def surface_to_volume(
pial_mesh,
wm_mesh,
labels,
volume_template,
volume_save,
interpolation="nearest",
):
"""Projects surface labels to the cortical ribbon.
Parameters
----------
pial_mesh : str, BSPolyData
Filename of a pial mesh or a BSPolyData object of the same.
wm_mesh : str, BSPolyData
Filename of a pial mesh or a BSPolyData object of the same.
labels : str, numpy.ndarray
Filename of a .label.gii or .shape.gii file, or a numpy array
containing the labels.
volume_template : str, nibabel.nifti1.Nifti1Image
Filename of a nifti image in the same space as the mesh files or a
NIfTI image loaded with nibabel.
volume_save : str
Filename to which the label image will be saved.
interpolation : str
Either 'nearest' for nearest neighbor interpolation, or 'linear'
for trilinear interpolation, defaults to 'nearest'.
"""
if not isinstance(pial_mesh, BSPolyData):
pial_mesh = read_surface(pial_mesh)
if not isinstance(wm_mesh, BSPolyData):
wm_mesh = read_surface(wm_mesh)
if not isinstance(volume_template, nib.nifti1.Nifti1Image):
volume_template = nib.load(volume_template)
logging.debug("Computing voxels inside the cortical ribbon.")
ribbon_points = cortical_ribbon(pial_mesh, wm_mesh, volume_template)
logging.debug("Computing labels for cortical ribbon voxels.")
ribbon_labels = ribbon_interpolation(
pial_mesh,
wm_mesh,
labels,
volume_template,
ribbon_points,
interpolation=interpolation,
)
logging.debug("Constructing new nifti image.")
new_data = np.zeros(volume_template.shape)
ribbon_points = np.rint(ribbon_points,
np.ones(ribbon_points.shape, dtype=int),
casting="unsafe")
for i in range(ribbon_labels.shape[0]):
new_data[ribbon_points[i, 0], ribbon_points[i, 1],
ribbon_points[i, 2]] = ribbon_labels[i]
new_nii = nib.Nifti1Image(new_data, volume_template.affine)
nib.save(new_nii, volume_save)
def load_surface(lh, rh, with_normals=True, join=False):
"""
Loads surfaces.
Parameters
----------
with_normals : bool, optional
Whether to compute surface normals. Default is True.
join : bool, optional.
If False, return one surface for left and right hemispheres. Otherwise,
return a single surface as a combination of both left and right.
surfaces. Default is False.
Returns
-------
surf : tuple of BSPolyData or BSPolyData.
Surfaces for left and right hemispheres. If ``join == True``, one
surface with both hemispheres.
"""
surfs = [None] * 2
for i, side in enumerate([lh, rh]):
surfs[i] = read_surface(side)
if with_normals:
nf = wrap_vtk(vtkPolyDataNormals,
splitting=False,
featureAngle=0.1)
surfs[i] = serial_connect(surfs[i], nf)
if join:
return combine_surfaces(*surfs)
return surfs[0], surfs[1]
def mesh_average(filenames, fun=np.add, output_surfstat=False):
"""Average, minimum, or maximum of surfaces.
Args:
filenames (2D numpy array): Numpy array of filenames of surfaces or BSPolyData objects.
fun : function handle to apply to two surfaces, e.g.
np.add (default) will give the average of the surfaces,
np.fmin or np.fmax will give the min or max, respectively.
output_surfstat (boolean): If True, outputs the surface in SurfStat format. If false
outputs the surface as BSPolyData. Default is False.
Returns:
surface [BSPolyData, dict]: The output surface.
"""
if filenames.ndim is not 2:
raise ValueError("Filenames must be a 2-dimensional array.")
for i in range(0, filenames.shape[0]):
surfaces = np.empty(filenames.shape[1], dtype=np.object)
for j in range(0, filenames.shape[1]):
# Check whether input is BSPolyData or a filename.
if isinstance(filenames[i, j], BSPolyData):
surfaces[j] = filenames[i, j]
else:
surfaces[j] = read_surface(filenames[i, j])
# Concatenate second dimension of filenames.
if j is 0:
tri = get_cells(surfaces[j])
coord = get_points(surfaces[j])
else:
tri = np.concatenate(
(tri, get_cells(surfaces[j]) + coord.shape[0]), axis=0
)
coord = np.concatenate((coord, get_points(surfaces[j])), axis=0)
if i is 0:
m = 1
coord_all = coord
else:
coord_all = fun(coord_all, coord)
m = fun(m, 1)
coord_all = coord_all / m
if output_surfstat:
surface = {"tri": np.array(tri) + 1, "coord": np.array(coord_all).T}
else:
surface = build_polydata(coord_all, tri)
return surface
def fetch_template_surface(
template: str,
join: bool = True,
layer: Optional[str] = None,
data_dir: Optional[Union[str, Path]] = None,
) -> Union[BSPolyData, Tuple[BSPolyData, BSPolyData]]:
"""Loads surface templates.
Parameters
----------
template : str
Name of the surface template. Valid values are "fslr32k", "fsaverage",
"fsaverage3", "fsaverage4", "fsaverage5", "fsaverage6", "civet41k",
"civet164k".
join : bool, optional
If true, returns surfaces as a single object, if false, returns an
object per hemisphere, by default True.
layer : str, optional
Name of the cortical surface of interest. Valid values are "white",
"smoothwm", "pial", "inflated", "sphere" for fsaverage surfaces;
"midthickness", "inflated", "vinflated" for "fslr32k"; "mid", "white"
for CIVET surfaces; and "sphere" for "civet41k". If None,
defaults to "pial" or "midthickness", by default None.
data_dir : str, Path, optional
Directory to save the data, by default
$HOME_DIR/brainstat_data/surface_data.
Returns
-------
BSPolyData or tuple of BSPolyData
Output surface(s). If a tuple, then the first element is the left
hemisphere.
"""
data_dir = Path(
data_dir) if data_dir else data_directories["SURFACE_DATA_DIR"]
surface_files = _fetch_template_surface_files(template, data_dir, layer)
if template[:9] == "fsaverage":
surfaces_fs = [read_geometry(file) for file in surface_files]
surfaces = [
build_polydata(surface[0], surface[1]) for surface in surfaces_fs
]
elif template == "fslr32k":
surfaces = [read_surface(file) for file in surface_files]
else:
surfaces_obj = [read_civet(file) for file in surface_files]
surfaces = [
build_polydata(surface[0], surface[1]) for surface in surfaces_obj
]
if join:
return combine_surfaces(surfaces[0], surfaces[1])
else:
return surfaces[0], surfaces[1]
def test_io_nb():
s = _generate_sphere()
root_pth = os.path.dirname(__file__)
io_pth = os.path.join(root_pth, 'test_sphere_io.gii')
mio.write_surface(s, io_pth)
s2 = mio.read_surface(io_pth)
assert np.allclose(s.Points, s2.Points)
assert np.all(s.GetCells2D() == s2.GetCells2D())
os.remove(io_pth)
def read_surface_gz(filename):
"""Extension of brainspace's read_surface to include .gz files.
Parameters
----------
filename : str
Filename of file to open.
Returns
-------
BSPolyData
Surface mesh.
"""
if filename.endswith(".gz"):
extension = os.path.splitext(filename[:-3])[-1]
with tempfile.NamedTemporaryFile(suffix=extension) as f_tmp:
with gzip.open(filename, "rb") as f_gz:
shutil.copyfileobj(f_gz, f_tmp)
return read_surface(f_tmp.name)
else:
return read_surface(filename)
def test_io(ext):
s = _generate_sphere()
root_pth = os.path.dirname(__file__)
io_pth = os.path.join(root_pth, 'test_sphere_io.{ext}').format(ext=ext)
mio.write_surface(s, io_pth)
s2 = mio.read_surface(io_pth)
assert np.allclose(s.Points, s2.Points)
assert np.all(s.get_cells2D() == s2.get_cells2D())
os.remove(io_pth)
import vtk
from vtk import vtkPolyDataNormals
from brainspace.mesh.mesh_io import read_surface
from brainspace.mesh.mesh_operations import combine_surfaces
from brainspace.utils.parcellation import reduce_by_labels
from brainspace.vtk_interface import wrap_vtk, serial_connect
template_path = "Z:/hschoi/backup/hschoi/template/MMP"
template_L = "S900.L.midthickness_MSMAll.10k_fs_LR.surf.gii" # S900.L.midthickness_MSMAll.10k_fs_LR.surf.gii # L.very_inflated_MSMAll.10k_fs_LR.surf.gii
template_R = "S900.R.midthickness_MSMAll.10k_fs_LR.surf.gii" # S900.R.midthickness_MSMAll.10k_fs_LR.surf.gii # R.very_inflated_MSMAll.10k_fs_LR.surf.gii
surfs = [None] * 2
surfs[0] = read_surface(join(template_path, template_L))
nf = wrap_vtk(vtkPolyDataNormals, splitting=False, featureAngle=0.1)
surf_lh = serial_connect(surfs[0], nf)
surfs[1] = read_surface(join(template_path, template_R))
nf = wrap_vtk(vtkPolyDataNormals, splitting=False, featureAngle=0.1)
surf_rh = serial_connect(surfs[1], nf)
# Visualization
from brainspace.datasets import load_group_fc, load_parcellation, load_conte69
from brainspace.gradient import GradientMaps
from brainspace.plotting import plot_hemispheres
from brainspace.utils.parcellation import map_to_labels
atlas = np.load("Z:\\hschoi\\backup\\hschoi\\template\\MMP\\MMP.10k_fs_LR.npy")
def __freesurfer_to_surfgii(freesurfer_file: str, gifti_file: str) -> None:
surf = read_surface(freesurfer_file, itype="fs")
write_surface(surf, gifti_file, otype="gii")
ColCurv = plt.colors.ListedColormap([
'#A2CD5A', '#A0CA5B', '#9FC85C', '#9EC55D', '#9DC35E', '#9CC05F',
'#9BBE61', '#9ABB62', '#99B963', '#98B664', '#96B465', '#95B166',
'#94AF68', '#93AC69', '#92AA6A', '#91A76B', '#90A56C', '#8FA26D',
'#8EA06F', '#8C9D70', '#8B9B71', '#8A9972', '#899673', '#889475',
'#879176', '#868F77', '#858C78', '#848A79', '#82877A', '#81857C',
'#80827D', '#7F807E', '#807D7D', '#827A7A', '#857777', '#877575',
'#8A7272', '#8C6F6F', '#8F6C6C', '#916969', '#946666', '#966464',
'#996161', '#9B5E5E', '#9D5B5B', '#A05858', '#A25656', '#A55353',
'#A75050', '#AA4D4D', '#AC4A4A', '#AF4747', '#B14545', '#B44242',
'#B63F3F', '#B93C3C', '#BB3939', '#BE3636', '#C03434', '#C33131',
'#C52E2E', '#C82B2B', '#CA2828', '#CD2626'
])
# Load fsaverage5 inflated
fs5I_lh = read_surface(dir_fS + 'fsaverage5/surf/lh.inflated', itype='fs')
fs5I_rh = read_surface(dir_fS + 'fsaverage5/surf/rh.inflated', itype='fs')
# Load conte69
c69_lh, c69_rh = load_conte69()
# Load native mid surface
mid_lh, mid_rh = load_surface(
dir_fS + subBIDS + '/surf/lh.midthickness.surf.gii',
dir_fS + subBIDS + '/surf/rh.midthickness.surf.gii',
with_normals=True,
join=False)
# Load native surface
surf_lh = read_surface(dir_fS + subBIDS + '/surf/lh.pial', itype='fs')
surf_rh = read_surface(dir_fS + subBIDS + '/surf/rh.pial', itype='fs')
subjectDir='micapipe/sub-HC001/ses-01' # <<<<<<<<<<<< CHANGE THIS SUBJECT's DIRECTORY
# Here we define the atlas
atlas='schaefer-400' # <<<<<<<<<<<< CHANGE THIS ATLAS
# ## Load the surfaces
# In[79]:
# Load conte69
c69_lh, c69_rh = load_conte69()
# Load fsaverage5
fs5_lh = read_surface('freesurfer/fsaverage5/surf/lh.pial', itype='fs')
fs5_rh = read_surface('freesurfer/fsaverage5/surf/rh.pial', itype='fs')
# Load annotation file in fsaverage5
annot_lh_fs5= nb.freesurfer.read_annot(micapipe + '/parcellations/lh.schaefer-400_mics.annot')
annot_rh_fs5= nb.freesurfer.read_annot(micapipe + '/parcellations/rh.schaefer-400_mics.annot')[0]+200
# replace with 0 the medial wall of the right labels
annot_rh_fs5 = np.where(annot_rh_fs5==200, 0, annot_rh_fs5)
# fsaverage5 labels
labels_fs5 = np.concatenate((annot_lh_fs5[0], annot_rh_fs5), axis=0)
# Read label for conte69
labels_c69 = np.loadtxt(open(micapipe + '/parcellations/schaefer-400_conte69.csv'), dtype=np.int)
# This variable will be different for each subject subjectID = 'sub-HC001_ses-01' # <<<<<<<<<<<< CHANGE THIS SUBJECT's ID subjectDir = 'micapipe/sub-HC001/ses-01' # <<<<<<<<<<<< CHANGE THIS SUBJECT's DIRECTORY # Set paths and variables dir_FS = 'freesurfer/' + subjectID dir_conte = subjectDir + '/anat/surfaces/conte69/' dir_morph = subjectDir + '/anat/surfaces/morphology/' dir_mpc = subjectDir + '/anat/surfaces/micro_profiles/' # ## Load all the surfaces # In[4]: # Load native pial surface pial_lh = read_surface(dir_FS + '/surf/lh.pial', itype='fs') pial_rh = read_surface(dir_FS + '/surf/rh.pial', itype='fs') # Load native mid surface mid_lh = read_surface(dir_FS + '/surf/lh.midthickness.surf.gii', itype='gii') mid_rh = read_surface(dir_FS + '/surf/rh.midthickness.surf.gii', itype='gii') # Load native white matter surface wm_lh = read_surface(dir_FS + '/surf/lh.white', itype='fs') wm_rh = read_surface(dir_FS + '/surf/rh.white', itype='fs') # Load native inflated surface inf_lh = read_surface(dir_FS + '/surf/lh.inflated', itype='fs') inf_rh = read_surface(dir_FS + '/surf/rh.inflated', itype='fs') # Load fsaverage5