dot product with the average
face normal that is not less than (1-max_deformation); otherwise
an exception is raised. The rationale for this option is that
certain surfaces may be almost flat, and projecting the vertices
on a truly flat surface should be fine. On the other hand, surfaces
that are definitly not flat (such as full cortical surface models)
should cause an error to be raised when it is attempted to flatten
them
'''
self._surface = surf.from_any(surface)
if curvature is None:
self._curvature = None
else:
self._curvature = curvature_from_any(curvature)
if self._surface.nvertices != len(self._curvature):
raise ValueError(
"Surface has %d vertices, but curvature "
"has %d values" %
(self._surface.nvertices, self._curvature.size))
self._min_nsteps = min_nsteps
self._range_ = range_
self._threshold = threshold
self._color_map = color_map
self._max_deformation = max_deformation
self._grid_def = None
self._underlay = None
def flat_surface2grid_mask(surface, min_nsteps, max_deformation):
'''Computes a mask and corresponding coordinates from a flat surface
Parameters
----------
surface: Surface
flat surface
min_nsteps: int
minimum number of pixels in x and y direction
max_deformation: float
maximum deformation to make a non-flat surface flat.
The normals of each face must have a dot product with the average
face normal that is not less than (1-max_deformation); otherwise
an exception is raised. The rationale for this option is that
certain surfaces may be almost flat, and projecting the vertices
on a truly flat surface should be fine. On the other hand, surfaces
that are definitly not flat (such as full cortical surface models)
should cause an error to be raised when it is attempted to flatten
them
Returns
-------
x: np.ndarray
x coordinates of surface
y: np.ndarray
y coordinates of surface
m: np.ndarray
mask array of size PxQ, with min(P,Q)==min_nsteps.
m[i,j]==True iff the position at (i,j) is 'inside' the flat surface
xi: np.ndarray
vector of length Q with interpolated x coordinates
yi: np.ndarray
vector of length P with interpolated y coordinates
Notes
-----
The output of this function can be used with scipy.interpolate.griddata
'''
surface = surf.from_any(surface)
x, y = flat_surface2xy(surface, max_deformation)
xmin = np.min(x)
xi, yi = unstructured_xy2grid_xy_vectors(x, y, min_nsteps)
delta = xi[1] - xi[0]
vi2xi = (x - xmin) / delta
# compute paths of nodes on the border
pths = surface.nodes_on_border_paths()
# map x index to segments that cross the x coordinate
# (a segment is a pair (i,j) where nodes i and j share a triangle
# and are on the border)
xidx2segments = dict()
for pth in pths:
# make a tour across pairs (i,j)
j = pth[-1]
for i in pth:
pq = vi2xi[i], vi2xi[j]
p, q = min(pq), max(pq)
# always go left (p) to right (q)
for pqs in np.arange(np.ceil(p), np.ceil(q)):
# take each point in between
ipqs = int(pqs)
# add to xidx2segments
if not ipqs in xidx2segments:
xidx2segments[ipqs] = list()
xidx2segments[ipqs].append((i, j))
# take end point from last iteration as starting point
# in next iteration
j = i
# space for the mask
yxshape = len(yi), len(xi)
msk = np.zeros(yxshape, dtype=np.bool_)
# see which nodes are *inside* a surface
# (there can be multiple surfaces)
for ii, xpos in enumerate(xi):
if not ii in xidx2segments:
continue
segments = xidx2segments[ii]
for jj, ypos in enumerate(yi):
# based on PNPOLY (W Randoph Franklin)
# http://www.ecse.rpi.edu/~wrf/Research/Short_Notes/pnpoly.html
# retrieved Apr 2013
c = False
for i, j in segments:
if ypos < (y[j] - y[i]) * (xpos - x[i]) / (x[j] - x[i]) + y[i]:
c = not c
msk[jj, ii] = np.bool(c)
return x, y, msk, xi, yi
