def populate_hagmann_density(self, WM_img, ROI_img):
"""Populate hagmann density."""
def inverse_sum(elements):
inverse_elements = []
for element in elements:
inverse_elements.append( 1.0 / element )
from math import fsum
return fsum(inverse_elements)
# get surface areas for ROIs
import images
surface_area = images.surface_area_for_rois(ROI_img, WM_img)
# make sure our surface areas are well formed, this means an
# entry for every key
epsilon = 0.00000000001
for n in self.nodes():
try:
surface_area[n]
except KeyError:
surface_area[n] = epsilon
except Exception as e:
raise e
# for every edge...
for i,j in self.edges_iter():
calc_hd = ( ( 2.0 / ( surface_area[i] + surface_area[j] ) ) * \
inverse_sum( self[i][j]['streamlines_length'] ) )
self[i][j]['hagmann_density'] = calc_hd
def populate_hagmann_density(self, WM_img, ROI_img):
"""Populate hagmann density."""
def inverse_sum(elements):
inverse_elements = []
for element in elements:
inverse_elements.append(1.0 / element)
from math import fsum
return fsum(inverse_elements)
# get surface areas for ROIs
import images
surface_area = images.surface_area_for_rois(ROI_img, WM_img)
# make sure our surface areas are well formed, this means an
# entry for every key
epsilon = 0.00000000001
for n in self.nodes():
try:
surface_area[n]
except KeyError:
surface_area[n] = epsilon
except Exception as e:
raise e
# for every edge...
for i, j in self.edges_iter():
calc_hd = ( ( 2.0 / ( surface_area[i] + surface_area[j] ) ) * \
inverse_sum( self[i][j]['streamlines_length'] ) )
self[i][j]['hagmann_density'] = calc_hd
def test_can_get_surface_areas(self):
# the following compares images and test images that are known
# to satisfy the condition that an ROI w/ label n will have n
# voxels that border white matter... labels in these images
# are defined for 1:10
roiImage = images.TNImage(filename = 'test_data/images/ROI_50x50x50.nii.gz')
maskImage = images.TNImage(filename = 'test_data/images/ROI_WM_MASK_50x50x50.nii.gz')
result = images.surface_area_for_rois(roiImage, maskImage)
for label in range(1,11): # 1:10
assert result[label] == label
def test_can_get_surface_areas(self):
# the following compares images and test images that are known
# to satisfy the condition that an ROI w/ label n will have n
# voxels that border white matter... labels in these images
# are defined for 1:10
roiImage = images.TNImage(
filename='test_data/images/ROI_50x50x50.nii.gz')
maskImage = images.TNImage(
filename='test_data/images/ROI_WM_MASK_50x50x50.nii.gz')
result = images.surface_area_for_rois(roiImage, maskImage)
for label in range(1, 11): # 1:10
assert result[label] == label
def extract_hagmann_density(connectome, roi_img, wm_img):
"""Populate hagmann density for given connectome"""
def inverse_sum(elements):
inverse_elements = []
for element in elements:
inverse_elements.append( 1.0 / element )
from math import fsum
return fsum(inverse_elements)
# get surface areas for ROIs
import images
surface_area = images.surface_area_for_rois(roi_img, wm_img)
# for every edge...
for i,j in connectome.edges_iter():
calc_hd = ( ( 2.0 / ( surface_area[i] + surface_area[j] ) ) * \
inverse_sum( connectome[i][j]['streamlines_length'] ) )
connectome[i][j]['hagmann_density'] = calc_hd
def populate_hagmann_density(self, WM_img, ROI_img):
"""Populate hagmann density."""
def inverse_sum(elements):
inverse_elements = []
for element in elements:
inverse_elements.append( 1.0 / element )
from math import fsum
return fsum(inverse_elements)
# get surface areas for ROIs
import images
surface_area = images.surface_area_for_rois(ROI_img, WM_img)
# for every edge...
for i,j in self.edges_iter():
calc_hd = ( ( 2.0 / ( surface_area[i] + surface_area[j] ) ) * \
inverse_sum( self[i][j]['streamlines_length'] ) )
self[i][j]['hagmann_density'] = calc_hd
