def estimate_distribution(scalar_files, scalar_range, fold_files, label_files,
background_value=-1, verbose=False):
"""
Estimate sulcus label border scalar distributions from VTK files.
Learn distributions from training data (different surface meshes).
Estimate distribution means, sigmas (standard deviations), and weights
for VTK surface mesh scalars (e.g., depth, curvature) along and outside
sulcus label borders within folds.
Note : The number of classes, k, is currently hard-coded.
Parameters
----------
scalar_files : list of strings
names of VTK files with scalar values for all surface vertices
scalar_range : list of floats
range of values to estimate distribution
fold_files : list of strings
names of VTK files with fold numbers for scalar values
label_files : list of strings
names of VTK files with label numbers for scalar values
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
border_parameters : dictionary containing lists of floats
means, sigmas, weights
nonborder_parameters : dictionary containing lists of floats
means, sigmas, weights
Examples
--------
>>> import numpy as np
>>> import pickle
>>> from mindboggle.shapes.likelihood import estimate_distribution
>>> from mindboggle.mio.fetch_data import prep_tests
>>> # Train on a single surface mesh (using FreeSurfer vs. manual labels):
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> curv_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> labels_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> depth_files = [depth_file]
>>> curv_files = [curv_file]
>>> fold_files = [folds_file]
>>> label_files = [labels_file]
>>> #
>>> # # Train on many Mindboggle-101 surface meshes:
>>> # import os
>>> # mindboggle_path = '../../Mindboggle101_mindboggle_results'
>>> # label_path = os.environ['SUBJECTS_DIR']
>>> # x_path = os.path.join(os.environ['MINDBOGGLE'], 'x')
>>> # atlas_list_file = os.path.join(x_path, 'mindboggle101_atlases.txt')
>>> # depth_files = []
>>> # curv_files = []
>>> # fold_files = []
>>> # label_files = []
>>> # for atlas in atlas_list:
>>> # if 'OASIS' in atlas or 'NKI' in atlas or 'MMRR-21' in atlas:
>>> # print(atlas)
>>> # for h in ['lh','rh']:
>>> # depth_file = os.path.join(mindboggle_path, 'shapes',
>>> # '_hemi_'+h+'_subject_'+atlas, h+'.pial.travel_depth.vtk')
>>> # curv_file = os.path.join(mindboggle_path, 'shapes',
>>> # '_hemi_'+h+'_subject_'+atlas, h+'.pial.mean_curvature.vtk')
>>> # folds_file = os.path.join(mindboggle_path, 'features',
>>> # '_hemi_'+h+'_subject_'+atlas, 'folds.vtk')
>>> # labels_file = os.path.join(label_path, atlas, 'label',
>>> # h+'.labels.DKT25.manual.vtk')
>>> # depth_files.append(depth_file)
>>> # curv_files.append(curv_file)
>>> # fold_files.append(folds_file)
>>> # label_files.append(labels_file)
>>> #
>>> scalar_files = depth_files
>>> scalar_range = np.linspace(0, 1, 51, endpoint=True) # (0 to 1 by 0.02)
>>> background_value = -1
>>> verbose = False
>>> depth_border, depth_nonborder = estimate_distribution(scalar_files,
... scalar_range, fold_files, label_files, background_value, verbose)
>>> scalar_files = curv_files
>>> scalar_range = np.linspace(-1, 1, 101, endpoint=True) # (-1 to 1 by 0.02)
>>> curv_border, curv_nonborder = estimate_distribution(scalar_files,
... scalar_range, fold_files, label_files, verbose)
>>> print(np.array_str(np.array(depth_border['means']),
... precision=5, suppress_small=True))
[ 13.0869 0. 0. ]
>>> print(np.array_str(np.array(depth_nonborder['means']),
... precision=5, suppress_small=True))
[ 14.59311 6.16008 0. ]
>>> print(np.array_str(np.array(curv_border['means']),
... precision=5, suppress_small=True))
[ 3.06449 -0.76109 -3.43184]
>>> print(np.array_str(np.array(curv_nonborder['means']),
... precision=5, suppress_small=True))
[ 0.62236 -1.55192 -5.19359]
>>> pickle.dump([depth_border, curv_border, depth_nonborder, curv_nonborder],
... open("depth_curv_border_nonborder_parameters.pkl", "wb"))
"""
from mindboggle.shapes.likelihood import concatenate_sulcus_scalars, \
fit_normals_to_histogram
if not scalar_files or not fold_files or not label_files:
raise IOError("Input file lists cannot be empty.")
# Concatenate scalars across multiple training files:
border_scalars, nonborder_scalars = concatenate_sulcus_scalars(scalar_files,
fold_files, label_files, background_value)
# Estimate distribution parameters:
border_means, border_sigmas, \
border_weights = fit_normals_to_histogram(border_scalars,
scalar_range, verbose)
nonborder_means, nonborder_sigmas, \
nonborder_weights = fit_normals_to_histogram(nonborder_scalars,
scalar_range, verbose)
# Store outputs in dictionaries:
border_parameters = {
'means': border_means,
'sigmas': border_sigmas,
'weights': border_weights
}
nonborder_parameters = {
'means': nonborder_means,
'sigmas': nonborder_sigmas,
'weights': nonborder_weights
}
return border_parameters, nonborder_parameters
def estimate_distribution(scalar_files, scalar_range, fold_files, label_files):
"""
Estimate sulcus label border scalar distributions from VTK files.
Estimate distribution means, sigmas (standard deviations), and weights
for VTK surface mesh scalars (e.g., depth, curvature) along and outside
sulcus label borders within folds.
Note : The number of classes, k, is currently hard-coded.
Parameters
----------
scalar_files : list of strings
names of VTK files with scalar values for all surface vertices
scalar_range : list of floats
range of values to estimate distribution
fold_files : list of strings
names of VTK files with fold numbers for scalar values
label_files : list of strings
names of VTK files with label numbers for scalar values
Returns
-------
border_parameters : dictionary containing lists of floats
means, sigmas, weights
nonborder_parameters : dictionary containing lists of floats
means, sigmas, weights
Examples
--------
>>> import os
>>> import numpy as np
>>> from mindboggle.shapes.likelihood import estimate_distribution
>>> do_test = False
>>> # Train on a single surface mesh:
>>> if do_test:
>>> path = os.environ['MINDBOGGLE_DATA']
>>> #depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> curv_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> labels_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> depth_files = [depth_file]
>>> curv_files = [curv_file]
>>> fold_files = [folds_file]
>>> label_files = [labels_file]
>>> # Train on many Mindboggle-101 surface meshes:
>>> else:
>>> mindboggle_path = '../../Mindboggle101_mindboggle_results'
>>> label_path = os.environ['SUBJECTS_DIR']
>>> x_path = os.path.join(os.environ['MINDBOGGLE'], 'x')
>>> atlas_list_file = os.path.join(x_path, 'mindboggle101_atlases.txt')
>>> atlas_list
>>> depth_files = []
>>> curv_files = []
>>> fold_files = []
>>> label_files = []
>>> for atlas in atlas_list:
>>> if 'OASIS' in atlas or 'NKI' in atlas or 'MMRR-21' in atlas:
>>> print(atlas)
>>> for h in ['lh','rh']:
>>> #depth_file = os.path.join(mindboggle_path, 'shapes',
>>> # '_hemi_'+h+'_subject_'+atlas, 'travel_depth_rescaled.vtk')
>>> depth_file = os.path.join(mindboggle_path, 'shapes',
>>> '_hemi_'+h+'_subject_'+atlas, h+'.pial.travel_depth.vtk')
>>> curv_file = os.path.join(mindboggle_path, 'shapes',
>>> '_hemi_'+h+'_subject_'+atlas, h+'.pial.mean_curvature.vtk')
>>> folds_file = os.path.join(mindboggle_path, 'features',
>>> '_hemi_'+h+'_subject_'+atlas, 'folds.vtk')
>>> labels_file = os.path.join(label_path, atlas, 'label',
>>> h+'.labels.DKT25.manual.vtk')
>>> depth_files.append(depth_file)
>>> curv_files.append(curv_file)
>>> fold_files.append(folds_file)
>>> label_files.append(labels_file)
>>> scalar_range1 = np.linspace(0, 1, 51, endpoint=True) # (0 to 1 by 0.02)
>>> scalar_range2 = np.linspace(-1, 1, 101, endpoint=True) # (-1 to 1 by 0.02)
>>> #
>>> depth_border, depth_nonborder = estimate_distribution(depth_files,
>>> scalar_range1, fold_files, label_files)
>>> #
>>> curv_border, curv_nonborder = estimate_distribution(curv_files,
>>> scalar_range2, fold_files, label_files)
>>> #
>>> import cPickle as pickle
>>> pickle.dump( [depth_border, curv_border, depth_nonborder, curv_nonborder],
>>> open("depth_curv_border_nonborder_parameters.pkl", "wb"))
"""
from mindboggle.shapes.likelihood import concatenate_sulcus_scalars, \
fit_normals_to_histogram
if not scalar_files or not fold_files or not label_files:
import sys
sys.exit("Input file lists cannot be empty.")
# Concatenate scalars across multiple training files:
border_scalars, nonborder_scalars = concatenate_sulcus_scalars(scalar_files,
fold_files, label_files)
# Estimate distribution parameters:
border_means, border_sigmas, \
border_weights = fit_normals_to_histogram(border_scalars, scalar_range)
nonborder_means, nonborder_sigmas, \
nonborder_weights = fit_normals_to_histogram(nonborder_scalars, scalar_range)
# Store outputs in dictionaries:
border_parameters = {
'means': border_means,
'sigmas': border_sigmas,
'weights': border_weights
}
nonborder_parameters = {
'means': nonborder_means,
'sigmas': nonborder_sigmas,
'weights': nonborder_weights
}
return border_parameters, nonborder_parameters
def estimate_distribution(scalar_files,
scalar_range,
fold_files,
label_files,
background_value=-1,
verbose=False):
"""
Estimate sulcus label border scalar distributions from VTK files.
Learn distributions from training data (different surface meshes).
Estimate distribution means, sigmas (standard deviations), and weights
for VTK surface mesh scalars (e.g., depth, curvature) along and outside
sulcus label borders within folds.
Note : The number of classes, k, is currently hard-coded.
Parameters
----------
scalar_files : list of strings
names of VTK files with scalar values for all surface vertices
scalar_range : list of floats
range of values to estimate distribution
fold_files : list of strings
names of VTK files with fold numbers for scalar values
label_files : list of strings
names of VTK files with label numbers for scalar values
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
border_parameters : dictionary containing lists of floats
means, sigmas, weights
nonborder_parameters : dictionary containing lists of floats
means, sigmas, weights
Examples
--------
>>> import numpy as np
>>> import pickle
>>> from mindboggle.shapes.likelihood import estimate_distribution
>>> from mindboggle.mio.fetch_data import prep_tests
>>> # Train on a single surface mesh (using FreeSurfer vs. manual labels):
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> curv_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> labels_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> depth_files = [depth_file]
>>> curv_files = [curv_file]
>>> fold_files = [folds_file]
>>> label_files = [labels_file]
>>> #
>>> # # Train on many Mindboggle-101 surface meshes:
>>> # import os
>>> # mindboggle_path = '../../Mindboggle101_mindboggle_results'
>>> # label_path = os.environ['SUBJECTS_DIR']
>>> # x_path = os.path.join(os.environ['MINDBOGGLE'], 'x')
>>> # atlas_list_file = os.path.join(x_path, 'mindboggle101_atlases.txt')
>>> # depth_files = []
>>> # curv_files = []
>>> # fold_files = []
>>> # label_files = []
>>> # for atlas in atlas_list:
>>> # if 'OASIS' in atlas or 'NKI' in atlas or 'MMRR-21' in atlas:
>>> # print(atlas)
>>> # for h in ['lh','rh']:
>>> # depth_file = os.path.join(mindboggle_path, 'shapes',
>>> # '_hemi_'+h+'_subject_'+atlas, h+'.pial.travel_depth.vtk')
>>> # curv_file = os.path.join(mindboggle_path, 'shapes',
>>> # '_hemi_'+h+'_subject_'+atlas, h+'.pial.mean_curvature.vtk')
>>> # folds_file = os.path.join(mindboggle_path, 'features',
>>> # '_hemi_'+h+'_subject_'+atlas, 'folds.vtk')
>>> # labels_file = os.path.join(label_path, atlas, 'label',
>>> # h+'.labels.DKT25.manual.vtk')
>>> # depth_files.append(depth_file)
>>> # curv_files.append(curv_file)
>>> # fold_files.append(folds_file)
>>> # label_files.append(labels_file)
>>> #
>>> scalar_files = depth_files
>>> scalar_range = np.linspace(0, 1, 51, endpoint=True) # (0 to 1 by 0.02)
>>> background_value = -1
>>> verbose = False
>>> depth_border, depth_nonborder = estimate_distribution(scalar_files,
... scalar_range, fold_files, label_files, background_value, verbose)
>>> scalar_files = curv_files
>>> scalar_range = np.linspace(-1, 1, 101, endpoint=True) # (-1 to 1 by 0.02)
>>> curv_border, curv_nonborder = estimate_distribution(scalar_files,
... scalar_range, fold_files, label_files, verbose)
>>> print(np.array_str(np.array(depth_border['means']),
... precision=5, suppress_small=True))
[ 13.0869 0. 0. ]
>>> print(np.array_str(np.array(depth_nonborder['means']),
... precision=5, suppress_small=True))
[ 14.59311 6.16008 0. ]
>>> print(np.array_str(np.array(curv_border['means']),
... precision=5, suppress_small=True))
[ 3.06449 -0.76109 -3.43184]
>>> print(np.array_str(np.array(curv_nonborder['means']),
... precision=5, suppress_small=True))
[ 0.62236 -1.55192 -5.19359]
>>> pickle.dump([depth_border, curv_border, depth_nonborder, curv_nonborder],
... open("depth_curv_border_nonborder_parameters.pkl", "wb"))
"""
from mindboggle.shapes.likelihood import concatenate_sulcus_scalars, \
fit_normals_to_histogram
if not scalar_files or not fold_files or not label_files:
raise IOError("Input file lists cannot be empty.")
# Concatenate scalars across multiple training files:
border_scalars, nonborder_scalars = concatenate_sulcus_scalars(
scalar_files, fold_files, label_files, background_value)
# Estimate distribution parameters:
border_means, border_sigmas, \
border_weights = fit_normals_to_histogram(border_scalars,
scalar_range, verbose)
nonborder_means, nonborder_sigmas, \
nonborder_weights = fit_normals_to_histogram(nonborder_scalars,
scalar_range, verbose)
# Store outputs in dictionaries:
border_parameters = {
'means': border_means,
'sigmas': border_sigmas,
'weights': border_weights
}
nonborder_parameters = {
'means': nonborder_means,
'sigmas': nonborder_sigmas,
'weights': nonborder_weights
}
return border_parameters, nonborder_parameters
def estimate_distribution(scalar_files, scalar_range, fold_files, label_files):
"""
Estimate sulcus label border scalar distributions from VTK files.
Estimate distribution means, sigmas (standard deviations), and weights
for VTK surface mesh scalars (e.g., depth, curvature) along and outside
sulcus label borders within folds.
Note : The number of classes, k, is currently hard-coded.
Parameters
----------
scalar_files : list of strings
names of VTK files with scalar values for all surface vertices
scalar_range : list of floats
range of values to estimate distribution
fold_files : list of strings
names of VTK files with fold numbers for scalar values
label_files : list of strings
names of VTK files with label numbers for scalar values
Returns
-------
border_parameters : dictionary containing lists of floats
means, sigmas, weights
nonborder_parameters : dictionary containing lists of floats
means, sigmas, weights
Examples
--------
>>> import os
>>> import numpy as np
>>> from mindboggle.shapes.likelihood import estimate_distribution
>>> from mindboggle.utils.io_table import read_columns
>>> do_test = False
>>> # Train on a single surface mesh:
>>> if do_test:
>>> path = os.environ['MINDBOGGLE_DATA']
>>> #depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> curv_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> labels_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> depth_files = [depth_file]
>>> curv_files = [curv_file]
>>> fold_files = [folds_file]
>>> label_files = [labels_file]
>>> # Train on many Mindboggle-101 surface meshes:
>>> else:
>>> mindboggle_path = '../../Mindboggle101_mindboggle_results'
>>> label_path = os.environ['SUBJECTS_DIR']
>>> x_path = os.path.join(os.environ['MINDBOGGLE'], 'x')
>>> atlas_list_file = os.path.join(x_path, 'mindboggle101_atlases.txt')
>>> atlas_list = read_columns(atlas_list_file, 1)[0]
>>> depth_files = []
>>> curv_files = []
>>> fold_files = []
>>> label_files = []
>>> for atlas in atlas_list:
>>> if 'OASIS' in atlas or 'NKI' in atlas or 'MMRR-21' in atlas:
>>> print(atlas)
>>> for h in ['lh','rh']:
>>> #depth_file = os.path.join(mindboggle_path, 'shapes',
>>> # '_hemi_'+h+'_subject_'+atlas, 'travel_depth_rescaled.vtk')
>>> depth_file = os.path.join(mindboggle_path, 'shapes',
>>> '_hemi_'+h+'_subject_'+atlas, h+'.pial.travel_depth.vtk')
>>> curv_file = os.path.join(mindboggle_path, 'shapes',
>>> '_hemi_'+h+'_subject_'+atlas, h+'.pial.mean_curvature.vtk')
>>> folds_file = os.path.join(mindboggle_path, 'features',
>>> '_hemi_'+h+'_subject_'+atlas, 'folds.vtk')
>>> labels_file = os.path.join(label_path, atlas, 'label',
>>> h+'.labels.DKT25.manual.vtk')
>>> depth_files.append(depth_file)
>>> curv_files.append(curv_file)
>>> fold_files.append(folds_file)
>>> label_files.append(labels_file)
>>> scalar_range1 = np.linspace(0, 1, 51, endpoint=True) # (0 to 1 by 0.02)
>>> scalar_range2 = np.linspace(-1, 1, 101, endpoint=True) # (-1 to 1 by 0.02)
>>> #
>>> depth_border, depth_nonborder = estimate_distribution(depth_files,
>>> scalar_range1, fold_files, label_files)
>>> #
>>> curv_border, curv_nonborder = estimate_distribution(curv_files,
>>> scalar_range2, fold_files, label_files)
>>> #
>>> import cPickle as pickle
>>> pickle.dump( [depth_border, curv_border, depth_nonborder, curv_nonborder],
>>> open("depth_curv_border_nonborder_parameters.pkl", "wb"))
"""
from mindboggle.shapes.likelihood import concatenate_sulcus_scalars, \
fit_normals_to_histogram
if not scalar_files or not fold_files or not label_files:
import sys
sys.exit("Input file lists cannot be empty.")
# Concatenate scalars across multiple training files:
border_scalars, nonborder_scalars = concatenate_sulcus_scalars(
scalar_files, fold_files, label_files)
# Estimate distribution parameters:
border_means, border_sigmas, \
border_weights = fit_normals_to_histogram(border_scalars, scalar_range)
nonborder_means, nonborder_sigmas, \
nonborder_weights = fit_normals_to_histogram(nonborder_scalars, scalar_range)
# Store outputs in dictionaries:
border_parameters = {
'means': border_means,
'sigmas': border_sigmas,
'weights': border_weights
}
nonborder_parameters = {
'means': nonborder_means,
'sigmas': nonborder_sigmas,
'weights': nonborder_weights
}
return border_parameters, nonborder_parameters
