def spectrum_from_file(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization=None, area_file='', verbose=False):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
verbose : bool
print statements?
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6)
>>> print(np.array_str(np.array(spectrum[1::]),
... precision=5, suppress_small=True))
[ 0.00013 0.00027 0.00032 0.00047 0.00058]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas,
verbose)
return spectrum
def spectrum_per_label(vtk_file,
spectrum_size=10,
exclude_labels=[-1],
normalization='areaindex',
area_file='',
largest_segment=True,
verbose=False):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues
if None, no normalization is used
if "area", use area of the 2D structure as in Reuter et al. 2006
if "index", divide eigenvalue by index to account for linear trend
if "areaindex", do both (default)
area_file : string (optional)
name of VTK file with surface area scalar values
largest_segment : bool
compute spectrum only for largest segment with a given label?
verbose : bool
print statements?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> verbose = False
>>> spectrum_lists, label_list = spectrum_per_label(vtk_file,
... spectrum_size, exclude_labels, None, area_file, largest_segment,
... verbose)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum_lists[0]]
[0.0, 0.00054, 0.00244, 0.00291, 0.00456, 0.00575]
>>> label_list[0:10]
[1029, 1005, 1011, 1021, 1008, 1025, 999, 1013, 1007, 1022]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.guts.mesh import keep_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
points, indices, lines, faces, labels, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[
ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels
]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i, x in enumerate(labels) if x == label]
if verbose:
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = keep_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size, exclude_labels_inner,
normalization, areas, verbose)
else:
spectrum = fem_laplacian(pick_points, pick_faces, spectrum_size,
normalization, verbose)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def spectrum_from_file(vtk_file,
spectrum_size=10,
exclude_labels=[-1],
normalization="areaindex",
area_file='',
verbose=False):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues
if None, no normalization is used
if "area", use area of the 2D structure as in Reuter et al. 2006
if "index", divide eigenvalue by index to account for linear trend
if "areaindex", do both (default)
area_file : string
name of VTK file with surface area scalar values
verbose : bool
print statements?
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6,
... exclude_labels=[-1], normalization=None, area_file="", verbose=False)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum[1::]]
[0.00013, 0.00027, 0.00032, 0.00047, 0.00058]
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6,
... exclude_labels=[-1], normalization="areaindex", area_file="",
... verbose=False)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum[1::]]
[14.12801, 14.93573, 11.75397, 12.93141, 12.69348]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas,
verbose)
return spectrum
def spectrum_per_label(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization='area', area_file='',
largest_segment=True):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string (optional)
name of VTK file with surface area scalar values
largest_segment : Boolean
compute spectrum only for largest segment with a given label?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> spectrum_per_label(vtk_file, spectrum_size, exclude_labels, None,
>>> area_file, largest_segment)
([[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]],
[22])
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.guts.mesh import remove_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i,x in enumerate(labels) if x == label]
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = remove_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size,
exclude_labels_inner,
normalization, areas)
else:
spectrum = fem_laplacian(pick_points, pick_faces,
spectrum_size, normalization)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def spectrum_from_file(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization=None, area_file=''):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[4.829758648026223e-18,
0.00012841730024671977,
0.0002715181572272744,
0.00032051508471594173,
0.000470162807048644,
0.0005768904023010327]
>>> # Spectrum for Twins-2-1 left postcentral pial surface (22)
>>> # (after running explode_scalars() with reindex=True):
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'label22.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]
>>> # Loop thru all MB 101 brains
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> for hemidir in os.listdir(header):
>>> print hemidir
>>> sulci_file = os.path.join(header, hemidir, "sulci.vtk")
>>> spectrum = spectrum_from_file(sulci_file)
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
faces, u1, u2, points, u4, u5, u6, u7 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas)
return spectrum
def spectrum_per_label(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization='area', area_file='',
largest_segment=True, verbose=False):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string (optional)
name of VTK file with surface area scalar values
largest_segment : bool
compute spectrum only for largest segment with a given label?
verbose : bool
print statements?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> verbose = False
>>> spectrum_lists, label_list = spectrum_per_label(vtk_file,
... spectrum_size, exclude_labels, None, area_file, largest_segment,
... verbose)
>>> print(np.array_str(np.array(spectrum_lists[0][1::]),
... precision=5, suppress_small=True))
[ 0.00054 0.00244 0.00291 0.00456 0.00575]
>>> label_list[0:10]
[1029, 1005, 1011, 1021, 1008, 1025, 999, 1013, 1007, 1022]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.guts.mesh import keep_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
points, indices, lines, faces, labels, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i,x in enumerate(labels) if x == label]
if verbose:
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = keep_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size,
exclude_labels_inner,
normalization, areas, verbose)
else:
spectrum = fem_laplacian(pick_points, pick_faces, spectrum_size,
normalization, verbose)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def spectrum_per_label(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization='area', area_file='',
largest_segment=True):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
largest_segment : Boolean
compute spectrum only for largest segment with a given label?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> spectrum_per_label(vtk_file, spectrum_size, exclude_labels, None,
>>> area_file, largest_segment)
([[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]],
[22])
"""
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.utils.mesh import remove_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i,x in enumerate(labels) if x == label]
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = remove_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size,
exclude_labels_inner,
normalization, areas)
else:
spectrum = fem_laplacian(pick_points, pick_faces,
spectrum_size, normalization)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def spectrum_from_file(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization=None, area_file=''):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[4.829758648026223e-18,
0.00012841730024671977,
0.0002715181572272744,
0.00032051508471594173,
0.000470162807048644,
0.0005768904023010327]
>>> # Spectrum for Twins-2-1 left postcentral pial surface (22)
>>> # (after running explode_scalars() with reindex=True):
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'label22.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]
>>> # Loop thru all MB 101 brains
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> for hemidir in os.listdir(header):
>>> print hemidir
>>> sulci_file = os.path.join(header, hemidir, "sulci.vtk")
>>> spectrum = spectrum_from_file(sulci_file)
"""
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
faces, u1, u2, points, u4, u5, u6, u7 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas)
return spectrum
def spectrum_from_file(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization="areaindex", area_file='', verbose=False):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues
if None, no normalization is used
if "area", use area of the 2D structure as in Reuter et al. 2006
if "index", divide eigenvalue by index to account for linear trend
if "areaindex", do both (default)
area_file : string
name of VTK file with surface area scalar values
verbose : bool
print statements?
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6,
... exclude_labels=[-1], normalization=None, area_file="", verbose=False)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum[1::]]
[0.00013, 0.00027, 0.00032, 0.00047, 0.00058]
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6,
... exclude_labels=[-1], normalization="areaindex", area_file="",
... verbose=False)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum[1::]]
[14.12801, 14.93573, 11.75397, 12.93141, 12.69348]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas,
verbose)
return spectrum
def spectrum_from_file(vtk_file,
spectrum_size=10,
exclude_labels=[-1],
normalization=None,
area_file='',
verbose=False):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
verbose : bool
print statements?
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6)
>>> print(np.array_str(np.array(spectrum[1::]),
... precision=5, suppress_small=True))
[ 0.00013 0.00027 0.00032 0.00047 0.00058]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas,
verbose)
return spectrum
def spectrum_per_label(vtk_file, n_eigenvalues=3, exclude_labels=[-1],
normalization='area', area_file=''):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
n_eigenvalues : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Spectrum for label 22 (postcentral) in Twins-2-1:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import laplacian_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> n_eigenvalues = 6
>>> exclude_labels = [0] #[-1]
>>> laplacian_per_label(vtk_file, n_eigenvalues, exclude_labels,
>>> normalization=None, area_file=area_file)
Load "Labels" scalars from lh.labels.DKT25.manual.vtk
Load "scalars" scalars from lh.pial.area.vtk
7819 vertices for label 22
Reduced 290134 to 15230 triangular faces
Linear FEM Laplace-Beltrami spectrum:
([[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]],
[22])
"""
from mindboggle.utils.io_vtk import read_vtk, read_scalars
from mindboggle.utils.mesh import remove_faces
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label==22:
# Determine the indices per label:
label_indices = [i for i,x in enumerate(labels) if x == label]
print('{0} vertices for label {1}'.format(len(label_indices), label))
# Remove background faces:
select_faces = remove_faces(faces, label_indices)
# Compute Laplace-Beltrami spectrum for the label:
spectrum = spectrum_of_largest(points, select_faces, n_eigenvalues,
exclude_labels, normalization, areas)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
