def makeLD(self, vesselgroup):
ldvessels = krebsutils.read_lattice_data_from_hdf(
vesselgroup['lattice'])
fieldld = krebsutils.SetupFieldLattice(ldvessels.worldBox, 3,
self.bin_size, 0.)
fieldldFine = krebsutils.SetupFieldLattice(
fieldld.worldBox, 3, self.bin_size / self.fine_bin_subdivision, 0.)
return fieldld, fieldldFine
def from_vessel_file(filenames, grp_pattern):
dirs = set()
dataman = myutils.DataManager(20, [
krebs.plotIff.DataTissue(),
krebs.plotIff.DataGlobalIff(),
krebs.plotIff.DataRadialIff(),
krebs.analyzeGeneral.DataDistanceFromCenter(),
krebs.analyzeGeneral.DataVesselSamples(),
krebs.analyzeGeneral.DataBasicVessel(),
o2analysis.DataDetailedPO2()
])
f_measure = h5files.open('chache.h5', 'a', search=False)
def cachelocation(g):
path = posixpath.join(
'FileCS_' + myutils.checksum(basename(g.file.filename)),
g.name.strip(posixpath.sep))
return (f_measure, path)
#run with grp_pattern: iff/vessels
for fn in filenames:
with h5py.File(fn, 'r+') as f:
d = myutils.walkh5(f, grp_pattern)
assert len(d), 'you f****d up, pattern "%s" not found in "%s"!' % (
grp_pattern, fn)
dirs = set.union(dirs, d)
for group_path in dirs:
if 'vessel' in grp_pattern and not 'o2' in grp_pattern:
vesselgroup = f[group_path]
ldvessels = ku.read_lattice_data_from_hdf(
vesselgroup['lattice'])
fieldld = ku.SetupFieldLattice(ldvessels.worldBox, 3, 10,
0.)
phi_vessels = krebs.analyzeGeneral.CalcPhiVessels(
dataman,
vesselgroup,
fieldld,
scaling=1.,
samples_per_cell=5)
print('bla')
import nibabel as nib
new_image = nib.Nifti1Image(phi_vessels, affine=np.eye(4))
common_filename = os.path.splitext(os.path.basename(fn))[0]
new_image.to_filename(common_filename + '_vessels' +
'.nii')
if 'o2' in grp_pattern:
po2group = f[group_path]
#sample_length = 500.
#data = dataman.obtain_data('detailedPO2_global', 'po2_tissue', po2group, sample_length, cachelocation(po2group))
data = np.asarray(po2group['po2field'])
print('bla')
import nibabel as nib
new_image = nib.Nifti1Image(data, affine=np.eye(4))
common_filename = os.path.splitext(os.path.basename(fn))[0]
new_image.to_filename(common_filename + '_po2' + '.nii')
def from_vessel_file(filenames, grp_pattern):
dirs = set()
dataman = myutils.DataManager(20, [
krebs.plotIff.DataTissue(),
krebs.plotIff.DataGlobalIff(),
krebs.plotIff.DataRadialIff(),
krebs.analyzeGeneral.DataDistanceFromCenter(),
krebs.analyzeGeneral.DataBasicVessel()
])
#run with grp_pattern: iff/vessels
for fn in filenames:
with h5py.File(fn, 'r') as f:
d = myutils.walkh5(f, grp_pattern)
assert len(d), 'you f****d up, pattern "%s" not found in "%s"!' % (
grp_pattern, fn)
dirs = set.union(dirs, d)
for group_path in dirs:
vesselgroup = f[group_path]
ldvessels = ku.read_lattice_data_from_hdf(
vesselgroup['lattice'])
fieldld = ku.SetupFieldLattice(ldvessels.worldBox, 3, 10, 0.)
#fieldldFine = ku.SetupFieldLattice(fieldld.worldBox, 3, 50 / 10, 0.)
phi_vessels = krebs.analyzeGeneral.CalcPhiVessels(
dataman,
f['iff/vessels'],
fieldld,
scaling=1.,
samples_per_cell=5)
#a_abs = np.fabs(phi_vessels)
#a_max = np.amax(a_abs)
#n_phi = phi_vessels/a_max
#visual = (phi_vessels-phi_vessels.min())
#inner1 = matplotlib.cm.gist_earth(n_phi)
#inner2 = np.uint8(inner1*255)
#result = Image.fromarray(inner2[:,:,:,0:2],mode='RGB')
#result.save('out.tiff')
print('bla')
import nibabel as nib
new_image = nib.Nifti1Image(phi_vessels, affine=np.eye(4))
new_image.to_filename('myni')
#for i in np.arange(0,phi_vessels.shape[2]):
# plt.imsave('img/%03i.png' % i,phi_vessels[:,:,i])
#scipy.io.savemat('test.mat', phi_vessels)
#np.save('mydata.tiff',phi_vessels)
#pydicom.
#pydicom.write_file('mydata.dcm', phi_vessels)
plt.imshow(phi_vessels[:, :, 20])
plt.show()
if __name__ == '__main__':
filename = sys.argv[1]
grouppath = sys.argv[2]
with h5py.File(filename, 'r') as file:
vesselgroup = file[grouppath]
ldvessels = krebsutils.read_lattice_data_from_hdf(
vesselgroup['lattice'])
wbbox = ldvessels.worldBox
graph = krebsutils.read_vesselgraph(
vesselgroup, ['position', 'radius', 'flags', 'pressure'])
graph = graph.get_filtered(
myutils.bbitwise_and(graph['flags'], krebsutils.CIRCULATED))
fieldld = krebsutils.SetupFieldLattice(wbbox, 3, 50., 0.)
print fieldld
edgevalues = graph['pressure']
edgevalues = edgevalues[graph.edgelist]
thefield = krebsutils.CalcIntervascularInterpolationField(
graph.edgelist, graph['radius'], graph['position'], edgevalues,
fieldld, 1.)
volfraction = krebsutils.make_vessel_volume_fraction_field(
graph['position'], graph.edgelist, graph['radius'], fieldld, 5)
thegrad = ndimage.gaussian_filter(thefield, 1.0, 0, mode='nearest')
gradfield_ = krebsutils.field_gradient(thegrad)
thegrad = np.sum([np.square(g) for g in gradfield_], axis=0)
thegrad = np.sqrt(thegrad)
del gradfield_
ldvessels = krebsutils.read_lattice_data_from_hdf(
vesselgroup['lattice'])
wbbox = ldvessels.worldBox
graph = krebsutils.read_vesselgraph(vesselgroup, ['position', 'flags'])
graph = graph.get_filtered(
myutils.bbitwise_and(graph['flags'], krebsutils.CIRCULATED))
print 'vessel ld:'
print ldvessels
''' this splits splits space in lattices of 300.
the second 300 adds a 'safety layer of 100. mum
so we do not consider the outermost data for calculating the
actual mvd
'''
fieldld = krebsutils.SetupFieldLattice(wbbox, 3, 20., 100.)
wbbox = fieldld.worldBox
print 'field ld:'
print fieldld
z = fieldld.shape[2] / 2
weights = krebsutils.sample_edges_weights(graph.nodes['position'],
graph.edgelist, 30.)
positions = krebsutils.sample_edges(
graph.nodes['position'], graph.edgelist, graph.nodes['position'], 30.,
krebsutils.VesselSamplingFlags.DATA_PER_NODE
| krebsutils.VesselSamplingFlags.DATA_LINEAR)
eps = 1.0 - 1.e-15
x0, x1, y0, y1, z0, z1 = wbbox
ranges = [
def sample_vessel_system(goodArguments):
filename = goodArguments.vesselFileNames
grouppath = goodArguments.grp_pattern
with h5py.File(filename, 'r') as file:
if ('vessels' in grouppath):
print('found vessels!')
vesselgroup = file[grouppath]
else:
if ('out' in grouppath):
outgroup = file[grouppath]
print('found tumor of type: %s' %
str(outgroup['tumor'].attrs.get('TYPE')))
vesselgroup = file[grouppath + '/vessels']
else:
print("unknown data structure!")
ldvessels = krebsutils.read_lattice_data_from_hdf(
vesselgroup['lattice'])
wbbox = ldvessels.worldBox
graph = krebsutils.read_vesselgraph(vesselgroup, ['position', 'flags'])
graph = graph.get_filtered(
myutils.bbitwise_and(graph['flags'], krebsutils.CIRCULATED))
print 'vessel ld:'
print ldvessels
''' this splits splits space in lattices of 300.
the second 300 adds a 'safety layer of 100. mum
so we do not consider the outermost data for calculating the
actual mvd
'''
sampling_lattice_spacing = goodArguments.sampling_lattice_spacing
fieldld = krebsutils.SetupFieldLattice(wbbox, 3, sampling_lattice_spacing,
100.)
wbbox = fieldld.worldBox
print 'field ld:'
print fieldld
z = fieldld.shape[2] / 2
longitudinal_sampling_distance = goodArguments.longitudinal
weights = krebsutils.sample_edges_weights(graph.nodes['position'],
graph.edgelist,
longitudinal_sampling_distance)
positions = krebsutils.sample_edges(
graph.nodes['position'], graph.edgelist, graph.nodes['position'],
longitudinal_sampling_distance,
krebsutils.VesselSamplingFlags.DATA_PER_NODE
| krebsutils.VesselSamplingFlags.DATA_LINEAR)
eps = 1.0 - 1.e-15
x0, x1, y0, y1, z0, z1 = wbbox
ranges = [
np.arange(x0, x1, fieldld.scale * eps),
np.arange(y0, y1, fieldld.scale * eps),
np.arange(z0, z1, fieldld.scale * eps),
]
print 'histogram bin ends:', map(lambda r: (r.min(), r.max()), ranges)
mvd, _ = np.histogramdd(positions, bins=ranges, weights=weights)
mvd *= 1.e6 / (fieldld.scale**3)
print 'result shape:', mvd.shape
print('average mvd')
print(np.mean(mvd[1:-1, 1:-1, 1:-1]))
''' new stuff '''
from scipy import ndimage
bool_field = mvd > 0
bool_field = np.logical_not(bool_field)
distance_map = ndimage.morphology.distance_transform_edt(bool_field)
distance_map = distance_map * sampling_lattice_spacing
# fig, ax = pyplot.subplots(1)
# plt = ax.imshow(mvd[:,:,z], interpolation = 'none')
# ax.set(title = 'MVD')
# divider = mpl_utils.make_axes_locatable(ax)
# cax = divider.append_axes("right", size = "5%", pad = 0.05)
# fig.colorbar(plt, cax = cax)
fig, ax = pyplot.subplots(1)
plt = ax.imshow(distance_map[:, :, z], interpolation='none')
#ax.set(title = 'Distance Map \n group: %s, file: %s' %(grouppath, filename))
ax.set(title='Distance Map \n smp_logitudinal: %s, lattice_const: %s' %
(longitudinal_sampling_distance, sampling_lattice_spacing))
divider = mpl_utils.make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(plt, cax=cax)
basename(filename)
with PdfPages('distmap_' + basename(filename) + '_' + grouppath +
'.pdf') as pdf:
pdf.savefig(fig)