def test_h5support(self): sh = (20, 20, 20) msk = np.zeros(sh) for i in xrange(0, sh[0], 2): msk[i, :, :] = 1 vg = volgeom.VolGeom(sh, np.identity(4), mask=msk) density = 20 outer = surf.generate_sphere(density) * 10. + 5 inner = surf.generate_sphere(density) * 5. + 5 intermediate = outer * .5 + inner * .5 xyz = intermediate.vertices radius = 50 backends = ['native', 'hdf5'] for i, backend in enumerate(backends): if backend == 'hdf5' and not externals.exists('h5py'): continue sel = surf_voxel_selection.run_voxel_selection( radius, vg, inner, outer, results_backend=backend) if i == 0: sel0 = sel else: assert_equal(sel0, sel)
def test_h5support(self): sh = (20, 20, 20) msk = np.zeros(sh) for i in xrange(0, sh[0], 2): msk[i, :, :] = 1 vg = volgeom.VolGeom(sh, np.identity(4), mask=msk) density = 20 outer = surf.generate_sphere(density) * 10. + 5 inner = surf.generate_sphere(density) * 5. + 5 intermediate = outer * .5 + inner * .5 xyz = intermediate.vertices radius = 50 backends = ['native', 'hdf5'] for i, backend in enumerate(backends): if backend == 'hdf5' and not externals.exists('h5py'): continue sel = surf_voxel_selection.run_voxel_selection(radius, vg, inner, outer, results_backend=backend) if i == 0: sel0 = sel else: assert_equal(sel0, sel)
def test_niml_dset_voxsel(self, fn): if not externals.exists('nibabel'): return # This is actually a bit of an integration test. # It tests storing and retrieving searchlight results. # Imports are inline here so that it does not mess up the header # and makes the other unit tests more modular # XXX put this in a separate file? from mvpa2.misc.surfing import volgeom, surf_voxel_selection, queryengine from mvpa2.measures.searchlight import Searchlight from mvpa2.support.nibabel import surf from mvpa2.measures.base import Measure from mvpa2.datasets.mri import fmri_dataset class _Voxel_Count_Measure(Measure): # used to check voxel selection results is_trained = True def __init__(self, dtype, **kwargs): Measure.__init__(self, **kwargs) self.dtype = dtype def _call(self, dset): return self.dtype(dset.nfeatures) sh = (20, 20, 20) vg = volgeom.VolGeom(sh, np.identity(4)) density = 20 outer = surf.generate_sphere(density) * 10. + 5 inner = surf.generate_sphere(density) * 5. + 5 intermediate = outer * .5 + inner * .5 xyz = intermediate.vertices radius = 50 sel = surf_voxel_selection.run_voxel_selection(radius, vg, inner, outer) qe = queryengine.SurfaceVerticesQueryEngine(sel) for dtype in (int, float): sl = Searchlight(_Voxel_Count_Measure(dtype), queryengine=qe) ds = fmri_dataset(vg.get_empty_nifti_image(1)) r = sl(ds) niml.write(fn, r) rr = niml.read(fn) os.remove(fn) assert_array_equal(r.samples, rr.samples)
def test_niml_dset_voxsel(self): if not externals.exists('nibabel'): return # This is actually a bit of an integration test. # It tests storing and retrieving searchlight results. # Imports are inline here so that it does not mess up the header # and makes the other unit tests more modular # XXX put this in a separate file? from mvpa2.misc.surfing import volgeom, surf_voxel_selection, queryengine from mvpa2.measures.searchlight import Searchlight from mvpa2.support.nibabel import surf from mvpa2.measures.base import Measure from mvpa2.datasets.mri import fmri_dataset class _Voxel_Count_Measure(Measure): # used to check voxel selection results is_trained = True def __init__(self, dtype, **kwargs): Measure.__init__(self, **kwargs) self.dtype = dtype def _call(self, dset): return self.dtype(dset.nfeatures) sh = (20, 20, 20) vg = volgeom.VolGeom(sh, np.identity(4)) density = 20 outer = surf.generate_sphere(density) * 10. + 5 inner = surf.generate_sphere(density) * 5. + 5 intermediate = outer * .5 + inner * .5 xyz = intermediate.vertices radius = 50 sel = surf_voxel_selection.run_voxel_selection(radius, vg, inner, outer) qe = queryengine.SurfaceVerticesQueryEngine(sel) for dtype in (int, float): sl = Searchlight(_Voxel_Count_Measure(dtype), queryengine=qe) ds = fmri_dataset(vg.get_empty_nifti_image(1)) r = sl(ds) _, fn = tempfile.mkstemp('.niml.dset', 'dset') niml_dset.write(fn, r) rr = niml_dset.read(fn) os.remove(fn) assert_array_equal(r.samples, rr.samples)
def test_minimal_dataset(self): vol_shape = (10, 10, 10, 3) vol_affine = np.identity(4) vg = volgeom.VolGeom(vol_shape, vol_affine) data = np.random.normal(size=vol_shape) msk = np.ones(vol_shape[:3]) msk[:, 1:-1:2, :] = 0 ni_data = nb.Nifti1Image(data, vol_affine) ni_msk = nb.Nifti1Image(msk, vol_affine) ds = fmri_dataset(ni_data, mask=ni_msk) sphere_density = 20 outer = surf.generate_sphere(sphere_density) * 10. + 5 inner = surf.generate_sphere(sphere_density) * 7. + 5 radius = 10 sel = surf_voxel_selection.run_voxel_selection(radius, ds, inner, outer) sel_fids = set.union(*(set(sel[k]) for k in sel.keys())) ds_vox = map(tuple, ds.fa.voxel_indices) vg = sel.volgeom sel_vox = map(tuple, vg.lin2ijk(np.asarray(list(sel_fids)))) fid_mask = np.asarray([v in sel_vox for v in ds_vox]) assert_array_equal(fid_mask, sel.get_dataset_feature_mask(ds)) # check if it raises errors ni_neg_msk = nb.Nifti1Image(1 - msk, vol_affine) neg_ds = fmri_dataset(ni_data, mask=ni_neg_msk) # inverted mask assert_raises(ValueError, sel.get_dataset_feature_mask, neg_ds) min_ds = sel.get_minimal_dataset(ds) assert_array_equal(min_ds.samples, ds[:, fid_mask].samples)
def test_minimal_dataset(self): vol_shape = (10, 10, 10, 3) vol_affine = np.identity(4) vg = volgeom.VolGeom(vol_shape, vol_affine) data = np.random.normal(size=vol_shape) msk = np.ones(vol_shape[:3]) msk[:, 1:-1:2, :] = 0 ni_data = nb.Nifti1Image(data, vol_affine) ni_msk = nb.Nifti1Image(msk, vol_affine) ds = fmri_dataset(ni_data, mask=ni_msk) sphere_density = 20 outer = surf.generate_sphere(sphere_density) * 10. + 5 inner = surf.generate_sphere(sphere_density) * 7. + 5 radius = 10 sel = surf_voxel_selection.run_voxel_selection(radius, ds, inner, outer) sel_fids = set.union(*(set(sel[k]) for k in sel.keys())) ds_vox = map(tuple, ds.fa.voxel_indices) vg = sel.volgeom sel_vox = map(tuple, vg.lin2ijk(np.asarray(list(sel_fids)))) fid_mask = np.asarray([v in sel_vox for v in ds_vox]) assert_array_equal(fid_mask, sel.get_dataset_feature_mask(ds)) # check if it raises errors ni_neg_msk = nb.Nifti1Image(1 - msk, vol_affine) neg_ds = fmri_dataset(ni_data, mask=ni_neg_msk) # inverted mask assert_raises(ValueError, sel.get_dataset_feature_mask, neg_ds) min_ds = sel.get_minimal_dataset(ds) assert_array_equal(min_ds.samples, ds[:, fid_mask].samples)
def test_surf_voxel_selection(self): vol_shape = (10, 10, 10) vol_affine = np.identity(4) vol_affine[0, 0] = vol_affine[1, 1] = vol_affine[2, 2] = 5 vg = volgeom.VolGeom(vol_shape, vol_affine) density = 10 outer = surf.generate_sphere(density) * 25. + 15 inner = surf.generate_sphere(density) * 20. + 15 vs = volsurf.VolSurfMaximalMapping(vg, outer, inner) nv = outer.nvertices # select under variety of parameters # parameters are distance metric (dijkstra or euclidean), # radius, and number of searchlight centers params = [('d', 1., 10), ('d', 1., 50), ('d', 1., 100), ('d', 2., 100), ('e', 2., 100), ('d', 2., 100), ('d', 20, 100), ('euclidean', 5, None), ('dijkstra', 10, None)] # function that indicates for which parameters the full test is run test_full = lambda x: len(x[0]) > 1 or x[2] == 100 expected_labs = ['grey_matter_position', 'center_distances'] voxcount = [] tested_double_features = False for param in params: distance_metric, radius, ncenters = param srcs = range(0, nv, nv // (ncenters or nv)) sel = surf_voxel_selection.voxel_selection( vs, radius, source_surf_nodes=srcs, distance_metric=distance_metric) # see how many voxels were selected vg = sel.volgeom datalin = np.zeros((vg.nvoxels, 1)) mp = sel for k, idxs in mp.iteritems(): if idxs is not None: datalin[idxs] = 1 voxcount.append(np.sum(datalin)) if test_full(param): assert_equal(np.sum(datalin), np.sum(sel.get_mask())) assert_true(len('%s%r' % (sel, sel)) > 0) # see if voxels containing inner and outer # nodes were selected for sf in [inner, outer]: for k, idxs in mp.iteritems(): xyz = np.reshape(sf.vertices[k, :], (1, 3)) linidx = vg.xyz2lin(xyz) # only required if xyz is actually within the volume assert_equal(linidx in idxs, vg.contains_lin(linidx)) # check that it has all the attributes labs = sel.aux_keys() assert_true(all([lab in labs for lab in expected_labs])) if externals.exists('h5py'): # some I/O testing fd, fn = tempfile.mkstemp('.h5py', 'test') os.close(fd) h5save(fn, sel) sel2 = h5load(fn) os.remove(fn) assert_equal(sel, sel2) else: sel2 = sel # check that mask is OK even after I/O assert_array_equal(sel.get_mask(), sel2.get_mask()) # test I/O with surfaces # XXX the @tempfile decorator only supports a single filename # hence this method does not use it fd, outerfn = tempfile.mkstemp('outer.asc', 'test') os.close(fd) fd, innerfn = tempfile.mkstemp('inner.asc', 'test') os.close(fd) fd, volfn = tempfile.mkstemp('vol.nii', 'test') os.close(fd) surf.write(outerfn, outer, overwrite=True) surf.write(innerfn, inner, overwrite=True) img = sel.volgeom.get_empty_nifti_image() img.to_filename(volfn) sel3 = surf_voxel_selection.run_voxel_selection( radius, volfn, innerfn, outerfn, source_surf_nodes=srcs, distance_metric=distance_metric) outer4 = surf.read(outerfn) inner4 = surf.read(innerfn) vsm4 = vs = volsurf.VolSurfMaximalMapping(vg, inner4, outer4) # check that two ways of voxel selection match sel4 = surf_voxel_selection.voxel_selection( vsm4, radius, source_surf_nodes=srcs, distance_metric=distance_metric) assert_equal(sel3, sel4) os.remove(outerfn) os.remove(innerfn) os.remove(volfn) # compare sel3 with other selection results # NOTE: which voxels are precisely selected by sel can be quite # off from those in sel3, as writing the surfaces imposes # rounding errors and the sphere is very symmetric, which # means that different neighboring nodes are selected # to select a certain number of voxels. sel3cmp_difference_ratio = [(sel, .2), (sel4, 0.)] for selcmp, ratio in sel3cmp_difference_ratio: nunion = ndiff = 0 for k in selcmp.keys(): p = set(sel3.get(k)) q = set(selcmp.get(k)) nunion += len(p.union(q)) ndiff += len(p.symmetric_difference(q)) assert_true(float(ndiff) / float(nunion) <= ratio) # check searchlight call # as of late Aug 2012, this is with the fancy query engine # as implemented by Yarik mask = sel.get_mask() keys = None if ncenters is None else sel.keys() dset_data = np.reshape(np.arange(vg.nvoxels), vg.shape) dset_img = nb.Nifti1Image(dset_data, vg.affine) dset = fmri_dataset(samples=dset_img, mask=mask) qe = queryengine.SurfaceVerticesQueryEngine( sel, # you can optionally add additional # information about each near-disk-voxels add_fa=['center_distances', 'grey_matter_position']) # test i/o ensuring that when loading it is still trained if externals.exists('h5py'): fd, qefn = tempfile.mkstemp('qe.hdf5', 'test') os.close(fd) h5save(qefn, qe) qe = h5load(qefn) os.remove(qefn) assert_false('ERROR' in repr(qe)) # to check if repr works voxelcounter = _Voxel_Count_Measure() searchlight = Searchlight( voxelcounter, queryengine=qe, roi_ids=keys, nproc=1, enable_ca=['roi_feature_ids', 'roi_center_ids']) sl_dset = searchlight(dset) selected_count = sl_dset.samples[0, :] mp = sel for i, k in enumerate(sel.keys()): # check that number of selected voxels matches assert_equal(selected_count[i], len(mp[k])) assert_equal(searchlight.ca.roi_center_ids, sel.keys()) assert_array_equal(sl_dset.fa['center_ids'], qe.ids) # check nearest node is *really* the nearest node allvx = sel.get_targets() intermediate = outer * .5 + inner * .5 for vx in allvx: nearest = sel.target2nearest_source(vx) xyz = intermediate.vertices[nearest, :] sqsum = np.sum((xyz - intermediate.vertices)**2, 1) idx = np.argmin(sqsum) assert_equal(idx, nearest) if not tested_double_features: # test only once # see if we have multiple features for the same voxel, we would get them all dset1 = dset.copy() dset1.fa['dset'] = [1] dset2 = dset.copy() dset2.fa['dset'] = [2] dset_ = hstack((dset1, dset2), 'drop_nonunique') dset_.sa = dset1.sa # dset_.a.imghdr = dset1.a.imghdr assert_true('imghdr' in dset_.a.keys()) assert_equal(dset_.a['imghdr'].value, dset1.a['imghdr'].value) roi_feature_ids = searchlight.ca.roi_feature_ids sl_dset_ = searchlight(dset_) # and we should get twice the counts assert_array_equal(sl_dset_.samples, sl_dset.samples * 2) # compare old and new roi_feature_ids assert (len(roi_feature_ids) == len( searchlight.ca.roi_feature_ids)) nfeatures = dset.nfeatures for old, new in zip(roi_feature_ids, searchlight.ca.roi_feature_ids): # each new ids should comprise of old ones + (old + nfeatures) # since we hstack'ed two datasets assert_array_equal( np.hstack([(x, x + nfeatures) for x in old]), new) tested_double_features = True # check whether number of voxels were selected is as expected expected_voxcount = [22, 93, 183, 183, 183, 183, 183, 183, 183] assert_equal(voxcount, expected_voxcount)
def test_volume_mask_dict(self): # also tests the outside_node_margin feature sh = (10, 10, 10) msk = np.zeros(sh) for i in xrange(0, sh[0], 2): msk[i, :, :] = 1 vol_affine = np.identity(4) vol_affine[0, 0] = vol_affine[1, 1] = vol_affine[2, 2] = 2 vg = volgeom.VolGeom(sh, vol_affine, mask=msk) density = 10 outer = surf.generate_sphere(density) * 10. + 5 inner = surf.generate_sphere(density) * 5. + 5 intermediate = outer * .5 + inner * .5 xyz = intermediate.vertices radius = 50 outside_node_margins = [None, 0, 100., np.inf, True] expected_center_count = [87] * 2 + [intermediate.nvertices] * 3 for k, outside_node_margin in enumerate(outside_node_margins): sel = surf_voxel_selection.run_voxel_selection( radius, vg, inner, outer, outside_node_margin=outside_node_margin) assert_equal(intermediate, sel.source) assert_equal(len(sel.keys()), expected_center_count[k]) assert_true( set(sel.aux_keys()).issubset( set(['center_distances', 'grey_matter_position']))) msk_lin = msk.ravel() sel_msk_lin = sel.get_mask().ravel() for i in xrange(vg.nvoxels): if msk_lin[i]: src = sel.target2nearest_source(i) assert_false((src is None) ^ (sel_msk_lin[i] == 0)) if src is None: continue # index of node nearest to voxel i src_anywhere = sel.target2nearest_source( i, fallback_euclidean_distance=True) # coordinates of node nearest to voxel i xyz_src = xyz[src_anywhere] # coordinates of voxel i xyz_trg = vg.lin2xyz(np.asarray([i])) # distance between node nearest to voxel i, and voxel i # this should be the smallest distancer d = volgeom.distance(np.reshape(xyz_src, (1, 3)), xyz_trg) # distances between all nodes and voxel i ds = volgeom.distance(xyz, xyz_trg) # order of the distances is_ds = np.argsort(ds.ravel()) # go over all the nodes # require that the node is in the volume # mask # index of node nearest to voxel i ii = np.argmin(ds) xyz_min = xyz[ii] lin_min = vg.xyz2lin([xyz_min]) # linear index of voxel that contains xyz_src lin_src = vg.xyz2lin(np.reshape(xyz_src, (1, 3))) # when using multi-core support, # pickling and unpickling can reduce the precision # a little bit, causing rounding errors eps = 1e-14 delta = np.abs(ds[ii] - d) assert_false(delta > eps and ii in sel and i in sel[ii] and vg.contains_lin(lin_min))
def test_surf_voxel_selection(self): vol_shape = (10, 10, 10) vol_affine = np.identity(4) vol_affine[0, 0] = vol_affine[1, 1] = vol_affine[2, 2] = 5 vg = volgeom.VolGeom(vol_shape, vol_affine) density = 10 outer = surf.generate_sphere(density) * 25. + 15 inner = surf.generate_sphere(density) * 20. + 15 vs = volsurf.VolSurfMaximalMapping(vg, outer, inner) nv = outer.nvertices # select under variety of parameters # parameters are distance metric (dijkstra or euclidean), # radius, and number of searchlight centers params = [('d', 1., 10), ('d', 1., 50), ('d', 1., 100), ('d', 2., 100), ('e', 2., 100), ('d', 2., 100), ('d', 20, 100), ('euclidean', 5, None), ('dijkstra', 10, None)] # function that indicates for which parameters the full test is run test_full = lambda x:len(x[0]) > 1 or x[2] == 100 expected_labs = ['grey_matter_position', 'center_distances'] voxcount = [] tested_double_features = False for param in params: distance_metric, radius, ncenters = param srcs = range(0, nv, nv // (ncenters or nv)) sel = surf_voxel_selection.voxel_selection(vs, radius, source_surf_nodes=srcs, distance_metric=distance_metric) # see how many voxels were selected vg = sel.volgeom datalin = np.zeros((vg.nvoxels, 1)) mp = sel for k, idxs in mp.iteritems(): if idxs is not None: datalin[idxs] = 1 voxcount.append(np.sum(datalin)) if test_full(param): assert_equal(np.sum(datalin), np.sum(sel.get_mask())) assert_true(len('%s%r' % (sel, sel)) > 0) # see if voxels containing inner and outer # nodes were selected for sf in [inner, outer]: for k, idxs in mp.iteritems(): xyz = np.reshape(sf.vertices[k, :], (1, 3)) linidx = vg.xyz2lin(xyz) # only required if xyz is actually within the volume assert_equal(linidx in idxs, vg.contains_lin(linidx)) # check that it has all the attributes labs = sel.aux_keys() assert_true(all([lab in labs for lab in expected_labs])) if externals.exists('h5py'): # some I/O testing fd, fn = tempfile.mkstemp('.h5py', 'test'); os.close(fd) h5save(fn, sel) sel2 = h5load(fn) os.remove(fn) assert_equal(sel, sel2) else: sel2 = sel # check that mask is OK even after I/O assert_array_equal(sel.get_mask(), sel2.get_mask()) # test I/O with surfaces # XXX the @tempfile decorator only supports a single filename # hence this method does not use it fd, outerfn = tempfile.mkstemp('outer.asc', 'test'); os.close(fd) fd, innerfn = tempfile.mkstemp('inner.asc', 'test'); os.close(fd) fd, volfn = tempfile.mkstemp('vol.nii', 'test'); os.close(fd) surf.write(outerfn, outer, overwrite=True) surf.write(innerfn, inner, overwrite=True) img = sel.volgeom.get_empty_nifti_image() img.to_filename(volfn) sel3 = surf_voxel_selection.run_voxel_selection(radius, volfn, innerfn, outerfn, source_surf_nodes=srcs, distance_metric=distance_metric) outer4 = surf.read(outerfn) inner4 = surf.read(innerfn) vsm4 = vs = volsurf.VolSurfMaximalMapping(vg, inner4, outer4) # check that two ways of voxel selection match sel4 = surf_voxel_selection.voxel_selection(vsm4, radius, source_surf_nodes=srcs, distance_metric=distance_metric) assert_equal(sel3, sel4) os.remove(outerfn) os.remove(innerfn) os.remove(volfn) # compare sel3 with other selection results # NOTE: which voxels are precisely selected by sel can be quite # off from those in sel3, as writing the surfaces imposes # rounding errors and the sphere is very symmetric, which # means that different neighboring nodes are selected # to select a certain number of voxels. sel3cmp_difference_ratio = [(sel, .2), (sel4, 0.)] for selcmp, ratio in sel3cmp_difference_ratio: nunion = ndiff = 0 for k in selcmp.keys(): p = set(sel3.get(k)) q = set(selcmp.get(k)) nunion += len(p.union(q)) ndiff += len(p.symmetric_difference(q)) assert_true(float(ndiff) / float(nunion) <= ratio) # check searchlight call # as of late Aug 2012, this is with the fancy query engine # as implemented by Yarik mask = sel.get_mask() keys = None if ncenters is None else sel.keys() dset_data = np.reshape(np.arange(vg.nvoxels), vg.shape) dset_img = nb.Nifti1Image(dset_data, vg.affine) dset = fmri_dataset(samples=dset_img, mask=mask) qe = queryengine.SurfaceVerticesQueryEngine(sel, # you can optionally add additional # information about each near-disk-voxels add_fa=['center_distances', 'grey_matter_position']) # test i/o ensuring that when loading it is still trained if externals.exists('h5py'): fd, qefn = tempfile.mkstemp('qe.hdf5', 'test'); os.close(fd) h5save(qefn, qe) qe = h5load(qefn) os.remove(qefn) assert_false('ERROR' in repr(qe)) # to check if repr works voxelcounter = _Voxel_Count_Measure() searchlight = Searchlight(voxelcounter, queryengine=qe, roi_ids=keys, nproc=1, enable_ca=['roi_feature_ids', 'roi_center_ids']) sl_dset = searchlight(dset) selected_count = sl_dset.samples[0, :] mp = sel for i, k in enumerate(sel.keys()): # check that number of selected voxels matches assert_equal(selected_count[i], len(mp[k])) assert_equal(searchlight.ca.roi_center_ids, sel.keys()) assert_array_equal(sl_dset.fa['center_ids'], qe.ids) # check nearest node is *really* the nearest node allvx = sel.get_targets() intermediate = outer * .5 + inner * .5 for vx in allvx: nearest = sel.target2nearest_source(vx) xyz = intermediate.vertices[nearest, :] sqsum = np.sum((xyz - intermediate.vertices) ** 2, 1) idx = np.argmin(sqsum) assert_equal(idx, nearest) if not tested_double_features: # test only once # see if we have multiple features for the same voxel, we would get them all dset1 = dset.copy() dset1.fa['dset'] = [1] dset2 = dset.copy() dset2.fa['dset'] = [2] dset_ = hstack((dset1, dset2), 'drop_nonunique') dset_.sa = dset1.sa #dset_.a.imghdr = dset1.a.imghdr assert_true('imghdr' in dset_.a.keys()) assert_equal(dset_.a['imghdr'].value, dset1.a['imghdr'].value) roi_feature_ids = searchlight.ca.roi_feature_ids sl_dset_ = searchlight(dset_) # and we should get twice the counts assert_array_equal(sl_dset_.samples, sl_dset.samples * 2) # compare old and new roi_feature_ids assert(len(roi_feature_ids) == len(searchlight.ca.roi_feature_ids)) nfeatures = dset.nfeatures for old, new in zip(roi_feature_ids, searchlight.ca.roi_feature_ids): # each new ids should comprise of old ones + (old + nfeatures) # since we hstack'ed two datasets assert_array_equal(np.hstack([(x, x + nfeatures) for x in old]), new) tested_double_features = True # check whether number of voxels were selected is as expected expected_voxcount = [22, 93, 183, 183, 183, 183, 183, 183, 183] assert_equal(voxcount, expected_voxcount)
def test_volume_mask_dict(self): # also tests the outside_node_margin feature sh = (10, 10, 10) msk = np.zeros(sh) for i in xrange(0, sh[0], 2): msk[i, :, :] = 1 vol_affine = np.identity(4) vol_affine[0, 0] = vol_affine[1, 1] = vol_affine[2, 2] = 2 vg = volgeom.VolGeom(sh, vol_affine, mask=msk) density = 10 outer = surf.generate_sphere(density) * 10. + 5 inner = surf.generate_sphere(density) * 5. + 5 intermediate = outer * .5 + inner * .5 xyz = intermediate.vertices radius = 50 outside_node_margins = [None, 0, 100., np.inf, True] expected_center_count = [87] * 2 + [intermediate.nvertices] * 3 for k, outside_node_margin in enumerate(outside_node_margins): sel = surf_voxel_selection.run_voxel_selection(radius, vg, inner, outer, outside_node_margin=outside_node_margin) assert_equal(intermediate, sel.source) assert_equal(len(sel.keys()), expected_center_count[k]) assert_true(set(sel.aux_keys()).issubset(set(['center_distances', 'grey_matter_position']))) msk_lin = msk.ravel() sel_msk_lin = sel.get_mask().ravel() for i in xrange(vg.nvoxels): if msk_lin[i]: src = sel.target2nearest_source(i) assert_false((src is None) ^ (sel_msk_lin[i] == 0)) if src is None: continue # index of node nearest to voxel i src_anywhere = sel.target2nearest_source(i, fallback_euclidean_distance=True) # coordinates of node nearest to voxel i xyz_src = xyz[src_anywhere] # coordinates of voxel i xyz_trg = vg.lin2xyz(np.asarray([i])) # distance between node nearest to voxel i, and voxel i # this should be the smallest distancer d = volgeom.distance(np.reshape(xyz_src, (1, 3)), xyz_trg) # distances between all nodes and voxel i ds = volgeom.distance(xyz, xyz_trg) # order of the distances is_ds = np.argsort(ds.ravel()) # go over all the nodes # require that the node is in the volume # mask # index of node nearest to voxel i ii = np.argmin(ds) xyz_min = xyz[ii] lin_min = vg.xyz2lin([xyz_min]) # linear index of voxel that contains xyz_src lin_src = vg.xyz2lin(np.reshape(xyz_src, (1, 3))) # when using multi-core support, # pickling and unpickling can reduce the precision # a little bit, causing rounding errors eps = 1e-14 delta = np.abs(ds[ii] - d) assert_false(delta > eps and ii in sel and i in sel[ii] and vg.contains_lin(lin_min))
def disc_surface_queryengine(radius, volume, white_surf, pial_surf, source_surf=None, source_surf_nodes=None, volume_mask=False, distance_metric='dijkstra', start_mm=0, stop_mm=0, start_fr=0., stop_fr=1., nsteps=10, eta_step=1, add_fa=None, nproc=None, outside_node_margin=None, results_backend=None, tmp_prefix='tmpvoxsel', output_modality='surface', node_voxel_mapping='maximal'): """ Voxel selection wrapper for multiple center nodes on the surface WiP XXX currently the last parameter 'output_modality' determines what kind of query engine is returned - is that bad? XXX: have to decide whether to use minimal_voxel_mapping=True as default 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 start_fr) 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 add_fa: None or list of strings Feature attributes from a dataset that should be returned if the queryengine is called with a dataset. 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 parameters 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). output_modality: 'surface' or 'volume' (default: 'surface') Indicates whether the output is surface-based node_voxel_mapping: 'minimal' or 'maximal' 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') Returns ------- qe: SurfaceVerticesQueryEngine Query-engine that maps center nodes to indices of features (voxels) that are near each center node. If output_modality=='volume' then qe is of type subclass SurfaceVoxelsQueryEngine. """ modality2class = dict(surface=SurfaceVerticesQueryEngine, volume=SurfaceVoxelsQueryEngine) if not output_modality in modality2class: raise KeyError("Illegal modality %s: should be in %s" % (output_modality, modality2class.keys())) voxsel = surf_voxel_selection.run_voxel_selection( radius=radius, volume=volume, white_surf=white_surf, pial_surf=pial_surf, source_surf=source_surf, source_surf_nodes=source_surf_nodes, volume_mask=volume_mask, distance_metric=distance_metric, start_fr=start_fr, stop_fr=stop_fr, start_mm=start_mm, stop_mm=stop_mm, nsteps=nsteps, eta_step=eta_step, nproc=nproc, outside_node_margin=outside_node_margin, results_backend=results_backend, tmp_prefix=tmp_prefix, node_voxel_mapping=node_voxel_mapping) qe = modality2class[output_modality](voxsel, add_fa=add_fa) return qe
def disc_surface_queryengine(radius, volume, white_surf, pial_surf, source_surf=None, source_surf_nodes=None, volume_mask=False, distance_metric='dijkstra', start_mm=0, stop_mm=0, start_fr=0., stop_fr=1., nsteps=10, eta_step=1, add_fa=None, nproc=None, outside_node_margin=None, results_backend=None, tmp_prefix='tmpvoxsel', output_modality='surface', node_voxel_mapping='maximal'): """ Voxel selection wrapper for multiple center nodes on the surface WiP XXX currently the last parameter 'output_modality' determines what kind of query engine is returned - is that bad? XXX: have to decide whether to use minimal_voxel_mapping=True as default 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 start_fr) 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 add_fa: None or list of strings Feature attributes from a dataset that should be returned if the queryengine is called with a dataset. 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 parameters 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). output_modality: 'surface' or 'volume' (default: 'surface') Indicates whether the output is surface-based node_voxel_mapping: 'minimal' or 'maximal' 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') Returns ------- qe: SurfaceVerticesQueryEngine Query-engine that maps center nodes to indices of features (voxels) that are near each center node. If output_modality=='volume' then qe is of type subclass SurfaceVoxelsQueryEngine. """ modality2class = dict(surface=SurfaceVerticesQueryEngine, volume=SurfaceVoxelsQueryEngine) if not output_modality in modality2class: raise KeyError("Illegal modality %s: should be in %s" % (output_modality, modality2class.keys())) voxsel = surf_voxel_selection.run_voxel_selection( radius=radius, volume=volume, white_surf=white_surf, pial_surf=pial_surf, source_surf=source_surf, source_surf_nodes=source_surf_nodes, volume_mask=volume_mask, distance_metric=distance_metric, start_fr=start_fr, stop_fr=stop_fr, start_mm=start_mm, stop_mm=stop_mm, nsteps=nsteps, eta_step=eta_step, nproc=nproc, outside_node_margin=outside_node_margin, results_backend=results_backend, tmp_prefix=tmp_prefix, node_voxel_mapping=node_voxel_mapping) qe = modality2class[output_modality](voxsel, add_fa=add_fa) return qe
def test_mask_with_keys(self): vol_shape = (10, 10, 10, 3) vol_affine = np.identity(4) vg = volgeom.VolGeom(vol_shape, vol_affine) data = np.random.normal(size=vol_shape) msk = np.ones(vol_shape[:3]) msk[:, 1:-1:2, :] = 0 ni_data = nb.Nifti1Image(data, vol_affine) ni_msk = nb.Nifti1Image(msk, vol_affine) ds = fmri_dataset(ni_data, mask=ni_msk) sphere_density = 20 outer = surf.generate_sphere(sphere_density) * 10.0 + 5 inner = surf.generate_sphere(sphere_density) * 7.0 + 5 radius = 10 sel = surf_voxel_selection.run_voxel_selection(radius, ds, inner, outer) # in the mapping below: # (tup: None) means that tup as input should raise a KeyError # (tup: i) with i an int means that tup as input should return i # elements qe_ids2nvoxels = { SurfaceVoxelsQueryEngine: {(1, 2, 3): 13, tuple(np.arange(0, 200, 2)): 82, (601,): None, None: 126}, SurfaceVerticesQueryEngine: {(1, 2, 3): None, (205, 209, 210, 214): 36, None: 126}, } for constructor, ids2nfeatures in qe_ids2nvoxels.iteritems(): qe = constructor(sel) qe.train(ds) img = qe.get_masked_nifti_image() assert_array_equal(img.get_data(), qe.get_masked_nifti_image(qe.ids).get_data()) img_getter = qe.get_masked_nifti_image for ids, nfeatures in ids2nfeatures.iteritems(): ids_list = ids if ids is None else list(ids) if nfeatures is None and ids is not None: assert_raises(KeyError, img_getter, ids_list) else: img = img_getter(ids_list) nfeatures_found = np.sum(img.get_data()) assert_equal(nfeatures, nfeatures_found) if constructor is SurfaceVerticesQueryEngine: expected_image = qe.get_masked_nifti_image(ids_list) expected_mask = expected_image.get_data() check_mask_func = lambda x: assert_array_equal(expected_mask, x) check_image_func = lambda x: check_mask_func(x.get_data()) and assert_array_equal( x.get_affine(), expected_image.get_affine() ) check_mask_func(sel.get_mask(ids_list)) check_image_func(sel.get_nifti_image_mask(ids_list)) tups = sel.get_voxel_indices(ids_list) tups_mask = np.zeros(expected_mask.shape) for tup in tups: tups_mask[tup] += 1 assert_array_equal(expected_mask != 0, tups_mask != 0)
def test_mask_with_keys(self): vol_shape = (10, 10, 10, 3) vol_affine = np.identity(4) vg = volgeom.VolGeom(vol_shape, vol_affine) data = np.random.normal(size=vol_shape) msk = np.ones(vol_shape[:3]) msk[:, 1:-1:2, :] = 0 ni_data = nb.Nifti1Image(data, vol_affine) ni_msk = nb.Nifti1Image(msk, vol_affine) ds = fmri_dataset(ni_data, mask=ni_msk) sphere_density = 20 outer = surf.generate_sphere(sphere_density) * 10. + 5 inner = surf.generate_sphere(sphere_density) * 7. + 5 radius = 10 sel = surf_voxel_selection.run_voxel_selection(radius, ds, inner, outer) # in the mapping below: # (tup: None) means that tup as input should raise a KeyError # (tup: i) with i an int means that tup as input should return i # elements qe_ids2nvoxels = {SurfaceVoxelsQueryEngine: {(1, 2, 3): 13, tuple(np.arange(0, 200, 2)): 82, (601,): None, None: 126}, SurfaceVerticesQueryEngine: {(1, 2, 3): None, (205, 209, 210, 214): 36, None: 126}} for constructor, ids2nfeatures in qe_ids2nvoxels.iteritems(): qe = constructor(sel) qe.train(ds) img = qe.get_masked_nifti_image() assert_array_equal(img.get_data(), qe.get_masked_nifti_image(qe.ids).get_data()) img_getter = qe.get_masked_nifti_image for ids, nfeatures in ids2nfeatures.iteritems(): ids_list = ids if ids is None else list(ids) if nfeatures is None and ids is not None: assert_raises(KeyError, img_getter, ids_list) else: img = img_getter(ids_list) nfeatures_found = np.sum(img.get_data()) assert_equal(nfeatures, nfeatures_found) if constructor is SurfaceVerticesQueryEngine: expected_image = qe.get_masked_nifti_image(ids_list) expected_mask = expected_image.get_data() check_mask_func = lambda x: assert_array_equal( expected_mask, x) check_image_func = lambda x: check_mask_func( x.get_data()) and \ assert_array_equal(x.affine, expected_image.affine) check_mask_func(sel.get_mask(ids_list)) check_image_func(sel.get_nifti_image_mask(ids_list)) tups = sel.get_voxel_indices(ids_list) tups_mask = np.zeros(expected_mask.shape) for tup in tups: tups_mask[tup] += 1 assert_array_equal(expected_mask != 0, tups_mask != 0)